diff --git a/examples/jupyter_notebooks/MM_trp_no2_l2_func.ipynb b/examples/jupyter_notebooks/MM_trp_no2_l2_func.ipynb
deleted file mode 100644
index 38306541..00000000
--- a/examples/jupyter_notebooks/MM_trp_no2_l2_func.ipynb
+++ /dev/null
@@ -1,199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1c1a4942",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "502b2359",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys.path.append('../../')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4fe2d1ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Please install s3fs if retrieving from the Amazon S3 Servers.  Otherwise continue with local data\n",
-      "Please install h5py to open files from the Amazon S3 servers.\n",
-      "Please install h5netcdf to open files from the Amazon S3 servers.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import driver"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7373a0dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "an = driver.analysis()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9b60c80f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading TROPOMI L2 NO2\n",
-      "/Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/*\n",
-      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/S5P_OFFL_L2__NO2____20220430T222541_20220501T000711_23559_02_020301_20220502T140952.nc\n",
-      "qa_value\n",
-      "nitrogendioxide_tropospheric_column\n",
-      "lon\n",
-      "DEBUG:root:lon\n",
-      "lat\n",
-      "DEBUG:root:lat\n",
-      "qa_value\n",
-      "nitrogendioxide_tropospheric_column\n",
-      "DEBUG:root:nitrogendioxide_tropospheric_column\n"
-     ]
-    }
-   ],
-   "source": [
-    "an.control = '../yaml/control_tropomi_l2_no2.yaml'\n",
-    "an.read_control()\n",
-    "an.open_obs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1b065328",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('20220430',\n",
-       "              <xarray.Dataset>\n",
-       "              Dimensions:                              (dim_0: 4173, dim_1: 450)\n",
-       "              Dimensions without coordinates: dim_0, dim_1\n",
-       "              Data variables:\n",
-       "                  lon                                  (dim_0, dim_1) float32 nan nan ... nan\n",
-       "                  lat                                  (dim_0, dim_1) float32 nan nan ... nan\n",
-       "                  qa_value                             (dim_0, dim_1) float32 0.0 0.0 ... 0.0\n",
-       "                  nitrogendioxide_tropospheric_column  (dim_0, dim_1) float32 nan nan ... nan\n",
-       "              Attributes:\n",
-       "                  quality_flag:        qa_value\n",
-       "                  quality_thresh_min:  0.7)])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "an.obs['tropomi_l2_no2'].obj"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "28adfe97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "from cartopy.util import add_cyclic_point\n",
-    "import numpy as np\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "74112ff5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:matplotlib.colorbar:locator: <matplotlib.ticker.FixedLocator object at 0x7ff68d7943d0>\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lon = an.obs['tropomi_l2_no2'].obj['20220430']['lon']\n",
-    "lat = an.obs['tropomi_l2_no2'].obj['20220430']['lat']\n",
-    "no2 = an.obs['tropomi_l2_no2'].obj['20220430']['nitrogendioxide_tropospheric_column']\n",
-    "\n",
-    "plt.figure(figsize=(20,10))\n",
-    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
-    "clev = np.arange(1*1e15, 5.0*1e15, 0.25*1e15)\n",
-    "plt.contourf(lon, lat, no2, clev, cmap='Spectral_r', extend='both')\n",
-    "cbar=plt.colorbar(shrink=0.6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "071c35f8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/jupyter_notebooks/MM_trp_no2_l2_plot.ipynb b/examples/jupyter_notebooks/MM_trp_no2_l2_plot.ipynb
new file mode 100644
index 00000000..7fc3315b
--- /dev/null
+++ b/examples/jupyter_notebooks/MM_trp_no2_l2_plot.ipynb
@@ -0,0 +1,1637 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a6bba673",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Please install s3fs if retrieving from the Amazon S3 Servers.  Otherwise continue with local data\n",
+      "Please install h5netcdf to open files from the Amazon S3 servers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append('../../')\n",
+    "from melodies_monet import driver\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "from cartopy.util import add_cyclic_point\n",
+    "\n",
+    "plt.set_loglevel (level = 'warning')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fc334ba9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'analysis': {'start_time': '2019-07-15',\n",
+       "  'end_time': '2019-07-16',\n",
+       "  'debug': True,\n",
+       "  'output_dir': '/Users/mengli/Work/melodies-monet/outdata',\n",
+       "  'output_dir_save': '/Users/mengli/Work/melodies-monet/outdata/save_intermediate',\n",
+       "  'output_dir_read': '/Users/mengli/Work/melodies-monet/outdata/read_intermediate',\n",
+       "  'save': {'paired': {'method': 'netcdf', 'prefix': '201907', 'data': 'all'}},\n",
+       "  'read': {'paired': {'method': 'netcdf',\n",
+       "    'filenames': {'tropomi_l2_no2_wrfchem_v4.2': ['201907_tropomi_l2_no2_wrfchem_v4.2.nc4']}}}},\n",
+       " 'obs': {'tropomi_l2_no2': {'debug': True,\n",
+       "   'filename': '/Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/*',\n",
+       "   'obs_type': 'sat_swath_clm',\n",
+       "   'sat_type': 'tropomi_l2_no2',\n",
+       "   'variables': {'qa_value': {'quality_flag_min': 0.7,\n",
+       "     'var_applied': ['nitrogendioxide_tropospheric_column'],\n",
+       "     'fillvalue': 9.96921e+36},\n",
+       "    'nitrogendioxide_tropospheric_column': {'scale': 6.022141e+19,\n",
+       "     'fillvalue': 9.96921e+36,\n",
+       "     'ylabel_plot': 'NO2 trop. columns',\n",
+       "     'vmin_plot': 0.0,\n",
+       "     'vmax_plot': 1e+16,\n",
+       "     'nlevels_plot': 23,\n",
+       "     'regulatory': False},\n",
+       "    'averaging_kernel': {'fillvalue': 9.96921e+36},\n",
+       "    'air_mass_factor_total': {'fillvalue': 9.96921e+36},\n",
+       "    'air_mass_factor_troposphere': {'fillvalue': 9.96921e+36},\n",
+       "    'latitude': 'None',\n",
+       "    'longitude': 'None',\n",
+       "    'preslev': {'tm5_constant_a': {'group': ['PRODUCT'], 'maximum': 9e+36},\n",
+       "     'tm5_constant_b': {'group': ['PRODUCT'], 'maximum': 9e+36},\n",
+       "     'surface_pressure': {'group': ['PRODUCT/SUPPORT_DATA/INPUT_DATA/'],\n",
+       "      'maximum': 9e+36},\n",
+       "     'tm5_tropopause_layer_index': {'group': ['PRODUCT']}}}}},\n",
+       " 'model': {'wrfchem_v4.2': {'files': '/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*',\n",
+       "   'mod_type': 'wrfchem',\n",
+       "   'apply_ak': False,\n",
+       "   'mod_kwargs': {'mech': 'racm_esrl_vcp'},\n",
+       "   'mapping': {'tropomi_l2_no2': {'no2': 'nitrogendioxide_tropospheric_column'}},\n",
+       "   'projection': None,\n",
+       "   'plot_kwargs': {'color': 'dodgerblue', 'marker': '^', 'linestyle': ':'}}},\n",
+       " 'plots': {'plot_grp1': {'type': 'timeseries',\n",
+       "   'fig_kwargs': {'figsize': [12, 6]},\n",
+       "   'default_plot_kwargs': {'linewidth': 2.0, 'markersize': 10.0},\n",
+       "   'text_kwargs': {'fontsize': 18.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True,\n",
+       "    'ts_select_time': 'time',\n",
+       "    'ts_avg_window': 'H',\n",
+       "    'set_axis': False}},\n",
+       "  'plot_grp2': {'type': 'gridded_spatial_bias',\n",
+       "   'fig_kwargs': {'states': True, 'figsize': [10, 5]},\n",
+       "   'text_kwargs': {'fontsize': 16.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True, 'set_axis': True}},\n",
+       "  'plot_grp3': {'type': 'taylor',\n",
+       "   'fig_kwargs': {'figsize': [8, 8]},\n",
+       "   'default_plot_kwargs': {'markersize': 10.0},\n",
+       "   'text_kwargs': {'fontsize': 16.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True, 'set_axis': False}},\n",
+       "  'plot_grp4': {'type': 'boxplot',\n",
+       "   'fig_kwargs': {'figsize': [8, 6]},\n",
+       "   'default_plot_kwargs': {'markersize': 10.0},\n",
+       "   'text_kwargs': {'fontsize': 20.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True, 'set_axis': True}}},\n",
+       " 'stats': {'stat_list': ['MB', 'NMB', 'R2', 'RMSE'],\n",
+       "  'round_output': 2,\n",
+       "  'output_table': True,\n",
+       "  'output_table_kwargs': {'figsize': [12, 6],\n",
+       "   'fontsize': 12.0,\n",
+       "   'xscale': 1.4,\n",
+       "   'yscale': 1.4,\n",
+       "   'edges': 'horizontal'},\n",
+       "  'domain_type': ['all'],\n",
+       "  'domain_name': ['CONUS'],\n",
+       "  'data': ['tropomi_l2_no2_wrfchem_v4.2']}}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "an = driver.analysis()\n",
+    "an.control = '../yaml/control_tropomi_l2_no2.yaml'\n",
+    "an.read_control()\n",
+    "an.control_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3dccf46a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading TROPOMI L2 NO2\n",
+      "/Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/*\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190714T231100_20190715T005230_09074_03_020400_20221105T205731.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T005230_20190715T023400_09075_03_020400_20221105T210613.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T023400_20190715T041529_09076_03_020400_20221105T210615.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T041529_20190715T055659_09077_03_020400_20221105T210617.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T055659_20190715T073829_09078_03_020400_20221105T210619.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T124258_20190715T142428_09082_03_020400_20221105T210621.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T142428_20190715T160557_09083_03_020400_20221105T210623.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T160557_20190715T174727_09084_03_020400_20221105T210624.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T174727_20190715T192857_09085_03_020400_20221105T210627.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T192857_20190715T211026_09086_03_020400_20221105T210630.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T211026_20190715T225156_09087_03_020400_20221105T210634.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T225156_20190716T003326_09088_03_020400_20221105T210637.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- satobs\n",
+    "an.open_obs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b3a0da47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrfchem\n",
+      "/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*\n",
+      "**** Reading WRF-Chem model output...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- model\n",
+    "an.open_models()\n",
+    "lat = an.models['wrfchem_v4.2'].obj.coords['latitude']\n",
+    "lon = an.models['wrfchem_v4.2'].obj.coords['longitude']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b1e6bcd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "model(\n",
+       "    model='wrfchem',\n",
+       "    radius_of_influence=1000000.0,\n",
+       "    mod_kwargs={'mech': 'racm_esrl_vcp', 'var_list': ['no2', 'pres', 'height', 'tk', 'height_agl', 'PSFC', 'PH', 'PHB', 'PB', 'P', 'T']},\n",
+       "    file_str='/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*',\n",
+       "    label='wrfchem_v4.2',\n",
+       "    obj=...,\n",
+       "    mapping={'tropomi_l2_no2': {'no2': 'nitrogendioxide_tropospheric_column'}},\n",
+       "    label='wrfchem_v4.2',\n",
+       "    ...\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "an.models['wrfchem_v4.2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "58f836ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:52: RuntimeWarning: Mean of empty slice\n",
+      "  no2col_satm = np.nanmean(modobj_tm['no2col'].values, axis = 0)\n",
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with TROPOMI regridding 2019-07-14 0\n",
+      " no2 satellite: 0.0 0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:88: RuntimeWarning: Mean of empty slice\n",
+      "  no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)\n",
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with TROPOMI regridding 2019-07-15 0\n",
+      " no2 satellite: 0.0 0.0\n",
+      "Done with TROPOMI regridding 2019-07-15 1\n",
+      " no2 satellite: 0.0 0.0\n",
+      "Done with TROPOMI regridding 2019-07-15 2\n",
+      " no2 satellite: 0.0 0.0\n",
+      "Done with TROPOMI regridding 2019-07-15 3\n",
+      " no2 satellite: 0.0 0.0\n",
+      "Done with TROPOMI regridding 2019-07-15 4\n",
+      " no2 satellite: 0.0 0.0\n",
+      "Done with TROPOMI regridding 2019-07-15 5\n",
+      " no2 satellite: 0.0 0.0\n",
+      "Done with TROPOMI regridding 2019-07-15 6\n",
+      " no2 satellite: -1309892300000000.0 1.3650907e+16\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with TROPOMI regridding 2019-07-15 7\n",
+      " no2 satellite: -3913532600000000.0 2.2299026e+16\n",
+      "Done with TROPOMI regridding 2019-07-15 8\n",
+      " no2 satellite: -2818903500000000.0 1.8684126e+16\n",
+      "Done with TROPOMI regridding 2019-07-15 9\n",
+      " no2 satellite: -1082265660000000.0 7392744700000000.0\n",
+      "Done with TROPOMI regridding 2019-07-15 10\n",
+      " no2 satellite: 0.0 0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:88: RuntimeWarning: Mean of empty slice\n",
+      "  no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- paring\n",
+    "an.pair_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "37dc4d2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 -122.3 -122.2 ... -60.37\n",
+       "    lat                                  (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    x                                    (y) int64 0 0 0 0 0 ... 283 283 283 283\n",
+       "    ll                                   (y) int64 0 1 2 3 4 ... 436 437 438 439\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 7.279e+14 ... 3.10...\n",
+       "    no2trpcol                            (time, y) float32 5.608e+14 ... 6.98...\n",
+       "    latitude                             (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    longitude                            (y) float32 -122.3 -122.2 ... -60.37\n",
+       "Attributes:\n",
+       "    description:  daily tropomi data at model grids</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1dbf4256-0d90-484d-ae81-4bf6c45e21ce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1dbf4256-0d90-484d-ae81-4bf6c45e21ce' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>y</span>: 124960</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e57f8b8a-7d55-4cc3-a36d-75dbac61da7b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e57f8b8a-7d55-4cc3-a36d-75dbac61da7b' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-07-15</div><input id='attrs-e6cbed71-784c-44e5-afe3-a5b504b7a38f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6cbed71-784c-44e5-afe3-a5b504b7a38f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb75667e-4fbe-4251-a518-8b4fc0a12e04' class='xr-var-data-in' type='checkbox'><label for='data-fb75667e-4fbe-4251-a518-8b4fc0a12e04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-07-15T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-122.3 -122.2 ... -60.52 -60.37</div><input id='attrs-e703b7af-b93b-4410-8dc4-0d5e1e46ce9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e703b7af-b93b-4410-8dc4-0d5e1e46ce9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4d30e4b-1f7f-4bc3-8e62-491852e79080' class='xr-var-data-in' type='checkbox'><label for='data-d4d30e4b-1f7f-4bc3-8e62-491852e79080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-122.282745, -122.17581 , -122.0688  , ...,  -60.675873,\n",
+       "        -60.522675,  -60.36969 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>21.19 21.22 21.24 ... 50.24 50.2</div><input id='attrs-1726c75d-e287-48c1-b2fc-d539c10c1dd2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1726c75d-e287-48c1-b2fc-d539c10c1dd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28db5e3d-8132-4386-a41e-a4fe3a5664b8' class='xr-var-data-in' type='checkbox'><label for='data-28db5e3d-8132-4386-a41e-a4fe3a5664b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([21.18737 , 21.215775, 21.244072, ..., 50.28482 , 50.24335 ,\n",
+       "       50.20171 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 ... 283 283 283 283 283</div><input id='attrs-2073a151-cad2-498e-aef7-0949c2e251f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2073a151-cad2-498e-aef7-0949c2e251f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75ea5a4c-7734-41e1-a432-3e948fe5de57' class='xr-var-data-in' type='checkbox'><label for='data-75ea5a4c-7734-41e1-a432-3e948fe5de57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   0,   0, ..., 283, 283, 283])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ll</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 435 436 437 438 439</div><input id='attrs-e98ee656-09b0-4777-96ef-d84e81b4ff7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e98ee656-09b0-4777-96ef-d84e81b4ff7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-314c8e86-8f27-435a-93ac-7f308c8a2d27' class='xr-var-data-in' type='checkbox'><label for='data-314c8e86-8f27-435a-93ac-7f308c8a2d27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 437, 438, 439])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9086d988-2a04-4478-97fd-ead59307d16a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9086d988-2a04-4478-97fd-ead59307d16a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>nitrogendioxide_tropospheric_column</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.279e+14 5.024e+14 ... 3.106e+14</div><input id='attrs-08a93538-3c8d-43a9-9ed4-442963066df7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08a93538-3c8d-43a9-9ed4-442963066df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf051f07-6743-48db-bf5d-6010cdab400f' class='xr-var-data-in' type='checkbox'><label for='data-bf051f07-6743-48db-bf5d-6010cdab400f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.2789676e+14, 5.0237021e+14, 6.1511361e+14, ..., 2.7740053e+14,\n",
+       "        2.5191384e+14, 3.1055362e+14]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no2trpcol</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.608e+14 5.617e+14 ... 6.985e+14</div><input id='attrs-61dcf427-7a35-469c-9c48-1fc0321d3604' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61dcf427-7a35-469c-9c48-1fc0321d3604' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c59b24fd-55a9-4834-b6e3-23a399f6f7db' class='xr-var-data-in' type='checkbox'><label for='data-c59b24fd-55a9-4834-b6e3-23a399f6f7db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.6082549e+14, 5.6167260e+14, 5.6204234e+14, ..., 6.5825655e+14,\n",
+       "        6.7548454e+14, 6.9849134e+14]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>21.19 21.22 21.24 ... 50.24 50.2</div><input id='attrs-f58c0059-6d85-4cf7-b780-af9c9321c01c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f58c0059-6d85-4cf7-b780-af9c9321c01c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ca7798e-7756-47b5-9356-bb5ab1872612' class='xr-var-data-in' type='checkbox'><label for='data-0ca7798e-7756-47b5-9356-bb5ab1872612' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([21.18737 , 21.215775, 21.244072, ..., 50.28482 , 50.24335 ,\n",
+       "       50.20171 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-122.3 -122.2 ... -60.52 -60.37</div><input id='attrs-cc418d5d-8be8-4df3-95b0-77cc3f4b2b7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc418d5d-8be8-4df3-95b0-77cc3f4b2b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1729b7a0-c606-4e85-bfa6-27f6b5c575d3' class='xr-var-data-in' type='checkbox'><label for='data-1729b7a0-c606-4e85-bfa6-27f6b5c575d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-122.282745, -122.17581 , -122.0688  , ...,  -60.675873,\n",
+       "        -60.522675,  -60.36969 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20ab2140-8e27-47f0-b067-37e25c8e1eba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-20ab2140-8e27-47f0-b067-37e25c8e1eba' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>daily tropomi data at model grids</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 -122.3 -122.2 ... -60.37\n",
+       "    lat                                  (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    x                                    (y) int64 0 0 0 0 0 ... 283 283 283 283\n",
+       "    ll                                   (y) int64 0 1 2 3 4 ... 436 437 438 439\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 7.279e+14 ... 3.10...\n",
+       "    no2trpcol                            (time, y) float32 5.608e+14 ... 6.98...\n",
+       "    latitude                             (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    longitude                            (y) float32 -122.3 -122.2 ... -60.37\n",
+       "Attributes:\n",
+       "    description:  daily tropomi data at model grids"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paired_obs = an.paired['tropomi_l2_no2_wrfchem_v4.2'].obj\n",
+    "paired_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "02aa8f8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paired TROPOMI NO2:  <xarray.DataArray 'nitrogendioxide_tropospheric_column' (x: 284, ll: 440)>\n",
+      "array([[7.2789676e+14, 5.0237021e+14, 6.1511361e+14, ..., 5.7081149e+14,\n",
+      "        5.9785468e+14, 6.4125546e+14],\n",
+      "       [6.7514457e+14, 6.3476610e+14, 6.9058511e+14, ..., 5.8134932e+14,\n",
+      "        4.8917876e+14, 3.5947947e+14],\n",
+      "       [6.7086309e+14, 7.4024929e+14, 6.8229354e+14, ..., 6.3158803e+14,\n",
+      "        3.4523021e+14, 4.4847257e+14],\n",
+      "       ...,\n",
+      "       [8.0331162e+14, 7.3679949e+14, 3.9228205e+14, ..., 4.1725221e+14,\n",
+      "        4.7237195e+14, 3.8848184e+14],\n",
+      "       [7.7998539e+14, 7.1245521e+14, 7.7136734e+14, ..., 4.5094415e+14,\n",
+      "        2.7837158e+14, 6.0029832e+14],\n",
+      "       [5.4706535e+14, 1.0423989e+15, 8.2307787e+14, ..., 2.7740053e+14,\n",
+      "        2.5191384e+14, 3.1055362e+14]], dtype=float32)\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2019-07-15\n",
+      "    lon      (x, ll) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n",
+      "    lat      (x, ll) float32 21.19 21.22 21.24 21.27 ... 50.33 50.28 50.24 50.2\n",
+      "  * x        (x) int64 0 1 2 3 4 5 6 7 8 ... 275 276 277 278 279 280 281 282 283\n",
+      "  * ll       (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 11667814000.0 2.2299026e+16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# plotting of paired data\n",
+    "# 1. paired TROPOMI NO2 trop. columns\n",
+    "paired_obs_stack = paired_obs.set_index(y=(\"x\", \"ll\")).unstack(\"y\")\n",
+    "no2grid = paired_obs_stack['nitrogendioxide_tropospheric_column']\n",
+    "no2grid = no2grid[0,:,:] # time, lat, lon\n",
+    "print('Paired TROPOMI NO2: ',no2grid, np.nanmin(no2grid), np.nanmax(no2grid))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dddbb49b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zysdufhqLfI513pkPq+oRkZpqLCUOsFhKWepKxAYn/lKCTRP3jUWsVAeta3op2Oodi2WOOEN7TGwBj/++Cba22wUA+9G0ZDFW4U0ly9POz95yUGdKeAb01SE3yazjNOCnAeBvA8DHFNt+AQD+XQD4z4h1b/R9/6eq1YphtXpUTjbYyjmEmKoRxyVZaDmLrSaG3CLCrWXjfUq2w/uU3jft/WgxRZvjjAknlLEo4kRSaM+wuMhp57Tzecd1wsskIUdZsIPQlDKmU1y+coksr5Rg+daI7wCeHi2A5yi/7T1vhzth2yIevAAA6ricU3Oz4+UA9PVdZREe3o2SXAoAw08NOBXmMBjqceXOWPR9/0td1+2Pl3Vd980A8HcA4J0A8PsA8P6+7y/2fX/52no+q2dj3AV9DZmKpbJUgGtcuHNEuNXVXFsv6/5TmAecOkZqxHsqz5fjWLFME8aJpBYiQSOEtUnYqKnFYvfz1LRmlBV8/56D0D//FPzi8X+iigGPLdzUtGIxwQLeEVbqkL383V98Gb784U9tzx8elYHFdzh+bF2Py6bEd8rFn9pGotb9dLbgphF0pgU2ErgonxZzd0EHALgmwP/H4ILedd0TAPB/6vv+Utd13wEAH+n7/ruj7X/62vaxC/pVAPgVALgKAP/Pvu//YYu6eiu14kxZDNUU3zXn8B4zizrF2G7tKcu6W8OduWMRyylhFFupaolwKcFaWH9m48Ti98lzZxd/43psuZLvTDoWjpGqdxCowVJ8bN/rWyLYeE5BJMcx3gFsxcbZyuMBhP17DkIPLyePtxxTvm1dl8R36rpL2w3JusaRU7j4Hh6N913oH3Dbej/CqU3XdXsA4AgAnOm6Lix+O7/Hgtv6vn+x67pvAoBf7Lrus33f/6vq9XML+OoxZdFdk1SjX9KQS3HeNY6pzUiuPU7Ne15jrnJHh1trdLR2vdRmYbaIauvUYzWE05mNEzvEd4DK4A6QFmwaa22wqn/pfX9DlYQttkAHcII1HMsdZyynMrIH9/NQJhbxUhI3jQdAC7hrm1OHdZnajMLb0HmiTezqonwYVs0C3nXdXgB4ru/7rxe2/2lAFnDL+qK6ugBfTdZRMOXGfGMsmcy545ccp/a908aGW93ytWWu47PorCYWi7bWFb2mcIqFvGaO81Qiurh+EvG0Zl9639+Azz553Q5X9B9+64nF31/6weMAsFMU4+nEALaFOOWGLsWe44zoONN77Eo/hvh26tFSgPvsH21JXV8X38OxagL82u8nAeBv9n1/ptsyg39r3/e/Gm3/0xAJ7K7r3gEAv9/3/Ve6rrsFAP4ZAPzFvu9/vXpdXYDPD03WyXX7UNQQ39rGv5a4nzKl52XxHvAs6s4cyBHfmn25ubpLxGCJIKGS0GkIVvAgfLuDHxK3f+KbvwfueeimJWEdiAV2LKSpOPAd5RICO14uXVfuuj328P1LngXOuMTeJW79nhe1BbXHlNdj7gK867p/AABHAeAWAPgdAPgvAOAXAeAnAODrAeBtAPDf9X3/4a7r/nUAOAcA7wCAPwCAL/Z9f0fXdUcA4P8NW3FK1wHAj/d9f6pJfV2AT48Sy+g6jtZyVtbcxlibhG0q4ru1gK31UdO41a/LMztlvGO7kxrzFVvc0QHyBHiNe5ZrBQeILOE3fiC57Zd+8DjcdO8tsPt9mwtBTYnneL5ygOV5u6Xp2bipwABo7wTq2sVtK3bhd2v5uODQkBb4NymfnFlicvCs6/WYuwCfGy7AR6bmPMpSXPBUhXnLDxx3PWrGhmuFeM2s53i/IY6pPccxrNmrJhinEBc+dB2mfg9LBDgXh80xtgAHKBPhgc1DD5DL+0sfAYAt1/JdR+6Az996MymkgwgPid723/gBuPzaI4tl0lRtqQz1GnA7v65TX82BqbYb64R21hiuj1Daj5Lq41ZyHesswP+b7n/X/6fwv/zxvu+/NNQxXYAPSGtRwrlLr6OLukUI1mj4S68x9bFILZOocX9rZ1+f6iCQU5+pC+octIna8DJtGSms2bdzr73GXZ4jnkObsoZffu0RePcXtzKWxzHZ3Nzj1PkFb4I44Vw8aIET0YV9LFCDt2GZu6JPlzHbm9xpS1edWonWcq5hSux75vVl1lWAd1133TfD3jf3wNvgV/qXuvQelY7rArwdYwhuqQ5TnK9Z6qjnWsctQs9q2aXKtM5HTm1TOi956vgS1HW2HCt0SFOdn9yp4+LtpmAddqbHEIKfO0ZKmGunJZNcpsP83QDLbtcpK3nO9cgV4Dip2ZIb+TURfuri6SWRXivxXIAT7FZS7V9tEe4u7fUY+vsw5KB4DmPlWMnJA5NCOgeqb5A76L+ugnxdBfi/393R/wa8Cr8HfwjvhX3wX/WfGUSEuwCvyBQEd6oeUxThErVi4EvdznPLSbmoWzKutxDgJQML1meJ6gi4oHbmTi3XZMn1nBLgFsGmeccs7vExVLz15qEH4NTF0wCwbRVPWeupgcBacfOlFnCKmiLcBXg7hsqQzjH1PtaQ5Lifa69fSoBbytLWbdVYRwHedd3XfiPseeM/gz8Fb8BV+Pvw6/Cv4NVdfd+/1frY3vPNZI6NKtWQTF2Q59YnR7jWjBMqdW2qtX/JdrgOqW1T23Gd6/jDOZTrcspV2HFShOeWs2xrpyHLwSrWrHXgrPxYbId6xPU5s3ECALZjwCWhGoRnaDtwG6KNn09dD+n+pODaYuq+W+ZXj3HxbUe6f60Hdmu4MK9jSFaNfgl33WpZv7V1WSdhvsp8Pxx44y3o4cbua+BG+Bp4T78Xvhu+4U0AaG4Fdwu4kqk0kCVxuWNk7R4yi2hu1k1NA2vJei5d99SxajT2La933LHRWs8tbmOpeYpL4FyBXYA7JVhjwzE15wGvCVd3SmQG0c0R2oDUtZIsZJoyrFgH/rh2OB5kcGv2cFDTkY2Vf2IdBbWW1Ew1JQloc8R6XJ+S+7ZqInzdLOBd1930DbDn5b8K3w5f0+0CAIAr/Vfho/Av4DfhyvV93/9B0+O7AKdpkbW6JnEdWsQP4/1r0UKQ12gELQKb25+j5oe5dqKSEssQrlOt+zqEOHYBvj607pCnBGFKhE9BgKesiTG4vjXEt1QPqv1slZFcmgccQ3n0xIIQC3HLVHSOHs5bY67t+pBGixZIOWU440hJfHZOLhnrem7b2mGDY7NuAvy+7rb+G2EPfGf39UvL/3H/ebgOOvjv+0tNreAuwGG1Ritrx0APFddecpxcK7fFop0bo1Qrhp1ar60DB57XFmC7U63tvMRlnNk4wdbDIng9LtypyVCDLdpkbJhci2kNSyslXqQkcxbrNxbL1L6cyzblTpoTH64d3NBcB03H3wX28AwxHzhHiVhO9dWm3C/NEc9aYa45Zozm2mkG0LSUGEGmLsjXSYB3Xfeeb4K9v/Gfw7fDdd2yzv7D/k34a/Bp+E24cnPf9680q8M6CnAqJm1VOvw5SbXCtlIjlSP8NKOOmnJyyXHr5tyUSo6vLVv7MdJ+TFLlpTrWlndCY3UIHaQa991FumNliLhQykKbEt8AtECkptMam+OH3yDn9w4J13DbEYtvLokcl1ld0w6lBK9lkIIKhYmXce15mBINYHlGCBfjw4Dv21y+C6m+2pQFOMdQ9U71rTg0uWqsx+bqUVrWGKyTAP+O7o/3fwbeBXd0N5Hrn+x/G74AV+Cx/gvNrOBrIcC5TsycGuocFx1rVmu8jdYCm+s+VMN1KHWcWvHgJWhjwGvF3HNIFpsaAjxlCbPCWS5buBhOJY5wLvj1sZEa7EqRmn9bKqtFTDIeSJMEeKg7NY1aCskqPpTAjds1jdXbxfd4DNEe1RSZc7B218B6nhbjSUtrNweXH6h1iGFr1kWAP9T96/0/hM/Bf9z9SXabt/oePgKfhn8Fr35T3/efa1GPlRTgFqvBunUgcxonragbMwlJTizOkHUc4kNrGXSIt+FiFgM13pGcTktKgFvqxllGtPG7U2WKQnhOVqjacM+TFBucQiO+A9wUZtJ6bnvNtpw4jdudk+fOms4hkGqLxhbgUsJIF+DjMJQAB6hrQa3peTdFcl3Ntd6LJZ6aGvA94fpaU8xvZGEdBHjXdd1heMdb/zYchG/o9ojb/nr/CvwSvAi/3P9OEyv4SghwPPpuZR07izmNHEXKdX0INMfXJu6Y4sev1AuAwhJPOeUpwbQCdFVF4RQF+NyoeQ1LM6JjKOEa5tXOwSLAqX2o+b4DXFZhzvpPEazkgZw5z/GxSq3/QYBz1u+cgT2nPnNoA8fuK60CVlGqMTC19OKsSWtBvg4C/MHuT/afgS/Dj3SHVdv/zf5X4fvgPfDh/p9XF+GzFODYwl3qYjeHhnsoar/gYwnxnJjoGscviQFqWa+W5aewWIo0QjrebggBuqri3amboM2S5Tt2zQbYnl+biouOBTcnUPE6TqDnCnBJOG8eemDpm4zfb0oIc+VJAtwiqLVWf427eO5z4SK8La3DkmpSI1HsOgv1qSQRntr9qN1fX3UB3nXd7nfDjV/9j+Bb4R3d21X7/FZ/Bf5b+JdwEX7vul4SzDn1mYMAl1zKqQ95jhifasM9BrkJLrh9xiAn5qhWwrVcET6lgYGax5y7m/eUkFycnTxKB1ji/bWii0tKhoW3lideuEGMsW7l8i4dJ4hbPEBuLb/E+q3dX7p/3LORGjx0Ad4WbvBkjLawlhCaSkhfKTX7NCXXtiTrOleHKd+L0udw1QX4D3WH+lfgD+BE982m/X6yfxb+NNwCf6v/taQVvOu6BwHg/QDQAcDf7/v+x9ltpyrANZ33VPbUHIZsvKfo7gxQZyqzKZ4Xx1waVw7po1KSzARg+paFdcLF97hoM/2niIUpxiK6Y7Dozv0Glohv7tglArxUfOeWEZMzJSPe18X4MAw1ywFeboEL2wvrVoWhE5NZpiPLyTqfuv9TvHfWZ3OVBXjXdXu+Afa89iH4Nri+s7UTv9t/Bf4W/Bp8Hl57W9/3bGPedd23AMB/BwD/BgD8IQCcB4Af7fv+ErX9ZHputR7e0o+t1SW2hCm+sABlc2bj7cc8R2mqr5ip3gcrKSE+1nmWDnA89vD9O7xg1ll0cvPeTiWT+9QHbUrbeOn8KKscd79KrMIStTOeW6HOJSdMrIb4ppZZy02FKVgGXShaZKlfB4bONs99uyRBrS1r7OzXLaiV8dwKdy2lPpA2ETFVxpT7j7kGtFXk/wDf9NrXwm6z+AYAeEf3drizvwmOwTd8FbYs2xyHAeCpvu9/HwCg67pPAcAGAPxX1Maj9ZBqPLTYZa/WR6xGwz5V63YOJS/u2PGzpYK7ZoxoK1o+ZzXOWVs/TrhsJXLit5/qfSkFP3uU2B77/ZoSmmthaddTzxd3PDwAEh8zTNcXDyilxPf+PQcXf1++Qg6kLyj9BqbivWtAHYNaFsd84/hv7ruvqb+lz4CPa+0XxM9Qbr/CItBrJZ6bMlPzKqgpbObabyyptyX2nZv+K7WOO46GVRGu6zDoI/E/wG/8/b+778j7b3u7LvYbc/fv3wJ/9Yuf/sRPdl3sFv5I3/ePRL//VwD4a13X3QwAbwDA9wAA60Y+WM8t9+FPfbRqfmjizlSqI5dqcObYiHKkRnil9Wc2TsB9F+iM27Wmt6LqQ9UrV3gDrK7ImwtjXv8pCH1JjOP18XbUNq3PY6zrJFknwzqN+zj+5tSOMw0DSpRQjGO5AZbFd/wbC/GQqE0j1sYQMLhOOOkcJo5nj69TbfdyjtpCVhocSh3DUodVEd7SMyo9t60HJFuIllUQQlb37VpW79DHqz2rjSasj6vPXFh3QZ7JZ/q+f4hb2ff9s13X/Q0A+J8B4AoA/CoA8C7rLWPAaz2MQ3UUpiCy5hqPbIlpqi0EckY6p+Ain0vtUXLpmSuZv7vF+7RuFl9NXKlWaE9hIGEKUM+Qdho+yiMGL5Pmr+eyoMdIAhyDhXgcC86JMc181Tnx6VwyOaku2gzppftzpCzmtQVtiRW8Rn3m4u5eMkg0VPtWMyfOlEPjuG9uK2s91R/JtWanvnmtRedU7qGWX/hbf3GVY8Af+bv7jrz/T7z9j2bt/9Tvfwn+6hc//WOSACeO+dcB4Lf6vv+75PqaArzlw9ZKhGsa67m6BQ1JSXbJWq5BU5/be0gBpD3neLsxBn801ySu18lzZyctIGsPEGgyLQ9xPdZh4EOK3Y6XSc8snvNaEjtakYtF+OUrl9TCHM+jrYlhljKxc3W5+rFTAADwM9/x5xaiNswRfuriaZOA1sAJfI34ztm2JkPHLs+RlACfSltUc1aYORsFatAiyS/VVg9t6Z3T/XQBzqMV4F3XfV3f91/quu42APgFAPg3+77/XXLbUgE+xMOVaoRzP2ZcchzHhiTcwrWtFY/DHVPz8UptU1sga61qrcj5oLdw2ZLQXvNQL22HbJWsvZzVVTM9Us1neZWvsYR0PbnEdxosVuaU4A70zz8Fbz75DOw6cgcAAHz+1ptNIjzUiZrSLCzDMemxAP+tE98F+2/8wI7ypalE4+NaY50DFlEdth/KnZ1iKBE+F4t3TE57MqU+3Fymap0CqbhtbnlsNLAmexuDOdzrxx6+f9WzoA8lwP8JANwMAF8FgP+k7/snuG3NLdYYD1Iqs2wuodEeI0uw9phzGBXl3IQAtmLAYYPevkUdNNvgRn9oITHUcXKus+RF0ML6LLkCx+us5zL3TpwGKQlYzbmtQ5lzQWuh1iZs0wx04G+JRphb3Kaxq3ksgvvnn1r8/eaTz8Arj78En/3wPwEAgHseugk237e5tP95sAuyeGAA1+XylUvwxHf8ua2yH78ezqD2HkAnOHNinbHA1CaRS4nv1sJVuhY1489rnYPmetSqt7XtmlpCylTm6an15Vr1fywedXHeHrxvWM5562lnuxkSqU8cmEPf3rHT9/2f0W6reuOm4PaZO/0HtX+rkefUB4BK7DP2da2JtpGtdaycRhY3elQnOl5ecn84F9ZWDBGTxTHUR4SLqa1d9pSQBDU3MFl6Lhprd813RQMXD69NmMi1+1zyNen8Use0fG9yso4Hy3MsvgOfffI6+MLnvgIAAJ/68CtwD5yC/e/bBIAXzccBQDHp16zbpy6eBgBYuJmff/Ys6em0bfmWn41c0cYJZ0owStd5TGs4ZujBgBTh2kjZ43GivCGS1wWm2m4DTF9cWWeFsBqOUliEc7ztVK+rZYqyKZ3D2AMW64rogv5H//iB/s/+va3py6bUyGkbDUtHrGXcJseUrmlLNBapMa+FxcV0qmjEt5RMJaBx4U9tlwuuX6mbtSScpn4/c2l9fmNfP01YhxSmoH1uNOdnuRbaZGepRGcAwArvwE/f/vjS79ve83a456GbYPf7NuHUc8siPD4edlXH7ugA2wI8eA5NKZ5ZY4HNGexoLX61uQJK6oHFM3cdqGNokuLVHsBYxfZ57hbPWgPeqb6KJaGv5ZhDMqd7HF8bd0HnyUnClqxTSoDf/Zf+m1rHqkaJ5dtixZCOa7WyaMpfJTT3qIar7JBMJZkbJlUvq4WwxnuQQ4m4k1x+5/J81WAIAT6FAQ2NyNYIbkt7bSmXOwYlWKWkZxRWAQ6wLcJ3HbkDugN3k4nc4sRtwbINsG3tBliOI28tvK3HSM3vXWt+87FjqmPBXmPQIfd8WmaPn6OnmJOH1oV8zvN4T/l5cwGuY3ABftddd/U3f2e1YxWR87GvbcXA+0nxqzllzh2tlQeA/kCXxmQGpv6BHVq0tErKFcqjRvZzRvtLXYmlfZw2jDmApknIJw26pgReDQFOPdPUMbXiME6EFovwL3/4UwAAcNO9t8Arj78Enzj91R373vaet8OdR96Cdz50z4513cEPkceTMpinGNo6bp2SjBOxlDU3Je5bwwltTb0s1m5tPVJMWYCnmHr/oTUl/YUayXZrWMCnILpT5CSSqw2+Ti7AeVoI8OTbNYXGSNuJwdtoyBV9KfE9dWq7Q3GdymP7Xicz+G7efi15T+T2KFnXLKI1NxN3oIVIbVG+lfi4Wpd1al/qN8eQbndDDmi40N9Cex1qXDP8PKa+A3gd9ZtzW6fqiuPfrW0+d/6UG3qcfTzmiRduWIjw7sDdALCchO2Vx1+Cm+69BX7wXoCf/eHfXtr3C5/7Ctx55G2s2I65/Noj1+qy9TtHUA39TbQKb/w7Frm1rOVWLO7mlsRouftjNNemRHxPoV2tIRpLB6NroMkbYu1bSVCil+tnWHSFJcv52FrFwtiDBGMf31FOQzb2Q62J0aXWUdu1jo2kaJk4agqkxDfnLhlcIZ944QZxipyWTNmaOgU36hrPa+3Ycet1mcJ1pBjTfXuOx+Sul2TV1gyUSsnWUvUpCZeg0Iib0LYCALz7iy8DwLYFHBNbwn/4LXY2lAVPfPP3LKzkn7/15q1lKDZ86mivIbdsiPMc4lic51kNa77Wq83KlNpnCupblnKXtgjwmiJSk3uHW2/F8o2v0R+gzm3OghI/S0NoLup6uQWcZxQX9DAP+NginMMS41eadKekbpgxPjSlczWnwLGMoXP45pPPAAAs5qYNcAJcGz9eg1qeFKGsGvVqnSRwCHBHovTDUipWV3HgS0OrRHRTGtTgXMPDMmkfiRr5QnI8pbTW183b37WYf/uVx18CgK0M6ACwyIIeoxHg/aWPLNplAFhqm+fCFBKtpQT2UGJ/StndteQOiLWAm/oqMIZwmio1RLh1ACJ1f+ZMy2eJu1YuwHlGFeAA02xcUo30FDqKc44NlxIGcWze/i7on39qIbwDQYAH10ksvrW0SPw1xLE0aOJa50xpbPhUrsMUOoe1KLHkTun8a7s8p9zTqZAZjWgP25XGgu/fcxCufuzU1rzfgvAOaAT41Z/5gaVEbXOzfgesIrzm+Y0dL04x9lRmJYzdxmhikiXL+BT7zSmGattz4r1bCG0pCVxqXvdWx2713Ejn4AKcZ5QY8KmTaiTGbLzn1EmXLGWBVKcGZ/CNLd6x6IZFx+6GpXJT7nEtrqPVhdlSD+tHLBXPOofnqAVTPe+p1stCeOZSycqmyBTybmAxHV9HTmTXSsSGkcS3hTeffAbgyWfg3UfugM3bDy6s4QDTEZS1aGX5xr+HvG7UMed436bS7tz34KNJ4RVvE6yynICaYqwybv+HuvbctW1lzaas5dS94O6PZd5yx0lhesum2HA4dbF0aqmO4JawfhGO7bt5eUXUieMSDHEd0Kl8iDXgDrhlv5gxRp+pj0ut973FHOJjDkrM6Znk0F4/arspiF8pXKVmBm7O4k0lZdPk+4jrhcVSPP1XzKmLp3e0t5evXIL979uEm+AUwJOvWE+LZPcP/RwAAHzpB4/DO4/sXE9NgQUwLYGnGcjQ1lfrSr5qWN4fzlOklCl5DmpFuKWslt9bCapdmvq1rXGMluWW1B/f93A9amSUp47lTAeTC3pgriJ8ThbpsbDEKPICPJ8hrN4xofFLuY9q6pJjNaQyMWvFjvXaaBpfi7uVJc5Lu4+zTMt8ACkBbjm2NhfH0Lk2SmiVM0SyfnMCHIAW4SHJ5Zc//Cly6jGMxg0dYEuA33TvLbD7fZtL8eABKuN4KxEuuU+npgyjsNSzlgC3Cn7LPjUpDeWr8S6mQj+o4w3Zp9MmXZO2GRNpQHWsvvGQAlw6Vsk9KjmHlMt9C0MIhbug87gL+oQZuwGrRW7SolLhDWAT37Wsn0F8hzIptK65lBVMQ9gndz+K1qPpmrJzYuFqWsq112Cqnj3c81jLMqQJ38HH4p7/1LPPdZ6pjqC1o13D2p0613i7FCX35+S5s3Bm4wS7Pp6GLOadD90DcPrxZPk/dd0xlQj/up89D/2lj7DrY1E8ZNbwWttpGUoEU/UeKm5b83zW+NZKIRmp7acUHoMtttK3Y4rflZJEptz+U493txofciixhkt9kFSuAcsxnGmRZQEHmO6L5rSBiweX5qy1MrT1G6BeduPS41sESoz0MWyNRVB7e1FGjWSBOc9uzowEmkEoS11qZjYvQUqiJr2nue772BqKZ5nYv+cgAMBSwks89zeF1gp+6uJpcf0Ycc01KJ16SzvfuOXYY1m+a39jpWebsmzntEW5A91Ds27fvly3+hqW49I+T+17lFsfTUI27jpzZVjq4xZwnklZwKdqNVp1Utd96EY/Jb65hEG1RHtNNKKh9kdfKi/VyacYcpST+ziEZdqPh5MmFWepeU5yRLFGPFrei5xBAO22rWJRA3G5lLVc4xFQUjfKvbp//qml3z/4U18Pn/rwK2JCtp+67hj84E99/SLeWzoeZqyYZ41refjWSN+WVtnONZS4sbdy88fvY41vnCWUi2q3aoSDTYGpfee0bW+JkUE7LdhUrLEtY8NrWMKpcixJ40KZzjTJtoADTK+BmSKtXNNbJGjQsPfo1eJsvQHcUYo7FznisxTJ0qaJcW1tTZAEhnTsMefKHHqKjVUl5YrJUfpMcs+99AxqYstbvc9DJoeztlGUFxEWVBqhFXsfhRhwPOVjgBLjt73n7XDPQzclBfjJc2fF9UNg+dYE93xKgMcCvtQCbkUayMDW9LEs5Jy7d0soTxINcxDfY2H1qKuFdl7vltN4tYrBLqHW+ebWz3p8t4DzTMoCDuDWLA2tPxYl19/iKhpDWSNi64MmORu1TdwB4eIxc64nJWQ1H31udB7z/T/62tYfh7euTY17LlkarZ0Wa2xRKVOMiRs7brAUKWmO9Cy0yiRMvQ/ad7TUFTVV9hgi3BJHG+rHie/wNye04uXH9l2Cd1/7O572MfDd57emFYvnCr/zyFtJ8b2VG0PcpDkWwRu+L5z4xuVaRaz0vbPCnZdUp6nEgtfAMrCMmWPbPQZUm0QNoNa+npz7c8vv/pQtvDUyvEv9NNdf86b47XMRPi0s98NqTYuJRXgsplNW78BQLuickM1FciktEd/YGlAqJLhMrdwUKGNRqy5WEbRO1HQppdbVELwaq3mqLnGdqH1rI3kltL5mxw+/Acf2vQ7v/uLLC+v3m08+sxDhbz75DOx+3yYAAOwCgHceATh28ENLZVz9mR+AXUfugA4tD+9jjetYUobG7Ty1f4Cbn5vaFpNThxbTllkt+NRzxj17Q1i/5xC3baX0+1X7mrQI7dEgDfJTU2pNidL+R+vnesrXziljtVrDa8zd2qVBY9VsAdUJaJWEDVPjXmo6v9hCkkr6FLYtqR+1b255XINduyHnRndTseHxslrPq9bqOiQtO7XaOG9NzLhUhlbw5oqzWuI47gRp4+FbWsq5OFZ83FQ28bi9jQc83/3Fl+HNs8/Am2j7IMLj6cPg1psBAGD/tW1OXTwNm7e/a9ti3n8SLl+5tGjH9x69nryWOeQOLGrivVPfnTMbJ5KJ5OLjcVOr5QwEtMhgXjogMTbhmYqfgfi5kAayhvRqsTCVgWwrmncSX/sa37Eh5vy2UEN8Uwxxji2T2znDUBQDHjPXhmhOtLJcWj9uUuxiCVKHpcSybIEaYLAcG3f+a328LO552oaXe5Ys839r5tSc+ryopWgH/FpbmaSReG04Rby+dt20cPUbw708psTSwdWbs2pSs0yEmO+rHzsFAACvPP4SAADcdO8tW5bsA3cDACzN2x1nS4/XAcBiOZ7nu5W7s1ZMSd+SIUSo1Ro+xnzdWjTtgWb7qTOEdb12+x0PTo09QNzyeUjlfRlTKKaMBCniezd2fh1MTn08BpxncjHgMVNya11VWl5fizs61eEomZol3m+MqchicjpTKWtirmjI3S81T2nqXdVOcaER3znrp9SW1Ox0tX6WuQEfLp+CRM3zzhHf4e9aIRm1yO0sc/W3xP0+8cINsHn71t9LMd3wEtwEz8DuA3fDqedehPPP3gIAO9thLLQBXrz2//RmpAjg5GnB9T62fuOwpxKPrFT7P2WxTYE9Q2p6Wq0bra7TWNc/FSZTC01fYCwRXjKvNsDWdRrK2o2PQyXXHSvZrpOHt7zOjo7uVDq7AMPFjXHxcrXqY91Pe/3jMrXC1TqHJLVOK75zRpXHEt+5wl97X2t7Q3CdaezeaUGymNWIJde6vuNlY7dHNVyxJShX8zAgeWbjBJw8dxaOH34DTj33IvzIkTsAHv/U0v6vPP4SvPPIUwBw247cHJevXIL9ew7C/hs/ANB/Eo7tw0IcFsduSc41pAZzW+YPaTXd15jEyTsDKct4Te+PIaj5rR6KqdWNC1+y1rNU1M4JfK4tha8krltnmnfaUM0FPbDqL9wUqTG1k7ax1VikcqYpiztVta3gnFt4Cs1H3eommyPCJZfcFKmsmZbGWvOB0WbspOYLp+pXiylMf1ZDgGvc3XPFYsp9XnoftM+m5OoondvYAjxgFS7xOglKgAMAbB56YPF3eHb3Hr0KZ77vHXD1Y6cWbugAAO986B74/LV4bwBYcj2P5wvvDtxNWMN3MqYI5Vy9h4yBLjl/i0fZEEjvE67rUMIw9e2cmkCdI2MMQlDfc8vg9ljWXGv9uH1b5NqxUHJ8d0HnaeGC7gLcyaZEgEtug7UEeA331RpCICfOS7N9+KhJnf8xRkutIpxbX4spiO8alIpcjpI4UY0Ar/HuTUHEBFIiu5XVHsca9pc+sjT/91LiNVgW4IF4PYDOmtxaiFMZyc9snACArYRx+PeQrJIlnHoupyByuQSKGlrmq6hdduscINzxrMccup4pxo4R1xz/5LmzTWPBSwYHLLgA55l0DHhgSvGbTlsscY24kxXPF57qBOaM4sb1CvN0D9GZis8zJ5FbaEBPnjsrnnfuezbEuyl9BDjRPcRHdu5tk7aTimM+OXEcL7d0uizHL2FqHUEtLQcL4vekO/gh2H0QAPpPAsDO5GuBOMP5FOK9pedj89ADsHlo5++T584CAMDxw4NUcUHtbOZjhnfleFANQUnoyVgW+9w8EEOSGyoX/z32MzK2O7VWfANsX68W/RlN32Xsa+XYafJ2zb2j6+jRfCg5i3joKGqEeOpjgNfH9aIs6lqLFRYBOOY6NL4apJH+sHz7vZGtklvHXa4TZemVPgZTarBrtRfUcxK7so3dNtUQlRaRLLmO10LTUbNY2fH7Oab12zroEdAMeljh3tf++acWGdDjNjQW5NygaFjeKrs3NSDJXYPY3T6wLbynP/0WN/gao/1OYnf7loPHUxzoouLWa5QJoG+LpnhdnPrkurpLfTVPhra+dF33HwPAvwcAPQB8FgB+uO/7P6C29ZZl4szFfVbq5IfOhNTZ07hCpsQEZ/Gj6mlNVkVtQ4lvfK54X+lYVMy3dtBBek6mMPcmVa/aYnjv0atb1x7oednHfoeGFJIWQcx1PFvXIfWeju12rklaFVMaB47B7eTJc2cXrtgUsdDG3jcpTyWrwMuxDHP7hGWx5RuAbl+11IwXT52r5ThUkjdq/xp1TyWUayFyS2lVF40lPbVuStepFVM7x1Z9F4tALuk31Kw/118au2/nbNN13T4A+I8A4F/r+/6Nrus+DgB/CQB+mtq+2ds2tqVpVViVayiJb4Dl+W6lkX8uCZr04eDWcR3TXGsV7vBoLRi4HjmDLttuUMvlckyh0U5lS4/bEG0naKoxm1OLuwzgeuWIYmm/eJ3FrVRTnzmiuQaxyMYxzyHWELuiA2wJWCxi4xwRKahYbLycEvgSeBqxUB61v0Zwp74jgdoW81xXdKkeqTqWtmWa/afgZhyoLXTjcysZkKTKW2eGHpDQTKtaUnbJ+lSS29q4rpoFuwHg+q7rvgoAfwS25/skN2yGPyzrC/cRs1omcMdH0+hTnX4tnNXLIgS4jqz2WLAh1y8lgsLxNzceqPqxkigZ6eUGHLD4PrNxAu67sL0NFXaw9KG+MJ22Z6odN1wvjceF1WKeK6THEt+cO3mumz1GK74BAD7+Ezcununzz24LVO13dQoDGFh4lwpLzfej1AKuFfrxtpgc8R0fV9rGMtgrDR7MOTlXitz3MRVOMnWkgYLSxGzW/WrBtXe1+hzWcuYQ1ufs5MC3vgV33vxW1r5f/K0e4IvwbV3XxZnJH+n7/pHwo+/7F7qu+38BwBcA4A0A+IW+73+BK7N6FnQOF+Lrg+aDFXcYpBjw0HGwfkwpS5PG+mSJVQWQO1mpjqbWGmZ5d4IbNq6D5Lqai1bcD/XuxwN+6zQX6dBorN3SsqmicR3XDu7lnLPlHaWeaapec7n2Kbhp2ixok36G48THrm39thB7h8XLALbPhbounMdBaXKuuQhwSVhznnTUttT2q0oqjGZq10CaSaWGGK4t/GvRKsZ81bOg/8Kfu/v9f/LmvCzo//NvfRl+4MJnxCzoXde9AwD+BwD4PwLA7wHAGQD4+b7vf47afpC3yTvAjgTuXJTOv2qJ7cakPsRatOeQqqPU0QZYTsB234OPwqsXdi9ioJe2EazquXCCN2bIrOvSx9LboHpwA0dSEkNL2VohWVvcUx1MblCBqhf2xrB6zYQpt/CxqHeLWoZduIe4ZgDpWOMacGJSSh5Hzbwhlc2VmTovzvqcGizQDghQ63Gme+p42OMg3HvsyYLfZ60FOGcge0hSoTESlHeVs+wFNYVrIhkBppD7phXep5ks9wLA5/q+/zIAQNd1ZwHgCACMJ8D9YVkPcmMNc4+VsljhjrLVVZYTGlKHM5VtmDomVXe8LGWZj9+vHZa0SHzX/ihZ49VbxEwF4fHYw8vn7W1OW1q4anLvQE78eVxHS6x7Lrllntk4wc5vvffo1SVhjZMuBjTim1o+hHimSCUjk1zAU15Hqe8Ltihzx7AIb81yLJi1xPvF9Y6t3xrie4//DsnYtDlRNHkcSgRwLTQhNaUMLUapHBm1vBq0ySanIL4pan/zOe0yBXE/9vEdki8AwN1d1/0R2HJBPwYArBv5NN8ix8TYmdJTH2PKomAR4ZL4tFjj4rpQnRZrbGsoJ9WJzZmSJoYT6Wc2TiQTF4WkTfH+VCKnEizPXUsrdcgS7cJ7NaBEAgXXBlgsWNrcErlwYiQMloUpuOI4b4CdQjBch9ABpN5/y4BErmu1lEE91S5RVtpQTungbM3ka9K0YAHKPVxL2M9SBmU1x9ewBOpbk3qPpHCvqRMPPuTuPxa1hLC2jLnc00CLubgdR6Lv+1/uuu7nAeAzAHAVAP4FADzCbT9YDDiAW6RWFekjxLkIamOnc5JCSdSaWzU3LtCC5dyo+FHOfb1mPHiNd3poD5mxB6xWldadUWvM9RAdRs05S54C1EBYGCCbavw2N+BItSucENe2w5YpwIaKD8dYErZZ67R/z8HFfO4cqTprvlOU8JbukfXdqhXa5cwvGZ6FWudmmV4ste1QiWyngMeA82hiwK2s3hvsDIqlk5jqCJRkO5f2o+qYqkvoTHIdyCFcNk0WckWMd80PR4545TKPDi2EXXi3QZNUcEiGsMhpEp5Zr8eZjRM73mfLdGKt0Qhval3clmrbz81DD+zwCsB1SVnRa84JTlEyGMsJ/9jtfP+egwAAcPnKJdi/5yD0zz+1WNcduBs2b4fFeg5tUrn4GZPOKcd7a1UY29Ifx/LHv4eoz9TFv5SLJjcxK+6nTMH93FkNBrWAA3jnd6606PxJrttTdAnDH7ohO8SSuAlWNOndqv3BiEW09Z3WxoFLGU6lsq31mXqnYqpgF2jJfbXkXbHcl9aJoVKW9pKkcxrPlKkIcYsXDTWImRrgjLcJ8fFakatNolZTlKcSuQViwc3FdmPxjYU1FuGB7sDdpAh/4oUbxOnKOEGe8tzAy2I0gxLaAXSqDiX9g1Vo58cYBBh74MEKNcDPiXBt/6hV9vGp4BZwnhYW8MEFOICL8LlR2uGTLBI15icFsCWA0wp+S4dDKrsEKeMy1XEfQoTXen9bJGTTWNWpbah8AlPsZGg6QWMkBdIm7ylBe06ppG25tBC++D2WZj3Q5HzA1G6TNMI7riPVzuMyTp47S9YzR4BbXNutaObcTpWdEuBxPLg2aVtsIY9/x8vCMSzPQk4Cs5xEiTnUbt/mLMxrfa805cztOknzfWMBHlu2S9zOqTZ8LoLdBTiPC3BnFEo+ntI0MRy1LF5SXazCfwpWp3j6j9wPoSR6tXNqlry/ltgsqYzcOmjd3qnrO5fYca4jZal/7bj8Wu9PKklU6rhT86zBYhQPCpVQM99FSBKXQjNAYIkVt6LJ6m4V35wL+xju7jFYoAdRHwvwQBDiQYRz86rjdamB55reKdzgYmrQcaz8Dy2wDGbia1RrcHGu146j1C29VHxbyxgTF+A8KyPAAabfiV0XuE5szQ6n1iWwldWbqksLAT7ElD41Po4lYjpXhEofIM6tK8eKDcBfo9LznnObZTl3a4iA5rrUcknnOuAtOuY13eglKBFe61ilbVLcdscinBLMWktnSwHOEVvYjx9+g3X/1sJZw3PEd24COCp7eqgDZz23ZF63PjNDC7fUs7ZqQrIVq54gr0RAx9/9EuOBC/DxcQGOmHOHdlUYqpNJkZq+RBJUFpfzgNShqCH+czq71Ci2NY7VKg4lMUZ9cChXLaq8mq7plGW+tPxcl/cpiG+LhYOrr/Y8NIMlXB0x1szlEpp2QNOOWVzZ8fal3kCx8AntRKkgHWJGhlKw+zyXnE0i9zw5kawVvlystEVsp/ZLCeLSAQPr8QB0oWIcNdyhtfHgLY4/Flprf83zs3o3zMVarskho+1b1PLcmzouwHlcgDuDMLa7NRWLW2LtDlBTqeR+0KwDANoOjHSu8XUJf6dEFXV+3IcpN+mZdlsNqRitoZnaccPyVrF4qSQ1ta+F5b3OFeCSe6tUp5KBPw5qQBDncJAGMriBP2yRnbogT1F7QJi6RgFOHKeuYa4At+4b0MSH799zEK5+7BS88vhL8M6H7oGffPO2HceXXM65emmnitM8f7mDcpb3f66MleNj6GR4oYyp3q8SsTxkMtzauADnWTkBDuAifMpIlukWnVNMaSdMM49pXHboCGuER42kb1qLIXcfamQgx6TOHd97S6ZRbnuuXt42zBOpY5XquOV0qKnjSQI8VW5LAU6hSaI49qCoFk2Ij/ZcNGFRLUOnctGIeGyFD6Ss0lyMd0z//FPw5pPPwK4jd8Dnb705mXxNigdvQa5HyVTFWk1yhbBmW6kMjTFiyslJW9FKiNc6Rm1cgPO4AHdmg/XDmiNGNVAdQu3xsJDUWKZLsQhwjOQeDqCfv5IT4aUfe84FPMe6is9VI5qmkjV81dEK8FTG5BpJhXLFN0eLjM4aC3gKqp0bQkyVHCenrZMSDLaOI69NLMJjyzaXxRwARPGNt7W6qlsGijkobxPp/asRhpLLFFyp47Yypz3Mbdus7WK8/1y+lamEs0MIZCmzupYhBboLcJ4WAnz0N2kK8ZVOGSUd0tCYt2rUOct3CkujxwmOnM6LZnlKaMfEH4CUtTpVPiUOcj8upfNpctmiSxKD1drW4Ul1NsP/JW2KRXxbw1w0HWZuHcWrF3YDbGz9jZ9Zy3WgxFMqKVjNBG0lWHIHcMvnJL7xdYuFMhbY4Xf//FNL83zj7eIs5wFqyjNKlEv3v3SwKWdwkxKdY3o2DDFAy51fvFzbPuUej2rb4vOe27dP6gNYZnfRGi4oSsXzlKzjTn0m8Ua5CJ8vLT7QNT+2uJOpLZtqdDVWO2qZNi5OOyJfKl417xtXdiqWXIpdBtj5kavx3odrF4uZuAPRwmrp8GieZY2lpnXH19q51WyrFfQLq3ckvvH2uc9sbFE/dfG0KZZ5CrHjufd8yPe8xbGO7XsdLl+5RFq5uwN3L/2Ot4st31RseLyMSr6WM1WoBBZx0jfeOtAiYbVop/od8T3WDsDm1MEyWJfzzGm8Eqh9pk44hzMbJ7KS0ObEapeK8dYJbJ15MboLeqDEHdUZl7HFTa3EQyUDAbgOOOuv5pipj7xF3NYYOZXiulMjx3GnKzf7eFwWgO5albrZWXH3dh5OjA4R18iVF5fbut3Clrz4eDjfRAu39lMXTwMA3S5qkm1JFu5a8cKSxa2Usb9LGGp6MGyx3r/nIPTPP7W0TSy+KYEetsciPQa7pwMsW9FPPffi0rrU/Uy9P7Xvq6acGm29xRsjtb92v9KwF62g1nyr8D6rgpRjRpOXpnbfiqsLd/yhcBd0npWMAadw8T0vxu7otBDgpefEiQ4qi7Xmw2ed91qTCE1CI/St011pB9e04lraN96mtFMlkSsynfrgwQ6rlac0lCa3AysNMOVCZfqu5TKOqWEtbUFufHHt71l83YMA37/nIOs2HotwSlRjgf7mk88AAMCuI3csLe8O3L3Y9vO33ryjHBxrTs0pnkvufc0dqEvtkxNuRJErxrl9x+g7aZ/zFoNhU/0upuLFhyCnn1YbF+A8ayHAXXzPi1YfkCFiv169sHtJLLY+F4DtRpWzjlPx3gEuE7v0UZNEryUjuhZrOIm2E9CiM2O1qEv7421bdDRSQq3kmFw5NSz8OV4Aln1KOuJal3Ercfk1xIiWViJbQ0235VaeD5o65OwbwIPBXLbzQJx8LcRqc4nWgpgOGc7D3zGxCI/X7f6hnwPoP7mIJT/13ItknfAc9SVoXLzxeo2llisP71My8FpLnKba57GNFxh8/WoOVGsHP8YQ6aHfYpmlpRUlbu6luADnWckkbM58ye0gaj7uQ3yY9h69CifPnYW9R+XtuM651vIe70vFekou5/F+oaywvfZDRe0blteY/kuTyERrmab207rMWaH2L7WCWo5Vo6NR4mZp6eBy98HSEW3VsaLeT21949+1LNBcmRzcoMA6YR1w4cRMSshrrnWN668ZCImTowHw1u8gmgPY0k0t6w7cDbtgS4Tv/qGfu7bwvQDwFED3Xtg8BHD5tUd21Ieqv1WMU9c2db+o/XOOM5RbvPZ5HSLUpSatBpGt97QEbARIeeTFTMEV3BOvrQ+TE+CekG11CR/1Wtl3ayYMkqYrq/kBtY7yA9CW79R+1EgudoO3YHErp84t7vxYr2fL2DRtBym1ndRx2Xv0Knz03pcAAJJz8qbqEMqr5WppHczRlIXJKdu6j/Y+DuEKyT2vrY9dav22zklNHb+kTY7bRqvnRdg+LE89i9yzUtLWW66/lPk8Ft4L93KmHMrivevIHUv7Xv2ZH1iI8O7gh8jjXr5yacd0aBjrNGXWsJyWA3QA9DOjbbM0AzUpC/cUhfjYLuG5baLk2adN9KpxM3cx7LRkcgIcwBOxTZ2hXSMtgpvrBKY+qNQ+qfOsGSPHCdzcbOUl21Pvn2VkWPqYSlbGVGcu7GPZL4V2vxzxHZYfP/zGwrJ1bJ9u2h/NNdS6f6fKSx2nFS1d9luUV+sZw/etxPI+psv5WJRYTaXyJC8nAP69tSSrs9AduBsAuZhrCML7lcdfWiz7uh/auR22rAPIgzDcd1jTbsfUfu8tg6O5njg57//UBLd1QGQMUt+rVtogJdJdiDstmN4bGOHW8OlR86OisZjEnZeUENd2dHJiA2udt/TRw8879ezH+5eKb2n/Ulesxx6+f0ece6nLH8WZjRNw34VHR3H101rmzz97PRzbdwng1ptZq2KJdd3q8pzr6ju2lbtGuZoBi2BBlTruNcIfUuVx7V1oN6X2jppuykLuvpbBUk74ap+5HFKWb6vrteabI5Wz5YZOz+0NsNO1HMd8U8TCW+LNJ5+BXbAl9ENSOC45HAf3vrQm1e5ZBgS0aIR+qi3WXCMqgWILSgZnS/aNRbQUEkSRM92Ydb94X9cfTksmLcD94acZY2BiKNc8S3n4w8R9qMKHk7OcYIFTwyU5B+s91STrqOVaZRXfAPw1khLRYfd8vH88x3FcXviYj2F10Li7fvDxW9h9OPfGGh2jmp2r3LKGcvcuOdfUMkzOs4YFJ7d/ENhce6bpkEviSRLn3HKtoLdYfKVrT70ftaylmnoA0GIcn1uwGHPXBmdAp1y8Qyx4EL6xEI/d0S189snrFn/feeQtcptXHn8Jvu6Hrrmk95/cMQCAz00zNVlAei+HsIpzaNsj6pmw7KcdEIjh3psWIXcBS3jM1Cz6KUr6R9r1jlPC5LKgx7gAnw65jW/KPa+kDMsHSXIzTIkh6SNFiUXug3lm40RyCi8rQ3wgpHm/qYQn1HZ4fU6m9Fcv7GbPV5P8LUUqPpQayNF01Epc5jUdopRV3MLUXRQxJfVNeTCkLLNWcvfnPIVyBzZTGblzsIqDlPeTtr0uJeV2niIWqViwxtc5iNt4Pu7Y2hyIy+Hm+47BFvHY+v3ZJ6+DY//qH6vPpb/0EQDYOe94qBfF+WevVwnqVJuU+u628J6ijmXZpwVU1vz42tcS4anBEu6a429OzcGxoXFxvRPPgs6zdlnQ3QXdTotrVvIxauU+ZcUSL8sRBOB9F3ZmMg/r4+XBWhssvdoRWe7+SXNVliLVLfdDFbuhh2sfjmNxD9N83FPPfUpcp9Zx20keFDlWaKqeFot0bF21PO+58YFjd8ByYzOxq+jeo1eruZBSdczZnwq/CVDtasoaG2/DrZPmgq7pGquZOaKla3OtMqlrTV3HIJ73H7h74eoNsFNkx+7oOBs6NSd4SM5GuaZzlm+KIL5DXcOxwiABJwgB0m2NpU2iyi0dTNQ8Q9K7P7blN36eqMGhHEOEtMw6aD329dGCB//HnO7LcQAmLsAdO6s4YEG5/+VOjaJdzq3jpi3DH6Hzz14Pm4d0ddNYhakPhfbjkRL0lg8SZe2OxS81zRrAcgcn9gII13OoeGDuOJpORHwOKRdLzWAOdVzpd013buo4KQv/VK0cFg8AatCBu1cpC7kWbQfVmkUcu3vHrsJW63jK+j1UXGqMVoBpnlGLSChJnBaQBjoAdlrC47+xVTxsH7ajLOEhVvwm2BLii6nHEsTCO0AJ/RQ5AlYT+lXS5lGDoVJ94nVjt3VSNvrWcO0p9y0a+1qVYBXh2qRsnrzN0TD5N8et4E5A2+nLGWWX9pc6AWc2TuyIZQ71jJdj92mqgdY01pT7tzWZGlcWt54aILBYsGPXe1z+Yw/vvH5aUuee6phrwg2wFwNVhsaqEJcpWVm1AwHc/rGItopnbKniRDo+XityO9/aQbUcgU116mtZgCyiNhUnriEl2OOy8TapwdCSwdJA6bOF70s8wFGjfhY+f+vNW38wwjsFFRcOsCzG33zymYUL+k3wA2oRLhG7oFOW/fia5rSF8W9pUDLXHZ0T4VQ7oPE6ag03qMZtayHnXDSDJHMh9r5rqSm4RLouxh3MpGPAAy7Ax2eODa825kyKZ+Lid3M78rGY5VzKc+fpxmjLSMVVU67kufXkjqspR9qe8yLg3BC1bu2W+mFSsXWaTl7qmbQ+43i5haE7ozUt/rhcjMVa1tI9Vcp8ThELSs5dXIonpSzbmuRjUl2o8yiZI5wbpML3QXNf4npRdZKus+YaaC2WkvimkrFJYBEOcC3B2s+eT++LLOBB3FOx6RLS1J/ce5x6t7jtagzMWAbPxnRHr5FDB6Cd987crN+lIji3vzMX8e0x4DxrFwMecCv4+EwpJkqL1IFPCZfYvSrlBmyxLlIx0GE59TcFJQylRGmlbJW18xy5JGs5Yrp2J8taDu7Uaz+Y+Hw17o2aelDLUy5/qWOXWDCwQG11v0qoNVgRlyNZyiRrX+51znF/DttLIjeIuWP7LonxpFzZKY7te33JQirFjlspeSek43MinNou3l4CC9USF+LY7Zz6HegO3L0Q4buO3AFvPvkM3HTvLXD1Z9JW8O7ghwD6T2bXUZp5JAXnZYP3H9vyOuaxufdoCPEd7zuXPl+K0rhvbRJZn8ZsPem67nYA+O+jRd8EAA/1ff/j5PZzsIAD+IM8JabeGNcY7dVYXXLKpgQeJeA4i1yKHBGuzSyecqGX9k0dW5ozvDTWXcPeo1fJKc4CJ8+d3XEfqEEUrcue1uqtFbpaS06Nd1dTL+2zS3VUKBdvyyCXpo4510Fz/Vp3WLVTEnHTZQHIGa2lMqjtjh9+A47te30py/cTL9yQZe3W3B/uHkihHJJ7fcnggGZqNkmAYzFNxX5r9gPYtoLjZGwaN/TLrz0i1oVLzKcRhJS3j+XdsL73qX21nhJzZwjPIep7WLOfXjsMoIYVWut5V+t4Q+MWcB6rBbzrul0A8AIAfEff95+nthnfbKHEreDTISdudSioxlr77KQa+hojyTFSnVJW95gS93MLYfS4NJs7VSYXJ25JClfCqxd2A2zw689snID7Luji6UN5mo6hxS2+VEBrYt5TWN8BqdOG8x+Ea5j7nuV4pljQejC09AawCu9AjgtxsPji7OrBahyOtRUfvB0rXBJTzXW6Ja+keD8KjeXaYrEPwrtk2jYAveCm9qNEeOx+DgAqF3QAgP03foC0gqfupWYQI9y3lu8E1Z5KruypZ2loplAHDVI9W4pNatAk53myWL8l44O0neNEHAOAf8WJb4AZWcAD/sBPk9IPSO2PkMX1OKcxjy2m1DOZEh6p5zhlzeMEK7UvBR65zU3kZimHKjdc/9JEb1S9pCRy1njwHCyWOs5iO0THrPQ42vhOAFh6Z7T3N+cdTe3T+rqO0akuseZyidSwBZdyMdeitd4D2ENHtMeOqekmr4G6lrku6rEAp6zfuQnYLr/2yKJu2nusndMdIJ1/QUNND7ch2gGuDni7WnUZKhyotoV6yPKtRovcmWrmloRt1S3gT/2Xx9//7ftvztr/H//KC/AXf/zCJwDg1mjxI33fP8Ic7ycB4DN93/9ttk4uwJ1SaojvuBztxzHXxTfAHS+1r0a8tUwco4X7aKSWU5S6s2vLaTHXuZSgLdVByrlvWuu/9HxLrrZTspZwlonU4EOrfAUSLa7blNxZpanCrEnUwj7H9r0O+2/8AJy6eJrdzlq/2rNZaK57Kzd0Cew5UFOAA/AiXJuAjaPGvQ5YvOUsYjUHLrRl6Pd2iMHWIUX4UMey0sKFPdWXSgn1qYtwF+A81wS4ygW967qvAYAXAeCOvu9/h9tumm+OwJxc0UsTMaxDIgecbKV2mTW2i5ESAuGya34AtJbs+JjBwvzYwzst9fy0YLqRYfwecoKKWi6J3JJM8BTSOyR1+Lh4b61bPYe2k6V9zgJDdyRLB54kr5ExO3QWy9jUBkNKxBIl1oP4BgDYPPRA9nSBufXTeJCEZSnrpuSGHuoV54CIRejmoQd2LLNCCW2N+A7zgnPzg7ekxvRsWKDlvjM1BsUtnjotsbThOZb6IdvPKYrvVhqBEt9cP4obmChNAufMhj8HW9ZvVnwDzFCAz4laUzStIpqGW9PJr/kBsJYV4oJDfbArntYKL7klp0hl4H71wu5F7LL0THEJ1qS4bO085NIHkbMgbnX2x2merPH6KcL5t7S4TFEwWt8nTSe7Vh4G6wBH6bZTIhUTHa+//NojsP/GDyyJ76kNPADo7i2XZO78s9fvSMAYRDdephHhWFjXcDPH4luaouyme2/JOl4gnPvmoa3f8b23ejEA7PyetXA5L81NMSTh/Lln1uoJMLV3cSireKo/M4ThCg/MU3lM5mQ0dKrxbwPAP0htNDsX9MCcHuicqZkopnrOLUa047KnOMoasLgsUxm/NfFxFjSx1Km47RoZPWNrt+UcNEmY8LZSPPxUPn6t3J/H6HxJA2M14zNbM7WOa8lc2ZqyA9os1nF9OLfdHEGmQfNslz7/kgCnOHXxNJv5nBLZta3W2nnBL1+5tPBcqAE3yJuKq8fUuKe5uSCm9K5rhLM2lCcwRvs5NPH3ZQp95Zzkt1M3rLkLOo/WBb3ruj8CAL8JAN/U9/3/Jm67CgJ8aiItx3WcE0mYKYgJCusHrkZMdI1ypLI15Vs/fJJglz62JeebE/MdsLqZ43XSu1n63sYCHB831GsK70uNzl9LcWZFema1XivSb6ms0vd+Sh3xGOscv9bnIWcO4XAMSQzUfC5biXnpeJSlm0KyfFss3LUSrwUkgV8qwiXvKpygT8oIX/N+ptqXKeVksGCNC9cMULek1vE07bm2n9Lye18jHHTq4hvABbiEJQZcXae5CnCA6YrRnIZg7vHeLa3g0vHGGnhJHV96BqR9Ux/hFudbI/laCk1HIiXS4m1S10HzDqYSgaWuu+ad5e6nxSqTI56oY9aiJGQi3h6XaXG1rJm/gTqWZZ8aWO6xVaimkrONPbCjPfcWLrcay3dMLMQ5IW0V2dL28fzqqXnDcTnkvt17dxzj5LmzO+6BNl5am8CuxTOWajPmIMAtbekqWrmlgZNXL+w2Gwlq0ErIuwAflykK8Fm/xVOxcAW4eW0t++FlUzo/Cc3I86p8NAB0I7aPPXy/GDuptR7Wvm742aTc0i3Ucj3WLAeoI74BbKPmFve3mPie58ZAlnZea3REpWte+nzi6zLUeyCVN5ZXleZeS9tQ4jyO6Y7dz2OxNqYIxzHp0rdkbFGljQOP4QR2WJ4S3+HvWEhTlm9cTrwNN3f4Vm6Knfefahep+xLuHRfCwCENCmmRnoWxnxMNVPuiHfgYE0vbmBqcTZUzxjRe6yq+neFZHUWkpKVozykXv5hzflG1oqlG53ZMMa8dYMBZg1MfUmp9fK1qiQIuc6cGSqjjOuGkY/E2pcl3qFFx/JHWWL5x0hSqo4DrHM7Hmsl0UeeN7QypYfnUOlcxcf1qWl9wW5DyCOCee6vl3dppnPK9oYhFDRZFVJbzlKV2yOczrl/OoFwKqv2xWr81cPHhkos2BWftlpKxURbxcPyw/okXXly43Z88dxb2Ht15bOt1weI5PDfxwAonxmsOAg0dwpBDSds5RQNGi4HKeKA7/j6fPHfWFIIwNFMzCDrTZ9Yu6IG5PfRS8oa5Wb5jSjtIQ1qduOm7Ql04LDFZqfKsYlRrMW9JnEiuxnlJxGKbSxgXr2sFdd01gwB4v7hjWzqlE0ULwSSFAtToDEmDEUM837iuUxfdOdZGAHnOa0pABeZ6PVKWxBzxjV21AXbO9R0v50Q3tY81LlyT4I0bYPng47cs/s4NcUoNIsbLqWcOu8pvDQ7cUCSepyTAtbkwNLQWl7n9Lq5eNfpxXLssXdcpDlAAzMuo5i7oPO6CPnFSMaXUOsodeK6kBECqk1vzI5BCMy1WznrNdjmd2thKeGbjRBMBpyEWlvijV9pZ554fbHEudZPHFtdUOAE3fzm3HSW+AbY68CX3ThMn3RLKO6BmXagQjal2quaIJL7j6zw3Sw4W4Wc2TgBsbP1ds508s3Fi4X5OJSCjph/TWL6P7XtddC3HaLKh799zEDZv3/q7f/6pxfLuwN3w0Xsv7RDhAMveKOF3ihxvhWP7Xod3f/Hlxe8eXga49ebF9csV0B//iRuTx24NF8uM14drbdmnFTUHC1qR8oAau36Ok8NKWMABpiNc52zBrkHOx69G49lqlJiz/lFwx9aKOA7Jui4dl9qn5Sh6KL9mB0grjq3vXa61VZqdgBuQqJ3oKjXoUWNQhHvWtRaJOWEVHVMmZeFOPYurch1iKMtoifv5qYunYfP2dwEALCy3MdjCHVyx8fLcbOgU2Jq+f8/BhfD+8oc/BQBb84P/1onvAoBlKziFtR2ntsfPWrgPP7LrC/DlD38Kbrr3Fth15A4A2BoYiK9laXs5pfCelDdGKjxP2pYrvwZjDvZOhdJB4DlZvwHcAi7hFvAZsI7CW+qkt7B6tywHYFnY1bCIl4jveL8a56ixbuWMfOMR+9LOjzS3dyDEMGLrHUD6PeQ6RXH8mRZu2rU4FlLzHmivWapTUMMToUY5ObSyekvP9JQ66yVoBnqm4J7bCkpoU3HxpeIbYNtSTVm48TKc8K4GnMV8cZzuvdAd3Mp4/nU/+6Ht/RZ/nV1cB8pLIOVWnto+jv2O78fm7e+CL73vZ+ETp78Ktz35Ctzz0DMLEY4pGbgc2wpOHV/zXU0Nng5plebqwrXRc/Oc0bBOgw3O8KzM04WtYXNuCKT40qmDO++hsZ5TQ5Yj3jTU6uhbjx9/ME8CnXinlhtaDQH16oXdABv577HVZR0PcOSKb+q8rXF+WkqusySwU2XWeJ/HsJ5LAy4l7+SU4k5LGSqMYUhxVHJfgtjePPTAQqQeP7y1Dlu38THjZSXiWzOlWSz4tXN/awYhuJCQnPsXBiGufuwUfOL0VwEA4Auf+wp86sOvwHefp7fXljuVd6+227hGjFu+LzXfb64Nb9X3nqPH1dys387wzOdpNjAlwZrTIFmmRxoabYzSXBrKOKkYgD52WxtDjNF0YKROfe4x430ptPF+of6Sm1yuS691H1wHTS4F7r3KEd9cedoEf9zzpKXUdVrj3VHa8aP2LalTyfGoa2+5btwUX9y6HEruZ+783tqQhRqeES3Fd2p6trA+tPWSCN1KuBb/rROEcV3ibPMA2zHhWFAH0Xz5tUcWy2JRTe1DWeBDdnMt29Op0ddNevdT95HzQsDceeQtsRzNezWVQbBUWBMV/01tR1E6cMV9t1Pgfah6hN/Ud9Dah019n4eilSeW48Ss1BM2RdGaW5+pnUcg5bqLl0v7TYFtkSRvx310wt+5Ipz7sEri23rMlPu/No44dq3Gy6UPs4V4H+vUaBq4jztlMU9lXA/rtW6Z1HHxttqOVq4Ysr6HWnFmKW+unRvK2qlZZ6H0GudYBKX33zrgN2b4goT2msTu2OGeYmt2LHol8ac5JmWxltzaJWv4qYun2eeQy9YuoXGlThHqsohLP/Iy3PaeVwBgS3y/86F7FvHfq4J1YFPbHg79bnH9t7j+Ut1zDEmabafYz8e49dvRsDJJ2GKm/nJyrHsCt6mQsvBS5Lpnl45qc2VoP9ZcJ8tSz5rnBLBtmSqZm3wotG7cXB1TVhPL4EhqoIUqd45CmCM14Dc1UVibWHjVsAZyXj7aZxKvm8r1x7HPqYGT2LIcrNSSkI1FMGcJ5qzVcfkpMR0j1SeVjb2F5Zibru3dX3wZ3nzyGQAA2HXkjiXxPXQdW7JK7SrHlAdShxTpkhfc3PAkbDyehM2ZNCkxMWWozrtWpGi9AjSW7xRaiyR1L7Quhda6lZ4TrmeYQkiL5UOrmUfcIk4tz3hKfGvg3Bol8LNY4r0xNbAXhrRNa8aOR611bEt4UcqVf+iYbwtaj4VTF08v4sCPH95yB5dELU78hjm273W4/NojpPV7/40fWIhwLOJTydw4K3fK6m1x4ba4hMcspk47cBB2wXbWcyCyyKfK5AY1piTShxjgtLbd2jppPRpzw/CGgOoTtBLllFec42hYSQs4gFuRx0Drcj61Tn8qTjTXusitx9tMGU0H2trJ1ny4tYkILR0A6eOIM5nHda2FxYtAsw+1n9U6ya3PiRVM7deqsyYNMDh2cjr1ragt4LnpwCyu2WGfuIycfQF2uqBffu0RNvEaXp6a/ixVB2q7VB6BHAEez/vdHbgbAOjp2yzg3AupOrWiRT+mdj/KYhjJ+Y7HBova12MO7uYxcxfgbgHncQv4CjG3hsWK1NGfkvgGkN2wLfHTFvHBxcIOIR5qd2opK3pqe+rvUDeAnXHY3IeNmkKHgnvfqOVUxyKuW7xO+x6nniMqBr00PEETp5cS/tbY3zGQPE0cPeG6pTrSmmt7ZuOE+t2k6mA5FgBtLZbAMdVWEa7JTq45drCE95c+AgAA+w/cDcf28fHQ8XGpOqfOIZ4j/Ng+m9s3NaVbar+wT3fg7sW85FYoV/xw/DGt3rXavNyBydptcw2PrNoivLSPvOr9bGferKwFHGA8K7ils78qWBJHzQHLR9HSYdWK9FKXzjmLEGpwImW9zokDT+2TimWVsiiXJJCT2g5p0EbrqSE929qY9rkw13dgLGpZx3KPW+MYlpju7UzgwxOE9MI9G+T5xan9OEsyHhwI22ze/q6FEP78rTcv7StlLsciWGOBXop5v/1dS+fHnaPVs0AacGktzufULtbIj5E6X82MIC3R9K+5Otao39yt3wBuAZdoYQF3Ae5UweoqO3VquCFbYq7mTM3Os3ZEHVuMtS7r3P4U1DOg/chKcWGaLOuWkAeKnOc3lTdgbu8wwPzerVzLcS3mHjIDoI/tboUkIjVWc434prbnyo7X799zEK5+7BTsOnLHkgCXxLMVrRcChUaAc8J6DBEOsHNwc6x2svTYtUOEuG9lDZFMCfvcPoCL721cgPO4AM/ARfgwzLWjnpvIBMBmLbJYLeduvQbId2ej3L3xunh9QCOopW21ZVugBgbw+hhLMruWsYdTTq5jYa7vUAm5bccYwrvmwF2KVqKciieP4UR4bvyzVL4mbjxkId915A74yTdv27F9ren0QlkA9a99SlSPHRceU5JPoXXbW+LtpPk+aL27qH24QWosuDXW7KE8T1dBhLsA52khwK+rVZCTR85LO1THch06sLkfOanDGl+3MxsnyG1DFtH4by7meE7g80/BJc+iBDG+TjH3Pfjo4p8E976lyrYQdx64esXCP/wLHcf4/uPnJNDi3eTOf0iszw/eN2bsc8lhjDq3Pmb8HONnWLpnc7p/ITN6DCeytUnSqDJz6hXYv+cgdAfuXkz/RVFTLKfK4mK4LQJbKnds8Q2ga6dL2rxcuOPh5XHd4m8VXh/+UYPb0rf5sYfvF/O8SNN74QFuan/ue19bLK+C+HaGZ+Ut4ADbiZpaZmsckqkJtKnVpzZSEq74d451XBJVc7aEW+E8AWqSY40OSPHeUjn42NoEa/h4uA0bAk07qc1/oM2lELCeJ+c5MXW4Qbfwu+bUZto8ADGl11EzaJR6fizXQMrcDVCWvTxGEsZUdnLt8YKbOI6VltzL43NKCfYQb27JQm7JeE7FkUtWdWwll+LQLfWaGloPk6Had6qtLc07A7D93dIMWlNiPWUB57bn1kvHrWkVXxUB7hZwHs+CXonWjVxrl5cpCd25dHRrEIuN+O/4f2oEOd4GQ30MzmycYDNzT4WS+nAf/NJzlD6smg95SHJGgQWw5YNLbYvvcfyccNsDwNIc6bXaGOk+cAK7xrOoLQN3Fql3Ctd5Lkh1tVgjuXeHa3esAyIckjjm6hSe5ZPnzqqeLap8bjosjfjOAQt3aaowaj9t+QEpwRq3H07OFtcprDv13IvXlqbFt3Td421qWM05S7hUdioB3BQEujWp4VDGIc4rj6tTDbjv89a31Rb2pV0vWdDjZSXf0lUR387wTEfJNWSoxDZDvogWK2pLpjQYMBSSq3j892MP308+d0vXbGPntpwYx+JjTKGhEc+14lFTllHNe5dK1CaJb2rAQGvpjo+Nl1HH0NJKfGNSAqnV+4/d8DWd1bm0RZLFKZzTqxd2w3m4fml7TbnUACB1vFqWb0nkWL1IuGNyYsriqqwRwpyl2iLccRmp8uL1OBt6KrkaRSgjFuG5Aw/Udeeyn2sFb7y/dXttXacmvnNDslpT23Mxfr9Tonbv0atw8tzZxT6t+s74exz3A4bM0O44FPPosVRgiIYtlXSpFnOy8MwdqdMqCUUAnduw5EpFLZuqi61WfEuCibK6Wj+SJ8+dhb1H6eNR05VxnZDc9iLc81CHUE7KxT3Hss6Ra0XRiFnLc2cV99L2tes2Fqn7gj1s4pjLlBcNgDyQFEM9I/G7UXuA79ULu+G+C3T4Reo4kvjmxFYQbJz4xEI5nlsbI1m7S2O08THDFGH7D9wNAC8yeywTRHf//FPQw8sAAGSMt8aajrfhXMwtngepe2QR4lSdpG2HwhKCJsF9F2oOemrjv6l6SNtJ/d+4bYnDawB2eoO16EPHAwLueu5MhbUR4LXdwrnyWo6mWVwMU2XkNOJDWL5yaWX550SCxSpFgcuhxLrmWZqaIJfi5Km/uf0DmkEtLq5aEhjcsal4frxeqjPnIi09N2c2Tiy5lpdisVprXZFLBX2q/JJjT+XZt4Kfj9w2i/KwiI8RW9XDtTqzcWIhiAGWv2e1hXdcJq5H7rGwwMshCE5KRMex0tT6sG+OlTreJ97v8pVLsP/A3dA//9S148qiObaYB+H+5pPPAADAbkKAp5LEaWLCKct3/FtDqcv6FKzbgdZhMK3aNmtdpe8iBzcziTafSg6SkLcO5g+VPd1ZX6alohojvVBWcTn0i6kRmClxiEXQECK6tpuThpZusVLZJce17juFARDttbC4zePnJY7Nlo5HJW+JBUa8TjqH8Jtz59XAxdfF5HRCKCs6vk61wZ200gEf6j5S1iNqwGYKz3wp1PlTnh/Wzq4knql36eS5s/DYwyd27F8Kd24LayfQLswacoRbSiBzy+MYarx9EN41YsrJ4956s7hdLLwBtsX3UNQQwKnEblMS2RI5wrSEGm1gbn213+4U1KA6tlDnWpYt30GP/XbGZi2yoMekBPhUO3kpAW4R6NI2tRnKaj70/atxvJp1TsUvp7YDsOdK0FpPS9C69knx3RKWQStcL235r16gE6tZoM4nZY3nrJol9631O5wS+Jp2rQSLW2wNNM+3tVMphb5oB6Fz0LipAvBCWnPNNSKccy+PwSIWgBfdHLGVPEY7zRhXZqoeGgHeHbhbdT7WbPBS1vKYmnNxD/FODimitXDeVBirxxW1byB131L3gkogq2FMMSt5EEnt5SoKcM+CzqPNgt513R8DgP8PAHwLAPQA8CN93/8zattpqs2GSK562v0BhreAaxrg1LZjDC4MJVAtoiLX/Z5yp+S2GRupblxnozRRIZd0zgIX80YJWSmpGYfmPsbbasqhto2vce0PdUqchvUhHj64/S7qsVHefrV81sfsCA9teaPOFS+jsuUD0O+KJu8E9Q0sueY47EQrvrELM7Z85og4LpabEtwYjfjFpCzlXP1wvUI51uOXiO8cNAMg1DY1RPScLOM10IZtxdvkeNLFZZdmta/lYZp7zJxvrWV60pLjOGvDwwBwvu/7f6vruq8BgD/CbTgNtTAjph4TMhUBWEoLd/ncMigXUbwup/zcgQBq31RZOOYrFc+cYwHdcm8Vq5EkjlfFxy396HH3UbqmFrfzeNslkVMY3x0Laen4uC7YGm6BOveaA07Ue2O1Qk3NYtUaS0iEdO+0idpy6iVZ1ACGHeCgRHcsWKlEZVI5l69cWvrbKr6tcBb2uB7dtZjx8DdHyiOgpM5aq2n8t+U5GOKZWZW2hPq2Wdto6XrjATK8LRcWFdr2kO+Em4KwBBfFzth0XbcXAL4LAP5dAIC+7/8QAP6Q23411JqRlJveXGltBS7BelzsfjWUK7sGLHbGIOc+agWctG3r5weXm5q/OxfJ3VnyFuCs3njQ4CRsieYQ6507Sr+1X3pgAHe0uAELTaxwihqDY0PtMzc4IZAjEEIHOX7O8SwBQ5KyrqXigjEaV/OYOFHZriN37BDjFqtxjrWcI0w5po0pxwMLsQjnkCzycTI5altu2jSAnfelxpzgTh7hu8AN1GlDRGpBTrW5QX/TSuO+ayB9L1sczxmeXd/6zbDrW/Zl7XvdV3YBAHxb13VxXPYjfd8/Ev3+JgD4MgD8VNd1fxIAPg0AD/Z9Tzbuq9+jWQOm3jGlLCRWt/IxzrG2qzkeHQZo61GRk/UzHpjC50+5mHODJIHjh99YdOCeeOEGtpPNXU8pPgvXIa6HdjCK65RY8yWk4pdzBy64e5iK79V2JjisQrBE0DsyuZ3lIL6Htu5J80dr9tWSEt+xpbh//qmF8N515I5FtvBdR+5IHicIbSx8gwinLNWU4OXqiNcFMR7K5izh4by0xO1wfLzcZHJxeRTcgIvFoo2f33VzQ7fAtcGa3Bo5UPchHvjlZgmi+hVh3dTErc8V7iA+k4gB3w0A3wYA/5e+73+567qHAeCvAMD/ndp47ZKwxfhLNTw5SUWmZP22gj9+NeuvKS+1Tbi2IXkK556usYhbBTjnzl86uMGtq9H5SNWZum61OhXx4Eeue3lJ7G9JJ07rxcI9d6nr7GwzVZGSSr4W11ubbE0Cu47HYjUW4Nh1OxbcnPjG28W/A1IMuMZyv3/PQbj6sVNL9YyPicV3fB4p67zW4o63o+otCXAA+l5KydtSz+7QiRLnzphtJtXOSwI8ZkpiXBrYXiVWPQnbPz/7H7z/2zMt4J/45EX4C//+x8QkbF3X3QoAT/V9v//a7z8DAH+l7/vvpbafl5qpTMtEQg6N9XrPvaNdw4rPCRerF4G0PkzZldoefyy5+uw9enUhvrkOmmRNrRFPb01io0EaJOAs3zkj+3TW853baedIr4FkYbHumxq4kY7plFMi0ltMIVZjXm885RgWobGrdrB8U3HTsdgOIryWuznVFkpieNeRO6A7+CHoL31kaXmYMzyXkinUAqmY8Zz7qH2eXHzbGdobhrOGa77tUxK6bqhztPR9/8Wu636z67rb+75/DgCOAcCvc9uvtQUcoOzlajX3bm3m6B46Vqz6VClxeweoex01CdrCNqnOutbabrHMjyXasKUhnuvbEgNuCRnQoHXfb4VbrVcL6Z3WiC7smlySeVmyKMfClsuCHruQc+slcCy4NZGZlBV9/56DAN17t+vy2iPkthzWmHbNPjWSywUslm7O6j01b4+p1ScwpPiO3d6H/tblkPo2r4v1G8At4BIaC/i14/wp2JqG7GsA4DcA4If7vv9daltXN5mEl9FjRFaDqXtDaNzIrdQS59T+4SOcmkvUIqitcdmlcG7T3PzaO+p1Lfs51zYMNV+qNFA4RMfMhXcZ0j3ixHDcCc5N4BaXqYnr1oromom7agpCjCRG4zjtkrokXcH7Ty5EuCVRG4A+UVzszo6t6tT51crynnIl574d8fIxxC73zoXlUxThNdtg6fzi9ib1rdLM7jE03o93Sun7/lcAQDWIMZ0nfyS4zqlWnMzhhZ1SA6dlyDqXCliAaV7jXHd/rdu6db8Ajg0MvykLesguro13rw11bG4aJ+0gTuySTu0jXc/cRDWhnaOON0aiLkePRXzjkBCrcJe2K9nGiiUWnCMWq5IQ5dYFwcvFQdcS/8vW++26LKzg/Sfh1HMvkvWOreCpmHQKKpY8ntYsLv/YvkvVBzwk6zZHzRjwUrFMvUfrkAleI8K13yqcPHTIPvUULPDO+jI91TARpM7LmK7nFkvtFAXiFF3LW1iANdRIohaOn3NdOcE7FFJCNkyre8Ql98qJtX/s4fvJOb+xlwz2nsH7UMloLO1N3PGJ98OeBBrX8Clac9YF7cBInO1cs71FIFD3furPg0UkUhZlHEteI146py7LQnpb9KcGFkri1UO2eACAXdeWxcnf8DUpEeThOeIGZANjziVvAecxyMmPMNVzi5HqGPori9wyggiPQ7QCVJb0lqSmOXXh7bRkOipoRLgOLtcAjGn1Lk3kNbbwHfv4tcAJvixZ3VPl1kjclgtOjpWKsaZEPF5uPXZuual7IAnO1D1KuXFrR/pTcFO3xIJa6tSE/UNHhpvzWSu+nfGgQi7iZXFHuLYHg8aap4nd1lrXcVlDPXvS3NhDYhH5sQi3uqVT4ER0u4h1nDt7ybXKGdyrLVDDcyeFSuWizYdg2X8OAj0H/D3D052OxRSnRnNWh9VQQw2ZemywBNXJnvP5TIVanV0udjp13Pi+hr9rWIi1MdZayxzezuo6WHqdqczbsUs7tw/lUcDFfGvFd0zuAF5qP2r9mY0TZGcGWyrCspjanb11nT6olgWvRZiANB1UipQ4tx47JehxvG1uDHoKyvW8tiCXRDMVX05tw+1nIT5GsLhTmeElJC8CC1LMd258tWb7FuJbizUfwhys5dS30/qNPLNxAmBjPGPXXBIsO/Nm7bOgx5w8d3YlxenQyaumRKnlv1UW8ZRLND5mi3CCWoMxWmGgFWCcQLbUNZWpnVpPiVVJoMbramQkp9BmkU0NDITzogZvqP1rkpr/eV2wdJ5b3Rtqrm0qkVRMyhqeE8fbgpqiqVR4S5bhY/te3xGnHVuYsQu8VAcqNh0LYm45h2bOc2odxnIeGGtm/Frx4GOR60kwtTY0tC/hW6r9RlpmCXHa4VnQebRZ0C2sjxJTsA7CdB3OkUtsNYVzlyyv2m257SR3cau13XJcK1Yrhta1HO+TGsw4s3Fi6cO/NRf67h2j3zWeHU54c/cnx119SeDB9XDy3NlFnB0+1/jYLYnr1MqaOCeG8v6oTWoQZWpCoBSN6JW2UT3j3Xth/43b04ztv/G9cOriafUxuPXcPikLeSprukV8A5S7p7dg1cJqUgNqYxDHdsffTkpkU/lOHGddGF+RTIhVdTuZU5bjGvcAx1CXlFlbtHMZr2uUKZU1lRwAGqx1lUR4zGMP37+wBIdOAvVs4N+xmz8O58D7SPOIUlDCWOqEaL10pEGOIduC2JIVd8bH7iTmonnXOMa2Dp9/9np2KjHKTV6abmmK9y/lTm+tcyux9sHHb4Hjh1+E88+eXRIrQXwD1IntpqDmSw9YxLd1vvDWg26pqcHw8hjKi2AMuPutrdMU3snzz14Pm4fCN4/PpyItd2u4sy5MvzfuVGEOwgugfsxPEF2p+SaHzhifcpGWtqHWt6xzagAjHDsWuKVokqFZLdOhU0AJ54A2PIByV8edhZQVfUc29AJevbAbzsPORFanLp5edMy4hGytmZqFZt3A8bNjJncaYzCYihWX4n657NyBUpEW349YdGNyY9BrCUpumrMh65ADN3DCPf+tBjsouONI11nyJJhiW6oZUOaSjQLstJpzs4g4ztzxGHCCOVrB52ThrEVNt/IaYjZ3irgYbdZxLkbaGu8vJXazoJ3+KO5QSvOIUsQfXm5Oa2of6X3GWd+p40vvViobufRctepIhIEBaVqfKXbcAimRNrSIwzMCzMWbKKZF8ioLte5ZyT1IDQBpp8FqjTX+2QIVHy5Nf5YrwPExuDqsQjiKxtWeEt7xtcVzsceJ8PB1x8dKvactBz6lsrkEpaX9axfg7fAYcB6PAXeWGNpqOzVKRWO8b6vEZqV1i9FMwaXp6KamF9PW2dIJPn74Ddi8/V2LjsaxffxHOy47d9qxAPWxlwY/qMzpEnGctXRMgJ2DAdR2JZ2L7fJ2L3kjxNe0pZjIie3n2rBUpvqhsCZDm6qV3yq+w/9TO4+S9jQnG3ZYju9r7nukEdct39E4wzsHlXCtRHzPNfcDVW88eEAJa8357t9zcDHn+iuPvwSfffK6xbo7j7wFAJ+Cm+69BXYduQP2XxPj8T2I72HqPR3iHebqgAeiz2ycGD3U013cnamwfqpNwdgNhIY5WmE0cMk5MDWm4BrqGuZYpfE2Ukb0rfmebefCCc1SC34OLeLic8rCAxCpa0J9vEO4QyDedyvJ205Lfi5SR4KLwcvpjGmFmLXsGgOIU8tvUSsb85jCt4XHRMt3eohnICVwtKKZiknGnkHWelkFOxefbYkJ11JrirJchvImkIQ4JojvL3/4U/CJ01/dsf4Ln9v6/3vhJbgJnoFdsGwRp8oda+Av5cWmDaOzJJAtcUV38e1MCRfgM2VVLd4pd2GLdTk3O3hNciy6kjXbIlq49ZTl0Zpx3ULoHFy+cmkxmv/Ecy+SZVqt2pSlN7eeGOs0Y9wUZvg5jePaanQC9h69usPSUOtatLaGauLtU/u1dg1vPUXb1KzMmKnVD1+z1P2pfY0psUMJvZS1vJYwDMcutc5bpysL+8Tbp2KZ8bGoddT6XKaS9Ry7m7/55DNNjjP19mT7G6mbpUbqD0ieZprtPKbcGZvVVHEVmIMVfN2Ik31pLOSasmqS4y5tqYclbjyF5P6be2zpmm+7zG0J79wM3XTGcpsFnRtwsH7sw4c7TnSmpcbHHyeki8FW/Fxad+Zy60cNxtVgSIu6z4++hVU05LjTtwDXW2Nt5TJ0a2OI8XZUdnupHpo6WuYdz7Waxy7UWMBfvnJpUm7rlvnTrV4Dt73n7fCFz30lp1qThgrtk765wYuPGli1gr+tqf5iPCDuOENyXXoTx5kWmoGR0PgP5SmA3cBz3MKpMgHykqtJ9aPq2pK4A0NNhRSQ3O4BdlqYMdRHnNvOspwjPIebhx4AAFvytriMkoG+xx6+H85snFh63uecLEyCOkeA+u7NqeewFtK7kOL44TcW/6ZMqg2m6q89L819oa5xje8Cd+9yY3FTIu+JF24widFQP2sdrWjqtH/PwcW/GEpkpwRsy2zlx/a9vviXg0V87zpyB9x07y1w55G34Lb3vH3HempZDJWIraQ9GQLpvQvrckLpuH3c3dyZOp4FPYFbwZ0cari4ayzUOXG0qQzqWrfgli7f1HFbC8tUlnJtW2C1rmNKYttWTXTHSKEkrY815HXVWoRbupu2OH/KdVuKXa0R1zqUR0NOXcM+Vmtv6T1PzfluQYrx1grZ1PZSErSwrNRS3lLY44GHkIg0uKHjJGwAIREbLBKxdQfuXhL4mvOdghjXDoaf2TiR1dfW7C/lXHFhvoxnQefxLOiOU5mSrLq1KU2klruP9vyx4K7dsdXUrfSYUkc5fhY0H+2xB+fCVGwAW3N8Ty3+r7b4sbjVlw7W4EGfVhb3knvGZecufQas941KLCZtE9ex1VRnpfffeg1yEteVeD9opp7Sll/biyII4thFPZ7ajMMq3OOBC6sIH2rebyy+Y3YduWMhwoPgjonF91xJDeqH5SFBqZVXL+xOuppLLubufu6MyTSUx4TxWPDVppX4rpnVmepMUi5bUlysJga9dgb0kk5w6EDO4f3jzi+enizuYFjPiZtKLc62bkmWRnW4Wwj3FpZHS7JALqmPlM8g9c7UFt/hfxzDa9mfWl5yP63nGAuhUuu9Rsxz1AqrsYhvrr41Ytq5jOna95sbbKw9zzmVeI0S4VokIY3Lw9ZxjQgvEd85cd6Y7sDdCyv4riN3wE1AJ2TbdeSOxd8a63fNQbja5Bg6tF6EKRGdEtguwp2xcAHuOCMSf5ikLKBUh5BbRmVRz3U75/ZNgYU9V0bcWYjPNe5AxIK11LIluVtqrN94mZicboOuQ+wuTrm9c4I7XCvqXuHzmup81ENCDWRJzw11P6bi0i91qilL+JD1CYKAE9RaF2v8XnLi1irSU/c9lVWduvaazOZUVvJSYZ4zuEZlSNe6nXP3LhaxKUG6JZovod92ESxlUddav0st39S5Ulbuy1cuidbvYNXun39qIbTffPKZJdHNWb5TAw1zavO5/kdYfvzwG3Ae6G9e3F5rM6I7zpRwAa5gDla4OVIzq3eNeuRkJLdajTXbS8LZMlWH1OmUrOecFbCF+y/u+OLjUa5pUnI6DVRSpkDsXh5bmSUPAu7exi7iVFNLlRnvE1/H0Fk+s3FiSdjHiem0FrhcC6s2dndM8P2wWEWHFt85YizXWl6DlJC0WFKtU2aNadWTBj4CVOK4OEN5+Nt6HrXOm8qWXgLlLq6xXIdtctzGpeNYy7IOAkgu9JTYlsR3TCyydzOCW2txn6Llm0LbhwgD8SnjQCrJqeNMFRfgIzGl2OOxaBVXaaF23DUnxvC5SlZvi4u3ZOHW1FN7HOqYmrIt1p+US31cNnf9UoM60oAGHi3H15dK9EKJPUmUc8fHVn48CHIeQszs2SXX9jMbJ5LZ4S1o3VZbz6ucCyW4ubAO7llp1TZb3gXNfRhaiOeK7lgYcZZsgHQyspR1lyPVXpXEYmu3oSzO0v617yc1gFOSfA2g3JpsFd81piTLmescwJbhXFuWJNI12eCl6zHFAVKpXaUG/TVtcGwhzzGkALiV3BmP9VaABmpbwdddfFOERlQToznE9cvtiId6auKuNWWFukjHKmUqz2N8Pmc2Tiws4JpzTLkXx8fAcB/hUJ8germ5tzWeCnF5eL+T587C3qP8oFTsynvq4mnYPPQAnLp4+tq6+h0tqWMeJ75ZtvLXPU5NNLkVwjvb6l0oFTytaWFBk4RniYCT9m0pOuJzkTKAa8pJZfS25HXQHI8i9UzWnIe7huXaWra0vgRJPIf47lTyNEqIlwp9a4hBS7i+SU4oENf/Du/FUthYZkI3xxmaafS6nbWhREimrKOl9UjFVId6SmVaRm85tLHgNYUCdT2l5G/xfhKpjmMQtlsCdFkAceKbS5aV61GRmm4Mx3OXZEGPOxKhHMmCHTpRYa7xrb9hsWxbhNs76VqLLF43V4sB93zEz3l4Blt55GgTJaW2a2HhyilL6uhL6yQBbRXmFhFXkuQt3kcrbnK9FDh3ds3xrNOMlYg1TXZzgLZimDsOXp4be47RupcHUvHgOaJbm0+h9HkvQeN1xBkYcH8E513ZMVCKvs8nz51l+0fUN9hxxsAFuIExY8FXwWVda6GcKyl3aQvUvtpBg1y04n6Ie2V5VkoHJXKzoGr2idsLXujvHO2PRXksvjGlIjyAO2qSwFseMNiZjd9Cq06hJS8CdlWv/XxT9yVn2ippf47ace1UQi9pW2pZSgSVWgQl0cFdNyl3AAeXkTuV1ZxKXlfTWpmTvC2XcH8kQXhs3+uwf8/BhWV48/atTOIhS3othppaTENs/Y4zp1vFO0XuNSv1pGjhIUOJaU2uDkqg4+8rFzbmceHOVOj6vmdX3nXXXf3TTz89YHWmT8uXd0j36jHg3JG4dSlqW8BTx7AMgpSKQioRF1e32u7pJfcEQGedk2Km8XGlZGDxNqnjSVBimnvX8b2V5hjFx0i1H/jc43hvjiDANZ0j6t5IFkwAOjt8zbjzMWhp5Q7UtFLnWrJa5tmwZNHGBFHCiRPOjZcS5KmpqKSkixjtNUq9M3igwmqFDqI1CFXpntcWRtrYfG12eyzAAbbuazi3XOJ7nSO+SyziGlEdW75bWLlzGSsuXHJJB7D1f7lttd9iadt1puu6T/d9f9fY9WhB13WP/POz/8H7v/1b9mXt/4lPXoS/8O9/7Mf6vn8ocZzLAPAaALwJAFel67maSq8hLa3gqyq8A1KnN8cCkTNgUdoJ5YSxNumalpwRYWp/axxWKllZyq0s15KD3fxDmTmunlzdKCwfYer53Hv0Kjn6npq+LC6PInbN/+i9LwEAwP4bP7C0zclzZ80iL2Ud4663d1b01LRmWsvi4trjdfH6lsTiJhYi4e8cyyAlaLCIioUZ9bxL56799lgGu/DfKdEXBGttOC+XGOm8rG7k4Rhb274Ix/bdvL2yUHyHOrTKI5Aifv7iv/HzWRLTLYVWlMTOj5GgjRtY595PynOHCwXU9K/isDH/ljkD8N6+719KbbTaiq8xq26xbgHVQcTrLZ1DqlOpuR9Wq4E0cJCi9PlInWOJa3qptRvvp7mmeLCFK6+GkJEGDiyj5XFZ+G+N+A7bxeuoe0hZvLmOZpwBXbJsp6xnAWqaokU8vmLu8znR6hw03ho1jxWXr81VEdbXvgaStZv6W2Jh+e7eu3Nl/8kd5XBCp1Y7j8sEoMMIUmhF36nnXtyRkE0zHZqW0phwLAApazguo2Xs95SomTGdE9o1ruUQbZU2rIdbH7uoa8LSTp47K3qNufh2psR1Y1dgjvhLbCNugONER9y2Q5ByV8ZxSZbOLcD2eebGlKb2i9fnurjjc6J+x8tTliNqH2o76VxScJZuipLrr8UivqntA+GaSd41py6eXribB0JnI55vGMN1plOZzs9snCDj6oZAeobmRO4gEjWvNB4g4cS3BH4nLPULdaLqFrN/z8ElK7dEvG3sogzde2nxnVhX4pKshYqBr5XZPexfWn/rc6eZI3z/noPw7i++vLhnkkU8laW+htijjhE/T1rCPjn7YmqK7yGe5Zihs6aHOb45A0K83PLd8RhvZ2R6APiFrus+3XXdB6QN59/DGZFV6CAOQamVNQWVyAOvj+shIW0z1P1OCd14G+7cNW7rmnpQ23EiOl6vGdHG5aXqzHXaNM+V9t5ZPt6cmI6vW/jbUi6Or447rMcPvyHGX+dmXaa2De564RyGtHznhG1MAS5BmcXts7QjrG3vpONgi2tOnd79xZcBDqTjYwG2xXf//FPJ6ZsWdO8F6D+5tAiLlSHcbONrQyVWi62YnFDdvP1di2W1pqLCWNuDuBwcw335yiXYf+MH4Ni+R8j9cy202oR0NUVp//xT8OaTzwAAwO73bS6FRoxFfP2G8hyo/a6kwuAA8nPxSNu5gczh6L7xTugO/Im8fZ8DAIBv67ouToz2SN/3uBH8zr7vX+y67usA4H/uuu5i3/e/RJU5vx7ORBgzI/pcaRGDSMU6pralXKBrd/hLytKIHe5atjwHrciXBJt2kOT7f/S1pfWlnQNKlJS8v9xUZFRoRY1nK1zPlAcAzqAuESdZC+0Zrn8oaxXczocECwmtsLCIYgx3j1Jz6HJ1kOrCWR8DnKAOv8mEawc/xB6vlKEHkLQDFkHY9vDy9rIoQVnpVGklU58FdsRwB3HafxK+4ewvwa4jd8Dnb91ap41N5gZ4SgefLMJ54XkBL8Mrj78EN917S9Gxa4Cv3RDiu9UglXZQPqyLQ6q4/bSx3o7TiM+kkrD1ff/itf+/1HXdOQD4NwDABfgcmaslKAZbBFseJyBZWKlryolL67VP3S9tubjOXBx4TkIhark1ll6qg9ZqjssKf796YfdCsFgze6c+9Nw5WNB+5ENduPJTg3jxeeFzoqZXCfUK67h4+lh4B1e/rXnYd9Yd/z0FhmhLaqCdAitQIj6kwTB8b6Xjc5ZFKt43BltIY/rnn1qI6+4gslp3791alsvCFX1nxumFdfnQA4t5guMOvrZ9Cc9/vL1FtIQcDtK0Y8ECu+vIHYtlnDVf+z5qpkOjMp5zoi92if4j/+ez8MST18GdR96Cm+AZ2P++TTareW7ixyF550P36D0vlOQkiStJriZhnb++Nqlwulcv7Ib7Ljy6o53C+4X3EICfIlMzc4jjtKDruhsA4Lq+71+79vefBYAPc9vPW9mNzBBW8FUQ3+H/oc7FEg+Z2n7sAZCU1ZkSedqR5xy0otVyTE7Ef/zCjeaOZm68vWXQJM6kGovx+x58NDmwE4PnCA+/NfeME9+4zgB8p8viZTGGCNcOHmkYcxCBmpIq/A0AS6IulRxLS+z+GeDOv9Tll4ubZS3aXGx3AfEMAftv3Cr/8ms73aNx53zzECwNVlHPSdzpT2UOT03hlbqH4Zrtv/G9cPm1R3YkYgMAOA95FkvKI4NKnpYSge/+4svwi09upQ/6up89D/2lj4hTikkDHVZBWMP1HD+vFuFtiXXPrWttES61K2NNSYaxts1TqbfjIP44AJzrug5gS1//t33fn+c29nnAC5HmCp67eC6BcvEeCq21dSr3hxPMuVZpjOYelFyLlODXuJ1bzy9HUOGyY3dtLhSBe16o5GstBuRSbvyxONg89MDSdnOfq7sWrcW3xdU3FQteo6Ns9ZahjqnJaB6Drd8t3cktnLp4esd7QRG/K9SgJ75vOeEFnMU5vqZ4SiutVdk640F8fIBtYbl56IEdiR6pfb7h7C/B7h/6ucWy1D7WeknHtpKbWM3izm4R5tayWkA9P0OKWutUaFT7JZXhVvA8Vn0e8Kf/+d97/7d/e14M+Cc+8RT8+b/wnyfnAbcwDQWyYoxlacl1qW0BZwksuTaUGy11XOk6TMWddux7VTtW3BKDTx0/x9WfqgNVN4ptsby8/szGiYW7arBo43K5zOfh/9aeMes+uGdhiOvUouOaWyY3eKRt84L1LXafTQkYyvW8v/SRSYhwjfjG4Pfr1Qu7d1idS1yosThLCb1YuGvCGKzPTrjX+/ccBOg/mbTAPvHCDXDsxHcBvPbIwvOACxnS1GcM9+hF3gHC+p2TgC2+ZnMT34GhRXjpsaSBL3dFd+aAW8ArQHW2x+okj9k5T4lKq/Cl3DUDlpFTDTWvGy4LCzKLUMw5tlSuNXbbetyUEC69zqmy49+pcsIHOhbOMS1EtFac47wJ+LqGup88d5a0gIfl6+CqN5VBtYA0UKi1VJYKcO310Mz3TCVMiwnxy4E4jnkKIlwLZQVPXceUy3m4fljUxfHqKeGG14fkbNxzwlnr8aABnkYsZGK3WN1zn9PU91zzXAY0sdYWK3huBvRcAR7Xf8i50qlBpDG+F1TyNbwux4DjAtyOW8B53AI+IyQR2lIgUw2VNZNkKZIbr5XUHMapD4b2epdYb1PHTYlvSz1T4OtOWXMkSizzOO6UyshNuYFzlmPLNdc+Y/H53XdhOQY70Ep4x39rj0HlUIjLCrHyAACbD28ti0X5uojwGmg8bErRlFvD+s2heR5iAVE6L/KciIWA1sMGTzcWiL0H+uefgndfW05ZXGPxRcWMU99A6f0O++Bp0KjnO67zqedevPaXLpZbGhy3tDubhx6AzUPbAyAaS3hOgjMJPC1eDjn14aana401QWRLtnIwbP0tiWY8U0dgiHbbcVrgArwCU5+SbCjxXaPsWm5o8UCEVdxSItRiZaOOpbHctkYjxnPvIWVpT8Ug4/dGeoe0AwM5MfK1oaYoixO34eRr8TJNzG4oC2e3jq+3d0TKqDFwMcY9SD3jnFgLcCIidtmNreDY+h2WvfL4S9d+fQq+7mfZHDST48zGCTh18fSO60R5oVDsyAZ/4OBO0d1tJVjbv+fgkiV889ADi+XH9m1bYUNcdirJGRfjHcrD++NzxPdeKwqpY2vdzrf+PnttwEB1OHP9ALYFNhba2No99vzfQzGmxRsT3qnjh99YvHvYw+s8XE+Kb8eZM+6CXompJGOjOgpjxotaGswc8W39gGit19Z9LGXWcvXO2Vcbr10LbLnNjZOm6my9FmF7PIUXZ/3OGVjj5geXtqWgspxzc44PxVCu3tx9xus1gxUWalpS5uR5QFlwAXZav1PCO2ZbgG9x0723wO4f+jn40g8eXyybqjCnBDjl0YVDpI7te30rQ/jxfwIAAPc8dBPsft/mcuFxBvj+k0tTtKWIPVs0WCyylEXZIsBLyUlqF1NqDcdT2NWAun6517glU2un4vY8d6CAS0zqyLgLOk8LF3QX4BWh3GeHEDpYdHMW2LFi0iWk0XsrNV03pzCAkZsNXVNui3NKlVvbzRtfC002+dQc3jn5HEo8YCwiHNdpFZHEd2oAZuxrMoZViRpszcm1EUglXgsi3CLAKfEdlklc/ZkfAABIblcbynMnNSgUBHg8HdqC/pNblldqnREu2zg3nVjAGocuxYDn5mPhwM+sNE95ippu6RhukEI65hQENmZqghuATx5ZOpDpceA2XIDzeAy4w0JlvB27Y6pNYJMrvmsJd4pSl/VWSIMFVCeRE6epfSlSQjRVxhBhGtIznxLfYRtczxrnxVlxY9f00PHHFnru3s3JyspR8s6M3b5NAXzuJcnXKPEdRBsW5Lvft7lDjIfka28++QzcdO8tS9tj8T1lpKRQgZyBDu2UaBLUNGE49hzHFVMCcUs47rT2SoKxVawtlSskrnNJxvkU2ljyXCFde05vDal+kTRQOJZretwPid+tuX/fHEfiurErsErg5EhDWU7DsbjjjemC3przz16/+JeiZsxzzTK4jpz2GdLGnHPbtuSxh+9XCd8UIZYfi1FOhIXlOSPgVJ1rnENMqG/ssh7OB8eDc+CO6ZDT+NQA52mIE87he42Jt61NyYBgDdd17Xa59dTO+x3E9xMv3ACnnnsRTj33Ivzkm7fB52+9GS5fuQSfv/Vm6A7cDbvftwm737e5iHXedeSOpUzoAFuiO/wD2GnV7i99ZOlf2GZo63eAajfiNiB2kY2vZ3/pIwvL/daCTy6VkZovW8PmoQeWvnucwAvP4hMv3LB0Lz/4+C1w/tnr4YOP37L4F+DE6LF9r7PrStudUxdPL67jmY0Ti+OE/7XCPycTek7G8vAv7B//jv9uDdfe5A6USLPODAUW4o6zyvgTvmJwMb5TbMxKR7YtFkDKYlHzmox9fXPvtya7OM5uriUkYmt9naXBhpDp3CqgY8s0JuVKT8W6cy52kvU8vu4LoX5hp8U8Zo5Wcc6aWOJtUcJcrl/tem4Lh+1s2MGaxseuvrhkOY+TtGERLrmtv/nkM0sWdHhyW8RyIry/9JFFmbWEehDI1LVNebpsWVMB9sdx39cSrlHHyLWGU1MMUgnW4m9qShSG7VuKR23bFLvqx9nRp4Ik2rHFu/X1BEgnwdMmyZtKu5cTQjOVujuOBY8Bb8BQGdE17sNjxTKXuEJa0Vq/pxDXPRZSPHm83JJELJVzAJfdir1Hr+5w27aQmv+bEtypuG98HePtNTH4mgGDOCvs3KdiyX1G1tn9PBepzeUGRSkBjqfg4pK2xaI8FuGxaA5Wb7wN3g4TW5tbiPBU7DcAPRf4/j0HAbr3JhOnkSL8mtU8xI2H+nz8J26EvUevqsSUxpppnXs7dUxcTk47xHkscYONMaXx39p4bk0itViID+2GXssLZ47fkRiP/7bjMeA8HgPuOAjtR2LuHXUs2qyJ1Dj3exwnLll+LXW0CqqSRGaxpTuUFZMql5sijCpLM5UYddz4b+raWM8/3nbu4rsGQ2VojxnT8tLi2Fb3U5w9HceLB5f0/vmntt3TYVtgX/2ZH9hhJe8O3A27rv2dSvQGUE90Byv1Ey/csIizDtMflZR5/LAsvKh47nD9cNK27//R18x14AZR9t/4ATj/7LKole4/NzAzpFhr6RYdu5CnEq2lxPQUM51rmVsYk+PMGbeAN6KVFZyaekdjAR/a4lvbAq7JzmolFqDYQom3www9lVdcj9x72rLONdz7U9Z3q1Vd4ypOwU1Rxm1HbVP7/ees/HMfWAqUWMDHEN8A4wlwasBFaxXlyA0H4tplnNCNm8IsFuDxfNnx9tppukq5/Noj7PclvHtUYsv4egWLdirWOyXosGU8Lk/rUkzdR1zu1Ny7AWjLZex2r8lhoE2uhvepyRgJ2ADKBkNqZ7gfC7d+5+EWcB63gM+c2gIo1fEca+oxAJ1FCscW5n6sSjrCcTZqab12eSmae5Z77FVxu68h7iVhrZkube/Rq/DYwzs/8rnCWzOocvLcWdh7dDr3sZbwrZUIcWgRnmpzagv0EuuU1g05tU2qDpToicU0wM7Y8Fh442X9808t3Mx3HbmjqRjfsjgvC93AllBdTgwVnrnS2TjwN/D8s9fD5qGd24R1oW6SJZp1i+8/uTQXuSbj+9CEvCE410V87gDL5yhlfKeQBHqNPskQtJgFZq6C23HmiFvAGzFUHPiUkTrEqfhCKzkfjqkImRIoC/66gq9BzpzfWvf1WDBzgj3nnmi9G0LZLUSnRszWFLza91A7ld6qeAXEcG7BGmGd6qRrrKep2HBKAO3fc3CH+A5ga3d34G5ynur++afIac7C3y0EeSxIsbCVnrGw7eahB5Kx3wCy0KPmFD918XQybpu7l0sx+pEAByizgo/lfRLIGRACmLawLqGGgJ57DLhbv/NxCziPW8BnhAsiubMSdx5X9WM4JLWeNc1zG7axZAPPPRZGSianjV+Xjokt5Jwgp0SjVXxbvFfwtsEi1wJNh7pmp7tWmMxYQmAKruiWdbnHSp1jWB/EHhbfsei+fOXSUqz45SuXWCsmtpjHv0MmdFUsOJoSDAvRQOraUfNWx/tsxY/z+3MJ7XBZeN7wrenHdopl7bN36rkXyaRvJc8vdS2GJCd0YpX6G3E/qlYb1MKyTuHZy511xwX4xJnKfM4t0DT0+APDbW9tzLnrZ8kCPjSUUCmNC8fgOHhJaAbBy12rVEKzHGux9fxyBf7Jc2fZuYApN/aSc9EIR8rSlLtfDtJgQC1S4RdjJVoD4Du3Q3VUW5ev7XRj63cQM9R0Zdi1HFu48W/M5SuXYD/hng6w061dQ5yVfWvBJ0kRnord3fI82blcm8QuuD/jY1DfMK4dio+T+vZhV/rjh9/YEaueK4am5L5OPcOrJLZjWghvXH5rXHw76878VdyEiUeHS4XR2K5eJZTUnRPfVPIfSydirOR0gZpCP762koixnLNW2Fszd0tZxDVIgxA59ZLmn44zq1P15M6hxj2VstZbyihtM8ZI3miZMnDsNnFVOpFWF3dsxcXimxLXqcRY1FRmoSxqeUw8HRmAPjv65dce2eHqHeoSDy4EuOdNI77xs0JZb+NvWEj+FuDErjWOH4Ce79wi6Kzibyi3Zq6fAJCeRmyspGk5uPWYxt3PnTnhMeCNmaIldSy4zksLC08Na/jU4MSwNV7XGnOrjUfG+7ZOVJd7Tlprbrxd7oCJxc2+9JrVFqOagZwhYtBLn/cpQXWcU51paVoobgo6aWq6VhZ17fzIGJwpPU7AFn6nLN7SlGVc0rd4kCCIcJy9PGVppNZxyUW19zjeLuSYkObBjp8FqyAO+1unMtQKam1W7ZpTKeLp8QKW6cOmIMS1me2la7pOIh3nY3HseAw4T4sY8OtqFeTQ1GoQptqhtMB16Mf+SOw9enUW1/fVC7t3WKap9Zz1lFuXOmaKMabGotyx8bLHHr5/aTn+H2D7/ML/3Aj6fQ8+mjVHeq4LPAV17Mcevn/Hcu19Dp25+BpIzwm+xi0yoFN1wXWwlp/z3LeCauu0sdWp7WMrKodFfFvb5SdeuGHpX2zJDX9TwiYIYSobOgA/hVnMriN37BDaYXmgO3A3e4zLrz0Cl197hLTsY7BYDAL2o/e+BB+99yWy/LBN/C+UJd2TvUevipZvjeU9dYycAZn42KXb4+OXDhDl9icsots6xZmV+L6mvCq4bcbuVw2Ni29nbkyjV+IkSXUgx5xyjKNWnaxJQWp+eOIs4wBtPBpSbuPadfHvYAWMBXvOvchx1W/5HKYEWLw+7rhy8dNYnEkhI8EShacCq+1+zt2rVNmUmJWs1djSFh+fKm8Icp5TTduoZapWo7gNtAqUXEGj2Y+bBxxPGSWBLeDSnOGUyLYQW9X37zmYjEOn0CT+ouK7uWslobU0YyFGCXPs3q61TgdSniWpNkaqO65rDULIQ3i+ju27lLz22nvT0kIuXY8ptk2O4+QxLcW2onBuqLXcgqdsvcUd6iHcQ2t2ohfxwAOFEmjde1Mx0FQZJQMinOs2NwBgKReXI21HUTL9lDaxHWd9BuCTzQ3xzKRc/zX3A18D6nrmWqNT76Il43qoAzWYUmOwYIiEarlo6sZdZ85FOTV3tAZKiGhcZ1Mx3RR4OjJufQBbv+P/AZZjyykhjgUzd/0CnCjD1tIQa1xbYOWIam491QZYBj+1xM+g1sU/VV687ZYItw+yjEHqPeTc9F2Ue+y3M09cgA+MlPRJg5ScaYpWcIyUxEabwKZVRxkLkaGuJyeYSo9NXWtsFeeOk9qXW1ZqaW+NNLDBWbVhY+u/LbG7fG5U1niL8OYs8SVg4ay9tpZ7pxXfpWgEAPb2CMusTLkjmyOu8DZaV/acumnIcdvddeQOMb47tZ9kMY8HAWJrOCeYAezPNBWHfGzf68mEX7lJyyRBTb0/WLByISGBVu10zUHzuWZC14YJTLmdchxHz7TV2gqBreC5HV2tWJoamjpyI8C4M5IaKc7N7BrXdQoDGa3q0dLFOMd9ONSntJPBWW5TIo4acEnNcx5iM6npiEoGObhtSjwXKDgPGhzGIJWTEvfWexgPJJYyhzaxhJx4Wq0bcyu4LNQBLtkaJ8KxwObENhf3rQULOG2yLu48rYKQmgnEmsgPYGf7mNqe+v5I3ka5IlraJ3cAIvc5xl4PU8iM7oLbcVaT8VXGmtFS2E1BNMbkugZrO4LaD1POByzXnTm4K9dyPy5x6y5BG+9Xy2L/0Xtfgv17DkL//FPXOswvAsD2vePqI3mEcL8xnEA/ee4s6X5usShjpKnMKKRrissK9cXH5s6fCnewWsynQHgOp1KfUlKDhlrxLa2v0anPybgdYGOvb70ZAHa6qO9G4pyziscifteRO/ika8zxcVw4Jb4++Pgt4rUuSdAVRCyVXAsfMzzzceK3VuEeUn4J6tiUNxGVTK6VuCwdTArCewzx7YLbhrufO3NlWoptDbDGfdfaZixyO8U5U6MEUnFlrWmVqC0gWVc111sbspB6rnJdzXF9jx9+A979xZfh6pO/BAAAuwDg2L6b1Z0fKY4b/9YKzZA06YnDNyxZurXXi4tTxpZ1KV5csn7HUxPF5xKOgwdIpPPllteeu7ekPMsgyqrS2oLdyvrNWSUly3IQwbEQj6cnk1zLSy3eVJ1S1nu8nEvwprGoWizJr17YDU8c3jnNWaptTr2LcTsiHTvloRc8hXJF0piu10Nbv114y5TkenGcIem6bhcAPA0AL/R9/+e57aar3FaQYBnFneXax4hHmscW5yWNZm6HUvMh04oBiwtwiZswZ8mN6yDVKfej1NLdXrJYx8/p+Wevh2P33gz737e5bQFHnVeN9Vuy+mot+oGQPffYvktLz0gNEaiZzoyyysd15KYmwoMQ4TprY8zj43qHsA05Fm5tfgwNJeXXyKjOiRpKkC/N1X1NjGOBHVvG8VziHLGwj48R/tYIL8nSnZNdPYYT4XjqQACAj//EjabBtTgjO+dhpAmZwVbyeDu8P9detSJcv/j/HGqKcG4gyttZHdR30K3fzkR5EACeBYC90kYuwEegpSjeEvjNis8iJ3400PrjpLU2WD0XNKTOv6UrsOUccExzTpw3Bj+nH3z8FgD4XfjovTcDXLm0NI+wVK+4fE3CrrA9N6gBsN1Bunxl57Q11jhH7loFizcXrpCqIy77zMaJHQnhwnH2HuXjOSmPBK7eOc8Znvool3W2erQW39b9W1rKQ9mUuKWs4hyUEN8Ob0lDTRkWlllczKX9c4Td5qEHAADg47DdZqSs0BjufdR4PHHeRlzuCM5KnvK6wXXNoVR81wQnQ5zqdIeO45TRdd03AMD3AsBfA4D/RNrWBfjAxBapmmiSJ41lDZdGyafQsba6/AHwwklCY+m2uH2Haycl5glIVlwp1g/XX1tPrqyUVTrEV9ZyU9Y+81jEh85xbuZorZu/hR2j/Rvbf1LiG0MNpKRc9S1Q982a/HAdaJl9vMacyli0aMvStEOYHAsjtoprRHU8x3jYvsRKTU1fJrmpU9nQc8DeM1pxSyUyzQkJ4doyLkzmvgcfHf07b3knhnI5d/Fdjlu/nYny4wDwfwOAG1MbugAfgZpCWPthm4Ir+hQGAwL4o5wSfbi+NeK8OfEDYBs40biUloy4S50rjlT98WAR5/osxbtzdcWWcGpgiqpLfHztc4HLWcR0X+BjuvFxU9cybHffhZ2u69uunbbBh7HfPwtjd+DHxiqqtcKK8k6g9tUel0ocJkFZlK0Wy3gubwqcVT0W65wbOlVHLMykecNTyzRIVtKc2VQwr17YDXA4a1eSvUevwpmNE0vGhTBYgN3P8btcez50bv+hrOG5gxuOjsU3fSO9rePEvPj7X4Cbr3RZ+/7OGy8CAHxb13VPR4sf6fv+kfCj67o/DwBf6vv+013XHU2VOZ9e2ApRM0u2xWI6NlLM7lBIggpbjOOPqCUWHECXGEyCsx6n3PsAbNYvyg1ZUydKYHJoRLB0rPg398xorgveXgqNyO04xa7lmrhHynIteSRo4sdjNPGZtSid/g+AbiNK24m5CXju2tXszMcDTFz52qzqWLynpumSRGncPoftKGtziiDM8dRm8d+WOPFj+y4lE7Fhwpzf1N+Y2BNACtmQ2gnpGZe8JXLbA3w8SmhbY7/xYEwO1Dd+KOFN/e04zsrwmb7vHxLWfycA3N913fcAwNcCwN6u636u7/sfoDaejjpzTLTsSK8quNMZT/fCbathCMs+5f5NxUCHBDshCy/nSh1DiR68zDJopHWDxnXgLNcpJAs7deyUKK0xULR1rXSx1VQ902Vv3RNuYELqkGtDHeYkWjnm2EZapvfKsfRx63K9ZOLyUlbgWHxiIUodOxbBksWaEuexCA9Q84xLx8Tkim9cBl6WaqdT94YbTOTWteg/WMKjYqTBAQ6pXK34rmURd7E9PJZBaMcZir7vPwQAHwIAuGYB/8848Q3gAnw0SqzgsRijmEunM6eTP3Tykppu27lYre+Y0uuldTdP1c8SQ26JNYyR5tmOn7c4fmxrmjG+ThbrO4WUHM6yL96euyext4HWQjYVagmDubSBrch552vEj8cEUcmJTYzluLFI7p9/CvYfuFsU4QFNvDjeJ5RrcSfntuXOHXuPaLwfNEk8NTk9AOzvCyfqA5Q3hOaZTF0HDsmTg8uGXpqkLQ65cBFeTuobNZdvmONoWe9eygox18ZpbBGucbPErnqxiAtJZnKxdJKk5fHvrU7eiwCgTygjdcZiocdlzY6h1ms6i9Rv7tpw1w3XlZu/G7tGhvjtVCw2Vy+rxT4nCaE2jwK3TWogAe9X0qYM1SHlnkXOq2GObSSHJcZbu3+p+I4tv1akzOcByjodRDVeF/alpjKTjpFaHsO5mXPbUlhDDcYWfKl3iJpyrqY7OVUel7ej1mCSRah7/LcdatCYw5OvOXOg7/sLAHBB2sYF+ARJdeZzrYPrTvxR1n5QpY8oF79rIUd8S9tR51W7wybFdGssrrkCFB+XcsHnYsHDOkqIndk4AbARpuxKJ4/DcclYrGrfTyqcIHX+3P7SPlzIgvV4QxHXM8fFPi5nndtEq8CubfmuXRaeFxxAdhcPYTgx2jhyaT5wybWcg9ont02m9qPeh5w+BEc8OCm9ezgcQZpOMtc9XdpWEt+loWZUnaQQkLEHSuaIKnzNk685K8L69k4mADcl2bp0GnM6+DU+aKVlDHV/YqGqqQ91PbWx31ZLM7dtCiyKU9tqypKEJddZDNsuspYXxrdrrK+aelnRCnfLM2uxRowFFc6wLu2mhVjkSANztcR3zrRiAcqCHJdHJUHD1u3UNGNUbHaqzvgaUXN7h+Wbhx6AUxdPs2XFx8oRaFJb33JgHrePXD3ia1UyEKNJEojXt8R6r1x421j3wVJnPfEnfmRKXFlXiTE6+xorePzhLY2ZS1m7KUtlzrOg+fhj112p80aJHGqf1O8gljlrMXV9Ndc8JcSpY4Rl8TReYbk0lZjmfmi24eLhOcs+tV4DVZfSnAJDkTP4U8OjZBWREq/VItf1PAaLW6uIS4lvfAzqd0CyZMa/Qz03Dz0AAACbhx6Ay68tZqbJFqL4uFw7kUPNmVgo8PWi6pvjoSENGGE39Nq0mC7N2UbjPefJ15xV4rqxK7DuUA3KOncUhyYVX8Z9YCkXZIugsbpex4KtluVUEi2l1mmpPKpsyr0aD0RQ5x4PDOB7EL9bkjjnzsXysT+zcUJ1Tbgy43pwdQqDA/G5alzQtc8Ld0wLtUU9db+15+zt6PZMD3MQCsf2vb74Fy/D28Ts33Nw8Y9aHtCI4PhaSd+FWJjHZUuWbw5p3nROfMfLcgdptdMZ4ncp/l3r/ZKuQfxMHNv3umqe+aGs4kMfa11YVyOUs374Uz4x1qnhmcq54tjweFmKVBwyt4yyblIiFMO5/pV0higrsyU2WVt2jMXyrYn1xWUC7MyATv0doDqjFgtRatvY1T01ACFZ8PFxtPeHO6Ym/EVrbSt5n/ExUp4X3HoMNYizTlBTLlLztddA69KtgRLhT7xwQ1GSNyuvXtgN58GWIC2I8GP7tpeV1rfWd5JqJylLONeelr47qfePys2ivd+1B5hwmELs1VAzx8E6oulbUOs8+ZqzargFfAJYRqLxslUBd5THEOc5H/H4Y8KN3Ernoj1Pi3iwXEPpmYr/znX9ouoRW7M5ESW5m4e64Xei9vuQc84acR93JDjvg6EsuNrjjPFOUl4PlmfEum4Via25eAqmlta7HNGZEjbYOg4gJ1WTEqnFSB4C3HPPbR+Wh8ECzXFbeiekPJ20g4zheTnzfe+Aj3/Lc3Dm+96htkRr3lm8T8wTL9wAH3z8FrUXQ/w/BfUcAWyJbM4D49TF06p76uhoPaDrOHPAn/AZs0oNVO3R9trgUdua8XgaUqPFqTqUxhNbrMHcNZLIcd8vff5rxpNxrvEAy9cuHDNMYaeBslLViOHMeW5reUZI9anRrkn1XEdL+FzQWDzj7OZcFnRttnMuVpkLZQHYzhuBBzZwWVK29CdeuGGHxdcixGOPGksiScmbJrQp0rft8pVL8G7Ymnv92L6b2XrHsdgfv3DjjjrEZXL7x9dXSiZIeXTgbOwA6YGYUxdPLzwXqIR/1DFx3Zw0kmdfvE28nVu/nVVkdRTcirNKYjtGk3hjCnDCIHaJtexXUg/tcsk6rHGJ57ZNodm+RPzVIu68hk71kB/6VMc5nG/stk4t00Jdc62XBOfFkNpOWxfuPZJCO7THwXWcE1SoRu4gSE2BEITJBx+/ZYcgkqbnqmFBTIlrbr32/MP11Q7Q4Zjw1AACNzd2TlZ0jfjWvDehHFwe9Yx9/tYt4S1NMRYvx+9uXCaX2AznYZGSq6Us8dqke5Ztw72yzA/uyMytbXacUlZT1c0QzqJVy9o3dUrF2FDuulRHQhIHWjGKyyyhpAyL1VoL9wxL5UtCMVc0YpY7m1uCMJ4W8LGH72enCsTkuPtLhLnJQ/1SbtWasmu9I9Kg2d6jV5MWoVTctrTP1GjhERBfV02c+5BgwaERH7Xm0ua4fOUSawnnxG4J+LmnrK3h2OG3ZhAibCMlX+MGBTghjr8t3DOEBffeo8vrpSzwGgswd2xuejxpfStSHg3HD7+xyHS/PXuGW74dx8lj/C+6s9bkdmBrDUxwVgc2My3s3FbTyYmX1x4sKCmXG8jALphUop7Utcfu7tY5t3EdpURl3D4cVF1CHcP0Y8FCTrmjhv24DjFnQTp57uzCyi5ZmYLoD4MAcX3C3yVeI5JlVdo2RhP6oC1rKGoP3LUe+Ks9OAeQl3iNy/ytIeUCXJKkLFi7gwiPrd/x8Wq7CXPPPWdBDWLcYn0FWG57W0/BpHm+pGnA4m8p1WanRPjYQja2alNsrdtqv89snFgS4U4Z0rPn7ufOquICfEJQQmfsDutQWMScZrmWGh99jZUqx9qnienOJSWgw/rYNTFHhA9RV+128fWMz0WbpAh3gKUOcbwOd9SCuNY8u7HojsuMXedTz0kqf4CVOE6W2t/dMsvgnuMWYjxGum8loj2ABaglo3kqJhcgnXQtFW+dE2qz9+jVJVEqDTZQ1niuPvG7pRXeoa1IhbRwpN7ruL7xvaSupfV7PYT41mTnx9Z3jtCme/y3HYvB4NULuxeeYI6zaqyHunNmC2X1HCtWKMfFjtoOQLbEWc4Pi//cfWNiawTXqVtYcS/oYw9LkoZpPB4sHWhu0MSSGE1DLL7jzpr2PknHiV3nJaTnQvPMcDHg8f+4DGwJc/RYB+vGtIzHYIErWcuxZZhC455eKyu11tU7XheHhoR7UKM+cfhJOI5VhMe/NVCWau65ajENXGshi0MBSu6TW77zsYRSxQYAx1lFvHc0MWpkN14luHjIocnpGGgsj7nnUyKouHJCXTYPPQDnn93uZLR8HqXz4OqXCy6TizPnso4HtB3irURpO5drrSx7j15NWrU04hkTPyOvXtBPVye5olPH0JSp3baEMdsNK9p6agcjKWHDCeVSqOzUVNlcPHQsxiXRHpdhnXOce99ynhH8Xkru2dzxpbjj0nY3Z//gWo3b3fh3fL1bWn41ic7w82IV2Nw9056XW75tSIM7noTNWTfm0StxFrhAHwYpgVRKcOQK4ByoUWNpvbQ8fBy3rNpy00C5o1PHoNalOrrBApRyeddYfvF2seDkrhsXc0m9dxbrVEDqtFH3JVd8c+W33scqIqdATUtyDWoMTliEoIRV5IZjU8fA1lM857L2OJqYam2dMHuPXl3KwSBBfY85wZiamzqcS+73vbRvELyaqHcBu9tTlFixqX0sA0Xh+nHPg5TNPFUPpy7S4I7jrBPT6QE5KtZRfFNW8JJGuySWUuNCPibajjslZq2dfepY+N5w4lu6XnHnl5tuKyyTOn3hnOLYRq7eFNy7hhOxSSI8vh5SnCdHKqZT+9yFbS3Ph2Y77TZjUitsYUjiZ7VGHUvEUa67bus8ANZ6pQa+8N/agbUwUwI+lhRPjy2v3JRe1sE9nCciTvQoPVNh4DXsr0kUiWktXvE14zwpYit4DZdzpz5cjov4OXP3c2fVmWbvY81xK7cuM3PuSKpGOObUNS6ztSCXrgtVp9ouX5JVG4s9yzFx55ealzY+lsWFtnTQwRJbmVqvidvf9kbg66PpIFPnWCrsLAJ9yu3ZVAbOAPhOKYVWEGneDY3rdC24uG6ryzBA/friZzrHuyUgDXhwy6zvo2YmBsrLKfxPHY8KebF6Glm/pdprxU33xi3LiVWfQjb2dQA/fzgsCgA8+Zqz8rgAdyYNtmJK2wW0HYAcESIJbPwRadW5T1lyOddybbxwqdWzVNjjaUco8R0f/+MXbjS52ePfWhGpWRbXlzoedexUGAHeBx/HEgeOvQGketViquI7Zs6ukJq6p0TFWFnrS1zbcwYOuPwLuZ5VqYRclLUbx+NzWMV/Kl8EVT7VVm3FgG8te/XCblaUcp5k2muYe98oaiaG0+bocMqQnhm3fjvrgAvwiTJlq9FYaEVBTmfKImZTZaS2AyiLPUuJfCm+O8clXCvIags3gJ3zdHNYrTA4rEFzjBiqg6B9X6lBhDMbJ8S4ek6ka8+ROn6qflJdxnDdpp6vnPOjtq0R2pJLznG55zeQmtMYb1sCdQyt5b228KfKpBLQSTk+UnCWcSnchKqL9bjc8eN2g5vGlPNIovJcUG0ovmYlYVwU+LpQAxdxnPyQGfKd9sx5ANRxcnEB7kySkg6+xr03wAls7vg1pkuplfhFEpBagUb9LW0nQV13q0s+tn5bB6IsgzRhe27gxBoLytVHY3VPJbXLGSSwbmstJ7dOterAPVOagSDqPR/Ce4Wri/X6WSzeXHtjFb+a/VJlDm1R1LqAA9juw/b7Wi5EucE1SujHeSe0WOq1dLzIBbjF1FvcwA03cIRjurUZ8znmEh++quI0NYjoOKvOdWNXwHFqUiIEYvFHWQxyrDVSfbQWKu1xuBjSeB3+nQtnUZHqZ91mCA+Q2A08HP/MxomlupTWIyVe8XLroJF0nBI0sZ/xdYvrbn0+NMfLgauH9A6M0RkM9Rzi2McPv1HN8qyx9GKXcbydtQ2kBhTicuLf1vPEz2/uu089Wyk3boCttkfKNfHYw/cv/uXUKbftx9+QVs9p6lkImc657PlSJnSurDkyhgdSTSgvsIC7nzvrwrzf4hXH3dC3kawTnGXM+pGiRvmleWzxNCkU3/+jr5HLa1qCSj7GuddKKouCSxjGWciDK2Tu889ZZzWiEjZ2vnuahEypjOXaOqT2x8u5d6NFSIAEdufmsLZrsXu49Xy0YmFKViZNjL6G1BROXBskZe+mlnFzW3PLuPItgrlVsizN86V5z1XtDPE7JF3UvCPa9yg+ltSGxWVpZnUYk1yr9xTIDY9LLZsjY4b+OM7YuAB3ZgFn3cVQMc65jTsnvt/9xZfhR3a9DD/yLVvLugN3w7F9l9i4tP17Di6WnXruxay6cFhdrrl9LOJGI9qx+7ZmLuvwd5wEKC4jEGf/pZIIpaw8qc57rui3xoBTzyq+bnhQCN+n2GJXw01c2l5bVuo4mkEKvJw6T0rw58aET60DWDJ4gtsta8gLlzTMklGdO06J+7oWKlGbBBao9z34KHvtKZEallnDbaS2MQxAUvWwJlkL5XDtaAmt35twL/GzwU1BpiWVK6AlJddslcQq1aa79dtZJ7q+79mVd911V//0008PWB2HoqUV3DK10pzJ+WhhC1IQ31/+8KcW23z2yevgC5/7CgAA/OBPfT3sOnIHdAfuhstXLsH+PQehf/4pePPJZxbb737fJly+comd99WCpYNOib3cfTX1CPuE5GI5SaYsH2OcPAjXIyz7/h99beGymJr6J6czUOMdoqZeA6iX+EgTH60d3NJQy8uiVcz5lDu0cYfbcv+HiMXOsULXTLomCXzK4s55LGkHCym4d5UCP7MpCzY32Fby7OPvPS7Tklyy5XsjPSctEq4NmZtgDBFd69vREus336lP13Wf7vv+rrHr0YKu6x559FP/+fvv/NPvztr/F89/Fja//2//WN/3D1Wrkwvw6dNaGK+6q3vKQqYhFuAAAF/+8KeWxDfmex94GwDAjm2+94G3wdd97C+zItzycS51PeeS/0jrJQtj7Q871WHFUDGSnFU8/A7CgTrfGh2A2u8StmQBlF/rnGcnRwCXimbt/pLnyxwt4DG5nWdJxIw5vVKr+HNu+ijJ5T2Q25amBDf3DFLPNfWeU8eoMfikdUWniOvTKhRAGkgptX7HtH4PqG+m9Cy0qsNQxyoFJ2F1hsUFOE8LAe4u6DOgtUBedfEd/g8uvrlxR0+8cANs3r7lTv7OhwDg+D9ht5XEeSCOJQe41sE4nO4UaIRIqpOWu566bpzFtHSkH59LHIfNPbM4Vju4cMdlffzCjVvbNMjyW/IuUQL71Qu7d7i44r+5QRHu2pd24FP7U+3VULHoi447rM78vbnvkCRQp06OyIot3NgNPXXuOS7/qftiKRMLYqnskrrGbSl1XE0ZgZznSTuLSLhn8fZ4n2P7Xl9KwJZD6zm/NfHbmkFVjUeSpQ5TxMW3M3e6rvtaAPglAHg7bOnrn+/7/r/gtncB7qwFlHixjERvHnpg6Xd3AACAF+AAALe95+0LEX7be94Odx55C9750D0AsB0XfmzfJXX9NR2vlOhNxchKo/VxJ44S4ZZ6WIjPe1so89dhWfwtd272Hr1KfujPbJxYEuS1sLqN5lwzzf2oUTftdpT4zrWEW+qmif/OqUcrD48hkARMbnk1p2CMoVyLOVLZ17XxvfH1wQOzmr/jcnKuCxbEFlHFPb84pja0mWc2TiySvAViN/TQLkpu9Zxleoj55nEZsfBO3XfNsVtZ8zHa3BUxWnGueX64Y65SfLnjjMRXAOC7+76/0nXd2wDgn3Zd9z/1ff8UtbFPQ7bCcNOV5E5jMkeC1du6DyZ0Yk5dPA2XX3sE+ufJ9wkAtmLBv/v8n4HvPv9n4HsfeBt87wNvg3seugm+7mN/GT5/681w+YosusPIf2wBwB9r7l9c//hjGq/HSOvibai/NduXEDqF8bnhpEkc8bqwXytL7H0PPrqwvue8X7GHBreeu08tOk2lZcZ1zSmrxOpboxONn5c5dkxj0VFb/NQiWDEl8W0R5gFrhncK/A7X+I5w4Hc7de+kdiIuD8/kINXz5Lmzi++cRnyH/4/te910XVOeGXjQSDOoksryr2kThk7CFgRvrbYWf0Oo7wnnqcYZJYZirOM6Tk36La5c+/m2a//YOG+PAZ8RJclhcln1+HAt4SMZx6F9w9lfgp/94d9e2u4Hf+rrYff7NpeWUYKbcpuzWm84ao5k14xX07ofSsen4Kz2Qw4ySdmRKTSeBhyabVOxhylvCmvstfaZa93R4uotWfutXiFzwTL12BBIUzqmwO2ldB4l7SfOAxGHpmDLpeRKnHrOU++NNnEcV/aZjRM7wmpSop3aVvPsh0HSk+fOki7dlvuR+kakrNSWKe5yPRZatAcWN/QSL6mh48+1uPv5NFj1GPC/+vM/9v79d7wna/9fu/Av4G/96H/9CQC4NVr8SN/3j6Dj7AKATwPAAQD4O33f/2WuTB92WnFKBbSLb55dR+4AgN8mltGiOyZ0PEPHcsyOsUROvFqghsUMCypJVE6JGvOXU2iFoeTtoDm+1a19Kh05KXdBaRlzxTK/NiZn0KwlIeZXEmKcaNXAiW+AtBdQyotFAgvX0ut98tzZ5DssiUncDnDbxsLpzMYJOHXx9I5trM9fynuhxXR11G/MEO1CygMqtW/qflrc3lsyhTo4TgafSSVh6/v+TQD4U13X/TEAONd13bf0ff+/UtuuVk9jxbGIaZyMyikjfNziD/SP7HoZvveBt8Fnn9yK5Pju838GPn/rzQAJ8Y3BH/3SzlfLj5pGkEtztlJok85N+WMd3k0umRK2juXEbZd2AFNW7bkMbtQGx/euClYX3BQtYmSfeOEGlRU83kaTeCtVT8oa+uqF3QAb25bc40JCTE7gU3Hk8bqwLHY/fvXCboDDy2VL3wT8jOJ+Qbw+WPTD+thazYGPLw5qRXkztuLJ6Vk9KBFOWctrhA5QlvfU/pryNZ4NFqQ2GD9DkrhOeWZwx5gC2OPEcVaFvu9/r+u6CwBwHABcgK8bklinxLy7m8vgj1t34G5450MA333t9+dvvXnRMWwxV+kUkT7m8TUISee2ll9SXwvcmQzHCx3J+y48SnY6xviop45ZInC52D2L27dFZFJivdT9csridhVczgMW62/K1VlbjnTsVtnXLQMCmmzX3/+jr7HC1HJNufeMe8bwwC4mrKPeH007R33TpXc6rovFPT1O+BbuTbxNyeANJaq1mdQt5afQitwYbbvHeVhYBPVc2q651NNxLHRd904A+Oo18X09ANwLAH+D2366PSLHBBd3ij/QYT31UV5n8a0dfFi2hL8IALct1p1/fNniFOYOB0iL76EysI5BEN/9809Bd+Bu2L/noCjCcWbimNCZue9CyN477cytlPhNxW0Hq0BqajTteXOWdsmyhTt1KVfUFK3Ed22r1CpgzUitibdt1T7lJFiL2w1tvVIDAnj6stQ14dbH7wd+b7jtw7Yc0jlav9mSGE/VI942bBdczjUDLlzb0fK7J2VBzz2uxRWcur/aPBSO48yOrweAn7kWB34dAHy87/v/kdvYey4zgxOK0rzIjg5JhFPrNCPw3Ci8xsrUUpRrk7FotuOsQ5J76bu/+DJs3n4QTj33Inue1HKpY6MhZ/5bK/g5sdZx79GrC/HdUlxaxCslKnKo7WWzDuK7dhw2J75bWK1T5UgeQ7WPi9tibnA0FV/ObYOtypowE87lOF5X475z7zonDLn2FdfxPOjrVkNkchnUuWdXcm23fl/xYIomfp7arqbYnrvnjidfc1aJvu9/DQD+tHb71e+9OI6ClCjAMXaWjx0ehdd+9FtaBlLiq0TYxOe71bl9EY7tex32H7h7sU134O5riepuWGwbd5bwNcbXLnX9qXjsVoNRqXItidMs8ci1YuOxCKjRkePOQ3MP3Kq9BSeUa2RzlvZLxSBry7SIeG0sOAAvnFPEM1hYSV0Pzt28RCBJseRSsq54+7DtmY0TABv6NpDz2MGx61Q7bX0ec/fhaPF9xZ4NGqR8H7loYsG5ujiOMy28lzNDaliRPN67LpqPrGb0fgy0scnc+aWsRdu8uPiLSqQUd8SwGN089EDSJTuu+9jim+okaztBUqyn9Ty4JFAUlnhyzTZW67p1vxpMqWPKCRHJjTaHVKKrWKDmxFhb9gvHy8mPEWdEl46pjQXmrKRS2QGrZwhlTZXeVcqKmnpP4n00XjmS0A7rJWvuR+99CQC27ssHH79FrBtAWXb+uIwpUyNnBhUOFP7WtOlTHcx067ez7kzzzXSa4uK7HNxZsn5oNR2PmiPnFFKCnXi9xpqTqisWEZp5fUvOPVjAtzLzXoW9R3euByh3RcfvEtdZAkhfI01HKUeIp+IPpfqUuE9aLflhn6E6jFMS34GcJGZWC2JKVOZallPLKILYr+GCrnE7t+zPDZhS6wD49lRjsbRaL0tiysO6MxsnFu0jLkPTR8D7xV4M+Fppwh4oSucFrwF1D1t/mwFkF/fcELKSwVfHceriAnymWEQ07rS7+B6OksQzmniz3HpQv/Ex43qkytPWLyebLQA/bY5UD/ycx8f8+IUbd7wXlNW8VKBbxK/WEwFg+Z2WOoM5bq+5+5Yclzp+TVL1qfWe1YKbyi8WxyUWRMnSDrBtnTr/7PZ7R4mdVhnOa2GNQU8lhox/a4WfJawk3keqR4i91sYlU/UJ7R61bzyAaSGI8NirISQjzZ0FZGpWbhzrP2ViN3iJnHPJ8fBa2meD385x1gEX4DNG+4F0wd0Oi3UCYxWhUhkaF0nutyb5Tm2G6lRxrqGUsJaSG1qnGOPQdHSG7thxx8utR67FvSVSAioskFLnzIm60mdacjXnxEvOMVP7cHNFB8GPLZi5IjyIstoJ2ALS1Gqx5f2JF25YXN94H8oinnOuOV4jAY3Yy2m/qfcB54A4ee4s7D260wMqFYqEn+P4Wu+/8QOweQgW5acoDSuoQerbOYYIT3lSSSEGKe+JVPlcGRoPr9j7wnHWnevGroCTjwvrcSn58FIWFYzWRTGF1fIa9ok7XrXqEnP88BtZnahQn9QHP6wL/4es9OefvX6p81fDDV0D7gClOiFhe678Wu+/dnBGWpYqOydMg6tfLahniDpWeE7x84qFY+7zjBnb4nfy3Nml9wOfF3c9UgTLaPjXGmlwMojuWHhLGbOpJGypc095pkjtLm5/4+PGdZFEeVxGbhuH440t72Kr5zg8d9J0cC2QBvGGOl5qe42LudlSDfq+iDWUwnHWGRfgM0b6qLacZslJx9hJnZXYMmDpiOYKX8todlx3yhpjiefVYhUtWFjnohGwjz18v/gu3ffgoypLTgBbmHLJEd+ae4OvrWRJ0aKN66aeu6l32qj3VxKnWtEaDxbF/+L1Q1gAsfDOETxSOzekEA9/U/WRBhXOP3t95JIvC/UYS1iIRtymxFWqndKgHXTNffZOXTwN9z34KFsfy7ux6li+v1bhm9uGa9px7XaOs+74W7IiYHd0t463hUvMAqBPPpVCU06tOWJz6sSdL45LtB4Dw7k+xuU/9vD9cOriaQCwXRPL9HNDJi+Mz61GLHrAEmdeW/xq4xClfAQSqe1rnk8s4KT4Vssc9ymwkByDUG9NjPqUiOubqiO+L7nusrh9AgC478Kj6meaep5xmxrHhG8N6JV/g6j2Oy4rxwoeno/zz14v7m/NRD9EErbA2IOBqUH/0vwCteFixN393HG2cAG+IrjgHh5pNDpYNKSPnTQNV4z04a01JzCFNc4rjrfGcbUUcaygtSO19+jVHR/yzUMPXPtfZ13m4sO5+g71jlEu4CkRrolFrGEJscY85rhR5kIJhBadTWw9lQRd/IwHyx1+1kumI8zZNz6+9N5xIl8S/9S1qR3nXVvkc94EpYMccfuveX/xPpSAwVbpzY2t6Rm13xxcHnUcbruw7asXdi+SwKXQbCtl/o/fGYoxLeFjxX9rqNHu1W47F+V58jXHAQAX4LPHpxQbHqtlDhN3KLD1zNKh5uJRpWROUrnWEXTpOtSMFczp5Fg6R9Q5pKb7iqc4G4pW77n2WuV0OLmERVpXxlKwoLHW35IQTdpfmnpJsvpZ0LYduC7UwIU0jZSFFi7mJRm1KfCABD7XUxdPZ2U8x4T3N34HOO+heFkMbnO3ytz+fWbjhMnKjgntmmTtxvXT5OGwCmXNOzI2UxXfQ2LtC3lopONs4zHgjmPEGgeFE+IAbHco9u85CJu3vws2b3+X2FmVxDR2h/3ovS+ZrFe5MVvYUkO5LnLWHAvWZE8poUq5z8doOgl4Cp+aUM+LZT+ri5+UDCq1naVumnpI+RO0SaBq3pOhO/65idxK3NPxdbUO3GnYv+fg4l8NhogdB7DHG1vDeaj3LCfW97GH71+897j9wr+l90hqO6n48BoidF1iultR817kHttxHDsuwFcA66iij0KOR/zBijuRl69cWtoungrno/e+BMf2vc52jEutQTlWTZwoK7bcWBK2SEhihCtb63ouIXVCueR0mrppqOFRMKRHjFYwUrkDMFbXWQk8iKEV7xKSSJASpmkpmdNbsrCX1CFYv1vGnNcS5RZS92jz0ANZ15EafKS2yR1g07A1fdj28x63B9pwG/y+xC7umv2nPj/8KmFJ9uc4znTw4as1xF3WxyV0Zj74+C1w/PCLYgKjH/rl/wl+9od/GwAA9r/n7fDRX/k++ODjtyxtg+PkOOtQLSuD1ppZavWO2eoQb4vrvUev7kiQFtftsYfvF8V4Kkkc9Y7gQYf4eHgAIoUUe1uaxA8fPzfG2NKBy4nhD1hCH6jniiuLi/G3IGUBx67LFNZQEg24vZCmZMq591QsbgrcduE2CA8wLu/3IgCk48TDMbTWb01yPGmA4czGCfNMBdy72yovAR5Mxy7pmrqFdVwOBWnwKpWfpKVle8gEbFNFat+mZp12w4/jLDOtN9SZPFOKOS8VeVNAssQcP/wG/Oy/9tuL31/43FfgmxNlHT/8BtnppI5R69pJVhRsEUqJIa5TderiaTh+eOvvsJ5zHw/T3Ow9Ktc71SGN1+UkOAtgEZQzf7CmQ8/VIaeTOrb1RIqFpWJoMdgbg1qnQZsAirq32utujQHXeLyUWNOHxupOrvX4ibfjxDh3nU6eOwtnNk4kRbg0iMehzZ2haZ9DcjfNTAmxmJbqnTonzcDWEM/RuotvgJ2JTx3HmQ/ugr4iDDW6OBXxDTC9Ed4W/PCbfxVue8/b4XsfeBv84E99vdndfMj4OknIalyAY7f7gGaudC7muYZLs0Z4azveKVdejftqvL6Gm7+mLhK5LtdS/UvOqSR2nEM777N2W41Lt8alHF97ah5rzb3BdZHmMa8J1ZaFrOmpbUO7oGkfuPWpgZWT586qxXcM1R5Z3m3tNoHwTcZJ3qg+geV9K2kXOdbhmz0ENcOeHMcZh67ve3blXXfd1T/99NMDVscpZax5i51yLFbU0gRZLT/YuZ0zqkN8bN/rsH/PQTj13Is71sXCAnd6Q8e5hpsiZRGirj93T6hs9UFQ4Lql7gtn6UhZpazXodR1e4wO4dhWey2SlTyVAI1zKdfeX42FXhLe2ozokiimBHb8XlD7SvtI22iIzylkha/Zng5lndTGZ4dttaFEMZaQhtI2xOGJvZ7mIL7d/XwedF336b7v7xq7Hi3ouu6Rv/rzP/b+/Xe8J2v/X7vwL+Bv/eh//WN93z9Uq07Tf3OdbFx85zPW4AXnThx3eEo/uK0+2BrXYGuG5e0YUbqs2FrFQVmkNLGdkmu3xo1Uug4lUylRgwDSPY09C7Qi3NpxxmVb4thrdSLx9c5xDx4CrfClthvStZd6XjgBnzNdWyy6uW2lMmrML7556AHYPARLUwpqnxVLjHhOXgfrPlaPH27QkGpXOCEeoJ7LKb1zq0rO82Tdz3GcNrgLurOWpNxSxxq80LjL5rrU5rosc6PXtTpY1rhZquPPucxqLTWUS198nXOsRfh3LBZqzmNsoZZbsaYDDtCuE869AzVFN36mSqYIk/ar4e6tsaSH7VJ5J+L6xL8tUxuWPN9BkOMY7tS2Gnf0GDxgpLkPuB0IceIcqTYhdawhrcixFw0XOoSfn9IQJxeCNvC3KtyfWs9Ii2fNrd+OQ+MC3HFWAGy1oP7lwg1GUKI1dZxUtmZORMf/rGKFqv+ZjRNL1h1NRzmVCI3rLOPzDXO/p8rHaDpHNTu00v3MzXhOXWttojtqn7jMmvHfVFw0t66U0unLcqHENlc/zTIrkuU7RiuqteWlkO5vEKn4veBEBtUuW6n5TktW0PCbG5DE35Iaz4BbyXdSI75bavek8qbqNeQ4q4gL8BXDRxt1xIJpKh+aVGdNm5irFZrrpLUal3TeOEEgWWGxq2jscmqBi79OPUfxfO39809B//xTsHn7u5Y6+6n6aAZUNAMAWnLcZlNIdc+5tiUWRglL4jVNWZaBo6GFeGDz0APkcsnbwZJMLpdYWFsyy0vbhnWnLp6GUxdP79iHs4a/emE3PPbw/ebvLPVst/xW1y47t7zW36RVhBv8pQYdLWVZ74XfN8dpi79hjjMhNCKbghJLtRO0WMuKj58rjEJSJDyvd07WbdjYmSm4FNzBsZS7NTfyssVOY3mX0FrhtVAWv5oDVpLIb5GF2YImE7kGaS7xGlCJsaQ47nib44ffWMRBA2yJ8Pg9S7nOU2Xi40ru7loxj88tJfzxemrudM2xApp3kMsbwu3bMswp5bUUBFzqvIJ7fWldqaRhlCeM5r3mEm6uGlSbO6SArtF/cIOQ4/C4AF9BPPu5nqmP8sadEtx5yRXrtUkJpdx6xPN9l3SygoCP6wRAJ1HihKZW8EmdyHA+m7cf3Fpw5ZKqPCs140apAZ3aYjzl2k89Q/gcSwd6UmiyP1PbaOKmNWXG21pEfGq7889evxDfALyYsWZr19YPC/WcPA7ahIYl7unxc3Xy3NkdbUrr720tb5TQN9CWQ51XSjiH32c2TgBsbG9HtbU5bSyu096jqt1mR9zuce0dhyacaep9H8dZdfwNdNaaFm62teHEBf5A51I6YIPrRwnAUoFEWWLisiRREjrKJ8+dXXTWzmycuOaGXq+Omv226vjitb9vYber7blQYwAhXoc7htx+lLimyonXU9tz64eAiv/WxEVLYlLrko6nyKKOo8mqLonYk+fOshm9Jes5PoaV3NhyfMw4G3pONnV8L1J1SL2bqfZUK35S36bYK0hbXmgDUx4nWk+quB2I6yMlpsPHwuVNJSRsCtT6xmM0ZUnbWAYDHMeh8TfHWVvm+KFvMXI9lvWG6uie2TixiMmkOvexmA6kRABp5d7Y/ruGq7wFjcjIuc/cPpJArnFcC7gzqRkAswj8Flhini3bSckIscClhDi3TwrqWuZ6mOS400vnODTawYyA9LyFtkmyGufGRFP74AFECymvktz6ntk4AfddeJSwUqff89TgH8Ucv+ESLb9DQxsb3P3ccWRcgK8o7oaehnNvnSpxPadWZ40rodSpCJ3wLWvcVjKoy689srBcBStdILbYhU4zZSnlLOPxu6G5llq335S11iI2pGcz1ZlqZTWmjqddJpGqY84z1YKhRKKUhV0ido0+ee7sjjngtQMxWiEgDYyk9pXeqVJPgxjOOi65zFtcfkNbct+DjxaLDqvFk4sTptoHfE813764HOy+ju+vdP6U2Mdl5CYVm7MI17TfgdI8L0NZ0Od8PxynhK7rvhEAPgYAtwLAWwDwSN/3D3Pbexb0NcZHKKcnZOdASOAT/04hXWfcgT557iycPHd2R4c5ZaUL9cL1weVT24TlEto5grnya05fZRG91uUaJGt06BTjZ0Qj5nIthFOjZiZzPL81Ny/38cNvkB4iMdp7wP2mlqfcmVNQ12orMRydlT0QX4OQKR1P54anMNNmt8+NR5aSn2mf6/CuPPbw/ar3JucY1DE1xH0GyTVeczyrUMtps6dM7iCpFCpgodW1e/XC7mQIguOsKFcB4D/t+/4wANwNAP9B13X/Grfx/Hs6TjZuIV8Nhrbic1YHKY5S+tinXHzj9TWzmNe+bqk6paxsVkrqjsWytUxJ1KcGZyTrHPVbyi2gpUX2cQ0589YHYvG4f89BOLbv0o5BKVx2CN8A4M/V8tzE74h0DXPujZht/FqISAhJ0UwpFgjXBFu+43sRe9Bonw383Lb4fsbXMcfF3OpmjMNvcq2rAckKrhn8ibfLcUmfOpzXXWoQSzO4Yr0mc72GjjNV+r7/bQD47Wt/v9Z13bMAsA8Afp3a3t/AFcbd0NsgxfoNydAxXTH4mHGnNzfbsDTfcHzMvUevqjvNWtGVche3iLdct9wcEWN5Bqjyh3iGJCuN5GJPWVhzLT41YpVzsWQEB9hptd2/Zytr/ru/+DL8yK6X4SffvI09jkbs57qt1hzAkMS31jNLiqGPxTe1nWVKw5RLeO3v7NCDqZpjWrzlSq4F1T7keixpKH3HrW32nC32jjN3/tnn3g4Xr8t713/7N98OAPBtXdc9HS1+pO/7R6jtu67bDwB/GgB+mSvTBbhTjbgjssrif8zz4gTT2IMCofNfMtVPSkAE4c25lVoT2KQ6TyVzZ1PHio/JdXwtlqscqOPV7BRqrVx4ey4mNNeqM7eOLvVcx/TPP7X4e/P2d8HlK7Q1HL8L5wVLeC5YKIc2R3PtcUw6LpOKK957tOwcuAzvGjTPX8s2t4VV0xL3DbBTfGu/NRrPmrj94QYHU2VbyRXdtb2lNBbtubVjAB7a6Kw0n+n7/qHURl3X7QGA/wEA/q9937/Kbecx4CvO0I2hN751ka4ndksbS3wvOvso/jJFapqmFLFgObNxYrRnj4vJlZhSx6qFxc1iYZVc0S1l1BbfNeO4JSTxHazfAADdwQ8BwJYYj5fj9w5fB/xc5l7j+P/4XXvs4fuX/nHusqnBtVDfIJat7VlcfhwTDrA9LZblnsbW15yY5RRTagMC2rwNWvEdnoWhw6QwODwq/s5YvKSkwVULue2j4zjTpuu6t8GW+D7d97048utvtlOdVbZ+Dw2+jtyo+NjXu1YMKLUcu4JTcxCHTvtjD+dbu1pRkihHU+aUKBXTlv24OHHrPkNCTSvGQYnv8D8AwOahZVdq6tw4i3OMtr1+9cJu0yCXNCCyeegBOP8s/56G+apLia9xjiV8LlbInDwOFPH5LmLdL9izu2uv21DXtuZAGo6bt1IaZ+84zjTpuq4DgFMA8Gzf9/9Nanu3gDvVuO/BRxf/hmIOnaPaTPWD3WK0nhMpnBthTgfbUhfOaq9NTlVqqV2V5z12Oy09J83++NmsGT/K/aa2D5Zu/PxSoRuXr1yCy1cuAXTvZcs8s3FiR8Zhy7kF8d3C0hvKCtc+ZDYP2c25TMlWb5KwD0fugBy24pa0bVrLaQtLu4RUL64u8fc9eD1Q1BiMq03Os0WhuU8tEqdNHfeAdNac7wSAHwSA7+667leu/fsebuPVevsdkjEt0q2PvWofsFKGvtelSbxys1PXsGjU7OhSnTou3hkvs167OVhNqDpaYjxLKL02qWSA3PLU/NRBeG4eivc5u2QN33/jB7LqfGbjBJyEnUKTqnt8b4L4jqGun7ZjTc1SwN1nLIw1AxgA/NzgNaf5s5JqB6lBN2lAyPqO51rqNcfYe/Rq9jelhmU+9Y0Y874D2JNrzqH9dhzHTt/3/xQAOu323go4TRnbNXrVwR9z7fVumf1a2xnUuMcGWsTiWpPxWOsQW/80CYVWqVOmGXzACZg4i6BUtsYdFC/XCuzUOgvcnNaxW3SO+D557mxRkjFtVvoYbAHFy7n7cOri6aXrIAknTmRLUAMgJdmtW20f6jU1IYbFskY0hmnHqIFfqTwLOeI7NSBjaQNSaD1wHMdxYrxVcNaWqXWArJRYEGudN9VR49Zhaoibko5U62mVLHD3Az+j0n2jhPwUxX3KGhhIPd8aK2sgiB5ujujUfty6AJ7LPv7Nie8A54qd4uS5s3D88Btw6uJpOH5Y/wzmPAtBaHHiO0ANdnDTgB0/TB+r1nsZlyMNUNR4NzTfkvh6BS+I+y4MM30Zzlyeaqep92kK7sVaa3e8XZgaEyc6LJmxg/u2TamdHZopPB+OMyfWt7VYMzwx2uoxlY+9JB5rggVPLddDi6WMmuqpZUes1A11zGeEq4u2jhbXTlw23hdn14+zY+NkZpj4nks5CfAzGfaJXc5rEcR3CTnvJ9fJpr4tXP3CgMGQSM/KlAZiW9Rj79Gr0fPNC8d4ucU7KNWvsHpEScdNvYcB7QBbLhpvm6kPhDqOMy7eGjhryxw/hlMfSKmdNdhqDeMs4pLIjsVYypUXl4E771ycN6bVszfkM50SLpJ3RLwNXk8lrpOOQZUfhOLJc2dVz6TUoY8tZ1KGfmo9wE7X61I48S1N64eff0lA5MaA4zIpsXTq4umlbbBQ0r7v2GsAv7dYZD/28P3kNjXEt7a9o7wIcH21x8sdmNO8V3havNQ9SX2TtNdHO6iKt+Gm8AvrsAhvYfmmqBH/7jjO6uKtwhoxdfHmpKFcQNfhnmo6ZpuHHtjRwY/3S1m68ZROXEeLcgnXJFHixOIqd860rpqxQJLcYePrzV23WJyd2TixcPPF9yrl+h0EgSQOtYLx1MXT8PGfuLHYTTO2Hrew8nHXNIhHajlAvrdL7BqsuZbHD7+xYzADC+szGycANrbqlrrHrSzOXPnWtpob4NO0H9pwIG7gBP8dQ8V7pwYiNWjCPSzs33MQju27tPhd+q5wg4QBbbK90uduat9+dz93HDur2/NznBVnKh/fMaA6ZFh8B4KQsljTNeI75R5N/c3tN0cXxVRdtTHcoZxYLAVRFXd0g7AK67n4eDxvcRD3cVI8AFiUtZUJ/Pod5ew9erVKPHIoI7gCx8/u5qEHFss2b3/X1kJm2jEcN50SExohZQG3N7U63U+8cIMpEWKOS/9W3dt7nZR6/1DhGbXahpz943YzTvi39+hwoUcp4vdAsoa3RpOro1b7vs7ffsdZFXwecMdpwCqNCKfcqvcerT+HsIVj+15nO16Sa7AGjXjmwNeDuz5jXLPaWM8htX2Y3/qxh+/fMdd1sGxKnVncQQ37x8/pfQ8+Sk7DBbD1/gaxnjqWBSy+47qdeu5Fcc7vkmPF1HzeQjsnXZ/zz17fZBaDADUXOsD2gEduhvgctAkTKbjnMH52LckKw/OrSdyI4e5XfC2xNd7a/lvm5E49Q9w34PKVndbv0ueRup5zGjitzSr1dRxnSNa31VhT3A19GFbpGo/VuRhyflcuSRMnmEPHVptobNXALpYpK6C0PqcDx7k+x8fhxLVUz7A8tvKlyqHQJLE6dfH0zrjX/pPQP/8UdAc/tNhmG96i3SpBYYq4nePccqmM1AD59YwtsTjcIKZ2Pgotue0lri8+t9grBGCnBwlnKc9tozQitcb1pRIYxnUYe57vgBRS5DiOY8VbEMdxVMQWmFadj5zOVslUUgGcwCuV1An/zWFNljRlLMmfqEzT8fIYLr5YUx8A9MxE03FJSaYoqGekdiyr1vLWX/oIfP7WmwFg+9nWJBK0HidgffY0U5ABLA9EUPH0uZZIyaq9PWAiW3/jOreKBbeUGwbH9x69Slr049j2gGQR5wYgqHMeY6AiFtfS7AIatLHdOCxjKuLecZz1w13QHWfGDNlxCh26Vq7mUmco120wNY8zPmaweOJl8d+UJQQvt3bopy6+AZbvfwxeljoXfJ2s4vuxh+9fcs3lsDyjLV2kLcc+9dyL8JNv3nZNfMvUFA+577P23mmmmKrN2F4o1nc6DESlnmvO7R+/V5Zs3Zq6DtFGWdv5sH34t3nogR3LSo+XcuPXYH0Wh3TrLvmeu/u54+Qz/V6fUx13Q18dhhZuqfi3lp3e2tYKTUdPur6aax+sWWE6rLBsLsSdc2tSNck9PGDpwIU2C+9j6bCPbfGS3G1jKIuexcptmce5hNR3hJsbvQavXti9SKTHrteUMSCcxT1nECq+9jmzKqS8fqTt8bFaeopYPFbi8IQa1Ho+cgZjhmIOg7+Os4r4m+c4jhpJkHFx0RyxdTolSKzCKS43JUbieuDpXXI6tnj7+y48CnuP8rHjqWzCY3aQLC7n2N08pxwK3Bld/r1VLiVgcCd8bPENYHN7zcneXwJlCaUGa8MyfM25d7+mq681BGIstNNElVoQpUEuizjm4sutx69NzuBN/N6XDP6M/Qw5jrPauAv6mjIn16GxsmvPlVb3NpXt1nKf8PzctaHcy6Wsu2F5yJqssfbmPpPa/Wp1AGu9P5LrvVS+FLpw8txZUqSEDOXUfMOp87nvwUdF8Y2zJQ/pfp77rFsyRteEuzcBbVujcfVNnV/Iii/VbyqeXVw9QlZ9gPJ2mhoAweS891P61mpdyaX9c+Cu5ZSuDUfLMLGYOfUhHWeK+BCfM3l8JNpGi05ozsdcEmYay3cQStoEO7XInc9VCu3gLFOWeOkxyPUACPdeKwqkZ1bKVh6OtTw9El+v8EzF2binDJXtXCMqcoWHJXEYdc9KRCBX50WZyN0clzsV8R2QBipqiZczGycWidtqeMxY2vkWWea1FnhLiEUty3c8kDh2uywx5bo5jrONv6mO46ih3KdLXI6l6ZQCrcVSiQU0Ft1WAZDqbI4Vf5gqK/aEwELbKthT7vcarCJg/56DS/MDTwlOcA/tjj4F8LMxNYE9Faj47SEstZrvgEQYPKCQvit7j17Neg80njoUlODOEeFTF+6O4wyLtwZrzLomY/MPoZ1U54TKjF2bFhZxSdSkLEolVqzUMziV51MzjRMlvqfmqhlPg3Vs36Wl5WPVx7pdq7rmTkHGfTssifvw+2dJIMZZxq1MIddCKZQlHNPCYh2QxDJFnOmde57i/XNj21N1xOtbeiQN+XyV5C7R4O7njlPOfL84jpNJzQ9S/CFax8GMlmyJpbYuw9zcz1Rm93gZ12nUdnw0bu5TosWgVUtBQMHNRT0k1mMOYfVu3VmXSM3ZLhGeydx55AMtznmMQV5sCcfv19CDYprjxfeNMghYk+5xA8IlzKF9lnCDg+NME38rHacQjfBeJW+DoYQTTpaVQ+40SKm4Y5wlXTsl2Zw7QlT9uYEEjXvqGFbyqblwp2JZp1ZfgHR7h+936hxz3x1sCZ1SGzvWex5PwVX6fmnaee23IDW3eU1yz1u6ZzXb7hKPC+2+c/7OOM664G/pmiN1WqbUoZkq2usz9+sodUBqCinttGS55M6PnJNcakzLYm20FvupCG0NlvmFhzhua/AASa4rLwcnDqTrmTut2Jziw4d216VEsaXdC14XVBl4/9RzVCtMgDqmlH+iNmO03/h9ojyzxqiXu587Th18GjKHZcqdGmd4NNaQmmCX82P7Xl/804Kt39yUNpIoCh29uOMRrkXYj4tVDP/wupa0mIJGU97UhDY3dVerKb0095c6LvU8llgMqXrEnfhW4jt+1uPj4veAYmou5HOGupZxu8c9/+E6Wr0xNO2Nti+heQ7ibOTh+HMi9S5I+wXmeu6O42zjXy7HcarQwhIOICdfw9mhcccSu6BrjhfAnaQ4vpIqc+/Rq3D88Buwefu7oH/+Kfj8vTfDBx+/hSyrlvXCmvwoF43lhTpH6zFaPUMA7SzeFjEbH0uK7z557mxW6AQe7NF6rpTEYOP9tZmm48GAVR7sDec2pOXwzMYJOHXx9NIybuAxLKfCBvB+lpwE4V5L562ZUz5n8M/yTrbKAZBb9lh1dhxnWPwtdtzV3ElCWbZEl8MIypXOgpR8LXQU8XRNMZwQq+FqjDunx/a9Dv3zTwHA1nRXH733Ejzxwg1wHraTTXFJ3gKWTNDc8podtNwpd3KOUxtOMNR2M8+pe/ysljyj2mR/4Te2WFL7D/VNWBchMYbbrjQoSf0dwM9e/A5tHnoAzj97dsc+OVDiO2Ryzx2Mw893qpxWz19Oe5nyXKlxnFLc/dxx6rEeXz/HcZohdXJKs/BSnUjJssm5HFNorDlcnbn9nnjhBti8/SD0zz8F/fNPwf4Dd8OxfZfI2MlSUte9pQinyteeU80BkBRYbFizipfUVeo8l7rA1xoQweWkphjT1GFdRLWGvUevDpqAjML67FJt7vHDb8DmoQcAYDnRmwYqS338fO09enVHUstczx5Luzr0c5oaaOWs5tqBbsdx5oW/xQ4AuBXcsZPqBLR0J05tF4SW5JKeK76lYx8//AZcvnIJ9h+4e2nd8cNvwMd/4kYysVHsYh+SHw3RwbIex5KIL3Y/DVYtgOXzDVPLDZnpO0eMWMUyZYUDoJ+nVH2GstCVTKdY6m676mKidJq02uDBTGpQinrmT547axpMiO/tToGtD1uwbEOJ9NoDkZYype1zB09rvy/r8A46zhTp+r5nV9511139008/PWB1nLFxEe5oodz8OLFBLc91SacEdLxMEkxcPCO26pTEI4fjY2Gd2jbEi4fjA6Q7R/h6plwvpfuQKp8qx0KIkQ/XZf+egwAAcPnKpcGFOECdObYpEYGvfUlsfK2OcWm8N1dmSYzrunX6hxTh2EIt5T+Q2s444SUOBbKGR2jboxK493EsSlzhp1D/mCkNIjlt6Lru033f3zV2PVrQdd0jxz76X73/Hd/8zVn7//bTn4b/5a//9R/r+/4h4Rg/CQB/HgC+1Pf9t6TKnMab7TjO7KCEN0eJyzJFStDG21Au6nh/3GEtrdvxw2+IsesYvC123de6+0od2njb0k6dZGWS6nv+2et3ZLHfv+cgHNt3abE+EFvbLC6vWmoLfu31pQQ6F/dayzpVUobGO0pyMY7xrM3jw8WGU+I7HiTLRbrnNa2v+HuUW24Li3BuHPpUxLfjOCp+GgD+NgB8TLOxv92O42SBRYNGJNbIdM0l1cKWHMkSznU+W1hhpTK1xxvCGpKykqYsu9TyOKY47P/Bx2+5Zgm/tOjgY7Cra/idI8Rb3tvc+8INnKS2GwNOTEtZrinR3sIK79AEj55U+ERq1ggsvK0Z0CUPmtqDMWMPVlHH13gl1R6cDlDvZa6Ho1u/HSdN3/e/1HXdfu32/hV0nBWldVy/Jq7tzMYJuO/Cch0snS+rm7B2281DDyym6akxJzSuZyt3am0nE3eAczuVsXVWW058b4Ng3nt063e4TiGhEwBcuw83LIUDcJzZOLEUUz4FpGtS8g7OQaRylm+pwz6H81pFYs8T3D5R7dUTL9ywyNMQb1M7GVruu9xCxMfE7Z42FChnEKD2+8C9ezjrvIcbOuvG65/eBbt+M+99+/3P7QIA+Lau6+K47Ef6vn8ktz7+JXScFWWoDyzXgQhWmCC+cuL9LHPQpizjMVvC8Hp2v1Bfrq5UJuCSmGLu2qSst6lrWdvVXJvJl+P8s9fvENibhx5Yelbvu8AnrcoR3y0GQyzX1dLZbS0qapGyiFHn68J7OE5dPA3HD2//jqdoDO+DJsFgnJciUOP5tJbBDShqBXIOmoSTcT00+TJSA6MlA6Ya4S0tcxwnyWekGHAr/kV0lvCRUaeEuONw34VHF+K7BjlCSmvd5uJvOVrEJFNI55zTEdZ08FKeDZp9xYRkG+l6cpmj8TRZUxWrkkBNZRxvJe5ziY8hnZd/N6ZFbL0GoAcIuZkipobVC6c2msHjGoNntc+txjvp7ueO0wYX4I4zIOs2wKHJut1CRKWsO7gzqq0D18nSZgSuZT3SJBmyDCiE+lHLLJnrqZwAVEw4xjL/dNh+TKtyaiBDqpu0zmoBa9WWUIME+B6VTF22jgw9HRkW3/H/GmLvG+vc3xZSiSNj9+5UGUNRmrNBu5303QxtoMXy7TjOdHAB7jgDsq4fRRxLjNcFqE5WyyRaFlp08sJ1qSUQtWJZSvxT0rnE2dvjfTXiI9Wp5OBEtsZF1EpLN/EhkuxJrrXSfcJtF9WWtXAFdupDTeHIbdNSfEtIA4JjP2Nj1iF+P7XeKes28O84Y9B13T8AgKMAcEvXdb8FAP9F3/enuO39S+nswBtrpxSug5LTcRk6MzlHbHnOdf/WWJC5fS3bSMfgsAhvbR3i5VhY43juEBuuEd+hfUrVeaou6mOQumd4vfUbUJKIyqlLnGQSu6LH05CF2HCJWHzXHnzC8dGpTOKaZGgtnz3LIOTQGdlzvW4k3P3ccfT0ff9vW7a/rlVFHMdZTyRxqckk25rWlnSra3i8D2UN1wpr3JmN/1nryB03Za2XMpjvPXp10ZnfSs5nv9/3PfjoUvx3/C8+35peBRTUdV0FsW8JBcDXeMwYXWeZWDTH4htg2eItuaTXGkBNlalZn/KSitG8h0O8q7WmEqNmGoj/pfZ3HGeauAB3nBWH+oi35NULu0UhZinH0tmjkgnVFtuaOESpk8qJ6RpJhqTYcEqQawS1dKwAtmpLwh+gLIGdVGdNrGgNVkFoY+F8ZuOEOQ4/d2DHac+ZjRPJ9i/OjB7+YVIJGa1o81dQcHXBg3CldeTA4TWagdC4TSodcMzBPRkdZ7q4AHdIfOR0dYhjxKT7WtNqWPrhx9ZcLVxnMu6MaixAcRl4MEFys+awJE0rEcS1krxRHUfuHChBzT1LKcu8REq4jy3+Wh2/ZABN2pe6P9b3duhr3tqzYZWgBkEpUR5PSxbT6lpjF20sovE/jOaZk0RwiRU/1RZK9RnCM6cm3gd0nLb4cLXjrAElVq0pkIo9tGT3xdtz0/MEJOGnyeCrtcrgc5S2x53IXIuS9nh4P277HPfQMxsntiw9zPzfmrm/qVjSlgwV51zaCQ5x91Rej6m+6xxzjy0fQ9CEBGpxFnTKKl4yxaN1X4s7ObWN1NZyseLhN27vrCK6xbaO46wn3ko4LJ6Mbb2Ye6fh2L7XAWA75jGVZAhnAqYyA8fLpOtDJRCyWKXjzqH2PtRI9mO1JmnqY2UrHnzr7yAY43bHUu6Q1iUq/rk2Ndrf2AMmlbwuRc4zWuvazL19GpNWiSwtA58a4W11IU8NBuKwntJBy9Yx8I7jrA/ugu44a8jY7mUpq0aOkMLJhgBsceGaziTnHkl1zLSdLa0Izo2dDPWzxE1LMezYjbN2p9IqvlvFfbZOKjbGwII0xZhG7Jc8g858iUN74nZSK+xjCzUeuKJyU5SAy6sZx1772Z+qO/rY/QPHWQdcgDvOmjGUZ4NF9OWK10DoCFIiXNo+/p2ankcSqZrsvXg5J2asCcYs1qKS/aX9anckw7NjGRSpDb7m1nucYkyX1qE62C7WtxnLm6xGQsxWx9bEfHNoBglL4sit1DwHx3FWHxfgjoiPhK4eQ4lvjhLBIu1rsWxLHUONVccSxygJbW5fSSinrkOqw5nqKMaWI5woiSuzVkfy+3/0Nfj+H31NPVXSkFDnb41nHZvw7nto0ThMXYRzbUsYkAz/tO97yJ8xxAAZQHque7zMEiaUotRLyXGc9cJbCsdxqpNKeIM7KZZ4WinJFhXHjX9b4yFD5zVOxlbiMq+1esdx5doBDa5TKpWfWiZR0/37+OE3FnH8gZzY1SGSsJV0smvE7ufgonu9CQnZJLTvzVQGm6h2uGbozzriRhfHGQZvgRzHaUILC2noYKVEVipOMXRENfGMufNW5yQUoty6YxFOnXeqfK34ttTVum04JrcvFt9PvHDDkqVtjHhpqb7UgFHLDr02bMQTZzpjwWVYHxuNKHcx7jjO0LgLupPER0QdCknoUC7LWnfrlLtgQHL3o+a2xXHdOcmEMNi1M+WGKFmjU1g7ibWSELVwq8TlYfG9eegBOLNxYjGF1hAdZMorI37uckIJuONYzsciqscU361i8Z1xwYnXxgwJSb03Q8a713jW/X1xnPXFBbjjOFnUEEWcO66lM58S4RpwJxOv47aJBY8mSReVAViL1XW5VnxjKv47pxxcXkieFyzfFK3jKzUx3qlBJyvx4GYYbMCsukV73YT7mPfTIlDjkB08kMm1l1y29FpYXMwDrTKXr6LV3I0tjjMcLsAdx6lKKrkXXkYl/YrL0iYfs6IV6NJ8t1qrviZjL7UdNZ3OHMSKthMer//g47cs/qZESmuhdmbjxJJACcfTeifkPJvxeWqnApMY89nwBFR6piTCuXcUewvF77PkTaQpuwRpoOzkubPqdiK3PamZY8LfF8dZX1yAOyp8ZNSxUNIZ12ayjcVr7rFwdl/NdiliKy+XXE7rZj9VSuoZ74tDBajrOwULMPc8T010TqkuWqZ2DYdiCs91oEQoT2WWghzLeI3nzvtGjuPksH5fPcdxmsLFyUrCmrL8hnV4W66ModFkFY6Zi7iugda7gErc9OqF3fDYw/dHUxjVrh2N5l62EIolA0hOHcZKXnffg4+OLuBil3LuvaWWh+0pDyFr+I9l6sdwvaj7NdR7hOuyCqzSuTjOHPCvvuM4JDmxbtQ+Urbt0uy0WLzExwqulvc9+Ogg4tcSH0wlSNNeJ2kKt1UgiO+STn0rpOdbChGQnuNWor5V2avIlKzRQ0ANHtZ8vywZ0VPbxc/w1ETiWM+NNAjhOM48cBd0R83UPn5OW2q46KUEeal45CznMZos2jnZc7XWb0syOW2seAt3dc21jOtpFZXUPmGqsfgfvq6bhx5Y6qS3FpXSYAeVmC1etmqDIVNgTqEZOdSI/c8hbvNyxDdOzMatk+DaEu4dD9fKhafjOHPHh8cdx1GRm4EWx2lTAqakTnGZ3CBRcDGlOvJcUqKaFqFUXLskdq3zftcEHz+V6R1vKw22xM8GVfb5Z69f3JszGyd2ZJxvdR2sU4pNkanWK4dVOheJKbijp6gV7y15kFDbxGgTXLawEk/Fs8QHIBxn/qzHl81xnGK02aCx6NK4kaeOk+r45BynVfIgyv2Sq5/kTk4NWOBtaiJlr09NaYYtwTnHi69RuIaxKGnldpkKA5CgppXTDFg4DsXQItyax0KaEcIyYCnNbCFNTYnB28RthItUPVMf+HGcVcR7B46JsZLVOPMh19qbWpcLZ/nmOpOllm+tu3R8Pbj4b060txLhKau1hhoZ0sM13Hv06g6B8NjDO0VDyTWxWuCsZbsIXz9yB4vGEEI5IhxgeQAzbkutQjx3UPXMxgmAja2/w3WOEze2eO/8XXYcpxYeA+44TjOsccXaciiozmsqAdsQib1SMawp4Yj3rxlDL9UlJ3Z9CPd4TixwHgNaYit+7B6vOSccN/7qhd1wZuOEd9jXkLlZE1PiO57/O8YyNWMJ1AAZDhsK19zaBuTcq1XOR+A4znB478BxnEHQuGBTUKKQFPYbefUKHVDOgmPJ6LtUHwHKTVnTsQsicaqdwJK6YYuahdhaVmqBt4hmaVv3FFpPSu57cEGPyxhT0GvCdOJ2E4t16j2O31NtO4lDXO67wF/j2DIe4O4JXj6Wl9aYzG3AyHFWhdVqSZxBcDd0RyL+oNd8TnIEfEqMUaK7xvRX8TuS40aOxaQUpxzKkspsLdpzyqY698f2vb70O1z7IQcd3G18fZjitwzXp3VcuMYFPeV2boHzskkleNTAeUFpqRV2Uqssx3FWF3dBdxzHBCeEHnv4/h0doBodRyq5lRZpejEsvmu6U+JOn+Y6UK7c2B2a2if8L3X8pmYxxx33Y/teXxLf4fdH730Jjh9+I1l/bYJAbtqj0rIBpneNnTRzyDwO0N6b4szGicW/GNwmSu3j8cNvLL3H1Lap6R6lJI9a4gRslIXbGvIjbUdhbV8cx1lPXIA7jmOCyjwrdWLxOq6Dk4o/TnVquE4q1emTLDc1hHgcPxwSiXHXSOp0lsaPp+DiOy1Y66A53v49Bxf/Ng89oKqDtR6pBHk5HWkX4fNjShbwKTw/xw+/sSTIJVEutZX4PY+nFIzzJHBI7x/3rYjvJTUIWlMcr4LInsPgk+PMia7rjndd91zXdc93XfdXpG3n34I4ozBF1z1neLQf8JRL9tRoMRf4yXNnYe/Rnctzr4eU4E4TDx13kLXnWyvGG2Db3fyJF25YLAt/b96+vd3l1x6B44dvyIrDx14BlrrXjAd3HA3U1Fvcc0V9f2sIKjzgZcmQHr/L8fvKCe2tc8gbCLNOdxlfL2l7KfxHwl3PHWe96bpuFwD8HQD43wPAbwHAP++67tG+73+d2t5bCsdxmpMzWKOJD9R2ekIHMHQmU2KulvBOCdYzGyd2JBRKiUVqvdXtPNfqnZvsLD5e3ME/dfE0ACx33AEATj33IinQc+uKwffFInyc9vj13yK+BrG7fJjdYSrXKMSDl7abVD4LKudFKUNfNxfnjrMW/BsA8Hzf978BANB13X8HAH8RAEgB3vV9z5Z011139U8//XSLSjorgFvAHQCd1SX1rGAxpBHfYb94vaYuFosOAC9Wuc7mmY0TO6wtgVSitHif3KzeGit1iNfcv+cgAABcvnJpIXRbTitEWcK4+2HJppyCeqZadOwdpyUaz7N4m/CMx+8dft9wEjY8WBkoTYQY10HTdygdgHRk3P3cwXRd9+m+7+8aux4t+JOHNvo9198CB979XVn7f/Zf/n/h9179LfjcC//s09HiR/q+fyT86Lru3wKA433f/3vXfv8gAHxH3/f/IVWmt1pONu6G7tRCI7opi09Ox8sqbK3ZfjlXc3z8uA6S22ON6bXGZnE+zFRxlAU6NRBQOt1YfOw5X1tnOuRaOqVvqbVMLL5TaAYkw7E1GdO5/e+78Ghk1U6fD2UNj5e18FopKcOt3I4zXX7tuX948zv2fuPL+7/hbti962tM+/7BV16Dyy/8Mvzuq1+4vu/7PxA27YhlrJXbWwrHcYqYUiZhTV3i9blWHjwtj8VqrMlwnhLmXEfRkoxsy+L94rVfeW7eFFIHFN8fLgZUGpCxiGWpLtQgh1OPdRYk1PsZu4/jZVpaDnhLCdHieua+i9T+GPzM4HYAu6Vr2+vUs2gR3lxZPpDnONOl7/tX7vqWfwcu/sYvwLcc/POmfT/7L/8R3HHwe+CfPP0TkvgG2Ir7/sbo9zfAdidrB+v3ZXQcpzo1RLjGIlK7LpS7Je5ISa7QVndtrqMX4icpa7c2I3xO5y/X3Ty2RFnRigjOU8DS0U3FeVPLHZ4pxR1jpnA/ax4753227iOJbmpdiD3PqZcmAzn3jkrlSsskIU/VxXr/pAGDqb4nAO5+7qwnn37mH3ztO/be9gcHbrsHvvbtN6r2ee3134GXfvc34Nnf+IVdAD+R2vyfA8DBruveAwAvAMBfAoB/h9vYpyFzivCG3NGQmqqMIteakPNMhul2qH3jKXdagcuOO6vhb02HTrtdPH2Qxb1eWz7A8n2o1U4EC3+Jm2hcxpQ7yXPHr+8y1LRYEtR7L01lKLWXJ8+dXQwyctOL1US69xpRndv2c55E1DLcFmgJ54anNcODhI7jTIu+779yx8HvgV/7l/9Ivc9nfv0M/OnD/xb0ff+WovyrAPAfAsBjAPAsAHy87/tnuO1dgDuOMxitB2xqlC+JcIw1mzhndeFiGq3u1jkdypyBBdyJpQieCJZ7ot2WEtCpc3b30HLGFhbSPZyD8JHeiccevn9JFMYEwRe2GwtLcszUNnh7ztNHO/AYc/zwG4t/uOwabS2AJ6F1nDnyTz/993a9/Lu/Aa9e+Z3ktl9+5Xl4880/hMf+l49Qsd0kfd//477v/0Tf99/c9/1fk7ad9tfKcZyVQdthmYILnzbxWSxeU9PwaAVibPmm1lvKqgXnNg9Q1zOAio+lnpuSJHVTF2kOz1zuXamrfirOWhLhXJK0Vy/sZpMgWsrBZebM0gDAJ57TuqlL1xgnzqzhbp5CU68p4F6LzjrT9/1bXde99188e+aT9/zrZHLysB185tc/Dr/z8sU7WtXFpyFzquCjwY70YR/y+ajVwdBkE6Ys4JwgtQhwahCCsopJgxVcIrcU2nLxPhh8H3KfAeq8Ndtp9nGcMbBM3Rie6xKXcW1ejHDMII5TbQYnwLn8DTloBS3OmF5yzBKmMIAs4QLc4Vjlacgw33jrn+7v/BP3wztvOkCu/8Jvfxpe+J1fhX95+ZNq67eVabYQjuPMDq6TN/TgDFWPnCRxjz18f9aUOxwa99nHHr4fYGOn9SmVbIj6bRXemnri8rltp9bJm7pVyllNJC+OADUHdyyEa9YjRXifw/FzM3vH75vlvaPEq3b/nOO1IDcUyHGc4fit3/mVO7569Q+e+bPf+SHoumWN/dZbV+HXnvtH8Mr/dvldLevgFnCnCm4BdwDojl7LZ8NibcjpzKas4FwMeGl28fi3dHxpm1wBTpUR4MqSLOA17n9u7KnWeu44Y8FZkGtYvnPQvK+UtTlAtWGcGMVu6LWsx1O3QmupfR5TGxh1psU6WcABAP7E/vf2+77uW+G2dy2f8sXPPQ5/8JVX4Vcvnmtm/QZwAe5UxEW4wwlwLt6vBKsoy+3IWkW45IKucelMEZcRW8yoTi8uU3Jj545PucZb6lwigmtZkValQ+6kmeO9TrULQ4pwiyD2vAttcQHuDMm6CfCu677+pj+6/8Xv+a6H4Lrrtt6xr371DTj/T/8f8Luv/uaNfd9faXp8F+BOLVyAO6kPfOoZqem2hzuHpTGUVJm1sSZqs67LPZ5mW00ZoZyc+2xNQieJhtodWm/77MxRKLeCej9xksMhRLj0HONnHf9exRwMJc8o186M6ZruAtyRWDcBDgDwJw9t9F/79r1w6D33AgDAv3j25+GG62+Cf/YrP9XU+g3gMeCO4wxIbUs4J8pqiu9QntRpqpURPHWcXFHJXZshEhbVEsLWRHNWV/5cXHznMVdRZkFq76zPYogVj6ckq4X2GaZi07lcGetwfyW481/36+I4U+LXnvuHN75j7ze+9k3f8J1w9eob8OLv/Bq8/L9dfhvATzU/trcETjVauBk780JKHtTa+s0l8KnRWS3tNFmTGVktLxoXfGpbqU6l77Mk7lt0Qi3xqLXw9s7B1BLH1GDeEOItrn/8fOO/4+24AbC5i83a3lhjhN8E3PrtODvp+/7Kv/mnfhieef4T8MYf/B586+3fB7/4y3+zravjNebdOjqOM0niuG9KyOFOXuigWDodcWcvtkDjMnIyoGPCOXDiFXeWsUVcKw4xc+7ESq7mmg4p3pd6jhaeDdz8xhtArzfMhzxFXPgPizSgqGlbKFEbnl3LTAut2wN8LtoQC4s7urNFqu2fa7vvOHPjqV/96bfd9Ef3f/Vr3nY9fPL/9+PXAfzNQY7rMeBOVbxj6GjAHb24E2pJRPbRe18CAIAnXrgBALYEL7d/iQiv5UYaoCxGUr2l90pKtmY5jgVJQLulZXXw9rz982yd6jC8c7XqZZ3yLMfLiSu71gwJUxOr1rnLax0TQP6OOI7EOsaAB77v2N/o/9Ev/pX39H1/eahjTqvVcmaPu6E7OeRYgz5670vw7i++DAAAx/Zti/DalD7PXJZ0rXgPHgKl8dIBrqOW1UGbuSXZkakt8uZA/H7UPP+9R6/uyEVhFd8tsJ5jSnjjwb7Wwm+K4ltLzbpLZbn4dhyZf/jEX+4A/vKgx5xWy+U4zsrDDdI89vD94rzTeN0TL9wAm7cfhMtXLsETL9wgWr9boUnudf7Z60WX9ByxTNUhoO5suXh2BiJXAIwl3M9snMh+P7gs4XgbaWYCcerECb+37nre3sPIcZzVwN9ux3EGhetUU8upOOLQOTn/7PVw/tnfBYBbqifXsqI5fk6G9IUFacKdbsdpRY5wLxXtr17YbX7fOJEdo5mJIbRzQ1iOa8Mle4zjwsM1mJNHxJDUHJh1HGfaeAy40wT/wDqtKIlnrmmJo9zCNfP5zrFz7TiriDVmOVUGFyoSl4fdzikPlhqJI4eCSryWikOeS/8gNY1c6vszBaE8l+fIGZ91jgEfAxfgThPm8oF15kno/OSK8RqWNaoD5p0dx1kvqPhnnL0/wMV8T2lQTiv+uUFJgPWwzE4x+RvFVJ4rZ/q4AB8WF+BOE1yAO7lYEvnFHb4hxbjjOA5FENk4W3lqii6Ni3ptsNjmcnNI6500OWK91swV/n1ztLgAHxYX4E4z/GPt5JAjwCm0nRbvoDiOMwScCB/DAn7y3NlmFlx8LnFiOs5lfxVmUKl9DqVWdv+2ORZcgA/L9P1nHMdZK6okUUJ4R8RxnLFh26ERkyy2cKWO23DJgj4l1/sa1HbL18SYa/KQOI4zPVyAO44zW4Lr+ZL7pmcMdxzHEYkzrlPUEuapAdUpWL1bWt/HmBrTRbjjTB93QXeaMoWPqzNvQkdwlSwljuM4YxAnggvisFa8cQ1aWORrlunJ15xVxV3Qh2X6rYjjOCtDqvMyRRdNx3GcVYSylFqn16olSIMVeg7idi4i3HGc6eItiOM4gxA6bT4y7ziOMw5xuA41LRoVUxy7Nb96YTe7TfibKkcifBNauYLXFMsuvB3HqYG3JE5TViGzqZOHC23HcZzpQk59ds3bKHy3JbGN1+fEHuNp2jS4BXqZdZp/3XFWBX9bHcfJxkW24zjO6sG17cFqzok+qwjMGaCfmtAc29CAr4d/lx1n+kyrFXMcZ5L4B91xHMc5s3FiOSdHZDEvEcZztuIOJb7nfI0cx1nG32KnOWOPDjt6XGg7juM4Vrhvx30PPlrsMr7uwtOnFXOc1WM9WzPHcVxsO47jOE1ZfGfQTBZ4UH4u4toymEAJ55zzzElo5zjOtJlHi+c4Tjb+QXYcx3GmBPVd4jzlpiTOLXWh5lnHonxK5+Y4znD4m+84K4ILbcdxHGeuWET5lEiF2VGZ5DHxMk6Ur7srvuOsEl3f9+zKu+66q3/66acHrI6zyszhQzonXHA7juM468iq9Sc0AjyF9wmcErqu+3Tf93eNXY91wYfRHGcG+IfVcRzHcbaYq7Wcw63ajrNe+BvvOBPDxbbjOI7j2Fg1Ue44zuriAtwZDJ+O7P/f3r3bNgxDARSVMkBmSpnWWTNLZICMkhECMEVgwPBHH0Mi36POqQzYBRuBvCYl3RLbALAPUQ5EJMChErENAG1dz8URg3ztu9OtLyAXAQ47MBkCQHwRd8ndEw59c4VTVa/H0AU3APQhYpQD/RDgsJLYBoBjiXp03ZoE8hHgMMPkBgBcsksOPEuAU13kY+hiGwB4RtRdciAWAU43Xt9+h2GYfnjJ9W8ENwCwh72D3BoGchLgpHIO6LPL2F7y1NDP08f/h9OmwwIAmGSHHBgGAU4jWxxDXxLc/h0GACKaC/K17wMHcnBVk8rcRCS4AYCM7j7Y7csuOfRGgJOa4AYAevVol9z6B/IS4KRiwgEAjso6CPIT4DSz5D5wEw0AANALAU4oghsAAOjVWEp5/OU4flccCwAAAHX9lFLeWw/iKCYDHAAAANjGS+sBAAAAwBEIcAAAAKhAgAMAAEAFAhwAAAAqEOAAAABQwR/0HF7Hb3XDpQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,10))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "clev = np.arange(0, 1e16, 1*1e15)\n",
+    "plt.contourf(lon, lat, no2grid, clev, cmap='Spectral_r', extend='both')\n",
+    "cbar=plt.colorbar(shrink=0.6)\n",
+    "plt.show()\n",
+    "fig.savefig('/Users/mengli/Work/melodies-monet/outdata/paried_trp_no2_20190715.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b352bf65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paired WRF-Chem NO2:  <xarray.DataArray 'no2trpcol' (x: 284, ll: 440)>\n",
+      "array([[5.6082549e+14, 5.6167260e+14, 5.6204234e+14, ..., 5.0799893e+14,\n",
+      "        5.1312666e+14, 5.1356541e+14],\n",
+      "       [4.3689763e+14, 4.3674170e+14, 4.3980378e+14, ..., 4.4611728e+14,\n",
+      "        4.5124336e+14, 5.9029769e+14],\n",
+      "       [4.4412458e+14, 4.4394268e+14, 4.4651554e+14, ..., 4.3647317e+14,\n",
+      "        4.4146513e+14, 5.8461524e+14],\n",
+      "       ...,\n",
+      "       [6.1838302e+14, 5.8272177e+14, 6.3751018e+14, ..., 7.8216743e+14,\n",
+      "        7.7528978e+14, 7.7019125e+14],\n",
+      "       [6.0601183e+14, 5.6466828e+14, 5.7435168e+14, ..., 6.6657966e+14,\n",
+      "        6.9247543e+14, 7.3247808e+14],\n",
+      "       [6.1570953e+14, 5.6744309e+14, 5.6898807e+14, ..., 6.5825655e+14,\n",
+      "        6.7548454e+14, 6.9849134e+14]], dtype=float32)\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2019-07-15\n",
+      "    lon      (x, ll) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n",
+      "    lat      (x, ll) float32 21.19 21.22 21.24 21.27 ... 50.33 50.28 50.24 50.2\n",
+      "  * x        (x) int64 0 1 2 3 4 5 6 7 8 ... 275 276 277 278 279 280 281 282 283\n",
+      "  * ll       (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 209019260000000.0 2.06299e+16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2. paired WRF-Chem NO2 trop. columns\n",
+    "no2grid = paired_obs_stack['no2trpcol']\n",
+    "no2grid = no2grid[0,:,:] # time, lat, lon\n",
+    "print('Paired WRF-Chem NO2: ',no2grid, np.nanmin(no2grid), np.nanmax(no2grid))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "54f394e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,10))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "clev = np.arange(0, 1e16, 1*1e15)\n",
+    "plt.contourf(lon, lat, no2grid, clev, cmap='Spectral_r', extend='both')\n",
+    "cbar=plt.colorbar(shrink=0.6)\n",
+    "plt.show()\n",
+    "fig.savefig('/Users/mengli/Work/melodies-monet/outdata/paried_wrfchem_no2_20190715.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a3543089",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing: /Users/mengli/Work/melodies-monet/outdata/save_intermediate/201907_tropomi_l2_no2_wrfchem_v4.2.nc4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- save paired data ---\n",
+    "an.save_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1d44d3ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading: /Users/mengli/Work/melodies-monet/outdata/read_intermediate/201907_tropomi_l2_no2_wrfchem_v4.2.nc4\n"
+     ]
+    },
+    {
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+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 ...\n",
+       "    lat                                  (y) float32 ...\n",
+       "    x                                    (y) int64 ...\n",
+       "    ll                                   (y) int64 ...\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 ...\n",
+       "    no2trpcol                            (time, y) float32 ...\n",
+       "    latitude                             (y) float32 ...\n",
+       "    longitude                            (y) float32 ...\n",
+       "Attributes:\n",
+       "    description:   daily tropomi data at model grids\n",
+       "    title:         \n",
+       "    format:        NetCDF-4\n",
+       "    date_created:  2024-01-08\n",
+       "    dict_json:     {\\n    &quot;type&quot;: &quot;sat_swath_clm&quot;,\\n    &quot;radius_of_influence&quot;...\n",
+       "    group_name:    tropomi_l2_no2_wrfchem_v4.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c82c73f1-a5e5-4595-9214-f44896663167' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c82c73f1-a5e5-4595-9214-f44896663167' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>y</span>: 124960</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57186ef3-3d1e-442c-a30b-252e03c051ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57186ef3-3d1e-442c-a30b-252e03c051ea' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-07-15</div><input id='attrs-a12e6d94-61ae-4984-823a-eba18d9335d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a12e6d94-61ae-4984-823a-eba18d9335d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-684a36fa-7761-4819-830b-96f2967df278' class='xr-var-data-in' type='checkbox'><label for='data-684a36fa-7761-4819-830b-96f2967df278' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-07-15T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6045c86-69fc-42d2-983a-edb3ecdda559' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6045c86-69fc-42d2-983a-edb3ecdda559' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2597498-27fd-4f5f-906d-ca2010cd1e28' class='xr-var-data-in' type='checkbox'><label for='data-e2597498-27fd-4f5f-906d-ca2010cd1e28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d2effb0-3a60-4cc8-8ef0-a6fe06715b7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d2effb0-3a60-4cc8-8ef0-a6fe06715b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac04d53e-13e7-4f8c-995a-a72107d89217' class='xr-var-data-in' type='checkbox'><label for='data-ac04d53e-13e7-4f8c-995a-a72107d89217' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d134aa6-a39d-4aef-863c-fcf905d36bac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d134aa6-a39d-4aef-863c-fcf905d36bac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1eaaa509-5a4e-408c-aed3-eb65190f8f47' class='xr-var-data-in' type='checkbox'><label for='data-1eaaa509-5a4e-408c-aed3-eb65190f8f47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ll</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-13251f2d-1e1c-4061-98a5-0e3b7078e116' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13251f2d-1e1c-4061-98a5-0e3b7078e116' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9479deec-0f83-421b-88e0-f2f789ce6329' class='xr-var-data-in' type='checkbox'><label for='data-9479deec-0f83-421b-88e0-f2f789ce6329' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8bff7300-3db0-4d33-b7ff-716f726b0d1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8bff7300-3db0-4d33-b7ff-716f726b0d1b' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>nitrogendioxide_tropospheric_column</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d3ee562-60bf-4d65-9828-f36ed6c4654c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d3ee562-60bf-4d65-9828-f36ed6c4654c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0789e5a1-9967-4966-bf8e-350d1595f5db' class='xr-var-data-in' type='checkbox'><label for='data-0789e5a1-9967-4966-bf8e-350d1595f5db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no2trpcol</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b05a2c2-5fe5-4012-8231-ed3f21362618' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b05a2c2-5fe5-4012-8231-ed3f21362618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-504a7124-ddbc-4877-a6cc-1f1e6ad2a9b7' class='xr-var-data-in' type='checkbox'><label for='data-504a7124-ddbc-4877-a6cc-1f1e6ad2a9b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aff8aa95-a1f4-4f6a-b139-525de8eb9b33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aff8aa95-a1f4-4f6a-b139-525de8eb9b33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94fc8302-458f-4a0b-b24b-ef6b3467e216' class='xr-var-data-in' type='checkbox'><label for='data-94fc8302-458f-4a0b-b24b-ef6b3467e216' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9aef124d-fad1-4c51-ae1d-60a6b0ce777f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9aef124d-fad1-4c51-ae1d-60a6b0ce777f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c4703a0-69ce-46fa-b723-8dab505b317e' class='xr-var-data-in' type='checkbox'><label for='data-5c4703a0-69ce-46fa-b723-8dab505b317e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00fcbfa1-5a30-43e9-939b-1d8c501e1ef9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00fcbfa1-5a30-43e9-939b-1d8c501e1ef9' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>daily tropomi data at model grids</dd><dt><span>title :</span></dt><dd></dd><dt><span>format :</span></dt><dd>NetCDF-4</dd><dt><span>date_created :</span></dt><dd>2024-01-08</dd><dt><span>dict_json :</span></dt><dd>{\n",
+       "    &quot;type&quot;: &quot;sat_swath_clm&quot;,\n",
+       "    &quot;radius_of_influence&quot;: 1000000.0,\n",
+       "    &quot;obs&quot;: &quot;tropomi_l2_no2&quot;,\n",
+       "    &quot;model&quot;: &quot;wrfchem_v4.2&quot;,\n",
+       "    &quot;model_vars&quot;: [\n",
+       "        &quot;no2&quot;\n",
+       "    ],\n",
+       "    &quot;obs_vars&quot;: [\n",
+       "        &quot;nitrogendioxide_tropospheric_column&quot;\n",
+       "    ],\n",
+       "    &quot;filename&quot;: null\n",
+       "}</dd><dt><span>group_name :</span></dt><dd>tropomi_l2_no2_wrfchem_v4.2</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 ...\n",
+       "    lat                                  (y) float32 ...\n",
+       "    x                                    (y) int64 ...\n",
+       "    ll                                   (y) int64 ...\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 ...\n",
+       "    no2trpcol                            (time, y) float32 ...\n",
+       "    latitude                             (y) float32 ...\n",
+       "    longitude                            (y) float32 ...\n",
+       "Attributes:\n",
+       "    description:   daily tropomi data at model grids\n",
+       "    title:         \n",
+       "    format:        NetCDF-4\n",
+       "    date_created:  2024-01-08\n",
+       "    dict_json:     {\\n    \"type\": \"sat_swath_clm\",\\n    \"radius_of_influence\"...\n",
+       "    group_name:    tropomi_l2_no2_wrfchem_v4.2"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- read saved paired data ---\n",
+    "an.read_analysis()\n",
+    "paired_obs = an.paired['tropomi_l2_no2_wrfchem_v4.2'].obj\n",
+    "paired_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "112760d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 62682\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# output statistics\n",
+    "an.stats() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "298c607d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'color': 'k', 'linestyle': '-', 'marker': '*', 'linewidth': 2.0, 'markersize': 10.0, 'label': 'tropomi_l2_no2', 'fontsize': 14.4}\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 54825\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n",
+      "-2964024308269056.0 2964024308269056.0\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 65536\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n",
+      "Reference std: 951817500000000.0\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 65536\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 65536\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Q53ojQETTtFAoFPJHo9Eg380BG0sw5bkf3ZP1oJdE7VA1qTNSynlJveKD0Oe/X5RSeoUQvwS2CCH+nJQVNTzn/YANyTHli3HooffUzkYxICKEcEkpjZD7JPQpg19JKZcJIU4FbhZCzJNSLsjpiBUKhUKRM9I1zqcAFTabzRaPxx0VFRVomkY4HCYcDhdPnDhx8pFHHkk0Gt1miUQiiWAwGAkEAjG/3x/3+/3xQCCgBYNBLRgMWoLBoCUUCtlCoVBxJBIpGTFiRNRmswWtVmvAarV6i4qK2i0WS6umaS2xWKw5GAxuSSQSrehz4k3oJVEbgeWpNc1SyqtSP0DKtqsBX3dvAjJF0uMdALhTtI8t6CVrPrb26J8FXkK/cQH4CF0jWXnNCoVCsRuTrnG+EWgqLS11dXR09HW5XBQVFVFfX08kEuHjjz9m6tSp23tvEdtvOr8VmqYRDAaLfT5fsd/vr/R6vf06OjpIXdxut9bS0hJsaWmJtrS0aO3t7Vav1+tIJBKMHj3abbVaWzRN2xQOh9cmEolNmqZ9iz4vbiwLpJTpNKvPFhb05gep3YgGoZd+re90g2EmugkhStGFYBpR4ikKhUKxW5OucQbdwBYBjB07lmAwyJo1a4hEIiQSiZ28NT0sFgsulwuXq7MOyta7sa1QCoFAgLa2tvq2trb65ubm/b/99ltef/31+IABAzybN2+ONzY2FrW3t5cEg0HH2LFj2+12+yaLxbI2EomsCAaDKzVN24BeL70mJayccaSUiWQSW+p8+FHoJWtfgt4fWEppfqnJ8q8rgEOBHyWPoTTCFQqFYjdlm5aRXSGE8ALTly1b9tn+++9/r5TS3HbjjTfy9ttvs3DhwuyNsgesW7eO8847j9mzZ2+1PhKJ0NjYyObNm9m0aRObN29m48aN0bVr14Y2btxIS0uL02az+e12+4ZYLLY8GAwuQxdfWUlSWWxXjaIQYjxwK/AWsB64A3haSnlHp/2GAP8HHIGubnaTlLJlV86tUCgUisKnO57zxIMPPvgkgJNPPhlN0/B6vdhsNtIx8LmmqamJurq6bdYXFxczYMAABgwYkLranlxIJBI0NzdXbtiwoXLjxo0j165dG/7mm298q1ev1jZv3lwaj8e1gw46aG0sFlsaDoeXoBvsZehCImllgEsp5wghHkfPfi8HbpNSPiuEuBBolFLOFEIcCdyOHsI+QUq5FlRXLYVCodgTSNc4r0VPYkqAHn42DLLH46F//64qlvLL9ozzzigqKqJPnz706dOHcePGgR5uLjG2t7e3s27duhFr164dsWLFipOWL18eXLVqVVFTU1PZQQcd1Gq3278Jh8Pzg8HgQmApsFRK6e98Hinl88DznVbbgSHJ5/8CDkCfYz4qmcW9VBlmhUKh2P1J1zgngLimaVbANMyapmGz2SgqSrdcOnc0NTVRX1+/8x27SXV1NdXV1YwZMwb0xC4nQDQaZcOGDX1WrVrVZ+XKlROXL18eWrZsWfTbb791HnzwwU0Wi2WR1+v9DFicXNalzisDSCkfT3l5GHo99Cnoim1/SSaFNUgp86ILLoTYB11B7kL09pz3KNlRhUKhyDzdmXP2uFwuVyAQqBo2bBiJRAKv14vFYsHtdrNo0aLsj7YbfP3111gsFkaMGJHXcUSjUdauXYuUkqVLl4YXLlzoW7FiRUkgELCWlJR8HQgEPtU0bT56Yw65o0zyTjXQOSWpavYeeunaS8BFwBIp5fWdE9gUCoVCsWt0xzi/MWXKlLapU6f+4pprrsHpdDJkyBCmT5/Ou+++W3AJYU1NTVRXV2O32/M9lC5pb29n2bJlfPXVV9G5c+d6li5dWtze3u5yOp1LQqHQp/F4/HN0FbYVhWD4hBA/Bq4DJkkp24UQtcBXwHFSyiWGYlmyTWc5EM6HpKtCoVDsDqRrnP1ASXKu2dqvXz8ikQgdHR0kEglsNhtfffVV9kfbDTZv3kyfPn0KMuTeFZ988gl/+tOfuOWWW1i8eHF8zpw5nsWLF9v8fr/V5XItCQQCH0QikdnAF1LKxlyOLanvPRXwSCmvS65zosu4ni2lXJiy7znAGehh+Rb0TPSX1Fy5QpEhLJZa4EngQjStNd/DUWSH7sw5P3/ddde13n333Vede+65OBwOhg4dyvTp03nnnXeyOcZuY9RdJ6VGewVLly7lwAMPZPz48YwfP976s5/9rBqgtbWVr776avzChQsPmTt3rn/JkiXFBx10UHtRUdEnXq/3XeBT9KSzbHrXA4E+wOsp6yail5gZfbMRQhSh18IvAS4FzkWvz/4c+DaL41Mo9iR+ApwA/Bj4R57HosgS3Sml+t6//vWvEoB58+aRSCR45plniMViBVdKlUgksFqtvcY4JxIJli9fbmSHb0VtbS2TJk1i0qRJFqAskUiwatWqPl9++eWZn3/++fFz5szROjo6bGPHjp0bCATeAT4G5ibbf2aK/uh9sj9PWXco+vzzFjBLvBLAM0IIm5QyJoT4CPglcCrwQAbHo1DsmegXtV8nX/0ai+Wf2boACyHqgIiU0rPTnRUZJ13j3Aj0HzZs2BcLFiw4au3atWzYsIHKykrq6+spLy/P5hi7TTwe7zXhbNCTxlasWMHFF1+8032LiorYb7/92G+//TjnnHPKQfeuFyxYcPSnn3560KxZs67btGlT6dixYxcGAoEZ6HrcX+xiIpkV2KdTOP3w5LHXgDnXbE0mtJ0rhLgDPeIyHV1ARdVoKxS7zkSgMvm8CjgSvXIiG0h0AaSCMs7JqpGlQO2OnBAhxCD065NTShkSQpwP/D9gL3R9il9LKT/O4jjrgfuB76M3NpoGXJ+u45TunPNqoNTpdJYEg8HK/v37097eTklJCZqm4fP5WLJkyS58jMwSDAYJhUJUV1fneyhp4fP5WLZsGaNHj6a4uHiXj+f1evnyyy+ZPXu2/+OPPw6tX7++vLS0dH08Hn/T5/M9D8zpTuMPIcRY9Dnnq6WUC4UQ16GH1s6XUnb5hxdCjAH+DfxGSvnRLn8ohUIBFstL6JGoIvSb31fRtDOycSohhAbsL6Vcno3jZ5tU4wwMBz4EjgfmopeDTgWGSCnbs3T+l9A7Lv4CXWzqVeBtKeUf0nl/up5zH8BtsViKAVwuF5s3b6asrAyLxUIwmM/WyNvS2zznSCTCyJEjM2KYAcrLyzn66KM59NBDS7/3ve+Vzpo1i9WrVw8tKSm5YsmSJT/ZvHmzY/z48XN9Pt+r8Xh8JrB4Rx6tlHKBEOIN4EMhxAb0SMpfUw2zEOIYdNGUf6Nnai8UQrwL3Il+961QKLqDxfIq8MNOayMkexwkH0/CYun8230VTTttV04thJiTfDpfCNGOnttyBHpznsOT1Rs3A33RdRumJK8Tg9BVDe8BpqC3xv1/Usr/JI87AbgbGIWeh/JHKeWzyW1r0efQr0a3OQ+hR93+BlQDf5dS/rGzR5zmRxoITJVSfpF8/V8hxD+A/YHZ23+beZNyDXADeo+Jp4Brkz0OBLqRn4CeAHuPlPKe5FsT6OqPASAghHgKXbciLdI1zhrwxoIFCxYLIe695ppruO+++3j55Zd5+eWXufnmm9M9X06Ix+NYrdZ8DyNtotEoFRUVO98xTVpbW5kzZw4LFy7E5XJxxBFHcNVVV+FwOIqB4ra2NubMmXPkrFmzxn/00Ue3er3e2Lhx497xer0vAzOllM2djyml/IsQ4p/A0cBCKWWTEOJvwJdSyqfQE8N+jH5naNxpl5MMiamQtkLRbW4GRqMbKqOzX+c7+NTXQfQ2uv+3qyeWUo5PGqWDgZuAY9H70AeEEJPRje/JwBfAz4C3hRDDk2+vBPZFN9zjgRlCiAXAZuAd9C6HD6MbtNeFEN9KKY3Q/BnJc+6DbuSHoRvyA4FZQoiHe/h5XkX3XAEQQhwBlKFLL6fD0eje9xD0G5XpQohZyc/zFHo0Yz/gTSFEm5TySSnlmZ2OcQp6hUtadCch7OwjjjjieIApU6Zw+umn8/HHH3PCCSdw0003deMw2SeRSGTMC8028XgcTdN2+WZC0zRWr17NF198wYoVKxg0aBCnn346w4YN2yaKUFNTwwknnMAJJ5xQDBSvX7+eTz755MyZM2ceN3/+/JKDDjpoTTAYfCmRSLwJfG4IoyTnSlJT858GGpLP30YP33wPWC6EMBLBrky+VxlmhaI7aNrXWCwHAI+hG8IdtevzA68BP0PTtpELzgDvGWqAQogLgMellLOS2/4lhLgcXT3QWHdtstfAR0KI14EfoTf5WSWl/Fdyn1nJHgM/4bt584eklG7ALYRoBR5Ots6dLYTwoctIN+3KB0l6uy8Av+tGSPsfyXEsFkIsQr/5sAClwO+T18glSYflYvRSt9Rz/h39RuOCdMeZrnFeA+zj8/mqAQ477DCam5u54447aGtrY9CgQemeLyf0prB2NBrFbrf3OLM8EomwePFivvjiC9xuN6NGjeKKK66gT58+aR9jn3324YILLuCCCy6oiEQiLFy4cPgHH3xwzdtvv/2LpqYm26hRo96KRCKvoHvFbcb7pJTzU577k/+YdwkhbgQWoP/TvtGjD6ZQKEga2nOwWH4O/JPvPOhUwsB1aNq/sziSVJneemBep+3r+K4Nrk9KmWpAN6J7/8Hkfp3fd0LK67aU53H0sLhBgu9C+j1CCHEUusLhPVLKu7rx1tRoYiw5jnpgYydVx9TvgWR74MfQIwjf6yoquT26U+ccKyoqsoIu8KFpGoFAAKvVWnBeam8Ka0cikR6pmLndbubOncuXX36J3W7nkEMO4eCDD95ZL+ydUlxcbNRau2688UbX5s2b+fDDD8984403vr9w4cLyMWPGLAkGg9OAVzsnikgpZ6CHsCqBkk4/UIVC0XMWoM83b884f5nl86dGvjYAgzptH8x3YeMyIURFSgnWPujh3A3AOV28L7UKJGsRNiHE2cCjwFVSyicycMgNQP+UKhVI+TxJpcQ30A35Yd1t95uucR6CPnfogu+6UlksFux2O+vXr+/OObOKpmlmnXNvIBqNUlpamvb+mzZtYvbs2SxdupS9996bk046if333z9rn7dfv36cd955Reedd15dKBTiiy++OHjGjBlDZ86c+YeRI0dGNU17KBaLvYRerpUAkFJ27OSwCoWie4zju+u1hu6FOtFDq7bk9rkZPmeYFJGhFP4HvCqEmIY+53w5esLVG3wXer8jWdVxJLpnfDO69zlVCPEL9DnnQ9FDwGdneNzbIIQYBzwBnCqlzJRq1hdAK3CbEOJWYChwLboqIsB/0b/Dk3uiO9GdOecPFyxY8JkQ4t7XXnvNXHnjjTfy9ttvd/e8WcO4aegNAiSapplh7Z3tt2rVKmbPns3atWsZMWIEl156KXvvvXeORqrjcDg4+uijOeqooypHjRrFK6+84jz00EOvfv311y9va2vTDjrooOl+v/8Z9PkppautUGSOieiGz0j6ugY9zN0nuf5I9DazmeRx9PIjDbjXWCml/EgI8St0L7Q/usb+Cckk0UHJ3aLo2djtwHlSSgmQTCb7B/AXdA9zipQyFwbk1+jJcy/pU84mp0kp3+3JAaWUUSHEKejJcY3oZVP3Ag8JIYYBpwEhoDXlnHOklN9P5/jdaXzRVlFR4fR4PPWTJ08mFAqxZs0aYrEYLS0tBdOVKhqN0t7eTkNDw853zjOxWIzW1tbtzg8nEgmWLl3Kp59+SktLC2PGjOGwww6jpqYmxyPdmlAoxG9+8xsmTJjA+eefD8C6det49913tddee82/evXqopKSkjc9Hs8T6NnfmVQrUyj2PCyW1ejh4WkYSV8WSyn6fOaPgHVo2r75HCJsK/yR5+H0atI1znOAJ2644YaD7rrrrp/+8pe/xOFwMGTIEDZs2MBrr73G//73v6081tTH7S2p+2WKUCiE3++ntrY2o8fNBsFgkGAwuI2xjcViLFq0iNmzZ+P3+znkkEOYMGFCt8Lf2aS5uZmTTjqJ6dOn07dv3222NzY28s477/Dyyy93rFixothqtb4RDAafAmbk+geb7JbVH4hLKTfl8twKRcawWN4AXkDTHu9i2yXAmWjaSTkfVyeUcc4c6Rrn0cD00tLSKr/fv80cxI033sgZZ5xhSrymPnZ+3nkpKioyjbXxPPXRWDq/NtZ1JhAIEIlEqKqq2oWvJTd0dHRgtVopKysD9OSw+fPn89lnn5FIJJgwYQKHHHIIJSUleR7pd2iaxjvvvMO///1vXnrppZ3u39TUxMyZMxMvvPCCe8WKFQ6LxfJqJBL5L3roO22Vsp4ihHgCqEPvkvUlcImUckO2z6tQ7Ink0jgLIU4Gnt3BLgdKKVfv5BijgM92sMvJUsoPezC8XaY7xvlNl8vlCgQCVcOGDSORSOD1erFYLNx3332MGjWq2yc3DHQikdjmeSKR2OZ556WzsbZarYRCIYqKiqioqDDXFeocdEtLC+Xl5cTjcebMmcOcOXMoKSnh8MMPZ8yYMQXZizoUCuHx6EmY3Z06aGpq4o033og+//zz3o0bN9oSicRzsVjsKeCTTHfVEkJY0eflfgVMRu+g9SjwkZSyR0IGCoVCkSvSNc6fAU9OmTLlgKlTp/7immuuwel0MmTIEBobG3nuued44YUXsj/aFLoy2vF4HI/HQ1FREXa7nXg8TjyuZ7hbrdYdLrk23pqmsWbNGlauXMm8efOorq7miCOOYOTIkQVdo+12u7HZbKa331M2bNjA66+/Hp42bZq/paUlHo1G/5NIJP67Pa3u7pLU9r4NvZf048l1P0NPQOn+naRCoVDkkO4khHkqKyttHR0dDSeeeCLBYJA1a9YQDAbp6OgomISwtrY2XC4XDsd35YCG4TYeU5dYLGZ64VarFZvNts1jpo2l3+9n1qxZzJs3j/r6eo466iiEEAXp3aeiaRqNjY3U1dVhs3Un0X/HrFixghdeeCHw0ksvJeLx+OZAIPCgpmlPSym39OR4yXnm36PL/v3MKO0SQtwF1EspL1ZyogqFopDpjnGeLqX8TAhxbzIrHviulGrhwoXZG2U3aG5uprKyslvCKJqmbWWsU5/HYjEsFgs2m22bxfC4jfKtneH3+5k9ezZz586lpqaG8ePHM3bs2II3ygbhcBiPx0N9fX1Wjm+E96dNm+Z79913bcXFxfO9Xu99wCvdmb8SQlQBD6JrgP85Zd1j6PNL/zTmu4UQRUZIXQixP3ARej/slzP52RQKhaI7dMf9OfKQQw45FuCaa67ZqpQqS72+e0RP1MFSjW/n5CsjfG4Y6lgsRigUIhKJMHfuXIQQ1NbW4nA4TLW0rsLk8+bN45133qG2tpYf/ehHNDQ0UFxc3GsMM+jzzakRiUxjtVo57LDDOOyww8qCwSDvvvvuEc8888zor7/++tGDDz74OZ/Pdz96o42d/cPF0RPAbktZdzJ6X+qvk/WJRVLKRIphngJcgi6wf15ynfKuFQpFXkjXOC8Dnrj88suH33333VcNHz4ch8PBhRdeyLp163j55cJwMgxDmskwtMViMeelDcPt8Xioq6tjr732YtWqVXz11Vccd9xxRKNRvF4viUQCm82G3W7Hbrdjs9mora3ljDPOMMPXzc3NBVMalQ6aphEKhXJWY+10OjnllFM45ZRTyjZt2sRLL73046effvrsUCi0Zfjw4fdpmvbkDuTwqtGlDlMN60/QFX3mAKQY5ePRe7zujy64EJNSPpfcRxlmRcExcCr90LOUz1k3hR5N/SgKn26VUjmdzspgMFi53377oWkaHo8Hu93Oo48+yuDBg7M/2p0Qj8dpaWnpVtOH7rBo0SLefvttJkyYwFFHHQXo3uRLL72E0+lkwoQJ9OvXz/S0o9Eo0WjUfF5UVGR61h0dHfTr1y+jc7fZJBKJ4Ha78yrukkgkmDNnDk8//bTngw8+KLZYLG+Fw+F7gQ87G1IhxO3Acejh7eOBEcApUsq1ye3GvPQPkvt8DfwRWCClvLWTXq5CUTAMnMoDwM+BB9dN4Zf5Ho8iO2SklOr+++9n5MiR2R/tTohEInR0dGR8TrSjo4MXXngBr9fL5MmTMaTYjLnmQCDAW2+9hd1u5/DDD6eurm6bYxjz2tFoFL/fj9frxel0mvrkxcXFpqddiNnaRvlUJvtO7woej4dXXnkl9thjj/na29u94XD4Xk3TnkhttiGEuAa9beXrwAtSynXJ9cXohvg3gJBSrhBCHIuu93u4lHKTCmkrCpGk17wavQFGEBhSCN6zEKIMXVt7HHCflPLG7ex3CzBcSnluDofXLZKfZSFwu5TyP9vZ5xr0Us3q5L6/zFSliUG6btuDwJ2XXXbZAVOnTv3FiSeeuFUp1S233JLzUqquyFbDi48++ohYLMY111wD6DcBRqgbwOVyMW7cOObPn8/ixYv5/ve3lU5NndeOx+PY7XYqKipMgx2JRAiFQkSjUXPu2jDYNpstr3PTRki7uro6b2PoTEVFBT/+8Y9tF110UdWiRYuqHn/88Zvfe++9W0eNGjUjEoncg17P/E90/WGEEI5kX9mnpZRPCyEeQ2+OPlsIYTQMeDtpmM0kMYWiwPg9erML0Lsd/R4KwnseDRwE1CX7OPdm/oHeXapLknra1wHHACuB3wKvCSGGZPKGfrcqpfL7/USj0Yyog6Umlq1fv57nn3+e66+/ns8++4wFCxZQX19PWVkZkydPNt/z2Wef8e2333LkkUd2KWtp0N7eTklJSZftHY1mGIbBjkQiaJpmetfGkktjHY1GaWtro6GhoaAT2JLedPSRRx7xezye9lAo9DdN0/5ntK4TQtQDe0kpzX/WZHvLf6AngQXQLzKbVUhbkUsGTqUcKF43hdaBU3ECFeum0DhwKjagAdiC3uTC8JoNguhdA0MA66bgHjiVUsC1bgrNA6dSAtSsm7JVP+YdIoT4CrhVSvmCEMI491lSyheFEP2AFYAPPSJ1Onpv56OAEvTf0ET0bk3/Sj53o3uhDyU95yOT7z8avU/0xVLKOclznwXcAuyFniPyCynlGiHEJOCvwKfAT5PHvBw4A/23uwW40DjOdj6XFb3N40VSyveS685E7zs/Ovn6ZOB69I5f/+rKcxZCXA7YpJQPJF+Xo3dt3DuTEsHdiZ9+OGfOnNsA/vGPf/Dggw/y9ttvc9hhhxXMBXtX+zgbCWUff/wxy5YtM9fvs88+9OnTh/vvv58NGzYwefJkhg8fztdff80nn3xi7rfvvvvi9/uJRHbckCkSiWy31MtisVBcXExpaSnV1dX06dOH+vp6XC6XOZWwZcsWWlpa8Hg8hEIhEonsOnmhUMgMwRcySW/a/tFHH1U9+OCDg4888sg7iouLN48ePfohIcQBUspmwzALIWxgtrf8Fj1R7DzArwyzIg9cB/wn+XwyycRF9L7J3wLl6F5y53IJe3L9X5ML6FUHRuvA8cCqbo7lTfRcDIBJ6G0Pj06+PgF4D/1mYC903fqzkmNuklKWSSkXoDfoWId+Y3EycJcQYmzyGEehG+5a4APgbwBCiAnAQ8Cl6DciHwOvCCEMO3UIsB49lDwNPYz+RfI47wK37uhDJX/XzwCpIfVzgSeT569Hj7Rdwg76SkspHzIMc5IfoncKS/sGKB12q1KqRCLRrfrmzhgyn3PnzmXQoEH069eP2tpa4vE4I0aMYM6cORx11FGmV2y323nrrbeYOHEioMtZOp1Oli9fzj777NNl/bMhSdqdmwir1YrT6cTpdAL6TYThVft8PqLRKDabjeLiYkpKSiguLs7ovHUwGOwVWuUGFouFCRMmMGHChMrGxkaefvrpi5966qmfjBo1qikSiVwFvC6ljAEIIZzoF7hHpJTvGEZbocgxf0NvaQjwFrqHCLAW2BsoRTcanbEl149BNxCgt3p8Lvl8DtDdblVvohtJgO8nj5dqnF9HjzC9kAxhB1PbMAohhqAb0h8k9QkWCyEmohtWgA+MNpFCiFfQ80JIfo7HpJSfJ7f9CZiSPBboNwT3SCkTQogPgEullI8l930XuD2Nz/Yk8K4Q4kp0T/+E5DlIfua/Jj31NA4FQogj0G80Lst0jspuVUoVj8d32SitW7cOl8tFR0cHa9asoaKiArvdzj777ENtbS19+/Y1jW5VVRUOhwO3221qeY8dO5YVK1Zs14uPRCLY7fZd8kItFgslJSWUlJRQXl7epbG22+2mod6VMLhx81WIOt/p0KdPHy6//HJ7PB5n4cKF/d1u91ObNm3yjxw58i/RaPSRZMj7JmN/w2grFLlk3RS8Kc+D6OFq1k0hBmxKZmhv70dcBEwxMrfXTcEP+JPPw3Tfo/sUaBBC7A18DzgJuCgZ4j4GuBb4vx0ctw/QIaX0GSuklIsBI5m2PWXfKN/ZoQHAJCHEpSnbi9FbZTYDnpSoVhzoSNkvQRqRYCnlAiHEZuBYoAb4Qkr5rRDip4BDSvnQjo/wHcmQ+GPoksDP7Wz/7pKucb4MmH7//fdXArz++uvblFIVAt0Ja29P1ev9999n1KhRxGIxvvnmGxoaGthnn32or6+nvLycNWvW4HA46NevHwsXLqRv375beZXBYNBMFuvqHIbhzCTbM9aGolcsFjO96pKSkm4lmAWDQRwOR8GHtLeHpml0dHTw2muv8cADDzBixIjShQsXlj7yyCO3ffzxx7cdeOCBT4TD4b9LKVfme6wKRVckM7QvQff0uqIEuGTgVG7LROa2lDImhJiJrpZnl1KuTPZXuA5Ym0yahO2HfjcClUKIMsNAJw3u8p2cejPwZynlH40VQj/ROmDCDs7XXZ4EzkQPhz+ZXHcOcJgQwp18XQY8IIQYL6W8svMBhBDXAv8POFtKOSND49qK7oTwbBaLRQO2kqyMx+P4/f5sjK3bdCdb2zA2nQ36Mcccw4ABA4jH4zz99NOsXLmS2tpaSktL8fl8LF68mPXr12O320kkEpx55pnAd8a+b9++tLe3d3lO0D3nrhLBMkmqsQb9ewmHw4TDYfNvZWwvKSnZYbQhFAoVTPlUTwiHw8yePZt+/fpxwAEHADB27Fjuv/9+1+bNm/nvf/97yTPPPPOT0aNHfxAKhe6QUu6ofZxCkQ9SM7S3R6Yzt99ED7VPT77+APgdcPfO3iil3CCE+BS4QwjxG2AY8Gd0b3VH/A94SgjxKrAYOB94BBjao0+wfZ5CT2Kzo9+AIKU8PnUHIcTnwIPbSQg7FX1+e2JqcmmmSTcGbJRSPQNw4oknctZZZ3H77bdz1VVXccstt2RrfGljtJxM18Nrbm7m9ddfZ8sW/UZz8eLFAAwYMMCcEx47dizr169n/Xp9qqSuro5jjz2W008/neOOO44rr7yShoaGrc7b0NDA0Ufr0zOdx2JkYu/KvHhPKCoqwul0UlVVRUNDAzU1NdhsNgKBAI2NjbS0tJjh8NT8AUNnPNfjzSSGnvlFF120zbZ+/fpx4403OmbPnu267rrrjq+pqXl7zJgx84QQpyUzOxWKQuAwtu81G5QAh2fwnG+hJ159mHz9AeBCn29Oh/PQy5E2A6+ih34X7ugNyb7JN6EnbXmAG4BTpZTfdm/oOybZz/1rYKZRybEzhBAPCiEeTL68Ef27+FQI4UtZhmVynLtNKVUsFqO1tTVtdbBAIMCMGTOwWCxs2rSJ4uJizj//fFwu11ZG9dVXXzUTjLpSx+qOXGgsFjMVzAolTKxpmulVh0J6bwmHw0FJSQnRaJR4PN6rksFSMUrADFGYnUVVYrEYM2fO1KZOneretGmTLxwO3wk8sRvUbSoUil7GblNKtaP55q5uQIykr6VLl9K/f38uu+wySktLzc9ivGf8+PGsX7+e1tbWLo/dnQQ0w2suhO/LwGKx4HA4qKysNL1qq9WKz+ejqamJcDhMMBjMerlWNvD5fJSWlm4lGLMjbDYbkydPtrz11lvVjz322IBDDjnkjuLi4m+HDx9+kxCi98b2FQpFr6M7nnNbRUWF0+Px1E+ePHmrUqqWlpa8e87BYHAbFSvjs3U2hps3b+bhhx9m9OjRZoOKsWPHUl9fv1WI2ni+ZcuWHYqKpIvH48FisVBeXr7Lx8o28XicxsZGKisrTc+6uLgYh8NhduAqZOLxOM3NzTQ0NOxSBr+Uknvuuafj448/tiYSiftisdg/UiVCFQpF4SCE+Avbn3f/VkqZXo1UAZCucZ4DPHH99dcPv/vuu6+69tprcTgcDB061Cylyrd8p8/nIx6PU1lZCWydjb1p0ya+/vpramtrEULgdDrp6OigurqaLVu2MHPmTAYOHMjEiRN36NV2Z067K1paWigvL9+mLWUhYoipGDc7RlJZKBQiHA5js9lwOBw4nc6CNNQejwdN08z/h11lw4YNPPDAA/433nijyGKx/DcUCt0ppVy/83f2DCHEQcBIYIuU8p1snUehUBQmu01Xqo6ODqxWK263m/79+5uGdPbs2cyaNYuxY8eyadMmSkpKGDlyJCNHjjT3mT9/PgMGDMhqxyVN09iyZQt9+vQpyMYWnWltbcXlcpnCJ6kY89ShUIhQKFRwhjqRSNDU1ERdXV3Gu341NTXx6KOPRp599tmE3W5/wev1/j8p5ZpMHV8IYQcuBu4CXgRORFdBOg8IqWYcCsWeQXesxFalVMZjoZRSffPNN9x///1byWnG43G+/fZbzjrrLI499lh+8pOf0NrayldffUUgEDA/x8EHH5z1VoixWAyr1dorDHMikSASieBwdFYK1DHmqauqqujTpw9lZWXEYjGam5tpaWnB7/cTj+dPATMYDFJcXJyVdpwNDQ389re/Lf7ggw8c559//nlOp3PZuHHjnhVCZKrc4xT0mtZfSikvRS8jKUHXBFeGWaHYQ+j1pVTBYJD//e9/fPjhh5xwwgmcd955gG5AQqEQq1evpqGhgZUrV3LvvfdSXl7Osccem3PJ0WyIj2SLUChESUlJWiH8rgx1JBKhubmZ1tZWAoFATpPJNE3D5/NRVlaW1fPU1NTw61//2vrRRx+VXHTRRWc6nc6vxowZ89yulFMIIfqgNxL4TEr5VLI7lh9dfvGwTI1doVAUPr2+lGrRokV88skn/PCHP2SvvfYy2xs6HA5sNhvPP/88GzduxGazMWnSJEaPHs2bb75JLBbjhz/84S7PI6eL2+3GZrNl3Whkgra2tq20vHuC8XcIBoOEw2GzC1e6Rr+nBINB/H5/lz21s4nX6+U///lP5NFHH40lEolXw+Hw76SUq7tzDCHEJejKRTdJKb9KrjsU/eb4xynrrFLKuBBir0x2wVEoFIVDry2lMm4qhBDEYjHa29tZsmQJ9957L6+++ir/+9//aGlpYdCgQZSVlXHqqacyevRocz5yyJAhwLaZ3NkiH+IjPcFI/NrVpDWLxYLT6aSmpoY+ffpQUlKCz+ejsbGRjo4OsxVmpvH7/ZSWlmb8uDujvLycX/3qV8Uff/yx6+KLLz69pKTkq1GjRj0mhNinG4f5ASANI5zkfGA+fKe9nDTMdcBSIcRXQoijMvMpFApFodAtz7m8vNzu9XrrjzrqKDZv3kxbWxt2ux232511z3l7HZ6KioqYOXMm8+bNY9iwYRxyyCGUlpby1ltvUV5ezpgxY9iwYQOfffYZw4cPR0rJ4MGDOe2003KWvGQkg/Xt27egapy7IhgMEgwGqampycrxY7GYeQ7ATDrLxN8iEonQ3t5eEH2n29vb+fe//x14+umnicfjT8RisduklNttQJBMBFuDPtf8anLdqeg9a180uu8k1w9Cl1EMA++jtwr8AviplHKXtZUVCkX+SddzbgJqTjjhhHcAPvvsM+LxOLW1tbS0tNCvX7/sjTBJLKY3C0q9mTCSq0aMGEFZWRn77bef2T3q9NNPZ/Xq1TidTo488kjOOeccBg8ezHnnncePfvQjszFFLjBaOubbYKSD0egiW9hsNsrLy6mvr6eqqspMJGttbSUYDO7S38QQHSmE77m6upqbbrrJ9cEHH7jOOeecC4uLi1eOGDHir0KI6q72l1JG0bWFJwAkE8z+H/AlYArrJzsF/QJ9HvpyKeWj6F2AlqDPVysUit2AdD3nbwGL0+l0BYPByr322guXy4XH4yEajVJVVcWMGVlpzAHAO++8Q3NzMxdccEGXHrTH42HTpk0MHz7cXJdIJHjooYf4wQ9+wH777bfV/tsTJ8kWfr/f/J4KmXyVeyUSCUKhEIFAgFgshtPpxOVydSuBzhDD2VXRkWyxZcsW7r77bs/bb79ticVidyYSiXuklIHUfZK1zU+jd9/pQO/u85NkUpixz3j0vrVO9HZ690kp/5ezD6JQKHJCulexcgCjlMpIajK6HzU2NmZ8YIannEgk2LJlCytXrmT16tVYLJZtsn9tNhs1NTWsXLmShQsXAvD5559TXFzMgAEDtjm2xWLJqXcViUR6xXxzKBSiuLg458atqKgIl8tFXV0ddXV1WCwW2traaGlpIRAIpOVN+/1+XC5XQRpmgL59+3L33XdXvPLKK+UTJ078bUlJyYbhw4f/PBnOBkBK+aWUcjhwNXCWlPJM9D61ACQTwOYAV0gpJwJ/BK4TQozK9edRKBTZJd0rWRz48mc/+9kzAIMHDzZLqQ4//PCMG7r29nZmzZoF6JnDVquVgw8+mFdffVUfdKcLcDwep6ioCLfbzfvvv8/DDz/MvHnzOPbYY7Maok2X3lJGZWS55xObzUZFRQUNDQ2UlZURDAbNJLJoNNrlexKJBMFgMC+JYN1l33335aGHHqp48skna0aPHv1Xp9O5Zvjw4WcIIcwfkZRyZrLt3gHADCHExOSmq4UQ50gpVwshbFLKN4AtwPfz8VkUCkX2SFeloRg47t5777WCHmaeNWsWkUiEeDyecbGHzZs34/Xqyalr1qyhoqKC4447jpUrV/Lhhx8yadKkrbpBJRIJnE4n48aNY+jQoQQCAfbaay9g1yU3d5VEIpGV7yjTGKpfhdK72aifdjgcZhKZcaNWWlqKw+Ew/66BQICSkpKCUCdLlwMPPJDnnnuuYtasWRV//OMf/9ve3r5aCHG5lPJzYx8p5dfJpDDjrqMUOAF4TkoZS64bBrwGIISwKKEShWL3IF3POQFMu+WWW/4B8Otf/5qrr76a++67j5NOOinjF8WGhgZ8Ph+hUIgDDjiAU045heLiYo4//ng++eQTvF4vRUVFZnh79erVvPvuuwBUVVWZhjmRSOQ9OcjwmvM9jp1h6GUXooEzksgaGhooLS3F7/fT2NiI1+slFovh9/t7Rf14Vxx55JG89dZbpb/97W9HVVVVfTBu3Lg3hBD7GtullO6UfrazgAlCiOOEEIcJIe4HKoCHkvsqw6xQ7CZ0p5Sq3eVyVQQCAVNb2+v1YrPZaG1tzWgp1Zo1a5gzZw4//OEPTQ/J8ICffvppNE3jggsuMPf/7LPP2GuvvRg4cGDGxpApvF4vmqYVjEe6PXqTSAroNz2BQICOjg4A+vTpU3DtOLtLMBjk8ccfjz/00EORoqKiJ/x+/81SyvbUfYQQ5wO3ooez1wD/llJ+qrxmhWL3Il3PeS36HfpW2tqgZ0r3798/o4MaNGgQGzZsYP369WYCmHHOE044gdWrV7Ns2TJz/yFDhpjNLgqN3jDfnKqq1luw2+1UVFTgcDgoKyujo6OjWwlkhYjT6eTKK6+0Pvzww84BAwb8rKSkZO3w4cN/KYQw50SklE9LKfcDzpNS/lhK+Wlyfe/80AqFoku6E9aOa5pmge9KkTRNw2azZTxD1mKxcNBBB/H+++/rgywqMs9ZU1PDhAkT2LBhgzkGY/65EL2m3mCcI5EIVqu14OfFOxOJRAC9pri+vp7y8nIzgczr9ea1+UZPicViPPHEE0yaNMn2/PPPV4waNeoOh8MhhRDHpe4npdyYrzEqFIrsk65VHQKEuupKVVJSwvr1mW9rO27cOOLxOJ9++uk224455hiOO06/VhledSEa5ng8jqZpBTmPm0pv85oNDKlO4+/vcDiora2ltraWeDxOc3Mzbrd7u1nehcjnn3/OggULuPzyyxk+fDjPP/985d133z24trZ22oEHHvi2EKLXNItXKBQ9p1va2gsWLPg9wGuvvcYbb7zBxx9/zKGHHpoVw1hRUcH3vvc93nvvPTZt2rSVt26cT9M04vF4wRq/3pAMZoS0d6XJRT6IxWJEIhFcLtc22+x2O1VVVTQ0NGC1WmltbaWtrY1wOFzQIe9gMMjUqVO55pprzLIwi8XCsccea/nwww8rfvnLXx5VUlIyf8SIEXcJIcrzPFyFQpFFupMQ1uZ0OiuDwaCZEObxeLDZbLS1tWVNW3vmzJm0tbUxdOhQDj744G22G8pS2dKC3hU8Hg8Wi4Xy8sK9jkYiEdxud9b7WWcat9uN1WpN67vVNI1AIIDf78disVBWVrZVKVYhoGkay5Yt49577+W+++7b7g1nU1MTt99+e8eHH34YDYfDVwPPqvlmhWL3ozsJYZVFRUUJ2Dqs7fP5djkhbP369cycObPLbcceeyxjx45l4cKFfPDBB6xdu3ar7YYASSHSG+abe6PXbMh9duU1d4XFYqG0tNScl/b7/TQ3N+P3+wvGk/b7/fTt25d//etfO4wENTQ0cM8991Q+9thjdQMGDHjQ6XR+rhTCFIrdj+4mhBXB1glhVqu1x8YxkUjwwQcf8MQTT5jH64phw4Zx1lln0bdv321aGRZqWFvTtII3zpqmZb3RRTbw+/04HI5u/91T56UrKysJhUI0NTXh8/m2kYTNJfF4HJ/P161yu3HjxvH2229XXH/99Qe5XK7PDzzwwPuFEIVdr6dQKNKmWy0jXS6XKxAIVA0bNoxEImHW8Ho8nm6Htd1uNy+99BLt7e2cfvrpZn/l7uJ2uykuLk7bi8oVsViM1tZW+vTpk++hbJdoNEp7ezv19fUFFeLdEZqm0djYSG1tbUZufKLRKD6fj3A4TGlpKaWlpTmPxLS3t2O1WntcC9/W1saf/vSnwDvvvBMOh8NXaJo2TYW6FYrezS4nhPVEW3vJkiU8+OCDOJ1Orrjiih4bZijcsHZvaHZheM29xTCDPma73Z6xiITdbqe6upq6ujri8ThNTU14PJ6cedLhcJhIJLJLeQk1NTXcddddrscee6x6wIABj1dWVs5OtpxUKBS9lG4lhFVUVDg9Hk/95MmTCYVCrFmzxmzVl47nHA6HmTFjBl999RXHHXcchxxyyC4bhqamJqqrqwsufNzR0YHVai1oxa2mpiaqqqoK/ibCQNM0mpubqays3GZ6I1PEYjFTOtblclFaWpq1aRPj85SXl2ds3j8ajfLEE09o9913X0jTtL+FQqHbpZThjBxcoVDkjHRdzmXAX3/+85/PABg4cCCjR4/m2muv5bzzzmPIkCG43W46OjrweDx4PB68Xi8+nw+/308gEGD16tX8+9//ZuPGjVx88cWMHTuWeDxOIpHYpaSc1AYYhUShzzdHo1E0TSvoMXYmHA5jsViyejNhs9moqqqivr7eNJ4ejycrgiaBQACr1ZrROX+73c7PfvYzy5tvvukcN27ctU6n8xshxFEZO4FCocgJ6XrOo4HpLperMhAIVHbefvPNN3PmmWdulShmPCYSCebNm8dnn33GAQccwBFHHIHVajWNsrGPxWIxVb6Kiop2uhjezJYtW+jbt29BhWY1TWPLli306dOnIG8cQNf8TiQSVFZu8+csWFpbW3E6nTnNLzCStYLBIC6Xi7Kysoz8TQ2RlEzNnW+PmTNncvPNN/vD4fC0cDh8rZTSnbWTKRSKjNEdvUYbSW3t1IQwQ2qzq166fr+fV199lc2bN3P22WezPXEjw0gbhtow3MbzRCJBLBYzPe3UJRQK0dbWZhps49FY8iHrGY1GdymLPReEQqGCb8aRSjQaJRaL5bzsy2q1UllZSVlZGV6vl6ampowkjnm9XlwuV9YjF8ceeywTJkwo/dOf/nTm66+/fooQ4grgRZUwplAUNul6zp8BT06ZMuWAqVOn/uKaa67B6XQyZMgQGhsbee6553jhhRe2es+6det44YUXqK2t5Ywzzsi4ITD6D3d0dFBVVWUa7ng8vtVilHsZi9EW0XieDelPv99PNBqlqqoqo8fNFEaeQJ8+fQoq4rAjuiM6kk1S56TLyspM+dDuEIlEzCz5XN3ARaNRPvroI2655ZZAJBL5pqOj4xSlz61QFC7dKqWqrKy0dXR0NJx44okEg0HWrFlDMBiko6PDTAjTNI3Zs2fz/vvvc/jhh/O9730vaxegYDBIMBjcoTqYIfFpeN6dnwOmoU5ddsXzdbvd2O32LqMJhYDP5yMWixXszUNnjBBwQ0NDwUQjYrEYXq+XSCRCWVkZLpcrLSOtaRotLS2UlpZmPDyfSCTM7lzGYkiXer1eAFwuFytXrozPnj07EI1Gf61p2qPKi1YoCo/uGOfpUsrPhBD3SinNbTfeeCNvv/02CxcuJBQK8corr7Bu3TpOP/10hg0blsWh60YmHo/3eN7UCKXHYrGtFsOAFxUVYbPZsNvtWxnunRmIQs0gN2hpaTElLHsDHo8HTdMKcn48EomYHbDKy8t3Wprm9/sJBoPU1tb2OGqhaRrt7e1s2bKFxsZGWltbTUMci8VwOBzU1dVRV1dHbW0tNTU11NbWUl1dbSbTSSm5+uqr3Vu2bFkcCoV+LKVc16PBpCCEGACcC/QFXjLaWSoUiu6TsVKqGTNmMG3aNBwOB2eddRbV1dVZH7zH46GoqCgr5UqpHncsFjPnPGOxmOlpG0bbbrdjtVrN3tONjY0Fl6RmYHihvSWknUgkaGpqoq6urqBbWobDYTweD6A3bemq1Mv4LN1JAotEIjQ1NZmG2FgMj71Pnz7U19ebxriuri5tLz4Wi/HQQw+FH3zwwWgkEvmNpmn/llL2qMA72dLybuBb4EvgauAGKeWDPTleJhFCWABLTz+bQpEP0jXOc4Anrr/++uF33333Vddeey0Oh4OhQ4eybt06nnrqKQ4//HDGjBnDCSeckLOLaHt7OyUlJTnN3k31tA2DnVqWpGkakUjE7IhUaAbQ7/cTiURycvOUCfx+P+FwuCAbm3TG6PDl9XpNxa9UI9zR0QGw3QhAIBBg8+bNbN68mS1btrBlyxZaW1spKiqirq6Ovn370qdPH/r06UPfvn0zNm2ycuVKrrnmGs/GjRuXBoPBC6SUq7vzfiHEocANwLdSyinJdRcAh0spf5mRQSoUexjdKqXqqitVNBrl6KOP5oILLuDAAw/M/ohTaGlpoby8PGuCFN0hHo8TjUbxeDxEIhHsdjuJRILi4mLsdrv5mG8d8NbWVkpLS3tFSNuoM+5NQinwXRcsr9eLw+GgvLycRCJBa2urOW9ueNEbNmxg48aNbNy4kba2NoqLi+nXr59piPv27Ut9fX3Wb3jj8TiPPfZY9L777otEo9Hr4vH4Q+nMRQshqoE/AQ7gGqNUSwhxCXATMDyfc9rJa9c44GCgBVgCvC2l7MjXmBSKdOhWKZXFYtFAbyAQi8UIBAIAnHzyyTk3zKCHCfNt7AyMDPBAIGAmCCUSCSKRCNFo1MzgNkQ0Ug12rrzreDxOJBLpFV4o6OVeFoulYOfut4fRBcvpdOLz+WhqajLFeL7++ms2btzIt99+SzQapaGhgf79+3PkkUcyYMCAXZqL3hWsViuXXXaZfdKkSfYpU6b8vamp6UIhxLlSym938taJwCjguhTDXAacAzwppdSEEFYpZeZVXHaAEMIGnAf8C0gNrWnAbCHEOVLKTbkck0LRHXpUSnXBBRfwzTffMGLECAYPHsyLL764TSlVtilUoQ+jKUNXno4xjx2JREyjHYvFTENtLNn6PIFAgFAo1GuMs5HV3NtaWhoe//r169m4cSPr16+nvb2d4uJi9t57b/bZZx8GDBjA3nvvXZARjGg0yoMPPhh/9NFHQ9Fo9OexWOzp7Xm/Qoi3gI1SystS1v0WOASYKqX8qNP+RYBNShnJ1viFECXAVcBdyVWfAEvR58NPAg4FvgG+rwy0olDpUSnVwIEDqampoa2tjVAotFUpVa4wEq/69euX0/PuiJ4kWyUSCaLRKJFIhHA4TDQaxWazUVJSknFj3draisvl6hXGzqgFbmhoKLh5+85omkZraytr1qxhzZo1rFu3jkAgQH19PXvvvTeVlZUMGzaM2tpaM2mssrKy4EP1S5YsYcqUKYH29vYP/H7/T6SUrZ33EUL8E1gqpXwo+fps4GJgJvCAoesthOgPHAb8GD1i97LxnkwjhLgQ+G/y5X+BO6WU36RsfwOYDDwIXKu0xxWFSHfC2h9eddVVDXfccccxDz/8MAMHDgS+K6XKNYXYx9nQ0+6OMSkqKqKkpISSkhLKy8vNhLJIJILf76e9vd001obB7omxMkLsvSkRrCcCH7nC4/GYxnj16tV4vV7q6+sZPHgwJ598MgMHDsTlcpkdroya8rq6OoLBoOlJV1RUFNz/scHIkSN58803XXfdddcPpk2bJoUQ50kpZ3ba7WvgViGEB9gb3WP9F/B8imEuB+4FAsBcYB7wsBCij5TytkyOWegyhP9KvnwGmGLMLwshnFLKYHL7scBIIJrJ8ysUmaK7pVSlHo+ntqddqTJJOBzG5/NRW1ub0/PuCKO/dSbV0FKNteFZ2+12SkpKcDgcpsrZzuhNIW3jf6qQREeCwSBr165l9erVrFmzhtbWViorKxk8eDBDhgxh8ODB25T0RaNRWltbqa+v38YAJxIJfD4fgUCA0tJSysrKCvZGBGD27Nlcc801/mAw+HgkErlBShkytgkhfgxcCmwC3pVSPpr6XiHE/cAR6EbxFSlloxDiIuBkKeU5mRifEMKSnN/+efI8c4BLpZRfd7HvRcATgBc4AD3LXAmxKAqKdD3nZcATl19++fC77777quHDh+NwOLjwwgtZt24dL7/8cjbH2CWF2Mc5EolkXBXMYrFs5VkbHnA4HKa9vZ1EImFuLykp2a4XFgqFCnJ+syv8fj8ulyuvf18jp2HFihWsWLGCb7/9FqfTyeDBgznssMMYPHgw1dXV2zWoRjVDWVlZl3+ToqIiKioqTO+6ubmZioqKgv0bHX744bz99tulN95440+++OKLE4QQP5JSLgaQUv5XCPGclDIshKgTQlRLKdsBhBA/BX6Enik9GFgghLgB3YM+TQhRkomwcopxNZSPliWXroije/xLgc3KMCsKkV0upbLb7Tz66KMMHjw4+6NNIRte6q6gaRqNjY1deknZJBaLEQ6HCYfDRCIRswVhSUmJGWI35ucLLXmuK4wSo1x/j6BHY1avXs2KFStYuXIlPp+P/v37s99++zF06NBuCcsEg0Ez1J3Oe0KhEB6PB5vNRkVFRcEKrmiaxksvvRT74x//GA6Hw7/XNO2fqcYt6UWfCPwSPWT8MLABuF9KuUYIcS7wU+BU9MQwr+H1ZmJ8Qog/Ar8D/iSl/L+UUHbn/SYAmpTyi0yeX6HIFD0updI0DYvFQjwex+/3Z2l426eQyqhA9+QtFkvOx2RIipaWlpoh8FAohNvtRtM0swbcbrcXvGEGPfy+owhApnG73XzzzTdIKVm7di0lJSUMHTqUY489ln333bdHAjfGjWtVVVXaxty4ofL7/WaWeiGGui0WCz/60Y9shxxyiO2KK674w8aNG08UQlwgpWwC04t+T0rZKoRwopdZfZE0zBb0OuO9gb5SyjXJ92TSMC5E94yPAf7PMMzJeuwOkpniUsrPO78xHyVfCsX2yFpXqmzT1taG0+ksmMzjdJpw5JpYLGa21ARMPW2Hw1GQhlrTNFOXPFuZzEa4etmyZXzzzTdmtGPYsGEMGzaM/v377/J34/V6icViPU6+i8fjdHR0EIvFqKysLAiRna6IRqPcfffd/meeeSYUiUTOWb58+Xup24UQxcCj6C0qX0mu+xNwmpRy/2yMSQhRBbyGPsf9CfA4ukDKMcCNUspVQoi9gEnoSWF+YC3woJTSJ4SwSSlj2RibQtEdMt6VKle0tLRQUVFRMOUoHR0dFBUV5b2lYWeMcHttbS3RaJRQKEQ4HMZut+N0OnE4HAUTgQgGg/j9furq6jJ6XMMgf/311yxduhS3283AgQMRQjBs2LCM3lAZyWyZCMsbZYqFnNWtaRr/+c9/+Pvf/x7VNO0f0Wj0d1JKMwM6KeP5L/SSpgbgZOAYKeXsbIWThRD1wHT0euZU7kyO5XFgApCawTcXODHp8RcpHW5FvsloV6pcsiOxj3xQSFKiqYRCIXw+31YGz+iFHQwGCYfD2Gw2HA4HTqczbwbAaKVodHbKxPE6G+RBgwYxYsQI9t9//6y182xra8Nut2fsJk3TNLxeL4FAgPLy8rSbWuSKpqYmLr74Ys466yxmzpzpWbp06epgMHhaapcrIcQ49GzupcAiKeXH2Z7nFUJUAtcDB6KH0eegh7zPAn4ArEmu+wg4HzgSvTb7DCll7ufpFIpOZKwrVS49Z+PCWyidnwpVrQz0OVWbzbbdzl2GoQ6FQoRCIWw2W1486nA4TEdHR9oJVF2xPQ/5gAMOYPjw4VnpXpaKkdS1K59he0Sj0a0aZxSCpGkoFOIPf/gD4XCYf/zjH2iaxqOPPhq57777QuFw+MLly5e/ls/xpZRX1SY94rPQ662jwK3o4fZ2IUQFuudcCxwrpVyQx2ErFEAvLaVKJBJYLJaCMMyA2Uay0Ayz0SWpvr5+u/tYLBZzHjrVo/Z6vVuFvrP92XZFdKS5uZnFixfz9ddfmwb5sMMOY//998+6QTYwksAqKiqy8n9pt9upra0lEAiYSm/l5eV5+w3EYjGmT5/O3LlzefXVV83f42WXXVZ88MEHF1955ZXPHXTQQY/4/f7rUsPcuSTFM29PPh6JHlp/E73euj25n0cI0QLsh96L2kSFuBX5oleWUkWjUdxu9w6NTi4JBAKEw+GCU98yegz35HsyDLsR+jZac5aUlGTcIBjRl+7IngYCAb766isWLVrE5s2bTQ85lwY5FZ/Pl7OmIvlOGDOmIJYsWUJNTQ2jR4/eZp+2tjZ+/etfh77++uuVHo/nRCnlhpwOsgtSyqxulVLemrL+POCp5MsJUso5ycQyh5RyizLQinzQK0upCk2AJBKJFExiWiqhUKjH2ewWi8XMhk8kEubctdvtNtdnqqOWz+dLay41Ho/zzTffsHjxYr755huqqqoYPXo0Z599timPmQ/i8fg28/rZxGq1UlNTY5bMlZSUUFFRkbPfREdHBzabjUmTJm33b1ZTU8Njjz3mePjhh0c88MADX++///7nLFu27K2cDHD7GPraFwshnkLP0j4bfW46gS45OidZdvUYcIwQYqyUcqWqhVbkml5ZSmW0X8znBTmV5ubmgmtkYGRp19XVZTRpLhaLmWVjgNlIo6fz0zsTHdE0jU2bNrFo0SKWLFlCIpFg5MiRjB49mv79+xfE1Iahf56PTP1EIoHH4yEcDlNZWZl1hbFAIGDeiKR7MzBv3jyuvPLKYCgU+kc4HP5/+awlFkI8D5wJtKEb54OSmxYC1wGzgQeAH6LPQV8DTJNSblYetCKX9MpSKq/XC1AQZUuFlpxmEIlEcLvdNDQ0ZOX4mqYRjUZNzW673Y7L5cLhcHTre/B6vcTj8W1utDweD4sXL2bRokW0trYydOhQRo8ejRCiYDL0QZ86ML7nfP79jXFk04s2tMJra2u7nZDW1NTEL37xC9+qVavmBYPBM7vqcJUrhBCPAMcBfQAL8CTwH+BT4B/A5YBxp+0B3MCpUsrc1osq9mgyUkr15ptv8tVXX2VxmFvjdrux2+1ZK4fpDpFIxMwyLiRyWXdthL2DwSDRaBSXy4XL5dqpETVER2pqarDb7SQSCVatWsX8+fP55ptvaGhoYPTo0YwaNSov88g7w+jbXF5eXhBiOIlEAq/XSygUyrgXnUgkTP3vnn7WaDTKn//85+C0adM6wuHwyVLK+RkbYDcRQgwHXOgSnguEEDbgr8AUdGGSL4D7gAvQPe27gb8lm3YoD1qRdTJSSrVx40ZSDXa2aW1tpbS0tCCaBPh8PuLxOJWVlfkeiklno5dLYrEYgUCAQCBgyopuz5sOBAIEg0GKi4tZsGABX375JYFAgJEjRzJu3Dj22muvnI69u/j9frPTVyFFTTLtRWuaRnt7O1arNSP/52+99VbipptuCobD4SnLly9/dOfvyC5CCDvwF+BX6G0t/4euBb4sqXI2GhiA3gP6KSnlh/kaq2LPId34YCkQKisra/d4PPVDhgyhoqLCLKW6/fbbsznGbSgkXe1oNFpwwiPRaBSLxZKX8K/RuKG8vJxQKEQgEKCjo2MbbzqRSCClREppeslHHHEEBx54YMF9n10Rj8fxer3U1tYWlGEGKCkpob6+3ux2VVVVtUvfqd/vJ5FIZKwaYfLkyUXDhg0rveSSS/4+cuTICdFo9JdSykhGDt4z7gd+DPjQW0nem5T5tEgpI0KIMHoY/FJgjBDiTCnluqRWeKa1wRUKIH3P+YfAQzabrTIWizmMdnd2u53i4mLa2tpyqhC2ZcuWvHQt6gpDC7oQRCEMPB4PQMF07Er1phOJBKtXr2bBggW43W7TSy6U5K50cbvdZtvHQsaQAHU4HD2qwTZak2bj9+bz+bjqqqs6vvzyyxXhcPgUKeWWjJ4gTYQQ/wYuQzfS90gpV6RsGwlcja4i5gIa0UPe/5BSfpSH4Sr2ENKNd60D4sXFxSGAAQMGmLWpfr+fdAx8ptA0DU3TCqKUKpFIEI/HCypByahPLoSQv4HNZiMUCjF37lz+85//MHfuXAYNGsTFF1/MqaeeyoABA3qVYTb6aRfiPHhnHA4H9fX15pxxJJK+gxqPx3G73VRXV2flRrisrIzHHnus8pJLLjmgpKRksRDikIyfJA2klD8HLgT+3ckw7w9cxXeGeRa6ktgPgdeEEGNyP1rFnkK6VuVB4M7LLrvsgKlTp/7i9NNPN+tfH3nkEUKhUDbHuBVGjXMhXMyj0WjGan0zRSwWQ9O0gvDkNU1j1apVfP7556xatYr99tuPs846i3322YeWlhacTidNTU04nU5KS0sL6iZne2iaRkdHB+Xl5QVxg5gORUVFVFdXEwwGaWtrS6sdpTHPbAjPZHNs1157rXPkyJEl119//QfDhw//5fLly5/I2gm3g5Ty6dTXQoj90A3zheiZ2w9IKa9KbvslUAOs6vQeVQutyBi7XEoVCARoa2vj66+/zv5o0b0Wj8eTM8GHHeH1etE0raBCm16vl0QikdcEtVgsxldffcVnn31Ge3s7o0ePZsKECebfzO12Y7VaKS8vN0VsAoEAdrudsrIyiouLC+qGJxW/308wGCzIueZ0MLxhTdOoqqra7g2Rx+MhGo3mNNlt5cqVXHLJJd6Ojo4nwuHwtflq3SiEEHxnmB3oetw3GRnayaYateiJYiGgycg8V5ncikzRHeP8ya233rr0D3/4w3Wvv/46NTU11NbW5rwrVSH1TS60ntKAWe6Sj6Qqv9/PvHnzmDt3LgDjx49n3LhxuFwuc594PE5zczMNDQ1beZ6aphEMBvH5fFgsFkpLS3E6nQVlAA3BlJ7U+RYSmqbh9/vx+XxmaVTq9xwMBk3Z11xHBzo6Orjiiit8y5cvnx8IBE6VUnbkdACAEOIk9J7QCfS65xtTDHM5cBNwIrpxjgNh4Aop5ZO5Hqti9yVd4xwEShwOhz8UCpXV1NTQ1taG1WrFbrcTjUZZunRp9kdL4ZQuZUuBa1foiUZ1JmhpaeGzzz5j8eLF1NTUMGHCBEaNGtXl9+LxeNA0bbt/P6P5ht/vJxaLUVpaisvlKogQcmpXqN2BaDRKe3s7drudyspKioqKzP+hmpqavCnexWIxbrvttvD06dObAoHA96WUK3M9BiHEH4CBwM+Nxh1CCAdwF/ocdDUwD3gX3VgD/FBK+Xqux6rYPenOFe++q6666j8AxxxzDMOGDeP3v/89xx9/PIlE7qI4iUSiIC7UxmcuhIxxAyMRLFeGecOGDTz77LPcf//9dHR0cO6553LFFVcwduzYLg2zpmkEAoEdiscYXbJqa2upqakhGo3S1NSEx+MhHs+b6iPRaJRgMFgQqnSZwm63m95xc3OzmZldXl6eVylam83GrbfeWvKb3/ymv9PpXLD//vt/L9djSDbGuExKGU3WQQOcgd4P2pjHqgT+Dzg3+fqo1GMIIRxCiPG5GK9i9yNdz9kPtKR2pVq1ahV1dXXY7facllK1t7ebHZLySSGF1w12JaTd3NxMUVERtbW1AGZjk65YvXo1H330ERs3bmTkyJEcfvjh9OnTZ6fn8Pv9hMPhbn9nsVjMnOvNR/KYpmm0traa594dCQQCNDY24nA4Cqov+eeff85VV10VikQi1y1evPiBfI5FCPEocAl6U4y+6KHtb9CVxO4CPgBOllImkuIlxwPPAd9IKcfkZdCKXkt3SqnKja5Ufr8fm81mdqXKZSlVoQiQGJnahUI8Hicej3fb4zG80Q0bNvD+++/zxRdfEI/Hd+h9b9y4kb59+/KrX/2K008/PS3DbMxz9sS42Ww2Kisrqa+vx2Kx0NLSQnt7O9FobtoEB4NBNE3L+w1hNtE0DafTic1mo729Pa9RilQmTJjAiy++6Kipqblr7Nix9wghcv7jF0JYhBAu9H7QAM9IKU9GN7zDgDsAO/BR0jCXAMegG2wHUJU01gpF2qRrnBOAw+VyeUD3sk477TRuv/12rrrqqm7VTu4qhdIustCMc09C2u+99x5PP61XkBx00EFMnDiRDRs28OKLL5rzq11x1FFHMXny5G51BQuHw1gsll0Kl1qtVioqKmhoaMBut9Pa2kpbW1tWjbShV11ZWVlQyWmZJBKJ4PV6qauro66ujuLiYlpaWgiHw/keGgADBw7klVdecQ0dOvSnLpfrdSFETsMXyfKoILAguWp0cv15wD+BcqAd3XMGmAT8Dd1wLwRGJZXG8n/hUvQaupOt7XM4HKWhUKj8qKOOwmq1ml2p3G43ixcvzv5ogc2bN+c97GZ0osr3OFJpaWmhrKwsLfGR5cuXM2PGDMrLyznllFO26lwVjUZ5/vnnqa6u5uCDD07LK053fEYGdqZIJBJmC8Pi4mLKy8szfsPk8XhIJBIF054002yvoYWhz+1yuXZaE50rIpEIN954Y+CDDz5YEwwGj5VSbs7l+ZNKia8kX85CVws7D/g7sEhK+agQ4hh0gz0CWAYcKaVsF0LY8lUapuiddMeyfPTzn//8SdC9rAkTJvB///d/jB8/PmcGykjCyveFIhaLUVRUVDCGOR6PE4vF0pprXrZsGS+88ALf//73ufTSS2loaNhqWsJut3PUUUcRDoczlkcQiUSIx+MZVy0rKiqirKyMPn36UFJSQltbW0Y9aaMl5u6UBJaKpmm43W4cDsc2N00lJSXU1dURiURoa2sriDB3cXExf//7312XXnrp0JKSkgVCiFG5PL+Ucjp68lcbeoj7R8CdUsqrk4b5e+iGegSwEvi+lLI9+d4Y6HXQuRyzovfSnYSwkqKiIhKJhLVfv35mq0RN07BarTlpGRmLxWhra8taj+J0CQQChMPhjDUC2FX8fj+RSCSt8Xi9Xu677z5++9vfAjBjxgycTieVlZWMGTPG3G/+/PmsXLmScePGse++++7S+IxynWzLXRrZ4D6fD7vdvkuetKZptLW1UVJS0itkOnuC1+slHA7vUFBF0zR8Ph+BQGCXG2hkktdeey1+8803+yORyBlSyvdyee5ku8m+QAz4Vkq5RggxEd1jHgusBSZKKb8VQgxG7xldLKVcnny/VUqZ/7sdRUHTnbD2a9dff33r3XfffdW1116Lw+Fg6NChTJ8+nXfeeScn2drhcNicG8snHR0dWK3Wgrlot7a24nK5ugwZb9q0CbfbzfDhw01P/+WXX6atrQ2bzWaWLc2fP5/x48djTFl0dHQwc+ZMhgwZwkEHHdTjsW1PdCSbpIpslJSUUF5e3u3s7mAwiNfrNZPQdje629DC2L+srIzS0tKC+E7mzJnD5Zdf7guFQlcsX778qe6+P5nkdQlQDyClvKUHx7AC49EN8yHAJuBwKeV6IURf4AFgArpYyeNSyv9nvE8ZaMWO6M7V8oiHHnroHNDnLD///HNuu+025s+fn7Ns7Xg8XhCZ2pFIJK91oKkkEgkikch2PZrPPvuMuXPn0tLSYq4bOXIkwWCQfffdl3POOYdjjjmG8847jy+++IJAIADoQhvl5eWsXbsWoMd/Y7/fj9PpzOkUgMVioaysjIaGBmw2Gy0tLbjd7rRDs5qm4fF4dtsksJ40tDDaUIZCIdrb23OqbbA9xo8fz7Rp08oqKysf3H///X9rtHBMByHEaPQSqN+ge8AThRCzezCMQ9AN8CHoc9CTkobZnuyyNRc9Wawv8DshxD0AyjArdka6V8xlwF8vv/zy5wCGDx/OhAkT+P3vf89Pf/pT9ttvv+yNMIVCKKPSNI1YLFYwqmChUIiSkpIujV80GqW5uZmOjg5WrlxpNiipqalh9OjR5t8tFouxzz774HK52LBhg/n+cePG0dLSQjAY7JGRMhK28lUbXFRURHl5uem1Nzc3mwleO8JIMCuUEG4m2ZWGFlarldraWqxWKy0tLTkrZdsR++23H9OnTy/be++9b7Lb7Q+mU2qVnKu+DnCiJ2zdLqX8ARAXQnQ3TBTnOxnPSVLKlcnEsceEELcBj0kpD0D3rt3AVUKIy5PjsCYfd787QMUuk66FuQyY/sgjj5QDLFq0iFAoxJNPPkksFuNvf/sbzc3NWCwW8yJuPO+8GB2lUp8byVWp7++KQvCco9EoNputYJLBdtQecuHChVRWVrL33nuzatUq+vbty5AhQ6itrWXixIlmE5GKigpaW1uprKxkwIAB5vvj8Tj9+/fH4XDsUJRkewQCAUpKSvJ+I2P0XS4tLcXr9dLU1LTdzkyG4El9fX2eRptdPB6PmUjXEywWC5WVlQQCAVpbW6moqMh7/XefPn14+eWXKy699NJzly9f3l8IcaaUMtjVvsmmFb9Ev/b9Tkq5IfVQ6N2m0kZKOVcIMQlol1JKIcSpwNPohj8C/EoI8UfgReAR4Hp0LzrVez5ACLERiEgpA905v2L3Ja2rppRykRBixCGHHPLczJkzT3r//fe32t7W1saoUaPM0KfRc7mrJRaLkUgkzNeJRMJcjD7NnRer1UpRURHBYBCXy5XXtpGFVN+cSCQIh8NmmU9nadP6+nrq6uoYPHgwzz77LCtWrKCuro6Kigq8Xi9ffPEFq1atYtiwYcybN48RI0bgcrlMQ2y0dAyHw93OtDbmfQslaQ50z6+qqopYLIbH46GpqYny8vKtGj94PB5KS0vzfhOYDYLBIKFQKCPz6C6XC7vdborBVFRU5HUKoLy8nCeffLLi17/+9cRZs2Z9JIQ4Tkrp7mLXk9DrkH8kpTRbPgoh/h/QJqV8N/k67e5SUsqPk+8pAX6CbpjfRXdqrkgu5wHDk29pS+p0XwB4galAC/C8EOLvUkp/9z69YnckLeOcnJ9589NPP3UBDBs2jEQigcfjwWKx0Ldv34zMwXY21qlLNBo1RRGCwaBpoK1W6zaLzWYzDXqmKaT55nA4THFxMZqmMWfOHCoqKhg+fDg+nw+Hw8GgQYPMfQ8++GA++eQT1q1bx8iRIykvLzcf29raOOOMM7bJyi4vL+ess87qUQlUKBTCarUWzHeVis1mo6amxowc+P1+KioqzJvHQrqhyBSxWIyOjg5qamoy9ruw2+3U1dXR3t5OW1sb1dXVeY0oFRcXc88995T/8Y9/HPnqq6/OFUIc1UUt9A+AN6WUZo9bIcTP0ZO2HjRCzCldqCqklJ40hxABvk0+D0kp1wG/FUK0ArcBJcA0KeV9Qohr0MuuAoALXeBkqTLMCoN0s7UXAftUVlZGOvdzNjI+33jjjawPtrGxkdraWmw2m2nIDdnKzkssFsNisWxlrG02m7n09CLS1NREdXV1QXjP7e3tFBcXY7PZePPNN3E6ncTjcRYvXsyZZ565TS7A66+/TiwW47DDDutSXMT4X9hVD0jTNFpaWigvL894bXOm0TSNUCiEx+MhEAhQX19fMFn4mSKRSJgiMNmY/9c0Da/Xa2rN5/u3oWkaDzzwQPThhx9uDQaDE1O7WgkhbgX6SSmNed9fAZOBT4FHpJSNKfseBvwbONsog9oZQoiRwEz0EPlc9Hydk9D7P78upfxhcr+r0I2zFT2R7KdSyhnJbaOBFinlt9ueQbGnkK5xjgG/klJahRD3SinNbS+++CK/+93vWLZsWRaH+Z0qV9++fdMyHp2NdywW22qxWCymobbb7VsZ7e0dP5FI0NjYmPYYsoERcjZaVhqlMO+//z6ff/45lZWVXHDBBVspWhnvaW1t5bnnnmPMmDEceuihW4VuezKnvD0ikQhut7tXlSF5vV68Xi9FRUU4nU7Ky8sLJq9gVzCERgCqqqqy+vcIBoN0dHRQWVlZED3On332We3Pf/6zJxgMfk9KuQBACLE38DkwB6hDL6OaCrwmpdzU+RhCiJuAG4BjjGPsDCGEAK5EN8qD0BNv35JSnpTcfia6Jy3QBU1qgBlSyhOFEP3Rs78PRxcxyY30oqLg6E6mTpdXKiMDONskEomdJoylYnjNXc0dGoY71ViHQiEz+9Qw2IbRttvtWCwWc745XwYndU45HA5js9mIxWI8/vjj+P1+9t13X8rKykwP2NjfMOZGIlj//v23+V4y+Zl8Pl/B1MKmQzwex+/3m1ndRtJYIdX09pRAIEAsFqOuri7rn8NonGGotJWXl+f1uzv33HMt1dXVlTfeeOOs/ffff/KyZcs+TgqDjEFX9ypC92Y3dn6vIbcppfyzEGIT8J4Q4lgp5fydnTeZGHYzsBcwBHgvxTCfwneG+X3gWeAi4BYhRKmUcqMQ4gvgZPSuV8o476Gk6zl/BexVUVER93g89ZMnTyYUCrFmzRrcbjcNDQ289tprWR1oNBo1vbFsYnjZ0WiUaDRqGm/DEBYVFZlh7XxdeObNm0dxcTF9+/aloaGBpUuXMmLECHw+H2+88QY1NTV8//vfz5pnvCNisRgtLS05FR3ZVdrb27HZbFvJdEajUbOHdGVlZa8sqzKkN+vq6nKaMR+Px2lvb6eoqIiqqqq8/x/Mnj2bX/7yl8FQKPSjZcuWvZW6TQhxjJEElny9VSJYUkjEC7yB3q/5ICnlwnTOK4TYF3gKuFpKOUcIcTxwN3AAehj9HinltOS+dcBDQBlwArqQyawef2hFrydd4zwamO5yuSoCgUCVkRDm9Xqx2Ww8+uijDB48OKsDDYVC+P1+s99wLtE0jWg0SmtrK1arFYvFYhpsu91OcXGx6WVnwwAahnXdunW8/PLLVFdXE4lEsNlsHH744ehRNJ3Zs2fjdDoZM2ZMXm4eOjo6sFgsVFRU7HznAsBo8NDQ0LDN96VpGuFwGI/Hg81mo6KiIu9lYemyvYYWuULTNDo6OohGo9TU1OQ9+33BggVceumlwVAodPHSpUufB1Pn+r/Ai1LKl4UQlmQHKoQQRwDHAmeiRxjfQ28LeWt35oJTOmhVJs/1feAzdMP8XHKfA4BfAD9GN86/kFL+O7lNKYntoaRlnEH/JzvmmGOeeffdd085/PDDcTqdjBo1ip/+9Kc58Sr8fj/RaDSv3YE6J6QZ3nUkEiEajZJIJExjbRjsnngNra2tzJ8/n/r6egYNGmRmD8+cOZOSkhIOPfRQvF4v7733Hj6fj1NPPdWUNM2Vh9wViUSCpqamtCUh842maTQ3N5vlVDvaz+fzmf2oC6VL0/YwdMHtdnteb5KMcjqjpC7fmfvLli3jxz/+cTAYDP5qyZIljxrrhRC1UsrW5HMb8D/0BK4E8A9ghZRydcr+phFPFyHEfuhh7Grgt1LKe5Prh6HXXf8U/SbgHuCm7h5fsfuRluVIes7fzJ49eyLo7f/WrVvH008/zbHHHsuSJUuyOkjYtoY318TjcbPJB2D2Ji4tLaW6upqGhgbq6+spLS01s1cbGxtNVapQKJSW5OHs2bN5+OGHCYfDLFmyhKeffpqOjg4SiQRLlixh8ODB+Hw+3n33XdatW8dhhx221QXYMBq5klRNxe/343A4eoVhBn28Vqt1pxnlFouF8vJy6urqiEajNDU15SzXoif4fD40Tct7Ny1DRrWyspK2tjaCwS51QXLG/vvvz7PPPussLy+/78ADD7zOWC+lbBVC2IQQJwFvofdhvh44X0r5NnojC7OjVA8N52b0MisXuiE2DPYVwMWAA7gf+D8ppSaEKEvuY0k+9o4flSJj9JpSKrfbjd1uz5sUZE/C6pqmEYlEtlpsNpspDVlcXLzVDUcgEODll1/mmGOOMUudpk6dyoQJEzj00EN59dVX2bJlC263m2HDhnHqqaeyfPlylixZwmmnnZbXeXBN02hqaiqIUpp0MBpy1NbWdnu84XCYjo4ObDYblZWVBXUzYoTp6+rqCmpc0WiUtra2vPaHNjTTZ8yYwV/+8pdoLBb74+LFi283tgshbgCOklKeko3zCyGq0NXDXgBeBW5EFyqpQG+c8Rv0sPa56PPOXmAF8KCUslmFuPcs0nVFDwBunjNnzm0A//jHP3jwwQd5++23ufHGG1m9evVO3r7r5FtXuyfiIxaLxeyKVFtbS9++famsrKSoqAifz0djYyMtLS14vV7TeG/ZsmUrD3vvvfc2xVf22WcfIpEIhx56KKeffjpFRUWsWrXKDBnmM9QaDAbNOfjegNfrNVWuuovRBMJut9Pc3Gx6qvnGSMSqqqoqKMMM3wmWhEIh3G53zr8vwzAvXryYf/7zn9x22232mpqa344ZM+a2FOGRu4BTITuealKx7BT05LLb0DtilQEPAjeha3Q/DvwrOY4LgVuBj4QQ/aWUcaXDvefQnThxXkupDEWwfJEJ2U4jFF5WVkZdXR19+/alvLzcTJ5pb2/nkEMOwWKxmB2UtmzZYmaoDxkyhOHDhzNv3jw+/vhjHnnkEdauXbtVH+Z8YMwt5iuq0V0ikQjhcHiXxEZSQ93hcJiWlhYikUgGR9k9jIYWZWVlBZtZbrVaqaurQ9M0Wltbc9bZyjDMy5Yt46abbuKOO+7gpJNO4oUXXnDV1dX92uVy/TVVGSw5p5wVDzV53FZgKPq89mPoHrQFvZ76NCAM/AW4FHgTXfbzf0KIKjUXvefQa0qpDCOVD4+gs+DHrhIIBHC5XNvMo8fjccLhsLk0NTXx9ttvc9FFF5lh7qamJpqamvD7dZW/CRMm7PJ4dhUjzNsbREcM9bLS0tKMNWzQNI1gMIjH48mbgElHRwfxeJzq6upe8Tfwer2EQiFqamqymgFvGGafz8eFF17IDTfcwIknnmhub2tr4/zzzw80NjY+EggErsmV8RNC2NHntf8lpXQLIc5Gr3kG+JmU8rGUfZehe9hHpyamKXZv0v1VXAhMj8ViFQCrVq0ikUgQDAYpLS3lnnvuyd4I+U40JF+eczweN0VNdpX58+fzxRdfcOWVV27zeaxWKy6Xy8wc/vrrr9lnn32wWq00Njaydu1aKisrGTFixFZjyXeynM/nK/gMZoNAIGA29cgUFosFl8uFw+Ggo6OD5uZmKisrcyZdmsmGFrnAKLWzWq20trZmLZPbiEjFYjH69evHs88+u41sbU1NDc8++6zrggsuuPTbb78tFkJcmQsDLaWMAn8CEEIUo5dYgd5i8rHk+iKgH3ojjb3RPehdyhpX9B7SuqJLKRcBIw4//PBPAOrq6hg4cCDnnXceb731VtZrnI355nxdeHY1pB2LxQD9YrFhwwaam5tZvFgX/ukqtJcqz3nYYYcRjUZ56aWX+PTTTwHde25rayMQCBREuD8ajRaEXOPOMGrzKysrs/K/ZAjUVFVVmdMU2Q7dZqOhRa4oLS01M7kzPT2WapiN76YrPXnQZU2feeaZ0n322ecip9P5YK7ndaWUEcCQDm1K2WQDxqFncq9H1+lGCFElhOibzOruXX90Rdp0R4TkTZfL5UoVITG6Uj3wwAOMHDkya4OMRCJm2DQfdHR0YLVaezRH6fV6mTdvHhMnTiQYDDJ9+nQaGhqYP38+N9xwA1ardavaZON5MBjk7rvvpr6+no6ODiZOnMjQoUOpqqrCZrMRDocJhUKmjKfT6cxLGZPb7cZqtea9bCcdOjo6AKisrMz6uYwbgWAwmDWtaaOhRVlZWd57Ku8KhpJZpnpDd2WY08Hr9XL++ef7N27c+GQgEPhFLr3SlLD2JvSWllF0fe0bgDHA2+iCKFbgOvTM7u9JKT/P1RgVuSXdu67/Ai673R4BGDp0KAMGDMDlchEKhbjxxhuzN0L0sHI+s093xXNubW2lubkZm83G+vXrKSkpYdKkSVRXV/PWW29ts79hpEOhEOXl5QwcOJBf//rXHHLIIWadtdGcobq6mj59+lBWVkYkEqG5uZmWlhb8fr+ZUJZN4vE4oVCoVySCRaNRgsFgzm4iioqKqKyspKamBq/XS1tbW0b/JoYBKi4u7hVRix1RXFxMXV2d2XxkVzK5e2qYQW+R+tRTT5X279//otLS0gdy6UFLKZ8HnkDX414CfIku5zkGWANcmmwneTm6YS4BjjHer7K4dz96RVcqv99PLBbLicfTGaMbVp8+fXoUNvT7/bz66quccsop2O12cx5y3bp1/Oc//+HKK6+kvr7enDfesmULixYt4vjjj8fj8ZgCI+l4fYbcZDAYJBwOY7fbTY86GyFPr9dLPB7Pq2pbOhjZwU6nMy83EkYCVCAQMOeidzWs7vf7CQQCOWlokSvi8ThtbW0UFxdTUVHR7c+1K4Y5FY/Hw/nnnx/YvHnzf30+X07moA2EEHcBxwMjga+BpcB1yYYY16JncdsAN7AS3XDfK6Wcpeagdy96RSlVPudVY7GY6a32BMOoappmGmZN0xg4cCCjRo1i+vTpAObxW1paGDhwIAAVFRVommaKmezMQ7JYLDgcDtOjNiIbjY2NpkJTpupLjfKp3tD72Pjc+Qr9GglQhhe9q3PRkUgEr9fbKzKzu4PVaqW2tpZYLNbtWuhMGWbQf3dPPfWUq1+/fj8pKyu7L8ce9A3oyWEHAOOBnyQN81XAneiG+RXgt+gdq84CPhBCHKEM8+5Fryilam9vp6SkJC8X10xoet9zzz0cccQRHHzwwVtlVnu9Xu677z4mT568w1plo9NTnz59enQxTiQShEIhs32gw+EwBTh6enH3+/2Ew2Fqamp69P5cYTSAKARtZ/jOizbmorub0W18nlxmg+cao2Zb0zSqq6t3amiNntXxeDyjiXEdHR2cd955ocbGxvvnz59/fUYO2k2EEBXAOega3y7gKSnlRSnbP0TvlnUH8P+Ugd59SPe/+ELAF4vF7KCXUm3YsCFnpVT5VAfLhPjIoYceyieffALoHrJxQ1ReXs5hhx3GmjVrdvj+UCiE0+nssSEtKirC5XJRV1dn1mq73W5T3aq7c6G9SXTE5/OZUqmFgOFFGxndbrc7bS/aMFrGVMXuisViobq62iy12tH/p2GYE4kEtbW1GY2wVVZW8uSTTzqqq6uvHDNmzC0ZO3CaJGuhLwT+hm6Yn+tkmEegz0kDOJVh3r3oFaVU+QxrZ8I4jxo1CqfTyXvvvbfNtkmTJnH66afv8P3BYDBjF2Mjs7q+vp6qqipisRjNzc20tramHfYOh8Om2lkhE41GCQQCBZlJbkiAAjQ3N5sSrTuiUBpa5AKLxWL20d6egU41zDU1NVkJ8dfU1PDMM884Kysrbxg1atQNGT/BDkjWQvdHFyB5RUp5nrFNCDEYXae7ApiLnjym2I3oFV2p8uU5JxIJYrHYLhtnl8vFD37wAz799FPWrVtnGsBUQ7g97ykejxOPxzNuCA3jWlVVRUNDA06nE7/fT2NjIx6Px6zN7gqfz0dpaWlBz3caylBlZWUFpzNtUFRURFVVFZWVlbS3t+PxeLZ7c2RMS+xu88w7wogyuFwuWlpatvqfzIVhNqivr+fZZ591lZeX/2HEiBFXZu1EXSClvBk9U/sMY50Qoj962dWB6Eljf0Ovg1bsRhR8KVUikchbj+JoNIrNZutR1mhnhg4dytFHH83s2bP58ssvAbY67vYiA4bXnM3Pnxr2NrSPW1pauvSmo9Eo8Xi84Mt3QqEQ8Xi8V4TeHQ4H9fX1Zm5B5xujeDyO2+0uyIYWuaCsrIyysjJaW1uJRqM5NcwG/fr147nnnistKyv7y/Dhwy/O+glTkFI+bjwXQvQDngIOASTwZ+ANKWXh9jBV9IiCL6WKxWK0tbXR0NCQlePvCGM+Nt0Srmg0yttvv011dTVHHHFEl/usWLGCWbNmMXDgQPbdd18zM3t7GCITuZ5j1DTNbJMZj8dxuVy4XC48Hg92u72gs7SN9pVVVVUF2wSiKzRNIxAI4PV6KS8vNxMgW1pacDqdBf2d54JgMGgKAhUVFeXMMKeyevVqzjrrLJ/P5/uJlPKlXJ5bCOEEPgdGAcvRDfPLUkpvLsehyA3dUZwvmTp16v4A//rXv6ioqOCAAw7IeilVvueb0724NzU1MW3aNDRN4+CDD97ufvvttx99+/Zlw4YNO/WC4vE4sVgsLwbG0J92Op1Eo1Ez5B0Oh+nXr1/eohnp4PP5zJ7ZvQmLxUJpaSnFxcW43W6zy5XVau0VEYBs43A48Hg8eL1e9tprr7z8/w0ZMoT//e9/Zeeff/5/hRAeKeW7uTq3lDIohHgVGIAeyn5FGebdl3Q9Z4ne4swCWIxsSqOGd+DAgV2qXWUCQ9S/uro6K8ffEY2NjdTU1OxwzlnTNBYsWMBbb73FyJEjmTx5csbmh/1+P5FIJC+fvSvcbreZDGaxWEyPvpCMtBEazlcHs0yhaRrNzc14vV723nvv3To7Ox1SQ9mlpaW43W6qq6vzdgM2b948fvrTn/rC4fCxuZbQFEKMAjYk+0MrdlPSdUlbAa/L5eoAPVs7kUhQV1dnykhmi3x5zvF4nEQiscN2duFwmJdffpkZM2Zw8sknc+qpp2Y0cSsUChXMRdmola6traW+vp7y8nICgQBNTU14vd6c9ebdGYWeBJYusViMRCJBQ0MDbrd7l2UtezNGCZkxx2wI7bS3t+esn3xnxo0bxz333OMqLi6ekTSWOUNK+ZUyzLs/6Vq94UD/vffeexnoyU0ej4fS0lIOPfRQFi1axAMPPADA8uXLufPOO7nnnnt47LHHmDZtmtl7ePHixaxYsYLNmzebZSE7I1+62tFolOLi4u16hVu2bOHhhx+msbGRyy+/nNGjR2f0/IlEgkgkUjCh2UAgQElJiZkg53A4qK2tpaamhng8TlNTk6nQlC9CoRCxWKzXh4ATiQTt7e1UVFSYZW/hcDjj+ty9AcMwA1vNMZeUlFBTU4Pb7c6bgZ40aVLRn//857Li4uIPhBD75mUQit2WdOecPcDadevWlQBs2LABi8XCxo0bWb9ez+BvbGwE9OblH374IT6fz1yOOOIISktLGT9+/Fb1nHfeeSe//e1vueOOO/jggw/MbGEhBL/61a9oa2vj888/Z8iQIQwZMiSndbXbq2/WNI358+czY8YMRo8ezQknnLDLpVZdEQqFKCkpKYg2gIboSFcRErvdTlVVFfF4HL/fT0tLCyUlJebcaS7HaGiRF1KYvbukNrQwEsIMWUuv10tLS0vBqJ1lm1TD3FUJWXFxMTU1NbS1tWWt89fOOOmkk6xut7v8L3/5y0dCiIOllI05H4RityRd46wBNYlEIgqwefNm9t9/f9ra2ti0aRMNDQ3ceuutABx++OG88847XR7E7/fj9/vx+Xx0dHSYF/tDDz2UoqIis3zHUMz68ssvOeOMM0yD3qdPHz799FP23XdfbrrpJqqqqujfvz+DBw9mxIgRGQ2vd9WjOBwO8/rrryOl5NRTT2XUqOxFswoppB0KhbBarTs0CFarlYqKCsrKyggEArS3t2Oz2SgrK9thBCJT+Hw+bDZbwXxnPcWQWK2rq9tqvVHzW1xcTFtbG2VlZQVfa74r7MwwGxQXF1NbW0traytAXgz0BRdcUNzY2Fj73//+930hxASVpKXIBN0ppbp18uTJY996663T33zzTfbdd1/8fj9PPvkk//znP7NWSrVlyxbzcePGjfzgBz/A4XDw05/+lPXr17NhwwbWrVvH7bffzg033MDtt9/O4sWLGTp0KEOHDmXkyJGMHz++W+fUNI3GxsatkooaGxuZNm0aVquVs846a5uLZyZJJBI0Njb2uBNWpmlpaaG0tLRbFz5N0wgGg/h8PiwWC+Xl5ZSUlGTFmMTjcZqbm6mrq9thjkChY/Q13tnniMfjtLe3myImhfA/kknSNcypRKNRWltb8+ZBa5rGzTffHJgxY8aCQCDwfSllJOeDUOxWdOdK1jFs2LAtb731Fu+88w5Op5MhQ4Zk1VMxOjL17duXvn37btUc4vHHzbp84vE40WgUgAMPPBC3283SpUuZPn06gwYN4vXXX+eNN97g9ttvZ9SoUYwcOZKRI0cyceLELkPS8Xgci8ViXvQWL17Ma6+9xsiRIznxxBOzEsZOJRwOU1xcXBAX3UgkQiKR6Pbf2WKx4HK5cDqdhEIhs1dvNjK8jfyH3myYDYNbVVW1089hhLk9Hg/Nzc07rSjoTfTEMIM+vZJPD9pisXD77be7Wlpaxs6fP/95IcQZUsrCyJJU9ErS9ZxXAPsWFRUlEomEtV+/fkQiETo6OojH4wwYMICZM2dmfHCJRIKmpib69u27y8dauXIlr732Gl999RVLlixBSkljYyOxWIzTTjuNkSNHMmrUKMaNG8eQIUPMTlQzZ85k7ty5nHjiiRx00EEZ+FQ7J59duDrT1tZmziHvCkavaSPruLy8PCNGOhwOm53RemuIV9M02trasNvtZv/udDGEOQyZy95MTw1zKvn2oEOhEBdeeGFg9erV//3yyy9/kfMBKHYb0jXOi4D+LperKBAIVA0bNoxEIoHX6yUSiVBfX5+VlpHRaJT29vasqIMZrRvdbjf33XcfS5YsYdGiRUgpeeaZZ5g0aRKPPPIImqbx/e9/nwkTJuTEk9U0jS1bttDQ0JD3ciCjZrihoSFjn90w0j6fj0QisUtG2qgFLi8vL3g50R3h9XoJh8PU1tb22CC1t7dTXFxMZWVlr7xJyYRhNjAMdFVVVV5yEDo6OjjzzDODbW1tt8yfP/+vOR+AYrcg3SvuIGBwV12p3n//fTZu3JiVwWWzjMowNlVVVfzud7/j2WefZdmyZbjdboQQPP300/j9fp5++mmOOOIIqqur+ec//wnAunXrzAtJpjFC2vk2zKAn8LlcrozelKSWYVVUVODz+WhpaSEUCnW7jtfv92O1Wnt1ElgmGlrY7XZTe2BnLRYLESNyALtumEH/PvJZZlVZWckTTzzhtNlst44cOfKsnA9AsVuQruf8FTDA5XJpqZ6zx+MhGAzSp08f3njjjYwPLhAIEIlEqKqqyvixt8fixYuZPn06Y8aMYfLkyVitVpqbm5k/f7457/3jH/+YJ5980py3njx5MieffHJGzm94QPmu1TWmFLKttJUa7gbMjOSdXaB3hyQwIzKRKaUrTdPw+Xymse8N5VaGYTaS2zLp9RsJdvlSElu6dCkXXHBBIBAIHCulnJ3zASh6Nd2S77RYLJqmaTmbczbmJ7s7D9cTEomEOb88ceJEjj766B3u+/XXX/PJJ5/wySefUF9fzz333MOrr77KSy+9xMSJEznmmGMYNGhQt8bQVZZ4vvD5fESj0ZxJhxqNNrxer9lzekfGxe12U1RUlJP/jWxgdP7KRkOLUCiE2+0u+HnobBpmg3A4THt7OzU1NXm5Wfnoo4+4+uqrPaFQaJyUckXOB6DotXSnlGrK9ddfP/zuu+++6tprr8XhcDB06FC2bNnC73//+6yUUrndbux2e9a9yEAgwLRp02htbeXUU0+lpqamR0Zp9uzZPPbYY3z00UesXLmSgw46iHnz5hEOh4lEIjs1JKFQCJ/Pl9UyrXQwujrlIwvYKMHyer1mglRnzzgSidDe3k59fX1BZLT3BOPGNlv9mY1ubiUlJQUpzJILw2yQbwP93HPPxe+8884toVBorJSyOecDUPRKunNls0ajUSt81684kUiY0pzZwEjayiZNTU08/PDDxONxLr/8cmpra3tskA4//HAeeeQRVqxYwZo1a7jjjjuwWCzMmDGD2tpaJk6cyG233cbnn3/e5bxgoQiPBINBbDZbXspzjBKshoYGiouLaWlpwe12m9+XoaBVXl7eaw1zIBAgHA5n1SjZbDbq6upMI10o2ueQW8MMutRnVVUVbW1tZsllLjnnnHOsF154YY3D4ZghhMj/D1zRKyjoUqrm5mYqKyuzdre7cuVKpk2bxogRIzj55JPN+eVMnzMejzN//nzeeecdZs6cyfz582lsbMRms/HKK69wwgknUFFRQWNjY97nUI1wq5FFnW+MqoBgMGgqYhkNOArNG0wHI5N4V24Cu4MhaxoOh6mpqcn7/HyuDXMqwWAQj8dDbW1tzr+HRCLBL37xC+9nn332djgcPltKuWd2MVGkTbquRwBodzgcXoDy8nKqq6upra3NarlCIpHI2tzr3LlzeeaZZzjqqKP44Q9/iNVqRdM0YrFYxi+aVquV8ePH87vf/Y6PPvrIVNxauXIlU6ZMoa6ujmOOOYbHH388q5GIdIhEImiaVjANN4qKiqisrKSuro5IJMLmzZt7RaJTV6Q2tMhVVMJisVBZWUlpaSktLS1badvnmnwaZsCc389HRntRURFTp04tHzBgwHE2m+0POT25olfSnVKqva+88sonAU466STOOuss7rzzTmbNmpWVUipN07LSLjKRSDBjxgzeeecdzjzzTI444gjzIhGNRs2uS9nEuJk54IAD2LRpE5988gmjR4/mySefJBaLEYvFuO2225g/f37O2wT6/X7KysoKziu12WzYbDaqq6sJh8O0tLQQifQehcSuGlrkktLSUrPNYj5uAPNtmA1KS0spLS2ltbU156F+h8PBf/7zn4qysrJfDx8+/LycnlzR60g3rD0HeHzZsmX2/ffff6qR/GWxWHjmmWd46aWXeOGFFzI6MKNUJhPqYAbhcJiXXnqJTZs2ce6557L33ntvtd3v95vKYLmkcwLW5s2bOffcc5k1axaDBg3inHPO4eqrr87od9EVRsi1T58+BWecjbEZSmBG0lhxcTEVFRV5z27fGT6fj2AwSF1dXV6/W2MO2uFwUF5enpOxGBEDq9WalkiKpmkkEgni8bjZVz11MbYnEgksFgtWq3WrpaioyHy+vXMZof7a2tqc5y4sX76cs88+2xcOh49XJVaK7ZGucR4NTC8tLa3x+/3b1H1ce+21nHrqqfoBLZbtLkVFRds8dl6MfY057fr6+ox8UI/Hw9NPP42maZx//vlUVlZus0++ZDMjkYgpQZlKc3MzL774Is899xxPPfUUffv25a677uKUU05hxIgRGR+H2+02y5gKCU3TaG1txel0bpW5n0gkzLre0tLSgvT4If2GFrkikUiYXmw2ssVTm54EAgGam5uJxWIUFRURCoXMJRwOb/U6FAoRjUaJx+MZixgZ1xWr1YrNZqO4uBin04nD4TBfV1VV4XQ6zfXGc+O13W7P+Hf04YcfJn71q1+5I5HIWCnl+oweXLFb0B3j/GZpaanT7/dXp8p3WiwW7rvvPkaOHGn+oIyGFcZi3O2mPu98N2wsoP+g4vE4sViMysrKre6Ed3ZH3BWbNm3imWeeoW/fvpx55pnbnU9tamqiuro651nKHo8HYKelVk1NTZx++unMnj2bUaNGcc4553DZZZdlRN7UiFQUQo11ZwKBAH6/f7teZywWw+PxEIvFqKioKIhENoN4PE5LSwuVlZUFNS5N03C73cRiMWpqatL+m8diMbOJicfjwePxmM9TH43fstVqNW94HQ6HuZSUlGz12ljsdrtpSG02m+kJd3VDb7FYzOkvw8M2nne1LhaLEYlECIVCBINBU5c8FAoRj8cJBoPmttSsbrvdTnl5ubmUlZVRWVlJVVWV+dgTCdqHHnoofP/9969J1kCnPdcghLABDcDZwFJASinXdevkioInXeP8GfDklClTDpg6deovrrnmGrMrVWNjI88991zGwtrGj83n8xGJRCgtLe3yh2aEs4wfcOqj8cMFPYT00ksvMXbsWI4//vjthrCMNo19+/bNqfdl6EN356Zg/fr1TJs2jWeffZbnnnuOwYMH8/zzz3PiiSf22Ov1er3E4/Gch/R3RiKRML+fnSWChUIhPB4PNputy/roXLMrDS1yQaqiWGoGczQapa2tzVxaW1vN54aSmxFhqaiooKKiwjRcxvPS0lIikQhOp7Og9b6NqIzxNzLGGYvFTEPt9/vNGxJj6ejowO124/P5AEwPvLKyksrKSmpqaqirq6Ours50MLo697XXXuv74IMP3g+FQqelk8EthOgD/Aw4FSgDFgCHAyOklMGMfTGKvJOucfYCnsrKSltHR0fDiSeeSDAYZM2aNebd56JFizI6MI/HY/YB7kzqnFQsFtvqzjgWi6FpGjabjWXLlvHRRx/xve99jwkTJuzQ4zYkJHMtAGJcCHelq9KWLVsYN24c7e3t/OhHP+Liiy9m0qRJac+lGcpkhRJ2TcXj8ZBIJNK+aTAMjpHYZpRf5QOPx0M0GqWmpqYgjZPP52PLli1s2rSJLVu2EAgEaG9vNyM55eXl1NTUUFNTQ21trSnOU/H/2fvuMCmqtPtTnXPuGXKGAkElCgYERCWICiYU0xpAXfWnm3Tz57e62dXPVXd117gGFFQUMWBEFAmSJBcoGZ2Zzrm6qqvq98d4yx6Y0KGqe3qW8zzzoDPddau6q+577/u+5xyHA2azuc1rImlznU7XqQMzAdEkL0WtLZfLIRaLycGa/BsOh2XNeJ1OB6/XC5/PJ/9LfkRRxCWXXJLYv3//37Zu3fq/7Y1F0/RwAHcA6AZgCcMwT3/3+8UAIgzDLCjxIziOTohigvNShmFW0zT9MMMw8t/uvvtuLF++HJs3b1b0xKLRaMmdrYIgYMWKFVi9ejWmT5+OAQMGyEGcpMv0er38r1arrahUaD5ICrC1GngxEAQBH374IZ555hksWbIEGzZswAknnIDDhw+jV69e7b43lUrJ3OHOBNIEVkqqnUyaJBtQafoVy7KIxWLw+XxVLxOQYNnQ0ICGhgY0NjaioaEByWQSBoMBdXV1cLlcsFgs6N69O7p161aymlatBWYCUn6w2+2K9ZxIkoR0Oo1gMHjMTzQaBQD5c3/sscd4s9n8/z777LPHWjsWTdN6AP8BoAfwJ4Zh1uf97S4AeoZhfq/IiR9Hp0AxwTnscDjM8XjcP2PGDLAsi3379sni/UrvnEOhEKxWa9F1OlEU8fbbb2Pbtm24/PLLW+hbS5IEnueRy+Va/EtRFLLZLCwWC2w2mxywK4GmpibFg0cikYDdbsfevXsxcOBATJkyBbfccgtmz559TOqcpNWdTmen4TYD36eETSZTyfKtRK87Ho/LMpaV6Mwlz0Q15CIFQUBDQwO+/fZbORg3NTWB53k4nU7U19ejW7du8k8+rYnIopKMQ7Go1cBMQL63SlhNkoVnIBBAY2MjNm/ejOeeey7DsuwpDMNsO/r1NE3/EcDFAMYxDBPL+/3JAN4B8DuGYVoN7MdRmyiGSvVsa9raBw4cwJIlSxSnUpXSnJXL5fDaa6/h0KFDuPLKKwuiHpEad0NDA6xWK0RRlAO2wWCAXq+X/1V6ssnlci3oQWpg586d+Pe//42nn34aZrMZ69evR48ePeS/E7OJalN8jgahSvn9/rLPizQvsiwrN4ypda1qGlq0hkQigcOHD+PQoUM4fPgwvvnmG0iSBL/fj27durUIxoV4XhOqFTn/Qj+nWg/MBKSzvlIKbvl4/fXX8bvf/e7bVCp1AsMw0fy/fRec9zMM83je7+YAmA9gF8MwP67oyR6H6iiKSmU2m52ZTMY5ePBgWRZQr9fjySefRP/+/RU9sYaGBtTV1RW802FZFi+99BISiQSuuuqqoowrSKcy4feSgM1xHHieB8dxsnKYwWCQf8rdhSmV0i4E6XQab7/9Ni6++GIcOHAAP/7xj/HDH/4QJ598MqxWa6dyLyK8b5fLpehunlDWSABRIzsSjUYhiqJqFKVAIICDBw/i0KFDOHjwIKLRKKxWK3r16oVevXqhd+/e6NGjR1mBRRAEhMNhmUPe0XWQwHx0U1Wtgsh8VqMk8bvf/S735ptvfh6Px6cwDCOrpNA0/VcAYxiGOYumaQuAXwIYBWA7gOcYhtlK07Qm/z3HUdsopvtHR1GUBEAOYBRFQRAExRWHCO2q0Ic8kUjghRdegEajwfXXX190So7juBY7Y4qi5No0AdlRcxyHVCqFSCQi8ySNRmNJwZrs5CoBi8WCSy65BEDz5+t0OnH++eejR48euPXWW3Hrrbd2mrQ2qYUqfT4GgwF+vx/JZBKBQKDDxqZiQfzHlcpCkNT+119/jb179+LAgQNgWRZ+vx+9e/fG5MmT0bt3b8UXAlqtFl6vF5FIBNFotF1Fr64WmIFmmU9BEBAKheDz+SoqUvKLX/xCt3Xr1nF79uy5D80BGADAMMzPaJpeRdP0ewDGAlgNYAmA9whPmmEYkQRomqb1DMNU3uXjOBRDp6NSAd+ne+vr6zt8bSgUwvPPPw+Px4PLLruspAm9vc7wtkDq1xzHyZaQOp0ORqNRDtbtTVSkvlVNNa69e/fi5ZdfxsqVK7Fs2TLE43HEYrGifaiVBPlc1OZb8zwve0I7nc6yu9SVMrRIp9PYu3ev/EOEeAYMGID+/fujd+/eFctySJKESCQCSZLgdruPCVKky7nQHXYtgWQGCQ+8ktcWCAQwa9asdCwWu2LXrl1Lye9pmrYBqAdQzzDM5zRNGxmGyX73Ny2am8WuBTAVQArA6wzDvFGxEz8ORdEpqVSF0poaGhrw3HPPYcCAAZg9e3bJk3mpzWf5kCQJHMfJwZrneRgMBllw4ejJP5lMIpfLVY1XTFL5+aWDp556CgsWLMDFF1+Mn/zkJzjllFMqfl4knVqJem0+7Yp06ZYyCYuiiGAwCJvNVnTgzOVyOHTokLw7/vbbb2Gz2TBgwAD5p5qKbUQTnAQpcq905cBMQBYnZAFXyWvcvHkzrrnmmkQ2mx3H5NNjANA0PQ3AOoZhIkf9vjeAxQC+AvAugMfQ3EC2s1LnfRzKoVNSqYhST3t14yNHjuD555/H8OHDcd5555X84BCOr9I7NVEUkc1m5R+KomRVJIPBgFAoBJvNVjXVqHg8Lqe387Fu3Tr87W9/wyuvvIKLLroIixcvrtg5kc5qJZrAikH+LtrlchV1H+RP4IUutJLJJHbv3o3du3fj66+/BgD069dPDsZqNgiWArKL5DhO3kV29cBMUA4HulwsXLgw96c//Wk/y7Ij8xXEaJr+EQALwzC/p2naAOBhAE0ABgC4HMB4hmHW0zT9MAATwzDzK3rix6EIOiWVKplMQhCENhulDhw4gBdffBGjR4/GueeeW9bkUEwKvVQQK0qiJ0x21/X19TCZTBUX3hdFEU1NTe2Kjuzbtw979uzBueeei0WLFiESieAHP/iBanVp0uxULfnN/F10MbXoQg0twuEwdu7cCYZhcOjQIbhcLtA0jSFDhqBPnz6dTvzlaOSriQHNDktdPTATEA40uS8qBUmScOeddyY//vjjZdlsdl6+ghhN030Yhjn4XaPYaAD/B2APgKvQnNq+A8CVAD5nGOZvFTvp41AMhc4IOwE8u2DBgqH333//bUOHDoXJZMJVV10lU6mUhCAIbe5e9u7di4ULF+K0007D5MmTy54cOI5TnYtKURT0er2s0ZtIJGQTAGIjSLSFK9EdmslkYDAY2g0I/fv3lzvwU6kU7rnnHtx777246667MH/+fMUnqWQyCZ1OV7VMAuk5MJlMiEQiYFkWLper3YUTx3FIJpOtBmZJkvDtt99i165d2LVrFwKBALp37w6apnHeeed1ut1xR6AoClarVRZ26ayqZ2pAq9XC7XYjHA5Dq9VWlLv+29/+1rZ27dqLstnsYgCXkN/nmWWMBvA5gHcYhskB+DVN01cCuAiAE8Duip3scSiKTkmlikQisjtMPnbv3o1FixZh0qRJmDhxoiJjxWIxaLXaiqasQqEQLBYLzGaznP4mu2qdTie74agRqAlNqRCt6nxkMhk8+eST+POf/4wrr7wSf/rTnxQ7J1L/7izyoeTeJgG6tWxBa4YWJCBv3boVO3bsQCKRQN++fTF06FDQNN3pdMuLAUnvGo1GaLVaJJPJFnrc/w2oJMWKZLceeOABrF+/HoFAIMNx3ESGYTaQ19A07QbwBoBfMQzz6Xe/6wVgKYBzAOTyBUuOo7bQKalUgiAcs2PZuXMnXn31VZx99tmYMGGCYmPxPF/R3ZooinLtDmh24CL2dJIkIZvNygIcer1eDtRKpb5Zli1p9W82m3Hbbbdh/vz5YFkWkiRh1qxZOP3003HbbbeVRQmLx+OwWq2dZqKnKEoOutFo9JgULnF0It9NOBzG1q1bsXXrVoTDYQwYMABTpkzBkCFDOhV/vFTkB2biAU3qzsSD/L8BhGJF7D/VyhxwHIedO3fit7/9Lfr27YulS5fi008/Ndx9991LaZo+kWGYMAAwDBOhafotAI/TNH0rgDiAfwLgAKQYhmFVOcHjqAg6JZWqqakJHo9Hnqy3bduGJUuWYPr06Rg3bpxi40iShIaGBtTX11es7ptOp8GyrByc2zs34oqTzWZhNBrlYFDOpBAMBmG1WstOS4uiiP/85z/4wx/+gEAggDvvvBN33nln0YIq2WxW9rLujGlSURRla0WiWEcoZ9988w22bt2KI0eOoGfPnjjxxBMxfPjwijcOqYnWAjNBOp1GPB6vippWtUAWZgDa5X+XeuxUKoVkMgmGYbBv3z5ce+218hj33HNP8rXXXluVzWZnHFV//gsAGsBwAFsAXMMwTJKmaaoQp6vj6JzodFSqowPm5s2b8eabb2LWrFkYNWqUImMQEMUoJfyQCwWRRiwmOIqi2MJn1mQywWKxFC0pSrSTlQyEuVwOixYtwn333YcXX3wRI0eORC6XK2gXTJrA7HZ7RRttioUkSchkMgiFQjh06BB27tyJI0eOwOPx4MQTT8SJJ57Y4WKrFtFeYCYgz/9/W4BWWqJVEAREo1FIkgSXy9Xq88NxHC688MLY/v37/3fnzp0P5v+NpmkNgF6kFn08MNc+Oh2Vivgqd+/eXQ7MF154IU466SRFjp+PVCoFnucrVgsk11bOTp2YwpOuWVK7LqQGpiaHmJQ5vvzyS5x//vn4zW9+g+uuu67dIJ1MJpHNZjt1cxGpI2/YsAHbtm2DRqPBkCFDMG7cOPTs2bPTnne5IOnb9gIzwX9jgG6t56BUkOyR2Wzu8LM+dOgQzj///FQmk5nCMMwXZQ18HJ0anY5KxfM8IpEIGhoa8MYbb2D27Nk48cQTFTn20YhGo9Dr9SW7HhWLTCaDTCajyC6LKJSRNDmx1zQaja0+3OR7KkavvBSwLIvHHnsMv//97+F2u3HvvffisssuO+acOlsT2NFgWRZbt27Fxo0b0djYiMGDB2PQoEEYMmSI/Pdim+pqBUS60mQydRgsCP4bA3S5JhmSJCGRSCCTyRSlI798+XLxrrvuamBZdvjRBhnH0XXQ6VypstksNm3ahPfffx/nn38+Ro4cqchxW0MpzlflgFggKt0kRNLe6XQagiDAYrHAYrG02E3HYjFQFFUxLe9EIoEHH3wQn376Kd577z3wPN8iDU9EPyrtn90eJEnC4cOHsXHjRmzfvh0WiwWjRo3CqFGjIIpiC0MLlmURjUZle8WusoMuJTAT/DcG6HQ6LdPpiln05nI5RKNRUBRVtPANAPzmN79JL1u27KN0On3B8fR110Sno1KRVPZ5552H0aNHK3LM1kBSzN26davIxFqp5jOymyZcZtIFHQgEVNerbg0k3X3nnXdi27ZteOCBBzB06FBEIhH4/f6KC7C0hmw2i82bN2Pjxo0IBoOgaRqjR4/GgAEDoNFo2pyAc7kcIpEItFpth5zoWgAJzMXaRebjvzFAE+53oQYk5DMqZ2GXzWZxwQUXJA8fPvzz7du3P1rKeR9H50Yxs0kLKhX5V0kqFcMwWLZsGSZPnqxqYAZwzE5ObZDUs9oTuF6vh9PpRF1dHYxGI+LxuOzxW43dHRnzxz/+Merq6jB69GjceOONyGQyVQ9mkUgE7777Lh544AGsWbMGJ554In70ox/hsssuw6BBg6DRaMDzPOLxeKvGDzqdTua8BgIBcBxXpSspH/mBudgdcz7MZjOcTifC4TByuZzCZ9k54XA4IIoikslku68jnd6JRAIej6fkBRAAGI1GPPbYYza9Xv8XmqZHlHSQ4+jU6DRUqq+++govvfQSzjjjDIwbN071OnBHEqFKIxKJyDvZSkIURXz77bcwGAwQBAFms7mqnOKPPvoId911FxYsWIAFCxZUfHxJknDw4EGsWbMGDMOgd+/emDBhAmiabtV1qVBDC7IbcjgcNcdtPjowK4F0Oo1EIvFfI1TSUYMY6aUhi2elFqavvPIK/vjHPx5IJpPDGIbJKHLQ4+gU6BRUqn379uHFF1/ElClTMHTo0KKpRqVArfpva1DLXKMQkM5ur9crZznS6bTctV3J7AFRPSJ1/mQyiRkzZuCnP/0pZs+erep5CIKA7du3Y82aNWhsbMSIESMwfvx49OjRo9XXE0MLrVZb8AIul8vJHfGVdjEqFWoEZgLC2a2EolZnAGkQy29ylCRJXqiosXCTJAm33XZbdu3atc+tX7/+uMFFF0LVqVQHDx7E888/j9NPPx2TJk1CIBCA0+lUvQu2sbGxYqt6lmXlSaqSIHxMohmd//t0Oo1UKgWNRiPbZaodTGKxZiVBEuwymQz++te/4s9//jNOOeUUPPTQQ4pT5jKZDNavX48vvvgCuVwOY8aMwSmnnNJhICrU0OJoiKIoWyy63e5OvWtUMzATELMMr9f7XxGgU6kUUqkU/H6/nMYm9Wi17oVYLIYZM2ZkIpHIpTt37nxLlUGOo+Ioikql0Wh6iqKonT59OrLZbNlUqm+//RbPPvssxo0bh7POOgsURaGxsVH1lTah8dTX11dkdxONRqHT6SquHJXNZhGLxdq0YCQqZKlUCqIowmq1luxp3BF4nkcoFGqVynX48GH8/Oc/x3vvvYcDBw4okjVJpVJYvXo1vvjiCzgcDkyYMAEnnXRSQU1Kre2AikG+0pPb7VbNyascVCIwExC6ULEdzbUIEpBzuZxcRqqEe9f69etxww03xFiWHcYwzLeqDnYcFUFRVCqdTve3XC5n7NatGy644AKMHz++ZCpVKBTC008/jWHDhmHmzJmyXndDQ4PqHdQkIHm9XtXGICAp7WrweYmIRCF17mw2i2QyiVwuJwdppSZSSZLkQNDeuTQ1NaGurg5Lly4Fx3G4+OKLi74P4vE4Pv/8c2zYsAF+vx8TJ07E0KFDCz6O0uISkUgEdrtdtUVPKSCB2WKxVGTBSPi8RFO+Kwdocq1NTU3wer3tetIrjQceeCD7n//8Z813AiXH6VU1jqKoVAB6A6B69uyJpqYmGI1GOJ3OoqlUiUQCTz31FHr27NliAiY72m7dupV2NUWML0lSRTi22WwW8Xgcfr9f9bHyQTIaxWYHeJ6XlbusViusVmvZkylJoReaIv7nP/+Jn/zkJ5g0aRIeeeQRDBw4sMP3RCIRfPbZZ/jyyy/Ro0cPTJw4EYMGDSrq2skigqhiKQFCtyKNQNUO0JUOzASSJHV5u0lBEBCJREBRFGw2GyKRCDweT8WEaniex4UXXhjft2/fL3fu3HmcXlXjKIpKRf7DarWid+/eyOVyCAQCOHToUMEHyWQyeP755+H1ejFnzpwWD2l7Ps5KohIezgQsy1bFoziZTJbEodTr9XC73fD5fMjlcmhqakIikYAoiiWdhyiKSCQSRQWmW265BTt27IBer8fw4cPx+uuvt/naYDCI119/HQ8//DAikQiuuuoqXHfddRg8eHDR155IJOSJVSnodDp4vV5Zp1oQBMWOXSyqFZiB752+NBoNIpEICtkU1BJYlkUgEIDRaITH45E3LpFIpORnp1jo9Xo88sgjDr1e/2eapodUZNDjUA2F7pw5ABkAdgAUSdHpdDrE43FoNBrs2LGjw+PwPI/nnnsOoijimmuuOSZAEpUrNU0EKtk5TbyTK00nIRkIJa4xl8shmUyCZdmSdtLxeByiKJasX/7GG29gwoQJqKurw5dffikrxoVCIaxYsQLbt2/HoEGDMHHiRPTu3bukMYDmey8Wi6nW7yBJktwcVQ2bxWoG5nxIkoRwOCx3wdf6Djrf+7s1OddiBUqUwHPPPcfff//9u1iWHcMwDF+RQY9DcRQ6y+bQ7BEKADjppJMwZMgQmM3mgqXnBEHAokWLkMlkMG/evFZ3rq35OCsNQRBAUVRFdug8z8uLmEoinU7DZDIpco06nQ4ul6vFTjqZTBa0GyBqZeWkiC+88ELU19fjyy+/xLhx43Drrbfitddewz/+8Q9ks1nMnz8f8+bNKyswEylFt9ut2n1BURTsdjscDgdCoRBYtnJWu6SOXu3ADDR/Dm63GzzPI5FIVPVcygUpHQmCAL/f3+qc5nA4IAiCbFRTCVx11VX6E044oY9Wq/11xQY9DsVRaCQUALw3bdq0pQDw7LPP4qWXXsInn3yCSZMmdTihSZKEpUuXoqmpCVdddVWbXD9RFFUPmkQZrBJgWbbiVoikU1hpsROdTge32w2v1wuO4xAIBJBKpdpMT5Idhd1uV+Q7pWkaTz31FLxeL95//30MHToU8+bNQ/fu3cs6LuEz2+32ipQ6zGYzPB4PYrEYksmk6uldEkCsVmvVAzOBRqOBx+ORGzNrDYSKSBY8ranHEZDFCGmIqwQoisL//d//OQ0Gw49pmj6lIoMeh+IohkqVtFqtxlQq5Z44cSJEUcSRI0cKolK999572Lx5M66//vp2ub6VcImKxWLQarWqT1QkpV3pFCbR1Va7E53jOCQSCQiCIPOo89N2mUwGiUSiTRpXMeOsXbsWq1atgtPpxKRJk7Bo0SLs378fzzzzTNnXke+hW8kUK7FkVLNRLJfLIRQKyRrOnQ3k/BwOR6f2884H4bHzPF+UaU4mk5EbQyvVrf7WW28Jv/jFLw5ms9kTGIapXKrmOBRBoXdJIwD3kCFDtgLA/v37sWrVKkQiEdm9pi2sXr0a69evx7x58zoU4ahEQ1ilds5EV7iSKW1S16zEDslgMMDr9cLpdCKZTCIYDCKbzcrnEY/Hywo6giDgiy++wMMPP4yNGzdi5syZuPnmm3HCCSfgnnvuwdNPPw1JknDdddfhjTfeKGmMdDoNjuOqUvvUarWyals4HFa8aaizB2ag+dkgWQRy73RmcByHYDAIiqLg9/uLmkfMZjOMRiNisVjFmuFmzpypHT9+vEen0/2+IgMeh6IoNDhrAMR27dp1MtA8+VqtVmi1WoRCIUSj0VbftHPnTnzwwQe45JJL0KtXrw4HUTutTTyQKxGcM5kMzGZzRSf9bDYLiqIq6jFsNBrh8/lgs9kQjUYRDodlHfFSxTf27NmDf/7zn/jkk08wceJE3HbbbTjppJNafJYURUEURQwZMgSXXXYZ5s2bh3A4XPAY7RlaVAokvavT6RTt5K6FwExA2AGRSAQ83zl7l8iiNxwOw263l5xlcTqdyOVyFas/UxSFP//5z06j0XgTTdOnVmTQ41AMhc5K9QByxJXKYrGAZVno9XoYjcZWA+qRI0fw2muvYdq0abJBfUdQuyEsl8tBq9VWZDKuBoWK1JorvQukKApmsxl1dXXygk2SpKJ3g4FAAC+88AJefvllDBs2DLfffjtOOeWUNhdsWq0Wv/jFL7B582Z89dVXOPHEExEKhTocRxRFRCIROJ3OqtsaEo9ts9mMYDBYdoCqpcBMYDQa4XA4EA6Hq0o1aw0ks8GyLHw+X1npd+LdnEgkKubY5fF48Mc//tFsMpleomm6NmoHxwGg8OAsAXhn/vz5C4Hm5hyfz4f77rsPt9122zGNDtFoFAsXLsTYsWNxyimF9SNIkgRJklT3Oq7EZMzzPCRJqujEz/M8crlcVWt3xEK0vr4eWq1W7uzuKI2XyWTw7rvv4rHHHoNer8ett96KqVOnFrzzHjZsGFatWoVHH30UXq8XTU1NbXZDE3lFo9HYaeqchFtNOrlLTfHWYmAmsFgssFgsqqT4S0U2m0UwGIRer1eMDqnX62G32yvK9Z42bZrmjDPO8BiNxj9VZMDjUASFNoSlAZi0Wi0vCIJBq9Vi+PDhiMfjyGQyaGxsBDHDYFkWTz75JHw+Hy699NKCgy2ZWOrr68u5nnZRiYYzALJoR6XsKIHma9NqtarrJLcHlmXlpheKouTUMbHmPDrYiqKIjRs34qOPPoLD4cD06dPRr1+/ss/j2muvxcaNG/HCCy8cY6RRqqFFpUAkP51OZ1GLh1oOzARERUwUxYrygls7D6IH7nK5FNdGJwwBnU5XEZVCoFlB75xzzkknEolzGYZZVZFBj6MsFEOlWnL++ee/DgBvvvkmFi9ejOXLl+PUU0+V07eEy2wwGHDRRRcVtQvuSjSqSqe0BUGQRUKqBdIEli/yr9fr4fF44HA45Ho0SVvu27cPjz/+OD7++GOcddZZWLBggSKBGQAeeeQRjB8/HuPGjcMDDzwg78SIfnhnlo80Go3wer2Ix+MF04y6QmAGvlcRI/dSNUA+S57n4fP5VDEtIentTCZTsUY4t9uNP/zhDxaLxbKQpunKSxYeR9EohkoVsVgsjnQ67Rw8eLD8AOn1eoRCIWzevBlLly7Fvn37cOONNxbdMZzJZJDJZFRTB6uUqUapmtblIB6PQ5Kkiu7Uj0YikQDP821+f6SpJhAIYN26ddizZw9OOeUUTJo0SbWFzJIlS7BgwQIsXboUp5xyimKGFpUA8YY2m82w2Wxt3ktdJTDnQxRFmZtdyWsi3vTks1T7+c1ms4hGoxWlV918882ZL7744p8bNmz4SUUGPI6SUegdsR+AA821Z/mmpSgK8XgcvXr1wqeffoqdO3di3rx5JVF51KZR8TwPnU6n+gNHds2VCsyiKCKdTld1YhYEAalUqsMU3e7du7F48WLE43FceumlmDx5sqqBcs6cOdi7dy8mTJiATZs2YeXKlTURmIHvNbmJrGhri2gSmO12e5cJzMD3XeyJRKIiSmqiKCIajSKRSMDj8bS7GFISpO+BcO0rgXvvvdcM4Ic0TY+syIDHUTIKDc4iAEGn0/EAMHDgQPTu3Rtmsxl6vR4cx+GTTz7BZZddhrq6upJORO20dqXMLiqd0s5kMjAYDBWXCM1HPB6H1Wpt8xwCgQCeeeYZfPDBBzj77LNx4403ol+/fohGo4hEIqp26NrtdiQSCXzxxReYN28e7r777k5L2TkahAtN5EXzJ/D8wNyW4l4tg3Cgo9Goqt8Xz/MIBoOQJAk+n6+iNESg+f4UBAGZTKYi4/n9fvzqV78y2Wy2RTRNV2/SOI4OUWhw7geg54IFC14GgKFDh2LChAn4zW9+g0WLFuGbb77BzJkzMWDAgJJPRG0aVSXqzYIgIJfLqVKnag1EqrOasozZbBYcx7V6DjzP46OPPsJjjz0Gh8OBW2+9FWPGjIFGo4HJZJKNOQKBANLptCq7B5ZlkclkcNNNN+Hjjz/GCy+8gMmTJxflpFZNaDQaeL1euYlIkqQuH5gJDAaDTLFSuoObPDukJFAtvjupP8fj8YrRq+bMmQOapnvo9fq7KjLgcZSEQmvO6wA8vWXLFutJJ5301y1btgBo5tXedddd2L9/P9555x1QFHXMT6EIBoOw2+2qBbampqai5PZKQSqVAsdxFTNYz2QySCaTVes8liQJgUAAdrv9mM7ivXv34q233oIoijjvvPMwaNCgNo/D8zyi0Sg0Gg2cTqdiWQBS/8/31A0EArjuuuvws5/9DJMmTVJknEqAUMA4jpO9yLtyYM5HPB4Hx3Hwer2K3OckjU3coqqZdSIgzm9KXWNHOHToEGbNmpVmWfZkhmG+Un3A4ygahQbnkwEstVqt7lQqdQxX58c//jHmzJkD4Hu+siiKLYK0RqORf/L/n4iChMNheL1e6PV6xW9OURTR2NioejNYKBSC1WqtWFqbNM1Ui6+bTCaRzWZbdD+zLIvly5djy5YtOPXUUzFp0qSCFkRkJ0PkR8ttyJEkSTYmaKseu2PHDrz44ou45557OsUE3RE4jsM333wDvV6P7t27V03ZrNIgWQNiM1kOSBOWyWRqwSyoNiRJQigUgslkqlgm7N///jf/6KOPrs1kMmcyDNO1DLa7AIqZkXRoto7EkCFDEI/HkUgkYLPZcNpppx1TayZBXxRFOViTf8mPIAjgeR6CIMhiFcTOsbUfnU4nB/diwHGcKkE/H6IoguM4Vb2o88FxHARBqFqDE/nO8nftX331Fd58801YLBbMnz8f3bp1K/h4RIjDZDIhGo2CZVm4XK6Sg2YsFoNOp2t3dxmPx/H000/jk08+waJFi8p2uFITPM8jEonA7/fLaW2v1/tfEaBJ6jcYDJbsuEbYAqlUCi6Xq9M1BuZfo9ForAjl87rrrtMvWrTopEOHDs0D8ILqAx5HUSh057wawPN33HHH8IceeuiWyy67DHv37sWll14Knufx8ssv45VXXin5JMjOtnv37nLQJv+SOi75b9I4ptPpjvlpa6JKJBJyKlAtpNNpsCxbseAciUSg1+urVm8maWiHw4FsNis7j02cOBETJ04sq7mPWPKRxV+xu+h0Oi0vHDoKXoFAAFdccQV27tyJJUuWFKxoV0nwPC/rOlssFlkkg2QtKuFN3hlAyhRut7uo8pcgCIhEInIA7MyfVyqVQjqdrlipasuWLbjqqqsi2Wx2EMMwhYvTH4fqKIbnHHc6nbpYLFbXt29f+P1+BINBmRvYnmVkRyC7gkI6vUnQzuVyx/xoNBro9XrodDr5X51Oh0gkArPZrGr6NxQKwWKxVCTFTCapurq6quycOI6Td3EHDhzA0qVLodfrMXv2bPTo0UOxcUiXMoCCd9E8z8u7ykJ3H7lcDr/61a9wySWXYNy4ceWcsuIggfloW0USoEmdsjMHHCVBqGU+n6+ga2ZZFtFoVPaz7ixp7LYgSRLC4TCMRmPFFt6//vWvk2+88cYrW7duva4iAx5HQSgmOC/98MMPd0ydOvW+Rx55BOeccw4A4O6778by5cuxefPmkk8im80ikUh0aCnZHiRJkoM2z/Oy1rQgCEin03C73TCZTDAYDNBqtYo+pGTnX19fX5FgGYvFZMOESoPUcvV6vWwHeuqpp2Ly5Mmq1G3za9Fk59jWdyeKIgKBQFn+wOvXr8dzzz2Hv/71rxWn1RwNstBoT8qTyEz+NwVokjVor3mKiCSxLAu3213177IYkMV3MQvMcpBIJDBlypRkIpGYyTDMp6oPeBwFoZjgHLbb7fWJRMI4ffp0ZLNZ7Nu3T76Rytk5p9NpZLNZVbqceZ5HY2OjbNdGul31ej0MBgMMBgP0en1ZQbWSKW1RFNHU1CTTkCqNVCqFffv24eOPPwZFUZg9e3ZBdqDlIpfLIRKJQKPRtJqaVKppaPPmzbjwwgvRp08fLF68uKi6uZIoJDAT/LcFaPJdk3vhaJB7RavVwuVy1WRdPp1OI5VKVSy9/e6774p33XXX3mw2ewLDMLUhBNDFUehduxPAX66//vpngWYXIMJzvv766zF48OCyTkJNAZJcLgeLxQK73Q632436+nr4/X5YrVY5NdjY2IhAIIBYLIZMJlM0p7KSwiPpdLpNm061kcvl8Mknn2DJkiUYPHgwbrrppooEZqBZlMLn80Gv1yMQCByjHJVKpSCKYtnZhJEjR2L9+vXQ6/UYO3YsNmzYUNbxSkExgRmATGVT0hO6M4PUjjmOa6E/TnoVSJd+Nb26y4XZbIZGoylYX71cTJs2TXPCCSf4NBrNHRUZ8Dg6RFFUKrPZ7MxkMsdoaz/55JPo379/yScRi8Wg1WpVqbHE43FQFNWuW5MkSeB5XhbU4DgOOp0ORqNR3l239ZBXMqUtSZLM1650mi6RSGDRokUIhUKYM2dO2QuyckBq3kajEU6ns0UNXKlFSy6Xwy9/+Utcc801GDFihCLHLATFBuZ8/LftoPN57DqdDrFYDDzPq65nUCmQ6/P5fBWh+u3fvx/nn39+kuO4oQzDHFF9wONoF8VEEx1FUcdoaxNd5XKgpq52IcpgFEXBYDDAbrfD6/WiW7ducDqdoCgKyWQSjY2NCIVCSCaTslczQTabbTd4KwmWZaHVaisemHfv3o1//vOfkCQJN910U1UDM9CsHOX3+yFJkvzduN1uRe8hnU6Hv/zlLxgxYgQ++eQT3HvvvarrH5cTmAHIVLTO5ImsJnQ6HZxOJ4LBIJqamkBRFPx+f5cIzEDz9dnt9oppb/fr1w/XXHON1mQyPaz6YMfRIQqNKI8B+MP8+fMXAsDMmTNx6aWX4r777sNtt92Ge+65p6yTUCutLUlSSZra+cHa5/Ohvr4eVqsVgiAgHA6jqalJ5uJmMpmKpLQJT7OS1KlcLod3330XL7/8MkaOHIkrrriiqs5X+SBqYqIoys1/ak1goijigQcewDXXXKOaxV+5gRmAnCEyGAwIhUJdPkCTJlCO4wBAXlB3JRCefjqdrsh4t912m9lisZxD0/TUigx4HG2iJCrVzJkzkclksG/fPkWoVI2NjfB6vYqnboqhaBUKMiGwLAuWZRGJROB2u2GxWGAymVTbQWezWcRiMfj9/opMQKFQCK+88gpYlsV5550Hp9NZNZnQthCPx8HzvOwXTXZSanwHu3btwsyZM9G7d2+89tpr8Hq9ih2b4ziEw+GyAnM+JElCLBZDLpeDx+Op2bprexAEQd5ROp1OxGIxGI3GdstXtQqycKtUE+gHH3yAn/zkJwdZlh3MMAyn+oDH0SqKeWpXrFu37l4AePDBB/HYY49h+fLlOPXUU8uWWVRr56yG2QVFUdDpdLDZbLDZbHA6nbBYLGBZVk6xplIpxRtziDJSJYLj1q1b8fjjj8Pr9WL+/Pmw2+2dbldC/L9JfZEIjgSDQVVcjIYOHYq1a9fC6XTK3GslQAKzy+VSjCNPUZSsUU7MMroSstmsTOcjdCO32y2zProa9Ho9rFZrxdLbU6dOxUknneTW6XR3qj7YcbSJoqhUDofDHI/H/TNmzADLsopQqQRBQCAQUIWyomajGQB5t0aOL0mSvKPOZrPQ6/UwmUxy52WpIJ9xfX29qgFSEAS899572LBhA2bMmIHRo0cjkUhAFMVWKSvVQmuGFgQkk9MRJ7pcLF++HBaLBRMnTiz5GPmBWY3SCKEcAYDb7e5Ui6tSQNgVmUwGLpfrGJUwoptdqEBJLaE9kxk1sHfvXsyZMyfNsuwAhmEaVR/wOI5BUVSq1iwjy6VSqUmjUtPDmQTi/EmVoiiYzWaZsmWxWJDNZtHY2IhwOIxMJlPSyjeVSqkaaIDmTt9nn30WDMPguuuuw5gxY5DL5ZBOpztVqpAEHFJbPRpmsxk+nw+pVErVncb69etx9tln46WXXirp/WoHZqD5fnS73XKau5Z30GRBxvM8fD5fq/KdRqMRZrO5YjvMSiLfWrISvQQDBgzA3Llz9Q6H4yHVBzuOVlF1KhXLskilUorW8IDmSbyhoUE1ilM2m0U8Hoff7+/wtaIoys1jPM/DZDLBYrEUZMZRCdGRAwcOYPHixaivr8fFF18s6zeHw2GYTKaSjAbUQH6Qcblc7X52oijKdVe1bAGffPJJ3Hzzzfi///s/3HrrrQW/rxKBOR+iKCIcDkOv13cqJ6ZCQbIhheisE3enrlp/jkajctlCbSQSCZx11llsPB6fyDDMetUHPI4WqDqVSi0aFc/z7ZphlItihEc0Gg0sFgu8Xi/8fj90Oh2i0SgCgQCSyWS79elUKgWTyaRaN/vq1avxn//8B6NGjcKVV14pd4eyLAtBEDqVZ3A6nQbHcQXVv4l6lMViQTAYPEa0RAnccMMNWLx4MR588EHEYrGC3lPpwAw0fxYejwfZbLZiohZKgPguJxIJeDyegrSxSbaAeKt3NTgcDrAsW5Frs9vtuPvuu012u/0pmqZra0XXBVCSK9Wdd94Js9mMAQMGoLGxsSxXqkQiAQCKr3JTqRR4nlelVkrEQMrpMCfCJ0T602AwwGKxwGg0yhMQGcfj8Sje2MZxHJYuXYqvvvoKs2fPxtChQ+W/EY3q1up61QLpWC1FkIGIlFitVlWa6kj5ZOPGjTj55JPbXEhVIzDnQxAEBINBuR7fmUGYFnq9vqQOfGKQ4ff7u1y3eiaTQSKRqAhzQxRFzJo1K7l3795bdu3a9byqgx1HC1SdShWNRuVuRCURiURgMBhUSclyHIdoNKoYRYukvYkEpcVikevVRPFJSUQiESxcuBAAMHfu3GOOH4/HIQiCKlrnpUAJQwvCUVeLbsVxHGiaxtixY/H8888fs6ipdmAmILXbap9HW8i3C3U4HGUtImKxmHwf11oqvz1U2rlqw4YNuP7664Msy/ZlGKYyhOvjqD6VSs20tlrNYJlMRtGOSZL29vv9cLvdEAQBTU1NaGpqUnznevDgQTzxxBPwer248cYbjwnMpAmsGo5XrUGSJESjUbnrvVRotVqZp62GBrXBYMCHH36ITZs2YebMmXJGCOg8gRloVp3yeDyIRqOdLu0riiIikYjsZ1zu7t7hcEAQBGQyGYXOsHOA1Jw7KokphTFjxmDChAlGrVb7U9UHOw4ZVadSkfSpkmlbonfdrVs3xVfMaqaa85HJZBAMBmWLS6vVCpPJVNb1fPnll3jzzTdx6qmn4qyzzmr1WOFwGAaDoaJKZO2hEHvAYkAsKFOplCoa5Q0NDZgxYwYGDBiAV199VQ7Mbre705QIgO/TvmqI/5QCQoMymUyKNq2VUw7p7KhkhuvgwYM477zzUhzHDWYY5lvVBzyO6lOpBEFQPMVIxEfUSGXlcjlZiERNpNNpeL1e1NXVwWq1IpVKoampCclksmgqhSRJ+PDDD/Hmm29i1qxZmDp1aqufDcuyyOVynaY7mzQwKZmWpChKFo8Jh8OKyyJ269YNK1aswF//+ldks1kEAoFOF5gBwGQywWazVV2Hm3CXI5EInE6n4mI3er0edru9S4qx2O12cBxXEeGVPn36YO7cuZTBYPij6oMdB4AqU6kI3UnpHS5J96hBN4jH4wCgatqX53mEw2HU1dW1+FyIRV42m4XFYoHVau2wJMDzPJYsWYL9+/dj7ty56Nu3b6uvIyIHDoej6qlX4HtxGjUDG/mcLRZLQZ3AxSCbzWL58uX49a9/jXfffRc9evRQ7NhKIh6Pg+M4xTITxUAQBEQiEZnDqxZVkHDjdTpdpynXKAWWZWVKp9rfXzwex6RJk1LpdPo0hmG2qDrYcVSXSkV2zWp1zyqN1oRH1EBboiMGgwFutxs+n08OptFoFLlcrtXjJBIJPP300wgEArjxxhvbDMxA84JGp9N1isBMJlOr1arqjpPIfpKUqlI7q2w2i0gkgjPPPBM9evTApEmTcOjQIUWOrTTsdju0Wm3Fd5YsyyIQCMBoNMLj8aiq6EVqtISK15VgNBqh0+kqQpFzOBy488479Uaj8e+qD3Yc1XWlqiVNbQCy85GatWZiqtFeapl0HBOaSDAYRCQSaRGkGxsb8e9//xtmsxk33HADPB5Pu2OmUqlOs6uIx+PQaDQVqXtrtVp4vV5ZvKLcFC8JzG63Gy6XC6+//jqGDh2KM888E/v27VPorJUD2bWKotiiiU0tECGZWCwGj8cDu91ekR27VquFy+VCJBLpUm5dFEXB4XBUrDls3rx5BpvNNpKm6XNUH+y/HFWlUhHzgvYCR7Eg6VA1dKiJzrSa6jzxeFx22ikUoijKTU5Go1F2lBo+fDjOO++8Dmv6JOXXGRSVMpmMnKarJD+V1D5ZloXH4ymppyA/MOfv+DmOw49+9CP89Kc/LVlJT21UggOdy+UQiUTkQFkN/jExLelMWvFKoJLNYe+8847485//nGFZdgTDMF1npdPJUFUqlRo7ZzWbwdROaROOZ7ENWRqNBna7HXV1ddi7dy9eeuklnHzyyZg+fXqHE2A2mwXHcZ2iOzuXyyEWi8Htdld84iY7EKvVilAoVLSzVVuBGWguRzz66KPo378/3n77bTAMo+SpKwKtVguPx4N4PK54gxG5r4PBICwWS1W+XwKHw4FsNquKYlw1YbPZwHFcRdL206dP13Tv3r07gLmqD/ZfjKJ2zmaz2ZbJZBz5VKp0Oo14PF7Szjkej8sG8UpBjWMClXGGIs1epWYSvvjiC7z77ruYPn06hg4dKkt/krri0ai00017EEURwWBQVvGqJkg2qNBmtPYC89G48cYb8dZbb2HlypVlsRzUArkWpahHROOc53nZ3rPaID0GXU09LJ1OI5VKVcR3fc2aNViwYMG32Wy233HPZ3VQ6J15BIDH5XIFgO+pVHfddRfi8Th8Pl9Jg6shQKJWvZnsmtW66Qn/tpTAJEkSPvroIyxfvhyXXHIJxo0bJ++kNRoNAoFAq242qVQKWq226k1gpA6p1+s7hayk2WyGx+NBJBLpUMCimMAMAI8//jgmT56MqVOnYv/+/QqdsXIghhFKUKw4jkMgEABFUfD7/Z0iMAPN12gymQrWQ68VkAV2JURXJkyYgBEjRlgoirqp0PfQNK357t/qd53WAArdOe8G4DKbzQZCpcpkMnIKRafT4eOPPy568FAoBJvNplhHriRJaGxsVMXBidTj1OoeZlkWiUSi6FWvKIpYtmwZdu7cicsvv7zVjmxBEOR6KnH2IZKYnUGcgdTLfT5fp9rJEKoV+cyOBsuyiEajrfpKd3Tcyy67DNFotKTnRm2QxZIoiiVxzMlCM5lMwul0Vj0r0xo6U9ZISRAd+UpkBXbt2oVLL700xnFcb4Zh2uwmpGnaAOAUAFMAnA3ABOB5hmEeVvUEaxyFfnvdAPD5VKrDhw9DkiRoNBqEw+GSBldagEQQBFAUpXhgFgQBuVxONTlQoJnKVCzXlud5vPzyy/jqq69w3XXXtUmVIg04Pp8PHMehqakJwWAQZrO56oGZ4zjZdagzBWagmWrl9XqRSqWQSCRaUI1KDczkuC+99BKef77ZR6ASXbbFgFCPSungJhrmLMvC5/N12sBXaX/kSsFgMMBgMFSEWjV06FBMmjRJq9frf9LWa2iaHgLgPgA/B3AygNcB3Avgapqmf6X6SdYwCp0NJQAfECrVkCFDoNVqMXHiRIwfP77kVK/SaW01U9r5blFKg+M4CIJQVHqZ4zgsXLgQoVAIN9xwQ0EmHERX2Wq1IplMys1g1QLRUnY6nVVfJLQFnU4Hr9criz0QrnupgZnAaDSiZ8+eWLFiBcaPH49QKKTwmZcHYr1IGBWFIJvNIhgMyouazvqdEhgMhi6Z3rbb7UilUhVZ9P3sZz+zaTSan9I0fUyjDE3TNIAnAQwF8CyAeQzDPMgwzDIAvwZwteonWMMo9OnRAbjqkUceEQDgnXfewfDhw/Hmm28il8uVFBDJalXJgKeW+Egmk1G1m5nUmgv9LLLZLF544QVks1n84Ac/KOrcJElCJpNBc7Pl9+5dDodDVSGI1s4jEomUbWhRCRAudDgcRiAQgCAI8Hq9itxrY8eOhclkwrnnnosPP/ywU1F8tFot3G637ObV1nNOaGiZTKZT2YwWAofDgUAgUBFxoUpBp9PBYrEgkUiofj/17dsXM2fO1C1fvvxXAI7eQf8EQIBhmItaeesIAO+qenI1jkJ3ziKA1y677LKXAeCtt97C4sWLsW3bNlx44YUlTeqERqVkcFZj5ywIAnieV23CEQRBluMsBJlMBs899xx4nse1115b9KIhnU6DoiiYzWbZCUur1SIQCCCZTFZMJYqM1VmETzoCcQ5LJBLtBqpiYbPZ8NZbb0Gj0WDmzJlIJpOKHFcpkIVbW+IdhMWQy+Xg8/lqKjAD36fwSY29q8Bms4Fl2aIpgaXg//2//2cURfEWmqbrye9omrajOQD/Lv+1NE33omn6d2hOc69V/eRqGMUU+ca/8cYb5wPA7bffjvPOOw9nnnkmvvjii5ImdKXrzZIkqRKc1e7STqVSMJvNBX0WqVQK//nPfwAA1157bdGdzaSGmG8uoNFo4HA45Hp0IBBQXUg/m80inU7XlM8uSWv36tULGo1GUblLp9OJ5cuXo66urtOltwHAYrHAaDQeI3FKnNPMZjPcbndFMy9Kwmg0wmg0yrr5XQFE+4CUYtREjx49cPHFF+usVus95HffNYiFAMynadpA03QPmqbvBvAwgBMA3MIwzEJVT6zGUWh03A/Agebac4sJlUxYxULpenMul4NWq1W8qUjNdJcoigWJjjSmgItfzuHRZxbBYDDg6quvLumcEokEzGZzqwsYUo92OByIRqOIRCKq1KyI2YGaRgdKI7/GTLSgASgaoD0eD15//XX07dsXmzZt6nS7OIfDAVEUZVe0aDSKRCIBr9eruGlINUDESSrh8FQpWCwWOTOnNn74wx/qBUG4lqbp3nm//hGAgQC2AFgDYAyADQD+DmAp8D296jiORTFpbUGSJAqAPCFJkgSdTldSQFRaHUyNerMoiuA4TrVUXTqdloXr28P9n2axvkGLLzSn4sorryzpfHieRyaT6VCcxWQyoa6uTk51p1IpxQJQpQwtlERrzV+kWQpo9r9WcmeSSqUwY8YM/PznP1fsmEqAXHMikUBDQwMkSYLP5+s03OVyodFo5PR2V7GWJKp3RzMN1IDP58MVV1yhMZlMvyW/YxjmKwDnAbgCwEQ0p7L/yjDMSoZh+O9e07lWoZ0IhUbVAQDY1lypjEYjDh48WPTASqe11UppG41GVSg+hYqOfN2QwKuMFgCFLQKNKF/8AoTwVh0OR0HXQh5qr9eLTCZTkpxla6ikoYUSaK8rmwQrQiVUavKzWq14/fXX8cgjj+CBBx5Q5JhKgHSpi6IIQRAKvpdqCSaTCTqdrtPV/csBYZlUQphkwYIFRkmS5tE0LadSGYYRAHRnGOYAwzB7GYaRt/E0TXtoml5A0/RcmqY7n1xelVHM0/Vpa65U48aNKymlVQs0KjVT2izLQqvVtrvbTyaT+PHL+wE0f76iROHv64ofK5PJQJKkoruiCSXGbDYjFAqVtQLPZDJgWRYul6smUqCZTAbRaLTdrmzClaUoStEU94QJE7B48WL8/Oc/x6JFixQ5ZjkglLd0Oo1u3brJ7k5dZYeZD6fTiVQqVZFGqkqASBlXYvfs8Xhw+eWXUwaD4TdH/elcmqavyP8FTdNno7lbew6A3wB4nqbp61Q9wRpDoQphKQBGiqIgSZK2e/fu4DhO7nDU6XTYunVrUQMHg0E4HA5FUtGiKKKxsRHdunVTbOInx6yvr1d8hyBJkqw41lbwT6VSePSZxXg8dxVyeYw3kw749AdAXYEqn0QJzO12l/VZC4KAWCwGQRDgdDqLOhbp6PV6vTWRBiXa2oWeL0nXA1C0ye3VV1/FuHHj0KdPH0WOVwqIDrXJZILD4cB3c4DsZFYr3fbFIJVKIZPJwOv11sRCshCEw2EYDAbVslaSJOHQoUP4+9//jnfeeSeXy+X6MwxzGABomnahuWepEYCXYZhvaJr+F5opur8CwAE4CcAihmH8qpxgDaKYmvOin/zkJ/8EgMsvvxwLFizA448/jvPPP7+kHbCSaW2e56HT6RR9kLLZLAwGgyqpO57nIUlSm3XXTCaD559/Hmup8aA0LT9bUURRu+dEIgGj0Vj2IohwXq1WK8LhcMFdoKIoIhwOw263d8nADLSsQSu5o7z44ovRp08fvPbaa9i4caMixywUhLtMRGLyO/xJxoBkQ7oaLBaLrAfQVWC32+VmPiUhSRL279+PX//617jooovgdDpx4YUXcjqd7pfkNQzDRBmGOQhgJoCbaJrug2a1sEcYhvmWYZgQgE8B7KZpeqSiJ1jDKEbC54x//OMfDgBYtmwZJElCPB6HXq8vejKSJEnRhjCe5xVvBlMzpZ1MJtsUHSECI2nKhq3iUPBiy9dwIrB4B/D/Tul490yawPx+ZRajFEXJtJpYLIZgMAiXy9WuOEUsFoPBYOgUhhYdoZTATEACdDgcRjQaVTR9v2LFCrz66qtYt24devbsqcgx2wPpqCeGFa09pxqNRr5eNbTsqwmy+AiFQjCZTF2itq7X62E0GpFKpRRx7CM2oMlkEj/60Y8wZswYvP3224QOaHnzzTevpWn69wzDHAEAmqb1AGYDOMIwzEGaph1o7mUiq87rAHwDoHh7wy6KsqhUFEWVRKUSRREURSk2eSldb5YkCdlsVpXgnMvlwHFcq/VfjuPwwgsvQBAENPafi++a449BIbtnsnhqyy6yHOTvokOhUJviJel0GrlcrsWuq7OinMBMQFEUPB6PXAJQagf9wAMP4KSTTsIFF1ygumYyy7IIBAIyZay9e8dgMMBqtXbJ+rNer4fZbO5S3Gci61nO7pnQ6ZqamuQekldeeQW//vWvZQlhr9eLiy++WNBqtXeR933XnW1BcxYWAG4DMJqm6cU0Tb8C4M8APmQYpmvdSGWgKCqVTqfjAWDgwIHo3bu3zJktdmXZ2WlU2WwWOp1Old1AKpWCxWI55jPjeR4vvfQSstkspl18NV5jdODaeIbI7rmpnXmadNaqtWMlu2ifzweWZREKhVrwoomhRS0IjSgRmAlIgM7lcoo14eh0Ovne+NnPflb28VoDyXLEYjF4PB7Y7faCvjfCcS7WIKMWYLfbq64/ryR0Oh1MJlNJ3eiCICAej6OpqQk8z8Pj8cDr9cJoNLY6T95yyy12rVZ7A03T+Wm7+wBcSdP0QwB8AIYAuBiAC8B0hmEeK+W6uioKjar9APRcsGDBy8D3fs6/+c1v8Omnn+Lw4cNFDapkvVkQBEiSpGggVSulLYoiMpnMMfQpURTx6quvIhaL4eqrr8a/t1jQ0Zze3u5ZFEXE43G5gUdNEGMIk8mEQCCATCZTE4YWBEoGZgKNRgOPxwOWZRWj5TidTrz11lv4n//5H0WOlw+e5xEMBiEIAvx+f1ELXZICTqfTXUrAA/heZasrcZ/tdjvS6XTBAkMkCxQIBCCKInw+H9xud4fPSn19PWbOnJnTarV3kt8xDPMlgJsApAHMRXP8OY1hmLMZhll3XJCkJQrt1l4H4OkNGzZ4xowZc9/69evlSf+VV17B0qVL8fzzz4OiKGg0GjllrdFo5CCcHyTS6TQ4jlNElJ1lWaRSKXi93rKPBajrCZ1IJCAIQovrliQJy5Ytw+7du3HDDTfA5XJhxovAjkDHxzvBD7wz79jfx+NxCIIgNylVCsRLluM4OBwOOJ3Oio5fLNLptGxXqUazmiAICAaDbfpBl4rnnnsOuVwO111XHvOEND2R8ofFYil5MceyLGKxWEk+wjzPg2VZsCwrN5kRta5sNisHEkmSWvwc/TugOR1NbBMNBkOL/zeZTDCZTDAajdDr9QVdqyRJCIVCMJvNin6H1QRx4Wrv+czlckgmk2BZFhaLBVartej58NChQ5g5c2aS47heDMO0sP6iadrCMEy6hNP/r0Gh25r5AJZOnDjRBTQ76eTj7rvvRjabbfGgiKIo/wtADtQajQbZbBYajQapVAoajQZarVaW3ix2clC63sxxnHw+SoI0UBDpR4IVK1Zg+/btuO666+Sg3VrALRS5XA7pdFqxJrBiQCbAbDYrP9SdtUNb7cAMfO9mFQqFoNFoFHPf0mg0uOmmm9C/f39Mnjy5pGOIoohYLAae5xXJGpDvPRqNyqWMXC6HWCyGeDwup8zJ/8fjcaTTabAs22IXp9FoWgRRg8HQgolBFv75P+T3QPN8wHGc/EP+n8xPBFqtFg6HQ/6x2+0t/t/hcMBqtcrKYSRAd4XmMJvNhkAgAJvNdsw8x3EckskkOI6D1WpFXV1dydfcu3dvTJo0Sfjggw9uA/B78nuapimGYdI0TVMAcLzO3DqKyTnqKIoSgGY/Z2KiQFEUxo0b1+4ujQRpoi6Uy+Wg0WiQy+UgCIL8Q9LTpN6r0+nkn7YCN8/ziloOZjIZVSwMM5nMMW5G69evx6pVq3DVVVehvr6+nXcXjng83upDVwmQoNyzZ09ks1mEQiE4HA6YzeZOVXeuRGAmIJrlJEArIVt65ZVXYvfu3bjooouwdu1aDB5cnLgSyXAYjUb4/f6yvptcLodQKIRAIICmpiYcOXIEqVQKiUQC6XTzxkiv18t0LIfDgd69e8vBjwRiYh2qNCWSgBjjkHs0nU7Li4R4PI5IJIIDBw4gHo/LDY4kre10OmG32+HxeNCrVy85tduZ7ulioNVqYbFYkEwm4XQ6IUmSHJRzuRxsNhtcLpciC5E77rjD+cknn/yMpun/YxgmBXwfjI8H5fZRaFp7NYDn77jjjuEPPfTQLXfeeSfMZjMGDBiAxsZGvPzyy3jllVcKHjQcDsNisRxT1yXBmwTw/B8AcqDW6/XQ6/Wy/rNSKWiS0vb5fIrWSlsTHdmxYwdeffVVXHLJJRg2bJgi4xDnpHIn3FIgCIIsdkICEM/ziEQi8uTcGXYdlQzM+chms4hEIorVtiVJwty5c8FxHF5//fWC35NKpeRJuZhFKOGrNzU1tfgh0qVOpxN+v1++vu7du8Pj8cDpdKrq6qYGSEdyfuAOBoNoaGhALBZDJpOBXq9HXV0d6uvrUV9fL/93Z/cmJxAEAU1NTbDb7bKCoM1mU2Uhff3118c+//zzX+/atesRRQ/cxVFocE4AiDudTl0sFqubOXMmMpkM9u3bJzfUfPll4fS0QCBQtMpUfsDmeV5OV7EsK6tfkRpTqTcXUT1TOiWczWbl41IUhf379+P555/H9OnTjykRlApJkhAIBOBwOCpuGk/qciaT6RgFovz0qcfjqWqDGAnMXq+3KudB6rs+n0+RxSTZ6RTSuyEIgmz56HK5Orz+dDqNw4cP4/Dhwzh06BCOHDkCnudht9tRV1cn//j9/mN8nEmtsispbAHNTAuitx8IBNDY2Cj/BAIB5HI5OBwO9OrVC71790afPn3QrVu3TrEozQfpNQgGgwAAv9+v6gJq48aNuP766xsymUxvhmFyqgzSBVFMcF7KMMxqmqYfZhhG/tvdd9+N5cuXY/PmzQUP2tDQoMhuN51Oy+YRpL6Uy+WOaQop9OGIxWJyKktJkHqVxWJBY2Mjnn76aUyYMKHkemFrSCQScgCsNIisZ1upPlJvJ17S1dhdVDswEySTSaTTafh8PsUm7X/9619IpVL40Y9+1OrfSbOWxWJp1d6RSLweOnRIDsihUAhGoxG9evWSg0337t0Loua1t1irZZAFsN1uP+YeJpmFhoYGHDp0CIcOHUJDQwN0Oh169eqFPn36oHfv3ujVq1fVHNkIWySZTEKn08FisSAajcoudGpi9uzZiV27dt24a9eu6ovF1wiKCc5hh8Nhjsfj/hkzZoBlWezbt0/WTS505yxJEhoaGhTRwY7H47KwO4Eoisc0hWi1WtlQva1gLUkSmpqaFE938jyPUCiE+vp6pFIpPPHEExgwYADOP/98xVaqJKWsdDq+EJDdYCFduqTWaTabC+bRKoHOEpiB78VhcrkcPB6PIp/BW2+9hdmzZ+Pdd9/F1KlTW4yVSCSQyWTgcrlaBIVoNIqvvvoKX3/9Nfbt24dsNgu/398iGPt8vpLPr9b01AsFaXqrq6vr8LPJZrM4cuQIDh48KAfsXC6Hbt26yTvrvn37qr6AEUURqVQKqVRK1tcmWUsiw6s2s+KDDz7Az3/+892JRGLo8VpzYSiGSvXsT3/606H333//bT/60Y9gMpkwaNAgHDhwAEuWLCm45kzoJUo0QJE6bnsrUdIIQjo2OY6TpezyKRUcxyEajSper41Go9BqtTCZTHj22WdhMBhw5ZVXKrpSJSYESu/4OwJZeBQzARNpSI1Go1jTSXvoTIGZgBhHkE5gJe633/3ud/j73/+ODRs2oG/fvsjlcohEItBqtXC5XMjlcti/fz++/vprfP311/L3NnDgQAwcOBB9+vRRvBxCMlvlBPnOiFJNJIiZzsGDB+WfZDKJXr16YciQIaBpWtH5RxAEpFIppNNpOYtx9DNAFvZqS7CKooipU6emv/nmm1kMw3ys2kBdCIUG55MBLDWbzc5MJuMcPHhwC23tJ598Ev379y9oQKXqumQHXqxrFOlMJF2boijCaDTK6XAluNcE+Tf+kiVL0NDQgBtuuEHRtG4xK3klIYqizOEtVoWM3DvZbFbVOjRpfupMgZlAFEU59avEokoURcyePRsulwuPP/64XOc/ePAgvv76axw8eBA6nQ79+/fHoEGDMHDgQEXv9dYgSZIcyCq9cFQTZFFaDs0I+D5bt3v3bjAMgyNHjsDtdoOmadA0jT59+pR0/HyOstls7pC9EYvFZA93NbF48WL85S9/+eyLL76YqOpAXQTFBOe3LRaLJZ1Ou46mUj366KMYMWJEQQNmMhlkMpmya6OkE5jouZaKXC4n6wkTPV2z2ayI4D0RHdm8eTPWrl2LG2+8UTGxFKD9GpiakCQJ0WhUVocqFYTOkt/hrRQ6c2AmIFkkQjcrF9FoVE6j7t+/H01NTejevTsGDRqEQYMGoWfPnhWn2JEFaldLbxci5FEsEokEdu/ejd27d+Prr7+GXq/HkCFDMGTIEAwaNKjDZ4TneSSTSWSzWVitVpmn3RHId1TuYqMjZLNZnH766elEInEKwzDbVRuoi6DiVKpUKiWbIZQDIheohAoWz/MIh8Pw+XxyBzjLsrKoRiniA4SW1dDQgDfffBNXX301+vXrV/a55oM8iErVLgsFSZUpka4kFCO73a6YAlMtBGYCsgvzeDwl68OHQiFs2bIF27dvRygUAkVRcDqduOaaayquEtcaumJ6W+0+D47jsHfvXjAMg927d4NlWfTv3x9Dhw7FCSecIGerjuYoW63WVrX7OwIpv6md4fj73//OP/XUUy9u3rz5B6oO1AVQcSpVa01cpSAajUKn0ynSTJFIJCCKYosFgyRJcpAm9Amyoy5kgkmlUti/fz+WLFmCmTNnYvTo0WWfZz6q1QTGcZy8kFFq3Fwuh3A4DKPRWLYeeC0FZoJSKFbkmduxYwcaGxtRV1eHYcOGYeTIkXj99ddx66234osvvsAJJ5yg8tl3jK6a3iZKWmozJERRxOHDh8EwDHbs2IF4PI5BgwZh2LBhMlWLBOVyG/jU3j2HQiFMnjw5w3FcH4ZhgqoN1AVQcSoVUSYq1y2pFK50qccSRVHW/eV5HiaTSZambIs6tHfvXrz22ms4+eSTce6555Z9jkcjGo1Co9GoXifKB6HcEGEJpY9NPIRLbRSrxcBMkEgkkM1m2+UGC4IAhmGwadMmfPXVV+jWrRsGDhyI/v37o2/fvvI1S5KEa6+9Fps2bcLatWs7hZc2mfyrwShQC6RmTHQWKgFRFPH111/jyy+/xNdffw1RFHHiiSdizJgx6N69e1nHJoJBaneP/+QnP0m8/fbbf925c+e9qg5U46g4lSoUCsmyfaVCSToWOf/6+vqCjiUIAjKZjCxNaLFYYDabW+x4kskk/vOf/8DlcuHyyy9XfCVKKEmlmAyUCrL70ev1qi0IiG0h4WsXUx+t5cAMfN/BTRYn+fdiIBDAxo0bsWXLFoiiiJNOOgnDhw+Xszmt0dKSySTGjBmDa665Br/61a8qfTmtIpVKIZPJdClxknQ6jXQ6rfo1Ea2AZDIJrVYrd15/9dVX2LRpE/bs2YNu3bph9OjROPHEE0vq4SDlPbWbSxmGwaWXXhrOZrPdGYbpGn6cKqDQWWwngGcXLFgw9P77779t6NChMJlMuOqqq2QqVaEQBKHsphSe5xXT4CX2kIUeizwYVqsVPM8jnU7L5vRkN/3222+D53lcdNFFigdPEsAcDkdFlYeI3rCaaUlSK00mk3IdtpBAm0wmZWeyWgzMwPfWi6FQCOl0Gnq9Htu3b8fGjRtx+PBh9O/fH9OnT8fQoUNlJ7b2Mhg2mw3vvfceunXrVuEraRsWi0Ve2HYVhyez2Sz3fqhlM0uCssFgOGaXPnToUAwdOhTxeBybNm3CZ599hvfeew/Dhw/HmDFj0LNnz4LnNr1eD51Oh0wmo2q2haZpDB48GNu2bZsL4DnVBqpxVJxK1dDQUHZdI5VKged5RaggRPKynG5horyTSqWwbds2rF69Gtdff33ZaabWUI3dB1GYUkp2shAQ84SOGqW6QmDORzgcxsqVK7Fr1y4YDAaMHDkSo0aNgtvtljniJJAX8l0IgoDf/va3uO666zBo0KAKXEH7IA1wavNqKwk1NO3zOcpGoxE2m62gbndRFLF3715s2LABu3fvht/vx/jx43HiiScW9HwcLTWsFj7++GPccccd27PZ7InHRUlaR7GuVBLQvMqXJAkURck3USEgNpLlfukcxylCvSEmG+XWi0hDRjgcxqpVqzB58mRQFIVYLAar1apY0CD0tUoGZqLJ7Ha7KzqZEv/YcDjcJtWqKwXmb775BqtXr8b27dvRvXt3TJo0CWPHjpUnZJZlEY1G5axNod+/RqMBwzCYO3cuPv/886pJRxLo9XpYrVbEYrGqSM2qAaPRCI1Go8iOM5fLyQtws9lcdI1eo9HI1LlkMokNGzbgww8/xEcffYRx48Zh7Nix7Z6jwWAARVGqZQIIzjzzTFit1t7ZbHY8gDWqDVTDqCiVitjLlasORpowyuVNFmMcUMix/vWvf6F///648MIL5XQUWflardayFwGEW6y21B4BcdMiQgbVAKFaHa3JTQJzJXfzSkOSJOzevRurV6/GwYMHMWzYMEyYMAG9e/eWG8Q8Hg8SiUQLg5diEY1GMWrUKFx00UX429/+psKVFIdqmrSohXJ3nPkcZYvFIi9OlUAul8OWLVuwZs0aRCIRjBw5EhMmTGhTcyGdTsvZOTXxr3/9K/vwww+/vnXr1stVHahGUVEqVTabRSKRgM/nK/mEiQSeEs1gROGq3AlCEAQ899xzEAQBF110UQtuKQnSqVRKpn6R1WkxUEqVqBh0ZGhRKZBGFbvdLvvQkiacWgzMuVwOmzZtwpo1a5BMJjFq1CiMHz++xX1DAhjLsrKncDnfO8nofPrpp5gwYYISl1EWiLJdJZsa1QZRfCumnk44yhzHFSUcUgokScJXX32F1atXY9++faBpGqeddhr69OlzzOsq0YUejUZxxhlnZHie78cwTJNqA9UoiskFrli3bt1qmqYffvDBB+VfEipVIRBFUZFmsHJsIQmIBaUSab4PPvgA4XAYc+bMOaZhSqPRyKlIspAhvzMajQVdRzWawDKZDLLZbKcQjtDr9fB6vQiFQkilUpAkqSYDMwnKn376KQBgwoQJGD169DGLQ2LpJwgCNBqNnDYtB6effjree+89xSxKywUxoUkmkxWlA6oJu92OSCTSId9YkiRks1kkk0kIggCbzVaRBTBFURg8eDAGDx6MhoYGrF69Gs8++yx69+6NSZMmoV+/fqAoChRFwWq1ykYZasHlcmHatGncW2+9NR/A71UbqEZRUSoVuRnLScsmEglIklT2A51KpcBxXNkKSgzDYNGiRbj88svh8/k6PB4RNyHSp8S4o70Hs9IKS6UYWlQCsVhM5spWKrWvBIiE68qVKyGKIs444wyMGTOm1Vpivv81uZdCoZBi/OBMJoPXXnsNV155ZdnHKhddUdqzPVMM8uwT5oPNZoPZbK7q4jcajeKzzz7Dpk2b0KtXL0yePBn9+vWTd89qN+7t2LEDl19+eSCbzfY47vXcEhWlUilFo1JCh5hl2bKbN2KxGN544w1MnToVDoejoHQWRVGy0hjp8iSi862lu0kTWKVSy0QMxOFwdKoJk9TjevfujWg0ing8XlHbyVIgCAK2bNmClStXgud5nH766S2avI4G4a+bTKYWtUuyI1NicbZv3z5cf/31sFgsmDNnTlnHKhdELjIej1dcglYt2O12hMPhFk17JBOSTCZlv/hCs2Zqw+VyYdasWZg4cSI+++wzvPDCC+jduzfOPfdcWCwWpFIpVTMbJ5xwAvr06aPbs2fP+QAK5+T+F6CiVCoy8ZQTXBsbG8vuziV162IdrY4+BrGAnDNnjqw1XSzyd9JE8Ss/lRSLxSBJkuoOQuRclDC0UBrEk5iksgVBkOU+O2OAFkVRDsrfif1j7NixbaYIJUmSRVSObnwjf1fye7n//vvx5z//GVu3bq06D5o0HZJdZFcA2T1bLBZF+k0qiVgsho8++ghbt27F8OHDcdJJJ2HQoEGqnvMbb7wh3XPPPZ9u2rRpkmqD1CAq6kpViP9yeyBpsELVvNpCOp0Gy7JlUTk+/vhjbNy4ETfffDMymQysVmtZkwtZXScSCVmFS5KkinJClTS0UApHB2YCYrnY2QL0vn37sHz5csRiMZx++uk45ZRT2q3bEaqaJEnt0tWIRacSDmSCIOCss86Cw+HA0qVLq/7ZVUPxTk1ks1k0NDTIxjmFcpQ7E7799lu8//77OHjwIEaPHo2zzjpLtc56lmUxYcIENpPJnMAwzD5VBqlBFLr9fAzAH+bPnz/8oYceumXmzJktqFT33HNPQVSqctPaSjWDEVWwUrFv3z589tlnuOqqq6DX65FMJsu+cSmKkqVAU6kUAoGATPOqRGDmOE7upK/2ZE3QVmAGmhvtSJMYgKoH6HA4jPfffx+7d+/GuHHjMGnSpA6DKOEuW61W2Gy2ds9fo9HA5XLJEqrlZI60Wi2effZZLFmyRBHdgXJhMBi6RHOYIAhIJpPIZDLQaDSwWCw11RuRj+7du+Pqq6/Gzp078eGHH2Lbtm0488wzMW7cOMXnI5PJhIsvvph65ZVXfgjgZ4oevIZRMSqVEnrYSjhalZvSTqVSeOyxxzBq1CicddZZcnpVaTnCVCqFUCgkN5cUIzxRLIivsBqGFqWivcCcj2rvoFmWxcqVK7F27VoMHDgQ5557boflDUmS5OtzuVxFZZKSySRYllVMiIYEE7/fX/axykG1XNaUQC6Xk78XwlEWRbEiOtVqg1jfHjx4EJ9++in0ej2mTp2KE044QdHr+vrrr3HxxRfHM5mMj2EYXrED1zAqRqUiK/RyvlCe58sOgtlsFgaDoaTALEkSXn/9dbjdbkyePBm5XA4cxylenxVFEclkUl5AxONxpNNpOJ1OxRWeSD2TNKl1BhQamIHvd9DBYFARK9JCIYoiNm7ciI8//hhWqxXz5s3DwIEDO3xfLpdDJBKBVqstKY1rtVplGo4S13rbbbchHA7jjTfeqGoQIZr1pDmsFnA0Rzlfg0Cr1UKv19e8jjh5pgYNGoSRI0di9erVeOONN7B69Wqce+65x3CkS8XAgQMxePBg7datWy8A8KoiB61xVIxKxfM8IpEI6urqSjpRsoIrt/5ajmXl2rVrsWLFCtx8881wOp2IxWJyp7WSiMfjsvgH8D0vMhaLQa/Xw+l0KpZaKsSqsFKQJEneyRXLYxYEAaFQCBaLRXU1s8OHD2PZsmVIJBKYMmUKRo8eXVCQJVkmIqZS6udNdpkd6Y4Xgr179+LEE0/EU089hblz55Z1rHJBhFfUWIQqBUmS5KCcy+VkH+XWvn9SS6/13bMoii1oVclkEitWrMDGjRtx8skn4+yzz1ZkAfLmm2/i3nvvXbtu3brqq+R0AhS6bN8J4C8LFix4GWh2QjnxxBMxcOBAXHfddRg8eHCHByi33iwIAiiKKusYJMiV8uCHQiF88MEHmDlzJpxOp2x2ofSqOJfLIZ1Otwj4FEXBZDKhrq4Oer0egUBA5kqWA5ZlkU6nq64ABrRM9ZYiMKLVauH1epFKpQrWei8W2WwWb7/9Np566in07t0bt99+O8aOHdthYBZFEdFoVNZFL7dEodVq4XK5EIlEIIpiyccBgAEDBuDee+/F7bffLtfvqwWySyMMhc4E0rAZDAYRi8VgNptRV1cHm83W5vdvMBhkl6dahkajkXthgGbHs1mzZmH+/PkIBAJ45JFH8OWXX5b9nZ177rngef4kmqb7KXDaNY+yqFQHDhyA2WzGokWLOqRSpdPpslLA6XQa2Wy2LNEQIgBQLOVJFEU89dRTcDqduOSSS0BRFJLJZAuhCKXQnogBQS6XQywWgyiKcDqdJe2eSMajLVOJSoIEZlJLLWcBRvTbHQ6HotQchmHw9ttvw2g04vzzz0fv3r0Leh/JGBkMBsUV3qLRKACUXVYRBAHnnHMO7r77bkybNq38EysDhKFgNps7RTo4n6NMUZQs91vo4qpSLk9qoy3fe0mSsH79erz//vvo378/Zs2aVVa55X/+538yr7766v3btm37rRLnXcsodKY4H4BDEAQ90JwKTSQSMBgMiMfj+OCDDzo8AJEiLBWkU7scEKeXYrFq1SrEYjGcd955siNXKpVSPH3KsqycKmsPOp0OHo9HdsKKx+NFrVolSUIkEpElRKsJEphJar3cdD35bGKxGFiWLfv8EokEFi9ejMWLF2PMmDG46aabCgrM5B4JhUKw2WxwuVyK04QcDgey2WzZ16nVavHhhx9WPTADkMtEyWSy7KxAOSDfX1NTEzKZDJxOJ3w+X9GKXqS/RYl7sZrQ6XTQ6/XHXAdFURg3bhxuueUWcByHf/zjH2Xtoi+//HKzTqe7iabp2ufUlYlCP4C7AYTNZnMcaP5CdDodunXrht69e+Mf//hHhwdQikZVKkhKu9imp4aGBqxYsQLnn3++XKfOZDLyzaoUiKiLw+Eo6OEn1Cu/3y/XILPZbEFjxeNxaLXaqu9MlA7MBHq9Hh6PB9FoFBzHlXxuGzZswKOPPopUKoWbb74ZZ555ZkHnSFTWCGdcLeN6jUYDt9uNaDQKQRDKOhZFUdi1axfOPPNMJJNJhc6wNORTqyoNot/Q2NgoZ+q8Xm/Jil5kt61EGaraIIphrcHtduOaa67BWWedhbfeegsvvfQSEolE0WMMGzYMfr/fAGBKmadb8yhmdWIirx81ahSGDBkCQRDAcVxBK1xRFEveOUiSVHZw5jgOOp2uqACQy+WwZMkSnHzyyRgyZIh8LqlUSvHAlkwmodPpil48aLVauN1uOBwORKPRDut1pDzgcrmqmmY7OjArvas0GAwyLziXK06yNx6P47nnnsMHH3yAc889F9dee23BpZBsNotAIACdTlcRWhBRoorH42Ufq3///ggGg/j1r3+twJmVB4fDgXQ6Xfaio1AIgoB4PI6mpibkcjl4vV5FGu6AZpMP0khWyzCZTMjlcuD51plOSu2ir776apvJZLpZiXOuZRQzI65Yt27dvQDw4IMP4rHHHsPy5ctx6qmnFjTJl7Nz5nkeWq22rAk8k8kUHfhWrFiBbDbbIt3HcRwkSVI0HSwIQtkatkSPWRRFBAKBVicCnucRj8fhdrurqsSkdmAmMJlMcDgcCIVCBU/y27dvxz//+U9QFIVbbrkFo0ePLtg5LJFIyN7ThWZAlIDdbgfP82U3HhmNRjz++ONyg081odVqYbFYStp9FYNcLodoNIpAIABJkmTzGiWzYvm751oGydal0+l2X1fuLvqCCy7QCYJwHk3TBTX0dNUUeDFUqqTVajWmUin3xIkTIYoijhw5UjCVqrGxET6fr6QAnUqlwPN8yY0vhIZVzE7m8OHDePrpp3H11VejX79+8u9L8WztCET1SSmOLqHs5CtPEflHm82mWpq1EFQqMOcjv9msrfGy2SzeeecdbNu2DWeffTbGjx9fcHAVBAGRSETWvq6GlSXHcQiHw4pIvV5zzTVoaGjAe++9p9DZlQZC4VEjA8HzvGymQoRD1PzeiMuTx+OpOSnPfLTVGNYWotEoli5dim+//RbTp0/HSSedVND75s+fH1q5cuVvGIb5Z2t/p2maAtADwBkATgQwBEAvAP8D4OOu4HBV6MzYCMA9ZMiQrQCwf/9+rFq1Sjay6CioSJJUVkOYEiltrVZb8AMuCAKWLl2KMWPGtAjMPM+D53lFg1s2mwXP84o2l5nNZvj9fnAch1AoBJ7nEYvF5BRotUDq6pUMzABkbeNIJNJqiu3gwYN47LHH8O2332L+/PmYMGFCwYE5k8kgEAjAZDLB4/FUzWNayfT2/fffjyeeeEKBsyoPxPdciWsiIM8EWRDX1dXB4XCo/r0Rj+Ra3z2TXptCszQulwtXX301pk6dirfffrvgXfSVV17pNZvN89t5yQ0AHgJwNYDeALah2dXqdwCuL+jkOjkKXY5qAMR27dp1MtA8yZKVZigU6vCGI/XmUtN8RIGnVBSrpb1q1SqwLIupU6e2+D2pNSuVrpQkCbFYTJUUqFarhcfjQSqVwjfffAOdTocePXooOkYxIIGZ47iKBmageWJ0Op1yZzvROxYEAZ988glWrVqFU045BVOnTi14AUeuhxioqGlKXyjsdjsCgUDZ2vFEKOjtt9/GaaedVlWHMqvVKnuvl/oZk2ZQ4idPskeV7rmwWCxoampSxDq3miCNYYUu9CmKwtixYzFo0CAsXboUjz32GGbPnt2uPsbpp58OiqKG0DQ9iGGYr/L/RtP0YwBmA/grgLcYhtmV97cAgFsB/Kv4K+tcKHSGrAeQoyhKApq/HJZlodfrYTQaO7zRyrkZRVGEIAglp7WIJWOhk1UwGMTKlStx3nnntagrC4KgiAd0PlKpFLRarWqymRRFyUIIGo2mauIO1QzMBBRFwe12I5vNIpVKIR6P45lnnsHmzZtx5ZVXYtq0aQXfYzzPIxgMQhRF+P3+ThGYge8XIYQDXw4EQcAvfvEL/Pa31aWbknptKbXnfOGQRCIhS2yqqVPfHo4W86hVkMawYhstyS56woQJeOmll/Dxxx+3OR/p9Xqcf/75IkVRV+f/nqbpAQD6AZjOMMzf8gPzdzAA2EnTdO2ufr5DobOkBOCd+fPnLwQAmqbh8/lw33334bbbbuuwC1EUxbKawcpxouJ5HhRFFZQWlyQJy5YtA03ToGm6xd/S6TRMJpNiK17iYON0OlU1tIhEIvB6vaivr5flEYt9qMpBZwjMBBqNBh6PB7t27cLjjz8Oo9GIm2++GQMGDCjo/ZIkIZ1OIxQKwWq1qsJdLhdGoxFGo7HsVLBWq8Ujjzwid9xWExaLBYIgFEwVzOcop1Ip2O32kjjKasBqtSKdTleVw10uKIqC2WzusDGsrfdOnDgRV111FdavX4+XXnqpTQ74ZZddZjcajTd+V18mSAE4BUCLm5Km6RE0Td+L5t30iwzDVKbNX0UU2hCWBmDSarW8IAgGrVaL4cOHIx6PI5PJoLGxEQzDtPn+chq6SCqqVOs1MkkV0gm9YcMGvP/++7j11ltb1NFLaSjrCNFoFBqNRjWLPEmS5LoaGYMEl0QiAafTqbq5PQnMPM/D4/FUPZBJkoTPP/8cH330EUaOHIlp06YVvOsVRRGxWEy28ezMTT2kY9/tdpe9q7/mmmuwd+9erFy5sqrfXzqdRjqdblcDXhRFpNNppFIp6PV62Gy2TpPVyIdaTnaVBM/zZbtuRaNRvPzyy+B5HnPnzj3GGU2SJEyaNCna2Ng4i2GYVeT3NE2vQnNwXonmevMJAOoApAE8wTDMcpqmKYZhappYXujTJgBYcv75578ONAuUL168WKZSdZSWLSetzXFcyRNhMSntRCKB999/H+ecc84xDW7pdFpODysBjuOQzWZVNWggacD8ayFNKR6PB/F4vGhlsWLQ2QJzNpvF4sWL8dlnn2Hu3LmYOnUqotFoQTsYjuMQCASg0Wjg8/k6dWAGIC/6otFo2d/vX/7yF/Tp06fqjUxmsxmiKLa6e87nKJP7rbP0AbQGUkevZVESvV4PjUZTcDajNbhcLlx//fXo2bMnnnjiCeza1TJDTVEUrrjiCrNerz+6wes6ACEAvwcwFs0Ny6QZ7D2apjW1HpiB4qhUEYvF4kin07K2djweh16vRygUajf1FY1GS+4UbmxshNfrLSkwFrO6W7x4MVKpFK699tpjtGOVdMqRJEmmNKm1c2VZFrFYrF3qGlGxAqA477mzBeampiYsWrQIOp0Ol112mWxJSHbCHo+n1fuDpEdJ+UHtTIOSIJkTg8GgGEWP2L5WC0Tj2ufzgaIo5HI5pFIpWcPAZrPVhBc0mQPsdnunsWktBaRRr1x/AUmSsHbtWrz//vs444wzMHnyZPk+O3LkCKZPn57kOM7LMMwx9VOapm0AKIZh1CXEVwGFzpr7ATjQXHuWPziKohCPx9GrV69231wqjUoQBEiSVPKum+yaO5pQ9uzZA4ZhMGvWrGNem81m5cYqJZBOp2WXKTVARBXcbne7nxupvxKXq7ZUf4oF6UDvLIF5+/bteOKJJ9CrVy/ccMMNLbyCSbq/tWYjQRAQDofBsiz8fn9NBWbg++awVCqlSI/B//t//w9/+MMfFDiz0mEymeQFUyQSkT28/X4/XC5XTQRm4PsMVq03hpnNZrAsW3b9nKIoTJgwQa5DL1y4UK5D9+zZE/369csBODv/PTRN30vT9DkMwySPDsw0TVPfBe2aRqEzpwhA0Ol0PNBsjN27d2+YzWY5vdEeSk1rl9sMVojRRS6Xw7vvvotTTz21VYlGJelTRLdXrSawfEOLQhYTxGSAqGiVK85PAjPZjVZbhezTTz/Fa6+9hnPOOQcXXnjhMelo0sGdyWRa8DZZlkUgEIDBYFBU87vS0Ol0sFqtivCETz/9dPzpT39CY2OjAmdWGniehyiKaGxshFarrRhHWQ2YzWbwPF/R5kylodFoYDKZFLPE7N+/P+bPn49kMol///vfCAQCAIDLLrvMabVaj05trwHgyf8FTdODaZr+J4BPATxJ0/SlNE13TmPwAlDo7NkPQM98P+cJEybgN7/5DT799FMcPny43TeX2q1djvhILpeDJEkdvn/NmjXgeR4TJ05sdfxcLqfYrikej8sLGjUQi8VKMrQwm82yk1OpAv2dKTATEZlVq1Zh3rx5GDduXJuLIZJBiMVi4DgO8XgcsVgMbrcbdru96t295cJmsyGXy5W98LrsssswfPhw3HPPPcqcWIEgHOVgMCgvPO12u+z2VKsgUphdYfespF+1y+XCddddh169euGJJ57Azp07MWPGDIrn+fNoms6vi77NMMzLNE33omm6+3e/uxOAH8CjAGIAfgrgRsVOrsIo9O5mAFx32WWXhYHmB/XCCy/E8OHD8eyzz6Jfv36Ix+NIJpNyDYgoX+VyOYiiWNIkV47wAKlDtTduPB7HypUrce6557Y6TjKZVGzXzPM8WJZVrP53NPL9sks5X4PBAJ/PJ0t/FmtB2VkCM8uyeOGFF7Bv3z5cf/31GDhwYIfv0ev1sFgsOHz4MHieh9/vr7qVplIg2ZFyOe4UReFvf/sb/v3vf2Pfvn0KnmHryOcox2IxWCwW1NXVycG5q7g8ZTKZmqZVGY3GkjjP7UGv12P27Nk466yz8Morr2DLli0YMWJEjqKomXkvI5PcjwBMpWn6dAATADwM4GWGYRYA+A2AHyp2YhVGoUWa+QCWTp482QkAp556aos//uIXv5D1m0VRhCRJ8n/zPI9UKiWnosgPcYgi/x4dUMp1omJZtkOa0vvvv48ePXpg+PDhx/yN8CpLpXDlI18JTI3ARQwtyuURa7VaeL1eRKNRhMPhghrFOlNgjkajePHFF6HT6XDDDTcUvBAiNB2iGlXru+WjYTKZkE6nkUwmy1ocnn766Vi7dm0LSVulQYJyMpmERqOB3W4/xq7RZDIhkUiA47iaXkTpdDoYDAZkMpmapVURznMmk1F040FRFMaPH4/6+nosXrwYffr0sR04cOA2AK8AAMMwIk3TIwGcA+BJAE4ALoZhPsk7jAvA1zRNmxmGUW57XyEU00GhoyhKBIAhQ4bI9VOKojBmzJg2v5hsNiv76wqCIP+QVBvZWWu1Wuj1elm7lTyMpaTDc7kcBEFod9d94MABbN++HTfddFOrk3EqlYLZbFYk2GQyGUiSpEpTEem6djgciqTLiUdwPB5HKBRqVy+6MwXmI0eOYOHChejVqxcuuuiigjIuoii2EEjR6XQIBoNIp9M1O1m2BYfDgWAwCIvFUlaNdsyYMTh06BDC4TBOPvlkxc4vn6Os0+ngdDphMBhafTbzXZ5qOTgDkJ23qiEnqhTMZrNcclBS2pjneRgMBjgcDrz77ruIRCKTzjrrLM9HH30U/u5lewH0YxhmBwDQNL2PpulrAXwBQA/gZgD/rMXADBQenB8D8Icbb7xx+EMPPXTLzJkzYTabMWDAADQ2NuJ///d/8corr7T6RlEU5d1xe5M88Qkl9Ih0Og2e5xEKhWAwGKDX6wuuM3XUpS2KIt555x2MGzcO9fX1rf49nU4X7OHbHsgixu12K/7wSZJUFk2tLZBUaCqVQjAYbJXK1pkC8+7du7F48WKMHTsW55xzTkHnwvM8IpGInM4n73G73QgGg/I911Wg0+lkY4xyqS8PPfQQPvzwQ2zYsKHs710URaRSKaRSKRgMhoKFU8xms7x77qx85kJgNBpldkOtXgfZTJV7DaS/gDRk/uUvf8GGDRswZswYzJ8/H88//zy7Z8+ecwC8DAAMw8Rpmv6UpukXAbwE4P7vfk5AszbH0wA+K/sCq4RCg/MIAL985plndEDzZJjJZLBw4UK5RtkWCqFREXnN/MmQBHKTyQSO45BMJmVfZ6PRCIPBAKPR2OqxWZZtV+Bj/fr1SCQSmDx5cqt/z2QyiomOJBIJ+XyVRiqVgiAIZU+2rYHsTjQaDYLBYAtRB7IoEEWx6oF527ZtWLJkCc455xxMmDChw9d3xF3W6XRwOByIRCItgnZXgM1mk72+y7kff/GLX+Bf//oXXn31VVx66aUlHYPI15LekGLV9/J3z/n0uFpDvkdyrQbn/NR2sddAhKJYlkU2m4VOp4PJZELfvn1x3nnn4f7775dLi1qt1vTHP/7xanwXnL/D7QAuR7PZxUkAvkazK9WbDMOEyIu+EyapqeJ+MSIkSxmGWU3T9MP5Up133303li9fjs2bN7f6XtJBXKwaFiHp56etSKqDKGxxHAedTifrCRsMBlm6sC2/UZZl8fDDD2PKlCkYO3bsMX8nvqtKSB+Snb8SHrtHg/j3quF1ezRYlpW50waDoUVgrmYqbtOmTVi2bBlmzZqFUaNGdfh6URQRjUblBU17n1s0GgWAqjoyqYFCZDALwf/8z/9g8eLF2Lp1a1H3di6XQzKZBMuyMJvNsNlsJT8bRFbX6/XWdJZDEAQEAgHU1dXV7GKwGJ9novRGGocNBgNMJlOH3gXhcBhnnnkmy/O8j2GYFm3uNE27AcS+q0XXodnnuQeAKQCGA/gLwzBPlXudlUQxs/oZ48aNOwcA7rzzTrAsi3379smUpbYgimLRD05bzWBEDMRgMMBms8mvY1kW8XhcFi2hKKpNNaNVq1bBYrFg9OjRrY7Nsiy0Wm3ZgZmoZNntdsUDMzG0qJTwgslkgtvtRiQSAUVR0Ol0VQ/Ma9euxXvvvYc5c+ZgxIgRHb4+m80iGo3CbDYXVGIgNdpCuPK1BOKKRIJjqfjRj36Et99+GwcPHkT//v07fD3P83IamrhDlRuI8sU8ankRReabWm4MI6XLtpr0SINtJpORX2MymYoyj/F4PBg2bFh6y5Yt0wG8etSf9QBuomn6LQBDANyL5oawRwHsAPBLADUVnAt9OnYC+EtrPOfrr7++XV/OUgRIcrkctFptQelw0jDg9/vh9/vlhUJjYyNCoVALB5h4PI41a9bg7LPPbvPYRHSkXBDlHCVrwcD3KWWz2VxR6T+DwQCtVis3ylUzMG/ZsgXvv/8+Lrvssg4DM1kkRaNROJ3Ogr2zNRoNXC6XXFfvKiD9BOXqqrtcLnzxxRftBmZSQwyFQrKUaF1dHex2u2I7RKvVCpZlIQi1bUJE3KpqGUdznknpIhgMoqmpCdlsFhaLBfX19fB4PLBYLEXfBxdddJHLaDRe0cqfrgJwE5pVLFcB+BWA3QzD/IFhmN8AcNM03TGvshOh0LT2yQCWms1mZyaTOUZb+8knn2zzIW1qaoLH4ylqh1eqZitRDyJNXkenTj755BPEYjH84Ac/aHWC5jgOkUikLKcVch6BQAAul0vxbtJ8zepKBcj8GrPdbkc4HIbD4VB84VEoWJZFU1MT+vTp0+7rSIaBoii4XK6SMhgkBVtuGrizgQTLcs1X/vGPf8DlcmHevHny70hQTiaTEEVR1pFX6/OLxWLyoqNWQcppRFK3FiEIAhobG2Gz2ZDNZiEIgpyuPpoOVypCoRAmTZqU5nneyzAMCwA0TesALAfwd4Zh3qBp2sgwTJam6c0A/sAwzCKacZZWRQAAh0RJREFUpl8BsJdhmLvKPokKoZhli46iqGO0tQVBaFPlRpKkknS1S+36Y1lWbhIjxuYejwf19fVIJpPYtm0bRo8eLatBHb0wUUp0JJlMyg1rSoJlWWQymZKFRkrB0c1fRNIykUhUbaVvMpk6DMyZTAaBQAAmk6ldOlhHIFmUWldyOhpEyKNcAQyWZfGLX/wC2WxWtiQNBAJIJBKwWq3w+/2q04S6ikcyaQyrJZDSIqFeksYuh8OB+vp6uFyugvwNCoXX60X//v3TAM4iv2MYJgfAgu/kPL8LzFYADWju2gaABwAcY5zRmVFo1HwMwB/mz5+/EABmzpyJSy+9FPfddx9uu+22NiX9SPArJTiXsnpsyx5So9Fg1apVGDZsGEaMGAGdTodoNIpAICBPULlcDhzHlb0bzOVySKfTiq/iCzW0UBJHB2bygOn1elUDdDnpVkLxisfj8Hg8ZXMvya6bsAW6CvR6PUwmU9lWkLfccgt4nsejjz6KpqYmpNNpOJ1O+Hy+ipU/SFOokjKS1QBJC3d25TNJksBxHGKxGJqamhAON9OOXS4X6urqoNfrFdspt4YLLrjAodPpLj7q138HcCdN07fTNH0BgGUAugN4GwAYhvmcYZhfq3JCKqGYbu240+nUxWKxupkzZyKTyWDfvn0ylao1y0jCJa2rqyv4hCRJQkNDA7p161bUl5uf0j56MbBv3z48//zzuPXWW2XaBVnxpVIpOQVDdtrlQKl0YT6IxRzpbq0E2grM+SCWnA6Ho6zmoo0bN8JoNEIQBPTp06fkJjqe5xGNRmURCyU7X4lABrEr7AogXcKlsgkIR/mRRx7BQw89BIZhFFHUKwVKlaSqjVAoBIvF0umaEElAzmQyYFlWzkyaTCbodDr5Myf3VCFd26XiwIEDmDVrVoTjOB+hR9E0TQGYieZasw/ARwB+zzDMIfI+mqapWvJ5LqbVd8W6detW0zT98IMPPij/klCpWkMphhc8z7f4sgsFqSsfPSFLkoQPPvgAY8aMaRF48zu/eZ7HN998IwvsW63WklIxRPFMad5xLBaTHYYqAeJuBaDd2jZRfguFmumEpUwokiTBZDKhoaEB2WwWH374IQYPHgy/349hw4YVlIEg6dREIiEvFJSeGIg9XrkSmJ0JWq1WVqgqptuZlLLS6TSMRiNuv/12Wd2vWiDueNlstqY9ks1mM9LpdKcIzqR3gPTtEA5ye/RNIsms5vfQt29feDweNDQ0nIJmdyoAAMMwb9E0/c53dCrNd//KAbmWAjNQ3M457HA4zPF43D9jxowWVKpgMNjqzjmdTiObzRYVrJLJJARBKHoFHg6HYTKZjklLE/WoO+64o81dJ0lZulwueQKWJAlWq7XgepkkSQgEAnA4HIrelEQTuVKCGPmBuVBVM8LnJvWlchAIBPDll18iGo1Cr9dj1KhR7daXRVGUO6pdLpeqzTRkV5AvyFLrEEURTU1NBfHlj+YoW63WFu/J5XKgKKpqFo6ZTEbObtQqCHdbDW2EQlAqBzkfyWRSfh7Vwl/+8pf0008//cjOnTvvPvpvtbZDbguqUqlKoVGVUm8mK7yjA4MkSVixYgXGjRvXZmAmilGkNmk2m+Hz+eB0OuWu4EIccJLJpLyyVAqk0aIQAwolUEpgBr7fQUejUXBc4T0XGzZskP+bGKX4/X6cffbZGDlyJCiKkgM1Ob98cByHQCAAjUYDn8+neperVqtVxOGpM0Gj0cBqtSKRSLT5GlKeCgaD0Gg08Pv9cDqdLQKzJEk444wzsHDhwkqcdqswmUwQBKGmewMoilLUI7kQCIKAdDqNUCiExsZGWbWtvr4eXq8XVqu1qHncZDKBZVlVn5Hp06dbjEbj3Nb+1hUCM1CkK9Wjjz7qBIBly5YdQ6VqDURXuxjwPF90XZVl2VZT2gzDIBgM4sorr2zzvZlMRjbdIKAoSlYdI+IJyWQSNput1W5ukuZTcsWutKFFRyg1MBMYDAa4XC6Ew+GCFJsSiQSWLVuG7du348ILL5QzJWRBN2jQINhsNrz//vv47LPPMGvWLPmcJEmS7UmV2K0XA9K005XS21arFU1NTccsjPNlc61Wa7t1fIqiMGPGDPzpT3/CvHnzqqJ0le+RXMuiJGazGfF4XNX+EkEQZPZHLpeD0WiExWJRZCPQkSCJEhgxYgS0Wq2PpulBDMN8pcogVYaqVKpiaVSiKEIQhKIDemtd2mTXfMopp7RZq83fNbcFsiv0er3gOA5NTU1IpVItVoXxePyYFF85IM1Y5IFRG+UGZgKTyQSn04lwONyhcIfVakX//v1x5MgR/P3vf8fatWsBNO9OSQd4t27dMGfOHDQ1Ncllk1wuh3A4jGw2C7/fX/H6IuneTqVSNb1DywfZPZMMEcuyCAaDiEQiMBqNso9yR8/y7bffjv3792PZsmUVOvNjYbFYZAGgWgWRIVb6/iJliUAggEAgIG+E6uvr4Xa7FXPhA77fPasFjUaDyZMnixRFnafaIFVGUa5U8+fPb9WV6p577mnVlarYtDbHcS3sIgsBSWkf3Ti0a9cuRCIRnHbaae2OJ0lSQas7EqRJqjmVSsFut4OiKHAcp+hKXU1Di6OhVGAmMJvNEEVR1v1u62HXaDQYM2YM+vTpg+7du+O1117DwYMHYbfb4XQ6ceqpp0IQBNhsNpx22mnYt28faJpGNBqF1WpV1J6uWGi1WtjtdsRisS4jTmKxWOSmSKKFX2xTpMfjwc0334y//vWvuOCCC1Q827ZBjHHS6XTFmA1KI99IopysGXH7IztkURRhNpvhcDjatONUCiaTSWZyqDXOtGnT7J988slcAA+pMkCVoaorVbHd2qWIj5Auwvxx8nfN7e08iVRnMTcP4fhms1nEYjEkk0n4/X7FbkCirFQJyo7SgZnAarVCEAQ5xd3Wcevr67Fu3TqMGTMGd955J5555hns2LEDF1/cTGEk32n//v2xZcsWHDp0CH369OkUHr5EMCKTyVRNKU0JkE73VCoFiqLk+n2p98Ldd99ddSENq9UqL+JqdeFkNpsRDoflDUChyPcbIHVrs9ksN0tW6vMgjJtcLqdaWe60004Dy7JjaJq2MQxTHmG/E6KYHMaKdevW3QsADz74IB577DEsX74cp556aqtfuCRJEEWxqDRJKc1graW0d+7ciWg0ilNPPbXN95UrOmI0GmE2m2UjgUgkUra+ryAIiEajFTG0kCRJFg9Qw2uacJUjkcgxjSGSJEGSJPh8PjgcDhw6dEimKg0bNgyvvvqqnOYmmYopU6Zg4MCBnSIwA9+nt4nhSq1BFEUkk0k0NTWBZVk4nU706NFDLi2VCr/fjz59+mDjxo0Knm1xIEGomObEzgZCDSvkGkj2kIiCRKNRUBQFj8eDurq6iuyUjwZpbFMztW2z2TB8+HAWwFTVBqkiVHOlEkURFEUVfEMQknsxFCpSH8tvxJIkCZ988gnGjx/fbuAtV6qT1Nr9fj80Go1cy2mraayQa4lEIrBYLKrXUUlgJuYOajy0JHiFQiEkEgmYzWZ8++236NmzZ4ssx8knn4wjR47g5ZdfRp8+fXDxxRdj//798Pl8SKfTsrOX2hKQpUCv15fEE64mSGduKpWC0Wg8RsuZ1J7LuZ5Dhw5h7Nix2LhxI0aOHFn+SReJfCnMzrKYKwUkuLV2DSQgEy9krVYLs9kMr9dbVb55PoxGo/z8qoUZM2Y49u3bdxmAN1QbpEpQjUpVbL2ZNHAUs9PmOE4mvRN89dVXCIfDmDBhQpvvI52K5aQj4/E4LBYLdDodNBoNHA4HfD4fstmsbGhfDBKJhGwgryYqEZgJyOqdZVnZ4nHHjh0taDtutxuff/45jhw5Iqez+/Tpg1wuh0QiIVM5OltgJiAi/519lyYIAmKxGAKBAARBgM/ng9vtPiZTRVyeynHi6tOnD2bNmoW///3v5Z52yTCbzbLyX63iaDlPURSRyWQQiUTQ0NCAZDIJvV4vO/LZbLZOE5iB5sY2QRBUdXWbNGkSeJ6f+Z1CWJeCalSqYuvNHMcVnXppLaW9atUqjBo1qt3Am06niyLVt3auHMfB7/e3+D3xOWZZVu50dTgcHS44SH1Iydp1a6hkYCbQaDSyxvWRI0ewceNGhEIhDB06FN26dYPX68W8efPkjnqSnjMYDHJWojODLMxisVinlPYkErVkMdqRuIVGo5HpSOVIcd55550ytaoY+V6loNFoZL5wrTaGkYV/LBaTxUEMBoPc1FUtsZdCQSippC9IDfTr1w9ms1mfTqdPALBdlUGqBNWoVMXSqIqtN5OUdr7M3eHDh3Hw4MF2O7QJfapUKUxirNBW0CWdliRwBwKBdusu+YYWagaiagRmAq1Wi/r6evTr1w9Dhw7F/v37sXr1ajAMA5Zl0bdvX9lIIxKJwG63F2XCXm2YTCZoNJqqN0Llg+M4hMNhhEIhaLVaufZYyIRutVqRyWTK2nVOmTIFNE3j3XffLfkY5YKktmtNMIbMqaFQCJlMRi4LEVEQi8XS6QMzgdp1Z4qiMHnyZK1Go5mu2iBVgmpUqlJ2zsWscHmeB0VRLVZkq1atwogRI9qtlxF6QqkdhOl0Wm52aA8kCGazWUSjUbAse0xAJ3Vmu92uqhxkNQMz8P0iLpFIYMCAARgyZAhWrFiBtWvXIhaLoX///tBqtXK6tTOl5goB8RIOhUKKckWLBenbIPKJNputpGY/Ur9MpVIlu6tRFIXVq1dXTA++NZBnXE0xDKVAKE+kpGA0GmXhl2AwqKjtYiVhNBoRjUYhSZJq5z9lyhTTe++9NwfA31QZoEpQzZWKaCMX8nASJ6rWHKXaQjweBwB58ggGg3j00Udx0003oVu3bm2OEwgE4HQ6S3pYiQ5xIQpYR78vHo8jm83C5XLJY5ObVs2AWe3AnP9QplIpuTbrcrmwZs0a7NixAxaLBWeeeSZ69OhRkxMQAZH1rHRzGMkiERERm81WtvkH0cyvq6sra7GxdetWJJPJdpkTaqISOs+loDUOsslkgtlsPqa8FwwGYbfbO/0Coy0Eg0GZN68G4vE4Tj311Gwul3MzDFPbvqF5UM2VShCEgr+MXC4HrVZb8CQgSRIymUwLl6nPP/8cAwcObDMwA831TOJGVQri8TjMZnPRu24SGFmWRTQahdlsluXt1KxTVisw5wdk8m86nZaNRIjK2vDhw2EwGBAMBtGzZ8+KnJuasNvtrcpgqgXyHCSTSbmZUKkdlk6ng8FgKFvM46WXXsL777+PtWvXVmXhZTabEQgEiqZ1qoFSOcikdl6rwdlkMqnqUuVwONC/f//Unj17zgDwviqDVAHF3K1njBs37rdAc7PHzTffjGnTpmH9+vVtUqkKTWsXO5mR7j+S/kwkEtiyZQtOP/30dt9XiuhI/jmyLFsWLYDYrbEsi4aGBtjtdtUmDKLSVY0dMxnrq6+aJW+3bNmCRYsWyUHb6XSioaEBmUwGI0eOxLRp0wAca2xRa9BoNLDb7XJWRy3kc5QzmQycTid8Pp/iVpk2m+0Yqdpi8cMf/hCbNm3CmjVrOn6xCtBqtTAYDKrWPduDEhzkShhJqAmj0aj6+Z9zzjnWrlZ3VpVKVWjgKTY4ZzKZFhPR2rVr5Yaj9sbI5XIlew631wRWDCiKklOfsVhMFfcZEpi1Wm1VUtkAsGfPHqxZswYffPAB3nnnHZxzzjmgKAqpVArRaBQej+eYOlQtp7QJLBaLTNVTGqIoIpFIoKmpCRzHwe12w+v1wmg0qvLZGQwGaLXasq6lZ8+euOSSS6pKqyKNYZUCKTNEo1E0NjYiHo9Do9HA6/Wirq4Odru9KLWufCOJWgTZRKlJazvzzDONRqNxlmoDVAGqUKmIAlShgaxY8RGWZeUaEs/z2LhxI6ZPn97uzZ5KpUoWsiBcw3IN0ImhBTGI4DgOkUgEHMcppkFLArNOp4PT6axawBs0aBASiQQ+/PBDuSkpHA6D53nZDzkej8ud6l0hMAPfN4fF43HFgibp3iUUwEo2zVmtVqRSqbLu/VtuuQW//OUvS7KQVQKkKSmXy6n2uRGqE8uyMnXIZDIp5svcniBJZ0clKFUjRoyAIAh9aJr2MgwTUmWQCkMVKhXZNRcyMZHGiEJ3zjzPQ5Ik+fXbt2+HRqPB8OHD23wP2cmU0jlKditKBLpUKgVRFOUmNsLlFQQBoVCo7JVlZwnMRB0uEonAZrOhW7duWLFiBTZu3AiXyyXX/O12O0RRbNPVrFZhNBqh0WjKzooQml0gEIAkSfD7/RWRds0H8UguZ9c2ceJEfPbZZ1Wj/+QbSSgJURSRTqcRDofR2NgoK5L5/X74fD7YbDbFrrkrpLaz2axqx9fr9RgxYkQSwGTVBqkwCg3OhEq1EABmzpyJSy+9FPfddx9uu+023HPPPS1eXGy9mYikFwIiPELSw8Q4ob3xyMq/lJR0IpGA0Wgsm+pEDC2O3iVqNBq43W4YjUYEg8GSJ8HOEpiB5msKh8NYv3495s6dizFjxsi+1PkrfyLxmUwmazZl1xrI7jmRSJQ0mfI8j0gkgmAwKHOUnU5nVYIbRVHy7rmcYzQ1NeFPf/pT1YLL0WpbpSKfg9zY2ChrLajNQc43kqhFkOCs5vc/depUp06nO1u1ASoMVahUmUzmmG7qtkB8cQulOgQCATgcDhiNRhw6dAjPPPMM7rzzzjYbtQj9qZRUIM/zCIVCZaemBEFAMBhsQaNqDaRO5XA4ipIW7QyB+ej6sSiK+Pbbb2EwGOB2u6HT6eS05tGvJUIL7VlM1iLC4TAMBkNB3c5Hc5RJZ3tn+DzIM1TOcxAIBNCzZ098+OGHmDhxosJn2DEIjTI/c1MoWuMgm81m1Wr9bSEej4OiKFW1qtWE2pSwHTt2YN68eQc2b97cT5UBKgxVXKmKqS0RD+dCkMvlIAiC/HCtW7cOw4YNa/dmzWQyMBgMRQdmUlMn7kqlIt/QoqOb0mQyyUpZ8Xi8oFVmZwvMO3bswBtvvIGVK1fKiyLy+ZFAc/Q5Em6n2l3OlYbdbkcymZR141sDaR4KhUKIxWIwm82oq6uDzWbrFIEZ+F4Ks5ymKr/fjzlz5uBf//qXgmdWOIpJbRPKUyKRQCAQQDAYRC6Xg91uR319Pdxud1VEQdRW21Ibaqe2hw4dCkmS6mia7qHaIBWEKlSqYtPaha5k81PaiUQCO3bswCmnnNLm64lUZyk8TZZlIYpi2V69xRpa6PV6+Hw+cBwni5S0hc4QmIHvg+3rr7+O1atXQxRFNDY2YvXq1QgEAse8rjU4HA65oaargKTxW0sJEx/lQCCARCIBq9UKv9/fKd23gObGsHKlMBcsWIDFixfLVqWVBrElbe0aSOYiHo8jEAggHA5DFEU4nU7U19fLWa9qfjd6vV51Iwk1YTAYVA3OGo0Go0aNSgGYotogFYQqVKpCaVTEO7bQXW2+0cWGDRtQV1eH3r17t/l6IjpSrCAEUfQqt4OaiA0U242s1Wrh9XoBAKFQqNWdFwnMer2+6jVmoNkmcO/evZgxYwYuuOACUBQl+wNv2bKlw/eT2ns0Gq1pJ6GjYbfb5UZA4PsFY1NTE9LptOxmpjRHWWno9XpotdqyJtcpU6bgrLPOwoEDBxQ8s8JBjCRIf0NrHGSg2SmN1Pkr7YPcHvK7nmsRBoMBuVyu3UxSuZgyZYrbYDCcpdoAFYQqVKpC09qE31zIzU9WjEajEaIoYsOGDZgyZUq7700mk7DZbEU/XMlkEgaDoazaCOm09Xg8JaUnSbNUPB5HMBiE1+uVP9P8wKwUBasUkHR2JpNBKpWC2+1G9+7dsXXrVhw+fBh33nknPv30U3zzzTc4+eSTOzyewWCAxWJBLBbrMvQqQqlJJBLQarXyveV2u1XVU1cDxK2qVKUnjUaDt99+W+GzKg4mk0met4gPsslkOsbXurOCqIVVU7O8VBB1Ro7jVFMLGzdunFav13eJpjBVqFSFprWLER8hHD+KorBnzx5wHIcRI0a0+XqO44qSECXI5XLyjqZUKGVoQdS0LBaLXPcSRRGhUKjqgZkgFoshHo+jrq4OiUQC7777Lt59913MnTsXGo0GBoOhqMWJ3W6XG3C6AgRBgCRJaGpqQjabhdfrlXnetQaz2SyL+ZQKSZLwhz/8AVu3blXwzNpHvg8y6efQarXw+Xzw+/2yKEgtoBJdz2pC7Z0/TdPI5XL1NE1X3qdUYShOpZIkqeC0NvFwLgT5Ke1NmzZhxIgR7b63VKnOWCxWNj8xFotBp9OVXa8msNlssNvtCAQCaGpqgsFgqHpgXrZsGRYuXIiFCxeisbERFosF5557Lr755ht5UfL111/j008/lU0PCplQSMYgFovVdHo7l8shFoshEAjIntYGg6FmgkBrIE1V5TSGURSFlStX4umnny75GPn3USqVwiuvvHIMFa81DrLBYEB9fb0smVlr7mdAc/ZBr9fXdGpbzXPXarU48cQTWQBnqDZIhVBocB4B4JfPPPPMxQCwe/durFmzBr///e/x8MMPY8+ePfILyYNTSOAodOcsiqKcCkkmk9i9ezdGjRrV5utzuRyy2WzRwZFlWQiCUFbKKJ1Oy4pnSgZPk8kkd/ZW2z7uxRdfRENDA4YPH45hw4bhk08+wfvvvw+tVosJEyagX79+eOaZZ7B161ZMmTIFffr0KcoyjqS3a7F7O5+jTFEU/H4/nE4nnE5ni9pzrcJisZTNF77qqquwcOHCknbgiUQC9957r9xoaLVa4fP58NlnnyEYDCKVSiEYDMocZJPJJHOQrVarbIephmxupUCMJGoRer1e7jVSC2eeeabDYrHUfGpbcSoVqTd3NBGTdF8hO9T8lPaXX34Jn8/XrosRkeosJp1K9LPLCao8zyMej5dcZ24LJJVttVpRX18vS35WGqIo4tChQ4hGo7jiiitw8sknY9KkSbj88suRTqexZs0aeDwezJgxA7fffjtmz56NcePGlTSW3W6XzUZqARzHIRwOyyUHYmpA7m+dTgej0VhRjWc1oERj2OzZsxGPx/HRRx+1+vdkMonDhw+D53n5d2RRs2HDBtliFmheiI8aNQrBYBCffPIJOI6D1WpFt27d4PF4Wp0HjqttVQ/5dWe1MHbsWEqv15+r2gAVguJUqkKt2YppBiNGF5IkYdOmTRg1alSb7yP1pWJ3v8lk8hgFq2JAmrScTqei6TISmEkqm1jMhcPhigZonucRDAZhMBggiiJ2794t/83v92Pu3Lnwer1YuHAhQqHQMbX+Yhc8pN4ei8U67W6TZDKCwSAikQiMRiPq6+vb5CjbbDbZc7mWUW5q22az4bbbbkMymWzxe/I9f/zxx1ixYoXcy0J0+mOxGBKJBLp164YtW7YgEAigsbERkiRh5MiRiMfjMle8vfutKxhJiKJY05QqNT/7E088Eel0ug9N07Wp1vIdFKdSFdqpXWi9maS0iSJYJBLBSSed1Obrib5tMTVj0tRWahNYvqFFueYY+SCB2Wg0tqgxm0ymigVoSZKQTCYRCoVkEYbx48dj27Zt2LFjR4vXzpw5E3V1dYqlo41GI4xGIxKJhCLHUwrERzkYDCIej8NisaCurq7DHge9Xi97JNcyzGaz3HBZKv785z/joosukv+fBODdu3eDYRiwLCsHZzIH7N27F5FIBKNGjcLhw4fhcDjQrVs3uFwu9O/fH16vFwzDFDR+LQt6dAVKlZrzlsFgwMCBA1kApaXtOgmKolL961//MgPArl27wLKsXDd64okn5BcW06ldyO42m83KRgKbNm0CTdNtvo9wSN1ud4GX1Yx4PA6r1VryjpfUEosdtz3kB2a73X7MpJ8foL1eryqNRoIgIBqNQhTFFvKnQ4YMQSQSwfbt2xGPxzF8+HBZpY3jOASDQfTv31+Rc3A4HGhqaoLFYql6MxURDkmlUrJ3c7HCFDabDdFotNOKjRQCjUYDo9GITCbTobhOa70GxH9948aNGDJkCKZNmwaKohAMBvHGG2/gzDPPBMMwCAaDsNlschDdunUrxo0bB7/fj3feeUfOchEjnLq6Ohw6dAiJRKJDiUuTyYRwOFz1xspSQTySa5FSpdfrZeaJWip448ePN+/Zs+d0AK3XTmoABX0yDMN8CWBQazvnDz74oMVEXEinNpHHK2SyzWQyMJlM4DgO27dvb7cRjPAWi6GpZLNZcBxXkooYeX9rhhbloKPATECsJ8PhsOIprmw2i2AwKCuW6XQ6OR3rdDpx6qmnwu/34+DBgzJ96v3330c8Hi+5ztwaSBCMxWJVSweLoohkMommpiawLAun0wmv11tSYx6hltXqro2ANIa1hfYaQ+12O44cOYJDhw7hoYcewsGDBwEAXq8XV1xxBWiaBs/zcsra4/FAp9PBarVi2LBh8Pl8sFgseO+997B//355B2+z2WT9hY7QFYwkOI6ryRJJherOOqvVWtNNYQVvFRmG4UOhUOr+++/HnDlzQFEUKIpCKBQCRVHQaDTQaDSyA1Q2m4VWq221OUwQBFAU1eEOm3ikulwubN++naQrWn0tSb8WIwpfbhMY2Vm63W7FnGgKDcwEZrNZfk++jnWpkCQJiUQCmUzmGKMOci6SJMFut2Py5MnYu3cvQqEQdu7ciYEDB+LKK6+Ur0OpVbHFYkE6nUYmk1GMnlYIiJ1lKpWC0WhUTKjCarUimUwqWgKpNLRaLfbs2YPPP/8cXq8Xp512GjQajbxTpigK2WwWgUAAdXV18oI5HA5j//79aGpqgsvlwkknnYQ333wTc+bMkTW8dTod6urqZMc6vV6Pzz77DI2NjVi4cCEaGhrA8zzWr1+P3r17ywHKYrEgl8u1uThnWVZeHFEUJae2q52RKQVkbi1G/rgzQW0xkpEjR4Jl2TE0TVMMw9TeCgZFBGeapo133HGHCwDOPPPMFn976KGHcOaZZ8ot8rlcTnbXIZO0TqeTH7RCJTuz2az8MG3btg0nnHBCmxM+WUUW09CVSqWg1WpLagIrxtCiUAiCgHA4XHBgJrBarXJDmtfrLTkoElUziqLkQL9//35EIhG43W707dsXQHOQJt/rgAEDMGDAgBa7ZVI/VAqkOSwSicBkMqluCCEIApLJpJy1KcXRrD0QlapieP6dDdu3b8fWrVvRo0cPNDQ04Pnnn8cVV1wBvV4PSZLw5ptvYteuXXA6nXA4HJg8eTK6d++Ot99+G7lcDk6nEzabDS+88ALmz58Pp9MpZyI0Gg169OiB3bt3y9r4PXv2BMuyGDhwIKZNm4adO3di8+bNGDZsmNxIZrfbEQwG4ff7W02nv/7669izZw8cDgc8Hg9cLhdsNhtOOOEE1NXV1Vx6m3CGa/EeMhgMqvaS1NXVwWKxiBzHDQFQWCNCJ0NBMw5N0ycDePuZZ57RAs2NP8QykmVZ9OzZU97RkJovmcyIKEkul5P5xyTtRGpFBoMBBoPhmF0f4SlmMhl89dVX+MEPftDmORYrOkImYJ/PV9JDWayhRSHnQ7qciwnMBDabDaIoIhKJwOPxFP1+Yv1ps9nkz3HDhg34/PPPYbFYcPbZZx/jQ02a/w4fPoxevXrJf1NjkiNyqslksiz1tvZAFpXEo7dcq9C2kO+R3Bkn1mQyiW+++QY+n69N29cPPvgAM2fOhMfjQV1dHR599FFs3LgR48ePx5o1a5BIJHDHHXfAaDTinXfewdq1azF27FjMmDEDHMchEolg27ZtuPLKK2G1WmW/dbIL7tGjB7Zt24ZkMon6+nqMHTsWY8eOlcfv06cPdu3aBeB7tzNJkuB0OttsSj3//PMRiUQQiUQQDocRDoexbds2rFy5EiaTCX369EGfPn3Qt29fdO/evSr+2cWgLVOVWoBerwfP80XpHxSLkSNH8itWrDgVXTk4o1kh7Hfr1q3T0zT98IMPPij/YcmSJfjf//1fvPLKK5Ak6ZiGMIqi5F0zARH60Gg04Hke6XQasVhMbjQxGo3yqtDhcGDz5s2w2+0tAkA+crkcOI4rqiGLdNmWsiMiftV+v1+RG4sEZrPZXJIWOND8OTscDkQiEUSjUbhcroKOQ1L72Wy2haxkJpPBJ598gssvvxw9evTAwYMHZR6pw+HAuHHjoNVqEQwG8dFHH2Hu3Lmq+bQSEJW0Ur+3tkB8lAlHtq6uTvXducViQVNTU1H2qmohl8vh8OHD6N69O5YtW4avvvpKzpycfvrpxxjbNDY2or6+Xl7E8TyPESNG4MCBAxg+fDiCwSB69OgBrVaLvXv3IplMYv/+/fD7/Rg9erS8eM9kMpg2bRrGjBkjH5vcsz6fTxYUIaUsskOmKAq9e/fGDTfcAOD7prMdO3ZgxIgRbX6eVqsVVqu1xTwSiUTk5+/gwYPYtWsXPvroI2g0GvTu3VsO2L179+506W+DwYBIJKJqgFMLJJuqZlp+woQJzs8++2wCgGdUGUBlFDrDnQDgSQA3H/2HCy64AL/73e8AND88pN7UFkgzGOnANhqNciMHz/Nyg1Umk5Frztu2bcOIESPaPC4RHSn0BuU4DhzHwe/3F/T6fBBZRqWERpQIzAQURcHtdiMYDBZUfydqVnq9Hn6/v8X1EP9aj8eDUCiEl19+GSeccAJ4nkcoFEIoFMI555wDg8GAGTNmwGg0qj5JaLVaWK1WWeilHBCLQFJ+sVqtcLlcFfNQ1mg0MJvNZVH4lMKOHTuQTqexdetWcByHu+66CxRF4amnnsKOHTvk4Ey+XzKhkkbITCaDPn36YP/+/Thy5AhEUcSqVauwbt06GAwG9OjRA+eddx769u0r19l9Ph/OO+88uN3u/9/em8fJWVX5/+/q2qv3PUlnI9tNAtkgLAJhEUGBYVFEVBZhmNFhHEe/jjOjzvenoF830HEbFccNXEFUwMgqEGQPYV/zkJB0QkLS+1JVXXvV74/73KefrlR11/LU0lCf16tfVV33WW7dus8995zzOefwy1/+knnz5vGud73LuI/T6eSEE06Ykmwo/bdJJ50lk8m8OCcwWUhCCIEQApDrw/79+9mzZw979+7lscceA2Te5iOOOIKlS5dWRepPlcpThZrONqi+l0o4r1271u5yuTaV5OJlQK4zTEOGUx2Cm2++ecrOdiYtIB6PY7fbD3nQFIPP5XLR2Nho1FMdHBykt7eX4447jomJiUN8jirpSK6CVmmKTU1NeS/EVhW0UDAL5nwXlWyw2Wy0tbUxODiIw+HISDpSIUF+v99IbGIWqr29vcZ37O3tJRaLccwxx3DyyScTi8XYuXMnDz74IOPj40ZpS3XvUqOhoYH+/v6CH2pVJjAQCJBMJmloaKhYucb6+noGBwcLcmNYASVs9+zZw6JFi9i0aZPBoH3uuecMToE6VkGFH42Pj+PxeIxQt/HxcSYmJmhoaGDOnDl85CMfMbTNe+65h3g8zpo1a4wNfEdHBwBPPPEEBw8eNISzGouZKpmlj9k73/nOKd8rF7jdbqNuujrH5XJx2GGHGVEoiUSC3t5eXnrpJW699VZsNhsrV65kzZo1LF68uGwbukxQFsbZKJxLnWd71apVRCKR5UIIt6Zpsy4oPK8456OPPtoLMkNYOBxm9+7dU+KccwmjymVRVVpNR0cHO3bsoKOjgwULFhAKhYwFQcW9qhJ2uZoGJyYmDKZmPlBC3aqCFqUQzAp2u93QeBUJTyGZTBo1kzMRnRKJBHv37mXVqlUcffTR/PGPf2TRokUGGczpdLJq1Sq2bdtGX1/fFOFcDthsNhobG42NQa6LsEocEggEDK5ApXOUq9+m3Cx0BSVgk8mkkXkOYHBwkHvuuYeenh6efvppGhoapoRLNjY24vP52L9/P4cddhjhcJiWlhbC4TAdHR2sWLGCl19+ma1bt7J8+XJ2797N7t27DetXuvC84IILOOeccwzeiBmFWGPyOd5cSCLbmmC321m6dClLly7l7LPPZseOHbz00kv87ne/w+12c8QRR7Bhwwa6u7vz6qcVUBaM2YhSk8L05ECBAwcOrAGeKtmNSgRL45xz8Z/lEt8cjUax2+04HA7DpO3z+Whvb6ezsxOHw8Ho6CgDAwNGQodckEwm8fv9BYVOWVnQopSCWcHpdBox0MpXF41GGRgYMMrlZTLN2e12GhsbufPOO1m6dCnnnnsuAwMDvPDCCwQCASYmJhgZGWH//v2G5lNuqPCxXHbdKjFNf38/oVCI5uZmOjo6KqYtp6O+vr6iGcOcTie7du1izpw5AEbCmf/8z//kkksuoa2tjccff5yJiQnC4TBDQ0P09fXR1dXF4OAgQ0NDdHV1MTQ0ZFij6uvrOf/88/H7/dx8881omsapp55qmKjTx/2kk07C6/Vy9913H9K/cvxG+WTbcjgcrFq1igsvvJDPfOYznHHGGQwODnL99dfz05/+lGeffbas1dRcLpdBrJptsNvtBmG4VFi/fj3M0kxh+ThO6mKxmB0mTVzJZHKKQz8Xs3YsFptRmCqW9tjYGG+++Sbvfe97jTa73W4wisfGxgiFQgwPD+Pz+YyqM9kwPj6O1+vNm9gRi8Xw+/10dHQUbcIqh2BWUPV3h4eHjbSRLS0tM1oNNmzYwIEDB3jppZc46qijaG1t5Z577uG73/0u7e3t2O12TjrpJLq6uipCRlHkN7/fnzVDlzlG2eVy0draWpXMaLfbzdjYWF61za2E8rlGIhEaGxunMJ9TqRQrV67k8ccf5/XXX2fu3LlG4ps5c+YwPDzMk08+yYsvvsi+ffs49thj6enpIZVKsXDhQnp6ejjzzDNn7IPT6eTKK69kdHS0xN82M5Rpu5Dz1q5dy9q1axkZGeHZZ5/l/vvv56GHHmLTpk2sW7eu5GS/chCrSgWbzWb4nUsV83/ssce23HfffccBPyrJDUqIfEOpHCBLRoZCIX73u98RDof5wQ9+wBFHHEEikZh2gVHJ2qc7RhUTaG9v55lnnqGjoyOrhhaNRpkzZw52u51gMMjAwAAej4eGhoZDtMJoNEo4HKarK78a3FYWtCinYFbw+XyMjIwQDofzCg9ZvHgxf/7zn4lEIhx//PFceeWV7Nixg2QySVtbW0FkOiuhwqpU2JOCypOu+AmlSm1qFcw1kpubm8t+f6Up79u3j46ODh544AFcLherV68mEomwZ88e4vE4S5cuxeVy8fLLLxOLxdi4cSOnnXYab775Jm+88QarV69mxYoVxncCjLmmLDfTbWyvvfbaEn/T7DCXMSxUmLa2tvLOd76TE088kaeeeor777+fRx55hJNOOom1a9eW1C892+OdY7FYyYTzmjVrbE6n84SSXLzEsCSU6uqrr+YPf/gDiURiWq1MZfyZTtOKxWJG+JWmaQaDMh2KuOJyuYwkFQ0NDUb4hYoXVqaT8fHxvElgVha0UILZ5/NZFhs9E8LhsBFWFQqFDDJeLli9ejVOp5PNmzfzxhtvcP75508JqSllXtxcoHzPY2NjeDweI2691DHKpYDP52NwcLAieZ7tdjtdXV28+uqrRratrVu3kkwmaW5uZmBggKOOOgqfz0ckEiGVShkbM4fDYYQa+f1+o7RrOnKdJ7/85S/ZsGEDa9assfQ7zgRzIYliff8ul4vjjz+ejRs38uSTT3Lvvffy8MMPc9JJJ7FmzZqSPDOzPd65lH1fvnw5kUhkoRDCo2narMqZm+tMUaFUh+Dcc8/l9ddfB2Y2a+dielHlIUOhEL29vaxcuTLjcYFA4JDQI7vdTlNTkxGnOjAwwNjYGMFgkFQqlbeAVYzeYkNdyi2YFXltbGyM1tZWmpubaW1tNWI6c8Xy5cu5+OKLicVifOtb3+LRRx/ltddeIxKJTIk5rRRcLhepVIq+vj4GBwepq6ujs7OT5ubmWSOYYZIYVs582yrhiopJ3rt3L16vl5NPPpn3ve99vPHGGzz77LMsX76cI444ApBCYP369QY5UEFl5iu2RvJvf/tbfvOb3xT1vQqF1VWeXC4XJ554Ip/85CdZv349d999Nz/84Q958cUXLS+BqlJhzka/szkZSSmgl3ENAoeX5AYlhKWhVDOxtVV8czYok3ZbWxuvvvoqPp9vSpyj+TrxeDyrsK2rq6OpqcmIie3v78/bDBuJRJiYmCg4g5hCuQVzPB5nZGQEu90+JXbZ7XZTX1/P6OhoXhnEuru7ueSSS3jllVd45ZVXGBoa4q9//SvLli1j48aNZWdrK6hwKJUiViW9mK1Q+cNLmW87FosRDocJh8OGlauhoYGjjz6al19+mVdffZX169dPCSPKhHSegfIdqkIShboRzjzzTH72s5/x9a9/vaDzi4HL5WJ8fNxyDoXb7WbTpk0cc8wxPPHEE9x555089NBDnHzyyRx++OGW3Kuurg673V7U2FcKqvZCrimdC8HatWvr3nzzzQ3A0yW5QYlgWSiVIpBMJ5yj0ei0vtZ4PH6ISTvT5M016YiKp25tbSWRSDAwMEBzc/OMMYGqoEVLS0tRC365BfPExATj4+NGqEv6+DQ0NDA0NGTkK84Hq1evRgiB3W43srmVy2+ukB6jXF9fT2trq1HXejYXklAEyHg8btkipRL7KIGcSqXweDw0NTUZ7iCFk08+mUcffZSuri7DvK20mUw5CdJhRSGJs846i0996lOHpIMtB8xVqkoh4NxuNyeffLKR3vQvf/kLjz32GGeffXZGBSRfKGLVbBPOMKk9l0o4b9iwoenBBx88BvjpjAdXESwLpVJaczaBmUgkZjR7q0IDiUSCnTt3ZjRpJxKJnOuYxmIxQqEQra2ttLW10djYyOjo6LTmXasKWiQSCQYHB8simFVO7UAgQHt7e9Yc4zabjZaWFgKBALFYLO/7KP99c3NzWQWzilEeHBzE7/dTX19PZ2enkQK2oaEBv98/K816Ckq4TVeGMReoDczY2Bj9/f0GC7mlpUUVAziE4Z5KpViyZAkrV67kueeeo7e310gUko+PVJm2C8Xy5cs5+uij0bTKpEJWZRhLCY/HwymnnMK//uu/MnfuXH7+85/zwAMPFB1OpIhVsxFKOJcKq1atwu12H1+yG5QIloVSzUQQUsdNp+2qZAa7d+/GZrNlNK2pkpS51IxWWqTaEHi9XtxuN36/39Ci0xNRjI+PGwt+oYjH4wwNDVFfX19ywaxScLpcrpxCvRwOh5GDe6bc4Pv27aO9vX2KRlpOH7PKZBYIBIz460yhU+qz6RJJzAb4fD5GR0fzTuOqBLLSkO12Ox6Px6iDrBJ/PP3002zZsoXLL798iptH3WvDhg2MjY0Zv3e+v7XL5SKRSBTFet66dWvFeAxut5tQKJTTxr9Y+Hw+zjnnHA4//HBuu+02du3axXvf+96C3UQul6tGCsuClStXMjExsUwIUadpmrUO/xIip22xHkq164YbbrgAZCjVE088wVe+8hVOP/10XnrppRkfyJniOBUpwOl08tprr7F06dJDrqcW61wennA4TDKZPIR9WVdXR3NzM21tbfj9/iladCgUMjYIhS4Q5RLMqn710NAQjY2NeeWF9nq9OByOabPzxONxfv/73/Piiy9a1eWckUwmCQQC9PX1EYlEaG1tpaOjI2tGL5Xxa7ZmSlJQz0cuWoSyJoyMjNDX10cgEMDhcNDR0UFnZyeNjY2GH7i/v5+f/exnPPDAA5x22mlZQxMdDgft7e0FM5YV67kY7TkSifDb3/625BpsJlSCWLVkyRKuuuoqmpub+fGPf8wzzzxT0P0dDodhnZxtKDUpTLf0JYClJblBiWBZKNWNN944rXCORqPTPvQq8YjNZuP1119n06ZD85VPTEzgcrlm9E0kk0nGx8enFbIul4vOzk78fj+Dg4OGabSYghZKMKskKaWC8omrGNV8fTUq9EzFhWdi0D///POkUimOPPJIq7o9I8wxym63O68Y5bdCjWQV8xwKhTJ+h2QyaWjHKq5V+ZCzPXsvvPACf/nLX1i5ciUf+tCHSq4VKuFc6H1SqRRXXnklc+fO5dRTT7W4d9NDcVTKTazyer28//3v5/nnn+euu+5ix44dnHPOOXltkhQpbybSbTVCrbe5JLEqFCtWrEg+8cQThwM7SnKDEsCyqlTTMbUVMWW6RTMcDtPU1MTw8DAjIyMGA9x8jWAwmFOihkAgYNT/nQ4q05TT6eTNN980tI1CUC7BHIlEGB0dNRKZFKrhq7CzsbGxQxjpqVSKxx9/nGOPPbYs1Xfi8TjBYNDgHBS64VDac7EVqyoJj8fD8PCwEfOsOBbhcNioPuTxeGa0lMTjce6++26ef/55zjrrLDZs2FC2/o+NjRXMevZ6vZx00kncd999ZRfOMKk9l5tYZbPZWL9+PQsXLuTWW2/lRz/6Eeeddx7Lli3L+Rqq77NNOJs3FqUSzkcccYTvqaeeWgvclqldCOFDataHAV1ACNgDbKtU0QzLQqkSiUTWSZFIJKYll8TjcRKJBC6XixdeeIHOzs5DYosjkYhRuWo6xONxJiYm8qpSpUzZAENDQ7S2tuY1ScohmFOpFH6/n1AoREtLiyUPoNLS0stL7tixg/Hx8SnF7UuBWCxGIBAwkj8UmzjE6/Xi9/stZTyXG06n00h+k0gkiMfjuN1ufD4fra2tOVl1RkZGuOWWWwiHw1x55ZVG3uxyQKWTLEZInHrqqWzevNninuUGlW2rHH7nTGhra+OKK67goYce4ne/+x0bN27kXe96V06bBVVEZTZCCedScUZWrFhRV19ff2ymNiHEe4FLgAYgBqSAbmAh8BMhxDc1TRsrScemgWWhVNOZJJS/OdtOOt2kna41Q+akI5kwPj5OQ0NDXlWq4vG44YdTaUBbW1tzWlzKIZjj8Tijo6NGmT2rdpfKvD04OGj4oQG2bdvGunXrSvagqDrK0WiU+vp6mpubLcmcVFdXh8/ny9nCUk2Ix+MG5yESiZBIJGhvb8+aOzwbXnvtNW699VYWLVrEZZddVhGCnEroUahwPuWUU/j6179eEReFqpRUiZzxCnV1dZxyyiksW7aMP/3pT+zevZsLL7xwRoXD6XQyPj5epl5ai1JvLIQQJBKJIzJ8fgTwf4FHgD8Ae4EBTdMmhBDNwP3Ai8DvS9a5LLA8lCoTZiKDKeGcSCTYvXv3IcI5Go3OmBpUXScej+csJKPRKH6/n9bWViN0pKGhwcimNRODsByCWYUQKfat1WYfh8NBQ0ODYYocGhpi586dHH20tYVcFKN4cHCQkZERlblnSrEFK1BfX08oFKp6Yoxy9agkOUNDQ0Y2OpVQJR/BnEwmuf/++7n55ps58cQTueiiiyrGXPd4PEVl2zr66KPp7++vCHdAPV/lrCyVDfPnz+djH/sYc+fO5ac//Sk7dkzvLi1HladSweFwEI/HS3b9ww47jFAoNE8IkT6p/gV4SNO0T2qa9rCmaXt0wVyna8s7gPLmk9WRz6qYNZQKpi8XOd0O2Gy627dvH4lE4pD0gKrO63QLlUpZmWtJRxUbnKmghdvtpqOjg2AwaAitdJRaMCvTpiKp5Rtekw/q6+sN3+a2bdtYvHhx3gVCssEcozw2NqZqrM74exYKu92Oy+WqSvOeqlOuYpBHRkaAyRhklSDH6XRit9tzZiyHw2F+/etf89xzz3HppZdywgknVDStqtPpNEKqCoHdbiccDvPaa69Z3LOZYfZ/VgPcbjfnn38+J598MjfddBOPPfZYVlZztfU9Hyi2eSnTeLa3tweA9GINI0gT9hRompYUQpwNtAJ/K0mnZoAlVan+53/+J2uaS6UhZNOcVbJ8m83Gzp07WbRo0ZRjE4kEkUhkRjNlIBDA6XTmZEpTiUamK2ihwlJGRkYYGRmZQsAptWBWsctOp9OSMpUzwczefu655zj33HOLvqYSyoFAwMgmlq+JtlCotK25ZJErNZRAVibrTDHImaCybc00n4PBIL/61a+w2+189KMfLXvWtkxQ3JBisrZdf/313HrrrTz++OMW925mFNt3q2Gz2Tj++OPp7OzklltuIRQK8c53vjPj3Cm177ZUsNlsJU9BumzZskR/f/8qpJla4TbgaiHErcCrgBNoQxLDDgN+pWnafSXp0AzIddVXoVRfBvj2t7/N9ddfzz333MOnPvUprr76aiNHajpUJaRsAkYVugDYtWsXS5YsmdKeS9IRFYKTa4GKQCBAKpWa8fi6ujojD/Xw8LBR8lLFFlstmBUjXQn+fGKXi4Xb7aa3txeHw5G1ElguUDHK/f39hEIhmpubaW9vzxqjXAqoghiV0iAUyVDFIPv9/qwxyNmghPN0mkQgEODGG2/E7XZz2WWXVYVgVlDEqkJxyimnsG3btorErivhXG1Yvnw5l1xyCU8++ST3339/xrkxWzVnKEumMC8wJe2kpmlbgY8Cg0An4NXf3wKcoWnaF0rWoRlgWVWqQvzNiUTCiMuLRCIcOHBgSlawZDKZU9KRsbEx6uvrc2LoqoIWys88E1TKS5fLRX9/PwMDA0buaiuhzOyq2EYltL7t27dPKQuZD5LJJH6/n/7+fqLRKK2trQURmqyAzWYzCkmUC8lkklAoxPDwMAcPHiQYDBqx9B0dHRlrjE8Hc67nTBgfH+eGG26gsbGRiy++uOrCZ4pNhXnkkUfi8/l45JFHLOxVbnA6nUZBlWrDwoULueSSS9i2bRv33XffIQJ6NgvnUvudV6xY4XO73Zn8x22apv0j8Engi8AXNU37kaZpO4QQFQv7yFU4TxtKtWTJkmn9zdOZtJVG9cYbb+B0OqeEfahkFNMtapFIhFgsllM2rkQiYZio8yFWqeQQiphm9UIYjUYZGBjAbrcXFONrBfr7+9m/fz8bNmyYNnNYOhKJhOFDTSQSdHR00NbWVvFEIF6v18gSVyokEgkmJiYYGhqir6/PiNPu7u42cpwXSuAzF5JIx+joKL/4xS9oa2vjQx/6UMXHOhMcDgfJZLJgv7PD4WDTpk1s2bLF4p7NDFV8p5SCohgsWLCASy+9lKeffpp77713ioC22+0kk8mq3FjMBLUpKhWWLFmCw+HIJJyvE0Is0DRtQtO0IXPdZ03TKjYJLAml+t73vjdtGFU2LTMcDhtte/bsYcGCBYYGrky8ra2tWTuVDwlM+ZkbGhryFq6xWIzh4WE6OjpIJpMMDQ3R3t5eNHNapeAMBoO0tLRU1E/0zDPPcNhhhzF//nz6+/tnZNiresDhcBiv11t0jLLVMBPDrHQ/JBIJw39cSAxyPnC73UZ+eIXh4WFuvPFG5s2bxwUXXFC18dzK76xi2AvBD3/4w4qFxCnTdjVufEAyuS+99FJ+9atfkUqlePe7321EnMzWTGGl3hAtWbKEcDi8OEOO7f8GhgCEEHZN0xL6+x7AA+zSNK3sVXUsCaWaP39+xoUplUpldfAnk8kpiQr27t3LwoULjXZFnpnu4QgGg0bIyUxQBS3yXaiVYFam7IaGBnw+H0NDQ0WFLCQSCYaHh4lEInR2dlZUMMfjcV544QWOPPLIKVWeMiEajTI8PMzg4CB2u91gGVeTYFawyrQdj8eNYikDAwPE43EaGhro7u6mtbU1p0IshcBcSAJgcHCQX/ziFyxYsID3v//9VSuYFYr13S5atAifz1cRLXA2mId7enq47LLLeP7557n77rsNDbqatf7pYLfbS8rYbmhowOv1xoApNTo1TbsbcAkh1mmalhBCrBFC3A7cC9wAfF4IkTkhfQlhSShVtjAqVaMzk1armKgql+3+/fuNECqlUU5nqk4kEgQCgZy05kILWqQLZgX9RzZiU/NFOBxmYGAAl8tliQZeLLZv3w5glOisr68nFosZC6uKUR4aGjIqYHV1dVkeo2w13G63EaqXDxSZTPnQzTHI3d3dhpWj1L50VUhCxYffcMMNLF26lPe9730VnzO5oFi/czgcpq2tjeeff97CXuWG2SCcAebNm8dll13GCy+8wJ133mkUD5oNfU+HmbFtJZSSGIlEmD9/fow0UpjuV/40cIEQYh7wZSAMXAz8Djgd+A9LO5UDLAml+spXvpIxacVMIVRKW9y/fz+AUXRcVSiZTiNWoTIzaQ/xeJyxsTHa29vzEiTZBLNCQ0ODkbQj12urMpbhcDjnDGTlwIsvvsjhhx9ujKVKxqKyrQUCAZLJpLEpqXR4Uq5QXIGJiYkZmflKIKuNHEi/dUtLy4zM6lLC7XYzPDzMn/70J5YsWcJ55503a8bf7HcuZDPh8XhYsmQJTzzxRNlygyuYqzxV8wYUYO7cuVx22WWGiftd73pXVcb55wKl9ecbTqUEsNqMq9e+vj62bNnC/v372b9/P3v27GkBPgH81XT6euD9wIVMhlBt0E3fzwkhHgE2U2YBbUko1XXXXZfx4cvms0kmk1Nq7+7du5eenh5DOAQCgWmTVESjUaLR6IwksGQyaRQRyOfHVoK5qakpq7/MZrMZITEjIyMzmmLi8TiDg4MkEgk6OzurRjCHQiF27tzJmjWTPAn1XcbGxhgZGaG+vp7Ozs6qiBvOFz6fj1AolPH3URaB0dFR+vr6GBsbM8Lnurq6aGpqmrEGealRV1fH5s2baWpq4txzz51V429FUozjjjuOrVu3Wtir3DDbEnooAf3KK6+wZcuWspe+tArTmeSVpVblTxgdHWVwcJC+vj4OHjxoZHVMJBI4HA4jgqe/v5/ly5dz+eWX88EPfjBZV1e3K+3So0CHpmkvA68DLWk+6SOAnaX4vtOh6KpU55xzjhHnnI5sLGpV7k7tSPfs2WP4m+PxONFo1ChEkQ5FAmtqapp2R6uOc7lceSUTMAvmmc5TyTuGh4enJaZNTEwYxJ5qE3Dbt2+noaGBBQsWGPWyVW3g9vZ2EolE1SRjKATKraKqoimBHAqFiEQi2O12vF5vxVjy0yGZTHL77bcTjUb58Ic/XHX9ywXK71wop+LYY4/luuuus7hXuWG2EavmzJnDxRdfzI033khdXR3d3d2zwv2hoPKZK75RuhacSqVwOBzY7XYcDgdOpxOv14vdbs+aZ2PFihVcc801xv/RaNT+hz/84XDzMZqm7RRC7BFCXAP8L3CNEOLXwM3AXOAypKm7rLCkKtXixYsPGRhlzsq0oJhN2qlUin379nHssbJgSDAYxOfzZRW8ExMTRpjJdDAXtMhVGMZiMYaGhmhubs5ZINlsNlpbWxkcHDwkJjuZTDI2NkYsFsurNnE58dJLL7F69WqDNe5yuWhtbTUEWS7M7WqG8tuOjo7icDhyroNcDbjvvvt44403uOiii6retJoNqpBEoXjPe95juJDKval1Op1FJVKpBHp6erjwwgu56aabOOyww1ixYkWluzQFqVTKSOaULnzVe8VVUmRf9b6urq7oObBo0SJSqVSmZA6fAP4dWeBiNdAMnA+8Bnxf07R7irpxAbAklOob3/jGISdkq0SlNBflA1SM5Xnz5hmJHLJVX1GJLtrb26f9kVRBi1ILZgVlCh0cHMThcBhEmNHRUVwuV1lScBaC8fFxdu/ezfr164nH44dsIGw2G/X19QQCgWlD2qoRKu2r8iFHo9GqZpan48UXX+TJJ5/k8ssvp7OzE7/fX1UZwHKF0j4LFa49PT1cdNFFJejZzHA4HDMWv6lGLF++nCOPPJLNmzdz1VVXWZ4waSao4huZBLAqH+xwOAyh6/F4DG04lUoZVQFLgQULFqgCGEY4lRDCpmnaE0KIf0AWuUgB+5F5txNARajvOQlnTdOeF0Is++hHP/rf3/zmN/9l5cqVeDweLrnkEo466qiM4SrZ/M2RSMT4UQDefPNNmpubqa+vx+/3Gz9UJoyPj+P1eqfV4qYraJENxQhmBYfDYVSzUnWSi7leKaFilJ955hmamppYuXJl1jH1+Xz09/fPihrJqnhHKBQy8gurGOShoaEp866a0dfXx+bNmznzzDOZP3++QVabDeSkdNTV1RWdM/kLX/gCbW1tfOpTn7K2czNAJcWoZPnIQrFp0yb27dvHn//8Zy666CLL+5+NgKVC/1RdbyV0XS7XFA14uusq7boUc72+vh6v1xsPBAJzkQIYUwxzGHhV07QBPcb5q0hmd1wI8RDwA03Thi3vVBYUHUql/HbpyGYKNZu0QQrnefPmGf7ObHHI0WiUcDg8rfagEo14vd6chaIVgllBMTyHh4dpb2+vOsGsCmoMDg5SV1fHvn37OPzww6ddNM01kqsRaqOhYpAVz2HOnDlTYpCzZduqNoTDYX7/+99z+OGHc+SRRwKT5KRqzPecC4qNd45EIjz44IPWdShHlCq0pxxwu92cfvrp9Pb2sm3btoKuYSZg+f3+nAhYbW1tzJkzx8iS19LSYkR5OJ3OGQVuObKz9fT0xIApdYmFEHXIcKr/K4ToBr6DNG9fCzwAnAZ8rmSdyoCiQ6lCoRDXXXed4TNWiMVih4SvqIIAZrP1m2++yfLlywmFQoaTPx0qBGkmEpgqaJGr+c9KwaxYvw0NDcTjcSYmJiqW3Sgd0WiUQCBgMNybm5uJRCLs2bOHU089dcbz6+vrjZzildbc1I5dacipVMrwH0/HrPZ4PAbRr1q1oFQqxa233orb7ebss8+e0k/lKplt1Yag+JjhdevW8fvfl73WPTDZ99nGuXA4HPh8Pv7u7/6O2267jYULF05JjQyT/t9sJuhCCFhWQJHBSoXDDjvMpWnaorSPVwIfQBbB6EGWltyoaVoUuEtPSnIf0i9dFuRqp1ShVE4hxPe//e1vGw2/+c1v+MY3vsGf/vQn4zOV5SVdo45Go8YPC3JnduDAAU466SSCwWBWoaoW4ekEqCpokaufWWW6KlYwp1Ip/H4/oVCIlpYW3G43yWSSgYEB3G53xRZT5dsPBAIkEgkaGhqmFPvYsWMHPp+P+fPnz3gtRcyYmJjIKYe51VBmXSWQIf8YZHMhiWpdaJ988kn27t3Lxz72sUNcCMUSqyoJp9NZVKa2devW0dvba0RElBOzNdtWXV0dqVSKVatW8frrr3PLLbdw6aWXGkmflABW1gHlA7aagFUISj3mixYt8jidziVpH48DCzRN2yqE6ATadcGssATYW7JOZUDRoVRnnHHGIaEOqthF+g9rLg8JMh1hLBajo6ODSCSSMWQhmUwyPj5ulG7MBFXQorW1NSefohLMxeazjsfjjI6OYrPZ6OjoMO5dV1dHa2srw8PDZc85rawTqtReQ0NDxmxW27dvRwiR88NXX1/P6OjotPHnVkLVQQ6Hw4TDYSOhyEx1kLPBXEiiGoXz4OAg9913H+edd17GMEKXyzVr/c7F+m5XrFjBFVdcQTAYLLtwLnZjUQ5kI2CFQiEOHDjAUUcdxZ49e/jrX//Ke97zHkP7dTgcVTmX7HZ7SePL582bh8/nSy8duU8I0SuE+P+ArwFfFEL8FPgNsAD4B/3zsqHoUKpbb72VpUunmO+NmFIzlNDo6JhMUdra2srll19u5CrO9OAqkli2HNv5FrSwSjCHQiHGxsZoaGjIKLBcLhc+n4+xsbGcy1MWA+WzDwaD1NXV0djYmLVcYzweZ+fOnXzgAx/I+fpqs2VOHmM1lLavBLKKQW5vb7eEjJapkEQ1IJlMcttttyGE4Igjjsh4zGwuaKD8iJnWhVzgdDr5+c9/XoKezQzV70ojGwFLlbbMRMBKJBL4fD7q6+u56KKL+MlPfsKqVauyzrFqgcPhKCk/pKenB5vNtjRD0yeATwGvAK1APfBB4CDwE+DOknUqA4oOpYpEIvzsZ1NLPcdisUOIXbFYzJg4Ck6nk3nz5jE0NERbW9shN1VkhGyhVZBfQQsrBLNKbhKNRmcsjdjY2Mjg4CChUKhk4Qyq5nUwGMThcNDc3Dzj4r1nzx4AFi9enPN9zDWSrRTOilSoBLLyazU2NlpucTAXkqgm1vYjjzzC2NgYH/7wh6c9ThGrZptwhklTZaFVnu6//37efPNNLr30Uot7Nj1UCcZyMLbN/t90QZxMJo31U72aTdCZ+qYsLQDd3d28+93vZvPmzfT09FR1aGSpfc7z5s0jFotN8efp4VSPCiH2AMuR4VNRoF/TtN0l68w0KCqU6uKLL2bJkiXMmzfPOFaZI9NNc6rWbTqCwWBGzVMJwekWaZXlKRc/sxWCWbGdnU5nTrHLNpuNlpYWhoaGcLvdlgqERCJBMBg0al63tbXlbK7duXMnhx12WN7aqNfrxe/3Fy3cksmkIYzLmRTEXEii3LGf2dDf38/f/vY3Lrroohn7NBtMrNlQLCnsxRdf5He/+13ZhbOZsV2sO6TcBCyVdEdh48aN7Nq1iz/+8Y9cccUVVbVBNcNcnaoUG6Kenh5CoVCnOdZZ07SULqD3AfvSz8lQZrLkyHl11jQtBmjf/OY3+ad/kq7nRCLB4ODglAFUMW7mH16ZtNO1YxWXmkkzDofDpFKprAtWLBbLuaBFsYJZmYz9fr+R1jPXSeN0OvH5fIyPj1uyW1XVuNRmp5C0k7t27eKoo47K+94qJCkUCuVNDFO/tUoI4na7DVJXOf1e1SScU6kUd911F6tWrcopk5PL5WJsbGxWxt06nc6iTJXr1q3j85//fEWsHvkUYzBnwMokgMtJwHI4HFM2czabjXPPPZfrr7+ehx9+mFNOOcWye1kJVZdaWQushsfjwe12R0OhUCfQpz6frmZzuQUz5CGcARKJhB2k5mOz2QxBbEam5COxWMzwO5mhTKTpP4AigWUr8agSjeRS0KJYwZxMJhkdHSWRSBScf7mhoYGBgYGspLdcEIvFCAaDhMNhfD5fwUSz8fFx+vv7D+EJ5Aqv18vY2FhOxDAV8hQOh4nH47jdbiMWspJVnsbHx6tCwL366qvs37+fj3/84zkdr7SnbGlxqxnFZgpbu3atUaRFCFGCHmZHOnt4pgxYSjlRQrdSBKxMMdper5czzzyTP/7xjxx55JEzVmurFFTOiFJtxNra2iL79+/vwSScqw15PeGjo6NzAVatWjXl82uvvZaTTz7ZcOSnC2eVeMT8UKZSKYLB4BSCmEIgEMDlcmUUZOaCFjNpP8UK5mg0ysjICB6PpyhSV11dHc3NzYyNjdHZ2ZnXdcwxyvX19XR1dRX1gO/atYuWlpaMPv5coHJuZ9IkzDHI4XCYRCKBx+OhsbGx4tWdFJSGUig5ySrEYjHuvfdeTjzxxLwYyCoZyWwTzkorLFQbam9vZ926dQwMDJRFOJsJWNFolEgkQjQazUrAUm6rQiIJSgUVTpXO8BdC0NPTwwMPPMD5559fuQ5Og1L7nbu7uxO6cH6mZDcpEnk94e3t7fsANE0jlUoRCASIxWJ4vV7DlOP3+w0h7Xa7DT9Zukl3YmLCSOlmhkrgkY0EZi5oMR2KEczquwWDwaJZ3Qrq4Q0GgzOahJXfPhAIGEx2qxjfe/bsyVioJFeYayQ3NzdPiUFWrgiPx0Nzc3NF6yBPB5XQo5LC+ZFHHqGuro7jjz8+r/NUSNVshNKeC9WGnnvuOUv7k60AQzoBS81hVYKwlAk4rIQyoadbOG02G2eccQY//elPOfbYY5k7d24Fe5kZdXV1JRXOPT099meeeaanZDewAAVvv202G6lUyiAtwKRvtqury2DhBgIB/H4/TqeTwcFBwuEwdXV1RCKRjJNChSdlqw+dS0ELJZhbW1vzNiOrmGnA0hhlm81GU1MTQ0NDWatumWOUU6mUkfbOyoWgt7e3aF+Tx+NhYGDACH1SMcStra1VpTlkg8vlqlhCFYCRkREeffRRLrzwwrw14GJ9t5VEsckl9u/fz8DAAOvXr8/p+GwELPU+GwFLmZ/VPE4mk/T392cNTaxmKOGcbuWaN28ea9as4a9//SuXXXZZhXqXHaXWnBcsWOBFZgKrWhRlG0skElOEn7nUl91uN3bKu3fv5uGHH56y47TZbHR3d7N8+XIWLlyI3W43TKGZwqKUn7mlpWXaBS0SiRgJSfIVzOFw2Ei0kS3uuhg4nU48Hg+BQGCKryeVShkFxG0227QxysVgdHSU0dHRvEKozH00hzxFIhFLY5DLCVVCslJ+53vvvbfgcn7F+m4riWJjhn/zm99wxx138Le//c34rBACVn19fV4ELHXMbBzz6YTcqaeeyve//316e3sLWhNKCbvdXtJc8vPmzfM4nc70FJ5VhaJW1XRfRnoO2ldeeYWtW7eyZMkSjj76aFwuF3V1dUbs7/DwMJs3b6ajo4PTTjsNgObm5oxhVaqgxXQm5kIFs8rdrRjlpTR3NjY2MjAwYBCq0mOUS+mb7e3tpaWlJWcfpzkGWVUTUzHIExMTJJPJWSeYYbJSUiX8znv37kXTNP75n/+5oN+5rq7OMPnNtrF3OBxG+tV8oAhYixcvZvv27YyNjZWVgGUOp6qkK6QQTCecW1pa2LBhA1u2bOHyyy+vqo2Hii8vFbq7u3E6nYeV7AYWoGjN2Wz2TRfO8+bN47jjjmP58uXGQqIKYnR2dhom3N7eXv785z+zZs0ajjvuuEPuk0tBi0IFczweZ2RkBLvdTmdnZ8nZlKp+aX9/PzabDZfLRWtra1ke+r1797Jo0fSbxVxjkGdDIYnpoBJ6lHux3bJlCxs2bJiRMzEdlPY8G4VztjSe08X/KiVgwYIF9Pf3EwgEaG9vLysBS7GHZxtmyrZ10kkn8b3vfY9du3YVHMFRCpTa56wTc6vP2W5C0ZqzecFWjGLj4g4Hc+fOneJrUkzrVCplFIZQf62trYcI/HA4PGNBi0IEszIlq3SOPp+v5A+5OXFIJBJh3rx5Zc32tG/fPo455piM/co3Bnk2FJKYDi6Xa0qChnLgwIED7Nmzh3POOaeo6yjhXG0lSXNBIpEwrC7TZcBSLiAzAUvNxwMHDtDTU1534WwtHTmTkGtqamLjxo088MADLFmypGo22qXOzNbe3k48Hi98h1wGFCyc0wdOPWzm3XwoFDJ8q2rnGY/Hef3111m9erWhCcfjccPnoUoT+nw+EokEo6Oj0xa0KEQwJ5NJxsbGiMVitLe3l1y4qJrD4XAYr9dLd3e38X+5hHMoFGJgYIAFCxYYfSomBrnaC0nMhEpUeXr88cdZuXJlwWFsCoWah0uNXAhY0WiUYDCIx+PJSsDKBrfbzYsvvlgRDa/UxRhKhVyIVSeeeCLf/e53ee2118oeQ54NpU5E0tbWRjQabRRC2DVNq0qTSFHC2fxAKZO2+j+RSHDTTTcRjUaNFJwej4f29na2bdtGJBJh2bJlNDc3E4vFjFAhr9fL6OgooVDIKHWYTYAVIpij0Sijo6O4XK68Y47zRSwWIxAIEIlEDolRVolJylUj+Y033jAsFQMDA0YMshrfQsahWgtJ5AK73W74MsuRcWpsbIyXXnqJK664ouhrOZ1OxsfHLehV/lBjls0ErZINmeN/zQSs0dFR3G53wRnaVq9eTSqVNZFTyaAIq7MNuWigDQ0NHHPMMWzZsoUVK1ZUjfZcV1dXMuGs82ciExMT7UC/5TewAAUL55n8zeFwmBNPPJG//OUvdHd3s3TpUvbu3cvw8DB2u51nn32WBx98kMsuu4w5c+YYE0LlrT548CDhcDhrBpt8BbNKehIIBIqu4TzTfcwxyvX19TQ3Nx8igMtRI9kcg7xjxw7Dz9nU1GQJ8axaC0nkAlXlKRqNlsU8vHXrVnp6egzLRTFQC26pykeWMgNWseFUn/3sZxkYGDik2E6pUerQnlIhVw30hBNO4KmnnmL79u2HJJmqFJRwLhWam5ujExMT3bzVhHMmf7OZSR0KhVi1ahU9PT089NBDBINBTj/9dNra2rj22mv5+Mc/TjAYNEyqZqhygXPmzGF8fNwIr1LCJF/BrMzjqVSq4BScM0GFGgUCAZLJJPX19TP6sRsaGhgeHra0RnKmOsgej4exsTEWLVpkaT1cRWirllzV+UIl9Ci1cA6Hwzz99NOWZWNS2mkx7OFcCFgq/EhtJK0gYNnt9qJ8/S0tLWzbtq3g8wtFqYsxlBJqMzedcPb5fKxbt45nn322aoRzqRnbHR0dyQMHDnQDL5bsJkWgKM05PYxKabnqIXe73XR1dXHCCSfwyCOP8Mgjj7Bp0ybDHB4KhQ4hepkLWjidTlwuF8PDw8TjcZqbm4lEIoYfOhfBrI5XIUBWP1jpMcoNDQ2HpCrNBqfTaZjLihEQmeogKxeC2ogcPHiQdevWFXyPbKimQhL5wul0EgwGS36fZ555hvr6ekv9ebkI51wzYGUjYJUCDoejqDFfsGABe/futbBHuUGtWbNROCtS2EzckLVr1/Lzn//cqBRYaZSasd3V1eUAukp2gyJhiVlb7SjV/4ropCZxS0sLRx11FDt37uTuu+8mkUgwPj5ukEEUMhW0sNvttLe3Mzo6Sl9fH6lUivb29hk1hlQqhd/vJxQK0dLSYjnxSmVDCwQC2O12mpqaCvLd1tfXMzExkbdwzhSDnK1Kld/vx+/3TyntaRXcbrcR6jbbFq1yJPRIJpM8+eSTHH/88ZaaoJVwNvt/0wVwegYsl8uVFwGrFCjWPLxw4ULeeOONisw3JSzKWbzCCuRqHlZ1nl9++eWMUR3lRqnN2l1dXR6bzdZeshsUiaLM2kpAppPBVOUkmMxTPXfuXBYsWMCWLVtoaWkhGo3S3j45LtMVtKirqzOqIfl8vhnN0vF4nNHRUWw2Gx0dHZb6Q5PJpCGUrYhRVibneDw+4/cqtA7ywYMHcbvdh9TYtgLmDdpsi7tVi2ypSCcgc5kHg0HWrl1b0PnZCFiqlvnExMS0BKxq2zCpYgyFCtdVq1bxpS99qSIJZLKlwqx25GoettlsrF27lhdeeKEqhHOps4S1tbU5vV5v1cY6W2LWNpPBkskk0WjUKHQRi8VIJpOGqff0009ncHCQhoaGKYv5dAUtwuEwY2NjzJs3j4mJCUZGRrKG/IRCISM/t5W+XHOMstvttiwESzHUQ6FQRtZzphhkj8eTVx3k/v5+uru7S7JQK7/zbKyUpEhhxRRjmAkvvPACK1asmDazXSYCllkrzkTAUmlgu7qq1iqXEeZiDIXMl46ODv7jP/6jBD2bGaX2gZYK+ZiH16xZw4MPPsjQ0NAU5akSUES2UqGlpQWXyzWnZDcoEpaYtc3JR5RJe3h4mH379pFMJvF6vYZmGIvFSCQSRhyz2h1lK2ih8l2rtJoul4uRkRGDEGbOezs2NkY0GrU0BWc8HicYDBIKhfB6vSUhlHm9XkZGRox83plikH0+X8F1kPv7+0u6iCvhPBv9zsWyh6dDPB7n1Vdf5fzzz7ecgKVi9WejO6EY4QySsX3OOedwwgknWNyz6VFqH2ipoEqk5oK2tjbmz5/PCy+8wKmnnlrink2PUpu1W1pasNlsVbu7LejpUMkGVKyoWXNWtZv37NnDPffcg8fjoa2tjWQyidPpJJFIGBrimjVrWLx4sVHaMf1hTRfMIHdTra2tDA0N4ff7aWpqIhaLMTIyYoRhWeETMsco+3w+SytUpcPhcBj+dqUtFRuDbEZ/fz9HHnmkRb09FC6XqyzEqlLA6XRaliksnYC1fft2QOZT7+vrs5SAVWx95EqiWCH3t7/9jblz55ZdOM/WLGH5avxr167l8ccf55RTTqnoxq8MoVSkUqm3ls9Z7daVlqdMVYqk1NzczKpVq+jt7eXgwYMcd9xxOBwOI3YZYHh4mFQqZTCp081+mQSzgs1mo62tjYGBAWKxmMEUt6K8oopRjkajNDQ0ZIxRtgLpdZCj0egUspuVoVWDg4NZ62NbAZX9rVRxt6VEPuxhcwWkTFpwOgFrx44drFq1ijlz5pTE/6s00NkmnK0ghVWKsT1bNed8hNzhhx/O3Xffzb59+yyJyy8UpRbOerro1pLdoEgUJJyz+ZsVSUkJ6vXr13P77bcTjUZZtGgRXq93Spas8fHxKSFYCtMJZjNUxqF58+ZZEooUCASMrGRmk7lVyBaD3NLSQktLCyMjI5ZXpVJks1L6j8wJPabzrVYj0osxFJsBS/12oVCIXbt2cemll5ZMeM7WxBjFpsKcO3cuBw8etLBHuaHUwqJUyLffPp+P5cuX88ILL1SFcC6V66a5uZlEImFd4geLUbBwNvublQBVJm2QBC+Px8OFF16I3W6fkuZRxQZninPORTBHo1FGRkbweDzMmTMHv9+fc2yxGalUyqiMlUqlaGhosET7Tr9Hphjktra2KYQyNQGtLiQxNDSEy+UqWRYyBZXQYzYIZzMBKx6PE4lEGBwcND7PRMBS73O1DGiaRn19PQsXLizZ95jNwrkYV8Lq1avRNM3CHuWG2UoIK4RYtWbNGu644w7e8573VMwyo6yzpRLODQ0NxOPxygd0Z0FBwtns51ILshJCTU1NRqpMJYAUE1tlzIrH44yPj9PW1jblh1dM62yCWYVlBYNBWlpaptxX+Z9zgTlxSF1dHY2NjZb4ds3jk2sMsoLNZsPtdlteSGJwcHDail5Wwel0VlUxhpkIWHa73RDASgNWQtiKsdq1axfLli0r6bjPVuFcrHn4ox/9qIW9yR2zVXM2k2ZznY8rVqxg8+bN7Ny5s6LFMFToXSnQ2NhIPB73CSFsmqaVP2H7DCjKrG0mgykhZLfbjdhLRXS68847OfPMM40dnGImmwWwEszZQpQSiQQjIyMAU8hZNpuN5uZmBgYG8Hg8M2ZMmpiYIBgM4nA4aG5utsyMXGgMshkejwe/329pIQkVdlZqlLsYg4qVzRR+pMzU6QQslQM6nYCVTCZxOByWbYpSqRS7du3izDPPtOR62VDqONBSoVghNzg4yJNPPslZZ51lYa9mhlq/ZhtD3maz5V1Ewul0snLlSl599dWKCudSkh5NssINVF1Vk4KFs9PpJB6PG6Y+FWqktGaVKnN8fJzXXnuN97///YD0M9vt9inp4WYSzMrUXV9fb4QbmaEydI2NjWXUEpPJJMFgkGAwiNvtPsSkXCisiEE2Q5mGrSRWjY6OlpQMplCKYgy5ErAyhR/lQ8CyOpxqYGCAYDBolEEtFWaz5lyMkNu+fTvnn38+kUikrEKyECFXLShEyC1cuJDHH3+8hL2aGaXUnAE8Hk9kYmKimbeKcFYLcDQaxel0TjFpK9axzWbj0UcfJRgMYrfb2bt3L6FQiFQqNaUs2XSCOZVKMT4+TjgcnpEc5vV6mZiYIBQKGfG2iUSCQCBAKBSa0aycK7LFILe2thYtlMyFJKwqxjAyMsKKFSssudZ0KLQYQzYClnqfjYClzM9WLM7F+kDTsWvXLubMmVPy/MSz1QeqnpNChXNnZyexWIzx8XFLC7nkglILi1KhkH739PQwODg4hUtUbpQ6EYnH44npwrmvZDcpEEURwpT5VmWHstvthoaryhTCZH5hv9/PnDlzWLlypUHGyiaY4/E4IyMj2O12Ojs7ZxR8NpuNpqYmI945GAwaaUSLiVFWplMlkK2OQU6H2+22rIyhClUrRdrOTMgmnNMJWGZBnE7A0uus5k3AKgZWa6C7d+/msMMOs+x62aB8t7PNzAqT2nMhv69KqDMwMFAR4TwbN0SFCLnOzk4cDgcHDhwoy3zOhFJvhnw+X3x4eLgqGdtFEcJisRg+n89gZquY47a2NiYmJjjrrLN48803CYfDrFmzhmAwaJC2QqEQ4+PjhwhmRdZS7O6Zyi6mIxKJcODAAVpbW6eEbeWD9BjkVCpl+I+tDnVKh8vlYmxszJJrRSIRotFoWRYw9eAHg8FDTNHpBCwrSxBaASuFczKZpLe3l40bN1pyvekwm82sxQg5lbBoYGCAZcuWWdyz6VFqTa5UKETI1dXVMW/ePPbv318x4Vzq8fb5fCmgKhnbBWvOinXtcDgIh8N0dHQQCASor683KkK1trbS29uL3W6nu7vb0OAmJiYyCmaVkjAWi+Wcu1rFDgcCAeLxOE1NTYTD4Yy+6VyukykG2VzUo9RQvnwrfLeKoGUFwSwXApZiSLvd7mkJWNUGZR62QgPt7+8nGo2WNITKjLejcLbZbLz22mv09PRY3KuZ8XbSnAFDOFcKKpSqVNBdT28d4VxXVzclV7ZafMPhMJ2dnQQCAdxut1HP+bjjjjO0t2yCORqNMjo6isvlorOzc8ZFcroY5aGhoSm+5+mukS0GuVIaXaG+20wYHx+fkcFuxkwELCCr9qvmxOjoaM4hbdUC5bu2QsgdPHiQ9vZ2y0uUZsNsFRbF9ruSmtzbxecM0u/8yiuvlKBHuaHU4+3z+WxAVRYFKEg4qxAOl8tlkAWUaVvVOe7s7DQyRjU2NvLKK6/Q0dFBPB6ns7PTEMyK3R0IBGhubp7R12qOUbbZbDQ0NBySgKS+vh6/359ROBcSg1xuqGxbxQrnTGFZpSRgqdzDs9EHqrRnK4Rzd3e3Rb2aGbNZOBez6J566ql84AMf4KqrrrKwVzNjto53oZpzT08P4+Pjlod45ooyaM51vJU0Z+VvdrlcBAIBWltbGR4epq2tzSjXCNLEt3XrVoLBIENDQ5x44olEo1HGxsZYvXo1iUSC0dFRUqnUjMJRCf1AIDBjjLLb7TbM406n05IY5HJClTEsFMr8PDY2NqVetJmAZRbAVhGwVPjSbBTOilxVbIhdX19fWbW6t5uwUKirq7OMm5EPZqvmXOh4t7S04PV62b9/PytXrixBz6aHssiVCvX19Q7eSpqzCqNSbGUlqJXP0ePxMDo6yrZt27Db7WzatIlbb72VpqYm+vv7eeKJJ+jo6CCVSuH1eo2Y6Ewwxyi7XC5aW1tn1ChVti3F9rYiBrmcUFnVpkMuGbBUURFlgi60AlI+UOSqah/jdFgh5FKpFH19fbzjHe+wqFczY7YK52L73djYSCAQsLBHuaHUwqJUKHRTYbPZ6OnpqZhwLvVmqKGhwcFbSXO22WwkEglisRhut9tgYY+NjdHS0mJU+dm7dy/nnnsuPp8Pp9NJQ0MDK1as4KmnnqK/v5/ly5dn1X4TiQTBYNAwl+dCEDOHPCmmcnd3tyUxyOWE8jlPV4DBnAFLZbdKJ2Alk0laWlpKnlfbDKs00HLDiphhv99PKBSqmbVzQLGac2NjI36/38Ie5YbZrDkX2u+enh7eeOMNi3uUG8ognF28lYSzysykkmXYbLYpCUkUe9vr9XLw4EGOPvpog4E9NDREPB6np6cnI2kmHo8TCAQIh8N4vd4ZY5TNIU/mGOS2tjYGBwdxOp1VK5inI2ApopvT6Sw4A1YoFCppNapMmK1Zq6wI2ejr6zPcJeXCbA3tKXbRveiiiyqyAXy7CuetW7dWxF1V6vF2u912p9NpTcYni1FUEpJYLEYkEsHj8RjFKFRFqUgkwsaNG9myZQvDw8P4/X6eeOIJ+vv7WbFixSGxt7FYjEAgQCQSob6+PqtQzicGuRSFJPJFOgErXRCnE7A8Ho/xvRsbG4vKzKNSqpYTs1WTq6urK8rPD7I8ZylKjU6Ht1PGKjP+7u/+zsLe5I63o3Du6uoyImMqQQor5Xjr/KPymRbzQFGFL5T5VSUj8fv9qgwXfr+fNWvW0NjYyMsvv8zChQvp6+vD4XBwxhlnGNqsqqMcj8epr6+nubn5EE230BhkZXIvNdLjfzNlwDILYJfLNSMBSxUNKQYqbWk5oRjbsw1WCLnx8fGyh5G9XTXnu+66i1deeYV/+7d/s7BXM+PtKJxV1Es4HC67cC5HNT2n0/nWIYSplIF1dXUGs1hpgDabDb/fT3t7OxMTEySTSZYsWUJTUxPd3d1GcQy1E0smk9TX1x+SCcyKGGSXy8XIyIgl5phMBCz1PlsGrGIIWFaYh5VVo5yYrZqzFULOXLO8XHg7CguQxS9uvfXWmnDOEcWsf2odq0RJ2FKPt67clScpQZ7IWTgLIdyf/OQnDwf45S9/idfrZeHChRx11FE0NjYSjUbx+XwEAgEmJib4wx/+QDAYpLm5mfr6epxOJ83NzSxbtswwaafHKCuhbVUMssrZHI/HZzRtmzNcZSNgmbXfUmfAUm6DYmBFrHS+mK2anFWac7mTY8xmYVFMv71e71tSWJQShfbbZrNVbLyhtGZtnSc1e4WzEGIdcOeNN97oBJnYPxKJcPvttxONRvnyl7/Mhg0bCAaDjI2NsWXLFlavXs2aNWsIhUIEg0EGBgZ4/fXXOXDgAO95z3uYM2eOsZCHQqGSxSArzV6R1bIRsOLxODabbdoMWOX0JRZbKSmZTBohbuXEbNacZ6tZ++0mLEBqc5UiHs7G8S527arkZqiUcLlcpFKp8i6SOSJXdfR64Ev33Xdf18aNG7/0+c9/HpAC5N577+U73/kOP/7xj2lsbOTpp5+msbGRU045xUgc4nQ66erq4thjj+WOO+7g2Wef5eSTT2ZiYsIou2hVDHI6ASsSiRgZyMwELCWAFQFLCeBqQbFCTmndldCcZ+viZUUoVU0454ZiF925Lhc/P3AAhoagjBEJsy25joIVlopwuDIlj0s5vx0OB6lUqirjPnMVzquBn8Xj8Wtgkq0NcMYZZ/Dd734Xt9vN+Pg4Pp+PUCiE3+9nYmICt9tNe3s7dXV1xs5rYmLCMIN7vd68hWI+BCxlzm5tbS1bCUIrUKxwVqSscqckna3s4WIXr0QiYVmpz3zwdhUWZw8MQH8/3HgjfPrTFvZsZszG+Q3F9dvj8bwlNWddjlVfmkggV0mlAf8Yi8XcgCEEk8kkmzdvZvHixcTjcVpbW1m1ahUjIyPccMMNvPjii2zfvp2nnnqKrVu3cv/99zM+Ps66detobW3F5/NlFZbKLKvyaI+OjjI4OEhfXx8HDx5kZGSEYDBIIpHA4XBQX19PW1sbc+bMobu7m/b2dlpaWmhsbKSuro6HH3541ghmmEz08sADDxR0fqWEs9JAC+13JfHoo48WfK4a73KH7aVSKR555JGy3tMKFNXvVIrEt74l33/721BmYTlbx7uY+V0ps3ax/Z4JukyoSsGQa6f+Efjseeed948AX/va1/jCF77ARz7yEW666SauueYa7HY7w8PDOJ1Ozj33XNauXcvw8DC7du2it7eXffv2EQgEOOaYY1i+fLnh/41Go0xMTOD3+xkZGWFwcJCDBw/S19fH6OgooVCIZDKJ0+mksbGR9vZ25syZQ1dXF+3t7QbhzOPxZGRxK9ZzuRPkFwulWRTab3MVqXKi2H5XEspdUwiUG6ESm6Fi+l0pFNXvhx8mOTIi34+OQpmF5Wwd78997nMFn19Js/ZnP/vZkl1bL+ZTlZpzTiuJpmnPCyGW/cM//MOD11577fGLFi2ivr6eCy64gOOOO87w7aoazx6Ph+OOO87QfEOhEKlUira2NhKJBAMDA1kJWOb4XytMGm9XM6syiVeisMdsHe9iUCnN+W2J73wHuyJLBoNSe960qbJ9eovD4/EwODhY6W5YjmrWnHPe5muaFnv++ecbAN73vvcZvmLFsFZpJmEyx7UiYJlLDg4ODjJ//nzcbnfZzMxvR2GhhPNs9UnONlRSc35L47zz4M9/nvqZy0WdeqZTKbjjDkgfh3PPhdtvt7w7b/nxzoK3Klu7moVzXp2Kx+Mr1Htz4QUlZNVnXq+XlpYWOjs76erqorW1laamJhobG3n88cfLSsyaqQZxNaOYfqsNSSVy4b4doYRzTXO2GF/9KixcCOZkOtHo1GPM/3s8sGiRPK8Gy1DJOOdSQl+vqlI42/LRKp9++uk/f/jDHz6nmBvOnTuXAwcOFHOJGmqo4W0EbzLJVw8e5JRgEN8069WEzcaW+nr+a84cQrOI/FlDZWGz2Q5s3759XqX7kY68hDOAECKlaVpW9eiaa66xA2uB9wGNwCAwDDz6xS9+8fki+lpDDTW8nWGzfQz4DpApJ20E+CSp1I/L2qcaaigRLN1eXnPNNV7gP4C7gVZgPfBp4CjZfM2/6sK7hhpqqCFfPAtEs7RFgGfK2JcaaigprGavnAq8GzgSqTEvRIZhBYGtwN8jY6bvsfi+NdRQw1sfG5lcs1JACPACNv3zjcC2ynSthhqsheVm7RpqqKGGksBm+x3wQaRQ7gc+hTRzdyNN3b8llbq4Ut2roQYrUYjmfI3lvaihhhpqmAmp1IeAD6V9elsFelJDDSVH3ppzDTXUUEMNNdRQWkyrOQshLgA+A/wLsjLVamAcaGcqMWOFpmlvCiGOMh33KnCxpmmafq1ytP0RuBKYA/QiE5ov1Nu+CnxOP3YPkACWZGjL51ir2w4H3Hp7t97Wi2S+25B+thSS/PJKFfT7f4HrgCYgBvQBbXrbTUgriw8IA3uB+VUw3n3AXMCFJESm9LGNAS9U+XjfAfw70s86AewHeqp8vA/o78dNbU69DWA30ky9RG/rADqBpP49UkyuLxcj55xP//4XaJp2N4DVbfra93Vgnqnt/wEfAJbq36cD+bxOAP8JfKRCY2xuW6OP3eumtseR3B+3PqZvItfI9PUliVzXNWB5mftdTFum/8ste4puS0dGtrYQwi6E+DfgN/oxtwM/AlqAIWAMaNY0rUH/e1MI4Uk77ufA3fq1ytF2H/LheT/yoelBPlgtwC+B3+uD0o1cnOcgBYm5LZ9jS9H2mD7ebaa29wG/QIaljSHD0lqrpN8/Bh7V7x9GCoNjgd8ihfbv9b6mgGX671Lpfn8HKRw+qo/pBPKhn1/l4/0X4Av62LYgsRQ5x6t1vK8BFgEr0tqWAFcB/wYcph87V/9rRgqSTyD9ys36+tIM3AjcjBSWm4HNQghnCdr+Hbn2LTO1/RUpAK5BbvjnIjf/a5Gbpu8jhXy5x9jctgsJl6ltC1K5+hpyTif1MU9fX1zA/0E+G98vc7+LaUsf7xbKL3uKbiMDsmnO30FOum8gd4phTdN+DiCEaEMuaKcjQ6YUTjUfB/xQCPF/9ONsZWj7rBDi/cgfcBlywT2gaVpMCLEDOSn3A5uQG4yU3mejLZ9jS9B2lP49EkDA1GYDnq7CfqsJ9X3geORCOoJciOv1tpuBdyB36guRYXXPVMl47wfORBKJDiAjDKp5vDv093/Qx/sAsABJilK1RatpvDuAS5CL5odMbSHk/Nirj3UCqa39q35eI+DSNM38rN+ttyc1Tft7/bt+UAgRRYZu1lnYlgD+GaltnqzahBBfB85BWrfW6P1+UX//C+BCoKuCc9oGrEJaDy8ARvW2B4BPIqNl3qOPvyPtvKer4FksqC2935qmxSi/7LGizSxLgezC+Wv6bvVypAntJQAhhNrB9AG3CCFeB/5L07Q7gJXA9rTraEj1PVWmtu3IBfcTevvl+ucrkYIj/Tz1v2q7O49jrWwbR2r83wZOBN7Q25SQuwpYjJyAWoZrVqLfxyMX1f9kcmOh6f08Sm8zn9cIHIPc2FV6vFXbOchNxfOzYLznIk3v5rYu5KYiVYXj7QbuQoY3xdOOs6f9P6oftwtpDbhKCHG1fo0L9H4fpR9rxoj+HVMWtg0j3RsL9H4DoGnaE0KIQdN5ykz/PHAGcvyP0Q+vxJweBm5AjmOIQ+eteV18mrT5ro93DBn2WolnsdC29DmthFy5ZU+xbbkJZ03T3jT9a0c+4CB9QQ/q77cgJ+bvhRBHI3/oCaZiAmkSooxtw8DVyHjrm3Ubfz3y4Uk/T/1vbsvnWCva6pAL6e+QY51Cmoh9yPGOAE8CtyAXgeOQu/qBCve7GWkKmwC+idzRnog0zdcjNQvzeeo66f0sd79VWwtSy3yIyTlVzeO9F2lyPBJ4Te9rA5NxvtU23mpM3cg5rdqSacdG9c/UnBlAjv8/IdeYjwghvm3qtxnp17GiTT17Lr3fmc5zIef//2qa9rIQ4r1MHf9KzGnVbzdyPCfSzjOvi8cy6UJT8/2fkBaADyNdEQNl6rcVbenjrY5PP7fa26Ygl1CqBHIBQNO0l4BThRB/AcY1TbtdCPEAcLZ+E2/auT6kibaujG1jetswclE9S++bK+08m+l/1UYex1rVdhZyjJ/T22xIU2tA78sY0nxWh9wdDiG1uj0V7ncIuQjcq7f59b4t0vtiTztPXcfcz0r0W7UtQS5AESbnVDWP9wRSWzsNOB+Z5GdQ/8xZxeMdTTsu/ViX/hrUv8OTwIu6uXIUuRFR60t6VRHzdaxuU/1Ob7MBRyO1zE/on5vnu/q/EnNa9buOqeuki6nr4ghyTj+kf67G+xmkeX4xcr6Xs9+FtqWPt0IlZE8xbYegLtOHaQgDAkAIcbwQ4pP6/6/o7YoBuF0dZ4I6ruRtQogPAqektbmRJrPtSKKMuc18HdVGHsda1bYK+YP9P+BLep+PRfqybEhmvPm8BmBfFfT7OeT8eTWtb/1Is5kr7bxWpN+r0v1WbfOB+9Paqnm89yA1hI8geSBR/dhHqny8DyCVANXWnHZsK9KKsQ1Yh5z7qq8CuXkK69+xjalQ37EUbb0cqry0Id05jwIOTdOUr387cvy3mv6vxJxW4+0xtXUhrSzpc/p1ps531W8nk/O9nP0utC19vBXKJnssajsEuQjnMaBRCHEVckG4DvmAPSSE+ADS7PdHJPGgUQhxlc6EvAo5Sf5WjjYk9d+n9+1B5KRcj/R7LUXubAVyt9iNnJSPmtv06+R0rIVt1yI10M8hyWwp5E7qX5EPlR04F3gYKVB8wH9XQb9H9fnxaaSA6NH79n2kFg0yrO0xpP/OjmR3V7rfqm0NkkBlbqvm8T4CKSxOQQqBhcjF9adVPt5dej9VW6P+PVYhn1mHPsY/Qi6wHYBbCPHP+v/LkevLdwG7EOK3QgivEOK3yPXrByVq+ytgM7Xdpvf1M0h+iHEecJk+/uEKz2mB9GE6TW1hffwvRD6n85EKwPcwzXchhEv/XjYkobDc/S6oLX28KyF7LGo7BNMmIdEJYf+EJMj8CMlU7EdO4C6kyelTmqb9TT9+g+k4DfiopmnPlLHtF3p/FyJ3hg5k2IAGfAvJWjwc6b9LZGnL59hStHn09i697TFkaImHyRjFrgznVaLfvwG+iNwQRZG79na97Q/AfyF36WEkyW1ulfT7W8jwowmkhmRuq+bxfhIpgJUJbx9ysa3m8R5EEquGTG1qQQVpEQjox+7V+z9fb9sBfMy0vnwQ+Il+zATwj5qm/bYUbfra91/IDXMD0oztRJrfQa4tbv39BNLydV6Fxtjctg65wd9pavur3uZlMj5ezQ3zfA8h+SNnVKDfxbRl+r/csqfotnTUMoTVUEMNNdRQQ5UhF7N2DTXUUEMNNdRQRtSEcw011FBDDTVUGWrCuYYaaqihhhqqDDXhXEMNNdRQQw1VhppwrqGGGmqooYYqQ004VxBCiNr4Vzlqv1H+qI1ZDTUUj1zSd9aQI4SsivVJZEUvLzKe8A7gak3TBk3HrQD+B1m2sLdMfXsQOBm4RtO0q0t0j15k+s4rNE27Icsx6bF7CWRCkyeAL2ma9mQJ+vUgBXx3IcQxwA+RxQTUZ6r/p2qa9qB1vcwPprG+SdO0D2Vpm9JHIUQPshrTWchY4giyyMPPgF+ZMl4hZHm7zyATbPQg5+n/At83H5ehX+9GloI8o8ivWNUQQtyAzNZ2o6Zpl1e2NzW8FVHb4VoEIcTfI4slnIhMThBA5qj9ODKbmkc/bi4yd/Pplelp1WAQmeBjDJm45GzgYSHEu0pwrwHkRmk81xOEEBuRudmPSmvar/9FLOtdcfigEOKUmQ4SQqwFnkVmnlvGZD7iTchqRn8QU+vK/gL4MjJDVxRZOec7wP83zT3eh6yusyL/r1FDDTWYURPO1uG/9NdvAo2aprUhKzXFkKkKP6C3u5GL4tsdF2qa1qNpWjsy3eQjyHH5uRDCUouOpmkXapo2X9O0/87jtAYyPB/6deZrmva4dT0sGt+fbsz01Ix/RKZr3A4cpY97EzI7VAx4L/ocFkIsBD6on/5OTdNagX/X//+MEMKW5VZNxX6RGmqoQaJm1rYO8/TXIb3gN5qmPSiE+AwynehBIcRiYLfpnN1CiBs1TbtcCNGMFOxnIRfRYaRJ/DOapo3AFJPqeuD/Imu0+oEfapr2ZXVRIcR8pNn8dP06X8nUYSHEuUhNaCUyp64GfEXTtD/p7TcgTXfX6fc8DmnG+4QQ4nBkHu13INNI/kd+wzUJTdP26ekSdyIF9enInOgIIc4AvorMhT2EtE58XtO0oBDic3rbc5qmbTB9r88CXwMe1TTtxExmbSHEifq4rENuCnYD39M07ce6JrrFdL2UOjeTWVsIMQ+ZvvEsZBGHV4HrVIpI/RjVh8uQWuiVSNfHbcDHNU0b14+7HKm17tE0bXGOQ3gE8C9IzTYTzkNqyyngvZqmbQfQNC0O/FSfl/8FfFoI8XVkOtY/AD5N09Q43ImcBw3IdKZ95huY+g2wSB+nK5Dm8C3IufVLZC72cWC9pmnjQogzkXnlNyDTfD4AfE7TtNf06y5G/jYRZFGMHyLLZu4E/k3TtHtzHCPVz27g68DfIXN67wCuTfutmoFrgPchczrvAq5Hzo+MKRXTnu3DNE3r1T9/ENPcM43THcj0t19Grh136+N1CfBZZF7xO5HpHUf1a+X6/F8OfAr5m0eRrosvaJr2UB5DVUOFUdOcrYOa+F8TQjwvhPiKEGIT8ANN0z6jLyJx4KDpnINI4QnStPgPyJy348hF4e+RhTHScRtycXEhc/9+SQhxNoAQwg3ch1yQVSGQHyHL3BkQss71H5n0p9qRi97NQohFaff7JLLggh14TgjRiVxwT0Xm5J2DFJpzphug6aBp2utMLm7H6n08DblAHYU0w3YgzbJ/0o/7FbJoyHrdj6/wQVP7IdB9r3cCJyHHMIkUmNcLIU5ACoJB0ylZTeJCiC5kRaUrkEIrhlw8fyOEyLRh+RJSC/UhBd0lTDUVB/X7Hch0vwx4Sn+9Whc8maBcBU8pwZyGX+uvzUit+kXd2nC26ZgTTf0bynCNILIUIUgewX4m81CDzIn8FeQc2q8L5kuRQmqT/nkjUiBuFUKkV+9xIOf1kfqxRwB/EbKWfE4QQtQjiwxcjnSlxJCbs9/oObYRspDFw8g5vwA5F1YiNz4/yPVeOeBYpHDuRG7S3ovMnf4/yHGoRxar+GqGc28j+/N/HlL4r9P77kZuDu4SQhxmYf9rKDFqwtk6/DMyiT9IQtjnkQJ7j+6PRtO0fUhNU+EdmqZ9Wjc7xpEa1zJN0zr064EuqNKwB/lQz2dS2CsCzvnISi5J4F2apjUhBUd6Qe8lSKHyLaS2185kmbx0P6sNmai9E7gJuWnoRO7a1yLNmV9mshBAoVDaWJf+qhbzT+um1W7kGJ0hhNikj+f9+rEXAQghVjK5MN2c5T7LgWeQxS9a9L/H9LZjdZP1hergGUziVyM1n93IMW1iUtj+P92KYYYXjJKOd+ufGeQpTdNu0e/3DnLDl5CCvJnMGzmQhC6QxWAyYZfp/YL0Rl1Qfk3/91e6xj0FmqbdgtSKAfbp3+EW0yEuJDGyFXifvon8LnJu/QQ5bt3IOdkCfDvtFnbkRqQNKZCeRxai+C9yx0eQYz8ErNA0rQU5/0HOaZA1mtcgNxob9OfnI3rbVTpJ0Ap0AB/RNK2ZSYvDCmThjRbTZydmOHe65/80/fX7uuuiHWkF2Yzc+NcwS1ATzhZB1/xWIxnYdyE1PZAL48+EEBdNc25U07QLkdqAKs+phENDhlOu1zQtoGlaP9JXC3K3DZPC/EFN0+7Xr38DsqqL+Z63aJp2PHJnfiZSyLRkuedWTdN2aJo2oWla0HSPW3QtK4U0Fcayfcccocx2diGEj0lt/z+FEPuQRDql1Z+qv96ov6rxVVrzZmUOTIemaQ9qmnYKsoLZyUhhulhvzjTe0+Ec/fU6TdN69bH4KlJgOoF3px1/m6Zpr+uuj7/onzVSOPxIVjXApUKI4zMco9xXnizXyLoOCCGWIzdAbUhtOB9hmI5fA2iaNoAUOq1ILfvTmqbFNU0bQlY5AzhdkShN+JqmaSFN04aRgh3ghDzuf4r++kdN03bq768GujRNU8JN/Z4/0TTtOb2/v0Rqteb2YhFk0mLxhP4aAn6uv1e1oTPNjemef9XPjwohbkeuR1/UNO2DmqY9ln6hGqoXNeFsEXTtt0XTtJ9omnYWcuE5C+kbA1l2c7rzr0Qufs8hFyilhWb6jcwm14m04xQpx2w+B8mMNt9vjhDiDv1atyIXLsVATr9nuon1kHtomhYhs7kzH3TqrwPIjYLqRzdyk6NqRsOkj/9WpIA6XAixmkkh/ctsNxFCNAghfq339x6kSTHbd58JSss3uAR6qJHaDKWbmqf77QqC7i/9G1IL/Z8M19uvvy4lM5aZ3u9Tb3TB/CBy3IeBs3TBWCjM80iN26CmaQHT52ocHcgNgRlmP7eazy153F9dz5inupAbyNAvMzfE/H8210E2ZOP1jJj811HVL1OYmpqPmch3WeeQpmm/RlrtxpB1yb8LvCyE2JrBXVVDFaMmnC2A7luOAG/o/lilDd+FJJLA5EN9CKFECHEE0rTXijR1z0ESUrLBbFZMv55aPHvSPp+X9v/3kJuH3wJtmqYdw1Tzphmhme6hmynbp+nztBBCzGFSeDyJFNBqoTpS0zSbpmk2JBPepmnaPwFomjaB9HeD1CBX6ufeTXZ8AbgY6Vvs0jRtLZPai0KutVSVwFhs+i51SBIgHLpJmu63KwYf16+9gUNN0w/qr2uyaNaX6a/jwNMA+jy+FzlvhpEukhdm6MNM38c8j9S4dQghzNYK5ReNcehmb7HpvTLR5rMhVPPWeBaEEG1CiCuEEEfrLPRDfs+0fqX/ngrm2G+ze6c5y/GJHD/LhJnm0DeR/T0G+Uxs199/I8fr11AFqAlna/AkUnurA34shGgBIyTlEv2Y5/RX84PVpIfArEbukJPAPl3QXa4fk+9v9LD+eoIQ4nS9Hx9lUlgoHKG/Dmua5hdCHItc2DPdM30BUPc4XwixXl/UvoA04+YNXRD8CDkG+4C7dLOvMtH9uxDCqbOi9wghDqbF9irT9uX6602KMZ8F6rv7gWGdTPZO/TP13Y3fSQihfqdMuEd//YwQYpE+Fp9FCo8o028SLIOmaS8jN1yZ8Hsm+RA3KhKVPqb/DPwfve17ugUEpM9zMfI7vEfTtGdz6IYas3ohRF36mKUxnR9Hjr8duE4I4RBCtCHNzAD3mPqi8HkhRKMQoolJTsYj5I4H9df3CiFW6e//DWlK/o3eP/V7/qOQseEIIS5GCjeAP2e59qjp/fH6eccgwyjLBiHELUiT+feQce3/jR75gPRz1zBLUBPOFkBfRD6h//teYEgIMYgkWK1HLkIq1GGASRbrY0iC1dNITcGLJO0MAyrrU76xo3cihZoDuFcI4Qd+zKGmaRWn+wkhxAhSc1Q+vpnueQPyuzUjF4BxpEDqz6Oftwgh9gkhDup9Ox+5uF9pEqzXIDcGH0IufruRpsk+4FHTtR7W25QJMKtJW4f67uchx/pVJi0b6rv3mo7fR3ay1TXI771U78M4k6Frn9c07c0s52WEEOJCfVwKiaO+mjT3BRjz8wKkOXQZ8KQQYghp+vwBcq7ciSSXKSa/mal9q94n9ZdOclPo1V87kISqj2frqKZpISZ95f+k96UPKQSHmSSXmXEk8vkZQPIRouhsZiHEO1T/st0TOW815G/8shBiFGkCBv27I4Wahpxnzwshxpn0Df+PpmlPkQF6KNw2/d+fCiFeRm4ccmXdW4XfIn/PK5HPzCiTm6+M0Qs1VCdqwtkiaJp2IzI+969I01ej/nozcJymaa/ox0WRoTQHkMJkTCeTXYyMuUwgF/m/Ry5wzfpimWs/EsiF9fdIf9QIcuOQvuP/d/2YceTG4A9Mmr1OYxpomuZHapr3AGH9u3wQKahzRQfSLN6JXED+DJxgjlvVNO1upNB+En2skAvMGWbNWNd4lEB+NdsCasJ1yFSUQ0hrxV+B/9TbTtOvuR8Zu6z8exlDqXTG+FHIhb8PyUp+DviwpmnfynTODKhHjkvezFr9d/lMlrankSz27yJ5EA3IzdBjSNLQuaYxPct0qotJf7/6y2ZFeBz4KfJ3ijPpD83W3/9FEqweRv4OQWR433Gapu3IcMr7mRSALwJna5r2jP6/29S/bPebQBIAf4ncADiRc/Zi3VerxvAdSCH9hn5dDRnC96/TfR/kM/w35KbBgUzwcscM51gKTdNuRfqaH2WSoPkU8jvemPXEGqoOtlTKSrdXDTXUUIN1yJbco4Ya3uqoac411FBDDTXUUGWoCecaaqihhhpqqDLUzNo11FBDDTXUUGWoac411FBDDTXUUGWoCecaaqihhhpqqDLUhHMNNdRQQw01VBlqwrmGGmqooYYaqgw14VxDDTXUUEMNVYb/HxkXVfpYmRJ9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting based on the paired data\n",
+    "from melodies_monet import driver\n",
+    "an.plotting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c2ea616",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mm",
+   "language": "python",
+   "name": "mm"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/jupyter_notebooks/MM_trp_no2_l2_plot_wavk.ipynb b/examples/jupyter_notebooks/MM_trp_no2_l2_plot_wavk.ipynb
new file mode 100644
index 00000000..2888f64a
--- /dev/null
+++ b/examples/jupyter_notebooks/MM_trp_no2_l2_plot_wavk.ipynb
@@ -0,0 +1,1782 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a6bba673",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Please install s3fs if retrieving from the Amazon S3 Servers.  Otherwise continue with local data\n",
+      "Please install h5netcdf to open files from the Amazon S3 servers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.append('../../')\n",
+    "from melodies_monet import driver\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "from cartopy.util import add_cyclic_point\n",
+    "\n",
+    "plt.set_loglevel (level = 'warning')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fc334ba9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'analysis': {'start_time': '2019-07-15',\n",
+       "  'end_time': '2019-07-16',\n",
+       "  'debug': True,\n",
+       "  'output_dir': '/Users/mengli/Work/melodies-monet/outdata',\n",
+       "  'output_dir_save': '/Users/mengli/Work/melodies-monet/outdata/save_intermediate',\n",
+       "  'output_dir_read': '/Users/mengli/Work/melodies-monet/outdata/read_intermediate',\n",
+       "  'save': {'paired': {'method': 'netcdf', 'prefix': '201907', 'data': 'all'}},\n",
+       "  'read': {'paired': {'method': 'netcdf',\n",
+       "    'filenames': {'tropomi_l2_no2_wrfchem_v4.2': ['201907_tropomi_l2_no2_wrfchem_v4.2.nc4']}}}},\n",
+       " 'obs': {'tropomi_l2_no2': {'debug': True,\n",
+       "   'filename': '/Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/*',\n",
+       "   'obs_type': 'sat_swath_clm',\n",
+       "   'sat_type': 'tropomi_l2_no2',\n",
+       "   'variables': {'qa_value': {'quality_flag_min': 0.7,\n",
+       "     'var_applied': ['nitrogendioxide_tropospheric_column'],\n",
+       "     'fillvalue': 9.96921e+36},\n",
+       "    'nitrogendioxide_tropospheric_column': {'scale': 6.022141e+19,\n",
+       "     'fillvalue': 9.96921e+36,\n",
+       "     'ylabel_plot': 'NO2 trop. columns',\n",
+       "     'vmin_plot': 0.0,\n",
+       "     'vmax_plot': 1e+16,\n",
+       "     'nlevels_plot': 23,\n",
+       "     'regulatory': False},\n",
+       "    'averaging_kernel': {'fillvalue': 9.96921e+36},\n",
+       "    'air_mass_factor_total': {'fillvalue': 9.96921e+36},\n",
+       "    'air_mass_factor_troposphere': {'fillvalue': 9.96921e+36},\n",
+       "    'latitude': 'None',\n",
+       "    'longitude': 'None',\n",
+       "    'preslev': {'tm5_constant_a': {'group': ['PRODUCT'], 'maximum': 9e+36},\n",
+       "     'tm5_constant_b': {'group': ['PRODUCT'], 'maximum': 9e+36},\n",
+       "     'surface_pressure': {'group': ['PRODUCT/SUPPORT_DATA/INPUT_DATA/'],\n",
+       "      'maximum': 9e+36},\n",
+       "     'tm5_tropopause_layer_index': {'group': ['PRODUCT']}}}}},\n",
+       " 'model': {'wrfchem_v4.2': {'files': '/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*',\n",
+       "   'mod_type': 'wrfchem',\n",
+       "   'apply_ak': True,\n",
+       "   'mod_kwargs': {'mech': 'racm_esrl_vcp'},\n",
+       "   'mapping': {'tropomi_l2_no2': {'no2': 'nitrogendioxide_tropospheric_column'}},\n",
+       "   'projection': None,\n",
+       "   'plot_kwargs': {'color': 'dodgerblue', 'marker': '^', 'linestyle': ':'}}},\n",
+       " 'plots': {'plot_grp1': {'type': 'timeseries',\n",
+       "   'fig_kwargs': {'figsize': [12, 6]},\n",
+       "   'default_plot_kwargs': {'linewidth': 2.0, 'markersize': 10.0},\n",
+       "   'text_kwargs': {'fontsize': 18.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True,\n",
+       "    'ts_select_time': 'time',\n",
+       "    'ts_avg_window': 'H',\n",
+       "    'set_axis': False}},\n",
+       "  'plot_grp2': {'type': 'gridded_spatial_bias',\n",
+       "   'fig_kwargs': {'states': True, 'figsize': [10, 5]},\n",
+       "   'text_kwargs': {'fontsize': 16.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True, 'set_axis': True}},\n",
+       "  'plot_grp3': {'type': 'taylor',\n",
+       "   'fig_kwargs': {'figsize': [8, 8]},\n",
+       "   'default_plot_kwargs': {'markersize': 10.0},\n",
+       "   'text_kwargs': {'fontsize': 16.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True, 'set_axis': False}},\n",
+       "  'plot_grp4': {'type': 'boxplot',\n",
+       "   'fig_kwargs': {'figsize': [8, 6]},\n",
+       "   'default_plot_kwargs': {'markersize': 10.0},\n",
+       "   'text_kwargs': {'fontsize': 20.0},\n",
+       "   'domain_type': ['all'],\n",
+       "   'domain_name': ['CONUS'],\n",
+       "   'data': ['tropomi_l2_no2_wrfchem_v4.2'],\n",
+       "   'data_proc': {'rem_obs_nan': True, 'set_axis': True}}},\n",
+       " 'stats': {'stat_list': ['MB', 'NMB', 'R2', 'RMSE'],\n",
+       "  'round_output': 2,\n",
+       "  'output_table': True,\n",
+       "  'output_table_kwargs': {'figsize': [12, 6],\n",
+       "   'fontsize': 12.0,\n",
+       "   'xscale': 1.4,\n",
+       "   'yscale': 1.4,\n",
+       "   'edges': 'horizontal'},\n",
+       "  'domain_type': ['all'],\n",
+       "  'domain_name': ['CONUS'],\n",
+       "  'data': ['tropomi_l2_no2_wrfchem_v4.2']}}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "an = driver.analysis()\n",
+    "an.control = '../yaml/control_tropomi_l2_no2.yaml'\n",
+    "an.read_control()\n",
+    "an.control_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8d9bd353",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading TROPOMI L2 NO2\n",
+      "/Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/*\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190714T231100_20190715T005230_09074_03_020400_20221105T205731.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T005230_20190715T023400_09075_03_020400_20221105T210613.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T023400_20190715T041529_09076_03_020400_20221105T210615.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T041529_20190715T055659_09077_03_020400_20221105T210617.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T055659_20190715T073829_09078_03_020400_20221105T210619.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T124258_20190715T142428_09082_03_020400_20221105T210621.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T142428_20190715T160557_09083_03_020400_20221105T210623.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T160557_20190715T174727_09084_03_020400_20221105T210624.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T174727_20190715T192857_09085_03_020400_20221105T210627.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T192857_20190715T211026_09086_03_020400_20221105T210630.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T211026_20190715T225156_09087_03_020400_20221105T210634.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/S5P_RPRO_L2__NO2____20190715T225156_20190716T003326_09088_03_020400_20221105T210637.nc\n",
+      "Reading tropomi l2 no2 data:  qa_value\n",
+      "Reading tropomi l2 no2 data:  nitrogendioxide_tropospheric_column\n",
+      "Reading tropomi l2 no2 data:  averaging_kernel\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_total\n",
+      "Reading tropomi l2 no2 data:  air_mass_factor_troposphere\n",
+      "Reading tropomi l2 no2 data:  latitude\n",
+      "Reading tropomi l2 no2 data:  longitude\n",
+      "Reading tropomi l2 no2 data:  preslev\n",
+      "DEBUG:root:preslev\n",
+      "Working on TROPOMI NO2 pressure\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- satobs\n",
+    "an.open_obs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a651f657",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrfchem\n",
+      "/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*\n",
+      "**** Reading WRF-Chem model output...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- model\n",
+    "an.open_models()\n",
+    "lat = an.models['wrfchem_v4.2'].obj.coords['latitude']\n",
+    "lon = an.models['wrfchem_v4.2'].obj.coords['longitude']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "52391527",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "model(\n",
+       "    model='wrfchem',\n",
+       "    radius_of_influence=1000000.0,\n",
+       "    mod_kwargs={'mech': 'racm_esrl_vcp', 'var_list': ['no2', 'pres', 'height', 'tk', 'height_agl', 'PSFC', 'PH', 'PHB', 'PB', 'P', 'T']},\n",
+       "    file_str='/Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*',\n",
+       "    label='wrfchem_v4.2',\n",
+       "    obj=...,\n",
+       "    mapping={'tropomi_l2_no2': {'no2': 'nitrogendioxide_tropospheric_column'}},\n",
+       "    label='wrfchem_v4.2',\n",
+       "    ...\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "an.models['wrfchem_v4.2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "150101a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:161: RuntimeWarning: Mean of empty slice\n",
+      "  no2col_satm = np.nanmean(modobj_tm['no2col'].values, axis = 0)\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:199: RuntimeWarning: Mean of empty slice\n",
+      "  modvalue_pb2   =  np.nanmean(modobj_tm['PB2'].values, axis = 0)\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:200: RuntimeWarning: Mean of empty slice\n",
+      "  modvalue_no2   =  np.nanmean(modobj_tm['no2col'].values, axis = 0)\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:226: RuntimeWarning: Mean of empty slice\n",
+      "  no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:296: RuntimeWarning: divide by zero encountered in log10\n",
+      "  f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:296: RuntimeWarning: divide by zero encountered in log10\n",
+      "  f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:296: RuntimeWarning: divide by zero encountered in log10\n",
+      "  f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 0.4779266 max: 4.3222723\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:296: RuntimeWarning: divide by zero encountered in log10\n",
+      "  f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 0.1657031 max: 8.092252\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:296: RuntimeWarning: divide by zero encountered in log10\n",
+      "  f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 0.28881785 max: 7.060148\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:296: RuntimeWarning: divide by zero encountered in log10\n",
+      "  f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value=\"extrapolate\")# relationship between pressure to avk\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 0.38660237 max: 5.198813\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:323: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  amf_wrfchem = nume / deno * tamf_org\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done with Averaging Kernel revision, factor min: 1.0 max: 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mengli/opt/anaconda3/envs/melodies-monet/lib/python3.9/site-packages/xesmf/frontend.py:693: UserWarning: Using dimensions ('x', 'y') from data variable nitrogendioxide_tropospheric_column as the horizontal dimensions for the regridding.\n",
+      "  warnings.warn(\n",
+      "/Users/mengli/Work/melodies-monet/MELODIES-MONET-1/examples/jupyter_notebooks/../../melodies_monet/util/sat_l2_swath_utility.py:226: RuntimeWarning: Mean of empty slice\n",
+      "  no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- paring\n",
+    "an.pair_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d8f16fdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 -122.3 -122.2 ... -60.37\n",
+       "    lat                                  (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    x                                    (y) int64 0 0 0 0 0 ... 283 283 283 283\n",
+       "    ll                                   (y) int64 0 1 2 3 4 ... 436 437 438 439\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 7.235e+14 ... 2.73...\n",
+       "    latitude                             (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    longitude                            (y) float32 -122.3 -122.2 ... -60.37\n",
+       "    no2trpcol                            (time, y) float32 5.608e+14 ... 6.98...\n",
+       "Attributes:\n",
+       "    description:  daily tropomi data at model grids,passing at localtime 13:30</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-583e0489-ee7f-41f6-ac8d-e446eed489ce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-583e0489-ee7f-41f6-ac8d-e446eed489ce' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>y</span>: 124960</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-007341c4-25f1-4cd8-a8c4-24b2e4b11829' class='xr-section-summary-in' type='checkbox'  checked><label for='section-007341c4-25f1-4cd8-a8c4-24b2e4b11829' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-07-15</div><input id='attrs-6e31b63d-1c1f-42ea-b4d6-ece3413582e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e31b63d-1c1f-42ea-b4d6-ece3413582e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f21917bc-2a80-416c-a2df-00659ae86eba' class='xr-var-data-in' type='checkbox'><label for='data-f21917bc-2a80-416c-a2df-00659ae86eba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-07-15T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-122.3 -122.2 ... -60.52 -60.37</div><input id='attrs-cac2825a-29ea-4312-80c3-d9fda6087d61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cac2825a-29ea-4312-80c3-d9fda6087d61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e7ff44-e865-4fcb-b094-1dc2991c52d6' class='xr-var-data-in' type='checkbox'><label for='data-69e7ff44-e865-4fcb-b094-1dc2991c52d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-122.282745, -122.17581 , -122.0688  , ...,  -60.675873,\n",
+       "        -60.522675,  -60.36969 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>21.19 21.22 21.24 ... 50.24 50.2</div><input id='attrs-8b52cda0-8623-4024-b766-8442dd5d3a0c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b52cda0-8623-4024-b766-8442dd5d3a0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62140b09-3427-4893-93d7-b32102106198' class='xr-var-data-in' type='checkbox'><label for='data-62140b09-3427-4893-93d7-b32102106198' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([21.18737 , 21.215775, 21.244072, ..., 50.28482 , 50.24335 ,\n",
+       "       50.20171 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 ... 283 283 283 283 283</div><input id='attrs-5a70ddd9-4d87-4081-8bac-4bd63a64d82e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a70ddd9-4d87-4081-8bac-4bd63a64d82e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-747f095f-6ecc-4ba2-a515-20b3708c7177' class='xr-var-data-in' type='checkbox'><label for='data-747f095f-6ecc-4ba2-a515-20b3708c7177' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   0,   0, ..., 283, 283, 283])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ll</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 435 436 437 438 439</div><input id='attrs-37606f1d-2116-48b4-95f5-f4d8e1ea3331' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37606f1d-2116-48b4-95f5-f4d8e1ea3331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128fe118-566d-4842-a75e-a5a0a23e74ea' class='xr-var-data-in' type='checkbox'><label for='data-128fe118-566d-4842-a75e-a5a0a23e74ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 437, 438, 439])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cba17129-09f9-47de-b001-b340b32df85e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cba17129-09f9-47de-b001-b340b32df85e' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>nitrogendioxide_tropospheric_column</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.235e+14 4.96e+14 ... 2.739e+14</div><input id='attrs-1ebaced4-b4cd-4861-bf90-d07b3511b2b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ebaced4-b4cd-4861-bf90-d07b3511b2b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-302ee971-2726-42c4-a0c4-266a8f9ae845' class='xr-var-data-in' type='checkbox'><label for='data-302ee971-2726-42c4-a0c4-266a8f9ae845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.2349825e+14, 4.9603873e+14, 6.1336596e+14, ..., 2.6179896e+14,\n",
+       "        2.3501016e+14, 2.7385837e+14]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>21.19 21.22 21.24 ... 50.24 50.2</div><input id='attrs-6403d9ac-cc59-4656-8498-90768dd2c75d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6403d9ac-cc59-4656-8498-90768dd2c75d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc59764f-0512-4c34-9923-e3c3ab54fb9d' class='xr-var-data-in' type='checkbox'><label for='data-cc59764f-0512-4c34-9923-e3c3ab54fb9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([21.18737 , 21.215775, 21.244072, ..., 50.28482 , 50.24335 ,\n",
+       "       50.20171 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-122.3 -122.2 ... -60.52 -60.37</div><input id='attrs-afd75d25-8529-41f3-8f68-7cd0a5332d5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afd75d25-8529-41f3-8f68-7cd0a5332d5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-733f6609-5790-453f-afb8-557af870fcca' class='xr-var-data-in' type='checkbox'><label for='data-733f6609-5790-453f-afb8-557af870fcca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-122.282745, -122.17581 , -122.0688  , ...,  -60.675873,\n",
+       "        -60.522675,  -60.36969 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no2trpcol</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.608e+14 5.617e+14 ... 6.985e+14</div><input id='attrs-28c4570b-0fa2-457e-895e-75e1329488c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28c4570b-0fa2-457e-895e-75e1329488c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a319e020-8ee9-4fb7-8dbe-35e83cf58ea8' class='xr-var-data-in' type='checkbox'><label for='data-a319e020-8ee9-4fb7-8dbe-35e83cf58ea8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.6082549e+14, 5.6167260e+14, 5.6204234e+14, ..., 6.5825655e+14,\n",
+       "        6.7548454e+14, 6.9849134e+14]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8c05be2-fe39-414c-b40f-7f003a65086a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f8c05be2-fe39-414c-b40f-7f003a65086a' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>daily tropomi data at model grids,passing at localtime 13:30</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 -122.3 -122.2 ... -60.37\n",
+       "    lat                                  (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    x                                    (y) int64 0 0 0 0 0 ... 283 283 283 283\n",
+       "    ll                                   (y) int64 0 1 2 3 4 ... 436 437 438 439\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 7.235e+14 ... 2.73...\n",
+       "    latitude                             (y) float32 21.19 21.22 ... 50.24 50.2\n",
+       "    longitude                            (y) float32 -122.3 -122.2 ... -60.37\n",
+       "    no2trpcol                            (time, y) float32 5.608e+14 ... 6.98...\n",
+       "Attributes:\n",
+       "    description:  daily tropomi data at model grids,passing at localtime 13:30"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paired_obs = an.paired['tropomi_l2_no2_wrfchem_v4.2'].obj\n",
+    "paired_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e18ef01b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paired TROPOMI NO2:  <xarray.DataArray 'nitrogendioxide_tropospheric_column' (x: 284, ll: 440)>\n",
+      "array([[7.2349825e+14, 4.9603873e+14, 6.1336596e+14, ..., 5.7689363e+14,\n",
+      "        6.0003109e+14, 6.3977397e+14],\n",
+      "       [6.7960509e+14, 6.3695895e+14, 6.9290855e+14, ..., 6.0556381e+14,\n",
+      "        5.0062444e+14, 3.5757948e+14],\n",
+      "       [6.7707462e+14, 7.4915027e+14, 6.8800162e+14, ..., 6.5404883e+14,\n",
+      "        3.5644709e+14, 4.3860421e+14],\n",
+      "       ...,\n",
+      "       [6.8593017e+14, 5.0628957e+14, 2.9182236e+14, ..., 3.9030931e+14,\n",
+      "        4.2706917e+14, 3.7428959e+14],\n",
+      "       [6.0558690e+14, 4.7553620e+14, 5.0021437e+14, ..., 3.9215451e+14,\n",
+      "        2.1985196e+14, 5.0861258e+14],\n",
+      "       [4.5287269e+14, 9.3725454e+14, 5.8742939e+14, ..., 2.6179896e+14,\n",
+      "        2.3501016e+14, 2.7385837e+14]], dtype=float32)\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2019-07-15\n",
+      "    lon      (x, ll) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n",
+      "    lat      (x, ll) float32 21.19 21.22 21.24 21.27 ... 50.33 50.28 50.24 50.2\n",
+      "  * x        (x) int64 0 1 2 3 4 5 6 7 8 ... 275 276 277 278 279 280 281 282 283\n",
+      "  * ll       (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 6125829600.0 2.7289909e+16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# plotting of paired data\n",
+    "# 1. paired TROPOMI NO2 trop. columns\n",
+    "paired_obs_stack = paired_obs.set_index(y=(\"x\", \"ll\")).unstack(\"y\")\n",
+    "no2grid = paired_obs_stack['nitrogendioxide_tropospheric_column']\n",
+    "no2grid = no2grid[0,:,:] # time, lat, lon\n",
+    "print('Paired TROPOMI NO2: ',no2grid, np.nanmin(no2grid), np.nanmax(no2grid))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c1a70dc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,10))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "clev = np.arange(0, 1e16, 1*1e15)\n",
+    "plt.contourf(lon, lat, no2grid, clev, cmap='Spectral_r', extend='both')\n",
+    "cbar=plt.colorbar(shrink=0.6)\n",
+    "plt.show()\n",
+    "fig.savefig('/Users/mengli/Work/melodies-monet/outdata/paried_trp_no2_20190715.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c35fe470",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paired WRF-Chem NO2:  <xarray.DataArray 'no2trpcol' (x: 284, ll: 440)>\n",
+      "array([[5.6082549e+14, 5.6167260e+14, 5.6204234e+14, ..., 5.0799893e+14,\n",
+      "        5.1312666e+14, 5.1356541e+14],\n",
+      "       [4.3689763e+14, 4.3674170e+14, 4.3980378e+14, ..., 4.4611728e+14,\n",
+      "        4.5124336e+14, 5.9029769e+14],\n",
+      "       [4.4412458e+14, 4.4394268e+14, 4.4651554e+14, ..., 4.3647317e+14,\n",
+      "        4.4146513e+14, 5.8461524e+14],\n",
+      "       ...,\n",
+      "       [6.1838302e+14, 5.8272177e+14, 6.3751018e+14, ..., 7.8216743e+14,\n",
+      "        7.7528978e+14, 7.7019125e+14],\n",
+      "       [6.0601183e+14, 5.6466828e+14, 5.7435168e+14, ..., 6.6657966e+14,\n",
+      "        6.9247543e+14, 7.3247808e+14],\n",
+      "       [6.1570953e+14, 5.6744309e+14, 5.6898807e+14, ..., 6.5825655e+14,\n",
+      "        6.7548454e+14, 6.9849134e+14]], dtype=float32)\n",
+      "Coordinates:\n",
+      "    time     datetime64[ns] 2019-07-15\n",
+      "    lon      (x, ll) float32 -122.3 -122.2 -122.1 ... -60.68 -60.52 -60.37\n",
+      "    lat      (x, ll) float32 21.19 21.22 21.24 21.27 ... 50.33 50.28 50.24 50.2\n",
+      "  * x        (x) int64 0 1 2 3 4 5 6 7 8 ... 275 276 277 278 279 280 281 282 283\n",
+      "  * ll       (ll) int64 0 1 2 3 4 5 6 7 8 ... 432 433 434 435 436 437 438 439 209019260000000.0 2.06299e+16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2. paired WRF-Chem NO2 trop. columns\n",
+    "no2grid = paired_obs_stack['no2trpcol']\n",
+    "no2grid = no2grid[0,:,:] # time, lat, lon\n",
+    "print('Paired WRF-Chem NO2: ',no2grid, np.nanmin(no2grid), np.nanmax(no2grid))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b06474d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,10))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "clev = np.arange(0, 1e16, 1*1e15)\n",
+    "plt.contourf(lon, lat, no2grid, clev, cmap='Spectral_r', extend='both')\n",
+    "cbar=plt.colorbar(shrink=0.6)\n",
+    "plt.show()\n",
+    "fig.savefig('/Users/mengli/Work/melodies-monet/outdata/paried_wrfchem_no2_20190715.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2bfcfe16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing: /Users/mengli/Work/melodies-monet/outdata/save_intermediate/201907_tropomi_l2_no2_wrfchem_v4.2.nc4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- save paired data ---\n",
+    "an.save_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1d44d3ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading: /Users/mengli/Work/melodies-monet/outdata/read_intermediate/201907_tropomi_l2_no2_wrfchem_v4.2.nc4\n"
+     ]
+    },
+    {
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+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 ...\n",
+       "    lat                                  (y) float32 ...\n",
+       "    x                                    (y) int64 ...\n",
+       "    ll                                   (y) int64 ...\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 ...\n",
+       "    latitude                             (y) float32 ...\n",
+       "    longitude                            (y) float32 ...\n",
+       "    no2trpcol                            (time, y) float32 ...\n",
+       "Attributes:\n",
+       "    description:   daily tropomi data at model grids,passing at localtime 13:30\n",
+       "    title:         \n",
+       "    format:        NetCDF-4\n",
+       "    date_created:  2024-01-08\n",
+       "    dict_json:     {\\n    &quot;type&quot;: &quot;sat_swath_clm&quot;,\\n    &quot;radius_of_influence&quot;...\n",
+       "    group_name:    tropomi_l2_no2_wrfchem_v4.2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7b6be188-1be6-41b5-ba95-76dbb23500b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b6be188-1be6-41b5-ba95-76dbb23500b2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>y</span>: 124960</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a7489862-3fdb-4c82-a301-fa50a3aef25a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7489862-3fdb-4c82-a301-fa50a3aef25a' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-07-15</div><input id='attrs-1ba82cb3-5497-4694-80ac-526832b29965' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ba82cb3-5497-4694-80ac-526832b29965' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3833cbe9-1a91-4c5c-afcd-27232a9b0e87' class='xr-var-data-in' type='checkbox'><label for='data-3833cbe9-1a91-4c5c-afcd-27232a9b0e87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-07-15T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c6c4f8aa-07d1-4503-92d6-03a791b575b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6c4f8aa-07d1-4503-92d6-03a791b575b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e34fcbf3-49b2-485a-8821-5774fc87d3c7' class='xr-var-data-in' type='checkbox'><label for='data-e34fcbf3-49b2-485a-8821-5774fc87d3c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-44430c7d-3162-4d91-8542-44c1dd318246' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44430c7d-3162-4d91-8542-44c1dd318246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-540b4c02-0a5e-49c0-b523-38708069f327' class='xr-var-data-in' type='checkbox'><label for='data-540b4c02-0a5e-49c0-b523-38708069f327' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36612cfc-9d47-4622-8532-6c3cc9618274' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36612cfc-9d47-4622-8532-6c3cc9618274' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-878c1cbf-13e4-4469-aeb2-d764321c8dd0' class='xr-var-data-in' type='checkbox'><label for='data-878c1cbf-13e4-4469-aeb2-d764321c8dd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ll</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6254d7fa-304a-42a2-9cd7-7c822d678c00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6254d7fa-304a-42a2-9cd7-7c822d678c00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdf79117-6abc-49bb-8b28-fe9485e1993a' class='xr-var-data-in' type='checkbox'><label for='data-fdf79117-6abc-49bb-8b28-fe9485e1993a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b582210-38ca-47b2-9ba6-18cfa7e6a350' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b582210-38ca-47b2-9ba6-18cfa7e6a350' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>nitrogendioxide_tropospheric_column</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8bea4558-d241-4a96-8a16-1d2cf7daf3c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bea4558-d241-4a96-8a16-1d2cf7daf3c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a93fa19-8f28-496f-b8d2-18ff6c9712ac' class='xr-var-data-in' type='checkbox'><label for='data-1a93fa19-8f28-496f-b8d2-18ff6c9712ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a06a3085-c82a-45f3-8c34-7f2402754741' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a06a3085-c82a-45f3-8c34-7f2402754741' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec7e5506-21f7-4f96-8f0b-b2cd8bb0d23a' class='xr-var-data-in' type='checkbox'><label for='data-ec7e5506-21f7-4f96-8f0b-b2cd8bb0d23a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de7ab4e4-da21-4ee6-8453-61eac787e9db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de7ab4e4-da21-4ee6-8453-61eac787e9db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb1e4843-54d1-4bec-bb10-77b096f3d966' class='xr-var-data-in' type='checkbox'><label for='data-fb1e4843-54d1-4bec-bb10-77b096f3d966' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no2trpcol</span></div><div class='xr-var-dims'>(time, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e07a3aa4-ed8f-4d9c-b920-b593aeaf458c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e07a3aa4-ed8f-4d9c-b920-b593aeaf458c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43e1932f-0da0-4509-b91c-005ad37285e3' class='xr-var-data-in' type='checkbox'><label for='data-43e1932f-0da0-4509-b91c-005ad37285e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[124960 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1ba0dae-2de2-4223-8dc7-7995d5354d08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a1ba0dae-2de2-4223-8dc7-7995d5354d08' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>daily tropomi data at model grids,passing at localtime 13:30</dd><dt><span>title :</span></dt><dd></dd><dt><span>format :</span></dt><dd>NetCDF-4</dd><dt><span>date_created :</span></dt><dd>2024-01-08</dd><dt><span>dict_json :</span></dt><dd>{\n",
+       "    &quot;type&quot;: &quot;sat_swath_clm&quot;,\n",
+       "    &quot;radius_of_influence&quot;: 1000000.0,\n",
+       "    &quot;obs&quot;: &quot;tropomi_l2_no2&quot;,\n",
+       "    &quot;model&quot;: &quot;wrfchem_v4.2&quot;,\n",
+       "    &quot;model_vars&quot;: [\n",
+       "        &quot;no2&quot;\n",
+       "    ],\n",
+       "    &quot;obs_vars&quot;: [\n",
+       "        &quot;nitrogendioxide_tropospheric_column&quot;\n",
+       "    ],\n",
+       "    &quot;filename&quot;: null\n",
+       "}</dd><dt><span>group_name :</span></dt><dd>tropomi_l2_no2_wrfchem_v4.2</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                              (time: 1, y: 124960)\n",
+       "Coordinates:\n",
+       "  * time                                 (time) datetime64[ns] 2019-07-15\n",
+       "    lon                                  (y) float32 ...\n",
+       "    lat                                  (y) float32 ...\n",
+       "    x                                    (y) int64 ...\n",
+       "    ll                                   (y) int64 ...\n",
+       "Dimensions without coordinates: y\n",
+       "Data variables:\n",
+       "    nitrogendioxide_tropospheric_column  (time, y) float32 ...\n",
+       "    latitude                             (y) float32 ...\n",
+       "    longitude                            (y) float32 ...\n",
+       "    no2trpcol                            (time, y) float32 ...\n",
+       "Attributes:\n",
+       "    description:   daily tropomi data at model grids,passing at localtime 13:30\n",
+       "    title:         \n",
+       "    format:        NetCDF-4\n",
+       "    date_created:  2024-01-08\n",
+       "    dict_json:     {\\n    \"type\": \"sat_swath_clm\",\\n    \"radius_of_influence\"...\n",
+       "    group_name:    tropomi_l2_no2_wrfchem_v4.2"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- read saved paired data ---\n",
+    "an.read_analysis()\n",
+    "paired_obs = an.paired['tropomi_l2_no2_wrfchem_v4.2'].obj\n",
+    "paired_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "112760d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 63214\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# output statistics\n",
+    "an.stats() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "298c607d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'color': 'k', 'linestyle': '-', 'marker': '*', 'linewidth': 2.0, 'markersize': 10.0, 'label': 'tropomi_l2_no2', 'fontsize': 14.4}\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 51784\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n",
+      "-3295428381801185.5 3295428381801185.5\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 65536\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n",
+      "Reference std: 1091791700000000.0\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 65536\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'tEXt' 41 57\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 110 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 131 65536\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9\n",
+      "DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 8192\n",
+      "DEBUG:PIL.Image:Error closing: 'NoneType' object has no attribute 'close'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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X3M6fK9p/K8qlPjmoSPB1xofR4KnXoYb+fcaYu4vP2ujOwuCr9BJu8hSe9YKi8kFUd/ogcH1B8XhalAuDgdd7nuLU0kAbkSfKA57e/jcU798lItcV9buOfl/86Gnnz6H63aUBV3K7v1Dcs5JC8ssiMln8/m+hnqAlVPXhmwE/TZ8a8u+MMV9Pzu6X0f5gA79VjD8TKK0INHA0er79YoghhnhxYmgUD/F88YeoUL+HGmdNYINicgd+w6gkU+lJ/W7U63UWcKuIrBTn///Qrc5PoEF1T4df5lTqBkX5K0AZCPevgY2i/DtRj+OJoo5lXX6qOPetwIqILKNqEJegE+2vlmUXMljHROSZNFw/MfD3fcXE+3n6BtMjA3zg54r3oKoNI8ADIrKOBgfCM7fZ6fgvRZ3+CapQ8TjaRgvA893mfk4oeLd/TX/7/r0Dn32oCDDsvVD1B9Agt5cAiMjvFPelvO8/Rt/DOgPcO0CJKIPkvhocKt7/DbBeLqaeAh9AecEu8I8isoHuJLgorebDp53fBn5PRJrF9yx08VJywn8OXRhcjXqFN4AfR72x7zyNJvOCoFgQ//zAoX//NaClPCWMMV369JofR9tkAeUxr6IGOnz9+8UQQwzxLYShUTzE80LhnfwB4EfRCbyURfoS8E+NMb9w2vl3ABejEeMH0C3ytDj/7cCbC6/Y0/3mYNDd6fgFVFXidpQeMYIaw+9G5aAG+bh/jnopP40aGY3i/QPFuQ8OlDuNbqs/bfKGgit7b/Hv54tjS2jkPTy5l/hZwRjTQXVy34tO+C6aHfAHjDHve45lfQr4TvSeCWpM/AXqQXva9v8ao6RQZMBfPo/vT6D3peSNvn7gswanUiLm+OrxLrRvJai3+inpfUUg3PXo4uoguoNxCF2QvO1JPPu3o89RE32GPgi8wRiTFeXdjXLT/wa9Xxb63Hy7MeZDfHPgKvp0Dnj2tJTnDWPMn6Ke6ZvRBUIbbaNrjDGPFqd9vfvFEEMM8S0EeZY7r0MMMcQQQ3wNIZqY5ZeAzxljXvXC1maIIYb4ZoWIvOmN8LGPP8vzHSADt1igD/EcMPQUDzHEEF81BugMT/f6atMID/F1hoh8z7O4j8/LO/0syj1WqI8MMcQQLxBE5HUicpdohs4HReStT3KOIyL/XUTmRbOs/o6IuAOfv0o0q2pLRO6QgayqInKRiHyp+Gy/iLz+9PJfSJweMDLEEEMM8XxQ0hmeDk9LQRnimwI1nvk+Tj/D50+FZyoXhnPSEEO8YBCROTTe45+hweOvAf5WRC4zxjwycOrPonFCL0fVn/4K+G9oAPd24CNoltWPopz/vymC2UvJxv+Oao1/B/AhEdn6zaIIM6RPDDHEEEMMMcQQQ3yT4htFnxCRa4AfHpTnFJE7gHcV+QLKY7cCf2KM+T/F/1eixu4MGt9zoTHmnxafWaiU6t3A9wI/ZYx56UBZlwAPF8GzLziG9IkhhhhiiCGGGGKIby0kImKe6TX4BWPMLacZxHvQ7K/3nFa2RV8qEzTQdQpNhHUZcEJEPlKoQN0EREXg8GXAoyLyHhFZLgzuxjeLQQzPsFW1e27OHD55ui78EEMMMcQQQwwxxLc0Dhtjdr/QleghCGBy8tmde/z4M5/zDBCRLSiF4s+KPAGD+DuUKvEFVDWnlAgNUMP429DETN+LUi0+LiLnFJ/9E+AHUbnE7wM+JiJnG2MW+SbA0xrFh0+exNxzD2xuwsgIJk3174kJJAhgfJzM8TAiIIYuNXQBYfBMyOKKQ5oJW6diHCsjE4uIALvb5vEH7+Pw0kn+9/vey/e85S185Y47+G+/9Evcv38/l192GRZ9EVNjoNmxyW0b37fwJSQk4LbbbyeOulz30itwRPi/H/oQdpZx1kWXEabCXXfexmOPPUpjZJSf/dn/QIUQC4MAxhjC3CXLBb+5jDU5jpAjQIZAGGJ32pAkmNFRErfOWhgwGp3EdQW7XutVMMUiwcM3EYghNxYZNpbk2ORYGEJ81jZdmk2wbaFWg3o1J2pGpLZHktmEIczO5Fg2CIYqHU4s++SWS60uLC9DnkOZ+2piAlwXbDunLm1CfDLcgZYrYXAJcciwMRggwiPHokLIwpqHJTAzGpOXrSDgkBPGFraV49haahwb8jjFay5x2+IutjU22DbSRqYmYWUF0hRmZpAwhHZb+0uzCVNTJLUx4uoI1dYytFvg+eQjoxzdHMOvWtTrFmLluCZFopAsyaBWx5ZU2zFNMGvrkOdYjo2ZmIDFRW0Ez4duB0lT8DwYHYWTJ7Xz2EW2y04Hbfg6hCEkz6BA5jhaVp5DlunLGJiZwawV+v9TU4RUsCXBMjldqjidTbAFCVx8Um1PY7CSBGwL1tYgivRGGoPYtv5tWUR+Hcky7O4mSW0Mr+IgroNBaFNFAMeE+JJTdGTIc4xt0yXAIcUj7fVjKfo7QBwZcsvFd1Iyseh2BMc24LlkuBj6fcshxS3kb7vUWN8Q6kFK4GcgFh6RNksnxqp4SJ7jbq7rl6NI27bZhEoFVldhaUnbs1LRjisCW7dCu013bI623SDpJPzBHzt87vM2992nXef5YG4O/tW/gu/+br3N8/Owumr40pcMd90Bd94NO3cKS0tCtwvv/IkMEb3VV5+zxjn7DONjhgOLo9x6t8f4uHDddTrPHDyov2FZ2o22boUtW+DIEb2tnqddLAyh24Vduwxf+IL+zugoLCxAq6VNND4Os7Nwzz3w0EPaZOeeq7f0oovA97XLAVx1lf7mxoZ+17b190AfuZERLS8IDEtLesy2BcvSudS2tRsfOqS3Yn1dj11yid4K29byswwOHICxMS1zff2J7WtZehsdR/9uNPSaH31U5+y1Nf2s29VHc/du7Qoi+opjfRRnZ7WcZlPb0Dpt39JxtLzy8VDVR0OFDgYhIqBGRx8DIMHFJsU+PZdKq8gCbdvaGEmilZye1oo4DqZsgDhGimN4nr4vL2ufTRLyJCV2qrhpF2u0gVgWnDihN63E2Ji+J4n+Zq2mDbuyohfkOP0Hjb6QefnexcfCkOIBsLJsaFQzvIp9yvdAaLWgXsmoWR2MgF2Ot+PjWv+NDTCGZGqWJMyprJ1APE/bIY61rlmmnWKwE+S5dsCREeh0MCMaDvB3n/wkk2NjvPTii7XTN5vaNpde+sSO8ixhjOGD73sf3Xabt73iFdQtizCKOHT8OAvLyxhj9FWcX6tUuPKlL8VqtweaQvR+lpNk2ZnyHKpVfWiyTNsky/SeiGj7uK7eq8lJLaNXpOx63hf1zYHnrT4hIuejBvGN9HX9B/HrqIH7FdQo/l3gu1DJyAj4O2PMTUVZv45qmF9YfHa7MeYDRTnvE5GfR7Ng/vXzqevXGs8c1GBZ2oE2NiDLMGecgVSr2vmWljBehWRyhryX5Em77uqmRzcWLFtY3KwgGEZHDRVCcDL2nHcB5/gXcvs9d/HKV7yCmW27+cgnP8UNn/403/tP/jkHH7qXl1xxOVdedplOWK4hzTMCSUjwEAwvueIS/vbvP8VHP/WPfOd3vJY4TXnk0UfppBk7RhrcetONXH3FFew/fIQH7vwijz3+OFMTE7z2Va/i2P793H7HHbzp299A1JgizatYxU6CSI7xK9gEeB6FEemDgbA2TebkGFwcYmwyXDLsvEtibGKrhtNex3YF27OxBGI8UlzqdX3+5ucNa2vC3r1CMwnoFBPdnj3g2wk2GRk2XRNg+xatJsSJ1s119bk1RsfzzU1hctIitIOnSI1m8IgwQEhAlS4pDjkOFTqIgemRGGMEA7Rin9i4+D44kpJ7ECbC3XdYXHVxhCsxVhZjAZdOH2epvpsvHJzk6qkYuzGG6XRxRHQgCkMdNM87D3wfN8twN1YwWUpSHyOyqiy3RpjKTuLHhli24JFiuh1MmuNbOUeXhGrFYrKRYxyPcGYHSSpIHFEnJ5ncorZqEFCpFoNkmiJrazoZGqMDu4jOwsZovaJI+3UQaB1L62MQaarHJyf1HMfRyQZ0Mmy3IQyp+AbEwuQZ/uYiUquq8WsMbani08XBgGPTkRrWVAWPGLCwFuZ18hkdBRH8bhfEYBp1nFqNruXhdFt47XWqE5Ow0YQ8w4yP0aWKTYZnJwhQ49QcDiE+IPiE2MbgZ13obpCPjhISkDgWS6sGSRPcWs5YNcX2hExcUhw22jXsPGG0HlKr+aSZDSbHIQEEWwymWsEhwTp+pN+u4+M6wdbrumgB2L5dxxIRTBBAECBFm+eWsLYuPPigz333w+OPP3+DuHxG6nX45CfV9jEGtmwRfu6dXcbsJqsnuvz+3+7ivvvVWP2Ob8u5bPY4lsl44Eid/QcqxE6FW+902bpVu8mhQ/rcbdumXWJqSrvU4iJ88Yv67G7bpuc88oh2Hdc15Lkaqq2WsLmpTbRrV68pEIFXvlJtkxtu0LJbLbjvPjU2KxWYmYE779TuOzamv22M2jSlQTwzo91xop7g2DYP7rd6a7/lZe3CX/4yXHyxlrG0pEbrfffpNUxNwRln6OPQavVvpYjWqVrV3yrWYLRa+vtJou3jOPpZaYt0io3VtTWtl+fp59PT/bI7HS3DtvvrzbJdysdvY0NtLgCPLjk2CR4WGdWB3VsBXJ64yDWgjdjpYFptpDCSpLyBvg+bm5hEL05GGn0jNoq0slNT2mAzM0jVwiQ2pruJLCzoBU1O9i+qXJQ7xXwoop05jmFiAmPMaYatirUbHFIchByPhJBqcVUZOyY7bGbV/iIagxFtaNuGbjen9NGYSgXZ3OyvmABqNdwsxm2v680rr8t1+wZxnusL9Gb7vp63sgKVCrkRNroxtVqNl15+uR4PAj1n61a+GogI3/cDP0Da7XL7V75C59gx8jRl75YtvPKSS7QTVKv6Ghtjc32dz3/hC0RhiCvCZRdeyNiWLbqCm5jQDm9Z/Y6zuNh3jMSxvpf3yLL0Wh1H7/sQiMgr0AC53zLG/LenOG0O+HVjzL8pvnM9cMAY0xaRR4C9g0VCz8/5CKq5PwibJ3ryXjA8baCdiBhz883agVyXaGorqRtQDUDyDNIU02rB2BixGxATYJFjk+KQYZmcVBzS3KKTuHiOwckjLFuwrRzb5BiBO++8k/nFJTrdLpNbdhKGMeedezY7Jup4nqp8GKBLgF14wmI1RVnf7PCnf/pHXH75lYw1KuzdtZXPfvku7rv1C/yzN72Bh48c4cJLL+V3/uh/8oP//Ee5eN9u3vtnf8bRo8f4wZ/4Wea27yIM9bkbGQFXEmqVlMgE+HTV6xZ2iVMLbAu7GkCeE+JjLAdPYlwSpNXCNDeJRqYxloVxHIwXYLC19sbgE7K07tKNHaanBVdijEBmXNJUcByDiAA5NhnNTYtWx2JmOse2cvIU2pFDtQJiGbQfaV8SEnwSQoKB42oQO6QIhjY1/OL/NlVqdFjbdGiGHhOTFt2uTnIiOiGXE5wxhsOP51QDw9xsht1cx/FtMt+nKw0efThnfFKYmRGEnOrKMaw06c+g4+M6MRkDzSZRMEriBZjcIjWCnaf4EuE5kIiNyXLcuEPHG8daW4I4pTu5nTgRRoOY3LJoth1qlQzfSXDIkcKLJHGMrK0hpScgDPWlHVongvFxdfm1Wtq3zz6bnsXyJDAzMzqoJgmyvq4TX+kJLa2UIOi7wVotsCzM6ChtU8GRDD/rIqurMDZG6leI8LHIcZbmcSwD4xNgSX9kKJ5LYwwmyZDlRTrTO8ltF8kyaqtHkZEREr9GZAVUpfuEUaVjfHIsHJPgt9aw8gzjulCtkuJgWi2i2OLg2gQzs4LvGSq+LshAMAaSOGfM69A1Fe1PJub4is/MSIhBWN5w2b54J04S6eSye7dOTrWatu3Skk68ItoXHnoI89KX6v/tNgIcle3kQY12W/jVX4XPflY9qpYFP/AD6rk8fFg9vs8mLrhahWuv1Vu6vKy344or4JxztBuWRuQFF+gtz3M4/5yUh/ZDcz1nYc1j7169zXGsnufHHut7LRcX9fv1uq5lSsMbdG4tHW2jlYhGLefAsYD9+2FpEVZW4YwzBM/TjYw41uZqNvXva6+FBx/UbjU5qc3Xbmsd6nV4+GH1Jm/ZAmeeCf/4j2qMj42pQ373jpSKn9GKPNptod3W7rlnjzo0KxXtqgcPalmFA5RyE9Bx9LW6qkZylmkbnnmmXnfpEZ+bgx07+jZGraZ1sG1ti3I9mSR6Tq2mZZbtVG4YNBr6u+UCYWJCu025prJtPbf0FPt0EIQMC5usePafmGe7t8s4cCzKXez1ZZxGFXEL9ajCEMzFomt8KqaLZUm/v3a7WumNDe2QrquGIJSu+eIHRa38svLLy9ogrvsEr3CE09vNKY91CDA4UFydQTBG8AhxSOmK7sJ2O4YRt0viBCClaz3HJ8Sl6HhLS7pjVrr0KxXdTVpf12up1xFj+h70EpWK3rSy3vTvlxH4xGdu4ttf9XKcbMD5aFnaDnL66PNVwBhIEvbfeisHb7+d6976VmqdTm83rWe8o+Pjbffdx0qnw2VnncVsWZfyVa3275nv630sUc4HlYoubpxTfYQigjHmm8JYE5E3vTEIPvbxZ0mfcI4ff76BdjvRJFQ/UwbRPcV5/xl4KZoVdhLN0vkBY8y7ROQyNLnQ96Le5l8Efgg4G9iCZmf9OeBPiuO/C5xhjFl/LnX9euGZPcVZprOI7+NvLOE3mzoTFEv/bGySxKmSGYdAQoS8t31lBN2Mt2wafoibhEizSToyUSwbDJHxufjSK7lMMj726c9zz51f4Xve+AZu/cwneXRynG07dnDuOediOzYBYc/cs8nIyWk0RnjTm76TP/7j3+f6617Jyy6/iIpEJCbj0aUlXvcd38EXv3wLF15yJWlnnQ985KN86b6H2bHrEuJsFzffDH/8x7cwM3MhY2M16nWXsTGXbdvgzDNcLt2bIo89RmICNibOpOH5tNqGLBdGxgRHisGtWiWPUtz2GpYl5FPThEXz2ZbB31gk9Wv4lYD1TfUaBZ5DJcgQMbgOIAbLJOQ4ZOJQa+TMNNoFpcEitYRux8bNY0bqGaFUerfJ4BANLLgsEmyTYZlUPeBC4VnpTxMJNqONDLdu6ZhmMjrNnC3bXV1A08bGYAlcuEeZ9JHxePjkKLVqxhk7DQ1pc+k5aqAnxsWTRMtKUx0sq1Ud5AqDGNfVhURukAfvZ7W6nfX6Vhw7YGYkBNdgLN1mf+Qhodmaw7IMlbWcaXed6nQHxsYIGg6hcUhyF68wCFNsYqdBdWSA6jA2hllY6PVhKfeKZ2e1H5cuttMRBDqTr62pFyk3ahDX61puuTcMOtB2Ov0FwNiYeqvn56lblhqiIyOY0VFwXWyTURWNK4hGx0njDPAJTNy7PwZ0cssyre+WLVQ3lnRSGB2FLVswzSZuexGnMULqV4tnAnIcXFKCzVUy28WtOOC5mNwmE5vIBKysWlQrY1RrCTsapdNEjY2ATkEJyaj6MR1qrK4KtqRsHc/ZOdUlFA8Rix1eiEyeAxilV61o4kAR6XtiHn6Y3j7+0aN0Wxky2iAwLYyo8T0/D0eOGC66CO69VyiHmQMH4LLL9LV/v3plS5vkqdDpqNF29dV9r+Z3fze85jVqCJ9/PrzsZWrk1WraRW+9w2FpSf+emFDP8MyM3krH0dseRfp3kqghu7mp55WGQ7kOK51uy+Jhi2FpxfAdrww5uuhx9IRNlsFXvgJ79+r11etap9KZdfHFSkVYWdHPpqe1PM/TxcG552q9RkfV0N+/X22wIIADjzvs2OFw9KjWtdlUz/vBg2pI27aWG0VaXhzrbxw6BPv2qaHeaum1lzvKjqOP8/Kytm3hNOXYMfV6l7ZUyVTKc300XFfrpDta+ne73Td0S6pJWY9aTX+jtGvS9HTnnWBhsIsRMcXBIS+Ww9ChQkC3dEP0xrgYj5rp4m0skdTG+rNeabVvbmJ5HjXb1me5bKg41v9FtEMliVawHB82N/vGsuNooxrT5308yQpO5y7IEVJsEvyCsNanBwSERHiEiUMSW9RrDqWTw3FhqV3RjaVemRk2GSE+lpXjjYxondbW9Do8D1ZWyBwPa2JCaRaW6PUMPkxJArOzvXoI0AotKn6OZQxZdxMnibVDOY4+lF9LY7jXSMplOvdlL+Oca6/lIx/5CG99wxv0fh0+fNqpwlUXXgjAl+64g9tuvpnzdu5kx7nn4ha7AD3KzOlOj3K7peQCDQEqnzYK/L6I/P7A8X8DvA24ufAe/xbwv9CssRHwp8DvABhj7hSR70IpFn8J3Ae8pQi0Oy4irwF+D/gNNNPnW75ZDGJ4Np7ie+/Vh0pER9Nya6XYfjJJShYmWBUXU63ToUKFLhkQmyoAtsT4JD0+K0kCKyu6OVSpEY+M40vKowcOEFRrPPzQfr5yyy2IZXHy5EnO2Hs2P/ovf4yRwDnFK1DSGnIEm5QjBx7i2LFjZHmOiEWj0eDySy7HCIRSpdPpcvjQQW6/6y6OHYt44IFj3HtvjaNHHyPL/oRymDnjDJ2kvu/7cr7tmk3GvRZxmEOeYWcxqfgk49MQBNiOenYrRMpFzg1O0qHrjZLmNgsLwpYtEOSbZJHh8ZURNjb0d1wXzjrLYEuOmBxsm7CVII6NHwhV6RLikw+sXXSRbLAswSq2C3NKzey+78EloblZjHNjEZFV6bVTQFwyq7FQD7yataZnXNpkBER0qeAWfnlj4NhKFccTKhXBdfR+Zokhi2KCmo1vxdhGvTc9z+mgV9W21UiEPk83zxHfx7Ra5MaQNcaIJKC9aQjbhh0zEXbY7m/7zc6CZbEZ2ixsVJmeNlTyNp6JdXBbWNCZvl7XPlruSTebuvovZ/rNTWR0VGffTqe/59to6M1ZX4fxcRJxicWj6mZIu9hbtu3++U/+8PTcXbkfkK1vktsOThZDUCGpNsjFxtlcgyxFRkewl5cQ34NGAyNW734YkR7X20oSJE/JvIoaB1EIKysks9uQbgcnjUhxsUdrWHmmVkgc67ZttUZWrdGVGrkRLBNTtbUPdYtdHs9EtPMaYgkWKRVCOlLDmNIrpVz3BJdcbCqmg6QJUvL+1jeQiXE15qNI9+dLb1meQ7OJaTR6W5b5eRcQ2jVd9KVw3/3Chz/8AHfddZz5eYdqdY6rrjqPs87Sdc2JE/A3f6Pe1KfD/ffrM1zihhvgR39UPaZnnaWvq67SbjA/3/d4JokOd1NFwuJWS43P886DW27Rrue62qyNhnbr5WX9/vS02lNTU7oOyDKdj0dHwfdyjhyGk4sWJ06ovXL0qHpgFxbgyiv1d+fn9fsl53bnTrW7Rkf1t6JI7a0779T/Z2f1N/Jc67C6qvTOlRUtf88eNXQdR8soWQOrq1rW6Kh2c8vSV6fT/xvU+F1d1fqWnu3ZWa1Dnve9vSJaj5JOW7IENjZ0/djtanusrakhPTXV21DpsZvK8S3P9fMk0e8MOiJFDFVaxLikeDgkOKRY5L0RMMJFMNgYHJSiI1mmDS2ilXddbaQkOcXzCKglPjenBlgZd1BSLcottHIFY4yurgbpV+WCemNDO0XptTwNER4xHkLeG2vLZ0yL1sXhZC3EOA4ZDrrkTXt8Y/Us51Ro96Sker+0vq6/OzICYagLBKdGsDaPnad6PYNj2MgI67nh4YMHaVQCTp48STN0yPAga/Lt559FrVbTTns6jOl7Y8tth40NHXt8H5mdhXabfH2DaHYntu/qmlmeSCc5HUeOHOHAgQPky8tM2DaXnneeLroLWgytlnawIMDU6xw6epQjJ07Q3dhg98QE51xxxRMJ6yUsSweDJ/n8xegpHuJZGMX5sWM9gp+UgTIl4a9c8heRHMa2ScUhNxAbDyuOcLIYu+rhSA7GkCOE4uOaGFd0Is2xSY0Qi6+BQpLw8H33cuvnP88X776bkYlJvGqVb3vNq3nly16mHksCHCJcDB/55A1cfOnVzNVr+K1VuiOznFxz+fP3vY/cBMxt3UYQVNmz5wrW1tRTMz9/mD/5k9/j2LFfot328f0K4+OGM8/USeXtbxe2b81wshA6bRaSKRaXpIjVEmZmdHz0PPVTWMVq3SOhS6DhdTk0Ny1GKjG+lxGZgIMH9Rkr6ViBD1XpMOcskU3MEIpLnluMel1iPPIiWMohwyLDKgbMGA+7GCAzLEK83vZbf1DVsdpxTO9YjQ4RLmAISIvDJW0DsiJQEiCkgkfY88p4pJhcP7UFJI5IU0OS28RWQCUw2FaONbhyKQ2zklIwNgFrq8joKHluyMOY3PFwKw6p4xHFNotrLtMjIQ27qzPyzAyyuKgD35YtWla1qjcgjvsXGkU9Ly1ZpgM0EIpH3ompHLgfGRslGZuEkREckyEb63ozgkC/Vxpv+gDoxFJYTGZ2FmwbyXNMEe0kIn3XYfnbcawTb+FaNI5DjKc7AWmK02mSRam2TRBgeR6yvooZG1emTXODcHonrqVUoRxoU8Mig9zgNlfBQFIfw3UMbrep1yqChKEGDXmetnunA2lKZ2o7dtTFDTfpTO0CsbBIqEjU6yspNmFeYXlFi6tWtU+U3tHS0MlzCNwULAED1cVD2GLIbYdwYivexiJO2FGr7OGH1aIsx5kzz6SVuAR2hj3eoJs4ZG6FOM7x8ogTaxX+8v23sLl5Oe99r8/113+WRuM6kkTn4mPH4B/+QT2sT4fjx/te5npdaQY/9EPw1rfqtv955ynTY2ZGDbW779YqnjjRd57ZthpnjqPtsW9f//rLYLLSV3DihP5O6XTyPO26g9i3T7vGgQPwuc8p7WFlRcu77DLtQuUO75vepJ9blhrdxmhzrqxo+fv39+25K6/Uch95RO24n/gJ9QxvbPTrJKIGdhk4V97LsTE19svH6bHH9NEpDeO5ub63vVjTlBsXrKwo8ygItE7lTnXZPtPT/RimLNO/Bx1y09O9UBXqdf273GyYmuo7OcfGtBvX66XtYnTNSYIUAWkWaeE8KGllep5PhFtSFcpxQQfFvrG6sfFEL+KOHdrZyn7ruv1xpqRMuG6fj1p6nbtdPeb7elNK/sppRldZyy4+6sRIC3pgSV96opHm0yXFLpjGdkHNk+I6u7jqlumjDLorVzClO7+MtQCoVrlrfp56fYSP/MWfc9bZ53DN695MfughZrfM4Gzd2nf7h6Feq21rucWNNydO8NBDD3Hi+HGs0VG95ixDRMjzHCsIejyZGJe0MUFucgKJefklF+GnqXaoYrx+Sjz6KAv793PfwgK252GaTSZnZjhvfBx3fFzrV6/3qWwbG9x8//1s2bqVvWecMdD4xbWL6CBQ3s/TMDSKX5x4xj2D2DjI1BZcK9fBY3CruV5X5YliTy6rjZDVG/gkeOvF1m8QgLF0FIhjLM/Tibhc+bda2J0Odpbhj472tlnPO/98zj77bF4zP8+v/fqvs316C1e+5BVAzvJGF7/uk9kBNl3uuPthdux5CZMz43T9cZaX4eQinHv+v2RhwXDvvR9nYeFzzMzczvnnv5ODByFJEtptw/T0f+Paa/fw8pdfz67tOxkfSTm54vPYY9DaMGyftTh4dIqFJQvLUu/T5OTA82sMnkR4aN8ToEqoHlTLwh71yHFJcBCBs87KNaitG2FcF8fKCSzDWncHdm4IvAzfidCBMiLHxikGuxQpPPEhAXH/HuEWPl9QIkWMYEjEx3EMjkmwJSMiIMPCKwKlDAJJjKQFkbii3scSNTpgF6Z+5mKTgSVE+FTjTaTVwglD3Q6c3kUsHpicKhHdyMKyDL5rE45M49Ua2CaH1VXC8a3Y3SZu1Cab2UYiPrKxgBut4eSGM8Qgm2Asm00aVNc3cRoNJAgw7XbPdSW7d6v1U3pwxsa0/5Sh/7kuxIIg0MFy9y7odrE7bRIsqBffKyU9XFf7eLOp71NT/T3eclJrtTAlETXP1QOb67MhDzygdTjnnF7fYGMDcV38RkM5gEkXE4bYSQJjY7Slzsm1gF3jFrntEFsVmJ2k2pzH6rQxto1VreJXHeh0cEhIxsZwkg6O6ZBZAaZW08dpc1ON9YlJjCXKHazVAKFCjvgB0vBpZE1C45FYPm2rDgJRXDhsDGyZzlhZs1heBtcVRkd1PlWnkiEMYaxhmGxEpOL2gpmsSoVqZwVWNQiXSkWtpm3b1GBYWoLVVTJrjI2xKSbIqHRWeGhpko2kxokTDjMzsLKSMDfn8ou/CAcP5th236n1wANaVLml/1T4mZ+Bj38cXv1q+NjH9P1P/xR++IeVQjE/D9ddp97PuTm91QsLOpSVju2JCX3eo0iNzdXVPlV0dlaNt5JHW3qYu111zO3bp7vMpTKSMXDbbX2j0fPUcC1tjI0NNfpHRvQ9y0zP+RbHwokT6u0eH+8H2q6tqSd4//7+GigM4TOf0Wsq1klMTqp99+ijWn4Zs2RMn7NcBs7V6/qZ5+k15rkuQrZv1++W3/f9vkc6z/VepCm9YOIs619TFPUXGo1Gf/0KfVutjM9MEi2/5GWXnOs47l+P4wiWZcjEwSLDGIOFwSImFQ8QAtrkhRuhNDGlJEyDVnZtrW8kD6K8oVu29C+sXFmsrvZVLEoZjWpVG2Jmpr+q6XT6Y9JpGNzprBDRISAHAiIsMnIsJMvB0jFaaXJChPL67SJGpCzHLhwyPcO/jFYMAu0QXuFVXlrSrYG5OW3UToe79u/n726+mWa7wwUXXMr1b/hOqo/eh8RhvzOUCALtNO02+7/4RU4sL4PvY4Uh+3bv5txrrtHyB3m75U0vxlmTpIR+g0wc3KTJLZ++kXBzk+179nD+K14xKDXyRExNMTs+zuz0tLbr5ibL8/Pc8uCDpHmOaTTI223sss2jiFSEheXlvlFcPjiuqw/GUxjEQ7x48Yye4uzQYczYOHap8ZymmMVF7fx6lm6LeD7p2AQigrO+giQxMjGho22W6UPRbve9cuXvlqPi5qYe831Mva7/J4mOlHnOI489xsMHDzI2MsLf33gjZ515Jrt37OBzX/oye848lzd/5z+j2VTivWVbPaWVOIb77mvy27/9n/C8y2g2v8J1172TJHmI8fFx5ucf55xzrubDH/6P/OAP/hojtRWa7TJQUtFua9UuvfTUZzbP1VvhSUqGjS1pz88aEhRcrxyLnBiv5+2Nch8Rg5DjmJRO6pFmVrFDZ/Al0u1kXJxC0ksweEYHw9MHDUPJUdNf0O24RPmlOaSZRe74vSA+HVAFECQO8ZtLWOXsfvqAVASO5N3wFHmv3oxVyP6YglApm5tKfzAOcSKMVFKMZdFOPFobGXOcLDsXxvdJqqNYnRZWnmHVqxqkWPKijaGzFmI7FoGbQRiS1+vgOEiWI0cOq4XQaAxEhJg+XQOgUsGU1koUafS566qhW3p1Ss9CeV153p88B6LFI/EQk+PkMaFUcSVB8pw0yvA+8kFkbVUnz0sugTPOQMo9+TIYxbKUylHshydOQIZNkHWQxUWd1up10vo4bGzgLJ3E2lzX7zoOBAG5ZZNMzGKsU/uoQSdHN48JrUrvM2PAzSIcy2hcjjH6bOWGzHKIG5NYktONLDod0Uc1SEgyiyR3GB3tb397HtTrBuKETGxcX3BMiru6qD/nukhp7TSbsLCgRvrWrYhtY+IYMzaGjI8jGxtknk82Osntdztkxu4xWDqdJm9725uL3ecHOXaswoMPnsGRI+rJBDWi7rqLp0QpmfaLvwj//t/rsc1N3SkdHdUt/CuvVKNtZkab+LHH1MCcm+s7xkqjeGMDLrxQyyjijnnoITWcy7VYs6nHSnZZSW24804t69xz9Zxbb4UPfUgN5zvv1DKuuELpHWNjWpd2y1D1U46ecPjyLfBrv6ae3n37hH37tPt2Okoj2bZNH8VGQ48dPKicafXMGqpVaLV0d6vV0nXKgQP9oLjSo71jh7bZli19IYWJCV0slCEBxqhzrd0+hUVHlpkeRcNzwXH1+w89pPUuBR1K7nq7farcm+/rb5WiLr7fZyiUdmnpfVdD31ClTdcEdEKbLJNT1q4lT9cmKQK/TXH8SWCMXuSgcVxGDhaKD72xoZTIKPm6jUZf3rHkvAyOoeX4MXAsQ+jiP0EtpotXOD9c1tcFj4jJ0YSuVGGAHCGFBkeKhzEQmK4GU5tcg3njgkZWBhWX7vfJyZ7LPUlTPvbxjzO3bRsvvfZaECFsRiTVUbzFo/hri31dQKA7P88dt99OtLKCqVQ4d9cutp11Vj9CtPQml4GJJVEc+nSS4iE0k5O9GyUFde3wygp3PfAA1113HaNbtjzpM40xcO+9ej1RpL9VolxJFXEbJEk/sjMI9IGfn+8HQdo2vPzl/U72JBh6il+ceGb6xP79vShWGRlRA6jTIbccrMAjbEyTWu4pD76QU6Wreo9h2DeIy0GkNDxENHipWu2rE0Av+CbDJhVlIgfo/m2z2WRsdJTF5WW+eOutnH/BJcztOItg+TiWI8jYqHqvspwshRPrVb78Zbjvvph/+Ic/Z3Pz/WzbdiU7d15GHJ9kbu5szjrr9Rw8eAdTU3DBBZfRaAgTE/pMOU5Jk+g/b6XHYnNTn8tazeiOmR1TEdX6jfDIcLBJ8YlQc9SiayqsrEovXmptTbBJqHoZjXpOJD7lAKib7glWYcRaacFvKx/kor3L7XUNMsnJsREyElzC1KW5afUiuS3JqUmHuOBii8kJoqbeK+jL2IAOoKXIabmHXgRB9LwPoG6owvPZo9AkOV5rFRkZwdg2zbTCyjLMZcdwnZwsqOE11MgTYxCT07HqgFFfdS7UNk9ihd3+ltjCAmFtnGRkiipt7BPH+4N/lqnBaQzUasiBA9rAQYBBiGe349g5VpYipVuvnEAsS43r0j1Vuvs6HUzJGxgfV1oCFEooVQyQJvDY4xaz8VHGWcNdXcRJuyrtNDKiBnzZfiJkls3m5BlUnBg3i1TFZXMTqjWSoEZEgEvCatPGsQyNzgJBa0UtiEw9Vubsc8HVdo5MBYRC9k0n9TY15agXT+PGWk7g5YzUlP6ytCqMjYLjCrEEOCQYhE7k9uKIQKs8Pa1NVIpq+D5MjGYEVkiGQyoulXQDOysmqlJKoJR+ajb1foyPE+YuS+2Aua02dtQhW2/S2XE2d93j9BxujzyiRmoc623QoPNP4fvfzpEjqun7hS/0qYRPh6uvhve/X72pJbpd+KM/Mhw8qDq+JX+4fD5WVrTKW7f2nYCTk2oUl5rEpcyrbWt9Su/myIjOuwcP6jkDNggPP6zfKY29/fvhAx/o22Hnn2/47d9Wj6yqZRn27kyI11ocXQz49Odc/vefu+zZo0bx+LjaZZ/8pA6dV12l7zt2qC13+LDaZ/VqRlARpqaFBx6QnoE/P6/1KRU0oK+oYdv9V7nTXnKLq1VtzzguFDZGYWZa6VkbG8pDnxpLiKWCTUJuLJaWhZER8DydI0pDPAz79sbISC8EgDA0JIlQr58yzGnfLjYqNczF8MADsGeP9IQWTqeG+nSJcfEJcQYX9ZxKtMAY3TYoNW7HxvoiymXFFhb083KbIEm04uV8Vmj59kjgoN7ZIobBnKJCodsySREUC9BGjV8xKSAYsXvGvUVGikeeyyltYkzO0gLsco6qqkYp/1aijCS1rILzv85d993HUhRxxugoe846C/eMMyAMMUeOkPk17LkZxHOZf/RR7n/8cew0xV1f56qLLsL3fW2Pbdu0/DKArdvtP0B53p/zB6lok5M6BjoeuWX1lENu+PSn+c3f/31+9Id+iPlDh/jp//AfTvVQDyKKlOtUr/djQUq6WpKUwv36u51On94JynEqnXAXXXSKJvGTYWgUvzjxjEZxsryC5diIZekAkufkCG2p4RHhpiEsryAT4+ReQIYQE+AWBp3udbqIJX3dg06HrN3FmhhDmk31qE1N9UY802jQTV2ssIPlO9ieTYSPMVBBlQY2wpRP33Qzo7UKV1x6EbX6CLEEVGgX0b3QMVWiSDVQl5bgL/7iAzz88B0kScq+fXs5fPhBZmYuotUyzMxcxa23vovduy/mJ3/yZ3Dd/uhqWbrQHBnRRebMTF94ACDpRLiSUQlyQqrF4KdyaDEelsnwJaZLlWZTkCxlqhER25VCb9IUSRciVVDAxybHI+7RGQT6+6OlXtEAQS8u+HQOWY/TbBklbhgRNjYdOrHD+LihJl3sguMtpRchy3QAKXR4yfO+9pRl6SD1dBG6WQYnTpAEdbKggiPgRG1wHDLLplsEYNob6+TdLunMVhzJe1v2vfnCGEyek2xGON1NLN9V4fjlZcQYMjcgqo8TeGAlkdIpOh29MfV6L4uBNJtKbSgijLPpOSTsYqcRZmxMz/e8fuBMGPajkYvFX8/7UUjsJeIQEwAWQopV3LcInzC0enTDzWPrTJlFAl9gdAQ+/3mdRGZnyaemObZap1rJqfopXnOFfGSMVmhjWYIdOPjrC+RBlRQPP4A4tzEZ2KbgAK8s9/ex85yuXccmxZWUzAgpHnbUwZVUhfrbbTIjxHaABD5rh5tY9SqNKZ9M+h25FNUoJcaKjRuWl6Wn5R8E6mXcssWwbSZlejwmFg3UtDstKo/eq65I2yYzFqEEVCXEEshsh424QsPp4liGqJ1yrD1GFAlJAkmiCWqmp9X+WFjQrnn55fMcOHA/x487PP644eMfH2V9fQ8wBk/u++vhh38Y3v3ugQPG0G2lrG06nDwJDz4o3HOPdvvJSTXWRkb02hsNbY9KpS95NjXV35HesUON29KwLaVRl5e17kmiRuXjj+v5MzOqnnHhhfpoffSjfd7uvrMyrrkaLrzY4toru3hZxIjXwVtdwG63WLam+a8fOpsP/Y1w/vnCy16mv9vtqv5wva7354EHtPm7XW3HX/rRY3RznwcXpzh2THoUkYkJPb8c/stgtpKqUhrwJU2ljC0rnXFlcJzaj4YRp0OEj2OlVO2kF/3QU4cwgpUlJHZAJw9YWurT+Mu1bL0O9Vr/lg5SWEvbJMv6tlaa9hN82XY5u5zaH4SMoNBm959Ex7j3jdKwLXdySmOq0+lfdCkvWC6iS+250his1bTTlMF5g44DIMbCgkI9o2AU4pBjExRa8gbh5EbAVC0ksatqPAu4xCQ4rK9bdLuC7xfKaVZCYELIMqyscKmvr/eTVJRyiMZgHIeP3nADl190ETt27uxHjVqWdsi1NQ4fP85BNajYMjbGhZdfrpUfFA4/PTgtLhQpjNEyFxd7kZ2mWGWXyb4Q6e2k+kTaS7pdjs7P8+Wbb2az2eSfvfnN+GNj+mA82ZzTbOqqrozlgH5HKCkiKyv9xEsXXKCfzc/3RcyfhbbyN51RPDb2sY/v2/esznduvXVoFD9PPKNRHG20ifHwJcIl742iSWrI2xHpyBiOpQZCahwWlh1cO2NuPCIWj8TYWHmGa2VkomI5vknoUME2qoSA0UgOeTLXTyGPk+WQdFK8cEMzfI2M0urapBjuvv8uVlabjI/WmZsew7cMZ+zeQ26EduTw0OMeJ06oYfzud7+T6elzMWaFubkLOHToMU6evJ/t218GeMRxzJVX7uS1r/32Hq9wdra/217GFwxSxeLI0Gll1EctbAscK8OWjMzYpMbRCaEYP0ya4psIS3Iio/uwHjGxuJRbY9UioMIYcEymgWuCcrQ7nf7MNRABXVIouqILEifP6cYWxqvgSoxnElIjhKYC5AQS4bbWkTIsvNwmLI3fUg+p3dYL1z1S1ZEq5ciehI+V55ClBufIQVV2GKTKiJBnmf5mt0tYmySvBFTzbo8HJ2nad0laVqH57PaN5jRV0fzJSZUAi2OoN8C21Gg1HtWFQ1hjo6SVmnKow5bqkkYRZn0ds2UOY1tFZqsm1sQoSaK0C7de6H/mGWkuRG6NauFtSlC+IkhvWzam79kvLzPL9B9HYirEJAsruCRYvkM2MU0kgXIje98XssyQxjkjQUScWhw/IcSxxXlnhmCh2RA7LtsnOtgba4hlkGoVWVlRNYeqeq7jSHAqNnaW9BKOSKG0UVrtplYjFp/Ur5Cfppte7iSvrfW3zycmBBFDlukclCTSY0VNTxmmpqHTEdI4Y+fEJqFdwzl+BKe5SuecS6majqoDpClmZRWZmiR2KnQyn4VFi/0P5LieejN37xY+//kyUYXp2f6Oo93innvg059e50MfOszCwirQAV5DtRqQZY8TRY9CLzLfUK3m/PzPn8d//s9bldPY7pBgs9G0WO94TE6qmkXpmSzXiNu29QUECuZLzzB03X7irCLBZ8+gvOeePv+1oJrT7aoaRsGAYXVVy77lFqUiHDgA3/3yRf7oZw9iXXgeiVejHq/idZvKQzhxgqSb8HD1Mn7zj2p4nrCxoY6viy7ScsuEZIcPq7f5kku03q97ndJMjh5VWyEp/BTl+tf39bulg++88wYNUb0+39drOPfc/jBRfr6+3o8jM8awZaRLLcjIRYOhA0KIYkITEKzNI40aea1OlFiIWIhbhg9bGGNhG3UERBJgooiVpsfIqLCyon20HJqqVa1Dtaoe43KMfCJyqnSwoBeGZmGIcLGgiK+Anlh9qX/X7WqSqjLDWxz3E2KUxPDpaR2jNjd1nCykNGSQjnVKTYQEp/+bqGRcRIBHUiT0gW5s0Uk9qk5C4lQQETwJSXAAu7cgUG61Gsw+Uf86Wi2kDPYrojSTNOXj9z3K7h3bueT8fVitZs/ojJeXufsf/5G1ZpNdW7dyzp49JXlbjfySg1suGixLtwsGFTiOH9cHtOzkBefQBJVCqxCVxEySfkaWOMasrsH0FDSbrC8u8Ud/9X5+4cd+DLtcLfZomqchSVTXsAy0Lu/LxESfJF+iXu9zc7JMeUvPAkOj+MWJZwy0c9II14o1g1cZaAC4Y2Pgg5e3ycUhEZ9MLCanleuoLNoYL88xa2uFAeQrrSJNqUQr5FGCiTUgSjD9VJtpquLiJYzBjiNsyTCNOsZ1CSXAWDm2MVx++bV88Uv3cWL+OK4tHD1+glsfWGRiYgdXX302Z++KOO8sw2YUcPLk/8fq6jKdzgonT97DyZOPMTo6yrXXnsOdd36BNJ1nZuaVveQBpcCGbRdUzGJ3vdxRMwbaHaHW0DTNSZQzO6ERxK22zXpTsG3To1itrTns3Gmzvq48v9lZGKk5OE5CoOFfhYclI85tbt9fZet0zPYpDeow5VZ86bkHTUXd6RKOzOBJ3PNENKOAuCW4tsXWMXDEkHZSNiOPsTEXqxJj57mmZN6yRQ3ucs8aeu4i0+3C8grEEbJ9u342yL8rJ5RmE2vbNqyiwUwp0RPHapQWlkOnPoWpGmrROqQZ2cYmVrWCVCu9BjcFXcMsrxBO70QczVhl23bPSsmqdULfxdgOnpXgmQRPQmjUC4WMgjtdGObG88jFIspccttXTeSZcQLa2FGbLM3IV1YJx+awXRfXSgpVVEt/m4zQVDBIkXSlz38u1wxl8FWSCBsbHq7tsBnW8N2cM8dDsm6CCSoY2yIeSJLhSULNT7DJmV/3qI7azNShY6nrzG/AeAUeX6xTq9UZddvUW+puizMbuxti+x7B+hKhvZXEr2JNVPCillpKk5NKbykmO9dWSb8Qh2BQzkkMzFTZNp3imIQONZaWoRIYRkbUQ/XwwzpXzc4aFk7qkJBlBtcGsWxqdMnHauAJddNClpdJUuXQ2yZH5ufxxsZwGw1GZ3J2mXUOr49wvD3BAw+oituJE+rx3L3bUJMOER7emMWrrgxx7FHuvfcSFhYAMuAWLrooxvd3sn//tzE3B9/3ffDXfw133mn4vd97kPn5+3nNK6t89xsuJsfCTmMcY1hddtm71+p5XZeX1dAsZZV37tRHo8zMXao/raz0+cXbtun72po6zG67TdeOl1wCb36zGt3nnadz9b59+ricOKG/cc45RQDh0gzvePcM555juOJKOO/caaZGRzCWw8bUDI/cF7H8UJeTJ6ps2So89pj+1uwsvOpV6gzb2NAdrauv1rrNzCh/+ehRtQnKbHv79vXtGcfRMqpVtV1K73G5KRXHKlFZjoXdbj8x5Pq6ntPp6G95rqHi5SCQGyEz+pxYnTaVzhIGIfcCBFXtiXCoolJheWromAorTZc0Nri+YXLMZnwkY33T6e2+a/4JnRtqNVFaz6Zh51Rbg9IKeUkLU6hRSJHJMyTGIyuUd6q0CVFOv0WGFdjYQYCVpjrWOXaPisXqqg74k5N9cnMh8WaKeTEJGtijdaxCUUmKQWHQ5SSYUwxipUsoiSMukvnYpFq8Z9HpOLRbMD5uSIyHZRJ++3d/k7mt27jqqmvZs+csjFEKhp1HGMsmD+r4pVFaKk8kCZ/94hd540Xn4rge1tKCUs1OnOArx4+zev/9XH3BBUzOzvbTFu7Y0eczlaugPXv6PN5BLm6p43z8eH/VNzoKnQ6dugYruxLjkmNcV2mYReKvKBght6pUJhweO3qCqTPPxZqa0oet1ApsNJ7cOCkjVktdwSDQB+GRR7TuZWr5Wq0flfsMlIkhhnhGozha2SRYOqYzVLkSg56uq6Qp9tQUlmPwUD5wmW3OJgXbIp2awcLGK0TJMRaVtIUddYiDBkltlMrKcaTeKAj5pk96K43xPFeuse8TUkFIcV1YWnXoxnD06HGWltY4fOQIc3O7cXKbRx99kOPHD1KvBwSB4eRJiyRZ4Jxz9lGr1VlYOItf+IV/gzErBEFCmt7H44+v4ftLhOHZvZ2wkyd18C8doyUnrvSolFJEvm+gYZEQ0OnARhE72Khm+G7OfNul1TLcf7+WVSZKKwPjSsMkAxJ8Mtvi0gsizVRkRA3iMqijzAFbqCZYQU7VdEiMS0iFSrjKTDUnT3Py1XWojdClSu54jFXU+EndCvaoPehy0fc41gF1dVX5s+NbEW8EP9lUPVoRUreKkxvVyS25XeWKoVqF3bvJOxEmS8njDK8QuLfyXCdCy8J4PrK6Qu7WyGwfryi7J+1h2cjUJFUrJDYOXaniSkxQSIXZWUZlRRNzWL6nXmbXVc9pAZcUs7amfLqxMaK5PYjolqqFITHK3HaqNrbV5sjqBLXYJXAswtzHsXJyESxj0TFVWi3RZndsLAPLK6agPAuOo/e03FZ2XZibSNgya5OLS5RXqbKEl24Q2TUy42HQvpQkDoLN9LRhYtrS+BwMljHaI0TwbBipGpLMpi0+tckpXWdstMhbXayVZcR18daXyMtnKQg0mixNMX4AojJQGrgZg+iWrRTb3B2qRLF68BxgZUU9xJKnBHnMsdUqY2Oi3YWczlrMrh05li20u2rgE0dkxiKuTVFNlb8d4rPOKNvMcaSzTt4OkVodVld5uLWdxU317to2fO/3qg3SqOZYYZcNu0IYWbRawv3313j727W76fNnMzPzUrZtg/PPF177Wn00LrpIDdeTJ4WLLz6fCy88n0r9CLffux+TdDl4sMnMyBRnXXAxFgGtrvR2nqemdJ4tH4OdO3XutSxVhLjvPuXD+r6uz7pdDdLzPHjta/uiG4cOwRvfqDvNr3iF/n/vvfC2t4Wcf37A4qIuMMbGlI6wugr/+FlBrJLOHxSOMId6vYK/1XDhxbCwYHAc4bzz1Pi+994+c6jkR2eZeqCPHu3vGIN6jpeWdBhfWuonMGm1tH4XX6z12LmzT2+oVPpCDKWChucZLAsmJ9VbubFhcGxozFhYkmG3NqnGKzAxTrc+jWW3sVOlBOQpuugVi06hzZuIzca6YXLcsLKm2TWXc4fxCZiakl5mvTInhto4hiAQgsCinVepmQ5GALFwiIixCIgKmTONUXGIMVi9GA8KhWMbUe1tx8GarlLNWpg813iAmZneeHLKvmqlQp4ZwszBJ6JrVaiaLrK4gJmcRByHXJQRbJOTYpHgUCFGUCpgh2qvZE3L7hEQ4hNTraZUqkKOQ5JYNJs+P/GOn+P97/sf/Oqv/ifm5vZSr2Zkec5505O89m0/wNT0RJljXG9cHHP3PfewfXISr1hVrKyv86cf/CCXn38+11x3HSMTE33qw8mT/eC0ckwHbYMs0w4FOqYMGsalE6ukWRSymdW1E5iZGTpSxy489qZI/GUQPnTTPYx3ThAEwtTYKG9/w+vUAVcE6nLkiAbEnQ7X1fSPJY4eLb0ROkeurenDUGYfXVrSjl4qAw0xxFPgGekT6UYTS9Co8nZbZ4vS+HFd7fyFRjH1OrEdkBlbU9gSqxTOKdENRsvqdpXcXzx0UWOCLKiTez4VQpw01g7t+zoQnRKkAKnRbae1dYtOB26//bPs2nUp1WqFBx64hShKybKERqNGksTF89JRrVerxsTEBNu3n02S1EgS4aGH7uRd7/ppXvrS13PPPbfwS7/0PhqNKpWKPlMljUxZC6WuryGKpJicMypEhLjkxiXPDQsLRcyDMUyOpjx+1MX3YXTEkBmh2xVGRgwz0xr8VnqJ9WVB4fHNCx0La3MTiUO6Y1uo5F2V4Sk5Vysr2kajo3o/VlZ04JmaYjOtwNo6STtm2Z0jqKhhV3MjastHVLZrfPxU72+rpa4nzyN2q+AHuIGl2pMIbWp4kuCbAW0lY/rbXZ0OJsuI8LElx80inYGLdJq5UapHQIgdR7qVVqYEo+SE16hszOPYQt5okOJgD2g1Ywxh6iJWjk+CbG5iqlVir4LBws9jXU8ZIU8z7HV148ZelRiXGl0W1nwcTwiqDiI5gekWer0Bq6vC9EhE4GckmbDa8vE8NQICP8MjIswDDFaxfXxqEAz0t59LDvr6akatYvADzR63tCSkqdEkezM5VUt55Wku1JJNyDPlBQ9EEHXRXZkqoSZfyWysOMaLW7p1Wy4kyz3uMoDVGDp2A5cYk0OXCpYlA/U1VOjy+HGXMHHYtUs/62wmpLlFY8QqAkzV+B2ppdSaJ7DKnQXb1vu7vKzP7uqq9osdO/rRqcZAp0MyNknkVPEkITc2jx5yefSAMDIivTimblfnuh074MABw003wYc/DCBccIFKqr3xjYZtWw2LSxpIdttt6gl91av0+7fe2ldqOO88CMPb8KwtdKKtLC0f48CBuznvvJezb99EOdywtKTG4fr6Il/5yn727t3Gzp1nMTKS4/vC0aOwuChFVjbdBbrvPq1Xt6vSZ3v2aPzpxgb83/+bYllfQaTL+LjNq17lsbzc5eabDbXaZVxxxSRHjugc7vv9RB5lso0zzyw0gXd3qdQs7rrf49AhehKRStUUNjYMJ09qnxsfVym3KOrR7HvBgQsLWm4pQT8+/sQg/Lm5PuNmfLyvYKWPuSHwcuYXLFZX4cwzBS/vkGYWlZrgEZMkFpEVYNnFoksE28RUTGGwVKvEQYNYAkBwUXpAQEzXBPh5SJIPquYM9tE2IT59XXa915vNnOlpqFghmVHleE8SNXaLcOWSmuaQU5q4Bs2UJ2hCDQOnSF72wh2K9wQLQXpSmRGaWMMlIcalYjTuRQb4sx4qs2lKz/RAmapGUcMmIShoEJG6mIokHcLGhgyIPJgic7NgkeE5Kbfdfgc3f/yv6UYxmevw8z/yI9S2buXvbryRc2ZmOHfHDhgf54tf+ALkOS99yUv6dMUtW/qKPJ1O30OeprpSqlaVg2tMP6Pcrl2nDnTr62pQWxYmz7np3nu57qKLtGzLIsEiwcfNQtXebsc8/sgDtDOHl1x2KcHhR5DAR3bu7NM+Dh/Wzrd3L0+L227rqw0Fgdb12DHtrPV6P2r3qqv6q8NngSF94sWJZ/QU58Uqt5fVDvp7aZ6H0dGXyHgYXAKTIN0N0k5ELhbh+CyOZMWgJFREA5qkyGGeT82Qi4VJDIGbYdHRQci2dfDsdqFSJfJqZJaDmByPlNWWj9jC2JgpMiqtc/75I+S5MDb2SrpdQ55HHD58KwERuYBl+UiecPtdt3LxxdfwgQ/8OhdeeBXLy5ucOHGcK698NUePHgJsbr75Js488/XMzen4sLmpz3dpg1ok5Nj4vhrHgmYF0+1ww8oyYAxWYcxsdDx27ITRkYyG1eHIUgXftxkdyamWBrHRrTQR9cKBJtNQwXaD1KvYuUfNjjGdLjTX6VntpZ5ukYo4G58kjoXAGBbnM5Y3JvEDYdt4RC3IcZwczzHI3Jxe0OlanaWnYGQEz/MK14xeS1q8WyY59fyyjxRZ7KTbJciahcFdnBerIK6VZdTybt9iLOgVofHIRagQ4UiK8SsYowoQSRGNbWF6QYKVzYLnNzqqk5YxuCYjNDZmSd1gsTfC8fUacxOq26zbmDpWTI9EpEZwRSctRDS1tVXIjzlCZiwsG8aCkMz2sBwVzo+oElghBkNkKr1JS2XztFradNIziuPUprPWzwQ7PW0IOzntzYyaiUiMR4ZeZ9KJcSXFZJl6rIpMfX69QeZXlItfGL+J7eOFyypnaCl3nyxDlpZ04VkEpdT27iWqjNDJfVbXYGLCnBLL0qXCtm0Jmcn1fhto1A0OIYm45LhqFAVFdxgfx5QSUAMydlK4Ls1tt/UjoozpBYputl2sWZ/UqxC3E7bOZnRDl0OHdN5aWNBTr79en70xZ5PADnjwQY+HH1aObqUC3/5tsGumy8njPhMTDtdcA3/+5zpHXnSRGsJrazofzs+DyJWsbaqHtlbbxZ49O3nggdt59NHbufrqWbZt28nS0qPccMM6W7dO88pXvoz9++/mH//xHxivp4yPGpY3PbJM8P2YCy98GSsrVVqtntgIO3fqPP7Zzz7CXXcd5tprbR5//BrW1qrU68r53diAs8821Gp3MT19Jysro/j+lUxOKl97166+Uex5hepFI8ALcq65OueqiyKS1NLFXd1CxDC3JefsPSlJ5pCLxcKCIUn0EavXpceHdl1t3y1bdMgYTMwyqG08mLm4zHDcS6ThxGybdZiacordLp/uWkbFi7AkwV9ZZl22EHl1JsYNFTqYjQ3Sah0mphExBZ8dwJDhUCkoDpodMS/6cd8JCcXuCUadBv3oXCoVqFdSPGJCKmys5Uw0ImLHx0hp8lo9rfZ44LchVzlDYvzCGD49AK48s93L2Or0jnukhbpwjosQi9cL6lOPcFjswlTwi0Dx/vX0lSjQ/U9iHALCgkyhx0dGTCGuIL1Fgk4tNik2V15xJS+/9DxIM06urPLvf+PXee0rX8l3vfnNOCKsHTvGFz/6Ua58+cuZLTuB7+sNPnmyv71VxpeUnuBShQP6Ther3/ZZlvGlL32JB++/n+bCApdfeimtlRX2XnQRN3zlK7j1Oueedx5zo6Pkqc2xdZt7H7qVVmeZ61/9aqanJogyIdx2JlYS4bgVHFvUaz07y7PC44/3dRE3N/u6jdDnL8/NPSeDeIgXL57RU9xsJtToqhlUhqZPTek2dZZhVlbB9UirVaJgnCpdrDgkz3M63ii+pdq6IUFvy7rn88oy8lyInQCHFNukWKW0mzEawFCrqfZttV6Q/VMSfOLcJjEunmvYbMHRo0dw3Yi9e8uVVKn+4GKhnsQyZbGYnE5WodWCe++5h6XlDe69dz/Ly/McPfowZ511DQsLKbOze3nJS97C9u19Tl4Zpe0QF8Ogg6BeQ5ucDJvI+Dgmxs5SVloelYZDq20V3DuDiROSFMS1GfdDLMk1iK7bJaxM4NgpDnkRmOFimwwxGaFUqEjhBcltgs6KrvYH05SKYIIARkbJV1axGjWkUsGIlmWRkmLjF9nsnhAPUmy5mQEjWVZWCi7AHIiQ5ZrZzyEm8EBs61SN4zIgUPW0+q6oMjpLyaA665Yr+dFROvhkRvnSAaF6wvMME4aE1XFVSjCCSBE8UwqnqhArJgxJ/Drx2AxGLCp5G4tctX3FIzUOFimO6H3yCXvTUYc6p0fqJJ0IRKhUdBLOsXAKRZCwt+0pvTicjY1CmquR4UpCgtfz8BgjPcW3EpYFYyMZdS+isjbfkyHK3IDU02A8a30NJ0/6up6eR+xWSHGprByHiXFyx0VEtazTVBAblZ5bWUZECFNNGe3Vfeh0SGsjSKOhwZ3F85+LhcHGJSbDwu22yZMU6jWWmwF1P6ERxOTisJn4pKkQBAZXMkyS4KZdHBudlE6eRDY2wPfJs4xwz3l43Q3VOu+GsGWWlj1Kh2qR2bHcgodbbjFMTKh8V7utu52zs9CYf5gEm88e3M0//UGHtTX1kr7udfD618Nf/qUawnmuAWzNps75W7aoZu/0tHpwWy01WDWA8NRY0bPOWqLVOk6jsYdabYTV1X6OhssuU2/t2poG/5WZ5R596O8Yq9nMrwV8+csWO3eq53h83HDixJlsbu7qBSjOzfXti/vu042Tq65Sr/Tx4xvceOPteF7OzMxFnHfebE/eeu/eXrwxExNQryT4JiSWAFciUqMGnCsp2dF5aDaJzr4IEWFp+dQkJ3EsvSzmX/6yLjwsSw3vM87QzcAysHFQS7iMvZqY0GvIsn7yxiTRxZWXtckyoeplmPV1OvVZHEf1dhHopm5B4/GLHSdbg5BJ8UykyYYkhTzn+HKFNOvXtd1WuyeOdVelp0csKjNpS1bwhXVvzU07ZJng+cUitZgTErye97UcqkQMVekSiY9nosJ9YzBiKHOVqsGb9UoSNJmSeoqVx+wVxMHTPcun+rn7KI938DSV8gDsIsV6ilXIZ9rFo9ov7dSxOydYP0kuNunIOJ+94aO88du/nSzP+YebbsL3fV551VXa6eDUKMtirDcDBcu2bdo5Vla0sffu1RtdGJwLWcYf/MEfkAQBl159NWeffTaHDh1i5Ngx9k1Ps+Pqq2F2FrOwwIM338yh22+nOjmJPT3NS667DnffPrAsuh3Dnbd8ieXmBq9+zetxkg4V0+0Hxj0bHDigq9w0VRulFM/2vL5+8eWXP60m8ZNh6Cl+ceKZdYrLbQk9WUfJAZJaWETla96zuEhUodtJpZe5DCDIC7k2p4jcd5MI2dhQ6kWprTjoolhZhalJ8DxyIDQV3I1lEEhHJpQjZmI8SUnF5ROf+CgXXngpW3acWUicxUUK5H4yA1BeYHPD4Do5lZoN3Q2OnFzhD//nn7B//wH27v0h4jhg9+7vIE114pqd1XGk2A0qBiRtO73urEjN4bCxYRGFOR4Rra5NUHdojNosLRVbkQ7ESZ8S7JASmC7S7dL1GqROpainpXy4NGV1zWJ2PMKzMqykmImiCIkKT4Ztq9Zwp0MexYQXX4O7toArGUxMqPSbqLciR7WV/LSN55ie/q6A0lWiiMQ4GGNwTYK1uKADzO7d2oLdkDizsDwX1xnoP6V46aDr0SpUHsqZudSyjdTg7A1gxSycdUPCyW1YnTa2JyqNJ5o2JMptUuNSka4aWMaoLufICCSJakNnEKyfRKYmlcIRRkTT2/AkQVptYuOQN0bwJC6msHIifSL9QUwGJseIU5wihUetTIDSV51oNnUQVXp2jkOGS0Scu0S5hzHC6qrpOWJsW5MN1CsJgRVjRHBbG8TG1omtNqGBpWmKlUS6ndvcQEZHSfwqYe5RP/mY8rv9KvHYDFUrYmnDI01hbCSn4mp62zASVlsOk5uH8CUhH58iGplADDSbhmoNbEfIcKiiC5ncWFhhm9AfIc0dkkyo+lkx+UckvaAlbQB3cxUnC4mq41QWD2MdOgTLyxjPo3XdG5STmxTybZ6Lb8WkuESZx9qazr2tFiwtQm76PNIy0N3NQ9qrEbc+NMK99wof+IAazJddpjtFf//3Om+fe67Op1/6kmaBiwsW1t139+NHXVd3WN/wBnjJS/R7a2v9zRZQXvL8fD9hxTnnFIkTnTat0KEx4fE//6ca7lu3Gt78Js0E+HeftMlyVbIoYwfKIL09e3T4TFNNOz02pvP1GWdove++G5aWDFl2L46ziOfVuOSSa3BdizPPVBrJli2QpTlxZBifECwx+E2VnuhWJllaEaJOTruVc+nZHbAtWolPK3RptfQaH3tMlXhK6bmSGVepaNuVC4rZWX08O52+3K7yifXcKCqEBQonoyU5luRUpKtKOLny02teTEUiMhG6VBHAMTFRrkmLOh0gS5gdj4mkeooHGErxB8HzckyUsNZ2iSNhackwPSNMTRnqXkIiLuVulhTzDoUqUkl3A6UdlD/R6ei1T0+pPORmy8JxjCY+sXNE+s95UHh8EzyqdMmxCg3kqKcT7wxIaObA01lU5Wc5FNr2avyW86VNVlDGCr5/GrC6qgtGVZI0/bGKVFV8TM5D995Pnmecf9ml/N2Nn+Y1r3g5lWI1Jp1OP0d5mUhrZQUzMUGUWNhRG2t0BNtztOELbnDvpi8u6v3RiEftMCXnenERs38/Bw4d4li3y6YxvPZ7vofq1JT+5toaRz79aR7MMvZeeSVbgoA7T67wvg/+PXNzo/zC29+Bd+IQVmtTo1fPPvtpWm8AeQ533KEdt0x3Wa6cXFcf8OcRYDc0il+ceEajuNOMioxAfR5UD0YTLbSlhoqOq2EciY9FSqXgFGuWNV19x3gahV5kbwdIckOcebhWhslzvIIqprwxlDdVbHdn2IXGYYjd0qQT2eQMlq1D1+e/8AX27N7NjlIloUCMQ0TfLaTrfx14/uFzd/MTP/UjvPkNbyPOqiwtVZma+ndsbgqveIXuJE1M6DiwbVufnyei7F99blS2yhiBPMdJu9z7sI9Xsdk6B7W6BqOIyZkYSXEkU96YUASDmN5AqvV1Wd1UuS4NpDUEgWGiFlFZOqILlEKj10zPQKOBtDaRY8f01szMQBBoVsE4JgoaJFZALjYiBr+5DN0Qd3oM1TOi3MEjNTZ5GEGc4FVspNHoKV5gTF/uptx+K/mrpeErooumKNLvPfZYn2u8uqrlTE1p8GQRVFcG8AFqGCY5VpbgBeqBFiBpR2web1I/Yxon7iJpkVGiEKg3RYIMKftcmiCtTeKRKdzNVSzbQjyPzPMJJcAiwyctotAtwlB6cs0iwvw8RN2Mc87oEkmFLC9oI5YhkAhTbPvKAPcvDKHdNsyNd1hc9zDY1BuWZvbaEjE6bve86qXaXRBAtaoqCwk2sdH7bolho1mwDyZyarSxygQqIpilJWKnShZUqTQXkSIDVyo2WTui440xUsux4i4my0iro9hxFztPMUFAikOSCpHxcE2E31rBGR8ltDUQzy8Wsl2CXjCoKbavY2wS/CKwaBF8D6ta0WQsS4uaWavTwUxMYJotwJCNjBLuOZ9KQRdKsYmokGXwyMNQdRMmx1JOrldYXVNPtEiZPEeYnjY0N3JmpoWXXCv8+I/DpRep0O2hEz4jQcyO3RbYDo8/Ljz4oKo8ZJkGoz3+uD4yExPqnVXZNzU0zz5bPbml7n/5fu+9Op/nuRrS2ya7nDHRZMWZ5oYbVLXiLW+Bbivjgj0dPvzpOocPSy+3Q0lJKJUUw7CfLK3VUs707KzyoBcW+nJjqqK3ybFjt2JZObt2ncdLX7qN2VmYbESMVVWKyiwv8/e3b2FtKWFsS4VrL95kod1g+0QXr71Cd2onR47ZjNYTRqoZhxcCHn9cCAL9/ZMnda1bxjaV3ugk6adsBn2UV1fVO186BcbGtK6DimQzM4VxTEZqHNbXdaE4Vk/wJaJjqiAWrZbpeaM1CaJQDXLGJvr0AGXbCotLQp7romx2tEMmNrlx8ExEZqBtqthOTz8FKHND6fyyuSk9CTfoh1+UGf9KGu3kpAZbJp2YKLXZOtEltKrF7okUS+isIFxoQLldZC0FQ5caNdrKJ0YdEC5hMfPZeCSkOMSF6kZZ27w4t5yXKsRF6mcNBOxDk5qoDLJQrWRomkpdBARZh+P33MmBxx/nNS95CTc8sJ8te85nz5l7CoO9cCQM5tZ21Xufxhnd8S14kuKZWO+B59ELjCkpUKU+84EDWqUyGdfsrEqklbFGo6NkjsM/PPoo17/5zdz1nvew/NBD7BLhkVqNytat/PGH/5bNNOftb30Lb7zmJdhbtmCdOIY0C8rd2Wf3t1aeCa2WGsblHGTbWiffPzV7z3PA0Ch+ceKZ0zw3m72sO6cYxcb0stJFohtHtaxFig22hSOmHxA18J3BrD5FBZSrJRXCrkXcTZkdi7Gt8iuF4SE6GMWFAVMOfmWqZMck2KIBcDd+5jNc/5rXlBfRC14rM70ZLLpU8IiYX1zkXb/3+2zbupUTxxe45Su3cdXV30+rdR1nnnk2u3cb9u2Dhx+WXrDJyIhGlNf8ENtkbIRVXE+fwyTRIJwy6Vvg54z5XSAnF4du7tNaz/GDQq3CsnAkwyHpeRnKATfOPcJQimAtw8Sobss7nU0SXM0mtnyUuDaO5Blu2FTt2nLPt9RXmp9nM6+yPHYWE5NCJW9DmnBkrc4Zs2EvqKOCeu5jp4JJczwnQ0rX+CBKxf3BzEllEFUJ28YcO6Ye7FpNB+DSEigax4gQi4PbbSOOTW7ZpOKxsuliOTa1isFgqEgXRwxRBDfcFHDJxTlzUzFu1O5x22m1odPGzGxBkojM9ohtH0sKUbX1NeyoCzMz5FL2HaU621AE1zm0uy5BAIcOqZTe1q0aWGmMkMSGdsswPmnh2SrRVmoNp9hFSaWevxp0WSY9RSO328QOW0RjW3oLkU5HF1uzsxoolCbKtcxz2DrRwfaUJlShq9rDa2s6UU1Okjk+1uYGEoVImdGpUsE4Lp3qBMcWPabGcxpuF7ezqUohpVxCkaykPbENp72OE3aw0hiZnaVrV8ixqJpQFV+EXlKZGh26xivkrqCet5DlJUyzqfd2akrjBYr9dbO6Sufcy3EDR/nRIkie0bVUyzUg7qkJzC+5HFv0tI9WhIduXuBLD08hlvBjb7cYGzXUpIUFHDoiJJmwe0uEFbbojG4lTS1aGxlH5h2WloTDh9XQW1zU7vfww301rX37dDFy1lk6r6+uKv94zx6d0w8doqd4MD+vht9556ktsHevcn5Lp9TRoxCFhtdd2+HRowGHj9k9Se/5eb1dhw5pog/PU8m0rVs1p4vj6HgyPa2/rQF7fZ3offv0sXr00QeYnT3O2WdXOPfclxCGDkuLhtWVnHvvhV1n2NRqhuuvbvLFO32WmwGVilJSrr8eDh0ynDxhWF4V6nXtezt39pNWdjr9JEXLy/r7k5NqIO/fr+3X6fQXD2tryrmfmVFptMFhveQng04Rpee51D2emjKsrkDFS2g0LJbXbWq1fjKyEg4JThbRymuIZZFlOTVX9Xw3uzZBoPzdqnToMJD1gz7tY2wUwkifw2pV63DypK7Rs0y97mvLSrFS1ULBEU2k1O3tMCqJIsciKHYFBVO4djQIWndNcmp0iPFwC4cQxdyjwX1KrQgLqkRQqFBklMax0JN4xNClCghShPb5RHR7AYa6Y2NlCbllE0vApz7xt+zYvosr9+3mk3/9Ia58+cup7jwXkhhsh5oVairoUn4kDHV+9H26Vg1sIUi7SLeNlNn8BlMpb9miD8viot6sarWvRrFzp3byMotcIez9VzfeyOTkJOd5Hts7He2QZ56J2b6dP/rQh/jOf/GTTG/dQtqN8JbnsTfXNbC4JOc/iRb+U2JxUR+2dltfrguveAULCwv84i/+Ilu3buVXfuVXnnVxQ6P4xYlnDLQDel7aJ2BzEywLv1bDWzmB5DlepVLoCp7Wl8qZz3HoWhXl+Rr19InrUvehXhE4bWEonQ5OnpPXG3So4ZBiSUZe6E16JLh5BKtrMNLA+D62bWPKnKSFkHpUUDzUCa3UhqizyX//9V/lV/7b7zFeEd77vvfhu8LcDkMcOywu6nb3xz8Ot9+u8j+/9mtqV6htp+EUzU0InITp8YTcqlCrSS9QS0Rom1ove+jmJriBhUeb+tKJ/p4kBbfVaBIG3aIv4+gMUTfHGdFt9swITrhJPLaVvBsRLOxXr+uWLZqgIo41q9vhw5pe1HWpm5hgbJOECmkGdrvDZN3ngccCWh0LyXOu2boMQYA30dDBsAyqOB2lq6iM0Cnv7wBSI1gImeVh12pY1SI708iIDljLy4SVMdJGnYwMb2ONcGwLKxsuWQaTskllcZlj+VZqdYepRkLg5rzldZ1eHXKnRjcPyI3B8mz8bpckd3DyBCfaJGhAZmzVbW7UMHXl7EZFMJ96blwgJgccyZmodDm+EjA9XXqXDKurQhgKjm3YPhOTWMrzLtOzpmJDTztECvZIn4qxvl5sM1dHSKsjlHx3weBUBNvWid0Z80lzp/ieIZNK4YMSmmmNamcZz/WQ889XigMO+eg4Trepz1wU9dq/tnGSnROTSHMdO08QKYLtSrH7YkFTjTfApJDq/yZNqUioiVFWV+lO7yK3vaIWGkBopykWmVJ+iuQA+eQUsvsMZGkRY9uaoKXVQrZsoba5AElAWh+jY9VwF48hIwZTrWGAzazG0aOGtXVdRluWLipfcVXE63Z8ibVt57ERTZJ1I6Sgcu/eachzA50YEyWAxcOPqmet1dLn8+GHdV494wx48MFe1m+2bVMv6LFjOiSdfbY+0+UmSJ5r85w8qbbD2JjaD8eO9XeNyoRnZZ6ZzU3hE5+rsW2beqdbLf29z3++5zug0VCFqTzX9+3b+9LgzaYaxKW3tV5X4zQM4du+DRqN80mS89m3r8Mdd3weYzLOOecCZrfMce75pYSccGhtlC/fUaafV5tFPaHSS1By/vnKrz54UB/HYo1FuXYtFR8ffbSvOHHWWSpDe+CAGvT79hmWl/X8atUUBgTkuTnFQFZZe03znKZa7sqyYet4BwxYtsXkpD9AR+p/OcciMS6ra8LkpMHzNC7CIHRzm27Xolot4wE0pXOuUQOMjtp4eajlBT5gaLdVMaRMuqYsQGFub0wiLlHq6DSFA2JwSIoAwE7PkRLiIngYDS3EI8OQ0kWwyDFFHEiG3YszEVBZzWKEKIP5yjEzFb83l6nihF04KkrYOERkue6UBtIlF73OxTWfOFzm8OFbOGtuhobE3HDTZ3n1m99MpVolISI1Bm/5KPN5zlyjhkxPq76yZWlMyvIK2XQdjE3eDXFaLUxBHO/dDcvSDrm42A+MsKx+MFylog9Ht6uTXJFS8Z9cf72eX6vBzTfrw5fnvPujH+XiCy5g2+4tALiuT+7OQRLBju19idDnglKIu5yLpqf5L//lv/DLv/zLfPCDH+T6669/buUN8aLEM3qK202dvN0ikXAPp3+vdA+IqBshSXSLvNXSYwN55M1g+ssiJaa47qkuhhJlRIfnkaRCGqfYFQ/byk/xXHdz9TxWJOILt91F1XO4/MLzyS0dPBJ0KxsMAR0ccv777/4u+w8cYnx8kr1795IlEYcOH+HGT3+WN7zhP7N37xu56sKI+UWLO+7zSFPhO79TJwXXNdiWtkGrLWw2DbMz0GzpFrzr6sRaJkXa3FRjqdQzdu0MO42Vi+2qt7xrfDWesxTbtXn4cY8stxgZMcxO52AJDgmBiehZ2e02sr6OcRzCkRlwbXw7RyzR46DuKdclEweDbmcm4mKTkOQOzTDgsYOGfTu7TIwLnDgOnqfBFoMBdOX9KPeCS+WLOD6FPpHRn6iEnFq0phSO6gS2neEY5cqGzkjPa2uwcIkwSY7JDBVfzUH18FtkmeCuLCBiENvGhCHtuTOprJ1QD6jtEE5tLzy4SuuJ8AmWTmClMcZxMJPTSJaQWy6ZZRNJBYcElwgLSHKL40s+1bp6ocBQs7rE4hIlTs/QsSxotwwmTWmMCBUrJMQjHwjgOe3heOKzRY4UrMMci0qx7aoJCCpFClQNtoyNz8aGwfZsalU1qFW7Wik0JssJ6xMYsfEefQDn2GFkfl6tvWPH9P7s2dOfzMbG1CW4sqITHWDGJzCuh+RpQZ8WvdhqlSTWBAGBROSuj2l3sJvriOlPmmmlRjQ6i7e+iJOGiOtiwlA7/NgYkqbk3ZDO6ByrTYc8SpgZT3A9w8lVn9FaRrejlKkTJ4TRUTUO44IhMzsLlhjq8Qr4Pql4HDzmUq0I46M5RizuvU+K7LKGWk0zubXbusOzvq4GYKvVpwRUq+qJrdf1EZmf1zTJExPqFPvCF/S8PXvUO7q0BA89pPWanNQuv3evGoplzoOZmX6K6m3b1PAsE5yVCUJ27NChsJR0PXmyT1sof3tmRg3Ko0eV3rC8rOecfXbfs33HHffx8MPz7Ns3yhVXXIVtqwTbnXeqFB1oOUeO6HuZmc/3tS4HD2pbbGzo9aWp2jzLy6oVPT2tlJM81++WOX727dNF286pNl3jY7uatTMMNUCs1YbJCWFx2WJkVK91fFzr7Ptaf9tSvq56XC3dpSqF06R4OozGoJiCmlalDUghyigF81ZjAjRhhvpuLcmJjc/yslCvGyoqCkS7pc9XqTa2uEihtWwY9bocXQqIE4tGwwwkZ1INbxE1eL0iHqZbBNpW6BRjTaUIjFPXixrUqhrkEdOlQpUuxigdqSpdDWCu1TB+gBGLCKcICk97z1VmpCfJGaY2y6sOM9NGs6aS8pFPfJo7bvkcb33bP2XnaIV7bvwUr//+76dbn8CKQtwsIg0qfPTvP8nuM/bw4B238ENvexumXtffKCRSH7j1VmzPZ+9ll5N1Q5x6Vb3KcdzPa15mrxqE66ox/IQhz2hnP3lSH+LxcW30Rx4By+IX3/9+fuHtb6dx8cVPnsr5+WJ9XR98y+KH/+zPOHrsGDfeeKMmfXqO+KbzFG/Z8rGPv+Qlz+p85yMfITNm6Cl+HnjG3lgh0kFp8GCpfVqS5vK8v9/meYU7YKVIwVvvB19NTJxiHmQIqVvBI9XEC6U1OeidtO3enppr5zgmRBXadQ2eFozn3NJLyYm56soref+H/pazL76mKKRkeenfoalgk/Iv3/HTWLbHL/zCv+W2277Cy666giOHjvL61/8gV176Wma3gtPwuXA7bEawY4fp8YlBkDjG5BnjFcP4xiJxe4IgGMHzwIQhE34GlrAeapR9t1sEp1ShsnaCdl5lw2kwm51ExkaohKuYzTaLrSqV7VP4gcXKCpxxhlHtzSISuSM2ua3eBz/IelmV/OYiudi0p3YSEOHUauRJShiM40tEO3RY3vTYOhnj2wkJDpnlUa3mXH5hDFikYYyVGSRKNNVpGawwuJApUfLOys/CkDxJEOgZdCAkbgVndQ2v1SGbmaVNldraYSq1mKxW79FhbDI6ibDSDphwoWLFpBm0kkAlk7NZam7CpNfBhCGSJCTjM3iSFjJu6oHJjBrEPjFMTWjiDrHoUMOO1rHTFtnoBK6JSMQlpaZeHgvGJ+2e12ttTchGqwReQsWNqY4rJUiMwa8Y8gxcKT1BqabtltPjy3NVPCkm/7TwABksMFlBUdBTlW/oU6NNl4Ac1U91JWFuLO3J8+XGoouLd+RR7E4L6XbxNlpIHEGWIs0NfR4fe0yf0W5XjWPoy6fceWc/EU+7DZ0u4dkX49kZTmtDn8XHHoO5Odw4xk1TTK1GNjJFLg7Z2Ax+Z7XnnTZYuHlE5lWQRx7CmZ4kn5whHR3HszLNZFXR5AbumMfqI0vMt0bZsidgdjIlxqW96fCVr6iRUqupZ7J0gGeZ4MVNzPoKq9UdeGN+8buQi90bkko2TanNu7iof198sRp/l12mxqA6vJTXGoZq8B45Itx+u3b388/X18SEBsAdP67fXVxUI+/ECXrP9MqKelKrVT0PdN2xtqbBfLfdpkay62rAXqlMZ1l63gUXwI036rHNzSJxSUOHz5J/HEX6O6X3Norg4MEL2bXrQu68c5377/8MnY5h69araDTG2LpVDeODB9Wonp/XrnDWWWqYrqzAhReqN73dVorE3r3qvS45xmtrui4qnP4EgdZNudHCocUaa6uwY6d6gvNcZTV3NFrYqxtUp6Y4tFRndjIizRwwNqQZq8sWaWZhO2p4b25Cy6oUQ79Qd0M8SUhz2GgaxkfTfmAcFSp0sIGctDCQbeLYsLFhMTVlqa6+lLFVgkdH1WYCjzDU3ZzStlO1QqHjVpmc0mNuHmLEJkpd4hhGKjrLRATqSMBDMAR06RYSbQ4pFjkZfl9+tNAcTopp1hhYaTqMNBIdA8dznffQxB2aObPTGz0MsNR0ybEZG8mpugnbZzNN7ITLkeMnmZqa4x3v/C2Wlx/g8Uce4Tu+8zsxrRZ+pYJxHfIw5m8+83ne9G2vRYIGnW6X24+cZPvWbdx3z13YaRfabfZceCEAn/rsP3DhRZcyi43XXusHrXULRYhiEX3K+A96E9NUO6zn6Qqo2EXSG9eBxx7D5Dmf/tKXeNvrXkejlFH7WmJsDK64ghs/9zlOLizwkz/5k8/LIB7ixYtn9BRHzQ7uwMq1N/usrOj/1Wp/q6Ms6ym2PQZ/qYzMNVhYeaor0pWVvoL9UyAqQv4CYmI8UixKEXdj+kEexx7bj23bbN91FvFpEjydTk/Fi1e/+gKSJGTv3gvYu+cMfuodP0LmXkDUTjlyXHj8sIslORecl3HGGYapWbd3eWkqtNs5o3XD+oawsSmsrwtbt6rXIkshySzuvFMYH9cF9dRUEbOWx5g4xQuENBeMpb9TIeSRowFpblGtWYyNCa6d4UgGopHUakAmeKJe0TACq9PCi1rkM7Mklo9lDE7WhbV18pFRYqdCIgFLS1oH2zYEVkKYe8UuQEaKjWtijp30qFczJkYHts8GssTlmVGhCaNeONcpZvk0JUe1bnWSyAoZuBw3DbFMrnzXPMDPOlgWdByl2li97UWL3KiedZQI3cgm39jEGwnYjNTFt328o9v0RjCeTz4+QWRpMJhLgmQZR1ZqbBntUPFNsX4TjKNayJLGeO01wsYMtp31tjg7VFld1WCb0VHB83QrWIkKaamQTLMpRLEwMalBQS4xkmW0soDAzVQ+qccN1D5J8e3SQAYgTsiXVolntpO7/T7qmQjIMcbCkYQuVZXiK5Ybrkk4slxlohLSiJcxrTbtqV0EN3wU5+EHVA6tlFHau1ddkydPaodvNNRKLAmulYpOZKUbcHZW1Ug2NrSOtZpO3FGk7toyMsqytJySPiEWaVDH7TSRY0cxMzOku/cQFh6yVBwsk+GkEXLiBKbV0lTdO3bRrc0U40eRHOO4ENgx3S4sLNv4FZtGAyacJm7SZoEtjI0LcSy9QLaSu7t9O+zYmhFIl1ZWZXlFJ91KRakMIoWWf8ewfYeef/vtOh5s2waf/KQGye3dC+94hwbafexjauzWavBd3wW/8RvKL77iCrUXSsVB19UhbGSk30SVitoKy8t9isbnP9/3CpcZ43wfvvSlPuMrTZX24fs9eiaTk1qPcmNmfl5vYaleZVmGQ4duY2Vlg83NLQTBhXieGvFzc3pLzzpLf291VV9jY1rnW27R67v4Yh2nSp5zeT2lwoRK6Gn9xse1S0xMQLViIIlwwhaHNiaYGMsYC0LEqA/DsWG97TAaJNhpROIGmnlTQrJcWA8rbLZ1l216GjxPdwLDyFC3uoirqgxdqkq7KBRgsgGFIzEZMT6BRISFZnw58wR0dMel5RAEOm5lWV+Wt0zEqfaTjt+5sWk26fOdiTVhD9WC6qEGdumz9goesemVAGDhkBU7STadruBKiu1rgO7/n70/D7ctK+t78c8Ys1/9Wrs9/alT3alTLVVQgBRQRV+gIiL8bAii4lUT771oYpJfNCGJGjU3if408QnRG7nRYKKCiBagAgICRQHVneqrTtVp9z67X3v1sx/3j3fOtfYpCilNfH7xkfE8+zln77XWXLMZzTve99tUzGRaYP3q7bTA6TKjcXQyxTHnWGxtbfLgg/fyule/mkcefph8uMMLDx8hbS/i6BS12yWfm+PDH/0or33dnSi/SaXYHJy/uE53/QL1hVs5Mh+T9QZYjfpUvu4r99zDcG2N21/0IlQhEzmVbtnbDh2SvwtGZrb+l7u9blf+1mqR+z6f+vVfZzgec8vLXkaz0SCfm6P1VyTBfb02Ho9J05RqtfpXDoq/kSn+m9WUUkeAfcaYLyqlDgG/AMwD/8UY81vP+zhfLyie9Ee4pfJEnsvMGgQS2ZVkrr36Nnvtu/jqwnFUSLhpsqnqwlTI03Vl1t3z+We3snhTEhRiPDJsskxJid0p43PDP/2n/5AXvOBFXHXVca699rqpkUYYzio299zzJBcuPMGb3/RarHSXdqvK2Q2Pi+sOz5yGTkdx7RUhh5YE75WjiYxDphw0Gd2eRVjorHY6oijg6ZiKneA6OU9dCFhZs6eSTEpJFqxRNyTjhGYlJtRibV2Nd0kdj3Szh9neJrn8BL2RjW9FzNVjJroicAAbTKvNhAq+mmANB5hJiOq0iCwpe+k0Yb3nEUVSbnQdRXtOkUcxrpUSuGCpWSnM7Lm/aix61LoSFHRyfUmmeDyeeX0oZaiF21LGb7eFSGnEkSktJL4i5RYl0hCdG7JuDysRE4d4bh+xFrWFUv1CYQrCs2g6d5wBqVsB20LnGbHlC4REmamGp89EMt/GprQQdVWMTcrYVOn1FWQJ9SDDC8CLh4ytOsZ2pESqFIqMSagYDDSWZejUC3b2V+uuYHLI8pkUFblh0M+pVWVYjBMXYwzLzQmWnt3nkrxjqQQ7SzizXuHA3ATb1UWpuHAZy7cIcpFHSxsdLCVQkgyNrXLJNuJAZvA3zzNZOIz94Q/iffqPZQyeOCGr+fIy6rrrxB2qBJqC1OXLyo7rSiSytISxbZIMzCjE0RlqMpZqQZJIQOz7EliXLO8gkMWv3xd9a2Mo9KIwrku208fa2YQjR8iDquCbV1Yw9TqmXmfkdnhqp801zTVslXEuXmZpSVEdXKSU4ouNy8aWImt28NyMcKLoDmzm5yVDORgIZlgpubx2W34Gg9k6HYYikXb8uIx910o4uBix2q3Srkx4/29aPPSYy7Fjih/6IYFIPfMM/PzPS7BcqgdedZVMVSX/p9+XIXL11RJQra7OOD7ttgSUWsODD87I+1/84qyiPBhIcLu8LEHZhQvy+U98QgLUY8fkfaUtM8yUDx3HTDfbX/mKQEXKIDuOL/LBDz7E4cM2c3MvJcuCKQxCyMDyCD/4QTnvalX2TfPzcr1XXCEbhsFAfra34aab4C1vkXM5e1bw2vv2yVzQ9CYMRppsMObioMpkojBKEycCfbjumpzAN4SjjHC9R90NGaYBY3+OQwsTVncDWrWM/tii3rSoWhPizCbTDrbKyIxUTewsJlSBwMOk/o9XmH4YNJoUj7CAN1iySuQSoGoFKD3tE6WudZmJzzIKMp5hY6NIYFhMY2u3wBAnUy0m0WDO0ERU0OQEpflUMafGOLiFRn5pVuIQY5EyGFpMYs2+diTwGkqqrrS0cL8bUy1gGpqTD53kDz72J1x97AgnrjnOhXMr7DtyDTdccxQ3nRBpsS20sgm/f9dHefVr34TvV0RPX2VUC/OQLE4ZWXUCJpjdPhv5PM2ORW3rLKrd4qEvfpG5o0fZd+CAOIUWxFzyfJYose1LRbD3/l5WGOMYFhfJlOILX/gCq489xlHXJWm1OHnqFH/3n//zWfwwGmGShM/edRe7Fy/y5ne/Wx7OX3NbXV3l5MmTbGxs8O3f/u3UCrnZbwTFf3OaUurlwEeBjxhjvkcp9UXgRZS5V/huY8x/fz7H+rrwiUveIOwx6filAUMpx7WHMFY2s+ffdCpeo0Vaau8bS0Bdeby/oJWvCnRCrDUBUuOI01JtFvA++eTj1OuLPPXUGQ4ePEy73QIuJbTedNNV3HDDVdi+oWZVyYxi30JGZ96mVldMJobtvs3++QRrsIsOQ3zLIZzfj9LQqCuqNauQVhbhfm3ZrG5bNFsWRkuC7oor5BI9V/QvLTKqtVgmSmNQWSo7dnuEWuhAw2enb8gwhLkNxFTMhHhunkT5QnBxYmJtQ2WONFAEOsE2CVGiOXvB5dByTLUt2c7eUOMazTi32Bq5NCspreql+LBJIQNUtRMx5PB9WRGLlFLemSfOLExe4PdUjqOFlWTCkGgQYxpN0iwnj3OGicJUAzw7wSPBAraHLm5ljporWDXXSPicGcXqlsfBuRClwSiXceLQbmi08lFGbEI1EeOxheVpud7CtlUpASdYacyXHq5w+aGYuWbCONYQGCxL4RXY7di4rPXb+L6iWtNTeLatMhpejueVkmhC5tx7l+w9rlentwJqFfADQzpJmIw0ftUiwSYMJWCZGB+VG7TK8U2INilGW2RKNocUWFM/GmPiESrP0GGGyRJI+tDrYXMOBWzPX8VTO3McvzrHcgAleO/x/itlMX71y0muO0F65HKcaIS1cga1tYV57DGRa4uiWXqxjB5LvdJCMi80Hlql5EGVCz2X5cYAZ7Ar5jKlqsSFCxL4zs1BTzZwNJuSOdIabJtQeXDmPP4ffBAuXkRddhn64EEJvF/4QkZOnYvrNlsbNucvGK5cPE9v7iiZb5OvX8QoYcoDOLki0BX8dgq2Q6uRkhXZtfPn5SvHYwlYt7YMvZ5gYa++WvDzpUjK4qIEtM88A/W6w/47bZaWwLYD/v5PGB56RHHZZdCo5Xz6z+D+B8RdbmsL3vEOePxxkTK7/nq5hadOSTBbGoMYM4NsXHGFBLG7u3KLC78jTp9mSsYbDCTovflmgViUChBZJrrFjiOPrPS8KZ3uSnKepXJesLBKZC/zwhfanD0r0/JgAJub+3jLW/bxoQ+l7O7ezalTOZ3OK7n+enmPZQmCJooMeS6BeLUq5NLFRfj935e9VZ7Lfb32WnnP44/L76U07O23Syb9277Np14zpLrN0cWcqh6L0kiaMcwDKpvn0ItzpEGVnrfE2DWsXwzZ2s3Y2h9w+ZWa7V1F1U/JUouJDtjuStesVkWNpT+CPA8IQ8XCwozvG2NPVYwyHEJVEvYEf5/nhgubFRaXFLrICpeFjnK52bsmKGVYXCyqNiosKo0WsWilTDPTKZoJBYG4SJiUcqFSefKxCuJcGai7hJgCBlevxfhGkyhRdwrxccgKwp0kFmwyqozIspyf/jf/lkcfeYirj19L1XfZvzDPiWtuYK3rMcnBtlJclTJJ4YMf/QRvfv0b8LOEbNQnqzSwlMRHBtBxiJ9GRM0FdA0OZLsoHYgvgDHYSweJu104cEBMkaot7MBBra/NbtTegLhkkna7xQKdz4i/loU1GPDyY8ekYycJD58+zatLRxiAbhdz//186L77eO2RI3z65En48IfhXe/ir7OFYciXv/xl3vzmN/OTP/mTnDp1iptuuumv9Tu/0f5a2r8EqoCtlLoGuBV4DPg48GPFz/+soDib/WIMU+up0iYyCGainoXN795geEwwlZCRMtZoGrpDEeSWmollrRCeMzg2CHwiwQW0lM+NBH22rS7ZVCql+P7v/2FqtQat1tw0IP6q67Nljb/nHsWxY1Ve8qIMZ9wnCFKW2y79wGFuXhNbPnZzHqeZoLQmICbBJrVs6mZEZHy0gjRM2Y1ElH40MtTrIql2/rxhMIC5juHQYow73kWTY/s+7tZ5sG1SxyN2a+T4PHHOx9UZh/eP6PYt0gTGqWKr74lWepaQm0wWnyeeZLV5gt16c6rHPr8MRhsMEiT3hhZermg2DQ0/RtmaEK/IWsjT8EvtTNeB5WXJGo9G8rcwZBKagl+dUzVj0Uo2xWRYqZDpmuhs6pjd1KFmhxjsYvMiE6ilMib9jNqCZlQVQfUKE8gNWa4ZJjZVL6XqxDhN8ZFjt4+XxOjWPN2RS6IM3dhB1Q27fU2aWywt5KS5RWCPadRyumMfr+FzYV1UQGo1Q65tfDfFNwOsuYoYzOSlvBQo3yVJZfGv1wFtTbNRPiNiPIwReEeUKKpVYcy7OmWifRr1kO62heMbOh2FaxtOnxbzjHZLY+GyzEWqDRk3UQRzdV9AESbDG+ygHIdtvYzvpPjzNSYLRwm6q1hZwpzdp3Pcge4OebVG7ouJB7ZLqmyszjxRax+7fZtKtY53VRtvaQM7HGL6fXlehw9Lx19Zkejq0CGxY3ZdeOopvIOHMEkKG+vUjt6IsRyBnYRCnpsyyCoVuXEXL2I6HVS1Sl6pMlGV2dgOKpBmcPXVmNKWbW6e3K9gLJ8DBzMW52IarYAt/xbmOtBhjL09YTx3BGNgfd3Q3coZpj43HlYMd0WtY35+Vlza3JRx/MlPwm23RuwOHa66SjM/LxniSkXUos6fl4Cv35ef++9X3HOPSJYdOaJYXobtrZy6GhFNAq6/zuKqK3KW2jFKe9xxh2Iykb5x4AA8fcqwuyvTX78vmcsDB2TdF53hWVm+vO3PPCNZ1htukClvdVX2nFdfLeefZfDEE1JNuvxyNcUeGwN33inv3dgoUTAWf3z/EtdeZzhSvPf+++U2l7JyN95ok+cv58CBP8eYCYuLAUki53nttfDFu2X+fPVrhNz44INCNnz5y2d6xXffLZl435cM9u23w4/+qHSf06fhW74FHn1UgPFRBAf3ZVx/pbiYdtdCqtEaX9w9wvWLitruRY7OVcn9Kge9AdbmGhrI3ctRnSo6zplEuehSz0vFp9cTvkHFThhkPlobtrcLzLARqU2ihAhL+L86xtE5mRIzJWUblveVK4gsM6ORmha/nr3U2KRYOiPCJ8XFIicnL4jaupBgkxC8woRSf18MbWzG1NAmwVMxIZUpjwAMMT5VRkAiAbQK93yvwLTK0/FIWF9f5eQD9/PfPvSHvPZ138n3fefbObS0ROzWqKiIXGXMzSlIU9TmOlme8bEvfpFvf9M343kOyhiszU2wc/B8UBBjoWtNHDIScjFsoliLLUnSuJWAiSUJr2xuXkxV7Ayn2ZxBq8q2fz9TByOAulTg6IomvTJG4oQSt9hsshSGfOXxx7kaIM/JBgPuuvtu7rj+ev7onns40m5LGSIM/3KybH+J9vDDD7OxscG3fMu3APDTP/3T/Nmf/dlfy3d9o/21txuAEfBO4H8r/vbrxphfVEr9APA8nWCeB3zClB09zyVVUQbBIIMgCOS1UsLLkhArmVp+CDsYipjaJPgqQiszHfyq1EKKY1kV5ue/CoB/KR5ZMcFjEtr0+zJ52zY8u9Kxu7vDL/7iz9Pv97njjtfy5je/9Tmv86GHCoh0nvHal43xehtywHZHWMv4gCEoCVNGkynJfKfGnmrRam0Y7GbUvQitNWs9n9VVCYo7HcM1xw29vqJdTahkPdydDVnROx0JTCyLLMkJ6/OQZ+juDiE+F0dNnKrDwpJmd8fQbmS4rkxjmSrTJQmMxwxUk060xuPnA57ebk/xgZ1qRNUXq+iKGhV4bBdV2IkWPiRTfBvIBiQ3Cj8do2ybRNlEeKKY0dvCciyR7un3UZ5HrF1St4LOU7q7Fl5V47iCg3UJRdpv0Be5roUFhrqGQVNhNA3MS/a2Q8SocL+qmDFpYjh5qkKjbmg2DI26wVI5UWRY3Q5YnM+oeyEhAVEsfUcCE0XdDbFUhhOPiGvzBNvnie2AsLZAniscx0y7W56raRGkUjF7skkKUrEIx7Loj2wCJyaKNDs9i8sPhCTKRQ92McMRq+ogBzlPUHen8kbpKCK3XVydoSrFIrG+jgkCTKEvrTyPvNsjm0Q4pJjNTVnwtMYU7hKTyhwmCMiNwuttkDfbGNtGmxy7kJnb2PUE820bWlafYPM8bG3JmOt0pNN3OrIgttuYY5ejjCEyDnmSY2+t8pX1w9TnXRaTiyyGZ1FXXy3scduWhQ9gZ4fw4BXQbuErScmOCky5Q8bE+Phb51HVCsYLyHI4v1Ol3YIoVtg6o2qFpE5FlFaqGelOD+05rHQDdnZtoshw5ZXytb2eYmdH1sosu5QItrEB11ydYWvDMLSpVGaqDdXqDLbQ7QrB7NFH4Z/+U1Fn+L7vk/lptyuqCloZeqHLOHFZW82xbUW9qaawof37Ie6FXNxxqdYtOh3DoA+jAnpZGjXu7IiTYVlMu3BBYorFRcW5c3Lehw/LNfzrf2146CH4kR/KmV9U7NunCgtluU7xTFBccw0cOCDj5ZFHZnhmrcWpzrLkmKVyx4MPwnic8KlPfZJO5w0EwUz2zWQ573jrhA9+LGB9Q9NqSQLwmmskK3zddbJJ/M//WfDUTz0lx7/zTvjUpwwnTkgy4uxZud5HHoE77zS8+JaU86sW2zualjdh8ZBLZ16zs5Wz1IpwKja5sgQSoTKUSYlSh8ruCmHnAEZb+CpEY8iNYZS42LYm1xYW6VQXODATzm9VqDcEupamBpMZGk2oBpArpnwAgS/M1ogwFGfJwvPnL2xWoWG01wCqhG5ESGXJJhFJttwhyxS2bWZzR3EMF5mjrBlFHIVU6Vwz4U8/8Qn+5FOfohIEAr9aWuLmm27iyJWvwFKKhWaEGU/EqbRaI1cCyQojG9+M+dJHf5fbXvc6rFqDEI+ACWFq44+7UK9NoRhjAnwiQhXgm7Fkv8uE1tYWa1tb9NKU49dei1GKBAvbZOidbblxZbNt6Uy9nsxnha59hibtjcRJtF6fkXlKC8S1NX7lt36LhWuv5Ttf8xoe+fjHOe04tPMce3OTdDzmqaef5l2vfjW85jWyoz10aAbg/x9sf/InfyLW16985fRvOzs7fOADH+BHf/RHgW/AJ5RSbwX+gTHmq75UKWUDPwN8L5Jc/U3gHxljEqXUfwTeseftGhHbfRlwO/BPnnW4KvA9xpgP/A+c6wAIjTELSqkPA9+CZIsfAHaB2Bgz97yO9byDYpiJWJa2UGUE9axOapBBnj0rER1FsqDNzYHniClyoCKZMkqxzjSdCoFeqlQBMUGRty7LY6WKmwSIxsgiWBjq0O3u8L73/TIHDhzine9899e8zuFQ7G73LeYEdog9HmF2dxkvXoZrJ0WmQDKraSryYEVSnG53xkFYWjKcPCnjN00VcSzjuF435Jnh1mtHaE9snJzVc6iSUCipbtGGLWyiEmx2+i7YFpaj0SqnYU/o7iqiRKNdTZg6tFoKz4ox45DuExss7HMYqjpbkyrbXYtKBeYWFbWGZtTPWG6FaFsVzoKimRnhMZxIRubowrjA0UkWPkcLieXZNy3LMFlGaFfwL54FIFQB6dIBfCthZdPFsTKW2jHDrILOYrxsjJuMRRtzcZGJUymshScYtEgf7fmKvOB0YgS/m2QWgSc9IDIuWzsWUSKWxAqKbJAiS1JGA4PRNp4vREDfjPC2LjJaPAq2TZKIuoRlQacWozwLikUzTY2QeQrykgCdFTqJcAeb2M0aqeViMNgmpze0qFcyTp132ew63Hj5EMt3ce0MO4vIun1y28HU6qgwwh3uzDaTnsfuyGI3rVPXQzqLDmo0EiKaMbBvH2pNSpbGtknnFhmMbSLjUK2L5FOFEZYCM5lgohjVqNPPKySpDQikp2oG6K0tGEulhgcemIJv4/YC5/RRDi1EeNGANMqYtPdRffTLxPsOY6p1/O5F2cyU+mKFEU+2tG/qJlZWgMYEiCNeVGyQheCkVS5cv6YmUBFfvEfx+x91eclL4PrrBZpwVW2VPIo5Zw4J2bQCzXpOwITT62Ib3OsJCuRlL4OPflSypqU7XckRNEYygpubsh4bYwqdYAmCarUSNWI4dUoUF668UlQJHn8cjh0zBQFLAuF9+wQ7vtuTsT0/B7s7Kb6boz0X38mxojH9vMrGhgSPV1ypWF5WxFHOaCSOaesbs8B8OFSsrMzgl4Nexr6lnNcsPIhzcJmL+gDnz2Vc21lj21nE9mbwNN9JGY403V01Pe7OjkyftRq84AUSJIMk9z/xCdD6GR544DR5/jIuXvwCN90Eu7sZo9FruXBBAvlqVebORkMC3O1tyWq/8pWSeb7uulJKzrC+LtlaMcYwHDsmuORnnoGFedkItDuKpu6x1Ij4s4cXeeRRxf/xf5R7MsNVRyKswMHEKSbL2O0p9s8n5I5LbBxQ1hTjW6+LzrCrBMerjMidDbIK29sKz4Nq1dCwJ0xUabwBDgka0RVPChdFkKWsXI9EeXJvDRP46lmvnJkIGDOhwOfv+YxFRpLb7OxAozHjuMyaGIHYJp4Scj0V8b7f/O+cOf0ML7/pOr75TW+cYkMSLAmajY8xikCNSXFwVCp6yLlgt/t9jePAfXf/EW941SsoXQFLHoLCoMMJxvPJctjoB9SaFlpnVPMxardL0prDVuK6unnhAusXL3LdrbfO7kIpsQgysEoJj6WlmU3g3JysDZtbhAsH8ByDzlJ5r7hQSeC8vQ1BwAc//nHe+pa3CH7o8suZPPIIv/67v8ubrr6af/ibv8lbbrmF/cvLPHLmDCdOnOCOV7wCdeLEzBpyd5csz4k7HcIowrZtut0uy8vLuM+lmFS03/iN3+D7vu/7Lvnbfffdx9zcHEeOHJGn/7c0KFZKWcB7gJ8FHjDGvOQ53vOPgR8A7gQ2gN8GHjXG/MRzvPf/B+w3xrztOV77MeBtwCuNMclf9lz3HOcB4HrgV4AfQgLhy4rffwD4nDHmFc/rWH+poHhvew6lib1krTITWYgmUQYcpYfHeJSzWB/jWGYGodjYkI5er09hGAZROdAYRirAJsMjJsHBKrQn8kJYfe8kZpMw2N3iXT/4bprtDu97338pJoo952suvQyPCZZKiXMXf+M8aa1FWmtOWc4GJa5ZQ5mgy7ImyMJT2oomCTz5pGJxUdybjDEcXZzg2LOgT/X7M0BkkYGfhAbX1aiFOaKFQ9iTHpYyZNU6oQro9wyDvqE/1DTqis4c1PwYJ0vYGrjEmU3Tn9CMtuDLX4Ljx9nxlhlaDVrzNkEuGQZxQHLxiC6BsvyF4jiue4mDXaY0yuTsTKr4k22sJGQl38/Ckqamhqg8J0MzUTVGvYQ0k0Wr442wslh0bG0bA4wKTWPXhDOlEyXyRVsDD9C4rsynjUaOzlM2uzaViqbixNgm5mI3wAsUliW4wZofM45ttKUlE4QhyEdC0lEGnSX0Qp+GH5MXC1Dp0razLc+y0zbYaUhqeRhdbvwkCNVFhxe1DZ9AReg8A4PorO720a4tdsqDMTSaoi446M3KjGULArJ6G7W1gW41JFrLc/KdXbTvSr+NY0yrRaw8rLUVsmqDOGjgDXewGlUsSzEKZdO5fyljYlVQoxHuuE/Y2Q9a4a48g3vxvNi8PvmkBOZBIJvQAwdkMavXyVsdQl3BXz+L0bZsDlxbxPcvv1yyy46DCQLi5hKWcynMakSAMSKaZZHhEbG1Y9EfaQ7tSzi3WcVxNWmU0uuLOm27LQHPgfwc+SQi7iyLHjE2WS42v7u7IjEWRbLZ3NiQ76vXZdooA9qjR6VK6zgzf5lqVex8S4e70plWk3NwX8bZVYc8F6JaHMsefXl5tvefnzc0uudQVZ/EqWK7iiRVJFs91kc17MDliDpH2F5GBx4qiXngyQrNpmbZWqdiJ8Tz+7HTMQ89GbDdlXnk8KEiiTBRRKOUlx+7gL+vjS42GpNIyvXu1iphbY4khjROadcyPFfGZa40Q1MTHfdJJBuCujdV4xsMFA89BL/4i9BodGm3H+L48ZexvW1Rrf4xy8uv5/Ofl8Dwk58UWEqppFnKzd9yizjx3XFHkRDsRHTaOePU5xOfVFx/TUye5nz6M5rv+0GbxmiFcSYQpv5GyKQ6R7ut6XZlo9loyLNcWpLjnz8PgQ/zC2BrmW1XVw3VpoPnKba2DK2WvAdkw5wkkpzQKsNKJ8SJZpz51KpFyYtL53uXkDjRKAsclUqSJY7pjxyaLXlvoEZEymdv4gWevdyZQiFZ4BGiLRwyIcAnZGvg4/kK21bPCohFMcOgScKMPEr5w4/9No898RjXXHE53/M9f4d0khPEu9BoYhSMTTCdl5QyBJtnSdoLZJZHoCac3aiKQpED586eobv+DC+79dbpqYf4WKRS4ewNMY4LgU8YQm651NwIZ9wn648I2/twVIp2FOvdMSvnT3PrNVeUZVixbo/j6aYYmEEnYVpBmqpR1Osyv2xuirGU0iReFddV05v5wAMP0O12ueOOO+RvUcR//Mf/mP2dDo+ePcvj587x/h//cc48/TT1ffv45P33s3DFFQCM+32iPOd9v/u7vPfnfg7b8zh//jy1Wo0vfelL/JN/8k+eU3ninnvuYX5+nsv36CsnScInP/lJ3vCGN0z/9rc4KP4VBI7waeD1XyMo/hLwPmPM/138/iKE6LZo9gSVBQHuQ8DVxpidZx3jcuA+4IXGmKf+suf5rGO9C/jPe/70/wX+LQKpsIBvNcZ89Pkc6+urZj+XzFrJzChVKPa+vfhXRNVBFTv0uMAAZ5mMIz9QZKogyhkj6Z1K5ZKAOMEiMj72uI/rGqr2RKrYWEX5yUyDElBTKECMh01Kd2eLF9zyYl51x6v52Mf+AK0dwjDklltu5eDBw6SpZGxWViRptrzo0vFTPJXA8hIOYBMyJphZ+lourabs8ONYMtP1ejFvpDkH5sekdoV63SIMDUuLhsCMSLEZ4wsi2iRSEhf/Vmg2MY7DIKtxKj7BjZ2UNMmJTYWGE4pDm7LpNGFfLSTtjQTD6/hSpE5zJqOMWjWl6sbkvRj1qleROx5Bqkn7GdV4hLW9gfI8rE6HQEVFXtZwyagv9aDSZ42lwkCFOJZnrG1GmcOX71WYvMMrXpbRDlPcPMOMh+hogjU3R5bm1OoK1xa5pC8+2ORFnVO4Vo5pNMlrDdxCCztOFZt9n1pTo7TCriqWKgJn6HYNaQqHD+R06imtps1gCP2+y8KCzeGFMefWCnJeW8h0rlfKKCVMTJXtqC7ZWxtSHWBQmJ0uduAQV+cwhbzSYifGVwnGwOqwQsXLCColilDcrCIs8iQn2FklmF9A2RYmTsj7A5K5fZjOIlGosOIEt+WTKwuLUKKNUrFFi8SbURYTXcMs1/HSEW4ak9sO4/lD+FsXZHFttVCA29uEWkCeiK60NRmgAxtj+Xge1BarjMhFCiuoE7oBlc0z4nDYroPaD0mCOX5cMjfr66iHH5axPJmQRSkom6BpGKg6Vs1Hj8cEZ55GBQGmYGbnk4h0/xE8K0MsaEAZ0Vp2iclHAoZ1PcGlL9qGxSWf2Eh1pF43jICj+2Pchj8NNpKogZX32B05tFRMnueEuUe3a6hWFVdcMcPVdrsSXIFger/4Rclknj9vOHBAMLmlrGqnErI8p9juefgezNVCMm2zsGxh5YaVtZzdoYXvM5VkO3pUHlOWiXLFC6+ZI/Bz/AJrZNtgFlrsa2ZsDW0m/jyulQMZylFce618t0sTlaZsbhpWVgJuvGrMk/jMNQxXHBHsapYZssxmKz9MR+UERsQnK05KnicoleI5IUZnEPehn0t02myiOx0auoexFZNmpUC9jsktxdZmgG0Loe+7vxuOHm3zG7/xCn71V+W6ajWfI0cyHMfiK1+Zif9MJpIdf/3rJUheXoY3vlGU/B57DNLUpVoxnLgWvumbIE80RxdDXrxvHdtvkdeaDHcdEsti4UDGIE0xRipbriPz5bXXyr1O44zLD+XkWYajtWgBZ5oo88n64PuGpSXY3ICaPWYYumTKJk1LKouFsQIsK2eOCQkerhFowN42Tn22tg3khmYlp9mckLk2c27GcGLT68HSYoClpQqyd0Xr98FxFJXAoFROhoVBnOjygnAHEBof3zNMQk0xVPasikC4y+fvfYjxOORjH/so3/Ud38bf+f+8nSi3iY1F4mjC3KGKIcElyWA0MjQaMleHbcHvKiWbybl58PMxF3fGnD3zJG+4+jJGuUtFxyg144kAhM0mHjFjbKrxBplbxc4VynVJ2gE4NnZvCxUE1OMhSXeHbGcXsgyrUROIV7H+J24FOx7DwgKq1ChuNKTjlDIwZXxQr5P1R8TNefII3GQka36lwvr6Oh//+Mc5duwYS0tLWJbFD/+zfwbADQ89xPv/n/+HpFbj6PHjALz6ttv445MneercOd77jndAmvJtr3oV94YhP//zP8+73vUurrrqKl7/+tfzXO2xxx6jVqtdEhAD/Oqv/ip33303w+GQY8eO0W63n/Pzf8NaotTXj+mfI/D/OWPMahFofq2mYY+otuSH5oE2sDf4/XfAe58dEBftF5DA+n8oIAYwxrxfKdUFXgV8yRjzXwGUUn8AfOD5BsTwfILirS1Jl+zdcSklM+ezssxZATEQM8yYpPBpj4sAudTrbDYNlQBS5WBIhMxVKlvIBQIKW2WkyQTbxJjcYaz2+tuXwZwpBHLiwlte4RQZ0IPHjtOs2Pzhh/8bYZRy0y0vpl5v8X3f910cP34j3/VdP8TRozfS6Uiydn3Tou9VqPopC824YDaLrWiKR4pLMk4YhRlRqqdxYyn9pJTFM2t10SHWhtHQoFsTiIYwScgXDpBYHmq3h7u5CWfOYLIM+gOifUdZWHmYhWs0k/gQGBvOnydrOnJsr4JpNtkY+1xc8wiqmv2HLOJYkycWQV0R5RantwOUbtIZjmgHExy3wqLZQO1IMGuaTYzSxZZCFU5/hkBogyK9s+cuA3to3jEZihCfSjbBRvHy23SRDYyxahVS45DWPbZCqPYijJUxxqHRECUHxzacHcxxZWsLbIvM6EJrNKFi56QNh52uLrRY1dQBK3ASsihjMlCcHfnUG5IlGo8hDDVx7mHbhqTgclT0mBiH1LikeCSJYWNDUruXH8kwWqAlqb9IlqXkRqoYbh5jq4TMkrLkgrODsj20UUV/NmI3rhJ8N8UsLxMSkKcGy7LIFtpU1ASNQTuaXuhRNRMmymZCFYUB1xCYCREece6AAdvkRLHGTnMY76KCQGxqq1VUnoiNd6+3x9GhSW4sznAZyyYkyEFjaOgBbG+hfF+INTtd1PwcE6sq3+s4mCeeYLibEfTXRBrv+usFFDo3h1aQ1Fvo8QBlNDk2jskwRWQUdZYxng9hRKp8bMaF6YiPnU2w+13i5jxeYEMUE+sanivnT55jeYpmCypWyAiXc+s+hzxDwxcHv+1xgOV4OApwLJw0YxBmBIFFryeBXOlIdmC/IelPsE3ChWGDgwc1+/cb9u/LcV1Nd9dw332KTgdWV32aDcP8nOHEZWMiHRCoiNTkxMal3lRcKBQhgkD26KXj3NZWgSF2qgVGNcY1MePcZ3NLs9CMOFjfxYQhll1HkxKbAr+qRH1gGHsoMq6/Fh5+xOf4sRjtWrieRY0xa7sBjQY4VkolG6PCRDTEskyCjkWx5l3vebhhRHuyJiVkpeRmKIWyLIJOBzMaozCk9TYmz/nSvRLsLyxQSB3OtJ17vReysvIparXXYtsJN920wdbWfg4dUlPb56uuEqWKf/Ev4EUvgrvugne9S2QMn3lGpof5eZsvPdbgygM2B554iAvN62juF6xtnlo02SUJ5lm5qDh3JueVLy5gWnNVdnY0vjekOtog7OxnKwrQWcrRJZGJyB0PbTKOLoaMVQW/CmEkmH+tJWjXSuH7NjsDhe0oqvUKBXrkkryO4xiWmyGZZbM7sgkjC9uZVf22toWgqacToKxFYiJjMBh8EwqkTgkMLcMqzIpsJqFmPJ555Bgjc6mnxoTG52f+zS/x4he/jCRJ+Lmf+7/wPI+xgY0iE15xYnqpJkgTLCfF2E6hdGaoOjG5NiRKyLkhFWydYGnDQw/dz6tvfxUq7hNsr2Dm59F7q7iGQmUdcdOrVXDiCHIHs7WFt7BIqhNUuyU68i3AJGjb5kx+iEP9s9iLc1NzrVjXUJsXsEoYg2CUZPAoJbuI8netsfq7WJaHk0XQrE2TbK9//eu54YYb6Ha73HvvvexsbpJcuECrsHZ8z9/7e3z+4YcJd3extOZ9H/kIlx8+zFyrxQfvuYe33n47H3v6ab75LW/hmWee4bLLLvuaocxHP/pRjhw5wrXlbrVoaZrya7/2a+zfv5/Pf/7z3HXXXdx2221f8zj/f2u+LxPRX3Mzxqw+j7f9EfATSqnPAX1mOOEp6F4pdTtwkEszuOVrxxDoxeXPfu2v2owxfwD8wbP+9va/7HG+flBcMkb2tiSR2bI0dChmHVvl1JgAiLkECSEeWcG+LbG+5IIltkuxq0pllpkMQ0ycMqmLc5abD7BMzsSpQaF8YO8ReCsLZIJjdguLzRSPmICIH3n3u3ngoUdZOnwl+/YdZDKBF7/4LURRhmVZU2O+xx4r3aMsjhyx8GoenpWI2xhWcYkKXBdXQdWZSTUPh5JtPnJEbsuZMxLfX7YvZDSxuBguYduKhSxGaUXi+Li7PUyjQdKYI13cT7Z0AKdVxRiDlaUktRbd2iHuPh0AmiuvVhzQY85ftKlXUq44GKEV7EQOlq2p1iA1kKRQ81IaFYMax9jDgWQGi8B2EDmgfFwrx1PxFK6QYl+CpjMUGDvflwsdjfaoiigyo/FVdEm3yIp7lBuFUjlbPRvHVSwuS0xUUWNecGiEHo+IOvtw0zHuYJth5rOetclTwQ7nuSE3QuBZW5MExPyCA5nmvgcsWm1FbmaLjlTrxFTF9+VvoQlEVrMgQCZhSqOm0bbIISkjzHnLsti/32J9tTBP1A6O7dBsa1wVk3tVnEEXixRac6xtWbi+pl53GSl32v2THIZDh2YTQu2hMWRKEVRKvK0Bk+KamIgq/ayO1nJfdncNy+0EMoteVkV3HBiPcXWC0hbGL8xxGg3GBNijHtbODnquwsKixlYW40nGetdjYZ9FZUGgC7nRJEuH8BBThTzOIB4Ima+fMVq8jEa0KRWAI0cwvo9q1HAnPbBtGk0Fesyg0qR74CYWvAHuoEuuW+RVH7vgAwTEhBhS28erhRggtRzcisImx/g+IV5hqZ0zF0xQ5BxsJTSrLpZjSfUEw7zVYzTIMWhCfxHHBTVMGY00liWBnTGK+XnY3lIc3jdhbVOxsqJZXJQ+Gw0SjKcYj12SRAh1Bw9KEJek0GhV+fSnoFoNqFdzHE/G65VXSga6FNWZTKDRMBw8IBt5rTLRe01SdBZS3V4RVOmOJqk2SasdEqMJTIwz2CWxU0xQw1chTsWmYUL8zXVeus/i7PgQ4dBFa4WtLZIU2pWQycQmzxX10Y50Dt/HzIv+mMozlpoxce5BYsvr47G8p5xci+Bks+/y2KkKv/Z/a554UhQ2lpbEEGR9fS9XqgpcyXj8KRzH5u67FxmP/4AHH3wNUJtKl5UEQ4FjwL/7d/CKV4gyRaMB3/zNIi330NMV6i+4nsMrDzMx1/Pwkz5H99vMBRaYnP37NVpbPPhUjauukqB2fydkMnYxvo9npyy7E0AMYZJCd7zbhfm6QtkKtEBtLBJyFI2GjZtHGK0xdZc0FYKi65YQOVGrmJuD+U5GogSSFVQV48JFsF6f8bv3LncCjYiY6ApBICN5EFeo6jGx5U/fW1ZDg0Bj2/LHEn5iWYrf/8gf8eCD9/Ptb/0ujh+/YQqtKAP2hQXDxrrCWXBoNRJS5RMwwTURqiIaxzEeWzuGdjUi8A2psslxyMjwdYYiJ3UDrAVnuiaWlxImiu2hw2Irx1EZyipI0kA0f4BUOVSNaBgH6UAwxlqj5zscS4awkUla33VhMqGit8jn5lGb67ObtW+fdK48n/Jk6HZlYwd4g61C774mgXWW8d6f+il+7md/ln0LC9MF9U9+67dY39igMzcHR49y+/XXy428eJHXfs/3wGTCr77//Xzq5Ene+u538/oTJ3j7299OoywbPUf77Gc/y3XXXcfhUgYG+OVf/mXuuusums0mN998Mz/1Uz/FlVdeOcVjv/vdX5uH9Dek/XXqFP8ckhW+BwmKfxH4dqC35z3vBP6rMSb86o/zDuBjxpi153jtr9SUUncCrwPqfDUa1BhjfuD5HOfrBsXiKc+lM4XjzDp+CaWY2QFNg6cEq2AJz5oY5FjInrsYtFrLzBTH4Lpo3xeZLjTKUzCJqYRCTtpLIJgFaWJkINaakwLoIOSCz3z5YU6cuJ75+cWpapwI/ltTvGG/L4titytB7YkTZdzvFBOhtFIrNIqmSW3SVE7/2DEJyFxXyqoHF0PQiv7IZqE+wTUh9nCCYyvMk08x2o3YDmvsjlrU9x1gfz4h7OwjzD0sS/CT3lyNl+1PIInZGgVsjwKO7k+oBylKK2IctvviFLawz2J7RxH4hkpdE6oaViOQC5yEeINNwtoiO0MfJ4aFdsaIYKq5G+LiAVFm4+kYR+WSsZ9IyT/3AqJJLjJtYQhOjrLKzYiHS4JDRGx8qmpCzeuzk3nkfpXujrDBM8vCqtXo+QtsbyuyvsLX8szPbSuuvFLR8GPSMOH+xyuk6cxfIk0VW1s23V1Q2rBvX4n9VJQGbpKkMKyuQquS0K6nbPQ9Gk2LXk+h8hS74vLkk3D4sOHIETnG5qZgcTsdWJiDwImJjUeW2xhtEbb2k2QxuXKYW1B7yJ3SSv6pbZeGAA55Tql1z/x8jcAOSWPD2W6VhQVhyoP0xbk5xYV1jyWvi+/bTEydXuzTbuZUxlvYkx60WoycBq6JsFtVciOGL5XeKrm28CyLY1sPks1fi7FcQT1miiDqiga2HWBNBrAh1m9N14VNKX9SrcLBg6wNqwT5gOZwOKsE7e5S1Ro7kNSXbtZRjgYl1rRTwms5Fl0xUUiwMYXzJIBPhMmNVBqMTWXrAjrPaEYReZTC5Ueh28WqVGh0hJ2UW2PGpsI4dul1c47uD+n3fTxPAgo/UJzuzzNI5D6eOiVn8tjEw/Pk2fZ6Uvq/cEGmqxMnYHVVnuGTT0K9bnHoENPn4Tgz9JBlSaDqeqLy4pBhyMnSlKw/RCsLZTKYa4NdwaQZ/s4qanEeFYXYqSLxq8TKRZFj2Yo0qEOrybx22e0aLBLmGgmj1MfvrlFpNhiNwCQpajyCc+cw+w4QLh+l0ttCzc/JmLUsOcntbcz+/awPKnhRRG28yejAVdz1lRr33SePZ21NguHTp8Wh75ZbhDz36KNwzz0AR8nzo1NZWbg47duyaZvNgQ88AN/5nfC7vws/8APS9z/zGfiv/1X6/lveAt2+hX/lDViba9SrB+mOfTZ2XIKaYn7eEIaK+XbG7o6mMy8Y4lYNEjVHmirCxKfuhGRYjCKbcJKj85zVLY8jy6WkpyrgTBa5slnbDcSSuTHLsWwW+700gU67nOstWtWIrAho5+bkfaOR2WPgoab5HpFYs9FKTEFCRD3hYq9Kp22mUWdcJBVKq3GlZkS+lZUBH//ju/nX//qXqNelr5UqIbZdqCepnGPLE3IUYVERnVDFkLO9ZWi1wbYMh+fGDE1AprLifCrEeAzDDAdxOB2rAK8wxpqOS8ehEmTSX4vxmqABTW+ksYddKjW5gYlbRS0FpI8+OhsUjQam35ddEaDSBKu7zXRnD1JSKcn3QcDUNrBanYL+zfa2lCgOHcaNY1552238ox/5EX7ux36Mi7bNgUaDl33rt3JmdZUHH36Y0WRCmuf0ej2OHTsm63+txt99z3t44skn+ePPfhbP9/mxH/sx5uZm4gJZlrGysjINgieTCfuLLGuv1+PWW2/ltttu44477sDzPPI85wMf+ADvfe97Adjd2+m/0Z6r7UNgFv8ngFLqdcApY8xoz3veBHzb1/j8m4Bf+p91MkqpnwL+xd4/PestBiHcfd329TPF6xsi6L3XmKNMyZXfFoaQJKiyZlQ0h4x4SrK7FGoR4WHvwTuxp0PDnisqMmRKKcweDeQcVcjKTLAxuKSMqTApstKKjIAx5CmNRnM6ST6bV1iatYWhZD0OHJDJ6tgxyRLZtpoqxZWJmSiSyazRmEoz4zhg24Z4nDLf1kSxRacRcfqMxajn0GnCQtvBcXImS5ex6lXYzevM955mf6WLpTzG45xz69BoGh54ALL+mNffvMHJnYP4NcmEOYHF+QJf6XnQmTdcvKhwszEHF0sL7IgMG03GRHmYoMauXRfDj0pCxZGMnVu4wCXKxTGSfe/tyt080AklU4ODn+egdOHilDNKA3bHKftakVi/EpMUmYzuriatV9nYsrCTIXHqoJRi7UmwbY+jRxWrq6UcUo1QgatErzVJYHnZRWuXSlU2HqORZO9qNVnQBY5rOHc6pz+ycBxZBPNc3rewABdX4cmhy+XHXC4/FDEKIajZbG3ZjLoSLK1cEMW9TgdaLTMtyY9G4NYU9z4Iq6sWS0uGF70IUssVxHFeQmWEzAmGim0YTBS7Y5d2WzLTcSxSfKXJQGJ8jAvLyxKQlZXvcj9Zq8EorVNNQ7oj+XxU0Ti1JnYeopSimg3JlSUwpaKEajodwkizu5Ozv94g03bx7A1eOoDz56BSwW5psoUlqHiysyudJpSSktxgwKIZMfEbjCv7qWyvyICpVFCeN6sKKAWWjYUR1RAjmyKUzA9jqviMJVOOZkgVTI6vQqzxCN3vU0EYrqaQOdA7O6j1dUYLh3GGXRwTQqeDBk49aRiFkOcWYe6BkXW5gCTyzDOyFue5YXERBgPDwcWY1S2X7W015f6MRhKgrK4aKhUhjD39NOzuSoDdaglJr9GQ/5fKFY7lsTSfYjsQKcl2m6DCxG9IxWtnBSvPUf0elclAYoI8F9Ki5yB+kjkxBZzFa4LJ0XnGcmWI7WiIY5zuquAzd3ZoVCqYicJcvAjz81jnzlAJx6jDhzFhKPPsZIIpL+6hh4muejWndqocPnyQLIFHHjGcPCkZ1b/zd+TaP//5mVbzgw9O45tnNQM8DbzwuV5kYwM+/Wn4kR8Ribvf+R25X7fcInOn1vAzP2fxtu/IOXbVER5+THHsGNRqFrZtiELBgjeDnENzE8JEc3anysJiTkWFGGUzHOToVlAY7uQoFIEj0JIxhRY6EyYEUilUMoZBrvPiRSVyc0bGdDNIsLOU9V5AnmsmUcDc3CwrPD8P6STGrVigZK0yRk1l/yoVMEpc68DgOBAEwj+omDGmgJS5xIS5z/rGDKpx6tTj/N7v/Qo/+ZM/Od2kue6MBJplsploNGz6eR3byi9ZKy2Vs9yOmJgKlkoZE6CLuTgnwyXiU5//Mjfc9EJCKgSMColNw0T5eERowFcxbqAKabocbYQMn6Np1SKsqo8yElbbpCTRGL8su+U5ploldJvYvS3sZDLz/263ZUKNotnECLJQJoWYwGgkP55Hbrskfh0nE1T2C6+7jmfuvZdf+c3f5D3/6l/x+Ec/ytU33si1N9zANdddx/ve9z4OHjzIT//0T/PSl76UxcVFarUa11xzDY7jMByNaDSbXLhwYXrPHnnkEc6cOcNkMpkGxVdeeSWnT5/myiuvxLZtXve616GU4sd//Mf52Mc+xo033sjRo0enn//P//mrKv7faJe2dwLfpJR6CzCHyLP9x/LFwnK5Ddz77A8qpRzgJuCL/xPP5+8iYeMa8DkE72z+wk98jfb81CeK95g9Ae8M2csUL6iKLXKGyDJ5xEXW1nnWkXMqjKf57UtC+mefz57vzFBkKBzyIjssFr/lAvzsjLkxcOHCOX7hF36aX/mVX2N7exaMtNsyGYWhjNdPfcrwG78hWaW3vU0sTS+/XBLYZ8+qKX/AtssAWI5fr8skt7Mj88LVRyc06zmPn61w/IqE+mgdJxyIvNn+Q6jeDk+f87j3/CJ33JZQ8VNcB/Ik5/HzNYKqRitDGBq2tgUc0mzOMtNKzbRz0xTqNYNnJrhWjlaFTXNx/QmayPhU0z5bkwpKa1q1BFuDCvYQ6nwfU6TK5DMBvgqnqG03EByZiSVrXZbmvqoveD4X1u3pBuLcOcPNV0/w3RyjFKGu0N3VhCGsrIgagJgaCTt+OJQS9sGDMs+WnLSNjVm2r9eTzUq7EvHY0w5ZLoGx60qAu9wOiSaGh5/2SVO4+WYJwodDOW4UFW5WQcJ8M2Y3CtjdyahWcjxPs75lkSUZP/4PLE5cq/mO7zDcdJMoUlSqEkD1egU+c5xQ8XJSo5iMoVVNWN/1SHKrMJYpvquW4QeQ5RrHkSyxZKglHq1UDBUrJEWTGQsrCUl0gK1iPBNjIdrI7O4Sa4+kMY/PRLSdJ2PyXBY4r7eOareJKw1yFCrL8c49jdJq5m1er886U7lYOY48z2YT5fuYNEU9+uhUIDdrtJioChUdojDFBsilYiaoLCXUFXIlcJqQoKjiSN81xuBlE1LLJcltFDk1PZa+aooRm6Wo9XVMo4HZ3SUbR0yuuIEkU3S7CseZBTkSAMvpx7EEejffDMNhXhSaFJ1mxuamYqtrsbEhVQGtJQisOxNuuj6j1dbc/0TA1pZIvO3uCna2SIrRbDJ1Ojw4P8bybfLC4dAlwgxG5GjQFl5/g27eJKvUWayMUVub07nR1Bvk9ToTVcEjAhR5mOBlE1QSz3bVvd4MqnbvvZiyEnfDDaA0Z07FNPWQ9tpjAuwNQ1CayG9w4aJmq2sTzNewBjscOZDyXz8+zwd+W6M1/OAPyrV927fJ5vPrtxxZr4bAceDwJa+22zLfveY1EiB7nihTWJYEyKWL3y23wEteIu95+9vhmqNjtOdwYc0mSw2HFyYEfj5VbAnxGfVzbCtDeb6YLW6fJwma5JUGtooLglvZt0rwHJAlbHc17Y6oRqyvMzUyiiJwnRzfV6SZqFYoJeOuXF4UBp9xoWKkWV+XeTdJCo6CNRGidWHSMRyWx4AszcRIxtO4ruLhh+/ngx/8MFtrT2I7Pv/sn/8n1tbtKTxDijACDyszyr4vXSAIoO7HGGXIcHGJADMljzskApPAYhSm3P35TzO3dJQT114PGHwmOIkY7eQUOvYY7CJrLKR0jZuNObtV4ZC9zri2yE63KBrpUBIlwy6f+PSf8aKjRzl49dXklg2bm2Taxk4k6AbkoVcqssMsMwB/Uc+yXcbzh2DnIv1kwu//0R/x0hMn+G8f+xj/+7vfzYFGA728/JyGHU888QT33Xcft956K8eOHQPgLW95CwDf+73fy4c//GHe8Y53cPz4cQ4dOsTdd98tWs/LyzzxxBM88cQTvOc97+HLX/4yzzzzDPv27ePnf/7nee9738uLXvQiHnvsMVZWVmi1Wtx6663/66lPHD36kT9805ue1/vtX/3V/xk6xe8CfrhUn1BKfQz4c2PMv1JKBcCvIRnfCPhPCKHOFO99BfA7xpjl5zjuYeAsEHwNaMVf5VwHgAMcMsZs/g8d6+sGxb2eRBS+L+YSysLaY7xhgIkRtq+dp7PaERJghYVUmlNYX8ZFydUhKrDBxbHKWnOvJ7NYEJR4hOlAS1FMqFBhVEwPNuZZguxlK+J0HnjgS5w8+RRveMP3kGWykIah/FtKL3kerK0ZbJ3T7miWshWOdnZxmlWGnSN85V6R9Tp9WrQ7Dx6Uyb5en2WPZzwDQ8XN6PYtLrdPc6DSRdcqhJUOTrMClibMPMJRhhdosDQqy6jsrnAhWSZSPoEOWUhXOJscYhC6aEsWonpdMRrmJInBcTWtlqGyvYLtWah6jVwpQgJc0UaYPcfBQGyxa03Bk4GUs/ZMYJNRDnmCTywkrfJ5WNbMR3fPM49wyNEEVobKRBw/1S4qT0gyizjKiSIYRD6DoThIXXGFPOetzYzVixZb26IWMByq6YI6HBYZ91gVlrmGZlNKmmWgPRjIIh+GcOGC4umnJX6b6o2aHNfJCKo2lar0jbNn5dhXXz2TCC7hM4tzCYGbkSbScTZ6Pl+5TwKym19gePH1YybGZRI7hOFM2zRNZWOllCHwRad6ZVUgHVdcIdc0GkHNjfCshPV+hVpdgrAyO6lLbbcie5pmmu1Nw77OhDiC/m7Gwd4jmCuvRvtyz+PUQkUhbjYhdTx0EmPnCSrLGLeWBPqCQYPI321tSQctpV+0lkxPWS/ft0/wgZ4LwyGqvKGdzqyDdzrgOCLFV4zZSe5j97YIjU/WaGFbhqoSuboYi6hwAOx2FdUgwSiLfl9cFdvumCzOyWxHTD+2tuDpp4nbS2SXXckwC7j7i4r9+wWqGATyllKwpTTuiCJxgHvjC1Yxjsfp/hxbW4onnpA+cviw4eABg2tC8lwLRdrqEY4z/vjeOaottzD3UFOptnpdssm2Ddddp6hZQ3LtkCoBqQZmhLW9SaI9sCycPBIN50KCymxvTx2FlFKkymKCT7VMBOxV7ymqbFPsjTGYjQ2izjLj5j6GqU+7oxiPRdP42vkNdLuOOn+OXlJlp36Ye+/VzM1J307ClNZwBXVwP39+tzPNThojWeJ77xXYRLcrf9vLi3ju9hhwDlgEXoDvw0/8BPzLfwm33ioOenfcIUmEz3xG5tPbb5fNXqmuOR7LPuymmwwH9hssWxNOMjremGHmY7k2tkrxzYR4fZdxZY7EOFQbsjq42RjLVsTKITc2PmMyHIaxS7crXXphLsPV6XR9KV2Gh0P57pYvVTHRzpdtvUUqG7Vi+bBUhk3GJHMv0byv18Hz9qYC1DR3s729ycfv+n1uvO6FdJZfwCc/+RHuuuu3OHH5YW4/uEja7fLN//t7GFhN1ruiu2xZUKup6TywuDjjsSsMgRpO1zeFwiMssrqiOy4GUoY/+PDv8rZvfhOhqTAYWzgqY6E6Qu9slfqVoBRxcc2anND4kGXU7JA0U5gkJlE+VhaiwzFJfY7KZEdumtY8+tBDnHr6afYdOMx1N7+YIBmiJuMZB6icV57dni3bVrxnfTjkkfPniba2WLzqKs6srjKXJLzyJS9BLS5yiS3t82jPPPMMrVaLixcvcuzYMYK9eEeg3+/z2c9+ljvvvJMvfOELfOxjH6PVak0zyW9961sxxkwVKQ4dOjSFaP5tD4r/JjWl1O8CbwTmjTHPWQN73sd6XkFxAZo0oxHR3H5sx8xoWcaQ9weo0UjcvubnL7F7zrBQRYAW4eKQYhWZ3rjYCVvGyKDRWtKCewfZnuMBpOhCMVhY3RFesfxf2neTRBaDz33uw/T7Xd72tu9jZ0d4ALYt8Xa3K4uEUobL5nZ5/b6H+NPHD3PtkQGR8fi/PnCAynyVhQVxiDpwYJYtLhcU15X5o5QHWloS6MOhQ3D5MXCtBB2FpI6PthW+CdGTMbguu5HP9tBnft5QsyYMJ5ph5DJfnbC2GzDqpeg8obFU4elnRG1heSFjrh4zSn3a9RDbSO4EpRjjkeGgSQiImFDBJcLhOSYtz2PqcwpTwqFOY+xccKDG80iMgx0Np8xltBbrz0SOmVs2dhYT41xCfjTA0+s1HEdY7KU01nI7ouLG7PY0taphc+gziWxRfnAlg+z7Ei8cOpijtcLRGVu7FmtrciwrjeiOHHKjGY8VGxtiHFDGGklieOktKePIQtkWaSp40/n5mZBKtysxiKwdhoqfUrUTqn7GQ0/5dAc2vi/OXmkqgftwKIusZc2kq2wbGnVD2x2hLEN/ZLPR87FtCeoHAxj0cvbNx4S5S61e9ON0JuzvMyI1FuPEo7sDhxcmZLaNCRP84Raq3SSxfDI0jomxU0kzq3qd3HbJPB/bSLY1VzIWtJIgezqWShmP7W35vd+Xk7MsOHGCkd/GSmM8E0r5f319ltYSFxNoNkmcgAyFl4UQRcTGYawqhJGm2TL4Ki6k2XTBzhe5RJuEOMyJjUM8Tllw+riDbcEgzy2QaTnv/thmbcuh25VN1O7uLLgqp6rC5A/LMrTbsLuTESeacJKztWOzuSnB2U03weF9MU17hHP+GdY6J5ize+ReQN/UOX1G0arEOBWbLNcMBlIy77TEQVBbYr6xuWkKJ3NFHBueOZVz1WUpVTXCKIUTDlHjsSzoQUCeG8xOF91qoArJylwpimEqM9VkIgHxZCK7bdeFZpNcadJRhB3YJIsH2LGXOXMWFhYUnY4pNLel3f1FyfwuLspjvfFGxcWLIh+XpnLvbrsNvvQl+LM/E+vmrS2pzqyuSnJvfV3u78qKOPvtkSJ/VltH6wf4wz+s8oY3fBNaay5cEF3jSgW+//ulq9x8s1R7Oh0ZO54nAfLp0yLBdvRomRE1VOyEtW2LoKJpVRMqVgRpSqhkrlTGMEp8Tj4Ex6821Ko5Yayw0xgvkE14ns94HSXBrZzPQL5rNJoZoQ0Gs1wLcUSaa3aHFlqrqTOg5ylG/ZR2R5Hl8iOeNbPjDwYy9j0PPvvZ+3nm6fv50pfv4fz587z61d8GoxXGzzzCD8/Pc9n118MVV2DabUZHruFCtwbIMUtZ33I6Dvycuh4xUgEOyRQWaFCFopNNhZA/v/dhxuMxd9z2MtZ3HBoNTbdrCKyYVrJJwIRxex+uixhyFHNyNpiQJxmq3cRWGRM8SW7kEyyTkSoLMxqh/AraUSjgD+66ize+8IW4Zeq8JHBsb8uJl/25CJT/y4c/zDu/9VulMxXykyeffJK1rS323XQT1x08yOkHH+Sjn/883/W2t/HQvfeyf3GR6nXXsXbqFGurq1xx883MLyywfe4cllJcdv316BIk3ul8rU76F7YHHniAxx57jNe+9rXcc889HD169KuUKPa2bwTFf3OaUuoG4MPAGQTGsQVccu3GmM8+n2N9fUxxmpb+qqhWC9/JL309itBJDIsLM+/VcqfFpaL+FWZqBRkKt8A7TdlulYrsbrvd2fH3HM/ANAOaoQq5tDLbeWlBvyxtra1t8D3f886pnni1KoFNqyX/v/pqmAwSXnnFDrWtHrc37+Ox08tcWDrOD/+4zZNPykJy/fUzDF5JuKvXZY5YXZXXDh6UCfiWWwqznZ5hXyPGBC4UoI+xqmFpxWRg6MUueWYkYxZo6kGKF8DuqEKtoWhUNX4ywY52+NDnW9z/VJ0f+zEbx7fxfUNMgLJSdC7lLC8dkzkiEST3e/wcOfSi5Zc+RyEWGqrEU3pGEqacXbM5Mm9wbFC2LeS7JMECholLblzqxChlSLWLlUs1JIzE6yGODTffPHPLsgIHk2U8veJy4jpNZ14ME8q911WHJ9ieheW7VOwEV0sK/sCczXxTE45zMcVYh52u4pZbDLZleOopwSruW8xptm1Or7hsbsoxS5xoIaIh5xdOobSQJDh+xqOnXK67csLxqzISZYFRgstOdJHMk5JnqyWfyzIZHjtdhbNYwQYyW1QOajX57jSFSk2Rape6HWJyTxwKC37K+jo0m1WUMgwGisUliFRFAijLEFY72MqRZ2LEGtmajAVXatvoMEQPeiXNHp0Vjg3jsdz0AkhvSixHoXdMtQqVCqbbhY0N7EVHPIrHPXFIGI0oUvSXaIfbpIAmUQ5OOsY1Ca6TQLsy7UubfYegosB2QIFDVBCQFPTHLDsDtFYYxyHNDLmypxm+3kiVSWlWC4m0SmU25oJAnnMYQq2S8fjJjFdefZFzo3l64yr1uuHGG+FNb5JN7NHDikMHqlx19DidZQuumGfBHjLvhsxdC5u7LkElYxJrlpflO3SeY+uU8+se2Z5y+3gsFZXrrxhhbW+hdY6uVmThL3dIgEpTovocrg1WHBX1cjUlFhuTy2fKUkW7Lc8JMKMJq1fewUL3Kexen+qhRS6/XLO9bchScGwgCdFRyLFjTa48HHLmgseFC3oar/T7EqxevCh97IknhFR46pT0tx//cekio5Fc7wteMNszxbFkkr+ae7HEd3/363nlK0d88IMf5G1vexvz8/De9wqsqdx7feYzEivdcoskC3Z3pRs2m4JB3r9fxk+eK3LLpVrNyJOUtW0H39W0WpJC8U3M9tBDFcHuE0/C8eMWdT9mHEPNxIBC6xzcsgJZ3mE5n71qoqMRrK+L7FkUQaulCCMhZB7dF2KR0BvabPZ98hz21Qa4mxsY2yGfXyRUlZkCEdIX8lyOf/z4TTz99CkWFw/zzDNnqdUWeNcPfDft9SdRD53EbGygTp5EHT2KU+vQaAb0BxZaQxiKHFxJDM0yjV338QmLDWVK1Fvn6dVtFmoWJ594hjCccN0Vx7j65luZ4Ew3A3NzCr22yajSxms2yVUFxWj6MBUQVVv4k10iY2GpDJ+ItJCUM1tbWPPzmDxjhAfGoapGHD54kK5SLJWZgL2l1iRhLxv9R3/6p9HAO1/zGpKdHb509ixd3+cFt93GDZddJjftC1/gwsoKBxcXueuL9/COO27nf/vn/5xDhw/znje/mRPXXsuTKyv82q/9GvPtNo5t869/6Zf4lpe8hG9+1av+SkFxkiQ8/PDDHDx4kLvvvptTp06xtLT0lz7ON9r/su3+4t8jwCuf43VZvp5H+/rqE4OBYA1F7+jSF/NcBki9TlGDLD60Z0bdQ4675KOFebBLOgP5lp8tWQhKyWyxB9M8wRcmO6KP6xIXfvQi15YjuL9hUbKfn7+c4XCGT6pWZbEcj2eY4M1NlxX/GKOrDzE5kPE77/e53FGcX5PT+KZvmnEKyjYeM8WGVipMg6WFVkKjEhPrgKXqCJ2LSUeqHHwicqNIPQfXc5kzUMlHaJOQ5p6IeSiH0Qh2tzMWF+Fsd57xMOdFr4DXvxXabTOFW7kqJsodueLJBKvXQzWaxNV5UrKCLX9pmz7BJMG4HkorTJISRRa2SQhxsFWKTS44VRc2+z775mKU68J4Mn1M5y4oFvaBUS4VOyY3NhjBQq5uuiwvw+qFnN3tnH3zGZVAoC6WyrAsxeYGLC3lKKWncn1VCxwVY4jRliUabYDOEgIFfgXWewptuWgtVrIqiTl2CHpzLjvrOUmaU6lorrhCgpm77gLbzjl8CK4+LhnctTXJ1MzNgeM42JZNe17RT+s08gRPTejHPk03pFl18H2X8ViCtD0w+2nTaVzcW28a74xGQgBLE+h0FDs7PrU6aFtMbEqn1DSV8n2JMZS+abC1TT3to1yFQqNHfUyaEvpNfDtDWVoijxIPsrVV4k8Ei5rn04jSNJsYy0GffrpgE7rS+RcWiP06zqgnfP7REJoNzOHD01SYBHKS5oxw0OSQZ4I9bjYIteAUFTkeCfVKJg6GTl5Ui2zOnDYcWIxptxVxWAHPx9TmcEgkC2YiEnwqFbkvrZah35esnudBryf3rNUyXHYwYWMlor+Z8sbrtlh3D2GMhx0rIbEhQc/fuXOLVy48QnP7DONXfycVOwQUue2Tpyleb5OgcYidXacQcxBTBNt1GY6FuDcaGTY3Z/CeZtsiVHW2VZV2I6PmRrhZJJ1id1cW7FoNf7wNiSv3bTgkrM6zthtwZG6I6m6jqhWM7ZAYi83GcZaDGoPEwzlUYaltcDMXopD4iSfoNo8L33j3LMncMpat8UZDWmlMjEsQjlhfn+eBByQgfcELxKjjzJmpoA9XXy3B4M6OBMwveIG8VhKNSyjP6dMCV3k29ligS1CtVjl27Bi7u7tYVovJZBYQ1+siS3nZZTLHNhozTeTBQObP+++XwNh1BYJaq2tcEhI0bn8b01cM/QW6fZckNVRqmmuukXlLFMEcKrWUDLBNQqR8zCVbfxmYti2bq50dyaRLV88hV2RGTF2KXA+W5eP7HuPU4HkFpMSu4Ow/gM4zjDKQG8YTNeWbp6mhUon5yEfu4uTJe9i//yBLSw1+5mf+E9XqYR55Bk5c8wLah46gzzyNWV9H+T5uHrJo75J7DYZjWasaDabVMaUMNmmxPmpGqcWn//wzvPBFL2GnP+TO17yKi5s7tGpzrGy7tDuywbh4EQ4dMlgL88ybIZnyqcY7ksyan4cowngenkoIqx0cYnI0IT6aHEdnZHOLhDqgGuQEUY/IbqOA5aUlvnzvvezu7vLAQw/xlttvp9Nqcc3Bg2AM2ysrtBsN3vPzP88Lr76ag1nGJ/7+38c4Di/69/+eVmG8AUhHiCJecegQn951yCZ9/uyRR3jX93wPZx9+mO5gwGPnz1OzLH7kO7+TZqcDScL3WgWGcC/h/3m2D37wg9x0000sLi7yyCOP8OSTT3L77bdz3XXX/aWP9Y32v2z7ehn9553x/7rwiazfZw+d4atbaftYpuEmk5kRR1F6lYVRFbCH2X5+iiXeC/YNw6+OOHx/6rc6Q3XNWoHIJC4KTgBZJji8z372Y9x5552XWK9PJrKGbW3JxFwqy21tyWJw8qR85bXXyuIxNyeXlySy7qWpnOJwKCXIEqfquoaF+ZyKGqMMIqlUSBAoyxKJutTgDrYZ1ZexdY4bj8i3tngiPsaR5Qg30CTKo8KYEJ+1TRvShIMLMZEVoFU2xWJrciI8LAW2ylCjAcYXRjYFpCvGwVXJ9J6ZPfduRAXb1rjpkJGpkScppBn4LjVdSB8ZRaQrUtrMcybGIzcGlSU8/LhHrWpoN3MWWgkTAqqbZ1DVCmm1RoiQi+zSuhmmPeDptSq2NnRqMaPMo2qLpIftGCpEEpAryaTtPeeLXZfU2AQVzVNPGRYWlRAmJ3D4YEa+to67PI9ddbFtCWZcF6xwSJoqtgcOmXbQWhWxoWCW19flDmWZPOulJQlOW96Y3Z6iF/kcODBTnyjxiu12AQ91U1wPBiNragHe7Yq0VrsWszkIaLZUYexS+i2YKd6zVlOMx7K3jGNwkgEVHeMstqhaY2JctMnwwqFYSG+uC/twYUGUXx59VKIQqfPP6ucF0yjvDYiOX48bD7FGw1mWUikSvw4VHzspWKhKg1+oVJSVokaDsQowSqPJcE1CGCkqvXVoNclcn1wpUlwCRO80RRdYSAu9ukLuBVQ7LrkSbGTpRGmRkmeKra7GoGX4ZxntekKEx2ikppzQycRw7dERW5s5NTWmGhhoNLj7fp8LF23Ons65844JqRNwxa//I9rxJuaqq2Rnu7DAg73DtNyQQ3Nj0lqDe59sSFCxaohjaHU0L3qRwh9ucHqrztbAJ88FVtFoyDhvt2Fx0VDZOY/lWqhqFVViK7e3RRO80SA3kAZ13GRMGmck1Tb+zgph5yCxctjYUIxWuhzjGbaXrmFZrRMGHVpBjHr8MeKDx8jnF/GUyN+o9TXM3Byh16S3C72LI6x6lTMrDsYozp+HF74QfvInZc7KMsERO45wIUqfBdcVMvFkAl/+8ixRUIoFnD0rWeXSRrtsngcf+hC87nUpX/jCF3jFK17Bo48K7+/Vr5bPX3mlwDPqdZmyx2ORhEtTmSfPnpXM8Y03ynnNzxfwB5VT8XNyZRVKgIKhLgPacJyxvz1mrKooJcouvpvhulJxVCbDoEmVPXWdhBlZUinQpKgk5sJWwNycmmLUS/1rmHGPRVXMEKgJVp4xSmwu7ggsynHg4oWT/M5/ez+3ftPtvO7130yWCXxBkv5qqlq6uGiobz4jG9huF44eZWQ12N2F4UTLOqhl7llsxziewlMJES6ajM985tO8+CW3UfdE7UIhMMQUwT2XmxKhiBjsYQ8v7pP4DVQS4eQxqtnEjMfkyiat1IiVQB48Qiwy8kK+lP5ANrz1uuh94wo/Q6VkUUSWJPzML/wCL7/hBj79pS/RWlzkussuw40i1ldWePVLXsLS616HWlmBP/oj6XDvec9s8RwOMfU6X/m932N7ZYXajd/MzQd9gpaH8n0+9Fu/xaOnTnHrbbfxujJgvewyKUUEwV/KuGJzc5O77rqLzc1NRqMR3//938/CwsJXYY7/ovYN+MTfzvZ1M8UxDh7J136DUrKFL0F/pRxDafNWpDUzbGLU1Mc9YIJlcqZeyb4/s4YssQ4gM+eeLLIqv/NZLcUixcYixSNibInVchDYU0ikbc/Wed+fidIniSwaDz8sE/orXiGXcPiwZFDKzWmZUdnelsu77DKZ1GVfYKhVDQETktSCJMFNMyatZTwNtjE4pDgWUK0yGWSMMp9O26XeSthPRmxXQJlCNEehSVmsJ3jbq6xuHaLayKlXUiGDEOARStbcwNhUqAQVwtwhNxComN7YxvJtIu0V8pESkFYYkovnGkkK2tioNGKSeOS5Q8XAmJqYaOSKQddweC7DGIMmxclTtDbcfGLCeKyoVXKUgipj0oVlbJMRUkGWIZE4cgsYjcIwiRVZJj9PnfcJAqh0DIEfE1NhWCwIrolBW0S5hYtkYpeaMUbJ/2+6SrOy5ZBGGjtPaddzTH2eu+/R7PTh9a83ONEQbzzm1HaLWiWn3TZMkhxtWXQ6kMQ5G+uKjXVFtWoIKoIfLjO49qJPYy5HT4Rc0+0WQasjC+nGRpHl8TJ8O0E1KuzuCnzCtqE/tkl6mqTQvg8C6eaPP25Ynk+xPIssE53kanUPAsmp4lUFgBqKiBmZcklch2reRV11FbnnFThVhSmlNfp91PnzEqnv3y9RSK2GbjWE0AYi2j+ZCDmsWsWZ9GFjhV5lH5PmAguNhFD7mHoFJ5ngZiFsbuLPLxBaFTIcRpnNZk9xqD1PbjmgRJPYIWREBdeEKAO50qI0Y8cop0S7S6nbL/gQubKI8YmTArFhhRiTY9k29d3ztBst1voVhpHi4JxcQ7UTsDtqYNci9HBIkgVcvGhIMs1P/JTD+//DgPrLb8YszZFfdRzVaqK2t7h+OaEb1lmJmizoiGZLcTA/xwv6p3lircGfP/kCfvZn4cZrmnzXd8S4NUOSKCoVGf/Ly3KOWovpgffUI9i9J+XBHj8Oy8tMMhebBO1YxCogs1z8IMImQS0u4ucx61sWW1s2Zy52+NhTNX7ghyysp7fo4hEtNVheXMLTKcnuFnGzjUcqo2d7h7ge8OTpKhtrDW57cUyv57B/P7z2tbKp73TkXNfWpAtce63IzS0uypy3tCRxRr0uwWzRDab6ubWaZFCfa6p/7DF44xtt0gI/evAg/Jt/I8c7c0a+d2tL8N6FKRmNhprmS664Qjag29tyXiXEIUkUtonJcJgkNs2mKTSjRUlicdFiQoWamJiTBhXyKMVSKUNVEYlccnzHoJwZmqWcu42RNShMbYKAKRGz5DrE8WzZKWO4hfmcqpdxbsvj6WdOcfb8CtWqxvMUX/nK/Zy4/lUoVeVzn/ts4YiXo5RIeBqjUMrwxBMKnScEpiDFrq1hcrjYDWj4CXFu0anFTIzP6TxE2ZoMWwJ94Oj+ZVpemfaR5hVZ3lxZBIES6UDH0PYm6FEXlcZ4hZQpqnCYq1aJChK2b8bk6AJTHOAxIcdiUl0iYIz4zAoB3CIDDbnrk2xvc+UNt/Irv/XrvOvtb+et3/mdmIcfluj/5pslI21Z0il+9EdnleQzZ0iShC8/8AC7acpLrrySW154K4POYbLJCLUrutjf/rrX8Zo3vpH7HniAqYi054kM1NdpWZZx4cIFTp48ie/7fOhDH+JnfuZnLtEu/hvffF92r8+n7SU6fqP9pdrXDYr9vyggBpl1lpdlFgnDGXlrj7uMQkpdOS4BYyGGwTSzAsjn+/1ZugJk5SkD4pKUUtaYi1Y+9rzAFvuF7JEmoz8cU6kE03dVKuD7aiq64LqCu6vV5Gte/OJZGUsm8xkpq9mcZQjLU40iw/ycVPjDEFqeCKj6JoRwCK5L4GSCJ0zTGQ7Ldem0XCpphFaKSa2Di8ZgqCJZ5kHsYLmKwE9QB5Zpx4o4s0hzQ6IkE5LhYKuSGaMYUSGMYdjPcbCZpA5535ruK/Lc4DsJqZo9diEe+tMS9eKi3PYsA5MrBr2MdjVmmPkMehnz9YRz2z6NpiLP4Pw5w01XTVCWBLy7Yw/H0wx7hqVlIMqnAXE5tYeJYJ491zC3FJI7LtqzcFWM0glh5op8lYIsz0kQ0gm5KVzqFKnlMcoMflVzsKFpVCw2tm2qNc1Lvgm8nRUitR9v+yJmrsPh5ZiLXR+VJ7iuYHSzDLSluGx/iI4ytgY+/dRmMskZDBQHDypyo4kzPYVNFAID0z1fiQ2+uOliEo1TUVPJvCyToHluTrOzI89gaalknCs6LUPgp0S5w2AgZKdSjm441GxtVbnhqhG2NqQ4pMamaoaSdcoyEmyi1MHaXGPsdsjyhEW1glpakihnrTALmkykY587R3j4KtzBFjoIZpIpW1tQr9NwQ7wgQQEBIWbQk89aGjM3x0RXCle6hMgOWFyEyNSK4agY7pFe3B4EDAYGS8Nliwa1sCCyjSaXLJ5SXNgO8D1o1HLqakhtEdCg+330ZCyycu0WlpWzuJDRqOQ0x2uonsFTmlqtST+ssHLOZTLIePnLNTvbhle+TDEXjMhOXMf6pEKrNocyGq/RYuI0qDoZvV7GF8/WeOopRbV6hMsvO8jETtn+vGJr0/D9P5ey0Q9YCkQPPE1lDrBt2QBjRJrN9mxYXMQsLMDamty7hQWM46KikGDcI2+1mShfiExJTlqYzvT7cryX3e5i6xT7wfs4fGiLwcceg2qCufMN2Kur2FdcSdxeJO0c4OxWlX/x40I4PXLEotN2OHTQMByJrOFoJOvm+roQ3UpKxg/+oDzmMuFWrUpgOJnMcPeHDknfK8lfX/nKpVPxpz4lqjt7W6MhUm8/+ZNCojtxQj5/xeWG4cCwtE8y/ysrhocfFtjS0aPynpMn5dw6nQLPjI+TRpx6UlOpKuYXFEFg2NqSrrywoKESUFUT6taYsOKjs5Re16CUZn5eY4wijmeyh3Nzs+JlmQQZjZhCK8plaGVlJnS0uiqf63YVW6M1PvulJ7nlhddy5LLXyL1QhhPX3E6jOatEaQ0eETmIHbxRDIcC4/M8mG8nOCoVT0djk2bCj7GsnAy7IGAKxjfCIyicYSdFCOwUjo9jVaHChEqh8W8UuHMWg7HFxAtg4QhWNKHiJDPy+tYWyvOo1PeQ2RcWMNoiUPI9mpzcwPntCgsLBoucNDfsdF0Oz0+wNCTtRf7jf/plmrU6br3OE888w1XHr4FNEWY2SUK+siqVKMuCpSXS9XU+d8899LOMV113HbUSYpnGNDZOXVqBznMaWnP7zTfLgrq1Jbsz0e2cvm13d5dKpYLrupw8eZLHH3+cj3zkI3zbt30bb37zm3Ech9e+9rV8o/3taUqpBvCvgNsQR7tnZ06NMebr7654HvCJvIBP/IVtL9W3FPStVmUhLrVmhkPyMEZ5DqpWk5RtGM6kXcpoYzicyX/Nzcn/S2uzPC9Xpen35kVRKVWWTDLkZAhUYWVtm7W1i7zgppuAHINYqJakLteVQOfCBTl0qyWnND8vAYw438lp2bY4HpUB8mgkpzLXyhiMLaJISGW+nXFwPsQqHM+MkUBO9XvElk/qVHDGu5h6EzUeoF2HvFIhIigCYsMo0qxsBzRrKYuNiBiHYeyxsVGo43mGhWbEUidFKdGetEiK0poItu/uGrQCx4WLOwGOleP5irmWKG2OqQKa0TAnnOTkmSFMbObmVAE1Mexrj6n5OTE24Rg2+y5HFyZoVxNmDqQpq9seC3M5FTvCynPOb/soFM2qlL53d2G+k9NsacahaIuWUJZazeDYGUFhLxopnxRHFAeCnIob46jyXOXZ7O5qmk3DfDBEAYPQpjv2aDUV93wJalXDDVdFBP2L7AYHyKMET0c4rRoJDttbhmpN4/myuIEhN5bAK7Z36fYsJqtdgusvp9qSMmOZHW63Z7rWZUFkr5FEmgruMs9lo1WrzUxfyr1eqyXDolSxKOH4/f6lwXYZLNi2ETJRBjqLCTbPooo0WF5vElpVBj3D5PQa7s5FDoxPSWQt0fhsbGoNnQ6552P6A3QuUZ4CiaBqNahWyW2H8eIRMduQHixbSqWYEGCJmjIRFSm5JinGsqd4qBLgZJsEL98DhbIs1GiEKeTdjCXayKmyGMYeOzuKhQVDzRpjDfpT4LbaFPiDqVZJsUhSSwikm5uc2m2znbWJYrjqUIQf7rCZzjFMfSpuxvJBzVfu1dSrhqsvCwkmXXLH5YnNOR5+RPH+98sG5kd+BG49MUD2z4ZJaFjp1QRaUmSJtYaHHhJiZ6uekCaKcWzjqxg77BPEPSEBlw+v1UIFAcQxxrIY6RrdnoXrSl/a3ZV/L16UKoHrwmvuSGg/+SX02ipqd5fhS1/Nw18aEWUO+65fYOx32N1VPPqoZGUPHhT8b7fLlED2xBPy2I8cgfvum2F5jx6VPri1JX320KGZdvcDD8y0mUvZyY0N+XyJF15elgRC2T73uc9x66234hZJkJK49+CD8t1aGw50Jlzc8QhjPRU7iSK55oMH5bttWyAGtiWEzGFos7OrObIwITQieZdGGVmSYfkuo5FiPM7wHOjMK2ydY5GS4uAzYVJUqPLcFJrmsLOjpktRiXIp1QlLKbpSPKGU2cxzw333fQbfb7Bv380sLMDRpRGrOwGOC21/QuYEVKyQWLmFNKiMFxBde0xOJRCjKaVEgUXnKVEm4zeK1EyPXYc4pIWqkiQtnAJbnOZKCLWByNWogl6eIXC0CJcKESEueZQQbF0Q/odlQRiiCjijKdnnuWD9E8vdU8ETB9qy2hqlNv6ki6nVGYYJv/OB9/Mtb7yTz9xzL9ccOcbKmdOcPH2WH3vP/4ka9LEnQ5J6Bz/uo8ZjjOfxu5/5DJ/+5CcJk4Tv/I7v4LU33ij6xiV+7Fkt9uroaIzVbnDmoYdI05TLDx/mfX/0R7zlne9kOBzyiU98gj/90z8lyzK++7u/m6WlJW677TasMs74n9j+l4NPHD/+kT985zuf1/vtn/opsjz/WwOfUEr9d+A7+NpIX2OMeV6d5Hmx8fYctjyDS3/PslkKda+yvjES1BYrv27WZ6DJKJpFBzCLNvaqIpRZ5FI/rSQH7QnCTZoTd5axyUiU0HYCYhwydLjDFz/7cW676SoMYigyGkkA6zgycXU6MzzduXMzAYwyIV2pCJkrzyEaZ9TrUj6rVSHNDK5O8V1IUwtHGw7MhejJSFz+0hST54QLh3Daczgmx0ozrGjMatrGri7SCFJyo6kwEpJVCJs7Fu2Ooe0nJAkMxoZYG+bnRJbNTwYspjuovA2WhW9EcD3CR5ORomk2pPQ1UVUOLKW4Kha2M/5UsSNJDNsbKcePRNz3eEAYi6Sc60r538UiLDQuG5WI3MDTa1UO7M8wygLbYWnJAFo2I8WjW2xFKMdmPJQQKZwYHF9PFcDGY1moAx2ic5mUV3Z8mnOSaRQYgcZyfFLl4poC6+r7LC+LNW6KhVKKzPKYTGCuOuZVLzYkKXjddXbsRTIsUtcFYxFsb+BYEDUOiH5y33BoYcJWzyU2FrZdY6ir1NpD5o9U2RlL4FJiiM+ckYChUpHue801EiSUknxal3JWZlp5aLUUjz8uC3+tNotRSwnoPGcaEJUZupKk12xKADM3BzUvwcvHqJ1tCEPiziKWytHjIdUgozpcB7sL1RE4TVnh4xhaLUy7LWNtYwO1fz/atskrxeazHMdLS9ML1XlGzYyYZC6ppbBVRkggUAg0WuWFxKIY8OTjoTgf1jtTcwoAnYSy+Pb75FhYvgPVKsbzSbf7DBv7cT0hXTbcEHfZp9RnMPU6VKqSVV5eZmLV0ORYeUKuXVA2LC9zbNGwnMVEmU2zt4a11OKxBw29WNFu2zxztzynOFbExsFpzaHGY37l34SsbPl8753rNK9cYvVCxqixQ97y6dNg2VzE1hWaHRvHMUwmknk8ckTMHs6v2GRRSpwb5hdcbKvBuNqk5iTkxuA64NhmNkdubxPMa5y6RZwomg3FfFOTIrrXJaF/q2tTO3Ej3uI8jEbU5issvuIwv/TLFtVnFO98p/SLm26SfhFF8jkQ7PCtt8rvjz4qfTfLJEAtnSHLqXZ+XoLeel1Id3sxxY2GfGZzU14v3W6/6ZsuXQpuuOEGTp48yQtf+EJg5uq7f/9Mfnk0EUObi5HHVYcmrG1abPZcwBAEMv/WarCzbTg8P0J3dwjqC1R8l0j5PPq45uhRRbVqEQSGzBgmWrHQTGlWhMuSI+5uBs2k2DyDkbml7QKahQXZ95X0lUZDssGlfnIJlyhEVuj1dvjKVz7H8eOv5ODB5tSt+MJWgFKwsqJ5alzFtg033eBj2TklYyNgzIQKDS+iO7RxTYylc8ZUxMRKabp9Ra0ukIcsE1iWRU5UqCk5JDhkKGOwVI4Vh+T9HhO9D8dTOErmzAQXiwyfeMq7cD2NqlbJ4oxcuTjt4mHkuRjJKI1lKdYuXiTWHvuXFnFIMVmGo00xr8KD932Oz//Zpwjqdc5tbLNUqfCf3vc+fvTHf5Kg0ebLJ59i38HjrJw9xbEjh1GeInV9TKC5+9FH+c3/8l947RvewLe/7W28+LbXUVUh7HZn2uejkcwHWsPFiyjg8See4rH1Ienu0zx55gz/4B3vQCnFVfv28du//dvMzc3xmte8hh/+4R/GGHOJRN432t/69sbi398A7oE9Umd/yfZ1M8Vpvy8qwMbMRB73ArW2tmRG8byZGYB8WBZbkKxPySKCWXDreRL4xvFz7hynzfNm2/vFxRmUIi90kcv6WBBcUmb5409+kqBS4eUvfSkghgKxEXLPoHAksm05hdFIPlqr7RVTn90bhQi7Z4Vir2ci1roBXiATNianmosNL1mClSYFw0uuOQOSzMHbvIBqNoiDGrFxqZgxanMLU61gqjUyNP1dqNcz+kmVJNE8/LDhxqsjWmYHpRVxW2rww+0QlWfUarC2W2F+3kCWEoeGKFXEqUWro6nGuzjpiLixQKJcKkzIlGYQ+Zw/b7j60AhjFAk2ludN91q6QGpbGCwyzm3XMFnOvoWIRAkspcKkyFxc2o9SZWNyQ5bmKK3ZmYht63gsxhytlmTWe7uGzS3RgxWL5JLZXZIlmWbqLEsCYpG1zwmxsU3O2fWANJMNQ6tpsLOIcxcdjLZYmkvFxS+NMNpiZ+TRHbloLYFFqSqUZWXWV0qiSTJjyK+uShcMQxkC9bp06VLzWioOhuPHJesl1q2KalXR7coxxmMJSubmJAjZ2ZHvtm0JdMr9X5lV3t2V4TPfSan5qTighSGcOkXSWcQ4Lk44kHHZ7e5l20g29oorZs4FWosm17XXokYjYssjrc9JiXZnWzKaYQgXL0oQvbwM4zGqSGOZYqzHXo1MOQWJ0xQ27nbhtJUSKa8g2DnoaII36aIadca6SjDcxg5HZI0mka6wuxlTbXu4nvABtBLMvTKyMOepwd9eQWcJan6e0K2TRRlB7yJqYR60JsoscmXhqITQ+ARmwuOnPcLM4fLLcsxkwmNnKlx+haIa5DjhAL22yqdXr6J37ynecPMGn3jmGC+9JaZ1uMHqiqG6WEVXfOr2hMz2cFRCbiDKffJcMtm5geH6mCQy1A828OwUnj5Nr32EszsNGg3Y1w7x3UwC/dwI9lsrJsplNFSo0YhatMWKOsjFHY/FRUWzKeSvQ0/8KY20Cy99KbRaPLPiYQUu994Ln/uc9MXDh2fue44j8ILVVQlyr7lG+mS3y9QJsMwmX3mlzG8gj/yxx0RbuF6XitnJk5IcmEzkM2kq0/rb3y599Y1vlH/37YPTpz/Fq171KmBWbZufMzjxgOz0ecJKm0o+5KHuQSqdgMX6mEw57I4c0lRNxYpaLbBUSiUdMDaBQE/iiFj7aK0KvK6MlzyfTfM2kl1NjFMExzYz4bschSHHmgaepdrLszel5e/tNnS7T/HYYytce+3tWJbEcI2GDKFyU+F5Mj5VnnLiipBIVS/JTxkDVYaAYqwCmEL7BMvrkDIyFdbWNfPzYFtAUVn0mYgaSzyRDG+B3zN5zqS1D19FaL3nywryeYQtMEVSdJ7D5iax38DxLbQlGC/jOIxVwL3338/7//2/5Y7X3cm3vPW7+Q+/+LOEO9v8+Ve+wo/+3b/HB37v91jcd5A3veZ2HnjwQf7RT/xDHN9HnT8H9TqmVqeX+MShYd7poWxJUDAc8usf/CD33X8/33HHHXzTq16PV3XJkwzGY6yKL5NbAecyts2oMo+7s44Tj/iXv/Ir/MAP/Aif/fQnOLQwxxceeADPtgldl299+9s5ceLE144R/hraNzLFf3OaUuo8UDXG/NVErPce6+sFxZP+BK8ozZMkUoKxbBiPRLC+ZLGXKbTnCm73BsyNhszUZSTi+zMsAsyig7I5ziwQL0VLLYtLhIdL+6K91kBIUHz19bewf3EORUZEgJNGxMohMw5ZLh2/25VT0FoyHeWpKJUSEE2JbwkOKXahIekRxjZxbISgpcZY/R7GKNJGC0dlhPhYZGJcgItDgpWlJOMYU6sT47G9ZfB8ycyixOnPMQmJsQjzgN2eEMBa/hjbZBgDoapgkfDgQxZnVmz2LRuuuSqn6qcMhopT5zySBG66coz2LMLUoeYnpNpha0tTdWI6jYSJqiD4a8n8pZlmbV1zaDkh1xa5UWIbqiSwTYxNajQeIRn2NEcDRZ6kWP+zHNZ2PQ50Ir7yqE97zmZ+zjAZZlSbNr1eKYY/e4y12mwjohW4nmD6dnZgODTsX87Yd8DCY4xTZFUS3GLfUkBVMtjdNYwjeYA1ejStMWZhgRzNYDfD5DntpsFyLJQqF05Nhrh/PfaYYn5eutzqqsDZynPsdGZl1zLQkL5jcB3D8WsUNTfGpBlrvYDtbVmMl5Yk8O12Z/yHZw8TwX4aqQwM5DOdDtTcCW44QpWSJ1EknbXcGB45wtQFRCmM42JyA91tVK1GEtSJc5vK1gWUMqhCUzefhJi5ebQCNSzq2tUqZjwhaXREYrg4GQMkysMJLFQp0ZbnJKOEvFpDT0bYVi5OiEqRYpEVjHabVEqyJsXa7ZJmYNpz2OMeY6+NFYcox8Z2YUKVwVBTr8RYGpTJ8brraMdmUptnkrkooGaP5XIHA6LIELeXUUqyn2EIWis8z0w3KN7WKlQrmGoDd7DJymSO5af/XOzLW01Uu01ibM7025w9bzG3aNFoKfZ3Ilw7YxTa5JZNRYfEVoBBjCUG2zEX1hyGw5wrF/s4kz7W5UelVE9GpmySRJEkoq28vCxzTK+nGA0N1WgHq+bh1j0uXJSKzHAoU+QNC4XIcKnX5Tj8h/8gfaV01K3V5Jp7PQn42m3hVa6tyccaDcEAz89LJnk0EnLw6dMyVZaQCNsWl8CS33TwoOiyLy5KF7twQaTUjJHj/fIvS2D6gQ98kte/8mXk5y/wmQuXc9110Aw3cKKBKBBccw1RnGNlCWFj6f9l77/jZUvP+k70+668VuXaVTucfXLofLpbakkttXJAQiAhgwFj2cbG2GMzNtyLw8x1GPAAtsf43sEexgYDBmcGBjCIDALRAhRaodW5+/Q5ffLZeVeuldc7fzxrVZ2WsVuAbfC13s9nf/Y5u6pWrfCG532eX+Cf/jOTvX3FseMGq6u6hFmInfLR9RSriLg5qGGaBvW6bDyzTMbChQtisiPawEoMT1UoEJxEo+YzJvYKvp3h2jnzzGM4WkqoVXSURx+V5ajSvh4M5L5tbX2a8fiQe+89jm3ftYA1NBovdy6ez+X//f7S6KTbFfUMSyVC845hMi7orxQoQ5d8FxNFTq3EChcYTAsfUHhGRIFarrMAWYYajSoyyHK9rGzwHIcqi1AoxQxf4BZJga+EGKtKhymtdWnBbIKCv/0d38F4PEarFqePrfDZz32a93/ZlzEZDvFcn5lyeNe73sGJE8ewdAaOQzqNsG5dg/V1lGGQo8hyhRocYseS9f1HP/zD+K7LG1/9as6fO0fcXsUcDzCj2QI2oatSWJaR2x5h/zjBzmWeu3WDB97xDv7OX/7L/C/f/u3k4ylGp4XWCqO95Cf912xfDIr/22lKqb8EfC/wjVrrH/n9HOsV4ROWztCq3AiXM4xOM1kwq1nBcZawh35fttGVbvF4LLNzGMpWuyLTJcnL8MaLLfztzXUXUmylSKXMxuOxzPRxvASDVcbyZdOAZVmcWG3DbXq9g7hGlhX0GxGRcnnpslpk8CoOQr1e8QQtQkyqrEOFG4vKMp1SkiFsuyGZbZM0V8tQTcpbPkuPeIdUlJnTFCuaMQvaOGbEak/CZk8JwcKgYDY32Jt4rPQRFyuVoNDEyiGa5phmDIbm1fdmnDuRYDd8zNkYY5Jh1/rU64oXX4SXPItzp1K2th02j7o0nIhj3RxDaQqlcLQYdWilsMgplE/gFWJB7SSMQ5+gWVAo0cK0S8NQuTMvH2sa2D0UU4paTRMlFlkWc+pYziS2GI8V44OCnX2N7Yj0Wbe7xGdPJlVcpzmxGvL0ix4YBrWabBg2uslCjTrODRJlEzAXzHYBeaHICgOtXTod6UqW2UQrn1vbJvv7iq415a7eHhwUGK4DQUBSWGgvwFdzcHyCQOSoskzx5jdTZqo1JTyUNBU2fGUOd+ECXL2q+St/ekDgBwxnLpOZw8qKQmtZxIdDRRzL4r67KwGM68qa1unIOjccgusUtFuU5fUM0yoF7LRGDYdLzW7XlbKjacKVK6i77164CWTtgGuDgG4jIIgPcaI9rO4K4foJPGLMPaH+qyObxLHGdhVm6VynRiMhxBkWhc4pLJeiK5muFAtDR1gzGb8KcMIQQgFuZtogxcUjKc10ZQwUQKFN7MGuWCBrk1h55HVLFBWUgZXMyJ0aWssGI5gPqLdNVByjhwP06ipFFGM6BpOxQrUCyZbVJR833ZVx4loFl7cMQHPmNBS5ENry9ib+fI9iZ4erao08g9n5R7hyZYZnuKw2HVQSca4+p7cSUOiCzHCZpD4qk8DxpZeg261z113Lx3DzwOeZFzTveLtB3fUxzYDhtKDIoVeLCLXP/r5V6lsrOh2BYjWbMifMVBcysKeVK5vm6FFo1zNo9f4DTdY/9aekOvH88/DUU9KPtrYkCF5bk6m3KMQ6/bHHBBs8mcg4O3FCflfItevXBWbx3HNyLf2+XOMddyyxx+fOCfG/cgZ/4onKzbF01itS8p1dDENxaiMimVsUngOpIelqw8AuElLbw07nfMuft/jMswGWo3EdifF2djUvvADR/TYbG8L5qNVkTKysgGloCi3nsbNTmu2gOXJEESsflxnKtpnELrd2c1zfptFwaDYl8B0M5LdSsLWlF9rJhiFVqDvvLPjUp34d3z/F29/+mkrAhbW1pSjSbYaqHDkiY3U0knsymcg97/cVotWd47kKe8UolYQqcEdKgse8xOVbZPiG8EUsMgykYhIVDobKCUzkxCvjndkMFQSyplZkhUwCVjQEKpK5PE/hcHeZ/p7PSdY2MYYT0k6f55/4NCeOHuVd73wnP/Wzv8QLFy7wvne+k6cvXOMD7/1qXn2shup2mBkNwlgBDkYqADntN7Hnc3StJvfDEHdZej0wLR549auJ9/c5f/w41Gq4Rga2AeESdqnbbVkTwxCVFqg0RmnN4XDI//kP/yFf9q53gdbkQYOw8PHrvzuE5xfbf7ftXwPfDPyQUup7gQN4mZXvF0y0e8UeZ04n6Hqdar8UY1NoE2+2s9zVrqws6fYHBzJYK5BWq8Q43mYpvHhvni/TcJU4dyUEDEtLsErQssQo6zgWEk67vZyxbyPg6du+JxcRsYWyr5CZDG7tO1y+qhgOhYRSOrQuTuX2pgCbtBQFM6jIFCLLpdCGhadlgzBXQXmXhNmvS0UOYzqWcnS7jVrpYiphW1c3McXGIqXQCuU7HA9CpolDgcZ2MnJlcm3LIcsUeQadriYbF9y8BWudhJWVgMj0CEwhBj30ELTqJp6e0O95uGQYRbbw/8uwMedj0BDXOngqxStmrLfAMqQ3+Uoxo7Z0HSSmUAaWTku5LSFfGUmCZWmCOkxnUFgOa2ua/Stz2o0Cu7PG9o6BFbiMDkCVXMzZTFjfaSqLzeqqZjZTXNv12TymbxPXV2LUURTEhYWhc7b3DDT1/2AfJcYc0h2OHlVoxHQjimBb1ak1FBuDp3GyGM6exZoeoGyfsLcJaDY2JAs1HMpnlNK0/IQwgqxwiCLJnFXyVnfdpfmqr4T69gFqb0JzZZ04dZhOFZ02DEeidxqGIvnX7Sr6/WU5+MIFKYV3OlJB7QVzTJ0ymQXUG0IkUtU4yXN5YxjKTZN67zJ9lUlPP7MyIru5jZGnFO22FHKisk5cmkuowSEeIiumbAej0iocjXAqsxw/IPXrqBJGYxweoPL05QoxFV/AsgSbqDURPjaxENWwcFTKvLmO0gWmpfGzKXowxDjYIz5xDh00sFSBpyMUHkaWwIGQdfJZyP4koLbmYxgGzVrGfG7g1xS7u0vY02gE3SDlzrMmSW6RJgXTvYi/9z0+D75Kcc+dHY5t5KzVQrwXP0fUey2nNhOSyRh76KI7LeaqxnQMe4cyLVYKkXkuwSLI82q3K/kyTb+bs9GKMAxNpFw6DoSJwTxzGc8MsmwJubnwfMHhbs4DrzFZWVG06xl2MqcYDOmurxBnFq2ehatj9N6E5281cbs1Tp+W7/Y8ydgWhWR5P/QhOTffl9eSRAJhmZI1586JY9ramgTyH/uYYIdPnBC3yY99TPpwiZZhc5PF+T7xhBzv/e+XGOz0aSHnbW3Bl7xT8xVvOWR94zRXXnyR46fPsbFe4BgRChOuHaBnM5EHXFnBaRqkgwnbepV+P6fA4kh3jucqjh2xeP5FG8uScV4Fr5ubiIPf4CaZ02AwdTi9GjPsNJmH5TyvRa5QKaivaIp9OJgJ/ErwwkKkTFMJ+g/3C6K2weqqbApGo20ef/xTvPa1X4LretV+cxFEF8WiALNoVaK2yr+cPFlaQyO8Do0hChKGLYo5aJKS/GyRotEoNAkOjijXkyIbRJsce7ItEDzXli9zHJJOH6dRoE1TAuPRSCq2rouaz0mCDqrIMVRB7NRRzoT5lUs0StzH4cShmA1Zy7f53GOfpNVu821//x/yF/7Y13D+zClu7tziz33wj9JfO0EWTzByRIvekGxYocEj4UbWZ5097DxnbjTxVUjaXkENdpmvHOWtb34L//5nP4Tu9eDwcFE5WqzJeU42TcgNGy/PMVoNbl27zjf/lW/mH333P+AvvuMdi8qfMxviZCF0vnBt4i+2/67bDwPnkIgqKH9ub/9xSMTntVd2tPN9IuWXGg85rk5Rtl5mamcz+cnLbGy3K/qnlejl79QqdYnbW0Xtr2ahCoiZpmDbC0ethb6YbUu2rAyGlWm+7KpzFKNYleSL6hXBp6UpHA5FquzOO5el+yyT2KMyCVvG2ZoEF4sEs2TeV821UiydMkwDPEv4wLHyidF4KqYgR6UaWwRw0YMBY7ND7Ci8ukKpQvR4gRyDNLN45nnFkSM+7UZBnmQYdkGsXMEHZhmdVoGlMsLCo2cPCffnPPH8EU6fUYtFcjCA69dNTrcVbidhMPeIQpu8MDjem+PbETmQmA6+DimAWWIRxQaNIAXHo+XMUWjywmCcBBROgAIOJi55knGkGxFOMp675HH0KPT7GifPsbdvoOtdktYqT2xBN1ULZ61GQ7Kl167JIry6Kgv7rVuawM05cszAVTG2zrlx6NPvmxiG5vDQw3UKLNdmf0+j0QtUTRSphTqEaUpZNookOK6yZP0+PP20wS9+tM7XvuUE3Z4pm7koJspdUCbxNMHKNVoL/OTCBfDtjMKwCFyN5Yo81OnTarGBskywkjl5bxVlW2SFEFlcF+pWRO6bzBObtTXJdN0exAwGcg+UktfS1EBTY7MxpuHExFkN5YJVrwtG+PBwkVrXO3vMu8fwh2PMixeXrhI7O2DbJGvHUJ5ojCfaIcsSISyCQCgaDZRhYI8O0NMpKo2WrjTlQqYAx/devtmthJaHw4XzjZ7PUWGIqQzo93D3rhPXuhS1RrmZTtCWhU0ick1hSNReg9YanorItEmCi1Ka4ytzKBowycgzTXLuXlpK8exFk40NqNVs0lCxfVkvyGNxLEHbM7ccTq+MOJg3yLXJrz0W8I53ah55VciKN8W5ehF+9XH0618PloFqNbDbbSaxzWDXoLdqkBby7EAuM89lqpvNZI7o9RbiEqx0CkzbZJQFvPACdDsiI+a6MBw7PPusSJRduCBB1Nqa4p72Dq7TwMeCeI6aTck6HZ6+5FFg8NqjBsZwxPzSTXaSVZgK5KGaFqUSI7f//HnZoHU6y/zD+98vlQvLhJOn4Jd+SQLfV79a7lVFwnv+eXn/PfeIJfbzz8NHPiLj5uGHZV6EZSZ5d1ce/9YWbN0seP1dQ/pHjvLJJx/j1NFNdOijPRtGQ1SzSRZnmHfdIxhfpSjcJt39IZZOuMFRtg59TvRnNOqKs+csXnxRcXAgWfR2SxPcegn7lz+EefUytUZA58QJtGGy8tVfS6ikrJ5lyw3zcxdtTrV2ubbrkUR11tdNPE+SF6ORXPtGLyWNFFevOty4sc8nP/k07373+2k0yk1peY9braWc22Ag8tMVeqGS5qy45dNpFXR7WGYByiBCJgeTiKQ0k4rxShKei0MqTq6AUZLkFmtXq4VRGhbpIEAFAVlhYk5DzJqHNk10swU7uyjHQacZbjaHg33C7hGMNGTr6hW+9Xu+h9W1TdZX2tx73738yL/9txzb2OAvfuVXcv+997J26h7OeLA9GfOeB95NoeGlQ4+T65q8kOF+tDdbaIjPCWh2YJ73aKVD3OkNopV1AjNB9XvU5wOYTmlYFoXjYqyuogcDqfYoA2qy07ADi9SsEyYmF557nl/6qZ/gZ3/035I4bXQyQVnGcoAFnx/X/NdrP/uzP/sH9t1fbL+n9t7y948AnwCS/8R7/5PtFYNiZZkLzcRCK6lJVlvo2WzxvsIwoN5AJQk6z39nZqhhLBy2Flo/Vat8N7tdGZGlhZQuGRK60UD7oqAflTtvhSZzfSSfCrlWpaOREC8cRwruFdelOqVaTSay06eXBnqmSZmtWEoH9XoAqpR6S8oyWEFKvjAhSQuHW3sOea4JAkNUK0p9qgQL/LoIpesZ+D6F4+IGHmaaojFxSYgzxWjukKaw3prz6js1SW5RmC5RYjNxHUwDWo0Cv9SnTHcHpInH5bjP9u4KzeZS5a6Sn93cVOB20Ta0Pbg5Exb92Mzx/QnmdIpj2kTtNQrHA6sgHhWM5gG9vkFmiQatBqJYflfEtE7HYq7qZIbixLEUoygoCoeDkc0+p0gOC8JpgWUZbG0tCTLttmSmqgC52SwRNQkc7UVYhkOGiy5iMAzSFDwzZq1dEGkbDDAtRaMmahc7u4oTJ5YGLFkm33HqFDi24D5tS9HqO7zpTXD8uMJYWwVXYDc6L7CymMJySHC5ekPzsY/B616nWVmBDXOf6dTgpeHqbVkjjWnKBmQ6U+hgjpcNwLGx2iskroXtQGZ41FuaoNAEes5LU5/Z3GQ4lOu2LAk21takbH6wm7NaC3G2rrLThk5ApgABAABJREFUOEurneBq2WgtQNjDoZQt203qj/6CpJmbzSWUqNxQOrZBHs4hneNpXUJLNEYWSQZqNoNmE7PCcti2dP7d3SWMaWVFFqYXXpBxORgsYVFl5lqPRuhOl+LIpmDUleLAqNEsUurExNonVAI3SnAJDZdBrFH7itVVRWQ5zKYQhwVHuyHqcBfV6aC7Kxh5jmMqDAX3nZ6THIww8agFHUZDOHcs5OI1j0bDoMhyevWEWnLIRl1x7abJQ2dTNo6aNM0YwzLR6xtsrWman3wGjpyDZEphOGTuCrkWiNDOjmZvTwwnKnPAyWSpwnB70arp5yidcDjyuO8+MVEQDLyMkzyHa9d0uRFUHD1qcHV+DLULp+vw3JU6a2urXHlGNGJtW2FaCrpd/FMxp+Mct27y0kvyaO+5u+CNr035mV90+NCHFBsb4qB2eKgZjRSPPqq4cQNe9YDmH/+DOV/+1QFXriquXJFzunFDsMOPPALvfa8EzIMB/ORPSv6iOueDA7h0SfOqV1V6xorv//5lFxwMLL7p7x7jJ//WZzFyCe5GI7C1TdO2odHg5qCBMbOpBZrRoMA0FYVTwy3mnCi2mK0cJTLqDA80fmDQ72tcYrrGkNrhNuaP/SjGv/oXqMEAnabwDd/A6AN/it39Jhq1cJ3b2YF+D86fmTNNV9i059TbEYXjUSDueCLLBoO5h+GCYaRcvPgx3vCGr1iIJE0mpb2zFPMq2Cu9nsxPjiM/87n89HrL4meawsGBot83SplHaSIPp6jKgXNdSkuqoIRQCKQuxEeh8YikDlktVKWwcrB/HV1aFKbaRGU5V/PjrBkFkaFp6hhHg0PMv/i//y8+8Zu/wc3Ll3n8qad46epVPv5TP8VP/cAPMN/epl3q/j/y0P0kYc7GtReg3cZIEk41QnSR48Qhmx1NplxsEpTOsPOYwChITIeUBnYY4YUjtO+iCg2ODbrgs48/Tu/U3dQczR2NukC92h0u79U44e1gAAc3L/K9P/yvOTzY5//4R/8EW4VATJwZeHtbaMtC1Wov8zr4r91Onjz5B/bdX2y/p7YFrGitv/H3e6BXBuxUcCA05nCwzPJWOOIK57u7R2TWMTKNW80a1XvFA7nyaV0e23FkQa5SM9X7K9WKijzU74vjjhaf9hyTCGGGGyVVqiggTEzZpRqKg4MD/s7f+Rv8yi9/lLqVsTtwyJWNLcpQrK8v1SbyXBYFrWXRq9yrqovPMDCo5MyqSU5eM03F+jpkaU4R56CdBZNYo0rLW9GpLDpdckxMpXDcDM2MOTW0pXAcTTcIGc9sDiYOcaw4fhyCpoGjRFs4xCPGJdcGY6/GMJIFTCmIIhGK9zwhp3meplEvJ/JZQcuek40N9LxglsWk/QKz3ye2GhjzGfZkwNxbQdsuTU8WeMuSDEqVQJRsppRR7SyksGwcxwYsGkHO7r7MY44DugAjTfnE4w4nToqyRJ4LSejGDXnPcCg6vKdOwdlzitwTAqHPDGVAt1Pw0mWDWuCwvlbgGDkpmtVVUEVBNs84ddJlNBYM8Llz4Ds5vop4+mLAiSMJq+05JCmx08cwFXffkWNsb8FM6OaGUhzOXDItlYNuR/OONyYcWS+YZQ7XRmskSU6/r7njaMje0CbRNu02i3uU1Lpkjz2G9ZnHMFZ6uH/8z0IZCOaFmBeMizrn7pDFdzoVS+FSLGJBkjp5GpquJvLPsWZkZKZFlJoEu9dgpbuk+J88KeNrY0PGTLli6+lUsrdZhhUNsSyL3LKJ8PCH2yhdLMeVbS81uLSmZIPJ62UlZpGKv/fe5cXOZktSq2Ggz5wjdXwy7RCokEj7HOmlkGfkmUkK2BZUigC+p/CPQKBmpa62S6uucWsisaVWVwljRY5PlBmMDqUCUbfn5Gs9plNFONac3pjz5AWfU6cUuVZ4jqbj5hhZm8ZwQO98C9KETBtkQZtsf5tiPKFuJzx3/x/jDp0xrm2QJQXhTNPpKLa3NStdTbOpKAq9cLjOMsWRIxJYVnANx4FEOwuFhiqzKCV4zfXrskE9ckR+Hz2qeeIJxdqaXgRdgS9awJahueMOTcuYwVwmIHV0k+NAnmmeflrc0U5vhNQ8Mel43/ukG2xaO4xuTRl1TpHmJr/6qzCfG3z/v6rzwgUZcxcvyuMbDGQjurcn+5wq6/mZz2i+53vkUf+TfyLEt5VWxuvunHFtx2G07vNTPyWwrCtX5Pq/7utc5vM3sPczYy5zinbX4Pq2xUodNhoFaSzZat9JqfUKZrknaivKJbHX2D8QaMlHPqJ4wxs0Z44l2MN98h/45+x2T3PkI78Ou7vobpd08wTDN30lB6sPMBwYdDqS/a0M1J58SrHarwk5VLUYDwrafkxhmxwOZIzZ9nJj8+STv8jGxpfhOMtunqZLF78KRlGR7GxblrA0XeZzdndZGHe0WjIcFAUeUamT77KUpJCMdeWEWWCglVg1G+QibYiSYB9JqsS4BFpUldTqKgWKuQpwJ4cUts9p5wYxfYazAJXEFPVVfuNnfpKGzrhy7Rq9RoPv/PN/nje95z14jgOXLy80pXEc9GSO1Wyg+z2p7NbrmOMB2DZxAnq0j7vSANfD0jn2YA8jiaF/jLSAtLlGsHsVFTsLwf9p/wTvePj1/LW//s08cP/9fMV73sMbH34zhunQ6WR85z/8P/nkpx+j3mjwNe/7Cr78Pd8MWpNphVaKLGiQBxZGlqD+AANigPPnz/+Bfv8X2++6/W3g3yqlPqi1/ne/nwO9YlA8Ux5uPMPKIsHwtttLebWKADcckjS6YBi4s8OFUgXt9vJAFXN+OpWV5sgR+Xwcyza+KGQWKjEL1X47xSAu5b/M6YhpaDFTdbpdC0NpMg2ZdtHaYJZAYGkMQ/P445/h+77vx5lMXRLXoVkKdVQx+e1Sy1LaXCoLLJPcGqEKCRHNYU5aioJJFkLIVIahMEyDWaLRtpBIDCWi8oJCFtKDkaQkqSIN2mjloJRCIYoTvidZALtms1naolbl0hSHTIu+Z6YM5iFEUcG5tQmzSQ1Ma+G8NxpJoL+xmrO6konUleVQYHJXbx81HpL2jxDXj4ASYFxRa1EEdYp5ARom46VCQrVZqEqGh4dCHCuKYGEZO58rPM/CMJfavnFssLLicdfd8pn9ffn82bMslAI8T3PiuNxlw9AYeUZu2kwLm2yeEqWafh9sI8dXMRkWFimZttg9sLh1y+K++6Qyt7cvPcZ1TR6/UOPs0YiWLc4aqtvBMwrIU9jeEVy6aVLkGsNUdNs5YWYyn2saTcXGmsVwKDbUtZrixGrMY4+Z1H0X2zMY70vgU6F8jNEhSmvUCy+g7jaoX3mO8Pg5Eq9Flmn292XTU6uVC6lVcGJdFBamDRcjS8hzjWObZSBwhNisY6kUnylqcAi7O8uN6Gi01KcCKkyQNkyijVN4w12U74JhYNo2rmGCuypMqgqXvLoqD+LixWUa3zSlbn7jxlJV5plnlt7mFXtpNJLxaVlMEgdDgxvtMmmtU4QJE20RZ74QKMeaO09GFJaLqXNMlZMaHnOrjqGEkJelmrFRxzA0gYrwP/ubJK0e3PUA3qpZzgcGWom+a9cZwcEh3fYJcffLYB5Z+J5HYIPSOfZTj6NtG5wAY/tTYtxw4gzuHcc5FoCbTkgTje8bOK5ia9fj8BAevCPk8i2XNDdIHSFV7u1JP1xbU1gWC3dDkH6cpjKt+b4Eze2mzAmBm3PH0YirTsDunuLIEU0yz6g5mii0OHsyo96w6bpzWuMbQqbcz8gffDWpXaNIUszJCFW0OH0iYzCxaXgZ3/7tJYzCCPnlX+swmvqcOwftjua55xRPP635F/9CArC/9bfgyScVH/mIBHqvf70EtnfeCceOSTb4E79d8PwzGss1+c7vFHWKL32Pyfp4mxMnDQ5HJ2m1bA4PFS+9BBdeyHlk9msc3vUIad4mGD6L2TvP0WOK6TQg0XNObSaMUxOdaRwrQ5kRoeExGJo4vsXGekaWGrz1reV8kVvUzJzn6uf54A9/KT961zOcc2vcvPfdpA++huGJh9na1ezt/TrNpsF4rMhzTa2m2N0Vmcc77liqEA6HSyOcVksviHUQcf/9b+Cuu6zFJj9JJLCtzDuUWiYCQILmLJOfIFjw3xbueKNRJYpkEhselkpwiEmxX5ZEqdTIWq1lUqXAINEOrkpQWi+0in0VomdCTFfr65izKX4ByjYxrRzd72NgstLJqM+m/OTP/zZt7fOl73gdX/vWt4r6hO9LB62kRrJMduJK4Y4PwTeW63IUlcDrQyzHZ4se6wdbxOun8Kf7qGYD9mPcwy3yTg/rcAfVacnkHop8XG2yy2ve9BZ+7uFH8Gsu7/+6r+NVr3s78eGMF154HCsI+IF/9oP0swR/dZU4AVNHmLvbzFePYxopkfKwHBfvv4AZx3/z7Xdj8/zfX/sgQq7710qp7wcG/Jci2gVEFJYBRSm+pZSA/fNcIp1yUHlxLItqu73M/CaJDDbTlBm6WkkqsF611V5fl/clySIYLoAUgUJIUJpiGQX9fIeVXkFq+OQoktRg78Cg19OLaosgLg54+OFjJIleLFiGoUuYknqZCV+lNTsayekL6VsIETZpiQAzURTEuNhkOCSMpyajyKXb1fhmit9KMZVIiv0O4BFIYrTXZjI1iGNotcGyFIOJjWHYKNPgxRcVm5tyq6rJutovNP0U09IEvkXXmWNOJpxbSZgaDRo1zU3X49JLElCbhslKV5Mqg8CMmIcmY2OT3qlVcsPG1RGGLsgLg0nmUSSapHSeGk1Uic9lIZs2GEhMdP68TOyTyXJREOydxrZl/h2NhDAThhJL7e3JQnXkiGCJJ5OySOAUnFgNef6yxzQ06dY1690ZMTaY0DASTEvj2Zq0VEvWFBhZQuDbnNlMcPKCwdwBLJIERkPNXacSutaELM7QQR0dxRg726LBW5I4izzn4k6D4X4qMBNbYxYpYWwxn5vkOTiOYn2lIADe/doDIu2i/Qat0zbpPMVuO9y8qTCjOSvDQ2En1euo0RAri4mLgvHYEBGVRorjFugC/CLFGRwSddaxLY1lg5fM0bsHKF1Q37smHbbCWNxxx9J9wDDQBwdCYmk2pXOUsCQjTXF3b0CnDYZalEDMStllY0OOWxQydiux1qJg8fCuXl06kpRWrQyHC3cGvboqJgCYmK0aWeoznts0GnVM4KVb1svw48dPKHZGQcmRNUujy5yVVo5lStUnzhXTibgehsrHOX6GYhZhD3ZIVzYARahLfKFSTMwue6pLd7WSaNYEToZHJEodRYG+6y6YTLDyHNpnwVDEfgcjivBH29z0jnJybY6pcvbGNo4j/XN3GnDseE5gxxS5ZpqJYcNq+V0V5Fopuf2VGI7va7a2pGrUrSWs9RS54XBr4HFidcbeXk1IudpE65z5XNGsSfR1+RY80DNReY5OU+aZw1NPaYpY8/DJkPODJ3kqf4RGC7b3fGZzkQ3s9QJe+4jGKiCJEq7terz5zZr3vjPmf/zAHt/7r9t827fVARGD+MxnBDLxvd8Lf+kvFZzuDKlvtvjgl0/g2Wf56Vuv45/+U4tv/3ZNf34FohDz+ac4qXKy9E6KQuYkW2WsWQd4xy1+6/EpN603MXnKKmMvRaFMDDTTucFu6NHt2DiuIs2q4FLjOzE1HVFfVWQHA26MTrI12eDnp28jVR7/5t7/jXiesdmYoKJruM9/CttuEgRvZ3PT4NQpWTK6XcnRVJvx8+dFiaTK4FNCLQ4OZC9Y6Y8/+6zEgL4vP1ov9Zsr89TxWIZQlWGuYHcVdEOURGQ+G410Cd0yaHjL6p5GrJo1imYd9g8toggansD/Cm1g5xFYCj2b4RgGkd9hRoBbM7BLIq2u14m1i23ITBjj4RDRjA559sqLXHzuE/yZb/qrFLvXMWqeaBw3GjLOt7aWmpC7u0v7woMDuYH7+8v5wDQxXYujxQHMM4xLz6CLHKPfk7ktjoX869iUJUm5Ob6PMZ2i5xPMeg8O9/gH3/It/ON/9He5eZBw8+ZFvvu7v4e19c3FfbEcgwgH1d7AG+ygVzpYJOBXJixfbF9sX3B7323/rpc/t7f/fES7GXVMM8X1FYaGhUev5y0k13SrhapM3qsFeH9fFtp2W/5fKZ9X5LjKL7li0ne7C/kHyc8at5WgFBk2KqgRu01sQ9zaCm1i2qZAIchFB5iCf/ljP8m3f/vfoNNZ5Uu/9B0SS6iSCKhN0dLNZWJrNJanJRhiXVW7Fhq2LhGgiAgWbOFU21g16NUlAEy0DYaNRlMjfJl+b/UFSbOLoWHVmqFrmr1JQFqY2LaJacsc1evJJJymcnsrhTvRX7UJVMHOnoFSDTzbp9FIWHUyPvuczzyWrIjvK5JEcWvX5uAA6vUax49D25ewMtea/WlQBt7C0D44EPxdvQ49d+kS3GrJYxX5omWgXP0cHkqiwXXkfuWZotuVLhJFstCcOSOLkOfJ/6WMqGk0DK7uBjTacGwzYx6LffT+0KRbS9AKdkcena7CMBS2KhcEy0GliqBj45gRtSQhskxWV3ICFaEMSKkzxCUd5Ky3Y7RhiGpJs4U6PMAoCtpuiNWzGY2gv16w4sUUuzdBa8L1k6AMNIoEh+vREZJYYUxyTm0mWDUbR82pnXHILqYio/GqV0mHiiJMz6ZmxVh9WTQMDCwSLFXgN0BnDQqtsEgwdMHMahCslUoqw6GsyIPBEshpWXB4KOTSVgs9m6EaDRY2gSUb3cxT2N+TcTWfL5VhVlYqLIDs/jod+Y5WS75TqSVNH5YA08NDGZfr61LqTHLy7hqp4YoGcSiyeRUm83bDSdNcZtLq9WqDp+m2CwIjFpk3CorCYKUWo5QNGpLNMwyG0q86Cbh2gZGEFI4ExpYlwef+fqkz29J0eiZzamKwsX+ICkPUygp50CCyxFxBoyjGE/xiQGO6RWEWpL0+jSAHI8G0DGaRhW9l5EnOYGyytZvywB0hc6PByorgyKNIbsuVK9Wt12USTnDo4dxl69kBeaA4ccbi08/W6fUVK8EMbTnc3JFKhGnahDHc1b8Fpslnhqd5Vf86ZhZTC0yevmyxtXuEB04G6APN3sDm9BnFd383vO1t8t23binC0KXZKOiuQDjX3N3b46MXbP7Mmy7yySfv59oNk7090d1+97vhJ34Cfvzf5nzv/3SIPdqlcGvMuxucDgxOnND8m3+jecvrNzmpE9btI7y6fY2srvjJ546x0YK/+LdyCh7h1kv7BIHBbObS7cq+bXUVEu0ymZm4riLLwUgTbtyyqPkFR3oZlqUw0WSZZrCbcm1+nDuOzfmlz9W5eNBl85hmPtecPGnS7bbZ379AENzLsWPr7O+LRKLjCFSsEig6cmQ5d+7cyjiymjFNPfZLSNfKCgvIRSW5Vu0HlVpu1O0y1rOsJdG6et98vtxTVo6BlQqNUjlggFZYKkOVy0ioglKDOEID3a4sufbuFmZHxp4aHqK7XeZBF2//Fo7l4FiW6IqXuB2twTOWznW+lpLnpf09nnnhAt/0jd9IlznceRb15JPLNRjk4vf2lj7Xw+FSiebFF0t7UV/mjCBYXuhggKELqNdESen6dQhqEEXoktxOFC2Z3VqjVlbw0gk0G9xz9hx/6uRd1OsujVYfU2Xkpb5rJe4ZZFMibRK2+vg6A899uTD0F9sX2xfWvuE/14Fe0bxjPC6wtFglRLrUIR0dLvC/utFYlFpVrbZUQ6+0fRznP3S6W11dOmTw8hA+QxHhi/h9KXpefhCj9IIvCkUYKqKoDMhc6HQ0tpGRY/DjP/6jvP99X4Ef1PEJScqSlEFBqm3299WiFFYFxSC2x2EoDOiaUeo+AnZJ5AsXrOIUo8i5uR8Q1AVrl6aKgwNZFH17CbK4HVWWoRjHNZ55VtPtSvDoOIIBXlkRzeM4rrTaFUePyvxQBRZpKsL6FTSsVKjjxrUCx6uy4Yo0VTz5pCwUm5tLS1jXFVH5KIJCGzQaslB63jIzUpUgB4PlPsayZL48dky+++BgibEbDstMii5428MRexMXwzIpCvl85RhVWTufOiX/zjLN0d4cy7ZIlMPeHqzUY5yahZFnpLnGdCw8HTNKfGxTY5gFhtKYJQIvwoFCEQxvYTTrqJIktm1t4tZMTBM+8nGPtz40o1HXZFgkhoenQ6y9LbJ6C42BFU3QhcLotpgbAQUmaaaYTsVkxCoSksMJB7sF3Z5JP75GePJuXCdHF4p0ElH7zV9B7e0uBGGLoEZ85m6UqUoJJhuTHD+dUxwccDk7xv7ApFGHkxsJ/sE1tOmgAhdVZnJVle7q9WTlrdXIo4TYCvDNFDUeL3dQ/b6MqxdekIdU6UpVraLXV/pgw6FEAFWFpyLrVZmkalx63ssUKbTrkfpSY05w2d8XrGTl+Hd7K9UIFxh9x4HAicvRDJ4SgnCaKUahjedpwrmm1dAkmcF4boGCTi3DnQ+I7DqFLwmAPFcLZThHJdSsmEIrrh0EHGlHaEcgTtXo8/WMkBruxWcw6j5mty3ueZZLZNfwdEgYKi7e8Dl1LONw6pAkCt+MWWeb68UmUWoxGsltqrwTikKzUdwgrvewjYzASSnqbSFHFQWz5hq7I78UBtEMBgrfFxhGxW9oX/o0H3q0RaMBb/3yOgdpg04b6jXNzoHF7tDmzGbEky/WuO+8IFp+5EcUX/mVgnTZ2FjOZUEAySSi3YFrWy4Hh/Ad36F4/jmBBoFsUJSC15+f8ue+ZogxHXP8jM1Pfu4Mn/iY5p//L1cwB/v8xNN30j/d5KGHIHjyE/Tim7Czg+r1yF/7On7it56ke+R9hKG50PPt94WYd8cdy0LHhQuaVgtObYS4vvArXFISrBJiYPLYpzTf/M3wjd+oeEB/ju4KnHp1l72kRebU+OjHH+W1r30n0Synt6rY2TVIU8XJkzKfXLsm3fjuu4FCTHpM22S/NNDxfcFlO470ncr/6eBAPt9u31aRKyuO7bbcp0qjfH9fnjks5TuroWFQYKQxqeVhKs1okLHSynGMbJEZCXEpKLWni5yankpgWY61GQEqy/DCIYnXwB9soXo9tFLkmISlCpSpC5Iwo+ak/MTP/CL33P9HuWNzihsKjkNVWd9qp1pp+ddqcoFZJoOzglRU4tXVBbkuC5ehnZ2FFIl2PWaqTu3mBTANVKcjQfXKitzMyqLw1KmFV0HW7GBkKcbBnqyh7Q1sz8JQmbiHFhl6/wD6PZRhouq1l89bf4DtD515x4MPfuhnv/Vbv6D3W9/wDf9dmXf852xfAHxiTqYV5v4e9SxdQiNWV2E+JzNdUr+Dt38DNZst6/0VVCKOl7tQkJmm3AlWwbAEjMLHzTFxSEuzDEWxOBO9WOQMQ6AQ9frSGlhyxRZRFHH06DFWAoUmfBkEozpWVfIUwoS4XlmWorei6QQhWrmEuCgKNBYJxW3YsAIbTaI8eqsadIHWJpYF6+u6PG+4HUBRIZNTbNrujHMnHQrDpF4Tgfd+XzJtRV5Qc3NmsY3rLhXtPK90TTKW4vX1kkRXq8G5Ow1sI8UjYk6N4UhIetOpJDCrCtd0CrZt0GzJsa9ckcfpeTJXrq4KEa4ijti26HlW4vXXr8sce/LkUj3P9ymlkQwu3AgEW1fAZKJ56SVZRFy32gAIXnE81mxswLXdAM+jJOEpEuVhak0Wp+xPHDQmKys2k0FElsOpzdscn4CAkChVS2CgZVFoTS/bYjZpYKz2eMtbwLl6g/E12O/dSX9VcRDVQG3Q93JMpWEULy7EsnNyXWBFc37rt2zuPO/Q6Wr8tsc9qzGqUYdLrsDhCw8jnhNMd+Geu2F3RU6s1cLodPBUQqg9XFIKlDhbKYUqCsaHGUf8McecAzgAbduk7VVmk5x6tIfjuuiyPq8qvcA4xux28DBAuUvJop2dZYVmbW1pqnN7iyJ50EGwzP5WWOLK07eCbJTGIHlJqNFZileInbdSqix/irpKp2OSpmqxplbro2A5pQ9VTtOgGU5t7CKh3ZDKTYSLYyU4Zs7+SMhYTX9G3clxHNFzNfICo8hxHIi0zAsCzxGlCKVsVu0p66sFq/2A3YFPrSba4q6VkWGjd3bZZ5Ne0KHuCtRMT6cYXRc0vHCtRhbnRIni1pbC8QvApNMqiOyjtHODa9ekq9Xrmm63TMID6A1uvlSQFC5OOoVA41obbB5TzGayGRocwMc+ZrC2JnbkW1uiOXzxIpxavQud5dz9Ko94/yo/+4ketZZFu604cwbqTdgZBxRaMMOWBV/91ZqXXhJjG8uS402n8OCD0Dxi07QjTrVuEY8iJv/DKt/yN+qLKXk8FuIfnkf/bJu9qzbbmcPXfU3BvfeacPIEnVbK1w8/wtXZJrWDPjur96LqJ1npPIsejfjQj9/gw0+3+M7/9w32Vo8xn+Sle5ri7Fl5/mEo55XFBWmkub7nCyKnHZPaijhR1OsJk7mFmcPXfY3i7a+acu7JT9P48Z+CfxnSsSyK93wpV5pt7jk+xXRMdKExDJ+bt+DaNcXGhsR3FVTrztpN7GZAHINdswmtJmnGAv4BGs+T53D8eKU2oZlNwQ9EBSSOZbhUSUuBU738bzLvlUZzU4XjeCTTgk5H02mkTLOAhhvhkJFLOItFQo6JNkzmNDDI8EsF/DpzMsskaXTwiRe2eTqMKJod0TlWYCUhL75wjY89/nG+6v1fgusnZLaPncxlhVxdlWyuTPiwtSUa+fO5XECFGax+V5WoahNe8Q2UkvlkNFrihpst6K2I5XySLAW8KzOu23lE9TqWKmB4KPqY/T4uETEtCixqeo6azci6PSKjgWOD+4ckIP5i+2+rKaW+7RXeorXW3/kFHeuVMsV6PF5q1sQxdDro/X1ZQEt6rkah0kRsn2u1JT6pYiXYpRB5hS8u7eM0EjDmVNqOlTHG4jpwiAEtGb1yakm0U0IrqvMsf5Px2U/8Bnfc+1oajTrLQLrMcull6bPa/ee5FjKao1htx2AZFJhUFrVWCZfIylKvRUaqbQyd4aiMVJvsDR38wKTmxvjqPwxINEimUGekOJJ9Vjmxdtk/UAskilIaS6dEmYXGJMuqQHa5oTcMTa8HyTwlTCw8Xy2yIPEoItE2jm9SFJKBrmDbFTEkiirJWX1bFk+srre2ZA48ckQ+E0VL/FwYyiOs4qbqcYIE1pVgiG3Ljb5+HXo9zcGhKmMxxdZWhdkWnHcUikX25lGDsBTkb9cSak7K9f1ASoVKc3ZliOHaaODJCwFnzpZdBclmGHGEN97FpCBScjMtI8dwbCLtMp0rphPBsHXbBZlysCwJ1oBFwMmNG2jbRjfbqOkYZVnEXovCcXBG+xjhHFb7sL2NbrXJMckHE7JWl9rzn0Fdvy43oNsVcVMkk7iT9zF8m5VWRqhFE9nLZlhGjsoyioNDsv4GtgXRcM4086h7uaiXjHdRrTLlVq3Gzz4rEWcUyUJXMTIr6EM1KCr756pMUK3g1YpfsYdsW35u3JAsz3QqcmuWRd5bgzLbnWqbHBMw0ErGJMiGbjZTFMXSCSzL5CsFmlNl1DTdWkzDmGNPD1Bao3t9QrOGQU4812yPPGpmxJHVnMSwyXCBgiAaYpiKi1t1cGxcV2GqDA4HPL/f5UR7Qj/fZrp2hnZHvmv/QDEcmxw7CoODgqP5FZyGjy4KdJqRNle4elCj0AYvvSQb7FZLxoHjyK1Ik4Kak7A/cvADgU8Y5ISh9Kn77gjZGgQ0W3KP9GTKbG/O4zf7vP4Nks1UFKw2Y24dumxtG3zmM/D00yKn9v73Q6MhrppZBmeOhvh1i/Hc4vnn1cLYZTyWeWqzM+fyTsBv/bbCdRVhKF1jc1Me6cYGBGbEUeM6Zl7wP/zNLu98c8q3/dMNXryoFuP1L/8lzQfePuLh+6akXovZMCHFZDBzuXHL5J1vmGM//TnpY5aF3jjCTf809UDT+tSH+Vcfv87peZ/Xf+IfiRTGn/yTHKg+V7ddLl4UPfj77pN55splzWvOx4wil8OBQZoWnD1dUAsKgmzMzc/u0Ur26F/+FNmLL2Hduo4aj4RgmefwznfyYdvmQdelf/w4+fkHmKycZG/klLANyQaPRkJqPXOm4Ix5GXc6IGn1SVaPCgyuCNkeOESJiWHI5iLPpY/W/YQi1UwTh/FYLWD2lSz37SRspSRFMp7IHNRoQBLnuGaGY4GpEwplEisHnwgTTYK10Lr3SIlLtWIAW8e4OhF1GEPcOk0tdlMa0IMBqdsg8xu4RJi3bvDTv/AL3P/II5y+886XGVcBsknOMnLDxtjdoXA9rL1tmcRrtaWl5upqaRtoLkuS1TxRsQ6jSOaDClIxmcgCUW2k771X3rO1BadPo4sCPE+qXM3mcrO9tYU+c1am7DJmMBTi1Kc1ut6QhMMfovbFTPF/O00pVfAfxw0rJCj+gtibryzJBhKNzefQ7QouE2AuA0wpJYO5Yl0FgSzOUbQEE5Z4x0XwkUrGOUeVWo7GQuHAUILPssnIMKjMfS1yQmyKwiBJZJe/tycLgWC6IEk1e+OC+7wWuV5WgKqWZS/XG221BAfrGxGJctClRE6NOTkGSUmQ8EiApLTusJjPFZOZy8qKh2lqWm0ATa5stE4ku6UybArRrtBgkXMwsYgS2OzGhCrAUTFrK+K4p1CYOuVgZBFGxgLjLE5VgiSbTDRPPgmPvC5jpZkSFwWT0F3gf6PMpJKDcxy9gFmIZJuq5kpOngSTnO0dzdWrVlkGlsq/bcumvl6Xx1mJEFTHqvhXo5Fkfe++WxbipRCCptXUbHZnZCmQ2tzYdRfZ7vFYc+pIxHobLt9yObaaMJ85ZIVZ+r3YrHQtcbAzCvwiRM2m4LQZTwwmc5O9A02/pzHKsriRTKDXhe1tnMkWUeMufu7RGm94MGLV2yOYTulryJw2Vm0FZWQvg++IZ69giXVeEJk1/K4NCgpc1GyKMZugMchS0FZAsTdiXvhcu25x+viAvLeG2WzKxrBaTHo9jNGIdX8EzSaqKGjs3lpidrJMHvB8BrogH88xXJcGIeYsRO3vwemT8MzT4hDW68FddwnOME2lA1cg3koeMYoEApEkAm0qN6aLmb2q2NyOdahwCNVuRyl0GdEaqmBODY8QZ7wPWQ7dTlk5gXnhAdZCdrzC6oMM9dv9ezZaITU/R2FTuGtEOPj7N5hog8xv0G3lnHGmmHvbhNlRLLvA00sJyGv7Hto0UMj4N5Sis26wNUm4uNPAOWOThxlWPcO1NXZhcq4Xo7XLyX6K2i1ge5vCsBi7PbbCBhoxjahUJSxrqUMslRhFFhv02gnjgcbVEfXVgLV2RtZTPPViwJ13wmAIvWSbwcxiZjZ500MRhh0QhpDnBld2/MX+413vgre8qeBgYDCdSgVGKfneLAezSNneNnEcVVayhUi43olAC6k1TQ0uXJAxVVW7q/jl4Yc9Lg1OU2QFD71T8be/x+J//B/h539e4FKvehV8/ddG3LE6E4t0r4buNJgMwFQz3nHnFlYWSP+6fl3cyba3WXuNzQ19mvT+d5C/8PMcf/63sT73GfJWk/ir/hihURCGgvcWGKsQ3k6fVhzMfOZzTbutGQ1FB/n82iHe5cc496EPSabic5/DMgw4exZ9333o++8nWz+GfbjD2zsdfvoXfoE/ur2Nsb1N/c1vJVm/l+1tk/19qfCvtRPW1iw6bcX1g1P0Txylng0I5xptwNbAl4TCIWgt+tGSMJDqR2YZ5KFmMlG32bovkwnVHLfWSVCmwrbthRO36xnoRDGNDaazGkEAdS8FpckxsMsES4GBjIAUh4QcgyJM0EYuG4F2G8eWNVcbJlgWqtXCnc9xM8EJ614Pb2WFM+02xbPPou69j9AKxFo9y7BW+hRpht7aJrzzAdxbl2Vs33OPzHOmKUSP8VjmCqVkBzgYLBfIymFWa5lDKjkk01zONa4rx5tOZVd2/boE3bYkMNR8LjdvOgXHIZxmOJMDjFadqL6KryMh7SuFsr6oNvGHqSmlHgK+H7gHeA74E1rrFz7vPRbwXcCfRmLJfw38z1rrtHz9zwN/C+gAnwW+SWv9fPnanwD+DrAKPAr8Ba311u/jlD/Ky4NiC2gBdwO3gJ//Qg/0ikGxBnAclGUJC7bRYu600IaNG41wwiELBXSQQVa1avGvZKTa7QUOQENphiETRYVP7XY0dTtBU6DxgaLEFysoM1OSMZXxF4YL0ixJkjAajUtfAr04paJQC6me9XU53TCUjFa7rdCWv+Aa5VjMdZmlpMBR6W33wqBA0Q3mOI5sCAyd4hg5SUkKPJh52CqlEWi0KkknE7CNjJVGRqh8cmwMChJcDFUQ6DnDyKNm56w0csKaj2VBHIvV8XgsAv3nTud86dskC5FoC63MUgJaY5sZq72M2Ujz0d82OXuqoNMziGPFrVuSsckyiVauXAHPNTixmWO78mjckt9QuQaHocyJnifP5dQpyfiNRnK/azV405vkvoShnONwqIVZ3VCcPdsgDOHGjsynW1uSVDCUkOeOHYPNo4rdfZ9btyT5cOqUxp3uE9d7NKwQW+WSsYwc5oXLc1cdjh6F1W4mE2oao7JMNmFlOjxPC7b2bd6z+jjuyCLJO0R5wDi02T5ocedkl4aaUqyukwUtigK8LJaJGVB5jjkbUpBhtppCtqw1yGyTK3s1nv+EiVItTiQvcu+Hvp3uW97MgXMebc+h15FOdnAg5yMdTCZ+9DJjW91E3xc3uNEIe29LFsDJmAKFubuFsbICzz8nwe3GBtqyBW/c6UgEF4aLTJ64nHgLer2OotIVJYXVVXQcSwn1d4JWVDrFGxty7qKpJ5Fev19qbYNqtWQ8gehra8VwJJniKlldsfzjWL5KhrvI17kojLxgMlM8c8nlodXrmEbBen1KER/C87uoc+fAVATFjGna4GDisLsD585pbmwLXn5jQ1RkZEZwuOfOnOksZBbbbPRinPkI3WziuTCY2vT1mNwLsEp4Sd7oEk499i7Kmn333SIcUu2TxmM57wsXNA/cMaPuy1xVd+DWFc3uxYLjrQlZYXD+dI5naty2g6MaXLloc3XbJehoVhyZ8i5dkmO/9JLm8FDGwZljGZ4FL91wyryByL65dZcoz1ntyyY/DDVde0pu17i8WyeOFVevCYa2Xl9SOK5dk+s4dky+z7ZNosjkDW+C7+jI4/2O75CNbBDAYejzkec3WVvVdA2p4mgNh4c1/NWA44Nr8Iu/gPqlXxJbu+PHsT7wR1h/7x/h0rbP+r//fk68+DS60aDYPMa0scnelrsoXoQh3HFW8+q75lw/qOG6mqPrKTdvGTx89xTzR/8NxY89i1F34cIF9JUrgsX/4x9Ev/rV5K0VpqrJ5chi7/pVjOtP8r4/8pVgmuh+H9OxGFwbUwQdul0ZUkfWLVbcgpu3FKePxGR2QOiu4mYF00mKpTXPPm3R6xtsroREmUuCweEhrK0FjAYaZRocPSqbl2eekaHQ7bIIfpWC3aFD4GTMyvzQ+rpwOQrTIZqlHG1NUYascEprImXhLsyfZPc40z4BIZYqUK6CqWyiF9Wg+Ryda8LmKsHBlswb5c7z2jPP8OgnP8mXvuY1hEdOg1nD1gmRDhhOJODvdMBtxdT2rkISLrPJq6si35hl5Ct9zMkEkgR144YEzlU2+fx5OZftbVnHq/Lhgw/KnFNh6Sq3E4C1NaLMxipirGS+5DpsbcHGBt7gkPnqcYLJDkF0iJpMlhaRFcbvi+0PvCmlPOBngG9DAt0/D/ySUuqs1vp2mbO/BvxR4M3ALvCjwN8D/rpS6oHy328ELgF/F/gXwOuVUm8Afgj4APAR4G+W3/e63+s5a63f9h+5ltcBvwl8/As91isGxak2sXKBTagsw5iMqJUBiBoOlwNC2GHy72pgTyaLDDOtlkSucgGgFDFLMozjlGLsOkdrja00FnO0VqWnvIVWaiFeUSXkKmw/QK1W43Wvew3Xrn2O++9/gDCUTWq3K4toWRWm02GRaT7Y1wR+QbNtLjJcRV6QxTmmb4PSFEC+oHeZhKpOGFffaVNg4RGJh71vYOYwVz4+EXmUgrIpHJ9QFUjx1URCC4sCgwiP0UixE3usr2mefV5c2nw7Q+UZ6x1NlnhYBztMC4fcD2i0DArlcHgoElR1L8drKuyGzbvellOb7RLHipHd5a5TBduHHoPhkpwUxQbDmSwAWgtfwjRZyMGJzJDcq0uXltX5KkgGCSSOHhVcXr0Ouzuad75+xsXrHleuWCWsQr5zPBYr541eSitI+eyzPvWmfF+tVpKXzAInmnIQ+qR1h7oHcWzSrpsUhsXd96gyGLAZJwa9/atYUyGbqU4HRiMsx+S0s8V1etTThKl3nHmoqHdi6pEr1EetiRNFXvb+CAejvcpse0TbnuPMR9Dvo+MYx9GkmaIwTDaOKBrNgjQp2ExNjPk7oV6n9pbXkiUJ9s2LclMSMaLg3LklyyxJlvT8IJBy4q1b5CdOs909T6Nh4UcjRpHm2rDDA5RBcZaJpvKRY8Taxtu9LgoTUGaTygezvy84vzJVWxw9Tmz4aMPEM1IMlaDLgFj1ektiTBXJHhzAygq6ZIBlzQ6RkshTlTAmu9ychgQorUlSGTAVzKffl2GepsuE0v6+ptXSBG5Bgk2RxDTCPd5wBHlzlYYzTYo770FrsErR8I99JGVrDz74tRmx6XP+7lwqSsqQ8nKecXlfcNUb3GRFxeiBhc5lA9BeWaHdEOvsNJFStJpOmaUByjA4uuly5aqgUY4flyDo8FC0e9steObpgrvnz3LNP4W/0WatD85ajfSmmGqsWIewC8naEeKdPZSVsdntEHRcdvYMgjr8zM/Aa18rc9ZrX5Vx/VohyuQxPHfR5tnnZOw98wz81E9qVrwJajSiYXvYvmJ24xA/DNH9E7x4q82xY5qNDdnofu5zct5JsuRPaS1/n07hrW+VZ6K1mIR4njz2556DH/5h+Jt/E44cUbz4olhRVwY7nK6j/FUZ3J/97MIuUo1GeBvr3EgS3rzWRk830N/wZzl4zZcxCUsogC1OoUrBZGZwc6uGUuIA2W6a3HM2JB0k2O0m5myI/oo/jr73Xg4vD7GOH8V+/5fy+CWLm5c+gefvoVSXt33dnyGYH5I6NXZijyg1sEzNzVsmh9fVbcgiE88z2dqBwcjizjtFHrLfN2h3MzaCIZvmnBscRbsu6URTpLKmaK1otNTC0HE4ZKGoUS1lFRKw31fs7tqLeasoZC3p95F1JIpRhwcLAXyTgkKJ4kbV/GhI5DbFodQwluTWMpOjfB8jz6ll46WE4q1bcP/9bDzwGlof/g3++F/9q/z97/p+AkeD6ZasGo1bM6XyuN6HmSsX9MILktWt1cj2D5nSQN9/itpGin39MotM0/q6ZEAqssjGxlKfvMIZVyQ9w1jKuZWkem8+RysD5ZY7QtOUY4xGGM0m/mwP3WphDAcy5wwGgrkqN9xfbH8o2tuBSGv9w+X//6lS6luBLwF+6bb3fRXwv2mtLwIopf4O8AtKqf8JOEcJcqSELwCz8v9fCfyE1vpXys99J/DXlFLntdZP/ee8EK31Y0qpF4G/gQT4r9heMSguwhhsYH9P7JtBCHVVG0kAsXBkqNirrRa6kn+KY1S9LoP74ICs3iL1G2VAbCwyuoK4sOn1bBy7IC8kMxRFy7I9yORuWTIWK/hEeQs4fvw4jz56EYOMrhfT8ByGUxONIXAJVUBJmlvtl/I2hITKxyJFURAZHkrlBJW9NQYZJnmJAROiW1WQlmNFiBYrhqYwXLRWxDiYbkqgMnw7RrEMrm0yKnvuKDUIHJNZ6KBMk/vuKxUgxha+C9FY0e4qZmwSTRLMLCfTNsrQrK+k2GSYFMwjg73Dgk5TaNRuU9EnZa59/FDhltjlwWBZPT84kMd18uQyRmo2ZTF44QVJGFQbj8lEfp58UvgVFYO725XF9PVvMLAdk9WVDNNStLomhiGOXJ2OHKfm58wJOLKWEqUmN24YOI4ExzkG2/5JyDLiRJHhY9swo+BgX/Hii5qLF4W0p5TBm+5aZb1lysgbDoWl3e2DbWNj8uK2Q2uS0WsX2IGNnmimoU/NiKRkrOVZZtjsjW22Jw3uPz0l8AowTIhi1GiE6vS5th/QbJk0GxmumWM4JwSqsrqK7+QQTpf43YMDiQwaDemg1YJ2993oPEeHEarRkA1R0CGa2zSvPoOVDug7Dr3zdZR5v9zcIKDwApLCwo+HGNGchdxDEEg/0w6uUpLJlZIJ5niIt38JikLcoTod6K7Awb6s4JUkSZJIFsiy0AcHFKaNymPM/ICgo0vL2SUmHxSF1qg4ZW/PolAWjrOEJx4cLH1FjhwBy9RMxxm+WRCkY5Rjo1ZXlzaSgwHUahStDvPCYzCCjXaMMZ3w7jP7ZCcVKj9GPZ2jLl4U+JYfQKOOMk1OrqwypM1vf2aT44ef4/TGnOi+h0QySpv4Lz2H4Tq4nkehZWa2bbg1dNjbX3KMigI2NzXtZsH6mkInKe/vf4YP7z6A27BwEkXPHNJoduh2oDe4TnLuHgrHYT5VPHvQIAgka14m53n+eTHL2NuT8n6aWpw5mzOaKj7zuOLipYKdHdGt/bo/pulaY+xf+WVUlmKfPw+TCfYTT8JrX4PhpsSR5od+SPEn/6Tc67W1ZRWm25W4ZWdHxnK7LTHthQvw4Q9LYH54CD/3c0Lwc1146CHJHF+7JgnB8VhwwFujGkeOKOxGE72+Dtevo7Rmev06v/4bv8GZP/utTL/yL5A5YNU8br5o8MzHWWCyL12SjPX161I1atQK7jo5xzYNmMzwb70k8+r73o9+7cPcOAxon3ySX3lhm9qjv0rQbvOqB9/MxvhF3KZHHk2I/Rba9elEOxxkPqO0TbOjiDNJbs5mUgHb2Fiqg8xmy/xMmlvMsxb1tQYb5LgqJfN99g90SSiWamKrJcO1Gh7Hjy9x5kLwXhZDS24vw6G8Z28PHMdAeT4OFpah0IUmMR0KbWKpRAh0gKFz/Nm+6AmX8ALtuhR+gDGbylpbWert7sp7dnbg6af5vp/7Oc6fOs03fsu3sLpqkF/9NPr0OYZGh5VWiq3nMBqjtndEs7zqIKXt4nwCT6TnWd8rOFXES1yIZZXyHbx8wq8Yk4YhN+TyZclg1WrSiT7xCVlQOh2U44gqjm0vO2fpZquVElvwWzdlg3r27HKwfLH9l2ip+gKIi78Dbvou4PnP+9sLCJTi9qDYAOa3/b8Aeghc4peBl8rP5cA+8Kb/yOcqLYJzwO8pKFZKHf8d/uwCjyDXE/8Or/+O7RWDYreImBsdzNVN3CJeZoMr0pzvL0GEa2vL3SSI7/nq6lKWzDSh30eVe1oQR7g4VotKr0jmQGEZ5PnSxbZqFRIjDJfksaVilGZwsEW/U6PAYlpY5EmGoXNs30CpAlunpQ6ABl0wSQMK08MxUxyVUZQEANO3CbUN2ihvVAZKY5LhkjBf+Novz6usRJGmhqgqtE0sIyf3LPISkSxP32ReysSBorA0nhezKbLPt8E9FW5gkZWZC8PQZJmFZxsYWYRvaG7tWgzmHnccnbM3dmnpAc1oDkEbrUTebjA0iOLluTabkrkZjZaV/Js3ZVGrKmKNhizkL7ywhIFX5OVKlq0qABSFLLKDAWxFASYZd58K2Z8HOK7Bww9rokiRRBpLaQ5GBbZrYLuaIM9ZacTs77m0WwXdpsIycrRWTGJFyw3JDA/fU6ytljC4nYI7eoekOwN0ew6dppCn+qscTH2yyKbeKjhpZ9RqipcuW9iOom2OaTDmcnESb6TwPI1v51hZyIYb0dgMsByTuekzGSuSyOVI22Nr38E2coz5HEPnGC5wY3epqfXUU8ssy5kz6AcekPLn/r7o5bbbAoG4fp3PTc7Q6cDx3hzTsQj2rnGq0KiOgzGvS9b76adgfV0c2TQox8HOI9StW6CLpdkGYIyHOMMRJGXduiLFFgXGXXctXGnSJCfxfAIRrpaVv4oI8xztOGTaJK21UYGLTU6o/HKMSt9J4wKbhMJU7A09lAVx2SeqsVpBEA2jgiEoVtoipZcPI6ztLVlQq6il0QDfx7JNgmJCsOJgKk3a6pK0+hRpzuEhbB5exGg0SI6dxr50AZUmcOoUxbPP81NPvJr3fEmOf++97E0imrs7PHl4nHpdc3+awOCQ7NQZsBzi5irDiUnNL9hDJNZaLc1LL2natYxRBqc2xFnrOf91fMnbp1jXLpCmGnseoNMhdmFwtXYP5o5iWtioPCVJ5HqPHxfM7/Z2NZZEHlHglYrRSPDXzz3nLHxX0hQ+94TBpddp7rl0GasRiMuG6zK3Wwxq52i7HqNBwSOPWDz6qJgTrqxIDPN93yddb31dpuTKkvrKFXk2H/iAcCh/+qfha75GgvVWS8w8qo1wxdEUJzjFbOzwxgcfxn3f+1A/9EM8XRRc6XZ5+//nf+UzV1e5+myA4whMtdmU5OLKinz+6adlD+j7EsedOGHy8c8FnDldsHutxkO9Fp7jciNY5ed+8kXm4Yx7zIt86QN3YbfX2K6dptOG4OkXMRsByZm7KeIc1dQk3TWC3MDJhcBbry/9oipi8V13yfe/+KKc12xGqTBkM00qtJ9Ju5HRb+bMMpcKpl8JMkwmco8qmf3VVZkHK4jNdCrvr5bDw0M5B8uCQVLHaJzFJcKOC6a5otkoKDCYUhPCdmDhkElwGIZCKIxjYruJ49cwD/aWchfHj8uN/djH+P89/jhmlvG+o0cFF3ztGnlvnWvjNiesm2TeKmGo8C+9RNFoYlY6cq6LLtPezY0mb24NUJ6HulzCqSr84G3yizSbcrNKohxFsaz4VrrmngdveYuUH0YjGc+VNnp1nOomHRwwWztNbX4JDg/kb5VO+hfbK7f/eo52NV4etFL+P/i8v/0cApX4LWCMwCAAPCTwfQKBXrwA/H3gJ5RSry4/99NKqX8JfBqBYdTKz/1e2+X/xGsK+OQXeqBXJtrV69SiySJrVbEP9O3U8jwX3GTlkVmmigrPJ8LHU9GidFRBETxCcgziwmM+X/KGKkWAolh6F9wuuTqfLyHMprn8yoqk1ww8nhmOAU2eCsbV9iwa1hyDgjC1Gcw9GnUYDA2SRGPbBr2eRaJFCzmcayYzUXDo9WQyNAxbFn+ssohcoEub5ErVwrah7cfYVk6qXEyVk2kTU4uUzlxJytUlokAUJhQ5hjIxfZvBUFjUnY7gc5tNqNsRza4mxWU6NzAMRcNOcAyRklvrZdx63mE2NzjeC8lVwFx18fWcTDvkSuH5irCEnpqmEPDCUDOfK9bWRAnioYcqaKmQmA4PpXq2siJzc5rKQnzvvUvepbh66VKmSMgpkgQ0yU2HTrRNaraYU2M+h37fYPvAJ8vBciDLFFevw2O7ijee3Ma34IWbaziGot6xWO9EOIbGJaRWh1U7Ya1ToxHuUuu46LkDsTic6P0DppGF4xkEKgHTot9MmOFz7mTCjVsGl/bb9HstLENz88KcWsvi6KamFkgK0XMyZiogL0yaTkgw30KNTU506qWDSg7KB3uJlS9u3ETlOQsZgPvvJ7+5JdWCLMe5cgE9GAi7+uhR1mswjixUJLs6Y/uWdPqq/FGlXS2LrLuKmo4xXrpE0e6j2x3UrRty4ytznOkUs9eDoCTdVeL8nY7saEodcTuVigL33ispvPF4iUEuClS/j441SucY0ynK0pieRYbDeCwW1YalGIxdDEMMX7a2lsz8KhYvpwwMgwV8ibqLrTK80SE06ujhULLa9TrEsQT/eY7OU2K7QaAj7OkhSllkyqbftVGd03DrJrge6akzOJMBNBqo8/fxOqegqSa0A5u8bjPc9nnD3UMMx0Ynp8iUjXYcktQgUw6zTNFqSVbRssDME06sj2kfb6KHY27uNDm5OeehuzTp3oit9l184nM+6RU4e0Zz6WIByqC/WpXeHbJMbuloBP1OwmOXbaZTyT72est9U/WoT56U/49Gy6l1eGuOfv4FePTXYG0N/Z73kDz8EIdpk+sXTOYxPP6k5B6yTBKITzwhY7dSqeh25fiVRvSFC1LZ+Z7vkXH72GPyzI4ckc+32yILd+SIZDqfeKKq+tvs3HeCL3/zO/nwr/86DwPv/Zo/RnZklRc+1eXmLckGVwJDeS6wjU5Hutjzz0s3PXYsI0leIklu8anPCLZ8e6yxigZ+WHDf+fs52K/xjte/GlcnFPsHrKs93Odeks3uU09h9frs9+8lHLs0m4q9PRkis9lShrdS7yg53GxuLvdc8/lSGVSM3uQ8BkOT2RiSXPgWjcbSf0op+ZxtS2AfhksVIOFxvFzwARbQWTxPfupmQYxNLU6xdIFDzhxfOCGYmMxEYaLbJcxMXFvjFwlqZ3uJ4SiliXS3C7Ua4xdfZM+2+e3plEe2tlBraxwOTXp3mUTFGkaaM8l9intfR7B9SeTQ4hgefBCda5iMYDpFVXjfzU2KNJP127ZQtwfFlaMlLEuMTzwhjOwwXL52O8Gn01lqnleqFeVcqZKE2sUnhHQcBDJgSpWeL7b/Iu33qj4xh9KUYdkCYPp5f/v7SFb4k0hQ/D0IpGIE/APgmtb6cwBKqb9W/v01WuvfKCEW/6o87g8Dz5av/17bfyol/ikkOP+C2isT7Sqmqe/Lt5omOstIuuuYkwGmZcNKD2ZTdCkHoWo1dFFg6IKAGQrZNWogxSHFxKDAIyE1C+p183bI8WI+qKRXF+eiZfyVTr10OjLRC7EOtFJ4QZ00lWxwxwtJPYsEg6jc5OSmLBizmZhurK9pSCKU9sgKSFLI4pw0Nen3ZdKVjLQWQp8Bo8glL9TLYFaLc7ddkiIjUCGZtpjMTKahx8oK2CoVW1s8KpiNT8Te2GES2oK7a2sRia/FuI7BJPGYTQrWuwk1U2NbGsfW5Lni1tBnvRWysZYzLTxcCjwibJ1xOHUxTAPXl6z1+np5I7OEaKbJc5fVVY1j5XQbBY4rWcP9fdFNbrerrJFc+8qKYOlk4RGTkX5/mRUELTbLI3Bsza2xA/ka+QDSQi3M07qNlNFEEYZWmVzVPHBfzmi8ztVpwrn1Mfsjl66fYTk2Kp4LMVNr1HjE2oolDktFQb7SJyxc7CIm659gPDEJp+B5Fk0byXRqRWa6tHqKTGk2uwKJ2WymoGNiu8aNgUeW+2x2I6bTgklssdG3UZ67ZFlXEh2+vxR2VorJ1SF1v4bheRSHQ4x2iyLJyA0x4tAnTxG318gbbbzpHmv2If0iQzm+kOFKuUI1GsmNXF2VyClJMF68QO7XMF0X11WQp7JL2d9/ueB+5YK3s7OUhHvxxVJmxFzarFfySydPygp+cLB0mJxOcVxXYCiuC8rF0zEzZRH4BlYWEWUWpmktnOqq4CtNl6IWFenr+HHwPM18Lo8rx2J+7E68/RuYvr/cdR09SmTWsB29ULTQQOw2GO3ntDnErTkUzRaJ14RC43SaTL0OeQjTucn9R3ZQV6+Sjzyi7iYhDfSFZ9BnT5ON52T1Nonlog3pvzs7mp0dWFmRbKNp2qxtuLhugrJn1HyTkBZFmnJlfoxmy2B1VW778y8oXNfk3Dn4yEekS7zxjTIWwlCCwXOnDM6dyvnNj1vs7cnfX3pJOEqf+tRS6eX6dblnL74o8Ib05g7q+DHRl333u0nf9DbCzbuwTANDuKBkGfz2b0s88rGPwdmz8rvTkWqPUtJlK5zrlSsSsH7JlwiMIs8FazybSfb46lXpFhsbctwPf7i05z4u53XtrMXXftN3kewOufqnv4HLzxqcOAmmJXuuygVTKTneL/2SZGe73Se5dm2bNPXo909jmm/F9xX33SddMU3l/LIM7n8A5m6XrUHB8b6Be+2KHPTYMTAtlG1Rd1KwXZJE88wz0n3X16XaI0NTqo67u2LUURlC5rnASFot6XJhKF2+7UYkkcvhyFrA85QSGEmJBEApOcfRSNaXiqx9e0B8+/rUbi+huFEEeIEMP08znaS4JmjXQBki35dhYWZztGmR2z6FijCSEL2yIgYceS6DqVaTQeF5fPsHPwhXr3IwnfLoRz/K2976Vrb3j9LthjiBjbZc9gdQGzyFcWZNBl8pyv/Y3kma1pyzvQlWNJPvsCzmZ87jHd7CTMJlivz2liRLIknFUi8NPRbt2LElpAIWc8qiI5YEYXX6tByrsri0XjEE+WL7r9+eB/7i5/3tTuD/+Ly/bQB/X2v9/wJQSr0buKi1npVwhsFt75V8KCRKqRXgN7TWP1B+rgn8VeBzv49zPvU7/K0Ahlrrye/mQK/YI9VtoqO63YadHVm8phPyepsIG8+IMT2/VD4vD1kNiiiSXW45i9gkOIBGEZVZdscRrQdYZpwqC82qslKpTiWJjL2KXGHbMkkZBihyQHPq9B386od/lfe+6x3kKAxyPEJiHDAs+n21YMtrrfA9TaYT4swhDjWW57DeUovvrlQZdnag28zotgrGobsol8HS0wQN/ngXwzFRQQOvZuLXZPettYGioMBClVjNSHsCX42UZIftkH5DMU8MDrZNDg41D94VU1g2yjIoKAi1wjVieiuaDJ9eN8FK51h7+1CrYfh1moEBBdiqIMZna89Aa4XnObRbBW4DxqOCmpPSqOckOGSFxjDEmc+xl4TE0kMCpbRkv4YZJ2pDuhsdWtEO88xkanVZaWXgeHgq4pkLNsOJRaEFT3zypNyjOLIwjJws0xwcQH9FYxwesmZljFbWSZOQE+sxSimKWzui1NluU2QFmd/COjjEUALFMV2XwIh48qJPvWXSW8lReUGhLXwiYu1ijfZw0zmG2+ZzVxqYUUGja2N6DXwzwp0f0miuoQyDma4TFbC2qvHyUJQhZjMJPrWcx2L1BJjPsZoBNw+Oc2T4NFluY3ZtaLawnnsWy1Yoz8Ot1dGXbqLyFMN1MD7zGfQjbySLc6xGYykjUMkXNRpQFJiejbl7s0wxlvqiVaqxIsZU7G1YiqlqLaXVSlXCtiUVWGWQK1ZlBZ8AiZLiGLpdUm2RuQFojX94A9obDGcuaaKZzkUb9/TppVN7RR2o1r+zZ0WLOnASum4qBikaQscnD+qY89EyshgMyPtNwciLgKFILLsmLWdMnlhkuSLHQa2usTv2GO8V1Pyc4dSWvUF9jZHZwzAs7uxlbJgFO/WHsG9tM3HXcXMPM4SDA81oqDl/n3xHlkuZPY1ysCxuHjoM99YkS1iYtNsm8xA+81m5hRWUu9mU4OnJJ+G975XH4DjyaB5/HHo9myCQ+WswkJ8jRyQATRJ5fJ/9LDz66BLb+/jj8L7JcxQnPPR734/5lrewe+RBPne1RxxLHPIbvyHfUWnrvuc9y6rZpUsSyB4eLiVl9/elOx0cLPHC3/3d8IM/KLJsP/qjwg2o1SRY//IvX25u6nUJcp98ssn4j75dEpY/LRyDlRW5/uefl/dvbsq12bbmxo1PUKuNeeyx82xu3k+7DR//uHzugQeEUBhFEjg7jpzf1haEocELLxgMjve582SAHx2SJznJq9+IV8xReDQs0KMJ95+Xak6vpxmNymSJTtndM9FaceGCbBocRzYKIPe839ekqXTzsRGQ5pp2e8lBiyK5rxUMKI6lX6+uSsz33HPLPl+p9RwcLA1qQO7J+rrc06WLusJwHW7tyedWVgCdY40PUTpDF+B1LRLtUBQRTp6IcU8UwdoaKs+J2+uoP/I1OC8+i/J9Vup1vv2HfoifO3aMB9fn7BcpX/XB/5n3v/cdfP03/GWyjfspDi9jrK3KHJGmPHTPnHSeoutd0qmLMz2ANMUZ76NtRyBYv1OQappL/eLjx39nDLBp3ib8zjJxoPXCz4CtLZkgqoW1yjR/sf1ha78ONJRS34SoRPw5BNrw6Oe97+uBR5RSXwmsIPJs31++9ovA/6qU+r8RSbdvA24AzwBvAP6vUhliCPx/gQ9rrW/+Xk9Ya3319/rZz2+vGBTnrS6RU8NXsRCaGg30PCIJ2gRmjDM8IM5NDqwufsMiIMakELvm+ipuPcEkW9hyzAkw0NilnHl+G4FN62UCrHJwq/SLq0lKsrzLMRjHkq20LKB02zp1+jQ3b14nzsEwbflmnWCTinzY3KHTkS3+ZAK7ob+QK9vogXF71qrMTm/2IjZ7EGmPMKySbvrz7pakjeP2GiYpaWGxu1+w2kywfYN5ajOZqJKUq9FaESYmYSILy4kTinyUY0URa22XfjPh2LGAgkAycrOQfrZN2N1kpGuIwpam7hp4node3YQoJA8z8lod34wIdYCpcxpuzuWbDnmu8O80qJkRK0FBjiLVNmFi0nJCWoFNlkLTTximAQcHsNYMCdDsTQN8X9HuWZxoGjjelAOzz3ycs5lcZv96jeDoCruJi2lDmgm+8qMfVTz0kOAPGw1FmFhYFtx3d07NzTAzn3wWEpgJQd2BnVtoNNPCw1E5xmjGdtKlvlrD9hpY8zHe9ADdbJIYNVZWjfJeiDnHPBMYQG14A8IICo23e4kvec0J0JpUOUypszcNILHpNhQOEdO5Iopc9q6FuL7NBgYm+dIlrvKrrjADRUHN0wS9GO2u4k4n8MwTYNnktgU1D27exLh+XTrRHXfIqhqGqO0t8v5R9EoPOy7B6OOxvK9SbDFFp7TCBC7MDG5jl6q9PTmXyWSZ4qqOU8m1bWzIsarA23Fkh1fhCMtISHc6cHiIXa+jPFcIp522JKZzheMYrK/LuvbssxL8BsHSLG9lRX5PpzCbaua+w0q9wN69ier3qB1eB09IuIxGomfa6xGYMcVwn1TZZK1V5lok7A6KVVaac3JHkeBgGgW1WsFkajKamQv/ksHExJgOCZyEg4Mefs1CGxaXk2N0ahX8Cdb6BafWQgxgmvpcumyWcoWignPipGIWWTSb8PzHJNA8d07mm6NHZW2/eVMgCefOwSOPyN+uXpV9xngs1375snzm2DG5/bOZBEoVX6Dblczya18rj/WTn4S//tfhk4MvZ/Poc6z9mb9MO9ml1WvQGEhVptFQPPywxBUV76pWE+hDNY/t7cHDD8vjriQn81wC8orv+cQTci4nTsAHPyiZYYA3vEE2CMOhPNdK3SLPT/NjP/Y458+/CpCgtsqKnz8vAe+VKyla/ybdbkar9Xre+tYmrZboIu/uynW+8AILh8sjRyRL7nlyH6NIXm+3od4yubDV5I5TPkWSYOQ5Y9XAVAW+DlFOypoz4uJhl09+vOAtD4fs79cYHiqOricM5i6rq3KPd3dlo/bss/DOd8r1n1yfkxg+aSok0PXunKefrmFZS5dOqDgc8vnnnpNjGYZsOnJRilwUkCqd6ypRk+cynGu1JXSjigsrVRbbKDBcCx1lJEELhhMcW5HXmuTKxUCjfF8qs3mOpwt0v4NafzN0u6hLl/h7jzzCxx5/nPe89730mk0+8s//d77mr/1V7rz7Pl71qrfysWdPsHJxwoOP+LjRFHM8ZnfWJJsrjrvhQhLAVlmJ8dcyL5w69XJsiGlKOeQLbZUfQadT7gCkg+pK/kmpJYnwi+0PXdNaR0qpLwO+D/huBBP8Aa11rJT6ReA3tdZ/D/iHwA8iOsAx8APA/14e5vuRQPlngSYCsXh/qWH8UaXUP0ZgDR5CyvtTv9vzVEr98Cu/6/bL0t/4BR33lRztivF44TqXYlHkCne4S1LrYjgGtkpJtMs8tajbMbYhATBaE5bmF7bKsEt9xhCvJNlBZcohnzDJc1k4arUluS7Pl+t39f/Pb7a9lHgUCl/KT/zYv+ErP/ABCq+DUyToPCdKbZTniOGIEsqbLsTO1jAUs5lkO1xXMshpKhPdzo7gbk+cEHZ5symZ1NLjFYuETDuLQLoi3dXr4BkRFJCaHkppnDwkUw65shZSVpVcnFKqxLRpwpkmKxT1uvwtDEuHzqEmyxWmKZ9znYKOM0ONBiSmT9pYAQUuc2LtM5sbkuHuSul4Ppd5qtpk2LamFsD+gdz3ek1jFCnGfIrlO8wIUIYimSYcTl1qNVkk7jk9F7MBHKxc1AKGqY/pOyjDYH/israqeemy4p//c5FG+vqvh9c8VOArOTdjMqRIMqJGH9/JCUa3CJtr2NMh9mxIHmfsOkdpqjF0Ohh7OziTfVS9juF7ZK0VLNcizCx2xy5ZAsdWQ2xLow4PRXe63SVUotJwuJOx2o5wHSE7ZkVBagR4KmJrx+TmnkOzlnPavsYwDmhkQxrJgYAkTUHF6wKM2URScaaJjmOyvQEXt2qcOaewb11DXbsmkcWDD0pn+K3fktSa6y6Bt2VwzIkTkkafzyUq2NhYlh2zTCId04Q770TP52jLXpQ3ldYwHkk2uwK2KyUY3el0aZjj+6L4cHCwdJscjZag8bNnYTBA93rguNKvTVP0iHXA3r4Y5sAyRq+Cg0ZjSUzq95dQGpsYg4JYi9Z4TYXEuBjjAc6kZET1+2jTJMRlMLLIUkW7o1BFTu3gCvPuUbRpE0WaJNL0WzE3DwLaHcHeT6ci8/fZz8Lrzs+5+2zGaGoy1TUhi80kqbW5Kbe/0tOu1yVAC4JlAqxel9cqp+yKzNtuy21qNiWb2mgslfVe8xrJDM7nS9WN6XSJtBkMJPi7dWsZEFUKdMOhHPNbvxV+/Mfl+yYT+Ct/BR6yn8AMx4zueD2Oo/nQL9rU6wIJuHpVukwFW/joR+Uazp6VLvkqiV0XCYVLl+S7bHsZIKapqGJ86EPwa78m5/TwwwKrmE7hR34E7r9fAu7Ll8GynqTRuEWt9kZsu0GSyDW+6lUTXnjh4zz7rMXq6ps4c8bBdeUz3/RN8O//vbzvnnukj3z60/C2t8nf5nOZs++/X157/nn5vrU1+XsFzTJNOZ6bjnnVmQkDc4UktwgaFpcvw+BQc/y45uYtxcaGepmCzq1bcsxeb6m5vtYvUIZGY3JwALu7miNHFMOhPOetLfneyoytGlaz2VKC3HWXLukV0bjivlR8C8takpKr4Vm9BzQrK5qOFy6qIzd2XdYbM1xPSpjTQrTya2rOT/7ov+NLzp/nE089xXu/+qshTcmvX2cWhnz6E5+g3+1yqtnkRrfLxy9d4blLV/mbf+1v89inFN3RZc7fnWIfWSVymmTaYPLCFmvrYCYxhWmRNlcwLbD2d9Cez7xzFMdVLyO4/67aSy8tZSnX16VTTSakYQa6wIjmmK99aCn+/Ie0/aFztHv96z/0s9/1XV/Q+613v/v/7x3tbnOx+089o+r1/3yOdikGCk2Eh0OMNmzilXWsPMba3yJp9rGtmK4TUSiDWLu4xBjDIX5RoDtdIuUR4+KQ4hEttHkVRRkqS8RbEedu30AaxtKRtppgbm+GUblG6/IOSPD+Fe97H7/x6KO8/d3vZxj5hNMc2zOplZlfA41JiiLh1qGL5xuLyrjWkuGsmPRHNqTUOh5rihzyOKXmJGgkyC+wJDMWKSGmmQndWs5kYrI797CdKvGniA2fojBQCNmtG4Ro28ZqWySJXGcQKKJI4bsyEReFXLdoM0uQXCUsDcNgRgPVqS0mWFCERYBCMCdRZDAe69JhW/rPZCKfT1Nwijk1xyYMbQJfkcxhlnaoOWDZClMVNGs5o1mBq1Luv9fCilOc7V129TozmnheHcMqsIuCel2xXtN4xLzwrEm77fDa18pC8rknDO66M0AXmtG0Q5IpVpyC1mwH0hgvHJA2W0ycJvnHH2PlnhqOa6D0FFoOHMbkVpuwu8n+1MOdTOl7AzYtk7Rew1IFUeGi2qu4KifCxdA5N7dM/MDi5qFPLShoNJUAxBGFjmYP7BrsbIGZTjlyvI6+MkanKUynFGHEfPU09e2LaF0ssLp6PCZvd9nIZ0TPXMfJhxKZNJtLfN7b3iYr7NNPyw33fYlsoqhMdQ5kEbn7bnktjpfg0MFgERAXWU5uOiS5A3mG4yjsVhuKAp0kqI0NtO8v7LfiZh/Td0RIfzyWAVOltnyxOybPJSLb2CC0ahhK45IKLVZDkUOno5jP5bCV9BQs2fdV9qtSjzGUWFSrLMcsIjwVUVi2/G74aKeHGg3RRcHMaIISucQwlEDm4MDkjjvO4NtibRzUDPwAEgJWevLdBweCbe/aYwKvxnDuwfgm9f4a+zeWPJ8kkfMrYZULpEolrx7HEghduSKJ/LU1FkYWN25IQFavy7UZBovzjCJ5/eJF+dz29tLA88IF4Q9pLY+02jRUxPzK26XTEUWIRx+VIPvOO+X1WfsevK3LbO9CFmtGgwIwefBBOd7Fi/IMfuVX4NWvloz0/fezmEPX1+X8fvu35bp6vQreIN3q138dfuzH5DxAvrst3QjLgte9Dt7+dvjN35Rrms/vJ03v48aNT9JqzWi1LA4Ocn7xF30s613MZgbXri1ROnfdJfe624U/+2clcDcMCcQr6/haTQL0rS2Z332fhZ55xcFSSt5nWXBlq8nOyKe9YuG4ivCy7FW7XbXYX+a5SGVWWXLHkXsxEm4Z7ba4C3reMkg2TcXFi0sonlLSB69cWYo/1OtyjCpbXKlQbGzIMw7DZQxYKfdUz7y6jnPnpL8UBdSCgrYzx9g/QFkmYX2VY70QtbsHrghLO3nEjYFP2rL4uV//db7vB3+Qb3z3u9HPPcf16ZRffPJJrj37LH/3Ax/g0Zs3+cbv/m7+0td9HVnvNfT7HtuHNm963Qz7wECFBUYtINi9hQ5q+He2MbZuMt84DZaFQyx6TEFA2uiitSZLChznC4ohXt7E831Jsql2YSsrmNMh0am7sRwD8w95QPzF9t9E+1fwH7V2/j23VwyKY+0RMMctJcXcolSfUAr6K/jRHD1PCRureMT4SVlDzAuK7grKUHiVNmN5TIOCgHmJ99XMCaiCeNPUL6vcTKdqwfz9/IAYljALRY5NikmOiSarNUgNl/E0Yz63WO1kuE6MKv3kIwIKPAZDje1AvS66EpbOSHBZWVGY5NjkKJ2zM/FwbcVaJ8EsNygJ7kJmrdZwqNUFK21SoCwI2jZBGywl55Vrk3lss7sni6/nKVLDx1SQpYo4hlpNrr/bFfJIFEnm2nHAshS+L8FtRRypshaWofFUSIiP1mJFnUYFq+0UjrmMJqqcwDWNhloofoxGisuxj+uWyhGFYjB3CcrsscRkJvO0RqMFvpkQOCHUTdJsg+QggDL7Va8bOP7y4UXa413vgde9QRYV05SFOpun3HNHStfTzA9DOkmK1fIxrDr7Y4srL7icO6sZ3PMIccOiW48XDoDO/R2u7Qe4o4yGGlIUGqUUtmdgOzn6cEAUB0ztLv12hmNF2Nu36PqbFHsHrKe7xMYR3PmcWfsoueViGwVWlmCOQ+7rzLBvDGHXgFab+PhZlGmipwlWGpI2O+SjGcUoIvANjMkEOwfbzjHDXVmpNzYkLaaUpPCee06iF9NEnzy5NNuo9LhKQVQ9Hi+tmgcDkS+87z70aEQe1Cm8gBQbV0eY0yHp3CJr1rEaTYgjkfvTEAdd9NEWto4x4jIKqQRYywztopkWrPbRhoVCUWhFhkGoA5JUMmjzuXzMMCRLmucSrEyny2yqUkIQ7feh6YjMU46BN94GIG2tkVkmUQxWWlDzfZGs69UEz1i2fl8UXy5e1Jw5VdCup2VfcpEYXtzXPvlJzZkzmtedSfngyY+DUpjdu7g5kGCv2vRV5W/RKJcxMxotSYK+vwxeTVNu1eGhwCQqXqXWcszK3rwSCdnelvc89dRSnjDL5Fj7+3I9L70kQek998ixbt2SY5TJeWo1CWgfeEDO87HHoChs4A5cV4LZ48flfR/9qMA0lJKs7tveJnCG0Ui+r8pIVgH8mTMyn4zHMt/s7SnW1qQ6vrW1zFwPBvAn/oQQ9l7zGskaP/HE8ppF99eg13vDIni8+24J+CqyXVXB+/jHxenyM5+R8773XjnuxsZyY3J4KCpe1fdfurT0glBKgv5uV+5bUci8dPo0zOc2g6F83zPPyDPY2ICzx2JqtmLnwFk46h0/LvcbZI7c3ZXrvHFDjnvypPSBqhKg9ZI7YVnyLLrdpRTlZCKfr9Q9ZrNlFrrCDlcVh6oYZFlLDHoVeBuGzPVoxbxzhFoyJByEZL0W1qqHryKxMbcKjq3GhCm4vk9/YwN7bZ3v+rEfo9vp8FVf/uX8s2vX+LUrV6ifOse/+yc/wCnf4ZcPZhx17qY9vkY2ifjs9DQtJ+T8bAanT6OeeopsnLB39D5q2sAb7mOmM/Ab6DTH3rqG6dUxmzXQ7f9QYuOV2pUrcnMqXdHSVAmlMJoNgpsXBUT/xfbF9vtsWus/81/iuK8YFO/uKVqeQ7OeYRQZ+vAQnWViCFCpUvji3gagbZuo1iW3AjBAaYWhRBXh80P6CF8W4kItYoPK8hSt0Rj4viy+Ff6/kjwEXq5YgcIkI8HDIMch4947zjLZv8TpU6dJcEgw8IgJdU0WURLWugUpDg4Jg6GBcjxcV7RyPSWGG5FyaHaM8nssojK8N0qBNoecgoR55pFhoywLN4/YO4B2K8fyRIYuzkwGQ7UgzlQk30rjtShYlPFET13TDhK0JU5N47Gw+Tf7CdgW3Y5BFMuEv9HLSSwXtKh8NGoa7IRh6NLyYrpBzjwyOJiJpfVsJgvA5qZkn21LU5q9sbJSSmkhi0DFMwtDSB2XwnbQmAwnGtMG19fYtloYqhzrhZi2YpbYjMYmg4FmMlGL4OFwbPNj/97gPe/KObMRk7tttqY2SmdsNCM6d2tMBa0jUIz30ROFatQpMLg5DDAMTZwq0hBOBAcovwemxRyXkdsgI2ejHXIwtqkzQ9WbdKc3SfotYv9eCsdnnMLujZg8mtNrhKw2YxpBXqZ/EtjdJXNr5OsNPB2y5R6lqXOcukeea8L9EK9uY3Q6mErB5Rty0yqmWbMpnfMTn0DPZmT1NlaalumqAo4eRe/sCPShBK3q8YQizsjXjqJWN7GVuNll3T5FFKPnIY4REXkNgnab/4e9/w6XLTvLe9HfmHlWzivstXaOvbtbrW51twKSWlkgsi4gjgw2YMA+BnwNGC7Hx0kXDOI5DmAbY4OxTLARmBwEQiinbrXU6tw755VD5Zp53D++mlVbQj5qOOBrcI/nWd17VdWaNeeYY47xju97v/fNQk2aGsR2ETPROJMByvdJMwWGTagcLMvF86dVoFNN4nyhy2yHcXMVT0WMYgfTgiQ1xOFvyp/MuZLzzAQMh3pGlQhDiba1m5rxMEEpmxgLU6dYZoqqVtFRjGFCbyhKF9HWDoW9c6h6HasZE2sHQ8X0+yZbWwYLC4rFRajYE1LtkGISR5qtbXEc+8QnoF5OeOm9KVbmwn33gNZc3ymwsWXS7YoU2v6+dO2RIxK9GwzmxVN5nWJOd+j3BXhduybr+caGPBtxLIAmd3sbDGQM5zTxclmeP9OcK1GFoRS17e9L0mB7ey4LvbAg37m1JZzkS5fgzW9mRofq9yWhEIb5synnlKtEgJxLksjx80LY0UiAXq520e9L1PbsGU2cKjY2pLgvZ+h85VcKAL95cy5De+bMXLv4Z39WzvENb5BrzsFxDjrPn5/TErpdAa6veY0whR56SMDwhz4EP/ET8jh86lPyWr6heve7pR/z8xwM5NxyF81pDSYgnzl5ch5dz81HHn9cIvl33ulSrWjSHTUTZkkSuQc53cf3Rchhc1OOm4vIgJzPc8/J9+c0vJ0ducYXvUjGQK5Ilq9DOesw3/zkxYyjkdw305TvytkDaSrH2d+HY8cMtsclklijGg1RZQoSDN9iZ1TA0jG1UoLKYp548iYrK0f5sje+ieWDJ3jLq1/NT/7Ku3nnT/80L7rzTt5//TpvP3Wa1QceIEgUdz/5CO1Fg8yx8fZ3+ZLFS1NToYJ0imnixQM8I8LyyrhdMdh4PG1x+HSFWj3EytJ5wfyfth0/LoM1fziyTDogp4/dddcLihMvtL+wppR6CfAVwAKwAfyW1vqxP80xvujoXGwnqDhBY6AtC91ZIEoUTiJRSUunmCrBmkLe2LCxPY032iENEoL6Io4KZ5YleUIm0wqDjBR79tyEIfgqpFiSIrXNrjfbcOZmHbmMou/LRLOzIxOZZ0mEF7JZ+vfq2ib33vdSxjgYxGQ4jDNrqlIBqTLxRHeBCS5uWexXt7c1K40QZWvQ4KoIWyWkmFPveguNwiUk03LlEQ5hZglIKNmElilys6Q4OiLFILUsFhfleuRahIWcpcJTHo8VwShltRFwa69AkimR60k1kwksLWQMxwY31kwKRU2zlVC1QgLXp9dXFEuK3tBgbU0m3n6/gOfBVt8jCjUZBvW69HOrNVfXCQJYbYVox6I3tNAaqlU1s+zt9YQ64nlQLik2txSNuqZZT9EKhkOTmzc1R45INHujV6BSSik5EUYxpuB7nL8g0TKQ+fahVySUKibPXapSqxm0iiPcnTWGV4eoO87g+RJpxPSwhl3GpRa+EbDSzFBRgJFlZAsek3ABb2eLW+kiqmjheQqFyX7foGPuYTgWoVEkKjcxsghtyEq4saHxVMShyhrm7jZKNecl5VP5MqvdxspGsLXJsoakdoAIl6RUoBhtokZDePIJoS7kCitKyaptWaSuj9FokBYrRMdOY4x32RhV2NpyWE5u0HElQkyrBd0uQW2JflpkfwPGw4w7Gut4vgGlGoZpYkUDMAw87aIA1zHQaPTGdVS/jy4WUMMhpc1NdJIQL6ySdpagtwntliCXOBYTHUNszQ00MTZxmBFH4Lsi51epQKmkZinkXK87SzVKaUpOhOM47O6KkkOcKhRgmxmmTmEwJLUtTJVCr0/k1PCdlCAz8FZbJOkOVncPt7qF0VwgxqZVSdjfd9BabNm1bTMeK1w3wzdTLNOlXoev/moouxlxrFjvlbi1pmYW06ebUChKoP7aNQG6eUYlF+oYDGTeKBQE7Gxvy9+eOyfjM3enV0oA5ItfPKdV5Yp4Bw8KAMqHjOvOwZjnyXPVbgsmENqSgKYoEpC+sSHnA3OwdPz4PPMcx/I9S0sCBh9+WI5RLApYjSLIh9zurkSOhf8r37m4KFJlS42IrR3F6qpDGAo43diAb/gGuc6lJfj2b5djb27ODXy+8RsF1J4+PZ8DgmCusvOZqSLHiRMCHMtleb4fekjev3VLhlsUyX04d06u/2Uvkw1FoTB3hnNd+cm9H/b2BNDu7kqfO45E2I8enctz5xK6YpKqcF3FyoqA23zvp6d1YzmAzWkzGxtynDyImSSyOel05LsdR67pox+dBwN2duY8+jxTUK/Pi/FA1qRHHvkAjvMazpyZb65yyc5aDfb2nsQ0Pc6fH+GpbQp+yk/+9E/zN97+duI44qN//MccOHKKe150lsT0qTc6fM/3/p/4dowTDlHPPst3vfWtvOOnfopDwEq9TqVc4vFHPsZjTz3LL//SL/Lj3/3/4egrX4ubE56nIXA1mcDJk6iVFRrlkjDtpqYhRzlP9+ZBaq3b9Oj+tFHivBNu1x7WWnZcuQNUHr5/ob3Q/pybUurf8iel5P6hUupfa63/38/3OF8UFKc49Cc2BTfDcR00YJkJ9EYUopCkXAchLEwLzablZsUiYdHHIUGWbkWCSYiNTUSSGuwOTKJIU6+rKV0AtPboJ6LwUCgwA2Z50UNOjYS59KpliQ6qSK9FJFPwOh50cabVAgYGDiPMyRAVhuhajUxZTPBRZEKr0Jp+X1GvK0apx7g/pJp1mTQPYJhCXrHQuEQoIMJkZ99CWa4Ivzt5gaDGyiJ0pglx2esbTCKDckXoD0rl8lWaxVpIMDLoTRwWFzWeZ/LkxRKWBc1ajGulmK5L2wMVx1QKBoWCTRqlOFnIrT2fQskgTQ32+zLx5mL2eaFHlimuXpM+3tuTvssn9b09KSrsxz4FW4umbCbR+jiWRU24eRqlNTVvQtF3yaIEFWeYtkWpZHDksMIypUhwMFCsr5ucOuUzHKWUpzJHGxuSntVJhrpxg/DoQQzLobcfs9ww6C2cZANFp7uHG4xxkohEOUxaB0GZTLBQ4YRx36G3l+I2PcLUIk2LFH25jsuXNJ6dUvAMzFIF3zAwHckQKEMziTIsx+RQrStFXt5RXL+MU/VlZb51SzqwXCbKLKLEwC1W6Y1sdFSgYoZ4WQjEjJsr8KoDFLeu5GRwkSn40IfQ99zDZtamXEkpdNdxNm6gamUW6gltp4/GB6+EPn9e8u8nT2J6I2oFaNdHhG4GwxFZb4K1JIuTmuZ57ckGM5eX0YjQKzJpHEJHMdVf/vcY4xHKMHDrlwSBudPiuanmcpopVKWEARSnGZ4CA1K3QGx5NOsxGTblsqCJ9XXR9W61pFCJKEIbFq6VUSiYxLFQelLHZmsHWg2HYtHF7O6hsgylNJ4KCN0i0T7YPoxXTjHZ7FNxHVRq0B0auAWL1VXY2kyoOBF6f4DdHXAtW6W17NLuKG7ckK7upi5bXWaucNWqAOFcJ/bwYQF8Mr4FgO7uSnY3VwMAmVuuX5ffT56cuzbWagLMciMbiTjqGaXAMOYbhno9L6zVJIk8MwUfxhMYjyXqnfN29/YEvIIAtiiaTsSWgMdjx2ZuvFy/Lq+Vy/LcLC7O1QzyedAwBBzv70vkN5ePzZIM307p+i7f/r/De98r5/GiFwmA3t1l9vvy8hyMvuENQtNwHHHC257KiH3oQ6JWobVI0U0mAtBdd27W8pGPzE2Ypp4TXLgg53blikSl19bkWnJL5fx8P/1p+c6VFWYZp3Zbvn9KSWU4lMcz57Xnc9OtWwKw881L7qXz678u93RpST6f39f8e3Ld4o0NuRebm3PzDTEmkkJEx5HzrFblHBoNGWd7e3KN3e6M7cTDD/8WZ87cy5UrVSoV6eOdnYQnnvgQcdzlgQeOUakMWFlZoVY7w2Qy4V/92GH6kwmHT9zNi++6m8XVE5iW4nd+6/coOgrLUsS4qGyM86K78RL4zu/6Xo43SnIDPJ8DCpqFAscdi8599/Lk1TIdM+LoHR7O3iYqt528dEluYruNdr1ZSrBKn+rkAngn5OLzMPr/06aUPEjXr0sHvsAl/rO3P42j3Z9lQ/OXuCmlvgf429NfH0Ec7o4ADwDfrZQ6r7X+qedzrC8KipWCajXDiUdYm5sYtdpUf6kxi5C6BCgg0QYhDgUlci82MREuETYFPcElJkQRKh9lJPg+VMqAkihpvokcjeaTWw6GYR5NbrXmQulSZa0Bg1QpUhIyDCIs4smQEiNiLCmoB1ShSFIokygLkwRbR1MzDShZEyqLGQkmWWaQGQ6G3aCkJqLqhkSXJ/i4hJhoGvWUOBNzg8T06PUNTBM816HX1Zi2gecbFCq5NKxY5WI6HFyQgipLO4RdEZ+X6IsmihRRZNEfGgT7snFIU5fJRNFqZpT8gPU9b6oBlNAsJCR2yq2tAv2+9KVta/b3YWtLALFtz6MsefrYdaVPd3bAtVNWFyJi0xPTizjFDCfU1QAVp0T1JQJVxCLGS4YYvR5pZxHbjHFNTZQ6+L7DeKxpNtW0wt9kfVOiJlEkkRzHsXhq+wDqXMZKfYcHW5cx05MkE4eVwgBfDyE1GNdXyEybTCssnaLQmGlII9rEcEqoUpmqDe5wl2E34Xq0yMlT4FsZzvYGOsoI7SrmYMK4sUQhHbGxplG25kDFoDjYJPWWsZplWRmnJf16KpZtmxaDuMJu0mGcKoJb0GlaLNUSzGYNy4F4fYdYuQzqB2kcSkV/+vp1blXv4NN9RV+f4CtfVqecdIlHEc7TD2Pt7IiSxZk7YPkA6uBB0koNOwxR2zdRQUAh5+YdPYre24NWC10sorIMneeVz52Dep1hcZXHHrPY2YQvndhURiNoNlG5dFyxKCt5rwdhSNpcJMHE18G8bNd1SWIwrBSHmHRqQRviceCAYntbzFlSbWK5DiAydvWKRZjYGAb4vqbqjDG06F2rXD2jXsckQ6MolE3STBFMMq6PWrR8AzWRzdRwyndfWoSamxInRUZDEz8csbHhUK8LeMstxgsFGbeDgVAcpOB2znvOs7T5eP/EJ+RvbFvGfG7IeejQ3Efg3nulm0YjmW/yusck0fR7mpUD0O4o9vb0TNGiXpcopmlq9vcVNT/k5qZNGBnccYfGsuS8n3kGLlxQs2K4nZ08nT5XpMht7q9ckeF4771yLhsbAg5zCsdkIvPgoUNy7UqJbvGUvkkQGSw0I2xLU687vPKV8PVfP6crPPoo/NEfSdQ9r/fMKTGtlhTvPfqoAN4LF+Btb5N/nzw5tVrfYkYD6/flHHJzi1pNrunMGZlfrl4V4Hqb0AkHD8p9eeQR6YtuV4DqYDCnrdy6NQebOUVlY0O+Y21N+j3P1BeLArpHIzn/alX6cmtLhuBgKhhTqci5Pf74PGvQ7cp35rWxnY6MoUJB+N7nzs0l2HKd6Js35TtzX4t8bj158hVcuXKVEyfuYnPzGbS+QZYZvPa1d5OmbTodi3J5SrUJRrz7v/42P/MzP8LXftVX0R29i6NHjmIYH+Jv/s2/w6m7vxytJSuptEYnKYQJiVtl9fgx+sMEU4EyPDw14ejdd7N654u4ulFEK7jSa9BKYGIfZiXcx7hxXZzkpuTtNAPdWMAoVUgmMc5oH556CpXbEm5vSyfmvJ0/S9vZkcFx+vS8uvWF9kL7829/C4Fof11r/Yv5i0qptwO/AHwX8LxA8ReVZOv3MxHx379F4hTJiiWUykix8HRIokwpqNEh3bCAYRliiqCSmZRbhIMVBdjhAF0sM1Y+vb0MWyXU6pog85kExkxVYTyWZ7FanZt5jMdzkFwqyUSZR3uGQ03Dn0haWBnYBEwo8PEPvIc3vebV8qGce5GmJIYtLmlogtRhHFqUnRAsRaoc0CAGtyGGzlBBICoEcYxqNtFKipEyrbBIGesi/aGBZWkB9jqjpMY8e8Wj2rRn+sv5om2Qkmklk90UlUQRlOwJGoNh5M741Ts7UCxofF8ThMJl9ry5sYgiJQtjzl+y+OQjBmfuUDML2dFo7vTb6aiZ2VCnI11x4YJM7IWCLDbjMVhmxtljEwoVi1RZWDomi2K0ZeMaKalhEuOCTrCC8TSdanBj02J736JdnBCMEprHGuzvSzQlNz755CdlQT58WHPPHQFHl2MsR7He9WjXEiwbzCTEGPTJooRJ5zCpVowGKbYpka8gUhSMAL+3MRunk9oSWaGCIiHVJgUVoNKEVBtEVhGQKLfX2xC5LlUhm0xwfItyVVEwQoydHaEW+AVGoxRQFLwMo1gkMjwmqU0yiSlOdnA9A9VswM4Oz2zU6VFjqdjnkLeBGgxgc5Pw5jYfzR7kN586zl13Gbz0gYxCQXN08DgqScjaHSatVZQCN+ijRyOybh+nvysFeDduCEAvFAgPn4J6DZcIdnaEdtNsTp3nFHGsuL5bYG8PTh4MqY7XUbGoUczSEqPRjBSslRLjkGCIKpXExS7L0KUSoVdBKwN/Whw71D5gMhjm8seSDRK3R5k7DJLZ5xXIYur7MrAGA7h2DX3mDCOzyvmLJs1yyNJCSjfw2d0V295GQ8+CSJalIEvZ3VPccSQgMlxurRuEoZppk+feQLer0mxuynjLaQm2PZduvnhxbr+b6wXnerYSlZRrqtcVxaKeRRaDQIpd40lMvao5digmNP3bc2Ls7Ci6GxPO1m6R7e4TnbobLJNwZ0iibApNn+FAE8QmxaI4YY7H82gnaK5ehRMnFI4jqjc3bogzm2ki3x/D5qbi8cfndUz33CPgantbwH2WCWhdXhawXCppSm7MuUsWl68YfMd3SEHdl32ZXPc73ylA7xWvEGCap/mbTfn3+rpEjXMxlJe9TPrkmWfmxX2WJX3ueTKH7O8LuO105O+iSIKE030aJ0/KORuG4CTLkr8bjWaJD06enEeoazX5TC6xBhKpHo/nlvNxPPezCcN5UeOtW3PVwdz4yTBk3svdio8fnzsF5oV9W1tyLjk/OafATB+V2YYmLz5VClxXc/o0fOCP/jFf87Xfxgc+/HGCIOK5555hcXGJM2dOMhjc4tChY+K+qhW/8evvwlIZr3v1Q/R6XVJM7rvjXl561zGGTpPtvovWYj9dLQSoIMQ2M8Z2BV9NmMQ2V65bnDo0ITJ9TNKpU55DtDvAvnkZdfddQpUixdu+iZWEckHtNjqMmNgViEPW+2WOxOdQ3X3Z2FtWXkH7J93rvlDLaRKOM79pWssAShIZlGfOfPHj/E/U/qeTZHvood/+nX/3757X56077yRN07/Skmy3N6VUAARa69oXeK8HOFrr58Xd+eKRYp2J9FhtGaVTUZLQhlQ5ZwaOEWIZJhPlo5TGSiY4lnB2FWCRYRGgbU2Qltjc8bFdk2JZ6pki5RNNlRdyJzutZdLZ2ZGJKMezeRQj53QJoBR1hrH2cJTGnZbBPfaJP+ZH3vmjvPGhV03d45iZF1i52w5gDzcx8YntIoZyQGusUR/LNSEcw2RMUm9h9HoSpRsM0OUKmRIO5bUtH9cXWoRlCe3AVxP6Y4ti1Z7yc2UeUip3mDIZDqHZ1Cy1ErBNXFcRa580TpiM5fRMEzptppq1ETrz8HxFryeKC9UqRLHBxz9q4fomd5yV1zqtDNcISSoZBc8lTs0Z9/HWLVnU7rpLokG9nnTLwsJUIq6hCLMC/V0xGdkfmlQqrrh8ASIml1FQIfgWkNEdmmjb4cRJTXE05LFug2hdQEm1KvewVIJf+AXY29O0WxnJbh8vuIw6c5pDlS6xWWB/6DIJXXRaouTHeIZB0I1wTKiWEhJMwCFxXEbFIq6OMcMRzqBLosB2DW7uF0krLmHokkYZC40RER7ROMarVPBGIXqwSUV32dhrYI3HKCNB97rQbBJ7JYK9IRtZm6PtGH/SxYkGYoEcT2ChOV8NGw0OJUMu71jYBYvYKuFsXoReD/fIMq978vd58UoJb/kIHzz3AKfOKNTSEvEoJCq2KWQjjPU1WXm3tpjZLAL62LFZFZhjZZCG0O+iFxYYUsY1JO+eaAvHillc1CwvZKhxSOKXsbwIldtI5ymWKX1CsjjJXD5iSo4UNnZKiMtQW6AzdncVFT+kYGjGlkerpVFolJE/4XomBCm/gW63oT+A9Q2UbaHvOEuYmGxP083KddmfaPb21Cy7WlATdroWqWHj+9Dtmhw+Ak9c8KnVNGGoZkY9eWa3VBLwlMtj5brfrda8IKxYlM+fPi2AJjezyP1NLAsOHNB0Orl2sGYyFnBWKAhtyNrdJD1QxzIzwtgEAxwVYZCigKWmRcdPsHsaTh7G3ruB7ixQaFskQURiOLTKIb2wgOsbKCXfv7cnz8f584IjtrfhxQd3Mcs+3W6Bz3wG7r5bc2gp5MaGg1JihNFuizqC7wslIpdCO3pUIq8gG4RqVXFjw8EvyN888IAAy/FYAO473iHzag6q41j6bmdnzp9+4AE5Vrcr55vTCj7zGYn2VqsCEms1+N3fnfOE89qPvLAvVw7KdX4dR647p7/k+sTjsWT4cyWPzU09K4g0TRkvL3mJvJdHdHNJNsuSe5brQm9szBUgcorHaCRz64MPymOXK4Dkms65HnGuFJE7GufKI9WqRInzgu9mU/pmdVWzvDDgg//pnxN86Ne4Vm3x9d/8k2j9Yk6dOkm7bXNoBco1xe7uOX7zN3+F17z2zZx/9kmOdjrc++a3kBVrPHvRZh0DJhlJIo/m+fNw9g6Xmh2QDccUKiZKKXw7YWHRYWtYnBZqGzNwHxslCu1l3OmC6q5fZVRZpFxLUFsbctNtGzuI0MqgXXVIew5Wu43u9URxR0sxrrHfRRVvS7FMHW557jnp8IUF6bhnn5VOcRzZaeVW7iCD5YX2QvuLa5vAilLqLq31k/mLSqm7gDJw/fke6IuC4kJ3jajaFGmy/V2072Mo8LtdUstB1atEhph0+G6CO10oclXlEA+PkFRZZL5Nyzen7xr4TkZCxES7M3tRuRB5zsT5ba4Jmk/ahYJElbSWSKnnpGQ4U+voAlka870/9A/42Hv/YA6IlfoTaSANUKtN/fQsPEYCtF0NWYwuFAndEsoyCdoHMUnxVAxoDK0wSFhux4wTk/FY0y4HWCol1A6p5eTiHDPdU+EgapaWpjQGlU751gkZNqQphcEmkWpiZQaejokTxY1tH02BA82A4dAgy2zEBVQ2BMdOmiSJIstkEt/vGTSqDpduQBwranX53n5fog4HD0of5kpdQaBm0Zt+T2MamiQzAAEiTAJSBf3Am+IngwAfDZg6oVKIcKyArT2fgdmh2lbsd+dRmigS56ybNzUvulvzxoPPsfrwbzP+a98GZhUzGrC3FYFr0Kll2FaGNixCNJUaWMSk2pQx4yUSMU0zYm0xsppY9Ro7V/vYFZ9SRYMyKZY0WWYwwcSIQtF53otZLo7w4j5xd8SV+Ail1gTX00SdFdT2JqHXoJf41DsmmWOiJzJ2lO/PV/Z8TJXLFJXiruwWKAsOH4OPvj+v/oE4pqH24dKYLzuVwI4DGwmq3uHmJcWhky7+hz/MzCVmWrmkk0Qspk+fJvDrJJYHKLTXwI4njLoBQcGhYgd42ZBA+eAgGyEDzLVbqII3D6d63lR3WM8HvuuicpvVnR3Ccht8F1fHWIimlNrZxi9WiP0yifJYKApVyU0nWGjEkmcuuag1ZChiZRNXFqGyhEoi7K1bvO+ZVZZWDSoVGaO7u2rGz63X4embBXo9eV5aLQFezz0nOrSGKdHVPGuUF891OnOaQ64Jm0c8l5fn1KtcwaZen0cTq1UYj8WU58wZzdHFCZsll1FgUvEj7jyWEZmeyFEv1CmlY8ZWheHEoOCItjVoTBK6fQtnPMLc6UFjkWtZg45WlI0xN/eLeHsBS9URJhZGYmDaiiy1GfQzCi58+WsnXNsqsLauGFp1msaY06dk42uaimLc48E7XA6sVBkOMpYPGNRqAkAPH5a5cGdH1F1yTuyzz86L9Lpdib5+/dfLXHT4sFAapgFDqlWhD6ytMTUnkn78ru/SfNVXzd38tFYzKkeSyPFza+ScznD2rIC4pSXZdOcucbmKSacj52JZc16yZc21hJeX5dwGA7kvS80Yy7cIQsVkohmNFEEgn+/15LuTZL7J6fXkGCsrMrTzAsEkmdNZx+O5AUtOtckNSXKzkHZ7zk9vNuUYeZ9Vq/Lac8/NC/NuXf8jfub9/4a/EQV80zPP0Dhxgtj4NE9+7at45NPv4+KFHr/5q+9nr9fHdl3+9t/+Hu655372XvmNpKlmfRhiZi7FsiindDomw4ncr4UFMEzF2CijqkWRRtUxJhl2FrC26YBpsNSMuHTV4Pw5k2pN8ZKlCGuaPUsyxUc+5dJq2twfrTPyW0zaS9RqCmPQpdRfo0cJlxB/WtEYVttYkwFsbsx5Iu12Hhqf74CuXmXGz8s17D75SdlN5IT/PE35Qnuh/cW03wC+B/iAUupnEE7xYeDbkRXvN57vgb64o12vR4KFoVKySUycmWjfxzUiUm2iyDDJCJSHgUZgnihLGGgSLJiZPANoHCJibZJq+3OKzkTrUt322Xkxh+uqWfTBUJrBEKIQWo0Yw1Ckc10LPvShD/DK++6kepsX++yIWs+/YUqDsJBoeP6ZRJuYWcqNXR/Pzlish0SYRKmNv7/O0KxCwSdKTIYjRbuZYpspKRZJZpCmarqZFprD/r7ME/k35DrEjhGTJRrTt1FKkSRqahOqybKps16m2JbMPvVCSMlLp7JkUHIjwsgkTE3CMJ9M53JDpglRqBkNUoJYQG6lojAMTbcLwSjFdk0aTYXngW3EOFmIxuD6lke3b0yjOnpGLZP0+Tx6n2uUiu6xKFRIVFpAepZpPvlJqeR+7jk4YV/ln73mj7Be+XL2z2/hnznEsHGIOBM1hHIpg6nWs6UiEiziWDEeQaFiUDQCosymN7bp9WT7VS/GxJjcuGnSaEj0LMs0+1sxjZZBsrWHFQ6puqGoREx1/H71j6q89tbPwytfRfHEMhkmw+0xkTZpNTSWZ4vNc5ahcu2/XIYgb44jq3suGP17vyeo4dCh2Xjj1i1Z3fJ8/+nTgjI2NmRhKRTQKyuyeBQK6PYCj+4fpbXi0WzJs+RkAaHh0e9pquM1vLKL4TsY3X10HBN3lklNj25PUQ62KY02xUp1KjKsc75NuQLl0uw5YGvrc2wite2gW230NBK803eItU21oqdSiWJsUyxAyYumBbQiQRgmBpnjYat0utVzMJIYNx2yOSyx3bWp19VMevDGDemGnCaVzwWGIVG706fnKf08Xa+1SJltb+fR0DnlKgc+efEtzNPfuU9JkmiqVQFYk1HK4cOKWsOYjeudHU3RjuhUI1IUE2QxV2SgNJk2pyBLgHo6ibi5Bs+ct7FtRbUqxXh33AGDvqZa1VT1PsvFAbRahJnNzrZG2RbDEXTqCUbBQ2uRYqvXYXlJE0ZqytOeesB4IU4yQWcZ/c0Jl4IDHDwI4xGUyvDpT8vGNqdGVauikZzLO+amIz/xExKZP35c+qPZFAC7syPA9sCBqXNmD9rNlFZb8c53wpGjaiptprh+fS5vlxcw1moyvM+dm4PPvT0Bxt2ugMojR5gFMx5+eH4vc8zUaMg5bm/LOa+uwktW1jlx1uHqboULVy2CQJGmAobzR0rMlubGJUkyB7q5vfWhQ/LZfn+e6MnNHfNyglZL3tNaNhUPPijX2evNHdpzI49+Pzdk0RxYusbj7/9ujsY73Hr8k+wBLcDxfer33IN96g681hJ/4699K4urh9jdm2cLcqe72+uibHtO3xiP5TP55sHzNFU3AMukP1QUnQTPyQjwcfWYRNns9ywWqgGTUFF57lMkZ+4idXwuX9ZcPpfwpe1Psda4k5HXZKkZU+rdxEhikkabfjejPr6Fce0aydm70baDNZ5qzQ0GqFpNTlCEr+Um55v6XL4jd7w5dkx2YLkr1l+y9gJ94i9PU0pVgI8Cd3I7gBRA+RzwMq1173kd64uC4rV1OWyhQKYVajImcMuklo+Zhri9LSblNqZtTI0zRMkBIMAlxSYHuh5jYmyx2YgVe3vy8Oc8uvwagkBPfeQ1SmeMA3M6gQinsFaJ8VQoZg6EGFoTa4NUOSiV8oH3/SFvfv1rZyldbu+lfPUsl0mVQYo5s5qOcfCZkGkDc2eT2CqgC0UsBwLlozFkcUxSSFNMV+ExH3MRJuPEp7+fYtgGxaKa6nuq6YSaURvcxK15ZIUSUWaRhCkFPyVSPvtdkzQVKSVDSX+NE1d4h5kmNWwGA027qTFthUIzGWu6fWtWJDMczheerS0YDDSdWgSmSZyZOI4sPpZKCMcZ2jQYji1AzWxMfX8eUfM8oXmYhkT388ltNJLFLm/i/KRxbE0UKzY35ZqX2hG/8usWSaJ48O4J9x/cgtUVTMfixg2NTjNKfsqlqwavuC8gNl3CyMQ2Enw7nRZN2iSxFEImhs1gZNHtGdPiFk3JCtC2w3BkMBgqXFdTr2mqo1tYarpi7uzISnryJLn11Kf+cBcjDDjrXWTnJW/G9RWNyRo3siWqVUWhiHBCua0gbf5wzJFcvqoePSqd9u53C3/u6lVZMJ5+WoiVriufiWPJAZ8/L5bM7baEt9pttGEybB/Bd1L6Q5N+6FAqKUajjGY1xbYyxjtjfDshK1cwFDAe4Y27ZI2WbEp7A9xkLEhlSnocUSBTsuIbcUhh76YscPv7cj62DZ5H1mgywZ9NJxkmou2STZ2vFFobYtuuZMzH2Hg6ZLPv4/nGzB5Wa+EGW90dYrvIztijPxTnxiia69PmsoCtlvxMJgKYGw15PwdZuS7viRPy+sbGXG1FNp0Cas6elcu+eHHuSpbPArmBxHIrwi8aVEsJk8wlimQjO9wJcV3NcicBwyDUNt7WVYLOYRyVsDuw2etZ7OzMQetwKM9DpQKLnQxliInKanOCY2YEmY3OMmJtMxgbs2LctVuwtg53nY45fkKhDZPNDc3S1me5bh4hKtRp1MF1Myp+gnPtAmbJ55Ze5tw1j3Ix484TEyKjyK01yaYdOyYbjfe8R4r0com2p58WkByGzHSgTVP68eGH4bOfhde9Ts+oKK94BSxXhrjddQZjm4vxKlFsMhhIxrxchvPnha61ucks06e1gOtr1wRUXrggGMn3JXrrOPJ525Zz63Zz7KRnlJeVFbh1S3TPH3+cmfnInXfOTYDyafzgQbnXTz89zyQcODAHnFkmYyp3q8sf1Zxb3mwKkM855I3GPCGUF+ZZlvTb4cNzAH7zpvx86etDjsY/xdq7/x3t7VucDgJelmXkE63+gR+Er/s61pI2g4mFYRgzAD4YSFTd92Vj0O/PHfRyDBnHc7lfzwNLR4xDkygxhEqYBKSWy35XCrAVMA5MMSEhwrt+ARUGKNclu3GTC4W7OeRtYY+7xNtdwuYBsqPHqF5+DOX7qHZ7Ku/SlRMqFsmSFBoN1JXLstG2LOHfpanMcfm6mkeLJ5M58X1paa7F+ZesvQCK/3I1pZSHRIu/HOgglIo/BH5Saz18vsf5ovSJrNki7E5IKaDSGNtwcMyUQKci3l9dQBsWydSdDlJiHBQai2gKinOQXCCHqflmU2tAi7lHqHxAUfIj0knKzsDlQGOCWfIplcyZMH6GxQQLhwATDXGEM7UbGtslkttwfv7PUNtkysDwTdwkQCtFioXSGRkQ4aFUisbAUhm6UEB1R6AzlFUAU45kkKFMTWY6JNogQhQlJJVsYVkZC+2EFIMMmzgWYOr7UAu3GKoSW+MaFUtRsAMKBdFITjNZBIKJxskC1nddvEJR7HODhMVqyGPnLY4cVbhWjIlwbL2Cy2JBrnE8nhd+WJYsHqurCq1dbCLKxZgIj7U1TbOc4TsJmWHTbMrkmxe3mKbMa4OBHG97G2qFBK9gok0ZMo4zczqeZf6tcR8r6HMrWWZ/36RY1ISRwdd+ZcL6tkOj4fPk/kGM5xTNpmalMZZ7WPBolDW2qTH3Nohijz2riePas4r7pfqYWNnoFBkroU+hoNBacXXN5eJFzWgCx45JwctklOH6dYpGgMoJ3a2WjIftbVQc85JjXbLRmPToAzSqils7Hv10Fa0UYZxRJcaZalp//tYxw8AglaI3rUVp4bd/G37nd4RcmXshnzghNs3VqkSpZReI7nSmeekK1GuklsuIIp6esLtrUSkqym6AbWv6Y4taTeGbAQEeVGsME02ZEJMMCg4j9wA+IbaRQa0A2p+FvXSWYegUS6U4YR/291F5pWW+gFcqUK9jpAnFeA9cF60MJrNnOSHGJsocBkNFtZhgGQmmyoSfrDSVKtiExIg6xXisARO7vMDurqbRgEpN1tG8AMv3ZdFfWpIxOBzOZdNMcy59ldftXb0qnPj775fuzXXMfV/2FqWSKNzl9UHjcZ6p0fhuxpkjAcpQmP19HM9nvVsmyRSWShhtjeg4XeqeYnfY5Na2y9mDfYztbdxakz/8ZJXOgjxgSZIXy2lOnJBnYHtbUa/BoYUAPI/1nQJLtQnetef4+HNN0lKF+88MaTSKmNvrHDtgkd1/AJ2Btb+DqlY5Zu7w+9t3srxqcaARUhpvkvQynMUyhk5ISnWsyOTECWhUwUw129say1LccQcY3T3KdZ+v+1qbTBts7yiiGNbWFH/4BzLndjqa9XXFi14k0dvlZeHt9jYnfNNX9rg26XDuqYR0JaPiVHn2ksUr77zGqLUKUcLj5zx2dhRLS9LXL3+53JPcRKTVkvv58Y/LPTh2TIrj0lR+/7qvEzBYKglA9jwoF1LOnslotAze/yGThQXNcKi46y6JoJ8+LeohR48KME5TkQN/6UtlY3Tlivz/1Ck5ZqvFzN1vf39e5NxsSrDg/HkZ9hcuSB8Ewdz4ZDCQ+3m7cUepJIC6UpkrURw6BK9/KOFlN5uoZMzEMPjgAw9Ap4NeXCSpd0gPnuDKXpvdro1lS+Aht4dO07nyRm6hnm+wcmUl05RNRi5NemghoVmOGGYFlNIUrYQUjdXyGY0NkkTle352BzYLC4fwx7voW7dQKwc4cf5h4te8gWEAT6ewsv805efO0TOK1CZTLpJS8lCOhVyfOkWumycortZZ1JvSIefPS4fkIWwh5MvF5R7r+U7ihfZC+x/QtNYB8OPTnz9z+6KRYq01o0GGIsZPJ+gwZOzXUakmS1NsRxErF1B4TLC4LRULhDi3USg+5+iAJsvmfvcSYcoZyZKydInIUER45IDaZUyGIbJSBEzwcEiAjBibz3z2SZI45GUvuReLGFNnKK3JDINEWUQ4IsdGQpZobu36VOsGJUdc9xJc4hQGPU2hJK87t3GlM0zUziaxXSSt1HBUOAX8Bu7UuS/GwkXk3tSUezgaaPpjm3LNZDwWOoTpSLQ6zRT9vsK2oeBrPDMkDmF35Ap1wR+xue9hOiblcgbKwCATkoqWmPhkMk8jVquSXswXAtvO6O5DqwmXroh28cICNGoZWpmzRT6OZWHodmViXVyU+TFPR+e8vdz56fYmwFrP7lPBjbFtTYo95SRqSsaE3sgi1A5xmGGEYyLTp1xVODoEDaPYwXZNrl2DYgGMKXMhZyiMx7KIlMuyoD76qGI00rz6VRndniGLTSOiMriFGvRJSxUxlOh00EqIPWprAwyLsNamF3j4BcXOjjimNRpSYGWaGkWGpwM0ikwpMbFBoTPY2jWpNQzKf/zbqPe/D3XuOQlT5WGks2dlt3DokKzWtRpqYQF99Sq62SazXaLOMuHqMbLMZG9PrqtQmEapvAhntEdWqRAbHiYpDiEBHhoTFQUUeuvQbDIySpCBM9jFiUdzZwEg2+/JzXFdKPgY+3sC4mEuyVCQnZWOY0kXNJtETgGTjBSFTUqELYA3DDEMTWbLc+8gz0euDw6KsfZJUmNmeZsrRAwGCtedp64XFgRwPP20gNrhkKnSgnRd7qWSG/ZEkYAJw5Bj7uzIWK/X50Yce3sS6cxB9N4e1EoxJw7HaMdBGVBIh1y8YnJtwwMFL797SHblOj/3ex3ufbHmxfebGBWxivd0wKNP+3zqUYMDB+R5ajSm63+Q0SgG4HpcuKhQSuHqMZ0FRWx4OI4SlzY/wI+6uOlETFYuXEAtL4PnkZSrDGOPnT1zZke9uwuNekZ1+wKTXsxg4Th6d4fdpErncAlQU5qSFn32dExgFCnoEdb+1hysWNbUAAh61/Z46laD1VMlrl2bFzEvd2LW1zW7PZtX3z/CthWG0pg7W/DBD8rN2N0lO3mK7NUPcTE9yjg0uXhx7kS6tSX3o9ORMby3N4/k59zu++/XPPwwnD2rZvd6YQGOHtVcu5rxkpNDyuEOF+OD3FizKJaEJx3HUkB48KBgsbyg7tIliXCfPCljp9+XBE2pJPcH5gV0ucGkaUqG4okn5tT/3N/i8uV5EWCpJN+TA+LcIjyO9dRARWQv77xT853fPGLh//pBdp9+kitv+gqOftPf5upjfXo9TePsMt2e4okn5Fp9f24t7Thybd2uXFscy3WNRtKnMHcqdBwZ8+1GShLEJKkiVg4tty/3vtgk0jbb22omGmEYGQU9AtvC1olEjHOR7IMHiZ94GnPYw8ilKHd30csHUGkCUYTa35f5rFQi29uXvz96VE6kXp/vbn1/fpNbrbkP9okTf6ll2F6IFP/lakqprwe+RWv9pdPfXwL8IvBPtdb/9fke54tGigGKXkoWSAo+KlTRWMQYBHFG2x7hqIQQhwAXAJ/JlOOrcfW0SltpYlwyhNs6rVOfukHm0mT5+FPT/2oypFI+0nn6HkIKU7qFRivhn4Y4WFOge88997C3cZXf/fV385Vf8irMNBK73Hqb2LHRGNNPOhiWptlR2CpBkWFpjcUE34ByfQrw9G3prOm3GK1lbC1c1hgPjwkG4hqXofCIMMgoMBbjIMAru1SLAds9h/JwB09n2HZZonmGQaFug85ItUmkPXCg4WpcQmJcWq0MhwA0jHUBh0Dsq1N7VhDcbsvkff26gAnZ8Gs2NoQTmEwiFuqwue8wHivC0JzxAqeeFTP76aUlWTDieF6wpJQsfLlJwVSpbsrXZHZvLQts16RSTEmVBq3o9hSpbTIcpJy/pjl5EupNg409+OAHFMHY5Wu+MqZZTEi0wfEDAdt9l1LFoOBrwhDOX1CcPq1ZbkfE2gYMzt6hWbseUdxdx+ssMQkVemuLNBygByM2naOsGGuQJETaJZtMGFMnG8fshSJrNhozpa6IXvSgl7FUD4iw2ek5FEsKTAsUGGmEpSOSpEiaKvYrB4mqJ6kuRvgupO/851iXzxOduRurVWN4s0eoXFqLDngONFs8ubPI9W6F5JJBZzB3K8sDNUkCvYlNpdLGVrFEE6dFnkLxUSgbaIh+lkGGPdjFHvVAKfTO7pQbbBLWF8mUGJ8YOsNvd2Ag+WU9HTgKIAzRYUxY7eAZGkunaKVm0XKXGJOUyHXxmQBjcR2cKlDkG+IxDtevwWAow8Eg466TAcO+j2WpGTc4SWTc5XqxWgsoyDIZh+WyrKkXLkhQPbee7vfn2Qnfn4lqkGUyNpeX57zPQmHK33dstvuK1XbAOCvw6LNlVg/BsmPwnvfApz9d4t7Dy3zwsTK7loezGHLHHYr9PUiyApNA6BKDgdyn/LuVMnjiOY/LV+DuFwmvt+A4mKM9Lu85ZAjQTUo2cX2B3iglHmcsnzgFtqRZEkzSMKFaNdjZUbKZVZql6gRrZQmno9lfd+lNFmnVE7JU+tZ1NXu3JrTtLquXPoht2+jhCN78Rvb7Jp4VYRsBQX0JLEX9iOYlh2xMK2KlPJK0uGHh7G+yunMOvbiEDg+gtrqoMJSHenNTMhsLC6h/+28wXYel13V47FKVI0fmNtFpKhuSJ56Q30ejuXnKyZPw7DOazQ25F088IeP8/vs1994Ljbqm4kZcvRBz9q46nfU1brDKeCyFwd2u6AU/8oiMmWJRosT1uqho1GqzPd1MoSSXwRyN5P2LF+eb6uVloWJEkYy9556b0y3W1+X1Eyfmkp/1uvR1tSKundeuKyxLk2WK69cVu32X9itfzXv6XV51/JX8ym8VuHq9hGFA/Nm5Z0VuPLK4OC+gKxTmChmFgvRhsSjfCbLByGls7TYEgclwYs5UQCK7wnBHU3RiMC3SVM3q3va2NXefFBfKfuRQnexLxisM0Xt7WKsH4EaKNgyU4zA8cS9/9EiFrzrwKMZ4KF9u26KPfu3avGo8t/Gr1eTmfOIT0lmOM3/oXPdzahVeaC+0v8imlPpG4JcArZRqaq13EX7xSeAXlVKZ1vrdz+dYzwsUY1mk2CgkbR/jYllQKhnEuNhTW+VcmCnSLooEh4woUcSGg23EFNRkGom1CHGwSVCkaBPinMeIcKEsUgI8IiwipuSweAyuP0tfKqWITRtPRWTEJFiYpNhEtBYP8rrXv45fed+H+Lqv+gpsS5LAGRbzYj+bFFOMRzBRWBjErO25YFqUihnhMKFdjcj3i/nCH2kHOxrhWYChyJRJgIsh5XYEUzUMgwxTJ6RYpFgMModSJcbVGdbVS7OQq2o0sH3QkwDVHxHUV3FsSVubpKAzQlUg1jaTQco4BNcvzaJnefQ2j9bUanNt5/V1uOvYGK9k0R07TMZq5hy1vz9X2Vlfn3PYFhdlkci5e5WKaLcmiZrpfuZ6sZ4nc2OrNeVsRikVa0wpDYh1iyxT+EZApy6UEt8aM97c5rFPN3nN8U0OmjEHXlLCqFUxbKHZODpgN3BoV0MUmsGuxUIz4kDbRmuHBBunvw2Zpl5boFR22OofojoeUJvs4ERd2UApgxCXSeaiJhq3fx21vg6HTzGstfETRd2fkFkuvaExs/eNItgZT+k+NoxCTdFPQClsYm7s+BxojMEw+Y1Lx/jX/7nJO77uCK/61uOoCxdJb65jF8tklbuoHKwT9QOU78HeLqPmKvVigcqKmimsZJmkoPP0LmjJJtQNqkWLZ8/DoUMerpOhlfASTZVyqBNgKbFrzqo1smoRjSLNFIPQI8lMyr48RcrMcMc9lFWc73ym+WWtxCBARRG2HZKhMeo1pjqDgGRJQmz8qQueZE4UsfZguvHRiIX7gVWFjuOpGotHqHzSTHjmORDIadlpKiAk14PNMhl3g4EAiVpNwFde+J4rVoSh/D+P9lWrsl57nvyeSwFmWa6pa9GNSuxsZ9wVfYqUM/zTn6xwa01xzz02ca1NuS0A/OpNi4cfTnjxi2Myy+WXf1lS+K2WUCRLJTm/7W0YTQwaTYl2S32RRbHY4dgJAVxKgatiTAwKyQhf91FWGfa76FaTWFko1yQJFbWaFOqVyxrbUYy0j3ZNjjd32Yw1qlhDk5FlBvrKVe5cfwT32kVUsYh+5StROzuwtY21cpq1rkPbH2NmiejGa4U2LKLMoBBHOFtrEg28dQuGQ9TFi5JBOH1ashuTCTSb6EceYX99QvrV30bpzV+J6RU5dEik62wbHn5YNtyHDglV4umnxaFudVUOZRhw5njEG1+0zrs/usqrX21y552aZBJzqDHBu3SB4PjdRIbD2kbGRnCQJ5+S4t/HHxdZuFJJigNv3JBosWHI0G23JTq8tjY3W0lTGSO5UUuekdBaAp+3bs15xQcPyjkrJWPMNOfzWc4Xv+ceuPWZd2GnW/z1l30lm286yUc+ZvDII/Jda1djKpUy/+cfvpf3/c134vTVjOeeK2Ssrsp37u7K95TLQvm4+26ZX3Np72PH5PqUmmvIw9w9LwjmhXnb20LZ8f0ay95cyrRUEl1rTINnrxZo1FImoYlLinfkCJeejfAmitKBBuUswzAN9GBAqe3x8vsTglsexYUiutWCfp9Le3WWSy18M5YLuXRpbo2a+25XKnJDbg/J/z8x/Xih/cn2p3G0+1+v/QASaf0XQK5l9lvAjwI/BHw/8OcIiqe2j5PAnaZH8xR5Lstk4BJOLZYNAl1gv2tRKkpNWpZBqaiwdUqqjKnerJpyD6eV3TPWpiLBnZWv2QSoKS3CjAMS2wFTzRQezKJLrN3ppzURrizQgFtd5Eu/4v/Fr/zu7/A1X/01GCR4jAjxhB85yehPTOp1TUlNiLXFRs8nxaReUVIUUXJIyYCEFHMKUS2S1CCOJGqYFKpkM2pHSIxEMA2tMXXCbt9lMjUeAM1eT3PAM9GnTklId0qeTL0CA6vBs9stmnHGkt/D9hVds8b+PpSrUPJinIJFmBn0+7Ixd915VCPn0e3uztUhlFLs9GwOllL8KWAYDuc8wJUVmXArFYmiXLkinykUZBIulTTmNDgAohlbqcgknmV6an6gcGyNbcHerkHBddkcWHz2U5obNxWnT3u87EGRANiblBhbFg++VOHEPmubHpfW2hw/Aa26BkPz9EWPheqYqpegtKYU7pFRY3kZEjIhpFQaeFmAqUf0dw1qNYvieIAdT2RnUCzibG5y0N3mM5uHSffhgWMyPIZ+m41Ng0IJNmMvF2Rgf1/TaWuWmhH90ObmLZMsg8VmTNMLGYwsCgXNwiKkkUm0PeDF5Zv8yEOfYa35Wnj04zj/+WfgzW+G8RCjuwueh5sksDkkW1hkNLFnkdFcZjAI5J6k6dQsIUu46+gIZ7yPl6YcOGmhkoierjO0m7RqMf7eGqFeRIO4JCJc52HgsrVncvUqrKxo/FZCxU9FZ1mpz6kEn9glbDvASkJ0uUxmmExUge1tg6U0xLOYZkOk4NAnZIyPR0CKSZzlhbQGaSYa3Vv7Im0GDpalqdUUG5uKjY25WkQeARuPhVmSawjnwaetLQHOhw7NgUVOmRHr8nlEsFCQDVmui507tJVKcp2f/jT0eppeT9Z0yzLQhx5kwdX8H/8Arl3Ts5Q8CIXGMExiZXLuikQz3/pWUYOpVuV8HnlEsbkp9+zZZ+E1r5GNJAhw01qey1wL17K8qfKBg9Z1DjfAaNamD6mi25Xag+V2xNFVWN802R566FRTKClUsUbziObmpsGFpxSVKpwqKpwP/jG85/fQi4tkhRLDr3o7O3uKllJT9Rm4+MkuPaeDV1AsLmiu31Dcf7ZMbWMD/alPiYtZFEnx1J13CoLMrfUOHiSpd3i49xJ+9UMdDv/YgG/7pl3GdpNDBxIs22N9XQrr8g3eqVOywc754JMJLC25vOfxFRaXxBylWY5wSwH2ZIBx8gQFFXBgxeTJS2X6fckc5qo9m5tzQ5FaTU4zp26trwsgjmN5L47nihvjsYDMPPOQK5FE0dyXYmNDaBOrqzIPTgVguHVLxkyt9h9517suc2Z1xI/eeyfmB/+YhbevcPddJR5/XBwYzXKB50bwhtd9OZd6LcJQ6kN8f24Bnms8LyzI/Lq0NLexzq8lL+rLtadlvpWWsxL6fbmW3IlvaUnON99MHjoERTfGshUsmxQ2rjHxWlhhxpa9zApDFsxtznUPMurb1ConKJdSVhf3sG5cZaFaRa3U5WQWFhhPQP/qx3ifdy9f/rJdccnM0ze5y9biovy+ujqfW44ceR7A4oX2QvtzayeAntb67+cvaK33gX+glPrfgdPP90DPDxQz1e0O9LQa/fMPInzeMb5wHQ1o1UIslZEokbWKtc1Ql6ax5ARfjQnwybSJ1obIPX0B9k6MB2i0ochKjdlHbrdmT1NZBNM4o1MZYyolxWvYOI7F2bN3cfHSZe44toI5jRKn2qTqR5iuiWGYBNolmEAYWzMalKVDDMtgoj3UjEIhBiHRxGAjqNJuZEItUIFYS2NLEZy20UmCMg1qxYBKySQzHKIwo0IfczKESUq6eACKRVQ0IcSl7Ex46V2gwpAkSplQZH9Ps6JuMRlVCCji+pLmr1SYVdPnWpyVyjzKkKcahQbhsLY3L9hIEolS5BmvXLP40qXcyUsWpRxk1L0RJdchNexZsclgoKlUcgcxTZZmFK2EStmh6KeMI48zd2iOHsmIEpO9fYNmNSWKMkp1F2UqslKLlWbIspYiwBSDixc1nWpAx9glfnYL4/RJ0kYH0BjdPTzXJvVLjBOPtV05n05hC2swEl3qWm0uedZuk9pljnopYaBJ+mMGlaNUiynqgI1hKArmBIOMqODRqoMKA1KzQLGkOLU6kkj6MODqVg2tFQUnQyvNc5c9nnrKY3+/w8aBe3nDS2Gy6uNU/VkeVGUZXL2KfvZZOHOGqNigULeoIn2dR6VtW9NqwUc+IqnZVtPkcrFMveZTsDX1jk1h4zJVN6JQT/BURLhwAE9HJNoiVM6MlKR0ytamyIxZFlgGRMoCt4ijpjIN01oCrRRptY416sJggNFsUSSksCj8WAmUpziks61wiQkBLkpr/ED+Lmu0uLJdROsi1Sp45HU6aqYkAQIO0lSuu1QSILK3J9E+yxJwlVNICgWJmrXbsslLUwlMSbFYHnnTnDolANTzIN7tY6UZyq9y7Zoc8+JF+MT7hiSGyytfa+O6UkRVcUJMLF78IpM4lSj2S18qEqvttozrl94bEqcmYJHFGQc6Kb/wX23e+0fzaKLjSET4U5+S10xTKAObmwLatrfhda+T1w8dkujk5iZUKoruvgDAZ56Bag10anHSv0Fz0ePCbp1jC0Nwy1j728SOz2F7RLM9Zrt8nIvdQ8Tf+H2svOYh1PYWxtIClXiXwu4NJgsvoVaDLPEYuhY/87OKfg/+w09M+I5v9/mD39DUDBN99JgAmn5fAM/6Oty4gT5yBDwPlaZYhw6wMvJ454O7hE6ZW/0mrqUIUgPDkEK7MJRryiP57fYcqOZZqGeesdjdhe/6LugPDbr9It1+lfYAjobPUrp+jfbB1xGGNufPM5O2G49ljhoOpa+DYF430evJY26aUoS3syOfzd0NjxyRcbO1JefU60151Mtzt83FRfm31vIdeZbhypV/DryUo0f/JsfvTFg7u85K/xnUhXNE+k7GY5cgmI6vTzzGybNv5camO6u/yCkO7fbnMglyXeXcirxalc9N1RNnlN3bn5U8owJyjbnnTk4BqVTmnPpCwcY0YjIy9EIHMoe6FeCO1tmLO0Stw9yVbeMUBgyrB+gNXfbtRRqniphZJHSI3V1Up4Nnphw743I8+SyqV4YDy/Kwrq/LA3LrltyANJWLynlFq6v/t3jihfZC+3NuY6ChlDqitb6Sv6iUOgVUgN3ne6DnDYoBim5KGkZM8ABjSoKIUVoK6jwViK4sJrahxd6ZhAzFeGwwGudFDBYTUzSEo0jT72UsNUJSy0F/wVOSgo44TGmWwqlwfg6P9Yyf6HoGE1X+E9j6xIlTfPCD7+PUsVVswCQlQ3Frz8PxRG80TGx6o2nV7jR9aRQdkliLC1s9EQ4gBiE+ZkFT1iH27jZJpcnE9TBViiIjwqXACB2MSEwXfBePmJG2SXsjqkWw/Cb9xGFvXKBtxvglE5+QQLugwCfCGA5Q7RqHlkOyqEE0Mhl2oekaM1pXpSITeV6g5PuiQSypd4kWxbHMW9euSZQlF7SP47nFbV5A3GxKKrTbnW88ikXFhZui13rooPS5UlOFgHBCkBgMQpd63WSiTap10LZLZwHsyQDHHzCqLLO9axCmNgfbE0yVsR/6bO1Z1GrWtMhPonSL5g4Lxpio1OC86tDouzz52YSXHduh0PaJnBIZNsqA5YVUaA9mmYqboMNQrIvz/PzuLnYpoVYqkxKgQ1FzqJcmlK0x6dYuLhMMwHGc6Wq0Sez4xNUOftSlGAVgJDSzfaLOQfpxASMJMTTs7xj8i58wedOb4A1vAK4EjF/xBpzRHqbK0OvrcN99ZCdPYZSKpKpAdztinLgsLmr29xXvec98M/OqV2lqVaiUMqq6i/HRD6EeeIBQdYhKdexwiKlDDAUFHUh1eKEyJQUJp93wbI4cFQk7z9XYlsbQGVkYQa87TyG0WhQs2e2pYpG0WGKsfQo7N1DVCtp1PudZ0sAYH4cYh4hJYjE06oysBhWlsG01o/KMx/IsFYtyXbklcB6tC8M5R7jRkGduMBBQcOLEXK821/S27c/d9PX7Yst8993ynmxsNFHLQwcxJJrdXZk3vvqui7zinkXa1QE7SYNMw2Si2Mx8ybQYEXGY8cjjLpUKvPWt4k77xBOQpS7Xr2VMQjhwwODihZTf+z3N088oTp4UQBVF8FM/JQDwta+VOW5tTaKbaSoAK47luq9dm1OaWo2MONa87qUTjr4qJd7aJdveQxsVrMk+R5Y8zCRGJQFpqYpva9JCjWqjyqiruHElxj9SZfCit1K5/jRpf4TxxJNYJ07gZAGPnS/SahssH3T4ru+S+cAqOnzJy1Jiu8Donpdy/pmMIye2qbnTSsfFRTLXJ6h28KOe5O8cmzvL22T1OuuxTwW55iAyZ9mOTme+Mdnakvkml97LXQWPHBEO8Po6PPaYjedpfE/TasM//o0zPP6RZZZOmbz4xVIo+fTTcy6w581FDmCuZw0yly0sSN8+8cRM4AWARx+VY9Vqc2OYq1dF0WJhQfbOm5tgmiG+/xiGEeH7X8J4/CSdzhavetWXsLcHH/64xU53lbe/tc3atsWHP2ZgmopeL+KjH/19bt58kje84f9ga0vOsdOZ117cbih5u767ac6VLPLNW6uV67zLNecbyiSZ13zkxatxrDl/XjYDxaKa0YlMExJsTJ0wyTzGoYkeBjSDkGEA6/tVJg2flcffT+HlRag0yHb32d0Z0HF6ki24fBl97hzRkTuwDx3BDEboAwdE3zwI4E1vkgfx3Dm5UeWyXMxoJDu/z6/CfqG90P5i2+8Bfx14WCn1awgIXga+5rb3n1f7U4FiZVuYYYBDRKRtTDImqkAcpGTKwHZdFBqbEEtJKVuMQYxPoQiFop5W1c6XWtfVHOwExNoi0xKF/kI635JOshhrE4WafUZrsM2UatX4nOPeLqqRZSKdleAyxMEgxVQJ7UZGPO2CfIIql6ecRzMTNQTb5VBjIABnHJGOA4LGCraRMcksBqUlCk5KQY1JtEmChacC2d2Xy8SRxc6ORas0QTkZ5ZpFqg2GdgFlK8qmMI+1hlhb0q9GRmo7rKtlanGMaWqCzKI/caj4ITvbFpWqLDhJoqeFJlIINOxnOJbCb6qpTaoAD9eViPFwOFc3qNXkGI1iSJBYNBrmjH9ZLEq6eDiUaEqlAs26qF30+7LQr3ZE01kZDlacAvL3tg3PPGugkpiXvMgiKzZRWolmrJqQGgYRHp4LytA4KmZ3ItEhrSGqtoirGS0dsVTsMRxVOHLC5+p4kc4ko1oQ++xuF+rFjLbRJTI9SROaQ5xymdwJYtw+hGkJZ3xiuGz3bQ4eHPPLv11g6YDB2dUU30wkExEnxIYPzWV2uhajbRfLWCTNmKpOQKUXMpzEFEsWKwsJ+32L7W3Nf/kv8M53KiqZiZFGhKaPc+0iZrtBuNODVgfTMckig2xnn3hjRPGuI1iWyenTmrPHJnz2SZPHH7O5+8SY1eYtLEPD2bMMKssU9RhlQ5LYhHgCiJHiP3N3m9R0SJwCWaEo46/qUZiWweV5WNO15wi02QTTRE291XW9jlLyXI07h+U5FF0UPGH1yzHICJHdUpSlJEFCpxwzSgo0m1JElxek50YInY6MoVzwQtRQZP2sVufRuiNH5J7mlr25zNqFC/JcLi/L63E8lR1UGVVnQmgUpueksVWE9ixKSnP60ISnLvn83meWObScENlNGo25FFiuYGH4ziyrsrUl4Mp1hfv5Xd+tsG2TOM6tpB0x06jKfPGJT8g1njwpQOvpp6V777pLwFq3K9eXy4hdviw822/6JmhZXVZP2lzf9DhiXqNgpGg9Qu0N0M0m/vplxo0DBFmJ9XVFpxKgDYNIO9QbcNd9Dq5dQSlIHZ/w0DKRVaKYdgnNIiePpxhrN/iWf3yQYsnk1a+GlQX4wW/Z5Cf/1RL/7b+VUGnMH/+Ll/Gimsh8qPEYCkV+61PLvOaNh1hopoTaxIwjzDTEt2LWByYPPyxDaTgUekqxqHnoIQkWXr6s+PjHJYiYF5CKi6HmvvsET508qSjEXXQY01pt8453wHvfW2VjQ/rsuefkHq2uzmX71tbmEen8Hrbb8vn8fp46Jffw7Nm5y94TT8hnVlbk92qVWRZhYQFM81Gee+4SFy78N8bjXV772vezsrLIM8/0OH1a8/TToiDy2GNQKHizzdvWFqyt/TC2PeE7v/OnZhvB3CRE6izkvCqVuVGI78v4OnhQxsXWloxtd8oCzNVXikXpv1xvOddVzvnSjpmw0DHp9xUHD2r0dO72XCgUQcUZ5fEmqlQnLlqE3YzKqkknu8yvPnyYN3U6dK5fh5UCcaxpp5vQPICecuPSi5dxnvkseuUAiTaxPvUpAbw5L2hxUQZ6FH0uoT+3E3yhvdD+x7UfBF6GFNZ9x22vK+Acwjl+Xu1PBYpRiszxUWHIYKCIPQ/LRipUU1BKQG+qbGJbuIY20RSrakDjurfvICUhO6FAmomFaLE4Mxz7nM8plYv8q1m0CERaTKExjJRM2bO/6PXk87YN6F0WGkIadYiwVEyGmqacFeOxTJC365Ybhkml7FGwpAo7jmJs38bs7pNNAizfoFVOQCssQ8xDdnoOjmeSuTYojacnmLZBvaH40EeLOA68+MU+/QEk+9BpaVSaEGiDzC2QJLDfhVrdxLAL1BbAICVVCdp1qdYVju3i1z63TwAsS9PrKRwro1zMRCtaySKwsMBM4zlJ5sVJefHGxp5o0UoNo+bWLcXZs9MoVxGKJTCNDJuIGJtaISbTFpHhE011mDv1BG0pWqWIBJN77gYni+hFBeLEIIo0jcKEwcggSG2a1RQjDcn2I66MiozDjDQ1sW24+86MghGQ9ftc6FVpLxh4ToLjWExik2IiC0W9DpOJzcZeFW+4w4F6gGkrGDmzSIW3v05SrlOwMrxwQuFQGceBr/vqmMRwUUaVxK6yvpbRKfdEvqtq025nlJIYZTsYhsbXY7a7Djc2PAFltmJnx+L++/WstuTTn9bY9goLbpel4oinzfs4HG6TuT6Pf8Lh9KkM11fQaVHLYkaTjEceMbhxA5oNjy+9+xru9k1UsykOelEs7pGTLpNCSdQfPBubmEwjKiZlF1WvYKdj/HBEttdHNWqgNGOKgMbeuI67syYoxjDmaCaW41Ovo5Uw+11CQlyMqYygQ0IKM9UYybIYZFh4rsLWmtHEYn8ih709BZ1lcwpPGErkt1SacyDzFLI4VspnBwMBkXlk8dYtGefCuZWNXpJoTp1IWWmGBIZHkTEaTYBLnIncYkEHTAzNeKj5zHNF7v0STaEg1slhKEAd5BlYW5NnpNEQO3LPg5/+aTnXPEIHAmhBgPShQ/OoY84ZPXsWfu7nhJoBYqv8gQ/wOfPL6dNynF/5FfhH32MS3tzm1mSFzn2HxbyDE6gsprebcnw5oKKHKCvCNGzWtiwW2wlrNxMe3bJYXoblRQeVxPz8ew7yFV9lkCmPwHLJ9kI++qjHb/76YZ55RnH9BmxuZLz5Sy3+w0cP8DVfo6j5I773yW9h99hPEO9t4kx6UvRbKvK2O5+CdIExh7CtFGV7oFxUL+VjH9W8730ilfiZzwhv91Uvj7l6Eeptm3PnNJcvS+ZnOJXMv/deeMPrNR/9iOaHfsjgyBE4cqjKt37pLYJhwpPrNqdPw6tfLX167pxgrsFAxtOHPiQ0jYMH5fW8ADgvUNvZmUeRc6m+s2elZCOnGPT7sok5e1bqlSaTCe9+999CqQDbPoXrrtBsPjilM3Q4evSVfOYzv0Ct9mbStEOlMqfIvPSlIXH8s9y8+RT/2//2L9jbq/Pcc/O1K49MJ9O5KpdazHWUk0TGeb0u/19fl/GW95fryphaWpLNWa0mgPjKFTmG1jDoKe46FbG+5xKMUpqViEJD4TiaEIdu4NCLF1h99hG4+wGMwwfZ3HYoGW0eOniF9PwaQecYBScl29kk9Ks4fglja4sds4PXWuVqsIjdi1g5ZKAOrmLqBN3tyuZpb09uxoMPflHo8EJ7of1FNq31llLqXiRa/BDQAPaADwLv0lpP/vt//bnteekUf+6XT601E+EMZ6YzNZKwIc1IwoRYOfi+pFJdM8JVMcaUMywLtURGpdQuJlJCx4jjOafq9sI7RYIkhy0+n3icu2HlG9XbOgmt56+9972/x+tf/xayTGGaGZbKprJxamrXLoC+0RDKQZ72ShLRpfTcjNLuddTONgOnwbh9hGo5xUOiwsluj8y0UeUipqUYa49xX1MpRKxtWex0bWp1NSuuaNQ1FT8U4wMtzm7s7hLUFslcKTk2SPB0RIBNhsXlK4p+T87nyGEIQiiYAbsDF78oDnpKybU8+qhM/EtL0ge5wtL162oqMSS/e15uTCRudCdOKi5cgE494shqQqYhNn2Y2nmnmNhEuCSEiWJ9v0CzlqJsE0dHpBq0MvAJ0Rk8e63A2qa4Lx1oh9SrKVFm4++tsWe0CAJwC6bYA1vgmjG+GRIplwSLYKJI4oxyGcLYZHub2Tip1aDsR2S7e6jr1ykxQi0uiP6rYaB3d+cuEO32NOc6RWNxRNRcIjJ9kkSA0fEDE9b3XcJQYZqaTj2i6KaM8XGJCScp2BbjyObmTcVopOi0M0rGmEee8LBck2pFc9K6zFM3a7zs/pgL+y1q1phop8f5Xovjd3jYpqZRDHnqosdnPwtvfmjMeAiDjSEva1/GPHZInKNu3CAqN7Acg0HqE5kFyvEudhYR1joYWcz6nk+hJJJ1SolRB0qkC11CbFLUZDInKNq29EelAq5LgiJTAnhDXExS0ikv3tApFjFaqduMdz73+cuyOR3ndmm/XBEC5gVChYK8d+yYgJ3RSM8kAINA0e8LSOj15NmzLDh+LGN/J2F/YBMEsuC36ikrq0w3K4EoYOAKcWvYZdCHq+Eily7Cf/55+P7vVxw7OuWJXhUL8pUVATcPPyyg6YEHpIvW1wVI/fRPC5/581u7LTzX0UjO7+mn5ZoqFfiv/xXe+MYv/Dfj8bzrCwX4/u+XZ/BNx8/TuaODF/eJC3U+/ZzPi5vXSdrLxMrBc1NUlpEqC3syoHujT7ewwqUraqapu72Z0etpXvMlESE+V64oWi2JXI/HMge89/cjvumJH+D/zN5BeblM+Oxl7rnfYvlUhcz1+JbF91B98Ayq36dHGcvUuEdXUKbBp572OXJUUSpohoOUq88G/NiPKWKnyGSQ8PXfaHL4CNx3Z8AfvNfm5obFz/ycwfXrasaF9X14xzs0L7lrwjd/m8uNWyaGAT/0Q/CWtwhQvHBhbvzx3HMSVVVqXgfxgQ/Aq14l/w4CAaiVioBKkP68dEnGzh13SJTZ8yRinfNxFxYEFH/sY+9ibe0/8uCDX0G9/iImk3uJ4/bMstq24fhxzc2bf0iz6bG2FvGJT9Q4c+Y0vl/h1q1fwPf3OHnye6Z283Kerjs3/Gi3pQizWp0fMz/X3InR92W8Nxryk/OwbVsiyLn0b643X6nM6RMLC3MK3c6Opl0cUZjsoWqyGI4oYu1v4+1vkGnIjhxld+jRHVosZ9cpBvt8ZnyaceygBwNe0rpKr3qQxeFFxrsBRquGcfgQwf6EkSpRDrYoWjGpaeNWvLlV4F/R9j+dTvGb3/zbv/Mrv/K8Pm/V6//L6RT/ebU/XaQYmaQcByJsUjQuESaJpFh1ShwFqHKRGA+dGQxHsB371BoKz4pRaOE+Zorh0KJcsjBMGXfz5+t2IK5h6h72+aqHWs8LFT6XwqRnRfaKdMrh8rh8eYzvF2m1FMoxJU6tJMparQBKOM7jsSII5PjVKhDFZJbJuHUImqtYOqUx3CHK6iT9AbYFVtmHMAJTNImV0jhFi4k26SxlrCwF7I9dCirkwIItsTadCP86izH2dglLDTJ3XkGYaYteYqKjmCBMWWpp7jgYYpiKmzsuXtEmsz3aHT4Hq/i+RBM8bxq5yDIa5ZhUudRqzHRiq9W5DvHKCjSLIWCyvOyQxQqLjExBojNcwpkGs2xlXLCg0xYetdawN7QZDqXjWy2LsjWm1cwolA0KTky1lBJqIcwlrUXGG1AuZ1iuwTCE8+cC7jWfJDt+BKNoYQLFdIRKAsJ0YSb/ljvtFYuarGhhdVpklTLp+Sexogjd60O5RFpvkvbHOOOu7ESyjKC2SFxbwVMRDiLllxk2ly8LfzSOFJajOHwwo2imTPAw0IS4ZGZMEmboOCaOHSoV0edt6B1eeleN9VEVS8d0loq8PnqCbL9K0y9SGO8y7tR5xfI+lqFIChXOX3F5yR0jvmR1B51pru0ZnCpcIbrrpXhmIpmQgwfpDVx8I6Kgx2TKJa60sJIuqTZIrQLNNhg6oqDE5CYw/Jk29uwh8TyoVtEXL0qRjCtFcuzuYpQqxG5RgLFmag5ikGAxGFgUbEXFDbFVSKzmYzPfK+egxXFkwd/enptr5KA4CGaut+ztCVDTGuqlmOVFTWI4bG/LIr+xIfNArycblYsXFEdWRTO81ZLMU7Fs4qsJaQzjzERbFv5oh4GqEiRNKh1NuSub23f9J41hatxswih0uXLF5MKFuZ9KGAo4+a3fEpOEKJJzf8tbBCDnAESpuQb4Jz4h52rbItf1Iz8i1/zqV3/hOfM7vxN+8zelHy5dku97/HF429tgLWhzcH+drcoxzHFCq5YSNxYouTFjbZJpC2v9Cs5oQFptUM32UEaBUqmJa0RcX7c5f95gcRE+8kmHVkszGMGnP614+cvFMv7IEThyxOZtr/8H9AcRG59W/IsXf5TvHv0W5t4qweHT/L7x1/iyW4/h3X0aM/MYDTPCyEW5Ni85O+bqZhHbMagUY06eNvjhv7OGcfAAp679EXoyIf7oBZ7l27nv/iYnexGG5fLwI4p3vEM2AB/8IPz9v6+Awnx+y+Df/TsBsK4r4M4whO7Qbks/9Xpzs46zZ2UM5Y6HnY4UKF67Jvfj2DG47765+s61ayLj5nlyr7pd+bvFRTh58m20Wp/l/vu/mWef/SMeffQfcffd/5JOR8Z4uw2uqzBNh3b7IYrFTQYDRbf7B6yufgnnzhl43t9lOJwXyfV68nf+VF00V5ZpNOQ9255nRcJwrjBx5sxcWSP//9aWgOC8+E6CHXP6RG6JnSTziHSgfBK/A9rFiQPc8QYqCgkrLQydooMIr1hksQQf+uAB3ng64SX1dYaBwbXyAmFpicV0nfOjA3T8LbLLG3hLy1idBtV+j/c/e4jxSLO4qHjJskH5ry4e/l++KaXuA34auAN4Fni71vrcF/jc9wHfB5QQo4y/q7WOp++9HfhhoA38PvBtWuvB9L03Av8KOAR8cnr8jb/gy3pe7U8NikEe5ijIAIPQcLC0IsUmMw2o1nBUgE1E5noUXEWLCYmySTCwCYnwZhGTL9TkaDEKCLXYzaZ/whFPWk6RyCcimOIAxhLZVFLc1WgcZjy+Sr1+liyIsB1NmLloJen/mjfBsE2SxCVNZUID2N1VOJZJmYiKN8bY2BCCWxDgmCm6XoE0IR2MuRm2KLsWjivRbdcKCROX9U2Let2iUNTEeEwC6PdMFpua/aFNoehRaDkYaYalYxJl4yOKCHuhS3ckVSOFCgSWPe0hmWh9X4kLlp5vJVwdcLiVMApMNnc9PN9gOLAJE5FXy0GHuAiqqZarYnvbE55uXVOpO5xfl8h8rQamERFNNMPEZTHbxGg3wDSJETClxmM64YRWu05gFkTNQQv1wPOgULKJU/D3bpC4ZaJiHW0b3NzVVMqaZkPz4L0JijPE2iTSjkQtLZfr22We/ZTiwQf1tHBLMZlMtThRZCl43XV2WqeJY427N6SQhFglhbW7jfZsrsYHaBhdnCAkNiFIHdqVDHd/l7TQ4qUvtUgSQ+xVFSjDYIKHO9XMHlHAdEwK6QBFQLC4yH/+zwbf8A2KUfMQBAlNM2bZ38cY9AiOnGEtbpNi0TjqkuwFPHpliYc/mtBopHzTHZ/CS46S1Fukysb1XZ69scRdQR9VcNBKxnvVj4gShS4WcJRBnCqu7ddoNA0KTLCUbBUzDAI8fMZoDBGtyx0Mpr7f2veJtYUD6MFA1AUGfSzHJcYhyxQbPZ9KVbHfg5oXYk0GqDDErlZnauSgZjx1paaZo2lE8PhxASQ5FSLXfQZyp+2ZokRo2AwvZ8QJrG/Is2wYki6OIgEx13qKNHM4UxB5x9MnTCwrY29cQMcxo8CiUwvhyhW02WDSPkFvyyCKFAsLGVYyYRL7fPwzRf71v5bI8HAoAOptbxNK5NaWAJsLF+TcLl8Wi+Kp0Revf70Alx/5EfiN34Dv+z74T/9Jssa53fn/XfvhH5afd70LfuAH4Hu/F/7tv5VppFis84ovq1IdJFy95ZNFIYXPfBTznrsoDdbQ9TosNDAmHs89p9kyznKklVErpyz7fe4+bsMbfUbDjGcueVy+DGEAjYbGGPaoHS4TTzK2Nk3KR1pcflze+7Xd13DPkQEPjM5jveG1vCgzMLa1hDbvfIBqI2GwPaRLDadpzAIItpHy4cccVo4c4j2/HdP50rvxWyX8//JL3Fr8a4z6DrYtEfnPfhZ+/uclCv+JTwiQyzNledvfh7/7d+HHf1x42ZYlm/acXjAez8QxZoAwjiX62+1K/x88KPfws5+du8YJtUq+8+RJWFqSwsxGQ1578MHL/MAP/B7l8l184AO/w2te8zeIIo9SScD2woKMia2tBk89dYE0PYHvJwwGBS5ceD9LS9/I6upc87hQkHG1vS3nmPPoQV7PnQpBiueCQDZmuXuh4wivuFgUoLy4mFMv9KxwzzDUTIEjp1wcOCD/Xmhr7CxgvVsgyxTFok/Vy3D8CNcEDIMsijH2drgZNLjzbpOnbi1yl/Usru9x8PAibj8kLK1yYlmzP2lyS63y2Pvq3HWX4vSxMoeWYoLMoVoVg5li8YV6ur+KTSnlIRq//wj4BeDbgT9QSh3XWqe3fe7Lge8BXolQFX4V+IfAP1JKvQj4N8DrEVD974F/C3yzUmph+tmvBz4A/FPgV4BX/Q+5wC/S/tT0ibyle12y/X2CxjIFK2Z9R+S6ajWFUpLKNcdDMsNEuTaOSgi0O5WNUhgqxdAZsXak+n3GO0Y4wmQk2iC3h/5CxXfwuRGrvA0Gc4kxw9A4THju0g02Nja5/4FXkSSittDrKYpFcBw9O9ZwKK/t78+BsZxTytHOGLW5hup2BUBX6kRuBa/iEGUWlpFhoElNmwAHU2lSbbC3p0hTqBRiqsWESebT68/50zYRxCn7Y5tS1RIB+WwsUnbKJsQljjUVe0KkPDLElnk0gnCiWWpMGKcuO3uyx5lMNKMRLLQzUm1M7UTVjAYSjDMKZZN+XyIWUjisiIIE20i5teVw4gQMB5okFeBdKmTYyQjLUkSGj6dCtJqywrt9rO11skNHuNktU/YitGFgWgaWrTCziMRwcLbXsGtFNncNfEeTFUrsDR3azYySOUErMaHoj0zQUCxkKKWZqKKMG60IQlkgVZbQqcVklkNh8zJmEoFtM3QabE3KdFop+xOPJX8f27H42Gd9DNvk1NGEcT+h3dLEtk9x4zIpJqPqMlkG+xOPdjPDN0JiLDxCQAwp0ql0YJjYZKaJpwPWdx1ubRikw4D7Du7g112iIOOX/7DBIPZ4+zemZKnGck0BejsJh40bWK0qE7vMXt8mTUUBpbefcNDfIStWyCwLV4UY5DrBLpnWuHrCOCuiDANDZZCl8sQYpmQnCNCYZFphrt/EMdK5M5UyGLcP4VkxZhITpCaDxENnikJJ4amQNNHc2ivQqQa4ZoptaZH8Mwvo3I1SKXZ35bAgC2OrNZe9unSJmftbrjF7e+Q4B9SBiGfw4hcLEDIMZqYEuRuZYcBDD81VYe46McEpO6SZQTwRrZuyE8L169zYK9A6VudyrwVA0U8xDLi1bvBTP6XIMok2DgbCzTQMAVC/+7uiOHHokLimPfKIRKxBwM4/+Sfw92fql38+7fJlUaw4cAD+3t/7vDfHY0E8GxsykaUputVi21vl6n6FA7Uxlz47IG4tcviwMVPycF0oegnhMKYXuiw5+9zYtFkqDUhizfufWeTadYNiWfEP/6GiXNb82r+6wUvvi+mvDfno5nFe8WoHw4KiFfL+jzl0lixqhYhWE3wzQqP47T/2cVyTA52IcKPL0xddvj7+Jd574TAv/447uZEewHRMHn0UfvEXBRQ+9ZRE7v/ZP4Mf/EG5rEOH4Fu/FX70R2WD8Q3fIPPh0pLMv8OhZAtyrnCu4T31m+GRR2b+Ikwm8n6lIj85LefaNfg7fwfU1m/ROVTm9AMPsbamOH9+wo/92CtYXPxqwnBCkihe/eofZWlpTmvIVSLOn/9Njhz5Mmxb8dRTn6RWezk7O/OdkGHkoFV+z0FxqZQXQ88LM/t9Adzb2/Nod86xX1ycSdaLpXMbWvWE/a7i6nUDy1JT1pOeOYhalpr9vWOl7HcV1ZoYnxiGws5GeN1tdKlC2uvTC1w+8oGUSttlccWCWo3tbc39Z0doy8Hd38Da32Hv5pDP+i/n5NGU933E5e67NaudiK2ug2GK+tHqqqxhf1Xb/6r0CaXUlwL/Wmt9/LbXLgDfrbX+g9teezfwpNb6h6e/vwL4Na31olLqnUBTa/03p+8dAK4ALeDtwFu11q+fvmcDXeA+rfVzf9rz/fNuf6ZIMYBRrzG2qwCMsfErin5fROhLJSjYMYZroFPIlE2AjdXfRhkmcbFOpkw0it09jecpSqU5+NZIZDi33qxU/vvnMZnIJCHRGhm/YpKRA2WFRnHm2AquV+Q3fuM3efnLvwylHMrl3B0oxdcBg9jHNM3PqZTPW6Mp52Q1GoTlBplpUdhdo/jIJ+DYMdyjR0EZpMMxXW8Zp2Dgm2MSZVEpm0RhhrJtAizSDOqVBMeIMVVGmsDm0KVeycCQCHwcJhjjLmmtgzJF7ipg6lagMrSWxbBUMbi+XcS254VBAOViykozYLPrMoksCgVxpVNAyUswiTBrLr2+we4OTAKolDQHvC1uRgtMJjatWkwWayZaCtcSt4TDiGI6QClFZpikGJglD0YFTDIWawE6jkkSzeaoRn8v4Y7yOsVmlS1vCTNR+K2M8UTRLkTYrsLfuU5U7ZB4JSxiPC+lG7jsbivqDZn4LSKhB2vFUkszSSx8M8HYugm9fVLTIVw8ihf2ONoIUYaBa08wDBdtwNnTGcNhSnW8Qd3I0FabSBlknQV2eja9rke7remUMlwCYuypBJmHQjOeQDwMaesddGuFTBts7jv0B4pKMWXxsMHNwQGag31qxoTtPYPdvsZ+9gme6a/QPF5jtR1hL/oM9FF0GBCPJfqptRbL432D8bBFKZLizMS0MFSGQYpLwCjzubJZYmNDcegQNKsJbjQgGwZMKgvYrp5yvhPcYQ8KNqxtixV0mqB2d/AMC2UqxtUOvdAlw6RkTvB3tzCadSzb4EhnJMPMkOfHtMDXEzItboMak1ptXl9w8+bcljZPpNwuy7a7K9G6Z54RYNBozNUpQEDy7dFWpSRqVirN08cHDgiY2O16WBONVlAtZlTNEXocMl48zPrAZ3PDYW1tqgx1UHFwMeLoEYe3vEXxsY8JCHntawW4f+ADUsA1HArAajTgySflfPI0eJrK5/7e35unsf882tGj8GM/9t+JMnueOMkVi5BmMB6hq1VKlscZZ58oMzh91iDwEra7DhcvSp+vrkK3a9FuGkT9DH/BoXfT59bjAa+9f8hbX7vPs++7xXf9+7t5y1vgwx/WJOOQ3lbMMx/c5pX2RzFPvYknBkc5c6bA4pJmv2dgZ7Ck14i0wS1jhTe+MqAbFnj2OYef/9UOhw+mnHvzt/APfs7nn39lytZWxn7f5Gu+PGYysfiX/1Jx332aQkFUJ3LVj0ZD+ve++4S6kru1bW7K9eQWzbXanE6TR1zjeC5dliRzreHxWF67+2451vKySE7qwRX2//ARVl5zD/1inXPnNAsLP0y5HPLcc9/PHXd8/awYtNebj8HJBA4d+gqefvoPaDQaKPVKQMBgFMn9O3hQwPeBA5/rOGcJ5gRkQ6i1HDtvudRcLqWmtXyf1tI3GxuQZSYNP8A0vBndrVZMSJRFvy/HmUxkU9rrmVSLIYOeRb9v0OloUsOBYpGb+z5h4FNtmFROQKtjcOSJX+PayisZdJs8+nSBcgma7YMstR3q5hYHgy6VYMJXvWWZi1dNbmx5PPWUPBevec2fXCNfaP/TtVg9jwnrCwD/08Dng9NzCJXiDz7vc7/6eZ9ZUEo1pu995LbvuKWUioDjn398rXWslLoyPf5fXlCslBRFxakiw8JxoNWUyKICEmWBaeOZE2Ji4UCWa/g6IDaEJ6yVOTV+ANB4jIlwyRCyUqUyn0jy3XAeKcgVKAxDz+xh5+f2udFtceOasHJgiVe96sv53d/9Nb7iK95KqWSKQQEWY0oMxzIR397ySPTuLoyVQVlnOHUf39EYuzvC1bQslGWROQ6BV0eFJmkKE6OIQuM5Mb6jSKZqG2mSEScaoyiG1tpUdJoR/aFBFpuUCjGeb5D6LQwMbMbs9mzSDBZqEQEeJWPMXurxxBOya8/dvU6dEl50vRSjTQvH0WRTSaA0BcdIJNinpUDMq5lUKj7dLriuxa3eAmdPRXh+yjh1GAQGtp1biCpaRZOKMRE94EoFS6fSOe022nGIA4NJVqBSyThYmTAsKNZ7y9R6QyrFAVmxjK0yim6GiaZkBtDvgV8gxCVIFbViRrGo8H1Fbz/DdhXFogM2xKHGVRmOoxhnBbwgJivWyZYPgGURui2sqQ2xaampkBqU3QjXMsFqEYYpTpqhVEpi2tRr4GkABVoTqMLUkIYZjadUUAROAaWa+ASs7XkwHNIkwHQssltDWoZIIJmdBn/ry64T9yYUC5rF/Zu89wN1Xv4lPrYjWYPdXU8Wv0pMpawJQxvPEyvy8QRKkcbyQWuTTIHSEX42xjZ8SiXJFASJQ39UpFzyGIWwYAbY4x5BqUHi1dBpCqc7EEVox0XXVwQxGCaXr4mzZL0OZsXBKDYZ4VEgwFAQTgsdRYEiYqTF5S7XCs5lsJSSMbe7O4/w5u6KuXHBlSuywOeR5LwVCnMKVW5YMBwymxOyLDeemdcyLCwodrah5gWUnASVQhZGxF6BBJuSA7/2a7C6qpmMFTu7LkeOCBh+yUvk3PIizcOHhc/6nveIrNgv/dJcGaDTEcWE3L32x39cwM8TT8g1/viPz7Vw83niQx+S6zt79nlNoTOThz/RDAPKZQbrQ/S58xSOLzNWFQq9bbphGavgojo1Ko6iP9H4voD6j39cCgg/8xmD3/kdgyMHC5w8rXjTmztc7xkkYYn6Qy/iZx9UPP2ZgH/4NzZorDZ4z8MF3JPHeNd7X8U/Lae0PEWcKIZj0Yze3va4zkHCYYLyTGK7SNnJePDuCdHbCnzoQya/8QcFfvY/woc/bPL2t2m6527ya7/e4a9/c8KrX2mytWOwvCz0lTe8QUBkuy1qH6urItM2HMo4unRJih0nE+FD33XXfC0wTYmyp6lQWrpdmbNlbAgo3d0V9QqtZfPxH/7Dt+Owxv/3m/4uF993mV/67ASl6qSpx/b2Qer1b+WTn/xHaN1iaen7yIFErydgO44Vjz76f3H06Jtpt1+GYcwL4Cxr7mhcKs1tonPnRseZyv5No8i56dvy8pxbf/SoAPqtLXkGKpX5xmw0UkSRz+rBOXdZa41OU2o1c+aCKXrRmuvbFvVqgjYc+j2o1S2iUNNqSCG3lZi85j4XtbuHvvskp0dXoJrRuvEEVr3CuRuHKK4mNIoux5Y1/azFY0+aU2dBzZkzeWG2FMa+6lV/pWvt/qdqCRYDyv8jvqqImGHc3sbcXhDwhT+X/7vwRY5RROgWX+z4/39pf2ZQDGB7NulInHNAkSUZg6FJpaLxECOPAOGX2lmIub8L5QKOGQGSKg/wYGoTGyAh3hyI5othLv6fyyD6/vx1z0tEwQLv885OSwEYasrPFee9TsfhG7/xK/nwh3+fY8dOcOLEabRWM/5gnvbNz2E0kkWx2chYbCf04yamE6LWr4PnoacK/dowCPAx0PhuwiSw2NqHZlPjmxGBcrF1KPX7no2TRdzaK1AsGjiOZhA7JIBr66kYlkWEjc4tdquQJooYjdYGt/Z9Uq05dUpTLKrbpNlEs7gXeEQKCmUBfLmuet2LKfgZ3ZFJd+zRaKmZ2odpgF2wGQTguyFVK6FQt1FkhNrhQCvGVikJRTKviJMl6N1dJrVFLNvGIsJzU2yXKQ/XpxsY1K0dioWMoNyUcaJTzCwDAzJloFpt0uEEY9GGWBFMXRMNQ1MuJDhGisZGGQZ1P8BAM9E+3u4aWbnKwG0yGbo0qzGOSqbjSRNhAxo3mWDEEW4UE5bqWAUTvbOD8g0S30dHCVt7QgU5tBiTKItYy0yfYGGQQZpQ3b6Erje4NaiRRRGp5dMPCujuhNOW0DT0IETXaphuFWt7Cz3YZfWVR/kWY0KGQYjL9q41WyS9okWjHpElY8rlAkoJhzL0Qpqe0CcyDMa6wPqOQZaKpXGlIulo17awx0OqnoFx6zp0WjhpSGZZiLhKjDKmgtVTqgXNBieOlYAUdwr6NWAoddtTk2GS4hJN6U6ySO/sCPi9cUOKxQqFafSqJoAzDD/3+UmSuSSg6woY8Lw/GWXKgXa9zszlL1ePqdU0Gxvy3cePK9qtjDRzyUyIrQL7hRoXz5tUqwIm3vY2TRDA6aMRhgHjxOHmTc0v/qKiXBaQVa3OC+m+9msFAMcx/M7vSPHcAw9IZPLJJ+fSq889JwAfhPrwt/7W3ODmyhX49V+Xc/+O73j+wPi/20yTYWmJ9dUDHO6kjLoxbqPOopmis5C9iUmcwvHFCbuXM559tsqVKyI3lzu67eyY3Pki4U//3LtchuOYI2ddjp+Af/L/Y+/Po2277vpO9DPn6tfu9+mb2+rq6qq3Jcuy3DcY29iAaUISQyhISALUeKFIX0mlqHohIQnj0bxXKUgIlQQGJCRACBgM2Ma9LMuWJVm9bt+ce/rdN6uf74/fXmfLQIgpTAoHzTGOdO4+e6+9mrnW/M7v/P6+3x/yCb1NvuM74D1fq0mTAssu+Pf/yefUqXmM+2/9lkx6HnvM5uJFm/Pn4Xu/F4Z9w1teU3DL+pSN9wV84AOGH/xB+Ib3GtotQ7ZU4Tc/oHjN6x3uPJuKU8q2x7Fj8HVfJ64d9bqc69JW3HFKokNeW1qCf//v5X3Hj8uKRL8v/e+lIPTsWXn9mWfkdd+X6yZL+58nyyz+7t/+Gbbr0B8ssrFxkV/8xY+S5+vceusaH/nIE7zhDU9h2zGNhmI45IgI2N+HW2813Hnn36XfH1CvC1CPIukfN27I97Tbch+U/Xc4FNCcJLPC9GRuNVgWFIbhvGCuKOaexDdvyr1SHsP6OkfppZ4HWe4SVMGyDFevGtbWoNcTH/iTJwz9rnxuuTZFDfrkQRXPKZiurOFEM13g2ioqz1GDAdcfCVnIDU17zLOXmvSjmDe/0jBRLQYz15RTGzEN37C77XL6jOb55w15rrh5U2QwL7c/ke3/rvvEBAh+12shMPqvvK8EtaP/yja+1O1/WZpS6jqwZoz5kvDuHwkUK63wSNFFRqI88iQnDBRaSTiDJiFkRIRPavmohQUMkODNYF88s4Aq2Xt1FMoQBKaU01Gvy0M6y+RhYlnmJcuYFhbRbDv2bCs5IdPZN7hkOChSwKC1oVLx+IZ3vY3Hn3qOp5++wMKCSGfCUBLVBgN5eJWm8xJGoIkSTTucwniC9lxYOQNXrzJdu4Vc18Ao0qQgnhYENcPCgmiUMzeQZTIC8tzgWQmp5bGwIKzIeFhw9105ReFweGjYaEcYT2GbjIKcHFeWqseKXuZRrxsWF0ArKUiMjIdHjLE9HJ3i1aE39uj1ClrBlNQKuXAFGrUMt6lICwWuRz6e2bLZKa5tYZmM9UZCmmlu9gLWGlNcW4TVznCXNDVMWysYo3CHh4yrTfzFZUIKpgpSU8HkKZ2BS7NRoC3F2mKCrQIKbIpC0+8VbDQzrncCglARhgrHnmI3PJSSoryAqRTaqYIoNlw/9Dh9QvrJ9UOP9dYUZeVyXXsd8vYCaxsa8LBUgcpSGI/xbAHd3SSEXkq41MLWRoo325tiip/mHA4CCTJJYZR6s+hrAVa2De22xtMF2DY3r2VcHFi87twUJxpAy8cUoC4M4bFnMffdjzo8xFcdsqDK5Ja7sbKY3MAoD8hzjTFGglZGil5f0V7wsFwHZ5bOuLqqQPtESYE/2odWm4qe0Axdbuy55IXcI2EIjSq4lQq+yUXu0OmgbRtdvmFxETPTA5lOh+mZe3AdG394AFGEabSY4BNYKb4doZWZxZW7gGKCc6TlLIFuWXH/tV8rA6bWcp9MZrxAWQhUptCVnsRra3Ivle8JQ44ibHu9OeMURXNL5d1dODwwLC8b7rxTMxgYav0t9kcVjNEcXxxjVdep1RT7+8JmnTsHp1anBHEPx4Wb0xb7qz5vfztcuiTL+A89BK9/vTCKw6EAv8lE8b3fKyB+NJLjuftuuUemU2Eoz56d66O7XTmuZ55hFkokx/SBDwgDfXAgjGepT73ttj+AHf592lIzJk1dcqNYdQ9xCFC7B3BwwOLqGnFziTi2ueXWlPWVnN/4Dc0DD8i+fdu3wdd8DbzhDfIce+vX16hU5JgB3vteCUMqJ2fTqeaJp0qbOsO1azIROHlS8cgjchxZJqlr/9f/BaOe4bu/O6A/0Nx/X84//ls97rm7zeljGcGLT3L6hef4J3/xldQbtzCMAp45LwCqZO1LN4jSg7coZNKeJNJPgkBkFQ89JNdod1f2x3HkeM6ckdfGs2dYGArAHo9lslOuJm5t/QTHjp3h+77/7Tz00P9AtXqC1772vRjzH9jauopSPhsbd3PyZJs07TAY/Ht8/88dOfiITZrF+9//s2SZol7/M2TZXCdfWlqW/tOlL/zCguxDZ8aHZdnceULreR8qV0JqNQHgrqgdsG1hkMu6llJidP26nAelIJrKe2CW4mdibJXSqFoks0nttNLGTqYY2yLUEv5iClmx1N0uBnj7vXvk5lauFOu8YTln8uwW157TOKeGJMYFbHA9Fpcz7j62x5Pbyywva2o1Q5L8iZDcvty+vO154Lt/12u3Af/f3+d9t/2u92wbY3pKqS/620xT7AHnZ59770v+5gKngGe/TPv/u5vid3uJ/gHtjwSKAbTn4Fy9Rt5cJXEquCol0KLJFCY4wCZDEROr0usUchwms3DaeRNzffEthXhaYDv6aNlMwgDMfMmVFPG0kGK1HI1PhEUx454LLFJSpLreIaG0eEvwOHfbPfzjH/5x3vymr+XWs7cxmRiyTApyyiIisdWRfdzr+7h6gj/oyc4cHqJcFxWNwa+gi4TAVvhhwd6hR1pobJ0znhZ0+y5FYWSZr56hPZtC2Zw5Iw+o0MRU7YRq6FEQkJFiZTH7PY+19ojUCnBdkakIS17gEzElRClN4fhgIM5d0kzhB9BUCq0MlkrZ3HTJE3CSKWlnyHPb69x5a4rjOAxHFr5O0BgubFelUCQYkmmPw77GUTmNeoNUefhEGGOjKy6HvYKx47JYF4O+SeHQH7pUKuCpGHIweYFRBbHliYl9rIhTxYnFKSiIE8WgqLEwvkHF9zFa2FBfCaveqAmwtXuHmNYSxxdjUjwmsUNe1FhZUazkB8DarDIGGRWjCGO7TFvrOIGNUVVQBr27i662GNAgiQpWWimthoXn5NRCw+FMjyc+uiUDJJOpZHEdDRwLErpZFdurkk9jnr3g85q77sQ/POTZw2Uq44IT4T6ObaMnh+wmTaxozG7fwqoENBoCDHzf4DhyPxRG0TmcOWrMJge5FzLxjlMmtS01ErZ3FJOJQ6Ui71UauQdUIYghz2Ud9uBAOkqew/4+xnZQ526DoEI+GmBNI1SWos+/QFAY8pU18vYyxnVnE9aUTHwqjtK0PE8dsb3Xr8tPGM4nkLYtQDLPBdwMBgJM4lh2p9WSz5ZArN+fs8FBMGf+BoP5EnOrBZZWtMMpjnJwHAfbClhyc9x2SKYr5J0pzz8Xcts5ASQnliYUtsv2dIXDGwU7uxYXL4tzyblz4iW82oyo1hTHV+HiVZvN4xbDoeHgAJaXhS08fZqj2OrpVMDzYCDH+dxz8gg4cUJOe7stAAeE1VQKfuEX5DNXr8qS/utfL0Vftv3Fq2D/pfZn/4eAv/nmz7LwwCnSjRXW7KkkEIYh7Gxz0A8Jh7t86uMev/Kfb2U6FZDpuiJ72NgQd4TxWED65uZ8YvP3/p6sSJw5A6++L+O9bxvy6vurLC1ZVP2Mj3zC4Zd/GR5/3PC1Xwuve50Ub124INfy737jJR76/n12Vu/lmRcc+lOHi9dirlwNeLxyD9/0nttIP1vwW79tc+qs4b77hKU/OJBrXOrES/Y3iubygE5H+v+/+3eKd7xDZC4/93Oy72trIoPJc+kfpSynlCuUzhatlvTLpaUf4LWv7XL77X+Jp5/+/9BsbnHzZot6/ds5derz5PlFbPvbuXbtOba2/hmt1inuu++9LC/77O/Ld2bZHmFY8K53/TMefVSuX1lcZ9tybT1PmOFS0zydyrHGsfTrjQ2ZaK2uzuUeu7tzAibLZkmq9nwbh4fSB8sCvXJMKqUny82UilvI+JZCpnwsy8WiwKAZUyPJIUpdaj4EJqIzcaiaHgQe4cIiKkng8ABLFWxa2xQrDfTSIo9fWWCzbbPkDPHbhqvXFPWazaCb8eDpfUbBCmkGldDwh8AbL7evjPY7QE0p9T3AvwK+C/CBj/2u9/088P9TSv0ysAP8b4gtG8C/Az6ilPrXwBPADwH/yRgzVkr9J+CfKKW+DtEo/2/AM8aY38cZ/svSvhkB5F9S+yODYlyXfH0TJy+AmFx5REbM/xXC+hWomc5XisiYBXIERCgkFiLCwWBLgY+S9KlmmDJNLMDGGIXniauFx4QMF0WBM3MvFiAsINwhxibDosCjYIqDQc0YY8FNBRbKtfie7/nLvPUt9/Px33kYu7rMYNDnuec+RrN5igceuJvz5z/FxvpJNjZW+M1f/WWKwOPsLSfnVMf16wSdDuahh0grTex6hcgNWVnOsU1MlFhcvGLjBIblJUMY5Diz488QezXQbPdCXNcQhAqUeBRja5oLmkjJfnuesOeOXRAwmZ3JHNukTCeG/sRhtRmTxBbGcqlURO9tmxzfz3H9DHBxVtu8YhUspTHkVCoKMkOuPCwL2o0cW1s46YRWPSCfaUuVicmVTadn4Xg1qgta/DOxwSg8nVCruiitSHVA1I8IJ13yZo3OABpNOLkaYSGFUgrwVEoQDaHZksEEl37kkiUFK9UxtsppZgNuxm1WsggdDbCNplpp4uc3UYNClNqj0RxtVSqQ56hc7A4UiiDISfHIXZ9gsIe15GBVJWQljXMCJeucjZrBysYca8uA9viLIYttw14aUKlAYymnTUZhNNNxQavo8sBpC6fWhLe/nbv6fYniHS4TBS2qh1u0VwJuZk10aNHpQNVNaNYNcSz3aVHMAiUWJxSzCRvKoLMElSQo3yNVHjk29SYMxjKAVqvguhZT4xPuXUUVhaRNlcLYzU2hrLo9opPncIMQZ9gjLTSJV8NzI6hWyQtFx1qibadATo5FRinSlxjsWtVjMp3rcUsrqRIw2vbc07es2i+KuW1WWZjkuhx5gDvOvFip1IyWhbqWJduWvym2eyEbTsL+bsG0viRgck8Y0nEhiZg3bsjV3tqqcHgozN3BgcX6uuGhh+DM6ZwgH+OOO3i1NokV4Lhw350Rjm+TL2tOnnSOwMcsAZvTp2fL0cvit6uUMHeOI2Dn0iUBY7Ytp710rnjFK0RiUqkIQLpxAz78YSlS+sxn5P0PPfR7H6nGwC/+ImxvK5wLL3DydR4HV/sU7QwVBBQ7e+RLq3TGFaL1u3ntN434Xzc0Fy7Kh08dS1jecLnnHmFnS1u8mzfFck5r+OEflmNTCn79Hz1NcSJhMDlBs7nInadiGI05d8qntuhRrcpk6No1kT68+CJEx2/juYXbsArDmx7sceVSxm89LGz0+x/3+MLzHhcvGj76UcUDD8D73je3G0tTmZzs7QkgfPxxAZXb27CyYrjrLgG1q6uGf/SPFA8+KDZ2i4vwuc/Bpz4lwPj06fmqxaOPyu9lfHOjIf8fjzf56Z/e5Lbb/t+Mx8dZWzvD4493SJJ/z3D4AnF8C8ePn2Q8tjh16q/R7/8Sh4e/hOd9K64LCwsRH/zgh9B6hdXVzSPHhcVFWSkIArnFyjmo1gJgx2PpP82m7Edpq5bnsm9JIhOpUqpRRqOPx/LvMgynPGedzjxZUUKmIFUuN/bm98/hIYDGcTS+L+c2ikDnKdpAohwWo6v0m8ep6YROFDIYhDRsTYM+Vr2CPRhQtBe449YU73MfJXrd28g7EzqHFRZVB28hxK5aLDhDCmVjhaX88eX230szxkRKqa8BfgL4Z0gB3dcbY2Kl1AeATxhj/rEx5leUUqeA3wLqzG3cMMY8PgPVPwOsIMly3zH727ZS6huAHwV+DngM+LN/jMfzyB/m/f+3Ldle2kQvVdDypti6IC4sBrEvhUNGHhaWKrBUik1ONivfAfCIcMjJjCIyIYORWMwEVgwKUlyMUWSZpIz5ajrzLbaxZ0xxhI+ioDAS+eyRkigHFylKE8AsIg2LggifAo0xin/5L/81n/vco7z97d/IW9/61ezuvsBTT13kqaee5uDgJrfccoLnn3+UIpnyy7/6n7nr3Dn+2rd+K3/5jW+ce+30+3D2LJntkSiPIqhgZkVbQX+Hx68tsLpi2FiWUIYCmKgA3ySzEBQb26R4xKAVExPgmJTdjoPriTOHUqI1loLEKSnC6BlgGHlSVNFU+E6GRwSFIbNcMmOhspTC9tCqoECL1EJpMqNnYQ0VwBAwpTeyMUpTDxOSRHMw8GktSHHYZGxYrY3lsygKy8U3kdjOjVzieGb7FmhAUNF0mNLKDilqNbStyS1HIqiNwS5iHJWjs+SIPkpnV1VlGV53G7XQZlp45DiEeiLfXVj4+QQO9ufUV7uNKiufZmamxd4emV8lbq4SWvFMLatRGGLs2e/gks6y3xx8xtgYprjEmcvFi4Yz61PZb+NQDxMsVcymXEL5GSz2DxQnsotYvQOmXoOxrnHAAieqXTrWEpPYIs/FEzoIZMWgKMqiu5kuN4yp+JnYoNkuZAWTwwmVhYDCcXFJ2OvYFNg0axmBk8wcSQBlCPZviLSo3xddTmnbUt6Ex4/P0VuZEa4U3HEHgzzEs3OKWTy7RUqGTY6DyQo6PYtKVR0lfpXsVq83ByKlTrgMEiwZ5JeyoeVKR3nZfF8AvufJOdjfl922bXkN5P+1moC76fSL32+MgNInnpDLfuWKsInixmI4dgxOnyxYrEwIOlsoNfO6sjQTXeGgY2FZhqVahNaiVZ9MJOVyPJbvk/h5xc6OpMQlibx+/LgwxQsLAoQ7HQE5q6ti8Va2hx+GD35QjuEd7xCN76OPyv6+9rW/ly3+yEdEF/21XyvSpr9192+x+dW3o6ZTeqbO9GBIvnmKpUXDRz7lsdexOXlS8YlPwKUXUn7oH6b85scCbrtNccstAtQcR2Qek4non//H/1G+6/bb4c8/dJnv+9YDnh9u8vDlFc6czLn/TJ+6MyH2Glzr1VFKsbsrsc57e3KMq6sC9E4cN1ScmEtbHh/8oPTpKBKLtZ/6KXjmGcO3fZvEdL/udXIOOx1ZaTh3TkDyz/yMXM+77zL80x8Y8/7fCdk8ptjdlZPTbBq+4ety0tziZ35W9PSVyjz17sYNmWhcviznd29PrsXZswI29/YK9vZ+kmPHNjh+/OuxrAM6nX/D1tY6lcr7aDbhypV/wunTTapVj1On/jyu63Pz5i8xHD7Fxsa3otStVKtybHt7suJw4cJ8taPs06Xsrt3maD9LRnhhQfarTL6z7XktS3lflPphx5mn2ZV9Lo7lGq6ulsclx1h6hZcstuiUxZs57G1hLzaPio4TPFwrwer3GNlNJlSohBKqVS2GqDRhbNUxhcGywe53mFaXiDPNYraLiiNUWQ3baPxX8cFXavuTZsn2zne+51f/w3/4tS/p/a2W/acu0W4WOPL3gdcBTeAQccD4IWPME1/ydr4coBhmy1bGEAYpXhEzmtpYvstwrFAqZ6GaYqmcBBdjNDkWFjm2SrAomBQhCkOnJy7FzaYUWnlEJHgMRxpHpbQqKRbF7/n+AkWeFujOHrQWyFwfTT5j3nwcEtxZURFAgk1/6tPtGhoNxeOPP8aVKzdRyubHf/wf8Ff/6o/ylre8kWPHDHV7wmgipUe+X+AXEWr7JuoTn5AnXbMJ6+tkm8dJvSquY1BZylRX6PcUjs5o1mVf+iObONUsLEKcWcSxyDWqFWHItSoYjDR1J8ZyIcciUDEJzpFvbjoDcIqMwEQYFJEJ6A01NWdKPeuS5pqosUyaWUwmBa0woTN0qLkJlbAg1h7kBeFwD+o1InwBidpgx1N0NMGqhxiliVTAZAL2oEcYQlptgpppf2ND5lTICo1tYmxLlvPcYoprGdG5ZhkRLmgLT6XkaKZFwP6hwjYpm/om2ncx1RpGKeJEcXCoWGtMSN0aTm+P3PEwlSp+PkF1uwJGtWZaX8V1CqztG6jBQEaRpSWJzt7dZdpcI/d9lKVwZ6sKMT55ZqjoCbYWwGiMIcXCJacwMCEkSTW+neGqhEzJKoaeabwLpWf9S6yROh24xb5CEPUYdhKetl7B3XcZAjPm2mGVnY5LHMvunTghwGF3V0ClxMMabJWjtMKyFa1qLMEYiUfVnooWGpduzyIr9KygR5Yu/WKMow3s7QpjLihu7pnW7wv1NmPQSRLps2USwOYmxnGY4uMqKaxTGAqjZ4Ww+iglrPQaLsfCsqiu1YL9fXPkM/zii5IKubIyLz764meLgIFKhSMA2mjIZ8uQj1Jj7Hmyu8OhsIm33CKD/pUrEvV76ZIArdtvh098QoDaW94C3/5nJiwugRVP8Q+3UOMx5vY7GGk5N4GKUEXBxATYlljfZXg4KsUYxTSCYeTS6YrdZBgK2/rJT8oE4Pbb5zrXVsvMwIiAx35/nnC2uSna3peegzI5z7bnzHoppzg8hHd/jeHVrzacPAVZZ8Cb3whrp32WOs/z4gWL43fVqS76/PSvLHD+koVtwzd+o+LDHzY88MoMbAvL0kSRYW9PZBJ5rphMDBsb8Ju/CWdvNbzzXZpv/mY4czrj+DHF2qZmf19cLO6/f+6zW/qid7vCiv7mbwpL/g3fIACt1xMgp7VMksZj+fv6umEyNli2RE//5/8sjgUPPyxBFM2mXPdaTY773nthODBMI3F9sG1hpr/u7VNObGZsdXyeeMrh0UfnaXel3G13V/rD4qI8ltfWBHiX0p7778/4jd/4Ycbje3jXu97N6dMZ/f5H2dqyqNXewsc//qMcO3Yf6+sXgSlf9VV/mR/7sW/n9tu/im/8xu9iPJbJTbm/q6vynWU/j+N5wly5GlJOSED6+cmTcsy9ngDmalX+XrorbWzI8ZaWbuXE0vPk9YMD2dYdd8wnj7u7c25mPJb+b1lgqZyDjubOM1OC0T7b1jGiqaHRhFYYkSgPjKw3ajLG3YzuQHPr+pjdnksjPcBdbhErj5v7HjYJx/x9rDyFOEadOPHFqVn/nbWXQfFXTlNKvR34NThafn9pi4GvMcZ85Eva1pcLFGdZubxT4JiU4QjyTJbiYy2hHZ6JUMqQTxP0aETUXsO2CnIsBgOFMjn1miLDQmuDPbODymcODAIL899zxKUkYn4GbIkhPnqnwSLHIcbCHEHqcVHh4EAdFWVMJoqf+ql/Rb9/wPPPX+Q97/nrvPOd5zixNKaIEjpJlc3FiCzXOL197GuX5WnlukJVrKxQnL2NidvAmQwwQB7WUGnM1mHI8kJGhk1oSTBCnCm644C6PcHv76LaTZTnkRWKOLdQrkOAMLEJLo4SXjlDE5sAMISM0fv7FNUaeVBlNLWJYkW1ZkA79AdQ8xOqfoo2BhNFpL0xaa2Nn42xJiN0sy5P1MEAs7ZOUlhkfhWvt0PaXCbzxVrOKlKYRnjDfXS7DcaQZFJwSeCjTUGeGW52QyxtWKjFBN7cxiMfTYiaaxjHwx4eUkwjnDSiCCuYVhtHZUxVgJUlTJ65SqVp4S03IUtFS2nNLPTS9KhaxeS5JOrtbMtoUIr7lMJkGUYpsB1y5Bz6TJnmHs9fcHBUxt1nIgotE7QMi9i4uPkUW8PhyCFwchxfYo9HU4s0MlQbCktLamKGy82tgkknYikY0a5E5MZmz9mg3tAMhobFWkR35JIrZyY9EFBc6mzz3BB6Ka7OsJE+YtDkyqZAETJBHxxggoCbowbX93wWF6HZmrFCo5xmEFMbz+ixRkPQm1IyaG1tyagdhnKdfZ9Zost8/bfXw7QXMJaFAhIsBpFMRttt2dTenvqigTiOBfD1euKy0jkU/fHungDDyUTuP9sWcFgC5kZD/l2pyCAeRQIyZDvzMJDRSHax9G/d3xcgUl7i7/ouYepu3Jgnnh0eyve86lWGH/0b13noVbMxIU1RWnN0MN2u3PTDIdOghSoy3EIKD40Su7t+H+pNzZXrNnt7sq9lzLh4oZsjVlomNiIF+OQnFWtrcqpLgFLOSd74xi+2jzRGtre3J7+fOjUrxjOGYjhimrs4zzxONk7Qno1X8+DggNxoinabbOMU+3GDvQNNtQY7O2rmWyss+fZNw+ceLfiJf2lx550KSxe8820Z7/yqjA98UGEFAQ8/DG+9Y4f2sZAsqDEeKz7/+Zne+NVSx3Hlikz8PvEJKWgMAkn6C0M5/tOnZ0qd7jz8Yn9fbMJeu3qZdHUTd9LlqWst6osOWgsZUKlIOuArXznX4u7vCwM/nZYaXMP6YsKZ4ylPnA+pVPVRMl2SiKRlZ2dedPfxjwu4fuSReRTyyoowqmEIjzzyY/j+Dd7yln/KM8+cZ3//Ub7qq76dbleKuM+f/0G++Zvfy3B4njvueCu+3+T552UbH/+49PvXvEbugd3dOUtcJu6dPi3XtNOR/SnBre/L8VQqsl9lQWd53paWZFvlvNVx5vfa0pJY7924IatxpXWh1vI9ZeBH6eDR7cL6Sko9zEkyxZLbY39cpdkwRCpAWxCqiEi55JOMg5EvOmwm+DrDDIboSojpdpgsnWAc2XgeDLaH+NM+xnZZuWflS8IGX6ntZVD8ldOUUl8A7gKeBH4cuAmsAd8LPAA8aYx55ZeyrT+6prjcULmlAvI0px6KO0KiA0CL8X/h4uuIxG9C0EKZnMIobHJWawkpNoly0agvYnYl1NiQzyQXvxsY/27Yro9eMUea5gSbBA+PhCkemrJYwpBnBeOxRVEY7r//Xp5++iJ33fV17O99DlPcxpUbDue8G4SHz2FFdWxjYDLBlOtas1xRNZ2ie128tkNuO1gOpMrCOBWqlRydZzT9mAzRgfp2xFIjxgJ2k2MkE4eKMVT8lKqVUpAREaDGQ5RtSLyQTBX4hegiVegztQKc5XVAkRgH3y9QFgQHN8iqTdq1EKU1kfEkJ1C59N0FWlaE7flQ9QUI2fZMGDrAAZKgznTlFJZK8ZhSYIk1mW9DsIIZCC1hWQ55ewmbgrRQWL1DNpsF2hVwFeGQGwdsizAfUBnukNYXsJIxSaVGMcjwbl5FZRPM4iKulWLbOb2VDQhsMtdBewV2UMd5qetInmPOny/Lw2eJbWpe8q01cbWNY0E0LggqBjubyoTLVpxaE/ZVachRJHiSXlcUbA8qLFcmNOoFNgXdoaYfWWgtWr0nnzC88twYHQJpQtONCZwxOo4wJ1aJrRpFF5QNrQVFWoQsLCToYsqkCCgKAcLlcqk37lJkNo6eoOIIS9vEjWUKIDATOeZmkxSXRuBSWyhQccSFKz4NP2J1xRCaMcb3UZYlx++6mNIvLQxRJV1VIsxqVc5dryfofHERdbCPynOi2iJprYUfKGzbcHgA7QXFyooMtmXVfwmQy5XUEytTTGbY2/aYTGQQrVbnWmOlBCSCDOpZNtdTdjpzTaZlzSNyi0IARBwLoCitsp5/XqJ7P/rReSBCyaK1WgKY9WKbqJLhBSIhMoeHc2/H0gMuz/GTXflgEKAowCgck9AfV/ilX5WJgOtKCmatprjnHgHfuzsGSxmWlxQ1N+bSVYsoco6CGE6dkv11HDm+TmcuHXjd6+b66jyX4ywKYb1Pn5bAmos3QxaTG+hbb+fJ8xWSuOAVrQH1jQ2uPtHH1E+xNO7g6zG72xWeeNLiFa+Qc+t5MlHY3oF778y4526F0hY/+rd3cHyLn/2PdX70//R43/sMP/zDisc+t0IQFBJSkxre9CZhcrWGIje85tUGbWu2tiRNbWVFUkgnE7kWd98tDHGey/VOEgF9W1uKX7h+itEQVhbb3Ht7wtZNm3PnpB89+6zirrvks3fdNXeNmE7nZRvLS4YotfjAJ1zW1qTIcXPTcOqUAOizZ9XRikMJyK9cEfeI0sKtVBJJWMj/xHT6Ad7//r9Eu93mjju+k8FA+t21a7/EHXd8E0lyibvuegd5XqHTkb565co8Jno8lu2W0v2SO7KsebpeqzXnTDxvfm48bx5e4/vlSoP8vez/UTQPi1leFleaOJbJVxwbWi3pI1qbl3gbC8GzuipjX6FsctvGDwzokBU1RimbbPuAJ7ubvOpuRagiDlOHydhQ5LC87BPoCVFziaJQhMEUd9zj0m6LtU2LxWWbGzsrbBzTfyAeeLm93P4btzNAArzFGNMrX5wV9e0hgSFfUvuygWKYkU/THDsekVt1lAU2CQk+ea7odi18y8OvGBxXdMLRROFXLYqiINc2RkvxVoo78+u1Zjua4pBQAGMCAiJiPJyZmCADUnw8IopZNLRLPHOgKH1zQ6YmBAWamADxu0lsB13XxImi09niTW96I5/97CNMp1v09j/BKx94HSpbxapK4IJUEAaYOCbFxc4j9Iy1VDdu4IzHOJ5HsbyM0gUoLd6UWAwTl8OOZmNhjHEgRdjAlVZCmud0px7K5IxVIP6wFBRBALk8dY0xAsZdF1XkOJZ4NMtgbrA7O+SNZYrFFZx4ApMesVPlYOCz2RhiXTmPvXKSQdomL6Ae5iiMTCMWFmB/n6i6hOcUOGpMhiYy/oxpT7l+GFLzU2q1NrpSk3hlWzMxAZbOMUtrM1Y/wSPFNgVZnlHp76DqNdJqEzuPUe0mvjEYtwYrTQHmSiz+pngsrGTYJkPrjCxT7Ax8mrVckhIxqDQVqrTXg+PHMVEEnkfsN3DtAuXYDCcOvZHNituhay9ycKhQpmBtXePVNRYJkbHwVYoxGWkO40SS/CwUJi+Yag8v1NgZXLliaDbFRu25q1WOHZMBbaI9drM6+12L2zs5aEOSqhnoM1gKkQylBXsHBoNU8tfchBdfcAicKmsLMWmlTZxnNGqKsJjC3g7KGKbNNfRwiNPbw2q2GXhLnN+ucvoUVLMYpSyMtlH9Q0zp72RZEIRHa/YGYT6ONArPPSdUYInMS0RZq6GrIRa5yIR0QXXJZpwHgHWkfxyPBRCnqVyC6VTRbFaYTCA1cxa4ZNDKyUypPS4tFkuQ0G7L34tCGLOS0O52BSAEgbw2WwRgYcHwvd9rWFtT/NzPiYS6yA1Ly/BVb4O77lQoq8JTV+GuW6ZEsYXTWCcYHWD5DqY/oLAcrDynsBzyoIKtQXW7pI0FenHIzZuGOBZ2cWVFrnUQSALnsWOwsKDYXIxwLMMg9lhat6i2FEtLAppLiXspPYgi0eRqPS/AO3dOjml5eX4JRiOZTFy+ZvHk4AS1bcPx47CyrAm7QxhmHEsucmW3zujMCs8+q3B86PcN29sC2ptBhO3ZDIc25897/Pn3CZDcKtY4Ft1gfTHjH/5Dj7e/raC3l7F/4PLAXQnDwOXDH9Y8+KAUCoKwz4u1mKDh8obXK175SnXkrNBuy/F94hPwpjfN/XvbbQH/m5vwoQ+JlvieexwuXbI5dypmd8elVlecOycSh8FAjnl1VVjzble6cRiKQ81yI+PO2zUFmloNdBZxuO9y7Jjm/Pn55+p1Odf33SeTixdekJ88l34VRQOi6DmC4J287nX38vnP/x2Uuolt341SeyTJAUtLB7z44uep1d5z1D9LD+3NTTm+UiqyvFwC89KpSK57ubKwsjJnccsi1V5P/h8Ec5D90iCYpaX5SonjQKsm3qSDyMN1C7mHcsXhoWGpnZMbi25P2ORy8WdzJca2IDEO+3uKyvLMh3wyodJyOZb2iaOQLPRxqxbH3BTH0wQ6YhJrJjMN8810lbX6iHOnE2IcfJVx9lg2T+h5ub3c/mS0x4FX8HsDQwpk+Hv0S93Ql00+ATIAxqMEMwNZBZYsVxMQEJMamyh30ORUrZgJAXGiGQ1nS8gVRbVSEKiICG9WWCbWawESZGCQ6N2SD1YzNljPjn4umIAcTYqLRyzvLgoyo8m1jVKGBI+AKcYI+EyxmeYWv/3bH+aOO95FHI/43Od+g2/7lq/HsiE1DtEoQ23fZFJbITIe01FGM4g53p6gbt5AlQVOq6uY1VUIK+RKMyWQvTWGkCmFUqgZB54aHyjQKscmx57ZzBVAbhQ2AqSUmTHkShMrZyYRKWbFigU5mqhwMMrGiqfY0wFppcFBz2WaKjb1Lr6VkoZ1+jQYTxTLiwUBEcV4hO3a7I4CHMvQruUYrciUhSoMdA5Iaos4riaOFaPIplnPQCkCE4MRqiu3HFLlUgCuSYlxCVVMgaTLTQlwevs48VguVqtFPhhj1StHI4NBJguJ8SkM6GSKk05JwwahjoSxHo/JpwnRwhouCc54CJ1DJqunKVwfpQzK5AT5hKLXJ2ksMY003mAXlpaxHVBG+gBZys19l+V6TH9iY80YZMeX4I7eUFYRjFEcHCg2NkTvLh7astuOiel1CuyKj20bKlZEqhwGY4ciyViop/QTn35fzRhURTTO0ZahiDOMgdxyaNQNNT1BFxmq08EUBYlfx1QqYmmXO3jbV9lyTuBWvaOl21qloGpNsFNZU06mKdtmjfaiprp3BdWooTzvyK7NLC/LiNztosp1X5DRPs/JKzXiyOD1d4kWNugkdfxAwEWvB6WneDFzgrt8eb78XUbVlkAgDOVzQSCDfSqSxKPl3lpNXisB9vHjsitJIsBid1e2ubw8l2GkqeG0cx2/6TN2mkRTg9rZJr5wHSd0qJxcIkfzoeePceas4rlnCxoti3tPDQiTDqMsxF2u057ukDfa9PIqVpGxUEvZ62g+8kiA1orbzoK24PjShP2Bz2FXi7f3S4qIS+eNTkc0o6WHsdZzj93l5TlQThJRtJw+LT+lt+3y8nzy8LGPyd+efhryKOEdX13QrGQ4uuDSTkCrlvKx9w+pnFzieKOHbcMvfajFXXfJZOL22woW6xGJcYkiQ+AZkszimectKAx33hLhXX0BR+c80rmV/ajG8pJBK/jtD2ne9jZot6Wvlkz+LacNC5UJUxOQZpqf/EnR7Zaa4k99Cv7qX5178O7uyvX+whckFMVx4Ou/Hm49ndNqG3Jjc+0a/MiPGB5+GL7jO+A7vxMuXxb98e6u2MY1mwIcb711LkFwnYLFds6V6zZJIkWQx4/L+8oExevX50Vs167JOU+Sgp2dH+SOO97O7u5p8vynqNXOsrj4LfR6P8upU6uMRpfxPM3x49+Fbcs1q1ZlUlZa6cG8L3c6cx1ztyv9/dgxuQeCQPpslsmkJ03nQR8LC9J3yqCaWm3u472wYGYFi3INbNswHBoqfkbFirm6X6HVVpg0ox5mTAofgNHQHLm3jPsZgVegi5wwOoRWS2pECpfeQUql7dKwpySZojPyqDfAMhmeyuiOLCqTA64XG7QXpYDbGvXxJn1UszlfHvrvuP1Jk0+8+c3v+dWf+IkvTT5x111/6uQT9wAfQsDvPwF2gZPA3wPuQ2zZXijfb4y59l/a1peVKVYKfDsnTzOmBBilSRE98cSEZKnBtxNy5TCZ+RfHqYDDjcWMVLt4SoIbFAaLmExJiICZ85n4JOQzFlVioQVwpTgUaFkGh5lcIp7LKdIUezJB1xtMrApgkWPhTvoQxzjNFqnt4vv2rPChypvf/C2Mo5TFSoRTJNzoBnTjE1QdWGqm3LI0wYwn3IwWWTkZYC8tQa+HGo3ItvdITp3D1hmeiomNR1EopsbBszJQBtfkOGZEnCos18JSOeXCVJZrOpOASligtMIddtAqJ681MUBlFgCTz+QpFgUVFZOREnkBytF4vX1azRVWnQKjFohm7gsVDAvhGKvIJI662kSNuqzYEfg+qjMgaq2SWx5aGdyFBSomJ1Wg/ICaneJ39yCJMbu7mGpVJCS2g7YcMqvCY8873LO4hWr5FNqlcFyC5JDCZNBsQLdL3u2TNxdJ3ODoWinANhnT3KC0ZpSFxAPFsjWCdIIZj9ix1lhoKwIVMyVAOQl2u402GUqluEXCtW2H5fUqaiHEIyFI9shNTDLq47hg9Xt4WlNUa9R1hen2iJYvlFKMR5JAkRlOt/sCuNubuK4n7KGb4TuK0cjCGPAtzWJ6A6ubY2ybbGkNh5xqRaP8giiGQS+nmg+4sN1kfUPz+OMWd90acfIUmMEA7/xzKA1qceGITorqS1iejT04JNMuU72EdewUqmNTcyIOBtL/bVvjVx4W4xQAAQAASURBVByMpyTx6uaTHFucwq5C7e6irqcy6tbrMhrnOUW3hy4BcTkyzybBVhoTXr8iBWmmYLmxSOE2yZRLkqijAd6Y+VJvGa4Tx/I1lcpcb1mCYqXm1flRJECwXhesXgYd7OzMrajCUIqPplMhuUuy+1OfUlTuO85GGLFzDYaxy9LhmM1rn8a0Fujd/z4+9imbGzcUr3llRK3q84UvKJqm4N6zDplpY6KCq4Mmp5o5rUpy9IRZbuf8mXdK6FDAVGoWdnepLCwCdYZjidf1fWEJXwrczp8XScHDD8u/3/lOOZYLFwT8xrGwqsePS9Hg0pJclpJ401pcIsZjePZZKVSbjhT5QZftaZu1VsRBR7PcMLzjnps8OfD5wCdqbOptXnl3jQ9/1GJtreDO2haFVaUbufhVm04nI4lz0kRxx4kJrm+T3XkvOzsZd9d26SSGzAvZWE5xHI/h2OLHfxz+wl8QcK41PPkFWF8Xp5r1dXjwQfgX/0JAWKkFbzYVly7NY7vzHO65Zx7DfOYMjEYWaLh5XSY3FSeh33cp8rKo0nD9umiXn31WAOn99wuALFVAWa457OkZYJTvSFPpxo2G6MwvX4Y/+2cTPvCBH+DatR7T6QanTm2wtvYPOH/+f8VxTjAYpKyuHufUKfjCFzI6nad59au/h709j91dKeoUyYIc4/PPy2uWJSB3MplLJECOeXlZMGMYyhyzbLYtfbyUHokMxRyxx74/vz8UpTe/oduB9gIshlP6sUfVURxbmWUBuHK/1qyRjH0NC4cUoxRJUKE7koJXC5e1JEY5DrsHFs2WTben0A2PmhMTBgVJ7tLt2iwsGFq1MUl9mQ2ToTAyBtR8qLhzLcrL7eX2J6c9guDZd81+fnf7zZf8bvgDsO+XFRQDsqy/t4cKFzBeSDYDqaoo2IsCdGATTPZxi4Si0SKvBnhVi8HUwbIMOBXyAqJJzmIlxVfpF22+ALKZ57BHjD/zOgbwmL9XARXE2DFDEeETeqA8jxgXZtAzwSOvtPHDCK2UAMtKhTi+Srt9ghdegMHA4SC0OLYUcfp4zuFQM5koKk2HhABPj1kNxyjbxjRbqHoDMxlj9/vYSY/Uq6LJiQYJ24cORZRz5owidDIwsDeQWf6yGx9x/QCJ8hhPFOFgF7dik1aaaFsdVclnRjyrLJUyGUOe5bTrGeM8wLEyeiObdqWBaxVSaYwCkxMSAYap8XH6A7y8T1GtYypVJsYlZIpaXCAgpeh3MJ5H7oVMcfGMBKAYbVOkGVaWUiwskoZ1TKuNR4IVJyjH4lXukySVkyTawUlGKJOB72E5NsWhMJS5W0FR4BGTYIMRyzgMHB4U2K6m0TD4vs/B0MMqAloLDaa9gBtdRdVNadUS9qMaVdfD8xQpIs0ZDGBF7xBVWngVA40m14cW05s551p7MnJfuYLe3WVtaYnk1juwHIXSI5LcoUhhvRWBthgunERZ1pEVsrIs2vUYQ8o089GWw5XoFJsLU3xXwmOizMJliqMLsnjCklIEbRtVyzgcuNxxhyFwxBJOhTYc32TaXMMdHGD7AcZ1KIzFaKip1dtoX1NTGouc9daU3Z7HwoLB07EkVU1jnP4O9gytmsVlLN8Vimw0+mJ/tCwnaa3hWhoLI7QczJM0ymo3x4EoxigLLxtjXA04DAbzgrNaTT5SOlS47nzp1/PmThPVqrxeOlfUanPnANedexWHofx+9arsVrst2yod5KJI3jMeQ7/q89sfhc98Oue1ty3yF9/xdrJbb6dGyp13ezQWoFYtcOyCBx/UaL/FThxAYOO5BUbXiHq72Okhme1TOBrXEslVZTg8Ek0bpbiyF7BzqHnueXVU1Le/L0Dn0iUBuFJcJkzheCyM79NPS/qf58Fjj8k56HTm3rJay/vuvlsK8W65RWQWYQgL1YgLhw4PX1zlgfsK8k6PO8+6GMfniexuMrtg84TD008fZ+MGPPCA4RV3JFzsHuNn/3fF7bfDu9+taLddtl4Ez4fHXwh4lfc07t238dRzPq+/r0mQQLOZ0Jt6HFtN6U003/d9cyu5Wk1x63KXzz1dZXvP4Q1vMOzuwtu/Cs4en/KBj/i8+c2Ka9fEZ/jmTSn4W1sT8H/33QIit7eF6S1Ne4pC8X/8tRdJB8d4+xtcqqHL5qbFwYFMKN70JukHZSiH78s5W1gQAFxOksprUALnSkVA+w//sMvf+3s/hDHwoQ99np2dZ7ly5Vu4446vQqkT7O9nrK3dOmP8W3z60z/DyZN/jlOn1rhwYS4BqlblWGY1qUSRHM8b3zjXP5cTpbJA7soVuUdsWyZ2/b5MDIpCzsNrXiOrAFtbDq2WwrUzdJZhEgvfiVEa3LpN7kOhXVLlE1QMMR4FFg4xGEOsPCpkWKagO7Co1Vwy5ZDOnDAkht0hKSyaeszGYk6ifSoVOOy75E2H0big3oSlJSXOKwRS70BKoqyZW1SBsV2cPyhx5uX2cvt/pvl/iPf+gR34yyqfKFs+GqPSlMTYxCrgxfMyYCwsiN9uQDSTA8hyeoFFnoMpcupuzKTw2T/QLFYnVIMCoziKEpAiOfGJNcbCJjtKP/tSW4xNcnQOxffXoqBAURhFgeK3fueTrG8cw/fPcXgodkzLCzGbjQnFJCKqLaFNhjVjwgo0RltYyQTfNShj4JFPS5HTyZPgumSTmFFtler0gMO8iVtz0bYGA56V4s4mABOENVXDEZEVgutSyUeo4QBTOrVrTVLYZEaDpQAtnsRKMRrkBJ4hcMT+TFkKz6QkOOgixzncEa/J0ujVssBxKJaWmagQv7eLRU5Sa6O0wikSVFFQdLqMqst4ocNuz6cZTKn2bzLuROy1z7G2LO4gX3je52xli9qSTxw2KZQUTmapwuvuM25tyMPbzwjDAm3EJSLDwTGyFGjlCTd2HTJj024LGxVHcNgpUEqztKRmJvcz+YyriXMP10oJVIw2s6qlTodo6RjG9wkmHTh+nMKAMjNT3HJNtN1msnGG3OiZdEXPQjUE5EgEqzkCxaAoCiNFou0psRZ6xyfGTqdyHRKXmz2fZjXDq9jYWYyxLA56Nn6gCXzo9Q37OwV3n5niWgXs7xGHLayqj51MZT3WtiEIyMMqU1UBIGSMMfD0+YBqU84HiB94WIxQBwdsmTVa3oTKYEfQZRDIqFytYq5flxG+XkdF0Twyy/OEFlMKk6ZHNhG57UlfBDo9i9FIUt9KkFq2RmOe5KWUgF3bnrPDZX1bsymsWpLIv42Rz/q+aDbLCPSrV8WJoCjEtq50eahU1BHbdnAAgZ1yYjVCRxNUr4exbfLWItcPQ9bz6+ywzI3Dykx3qmi1BLDUvSl3qWewlxdQh4dCMU4mc9Hn0hLG8yiabT5/qclgZPGv/3XJgBueekpOre8rTp4UBrj0zt3YkFtrd1f60D33iCPCxoa4GLz73QK0wlBY9gcflP//yI9ISMb998NTXzD4Xk63b3HqlOLEZs4qN9GNOgdRjdaCJH5ubclup6mw1T/3c7K9L3xBzvm3fut8Kf/ksZQXz2s2jymWa1MmhU9wcI1RbR1lKZ55wUYpzelTBZ9+RPTAlqUYHMRs1IdExmN/HLCzZ/HhD8O3fHOB4xjIcpY33Vm0t5rVvxq2t8uCSXVUTDadGkYjOHWiwA81P/7jorP+Fz9p0JawzQ8/LDKKhx6SazydSox3oyHnLAhgY0O2mc9Y5o9/XPpQOSkrXUlse34tKpUJ//bf/h3W1r6ahYVVdnZusLT0Ddx5Z8TFiy9w7Ni9OI4USl648MWTsij64p96fR5bPZlIPz91Sq7Bk09KfyjDPRYXZYKxuCh2fkVh6B5kbG4aXC2uQo5JsYtshvwXwdKSzhklmEadRDlYs9AqA+x2PWoNhVE2ShVMJgrL1kcFqQLYDY4jfSObpkymikrDxp8NgaUMKhfHSra3FY2GmSXsGSxtcJW4QPmBLWPWn4L2snziK6cppU78Yd5vjLn6X9zWHwcoLgoYjzJGA3BcRcXP8FUy0xlrpniAZjw0JClUqxrfTnC0FI1NIovhUPRNawsJxtI4s4S8AvEkto1BFfm8ZP0Ps3+ID62ZeVs4iE41JiTPDYcHsqR14eIzXLu2RRQZarUTrK7cwe23plTtCbEO0dMxdhFjbAer30e1myJk832i5hoELn40OKoQMgZ2hwG18S6qVsVMItTBLr2FMzRXXHFoMBGJcjEY3DwmzwsiVcU+3MZvzQqnZs7uJgjJJjFZrYlniUNHPLOiK69bSCza6zgSpFI6ye/vozodebL7vozUq6tHLvJJoTDDCfaoh3WwdySaG7oL5GGF0UST54rFRoalMrpjj3olwVgOnomx8gylDKl2SJRLSERhFNNC2G1b5Rzsw+eechkM4PWvNbQWNJ2OPIOyrGR9DI1KSuhmxIWNf7DNjlrBr3v4PrOqcXlwjzspm9ZNrMXW3MKgtCZ45BHU2hrmYx+DzU2Kb/4WlClIjYWTTUkaS6S2zySy6fXk/GkNSwsFcaoZDJR4+CoRylYq6mi5H2BpqaDqJlgmIxqmFNOEERW8is0k83juOcNrXxHxwlUPZWk2FmOWmjIJynPFTk80zW40hOEAmk2SsIFlUqxYrlGhFJPcx9u/QbywhrEdimK24pHAeARLrYywGIrgyLalP5bodDgUJNFoiCOLV8Mbd1BJgpklb6iZVYQJAky1iooizMoKU7uGd7DFtLZMd+LPgjQU/f58MIUvDuYoW2lZ1ukIKDs4kGX0KJoPxvPrLexcryfHVOJT1wXLJKyvIVaFgTpKMOv1BLycPJZx+4kJlWJIuFrHa/jYeYIz2EfXqlzt1rmxK3ZgxhhenIWKfv3bR7T8BC5eQJVVXy9JBMzP3EoxHDGprvLksy6/+qtyq5y5xXBze25oce2aAKh2Wx1prMvCqzKcoTzWq1eP7M25dKkE+rKtLBO2uCgESN9yCzz2mMGyxGmh6kV88IOK2+5y2dyU+6XTkffv7AgQ/sVfFC/bd79bWFrHkXP/y78M7/2aiIV6xkc+7XPrOYvr1xV33F7wWx/IqdQ0b30zfPB3NOduKzh7PGLrUIqTjYHFdk6FMTqZcmm4zAsviKdxv1fw2ldO6Ec+OTZFIYDRIkUpQ1a4PPEkrK0pHnlEgO0tt8Axd5vnLoV4ixU+8bDDa19rjuz5zp9XR/HN7fYsc8bkLFQmeM2Q/QNxgwkCkfOUfsRf+MJcB+37AgYXFuR325ZznCR9fu3X/g1JcoFXveqNPPZYi2rV4t3vfgtXrgiLW7LBjiOTl8NDYXjzXLYdhtIPDg5kUnLliiTtlU5MocwhWVuTx9G5c9JPq1X52+ZKTLMBVuDS8KZEePidHaxGhULbTAmwSfFJiWaWnBqY4uMZkY1lGZAXeJ4hV85RYaDWahZZbkijjDizqVZhOsnYuq44dcbCsWE0lom0yaSu4aDvCpNvxhTKZu/AYnM1pdA2Whn8mveHGmu/ktvLoPgrsymlXKANHBpj0v/a+3/P5/84QDFAMoopkpzB1MarWAQ6wqYgVi4gDFuc2bh7N+iYFvWWTRAoUmWRFA5Rqmk4UzLlYs88GlBlAZ2FZTJ0rydPgPLp8/u1l+7/71r2ibFmjPHv7fc2KRYZOZoMjw9+8Lc4e/YdLLShXZVKoknqUXOmRFqWmnwVU4ymKGNwAnkyptg4ezdFNDnzKVKrq5gsI/Gq7HQ8VuwD7FpAbFek0EFnUoCXWTj729wwGwQVxXK+Q+zXyYI6DsKOG6XFio0pOTapcgADScZh32G9PSG1AtxJj9xoMjdAJzHBzhXUzg5mttaoarU5onEc0mqD2GsQ7l8RoYkx4PuYeh3jOHJ2Eo3f2SJZWAVXSgTlGaLRyQR33CNvtEj17EFqCvyDm1hFQhbU+dCTS/zjH1JcuQLvfa/iz/95KaYprZ46HfHmXFsDkEI3W+dIDXpBhi2McJGTaU9Abq6ojXco6k1sXUC3B7/+fvS/+3kZsdptstvvJL/3fqzlNkppTF6g84RRHrLT9UHJIFp1EjydMKYq6WpxRGEM41SCTvb356CnypAUi92uzzRSJDFcv2F4w2tzsCSkpaLGFLbD8+cdpuOMV96VMU49HBNTCQtsDNe2bb7w+Yz77oPmui/FlyYjQSz18kKxvyfLzrW6eL1ORgWWLSxU05+iux2saCwHMRpJgV0UyWhdRrSdOkWubNT+LrpWxQyHR1FxajhkGhluWsdQRcHxlRhLy300xaczdGfMnDrSArdaAhp+v8dFmcz1u0MqyoIspeYhBXt7AjyeeUYq+md1f9xzjwRkeHbOYc8SmcysyOl/+V8EWL7xjcIyP/CA4dZbMr7pPTHdaUClqtFJzKcetbh8zaZeV5w9kxP40B8o1tcyTnEFa+cmamtLvK/7fVSlAhsbmDNnpLDWqvDwwxIocdddsNKYcO26Yq/nURjpx64LWSbSgTSV/So9itttmRyUgS2ly0KWyXuXluRx1mrJz7/9t3IONjdFw5skMse7966cDf+A3WKJStXi/Hl5/yc/KfPePBfZw2gE3/3dcj6ffFIS5h5/HHyv4K+8d5eJrvFrvxPieYpf/EVFvZbzo993haXbxd3kE59UvPWt4mqgMNhZRKxCsQybpHzhCcPikuK2W3Ku7vhEk5xazbDQLBgPC6yKj+8bfBUznFhkOGxtCeN/y/qEyASMRwWdy3381SYXLll8+Ldz/sHfiYlUyPnz0n8mEylMlf5hcAcHvPLVDvvTGtWGBhRxLEWN3a48Q3Z2BKiWGuT19Xlf2tmRbfb7n2Zjo82rVw7xT93Pj/7zR9nYeIB77vGPXBxKttd155Ov0gL8ta+du22U33XypDDFZXKjZc1jpw8O5vHNGxtwx6kJ1SAnUY54kxtFmA9QeS7R7XGPIkpQ9SrGD2YUjtyDSWLw3OLIj1+GNnO0clKrKbpd2Y9GEOP4Nlgajykag5VMSccJfW+Fih0ROAXPXfHIcmiEOcf0DXTg88SVJituj+Ulg7XYRP0p0hO/DIq/sppS6lYkOvrtiDQ4RbTEf8MYc/FL3c6XX1M8a8rziHPQrqHTBdcNadRSPBLx3kXh2yl6fZmFOCPLDWNVlahKldL0BPQNhgqNTVDVBERkWOJTrJSMBL9fK0demK+vtttf7HsDuOTYTIGCeGZrZpNhUMT4ArooiOOISxdf4KFXvRYfC4eCzCiyOGVi+aBk2UtR4NgG0xugBinGcVCWR6EtDhfPEU1hI76OtixwXRwMxxp9+nEVK9VUkg5mNIGFFpZtoewC1pZZzGQ2YFIfb3iI62vIc1IrQJNR6e6RWD5FvUnAVPTTbkplMcUoyMmJw6ZA/yghONiC4VCkGO02rK1hyjXH7W2U5+H0eli3nEEtLWG6XRltZ+czt12S3KHSvwG2hSoksCVBkRkHTE7h+gz1Ojs3ZcW+3zO0Dy6iiz57m/diK7j3noL/+fsTvvO7fR59VJitRkMu03isqFdzlusRbm7YPvSwfZtRr+DkeswksYAUx4atQxebjHW9x2HRxlGKUPUwkykf/LDiE098Ez/wt07j9vfInn+R4sztWPUqRVLQSzwclVFkPs9e8kAZllcM/b7GrgO+hwGyOAUUtipQSpZhDw9lcF1agm7s4+gcbIWXTzlR7bJwzMEpQmxl8F3FxZsVFmoJrzwrUdmDxGN7y2AlKcunauRZTqt3gVfUE7L+Et66ojAWSapxD65z1RynmBWd1mpzJ4hKYFhqJFzZ9rBWHIJMoRoreDevo8IAk6ZziUSZflEUWFYO+3tQrTBdOoY16OBevQaDPoHncfr2ttxK+/siN2m38T3NQs3Qm3hHyo5yeXhpaQ4A8nyuxywZ0r09+Wm1ZKKj9bwQyZ0VDOU57O+rWUKeAJiDA0nzuuMOxfq6jW1Lt63V5O+lhtPz4D3vMXR2Us6sTdFJSqsVEsWKRz7v89nPy/dWKoamPeQ25xpFJWWyci+T4gTVQqyv2N6GWg2TJEdf0PfhUnoCsI8cDdJMc7q2x6m6hanWueesxcEo4MUX5XaZTmWZvCw47HaFTQbpN4OBMOYPPzz/2vK8vfKVAsbe+U65H04sjpmYkKeeUfSGNsPJKpUKbL0o57j04i0Lzb7zO+Fzn+MohOOee0Tb/I53wL/6V5of/ZlF/to7nuetr7+Vy1se//AfGq5cKGjUDVHucibconvHOq6rxQ5T5fTzgNH+mLXoCmlliaefX2L0mML/Ojh+PGYyVYwTh2w6ZS3fI7LW2OmEJInHYrvAyRPOnLbodgyR8Tg4gAKbdiPnwpbitpMx4dti7P6AseXTakLo5jz9vMPWluHWW+UYn7m+hLo8s+9L5TymqQDN0UhAaprKcZeSh+vX5wC5LJyrVh/iscfgTd895vTygG/+Zo9PfOInOHXq+9nfFyZ/YUGY525X7veS0T99WiYzZU7O8rKc49JZYm9Pfm+15PpNp8IUB4E56q+5GzDC4JgUE8e4riaxKjh2TL/vo8M2eUUzOkzxjY1xfYq8YDRWpKlY05WSJNuWcvTQmlKvhxgjt3qawmHXQ41EvtgfuCy3UoxXwbEsKlaGpwoAVtY0nQPD5lqGzheh2+EVK9uo0vLlZRu2l9uf0DYDxI8g8c7lRMYFvg54rVLqoS8VGP+xMcUAN64XhE5KJcxJlAiYfMakyBKbTYxjUuIICi/A1xFFYkhwsGyFp1LiacFEhTT9SAZXFBZGioNe2l66ny+lo76EFuOQzNIBNTk+ESkuambT9rf/9vfz/d/zV1jbPMthz6HeEumAZ2corRhNFHEEjSa4Vo5fTEXaUfrr2Daj1OPyDYezC4d4B1uwvk4SNGAyRscTxuEyewMfbSmWWym2K5Z2BkWWQjQ1LFWmTHEIVYoZjdG2hcoS8XturqBUgTWzdbMoiI1LhoOaeQxbFKg8I3/hEjTrWOQc0kaFFdpNWQPX0RT1qU/Jk39lBQ4PyXODWVyAWoN0cQ3bJNzsBLTqOaNhgVe1CdMh/ugQipzC8ckXFoktn2KaUtguwaSDncdktRa55RBNxYKs6Yz56Kd9wrrN6nKB54KyxfZtOoVOp2BxwXDc3cWtudyctEgyxepSQWYk+bCzX7CmbjL1F8B2cFRKLe/TH2v6ps5qOMJ55OOYVov03D0MVIPAL1C2xWBsz+JiDUuLBq+IsGyOilkAHJKZ04kvEqBCHQ2QMl8w1MOMNNcsLMA0UugsobEkdNELLxhWVhS2rSjSlOVGgmWDKQrMKMIZ9ygcl/TCVeJbzmGHPo5tcH3NNFHc7AQcX5qSWD4uKVdv2BjLpiikfxdZxrENQ4bNdGJYzm6KALtex+ocQr0Gw6FoZpeXZbQvTX8PD2WwiyLxCE9TKdt3XUk/mOnNUQqDhLEMp1KZPxwqmk0zCzFQNJtzgFpqsUtbtjKgwrbnxVFxPJcLhEHBeAKtlhRJPvOMdMEf+zFZZj9+XP59993in1tOCOJYtMdXrsit9uoHDEvNiNOtHvk0oRdusL1n8/DDcp2OHxeZhVKG208l4vtdKPY6FscOnkDddoaxqhPuXkb/+q9BJcTccw+X/XN87AtttGVx8qQAnkoFFpMtMsvjyqBNnIiW9upVmbR0u6LAKCcHZXLdCy/I6d/cFDeDZ56RCeH73ifHkmUCkJtNeNvbSkZd5DzPPy8ThjJdb3VVfpQScFaWCVy+zJG0ZTKRc/bBD8JXf7Wc8499DL753RNec+eIvL1ENolZrQxw6wETKqg8pdsxFEaxtKLwVMy1LZuHH/M56d7kwdozbG3B4+23cV/9AsdOOZhFscHrT2xqSRen5jGxaxTGkmtvGTwm5KnC2dtCmZxskkAYcuiuM+xlnAm36as6lyerZBmcWM+4eFnz8U9qFhYUb32bAEHPk0Lafl+A686OaM8nE5mw7u8LOxyGch0+8xk5354nr29tyWfbbTh1IufeO1K+46/+ZS5fvsFP/dRH0Hoemwzw1FPSn3d3ZxbggTDCpRxoff2LZTJai0xieXluU7iwAIu1Mbn2RYaFeJmXZQ3VKvi+wVI5SpnZ80dWROJRwiS2CSoa24bBQD67vCzXu0xyt62Cbk+eNWW/a7dnjjA1Q56kVKx4lpooUsQUC09lDKYOO12PVssQ+FApBqiDg7ll458ilhheZoq/kppS6heBbwQ+C/wAcBk4BfzvSKLdfzTG/NkvZVt/bEwxwNpSTjrJsFVBSoHBIqKCMgWaWAqryHFMjMoLtNIUhWF4GDPWVTbaGb6K8O2cFG/2/lR8al/KBoM8VeRpOS/t/hJz2T1SbFImhHgzNwtxeLABzRve+FZOnTrFFIfmgjVbBjZMIotqJWM5jMhDTYxHZmzS/hg3HpG7AVlQxbYttONx9raCfn+BSj2nsrODm90QdNBs0ggyKv6EXDukykGTYpGQG4fAjrBrAcpAtbON8QLSWpNcKYJRB2frMo5vYXx/ppXOSYyNKXKCdEie5DhZBM0GkRXSO/5K3HhA3U9YdDS5YxjmFRwrw/fAarXm5eHr66hcluUclWM9/TlMvc7xlRWyaU5qV4lin6pvQZ6R94ckp9dQtsvVKxYoi2WnS8WNGIdLjPcilrLr+PGUa9VzdEyV++5NcOyYUerQqmXk2j0q/tjY0Og0wrVrOKMeK6HNYVbjsGfRbsvgs7ma4tAimtooU1AtBpgsw2vUmR76PHbF5czy7TSWPRzHUHcS0v4E1y5YrlVYCUHNlvIv7AQ0FyyKHNIZcWgKh4qbcDg0LC0pXFdOzfa2DDZrSymrzZjBENxBlz6bGDwmOwKeNzcV7bYMsNOpw97IoVnPcXUiA6lTR/d62Hfegm8bpr5HrFxiYygsBQpGRQVX58T4jCLR8y4tyW2wumlh6xiXlNwNGIer1NSIyHgU6w2cIsGt1QUhXL0qB7qyIvfP4uIRulRZJrSaUjKh+/znBeXecYcU2ynRLwdBzvFNw6ibzWzWfLa3Db2eOkrraja/OMK5EGvpI7VTrzd3pQDwbMOJlYSJ8Tg8FFB58aKEMJR60N1dWb6+7z5zdMtrrY5YuNtug4VgSiXtovYP6fZdvjBVPP9iGbVruPNsgtKadstwabciUcGR4c5bJhSrd6GUYm/fZrG5iftt30USFzz7os2zz1e5eFlTrQqwnU4Nd94Jb70voB0m6GDEjW6FXl8dpdTV6wLWFhbkOrmuHPvZswKkPvtZWZ4/PJRl97/+1yVW+TWvgTvvlPji3/gN+Jt/U3Szx4/LBGFnR87vxYvy+NjaEreLUv309NPyHaW67Pbb5TLfd59EKt+8KZ7AjzwZkrshDzwANVcICm1cfD0hMjar9gGq2SDGZ0KVxY2C43tw2Fsnqe2yPn2EZP0+6ieOQ5gR4eOoGJMZpkETy0rF0k5BoWFqKhg0O/uaWmWDZv8KzrBLWqlSa9vYoc1Bf5FF3WHZH/DBzzT4rd9yOHHC8GfeM+H4plyz0UiK6/p9+XEcw+Ji6fSguPVW6QvXr0sfWVyUc1pqgsdjkVjYtvz9xQsWgWc4tnmW69d3GQz26PWWsW05n9evywpDWchYGrRIZLuc+9KibXl5np2ztjbX2y8vC4t9fRqytiJsb6Ui90ccz2UW+/uKhQUbx8pxVEKKh+MowpZhhQljQkATRTJ52tubO79cvy7Py2q1jIaWfRqPZytwxRR7dICqVymMQ5oqDkc+i80EFFi+xWI9ZjKxaXoxU6tCWI3mM7iX28vtT257K5AB7zTGdGevvaCUehTYQSQVX1L7YwXF2nPIU0WKRpHjM8EyBZMIvGkXKwxIvCpZWMUzyazopqCxYLHiTsiVTUoNhxSvSPC0hEVQFPMvKUeCNJWnThmN5Pu/Fzj/l5qRmJCKEk/SFE38Eq1xq9lk+3BIayFgPJaUq0pY0AjkodXPqzg6YzhSjCbg2UusLtewlCEuQq7vaRZqCdUwo1J1KCorFGtNoil4/V22hzWavS6x8ikqLgu1MTkWMQHFJMILIOzvsBvV0cE67Yq4NTgmg0oFc+edMBzBpENQTTGui5tJlUjh+aR+nUnQwCgHlWe0/Ck6m2JfOI+xbKKzr2TUi2jFO1hJXyiXMpv20iV0GOKWZdbr65gsJ8sMmoLW8AbF5jFxP97cJN30cdRsCqQLbu5YxLU6jdMOldEB9SpAFZwWJya7HCQ1RskCC9WCZpgfRXlDqbc05KlNkWpWlxdxtSS+GVNGPisSM1OA5wYdxRTZCJ3n+FHEbasGlgu68SnGcYRjBSjLwmpZJFHK3p7HxlKMhUSmNsKUONK0ailpqkArqnaMMgWDicMLL0h4RxzPB0bLcujHiuEo4RhTTjk3uJQeQylzVJmeJDLwvYR4xSFD+6K3NmUpuNZkk4zDscPCgsJSBa2WxrYV6bTA8Q0LCw5FYdjbE+Aj7KuHJqfZMmhtUWDjq4ziuedFe7+5MY/gArlfhsP5vVSplNqFuVnq5cvzCrkwJJkJjpQy+HaKv2BQwwFZBcyqz2BQFjYZPE/AyLVrwmhFkRx3CWZKWzYQ4JZmFpd3AgYD+epr1+Zs8s6OABrfl1t7d0fOq+so6ovC0pXpeItxB92wuahO8/GnFE9flEr6alXRaipalYzPPOnj+BYn11MunLc4dlzz1MUqjYYA2EYT/sbfrfLLvywpcZWKwrLku1stAeDf8K6IpeEW02GDWmDRzDtc2kl44oWFI6DU6cgjaGdnHu5QztU3NgTMvvCCXJbf+R05N7/2a9JXbr1VwMzBgXgFVyriSPF93yeA59FHBfju7goIyzL5nG3LfMd1hUHe359rlZtNOZ/vex/U6yJHuHBBvID/0neE5A2HLErQUR/dWmJi1bl5zWXjuEZZBo+Y0ydshks2E+sYzdcUHNsAozMJ3DQFShuCmkWUWEBOgo2fTjBGs9uFlUWXRrFHN1nBPXaO4tgdWEVKFhdgIPJbHFoVtm86VCrwxtv3uXAz4Md/IuDv/vWEp59RnDmj0FpAZxCI1nw4lKJPyxIDkY0NOecPPyzv2d4WScqtt8J//I8fIgjezGhkc+MGjEafYmeniuud4cSJPr/4i7/A133d/4s0lWtw9qxcoyyT/jEYyPmNY46iph1nft4rlfm9XjLNZdQ5wO6+Js/NzGJQxpggMEeR6UqBQTF3rDekeNhkzFLICQJDlimCYO6TPJnMo6FLGdrq6tyCfOvQ43i7QVJYxJGD70O7FsvKLIXYLlY1tWpMTIBDgqlUUNXqvHrw5fZy+5PZbCTmuf+7Xu/PXrd+zyf+gA39sTWlhAEymSTUxXh4iAvFKFwRuyaVU2Azweago7CSCQbQrku7lmLZGg72MKZAlV48cfzFXjsgT4TSl6f0fyqBxn8NGM+eGnpmqupQADGanBiPN7zudXzikx/Ddqs8+OBD1MKcnX2Nbbk4LqRJQZIbSeOrWBhj042qhF5GtyvgbjSwOXdLgZ0nXD8MqDeq6DwlqW+w7GZM4xZR4tIOIlSvi1VvoC2PvXFAnEQ0mm1qxhPQhJ7pnwvUwSFFIMau2hSo8RjV70GziQkCVKWC69io/r4s4boLXN/VnGlkqDNn4MUXsV94huXDPdyVtoBerSn9tlQcy5O105HRZWGBQtnkcY41OsQsLxMZj+BwizSoo0OLbAbKFpctXA9aLZupVecgq7HcyLEsMEphKgodK9RYURhDQMIUC9AoJewPRlS0dpGSJArXLrB2LzIpfCrrTTJjM4ws0tgQFw5VO2UarBH2b84d8tOUlk6h6dHtx8Q6pB7m7HYdNvRNsmKZ1HJwSWgtQG+Y8+x5h+nYsLah6RjDcbXFsWaOa1fp9BwOD6UrNpuQpdBsKJaXDIVaQ2tNpZczjmxq3pRq6FAYQzKasuxlTHSTwggDbPb3pV+7nszBioKal1MJCqbGJ48ydvZcllYsRmOXYigDn28lnD6muOE61Kpg2aCwBLDuXyOutSn8EG6/H3sywu/enNshlOa6MDuAbD6alma7e3tyL4EginodT0+ZBC2UPSt4VTZFfZkgz6mZAseRtLfVdkxuHGo1zfq6ALfyNi3BWxgKUDbGsLYqTGJRiLWZZQnoHQ4VvZ4Ai8cfF4D4+tfD7adijr01pV6vkmXqSMe8sgKps0nHwK/8tuHhTxoeeq1iYVGKu86ehdwNeeY5OHlK8eHzNkvtjBde0NxxhzxStreFFPvkJ+W+PXNGGNr9ffmOu++eLe6sBtx7ro2lCq5eh1bVx606uA7s7Mo+l1KGOIZf+AUpBLzrLvmbUmKZVi5wTafC4O7vC8u5tSUA7rOfnVvXXb06LyCbTmVZ/OpV+ft0KhKTGzdkv8u5zG23zffl+HH4Z/9M9Nuf+0zOQw+KraBOYi5fC7lw3vD6V+XkTp2AmIFj4/oKq0gptAOmIM00q60Yy23w4uQu3D6sqj38lk8w2pfUNEvT8KcYwCMhT1PUjZss3XI3hbJwlxqsmRHXr/s0Goof+RG4J/kcX/stIcE9d3DpAsSjlDtuUywutTk7nvLu9+Zc3gq49VZJEpSwCyOGKiphOrHY27NxHJn35bmcJ9ede/V+5jNy/p9++v2cOfNWLlx4DK1tXnjhWR599BLHj0e8+OIXGAyGdLvy+CiL5ep1uR2Wl+W7S014OcEpV0WSRP5fBnKUMeeHh7OYcN9QD1N6fcX2vkOtZmTSYqaMpj4KRZLK9bMwmNkKK0CGBXFMLwpoNuX4pVBVoZRhMpGCwySR78oy6Qsg+7DSiDDDIUVzmXgExrKoOwkuCalxCMyYTNkYFFU1lnEY5uPoy+3/0RZF0qe/lPZHUL1+pbYngNcB/1wp9TeMMROlVAj8MBACH/9SN/THqimGWeLVSDSZNqK7HMUODT8i0hU0OQUa0CRxQTGaUEs6OIt1utMA7TtU7ATXxKiX+hq91Cm9KOb63TIWqKy2Lwf7EhiX7PHsuAwcfV7NPmuAGNHjmpc4U2xv3+T6tcvcf8+r6A0tKl5Oqy6OH0UOW90KjaZmMpn5roYRJjNsHfoMh4blds7OnqZSU1QqmpbqUAlyMssnGO6jw4A8zbgxWSDDIgwMbtXDs1M8lRCpEE0uHsYoMOLXnCgPhxSHVFK4igg16JG6FazAQY+GqDgmrbfoZVXicUa1aqj5Gerpp+BDH0JdvizCw/vuk7SzlVW0Aq5fR7Va8uQ3RoqP6nXodjG1OhO3ga8lDWwQuUwzh1qY46kEWxVMYzgcB4RVizQFX0XUwhyNITeaQRqQ5UrsknSOx5QY/0jPmyUGlSY4ruGg77DeisRvtOcTx4aVRgy25mAUkCSKpXpE9fnH0JYmri/gWAWq1UIFAUynJElBFDSpHlzHdDokt9yOCgMZGJSNomB33+byDZcTqxHHVjOGhYRejEbzJdqLFwXAHDsG527N8ZnyO5/2efNrYtyaR45olbNxxII/hlYTVyU40ZiJ2wStCRlLgl+WkVg+ubLw4wFmMCRtLRElFvsDj8FIE4awuKi4elUippebCYutjKhwqVoxUk6pidVMCwI4xDikqDRFHR6KXrhSwQgalU5dUrY7O1JZ7rpyf128OAfOjYaswVermCwnT3OseAKuS1ptEeuANJ2TzqGegO3QGTjEsSJJ5ixmHMtAXTrm3bu2Q305pBsHXLtp02hI9yoKkRY89hg88YTi1a8WwPqqVxne88obLFYm7IS3cNARv9XhsHR+kPcFAdx1vE/N9Dl013EDi8ceg5//ecVb32pm7oOKT31KAM0DD8xZxmYTfvInRaccBHIa4lgY6W/8Rnlvyf62WvK+xUV4wxvm8/V+fw5i9/bkcVRGAIehsJa1WinFEJeKCxdkTlI6eUBJ6BtOnBD/2J0dePBBsS1729sE7LRa4usLcwfCsghsMBCm0xh5ZC4uQuil+JMu9sEu1+Jlqlef5Wevvo6ve6/NenNKUNWoIqNZN1y+aaO1Yn3FgCrY7QXEiWZ/TyRVd52ZkhmbxmRbJF6OQ25ZTFQVTUFoZMUvw0IXOb044LCjSCYpn/mspt7UfKP5JfIXLhCEFuYVr+Bm83bczg4NP4E77+Dxiw0Mim5XHdmltVowGWU0awUvXnJoNBXnz88lKmWxXcmddLuzlYRF+M3f/Ds8+OC3o1STn//5v8L6+htYW7uXN7/5q7l583He//5/zjve8e28/vVvIUmEE4giuc5iiyeAd39/5lIzC7Dp9+dFlY4jr5fFuIuLcj2q1pTctsEoJuOcoGKBttCkJJOcTPs0vSmxco/AcDls2WS4xMS4WKogxSHPLTodI7UMs3AbsWQrNbHSJ8cjw+JCjqtSUmxsMmyToYG8gGls0Z/aBBULxxOnnJKnVn9KC+z+pGmKX/Oa9/zqD/7gl6Yp/uqvtimKP1Wa4ncB75/9MwcOgEXmDPHXGmN+40va1h83KAaIJxl5npPjYApFNo2xfBttadEHI4J/n0iKwYwRTalSaEClCWp3V6igcg02jucir1JSUa5Vw5yiGQzmJpcv9YSaTmEywbTaJJY9C4gWkJyiZhZYJZFu8IiwyXnx/Hlu7Ha55/634LrqyIg+9AqKWfGE1uV9lBESERmP6bjg0jUpICyNMOJRxvFGF2s0WzOerZcax8FUqoyocmm8SrMFGy2xVpOSwOzoGAujmWqfkAitpHirQIv3rFKYvMDui4lpYTvse+s0KgWuUwjwNzY6jbA7+7h5DDvbFEvLpMub6KuXcU0CnncU42zimMwJsS+/iDp2jBuTFr5jqFQMRV6QTyK2hg0uvmh486snPPmszfJxn9U1CfAAKd7xi4ipCukPYDix2FyK8J2CRLmzIswMm5SpCRiPFdEkZ605xbEl6S9XFvEkl9S6pRxbGya5TxzDUu9F1PY2WXMRc+oUznQo7gtaU9SbjHUFCoMqMmxbM5oqbNfCsQsUObbKmeY+NWuCMXCzG1AJDGlhoS0Bx6KRFV3gjRuG5SWD0lLc0mwKcC5BmmXNBkuV4RVTbuz5rBXX8RbrKNdFbW/LqLu+DnGMmUZEC2uMY5vEOGilmNkjz6ykoFo1LLYNYKiYMWp3l+vqGEHTx/fF58w2KS4ZSabwkiGq1z2it5LmEs64R9Jcxhl00OOhTP+aTelakwncuIHK8xk17QtiK1MB8vzIay3VNpEKZpHI8rzY7XiMYmcGlOeD84svyjlZXBRwcfNGjmVSqk0bx9NUg5yaGuPkUw7UMpeuaD7zGcXaGtx+zhAncHpxQGt0ndH6bUS5eLMOhwKGy8ImkRUY7l68yWb3Gbq3Pcgnnqhz/jzceYfhsKt4+GHF1pboeWs10evevAlPPmn4/OclDS6OJYjh6lUB62fPyvzg3DkBx7VaGQgxC8Y4KY+Xfl80njdvyqPnxRdlHz/5SQF1YSgqpTIrZTQStcrrXw9PPCEuER/9qCyTv+IV0j2qVYhjxec/l3P7bQV33q356X+tedOb1BepxUqv58NDYYevXp3XVw6H4Nsp6wsJlm8xSjyim4fUujdoVlO45y5GA0OQDmjWC6jUGKYutquxVTormjMEJpLnnFIUXbF7tFSOyjIKo5gsHgdTUO3cQPneEXVZFIY0rDP85JNsPz/g//idO/gb73mBsx/8P+HNb+a5za9iSsCVeJXNWwNW1xRPPKE4cULxwQ/KxGM4LN0tDZ5KeeoFm0ZDY1lyzOfOifymrBcdjebynU4HLl78WSqVEcPhiGp1jStXtllevpWFhQE7O89y7NhZHn30V/j7f/8/srnpYVkCwi9dkj7m+3Jey2hmy5L+XEaal24sIMNTszlfxEyinPXWBGVJDYpLPCuKlhGoUJqAKVNCXBIybArsmQxDEYaGgAkRPpoCoxTa5DhkJMoVMsGUd6E64oImE0PNjchtF/HmTxgnUmNhUdDrG0Zji+UVeYa1aimBjjGOJ6mYfwrby6D4K6sppf4C8GNA6yUvD4G/aYz5qS95O/8tQLHJC/LRBKUMo0QS6Vy3IFFlIZykyqU4FFhYZLh5QjZNcENbdFdlfmrJFpdCquFQpuBlybtlzQftw8O5879S8nQsPYvK7cBMu1XM/gsJLhkWPhEJHgWzUl1khfvhT36YpdWT1GpnaDcKCqVQSh2R1lrPfSMBbIRJVcbQ6xou3PDx7Yy1yQUmyydYbWX4RPTHFtgOzYb4gsaJYpi42KFHdbQNjotxXFSekjm+WKtZFqlXxapXsDQwGJBlhiys4XV2JMBhhsxMo8E0aM5AZ4IuCiaZh28n2NoQGxf/cIs0aKBCD3c6RI1HR4EfRXuRfO8AZVnE9SW8rUsUQci4fZx4nNFsFHh7N8j3OlgmQS0uQhiSh1U6pk1hDDU3Ye9As9l5CuuWE3SKBspxQEtVtZ5JCHSeoG1Fpl1AluW7XQg8Q6VqcFRKHmeMYxcnsOkeZGwU19hnmTpDat1rqNITK0mk77TbYkN3eAhFQWa5pEGT2K1Av0/FSdD1CpZ6ScCL0ezsQDJNObkSEamAtLCZTGZs1dhw43rBnacjJhPYG4WkmTgo1GoCfvp9AU/tVkFFj5kQABZ+PsYxiexnkswRVhhSWDZxfRGnd4BpNNgehMSJVPFXij7r7Mi5KiUPnQ5UKiROBeOI0X6RFLizvpd3e2ROiBsNULUaeZyCKYgrLaw4womHwgzlOaZcjdnZkWTGhQVBFN3uXPtQri3bNmZ5hZGuyt2hZiswRjEYyAQChC0+OJjZaC3J7ffSeWq1aqjXkUTCZIDqdsmWVhgVNSZT2NtTbLYnKN9lMNIMezmFtqhWNUoJi/fUUwL+Pvc5mV+cOZUR1jRLtZi95w74nh9c57v/MnzH267x3T+4SWvJYX1d9qdaNUf+s5cuwa/8igAw21bcvDl/zLRaAoYffFDAbJYJCGu35TqX84Zr12T+Uco/Dg/llF64MGcatZbCuvKxVBYpnjghEy0p8jS8evUaj2xtsLxqs7UFRWZ48K4h6f6Ah6+tc+52C98XEH7xoiz49HqiV15ZkQCHalWOIU0VFy+aGXBTnDiWc3/1eZZ3noKNDdLWMrsdh77V5li1h7vawg5sYhxsctyp+OgyHkta52RyVEk4dhoyMYpj8oOOLNAZsTBUvg9FQbS4gdU9pDt1CZ75HNWVGnz2UaHM3/AGkrvv5zCtkyYFfjZmFCzy+c9r9vbk+q6uGt70JjnnvR489ZT0sVpNHvOveIVMQK5fl1updGYoi+JK2UMJUkV6UnDjxhPcvPlLnDr1RiaTCYPBIwTBGn/xL/5PbGyU5062MxjMtcIgQ87CgvSBPJffJxNDryf7ePz4vKiuEhR4KiKdFY0nxqHA5uAAGk2FZwvJ4xKTICuAKQ65Ea9zz5M6i15PvsdTMQUW7mybubFRacLBKDg6ZjkHilYtAcsBBdpk9AcK17dwXXCJyZOCrU6AY+W0mkYK/Gr2l2ri9N9dexkUf+U0pVTVGDOaBXe8HlgGdoFPG2OiP8y2/puo55WlsfsdUhyoekwzV1LWAMckZGhSHCyVk6NwTIJOIzxdYAotQGZ5WQbokg4ZDufVDUdfpOajrNBpcylFWaVTFPNkr1nFk0WBcG4CkHMsfGI0ZsZuZkhanIQ2PPT6r+LDv/nLvPWtG1iOg1IFBo2VJ+z2Aqo19RJLZINBZvMWOe0GnIrHTFVI4/g6a16M6vcpooRw9Tjj3OfKYc662kb7Lu2KIXUs8uYCfjqiSCNip4KnM1S/D2GIW6th8hSjHQgCrPEYy8pFblL6ZKUpmfYwRhhLm4wJAfE0xwtBq5wKU8xCGwvNJLYYTQPaagy2S2E55IWF2TyOnUUE/UNUu4Fdr+EWA/ATjF1hsn4Ks3Er5DluPCTLFIM0JE4hLWyCqs2x9QlWdYPE8qm4OYaC/Y7FQc/j1EmDS0Iv8mhWUjwdERsfpTQr7QRXJbNrZshdRaw0VT2mwTZKw2ZxU8765qaMlFofRWIZy8IojVpcIlOa1DgoCjwy8naTpMgBhU88Y3EyPvukQ6Wq8Ha2GOYO7opDq5oSBgF5ITZf99yWYDkW1d5VagtLDHSLbk+YZMeRAppqVbG7p2m3a7iuQVFQxAmMuzKSlsC9VhN9ezTFC6fQqBHhoJSm7scs1VPUcIS6eihGrP0+JAnG86Dfx/EiuU8aDUhnkXD1OrrIsYoMoy3UZCIOI50OQX8Pg0YPB0Kxui7mzFkmxiU8OERdvyboz/MEDQjKlftpYQG0rErUZrFdE13BJsNRGV7DwXXFz9jzZBl6d1fAsVKimd3bk11dbiZMJwWHkcWGl6L297EWFtGq4OpVTaNhCLZeRJ04wfndJnlhU6uJVng6ld361KfUkYb2wx+GzW+DyxdS/u2/89ndPcarXw2Ob3g+OsmJWwT85rmws5XQiEtILeHicXj0UZfPflbAdb8v2tx3vQvuvVfmH1evyr4PhwLIb7lFGOBuV1jDxUV57HQ68r4LF2ReX6sJA7y6KvrWrS35fxjKfo9Gho99THH7LQl33ANVJuhnrvL6lSm99hkWF20uX1a8uF3nxo06xii6XcNtt82Lzy5ckDnLyopcokpg+MIXJOjl3nsNK8swmUKzabjtlowlZWPW70cVOc6gz+pCnZXaBMcLMdMe2DXC3g6m0UZ5Hv39mMBYeJ4nhACgul38RYf9ccCiE7HvbdDdSzhb34W9ffR0gjIF3nQKGxss7rxI7FrEK5s433gcazqm0BZx0CLPBPx+/LNL3LwJa2uGF19UDAaGk2sR/+YnIdMeuVG86lUCPF/3OtF7P/usgOfFRekTQTCfa4K8fscdoi8+fVrO1XCouXr110mSfS5ffgdPPfVvWF39p1y9+vf5vu8z7O8L8K7XZdVgaUm20+/PJRTlKk6pX677CX2jCUP7SNEXBLNEVxOiZpPHIslIlUWrLfpulARHRQhhFOPPlE7qSOLvOAULCzIZHKceVWvCVIeAYTK1CIJQHn2kmEKzsytCCJMpVhcEbPdHNl4g+v3dXaj4Fs2GwnNBWxrfjlHWn15A/HL7imtPKKVuAN9kjPmdP8qG/pswxYAM+rFEDudGE88q2ePIMJkYmk2FthRg8IopzmRwFBhhmm2UJQ9fimImkppN1Vst+bfvCwBKknl1RJLMtcdhOJdNlGuML6moNUCEN7NhU1gzjW6GFB54xEQER6zx/v6Uj330w9x66zoPvOIOklmyUBTJg8a2wVIZPjFgiHMHd3+LtLmMbRL0aABZhjUeyT41m7C2RmHZzEwVKFD0xjbjqWajNsY63BNf1aVlCc3o94VlNAYThBSNJkYrCqWxTY7a20MlydEEIIlzksV1fBWhgQkB3ugQO54QhS3wPZw8JsktlONQ5DmZ5dHtKIKKphlEuKRs79scXuxye+/T6I01stVjFM02rmOYKB+Ta+gcYO3vogOX1KtjL9RJ7YBAx+x1HapVQ+AkM3ZeEauAPJcUJq2gVgfXLgBDr68ZTyDwhU3ROidEnELGBAQmwup1wJZzN4ngc9dWWF7VnD2VoocDzMEBZmlZgLCtybVFMYlwTUIRhiSZhd/dIV7cxFgWVaR/HQ4cDoY+q80JjMbspAvYlmGtOeXzz/qc3EhZbKZo18FJx2RJQde06Axs0nQueT84EKuoNDUEXkEjTHBHXWHyk0RG7Tie2wm22zICb25iNjaErVdaZpLjMeq5544oVxPHpOsnsVWGGg5QpWAX5J44dkwQ2tWrsr5/6ZJ8RymFeCkNVn6mZIh//deFDl1akntoOpX93NiQ7ZZJFfW6TDpXVoirbcr0yVTNXFyMkTS+fTnUklkNwzlTG03EY9Uj5ri5ilZQLC8zshoYNKrI6XYKoswmtBKabUV35HLhgiyV/4f/oHj66blMulxMOjgQqfwb3gB33pGzcxP++U9qnnhCbo0f+AH4c2+8SWUpIPIb+Drlp3/a8Df+Z59qVeROzeZMp3wXvPe9cgyzMMgj14OVlfli1XBYRpDL6R4O5TM7OwJc3/te2V657O77EuMMomuuWRNuUy+yH55gc/cxrO4hxT33UoRVnthdIyssQi+j07cYTxRnzyrqdVneL3XMIIDwVfcmLLYLDkceeZKh84wnnvcZD3LecN+YxXVPUh0tI9aNoxEZNlYtwDgOUwL8Ysq1PZ9NtQVaM0psak4qIC6ZUlqM/P/Z++9w27KzvBP9jZnnynHnc/bJp+pUTiplCSWEJSMEGAxuaAu3cRundnhM33vx49CO3b7d18Y2xjbBgSu6MVgSFiLIIIRUpZJKVVLlcPI5++y4cpp53D++NdeqkoUR7b5INDWe5zz77L3WmmvOMccc4x3f937vO9k8i9rZoVBzuGUe45d/3eaXPxbyre6v8cfeeFUoAh/8dsxqCRUGfPj/C+/7oEW1fwOaDfTBAaON2zgaudy4AU8+qbFc4ZqXihBHKW86f4TfKvHrnyuyuaW4fn05jopFeZyefFIUBdfXBbjmWQDfzzm/fcLQYjgsEccQBId86EMTfv7nf5osO06z+QPcuvWvefe738Ddd58jDGUM5JbRKyvyqA6HMt7yJWcefyDLNI4p7ov1uqLV0gvWX7EIBWOG0hlharLfc1FK0WqkOEayKPB2iAjxiCJFlqmF5bmLRIW1MkgStYgaR5FkJCoVAeiOldGuxwxmDv2+KKis1wO0bZNkBkGgqBYjrDRiFNpoy6ZqzRhnPqalFqorf1Dba5Hi3z9NKXWE4Nnmf+2xfu90Vmxb+KiYdCc208CkXpNqc9cDV0WkmKKqEM8n2XodrRSh8sRViWxZsZKnjQ3j1X6xILNDrlWTpksg7Diy2BuGKCzMo84KwRo+IRnhgl0c4GGSzScnl0yb87gvtNsu3/dHvonPfek5PvnJX+Ge+99Atb6CaWrSZK6BicUUE5MUxwiJ2pvCAzNc4rCIe/05uc7JZOHFGp2/G7a3MW2IlY1bNHGKCjILLIug2MQ0bazZSFLd5TKUSiSJ5la3QLFiUnIjMqUxc7V5x0E3m9i1GiYB08zHNaT4MfWLZK5PYokklzUYY9gOseXi2zFaa9ZqAabKyGYJcRxRa9VZbxRRh3eji0XiYhunf0DoV9C+gTPYR0VTBvVNym2fgg6Ymh6ZVgSxQRAqbh1YtJomnhWxWk0w1YSpUaRQgLIbkRoOnZ5ByQ5pVzMaVbH7DnEXnLmp8gGDWeZiF+u4yRTd79PvOrKITDQqCsFxmFDAPBxgOpaMxUoNUESDKTujBmCw0t7GMjSGzkiUgUVGvRzTTPZJnRpRzSfdk4jvLCwSxym7PY+rFxMeuDDEqXn0ZzazSKrBfVfA89OXCrRac3o7XaolBwctG5qLF+el5qaMhVzrKctkpRsM5IP1OsnJs0Smh68nqIMDeU+5jJrNMJtT0loNezZlsXrmlW3PPSdjPYrg+edZCDx3u/JswPK9uV5av7/0rnXduTVWZ+60MQfHTz4p4/b6dUFh994LDzyAe0I2nYnlkpkWqTKIEwOtBQSCAIM85Z1loLKMctKlYY6ISg3UUQK9LkYcU17PmDlVnMPrlGMxGcn8CoZZxq9E2CdMLr7s88UvymnfuvXqqScvvLp+HbLU4MKZkKNDG8eCv/0jIX/ovQaZ08AL9njhWc3zBw2KNfj+71c8/bTcmqeeku7b25Pfz5yRFP3Zs7kMnXRFqyUgXOvFFIbvy5Sltbw/35e89a0Clm+7TWMaYGczDgYeZ88qZtfH0FrheOcSqnsEzSbGbecxsow7pteZlFbZ7TpsrcdEmYNpysbg4sUlxzXfaz3z+IzqZpkghH7f5p67TVZXNd62CWmCjk2s2QDTc4TmkKbYrsw3EYZYtyuD9oqBkdUxk5hCYoC2SLXCSGMyp8CEMukso2HHqIM+U11huFvgfR/wWfvNPtloDKdPsTerEAQFVOrynvavczj+JiobG6jpBM6cI5sqrt00uHZd8ebXh6xumUwDg7XKhNj0uHx1le6zilYbHn1UxtH6+nLflyddWi1R75BHa4eXX/4bNBpv4cEH38wnPvGX2Ng4wR13/EnuuusOXnyxys/93P+Obf8wly75fO/3wi/8wjXC8LsXus/549FswsWLerHEKKUoleR+27acT6ejKFUtHF/iMvt7WjKIZoyRKQIlTpnjYcpWM0ArA1vFxNoU10plEOKjyEgSgzheMpZGobfISOQmLrm6ouPIWN/ehk7HYL8n4eVKRR7ZYeQxmithtOoJWZyxM/RpVDNSFDd7BRoNuaY/yID4tfb7rv2/gb+tlPoR4GeA3d8tbSJvv3egWCkSyydLYlqlEFXUdGceth3jWyl6DkQtUpTrErkFEgQIOYQYGkDLLGRZS4X0nF8MMivkoqg5xzgMl+oTefik3UZ3e+B7qGLxPzvVAHcOfRUJJgnz4r00ZTw1aJWmRMojw+D1996B4nb+H3/jb/LX/9rfwQunpIUyngpIcObXlDCYWEyHKat2F8uZh0Lvvht97ZqENJRCra7ivfwMRCPSc+dJLQ+fmfjaKaBexyMjNhTTYotCYbbQxrAsxfHWTBQItA2YmLno5bxN7QqZ4TEaakKlKBdTQtNHmQqNQYJJ1tjEVQE+EZF2mEwNdD+irQ+w1ldBmdgH11FzP1MVx6g4JK03cbMQK5kSuUWsNKXRuUrQuI1bg6rYEicaczBg2xix4inUIMWddaF0jqmSCnPXiCBJKToTivV5hFEpjHRGEqZYLhwMXKqVokRmVIg7lQIybVrEts96eoPyCujNLbJMkSUZ1794RFZrcna1jyr6mN0OZsFHmRkb7QzfDDCnEyYU8VxNapgEmY31Hz+CuXMN3vs+jGPbGIam0VDUaoozJzJMQzP87Et0bjTYisfUohS3sYndP+CGPs7NXolz5zWunTKamKiDEOvGDlTFZQ7TFHBaKsnKlcs4xPFCCSIrllGuj3Gwi9HaWEo5xLHk7k+cEF3U61eWtCKtWaxqIlEgUd1ckiDPqHieoLajo2UIN8+/59kV2146EUwm8oy5rqDA6VR+3rolQPz8eVl94xgrHqBrbQzHJTOdhS201ooo0vMIqQDN4HDMdmmM0TnAGg8EfVQq0OmggMI6sLcDkwlhY53nbzrMHA/XTPnJD7t85CNqodjwlW06Fb3aJ56Q5+Q7v9PjjfdPcKdd3nVqyJbp8a//4ypnyprV4zMeeEBTrSruvU/T7crl/uIviuLIXXfBRz8q+4VqVaaU9XX5Nxgs9twL44StLekOy5Lunc0ETPu+fPaee8AyoWiMMSxoNTMOOyabx22cJ78oz/fKyjIAMBziD/bxVqr0pg7XdhzqdXj2Wc3urprrZsstfuIJOd9WS1F0Jjx7q8RnP6v59KcVDz6ohVl1uk7NijFrdZRjLs2PplO01mSFGioNCJRCmSmZNrg5rLLZDFHjAC+JiDFJvSJ6OiNUHrreAK054xzxPxR/BV3bJvmeDazaObrU8H3NWnkiRcqtt7GiNftRjaYD2SxhFJc4d05c25541iN9SjYSXx4W2NzUjCdSuPncc/L3/X2hpBw7xoK2tr29TBA+/jisrm7y1rf+C5555hn29n6VU6duw/MMnnrqr1OpfC++n/KDP/htnDjhL4rkDCPioYe8hUlOocCcsgMbaxlFN+Jo6JFlAnwt2UcsMge2LT/DEFYroRRQGxbZLMT3NLFyqJRSosTEt2Nm+PR6UKqai7oUjYltLxl/pinLW5ZJBFxrOacbN5bJn9zeOi++zC3HTVMTxzLVpKkoTpQn+zTqq5K1DaHZVItH/rX2WvvKpkRY+x8AH0KUHf6x1vpv/DbvfRD4UeAO4EXgL2itH5m/tgH8C0RGbQL8U63135u/tgL8M+CdQA/4O1rrn/gdTu37ET3ivzn/t9AAnzettf6a8O7vqSK35ZokUchMuVik+HZMrC0KKsWQxJq8UWtslWEjQD/CwckC1NGclJe7IbguC+uzPHLm+xKGCsNXK6YH803DvOIktjwSv4FPyCu7ztCaElNA0vrpXBM4VB6uFWL5uTKFQYyLSYidZdx1+22k4ZCSBegxYGIQ5leEU/Jwii6h9jCiPubzz6JmM2alJun7vouCEaDKJfRTT4HnYWQpxaMb4LnL6GEYorTGbrZIDYNsNCYq1Dkc2JTsmGopRoch/ngflWZQr0lEPE2hVsM3RLqrUEEKN2KLg55YVrdaYkpi2+AHfczDXfwgwK/XoVxGdyImqUvBtVDttoClQgHGY+K0yCj1aWZ9Dlih3L+F1fIItk7zwssW62sRdS+D0QBzcgsmE2aXR3w8eAff8a1rlMMJSZygHAfPzQCDo5lHME3ZbAi3e6Y8TGboDNarEwydYWrQGKR+EZ2KdFBklojLTbAdynuXAc1VTrLx0BbupEO6dYJU2RztJfiuzdoxh5JSgAPDAUwHHBVWqNHHvvQSxsvPo+MYo3NItHaamzuKOBZJrFjbWLamdc/WPJ9excwyDNfGWmuzmsFgpCFLcYIhTRJ0yyFxjuMc7QqgrVYFUXW7Sx2ufh8dhgKGVlfB8QiLVbzuPl4ygdkUvbEhMnO2vbR8yzJBgPNoH9euLXO9L70koDXPrIDcvxxBNRpLHak8i5K7CeRUpGSeAslztbl7ydaWfO/OjqCTalXoFbZNliTEThlDpTRKGXtdZyFLZiQhceyw1ozR1w9hMpPPzmYsKitz5QuQoslul/2gzb/81zV+8icVYfjqynjH0bzrXfBbvyU6x69suQLBr/wK/OUfgh989wCz4NPVDVaPe0SFk6TtlEY1I0pMLJ3gOQaXdgw+/GG5pPvuk+55/HHpomJRuuy++4RznNvuGobmrruEAXPhgkipra7KbZ7LoS+sqR1HsbpS5PT6BGXCZmMKnQnGiRNLNHT6dP5mSFPU0SHbakjjbJvDiWzs82FQKgkw29wUg45CuUyvL135jren1JsmtzUOMX0Hq1kjMxy0q1j45hw/LtPq5SvY012SxibOpIsdB2TKpFnfxEgjzEDGmjUZYGURzuOfo3xjh+jbvh3D8bGvX0E//Hqo1bAPD+D0aUo399mJPbp7cKY5oDidMCm3CccGYb2KY2WoIOXqdZPTpwXcDocy7M6ckb565BG5xs1NOd2dHdnz5bzfSkV4xSBg0bLkPYeHimr1Ls6du5OXXvolbPsp3vKWf8+dd0qGodsF00zZ2/stfvmXP0KzeQrXlddAQHejkQNMk1ngUK0u9xE5PaLdltt26ZKcl2FIoKV3qFhZgcjwmE4VpWKGchymU0WkHExTUawsE0Z5+UzuXnd4uNRdzudqw5Cppz6vt88y6a8cxOfLXhwjCj7NBOXY7O2LK55R36B7qCkUpJ+SRM+TQ98QrIHX2jde+zMIWL0DsIFfUkpd11r/5CvfpJSqIhJp/xh4K+I49x+VUndorXeBfwK8BHwbcBx4VCn1pNb6l4F/AwTANlIw92tKqT2t9cf/C+d1/nc47695QP+egmJlKOEKRylhZQU/6OEXCqg5bSfERmuFl0wxLHOxOHuEAlhyAUaQiNVkIou248iKk5dxO84ykjafFRbM6DhGNZvYSgHpvIhh/qpSSxX2el3Uk3tHslDbSuRxbFPUMxAnoBif2PB41/u+m9/43KM0yy5veuj+V1y10CeKTBmEHq6VoWYzUVzIMpx2janfQPka0oTpQ2/F1QGmMohba6SzGP/o1quvIYnxHIOwUMYk4lg9wkhisjAjNR3hER8ckIUxquSjunO+bUEWzzSFdDqDYg3BtxpDpTiOSMolTgNVKRFGJv7lZzFsm+zmDh/97DEevn3Eqc0Qtboiq2y1StUzKMQzgsIxNoIB0+oWM2VQnB5w73ZAWqqIjJpTQ616DLsxKw8c8scLu1BegcsHuIeXiNaOkTUrZKUyBV/TKoSSP8gy3FFH7l25hDEZoyYT8Hx0vU5o+mS1MqZO8LIJytWEWhP7Zexowma2xzgwKSiN/ewXUaurFC0DNQU6TUETc2vjYqXAOMrohw6tLEGNRqROAXNtlZI54a2vU1y+JfQMAP3yy/DE5xe6v0a1SiHLuKmOk+mIasPCczVmf4IxlVynGgWy0uZVOfmONk1lfJfLZO1VlGOj0hQzDvF3r6GnU9kInTiH22jIfc1FVC9dWhbBZZmghjNnlit6vc7C9UC8cZdc4JweIaXtc/6yWp5XHk3Otcdse2kD/uyzEo7MraFBBIYdB7a2cFwbpQJmgcEkMBd1n6apMG0biwy0hV81UcME3euJ3TQsBV9zyhTAeMxGvcC3f7BGEMCjj2quXRPbX9OEVhP+1A9q/v7fV/ylv6R57LFXg2PbhgcfhNAo8KsvnuSN7/Q42Ms42exjNOusNDJqXsjBXkppMqI4GvIfvnQ729uiQvFTPwV/7I+xUEPIlSTuvhu2tjQbG9JFR0eal16SNPq5c5rBQLG6KqDq6tVccUP2Qe029AeKK2aJtXaK3dnFaNThcLIU2f3wh8WKrlAQ6YW9PZzeIY21BNU6QaNh8dRTYg+9tydWz1GkufvOhLvPZ+zYLt0u3HkuxbChE61wpj4kiiakTgGieBlmnd/3Wes4hf3L+L1d0tUNUAnmZEJ5/5K8Jwyh0yG2fI5UA/tt307t8hdxSMh2DtBpitrcZObX8cpl1I/9GNYf/xDuZEpjs0IvbuE0Wyid0h/BsA/1lo3lGtx1F+zuap5+WvHmN0MwTSgWTQ4OpN/yYQ3whjdIf77wgtBSqlUZqr4v/xct6t9gbe0ab3vbH6dQiPj5n/8pvuu7/gnPP6/xPEWrNeZXfuVfMZvt8i3f8n188IP/GydOiPbxuXOyJ8l1j4tF6aogMAknLGoHcq3iJJHz2d5eboKGA82J9gSNheF5iOJSgEFK4haYTMSEJjf+yO2h80hz3nLN78FAvvfOO+VnXhIwmSzqirFt2RfnDnvVqolhpjgENJs+hwcScT/Wjjjqm5QKGaZlYhkapV4LFb/Wvmr7PuB/01ofACil/mfgzwE/+RXvexMQa63/7vz3X1FKPQp8JxI9PgdchQV7NQOmSqki8B7grNZ6CAyVUv8M+AHgvwSKv+n/gmsDfo9BMQArK1jTKSka7foYaHQiW2wvDGXGC4Jl6jZJlkr0uXKE68rMkKbL2TFfSGHpIpAXDk0m6PGExC9jF2w0UgVsk4h26xyIoJR8f6m0PFZTeNsKJc5qxPOiukgMKMiwiWmWTN7xljfw6GOPSPEXMxKsOU86xQS8eEQ8hYKh0aMRFAoYRZ/S+BD6EXo4RHl19srHUaZJtZJRcEJJA+SFT45LECuS2ELZUh3sJGP0YICKIqx2W2Syzp4jLVbQSuG4LuloSoiHl4WkswhtCZnTMA1Qei7knhLikyYat7OP6VfRW8cI3SL2WfjeU3sQJ6hJAllrca/04SGm72MXKgwnJlGaUqpA2lwlwBa8GET0xzZrTWgWHW50mpS9GGOQMnDOUj8XYoZT+lOLtj/FUplwvzXwpS9hWTakybKiZn2dRBuEocJ3QzKVEODxqc9YnD2rOV7sYgdCT7BqBZpxiNqfLTILhjWPhnY6S1vwbheCgPp6kb5b5ura69n4k8d4bq/B6iOPsnHnCMc0OFldYey2sbSJlYZLIwwRrYYo5vjGfLXUBqkqEVZaWG4BO5ygc7WJ1VXU0ZEgmCxj4SVcq0EQEq9tSUR5Hp3NUk1YW5FoWn8o3zm3YF6M21y/2/MEDeS+wrnMmmEsIvwLsJk7RqSpfDaPFtfrsqLmoatCYSm5kLvj1WrSh+WyfP7oaGm5HoZzoCXFUp4p/NR+fx7ZMqSC3u3eYlZp42kwOx05p2PHlm57s9mizF93u1gbG7zx3hnVqke7bXDxonRDvQ4P3pfx1gdn2GWPX/h5xVHH4Pu/X/OZz8hTPJ1K2n06VXx0VOTPAMePG2zWfYJpyq3E4Wd/XfG6C2PuOA7JsTa33YIf/afLbnj0UcGlue3zW94i6gd3nBW3zgSLrfWUkysRV/Z8XnzRoF7XdLuiJ5zbBA+H8+IwP+b4liJKDA47Bqp+jNLR1aVu9Mc/vjQsKhSWY+XkSZhOKRgzto+XqNUUjz8ut+LCBXj9bX1qxYjeqMVadULHDnnsiTr33q2pmT1Mw8IfH6IzjbG1uZzzggB1dESx0ZzLd6WYxMgvhpzHrVsyVgBbw5FzP7Y9oH3/aSalNt76Ouqxz8Gli/imCXGMft3r0E8/g3HvO7lxHeoNzWQCK/qAM2tFXt4tEwUxVy9n3HG3ycaGolQSO+OtTc3uzZggsGm3WWgSg/z8mZ+Re+K6MjzvuEOWjCtXBNAeHn4TzebP8au/+teoVs9wxx0P8PM//2dptVY5deq7eeGFZ/jAB/4YSrVpNOS+JIkA7tzkMUn0XOBIzSW6NXt7eURX6Ds5uyhXMVxdlcdsNjM5nJZplGYoYjQ2k7SAYWhMQ2zI+/1X0x5yC/c8wZPT/7NsKWt/+fIySvxKmbgbN+TxyyX6RZ5OUa26pFpkONfbmkQ57PU8okhT0hk2CdYfUF3i/xu2+CsoBF+1fWUxoVLKBPyv8tYUuA144RV/exG48FXeK7X8r24ZcHb+/38I/HPgzyM0jP9Va/1ppVQZAcnT3+Zzv13bBiZa65//KtfyQ/Pj/ebvcAzg6wGKTRMFeETgmOg4JhuMUJWyFHnMOZF6XtWg5u5pwJIbqbUs9LOZrCy5Kr1hyIKczx654v1IjAlUsYC2ZSFOMYiw8Ov1ZQT2lU0Lq1jUcNQ8OpzNFz1Nhrkw0YhxMEkIR0eEkUw+GQamyjBf4WDvFU38OEYbRXS5gqrXlunvyYTEcIiMEo1SjNO5hhWw3PL7PqFdIlMWyoyJQ4VtKTxmUszUWiONM2yVYnoeSZgQFyx8RE7LHI0oRgO01ijToTuyKfgJpVJMioUTT8lmMyi7YNlox8G/9iLMZpira+h6HUpNdKqheyT9naaoWg3tuswoYCYJdtXDGEwZ3pqxF9a542xAZHqMY4dydAixR2wXqLUMOh2f/X0xapnhsVp1aY32uLm/yYp5RKFq00vLPLl/gROtMSfvLJN6PiEuPjO0MogygyyzMeYSR+98S4hDjEpMsOskcUrklfFtU1aSPMyTy59lmciU3bgh404pbBIa3oTCZoE0O86d6WV2Kw/yZV3jtuQZvL1rOMc1ZHUMy4J3vAP92GNw7BjaNEnWj2M4JplbRKOwSPCMmIwMHYaiDtFsLhUn8ghtlskqWihgrq5iziZQKS8Ecc2ij7d/HRXXRW4PlmY2gagG4LqyOl65IoTL3B1CfGHlM7n923Qqn5trzhIE8v7DQwHReT4+ryjKOdD7+wKIZzNBo1kmIatuV6gg9bpcR7EIkylWHKFrLTKrQDyn/D/9tAC3ijPBqJUodA7RaSwygwcHS/Bercr3NhpCBZpTN/xiSsFJ5hEtxQc/KF9//IRBEJsUCUApVlou732Potcz2NvTDIdqQbculSTAfnI7o1RRPPppm3/9b+DGDZvz5+t84AN1Wq2lkxkIwHjmGSm8s234zu+Ubh4MYBrZJGFM1ZdCYcvSbK9Ap+tzeGhwdCR89GJRjpGDmdVaikOKqQymhqJ4dA0jiVlIdZw+LXyNdls+cOUKjMfoSpW01iCxClSqYFqa171OcXgo6je9sMiNUZ2Vy1fZzRS240pNQ+bTMHuotA7NJirfBKWpfN9kIs82yDip15fUmVdWdc0dElWacpfzIhSK6JfAD56Wzn3gARiP6V/rUxrvYd1zB4bj0E4PMKsNHMdkp2tySa3Ru6FptRS1ckL7gtiYzzKfclmKVhNt41dhsiP7Sc8Tqsrzz8v+6Q1vkP1YILedXk+K7EBORZhIf4TLl/8whcITXLjwQR5++Ij/4//4Eb7whU/wQz/09xac3VZL9pKXLsn3DAZCyymXIIzg8FAzmSjW1mB1BSZzWr1ty5DNzVjSVC80jF0Xskyod4bWaDRJIlmMYlEyHfneut+Xc8gFkl6ZxMml+i1LgH+eMM3bKzGQ78sx8yUGpF+aDXFCxVZYSL+LRqpBomw88zXqxDdi+93YPP9XtncDn/gqf/8sUOTVgHWK2Ch/ZXsEaCml/hQSRX4bQrv4d/PXNfA3ELONC8DHlVKPaK1/Xin1m8DfVUr9OWAN+JOwgFG/Xftp4Abw81/x9wz4nxC+8e/ESwa+HqAYlgs1iDxMaxVj2EeRoqpVdJIswK625hzWVxpz5E4AeZsbFyyqG2z7VfxMDANl21hZBEqAgU2CiTnXJ5YosALQoBWoOEaPx1CtkpoS8bUQW0yHiGz+mVy2zSHCr9eweru4kz5ZsYBCEyHf5+gAS7FIUYbn7kTFEbNhxlR7tKsG2i+jjmZYRobhWOj5gqMBBgOsaUjYXAfLxFPgHF4jLDbRpoUZT7CiKSqOQGdYjovWEVpnkmav1dBRRJwqVBKylnYwZ4rMrRGmDsr2sGddlBfgOAqzUYPZRAqd4gjV7ZBduUp47DROsSwR5kEfHcdEzXUGQ4vwKGF9JcNqOlQbJq00Ypz63NoxKJWg0SyQmBY7exZHRwblMqw1Iza8LmkCxXIJVWnTmGTgt4lUQjFNeOitBaK0xMR3QYNKQjITQuXiFsT58KWXTVqlkM12KNGcJIFeD6NSwVHpkh9brwsYnEyk+LNUI+5N8Q4OxJRgbY1s/4hwZQvfnGHEAdNj26xMJ0z2Um7FG5yo7GN0u/DSS+jBQEJUd91FeuMWpgnOuANWncDwSZSD1uAFA8zBQEA4BrPaJt6LL2Pu3BAx3GPHZLwGgaCs3Jd2zgenVII4lrS61kv96UKBha94lkm+NEd8nresCKvV5D2VylK7Oc/GxDF6MlmY2qg8d5uHvLpdeY6KRQG/584tSZR5AZ5hCBCuVpdezq5LUmvRD33GXZE2M03hum5uzvmSFHnxoma7bdKaXpH7MhzK6p8kcsx5ZF23V7hhn2AjOsA6OqLkr7Gy4vDFL4ousecJb3R9w+ee0tyzh4y/+udn3HOXTaxtPv95+IVfEMDw3d8tuP6jv2jyh9/v8eSTmj/1p+DXfz0PrCv+1b8ScHTihJzSvffCu98tdImPf1xu/daWdNfegUG77TJKbGrWEBXMcLMJD50OudKv4TgGvZ7scxsNOZ5pwiRyeOFiwtYWtNoZxqQiSPzsWUHfv/Ebr86EWaKgEjTX8QaHOO2AQBXm4Ehzcjul5Y1JZgm7HRv/xAla9ZTRQHOncYDjxajEWdJTcvWeS5fkZ6Mh4/DmTRa7As+T/49GcOWKOFyWSuh2G6NSoTe28AomUaFG4enHMLcaTIwKxd4OTr3IsHYHxfoqrpGSZAYFwyIOM7ZXpmSmg2W57O5qjl36PNlDr+PzX3Y4dSzgcOQvhoDva+65R9Htyqlduyan+Qu/IKd47pwM6ySRn1/4wiPcd1+Z++67nU9+8meZTrcYjx/ht37rIxSLx9nauo077ng3b3jDh9jbEyZSHMsQzhVM8oKzUgkq1pSJ5wOK/X0ouQkFPyGKPJZOyJr19XnJhZeilbnYSwaBploFT83YGxQXojNZJq6onieOhXnL90B58qVclp8gn7l5c8l4yffD+fk6jtyuixdluPh+TvmAWaCouQGR4ZFmhuyZ/RBLpWA7/M7447X2+6T9n5Jkm/N6v+rOSCk15NVR5AIw/irH6CqlvhUBvX8X+CQCWAdKqXXgnwJ1rXUMPKGU+lHgT8zf898ghXZXEN7xzwLv+4rzUMAv8+oI8rpS6vJXnEoRqAD93+m68/b1AcW2DbOZOGehMFWMWS6I3m6SoE2TWWNTvN71fHs8mQigGY2W1QOvbK/cLvu+LP55RMPzoF4nNQxC7QmXS4GJLP6xlknAIZFqXRwcR/ihKIXGJMImwsMgwSfAJnc98wGTEA9bhZj1Ok7JARJS5pFmDb2pT9Wd4Y66aMAqVsiimFrNppRF7A4KVEsJReOIKHbIWuu4KhG73bk8lzkcUkjHKMMmMwxYWaFwdEgyCUiaqyTlJvZcK9glIMNihodeKWNN+tjRDMsyyLIEQ2WoehNjOCI16iRWgbh9Evfmy9hGJhuRKBJQ5Tjw4IMYsxl+fw+ShOnx81h+ih2GWGpK3dC4qxZxanCj47FRC7h4w8ENOvQmNQpmilJjTEPRqEu6rtsFq+0Q+TUsU6OiqQApbRB2QqrWGGVbGIUavlJ4TOXeTHr00xIzBc1SBJbNhdMx8TRmZ9+iakhxUtmwUJ6HqZOl8kijIeNhLjWWBhL9p1gUK+xOj5tqi2aqMUxNkhmoIMRwbc51P8codIiKbbxHfg19+wW46y7RGq7VSCst9GSEZRl8+RmDJjusrWoM1ybdP2B2+k5IU4oH1yh+9lcFPG5vL4FfrSar22OPyd+2t+WcDw4EGDUasLoq1Jt+HxwHNZdlI46XrnhbW4sCNR2GEvXLrdpcVyKP+eYgv/ZKFd0VFzI2NgS89/uLcnc1Hi8twUD+n0duX3pJPnN0JFFMpRaivTbQqtepOiajxGcwyMGxnkvricauhwGNE3K+8+9ROztL4N1sYhiKzVaMMlpwtA86o9kUDP2lL8E3fZNEDHPAHUVw65bB9Ys2/+AfGjz1rOKOO+SSjo5ytzHZKzzyqMH2tua226R7nnoKzp/X/Mt/Cb4vafw3vlHcyc6ckVM6c0YAfl7spLXi6Ahs2+DsmTJ1PcPo97HTDitNhyQtks5rgk1TCvBWV8E0DQ4PHa5cS7m7sQsqETOWp5+GnR25x6urMl4sa6HvZnUPCc+cxw1HlGYDNjfWGY4U8ThBDXo8d2sNVfBIx6BTg7XaDNOrkHWOMMpFyTZISFOCCHEsx8/55NOpdFS5LNzxel1ev+ceUrfAXtjg0qjFW71LVD2TaWTg6ymdc2/Ee+nLfP7aWd5y/wlGhyHt0cuYBzGsrRErG42NHXUZhS41OpisYtsm8d33Uzq6juOe4+r1jPvuizjoOxwcSHFrEGiiSPGpT4kV9pe+xILu8PTTMlXdfbec/pve1ONLX/pxptN3kSQ3uXLlMseOvYcPfei/5amnfoYbN67x8MMf4oknZNx0OrL5yQv7codBkXRTDHSBTkeK0La2YDCwsHxzscSk6ZI51GqBRUaKplWKCENFoS4uHiEFikVNFInU2nSqabXm2sKOxnUVg4Ec0/OWVP3hUPaxxeIyITqbLRlgg8GyxrxQkCh6HkOaTnMVU02WwSDyF66ccQxTx8OyNKXXCuxea//l9gJS1PbF+e/ngee+8k1KKReYaq0fesXfHkGA7DpS1msC820eMRLNBWgCf1RrPZ1/7u8CX3rl8bXWWin1v7KMaOv58U78Nuf9c1/rBf7emXd8RcsOO6jJCF2rExli5OEQYc7GKKWESqH1qyNaIJN0Pkv8dq1YlFltOl1WQeQWv/PrCedpV1OnJEGKG41QxYJQNopFAr+KylIcHaMMNQfw/3kLcUgwUFqTAU888htEUcj62hqHnQ7KLnJspc3m6hratPBdUbWIMxN7OsAYj8hqDQK3jEnKaKiJY8VKI8FUmlTDLLHwevuYhbn0XBAQOiW05+MlU0gT0kSjKiUC5ePEE2zbIBuO0LaN4XtorYiVjRWOCewSvhFjZOlC+kpPpkJtKRaWkb7dXVl1qlXpt9tvlzxklqGjSKKKrktarZMe9bEtSQvOGhs4lmY00hRGB6jJhEltnbRcZzQyKJc14TTlsGvherCxDpX+VVSWkbXa6CiWBSbUDGcmK0YXY6WFMg0SDEwybh55WK5FHGmCQFNkxobaRWtNlxqmArNSpDuyWc92cHOVkY0N2TDNmx5P0GGEOtyX1xsN6PWIqi2SgoR/1P4elqkxG1XicYTh2ahej6C1iTZt/KBH6nrY3UNUMOPyZAWzXqHopbT8qYzXF18UtYjr12WlzaOheRl9nr4uz+kSuUrE3p6k0S9cWNAfkjDGPNyX36tVcbDLLbWSRFbkW7fQd965tEfv9SQCnCToanWh8qDCEG67jUSb3Oj4bNZnOF35zjTVGCstaLWWYYN8g1qtymo8HMp3Hh2BL2YPeSRTra9DpUJqGESJxcHQpVo1MAyNowOmsUOGKVE1K8RVEfroCOP6dZhJNF/l9mGOg2400L0ek8YWqbLpdhS39gyuXl26l+X75pzf+bnP5QwEvTC7FDENxZveJKoRWSa1gdvHMt771gmZVnz+mQKNhuJzj8ljsLYme42VFSgUFO32nGUQJaSpZv/QYm9fce6cXP7x1pSCm2Du3xJTi9WTXDso4HqKTs+gXBFDhm5X0uCGoTGiAPNoHyeZoZII82AXYzKWixoO5QLf/nb4xV+Ukz59GgyD9PRZiRWMYurFmHGhhZrNuHQxo14IsBo1Vgpj/Gsv8FRwlsSrcM/xDpapeEUYU3YJ589L9qNSEUB+44a8vrUlnTq368sMk+d3a3h2wmnzurynVoMvflHG+7d8C+zskGAydhoUhztYRR/tF7g+qNKqhfSGDpNOwMoJn5ITE6cGnhGikoREzXWuMXnpZcVWK+DyjovtGrz4ojiGPvaYLAu5ouH+voyDO+6ANN3hU5/6YVz3LN/zPT/C0ZFJvy/vsSyJsrqu5uBA8ba3wR/9o0szmVZrSVvI5cnyYHqjIcPdMGA8yihXhE+c881tFTMZZsSGO69XzSgwJZibO2kkyAJCp+j3oVbNsLMZvVlhLvqisCxNEEAQiClLLg2YZXI+a2uLZAz9/tI8JE8YHR4uCxLz5bNYBNfJWG9FYLt0e2L77fssXPcajd957f6D0r7RzDvuvff9H/uLf/EXv6b3f+hD//8x71BK/Q+IHNu3zP/0CeDHtdb/7CveVwD2gA8iXN7vB/4OEtmNkQjwR4G/DJwCfhX4f2qtf0Yp9evAbyHSag/P3/d+rfXnv8r5fBCoIhSN7vx4eRO2qxT0Pfq1gtmvT6QYoNlkYhZxjRRv0kcrReyViPwqLtFyi5uDhVyb5mu5rjxq3GwuZ7fhEFyxOVazKW65DMMhql7HzkK054JpLUp/3Ux0cNRoBLWa8ECBFIXSMrXFmHMSRoyXBuijI9784EPs97sYSnHq/J1Mg4hbVy/xK5/8FbyCi2lZhGHIhXPnODmboVdWCMwCQWyTpBbRbMpquoe6EaLX11CDId2ghVNYo84Eu7uH2tnBvXFDImlbW+hTpzBfekkixysrKMcB18Wo10XSLY5R/T5uowHjEYW4KyAn13KeTlHuvPLHMmW2zQ0gtraWZhJ5GbRlLQCeXlkBQxFuHsdMJhhkWFkq6gyWzVFaZ6MYUY+P0IaBX3YYBzbNckyxDBgWhqnoeZuU/ZhIuVhOjBqP8OMhntKoVouZWcDUGSkGWhnUGxrSkGIpErWCKAJVQfV6tHQHUOhhn3KrhTqcFys6zqsFOIMAtXNTQCXI9UYRTCY4UYS5sUUnLKFUmcZ0B8hQ1QY3Dlx06lAPMgqFSIBWGJLW6ljX+pwoHREnI5y9Ltnps8y8EoW1dfj8Y4KqymX5TsmXCtio15fhzc1NCSfu7uYVPrIqzomUGpPpXa/D7dzCHA3k874v494wRAfMsiBNmdz5EP7gEMMcSniz3ZbvnNOKcmqC5bqcbAyXYLxYJK2tkMQRzt7ekq+fpgJ8SyUoFFCtlpzf8eOwu0tseFjhWBRQcq6y7ZCYFo26mK6kqWIQ+YuoGoDvu7RqirSygXH7CoXRHgbzOaDXk6Ky/oCs0SLF5vkXDDY2xCXwzBk5xmwmwfD/9J+k8MowlqpuDz4Af+oHAh75gsdvfGrZ9bPZslq/WDa4dlhksznDdTJOrs54qV5kc1Nx/+1T+jOPp5+VmoTNTbEJLtcSnOER6+tr83JaoXDEts/MzCg4Hka5jG+EnDhRIIsiNtoQaI+LlxQXLsjtW2lripN9zLUy+vqASBuktTau1qjV1VxTTMJ/29vLgsdajbQ7wA8CKtMhUXET2zEwbYeVrQzrxi3Wq12MK0fw5S9z9/A3yb712zDHajHWuXRpfjEieKsxCBob+Dk1ptcTaQcJa8JshlGtcsdKgr55UwIGJ08unRTzcew4WGmKX1RkIxsOD0n8EokqE+KxVp/S8YrE05jItrAOdxl1Rkw2z9P0JhizAcr1uPcMXN73JcNgS3T9+nWhrjz/vDxSSQLvfKfseR977Ek+9rG/w3vf+y9oNBqL96ytyWPR6Qg4/oEfUHxiHme6epUFvSdNl5rE3a5cSqOxlOnOMqE6FIuKkpoSGD6mI3r0sXawimDMOcIG4uAqdSlSXxIDWplzzXMARWZ62InGssS2u9nMvXkkSJIrLopsnNyOPDo83xsvzDu63QUTKk/6MB7P6361AaZFHGdUKsbiuRmPZXp4rb3Wfof2o8Aq8DgS7f0p4McAlFJvAT6htS5pradKqe9GaBKbwJeBb9Zaj+fvfR/wj4ADhNrw/9Fa/8z8O34QAbl/CdgB/sxXA8QAWuv/MD+eRgrt/v1/7QV+3SLFgCz6cSwLeqeT+50uNVHlJORfrijxu2n5tn7+HYnrE+LgjzqoYB5Vq9VQhQKhtsgwcLNAojNhKEDlK1TMUwzSMMYe94VvbFgY4yHK91GmSaxNtCHKFimKAB+DVArg5nQN+n2efOIJhoMBb3vzW5iV2sxii3IxJY4yvvyCR/xbj3H3mQnVS09iXL2MajZJjm0Tfef3ko2nFH7yn6F+7VckrfpHvgtuv00KCnd20KYpYMV1lylPpXIhyqUMmGXJzGpZssjmFR6FgoRSgkBmyhxd5FzwMES321KYVq8TOQVMnWL2uqhgRmfs0qHBsdM2aQy+nmB2j0g6fa4E61ibq3i+IkyEbiHcOsV0mtIsR9jjAcQRN9JNbFuz2QwJsRlNLSxLKr9NIixS4sRE7dzgxqM7HH/TFv64I36wud2T6wq9Iacn5EoMIH1x5Yr0R74Jm69GWkiMaNdjOLM4GLiYpuJ4Y4x1/TJpqcLF9BSDTsqdZ2dMIhv/6AZFLyNZWSO2fXwdopViho+jQxiPsBNRh1A5UbBWW96LPGTz4ovy2isL2vKivI0NOHWK7PkXSFc3MI1MAHatJkVqKytyL5tNePlloSOcPi0FkXOzm8QtELtl/GsvonxvqfaQ241HkYwFz5PzypHrHOwktkvWWsWu+MsItW3D3h7pcIJeW8eOpugsg2qVuFBiGjqEmY3nS99Pp2pR75ir05W9kMywCSNFpaTx1QQzTVGHB+heD336NIFV5vBQs9d1uXZNZJG//GWJ3r7znQJWf+ZnRIs4b4YhXOD//cMplQocdgxeeAEqFcXOztIVTGvNbbcpNtYSjjfGGN0jRtVjfOk5h9tv07iu5id/ymAyUayvy6156N6IVj0myFy6XUUYi8KGUrCyoimrEeaRANhsY4sr/TrKMmm1lpFIw1AcHmhOtIZCXXr00WUEvlSS/r96Ve7/N32TfPHhoTx/Z8+iw5DUKZAZFp2kilYGTX2EPRvSGTrUkwPs3uHSoOWNbxTU1GjAJz4h2aA3v1nmknIZkoTsxEkYjzHCYLFJyu+1ns1EOeXaNZIoxTBAnTyByuUTgkBQ6/o6XLiAVorMsFC9DrFy2QtrJFg0mgadrsKxM1bqMdbhPmb/iHRtnajcwuwcshfXaDUUhsq4elBiOFoWKo7HUkx3cAAPPyy0iatXn6Hb1Wxs3LXg4vb7AqB9X3jIOUj+K39FujFnIyklwHllRTMYCOBst9VCgEVqvoXGkS+LySyiVs0YzFzCUYLpie32dnvK/kh4w9MJ2ASYCsaxS7EEtgVurlCkhBhsE6KRIm2fgElkESsP25bi0IMDPbcFV6+iT6Sp7HVns6VU+Ve2nHHounB8dUp/4hFEilZLLZbYJS/6tQavRYr/oLavX6QYZHLP7XnyyuZcgi3XQh2Nvrbo8FdreZq3WATDwAIsIiCTBRsW7mBud28pHprPgHk+6hXUCVOnmEYmkZt+H722RVYoid5qFGHPZlAsovt9zEaDYtJDzVUNAuWS4GL6ivvuv5+nrlzh3/3aJ3nD697Lsa0az77k87f/vsETTxho/WZsI+V1usg/a/4qlad+CtN1sS/cwdX2Q/DeH2Lj3ofxpx16x+5iXL+D6kZIofgi9tEeOgfFgwEqJ8flkaEoWoph5hJYjYZE4+ZuZIsNxRwEUy4LqGq14No1sloDw1Qo08TRCYcDB63atFciqk2Nv39Ev7PONLPZbrvEa5vEG6dp3OqxfzBCb7bECjuGyVhTqWo2qgFmGsJ0AKZJpZTRKgVMKOIQUnJS+ocpoedQLSRc7xVk37J1gsf9U1jDQ07oQ8Jim9iu47o2XjQHil/NosmypJgpiiTEOKfb6K0tYmXLJqnfo1quULH2UM0GmBbh6Qs4RByPpox8g2u3LNZXM7qFTS4OHU6tmZhZSpQZpKYDZIT4UPZRzLBK8xL5gwMJ6+SSe5Yl4zWK5JmYTASs5u5xlYrcixs3MFbaGP0jdLMp4/v4cVnhj45kZW+1xHFwMJBnbG+PbGUNffw4ZppgpTHq+DEBSQB7e1I81e0uqnSUNwfMee54zl82HQcqJVJTEeFAwZXq1FUPj2uoYRftOIvszjRwmbkFTEuq3BNhcSyitTkveBpamCpFK4v+UKF9h1ImwrCpaaFNF98I2fKHNM80efzzNs8+ay6c4z7xCdlPvLImrVKBd7xDQPFwbNJsiz3vhQtQKGjOnlU89pi8d30l4cRqTLFskGJhNpuUDq5zfO04FTvm1pHPvffC449rCgXFeAwvXLR56OwQswD1osEkcRmN1XyvIz6dZBmz9ZP0eiaTicYpiH+kUiZXrxqsrWn2DyBLCpyxr2Hefz888wxqb082qDduLARq9a1b4hpomnPSawGlIUtSdLXCejpgf1wkmMa4/UPaUSTZhVYNrlyRaO4//+eCIq9fF7rD+fOys7jnHumIrS2MNAGdycaw20VpjdZa5oVmCwyFPn4co9sjaq7jDg6kszOZBwkCKcgD6HQw1td5wbjAlrPH2oqLGvSxDmPKlkNQXGEQFJhZ27TqHt5oQObVMBt1/P2Qg0GZwdji6lU4c0ZTLMp8nDOQHnpI9oo/8zP/kL29S9x//48tRGaEpyuFc8OhaFRvb0tgPAePx44JILQsWFnJKBozfM/H89TcREMvEmXVqqaopux0CzSaUHFSLJXSDI4YOWX2ewUMx2K359KqzIgSj3JZo3Eg02R92L2ZcWw14ijyqFdjFCmeDudwWFqIhXJs7Lk2Uk5tWNA0bL3gBMtjL32SJMs4Uk77n5fUMJ3KNdw68jCNjDgxmc1kaXjNwe619lqT9vUFxbkw/7xobAHC8sU5TZcRv1xm7XdqjiOAL9fyHI2WMlw5uC6VSItiKOGqCFNpiZLkhXlZJmDR89BzkUgFS/AyHKJPnQLACsao2JQFwXEgSbj63HP8vZ/4Cb79A99GFokL3Uc/8QnedN8DfOHFl/nmt72db/mWd3HvPfdw/vx5Hv/il3npSsRhP+G+e4ukaUyavoFf/uU6+/6buf8HP8EfH7+d5kufI97tcHlm84XPNdi2LvA937RPeaWMWVMMBi6XjHu4sFUQk5C536x2HNThoSyutRoLoc/8nHPZrXn4QafpQrNWzS2i9M6O3KP9fdjeJi2USPcPsMJ9VLNJvVohTiwYDIirbbLj29TjgLYxI8BnODGxjZRCyWIzHaOyfSZmm4Neyu1nY/pTl4PMYkUfYTabBJlDgQC0WDlHOFjxiA29T6DqxFYT20i5dMng2GrKm98IbVKsqMq1UYndmzb33ddAFb+CJJcrK4h4qKyEsNh86bnUmXJtYlUk9irzwrEajo4IZxmul3Iw9ql5M1bqGW1zSBpm1NolttoxgVHE1QEHI5tKIabY3SFtr5GZFkply8hrLqW2vS1jfzCQv+3uLuXj8rLzclnul1RmzaUVFGmYEJ4/R+H5ZwTEgBzr85+X466tCVCeb+yi3hSlNG4yRU3G8tp0Cq0WaaHMbzzd5M0PBngFE727KyYpu7sCdCoVqNdRzSYmKRkmqTYBhaenqMERemUVhWhMh5mNoTMq0wFOGOAUK8wSk17PWzzqaSqXqTWMpyaVioltgpUF6CQDz4HJeF4Aa6NUTFJtgrb50PenvPMdAqY9Tx7ZoyP42Z+F//Af5DEuFGTf8/DDMt0cHMBWc8KXXyygtVokU6ZTWC+NaFeEJ9wduLRKEYbjsF2bgWWyvSLqQ52OwXgsj9HKquIwrJMdjQnsMv2BQaEgIGxjLaPAPo5O8Pr7rLZWcBsGL19StBomNT/g9CmXXt9kfU1jZhmg0MqQqOvcSp0kQW9tQadLhoHxwovoWo1p4xg3dwqgC2zWJsxGUNEJq/6QoNIgdWJMxyQ0fMxRF+vNbxbHTMuCF15A/cRPyPh64gkBxq8kn85VfHSvJ1rknkeqDZLuiLhdoWjFqOEQY3UFz9AyN+RtOkU3GjAYkB52yKoN7EuX+NzBeb75m5usHe1wVDlFikmjmmIqEzcIeeGay45qsbLS4LgZERk+btuBEDbrIxy7xJefUmxvyybn/HnZ3PzIj8Cf/tOwu/s53vWu/wXTzAHuEuzl4kW5yePNm9K1/b6Mw5xT2+sq3BWRfAx9KYhDa+JE4SrRWrcrHmuNgEQ7mEZGgEda36SaDCjUUrTKCFIXrQwsNI4OMVUKhsKpmwx8D89TuO5srk5kMkx8OkcZBTelWkhQroVLSDAv9DdIKNgpsXZJ54m+VmnGKJbXx2PkXMkNYdTC9jnfPDSbMuWFoSE0ivmUM5lIPOS19lp7rX29QTHITJXrzJimTMydzjLF/0rd4a+lRZEAnDzSm4uBar0EIVGEValgDfeXxU2Osywoyy2DKnNAdHiILs91lPPivSAgdQuYtao4Nx0czJ3vbE6sr/Ojf+vvc3M45JknH8PzPP7eX/gLXOn1+O73vEvc9MIQJhP8NOVNd14giyLMyZj0O07xpZeLfPCDv4xS72U8Vvzmh2/x4OQkb+dzmL/0Me754TdxfHCTM8U9ZrXzfPjXj/Hud2s8K6XVMJg5xxhmBlFqsVaeYE2HAu4dB9Xvi1yZaUohU562H0lRnhiEOGDZEEdSsDUYoCsVZo1j2MdPYwcjnFGPuNkgnvk4nosJJLaJbtbnsnUzDFsT4jAeGyidUiho7P6MYucqKk2pNvZZNyNms22U4TELTW6kbRphl8E4YeZUOH1cFhlNBp7LaPU0s8CgbGZsrYR4hkGmFavVED/UkJqcPqE47X+VsZEXboIUsOXZgjwCPhqRWC6xWcA96kDTB9OYg02DCJfIgCjMSFNNJyhRsTOychFzOMB65hkMy8I/e5bU9WjVMyJtk5aqmMGEqFTH1nN+fD5Gz5yRfG5OYdBaor65IOkLL8i4yvnxeZFppwNBgHXqFNbV50UwtdWScZtzlDsduZfNpmgfa00hm0iW48QJUCVBifPNgRlMWN9e56VOiduiHRzHEdSgtYzXPNesNVl7lZl2KerxwlhDNRtMcXGNiBgbdeMG1nSIWl3BqpaZqQI4mnpdc3ioFhHdXD0R5PBSZe+y2kqZKhvV8NFhRKYsIm1gkWBmCUcjl+5A0r/XrsmlrK7Ce98rtOqcLfPmN2v6fTnuG96geOL5IufOwYsvKp57Do4d06yuQskOsbojKFWoVxJ0b8B0dRvHyoi0i0bRaCnuvBM++cllguHcOZM4ri58T3JN2WoFzMCE/UPUqVOEdona4XXuO1UnzAStlcwZcdHHtTOKVkScrLHbdTn+8Bswf/Gj8LrXkb3+9YTlFt6LT5H6ZZILdzMbRMwiB1vFXN1zKVQqjPoZ/mYFy4rx+vvoiuTpE6OAsb+LTgIOt+6nWnsW74lHl1m6blcoFKdPizvhnKCq9/YIGusM7A1K00O8hseB2sTvDCjtPgO3344+OpprhylAC888TYmOnSY7W8R46RmO3C1WVlK+7eGU931PmV/9V76YBTkWWhkcDWyiIMMwNFeuGNxe20MnFZ56FmzHolhUJOaUY9UhxTdW2d+P+dEf/VGOHTvHlStTvuM72jz11ADLKnHu3El6vSV9Pk82PvGEAMeHHlrENQiCZWGZyDFr4llEOssYpS7K0CIVac447Pkca8X4tiLFJjN8lMrINPgEZBqptUATZB6dDnzucwb33AObmz6WqUGBrSKMJCKJNb6tsVRAiA22xeZqShqmjAKTth1gmFKal+n5dyqLwTCXDFdUyh5pklEsmqysgKendGe+2EUXZSmFJWPu6Eh+5rEmx1my53LZ89faa+0Pevv6copBntCLF2WmyqO5OSDIgXFOYfjdnEuttpwV8u85OpKf9brMLLA0MMi1aYJAwMr8OwPDQ6kMh1S4m/Pzy3p9pvUNPDvD7BxIMdsrXcW6XTl+sSjHzCOU4/GSopAk8ve5Bi1ao0+fJqm1+HM//HN8/JeOuHlzxB95+Nv4t9/1WZydq+j1dWat43jDfYz771v4iapCgdHlfSZmhVl9k2vXZWI8czKlovsYpjnnBnfFbMS3MWYTCEOU56ErVYhCCS30ekyr6zjDI6ySt8i7RaUGaaWKR4QRhejxmDRTzGqrKMuay8CZ84ISTYxNhsKZDUm6Q+E1Xr6CMRujnnpKQKBScOwY0e13g+tiq4S4WMWajUgdn7TaYJJ4dLrCu6zXoVpK2WgEQgvJMwKv1LH+7Vx8tJa+f6VOUb4Bc10y10NlKWmrzcwUgp2rA0ZDTWo4FLMhdsGhM/XRhonnKQxDM53ArZ2Uu05NsYmIbZ/YcMm0hdvfxUxC4koTwzGxR33Uwb7IbJ04IWPj2jV0HM/dJ44LwMx5xb3e0g46pyTkwDnnELfbAl7z0vS8ICq/vlZracSxu7vMxoxGAoAODnICJemFO8mGE4yDPUzfke/e3V2qxtu23Lc77kAbhhQwKkUGzPDJMHGZYSHmDzpDVC6CKWm9xcyQCG2+H9FaCtNy562jI73ggdbrinJ5uY/JTfUmoxRFRhCZ7O4ZGIbQJqRmUfPQQ3KaN29CraZYX8uoVTVBZHDpklzybefh6jVJow+HUK1oas9+Bu/4KlG1jTEdYZYKTD3h6CYhBLGJ64pR4Ec+Ira8m5vS/aWSdL9hCNAqlaBRnHH75hjTVqi57nqG4gsvlEhSg9tv01SDfVK/SD8qUiuGxIZoexXCPurZZ8nKFbrH72UwMjg+fAZr94YQZJOEyC6imy3YuYU5GzEorFFaLWACRhYxcyr4KmSmPbxkQjRLuXzTxv/Cpznx+X+P8XM/i5rPO2xswPd9n4yV++5Dr6ygV1bBcUiVQRhANAwo1D10prGe+xKGbWGcO0tmWiTKxhl2ZL4bjZjaZcx+l720TXl6QOmxTxJ/9/cxPAywCxbT1GOSeBxbCbl2DQ4HDtWa4uBATqVaTFlrhOwNCly5KhF9z9NcvfppxuOM06cfoNerLGIZEjcJODryuHVL6DRXr4pgTi6kUy7LY5AkMqxXVpY83LU1zUYrwvdSNCZJorFNzSRyMG0FKCyVEAcJkfLEWMOEmzcz1uohpqGYDCJWimNir8pzL9ucPRXTnRbwC4qSF+OqkFC7gCad2ygrMiwdY5ERYaNRmHMvVK0UgfaJYmMxvSXJ0ukufy5yXx6dZkynmiAyF6JLeQnF7u4SEJfLcr22BSiRHLS+/uGxb7j2Gqf4909TSrWBjtZ5uvRVr20Altb6+tdyrK//ozDXU10AxdzBKnesy8Hk7xacD4fLNDPIom+aSypFbpuqlHynCIwuOMY5+HBLhhhGgPCQ5+XIhmniJxO04y9BS/49pblzQB75zs3pc6JXFC3Bf25T3etJlOXaNezxmHOtb+Yn9x6nWHwP4/Kn+Xxrgzfdc4GkvcGX9rcJWpr1gsnaakrNGkOnQ8mYYZExDNfZ2lK06xnKNrnRqVMqQVkFmH6JFIsEhR/35VoLBbRpEvg1vKNbqFqN4vgQHQeQWESNVbJMYZoZZKCjAFwHValgAsUrz6GqFTAMMpQYiqQJajIgaa5iGybu9YvwyCOoj31MrvfiRVnc3/MedLmMfflFOHOGZOM4MS6hW+HSDY/uy5qH7g44OvQolWGrHVLwUjLTxrTt/xwA/5dsLZUicQpEEbiVIma9LivK4aHwwG2XiSqAysMmilD5zIBwBkapgs4ipjPFYKRotQSMRdOYt93dw05FzzRWBpltiYNyoYRjusSGSaQ9UquIn0uixbHwOh1HChZXj+PsXEEFwVJw9Px5eR6iaPlsXLu2jNrmUf98bG5sLFVbomhpvmAYkiM9fVqejXyc5yB3YwM6Hcz9XczRSL7bnvdDtSrgfTqVUGyrJZ8fDsXp0HHI7UQVYk4SIrSK2TRjEnqs18XS1mfKJCvQmUetmi3Fyoom14p3bLG83diQ07x1S7pp6SwG9bpJlpkLKnytJgDYtueBTi9CuRYnTxpyrx1NvRRxfdfl7rsgiTX/8Rfh4QcShgOb440xzriDsbXCrNDEOtwHpYgsF8eN0SgcK+PgwKNUgmbDZHtbuvTCBdHHzfe4+XkfHEC34nDMm1K1A8nUzA0ybj9zN6ntSeKitEIhHdIsBXQnHmUvwonGqE4HXa3y2M0tTmyZpBlMN89QqleI7QIHlzu04pfwzsxgaw3VMymTgmmQhRF6NIRGCQyNn43Rzz6LZxp8+fLDGMn9bL5rhHtsQ5wY770Xrl1D5RrX88K60KmQaANQFLwpru0yPZpSrmiGp+7h+k3F7aMBUWOFKDHp7oGVdmmerVN46UWyvX3WNk6wZ22SvPF9tJ/9AqG7SqW1SmHvOuNgld1RwpnqkNsLEY/tn+Te2zW3Og79gWalaeB6AuAsC375lz/K6173dorFGp/5jGxGtJbhHQQQhh7V6lJrOI5l/OTsu253GQvRWvZ529v5o6WYhoqCleJkM3YHVVoNjWUkTEYGq9WASNmogomRpbz4Itx5eoYyihyOfKrllMYqxEkFDMXdG4fgVmn6UmCdaJeJtjk61DSaAnKNLMYzQkYzh1i5VJ0phtLsDUpUqqAztVDiyzWIh8Plnj5vuZBMsWgQzY1L8vrXa9eWj3ce82m3wTYSTBJAYZre77yevta+7u330NHu90WbG3j8CPBXgBIwUUr9G+BHtNb9V7z1MUQb+WvCu19/UAyyZb92bWn4Dkve5X+p/XbR49wEodORhb9YlL+F4Vy6S8kKmtuX5vzaucsZw7k0VbstS3VuaWrZS6DreZhZSqI1U6+BF0QCJkBm3kplqf05Gsmxy+UlFSR37ctnvPnv2XDI7PCQ73//3dj19/Dklwz+9B+9m3bpIj93+RoX2vfi1nxqUY9jZpfSwQjV6ZAqxez47RxmDY63pli2yMUlKNaMI5xpjFH0idOEtFTFUQlj/xQFPcXsHMqu2PVJWqvYWYS2bSaBYhaWKZZMLCMh1B46CPBGA2iIqoVWBvHJcxDFEM1AGSQZvHCjzhcer3P+vOLM6TqNu99A6agjec3nn5cisEuX4ItfJEsyCCKM2Qxrbw/T9wm3TlONR7hrDYyDHTaaG+z1C+z2fNbWwFHgz29/HGmsLMToddG2zazYRutXy1vnLYrmIvthhpnOJczqdTKt4OgQ1dgA28YmlVin1lQrNtZcgO/wyOSwI4oZ3a5mMta88d6Awu5l1EqbuLZJZnv0esLQSNtFNpshHjGERzAayqbpFbQFlMLwfeyjHqyto9JYuJwgoBlQOYBXSpDYwYGsdt3uEpHt7QnnOw8RSam6UDRyx4hc9zeOBVgfHCxpF43GnDpjLTeNeVit2RRAnT8PV66gy2WywQhqdQzHBDQWqSi0IJGwzAYSxQQbSyd4BFhGyvZKiKE0M+1x2LVIEo3WioobUXAUSeYwm2nGY7DtXLFCLnE2k8vSWi73s58VRYDz5xWnNmbsHjqkccZsZmBZmuLsiKO+y8qqTbGoUXHMhfMmm9MXicrbGJcvE22fIG3X8F54EnOlyYtHDYqeQ5uMcKpJcWi2FZN+wrPPKgoFk2JR9rInTkg3RpGImThOvkc26YRlfA/M4UQ2DOduwzBt0JqDQ0WzaTF16hTVBM/T9EOPip2RhA5+NGGlNOXH/knGn/+LBpPI55lLbR5Y2yFcP8HL6XlOVgMcTIzGCkmqMJTGdU0YQzYbgZmSdQZ8KbmHu+q73H1Hgnv1kBvnv53TD93D4Qf+BO2rT6C2tpaIvt9HTSb4DxZgMpECuzjGWF2lkvUgKVGd7nL3uguWRTbogu2RNCpc3CvhR1OKyoA3vpFMexx/6jFUu8XNoIl95jQUM5zKhLYTYo17uGmKqte5P73Gl29uc+HsjE895jPrR9zxgCYIFB//+Md5+OF3s7pa5Pp1cRX8zGdks3TsmEz1jYYsI42GRPCjSMoo8s2TUvL+s2dlHC2UHOfziO/BOHIpXL9M83SJzHAwzIyqMyHBFhqDsrCMmPtvj1HAyXqfJEpxfQ9Tp2jbZIJPVCsAUliKVsSJBmUQxWLwIntem9nUxHcy+iPFvlXCtiQCk2m1iGIHgYypGzdkKckTnfmylSsy5goTWss1b24ul6s8JrT0a7EwNRRKJt5rqhOvtd+f7c8hWsZ5KwF/GvjDSqk/pLV+9hWvfc0R/28MUOz7S7rE19pcl8WqNAfGOpcc0xrVbC40NXHdJeDWWgBqXt1jiZ0yfkE4YYeHcvw815RvucdjYqdI0ljDy4uUTBNr7xZWrQaNGnSzZRR6NBJwkWUyO+VWuWEo55g7rOXlwvMWJwl/9sd/nLffey9/7Ju/mT/37afkeGmLbZXyyV/6aYyTF7jwtjfhWzXUramoQjgOnbRCu5Vh24pbhxaZZdOqJTgFE0ZSQGeWCnhGjKlT7N4B+AX0nDvtM4Nghh4MUVlKEbAShZFUudUvUqwYGFiEqYWfJHDrFklmkrVWsMIpcWOVWWrT78OpU5puT/HSS5qPfASgwg+85y3cdeJzmI88ItHQuSTaU8U38tc/9R38+A88Tm2tgLGygWtlbNeGzNpV7GEBPwio1fxFpEcyCBbZeEKMi53MpM+B1GsCaiGNBcjqt7eH12gR2QUcM4NU+l8/9RTceSdhdRXPykAHRMpBoRmNDSwrZZzaJLFNpak4X1dcvgy1qubUdsosdggTAz+K6I8MvArUappyWeEaCU4wQs9m6HIFTAsjdxHr9WS1yjm/wQRWmgKalYE+PETpbKEljGHI2F1Zkc/durW0eM65BSCRXdeV9zrOAvhjWUKCBflbqyUAulZbGjjMZkttqrzoT2t5b6OxtJ4+exY9nTErtNC2hz03I4pxMYgwiUmxcF21SNaEsUEUm1hmzMz1cYmwiFhrZETaIQg1/b5Hp5/gF1KyzKBeVwup4tzFfXV1QfVndVUvtFhNE/qBj1/KCEJT0uenQ0wyTvj77AceM60Y7Abc0+6za97GifYMr74GBYNZOEZPpoSzBrvDIi1HUQUO+xmJNqhWFdd2TWxH9geTSa5bK1iy15N0/daWnGeawtVejWpF47VKONMeqXKw4hlJqmjUfYpqigFMsgKYcHho4G34jGvHudrf5sKpAX/t9C0iZ410EtGbFTjQq4wHCafvtHEMQ3TdgaOeg1V06IY2tZKikIxIghRlWTzgPYOyaty2PmW2eZqCGpMFVS5ftBk1H+SUqWSMnDsn7oW1mmzMci77/j56/4BZY4vCcG95gQcH2Ds7WNdv4t1/L/evbmGPYuLT59jpFzmmbqFvuw116SJh3GayO6NRd0mbazz9mZjGzhUqF1KsL30JN4y47d0rXNkpMRlp7q3s8dgnnuSJazf4Q+/7HgaD4qLm88oVGQdaLx57plN429vgl35JHpHNTflbXrOapjK8n35aHo9TpwRUuu5cndIV8m13/TxtKyIzUgJ8sjAhI8PzTTJiNAYRNmHq4LoBvhqTZRlRZ8Ckvsl4rKnUDNJUpN16PZN6XaG1plbL4yQKw4CDQwOQTfZ4LFKFti3xk35fzjkXYnKcZT167mk1GMiSkvPp63VZaixLPp+XyWi9VGUJAuaZHZu1ra99yX2tvda+wdoPItvOvwr8EvAWxFL6GPAppdQ7tdZP/W4P+vXnFOet05FVLm+l0pKL+5U6xeXyciYsFOSfaZJgkCkDO5yK6cYrisgWhXewlAwyjIXmbmzYWL0OKpgJN9i2l96YeUS520U3m8ItFruhVxcJ5gYHuQ5OsSi0jLySqFBgcPEin/iN3+AD992Hn6OFvM2ByygMefTJJ8mA97797dIXuRLBaMTNW7d4eTbjm977Xnj8cbTnEZ44j6qWySyXQLtkScqon9BYcQUYZhn+0TXMWgUcF5QEdzNMrHCMlYnKQYZBlFm4oyPRa563pNGSaGoY4hztyQxdqZDtH5A12xiew8wq8dTFAmfOKKp+yGQMnZHLLFT8u38nUb33vx/evfEM5R/9uxi3bkkh2bd+K0mjzSMn/xvU7ecxLIPzZzKq0x1it0g3azAZaxxH/jXLMbaRMhwqyrM9ZisncXWANTjCcF2yQhHt+Zi2uezbw8NlSGV7W8bTaCRFhy+9BFtbUtmvU5JyndR0SJUCbRDEJkmiGI1EbP/wEK5dU5w+nXK8NcPSCWp3V/RdbYdJZZ1+WmY8Bs9TtBsJXjIimiQowCla4kiXhz1bLYkIHzsm99owiPwqYaWJ1T3CvfQMhtaS5s7B9O4uc89ZGaf9voyR229fWnvlqv3VKrzpTWLSkCuLmKaA3LU1+fyLL8pYzQVP51q0Cx5zuQyGgc6LUgsFEmXRmRTwS8aiUEdrsU/31IwYmxiHyUQJoJ3zIDULlUTp126E64upgFIQTmNsIgYji/2ew3CoFiyk3FJX9gWaRl1zYmXGl1/yOX5CMZko0lRzdCTTwkZDCpbccYfU8UmChFE3pqJGTIsrrFQC/FuXRXd7MiGotNGmQzcsYNXL7O8rOh05VpoKGBsOl4BKKfnbZCK386GH5JbkaofFIpw9o9lcT8mUga+mBFN45pJPHCuUIQD7kUcU588rLl0Slz2RJ9a4VkLVC8kwmUQ2486ULeeQvVGRrFJjpTTDKVgYCpKjHlG5he1oSDMGU5uyHkKxBEmKQwjjMbPKKoXRPrrR4FbPp2SH1G4+S3L5Otb2pgQVSiXCWpsoMfDqBTANZtrDzGLcwx2MQQ+1tip86+EU+2d+muDt74V778W4ehkaDaJaG2UaFIwQ1TmSZ311lXjtGMmlq6jDAw6cYxx74iMY99xD/PZ3sjso0u3BwYHc85/6qb/K9vYa3/u9f3mh1jkYLOWzRyPpq8uXhWa/sSGPxsHBUoLMdWWYe558JldlqFTEortUkjFzcmVMajoYKkWhmGXu3O1aMZlAo6GxzZQCAZG22O16hKHGdzKa9ZRJYPLcCyZRBOvrimolYxaIWU2xqDCymFlszZc2ke3La2eHQxlLaboUCYoiuY52W167eVPYVJa13Lvmy+BwKI9otSrTQ6XCQpGl1ZLj5qxAkCmi2YQ77/zdL9V/UNo3Gqf4ttve/7Hv//6vjVP8Iz/yf39OsVJqDIRa6+Yr/nYe+CRiGHKEOO99BNjQWptf7Thf2b4xIsWwFFzMuZa5K51pLi1lgXnFxZKKMJ3KLJCmGMqAlTVZwSxLPptb/riuzAi5jVWlIjNJlqGiCDOL0XGCkYs25p6e+SxiWbJYHBws09O5+0C+lfe8pUtYvlWvVGRl7PchSahsbXFsZYWp1vxnAgnzKHZZKd7z1rfKsQYD+bvnSQhgPGbr/vuJr13jiU98gvs3NojrLZQlebGgXGI0yWgXA+rWESRlArsMUYiuVgmUi6EMHJ1CnDKdJpRrPikegS7gB108z5XS+XJpYdqhDRMmM+zpPDRx7RqcPYva2kRrC+1bFDr7PNxIiPQaoa7g02OrEdONynzv98Bkqmk0FEH5PMW/+JcxPvNp+M7vhNkMC3iL/wX6RkqvdorsqRfpOzazY+s4viYMNJsrMdqyiRILdWuHp3e3edMJg2w8JR11sBsFtOui0DAZS9Q7TZlVVrFKDRytZSwBKIU2DPRgQHj6DlQcYaiMxClDEJIVHLFkVcb8cjXnz4NtZjSbEtlBK6LOCEtPF+DRKBVxihZZT7qu2YQoyChkIf7BXAeqVluMWfp9WfXabXkGPA+GQ+xxF2d4hJ5OxYr57FlRBwkCcWJMkqV01nS65LXnxFpg4Q978SKLqrDZTH7PQ1MHB3Luq6tyjPz5y+lLliXHiSJ0oUCqDWK/BqbQIywX0lTjqwAU7Pc8mpUYbSC27aQ4w0OiwyHOWpVCqyVScsogiF2mU422bDp9xcqKnJpSNuOxTa+3XOiDQE5pbpRGuw0XbtMMBppR6rPaThiPbWo1Td2bcXo1JVQehz2XcAKD4QZPfi4is0y+5fU9vOYKdSvEME10e4VoltCJW1i7Y/ruGiOrzGpZLR7LnR3pquFQvv/yZZleVlflVkYRPPCA7Ct2dgTo5i7YzaZCGZYI4bhFRtOIViPDshS1YsTTL3n4Hvzar2laLcVgINe8Wg/oTVwOxkV2dhR1upxsj0kba9QbJkFqk2URaaZITYMDa4OCYXBwmJEkEol0Sz7l7i5GFJJOA6L1bQrDPWbFJsNDRdvrQakM585xVD1PVffxn38C1tawCw6JWeJW36JeihlOUjYKY1LHZdQ6STyM8aoOVwYVTvzQX6X4+V8nffxRsgt3QbFIaf8yg6Hi5sZ52k0L6w6b2K0QHg35oX98L3/rb2pO7T+Kevd70Pt7hDONo2LqdYcz2zE7exlhOMN1Wws5sRdflOk9H55hKMAw17qeK0nSbMr9KZeXSZJ6fblstFqSTx2OYH1dU/RSLAWKmAAfj4BxP8UtWpTdkCxzSBLFZAC6YBJnCp1lNBom3a7BXsekYCc8dG/EXsfh8BBaTVitBAxjn9EYir6mUss4ODIWfkhiGiM/Dw+XiR3XFRvqYlHG+t6eWrh91+v5hlIAtGHIMpVnLnLrZ3h14lJMkuT1O+98dR36a+219vuw7QBnlFJv1Vp/GkBr/aJS6h3ApxAe8Wf4XVAn4BsJFOfp3KOjV/89SZbbYVhGynJQmhfJ1WoowNQJaGvJA55HgxcFfbnBfRAsVR+iSIq2DARo5Dm33EA+1/WBpY5NDrLz/FS/L+eRR9aLRZm9ZjM53xxUT6e86c1vXmrkFIvLiN5gQH8248WdHR4+fVrAyIULy+iebcu593qcXF/npWefRZ86ha0y9OEunDqFZYJjKyxDo/b3UFcuU9jaQhcKRNpCVYtYWQRRiOvauOmMrBswbWzgJWOMg/3FzKkLBeGypin2bAL9jpxnXt10cIBKU2zHQQ8Vs8YmZjzDTSbY2iTzXbI0FYndUoqKIjoTl/7YonbqLOZMNHBVFIFpEt92J+XVNr6aEW/dixFOqY12wKljbpTITAcnC9g5ctjEoO4HXByv099RrFsmG8Eu6eY2tg2q30MHAYlXItUGs6miZBVw4lhssJViosoU2uBpCL0Sw8DC1gmJ4RGMLMplhUK6fGUF4lhjxDGjkcVRz+Js9RDXmKHSRKLPjgNBgJ2ktCsz4tgjSRWdnkHRS7AqFZHWGhxINiIIZPWu1WQ1u3pVQlpak6YafB/TDNHb20uOb7G4tD7OMgkdDYcSpvQ8kUZoNCSU+eKLMk5vu01eX1uT72k2l0hhPF6SEVdWluors5m8p1qVZw1IDZvE9rGzCLpdsnod2yrQ6SpK9RTbhFotJcxcnN/4JGr/Ftb586gzZ3BXfBj3Zcap1UgxmGETBAb1+lLeLAyFFdJsCoB58UUZ9s2mPKr1uvwLQ/ELWW9GHPZtVowOWa3GYGIzNFx2bhkLualhP8My4dsuvIR74yVs617SqMSlfoX+xEZRp91WvLQnHPnuTZkmbr99yezKQYjvy+Pf6UjgM4/25VX+pRJz9zG5Vc3mstBL6oYVvu8SzDTrrYj9gUezCbd2Nb2e4i1vEQWO4RA21x2UTpnFljiumSZf2Pd54lqBh1+vxAXQ91kpZGhlUStr4iTjuL7GdOUYmbLxjrqoehU9HmNmGf7hDTLDxGs0cRxFljok2uCgazG9dJP2CQXnzqFrNQzbxkBRq2YcDTxWCyPod7E8j0LFYVYsYFsJdxavoMce07e/n8JTj6KuX8G8/Ryq1SIrF7AMiJVHVl9nNDUY2kU+8O0GP/wj8O63v4F3vHHGzmGLiz8+oni2zOnTAZ/97Ce5/PLTfOADH+Tuu99BFC0FfZJEL/i0h4dq4XNTLstwzw0Zc0VO05ShnUswW5YM83PHZ4xrLqORQVEFJNMBWaONS4BBxno94PJekZFp026lZMrCdW0GAxONolRWXLsGGxuaq1chjmXOEEtmzeNfVBw/7i+Wj5HtLOhfc0VOCoXl0tJsyhJYq81ZVfUUzzPQSrG2lmsMy6Oci8zkG8Qc9Ofmgzs7y73yK68/V+qYzZZydK+119rv0/Zvgb8F/IZS6uNa628F0Fq/rJR6O/CrwPb8vV8z5eEbBxSDzAZHR/Lk5lvmuQvb4ormHFyV20EbhoRrhkNiwyGzCrhZKLPERCTHFlzIfHbItYxz57ZcuDIv0PP9vBphaahQLi+L4ubROpJEZqI8wmaayxDGZLLM3/X7Uo0zm801peZb+jxSmOeDw5Aa8JHnnqPWbnP+zBk5x5zXnOcCfR+ShHONBleeeYZTm5uoBx9EZymmmVBwtOhmbmwwr1QiLZbJUoWlY8aRQ/GFp7HSGG7eRNVqWOdtMsfELJeJvDJEMc6gL+cXRcuixTRdStYliRh99PvgOHiFPlm1Rmr4mGGInoXElVWU1phxRGfmkGnFRj3AuHRx6U8630hIkRY4yQTz+atMT93JkXGcShLxwgsayzHJUo+TJyENClw4PubloxoXrztczNZ504N1ypnLZDdkkxDDMNFZShbGXLlqc/dpDRrC7pgZHq4jtqtGGmMaKa5jEkYmRTfFVCHFw1sYjToohdMq0hvZ9MYupZKiUNAkXhGjaBN7JWxvnjXY20O7Hll5hXZb0e1Bv6fQ7SoGBVylACXj1ralX599Vu5tPh7DEH1sm6C8gjPq4SQTsiQl8BtkhkWxvSKbiWpVKBHPPy9jzplLqIWhhFwdR8xIhkPxwIXFZlLltCTHkXPY3haUl28cTVP+nksGui5W0UQd7KBMhWo0MMhQSlOrKa4eFGm2oGJNMEyNuvdueBoB6R/7mKzU588LNyAIMJWiisLLLG51j9NsmtRqktZNEs3BgaQv53syskxwfr0uw89QGjsNyOKIRk2hVQMTzVp1hhEFeNs1hvszMqfE3Vtd3EkPaj5kZSJHoVoVij3Fo0863NpVfMd3QDRPzb/wgnTfeAzvfKd8p+MI0Oh2BbSXStKdH/2ogOfXvW4h18zp0/L/KJLruf12ubWf/ax0/8MPw9q6Is1cKoUYg4z3vzNhY93n5k3Fm94kGQlHzxgnRfauzlPfYYi31eLCnFpx/jzEsQA0rRWOEdIfmdiZi9YK08iw0hA6ohyi53n0xC7QmxUx0ohyxURNp9gq4zNH5+Fsxnbni5j6Fto0cYdjrNUNtPaYhi5Z+wTudIAdTikXCsSYpCdPYaYJngoY3vlGep2M406ECqaUky61ZI+JvYblmxR8FytL+eD7Iv7wexSqe8SnL23wt366KrJ6o4j/+U//j7zhv/3v+bZvfisBZV6+yMK1sNkEM0uYRYovPmlSLGoOD+Gee9RinFSrMs3mfkRxvLR2LhTk3vV6cP3Ax3M1q7WAIHEwG20SZWNikOkEkpSTKxMmoclBx6dUEmm4Utng6EiWhI0NMLOYzRXNQc9hNJLN3YVzCZWoz0GnyZkzQh/KN3uzmSwdedQ6zyrk8vi5QuhobOCZIbsdbwF6QZ6HvT35fc62olRavi7ZCZlWB4MlMM6XN62XMZ3X2mvt93H7X4A7gO8GXmVmMQfGDwE/Dfyh381Bv7FAca4ingNT25anem5LlFguZiIOcYShLLSzmaxArotdL0MWiBtULhyaG7rn+SXJSy2L7/KWW2zlFf5ZJsfOqRQ5cC6Xl1SG3Aku38LnUeZcYq5eF67oK68jt5E+PJRrXV9fbvvnM/mfff/7+Xef/zx/9o1vpJjTMQqFZbk0gGkyHY/xgkA2BVeuoJIEY32baekYs0lAe9aHQR/VbmNmCWowQNfqzKY25o1DShefgqtXUaUS3q1bsno3mzhJAnGCCoMlrzqntti2OCPE8fIeGQZxrUVUXUMZEq1XDkwocbSvWG9MORi6eEWTmh1w8YpNpXUfa5td/M6OSHu1Wqh6HWU7TKwWVLco9ndwSjWeu1YiThWepQkTxWAIY3OV2UFKq5Zy220K31fUNwq4KqLRnEJHo8sldKGKqzRnjwekGTiHh9yKNlhtRWjt0BnYhKEDaGqlFL9sEqQm3aGm0GhjdA8gSfCLRSbmKs2mzXAot7fvFrAqBdJxRtPPyPpDsdFNUlQcMIsK1GqK2UyRapjYZfyD66hf/JiMv60t0abOlVBy2bNCAbN7hFssYxQ9su6IWX2D1HQ5PNRsPX8RNw+d5RpUhYKshDduLCkazSZprUmGwo5ism6PePMk7u41mE1lLI/HywK+uaGLXptTkKZTVK+H7vcX4VrDssjqK0yMImlm8PxzUK5oBkNFsQSVySGGYxBW2qiVLRz3RRZSHNOpfM8b3yhFraaFajTIuojLnqs4dkwv0rtKabpdibxF0TJV7jiwWhxhRjGGZWDqWBhLwYSkWCGLMqzODVrhjNFuwsGJO1ktZFiVAsZwiH3zKlcPC3zquRX6A7Fr/rf/Vh6ruYs2L78sXfvWt8qe75lnBFhdviy3LVc6UEpYKdvb8jjs70uUOJfJKpXk3Oe3mr09eOQRuVXnzsH6momOU3b3TNbXFbU6XL6ouftcQKAKdLpi4Xv+PNQSxVGnx8RuEEwzrl0zaDQUg4FJpQLTzKNWmtGfrRH3TVrNjHR1nesHDsdXuhjdDlGthTPqsdIyyIoeCo0uWEymGe94S8wkMDlYv4f6lSdxPYWaTrCuX6K6ugqWDxMDqlUCXGwirEGPWbGJR0J60GVobbC1KnOUKhTIClVmicXugclxa4Jha9yShSJhknoUDJPu45d55hmFCv4of+Ptr+ebz1/gxFYL+h3SRgGlLJJEgHEUiSSfo2IeuFfzqd+yeMtbWLjYjUbCLc7l/PKyje3t5dQL0v+gSFJFb+xQb0CMiaenGGTsDwtkWmHqBM+K6fc1w6FifV2GMwiQNQwIAgdDZQvTyVoNhlObjbUC7XLKaGaTu7Hv78sjkKshVqsyVuJ4KZk+Gs2dF7cMuhN/AXwnEy3KObGM08FAsbamUUq+czaTotR8GWq3ZYNpWWBZisPDZWnO6ur/dcv2a+219vVoWusQ+B6l1F8Fql/l9SPg/UqpBxBu8dfUvrFAMUj+6OBAFuu8or5QQE0mqESTKQ9LzbmT47Es4obxqkI63WwKiaTVktkjL9XNvTxfCYbz6p3mnKudZUtg6zgCHPICvTxv2+ksi4+UkmPnW+9XuuPlK3i+mtdqS2e9M2eWuVkQgCNlwWyeOcMPv+51co2z2RJQ55+f6x2fev3rqT38ML/54Q/z8NYWJAnO4IjSzV32Nx+g2N6mMAdPURxz0yyy98JNpv09zC89Ar/1W1KFvLGByjL07i4Xd3f57y5cQOWimCCg6+RJuc5yeZnKtyWdHnkV/JsvYVWLJI6PlcWobpdypUJ5zUUhkS8zCVG+Sblq0B8o6utFvGoNanWy2YzAruCbMcWDK4S1NXShhBuOOV0ckxkhj1+s4VR9KlbKza5PvW4xCi2KRTBVip9NsVVK5nhkxSoJHm73gKC6StHXmAh5r5H06PdKtCojXLfKwUHOvzexPCVpWg29qETDm2KN+0TawlERR0OR9WrXQuKZ5mjfYb0RonshU7uEa0Soso82C5iZIss0OTPHZYp68ouo48fl/k+n8NJLpJjQWsUcDknP3U6sRUfJ07GA0iTBM0JmmJxQO5gXzqCuXxdUNrcB5vBwqd20tSX3ybIwCy5og8w0iNwyzqUX0GEglA/fX4Zh01SiyXkRaj6W220pwOz2xYnDMAlwKR7dRNWrPLwxRFcqBNsVotREf+kifOkLuCsrqNtug/vvl/N7/vlllmMeag1O3s6vf67EyVOK/X1JObfbmlNrMwLtopWJt6G5ckUt2CJKyeGcrTK1QoBhG1iTHubhEcpSmAdHhNU2e90C222Y7E8pX3ySoFzC398lKdWZGTVmrDIaK6ZTzdmziiCQ07p+XaS+trflcc8ja7/6q1LANad88+STApTPnVtyjl8JWBxHHtlcU3Y8lseo24XnnluWIvR6BnHs4bpw7Rm5dZWKweeeKmKasme+/34BTp9+vs1992m20Ww2A67vOti2RbUC3Z4UjM0yn6IbE3sGRjTD9mB7LYTARA0HGCkElQbeUYe4sYbp2VgqpdEyCI66rKsh/XGZJ4PbeGjtCCwHdbBPfLKOl83QjkuGwiNCHx6gqlVUmpLEKU7RYjU9IjBWsYkxsgRDpRSIuX7FJ459bEsxGis2Vh10FPLyYZn3PnCND73viN1r7+K/f+c69somwQtXOVi7h3IiJjl5vKRa1Xz6NxXvfJfDeJzwhvtDnr/sLjYnlcpyGq/VZBOztrakGeQlJrn6ptaQaJNpMJf7tsRuuV7XjCcGmWHTD2zW1uQRy53wptNl/ERr2N03FhQFwxBgPpsVmY6Wy0zOyltbk/OqVmUs55bUKysyjgxDxkGesMxLDWpeQG9oMptaOI6iVNK4VkaUGovoch79zRNGpaIWqT5D9L8PDjSOo5YqPq+119rv86a1vqGU2lVKPYwoTwDcBL6otY611l8Evvi1Hu8bDxTn+dLRaAk05/6p1nQK03DJ1S0URCt1zqM2tCbnVGv0YtutLGtZRZ/PTjn5Ko/EZtmS+5unkO25LnEuV5Xnonx/SZ+YR7EXs1GxKD9ns6U5iGlKmCJXqMiJiHmULucf59o5+fnknOhGQ4B4DvABKhV81+XoySf5Bz/2Y0w3Non8CpPKBmUGHN4YMYlcVvwB1ayPVS7T3NykUVxFcZbWB+7h1IMPY37mN8Xu+YEHSIpFguefl0h7rp6xsSH3IVfDD8NlJN/zsOtlLEORra2jgoDOrEK9pEmbW9g3LpNZHqyvUq1mTGMHg4RjqwHVqs/l6x6tcpvNlRSzXscnITvooLQimwWMMptpUKTeNChi8e7yEUmq6RwYWN5pmk0BUmkKly4qqtUC9ckNsnKNtNrAvHUTPR7gHx6gTQvaLXStRqls4sSaZLdL6JSYTg2yTC7puefg9GnNanlGd2wzjW0qrottG8RFj3ZBon0v7LpUnCl3rdzEMsukkwAqZdJajUybaK1xbMULL2huPz7FHXVExu/UKenHZnMRJjKjmMRyoFrDjAOU7YKRwIsvQaWCtiyu73usDV7AtOac6Hz1/8Qnls/JmTPCH8759oDyfaw5GdOtN1G7O6hyWTaSL7yA2t6Wz3a7Mv5XV0m2TpJEKd7eVTg4QBdKjKub9Mcma60Yz0hQKy00MGltYWiYRSajEWS3PUj92BrKstC3bokpRLu91L0yDEGX992HN+7wLW8xyQyTm0c+O7sG/a7m+IkCrVJAmGn0vEBtMpFIoQxLzc0dMBoxq+4AVa9gBgX0rVuY1SoFX9Pc9IhmIcVkhDEaUxnvwEMPMRpmXB/4BHZGtWJwdCSR6Fy8YzoPoD/zjOwBtZauvvPOZQo+V64DuZy81vfcuWU0cDpdMrVKJQE5k4mwXTY25Nh5DWSjIdNILi2XM8KOjliA9Y99DO66K6NSVvT6iuHUYm8fisOY09sJxxopNw59qg0TR2mywx6qJlKBThqgphP05ibWrVtEa8foF+pU3QSlUqbaI8Og0Mgwdvu0glu0jvskbonY38Drd1D7N9HlIioISFhD+SY021w+KNBeUWSGiZpEsgHWMYMh7B76rLdj6lV4+5sCnv7ylzl9+x380jOP8/gXDnnLQ3fysz/xj3jzqVN813ffxanzP0whvkmoPHbdU+wfGgQ3xMjF8+D6dc3GuuZ7vnXK1aMCfsHi0iUL24Z2W4oULUumadeVfs2yZc11nvDLaRV5DXevJ/cmSWClpWm1DFBLbu/e3lINQik5di46lDPiNjcX5R6vKnLLDWeuXVs+srkK43S6VE8cj+WYeQQ3X6IqFXmt14OC59KuR6w1p0wyn3Qa4RRMgknC4dBhZ0fTbC6ToEopXB2C6eAQEVkeGxvLJeu19lr7/d7mBh7/L+Av8Z9Hi4dKqX8E/E9a6/RrPeY3HihWSmaaKFpGcHPAmTvc5YQu20awr0aPJxAFBPU1vGiM4dlgmMvtuNbCSzZM4VSa5lLQsVJZFvrls10eZc6r86vVZZgqV1UfjUSiajYTbc/cxS7Llsc0DHn/ZCKhopxKkVM7THOZO1NKQkNra5K/za2J8mvtdJZUjekU7fscDIe84cRZCrd9L7Oshg5N6ttwoiqRI6Uz3MkRtawLrktQbhFcc/jFT7XpH/1h/sS338exwycwGnVUllG87Tb+U6eDORzy4L33UpprOS9kvzY3BcDPI9e60xGzhloLw9A0swkhRTAswuPnMOIIM4qwXRPfy3CIQINpak6fNhgOfcJ4TMGFAJ8r4UksC2ouqGGfetancPWA8M770ZX/H3v/HS3Zdd93op+9Tz6Vq27d2Dmg0Y1GZCMSiQCjKJKiAmlZwbItLfs9+dkeymOP47KlGen5aTRLfqOxNWNpbDlIlpVoSRQpEQRIMCARQANoNNCNzt23b6ycTt7vj13nFkRRFJyeSRl7rUajq06dOnXCb//27/cN82TtNnODS8hmALhazL6quGvhElKUUJ6PdeU89q4VmKtAv4VYWIAsQ1WqCARSZVimINq9F28osUzFLYfGbA09DMNgPJYMk4hqJQPXQsU+wjSJhglBmnFwKeTAkmQiPEbpboxogidDRJZhyQRUitreQiQJJ+oCEVmktovR7WqpwHpd38P1OrguQgisSR8adTh1CqNe11nY0aM6cd21G09JkvphuHwKdfkyotfTxLx2W5ed7rtPPyv796N8X9+L1SpCZwywtYXstODYMe0nIA3Erl2oVksvgvJ7djjE2l7Dqlb1vdjtIq9epgJ41QaDYBe2b+AxJsVAoLAJGScOtlBQKBLYyxijDpbnaxJltzuDCR04MOvKGAZhpEgdh2JJsN+KqbgRl9Zc1tddwhBATAlVM2XCa1cVJ05AKoqc3nY4Pr6AeuJx/dzMzYFpUumuoy5egsM34Zx7lWDuIP3rCVvWEhPPoOoGvOt+iTQctrZ00rW6qhOgXk8/rp//PDz99Czp6fVmFIDlZb29UmqHuHT+vNjxANrY0I9r7iSWj337dIJk27ow/+yzMwypbc+c15aW9Pd8/vP6/9/3PmjWMipWiFM36I4d5hehoTZodSokzQLrW9AojnB9MOcczM423eIuoshmsWYiwoB4fhlpWxixIsnAVCmoVMeprS1oNlG9HgiBGU0wFyuE+27AjkdQryPabZxRm8hoMBIlhMjw1ITQ8pC1KnLYIwhigqzIgZUxYabJggk2F65t8cLp3+aXf+VfceH8G7h/7a/zV//W32HJFKgwZjzvQb/MyGoyZyX4RZtuV4e/Tkdx280Jjm8APguGYGNDUCpm1OsZ2y1twb2xMTMudV19vrtdfY4vXdLGHTkxstPRtZd2W+N6owh63ZSCk9IeOZTLejuN3db3Ro7XLRb1I5x7NOVu6HnTMF//7d6tQ2eS6Md9KuYD5NALncC+eSoYj6eY4oF+ZKaCLSSJnBJUHYQE10kZjC3cosFSAdJRwNqWi2kqSmWBEIpB4tHtKhbnDGyhpf0c55snK46iCPtrpUnfHl93/Mc42v3/S0n3v+WYJsS/BnyUr68wUQH+PnCHEOIjb1Vf+JsvKQa9XL56VRO5trd3yF2iXp/JsA2H0yQ5I6svYno2RsmjkE1AZISpQSZt3GxK863XIU21HrFnAAqRJ8Hdro46OT44773lf+dku9ztyzBgMtFJ9ng8w93a9qxv2unsQAx27IcsS7+fC0hOJrO+Wb68z/Wncv2gRAvKY9v6/4dDHXmzDHH1KruKRV4szPNGy+TiRYOHH1YcPAhCSK5dU3zqU5LrF0r8L39liL9QJTUcbjoccHhXwleecznZ2UdWU+yfH2MYBg9+7GPgeSSGwfNPPMFwCus4vrTEwoED+pjysmqvhyiXyfwyQmjcbIoJGCildTm9pI9QGVnmoaSDmro8RYFCWqCU4NULRUwTGnPQbsGFJ6/wgXf2qVUU5vkzZM0FokSCUFCsEq7sJ5xIilKx0pgQY5Mmimy7Q1qfx1hcwRqNEIopvMYgrTcYpx4qiMgyRdlPsUVEo6AYLniEZoHmguDqVT3xza14pGFA5noow8AWKUUrxc4MslQxwtNNBJWy2XdZqSkyQ5IpQShcXARhdQHDsxFpTG9kkRR9FqIzOqHN75XlZT3bZplOXvOORT47VquITpsl2WW7uJfU9TH6/Zk2U04izXG7V67oDO7QITAMbU0+rQDv2Iy3WkReFRmOsWRHY4hzSbd8xs51vptNvagAzDhFdUYkdpnYtDCEwiAFNeW/GpKKOwHfI6kWieME6+hRxLVrmpCpJTwQkwkUi6haHSOIkVlIP3IxREaSSWSWsLFhTBMBxaVLWq5NryMVD74zoeaFGBJ2+wG0Jdn7P0BWbxLIAiIN8WSPyS33YA9aXNl9P1sdk9S0OdAMKGHQ69iQKlZW9Lo355G++926mpvr4Z48qYlxvj9DqKyvw3d9F9x6q6JWiLlwzaJU0omS5+mELJdqy+W/ps0u6nX93vnzGls8Huv/n5/XLfduV39mfX0mQ10s6luj0zG5cDVFqITTFxT79wsGlQXiCIx4ws032TgoAuHR7gsEi8xnPZJKiWiSIcOMsLkLY9SnIEIyWSRTBm5/CoOo1+C111DSJCrUcLev78TjWCVYF8+DyoiKNZAGk27EYiFEnLuAOHwMZMbErREmBvVqhovS8KwLF3EaFT503x389b/1t9hVKRIvr1Crf5Qr68uUjqRERYfzZxW3HaogYoPexNlpyg2HStM0BgkFJyUxHFxXh8p2S+AZCTcdk2xtiyk/W+0kBZapJfE2NqZY374Ov3l1OJdv6/f1LT+UFn3HwjT14mUqX71jztJs6v3mip25CuJwOKup5NSLKWcWx5m5sOefBW0ismuXmqpV6Pttzx5wHEGno5ibm9FIdMNSYIsEgdJ68qbHqCcxLCjbY0auy/xcQtWPCHCxRUyCwfy8iSTTkBaRgPhvnxQPh0O+//u/nyeeeIJ2u42RQxTfHm+Ptz5+APhOIAb+P8C/AS6g0YqHgD8H/HXgg8CfB/7vt7LTbx7zjjePnJFiGNqtzTBhSvoS056XCkMNi5jCLFTe68yrsjn9NgzBsvTnul1St6A1acn0TJczE95cgc71hnMy3bTSTKGgLU+VQrTbJG6BtWSO+UqEM+nNAGBhqGfPLNvRJ2ZuTu87N6BXaoabHg5ns1+O281pzDlMI9fXzY1D8uS8XCarVvmtzz3B/XfeT9WzwHa4rhZ5+XWHT3xC/6zf/m3FfbeNEf0+8voq0tZMj0SYZIMRZhoglpZgfV23u/PK9MoKqtfj1LPPsjEaIapV9tTrHN6zRx9PbmltmloCbRwRVBcJU5NgnDFvtklsD/fcadS+vfSNOsIykVLrsWq1gankVZSxuz6gEVxFhhFOMppWpW2C6iKyUkREAWJ7i8vREsnUStj3oVbOSCYhdncTyzUZ+POMx7BQDXWjQUrGqctWy8D3FcE4o9FQ+HLCRtfBsK0dFb7Ll6FaFdyw0KHXTumOHQ7PdRhVl+l1MubZpG3M41dMPCshDWNSw0Eagq1tSRzBfDXEdgWjyCYIBGkUU3ATGv1LyNdOIw4e1NfbtuH553USO5XtY35e38P5tW61oNulnxUIFvbSjK8jnn5adxPSdEZl378fTpwguuE4qy2L3a1XMA7vR/kFRBKTDUZIy9D3bxgSY2KGI8gykloTVSxipBFpECJ8LaW1o+ttGGR+kckYerFLsWphSm3bHCqH9S1JFJvMNTJ8J0UBTjLEWF9DKEVkegjbwlSxrkzXamRxTDKOyBwPsdAkaAckXpGClxLGBheue4QhrK6KHZMGS8RUq7CwqNizEOnEOTMIYoPxSFGbM5AG+MmArNNhUt9FlJqcPg3HD41xLcWZKx7j0GA8FjuGktvbGks8GunK4Msv61PreRrTe/fdWh7ud35HJzsf/KA+/XfcGtNuCza2TVot/XnP0xVJz9Nre9vWIUYI/dl6HX7pl7Su8enTOtQ1m/CDP6gv49NPA2hjiVxf9tAhfU8WCmAaGd2tGMsWFCoWli24eFGxb6/C8xU+Y+JY8MLrPkcOZdSTLSgWyIZjImFzfrsKccTeXSm2KzCjgMDwcdMRtNqM5/dhTAZ4Z19Gzc1DrYoY9BHb21Aski0uMfHqeMMtRJowcObohh5z1QAT2GwbFN2Urb7N7sZYwzfOvM5Fx+Fv/eRP8o/+4U+ytOcG0szglZc1fjtJYWkuRiQRqy0f0xY7FIpOW7FS13gV0xKE0ptWXAXdrq6eraxovHFuzkGW4bmQCYkQgiRRDAaajNbrzYxOcxm9XHJvPM7JfDuCQLRaeipot/V1zOslhYJ+PCsV/dpgMEPABcEfprDkNZNcwEiHcjWlpCgMlVCqWTvcWctIUJhIYpSCrbZFHAuWFjKEyFDCAMSOLnOxCCYho7Gg6CQkpo1LgEKQYGMTIlEI10NY/+1rYWma8iu/8it83/d939Rd75tvfLOZd+zb9+2//cEP/s5b2v6f/lMTpf7Um3d8Abgf+BGl1NdNeIUQ/0/g54AvK6UeeEv7/aZMimGnV5A15ggyCzscaIeynLg2lVsT0yW5SlNUu62T36mLXSYMRKmA8H2tWJFbIk11V3e8Ly1rNjNGkZ6ZcoBhEOyYeCjQ0bDV0hUvQNWmLXApZljgJNFlhSTRievcHHQ6qDSd1fjzCJxLceX43RxMlu/ryhUd5XUv+Q87+43HO/jUdDLh8y+e5O6/8KN4UR9RLHDqcpGXX5E8/jg89LDgIw+0qfSuIq5d1b+32dRZqeOg9u9HbW0jbjisjSFWtSrETmkrivT5yjIuX77MuevXwTSpC8Gtd9wxVV1IEGlKimDTXGGQFWhUEyrD60wKc2Suz+qqnsjm5vTEdO3aTJo6SxUrtSEP7r9KPLeE97u/jnHHrVCroTyPyWuX6TUP0sw2CUtzJLV5JqFgMJL4niIJErauh9yy3GJY3c3GlkGrNfPGsKxcxU9RlGNCHHoDiTSEhnyLhG5X8NUXDDodwbFjGQvVkKVGTGx4ODJEIQhwQEgsQmK0WYhSoNCkyRxlE4aCtTV9mXYvRixbmxjbuiwpms1pNl/T91PuNnDmjIYBFAqorS2dMYEmm168qHu0tZq+7v3+TBssSXQ//ujRmfW5Uqh+n3D3Ycw0IA0TpO9i9lqISoXEL5FJA5MEEIyFh27QZmRITGKUktjhSH+HbRNkJte3XVbmAmLT21F/TFLJ1tZMcrlUUpTG69i2hkOpICCLEmQ40Rbs9TpJCsORoN/NMEo+1XKKMLRWcKcDG9uSxSWDLNO42ijS2q37d0UEkSSV2i0viiDuDLhBvIFZKxGWGyTCIbNdXCastlyW6wET5XH6NcHqqqBc0ffh/v0zXuHp07Nq8Oamvj9zaP0LL+jT/tGP6mSpXocnntDbfOd36st2/vwMMrFvn748b7yhz0+ppBO3lRX9+sWL+hJdvDhTSGg2dbv9hhsUr53KOFE5Q7Z7H+2xR7EEUaTVOdJUKxHUKwphSmxbMBklFJ0E30mZKFcnQ2GC29vELjkYwYi4Nk+UGrSHNuXRKm7NQ3k+tgqh3SVyS7jnT2u8+dISgfRYHxSpORMqboh0p64QSpEuLJHFKWYSEBcqhMLDIsBWMVFmsNX36HQEly6lvP9EG/PiWX7203/AIx/6GMeP7WOt5VKoGBSvnCbbs4/W2Gd8cY1dtRHRwh4uX5W4RZNhX7FvX4YnJrSHLvVqSipMWh0Dz9OLpVYL5usxvZGJZ0Zaize2aFYi1to2aSpyA042N3UEXl/X5z/Xss6r+lPHdUAn0QsLEMdi57Frt3e8bLBtnQi77k5dYAfpl9c72m19XXODjUplFuINUqp+SC/yaFQiUmGSZQIrCTBNRZRZbPdMLJFieQa2PXOO1Mn2bG4WAlxGRNhkWIDCJCJDYk8NSQSKQtF4W4rtLY63k+Jv7iGE6AAWUFZKZX/MNibQA2KlVPUt7febNilO09mMAkwaK6SOhycmqEQ7NjkyQQwHqCkZKVQ2pkhJkAgUJhmZEljhSFenCoVZySYvT+ZRBnQky3WN88iRszPCEOU4jI0iZhpiDdrIXKUir5ROj1tsbc1k1KZVYQU73y+k1JPL+fOzz02BaiJPwvNjabd1BpmLTuZ2TjnhTqmpMvyAL12+jNlsco/rQrFIWp9DlMsw3yT1i6jhGLO9gcw1hbJMH1+zyf/xqU9x/wd/mEPzLgU13f+lS3qb+fmZDvM0+c2xzq3nnuNkEKCGI1zL5M5bbsEolJCGIKgukkqHbk8nGVpsXnDx4ox9n7uD+b7OBa9fh6ofoQyTw0cEu51NGqWQtmxQePYLtJdvQuzaxetn4OILHT5+/3VGe4/i+BbdrmLYTzm4NGa1UyBVBqapqFRmDYFcrc+x9H1tigQhMmLlAAqpEs5eMHnmGcnttys2z/d5VD2Gd+M+xnuPMEx8il5MJiyE0NfVIWCzZSMsk3JRgYAwEjvVok4HKsWYqhuRdvq4516FQR9x9936x49GM32mINAHWq8Tmh7mtcvISklf+81NRA5YvXRJf2ZxUV+fZlOfzJxpMzc308LOuyjtts7OgkC/t7CAKhR2ZnDlOATCRqCwVIogI8EgxkVTWBUuE0aZfob0xKyfk7widvbsFBFUmaCkxOh1sEfdme53TvKzHSalOVY7Ba0vXA8YZT5nz4qdalmtNrPnzQU2lJrJb1nW7OfYZoaVhagkIdgacPL6HPsPGMzPK3wRMEx9tlpyxxAkN/vT2E2tixyG2gXumWf097/wgibXrazo5PnRR7WNs2lqfeIcK3zLLVqNIor0fnMVyFy9YjzW9/XNN+twkxOwYCa1nqNp5uagXss4sDRhfVPg2RmXN1zcgonnQdkJuLzusLGhP3fkiGDPHqWvTdAlMjW0amNbsstcR9QqJNLFjQZgSFQQMHFqZLbL1pZiuR6QmA5WEmBNBohigSjIEJMxqe2S9PXisVKVWOM+aioJmc4tsDX2Wajoan0gHBJM3PZ14mIVLJN2z+bSJcXa9VWuXnyNbnuVj3/fD7C026RkTghwcII+b5yXNPZ41EsJnZ7B575oceL2FMM2cCxF1Y+4tu1g29ouWVddFa++qq9TpSIwiCl4CmFbtFsK2xFIqUmUJjEiSxlEDpcuiR102/Y2O1Jp5bL+/9de0+F3MkoZB3KqJCJ29IObTX0/+r6+D3NT015PLwhzPnc+8vfm5mYCQoYxI+2VnJAoFijTmiZh0G2lREGM7VlMQj0/5RbVpiloNrWiTRgqwlBMCXwKnxETfEDgioAQe2eBq5DYRobj/7evEn+rjLeT4m/uIYQYAyOlVPNP2G4bKCilvLey32/eJyR3VQPwfZx0BChUJpDBGMvzAa2ZiZSITOGpCUiJ+aaE1uj1EMUiyrZ1icYwtPFHXqnNiWxxrJOJwUBjmU1Tz5phqKugrouwNYs3SdTss8XizLh+eizKcXY0jkW1CkFAGqWkbgFbJZrcJIQW07dtlDAQZDMzjxw3nGs7DQb6+3ILolyrGGbSWUHA/Q89xCtXrvDk5iYP7tuHYduQJWSOQxAIxoFL03b1cQsxI/1duMD3P/ggP/4LP8HuWo2//OijuJ6nZwDP+8MJeJLoa+J54PnU776bd5kmE1kgfe1lnjl9msAvAYrbbrHwGstUy4qCm3F9w+D11xW9HgyHgkOH9MR+4ID2r1heVtxzD8zN2Vy/rnVqryfzdLoB16/EiOp7uH1PSDcxcOyEj77jMgUVUarF2vajbLFcmSClYL6paHV1MhrGJpNJj05nhGUJwlBgB338dExaqRMbLlksCBI9MV28KDl3TrIwl3H33is450bQ7eIt9ghtG5EmCMtAoXFwIT6YMBoqRJowV4kxHIuCrcgQOAsWYWSz0RUsNUoo/2bEr/+6zroWFzVew/dnWtrTRNZyBVm9QWj7yFINazjUcgW9Htx2m77uu3Zptla7rcl5QaBVWUATSicTvSLJ+/b5fTu1YAuFg9HfwJRDxNISLjGxMgkDhd9Zw7AcRpXdbG7p3yplkUYDWi1BHOtENU1nyguNhr6tEuFoUmW5TFiuIIUiVRKDjI2ex9o6HK5BqSR4/XW4fM3fucWm3jQ7CCYpZ65dhYI+/HytnrP2I0MipcdgAEFWYmlFUbADBtsZsStojzXH9sknZ/vPMjh1StFpw4GDsGeP4uRJsZPUvuc9+lRvb2uy2wsvzOSgc3m222/Xa/f5eX0eNEpLcdNN+jtyVNSNNwpeeWWmjHD4sH6Mc4WJvXv1raC1ZSVbI59PfgY+8AGBN9U6tixFuTBiqRiyf1+RalVimOBMuqSTkNAp4QRdOmKOIFI8397NSjph2d2GgkcWJaSFCjLNcMWYvfMwVh4giMwiEGD3eqjKPCqOcfrbuL5H0c4giXRHLlNknS6BWcR0DUYpFK1pPFJwJVpiLktZvWKwy9/m8JLFv/3Ff8ADdz/Ae7/9B+l0TYxNSBs+War4/DMVdu2Gy9cEzj5FtRDz4Q8aqJdfprt8nNHERhgu5fJMTi0IFLVyytKS5AtfEHz4w2BZlkbSoRekxSkCTasNWozH5o4L3uamfu/AgVlnYDzW2xqG/p6DezN63ZS1bSs3TJ1qAc9Ic6Arzp2O3tdgMGv45RjhXLs4y2aSabm4UhgqxLyNbSt6XS2Tlqaw1TZQyuDAAjSsFCOZEKQ229sWrqt44w3F8RsTfDOjYsWEyieMJUFW3FmY6cshSTEwiTV8wnlLOcHb4+3xrTKuAQeFELcqpV76ehsIIU4AdeCNr/f+1/3MN22lGDRJ6upVEsfHqOg+V4yFTGIsmemZYjwmMSxCr4rbWcdo1LQZQj5SzazOhEAlGcZkhAjDmSdm/vsmEx2ppkBClffVJhNEnpBKORNLDUN29H+kBM9jYhUQQmBEE9RoghUONcTCcVC1mpYFy5vsSkGq9ZfiVFeuzWEHGYYzXaYc3iHlLMJalq4Upm9SGMlb6GHIT/36r/OBRx/ltkoFtba2Q7DKDh5i4M9TNCPM9pau+CgF589rd7OFBRLgH/3czyEMg9tvv533Pfoo/lRXKgPiTgd7eRk1HCFMg6hYx7xyAVmvap3jPKORktQw+OrJk3QHI5JEcsvuZQp7jvHaGxZCwKc+Bbt3ix2ZaC1xpbj33hxmLRh1QzbW4YbjNocOiR0p58OHQaYRk1cv0DxUwSx5qPUNRvUVXr/o4XgS39d40Vo5IY4VTzz+SU7cdpg4E0SZBVmCTUCCZBQaREGKYSkcYq5P8XsvvqiYjFP29D/NX/3h95HdeJzE8ckwcBnrexGFFBnDkUEYKOZqiSZzqpgkymgPXFJMSmXB1pbGEC7UY0qf+TWMQW/mz1qt6qwo1/eaTHYqxkpnSohLl2YYYyH0tW029b1w4YIuaTYauhLbbOrtQEOHqtUZ42d7W99X5TJZUWeAcQqZYWGIlF7gMR5m7KpPQAgGic92W9BozGDP29tw/bqGLpjmm+WrFdUq7GoG1Eu6k0O/T+b5ZLZHPI7o2EtcvCyoVHRl9o9ybGZEqa0tQb2uk9L8u/NHo9PJbXLVFAUlpsgq/eF6Hc6eFbRaukr7+uv69F25Ag8/rBPq//P/VPzU3x/THrvs2Sf59Kc1lObUKSiXFaPRjNN7883w0EP6mDY3Z1QDy4LDh/UxhwFcX4Pbb8tIJgnrLRvXg35f7zcMZxjWK1f0eYxj/drSkr5EeXu909FEv8OH4cQJUGlKrZRQL8dYJljuVD5y2nqJuiM4cIBu4JBNIppOH1ktQ6YQ/R7ZeExcqmPGE0Su256lZKfPMLnhNrzudYw0IsYGx8Z0Db2oyjJUrYYYDMjcAmO3ShAaDEdQrytsEWMSY2YJbG6RNZt0kjLpcMw//Pt/mXfccgt//i/9ZRSCbuBx9bpBmsKu5ZSKFxJj0WsrpK1LqJ4vOXNOcsvhEdtBmf71AaXlyg4UoV4M2WqbxJOEr75k8c77JdWapp8nqYbBaJjCjKBZKOgQWijoMN/v64VNHvrL5ZmH0mCgvydvih08qGFeYTiTpc/hEGE40z3O1SpAxzClZt0EIWZyfr3eNPxHMcu7DDIlOH9eUKmw84xdvw4rKxpXLkWKyDLaPZNKWaHCCCFhMDIYBSZLSxnjQJObc07314NI5Iu9t8dbG29Xir+5x1Ru7f8FnAY+qpR642vevwf4V8BB4J8opT7xVvb7zVspBo2jHI+RhTLZOMBIE+xqBSyJwkBMBSMN28bvaIwrWUaArdUPyEBa2CJGqGmrN49e47GOhnGs/10o6Ag4rRinSUZoenh2pjG2OYOi39ezf6Wiv+9NApZePIJeTwe85gIwjcTFIqHhYRGRYCPIsIcdIiwyv4gT9TS8I7dRzksOGpypX89lrJTSyVAeXdNUR+ZyGfp97p6bo5DLvwHpoRuQ21vIC+cpN7owN4cqFIgtD9HtYRYKOsovL7P14ot85IEHOHHPPWSmyeeffRYzy3jgoYfYPHeOn/rX/5oPfehD3Pnwe/AMA7O1AZ6H6Pd1hVwY2ojCsRlRZnHP/dxRDzCyiOfPXuPC41/AMyKubR7g0KEbd4rs16/rhOCe20MO7o75vc/7jEODuark2x4esDYyGQwMkgS++tUcNWAy8OYpDMaUwjZKKc69IagtSKpVkd8KJIlB0YfqXIO9B49hq5AkFbSGNrVyRmqYOMSkUYaUGVc2fWpLxk7F5e/9PTh+/FZ+5skn+JEbCoy7hq7sxD6gqJTBEIqyM4JkTEYJl5BJbNIdOxQqOQ5QYVk6YSwyQt75DtT58xpCE0V6oXb58kyCICfaTSaI9XVdSqxWZ3Cay5dn+JNc2WRKTmUSEBTncBCI06+i9u7V90mjoavHpolKEq3GogwyYeDIEKFCEgwcG0TJoBsVKDsTpMgAAxQIlQGKRl1rMecQ/DjWi5Ddc2MEimJBkiIwMkWIrYXbrCp9IZgMdPKwvT2T7c6FZUDDNBrVlFbP5IYbZo7vuXXyVECDYlFXDXN76NFIkSRiR5LL9+DGGxVXr+q95oqHBw/qU3vhgq7gnr5UYPdurQbxH/4DPPII3HuvDgVKweE9wdSoQVBvCGzLpFyWVCo6kbl6VXHlsmJ+AS6dCXnktjaBmGd5KSbKTILIoN3Wx7+1pYv8U94uv/RLcOyYxhJfuaJ/fxzryrthaPJdjsIqlyWWkPQnNvPlcIZBKhZhY4NV/waszYSa22FSrJOaReJU4hgZSkpiv0pqukjfgdGI0K7iyQDjyGH87nVEtQxZirW+DqlJ4C1j1YuEysbdvoYo+hjXLlOs9bEbS8SRQ7TVxS1lpMJCigQ5JdFWRJ833nieR+67D8u0GE0MtocOy7UJ+/f5hKOEjQ3JWjfklsMdCo6gm5YpeROsq5e4vdFgY7ibhWbIvAuJEZCmLgVfL95qtYygs8n3LF1ADuaIKvv18+bBwYOSa9fEThJqGPpe29zU1z8I9HleWdGhdn5+1vHodPJq9Myb6fLlmThQqTSDSuS62Z6nt+909KWoVPRjmpPryuXZOhTUTsJanzPwzIhIOezdqy9nzp9uNmHQU1RrCluFhMKjUhWkqWS9ZVOtQrMe0lQxAT6mqXeaLzCn9I+d5P1tbeK3x3/JIYRwgSeBf6CU+swfs80J4H9HWzGfAf6aUuorQog96ET2zcMFHldKvVcIMfya92zgklLqhq95/R8D3wscBV4TQnwVOD/d10HgZvRaeQutTvGWxjd3UiwE7NuH0WqRzi8ylh4OE/LCkpISEceaBFcu64qnbeMSkWaQjALiQo1EmrhqgtmfghXDEDXS5CGRJxXDocY7TlUhjHCEb0pdJXbdWbSpVnX0/FpXvDdRlkUUadc9BNRqWkQd3WK0ibRUWKGArRQiGZENhgSVeRwLRLetvyOvRH/tkj+XpCuXZ9G7XNYRWUoevPtufvexx6jW6zTLZVS7Q7prL1a9plufbgHDEFgyIDPFzIu21+P1117jXY88AraNFIJH7r2X9sYGn/vc50hGI/7XH/1RhO/zG//mX/Cxj38c0ajp7x+qqevgmLRcJcWk2LqGUV8hlAXcdofbD+7m8JGbUcBnP3uVJ554nMEASqUqjcbtBIEkGkUMrvYoFDyMqMd+q40zTKhUy0xCxTPP6MliYQEGA4FTdbjcNtl/1KPbl5QsuVMI9TyNJfachCxI6PcFo9BmY2hTrSrq1YgEC4cQg5TQ9FjflGQIymXF+rrG6v3Ij0C369Fo3MQv/ZtP86EPvQvT9JmMUjwrxiUjwiEwS1ApYpIQIMASFMsSOcXegmJPc0IaZahQy61FNxzDKpURg74+j/mflZXZ7BrH+tqururZ2DT16zffrO9J358xhKbuc0JKnEtnUNUa4q67ZqWpc+fAslCFAkpIGE8QWYekuoAKwRl1UJUGRhbQbrlcviK44bBH0QpxbEGaCZhEmGQ4rkHJlVztWkwfJdptxa664oZGC2NikxZKxEGCSBKGlPAsWJmbEEQGZy65FIsaqrC9rROPlRXdejYNgdHeIg0bnNtwCEN2cOG50IaWA1fUqlAO19keuoRWVbepCyHLbpfRyCFIKjSbgtVVlZtjsrKiK7DFouDmm8E0FS+8oB+nlRWdJO3eDQfmhxi9FpQreEWTzZ6trcJlwmhkT+XLFXt2ZTT9CfNmi2P7G5Q6A5JowmTTpdd1CWJFEAguXNBt9l27dALueZq4d+GChmDs3j3z9zl1Sl/O119XO2YTWu7Z5PixmDQPP+OJ7gR0u+w1ThFV5xB+lYqhZSvNLCXzPUS5gpWlyMGA0KhilBsEoUFm+RiEbFFm3kzxZMTIKhEMMxpyHebn8YwU0kTrslertKICFg4NtjGTNjIp01YuXhZi1+cwVEaUmXzltau8+PzT/N1P/CSvnzM4UriCc+0KRqGEdeRWasUQK4gQvRHD5j6itSGJGiLml7F8h8XxFikNTJmRqAzDUBSMMREuvhlTqGeYY4nKMtJU438nY2hUIpaWHN54Q1/TRmOqQTxNZvPEdzLRj8W1a/qaJ8msNjIa6fVpva4rynnNIq+DvPGGDve5+sWBAxBFivEYPE/H7JyoBzN3Q9NULC9BkuoEtzdxc8TTjsDQcDiVenMERCFj6epjr8NwoJifl0QxTJRHluntcwSf/r068e60oVafSrm9LQX89vgvNIQQR4FfAO78BttUgN8F/r/Ag8AjwO8KIW5SSl0Bim/adj/wFeBvAyil3vxeFXgB+LGv/Q6l1HUhxLuB30AnwXe96ZjyxOky8F1KqfW3+vu+uZNi2BF9NICCGhNhEmBjkOAQI2o1UiWIpIsrQg1XAALDxXVTnI7WRBJv0hFWeYSYTPT+c/e5wYC0PoesVBCt1kzrJjfgGA71v3OtnbxKm4tJjkYaz5mzLfJKdKmkdXynygV+Nka0dN9UGQbC87AdgTCnJa5caeLNJMB85G3PXBlj6oqWq1KYpsl3fOxj/PYzz/CB48exBl3oe+D7xN0x2yHMF8eYRRdjoakV/aYJvppiTtXUAIEoor68zLvzyA4gBAeXlvjMJz/Je973PoxGgyRJMNfXwTSx2xuMaisoaeAGfdJCkajSwO1vEaUGV7plisU9vO99e9Fw6zYnT36ey5cVV65IQGDwOu+5aY1rqylfOjOHUbzC00/DrsWEkpXxxSdtXE/ijtusdxxefcNmoRrSm5gkhkscz8hZ29sC21Zsb0+4elVXioQQtAeOPtXSBaFQAlwtKrGjmtfraTkux4Ht7b2sru7ik598knfc7nP/sQOY22tEtQN4ckKERaIsJAkJDrYas7Xts1jThLPM0KUaZ9gC1yXcewgji8nqN2OeOzMDQF6+rGfbKNLlq9zsYntbz9xzc/q9alVr/3qezqoKBTh7VmPYAXH4MGI0hm6XoLZE4pWwl2OsURsxGBC7JaLKAoZIKDBBmCmJYWux/4HJ/AIsLqb0OxmX1l1279bkpnAo2eg7LDQ0KLfgpQxGBuWyoppsYaYZfW+RihdpPGOlgJQpjjNgrVXm5KpPMEzxy4JCceboNZUwnhpnCJRa3pno41hLm+3fr5Pm0UgnKqXC1Aym3uDMVy0uXBA8/DCslPt4IsEru3hZxPWWo4mcczFL5RGOBTfeWGF1dWqYMVbccWRCVXRZ9Auc3ypTKij8579EcPwdpH6Jy1uC+fKEp1/w8Ity6u2jSKMYghAzixgEJpN2H7e9SnjXg3Rbis0tQaWmk+1f/3VFs5lbPguef15XvC9d0o2AXLZ6eVkn7Z4HDz+YcfYNyYWL8NB9EZ4Z4zomiXRIM40aFeMI9hzA9CycVotk0GHL3k3NAyOcEPl1FCZubxXTd4iERPS6GG6Nwcii7BksFEc4G+so38cvO/TGMEwNnERgoiFjWbVOlBnYsWASCFK/iVWsA4oSIde2G4i2pGYP+e1/8U/4yle+wl//63+D4lyJzhXFhlmhaLuEh25BBAHG1XOIgpa/FGnCXOcN4lINNQgIrCWMWglXxOCaqFHAXNUiMnxkv4vRvo7YtYKyDpAVCmSmQ7kqGI8F565qUuLCgr5Pcrvkw4fzRFhNtYcFu3frx2xKAaHRYAemlXObcyGi3NDzzYYeV69Csaj3d+gQO4S8vLH3Zgn7JIEwEFhpQGq4jCcz0l25rEmqy8uKVitfDwsSw2QySFmsRkTKplozsZIJw6FP0c/oD2A0llQqagdz7Qg9f1Sret6bIgjfHv/9jfityN39x0BEhBBLwBeBnwB2fYNN34lWfPjJ6b9/XwjxFPDd6Orxm8c/B/7Z1I75a8fPAJ9XSn1d3IhS6mUhxDHgI8C7gRV0XrsKfB7490qp6Ot99o8b3/xJ8Zv7QUmCGU3AKxBnJiqcgGNhttuYpdIOoEsqhU9IloPA8uV/p6PxwqZJUJ3HqUQYTEGBU0UF4Ti63Vtfwk1GGhoAM9BkpzPzB81LDWmqvyPLUJcuzZb8U32fDBijCS0GKROjQNYsYhPpSQ2FMRgg4jdpJueY4ZzW/+YRx/q7i0V97Hn/zvP05/p9vu3IEX7v6af58D337Ihv2lLStLoYwoI0Q3U6uso0HcK2tSKBYaAsBzmVX9phF8UxJAknbr+d7V6PLz/zDJd6PV67eIn3v/M+HrrlFiaFBkpaTOb20u8rzEFIs5QRNRYRYxNnMpMv0o5gdd7xjkcYDuFf/kvN7r/xRkVjecTxxhpPXd/H8dtNvufOC7jJAHP3CqPE4uVLZa5fF1QWFfsWJxypb6JMi6E7h7QMSl7CIHJ55RVBlumE6tSpmWR0ucyOjJqrxpik+DUYBjYbbZswVJw/ry/3HXcIdu2CZtNge/tdnD9/hZdf+So/8gMPUDAVejkisVMt/1VrgGNK5ucz0sTEHnexTDTBs1RCZRmWJVHdACMco6pVrTxiWTopFkInw8COfl21qv9/MND63ULoH3Hxou4LT/ehdI+WLEqIGkskwsaWCRYRUSII7BqF+SJJajIJIY4MCpZBwRNEvkWaSkxTV7xMU2N0dy+nFK1YY+Fdk8l1RdIwKXgB7mid9dICtZqJ16zgJX24+ipkYJWLZI0mwdwypkiouoJdUnDunMX1NZifVxQK+tEajXKtVy2V1u3qW3ljQyct+aSeZfrxunYNrl1M2b0MN95qcPQoVKsaPoE0iYDIKDIM9DJ5ZT5m3ulpPfFJzEG2WPIjRlaFsWGxZ/MF7LOnOLRrN2v3PES1WkEeO4IXtLm6XqDJJo4os3ePy/Mn4Z79G9QaRSarY/bsFay1E566MMcjD6Qwf5QwNhDJmBO7e7y0sUSaCR66P+PY4ZR+X+vR9nrwm78Jf/Nv6kTsttv0ecjh5IUCWJMeh3c72LYPlo1RNDGGLR0HKyWkbRE25iGJMdauIFotzPkmDWeosaeOhZ2MyWwXUasBGZmw2OrWsYMIfBNz2MFVIdHiXlAZ9qDFCpukRpmNkUMl6bBt7GPZDkmkr00g4pCNts1itkY2N09g+giRkWWwvgbzxx/k+244hpr0MVyTR+8ckNg+sr9AQYzAlahSARVH4Hn4p56D9XXMpQAWF7F6a4Rpg6yq1UnacYmCJ7FVBEIRleqkwsdxMpLUIFaw3dLKEpalyDL92MzPC1ZXZyolueOcZcHammIwEMzN6WQSdNKbx4v1dY1Cy4l1YajjwVREBcvSi7luJ6Xkg60SpHSo1xWWJVhYmBE6h0OdoC8uCs5czglvinodymXB1pZ+nNMopegJikVJqwXDLuzdA1KAISBFEJsF6pUQECzWYiZVjygS2JbC8xSxsBl2Eiw7Zntgs7T0Hzflvj2+OUdu+f5fewghDODrsTJToAscVkp1hBD/wzfYjQTGX/NaBhz+mu/6duAI2mDja4/jZuBjwIFvdLxKqRj49emf/+zxzZ8Ug05CpwmuGA6wTAMrl5XKXb2mIColhHZ7m4wQhSLjUh2HQDvmzM8j0EmzxzT6RdFMpX84xMgSPKG0sHpucTwa6aiZR85ctX1mNaSjZBShSmXGlWWUbeGKgBRT4zRVhElKInSSb4gUkaQQx2SOC6ViXtqckfnm5vT3penMGjpXgvc8Hd2nZEOk1MclJZRKmJ0OxX5fZ54nT8LttyOOHEFmgutBmbJMKXo+gvGOJnKSJIyiCK9SJYkUVrWmW/uVis5MOh19TFnG3IEDPHDsJh7sdhgpye/+1m8S3HY33dBn2JMUClD0U0Z9yebApVgWeL5g7/yYK8plY0sSReD7glOndKXs279dV8luOhTSqArk2GTOG3LlSgXXPUgwTlkIYqLUIAwFFy4IajXB4SMFXh3uZzIIuXPfFiI1uNKbJ4wUW1tip4ozFTTZcdUGPREZrkkmIJYOhisplQT79imWFiGKc6teRaej5bd27drDcFjjVz75+/zZjz4CToEMA99Q7BeXydIqkVHEisZY8VjPZoMhaqriERYbSAWWVCjbgXJJL75WV3cq9Jw7N9Nv6vX09bbtHQ9ZVSgwcmpwYx3vhqOEODi9TWTRR2ys0+5KjO01qqqDmJsjqc1BqtlpXVGkWE4gEgQTg07ksbICw37MpWuwvKIv9fXrgs1Nk0OHBAUPDu3XxhA3RBeQ2X5Sr0hxocj+zcuop88iT59CPPgAsl6DQoGJUyGVDpZIibEwZMZ8PabftHeSWynBsWePkWnqR+vqVf3YnTypH4l3v1snIdvbWlLtwAHF5qbFF5+zuGXPGp6zyHPP6a5AoVDn4N6IwnCD4mDAUpqi7AKi3dc2Yr6PNZkwvK64euN7WFySiNUrcO4cotmkJrq0ejUKRp3K8Dy7i6sIMjIEr57W6xVzsUnT6yN7q/Dpl1nOMj567Bgvrb2DPfuXMIHILPDi5QK9nlZaeegRiSUVX3lacX1N8Nxz2p37+HH9u0Yj/Whfv65VLZIEnjlps2suxLEl3U1Jt2NTjVJWlmwCswAIbEJIQsLmLiy/SORV8cYtknKZxHBBCmQSIbKYoVUBJLU5hZMqLm9CyddwKQYDRLkI5QrKdZHjMb4NsbdEXSpiYeEzJmPaBIssoqVdSAHbG4rlyphkMCas2rzr/R/l//rEJ7j1oYdQvXXSUYCzexlRKOiVjlKElXmUENhXLyCGQ4bv/qiWAwz6OCLCLXsolcLGJo3GPGFsk2YxZpayHRRwEhvDckj6E/ppcUpkVCw0EixH8MY5g9KUQJcTOicT2Dc/ZqPvsbSkE+bcYvv8eb1tu62Jm0ePapm+XJo+y3SozbHGU5NUds1FzM9LEBmZkSCFiVKKdlv/1IUFMYX0ayjMdKrIxZCYTPTi0PfBNRJUnBFLDS9aG1h0hlAqWYipMCKAMmwkCaGycUSkJQ7HAisLiQ0Xu2ABIpc8f3v89zn+U4l27wE+/XVe/7JS6n5g8hb28RVgTgjxl9BOcg8Bj6Jd5948/gbwvyqlwq+zjx8DfkEptfX1vkAI8eBbOI6doZR68q1s962RFHueZqFUKgRzy0jALDvIfPm/va1bybaNqtVJDRPDspEqxVeTGS439/Usl3VoyX03pxFOwY7Kg8gp5543k1jzPP25nAwHWuliRxYuQ5RLCEtiiRgt2hVjkWhJ4FTP+qk0AYGUMW7SxuytzzR78qSoXtf4Zq3Ur783y2a+orattxsMdBU3xzlvburj8X0mQvDa1ascvfdeVKkEm5sMirswDYnhGSCKqKKGa4iNDe5ZWOCTv/qrfPyhh3BME7W8jJqfn/UE5+cRU8tplabaDc8qYo27vPjGGb5TJYSJwZNPwssvK370RyW7FhWJErgqoBd6XLrm4xkRg4FFpSJ29FrHY/3zy2XN+vedjJOXFrm05lIuC8ZjuONWhWULgkCfv7vu0rjLchk2NhTlsklqF3B9Ses1WF3TE1oY6skp55vVarN1hGVBP7BRqUm1FJNiaWNBN8EoRmz2fNJUT5STYUa1qqEClbLPvqX38B/+4DHuvfc+VuaKTISLUV9mEhv4ShPZ1CQgqc5hmCGy1wXHwTZ7iF4EW5uMayt0B7A03MRYWpoJneb44lwSr9/X12Fq8iEMA7FcwjEyjNY2fpoiSiVCLNhzkEKc4SRjsonNOJQkWwGFmoto1qi2WhgTQa1qUShLRqEijgVSxRypbuMWq9SLBnuMLUY3LSEMycWzGfKVF5HHD+Ed3cdYeFoCsd3i5NYSh2+fp3L0MCJP6CsVnEOHUK4gxCLFojsUDAaKZi3ihloXM4tQtoPsBoz9OdbaHpub8IUv6DWY7+tH9o039KN6++16TdBuq+kjoviOb4voBQ67mh3mmzXOXxCsr0O9bmHE4Eip9Z1Pn9bZgW3rHa6u0jxyHOPWKk7QQ954BCoaW+9mAVl3yFVVInnxCt5Dd2GZGZeu+4wmmuXfH0rqow7OyZOo575K2h8iB0MOPNyktbVMqWrQ2jYwDMFNN+lHUgjB5csmzz6nMcUPPqi1j69f11JxjzzCtKqnlSwWF+HSRZ9hZjJfSGl6fTpjh67VZGQE+CJiHFsoU2C0tvGmnQgj0T17ZTmoSYyRRkS+vulFMcE3IzIpCKTD3vkJAgtheYhej2QUIAoW5mAIKkMWJKMJVAvxVK7PIMKiUIgoBDFmlCIck33zEUpIMqOCGCbccfOtPPSe98CJE4h+H9MbzcyKKhVYX8cpjEgrdcTuFcTWBtGVNTbjCocbA9RIc20ip4gQJrHh4lkR0SBmbJTwF1y2t6HcFIzMKo4tdtBHjm2wd1fMgYMGGxvanXAwgBtv1CHzV3/V44479OImJ6OdPasr9WtrcHR/wPwuh40tgRCKN97QMAvQj2G/ryvEr72mb6fqkoUhQq23XclYHxiUChnzTcnmpmA8VuzaBUkMvT40SwFhCMd2RWSdPtl8kxiHrS0wpYUlUwaBnrNy09U0FRiG2JmulBK0OgZztYQQbc3u+YJYeECmTXNQSCl4Cx30t8fbY2dMiXP/WXeNUqothPgw8LPATwKPobG/vXybKeHuXrRV8x8aQggPDbW45xt8zefZsY/6kw+Jt5jvfmskxW/Ss/GiIdlwRFRtYltSSwt5nq7SGgaRdLTubzjEmOgEOBWCCBvXmzrbGcYsIQadTLZapJYLcYaRE9ksS0dA00RV6zskOPXmz+aW0AhtNQ1YIiXVdWEiHBQCX0yQYZ+J0cBlTITWcZVJ9IdV33NGRhiSdbpkaYZpSGht68mk09ElCt0n1J/LslkVeyqhRhjywYce4oU33uCLZ85w7213oJYWKYwGyGoZJYROYIYDrZixtETRNHm37/Pa5cvcvGePPq+jEZnnIxwXEUcaLz0aocIQ0wkRRkKaJnzgHe/ArpXYJ4d873d73P0O8D2TRBkUjRFSwfpahmFIWl2TWlkRpZIbbtBJ6mSiJ6lWC1456/LC8/Avf0nQaCjuvFNRrcDevQbLC5o01WyKHcmjtTXodRS3NNZ57cocl69buK5k926939zoIRfav3hRI0/iWOdvSQJHjkgGY4OSH5FKB8MyUaZJY07rJfs+7JkLMGxjOgkJPCQf+tCHeeqpp9i4Krn19jvpBi79ToKfBiytGCSWhzHuowyLuDpPWqrgyghRAlWt4A9HFEoZIts36xDkP2w8nmlELy7qP62WNoipVinKiCBzUP0Jlq1jmLN6EQyDsV2h79coqzFbUZ3LWz67Vlc5UN6GpRVi0yISNuPQIAyg6o2pxGukluRa2wLDpLawRNgTzM0pjt8sePm1OziUSurGiEJ7FVEqQaPBspIYwza6u4Ze0E0m0B+Q+FVSbEwSlgoTlnxFLG1CmtoaRCV0jDnaHf3TPvtZxfXrurIahnoR02rpn/6Lv6gl0d73XqUNZk6+SKl4nFJvlXNrh3ny8xmDscmRI3DPHTHiwhXobevS3HisL/Yzz+ik+OhRxN3vpHz9NO7TTyLuPAHf+73a5tzz2HP6NLtNk847bkONx4Tzi5x5xeSll7Q04Hd8BKwrW6hdu/kD9V6SVPBtwW9SqlmEUQCJze6lDIStTTfKuhFw+bIOFx/+MLzznZrMlWXwvvdpFYqLF/VvtaZCNLWqYs9KQpSaXA8bLMynNLrrSCxQLn7nunbVbDT0yi8IyAol0kqDTEm0CZ2DMBLMSgU5neqm6RWWmaGARAlkqYjTazNJmgSNPfhJn0JnFccpIqa8GCsNMfpbCL9Is1nASiLUsEdUahAHCb3QpegNScjwPU8DphcWNH6gWs0xU1rD3fMIsfHeOAc33cTcpUv4B25CFRcQZESjkIlR41x3H7tK0EtMSkWHfkcQ9hV75iaMUpfBSGG72sr5ruNjuqHHCy/aVCqCS5d0neDmmzXk5uhReOd90GgINjZmi+MpRxXbhlI8xg3GzDerTALJwoIm3h44MEPznT49k/1WpkU3srDMDEMqXFuvxD074+iNPuEgptt1mK9GOHXBdstiydpCWmWiuQWkSqcwOkmxLAkCiWlqNZcpRYCJluCfuu1NlTDKEpElCCnZ3DZ2GoseIYGwAYnj/GfMu2+Pt8d/4hBCOMBYKXXnm177CvBmpYoPovHC7a/9PPAwsKqUOvUNvuYKf3xSLIHd/1EHPR3fGkkx6FlFCGi3kYUCrpGCmlovC0Hk+GRK4qQBQkjSUoExHi4TpIIMQWqYuroMO/Jn+RkVloVR9EkdD8S0IjutBpMkxGGKIUzMNEX5PmmUIlyXxCqQYuMlI0So3cgs0qldLrhEWsZKQFKq4RITYQMCNQ41jjfXnApD/cdxoNdDSknmFSEc66it1EyZHfSsWS7rz/b7s/dzIdc45o5ymSvdLmdefJ4jkxBzroa5eY7J3G6SRJH4NewgRLZaMBhQBV6XErW8rC1DyxWsNCDpT7CyFFGv62S53SHqT7CTCVSrZFKiNjYQpknBdbn5QIlr3QJWFqEERCnUq4rVTVhcMRgMdKK6vKwD/7lzOmFIEtjYEJy4E/7M9youvjpkdLlN6cZl2m2TKIAjNwg2NhWjkZ44ggBuvlVCZYnFCZQbcmoQsgODRkq9VsgFO+JYT5JXr874lAcPGqRCACkuAZHSrG+vYFCpCJRwMAmQpEzwdtyj7r33flZXV/nMZx9jz55d3HjkKE4qMaIQo+ztEDKNIMBMTOh2UFmGMkzC+iJOFminujDUCUSno09ODo/JhYEXFvKerv5xwyF6KRRDkKFaLb3CCAI8K8HrX4F+nz2d16lEPlmaYpQrCE93GcyNK/iDPtnGJtk99xI2V8hW1zj5qiDOwDIFxw+H+MUR1lef4u5CEZEcIR4kyFIRNZ7ApMXypUuIXKx4enIz1ydWtnbSSsaQxARBiDvqYLg+fr3OeAyj2GBrIDBEhlCKjQ1t8by8DD/907C6qvjB7wn4+X+mCA1dte93FfPDdaRYg2fWyZoLPP1azFeecbl4ET7yEWida7P3+hU4/aruMjUa+pwtLelnZGEB65UXsb74hGZNDYfw1FNaENi2Ndj3lVeo/Zk/A0qRlSqI1/ezd+8Jjh6FQlEwOHYXBhnv6v0e4sA+xNJ3odKUUs3kpXO6/Z0kYseYMpefPnBgZx3PE0/8Po888m527zYwDN3N2FjTSiWtgcNtx0Iyw0Mkiqif0gtsvOoybuc6Zq/HuL4b77WT0GygXBchBNnmNkHmUQy3IVO4lQWyUQ/HMEhsb+r4qfH0YQBeb53YrxKVqhjVGn4WkqYRl9plmnWPopighHY3FMM+hueBbeKImLVxiXrJwRURjgeWGyFWt9hl2xif/ay+J/bv17F7a0sXGPIEfn0dT26hwgBx+jWyaoXuRoDt1nGa+7l6PmKpqUnIg1ZEoajYblu4ZswgMji76jEaaVm8cVc/5zUb3jinGI/14sU04Z572MEVFwpw9zsSwgDCyNrBBXuefqw2N2FSarDRgVpNceGC/tzyst5HtapJk5alkWQ5rSNN9cJ8vh4hbYlFxlrbZb4aYNcsjEHI1Q1HW4TPQT9apEyEZ4Rs9H3KhZj98zGRMHEcd0eVJa+5KKXY2pr9ezQSDIeK+aaLJyKaTQPQDneZ5e1IsZnfOjP82+NP1zCALwghPgp8AfhBYD/wH960zV3A03/M57/RewAopfZ9vdeFEPcCP4cmAgrgOhqm8ZbGt84jkzvMTZ24aLdRU8JbgsQmRUxtSMNCDau7hV8q7sANiuOuVnsoFDQMolRChSGZ6SBqFWhtIzptzNw6S0qtwiAEYjLBiYZQ0LjO1C8S4OBGI2htsS2XWaiZZHaJFAeXiAgLmwgTtUOaU4ZFLCyNZwYoWJDKnSRWSblj2QyA62AFo9k5yLHE+cjVL/ISR7//h6uNrgtLS6xUKnzp5Ze56TYThUKkKdawC1mKW3AJlvchBgMcIbBNk+D0aYhj7GsXYG5OY57dAkqajPHwZIYU4E66qG4Xo1wkLJcJHRfbMLSucxiyUpOEwqE9tuh0BblW7NbWzN1pY0MH/3JZE1suXdKv51DaQtVhvlKktitlu28yXwrpdy1WV82dxAJytyuJX9TQ2Xyisiyd80whk3zlK3qCO3p0JsG0saFPn2VqG+MAj4nyEXFIxVFstW3qTRPHSHYWWoZISZWlUX4iozm3zO6H57h+5QzPfvGz3P/AQ0SehVISaWXEmY0TxAR2FXfJw4gjZLuNSwhhoKEyQiA6ndkJmp9H5b7UwyGiWNSzdu4XGwTahMX3oVQicstkUYzl+CSGjWwYWJ1tzCShIQSUNHhSTbsNYqEJjRqiVieUPlII7KUGh8cpe/amFKM29slnYdNB7dlD2NyFoRK6dh0iQa1awLQsOPkSnHpFlz3n5uDGG8nmV0i9AlkmiVOXqBNT76/Rj0xGnkOY+QzHBs89p9vaRTXiiNfikYd28zM/a3LhAnzf98Ej9wU8uPcKrBm4hw7R72acOSM4J/ZyX3MV8dhnGf7QX2H4isvyEvylv6R0S3t/DX7vNHz+8/C+96H6fWg0tB70ysrMeUMIvSp6fkp8PndOJ8aNBmp5hWT/YbKFJewLZ7jnfpPLFV0hbLUgS6FZCnm68W2snxV8cGmIXxYEsctTX1bcdLOkXlc5z5WNDV2dLBZzM0yFED3Onv0s9977fqJIsb0NflES4HLx8gUuihHlQp3D+5ogLVSmLZ3jyhySACUsLs3fyd5yF+P8WWg2sVyT0SRkWFshTG2K4w38eESQLNKbWJRKiigWVFSL1PEZNg9gZWOcUYfUtIAMo9tmV1Ww2isiGxJXRdo2uNLEUaHWgFeKRiFgEBfILO1SYZEQLa3QKJa0Es8ttyAqlVnF2HGIpY3heEghUHFCVigTNxawjYRSuU5iCpQ0ae4xWdfFb7qBDS0dK5pNR59DFbNQS3n1DWdqAS74aqoBtPljMj+vtc0vX2bHZbBUsnfc62o1fT0GA71guXZN/7vZVFw8PeHEoR7n+guMJ4JmU8cOIfR69epVHXLzjpTtGXTGHsVChhCKxUZEMOUrlQoBURxTkiGZYWBgkiUZWNAsTghiQWYIUiWnMUUf+3A49YZyMgypY2iSgOcqSkWFmWreSg6RsCzFcKjJe3/UFOft8fb4rzeEEA8An1ZKFZVSYyHEx4H/A60I8RLwPqXUmzWI9wLP/DG72wus/Ud+fxOtR/wD6GQ4RUvC/cOv+d5vOL51kmLQEazT0RAGw9DyYY4DCajxEJFply570te2zLlNdJbpZLjX039sGyUEqlIhcOuQZfhTSTPCUEMxphhiCgWdscXxDuHOJMMhRtoSY2mOBRQGEpsMRYBCIFAkGCgyYuVih1qeLEOQIHSyLIS2k56aNGBZYFqoJAUyrcFsGH/Yve7NI68y51E6N/+YTGbZYpLw8unT/P6TT3L/HXcgph6kVntTqyEYCg8Fg47+jUIg5+ZQu/cwkT5u0EP0OkTlRQwzQsYJ2XiCMWWvTA4exxp1efSOO/iNLz7FB9/zPooiRpgmQgpdKXcSRM2j18+tbwWvvqqTi8OH9aGPRvqQGw3dPtbJg0CaBtuTKsMNg9VV+PK6i1Jw7Cb985aX9bZnzsCLL+rXVlb0KV1e1p3yJNFqBo2GJsoVixpykRcPHUffWq02ODWBaackWIwSl25Xn5b107B/n0ujONZJu0qJUhNDKCyZYBgZ40Bw4MAB0jTh8oVzHNx/gIwMpSSp5TOoFjClIsLGMRUyt8IqlXSWVatpWMuUOq9ME3XzLYg0meG6p5q0JIn+AVNbrRCb1HCQWYAyBZtdh0lms2dxETfL4NIlVJyg9u4nQ0IUYwx6ZNUGqlqnoEYoBHLQ5rbiCDZTJmaRF7JbOepv8cIrRbb6AQcWxtx0U59RdZlRJ8TvbGO9/hrq2Wf1sc3NQauFWa9jZiFp2MdZWCJb8mhXbuXCBcGF01AqKt73ft3W1s+dQ3gu4fhNiscee5P7VmrDWmGqIQ7Rdpd0YPHUSZPbfvA4heNrlH7q7/AX77qP+T//vew5WuDyZeAnfgJ15RTillv04vamm2B1FdXtannGfftmVflr13acL3NRWnXbbazvu5tnW3dTevk0h6yMp0tHcQY64bpyRSsJgM9wqKW0rnRK1IXiqacE99wnaLUU/b7CcSaAj+sq3vMefX+PRnD16hnuuOM+Wq1TgMZJVyrw+OOf5b67jnP29Of5/JeeZmFhgU984icolAUXL17m5//ZT/Gh936A7sY6D7zrYfbuWsGQFhw6hNreRkhJ1QlACT77bJHEWuSddwQUh+usqz1MQpNKKaV3fUC10CFb2IswDUzTJQhS0ijDlBZy1GOp4SBlSjROcMJNhGOhXJes1UHUa9iGomKMGaY+wUTRLER4p0/izjUYHzmCl6GLEDfdpOOT42ANR4SLK9jDDltBhbZ7kBtKA4aRSRwLtrZ1bFxYmMnCj8eKtTX9nDqOmNrAW1Sr1rTiPqv27tunY8uTT+rPnjypX6vV9HqzVNLnOcv05c+VaBYWZmqY417Mes/jmcsu+w9IiiX9Xh5HPE+Td7e31U4ozt0NXcYE+BgEWMQIUsSgx5IrQbhEysQ0MpxhC1kqIg0Dz1BM8Mgyg1ZrJunm+zthmT1zI9pDC2U6RIGiWQmITQeDmExlgNwRRgLehk68Pf6rjq+t1Cqlvsib9IeVUp/m6xP28vcf+Qbv/YW3ehxCCAn8FeAfAhV0QvwF4EeVUl9rEvInjm+9pDhNdXBNEoLKAjLLsEWImKuDEKTSIBYWrorIhCDEJpEGRTVGuS5BqYklwIwGCMPANyPEZILyPJTnaZc8NNwitAq4OcDCsjRBTwjE9H2BwEBhEZNgkEz1k0McDHRiZREgLYmwCjoZQRDhEpHhEiLRMjsijIkTSdLci7V9HeU4OGkws/mCPyrNlhuLyGm1udHQJQ/X1ecpCCDLuH3PHn7s+nU+9+KLPFqfI9p7CKchEWGgCXh5mSEIELaNHI8Rmxv4tRpsbiDSlOLaeZhr4G9v77hoqfEYr72qFxiGwUcffIDPP/cUjz78MAGalOgSgBRMxrr9ePSormaUy4K1NV1tOXRIT1C9np50crPATgfW1w2uXdMJc6EA+/dkLC+kXFmzWV7R+/Q8uPVWvc2lSzrhyH9+raYwTTh2TFsK55LOBw/qz+U+GIUCRCFEiUnFDplMFI5j7Qjx1+uKNIN0FEIakpVrWCpgODKpFmMMQ1IpZEgUNx4+zKcee4z67uP0u4rlWoAQKYOBQa0YY1kRCSZmuUIYCxwSJnO78DavIrNUL5Z0JkBkl5BZghUEO1Ahrd2UIvp9wmIdo1DC3lgnu3QO5hoMCkvYKmBZXUaeXNf620rSP3wXnWuSKDMYjD0O73MxM8FgKGmk6yi/pBVHsowkTDArJZ5drdPOarRSm5ZyiVPB5pkh73zHBLdzlc2kztzSLoxKVZ/44zfD0iJbI49EFSkVfQxl8MUvC1Jg70rEzYcy/u2/k0SByXd/JCazHEahg3/kMEeNP3zLYxiwaxdRBGdOweuvVii+8VVe/lSdjcZ5DrbeIDt8hNgtc/89CfaV57nlzl1Y/+TzyOurcP99cOutjHcdxiu/gpifQ506pWFLcayxDIuLM1fIl15CtdtEx25lOJ7n1G/bVINFbjiRUrYN1tbh7rsnPP744zz+uM14fDN33rnAzcczGnXBa68LTp9+mvPnr9Ko12hfeIaXLpzjvd/2MdbXYlqbJqdOBZTLknPnTvOJT/xtXnzxOl/64m/x7vsf4Jd/6ys89ti/4syZG3nxq08xHI1ob1/mf/yxHyDOBMWiy+6VJT71+7/LtWvrGOUadmEfi2oDamUCy6LQau3wEt5zY5fUdgmsJqpaR20LmrUAaUiclQqpUpAmSFNom2DXZGx7mCbEfhlHREgg9Fwi0yYzHZSQxJUSJBnjQcpiPcSQGQJFMFFYk5T/sVLnd8+PuO/EcXZNWjNdsiiCw4dJzRJcfIO03MB3DYw4ZDKRBMpBGlJL7l3Tt8BgAGU3ZKlpsLphMh7rBPbMGS0m8vLL+lkXQlduX39dX9LVVb3WvHpVd6HOndOSj/v3azx3qaRP05kzszCaZTqhLnmChcWEjZa544TXbM44DGGo1SRqlYzNTalNPYopnbbEaLj4cgJCYSlNgFaTEa1hkWIxJC17WCKBagUFRJgkmEgy4lTPEXlybts6FvaHBqpUwp6qi5argnHqT40+DHxfGxVJlZAKB1A7xLy3x9vjT+uYKlD878BxdDK8BvyYUurf/afu81srKc5hAaMRwnVxexs7DCqVZZrlXC4jp5aWQiksEaMQBMLBcjNRnM2NAACsPUlEQVS8YQcxHpN4BVK/rKu7nrcDS1CASFOM8QivbADOFIOXYeQwB6WwhW4XZggipsYMRDtVYoUBSCb4GKREOLgEWggfCDE1+Q9dfU0zcMYt3ElvqhY/1JN0Xh3MR17qcF392/M+4JtHrjcGOqo2mzz+d/8u58+e5fIbZzggtHzdVLaBrNFEVKoI30eNRqRKkZUrOu13HJRtI6ScaTRPz5O2Teog5ucRjTmE6bB+7QrxtSsUllcYGUUmykOqGGkobj86ZrvnMYkkrjuzQz09XcvNzc24hL2e/nlBoCtzjpO3Ek1eOmVSrwNT+aoDB3TlrtnUVeAnntCnaH5esbysX796VZFlYkd2eeqKzdLSjE/pWTG+MWGSunhWzDiWNCspaUmQShtDQkoVJRQWIaZQVN0J1mRC5hc0abLVAik5cuAgly+9zvHD+xCZwo96iEoZQ+qHLlVafs/NUmjU8Y0IMd9ETBX4syRD2jaGb2FMIuLGAmY4Jio3sUyFtPXC0JaS1sQnrt5IcSVjMoFkHLNUDhA9SIpVsq0uSsD44joH4iukrk+rtI/CKMJKJhSLRYRpMNxs89zaAippcN+DNqLT4uN3vMF27TBXvmKwaxcYpLS7Bn/xb9Rg4vGRjyjee/M7+WLpezh01GbXngpFL6PuRoRFh9j2+dJTEtS0DRz1+dVPWvyzXyhxoLxOcHvCZ17dTa8veNe74Hd+B/6n/wk+8Qn48R/X98VkAl/+staZXl01+PJjx/kXH/s9FuIOrZsfprsV8ap5J7/7M1V+8zfv5C/+uYi/0ZY0fuXfMTl6B242wZUwuv1e/C89hswT4iiCp6ewtR/6ITh5EnXkCInlcuGrbS5XdnHjjYKrV5d4yVzk1MuCf/Evhtxxx2c5evQjlMuCavUJTPUsl84uUC/ezGjYp1q9yIMPvotf/dVf4tiNH+DjPzLPa2cv8/zzn2H3rgPcddfdvPDil9ne7vLL//rf8sxXX6Uitmm4iksXnua++x7mIx/5EH/zr/1VyqUSMTaZkFM4lo47AgjijCcfe44Xv/ppXnzlJCdPnWZxrsbP/dRPISYTTYgFZBxqXfLRgPk5H6+9jvRskmKVXuhSHW0RexUudwvsro/wzITM91FCMMHDIiVRAstSRKjpolMho4D5hr7fTBVRjhKMYYc3+vMcacxRf+53WUlWEffeg+r3EUqRzC8h2x3c3mXS3oDKfEC/1KDv72IcpviewLIVjYbgyhV2pMqCwOSmRgvbbTIcC86c0cliqaQXuE88MQuDFy/quHDjjfrzxaJeeOfKlr2eTrjrdb3vxUW9vW3PIBadjrVj25xznycTnaD3ejqxVmFEfyzYs5gyTixkHLNYydjue9hVqclzSQLSICzP4XQGbA7rlHwDxwyJhDVNhxMibCTgWzFD02Yw0M/LmxGDkwm4rtqxbO73xY6s/XAISWLSKIQYBBiWgXbHfXu8Pf50DiHEvwX+TP5P4Dngx4HB15Nr+9Mlyfbm4Xk7smn4PrEwMZMY0e/qKDIYIB0HXJc4lWS2xBMhKYLQ9PGqBlSrSKWxeWoq/RhhThWF0dCMKZU3d4SJsPFUpM0RpuYJ4ynRyiAlRTJGa0MqBQ4BJgmxsIjRTGCFQhCSTT8TYKEUSFMifRsiqfuFcTzzI31zQpwDzZJEb5NHxxxH/PVM7nORzbU1mvfdx+utFgdGI73f8RhRKpEJi2huCTvuYZbLxJZFOBhilOtYlYoO7Ds4ZxdlaWJgpgTBsXfgygiJwlYJ3/s938Ozn/sc980vIEVGd2iRxiaFgmC7XyTOcoF8hRAa95YL3Jumfs/3NSRiqrK30xzI1ckcR+ME9+/Xxcljx3QFuFDQCfb+/YrbbtOYzcVKwOaWheeZmGZup6qve62mv7ffF0wmCss3UI6Be/40WRBRmF8kWtjDOBKMRoJGTRPwEtMmFB4iDvCHHUSSYJjGjlaTkpKD84I/eOJxwkM3IMiQaYqdTAitEiNh4MkJ47kVUBKvdQ2j6CGCQC/wPI8UgwShq0eWh5FETMpNTJGgpnhO5RgIBZbMCAYj3LCLE4QkT3yRYKGJd/QAWbnG+Ztuo9mEwZZizd/N2tUMLmxRPRLQseaoxEPkxjqFLGP/vnlKvsDcWEUtLePYZQ70LjC/t0cynOCfPYnYvULt4+/lQHSGkhzi71ni1htXKF54Gd9wkNU5RJoQoxcSD8ePEb96FuvACsb8Ad73yBK335fwngN9gkRDnHKEUC62cfo0BBOFowJePe1w8qTk4kW4ckXR345wlxsM73iQS4Mmv/QHkjvv1JJm58/D3n0C5+d/lvG+Q2SYXGqVKZdT6vYIUasiigVN+Dp1CuIYYRi6xaAU4a6DxIu7EU3J2c9qPPriIvzO74ipGcRLXLt2H5YluLl+jUfv9/jKa6f4+A98jPe85zu5+56PcPjwo/zVv/oPuPXWv4dfXMItGNxzz27uuON+NjdTpMz4oR+6CZGluCLgQ9/x/ZRK2gL4vvd+Zy6Gg+2n9AcxpWKITUYmIMLBnHIVPEty/6PvpHd9xOFDh3j2+b/PSy+/zr/+5X/Px7/7YzhepjsKUYQzbDFprCCyFCEFY6vC0196hXA8wHBN3vPQnZgi4+JmAVNk7F3QTowOAbGyabcEhbJDxR5hugb9jTEmCVYvIqoukKQS+/IrWKsXWB6aCNNAHNiPPHSQyClgVqV+WCWMq7sx01W2WUBS5sxrinot49DekEgYtAYae71//xSra4NSJmuTBQxLx4YvflGTEgcDHQ+Wl/Ua59VXdYU4TfX/l0oa87u8zFTHWCfPOT0jTfWlX17WIfHyZf13ngwPh3px7ft5KBY7i2+rbLK+oeiODYZDwd5miikVTSdGkmGimODgbl7HVRn4LsV0kzCqYJgWCTDZ8UgQZAgynD8U3vPFe25VHQWKINIW9DkicG5uRjUJhS7CWNbbgOK3x5/68b3M1CcUcAL47T9m2z9lkmxvHjkg1LIQaYoZaEck6nXUtEUnxmOwbcw0mkIWZilpioY/iMEAaZm64qoENgkIfeZSBObUQisFDDIkMQpdLojcMqYNJgnZVEwHJCYRAkV3bNONXGqVDMQ06wYyTBJSJIoAR7PawxhhSY2BXlyceYLmll5fbwgxMw7JcQdTO2wcZybdFUV6oTA3B80maZoS5eVR19XbKYW5vo4RDFH1OTAt3n3PPXz22afZMzfPoXsewLMCna2kqbYsLhaZCA/PL+AnI2Jhw6BHUJgjNCxGfoUhJVotc0pmM7i+rnehJYU0+cgwFPW6II51ATzHFbdaukKSmwb6vm59Li7qtYJladODlRV9ms6f15NHbuN8+BAUitC+NOC5KyUWlwSVitAJ01697dqaotsVO4L+c6WAYjVDmTbpoRsIt/q0qeNH2lmq0wEhDGzLYb40ITNMYttCVSp64SQl4/m9OCrASCISw8IUAnvYRfguoVfAaq9TqCqUIRFCUOxcn2LBhT63U33i2C1ihkOErUvlkV1EuS6eCAmxUEpgkECmNMnUFZQTeOHyEhkGvUOHuXIx4dH2OodWQo4d0xN6pSIwDJdhBM29NewLL5IUErK5EnJ5BS5dYqU20RJ9QkCWknglTFtR2dqE0ychHcHyHKwGFF95kvlf+6eI5WX2raygPvQhxNkJGEfBMHDOPI+zvgblMs6jd7Ph7cOQcNvRkG2h+PUvH+EX/q+M518U3HKL4uBB+PCHNazm534OfulntvihH0zp9xbIMsX+/boqePhAEbV7L4OhZH3tVXy/yKVL+7n9dvjhH9ZVxY6/i4albW5NU3HhgsApdCirTBNaDUPfLPfeqzshw6G2KS/YiNdOsnLDzbz/fRavn1GYpuAv/AUddl566Z1E0fPcdOwlFpYMJu0rHFmq8tP/889y3yPv5Td+49/za7/2OKVSkY9/vIxhmPz8z/8i733v+zl2bAUpDQoFgzjW6ilzFZN6PUErCMlprFAs1zTGO3Y9hgmUrTGRcqYLdwVKL4QLdkKhOCBwd/Gv/uWv89u/80l+6Ic/xrF7HuTWw3uxSGF9DWkaWPEEU8W81LX589/1bfxv/9u/5tDu3Sjb4zc+9TkW6gWO3/Ewb1y0aDZdfBkikwQjSVg2hkjhwyuv4R49ittQqMvXyMw5sknI1rDE4nYL27JQR49yzX0IcfJ3SBYWaKk6qe8zV+6Q+SVEppC+B6UlJrFk796MerTJK6/Pc8NRgWmKHQn2SkUvmi9d0smtUprP+bGPwWOP6emg1dKx5ckndTKZh02ldELp+3pxs2+f5hjkoQ90+Dx2TP/tefrvRkM/ljv7mS7gbVt/d16RzpDML0KSaC3iifJwRA6lMzEYg2UzXjoIStHdDFmqTCBMiPCxiUkxcAjJkIR4O7D8LNNdMtPUt2mWgeMoXEKCsUWaGjtThV4YKFxXTOcsE/l2Tvz2+NM/vpEk23/y+NZLioXQy/pUYy9lQemqmZBEpolVqU6dfwDLmqbEkCgDi5gMg1QYWKUKmVKEyiEOUkzXQAiBQ4BOobVcW4JJjKlVAoQgKdeQKAQZNtHO+3rLjACHQkFQKGQ4hCgk0c5kJ1BIJDEmKVKkSKl0xuU4M+uhyUSXNL4WQ5wPw5jKok0zx1JpJsMmxEyLbDjUs4Xnge9TmkzYuHiRaG4Oe8q+V4cOke7bT2x6uJZWwLAdl+OHDnHq/HmOb15F1WqI+XnUeIzq9Qi9Gq4dEOEgoxg52kaVywgyxmOP51+5TqEWk6b2TqVXs+2nk1aUMl9P2Gg7vPqq/gnTy4mU+t+XLmlIhBD6c/2+npBAJyfToiqlkp4UoghuuEH/ZKu7ydY1AyUl79jXom81CEOdUF+9Cmmnx8FGn/5SjTeuOJQKBnt2g52OiYYm1tY13H17qQmHTKkpoUobhOzfL0kMB4eQGJuxWcZnjPbFnBBhoYIRWAKnUGIUDHDdAsqSUKsSCBtTpGTKQNQXMEd9RJKQmi6G75AEMSIMmDgVRBJj+TYqzXj98gbXr57nwXfchqMy0klM5JZwxts4hRLO1jXubnj0ZJUXwyaNYkh9vAptufPY2NNu6g0HU9KxZHT8BJgWWTLh3LZPWplj3+kXcBZqsLg49c9SrPbKVI7eh33TXXhffgzjZ3+W937hC1rr6kd/VGcZkwk4DurQIb3qGI9JbroN1VzGXrtMWKpzuVVhY1Nwx+2CalVxcF/GPfdK9h3QurxPPw0PPKDY2NDH2XvmdcTH93DihE5qPCfGTCJ6I0m5vpdWN2R0/jorK5JCYX8OdWdpSaCK80SABTTqirlKjAiqpG6KVBqyMlo6wKB1jUahgNNsapOP8RC75BL3uywtNSkU4fnnFWtrgs9+Vi/E3vuum/nwO1skpoOIb+LqtQGvffXLvPLKST7ykb/Ed33XXXzP9/wPvPZahTvvhHvuuZ+vfvWz3HXLn2H3Lpv+QOIYMbaXkCoTAdgEhLhkmQEIQllACoVtACiyFEahwHMdYiy89iqiUmJsFvErVaxOF3PO5mPf8xEePvEqv/Lvf5kTP/LDmjTcaiFLJexxhy9+7nP0o4z3vfdB7nnHAn7F1ySzpY8y6m/z2G/9Eu//+J9DGhb9UDIeWBQZUKhWGEsfddN9SJHimRPSuWWIY60lvZVSuONh3K3TVGxFVvU4ftddPHPxArfeewOTxKKfNRm1ErBtajWfOiGXrhjYvsl1lmnMC556ClZWNOnwtdcE+/fDs8/OKrh33qkTxFOndKen3dax4fx5fQtWKjqU5iaopqnjxsrKTA5vdVXHi8VFDa2yLB2X4nim+NDv69dLJagXI9yiyXbboFZTKKWNQoxpWE9TxXwjwZIpsXJ2OBqhcjUJDg37W5hLiFIbVSqRYhBjIlXGmAJKaWWe0Ugn8blB6ZT3TacDhYLALgkqDclU4GeHVCfCEKWcr9ssfHv86RhBoO/dtzL+uNThT9P44yTZ/nOHUN/g7Akh1Dd6/7/ZyMsISaIjXRBAtUpaKCHabaQpdXtUKWKvRCIsnLAPwyGyVp0t9ZVuo3cil4IVkRk2AqVNNcgJEIZuR+1UhbUeZDw1cIAMZzqhMU3BHUZT0w6DmTGMmP43xZp6fGklCn2cbhaBFBqq0OvNaNDfaOSKGbmW7Zvl2kYjHUlzzPHUGe+1dhvz0CEOT3tz2dIyoaWrFgKtjGCO+pijPp95/nk2Xn+dh267jX0nTqCKO8RSxHhMHCZEpQbCkBhZjLAMBhObTivkC195kmPH3k+tJqhWdTVna0tPWJOJwnMUhZJgc1PQas0M3GxbV0rOn5/BqScTPfldvQof+ID+/lxsY2NDV1Labb19sahPw97FCSduiXnutQK2I5mbm7HD55vaDnbjasT+SpuhWaZOByEE44qunnuErLZdkszA8/XxLyyA7ykcMcFU2U61TiC0DnWuKjK92kopfvfTn+bRD3wUg4QUE2OKKVdpgpMGhKYHUmKOh2SWg2lBKgykSlGjCU8+9RwT02ChVuLmm47x7Fe+QtzpIKVEWRZpq03Pr+B5Hu87cStcvsKFwRzjMRwubeC/5/5ZNry2BpubpJUqot4gRZBcuMzT7SP8u19RjMaSG28UfN/3K3Y3xgilWO84VMwRhbiHuHQRnnsOPvMZva8TJ7QN2IkTsLzMxCljWQLj+jWtV7a0xMCoYJgCaZuIIMQatBAqYxIKVAayXEAUCpjdbV5YXeD//hWX1ha88PSLHItW+cv3/Crtj34vH/2u92PJiCxI8UYthoU5njvV46d/+u/zwAM/iJRKY5Y9fT9MJhmeV+Rd7zqBlJpUWu5cQhkmQW2Z//dP/zh33nE7k8mE18+e5d477uDRhx5CKMXIrHDmnODooZSNrsvjTwjSVEwTDsWePXn1UWCa+t4YDiMGgwv843/841jWPDff/Gc5ceJO9uyB3//9f8qf/97vpl7yGQqf8dgg6/WoFUIoFVFCZ1eJsghCQb8vaDRASoUQKTYRKTbjscKxMjwr1gvzLENtbOprW68TSgfDkoQxfN+f/W5+/if+EZ2tLdpJwu997nOEmWC+UuTHPvEJLMfRJ0vKnWc6y6B38TqPP/MlPvTBbyceBcQRpG4R25WkyqDb0x0H24hwtteQ25sI2yK2fV5u7+aWuWtYjsnwlYsYB/fx5We/wMPf/hGu9qrU5yAeRWz1nB1I1O7GiJfOehSKktFIUKvB9VXF1rbOLVstsaPZm3vavPqqxgRXKvq1Awd0yPT9HB41I9fl/OP9+/XazXF0gpwL9+QLKcfRi2bdwZqZhA4GOlbsnR8TCo80E1MraJgvjUhMn+2WIAwF9bqu1ua85WSzRVn0mFQW8URI1h8R1uaJE1PHTJkhk5Bh7DIeQ7UqpkobM7n5blf/u1DQv103BNWOkqBt626XFClMuSzF4tsudv8lhhACpdQ3xZkUQnyoWv32377hht95S9s/+6wJpP+pNs//XY9vzaRYKT0pDwY6WhQK+jXT3FmlK6XI0pSxUcYIxji2QgpFKqSu2wpm+sHSAKFT2lSh1RSAEBdJRobUWFJMTJUQ4GCTaOawyJPj2bDQOp4hNlrDGvK2KKQ4hIRTLJmjQowsRLQ7iFJRK0CMx29tqTca7SwI/gjZLop0Fvp1xjNra2wkCQ/ddhuVqdQVSYIoFbV0XZpClhFaRRiNeOaxT1Esl7n5kfeSComLfs6yNCPpj8ikQVKu4cqYMDNxhy1OXrxKoDyO3Hgz587lbnKCwUAnEXGsiS45bHo81pev2dSVmvX1XFNUH87amv67WJzJH+UkGSFm3iaFwrSSPIi552iXsVMlEyYFc8I8W6TzSzjT4/SCHkKlpH4JIwqItzs8v7kHvyBoliMW6yGx5dObuGRKUXECUsvBVbqVbXe2kJ6F9LSFRjK91mbu7AZsnDnD61ev8sBddyNVRlyokEkTJx4jxiNC0yc1TFwRaRiG4xALg3SS8MQffJpDJ96N7c1RrymEUBikuISILIV+X8v2jcd019f58ksv8W13362d5p5+Wt8Td96pT3YY6t5xr4e6/XbGN92FGPQwvvo0/Qc/RGlwHbto89pFl3pT0qhljJVHwYoxHvsM8g/+QLOR8nHokJb8WFnR2cbKCkNZJnRKFI0x1qTPMPVppyWGoU2tJqkUE5Iw1VbBo5RLazZpKji8OKB+8nPwyssE/48fQ0rovPwMP/13/jYf+/aP88byUR569AP4Pgy6E65c/BLDSGEZkmjc5+CN7+OllwvUappctbGRrwMHnD//rNaRbi7zzjv3UxAhcZjxi7/486x3O7zzrrsoC4F0HEZC8tBck2jPbs5uhYxGe6nXFf2B5JOf1JXJ955oUTtQp1IVdDr6nh6P4Z//8/+Z55//A44efT/vfe/fZtcuqPohabLG5tY5jh1/iJKbItwpHj9DV6ylRAi93B6NDJQ0CEOB76Md0kSCJWJiLK0NjECQkWJiExKlFt72NdRck7FRxEhCvKDLL/zCLyB8n0994avsnysAGR9+3we4/yPfgSmnq8Ovlzn1+5z98ou8eOUNTrzzz+K7Gc2mQoyGjL06UWYzGsG4G3F4T0i63WbsNRDRhHLZgCAg2WqTuj6mhN9/+mmO3fvd1OZMPBGQSH3Nz18ysSxYXMw4e0aRpJJGQ5tS3LBnwqmzNleuSSqVmfLM9jY8/7xgNNIx4sQJHRNcV//JUWRT5Teeekp3m269VS+Uct3ylRX9J4coWBbs2rUj1vOHOlCWNdUz91J8M9LFD5GSJRIkpAl0e2KHo6DNMvS/2y1FtRBhExFLhyhU9AObalVMk2AQKqXVEri+3CEX5xyLXBRFQ7e02kWWgeemIHSsmUwErgsmIVIoJGCX3D957nh7/Inj7aT4v8/xrZkUg46QOa42N63Ie2Ho9DNTECsbGY1JDQsxCQBFXKprabUoxAiGUCrikEAcgWmSSTkl0ck3fWGGQUqnLTAcA9sW2IR4ZowQkGIQKFdrvZLiCI0Tk2RTAXedFDtMdqrKJiG2ipGtFpFbQhQ8rO62TorfyshLGaXSH02Ks0xnB3k1+WuGKpX40pkzxGnKI+99r35xMiFyiyAlVhLMMtIg4MLqKq+eOsW3ffSjGELwr37119i/vItDt76bueKYUBbJPWQNUjIFv/HJx7nzxM0sLC5x/TqkqeDyZX25ymU9gQyHOndf18phO4c7Vd2jUJgpzbXbOineu1cnwFeu6EOs1XSLtN2eTSK33aawTD2BHNkb8vhTHo05wbFjetLrdRVzzgBXBAROlcw08Jno5U0Gp85a1J0hc0sWrUkBv2RgGAqPMUanReSUSfwSIoqwTIU10TAIfB81xbQTx4jhkOuDAS9cWWX38n6ONSuYjrFTClJAqiRGHOgENwyhVmNje5tr165xy83vYJLZlIyxttkWBqF0iCcpRTnWuPjtbRgO6V+5whe+8hVuP3aMXbfeqk/000/PVhugNXhvvBG1tEzYnWD3N9kq34B0LZr984hXXkHdcgvB0dsIYgPTsyj+/M/AP//nund3112IBx7QrLZ6XZ/03bv1dxQK4DiEXhmLhDgRtMYe0pR4nmA00m3xINBJTBjCuXNafmpxUe+uUtHXNZiMeOz3/wkPvutH+be//E+5fPmrLC2t8MAD38WJE3dRrXqUyzOnuJMndcUwN6LcvVtjSPt9/T3r62tEo5dwkiEpBpbv48qU4doab1y5yqPf8V1cunqNU6+vc/36Wd75zg/x6KP3UGDMWPj0+zoJNpOAw3tC/pef+wVefPF5jh//fu655xgLtT7nLp1lz74PUC75jIdbXLr8Je6+7QgH9uwliCTDyKJcSIilzWBoYKmQajEmFA6pMhn3UxIMSiVt0BCOYhYrIYk0pzEjQ05hWjEmDtFU9jHWhC2FlgHrt3HSCcK2Ub0eaZZh5rZs8s0x7euP4UDxO7/xe9w8V+TY7iqTlYO4RsKFS5L6kstmx2JPUXfkjILDdt/GiEOa2SYJBma9TCQdttom0XCTi9fPc/dd9zIYSdpdwcqKpN1W+L5gMk6pFyZcWvO1S+BAVz5brYwkEfi+ouQmrK4qzl60WViAV17RNuC7dum4MRpp1YjXX9ePQqGgu02rq9qs59AhXSleWNCV1+VlrVf+4ov6ddPUMabd1p9fXNT3YV5zcRx9D8UxlEp6cSrimP4QFqoxvdDGsrQsZhQJglBQLOq4lmX6e/NFf7uday3viPeglH4mYAadSJKZwVF+HIbU8qNSZQS4KKQ2e/Gh4CsMkWI7Bqb9J1/jt8efPN5Oiv/7HN+6SXEc6x47aNODnDxjWQggxKA9dCBOma+EpEIQZD6GSDCllkhjWnnx0K1iMR4TWkUsW5MVMgQpcgqf0PbM6VRhIkszVBRSdtNp69xgkjpsbwuW6gGplVeJ1Q4xyhTJFI6hkKTEU8ULO43JMshMiakyrULQ6fznn6N2+2tEX6fDdTV4zjBodbu8cP48ajzmgYcfxqlWyaSJjALoD8gqdQxTJ/Sj4Yjf+v3Hec/tt/D5l1+jObcPp2gxv3gbgpS5Skq7K6k0TBwbVi+dRqYR5cZRumNtbd3p6GrQePyHFN44eVJLKdm2TnqXl3VFudXaMRhkfl6/f+qU/tsw9IS3uQl3361bpnJKcj+4L+Geg5tYy/NIS3LxfMbB3TFWwWQ0MaDdwhcRrp1gZjHp/OKOscYkgJIVEUsNd8gyCJWNkiaWCrFUrKWhp5PjDiBxOIRCgbA8RzYa4yYjrXs9BTs+/coZ1tsh73nX3VhGBigyJM60spwgiJWNLWI+9fiXePSRRzHjgAvXbPYuRfhhFzUaEY8jgrndFEabGo7R6+mT1mohgM+8+CLvDENK29szWY9GQ68g/n/s/Xe8bddZ3ot/x5h99bL3Pvvs04+kc9SrJVuWjVwkF2zABmzyCeVCTOjthtwLF25ISEh+pJCQQBIIlxquAdtgYywbd8u2bKtZkmXJqqe3XVdfs89x/3jn2utICGxsk5+Rz/v5HO2ttdaea9YxnvG+z/s8QYC59jqSTHHf0w2Wlh32xk/gpqWVeH8gzX6dBbLlfWhdwKnT9D/1KAsfezvWQgf14hcLCrFtScvP9PUsC7OwQGE7rA4CalXDeKLwKta2pHaRgzvaIKvUiHKf4ycU587N+eYHDsDCgtjr3nnn23nkkQdYX+9z1VX/hFtvvYR9++SzTz4pxi+zrPDWlgCP06eFWz6T7p5xqZtN+f6qFVFvKzwrx4om6K1N8qIgb++gqFZJcTjy1AZvf8dv8k//6c/SsIXGNKVKkmoeeGDAgX01KhX4D//x9xiPexzc16bRbnHj9a/nt3/7X3JoyeX2m29k//XXYapL9LcKikKxtzMh1AEBMSEuhbIxKJwiYqPn4NdsXBOhXI/xVFELMgIdCwiDsnrlYozGJaZQFg4ZBs0099C6wC7lH92wj1fIAgv/b585LHp9Pnnfw2SWzUtfcBURAVFoOHG8oKnG9IsGu/dZVCoGv5jw2BGfSw6krK5bbI5cdq8UuL4iOrnBPQ98mJffejvZ2Q3ONQ5RqxkKI4Bu57LhqadL7m5HE0Uy31gmYZc6Q+rV8ZMRtm9zdLzEPQ8JFWj3bgGWv/d7MgZcfrlkkJ96SjKwy8ulI2ZVFszGyL0x4wqLaaEppcxEzWHmsAkie7a4CJal5u0amGcUBdNpgh+AQ0qMh0lzjOORpgLsk0SGhE5nDnTX1thWwfG8Zxbzmk0Zu7rduULGPMrzQkYeZxjHQ6mCNCxQjkXDnoJSWLXqc1cALsTfOi6A4q/P+PsLikEQk2WJfXOSyMhammvAjKwg0LQoYG3g0qxkBN48exrikpd6jgoBKiKME4v+PQEGhasSUhx8QhSqBLdZKakj4NclIsOl4JlZ2zRVUBS4bo5R8/c0BQZDJZd6YO5XsTfXUea5s7t/6wjDuVvF+aG1IIt2W5CIZVE0m/z5n/0Zr/qWN2BqC/hEsghQDoGZYK2tSibUsviT97+fS2+4iWOPfoHMUkxNk5e/7LUstFKyXBPnFtWgIIuG/L9/8qfYbo3Xfct3EkWKKJKsy2AAtq0IQ5mIzp6VcudkIjTVWeL/3Lk5yNnYEOBjjFTyS5MyLrlEDqdSkf9fXJSJ8ZqrciYTRZIpVroxQQD9qctkqjBJyr7FkLODgJqf4VdFZs8nJh5ETKw6zbxP6lWxfcVgZNEbe0JhUIaaFbLgj9GuM3c7HAzmxD/fl/txOBQZu/EY02hyNoEHHnoQK4/Zv38/ew5eiqcSEjwCIpSR+3YGih0SksxiMHWwTUy32CTb6LNZ3csOVtF5JjNrpwtbm8IzTRL+8oMf5MpKhT27dknaTCk5sUePQrtNsnMfJ04Y4seOsHzjXhq7amyNXGp14SP2N1IWmimx28CgeN/74JWrb6W74sH+/ai9e2XGf/hhkZFbWZmnvzpdJk6DpHAYDueZtqYXUa/mxDj0N4SvrLVY0s745FkmWqyrqyPe8Y7f4cEHH+JlL/sBDhy4eVuae8cOuY1nPNL1dVkft1ry2izrXPpE4Hkzp2xDuw1uNqEyXCW3HNJKCyue4pmYotkksXx+5Vd+mRMnT/OaV38LL7v15QQOnNwI+Jf/6qfodFY4evQsO3e+hTj+ENXKOj/6vd/BnR/5OAs1j8mkz+uvv54H+30+99RT/MgP/jCxbmAKQ7N3jLy7RDGZkNbaOCrHUoXwtv0U47ls9jT9TcP+PSmFASdwUSrHYOMakZbc6LtUK4bAzfBVTI5GRyHFNBSHQmXhqgRtWTO5l791GANxmHPXXfdQdTTt5RuxHUXFL/DDHnZgsxnVaLQtHLtARSGntqrbFaDm5DRnky7L3Yy1tQ0eP3GWpZ03ixBOLeSRpzy63ginEdCoFWyMfHo9aFZSHBe6QUQwOCfP0WRCurTMiAZPPanYtRviRFEJChxb8bZ3KA4ckN6FWXPak0/OBXn27JFbdab5CwI8Wy3JRkuPqNqWNyNNwNJU6xaWpba5zEoZ6vaECB9b5Rw75dJuw3gEgZeT5BZBIAv+2f03a/cwZnuq2gbIMyVN35dssFLyd+Ox2QbS5dVAUZQ9KrIN4R0bLAoMioqakisbpxY8x9W8EF9OXADFX5/x9xsUb21h1qTRpGi1CSsdfBWLZFEZOZboxJ73WoJDgcY2CRaGTOnSha4gR8roIR4mV6xuaFothecJ9cGiICv5fTYywafbIh6zUUyVLXQZKR55rkV/N8ipqCkFmmi7Mc+Umsa52Puur6OyjI1ej7e+//38xHd+518vzfbFwhgZfdfX5fdO55kgWSmZBRwHul0KpXjrO9/Jrbe/jh3NBrbOiXSArTIck6PyHLKM+x54AMd1aXS6fP7RR4jSKoeveg27F8WkYxgHNJqKO+54JxXH5d5P38XP//zPYLsOCQFZaljb0BgjE9Ijj8wkw6QEqrWUEweDOc1iMJDd3b9f3huN5HBOnpTPXHSR4NKZvqfrzu1e+3053H27cxrpBnmSolotQlWhGSTCYd702LGY0ahkOHkMmWRjtMkhjsQModWSc5dlMtvNZrhykWHSVNLfroua+dMag5lM5G9qNbKgRqx9KumQIw8+yOObm1x7zbUs71whNB5u1ON9f/k+rrvmOurLB7E9m8HIIU5goZ6gtMIYw9qWw0LXYOuc4PGHiA9cih/2sFwbHnoITpzg7rNn6VQqXDLTebr+era9hLNMZtaiwFgW5qKLGOmWUAvskqvJBNcynNisMA01y7UhbTcCt+SjPvUU3HcfyrIorrwKs/8AZ8MWTtXmscc0i0uqtN417GCVRlOjqxVC5bK+5c6+nlZLTAiKQowh7v3U+3n/h++gMC2+53v+T1y3RhRJlWHWJBXHcu37fQE847FkDJWo27Frl6z5Zk7t0yksdKUZT5MRFBNUUVCkObq3JeopdWkkzYzi+OqIj995D5MjD/Hw6pj9Fx2i293FVVe9jCNHPO699+3s2jngza95CXuLBK0E+dxz9AR3P32UHcsLbG71efuff4z/+l/fwUU7J5Bn2BYkmUJPRqh2i0R5MhblCZnlUhhNlskiuuKnpTOmNOxGqYNjS+XJMVMyy0fBtntmOCmwipR2LRUmk+t+ZV6/xhD3p/zsL/4Rlx9YIjn7IC/81p9n184CozTTULOrMxGTHTzyacoo9WnVc9Y3LcJYoVVBtVLwwD1/wd6dryDz6tTqiv6WgTTjyktCVsc1Tp622OqBbRleeniV9uSMLOK0FoOhoEKiXLxxDzMakdWbjFUD1zLc96hPGCquu07NbdnPzaziBQCfOyfjxs6d8lqrBSsduZfPnJNx27bncmi+nZIrp5R1Ezm2JIF6NadmCb0twifObLJMMRzKvZemso3BYE7JaDb/antHqzWvYoxGc3qFbUO7nqAtVZbRBJQ7pdIRSmGTkCHXdWND0enMdZcvKE989eICKP76jL/foLgoSE6exdYFqlZD2dL7L1JSPCNjbIBZvjTGwytiimmMrriEuoImR3K3Go+4/HsB0/G2M5AhQ1yIhM8n2pIirpOVzXMzRYoYi5wpAcbMle+ksvXMc6rJcEhw+lvi1gf0h0PefdddvOD667l8lhqbxUx67W+K0uaVJJGRd2tL/nneM7NH9bqghnYbajXSjQ3+7F3v4nWveg1Bp4OqVjBao6cT0XItCigK/q9f/2/8yD/5GWom4n/89u9y+IZXcNsrXkKaKdY3NNqCBx/6KK++7VZ+/T/+Mje86PvZe3CJ5aWCih0zCD2SzOLYMdmFwUCyv/v3y+59/vOScen3ZZJJEik9DgaC7RoNkWkaDIRq0WzK72trc5crkEnKdQ1ZJhnkJDK0/Ql7Djj0Qyl1VqvwxBPQ28y58fqs1IZTpHHBzlYijmAbGzJjdTpzT9k8l2z8rGETMI6L6rRlQB2NIM8ptE3i19Faszb0We5GWBqh7BQFDzz8MKfW+rieT//oE7zhNa/CrVQY0qCXBCzUpcHH0TnVjWOYM2cxrTbFU0dxb7yWojDoOJR9W1sTNDCZoIzhL598ktccPiwIcmVFbL19H3Xo0Hy/BwPCzi5cz3Bqo8JiM6KwPdY3NY26oemEZVVDkWgPhwyrv4n65CeEAHzwIGsXv5gj0Qqer9i/I6RdTUhw2Ry6+NMN2gxQjTpRrUuqHOwiYTBQJLgYA+fOKSxdsGMZ7rzznVx00SVcc9khklRx8pxLHMsCajIRwGvbcm+cOjW/rS++eM4n9TwBQtWqgI4ogp07MhZbKYnysVVGNdqSjbguaWeRVIkxRhLKwjlJFB+58z727D3Az//8f+Gnfupn+dCHfofFxcNcc4nPNx+qYR3Yhzp9GuIYtX8/aaH54Z//F+ze0ebs1oDv/b7/m4svXmbQL6h4KTtaCVPl4ydjCm1jbBuMZr1n4QUWrjunfcjYkJfjl2YyzKm7CcpzcYkJ8fFLF80MhULjq7i8FxW5V8H5SvmlRUEymLI+cnnqC3dzemORducQBw4IGItCQ6thsKyC4UbCkTMel12UUQ1yHnjE52d+xvBnfxQRRWPu+tidvPFbX81ENfjc54V326gX7FsKseMpxWaPaM8hqvd/XBaT3S79ygoNRliToVzMLKPQFsnCCv2JRbuaMjUVtKVQSnHsmNwb6+tyHjsdGbLOni3Ppxbg2OkIHWs4LN35VEEaFxhts2uXIbAiEhOQ5dDriSrETK5tPDYsdnOKNGeSuAyHil27ZNsinTZnv80a8E6dkvty1i4yA8B5Pl9rz5qHlTJoI2rHxii2xh6+r3BdmaOUyTEoUBalsua2isYF5sRXLy6A4q/P+PsNioFsHFEYydoqxHhD7JPjZ4DisFSSmMmtAdvUgUR7KAx5CU8dMjRpKaumwajS+gOUKkrTDoscjcLgE2GJtUf5Pbms6kt94uFQpHOkuizKxZQmIiL1Jh3lGQ5+PEJvSFrhV373d3nNS1/KFZdeyvboN8v+zpQ2/qZRcMZN/mua7QC2hS1nfqi2zQS44447OHD4Cm540QvK5rNCrJ7L73/n+z/En7zrA/zST/4Qew8fpJ8HTKYBrUZOfXiazzx2hNwK2HfJjdz9yT/hzFrKG9/4TexalIz8FJ+8sNnYkMsAkIYZJ89Y7D+g+MhHxMp1zx7ZtZl0c79flmfLRhjXFVAcx0KzSFP43OcEFO3YIX9ft6ZsbuZ47Sq+r6nX5dTcdx+86lUyKU0mcO7olD3dMUN7gU5X4VgFgZmikhg103dqNCQl0+/LuWu35fU8xwyHRLUuTiYVBVyXdDhlI66xqDawFJiFRUK7KvdMngkFqNGAJCV3bHSeo6KIvNnh3NCnW09JLJ+qCjHra0StXWz0bdZWDRvrOddcCe1FC3eyhWUrBlmVxhfuQYcT1NYWx48fZ+z7XFHa/plLDkm9t9uVRc7Bg9IcCBilUcDU+Dx5VBZy+5djgukGxnFZjVtYvsNiRVxW1No5nDMnSIIG1iUHeXqzTWxcLulu4ffOyv1i22IEUqnIoku70pQ6zjmx6nL2rCZJYccORRB/gfsfvJsXv+qN7FxpcN99ioUFw3QqfNGZGcOMbymlcZGnOn4cdu9W2yXzPJevnExE2mwyylnZachyRW9Lrm03mBIMhBMdtxcplEM0KUgKizjTkBecPmvR1j2wbJb2+AzXY86taa7ZuUaw0ibJFO6oB7UqOA4my/mj9/wl3/aaVxNqn2FUI8+h2ymoqSm6rEwlysUhJ0cJqDU2g54hTrTce3aBr0IiKoDBJgMKnDyjKMCxDGZ9DdPtEqkK/miNqLGMqxJMWfkCLUCpOE8E/MuJPOfU8YxqvMX73/ob3H7bN/Hw+DLcdoWFTsH+1hZOPMW02pze9Di74bJ3KaISGNTWFtPWTkxu+OSn7+HwpRexsrKDPDfScFvLaDkh7rg3Nx7a2oITJ4hGKce71+HZOfuyp6FapZiGTFu7yYMKnglZ3XKFn++4JIniiSdkfKjV5LGaDYPr6zJetFoQBKb0LRIJOFtn5EmG6ygKywIl1cU0gc2eJs+h01HbFIdaTR7/2eIlSeSn58lrM4pXUcj9t7Qk78+0hbe25jzjdluA9ozhNJtujTFkYUoYKYK6jTGKipsSqIizvYBKQ6gdYMpLq7Zl7i/EVycugOKvz/h7D4pJU+n2VyJYlClpXptlimfNKSAlqLT83aIgxaLAxiLD2ZZJU/hMSfHISim2qPDZ7Fk0GoaKk5RNeMLvcgmhtOOYcb6kVzwlNZY0xmBtywWLr11KjlN+PqdipqUhCDKCD4eQZWTtNnYQyKg5K8FvbcloW2b8vmgMh4IUPU9Sqc+O2WieZTIyl51vBZp33f8Qt7/8pehKB2+whqUKaDRQWmOUoh/D29/xNlaWutx6++sxhQXK4KqUY0dPcd+DD+KqCK/S4tWvfBlvfevvY7k+3/XmNxHhsTVwqOgYyzL0QpeWn3L0mOL4OZd6XTLHw6GAndl6oNdjm5ccBJIUnYHamYzRaDSXaLr5Zjh8yJAMQoZZQBQpHnxQKBsAV14ph//003D33fAtr8t48TdY7NldULMjVJ5hbW3A44/Ludm1CzVrLNvcfGbWfmGBVLucOZnRamtOrXtcsjtC97dQC21Cp46vkrJhU9QH3Emf1Jln9CLl4mdTqTdsbGHabU4N69Tq1rawfytexdpaY+K0+MgDbW55dY08zlgYHCFxqpiHH6Yy3ZJ9Wl3lvceOcfsrX4kThkxueCmeSrAefRg1HEpK/cABqFaJMkskCG2HMHfRWqEVnDotIGAygboZcEljjXYi2WgOHqTf2Es/9FBKsbFhWFJr7K72UeMR7NxJWpV75ukjRzh80X7ikr7kE7PRzzh6JuKeex5icXGJF1x/Da4nZKcTJ+D++yntlSGKZIG5smKoVufrE7ENl1t4aWnGYRar4HbboPKEBW+Ma4R+oiwLjCEfjtGOTdpa4GzPZzRWHNwTY496nF53uO+pDp2O4sDOkD3eGkV3AZWljB45xaeO7+IVl52luPhiLEvhh31Gg5xppUurnmErQ4ESg4ayW0HlGd5gnbi9A0fnJMYrLd8LChQuCZiC1Z5PqyW89RxFRFUqWabAMzHrQ492LcFTGWxuYRYWIM8olAMWZLiiAuFYeJNNafacobkvI4yBI0cMTz0Fv/u7P8kv/vN/hq2brPcc6tkWKgi4dHGTxK7wkfubXH4oJYtSdlSn3HHPIp9/RPNd3614+OG7eeSRB3j9638QXWTs3Z1RhDELzhiVRCgg9WvktosXjyjCiNNmRVzeOoa1gYuajEmcOud6Ho5t2L/PcPqs3tYdbjbnyoH1uhRO0nSWFzDbfYezdoqZ/B1FToGmUSvwLanMWCZlMNL4FQGgSSKL81kmuFKRbSTJM130QEC4yKjNm0hnvbieN89DANtNfiDb6fVgsTYmGacMTJNmS2zm63WDMYaNDaEcaQ2eK+NP4IPtfE3gt+dNXADFX5/x9x8UG4M5epSw2qUIKjgqKzuyKX3jbBQGB9EXVuT4MwmjkuoQlI5kBcIT00A2M+IwsfCRjUjxpMolLWWQVKlhrMhJtz3sQRQnRMPSs1Jy7XA+mWOmfTyD7YGZYm2sCdiK47lxx0xVfmbZBnPTkmbzS8v8xGXp/2+KRmPeJu262ymLOE1529vfzhUvehFXvuRl2BurqKwUGJ5NtMD7PvZJbrjxFgKr4Df/n//Bj/zoT/FvfvmX+Oc/+7MUSYSbxpisYCMveP9HPs0t3/BGAl/Tqmd4Wo4rR5NNM6wkZMtaYmNTMRpKdno0Ujz0kLianTolbneLi7Krq6uyO6dOCWa98kr5/6NH5fS88IVCydjYkHJ6mspEefw4XH21oduFe+9VvPOdol4GYiH7Yz9qOHwox7YV/sZJ7McfnVMolpa2u/yUUnOObrdL4Xmsjyv0hhZra9BuGVZ2GepOTKZKIxiTE+KRl1QchwS2tkibi7iWmKiIFnaKhWGS+7hWjktKgs1WXzM5vkFsHO57usslrXWqe7vs745onPoC3PVJZnwRtWMHyfHjfODJJ3ndq1+NSRJUeQxKuCWYdltm6eFQ7tJWi4G3QBIZKm2XOIbVDZvd7RHusSdxpgNUq0W/upPC8RkWVdY2HVZ2KbpWD5sMM57iBDbKsfnD932I4cYGv/WHv8cPfv8PMBj0uPnml/L//vFbuf/+B3j1a36aF77w2wlDzeWHMxY6BX/6bocdCzk3XJ1gXJ/VNYXWir17heTkVxS2JUY7hdKs92wsS21LMg+HUimo2jGNooeVRuTawbIVyvdRcUyeQ1prYBU5qe2DMYS5RxyDM1zHDywiXSXKFC3To9awKQZDeqpT9hO4dKsxZhoStVcYh5o0MSw4fdGMrXqoSYhxXIzrYCux/knQxFSYTuWxdhyFQ4JXUdvDhE+CptgGxSCmN24RUa0UJMrFJSUiQJNTQTS9wlgxiW3STLFSG8vCbSbb8uU23eUF46khGuf81998K9/5nd/G2lqNyw9IMiLQMd60j0kSijhlsjamYYfCZ7FtPnZfndyvsrX1ER5++Ai12hK33PLNeB6s7DQsRcexM9n/42YflZqmEcRMElcEXeItTmU72LVbc/y4UBn275fnecbL3dgQANpuy2LKdeWwZbgUKcAsKdi9G7q1iGkRkGaKMJQGvbW1eUWqWp0ZARmKOCUpHEZjtU1zcBz5eebM3DBoVr2YGZE6zryop5TcjzPF0DNnpEAThpK1tu05rUIaARUKQ0OPybVFbgfkBdgqY7OnsTAsVccMpjadNti2QV/gTnzV42sNFPv+69/d7X5poPj06Qug+MuNv/+gGCCOSRKRIpopOoifvI0iJ8bHIyphqCkb3YQa4RFvZ5aNgUQ52xL5BkUeZ2SpoVktyMuypDTQ2VSIpPObnLDUNXZISI2DMYrpOMf2FE0vLOkZdmkd7ZR2z3kJklL0aIgajZ77+BxHRkshwM3TpF/KIFgUkl02Zg6snx1KSbrVceaNY72ezCpFwSfuu4+FPXs5tHMX2ncw9SboOczvTWLu+/Q9vPL6K7nrC0+yb2U3u5eaUKkyjTRbU59Oq0Bpw2SacdcnP8obXvUN5QYUEQ6gyYyFKQxpZrG5YUgnMZsjF4Pm6acFj86o0pYlE+HGxlzPcya83+nMJiXDzp0COvJ8jmmlZ86wuSnC/pubYgrw9rfDo4+KYsEv/RLcfGOCH0iNdPNMwsMPF7y4/jDtlQpqqUTlZ89CvS5NiEWB6XQ50a8RJjbtNoRTQ6cSYQeyODMo7OkQ5Xs8ebrK3h0xuA6BmUpzp3bwt84Su3Vip0Ze6O0s03LQw/U0o6nF2iNrWEsLtPWAhjMlO73Gw/oaQuVz2eqddP/892BzA17yEopbX87TGxskoyGXvOBmGPRxjz0FWYpqNEgvuxJbGRgOJI1mWXPSY1FAp8N4M+axMzUO7xjgFDEjd4FI+UwTm6afkiYFlmVotjVWnmJ7CgYjrJrP2UHE7/z2b1Cr1vmBH/pRXvCi63nxTTfxLW98E7d8w2v44ActjBHr5xkn+OMfh1Y949tvGzCaaka6RZppfN/Qrqck05w013TrCYkSGUaXlKdOepxaddiz29DtKqp2iENGnokZjbd+GkuL/CJRJFrRvs/QbpPWuhw5YaGV4YqVLfzBGsYY8HyySh1n7QxRd4WsUKRenaqTYuIYbAeSmC21gD3aol4rUKbAiSakza7odvsVxCCooDAWCQ6TqVzbRgN0HmHbAoodMuFao7DJSPBE0rFIpOhjG3JL7icxGMqoEJMaTWE0aWzw3AKXTMaMZnPuB/zlRByTJgVZWvC+932M62/5RkYjuOMOw3f+Q8PZcxaXdDfwaw7un79dJMyihPC217I6bfIL/7HNt32bYjpN+PM//z84efIR/t2/+5/s3r1MHMP+hTGkGZtnQjInYGK32GOdJu8ucm7DxfPUtuPlrOH20CH5PcvmQ6Pryu/1emn37sjfLJSmF1FsRGpRufSHIsM2LH/aZdtHkghoXVqSbW1tyXBYqZSSggUcOSLvr63NucCzYXSmfjG7rjMN4kZD3psV/IyR7darOavnDFt9C6uUaps1A2oNWZRy9Khi5x6beq2gqqbb8nBqPEZVq3ON8AvxVY0LoPjrM+wv/pG/B+E42EkCJGVzXFF2ZUvG2CLfBqEgWWCLDI+0pFc4ZMYmLywsXeCU8msAyhXHu5yQFAerBMQBIZp5I4y4mOXkRuOZiByLVr2gULrM9KhS9ViAsFf+fYoijsAfjecSmJY1b1m2LBnwZhIKf1uZJa1LnSHY9lOehTGEcYzveaheTz47E9Fst7e5yy+97Tbe+8EPctniAlmsCCseWmkCQnLAsz2CIuePPvYQvhVy7f7D5KOQsd1lMLZo1yJck2ONxpjaInv27eX9H/kYfqPDC2+4Fk8nTKlQ5Iqz5xRra4ZGA44e99BasbIiuxTHc2vnel0mjtnEdMstIsbvOPNGmlny+/77DUtLUKsplpe3D53rL49474ddtKXxffje74WPfUyA8qFDcOyUS60m21vrBVS6MN17DZUljbV+DjVNsGwblabCm80yVJqwpxtBv0diarS7AXlaEEeGMHfwfUXh1rHSlIM7pxS5wSiLDJsKCcokJJ0uvb5Lb33GRzcsLsKZXsBSI6FWNbjXL+KsncHScLbYQW1ymusbD3D39Aru9l/GFW/usvd3/yXq7rsZXvMy7Cu+kc898EEubwSE/TH2JYfQTz4OGxvo0Yhs7z6KZhdjOzCZ4vTW0N02kVXFpDaVjmI5MwyCFVotqG+do+IWVGo1BkMHrXMWzTm8XkLR6qBwyAOH1775H/C7v/kb7Nm9i2PHj/GTP/1TvOPf/UcOH74YvdBl4sB114lCRVHAYjPi8addDhzQZJlNbFcJS8fJatXQqKTkyiaoZlRMVtKWZAFrDOzebVjeY3AHGxSJS2w3iIW8L7z4LIUiwxgjWf56ncQOWDtR0DmYcO1hg4lTgaRhiBlPYPdu8mqD6ECHz34m5YrLDEHFwlYpuL480YGPHQoVwpv0xVVwsUuBT6pdAiZYQGQcRlOXepDQqiB28crgKINBkxkblFBLCqOIkaZAn5j1sQ22g3ELfGKyUjN9pqzjToYUBtxaVSZ0LNTi4jPt37+csITqg6WxijF3fSzjFa+y+O7vVpw9q8hzw31HOtyy/zS6UWPy3/+Ad3/r7/PWH6tx88t9lpfhsccM3/mdDp/97G48T3Pq1Drt1jKOaxhTZRIBC21OnZJ1f97exR4ybFvMXZQSQJnnIrc2M8CYYX7XNdsKDjNtc1k8K6JxSqNpcFyXPPfpb+QkmZoJxQAyvLbb8vuOHfJ6ryenrlSiZG1tvuBeXZUxKc9lvyYTyStoLWPT4qIA7Fl+YcZ1n+0fSBa719Ncsjem24XFesRW3yIawuMnXQ5dqqkEBdcvr2IqTSY0hI6nhNZHvS4bmh3EhbgQF+IrjucHKNaaxAiI1ShyJYdVYCiwyiY8s61CYZER42NIt5vr8kIz7BdU6xrlOtuUiljZeI7oyKrSW947r1kvLq04RDLHYho6bPYCgsDQaaVoNTvFOQaDS7oN2o0xOBgctwBLz4lpeT4HsK47B8TPztp/sUxxpSKj+SwDXcoz3X/8ONcsL2OHIe+9806Wd+/mlmuumadaZxQKgCzjzGOPsbPVgm4XezCg2j9N2uhQhGPi+gKBl3DDq17NC03CHR/8KJWlKpbW1E2IbgRU4iFqmkJNmoYOH76Sgwev5P777+LIkWPsv/gQgZlSGCimFlnmM5koul1IU8ORI2o7yxIE86aUI0dkojp2TOgSe/cKxeLhh+X3V191hmOrdfbtreF60ijz+ONSZt3cVKy1fJQWV7WVFcnmXHWV/O0s07O2ZlhZEbOA9XU4ueqS5Qm76j4kMeHSfrxsgjXqQ72O8QUE0VnCNSmKjLTQjCObyVThmJhq1kMHLoldw7JzClMQFQFTA75OcEhp+AprwWFlBZJJghoMCNoeiVPFUQnemSOYOz9OduIUlYuupLZ1nCxMuf6is1h7djJp72Hr23+AzkMfox2eoZ0/xbFqwbjZwL1hN+N33kFjNAatsE4ew1rqUpw+I89Oq0W08yAPPaLZGrnccFVCveuzFD8BkcLyl1g1HaYDOHocPvtZw7XXadr7FX6rQqp9MlwGYcKP/OAPcsd77+C+zz3CZbtW+IXv/z5UHKEmIzA5uuviulVaLWh6IVHhcN99UG/Ca687Sz6tstDQJHmO4xhZmJocpcAeD0irDeH3KxhEHoGXY5OSBE0cW4wVUhx8E2H11lFFmThZXMS4LirPydwGOyo5aVzgnDyKmtmaVaukF19Gqj08YjY3DJdf51PrncSrVkWyTxlSxJynFihU2yOKW3jVQDRoVYrZOCc3k+PgDjao+B0mkY2lDI4vlaKtgU3NSwn8VCpUaKxRn0otJ8UhUTaNhiYxokFMmUVOcbcX+1SrJHikShbtFl+CSs2XEFtDm+r6Ol84U+d0P2Nlt8XqOUOnFvOFL/hceShhyVklnyTk61v0b/0WtlSL17zBwRutce1lGRddU2V9vcHu3V0+85mPcfDg1TiuYUczYm3TpzAax5EsabMpig+FdqjXBVTm+TxPcM01MqSdOgU7dhgqFRkqw9DQbRbYnsXaunxeKYPjuGJMX2ZnW82CwMs4t+HKPZVKw91wKNvJ83nydTSaZ51nDpyeZ7Yz0zO5tvHY0OspbrlFMtiDAdx5p+G222Q/NjaUmLCoWRVLuMz1umIY+3S74Dguy26MyjIO1jfIrRYFFqbTQSO9Khkewcx5cxYXdNguxIX4qsXzAxQDbjwhTzN01QelydE4FEzxtr2AilIX2CckICIugS6IBE+jmxHjop5lv5HilE5RLqrMDE8JsMrJyN428YBOJaRbMdtySZAQ4eMSo8qmmmdA25kv6axlfhazWtz5EUUyKrvutlHJM8K25/65rZaA6POb66pVCAJuaDZhPCaOY7xul+nysjRcRZEgT/u82yII8ByHE8eOcd1NNwkXFYWDYWotYpRFTk5ADAqycIhybKaFx2AkhhhOo4ufT1Bbm+iaS+FXyDLFtdfewp0fehu79x5gYtc4t2poOH2qizHKC/Dq4j5XrQr4rdUkUzPLFO/ZI+uFIBBDt0OHpNz5ylfKaX1wbRdXXJKwc2fCet/j9Bk5xUePUlqszlUNRiOZvG67TS5FsymT5MKC8I6VUrRbBbbK8MgwVNkctfGzjNSvkvtV4QYrhW1EJitMNHGiyQpFqxKx2FCEhU1iL1IUCncyQGUpWWOBJIXRZkJQAd9zsB1ouzGDqcPmwMG1WoSTjLad4rgZm60D8M0H2Vwv6D1ylqt2+4wTl3Nhl8vOnqL5ilth0YV6Ioud06d5eZLw4TvuYN8LXsAl73mrNNitrMgqYTBAd9roMMTkMVY24JrrFyjSpLSjtkj2XYrXX8XKU1asVYrROQ56U26/rAeZDZOdoGp84F3v4mSvx9vvuIMXX3cdVdvhX/3Ej7K4ezfqxAlIYlmgNZtUxuvsq0wpPA/d6+FUq7zp21s8eUQTuBl1LwbLx7FS2NrCLgxFq4u2NQQBeaooXFtoU6pgHFkEviJOoTex2dEM8Siwxn25eTCExsd9/GEs34Fdu6iYEOMrdN2B+h5Mab5iNjcpjKJiphRKoW1xMXSsgtD4FPjblBiNjU9IVKkwps40yvEDhWtSss5OXJMwwYe6S5BPGEZ1LNehCBOqQUa7pUkJCA1UEIOIoFLBJCm4Ng45BQpPJYQlHSwqFXW0MbLkTxI8F2konNOTv+J45OGc3UGd+z73KW49cCnVhTO87+5lXvlS2L/PgOuw91Abe7DJ2Ze9CVpdXp9rcZE7ucBub5V6PWaxW7D4bd/Df/7Pv8xkYtixQ3F6s0KnI8NUvy9r+CCQf489JkNWXopnjMcyNDUac0MWzzM4lmGpERPHwq8e9GHvYswkD5iGivEYmk2D58m2+iOHHHmuRXJNgPGMKuE4cx+embnGjLMchrDcjtnadLAsafBbX5eFs5jHmG1+8EtvEStrrWb7bFhfl4a9WTa5KAT0T8c5ri/JFrvcCSuO0GXvhkiKyqwUUsEhxiMlt+xnWUVdiAtxIb6SeN6AYt1poQYDjFKEBKXecIhLSlbmZh0SLAqS0nVupjfsIzbNMS4uSdl+J/JuQpeoloJvBX7JTTblVjU5EQEzI44CyVa7ZTOfKZnMOQKmAHIj9iKOymVkBBkdz6c2PFf4/pzr+VwZoCyTmWNGsO332Z4JZuS3rS0ZkSsV7n3qKfqNBu2ZhlCnI9s5H5ynKf+/3/otrr3+egHbeU5caeL0NqkGEcr3SXGY4uETUw18lDFUigkVJ6YYTwn9ZaZuA3fRIx3nxMOUekPTG9tced0b+aM/+VNe/01volaFJUJpOlNNrOoChQnwfZlUajXJEtdqwv1dW5NdOnBAEntBII12CwtyWh97DNb6LtqfSyjt2GHYu1cmt2PHpIN7OJTDX12V07V799xRL+lNUeeGjIJFOs0CT4s+dYZDpabIch9jUhwSCm2JFFaaUqAwloZoTJxYpJnHQrvAsm1yo1Ea0moLrTJRHfB9giWFF/fJ3BqPPW0TRxbLOw37mn3sPMI026SWTzSNWDj1KEzGLOzcybkVwz2DW9kdfY6rHn8H1jVXYZ56mvHSAYLDl2ENeqjNTVQUcduLXsSnVlep/tIvsQLwR38kvOiFBUyvB60WZscySoGVxQzCAFcl1Go5jEY4jiGptDDKIq3skOznZAt33MOgGCn45GOPUalWuezSS9l78cV871t+GD0azgWFd+ygdOtAdToopcgGU6wkZjVu0crXuP0aG+M2CHWAaxKsjTVMo4nu99Ab52B5mYndwNjgEQMG21OsDXxsT5HlKRUvJelNcQKLyHiYCGzfIRiuohrlgnN9HaUUanl5Xu9utZgaH39Rk+cFuaVJcKm3LGmKdRQVQgw5lMoSIVVCAjximoH0DvgkxDi4vTV0EsLCHjw7J9QN2tWIEAvHcYnKz7omZlp4TLTs29SqU9ET9KBPbjsk1TYOKb6KykV3JI2+RYGajDBKUxiF5TlgwGQZnFtFXXzRl58xNoaD1TWOj9u07SHDSsLhfRb/+OIhg4nFwYUxkd+Cz3yG8NqbiNKAez8lFsp5rrBtxdF8mXwE7bG4Jd566xu5556/oN3+5m2t4GpVeLrT6Xwdv3u3PJezZ7dalVtn5ga3YwfkKVTVlDEWjbohNh6NFmSWom5FFEVAUcB4JHSZmYKLZYHPlDEV8lwaNFdWBKDO+L5xPJdZO5+Svdb3OHgg59QZqNcNF++aMg019UWf8djQbCpIYupNuTa55QGi0bZvR8ok84kTg8kLVJ6Tpg6On4CCcz2flfoQazLB5DnF0jKptrFVTlFO16qsEGQ4BF+pDvWFuBAX4hnx/Gi0KyMbTcmhlFlLscusbIxHjo0mxyMmxcZDbFSfscou5w3JBPvYpbgRQFg22DklPE6wSqVikXyb8fssEtEiZUbnyHFJMGismWtdydVVM/WIWRPcF1OJ+GJRqUgWeTD4q1SLWYThnItWqzHOc1xjcMqZSJ3PWZ5O+YuPfpTf+OM/5v98y1u49SUvgWYT4ziYwohrVqldXACxcjny9BHyeMSVl1xCPokk4+FocF3yXJGkYCURea2OqzLuvv+zHLjkRt76R39GHEccP/Yw1+7fyzfedAUrFx1iUN3NkWMWlYrCsuZan/fdJwoS9bpMpFpL5nhhQcDtnj2CwZSSybPRkElPa8O5c/CFL0jps92WNUaWCadwaUl+P3xYurqzDMb9lFYlxQsMaQxhYlGrG1IVoKIpVm+TDWcHO9sJVpGSpwWFMeR+lWnsEI4zGi0Lz06JqZCkitHI0K4lVKyY2JbOcRWOSUJD7voYFFsDi4UFVWa3DUePiiXyVRdN2Bn0sIoc9cBnSVtLJIeuwH3sIZxwDP0ex6Id/GV6G42a4bX7v0CHLTkBF18MwB1vfSuv01pMPfbuhX37KMIIpWDgLJK0F1g4+RBqOiE9dIXot6JwfA2tLoPIYWPo4cZj9tQ2UbZFVmuSpJp7H7ibj3z8Tq6/5hre8NpXkRoBia5JpMFtOpV7zBhM2cWUlFKJ9miLo6dc6k7EUnWKabdRkzEmSTlpdlHreDS9CEvP6VCqfGin+CSZXbqIFfjDdSbUqAQFx9erHFwaYecJbsNH9fsQx5iZlECrNZcQKM0wYjw8lWw352ZG4xYpOkswjosuMnHzU4qJqYDSKDLcLGJzGtCqpXg6Iy95vxpRcjAbm4QLezDlOQUpjTsmQ/U2mdSW2ew77OpOsC3QaUqepEzdJjg2lbIfAaAwEOUWlfXTGN8jbS5gaxln1PoGubaxD+z9ymgU6+uwtsY//dmf5cRnH0Tf+I/43n/0Um7efzHp8l7GU1isTim0Q5FlvPN9FdbX4aKD4DkFO3drsmzmKKfY2Mh5wxsO8Qd/8CRXXKE5fny+ju90ZqoMc5+cU6fk8ti2XKKtLVNqkysWFsDVKRrD2U1pcJ4Jbdi20ByCQOE5OQ4JawOfNFX4/tw5c6YhbMxcg/j8sKy52E6SyFgymRgsS34e3DHBsiAiwDYpsfHY2jJgCkZTm/V1oWVNJnL9lxczrP4WdiNAaU2Sa2xLegvC3MMrJjjRGFOtobQixdrWnQZwiCjK+SWo2RdEJ/6O4kKj3XPuRwX4VeBbkBvy/cCPG2N6z/HZFwC/BlwBPA78pDHmU+V7XrmdNyPD+P8EftoYUyilFPAvgR9AukHuAH7AGBN+NY/lr4vnTaYYANuhyMy2I51B/lOgqahJmcuFDLsErhbu1ipptYXtWRQlT88CbApSXJSRbvCAGKMUWUnDcEjJsAmRLIDkewwJPnmZLwZKWG5IZdrDI8YkMbbW0ugDMhpPJl/58c8c7P6mhcysc6ScEVKluPe++3jg3nu58qKLUFpTK0HxRq/H1mDAD775zXTbbYzWqNEIlWWoLBM5r0YD8hzl+9iktNqLfPa+x7n4smtRzQCnzLkbFDEOfrxJXmvg6gwwXPuCm3HyhH/w5u9gMEp54IEP8apXfSO//Mv/miuuWOe73/ytHLpEcfKUhdYyYc4M/i6+WNYRtZpkcmYNMrWaULKzbM4FrFbhox+Fm240HNodkcT+Ns9YqblW6Hg8L2tiDLZV0KpmVNaOka3sRfkeTUfswT0zAqegWO6woIQnmCkXk4akfkM66gcWRWEzXgelHCxLut0btYIwApsE6gXKMli+g6sNqbaxbMXOivDfXTumfpnLCy4X9ZAMh8m0S39k0zpYUHdC3OEG9sYa+eIOrMDn7BMdkRwrNB/fuIw3fLN5Bi3mpjNn+OAnPsFtt966vXrId+8n372PxnADdeZh8gMH0ZYmokZftVBKUUQJS6MJsVWBPKXSsGAS8bY/ewd/+vGP8+rXvpZ6vU29WuFVr/0mDDGTqaGxdRJcCzOTl1AKplPSoIaDwY2GpF6NqL7I3h2rOHmMqjWJtY92MrLqAlbk46kQlGFSVmECxAEyxMM/d5J19tBZsKioWHS+Y8WRcza7FmKKJMdNR9AIRIbOzN0uVWEo1jexOi1iY/PRT3ncenNM7LlUS3dKJ4/lBkkSkuYSzmSMVa+irJmySIGnYhLboxXEqEKRaU2MD5iSsuXja02FKRk2MR4+CSkWoargdGw0FruXQhJ8bCKM46AdB4+ClIKs1FaPCskWB1aOWVxAJQluEaOUhdGa6Y79WOTYXylqajYxkwknxmN+7Vf+Ez/367/GA59MOLR0MRuPRawcrHBio8bmJuza6XDrrYbJRNFsGnY2Q4yBs/0KjzyqSrtjize96Z/w1rf+IT/909+z3cyq9dzwwvMEjLbb8myvrs7YZAU7lw1Hj2ouvdSwuamo1ZztYW11VapGWQaViqJeF0y/sGAxiYNth/aZEM/s1NTrJbWi/1cPfzCYs9vKAgcOKY6lmGDTT2uMNgpqdYXnB6yvGbSlKArN4iJUAkNNT2l1FA45Rikm9TYmi7FdhWuV43WRUh+sM6kvUdQ6YuqCUIN8IiICHEQi1CVEaQulnl9T+IX4mo9fAvYBlwI5AmZ/Ffjfzv+QUqoJvAf4L8A3AK8A3qOUusIYcxb4t8AlwEWAB3wYeAvwW8BPIqD7emAEvBP4v4Gf/7s9tHLfn0+Z4tmIWiBZI4zGG29iexY4DpkSKSS7BGkpLjapSCZh4ZTUCY00wSVY5MbFS0doxyLXYuvsEZLglYL7KUkpKVbab2CVTlU+EU7JIJYSq480yKQ4JkOb0mlOCK6C+L6cmKUyXFfOwXA4n2Ge/blZF0m9Lp9TiiwzfPjxp3n6ic/zQ6/7RqJeD5NlVFot7vrEJ3jo7Fl+713v4sff8ha++/WvR62vw8UXS6ardAUsPJ/QePhbq9z9+BNo3+YFV16NNdhCdbsYyyZRDhY5GRYeYmM3MYEACmXhEJPi4hHyO3/4NnbvOcQNN7xkmxWyvi68Pq2lYa7XEzznOJIZGgxkAh2PBRi325RNMHPVuXpdAPDqqik9S0R/dGYdvLpqWF+XjPFC16DzhEpQkE9SjO/i2WLnXVEhFAX5YERS6+I7GSpNyacxVr2KUZJBN0CoZ9xwtX15wFD3c+JUZJly5aFVimsSIlVBG4OVh5xcr6CKjJ3mDKoivHVTGM7EC2SnV2nkW+y4qEbR6ZIOY7St8dMRSaXBw8dbfPIThttf0OOKbygVSKIINjYIexM23/o/uWdtnZe+/nW0LroI/fSTslhc2IFe7IJtE7VX0I5mNLFoenJ/RomFG1jkysJTMQzHWIHDZx89w+c+dzefvPcz/Kf/9OtUrJQYj/4m1BlSNyMSv4ZrGZTnEmeKIlfYvsaOpyhjSDwRiXXJwA8w4ZRMO4xpEHg5jspIccQxspT0k6ZYgzKGvDBMVE0oGbk0VCYJ+E7GAeskRQHJ4gquTokzizwx2L7IsK31HBbqMYUzb2YyBgoUCQ4BCcykE2caW+dRlUyZbTZGdLdFvjEtVWak4VernAgfqVEZNDlBqTEc4ZKXfQu6tIp/dnHclOOIQWNlERtDn2ZLo7TBHfelCa9SmS+4vwLTjvmXGpJJwl++7d38+m/9d27/ln+D5Yx58YtexjQS3nyjIc/ezDHeIuXRLyh27bFo1gocFfPgFwKOHVMcPgw7dxZ83/e9iZ/5mT8lz2UX63VZ2MaxodebyaYrNjbkme925b2GFxEXHl94XLNjhwxrs+e8WhVJtYWFeaPcTIZ9NiyurMyLcrPxYkaZmJlrnL+OmC2at7YMn/+8KN2srxsalYJcWSSJmpmBytAapLgm4uSqj1OxaTbBNjF+2Ee5Ltg2hdIk2CSZTUVHZInBj4eoakXc7KYpab2NoxMiglLtyJQLQBcLgx14WPbXRCLzeRkXMsXPuR+/DvyxMeaTs/0CfsUYc+hZn/tG4DeNMXvOe+0O4C+B3wC2gBuMMU+U7+0FMmPMGaXU40j2+QPlezuAijHm6FfzWP66eH4tM7UuzTQKHFLyDCxdgHZAlXJJpY+dgZJpbKRMSkhU8mINQFHgbawJxzLLMJZHpKuA2dY3jvBISmBtI13ikm3O8cm3pZIAtDFUVfTMLG4czzPEM6/jL+OYqdXmivEbG/PukCh6JjA+X1H+PPKcnSa86pYX8FDN4d0f/jDf/OIXoy1ZSLzkttvYs7bGC194C1defbnMDktLkoq1LGK3Tu7UKJSNp2J0vcKLr7uSE088wYf+4HfZdeAAl7zkNpQlhiexcclLYopB5JYqrljYpkoAcYrH8s7dXHbZLX9FeMOyJBM0mcjhXXstfP7zoi2c50KPrdVkkty9WyTWOp1tB+tteafZZFkUBq3FUvrAAdlm4OS06wUVx3Bu6IJjsCo+Gxuwq51RcUKUgVTZ5K0FxBxZobTGjiaYis9IVXj8CYsDBxSBP7Ps9dBa021l5DhkmU1/C6ahYqU7IdEVQmMTmIjQeDiWYmUhob+Z07OWaU9XsToN3HDIsj+AvRrXbpPWGvjjAc7Dn6M4cwZ2rTANXdwdN/Jjr1nF1gbWCtGyajREeWHfAZr/17/iFZMpH/yTt3PD0gq7rn2BSLF1uuS2j13EWGnEVtykaick/QnVuo1bcTHDLaZ+m8JVFLUmSZaz9+J9/Npv/Rfe8pYfRmuXyDgoVbDQSmBjiuo0yO06eW8N+9RJ3EaLrLuIpiD26ngqxTIGM51gipTEa0LVYTSxMcpmklpUHHCiMbbvihGGKqUTjbh9JWt9Kp2CwrJ59PEqF19saNgTrCKDnkG3W1gmIyIgzjX9iWLRScG2aHWgwMEvq3S5gZAAnaW4w1XCzjKB2OUJeioFaA1Ap4NRiiKHiakwHCuGWxmH9mdY4QRwSCt1tFEERIRKzIMUlBYtwivOyEmQxWNhNDEOthK1HMdIc1VASKJcMttlpTMlNxZFoXEqNiZnXiWDOUXrKwmlcKoeB296E99lv4x3vevf8Mu//CucPCkc4cOHDY88IkPYrl3C23Vdm1Y94/HHYHmnxWQilArPm1EiNL6/xMbGOTqd5W2XOK2hEgg4PnduLqwwA7XdrmJIgNbyXCfJ3L1yplLRaomkY6+ntikY06mhVpNF8AwQay3jQFHMh9GZjrDjCLifbb/fF+3nfSspm+s2+/dbnDljkabSxBdFalvRMk0dmk0bZeXoLCFNXXpjF5V2WKyMcKsKrURI0DUxq8OABTalWbLUeLNrNWwzpcDZnqdm5lIBMQX6AiC+EF8sUvUlVImeDfyVUhY8w41sFrkx5see9do3Aw89x2c1MH3WawWSHZ4B6Jcopd6LZIp/G/hFpVStfH9FKfV5oAv8KfBPv+iBfJXi+ZUpBtI4J00yXJMRGxs/GaM9h1ypMjcjmRaXGNuIvWqGZJELNJ5JsEllujrvVolLDpdFgbCWJQuYYpUKFooc+zmzO9sexTPyWq8nI3GjMff+/GLR7coIbozMLOf/TRD8zVnmWYb4OcJY1nYTnrJthlHEZx59lIbWvOjKK+VDjQZFtV6aD5QDd6lJVFRr2xNwgotOQpzhFnG9i+VYfOTD7+c1r3wlofbJsLjnL9/Ny7/hJVCpEOOy0XcInBSvauGQ4qqMBIcP3fk5ut0OjnMxti2TVa8nWHznTjGUe+opsy3Icfq0TH62rbBt0b59+mmhyy4uSua/2Zw18ih2L0b0Jw7a1mxuipW0bcs2mrWUWlDQG2jCRNNqS5e5osDtr2EsB6vmE+Nx9gzs3xFh2YYpAZ4JialglACdNBVuo6NScsnviLa1AtAUOeSlv0qjIYCi0TBiqVwtaKgRemONia5yNt/B8pLBt2LSFIL1U/P7qdNBuS5mYRFjWZLpyjKRFyuvlxmNJCVerzO2GsRWlSIvqJx4nHve+f/y8te/DjodkkqLgdXBN1NqDYt8NBGOfK1Kr69wApumH5MVij9++1t505v/IcrkvPf9H+SuTz/Az//8v6ThhaRKLM4DJtib60yrCxR+lcBMsLJknvofjSjaHUKrRoVQZADXbArLptpyWN/QHNwZYrRGq4IiTFCWRjta7HCKApNlJMrj6HoNSxsWF4TziSmo6gitBOROqYhhIwpjNOOxLIxEzEUc8vK0AAxG27i9NaxWDW2JiYsCeV7CcFsSJbU9VLOJshTpNGe6PuJsusCuZcMo81jUG7hFzIZaouan2K4oEsTGEbt55QIKl1Dk2IzcI3o6ofADjLaxVA7DEa5VQKVKhChS5MrCjSeQJMTKQ1c9HDUfs43rob2vjo7t44/D7bcf5vv/0S/zHW98DWlhsbrlcMVFIf3Ip9VS9Ptq1sfL5uZ2MYqtLfjEJ+Cyy+R5XFmBz3/+Hj72sbfxXd/1H6jXBYSOx7CzG3P8pEY5NpOJ8H9bLeEWz8wxXFeGwX5fvms2JE6nch337AbPF8p8vS5DZKMhn+905BLm+UxHWJFl8rk0nW1jrnThOGDrHKuIMdpGZRlntnzsIiKI+0zdNs2lAKXEOS8qJYR7WwUr7Yhh5GH7Vpl5nCleGgImpc23QZmCOFHbts3no5QQB4Ml9wo2CnC0was+v3JaX2vxtZYphte/G760TLHkO597zn92PAcofg3wvuf46F3GmJec97mfBP458KJZxve89zrAk8DPAb8D3Aq8G/hD4A+AO4G3I7zhRSSD/O+B9wIngY8A/7Dc3LuAjxpjfu5LOqCvMJ53rauOqwnSCZqc6VSRaA+BMzZTfCZUKbDIsWA4JB9OyYxQGqpMcfKIPM6It93ndKlh4ZDiESHbmzX4qHJQc8gJTISeAeDzFxMzV7mZ+0S3K6/3es8NiGfucufH5uZcpLco5vU9x5lzhEF+BoHMMLMOM8eRdMezYzbb7FhG7doFS0s0KhVedemlTEcjmWk6HdAaPR6iz56R45gR84ZD1GiInk4wgxFmXPrWdjr48RhnuMWV+/fzqY98BG1yUBY3v+b1mKBCqmymiUNhFJsDh2iYg5HFh6Lgnns+wT/7Zz/L0aOPEApTgaefhieekLXBdArXXC2T7BVXGK65RgwuFhcFQN97r6wFPvxhuPNOiKcFx44Ztrbk0I+e8ejWYnSeUqnIZDhzxzq7ZvP5J1yUY9PpalxHrrWrEvJ2B+1orM01fBOycyElzUBtbBDEA9Y2XUwcCxVHGUyWYpKUXDkUxmJzE9JcHNw8M0GrAscyNBswGhnqdYNrFXTdodi/qipr1jJB3WWlNsLZPMck8TnbrzBa2I9RGpUkcO4cJo4pNjaZ5K54NNq2ZIV9H1Oryb2QpKA11WgLb7xJ04swtSqmWoEkIV3YSVJrU2WMNjmrkyr9sc10nDOewI5OSi0QB8dU+5xbXeNHf/gtXHvDNTzwwL1867eKfe9qP+D0GYtwUmCwCLu7cP2S268RZLCywsRtcrTYS5LLkjUHjvdqLCzAzuIstjZs9RTj1BPShMmxfGnetPIMBgPMcEgxGEmGzyok2+jEBNmI+uYJdC4VRKVUKW+lmc0DtZqZ9fwRRYpB6JFbDtOh8Oaj1jJrfY+iNxDXwlmdfcYTSFNsZTBKPDC9ySaOpzmwH4KqYrElI4eyLOp1CFVQ6tgo3HAgerVmgmtipHZlYffWyTMo3AA7nmzLcdmNAFOtMlUeztY65AWOScEYcu3gVRwZtQxM8YRCliR/lUb1ZcbBg/Crv/rHnDlzD/v3FVy0O2bProInT/jUqwVPPgmOYzh2zHDihCxU3/Y2OHLE0G4bXv5y+Tlbp73kJTfx1FPHmE5zWi2hPVgWPHXCIzXSuFyvy21cCqPw1FNy64Sh/MtzGU5bLXmG22246aqQpfqE6cRwYG+GZUG3EjIpm1vDsFSv9MB1JNNbrUoTbp7PJdXyfD5Er21oMsun0Da5ZeMH4NYDrE6HxWWbNIXTp2T86HbBdwt2L8Xkjofjy702Hhscx5RDtyIi2KbmGKXJSyOYqQkYUyXEKzWObAoh3eGJQTie/zWB1S7E13Y4xhj1xf49+4+MMX/513z2JQBK4t8igPf2ZwPichtbSBb5+4FzwD9GMr4DQAY7+AVjzNAY8zRCqfjm8j2Af2uMWTXGrAL/oXzvf0k8/5aaSqGShGISEtRcYlWhUDkuMUGpJFGgCPFJmyvlNJSiMExMAFmMVeTkuKUwfoZNTr698lLb5hsgTXhqBoBn+kEzFYjz9qlUkpf3w/CvdoPP2qGn02d2e8xsd2e2SSWtYdu+Sak5dSIIylRnU372+3Ov0WdbSGsNlkU2jkhbdfxogk4iAe7r6+zsdvnT97yH17361XiLi9JYB5g0lYnWdVFZRl6pk1gBHhFunpNtxah4St7pMjUBHafB3ffcy6/9l//Ij/zoTxAWNdbGrrhQORkqLRhPXJZ2uOQ6xyPCoPiZn/5RjrzpO7j33rtot/fQ69W56SZppMlzxaOPwstfmlHVKYPY5brLM7pdl7NnlQDbszLJrqyIBHO9oQjijNNrDpUKXHqpIo4rkECnbNQLQzlV7bac6hMnYEc7YddSgtEzOSRD4jewHIuNTYXTcKn7MXhdtDHsslYhc0hNk1wp/EBRIWZqLJSyaLeF4dJsKlzbotfLCbyC3sgSNQ8sbCvFsXLIx2TKYWyqtBxFJTAUQYNzW4bd3SkuKcrSGK1JBxMmdRvHcbBMRmZsNCk6l8ae3K3Qc1eYTmCPB4Xj4SgHVxe4uztEF11EduW1xG6NaaiJC49Ww1CzwK43cPOETEk1wFMJ0dSw0be5+YW3cfVlh7nlJS/j03d/mhuvvYrhpCANc5oNm1a+Sdhz0K0GliqkKlNWXVAGj4gDS5LRMCpiSoXuosIG9PIC2sALLhujZ9nPvMDaXKdwHOi0MZUqSVKQuQ7Gdeg2UyzbkBgXzy4wrlc+m5KdqzKhQDNIA0YjRacNrpOShTlFYlHzU1wN9Q4QjsANqNcTsOvMJBRMozGXPFlaKscBcc2k0cDZGjOdFkLlVYq0sUCKC8bgOlKBcsiJHR8dTdEkWEGVzDg45FitGo6JKLQDToWgVMyZIvQtANNpExcugUpI/Doagylyskzh2kZ0wympFJMpqv4V8oqRIea1r72OrY2jfOSuB9i58mKOHoX9BxTnzsGB5ZA4c1hYsDlzRp6/iy4CK54ShCGp26bV0hQlk6ffzzh+/Ajvec9/ZXn5JwCRUdy9W4CoZckQNjPwaDYF/G5tzU36Wi0Z2mb0B9+HI2cDNjcKFrqG8cCwc0fO6oZPowFnVzW7d2VkmUXNnjItKoxG4sqXZ9DrC/3CcRTNJjz5pKFWMyTTguFA02wptGXRbWaiRZT7ODrCsmwO7Y9Lh0WpOhbaxyEmszyiSUGrpZ8x7Bs0aVm1LNCMpzapBscHj4jYBGQ4+CoiRpRNrNnc89WgxVyIC/G3DCWdnW8FrgRuNsYc+Ws+5wFTY8yN5732KSQj/BRCpWic9ycWwlxYV0r1nuu9r+qB/A3xvKNPABDH5LGYZjgqIcMppdFKYIc04gXEKAwRHi4xifGZRBa2DTUnLJUTIMIjK8tXprT2sElxS6rEtsxaGM4ztTMNohJ8bo/yWSYpiBm4jUS2jFZLPjNLY1SrzzTe0Foyt77/TNvWNKVs6Z4317muvF5qrrK1Nc9ci52bfNeBA/KZ9fV5NkkpyUZnGZMzZ7jzU5/CCgIO7NrFoRtuoOiLjq4TWCIrZ1kClLWWJsegitnaJO7uprBsoQEUW3zioYc48dRjvOWbv4lpcwfa1qjMsDZwaLZEz1R6rWXgtygogGFa4aMfvZvpeMwn7voIL3jBy7npplfT6wngXViYrwnW1sSYYzKRLNXBg3KqduyQCTrLBMOcf+rHY3lN9EslS9SoJrRqBZPMp9M2kqkeT6kGBtvTUv4eDdAKTKVKpl3MZIq7eRblitxF3u6SWy4bA5c4URS5od7U+L6iVANDS0VeMpsVQzsQK3GDxhQFn7kbXnh1BIFPxYRYG6tynbpdotzFKULs0YCiPyBtLXEqauOEI+Jal831guuvztnYhD95m0Waw+23FRzqbuG3fHQ4xbRaqDSFzU0+fOed3Hb4MDNbsay7RKod0VAGppFNP/JoN3JclRIjgC5IBuR+hffeeRcf/PCH+Ne/+G+loz4akuUaU62xuQEo6C5ArpzyVlS4xIynGt9JqTo5ORArH5cEm4K8gDMbHjvSU6JWsrhIpmxObvh0u4qGFRJncLZXobOghdNpEjwS8rh8zmwLVRQU/uyZEam1mfSaRYJNRly45MbC0xF+OU4kSu5EN4/QYSkjp5TAEgXFYIw9HkCrhapUCJVHYcSCtzeyadcytJLGqAIbrwjRWpqlpqXEljEaf/MUSa2N7dkCziyfYOM4YWcFbemSf1qQYeEYeUYApqqyzR+2KHCKBHvYo6hWCZ0GFabl+xpdf5YR0FcQZ88WfOSDd/Dab3wtvk5Z79vU/ZQwc6j4BVHmMBjqbXn0wDdUh2cIdjQYZFUMwuH3fUUcGz7+8T/n9tvfwOKiDEXVqgx1SSLDoe/PF6wgxavhUF6bFbPiWIDzwYPlI9LK0EXGqXWPTqugUlUoVRCNM7BsTp8s2HvAwrYVUZjjOOCRcuRsgG3PpRp7mwWLrQTXm5k0VUgzRZYpPE8a7pYXM7JpRu44tNyQvLRtd0iJjCfPeJGLPr22yiZIUSxKcMHobaWkJJ0pcaiSUiU0IEclFGhscrA0XuWCtfPfdTwP6BN/F412/wl4JfANxpj+3/C5CpIhfiNClfge4F8DlxhjxkqpdwE14FuBBUR94l8YY35fKfWr5XfchgxvdwDvMcb84lfzWP7afX9eguKigJMnyesNYruCrXJs8m094dnKHMy2U50uSRWFUWWXuCmb8UDMel084nIK9YRfSopjUilf93rbjnHbNs3nnzvfl1RHqevLaDRvsrOsbZoCq6vzv6nV5vSKbndOgTBGQG8cb0/U2ymIJJkT4maivpPJXOgzy+YzytKSzCqTiZyz2XfNdJOVkv2aTPjEvfcyNoblXbs4dM2L8Jwcy0jW4hPvfz8XX/dClhaaRAR4gzVSp4KqBVgmR2+uo9KEP73nQb7pW74FhcEa9gkrXT7/hMP+/Yp6NRfVDiWLQgMEhET4KArh3E0y3vw93833f/87KQrhFXe7krSbZZU++lE5/QsLcriOMxfmiGNp0nvwQWnM8zw57OlUPl+vG5Z3wHgCR49AUDFcvDcl0DHjkaGlBkxqi8S5g8kyFmsJOYq8UHhWTh4nFKMJRWcBkxscT7E1cOhNzp/A1Hb13bbn3MZqFcajgiw1NNsWjm0ojGTOwrDgqs5ZOv4UfF+ap06ckHus24VajWwcUexcwZ4M2IiqvO3Te+h24OmnDe9+j+YP/8Cwf2mKM+pRjCdEi7sJ+qsUlkVS63Li8/cytW2uv+oqGI8lG2pZZGgiE6AohCIQRdh5gh1POJcvSMbfRPz27/0Op1bX+eEf/yWSVFOpKGZjR6OazjAoBU5p1y22t+vrYuW9qxOB54CyQBn8YkqWgqNzbEthlCqNeXx8E4uUmQnJlSI2Ab4KpWgSp+jeFspzSYMasdNAJyHTIiBKbYJA7gWtMkxZ9fGIUGHEqJeTVNt064kseJDFs9k2+olR4xFZtUGiPIYTm2C0TrNhoBLIPq5vErZ3YpuUaeFRcTLGoUUzSNHDLaKgje9Kk1WxsUXY2Q1JwiTUVJsO/niVJGhRuAEY4Z2ColCa1FjEmbu97hXXugKfkAyLPC7wixDlu4SqUjqgmXIo0X+lOPXlxvo6vPe997F+7kEe+NynuOyya6nXr+TgwZdz0UUQhmrbYKNWm6mtyPB18cGMLzxulUOfNOr95V++m8OHX8LevZ1toLu0JLf4YCCZY5CilzjIzQ07k0Ro8mtrcO7ciDNnHuelL92Pp22U8XFqLr6VUq8WpMrHVREpNkEyJp+EZK0OjsoJQ01ueyitmRXDoqiUbC9ytKWwyn6FMJyZhiosleOolDBW+FZGYTtUVEwOZJkm0x6eiolx2Ow521OAUgYw+CZknFUpjMF1EHpPuWgEQ5oaPCfHKIsKE3JsnMBF2Rd87P6u4wIo/iv7UEHoDwVwvjLAqjHmIqXUS4H3GWNq5edfC/wnYBfSjPcjxpjPle81yvdeh8Cs/46AYqOUchEA/Q8Q4PxW4H83xiRfrWP5m+L5CYpBGovyHGPbZYe3NNSJSoSs4hM8nNLkQ1MQ54rBxMGvOZJ1IsEjRTEjTkj3r080N/2YAWDblpH0r2lo25ZLy3MBx0EgdfRKZW6W0evNusXmqRIR3JTZZdtcgDmhbuZyN3Pjsso9M2aePpl1vozHz+Qw12ryb1aK6/VkW7OmwBnQb7WkbJwk3P3ooxzYsUxnaZEnn3ySrufx7377t/nJ/+172XXppSS+2E9HoeLEWYf9O6bUg5xYeTz+4IP8z7f/Ma9+9e1cccUraFQyxrFLrmwCN6PmpSRauvK3HRkAr5THio3Hu959J7t2fQNKORw8KGuDWff55z8vE2mlImB4JtXmefK678shLS/PzDkESLfbwivd0U6ojM4ybuzi6HHNnqWEXAsFwLbA9m1y16cfV8QSloLATFmKjqM7LYpWi1hVS5uGcuIzBltlaJMzzX2SRM28W9jYKDVPnfktMeuED3SIQ0aMh5XFmDhBWwr7oftR6+sYz5vrSl18MarXg+uvx6yuYg5ezJZqc/IEvOnbMi67yuVVV5/jx1/1hPhhv//9cPgwxeVXElkVvN45LA33HTnC8u7d7Nq1S2xplEGhGIQ+RivqboQpDO76WZRlMcw8JqcHLOx0+Ac/93Ps2nUR//oX/xUpFuvrkhEvCji4v2ChMkUrAZlhYpf9ogaHhEy5WHmMPRmRKfH5deIJ5Dm2ycGxMUGFAsSwpHzG0v6IotkmcWtoZXCJiYyPVaTYRu4lpXOUgQyXzU0xgGk2DY4Wk5285GwKXJf5Y2sk2b1atcDrnSWpdHCnfXSjijY5Sa5xPU0/9KgFoqViZs1RxpQygwqvd5aotZM8yVAa6m5KrEStJsHHNxFT4+HrhFHi4eoMx5bFeZh7qLygMjhL0V0g0gF2NEFnGYOihlux0baA+pl8XG4UeZiIo50Go2RBg1KlfbL6qgBjYYkZ/p/f+h+cOfkY+y56Ea977bdTqUnj4tNPy5dsbEg1x/OkSe7666Fdz9hcz/ncYy7NpmRDbTvm0Uc/zfXXv4yrrxZjHs8TsOu6MrzWajLcOc7Mn2iLdruD60pVqFqFe+55H9NpyNbWCcLpBrvqFX7oh38YU6mTRAV2IFUKSxVMJjndaoIyYkI0yjw2eg5pNm/Gm0nFpWlZjVIpUWJTbyjGE/nOahVcQo6ddKnULZpNUTxSyAJ3OHFoNnIsJX0gWWGXixmDq2L6UzE1MgYxq9EWg8jHshWOLYC7MIocp2zAM9TqF6gT/yviAij++oznLyhOEvI43rZDneVMDBAaH4+EGBejZEJ0EZpFkllYFmiV4xOXahNiA61K1YntmDXUDYdzasSsS+P8cJx5TXBmoDAaUba+y3szzvHsfE+n8pnZTFCpzHVRnx3GzDtPXFf2Z9Y6PeM2zwDys2PGfx6P5W/abfnO0UiOqVLZ9l5NV/ZShBEffd+72RyPecVtt/GxO+/kv/32b/Ovf/6fc+MrbkdbkOAxneptwOe6c871T/zkD/BTP/5jPPTQQ3zzG78T3y7I45SzWx79gWbHkqHZkpKxRQaKbSfBKR5hXPDOd76Hbnc373737/PCF97GK17xrRgjUkurq9KMM8sCHzgwl1l68kmZ4G66SU7ZRReJ8oDnzcX5LQtW2iG2a3FmVeH4Ft1aytqGQlmK3QsxBsU4cQlG5/DrHsb3iZVHgcUksogiVdrCKixSXJ0yTT3SXDMaqe1LNNNY3rlz1uglk3C1Kv9v65wk01TTHu7Rx9H9PjzxhLjAvfCFkh6rVOHP/hRuvRV1+jTFocMMrn4Jn7zPp+ZlXON+gT+5/yBvuOxJdiwr8mmEowrhlHgepl6fU3jGYz5w33284tZXkgVVjFL4JsEUhkHi43s5vkrRRQGjESbN6AU78MIhn/rUR7n/iSf4htu/m8OXH2A4hIYbono9nE6dXlKh1TQordjsWdu9ot12TkWLfbi1voqQfF2KKGGsa6SJoq5GOJ4mxcHOYnQ0haIgrrZ5/FSVi3bHBF6BUZo0AX/jNJkbEHV2UTADgtIam6YQ2AmF0mgl/QVSAXJLcw23VAmxpKJkNJkRTeRKMUJbmrykYERTgxf1qbR8pkrAp1+IiQ9KxhlQWHmKFY4xtSqOEo10hWFqKkJtUBmOSsUsSMT6SHMLb7hB0ljCtkQbW5uYIBmTOkGpbqLQqihNPmJ8UsxwSOi1cd2CcexScTMSFYAC385xgq9OG0lRwHvf+2GOH3saZRb5tje8lne9513cfMsbUcplOFScPClr9UsvnTO+ajV5Pp98UqhOO3fC1VfDBz/4AV75yldhWfPhTCmxZtYannpqnUcemRAE++j1jrK+/jtcd93LaLUsms0cx1E88sgD3HD9P6HeUNx4TcI7/+IP6W+c4Vu/43tpNHejtTTrHjo0zzekKbRaBa5VYAojDX4GHv2C4aI9KdPEotnW+EUoDqja4DqGcVEhHmekyqXRMLjplMHUIWi42+xHmwyF4dymw3IrxFgOg37BOHLZuyMi07Zo5mdC6ciUQ6+vtvXTq1WF1jnjkSKoCD1oxp2+EH/3cQEUf33G8xcUG4MZj0mM2HO4pORKpI6c3gaxXSWrtkBrnLIpJUWkkUAyWKJJLAYdwts7j+09Oy9hOAeb54Pa82OmG9xqPbNBIkkEjDYac7A8s3SKomfyAP6G4yRJZtpf8l3nUzf+Bjk2lJrPVrM275mOcb8v27UsKdEbgxmNKAzoem3ulJHnrK2v8+k77+TVL38l+c4DGMulMPK1gq8VzXqO6xbce//9fOITH+UF113HN9z8IpRfAwy2SUlTzShyyXKhU9jkGMdBKUrHQAHcmIJRf5MwLbjrrruoVi/jiiuuoFoVYHzu3Bzgbm6WogvSG0ilIpdiNJJJ+ZJLDC+4Yorle3zqHgvfV+zZY2jXM1bPGU6ctti7D/JC8cQTigMHSi3SOKdTCVmopzh1F5Mb1sZVKuU+SPZXMZkUNKo52uRMUhelFIMBeJ7YN88YKpZlZmZp2LYiDKFdiQjcjHQY0cy3UA8/DEePYm6+mfDA5bgP3cuZ7pUs/uh34N9/l6gafN/3ce9rf4HlFYvuxmN4Vx8ia+/A6m/RGygW1h9FOw4cPlRee0WuIDcW7nCLD37yLl768tvBDyi0Jpj0xWZ5MsGMpuiKh9qxQ07weMyqWsI9fZzYDXl4NOHT9z3A//ETP06uHHJTSsOZgo1NS9ZonixE/GICBvTWJvZimwLN2U2bVqOgGm5imm2mukKSaLQ2BMUULx6Sa5vMq2KVLmDTImAyNtRqCksXBCpCI/W9OLcZRj5Jqmg2i+1zX3PC0nxEFA5UWSkS2GywTUZW1o9UluHb6fY2Q+UTEDHFx8tj9NYGdqNG7FaxVAYFnOn5LDcTEtvb1jzWaSoSYCYjDRoUWYEqcjbGAUvthFzLGCV1K2nUcktjoRQbn3nl0ACro4BKoHCslFy5pTZOQmhc3DTEOI44t6Uav2pRKAufEMsunS6sr6z8bgwMBzlv/f3/xic//TCXXfk6lrsDXvKy7wIlgO6pp2QoWVyUYW8wkFvuc5+Tv7/2WtnWdAp33fVBbrzxdhoNyTCfPGnY3Jyba5w+bXj/+/45LzrU4ugjHt/4nQew6jaBPaXpF6halSLv0T99ipte9dMEgWbnUso7fvtXuefJ4/zIj/4ol156CIxc7yzK6E082o0M22R40x6x32Rz7NHpAHlKgYXv5iRGXEu1KdjYUoSxxa7lDE+nrPZcajWFR8g4cqjUNLmx6G0Zus2MxDg0nSmFMSS6wlZfU68b6taEQlnlcl+LYyIp2hSM8irTUAxBFJDlMi4EgSrtq7+iS3chvsS4AIq/PuP5C4oBJhMmUxhnLtWKwdFF6T43u88VihSfbNuu2SMmw8UhIsXFL13uZOAq5koTeT5vcPtSJY8WFwUtnQ+en9GObObKFJY1B7nP/tz5nz8fwM60jJMvgXqj9ZxwOxoJ+HaceffKbP/6ffkOz4NqFWNZhCrAHW5i+S5MJ8JpXV/nA/fdx/5LLuHAJVezNvS3eX95XoplqJxqw+LkiaPsXWrza7/1P/jxn/inOEpK2Wlc4I+2CFvLKAVpXOD6CqMVQdnUWDDL2ls4Oicp4L0fuoe9e2+gXq/jutDrifSaZFwVTz8tuz8DtLMspWXN9IHnvZCz5H+tZnjoIaFZHDwIvi8d/Wt9hzOrFp22oa4nVFoOniNF+KmpAIpAhUQmIEr0tvbt1tCh2VbbstS+r7ZlpwECv6BSgdFYyvCuC/V6QWW6gbV2Tq5RmkIUcbZ9OfXecapNoQbx2GPwm78Jn/0svPjFPPRtv8hTizezpDY4fEuXZi3jkSdczqw5dIoNKlbM4euruHlI1mjjqYwcjSZn0uvxmYce4taXvULMaKKM8VbCjslRdBzN69ejEZw7R3LDzUSNRTae+jwf/sRH+NyRc/zcP/v329nufl+A0GikWKhM2Zq4VBsWvi/NYwFTMSRQMeOiglIaZXJ5RBTi9pak5NOILGgQ5w5ZDq1qho6FYzmmiq0NjkvZhCb841HsE0Ui6+WohEDJ4jc0LrlycAlL2wwx6ZDGN5iOUlq1DKMsxmMDtk3bmaItaW6b0RUAdElbyixbGqxSRWIcak4MCtJU4ww2SLuL4ognqx5RpRiFxLVFsFTpfJeTMJNOlG9wiLfdHwGiwsZWOaowhKnQrDT5dg3LVyFJaU8/GhS0GilKaSxVbFe9AFSl8hUD4+EQfvLH/wV5+jT3fvZxfuAHf4F9+17PwoJwgpUydLsidffoo3IvLCyIM5yI4ShuuskwHhs+8pEPc/Dg7YxGkg+4/sqEVi3lE/d6rOy22NhQDIcF9N7NpdFn2GUUndd8N82rD2IVEZuTCkuthCQ3HD1b4+xZeW7bzYRf/S+/wr69+3jjN72Olm0YJJ4srrWiboXY0wGbqkvNzZhmLt1aRK7EglthUFnKMPKkcOamnDmnSTKLS/aFDMYuSQqLrYRJ5pOl0Axi+lMf10QkxqZStYhiGVorVUXFz8pFmCmbuY1ULYGZIJSw6AxVW3TPZ1GrfXUoMBfii8cFUPz1Gc9vUJym5GHMuPCoWkKl8EikgxfIsdDkxHhivGHCsslOngPJHD0rZtnTXm+edtzcfG4gWq3OCHCCxCoVycjOaoTwzCa52fZhrj+cJHOC7HONhtPpvCskz2X7s2zzXxfN5rxBD+Z1/Gp1nlWeAfI4luNL07lKRhBQTCbEQRu3BLRsbaGShMd7PZ44eZrdB1/KJXtr9EOPcWjheZL56PcN7bY4TJ05ejeua3HlFVeS4FAhgiInSRW2q0mUyGk5JCTKw0eygJlXxbYVpClZkvKFR5/kh/73n+Z3fueDBIEmjxPCQcLEVBmN1ExSmXZbAPDZs3L59u2bU7UrFckcN5ty6LVKjlGaPQshYWpTWM42LdwmYbNns9SOKSyfJ58UALu0LNQbyxKO4GAgE2G7LVnghXaBb8WEZq4eIr4aik4rR2UJOZaoFCjAc9CjIVaRouIYU6qZKKWIm0uoM6eFc7i8gnX/veQfvRPr6ivh1lsxvk86inA2V8kPXcpnHmmwdznBMikFmu7jnyboBLC8jFnaQZRrKhtnQCtOTiY8+fDDJPUmO/cc4JrdO+DsGej3UbYtJzMI5KRtbMBgQOZ5/PKdd2IvLvMPvuv76Xa7jIcGpTVBBU6dUqyvz253w8UXFWKqoMJt58cJ5aJi210yFHUXIC8MoaoRRprx2LDcjbBVTpaAsV0miU3NS/CmA6ZuExyPme16HBtcF3wVY5c0BWESZygDE+NTqJkqgDT+BYQUjltSKCz0sI8KfJRbghY8AhOjUqn2pK0Osa5sG4JUKmBpoWE5JKTYVIxUO2JsCiWANC+VcQIiqVGZucGQTUamHColCC9QqF6fqd+i8ANhrisDhSGOELtq0fXDZ8I48/FIcewMZdT2eZZroL4qdfh+v+Dhzz3BlVcd4vd//884dOjb2LtXgPB4bLZFc3o9ta1iV62a7ebYSy4uOPL0Gmm+ju9fxe7dsoiZTGA8ymnVc8LUZudOhevA+lpBNe5hFxGj2gqup1hfLag3FUEwt1F/8smnuOSig+zbk/OOd7yHq644wCROuGX/ToznEQYdHCWLDUeJRnqaQT/0GYwtdu6EjU1RoYhjAIPrKmyrwFcROTYWKWHmMokcalXpw5iJEFUqSkxhrAmZcrCLhHEeoC0L10oxpTOqJFxsjBG6WKEcbBIsxMUQNDYxFgatNXbV+4qv2YX40uICKP76jOc3KDZmnv5Tiry0gE5w0BjRGC4/ms/YBqb4q0D1/HMwI6iezwGeeZA+O2bZ3pkMG8xpFEkio+0MJD87wlC267pst8z/dTGZCGh13bmW8XM53Pm+fKa0p93mEs9SlkEgfztTyJhZPsGcnlG+Zmo1TH9AVGlheS5uPEZl0vXWMy7vfu+d+Drl4MWH2X/xZYDCcaTEPVOXsEzO299xB9/yxtdR1yGWNphpSG/i0GhBZNXwN08TtXaC4xKoqbg/1euYKCZPcyJ8/vFbvot9B66hVt/PNde8mcXalMPdTT5zcoVTZ2wOHZLDnGWP63U5pCCQCXs6FaB8+eXy+2QCl+wN6ZYyX0WcMgwdosLFcRSfvT/n+uugWoeaNRXDll6fc0mbUVGl3RY+tSjzGWxLSqCzTJkwWgz9LajUpKPd5LloZY8NO/0+1mCrbISsC0NGO2xNAilHNxMsLaopdjgmmhRY7Zok/8mwN1dRrksRRpg0hyLng0/u5+b6I1SclNHlL6R3Lmbfxv3YO7rSeVgu1IzS289Ekmse+sJT9M4dQY+G3LCwQPuTn5T3V1bk75aWtgHy5zd7PLU54KkjR/mRH/hBolGKcmws3yWOFVtb0tDYaICvIhwl43WMi01GhHBwfaJtkxxRV4jLTK4iihThKGWxk5JZz8yqgsEqMkwSU3hVhiMpN+siIRuEBJ0AVaJsQ+lAaSLObbpUG5qGMylVJ3T5/YbAxIAhxS6d56ReUWUqcnYbG2DZpAtLxFpAvUYWHjqKwHZwrIxYBdjjPp6JiGpdcuVgl1UpkSGUakOWKVIrwN84idVqUNgOMX4JmDS50WxuauJEsbQEtlXgqoRB6OJbOcrVgIVCNL8TAhQ5UWKJvJ7jUFHSzKt8f7ux8yvJPg6Hcm9/7v73cfnVr2Y0VrRasFiL+OwXXNnfWNHryfe87hUTHEexPvZpBxGn7/0LhsHNXHHTHra2VMmbLQ01kpBhErCxAfuWIhr1jEFS5YknpZHz2DEBn//wDROKKGU1avKZu+HAzg9x40teiZNF/MZv/XdWlnfwyle8grrrk2gPx4Gzmx67uhGF0sT4KGWwkpi7H3C5eG/Cslpl1NmH7ajSqVIURpQS98GE2Zisy/+X+zfPYWNDAPpiN2c6MUSJYrc5QbK0F1PehEqBVVYBzq7ZdLvCWaYcIZUS7WuDFpUU38J2viYw2tdFfI2B4lfCdR+C+3mOVN2z4ixwEIhsY8yXZmt3Ibbj+c1Omtkql8DOUlI+9J+l7GGUktLwLGM6E6mcRZpK2q/dFlSVZXMubxgKyHyu73ZdQQAz/eDNzbmmUKcz11V6rvC8+c9nC7WfD9Jn3zMazTWMnwugg+znbF83NuYA3ffn75USDQa2VS9UUbCtRVQqHqT1NvnCMgEZKo0wRY6p11FK0ULxPd/6MjBwxwc+xMUXH2IS2ySJoROEoBW9qU892qDmpXzqUx/npov3UQ9sPGNwgoBca2pWjFlcoEg0tpGyuPLdUpIuwWBx5uRRXvWq1/CaV72ZTLk4Nniux3i6h/pQcXFFJmzPk0sxmcyzw9OpgOVZp/tdd0mH/LXXgm/ZpHHOOHGZhh5nz8qpbbXgxps0Dz0Ee/cYDqw4KHKy9gpuomkXalsEZGPD0O0aKtaU2ApYWprJYhkqOqLaNfTHNtq2KIoMzwW7XhA6XSy/RkDCJLE4ei5gOlW0WwV+oMiMxjZiL2P5Fsq2iZICNx6QVJukrZ34m2eIm8sUSuOrhG7f4v0nr+fAAdiTKnLH5/S+m9nbnUpfWJpghkOiahfPLlBaMYls0FdywwsPoW3Ngx98F2mjwQpwydISXquFyjLMdArTKVdedBF3PfCnmNGAvNBsTCoUhTw2igJ3OiANFYUXkAeWuBwCHomoTpS1mRgXRSG0ASM64h4RsfJlvWZZJMpG4J/QDtISkGhdkPs+xihqVdBphB/1sBoucZKQ+VV0qTIxs9vudA3kGQrF1PjlM2nwKTPB4RRT75b7l0FpoWDZDvnOXYQEpd+YSD2asnkPB4poQlEJsElQtQopVXLjggGbApdQKlgqIcTDCfvUnClmoc2UCrlR9HvQaTtig07Mnq7YVYMGpcmw5LkqM8FRmXEXU/GIhAq2o5iMbQJLMbFqkIGalA2d9jOlz/82YYwsKHfsgCMnT3HNFadw7N2srSkee8xnZSckJYAXI09DPwqoqoKLd4fkyuYzVo0rDu9hMJAF4nAImxtGFGICn6pdsIHFY8cDDh40TCdiy/3ww7B/v2H3boXTCHDslPVhhO96XH3TbWhynj7yGJtnTvGPfuAnsa0CiwRwUaS0OhahCYS3bmvJBzg5V12eMYx8aC/QsKZkyiIvYLMH7Y5ICSb4s9MtNBk8Kogd+iDxyHODMbDZ00SRNM9FC/tQxpBmajv3kCuHNBUznogKSQqBiihsF20MSmWkuBRYWPbXBD67EP//iY/J2PPnwBu+yEf/OfBjGPPvLwDiLyOe35liEKB35oxkdmfWyWk6z4giZclIuXgkPEfOdk40hXkW2RhBWIPBnMtrWfJ97bZseyZ0aVnbtsjU64LGZq/P/j1XxPE82/zsfTk/xWOMAO3sS6iUdDqyz89uvpvpmI3H0G5jBkPS1gKOU3LYskw4B0mCsSxBh8OhbM+2IU5gOkH5Ptk0Jmkt4Wdj1lY3+OD9D/CGN34HYNBpjJOMSfwGpjDc+eG/4JbrruMlr3sdH/qLv8AJVsCycWseLgkuCcPYRZucepCjZpbVoxFTPD78oQ/wjTdeR1zrkmsH30RkheJDnwzKNYI0rR05InQJx5FL0O/LZRgOYf9+OYR9+4Q7HEUi87SwIKXcrS35bKdTKtx5MY2GIRuFuDons1x8lZDXGjzyuMPSkir1Vc22BHSno/A8yf5QKFQ0xvhVKAyjiWKxHjHKKmxuQl6A58hEqfKMvDeCVhvblfsvCQtIEzKvAq7PdJgRp1BkhnrVUFs7gtNbgyIXrWBtoVtNTKXKez7d5dRWlfaKxy0vytnZTUiNjdXfFDvr0nEhrzdIwox+6IuxyWKVPErZ6Wyy8cjnOHruHFG/T5FlmGaLQ9dew55Ohz/8wAd4+vhxfugf/xxWvSb0Gk9oCDqNSAsbv8QTce4wnDo0qhlKFWTKxxgxNXDJCZVHlEgPwJw9lKMx2GTkKDF5MaIMkyrJLoNha0vRqUypeDmRcVB5gesU5OhSmjErtyGNUdaghw48plYTZckzrsmpEJJlBYOkQpJpOvZAKiVG4dkFhe2QK0s4qudlrEFAN2VFSvbcxjOxqB7YPmfXLExh2LcUCthXGqlngSlAmZypruGZULatFCbNyZOMyGuKVbES7XSbrMwqqu2su11mn2XfJGwSMuNuU0tmQ9pMy/vLibNn5W831nN+/dd/gTd9+89zdrXCeAwve5k8S7WyN1crw42Xj/FckcMbZwEf+NDHufLKl2FZqswTGLTJWOlGxInF2b4sCmcMr5PHUl54TcLRsx61pkWjIb0hBgunpMfl2iUa9fnPv/mb3Hj9dbzi1leQG+H4Z7holROPczbHHq2W8PtlGC9oeSGjxKdRywlUTKJsQBNmrhhplC61WQbaNliYsoog57QoYNTLMJZNtSqLgplh0GhkyhzETKFmLjKktTmv2Kdot0ohUCWL6QuqE/9r42spUwyglHoNHHoffB5mrqB/JR4BXg6sd4wxzyE3dSG+WDz/QbExAoorFRmVZ3bKSs3d42q1L14/nOn7PlsWbXZ+zjfjmDXezfSHZ1QG6eASVPbl2nT2+/PvmYHlLPubVSZm+zRTm8jzmRbRPEPc6cgxzKghk4nQTTpttKXl82mK0RqjLaZ2jaC/iqpVSbwAkyq8zdOobgcch0QHOLZB5yknjh1jOJmy7/C12OTiDqikZPzeD3yUk8ee5PoX3MyN118NBqLCR1tAlmIb0W4FhaWEo0q1CmlKFBW8/yMf47W33iK2xr1N+nGA9h3WxhVOnhJ+5+qqYu/e+aGfOSObiGP5uW+fgONWCxr1Aksb6g1IM4ssk0uvlKHVmitbHDyoKOKEdBxTaXsMQ4fpVP1/7Z15sGVHfd8/3X22u7y7vW02zQwzGs0IiZHYLRCL2Z1IYJylSk6KONhGJqYqkKQgpoILTBaqbOLYScUYcJxUESAhBCkKEkQiUHZciBJD0Egz0mik0cxolrfd9+6761m780efc+/TaBAqHCMh3W/V1Htz371n6Xu6+9e//v6+X6pVY+XIatAsR2z2bEGj6+ZmIiLBINFhTKerCI3P2hocuNLgegYpDGVhn5cRAWmm6G5qpDDUaoaSisi0gwr7aKmIvDonHxVIAQf8xymNOogd2+DsE9BeJ0s1y42DzLsdvH/9Kej1SG79Tc7veQ31nRVm/JT1dahnbRypUU27eEyRhJSQ/R7+mROY6gz96jbcZhWXmNXTA5w0YtHdIJqZ5cGTD/HExfPs2b2b5U6H1735JuLMxXUNJTO0erAbbbJGk1BWEELjZyFxu49o1vAcQ4qin5SIIkO9kiKllcgaDCxfVEqDFFZPOM21oBWaQexaR8QnsZ5scExubhESUM45yhqRExZczLi4VoGBTkfSnEnQjjd2rEyNIKREmkp8GSOTEKUEOo7RpSqp9LAloAKQ+IQoUoaUAEXAEAeda0koNrsC11e4nsyZXQYlMsupB1IEkfHwN1Zx6hWEtGzg/kgyyEo0xAa9tIw/4yOUys9tFVocLKVIojFGokiJRZAPP7Zx+v1J+UAxhBRBV7HGHo0YP8fFMPajULx/fS3jdz7xEZZWy1y4cIIPfeijzM5ex4MP2iLXRsMOUy/aNmSHu4Y0CVr5/M1/+HHe8/c+x549gm4Xvvc9w+GXaA7uSwhqfn69gjNnDIuLBQVJ09u0C75aNcMT6ZhiEmHT3oEJ6Ywy7vnW3bzu9b+I0ZaS4bigSEmNy8qKVYmxQ6ShXDJkGUTDDCdw8DyDEAZjBP2+wHNtkeC2Vky7I/ErLoFvi0aNmOgTKexu4tKaQ6slckVMw/ycfSqLTcK1tYkG82xpSC8tEcVirKTTapncJFWMczpT/HTwHAyKBbxFWxO49/+Id90EvBljPvScue6fNTz/VcCFsCPO5qYdlX1/XMVPvW4D1GImeLoFQGF0cWnwXMzE1ao9fsE1hkJHx46AQTBJU14mIDZMduOe+kcz+VdoHQeBPWdBjP1xs1cY2llOa3sNi4v2s4V5R8FJrlbtcVotVH3GFnwVQXTuOiF8j4qMUK0G0nNxjSZ1XLKFRXBdhO/jVxykb/WVd+/fz9Hv34cc9NjoKXwT4ZISmxJXHbyO48ce4qWHX0K7vU6CQ9ZbwjGpLYzqbqD6vXHlvNZ5K7kuXhZy7IEf8mffvx+E4Xw4y7Fjj3HXf/w0V+vjHNofMTdnb6/wVSkmoQMHLC22XLa6qc2mbZKgJCg5Cf/3+5oTJyZrp4cfhvU2zLdSFhbgwgXLUxdJyKMPDFhe0mSZGW8GVCvQGflk0rU1jTohDG2Qd+qMQyTLVGd9tLZf3QMPCn5wRJANIkYmYISPYxICGTHXTJltJJRkjEaQSUlarpAFZfrtEM+D/XsTvB3zJHsPEHszDA9cx+mr38G3/L/OZ+7ez7e+I4hfcQPJda8AKZFH7+fU44qlToDjKzInQHnumF+epYZuR+O5IGcqOK6kPqNxREYnKiNm51C7dxLuuhLTbHHtz93IL7773bz0la/EuCU8meC6lvLyRLtMJy2hmy2UsMV1GoeRqhDMlvGkJsTHJBmDvsbzBKnJpapEgpS2WM52BTl2oPSJSHBQJmUwYEyLmPQo8IlwMFTyYrU+JYaUSHHyjHSCxOCRIoRhvhnjOZY6AYIQHxWNqHbOU3JCNgYu3ayKdhyyygwi54BKYwhMmG/Pm3HmODADW6dgsI6NKCo1xYwfIrTGGKs24prELoIQJDg4QpM25+zznotYV6INGsGQTlbDLytcmVJYo1cZ4ObunAZIjMvmwCVDofJam5UVMy4EK4wut67Xo8h+/WfO2J+dDmOt4ULoZityh/CJ85tUHL7+d/ngBz/Bhz/8SY4c+TqtVszNN9u+trhoh8iNgceZaIEfrO/lC998jIX5/aSpIYqs/PYth4+yuP4w53+4ym/c+n6rCDKEaw+EyCzh3DmIIskwdOj1BNFQM4gc1kPJH3/2jyBvsqH2aHZXePfrXsNwCCUR4Shb9BdHgniUsb5u5RCDwOqV+2aIz5BR4tDvQ5ba4DpJ7KKs6obMtjTagBYuGMNaW1gL5y0DeIZDlLk0m2KsyFkuQcUZEngZUqSAGW+0VSogcgUVKW1bz7bM2EfpLykUMsXzADZDec/L4HeA3mXe8W3gOPCPgsv8cYpniOd/phjsyH/mjB1dcs1dXPfJ7m+XSqT9JJUnWk9cxgpliuIchRTTZUa3S1v4KWfOsgkZtlCEiCJ7zEtVL6ScUBsc58l8ZyEmwXOhkdxs2vd0OpfXUtZ6rHRvqlWiXNvVS4bIKJpIuo1GEyeKS4PzMOT1N97I3bfdCb6HdBWRDDBIRr2UR+7/c7bvP8jR4w/wzp+/kY/+1kf4V//8X5B4AVIbVBYjlMJIa5pQmsn3eTsdvvzlr3DPfffz0MOP8ta3/Aonj/8fPvPh91J1JWFrB6d6cwwGknLOLV5ZsbdpK+LtuuD0aTthZ5lhbs7SKZaWyDOUtsn6Pc11V1lZrtV1l4vnEtxa2erj5nJu9ZphvpmiREaMj5SWYarzteega7iwolhYEJw/P7mO/FaIIrjiCsPcrCYQIxLt4HdWUN0OSX2OpNHCF4lVPsi1teNMsdYrcfwhwSOP2DXZzp12K/b734cvfhFe1jjFu656iF/4wH7YtYvzyw79x5fZu9swmttDv6/Z0RyxvBlQrxmrxiEjdJwily8g+n248koS5RHKCioPxDaHPhsdQb2SsFCPQcB6u82JMxc4/LIbJk+2sZbdUmiGlDC5wgQYRiagYkIEheOXYCQqYyUIhA0czeamFUSplNFCYlA2G4dAG0FAlD+b+WM73l60jm9qC2HA5o9tP8sp3qQ5L9QhsUVOmDGVKiNXCMiPEGa+3WxCExASC4+NTWUzzMLDFTEOMRIYmhIrq3DF7IhY+jgiJcHFIybMAtbWJbWazUDa9bUNuBQZ/nAToxxUr0PSWmB9WAahqZaBNALXRwqNNFZuzcnNbgaU0VrSblvFl5KvQRiyTLK8YnWwC6e2OLZdvgiIi02uVgu6XTN2X3QcQRjaIWTXLjtc3n8/49caDcaa262WpUycPdvn4sW/4Prr35bLMgpmZy0dKUkMWZZyzz3f4pprtnHgwHW89OAIVQn4wX0pL9nfQyeCYyeeoLbrMMYI5mY1YT/h7HnFzisUo5EYB+yDgbH5CGIqZYdSReK5ho0NW9w5HNldn9nZiQLH0pI95sUl289nZmDQt8WOBpibBT8wbGxYCtbMjCBLNI16Rmc1Ixa2/evlhIhgyyaiGA/bq6uMefVBYPL8S2FMZDP5yZOoN1uG8lx72giJW/0JSd9T/MR4rmWKCwjxHgN7gE9ueVUDrwKO3GKM+fKzc2XPD7wwgmJjyIUxf3SwW7jSbQ3o/ooEIS9tUQOEeLnltLnM8Ahjp4dCpq245uL7qVSeKsNWKF+ADViDwN5fUfi3tjbZP202J4uDLQ5nFI5n1SpGKVIjrX5z2EGst+1ny2UbjclcueDSjPpgwGvf9CZu/8od1ExK5pVIy3UckdLvaYKy5I4772BmpsYbX/d6/GSA4ylGsoKrQ863PbY3Y4zr4ZDhVrYUHxoDy8scPfoAv/vZzxNnKa+9/jDve/e7Cb0WS+wAJJubdqL2PNskhYGHUpNAYNe2hNmGphe5hJHdwlxfn5xm925r7dycSYj7MW7VY7Od8e3vBly9L+L67cv0/DnOb5RxPcFMVVPxE4xRLLUV1aptEyvTBlFkt4srFXuOUskGC81yRNm3Fs9+1EUuX7Rb6EphpCJtLTKgQqYljz0Gx47ByZOGh34w4vfef4or33UN6xdDjh8Zcts9FfbvzvjlN57nxAlJ89odzO7w8b/wedyb3oG72CTTgpG0Or1aS9bbml2tAW7+aGRGEKYOsr3KqtpOq2WoKEu7MaMIWa+hpSDB5Zt3381fe/MbSKWPwBbRFcolGpGrO5AXz0XIIn2fplbubXYWkzsqmlaLVBbqCwmBDomkj0CT4eSBr8DbuEgy08JXCaEo4ROS5EGxxsm1fp/KtzeAMCbPzrpIYUjw8UyYF/FZEw8pDCNKKFIkhnbXw3GgHozwRIpB0M3K+GaEdgPrNJebMbhbtIENlhEdmgCHhDCUpMKl37dFwAvNGC0l/U0wrkfdH2HihF7o4WVDZKuJL+K8uLBQ0JAkRhFH4AYKnYfyWWaDz0ZD4LoaN4tsK2TSCmaoyZDSaNiuXtQPjze3vITVtkLr8fIBsHmFdtv2J5hsli0uToRrigD0q1/+E95+099BymBcIlEu22s6feoonY7i4N6AXTu24yjD6ZUy25sja1CyWebi0kmuvvognY7tJ/2+7T8XLljaU6WSc3LjhPKMotUUkMRkyysE21ooT3L6QsAoF9z55je+xtdu+6984AO/zY03vhipU+IwJTI+jXLERlex0Mow2pBKDyl1TtHxqDgRWkgGPYPvpHTCgExLggCapRGJCDDCttXW4W993Q6huxYjtPRor1vKhlIm1+ERefNuHfnN+KfnCvzgORebPe/x3A2KxW5onYGjwM781S8C/wa4Tz4/grZnDy+MoBhswFhwbi8X7BbkUauabqOWp9mz2toqz6TXXK4VbS275QS6eTBsnu54RXA/HE74wQX1IY5tABtF9v9BwLjKC2xKssgCdzoT846iUFBKe1zfnxBsCxJb3l7awEj7eDLBMRnE0ViSzjRblt8Yx4h+z6Zbc8SR5pZfupn/8pnPkNVmMVGCikeEM3N0uoqKimhUE+76xl28/fVvJIs1Ttll5M2Mt78nVNH8t0plEhhnGTzyCE8cP84fffvbbEQR93z7O/zb3/sDDr7kbShX0W4Lzp61EmmFgl29LnK+sOUpzrU0C3MZa+uKbl/SbFq6xuKibbLNTcHiIkTDmBdtj60xmEkJL25worudHds1d/1vn21zKQszIQt7S1Z31J3IW8Ok7nI4hO98R/DAA3DttfDyl1uVs82NjJmKpjWvSDPB7bcbrr/esOcKgUvC+TWPBx+El7/McPYJa05yw4uW2H/8Dty5OulN7+L0eY8dzQFBd43uuS7VPbPIzQ2SRJPt2Ucw6LAUztCcV5R8a16ht3g2CrTNhIqQYVZCG6ioiDSzRhOJCsbWxAarJb3U7nHysVO86lU3IEyKwcEXI5xBFykEcamWC6Fpexat0ZlGI1Eba4haDdKULE4RjTpCyVyf1yLB2WJuYa/SGFuQhs7wndhKa+U9SeTZ05gA19jAWGPVZjAQEiCSlFJvmaQ5Ryyt9Nmga/BHHbxGhczzccjy7DRkuRWzMZKN1YzZ9CJ6fh4c33JPgUJbFgqdCmNtpE1IhmR9VCLLhM0SZor6jLX5xRhcEno9QSo9KhV7D0kqifsJjidxSw7G2IK1tZWMK9RFouYiF1cUrq9otWyXtwGxtQuWOqEUaDY2FX7ZeZJxZVEkV9Tfaj2RMR/2NSZNGcQuWSbGa+ZGY7LxNhhYT6KiW27fboeoc+fg6qvBVSG///t/zN/6pV9hMKpw5UFFmloZtSP33sXxE+d43999J7WZCu2+Q2Ss4UqSGEolQZB/3Y5j+0zuME6tZofqQrGy4ow4f77No48e5a27dyAefhgRBERveCu9tIy7dp41ZxuNWsYdX/8Kp04/wYULQ9773vcwN3eAZhN8T7PR1pS9FL+qKMmIkArawPHjgkMHNY7URKnCde13UCiVlBgwSEuMIkW5bBUqtj6nWWaQOsZxLTXIGPtaIEIyVWhqi62fyGXZoFx1poYdzwKeq0ExgBD/1MAa8HkgBA4BZ95gjPmzZ/fKfvbxwgmKw3BSaHc5ikSaTmaSH1OG/UxaZOtWbQGNJXFnQESAS4zJnfJkPvWLSz4/OWk+i41GdlZwXZuRLX4vSLNgg0XHsZFfobmk9YRPXOydFmnKQrtYiEkFTqdjo7ctHGijNabbJcVF1qqEMqAkrOlAZFzINMHaBYSStrImb984NvyDX/81PvfbH0U0m+Pr1J5HhGe3rWXKw0e+iy8VK6OQg1fuozm/A6E1vkgQhYlJUSS5NSgukN+DiWOyTPO9e79Lu9tntVvl0NVv59QpQbNpt4QbDThyRIxrDBsNO9GmqWHbtqIeUzDoZQgl2bULPv5xuOUWOHQIzpw27Ntv8H3J448b9ixGCM/ByVUSSp0l+hf7HEsP4AeKA/tSZJrQHSpqpZSLHZtl6nbFuNax2bTNXa+bsRPvyZP2tsOhxgyHXNW5j7Wdh6ldUaeh26z2y3ix5VyXGKF2bkP6HqcveCwvGa6beZR6KbMaxEphjh+H7TsQhw7C5iYrcYOsWqNSEeNMoQ26NDPuCCEsP7XTU5Qqkmq6iTPYxDSaJMrDJbUavrjc87UvcfPN72Soqnj9dWQ5wEiFIzKr5Qz2GQzDcdWjabeJgxrOqGu5t0qh6w1ifwYlEhS2AM8aGRguLYPY2LBZupKfMRJWy9gW1tmMcRGQukmIikfIUkAkfEyc4sqUvqniKENJhUhjzxALlyjzKNFnebPMbC3CKA9PxHl21vZYudEm9WcolQwjAkpihMiL+GL8MYeYPDAmSsmcACFFvsFjrYwbdU3gZqjRABm4OMKw1PFxyy6eC+11mGtmuDLBIFjZ8GiVQ1IZ0FlP2TEX00sDuh1NrekS+JoSIZFxibVHlhlKTor0FGttq2TQ60260mAwdmwnTcnNdeww0W7bADnLyBeVtt1LpUmWNk1t3zl8eLI59cMf2j7F8Bgf+fi/ZPvCLL91669TfdGLefz0KnfeeRunT/2QG1//87z5zX8bn5ByVXBxzaNalRMVCsmYvlCt2jX/6qphbQ08z+pQX3WV5uFjmusORcjNdaq6B0eOWD/pV7+ao7tvYscel/5mBq5HENh7qdW6/Omf/nsOH76aG254F/V6vvmmM04+Ynjty0M6YQnXt/0UbF/U2vaR+XlQIhnrDhtjr9PznmrFLElzNRJBigtIpInZ2FR4viTNZdp8vxjGDI1ShBGSYObp56Mp/mrw3A6KRR0WOvAt4BvAn2PM7c/Ja/1ZwwsnKAZ45BH7s16/vDDnM1SyL4ribMFPhsvlVR+KzG/xmQQXSWpNF0hJcfHyTKgBRvi5LNxlKBTFNrPv22svgt6NjSfbTBf6SgWXuLiXgjSnlP1cltn3FRzlOH5y+XkxQ/b79nXPQw9zC2rPZSRKaNdugbvGvs+UqzbDmCSTIkDg3nvv5a6vfpVP/Oqv2vYIAksHMMZaR/f6pI1ZYunxjTv+G29/xy+gBwm10YZth0rFzsxaT+7/aUqxjYEoNKjNNR4+8wS/+U/+MW97+2/QbL6IN73xFexohTz4aMCFJcnsrM2WrazYS75wXjMawfUvtfJN+/ZkNOuaxpzivvskQsLPXTfEqXikmYOSGVEkCXSfoagQOBklL0EYTRga/M4acmGeoSzbXFAUEcUG5UqGmc/qqmQ0gu3bzTjO90yIFopEu7iOptczRP2U3ZwlHcack7tYGdXYWR/gBYK5SmTVKHrW1rw3t4/F6Czy+IPIRn1Mb9EzM6TVBgNTQQ26lKMOquLTMXV6aUDgpgwjd/zo1MsJ1bpCCZu10sLqRTthD+2VSJWHgw1c7z36MDvmZ1lszuH6kki7yDTB9+wWsSgs/qyt4Zhuw/q6XYEEwXj/3oQhNBokyory+USEBLgkebbWZrQNtrhJYAjECAeNAQa5ZbM12RhhEPYa4pybXtCP0pTEr7AZ+tS9EC0V0lFEwscXseXorq6hZxqEfgVfWGc6B22NHAx0uxmtIMR1DE5hvwdExiHBUimEMDjEdIce7bZgxw5yaa8RymSkiSB1S/hJlyRTyMDLnSLzcUMLRqaMUra4MNIB7Q2JTjNaLYlyQIkYLy/WA+tGmaZ2GCi4roUj45kzT+0zrmt9WApxm9lZO7QUQ0+5bIvurLub7YbXXGOPWdB/CmbXcGiDaceBhj7H3XfdzhNDzcK2RYYjSRSNOHv2DA888Bfc9tXbWVp1EUIwN2dot8XYQU4KgzZiHMSXSpO8RbdrnSA9D77+9c+xuDDPgT3z7KzuZ/dVAc10FfcPP4049Rjmn32MtQOv4fQT1lkT7L0miT3ml770n4iijFtv/fvWZykxNIIQJTO6sc9K26rHtFqQJGKcawCYmTG4IibFY3MT6kEEnocj7K6EQueLKIcnM9kFJt8ZSHFt4Z62Um25uA6OdfEeZ8un+OniuRwUAwjxBwa+hpVhW3uxMeahZ/uang94fpt3XIrt28cKCoAdvXO+7DhIfBoUAW6cm8Ra/VGdK2NKXDJbDY/Gy52K4jxr5JDi58U3LhEJCoXOPe9TXDKC/DNP6oXF6CulDWoL+oQxk1muKGHe2JjsM4LlBKTphPRnNX4Y758WKIISmBTdKQVBgGm1YDRCrKxggjJZvYkRApNvY8f4MApxwxC51TSkoHcA/+uuu3CK2bRWA9fFDAaYWp2hrJA1Z0nClLIZsO+qF/PF//wF1tprvPeWW9heSNgphTEGI0RuhcokZXPJQkYIqyJBMMeLK2U+++/+kA9/7ONsbKRk2Ud49atfw/wi9HPb2bk5O+m/6lUQdWNmadOvLDIcGhZmM+KRZvUiXLNtneasxC1XMGj6IfgkCKlY6yi2z/WI3Roj4yB0itu5wNH2TnbXHSpuhHFcErfE+iaUhZWZmp+3ihXDISwvwcIinDvnc/G8YedCRGtecOXOmE/9ic/yyl4++bGEhXLAbH9I2u7y0NlFzldK7FosE5hlTomdnPie4pWLHvv27oF9+2wwGoakiSaJob+8wWraYOdimUY5pS4zCG3hz2wpY2lVkWXQCz2cEkSR3eK3E7jCBFU8kSFynq4Ahu2L7H7JIfvICkNJxWhpJ37RXrP9aytvHewzVmh6K2WfZ2NAKRLhUCw/IwJ8RpD7vglsMGEMdMOAapDgGA3ChsIzjPJiOsGIAI+IBJ+SlweZQcXqbVcqKKmZ8xJINP2VHuUZRbVatRMiYObnLLebaGyKYYqjC0OrrhGZw3LXpVaDihwhhCEcGQapyd0TBSk+pZJh1y6TT7a2sE8xYqXjUW9phOuAF5CiSI2TO/sFIAXKZATGmoPIeMjCjEALB6/fJqpvIzE+m32bJVaevcp+3wa4QSDGw8Kl6/2CH5wkE6lyIZ7MDQ7Dybq74A37vv3qtuL0aft+14oyWPf52i4O3HArb9uV4JQDVtcEDz20yZ13/hqf/vR/J9FWnWU4NFy8KGi1JoH3fC3i2KM+voiYq4yIVINti9AqhQwHJebn7TkOHdrDW97yNv7n//gySxs38Kn/ILj33pNcPRfxysFjvO8797Lz2pdy1YEqYWSzxKdOWbWJvXvhllt+mY997CNIYXBdgRSC9qhEqxLh+oJdCyErnYB+3w5fhQumdTsX4HgIDLUaaO2h0CSm4LRnVBjZJMIlKvhaC9YHHrWqRgqBzNVBjbFDuVJwxRVMMcWPwAd92B/B38CYz0wD4v9P+LGZ4p/itUwxxRRTTDHFFFM8F3DGGLP32b6Ip4MQwgO0MeYZOHdN8UzwtEHxFFNMMcUUU0wxxRRTvBDw/DfvmGKKKaaYYooppphiih+DaVA8xRRTTDHFFFNMMcULHtOgeIoppphiiimmmGKKFzymQfEUU0wxxRRTTDHFFC94TIPiKaaYYooppphiiile8Ph/mBO8f7cwcLQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting based on the paired data\n",
+    "an.plotting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c2ea616",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mm",
+   "language": "python",
+   "name": "mm"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/yaml/control_tropomi_l2_no2.yaml b/examples/yaml/control_tropomi_l2_no2.yaml
index 9b01eeea..44b7ae1f 100644
--- a/examples/yaml/control_tropomi_l2_no2.yaml
+++ b/examples/yaml/control_tropomi_l2_no2.yaml
@@ -1,23 +1,174 @@
 analysis:
-  start_time: '2022-04-30'
-  end_time:   '2022-05-01'
+  start_time: '2019-07-15'
+  end_time:   '2019-07-16'
   debug: True
+  output_dir: /Users/mengli/Work/melodies-monet/outdata
+  output_dir_save: /Users/mengli/Work/melodies-monet/outdata/save_intermediate
+  output_dir_read: /Users/mengli/Work/melodies-monet/outdata/read_intermediate
+  save:
+    paired:
+      method: 'netcdf'
+      prefix: '201907'
+      data: all
+  read:
+    paired: 
+      method: 'netcdf'
+      filenames: 
+        {tropomi_l2_no2_wrfchem_v4.2: ['201907_tropomi_l2_no2_wrfchem_v4.2.nc4']}
 
 obs:
   tropomi_l2_no2:
     debug: True
-    filename: /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/*
+    filename: /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/20190715/*
+    #filename: /Volumes/Meng/TROPOMI/20190715/*
     obs_type: sat_swath_clm
+    sat_type: tropomi_l2_no2
     variables:
         qa_value:
             quality_flag_min: 0.7
+            var_applied: ['nitrogendioxide_tropospheric_column']
+            fillvalue: 9.96921e+36
         nitrogendioxide_tropospheric_column:       
             scale: 6.022141e+19 # unit convert form mol_perm2to molec_percm2,6.022141e+19
             fillvalue: 9.96921e+36
-        #averaging_kernel: None
-        #air_mass_factor_troposphere: None
-        #tm5_tropopause_layer_index: None
-        #tm5_constant_a: None
-        #tm5_constant_b: None
-      
-        
+            ylabel_plot: 'NO2 trop. columns'
+            #ty_scale: 3.0 #opt
+            vmin_plot: 0.0 # optMin for y-axis during plotting. To apply to a plot, change restrict_yaxis = True.
+            vmax_plot: 1.0e+16
+            #vdiff_plot: 15.0 # Opt +/- range to use in bias plots. To apply to a plot, change restrict_yaxis = True.
+            nlevels_plot: 23  # Opt number of levels used in colorbar for contourf plot.
+            regulatory: False
+        averaging_kernel:
+            fillvalue: 9.96921e+36
+        air_mass_factor_total:
+            fillvalue: 9.96921e+36
+        air_mass_factor_troposphere:
+            fillvalue: 9.96921e+36
+        latitude: None 
+        longitude: None 
+
+        preslev: # pressure of the vertical layer center
+            tm5_constant_a:    
+                group: ['PRODUCT']
+                maximum: 9.0e+36
+            tm5_constant_b:
+                group: ['PRODUCT']
+                maximum: 9.0e+36
+            surface_pressure:
+                group: ['PRODUCT/SUPPORT_DATA/INPUT_DATA/']
+                maximum: 9.0e+36
+            tm5_tropopause_layer_index:
+                group: ['PRODUCT']
+        #latitude_bounds:
+        #    group: ['PRODUCT/SUPPORT_DATA/GEOLOCATIONS/'] 
+        #longitude_bounds:
+        #   group: ['PRODUCT/SUPPORT_DATA/GEOLOCATIONS/']
+
+model:
+  wrfchem_v4.2: 
+    files: /Users/mengli/Work/melodies-monet/modeldata/wrfchem/0715/*
+    #files: /Volumes/Meng/WRF_Chem/0715/*
+    mod_type: 'wrfchem'
+    apply_ak: True
+    mod_kwargs: 
+      mech: 'racm_esrl_vcp'
+    mapping: #model species name : obs species name
+      tropomi_l2_no2:
+        no2: nitrogendioxide_tropospheric_column
+    projection: ~
+    plot_kwargs: #Opt
+      color: 'dodgerblue'
+      marker: '^'
+      linestyle: ':' 
+
+plots:
+  plot_grp1:
+    type: 'timeseries' # plot type
+    fig_kwargs: #Opt to define figure options
+      figsize: [12,6] # figure size if multiple plots
+    default_plot_kwargs: # Opt to define defaults for all plots. Model kwargs overwrite these.
+      linewidth: 2.0
+      markersize: 10.
+    text_kwargs: #Opt
+      fontsize: 18.
+    domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['tropomi_l2_no2_wrfchem_v4.2'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      #See 'altitde_yax2' list below for secondary y-axis options
+      #altitude_variable: altitude  
+      #altitude_ticks: 1000  # Altitude tick interval in meters (for secondary y-axis for altitude (m))
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      ts_select_time: 'time' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
+      ts_avg_window:  'H'# pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
+      set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
+      #vmin2, vmax2 filter not needed as filter_dict option added in 'altitude_yax2' to subset the paireddf as per altitude secondary-axis limits
+      #vmin2: #0  #Optional
+      #vmax2: #5000 #12000 #Optional #Subset limits for secondary y-axis (altitude_variable) 
+  plot_grp2:
+    type: 'gridded_spatial_bias' # plot type
+    fig_kwargs: #For all spatial plots, specify map_kwargs here too.
+      states: True
+      figsize: [10, 5] # figure size 
+    text_kwargs: #Opt
+      fontsize: 16.
+    domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['tropomi_l2_no2_wrfchem_v4.2'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      #filter_dict: {'state_name':{'value':['CA','NY'],'oper':'isin'},'WS':{'value':1,'oper':'<'}}
+      #filter_string: state_name in ['CA','NY'] and WS < 1 #  Uses pandas query method.
+      #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: True #If select True, add vdiff_plot for each variable in obs.
+  plot_grp3:
+    type: 'taylor' # plot type
+    fig_kwargs: #Opt to define figure options
+      figsize: [8,8] # figure size if multiple plots
+    default_plot_kwargs: # Opt to define defaults for all plots. Model kwargs overwrite these.
+      markersize: 10.
+    text_kwargs: #Opt
+      fontsize: 16.
+    domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['tropomi_l2_no2_wrfchem_v4.2'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      #filter_dict: {'state_name':{'value':['CA','NY'],'oper':'isin'},'WS':{'value':1,'oper':'<'}}
+      #filter_string: state_name in ['CA','NY'] and WS < 1 #  Uses pandas query method.
+      #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: False #If select True, add ty_scale for each variable in obs.
+  plot_grp4:
+    type: 'boxplot' # plot type
+    fig_kwargs: 
+      figsize: [8, 6] # figure size 
+    default_plot_kwargs: # Opt to define defaults for all plots. Model kwargs overwrite these.
+      markersize: 10.
+    text_kwargs: #Opt
+      fontsize: 20.
+    domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['tropomi_l2_no2_wrfchem_v4.2'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      #filter_dict: {'state_name':{'value':['CA','NY'],'oper':'isin'},'WS':{'value':1,'oper':'<'}}
+      #filter_string: state_name in ['CA','NY'] and WS < 1 #  Uses pandas query method.
+      #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: True #If select True, add vmin_plot and vmax_plot for each variable in obs.
+
+stats:
+  #Stats require positive numbers, so if you want to calculate temperature use Kelvin!
+  stat_list: ['MB','NMB', 'R2', 'RMSE'] #List stats to calculate. Dictionary of definitions included in plots/proc_stats.py Only stats listed below are currently working.
+  #Full calc list ['STDO', 'STDP', 'MdnNB','MdnNE','NMdnGE', 'NO','NOP','NP','MO','MP', 'MdnO', 'MdnP', 'RM', 'RMdn', 'MB', 'MdnB', 'NMB', 'NMdnB', 'FB', 'ME','MdnE','NME', 'NMdnE', 'FE', 'R2', 'RMSE','d1','E1', 'IOA', 'AC']
+  round_output: 2 #Opt, defaults to rounding to 3rd decimal place.
+  output_table: True #Always outputs a .txt file. Optional to also output as a table.
+  output_table_kwargs: #Opt
+    figsize: [12, 6] # figure size
+    fontsize: 12.
+    xscale: 1.4
+    yscale: 1.4
+    edges: 'horizontal'
+  domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.)
+  domain_name: ['CONUS'] #List of domain names. If domain_type = all domain_name is used in plot title.
+  data: ['tropomi_l2_no2_wrfchem_v4.2'] # make this a list of pairs in obs_model where the obs is the obs labeland model is the model_label
+
diff --git a/melodies_monet/driver.py b/melodies_monet/driver.py
index ece76c30..02bf9b65 100644
--- a/melodies_monet/driver.py
+++ b/melodies_monet/driver.py
@@ -287,9 +287,9 @@ def open_sat_obs(self,time_interval=None):
                 self.obj = modis_l2.read_mfdataset(
                     self.file, self.variable_dict, debug=self.debug)
             elif self.sat_type == 'tropomi_l2_no2':
-                from monetio import tropomi_l2_no2
+                #from monetio import tropomi_l2_no2
                 print('Reading TROPOMI L2 NO2')
-                self.obj = tropomi_l2_no2.read_trpdataset(
+                self.obj = mio.sat._tropomi_l2_no2_mm.read_trpdataset(
                     self.file, self.variable_dict, debug=self.debug)
             else:
                 print('file reader not implemented for {} observation'.format(self.sat_type))
@@ -1144,6 +1144,37 @@ def pair_data(self, time_interval=None):
                         p.obj = paired_data 
                         label = '{}_{}'.format(p.obs,p.model)
                         self.paired[label] = p
+
+                    if obs.sat_type == 'tropomi_l2_no2':
+                        from .util import sat_l2_swath_utility as sutil
+                        from .util import cal_mod_no2col as mutil
+
+                        # calculate model no2 trop. columns. M.Li
+                        model_obj = mutil.cal_model_no2columns(mod.obj)
+                        #obs_dat = obs.obj.sel(time=slice(self.start_time.date(),self.end_time.date())).copy()
+
+                        if mod.apply_ak == True:
+                            paired_data = sutil.trp_interp_swatogrd_ak(obs.obj, model_obj)
+                        else:
+                            paired_data = sutil.trp_interp_swatogrd(obs.obj, model_obj)
+
+                        self.models[model_label].obj = model_obj
+
+                        p = pair()
+
+                        paired_data = paired_data.reset_index("y") # for saving
+                        paired_data_cp = paired_data.sel(time=slice(self.start_time.date(),self.end_time.date())).copy()
+
+                        p.type = obs.obs_type
+                        p.obs = obs.label
+                        p.model = mod.label
+                        p.model_vars = keys
+                        p.obs_vars = obs_vars
+                        p.obj = paired_data_cp 
+                        label = '{}_{}'.format(p.obs,p.model)
+
+                        self.paired[label] = p
+                        
                 # if sat_grid_clm (satellite l3 column products)
                 elif obs.obs_type.lower() == 'sat_grid_clm':
                     if len(keys) > 1: 
@@ -1165,6 +1196,7 @@ def pair_data(self, time_interval=None):
                         p.obj = paired_obsgrid
                         label = '{}_{}'.format(p.obs,p.model)
                         self.paired[label] = p
+
                     elif obs.sat_type == 'mopitt_l3':
                         from .util import satellite_utilities as sutil
                         if mod.apply_ak: 
@@ -1186,6 +1218,7 @@ def pair_data(self, time_interval=None):
                             self.paired[label] = p
                         else:
                             print("Pairing without averaging kernel has not been enabled for this dataset")
+
     def concat_pairs(self):
         """Read and concatenate all observation and model time interval pair data,
         populating the :attr:`paired` dict.
@@ -1279,6 +1312,10 @@ def plotting(self):
                         # Adjust the modvar as done in pairing script, if the species name in obs and model are the same.
                         if obsvar == modvar:
                             modvar = modvar + '_new'
+
+                        # Adjust the modvar for satelitte no2 trop. column paring. M.Li
+                        if obsvar == 'nitrogendioxide_tropospheric_column':
+                            modvar = modvar + 'trpcol'
                             
                         # for pt_sfc data, convert to pandas dataframe, format, and trim
                         if obs_type in ["sat_swath_sfc", "sat_swath_clm", 
@@ -1489,10 +1526,12 @@ def plotting(self):
                                 vmin = None
                                 vmax = None
                             # Select time to use as index.
+
                             # 2024-03-01 MEB needs to only apply if pandas. fails for xarray
                             if isinstance(pairdf,pd.core.frame.DataFrame):
                                 pairdf = pairdf.set_index(grp_dict['data_proc']['ts_select_time'])
                             # Specify ts_avg_window if noted in yaml file. #qzr++
+
                             if 'ts_avg_window' in grp_dict['data_proc'].keys():
                                 a_w = grp_dict['data_proc']['ts_avg_window']
                             else:
@@ -1582,6 +1621,7 @@ def plotting(self):
                                     altitude_yax2 = grp_dict['data_proc']['altitude_yax2']
                                     ax = airplots.add_yax2_altitude(ax, pairdf, altitude_yax2, text_kwargs, vmin_y2, vmax_y2)
                                 savefig(outname + '.png', logo_height=150)
+
                                 del (ax, fig_dict, plot_dict, text_dict, obs_dict, obs_plot_dict)  # Clear axis for next plot.
                                 
                             # At the end save the plot.
@@ -1811,6 +1851,12 @@ def plotting(self):
                             plt.close()  # Close the current figure
                             
                         elif plot_type.lower() == 'boxplot':
+                            # squeeze the xarray for boxplot, M.Li
+                            if obs_type in  ["sat_swath_sfc", "sat_swath_clm", "sat_grid_sfc", "sat_grid_clm", "sat_swath_prof"]:
+                                pairdf_sel = pairdf.squeeze()
+                            else: 
+                                pairdf_sel = pairdf
+
                             if set_yaxis == True:
                                 if all(k in obs_plot_dict for k in ('vmin_plot', 'vmax_plot')):
                                     vmin = obs_plot_dict['vmin_plot']
@@ -1824,10 +1870,10 @@ def plotting(self):
                                 vmax = None
                             # First for p_index = 0 create the obs box plot data array.
                             if p_index == 0:
-                                comb_bx, label_bx = splots.calculate_boxplot(pairdf, pairdf_reg, column=obsvar,   
+                                comb_bx, label_bx = splots.calculate_boxplot(pairdf_sel, pairdf_reg, column=obsvar,   
                                                                                        label=p.obs, plot_dict=obs_dict)
                             # Then add the models to this dataarray.
-                            comb_bx, label_bx = splots.calculate_boxplot(pairdf, pairdf_reg, column=modvar, label=p.model,  
+                            comb_bx, label_bx = splots.calculate_boxplot(pairdf_sel, pairdf_reg, column=modvar, label=p.model,  
                                                                                     plot_dict=plot_dict, comb_bx=comb_bx,
                                                                                     label_bx=label_bx)
                             # For the last p_index make the plot.
@@ -1866,7 +1912,7 @@ def plotting(self):
                                 comb_bx, label_bx,region_bx = splots.calculate_multi_boxplot(pairdf, pairdf_reg,region_name=region_name, column=obsvar, 
                                                                              label=p.obs, plot_dict=obs_dict)
                             # Then add the models to this dataarray.
-                            comb_bx, label_bx,region_bx = splots.calculate_multi_boxplot(pairdf, pairdf_reg, region_name= region_name,column=modvar, label=p.model,   
+                            comb_bx, label_bx,region_bx = splots.calculate_multi_boxplot(pairdf, pairdf_reg, region_name= region_name,column=modvar, label=p.model, 
                                                                          plot_dict=plot_dict, comb_bx=comb_bx,
                                                                          label_bx=label_bx)
 
@@ -2195,9 +2241,18 @@ def stats(self):
                         # Adjust the modvar as done in pairing script, if the species name in obs and model are the same.
                         if obsvar == modvar:
                             modvar = modvar + '_new'
+                        # for satellite no2 trop. columns paired data, M.Li
+                        if obsvar == 'nitrogendioxide_tropospheric_column':
+                            modvar = modvar + 'trpcol' 
 
                         # convert to dataframe
-                        pairdf_all = p.obj.to_dataframe(dim_order=["time", "x"])
+                        # handle different dimensios, M.Li
+                        if ('y' in p.obj.dims) and ('x' in p.obj.dims):
+                            pairdf_all = p.obj.to_dataframe(dim_order=["x", "y"])
+                        elif ('y' in p.obj.dims) and ('time' in p.obj.dims):
+                            pairdf_all = p.obj.to_dataframe(dim_order=["time", "y"])
+                        else:
+                            pairdf_all = p.obj.to_dataframe(dim_order=["time", "x"])
 
                         # Select only the analysis time window.
                         pairdf_all = pairdf_all.loc[self.start_time : self.end_time]
diff --git a/melodies_monet/plots/satplots.py b/melodies_monet/plots/satplots.py
index 98fa0772..8b407df9 100644
--- a/melodies_monet/plots/satplots.py
+++ b/melodies_monet/plots/satplots.py
@@ -223,11 +223,15 @@ def make_timeseries(df, df_reg=None,column=None, label=None, ax=None, avg_window
         print(plot_kwargs)
         # {'color': 'k', 'linestyle': '-', 'marker': '*', 'linewidth': 2.0, 'markersize': 10.0, 'label': 'omps_nm', 'fontsize': 14.4}
         if avg_window is None:
-            df[column].mean('y').plot(ax=ax, color=plot_kwargs['color'],linestyle=plot_kwargs['linestyle'],\
+            # bug fixed (AttributeError: 'Rectangle' object has no property 'marker'). M.Li
+            df[column].mean('y').plot.line(x = "time", ax=ax, color=plot_kwargs['color'],linestyle=plot_kwargs['linestyle'],\
+            #df[column].mean('y').plot(ax=ax, color=plot_kwargs['color'],linestyle=plot_kwargs['linestyle'],\
                                            marker=plot_kwargs['marker'],linewidth=plot_kwargs['linewidth'],\
                                           markersize=plot_kwargs['markersize'],label=plot_kwargs['label'])
         else:
-            df[column].resample(time = avg_window).mean().mean('y').plot(ax=ax,color=plot_kwargs['color'],\
+            # bug fixed (AttributeError: 'Rectangle' object has no property 'marker'). M.Li
+            df[column].resample(time = avg_window).mean().mean('y').plot.line(x = "time", ax=ax,color=plot_kwargs['color'],\
+            #df[column].resample(time = avg_window).mean().mean('y').plot(ax=ax,color=plot_kwargs['color'],\
                                                                               linestyle=plot_kwargs['linestyle'],\
                                            marker=plot_kwargs['marker'],linewidth=plot_kwargs['linewidth'],\
                                           markersize=plot_kwargs['markersize'],label=plot_kwargs['label'])
@@ -236,11 +240,15 @@ def make_timeseries(df, df_reg=None,column=None, label=None, ax=None, avg_window
     else:
         # this means that an axis handle already exists and use it to plot the model output.
         if avg_window is None:
-            df[column].mean('y').plot(ax=ax, color=plot_dict['color'],linestyle=plot_dict['linestyle'],\
+            # bug fixed. M.Li
+            df[column].mean('y').plot.line(x = "time",ax=ax, color=plot_dict['color'],linestyle=plot_dict['linestyle'],\
+            #df[column].mean('y').plot(ax=ax, color=plot_dict['color'],linestyle=plot_dict['linestyle'],\
                                            marker=plot_dict['marker'],linewidth=plot_dict['linewidth'],\
                                           markersize=plot_dict['markersize'],label=plot_dict['label'])
         else:
-            df[column].resample(time=avg_window).mean().mean('y').plot(ax=ax, color=plot_dict['color'],\
+            # bug fixed. M.Li
+            df[column].resample(time=avg_window).mean().mean('y').plot.line(x = "time",ax=ax, color=plot_dict['color'],\
+            #df[column].resample(time=avg_window).mean().mean('y').plot(ax=ax, color=plot_dict['color'],\
                                                                             linestyle=plot_dict['linestyle'],\
                                            marker=plot_dict['marker'],linewidth=plot_dict['linewidth'],\
                                           markersize=plot_dict['markersize'],label=plot_dict['label'])   
@@ -344,7 +352,10 @@ class
         dia.add_sample(df[column_m].std().values, cc, zorder=9, label=label_m, **plot_dict)
     #Set parameters for all plots
     contours = dia.add_contours(colors='0.5')
-    plt.clabel(contours, inline=1, fontsize=text_kwargs['fontsize']*0.8)
+    # control the clabel format for very high values (e.g., NO2 columns), M.Li
+    #plt.clabel(contours, inline=1, fontsize=text_kwargs['fontsize']*0.8)
+    plt.clabel(contours, inline=1, fontsize=text_kwargs['fontsize']*0.8, fmt='(%1.1e)')
+
     plt.grid(alpha=.5)
     plt.legend(frameon=False,fontsize=text_kwargs['fontsize']*0.8,
                bbox_to_anchor=(0.75, 0.93), loc='center left')
@@ -693,6 +704,7 @@ def make_spatial_bias_gridded(df, column_o=None, label_o=None, column_m=None,
         ylabel = column_o
     
     #Take the difference for the model output - the sat output
+
     diff_mod_min_obs = (df[column_m] - df[column_o]).squeeze()
     #Take mean over time, 
     if len(diff_mod_min_obs.dims) == 3:
diff --git a/melodies_monet/util/cal_mod_no2col.py b/melodies_monet/util/cal_mod_no2col.py
new file mode 100644
index 00000000..c9840e9e
--- /dev/null
+++ b/melodies_monet/util/cal_mod_no2col.py
@@ -0,0 +1,82 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+
+# calculate WRF-Chem NO2 trop. columns, for further pair with satellite swath data
+# contact: meng.li.atm@gmail.com
+#
+
+import xesmf as xe
+import numpy as np
+import xarray as xr
+import pandas as pd
+from datetime import datetime
+
+def cal_model_no2columns(modobj):
+
+    """
+    Calcuate model (WRF-Chem) NO2 columns for each layer, to pair with satellite data
+    
+    Parameters
+    ------
+    modobj         : model data
+  
+    Output
+    ------
+    modobj        : revised model data with 'no2col' and 'localtime' added
+
+    """
+
+    # calculate the no2 tropospheric vertical columns and pressure from wrf-chem
+    no2    = modobj['no2']
+    ph     = modobj['PH']
+    phb    = modobj['PHB']
+    pb     = modobj['PB']
+    tdata  = modobj['T']
+    pdata  = modobj['P']
+    time   = modobj.coords['time']
+    modlon = modobj.coords['longitude']
+
+    # presure: base state + PB (KSMP)
+    nt, nz, ny, nx = no2.shape
+    pb2  = np.zeros([nt, nz, ny, nx],dtype=np.float)
+    pb2  = pdata + pb
+
+    # convert the perturbation potential temperature (from 300K reference) to temp
+    tb = np.zeros([nt, nz, ny, nx],dtype=np.float)
+    tb =(300.0+tdata)*((pb2/1.0e5)**0.286)
+
+
+    # --- initialize arrays
+    # no2 columns for each layer
+    no2col     = np.zeros([nt, nz, ny, nx], dtype = np.float32)
+    # temporary array
+    value      = np.zeros([nt, ny, nx], dtype = np.float32)
+
+    # average between 13:00 and 14:00 localtime
+    localtm    = np.zeros([nt,ny,nx], dtype='datetime64[s]')
+    tdlist     = np.zeros([ny], dtype=np.int32)
+    tdlt       = np.zeros([ny, nx], dtype='timedelta64[ms]')
+
+    for xx in range(nx):
+         tdlist[:]  = np.array(modlon[:,xx]/15.0).astype(int)
+         tdlt[:,xx] = pd.TimedeltaIndex(tdlist, 'h')
+
+    for tt in range(nt):
+        localtm[tt,:,:] = pd.Timestamp(time.values[tt]) + tdlt[:,:]
+
+    # --- calculate NO2 columns by layers
+    # convert to ppm
+    no2 = no2 / 1000.0
+    for vl in range(nz):
+        ad = pb2[:,vl,:,:] * (28.97e-3)/(8.314*tb[:,vl,:,:])
+        zh = ((ph[:,vl+1,:,:] + phb[:,vl+1,:,:]) - (ph[:,vl,:,:]+phb[:,vl,:,:]))/9.81
+        value[:,:,:]= no2[:,vl,:,:]*zh[:,:,:]*6.022e23/(28.97e-3)*1e-10*ad[:,:,:] # timex y x x
+        no2col[:,vl,:,:] = value[:,:,:]
+
+    # add to model
+    modobj['PB2'] = xr.DataArray(pb2,dims=["time", "z", "y","x"])
+    modobj['localtime'] = xr.DataArray(localtm, dims=["time","y", "x"])
+    modobj['no2col'] = xr.DataArray(no2col,dims=["time", "z", "y","x"])
+
+    return modobj
diff --git a/melodies_monet/util/sat_l2_swath_utility.py b/melodies_monet/util/sat_l2_swath_utility.py
new file mode 100644
index 00000000..8bedb674
--- /dev/null
+++ b/melodies_monet/util/sat_l2_swath_utility.py
@@ -0,0 +1,339 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+
+# read all swath data for the time range
+# developed for TROPOMI Level2 NO2
+#
+
+import xesmf as xe
+import numpy as np
+import xarray as xr
+from datetime import datetime
+
+import logging
+numba_logger = logging.getLogger('numba')
+numba_logger.setLevel(logging.WARNING)
+
+def trp_interp_swatogrd(obsobj, modobj):
+
+    """
+    interpolate sat swath to model grid
+    
+    Parameters
+    ------
+    obsobj  : satellite swath data
+    modobj  : model data (with no2 col calculated)
+    
+    Output
+    ------
+    no2_modgrid_avg: Regridded satellite data at model grids for all datetime
+
+    """
+    
+    # daily averaged sate data at model grids
+    no2_modgrid_avg=xr.Dataset()
+
+    # model grids attributes
+    nt, nz, ny, nx  = modobj['no2col'].shape # time, z, y, x, no2 columns at molec cm^-2
+    modlat = modobj.coords['latitude']
+    modlon = modobj.coords['longitude']
+
+    time   = [ datetime.strptime(x,'%Y-%m-%d') for x in obsobj.keys()]
+    ntime  = len(list(obsobj.keys()))
+
+    no2_nt = np.zeros([ntime, ny, nx], dtype=np.float32)
+    no2_nt[:,:,:] = np.nan
+    no2_mod = np.zeros([ntime, ny, nx], dtype=np.float32)
+    no2_mod[:,:,:] = np.nan
+
+    for nd in range(ntime):
+        days = list(obsobj.keys())[nd]
+        # --- model
+        # get model no2 trop. columns at 13:00 - 14:00 localtime
+        modobj_tm = modobj.where((modobj['localtime'].dt.strftime("%Y-%m-%d") == days) & (modobj['localtime'].dt.hour >= 13.0)
+           & (modobj['localtime'].dt.hour <= 14.0), drop=False)
+
+        no2col_satm = np.nanmean(modobj_tm['no2col'].values, axis = 0)
+        
+        # sum up tropopause
+        no2_mod[nd, :,:] = np.nansum(no2col_satm[0:49,:,:], axis=0)
+
+
+        # --- TROPOMI
+        # number of swath
+        nswath = len(obsobj[days])
+
+        # array for all swaths
+        no2_modgrid_all = np.zeros([ny, nx, nswath], dtype=np.float32)
+
+        for ns in range(nswath):
+            satlon = obsobj[days][ns]['lon']
+            satlat = obsobj[days][ns]['lat']
+            satno2 = obsobj[days][ns]['nitrogendioxide_tropospheric_column']
+
+            # regridding from swath grid to model grids
+            grid_in = {'lon':satlon.values, 'lat':satlat.values}
+            grid_out= {'lon':modlon.values, 'lat':modlat.values}
+
+            regridder = xe.Regridder(grid_in, grid_out,'bilinear',ignore_degenerate=True,reuse_weights=False)
+            
+            # regridded no2 trop. columns
+            no2_modgrid = regridder(satno2) # , keep_attrs=True
+            print('Done with TROPOMI regridding', days, ns)
+
+            #regridder.destroy()
+            del(regridder)
+            regridder = None
+ 
+            no2_modgrid_all[:,:,ns] = no2_modgrid[:,:]
+            print(' no2 satellite:', np.nanmin(no2_modgrid), np.nanmax(no2_modgrid))
+
+        # daily averaged no2 trop. columns at model grids
+        no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)
+
+    # exclude 0.0 and negative values for model
+    no2_mod = np.where(no2_mod <= 0.0, np.nan, no2_mod)
+
+    del(modobj)
+    del(obsobj)
+ 
+    no2_modgrid_avg = xr.Dataset(
+        data_vars = dict(
+            nitrogendioxide_tropospheric_column=(["time", "x", "y"],no2_nt),
+            no2trpcol=(["time", "x", "y"],no2_mod),
+            latitude=(["x", "y"],modlat.values),
+            longitude=(["x", "y"],modlon.values)
+            ),
+        coords = dict(
+            time=time,
+            lon=(["x", "y"], modlon.values),
+            lat=(["x", "y"], modlat.values)),
+        attrs=dict(description="daily tropomi data at model grids"),
+        )
+
+    # change dims to "time" x "y" (multi-index)
+    no2_modgrid_avg = no2_modgrid_avg.rename_dims({'y':'ll'})
+    no2_modgrid_avg = no2_modgrid_avg.stack(y=['x','ll'])
+
+    return no2_modgrid_avg
+
+
+def trp_interp_swatogrd_ak(obsobj, modobj):
+
+    """
+    interpolate sat swath to model grid applied with averaging kernel
+    
+    Parameters
+    ------
+    obsobj  : satellite swath data
+    modobj  : model data (with no2 col calculated)
+    
+    Output
+    ------
+    no2_modgrid_avg: Regridded satellite data at model grids for all datetime
+
+    """
+    
+    # daily averaged sate data at model grids
+    no2_modgrid_avg=xr.Dataset()
+
+    # model grids attributes
+    nt, nz, ny, nx  = modobj['no2'].shape
+    modlat = modobj.coords['latitude']
+    modlon = modobj.coords['longitude']
+
+    tmpvalue = np.zeros([ny, nx], dtype = np.float)
+
+    time   = [ datetime.strptime(x,'%Y-%m-%d') for x in obsobj.keys()]
+    ntime  = len(list(obsobj.keys()))
+
+    no2_nt = np.zeros([ntime, ny, nx], dtype=np.float32)
+    no2_nt[:,:,:] = np.nan
+    no2_mod = np.zeros([ntime, ny, nx], dtype=np.float32)
+    no2_mod[:,:,:] = np.nan
+
+
+    # loop over all days
+    for nd in range(ntime):
+
+        days = list(obsobj.keys())[nd]
+
+        # --- model ---
+        # get model no2 trop. columns at 13:00 - 14:00 localtime
+        modobj_tm = modobj.where((modobj['localtime'].dt.strftime("%Y-%m-%d") == days) & (modobj['localtime'].dt.hour >= 13.0)
+           & (modobj['localtime'].dt.hour <= 14.0), drop=False)
+        no2col_satm = np.nanmean(modobj_tm['no2col'].values, axis = 0)
+              
+        # sum up tropopause, needs to be revised to tropopause
+        no2_mod[nd, :,:] = np.nansum(no2col_satm[0:49,:,:], axis=0)
+
+        # --- tropomi ---
+        # number of swath
+        nswath = len(obsobj[days])
+
+        # array for all swaths
+        no2_modgrid_all = np.zeros([ny, nx, nswath], dtype=np.float32)
+
+        for ns in range(nswath):
+            satlon = obsobj[days][ns]['lon']
+            satlat = obsobj[days][ns]['lat']
+            satno2 = obsobj[days][ns]['nitrogendioxide_tropospheric_column']            
+
+            grid_sat = {'lon':satlon.values, 'lat':satlat.values}
+            grid_mod= {'lon':modlon.values, 'lat':modlat.values}
+
+            # -- applying averaging kernel ---
+            # trop. amf in standard product
+            tamf_org   = obsobj[days][ns]['air_mass_factor_troposphere']
+            amf_total  = obsobj[days][ns]['air_mass_factor_total']
+            troppres   = obsobj[days][ns]['troppres'] # TM5 tropopause pressure, Pa 
+            tpreslev   = obsobj[days][ns]['preslev']
+            scatwts    = obsobj[days][ns]['averaging_kernel']
+
+            nysat, nxsat, nzsat = scatwts.shape
+
+            # regridding from model grid to sat grid
+            regridder_ms = xe.Regridder(grid_mod, grid_sat,'bilinear',ignore_degenerate=True,reuse_weights=False)
+
+            # regridding for model pressure, and no2 vertical colums
+            wrfpres        =  np.zeros([nysat, nxsat, nz], dtype = np.float32)
+            wrfpres[:,:,:] =  np.nan 
+            wrfno2         =  np.zeros([nysat, nxsat, nz], dtype = np.float32)
+            wrfno2[:,:,:]  =  np.nan
+            modvalue_pb2   =  np.nanmean(modobj_tm['PB2'].values, axis = 0)
+            modvalue_no2   =  np.nanmean(modobj_tm['no2col'].values, axis = 0)
+
+            for l in range(nz):
+                tmpvalue[:,:]  = modvalue_pb2[l,:,:]         
+                wrfpres[:,:,l] = regridder_ms(tmpvalue)
+                tmpvalue[:,:]  = modvalue_no2[l,:,:]
+                wrfno2[:,:,l]  = regridder_ms(tmpvalue)            
+
+            # convert from aks to trop.aks
+            for l in range(nzsat):
+                scatwts[:,:,l] = scatwts[:,:,l] * amf_total[:,:] / tamf_org[:,:]
+
+            # calculate the revised tamf_mod, and ratio = tamf_mod / tamf_org
+            ratio = cal_amf_wrfchem(scatwts, wrfpres, tpreslev, troppres, wrfno2, tamf_org, satlon.values, satlat.values, modlon, modlat)
+
+            # averaing kernel applied done
+            satno2 = satno2 * ratio 
+
+            # regridding from swath grid to model grids
+            regridder = xe.Regridder(grid_sat, grid_mod,'bilinear',ignore_degenerate=True,reuse_weights=False)
+
+            # regridded no2 trop. columns
+            no2_modgrid = regridder(satno2, keep_attrs=True)
+            no2_modgrid_all[:,:,ns] = no2_modgrid[:,:]
+
+        # daily averaged no2 trop. columns at model grids
+        no2_nt[nd,:,:] = np.nanmean(np.where(no2_modgrid_all > 0.0, no2_modgrid_all, np.nan), axis=2)
+
+    # exclude 0.0 and negative values for model
+    no2_mod = np.where(no2_mod <= 0.0, np.nan, no2_mod)
+
+    del(modobj)
+    del(obsobj)
+
+    no2_modgrid_avg = xr.Dataset(
+        data_vars = dict(
+            nitrogendioxide_tropospheric_column=(["time", "x", "y"],no2_nt),
+            latitude=(["x", "y"],modlat.values),
+            longitude=(["x", "y"],modlon.values),
+            no2trpcol = (["time", "x", "y"],no2_mod)
+            ),
+        coords = dict(
+            time=time,
+            lon=(["x", "y"], modlon.values),
+            lat=(["x", "y"], modlat.values)),
+        attrs=dict(description="daily tropomi data at model grids,passing at localtime 13:30"),
+        )
+
+    # change dims to "time" x "y" (multi-index)
+    no2_modgrid_avg = no2_modgrid_avg.rename_dims({'y':'ll'})
+    no2_modgrid_avg = no2_modgrid_avg.stack(y=['x','ll'])
+
+    return no2_modgrid_avg
+
+
+def cal_amf_wrfchem(scatw, wrfpreslayer, tpreslev, troppres, wrfno2layer_molec, tamf_org, satlon, satlat, modlon, modlat):
+    from scipy import interpolate
+
+    nsaty, nsatx, nz    = wrfpreslayer.shape
+    nsaty, nsatx, nsatz = tpreslev.shape
+
+    nume             = np.zeros([nsaty, nsatx], dtype=np.float32)
+    deno             = np.zeros([nsaty, nsatx], dtype=np.float32)
+    amf_wrfchem      = np.zeros([nsaty, nsatx], dtype=np.float32)
+    amf_wrfchem[:,:] = np.nan
+    wrfavk           = np.zeros([nsaty, nsatx, nz], dtype = np.float32)
+    wrfavk[:,:,:]    = np.nan
+    wrfavk_scl       = np.zeros([nsaty, nsatx], dtype=np.float32) 
+    preminus         = np.zeros([nsaty, nsatx], dtype=np.float32)
+    wrfpreslayer_slc = np.zeros([nsaty, nsatx], dtype=np.float32)
+    tmpvalue_sat     = np.zeros([nsaty, nsatx], dtype=np.float32)
+    
+    
+    # set the surface pressure to wrf one
+    tpreslev[:,:,0] = wrfpreslayer[:,:,0] 
+
+    # relationship between pressure to avk
+    tpreslev = tpreslev.values 
+    scatw    = scatw.values
+    wrfpreslayer = np.where((wrfpreslayer <=0.0), np.nan, wrfpreslayer)
+
+    # shrink the satellite domain to WRF
+    lb = np.where( (satlon >= np.nanmin(modlon)) & (satlon <= np.nanmax(modlon)) 
+        & (satlat >= np.nanmin(modlat)) & (satlat <= np.nanmax(modlat)))
+
+    vertical_pres = []
+    vertical_scatw = []
+    vertical_wrfp = []
+
+    for llb in range(len(lb[0])):
+        yy = lb[0][llb]
+        xx = lb[1][llb]
+        vertical_pres = tpreslev[yy,xx,:]
+        vertical_scatw = scatw[yy,xx,:]
+        vertical_wrfp = wrfpreslayer[yy,xx,:]
+
+        f = interpolate.interp1d(np.log10(vertical_pres[:]),vertical_scatw[:], fill_value="extrapolate")# relationship between pressure to avk
+        wrfavk[yy,xx,:] = f(np.log10(vertical_wrfp[:])) #wrf-chem averaging kernel
+
+
+    for l in range(nz-1):
+        # check if it's within tropopause
+        preminus[:,:]         = wrfpreslayer[:,:,l] - troppres[:,:]
+
+        # wrfpressure and wrfavk
+        wrfpreslayer_slc[:,:] = wrfpreslayer[:,:,l]
+        wrfavk_scl[:,:]       = wrfavk[:,:,l]
+
+        ind_ak = np.where((np.isinf(wrfavk_scl) == True) | (wrfavk_scl <= 0.0))
+        # use the upper level ak 
+        if (ind_ak[0].size >= 1):
+            tmpvalue_sat[:,:]  = wrfavk[:,:,l+1]
+            wrfavk_scl[ind_ak] = tmpvalue_sat[ind_ak]
+
+        ind = np.where(preminus >= 0.0)
+        # within tropopause
+        if (ind[0].size >= 1):
+            nume[:,:] += wrfavk_scl[:,:]*wrfno2layer_molec[:,:,l]
+            deno[:,:] += wrfno2layer_molec[:,:,l]
+        else:
+            break
+            
+    # tropospheric amf calculated based on model profile and TROPOMI averaging kernel
+    amf_wrfchem = nume / deno * tamf_org
+
+    # ratio
+    ratio = tamf_org / amf_wrfchem 
+
+    # exclude nan
+    ratio = np.where((np.isnan(ratio) == True), 1.0, ratio)
+
+    print('Done with Averaging Kernel revision,', 'factor min:',np.nanmin(ratio), 'max:',np.nanmax(ratio)) 
+
+    return ratio 
+
diff --git a/to_generalize/OutputPlot_Config.py b/to_generalize/OutputPlot_Config.py
deleted file mode 100755
index 9bb3b2d5..00000000
--- a/to_generalize/OutputPlot_Config.py
+++ /dev/null
@@ -1,353 +0,0 @@
-
-# This code is written to process monthly averaged map for both wrfchem and TROPOMI
-# --- Meng Li, 2019. 5. 9
-# --- Contact: meng.li@noaa.gov; meng.li.atm@gmail.com
-
-import os
-import numpy as np
-from netCDF4 import Dataset
-import matplotlib.pyplot as plt
-import wrf
-from os import listdir
-from os.path import isfile
-
-
-Basedir_wrfoutput = os.environ.get('Basedir_wrfoutput')
-
-'''
-===================================================
-Main program: wrfoutput, tropomi, and evaluation
-===================================================
-'''
-
-#=================Preparation Codes==================
-#---
-class file_management:
-    def __init__(self):
-        pass
-    def subdirlist(self, indir, keyword=''):
-        subdirlist = []
-        subdirlist_org = [x[0] for x in os.walk(indir)]
-        for sd in subdirlist_org:
-            if keyword in sd:
-                subdirlist.append(sd)
-        return subdirlist
-                
-        return subdirlist
-    def filelist(self, indir, keyword=''):
-        filelist = []
-        filelist_org = [os.path.join(indir, f) for f in listdir(indir) if isfile(os.path.join(indir,f))]
-        for f in filelist_org:
-            if keyword in f:
-                filelist.append(f)
-        return filelist   
-    
-#---
-def extractwrfcoord(lats='', lons=''):
-    # extract one wrfdata
-    fm = file_management()
-    subdirlist = fm.subdirlist(Basedir_wrfoutput)
-    ff = fm.filelist(subdirlist[1])[0]
-    wrfin = Dataset(ff,'r',format = 'NETCDF4_CLASSIC')
-        
-    # get some attributes of the wrf domain 
-    latdata = wrf.getvar(wrfin, 'XLAT', timeidx=0)[:,:]            # latitude
-    londata = wrf.getvar(wrfin, 'XLONG', timeidx=0)[:,:]           # longitude
-    wrflonlat = {'lon':londata, 'lat':latdata}
-
-    if (lats == '') & (lons == ''):
-        return wrflonlat
-    else:
-        xyinds = wrf.ll_to_xy(wrfin, lats, lons)
-        return xyinds
-    wrfin.close()
-
-#===========MAIN PROGRAM STARTS HERE===============
-    
-# GET THE WRF COORDIATE INFORMATION
-wrfcoord = extractwrfcoord()
-#---
-class output_config:   
-    def __init__(self):
-        SMALL_SIZE=12
-        MEDIUM_SIZE = 16
-        BIG_SIZE = 18
-        plt.rc('font', size=SMALL_SIZE )
-        plt.rc('axes', titlesize=SMALL_SIZE)
-        plt.rc('axes', labelsize=MEDIUM_SIZE)
-        plt.rc('xtick', labelsize=SMALL_SIZE)
-        plt.rc('ytick', labelsize=SMALL_SIZE)
-        plt.rc('legend', fontsize=MEDIUM_SIZE)
-        plt.rc('figure', titlesize=BIG_SIZE)
-        plt.rc('font',**{'family':'sans-serif','sans-serif':['arial']})
-
-        
-    def outputnc_2d(self, fn, value, valuename,valueunit):      
-        print('--> output 2d data for:', valuename)
-        ds = Dataset(fn, 'w', format = 'NETCDF4_CLASSIC')
-        ds.createDimension('longitude', np.shape(value)[1])
-        ds.createDimension('latitude', np.shape(value)[0])
-        dlong = ds.createVariable('longitude', 'f4', ['latitude','longitude'])
-        dlat = ds.createVariable('latitude', 'f4', ['latitude','longitude'])
-        dsec = ds.createVariable(valuename, 'f4', ['latitude','longitude'])
-    					
-        #lat, lon = wrf.latlon_coords(value)
-        lon = wrfcoord['lon']
-        lat = wrfcoord['lat']
-        ds.variables['longitude'][:,:] = lon[:,:]
-        ds.variables['latitude'][:,:] = lat[:,:]
-        ds.longitude = 'Edge of grids, West to East'
-        ds.latitude = 'Edge of grids, South to North'
-        ds.variables[valuename][:,:] = value[:,:]
-        ds.valuename = valueunit
-        ds.close()       
-    
-
-    def outputnc_3d(self, fn, lon,lat,time, valuename, value, valueunit):       
-    	ds = Dataset(fn, 'w', format = 'NETCDF4_CLASSIC')   
-    	ds.createDimension('longitude', np.shape(lon)[0])
-    	ds.createDimension('latitude', np.shape(lat)[0])
-    	ds.createDimension('time', np.shape(time)[0])
-    
-    	dlong = ds.createVariable('longitude', 'f4', ['longitude'])
-    	dlat = ds.createVariable('latitude', 'f4', ['latitude'])
-    	dmonth = ds.createVariable('time', 'f4', ['time'])	
-    	dsec = ds.createVariable(valuename, 'f4', ['time','latitude','longitude'])
-    					
-    	ds.variables['longitude'][:] = lon[:]
-    	ds.variables['latitude'][:] = lat[:]
-    	ds.variables['time'][:] = time[:]
-    	ds.longitude = 'Edge of grids, West to East'
-    	ds.latitude = 'Edge of grids, South to North'
-    	ds.month = 'Time'
-    
-    	ds.variables[valuename][:,:,:] = value[:,:,:]		
-    	ds.valuename = valueunit
-    	ds.close()
-        
-    def plot_2dmap(self, fn, value,valuename, valueunit,  mindata=0.0, maxdata = 0.0):    
-        print('--> plotting 2d map for:', valuename       )
-        from wrf import (to_np, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords)
-        import cartopy.crs as crs        
-        from cartopy.feature import NaturalEarthFeature
-        from matplotlib.cm import get_cmap
-        from cartopy.io.shapereader import Reader
-        from cartopy.feature import ShapelyFeature
-        
-	# get the cartopy mapping object
-        cart_proj = get_cartopy(wrfcoord['lon'])
-        lats, lons = latlon_coords(wrfcoord['lon'])    
-        # create a figure
-        fig = plt.figure(figsize=(12,6))    
-        # set the GeoAxes to the projection used by WRF
-        ax = plt.axes(projection=cart_proj)
-        # download and add the states and coastlines
-        # states = NaturalEarthFeature(category="cultural",scale="50m",
-        #                             facecolor="none", name="admin_1_states_provinces_shp")
-        states_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_admin_1_states_provinces/ne_50m_admin_1_states_provinces.shp')
-        states = ShapelyFeature(states_reader.geometries(), crs.PlateCarree())
-        ax.add_feature(states,linewidth=0.5, edgecolor="black", facecolor='none')
-        #ax.coastlines('50m',linewidth=0.8)
-        coast_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_coastline/ne_50m_coastline.shp')
-        coast = ShapelyFeature(coast_reader.geometries(), crs.PlateCarree())
-        ax.add_feature(coast, linewidth=0.8, edgecolor='black', facecolor='none')
-
-        # make the contour outlines and filled contoures for the value
-        #plt.contour(lons, lats, value, 10, colors="black", transform=crs.PlateCarree())
-        #plt.contourf(lons, lats, value, vmin=mindata, vmax=maxdata, transform=crs.PlateCarree(), cmap='jet')
-        if (mindata == 0.0 and maxdata == 0.0):
-            mindata = np.min(value)
-            maxdata = np.max(value)
-            
-        plt.pcolormesh(lons, lats, value, vmin = mindata, vmax = maxdata, cmap='jet',transform=crs.PlateCarree() )
-        #plt.imshow(lons, lats, value)
-        cb = plt.colorbar(ax=ax, shrink=.98)
-        cb.ax.tick_params(labelsize=18, length=8)
-        # set the map bounds
-        ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
-        ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
-        
-        # ad the grid lines
-        ax.gridlines(color="black", linestyle = "dotted")
-        plt.title(valuename + ', unit: ' +valueunit, fontsize=22, fontweight='bold')
-        plt.savefig(fn, dpi=300)
-        #plt.show()
-        plt.clf()
-        plt.close()
-
-    def plot_2dmap_ccolbar(self, fn, value,valuename, valueunit,  mindata=0.0, maxdata = 0.0):
-        print('--> plotting 2d map for:', valuename)
-        from wrf import (to_np, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords)
-        import cartopy.crs as crs
-        from cartopy.feature import NaturalEarthFeature
-        from matplotlib.cm import get_cmap
-        import matplotlib as mpl
-        from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
-        from cartopy.io.shapereader import Reader
-        from cartopy.feature import ShapelyFeature
-
-        # get the cartopy mapping object
-        cart_proj = get_cartopy(wrfcoord['lon'])
-        lats, lons = latlon_coords(wrfcoord['lon'])
-        # create a figure
-        fig = plt.figure(figsize=(12,6))
-        # set the GeoAxes to the projection used by WRF
-        ax = plt.axes(projection=cart_proj)
-        # download and add the states and coastlines
-        states_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_admin_1_states_provinces/ne_50m_admin_1_states_provinces.shp')
-        states = ShapelyFeature(states_reader.geometries(), crs.PlateCarree())
-        ax.add_feature(states,linewidth=0.5, edgecolor="black", facecolor='none')
-        #ax.coastlines('50m',linewidth=0.8)
-        coast_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_coastline/ne_50m_coastline.shp')
-        coast = ShapelyFeature(coast_reader.geometries(), crs.PlateCarree())
-        ax.add_feature(coast, linewidth=0.8, edgecolor='black', facecolor='none')
-
-        # make the contour outlines and filled contoures for the value
-        #plt.contour(lons, lats, value, 10, colors="black", transform=crs.PlateCarree())
-        #plt.contourf(lons, lats, value, vmin=mindata, vmax=maxdata, transform=crs.PlateCarree(), cmap='jet')
-        if (mindata == 0.0 and maxdata == 0.0):
-            mindata = np.min(value)
-            maxdata = np.max(value)
-        # define a custom colorbar
-        #cmap = plt.cm.RdBu_r
-        #cmap = plt.get_cmap('bwr')
-        cmap = plt.get_cmap('PuBuGn')
-        # extract all colors from the .jet map
-        cmaplist = [cmap(i) for i in range(cmap.N)]
-        # CREATE THE NEW MAP
-        cmap = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, cmap.N)
-        # define the bins and normalize
-        #bounds = np.linspace(0,20, 21)
-        bounds = np.linspace(mindata,maxdata,11)
-        norm = mpl.colors.BoundaryNorm(boundaries=bounds, ncolors=cmap.N)
-
-        plt.pcolormesh(lons, lats, value, vmin = mindata, vmax = maxdata, norm=norm,cmap=cmap,transform=crs.PlateCarree() )
-        #plt.imshow(lons, lats, value)
-        cb = plt.colorbar(ax=ax, shrink=.98,extend='both', ticks=bounds)
-        cb.ax.tick_params(labelsize=18, length=8)
-        # set the map bounds
-        #ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
-        #ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
-        ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
-        ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
-
-        # ad the grid lines
-        ax.gridlines(color="black", linestyle = "dotted")
-        plt.title(valuename + ', unit: ' +valueunit, fontsize=22, fontweight='bold' )
-        plt.savefig(fn, dpi=300)
-        #plt.show()
-        plt.clf()
-        plt.close()
-
-    def plot_scatter(self,fn, title, x, y, mindata=0.0, maxdata=1e17, mb=None, nmb=None, label='$\mathregular{NO_2}$ column',
-            xlabel='TROPOMI $\mathregular{NO_2}$ column, $\mathregular{10^{15}}$ molec c$\mathregular{m^{-2}}$',
-            ylabel='WRF-Chem $\mathregular{NO_2}$ column, $\mathregular{10^{15}}$ molec c$\mathregular{m^{-2}}$'):
-        from scipy import stats
-        #fig = plt.figure(figsize=(6,6))
-        fig, ax = plt.subplots(figsize=(6,6))
-        x = x/1e15
-        y = y/1e15
-        mindata = mindata/1e15
-        maxdata = maxdata/1e15
-        slope, intercept, r_value, p_value, stderr=stats.linregress(x, y)
-        plt.scatter(x,y, marker = 'o',facecolors='cornflowerblue', edgecolors='b',label=label, s=150) # s=50
-        plt.xlim(mindata, maxdata)
-        plt.ylim(mindata, maxdata)
-        plt.xlabel(xlabel,fontsize=18, weight=500)
-        plt.ylabel(ylabel,fontsize=18, weight=500)
-
-        xarr = np.arange(start=0.0, stop=1e17,step=1e15)
-        plt.plot(xarr, xarr,'k--',label='_nolegend')
-
-        y2 = xarr*slope + intercept
-        plt.plot(xarr, y2, 'r-',label='liner regression')
-        div = (maxdata - mindata)/18.0
-
-        if intercept > 0.0:
-            eqstr = 'Y='+'{:5.2f}'.format(slope)+'X+'+'{:5.2f}'.format(intercept)+'e15'#'$\mathregular{10^{15}}$'
-        else:
-            eqstr = 'Y='+'{:5.2f}'.format(slope)+'X-'+'{:5.2f}'.format(intercept*(-1.0))+'e15'
-        plt.text(maxdata/2.0, mindata+div*5.0,eqstr,color='r',fontname = 'arial',fontsize=18) # fontweight='bold', fontstyle='italic'
-        r_value = r_value * r_value
-        rstr = '$\mathregular{R^{2}}$:'+'{:5.2f}'.format(r_value)
-        plt.text(maxdata/2.0, mindata+div*3.5,rstr,color='r',fontsize=18)
-
-        plt.text(maxdata*0.2/6.0, maxdata-div*5.0, 'N: ' + str(len(x)),fontname='arial',fontsize=18)
-        #plt.text(maxdata/2.0, maxdata-div*2.0, 'slope: '+ '{:5.2f}'.format(slope), **csfont)
-        #plt.text(maxdata/2.0, maxdata-div*3.0, 'intercept: '+ '{:5.2f}'.format(intercept/1e15) + 'e15', **csfont)
-        if mb != None:
-            plt.text(maxdata*0.2/6.0, maxdata-div*6.5, 'MB: ' + '{:5.2f}'.format(mb/1e15)+'e15',fontname='arial', fontsize=18)
-        if nmb != None:
-            plt.text(maxdata*0.2/6.0, maxdata-div*8.0, 'NMB: ' + '{:5.2f}'.format(nmb*100.0)+'%',fontname='arial', fontsize=18)
- 
-        plt.legend(loc='upper left',fontsize=16)
-        #plt.title(title,fontname='arial', fontsize=16)
-        ax.tick_params(length=8, width=1, labelsize=18)
-        plt.savefig(fn, bbox_inches = 'tight', dpi=300)
-        #plt.show()
-        plt.clf()
-        plt.close()
-        print('--> scatter: r,slope,intercept:', r_value, slope, intercept/1e15)
-        
-    def plot_minus(self, fn, value,valuename, valueunit,  mindata=-1.0e16, maxdata = 1.0e16):
-        print('--> plotting 2d minus map for:', valuename)
-        
-        from matplotlib import cm
-        from wrf import (to_np, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords)
-        import cartopy.crs as crs
-        from cartopy.feature import NaturalEarthFeature
-        from matplotlib.cm import get_cmap
-        from cartopy.io.shapereader import Reader
-        from cartopy.feature import ShapelyFeature       
- 
-        # get the cartopy mapping object
-        cart_proj = get_cartopy(wrfcoord['lon'])
-        lats, lons = latlon_coords(wrfcoord['lon'])    
-        # create a figure
-        fig = plt.figure(figsize=(12,6))    
-        # set the GeoAxes to the projection used by WRF
-        ax = plt.axes(projection=cart_proj)
-        # download and add the states and coastlines
-        # states = NaturalEarthFeature(category="cultural",scale="50m",
-        #                             facecolor="none", name="admin_1_states_provinces_shp")
-        states_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_admin_1_states_provinces/ne_50m_admin_1_states_provinces.shp')
-        states = ShapelyFeature(states_reader.geometries(), crs.PlateCarree())
-        ax.add_feature(states,linewidth=0.5, edgecolor="black",facecolor='none')
-        #ax.coastlines('50m',linewidth=0.8)
-        coast_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_coastline/ne_50m_coastline.shp')
-        coast = ShapelyFeature(coast_reader.geometries(), crs.PlateCarree())
-        ax.add_feature(coast, linewidth=0.8, edgecolor='black', facecolor='none')
-
-        # make the contour outlines and filled contoures for the value
-        #plt.contour(lons, lats, value, 10, colors="black", transform=crs.PlateCarree())
-        #plt.contourf(lons, lats, value, vmin=mindata, vmax=maxdata, transform=crs.PlateCarree(), cmap='jet')
-        if (mindata == 0.0 and maxdata == 0.0):
-            mindata = np.min(value)
-            maxdata = np.max(value)
-        
-        cmap = plt.get_cmap('bwr')
-        plt.pcolormesh(lons, lats, value, vmin = mindata, vmax = maxdata, cmap=cmap,transform=crs.PlateCarree() )       
-        # color bar
-        cb = plt.colorbar(ax=ax, shrink=.98)
-        cb.ax.tick_params(labelsize=18, length=8)
-        # set the map bounds
-        ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
-        ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
-        
-        # ad the grid lines
-        ax.gridlines(color="black", linestyle = "dotted")
-        plt.title(valuename + ', unit: ' +valueunit, fontsize=22, fontweight='bold')
-        plt.savefig(fn, dpi=300)
-        #plt.show()
-        plt.clf()
-        plt.close()
-        
-
-#---
-if __name__ == '__main__':
-        main()
-    
- 
-
-
-
diff --git a/to_generalize/avg_trp_no2.py b/to_generalize/avg_trp_no2.py
deleted file mode 100644
index fd53b61e..00000000
--- a/to_generalize/avg_trp_no2.py
+++ /dev/null
@@ -1,281 +0,0 @@
-
-# This code is written to process monthly averaged map for both wrfchem and TROPOMI
-# --- Meng Li, 2019. 5. 9
-# --- Contact: meng.li@noaa.gov; meng.li.atm@gmail.com
-
-import os
-import numpy as np
-from netCDF4 import Dataset
-import wrf
-from os import listdir
-from os.path import isfile
-
-from OutputPlot_Config import output_config
-
-# baseline + lightning
-Basedir_wrfoutput = '/scratch1/BMC/rcm2/mli/nyc18_lightning/run_12km_five18_bmcdVCP_fog_wofire_BEIS_0.5ISO/Output/'
-Baseoutdir = '/scratch1/BMC/rcm2/mli/outdir_12km_noPM_baseline_bmc_cams/'
-
-year = 2018
-seasoname = 'm07'
-
-'''
-====================================================================
-Comparison Between TROPOMI and WRF-Chem NO2 Trop. Columns Seasonaly
-====================================================================
-'''
-
-Month = [7]
-MonthStartDay = [1] # start day for each month, 1-based
-MonthEndDay = [15]  # end day for each month, 1-based
-
-
-# Use wind speed as a criterion?
-UseWPD = False  # False: not use wind speed
-
-#================Preparation Codes======================
-#---
-class file_management:
-    def __init__(self):
-        pass
-    def subdirlist(self, indir, keyword=''):
-        subdirlist = []
-        print(indir)
-        subdirlist_org = [x[0] for x in os.walk(indir)]
-        for sd in subdirlist_org:
-            if keyword in sd:
-                subdirlist.append(sd)
-        return subdirlist
-                
-        return subdirlist
-    def filelist(self, indir, keyword=''):
-        filelist = []
-        filelist_org = [os.path.join(indir, f) for f in listdir(indir) if isfile(os.path.join(indir,f))]
-        for f in filelist_org:
-            if keyword in f:
-                filelist.append(f)
-        return filelist   
-    
-#---
-def extractwrfcoord(lats='', lons=''):
-    # extract one wrfdata
-    fm = file_management()
-    subdirlist = fm.subdirlist(Basedir_wrfoutput)
-    ff = fm.filelist(subdirlist[1])[0]
-    wrfin = Dataset(ff,'r',format = 'NETCDF4_CLASSIC')
-        
-    # get some attributes of the wrf domain 
-    latdata = wrf.getvar(wrfin, 'XLAT', timeidx=0)[:,:]            # latitude
-    londata = wrf.getvar(wrfin, 'XLONG', timeidx=0)[:,:]           # longitude
-    wrflonlat = {'lon':londata, 'lat':latdata}
-
-    if (lats == '') & (lons == ''):
-        return wrflonlat
-    else:
-        xyinds = wrf.ll_to_xy(wrfin, lats, lons)
-        return xyinds
-    wrfin.close()
-
-#==============MAIN PROGRAM STARTS HERE==============
-    
-# GET THE WRF COORDIATE INFORMATION OF WRF-CHEM
-wrfcoord = extractwrfcoord()
-wrflonlat = wrfcoord
-# extract locations
-wrflon = wrflonlat['lon']
-wrflat = wrflonlat['lat']
-xy = np.shape(wrflonlat['lon'])
- 
-# MAIN PROGRAM
-def main():
-    m = model_validation(year)
-    m.evaluatedata()
-        
-class model_validation():
-    
-    def __init__(self, year):        
-        self.year = year
-        
-    def evaluatedata(self):
-        year = self.year
-        
-        # define the outdir based on use wind speed or not
-        if UseWPD == False:
-            Outdir = Baseoutdir + seasoname + '/'
-        else:
-            Outdir = Baseoutdir + seasoname + '/' + 'wpduvle'+str(maxwd) + '/'
-        if os.path.isdir(Outdir):
-            pass
-        else:
-            os.mkdir(Outdir)
-        print('***Evaluation starts here: ', year, seasoname)
-      
-        # initialize data array
-        no2_tomi = np.zeros([xy[0], xy[1]], dtype = np.float32) #TROPOMI NO2 columns for further sum
-        no2_tomi[:,:] = 0.0       
-        no2_wrfchem = np.zeros([xy[0], xy[1]], dtype = np.float32) #WRF-Chem NO2 columns for further sum
-        no2_wrfchem[:,:] = 0.0
-        
-        num_tomi = np.zeros([xy[0], xy[1]], dtype = np.float32) #Number of observations
-        num_tomi[:,:] = 0.0        
-        num_wrfchem = np.zeros([xy[0], xy[1]], dtype = np.float32) #Number of observations or WRF-Chem, should be the same
-        num_wrfchem[:,:] = 0.0
-
-        no2_tomi_avg = np.zeros([xy[0], xy[1]], dtype = np.float32) #seasonal averaged TROPOMI NO2 columns
-        no2_tomi_avg[:,:] = np.nan       
-        no2_wrfchem_avg = np.zeros([xy[0], xy[1]], dtype = np.float32) #seasonal averaged WRF-Chem NO2 columns
-        no2_wrfchem_avg[:,:] = np.nan
-        
-        # summerize each day
-        for mind in range(len(Month)):
-            month = Month[mind]
-            daymin = MonthStartDay[mind]
-            daymax = MonthEndDay[mind]
-
-            for day in range(daymin,daymax+1):
-
-                Indir = Baseoutdir + '{:02d}'.format(month) + '{:02d}'.format(day)+'/'
-                fn = Indir+ 'no2_wrfchem_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-                if isfile(fn):
-                    # wrfchem daily no2 column 
-                    fn = Indir+ 'no2_wrfchem_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-                    print('--> reading wrfchem datafile of : ', fn)
-                    ds = Dataset(fn,"r")
-                    variable_wc = ds.variables['NO2'][:,:]
-                    ds.close()
-
-                    # tropomi daily no2 column
-                    fn = Indir+ 'no2_tropomi_wchamf_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-                    print('--> reading tropomi datafile of : ', fn)
-                    ds = Dataset(fn,"r")
-                    variable_tp = ds.variables['NO2'][:,:]
-                    ds.close() 
-
-                    if UseWPD == False:
-                        # add to summary array
-                        ind = np.where((variable_wc >= 0.0) & (variable_tp >= 0.0) & (variable_tp != np.nan))
-                        print('check', ind)
-                        print('wc',np.nanmin(variable_wc), np.nanmax(variable_wc))
-                        print('wc',np.nanmin(variable_tp), np.nanmax(variable_tp))
-                        no2_wrfchem[ind] += variable_wc[ind]
-                        num_wrfchem[ind] += 1.0
-                
-                        #ind = np.where(variable_tp >= 0.0)
-                        no2_tomi[ind] += variable_tp[ind]
-                        num_tomi[ind] += 1.0
-
-                    else:
-			            # read the surface wind speed file
-                        fnwpd_u = Indir + 'wrfchem_u10_'+str(year)+'{:02d}'.format(month)+'{:02d}'.format(day)+'.nc'
-                        ds = Dataset(fnwpd_u,"r")
-                        variable_wpdu = ds.variables['u10'][:,:]
-                        print('--> reading wrfchem u10 of :', fnwpd_u)
-                        print('    ',np.nanmin(variable_wpdu), np.nanmax(variable_wpdu))
-                        ds.close()
-                
-                        fnwpd_v = Indir + 'wrfchem_v10_'+str(year)+'{:02d}'.format(month)+'{:02d}'.format(day)+'.nc'
-                        ds = Dataset(fnwpd_v,"r")
-                        variable_wpdv = ds.variables['v10'][:,:]
-                        print('--> reading wrfchem u10 of :', fnwpd_v)
-                        print('    ', np.nanmin(variable_wpdv), np.nanmax(variable_wpdv))
-                        ds.close()
-                        # the surface wind speed
-                        wd = (variable_wpdu**2 + variable_wpdv**2)**0.5
-
-                        # add to summary array
-                        ind = np.where((variable_wc >= 0.0) & (np.absolute(wd) <= maxwd) & (variable_tp >= 0.0))
-                        no2_wrfchem[ind] += variable_wc[ind]
-                        num_wrfchem[ind] += 1.0
-                
-                        #ind = np.where((variable_tp >= 0.0) & (np.absolute(wd) <= maxwd))
-                        no2_tomi[ind] += variable_tp[ind]
-                        num_tomi[ind] += 1.0
-                else:
-                    print('--> NO wrfchem / tropomi file found: ', day, fn)
-                    pass
-
-        # calculate seasonal average
-        no2_tomi_avg = no2_tomi / num_tomi
-        no2_wrfchem_avg = no2_wrfchem / num_wrfchem
-
-        #----Output and Plotting ---
-        ot = output_config()
-        pmin = 0.0	# pcolormap, min
-        pmax = 1e16	# pcolormap, max
-     
-        # TROPOMI NO2 for comparison 
-        fnnc = Outdir+ 'no2_tropomi_wchamf_'+str(year)+'_'+ seasoname +'_'+'mavg'+'.nc'
-        ot.outputnc_2d(fnnc, no2_tomi_avg, 'NO2', 'molec cm-2')
-
-        fn = Outdir+ 'no2_tropomi_wchamf_'+str(year)+'_'+ seasoname +'_'+'mavg'
-        ot.plot_2dmap(fn, no2_tomi_avg,'NO2','molec cm-2', mindata=pmin, maxdata = pmax)
-
-        print('TROPOMI NO2 after AMF revision:')
-        print('    x1e15: min ',np.nanmin(no2_tomi_avg)/1e15, ' max ',np.nanmax(no2_tomi_avg)/1e15, ' median ', np.nanmedian(no2_tomi_avg)/1e15, ' mean ',np.nanmean(no2_tomi_avg)/1e15)
-              
-        # WRF-Chem NO2 for comparison
-        fnnc = Outdir+ 'no2_wrfchem_'+str(year)+'_'+ seasoname +'_'+'mavg'+'.nc'
-        ot.outputnc_2d(fnnc, no2_wrfchem_avg, 'NO2', 'molec cm-2')
-
-        fn = Outdir+ 'no2_wrfchem_'+str(year)+'_'+ seasoname +'_'+ 'mavg'
-        ot.plot_2dmap(fn, no2_wrfchem_avg,'NO2','molec cm-2', mindata=pmin, maxdata = pmax)
-
-        print('WRF-Chem NO2:')
-        print('    x1e15: min ',np.nanmin(no2_wrfchem_avg)/1e15, ' max ',np.nanmax(no2_wrfchem_avg)/1e15, ' median ', np.nanmedian(no2_wrfchem_avg)/1e15, ' mean ',np.nanmean(no2_wrfchem_avg)/1e15               )
-
-        # Observation numbers
-        fnnc = Outdir+ 'num_tomi_no2_'+str(year)+'_'+ seasoname +'_'+'mavg'+'.nc'
-        ot.outputnc_2d(fnnc, num_tomi, 'number', 'unitless')
-
-        # Correlations
-        cal = calstatis()
-        ind = np.where((no2_tomi_avg > 0.0) & (no2_wrfchem_avg > 0.0))
-        x = no2_tomi_avg[ind].flatten()
-        y = no2_wrfchem_avg[ind].flatten()
-        mb = cal.calmb(y,x)
-        nmb = cal.calnmb(y,x)
-        fn = Outdir + 'corrl_trop_wrfchem_no2_'+str(year)+'_'+ seasoname +'_'+'mavg'
-        ot.plot_scatter(fn, 'NO2 columns', x, y, mindata=0.0, maxdata=3e16, mb=mb, nmb=nmb)
-
-        # minus
-        minarr = np.zeros([xy[0], xy[1]], dtype=np.float)
-        minarr[:,:] = np.nan
-        minarr[:,:] = (no2_wrfchem_avg[:,:] - no2_tomi_avg[:,:])/1e15
-        fn = Outdir + 'minus_wrfchem-tropomi_'+str(year)+'_'+seasoname + '_'+'mavg'
-        ot.plot_minus(fn,  minarr, 'NO2', '$\mathregular{10^{15}}$ molec c$\mathregular{m^{-2}}$',  mindata=-3.0, maxdata = 3.0)
-        
-
-
-class calstatis:
-    def __init__(self):
-        pass
-    def calcorrelation(self,modellist, obslist):
-        #r = np.corrcoef(x=modellist, y=obslist)
-        slope, intercept, r, p_value, stderr=stats.linregress(obslist, modellist)
-        return r
-    def calmb(self, modellist, obslist):
-        minuslist = [(modellist[n] - obslist[n]) for n in range(len(modellist))]
-        mb = np.sum(minuslist) / len(modellist)
-        return mb
-    def calrmse(self, modellist, obslist):
-        minuslist = [pow((modellist[n] - obslist[n]),2) for n in range(len(modellist))]
-        rmse = pow((np.sum(minuslist) / len(modellist)),0.5)
-        return rmse
-    def calnmb(self, modellist, obslist):
-        minuslist = [(modellist[n] - obslist[n]) for n in range(len(modellist))]
-        nmb = np.sum(minuslist) / np.sum(obslist)
-        return nmb
-    def calnme(self, modellist, obslist):
-        minuslist = [(abs(modellist[n] - obslist[n])) for n in range(len(modellist))]
-        nme = np.sum(minuslist) / np.sum(obslist)
-        return nme
-
-
-#---
-if __name__ == '__main__':
-        main()
-    
- 
-
-
-
diff --git a/to_generalize/daily_trp_no2.py b/to_generalize/daily_trp_no2.py
deleted file mode 100755
index 56706496..00000000
--- a/to_generalize/daily_trp_no2.py
+++ /dev/null
@@ -1,780 +0,0 @@
-
-# This pro is written to read the wrf-chem output 
-# and process the TROPOMI data, to compare the observation with the model
-# --- Meng Li
-# --- 2019.04.11
-# --- Contact: meng.li@noaa.gov; meng.li.atm@gmail.com
-
-'''
-===================================================
-Import necessary packages and set the environment
-===================================================
-'''
-
-from os import listdir
-from os.path import isfile
-import csv, wrf,os,sys
-import numpy as np
-from netCDF4 import Dataset
-import multiprocessing
-from datetime import datetime
-import pytz
-from timezonefinder import TimezoneFinder
-import ESMF
-import xesmf as xe
-import math
-ESMF.Manager(debug=True)
-
-# import outputplot_config file
-from OutputPlot_Config import output_config
-
-
-# Get Basedir_tropomi, Baseoutdir, Basedir_wrfoutput, and Geofile from environment variables
-Basedir_tropomi = os.environ.get('Basedir_tropomi')
-Baseoutdir = os.environ.get('Baseoutdir')
-Basedir_wrfoutput = os.environ.get('Basedir_wrfoutput')
-Geofile = os.environ.get('Geofile')
-
-
-'''
-=============================================================
-Generate Daily Trop. NO2 Columns for TROPOMI and WRF-Chem
-=============================================================
-'''
-
-#==============Preparation Codes==================
-#---
-class file_management:
-    def __init__(self):
-        pass
-    def subdirlist(self, indir, keyword=''):
-        subdirlist = []
-        subdirlist_org = [x[0] for x in os.walk(indir)]
-        for sd in subdirlist_org:
-            if keyword in sd:
-                subdirlist.append(sd)
-        return subdirlist
-                
-        return subdirlist
-    def filelist(self, indir, keyword=''):
-        filelist = []
-        filelist_org = [os.path.join(indir, f) for f in listdir(indir) if isfile(os.path.join(indir,f))]
-        for f in filelist_org:
-            if keyword in f:
-                filelist.append(f)
-        return filelist   
-    
-#---
-
-def extractwrfcorners():
-    ds = Dataset(Geofile, "r")
-    variable_latc = ds['XLAT_C'][0,:,:]
-    variable_longc = ds['XLONG_C'][0,:,:] #1,285,441
-    cornerdic = {'lon_c': variable_longc, "lat_c": variable_latc}
-    #print(variable_latc)
-    ds.close()
-    return cornerdic
-
-def extractwrfcoord(lats=[''], lons=['']):
-    # extract one wrfdata
-    fm = file_management()
-    subdirlist = fm.subdirlist(Basedir_wrfoutput)
-    ff = fm.filelist(subdirlist[1])[0]
-    wrfin = Dataset(ff,'r',format = 'NETCDF4_CLASSIC')
-        
-    # get some attributes of the wrf domain 
-    latdata = wrf.getvar(wrfin, 'XLAT', timeidx=0)[:,:]            # latitude
-    londata = wrf.getvar(wrfin, 'XLONG', timeidx=0)[:,:]           # longitude
-    wrflonlat = {'lon':londata, 'lat':latdata}
-
-    if (len(lats) == 1) & (lats[0] == '') & (len(lons) == 1) & (lons[0] == ''):
-        cornerdic = extractwrfcorners()
-        wrflonlat.update(cornerdic)
-        #print(wrflonlat)
-        return wrflonlat
-    else:
-        xyinds = wrf.ll_to_xy(wrfin, lats, lons)
-        return xyinds
-    wrfin.close()
-
-
-#=======MAIN PROGRAM STARTS HERE=============
-    
-# GET THE WRF COORDIATE INFORMATION
-wrfcoord = extractwrfcoord()
-wrflon = wrfcoord['lon']
-wrflat = wrfcoord['lat']
-wrflon_c = wrfcoord['lon_c']
-wrflat_c = wrfcoord['lat_c']
-xy = np.shape(wrflon)
-tf = TimezoneFinder()
-
-print(wrflon_c)
-print(wrflat_c)
-
-# MAIN PROGRAM
-def main(year, month, day):
-    m = model_validation(year, month, day)
-    m.evaluatedata()
-    pass
-#---
-class model_validation():
-    
-    def __init__(self, year, month, day):        
-        self.year = year
-        self.month = month
-        self.day = day
-
-    
-    def findxy(self,coord_lons, coord_lats, lons, lats, iswrf):        
-        arrayout = extractwrfcoord(lats=lats, lons=lons)
-        arrayout = wrf.to_np(arrayout) # EDGE: coord_lons[0][0], coord_lats[0],[0], POINT OF (0,0)          
-        return arrayout
-
-    def extractloc(self, wrflon, wrflat, lons,lats):
-        londata = wrflon
-        latdata = wrflat
-        lats_1d = lats.flatten() # change 2-d to 1-d, dims of lons and lats should be the same
-        lons_1d  = lons.flatten() # change 2-d to 1-d       
-        idx = self.findxy(londata, latdata, lons_1d, lats_1d, 1)
-        idx = np.reshape(idx, [2, np.shape(lons)[0], np.shape(lons)[1]])
-        return idx
-        
-    def evaluatedata(self):
-        ot = output_config()
-        wrflonlat = wrfcoord
-        year = self.year
-        month = self.month
-        day = self.day
-
-        Outdir = Baseoutdir + '{:02d}'.format(month) + '{:02d}'.format(day)+'/'
-        # check if Outdir exsits, if not, create a new one.
-        if os.path.isdir(Outdir):
-            pass
-        else:
-            os.mkdir(Outdir)
-        print('*** Evaluation starts here: ', year, month, day)
-
-        # extract wrf-chem data directionary for each day
-        w = wrf_chem_process()
-        w.extractwrfdata(year, month, day)
-        wrf_omi_avg =  w.wrf_omi_avg
-
-        # no2 column and pressure of wrf-chem
-        wrfpres = wrf_omi_avg['pres']
-        wrfno2_omi_avg = wrf_omi_avg['no2']
-        wrfno2_omi_ppm = wrf_omi_avg['no2ppm']
-
-        # get the wrf grid cell center and boundaries
-        lat_wrf = wrflat
-        lon_wrf = wrflon
-        lat_wrf_b = wrflat_c
-        lon_wrf_b = wrflon_c
-        # extract the tropomi data dictionary including all swath tracks covering US
-        t = tropomi_process(year, month, day)
-        t.avgtropomi()
-        omidata_alltrack = t.omidata_alltrack
-        
-        print('--> total track number of tropomi is: '+str(len(omidata_alltrack)))
-        
-        # initialize arrays
-        no2colomi_avg = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # NO2 column for sum in standard product 
-        no2colomi_avg[:,:,:] = np.nan
-
-        no2numomi_avg = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # NO2 number for sum in standard product
-        no2numomi_avg[:,:,:] = 0.0
-
-        ratio = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # ratio of revised column / standard column
-        ratio[:,:,:] = np.nan         
-
-        tamf_omi = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # trop. AMF in standard product
-        tamf_omi[:,:,:] = np.nan         
-        
-        amf_model = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # revised trop. AMF 
-        amf_model[:,:,:] = np.nan  
-
-        amf_total = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # total AMF in standard product
-        amf_total[:,:,:] = np.nan
-
-        slantcol = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # slant column density for sum in standard product
-        slantcol[:,:,:] = 0.0
-        
-        zmax_model = np.zeros([xy[0], xy[1]], dtype = np.int32) # vertical layer
-        zmax_model[:,:] = 0.0
-
-        no2_wrf_forcmp = np.zeros([xy[0], xy[1]], dtype = np.float) # final wrf-chem no2 column for comparison
-        no2_wrf_forcmp[:,:] = np.nan
-        
-        # processing each track into wrf domain and average 
-        for trk in range(len(omidata_alltrack)):
-            print('   -----> prosessing the track of ', trk)
-            omidata = omidata_alltrack[trk]
-            
-            # cell centers and boundaries
-            lon = np.squeeze(omidata['longitude'][:,:,:], axis=0)
-            lat = np.squeeze(omidata['latitude'][:,:,:], axis=0)     
-            lon_b = np.squeeze(omidata['longitude_bounds'][:,:,:,:], axis=0) # 3245x450x4
-            lat_b = np.squeeze(omidata['latitude_bounds'][:,:,:,:], axis=0)
-
-            locind = self.extractloc(wrflon, wrflat, lon, lat)  # locind: the value of x_wrf and y_wrf, omi domain shape
-
-            # extract data in standard NO2 product
-            no2colorg =np.squeeze(omidata['nitrogendioxide_tropospheric_column'][:,:,:], axis=0)# Trop. NO2 column in standard product
-            tnx = np.shape(lat)[0]
-            tny = np.shape(lat)[1]
-            lat_trp_b = np.zeros([tnx+1, tny+1], dtype = np.float)
-            lon_trp_b = np.zeros([tnx+1, tny+1], dtype = np.float)
-
-            # Revised NO2 Tropomi product at original dimension
-            no2colrev = np.zeros([tnx, tny],dtype=np.float)
-            no2colrev[:,:] = np.nan
-
-            qaorg = np.squeeze(omidata['qa_value'][:,:,:], axis=0)                              # quality flag
-            tamf_tm5 = np.squeeze(omidata['air_mass_factor_troposphere'][:,:,:], axis=0)        # TM5 trop. AMF 
-            amf_tm5 = np.squeeze(omidata['air_mass_factor_total'][:,:,:], axis=0)               # TM5 total AMF
-            cldfrc = np.squeeze(omidata['cloud_fraction_crb'][:,:,:],axis=0)                    # cloud fraction
-            tpreslev_tm5 = np.squeeze(omidata['preslev'][:,:,:], axis=0)                        # TM5 pressure level
-            trplayer_tm5 = np.squeeze(omidata['tm5_tropopause_layer_index'][:,:,:], axis=0)     # TM5 tropopause  
-            slant_col_single = np.squeeze(omidata['nitrogendioxide_slant_column_density'][:,:,:],axis=0)# NO2 Slant column density in standard product
-            scatwts = np.squeeze(omidata['averaging_kernel'][:,:,:,:],axis=0)                   # averaging kernel
-
-
-            for x_tomi in range(tnx):
-                for y_tomi in range(tny):
-                    x_wrf = locind[1,x_tomi, y_tomi] # MAX: 299, SOUTH-NORTH
-                    y_wrf = locind[0,x_tomi, y_tomi] # MIN: 239, WEST-EAST
-                    # get the cell boundaries of each swath
-                    lat_0 = lat_b[x_tomi,y_tomi,0]
-                    lat_1 = lat_b[x_tomi,y_tomi,1]
-                    lat_2 = lat_b[x_tomi,y_tomi,2]
-                    lat_3 = lat_b[x_tomi,y_tomi,3]
-
-                    lon_0 = lon_b[x_tomi,y_tomi,0]
-                    lon_1 = lon_b[x_tomi,y_tomi,1]
-                    lon_2 = lon_b[x_tomi,y_tomi,2]
-                    lon_3 = lon_b[x_tomi,y_tomi,3]
-
-                    lat_trp_b[x_tomi,y_tomi] = lat_0
-                    lat_trp_b[x_tomi,y_tomi+1] = lat_1
-                    lat_trp_b[x_tomi+1,y_tomi+1] = lat_2
-                    lat_trp_b[x_tomi+1,y_tomi] = lat_3
-
-                    lon_trp_b[x_tomi,y_tomi] = lon_0
-                    lon_trp_b[x_tomi,y_tomi+1] = lon_1
-                    lon_trp_b[x_tomi+1,y_tomi+1] = lon_2
-                    lon_trp_b[x_tomi+1,y_tomi] = lon_3
-   
-                    if (x_wrf < 0.0) or (x_wrf > xy[0]-1) or (y_wrf < 0.0) or (y_wrf > xy[1]-1):
-                        pass
-                    else:
-
-                        # extract the no2 trop column, quality, cloud radiation fraction                            
-                        value_tno2 = no2colorg[x_tomi, y_tomi]
-                        value_slnt = slant_col_single[x_tomi, y_tomi]
-                                            
-                        # screen data
-                        if qaorg[x_tomi, y_tomi] >= 0.75 and value_tno2 > 0.0e-30 and cldfrc[x_tomi, y_tomi] <= 0.5: # QUALITY CONTROL
-                            value_tno2 = self.converunit(value_tno2, 'mole m-2', 'molec cm-2') # CONVERT THE UNIT
-                            value_slnt = self.converunit(value_slnt, 'mole m-2', 'molec cm-2') # CONVERT THE UNIT
-
-                            # add slant NO2 column for further averaging
-                            no2numomi_avg[x_wrf, y_wrf, trk] += 1.0
-                            slantcol[x_wrf, y_wrf, trk] += value_slnt
-                            if value_slnt < 0.0:
-                                print('Error for slant column here!' )
-        
-                            # revise amf using wrfchem NO2 vertical profile, start here
-                            scatwts_vertical = scatwts[x_tomi, y_tomi, :]
-                            tpreslev = tpreslev_tm5[x_tomi, y_tomi,:]
-                            trplayer = trplayer_tm5[x_tomi, y_tomi]
-
-                            wrfpreslayer = wrfpres[:,x_wrf, y_wrf]
-                            wrfno2layer_molec = wrfno2_omi_avg[:,x_wrf, y_wrf] # mole cm^-2 by WRF layers
-                            wrfno2layer = wrfno2_omi_ppm[:,x_wrf, y_wrf] # use unit of ppm to derive NO2 profile
-
-                            # trop. AMF and total AMF in standard product
-                            tamf_org = tamf_tm5[x_tomi, y_tomi]    # trop. amf
-                            tamf_omi[x_wrf, y_wrf, trk] = tamf_org # add trop. amf to array
-                            amf_total[x_wrf, y_wrf, trk] = amf_tm5[x_tomi, y_tomi] # add total amf to array
-
-                         
-                            # find the vertical index of wrf-chem corresponding to the tropomi tropopause
-                            if type(trplayer) == np.int32:
-                                X = abs(wrfpreslayer - tpreslev[trplayer])
-                                zm_wrf = np.where(X == np.min(X))
-                                zmax_model[x_wrf, y_wrf] = zm_wrf[0][0]
-
-                                # calculate the revised trop. AMF, amf_model
-                                scatwts_vertical = scatwts_vertical * amf_total[x_wrf,y_wrf,trk] / tamf_org # converting from AKs to tropospheric AKs
-                                amf_model[x_wrf, y_wrf,trk] = self.calamfwrfchem(scatwts_vertical, wrfpreslayer, wrfno2layer, tpreslev, trplayer, zm_wrf[0][0], wrfno2layer_molec)*tamf_org
-
-                                # summarize all columns in WRF-Chem from surface to the tropopause
-                                ratio[x_wrf,y_wrf, trk] = tamf_org/amf_model[x_wrf, y_wrf, trk]
-                                
-                            else:
-                                #no2wrf_forcmp[x_wrf, y_wrf, trk] = np.nansum(wrfno2layer[:])
-                                ratio[x_wrf, y_wrf, trk] = 1.0
-
-
-                            no2colrev[x_tomi, y_tomi] = value_tno2*ratio[x_wrf,y_wrf,trk]
-            
-                        else:
-                            no2colrev[x_tomi, y_tomi] = np.nan
-
-            
-            # Regrid from revised TROPOMI to WRF-Chem grid, conservative method
-            # Refs: https://xesmf.readthedocs.io/en/latest/notebooks/Pure_numpy.html?highlight=conservative#Regridding
-            lon_wrf_value = lon_wrf.values
-            lat_wrf_value = lat_wrf.values
-            grid_in={'lon': lon, 'lat': lat, 'lon_b': lon_trp_b, 'lat_b': lat_trp_b }
-            grid_out ={'lon': lon_wrf_value, 'lat': lat_wrf_value, 'lon_b': lon_wrf_b, 'lat_b': lat_wrf_b}
-            regridder = xe.Regridder(grid_in, grid_out, 'conservative', ignore_degenerate=True)
-            no2_trp_regrid = regridder(no2colrev)
-
-            ind = np.where(no2_trp_regrid <= 0.0e-30)
-            if (ind != []):
-                no2_trp_regrid[ind] = np.nan
-
-            no2colomi_avg[:,:,trk] = no2_trp_regrid[:,:]
-            print('regridded NO2 column', np.nanmin(no2_trp_regrid), np.nanmax(no2_trp_regrid))
-
-
-        #-- final averaged data ---
-        # averaged slant NO2 columns in standard product, WRF-Chem domain
-        slantcol2 = slantcol / no2numomi_avg
-
-        # WRF-Chem arrays for comparison
-        zmax_default = np.nanmax(zmax_model)
-        print('zmax_default', zmax_default, np.nanmin(zmax_model))
-        no2_wrf_forcmp[:, :] = np.nansum(wrfno2_omi_avg[0:zmax_default+1,:,:],axis=0)
-
-        no2_omi_forcmp = np.nanmean(no2colomi_avg, axis=2)
-        slantcol_forcmp = np.nanmean(slantcol2, axis=2)        
-
-        print('OMI NO2 after AMF replacement:', np.nanmin(no2_omi_forcmp)/1e15, np.nanmax(no2_omi_forcmp)/1e15 )
-        print('WRF-Chem NO2:', np.nanmin(no2_wrf_forcmp)/1e15, np.nanmax(no2_wrf_forcmp)/1e15)
-         
-      
-       #----------------Output and Plotting-------------------
-       # Configured in OutputPlot_Config.py     
-
-        fnnc = Outdir+ 'no2_tropomi_wchamf_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-        ot.outputnc_2d(fnnc, no2_omi_forcmp, 'NO2', 'molec cm-2')
-
-        fnnc = Outdir+ 'no2_wrfchem_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-        ot.outputnc_2d(fnnc, no2_wrf_forcmp, 'NO2', 'molec cm-2')
-       
-        outdata = np.nanmean(amf_model, axis=2)
-        fnnc = Outdir + 'amf_model_'+str(year)+'_'+str(month)+'_'+str(day) + '.nc'
-        ot.outputnc_2d(fnnc, outdata, 'amf_model', '1.0') 
-
-        outdata = np.nanmean(tamf_omi, axis=2)
-        fnnc = Outdir + 'amf_omi_'+str(year)+'_'+str(month)+'_'+str(day)
-        ot.outputnc_2d(fnnc, outdata, 'amf_omi', '1.0')                
-      
-        outdata = np.nanmean(amf_total, axis=2)
-        fnnc = Outdir + 'totalamf_omi_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-        ot.outputnc_2d(fnnc, outdata, 'totalamf_omi', '1.0')
-
-        fnnc = Outdir+ 'no2_total_slantcol_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-        ot.outputnc_2d(fnnc,slantcol_forcmp , 'NO2', 'molec cm-2')
-      
-        fnnc = Outdir+ 'zmax_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
-        ot.outputnc_2d(fnnc, zmax_model, 'zmax', '1')
-
-
-    def converunit(self, invalue, inunit, outunit):
-        if (inunit == 'mole m-2' and outunit == 'molec cm-2'):
-            #outvalue = invalue * 1.0*6.02e23/100.0/100.0
-            outvalue = invalue * 6.02214e+19 # provided by the TROPOMI
-        else:
-            print('no unit is changed', invalue)
-        return outvalue
-    
-    def calamfwrfchem_tm5(self, scatw, wrfpreslayer, wrfno2layer, tpreslev, trplayer):
-        from scipy import interpolate 
-        nume = 0.0
-        deno = 0.0
-        amf_wrfchem = np.nan
-        
-        f = interpolate.interp1d(np.log10(wrfpreslayer),wrfno2layer, fill_value="extrapolate")      
-        wrfno2_omi_avg_tm5 = f(np.log10(tpreslev[:]))
-                   
-        if type(trplayer) != np.int32:
-            amf_wrfchem = np.nan
-        else:
-            for l in range(trplayer+1): # add all tropospheric layers
-                if l == 0:
-                    deltapres = tpreslev[l]-tpreslev[l+1]
-                else:
-                    deltapres = tpreslev[l-1]-tpreslev[l]
-                nume += scatw[l] *wrfno2_omi_avg_tm5[l]*deltapres
-                deno += wrfno2_omi_avg_tm5[l]*deltapres
-                
-            amf_wrfchem = nume / deno
-        return amf_wrfchem
- 
-    def calamfwrfchem(self, scatw, wrfpreslayer, wrfno2layer, tpreslev, trplayer, zwrftrop, wrfno2layer_molec):
-        from scipy import interpolate 
-        nume = 0.0
-        deno = 0.0
-        amf_wrfchem = np.nan
-        
-        tpreslev[0] = wrfpreslayer[0] # set the surface pressure to wrf one
-        f = interpolate.interp1d(np.log10(tpreslev),scatw, fill_value="extrapolate")# relationship between pressure to avk
-        wrfavk = f(np.log10(wrfpreslayer[:])) #wrf-chem averaging kernel
-        
-        # add all tropospheric layers in WRF
-        for l in range(zwrftrop+1):
-            if (np.isinf(wrfavk[l]) == True) | (wrfavk[l] <= 0.0):
-                nume += wrfavk[l+1]*wrfno2layer_molec[l]
-                deno += wrfno2layer_molec[l]
-                print('error of ak here', l, wrfavk[l], wrfavk[l+1], wrfno2layer_molec[l])
-            else:
-                nume += wrfavk[l]*wrfno2layer_molec[l]
-                deno += wrfno2layer_molec[l]
-                
-        amf_wrfchem = nume / deno
-        return amf_wrfchem
-   
-#---    
-class wrf_chem_process:
-    def __init__(self):
-        self.wrf_omi_avg = {}
-         
-    def extractwrfdata(self, year, month ,day):
-        # extract the NO2 column and Pressure from WRF-Chem
-        # average between 13:00 and 14:00 localtime
-
-        fm = file_management()
-        keywords = '{:02d}'.format(month) + '{:02d}'.format(day)
-        subdirlist = fm.subdirlist(Basedir_wrfoutput, keyword = keywords)
-        subdir = subdirlist[0]        
-
-        if len(subdirlist) > 1:
-            print('Warning: more than one directories for day of ' + keywords)
-            
-        infile_wrf_12 = os.path.join(subdir, 'wrfout_d01_'+str(year) + '-'+ '{:02d}'.format(month) + '-'+'{:02d}'.format(day)+'_'+'12:00:00')
-        infile_wrf_18 = os.path.join(subdir, 'wrfout_d01_'+str(year) + '-'+ '{:02d}'.format(month) + '-'+'{:02d}'.format(day)+'_'+'18:00:00')
-        wrfdata_perfile_12 = self.readwrfoutput(infile_wrf_12)
-        wrfdata_perfile_18 = self.readwrfoutput(infile_wrf_18)
-
-        no2_perfile_12 = wrfdata_perfile_12['no2']
-        no2_perfile_18 = wrfdata_perfile_18['no2']
-
-        pres_perfile_12 = wrfdata_perfile_12['pb2']
-        pres_perfile_18 = wrfdata_perfile_18['pb2']
-
-        ph_perfile_12 = wrfdata_perfile_12['ph']
-        ph_perfile_18 = wrfdata_perfile_18['ph']
-
-        phb_perfile_12 = wrfdata_perfile_12['phb']
-        phb_perfile_18 = wrfdata_perfile_18['phb']
-
-        t_perfile_12 = wrfdata_perfile_12['t2']
-        t_perfile_18 = wrfdata_perfile_18['t2']
-        
-        layers = np.shape(no2_perfile_12)[1]
-        
-        wrf_omi_all_no2 = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
-        wrf_omi_ppm_no2 = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
-        wrf_omi_all_pres = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
-        wrf_omi_all_no2[:,:,:,:] = np.nan
-        wrf_omi_all_pres[:,:,:,:] = np.nan
-
-        no2_u2  = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
-        #pres_u2 = np.zeros([6, layers,xy[0],xy[1],2], dtype = np.float)
-        ph_u2   = np.zeros([layers+1,xy[0],xy[1],2], dtype = np.float)
-        phb_u2  = np.zeros([layers+1,xy[0],xy[1],2], dtype = np.float)
-        t_u2  = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
-        
-        no2_u2[:,:,:,:] = np.nan
-        #pres_u2[:,:,:,:] = np.nan
-        ph_u2[:,:,:,:] = np.nan
-        phb_u2[:,:,:,:] = np.nan
-        t_u2[:,:,:,:] = np.nan
-
-        for x in range(xy[0]):
-            for y in range(xy[1]):
-                # Get UTC time at local time 13:00 and 14:00 for each wrf grid
-                lat = wrfcoord['lat'][x,y]
-                lon = wrfcoord['lon'][x,y]
-                utc_13 = 13 - self.utchourfromlatlon(lat, lon, year, month, day) # Local - UTC
-                utc_14 = utc_13 + 1
-
-                # read the corresponding file, 12 or 18
-                if utc_13 >= 12.0 and utc_13 < 18.0:
-                    hour_u13 = int(utc_13-12)
-
-                    no2_u2[:,x,y,0]  = no2_perfile_12[hour_u13,:,x,y]
-                    wrf_omi_all_pres[:,x,y,0] = pres_perfile_12[hour_u13,:,x,y]
-
-                    ph_u2[:,x,y,0]   = ph_perfile_12[hour_u13,:,x,y]
-                    phb_u2[:,x,y,0]  = phb_perfile_12[hour_u13,:,x,y]
-                    t_u2[:,x,y,0]    = t_perfile_12[hour_u13,:,x,y]
-                    
-                    if utc_14 >= 12.0 and utc_14 < 18.0:
-                        hour_u14 = int(utc_14 - 12)
-                        no2_u2[:,x,y,1] = no2_perfile_12[hour_u14,:,x,y]
-                        wrf_omi_all_pres[:,x,y,1] = pres_perfile_12[hour_u14,:,x,y]
-
-                        ph_u2[:,x,y,1]  = ph_perfile_12[hour_u14,:,x,y]
-                        phb_u2[:,x,y,1] = phb_perfile_12[hour_u14,:,x,y]
-                        t_u2[:,x,y,1] = t_perfile_12[hour_u14,:,x,y]
-
-                    else:
-                        hour_u14 = int(utc_14 - 18)
-                        no2_u2[:,x,y,1]  = no2_perfile_18[hour_u14,:,x,y]
-                        wrf_omi_all_pres[:,x,y,1] = pres_perfile_18[hour_u14,:,x,y]
-
-                        ph_u2[:,x,y,1]  = ph_perfile_18[hour_u14,:,x,y]
-                        phb_u2[:,x,y,1] = phb_perfile_18[hour_u14,:,x,y]
-
-                        t_u2[:,x,y,1] = t_perfile_18[hour_u14,:,x,y]
-                else:
-                    hour_u13 = int(utc_13-18)
-                    no2_u2[:,x,y,0] = no2_perfile_18[hour_u13,:,x,y]
-                    wrf_omi_all_pres[:,x,y,0] = pres_perfile_18[hour_u13,:,x,y]
-
-                    ph_u2[:,x,y,0] = ph_perfile_18[hour_u13,:,x,y]
-                    phb_u2[:,x,y,0] = phb_perfile_18[hour_u13,:,x,y]
-
-                    t_u2[:,x,y,0] = t_perfile_18[hour_u13,:,x,y]
-                    
-                    hour_u14 = int(utc_14-18)
-                    no2_u2[:,x,y,1]  = no2_perfile_18[hour_u14,:,x,y]
-                    wrf_omi_all_pres[:,x,y,1] = pres_perfile_18[hour_u14,:,x,y]
-
-                    ph_u2[:,x,y,1] = ph_perfile_18[hour_u14,:,x,y]
-                    phb_u2[:,x,y,1] = phb_perfile_18[hour_u14,:,x,y]
-
-                    t_u2[:,x,y,1] = t_perfile_18[hour_u14,:,x,y]
-
-        # calculatethe NO2 column, convert the unit
-        for l in range(layers):
-            ad = wrf_omi_all_pres[l,:,:,0]*(28.97e-3)/(8.314*t_u2[l,:,:,0])
-            #print('ad', np.nanmin(ad), np.nanmax(ad))
-            zh = ((ph_u2[l+1,:,:,0] + phb_u2[l+1,:,:,0]) - (ph_u2[l,:,:,0]+phb_u2[l,:,:,0]))/9.81
-            wrf_omi_all_no2[l,:,:,0] = no2_u2[l,:,:,0]*zh*6.022e23/(28.97e-3)*1e-10*ad[:,:]
-            wrf_omi_ppm_no2[l,:,:,0] = no2_u2[l,:,:,0]
-            ad2 = wrf_omi_all_pres[l,:,:,1]*(28.97e-3)/(8.314*t_u2[l,:,:,1])
-            zh2 = ((ph_u2[l+1,:,:,1] + phb_u2[l+1,:,:,1]) - (ph_u2[l,:,:,1]+phb_u2[l,:,:,1]))/9.81
-            wrf_omi_all_no2[l,:,:,1] = no2_u2[l,:,:,1]*zh2*6.022e23/(28.97e-3)*1e-10*ad2[:,:]
-            wrf_omi_ppm_no2[l,:,:,1] = no2_u2[l,:,:,1]
-
-               
-       # average the wrfno2_omi between 13:00 and 14:00 localtime
-        wrf_omi_avg_no2 = np.nanmean(wrf_omi_all_no2, axis=3)
-        wrf_omi_avg_pres = np.nanmean(wrf_omi_all_pres, axis=3)
-        wrf_omi_avg_no2_ppm = np.nanmean(wrf_omi_ppm_no2, axis=3)
-
-        self.wrf_omi_avg['no2'] = wrf_omi_avg_no2[:,:,:]
-        self.wrf_omi_avg['pres'] = wrf_omi_avg_pres[:,:,:]
-        self.wrf_omi_avg['no2ppm'] = wrf_omi_avg_no2_ppm[:,:,:] 
-
-        print('WRF-Chem:', np.nanmin(wrf_omi_avg_no2), np.nanmax(wrf_omi_avg_no2), np.nanmin(wrf_omi_avg_pres), np.nanmax(wrf_omi_avg_pres))
-      
-
-    def utchourfromlatlon(self,lat,lon,year, month, day):
-        # convert between UTC time and local time according to lon and lat
-
-        lon2 = lon.values.tolist()
-        lat2 = lat.values.tolist()
-        timezone_str = tf.timezone_at(lng=lon2, lat=lat2)
-        
-        if timezone_str == None:
-            if lon > -100.0:
-                timezone_str = 'America/New_York'
-            else:
-                timezone_str = 'America/Los_Angeles'
-        tz = pytz.timezone(timezone_str)
-        d = datetime(year,month, day, 00,00,00)
-        uos = tz.utcoffset(d, is_dst=True)
-        utchour = uos.seconds/60.0/60.0
-        utcday = uos.days
-        if utcday < 0:
-            utchour = (24-utchour)*-1 # Local - UTC
-        return utchour
-              
-
-    def readwrfoutput(self, infile):
-        # read wrf-chem output, return wrf-chem data directory
-
-        print('--> reading wrf output file of : ', infile   )
-        ncfile = Dataset(infile,'r',format = 'NETCDF4_CLASSIC')
-        wrfdata = {}
-        
-        no2data = wrf.getvar(ncfile, 'no2',timeidx=wrf.ALL_TIMES)  # NO2 Mixing ratio, ppmv
-        tdata = wrf.getvar(ncfile, 'T',timeidx=wrf.ALL_TIMES)      # K,perturbation potential temperature theta-t0
-        pdata = wrf.getvar(ncfile, 'P',timeidx=wrf.ALL_TIMES)      # Pa,perturbation pressure
-        pbdata = wrf.getvar(ncfile, 'PB',timeidx=wrf.ALL_TIMES)    # Pa,base state pressure
-        phdata = wrf.getvar(ncfile, 'PH',timeidx=wrf.ALL_TIMES)
-        phbdata = wrf.getvar(ncfile, 'PHB',timeidx=wrf.ALL_TIMES)
-        
-
-        # presure: base state + PB (KSMP)
-        pb2data = np.zeros([np.shape(pdata)[0], np.shape(pdata)[1], np.shape(pdata)[2], np.shape(pdata)[3]],dtype=np.float)
-        pb2data[:,:,:,:] = pdata[:,:,:,:]+ pbdata[:,:,:,:]
-
-        # convert the perturbation potential temperature (from 300K reference) to temp
-        tbdata = np.zeros([np.shape(tdata)[0], np.shape(tdata)[1], np.shape(tdata)[2], np.shape(tdata)[3]],dtype=np.float)
-        tbdata[:,:,:,:] =(300.0+tdata[:,:,:,:])*((pb2data[:,:,:,:]/1.0e5)**0.286)
-        
-        wrfdata = {'no2': no2data, 'pb2':pb2data, 'ph':phdata, 'phb': phbdata, 't2': tbdata}
-
-        ncfile.close()        
-        return wrfdata
-        
-#--- 
-class tropomi_process:
-    def __init__(self, year, month, day):
-        self.omidata_alltrack = []
-        self.scatwts_alltrack = []
-        self.year = year
-        self.month = month
-        self.day = day
-        
-    def avgtropomi(self):
-        year = self.year
-        month = self.month
-        day = self.day
-
-        fm = file_management()
-        timeindex = '{:04d}'.format(year)+'{:02d}'.format(month) + '{:02d}'.format(day)
-        subdirlist = fm.subdirlist(Basedir_tropomi)
-
-        omidata_alltrack = []
-        
-        for subdir in subdirlist:
-            fflist = fm.filelist(subdir, keyword='____'+timeindex)
-            tracknumber = len(fflist)
-            for infile in fflist:
-                # identify if the swath cover the WRF-Chem domain
-                llregion = self.identifyregion(infile)
-                if llregion == True:
-                    omidata = self.readtropomi(infile)
-                    omidata_alltrack.append(omidata)
-                else:
-                    pass
-                
-        self.omidata_alltrack = omidata_alltrack
-        
-    def identifyregion(self, infile):
-        # identify if the swath cover the WRF-Chem domain
-        # wrf-chem domain
-        wrflonlat = wrfcoord
-        wrflon = wrflonlat['lon']
-        wrflat = wrflonlat['lat']
-
-        # identify if the file is located in the region or not
-        ds = Dataset(infile,"r")
-        variable = np.squeeze(wrf.to_np(ds.groups['PRODUCT']['latitude'][:,:,:]))
-        lat_ind = variable
-        variable = np.squeeze(wrf.to_np(ds.groups['PRODUCT']['longitude'][:,:,:]))
-        lon_ind = variable
-        ds.close()
-
-        # determine the location in wrfchem
-        locind = extractwrfcoord(lats=lat_ind, lons=lon_ind)
-
-        wrf_ind_x = int(np.shape(wrflon)[0]) # lat
-        wrf_ind_y = int(np.shape(wrflon)[1]) # lon
-
-        llregion = False
-        xtomi = locind[0,:]
-        ytomi = locind[1,:]
-
-        ind = np.where((xtomi >= 0.0) & (ytomi >= 0.0) & (xtomi < wrf_ind_y) & (ytomi < wrf_ind_x))
-        if np.shape(ind)[1] == 0:
-            pass
-        else:
-           llregion = True
-        return llregion
-                    
-    def readtropomi(self,infile):
-        # read tropomi swath L2 NO2 data, return OMI NO2 data directory
-        omidata = {}
-        print('--> reading tropomi datafile of : ', infile)
-        ds = Dataset(infile,"r")
-        
-        variable = ds.groups['PRODUCT']['nitrogendioxide_tropospheric_column']
-        omidata['nitrogendioxide_tropospheric_column'] = variable
-        
-        variable = ds.groups['PRODUCT']['qa_value']
-        omidata['qa_value'] = variable        
-        
-        variable = ds.groups['PRODUCT']['averaging_kernel']
-        omidata['averaging_kernel'] = variable
-        
-        variable = ds.groups['PRODUCT']['air_mass_factor_total']
-        omidata['air_mass_factor_total'] = variable
-        
-        variable = ds.groups['PRODUCT']['air_mass_factor_troposphere']
-        omidata['air_mass_factor_troposphere'] = variable
-        
-        variable = ds.groups['PRODUCT']['latitude']
-        omidata['latitude'] = variable
-        
-        variable = ds.groups['PRODUCT']['longitude']
-        omidata['longitude'] = variable
-        
-        variable = ds.groups['PRODUCT']['tm5_constant_a']
-        omidata['tm5_constant_a'] = variable
-        
-        variable = ds.groups['PRODUCT']['tm5_constant_b']
-        omidata['tm5_constant_b'] = variable
-        
-        variable = ds.groups['PRODUCT']['tm5_tropopause_layer_index']
-        omidata['tm5_tropopause_layer_index'] = variable
-        
-        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['INPUT_DATA']['surface_pressure']
-        omidata['surface_pressure'] = variable
-        
-        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['INPUT_DATA']['cloud_fraction_crb']
-        omidata['cloud_fraction_crb'] = variable
-
-        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['DETAILED_RESULTS']['nitrogendioxide_stratospheric_column']
-        omidata['nitrogendioxide_stratospheric_column'] = variable
-
-        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['DETAILED_RESULTS']['air_mass_factor_stratosphere']
-        omidata['air_mass_factor_stratosphere'] = variable
-        
-        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['DETAILED_RESULTS']['nitrogendioxide_slant_column_density']
-        omidata['nitrogendioxide_slant_column_density'] = variable
-       
-        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['GEOLOCATIONS']['latitude_bounds']
-        omidata['latitude_bounds'] = variable #time x scanline x groud_pixel x corners, 1x3245x450x4
-
-        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['GEOLOCATIONS']['longitude_bounds']
-        omidata['longitude_bounds'] = variable #time x scanline x groud_pixel x corners, 1x3245x450x4
-
-        # calculate the preslev
-        pleva = omidata['tm5_constant_a']
-        plevb = omidata['tm5_constant_b']
-        
-        spre = np.squeeze(omidata['surface_pressure'], axis=0)
-        aks = omidata['averaging_kernel']
-                
-        FillValue = omidata['surface_pressure']._FillValue
-        preslev = np.copy(aks)
-        preslev[:,:,:,:]  = np.nan
-        aks = None # to save memory
-        del aks
-        
-        spre[np.where(spre == FillValue)] = np.nan
-
-        for l in range(np.shape(preslev)[3]):
-            preslev[0,:,:,l] = (pleva[l,0]+spre[:,:]*plevb[l,0] + pleva[l,1]+spre[:,:]*plevb[l,1]) / 2.0 # center of the vertical layer            
-        omidata['preslev'] = preslev
-        
-        return omidata
-        ds.close()
-               
-#---
-
-
-if __name__ == "__main__":
-    # year, month, day
-    print(sys.argv[1], sys.argv[2], sys.argv[3])
-    main(int(sys.argv[1]), int(sys.argv[2]),int(sys.argv[3]))
-
diff --git a/to_generalize/run_daily_trp_no2.sh b/to_generalize/run_daily_trp_no2.sh
deleted file mode 100755
index a9067a57..00000000
--- a/to_generalize/run_daily_trp_no2.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash -l
-
-#SBATCH --job-name=mlitrop
-#SBATCH --partition=hera
-#SBATCH --time=08:00:00
-# -- Request 16 cores
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=1
-# -- Specify under which account a job should run
-#SBATCH --account=rcm2
-
-
-echo '=== Processing TROPOMI and WRF-Chem NO2 Columns ==='
-
-export Basedir_tropomi='/scratch1/BMC/rcm2/mli/tropomi_data/NO2_global/'
-export Baseoutdir='/scratch1/BMC/rcm2/mli/outdir_12km_noPM_baseline_bmc_cams/'
-export Basedir_wrfoutput='/scratch1/BMC/rcm2/mli/nyc18_cams/run_12km_five18_bmcdVCP_fog_wofire_BEIS_0.5ISO/Output/'
-export Geofile='/scratch1/BMC/rcm2/mli/nyc18_WPS/WPSV4.0/geo_em.d01.nc' # TO GET THE WRF boundaries
-
-echo "---> Data Locations < ---"
-echo "TROPOMI data are in:    " $Basedir_tropomi
-echo "WRF-Chem data are in:   " $Basedir_wrfoutput
-echo "Geophysical data are in:" $Geofile
-echo "Out data locations:     " $Baseoutdir
-
-year=2018
-month=6
-logdir=logs
-
-
-for day in $(seq 1 1 15) 
-do 
-    echo "Processing TROPOMI data in " $year $month $day
-    python CMP_WRFChem_TROPOMI_NO2Col_Daily_12km_Conservative_v3.py $year $month $day > $logdir/log_$year"_"$month"_"$day
-done
-