pagespeed-calculation-OLS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tools.eval_measures import mse, rmse\n",
    "import seaborn as sns\n",
    "pd.options.display.float_format = '{:.5f}'.format\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\")\n",
    "import math\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sqlalchemy import create_engine\n",
    "import warnings\n",
    "from scipy.stats.mstats import winsorize\n",
    "import scipy.stats as stats\n",
    "import scipy\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn import linear_model\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Group</th>\n",
       "      <th>Performance Score</th>\n",
       "      <th>Time to First Byte (ms)</th>\n",
       "      <th>First Contentful Paint Time (sec)</th>\n",
       "      <th>Speed Index Time (sec)</th>\n",
       "      <th>Time to Interactive (sec)</th>\n",
       "      <th>CrUX Performance</th>\n",
       "      <th>CrUX First Contentful Paint Time (sec)</th>\n",
       "      <th>CrUX First Contentful Paint Category</th>\n",
       "      <th>...</th>\n",
       "      <th>CrUX Origin First Contentful Paint Category</th>\n",
       "      <th>CrUX Origin First Input Delay Time (sec)</th>\n",
       "      <th>CrUX Origin First Input Delay Category</th>\n",
       "      <th>First Contentful Paint Score</th>\n",
       "      <th>First Meaningful Paint Time (sec)</th>\n",
       "      <th>First Meaningful Paint Score</th>\n",
       "      <th>Speed Index Score</th>\n",
       "      <th>First CPU Idle Time (sec)</th>\n",
       "      <th>First CPU Idle Score</th>\n",
       "      <th>Time to Interactive Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.jivox.com/</td>\n",
       "      <td>Business &amp; Consumer</td>\n",
       "      <td>0</td>\n",
       "      <td>790</td>\n",
       "      <td>12.70000</td>\n",
       "      <td>26.10000</td>\n",
       "      <td>43.00000</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>16.92200</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>...</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0.63500</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0</td>\n",
       "      <td>12.70000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.10000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.wish.com/</td>\n",
       "      <td>Marketplace</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>17.90000</td>\n",
       "      <td>35.70000</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>11.90100</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>...</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0.46900</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.paratika.com.tr/</td>\n",
       "      <td>Business &amp; Consumer</td>\n",
       "      <td>0</td>\n",
       "      <td>1230</td>\n",
       "      <td>13.90000</td>\n",
       "      <td>25.60000</td>\n",
       "      <td>29.30000</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>5.95100</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>...</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0.33300</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0</td>\n",
       "      <td>15.80000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.70000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.ataonline.com.tr/</td>\n",
       "      <td>Finance</td>\n",
       "      <td>1</td>\n",
       "      <td>490</td>\n",
       "      <td>17.80000</td>\n",
       "      <td>20.20000</td>\n",
       "      <td>18.90000</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>6.18100</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>...</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0.27400</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0</td>\n",
       "      <td>17.90000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.90000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.yeniasir.com.tr/</td>\n",
       "      <td>News</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>12.10000</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>28.10000</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>3.28000</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>...</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0.25900</td>\n",
       "      <td>SLOW</td>\n",
       "      <td>0</td>\n",
       "      <td>12.70000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14.50000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Address                Group  Performance Score  \\\n",
       "0         https://www.jivox.com/  Business & Consumer                  0   \n",
       "1          https://www.wish.com/          Marketplace                  0   \n",
       "2   https://www.paratika.com.tr/  Business & Consumer                  0   \n",
       "3  https://www.ataonline.com.tr/              Finance                  1   \n",
       "4   https://www.yeniasir.com.tr/                 News                  1   \n",
       "\n",
       "   Time to First Byte (ms)  First Contentful Paint Time (sec)  \\\n",
       "0                      790                           12.70000   \n",
       "1                      690                           14.00000   \n",
       "2                     1230                           13.90000   \n",
       "3                      490                           17.80000   \n",
       "4                       90                           12.10000   \n",
       "\n",
       "   Speed Index Time (sec)  Time to Interactive (sec) CrUX Performance  \\\n",
       "0                26.10000                   43.00000             SLOW   \n",
       "1                17.90000                   35.70000             SLOW   \n",
       "2                25.60000                   29.30000             SLOW   \n",
       "3                20.20000                   18.90000             SLOW   \n",
       "4                12.60000                   28.10000             SLOW   \n",
       "\n",
       "   CrUX First Contentful Paint Time (sec)  \\\n",
       "0                                16.92200   \n",
       "1                                11.90100   \n",
       "2                                 5.95100   \n",
       "3                                 6.18100   \n",
       "4                                 3.28000   \n",
       "\n",
       "  CrUX First Contentful Paint Category  ...  \\\n",
       "0                                 SLOW  ...   \n",
       "1                                 SLOW  ...   \n",
       "2                                 SLOW  ...   \n",
       "3                                 SLOW  ...   \n",
       "4                                 SLOW  ...   \n",
       "\n",
       "   CrUX Origin First Contentful Paint Category  \\\n",
       "0                                         SLOW   \n",
       "1                                         SLOW   \n",
       "2                                         SLOW   \n",
       "3                                         SLOW   \n",
       "4                                         SLOW   \n",
       "\n",
       "  CrUX Origin First Input Delay Time (sec)  \\\n",
       "0                                  0.63500   \n",
       "1                                  0.46900   \n",
       "2                                  0.33300   \n",
       "3                                  0.27400   \n",
       "4                                  0.25900   \n",
       "\n",
       "  CrUX Origin First Input Delay Category  First Contentful Paint Score  \\\n",
       "0                                   SLOW                             0   \n",
       "1                                   SLOW                             0   \n",
       "2                                   SLOW                             0   \n",
       "3                                   SLOW                             0   \n",
       "4                                   SLOW                             0   \n",
       "\n",
       "  First Meaningful Paint Time (sec)  First Meaningful Paint Score  \\\n",
       "0                          12.70000                             0   \n",
       "1                          14.00000                             0   \n",
       "2                          15.80000                             0   \n",
       "3                          17.90000                             0   \n",
       "4                          12.70000                             0   \n",
       "\n",
       "  Speed Index Score  First CPU Idle Time (sec)  First CPU Idle Score  \\\n",
       "0                 0                   21.10000                     1   \n",
       "1                 0                   25.50000                     0   \n",
       "2                 0                   26.70000                     0   \n",
       "3                 0                   17.90000                     2   \n",
       "4                 3                   14.50000                     4   \n",
       "\n",
       "   Time to Interactive Score  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          3  \n",
       "4                          0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"pagespeed_all7.xlsx\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1745 entries, 0 to 1744\n",
      "Data columns (total 24 columns):\n",
      "Address                                          1745 non-null object\n",
      "Group                                            1742 non-null object\n",
      "Performance Score                                1745 non-null int64\n",
      "Time to First Byte (ms)                          1745 non-null int64\n",
      "First Contentful Paint Time (sec)                1745 non-null float64\n",
      "Speed Index Time (sec)                           1745 non-null float64\n",
      "Time to Interactive (sec)                        1745 non-null float64\n",
      "CrUX Performance                                 1618 non-null object\n",
      "CrUX First Contentful Paint Time (sec)           1618 non-null float64\n",
      "CrUX First Contentful Paint Category             1618 non-null object\n",
      "CrUX First Input Delay Time (sec)                1618 non-null float64\n",
      "CrUX First Input Delay Category                  1618 non-null object\n",
      "CrUX Origin Performance                          1617 non-null object\n",
      "CrUX Origin First Contentful Paint Time (sec)    1617 non-null float64\n",
      "CrUX Origin First Contentful Paint Category      1617 non-null object\n",
      "CrUX Origin First Input Delay Time (sec)         1617 non-null float64\n",
      "CrUX Origin First Input Delay Category           1617 non-null object\n",
      "First Contentful Paint Score                     1745 non-null int64\n",
      "First Meaningful Paint Time (sec)                1745 non-null float64\n",
      "First Meaningful Paint Score                     1745 non-null int64\n",
      "Speed Index Score                                1745 non-null int64\n",
      "First CPU Idle Time (sec)                        1745 non-null float64\n",
      "First CPU Idle Score                             1745 non-null int64\n",
      "Time to Interactive Score                        1745 non-null int64\n",
      "dtypes: float64(9), int64(7), object(8)\n",
      "memory usage: 327.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Address                                            0\n",
       "Group                                              3\n",
       "Performance Score                                  0\n",
       "Time to First Byte (ms)                            0\n",
       "First Contentful Paint Time (sec)                  0\n",
       "Speed Index Time (sec)                             0\n",
       "Time to Interactive (sec)                          0\n",
       "CrUX Performance                                 127\n",
       "CrUX First Contentful Paint Time (sec)           127\n",
       "CrUX First Contentful Paint Category             127\n",
       "CrUX First Input Delay Time (sec)                127\n",
       "CrUX First Input Delay Category                  127\n",
       "CrUX Origin Performance                          128\n",
       "CrUX Origin First Contentful Paint Time (sec)    128\n",
       "CrUX Origin First Contentful Paint Category      128\n",
       "CrUX Origin First Input Delay Time (sec)         128\n",
       "CrUX Origin First Input Delay Category           128\n",
       "First Contentful Paint Score                       0\n",
       "First Meaningful Paint Time (sec)                  0\n",
       "First Meaningful Paint Score                       0\n",
       "Speed Index Score                                  0\n",
       "First CPU Idle Time (sec)                          0\n",
       "First CPU Idle Score                               0\n",
       "Time to Interactive Score                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Address                                          0\n",
       "Group                                            0\n",
       "Performance Score                                0\n",
       "Time to First Byte (ms)                          0\n",
       "First Contentful Paint Time (sec)                0\n",
       "Speed Index Time (sec)                           0\n",
       "Time to Interactive (sec)                        0\n",
       "CrUX Performance                                 0\n",
       "CrUX First Contentful Paint Time (sec)           0\n",
       "CrUX First Contentful Paint Category             0\n",
       "CrUX First Input Delay Time (sec)                0\n",
       "CrUX First Input Delay Category                  0\n",
       "CrUX Origin Performance                          0\n",
       "CrUX Origin First Contentful Paint Time (sec)    0\n",
       "CrUX Origin First Contentful Paint Category      0\n",
       "CrUX Origin First Input Delay Time (sec)         0\n",
       "CrUX Origin First Input Delay Category           0\n",
       "First Contentful Paint Score                     0\n",
       "First Meaningful Paint Time (sec)                0\n",
       "First Meaningful Paint Score                     0\n",
       "Speed Index Score                                0\n",
       "First CPU Idle Time (sec)                        0\n",
       "First CPU Idle Score                             0\n",
       "Time to Interactive Score                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b657cf8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JTiy6DPlIUhphp2mPvoPasnjepsoI7XerUzOIhOPFzr3jGdQJLf/ce+3WMSx4djwnsnOZv6ZsO13QvS0/btpbUaH+LpEhJfuWhLQM6pxF39I4LJTo8FpMv/5SPrnhMno1b/zbG1WSOjWDSCzddjVPXbfgQH/6tG3Kz9td/eO2Q8n0j20BQP/2zQkK8Kdm9YCKDfosRAYHkZBWsu0ig39fYvmiDm1ZsrPqTPzPSHESHF40jSYo3If0lJKX4x07nM+xQwV8ck8KH92Vwp7VruReRBNf9v6aQ16OJSvNycENuaQnl9y2qnGNg0VtmXwkvew4WMM1Dl57dW/eev0aHn14NLVCS14+W1WkpzgJKdZ+wWEOMlLKv6Q1NamA1EQnjdv/ORIOR44YIiKKjqfwCEvKkZJfQ6+8OocfFvhx5aVBPPJAdW6e4EpaN2lWwIqffCkogIR4w87tPiQnVb2vsGF1gkkutqRIcmIaYaf50bHPkPYsnruh8HHz1nWJiKrJyqXbKzTO3+PYEQgrdilr7Qg4llIygzfmqgJ+WuDg9iv8ePEhX666pejY3bXFcP8Nvjww3o9rJuZX2dl+cGbHatNmTn5c6jr3flrmS9YJQ1pqFc9oyl9e1es1pUpyz04bCmwwxrTGNQOup7W2A1CAa2YfQA3gZ2ttrLX2H8BM4G5r7ThgLNABiAUGAM8bY+q6tzsXuN1aG+N+3Bx4EWjl/u8KoBdwF/CAu8xWoLe1tiPwCFD8otQO7hjbAZcaYxoaYyKAd4ALrbWxwMXusg8CC621XYDz3XGV/sn4U2CkO9H5ojGmo/t9OdU+HwfWWGvbu+Mtfu1JG2CAtfZy4Dog1VrbGegM3GCMaVLO+3+jMeYXY8wvb7/9dumXpYINbdeS7zdux2n/XFPSfXwc1IkMYfPGg9xy3bts3niQ8bf2B2D5jzu46uL/MP6ayfz6yx7ufnCUh6P93/QZ1JYf5mwo83y/oe1p0aYe/33/Rw9EVTH6DGzLD3PLT3L+Gd362pcMvP9tqvn60LllwxKvXTe4CwUFTr5dtdVD0VUMX4eDxrVDuXrKZ9z52bc8ccFAggP8f3vDKsbHYXjm6qF8tGQNh1JcX3hf+moJnZrXZ8Y94ziveQMSj6f/6frOMzGqXSva1o1k8vLVng7lrDgL4Fh8Phc/VZvhd9Vk3mupZGc4ie7oT5NO/nxyTwrfPH+cuq38qtyllL+Hj4+DOnVC2LT5EONvmcamzYe4aXw/T4f1P9uyJIeWPV0zNL3FooV+DBiUx4czMnjiqRM8/3QgTicMHppHeISTiTfX4K3XA2h9Tr5XHJt9hrRj0beu2f7GGG68axjvvFB11no9W8t/cNB7kJN/fZTHnf/I563nXOsBAjRrbXn6nXwe+08es2f4kHvqiax/CtePz2bDeh9uHV+DDet8CAt34vDxvnFOvEuVvtRQqoRAY8zJFf+XAu8CNwLnAauMa752IHByZewC4PNT7KsX8LG1tgBINMYsxpXsSgNWllovb4+1dgOAMWYTsMBaa40xG4Bod5mawHvGmBa41t0r/rPnAmttqnv7zUBjoBaw5OTfsdYedZcdBIwyxtzlfhwANMK1piHusgfdMwr7uf9bYIy5GKh+in32Ai50P7fQGBNmjDn5s9/MYpc9DwLaG2MuKlanFkCJKQTW2reBkxk/60x4oZy3VwAu7xrLxZ3aArDhUCJRNYt+/Y8MCSIx7ewXmR/WriVPzlr4h8X4e40aex7DRnYEYNuWw64vM+7XwuuEcORIyQXm01KzyMrKZdliV9JkyQ9bGDKiAwDpxS51+m7WWm642fNfhEZe3IWhY84FYPvmw0REFv1SHl4nhJTktHK3a9oiEh8fR5mbf3Ts0pTLr4vjrhumkpdXtRYCH3lxZ4aOLl7XmoBrxlR4nRBSksq/WcCp6loVXBIXy9iernNv075EooqtFRAZGnTaGzzk5hewaP0u+rZvxs9bXZeJjuzWhri2TRj/6qmGlMp1RZdYLjrPVb+NpfqWqJAgks6ib0lIy2D9wXjynU4OHU9jb8oxGtcOZePhsjcmqAyX9o5lbHd32+1PJLJ026WWX7dHLhvA/uTjTF+0pvC55LRM7nh3NgCB1fwY0KE56R6+McsVnWK5pKN7XDicWOLy3KiQIBLTz25c6N6kETf16sKV731GXkHV6VuCwhykHymKJ+NIAcFhJTMkweEOomL88PE11IzypVY9X47HFxDVwkHXS4Loeolr9uM3LxwntH7V+5pwwahzGT4sFoBt2+KpUyeYkwNhRHhw2XEwzTUOnryZx+IlWxk2pH2lxnymgsMcpBVrv/QUJ0Fh5U+N2rI0h4E3Va0lEE4nPNySnFx0LB5JNoSFl5xROvc7P/7xjOta0dbnFJCX55pFFVrLMv6WHMDVj9wxoTr1G1SN2agjL+3KkAs7AbB90yEioopuqhIRGUJKUvmfW5rERLnG8i2um3cE1qhG4+Z1eO7d6wCoFR7EY69eyWMTP6wSN/ioFQ4pyUVJ5qPJUCusZKJryVwHd/3TdflrizaWvFzISIWQWkVl6jdyXfp7cK+haUzVTJSdybEaFm55+HHX5+isLFi21I+gP8/pWGU4tcZfpap6I7pUNVnuWX2FjCvb95619v5yyme7E3tnq/RCOsW/JTiLPXZSdNw+CfxgrR1jjIkGFp1i+wJOf6wbXDP2TruAm7U2B/gO+M4YkwiMBr4/3TanULyuBphgra2ad1j4E/r453V8/PM6AOJimjCuWyzfrt9G+wZRpOfk/uZafqU1Ca9FSKA/aw94PtEy84vVzPzCNbukS/fmXHBhJ36Yv5nW59QjMyOHoyllv7yu+HEHsR0bs/bXfXQ8rwn79x4BoHZYUGH57r1i2L8vpfIqcgqzPlvJrM9WAtClVwtGXdKVRXM30qptA05kZHP0SPlfzvsOaceiUjPgmrWMYuKDI3nwtg9IPXZ2bV4ZZn22ilmfrQKgS88WjLqkM4u+30irtvU5cYq2BNclzYu+r5qz/T5dso5Pl7jOvV7nNOGyPrHMWb2NdtFRZGTlciStZDsE+vtRw78aR9Iy8XEYerVtwppdhwDo0aYx1wzoxPWvfEZ2XtVY6+ijlev4aKWrfn1imnBF11i+3bCN2AZRpGfnlrse3Kks2LKT4e1a8eWazYRWDyA6rBYHj3nurtMzlq5jxlJX3Xq3acJlcbHM+dXddtll2w7g1uE9CArw57GP55V4PrRGAKknsrEWrhvYma9WeP6y9I9+WcdHv7jbrnkTruwcyzebthFb/+zbrnVUBE8M68/1H3/J0RNVa624qBZ+HD9cQGpCPkFhPmxdms2wu0re3bVZ1wC2Lcmi7YDqZKU5OXY4n5qRPjgLLDmZlsAQB8l78jiyN5/ojtU8VJNT+3rmr3w981cAunZpxugLzmXhD1to3boemZk5HD1ati2Xr9hJh9jGrFm7j3M7RrNvv+fHu/LUbeHLscMFHE9wJWy3LMlm5F1lLxVNOZBPdoaT+q3+PF/jYloVcPiQg4R4Q1i4ZfEPftz7YMnzp04dy9pffRk4JI/9+xzk5kLNUEt2NmAhIBB+/cUHHx9K3GjBk2bN+JlZM34GoEvvGEZe3o1F362nVfsGZKbnnPpzy9D2LHKv0wxwIiOHS/s8Xfj4uXev450Xv6sSST+Api0tiYcMyfGuJOCKxQ5uvq/k172wCNi81jXr79B+13p+waGQHA+167jW+TuSCPEHDBGRVTfhcybHamqqITjY4nDAjI/8GTQkz0PRipy5P8+IIVXJAuBrY8zL1tokY0xtINhau+83tlsKjDfGvAfUBuKAu3Fdyvt71AQOuf99zRmUXwG8boxpYq3dY4yp7Z6hNxeYYIyZ4J5V2NFau6b4hsaYc4EEa+1h9x1+2wPrT7PPpbguf37SGNMXOGKtTTNlV7SdC9xsjFlorc0zxsQAh6y1Hs9U3Pk4rFwLx1Oh70Vw27Vw0XBPR3V2lmzfQ1xMNHPuuJbs3Hwe/KIoT/vFreMY+9p0AO4c3Jvh7VsS4OfHwruv5/PVG3ltoWsh8GHtW/Lthqq33srK5Tvp2r0Z7824hZzsPF54anbha29OvZ6brp0MwOQ3FnLvwxdw80R/Uo+f4PmnXeVGX9SJ7r1iKChwkp6WxfP/nOWRepzKymU76Nwzhqlf305Odh4vPvZV4Wuvf3RTibv5xg04h4dvn15i+xtuH0RgYDUeevYSAJISUnnsjo8rJ/iztPLHHXTu2YKpX05w1fWJrwtfe336+BJ38y2vrlXRsk176HVONDMfc517j31YdO59cv84Lnt6OoHV/HjlplH4+frgMIZfth/gv0tdX4TuvaQf1Xx9eGPCWAA27Engn58s8EhdyrN4+x7iWkQz9/+uJTsvnwe+LNa33DyOsW+42uiuQb0Z3q4lgX5+/HDn9fz314289sMKlu3cR8/mjZl1299wWssLc5dwPKtq3BFw6WZX281+xNV2j0wvqtuMe8Zx6XPTqRMaxI2Du7I7IYVP7nat9PHJ0nV8uXwjnVo0ZOKIngCs3nWQpz77wSP1OJXFO/fQp3k08269lqz8fB6YWVS/r24Yx+h3XG13d//ejGjrarvFt1/PZ2s28p8lK7infxzVq/nxrwtdA2J8Wjo3z5jpkbqU5vAxnD8+hM8fO4Z1QtsBgYQ38uPH6elENfejWdcAos+txr61OUy7NRnjMMRdE0xgiIP8XMuM+10JsWqBDobeUbPKX0b688pddO3alA/fG092Th7PvVB0d+W337yWG2+aCsA7kxdx/70jueXm/qSmnuC556vGXZhLc/gYBt4UxKePpmKdlnYDAoho7MvSDzOJauFLi66u5QC2LM2hdW9/Sn+mnH7vMVIOFpCXbXntmhSGTgym6blVI3nr4wM3T8jmoXurU+A0DBqaS+NoJ+9P9SemZQHdeuRz/U3ZvPpSAF9+Xg1j4I57sjAGUo8bHry3Og6Ha6bVXfdXrYT7SSuXbqdz7ximfHMHOdm5vPTwF4WvvfbprSXu5hs3uC0P31L2DtRVlY8P/O22fJ57wA/rhLjBBTSItnz+ng9NYpyc291y+fh8przsy5wvHBjghrvyMQa2b3Iw+xEHPj5gHHD1hHyCa/7mn/SYMzlW16/1Ydq7/higbfsCbplYNcZvkdMx1gvXXZE/jjEmw1pbZvKy+8Yc9+NaJzIPuNVau6J0eWPMNGC2tfa/7pmCz+FaK9AC/7DWznAnxu6y1o5wbxPt3qZtOfsofM0Y0x14D9cMum+AK6210caYa4BO1trb3NvPBl6w1i4yxgzFtRagA0iy1g40xgQCrwA93M/vORlLsXoMAf4JnFyEaSVwi7U2+xT7rA1MAZoCJ4AbrbXrjTGPARnW2hfc+3UA/wBG4pr9lwyMPnmZ8ilYZ0LMaV7+c3NEbafNQy97OowKsfkfkxjY65+eDqPCzFv2IIPPe9TTYVSYuasfZ3Dnxz0dRoWZu+pROt7qnecewJrXJtH6Ee+s35YnJhE70TvrBrDu1Um0fNJ767ft4Um8ta2Pp8OoMONbLqbfwGc8HUaFWTjvPqZs7+XpMCrE32OWsftg3d8u+CfVtEE8Q9o/5OkwKsyc9f/g531llg73Gl0b7/H247Nq//ryP0o+XN8rE1ER9Q5VyXbTjD85rfKSfu7nZwAzfqu8tfaaYv+2uGb43V2qzCKKXaZrrd0LtD3FPgpfs9YuB4pnwB5yPz8NmFZsmxHF/v0drst1i//9LOC0d9O11s4B5pzitfL2eRTXpcClyz5W6rET180/HihdVkRERERERMTbFGgCWqXygnsiiYiIiIiIiIiISGlK/ImIiIiIiIiIiHghJf5ERERERERERES8kNb4ExERERERERGRSuH0dAB/MZrxJyIiIiIiIiIi4oWU+BMREREREREREfFCSvyJiIiIiIiIiIh4ISX+REREREREREREvJBu7iEiIiIiIiIiIpWiAOvpEP5SNONPRERERERERETECynxJyIiIiIiIiIi4oWU+BMREREREREREfFCWuNPREREREREREQqRYGW+KtUmvEnIiIiIiIiIiLihZT4ExERERERERER8UJK/ImIiIiIiIiIiHghrdUhEKMAACAASURBVPEnIiIiIiIiIiKVwunpAP5iNONPRERERERERETECynxJyIiIiIiIiIi4oWU+BMREREREREREfFCWuNPREREREREREQqRQHG0yH8pWjGn4iIiIiIiIiIiBdS4k9ERERERERERMQLKfEnIiIiIiIiIiLihZT4ExERERERERER8UK6uYeIiIiIiIiIiFQKp/V0BH8tmvEnIiIiIiIiIiLihZT4ExERERERERER8ULGWs2xFPmT0UkrIiIiIiLivYynA6hI2w7U88rvtC0bHq6S7aY1/kT+hNo89LKnQ6gwm/8xCWdCjKfDqBCOqO1ET33O02FUmL3X3kOv+fd4OowKs2zAc0S/+YKnw6gwe2+6i25z7/d0GBVmxeCnafrqi54Oo0Lsnngn7Wc94ukwKsz6kU/Q9OWXPB1Ghdk96Q5i/uG94/r2hyYR/fbzng6jwuy98W5aPOOd7bfjvkmcd6N31g1g9duTaPJv7xwXAPZMuJOmr3hx3/l/d9D8ee+t38677/B0CBWqwLvzmlWOLvUVERERERERERHxQkr8iYiIiIiIiIiIeCEl/kRERERERERERLyQ1vgTEREREREREZFKoTX+Kpdm/ImIiIiIiIiIiHghJf5ERERERERERES8kBJ/IiIiIiIiIiIiXkhr/ImIiIiIiIiISKVwWq3xV5k0409ERERERERERMQLKfEnIiIiIiIiIiLihZT4ExERERERERER8UJK/ImIiIiIiIiIiHgh3dxDREREREREREQqRQG6uUdl0ow/ERERERERERERL6TEn4iIiIiIiIiIiBdS4k9ERERERERERMQLaY0/ERERERERERGpFAWag1ap9G6LiIiIiIiIiIh4ISX+REREREREREREvJASfyIiIiIiIiIiIl5Ia/yJiIiIiIiIiEilcFrj6RD+UjTjT0RERERERERExAsp8SciIiIiIiIiIuKFlPgTERERERERERHxQlrjT0REREREREREKkUBWuOvMmnGn4iIiIiIiIiIiBdS4k9ERERERERERMQLKfEnIiIiIiIiIiLihbTGn8hf3APD+xIX04SsvDwe+Px7tsQnlSlz+4AejOrYhpoB/nR68rXC5+8d2oeuTRsAEODnR+0agXT75xuVFvv/4sFnYNFyqF0LZk3zdDRnr0/9JjzStT8+xjBj+3re2PBzide7RDbgka79aVUrggmLZvLdvu2Fr7038CI6RtRjVdIhrpv/eWWHfka6hsVwe8wFOIxh9qGVfLhvUYnXL6jfjbENu+O0lqyCHJ7b8jl7M13HbrOgKO5udSE1fP1xYrlh5b/JdeZ7oBYl9WkYzSM9+7nabMsG3li7ssTr1Rw+vNRvKG0jIjmenc1t82dxMD0NgFa1w3kqbhBB1arhtJYLvviQnIIC7urSi7ExbajpH8A5777qiWqVq1t4DJNajcBhHMw8uIoP9iwu8fqYBl24sFF3nNZJVkEuT2/6srD9ACIDavJxz0lM3rWAj/YurezwAYhrHM0jcefjMIZPN23kzdWl2svHhxcGDqVtnTocz85mwnezOeRur5s7deHiNm1xWsvjixeydP8+6gYF88KgIYRXr4G1lk82rmfaujUl9nldx/N4sHdfznv7dY5lZ1VaXYvrGdGce9sOw2EMX+z/lSk7S77/VzXtwdhG51JgnRzLOcEj674kPisVgEmtB9E7MgYHhuXJu3h207eeqEK5Hul7Pn2bNCE7L4+7v5/LpqSyY13bOnV4fvAQ/H19WbRnD08s+gGA+3rH0b9pU/IKCtiXmso9388lPSeHXo0acXev3lTz8SG3oIBnli5h+YEDlV21Mh4a1Jc+zV3j+n2zvmdzQtm6Turbg9Ht2xAS4E/H54rG9U6N6vPgwD60jIxg0hffMnfrjsoMvVx9GkTzSA/3eLd1PW+sK6fvPH8YbcMjOZ6T5eo7M9K4oHlrxrfvUliuVVgEI754n31px/hs5BWFz0cFBfHVjs08sfyHSqtTaQ8P6EufZq42u/eb79mcWLbNzomsw7PDBxPg58viXXt4cv4iAF65YBhNa9cCIDjAn/TsHEZNnV64Xd2QYL67/m/8e9kK3l25ulLqcyp3X9qXnu2akJ2bx2PTvmfr/rL1/PfEMYTXrIGPj4M1Ow7x7EcLcVoLwKXnd+CS82MpcFqWbdjDq597Znw4Ka5RNI+6x4kZm8sZJxw+vDhoKG0jXOPEbXNc40Svho25p0dv/BwO8pxOnv5xMcsPHiDA15fXho6kcc1QCpxOFuzdzXM/ebaOj/Qp1Xcmn6LvHFSs71zsOpeGtmjB7d2607x2GGM+/ogNSYkltqsXHMzcq67mXyuWM/lXzxybD/c7n75Nm5CVn8e935Y/NpwTWYfnhg4hwNeXRbv38ORCV/0m9ujOJe3bcTTrBAAvLvmRxXv2EBoQwH8uGEm7qEi+2LiZxxcsrNQ6iZRHiT8vY4wJAxa4H0YBBUCy+/EJa22PP/jvdQDqWWvP+NO9MaYv8DWwx/3UEWvtAGPMTe4Y3/9f/3apv+EAkoArrLVle/OibUYD2621m8+0LsW2a2+tfeJstitnPy8A31prK210iIuJpnFYKENenkr7BlE8Oqofl731SZlyP2zdzfQV65gz6ZoSzz/7XdGX+XHdOtC6bkRFh/yHGT0UrhgL9z3l6UjOnsMYnug2gCvnfkrCiXRmjvwb8/bvZGdqSmGZw5lp3LX0W25o27nM9m9tXEmgrx9XtOxQmWGfMQeGO1qOYdKad0jKTmVylwksO7K5RGJoXsIavj60AoCe4W2Y0GIkd659Fx/j4OFzLucfmz5hZ0Y8IX7VyXcWeKoqhRzG8ESvAVw5+zMSMtOZOfZK5u3bxc5jRW12Set2pOZk0/fjdxnZrCX3dY3jtvmz8TGGl/sP546F37IlJZlQ/wDynE4AFuzdxXsb17Do8us8VbUyHBjuaj2Kib+8S1J2GlO738rSpC0l2m9u/Dq+POj6gtQ7ojW3txrOpNVTC1+/veVwlh/ZXmbflcVhDI/37c/fvvwvCRnpfHXpOObv2cnOo0cLy1zSpi1pOdn0e38KI1q05N6ecUycM5vmtWszokVLhkx/jzo1avDBmIvp//4U8p1Onlq6mE3JSdTw82PmZVey7MC+wn3WDQqmd6NoDqWlearaODA80G4EN654j8SsND7uPZ5FCVvZnZFcWGZrajyXL32L7II8LmncmUmtB3HPr58RW6shHWo34qJFriTSez2vp1NYNL+k7PVQbYr0jW5CdGgo/aZOoUNUXZ7s15+xn3xcptyT/Qdw/7x5rE2IZ8roMfSJjmbx3r0s27eP55ctpcBa7u3Vm1s6d+HZZUs5mpXFDV9/RVJmJjFhYUwbeyE93nnbAzUs0qdZNNG1Qxn4+lRi60fx+NB+XDy17Li+cMduPvxlHd/fck2J5+NT07lv1vdc1+28Sor49Fx950Cu/OZTV9855ipX33m8WN/Zyt13zpjMyGatuK9rH25bMIuvd27h651bAGhZK5y3B49hc4qrHxr2xXuF288acxVz9nguwdmnaTSNa4Uy4K2pdKgXxROD+3HR+2Xb7PHB/XlozjzWHk5g8sWjiWsazZLde/m/r4s+Bt/XL46MnJwS2z3Qrw9Ldu+t6Gr8pp5to2kYGcroh6bStkkU94/rx9VPl63nfW9/Q2Z2LgDP3TSCAZ1a8P2q7XRq2YA+HZpx2RMfkpdfQK3gwMquQgkOY3iib3+u+so1Tnx96Tjm797JzmPFxolz2pKanc35H7jGift6xjFhzmyOZmVx/ewvXX1H7TDeu+BCuk919R3v/PoLKw4dwM/hYPqYi+nTOJrF+/Z6pI59o5sQXSuUftPcfWf/U/Sd/QZw//yyfef2IyncPHsW/+w/oNz9PxjXh8V791ZwLU6tTxNX/fpPnkKHunV5fGB/Lppetn5PDBzAg3PnsTY+nncvHENck2iW7NkLwNTVq3l3VcmkZU5BPi8v+5GY8HBiwsMroSZ/TgVWF59WJr3bXsZam2Kt7WCt7QC8Cbx88vEfnfRz6wAM+x3bLS0W1wAAa+2b5SX9jDGnSlD/1t8++TfaA6uAW38jptFAmzOIvbR7gNd/x3al/Ru47w/Yzxnr17oZX691fShefzCB4AB/woNqlCm3/mACRzIyT7uvYe1b8s36bRUSZ0XoHAuhwZ6O4vfpEF6XfenHOZCRSp7TyazdWxjUqHmJMgcz0th6LBnr/pW8uJ/i95OZl1tZ4Z611jUbcjDrCIezjpJvC5ifuI5eEeeUKHOioOiLTaBPNSyuenauHcOujHh2ZsQDkJZ3Aidl34PK1qFOFPvSjnEg3d1mu7YyKLpZiTKDopvx+fZNAHy7ezs96jcCoHfDaLamJLMlxZV8OZ6TXTj7YU1SPMknTn9uVrY2NRty8EQKh7OOkW8LmBe/jrg6rUuUKd5+AT7VoNhxGlenDYezjrEno+TMgMoUGxnFvuPHOZDmaq/ZO7YxsGnJc2xA0+Z8vsXVXt/t3E6Phq72Gti0ObN3bCO3oICDaWnsO36c2Mgokk9kFs6UyMzLY+exo0TVKOqEHorryzM/Lik8lj2hba0G7M88yqETrrabc3gD50e1KlFmVcoesgvyAFh/7ACRgTUBsIC/wxc/hw/VfHzxdThIycmo7CqUa0CzZny5xfWb3tqEeEL8/YmoUXKsi6hRg6Bq1Vib4Oo7vtyymYHNXG2+bP8+Ck6ec/HxRAUFAbA5OZmkTNf5tz0lhQBfX6r5+FRKnU6lf8tmfLnBNa6vO+Qa1yPKGdfXHUoguZxx/VBqGtuSjhT2MZ7WIaIu+1JL950lz8VBjZsX6zu3FfadxY1q3ppZu7aUeb5JzVqEBVZnZcLBiqnAGRjQohlfbXTFtvZwAsGnOj79q7H2cAIAX23cwsAWzcrsa1irGGZtLvosNqBFMw6mprLjSEqZspWtT4dmfLPcVc+NexIICvQnvGbZY/Nk0s/Xx4Gfj0/h8HBRn1imzVlFXr7rx7xj6Z6ZFX1S6XFi1vay48TAJs35fGuxcaKB69jcfCSpqO846u47HD5k5+ez4pBr1nCe08nGpCTqBnnuw2qZvrOaPxHVSx2b1U/dd+46dpQ9x46Vu++BzZpxIDWNHUc9d2wOaNGMLze56xcfT0jAacaGeHf9Nm1mYIvmZfZVXFZePqsPHSYn3/NXm4icpMTfX4gxJsP9/77GmMXGmK+NMbuNMc8YY8YZY1YaYzYYY5q5y0UYYz43xqxy/9ez1P6qAU8Alxpj1hpjLjXG1DbGfGWMWW+MWWGMaX8W8T1mjLnL/e9FxphXjDG/ALcbYy42xmw0xqwzxiwp72+fZr8GCAaOGWMcxpgdxpgI92sOY8xOY0wfYBTwvHt/zdz/zTHGrDbGLDXGtCpn3zFAjrX2iPvxNGPMG+6673a/11OMMVuMMdPcZXzc5Ta63+9JANbafUCYMSbqTN+z/1Wd4CASUtMLHyemZRAZEnTW+6kXGkyDWjX5ebfnL3H6K4isHsThzKJ2iz+RTmSNP2kWsxwR/jVJyk4tfJycnUqEf0iZcmMbdGdGj3u5ucUwXtk2E4CG1cOxWF7seB3vdrmdKxr3qbS4TyeyRjCHM4q1WUZGmTYrXqbAWtJzc6kVEEjTmrWwWN4ffiGzL7yK8R3KzuKsSiICQkq0X1J2GhEBNcuUu7BhN/7b+y5uixnCS1tnAa4k7lVN+vDurgVlylemqKAg4ku0VzqRNUr2jZHFyrjaK4daAYFE1gjicHrRtgkZ6YWJopPqB4dwTkQd1ia6vkgMaNqMhIwMth5JxpMiA4JJzCpqu8TsNOoElD33ThrT6DyWJblmSq0/doBVKXtYMOhuFgy8m5+Sd7In40iFx3wmooKCiC/RJhll2iQqKIiEjNOXAbi47TksKmeGytAWLdiUlEhugWdnGEcGB5GQVmpcDz77cb2qiKxRarzLLOdcrBHE4UzXTNnCvtO/5GywEc1aMXPn1jL7H9msFbN3efZHy8jgUsdnetk2iwwOIiE947RlOjesz5HME+w7dhyA6n5+3NitE/9etqICoz9zdUKDSDxWVM+kYxlEhJZ/bP7n9jHMe2E8J7JzWbDa1cc0igylY/P6vHf/Zbx918W0aRxZKXGfSlSNkuNEeX19ZLG+p/g4UdzQZi3YmJxEbqmrE4Kr+dO/SVN+PLC/gmrw26Jq/I6+Mz2DqBqn73Oq+/kxvlNnXv15+R8b8FmKLD02pGcQWU4blq5f8TJXdezA7Guu4ukhgwjx96/4oEV+JyX+/rpigZuA1sBVQIy1tgswGZjgLvMvXDMGOwMXul8rZK3NBR4BZrhn1s0AHgfWuGfZPQCc6rLd3u4E21pjzIOnKFPNWtvJWvui++8MttbGAqNO8bfL/RvAfmAAMMVa6wQ+BMa5ywwA1llrFwMzgbvd+9sFvA1MsNaeB9xF+bP6egK/lnquFtAdmOTe58vAOUA79+XJHYD61tq21tp2wNRi2/7q3mcJxpgbjTG/GGN+efttz15GVJ6h7Vry/cbtVWaGgPw1fHFwOZf+9Cxv7viWq5v0A8DXOGgf2oQnNn7MLb+8TlxEW86rdfpfZqs6H4eDzlENuH3Bt1z09ccMjm5R7oyWP5vPD6zgoqUv8NqOOVzT1NV+1zfrzyd7l5FVUHVnpP6vqvv58frwUTy55AcycnMJ8PXllk5deWXFj54O7awMr9+ec0LrMW3XMgAaVq9Nk6AIBs57kQHzXqBLWFPOrd3Yw1H+sW7p0oV8p+XrrSVnjrUIC+OeXr15cP58D0Ump9Mhoi5Z+XlsP1Y2ET2yWStmljMT8M9oROuWzN5SlNyc0KsbU1et4URengej+n1u+9eXDL77bfz8fOjcqiHgGgtDavhz9dOf8K//LuGZ8cM9HOX/rkXtMO7tGceDC+eVeN7HGF4dMpxp69ZwIC31FFv/ed3erTtTfv31T3lsFjd97Tr6vTOFkdM+IDkjk/vPrxo/NouUR2v8/XWtstbGAxhjdgHfu5/fAJzv/vcAoI1rwhwAIcaYIGvt6a7d6YUrSYi1dqExJswYE2KtLb1o0VJr7YjfiLF4Mu9HYJox5lPgi9/YrszfMMbcCzyHK9k5Bdf6f68Af6dk4g13+SCgB/BZsfqX9zNOXYrWUDxplrXWGmM2AInW2g3ufW4CooHFQFNjzL+Bbyh678G1FmG90n/EWvs2rkQkgH3loZdPW/HTubxrLBd3agvAhkOJRNUsmnUUGRJEYtrZX5o1rF1LnpylhWsrS+KJDOoVmy1Wt3owicVmRPzZJeekUqfYDLGIgJok55x63bP5ieu4s/UY2AxJOamsO7ab1DzXQsvLU7YSE1Kf1cd2Vnjcp5OYmU69Ypfr1A0KKtNmJ8skZGbgYwzB1apxLDuLhIx0VsYfLLzZww/7d9M2PJKfDnluFsDpJGenlWi/OgEhJGef+ovLvPj13NN6NE8C54Q2pF9UO25rOZQg3wCcWHKd+fx3f+XOCkjIyChxeVXdoGASM0v2jYnuMgkZJ9vLn2PZWSRmZlAvuGjbKHcZAF+Hg9eHjWLmti3M3eU6JhvXDKVBSE2+ueJvheVnXX4lo2dM58iJExVd1ZJ1yk4vvHQXIDIghKTssude1/Cm3NCiD3//aQp57lkq/eu2Zv2xA4VJ22VJO4it1ZBfj+6rnOBLuSo2lkvbtgNgfWIidUu0SVBhm5zkmsly6jIXtmlDvyZNufLz/5bYLiooiDdHjuKuuXPYn+qZL+jjzovlko7ucT0+kaiQUuN6etW45Pr3SMwsNd7VKOdczMygXo2Qkn1nTtFloCObt2LmzrLJvda1I/AxDjYeqfxlBcadG8ulsa42Wx9f6vgMLttmiekZRBWb4Ve6jI8xDGrZnDHTPip8LrZeXYa0asE95/cixN8fp4Wc/Hw+/HVdRVWrjIv7xjKmt6uem/cmElmrqJ51agWRfPzUx2ZufgGL1+6iT4dm/LxlP0nHMvhhjavf3LQ3EWstoUGBHM/wzCW/CZklx4niff1JiRkZ1A0uPq77F47lUTWCeGvYKO6c9x37SyX3nuo3iL3HjzF1Xem5BRXvqvaxXNrO3Xcm/I6+MziIhMzT9zkdoqIY2qIF9/XuXXRsFhTwwbq1f2BNyndlx1guae+q34byzr1y2rB0/U6WSSk2Rs9Yv4F3xo6uyNC9jlNz0CqV3u2/ruIr/zqLPXZSlBB2AN2KrcVX/zeSfn+0wsVnrLU3AQ8BDYHV7puYnI2ZQJx7XweARGNMP6AL8F055R3A8WJ172CtbV1OuSwgoNRzxd/L0u+zr7X2GK4Zl4twJSKLz6QMcO+zwnz88zrGvjadsa9NZ8HmXVzQwVWt9g2iSM/J/c21/EprEl6LkEB/1h6Ir4hwpRzrjsQTHVKLBkE18XM4GNm0NfMOeDax9UfamnaQhoHh1A2oha/xYUBkLD8ml7znToPAosWSe4S34uAJ1xoxK1O20zSoLv4OP3yMg46hTdnrwbXiTlqXlEB0zVo0CHa3WbNWzNu7q0SZeXt3cWGMay3DYU1j+Omw69L5xQf20rJ2OAG+vvgYQ9d6DdlxzPPrNZ3KlrSDNKweTt1AV/sNrBvL0qSSX7obVi/qwntGtOTACddMnJtWvs2YJc8xZslzzNj3I+/tXlTpST+A9YkJRIeG0iAkBD+HgxEtWjJ/d8n2WrBnFxe2drXX0OYxLD/oSsTO372LES1aUs3HhwYhIUSHhrIu0bUu1zP9B7HraArvrilaCHxbyhG6TH6DuGmTiZs2mYSMdEZ+/GGlJ/0ANh0/ROMatakfGIqv8WFIvXYsSih5eWSrkCgeaT+KiaumczS3aLyIz0qlU1g0PsaBr3HQKSy6xE1BKtsH69YxYvqHjJj+IfN27WRMa9cSvh2i6pKem0tyZsmxLjkzk4zcXDpE1QVgTOs2zN/lavO4xtHc2KkzN878muxiazYF+/vz7ugxPLdsKasPH66kmpU1ffU6Lpg8nQsmT2f+tl2Maeca12PrR5GRnVvuWn5/FuuS48v2nftKjnfz9hXvO1uW+FHEAMObtmTWrrKX+brW/Sv7fGWY/us6Rk2dzqip05m/Yxej27rarEM912exco/PnFw61HOtBjO6bWvm7yjqk3pEN2J3yrESlwNfMf1Tzn9jCue/MYVpv6zhzeUrKzXpB/DZonVc8eR0rnhyOovW7mJ4d1c92zaJIiMrlyOpJesZ6O9XuO6fj8PQq10T9ia4bpaxaO0uOrV0zf5rVCcUXx8fjyX9oOw4MTKmJfP3lBwn5u/ZxYWtyo4TwdX8mTJqDM8uX8rq+JJ9x53dehJcrRpPLPHMXaY/WP8bfWepdYWTT5y67zyVSz/7lLgp7xI35V2mrlnD6yt/rpSkH8CHa9Yx6r0PGfXeh8zbuZMx57jrV7fuqc+93Fw61HXX75w2hede8fUAB7VozvYjVWN5C5HyaMafnM73uC77fR5cd9G11pbuldNxrZ930lJcl9E+6b6z7pFyZvudNWNMM2vtz8DPxpihuBKApf/26fQCio9Ck3Fd8vuBtfbkohqF+7PWphlj9hhjLrbWfuZeJ7C9tbb0J6YtwJVnWZdwINda+7kxZps7jpNigM/OZn//iyXb9xAXE82cO64lOzefB78omnz4xa3jGPvadADuHNyb4e1bEuDnx8K7r+fz1Rt5baFrzZhh7Vvy7QbP3X3z97rzcVi5Fo6nQt+L4LZr4aI/yVUjBdbyyIr5vD/oYnyM4dMdG9hxPIVJHXux4UgC8w/spH14FG/1G0PNav70b9icSR17MeirKQB8OvRymoWGUcPXj+WX3My9y75jyeG9nq1UMQXWyUvbvualjtfjMA6+ObyKPZmJXNd0EFvTDvLjkc1c2LAHnWo3J986Sc/L4p+bXBOE0/OzmLF/CZO7TMACy49sZXmKZ77YFVdgLY8sW8D7wy/Exzj4dNsGdhxLYVKnnmxITmD+vl18unUDL/UbxqLLr+N4TjYT5s0GIC03h8nrf2Hm2CuxuGb8/bB/NwD3dYvjguatCfT1Y/mV45mxdQOv/PKTB2vqar8XtszkX+f9HYcxzD70C3syk7ih+QC2ph5iafIWLmrUnc5hzcl3FpCen8UTGyqt2zsjBdby2KKFvHfBhTgcDj7btJEdR1P4v6492JCUyII9u5ixaQMvDRrKwr/9ndTsbCbO+QaAHUdT+GbHduZeeQ0FTiePLlqA01o61a3P2NbnsPVIMrMvvwqAF35axqJ9e04XSqUqsE6e2vgNb3T7Gz7GwVcHfmVXRjK3tOzH5uOHWJS4jTvaDKa6bzVeOM+1tG5CVioTV33EvMOb6BLehM/73IrF8mPSThYnVo0bPv2wZw99o5vww7V/Jzs/n3u+n1v42uxxVzJiumsYfmThAp4bNJgAX18W793Lor2utnmsXz+q+fjw/tgLAdci9w8tWMDfYjvQODSUCV27MaFrNwCu/uJzUrI8l4xYtHMPfZpHM//Wa8nKy+f+WUXj+tfXj+OCya5x/e5+vRnZtiWBfn4smXg9n63dyL+XrKBd3Uheu3gkIQEBnN+iKRP7dGf4W6dataXiFVjLIz/O5/2hF+HjKNZ3ntfTNd7t28Wn29bz0vnDWXTp9a6+c8Gswu271m1IfEY6B9LLzsYc3rQl1373eWVWp1yLdu2hT9NoFox3tdl93xa12cxrxzFqqqvNHvt+Ic8OH+Q6PnfvZXGxO/WOaNOS2Zurxvl2Kss27KFn22i+/qfrM+dj04rq+dHD47jiyekEVvPjpVtHUc3XB2MMv2w7wOeL1wPw9f+zd9/hUVT7H8ffZ9NDCKTTewep0gQpKghKUeyKVyzYLqiI9Xrtior1Z79W7HJRryCCKEoRBSnSewudhEBIr7vn98cuKSQBRLIb18/refJkZ+bMzPfM2d3Z/e45wNeoFQAAIABJREFUM7+s4eFrBjL54aspdDp55P1ZFe3KK5zW8vC8n/hwmOc8sc59nhjnOU/M3r6VyetW8+KAwcy5+jrS8nIZ6zlPXNO+Iw1rRHFb157c1rUnAP+Y+gVBjgDGdO3BlkMHmX65+zzx4aoVTF632id1nJO4nX6NGzNn1Mm9dw5s2oyH+/UnOiyMd4dfwLqUA4z634kO3Kp8c7dtp1+Txvw0+jpyCgq5d2Zx/aZdM5JhH7jr9/APPzJx8LmEBnlee9vd9bu375m0jo/HYtmTls6/vy++3MPcG68nIjiEoAAHA5o3ZdSUL9ly8BAivmLKu+Oj+AdjzCNAprX2Oc90prU2wpOQu6vEMNi5numlJZd5ElSv4b4OYCAw39PzruQ+ooFZQBDwFPAD7qG0TYBs4EZr7aqj1im1//LiLRmTZ9lXQHPcP97+CNyB+1p6RfsueZ0/zz6mAts966QBN1hrN3mWBwEHgW7W2g2eeb2At3H30rsYdw+9N3AP5w0CPrfWPnZUzOG47xjczjO8dxIw3Vr7hTGmkedxO0/ZScB0YDPu4cVHetzeb62d6YlpFXCatfZYt4Gybf7EUN+qbt0T43Dtb+HrMCqFo9YmGr0/0ddhVJrEa++h9+x7fB1GpVlwzkQavfmcr8OoNIk330WPWff7OoxKs+jcp2jy8vO+DqNSbLttPO2/ecjXYVSaVUMfo8mLL/g6jEqzbdydtHjCf8/rm/49jkZvPevrMCpN4o130/xp/2y/zfeNo8uN/lk3gGVvjaPxK/55XgDYPnY8TV7y4/fOO+6k2bP+W78td99pjl/qr2teYgu/TET1bbTpuO1mjBmE+34KAcA71tqnj1reAPgAqOkpc5+1dsafiUs9/vyYtfaRo6YjPP/n4h5memR+vxKPi5Z57lRb4d1yPWUOAUffYvKYFzg4ev/lxVsyJs/0iHI2Vd6+S+6j7G0ki3XAfVOPoq5A1tpfgDZHlRt0jG1grc02xswGzgZmW2tHlViWCLQrMT2qxKqdy9ncEOCL4yT9RERERERERP6ynPh1XrNCxpgA3J2rBgC7gSXGmGnW2pLXNfo38F9r7RvGmDbADNz3Cjhpusaf/O0YY+4DvgROVdeWCUD4KdhOIOC/PzuKiIiIiIiI/H11A7ZYa7dZa/OBz4HhR5WxQKTncQ3gT19IWIk/+dux1j5trW1orV1wiraXZK2ddgq2M8Vae/hUxCQiIiIiIiIi3mOMudEYs7TE341HFakL7Coxvdszr6RHgJHGmN24e/uN/bNxaaiviIiIiIiIiIjIn2CtfQt4609u5gpgkrX2eWNMT+AjY0w7a63rZDeoxJ+IiIiIiIiIiHiF0/5tB5/uAeqXmK7nmVfS9XjuNWCtXWiMCQVigeST3enf9miLiIiIiIiIiIh4yRKguTGmsTEmGLgcOPqyYTtx3zwUY0xrIBQ48Gd2qsSfiIiIiIiIiIhIJbLWFgJjgFnAetx3711rjHnMGDPMU2w8MNoYsxL4DBhlrbV/Zr8a6isiIiIiIiIiIlLJrLUzcN+0o+S8h0o8Xgf0OpX7VI8/ERERERERERERP6QefyIiIiIiIiIi4hUujK9D+FtRjz8RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA/pGn8iIiIiIiIiIuIVTvVB8yodbRERERERERERET+kxJ+IiIiIiIiIiIgfUuJPRERERERERETED+kafyIiIiIiIiIi4hVOqz5o3qSjLSIiIiIiIiIi4oeU+BMREREREREREfFDSvyJiIiIiIiIiIj4IV3jT0REREREREREvMKlPmhepaMtIiIiIiIiIiLih5T4ExERERERERER8UNK/ImIiIiIiIiIiPghJf5ERERERERERET8kG7uISIiIiIiIiIiXuG0xtch/K2ox5+IiIiIiIiIiIgfUuJPRERERERERETEDxlrra9jEJE/Ri9aERERERER/+XXY2G/2trJL7/Tjmi6vEq2m67xJ/IXNKD3k74OodL8sOABGr0/0ddhVIrEa+/Btb+Fr8OoNI5am7j6txt8HUal+aj7O/T8/j5fh1FpFg58mrPn3OnrMCrNj/1foNFrz/s6jEqR+M/xdJ35L1+HUWmWDJ5A41f9s+0Ato8Zz6DIa30dRqX5Lv19pmzt4uswKs0lTZcxuI1/vv5mrptAn2HP+jqMSjN/2t30+uFeX4dRaX4Z8AxdRr/o6zAqzbK3x3H6DS/4OoxKs/Qd//1MBuDU4FOv0tEWERERERERERHxQ0r8iYiIiIiIiIiI+CEl/kRERERERERERPyQrvEnIiIiIiIiIiJe4bLqg+ZNOtoiIiIiIiIiIiJ+SIk/ERERERERERERP6TEn4iIiIiIiIiIiB/SNf5ERERERERERMQrnOqD5lU62iIiIiIiIiIiIn5IiT8RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA/p5h4iIiIiIiIiIuIVTmt8HcLfinr8iYiIiIiIiIiI+CEl/kRERERERERERPyQEn8iIiIiIiIiIiJ+SNf4ExERERERERERr3CpD5pX6WiLiIiIiIiIiIj4ISX+RERERERERERE/JASfyIiIiIiIiIiIn5I1/gTERERERERERGvcFr1QfMmHW0RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA/pGn8iIiIiIiIiIuIVLoyvQ/hbUY8/ERERERERERERP6TEn4iIiIiIiIiIiB9S4k9ERERERERERMQP6Rp/In9zt94+kG49m5KXW8CzE6azZdP+MmUCAx2MuXMQHTo1wOWyvP/WXBbM28jAwe0ZfetZHEzJBGDql0uZOX2Ft6tQob51G/NQ97MJMIbJm1bxxurfSi3vllCPh7qfTauoOMbOncbMHZuKln0w4GI6xdVhSfIerp/9pbdD/9MeeBrmLoToKPhmkq+j+eMOrzrEzo+3YV2WuL61qDO0frnlDi1JYcsr62nzSEcimlTHVegi8f0tZG3PAGNoOLIJka1rejn64+sR04I7Wg0lwBim7V7CR4nzSi2/sF53LqrfE6d1kePM5+l1X5GYlUyt0Cg+73UnO7IOALA2bScT13/tiyocU9foVvyz+QU4cDBj3yI+3/lTqeVD6vRkeN3euKyLHGceL26cwo7sJAJNAONaXkKL6vWxWF7b/D9WHt7qo1qU1rdBIx7q3Z8Ah2HyujW88fviUsuDHQG8cM5g2sXHczg3lzGzprM7I50O8bV4qv8AAAzw0uKFzNq+BYDrO3TmsjanYS1sPJjC3T99R57T6e2qldIztjnjWw/BYRxM3b2ED7bNL7V8RP1uXNKwBy7rIrswnwlrv2Z7ZjLdYpoxpuW5BDkCKHA5eXnDTJYe2uajWlSsT4NGPHxmfxzG3Y5vltOOzw8YTLu44nbc42nHCUfa0bjb8fttW3xRheO6ZeKVdB3YnrzsfJ6/5V22rNxRpkyfEd244q4hOAIc/PbdSt57eAoA513Xj6Gjz8bldJGblcv/3fYBOzfu9XINKrZpaSEz/pOHywVdzg2i76XBpZbPeCuPbavcr6GCXEtWmuXfUyKKludmW16+KZvWPQMZemuIV2M/UTf/awhd+7QkLyef5//1JVvXlz7+YeHBPPvxjUXTsQk1mPPNCv7z9LfceO95tO/eBICQ0GBqRlfjkh6PezX+Y7lt9Fn0OL0JeXmFPPXSDDZtSy5T5v+evIyYqAjy8gsBGP/wFA6nZRct79uzBY/fP5zRd37Ixi1JXov9eLrHtOCOlsNwGMM3e5bwceLcUssvqNedEfV64sKSXZjHxPXu8zpA04ha3NN6BNUCQ3FZFzcsfpV8V6EPanFsd1/ej16nNSY3v4BH3v+eDTvLtt8rt19IbI1qBAQ4WL55D8988hMuawG47KyOXNqvA05rWbBqOy9/+bO3q3BMd13Rv7h+781iYzn1e/mOEe76OQwrStRvwk3n0zAhCoDq4SFkZOdx1WMfe7sKIhVS4k/kb6xbj6bUrR/NqMvfoHXbOtx21yBuu3FSmXJX/qM3h1OzuPaKNzEGqkeGFS2b99N6Xn1xlhejPjEOY3isxzmMnPVf9mdnMG3oP/hh5xa2pB0sKrM3K527fp7B6HZdy6z/nzWLCQsM4sqWHb0Z9ilzwWC4cgTcN8HXkfxx1mXZ8eFWWt7TjuDoENY+vIKoztGE1a1Wqpwzp5Ck7/dQrWn1onkH5roT16dN6EJBej4bn1tL20c6YhxV5wLCDgzjWw/n9mXvkpybxns9xvDzgfVFXwAAZu1bwf92uxPVveNac3vL8xn3+/sA7M45yDWLXvZJ7CfCgeG2FiO4Z8WbHMhL4/XTx7EwZS07sou/oP2U9DvT9y4EoGdMW25uNpz7V73F+XV6ADB6ybPUDIrgqQ6juXXpS1isT+pyhMMYHutzNiOnfcH+zAymXXIVP2zfwpbUQ0VlLm3TjrS8XPp9/B5Dm7Xkvp59GPP9dDYeSmHofz/GaS1x4dWYedk/mJ24ldjwaoxq35lzPp1EnrOQV88dwtDmrfhiw1rf1RPDPW2HMWbxeyTlpvPBGbcyP3kD2zNLPjdX8tUud7KsT3wrxrU6j9uWTuJwQRZ3LvuQlLwMmkYk8HLXUZw/5xlfVaVcDmN4rO/ZXD3V3Y5TL72K2RW0Y/+P32NI85bcd0Yfxs5yt+OwEu044/J/8OP2rTitb5+bR+s6sD11miZwXcf7aNW1CWNevJo7znqiVJnq0dW44fFLGdvnUdIOZjD+zRvo2Lc1K+atZ+6URcx4by4APQZ35ManLuffI17wQU3Kcjkt37yex7VPhhEZa3jzjhxa9wgkvkHxAKbzbixO5i2cls++ra5S2/jxw3watQvwWsx/VNc+LajTMIbrBz1Pq/b1GfPwcMZd/kapMjnZ+YwZ8WrR9MtT/skvP7jfN956ZkbR/GFX9aRp69reCfwE9OjSmHp1orjypndo07I2d94ygJvv/qTcso+/ML3cpF5YWBAXD+vM2iqUjAbPeb3VBdzx+zsk56bxTvcxLDiwrtR5/ft9K/i6xHl9bIshjF/+HgHGwUPtLufxNZPZkrmPyKBwCl2+/QGoPL3aNaJ+fE0ueOB92jWpxf1XncU1T31eptx9//mWrNx8ACbePIRzTm/O90s2cXrLevTt0JTLH/uYgkInUdXDyqzrS71Oa0z9+Jpc+K/3aNekNvePPJtREz4rU+7+N6cX1++WoZxzegu+X7KRf/3n26Iyd1zah8zsfK/F/lfltBp86k062hUwxjiNMStK/DUyxpxujDnhb1vGmJrGmFuPsbyWMeZzY8xWY8wyY8wMY0yLk4z3DmNM+Mms61m/nzHmjBLTccaY34wxy40xZx5jvUeMMXdVMH+P59itMcYMO87+hxlj7jtOmUbGmCvLmX9aiXY6ZIzZ7nk82xhTxxjzxbG2e7KMMRcYYx46hdt7zhhz1qna3onoeWYLZn+3CoD1a/cSERFKdExEmXLnnt+Bzz/6FQBrIT0tx5thnpSOsbXZkXGYXZlpFLhcfLNtPQMbNCtVZndmOhtSD2DL+eL2676dZBX8dU/aXTtAzerHL1cVZW7NICQ+lND4MByBDmJ6xJH6+6Ey5XZ/uYPa59fHEVR8KsvZk01kmxoABEUGExgeQNb2TK/FfiLa1KjP7uyD7M05RKF1Mnv/SvrEtylVJtuZV/Q4LCDYx2mvP6ZVZAP25KSwL9ddvzlJyzkjtl2pMiXrFxoQDJ4aNgxPYHnqZgAOF2SSWZhDi+rl9/b0po7xtdiRdphd6Z73k80bGdi49PvJwMbN+NKTtJuxdRNn1GsAQG5hYVFyKCQgoFQSM8A4CA0MJMAYwgIDScry7XO1bc167Mo6yJ6cVAqtkx/2raJvfOtSZbIKS7fdkfpsSt9HSl4GAFszkwhxBBHkqFoJlg4JZdtxQJPS7TigSXE7ztxScTtSRV+VPc/rxI+fuc/XG5ZsI6JGONEJNUqVqd0onj1bk0g76G6vFXPX0mv46QBkZ+QWlQutFlLu+dFXdm9yEVPHQXRtB4FBhtP6BLJ+YcW9olbNK6R93+I+Dns2O8k8bGnWuWo9L0vqcVYbfpy6HIANq3YRUT2UqNiKT+Z1G8ZQM7oaa5YlllnW97z2zP12VWWF+of17t6cWXPcr611G/cRUS2UmKhqx1mrtBuu6s0nXy4mP79q9YZrfdR5/cf9KzkzruLzemiJ83q3mOZszdzHlsx9AKQXZOOqgu8vfTs25dtF6wFYs20/EeEhxNYo235HkmKBAQ6CAgM48hZycb8OTPpuCQWF7qRmakbV+i7Rt2NTZixcB8CabfuoHh5CzDHqFxDgIDDQUe4Pk+ec3pJZizdUbsAif5B6/FUsx1p7dFefRGDp0QWNMYHW2vLOQDWBW4HXy1nHAP8DPrDWXu6Z1wFIADYdXf4E3AF8DGQfr2AF+gGZwK+e6bOB1dbaG05yewAvWmufM8a0Bn42xsRba13lFbTWTgOmHWd7jYArgU+PWnc10BHAGDMJmG6tLZnsu/jkwj+ue4BjJjT/oFeAt4GfjlfwVImNrU5ycnrRdEpyOrGx1Tl0sPjLZ7UI96/n19zQlw6dGrJvbyqvvDCLw6lZAPTu24rTOtRn965DvPnKDxxIzvBW+MeUEB7B3qziWPZlZ9Axro4PI5ITVZCaR0hMca+N4OhgMreWfl5lJWaSfyiPmh2j2Tdjd9H88AbVSP39EDE94sk/lFdUjqZVJwsaFxpJcm5a0XRybhpta5RNbl1UvweXNzyTIEcAY5a+XTS/Tlg0H/S4jazCXP6z5XtWHk70RtgnLDakBgdyDxdNH8g7TOvIhmXKDa/bi4vr9yXQBHDXCnePlq2Zezkjti0/JS8nPqQmLSLqEx9ak40ZO70Wf3kSIiLYm1ni/SQzg44JpXvSJFQrLuO0loz8PKJCw0jNzaFjQi0mnnUudatHcucPM3FaS1JWJm+vWMKv14wmt7CQn3ft4OddZYdkelNcaA2SSjw3k3LTaFez7HPzkgY9uLJxL4JMALcsfrfM8rNqtWNj+l4KqlivlVrVItiXUdyO+ytoxyNlymvHZ4604+yZVa63H0BMnZoc2F38Q8mBPanE1IniUFJxu+7dlkS95rVIaBDDgT2p9Dy/M4HBxV8Jho4+iwvHnEtQUCD3Dp3o1fiPJf2gpUZsce/tyFjD7o3lfqwkNclF6n5Lkw7uJJ/LZZn5Th6X3B3K1uVV63lZUkx8JCn7i9sqJSmd2IRIUlPK/2zV97wOzP9udZn58XVqUqteFCt/qxqXSgCIjYkg+UBxPQ4czCA2JoKDns+TJd1/22CcLsu8hZv4cLK7d3iLJvHEx0ayaOk2rriw7EgNX4oLqUFyXvF5LzkvjbaRDcqUG1GvJ5c3PJNARwC3LXsLgPrhcVgLL3S6nprB1Zi9fyWf7phXZl1fi4+KIOlQcfslp2YSVzOClLSy7ffqHRfStlEtfl2TyI/L3D/mNUioSafmdfnnBWeQV+DkpS/msy6x6gzVjqsZwf4S9UtKzSS+ZgQHy6nfK3eMoG1jT/2Wbi61rFPzuhxKz2JX8uEy64n4knr8/QGeXnHTPY8fMcZ8ZIz5BfjIGNPWGLPY09NslTGmOfA00NQz79mjNtcfKLDWvnlkhrV2pbX2Z+P2rKen3GpjzGUl9j/XGPOFMWaDMeYTT9nbgDrAHGPMHE/ZgcaYhcaY340xU4wxEZ75icaYRz3zVxtjWhljGgE3A+M8sZ4JTASGe6bDjDFFmSBjzMWeBNsJsdauBwqBWGPM0BI9CWcbYxI82xxljHnV83iSMeZlY8yvxphtxpgjibungTM9MY07kX17egmuKbGPr40xP3iOwxhjzJ2eWBYZY6I95ZoaY77z9ML82RjTqpzttgDyrLUpnulLPO210hgz3zMvwNOOSzzPiZtKrH+v5/ivNMY87TlOO4AYY0ytcvZ3ozFmqTFm6VtvvXViB/4UCQhwEJ8Qybo1u7n1+ndZt2Y3N/3zbAAW/rKZqy95lZtGvcPvS7dz9wOnMg8qUj7rsuz8dBsNrmhSZllcn1oERwez9uHl7Ph4KxHNIjF/0TPdl7sWccmCZ3l900yubeLuDHwwL50L5j/NNYte5v82fsuj7S8nPKBqXqfqeKbu+YWrF03g7a3fMrKh+9ppM/cv5kBeGm90GcetzS5gbXoirvJ/L/pLWZG0n4GffcCwKZ9wS5duhAQEEBkSwoDGzTjzw3foPuk/hAcGcUGL1sffWBUwZeciLpz3PK9snMV1TfuXWtYkIp6xLc9lwtqqd+3JP2tF0n7O/ewDhk/5hFu7dCM4oOr2HDuWzMPZvDruQ+6fdAvPz7qfpJ0puJzFr7Nv3v6J6zrcy7sPT+GKu4f6MNKTt3p+Ie16B+IIcCcKF39bQMvTA6kR+xc9IVTA3atvZdn5g9uz4Pu1uFxVLzl9PI8//y2jbpvEmPs/pUObepzbvy3GwD+v789r783xdXh/yle7F3LpLxN5Y/NMRjV2f5YOMA7aRzXi0TWfccuSN+gb35Yu0U19HOmfM+al/3HuXW8RFBhA11buH48CHA4iq4VwzVOf839fzOfpm873cZQnb+xLXzFo/H8IDgyga+vSP46d270VsxZv9FFkIhVTj7+KhRljjtylYLu19sJyyrQBeltrc4wxrwD/Z639xBgTDAQA9wHtyuk5CNAOWFbBvkfg7sHWAYgFlhxJJgGdgLbAXuAXoJe19mVjzJ1Af2ttijEmFvg3cI61NssYcy9wJ/CYZxsp1trOxj0M+S5r7Q3GmDeBTGvtcwCeIaynW2vHeKaPf8QqYIzpDriAA8ACoIe11hpjbsDda258OavVBnoDrXD3BPwC9/G8y1o75KSDcR/3TkAosAW411rbyRjzIvAP4CXgLeBma+1mT+yvA0cPwe0F/F5i+iHgXGvtHmPMkTsJXA+kWWu7GmNCgF+MMd976jQc6G6tzT6ScPT43bPtUneTsNa+5YkLwE758MmTPgDDRnThvKGdANi4fi/x8ZEcuaJUbHwkKUf9qpyelkNOTj4L5rm7rM+fs55BQ9xP6Yz04m76M79ZwehbvDpS+ZiSsjOpU624l1ft8OokZVWN3ohybEFRIeQdLB4Sk38on+Co4uSWM9dJzu4s1j/lHsJUkJbP5pfW0fyONkQ0qU7Dq4o/MK97bAWhtarWdWQO5KYTH1o89C4+tAYH8tIrLP/D/lXc3fpCYAoF1klBgbtj98aMPezJPkSDarFsSN9T2WGfsJS8NOJCi2+oEhdSk5S8tArLz0lezu0tL4IN4LIu3tgytWjZy53Hsjv7QKXGeyKSMjOpE1Hi/SSieplhuUlZ7jL7szIJMIbqwSGk5pYeyrQ19RDZBQW0iI6lfmQNdqWncchT5rttm+lSqw5fb1pf+RWqwIHcNBJKPDcTQmtwILfi5+b3+1ZxX9vhPOrpcBQfGsnEziN5eOUU9mSXHZ7va/uzMqldvbgda3naq6QkT5njtWNWQQEtY2JZnez7HitDR5/FoGv6ArDp9+3E1Sv+WBFXN4qDe1PLrPPbdyv57Tt3wmjwqL6lEn9HzPviN8a+cDXPV1Lcf1RkjCEtpTiRlZ5iiYwp//Pp6nmFpW7esXO9ix1rnfz2bQH5uRZnAQSHwbnX+v6HkyFX9GDQJe6h1ptW7yG2VvFrMDYhkpSk8l+DjVvWwhHgYMu6ste763tee157/HgDaSrfhed1YsjA9gBs2LyP+Ljq4HmLi4upTsrBspc3SDnknpeTU8AP89bRukUtFvy2mcYNY/m/Jy8HIDqqGk89MIL7n/yqStzg40BeGvEhxee9+JAaHDjGeW/2/pXc1cr91TI5N42VqdtJ85zbF6ZspGX1uiw75Pvempf068CFfdyX6li3PYmE6OL3z/ioCA4crvjyFPmFTuat3Erfjk35bf1OklMzmfO7+4ZIaxOTsC5LzYgwDmf6bsjvJf07cMGZpwGwLjGJWtHVOZJGT4iKIPl49Vuxhb4dm/HbOveohACHoX/nZlz9ePnXrpTSnOqD5lVK/FWsvKG+R5tmrT3ybrUQeMAYUw/4ypM0Otl99wY+s9Y6gSRjzDygK5AOLLbW7gbwJCYb4U6mldQDd1LyF08MwZ74jvjK838Z7iRjZRlnjBkJZACXeZJ99YDJxpjanri2V7Du155hweuO9Ao8ReZYazOADGNMGvCNZ/5qoL2nZ+QZwJQS7Vfep8LauBOZR/wCTDLG/Jfi4zvQs80jPRZrAM2Bc4D3rbXZANbakt+OknH33qw0075axrSv3Dnnbj2bMfyi05kzex2t29YhKzOv1DDfIxb9spkOnRqy4vcddOrSmJ2JKQBEx0QUle/ZuwU7dxwss66vrEzZR6PIKOpF1CApO4OhTVpz27xvjr+i+FxEk+rkJeWSdyCXoKhgDi46QNNbWhYtDwwPpPPrPYum109YRf3LGxPRpDrOPPcQroCQANLWpGICTJmbgvja+vTd1A+PoXZYFAdy0zmnVgceXlX6AtL1wmPYne1+PfWKa8WubPdrrmZQtaLr/9QJi6Z+eAx7q1iCZUPGLuqGxVErNJqUvDT6J3TiybUflSpTNyyWPTnuOvWIac0eT/1CHEEYDLmufLpEtcBpXaVuCuIrK5P306hGTepVjyQpK5OhzVty2w8zSpX5YftWLmrVlt+T9nFe0xb8usf9RaBe9Uj2ZWbgtJa61avTNCqa3RnpBDgMnWrVJjQwkNzCQnrVa8AqHyeR1qXtoUG1WOqERZGcm86A2u15cOXkUmXqh8ewy/Pc7B3fkp2etosIDOXFLtfw2sZZrDrs26HZFVmVVLYdb/++dDvO9rTj8v37GNysBQt3H6Md0ytOinrTN2//xDdvu68S0u3c9gy98WzmfvEbrbr3J+9vAAAgAElEQVQ2ISs9p9Qw3yNqxFYnLSWDiJrhDLnhLCaMcl+Vpk7TBPZuTSra1p6tvn/9HVG3hYODe10c2u8iMsawen4hl9xT9iPagV0ucjIt9VsXf6m89J7Qose//1DAns2uKpH0A5j+2SKmf7YIgK59WjL0qh7Mm7GKVu3rk5WRW+Ew337ndWDejLK9/eo1jiMiMoz1K3z/OvzfjOX8b4b7moU9Tm/CiPM78eP8DbRpWZus7Lwyw3wDHIaIaqGkZeQQEODgjK5NWbpyB1nZ+Qwb+VpRuf978jJef39ulUj6AWxI30298Bhqh0ZxIC+ds2t14NHVpW98UfK8fkZsK3Z7zoGLD27iqkZ9CXEEUWiddIxqzOSdR3+1840pc1cyZa77Odb7tMZc2r8DsxZvpF2TWmTm5JcZ5hsWEkS10GBS0rIIcBh6n9aY5ZvdP0zOXbGV01vWZ+nG3TRIqElgYIBPk34AU+asZMocd/16ndaYS8/q6KlfbTJz8ssM8w0LCSI8NJiDnvr1at+EFZuKf3jt1rohiftSSU6tWteWFgEl/v6soncDa+2nxpjfgPOBGZ5hnduOse5aTu7ac3klHjspvw0N8IO19orjbKOi9ctTcqxAaIWlSnvxSA/CEl4BXrDWTjPG9AMeOU6M4K7PqVJyu64S0y7cx8IBHD6BpG8O7kQeANbamz29A88HlhljuuCOe6y1ttQtb40x5x5ju6GebXvF4oVb6N6zKR9MvpW83AKemzC9aNmb79/Azde+A8A7b/zEvQ8O55bbQkg7nM2zT7nLXXDx6fTs3QKn00VGeg7PPll1EmtOa3lo0Ww+HHgJAcbw382r2Xz4IOM69WZ1yn5m79pC+9ha/OesC6kRHMLZ9ZsxrlNvBn79HgD/HXwFTWvGUC0wiIWX3sK9C2Yyf2+ibyv1B4x/FBavgMNp0O9iGHMtXPwXGVVhAgwN/9GUDRPXgLXE9UkgvF41dn+ZSLXG1YnqHFPhuoXpBWx8dg0YCI4KoclNLSss6ytO6+L5DdN4qfN1OIyD6XuWsj0rmdFNB7A+fTcLDqzn4vpn0DWmGYUuJxmFOTy+5r8AdIxqzOhmAyh0ObFYJq7/mvTCqnWBbJd18cqmr3imw404jIOZ+xazIzuJUY0HsTF9FwsPruWCur3pHN2CQpeTzMIcnlnvvnRrzeAInulwEy5rSclL46l1nx5nb97htJaHfv6JD4ddRIBx8N/1a9h86CDjup3B6uQkZidu5b/rV/PCOYOZO/I6DufmMvZ79x3+utauyy1dulHocuGylgfn/Uhqbg6puTnM3LqZby+9mkKXi7UpyXy21rcX4ndaFxPXTePlrtcSYAzTdi9jW2YyNzU/h/Vpu5mfvIFLG/akW0xTCq2T9IJcHl3lvqTupQ17Uj88hhua9eeGZu7hv2OWvE9qftnrI/mK01oenv8THw6/CIdxMGVd2XacvG41Lw4YzJyR15GWl8vYWZ52rFOXmzuXaMe5P5bpCVgVLJ61iq4D2/PeymfIy87nhVuLr8H42oJH+WfvhwG4ZeKVNG7nHqL26TPT2ONJoAy78Ww69WtDYYGTzMNZPH/zO96vRAUCAgxDbgnhg3/n4HJBl4FBJDQMYPZHedRtHkDrHu6PtKvmFXBa38A/NVrFV5bM30jXPi1577vx5OYW8OIDxQNAXv1qTKm7+Z456DQeuvmDMtvoe1575s2oOjf1OGLR0m307NKEz/4zmry8Ap56eWbRsndfuobr7/iAoKBAnnv0YgIDA3A4DMtW7GD691WvLkdzWhcvbpzKC52vJ8A4mL53Cduzkrih6QA2eM7rF9U/g67RzSm0TjIKcnjCc17PKMzh8x0/8273sVgsC1M2sDCl6t0YYsHq7fQ6rRFTn7yW3PxCHpn0fdGyTx+6iisf+4Sw4CBeGDOM4MAAjDEs3biLL+e522/qgjU8PGogkx+5msJCJ4+8P6uiXfnEL6u30+u0xnw94Tpy8wt5tER8nzw0kqse+5iwkCBeGDOc4KAAHMawdMMuvpxXnHwf2K0l3+umHlJFmap0t66qxBiTaa2NOGpePzxDTY0xj1B6aGwT3EOCrTHmOWA38BHwu7W2zFXNjfvTyCLgXc8wTowx7XEnk+KAm4DzgGjcNxTpjnuIaNFQV8818ZZaaycZY1YDw6y1240xcbh7851lrd1ijKkG1LXWbjLGJOIewptijDkdeM5a288YMx6ItNY+7Nn2KEoP9d0CDAU2AlOADGvtqKOPQ4n6VTR/OXCDtXaZMeZ9oLFn/0X7M0fdoONIW3iSaS9Ya/seo92OXreRZ7pdOXUqeSxK7v9X3EnLKZ52am+tXXnUfgYBI621Iz3TTa21Wz2PlwCjgW6eNrzEWlvguS7gHuBM3EODzzky1PdIrz9jzDfAk9baRRXVEbADep/8UN+q7ocFD9Do/apzMfFTKfHae3DtP6kbd/8lOGpt4urf/sz9gKq2j7q/Q8/vj3nz8b+0hQOf5uw5d/o6jErzY/8XaPRaVRm0eGol/nM8XWf+y9dhVJolgyfQ+FX/bDuA7WPGMyjyWl+HUWm+S3+fKVu7+DqMSnNJ02UMbuOfr7+Z6ybQZ9jRlyn3H/On3U2vH+71dRiV5pcBz9Bl9Iu+DqPSLHt7HKff8IKvw6g0S9+586/368Uf8PrG/n6ZiLq15Zwq2W4aWH3qXAqs8Qy/bQd8aK09iHu47Rpz1M09rDvjeiFwjjFmqzFmLfAUsB/33X5XAStx3+H1Hmvt/uPs/y3gO2PMHGvtAWAU8JkxZhXuYb5lblBxlG+AC03xzT2Odh8wHfddf/cdZ1vH8gjuYbTLgJQ/uO4qwOm5IcYJ3dzjJF0FXG+MWYm7Z+bwcsrMBzqZ4p+Tn/XcrGMN7mO0EngHWAf87pn/HyDQWvsd7usWLvU8X+4CMMYEAc0o587RIiIiIiIiIv7AZY1f/lVVGupbgaN7+3nmzQXmeh4/ctSyp3Hfdfboda48xj724k4Yluduz1+5+/dMjynx+BXcw2iPTP+E+7qAR++zUYnHS4F+nsebgPYliv4MTCpR9gvcN9g4enuPlBf8MeZPBaaWM3/Skf1Za0cdtSzC87+AsjfZOHo7R6+biDsRW2ofnulGFex/OzDoOPvJNsbMBs4GZltry7tWogX+5fk7ev3yni9DgC+stYXH2reIiIiIiIiIyIlQjz+RkzcBCD+F2wuEKnPzPBERERERERH5i1OPP5GTZK1Nwj1k91Rtb8qp2paIiIiIiIiIiBJ/IiIiIiIiIiLiFU4NPvUqHW0RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA8p8SciIiIiIiIiIuKHdHMPERERERERERHxCpdVHzRv0tEWERERERERERHxQ0r8iYiIiIiIiIiI+CEl/kRERERERERERPyQrvEnIiIiIiIiIiJe4cT4OoS/FfX4ExERERERERER8UNK/ImIiIiIiIiIiPghJf5ERERERERERET8kK7xJyIiIiIiIiIiXuGy6oPmTTraIiIiIiIiIiIifkiJPxERERERERERET+kxJ+IiIiIiIiIiIgf0jX+RERERERERETEK5wYX4fwt6IefyIiIiIiIiIiIn5IiT8RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA/p5h4iIiIiIiIiIuIVLqs+aN6koy0iIiIiIiIiIuKHlPgTERERERERERHxQ8Za6+sYROSP0YtWRERERETEfxlfB1CZnlw7xC+/0z7QdnqVbDdd40/kL+jcLg/7OoRKM2vZo/SefY+vw6gUC86ZyNW/3eDrMCrNR93fwbW/ha/DqDSOWpuYuq2jr8OoNMObrKDbd//ydRiVZvGgCTT++Clfh1Epto+8nw7TH/R1GJVm5ZDHaTTpGV+HUWkSR93LGZc97+swKs2vk8czfMEYX4dRaab2fpWeV/hn+y38bDyDm93t6zAqzcwtz3LD0lG+DqPSvHP6JHpd/Jyvw6g0v3xxF/0GT/R1GJVm7kz//D50hFPX+PMqHW0RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA/pGn8iIiIiIiIiIuIVLv++d0mVox5/IiIiIiIiIiIifkiJPxERERERERERET+kxJ+IiIiIiIiIiIgf0jX+RERERERERETEK5xWfdC8SUdbRERERERERETEDynxJyIiIiIiIiIi4oeU+BMREREREREREfFDSvyJiIiIiIiIiIj4Id3cQ0REREREREREvMJlja9D+FtRjz8RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA/pGn8iIiIiIiIiIuIVTvVB8yodbRERERERERERET+kxJ+IiIiIiIiIiIgfUuJPRERERERERETED+kafyIiIiIiIiIi4hUua3wdwt+KevyJiIiIiIiIiIj4ISX+RERERERERERE/JASfyIiIiIiIiIiIn5I1/gTERERERERERGvcKkPmlfpaIuIiIiIiIiIiPghJf5ERERERERERET8kBJ/IiIiIiIiIiIifkjX+BP5m7vl7sF069Wc3NwCnn/ka7Zs2FdqeVh4MM+/c13RdGxCJD/NWMWbz3/HiKt6MuiCzjidLtJSs3nh0a9J3p/m7SpUqHtMC25vMRyHMUzfs5iPd8wttXx43R6MqN8Tl7XkOPOYuP5LErOSAWgaUYu7W11EtcAQXFhGL36FfFehD2pRvsOrDrHz421YlyWuby3qDK1fbrlDS1LY8sp62jzSkYgm1XEVukh8fwtZ2zPAGBqObEJk65pejv7Pe+BpmLsQoqPgm0m+juaP27jUydQ3C7Au6DYogP6XBpVaPu0/+Wxd5QKgIA8yD1se+yIMgNRkF1+8VEBaigXguseDiU6oWr/j9YhtzvjWQ3DgYOruJXy4fX6p5SPqd+PiBj1wWRfZznyeWvM12z2vPYCE0BpM7n0Hb2/5kU8SF3g7/OPqU7sJD3c9B4dxMHnLCt5cu6jU8m7x9Xnw9HNoVTOe2xZ8zcydG4uW1QmP5Oke51G7WnWshWvn/Jc9WVXnffNoZ8Q149625+Mwhv/tXMZ7W38utfySBl25rFF3nNZFjjOfx1ZNZVvmAR9Fe3x96zbmoW5nE2AcTN68kjdW/1ZqebeEejzU7WxaRcUzdt40Zu5wt12b6Hie6DGQiKAQnNbFa6sWMj1xgy+qcFzjRvWnZ6fG5OYV8sQb37Fpe3KFZZ+5+wLqJtRg5F0fFM27eFAnLhrYEafLxa/Lt/P6J/MrXN/bMlenkPzZRqy11DyzLjHnNS63XPrSJPa+sYqGD3YjrFGNovkFB3PY9uBCYoc1IWZQIy9F/ceMu6Y/Z3RsTG5+IY+/8R2bEituv4l3XUCd+BqMvMfdftdf1JPhZ51GanoOAG9OXsDCFdu9EveJuPnB4XTt14q8nAKev3cyW9fuKVOm75COXHbLWWDhYHI6z47/lPTUbHoPbs/I2wZQv2k8d4x4hc1rdvugBhU7tDKVbR8lYl2WWv0SqD+sbqnlSfOS2fbZDkKiggGoM7AWtfonkJmYxZb3t+HMcYLD0GB4XeJ6xvqiCsd1x3Vnud9b8gt58tWZx35vufcC6iTU5Oo7JwHQrGEcd984gLDQIPYdSOfR//uW7Jx8L0V+YsbefDY9ujYhN6+Ap5+fyeatSWXKvPTM5URHR5CfVwDAXQ9M4XBaNgD9zmzJqJG9sBa2bkvmiYnTvRq/yLEo8ecnjDEPAFcCTsAF3GSt/e3Ya/2p/c0F7rLWLj2R+cfZVqa1NuJPxBID/OiZrIX7GBz51tENmGutPeNkt3+M/XYCxlhrrz9F2xsDZFtr3zsV2zsRXXs1p279GK694GVatavH2PuHcPs1b5cqk5Odz61Xvlk0/erHN7Hgp/UAbN24j7FXv0VebgFDLu7KDbcPZML9U7wV/jE5MNzZ8kLGLX+b5Nw03uk2lgUp64oSewA/7F/O1D3uL+y9YtswtvlQxq94lwDj4MG2V/DE2s/ZkrmPyKBwCl1OX1WlDOuy7PhwKy3vaUdwdAhrH15BVOdowupWK1XOmVNI0vd7qNa0etG8A3P3A3DahC4UpOez8bm1tH2kI8ZhvFqHP+uCwXDlCLhvgq8j+eNcTsv/Xitg9IRgasQaXrk9jzbdA0hoWJy8G3ZTcNHjX6YWsmerq2h68nMFnHV5IC06B5CXYzFVrOkcGO5pM4wxS94jOTedD3reys/JG0ol9mbtXclXuxYDcGZcK+5odR63L5tUtPyOVuezMGWTt0M/IQ5jeKzbQK7+8XP2Z6czdfAoZu/ezJa0g0Vl9mSlc/ev0xndpnuZ9Z/vNYTXVv/Kgv2JhAcG4bLWm+H/IQ4M/2o3lJt+m0RSTjqfnnkzc5M2lErszdi7iik7lwDQN6EVd7UZzK2LP/RVyMfkMIbHug9g5PeT2Z+dwbQh1/DDzi2l2m5vVjp3LZjB6LbdSq2bU1jAnT9/S2JGKvFhEUwfeg3z924nPT/P29U4pp4dG1OvVhSX3v4ebZvX5u7rz2H0vz8tt2zfbs3IyS39xbtz2/qceXpT/nHPhxQUOomKDPNG2CfEuixJn2yg/vjOBEWFkvj4b0R0jCOkTumPkM6cQlJn7yS0SY0y20ievImIdjHeCvkP69mxMfVrRXHJuPdo26w291x/Djc8WEH7dS3bfgCfz/idT7894Y/hXtO1byvqNIrl+rOfoVXHBox5dATjLn6lVBlHgIObHxzOTYOeJT01m+vuOZ+hV/fik5d/YMem/Tx+64fc9sRFPqpBxazLsnXSdtrd34aQ6GBWPLia6M5RVKsXXqpcXI8Ymo1qUmqeI8RBy1uaEVYrjLzUfJb/exVR7WsSWK1qfU3v2akx9WpHcdnYd2nbvDZ33TiAG+//pNyyfbs3Jzu3oNS8+245l1c/nMuKdbs5/6x2XDW8K29//os3Qj8h3bs2oV6dKK66/m3atKrNuDEDuHXcx+WWfXLidDZu3l9qXt06UVx1WQ/GjP+EzMw8atYIL3ddKea0VewDrJ+rWl0E5KQYY3oCQ4DO1tr2wDnALt9G5T3W2oPW2o7W2o7Am8CLR6attfmVkfTz+Bfw8inc3nvA2FO4vePq2bcVs79dAcCGNbupFhFKdGzFOdi6DWKoGVWNNct3ALByaSJ5nhP7+tW7iI2PrPygT1DrGvXZnZPC3pxDFFons5NW0juubaky2c7iL2xhAcFY3F/Au0a3YGvmPrZkuns/phdk46LqfDnP3JpBSHwoofFhOAIdxPSII/X3Q2XK7f5yB7XPr48jqPitPmdPNpFt3F+GgiKDCQwPIGt7ptdiP1W6doCa1Y9friratclFbB1DTG0HgUGGDn0DWLuo4sTyinlOOvYLACBphwuXE1p0dk+HhBmCQ6vWB6e2NeuxO/sge3NSKbROvt+/ij4JrUuVySr52gssfu0B9I1vzd6cQ2zLrLgngS91iKnDjoxUdmUepsDl4pvE9Qyo16JUmT1ZaWw4fKBMUq9ZjRgCjIMF+xMByC4sINdZdXoSH61dzXrsyjrInmx3W363ZzX9jm7LwpLvo0Gl2rKq6Rhbmx0Zh9mVmeZuu+3rGdigeakyuzPT2ZB6oEw9tqenkpiRCkByTiYHc7OJDql6X+zO7NqU7+avA2Dt5n1EVAshpma1MuXCQoK4/PzTmfRV6d6qFw7owEdTF1NQ6H5POtJzrCrI3ZZGcHw4wXHhmEAHkd1qkbm8bO/SlK+3EjO4ESao9NecjN+TCYoNI7juSf/WXOn6dGnKzJ897bdlHxHhFbffFeedzvv/W1RmWVXV45y2/Pi/ZQBsWLGTiMhQouJKn8iNcf+Fhrl//AqPCOFQUjoAu7Yms2d71exNnLE1k9CEUMLiQ3EEOojrEcuhZakntG547TDCarkT7CFRwQRHBlGQUXCctbyvd9dmfDd3LeB+b6le0XMzNIjLhnThgy9LPzfr145ixTp3L80lK3fQt3uLMuv6Uq8ezZj1o7t+6zbsIyIilOiosvWryJBB7fn6m+VkZrrPiUd6AYpUFUr8+YfaQIq1Ng/AWptird0LYIxJNMZMNMasNsYsNsY088yPM8Z8aYxZ4vnr5ZlfzRjznqfscmPMcM/8MGPM58aY9caY/wHH/QnYGJNpjHnSGLPSGLPIGJPgmd/YGLPQE9MTR61ztyeeVcaYRz3zunqmQz3xrTXGtDvRg2OMyfT872eMmWeMmWqM2WaMedoYc5WnrquNMU2PdWyO2mZ1oL21dqVnuq8xZoXnb7lnebn18cz/h2feSmPMR552ywYSjTHdjt5fZYmNr84BzwcqgJTkdGLiKk7e9Tu3HfN+WFPuskHDO7Pk182nPMaTFRdSg+Tc4uFzB3LTiAspW7cR9Xoy+Yx7uaX5eby0cRoA9cNjsVie73Q973a7nSsb9vVa3CeiIDWPkJiQoung6GDyU0v3OslKzCT/UB41O0aXmh/eoBqpvx/COi15B3KLyon3pKVAjbjiZF2NWEP6wfKTJalJLg7td9Gsg/t0fWCPJTQCPnw8j5f+mcv0dwpwOatWoiUupAZJOcWvveQKXnsXN+jBV33GM7bFIJ5f7x4OExYQzD+a9OWdLT95Ld4/qlZ4BPuyi98392dnUCv8xLLQjatHk56fxxt9RjD9vGu5v3N/HFWty2YJ8WGR7M8t3ZYJYWXrelnDbkzvP45xrc/lmbXfejPEPyQhvDp7s4rbbl9WBgnhfzwJ1CG2NkGOAHZknNgXe2+Ki4og6WBG0fSBgxnERZet4+jLevHZ9KXk5pdOPNevHUWHVvV4+4kree3hS2ndNKHSYz5RBYfzCIwuPvcFRoVQcLj0+St3RzoFh3KJ6BBXar4rt5CDMxOJHVa6t1VVExd9VPsdKr/9bry0F599u5TcvLI/HFx8bkc+euYfPHDTuVSvFlJmua/EJESSsu9w0XTK/jRiE0r3ynQWunj1oa94Y8Z4Pvn1QRo0S2DWlMXeDvUPyzuUX+ZzWV5q2c9WKUsOsey+lax7aSN5B8suz9iagavQEhofWqnxnoy4mAiSSzw3kw9lEBdTznvL5b34/Jul5OaVTl5u353CmV2bAdC/ZwsSYqvWr7dxMdU5kFJ8fjiQkkFcBTHeO24w77x6DVdf0bNoXv260dSrG8Urz13J6y+OpFuX8i9DIOIrSvz5h++B+saYTcaY140xR2cp0qy1pwGvAi955v0f7p5xXYGLgHc88x8AfrLWdgP6A88aY6oBt+AehtoaeBjocgJxVQMWWWs7APOB0SX2/YYnpqILyhljBgLNcQ/P7Qh0Mcb0sdYuAaYBTwATgY+tteVnn46vA3Az0Bq4Gmjhqes7FPe2q+jYlHQ6UDKGu4B/enodngnkVFQfY0xb4N/AWZ5jc3uJ7Sz1rF+KMeZGY8xSY8zSt9566ySr/uf1HdiOOd+tLjP/rMHtad6mDl98WHW67J+or3Yv5LJfn+HNzTO4pvFZAAQaB+1rNuaxNZ9x69LX6RPXji5RzXwc6YmzLsvOT7fR4IqyX3Di+tQiODqYtQ8vZ8fHW4loFonRmaDKWjHPyWm9A3AEuJNDLickrnFx/g1BjH05hEP7XSydXXWGof8RX+xcxIj5z/Pqpllc17Q/AKObnc1nib+Q46xa1/05VQIdDrrG12PC7z8xfOYk6kfU5OImp/k6rD9t8o7FDJnzIi+t/57Rzfr5OpxKFRdWjRfOPJ+7f5lRhfs2HlvzhnHUTajJ/CVbyiwLDHAQGRHK6H9/yqsfz+fxO4b6IMKTY12WpMmbiL+sbE+ilKnbiB7YAEdo1Ro+eTKOtN+8pWXb76vZK7n49nf5x30fkpKayW0j+3k/wD8hINDB+Vf2ZMywl7jqjMfZvnEfl958lq/DOiWiO0fR7aXOdHm6A1Gn1WTjm6XbLz81n41vbKHFjU3/cpdfOaJ5I897y+Kyz80Jr81ixKCOvPvMSMLDgot6Ff/VPDFxOtfd+j5j7/6M9u3qMfBs92iigAAH9epGcce9n/PY099w1+3nElGFEu8if/2zn2CtzTTGdMGdMOoPTDbG3GetneQp8lmJ/y96Hp8DtDHFPQ0ijTERwEBgmDHmLs/8UKAB0AfPsFZr7SpjzKoTCC0fOHJV02XAAM/jXrgTagAfAc94Hg/0/C33TEfgTpzNBx4DlgC5wG0nsO+KLLHW7gMwxmzFnTQFWI372EEFx8ZaW3I8ZG2KryMI8AvwgjHmE+Ara+1uT+KvvPr8P3v3HR5Vlf9x/H1mUggklJBG701aROkIkQ6K2Bu6llVQQZG19/qzr7prxUWxrmJHsFAFQUFApBN6T0ISSnqf8/tjhhSSUJRkxtnP63nyJHPvuXe+Z05m7p3vPfecrsBn1tpUAGtt6Xs0k4H2RwdtrX0LOJLxs19MfuTkal3KqEt6MOKCbgBs3pBAZHRJT5yIqNocSEmvcLuWbaJxOh3lJv84vUdLrvh7f+68cSoFBb5zEE/JSyOqRsmV5MgadUjJq7huAHP3r+aODhfABkjOS2P1oe2kFbi76S85EE/b2o347VD5ExlvCKwXXOZKcf7BfILqlZxcFOUWkbM3i41Pu9+mBWn5bHl5A21uP43QlmE0G9OquOyGx1dRI8Z3xnD6X1AnAtJSSlIGaamW2vUrPslfvbCI88eXTPxRJ8LQoKWD+g3c2dqOvZ3sjnfBsKqN+WSk5KURHVLy3os6zntvduIa7jltNACd6jRhYEwnJrQbTlhADVzWku8q5LPdvnM7W1J2Jg1qlnxuxtQMIyk74xhblEjMzmDjoWT2ZLp7vczZs4XTIxry6bYTOaRWv+ScdGJqlG3L/TmV1/WHhLU80HkUrK6O6E7e/uwMGtYqabsGtcLYn33iQx2EBgYxdfDFvLByEb+nJFRFiH/IhUAoZnwAACAASURBVENjOW+QO4Ecvy2J6PolvVQi64eRcrBsHTu1bUj7ltF88coNOJ0O6tWpyasPX8qExz8l+UAGC5e5e+9v3JaEdVnqhoVwOMP7t/wG1g2msFQP9cJDeQTWLTn2uXILyd+Xye7n3OPbFaXls+/fq2h0Wyw5O9JI/20/yZ9twZVdCAYcgQ7qDWpa7fU42kVDYjlvoLv9Nm4/qv3CK2i/Nu72+/LfN+B0uNvvtYcuZfwTn3Ko1O2F0+ev5YW7L6ieSlTi3Kv6MPxS91inm9fuIaJByWRiETF1SN1fdmKjVh0aApC42z3u5qLvVnPpuLPxdcHhQeXOy4LrlU36BIaVHMtjzo5ix8e7ih8XZhey7oV4ml3SlNptfKcn3IXDYzlvUBfA/XkQVep/Myo8jJQDZf83O7ZtSPtWMXz++o3uz5baNXnlscu49ZFp7E44yKQnPgfcPYv7dPN+79vzzz2dc4e76xe/OYnIiNqAe8KZyIgwUlLLH+9SPXXOycln3o8b6dC2AbPnrSclNYMNmxIoKnKRtD+NPfsO0ahRPTZtTiq3D3FzaYy/aqXEn5+w1hYBC4AFxpi1wDXAu0dWly7q+e0Aellrc0vvx7izXRdZazcdtfyPhFVgbfEAR0WU/X+r6EK5AZ621k6uYF193ImzQNzJyKw/EhBQul+9q9RjV6n4KnxtjpLjiQMAa+0zxphvgZHAz8aYYVRSH2PMscbxq+HZd5WZ8dkyZnhum+jRrw3nXdqTBbPW0b5TY7IzczmYWvGXoLjhnVkwq2xHy1btYrjtgVE8MOED0g790SapGvHpe2kSEkGDGvVIyUtncHRXHlv3cZkyjUMi2JuTCkCfiPbszXafaC47sJkrm8UR7Aik0BZxet2WTNu9qNxzeEtoyzDy9ueSl5JLYL0gDixNodXN7YrXB9QMoNvrJbcfbHxqDU0ub0FoyzCK8tzJWWewk7R1hzBOU25SEKlajds6SE2wHExyUbu+YfXCIq64J6hcueQ9LnIyoVmHki6ZTdoacrMsmYctoXUN21a7aNzGt7psbkjbR5OaETQMqUdybjpDY7rw0JppZco0qVmfPZ73W9/IduzJdr8Pxy4r6dF8Y+tBZBfm+VTSD2DNgQSah9WjcS13EmxU8w5MXPzNCW6bSO2gYMKDQziYl0PvmGasPZB4/A29ZH3aPprWqk+jkLrsz81geKPO3Ley7AROTWuFszvLff2qf1RbdmcdqGhXPmF1aiLNa9ejcWgd9mdnMKpFB277acYJbRvocDD57Av4ctv64pl+fcWXs1fx5Wz3eL19Tm/BRcNOZ84v8XRs04Cs7DwOHC57fP5qzmq+muPOzsZE1uaFey5gwuOfAvDT8q10O60JK9fvoUmDegQEOH0i6QdQo0Vt8vdnk5+SQ2C9YNKXJdFwbEmPWWfNQNr8K6748a7nVhB1aRtCmteh2b3di5enTN+GI9jpE0k/gC/mrOKLOSXtd/FQT/u1rqT95q7mq7me9ouozQt3X8D4J9ztV79ureLycd1bs31PajXWpLyZH/7CzA9/AaB7XHtGXd2XhTNX0T62KVkZuRxKKZtYSd2fTtPW0dQJr0XawSxO79uW3dt8c7zX0sJahpKblEtuci5B4UGkLE2l3fiy44fmH8onyDOj74HfDlKzofuiq6vQxYaXNxHdL5LInr418cyXP6ziyx/c/5u9u7XkohGnM/dn92dLZgX/m1/PXs3Xs0s+W56/70JufcR9/K9buyaH07MxBq65uBdfz/H+FaKvZ/7O1zPd/TN6dW/JBaO6MX/hRk5r34CsrDwOHvXdxukwhIbWIC09B6fTQe+erfjNM+754iVbGBjXgR/mrKNO7RCaNKpHYqlb20W8TYk/P2CMaQe4rLVHBliLBXaVKnIZ8Izn9xLPstm4b2193rOPWGvtKmAWcKsx5lZrrTXGnG6t/R13r7srgfme8fW6/ImQfwYuBz4ExpRaPgt4whjzkacXYyPcycNkYDLwENACdw/BCX/i+Y+nstemtI3AHUceGGNaWWvXAmuNMd1x99qrsD7AfOArY8yL1toDxpjwUr3+2uJ+farFssVb6N63LVOnTyQvt4B/Pvp18brX/3tTmdl8+w/uyEMTy87edePEoYSEBPHgs5cCkJyUxqP/KJtc85Yi6+LFTdN58fQbcBgH3yYsZ0fWfv7ecijx6Xv5OXUDFzXpw5nhrSm0LjIKcvi/9e6Tk4zCHKbt/okpPW7FAktS41lyIN67FSrFOA3N/taK+OfWgbVE9o+mZuNa7P1iJ7VahFGvW+UnjoXpBWx6fh0YCKoXTMtx7Sot68vueAyWrYLDaRB3MUy4Di4+x9tRnRin0zD65kCmPJiPqwi6D3US08zBrPcLaNzWQcde7ok7Vi0sousAZ5kLLw6n4ZwbAnnrPvc1i0atHfQY7vRKPSpTZF08v+Eb/n3mdTiMYcbe39iemczY1oPZmLaXRSnxXNK0Nz3qt6LQFpFekMtjaz/3dtgnrMhaHlk+h/cHXY7DGD7btoYtaalM6nIWaw8mMnfvVrrUb8Cb/S+kTnANBjVuw+1dzmLYzCm4rOWp3+bz0eArAVh3MIlPth59ePEdRdbF0+tn8kbPa3AYB1/vWcm2zGRuaTuQ9WkJLNwfz+XNe9ErohUFriIyCnJ4aNWX3g67UkXW8vDSObw/5FKcxvDp1rVsOZzKpNh+rD2QxNw9W+lSP4bJAy+kTlAwgxq3ZlJsP4ZOf5tzmrenR0wT6tUI4eLW7mGG71z8HRsO+lZS4pffd9D79JZ89q+/k5tfwP+9Mat43bvPXs2193xwzO1n/riOB24exocvXENBYRFPvv59VYd8wozTQfSYdux5aSW4LHX6NSS4USgpX2+lRvPahMVGeTvEP+2X33fQJ7Yln738d/LyCnhyckn7vff01Vxz37Hbb/yV/WnbLBILJKak8+yUOVUc8YlbviCe7nEdeGf+veTm5PPSPZ8Wr3v1m0lMOO8lDian89Erc3juvzdTVOgiOeEQ/7zbfW7WZ0gnbn5kNHXCQ3lsyvVs35jAg9dVNBpP9TNOQ6trW7Du2Y1YlyV6QBS1Gtdk5+e7CWsRSv0zwtk3K5GDK90XXANqBdD2JvcQMqlLD5Aen0FhRiH7f3J/nrQd15rQ5r51UXbJyu307taCT1+9gdy8Ap56/Yfide8+/zeuvevYs7kP6deeC4fHArDw1y18O/+PjtpUNZYu307P7i356J0bycst5NmXSj77prx6DTdMeI/AwACee/ISAgIcOBwOfvt9JzN/cCcwl/22gzO7NefdydfjKrK8+fYC0jOO1YdEpHoZa/+qI5TIEZ7bfF8B6gKFwFZgrLU21RizE5gGjMDdu+0Ka+1WY0wE8Bruse4CgJ+stTcZY0JwjwPYB3fPtx3W2nM9y6fivk11I9AI95h2K46KZQFwp7V2hTEm01ob6ll+MXCutfZaY0wL4L+4e/BNB24vVW4icINnd5nAVbhvDR5trb3IGOMEfgHus9aWG/3dGPMokGmtfaHUskxrbagxJs4T27kVxFq8rrLXpoLnWgv0sdZmGGNewX2rsAtYD1xrrc2rqD7W2m3GmGuAu3D3hPzdWnutZ58rgSHW2mN1mbDDzvjjt/r6ulm/PUa/uXd7O4wqsXjwc1z96w3HL/gX9UHPKbiSfGuWtlPJEbOZ6dtjvR1GlRndchU9frjf22FUmWXDn6LFh097O4wqseOq++g68yFvh1FlVp/7BM3fffb4Bf+idl57D30u+6e3w6gyv0y7g9GLq/J6rXdN7/cqva/wz/Zb8vEdjGh9l7fDqDLfb32eG1Zc6+0wqsyUM9+l78UvHL/gX9TPn99J3IjnvB1GlVnw/d1+fS/sxN+v8MtE1L9O/9gn2009/vyAtfY33Im6yjxvrb3nqG1ScfcAPHpfOcC4SpZffgKxxJX6O7TU358Dn3v+3gH0LrXZg6XK/Qv35BqlbQPe96wvAnoe4/kfrWBZqOf3Aty3Q1cUa/G6yl6bCrzjKTfFWlvh7buV1Adr7XvAe6WXGWNOB9YfJ+knIiIiIiIi8pflsr41VI2/06st8se9QdkxA/+sCNy3M4uIiIiIiIiI/Gnq8efnrLXNvR2Dv/JM/nHswVZObn++MxCLiIiIiIiIiPzlqcefiIiIiIiIiIiIH1KPPxERERERERERqRZF+OQcGH5LPf5ERERERERERET8kBJ/IiIiIiIiIiIifkiJPxERERERERERET+kxJ+IiIiIiIiIiIgf0uQeIiIiIiIiIiJSLVxWk3tUJ/X4ExERERERERER8UNK/ImIiIiIiIiIiPghJf5ERERERERERET8kMb4ExERERERERGRauGy6oNWnfRqi4iIiIiIiIiI+CEl/kRERERERERERPyQEn8iIiIiIiIiIiJ+SGP8iYiIiIiIiIhItXBhvB3C/xT1+BMREREREREREfFDSvyJiIiIiIiIiIj4ISX+RERERERERERE/JDG+BMRERERERERkWpRZDXGX3VSjz8RERERERERERE/pMSfiIiIiIiIiIiIH1LiT0RERERERERExA8p8SciIiIiIiIiIuKHNLmHiIiIiIiIiIhUC5dVH7TqpFdbRERERERERETEDynxJyIiIiIiIiIi4oeMtdbbMYjIydGbVkRERERExH8ZbwdQla5Z9ne//E77Xo+3fbLdNMafyF/QsO6PeTuEKjNr+SM0f/MFb4dRJXbedCe9Z9/r7TCqzJKhzzB9e6y3w6gyo1uuwpXU1tthVBlHzGZafvyUt8OoMtuvuJ/Wnz7p7TCqxNZLH6TFR097O4wqs2PMfX5fv243v+TtMKrMyjcmMWTBJG+HUWXmxL3EmTe86O0wqsSKKf9g0ED/fe/Nm38fL20c6u0wqsykDrMZOOgZb4dRZebPu5fhXR/ydhhV5ofVT3g7hCrlsj6ZH/NbutVXRERERERERETEDynxJyIiIiIiIiIi4oeU+BMREREREREREfFDGuNPRERERERERESqhcu/5y7xOerxJyIiIiIiIiIi4oeU+BMREREREREREfFDSvyJiIiIiIiIiIj4IY3xJyIiIiIiIiIi1cJlNcZfdVKPPxERERERERERET+kxJ+IiIiIiIiIiIgfUuJPRERERERERETEDynxJyIiIiIiIiIi4oc0uYeIiIiIiIiIiFQLl1UftOqkV1tERERERERERMQPKfEnIiIiIiIiIiLih5T4ExERERERERER8UMa409ERERERERERKqFyxpvh/A/RT3+RERERERERERE/JASfyIiIiIiIiIiIn5IiT8RERERERERERE/pDH+RERERERERESkWrjQGH/VST3+RERERERERERE/JASfyIiIiIiIiIiIn5IiT8RERERERERERE/pDH+RERERERERESkWrisxvirTurxJyIiIiIiIiIi4oeU+BMREREREREREfFDSvyJiIiIiIiIiIj4IY3xJyLFbr5jOD36tiE3t4B/PvY1WzcllVkfUjOIf/7nuuLHEVG1mf/9Gt58cVZ1h1qhAU2a83DfgTiNYdrGtbyxalmZ9UEOJy8OHEGnyGgO5+YyYe4M9makA9A+PIKn+g8lNCgIl7WM/vJD8oqKuLNHPy5sexp1gmvQ8e1/e6NaFepVvy23tx+F0xi+2bucD3YuLLP+gsY9uahJb4qsi5yifJ7Z8CU7s5KJqVGPT/r+g11ZKQCsT9vNcxu/9kYVjmnTiiKmv1mAdUGP4U7OvjSwzPpvJuezbY0LgII8yDxsefzzEAAOJbv4/OUC0lItANc/EUR49F/nOtcDz8CCJRBeD2a86+1o/pj+DVrycLchOIzh022reXPjkjLru0c24aFuQ2hfN4qJv3zN93vii9fdE3s2ZzdsjQPD4qQdPL5yTnWHf1L6x7TkwdhhOI3h0x2rmBz/S5n117ftyaUtYim0Lg7mZXPv8pkkZKd5Kdrj69+gJY+cORiHcTBt6yre3LC0zPoeUU146IzBtK8bxW2Lv+b7PZsA6BXdlIe6DS4u16pOfW5d/DVz9m6p1viPx9/rB3DXpXH069iC3PwCHnl/NvF7ksuVeXXCBUTUqYXT4eD3rft45pP5uKzl5lG9ievSCpe1HMzI4ZH3Z5GaluWFWhzfmeHtuaX1BTiM4fvEX5m2e16F5fpFdOGRTtcx/rcX2Zyxp5qjPHl3XnE2fTu72+/Rd2axaXf59vv37Rd62s+wass+nv3I3X5PjTuHZtH1AAirGUxGdh5jHv+wuqtQqfEThtCzZyvycgt47rmZbNmyv1yZgAAHt942lNiuzXBZyztvL2TRok1cfHF3Ro6MpajIxeG0bJ5//luS96d7oRYV270yh5+nHMS6oMOQUE6/qE65MlsXZ/HbJ2lgoH7zQAbfEQlARkohC189QOaBQgyGEQ9FUTvat7+mTxg/mJ49W5GbV8Bzz31baVvedutQusY2xbosb7/zE4sWbfJCtCfm5ntG0r1fW/JyC/jnQ1+yNT6xzPqQmkG8MPWG4scR0bWZ/+1qJj//ffGyvoNO46EXr+DWK95gy4aEaotd5Hh8+xNFyjHG1AeOnNnEAEVAiudxtrW2zyl+vligobX2u5PYJg6401p77jHK1AWutNa+/uejPGYsZeI3xpwHnGatfeYU7Pt8oIu19vE/uy/P/l4AvrPWzj8V+ztZ3fu0plHTcK678BXad2rErfeew8Tr3i5TJic7n1vGTC5+/Or7N7L4x43VHWqFHMbweL/BXDXzM5KyMvjmwquYs2sbWw8dKC5zaYfOpOXlEvfx24xq1Y57e/ZnwtyZOI3hpUHn8I/537HxQAp1g2tQ4HInlebt3MZ7635nwRV/91bVynFguKPDaCb+9jbJuWm802sCi1I2sjOr5MvBrMRVfLX3VwD6RXZgYrtzmLRyKgB7cw5wzVLfSWIezVVk+eq1Am58Kog6EYZXJuZxWk8n0c1KknfnjQsq/vvn6YXs2+YqfjzthQIGXh5A225O8nIs5i82dvD5I+DKC+Hep7wdyR/jMIbHzhjG3378mKScdL4eeh1z921ha3pqcZmE7HTu/nUGN7TvVWbbbhGNOCOiMSO/nwLAp4OvpmdUU35N3l2tdThRDmN4tNsIrln4EUk56Xw5+O/MS9hcpq4bDiVx/ra3yS0q5MpW3biny0AmLv3Ki1FXzmEMj3cfytXzPyEpO53pw69l7t4tbE0v+Rzdl5XOXUtmcmOHnmW2Xbp/N+d8/w4AdYJqsOC8m1iUuKNa4z8ef68fQN+OzWkaVZfRj0ylc4sY7rtiINc890m5cvdM+Zas3HwAnh97LoPPaMPsFZt5f85vvDHDnai//OxYxo7sxVMfV5xQ8yYHhlvbXMQ9q98kNe8wr54xiSWp69idXTb5EOIM5oLG/dmYvtM7gZ6kvp1b0CSqLhfc/w6dWjbgvqsGce1TH5crd9+bM4vb77mbRzH4zLbMXr6J+yd/W1zm9kv7k5mdX22xH0+Pnq1o3Kgef7v6TTp0aMjE24czYfx75cqNGdOXw4ezueaayRgDYWHui3pbt+7n5punkpdXyKjzTmfs2LN58onp1V2NCrmKLIsnH+Tcx6KoVT+AL+9KpFmPEMKblJyrHE4o4Pcv0jj/mWiCQ53kHC4qXjf/5VS6XVKHJrEhFOS4fP6evJ49WtKocT2u/ttkOnRoyO0ThzF+wvvlyo0Z04fDh7O45pq3yrSlL+rerw0Nm9bn+lEv075zYyY8OIrbr3qrTJmc7HzGX1by1fWVj2/i53kbih+H1Azi/DG92bjG9y8w+AJN7lG9fPxjRY5mrT1grY211sYCbwIvHXl8qpN+HrHAyCrYb13glpPZwLid7P9smfittd+ciqSfx93AqUxcvgLcewr3d1J6D2jP3G/XABC/bh+1wmoQXj+00vKNmoZTN7wW6373jS/ksVEx7Eo/xJ6MNApcLmZsi2do81Zlygxt3oovNq8H4Lvtm+nTqCkAZzVpTvyBFDYecOfQD+fl4rLu3mK/JyeSku1bvR1Oq9OEvdkHSMg5SKEtYm7SavpHnVamTHZRXvHfIc4gbHUH+Sfs2ewioqGhfgMHAYGGrgOcrF9aVGn5VQuLiI1zArB/lwtXEbTt5n4cHGIIqvHXOrHo3hXqhnk7ij+ua3hDdmUeYk/WYQpcLmbu3sCQxm3KlNmXlUb84ZTi99kR1kKwM4BAh5Mgh5NA4yQ117fef6W563qwuK7f7l7P4IZty5RZmrKL3KJCAFYd2EdMzdreCPWEdK3fkF0Zh9iT6a7PjF0bGdKkbH0qa7vSRjZtz4KE7cX19hX+Xj+AuK6tmLnUfUFu7Y4kwmoGE1G7VrlyR5JGAQ4HgU4nRw4SR5YDhAQFYn306NGudlMSclJJyj1AoS1iQfLv9InoVK7ctS1GMG33fPJdvtdWFRkQ24rvlrgTCeu2JxJWM5j6dSpvP6fTQUCAo8J2GnxmO2Ytiy+33Fv69mnD7DnrANi4MYHQ0GDCw8vXbfiILnz8X3fy2VpIT88BYNWq3eTludtx44YEIiN957M0eUs+tRsEUDsmEGegoVW/Wuz8NadMmY2zM+k0MozgUPf5SUhd9++De/KxLmgS606KBYY4CAz27a/offq2Yc7s47fliOFd+O/H7l7VpdvSF/U+uwPzZqwCIH7tXkLDQgiPOMb3oGb1qRseyrqVu4qX/W38ID6buoiCvL/G5438b/HtTxU5KcaYTM/vOGPMQmPMdGPMdmPMM8aYMcaYZcaYtcaYVp5ykcaYL4wxyz0/fY/aXxDwOHCZMWaVMeYyY0y4MeZrY8waY8xSY0yX48T0qDHmHWPMAk8st3lWPQO08uz3eU/ZuzxxrDHGPOZZ1twYs8kY8z6wDmhijHnDGLPCGLP+SDlP2e7GmF+MMas9da1TQfzXGmNeNcbUMcbsOpJINMbUMsbsMcYEGmNaGWN+MMb8ZoxZZIxpX0G92gJ51tpUz+NLjDHrPM/9k2eZ0xjzfKk6jSu1/T2etlhtjHkGwFq7C6hvjIk5sRY/tSIiw0jZX3L7WWpyOvWjKs8+xA3txMI566sjtBMSXSuMhMyM4seJmZlE1wqrtEyRtWTk51OvRggt69TDYnn/nIuYedHVjIvtXq2xn6zIGrVJzi1pq+TcNCKDy58AX9SkF5/1u4vxbUfwYvw3xcsbhoTzXq/beP3MsXSt27w6Qj4paalQJ7IkWVcnwpB+oOIvn4f2uziY5KJ1V/fhLGWfpUYovP9EHi+Pz2XmlAJcRb75xdVfxdQMIzG75ParxOwMokNOLJP5+4F9LN2/i1/Pv41fz7+Nn5K2s61UbyxfEx1Stq5JOceu6yUtYlmYuK06QvtDYkJCy9YnO4OYE2y70s5t1oEZuzYcv2A18/f6AUTVDWX/oZJjYfKhTCLrVvzl9bVbL2Du8+PIystn7sqSW5bHn9eH7/7vBkb0aF/c+8/XRATXJSXvcPHj1Lw0IoLL3lrZOrQxkcF1WXbQN9uqIpF1Q0k6WNJ++w9lElVJ+71y+4XMefEmsnMLmLei7C3np7dpxMH0LPYkH65wW2+IiAgjJbnk/ZeSkkFERNn3X61awQBcd11/3px8HQ8/cj716tUst68RI7uybJnvfJZmHSwkNKLkRrrQ+k6yDpa9YJmWUMDhfYV8dW8SX96dyO6V7iRY2r5Cgmo5mPVMMp9NSmDJu4d8/rwlIiKM5JSS/9Njt+VZTH7zWh55uOK29BX1o2qX+R6Usj+N+lGVJ5cHDO/Mwllrix+3bt+AyJg6LFu0uUrjFPmjlPjzX12Bm4AOwNVAW2ttD2AKcKunzL9w9xjsDlzkWVfMWpsPPAxM8/QonAY8Bvxure0C3A+U79ddXntgGNADeMQYE4i7Z9s2z37vMsYMBdp4ysQCZxhj+nu2bwO8bq3t6EmOPWCtPRPoAgwwxnTxJCmnAROttV2BwUBWBfEfqVsasAoY4Fl0LjDLWlsAvAXcaq09A7iTinv19QVWlnr8MDDM89zneZb9HUjzvL7dgRuNMS2MMSOA0UBPT/nnSu1npWffZRhjxnqSnSveeuuto1d7xYAhnfhx1jpvh3FKOB0Ousc0ZuK877h4+scMa96muDfgX9kXe5ZyyeLneX3z91zXciAAB/LSOf+nZ7hm6b/516ZveazL5dR0Bns50j9u1cIiOvdz4nC6E4WuIti5zsU5NwRy67+DOZjkYsXcynsLim9pFlqP1rUj6DP9FXpPf4Xe0c3oHtnE22GdEqObdqJzeAOmbPLNRMqpElmjFu3qRvFTwnZvh1Il/Kl+41/5iqH3vEVQgJPu7UreZ6998wsjH5jC98viuTwu1osR/nEGw02tRzN5m2/cCloVbn35S4bfMdndfh3Kfk4O69meWct8dyy1yjidDqKiarN+/V5uGjeVDev3Me6mQWXKDB7ckbZtY/h02q9eivKPcbkgLbGA856MZvAdESx87QB5mS5cLkvShlx6X1uPi15oQHpSIZvmZ3o73D+tpC33Me6md1m/YR83jRvo7bBOmQHDOrPge3fizxjD2DtH8J9//uDlqEQqpzH+/Ndya20igDFmGzDbs3wtcLbn78HAaaZkAKzaxphQa+2xjjb9cCcJsdbON8bUN8bUttYea3Tdb621eUCeMSYZiK6gzFDPz++ex6G4E367gV3W2tKjb19qjBmL+/+3AXAa7ptUEq21yz2xpXvqfoywmAZcBvwIXA68bowJBfoAn5XatqKsSANKxlYE+Bl41xjzKfBlqTp1McZc7Hlcx1OnwcBUa222J9aDpfaTDDQ8+smstW/hTkgC2C/+89jRRf6QUZd0Z8T53QDYvCGByOg6gHtcioio2hxIzqhwu5ZtonE6HeUGvfWm/VkZNAwtudrYIDSU/VkZFZZJysrEaQxhQUEcys0hKTODZYl7OZTrfet9IAAAIABJREFUvvr64+7tdIqI5pd9vnEb89FSctOJqlHSsyGqRh1S8ip/C85JWsNdHS4APqPAFlFQkA3Apox97Ms+SNNaEcSn76vqsE9YnQhISym52p2Waqldv+L38uqFRZw/vmTijzoRhgYtHdRv4L6u1bG3k93xLvelB6kWSdkZNCh1O2uDmmHsz6n4s+RoQxu35fcD+8guLABgYcJ2To9oxPIU3xwvZ39O2brGhFRc1z5RLbj5tH5c+eP75Lt8NxGdlJNZtj41w0g6wbY74pxmHZi9ZxOF1nX8wtXMX+t36YCuXNDXfZvr+l37ia5XciyMqhdKyuHKT+vyC4tYsHobcV1b8Wt82WPe98vi+feE83lzpu8lq1PzDhMZXLf4cURwHVLzSnrrhDiDaV4rhhdiJwAQHhTG453+zsPr3va5CT4uObsr55/VGYANO/cTEx7Gas+66HqhJB+n/Rau2sqA2Nb8usHdfk6H4exurbn6iY+qOvTjGj26GyPPcSePN21KJLJUD6rIyDBSU8u+/9LTc8jJyS+eAGLhwnhGjOxavL5bt+ZcOaYP/5j0EQUFvvNZWis8gMzUkts7Mw8UUSvcWaZMaH0nUW2DcQYYakcHUrdhIGmJBYTWD6B+iyBqx7jPZVr0DGH/5jx8zejR3TjH0xabNiUSFVnyOXOibTlyxDFvFKt2oy7rwfALzwRg8/p9nu9BbpHRdTiQXPG5dYu2MTgDHGzd6J68I6RWEM1aR/HclOsBqBcRyqP/GsOjEz/SBB/HoDH+qpd6/Pmv0kcMV6nHLkoSvg6gV6kxAhsdJ+l3KmIpouKEswGeLhVLa2vtkZkligd4Msa0wN0Lb5Cn1+G3QI0/GNc3wHBjTDhwBjAf92tyuFQcsdbaDhVsm1P6ea21NwEPAk2A3zyTsBjcPQeP7KeFtXZ2BfsqrYZn39VixmfLuWXMZG4ZM5lfFsQz+Bz3Abl9p0ZkZ+Zx8EDF/w5xwzqxYLZv9fZbnZxE8zr1aBxWh0CHg1Gt2jNnZ9nbQObs3MZFbTsCMLJlW35JcJ/8L9yzk3bhEdQICMBpDD0bNmHLId+9vXBj+l6a1KxPg5B6BBgng2O6sii57K1MjWvWL/67b2R79mS7JxuoG1gLB+4DbcOQcJrUrE9C9kF8SeO2DlITLAeTXBQWWFYvLOK0Xs5y5ZL3uMjJhGYdSg5lTdoacrMsmYfdicNtq11EN9WhrjqtOZhA87B6NK7lfi+e2/Q05p7gzKcJ2en0jGqK0xgCjIOeUU3ZmpZ6/A29ZM3BBJqFhtO4Vl0CHQ7OadqReQllb/M5rW40T545knGLp3EwL9tLkZ6YNQfKtt2oZh1OuO2OOK/5aXzjo7fB+mv9Pl24miue+ogrnvqIBau3cW4v92lL5xYxZObkk5pedpzMkODA4nH/nA7DWZ1asDPJfRxoElmSTBvQtRU7kw5VUy1OzqaMPTQKiSSmRjgBxklc1OksSS0ZfiS7KJeLf36Iq5c+wdVLn2Bj+i6fTPoBfPbjasY8/iFjHv+QBb9vZWRv95i9nVo2IDMnnwNp5dvvyLh/Toehb5eW7EwsOY736NCMnYmHSD7k/V5j06evZNzYdxg39h1+XryZoUPcCeoOHRqSlZXHwYPlx3BdumQrXWObAe5E365d7mNA69bRTPrHcB568HMOH/atz9KoNkGkJRaSvr+AogLLtsVZNO9RdiKL5j1rkrAuF4Cc9CIOJxRQOzqAyNZB5Ge5yElzJzL3rc2lXqlJQXzF9OkrGTtuKmPHTWXxz1sYMvT4bblk6VZiu5ZuS986t54xbRnjL3ud8Ze9zpIfNzJolDtJ3b5zY7IyczmYWsn3oBElvf0AsjPzuCzuGa4Z+SLXjHyR+DV7lfQTn6Mef//bZuO+7ffIGHux1tpVR5XJAEoP2rAIGAM84Zm9N/U4vf0qc/R+Z3n2+ZG1NtMY0wgoqGC72rgTgWnGmGhgBLAA2AQ0MMZ0t9YuN8aE4U6gHf08xTzPsxz3Lc8zrbVFQLoxZocx5hJr7WfG3e2vi7V29VGbbwSuOvLAGNPKWvsr8KvnVt4mnjrdbIyZb60t8IwLuA+YAzzsqWu2MSa8VK+/tsBnJ/ICnmrLft5C975tmPrVre5p7B8vuT3m9Y/GlZnNt//gjjw00ftXkksrspaHF8/j/XMuwmkcfLppLVsOHWDSmX1Zm5LE3F3b+DR+LS8OHMmCK/7O4bxcbp0zE4D0/DymrFnBNxdehcXd4+/H3e7buO7t1Z/RrTsQEhDIkqvGMS1+LS+v+MWLNYUi6+Kf8d/wcrfrcRgHM/etYEdWMje2GsLG9L0sTtnIxU360L1+awpdRWQU5vDEuk8BiK3XghtbD6HQVYTF8tzGr0kv9K3Blp1Ow+ibA5nyYD6uIug+1ElMMwez3i+gcVsHHT1JwFULi+g6wFmmZ6/DaTjnhkDeus99vaFRawc9hpdPGvqyOx6DZavgcBrEXQwTroOLz/F2VCeuyFoeXTGb9+Iux2EcfLZ9NVvSU7m9c3/WHkxk3r4tdAlvwBtnXUSdoBoMatSaiZ3PYvh3/+H7PfH0jm7O9yNuxAI/JW5jfsJWb1epUkXW8tjKH5ja/wqcxsFnO1axJT2ViR0HsO5QAvMStnBP18HUDAjkld4XAZCYnc64nz/1cuQVK7KWR1bM4f2Bl+Mwhs+2rWFLWiqTupzF2gOJzN23lS7hDXhzwIXutmvchtu7nMWwb90jhTSqVYcGNWvz637f7C3t7/UDWLxuB/06NWf649eRm1/Io++XXG/8+P4xXPHUR4QEBfLSzecRFODEOAwrNu3h80Xuyb1uu6AfzaLrYV2WxIMZ/N9/53qrKsfksi5e3fIFT3cZh8M4mJX4K7uyk7im+XA2Z+xhyQHfGYP4ZPy8dgd9O7fg66euJze/kMemzipe99HDVzHm8Q8JCQ7kxQmjCQp04jCGFfF7+GJhyWnq0B7tmO1Dk3oc8euv2+jZsxUffHgTubkFPP9cyQzEk9+6nnFj3bNmv/WfH7nvvlGMv2Uwh9Oyi8uNHXc2ITWCePiRCwBITk7noQc/r/6KVMDhNPS7MZxvH0vGFkG7waGENw1i+X8PE9k6iOY9atLk9BrsXZXDtAkJGAf0vrYeNWq7z096XVuPGQ/vBwsRrYLoMKTySSV8gbstW/LhB+PIzS3guee/K1731uTrGDtuKgD/eWsB9903ilvGDyLtcHaZcr5m2aLNdO/XlndmTiIvt4AXH/6yeN1r024pM5tv/6GdeGj8B94IU+QPM/YYs5aJbzPGPApkWmtf8DzOtNaGehJyd1prz/UsX+B5vKL0OmNMBPAa7nEAA4CfPD3XSj9HOO4EViDwNO6k1TtASyAbGGutXXPUNqWf4+gY1wHnWmt3GmP+i3ucvu894/xNBG7w7CYTd2KtCHdSrlOp/b+L+3bcPUAa8I219l1jTHfcM+OG4E76DQaCjoo/BDjTWjvBs6+LcSfa4qy1Cz3LWgBv4L6dNxD4xFr7+FF1rAksBzpZa60x5kvct/EaYB5wu+fvJ4FRnr9TgPOttWnGmHuBvwH5wHfW2vs9Yx+uATpba481HZQd1v3U3Orri2Ytf4Tmb77g7TCqxM6b7qT3bK9N3Fzllgx9hunb/5rjQZ2I0S1X4Upqe/yCf1GOmM20/Pgpb4dRZbZfcT+tP33S22FUia2XPkiLj572dhhVZseY+/y+ft1ufsnbYVSZlW9MYsiCSd4Oo8rMiXuJM2940dthVIkVU/7BoIH++96bN/8+Xto41NthVJlJHWYzcNAz3g6jysyfdy/Duz7k7TCqzA+rn/Dre2FHLbrVLxNRM856xSfbTT3+/sKstY8e9TjU83sB7l5wR5bHlfq7eJ1nRtrLjvMcB3FPTFHa+cfZpvRzHB1jp1J/X3nUun/h7n13tE5Hlbu2kuddDvSqYNXR8b9bapvPgTJvTmvtDmB4Rc9Rqky2MWYuMAiYa629sKJiuCdAub+C7Z/BPbNxaecCnx8n6SciIiIiIiLyl6Ux/qqXBj4S+eOeAk7lvPQBwD9P4f5ERERERERE5H+YevyJ/EHW2v24Jwg5Vfvzyth+IiIiIiIiIuKf1ONPRERERERERETED6nHn4iIiIiIiIiIVAsXGuOvOqnHn4iIiIiIiIiIiB9S4k9ERERERERERMQPKfEnIiIiIiIiIiLihzTGn4iIiIiIiIiIVAuX1Rh/1Uk9/kRERERERERERPyQEn8iIiIiIiIiIiJ+SIk/ERERERERERERP6TEn4iIiIiIiIiISBUzxgw3xmwyxmw1xtx7jHIXGWOsMebMP/ucmtxDRERERERERESqxf/q5B7GGCfwGjAE2AssN8Z8Y63dcFS5MGAi8OupeF71+BMREREREREREalaPYCt1trt1tp84BNgdAXlngCeBXJPxZMq8SciIiIiIiIiIlK1GgF7Sj3e61lWzBjTDWhirf32VD2pEn8iIiIiIiIiIiJ/gjFmrDFmRamfsSe5vQN4EbjjVMalMf5ERERERERERKRa+OsYf9bat4C3jlFkH9Ck1OPGnmVHhAGdgAXGGIAY4BtjzHnW2hV/NC71+BMREREREREREalay4E2xpgWxpgg4HLgmyMrrbVp1toIa21za21zYCnwp5J+oMSfiIiIiIiIiIhIlbLWFgITgFnARuBTa+16Y8zjxpjzqup5dauviIiIiIiIiIhIFbPWfgd8d9SyhyspG3cqnlOJPxERERERERERqRb+Osafr9KtviIiIiIiIiIiIn5IiT8RERERERERERE/pMSfiIiIiIiIiIiIH9IYfyIiIiIiIiIiUi2sxvirVurxJyIiIiIiIiIi4oeU+BMREREREREREfFDSvyJiIiIiIiIiIj4IWOt9XYMInJy9KYVERERERHxX349CF7cvDv98jvtgkEv+GS7aXIPkb+g08e/5O0Qqszvr02i16z7vB1GlVg67GkG/fgPb4dRZead/SI9frjf22FUmWXDn6Llx095O4wqs/2K+3EltfV2GFXGEbOZZlOe93YYVWLXDXexcndTb4dRZbo13U3z//hn2wHsvPEuzrjRf4/rv/1nEmd8/4C3w6gyv434P2In+Gf7rXp1Et1u9s+6Aax8YxKtPvHf4/q2y+/ntAf9t/02PDnJ7+vnz1z+ndf0ObrVV0RERERERERExA8p8SciIiIiIiIiIuKHlPgTERERERERERHxQxrjT0REREREREREqoXLaoy/6qQefyIiIiIiIiIiIn5IiT8RERERERERERE/pMSfiIiIiIiIiIiIH9IYfyIiIiIiIiIiUi2sxvirVurxJyIiIiIiIiIi4oeU+BMREREREREREfFDSvyJiIiIiIiIiIj4IY3xJyIiIiIiIiIi1cKlMf6qlXr8iYiIiIiIiIiI+CEl/kRERERERERERPyQEn8iIiIiIiIiIiJ+SIk/ERERERERERERP6TJPUREREREREREpFpYTe5RrdTjT0RERERERERExA8p8SciIiIiIiIiIuKHlPgTERERERERERHxQxrjT0REREREREREqoVLY/xVK/X4ExERERERERER8UNK/ImIiIiIiIiIiPghJf5ERERERERERET8kMb4ExERERERERGRamGttyP436IefyIiIiIiIiIiIn5IiT8RERERERERERE/pMSfiIiIiIiIiIiIH9IYfyL/4+6+JI6+HVuQm1/AIx/MJn5Pcrkyr46/gMjatXA6Hfy+dR9PT5uPy1puv+As+ndqSUFREXtT0njkw9lk5uR5oRYV6xXRlkntz8VhHHyzdzkf7FhYZv0FjXtwUdPeuKyLnKJ8nl7/FTuzSuofXaMOH/edxJRt8/jvzkXVHf4xdQ9vz/g25+PAwXeJS/lk9/wy689t2JvRjfp56pbHS5s+Y1f2fgKMk0ntLqFtWBMslte2fMXqw9u8VIvK9Ypowx0dzsWBg+l7l/P+jp/KrL+wSQ8ubtoLl3WRXZTP0+u+ZsdRbTet3+38Z+s8Ptq5uLrDP67+DVrycLchOIzh022reXPjkjLru0c24aFuQ2hfN4qJv3zN93vii9fdE3s2ZzdsjQPD4qQdPL5yTnWH/6c88AwsWALh9WDGu96O5sQMaNycR3oNwmkMn2xawxtrlpVZH+Rw8mLcSDrXj+ZQXg4T5s9gb2Y6gQ4HT/UbSpeIGFzW8tjS+SxN3APAe8MuJqpmLQIcDpYl7eWhX+bi8oEBb1Ytd/D+64G4XHD2iCJGX15YZn1qsuGN5wLJyjS4XHDF3ws4vaeLlCTDHX8PpmFjdx1ad3Bxw+0F3qhCOQMaN+fh3u72m7ZpDW+srrj9OkVEczgvhwnz3O0XYBw8238YHSOiCTAOvtyyntdX/0qDWmG8GDeSiJCaWODjjauZun6ldypXgbsuj6NvZ/dx/dGps4nfXf64/srEC4io4zmub9nHsx+5j+tPjx1Js5h6AISFBJORk8eVj39U3VWoVO+INtzZ4RycxsHXe1fw7vayx4aLmvTg0mY9KbKWnMI8nlz/NTsyU+hZvxW3thtGoMNJgauIf8X/wPKD271Ui2O7++I4+nnOyx7+YDbxe8u332u3XEBE7VoEOB2s3FZyXjbk9DbcNLI3LaLDueqFj9mwe78XalC5uy4tqdsj71dyzjnB87/pcJ9zPvOJu243j+pNXJdWuKzlYEYOj7w/i9S0LC/UomL9Y1ryULch7s+Z7auZXMFx/cHTS47rP+wtOa7f3fVszm7QGoBX1y/m2z0bqzX2E3X/OXH0b9uCnIIC7v9iNhsTy7ffxMF9OO/006hTI5gzn3itePk9IwbQs2VjAGoEBhJeK4Re//dGtcV+Iv5M/RrUCeOpi4ZRu0YwDofhpdmL+WnzzmqM/q/HhfF2CP9T/jKJP2NMDPAy0B04DOwHbrfWbj6qXHNgprW2U6lljwKZ1toXjDHfAJ9ba9/3rPsPsNla+/xR+3kXGACkeRa9Y639tzHmO+BKa+3hE4z7WmC2tTahkvV3AjcAuUAB8MqR2E6GMSYOyLfW/nKy23q2r4u7Xq+XWvY8MBL4zlp7VyXbNeeo17vU8o3AJiAI+Am4xVrrOkYMv1hr+xwnztuBt6y12Uct/wpoAYQCkcAOz6pbgOuBF621G46175NljAkBfgAGWmuLTsH+OgN3WGuv/bP7OlH9OjanaWRdRj86lc7NY7j/8oH87flPypW75+1vycrNB+CFG85lSLc2zPptM0s37uKV6YspclluG92P64d259/TfSPJ4sBwZ4fzuG3F2yTnpjO193gWJW8sk9iblbiar/a6vwCeFdmBie3PYdJvU4vXT2x3DktSN5fbt7c5MNzW9kLuXvUmKXlpvH7mJJakrmdXdskJ/vz9K5mZ4D7p7F2/Ize1Hs19a97inIa9ALhx+fPUDQzl6a43csuKl7F4P+FwhAPD3aedx4Tl75Ccm857vW9hUXJ8mcTerITVfLnnSNu15/b2I5n427vF629v75ttB+AwhsfOGMbffvyYpJx0vh56HXP3bWFrempxmYTsdO7+dQY3tO9VZttuEY04I6IxI7+fAsCng6+mZ1RTfk3eXa11+DPOHwFXXgj3PuXtSE6Mwxie6DOEMd9/SlJWBt+Mvpq5u7ex5fCB4jKXtetMWl4uAz6bwqiW7bm3xwAmzJ/BFe26AjDsy3epX6Mm7w2/iFFff4AFxs//hswC9+fqm4NGc06LdszYHl9RCNXGVQRTXwnk/mfzqR9heWBCMGf0LqJxs5LPh68+CqDXgCKGjCpi7y7Dsw8E8UpP9wWf6IaWZyb7zsUfcLff432HcNV3nvY7/2rm7NrG1lLtd2m7zqTl5xL3adn2G9myHUFOJ8O/eJcazgDmXnI932zbSF5REU8u/ZH1B5KpFRjIjAv+xqJ9u8rs01v6dmpOk6i6nP/AVDq1jOG+MQO55unyx/V7J5cc15+76VwGn9mG2cs3c99b3xWXmXRJf5+6mOfAcG/HUdyybCr7c9P5oM/NLEzeyI7MlOIyPySu5gvPsaF/VHv+0X4kt654j8MF2dz+2wek5mXQKjSKV7tfx4gfn/VWVSrV7zT3edl5j7nPyx64fCBXv1C+/e5+p+Lzsq0JB/jHf2bw0BWDqjv04+rbsTlNo+oy+pGpdG4Rw31XDOSa5yo455xSUrfnx57L4DPaMHvFZt6f8xtvzHCf11x+dixjR/biqY/nVWsdKuMwhkfPHMY1nuP6V0OuY14lx/UbjzquxzVoRcd6MZw7awpBjgD+O3AMCxO3kVmYX93VOKb+bZvTrH5dhr80lS6NY3jkvIFcPrl8+/0Yv52Plq7mh0nXlln+7PclF9/H9IqlQ4PIqg75pPzZ+o2L68kP6zYzbdkaWkWG8+bfzmfIP9+ppuhFju8vcauvMcYAXwELrLWtrLVnAPcB0UeVO5FE5m3AY8aYusaYPkBP4KVKyt5lrY31/PwbwFo78uikn3Gr7LW8FmhYSb1uAoYAPay1scAg+MOp7zjgmEmz46iLO0lW2ligS2VJvxOwzVOvLsBpwPnHKny8pJ/H7UDNCra9wPNcNwCLSrXbL9baG0510s/jeuDLU5H0A7DWrgUaG2Oanor9nYgBXVox81f3VcW1O5MICwkmonatcuWOnIAFOBwEBDiLZ2FaGr+bIpf1bJ9IdL3Q6gn8BJxWpwl7sw+QkHOIQlvEnMTV9I/qUKZMdlHJF5oazqAy00v1jzqNhJxD7Mj0ravlAO1rN2VfTiqJuQcptEX8uP93+kSUyb2Xr5snsdesZjS/H9oCwOGCTDILc2gb1qTaYj8RHes2LtN2s5PW0D+6bNtllapfSEBQmcTlgKgOJOQcZHtm+Su1vqBreEN2ZR5iT9ZhClwuZu7ewJDGbcqU2ZeVRvzhlHI9wKyFYGcAgQ4nQQ4ngcZJaq7v9Hg4Ed27Qt0wb0dx4mIjG7Az/RB7MtIocLmYsT2eIc1alykzpFlrvtiyHoDvdmyib0P3x3ibevX5JcGdlD2Qm016Xh5dImMAipN+AcZBoNPhE8n3rZscxDS0RDewBARC77giVvziLFPGGMjJcp+qZGcZ6tX3ftzHEhvZgF2l229bPEOPar+hzVvzxeaS9uvT6Mhh2BISEIjTGGoEBJDvKiKjIJ+UnCzWH3B/vmQVFLDt0AFiavnG8W9AbCu+Xeo+rq/bnkRozWAi6hzjuO50EFjquF7a4DPb8sOyTVUa78noWLcxe7IOsu/IsSFxDXFHHdezCksdG5xBxe+qTemJpOZlALAtM5lgh/tz1NfEdWnFzGUnd14W6Cxpvx37D7Ir+VC1xXsy4rq2Yqbnf3PtjiTCap5Y3Y404pHlACFBgT7xmXlE1/CG7Mooe1wf3Kj8cX1TWgquo+JuUyeC5Sm73b1UiwqIT0umf4NW1Rn+CRnYoRXTV7nbb83eJMJqBBMRWr791uxNIjXz2OclI7u049s1vvPZAqeifpbQ4CAAQmsEk5zx1zo3E//3l0j8AWcDBdbaN48ssNauttYuMsbEGWMWeXryHTe5Y63dCbwFPAe8AUyw1hYec6NSjDE7jTERxpjmxphNxpj3gXVAE2PMu8aYdcaYtcaYScaYi4EzgY+MMas8PcRKux+42Vqb7okt3Vr7nud5Bhljfvfs6x1jTHCp53/MGLPSs669p3fdTcAkz/OcZYyJNMZ8YYxZ7vnp69n+Uc/+FhhjthtjbvPE8gzQyrP9857XMxT4zRhzmaduF5d6HTJP9DXzvL7/z959h0dV5X8cf59JQk0gQEhCTyD0Kk1E6R0LUuzd1VVXLNjFjiu66uquiriKivzEQlERBEG6qIio9B46JKGTBEiAzPn9cW/IpEEoyYTx83oeHpLb5nvm3Nxz58z3nPszEGeMCTXGzPKJv1/OY7p1OtcYM8EYs8YYM9btXL0fpxN1jjFmTkFf3z1W68zXcMu30hgz0xjT1ue9uMLdJsjd5jdjzDJjzF35HPoGYJK7TxVjzHz3/VthjOngLu9pjPnFLe94Y0you7yNMeZnY8xSY8wiY0zmx+DJwLUFLdvZiiwfSuKBlBO/Jx1IJTI87w8vI+7tz6x/3cXhtKPM/HN9rvX9LmrCTys3F1aop61yqXLsSjt44vddaclULlU+13YDa7RjQodHGFyvN2+smQw4HxZuiu3Eh/HF45vknCJKlmd3Wtb3D7vTDxBRMnfZ+lW7mP9rN5S/17mMd9Z/DUB86k7aRzTGYzxEl6pIvdAaRJYKL7LYC6JyyfIkHfGtu4NULlku13aDarbjq44Pc1+93vx79RTAqbuba3di1IbZubYvLqLLhJFwOPnE7wmHU4gqXbCesD/37mBh0hZ+vfJ+fr3yfuYnbiQ+2f9ZRoEsukwoCYeyrpMJh1KILhOaa5udqU6dZlhLytGjVChZmlV7d9GjZhxBxlAjtDxNIqKoWjbrXB7TexB/3Hgvh44eZeom/2eo7t8DlSpnfSitFGHZvyf795EDbzrGgllB3HtdKV59qgS33ps1nHd3ouGJu0vywkMlWLO8eNxiRpUNZWdq9vqLytFJF1UmlJ2Hctff1I3rOHL8GItu+Ac/X3cXHyz7jYPpadn2rR5ajkYRUSzZlVD4hSmAyAqhJO3LKu+u/alUzqddf+fB/vzwb6ddn/V79nb9grrV2Jd8mG27CjTApUhElipHkk+7npRPu35VzQuZ1Okh7q/fi9dWTcm1vlt0Y9Yk7+SY95x8b3tORYaHkri/YPdl797bn9mv3MXh9Lzvy4qbyPBQkvYX7NwccV9/Zr52F4fSjzLzj6yy3XtFe6a+dAd92jY4kf1XHESVzt6uJx4peLu++sAuOkbXplRQMBVKlKZdZC2qlCl+345FhoWSeNDn3ExOJarc6X/hUTU8jOoVyvPrxm3nMryzdrble2fWQi5v3pDZj97BezdfyUsKi0TOAAAgAElEQVRTCvxRVaRIFI+7slNrAvx+kvUtgQestfUKeLzXgd7ACmvt/JNs95rbkbPEHYaZU13gXWttYyACqGatbWKtbQp8bK2dACwGbnCzz45k7miMKQeEWWtzTTBijCkFjAaucY8VDNzjs8kea21LnI7LR9zOzPeAN93X+RH4r/t7G2AgMMpn/wZAL6At8JwxJgR4AjdDz1r7qLX2CuCI+/uXJ3mPTskYUwYnm3E5zpDm/m78XYB/uxmdOV2Ak93XCKgNXOxmXe4Eulhru5xhOGWB2W6dpQD/xMm67A8Mc7f5G3DQfe/aAHcaY2JzlKkEUNt97wGuB6a7WYfNgSXGmAjgaaC7W97FwEPuvl/inLPNge5A5rmxGOiQM2hjzN+NMYuNMYvff//9Myz62bl3xNf0ePJ9SgQH0aZ+9gyxv/VqS0aGl6m/+XeY2pmYuG0hg358nRHrv+fW2l0BuKNON77YvIAjGcVrmMXpmrTjJ25aOJwP4r/jxlo9AJiWuIjd6QcZ2WoI/4i7kpXJm/HmPwK/WJuwdSED5v+bd9ZN5/Y6ziXhzrhufL75p/O+7vJTK7QCceUiaD/pbS6a9DYXRdWiTeXilbEpWcatW07CoRQmX3kzz17UhT927STD5+/t5u8n0OazdykRFEz7qkWW7H1Wfp4TTMeeGYz4PI3HXjrKu/8qgdcL4RUtb49N45X30rnp7mO8/XIIh8/zhIfmkVXIsJYLx46kwxcfcEfTNtQIy+poKhMcwsju/Rj2y+wTGZznk8H/+Zpej7xPSHAQbRpkv470bluf6YvOvzYdYPzWX+k37w3eXjudO+p0zraudmgk99fvxfCVk/wT3Dn0jxFf032oU39t6wdWO3Dv21/T8/Hc95wjvv2Zvk+NYtqiNVzbuYUfIzx3FiRuYm5CPOO738J/2l/Jn3t2kFEM5nstLH2a1mfGinXFYk7bc+nSZvX55s+VdH1tFHeP+YZ/DepNnp9wRfzkvJnj7xQWWWsz53TL7yriu7wZTqdnA2OM5yTzzj3qdt7lZ4u1dqH780agtjHmbeA7YEYBY89LfWCTz/yFnwD34sxxCPCV+//vwIB8jtEdaOTTp1YuM9sM+M5amw6kG2N2kWPI9DlUxxizBOe9n2StneZ2Mg43xnQEvEA19/UTc+y7yFq7HcA9RgxwLiaPO4ozLx84HZHp1tpjxpjl7msA9ASa+WQ4lsfp5N3kc5wInLkmM/0GfOSW7xtr7RJjTCecjsuf3HooAfyCU78J1trfwMn09DnOLvIYGm6tfR8nUxXAjrw3v9Hpp3Z1x+YMuNgZFrpySxLRPmPuosJD2XUg/2TOo8czmLssns7N6vDrGmf42uXtGtGxSSx3vTXxjGMqDLvTkon0yQSILFWO3T6ZAjn9kLCMxxpeyYtA4/AadI1uyuD6fQgNLoUXy1HvcSZsLR7fLu9JP0hlnyy9yiXD2ZOef9nm7PqTB+oPhDXgtV5Gbsj6wPNWy/vYfnh3vvv6w+70g0SV9q278uxOT853+xkJy3i8kZM83KR8DbpGN2Fw/d6EBZfCa526G791Yb77F7XEwylUKZOV9VWlTBhJR1JOskeWntXr8efeHRw+7mRZzdu5kQsiqvHb7uL1zXkgSTycSpWyWdfJKmXDSDycmmubqqHlSDycSpAxhJUowf505/ucF3/N+tb/q8uvZ9PB7MPw0jMymLFlAz1rxrFgx5ZCLMmpVYiAvbuzPqns3WOoEJH9tmrO90E8Odzp5KrXyMuxo5ByEMpXgBBnlBO16znDhRO2G+rU9++Hu6RDqVQNzV5/SYey11/S4VSqli1H4qHs9devTkPmbdvEcetlb9phfk/aQbPK0WxLOUiw8fBej358E7+a6Zv9m211Vefm9O/otOurNiURVTGrvJEVQtl9inZ93tJ4OrWow6+rnXY9yGPo0jKOG//5WeEGfpp2pSUT5dOuR52iXZ+esJwnG/eD5c79SWSpcrze8gaeXTqB7Yf3FXq8BXVNx+YMaO9zX1bhDO7LmtZh4ZriN9fr1Z2a09/nnjOqwumdm3OXxtO5edY9Z6Zpi9bw1uAreW9K8bgvSzqSvV2PLl3wdh3g3VU/8+4qZ5r2Ny/qx+aU4nF+Xndhc65q7dTf8h1JRJf3OTfLhZKUXOABYCf0bVqfFycXj1EZ57J8A1s14e9jnI/oS7clUCI4mAplSrPv0JFT7PnXZa16RovS+ZLxtxJodZL1vt8p7wUq5FhfEdgD4M7F9y5wI7Ce7Jl0p+vE61pr9+Nkes3FGXY7Kp99MrdPBlKNMbXP4HUzJzDJIP/OWw/Qzmeuu2rW2syrl+9MzSc7hq/j7jEz38MSBdgnM4PwAmvt8+6yG3AevtHKzY5LAkrlse+ZxFgQx6w98RWTN/N13M7fzNcwwH0+712stTZnR+4R37jdzNGOwA5gtDHmZvc4P/gcp5G19m+niK8UWdl/hWLc/KVc+/JYrn15LHOWxnPZhc78OE1jokk9cpQ9ydlTNEqXDDkxB0uQx3BJk1g2Jzk3JO0b1eLW7q158H/fknaswCPmi8Tq5O3UKBNBldIVCDZB9KjSnB93ZX9KWo0ylU78fHHl+mw77EzCfPei9+k//1X6z3+VL7f8xCcb5xabTj+ANSnbqFa6MtGlKhJsgugSdQE/71mRbZtqpSNO/NyuUkN2uGUr6QmhlMf5821VoR4Z1pvtoSDFwaqDO6hRJoKqbt31jG5W4Lr7+6L3uXLea1w57zW+2PIzozfOLVadfgDL9u0kJqwC1cuWJ8Tj4bKajZi5vWAdBzsPJ3NhZE2CjCHYeLgwsiYbDu459Y5yxpbuTiC2XAVqhDr1dXntBvywZUO2bWZuiWdg3cYA9I2tf2Jev1JBwZQODgHgkmq1OO71sv7AXsoEhxBZ2r2uGkPXmrWJP+j/D3p16ntJ3GHYlWA4fgx+mRtEq4uyD4eMiLSs+NO5fdyxxXD0qKFcOCQfcB4OApCUYEjc4SGqiv8zOpbuTiCmXAWqh7n1V6cBP2zNXn8/bIlnYL3c9bfzUPKJTMzSwSFcEFmF+ANOPf2rU2827N/Lh8sXF2Fp8jZ+7lKuHzaW64eNZe6SeC5t57TrTWq77frBPNr18j7tetNYNidmnX9tG9Zkc8J+du0//Q/1hWnVwR3UKFspq22o0ox5u7JnJfq2DZdE1mfrYWcqhNDgUvy31c28vXY6Sw8Urw6yL+cv5ZpXxnLNK2OZsyyey9qe4r6sRPb7sg6NY9mU5P/rR17GzVvKdcPHct3wscxdGs9l7rnZNLZg95wdmmSdmzUqZ33h2al5HTYnFp+5DPNq12ftKFi77jGG8BLObFD1y1emQflIfkwsHk+c/vzXpQwYMZYBI8Yya1U8/Vo49desejQp6UdPOZdfTrERFShXuiRLthWPqRHOZfkSDibTrrbTXtSuXJGSwUHq9JNi5XzJ+JuNkyX2dzfzCWNMM5xMrGystanGmARjTFdr7WxjTEWcYb3/dTe5C1hvrZ1rjFkHLDTGjLPWnlXKizus86i1dqIxZi3wqbsqBchvooaXgRHGmGustcluRt4AYBwQY4yJs9ZuAG4C5uVzjEwpgO8kWDOA+4DX3PhaWGuXnGL/k00osRmn83UccAUQcop48lMe2OVm2XUBap3m/plxFuYn3enAPcaY2W6c9YAd1tpsHb3uXIClrLVpxphawHZr7QfufIwtgZdw6jfOWrvBGFMWJ8NxLVDFGNPGWvubO7/fEXcuxHo4c0YWiQUrN3FJ4xi+ff420o4e5/lPs/o3v3jyBq59eSylS4Twn7uvICQ4CI8xLF63jQk/LgPg8au7UiI4iJH3OYmnyzcl8tIXxWNevAzr5fXV3/LfVrfjMYYpOxaz6dAu7ozrzpqDO/hx92oG1byINpXiOO7NIOX4EYYtH+/vsAvEa728ve4r/tX873iMh2kJi9hyOIlbY3uzNnkbv+xdyZXVLqFlxXoc92aQevwI/1rtZG6ElwjlX83vwmste9IP8vKq4pXRAU7dvbbqW95qfRseY5i8/Xc2pu7i73HdWX1wOz/uXsNVNS+ibaU6HLcZJB9L44XlJ0vOLl4yrOX5xTP4pPO1eIyH8RuXsj55Dw827cjyfQnM2rGeZhWrMLLDQMqXKEW3anE80LQDvad+wLRta7goKoZpfe7EAvMT4pm9c8MpX7M4efgFWLQEDhyEzoNg8G0w6FJ/R5W/DGt59ueZjOkziCDjYdy65aw/sJeHWl7Msj2JzNwaz5frlvFmp0uZd9UdHEhPY/AcZ77QiNJlGNP7KiyWxEOpDJnnPDG1TEgIo3r2p0RQMB7gl4RtfLr6ZE100QgKglsHH+PlJ53hu517ZVAjxjJ+dDCx9by0bu/lxruO8cEbIUz9KhgD3PPoUYyB1cs9jP8khOAgMB742wNHCc09NWeRy1V/a5ezfv9ehrS6mOW7nfobt3YZb3S+lLlXO/V332yn/sas/JPXOvVhxqDbMMD4dStYs283raOqMbBuY1bv3c3UAbcA8Opv85m7bdNJIikaC5Zv4uKmMUx6yW3XR2e16589ewPXD3Pa9TcGX0GJ4CCMMSxeu42J85ad2K5X2/pM/614TbwPTtvw6qrJvNPmVoKMYdL2P9iYuou763Zj1cEdzN+1hmtqtXPbBi8px47w3DKnbbimVjtqlKnEnXFduTPOmdbj3t8+Zv/R4jUe/Uf3vmzyc7eRduw4z/ncl335xA1c88pYSpcM4b93Zd2X/bZ+GxMWOPXXpVkdnriqCxVCS/P23f1Yu2M3/xjxtb+Kk82CFZu4pEkMk4a55+aYrLJ9PvQGrhvunJtv3uOemx7n3My857y//yXUiqqA9VoS9qXw0mcz/VWUXDKs5YXfZzC607V4PB4mZLbrTdx2fed6mlaswshLnHa9a1WnXe8z7QOCjYcvut0EQOqxdB5aOKlYDvWdv24THevF8P1DTv099VVW/X117w0MGDEWgId7deDSZvUpFRLC7EfvYOLvKxgx2/kCtm+z+kxd7v/5bPNytuV7ddp8XriyBze3bwlYhn413U8lEcmbscXwwpIXY0xVnKGurXDmiduMMwdcNZx57i7z2bYRMIKszL/XrLVjjTGRwCKcTLhEd9uHgKbW2ttyvN5oYErOob7GmM04D+wIddc3cZc3Bz4mK4vySXdo60BgOE4W10U55vkzwKM4c8odc//921r7qTGmG85chME4w0jvsdamZ76+tXaP+8CK1621nd3OqQk4WWz3Aavd96Che4z51tq7jTHPA6nW2tfdGFYAl1lrNxtjPsMZBj3NWvuoMSbVWpv5MIoonAdZlMYZKnuvtTbUOA8WOfE++JQtv+UROA+wCMWZz64d0Md9/VT3mJ1969QY8w6w2Fo72hhzHzAY2JnXPH8593WXzXWXLc5RppzvRebre3Dm/rscJ2tvN3CltfZgjtf6EPjcWjvTGHOLW5fHgFTgZmvtJmNMV+BfQEl3t6ettd8aY9oAb7vv5xGceQBT3bJOt9ZOzlk2H/aCsxjqW9z9OWII7aY/6e8wCsXCXi/Tbc5D/g6j0Mzq8gZtvx/q7zAKzaLew6n9+XB/h1FoNl43FG9iQafKPf94otdRa9Rr/g6jUGy541H+2Hp+zBF4JlrW3ErMB4FZdwCb73yUVncGbrv++wdDaDXtKX+HUWh+7/MSLQYHZv0teWcILe8JzLIB/DFyCHW+CNx2Pf7aoTR6OnDrb9U/hwR6+QJ6LGzLqU+fHx1Rp+mPvv8slvV2vmT8Ya3dCVydx6r1OMNrfbddhfPgiJzH2EXWPG6Zy97I5/VuzWd55v57cB46krl8KU6WV87tJwJ5Tn7mDjl91f2Xc90snAdc5Pf6WGsXA53dn9fhdNr5uiaP/Z/P8btvGa7PsS7U5+cknE66TI+7yzfj8z74bJ/f8j3ARTmX+76etXYuPnVqrR3s8/PbOB1mecq5r7usc87XcH9+Pp/X9+I8cflUPRgjgCHATOs8jfmTPOKZjfOAkJzLfyP7+4mbKdgap0NbREREREREJOB4NcdfkTpf5vgTKXastX8Ac4wxQefokDWBJ9whvyIiIiIiIiIiZ+W8yfgTKY6stR+dw2Otx8lgFRERERERERE5a8r4ExERERERERERCUDK+BMRERERERERkSJxnjxjNmAo409ERERERERERCQAqeNPREREREREREQkAKnjT0REREREREREJABpjj8RERERERERESkS1hp/h/CXoow/ERERERERERGRAKSOPxERERERERERkQCkjj8REREREREREZEApI4/ERERERERERGRAKSHe4iIiIiIiIiISJHQwz2KljL+REREREREREREApA6/kRERERERERERAKQOv5EREREREREREQCkOb4ExERERERERGRIuHVHH9FShl/IiIiIiIiIiIiAUgdfyIiIiIiIiIiIgFIHX8iIiIiIiIiIiIBSHP8iYiIiIiIiIhIkbDW3xH8tSjjT0REREREREREJACp409ERERERERERCQAqeNPREREREREREQkAGmOPxERERERERERKRLWGn+H8JeijD8REREREREREZEApI4/ERERERERERGRAKSOPxERERERERERkQBkrLX+jkFETo/+aEVERERERAJXQE+C1/DrFwLyM+3q/s8Vy3rTwz1EzkMNn33T3yEUmtXDhlD7rX/7O4xCsfH+h4kZEZhlA9h878PEfvqyv8MoNJtufJK4cf/0dxiFZsPVT1Nr1Gv+DqPQbLnjUbyJ9fwdRqHwRK8j5n+v+zuMQrP5rkf4cXOcv8MoNB1iNlDn9Tf8HUahiX/kIWp99Kq/wyg0W25/jHovBeZ92bqnhtDgucAsG8CaF4ZQb8KL/g6j0Kwb9AyNhgZu/a0aPoSGzwRu+Va/OMTfIRQqPdyjaGmor4iIiIiIiIiISABSx5+IiIiIiIiIiEgAUsefiIiIiIiIiIhIANIcfyIiIiIiIiIiUiQC8skexZgy/kRERERERERERAKQOv5EREREREREREQCkDr+REREREREREREApDm+BMRERERERERkSJhrfF3CH8pyvgTEREREREREREJQOr4ExERERERERERCUDq+BMREREREREREQlAmuNPRERERERERESKhvV3AH8tyvgTEREREREREREJQOr4ExERERERERERCUDq+BMREREREREREQlA6vgTEREREREREREJQHq4h4iIiIiIiIiIFAlrjb9D+EtRxp+IiIiIiIiIiEgAUsefiIiIiIiIiIhIAFLHn4iIiIiIiIiISADSHH8iIiIiIiIiIlIkrPV3BH8tyvgTEREREREREREJQOr4ExERERERERERCUDq+BMREREREREREQlAmuNPRERERERERESKhLXG3yH8pSjjT0REREREREREJACp409ERERERERERCQAqeNPREREREREREQkAGmOP5G/uKF9O9Oxbixpx44x9OsZrErYlWubB7q1p1+LRpQrVZLWL43Itq5343rc26UdAGsSd/PohGlFEnemjrVieLZjFzzGMG7lCt77fVG29SWCgni9Rx+aREZyIC2N+6ZNYUdKMgD3tG7LVY2a4LWWF+bN5setW6gSGsbrPXsTUaYs1lq+WLGM0Uv/zHbMv13Qiqc6dKbV+++yP+1IkZW1U80Ynr2kC0Eew5erVjDyjxxl9QTxRvessg6ePoXtKck0j4zm5S49ADDAfxb9wvRNG5yyNG/JNY2aYi2s3buHR2d/T3pGRpGVKT8dq9TmuTbd8RgPX25YwnsrF2Zb3zayBs+07k6D8EjuX/AN07auPbGuaplyvNKuL1XKhmEt3DZnHDsOHSzqIhRYx+jaPN2iF0HGMG7TEv635uds62+vdyFXx7bguPWyL/0wT/w2hZ2Hi195OlWP4bl23Qgyhi/WLmPksjzOz859aVopiv3pRxg8ezLbU5MJ8XgYfklPmkVEO3+LC2ezMGEbAJ/0GkRkmbIEezwsStzOMz/PxGutP4pXYE+9AnN/gYoVYPJof0dTMJ1qxPBs+64EGcOXa5Yzckkedde1D00iopxry0yn7gAaVIxgeMeehIaUwGst/b7+lPSMDL64/BoqlylL+vHjANz03QT2ph0u8rLltOI3+Pw9D94M6NDH0vea7OfT3l3w0WseDh8CrxcG3u6lWVtY+TtM/MhDxnEICoar7vTSsIWfCpGHZ7t2oXNsLEeOH+OxadNZuSt3W94kKpJXe/emVHAwczdtYtjsOdnW/611K4Z27kTrEe+y/0gatStW4F+9e9E4MpI3FvzEqMW/F1Vx8tWpWmzWdWbdMkYu+zXb+rZR1Xnuwm40qFiZ++Z+y9TN606s+6TnIC6oXJXFSTu4febEog79jDzdszOd6sRy5Ngxnpgyg1WJuet1SOf2XNnUuUe74LUReRyl+HiqT9Y955Pf5H3P+WC39vRr7pSn1fCs8vRv0YhHe3YgKTkVgLGLljLhjxVFFvupdIiqw1NuWz5+05+8vzZ7W35b3Qu5KvYCjnu97D96mCcXT2bn4YM0LB/F8y37Ehpckgzr5b01C5i6fZWfSnFyQy/rTMf6sRw5eoyhE2ewemcenxl6tOeKCxpRvnRJWr+QVX9Vw8P458CeVChTmoNH0nh83Pcn6rK4GNq3Mx3ruZ+JvsrnM1F3n89E/8wq3xN9OtE2tjoApUNCqFi2NBcOH1lksZ+XNMdfkVLGH2CMiTbGfGGMiTfG/G6MmWqMqZfHdjHGmBU5lj1vjHnE/flbY8zNPus+MMY8msdxRhtjNhljlrj/7neXTzXGhJ9G3LcaY6rms260MWZQQY91Gq/5oDGmTD7r5hpj1hpjlhlj1hhj3ilIeYwxm40xEWcZ16/ue7nVGLPb572NOd339TRes4oxZso5PN5lxphh5+p4BdGxbgy1KoXT+78f89y3M3n28q55bjd37Uau+d/nuZbXqhjOnR3bcMOoL7n8nTG8PG1uIUecnccYXujcjdsmfUWvT0dzeb36xFWsmG2bqxs1ITk9ja5jPuKjP3/n8Ys7AhBXsSKX1a1P77GfcOukiQzr0h2PMRz3ehn+4zx6fTqageM+46ZmLbIds0poGB1qxrAjObnIyzqsYzdunfIVPT4bzRV16xNXIXdZD6an0fnTj/hwye88cZFT1rX79nD5uE/p++X/cfPkr3ipcw+CjCGqbCi3NmvJ5ePG0uuLT/B4DJfXbVCk5cqLxxiGte3JrbPH0XPy+1wR04i48pWybbPjUDKP/jyFbzevzLX/vy++jPdXLaTH5A+48vvR7E07VFShnzaPMTzfsg9/+/Fzek9/j8tqNiauXPbL4ar9iVw580Mum/EB329fzePN8v479SePMbzYvge3TJ9A94kfcUWdhtQNz15n19RvysH0NDqNH8WHK37nibadALiufnMAen01mhunjefpCzuTeSt47+xv6fP1J/SY+DGVSpXh0tj6RVmsM3JlH3j/NX9HUXAeYxh2cXdunTqRHuM+5oq4BsTlqLurGzh11/mLD/lw+WKeaOdcW4KM4c2ul/LU/B/oOX40107+kmNe74n9Hpz1HX0njqHvxDHFotPPmwFjR3h48J9eXvzAy6I5hp1bsm/z3WeG1h0tz73r5a4nvYx9x7lVDisP9w/z8sL/vPztUS8fvlp8bqE7x8YSUyGcrh9+xFMzZjKsR7c8txvWvTtDZ/xA1w8/IqZCOJ1iY06sqxIWyiW1amVr2w6mpTFs9hw+LAYdfuBeZy7qzi0zxtP9qw+5onbu68zOQ8k8/ONUJm3M3XHy/vJFDJn/XVGFe9Y61YkhpmI4PUZ+zDNTZ/JC77yv/bPXbWTQx7nv0YqbzHvOXm99zLOTZ/LcZXmXZ87ajVz9ft7lmbZiHf3fG0v/98YWq04/D4bnLujNnQs+o+/0kVxWowl1wnK05QcSGTBrFFfMfJ/vt6/msabO3+mRjGM89tskLv3hPe5Y8BlDm/ckLKSkP4pxUh3ruZ8Z/v0xz30zk+f65VN/azZyzcjc9fdon45M+mM1/d/+lJGzf2VIr0sKO+TTcuIz0X8+5rlJJ/lMtGYj17yXu3yvTJvHgHfHMuDdsXy6cAk/rNpQ2CGLnJbic9fiJ8YYA3wNzLXW1rHWtgKeBKJybFeQ7Mj7gReMMeHGmPbAhcCb+Wz7qLW2hfvvLQBrbV9r7YGc8Rlj8qunW4E8O/4K0YNAnh1/rhustc2AZkA6MKkogrLWXmitbQE8C3zp895uzut9PUceAj44h8f7Drg8v47VwtC1QR0mLVkNwNLtiZQrVZLKoWVzbbd0eyK7U3N3nlzVuimf/7qU5LR0APYdKrrsN4DmUdFsOXCAbckHOeb1MmX9WnrUjsu2TffacUxc7XQOTduwjvY1agLQo3YcU9av5WhGBtuTk9ly4ADNo6LZffgQK3c73/AdOnaMDfv3EV027MTxnu7YmVd+mo+laLOOWkRGs+VgVlknr19Lz9jsZe0ZG8fENU5Zp8avo311p6xpx4+T4WZJlQwKyhZ7kPFQKjiYIGMoHRxM0iH/f/vavFJVtqTsZ1vqAaesm1fTo3r272J2HDrImgO7c2V/xZWvRJDxsCBxMwCHjx8jLeN4UYV+2ppXrMqW1H1sO+SU9butK+leNXtZF+7ecqIMS/buILpMOX+EelItKldhc/J+tqW45+fGNfSolf387FErjonr3fNz01ouruqcn3UrVOLnnVsB2Jt2mOT0dJpVjgYg9dhRAIKNh5AgT5H/3Z2JNs0hPOzU2xUXLSKj2eJbdxvW0DOmTrZtesbUYeI6t+42rqO9W3cdqsewZt9uVu/bDcCB9LRinZG5aS1EVoXKVSA4BNp2tiz5JUfGgYHMPsojhyDc/X6lZhxk9jFVrQVH08E9Pf2ue1wdvl7pdHQtSUigXMmSVC6bvS2vXGAIRXcAACAASURBVLYsoSVKsCQhAYCvV66iR1zW3+hTXTrzr/nzsT71t/fwEZYnJmXrzPWnFhFV2Jx8wOc6s5oeNbNfZ7anJrNmf+62AeCnhK0cKi6VVgDd6tXh62XuPdrORMLyu0fbmfc9WnHT7SzvOYuzZhWrsiV1v9OWWy/fbVtJ96rZv6j61bct37eDqNJOW745dR9bUvcBsCstlX3ph6lYMvf74m9dG9Vh0p9O/S3b5pyPEWG541y2LZE9Kbnrr05kJX7d6LT1v27cRteGtQs34NPUtWGO87P0mZ+flzarz9Tla0+6jUhR+8t3/AFdgGPW2vcyF1hrl1prfzTGdDbG/GiM+RY4Zc61tXYz8D7wKjASGGytLfAnzszMNzdLba0xZgywAqjhZvCtMMYsN8YMcbP5WgNj3cy20qc47gvGmD/c/Ru4y583xvyfMeYXY8x6Y8yd7vLOvplsbuberW5mYlVgjjFmTt6vduK9OAo8BtQ0xjR3j3OjMWaRG+//jDFBecT6jZt1udIY83d32e3GmP/4bHOnMSa/DtVTva9r3PdynTFmrDGmuzHmJ7f8bd3tyxpjPnJj/dMY0y+fQw8Evnf3aexTtmXGmLonK7MxprdbH0uNMbPc98wCc4HLClq2sxVVLpTEgyknfk9MTiWyXGiB969VKZyYiAqMveMavrjzWi6Jq1UYYeYrOjSUhNSs+BNSU4gqmz3+KJ9tMqwl5Wg6FUqVJqpsKDtTfMqemkJ0aPZ9q4WVo3HlSJYkOR+UuteuQ2JqKmv27C6sIuUrKjSUnacqa9msbXzLCtAiKpoZ193C9Otu4em5M8mwlqRDqXyw5Dd+vuVOFt12NylHj/LjthzpL34QXSaUhMNZWSeJh1OILlOwnpTYsIokH01nZMcBTOl7G0+2dIaBF1dRpcOyl/VIClGl8y/rVbEtmJcQXxShnZboMqEkHPI5Pw+lEF0mNNc2O93hoc75eZQKJUuzau8uetSMI8gYaoSWp0lEFFXLZnVujuk9iD9uvJdDR48yddM65NyKKhOW/dpyKJWostnPwaiyYTmuLUepUKo0tcMrYK1lTN+BTBlwE3c1b5Ntv9c692bqwJu5r2W7wi9IAezfCxUqZ3UIVYiA/Xuyb3PFjZaFsw2P3uDhv894uO7e3J1evy+AWnEQUqKwIy6YqNAc7VlKaq72LDo0lETfek5JJcrdpnudOiSlpLJmd443o5iJLpvXdeY86mU/TVFhoSQmZ5U3KTmVqLCC36MVN1FhoSQkZ7/njDqNe06AHo3qMumeG/nv1ZcRfZr7Fqao0uVIPOLbliefvC2PacH8xNwZYc0qVCXEE8RWtyOwOInM8Zkh6TTrb03ibro3rgtA98ZxhJYqSfnSpc55nGcq12eig6f3mShT1fJhVK9QnoUbt53L8ETOmjr+oAlwsjEMLYEHrLW5hv7m43WgN7DCWjv/JNu95jMctWke6+sC71prGwMRQDVrbRNrbVPgY2vtBGAxToZdC2vtqVKt9lhrW+J0SD7is7wZ0BW4CHg2v6HDAG5m4k6gi7W2yyleD2ttBrAUaGCMaQhcA1zsZuZlADfksdvtbtZla+B+Y0wlYBxOJlyIu81twEenev18xAH/Bhq4/64HLsF5T4a62zwFzLbWtsXpGH7NGJPtKx9jTCyw31qb7i66G/ivW7bWwPb8ymyMqYyTKTjQWtscuMrn0IuBDjmDNsb83Riz2Biz+P333z/Dop97wR4PtSqGc8tH43l4/FSG9etBWKniNzzhTJQJCeHdS6/gxflzSD16lFLBwfyj9YX8Z+FP/g7tjCxJSqTn559wxfix3NOqLSWDgihXsiQ9YuPoMGYUF47+H2WCQ7iyXkN/h3pWgj0e2kRWZ/gfs+k3bTQ1QsMZVDuvS+z5p1/NJjStWIVRa3/xdyjn1Lh1y0k4lMLkK2/m2Yu68MeunWTYrM6Wm7+fQJvP3qVEUPCJTDMpHoKMhzbR1Xlg9lQGffs5vWLr0r6aU0cPzPqO3hM+4apvP6dNdHUG1G3k52gLZtFcQ/seltfGenngRWdIr2/C247NMPFDDzc9UDyy4M5WqeBg7mnXljd/+vnUG4v40Zy1G+n25of0G/kpP2/cwiv9e/k7pDNyRc2mNKlQhVHrsrfllUuF8mrbK3li8bfnQW776Xtt6nzaxFZj4uAbaBNbncSDKcU6Q/xM9W1Wn+kr1wVk2eT8pod7nNoia+0m9+f8/oJ9lzfD6VBtYIzxWGvzuzN81O28y88Wa23mbPYbgdrGmLdxhoPOKGDsvr5y//8dGOCzfJLbaXjEzeJrC5zLYbGZqTbdgFbAb87oakoDuWdMdTr7+rs/1wDqWmsXGmNmA5cZY1YDIdba5WcYz6bMfY0xK4FZ1lprjFkOxLjb9ASuMO7cjUApoCaw2uc4VQDftK9fgKeMMdWBr6y1640x+ZW5HTA/87yy1vp+rbeLPIZvW2vfx8kmBbBvPlvghMdcrm/bnEGtmgCwYkcS0eWzvpGMLhfKrtOYaDcxOZVl2xM47vWy40Aym/fup1bFcFbsTDrj+E5HYmoqVUKz4q8SGpZrqGqSu01iaipBxhBWoiT7046QdCiVqmE+ZXe3Aafz6N2+V/Dt2tVMj3e+ka1VPpzq5crz3fU3n9h+8nU3cuWXY9lzuPDnrkpKTaXqqcp6yNkm8VD2svqK37+Pw8eOUa9iBDXKlWdb8kH2udt8v3E9raKr8s261fhT4uFUqvgMZ40uE0bi4ZST7JEl4XAKq/fvYluqcxn7Ydt6Loioyrj4ZYUS69lKOpKSvaylw0g6krus7SNjuafRJVw/ZwxHvf5/+EpOiYdTqeKTJValbBiJh1NzbVM1tByJhzPPzxLsT3fOvRd/zUoi/+ry69l0cH+2fdMzMpixZQM9a8axYIf/s1IDSdLhlOzXlrKhJB3Kfg4mHUrJcW0pwf60IyQeSmFRwvYT15k5WzfSJCKKn3dsJcmt/0PHjvHthtU0j6zCV+v9O2F9hUqwf7ch87Zt/x4n68/Xgu8ND77k3LrVaeQM501NhnLhsG83vDvMw+2Peoks6olWcrixRXOuaeZ8qbE8MYmqYWEnvsWODgs90Z5lSkxNJdq3nsNCSUpNpWZ4ODXKl+e7W25y9w3j25tupP+nnxVJ23Y6Eg/ldZ0pWNtwvrihVXOuvsC5R1u+M4noclnljSoXSlKK/6fjOB3Xt23OVS2zylOlXPZ7ztN5uMOBI2knfh7/+woe6ZHre3K/STqSTHRp37a8XP5teYNLuGHeJxzzacvLBpfg/Yuv5c0Vc1i6b0eRxFwQ17VrzlWt3frL8Zkh6jTrb3fKIR4Y6wwoK1MihB6N40hJSz/FXoXr+rbNGdQ6n89E5U/vM1GmPk3r8+Lk2ecsxkCmvtGipYw/WInTOZMf30H8e4EKOdZXBPYAuHPxvQvcCKwH7jmLuE68rrV2P9AcZxjo3cCoMzhe5pU1g+wdvjn/5CxwnOznxhnlYbvDWpvidJgZ4BOfuffqW2ufz7F9Z6A7cJGbCfenz2uPwpnT8Dbg4zOJx+Xbwnh9fveS9b4YnGy8zFhrWmtz9oQc8YkNa+1nwBXu8qnGmK4UoMx5KOUeo9B8tmgpA0aOZcDIscxaE0+/Fk6GV/Pq0aSkHT2teVVmrd5A25gaAISXKUVMpQps3190TxtdlpRITHg41cuVI8Tj4bK69Zm5MfswyFmb4hnYsDEAfeLq8ct2Z36RmRvjuaxufUoEBVG9XDliwsNZmpQIwCvdehK/by8f/pmVDLx27x7ajhpJx9Gj6Dh6FImpKVz++adF9sFo6a5EYsqHUz3MKevldevzw+bsZf1hUzwDGzhl7VunHj/vcMpaPawcQe5w12phYdSpUJHtKcnsTE3mgugqlAp2Tv2Lq9dkw37/Dy9ZtncnMWEVqF62vFPWmIbM3L6+gPsmUK5ESSqWdIY4XxRdi/UHi+/wtWX7dlIrtCLVy4YT4vFwac3GzNqZfThro/Ao/tm6L3ct+JJ96cXrg3impbsTiC1XgRqhbp3VbsAPW7IPY5q5JZ6Bdd3zM7b+iXn9SgUFUzrYSei+pFotjnu9rD+wlzLBIUSWdpKtg4yha83axB/0//kZaJxrSwWqh7l1F9eAH7bkuLZsiWdgPbfuatfj553OEKZ52zZTv2LEiXlCL6xSg/X79xJkzIlpBoI9HrrWqsO6ff7/O4ypD0k7YHciHD/mZPc1b5f9NqhiJKxe4lwvd251Ov7CysPhVHjrGQ8DbvfinsZ+9emSpVw+5lMuH/MpMzZsoH9jJ6OyRZUqpKQfZfeh7G357kOHSD16lBZVqgDQv3EjZm6IZ92ePbR99z06ffAhnT74kMSUFK74v6Jr207H0j0JxJb3vc405IetgTWB/tjfl9Jv1Fj6jRrLzHXx9G/m3qNVjSY1/fTu0YqDzxYtPfEwjlmrz+6e03e+ta71axO/u/i0B8v37yQmtCLVy4QTYjxcWqMxsxKyt+UNw6MZ1rIvd/+cvS0PMR7ebX8132xZxvQd/v3iNafPFy5lwDtjGfDOWGatiqffBU79Navh1F9ec/nlJ7xMKTJnXrmzUxu++j33w9mK2meLlp54IMfZnp8AsREVKF+qJEu2JRRGuCJnRRl/MBsYboz5u5tVhTGmGVA+54bW2lRjTIIxpqu1drYxpiLOsN7/upvcBay31s41xqwDFhpjxllrz2pCMOM88faotXaiMWYt8Km7KgU428lN+hljXgbKAp2BJ4AgoJExpiROllo3YEGO1zzpHbw7LPclYJu1dpkx5jgwyRjzprV2l/vehVlrfVM3yuMMnz3szkN4YlIga+2vxpgaOEOvm51lmU9lOnCfMeY+NxvwAmvtnzm2WUdWhiDGmNrARmvtW8aYmm6MM8ijzMBC4F1jTKy1dpMxpqJP1l89nHkdi8S8dZvoWDeG6Q/eRtqx4wz9OiuZ9Kt7bmDAyLEAPNKzA5c2rU/pkBDmPHwHE/5YwYg5C1mwYQsXx9Vi8uCb8VrL69PnZ/tGtrBlWMvzc2fzSb+BeDwexq9cwfp9e3nwwvYs35XErE3xfLlyOW/07MPsm2/nYFoa93/vPNFv/b69fLd+HdNvvJUMr5fn5s7Cay2tq1RjQMPGrNmzmynXORkQr/+8gLlbNp0slEKXYS3P/jibMVcMJMh4GLfaKeuQtk5ZZ26OZ9zq5bzRvQ9zb7ydA2lp3DfDKWubKtW4p1Vbjnu9eK3lmXmz2J92hP1pR5gWv57vrr6J414vK/fs4vOV/s+My7CW5377gTHdrsVjDOPjl7H+4B6GNOvA8n0JzNy+gWaVqvBexwGUL1mKbtXr8mCzDvSaMgqvtQz/fTZju18PwIp9iXyxYYmfS5S/DGt54Y/v+bjjdQQZD+M3LWF98h4eaNyJFft3Mmvneh5v3p0ywSG8fdFAABIOJ3PXT+P8HHl2Gdby7M8zGdNnkHN+rlvO+gN7eajlxSzbk8jMrfF8uW4Zb3a6lHlX3cGB9DQGz5kMQETpMozpfRUWS+KhVIbMmwo4w+1H9exPiaBgPMAvCdv4dHXxrctMD78Ai5bAgYPQeRAMvg0GXervqPKXYS3PLpjFmL7utWXtctbv38uQ1hezfHciM7fEM27Nct7o0pe51/6NA+lp3DfTydpIPprOqOWL+bb/jVicjL85WzdSOjiEMX0HEuwJIsgYftqxhc/X+P/aEhQE19/r5T9DneG7F/e0VIuBbz4xxNSztLgIrv67l0/+4+GHrwzGwO2PeDEGZn9r2LUTpoz1MMVpGhnyspdy4X4tEgBzN26ic2wss++4nbRjx3n8++kn1k2++UYuH+PcNj43cxav9ulFyeBg5m3azNxNJ2/XIsqU4ZubbiC0RAmstdzaqiW9P/6E1KP+eUBGhrU8+8tMxvS6iiBjGLfevc5ccIlzndm2gWYR0bzfrT/lS5Ske404hlxwCT2+dmaHGd/3OuqUr0TZkBAWXnMPjy2Yxvwdm/1SloKYu2ETnerEMPMft3Hk2HGenJJ1jzbpjhvoN8o5ER/t2oHLGzv3aPPvu4PxS1bw9o8L8zus38xbv4mO9WKY8YB7z/lNVnm+vvsG+r/n3nP26MBl7j3n3Iece8535i7kpnYt6FK/DhleLwePpPHkN9Pze6kil2Etw5Z8z4cdrifIGCZsXsqG5N3c36gTK/YnMDthHY837UaZ4BK81c5py3ceTuaen7+kT43GtI6oSXiJ0gyIcZ5y/8Rv37L6YNGMnimo+Ws30bF+DN8/7NTfUxN9PjMMvoEB7zj193DvDlzavD6lQkKY/fgdTFy8ghGzFtK2dg2G9LwYCyzetJ0Xvz3pdPFFbt465/ycPsQ9P7/yKd8/bmDAuz6fiZq5n4keuYMJvzufiQD6Nq3P1OWai1iKJ2OVY4k7r91/cDL/0oDNOE+vrQY8Yq29zGfbRsAIsjL/XrPWjjXGRAKLgHbW2kR324eAptba23K83mhgSs6hvsaYzTjzw4W665u4y5vjZLllZuE9aa2dZowZCAzHyRC7yHeeP9/XyDyutXaPMaY18Lq1trMx5nmgNs58ghHAq9baD9z9XwX6A5uAVOBba+1oY8x9wGBgZ855/owxc3GGwKYDJYGZwFOZT9Q1xlyD88RkD3AMuNcdxptZ7hTgG5wOtbVAOPC8tXauu/8TQAtr7bXkwxhzq1vWwQV4X33fo5jMdcZ5UMp/gPZurJt8zwGf484C7rLWbnBju8ktVyJwvbV230nK3Aen7jzALmttD/eYU3Dq92RDmW3DsxjqW9ytHjaE2m/9299hFIqN9z9MzIjALBvA5nsfJvbTl/0dRqHZdOOTxI37p7/DKDQbrn6aWqNe83cYhWbLHY/iTSzodL3nF0/0OmL+97q/wyg0m+96hB83x516w/NUh5gN1Hn9DX+HUWjiH3mIWh+96u8wCs2W2x+j3kuBeV+27qkhNHguMMsGsOaFIdSb8KK/wyg06wY9Q6OhgVt/q4YPoeEzgVu+1S8OKb5PpzsHan8+PCA7ojZeN7RY1psy/gBr7U7g6jxWrccZXuu77SqcBz7kPMYufDLA3GV53sVZa2/NZ3nm/ntwHjqSuXwpTqZbzu0nAhNP9Ro+x8Vauxgnsy/TMmvtzXns/xjOU3lzLn8beDuf1+yc13Kf9V8CX+axPMbn1z4nOcQlwEmv7tba0cDofI6f83291efnzZnr3A7Uu072Oq53cIYfP22tfQV4JY948ivzNGCa7zJjTBRQ+izmLxQREREREREp3gKy26/40hx/UuwZY8LdodNHrLWz/B1PJmvt1zjZoedKTeDhc3g8EREREREREfkLU8bfX1gBHjRRLLhDhYvl+Cxr7Zk8aCW/Y/12ro4lIiIiIiIiIqKMPxERERERERERkQCkjD8RERERERERESkS1hbLZ2AELGX8iYiIiIiIiIiIBCB1/ImIiIiIiIiIiAQgdfyJiIiIiIiIiIgEIM3xJyIiIiIiIiIiRcP6O4C/FmX8iYiIiIiIiIiIBCB1/ImIiIiIiIiIiAQgdfyJiIiIiIiIiIgEIHX8iYiIiIiIiIiIBCA93ENERERERERERIqEtcbfIfylKONPREREREREREQkAKnjT0REREREREREJACp409ERERERERERCQAaY4/EREREREREREpGtbfAfy1KONPREREREREREQkAKnjT0REREREREREJACp409ERERERERERCQAaY4/EREREREREREpIsbfAfylKONPREREREREREQkAKnjT0REREREREREJACp409ERERERERERCQAaY4/EREREREREREpGtbfAfy1KONPREREREREREQkAKnjT0REREREREREJACp409ERERERERERCQAGWs1uFrkPKM/WhERERERkcBl/B1AYYr55F8B+Zl28y2PF8t608M9RM5Dze9/098hFJqlbw2h2eRn/R1GoVh2+TDaTBvq7zAKzW99htN8yjP+DqPQLL3sRWLHvuzvMArNphue5I+tNf0dRqFpWXMrMf973d9hFIrNdz2CN7Gev8MoNJ7oddT68FV/h1FotvztMVoMDtx2fck7Q2g86Xl/h1FoVvZ7nmYPBWb9LXtjSMCfm3W+GO7vMApN/LVDafRU4NbfqpeG0OjpAC7fP4f4O4TCFZDdfsWXhvqKiIiIiIiIiIgEIHX8iYiIiIiIiIiIBCB1/ImIiIiIiIiIiAQgzfEnIiIiIiIiIiJFwxbLZ2AELGX8iYiIiIiIiIiIBCB1/ImIiIiIiIiIiAQgdfyJiIiIiIiIiIgEIM3xJyIiIiIiIiIiRcJaf0fw16KMPxERERERERERkQCkjj8REREREREREZEApI4/ERERERERERGRAKQ5/kREREREREREpGhojr8ipYw/ERERERERERGRAKSOPxERERERERERkQCkjj8REREREREREZEApI4/ERERERERERGRAKSHe4iIiIiIiIiISNGwxt8R/KUo409ERERERERERCQAqeNPREREREREREQkAKnjT0REREREREREJABpjj8RERERERERESkSxvo7gr8WZfyJiIiIiIiIiIgEIHX8iYiIiIiIiIiIBCB1/ImIiIiIiIiIiAQgzfEnIiIiIiIiIiJFQ3P8FSll/ImIiIiIiIiIiAQgdfyJiIiIiIiIiIgEIHX8iYiIiIiIiIiIBCDN8SfyF/f4wM5c0iiWtKPHeGbsDNZs35VtfamQYF67/VJqRITj9VrmrdjIfycvAKBKhTBeuL4nFUJLc/BwGkP/73t2HUj1RzHydHHlOB5v0hePMXy19Q8+2vBjtvU31W7PgJotybBe9qcf5tmlX5Nw5CAAQxr2pENUPTwYftkdz79WTvVHEfJ1UURdHm54GR7jYdL23/hk4/xs6wfUaMtVtdrhtV4OHz/K8JXfsCl1F20rxTG4fi9CPEEc82bw1pppLN630U+lKJj2leN4vPGleIzh662/81F89nq8qmYbrom5kAzr5UjGUYYtm8TG1N1+irZgOlapzXOtu+MxHr7csIT3Vi3Mtr5tZA2eadWdBuGR3L/gG6ZtWwtAu6iaPNOy+4nt6pSvxH0LvuGH7euLNP5TWfKbhzHvhuD1Qpc+GfS79ni29Xt2GUa+GsKhVIPXC9f97RgXXOhld6Lh4b+VpGp1Z+KXuIZe7njwmD+KkE2nGjE8274rQcbw5ZrljFyyKNv6Ep4g3ujahyYRURxIS2PwzMlsT00GoEHFCIZ37EloSAm81tLv609Jz8jgi8uvoXKZsqQfd96bm76bwN60w0VettP11Csw9xeoWAEmj/Z3NAXTqVosz7XrRpDH8MXaZYxc9mu29SU8QbzR6VKaRkSxP+0Ig+d8y/bUZEI8HoZf3ItmEdF4reWFhbNYmLiNsiElGH/pdSf2r1I2jK83rGLYr7OLumh5emxQZy5p7LTrz/5f7nYdYMQ/+hNRrizBQR7+iN/By1/OxmstPS6oy919LyI2qiI3vv45q7Ym+aEE+bskMo4nmvYmCA8Tt/7BqPULsq2/pc5FDKzVkuNeL/uPHuLpPyeRcOQgbSNieLxJ7xPbxYZG8MjiCcxOXFPURTilx/t3pkND977s8xms3pH7vuz1Wy6lRqVwMqxl3sqN/Pe7rPehZ/N63NOrHRZYt3M3T3w6rYhLkL9APjc7RtfmmZY9nHZi41L+t/qXbOvbVK7B0xf0oEF4JA/8/A3fb8869x5r3oUuVeIAeGflAr7btrpIYy+ooZd2pmP9WI4cO8bQiTNYvTN3/T3Qoz1XtGhE+dIlaT1sxInlVcqHMXxQL8qVKonHY3hz+gLmr9tchNGf2tBLO9Oxnk/5EvIoX/f2XHFBI8qXKvn/7N15XBVV/8Dxz/eCCgoIboArCq6pWLllue97ZWpli+09ZYs+2Z5tv6enbH1azUrNpDLTTM3cxSUztXLfEVERcGcTFOH8/pgBLnhBTOESfd+9fMWdOTNzzj1z58z93nPO0PrVfOUb4lS+RaWvfKWOEXfn4B/lgj3+RCRIRL4VkSgR+V1E5otIIxfpQkRka75lL4nIE/bfc0TkDqd1n4nIWBf7ERF5XkT2iMhuEVkuIlcUkr/PRaTZBcrwoPOxL8QuS5qIbHT6V15EBonI0xe5n1sLWd/Ifj/3iMgfIvKdiAQWdf/59vXsX9nOafvrnd9HEWlil/tPEQktZLspInJTAcuj7X38ISLXXOD4F6wjEWklIv1cLO/tVE8pIrLL/nuqiLQWkfcL2+9fJSKPX8x5VYT9fSsiDS/X/oriumYh1K3uz8BXJ/PK9CU8P6yby3RTl/3O9f/5kmHjp9GqQU2ubRoCwJjrOzF3/Q6GvjGNiQt+47GB15Vg7gvnQHi2xQD+9dtXXL/8Q/rWbEEDn+p50uxMjOOWVZ9y04qPWRy3jdFNewEQHlCHVlXqclPkR9wY+SHN/WvRumqIG0rhmgPhySsG8diGKQxb9R69gsOp71MjT5qFcZu4ZfX7jPjlQ76KXsnoJtZH51RGKmN+n8otq9/n5c3f83L4UHcUocgcCM82H8hD66ZyQ+QH9KnV8rx6nH94Mzet/JDhqz5mctRqnmjW1025LRqHCK+06cXI5d/Ra95EBoU0I8yvap40salJjP11HnP2b8uzfG3CAfr/PIn+P0/i1qVfk3Yug1Vx0SWZ/QvKyoTJH5TjqdfO8tbnZ1iz3INDMXlv7n6I8KR950xen3CGR587y6QPyuWsC6xpeP3TM7z+6ZlSEfRziPDKtT0YOX8mPb+bzKCwJoT5562vYU1akHgmnS7ffsEXWzbwdPtOAHiI8G63/jy3cjG9Zkzh5rnTycjKytnu8aU/0W/mVPrNnPq3CPoBXN8XJr7p7lwUnUOEVzv04M5FM+gx8wsGNWhKw3z1N7yxVX+dZ3zGF9s28HSbLgDc0jgcgN4/PVMBMQAAIABJREFUTOa2Bd/xfLuuCJCacZZ+s7/M+RebksSCmN0lXDLXstv1QS9P5tVvlvDcza7b9Scn/cTw16cx5D9TCfDxpudV1u3H3sPHGfPZXP6IOlSS2S4SB8JzLfvx4K8RDFr2Ef1qNSfUN297sCMxjmErJnJj5CcsOrydf1/RE4B1x/YzJHICQyIncPcvX5KemcGao1HuKEahrmsaQr1q/gx4bTKvzFjC8ze5rr8vI39n8BtfMuztaVxZvybXNQkBoG41f+7p3oY7PpjOjeOnMn52ZMll/gLK9Lkpwkute3P3iun0/nkiA+s2I8yvWp40h08n8eRvc5kbk7dd7xIcyhUBQQxY+Dk3Lp7CvU3a4eNZviSzXySdGlnnZp93JvPi7CW8OMh1/S3fuY/hE745b/kDXduxYMtuhnwUwRPfzueFArZ3l06NQqhX1Z8+7xahfJ+4KF+XdizYupshH0fwxPT5vDCwdJVPqUIDfyIiwA9ApDEm1BhzNfAMEJgvXVF6Dj4KvCwi/iLSAWgHvOsi3cNAByDcGNMI+C8wR0S8XOTPwxhzrzFme2EHNsZMMMZMLUIenUUZY1o5/TtrjJljjHndRT4KKn8I4DLwZ5fnJ+ATY0xDY8xVwMdAdVfpi+CSAn/A9UCzfK+/N8ZcaYz5q3dGY40xrYCngU8LS1jEOmoFnBf4M8YszK4nYAMwwn59hzFmgzHm0b+Y/wLZdX438PVl3O0nwJOXcX8X1LVFKHPXWb8qbtkfj693Bar5VcqTJj3jHOv3WDdZ5zKz2HHwCIH+PgCEBlVl3e4DAKzbc5AuLRqUYO4L1zygNgdSTxB7+iTnTCYLDm+ha1CTPGnWH48mPdMKLGw+eZBA78qA9ZCpCg5Pyjk8KO/hiafDwfEzpacn4xX+tTmYepzYNKtsi+M207lG0zxpUs+dyfnby6M8xn501u6kOI6dSQYgKiWBCo5ylHN4lFzmL1Lz7LJm12PsFroEFlxWb49yOWUtrcKr1iQm+SQHU06RkZXF3Jgd9KyT9/e02NREdp46SpYpuCz96jYh8vA+0jPPFZjGHfbuchBU0xAYbPAsB9d0yWTDmrznmAikpVrBwNOpQkDV0ltnrWoEEZN0koPJiVZ97d1Jr5C8v4f1Cgll5m7ry9z8fbvpULMuAB1rh7DzxFF2nLB6oJ46k15onf4dtAkHf19356LoWlUPZn/Sqdz627eDnnXD8qTpWbchM/dav13Pj97FtXb9NfSvypq4GACOp58m6ewZWlYLyrNtfb8AqnpVZF186QhGdGkZyrwLtOsAqelnAfB0OCjn4UH2aRmdcIKYIydLLL8Xo0VALQ6mnuDQ6ZNkmEzmx26la1DjPGnWHduf065vOnmIIC+/8/bTq2YzViXsyUlXmnRtHsrcDVb9bY6x68/XxX3ZXqf7skO592VD2rdg+i+bSE6z2sUTKWklmPvCleVzM7yK3a6nWu36vAPb6VEr72/5samJ7Eo8Sla+e5SGlaux/ugBMo0hLTODnYlH6BRcYJ8Lt+nWNJQf/7TPzYPx+Hqdf25mrzuWnHr+DozBp4IV0PTxqsCRJBdp3Khb01B+3GiX75BdPh8X5TsUz7EUV3nPVz5X74FSbnShHn9dgQxjzITsBcaYTcaYVSLSRURWicgcoNDAm73dfmAiMB4rwDHKGOPq28pT9rrT9naLgDXACAC7R9fbIrIJuEZEIkWktb3uHruX4Dq7R+GH9nLnnoeRIvKGnWa3iHS8UN6zichIp31OEZEJIvIbMF5EOjv1OvtTRHyB14GO9rLR+XZ3K/CrMWau03sUaYzZKiJeIjJZRLbY++rqdPxZIrLA7iU43l7+OuBtHyfCXnabXcaNIvKpiHg4vX//EZFNIrJWRALtQOwg4E07/cPA48C/7B6XeXpzisgTIvJSUd83YCUQZm97n4ist48/U0Qq2ssLrSMRKQ+8Agy38zi8KAe2z9N5Tsf40j5vY0TkRhEZb7/PC0SknJ3uahFZIVYP14UiEuxi192AP7LPYRF5VES2i8hmEfnWXlZJRCbZ5fhTRAbbyz1E5C0R2Wqnf8Te5yqgRxED6ZdFjco+JJxKznmdcCqFGpV9Ckzv612Bzs0b8NvugwDsij1K93DrxqZ7yzB8vCpQueJ5MXq3CPTyJcEetguQkJ5EDRdfALLdUPdqVh+xhktuPnmQ9cejWdprLEt7jmXN0b1Epxwr9jwXVXWvyiSkO5ctkeouyja0bnt+6PxvHm3ch7e2zztvfbeg5uxKOkxGVmax5vdS1PD2I96prEfSEwn0Pj/qMLxeW+Z1Hc3opr15Y9tPJZnFixbk7UPc6aSc1/GnkwlyUaYLGVCvKXNjLtj8lriTx6Bq9dwvNlWrGU4ey9vjb8jtGaxe6sHDt3gx/rnyjHw49wv40Xjh6Qcr8PKY8uzc4v6piAMr+nI4Jfc6GZeaQmClvPUVWCk3TaYxJJ89S4CXNw38AzDGMLXfEObdeDsPhLfJs92bXfowf8gdPHJV++IvyD9UUEUf4lKd6u90MkH56i+okg+H7aHZVv2dIaCCN9tPHKVn3TA8RKjjU5nmVQOp6ZP3WjuwQVPmRZee4aI1/H2IP5mvXfd33a5//PANLHv9AU6fOcuSP0vXdAGuBHr5EZeWe+1MSEsisJB2fUjdq1h1ZO95y/vWas782K0utnC/Gn4+xF/MfZlXBTpf0YC19n1Zver+1KsewJePDGfaYzdzbZN6xZ7noirT56a3b952PS3Z5b2KKztOHaFTUAO8PDwJKO9N+xr1CK5Y+n5dqeHnQ3yiU/0lpRDoV/C5md+Hy9YysFVTlj15LxPuvJ7/zFteHNn8y2r4XmL5lq5lYHhTlo29lwl3lL7yKXWhO+rmwO+FrL8KeMzumVcUbwF9gK3GmJX5V4qIH1DJGJN/wqkNQPZw30rAb8aYcGPMaqdtawIvAO2Ba4EmFMzTGNMWK7j1YgFpQp0CeR8VkKY20MEYMwZ4AnjY7nXWEUjD6um2yu59lr93Y2Hv7cOAMca0AG4BvnTq8dgKGA60wAqC1THGPA2k2ccZISJN7TTX2vnJxA6cYr1/a40x4VgBufuMMWuAOdg99IwxHwETgHeNMV0LyOPFGAhssf+eZYxpYx9/B3BPAdvkqSNjzFlgHDDdzuP0v5iXUKyg3SBgGrDcfp/TgP528O8D4Ca7h+sk4D8u9nMteevvaeBKY0xL4EF72XPAMrscXbECq5WA+7F6g7ay00cAGGOygL1AeP6Dicj9IrJBRDZMnDjxLxb90ng4hNfv7MvXK/8k9rgViHln9kpah9Vi+pMjuDqsNgmnkv+WvVn612rJFf41mRJlXVLqVKxCfZ/q9Fz8Nj0Wv0Xbqg24qkrpuXkuqhkH1nLDirf5YNdC7g7N+1Fu4FODRxr35rVts92Uu8tresw6Bix/l/d2LOK+sC7uzk6xq+5Vicb+NVh5uHTPz1iQNcs96dQrk4++SefJ/5zl4zfKk5UF/lUMH0Sk8/qEM9z+YAYf/Lccp//GP5p7iIM2QbV5bNl8bprzDb3rN6RDLas32WNLf6LP918ydM43tAmqzY0NC521RLnBd7s3E5eawtzBdzCufTf+OBJLpsnKk2ZQgyb8GFU65+O6kIc++oEez06knKcHbRvXcXd2LqsBta12fdLeX/Isr1bBh4Z+NfjFRUDw78bDIbxxe1++XvUnsScS7WUO6lb3556PZvDUV/N5cWhPfL0quDmnF68sn5v5rY6PJjIuihk97uS9Dtfz57FYMv+G99IX0r9lY2b/sY1u4z/nwS9n88bQPkgZmuKtf8vGzP5zG93e/JwHp87mjZvKVvnU39+l9ixaZ4zJnlyooCuU8/KWWMHGJiLisAMdFysTmOlieVtghTHmBICIzAAKCkjOsv//O1YAxpUoO2hWmBnGmOyuMr8A79g97mYZYw7JX/+0X4cVfMIYs1NEYsgty1JjTCKAiGwH6gEH823fHbgaWG/nwRvInp30LJDd9ed3oOdfzWQRvCkizwNHyQ3wNReR/wP8AR9gYQHbFqWO/oqfjTEZIrIF8AAW2Mu32MdpjBWUXWy/dx5AnIv9BGMFLrNtBiJEZDaQHUnpBQzK7skIeAF1gR7AhOzegtnnrO0IUJN8QWFjzESsHrMA5qNHXY2SL5rhHcO58ZrmAGw7kECg05itQH8fjiS6HtI67uYeHDh6iojIP3OWHU1KZcwX1unkXb4cPVqF5QwvcbeE9OScobtg9RQ4kp50Xrp21RpwX8PO3L1mUk7Pt+7BTdl88iBpmdZwk9VH9hAeUIc/TsSUTOYv4Gh6IoFezmWrzFEXZcu2KG4zT18xmJft8HsNLz/GX3UbL26aQezpEwVuVxocSUsiyKmsNbwqk5CWXGD6BYe38FyLgbCpJHL318SnpRBcMbeXSlBFX+ILKZMr/es1ZdHBXZz7S81o8QqoBseP5rZ/x48JAdXy3iIsX+DBM69Zn69GzbLIOAvJiVA5AMrZUxs1aGQNF447JIQ2dt+XoITTydT0yb1OBlfyISE1b30lpFpp4lNT8BDBt3x5TqanEZ+azLq4Q5xMt4bbLT+wj+bVAlkTe4CE09a1NjUjgzl7dxBeI5hZe0pfD86/u/jTKQQ79fALruhLfL76i09NoaaPH/Gns+uvAifPWHX2qtMDO2YNGEF0Yu5Qw6ZVquPhcLD1uHsfMjC8Uzg3drDb9ZgEggLyteuFPHTr7LlMIjdH0aVFKGt3Hij2vF6KhPQkgr1zr52B3n4kuGj72ldvwP2NOjJy9ZTzerT3qXUFS+N2lqpr5/BrwxnS3q6/gwkEFfW+bGgPYo6dYtrK3PuyhMQUtsTEcS4ri9gTScQcPUnd6v5sO+iec/Qfc26mJedt1719C71Xye/j7Wv4ePsaAN69ZjD7k0vHvdkt7cIZ2saqvy2HEgiq7FR/fj4kJBV9GpwhVzfn/i+tr3ebDsZR3tOTgIrenEh133D0W9qFM7S1Xb7Yy1C+qaWrfKVe2Ytvl2oX6vG3DSuAVBDn3+GPAwH51lcBjgGIiANrDrvbgD3Av/LvzBiTBKSKSP6Jwq628wKQ7hRs+6uyIxOZXFrwM6f89tx/92IF2X4RkcJ6HMKF39uCOEdVCsq/AF86zU/Y2Bjzkr0uw5icn5GKWv5z5D1XijqWM7sHYU9jTPaYiilYQ7lbAC8Xsq/LVUcu92sHnZ3fiyz7OAJsc3rvWhhjernYTxp5894f+AirF+x6e7iuAEOc9lXXGHOhbgFe9r6LzfRVmxg+PoLh4yNYvjmKgW2t+dJahASRkn6WYy7m3Hi4fwd8vCowflZknuX+lbxyfs26p2cbZq/ddt627rLtVCz1KlWhlrc/nuJBn5otiMz39L4mfkGMazmIR9dHcOJsbrnj0hJpXTUED3HgKQ5aVw0pVU+J3Z4YS91K1ajpHYCneNAzuCUrj+Q9tepUzJ28/roajTlw2hqq7OPpxbtX38lHuxay+VTpvpEG2JYYS91KVXPrsVYLViTkrce6lark/N2pRiMOpB4v6WxelM3HDxPiG0DtSpUp53AwsF5TllzkU3kHhTRjTikc5gsQ2jiL+FjhSJxwLgN+jfTg6mvyNtvVahi2/mk1K7Exwtmzgp8/JJ2yHg4CkBAnxMc6CAx2753hpiPxhFQOoLavXV9hTVgck3fq28UxUQxpZA1M6NegEWsOW7/HrTi4n8ZVquHl6YmHCO2C67Dn5HE8RAjw8gaseay61Qtl94nSM51AWbLpaBz1/QKo42PXX4OmLD6Qt7fXkgN7GRJmffnrV78xaw5b10YvD0+8Pa0Hz1xXsx7nTBZ7TuVeXwY1aMqcUtDbb/rKTQx/PYLhr1vt+gDndj3t/Hbdu3y5nLnVPBxCxyvqE51QOgINhdl66rDVHlT0p5x40K9Wc5bH78qTpknlIF4MH8Co377J065n61erBfNjt5y33J2m/7KJYW9HMOztCJZtiWJga6v+WtYLIjn9rMv50kb17YCvd4XzHt6xfOte2oRZPeT8K3lRr3oAh44nnrd9SfmnnJubT+Rt1wfUbcbS2KK16w4R/Mtb7UHjytVpUrkGq+JLR2/+b37bxI0fRnDjhxEs3RHF4Cvtc7NOEMlnXJ+bBYlLTKJ9A6vHe4PqVajg6eH2oNg3v23ixo8iuPGjCJZuj2JwK7t8te3yuZzLz7XSWD6lnF0ooLIMeE1E7rd7HCEiLYHK+RMaY1JEJE5EuhljlolIFaxhvf+zkzwA7DHGRIrIbmCtiHxnjMn/bfpN4H0RGWqMSRORHlg94B64QF7XA++JSACQDAwhd3hpsRORUGPMFmCLiLTBGmp8EChokoavgWdEpL8x5id7H52AE1hzvY0Alon1BOW6wC6soFJBMkSknDEmA1gK/Cgi7xpjjth14WuMKay7UnIheU0AaohIVSAFGEBuT7mL5QvE2UNqRwCxF7FtYXm8XHYB1UXkGmPMr3Y+Gxlj8ke0dpA7b6EDqGOMWS4iq4Gbye3N+IiIPGKMMSJypTHmT2Ax8ICILDfGnBORKk69/hoBJTbxzKrt0Vx3RQjzxt1F+tlzjItYlLNu+pMjGD4+ghr+Ptzfux374o/z7VhrxPi3qzbxw69bad2wDo8OuBaA36MO8dqM0jOfRabJ4rWtP/FJ+zvwEAezD/5BVMpRHmrcje2nYolM2MWYZr2p6Fmet662poyMT0vk0fVfs/jwNtpWq8/Mzg9jMPxyZC8rEnZd4IglJ9NkMX77HN5vcxceIsw59Dv7Uo7wQMMe7Eg8xMojOxlW7xraVg3lnMkkKSOdlzd/D8CwetdQp2JV7g3ryr1h1vDfUesnc9LFF6TSINNk8d9t8/ik3Z04curxCA816sa2xMOsSNjJzSHtaV8tlIysTJIz0nhh46wL79iNMo3hxQ2LmdrtZhwizIjazJ7EY4xu2ZEtx+NYEruXllWCmdD5RiqX96J77YY83rIjvX/6HIBalSoTXNGP3xJKZ+DWwwNGjsrgv89Yw3e79M6kTohhxhRP6jfKonWHLG57IIPP3inH/FmeCPCvsWcRgR1bHMz4shyeHiAOuOexs/gUPIVXicg0hnGrlzK13xA8xMF3u7aw5+RxRre+li1H41kSE8V3O7fwTtd+RN58D6fOpPPIEqsndNLZM3y+ZQNzbrgNg9Xjb/mBfXh7lmNqvyF4OjzwEOGX2Bi+2bnZvQUton+/DOs2wqlE6HITjLoLburv7lwVLNMYxv26hKl9huIhwne7t7Dn1HHGXHUdm4/Fs+TAXqbv3sy7nfuzYuh9nDqTzqjlcwCo5l2Rqb2HYTDEpyYzekXe+UMH1G/CyEXfu6NYBVq1zWrX5754F+kZ53hxmlO7/vQIhr8egXeFcvzvgUGU8/TAIcL6PQf5frV1/nVtGcrTQ7sS4OPNBw8OZlfsUR766Ad3FSePTJPFfzbPZ+I1t+MQ4YcDfxKVfJRRTbqy7dRhlsfv4okrelHRozzvthkGQNzpREats57AWdPbnyBvP9YfKx29911ZtSOajk1D+OlZq/5e+Ca3/r779wiGvR1BYGUf7u/Zjn0Jx5k+xr4vW72JWb9t5ZedMVzTqB4/PHkHWcbwztyVJJ5Od1dx8ijb56bh5d8XMaXzzTgcDr7ft4k9Scd4vHkntpyIY+nhPbSoEswn1w2hcnkvutUM47EWHen782d4ioNvu98OQErGGcas/bFUDvVduSuaTo1CWDDGqr/nZuXW36xRI7jxwwgA/t27I/3DG+NVrhzLnryXmRu28tGytYyfv5KXb+jJHddeBRienVnQoC/3WLnbqXxn85Xv4RHc+JFT+Vra5Rt7LzN/t8v380pevr4nd3SwyzerdJVPKTEXuLDYc+e9h9U7LR3YjzXvWi3gCWPMAKe0zbB6PWX3/HvTGBMhIjWAdUB7Y0y8nXYM0MIYc1e+4wnWXG63Y/X2isfqIbbFXp9ijPFxSh9p52ODiNwPjMUKnu0EDhljnrMfRJFijHkrX/pqwAZjTEi+PIQA84wxzfMtHwm0NsaMEpEpdprv7XUfYM3jloXVm2+k/fdCoCowJf88f3avwPew5p3LwBou+hiQiPUAlNZYve3G2EGlnOPb288D3rKDqW9gzVv3hz3P33CsJzA77H0/bIxZ6/z+ichNwABjzEgRuRb4DKtH3E32+59ijHnLTvuonbdYYB+w3xjzUv73walsBS3/F9aTa48Cv2EFJEcWpY7sAOZCoBzwX1fz/Dlva7/uYr8e4HwMe53ze+F8/FbA+1gBbk/gPWPMZ/mOUw/4yhjTyQ4OLrfTCzDNGPO6iHjb9dvBrodoOx+eWA+56WPXzWfGmA9FJBCYa88JWBgTfglDfUu7Te+PpuXcce7ORrHYPPAV2vx8qQ/gLr3W932N8HkvuDsbxWbTgFepH/Ffd2ej2ESPeIY/DtR1dzaKzVV1DxDy6Vvuzkax2P/AE2TFF3W65b8fR9Bu6n0x3t3ZKDYx9zxJq1Flt13f+OForvjxJXdno9hsG/wSLceUzfrb/M7oMn9uhn77mruzUWyibn6WZs+V3frb/p/RNHu+DJfv/0aX6VkCQya8Vfoi3JfB/gefKJX1dsEhlMaYw8AwF6v2AJH50m7HCn7l38cR8s3TZox5p4DjGawhoC8XsN4n3+suTi+/NsZMtAMrP2DPteY0zDVPemPMsfz5spfvx5rnLf/yKVhDVTHGjMy37pH86W3dCliOMWYnVvDHlbvyL3A+vv16gNPfT2E9ETn79XTgvMCY8/tnB+W+t//+BXCeWfylfNu9jxUMy7+/ka4yX8jyT7CCmvmXv+T0dxenv3PqyO4V14ZC5DsfMMZEYp+nzsewXzu/F87H3wh0usBxYkTkuIg0NMbsweqVmj9NGi56qhprbr8x9j9ntwKfFnZcpZRSSimllFLqb61Mhv1KrwvN8fd385KIbMQaKhlN7kMWlCoOT2M95ONyOQV8eRn3p5RSSimllFJKqX+wy/nQBLczxjxx4VRKXR7GmF1YcwJerv1Nvlz7UkoppZRSSimllCprPf6UUkoppZRSSimllFKUsR5/SimllFJKKaWUUqoU0zn+SpT2+FNKKaWUUkoppZRSqgzSwJ9SSimllFJKKaWUUmWQBv6UUkoppZRSSimllCqDdI4/pZRSSimllFJKKVUyjLg7B/8o2uNPKaWUUkoppZRSSqkySAN/SimllFJKKaWUUkqVQRr4U0oppZRSSimllFKqDNLAn1JKKaWUUkoppZRSZZA+3EMppZRSSimllFJKlQgx7s7BP4v2+FNKKaWUUkoppZRSqgzSwJ9SSimllFJKKaWUUmWQBv6UUkoppZRSSimllCqDdI4/pZRSSimllFJKKVUydI6/EqU9/pRSSimllFJKKaWUKoM08KeUUkoppZRSSimlVBmkgT+llFJKKaWUUkoppcogDfwppZRSSimllFJKKVUGaeBPKaWUUkoppZRSSqkySAN/SimllFJKKaWUUkqVQRr4U0oppZRSSimllFKqDPJ0dwaUUkoppZRSSiml1D+DGHfn4J9Fe/wppZRSSimllFJKKVXMRKSPiOwSkb0i8rSL9RVEZLq9/jcRCbnUY2rgTymllFJKKaWUUkqpYiQiHsBHQF+gGXCLiDTLl+we4KQxJgx4F3jjUo+rgT+llFJKKaWUUkoppYpXW2CvMWafMeYs8C0wOF+awcCX9t/fA91FRC7loGKMDq5W6m9GP7RKKaWUUkopVXZdUqCntGvw/ttl8jtt9GNPPADc77RoojFmYvYLEbkJ6GOMudd+fTvQzhgzyinNVjvNIft1lJ3m2F/Nlz7cQ6m/ocavvuvuLBSbXS+MpsG777g7G8Vi3+gx1P/wbXdno9hEj/o3IVMuuSd6qbV/5FPUj/ivu7NRbKJHPEPIZ2+6OxvFZv99Y1m1P8zd2SgWHUP2Uu+L8e7ORrGJuedJsuIbuTsbxcYRtJvQt8tmuwcQ9e8xhEwsw9eW+8cS9kbZvC/b+9ToMn/PGfrta+7ORrGJuvlZmrxUdutv50tl//ws00zZjGvaQb6JF0xYwnSor1JKKaWUUkoppZRSxSsWqOP0ura9zGUaEfEEKgPHL+WgGvhTSimllFJKKaWUUqp4rQcaikh9ESkP3AzMyZdmDnCn/fdNwDJziXP06VBfpZRSSimllFJKKaWKkTHmnIiMAhYCHsAkY8w2EXkF2GCMmQN8AXwlInuBE1jBwUuigT+llFJKKaWUUkopVTLK5KM9isYYMx+Yn2/ZOKe/04Ghl/OYOtRXKaWUUkoppZRSSqkySAN/SimllFJKKaWUUkqVQRr4U0oppZRSSimllFKqDNI5/pRSSimllFJKKaVUyfgHz/HnDtrjTymllFJKKaWUUkqpMkgDf0oppZRSSimllFJKlUEa+FNKKaWUUkoppZRSqgzSOf6UUkoppZRSSimlVIkQneOvRGmPP6WUUkoppZRSSimlyiAN/CmllFJKKaWUUkopVQZp4E8ppZRSSimllFJKqTJIA39KKaWUUkoppZRSSpVB+nAPpZRSSimllFJKKVUy9OEeJUp7/CmllFJKKaWUUkopVQZp4E8ppZRSSimllFJKqTJIA39KKaWUUkoppZRSSpVBOsefUkoppZRSSimllCoZOsdfidIef0oppZRSSimllFJKlUEa+FNKKaWUUkoppZRSqgzSwJ9SSimllFJKKaWUUmWQzvGnlFJKKaWUUkoppUqE6Bx/JUp7/CmllFJKKaWUUkopVQZp4E8ppZRSSimllFJKqTJIA39KKaWUUkoppZRSSpVBOsefUv9wz/XuQuew+qRnZPD0nEVsjz9yXprHu3bg+hbN8POuwFVvfJSzfGS7qxh6ZXMys7I4cTqNZ+cu4nBicklm/zzjunSlS32rPGMXLWTbkfPL07xGDd7s3YcKnp5ERkdv0EeVAAAgAElEQVTzSuRyAJ7u2InuDRqQkZlJTGIiTy5aSPKZM1xXty5jr+tIeQ8PzmZm8vqqlfx68GBJFy2PTnVDeLFjVxwiTN++lQl/rMuzvrzDg7d79qV59RqcSk9n1MJ5xCYnEV4jiNe69gRABN5b9yuL9u11RxEK1blWfca17Y6HOJi+ZxOfbPktz/q2gbUZ17Y7TQJq8MiKOfwcswuAZlVq8H/te+FTrgKZJouPNv/KvP073VGEQnUKbsCLrXvgEAfT925kwva1eda3rVGHF67uQRP/Gjy6ejY/H7TK1z6wLi9c1SMnXWjlqjyyejaLD+0p0fy70rl2COOu6Y6HCNN3beaTTeefk+906UfzaoGcOpPGqKVzOZSShKc4eKNTb66oFoinOJi1Zxsfb/qN4Eq+vNOlH9W8K2KAb3ZsYvK2P9xTuHy2rodvJjjIyoSOfQ39huedqOb4EZj0poPTqZCVBUPuzqJlW9j2O8yc5CDzHHh4wtD7smjayk2FcNK5Vn1ebN8dD4fw7a7NfLI57+etvMODdzr3p0W1QE6mpzFq+RwOpSRRzuHgtWt707JaEFnG8PLapayNP0ilcuWZ0f+WnO2DK/nyw97tvPLbspIu2kV77nWI/BWqBMDcKe7OTdGN62q1fWnnMnhyQcFt3/g+ffDKbvuWW23f6A4d6BEWSpYxHD99micXLORIamrOdi0CA/n+1lt4bN5PLNhT8teazrVDGNfBvrbsLODa0tXp2rLEurYMDmvKAy3b5qRrUrU6A2ZNZfvxIwwKbcJDV7bHGDhyOoXHl/3EyTNpJV00AF7o3oUuofVJy8jgqfmL2JZwft1dEViD8f17W3UXFc2rSyNz1t1+VStuuyqcLGNYHhXN+MhV1PLzY+G9d7LvxAkANh6OZ9yipSVVpAJdyj1n67q1eLZXZxoHVmfMrPks3OH+ds9Zp6AGvHBVT+s83beJT3f8mmd9m+p1eP7KnjTxr8Fja2az4FDuvcmT4V3pGhwGwIfbVvPTwR0lmveieq5vFzo1tOrvmdmL2B7nov66dWBwuFV/V7+WW383tGrG2J4dSUhOASBi3Sa+/2NrieW9KMrad6JSz4i7c/CP4tYefyISJCLfikiUiPwuIvNFpJGLdCEisjXfspdE5An77zkicofTus9EZKyL/YiIPC8ie0Rkt4gsF5ErCsnf5yLS7AJleND52BdilyVNRDY6/SsvIoNE5OmL3M+thay77FdSEWklIv0KWNdFRBJF5E8R2SUiK0VkQBH2OVJEPrzEfN3l9F6eFZEt9t+vX+z7epHHfU9EOl3G/S0RkYDLtb+i6BQWQkgVf3p9NJkXflrCS/26uUy3fPc+hk765rzlO+KPMOTzrxk0cRoLd+xhbPeOxZ3lQnUJqU+Ivz/dJk/i2SVLeLVbd5fpXu3eg2cWL6bb5EmE+PvTOSQEgNUxMfSZ+iX9pn3F/pMneaiN9YXhRFoa9/04m75fTWXswgW83advSRXJJYcIr3Tuzsi5s+j19RQGNWpMWECVPGmGNWtO4pl0uk6bxBebfufpDtapuuvEMQZ9N43+07/izjmz+E8X6ya1NHGI8Eq7noxcPIOesz9nUP1mhFWumifN4dQknlg9nx/3bc+zPO1cBmNW/USvH7/gzsUzGNe2O37lK5Rk9i/IIcIrbXoxcvl39Jo3kUEhzQjzy1u+2NQkxv46jzn7t+VZvjbhAP1/nkT/nydx69KvSTuXwaq46JLMvksOEV65ticjF3xPz+8nMSi0KWH+ecs0rHELEs+m0+W7z/liy+883bYzAP0aNKa8hwd9Zk5hwA9TubVpOLV9/DiXlcX/rV1Oz+8nc8OP07j9iivP26c7ZGVCxEcOHv+/LF79LIt1y4XDMXnT/PS10LqT4cWPs3jgmSwiPrRut3wrw6OvZPHyp1ncMzaLL8a7f+CFQ4RXO/TgzkUz6DHzCwY1aErDfO/z8MYtSDyTTucZn/HFtg083aYLALc0Dgeg9w+TuW3BdzzfrisCpGacpd/sL3P+xaYksSBmdwmX7K+5vi9MfNPdubg4XerXJyTAn26TJvHc4iW80sN12/dKjx48u3gx3SZNIiQgt+37bMMG+k/9ioFfTWP5vmgeuaZ9zjYOEZ7q1JHV+2Nc7rO4OUR45bqejPz5e3rOmMSgMBfXlibW+dllun1taWddW37cu4N+s76k36wvGb38Jw4mJ7L9+BE8RBjXoRu3zJ1O35lT2HHiKHc2v9IdxaNzA+s+rPvEyTy/cAkv93J9H/ZKr+48t2Ax3SdOJqSKP50ahADQvm5tejQMZeDkafT9Yiqfr9uQs82BU6cYNCWCQVMiSkXQ71LvOeMSk3lmziLmbS19P+Y5RHipdW/uXjGd3j9PZGDdZoT5VcuT5vDpJJ78bS5zY/K2612CQ7kiIIgBCz/nxsVTuLdJO3w8y5dk9oukU8MQ6lXxp/f7kxk3dwkv9i+4/oZ9dn79Afy8bTc3TIjghgkRpS7oV9a+EymVn9vuOEVEgB+ASGNMqDHmauAZIDBfuqL0SnwUeFlE/EWkA9AOeNdFuoeBDkC4MaYR8F9gjoh4ucifhzHmXmPM9vzrnBljJhhjphYhj86ijDGtnP6dNcbMMca87iIfBZU/BHAZ+CtGrQCXgT/bKmPMlcaYxlh18qGIuL77vIyMMZOz30vgMNDVfv10Qe/rpRKRqkB7Y8zKy7jbr4CHLuP+Lqh7o1Bmb7Z+VdwUG4+fVwWq+1Q6L92m2HiOpqSet/y3mEOknzsHwMbYOIL8fIs3wxfQIzSUH3ZYH9mN8XH4VahA9Up5y1O9UiV8ypdnY3wcAD/s2E7PUOtX1tUHYsg0Vs+dP+PiCPLxAWD70aM5vR92Hz+Ol6cn5T08SqRMroQHBhGTeIqDSYlkZGUxd88uejYIy5OmZ4MwZu60bi5/3rubDrXrApB+7lxOGSt4eACl75FaraoFE5N8ioMpdvmid9CrbsM8aQ6lJLHz5FFMvvxHJ51kf/JJAI6kpXA8/TRVKlQssbwXRXjVmsQkn+RgyimrfDE76Fkn729esamJ7Dx1lCxTcP30q9uEyMP7SM88V9xZvqBW1YOJSTrJwWS7zqJ20qte3nOyV0gYM3db5+T86F10qFXXXmPw9iyHhwhenp6czcokOeMsR9NS2Xbc+rU9NSODqJPHCarkU5LFcil6F9SoCdWDwbMctO1i2PhrvuC5QPpp68+0VPC34/J1wyA7ZlGzHpw9AxlnSy7vrrSqHsz+pFO5dbdvBz3r5rue1G3IzL3Wl7T50bu4tqZVdw39q7ImzgoIHU8/TdLZM7SsFpRn2/p+AVT1qsi6+EMlUJpL1yYc/N3blF20HqGh/LDdbvviCmn7KpRnY5zd9m3fTs8wq55TzuaehN7lPHG+7NxxZSsW7NnD8dOni7kUrrWqHkxMYr5rS0i+a0s9p2vLPudrS65BYU2ZG2Xd74j9X8Vy5QDwLVeehNSUYi6Jaz0ahvLDVitfGw/HF153h+MB+GHrDno2DAXg1ivD+XTtes5mZgJw4rR7ei0WxaXec8YmJrHryLFC20V3Ca9it+upVrs+78B2etTKe98Sm5rIrsSjZOW7b2lYuRrrjx4g0xjSMjPYmXiETsGhJZn9IuneOJQfN9n1d6iQ+jvkuv5Ku7L2nUip/Nz5U3NXIMMYMyF7gTFmkzFmld17bJWIzAEKDbzZ2+0HJgLjgU+AUcYYV9+EnrLXnba3WwSsAUYAiEiKiLwtIpuAa0QkUkRa2+vusXsJrrN7FH5oL3fueRgpIm/YaXaLSJFD/c4930RkiohMEJHfgPEi0tmpR9ufIuILvA50tJeNvsB+Z4nIArun43indSki8q6IbBORpSJS3akc2eWuJiL7RaQ88Aow3D7m8MLKY4zZaKcfZe+nuojMFJH19r9rXeR1oIj8ZpdxiYgEiojDznd23hwisjf79V94Xz8RkbUiss8+zyaJyA4RmeK0TS8R+VVE/hCRGSLi6pvmEGCB0zavi8h2EdksIm8VVmYR8RGRyXbPxM0iMsTezRzglvOOVIwCfX2IT8rthh6flEKg71/7Yn1Tq+as3OvenkdBPj7EJTuVJyUlJ3jnnCY+pfA0AEObX0Hk/v3nLe/bsCHbjiTk3GS7Q1Cl/OVMPi8gEuiUJtMYks+eIcDLG4BWgUEsvOVOFtxyJ89FLskJBJYWgRV9OZyalPM6LjWZwIoXf16GVwumnMODGDsQWFoEefsQdzq3fPGnkwnyvvgbxAH1mjI35oJNZIkIrOTDYafPVVxqMoH5z8mKPjn1ap2TZwmo4M38fbtJO5fBuhEPseaWB/hs83oSz6Tn2ba2jx/NqgWy8Uhc8RfmAk4eh4DquZ+ZgGpw8ljeNINuM6xdJowd4eB/Lzi45eGs8/bz+2qoFwbl3NyxI6iiD3GpTnV3OpmgSnnPx6BKPhxOca67MwRU8Gb7iaP0rBuGhwh1fCrTvGogNX388mw7sEFT5kWXvh46ZUmgjw+HnduE5ALaPqc0cckpBDql+fe117L6/vsY3LQp761Zk7PfXmENidi4qZhLULDASj4cTr3AtaWS62uLswGhTZiz1zoPz5ksnl+9mAU3jWTdbf8iLKAq03dtKeaSuBbo40NcUt66y38fFujrQ3xySt40dt2FBPjTpk4tvr/9Zr6+ZSgtgnL7T9SuXJk5I0fw9S1DaV27VjGX5MIu5z1naRPo7Zu3XU9LJrCI7fqOU0foFNQALw9PAsp7075GPYIrlr6gUaBfvnM1KYVAv4urv55NG/Ljv27jf8MGEHSR2xa3svadSKn83Bn4aw78Xsj6q4DH7J55RfEW0AfY6qoXloj4AZWMMfvyrdoAZA/3rQT8ZowJN8asdtq2JvAC0B64FmhSSD48jTFtgceBFwtIE+oUyPuogDS1gQ7GmDHAE8DDdo+2jkAa8DRWD7tWxhhXvRudtQKGAy2wAnd1nMq7wRhzBbCikPxijDkLjAOm28ecfoFjAvxB7nv1P+BdY0wbrKDZ5y7Sr8bqRXcl8C3wpDEmC5iGHZwFegCbjDFHi3B8VwKAa4DRWIG2d7Hqv4VYQ5mrAc8DPYwxV2GdH2Nc7Oda7PPX7v13A3CFMaYl8H8XKPMLQKIxpoWdfhmAMeYkUMHeXx4icr+IbBCRDRMnTvyLRS8+g1o0oXlwIJ//WthH+u/jobZtOZdl+HFn3jlWGlatypPXdeS5JUvclLPLY2NCPL2/+ZLBMyJ46Oq2bu29WFyqe1finY79GfvL/FLYp/HSVfeqRGP/Gqw8nL9J+/sJrxFMpjG0i/iEjt9+xr0t2lDHt3LO+oqe5fikx2Be+XUZKe7uHldE6yKFDj0Nb0Zk8dir1pDeLKfYX+x+mPmFg9sfOz8g+Hfy3e7NxKWmMHfwHYxr340/jsSSafKWaVCDJvwYVTrnq1K53v7lF66b+Bk/7tjB7VdaE08+36UL41et+ttfQ1tVDybtXAa77Qi9pzi4rVkr+s+cSttpn7DzxFEeatXOzbn8azwdDip7VeCmr77l9ciVvD+4PwBHU1Pp9MnnDJoSwX+WreDdgX3xKV/6ho8qWB0fTWRcFDN63Ml7Ha7nz2Oxpe4H2cth+a59dH/vCwZ/Mo01UTG8fkNvd2epWJS170Sq7CjND/dYZ4zJDpUXdPVzXt4SK5DZREQcdsDoYmUCM10sbwusMMacABCRGUBBAclZ9v9/xxqO60qUHcQrzAxjTHaXol+Ad0QkAphljDkkFzcn11JjTCKAiGwH6gEHgSwgO4A3zSnvl4tzJnsAzZzy7eeiJ11tYLqIBAPlgez6nwT8CLwH3A1MvoQ8zTXGGBHZAiQYY7YAiMg2rPqqDTQDfrHzWh741cV+goHs4GMikA58ISLzgHn28oLK3AO4OXuhHfDLdgSoCRx3PpgxZiJWr1YA8/arF4r1FuzW1uEMu7I5AFsOJ+Tpih7k55Mz6W5RXVO/Lg9e15bbvpxBhht6wd0eHs7w5i0A2JyQQLCvU3l8fIhPyVseq4dfwWmGNGtGt/oNuG3m93m2C/LxYcLAQTyxcAEHEhOLoyhFFp+akq+cvsTnG6aUYKeJT03BQwTf8hU4mZ53CFDUyROkZmTQuGo1thxJKJG8F0XC6WRqVsrtNRRcyZeE00U/L33KlWdyj5t4649V/Hn0cHFk8ZLEp6UQXDG3fEEVfYlPu7gJoPvXa8qig7s495eaussvITWFmk6fq+BKvucNnUs4nULNSn5O52R5Tp5JY3BoU1YcjOacyeJ4+ml+T4ilZfUgDiYn4ikOJvQczOyoHSzcXzomcg+oCiePCtm3ICePWb3+nK1eIDz+H6tuQptZw3lTksDPH04chY9fcXD32Cxq1CzhzLsQfzqFYKcefsEVfYlPzXs+xqemUNPHj/jTTtcT+0EIrzo9sGPWgBFEJ+Y2aU2rVMfD4WDr8dJzfSkrbmsVzvAWVtu3JT6Bmr6+Ob+mB/kW0PY5tRvBvj4kpJx/Xf1xx04m3XgD/1vzKy2CAvlff2uGlwBvb7o0qE+myWLx3qjiKZQLCakp1Kx0gWtLqutrS7aBYU2Yszc3+NysWg0ADiSfAuCnqF38qwQDf7ddGc6wcPs+LD6BYD9fiLXWBfmefx+WkJxCkFPPoyCnuotPTmHRbusBXZvjEjDGUMXbmxNpaTkjE7YlHOHAqVOEVAlga3zJfhYv9z1naZWQlpy3Xff2JeEi2vWPt6/h4+1WT9t3rxnM/uQTlz2Pf8WtbcIZerVdf7H2uWoL8vMhIano9XcqLbcn/4w/tvJET/fPgVfWvhP97ZS9+Hap5s4ef9uAqwtZ7zx4/jhWTy1nVYBjYA3/BD4GbgP2AP/KvzNjTBKQKiIN8q262s4LQLpTsO2vOmP/P5NLC6zmlN+eo+5ewBsrIFVYj8PC8nShfGV//M6Re26cN//hRbgSyL7TcmD15sue17CWMSb/1fQD4ENjTAvggexjG2MOAgki0g0rCPvzJeQp+73IIu/7koX1vgiw2CmfzYwx97jYT5pT/s7Z+foeGEDuEOCilDk/L3vfxebrDZu4/rMIrv8sgiW7ori+ZVMAwmsFkZx+9qLm5WgaVJ1X+nXnX9PnuG1ema82bWJAxDQGRExjcdRebmhqPY+nVVAwyWfPcjQ1b3mOpqaScvYsrYKCAbihaTOWRFlfYjrVC+H+1m24f86POfN0APhWqMAX19/A+NWr+P2w+wNJmxPiCansT21fP8o5HAxs2Jgl0Xm/iC2JjmJIE6szc9+wRvx66AAAtX39ch7mUcvXl9CAKhxKSqI02XQsjhC/AGr7VLbKV78piw8W7cnD5RwOPu16A7OituU86be02Xz8MCG+AdSuZJevXlOWXORTeQeFNGNOKRnmC7DpqF1nvnaZQpuw+EDeOlscE8WQRtY52a9+Y9Ycts7Jw6lJdLDnjPP2LMeVNYKJOmV96Xmjcx/2njzOF1s2UFqENIaEWDgaD+cyrN594e3z3r1WqQE7Nlqfs8MHrMCfb2U4nQLvv+DgxruzaFjgo8VK1qajcdT3C6BO9uetQdPz6m7Jgb0MCbO+HDnXnZeHJ96e1jxp19WsxzmTxZ5Tub9bDWrQlDna269YTNu4iYFfTWPgV9NYtHcvNzSz277gYJLPFND2nTlLq2C77WuW2/aF+PvnpOsZFkqU/STYLp9/QWf734Ldexi3ZGmJBv3AvrZUzndtiSnk2tKgMWtiD+SsE6B/g8bMjcodbh6fmkzDgKpUsae/uK52PfaeyvN7a7Ga9uemnIduLN4dxQ3NrfuwVjWDCq+7mtb8mTc0b8qSPVY9LN4TRbu61kCekAB/ynl4cCItjSre3jjstr5O5crUCwjg4KlTJVXEHJfznrM023wib7s+oG4zlsYWrV13iOBf3joXG1euTpPKNVgVXzp683+9flPOwziW7oxicLhdf7Xtc/Ui6s95vrxujRsQdcz9wc2y9p1IqcK4s8ffMuA1Ebnf7s2EiLQEKudPaIxJEZE4EelmjFkmIlWwhvX+z07yALDHGBMpIruBtSLynYvhoG8C74vIUGNMmoj0AK6zty/MeuA9sZ64mow1bLPEJgMRkVC7Z9oWEWmDNXz2IHCpE0A4gJuwhtXeijXUFmA/VkB0nb0+W3JRj2nX5QtYAUuARcAjWHWAiLSy5wF0Vpmc3zy5M9+6z7F6JX51GYKzhVkLfCQiYcaYvSJSCahljMn/OMIdQBgQaffiq2iMmS8ivwDZrXVBZV6M9aCZx+3lAcaYk2J1DQzCev9LxIq90XQOC2Hxw3eRdu4cz85ZlLNu9n0juP6zCADGdu/IgOaN8S5XjhWP3cuMP7fy4cq1PNm9ExXLl+N/Q6yhJXFJyfxr+pySyv55lkdH0yWkPsvvupv0c+d4ctHCnHXzRtzGgIhpAIxbtpTxvXrj5enJiv37idxvdS59qVs3ynt4MPVGa9rFjfFxPL90KXeEt6Kevz+PtGvPI+2spx3eOWsmx9Pc07BnGsOLK5cxdfAQHOJgxvat7DlxnNFtO7DlSAJL9kcxffsW3u3Zl+W33U3imXQeWfgTAG1q1uLBq9pyLiuLLGN4IXLpeT0B3S3TGMatXczUnsPwEOG7vVvYc+oYo1tdx5bj8Sw5uJeWVYP4tNuNVC5fge61wxjd6jp6/fgF/UOa0DaoDgFe3txkByqeWD2f7SeOuLlUuTKN4cUNi5na7WYcIsyI2syexGOMbtmRLcfjWBK7l5ZVgpnQ+UYql/eie+2GPN6yI71/smYLqFWpMsEV/fgt4cAFjlRyMo1h3JolTO17Ex7i4LtdW9hz8jijr76WLUfjWXIgiu92beadLv2JHHYvp86k88iyuQBM3fYnb3buy6Kb7kKAGbu3svPEUVoH1mJIwyvYcfwo82+0moTx61cSedC98+Z4eMCtD2fx3rPW8N1rexlqhcDsL4WQRoZW18Cw+7P48j0Hi2cJInD3E1mIwLI5wpHDMC/CwTzr8sro/2bh51/oIYtVpjGM+3UJU/sMtT5vu7ew59Rxxlx1HZuPxbPkwF6m797Mu537s2LofZw6k86o5dZ1vpp3Rab2HobBEJ+azOgVP+XZ94D6TRi56HtXhy21/v0yrNsIpxKhy00w6i64qb+7c1W4yOhoujSoz7J77iY94xxPLcxt++befhsDv7LavheXLmV8n95U8PRkRfR+IqOtz9LYjh1pUCWALGOITUrihSXufwJstkxjGPeLfW1x5Lu2HItnSYx9benan8jh9rVl6dyc7dsF1yEuJZmDybk99Y+cTuV/v6/hu4G3kJGVRWxKIk9EXspvyn9d5L5ouoSGsOx+6z7sqfm592FzRo5g0BTrQvHi4mWM79fLum/Zt58V+/YD8P3mrbzerxfz776djMxMxv5k1X2bOrV4vGMHMjIzMcYwbuFSEtPPnHf8knSp95wtggP5cNhA/Ly86NqwAY90voYBEy72+YrFI9MYXv59EVM634zD4eD7fZvYk3SMx5t3YsuJOJYe3kOLKsF8ct0QKpf3olvNMB5r0ZG+P3+Gpzj4tvvtAKRknGHM2h9L5VDfFXui6dQwhEWP3kV6xjme/TG3/n54cAQ3TLDq74meHRnQwqq/yDH38v0fW/kwci23t2tF18ahZGZlkZiWzjOzFxZ0KLcoa9+JlMpPjBsvLPbcee9hBZnSsQIejwO1gCeMMQOc0jYDPiK359+bxpgIEamBFaBqb4yJt9OOAVoYY+7KdzzBmqfudqyeb/FYD/vIHu6ZYozxcUofaedjg4jcD4wFTgA7gUPGmOdE5CUgxRjzVr701bDmzwvJl4cQYJ4xpnm+5SOB1saYUfaDJuYZY763132A9TCULKzeiSPtvxcCVYEpzvP8OR/Deb/2unnAW3aQNAVr+GgvrCGmw40xR+0ehd/Z79FPwG3GmBA74LoQKAf813mePxHpgjUcdx9Q0d7feGPMXHt9Nbv+mmIFnFcaYx7MV+7BWHPuncQKDLcxxnSxty+H1fOzrTGmwFnCRWS/vb/s3qAu39f89ZBvXTfgDaCCvdvnjTFz8h2nI/CAMeY2e2jyj1i99cR+f78spMw+9vKr7ff4ZWPMLLEeqPKMMWYIhTONL2Gob2m364XRNHj3HXdno1jsGz2G+h++7e5sFJvoUf8mZMob7s5Gsdk/8inqR/zX3dkoNtEjniHkszfdnY1is/++sazaH3bhhP/P3n3HR1Wlfxz/nElCEkhIQio9ISAdQlWKEopgA8WCvSsqi/xErKtrYV1Xsa27lhVdZe3YERuiiKioCNKkE5pAEhIS0kjP+f1xhyRDEgQkmTj7ffviZebeM3eeM+fO3DvPPfecP6AT4zfT/j8zfrvgH9T2q2+jIu1wh3z+43HFbSTxMd887gGkTLuZ+Jk+/N0y8VY6Puyb52Wbb5+Kr59zJr75oLfDqDcpF/yZLvf5bvutv8/n988jGtvrj6bjw080vgz3MbD59sbZbl4d489auxuYUMuqTcDCg8quxUl+HbyNPRw0lp61ttazJ+tkOe93/6ttfchBj5OrPXzdWjvTGOMPvA984C5zX23l3Yknj7jcy7fhTGxy8PJZwCz331cctO7G2uIFRtRRj8rXqL5d9+MzDipbY+IKd2KtV7VFd7uXZwED6njNhdTSW7Pa+kycCUYOXl4Zn7V2Dk4CrTa9cSb1OOTUgAcnWut6Xw9uh4PWLaCOelYr840x5u/GmHBrbSrOrb4Hl6mrzvnU7NEITkL6mUO9roiIiIiIiMgfmfHJtF/j5c0x/v5o7jPGrAB+wZl04gMvx/M/wxhzB86kK3d6O5aDTAPaHcPt/WKtbTz314iIiIiIiIjIH1pjntW3UbHW3uLtGI61g3s4NlbuyU0e8nYcB7PW/niMt/f8sdyeiIiIiIiIiPxvU48/ERERERERERERH6QefyIiIiIiInXELwkAACAASURBVCIi0jA0xl+DUo8/ERERERERERERH6TEn4iIiIiIiIiIiA9S4k9ERERERERERMQHaYw/ERERERERERFpEEZj/DUo9fgTERERERERERHxQUr8iYiIiIiIiIiI+CAl/kRERERERERERHyQEn8iIiIiIiIiIiI+SJN7iIiIiIiIiIhIw9DkHg1KPf5ERERERERERER8kBJ/IiIiIiIiIiIiPkiJPxERERERERERER+kMf5ERERERERERKRhaIy/BqUefyIiIiIiIiIiIj5IiT8REREREREREREfpMSfiIiIiIiIiIiID9IYfyIiIiIiIiIi0iCMxvhrUOrxJyIiIiIiIiIi4oOU+BMREREREREREfFBSvyJiIiIiIiIiIj4ICX+REREREREREREfJASfyIiIiIiIiIiIj5IiT8REREREREREREfZKzVPMoifzD60IqIiIiIiPgu4+0A6lPnvz7hk79pN/xlaqNsN/X4ExERERERERER8UH+3g5ARI7ccxuGeTuEenNd56857oEnvB1Gvdh491ROaX6lt8OoN5/lvsTg8x/zdhj1ZvHsafS9wTf3TYCfn51Kv2t9t37Lnp9K4qOPezuMepFyy80kTfbdtlvx1FQSH/PNtgNImXYzFWnHeTuMeuOK28jogdO9HUa9+XzJPfy4PcHbYdSL49tvZeqKC7wdRr15IulNnz+un/Tlrd4Oo94sGvkIwxdM83YY9earEb57Tg3oHrYGph5/IiIiIiIiIiIiPkiJPxERERERERERER+kxJ+IiIiIiIiIiIgP0hh/IiIiIiIiIiLSIIzG+GtQ6vEnIiIiIiIiIiLig5T4ExERERERERER8UFK/ImIiIiIiIiIiPggjfEnIiIiIiIiIiINQ2P8NSj1+BMREREREREREfFBSvyJiIiIiIiIiIj4ICX+REREREREREREfJDG+BMRERERERERkYahMf4alHr8iYiIiIiIiIiI+CAl/kRERERERERERHyQEn8iIiIiIiIiIiI+SIk/ERERERERERERH6TJPUREREREREREpEEYTe7RoNTjT0RERERERERExAcp8SciIiIiIiIiIuKDlPgTERERERERERHxQRrjT0REREREREREGobG+GtQ6vEnIiIiIiIiIiLig5T4ExERERERERER8UFK/ImIiIiIiIiIiPggjfEnIiIiIiIiIiINwmiMvwalHn8iIiIiIiIiIiI+SIk/ERERERERERERH6TEn4iIiIiIiIiIiA/SGH8i/8O2Litm4Qu5VJRDz9HBDDw3pEaZDd8W8v0b+RgMUQn+nH5LOACLZuWxdWkxACec34zOJwY3aOyH6+7RyQzrmEBhaSl3zP2ctWl7apSZmjyYs3p1o3lQIH1mPF25vH+71tx18jA6x0Yz9b1PmLd+U0OG/ptumHERA0b3onh/CY/d8B82r9xeo8xJZw/kwlvOwOXn4sfPVvLivW8DcNpVyYy9diQV5RUUFRTx5JT/smPD7gauwaFNvWI4g/okUFRcxgPPfsbGrTXb7oCHbz2L1rFhXHLLfyuXnXtKH84ZnUR5RQWLl2/lmdcWNUTYh+3WCckM7Z5AUUkp9778Oet/rVm/pyaPJyqsGX4uF8s37+KhNxdQYS03jB1Ecq9EKqwlK6+Qe1+eR2ZOgRdqUbdbL0hmSE+nfve99Dnrd9Ss37/+z10/PxfLN+3i4dec+v194mm0j4sAIDQ4kLzCYi6a/lpDV6GGe0YMJzkhgcKyUm77dB5r9tSsU4/YGGaccgpB/v4s3LqV6Qu+8lh/df9+/Dl5GP2ffobswiI6tIjg4VPG0D0mhse//Y4Xli5rqOrU6bZzq/bNe175nPU7a9bz6UnjiWreDH8/Fz+n7OLvs522O7lPJ64/bRAJsS245NE3WLsj3Qs1qOme4dXa7rM62i7moLb7ymm7qYMHM6qj83nbu38/t302jz0FVZ+3nrGxvHPRhfzfRx/z2abGdZyo7q6HYOH30CIC5s7ydjRHb9K0MQwY3IniolIenT6HzRvSapTx93cx+dZT6dUvHltheenZBXz71Xpi4sKY9pdxhIU3JS+3kIfvfZ/MPXleqEXtVv1kePVZfyoqYNgp5Yy9oMJjfeYeeP4RfwrywVbAhKvL6T3QkrLe8NI//ACwwPhLyuk/tHENoJWxIpe1s3ZiKyxtR0SSeFacx/qdC/ey/tXdBLYIACB+TBRtR0ax95c81r68q7Jcwe4ikv4vnrgB4Q0a/+Hw5eP6wBadmXLcOFzGxce7l/Dads9j27jWJ3B2m8GUW0theTGPrH+H7QV7ODm2Dxe0T64slxgSxzVLnmRzfuM65xzQojOTO52Fn3HxceqPvLF9gcf6sa0GcVabIVTYCgrLS3hs/dts35+Ov/Hj5i7n0jm0LdZa/rXpA1buS/FSLf5gGtdXlM9T4s9LjDHlwOpqi84CooDLrLVTDnMb4cBF1tpn6lgfB/wDGADsA9KBm6y1G48i3puAmdba/Uf6XPfzk4ESa+1i9+No4COgCTDFWvtNHc+7D8i31j5ay/JrgQyc/fjP1toPD/H644Bu1tqHDlEmHhhsrX29lnUunPdyBM7XVBEwwVq7ta7tNXYV5ZYFz+VyzvQIQiP9eG3aXhIHBhHZruprIXt3GUveLuCChyMJCnGxf185AFt+KmJPSimXPhlJeanlrT9nEd8vkMCmjasT8bDEeOJbhHPyMy/Ru3Uc9586gvNeerNGuQWbtvDq0pV8PukKj+WpOXncMfdzrj6hXwNFfPgGjO5Fq8RYrkq6gy4DOjD5iUu5acQDHmVCWzTjmr9O4MaT7idnbx7T/n0NScO6suLrdSx8+wc+eXEhACecmsTEv1/A3Wc/7oWa1G5QUgJt4iKY8H8v0r1TS269ehTX3l3jownAsIEdKSwq8VjWt3tbTuyfyGW3vUxpWTkRzRtXYnpI93jaxYRz5r0v0TMhjjsvHMHlM2rum7e/8DEF7ro9MvEMRvXrxOdLN/Ly/GU8O/d7AC4YnsTE007gwTe+bNA6HMqQHvG0jQnnrLteokeHOO68eASX/71m/e54rqp+M64/g1H9O/H5Txu5c+YnlWWmnncS+YXFDRZ7XZITEoiPCGfEf14kqWVLpp88knNee6NGuemjRvHnz+ezIjWVF88Zz7CEeL7eug2AlqEhDG3fnl25uZXlc4qKmL7gK0Z37NhANTm0od3iaRcdzrj7X6JnfBx3XTCCSx+t2Xa3vVjVdo9ecwYn9+3EvGUb2bx7Lzc/P5e/XDiyoUOvU2Xbvehuu1EjOef1Otpuvrvtzh7PsPh4vt62jeeXLuWJxYsBuLxPH24cdAJ/+cL5vLmM4faTTuTbbTUvvDQ2Z50KF50Ndzzo7UiO3oDBHWndNpIrz3mKLj1aM+X205ly1X9qlLvwyhPZl72fq859GmMg1H0MmPh/J/PFJyuZ//EqkvrHc9Wkkcy474OGrkatKsrh5af8ue2hUlpEwb03+tN3UAWt21eV+fA1PwaeVMHIsRXs2g6P3R3A46+U0ibecv/TZfj5wb69cNf1AfQZVIqfn/fqU52tsKx58VcG3tWRoMgAvrtzAzH9wwht43lsbjk4nO5XtfVYFtkjlBNndAGgJL+Mr6esJbpX8waL/XD58nHdhWFq5/HcvHwmGcU5zBwwhW8z17C9oCqx+UXacj7c9QMAQ6K6MbnTOG5d8QLz05czP305AB2axfG3Xlc0uqSfC8P/dT6bW5c/R0ZxDv/ufxOLM9awfX/Vhasv039m7m6nfQZHdWdSp3HcvvJ5zmh1AgBXL3mU8IAQHu59DdcvfRKrrJY0Mo3rV/r/lkJrbVK1f9ustUtrS/oZY+pK0IYDk2pbYYwxwPvAQmttorW2H3AnEHuU8d4END3K5wIkA4OrPR4JrLbW9qkr6XcYnrDWJgHnAS+6k3O1stZ+eKikn1s8cFEd684HWgG9rLU9gfE4ydSjdoh2bRBpm0oJb+lHeJw/fgGGLicGkfJjkUeZ1fMKSTq9KUEhzlvbNNw5g9z7azltujfB5WcICHIRFR/Atp+9/8P8YCM7J/L+6nUArNyVRmhQINEhzWqUW7krjYz8mldVd+XksmFPJhW28R28B53Why/fcH6Irv9pCyFhTWkRG+ZRpmV8DLtS0snZ6/RmWLFwDUPO7A/A/ryqtg5qFohtZHU8cUAiny1aC8CaTamENAskMrxm2wUHBnDB6f2Z9d4PHsvHn9ybV+YsobTMSVZn5xbWf9BHILl3Ih/94Oybq7emEdo0kKjmNet34MeBv8tFgJ9f5dXRgmqJzuAmAY3uBHNYUiIfu+v3y5Y0QpoGEhV2iPr5uQjw96O23XBU/+P4bMmGeo33cIzqmMj7a5x9ckVqKs0DA4lu5lmn6GbNCGnShBWpqQC8v2YtJ1dL6N01PJmHFy3y+Lzt3V/I6rR0Sis8e/Z4S3KvRD5a4t43t6URGnx4++aBKm1Nz2L7nuwGi/dwjEpM5P21h9F2gdXabm1V2+WXVPu8Bfh77KeX9Unis02b2Lv/qK6LNqgBvSE81NtR/D6DT+rM/E9WArD+l100Cw2kRWTNuxVOGZfEm7O+BcBayM1xjgHtEqJY8dM2AFYs3cagkzo3TOCHIWWDIaaVJaYl+AfACcMq+Hmx56mtMVDo3tX2FxjCI52dMTCIyiRfaYlTrjHZt3k/TWMDaRobiMvfRcvBEaT/lHPE20n7YR/RSc3xC2x8P2F9+bjetXk7dhVmklqURZkt58v0FQyN6u5RZn951e+AIL8mtZ5XjoxL4sv0FfUe75Hq0rwdu/fvrazfgj3LGRL9G/Vzt0/7ZrEsz94MwL7SfPLLiugc2qbhghc5TOrx14i4e8XdYq09w92jLRHoAOwwxjwAvITTQ84FnAP8FUg0xqwA5ltrb622ueFAqbX23wcWWGtXul/HADOAU3EONw9Ya2e7X/8+IBPoASwDLgFuxEl6fWWMybTWDjfGjAbuBwKBFOBKa22+MWYb8F9gLBCAk5QrAq4Hyo0xB7Y3Awg2xvQHBgEZ1toQd3znAmdYa684nPfNWrvOGFMGRBljjgfudr9Pe4GLrbXpxpgrgP7W2snGmFlALtAfiANus9a+AzwEdHW/n/+11j5R7WVaAqnW2gr3a+48sMIYcwrwIOAHZFprRxpjWgAv4rTffmCitXZVLe16ift1k93v5dPW2ucOp96/V/7eCkKjqi4Fh0T5kbqh1KNM9u4yAN68bS8VFTDowhAS+gUSneDPD2/k0298M8qKLTtXlxDZtvF9ncSGhpCWW3ULT3puPrGhIbUm+f5oIluFk7Ezq/Jxxq5sIltFkJVedSK9e0s6bTrFEdsukoxd2Qw6vS/+Taraaey1Ixg/eQwBAf7cPnZGg8b/W6IjQkjfW9V2GXvziG4Rwt59nm137flDeOOjpRSVlHksb9sygt5d2nDd+UMpKS3jqVe/Zl1K47jlECAmPIT07Kr67cnOJzo8hMzcmvvm0zeOp3t8HN+t2cYXP1fdRvincYM5/fhu5BcVM/GJdxok7sMVExFCelYt9avltqWnbnLqt/iXbXy5zPM2yT6dWpOVu59f9/yu6yzHRGxICLvzquqUlpdPXEgIGdVu+YwLCSEtv6pMal4+sSFOUmJUYiLpefmsz8hsuKCPQkx4CGnV9s30ffnE1LFvPvOn8fRoH8d3a7fxxfLGe4vrYbddXu1tBzBtyBDGd+9GXnExF7/1duV2R3fsxMVvvUWvMZ63LUr9iIwJJSO9qsds5p48ImNCydqbX7msWUggAJdfP5xefduTuiubpx75lH1ZBWzZlM6Q4V34YPYShiR3oVlIIKFhweTleP/iUHYmREZXJUtaREPKes8M3vhLy5lxpz/z5/hRXAS3P1R17EtZZ3jhcT8y0w3X3VbWaHr7ARRllRAU2aTycXBkE/ZtrvmdkvbjPrLW5dOsZRBdL2tNcFQTj/Wpi7OJPz2m3uM9Gr58XI8Kas6eoqrjcEZxDt2at6tRbnybwUxoexIBLj9u+rnmz5kRMUn8edVL9Rrr0YgKDGNPsWf9utZSv7NaD+HcdicRYPy5efmzAKTk72ZwVHe+TF9OTGA4x4W2ISYonPV5vzZY/CKHo/FdLvnfEWyMWeH+934dZboBo6y1F+Ikzp5093DrD+wE7gBS3D0Gbz3ouQcSd7U5G0gCegOjgEeMMS3d6/rg9O7rhpOcGmKt/SewGxjuTvpF4STXRllr+wJLgZurbT/TvfxZnETmNuDfuHvouXv43QPMdj/+XWdb7mRfBc5tv98CJ1hr+wBvArfV8bSWwFDgDJzEGzjv5zfumJ44qPxbwFh3ez1mjOnjfu1o4HngHGttb5xEJzhJ0eXW2l7An4GXq22rerteDeRYawfg3JJ9rTEmoZY6TjTGLDXGLJ05c+ZhvjO/X0U5ZKeWcd6DLTj9ljDmP51DUX4F8X0CSegfyJu37eXjR/bRsksALn2bNDr5+/bz1NSXuXPWDTw2707Sd2RSUV7Vq2ju8wu4qvft/Ofet7nw1rFejPTodGofTevYcBb9tLnGOn8/F81Dgrj27td56tVF/PWmP179DvjTv95n9O0zaeLvx4DOVbdAPf3hYk676wU+XbKeC5KTvBjh7zP5H+8z5paZBPj7MaCL5y1epwzszLwl670U2bET5O/PDScM5InvFns7lGNq0tPvM+rPTtsN7Nz2t5/wB/bYd98xdObzzFm3jkv7OJ+3u5OTmfHNN42oX44A+Pm5iI4NY+2qX/nTZc+zbvVOJk45GYCZT86nV9/2PPPKtfTq256M9FyP42Jj9/1XLk4cXcGTr5cy7YEynpvhjAcIkNjV8vfny7jvqVI+mu1HScmht9XYxPQLI/mp7pz4SFeieoay6hnP2+eLskvJ21FEdO/Gd5vvkfLV4/r7Oxdz4fcP8e/NH3NZgudwD12bt6W4ooStBY3nIuyR+mDXd1zy/d+ZmfIRl8aPAuCT1CVkFO3juf43MbnTmfySs43yRnYXTaNlffRfI9X4uuj87yh0J/EO5cNqSbHvgbuMMW2A96y1m8zR9+MfCrxhrS0H0o0xX+MknXKBJQd6s7l7vsXjJNOqOwEnefWdO4Ym7vgOeM/9/2U4Scb6MtXdYy4PON9aa93vz2x3IrMJUNcYfB+4e++tNcb85u3P1tqdxpjOOGP8jQC+NMach3P786IDY/1Zaw90wRqK0ysTa+0CY0ykMebAmUr1dh0N9HL3cgQIAzodHLe1diZwIONnn9vw+we5D4l0kZdZXvk4P7Oc0EjP7F1olIu44wLw8zeExfkT0cqffanlxHVycfyEEI6f4PSG+PjRfYS3bhxfJxf3682EPj0AWJ2aTlzzqvuaYpuHkJ6XX9dTG72x147glMuHAbDx561Et2lRuS66dQR7d9e8ve7Hz1by42fObVGnXjGs1h84X7/zIzc+fimP1VPch+vs0UmMG9kTgPUpacRGVrVddGQoGVmebdfjuFZ06RDLu/+6Bj8/FxFhTXnqnglMnv4We/bm8fUS5yr6upQ0bIUlPDSYfXne69UxYVhvxg9x9s0129OJjaiqX0xECBn76t43S8rKWbgyheTeify4fofHuk+XrOefk8/i3x99X8ezG8Z5yb0Zf5JTv7Vb04ltcWT1+3plCsOSEvlxnVM/P5dheN+OXPJA7WM7NoRLknpzfi9nn1ydlk6r0NDKK2pxoSGk5XvWKS0/n7iQqnq3DA0hPT+fduHhtA0L4+PLL3U/N5QPL72E8a++TmYjuEX0/JN6c/bgqn0zrtq+GRsewp7f2jdXpZDcM5EfDto3vemSpN6c3/MI2y60ZtsdbM669bx49nieXPw9PeNiefL00wCICA4muUMC5baC+Zs1sPuxNPbc/px2Vl8ANqzdTXRsVeInKiaUvQdNzpGbU0hRYQnffuXcdrnoi7WMGeeccmdl5jP9dqfHZlBwAEOHd6Ugv3EMVRIRBXszqs7tszIgItLzV+SieS5u+ZvTy69TN0tpCeTnQPOIqjKt2zm3/u7cZuhwXOP4FRrUoglFe6sykYV7SwiMCPAo0yS06jyy7chI1r+2y2N96vfZxA4Mw+XfeO5j9vXj+gGZRbnEBFVNphIdGEZGcd23an+ZvpKbu3j+BBwZm8QXaY3vNl+AzOIcYgI965d5iPotSF/BTZ3PgXVQYSt4ZnPVMPP/6ncjO/dn1Gu8IkejcfxSl7pU9g231r5ujPkROB34xBhzHbDlEM9dA5x7iPV1qX72U07t+4jBubX4wt/YRl3Pr031M5Ogw3zOEwdP+gH8C3jcWvthtVuXDxUjOPX57QCtLQY+BT41xqTjTMjy+WHGWl31Pv8GuNFaO+8otvO7xHUKYN/ucnLSygiJ9GP9N0WcdovnGHGJxwexYVEhPUY1pTC3guzdZYTF+lFRbikusAQ3d5GxtZTMbWXE92lSxys1rNeWreS1ZU6iK7ljApf0783HazbQu3Uc+UUlf+jbfOc+v4C5zzuzjA0c04uxE0ey8J0f6TKgAwW5hR63+R4QFhVKTmYeIeFNOeOaETx4hTMXUKvEWHa7b30dOKYXuxrBbbDvfb6C9z53TgoH90ngnDF9mL94Pd07taRgf3GN23zfn7+S9+c7bR0X3ZxHbx/P5OlvAbDop8307daWn9f8StuWEfj7+3k16Qfw1tcreetrJ96hPRI4P7k385ZuoGdCHPmFJTVuBwoODKBZYBMycwvwcxlO7JHA8s3OD6G20eH8muHcljKsdyLb0rw/ptrbC1fy9kJ3/XomMGF4b+Yt2UCPDu765dRSv6AmZOY49RvaM4Hlm6p+6A3s2o5tqdnsyfZesv7VFSt5dYX7+6RDApf1SWLu+g0ktWxJXnGJx62iABkFBeSXlJDUsiUrUlMZ370bL/+8go2ZmQx8pnLkDb6+9mrOevU1sgs9x1X1ltmLVjJ7kVPPE7sncP5Jvfls2QZ6xtexbzZxt92BfbN7Aj+n7Kpt017j0XYJh9l2xdXarls3Xl7ufB/Fh4ezbZ/zeTu5YyIpWc41vuQXqiaVmDFmDAu2bFHSrx7MfWcpc99ZCsDAIZ0487wBLPx8DV16tKYgv9jjNt8DfvhmI737xbNi6TaSBiSwY6tzi33zsGDycguxFi64Yijz5jaeRESHzpb0XYaMVCcJ+MPXLm64o9yjTGQ0rF3h9PrbtcMZzy80HDJSoUWMM85fZjqk/mqIjm0cST+AsMSmFKQVs39PMUEtAkhdnE3SlHiPMkXZpQS5k4HpS3MIae35cyD1u2w6X9iqoUI+LL5+XD9gfd6vtGkaRcugCDKKcxkZm8T0NZ4X5doER7Gz0PmcDYrqws79VcNaGAzDY3ozeVmt81F63fq8X2ndNIq4oBZkFucwIqYPD6x91aNM6+Aodrnrd0JkV3a56xfoCsBgKKoooV/EcZTbco9JQUQaCyX+/iCMMR2ALdbafxpj2gG9gJVAXcM0LwAeNMZMdPcWwxjTC6dH2TfAdcaY/wItgJOAW4Euhwghz/1amcAPwNPGmI7W2s3GmGZA69+YLTgPOFTf/HRjTFdgA87EGXmHKHsoYcCBXx+XH+FzD9SxBmNMXyDNWrvbPYlIL2AVznvxjDEmwVq71RjTwt3r7xvgYuCv7gRkprU2t5ZemvOAG4wxC6y1pcaY44Bd1tp6z065/AzDr2vOu/dlYyugx6hgotoF8N1recR1DCDx+CDi+zZh+4piZv0pA+MynHRFKMHNXZSVWGbfuReAJsEuTr05DJdf47kCe8DCzVsZ1jGeL/50JYWlZdw5typPO+eaiznzBafn5K0jTmRsj84EBwSwaMo1vL3iF/616Ad6tozl6fPG0jwoiOGdOjBl2CBOf+7lul6uQS2Zt4oBo3vx4sqHKd5fwuOTqn6APv3t/fxp6L0A3DDjIhJ6OLeRvP7wh+za7JyMjJs4kj7J3SgrLSd/XwGPXf9Cw1fiEBYv38qgPh14+8mrKSop5W/PVuXGZz18KVfc/sohn//RV79w1w1jePXRyyktK+eBZz6t75CPyLe/bGVoj3jmTL+SopIy7nu5at98488Xc+GDrxHcJIAnbhhHE38/jMuwdMOvvPPNKgCmjB9K+9gIbIUlNSuPv73+hbeqUqtvV29lSM945vzNXb9ZVfV7/Z6LuWi6U7/HJ7vrZ5z6vfv1qspyYwZ2Zt5P3p/U44CFW7aSnJDAgmuuoqi0jNs/q9on5152CWNfdn4k3PvFl8w4dQyB/v58vXUbC7ceevL3qKZN+eDSiwlp4gyGfkW/vpzy0n89JpRoSN+s2crQ7vHMvfdKikrLuPfVqrabfcfFnP/QawQHBvDkdeMI8PfDZQw/bfqVd7512m54r0TuOG84ESHB/Ov6M9mwK4NJT9c1oknDWLh1K8kdElhwtbvt5lVru0svYewr7rb78ktmnFKz7W498UQ6tIigwlp25eZWzuj7RzPtfliyAvblQPK5MPlKOPd0b0d1ZJZ8t4mBgzsy673JFBeV8uhfq3raPPvqRG64xLk54oWnvuT2+8/i+qljyNm3n0enzwGgd794rpo0AgusXr6dp2Y0nmODnx9cNrmMGX8OwFbASWPKaRNvefe/fiQcV0HfQZYLryvjxSf8+ew9Fwa49pYyjIGNa1x8dI8LPz8wLrj8xjJCw37zJRuMy8/Q/ao2LHkwBSosbZIjCW0bzMa3Ugnr0JTY/mFs+zSDPctyMC4ICPGn16Sq6Yz37ymmcG8pLbrVnMilsfDl43q5reAfGz7g0T7X4sLFJ6lL2FaQzlUdRrMhdyffZa7l7LaD6RfRiTJbQV7Zfh5cO7vy+b3DE9hTvI/UoqxDvIr3VNgK/rnxPWYkTcRlDJ/udup3ZcIYNuTtZHHmGsa3GUK/iOMos+XklRXy0DpnZvjwJiHM6D0RiyWzOIe/uLmujAAAIABJREFUr605Y7xIY2Aa20yO/yuMMfkHJrOotiwZz8k98g/0aDPG3AFcCpQCacBF1tosY8zrOEmoTw8e588Y0wr4B9APZ4KNbTjj922m7sk9brHWnuF+/lPAUmvtLGPMjcBkYLd7nL8RwMM4E1IA3O3uZbcNZxKNTPfEHY9aa5PdCa13cMbiuxFngov+1trJ7tc61729DJwxA0OstVcc/D5Uq1tdy88EngCycZKfA9yvfwWek3t85J7Qo7ItjDEBOIm4SGBW9XH+3BN4/K1afZcAk6y1RcaYU3Em93ABe6y1J//G5B7V29UFPIAzGYpx1/8sa+2hpjqzz20YdojVf2zXdf6a4x44eIhF37Dx7qmc0vxKb4dRbz7LfYnB53v7huH6s3j2NPre4Jv7JsDPz06l37W+W79lz08l8dHHvR1GvUi55WaSJvtu2614aiqJj/lm2wGkTLuZirTjvB1GvXHFbWT0wOneDqPefL7kHn7cXmN4Zp9wfPutTF1xgbfDqDdPJL3p88f1k748eBh437Fo5CMMXzDN22HUm69GPNb4elUcQ93+/IRPJqLWPji1Ubabevx5ycFJP/eyhcBC99/3HbTuIaomoai+/KJDvMZuYEIdq291/6v19d2PJ1f7+184t9EeeLwAZ1zAg18zvtrfS3Fmq8XdG7BXtaLfALOqlX0HJzF48Pbuqy34QyyfA8ypZfmsA6938GzBB9rCWluKM35fbdv9DPisjnWf4twCXH1ZFs6twIeM2z3O4J/d/0RERERERER8mvHJtF/jpXk4RUREREREREREfJASfyIiIiIiIiIiIj5IiT8REREREREREREfpDH+RERERERERESkYWiMvwalHn8iIiIiIiIiIiI+SIk/ERERERERERERH6TEn4iIiIiIiIiIiA/SGH8iIiIiIiIiItIwNMZfg1KPPxERERERERERER+kxJ+IiIiIiIiIiIgPUuJPRERERERERETEB2mMPxERERERERERaRBGY/w1KPX4ExERERERERER8UFK/ImIiIiIiIiIiPggJf5ERERERERERER8kBJ/IiIiIiIiIiIiPkiTe4iIiIiIiIiISMPQ5B4NSj3+REREREREREREfJASfyIiIiIiIiIiIj5IiT8REREREREREREfpDH+RERERERERESkQRiN8deg1ONPRERERERERETEBynxJyIiIiIiIiIi4oOU+BMREREREREREfFBGuNPREREREREREQahsb4a1Dq8SciIiIiIiIiIuKDlPgTERERERERERHxQcZa9bEU+YPRh1ZERERERMR3GW8HUJ96TnvCJ3/Trn5saqNsN43xJyIiIiIiIiIiDcMn036NlxJ/In9AI05+yNsh1JsF8+8gfuYj3g6jXmybeCtvp/Tzdhj15rzEZZz57WRvh1Fv5gx9ipMXTvV2GPVmfvIT9Pv0Lm+HUW+Wnfo32r84w9th1IvtV91G9zn3eTuMerPmzPt89rgAzrFh9MDp3g6j3ny+5B4q0o7zdhj1xhW3kVM73OLtMOrFp1seZfCEx7wdRr1Z/NY0rll6hbfDqDcv9J/FwMsf93YY9WbJf29mwJW+W7+fXrrZ2yGID9EYfyIiIiIiIiIiIj5IiT8REREREREREREfpMSfiIiIiIiIiIiID9IYfyIiIiIiIiIi0iAa5dS3Pkw9/kRERERERERERHyQEn8iIiIiIiIiIiI+SIk/ERERERERERERH6Qx/kREREREREREpGFYbwfwv0U9/kRERERERERERHyQEn8iIiIiIiIiIiI+SIk/ERERERERERERH6Qx/kREREREREREpEEYjfHXoNTjT0RERERERERExAcp8SciIiIiIiIiIuKDlPgTERERERERERHxQRrjT0REREREREREGobG+GtQ6vEnIiIiIiIiIiLig5T4ExERERERERER8UFK/ImIiIiIiIiIiPggJf5ERERERERERER8kCb3EBERERERERGRhqHJPRqUevyJiIiIiIiIiIj4ICX+REREREREREREfJASfyIiIiIiIiIiIj5IY/yJiIiIiIiIiEiDMBrjr0Gpx5+IiIiIiIiIiIgPUuJPRERERERERETEBynxJyIiIiIiIiIi4oM0xp+IVJo8aRTHD0ykqLiUGY98zKbN6TXK+Pu7mDJ5NL17t8NWWP7z0iK++XaDF6KtaVibeO4ZPBI/Y5i9fhXPrlzisb6Jy4/Hh59Gj6hY9hUXMvmLuezMz+XMjl25rtfAynJdIqM5472X2Z6bzdtjL6pcHhcSwgeb1jL9+68arE512bi0jE+eK6aiAvqNCWDYhCYe6z+ZWcyWVeUAlBZZCnIsd78dUrm+aL/ln9ftp+sgf8ZOCmzQ2A9H/upM9ryxAWst4Se2JvK0hFrL5S5NZ/ezq2j/l4EEx4dVLi/dW8iWv3xP1LgORJ4S30BRH53+LbowqeN4XMbwaeqPzN7xZa3lhkb14t4eV/KnZY+zMe/XBo7yyAyK6sQtXU/Hz7j4YOdSZm1Z5LH+nLYDmdD+eMqtpbCsmAfWfMDW/AyOj0zkxs5jCHD5UVpRzpPrP+OnrC1eqkXdhrVO4N4TnO+aNzeu4tlVP3qsHxjbhnuPH0mXFtHcuPBDPtm2sXLdf0efS5/oVixN38VVX7zb0KH/pqExHbmj5yn44eLdHT/zwqZvPdZfnjiIc9r3payiguySAu5ePofUwhwGRsVze49TKsslhERxy9J3WJC2vqGrUMOxPjas3buHcYldmNTnBKyFPfvzuWnBx2QXFzZ01Wo1adoYBgzuRHFRKY9On8PmDWk1yvj7u5h866n06hePrbC89OwCvv1qPTFxYUz7yzjCwpuSl1vIw/e+T+aePC/U4sjd9RAs/B5aRMDcWd6O5uhdf8+ZDEjuSnFRCY/dOpuUNbtqlBk2NonzJ40Ea9mbnssjN79ObvZ+rr7jDI4f2Y2y0jJSt+/l8dtmU5BX5IVa1G7qlcMZ1CeBouIyHnjmMzZu3VNn2YdvO4vWMWFccst/AZh+0xm0axUBQGjTQPL2F3PFba80SNyHI2tlNlte2YatsMQlx9J2XGuP9elf72HLG9sJjHDO11qNjiNueCz52wrY/NIWygvLwWVod2ZrogdFeaMKv2naxcMZ3DuBopJSpj8/jw3b626/R286k9bRYVx418sAjBzQiWvHDyK+ZSRX3v8667bV/I3RmEy7aDhDejl1vf8/h67rY1Ocul7wl5cbMEIfoDH+GpQSf9UYY8qB1dUWnQVEAZdZa6cc5jbCgYustc/Usd4Cr1lrL3E/9gdSgR+ttWf8nvhrea0XgMettWuP8vmPAKcBn1hrb62jTDzwkbW2Ry3L1wEbgCbAImCStbbiEK+32Fo7+DdiugmYaa3df9Dy94EEIASIBra6V00CruJ3vA+HiCUY+AwYYa0tPwbb6wlMs9Ze8Xu3dTSOH9iB1q0juPSK5+jatRU3TRnDn6bUPIBdfNFg9u0r4PIrZ2IMhIYGeyHamlzGMH3oyVzy8VukFeTx4fhLmb89hc379laWmdClJznFRSTPfoGxiV244/hhTP5yLnM2r2PO5nUAdI6IYuaY8azd6xzgT3vvv5XPnzv+Uj7buqlhK1aLinLL3GeKufJvwTSPMvz7pkK6nuBPTLuqTtynTaxK5n3/YQmpKZ4fvS9fLiG+h1+DxXwkbIUl/bX1tJ3Wl4CIILb99UdCkqIJbBXiUa68sIzsL3YQ1CGsxjb2zN5ISI/Ihgr5qLkw3NjpHG5f+W8yi/fxVL+pfJ/5Czv2e54QB/sFMr7NSazL3eadQI+AC8Md3ccyaclLpBfl8srgG/h6zzq25mdUlvksdSXv/uokX06K6cLNXU7jxqX/ZV/pfm5a9gqZxXkkhsTw1IArOfWrh71VlVq5jOGvg0Zx8Tz3d824y/hix2Y2Vfuu2V2Qy7RvPmFizwE1nj9z9RKC/AO4uHNSQ4Z9WFwY7up1GtcufoX0wlxmD7uWr9I2kJJX1XbrclKZ8PVMispLOT++P9O6n8wtS99hSeY2zln4bwDCAoL5dNQUFmekeKsqlerj2OBnDPcMHsHJb71EdnEhdxw/jMt79OEfyxZ7q5qVBgzuSOu2kVx5zlN06dGaKbefzpSr/lOj3IVXnsi+7P1cde7TzrG8uXMsn/h/J/PFJyuZ//EqkvrHc9Wkkcy474OGrsZROetUuOhsuONBb0dy9AYkd6FVfDRXj3iILkntmPzXc5h69j89yrj8XFz/l7O4bswMcrP3c9XtpzP2sqG89uTnLP92Iy898gkV5RVcdfvpnD9pJC8+/LGXauNpUJ8E2sRFMGHKi3Tv1JJbrxnFtXe9XmvZYQM7UlhU4rHsnn98VPn3jZcOI39/cb3GeyRshSVl1lZ63NmNwBZNWPGX1bToG0GzNk09ykWfEEnHKzp4LHMFuuh8Q0eC44Ipzi5h+d2riOgVjn+zxvUzfXCvBNrGhXPObS/SI7Elt18+kqumv1Fr2eR+HSksKvVYlrJzL7f9cy53XjGqIcL9XQb3SqBdbDhn3/EiPTq05I5LR3LlA7XXdXi/juwvLq11nUhjolt9PRVaa5Oq/dtmrV1aW9LPnbCrTThOsqkuBUAPd9II4GSg5qW8Y8Bae83vTHZNBHrVlfQ7DCnW2iSgF9ANJ5Fap99K+rndBDQ9eKG1drz7ta4BvqnWhouPwftQl6uA945F0g/AWrsaaGOMaXcstnekBg/qxPwvfgFg3brdhIQE0qJFsxrlTh3Ti9ff/AEAayE3t3H0cEiKbsn2nGx+zcuhtKKCuSnrGR3f0aPM6PYdeXfjGgA+2bKBwa1rvtXjOnZlbsq6GssTwiKIDG7KkrSd9VOBI7BzYwWRrVy0aOnCP8DQ8yR/1n1fVmf5VV+X0WtY1VfWrk3l5O+zdOzbOBN/RVtyaBLTlCbRTTH+LpoPjCN/eUaNcpkfpBB5ajwmwPNQlvfzHgKigmnSOqTGcxqbzs3bsbswk7SivZTZchbuWc7gqB41yl2RcCqzdyygpKLudm4suoe34deCLHYVZlNmy/k8dRXJMV09yhSUVf1gC/ZrUnnRd0NuKpnFTu+ilPw9BLr8CXA1rv00Kaol23L3VX3XbFnHye08v2t25ueyPjuDClvzcvZ3qTsoKC2psbwx6BnRml8Lsti5P5tSW84nu35heFxnjzJLMrdRVO78yFmZvZO4oOY1tjO6VTe+Sd9UWc6b6uPYYNz/NQ0IACA0oAnpBfn1XJPDM/ikzsz/ZCUA63/ZRbPQQFpE1vwuPGVcEm/OcnpzWgu5Oc6xvF1CFCt+2gbAiqXbGHRS5xrPbawG9IbwUG9H8fucMKo7X76/FID1K3YQ0jyIiGjPShnj/AsKdi7wNQ0NIis9B4Cfv91IRblzoW/98u1ExdW8MOYtJ/ZP5LNFzun4mk2phDQLJDK85nlmcGAAF5zRn1nv/lDntkYM6sz877zfm/iAvJR8gmKDCI4JwuXvIvqEKLKWZR/Wc5u2DCY4zvlZGBjRhCbNAyjN8/5358FO6pvIJ9857fdLSiqhTQOJDKu9/S46pR8vfujZfttSs9iRdnjvibcN65PIx4vddd3yG3Ud3Y8X59a9r4o0Fkr8/QZjTLIx5iP33/cZY14xxnwHvGKM6W6MWWKMWWGMWWWM6QQ8BCS6lz1Sx2Y/AU53/30hUHkJwRjTzBjzonu7y40xZ7qXxxtjvjHG/Oz+N7hafAuNMe8YY9YbY14zxhj3uoXGmP7uv/ONMX8zxqw0xvxgjIl1L090P15tjHnAGJPvXv4hTu+5ZcaY840xs4wx51aL87DPcK21ZcBioKMxJsQY86W7DqsP1K/6NuuqkzFmCtAK+MoYc9j3WtbyPjxijFljjPnCGDPQvX6LMWacu4yfu8xP7na9ro5NXwzMcT+npTFmkbvdfzHGnOhePtoY8727vm8bY0LcywcYYxa722OJMebAWd1c4ILDrduxFBUVyp5qt/NkZOYRFeV5stmsmXOSeeXlJ/LcM1dw71/OIiK8Rh7WK2KbhbC7oCr+1II8YpuF1FImF4Bya8krKSEi0LPH4hmJXfhwc82TybGJXfgopXHc0py71xIWZSofN48y5O6tvb98dnoF2WmWDr2d5ElFheXTF4o55ZomtZZvDEr3FePfoqrHon9EIKX7PK/sF23PpTSriJDe0R7LK4rK2PvpNqLGeV5Rb6yiAsPJKN5X+TizOIeoQM8fah1D2hAdGM6SrPq4fnHsxQQ1J70op/JxelEu0UE1f3ye1+545gy7mSmdx/DI2o9qrB8Z1531ubsprTgm11aOmbhmIaQe9F0T1/QPnm1wiw1qTmphbuXj9MJcYmtJ7B1wTru+fLNnc43lp7buwSe7fqmXGI9UfRwbymwFd387n8/OvYIll9xAx4hIZm9YTWMQGRNKRnpVG2buySMy5qBjeYjz/Xr59cN5+uVrufvv5xLuvtC3ZVM6Q4Z3AWBIcheahQQSGtY4evb/L4iMCyMztdoxIS2nRvKuvKyCp+55j2c/ncZrP9xDu46xzHtrycGbYvR5A/lpYeNJjkW3CCE9s9p55t48olvUTEpfe8EQ3pi7lKKS2i90JXVtTVZOATvT9tW63huKs0oIjKw6b2nSognF2TV7JGb+lMWyO1ay9h8bKN5bc31eSh4VZZagmKB6jfdoxESEkL63qv32ZOUTE1Gz/a4/ZzCvf7aszvb7I4gODyE9q1pds+uo6/jBvDZvGUXFf9y6yv8OJf48BbsTNyvct47Wphswylp7IXA98KS7p1l/YCdwB+6ebofoKfcmcIExJginN1z1wYHuAhZYawcCw4FHjDHNgD3AydbavsD5QPV+/31wesJ1AzoAQ2p5zWbAD9ba3ji33V7rXv6kuw493fEDYK0dR1UPyNl11OOwGGOaAiNxbqMuAsa76zEceOxAovIgNepkrf0nsBsYbq0dfpThNMN5f7sDecADOL0uxwPT3WWuBnKstQOAAcC1xhiPAcaMMU2ADtbabe5FFwHz3PtCb2CFMSYKuBtnf+kLLAVudj93NvB/7vYYBRzoNrcUOPHgoI0xE40xS40xS2fOnHmUVf/9/PxcxMQ0Z83aXVw3aRZr1u7i+utGeC2eYy0puiWFZaVszM6ssW5sYhc+rKUnYGO3elEZPYb64/JzPmZLPi6lc39/wqL+uF//tsKSPnsjMecfV2Nd5pwttBjdDldQ47pF5mgZDNd3PJPnUuZ4O5Rj7u0dP3Lm14/zrw3zuCYx2WNdh5AYpnQew4NrfK/evuKMNr3oHt6KFzd/57E8KjCETs1j+K6WhOAf1cHHBn/j4pJuSZz+7ssMfPVZ1mdlMCnpeC9Hefj8/FxEx4axdtWv/Omy51m3eicTp5wMwMwn59Orb3ueeeVaevVtT0Z6bmUPMmkc/PxdnH7xICaPfYKLT5jO1vWpTLjB81zsgkkjKS8r56s5P3spyqPTqX00rWPDWfRT3d8fo4Z04YtG1NvvcLXoG8HAf/Sl30O9iegZzoZ/e9axJLuEDc9u5riJiRhXbT+NGr9O7aJpHRPOwmW+8/1fl+PaRtMmJpyFP/t+XeuLsb75r7HyjV9Gx06hO3FzKB9aaw8kab4H7jLGtMG55XNT7TksT9baVe4x8C7E6f1X3WhgnDHmFvfjIKAdTsLrKWNMElAOVP/Fu8RauxPAGLMCiAc8R+OGEuBAl4plOMkugEFU3YL7OvDob1bg8CW647HAHGvtp8aYAOBBY8xJQAXQGogFDh55+nDqdDRKcMblAycRWWytLTXGrHa/Bjht0KtaD8cwoBNV4waCM/Zj9UuNPwEvuuv3gbV2hTFmGE7i8jv3ftEEZ5/pDKRaa38CsNbmVtvOHpxejR6stTOBAxk/++bbDx1pvWt15ri+nH5abwA2bEglJiYUnLudiI4KJTPTc0Dv3NxCCgtLKifz+HrRek47pdcxieX3Si/Ip1Wzql4NLZuF1rj1yinTnLSCfPyMIbRJE4/B2Md27MKHm2sm97q2iMbPuPgls3EMRNw80pCTWXVkyc20NI+s/btn9ddlHpN37FhXwfY15fz4cSklRZbyUmgSDGOubDwTfASEB1KWVXUlvCy7mIDwqvgqisoo2ZXPjhnO7VDlOSXs+ucKWk9JonBrDrnL0tnz9iYq9peBAVeAi4iRXrmD/jdlFu8jOjC88nFUYBiZxVW95YL9AolvFsejSZMBaNEklOk9ruaeX/7TaCf42FOUS2y1Hn6xQc3JqNYD8GDzUldzZ/czYbUz0UVMUHMe7Xsx96x8h537s+o93iOVVpBPy4O+a9L2/zEmP/gt6UW5tAyu6uEXG9yc9KLcGuVOiO7AxONO5IpvZ9XokXlK6+58mbqesrqH9G1Q9XFs6BYVA8COPOc04OOUDdzgxcTf2HP7c9pZfQHYsHY30bFVbRgVE8regybnyM0ppKiwhG+/cuq06Iu1jBnnnP5mZeYz/fa3AQgKDmDo8K4U5DeesdR80RmXDuaU8539Z+OqX4lqWe2YEBdGZprn92diN2fSiNQdzjiV33y8kgk3VF0PH3VOfwaO6MqdlzxX36H/prPHJDFuZE8A1qekERsV6oz+DURHhpKR5flZ7HFcK7p0iOXdp67Bz89FRFhTnrp3ApPvfwsAP5cheWAnrrzj1Qatx28JbNHEowdfSVYJgRGe51UBoQGVf8cNj2HrG9srH5ftL+OXR9fT/rx2NO/UeHqQnzuyN2cNc9pv7dZ0YiNDwT3UdUyLEPZke7Zfr44t6RofywePXo2fn4sWzZvy7B3nccNDbzd06EfsvBEH1bVFVTvERNSsa093Xec8cjV+Lqeu/779PK5/uPHXVf43KfF35AoO/GGtfd0Y8yPObbufuG8JPdzpBz/ESbIlA9VHoDfAOdZaj3sKjTH3Aek4vclcOD3nDqh+RlZO7e1aam3lYEN1lTmUMvfrYoxx4SSxfktKLYnUi3Em3+jnTrhtw0luHuxw6nQ0qr8PFQdex1pbYarGbTTAjdbaeYfYTiHV4rbWLnInM08HZhljHgeygfnu3qGVjDOJR12CqOr9V+/mfPgzcz50rgYfPzCRs87sy4Kv1tG1aysKCorJyiqo8Zzvf9hMUu/2LF+xnb594tm+Y2+NMt6wMiOV+LAI2oSGkV6Qx9jELkxZ4Hn74PztKZxzXHd+3rOb0zp0ZvGuHZXrDHB6h86c92HNwXudsZ0az9Xl1se52Lu7gqy0CppHGlYvKuO822om7jJ+raAw39K2a1Xvvgm3VX3cfp5fyq5NFY0q6QcQlNCckvT9lGQUEhARSO6SNFpNrPrY+DUNoNOTyZWPt89YSsyETgTHh9H+jqrJFDLmpOAK9Gu0ST+ADXm/0jo4mrigFmQW55Ac04e/r636QbO/vIhzv/tL5eNHk/7EzJQPG23SD2Btzi7aNoukVXAEe4pyGd2yF3etfMujTNumkfy63/nuGBrTmR3uv0P8g3iy32X8a8M8Vu7bUWPbjcHKzFQSwiJoGxJG2v48xnboypSFc70d1jHxy77dtGsWSeum4ewpzOO01j24dZnnzMNdwuK4t/cZXPf9q2SV1DxGnNa6J/9Y90VDhfyb6uPYkFaQR6eISFoEBZNVVMjQNu09JgtpaHPfWcrcd5wLIQOHdOLM8waw8PM1dOnRmoL8YrL21hyd5YdvNtK7Xzwrlm4jaUACO7Y6vRmbhwWTl1uItXDBFUOZN3dFg9blf9FHryzmo1eciWEGDO/K2EuH8PXcFXRJakdBXhHZGZ6J28y0HNp1jCWsRTNysgroM7QTOzY7E5L1O6kz500czm0XPkNxkffHiXtv3grem+fsQ4P7JHDOKX2Y/916undqScH+Yvbu8/wOeX/+St6f74xRGRfdnEdvH1+Z9APo37M923dn1UgYeltohxCK0ooo2lNEkxZNyPghk85/6uRRpiS7hCbuGX33LsuiaSvnFvqKsgrW/mMDsUOjiT6+cU1K9s6XK3nnS6c9hvRO4LxRSXz+wwZ6JLYkv7CEvTme7ffuglW8u2AVAC3/n737jo+iWv84/jkpJCGFhCQk9ECoIgSkSJMOCmLBhgp2RUT0CipiL/xUUOygV/SCBVRsXBUbiCA2pEjvvSdAaAmk7/n9MQtJSAJBzW7Y+32/XrzYnTkz8zw72Z3dZ86ciYngxWGXnhFFP4BPflzKJz+6c21Wh6u6N2fGH2s5u24Juc5exmez3blGR/DSPZeq6Cflmgp/f4Mxpi6wyVr7qnFuyNAMWAqU5lTNROCgtXa5MaZLgenfA3cZY+6y1lpjTAtr7WKcXmc73AWqG4B/arTzecDlOJeenmxsuS1AS+Bj4GIg8CRtT6YSsMdd9OsK1D7N5dNwXt+i12L+c74H7jDG/OiOswGw01pbsOh7wD0WYLC1NtMYUxtn/7xljAkCzgGeBsYbY+pZaze4L9mujnOus6oxprW1doF7fL8M91iIDQCvDIz0x/yNnHtuXSa/ezuZWTk8Nza/M+qEf9/EoMGTAHjr7Tk8+MBFDLmjO4cOHeW550/stOodedby2K8/8F7vK/D38+PjtctZfyCVYS07sHxfMj9s3cjHa5fxYtcLmdP/Vg5mZXLXrPwf6+dWrcnu9DS2pxXtmXRh3Ybc9O1nRaZ7i7+/oe8dQbz7SAYuF7TsFUhcbX9+eD+L6vX9adzW+Whf9lMOTTsHUJqeyOWJ8fcjbkBDtr/0J7gslTpWI6h6GHv/u4HghAjCm1fxdoj/GJd1MW79Zzzb7Hb8jB/f7/6DrUeTuSHhAtalbef31JXeDvG05VkXz636inGtb8TfGL7Y8Seb0vcwuH53Vh3aydw9a+hfuy1tohPJtS7ScjJ4fNmnAPSv3ZaaFaO5rV43bqvnXLp254JJHCimwOQtedby2O8/8N75V+JvDB+vX876g6mGd7++AAAgAElEQVQMb9GRZfuS+WH7BprFxDOhez8qVQiiR816DGvRkZ7TJgLwSZ9rSKwUTWhgIPP638GIX75l7s4t3k3KLc+6eHrZN0xodx1+xjBt22I2pu1laKOurDy4i9nJa7mvSS8q+lfgpdZXAbD76CGGzneKYtVCIokPiWDBvq0n24xHlcWxYc/RI7yy6Dc+vugaclwudqYf4r4533ojvSLm/7qeNu3r8c7nQ8nKzGHsqC+Pz3tj8iDuGOhcPPD2uFk88OSlDB52PocOHmXsU85l9UktE7h5SDcssHzxVsY9Vz7yKo17n4T5S+DgIehyBQy9Ca648NTLlScLZq+mdZdGTJw9kszMHF4akT/azrjpwxja9yX27znMlFdn8txHQ8jLzWPPzoO8cP9HAAx5oh+BFQJ4+r1BgHODkHGPlI/vL78t3ky7c+ryyau3kJmdw9Ov559ff+e567hxxPunXEePDuXrph7HGH9D4o11WDFmNdZlietchdAaFdny6TbC64QR3bIyO7/fzf4/D2D8DQGhATQY7NxkaN+8VA6vSSM3LZeUuU4Bt8Ht9QhLKHozCW/6delm2jerw+fP30xmVi6j3s7ff5OfGsjAx07eC7NLy3rcO7ArUeEhvDj8UtZv28vdYz8v67D/kl+XbaZDszpMG3Mzmdm5PPWf/FynPDmQAY+Xrx6nIqVhbDF3nPtfZYxJt9aGnTCtC3Cftbavu9ddurV2rHveSOA6IAfnUtVrrbX7jTEf4BQBvz1xnL9SbCMEeBloj9PDbrN7en3gM5zLZr8D7rTWhhVc1r2uccBCa+07xpg57nkLC27XfQlrX2vtje71TgZC3OsdYK2tfmKsxrkZyBcF2h3bfgIw3Vpb6DaUJ5keg3MDizCc8ezaAr2ttVuObe8UOd0FDAV2FTfO34nLuqeV9DqcuD+Pbd8PZ+y/i3BO9u8FLrXWHjphW/8BPrTW/uAuxt6P87eQDlxvrd1sjOkGjAGOdal6xFr7pTGmNfCa+/XMwBkHMN2d6/fW2pN1H7Hdev4zl/qWRz/OHEnChJLui3Nm2zLofj7Z2NLbYZSZKxMXcckvQ70dRpn5ouM4es4Z5u0wyszMLi/R8tuHvR1GmVnU+2lqT3zO22GUia03j6DJF094O4wys/KSJ3z2uADOsaFXm6dO3fAMNWP+Y7iSi47J6iv84tfRu+59p254Bvp201jaX/WCt8MoM799fC+3LrzR22GUmbdbvUObG170dhhlZv67w2l9k+/mt2DS8DPrzP1panHnSz5ZiFo8fli53G/q8VfAiQU597Q5wBz34ydOmDca5y6+Jy5z7d/YRgZQ5C6y1tr1OMXEYx44cVn386EFHncpbrvW2k+BT91PdwJt3b0Lr8YZf664ZVJwinQnbn8LUKi4d4rp+3DGFSzi2PZOkdNrOAWzYp24rHtalxO34X78RAnbdwEPuf+dzHhgGPCDtfZd4N1i4vkR5wYhJ05fQOHXE3dPwVY4NzURERERERER8T0+WfYrv1T4k5Y4Nw0xODeruNnL8ZwxrLV/GmNmG2P8rbV5p17ilGoBI92X/IqIiIiIiIiI/C0q/P2Ps9b+jHPDEPkLrLUT/8F1ref4vbJERERERERERP4ev1M3ERERERERERERkTONevyJiIiIiIiIiIhHGI3x51Hq8SciIiIiIiIiIuKDVPgTERERERERERHxQSr8iYiIiIiIiIiI+CCN8SciIiIiIiIiIp6hMf48Sj3+REREREREREREfJAKfyIiIiIiIiIiIj5IhT8REREREREREREfpDH+RERERERERETEMzTGn0epx5+IiIiIiIiIiIgPUuFPRERERERERETEB6nwJyIiIiIiIiIi4oNU+BMREREREREREfFBurmHiIiIiIiIiIh4hNHNPTxKPf5ERERERERERER8kAp/IiIiIiIiIiIiPkiFPxERERERERERER+kMf5ERERERERERMQzNMafR6nHn4iIiIiIiIiIiA9S4U9ERERERERERMQHqfAnIiIiIiIiIiLigzTGn4iIiIiIiIiIeISxGuTPk9TjT0RERERERERExAep8CciIiIiIiIiIuKDjFUXS5Ezjd60IiIiIiIivst4O4Cy1OrWF33yN+3Ct4eXy/2mMf5EzkAT13X0dghl5uYGv1B/9EveDqNMrB85jN5nPeTtMMrMt6ueod01L3g7jDLz+4f30urWF70dRplZ+PZwmg/1zfcewJJxw2jwtG/mt+7hYTQb7pu5ASx7cRj1xvhufhseGMYfW+t4O4wyc27tzfSue5+3wygz324aiyu5gbfDKBN+8eu4b2l/b4dRZsYmTaXNDb57XJ//7nD6/Xqnt8MoM9M6jOfy34Z4O4wy81n7170dQtnyybJf+aVLfUVERERERERERHyQCn8iIiIiIiIiIiI+SIU/ERERERERERERH6TCn4iIiIiIiIiIiA/SzT1ERERERERERMQjjG7u4VHq8SciIiIiIiIiIuKDVPgTERERERERERHxQSr8iYiIiIiIiIiI+CCN8SciIiIiIiIiIp6hMf48Sj3+REREREREREREvMQYU9kYM9MYs979f9RJ2kYYY3YYY8aVZt0q/ImIiIiIiIiIiHjPSGCWtbY+MMv9vCSjgLmlXbEKfyIiIiIiIiIiIt5zCfCu+/G7wKXFNTLGtATigBmlXbHG+BMREREREREREY8wGuOvOHHW2t3ux8k4xb1CjDF+wAvAQKBHaVeswp+IiIiIiIiIiMjfYIwZBAwqMGmCtXZCgfk/APHFLPpwwSfWWmtMseXRIcA31todxphSx6XCn4iIiIiIiIiIyN/gLvJNOMn8EnvpGWNSjDFVrbW7jTFVgT3FNGsHnGeMGQKEARWMMenW2pONB6jCn4iIiIiIiIiIiBd9CdwAjHb//8WJDay1A449NsbcCLQ6VdEPdHMPERERERERERHxFOuj//6e0UBPY8x6nPH7RgMYY1oZY97+OytWjz8REREREREREREvsdamAt2Lmb4QuLWY6e8A75Rm3erxJyIiIiIiIiIi4oNU+BMREREREREREfFBKvyJiIiIiIiIiIj4II3xJyIiIiIiIiIiHmH+/o0w5DSox5+IiIiIiIiIiIgPUuFPRERERERERETEB6nwJyIiIiIiIiIi4oM0xp+IiIiIiIiIiHiGxvjzKPX4ExERERERERER8UEq/ImIiIiIiIiIiPggFf5ERERERERERER8kMb4E/kftmlRNrPeSsflsiT1DKHtlRULzZ/1VjrblmcDkJMFRw+5uOejGAA+fvwgu9bmUqNxIFc8XsnjsZ/Moz260DmxDhk5OTzw9QxWpewp0qZJXBXGXHg+wYEB/LRxM6N+mAPAy5f0oW7lKADCg4NIy8zi4klTji9XNSKcb2+9ntd+mcd/5i/ySD4lGfxQX1p3akhWRjYvPPQZG1fvKjQ/pGIFnp886PjzmLhKzP5qCW+O/ppBD/Sh2bl1AQgKrkBk5VCubDvKo/GfyrAbutK+eR0ys3MZ9cZ3rNtSdD8e89x9l1KtSiUGjngXgFsub8cl3Zpy4HAGAP+e+gu/L9nskbhL675rutKhaR0ys3N4YuL3rN1WNL9X77mMmEqh+PsZlqzfyZgpP+Kylmduv5Dace6/04pBpB3NYsBTkz2dwkmNuKILHZs4+T32/gzW7Cia3/gh/YiJCCXA348/N+7k2alOfj1b1Gdwn3bUiavMwLEfsmpbihcyKL1HeuV/5oycPoNVyUVzHdalPZc2PYuI4CBaPD/eC1GW3gP9unBeY2ffPfrhDFbvLJxPcGAAY2+4kJrRkeRZy08rN/HK178cn98rqQF3nN8WC6zbtZeRk7/1cAZFPdq9C12OHRe+mcHKEo4Lz114PsEBAczZuJlRs+Ycn3fdOc0ZeE4SLmuZvXEzz835meoREXx/6w1s2r8fgCW7knlsxixPpVSsZQsMk98IwOWCzhfkcdHVrkLz9+2Bt54P4Eg6WBdcdUseSW0sG9cYJr3sDzjDLvUbmEerjuVzAKbBj11C6y6NycrM5oX7p7Jx5c4ibTpf1Jz+Q7qDtaSmHOb54R9w+MBRbhnZl3O7n0VuTi67t6by4oipHEnL9EIWp+/h0TDnd6gcBV+94+1oTt+eJYdZMWkn1mWp1T2a+pfGFZq/fU4qq97fRXDlQAASLoildvdoAFZN3kXKn4fBWmKbhtPkpuoYYzyew6ncO6Ar7ZOcz86n3vqetVtL/t4y9p5LqB5biWsefg+A7q3rc1u/diRUjeamJz9g9ZbyddxLX76P5A/WYa0l6rzqxFyYUGy7wwtT2PH6cuo82oaQOhFk78tg48O/UyHe+Y1RMbESVa9v7MHISydteSq7P1gHLktUp2rElpDfoYV72D5+OYmPtSakTgQAmdvT2PnuGlwZeWAg8fHW+AX6ezD6M5Mpn4cYn6XCnwcZY/KA5QUmXQrEANdba+8u5ToigWutta+XMN8CU6y1A93PA4DdwB/W2r5/J/5itvU28KK1dtVfXP55oA/wjbX2/hLaJADTrbVnFzN9NbAWqADMBYZYawt/wy28zG/W2vaniOkeYIK19mgx8/oCo3B6ygYCr1hr3zzZ+sozV55l5r/T6D8qkvBoP94dfoB651Ygplb+x0L328KOP170VQYpm3KPP29zWUVysyxLvi1fX5g7102gdlQkPd6cRPNq8Tx1fjeueO+jIu2ePL87j3w3kyW7knn7ykvpVDeBuZu2cM8X3xxvM7JbJ9Kzsgot91C3zszdtKWs0zil1p0aUK12NLdc8AKNmtVk6OOXMOzqNwq1yTiazdDLxh1//uond/LrzJUATBiTn+fFA9qR2LiqZwIvpXbN61AzPoorh02kSb2qjLilB7c++kGxbTu3rkdGZnaR6R998ycffL2wrEP9Szo0rUPNKpH0e2giZ9etyoMDu3PjMx8Waffgv6dzxJ3bc3dcRI9WDZixYC0Pvfn18Tb3XNWJ9KNF8/emjmclUCs2koufnETThHgevrob140t+j4cMfHr4/mNvbUvPc+pz/eL1rFhVyrD3/qKR6/p7unQT1vnxAQSKkfS841JJFWL58kLunHlO0Vz/XHdJiYvXMqMO270fJCnoWPjBGrHRNL3mUk0qx3PI1d0Y8ArRfN5d84iFmzYQYC/H2/fcQUdGyXwy5ot1IqJ5Jburbn+tamkZWRROSzEC1kU1rmus4+6T3COC0/26sYV7xfN6ale3XnYfVz4T4HjQttaNehRP5GLJk0mOy+PyhXzc9p28CAXvzOlyLq8wZUH740LYMToHCrHwON3BXBOOxfVa+e3+XKKP206ueh+kYudW+GFRwJ58f0caiRYnhyfi78/HEyFhwcH0qJdDv7l7Ldr6y6NqJYQyy3dRtOoeS2GjrqcYZe9WqiNn78fgx+9lNvPf47DB45y8wMXctH1HZnyygwW/7KOSc9/gyvPxc0PXEj/Id2ZOObrErZWvlzaG669DEY+4+1ITp91WZb/ZwdtH0kkJDqQnx9cR3yrSoTXCC7Urlr7KJreUqPQtP1rj7B/7RG6jG0IwK+Prid1VToxTcI9Fn9ptG9Wh5rxkVw+YiJnJ1blgRu6c/NTRY/rAF1a1iMjM6fQtI07Uhnx6lc8eGMPT4R7WqzLsnvyWmrf24LAysFsemo+4c1jCKoeVqhdXkYu+3/YTkjdiELTK1QJIfHJtp4M+bRYl2XX+2upc18LAioHsempBYQ3jyG4mPxSZxbOz+a52D5hFTVuO4uQWuHkpudg/HVRpZQ/+qv0rAxrbfMC/7ZYaxcWV/RzF+yKEwkMOck2jgBnG2OOfSvtCRQ9FfoPsNbe+leLfm6DgGYlFf1KYaO1tjnQDDgLp5BaolMV/dzuASqeONEYEwhMAC6y1iYBLYA5pxvwCes0xhivvQd3r88lsqo/kfH++AcaGncKZv0fJRcPVs3NpHGnoOPPE5IqUCGk/J1t7VE/kf+uWA04vS/Cg4KIDQ0t1CY2NJSwoAos2ZUMwH9XrKZn/cQi6+rTqAFfrVpbaN07Dh1i/b7UMsygdNp2O4tZXywGYM2y7YSFBxMVU/KX4Oq1o4msHMqKRVuKzOvcpxlzvl5WVqH+JZ1aJvLtz87Hy8oNuwmrGER0ZGiRdiFBgVzTpxWTps3zdIh/S+fmiXzzu5Pfik27Ca8YRHSlovkdK4r5+/sREOCHLeYWaD1aNeT7+WvKNuDT1KVZItPnO+/D5VuSCQ8JIiai5PwC/PwI9PfHutPbnLKfrXsOeCzev6N7g0SmLXNyXbormfDgIGLDiua6dFcye9OPeDq809b17ES+Wujks2yre9+FF84nMyeXBRt2AJCb52L1jj3ERTo/kC5v25Spvy4lLcM5abI/PcOD0RevR/1EphU4LkSU4rgwrcBx4doWSbw5bwHZeXkA7D/q/ZyKs3GtoUo1S5WqEBAIbTu7+PO3wl8zjIEM96nNo0cMkdHOmy4omONFvpxsp1151LZHE2ZNc07orFmyjbCIYKJiCx/7jHH+BYc431kqhgezP+UQAH/+sg5XnnOOeM3ircTEl68rFk6mdRJElq9aV6kd2HCU0PggQuOC8Avwo1r7KJIXHCrdwgZc2S5cuZa8HIsrzxJUKbBsA/4LOp2TyDe/uo/rG0s+rocEBXLtBS2Z+GXh7y1bdu9nW3L5PO5lbDpEhSohVKhSERPgR6Vz40hbsrdIu73TNhLdOwETeGaVGDI2HSaoSggVqoTgF+BHpTZxpC3eV6TdnmmbiO1Tu1B+6Sv2E1wjjJBazpszICwQ41dOP0Dlf9qZ9a70QcaYLsaY6e7HTxhj3jfG/Aq8b4xpYoyZb4xZYoxZZoypD4wGEt3Tni9htd8AF7ofXwMcP91kjAk1xkx0r3exMeYS9/QEY8zPxpg/3f/aF4hvjjHmU2PMGmPMFOPuW++e3sr9ON0Y87QxZqkxZp4xJs49PdH9fLkx5v+MMenu6V8CYcAiY0x/Y8w7xpgrCsSZXtrX0FqbC/wG1DPGhBljZrlzWH4sv4LrLCknY8zdQDVgtjFm9gmbCcfpIZvq3maWtXate31xxphp7tyXFnjthhtjVrj/3VPgdV5rjHkPWAHUNMb0Msb87o75E2NMGB6QluoiIib/VH54tB/pqXnFtj20J49DKS5qNyt/X7ROFBcexu60tOPPk9PSiQsPK9ImOS39pG1a16zOviNH2XrgIAAVAwMZ1LYVr/1SPgpM0VUi2Jec/6V5X8phYuIiSmzfuU8Sc79bXmR6lWqRxNeIYukfG8skzr8qtnIYKan5+3Hv/jRiKxd9awy6qgMffr2QzKzcIvOuOL8574+5nodvP5/w0KAi870pNjKM5P35+aUcSKdKZPFv/dfuuYyZLw7maGYOsxauLzSvRf3q7D98hO17DpZpvKerSmQYyQcK5Hew5Pxev7MfP46+naNZ2fyweH2xbcqzuPAwkg8XyPVw0c+TM0mViDCSD56w7yqVnE94cBCdm9Rl3rrtANSOjaR2bBTv3tWfyf+6mg6Nape4rKfEhYWx+/BfOC6EOW0SoiJpXbM6n153NR9ccyVN4/MvUaxRqRJf3jiAD665klY1qpdxJid3YB9Ex+afHKgcCwdSC/8A7XddHr/N8uNf1wbywiMBXDck/7i/cbXhwdsCeOj2QG68O7fc9fYDiI6vxL7d+Z93+5IPFSne5eW6GPfY57zx7b1MmfcYterF8f3H84usq9eVbVgwp3ydNPFVmftzCInO/w4ZHB1I5v6cIu12/3GQOfetYeELm8nY55wYqtwglOgmYcwYtIKZg1YQmxRRpKdgeVAlqvD3lj3706kSVfSzc/Dl7fngu0VkZhf93lJe5R7MIrBy/mseEBVMzoHCV8RkbD1MzoFMwpNiiiyfvTeDTU/MY8vohRxZV/6KmzkHMgvnVzmoaH5bDpOzv2h+WSlHwcCWsYvZ8Ph89n6z1SMxi5wuFf48K8RdsFtijJlWQpuzgB7W2muAwTiXkzYHWgE7gJG4e7qdpKfcR8DVxphgnN5wfxSY9zDwo7W2DdAVeN4YEwrsAXpaa88B+gMFr5togdMT7iygLtChmG2GAvPcveHmAre5p7/izqGpO34ArLUXk98DcmoJeZSKMaYi0B3nMupMoJ87j67AC8cKlScokpO19lVgF9DVWtu1YGNr7X7gS2CrMeZDY8yAAr31XgV+cud+DrDSGNMSuAk4F2gL3GaMaeFuXx943VrbBKeH5iM4+/wcYCEwvJgcBxljFhpjFk6YMOGvvEx/y+q5WTTsUAE///+dM1h9Gzdk+ur8HwR3dWzLpAWLOZpT9IvqmcDp1be06PTezfhlxkpcrjNvoI36tWOpHhfJTws3FJn3+Q9LueJf/+H6ke+x70A6dw/s4vkA/yF3vfw5F9z7JhUC/GnduGaheeef24jv568tYckzw5Dx0+jx0AQCA/xp07DmqReQcsPfzzDmut588PNidu4/5J7mR63YSG4Z/wkPvP8Nj1/Zk/Dg8lV4P10Bfn5UCg7iivc/YvScubx6iXNude+RI3R6420ufmcKT//4Ey9d1JuwChW8HO3J/T7bj/N6uXjlgxzu/b9c3nzOGQ8QILGx5dm3cnliXA7Tp/qTXb5GECg1/wA/LhzQjqEXvcSAtk+xec1urrqjW6E2Vw/pTl5uHrO/+NNLUcqJ4lpWovv4s+gythExzcJZPH4bAEeSs0jfmUXPfzeh55tNSF2RRurqUvcPKFfq14qlepVI5iwq+r3lTGZdlpSP1hHXv0GReQGVgqg/tiN1n2hL3NUN2PnmCvIyzpyiJ7gvdf5oPfFX1y86M89ydP1BatzehLoPteTwn3tIX7Xf80Geiaz1zX/llMb486wMdxHvZL601h67huR34GFjTA3gc2vt+tIMZGutXeYeA+8anN5/BfUCLjbG3Od+HgzUwil4jTPGNAfygIKf3POttTsAjDFLgATgFwrLBqa7Hy/CucQYoB35l+B+AIw9ZQKll+iOxwJfWGu/dV+S+4wxphPgAqoDcUDyCcuWJqdCrLW3GmOaAj2A+3ByvBHoBlzvbpMHHDLGdASmWWuPuLfxOXAe7uKhtfZYt7G2OMXHX937tgLOfj9x2xNwLjUGsBPXvXfKF+dUwqP9OLwv/0x/WqqLsOjiT++v/jmLnoPLbw+WAeck0T/JGQZy2e4UqobnXwsTHx5GSlrhL4gpaenEF+jtcWIbf2Po1bAe/d7JH1MuqVpVLmhUnxFdOxIRFITLQlZuLpP/LFpMKyt9r2nLBVe2AmDd8p2FejnExEWwL+VwscvVaRiPn78fG1btKjKvc59mjB/1ZdkEfJou79mci7s1BWD1pmTiovP3Y2zlcPbuL7wfz65fjUZ14/j81Vvx9/MjqlJFxj96FXeO+pgDh/KH6fzix+WMHdHPM0mcxJVdk7j0PCe/VVtSiK8czrG/nrioMPYcLPmHTHZuHj8t2UDn5vX4Y5XzY8jfz9D1nHpcN6p8jC/Wv1MSl7V33ocrt6YQH5W//+IiT53fnGUb6dI0kXlrtpV5rH/XgJZJXNXCyXX5rhTiIwrkGlH0M6e8698hicvbuvfd9hTiI0/Yd4eKz+exK3uwdd9BJs9dfHxayqF0lm/dTa7Lxc79h9m69wC1YiNZud2zA9UPbJHEVe7jwvLkFKpGhB8f+KTUx4V0p01yWjoz1jk/1JftTsFaS+WQEPZnZBy//Hdlyh62HTxIQuUoViR7Z1D+qBhI3Zv/PXH/XoiKLvwjZO73ftz3tPOju/5ZlpxsSD8EEVH5barXci793bHFULeB93/E9L2uPRf0PxeAdcu2E1M18vi8mPhKhXq/AySe5fS83L3NGZbj56+XctUd+edze1zeijbdGvPgwDN2mOYzTnDlQDJS80+cZqbmHL+JxzEVwvN/ltbuHs3qyc53lt3zDxFVvyIBwc531CotIjiw7gjRjb3/vfSK7klc2tl9XN+c4nxvcXdcr1I5jD0HCn/ONKtXlcYJcfx37C34+/tROaIib4y8kjtGf+Lp0E9LQGQQOfvzx/TOPZBJYFT+CR1XZh5ZO4+wdYxz07vcQ9lsf3UJNe9uTkidCPwCnRMiIQkRVKgSQnby0eM3xigPAqOCC+e3P6vY/DaPdk4U5B7KZuurS6l9dxIBlYMIbRBJQLiTY3izGDK2phF2VmXPJiFyCir8lT/HBwCy1n5gjPkD57Ldb4wxtwObSrmeL3GKbF2A6ALTDXD5sctUj0805gkgBUjC6Qla8I4NBfs651H8302OtcdL3CW1OZlc93Zx96QrzSnzjcUUUgcAsUBLa22OMWYLTnHzRKXJqQhr7XJguTHmfWAzTuHvdBUc5MkAM909PD2qav0ADuzK42ByHuHRfqyem8lF9xU9CKduzyUz3UX1RuX342LKn0uZ4i7AdUmsw8Bzkpi+ei3Nq8WTlpXN3iOFx9Xae+QI6VnZNK8Wz5JdyVx6dmPeX7Tk+Pz2CbXYlHqg0GVf1075+Pjjuzq25Wh2jkeLfgDTP5zH9A+dmnHrTg25aEBbfvpmGY2a1eRIWiYH9qUVu1yXPkn89E3RWGvUiSUsIoTVS8pHoeWzmUv4bKazH9q3qMMVvVow87c1NKlXlSNHs0g9WHg/TvthKdN+cPKKj4lg7Ih+3DnK2U/RkaHH23dpXY9N24uO1eJpn8xeyieznXg7NK3DVd2a8/38tZxdtyrpGdmkHiqcX0hQIBWDK5B66Aj+foYOzeqyZF3+kK1tGtdmy+4DRX5YeMvUuUuZOtfJ77wmdejfKYnvFq2laUI86RnZ7Dt8Qn4VAgkNrsC+w05+5zWpw58by2RI2n/clEVLmbLI/ZlTrw4DWyXx9aq1JFWLJz0r+4wYy6+gqb8uZeqv7n3XuA7XdEzi28VraVY7nrTMbPalFc1naO/2hBqdHL0AACAASURBVIcE8cTHMwtNn71iA71bNOKLBauIDA2mdmwUO1JLOZbXP2jy4qVMXuzeR3XrcF3L0zsu9Du7Me+5jwsz12/k3Fo1mbdtBwlRkQT6+7M/I4PKISEczMzEZS01K1WidlQU2w9677L7ug0tKTsNe3c7RcB5P/lxx8jCQ3hEx8KqJU6vv53bnPH8wiNh726oXMUZ529fCuzeboiN837RD2D6+78x/f3fAGjdtTEXXdeBn75aQqPmtZxj397Cx759yYeoVS+OSpVDObT/CC061mfbBufuqi07NeTKQV0Zcc3rZGWemT34z0SRiRU5sjuLo3uyCK4cyK7fDnDO3YWHAcg8kENwlFMMTF54iDD35bwhMYFsm5WKK8+ChdRV6dTpE+vxHIrz6aylfDrLfVxPqsOVPZozY95azk4s/rj+2Y/L+OxHZ0zlqjERvDjs0nJf9AOcu/OmZJC9N4PAqCAO/ZFC9dvz77voXzGAhq92Pv58y5iFxF3VgJA6EeQezsbfPe5d9p6jZKdkUCHW+zd9KiikTjhZe46SvTeDgKggDs1PocbtTY7P968YQOPXOh1/vmn0Iqr2r09IHaeQue+bbbiy8jABhiNrDxDTq5Y30hA5qfL7S14wxtQFNllrXzXG1MK5bHcpznhzpzIROGitXW6M6VJg+vfAXcaYu6y11hjTwlq7GKgE7LDWuowxNwD/1Mgu84DLganA1SdptwVoCXwMXIxz19y/ohKwx1306wqc7uBCaTivb6EqgXvcvVbW2jnuSc2BY4M4zALuAF42xvjjjF34M/COMWY0TnGvH3BdMdubB4w3xtSz1m5wX3Zd3Vq77jTjPm1+/oaeg8P4+PFDWJelaY9gYmsH8PPkI8TXD6D+uc6ZrtU/Z9H4vCBO7G065YEDpO7IIyfTMv7GVHrfHU7dc7x/idOcjZvpXDeBWbffREZOLiO/mXF83pc3DeDiSU7vqCdm/MiYC3sRHBDAT5u28FOBO/X2Pash01eV78snF8xdS+tODZn43b1kZubw0sOfHZ837vOhhe7me94FTXls8LtF1tG5TzN++qZ83dTjmN8Wb6Z987p88vItZGXl8H9vfn983rvPXscND75/0uXvvLYTDWrHYoHdew8z5u2ZJ23vab8u30yHpnX47zM3k5mdy5OT8vOb8thABjw1mZCgQF4cegkVAv3xM4aFa7bz2U/5BdxebRoyo5zd1OOYn1dupmOTBL56/CYyc3J5fHL++3DqyAH0Hz2FkKBAXrn9YgIDnPwWrN/Op784f49dmyUy8squRIWF8NrgS1i7cy9Dxpc0QoZ3zdmwmc6JCfwwxPnMeXB6fq5f3DqAS952PnPu73YeFzVpSEhgIHPvupVPlqzgtZ/Lx5ihBf28ejPnNU7g64ecfffoh/n5fHzvAK56YQpxlcIY1PNcNqWkMnX4AAA++mUpn/+xgl/XbKVdg9pMG3E9Lmt58au5HDrq3bu/z9m0mS6JCfw46CYycnN5oOBx4cYBx+/K+/jMH3muT9HjwqfLVjC6Ty++ufk6cvLyuP9r5/3aumZ17jmvPTl5eVhreez7WRzKzCqyfU/x94frh+by3EOBWBd0Oj+PGgmWz971p04DF+e0s1xzey4TXwrgu8/9MMBt9+ViDKxb6cf0x/zw9wfjBzfclUt4ObzvxYLZq2ndpRETZ490jn0j8keLGTd9GEP7vsT+PYeZ8upMnvtoCHm5eezZeZAX7nfu4jzkiX4EVgjg6fcGAc4NQsY98lmx2ypv7n0S5i+Bg4egyxUw9Ca44sJTL1ce+Pkbzr65BvOe3oR1WWp2rUx4zRDWTN1NZGJF4ltVYvO3e0leeBg/fwgMC6D5EKd4Uq1tJPtWpPPTfc7xrkrzCOJblb8/zl+XbqZ9szp8/vzNZGblMurt/OP65KcGMvCxySddvkvLetw7sCtR4SG8OPxS1m/by91jPy/rsEvF+PsRP7Ah215cjHVZIjtWI7h6GHumbSQkIYLwFiUXYo+uO8De/24Cf4MxhqrXN8I/rHyNGW78/ag2oCFbXliMdUHUeVUJrh5Giju/iJPk5x8aSMz5Ndn41AIwEN4suthxDkW8zdhyfB2yrzHGpFtrw06Y1gW4z1rb193rLt1aO9Y9byROsSgH51LVa621+40xH+AUAb89cZy/UmwjBHgZaI/Tw26ze3p94DOcy2a/A+601oYVXNa9rnHAQmvtO8aYOe55Cwtu132Tjr7W2hvd650MhLjXO8BaW/3EWN03A/miQLtj208Apltr808rOe1Lmh4DfIVTfFuIcyltb2vtlmPbO0VOdwFDgV0Fx/kzxoTjFC8TgQycXnv/cuceh3MZbl2c3oN3WGt/N8YMB252r+Jta+3LxcVtjOkGjAGO9Sl/xFp7smsv7cR1HU8y+8x2c4NfqD/6JW+HUSbWjxxG77Me8nYYZebbVc/Q7poXvB1Gmfn9w3tpdeuL3g6jzCx8ezjNh/rmew9gybhhNHjaN/Nb9/Awmg33zdwAlr04jHpjfDe/DQ8M44+tdbwdRpk5t/Zmete979QNz1DfbhqLK7no2Ga+wC9+Hfct7e/tMMrM2KSptLnBd4/r898dTr9f7/R2GGVmWofxXP7bEG+HUWY+a/+6Tw+u3nbACz5ZiJo35d5yud/U48+DTizIuafNAea4Hz9xwrzROHfxPXGZa//GNjKA24tpsx6nmHjMAycu634+tMDjLsVt11r7KfCp++lOoK27d+HVQMMSlknBKdKduP0tQKHi3imm78MZV7CIY9s7RU6vAa8Vs2wa0KeE9aYAlxQz/UXgxROmFYnbWvsj0Lq4dYuIiIiIiIj4EuOTZb/yS4U/KWstcW4aYoCD5PeAExERERERERGRMqTCn5Qpa+3PODcMERERERERERERD/LzdgAiIiIiIiIiIiLyz1OPPxERERERERER8QyN8edR6vEnIiIiIiIiIiLig1T4ExERERERERER8UEq/ImIiIiIiIiIiPggjfEnIiIiIiIiIiIeYVzejuB/i3r8iYiIiIiIiIiI+CAV/kRERERERERERHyQCn8iIiIiIiIiIiI+SGP8iYiIiIiIiIiIZ1hvB/C/RT3+REREREREREREfJAKfyIiIiIiIiIiIj5IhT8REREREREREREfpMKfiIiIiIiIiIiID9LNPURERERERERExCOMbu7hUerxJyIiIiIiIiIi4oNU+BMREREREREREfFBKvyJiIiIiIiIiIj4II3xJyIiIiIiIiIinmE1yJ8nqcefiIiIiIiIiIiID1LhT0RERERERERExAep8CciIiIiIiIiIuKDNMafiIiIiIiIiIh4hNEQfx6lHn8iIiIiIiIiIiI+SIU/ERERERERERERH2SsbqMscqbRm1ZERERERMR3GW8HUJY6Xj7WJ3/T/vLZfeVyv2mMP5Ez0KYdVb0dQpmpW2M3LQe95O0wysSiCcPodPHz3g6jzMz98n5617vf22GUmW83PE/3bs96O4wyM+vHBznnDt987wH8+cYwGj3um/mteXIYzYf6Zm4AS8YNo+Eo381v7aPDGLbkam+HUWZeav4R7a96wdthlJnfPr6X+5b293YYZWJs0lRcyQ28HUaZ8YtfR9deY7wdRpmZPeMBnl7Z19thlJmHm0xn4rqO3g6jzNzc4Bdvh1C2fLLsV37pUl8REREREREREREfpMKfiIiIiIiIiIiID1LhT0RERERERERExAep8CciIiIiIiIiIuKDdHMPERERERERERHxCKObe3iUevyJiIiIiIiIiIj4IBX+REREREREREREfJAKfyIiIiIiIiIiIj5IY/yJiIiIiIiIiIhnWA3y50nq8SciIiIiIiIiIuKDVPgTERERERERERHxQSr8iYiIiIiIiIiI+CCN8SciIiIiIiIiIh5hNMSfR6nHn4iIiIiIiIiIiA9S4U9ERERERERERMQHqfAnIiIiIiIiIiLigzTGn4iIiIiIiIiIeIbG+PMo9fgTERERERERERHxQSr8iYiIiIiIiIiI+CAV/kRERERERERERHyQCn8iIiIiIiIiIiI+SDf3EBERERERERERjzC6uYdHqcefiIiIiIiIiIiID1LhT0RERERERERExAep8CciIiIiIiIiIuKDNMafiIiIiIiIiIh4hkuD/HmSevyJiIiIiIiIiIj4IBX+REREREREREREfJAKfyIiIiIiIiIiIj5IY/yJ/A9bON+ff48PxuUyXNAnm6uuyS40f0+K4YUxIaQfMbjy4Kbbsmhzbi45OfDaS8GsX+ePMTD4zkyaNc/zUhYnd3//LnRoWofM7ByeeGcGa7btKdLmtbv7EVMpFH9/Pxav38mYD37EZZ1xJ/p3bc5VXZPIc1l+Wb6ZVz/72dMplOju27rRtlVdsrJyefblb1i3qWhurzzdn+ioMLKycwG49/FPOHjo6PH5nds1YNSDl3Db8PdYuyHFY7GXxuBHL6F1l0ZkZeTwwgNT2bhyZ5E2nfs2p/8d3cBC6p7DPH/vBxw+cJSOvZsx8O6e1Eyswj2Xvcb6FTu8kMHJ3Tm0J+eem0hWZg7PPTed9euLvv4BAX7cdXcvmifVxmUtE//zEz//vJYrrmhNnz7NyctzcfDQUZ5//mv2pBz2QhYlu/+qLnRs4rz3Hn9vBmu2F/37HDfU/d7z82Pxhp2M/sh5791xUTu6NEvEZS370zJ4/L3v2XfoiBeyKNnDvbvQqX4dMnNyePC/M1i1u2h+93RvzyVJZxERHETLZ8Yfn96v+Vnc3+s8Ug6nAzBl/lI+/XOFx2I/lRFX5O+7x96fwZodRXMbP6QfMRGhBPj78efGnTw71dl3PVvUZ3CfdtSJq8zAsR+yalv5+lwBePj8LnSu5+y7kV/OYFVyMfuua3subXoWESFBnDMmf9+1qlWdh3p1pmFcLMM//4bvV6/3ZOintHfJYVa9swPrstTsFk3ipfGF5u+Yk8qaybsIqhwIQML5MdTsHkPqijRWvZf/GXtkVybN/5VAfOtIj8ZfGsNu6kq7FnXIzMrl/17/jnWbi+6/Y8aMuJTqVSox8L53AXjqnr7UqhYFQHjFINKOZnHjiPc9Endp7FlymBWTdmJdllrdo6l/aVyh+dvnpLLq/V0EH9t/F8RSu3s0AKsm7yLlz8NgLbFNw2lyU3WMMR7P4a96eDTM+R0qR8FX73g7mr/mriHdObd1IplZOYwZ+w3ri/le9dLz11C5cijZ7u9l9z/4MQcPHiWuSgQj7u1NpUoVSUvL5Okx09m3L83TKZRo559HWDBxL9YF9XpE0PSyykXabPk1jaVT94OBqIQKdBpWlfQ9OcwZsxtrLa48aNSnEg3PL3+fK5sWZTPrrXRcLktSzxDaXlmx0PxZb6WzbbnzOyknC44ecnHPRzEAfPz4QXatzaVG40CueLySx2M/Y2mIP49S4U9KxRjzMHAtkAe4gNuttX+U4fbmAPdZaxeWZvop1pVurQ37B2Ly6GtQ1vLyYPyrITzz3BFiYi3/GhLKue1yqZ3gOt7mwylBnNclh74X57B1ix+PPVSRNh+k893XzhfON94+wsEDhkcfrMgrrx/Br5z1Ie5wdgI14yK59JFJnF0nngcHdOOGZz8q0m7khK85kukczJ8b3JcereozY8E6WjWsQefmiVz91GRycvOICg/xdAolatuyDjWqRXHt7W9zVsOqDL+jJ4Pvn1Js21EvTi+2qBcSEsgVF5/DyrW7yjrc09a6cyOqJcRwS/cxNGpei6FPXsawK14r1MbP34/Bj17C7Rc8z+EDR7l5xIVcdF0Hprw6k63rkhk15D3u/r/LvZTBybU5N5Ea1aO4/rp/07hxNf51zwUMvfPdIu0GDOjAwYNHueGGNzEGwt1/gxs2pHDHHZPIysrlootbMGhQV/5v1BeeTqNEHZokUKtKJJc8PommdeJ58Jpu3PBc0ffeA2/nv/eeH9SXHi3rM2PhOt6buYg3vvodgKu7NmdQn7Y88+Esj+ZwMp3qJ1A7OpLzX51EUo14Hu/bjf5vFc1v9tpNTPljKd/dfWORed+uWMeob2Z7INrT0/GsBGrFRnLxk5NomhDPw1d347qxRXMbMTF/3429tS89z6nP94vWsWFXKsPf+opHr+nu6dBLpVO9BBIqR9Jr/CSSqsfzRJ9uXDWxmH23bhNTFizl+ztvLDR996E0HvxyBje3a+mhiEvPuiwrJ26nzcP1CI4O5NcH11KlVSXCaxQ+dlVtH0mTm2sWmhZ9djjnPdcIgOz0XH66exWxzSI8FntptWtRhxrxUVx190Sa1K/K/bf24LaHPyi2bec29cjILHxC87GXpx9/fNd1nUk/mlWm8Z4O67Is/88O2j6SSEh0ID8/uI74VpUIrxFcqF219lE0vaVGoWn71x5h/9ojdBnbEIBfH11P6qp0YpqEeyz+v+vS3nDtZTDyGW9H8tec27ou1atXZuBNE2jcqBrD7u7FkLuLLyo/PXo669YnF5o2eFBXZvywku9nrqBF81rcdnMnnn3ua0+EfkquPMsfb+2l5+PVqRgdwDcjtlGzdSiRNYOOtzm8K5vlnx/ggmdqEBTmT8ZBp7AZEhVA79E18A/0IyfDxZf3bKVm6zAqVi4/ZQhXnmXmv9PoPyqS8Gg/3h1+gHrnViCmVn6M3W/L/ym56KsMUjblHn/e5rKK5GZZlnyb6dG4RU5HOfuZLuWRMaYd0Bc4x1rbDOgBbPduVJ5VFq+BMcarR7x1a/ypVt1F1WqWwEDo3DWHeb8VDskAR484Z4uPHjFERztFwW1b/Ulq4fTwi4yyhIZZ1q8rfx8nnZsn8vXvqwFYsTmZsJAgYiqFFml37MdrgL8fgf7+uDv7cUXnJN75bgE5uU6uB9IyPBN4KXQ8tz7fz14JwKq1uwkLDSY6qmhuJ3PrgI5M+Wz+8bPO5UnbHk2YNW0RAGuWbCMsIpio2MI/YIxx/gWHVACgYlgQ+9293rZv3MPOzXs9G/Rp6NC+PjNmOj28Vq/eRVhYEJUrF91/F/RuxocfOAUwa+HwYedvcMmSbWRlOftt9apdxMaWrx/oXZISmT7Pee8t35xMeMUgYiJO8t7zc957x87+HinwYz2kQiC2nJ0W7t4okS+WOPkt3ZFMRHAQsWFF81u6I5m96eWrp+KpdGmWyPT57n23JZnwkNLtu2Ofm5tT9rN1zwGPxXu6ujdI5L/L3Ptu50n23c7i993OQ4dZu2ff8V7h5cnBDUepGBdExbgg/AL8qNo+ipQFh057PcnzDhLbPAL/oPJ3XD+vVSLfzV0FwMr1uwkLDSI6suj+CwkK5Oq+rXjns3klrqtbu4bM/HVNmcV6ug5sOEpofBCh7v1XrX0UyaXdfwZc2S5cuZa8HIsrzxJUKbBsA/6HtU6CyDOnTllEoeP6ml2EhhZ/XC9JQq0Y/lyyFYDFS7bRoV39Monzr0jdkEl41UDC4wPxDzQkdAxn+/zCn4/rfzhMowsqERTmD0BIpPObwj/Q4B/ofJbk5VrK4Ucnu9fnElnVn8h4f/wDDY07BbP+j+wS26+am0njTvlFz4SkClQIOXN618r/pvJTapfyrCqwz1qbBWCt3XdshjFmC/Ax0BvIAK611m4wxsQC/wZquZveY6391RgTCrwGnA0EAk9Ya78wxoQAk4AkYA1wyq5Vxph04BWcglwGcIm1NsUYUwf4AAgDvjhhmfuBq4AgYJq19nFjTGvgP0AbwB+YD/S31ha87upkr0FrdxyhQBbQHcgB3gBaAbnAcGvtbGPMjcBl7tj8gc7FxXSq3P8J+/YZYmPze/fFxFrWrvYv1GbgDVk8/EBFvvxvBbIyDc887xzk6yTmMe+3ALp0y2HvHsOGdf7s3eNHw0YuypMqkWGkHMi/TGLPgXRiI8OKvWRw3L/60SQhnt9WbGHWIufSrVpxkbSoV507L21PVk4eL38yl1Vby8dlazHRYezZm5/b3tQ0YqLDSD1QNLcH7+5Nnsvy0+/reG+qU0RqULcKVWIimLdwE9f0a+2xuEsrOi6CfbsPHn++L/kQMXGVOFAg57xcF+Me+5w3vrmXzKPZ7Nyyj9efmOaNcE9bTEw4e/fkX5q7d28aMTHh7N+fv/9CQ50vlTfd1Imk5rXYtesAr706gwMHjhZaV+8+Scyfv9EzgZdSie+9w0X/Psff5bz3fl25hR/+zL9s8s6L23PhuWeRnpnFoJc+9UjcpRUXHsbuw/n5JR9OJy4i7LSKfD3Pqk+r2tXZknqQZ7+bQ7L7sl9vqxIZRnKBfZdyMJ0qJey71+/sx9m14/l11RZ+WFy+LnktSVx4GMkn7rvw09t35VXm/myCoyscfx4SXYGDG4rmlfzHQfavTie0ajCNr69OSEyFQvN3/3aAhAurlHm8f0Vs5TBS9hU+9sVWDiP1YOE8b7u6Ax9+tZDMEk5sNW9cnf2HjrAj+WCx870hc38OIdH5xbrg6EAOrj9apN3uPw6SujqdsKpBNLnB2X+VG4QS3SSMGYNWgHUuAT6xp6CULed7Wf5xfd++NGKiCx/Xj3ngvj64XC7m/rKO96f8BsDGTXvo1KEBn/13Eed1aEBoaBAR4cEcTvN+L7KjqbmERueXDSpGB7BvfeG4Du9yCmXfPrgd64Kk/pWpfo5T+DyyL4dZT+8ibXcOLW+IKVe9/QDSUl1ExOT/BgqP9mP3upxi2x7ak8ehFBe1m51ZhXWR8ncqT8qjGUBNY8w6Y8zrxpjOJ8w/ZK1tCowDXnZPewV4yVrbGrgceNs9/WHgR2ttG6Ar8Ly7GHgHcNRa2xh4HCjNNTShwDxrbRIwF7itwLbfcMe0+1hjY0wvoD5Oga850NIY08lauwD4Evg/4Dlg8glFvxJfA2NMBWAq8C93HD1wipB3AtYdwzXAu8aYY9/AzgGusNZ2LimmExM1xgwyxiw0xiycMGFCKV6af8acHwPp0SuHyVPTeeqZozz/bAguF5zfO4eYWBd33xHKm68H07hJbrm7zPd0DX1lGuffP4HAQH9aN3IugfL38yMiNIgbnv2IVz6dy+jbL/RylKdv1Atfc+Pd7zD0wQ9IOqsG53dtgjFw5y1dGT+x/F1meDr8A/y48Np2DL34ZQa0H8Xmtbu5anA3b4f1j/H396NKlQhWrtzB4NsnsWrlTm4fXPjyyR49mtCgQTwfTz1jRx3gztem0euBCVQI8Kd1w/zLD8d/+Rt9Hn6bb+ev4eouzb0Y4T9v9tpNdH/pP1zyxmR+27SV0f3O93ZIf8mQ8dPo8dAEAgP8adOw5qkXEK+r0rISXcY14bznGxPTNJxlr28tND/zQA5p2zKJTSpfvYhPR/3asVSPi2Tugg0ltunRoRE/lKPefqUV17IS3cefRZexjYhpFs7i8dsAOJKcRfrOLHr+uwk932xC6oo0UleXj5MJUtjTo7/iltsncvfwD2h6dg169WgCwBsTZtOsWU0mvH4jSc1qsndvGnmuctg9rgSuPMvhXdmcP6oG5w2P5/c39pB9xLliJjQmkItfqk2/1xPYOPvw8cuAz0Sr52bRsEMF/PzVw+/vMtY3/5VX5avcLuWStTbdGNMSOA+nWDfVGDPSWvuOu8mHBf5/yf24B3BWgUGFI4wxYUAv4GJjzH3u6cE4vQI7Aa+6t7fMGLOsFKFlA8cGa1kE9HQ/7oBTbAR4///Zu+/wqKqtj+PfPekhCemEHghVeq/SUbAgWBBFL9gbKNgA0Yu9t6vYfZXrFSv2QpMmFkQ6SO8tISSBNNKz3z9mSCGhajJx/H2eJ08m5+w5s/acmZw5a9bZG3jKdfsc189K199BOJNuPwIPA78D2cDtp/ocuB433pU8xFqbBmCM6YmzshFr7UZjzC6giWtzc621KacQU8nHfxM4mvGz2/f++aLAyEjLwYPF2bqkg4aIyNIVe7Nn+vDok85vm5u3KCAvz5CWaggNs9x0aw7OAke4c2wgtetUjWq/y/q0YdjZLQFYv/MANcKKrxuJDgvi4OHjfxDOzS9g0apt9G4bx28bdpN4KIMFK50nDn/sPIC1ltCgAA5nuOeS32HnteOCc1oDsHFLPNFRweC8Yo2oiGCSksv2LSnFuSwrK4+5i9bTvEkMP/22hQb1I/nPYyMACA+rxhOTL2bSY5+7dYKPC67qzqDhXQDYvHYPkTWLB3+OjKlO0oHSlzzFNa8FQPzuZAAWf7+a4Tf1raRoT99FF7XnvPOdCaxNm+KJii4+sY6KCi4ziHdaWhZZWbksXrwJgEWLNjL4vDZF69u3j+XKkd25c/x08vLcP7nO8N5tGNbD+d77Y9fpv/cWrt5GnzZx/LZxd6l1M5du5KUxQ3n9218rJvBTdGXnNlzW3tm/tfsPUDOkuH8xIUFFE3WcisNZxZUSny5fx90Dz/7rAj0Dl/dqw8Xdi/ddTIl9VyM0iMST7bs12+jTKo4lx+y7quLKjm0Y3q5438Ucu+/SPSNB4h/uS3Zy8eVpWcm5+IWVrkrxDS7+6F+3fwQbp5eeNCn+10PU6Fwdh3fVOam9+Ny2DOnfCoCN2xKoERkMzn+LREUEczCl9P5r2aQWzRrW4LOp1+Pl5SCseiBTpwxnzEOfAODlMPTp3JhrJr5fqf04Gf9wH7KSi6uMspPziibxOKrk/qvfP4IN7zvH6I1fmkpY40C8/Z1VS9HtQji0OZOI5n96iGs5gaEXtuN813F546YEoqNCAOd7KjIymKTkspNzHP2slpWVy7z562nWtCZzfviD5JQMpjz8JQD+/j706tmUzMyqMQZlYIQ3mcnFybojyfllqvaqRXgT2dgfh7chuIYPIbV8SNufR2Tj4kq6wHBvQuv5kbg+i/rdq8513cERDtKSij9HpScXEhThVW7bDYtzGHiz3lfy96PEn5wSa20BsBBYaIxZC4wCph1dXbKp67cD6GqtLVUHbpyZwEustZuOWX4mgxJoCwAAIABJREFUYeVZWzRSRAGlX8/l5dsN8IS19o1y1kXgTLr54ExGlqnLP85zsPwM4i657RPFVKGaNCtg/z4HCfGGiEjLogU+TJhcOqEVHW1ZtcKbgYPy2L3LQW4uVA+1ZGcDFvwDYMUyL7y8KDUpiDt9unA1ny5cDUDPVg0Y3rcNs3/fRMsGMWRk5Za5zDfAz4dq/r4kpWbi5TD0bNWAlVudH9oWrtpGx6Z1WbZpL/WiQ/H28nJb0g/gi+9X8sX3zhxx144Nufj8dsz7cSNnNa1J5pGcMpf5ejkMQdX8SU3PwsvLQfdOcSxbvYvMI7kMuap4lsr/PHY5r7670O2z+n77/i98+77zkpdOfZpx4dU9WPTtKpq1rUdmenapy3wBkg6kUa9RDaqHVyM1JZN2PZqwe9vxZ3d0t6++WsFXX60AoEuXOIYO7cCC+etp3rwWmZk55V4OtOTXrbRpW59VK3fRvn0su3Y5Rxlo1KgG4+8cxMQJH3P4cNlLwdzhk0Wr+WSR673XsgGX92nD7GWbaHX0vZdWznvPz5ekNOd77+yWxe+9ulGh7DnovASvd5s4dia4f8y4D5au5oOlzv71btyAkV3a8N26TbSpE0N6du5pXSoaFVStqH2/pg3ZdjDlJPeoWB//uJqPf3T27ewWDbi8VxtmLd9Eq9jj7Dtf1//No/uuRQNWbCs763ZV8cGy1XywzLXvGjXgqk5t+O6PTbSpffr7riqrHhdIZkIORxJz8A/3If6XQ7S9PbZUm+xDefi7koEHlqUSVLv05aDxPx+i6RW1KivkU/L57FV8PnsVAN3bNeCSQe2Y+/NGWjR2HfuOucz3i7mr+WKuc3/HRIXw7IRhRUk/gI6t6rNrf0qZhKG7hcYFkhlfvP/2/3KI9rfXL9Wm5P5LWJZKkOty3oBIH3bPS6awwDpnuV+fQYPzoiq9D/80X36zki+/cX0u69yQoRd1YP7CDTRvVv5x3eEwBAX5k5bm/FzWrWsjlq/YCUBISADp6VlYCyNHdGXm7FOpgagcEY38SY/PJf1AHoHh3uz8KZ2zx5eeMbxu5yB2/JROo/7VyU4rIG1/HkExPmQm5eEX7IW3n4OcjAISN2Rx1oVVa1bfmo29ObS/gMMJBQRHONjwYzYX3l226jl5Tz7ZGYXUbqYUivz96FUrJ2WMaQoUWmuPDuDTFih5bcjlwJOu30dLMuYAY4FnXNtoa61dBcwGxhpjxlprrTGmnbV2Jc4KtyuB+caYlkDrPxHyz8AI4H1gZInls4FHjDHTXRV8tXEmDxOBN4AHgAY4KwTHnOJzsAmoaYzpZK393RgTjPNS38Wux55vjGmCs6pxE87LfEs6UUwVyssLbhmbzf0TAikoNJwzOJf6sYW8964fTZoW0LV7PtffnM1Lz/vzxWe+GAN33puFMZB62DB5QiAOB0REWu6eVHUmvSjpp7U76NEylq8eu4bs3HwenDanaN0HD4zkykemE+Drw/O3DcHX2wtjDMs27eGzRc4PW1/9vI4po87h4ylXk19QwIPvznZXV8pYsmw73To05MM3biAnJ48nXppZtO7/XhzFdeP+i4+PN88+dCne3l44HIblq3bx7Zyq80HyRH5fuJFOfZrzzvyJZGfl8sKE4pO2qV+PZ8yQF0hJTGP6y3N5+oNbKMgvJHH/IZ6792MAug9syS1TLqJ6eBAPvX0t2zfs5/5r3j7ew1W6337bRpcucfzv/ZvJzs7jmRIz973x5rXcdOM7ALz51gImTbqQ224dwOHUI0XtbrypLwH+vvx7yjAAEhPTeOD+qjMO3k/rdtCzZSxfPex6771X/N778L6RXPG48733wi2u957D+d6bsdj5+rx9WE/q1wjDFlriU9J57IMf3NWVci3asoNeTWKZc8c1ZOflc9+Xxf374uaRDHvdOcP23QPP5oJWTQnw8WHhndczY8U6pi5cwtVd29K3aRwFhYWkZmUz6cuq879l8R876Nkilm+mOPs25f3ivn08cSSXPzmdAD8f/nPTEHy8vXAYw+9b9jDjJ+e+69s6jomX9SUsKICXb76ITfsOcusrVWfszUVbd9C7USxzb7uGrPx87vu6uH9f3jCSoW859909/c/mgpbOfbfojuv5dOU6pv64hFY1azB1+IWE+PvTt3FDxvbuxgWvv+eu7pTi8DK0uLYOSx/fBoWWOn0iCK4bwOZP4qneMJAaHauzc+ZBEpenYhzgE+RN61uLE0tHEnPISs4j/KyqW83yy8oddGvfkE9fuo7s3Dwee7X4vTPt6asZfW/5s6iWNKBH1ZrU4yiHl6HltXVY8th2bKGlbt9wgusGsPHjeELjAonpWJ0dMw+SsCwNh5dz/7W91TmUdq2uoSSty2DR3c5+RbcNIaZjdXd257Td9RAsXQWHU6HPpTDmGrj0bzTCypKl2+nSOY73p91ITk4+Tz37fdG6t14bzQ23TMPX15tnnhiOl5cDL4eD5St38t1MZ5K6bRvnTL7Wwpq1e/jP1Lnu6koZDi9D5+uj+eHhfdhCaNQ/hNB6fqz6MJmIOD/qdg6iVrtA9q8+wle378I4oMOoSPyDvdi/LZtl/03C4KzKaHFRGGH1/U72kJXK4WUYeHMQn0xJxRZaWg3wJ6q+N4vfzySmsTeNuzjj3bA4h+Zn+5UpWJk+4RDJewvIy7a8MjqZwbcH07C9b3kPJeI2xlbFqXWkSnFd4voyEIpzooqtwI3W2iTX5B4f45zcIwe4wjW5RyTwCtAcZ4L5R2vtza5JPF4EuuOsCtxhrb3gmMk9NgC1gdustcuOiWUhcLe1dpkxJsNaG+RafilwgbV2dDmTe4wr0e4O4HrX5jKAq3BeGnyRtfYSY4wX8AswyVo7/xSfg06udQE4k34DXG2ON7lHR2vtmBLbLhOTtfZEI/Xb7XtrnmD131vDOvF0uPGFkzf8G1r+5nh6DXnG3WFUmB+/vofBje5xdxgVZubWZ+jf7wl3h1Fh5s2fRPtbPPO9B7DitfE0m+KZ/dv40HjajvHMvgGsmjqepo94bv82PTCe8atGuDuMCvNC24/oPvw5d4dRYX755C7uXn25u8OoEM+2+ZjChCYnb/g35YjZTN9znjp5w7+pBXMm8NgfF7g7jAozucW3vLO5p7vDqDDXNvmp6oy5UAH6DH7aIxNRC2feWyX3myr+5KSstctxJuqO5xlr7YRj7pOEswLw2G1lATcdZ/lJP/Vaa/uUuB1U4vYMYIbr9g6gW4m73V+i3X9wTv5R0jbgPdf6AqBLOY973OfANb5f13JWXVNO22kUXyJ9ophEREREREREPI8K0CrV33weThERERERERERESmPKv7kT7HWxro7BhERERERERERKUsVfyIiIiIiIiIiIh5IFX8iIiIiIiIiIlIpjIb4q1Sq+BMREREREREREfFASvyJiIiIiIiIiIh4ICX+REREREREREREPJDG+BMRERERERERkcqhMf4qlSr+REREREREREREPJASfyIiIiIiIiIiIh5IiT8REREREREREREPpDH+RERERERERESkUhirQf4qkyr+REREREREREREPJASfyIiIiIiIiIiIh5IiT8REREREREREREPpMSfiIiIiIiIiIiIB9LkHiIiIiIiIiIiUjkK3R3AP4sq/kRERERERERERDyQEn8iIiIiIiIiIiIeSIk/ERERERERERERD6Qx/kREREREREREpFIYa90dwj+KKv5EREREREREREQ8kBJ/IiIiIiIiIiIiHkiJPxEREREREREREQ+kMf5ERERERERERKRyaIi/SqWKPxEREREREREREQ+kxJ+IiIiIiIiIiIgHMlbTKIv83ehNKyIiIiIi4rmMuwOoSP37PuGR57TzFkyqkvtNY/yJ/A0Nan2/u0OoMLPWPEqDl59zdxgVYsfYu+gxd4K7w6gwPw98iuuXjXZ3GBXm7Y7TeGHDOe4Oo8KMbz6HuI8ed3cYFWbbiPtoMuMRd4dRITZf+oDH7ztP71/7W15wdxgVZsVr4z3+2NB51PPuDqNCLP3vnfQ95yl3h1FhFsyZQGFCE3eHUWEcMZsZHHOru8OoMDMTXvX4cyKPpgK0SqVLfUVERERERERERDyQEn8iIiIiIiIiIiIeSIk/ERERERERERERD6TEn4iIiIiIiIiIiAfS5B4iIiIiIiIiIlIpjOb2qFSq+BMREREREREREfFASvyJiIiIiIiIiIh4ICX+REREREREREREPJDG+BMRERERERERkcphNchfZVLFn4iIiIiIiIiIiAdS4k9ERERERERERMQDKfEnIiIiIiIiIiLigTTGn4iIiIiIiIiIVApT6O4I/llU8SciIiIiIiIiIuKBlPgTERERERERERHxQEr8iYiIiIiIiIiIeCCN8SciIiIiIiIiIpXDWndH8I+iij8REREREREREREPpMSfiIiIiIiIiIiIB1LiT0RERERERERExAMp8SciIiIiIiIiIuKBNLmHiIiIiIiIiIhUDs3tUalU8SciIiIiIiIiIuKBlPgTERERERERERHxQEr8iYiIiIiIiIiIeCCN8SciIiIiIiIiIpXCWA3yV5lU8SciIiIiIiIiIuKBlPgTERERERERERHxQEr8iYiIiIiIiIiIeCCN8SfyD3fLhPPpdHYTcrLzeO6Bz9i6Ib7U+oBAX56ddkPR35E1Qpj/3WreePr7omU9BpzFA89fydgRr7Jl/f5Ki/1YverFMqVXXxzG8PH6dby+fGmp9b4OL547ZzAto6I5nJ3NmFnfsi89jZ5163Nv97PxcTjIKyzkiZ8X8evePfh7e/PK4AupXz2UgsJC5u3cztO/LHZT70rrEtGEcU2H4DCGb/b9zvs7F5ZaP7ROFy6u041CLEfyc3h6w+fszEwEIC4ohnubX0w1b38KbSHXL51KbmG+G3pxfCmrD7H9fzuxhZaYPjWoO6R2qfUHFiWy/cNd+IX5AlDrnBhi+tYgY2cmW9/dTkFWATgM9S6qTVS3SHd04YR2r8ji57dTsIXQfGAQ7S6pXqbN1p8yWf5RKhiIiPVhwF1RAKQfzGfR1GQykvMxGAY/EE1Ijap1OO8V05AH2g/Eyxg+3r6aNzb8Wmp9p6i63N9uIM1Co7njly+ZtXdj0bp72/Slb81GAEz94ye+27OhUmM/FWfXiGNy23PxMoZPd6zkzU2/lFp/TeMuXNagHfmFhRzKPcKkZd+w/0gqzavX4MH25xHk7UeBLeT1jT/x/d71bupF+Tx933l6/wDuGd6Hni0akJ2bx5T35rBxT2KZNlPHDCOyejW8HA5Wbt3Hkx/Np9BabrmwG31ax1FoLSnpWUx5bzZJqZlu6EX5PP3YAHDXyL50b+Pcfw+/NZtNu8ruv6OeHXcRtaOqc8Xk9wDo36kxNwzrRmzNCK556AM27DxQWWGfkrG39qdLpziyc/J46tnv2bK1bHwvPHMF4eHVyM11fi65Z9InHD58hBrRIdx712CqVw8kPT2bx576lqSk9Mruwhmb/CQs/BXCw+Cbae6O5szc/OhldOrfgpysPJ674z22rd1Tpk3voR25/I5zwUJywmGeGTONtJRMGraow9inr8DHz5uCgkJemfgRm1fuckMvju/PnBMNHNKO6+4cRHJiGgDffLSEWZ8vr9T4/3Y0xl+lqlpnCnJKjDEFwNoSi4YCkcC/rLW3n+I2QoErrbWvHmd9DPAi0Ak4DBwAxgG5wAZgE+AL/AjcCvQC7rbWXlBiG9OAb621M47Z9vGW9zm6DWPMaKCjtXbMKfTlXOAp15+NgH1AFrAGeInTeF5OhzFmHJBirX3vL9reR8AD1totf8X2TkWnnk2oVT+Cay94gWat6zDm/iGMG/lGqTZZR3K5bfgrRX+//NEt/Dyv+EQ1INCXoSO7s2FN2YN/ZXIYw8N9+nP1lzNIyEjnq8tH8sP2rWw9lFLUZniLlqRmZ9P3f+9wQeOmTOzRi7GzviUlK4vrv/2CxMxMmoRH8N+LLqHbu28C8NaKZSzZtwcfh4Ppwy6jd/1YFu3a6aZeOjkw3NVsKONWvE1idipvdxnDTwfXFyX2AObEr+LLvb8B0DOqOWObXMBdK9/Byzj4d8sRPLLuY7ZmxBPiE0h+YYG7ulIuW2jZNm0HLSedhV+4L6seWEt4+zCq1Qks1S6qawSNRjcstczh56DpLY0IiAkg51AuK+9fQ1jrULyrVZ3DXWGB5ac3UrjgoWiqRXjz+T3x1O8cQHhd36I2h/fnsfKzVIY+WQO/IC+yDhfvo/kvJtH+surUbRtAXlZhlavddxjDgx3PZdSCD0nISuOLgdcwb98WtqYlFbXZfySNe3/7hhuadS113z4142gRFsMFs9/G1+HNB/1Gsih+Gxn5uZXdjeNyYJjSbhDXLJ5OwpE0Put/PfP2b2ZbenH/1h9O4OJ5b5NdkM8VDTtwb6v+jPvtc7IK8rj396/YlZFCtH8Qn/e/nsUHtpGel+PGHhXz+H3n4f0D6NEilnrRoVw05V1aNYhh0hX9GPX0R2XaTXj7OzKznbE/c+MFDOjQmDnLNvPe3OW89o0zGTqib1tuPK8rj384r1L7cDyefmwA6N66AXVjQrnk3ndoGVeTCaP6c+3DH5bbtk+HRmRl55Vatm1vMve+9A2TRg+ojHBPS5dODaldO5yrrnmT5s1qMf72c7j19v+V2/axJ79l85aEUstuvrEvc374g9lz19GubT1uuLYXTzz9XWWE/pcYOhiuvBgmPu7uSM5Mp/4tqNUwmuu6PUiz9rGMeWoE4897plQbh5eDmx+9jJt6PUxaSibXPjCMC6/tw/Rnv+O6B4Yx/bnvWDZ/PZ36t+C6B4Yx4eIX3dOZcvwV50Q/zl7Lq098W2kxi5yOKna6IKcoy1rbtsTPTmvtsvKSW8aY432iCcWZsCvDGGOAL4CF1to4a20HYBJQw9Vkm7W2LdAaOAtn4tFtrLWzjz4XwDJgpOvvfx3vefmzXM/rtcAHf+FmXwPu/Qu3d1Ld+jZn3jerANi4Zi9Bwf6ERwYdt33t+hGEhgexbvnOomX/GjOAT9/5kbwc91aMtakRw67Dh9mTlkpeYSHfbN7EwIaNSrUZ2KARn238A4CZWzfTvU49ANYnJZKY6axo2JySjL+3N74OL7Lz81myz5nQzCssZF1iIjWDgiuxV+VrXr0ue48ksz8rhXxbwLyE1ZwddVapNkcKihMJ/l6+HP1OrXNEY7ZlxLM1w/ktZlreEQqpWt+4pW/LwL+GPwHR/ji8HUR1jSRl+aFTum9gzQACYgIA8AvzxTfEh7z0vJPcq3IlbsklpKY3ITE+ePkY4npWY+dvWaXabJiTQcvzgvEL8gIgINT5O2VPLrYQ6rZ19tEnwIGPX9U6lLcJr8Wu9EPsyTxMXmEh3+5ez4DajUu12ZeZyqbUg2Vee42rR/L7wd0UWEtWQR4bUxPpVTOuMsM/qdbhtdiV4eqfLeS7PX8woFbTUm1+O7iL7ALn/8RVKfuoERACwM6MFHZlOL+MSMzOICXnCOF+1Sq3Ayfg6fvO0/sH0KdNHN8ucVYirt2RQHCgH5EhZV9jR5N+3g4HPl5eHO3u0eUAAb4+2Cp0fPD0YwNAr/ZxfP+zM5Gwbls8wYF+RFQvu/8C/Hy4clAH3vl6SanlO+NT2J1was9JZevRvTFz5q4DYMPG/VSr5kd4+Kn//4utF8mKVc4KsZWrdtOjW+OT3KNq6dQGQt3/EfKMdT23NfM+cX6hvHHFToJCAgmLDinVxhjnj3+gHwCBQf6kJBwGwFpLYLDzPRgYHEByQmolRn9yf8U5kUhVVrXOFuSMGWP6GGO+dd1+0BjzP2PMz8D/jDEtjDFLjTGrjDFrjDGNgSeBONeyZ47ZXF8gz1r7+tEF1trV1tpS1zhaa/OBX3BW2Z1p3IOMMRuNMSuAi4/TJsoY85kx5nfXT4/T2P6xz8t/jTGLjTG7jDEXG2OeNsasNcbMMsb4uNp1MMYsMsYsN8bMNsbULGfT/YAVrucAY8ztxpj1ruf3I9eyasaYd1zP/UpjzEWu5V7GmGeNMetc7ce6trkYGHCCZO1fLiI6mIMlDrwHD6QRccxBvKTeg1qzaHZxsWmj5jWJiqnO0sWbKzTOUxFTLYj4jOJLPhIy0okJKn3ArhEURHy6s02BtaTn5hDmH1CqzeC4xqw7mEjuMVVwwb5+9G/QkJ/37K6gHpy6KL/qJOYcLvo7MSeVKL+yl4peXKcbn/S4l1sbn8eLm74CoG5gFNbC8+2u450ut3Nl/d6VFvepyknJxS/Cr+hv33Bfcg6VrYhK+j2F5RNXs/7FTeQkl12fvi2dwnyLf7R/hcZ7ujJT8gmKLH6bB0V4kZlS+vWWuj+Pw/vy+WJiAp/fG8/uFc7EYOq+fHyrOZj9ZCKfjt/Pr9MOUVhQdU7MAWoEBBN/JK3o74SsdGoEnNrZzobDifSKaYi/lzdhvgF0ja5PzcCqdaZUIyCEhKyS/Us7Yf8ui23LjwlbyyxvHVYLH4cXuzNSyrmXe3j+vvPs/gFEhwZx4FDxsTDxUAZRoeWfvL4ydhg/PHMTmTm5/LCi+GKD24Z05/vHrmdw52ZF1X9VgacfGwCiw4I4kFxi/6VkEB1Wdv/dfEl3Ppi1nOzcqjVMx4lERgSReLD4/ZeUlE5kRPnvoQl3n8dbr43m6pHdi5Zt255Irx5NADi7RxOqVfMjJLjq7UNPFVEzlKT9xUnlpPhDRNYMLdWmIL+QqRM+4rUFk5m++gnqNYlh9gfOoTDe+PcMrntgGO8tf4zrp1zMtMe/qtT4T+bPnhMB9BzQgtdmjGHycyOIrFH2c7mIOynx9/cU4ErYrTLGfHGcNmcBA6y1VwA3A/9xVcR1BPYCE3FV7llr7znmvi2Bkw5KYIwJBPpT+rLjU2aM8QfeAi4EOgAxx2n6H+AFa20n4BLg7TN5PJc4nEm7IcD7wAJrbSuclwaf70r+vQxc6qp0fAd4rJzt9KD0czQRaGetbY3z+QaYDMy31nbGmUx9xhhTDbgRiAXautpPB7DWFgJbgTbHPpgx5kZjzDJjzLI333zzT3T/z+k9qBULv19zNCZuvPs83np2ptvi+as1Do9gQo9eTJ4/t9RyL2N4adD5TFu9kj1pVesbyhP5fO+vDP/5aV7bMpPRDfoD4GUctA6L5aF1H3LL76/RO7oFHcKrXtXKyYS3D6Pzi+3p8GQbwlqFsun10omV3EO5bHptK01ujMM4jJuiPHOFhZAan8eQR2sw4K5IFr2STE5GIYWFloT12XQbHcYlz9YkLSGfTfMz3B3uX+anhB0sjN/GpwNG8WL3oaxM2kfB33gMmCH1WtEyrCZvby6dPInyD+LpzkOZuOzrKlRP9ed42r47lif277aXv+CcCW/i6+1Fp6Z1i5a/8vUvnDf5bWYu3ciIPm3dGOHp8/RjA0DjelHUjg5l4fKyXyh4gsee/IbrbnqH2+/8gFYt63DOgBYAvPbmAlq3rsubr46mTeu6HDyYTkHh3/s96Gm8vB2cP+psxgx4gpFtJrFjwz6G334uAOePOps3p8zgXx0m8+aUGYx7/io3R/vnlDwnAliyaCOjBj3LLZdOZeWv27j7sUvcGN3fRKGH/lRRVWtgCzlVWa4k3ol8ba09eu3Yr8BkY0wd4HNr7Rbn1bxnLM4YswrnhSFfWWtnGmOOVzZ0oiNyM2DH0THtjDHv40yKHWsAcFaJmEOMMUHW2jM5251prc0zxqwFvIBZruVrcSbjmuJMfM51PZ4XEF/OdmriHOvwqDXAdGPMl8CXrmXnAEOMMXe7/vYH6rn68/rRakFrbclyj0SgFsckXq21bwJHM37286n3n0aXS7vw8i4MuqQjAJv/2EdUTPE3UlE1QooGpT1WgyYxeHk52LrBOXlHQDVf6jeK5un/uw6AsMggHnzpKh68/X23TPCRkJlR6jLcmKBgEjJKv0QOZGRQMziYhMwMvIwh2NePQ9nOt0lMtSDeOG8Id82dye5jknuP9zuHnYcP8e7qFRXfkVNwMCeVaL/ib1mj/apzMOf4CckfElZzd7NhACRmp7L60A5S844A8GvSJpoG12Z5yraKDfo0+IX7lqrSyE3JxS/Mr1Qbn2CfotsxfaPZ8WHxANH5R/JZ9+xG6l9Wj5DGVa8ip1q4NxlJxVUaGckFVAv3KtUmKMKL6CZ+eHkbQmr4EFrLh9T4PIIivIlo4EtIjLP/DboEcGBz1Rgf7qgDWenUDCz+ljwmIJgDWac+APur63/h1fXOCoEXul3EzvSqUxEHcCArjZiAkv0LKbd/3aMbcEuznoxc9F/ySlQQV/P25c0eI3hh3QJWp+yrlJhPlefvO8/s3/DebRjWoyUAf+w6QI2w4v970WFBHDx8/I9LufkFLFy9jT5t4vhtY+mK9plLN/LSmKG8/m3VqPrz1GPDpf3bMLR3KwDW7zhAjYhgcBVgRocHkXio9P5r3agmzWNr8OWz1+Hl5SA8JJDXJl7GLU9+Wtmhn9TQC9tx/nnO77M3bkogOioE51DcEBkZTFJy2fdfUrKzv1lZucybv55mTWsy54c/SE7JYMrDzo/Y/v4+9OrZlMzMqnX88zQXXNOLQSOdF1ptXrWLyFphResia4aRFH+4VPu4ls4vEOJ3OcdNXfz1CoaPPQeAAcO78vr9nxYtH/fcyAqP/2T+qnMigPTU4iFbZn2+jOvGn1tBUYucGVX8ea6iKdistR/grHDLAr43xvQ7yX3/wFmBdzxHKwXbWWsfdC1LBsKOaRcOJPHnOYCuJcY0rH2GST+AHCiqrsuztujr+kKciXAD/FHisVpZa88pZztZOBN5R50PvAK0B353Xa5rgEtKbKuetfZkUwD6u7ZdYb75+DduG/4Ktw1/hV/nr6f/hc4ccrPWdchMzyElqfynts/g1iycVfzN1pGMHC7v/QSjBj/HqMHPsXHy85w3AAAgAElEQVTNXrcl/QDWHEggNjSUOiEh+DgcXNikKT/sKJ3M+mHHNi5p5vzmeHCjJvy613mSE+zrxztDhvHUr4tZHl86/ru69iDY15eHf1xQOR05BRvT9lInMIKa/mF4Gy/6x7Thp4OlX1p1AiOKbnePbMbeLOdbcWnyZhoGxeDn8MHLOGgb1oAdmcefMdAdghsGkZ2QTXZiNoX5hRxckkR4h9L/XnIPFY9Dlbw8hcBazku2C/MLWf/iJmr0jCKqSwRVUXRjX1Lj80k7kEdBnmXbT5nEdi59yXlsl0D2r8sGICutgMP78wip4U1UI19yMwvJSnUmkvatzSasxKQgVcGalP3EBodRp1p1fBwOLqh3FvP2ndqcRQ5jCPV1PhdNq0fRrHo0ixO2V2S4p23tof3EBoVTJzAUH+Pg/LotmBdferiD5qExPNz+PG7+5WNSco4ULfcxDl7tPpwvd61h9r6qNyOsp+87T+3fJ4tWc8Xj07ni8eksXL2NC7o2B6BVgxgysnJJSis9K2+An0/RuH9eDsPZLRuwM8GZxKwbVfylUu82ceysQuPFeeqxYca81Vz17/e56t/vs2jFVs7r4Ryzt2VcTTKyckk+Zlblz+av4fxxbzL07v/jxsc+ZnfCoSqZ9AP48puV3HDLNG64ZRo//7KZcwY6E9TNm9UiMzOHlJTSfXM4DCEhzn3m5eWgW9dG7Njp/PwSEhLA0RqAkSO6MnP2GqRiffvuj4wZ8ARjBjzBr7PW0H94FwCatY8lMz2LQ8ckxpLiD1OvSU2qRzgvT2/Xqxm7XZO0JCek0qq7c1zGtj2bsm/7wUrsSfn+qnMioNR4gF37NGP3Dvf3T6QkVfz9AxhjGgLbrbUvGWPq4ZyUYzVwvK875wOPG2NudFWaYYxpDVQHjjd16xagljGmubV2gzGmPs5LVledILSNQKwxJs5auw244jjt5gBjgWdcsbS11p5ou3/GJiDKGNPNWvur69LfJtbaP45ptwHX2IbGGAdQ11q7wBjzEzACCAJmA2ONMWOttdYY085auxKYC9xkjFlgrc03xoSXqPprAqyroL6VsXTxZjqd3YR3vruTnOxcnn/g86J1r3xyW6mZq3qd25IHbv1LJjCuEAXWMmXRfN4bcgkOh4NP169jS0oy47t0Z23iAX7YsY2P16/lhYGDWXD1taTmZDN2lnM2uFGt21K/ehi3d+rG7Z26AfCvr2bg4/BiTKeubE1J5tsRVwPw3ppVfLz+jK5u/8sU2EJe2PQVz7e/Di/j4Nv9v7Mj8wDXxw1kY9pefjq4gUvqdqdTeGPybQHpeVk8uu4TANLzs/ho12L+r8tYLJZfkzbya9JGt/bnWMbLEDe6Aeue2oAttNToHU21OoHsnLGb4AZBRHQIZ9/seFJWHMJ4GbyredPkZudQo0lLkknbmE5+ej4HfnQmNJvc1Iig2KozgYLDy9DzhnC+eygRWwBNBwQRXs+X3z84TFQjX2I7B1K3nT97V2Xx8Zj9GAd0Gx2Gf4izKrDr6DC++fcBsBAZ50vzgccffNodCqzloeVzmNZ7BA6HgxnbV7MlLYlxLXuxNiWeefu30Cq8Jq/1vITqvv70q9WIO1qdzeCZb+FtHHzU3/ley8jL4c4lX1W5yykLrOXhVbP4v7OvxMsYZuxczda0g9x+Vm/WHYpnfvxmJrTqT6C3Ly91dV7qs/9IGrf88jGD67agY2Q9Qn0DuDjWWQUz8fev2ZB6wJ1dKvJP2Hee3D+An9btoGfLWL56+Bqyc/N58L05Res+vG8kVzw+nQBfH164ZQi+3l4Yh2HZpj3MWOw8ib19WE/q1wjDFlriU9J57IMf3NWVMjz92ADw8+oddG/dgM+fuZbsnHweeXt20br3H76Kq/79/gnv36dDI+66qi9hwQE8f+dQtuw+yO3Pfn7C+1SWJUu306VzHO9Pu5GcnHyeevb7onVvvTaaG26Zhq+vN888MRwvLwdeDgfLV+7ku5mrAWjbxjmTr7WwZu0e/jN17vEeqkq66yFYugoOp0KfS2HMNXDp+e6O6tT9/sM6OvVvwTtLHiI7K5cXxhXPyDz1h0mMGfAEKQdSmf7c9zz9xZ0U5BeQuDeF5+5wnju8dPd0bnrkMry8HeTm5PHSPdPd1ZVy/dlzoouu7EbXPs0oKCgkPTWL5+7/rNJiFzkVxlbBDy1yYsaYDGtt0DHL+gB3W2svMMY8CGRYa591rZsIXA3kAQnAldbaFGPMBziTgDOPHefPGFMLeBFn5V82sBMY59rGt9baluXE1QN4DmfVWh5wn7W2zFHZGDPNtY0ZxphBrsc5gnNyizhXH0YDHa21Y4wxkTir6ZrjTFb/aK29+djtura90PU8LDuF56XoeSy5zhjTFngJZ6LTG3jRWvvWMY9TH/iftbaXKzm4wNXeAO9ba580xgS4+tYdZ9XiDlcc3sDTwCDX8/SWtXaqMaYG8I1rTMATsYNan/mlvlXdrDWP0uDl59wdRoXYMfYuesyd4O4wKszPA5/i+mWj3R1GhXm74zRe2FBeAbBnGN98DnEfPe7uMCrMthH30WTGI+4Oo0JsvvQBj993nt6/9re84O4wKsyK18Z7/LGh86jn3R1GhVj63zvpe85T7g6jwiyYM4HChCbuDqPCOGI2MzjmVneHUWFmJryKh58T/T0HIj1F53R+2CMTUXOW/rtK7jdV/P0NHZv0cy1bCCx03X7wmHVP4pzF99j7XHmCx9gPDD/O6jJJP9d9fga6Hm+bJdqNLnF7Fs6x/o5tMw2Y5rqdBFx+su262vY55u+FHP95CSpx+8ESt1cBvU7yOLuMMcnGmMauMQp7ltMmC7ipnOX5wJ2un5KuBN440eOKiIiIiIiI/J0ZFaBVKo3xJ3LmJuKc5OOvchj471+4PRERERERERH5B1PFn8gZstZuwjkm4F+1vXf/qm2JiIiIiIiIiKjiT0RERERERERExAOp4k9ERERERERERCqHxvirVKr4ExERERERERER8UBK/ImIiIiIiIiIiHggJf5EREREREREREQ8kMb4ExERERERERGRyqEx/iqVKv5EREREREREREQ8kBJ/IiIiIiIiIiIiHkiJPxEREREREREREQ+kMf5ERERERERERKRyFLo7gH8WVfyJiIiIiIiIiIh4ICX+REREREREREREPJASfyIiIiIiIiIiIh5IiT8REREREREREREPpMk9RERERERERESkUhhr3R3CP4oq/kRERERERERERDyQEn8iIiIiIiIiIiIeSIk/ERERERERERERD6Qx/kREREREREREpHJojL9KpYo/ERERERERERERD6TEn4iIiIiIiIiIiAdS4k9ERERERERERMQDaYw/ERERERERERGpHBrjr1Kp4k9ERERERERERMQDKfEnIiIiIiIiIiLigYxViaXI343etCIiIiIiIp7LuDuAijSo9f0eeU47a82jVXK/aYw/kb+h33Y1cHcIFaZL/R00fPF5d4dRIbaPu5MON7zg7jAqzPK3xtPj0mfdHUaF+XnG3fTr/6S7w6gw8+dN5Kz7Pff1uf7R8Zx1n2f2b/3j4zlrsmf2DWD9Y+Np9qDn9m/jg+PpNe8ed4dRYX7s/wydR3nmcR1g6X/vZNjPt7k7jArxRY9XeOyPC9wdRoWZ3OJbBsfc6u4wKszMhFcpTGji7jAqjCNmM2v31HF3GBWmVd297g6hYqkArVLpUl8REREREREREREPpMSfiIiIiIiIiIiIB1LiT0RERERERERExAMp8SciIiIiIiIiIuKBNLmHiIiIiIiIiIhUjkJ3B/DPooo/ERERERERERERD6TEn4iIiIiIiIiIiAdS4k9ERERERERERMQDaYw/ERERERERERGpFMZad4fwj6KKPxEREREREREREQ+kxJ+IiIiIiIiIiIgHUuJPRERERERERETEA2mMPxERERERERERqRwa469SqeJPRERERERERETEAynxJyIiIiIiIiIi4oGU+BMREREREREREfFAGuNPREREREREREQqR6HG+KtMqvgTERERERERERHxQEr8iYiIiIiIiIiIeCAl/kRERERERERERDyQEn8iIiIiIiIiIiIeSJN7iIiIiIiIiIhI5bCa3KMyqeJPRERERERERETEAynxJyIiIiIiIiIi4ibGmHBjzFxjzBbX77DjtHvaGPOHMWaDMeYlY4w52baV+BMREREREREREXGficA8a21jYJ7r71KMMd2BHkBroCXQCeh9sg1rjD8REREREREREakcGuOvPBcBfVy3/wssBCYc08YC/oAvYAAf4MDJNqyKPxERERERERERkT/BGHOjMWZZiZ8bT+PuNay18a7bCUCNYxtYa38FFgDxrp/Z1toNJ9uwKv5ERERERERERET+BGvtm8Cbx1tvjPkBiCln1eRjtmONMWXKIo0xjYDmQB3XornGmLOttYtPFJcSfyL/YGt+N7z/mjeFhdB7UAEXjigstT4pEd56xpvMDLCFMPy6Atp0tmzbaHj3RS/AWWs87KoCOvasOuXa/+7dlz4NGpCdl8c9c2bzx8HEMm1aRkfzzDmD8PP2ZuGOHTy8aAEAgxs35o6u3WgUHsGwDz9gbWLpyulawcHMvnoU/1nyK2+vWF4p/Tmee0b0oUerBmTn5vHgu3PYuLtsP1++YxiR1avh5eVg5ZZ9PDV9PoWu0vrL+7VleJ82FFjLT2t28NJnJzxeVLpx1/ajW7sGZOfm89jUmWzeUbZ/Rz01YSi1aoRy9Z3TAGhUP4p7bhxIgL8P8QfTeOg/33EkK7eSIj99Y24bQJcucWTn5PH009+xZUvZin1vbwe3jz2HNm3rYQst//fOjyxevMkN0Z6a+87vQ68mDcjKy+O+z+awIb7s/rtjQHeGtDuL6v5+dHzklaLlEwb3pktD5+cZfx8fwqsF0PWx1yot9lNx3wV96NW0AVm5rv7tL6d/A139C/Cj40PF/asVGsyjl5xDWGAAqVnZTPhkFgfSMioz/BO673xX3/JO0re2rr49XNy3mtWDefzScwnx98PhMLww+yd+3LyzEqM/ucmD+9CrsfMYMenLOawv57U5rl93LmpzFiEBfnR4vLh/w9qexT0Dz+ZAunN/TV+6mhkr1lVa7CfTObwptzcZgsM4+G7/UqbvWlBq/ZDaXbm4TncKrCWrIIdnNs5gV2YiA2u0Y0T9PkXt4oJiuH7pf9iasb+Se3Byd43sS/c2zmPfw2/NZtOu4x8bnh13EbWjqnPF5PcA6N+pMTcM60ZszQiueegDNuw86dVRlSpjbRIJH2zGWkvY2bWJPD+23HZpyw6w99W1NHigMwENQshNymLb5F/xjQkEIDCuOjX/1bwSIz+5fSsy+f2dg9hCaDQghFYXh5dps/PndFZ/nAIGwmJ96TW+JhmJeSx8Kh5rLYUF0Oy86jQ9N9QNPTi5mx+9jE79W5CTlcdzd7zHtrV7yrTpPbQjl99xLlhITjjMM2OmkZaSScMWdRj79BX4+HlTUFDIKxM/YvPKXW7oxemb/CQs/BXCw+Cbae6O5sysXOrFu6/6UlgI/QfnM+yKvFLrDx4wTH3aj8wMKCw0XHV9Lu27FPDjPC++/sSnqN2u7Q6efi2bBo0Kj30IEQCstQOOt84Yc8AYU9NaG2+MqQmUd4AbBiyx1ma47jMT6AYo8SenzxhTAKwtsWgoEAn8y1p7+yluIxS40lr76nHWxwAv4hyQ8jDOa9PHAbnABmATzmvXfwRuBXoBd1trLyixjWnAt9baGcds+3jL+xzdhjFmNNDRWjvmFPsTCLyFcyBN44p50NE33d9NYQG8N9Wbe5/MIzwSpoz1pn23QmrXL27z9XQvOvcqpP+FhezbBc/d78Pz/8ujTqzloVfy8fKCw8kw+WYf2nXLw8vLff05qk9sA2LDQuk37R3axtTkkf79ufijD8u0e6TfACb9MJdVCfG8M3QYvWNjWbRzJ5uTkrnl2294rH/5/5Mn9+rNop07K7gXJ9ejZSx1o0MZOvldWjaMYdLIfox64qMy7Sa+8R2Z2c6E19M3X8CAjo2Z8/tmOjatQ+82cYx4+H3y8gsICw6o7C6cULd2DahTM4zLx/4fLRrX5O4bB3LjpOnltu3dpTFHskt/QJt4y7lMfW8hq9bv5fx+LRl5USfe+ujnygj9tHXp3JDadcK4+l9v0Lx5LcbdcS63jXmvTLuRI7tz+HAmo0a9iTEQXMX2WUm9msRSPyKUQS+8S+s6MUwZ0o8Rb5R9fS7YuJ3pS1Yza/zoUsufmrmo6PbIrm1pXjOqokM+LUX9e+5dWteNYcpF/Rjx2gn6d+foUsvvGdyLr1Zs4KuV6+nSsC7jz+3JxE9nVVL0J9arSSz1I0MZ9Lyrb0P6MeL1U993N/Xtwqy1m/l46RriosJ5fdRQBj77TiVFf3K9GsdSPzyUc196lzZ1Yphyfj8uf7uc/m3ezvSlq5l1++gy62b+sZlHvl9QZrm7OTCMbzqMO1e+ycGcVN7sdDs/Jf3Brszi84YfElby9b4lAPSIPIsxjYdwz6q3mXtgJXMPrASgYbUYHms9ukom/bq3bkDdmFAuufcdWsbVZMKo/lz7cNljPECfDo3IOubYsG1vMve+9A2TRh/3vMttbKEl/v1N1L+rHT7h/mx/eCnBbSPxqx1Uql1BVj4pP+whoGFIqeW+0QHEPdS1MkM+ZYUFlt/eOsjAKbUJjPDm+3t3U7dTNULr+hW1Sdufy9rPDzHo8Tr4BXmRdTgfgIAwbwY/WQcvHwd5WYV8PW4XdTsFERhetU5jO/VvQa2G0VzX7UGatY9lzFMjGH/eM6XaOLwc3PzoZdzU62HSUjK59oFhXHhtH6Y/+x3XPTCM6c99x7L56+nUvwXXPTCMCRe/6J7OnKahg+HKi2Hi4+6O5MwUFMDbL/vy76eyCY+yTLzNn47d86lbv7io4bPpPnTvnc+5Q/LZs8vw+H3+vDY9i179C+jVvwCAXdsNT0/xV9LvVGmMv/J8DYwCnnT9/qqcNruBG4wxT+DMSfTGmVM5IY3xJ8eTZa1tW+Jnp7V2WXlJP2PM8Y68oTgTdmW4ppz+AlhorY2z1nYAJlF8Hfs2a21bnEm2s3AmHt3tDuCAtbaVtbYlcB2Qd5L7nNAJnrsKt22TIbqWJbomePtA196FrPil9L8EYyDriPP2kUxDaITzH7SfP0VJvrxcZ7uqYkBcHF9sWA/AqoR4Qnz9iAqsVqpNVGA1gnx9WZXgHELhiw3rGRjXCIBth1LYcehQudseGBfHntQ0tqQkV2APTk3vtnF8t8Q5nMO67QkEBfoRWb1amXZHk37eXg58vL2KjrGX9mnDtFm/k5fv/LByKD2rcgI/RT07NWLWwj8A+GNLPMGBfkSElu1fgL8Pl1/Qgf9+tqTU8ro1w1i1fi8Av6/eRe8uTSo+6DPUvUdj5s5xVgxt2LCfoCA/wsPL9nXwoNZ88KGzn9ZCWlrV2mcl9Wsex1ernK/PNXsTCPb3IzKobJ/W7E0gKSPzhNs6r3VTvltTtSob+50Vx1crXf3b4+pfcDn925NAUnrZ/sVFR/Db9t0A/LZ9D/2aN6zYgE9Dv+Z/rm9YS5CfLwBB/n4kpp14/1a2/k3j+Gq1s3+r9yYQ4u9HVDmvzdV7Ezh4ktdmVdM8pB77spKIz04h3xYw78Aqeka2KNXmSEFO0W1/L19sOSde/WPaMu/AqgqP90z0ah/H9z87j/HrtrmODeUc+wL8fLhyUAfe+br0sWFnfAq7E8o/xrtb1vZUfKMD8I0OxHg7qN6lBumrDpZpd/CLbUQMjsX4/H1O45K3ZhNc04fgGB+8fAyxPYPZs7T0+2vLD2k0G1QdvyDnB8yAUOdHZC8fg5errwX5tsrmCrqe25p5n/wGwMYVOwkKCSQsunRy1hjnj3+gM+EZGORPSsJhAKy1BLq+0AsMDiA5IbUSo/9zOrWB0GB3R3Hmtm5yEFOrkBq1LD4+0KNPAb//XPoUzRg4csR5wnMk0xAWUfaF+NMCb3r0za+UmMVjPQkMNMZsAQa4/sYY09EY87arzQxgG84irdXAamvtNyfbcNX6qkSqtGOq5R4E4oCGwG5jzKPAuzgr9BzAJcAjQJwxZhUw11p7T4nN9QXyrLWvH11grV3tepzYEsvyjTG/AI2ApWcY9yCcWfAjwE/HaRMFvA7Ucy0aZ609tjyoJlBUc2+tLToTNcb8C7gb55Wva6y1V7v68Q7OSsmDwDXW2t2uasRsoB3wszHmAeBlnNNx+wAPWmvLy+7/pQ4lQURU8UErPAq2bSydwRt2dQFPT/Jm7lde5GTDhCeLD2bbNhjeft6LpAOGm+7NrxLVfgAx1YKIT08v+jshI4OYoCAOHin+gBkTFERCRok26RnEVCv9jfqxAn18uKljJ/71+Wfc0KHjXx/4aYoOC+JASnEfEg9lEBUaRFJq2RPVqeOG0SI2hl/W7WTe8i0A1KsRSrvGtbltaHdy8gp4ccaPrK9ClzxFRQSRmFyifynpREUEkXy4dP9uGNGDj75ZRnZO6Rz8jr1JnN2pEYt/30rfbk2oEVl1P5FGRgaTeLC4rwcPphMZGUxKSnFfq1VzniRcc83ZtG1Tj/37D/PSy3M4dOhIpcd7KqKDg0hILe7TgbQMaoQEnTTJd6xaocHUCavOb9vLXi7lTtEhx+lfeYmwcmxMOMiAFo15/5eVDGjRiCB/P6oH+JOalV1RIZ+yP9u3qfOX8PboixnZrS0Bvj5c985nFRXqGakREkR8Won//67+nU6Sb2DzxnSsX5udyYd5YtZCEqrIZdqR/iEkZh8u+vtgTipnhdQr025Yne4Mr9sLH4cX41a8UWZ9v+i23Lfm3QqN9UxFhwVxoNSxIYPosCCSjzn23XxJdz6YtZzs3L/PSXj+4Rx8wv2L/vYO8ydre+nkT9auNPIOZRPcJpLkWTtLrcs9mMX2B5fg8Pcm6uI4qjUJq4ywT8mR5HyqRRSfdgZGeJO0pfT/u7T9zi8qZ07agy2ENpeHU7u9M6mbmZTHvMf2kx6fR4dRkVWu2g8gomYoSfuLk8pJ8YeIrBnKocS0omUF+YVMnfARry2YTPaRXPZtT+TVSc6K4zf+PYP/Z+++w6so0z6Of+8UkkASWkJHCB0EggVBUKQoYO8V+9rWXl97dy3Ydu29rXURZBULulQ7gnQUVBABQ5dAIP087x8zpAMiSSYZfp/rymVmzpyT+/Y5zJy5z1PueetSzrvtOCzKuObIh6o9h93V+rVGSpPie6LGqY6ffixdWD/pzHzuvj6ej8fGkJtj3Day/PX6q8kxXH9X8Ndxqb2cc+uAIRXsnw6c5/9eCFy4s69de74qkuqWYGaz/J/3tnFMN+Bg59ypwEXAv/xeevsCy4Eb8HvulSn6gVfk2uEEaf7w2iGUHnb8p5lZPN7w3COBfah4Ik2AfwGPOud64xUtX6jgmJeA683sazO7x8w6+n9jT+AWYLBzLh2vZyB4xbxXnXM9gTeAx0q8Viugn3PuaryJPCc65/bDK4g+aGblv74OwNeTojhwaIR/vZnPNfcU8OxIbz5AgPZdHfc9X8AdT+Qz7p1o8mru9GmV4oq++/PS99+zJX+XOnkG4tJ/vsewa58jNiaa3l1aAxAdFUVyvTjOuu9t/vXuVO6/8PCAo9x5Hdum0rJpA6ZO+7ncY/c+OZ7jhvfixQdOp25CnaKejbVVdHQUTZokM3/+Ci686BXmL1jBRRcODjqsKndoj858Om9R0byUYfHgR1PpndaS0ZeOoHdaK1ZmbgpNjof37MzY7+czeOQLXPTqWB44cXiN6hW+qyYtXMyQf77I0U+/zle/LOX+Y4cFHdJOe2/5V5z69f088/OHnJlW+v6ia3JrciN5LNlcc74I2lkd90ilZZMGTJ5R/tpQm7mIY9Xbi2h6cvke7DH14+j40AG0u6MvTU/pxIpn51GYXXuKnuANB974ex7D7m7FgVc34+unV5O32bt210uJ5ahH23DsU235ZdLGomHAtU10TBSHn3Uglx58HyPSb2TJDys46XLvHHL4WQfy3O3vcuY+N/Pc7e9y5SOnBxytlPTFpGgGDsvnubezueneHB6/P67onghg0Q9RxMXBHmnhuJZL+NS8r0ukpsj2i3jb875zbutYs6+Bm82sFTDGOfeT7don/a09BR3wX+fcx2Z20DaO3d4ZtguwxDn3E4CZvQ5UtKT2wUC3EjEnm1liyfn7nHOzzKwdMNQ//jsz2x8YDIxyzq31j1vvP2V/4Dj/938DI0v8vVF+tR7/9Y4ys2v97Xi8nodFy3L7y4BfAPDss8+SXgn3GQ1TYN2a4jZav4Zy3danjo/i2n94H646dnPk50FWJiSX+BK55R7e0N/lvxrtOgVzsTujZzon9+gBwJyVq2ieVNy7y+vdV7o3htcLsMQxSYms3Lz9Hhu9mjXj0I4dueHAA0mOiyPiILewkH/Prr7hUCcOTOfYAd0BWLBkFU0bFefQpGEiazZsO4e8gkKmzP6Fg3q159sffmP1H1lM+t67KZr/6ypcxNEgMYENWcENHz1ueC+OGtITgB9+WUmTxiXya5TEmnWl89uzUwu6tG/Gu0+dT3R0FA2T6/L4nSdz2e3v8Nvv67nqbm+Kz9bNG9Jv75ozlBLg6KP35vDD0gFYuDCDJqnFuaamJrF27aZSx2/cmE12dl7RYh5TpvzIYYf2rL6A/4RT+6Rz4r7e+3PuilU0q1+cU9PkxL+0eMVhPTpz9wcTKy3GXXFq38rLb82mzVzxxjgA6taJ5ZA9O7ApJ3cHz6o6p/ZJ58Tefm7Ldy234/fpzgWvjgFg9rIM6sTE0LBuAus3B3duOa13OifuU9x2zZNLnP93Mr8NJXpljvp+HtcecmDlBbqL1uZspEl88aIHqXH1WZO77eGCE1bN5uoux5XaN6RpLwlG+P8AACAASURBVP63smYN8z1hSDrHHORd4xcsWUXTxkngdV6nSaNEVv9Ruv16dmhO17ZNGfvQ34iOjqJRcl2evuFE/n7/qOoOfafENIgjf33x+6vgjxxiGxbPgRfJKSR3xWaWPuB9b16Qmceyx2bR+vJeJKQlExXrDbFPaJtMnSYJ5K3cQkJa6aGmQanbOIbN64qLdVvWFZTrtVevcQwpHeOJijGSmsaS3CKWjb/nk9KxeEhJ3UYxNNgjjtULsmnTL/ie/EecM4DhI/oDsGjWUlJaFH9ATmnekLUZG0od37679+VrxtK1AHz+/vecdNlQAA4+qS/P3DKqaP+VD4+o8vjF0yjFsXZ18T3RujVGozL3RBM+juWW+7x/n527RcjLg02ZUN9v8i8nxdB/cO0sSAcmoiJpdVLhT3ZF0bgK59ybZvYtcDjwkZldCCzeznPnAyds5/FfKig8rgPKjltoBKz98yFvUxTQ1zm33f7ZfiFwDDDGzCLAYXiLkeyskmNSDDi+5NDhCv5uyWXB3bdL7/sLf7K0dp0dq1YYazK8IuA3U6L4+w2le0U1ToUFs7xefyt+8+bzS2oAazKgURNvnr+1qyBjmZHaNLiT97/nzObfc2YDMKhtGmf06sUHCxfSq1lzNuXllRrmC7Bmy2ay8vLo1aw5s1ZmcGzXbrw2a/s3OieP+k/R71f03Z/NeXnVWvQDGDV5NqMme3ke0CONkwalM37aQrq3a0ZWdl65Yb4JcbHUi6/D2szNREcZB/RIY+ZPKwCYPOsX9u3cmukLl7NH0wbExEQHWvQDGPPJLMZ84v0/3X/vdhx/6F7878sf2bNjc7K25JYb5jv209mM/dT7/9EsNZkHbzyOy25/B4AGyXXZsHELZnDWCX0Z+9ns6k1mB/773+/573+/B6BPn/Ycc8zeTJz0A127tmDz5txSw3y3+vqbn+mV3oaZs5ay995tWbo0+LkmS3rr29m89a33/3lApzRG9E3nozkL6dmqGZty83Z6mG9aSkOSE+KYtSyjKsLdaW99M5u3vvHz61wiv9bN2JST96eHwgI0qOsN63UOzj+oN2NmzK+qsP+UUm1XNrfcncstI3MjfdvtwdiZC2iX2oi4mOhAi34Ab343mze/8/I7qGMaI/ZL58N5C0n335s7M8w3NbFe0fGDO7fjl7Xrd/CM6vPjpmW0qptC8/iGrMndyJCmvbhr/puljmmVkMLybO9j0/4pXVi+pfgjlGEMapLOpTMqXJMtMO9OmM27E7z265+exokH9+LTbxbSvX1zsrLzyg3zHT1xDqMnzgGgeUoyj1x1TI0v+gHe6ryrsslbk01swzgyv11Fywu7Fz0eXTeGzo8Vfwf+6wPTaXpSJxLSkinYmEd0YiwWZeSt3kLeqmzqpNacBaAad4hnU0Yem1blU7dRDL9+sYkDryo9CKf1foks+WITHYbUJ2djIRt/zyexWSyb1+YTlxRNTFwUuVmFrP4hm25H1oxVfce9PJVxL08FoPfB3Tny3IOYMnY6XfZuy+ZN2aWG+QKszdjAHp2aU79xIpnrsthrQBd++2klAOtWZtKjX0fmfvUTvQ7ozIrF5ed3lKrRoXOEjBVRrMowGqU4vpwczZU3lf4yLqVJhLkzoxk0rIDlS438fEj234aRCHw9JZq7H9UwX6m5VPiTSuH3hFvsnHvMzPbAW5RjNrCtr+MmAvea2QV+UQsz6wnUB7Y1mdNPQAsz6+qc+8HM2gDpwPaqLz8Cbc2svXPuF+DUbRz3KXAZ8KAfSy/nXKnXNbP+wALn3B9mVgdvqPNkvJ5575nZI865dWbWyO/19xVwCl5vvxFse4nt8cBlZnaZc86Z2V7OuZnbyalSREfDmZcWMPKmWFwEBgwrpFVbx+hXo0nrFGHv/R2nXljAS4/G8MmYKAw4/9oCzGDR/CjG3RZFdDRYFJx1WQFJ9as64j9n0q9LGJiWxqSzzyWnoID/+3R80WPjRpzOEW+8DsBtEycwcugw4mNimPLrr0z+dQkAQ9t34PaBg2iUkMCLRx/DgrVrOPu9MYHksj1fzF1C/x5t+e8/ziEnr4A7Xvm06LE3bxvBaXe9QUKdWB659CjqxERjZkxfuIzRU7ybof9+MY/bzx7KO3ecQUFBIXe8PH5bfyoQX3+/mP33TuM/T5xHTm4+9z5VvOLpKw+eydnXlV/1tqRDDujCccO97w6mfPsTH06cV6Xx7opvv/2FPn3a8fq/LyQnJ5+RD35U9Nhzz57DBRd6c209/9xkbrzxSC6+ZAiZG7aUOq6mmbpoCQM6teWTq733581jit+fYy4ZwXFPeis0XzPsQA7v2Zn42FgmXnceo2fM48mJ3mT8h/XszEdzFwUS/45MXbiEAZ3b8sk155CTX8DNo0vkd+kIjnvCz2/4gRye7ud3/XmMnj6PJyd8w37tWnPV0P44YPqS5dz9fs1ZIXbqwhJtl1+m7UrmNqxEbv/n5zbxG0Z+NJU7jz2EM/vvDThuGl2zzi1TflrCgI5t+fRyL7+b/luc33sXjeDYZ7z8rj3kQI7o0ZmE2FgmX30e734/jycmf8MZfXoxqHN7CiMRMrNzuHFszcmv0EX458KxPLTX+UQRxUcZ0/h18yrObTeUhRuX8+XaBRzXuh/7NOxIgYuwqWAL9y54p+j56Q3SWJ27gYycmlPMLOvL2Uvo1zONMQ+eS05uAXe/UPz///W7Tuf0217f7vMH7tOBa04fRMOkBB65+hh++m0Nlz9UM67xFh1Fs9M789sjM71e+Ae0IL5lIqvf+4WEtskk7bXt1c23LPqDNWMXQ7RhZjQ/swvRibHVGP32RUUb+53XhP/dtQIXgQ5DkmmwRxyz3lpH4/ZxtN4vkRZ71eX32Vv47+VLsSjY56wU4pOi+f2XHKa/uhbDG+Kz59ENadgmbkd/stp997959B6yJy99cyc52Xk8euW/ix574n83cunB97F+VSZvPPwRI9+7msKCQlYvX8/DV3ifZx679g0uvPtEomOiyMvN57Hr3ggqlZ12zZ0wbRZsyISBJ8Cl58AJtWgGmehoOO+yPO65IZ5IBAYPL6B1W8fbr8TSvlOE3v0KOeuiPJ55JI5xo2Mwg0uuyyuaxmLBnCgapzqatlAPNqm5rKLVvETMLMs5l1hm30BKL+6R5Zx7yH/sBuAMvFVuVwKnOefWm9mbeEXAj8vO82dmLfAW3dgHb7GLX4Er/dcY56+cWzau/sDDeMNh84GbnHOfVXDcK/5rvFtmcY/PgfZ+DmcD+zrnLjWzFOBJoCteQXyqc+6iMq+5dQEPw+sh+CFwvV+sOwu4DigEZjrnzvYLky9T8eIe45xz7/qvm+DH189/3SXOuSPK5lSC+3Zp2nYert36tFlCu38+EnQYVWLxlVezz/mPBh1GlZnx/FX0PyG8k1F/+e61DB5yf9BhVJmJE26g2y3hfX8uuOcqut0UzvwW3HsV3W4OZ24AC/5xFV3uCG9+P95xFQMmlJ0KOTymDnmQ/c4K53UdYNqrV3Psl5cEHUaVeK//k/xj/vY+ktZuN+85jkObXRx0GFXm45VPEVlZfk7IsIhqtoi5y1oFHUaV6dF6eYhmyC3v0HbXhrIQ9fHih2pku6nHn1SobNHP3zcZr4cbzrk7yjx2P/5y02X2n7adv/E7cNI2Hi5X9POf8yXQd1uvWeK4s0v8/gneXH9lj3kFeMX/fS1w8g5e8zWgwm5GzrlXgVfL7FuKN//fNmPzt7P5CyvziIiIiIiIiNQ6LrLjY6TSaFVfERERERERERGREFLhT0REREREREREJIRU+BMREREREREREQkhFf5ERERERERERERCSIt7iIiIiIiIiIhI9XChXNS3xlKPPxERERERERERkRBS4U9ERERERERERCSEVPgTEREREREREREJIc3xJyIiIiIiIiIi1SOiOf6qk3r8iYiIiIiIiIiIhJAKfyIiIiIiIiIiIiGkwp+IiIiIiIiIiEgIaY4/ERERERERERGpHk5z/FUn9fgTEREREREREREJIRX+REREREREREREQkiFPxERERERERERkRDSHH8iIiIiIiIiIlI9NMdftVKPPxERERERERERkRBS4U9ERERERERERCSEVPgTEREREREREREJIRX+REREREREREREQkiLe4iIiIiIiIiISPXQ4h7VSj3+REREREREREREQkiFPxERERERERERkRBS4U9ERERERERERCSENMefiIiIiIiIiIhUj0gk6Ah2K+rxJyIiIiIiIiIiEkIq/ImIiIiIiIiIiISQOS2jLFLb6B+tiIiIiIhIeFnQAVSlQ5tfEsp72o8znqyR7aY5/kRqocXLmwcdQpVp1yqDDg8+EnQYVeLn665m3/PCmRvA9BeuZuChI4MOo8pM/vj/GJ5+a9BhVJlPZt9Nt1seDTqMKrPgnqvoems48/vh7qtC33ad7w5vfgtvvYpBE68JOowqM2nww/Q+J7zXvu9evprjv7o46DCqxOh+T/HSogOCDqPKnNvpC4b3vCXoMKrMJ3PuYe6yVkGHUWV6tF5OZGWnoMOoMlHNFgUdQtVSB7RqpaG+IiIiIiIiIiIiIaTCn4iIiIiIiIiISAip8CciIiIiIiIiIhJCmuNPRERERERERESqh+b4q1bq8SciIiIiIiIiIhJCKvyJiIiIiIiIiIiEkAp/IiIiIiIiIiIiIaTCn4iIiIiIiIiISAhpcQ8REREREREREakeES3uUZ3U409ERERERERERCSEVPgTEREREREREREJIRX+REREREREREREQkhz/ImIiIiIiIiISLVwLhJ0CLsV9fgTEREREREREREJIRX+REREREREREREQkiFPxERERERERERkRDSHH8iIiIiIiIiIlI9Ii7oCHYr6vEnIiIiIiIiIiISQir8iYiIiIiIiIiIhJAKfyIiIiIiIiIiIiGkOf5ERERERERERKR6OM3xV53U409ERERERERERCSEVPgTEREREREREREJIRX+REREREREREREQkiFPxERERERERERkRDS4h4iIiIiIiIiIlI9IpGgI9itqMefiIiIiIiIiIhICKnwJyIiIiIiIiIiEkIa6isiAEyfFs0zT8YTiRjDD8vjpFPzSj2+apXx6IMJZG4wkpId192YTWqqCyjabbt18CAGtksjuyCf6z8az/zVq8sds2fTJow8dDjxMTFMXryEuydOAuDyfvtzUs8erM/eAsDDU79kypIlNIiP54mjj6RHs6aMmbeAOydMrNactuXaUwfRv0caOXn53PHSeBb+Vj7Xx648jpT69YiOMmb9tIIH3phIxDnuvfBw2jRtCEBS3Tg2bcllxF2vV3cK23XZRUPo27sdObn53P/wx/z0y6pyx/zzgVNo1CiRvNx8AK69eRQbMr32G3hgZ84+vT/OwS+LV3PPyHHVGv+O/P36w+h9QCdyc/J5+NYx/PxjRqnHE+rW4aGXzyvaTmmazMQPZ/Psgx8X7es/pBu3PnIql536ND8t+L3aYv8zbjp8IAM6pZGdn89Noz/lh4zy788rDu7HUXt1o358HPve/WTR/ub1k7j3+GEkx8cRFWU8+ukXTF30azVGv2M3Hebll5Ofz01jPmXBNvI7ulc3kuPj2Pee4vxuOPQg9ktrBUBCbCyN6iXQ596nqy32HQl72908bCAHdfDa7ob3P2XByvL5XTmoH8f06EZyQhx7P1Cc39l99ubEvbpTGImwfks2N33wKb9nbqrO8Lerd6POXNrxGKItig8zvuWtpaWvV0e22J9jWvUn4iJkF+bx8I+jWLplFTEWzdVdTqBzUmucczz+01hmb/gloCz+vGtOG0T/nt518M4Xx7Nwafm23Orhy4+mZWp9Trn1tWqMcOdsmruOjDcXQcTRcEALUg9vW+FxmdNXs+zJubS/rTcJackA5CzbxIpXfySSXQgG7W/vTVRsdDVGv32LZ+Qx4fksIhFH+iEJ9D2xbqnHJzyfxW9zvc+e+bmwJTPClW+nAPCf2zfw+8ICWnWN5YTb61d77H/W368/nN4Hbr2uj+bnHyq4rr9yftF20XV95EccctRe/O3q4axbvRGAD97+hk/GzKjW+Ldn5rRoXn6qDpEIDDm0gGNPzS/1+JpVxhMj49icBZGIcfp5eezdp5CpE6J5/z+xRcctXRzFyKdzSOtQe4Z53nw/TP4aGjWED14JOhqRv0aFv1rAzBoDE/zNZkAhsMbf3uKc61fJf68X0MI599FOPGcgcK1z7ojtHNMAOM0599SuR7ndWErFb2ZHAd2cc/fv4us2BV4EWgOxwK/OucN2Nd6aoLAQnnwsgXtHbiYl1XHFxfXos38BbdoWX5RfeCaeIYfkc8iwfGbNjOaVF+K47sacAKMu76C0NNo2bMCQF16iV/Pm3HnIEE54461yx911yMHcPP4zZmVk8OLxxzIgrS1Tl/wKwMszZvDid6U/aOUWFvDoF1/SKSWFTikp1ZDJjvXvkUbrJg049qaX6N6uOTeePoSz7y2f643PjGNzjvdBeuTfj+TgfTvx6XcLuenZD4uOufKkAWRtySv33CD16d2OVi0aMuJvz9OtS3OuuvQQLr6q4sLkP0aOY+FPK0vta9miISNO7sul17xBVlYuDerXrfC5Qel9QEda7NGYc4/8J116tOLSW47kytOfK3VM9pY8Ljm5+HT5+FsX8eWEBUXbCXXrcMyI/flhzrJqi/vPGtCpLW0aN2D4oy/Ts1Uzbj9qMKc8+3a54yb9uJg3vpnNJ1edXWr/hQP78Mm8RbwzbQ7tUxvxzJnHcMjDL1VT9Ds2oKOf3z9fJr1VM247cjCnPFc+v8k/LubNb2bz8ZVnl9p//8dTin4f0acXXZunVnXIf1ro265DW9o2asDQJ18mvWUz7jhsMCe9VEF+ixbzxnezGX/J2aX2/7ByNce/8CY5BQWcuk9PrhtyIFeN+dMfl6pUFMYVnY/jupnPsiY3k2f2vZKv1sxn6ZbiL00mrPqeD37/GoB+KXtyccejuH728xzRoi8Af5v2EA1iE3kg/Twumv4vHDXvC76t+vVMY4+mDTjuBu86eMMZQzjnnvLXQYBB+3RgS25+hY/VFC7i+P3fC0m7di9iGsWx+K7vSOqVQnzLxFLHFWYXsO6zZSS0Sy5+bmGEZc8toNX53UjYI4mCrHwsuuYM7IoUOj57ZhMn392ApMZRvHr1H3ToU4eUPYpvRYecX5znjA+yWbW4oGh7v+PqUpDrmPVxzfrcWVLvAzrRok1jzj3iUbr0bMWltxzFlSOeLXVM9pY8Ljmp+IuEx9/+e6nr+tTxc3nqvpr1JSV49wkvPF6H2x7IoVGq44ZL4tm3XwGt2xSfH0a/EUu/gwoYdlQBy5Ya994Uz9NvZDNgSCEDhhQCsHSxMfL2+FpV9AM45lA47Ti44d6gIwkZV3OvL2FUc64Isk3OuXXOuV7OuV7AM8CjW7cru+jn6wVURUGrAXDxzjzBPDv7Pi0Vv3Pu/V0t+vnuAj5zzqU757oBN+zqC5pZjSi+L/oxmhYtIzRv4YiNhYMG5fPNV6VD+21pFL328j6Epfcq5OuvYit6qUAd3LE97833PkDNysggOT6O1Hr1Sh2TWq8eiXXqMCvD+xb2vfkLOKRjh+2+bnZ+ATNW/E5uQcF2j6tOB/Vqz0dfe7nOW5xBUt04GtevV+64rUW/6OgoYmKiKryJO3jfzoyf9mPVBryT+vftwPgJ8wFY8GMGiYnxNGpYPr9tOWJ4T8Z+MJOsrFyAol6ANcX+g7oy4YNZAPw4dzmJSQk0Sknc5vEt2zSmQaNE5n2/tGjfmZcMYdTLn5OfW3Pel1sN7tqe/876AYA5y1eSFB9HSmL59puzfCVrszZX8AqOxLg6ACTGx7F6U0XHBKdkfrOXryQ5IY7UCvKbvXwlayrMr9jhPTvz0dyFVRLnXxH2thvSqT1j5/htt2Kld52oqO1WVNx23y5dTo5/LZi1IoNmyUlVG/BO6JK8B79vWUdGznoKXCETV8+kf+qepY7ZUphb9Ht8dJ2ia0Kbek2Z+cfPAGzIzyKrIIfOSa2qL/i/4KC92vPhVzu+DibExXLa0H146YNvqjvEnZK9eCNxTRKo0ySBqJgo6u/XlE0z15Y7bvV7i0k9rA0WW/zxOGveeuJbJZKwh/d+jEmMxaKs2mLfkYyfCmjQPJoGzaKJjjW6Dojnp2+3/YXjgqk5dB0QV7TdNr0OdRJqTj4VKXVdn7OcxKT4P3ddn/FrNUX41/28MIpmLSI09e8T+g8s5LsvS98nmMGWLV4bbdlsNGxc/vPmF5Ni6D+o5n1m2ZHe6dCg5pzqRf4SFf5qOTPL8v870MymmNl/zWyxmd1vZiPMbJqZzTWz9v5xqWY22sy+83/6l3m9OngFrpPNbJaZnWxmjcxsrJnNMbNvzKznDmK6w8xeMrPJfiyX+w/dD7T3X/dB/9jr/DjmmNmd/r62ZrbQzF4D5gGtzexpM5tuZvO3Hucf29vMvjKz2X6u9SuI/2wze8LM6pvZ0q2FRDOrZ2bLzCzWzNqb2SdmNsPMPjezLhWk1hxYvnXDOTenRBzX+/+fZ5vZ/f6+Xv7/rzlm9p6ZNfT3Tzazf5rZdOCKHbVJdVi71khNLf72LSXVsW5t6dNDu/YRvvzcK/Z99UUM2VuMjZk160NY08REMjYVD7lauSmLpomJ5Y5ZmbXtY87Yqxfjzj6D+4YPJTkujpoqtUEiK9cX57HqjyyaNKj4A+bjVx7HZ49cxJacfCZM/6nUY3t1bMn6jZtZtnpDlca7s1IbJ7Fm7cai7TVrN5GaUvGnruuvOpQXnjiLM07dv2hf65aNaNWyIY8/dBpPPXo6++2TVuUx74zGTZJZsyqzaHvNqkwaN0ne5vEHDe/BlPFzi7Y7dGlOarP6TPt8UZXG+Vc1SUpkZYnhj6s2ZtE0eds3QGU9MeEbjkzvysTrzuOZM4/hH+MmVUWYf1nT5NL5rczMoslO5LdVi/pJtGpYn28W15xem6Fvu6REVm4s0XYbs2iatPNtB3BCr+5M/XlJZYW2y1Li6rM6t/hcviY3k5S48sMij2nZn9f3v5EL2x/B44vGAvBL1u/0S9mTKIuiWXwjOiW1okl8g2qL/a9IbZDIqhLXwdV/ZNGkYfm2vOjYfrwxfgY5NfBLkpLy/8ghtlF80XZMozjy/8gtdUz2rxvJX59DUnrp0Qe5q7aAwa8PzeTn26ex5qOl1CSb1kVITikedpzUOIqsdYUVHpu5upDMVRHa9Kx5XzBvT+MmSaxZWfK6vnEH1/Wepa7rAAccvCdPv3spNz98CilNa86Q5vVrjZQmxYW8xqmO9etK3wOcdGY+n/8vhgtOSeDem+L526XlC7tfTY7hgFpY+BMJAxX+wiUduAjoCpwBdHLO7Qe8AFzmH/MvvB6DvYHj/ceKOOfygNuAd/wehe8AdwIznXM9gZuAPzM5ShdgGLAfcLuZxeL1kPvFf93rzGwo0NE/phewj5kN8J/fEXjKObenc24pcLNzbl+gJ3CQmfX0i5TvAFc459KBg4HNFcS/NbdMYBZwkL/rCGC8cy4feA64zDm3D3AtUNFw5CeBF81skpndbGYtAMzsUOBooI8fx0j/+NeA6/3/b3OB20u8Vh3n3L7OuYfZQZvUFOddmMPcOdFccmE95s6OpnFKhKjocHXRfmPWbAY//xJHvvJv1mRt5sZBB+34SbXAZf8cw/BrnqVOTDS9u7Yu9diwPl0YP63m9DbaWfeMHMe5F7/MZde9Rc/urRg6xOvdEh0dRauWDbny+re56/4PuPaKYSTWq7mF3B05aFgPJn/s3SCYGRdceyjPP/xJwFFVncN7dmbszPkMfvAFLnptLA+cMByrWd8zVIrDenZm/PxFREI03GV3abujenShe/OmvPB1zZmD688au+JLTv/6Pp77ZRxntD0YgI8yprEmZwPP7nsll3Y8mnmZv1IYgvdlp9aptGrSgMnf/xx0KLvMRRwZb/9Es1M6ln+w0LHlpw20unBP2t20Dxu/X03WgvXVH2Ql+GFqLp371yEqOoQnjhIOGt6DyR8V9SHgmyk/ctbwh/j7CU8w8+tfuPYfxwcY3c77YlI0A4fl89zb2dx0bw6P3x9HpMSI3kU/RBEXB3uk1f7zikhtVCOGGUql+c45lwFgZr8An/r75wKD/N8PBrpZ8afwZDNLdM5lbed1D8ArSOGcm2hmjc0s2Tm3cTvP+dA5lwvkmtlqoGkFxwz1f2b624l4Bb/fgKXOuZJjMk4yswvw3rPNgW6AAzKcc9/5sW30c99OWLwDnAxMAk4BnjKzRKAfMKrEc8tVCJxz482sHTAcOBSYaWbd8f6fvuyc2+Ift97vedjAObd1IqdXgVFl4thqh23i534BwLPPPsvBlTwQOyXFsWZN8fcAa9cYjVNKz7/ROMVx653ZAGRnwxefx5L41zpJVKrT90rnpJ49AJibsYrmScW9wpolJbIqq/Rbe1VWFs0SKz5m3Zbi4aDvzJnL88cdU5Wh77QTB6VzzIFergt+XUWzRknM9h9r2jCR1Ru2/c84r6CQKbN+5qBeHfh2wW8AREcZg/buwBl3v1HVof8pxxyxF0cM9zoU/7hoJakpycAKAFJTkliztvwE+mvXeTlnZ+cxYdIPdO3UnE8nzGfN2k0sWPg7hYURVq7KZNmKP2jZsiELF60s9xrV5ciT92P4cfsCsGj+ClJLfJuf2rR+0YTeZaV1akZ0TBQ//+At3pFQrw5tOjRh5AvnAtAwJZE7/jWCO654I9AFPk7tk86J+3YHYO6KVTSrX/zvrGlyIqs2bu8yU9rx+3TngtfGADB7WQZ1YmJoWDeB9ZuzKzfonXDafumc4Oc3r0x+zeonsnon8tvq0B6dufuD4BcLCn3b7ZvOSXv5+f2+qtTw3GbJiazatHNtt3/aHlx0wH6chFlnGwAAIABJREFU/uoo8gsr7rUUhLW5mTSJK+6llxpXn7W5mds8fuKqWVzZ+Xj4ASIuwlM/v1/02OP7XMbyLWu2+dygnDg4nWMO8q+DS1bRtFFxWzZpmMjqP0q3ZY8Ozenatin/ffBvREdF0Si5Ls9cfyIXPTCKmia2YTz564vnsCtYn0tsw+KPo5GcQnJXbGbJ/d97j2fmsfSx2bS5PJ2YRnHU69SAmCRvmH1SzxSyl24isVuj6k1iG5IaR7FxbfG/lU3rIiQ2rnjhkR8+z+WQi2rAB8w/4ciT+zD8+BLX9WYlr+vJ27+uRxdf1wE2ZRafIz8ZM52/XTWsiqLeeY1SHGtXF99frVtjNCozlHfCx7Hccp/3/u3cLUJeHmzKhPreOnJ8OSmG/oPV20+KuUjtmuuxtlPhL1xKjgeIlNiOUNzWUUBf51xVz45bMpZCKn6vGXCfc67UzLdm1hav597W7TS8Xni9nXN/mNkrQDx/zfvAvWbWCNgHmAjUAzb4cyhul3NuPfAm8KaZjQMG7OAp21Jy4qAdtolz7jm8XokAbvHy27d16F/SqUshv6+IYmWG0TjFMWVSLNffXPomLTPTSEpyREXBO2/GMXR4zZgk+/WZs3l9plf+GtguzRuq++NCejVvzqbcPNZsLj1H05rNm8nKy6NX8+bMysjg2D278dr33pwsqfXqFR0/tGMHFq0tP7dOkEZNms2oSV6u/XukcdLgXoyftpDu7ZqTlZ3HuszSuSbExVI3vg7rMjcTHWX079mOWYtWFD2+X9c2/JrxR7kbpaCMHTeTseO87wH69m7HsUfuzcQpP9CtS3M2b85l/R+l84uOMhIT48ncmE10dBT792nPjJne8KYvvv6JwQO78sln86ifnEDrlg3JyAh2OPMH70zjg3emAbDfgZ048pQ+TP5kLl16tGJzVg7r11bcDgMPLe7tB7AlK5eTBxZPWzryhXN5/pFPAl/V961vZ/PWt977c0CnNEb0TeejOQvp2aoZm3LztjEfXMUyMjfSt90ejJ25gHapjYiLiQ60cATw5rTZvDnNy++gTmmc1iedj+YuJL1VMzbl5O1wLr+y0lIaUj8+jlnLMnZ8cBULfdtNn82b0/2265DG6b3T+XD+QtJb7nzbdW2Wyl2HDeG8t95j/ZZg8yrrx03LaFk3hWbxjVibm8ngJntxz4LSiyK1TEhhRbZ3bevbuCsrtni/x0XFYhg5kTz2adiJQldYalGQmmLUxNmMmuhfB3umcdKQXnz67bavg6MnzWH0JK9XVfPGyTx65TE1sugHkJCWRO7qLeStySamYRyZ01bR6sLiORqj68bQ9fHij52L759B85M7kpCWTJ0mCaz96DciuYVYjLF54R+kDN0jiDQq1LxjDH/8XsiGlYUkNY7ih6k5HHlt+WGw65YVkJMVoWWX2nGL+sE73/LBO98C/nX91L5M/ngOXXq2YvOm3O1c13sy+ZM5pfY1SkksOr7vwC78tqTmFN47dI6QsSKKVRlGoxTHl5OjufKm0sPQU5pEmDszmkHDCli+1MjPh2T/e4hIBL6eEs3dj9bcxVlEwq52nFWlMn2KN+x36xx7vZxzs8ocswkoOZnW58AI4G5/9d61O+jtty1lX3e8/5pvOOeyzKwlUFE1KRmvUJbpr6x7KDAZWAg0N7PezrnvzCwJyK7g7xTx/853eMNrxznnCoGNZrbEzE50zo0yr+tdT+fc7JLPNbPBwDfOuS3+32qP1ztxM3Cbn8cWM2vk9/r7w8wOdM59jjf0egoV+zNtUqWio+Hvl+Vwy/V1KYwYQw/No03bCK+9HEenzoX07VfAnFnRvPJiHAZ071nIxZfXvIv35MVLGNgujYnnn0t2fgHXfzy+6LH3zzqdo171boBu/2wCIw8dRnxsDFMW/8qUJd4cTdcfdCBdmzTB4ViRuZFbPv1f8Wtf8DcS68QRGx3FIR3bc/ao0fy8LrhhNF/OXUL/HmmMvfdccvIKuPPl4lzfuO10Rtz1OglxsTxy6dHUiY0myozpPy5j9JTit/XQ/TrzaQ1b1GOrb75bTJ/e7XjjpfPJzSnggUc/LnrshSfO4rxLXyU2NoaR95xITEwUUVFRzJj5K+M+8fKbNmMJ++7dlleePZdIoeOZFyezcVPNec9O+3wRvQ/oxEvjriI3J59HbhtT9NiT71xcajXfAUO7c+sl/w4izL9s6qIlDOjUlk+uPoecvAJuHvNp0WNjLhnBcU96vUyvGXYgh/fsTHxsLBOvO4/RM+bx5MRvGPnxVO485hDO7Lc34LhpzPht/KVgTPHzG3/VOeTkF3BTyfwuHsFxT3n5XTvUyy8hNpZJ157HuzPm8eQkrzP7YT0689HcmjdHY+jb7uclHNShLZ9dcg7ZBQXc9H5xfmPPH8Exz3v5XTfkQI7o7rXdlCvOY9TMeTwx9Rv+b8gA6taJ5V/HHw5AxsZN/P2d9yv8W9Ut4iI8tmgMI3tdQJQZH/8+jV83r+KctGEs3LScr9bO59hW/dmnYScKXCGbCrK5/wdvFdwGdRIZmX4BDsfa3EzuW1Dx6rg1yZdzltC/ZxrvPeBdB+96scR18M7TGXF7xSvB11QWHUWLEZ359eGZuAg0PLA58S0TWfXeLyS0TSZ5r22v/h1dL5aUYa355a7vwCCpZ+Ny8wAGKSraOOSiRP5zeyYu4uhxcDypbWL4/PXNNOsYQ8c+Xs/GHz7PpeuBceVG77xx/R+sW15Ifo7jybPXcejlSbTbu04QqWzTtM8X0fvATrz04dXk5uTxyK0lruv/uaTUar4DhnXn1otLz5x09Gn703dgFwoLI2zKzObhW0ZXW+w7Eh0N512Wxz03xBOJwODhBbRu63j7lVjad4rQu18hZ12UxzOPxDFudAxmcMl1eUXTPCyYE0XjVEfTFrVzmO81d8K0WbAhEwaeAJeeAyccHnRUIjvHXAjm79idmNkdQJZz7iF/O8s5l+gX5K51zh3h75/sb08v+ZiZpeDNVdcVr/A71Tl3UZm/0QivKBcL3Ad8BrwEtAO2ABeUXNjCf07Jv1E2xnnAEc65X83sTbx5+j725/m7AjjPf5ks4HS8HoLjnHPdS7z+K3jDcZcBmcD7zrlXzKw38DiQgFf0OxioUyb+BGBf59yl/mudgDfsduDWobh+r8Kn8YYRxwJvO+fuKpPjdcA5QAFeL72X/Tn6MLMbgDOBPOAj59xNZrZ1Fea6wGLgHL/HYlHb+M/dYZuU4RYvb76dh2u3dq0y6PDgI0GHUSV+vu5q9j0vnLkBTH/hagYeOnLHB9ZSkz/+P4an3xp0GFXmk9l30+2WR4MOo8osuOcqut4azvx+uPuq0Ldd57vDm9/CW69i0MRrgg6jykwa/DC9zwnvte+7l6/m+K8uDjqMKjG631O8tOiAoMOoMud2+oLhPW8JOowq88mce5i7rGavzr0rerReTmRlp6DDqDJRzRaFeqLLYYlnhbIQNT7r1RrZburxV8s45+4os53o/3cyXi+4rfsHlvi96DHn3Fq8Oe629zfWA73L7N7uZGdl/kbZGLuX+P20Mo/9C6/3XVndyxx39jb+7ndA3woeKhv/KyWe8y7eMOOSr7MEb+6+bXLOPYjfK6+Cx+7HW7W45L5ZFcVWsm387R22iYiIiIiIiEgoqANatdKqviIiIiIiIiIiIiGkwp+IiIiIiIiIiEgIqfAnIiIiIiIiIiISQir8iYiIiIiIiIiIhJAW9xARERERERERkeoR0eIe1Uk9/kREREREREREREJIhT8REREREREREZEQUuFPREREREREREQkhDTHn4iIiIiIiIiIVA8XCTqC3Yp6/ImIiIiIiIiIiISQCn8iIiIiIiIiIiIhpMKfiIiIiIiIiIhICGmOPxERERERERERqRYu4oIOYbeiHn8iIiIiIiIiIiIhpMKfiIiIiIiIiIhICKnwJyIiIiIiIiIiEkKa409ERERERERERKqHiwQdwW5FPf5ERERERERERERCSIU/ERERERERERGREFLhT0REREREREREJIRU+BMREREREREREQkhLe4hIiIiIiIiIiLVwkVc0CHsVtTjT0REREREREREJIRU+BMREREREREREQkhFf5ERERERERERERCSHP8iYiIiIiIiIhI9XCRoCPYrajHn4iIiIiIiIiISAip8CciIiIiIiIiIhJC5pyWURapZfSPVkREREREJLws6ACq0iFRJ4bynvazyKga2W4q/InIdpnZBc6554KOo6oov9otzPmFOTdQfrWd8qu9wpwbKL/aTvnVXmHODcKfn4SbhvqKyI5cEHQAVUz51W5hzi/MuYHyq+2UX+0V5txA+dV2yq/2CnNuEP78JMRU+BMREREREREREQkhFf5ERERERERERERCSIU/EdmRsM9lofxqtzDnF+bcQPnVdsqv9gpzbqD8ajvlV3uFOTcIf34SYlrcQ0REREREREREJITU409ERERERERERCSEVPgTEREREREREREJIRX+RKQcM2tjZgf7vyeYWVLQMYmIiFQlM2toZnuaWTszC9VnZF3XpSYzswPM7Bz/91QzSws6Jvlz/PNJ56DjqEpmVjfoGER2VUzQAYhIzWJm5wMXAI2A9kAr4BlgSJBxVRYzawL0B1oA2cA8YLpzLhJoYJVkN8hvX+BASuf3mXPuj0ADqyRhbj8zawWcQvn2+xD4uDbnaGbxwBFUkJtzbn6QsVUWvxCWTon8nHOrg41q15lZfeAS4FSgDrAGiAeamtk3wFPOuUkBhrjLwnxdD/N5ZSu/6HANsIdz7nwz6wh0ds6NCzi0SmFmtwP7Ap2Bl4FY4HW8a2GtF9ZzJ4CZHQk8hHfuTDOzXsBdzrmjgo2scphZP+AFIBHYw8zSgQudcxcHG5nIztPiHiJSipnNAvYDvnXO7eXvm+uc6xFsZLvGzAYBN+Dd+MwEVuPd3HXCuxF6F3jYObcxsCB3wW6Q3znAZcASYAal8+uPd6N3q3Put8CC3AW7Qfu9DLQExgHTKZ3fIGAf4Abn3NTAgvyLzOxOvKLfZMq/Nwf5v1/jnJsTVIy7wszaA9cDBwM/UVwY6wRsAZ4FXq2tBRYz+wx4DfjAObehzGP7AGcAc51zLwYRX2UI8XU9tOeVkszsHbxzy5nOue5+IfAr51yvgEOrFP77cy/g+xLvzznOuZ7BRrZrwn7uBDCzGcBgYHKYzi1bmdm3wAnA+yXym+ec6x5sZCI7Tz3+RKSsXOdcnpkBYGYxQBi+ITgMOL+iwpCf4xHAIcDo6g6skoQ9v7pAf+dcdkUP+t8ydwRqZeGP8Lffw865eRXsnweMMbM6wB7VHFNlmeacu30bjz3i9+KsrbkB3AM8jdfLodS1wM/tNLzi2KsBxLbLnHOHbOexGXgFl9ourNf1MJ9XSmrvnDvZzE4FcM5tsa2NGQ55zjlnZg7AzOoFHVAlCfW505fvnMss83YMw7mliHNuWZn8CoOKRWRXqMefiJRiZiOBDcCZeD2sLgYWOOduDjQwEan1/Bu67K09HPwhUPHOuS3BRia7OzM7FpjonMv0txsAA51zY4ONbNeF/boe9vOKmX2FNyz7S+fc3n5Psrecc/sFHFqlMLNr8b64OwS4DzgXeNM593iggckOmdmLwAS8EQvHA5cDsc65iwINrJKY2bvAI8ATQB/gCmBf59wpgQYm8heEauJiEakUN+ANR5gLXAh8BNwSaESVyMyuMLNk87xoZt+b2dCg46osZnavf8O6dbuhmd0TZEyVycxerSC/l4KMqTKFvf3wbhBKTpJdF/hfQLFUKjP7rIK2Gx9kTJXJzC6pIL8wzXN0+9aiH4A/7HdbPTlrm1Bf1wnxecV3O/AJ0NrM3sDL9/+CDanyOOcewpvOYjTePH+3hanoF/Jz52XAnkAu8CaQCVwZaESV6yK8OWBbAiuAXv62SK2jHn8iUsTMooHXnHMjgo6lqpjZbOdcupkNw7sBuhX4t3Nu74BDqxRmNnPrPCQl9n0f8vzK7autdoP2m1V2XqqK9tVGu8F7s6K2C1N+5eYUC8NcVbvJdT3M5xXDW4xlC9AXMOAb59zaQAOrJP7783/OuUFBx1JVwnru9NvuAefctUHHUhX8/C53zj0adCwilUE9/kSkiHOuEGjjz4sTVlsn6jgMr+A3v8S+MIg2s7itG2aWAMRt5/jaJsrMGm7dMLNGhGu+2rC332YzKypi+osnVDhvYy0UMbOi+cTMrA3hmusouuS8Yv5NUZiuFdPN7BEza+//PEII5vfbTa7roT2v+HPDfeScW+ec+9A5Ny4sRT8oen9GzFtdO6xCee702+6AoOOoKn5+pwUdh0hlCdPNkohUjsXAl2b2PrB5607n3CPBhVSpZpjZp0AacKOZJQG1dkW1CrwBTDBvtUOAc6jdE0eX9TDwtZmN8rdPBP4RYDyVLeztdyUwysx+xyu4NwNODjakSnMz8IWZTcHL7UDggmBDqlSfAO+Y2bP+9oX+vrC4DK8H+Dt4BdvPCM+QrrBf18N8XgH43sx6O+e+CzqQKpIFzDVvhe2S78/LgwupUoX53DnTP6+MonTbjQkupEr1hZk9gXddKJnf98GFJPLXaKiviJRiZhXOaeScu7O6Y6kK/qTfvYDFzrkNZtYYaOmcmxNwaJXGzIYDB/ubnznnQjPPGICZdQMG+5sTnXMLgoynsu0G7ReLN48TwELnXH6Q8VQmM0vBG44HIRqOB0XnzgvxFhkArzD2gt8rIjTMrJ5zbvOOj6w9wn5dh9CfV34EOgBL8YoPhtcZsOd2n1hLmNlZFe13zoXiS68wnztLfElZknPOnVvtwVQBM5tUwW7nnBtcwX6RGk2FPxGpkJklAjjnsoKOpbKZWU+gLSV6PYfo28mtQww7Ouf+Z2Z1gWjn3Kag46osZnYAXn4vm1kqkOicWxJ0XJUlzO3n53M10MY5d76ZdQQ6O+fGBRzaLvOHco0A2jnn7vKH/TZzzk0LOLRK4w8938M5tzDoWCqbmfUDXsA7n+xhZunAhc65sEzCH9rrepjPK1B0TSjHObe0umOpKv5Q9E7+ZqgKtxDuc6eI1A6a409ESjGz7mY2E5gPzDezGWa2Z9BxVRbzVoB9CTgeONL/OSLQoCqRmZ2Ptzre1iElLYGxwUVUufyeK9cDN/q7YoHXg4uocoW9/YCXgTxgf397BRCWVYufwsvrVH97E/BkcOFULjM7CpiFP0TNzHr5Q7zC4lFgGLAOwDk3GxgQaESVJOzXdcJ9Xtla4GtA8WeWBiEr+g0EfsI7Xz4FLDKzUPzbg3CfO82slZm9Z2ar/Z/RZtYq6Lgqi5nV9+d+ne7/PBzy+SglxFT4E5GyngOuds61cc61Aa4Bng84psrU1zm3r3PuLOfcOf5PKIYk+C4B+gMbAZxzPwFNAo2och0LHIU/14pz7ncgKdCIKlfY26+9c24kkA/gnNtCeBbX6eOcuwTIAXDO/UEIJnAv4XZgP2ADgHNuFt5cqaHhnFtWZletH4rnC/t1PcznFczsCrz5X5v4P6+b2WXBRlWpHgaGOucOcs4NwCvAh2kl1TCfO18G3gda+D8f+PvC4iW8L/FO8n82Eq78ZDeixT1EpKx6zrmiOS2cc5PNrF6QAVWyr82sW9jmhSsh1zmXt3UBOTOLIVwri+Y555yZOfDm4wo6oEoW+vbzhzxtbb/2QG6wIVWafH+1xq25pRKuhYPynXOZJRanhHC9N5f5w32dP1/cFcAPAcdUWcJ+XQ/zeQXgb3hfLGwGMLMHgK+BxwONqvLElhwC65xb5P8bDIswnztTnXMlC2GvmNmVgUVT+do7544vsX2nmc0KLBqRXaAefyJS1mIzu9XM2vo/t+CtCBgWr+EV/xaa2Rwzm2tmoVnYA5hiZjcBCWZ2CN5Kax8EHFNl+o+/Ml4Df1js/whXz5Wwt9/teMOdWpvZG8AE4P+CDanSPAa8BzQxs38AXwD3BhtSpZpvZqcB0WbW0cweB74KOqhKdBFej9uWeENFexGiVX1Dfl0P83kFvN6LJXufFhKiHo3AdDN7wcwG+j/PA9ODDqoShfncuc7MTjezaP/ndPzpEkIi259XGgAz6w9kBxiPyF+mxT1EpBQzawjcCRyA943k58Cd/rC1Ws/MfsabBHwuJXrjhGW+HH/1uL8BQ/FuDMbjrR4XmpO9XxArys8591nAIVWa3aT9GuOtfGuEb+XbLngrNxowwTkXlh5jWxdQuJnS7827nXM5gQYmOxT26zqE/rxyNXAW3hcLAMcArzjn/hlcVJXHzOLwiuxbCyyfA08550LRazPM505/4ZnH8ebXdHgFzcudc78FGlglMbNewKvA1nn9/gDO9ueAFalVVPgTkd2KmX3tnNt/x0fWfmbWCGjlnAtNj0Z/eFqOc67QzDoDnYGPw7YCIIS2/foDs5xzm/2eAXsD/wpD4d0fXrjcOZfrT1bfE3jNObch2Mgqnz+kuZ5zbmPQsVQWMxuJtyBENl7vsZ7AVc650CweFFZhPq9sZWZ7U6Iw5pybGWQ8lankdd3fjgbi/LkaQyWM587dgZklA6jdpDbTUF8RKcXMPjOzBiW2G5rZ+CBjqmQzzexNMzvVzI7b+hN0UJXFzCabWbJfNJoBPG9mYZokeyoQZ2Yt8W7OzwBeCTSiSrQbtN/TwBYzS8frefsL3vD7MBgNFJpZB7xVmVsDbwYbUuXxz5vJ/k36XGCBmV0XdFyVaKh/U3cE8CvQAQhFfrvBdT3M5xXMrC/wk3PuMefcY8AvZtYn6Lgq0QQgocR2At40HqEQ5nOnmb1awbnlpSBjqkxmdq+ZNXDObXTObfTzC82K4bJ7UeFPRMpKKdlDxR8KFKZVRRPwJv0eChzp/xwRaESVq75/83ocXm+jPnhDD8PC/F4AxwFPO+dOBPYMOKbKFPb2K/CHLR8NPOmce5LwrMoccc4V4LXdE86564DmAcdUmbr5781jgI/xVqU8I9iQKtXWBe8OB0Y55zKDDKaShf26HubzCniFzawS21n+vrCId84V5ef/XjfAeCpbmM+dPSs4t+wVYDyV7dAK8jsswHhE/jKt6isiZUXMbI+t83P483eEZk4A59w5QcdQxWLMrDlwEt6cMmFjZrY/MAJvLjz4f/buO1yuqmz/+Pcm0gIEUBBsQEAktAAhKE2liRUsIIihCIrYqIoNX0ER/aGgIopKEQWVpiJFmtKLtISEhPbCSxFEpUiJgBDC/ftj7cmZMznnJOTsycpe83yu61w5s3fguueayZrZa6/1PDAiY566lf76TZf0FWBX4G1VTcNSujfOkLQLsDvphgKU89wAFlbqtPkB0sTmDFXdtQtxvqQ7SVt9P63UlbnxNbgqRX+uU/a4AumG16zXy/ZLSh3fS/GMpHG2JwFI2pCyGiiUPHYuJGnZVr3QardCSe/NEZIWbdWbVOoevmjmTCHMk5L+YYYQ6nEIcI2kK0lFiN8KfDJvpOGruhgeZ/vfg5zfChhp+/z5m6x23yQVjr7G9k2SVgXuzpypTvsDXwHOtn1b9fwuz5ypTqW/fjsDHwU+bvufklYCvpc5U132JHWGPcL2fZJGA6dmzlSnn5O2wE4Brqomj4qpd2T7y1Wdv6eqGqLPklaQlaDIz/U2JY8rkLoy70ffKr/PUFZX5gOAsyQ9THp/rkh6TUtR8th5NPBXSWeRXrsdgSPyRqrVb4BLJZ1cPd6T1OwjhMaJ5h4hhNlIWo7UHc/ADSV0x5P0fuCLpBUck4BHgcWA1YH1SfVkvm370WwhQyiUpH4rVub174QFhyQBI6rtzY0laXPb1wxxfhSwku1p8zFW7Qr9XO+JcUXSq4EfAVtVh/4CHGD7kXyp6lWtiFujenhXiQ27WkoZO1skrUV6bxq43PbtmSPVStK7gG1Iz+8vtkuqjxp6SNT4CyEAaeuPpKUBqguCZ0h18HaXtEjWcDWwfY7tzUgrcm4jbQ99Gvg18GbbBzZ50k/S16otFoOd30pSY2sZSjpB0rqDnFtC0l6SJszvXHUp/fUDLpe0b7USZxZJi1TP7VfAHpmyDYuk8yRtV124dp5bVdI3Je2VI1sdJO1abZ3sx8mLklaTtPlA/21D7CDpOklfl/ReSW+W9LZqTDkVOJ/+jQcao/TPdQoeV9rZfsT2R2y/uvr5aAmTfpI2krQiQDXRN460WuzooT4Pm6LksVPSyNZnXjXR92dgEWBM1mBdYPsi4DvAdUDjb5iE3hUr/kIIAEi6Afig7YcltVbAfQcYC8yw/YmsAcOQSl/RWL0nvwqsC0yj//MbBfwC+FmrDkvT9MDrtxiwF6k242jgSdLzGwFcQtqGf0u+hPOuunA9CNgB+Dd9r90qpO6iP7Z9TraAwyRpf9JrN7H6aT2/NwJvJ10Ifdl2Y7ekV5MMOwCbkRqyPAfcAfxpqNWAC7rSP9dLHlcAJO0NXGH77mqV2Emk9+kDwMdaNfGaStIkYBvb/5b0NuB0YF/SZ96atnfMGnCYSh47JV1F2lp/t1In+xtJ22LXAm6y/eWsAYdJ0vmk12aaUt3lScDNwGrA8bZ/mDVgCPMgJv5CCABIutX22Or3o0gdKr9Y3a2c3DoXFmySVmf2i9erbBdRKFvSksB42p6f7bvypqpP6a8fzNrStRzwXHu3vBJIWoW+1+5/qw7UjSdpBGkrV+d788JWw4iw4Omlz/USxxVJ04ANqmYQHwU+T1qxuQFwqO23Zg04TJKm2F6v+v0nwKO2D6seT7a9fs58dSh17JQ01fa61e+HA6+0/dlqJfHE1rmmknSb7bWr378KjLG9u6SlgGtLGjtD74jmHiGEFrX9vhWpgUKre1yeROFlq+4cN+7u8dyy/R/gitw5uqX01w9mben6R+4c3WD7flIR96LYnknayvXn3FnCy9Izn+uFjisvttW6ex9wiu3Hgb8oNaJpuhGSXlHVutua/g3Bv6BfAAAgAElEQVRnirhGLXjsbF85tBVVMx3bL0h6KU+kWrXXmNwaOAHA9vRCnl/oQUUMqiGEWlwm6UzSF+dlgcsAqiXuL+QMFkIIIYSXLT7Xm+2l6rV6gjT50N4ttZF1JzucBlwp6THSSrirAaqto0/lDBbm6NZqFfHfSVuXLwGQtEzWVPV5UNK+wEOk2pMXAUhaHJitnm8ITRDNPUIILQcAfyCtVtm87S7zisAhuULVTdKbJF1abaFB0lhJX8udK4QQQqhZT3yuF+zrpLpi9wPn2r4NQNLbgXsz5qqF7SNI25d/SXp/tlaRLUSq9RcWXHuTahSuAmzbVtZiLeCoXKFq9HFgbeBjwM5t5QM2Bk7OFSqE4YgafyGEniLpSuBg4Oe2N6iOTbO9Tt5k4eWQNLKU+mm9RtLKwOq2/1LdPX+F7em5c9Whej4rlVR3sldIGkmahFjJ9t5Vvc01bJ+fOVqYC6WOK5JeASxl+4m2Y0uQruH+ky9ZCCGEJokVfyGEXjPS9o0dx17MkqQLSl/RKGlTSbcDd1aP15N0XOZYtemB129v4HfAz6tDrwf+mC9RfSRtB0ymb0vQ+pLOzZuqPpJWkHSSpAurx2tJ+njuXDU6GXge2KR6/HfgW/nihLlV8rhi+8X2Sb/q2DMx6dccPTB2hhAaICb+Qgi95jFJq1EVJpa0I2UVBD+BVMB9BoDtW4GPZE1Urx8A7wQeB7A9BXhb1kT1Kv31+yypu+HTMKuZyauzJqrPYcCbgScBbE8GRucMVLNfAhcDr60e/y9pK2kpVrP9Xfr+7T1L/+YYYcFV8rgSmu+XlD12hhAaICb+QgizkbS4pDVy5+iSz5JWBYyR9HfSl69P5Y1Uq6JXNALYfrDj0MwsQbqj9NfveduzmgpU29hKqTkyw3ZnQfpSnhvAcrbPBF6CtBKJsv7tvVBtEW3dFFqNtAKw8STtPzfHGqzkcaV4ktYa4NgWGaJ0S+ljZ6tUQghhARYTfyGEfkrfrgbY9jbA8sAY25tT1lhY+orGByVtCljSwpK+ANyRO1SNSn/9rpT0VWBxSe8AzgLOy5ypLrdJ+igwQtLqko4FrssdqkbPSHoVfe/NjSmr8+ahpM+9N0j6DXAp8MW8kWqzxwDHPja/Q3RRyeMKki6dm2MNdqakLylZvBo7v5M7VI2KHTuj/EoIzRHNPUII/UiaCGwFXNHW/GKq7XXzJquHpEm2x3Ucm2h7w1yZ6iRpVeB4YFPgCeA+YFfb9+fMVRdJywHHANuQtuFdAuxv+/GswWoyyOs3wfYDWYPVRNJCpG5525Jev4uBE13Al5FqxcMh9H9uh9v+b9ZgNZE0DjgWWAeYRrp5smO1Hb0I1cX5xqTX73rbj2WONCySdgE+CmwOXN12aingJdtbZwlWs1LHFUmLASOBy4Et6Nt6Pgq4yPaYTNFqVTUrORLYkPTe/A1wpO2XsgarScljp6QbgB1JXaeLa5gXDQFDSV6RO0AIYYEzw/ZTUr/SRo3+8gwgaQywNrC0pA+1nRoFLJYnVf1s3wtsU32RXqiErobtqgvxCblzdNEDtkt+/V4i1TE8IXeWulU14Q6pfopje5KktwNrkCYg7rI9I3Osur0OGEH6fvw2Sdj+Q+ZMw3EdacXwcsDRbcenA42fdGgpeFzZh1SO5LXARPom/p4GfpwrVBfMAJ4DFid9H7uvlEk/KH/stP1gxzVDSduYR9q+seP5lVR+JfSQmPgLIXTqt10N2I8ytqutAbwPWAbYru34dGDvLIm6QNIywO7AKsArWl9WbO+XMVZtJI0G9qV6fq3jtrfPlalm90m6CDgDuCx3mLpJeh9wOLAy6fUTafv9qKzBaiBpPPBVZn9vjs2VqU6SRgDvoe/5bVtNjH0/a7CaSPoFMBa4jaoWF+mmV2Mn/qqVwg/Q16m4SKWOK7aPAY6RtK/tY3Pn6aKbgHOAjUiT1D+TtIPtD+eNVY/Cx85+5VeA/YnyKyEskGKrbwihn47tapC2zHyroO1qb7V99Zz/ZjNJug64HphK38Urtn+VLVSNJE0BTmL253dltlA1qv79vY/UyXcccD5wuu1rsgariaR7gA8BU5u+Da+TpLtIW4I635ulbNO+APgvsz+/b2QLVSNJt9uerclACapV7keSOt2KQibGWkoeV1qqyZVV6H9T4ZRsgWokabztmzuO7Wb71FyZ6lTy2BnlV0Jojpj4CyH0FEl3k5qXnAxcWNpFwkA1DEsi6Qbbb8mdY36QtCzpC/UE2yNy56mDpMuBrUvaxtUi6ZqqWVCRJN1ayurFgUg6CTja9u25s9StmhjbznZJK3FmKXlcAZB0KrAa6btLaxulS1nJDyBpc2B12ydXk0lL2b4vd646lD52lkzSCNszSy2/EnpLTPyFEPqR9Gfgw7afrB4vS1px9M68yeqhtPd1G2Av0raSM4Ff2v7frMFqIulA4D+klWLPt47b/ne2UDWqtqGvTrqr3P78JmULVbOqFtDOwLuAm4EzbP8+b6p6SNqItCXvSvq/fo3f8iRpa2AXUjfY9ufW2K2i7SQdCVxq+5LcWbqh+nd3LvBP0uvXWhXX+At2Sdfa3ix3jm4peVwBkHQHsFZpNypbJB0KjAfWsP0mSa8FzirlPVvi2Fl1Xh70/VjKpLSkv5G6vZ8BXFbqv8HQG6LGXwih03KtST8A209IenXOQHWqPrT/DPxZ0pbAr4HPVFtIv2z7r1kDDt8LwPdI27VbX1AMrJotUb3WBXYjdZ5ur8O1VbZENZJ0P3ALaUL6YNvP5E1UuyNIE9OLAYtkzlK3PYExwMIUUiOuw/XA2VUH1RkUtl2UVEJgNzq24xXiZklnAH+kwElpyh5XIHWCXZFya4t9ENgAmARg+2FJS+WNVKsSx86b5/xXijCGVH7ls8BJkooqvxJ6S0z8hRA6vSRpJdt/A5C0MgV09W2R9CpgV9IF3r9IjSLOBdYHzgJG50tXi88Db6y635bow8Cqtl/IHaRLxtp+OneILnqt7XVyh+iSjWyvkTtEF32f1CSi1Dpqj9o+N3eILhkFPEtf7V4oa1K65HEFUsOL2yXdSP+J21KaWr1g25JaDRSWyB2oZsWNnaXUjZ4T28+SbsSe2VZ+5UpS9/cQGiUm/kIInQ4BrpF0Jemu5FuBT+aNVKu/AqcCH7D9UNvxmyX9LFOmOt1DusAr1TRSZ+ZHcgepk6Qv2v4ucETr4qddKdtmgAskbVvSlqc210laq8QacZUHgWmlXLgO4BZJvwXOo7BVcbb3zJ2hy0oeVwAOyx2gy86U9HNgGUl7k0qxnJA5U52KGzslncfQW31LmZQeqPzKTnkThTBvosZfCGE2VWHljauH15e0ekySSvry1UnS2cDawOX0v3gtYuJI0hXAWOAmClr5IGk72+dJ2mOg86XcXZc0HViC9NqVsuUJmFWHazVS17+iasQBSPolqWTAhZRZR+3kAQ7b9l7zPUxNWjcUBqvHVdDnQrHjSq+Q9A7SilQBF9v+c+ZItSlx7KwmwyB1016RVDYHUp3bf9k+MEuwmnWUXzm3wPIroYfEir8QwkAWBf5NGiPWkoTtqzJnGpZqMvOzwL+rC7zvkVYz/h/wedv35MxXoz9WP6U6NHeAbrB9XvXrs7bPaj8n6cMZInWF7ZLqNnV6V+4AXXZf9bMIBdZRK3RVXKuLb9H1uEodV1qdwquJzfaJ2+ImNquJvmIm+zoUN3bavhJA0tG2x7edOk9SSeNN6eVXQg+JFX8hhH6q7mM7A7fRVqC+gBVVl5AufpYCtgZOJm3peiswwfYW+dKFkEiaZHvcnI41jaQxtu+UNODzaHJXZkmjbD8t6ZUDnS+lo3apemVVXIlKHld6wQATmv2UNLFZqmql+3tt31s9Hg1cYHvNvMmGJz4XQolixV8IodMHgDVsPz/Hv9ksK9j+qiQBD9j+XnX8TkmfzRmsDpLOtL2TpKkM/CWl0dsNS1/5IOndwHuA10n6UdupUcCLeVLV6iBSrdCjBzjX9K7MvyV1/ZtIei5qO9f4jtqSfmj7gMFqOjX9phA9sCpO0vLAl4C1SJ1vAbDd5H93UPa4MhtJr6b/6/e3jHGGrbVSU9LhpI7Fp5LGzwnAazJGq0UPjJ0ABwJXSLqX9NqtDOyTN1Itiv9cCL0nJv5CCJ3uBRamrQ5JIWZCmiGS1Fmz8KUB/n7T7F/9+b6sKbpnSyh3SxfwMOkL5vakCaSW6aQv1k33CwDbW+YO0gWfBbDd9I7ggzm1+vOorCm6ZxPgvFLqaA7iN8AZwHuBTwF7AI9mTVSPkseVWSRtT5rcfC2psdXKpImJtXPmqtH2ttdre/xTSVOAr+cKVJPSx05sXyRpdWBMdejOEhYO9Er5ldBbYqtvCKEfSb8H1gMupaDmEJKeBK6ir1Nxq2ahgM1tL5srWx0kXWJ729w5uqWE7a5zQ9Io4BnbM6vHI4BFbTe6U3PJr1/Jzw1SYXrbH8udo1tKf/0AJE20vaGkW1urvyXdZHuj3NmGoxdeO4BqEmwr4C+2N5C0JbCr7Y9njlYLSdcBPwFOJ62M2wX4rO1NswYbppLHTkkfGup8Cd3QodzyK6E3xYq/EEKnc6uf0ry/7ffOu68l3I1dPneALtOc/0oRLgG2Af5TPV68OtboC6DClf7ebHSZgLkwQtKyDPI6FlKjcUb15z8kvZe0wnjAmpRhgTTD9uOSFpK0kO3LJf0wd6gafRQ4pvoxcG11rOlKHju3G+KcgUZP/PVA+ZXQg2LiL4TQT6nbnVodyAq29FB3YAu4+7q8pIMGO2n7+/MzTBctZrs16Yft/0gamTNQTVaVNOgNhYbXOuq8MOin6aulgZGSNmDwibGmN1AYQ9peP9Dza3yNxsq3JC0NfB44lnTxWkIJgZLHlXZPSlqStFPhN5IeAZ7JnKk2tu+n/83ZUhQ7dhbaBb1d6eVXQg+Kib8QQj9VrY7vMHsR8BIufkq2NKm+32AXr02f+BsBLEn5q6uekTSudUEgaUPgucyZ6vAoAxfgL8Fz9L8wKM3rSK/dYGNL0xso3G57g9whuux6208BT1HVSy1EyeNKu/eTxpkDSY0vlga+mTVRjarmM3sDq9B2bWp7r1yZalL62Fks21OAKZLOZoDyK1nDhTCPYuIvhNDpZOBQ4AekC4Q9gYWyJgpz44ECviQP5R+2i7nQGcIBwFmSHiZdLKwI7Jw3Ui2mF7zq9vFSV0pX7img+2tPkrQdqQHGi5JmAjvZvi5zrDqVPK4AIOkDwBuBqbYvBkoca84Brgb+QtWIrRAxdjZflF8JxYiJvxBCp8VtXypJth8ADpM0keZ3VwNSN66BOnR1Hmug0lfClf78ALB9k6QxwBrVobtszxjqv2mI+3MH6KIXcgcIw3JM7gBddATwVtt3SnoL8F3g7Zkz1en+3AG6SdJxpM691wGHS3qz7cMzx+qGkba/lDtECAMotfxK6EEx8RdC6PS8pIWAuyV9Dvg7aYtlKb4CdE7yDXSsaXbLHaDLts4dYD5ag76t9uMkYfuUzJmGxfaQHQCbzPbGuTN0WdEX5LZ/mTtDF71o+04A2zdIWip3oDqVPK5U3gasZ3tmNdlwNVDixN/5kt5j+4LcQWpW7Ng5QE1pA48Bk21PzxCpW0otvxJ6kGznzhBCWIBI2gi4A1iG9AVzaeC7tq/PGmyY2jp07QSc0XZqFLCW7TdnCRZCG0mHAluQJv4uAN4NXGN7x5y5QgjNI+khoL3x0UHtjwtqilQkSZNsjxvscSkkTQeWAJ4ndaAWYNujsgYLg5J08gCHX0nqZPxx25fN50hdUV0TnU5q9jGr/Irtkuv6hkLFxF8IoSdIWg9Yn1QQu33b8nTgcttPZAkWQhtJU4H1gFtsrydpBeDXtt+ROVoIRZP0KtuP585Rp+pGwqBsf2N+ZQkvn6RngXtaD4HVqsetibGxubKFMBBJKwNn2n5L7ix1kbQw5ZVfCT0oJv5CCP1IGg8cAqxM/+5qRXzBlLRw60Nb0rLAG2zfmjlWbSTtb/uYOR1rKklr2b6949gWtq/IFKlWkm60/eaqruaWpInpO2yPyRytFpIutb31nI41kaSjgV/Yvi13lm4ouD4qAJLuBiaTGlxd6PiC3BiljivVJMqgqjrMjSVpyNWLre2VpZA00vazuXN0W2krUyWtQ1/5FYDGl18JvSlq/IUQOv0GOBiYCryUOUs3/FnS9qTxbyLwiKTrbB+YOVdd9mD2YvUfG+BYU50p6VRSkfrFqj/HA5tkTVWfmyUtA5xAen/+B/hr3kjDJ2kxYCSwXDXh3mrWMgp4XbZg9boDOF7SK0iTR6fZfipzpjqVWh+15U2k7o17AT+SdCbwS9v/mzdWGEzp40rTJ/bmwtFDnDNQREdcSZsCJ5LqZa9U7UDZx/Zn8iarn6Q1SFu2izBY+RUgJv5C48SKvxBCP5Kusb157hzdIukW2xtI+gRptd+hkm5t+opGSbsAHwU2JxUAb1kKeKnpKx9aJC0BHAlsSHpuvwGOtN34SWpJAl5v+8Hq8SrAqBJWpEraHzgAeC2pYVDrAv1p4ATbP86VrW7Vhc+ewC7AtaTnd3neVPOuF+ujStoS+DWp7tgU4Mu2Gz8BX5peGldCc0m6AdgRONf2BtWxabbXyZts3kk6jzQ52+6VwGuAXUsZL6P8SihJrPgLIXQ6VNKJwKW03bWz/Yd8kWr1CkmvIV3EHpI7TI2uA/4BLEf/u+jTgcZPHLWZQeqotjhpxd99JUz6QSrYJOkCYN3q8f15E9Wn2mp+jKR9bR+bO0+3SBoBjKl+HiNNGh0kaR/bH8kabt49DNwMbE9ahdoyHShlpTSSXgXsSuqQ/i9gX+BcUm3Ys4DR+dKFgfTKuBKaz/aD6d7eLDNzZanJUR2PDTwO3G37hQx5uuU52y9JelHSKOAR4A25Q4UwL2LiL4TQaU/SRevC9G31NVDKxN83gItJnVJvkrQqcHfmTMNWbQl6gHK2vA7mJuAcYCPSJOfPJO1g+8N5Y9VmkqSNbN+UO0g32D622va0Cv1riDZ+24ykHwDvAy4Dvm37xurUkZLuypdseGxPAaZI+m3hRc3/CpwKfMD2Q23Hb5b0s0yZhkXSQUOdL6Wrb8njSijCg9X701WjiP1JpSEay/aVkj4AvBGYavvi3Jm6pMjyK6E3xVbfEEI/ku6yvcac/2bzVKtx9rP9g9xZukXSh0hbYV9N2vbU6v43Kmuwmkgab/vmjmO72T41V6Y6SbqT9EX6AeAZCuveWNVnXI3URKG14sG298uXqh6S9iR1M3xmgHNLN73en6TNgMPoa/zUem+umjNXXSSptIYevdLVt9RxpdpmONB7sqjPhdJJWo5UZ3kb0mt3CbB/k7uISzoOWJu022Rr4Dzbh+dNVa+Sy6+E3hQTfyGEfiSdDHyvs3NqKVpdU3Pn6BZJ9wDb2W703eShSNocWN32ydUX6qVs35c7Vx0G6+JYSpF3SXeQ6sIV+eWjajCwOv27/12VL1F9qknpA0mrHmZtU2vyxWs7ScsDXyRdzLa/fkU0GChZqeNKdPUtq6tvSSRNA9azPVPSSOBq2xvmzlU3SVNtr5s7Rwh1iK2+IYROGwOTJd1HqvFX2p3layX9mFSkftbKnIK+YP6r8Em/Q0ldfNcgdU5dhFSEf7Ocuepi+4GOic3lSZ0ASzENWJFUj7IoVcOg/YHXk1YebUzaElTKxNFTti/MHaKLfkP6XHgf8ClSh/RHsyaqSXVDb7ZJMdt7ZYjTDUWOK02f2JsLrXrEi5E+16eQvnOOJdUVbXTpEknHMvCKTQAaviL1BdszAWw/q44ChgUpuvxK6C0x8RdC6PSu3AG6bP3qz2+2HTPlXJzfLOkM4I+U2Zzlg8AGwCQA2w9LWipvpPoMMLG5MAVNbJLqMt4u6Ub6vz+3zxepNvuTak9eb3tLSWOAb2fOVKfLJX2PVO+1/bUr5abJq2yfJGl/21cCV0oq5WLv/LbfFyONow9nytINJY8rSJpO3wTSIqTPhWeaXsLD9pYAkv4AjLM9tXq8DqmsQNPdPOe/0lhjJLW2vApYrXpc2mKBtwATJBVZfiX0lpj4CyHMUtXAu9j2mNxZuqX1RbNgo4BngW3bjpXUnOWFqvutASQtkTtQzYqe2KSMi7nB/Nf2fyUhaVHbd0oqqV7qW6o/x7cdK+mmSatxyT8kvZc0MfbKjHlqY/v37Y8lnQZckylONxyWO0A32Z71GVCtrHo/aUVxKdZoTfoB2J4mac2cgepg+1e5M3RR41+fufTO3AFCqEtM/IUQZqlqddwlaSXbf8udp06SdrX968G6HBbU3XDP3Bm67ExJPweWkbQ3sBep21opip7YrFZSleqhqvvfH4E/S3qC1KSlCD1w0+RbkpYGPg8cS7qJcmDeSF2zOqkBVBEKH1f6qeoY/rFaHf7l3HlqcqukE0mr2wEmAI1voCDpPIbe6tvkFakLAyvYvrb9YNUE6p95ItWvB8qvhB4SE38hhE7LArdVW2baa+A1+QsKQGsCpaTVU7NI+qLt7w5WU6bhtWRmsX2UpHcAT5O2w37d9p8zx6rTQBObJ2bONGySrrG9eceWNSio67TtD1a/HibpcmBp4KKMkWrRQzdNWtthnwKKmuQc4N/dP4EvZYpTm14YVwAkfajt4UKkVbf/zRSnG/YEPk0qlwBwFfDTfHFqc1T154dINShbE5u7AP/Kkqg+PwS+MsDxp6tz283fON3RA+VXQg+Jrr4hhH4kvX2g4710R72JJG1n+zxJewx0vvAtJ0WpJja3JV28XlzYxGZxJA25HdT2v+dXlm6QtI/tn1cXQLOx/Y35nalOJRfgl7SZ7WslLWa7pIminlI1Z2l5EbgfOMH2I3kS1U/S4sBKtu/KnaVukm62PX5Ox5pE0k22NxrkXDGdcCVNpiq/YnuD6titUeMvNFGs+Ash9GP7SkkrkIrUA9xYwpdLSZfY3rb6/Su2v5M7U51sn1f9WeQE3wArOvopaGXHkba/BPx5gGPFkPRqUpMBABpeWmAi6b3Z3tWw9djAqjlC1cX2z6s/Gz3BN4SSC/D/CNgQuA4YlzlL1xU2rrSP/RfaPjN3nm6RtD3wPVLjktGS1ge+WcBOk5YlJK1q+14ASaPp24XSVMsMcW7x+Zai+4ouvxJ6S6z4CyH0I2kn0hewK0gXrm8FDrb9u5y5hkvSLW136ybZLvIiqKo/8iVgLfpfABVRgF/S4cA/gFNJ788JwGtsfz1rsJoM9N4s6e5ydYF3NPBa4BFgZeAO22tnDRbmSNJiwMeBtek/tuyVLVQXSBpp+9ncOeog6XpSrbQPAKd3nm/yasZ2pY4rkqYCY4GJpX5nAZA0kdQk6Iq272klrRp7F3A8cC/pe8vKwD62L84abBiqBkGX2T6h4/gngHfY3jlPsnpJ+gKpJuo7gO+Qyq+cZvtHWYOFMA9ixV8IodMhwEatVX7VRNJfgEZP/DHEarHC/AY4A3gv8ClgD+DRrInqtb3t9doe/1TSFKDRE3+SPg18BlhVUntR86WAawf+rxrpcFI3yr/Y3kDSlsCumTPVouq2OQEYbftwSSsBK9q+MXO0upwK3EnqcvhN0nO9I2uiGknaBDiJVLh9JUnrkS7OP5M32bC8D9iG9JpNzJylm0odVy4CngCWlPR02/GiahgCM2w/lYbQWYr5zmb7IkmrA2OqQ3fafj5nphocAJwtaQJ9Y8t40qrNDw76XzVMD9SVDj0kVvyFEPrpvMsqaSFgStPvvEp6klQwurWK8ar286VsKZE00faG7avEhqrF0jSSrgN+Qlq9YlKR7M/a3jRrsGGquokuS7qj3N6pcXrTa8S1a9U1qiZrN7D9kqQpHZO5jSTpp8BLwFa215S0LHBJQf/2bqkmVW61PVbSwsDVtjfOna0Okm4AdgTObVt1NM32OnmTDZ+kzW1f03Fss86OnE1V8rgCIOkc2+/PnaNbJJ0EXEr67NsB2A9Y2PansgYbpo6mLLOx/Yf5laVbqkn21hh5m+3Lcuap20ClVkosvxJ6Q6z4CyF0ukjSxcBp1eOdgQsy5qlL+5fmowb9W803o/rzH5LeCzwMDNl8oGE+ChxT/Zi0Gu6jWRPVwPZTpG6iu0gaAaxA+oxeUtKSTa9V1eZJSUuSJt5/I+kR2rqHN9xbbI+TdAuA7SckLZI7VI1aY8uTktYhdYZ9dcY8tbP9YMeqo5m5stTsR8xe4+/YAY41VcnjCiVP+lX2Je02eR74LXAx8K2sieoxVGdbA42f+LN9OXB57hxd9A5m74D+7gGOhbDAi4m/EAIAkha1/bztg6u7lJtXp463fXbObHXooa7E36pWj32edGE3Cjgwb6T62L6f/pO4RZH0OeAw4F+k1WOQLhCKqPFHeu2eI70nJwBLk7aNlmBGNWnbKgK+PH2vYQmOr1Yx/g9wLmlL7P/kjVSrByVtCrhazbg/Dd/KXG1f3hRYXtJBbadGASPypOqKkseVwZpbPUVqTPP5VtOIpqpqah4i6YhS6msC2N4zd4Ywb3qo/EroIbHVN4QA9DUVkHSq7d1y5wnzRtJyth/LnaNbqsmUvYFVaLt5VUqDAUn3kFaOPZ47S90kfQB4IzC1yUXNB1PVOtqZtIrqV6Rto1+zfVbWYGGuSFqOtJJ4G1JJiEuA/Zv8b1HS24EtSPVef9Z2ajpwnu27c+SqU+njCsxqavUQaTWcgI8AqwGTgE/b3iJfuuGrJtxPBJa0XUp9zdBgvVJ+JfSWmPgLIQCplhHwbVKR7IM7z5dQi6RkkrYDfgG8SNqetpPt6/Kmql9V4+9qUjHpWdvwbP8+W6gaSbqc1BHvxdxZ6iTpOFI32OuArUmTDofnTVU/SWNIz0/ApbYbvWIMQNJbSB0pVwOmAh+3fXveVOHlkLSy7Qdy56hbD40rs9UrlDTZ9vol1DIsub5m6XqhBl5H+WklDacAACAASURBVBUACiq/EnpIbPUNIbR8irRFZhlmr0tSRC2SdlU9IGz/J3eWmhwBvNX2ndWF+neBt2fO1A0jS/pCOYB7gSsk/YlU7wgA29/PF6kWbwPWsz1T0kjS5G1RF+iS1iV1bXwEuKOESb/KT4AvkOqnbQ/8gNQlthhVgfp9SV0bIW3x/bHtK7KFqteiko5n9pXSW2VLVI/ix5XKs5J2An5XPd4R+G/1exErOAqur1m6omvg9UD5ldBDYuIvhACA7Wuq1VQP2T4id55uqS7OTyE1vJCkR4E9bE/Lm2zYXrR9J4DtGyQtlTtQl5wv6T22S2g4M5C/VT+LVD+leMH2TEj1nNRxhddk1Zagc4A3ALeSVvutK+lvwPttP50zXw0Wsv3n6vezJH0la5qaVU2QfkyqCfcN0us3DviFpM8VMtacRdrqeyJlTagUO650mEDahn4cadLhemBXSYsDn8sZrCbF1deEAbv6GngMmGx7eoZItemhGngHAGs0ueRDCC2x1TeE0I+kW1pbLUpUTW4eUnUiQ9IWwLdtb5o12DBJeghoXxV2UPvjAlaMAbOKnC9BWg03g3SRbtujsgYLQ5L0LHBP6yFp2+g99L1+jb17LulHwAvAF22/VB0bQaoNtLjtfXPmGy5J95JW/LUc1f646WUgJF1BquU3peP4WOBY241fOS1pou0Nc+eoW8njSi/pqK+5EKmrb6PrawJIOnmAw68krRb7uO3L5nOk2vRKDbxSy6+E3hQTfyGEfiQdBfwV+IMLHCAGqZVTQo2cQ4c6b/sb8ytLePkknccQW7Zsbz8f49RO0spDnW9y/TFJtwNjOy8MJL2C1HBgzTzJ6jHIxWuLm95YR9Kdtse83HNNIukw0hb0s+lfQqDRF+gljyvtSm9q1Wuq9+2Ztt+SO8twSVqNtFPo+epG+ljgFNtP5k1WD0knkUpAlFZ+JfSgmPgLIfTTtqJqJvAcha2oknQ2qRPeqdWhXYENbX8wX6owJ5LGDXXe9qT5laUbqu6bg7J95fzKEl6eVpH9l3suLBiGWg1Xyko5SfcNcNi2V53vYcLL1gNNrVYlrfjbmHQD7K/AgbbvzRqsiyRNsj3k95omkDQZGE+alL6AVPZibdvvyZmrLoPdVI+b6aGJosZfCKEf26XWhmvZi1TH6Q+kL5hXA3tmTRTmxtFDnDPQ6CL1MbHXaItJ2oB0k6SdgEUz5Akvz2qSzh3guIAiJsZsj86dIQxL6U2tfktqItS6AfsR4DSg8SviBiJpDdpWjzXcS7ZfrOoZHmv7WEm35A5Vl5jgCyWJib8QQj9VcewJwGjbh0t6A/Aa2zdmjlaXbWzv135A0odJxc/DAsr2lrkzhDCIf9C/vma7f87PIGGevH+Ic0fNtxRdVHW8PQhYyfYnJa1OKlh/fuZoYe6U3tRqpO1T2x7/WtLB2dLUZJASHq8EXkPabVKCGZJ2AXYHtquOLZwxTy1KL78SelNs9Q0h9CPpp6SW9VvZXlPSssAltjfKHK0WA22vKGXLRQghhNBJ0hmkbaK7216nmgi8LrahN0PpTa0kHQk8AZxOmmzZmdQ44nvQ3FqUA5TwMPA4cLftFzJEqp2ktYBPAX+1fZqk0cBOto/MHG1YovxKKFFM/IUQ+mlNgrV39y2k+cW7gfcAOwFntJ0aBaxl+81ZgtVE0kFDnY9CxM0iaUkA2//JnaUOkqYy8N3z6L65gKu2cA2q6V19e4Gkm22PL/BzPcaVAgxSg7Kl0bUoJX0AeCOp0dPFufPUqepef4rtCbmzhBDmLLb6hhA6zag+zA2zusm9lDdSLR4Gbga2J618aJkOHJglUb1Kr83YEyStC5xC2g4kSY8Ce9ieljfZsL0vd4Awz7Yb4pxJ9VLDgu0FSYvT97m+GmXUGCt6XJE0xvadgzW3anpTq5ZSa1BKOg5YG7gOOFzSm20fnjlWbWzPlLSypEVKWcEYQslixV8IoR9JE0jbLMYBvwJ2BL5mu4gaeJIWtj0jd47w8pTe1bel6t54iO3Lq8dbAN+2vWnWYCEUTtKHOz/nBjrWRJK2BQ4B1gIuATYD9myNM2HBJOn4qibjQK+TbTe6qZWkjYAHbf+zerw7sAPwAHBYU7f4tkiaBqxXTZCNBK4uoUt4O0mnAGsC5wLPtI7HLpMQFjwx8RdCmI2kMcDWpO0yl9q+I3OkMJckncwAW59s75UhTm3aLnwWA8YDU0jvz7HAzbY3yZWtTgNtvythS15LVauq9f5chFQE/JkSalVJutT21nM61lSSvj7QcdvfnN9ZuqH0+q+SXgVsTBo3r7f9WOZItSl5XCmZpEmkhmv/lvQ2Uo2/fYH1gTVt75g14DB1jh8ljSctkg4d6Hhp3XBLK78SelNs9Q0hACBpMVKB3jcCU4Gf234xb6owD9q7NC4GfJC0zbnRWl19Jf0BGGd7avV4HeCwjNHqdq+k/wFaHQ53Be7NmKdWtmdtSa86iL+fNBnRWNXYORJYrmqGpOrUKOB12YLV75m23xcjbbNs/E2htvqvr5P0o7ZTo4AiPgPbJqD/NMCxxitxXOkkaVNgFdqu3Wyfki1QPUa0rerbGTje9u+B30uanDFXXcZIurX6XcBq1eNialCWNsHXqeDyK6EHxYq/EAIwq+vfDOBq4N3A/bYPyJuqeySNtP1s7hzdJmkh4JpStopKus322nM61lTVxNE3gM1JK1iuJm15ejJrsC5qbzjQRJL2Bw4AXkv/SfangRNs/zhLsC6TtChwse0tcmcZDknrkVYYfRNoX9U4Hbjc9hNZgtWgbVL6cmAL+k9KX2R7TKZoXdf0caWdpFOB1YDJwMzqsG3vly/V8FVbYde3/aKkO4FP2r6qdc72OnkTDo+klYc6b/uB+ZWlbpJ+aPsASecx8C6T7TPEql2UXwkliRV/IYSWtWyvCyDpJODGzHm6orprfiKwJLBSddG3j+3P5E3WNasDr84doka3SjoR+HX1eAJw6xB/v2m26byYk/RhoPF1xmC2DrELkbZt/zdTnFrYPgY4RtK+to/NnWc+Ggm8PneI4bI9BZgi6TcFrnLfh75J6Yn0Tfw9DRQzIV3iuNJhPOk7WmmrNU4DrpT0GPAc6UYXkt4IPJUzWE0WBlawfW37QUmbAf/ME6k2rV0JR2VN0X1LtNdCtX2FpCVyBgphXsXEXwihZVbDi+rua84s3fQD4J2kQsTYnlLVlilCR60jSF8uv5QpTjfsCXwa2L96fBXw03xxavcVZp/kG+hYU7V3iH0RuJ+0La8ET1XF6fspYDseAJKm0je2jACWJ62SK8XdkgZaubJqjjB16KFJ6ZLHFYBpwIrAP3IHqZPtIyRdCrwGuKRtYnMhUq2/pvsh6fO709PVuaE6pi/oHgWwfWXuIF1WdPmV0Fti4i+E0LKepKer3wUsXj1u1SIppki27Qc7JjZnDvZ3m0LSZtVd5eVtl7TSoR/b/5X0M+AC23flzlOX0uuMSTrS9peAC22fmTtPl2zU9vtipAZJk0j1gRpL0mjb95Fq+rW8CPyrsBVy49t+Xwz4MKmuU+PZPrbEGnE9Mq4ALAfcLulG4PnWwRK2U9q+foBj/5sjSxes0KpH3M72VEmrzP84tfojMA5A0u9t75A5T7fsRSq/8gf6yq/smTVRCPMoJv5CCADYHpE7w3zyYHUBZEkLk1aONb5APfAjYEPgOqovYyWStD3wPVLnxtGS1ge+WcAF0MPAzcD2pC15LdOBA7Mkqtd7JH0Z+DJQ5AW67X4rVCQtQ+pS2XS/I40tvyilGcRAbD/eceiHkibSv+5fIw1WI46GT0rTA+NK5bDcAcI8WWaIc4vPtxTd0X73vLGroudC0eVXQm+Jib8QQq/5FHAMqdvm34FLgM9mTVSPGZKOB17fsWIMgKYXAW9zKPBm4AoA25Mljc6aqAZtdcZ+a3vGHP+D5rkIeAJYsm1lMRS4orjNM0Dj35vAQpK+CrxJ0kGdJ21/P0Om2klqv2HSqhNXyvfkUmvE9cS40gPbKUt1s6S9bZ/QflDSJ+h/g6+JPMjvpSm9/EroIaV8oQkhhLli+zFSQ4jSvA/YhlS/sOlfKIcyw/ZTHVu1i/nSWeikH7YPBg6WdI7tkmpvzdLR3XAEsCZlrEL6CPAB0nfGpTJn6aaj235v1YnbKU+U2pVaI674cQVA0sbAsaQxZRHS+PJMKRObBTsAOFvSBPq+l40nvYYfzJaqHq3yQO2lgaCQSffSy6+E3hQTfyGEnlKtDtuX2WsdNXqraDWhebqkh2xf036u6iBXitskfRQYIWl1YD/S9ubQDHd2Hmir09V07d0NXwQesP1QrjB1qWppHilpuu3j2s+VsNq2xfaWuTN0UbE14gBKnvSr/Jg0AX8WaeJod+BNWROFObL9L2BTSVsC61SH/2T7soyxatED5YFKL78SepDKW/UfQgiDkzQFOAmYCrzUOl7KVhpJk2yPm9OxppI0EjgE2LY6dDHwrdIamkgaafvZ3DnqNsj781bbY3NlqpOkFUlb0Q3cZPufmSPVZpDXbqLtDXNlqpOkV5FKCWxOev2uIdUP7az91ziS3j7Q8YI+9zq72QM8Rbpw/7ztRnfhlHSz7fHtY6WkW2xvkDtbCCWTtHCpOzFC74kVfyGEXvNf27PVwGs6SZsAmwLLd9ThGkXaFlSEajLsEElHFDoxtilwIrAksJKk9YB9bH8mb7LhkfRp4DPAapJubTu1FHBtnlT1quo2fR24jLTd6VhJ37T9i7zJhkfSGGBtYGlJH2o7NYrU/bYUpwNXAa3ulBOAM0glFBrN9pWSVqCv8/SNth/JmalmPwQeAn5L+rf3EVIzk0nAL4AtsiWrx7OSFgEmS/ouacv2QpkzhVC8mPQLJYkVfyGEnlJtE12d1NSjfcvTpGyhalCt6NiC1LzkZ22npgPn2b47R666tU+M2S5mYqxF0g3AjsC5rdUckqbZXmfo/3LBJmlpYFngO6QOnC3Tbf87T6p6SboL2LS1QqxaQXad7TXyJhseSe8n1fjbHji37dR04HTbRWy1H+jfmaSpttfNlakuknYidUO/gjQx9lbgYNu/y5mrLpKm2F6v49hk2+sPdK5pJK0M/ItUG+5AYGngONv3ZA0WQgihMWLFXwih16wL7AZsRd9WX1ePG6vasnWlpF/afiB3ni76AamBybmQuuFKelveSPWy/WBH85KZubLUxfZTpK13u0gaAaxA+g6ypKQlbf8ta8B6PE6aDGuZXh1rNNvnAOdI2sT2X3Pn6aJLJH2EvoYsO5JKCZTgEGCj1io/ScsDfwGKmPgjrYjbib7nsyPQKv/Q+BUOth+QtDjwGtvfyJ0nvDwD1bEtqLZtTyi1/EroLTHxF0LoNR8GVrX9Qu4gXbKopOOZvXlJoyc225U4MdbmwWpVoyUtDOwP3JE5U20kfQ44jLR6pX3ivYQaf/cAN0g6h/Sc3g/c2tp6b/v7OcPV4B5JX2X2sWWvbInqtTepC+evq8cLAc9I2ofmd6lcqGNr7+OUtVV0AnAMcBzp3971wK7VZNnncgarg6TtSM2DFgFGS1qfVH+yiOYsPeAdQOck37sHOBYWMKWWXwm9KSb+Qgi9ZhqwDFBSfaN2Z5G2+p5IWRNiLUVPjJG2ah8DvA74O2lL+mezJqrXAcAaJTRMGMD/VT8t51R/LpUhSzecA1xNWilW3Nhiu5TXaSAXSboYOK16vDNwYcY8taqad2w3yOlrBjneJIeRmgZdAWB7ckkdtUvVVtt21VJr2/aA4neZhN4RE38hhF6zDHCnpJvoX+OvlDvnL9r+ae4QXdQ+MfYwaSteMRNjth8jrV4p1YOkLb/F6YEteCNL35om6XXAyvRf0XhVvkT1sH1w1Zhl8+rQ8bbPzpmpTtXW5b0pdzXqDNtPdax0b/wW5h7wW9IEe7G1bXtB4btMQg+Jib8QQq85NHeALjtP0meAs+k/sVnEl8zSJ8aqVRz7MvsFbCkT0/cCV0j6E/3fn03fBoukNwFfoNxt9udLeo/tC3IH6QZJR5JWwt1O34WdSZ1+G0nSG4EVbF9r+w/AH6rjm0tazfb/Df1/aIyiV6MCt1WNyUZIWh3YDyiiqU7JWrVtJX0N+Kft5yVtAYyVdIrtJ/MmDHOh9F0moYdEV98QQiiIpPsGOGzbq873MF0gaVXSir+NSRflfwUOrLZ6NZ6kKcBJwFT6auC1mrc0nqQBJ95LWC1XvXY/AybSNvlge2K2UDWSNB1YAnih+hHNr303S9WVeazt5+f4lxtC0vnAV2xP7Ti+LvBt24Ntj22UVgff3Dm6RdJIUoOWbUn/7i4GDrf93yH/w7BAkDQZGE+6KXQBaaJ6bdvvyZkrzJmk5UjfObch/du7BNi/0HIloXAx8RdC6AmSrrG9eXXx2j7wFXXxWjpJ1wM/oa9W1UeAfW2/JV+q+ki6oZTnMpQSO+RJmmh7w9w5wryRdCHwYdv/yZ2lLpJusr3RIOem2l53fmfqBknfAq4rdTVqaDZJk2yPk/RF4Dnbx0q6xfYGubOFEHpHbPUNIfSKJaD4Au6tlQEHASvZ/mS1LWgN2+dnjlaXkbZPbXv8a0kHZ0tTv2OqVXGX0H8r7KR8keojaRPSisYSO+QVvc1eqcjRBGC07cMlvQF4je0bM0ery7PAZEmX0v/12y9fpGFbZohzi8+3FN23P/BVSc8DMyjkhp6kc4c6X1AJiNLNkLQLsDt9TWgWzpgnzKUeKL8SekhM/IUQekWvLG8+mbTVcNPq8d9JnX5Lmfi7UNKXgdNJr+nOwAWSXglFTLKsC+wGbEXfVl9Xj0vwQ8rtkLdH9Wf7RLSBIrbZA8eR3pNbAYcD/yGtvh1wRVkDnVv9lORmSXvbPqH9oKRPkD4nilDwDb1NSA2RTgNuIE1ohubZk9SY7Ajb91WTSafO4b8JC4Y/km5Wnkdb+ZUQmii2+oYQeoKkh4BBGwiU0FwAQNLNtse3byORNMX2ermz1WGQGoYtja9lKOkeYC3bL+TO0g2trcylvj9L1rZdLV67hpC0AmkF6gv0TfSNBxYBPmj7n7my1UHSGNt3Sho30Pmmr5SWNAJ4B7ALMBb4E3Ca7duyBgtzrXoNT7FdbFOykvVK+ZXQG2LFXwihV4wgbS8s/Y75C5IWp1rhKGk12ratNZ3t0bkzdNk00va8R3IH6ZLiOuRJ+tBQ56tuqiWYUV3EtsaW5SlgBYSkqQyxItz22PkYp1a2/wVsKmlLYJ3q8J9sX5YxVp0OAj4JHD3AucavlLY9E7gIuEjSoqQJwCskfcP2j/OmC3PD9kxJK0tapNQbeoUruvxK6C2x4i+E0BNaq1Vy5+g2SduSuv+tRfqishmwp+3LswYbJkkbAQ+2VqhI2h3YAXgAOKyALb4ASLqCtLLjJvp/ySyinswgHfL2a/LrJ+nkIU7b9l7zLUwXSZpA2lo/DvgVsCPwP7bPzBpsmCStPNR52w/MrywhdKom/N5LmvRbhbQd/Re2/54zV5h7kk4B1iS9ds+0jpey06Rkkr5DKr/yf7SVX7Hd6JsKoTfFxF8IoSf0Ugc1Sa8CNiZNrFxv+7HMkYZN0iRgG9v/rmrCnU4quLw+sKbtHbMGrImktw903PaV8ztLN0jazPa1czoWFkySxgBbk8aWS203erVmKEe1kngV+hfgPyVboBpUE0brABcAp9ueljlSmAfVirHZ2P7G/M4SXp7Sy6+E3hITfyGEniDplU1eVTS3JF1qe+s5HWua9lpikn4CPGr7sOrxZNvr58wX5s5AK297ZTVu00k61fZuczoWwvwm6VRgNWAyMLM67IZ3ZEbSS/StEGu/YCuia3EICzpJfwQ+abvU8iuhh0SNvxBCTyh90k/SYsBIYDlJy9JXy3AU8LpsweozQtIrbL9IWnH0ybZzjf8sk3SN7c0lTafACzxJm5A6TS8v6aC2U6NI9TfDgm/t9gdVvb8NM2UJL4OkI21/aU7HGmw8aVVOUasZbC+UO0OYd5J+aPsASecxQB3RUkp4FG4Z4E5JRZZfCb2l8RdLIYQQANgHOAB4Lal7Y2vi72mghCLgpwFXSnoMeA64GkDSG4GncgaryRIAtpfKHaRLFiE113kF0P4cnybVims8SYvafn5Ox5pG0leArwKLS3qavrHlBeD4bMFqJml/28fM6VhDvQPonOR79wDHmmoasCLwj9xBQmhzavXnUVlThOEYcJt2CE0UW31DCKEgkva1fWzuHN0gaWPgNcAltp+pjr0JWLLpHdZ6ZburpJVLbZZQ+jZmSd+x/ZXcObplkNev0bVhJX0a+AywKqk4fctSwLW2d80SrGaSLifVe72RWJUTFhCSVrL9t9w5QggBYuIvhBCKU2KR89JJeggYtMNfKd3/qonaLzD7+7OxHfIkrUjaTv9r4KP032b/M9tjcmWrm6TXASvT/7W7Kl+i4ZO0C+l125xqJXFlFDCzyfVRJS0NLAt8B/hy26npJZW/KL0pUmim9psJkn5ve4fcmcLcKb38SuhNsdU3hBAKMliRcyAm/hZsI0hbYTWnv9hwZwE/A06k7/3ZdO8EPga8HjiavtdwOmmLbBEk/T/gI8Dt9B9bGj3xB1xH2iK6HOn1a5kO3JolUU1sPwU8JelrwD9tPy9pC2CspFNsP5k3YT1Kn+DrgRqNpWr/PF81W4owL0ovvxJ6UKz4CyGEgki6gwKLnJeupC2hQ5E00XaRDSEk7WD797lzdIuku4CxTa9ZOBhJSwDP2X6pWpk6BrjQ9ozM0YZN0mRSA4xVgAuAc4C1bb8nZ666VGUgjgXWJNUTHQE8U8qqnEG2od9qe2yuTGHOOlb89cRnfCni9QolihV/IYRQlihy3kylr/RrOU/SZ4Cz6V+Lq4Rth6+XNIq0UuwEYBzwZduX5I1Vm3uBhWl73QpzFfDWqiv6JcBNwM7AhKyp6vGS7RclfQg41vaxkm7JHapGPyatRj2LNMG5O/CmrIlq0F6jUVL76tOlgGvzpAovw3ptDZFazZEgtos2waslHTTYyVLKr4TeEhN/IYRQluWA2yVFkfNmaWwdsZdpj+rPg9uOmTK2Qe1l+xhJ7wReBexG6upYysTfs8BkSZfSf2zZL1+kWsn2s5I+Dhxn+7vVSrkSzKhqGe4ObFcdWzhjntrZvkfSCNszgZOric2mN6P5LXAhhddoLJXtEbkzhHnWK+VXQg+Jib8QQijLYbkDhJevVy7ibI/OnaGLWhcI7wFOsX2bpJIuGs6tfkolSZuQVvh9vDpWyoX7nsCngCNs3ydpNGlSuhTPSlqENDH9XdKK94UyZxq2XqnRGMIC6B+2v5k7RAh1ihp/IYRQGEkrABtVD2+0/UjOPCG0SFoY+DTwturQFcDPC6mjdjKpu+9oYD3SpNEVJdU0rCZXWlso7yrhdWuR9DZSx+lrbR8paVXggKavaJQ0gjQRXcKW5QFJWhn4F6m+34HA0qRVm/dkDVaT0ms0hrCgkXSL7Q1y5wihTjHxF0IIBZG0E/A90oSKgLcCB9v+Xc5cIQBIOpG0xfBX1aHdgJm2P5Ev1fBVK/teDywP3Gv7SUmvAl5nu9GdYVuqlUa/Au4njS1vAPaw3fSuvq3JsSNtfyF3lm6QdA2wle0XcmfpFkmLAyv5/7d398FylvUZx68rIBIxKYTB6GglvEyJgiAx6EAy2ljfCyjiWBWxlRdFFItEqRadgu1YiagZI1p1rB1QdGqBghV8QRA0o4YEKEkqioSJtoQKQRMECxqu/vE8S5Z1z1vOs3uf8+T7mdnZ3d+dzFyZc7Jz8st9/+7kJ6WzNK1z0YDts1VdQLOCxgQwOLbn7CwnMbDz4KgvALTLOZKO6Ozys72PpGsk0fjDVHBEksO63l9r+z+LpWlIkti+KsmzumqbJW0uGKtpH5X0kk5jpb759suSpv2OxiTbbC8unWOANkhaaftKSQ90im0ZUG/7GEkXqNrxt5/tZ0v6YItm27Z+RiMwldD0QxvR+AOAdpnRc7R3s1ow6witsc32AUnukKT6OOW2wpmacpPtI5LcWDrIgDyuezdVkp/WR7fb4ua6MfZVPbY5dlm5SI25o37MUHUjbNucK+m5qna6K8kt9RzDtmj7jEYAwIDR+AOAdvmG7W+q2okjSX+h6lZAYCp4j6TrbG9QdVx0X1X/qG2D50k6wfZGVY0jq9oMeGjZWI1ZXR/V/mL9/o2SVhfM07TdVf1HyQu7apE07Rt/Sc4rnWHAfpdkS89dOq2YZVQfQz+ne0ZjkjslnV8uFQBgumHGHwC0jO1XS+ocW/tekstL5gG62X68pIPqtz9J8lDJPE2pLxj4A0k2DjvLINRft7dr+2fLDZI+3ZavXxvZXp7kTNtfU59GWFuOwtr+vKTvSHqvpOMlvVPVDtXTigZryM4woxEAMFg0/gCgBWwfKGlukpU99cWSNnWOVgIl2H6jqp85Lu6pdy73uKRMssmzPTvJVttz+q1P91lB9ZzQfZL8V0/9YEm/THJPmWTNsH12kmW2V6h/c2za3upr+zlJ1th+Qb/1JNcPO9Mg2H6Cqvm2L1G10/abkv4+yf8VDdYQ2xdJeoakVs5oBAAMHkd9AaAdlkt6X5/6lnrtmD5rwLCcIenP+tQvU7VzbNo2/lRlP1rSGlWNo+7zhpG0f4lQDVoh6VN96nNUNVveMNw4jftx/dymY8sd90jtafCNJMmDqr4XzymdZUDaPqMRADBg7PgDgBawfWOSI0ZYW9t92ygwbLZvSrJghLVbWzQHr3Vsr06ycIS1dUkOGXamJtneNcnvS+cYhO6/d7YvTXJ86UxNqi9jGVFbjjIDADBZ7PgDgHbYc5S1mUNLAfQ30/YeSR7oLtqeJWm3QpkaYfsdST5Zvz44yfrSmRo22g6jNtzqu0pSpzm2IskZhfM0qXv36XTfedrPkZJ+oeoyqx/psX/eaW9nmdEIABg8Gn8AM6GW/QAAElZJREFU0A6rbZ+a5HPdRdunqDqCCJT0eUn/Zvu0zmUXtudJurBem85OkvTJ+vXFqptILfIz269IclV30fbLJW0olKlJ3c2iRcVSDEZGeN0WT5b0YkmvV3Xk/OuSvtyi5ntnJuoFRVMAAKY9Gn8A0A5nSrrc9gna3uhbqGo31XHFUgGSklxg+zeSbrD9xLr8G0kfTvLpgtGa1qodR7UzJX3d9mv12M+WI1XNNpzu2tgQ6zjM9lZV35cz69eq3yfJ7HLRJi/JNknfkPSN+tbp10v6ru3zOrtwp7mdYkYjAGDwmPEHAC1ie4mkzsyt9UmuLZkH6FUf71WS+0tnaYLtDZKWqhq8v0zSe7rXk1xWIleT6qbKG9T12SLpkjbcmmr7QUk/U9UMO6B+LW1vjjF/cgqrvzf/XFXTb56qm2//Ocn/lMzVhLbPaAQADA+NPwAAgB1k+wujLCfJSUMLgwmzve9o652j6Zh6bF+kqhl9laSvJFlXOFKjbN+c5PDe1wAATBSNPwAAAADTiu1HJHUuDOr+B00rjjL37Pgb8WZ0AADGQuMPAAAMhe3HJ3lorBoA7Oxsb1PV2LSkmZIe7CypBY1NAMDwzCgdAADQHNvnj6cGFPKDcdYwxdj+6/HUADQjyS5JZieZlWTX+nXnPU0/AMC40fgDgHZ5cZ/ay4eeAuhi+8m2n6PqZtHDbS+oH38q6QmF42F8/rJP7a+GHQIAAAATs2vpAACAybP9NkmnS9rf9q1dS7MkrSyTCnjUS1U1iZ4m6aOqjqpJ0v2S/rZQpkbYXirp40ke6anvLWlZkpPLJGuG7derutF3P9tXdi3NlnRfmVTNs71I0rmS9lX183HnOOX+JXMBAABMFjP+AKAFbP+RpL0k/aOk93Yt3Z+kNf84x/Rm+/gkl5bO0STbn5X0XElvT7Kyrp0u6WxJy5MsL5lvsupbb/dTn88WSbcm+X2RYA2zfZukd0laI2lbp55kc7FQGBfb5yf5m7FqAADsrGj8AUCL2D5A0n8neag+RnmopIuS/LpsMuDRmXBfUNU0+pykBZLem+RbRYNNku2jJF0oaZ2k+ZJul7Q0yaaiwRpkew9Jv03yiO0/UfXnvDrJ7wpHa4TtHyV5XukcmLh+N97avjXJoaUyAQAwlTDjDwDa5VJJ22wfKOmzkv5Y0iVlIwGPOinJVkkvkbS3pBMlfbhspEask7RK0vMlPVnSP7Wp6Ve7QdLutp8q6Vuqvnb/UjRRs66z/RHbR3bNoFww9m9DKbbfZnutpINs39r1uFPSrWP9fgAAdhbM+AOAdnkkye9tv1rSiiQrbN9cOhRQ68z2e4WqnajrbXu03zDV2T5R0nmSPiPpAEmHSbrQ9k8lvTvJL0vma5CTPGj7ZEmfSrLM9i2lQzWos9tvYVctkl5YIAvG5xJJV4sRFwAAjIrGHwC0y+/qYfxvknRMXXtcwTxAtzW2v6VqZtz7bM+S9MgYv2eqe42kJUk21u/X2D5S0mmSfiipLZdDuP5znSCpc2HJLgXzNCrJktIZMDFJtkjaYvv9ku7uHnFhmxEXAADUmPEHAC1i+5mqGg4/SPJl2/tJem2S8wtHw06u3tn3NEn7SNqQ5Nf1zbdPTdLKY3m290lyT+kcTbD9fEnvlrQyyfm295d0ZpJ3Fo42KbbfmOSLts/qt57kY8POhImpd54ulDRP0lWSrpB0cJJXlMwFAMBUwY4/AGgJ27tIOifJCZ1akjsl0fRDcUli+6okz+qqbZY0rW9N7dMwiqR7JX2//vs37dWfLccmObZTS7JB0rRu+tX2qJ9nFU2ByWDEBQAAo6DxBwAtkWSb7X1t75bk4dJ5gD5usn1EkhtLB2lQv4bRPEnn2D43yVeGnKdx9WfL4tI5BiHJZ+rn80pnwQ5jxAUAAKPgqC8AtIjtiyQ9Q9KVkh7o1DmuhqnA9m2SDpS0UdX3p1VtBjy0aLABsD1H0jVJWnEzrO1PS3qqpK/qsZ8tlxUL1SDbn+hT3iJpdZIrhp0H48eICwAARseOPwBolzvqxwxxdA1Tz0tLBxiWJPdN9xuLe+yu6lh29y23kdSKxp+qP998VY1NSTpe0p2SDrO9JMmZxZJhRIy4AABgbDT+AKBFOK6Gqcj27CRbJd1fOsuw2F4i6VelczQlyZtLZxiwQyUtSrJNenSH4/ckLZa0tmQwjIwRFwAAjI3GHwC0gO3lSc60/TVVu3Aeo3soP1DAJZKOlrRG1fdn9064SNq/RKgm2F6rP/w7N0fSXapmjk1rts9Ossz2CvX/bGnDBR+StJekJ6o63itVl37MqRtLD5WLhXHYIGmlbUZcAADQB40/AGiHi+vnC4qmAPpIcnT9vF/pLANwdM/7SNqc5IF+v3ga+nH9vLpoisFbJukW299V1Zh+vqQP2d5D0jUlg2FMjLgAAGAUXO4BAC1g++lJfl46B9CP7Xck+WT9+uAk60tnalI9Z2x9kvmlszTN9q5Jfl86xzDYfoqk59Zvb0xyV8k8AAAATZhROgAAoBH/3nlh+9KSQYA+Tup6ffGIv2qaqufC/cT200tnGYBVnRf1cd9WsT2/fl4g6SmSflE/nlzXMEXZXl4/f832lb2P0vkAAJgqOOoLAO3QPTNt2s5Lw06hTTfddttL0nrbq/TYOWPTfb5m99drUbEUg3OWpLdI+mifteixtxhjamHEBQAA40DjDwDaISO8BqaCPW0fp+qkwWzbr+5eTHJZmViN+kDpAAPS6s+TJG+xPUPS+5OsLJ0HE3KPJCW5vnQQAACmMmb8AUAL2N6mapeRJc2U9GBnSVKSzC6VDbD9hVGWk+SkUdanNNsHSprb2zSyvVjSpiR3lEnWDNsPSvqZqs+SA+rX0vbPlkNLZWuS7ZuTHF46B8bP9k1JFtSvL01yfOlMAABMRez4A4AWSLJL6QzASJK8uXSGAVou6X196lvqtWOGG6dxzygdYEi+Y/t4SZeF/xWfLhhxAQDAOND4AwAA2HFzk6ztLSZZa3ve8OM0K8nG0hmG5K2q5v1ts/1bsVt6OmDEBQAA40DjDwAAYMftOcrazKGlwKQkmVU6AybsMNtbVY+4qF9LNG0BAHgMGn8AAAA7brXtU5N8rrto+xRJawplwgTY3k3SCZIOrkvrJX0pycPlUmEsjLgAAGB8uNwDAAAMlO2lkj6e5JGe+t6SliU5uUyyybM9V9Llkh7W9kbfQkm7STouyd2lsmFstp8p6UpJK7X96/ccSYskvTLJ+lLZAAAAmsCOPwAAMGgHSbrJ9ts7t9/aPl3S2aouwJi2kvyvpKNsL5F0SF3+epJrC8ZqnO1Fks6VtK+qnx87xymn+6UKKyS9Lcm3u4u2XyTpk5KWFEkFAADQEHb8AQCAgbN9lKQLJa2TNF/S7ZKWJtlUNBjGxfZtkt6lalfctk49yeZioRpg+7Yk80dY+3GSneVWYwAA0FLs+AMAAMOwTtIqSS+TNEM0/aabLUmuLh1iAGbYfnySh7qLtncXPycDAIAWmFE6AAAAaDfbJ0q6RdIGSQdIepWkZbYvsv2kouEwXtfZ/ojtI20v6DxKh2rARZIutb1vp2B7nqR/lXRxoUwAAACN4agvAAAYKNtXSHpnko1dNUs6TdJ7WjAnrvVsX9ennCQvHHqYhtl+h6p5k0+oSw9IuiDJinKpAAAAmkHjDwAAFGN7nyT3lM4B2J4lSUnuL50FAACgKTT+AADAQNk+q6cUSfdK+n6SOwtEwjjZfmOSL/b5GkqSknxs2JkAAAAwfsz4AwAAgzar5zFb0kJJV9t+XclgGNMe9XPv17DzAAAAwBTGjj8AAFCE7TmSrknShksiAAAAgCln19IBAADAzinJffUlH5jibH+iT3mLpNVJrhh2nqbYXirp40ke6anvLWlZkpPLJAMAAGgGR30BAEARtpdI+lXpHBiX3SU9W9Lt9eNQSU+TdLLt5SWDTdJBkm6yvahTsH26pDWS1hZLBQAA0BCO+gIAgIGyvVbVhR7d5ki6S9Kbktw2/FSYCNs/lLQoybb6/a6SvidpsaS1SZ5ZMt9k2D5K0oWS1kmar6qxuTTJpqLBAAAAGsBRXwAAMGhH97yPpM1JHigRBjtkL0lPVHW8V6ou/ZiTZJvth8rFasQ6SaskvUzVaRiafgAAoDVo/AEAgIFKstH2LpLWJ5lfOg92yDJJt9j+riRLer6kD9neQ9I1JYNNhu0TJZ0n6TOSDpB0mKQLbf9U0ruT/LJkPgAAgMniqC8AABgK21dIOiPJz0tnwcTZfoqk59Zvb0xyV8k8Tai/J9+ZZGNXzZJOk/SeJPsXCwcAANAAGn8AAGAobN8g6XBVxyofPeab5NhioTAq2/OT3GZ7Qb/1JDcNO9Ow2N4nyT2lcwAAAEwGjT8AADAUtl/Qr57k+mFnwfjY/mySt9i+rs9ykrxw6KEaZPusnlIk3Svp+0nuLBAJAACgUTT+AADAQNk+UNLcJCt76oslbUpyR5lkGA/bMyQd2fv1awPbf9enPEfSSyWdm+QrQ44EAADQKBp/AABgoGz/h6T3JVnbU3+WpA8lOaZMMoyX7ZuTHF46x7DYniPpmiR9jzgDAABMFzNKBwAAAK03t7fpJ0l1bd7w42AHfMf28fXFF62X5D5VtxcDAABMazT+AADAoO05ytrMoaXAZLxV0lclPWx7q+37bW8tHWpQbC+R9KvSOQAAACZr19IBAABA6622fWqSz3UXbZ8iaU2hTJiAJLNKZxgE22tVXejRbY6kuyS9afiJAAAAmsWMPwAAMFC250q6XNLD2t7oWyhpN0nHJbm7VDaMzfZukk6QdHBdWi/pS0keLpeqGbb37SlF0uYkD5TIAwAA0DQafwAAYCjq45OH1G/XJ7m2ZB6MzfYzJV0paaW2N22fI2mRpFcmWV8qW1Ns76Lq+3F+6SwAAABNo/EHAACAvmx/R9KHk3y7p/4iSeckWVImWbNsXyHpjCQ/L50FAACgSTT+AAAA0Jft20baCWf7x0meMexMg2D7BkmHS1ol6dFjvkmOLRYKAACgAVzuAQAAgJHMsP34JA91F23vrnb9HPmB0gEAAAAGoU0/sAEAAKBZF0m61Pbbk2yUJNvzJH1C0sUFczXC9oGS5ia5vqe+WNKmMqkAAACaM6N0AAAAAExNSf5B0jckfc/2vbbvlXS9pG8n+WDZdI1YLmlrn/qWeg0AAGBaY8YfAAAAxmR7liQlub90lqbYvjHJESOsrU3yrGFnAgAAaBJHfQEAADCmNjX8uuw5ytrMoaUAAAAYEI76AgAAYGe12vapvUXbp0haUyAPAABAozjqCwAAgJ2S7bmSLpf0sLY3+hZK2k3ScUnuLpUNAACgCTT+AAAA0JftpZI+nuSRnvrekpYlOblMsmbZXiLpkPrt+iTXlswDAADQFI76AgAAYCQHSbrJ9qJOwfbpqnbHrS2WqmFJrkuyon7Q9AMAAK3Bjj8AAACMyPZRki6UtE7SfEm3S1qaZFPRYAAAABgTt/oCAABgNOskrZL0MlWnRWj6AQAATBMc9QUAAEBftk+UdIukDZIOkPQqSctsX2T7SUXDAQAAYEzs+AMAAMBIXiNpSZKN9fs1to+UdJqkH0rav1gyAAAAjIkZfwAAAJgw2/skuad0DgAAAIyMHX8AAADoy/ZZPaVIulfS95PcWSASAAAAJoAZfwAAABjJrJ7HbEkLJV1t+3UlgwEAAGBsHPUFAADAhNieI+maJAtKZwEAAMDI2PEHAACACUlynySXzgEAAIDR0fgDAADAhNheIulXpXMAAABgdFzuAQAAgL5sr1V1oUe3OZLukvSm4ScCAADARDDjDwAAAH3Z3renFEmbkzxQIg8AAAAmhsYfAAAARmR7F0nrk8wvnQUAAAATw4w/AAAAjCjJNkk/sf300lkAAAAwMcz4AwAAwFj2krTe9ipJjx7zTXJsuUgAAAAYC40/AAAAjOUDpQMAAABg4mj8AQAAoC/bB0qam+T6nvpiSZvKpAIAAMB4MeMPAAAAI1kuaWuf+pZ6DQAAAFMYjT8AAACMZG6Stb3FujZv+HEAAAAwETT+AAAAMJI9R1mbObQUAAAA2CE0/gAAADCS1bZP7S3aPkXSmgJ5AAAAMAFOUjoDAAAApiDbcyVdLulhbW/0LZS0m6TjktxdKhsAAADGRuMPAAAAo7K9RNIh9dv1Sa4tmQcAAADjQ+MPAAAAAAAAaCFm/AEAAAAAAAAtROMPAAAAAAAAaCEafwAAAAAAAEAL0fgDAAAAAAAAWuj/AR3NSlBuX1+XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_kor = df.corr()\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(df_kor, vmin=-1, vmax=1, cmap=\"viridis\", annot=True, linewidth=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_col = [\"First Contentful Paint Time (sec)\", \"Speed Index Time (sec)\", \"Time to Interactive (sec)\",  'First Meaningful Paint Time (sec)', 'First CPU Idle Time (sec)']\n",
    "sns.set(color_codes=True)\n",
    "plt.figure(figsize=(15,20))\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "\n",
    "for i in range(len(all_col)):\n",
    "    plt.subplot(5,2,i+1)\n",
    "    plt.boxplot(df[all_col[i]])\n",
    "    plt.title(all_col[i])\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To test data both outliers and without outliers, we'll copy our dataframe and apply winsorization for the second one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['First Contentful Paint Time (sec)'] = winsorize(df2['First Contentful Paint Time (sec)'], (0, 0.05))\n",
    "df2['Speed Index Time (sec)'] = winsorize(df2['Speed Index Time (sec)'], (0, 0.05))\n",
    "df2['Time to Interactive (sec)'] = winsorize(df2['Time to Interactive (sec)'], (0, 0.06))\n",
    "df2['First Meaningful Paint Time (sec)'] = winsorize(df2['First Meaningful Paint Time (sec)'], (0, 0.05))\n",
    "df2['First CPU Idle Time (sec)'] = winsorize(df2['First CPU Idle Time (sec)'], (0, 0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZmWmfv7Zv3z7t27dPkrRw4UI1bNhQZcqUUfPmzfXFF18oJydHNptNo0aN0uTJkxUQEKD69etr9uzZkqS0tDT17NlTq1atUtOmTbVhwwadOHFCkhQXF3dT8bdo0UIrVqxQWlqabDZbngt0V4pPkj777DNlZmZqyZIl+uyzz+z53pn+Lp7LXZ7HJOnFF1/U8uXLFRERodGjRysgIEDHjx9X8+bNtXjxYmVkZEi6OFfw0rzBkJAQLVmyRNLFVbz79++vhIQESReLujvvvJOLkigQd+TgUv3791dsbKwiIyNVrFgx1ahRQ5GRkfL19VXt2rUVHh6u+fPnq1y5cvZjypYtqzlz5uidd97Rhx9+KB8fH5UsWVJjx461r1g5depUvfnmm8rKypLFYtHbb7+tKlWq/O3S/SEhIRo2bJj8/f01atQoDRo0SAMGDJDFYlFAQIBmzJhx1StgYWFhmjBhQr47bvfee6969Oihrl27KigoSA8//PCNf2j/v+eff15Dhw7VrbfeqpIlS+qhhx7Sn3/+ec3H//V8nSUwMFDTpk3TxIkTlZ2dLWOMJk6cqDvuuCPfe9u3b69169apSZMmqlmzpsLDw9W1a1eVKlVKJUqU0GuvvaZixYpp5syZGjt2rD755BNduHBBzz33nB544AFJF4ecDhw4UJIUFBSkcePGSbp4tfO3337T2LFjnXauAOBKVapU0XvvvacpU6boxIkTKl68uEqXLq0xY8bonnvuUXJysm677Ta9++67Onr0qAIDAzVx4kRJ0tNPP60JEyaoc+fOslqtqlWrlmJjYyVdvMj65ptvKioqSjk5OfZH6UgXf8f2799ft9xyi+rWrXtT8YeGhmr//v3q2rWrypQpo5o1a9oXP7tSfHv27NEHH3ygxYsXq0KFChoxYoRefPFFxcXF5Vt4xJH+7vO6XNOmTTVy5EilpaWpTJkyevrppzVy5EgtXLhQvr6+euSRR9SoUSM99NBDOnnypB599FFZLBZVrFhR48ePlyS9/vrreuONNxQVFSVjjIYMGaL7779fkrRu3Tq1b9/eaecJz2Yxjhx3BQDXISMjQ48++qiWLFliH9LjKEuXLtWBAwd4/AAAr3GjKyTj5nzwwQfy9fXVoEGDHNqu1WpV586d9emnn7L6MgrE0EoAbhMQEKAXXnhBM2fOdGi7GRkZ+uabbzRs2DCHtgsAwF8NGDBAmzdvVnJyskPbnTNnjvr3708RhyvijhwAAAAAeBjuyAEAcBMyMjIUGRmpI0eOSJJeffVVtW3bVtHR0YqOji7wESB79+5V165d1a5dO40cOdIlCxEBAIoWCjkAAG7Qjh071LNnT/sKc9LFVXXnzp2rZcuWadmyZWrTpk2+415++WWNGjVK3333nYwxWrRokQujBgAUBRRyAADcoEWLFmn06NH2Z2GeP39ex44d06hRoxQVFaVp06blecaUJB09elRZWVmqX7++JKlLly5asWKFy2MHAHg2CjkAAG7Q2LFj8zycOCUlRU2aNNG4ceO0aNEibd26VYsXL85zTFJSkoKCguyvg4KCdPLkSZfFDAAoGgr1c+TOnDknm421WOA9ypcPUEpKhrvDAFzKx8eicuVucXcYDlGpUiW999579td9+/ZVfHy8Hn30Ufu2gtYYu9ozKwEA+KtCXcjZbIZCDl6Hn3nAc+3fv18JCQlq166dpItFm59f3lRboUIFnTp1yv46OTnZPjTzeqSkZPD7Al4lKKi0kpPT3R0G4DI+PhaVL3/lB98ztBIAAAcxxmjcuHFKTU1Vbm6uFi5cmG+xkzvuuEPFixfXtm3bJEnx8fFq2bKlO8IFAHgwCjkAABykZs2aGjx4sHr27KmIiAjVqlVLkZGRkqRBgwZp165dkqRJkybp7bffVnh4uDIzM9WvXz93hg0A8ECF+oHgDBuBt2HYCLzR1YaOoGDkSHgbciS8DUMrAQAAAKCIKdSLnQCeqmXLxtq3b69L+6xZs5Z++ulnl/YJAMD1cnWOJD+iqGJoJVCIBAeXUVJSmrvDAFyKoZU3hhwJb0OOhLdhaCUAAAAAFDEUcgAAAADgYSjkAAAAAMDDUMgBAAAAgIdxeiG3evVqdenSRe3bt9dbb73l7O4AAAAAoMhzaiGXmJio0aNHa+bMmfr666+1Z88erV271pldAgAAAECR59TnyK1cuVIdOnTQ7bffLkmaMmWKihcv7swuAQAAAKDIc+oducOHD8tqterJJ59Ux44dNW/ePN16663O7BIAAAAAijyn3pGzWq3aunWr5syZo1KlSunpp59WXFycunTpck3H84BYeKOgoNLuDgEAAACFnFMLudtuu00hISEKDAyUJLVu3Vo7d+685kIuJSVDNptxZohAoZOcnO7uEACX8vGxcOEOAIDr5NShlWFhYVq/fr3S0tJktVq1bt063Xfffc7sEgAAAACKPKfekatXr54GDhyoXr16KTc3V82aNVPXrl2d2SUAAAAAFHkWY0yhHbvI0Ep4m+DgMkpKSnN3GIBLMbTyxpAj4W3IkfA2V8uPTn8gOAAAAADAsSjkAAC4CRkZGYqMjNSRI0ckSQsXLlRkZKSioqL06quvKicnJ98x8fHxat68uaKjoxUdHa0pU6a4OmwAgIdz6hw5AACKsh07dui1115TQkKCJOnQoUOaNWuWli5dqltuuUWxsbGaN2+eHn/88TzH7dq1S7GxsYqMjHR90ACAIoE7cgAA3KBFixZp9OjRCg4OliQVK1ZMb7zxhgICAmSxWFS9enUdO3Ys33G7du1SfHy8OnbsqJdeekmpqamuDh0A4OEo5AAAuEFjx47Vgw8+aH99xx13qGnTppKk06dP64svvlDr1q3zHRcUFKRhw4Zp2bJlqlixosaMGeOymAEARQNDKwEAcLCTJ09q4MCB6tq1qxo3bpxv/3vvvWf/euDAgXrkkUeuuw9W+oQ3Cgoq7e4QgEKDQg4AAAf6448/NGjQIPXp00cDBgzItz89PV1Lliyxz5szxsjP7/rTMY8fgDdKTk53dwiAy/D4AQAAXCQjI0NPPvmknnvuuQKLOEkqVaqUPvnkE+3YsUOSNHfuXLVp08aVYQIAigDuyAEA4CCLFy/WqVOn9Omnn+rTTz+VJLVq1UrPPfecRo4cqVatWql169Z699139cYbbygrK0t33323Jk6c6ObIAQCexmKMKbTjMhg2Am8THFxGSUlp7g4DcKmrDR1BwciR8DbkSHgbhlYCAAAAQBFDIQcAAAAAHoZCDgAAAAA8DIUcAAAAAHgYCjkAAAAA8DAUcgAAAADgYSjkAAAAAMDDOP2B4P369VNKSor8/C52NWbMGNWrV8/Z3QIAAABAkeXUQs4Yo4MHD2rNmjX2Qg4AAAAAcHOcOrTy4MGDslgsGjRokDp27Ki5c+c6szsAAAAA8ApOvU2WlpamkJAQvfHGG8rKylK/fv1UpUoVNWvW7JqOL18+wJnhAYVSUFBpd4cAAACAQs6phVyDBg3UoEEDSVKpUqXUrVs3rV279poLuZSUDNlsxpkhAoVOcnK6u0MAXMrHx8KFOwAArpNTh1Zu3bpVmzZtsr82xjBXDgAAAABuklMLufT0dE2cOFHZ2dnKyMhQXFyc2rRp48wuAQAAAKDIc+rtsbCwMO3YsUOdOnWSzWZTr1697EMtAQAAAAA3xmKMKbST0JgjB28THFxGSUlp7g4DcCnmyN0YciS8DTkS3uZq+dGpQysBAAAAAI5HIQcAwE3IyMhQZGSkjhw5IknauHGjoqKi1LZtW02ZMqXAY44dO6bevXurffv2+sc//qFz5865MmQAQBFAIQcAwA3asWOHevbsqYSEBElSVlaWRowYoZkzZ2r58uXavXu31q5dm++4f/7zn+rVq5dWrFih+++/XzNnznRx5AAAT0chBwDADVq0aJFGjx6t4OBgSdLOnTt11113qVKlSvLz81NUVJRWrFiR55jc3Fxt2bJF7dq1kyR16dIl33sAALgaHuoGAMANGjt2bJ7XSUlJCgoKsr8ODg7WyZMn87znzJkzCggIsD9XNSgoKN97AAC4Ggo5AAAcpKCFoC0Wy3W/51qw0ie8UVBQaXeHABQaFHIAADhIhQoVdOrUKfvrpKQk+7DLSwIDA5WRkSGr1SpfX18lJyfne8+14PED8EbJyenuDgFwGR4/AACAi9SrV0+HDh3S4cOHZbVa9c0336hly5Z53uPv768HH3xQy5cvlyTFx8fnew8AAFdDIQcAgIMUL15c48eP17Bhw9ShQwfdc889at++vSRp5MiRWrVqlSRp9OjRWrRokTp06KCtW7dq+PDh7gwbAOCBLKagwfqFBMNG4G2Cg8soKSnN3WEALnW1oSMoGDkS3oYcCW/D0EoAAAAAKGIo5AAAAADAw1DIAQAAAICHoZADAAAAAA9DIQcAAAAAHoZCDgAAAAA8jMsKuQkTJig2NtZV3QEAAABAkeWSQm7Tpk2Ki4tzRVcAAAAAUOQ5vZA7e/aspkyZoqeeesrZXQEAAACAV3B6Iff666/r+eefV5kyZZzdFQAAAAB4BT9nNv7ll1+qYsWKCgkJ0dKlS6/7+PLlA5wQFXDtAgMDdebMGZf2GRzs2ose5cqV0+nTp13aJwAAAG6OxRhjnNX4E088oeTkZPn6+io1NVXnz59Xp06dNGLEiGs6PiUlQzab08IDrio4uIySktJc1l9QUGklJ6e7rD/J9ecI/JWPj4ULdzeAHAlvQ76Ct7lafnTqHbnZs2fbv166dKl++eWXay7iAAAAAAAFc2ohBwAAgKKnevXKOnv2rMv7dfX0g7Jly+p///vTpX0C18plhVyXLl3UpUsXV3UHAAAAJzl79qzLhzm6a/oBUFi57IHgAAAAAADHoJADAAAAAA/DHDkAABzsyy+/1Ny5c+2vjxw5oujoaL3++uv2bTNmzNCSJUvsz1l99NFH1bt3b5fHCgDwTBRyAAA4WPfu3dW9e3dJ0oEDBzR06FA988wzed6ze/duTZ48WQ0aNHBHiAAAD0chBwCAE73xxht6/vnnFRgYmGf77t279fHHHysxMVEPPfSQYmJiVLx4cTdFCQDwNMyRAwDASTZu3KisrCyFh4fn2X7u3DnVqlVLMTExiouLU1pammbOnOmmKAEAnshijDHuDuJKUlIyZLMV2vDgBYKDy7h0eWV3La3s6iWkgcv5+FhUvnyAu8NwimeffVZt27ZVZGTk375vz549GjFihOLj410UGXBzLBaLCvGfkA7jLecJz8TQSgAAnCAnJ0dbtmzR+PHj8+07duyYNm7cqG7dukmSjDHy87u+lMzFTribqy88uuNip+T68wQuudqFToZWAgDgBPv379fdd9+tUqVK5dtXokQJvfPOO0pMTJQxRl988YXatGnjhigBAJ6KQg4AACdITEzU7bffnmfboEGDtGvXLgUGBmrMmDH6xz/+ofbt28sYoyeeeMJNkQIAPBFz5IC/wRw5wPmK8hw5ZyJHwp3ckTvIkfA2DK0EAAAAgCKGQg4AAAAAPAyFHAAAAAB4GAo5AAAAAPAwFHIAAAAA4GGcXshNnTpVHTp0UEREhGbPnu3s7gAAAACgyPNzZuO//PKLNm/erK+++koXLlxQhw4dFBoaqnvuuceZ3QIAAABAkebUO3KNGjXSv//9b/n5+SklJUVWq1WlSpVyZpcAAAAAUOQ5fWilv7+/pk2bpoiICIWEhKhChQrO7hIAAAAAijSLMca4oqPMzEw99dRT6tChgx577DFXdAncNIvFIhf9F3EbbzhHoChKScmQzcb/XbhHcHAZJSWlubTPoKDSSk5Od2mf7jhP4BIfH4vKlw+44n6nzpH7448/lJOTo1q1aqlkyZJq27at9u/ff83Hk6RQGLgyabgjSUmuPUfgr66WqAAAQH5OLeSOHDmiadOmaf78+ZKkVatWqWvXrs7sEnCob4e1VvpHj7usP3eUU98Oa+2GXgEAAHAznFrIhYaGaseOHerUqZN8fX3Vtm1bRUREOLNLwKHCp69y6ZAKd9yRCw8uo6RRLu0SAAAAN8llc+RuBEMr4W6uHhvP+H94I4ZW3hhyJNxp25udVb3Cre4Ow+n+dzJVD4yKc3cY8FJunSMHABRHZ4EAACAASURBVACAosfVI1YkRq0Af+X0xw8AAAAAAByLQg4AAAAAPAxDKwEAcLB+/fopJSVFfn4X0+yYMWNUr149+/6NGzfq7bffVnZ2tsLDw/X888+7K1QAgIeikAMAwIGMMTp48KDWrFljL+Qul5WVpREjRmjOnDmqWLGihgwZorVr1yo0NNQN0QIAPBVDKwEAcKCDBw/KYrFo0KBB6tixo+bOnZtn/86dO3XXXXepUqVK8vPzU1RUlFasWOGmaAEAnoo7cgAAOFBaWppCQkL0xhtvKCsrS/369VOVKlXUrFkzSVJSUpKCgoLs7w8ODtbJkyfdFS4AwENRyAEA4EANGjRQgwYNJEmlSpVSt27dtHbtWnshV9DjWy0Wy3X3w7P34G5BQaXpE3AjCjkAABxo69atys3NVUhIiKSLhdvlc+UqVKigU6dO2V8nJSUpODj4uvvhgeBwN1c/080dz5GTXH+ewCVXeyA4c+QAAHCg9PR0TZw4UdnZ2crIyFBcXJzatGlj31+vXj0dOnRIhw8fltVq1TfffKOWLVu6MWIAgCfijhwAAA4UFhamHTt2qFOnTrLZbOrVq5caNGig6OhoffTRR6pQoYLGjx+vYcOGKTs7W6GhoWrfvr27wwYAeBiLKWiwfiHBsBG4W3BwGSUlpbmsP3cMG3H1OQJ/dbWhIygYORLu5I7cQY6Et2FoJQAAAAAUMRRyAAAAAOBhKOQAAAAAwMOw2AlwFcHBZdwdglOVLVvW3SEAAADgOjm9kJsxY4a+/fZbSVJoaKheeeUVZ3cJOIyrJzgzqRoAAADXwqmF3MaNG7V+/XrFxcXJYrFo4MCBWrlyZZ7n6QAAAMDzFPURKxKjVlC4ObWQCwoKUmxsrIoVKyZJqlq1qo4dO+bMLgEAAOBk7hg9wqgVIC+nFnLVqlWzf52QkKDly5drwYIFzuwSAAAAAIo8lyx2cuDAAQ0ZMkQxMTG6++67r/k4HhALbxQUVNrdIQAAAKCQc3oht23bNj377LMaMWKEIiIiruvYlJQM2WzGSZEBhVNycrq7QwBcysfHwoU7AACuk1MLuePHj2vo0KGaMmWKQkJCnNkVAAAAAHgNpxZys2bNUnZ2tsaPH2/f1qNHD/Xs2dOZ3QIAAABAkWYxxhTasYsMrYS3YUUueCOGVt4YciS8DTkS3uZq+dHHhbEAAAAAAByAQg4AAAAAPAyFHAAAAAB4GAo5AAAAAPAwLnkgOAAA3mTGjBn69ttvJUmhoaF65ZVX8u1fsmSJypQpI0l69NFH1bt3b5fHCQDwXBRyAAA40MaNG7V+/XrFxcXJYrFo4MCBWrlypdq0aWN/z+7duzV58mQ1aNDAjZECADwZhRwAAA4UFBSk2NhYFStWTJJUtWpVHTt2LM97du/erY8//liJiYl66KGHFBMTo+LFi7sjXACAh2KOHAAADlStWjXVr19fkpSQkKDly5crNDTUvv/cuXOqVauWYmJiFBcXp7S0NM2cOdNd4QIAPBQPBAcKER52Cm9UVB8IfuDAAQ0ZMkTDhg1T586dr/i+PXv2aMSIEYqPj3dhdIDnsVgsKsR/tgIux9BKAAAcbNu2bXr22Wc1YsQIRURE5Nl37Ngxbdy4Ud26dZMkGWPk53f96ZiLnfBGycnp7g4BcJmrXehkaCUAAA50/PhxDR06VJMmTcpXxElSiRIl9M477ygxMVHGGH3xxRd5FkIBAOBacEcOAAAHmjVrlrKzszV+/Hj7th49emj16tV69tlnVadOHY0ZM0b/+Mc/lJubq4YNG+qJJ55wY8QAAE/EHDmgEGGOHLxRUZ0j52zkSHgbciS8DUMrAQAAAKCIoZADAAAAAA9DIQcAAAAAHsYlhVxGRoYiIyN15MgRV3QHAAAAAEWa0wu5HTt2qGfPnkpISHB2VwAAAADgFZxeyC1atEijR49WcHCws7sCAAAAAK/g9OfIjR071tldAAAAAIBXKdQPBOe5QvBGQUGl3R0CAAAACrlCXcjxsFN4o+TkdHeHALgUDwQHAOD68fgBAAAAAPAwFHIAAAAA4GFcNrRy9erVruoKAAAAAIq0Qj1HDvBULVs21r59e2/o2ODgMjd0XM2atfTTTz/f0LEAALiKq3Mk+RFFlcUYU2hXE2GxE3iboKDSLHYCr8NiJzeGHAlvQ46Et7lafmSOHAAAAAB4GAo5AAAAAPAwFHIAAAAA4GEo5AAAAADAwxTqVSt9fCzuDgFwOX7u4W34mb8xfG7wRvzcw5tc7ee9UK9aCQAAAADIj6GVAAAAAOBhKOQAAAAAwMNQyAEAAACAh6GQAwAAAAAPQyEHAAAAAB6GQg4AAAAAPAyFHAAAAAB4GAo5AAAAAPAwFHIAAAAA4GEo5IBCJCMjQ5GRkTpy5Ii7QwEAoNAgPwL5UcgBhcSOHTvUs2dPJSQkuDsUAAAKDfIjUDAKOaCQWLRokUaPHq3g4GB3hwIAQKFBfgQK5ufuAABcNHbsWHeHAABAoUN+BArGHTkAAAAA8DAUcgAAAADgYSjkAAAAAMDDUMgBAAAAgIexGGOMu4MAAAAAAFw77sgBAAAAgIehkAMAAAAAD0MhBwAAAAAehkIOAAAAADwMhRwAAAAAeBgKOQAAAADwMBRyAAAAAOBhKOQAAAAAwMNQyAEAAACAh6GQAwAAAAAPQyEHAAAAAB6GQg4AAAAAPAyFHAAAAAB4GAo5AAAAAPAwFHIAAAAA4GEo5AAAAADAw1DIAQAAAICHoZADAAAAAA9DIQcAAAAAHoZCDgAAAAA8DIUcAAAAAHgYCjkAAAAA8DAUcgAAAADgYSjkAAAAAMDDUMgBAAAAgIehkAMAAAAAD0MhBwAAAAAehkIOAAAAADwMhRwAAAAAeBgKOQAAAADwMBRyAAAAAOBhKOQAAAAAwMNQyAEAAACAh6GQAwAAAAAPQyEHAAAAAB6GQg4AAAAAPAyFHAAAAAB4GAo5AAAAAPAwFHIAAAAA4GEo5AAAAADAw1DIAQAAAICHoZADAAAAAA9DIQcAAAAAHoZCDgAAAAA8DIUcAAAAAHgYCjkUKm+99Zaio6MVHR2t+++/X+3atbO/zsrKUnR0tNLS0pzS94ABA3T69OnrOmb69OkaM2bMVd83Y8YM/fDDDzca2nW5/DwGDRqk33//3WFtz5s3TwsXLnRYe5J04sQJDR06VDabzaHtAoA71KhRQ1FRUfbcFR0drZEjR0rSdeew9PR09evXr8B906dPV40aNbR48eI828+fP68GDRpoyJAhN34Sf2Pq1KmKj4+/4eP37t2rRx55RJ07d9aRI0eu+L7Y2FjNmjUr3/bp06erSZMmio6OVqdOnRQVFaXHH39chw4d+tt+T548qR49elw1vi+//FJffPFFvu0fffSR/fvZoEEDtWrVyv76zz//dHi+/SvyLwri5+4AgMu99tpr9q9btWqlSZMmqU6dOvZty5Ytc1rfGzZscFrbP//8s+69916ntX+5y8/j448/dli7R48eVVxcnBYtWuSwNiXp9ttvV61atTRv3jz16dPHoW0DgDt8/vnnCgwMzLf9enNYamqqdu3adcX9//d//6evvvpK3bp1s2/7/vvvVapUqevq53o899xzN3X8qlWr1LhxY40dO/aG2+jQoYNef/11++s5c+boxRdf1NKlS694TIUKFbRgwYKrtr1t2zZVq1Yt3/bBgwdr8ODBkqS+ffuqd+/eat++vX2/I/PtX5F/cSUUcvAoNWrU0KZNm7RmzRp9//33ysrK0tGjR1WxYkX17t1bc+fOVUJCgp544gkNGDBA0sWra/Pnz5fNZlPZsmU1atQoVa1aNU+7r776qiSpf//++uijj5SRkaExY8bo7NmzslgsGjBggDp16vS3scXGxiogIED79+/XiRMndM8992jy5MmKj4/X7t27NXHiRPn6+io0NFSTJk3Sli1bZLVaVbt2bb322msKCAhQq1atVLduXe3fv18vvPCC/Pz89OGHHyonJ0enT59Wp06dNHz4cEnS4sWLNXv2bPn4+KhcuXKaMGGCpk2bluc8evfuralTp+qzzz5T7dq19eSTT0qS5s+fr59//lnvvvuuVq9erffff1+5ubkqUaKEYmJi1KBBg3zn9+GHHyo6OloWi0UXLlzQm2++qV9//VX+/v6688479fbbb+uWW27Rr7/+qkmTJikzM1MWi0XDhg1TWFiYvY24uDj5+fnprrvu0vjx41W6dGl1795d3bp106OPPqpixYrdxE8IABRel+ewxYsXKzMzUwEBAZo8ebJiYmJ05swZSVJoaKiGDx+uV1991T4aZenSpfL19c3TXosWLfTDDz/oxIkTuv322yVJcXFx6tixow4ePChJysnJuWLO+fHHHwvMMT///LOmTJmiSpUq6cCBA8rJydHrr7+uJk2aKDY2VtWqVdOTTz6pOnXqaPDgwdqwYYOSkpLUr18/Pf7447JarZo4caJWr16t0qVLq27duvrjjz/UvXt3zZ8/X1arVVlZWWrWrJm+++47ffjhh5KkpUuX5nl9rUJCQjR58mRJ0vbt2/XOO+8oJydHycnJatq0qcaNG6cjR44oKipKv/32m6ZPn66jR48qOTlZR48eVWBgoKZMmaKdO3dq9erV2rBhg0qUKKHevXtfcwytWrXS1KlTdf78eU2ePFnBwcE6cOCASpYsqWHDhmnOnDk6dOiQ2rZtqxEjRkgS+Rc3zwCFVFhYmNm5c2eebdWrVzcpKSlmyZIl5oEHHjDHjh0zVqvVdOjQwQwbNsxYrVazd+9eU6dOHWO1Ws3PP/9sevXqZc6fP2+MMWbdunUmPDy8wP4utZ2bm2tat25tvvvuO2OMMSdOnDAtWrQwv/76a75jpk2bZv75z38aY4yJiYkxjz32mMnOzjY5OTmmU6dOZvHixcYYY/r06WO+/fZbY4wx06dPN+PHjzc2m80YY8y//vUvM3r0aPs5z5gxwxhjjM1mM3369DGHDh2yx1GrVi2TkpJi9u7daxo3bmyOHTtmjDFm9uzZZtSoUXnO4/LPcNOmTSYyMtIed7du3cyGDRvMoUOHTGRkpDl9+rQxxpj//e9/plmzZubcuXN5ztNms5nGjRubxMREY4wxW7ZsMe3bt7efw8SJE822bdvM2bNnTdu2be3vO3HihGnZsqU5evSo+eGHH0zbtm3N2bNnjTHGjBs3zsycOdPeR5cuXcymTZsK/N4AgKeoXr26iYyMNB07drT/O3XqlH3fpRz20EMPmfT0dGOMMTNmzLD/Dj937pwZPny4SUtLM4mJiaZ+/foF9nMp/4wZM8Z8+OGHxhhjjh49arp27WqWLFliBg8ebIy5cs75uxyzefNmU6tWLbNnzx5jjDGzZs0yvXv3NsZczHWffPKJ/XzmzJljjDFm165d5v777zdZWVlm/vz5pnfv3iYrK8tkZ2ebAQMGmD59+uSJ2xiTJ86/vr68n4LO+5Lc3Fzz9ttvmyFDhhhjjHn++efN5s2bjTHGZGRkmMaNG5tdu3bl+SynTZtmWrdubf/8hwwZYqZOnfq3/V7u8px+yaV8e+mz++9//2uMMebJJ5+0/22QkpJi7rvvPnPixAnyLxyCO3LwWHXq1FHFihUlSXfeeaeaN28uHx8fVapUSdnZ2crMzNSaNWt0+PDhPOPiU1NTdfbsWZUtW7bAdhMSEpSdna22bdtKujgco23btlq3bl2BV8ou16JFC/sVrerVqys1NTXfe9asWaP09HRt3LhRkpSbm6vy5cvb9z/44IOSJIvFog8++EBr1qzRN998oz/++EPGGGVmZmrTpk1q3ry5/fwff/zxv42rcePGys7O1q5du1SyZEmdPn1aISEhmjdvnpKSkvIcb7FY9Oeff6pmzZr2bWfOnFF6erruvPNO+7n5+vqqe/fuat68udq1a6e6detq7dq1Sk5O1tChQ/O0t3//fm3atEnt27fXrbfeKun/3QW9pHLlyjp06JCaNGnyt+cCAIXdlYZWXq5GjRoKCAiQdDF3DB48WMePH1fTpk314osvqnTp0gXmkL+6NAdv8ODBWrZsWb7RI1fKOX+XY6SLwzZr1aolSapdu7bi4uIK7L9169aSpPvuu085OTk6f/681q5dq+joaBUvXlyS9Nhjj2nOnDlXPZdrtXz5cm3bts1+Pvfdd5/efPNNSdL48eP1008/6YMPPtDBgweVlZWl8+fP58v5jRo1sn/+tWvXvqbP+lrdeeedql27tqSLua106dIqVqyYAgMDdcsttyg1NVVbtmwh/+KmUcjBY/11CICfX/4fZ5vNpujoaL388sv210lJSfZfZgUpaNKvMUYXLly4akwlSpSwf22xWGSMKbD9ESNGKDQ0VJJ07tw5ZWdn2/dfmttw/vx5de7cWY888ogefPBBde3aVT/88IOMMfL19ZXFYrEfc2mI6V+HjF4eS7du3bRs2TL5+/urW7duslgsstlsCgkJ0bvvvmt/7/HjxxUcHJzneB8fHxljZLPZ5OPjozJlymjZsmX69ddftXnzZg0fPlz9+vVT5cqVVbVqVX355Zf2Y0+ePKnAwEBt3rw5T8xpaWlKS0uzJyer1Zpv2BAAFFWXz2OrW7euVq1apU2bNmnz5s3q3r273nvvvXy/iwtSt25dWa1W7d27V8uXL9ecOXO0evVq+/4r5Zy/yzHSteUzSfZi7dLvd2NMvnzs41Pw2np/bTc3N/eq5yvlnyN3ud69e6tmzZpq0aKFwsPDtWPHjgJjv9bzuxHX+vcJ+Rc3i1UrUaQ1a9ZM//nPf5SUlCTp4tyw/v37F/heX19fXbhwQVWqVJG/v7++//57SRd/EX733Xdq2rTpDcdxqW1Jat68ub744gvl5OTIZrNp1KhR9rH9lzt8+LAyMjI0fPhwtWrVSr/88ov9mMaNG2vTpk3281qwYIHeeeedfH1drnPnzlq9erW+++47denSRZLUpEkTbdiwQX/88Yckae3aterYsWOewlKSypYtqzJlyujo0aOSpB9//FGPP/64GjRooGHDhqlTp07at2+f6tevr8OHD2vLli2SLq5O1q5dOyUlJalp06ZauXKlMjIyJF1ceeyzzz6z93HkyBHdc889N/wZA4CnmjRpkmbOnKlHHnlEI0eO1L333quEhAT5+fnJarVetciIjo7WuHHjVKVKlXx3nq6Uc/4ux9ys0NBQffXVV8rJydGFCxeueDcvMDBQBw4cUHZ2ti5cuKAff/zxpvpNTU3V7t279dJLL6lt27Y6efKk/vzzz+s6pyvlUEcj/8IRuCOHIq1FixYaNGiQBgwYIIvFooCAAM2YMSPPlalL2rRpo169emnmzJmaOXOm3nrrLU2fPl1Wq1VDhw69qSEHYWFhmjBhgnJzc/X0009rwoQJ6ty5s6xWq2rVqqXY2Nh8x9SoUUMPP/ywwsPDVaZMGVWuXFn33nuvDh8+rBYtWujll1/WwIEDJUlBQUEaN25cvvO4XFBQkGrXrq0LFy6oQoUKkqRq1appzJgxeuGFF+xXUd9///0CVzy7NLy0V69eatmypX766SdFRkaqVKlSuvXWW/Xmm28qMDBQ06ZN08SJE5WdnS1jjCZOnKg77rhDd9xxh37//Xf17NlTknTvvffah8KcOnVKKSkpatiw4Q1/xgDgqfr376/Y2FhFRkaqWLFiqlGjhiIjI+Xr66vatWsrPDxc8+fPV7ly5Qo8vmPHjnr33Xfz/d6XdMWcU6pUqSvmmJtd9KJLly46dOiQOnXqpFKlSunOO+9UyZIl872vWbNmeuihhxQeHq6goCA1btxY+/fvv+F+b731Vg0ePFidO3dW2bJlVa5cOTVs2FCHDx9WpUqVrqmNli1b2nOTsx7hIJF/4RgW48h7yQCKrMTERD333HNasmRJgYXwzZg+fboCAwOva4UwAEDhtH79eqWkpCg6OlrSxWfEFi9e3D7NAdeH/IsrYWglgGtSqVIlderU6Zqew3M9jh8/rv/+97/X9KBWAEDhV61aNcXHx6tjx46KiIjQmTNn9NRTT7k7LI9F/sWVcEcOAAAAADwMd+QAAAAAwMNQyAEAAACAh6GQAwAAAAAPQyEHAAAAAB6mUD9H7syZc7LZWIsF3qN8+QClpGS4OwzApXx8LCpX7hZ3h+Fyy5Yt00cffSTp4rOrYmJirut4ciS8DTkS3uZq+bFQF3I2myFJwevwMw8UfZmZmRo7dqxWrFihMmXKqGfPntq4caOaNm16zW2QI+GN+JkH/h+GVgIA4GJWq1U2m02ZmZm6cOGCLly4oOLFi7s7LACABynUd+QAACiKAgIC9Nxzzyk8PFwlSpRQo0aN1LBhQ3eHBQDwIBRyAAC42L59+7RkyRL9+OOPKl26tF566SXNmjVLAwcOvOY2ypcPcGKEQOEUFFTa3SEAhQaFHAAALrZ+/XqFhISofPnykqQuXbpo3rx511XIpaRkMF8IXiUoqLSSk9PdHQbgMj4+lr+9aEchBzhBy5aNtW/fXpf2WbNmLf30088u7RPAjalZs6beeecdnT9/XiVLltTq1atVp04dd4cFuISrcyT5EUWVxRjjkMt5M2bM0LfffitJCg0N1SuvvKJXX31V27ZtU8mSJSVJzzzzjNq0aXPNbXK1Ed4mOLiMkpLS3B0G4FJXu+JYVH300UdaunSp/P39VadOHY0ePfq6FjwhR8LbkCPhbVxyR27jxo1av3694uLiZLFYNHDgQK1cuVK7d+/W3LlzFRwc7IhuAAAoMgYPHqzBgwe7OwwAgIdyyOMHgoKCFBsbq2LFisnf319Vq1bVsWPHdOzYMY0aNUpRUVGaNm2abDabI7oDAAAAAK/mkEKuWrVqql+/viQpISFBy5cvV4sWLdSkSRONGzdOixYt0tatW7V48WJHdAcAAAAAXs1hc+Qk6cCBAxoyZIiGDRumzp0759m3cuVKxcfH67333nNUd0CRY7FY5MD/kgCKMObIwdswRw7exmWrVm7btk3PPvusRowYoYiICO3fv18JCQlq166dJMkYIz+/6+uOJAVvxNLK8DbeutgJAAA3wyFDK48fP66hQ4dq0qRJioiIkHSxcBs3bpxSU1OVm5urhQsXXteKlQAAAACAgjnkjtysWbOUnZ2t8ePH27f16NFDgwcPVs+ePXXhwgW1bdtWkZGRjugOAAAAALyaQ+fIORpDK+FtGP8Pb8TQyhtDjoS3IUfC21wtPzpkaCUAAAAAwHUo5AAAAADAw1DIAQAAAICHoZADAAAAAA9DIQcAAAAAHoZCDgAAAAA8DIUcAAAAAHgYCjkAAAAA8DAUcgAAAADgYSjkAAAAAMDDUMgBAAAAgIehkAMAAAAAD0MhBwAAAAAehkIOAAAAADwMhRwAAAAAeBgKOQAAAADwMBRyAAAAAOBhKOQAAAAAwMNQyAEAAACAh3FYITdjxgxFREQoIiJCEydOlCRt3LhRUVFRatu2raZMmeKorgAAAADAqzmkkNu4caPWr1+vuLg4xcfH67///a+++eYbjRgxQjNnztTy5cu1e/durV271hHdAQAAAIBXc0ghFxQUpNjYWBUrVkz+/v6qWrWqEhISdNddd6lSpUry8/NTVFSUVqxY4YjuAAAAAMCrOaSQq1atmurXry9JSkhI0PLly2WxWBQUFGR/T3BwsE6ePOmI7gAAAADAq/k5srEDBw5oyJAhiomJkZ+fnw4dOpRnv8Viua72ypcPcGR4gEcICirt7hAAAABQyDmskNu2bZueffZZjRgxQhEREfrll1906tQp+/6kpCQFBwdfV5spKRmy2YyjQgQ8QnJyurtDAFzKx8filRfuVq9erRkzZuj8+fNq3ry5XnvtNXeHBADwIA4ZWnn8+HENHTpUkyZNUkREhCSpXr16OnTokA4fPiyr1apvvvlGLVu2dER3AAB4tMTERI0ePVozZ87U119/rT179rAgGADgujjkjtysWbOUnZ2t8ePH27f16NFD48eP17Bhw5Sdna3Q0FC1b9/eEd0BAODRVq5cqQ4dOuj222+XJE2ZMkXFixd3c1QAAE9iMcYU2rGLDK2EtwkOLqOkpDR3hwG4lDcOrRw9erT8/f116NAhJScnKywsTMOHD7+uueTkSHgbciS8zdXyo0MXOwEAAFdntVq1detWzZkzR6VKldLTTz+tuLg4denS5Zrb8LbiF5BYEAy4HIUcAAAudttttykkJESBgYGSpNatW2vnzp3XVchxRw7eiAXB4E2udkfOIYudAACAaxcWFqb169crLS1NVqtV69at03333efusAAAHoQ7csD/1969B1lZ3ncA/55lA4YARZk91HqJiYmXtgnBmPGWQKOCt8U1KlY0Ek1R4yheJlERNVhalBpHG2+Zxpq2I0RFI2uTIolJoxkHGy9pURpvUTESHBZXuawKEvb0D8dtyKLu/ezLfj4zzux7zvu+v9/Ziy/f8zzPewD62JgxYzJt2rScdNJJ2bRpUw466KAcd9xx1W4LgAJxsxPoRyzkZiAaiDc76QmukQw0rpEMNKZWAgAAbGMEOQAAgIIR5AAAAApGkAMAACgYQQ4AAKBgBDkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQAAgIIR5AAAAAqmR4NcS0tL6uvrs2LFiiTJJZdckokTJ6ahoSENDQ25//77e7IcAADAgFTbUydaunRpLrvssixfvrztsWXLlmXevHkpl8s9VQYAAGDA67Egt2DBgsyaNSsXXXRRkuTNN9/MypUrc/nll2flypWZMGFCzjnnnNTUmM0JAFBke+yxa9asWdPndcvlEX1ab+TIkXn22d/2aU3oqB4LcnPmzNliu7m5Ofvvv39mz56doUOH5swzz8zdd9+dE044ocPnHDVqWE+1B4VRVze82i0AwPtas2ZNmprW9WnNurrhWb16uaLgjgAAIABJREFUfZ/W7OvgCJ3RY0Huj+2yyy656aab2rZPOeWUNDY2dirINTe3pLW10hvtQb/V1xcpqLaampI37gCgk3ptnuMzzzyTH//4x23blUoltbW9lhsBAAAGjF4LcpVKJVdeeWXWrl2bTZs25c4778yECRN6qxwAAMCA0WtDZHvttVfOOOOMTJkyJb///e8zceLE1NfX91Y5AACAAaNUqVT67SI0a+SotmrdlasvuSMX1WaNXNe4RlJN5fKIAXOzk75+nfCuD7o+WrQG76Ov78rljlwAAHSED3UDAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQCoon/4h3/IjBkzqt0GAAUjyAFAlTz88MNZuHBhtdsAoIAEOQCogjVr1uS6667L1772tWq3AkABCXIAUAXf/OY3c8EFF2TEiBHVbgWAAqqtdgMAMNDcdddd2XHHHXPAAQfknnvu6dI5Ro0a1sNdQefU1Q1XE6pIkAOAPrZo0aKsXr06DQ0NWbt2bd58881ceeWVmTlzZofP0dzcktbWSi92Ce9v9er1fVqvrm54n9dM+v51wrtqakrv+6adIAcAfexf/uVf2r6+55578sgjj3QqxAGANXIAAAAFY0QOAKro2GOPzbHHHlvtNgAoGCNyAAAABSPIAQAAFIwgBwAAUDA9GuRaWlpSX1+fFStWJEmWLFmSSZMmZeLEibnuuut6shQAAMCA1WNBbunSpZkyZUqWL1+eJNmwYUNmzpyZm2++OYsWLcqyZcvy4IMP9lQ5AACAAavH7lq5YMGCzJo1KxdddFGS5IknnshHP/rR7LLLLkmSSZMmZfHixRk/fnxPlQQAoArum35I1n/31D6tWY2P5b5v+iFVqAod02NBbs6cOVtsNzU1pa6urm27XC5n1apVPVUOAIAqOeKGn6WpaV2f1qyrG57Vq/s2zh1RHpGmy/u0JHRYr32OXKVSafdYqVTq1DlGjRrWU+1Al9XVDd+m61WrJgAAXddrQW706NF59dVX27abmppSLpc7dY7m5pa0trYPhNCX+vLdv2q825j07WuEP1ZTU/LGHQB0Uq99/MCYMWPy4osv5qWXXsrmzZvzox/9KOPGjeutcgAAAANGr43IDRkyJHPnzs306dOzcePGjB8/PocffnhvlYNe0deLuS3kBgCgI0qVrS1m6ydMraTayuURfbqYuxpTK/v6NcIfM7Wya1wjqaZqXDtcIxloPuj62GtTKwEAAOgdghwAAEDBCHIAAAAFI8gBAAAUjCAHAABQMIIcAABAwQhyAAAABSPIAQAAFIwgBwAAUDCCHAAAQMEIcgAAAAUjyAEAABRMbbUbAACgeMrlEdVuodeNHDmy2i3AexLkAADolKamdX1es1weUZW60F+ZWgkAAFAwghwAAEDBmFoJH2BbXwNg/j8AQPEIcvA++nouvvn/AAB0hKmVAAAABdPrI3JTp05Nc3NzamvfKTV79uyMGTOmt8sCQL9244035r777kuSjB8/PhdddFGVOwKgSHo1yFUqlbzwwgt54IEH2oIcAAx0S5YsyUMPPZSFCxemVCpl2rRpuf/++zNhwoRqtwZAQfTq1MoXXnghpVIpp59+eo4++ujMmzevN8sBQCHU1dVlxowZGTx4cD70oQ9l9913z8qVK6vdFgAF0qvDZOvWrcsBBxyQK664Ihs2bMjUqVPzsY99LAcddFBvlgWAfu2Tn/xk29fLly/PokWLcscdd3TqHKNGDevptqDfq6sbXu0WoN8oVSqVSl8V+9d//desXLkyM2fO7KuSUCilUil9+CcJVNlzzz2XM888M9OnT8+XvvSlTh3b3NyS1lb/v2DgcGdnBpqamtL7vmnXqyNyjz32WDZt2pQDDjggyTtr5jqzVs5FioFo9er11W4B+tQHXai2VY8//njOPffczJw5M0cddVS12wGgYHp1jdz69etz9dVXZ+PGjWlpacnChQst5AZgwHvllVdy9tln55prrhHiAOiSXh2R++IXv5ilS5fmmGOOSWtra0466aSMHTu2N0sCQL936623ZuPGjZk7d27bYyeeeGKmTJlSxa4AKJI+XSPXWaZWMtCY/89ANFCnVnaXayQDjWskA80HXR97dWolAAAAPU+QAwAAKBhBDgAAoGAEOQAAgIIR5AAAAApGkAMAACgYQQ4AAKBgBDkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQAAgIIR5AAAAApGkAMAACiY2mo3ANuiceP2y9NPP9WlY8vlEV06bq+99s4vfvHLLh0LAH2lr6+Rro9sq0qVSqXSmwV++MMf5jvf+U42bdqUU089NSeffHKHj21ubklra6+2B/1KXd3wrF69vtptQJ+qqSll1Khh1W6jcFwjGWhcIxloPuj62KsjcqtWrcp1112Xe+65J4MHD86JJ56Y/fbbL5/4xCd6sywAAMA2rVfXyC1ZsiT7779/Ro4cmaFDh+awww7L4sWLe7MkAADANq9Xg1xTU1Pq6uratsvlclatWtWbJQEAALZ5vTq1cmvL70qlUoePt2aCgaiubni1WwAAoJ/r1SA3evToPPbYY23bTU1NKZfLHT7+9dffsJCbAWXUqGFpbm6pdhvQp2pqStl++49Uu43Cqanp+BujsK3we89A8kG/770a5A488MDccMMNee211/LhD384P/nJT/J3f/d3HT7ehZ2ByEg00BGukQxErpHw/3p9RO6CCy7I1KlTs2nTphx//PH59Kc/3ZslAQAAtnm9/jlyAAAA9KxevWslAAAAPU+QAwAAKBhBDgAAoGAEOQAAgIIR5AAAAApGkAMAACgYQQ4AAKBgBDnoR1paWlJfX58VK1ZUuxUA6DdcH6E9QQ76iaVLl2bKlClZvnx5tVsBgH7D9RG2TpCDfmLBggWZNWtWyuVytVsBgH7D9RG2rrbaDQDvmDNnTrVbAIB+x/URts6IHAAAQMEIcgAAAAUjyAEAABSMIAcAAFAwpUqlUql2EwAAAHScETkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQAAgIIR5AAAAApGkAMAACgYQQ4AAKBgBDkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQAAgIIR5AAAAApGkAMAACgYQQ4AAKBgBDkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQAAgIIR5AAAAApGkAMAACgYQQ4AAKBgBDkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQAAgIIR5OgX9txzz0yaNCkNDQ1t/1166aVJkoaGhqxbt67D51q/fn2mTp36ns+vWrUqM2bMyKRJk3L00Udn8uTJ+elPf9r2/MEHH5zDDjssDQ0NOeaYY3LUUUflyiuvTGtra9vzTz755BbnfPLJJ3PwwQdvtd6MGTNy6623bvW5+vr6/PKXv8yKFSsyduzYDr/GJDn33HPbvld/+P075ZRTsmrVqpx44omdOl9nXXjhhXn22Wd79Jw//elPc+ONN/boOQEAtkW11W4A3vVv//Zv2WGHHdo9fu+993bqPGvXrm0XtN712muv5cQTT8x5552Xq666KqVSKU8//XROO+20fPjDH85BBx2UJLnmmmvyqU99Kkny9ttv55RTTsn3v//9fPnLX+7kq+o9119/fdvXe+65Z7vv3x133NFrtRctWpThw4dnjz326NHzHnrooZk/f36eeuqp7L333j16bgCAbYkgR7+355575uGHH84DDzyQu+++O2+99VaGDRuWa6+9NhdffHFef/31JMn48eNz/vnn55JLLsmGDRvS0NCQe+65J4MGDWo71/e///3ss88+OeaYY9oe22uvvXLDDTdk+PDhW60/ePDgfPazn80LL7zQ7dfym9/8JjNnzsxbb72Vj3/843nzzTe3ut93vvOd/OQnP0lra2t22mmnzJo1K6NHj+5wnRUrVmTSpEn57//+79xwww357W9/m5dffjlNTU359Kc/nYMOOiiNjY1ZsWJFLrzwwtTX13eq7g033JBvf/vbSZLVq1dv9eeQJHfddVduv/32tLa2ZuTIkbn88suz++6754033sjf//3f51e/+lUGDRqUQw89NBdccEFKpVKOP/743Hjjjbnppps69b0FABhITK2k3/jKV76yxdTK5ubmdvv85je/yW233ZbbbrstCxYsyM4775yFCxdm/vz5eemll7J+/fpcddVV2W677XLvvfduEeKSZNmyZdlnn33anXfffffNnnvuudW+Vq1alZ///OfZb7/9uv0av/GNb2Ty5Mn54Q9/mKlTp2blypXt9mlsbMyzzz6bu+66K/fee2/Gjx+fyy67rFt1H3/88dxyyy1ZtGhRlixZkueffz7z58/P5ZdfnhtuuKFTdZ999tls3LixbTTuvX4OjzzySBobGzN//vw0NjZm2rRpmT59epJ3RhM3btyYRYsWpbGxMb/61a/yyCOPJHknCD700EPZsGFDt14zAMC2zIgc/cZ7Ta38Q3vuuWeGDRuWJPnCF76QM844I6+88koOPPDAfP3rX8/w4cOzdu3a9zy+VCqlUql8YC/f+MY3st1226W1tTUf+tCHMnny5Bx22GFt5/hjlUolNTXv/77I66+/nmeeeaZtNPCzn/1sPvnJT7bb7+c//3mefPLJHHfccUmS1tbWvPXWWx/Y8/s58MAD20Ycy+VyvvCFLyRJdt1116xZs6ZTdV944YXsuuuubdvv9XN44IEH8tJLL22xVm/t2rVZs2ZNlixZkksuuSSDBg3KoEGDMm/evLZ9hg0blmHDhuV3v/tddt999269bgCAbZUgR6EMHTq07etPf/rT+dnPfpaHH344//Vf/5XJkyfnpptuSrlcfs/jP/OZz+R//ud/2q11u+OOO/LWW2/ltNNOS7LlGrk/tv3227eFn3e9+uqrGTly5Pv2/m4A/MMgWVvb/k+wtbU106ZNy0knnZTknTV67xdOO2Lw4MFbbHenbk1NTTZv3ty2/V4/h9bW1jQ0NOTCCy9sO39TU1P+5E/+JLW1tVsE4ldeeSXbbbddtt9++yTJ5s2b242mAgDw/0ytpLCuueaa3HzzzTn00ENz6aWX5hOf+ESWL1+e2trabN68easjb3/913+dRx55JP/+7//e9vyyZcty/fXXd/jGHePGjcvtt9+et99+O0ny1ltv5c4778z48ePf97iRI0fmL/7iL3LXXXclSf73f/93q3d9/PznP5+77747LS0tSZJvf/vbueiiizrUW3d0tO5uu+2WFStWtG2/18/hoIMOyn/8x3+kqakpSXL77bfnK1/5SpLkgAMOyMKFC9Pa2pq333475557bh599NEk79x1dOPGjfmzP/uz3n7JAACFZUSOwvrKV76SGTNmpL6+PoMHD86ee+6Z+vr6DBo0KH/+53+eI444IrfffnvbKE/yTpi67bbb8q1vfSv/9E//lJqamnz4wx/OnDlz2u5Y+UG+9rWv5dprr82XvvSlDBo0KL///e9zyCGH5Mwzz/zAY6+99tpccsklueOOO7Lrrrvm4x//eLt9Jk+enFWrVuWEE05IqVTKjjvumLlz53b8G9NFHa27xx57ZMiQIXn++eez++67v+fPYfDgwTn99NPz1a9+NaVSKcOGDcuNN96YUqmUc845J3PmzElDQ0M2b96cI488MhMnTkySPPTQQ/mrv/qrdqOIAAD8v1KlIwuGAP7AD3/4wzz++OO54oorevzcU6dOzcyZM7PXXnv1+LkBALYVplYCnTZp0qSsWbMmzzzzTI+e9/7778++++4rxAEAfAAjcgAAAAVjRA4AAKBgBDkAAICCEeQAAAAKRpADAAAomH79OXKvv/5GWlvdi4WBY9SoYWlubql2G9CnampK2X77j1S7DQAolH4d5FpbK4IcA47feQAAPoiplQAAAAUjyAEAABSMIAcAAFAwghwAAEDB9OubnUBRjRu3X55++qk+rbnXXnvnF7/4ZZ/WBACgOkqVSqXf3iKvubnFHfwYUMrlEWlqWlftNqBP1dSUMmrUsGq3AQCFYmolAABAwQhyAAAABSPIAQAAFEyHg1xLS0vq6+uzYsWKJMkll1ySiRMnpqGhIQ0NDbn//vvbHfPUU0/luOOOy2GHHZZLL700v//973uucwAAgAGqQ0Fu6dKlmTJlSpYvX9722LJlyzJv3rzce++9uffeezNhwoR2x1144YW5/PLL8+Mf/ziVSiULFizoscYBAAAGqg4FuQULFmTWrFkpl8tJkjfffDMrV67M5ZdfnkmTJuX6669Pa2vrFsf87ne/y4YNG/KZz3wmSXLsscdm8eLFPdw+AADAwNOhz5GbM2fOFtvNzc3Zf//9M3v27AwdOjRnnnlm7r777pxwwglt+zQ1NaWurq5tu66uLqtWrepUc25HzUBUVze82i0AANDPdekDwXfZZZfcdNNNbdunnHJKGhsbtwhyW/t4ulKp1Kk6PkeOgWj16vXVbgH6lM+RA4DO69JdK5955pn8+Mc/btuuVCqprd0yE44ePTqvvvpq2/bq1avbpmYCAADQdV0KcpVKJVdeeWXWrl2bTZs25c4772x3s5OddtopQ4YMyeOPP54kaWxszLhx47rfMQAAwADXpSC311575YwzzsiUKVNy1FFHZe+99059fX2S5PTTT8+TTz6ZJLnmmmty1VVX5Ygjjshbb72VqVOn9lznAAAAA1SpsrXFbP2ENXIMNOXyiDQ1rat2G9CnrJEDgM7r0ogcAAAA1SPIAQAAFIwgBwAAUDCCHAAAQMEIcgAAAAUjyAEAABSMIAcAAFAwghwAAEDBCHIAAAAFI8gBAAAUjCAHAABQMIIcAABAwQhyAAAABSPIAQAAFEyHg1xLS0vq6+uzYsWKJMmdd96Z+vr6TJo0KZdccknefvvtdsc0Njbm85//fBoaGtLQ0JDrrruu5zoHAAAYoGo7stPSpUtz2WWXZfny5UmSF198MbfeemvuueeefOQjH8mMGTPy/e9/P6eeeuoWxz355JOZMWNG6uvre7pvAACAAatDI3ILFizIrFmzUi6XkySDBw/OFVdckWHDhqVUKmWPPfbIypUr2x335JNPprGxMUcffXS+8Y1vZO3atT3bPQAAwADUoSA3Z86c7Lvvvm3bO+20Uw488MAkyWuvvZb58+fnkEMOaXdcXV1dpk+fnnvvvTc77rhjZs+e3UNtAwAADFwdmlr5XlatWpVp06bluOOOy3777dfu+Ztuuqnt62nTpuXQQw/t1PlHjRrWnfagkOrqhle7BQAA+rkuB7nnn38+p59+er785S/nq1/9arvn169fnx/84Adt6+YqlUpqaztXrrm5Ja2tla62CIW0evX6arcAfaqmpuSNOwDopC59/EBLS0v+5m/+Juedd95WQ1ySDB06NP/8z/+cpUuXJknmzZuXCRMmdL1TAAAAknRxRO7uu+/Oq6++mu9973v53ve+lyQ5+OCDc9555+XSSy/NwQcfnEMOOST/+I//mCuuuCIbNmzIbrvtlquvvrpHmwcAABiISpVKpd/OXTS1koGmXB6RpqZ11W4D+pSplQDQeV2aWgkAAED1CHIAAAAFI8gBAAAUjCAHAABQMIIcAABAwQhyAAAABSPIAQAAFIwgBwAAUDCCHAAAQMEIcgAAAAUjyAEAABSMIAcAAFAwghwAAEDBCHIAAAAF06Eg19LSkvr6+qxYsSJJsmTJkkyaNCkTJ07Mddddt9VjVq5cmZNPPjmHH354zjrrrLzxxhs91zUAAMAA9oFBbunSpZkyZUqWL1+eJNmwYUNmzpyZm2++OYsWLcqyZcvy4IMPtjvub//2b3PSSSdl8eLF+cu//MvcfPPNPd48AADAQPSBQW7BggWZNWtWyuVykuSJJ57IRz/60eyyyy6pra3NpEmTsnjx4i2O2bRpUx599NEcdthhSZJjjz223T4AAAB0Te0H7TBnzpwttpuamlJXV9e2XS6Xs2rVqi32ef311zNs2LDU1r5z+rq6unb7AAAA0DUfGOT+WKVSafdYqVTq9D4dMWrUsE4fA0VXVze82i0AANDPdTrIjR49Oq+++mrbdlNTU9u0y3ftsMMOaWlpyebNmzNo0KCsXr263T4d0dzcktbW9qEQ+soee+yaNWvW9GnNrrzp0R0jR47Ms8/+tk9rwh+qqSl54w4AOqnTQW7MmDF58cUX89JLL2XnnXfOj370oxx33HFb7POhD30o++67bxYtWpRJkyalsbEx48aN67Gmoa+sWbMmTU3r+qxeXd3wrF69vs/qJUm5PKJP6wEA0H2d/hy5IUOGZO7cuZk+fXqOPPLIfPzjH8/hhx+eJLn00kvzs5/9LEkya9asLFiwIEceeWQee+yxnH/++T3bOQAAwABVqmxtQVs/YWol1VYujxgQI3J9+Rrhj5laCQCd1+kROQAAAKpLkAMAACgYQQ4AAKBgBDkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAghHkAAAACkaQAwAAKBhBDgAAoGAEOQAAgIIR5AAAAApGkAMAACgYQQ4AAKBgart64F133ZV58+a1ba9YsSINDQ355je/2fbYjTfemB/84AcZMWJEkuSEE07IySef3I12AQAA6HKQmzx5ciZPnpwkee6553L22WfnnHPO2WKfZcuW5dprr83YsWO71yUAAABtuhzk/tAVV1yRCy64IDvssMMWjy9btiy33HJLXn755Xzuc5/LxRdfnCFDhvRESQAAgAGr22vklixZkg0bNuSII47Y4vE33ngje++9dy6++OIsXLgw69aty80339zdcgAAAANeqVKpVLpzgnPPPTcTJ05MfX39++7361//OjNnzkxjY2N3ykGfKpVK6eafSL83EF4jAMC2pltTK99+++08+uijmTt3brvnVq5cmSVLluT4449PklQqldTWdq5cc3NLWlv9A5PqWr16fZ/Vqqsb3qf13lWNmvCumppSRo0aVu02AKBQuhXknnnmmey2224ZOnRou+e22267fOtb38p+++2XnXfeOfPnz8+ECRO6Uw763H3TD8n6757aZ/WqEafum35IFaoCANAd3QpyL7/8cv70T/90i8dOP/30nHvuufnUpz6V2bNn56yzzsqmTZuyzz775LTTTutWs9DXjrjhZ2lqWtdn9aoxIndEeUSaLu/TkgAAdFO318j1JlMrqbZyecQ2H+T6+jXCHzO1EgA6r9t3rQQAAKBvCXIAAAAFI8gBAAAUjCAHAABQMIIcAABAwQhyAAAABSPIAQAAFIwgBwAAUDCCHAAAQMEIcgAAAAUjyAEAABSMIAcAAFAwghwAAEDBCHIAAAAFU9udg6dOnZrm5ubU1r5zmtmzZ2fMmDFtzy9ZsiRXXXVVNm7cmCOOOCIXXHBB97oFAACg60GuUqnkhRdeyAMPPNAW5P7Qhg0bMnPmzNx2223Zcccdc+aZZ+bBBx/M+PHju9UwAADAQNflqZUvvPBCSqVSTj/99Bx99NGZN2/eFs8/8cQT+ehHP5pddtkltbW1mTRpUhYvXtzthgEAAAa6Lo/IrVu3LgcccECuuOKKbNiwIVOnTs3HPvaxHHTQQUmSpqam1NXVte1fLpezatWq7ncMAAAwwHU5yI0dOzZjx45NkgwdOjTHH398HnzwwbYgV6lU2h1TKpU6VWPUqGFdbQ96TLk8otot9Krtt98+dXXDq90GAACd0OUg99hjj2XTpk054IADkrwT3P5wrdzo0aPz6quvtm03NTWlXC53qkZzc0taW9sHQugrTU3r+rReuTyiz2smyerV6/u8JryrpqbkjTsA6KQur5Fbv359rr766mzcuDEtLS1ZuHBhJkyY0Pb8mDFj8uKLL+all17K5s2b86Mf/Sjjxo3rkaYBAAAGsi6PyH3xi1/M0qVLc8wxx6S1tTUnnXRSxo4dm4aGhnz3u9/N6NGjM3fu3EyfPj0bN27M+PHjc/jhh/dk7wAAAANSqbK1xWz9hKmVDDTVmloJ1WRqJQB0XpenVgIAAFAdghwAAEDBCHIAAAAFI8gBAAAUjCAHAABQMIIcAABAwQhyAAAABSPIAQAAFIwgBwAAUDCCHAAAQMEIcgAAAAUjyAEAABSMIAcAAFAwghwAAEDBCHIAAAAFU9udg2+88cbcd999SZLx48fnoosuavf8D37wg4wYMSJJcsIJJ+Tkk0/uTkkAAIABr8tBbsmSJXnooYeycOHClEqlTJs2Lffff38mTJjQts+yZcty7bXXZuzYsT3SLAAAAN0IcnV1dZkxY0YGDx6cJNl9992zcuXKLfZZtmxZbrnllrz88sv53Oc+l4svvjhDhgzpXscAAAADXKlSqVS6e5Lly5fnxBNPzB133JHddtstSfLGG2/k/PPPz2WXXZaddtopM2bMyE477ZQLLrigu+Vgm1UqldIDf5IAAGzjuh3knnvuuZx55pmZPn16vvSlL73nfr/+9a8zc+bMNDY2dvjczc0taW31j1oGjnJ5RJqa1lW7DehTNTWljBo1rNptAEChdOuulY8//nhOPfXUfP3rX28X4lauXJm77767bbtSqaS2tlv3VgEAACDdCHKvvPJKzj777FxzzTU56qij2j2/3Xbb5Vvf+lZefvnlVCqVzJ8/f4sboQAAANA1XR4iu/XWW7Nx48bMnTu37bETTzwx//mf/5lzzz03n/rUpzJ79uycddZZ2bRpU/bZZ5+cdtppPdI0AADAQNYjNzvpLdbIMdBYI8dAZI0cAHRet9bIAQAA0PcEOQAAgIJjNpSUAAABnUlEQVQR5AAAAApGkAMAACgYQQ4AAKBgBDkAAICCEeQAAAAKRpADAAAoGEEOAACgYAQ5AACAgqmtdgOwLRo3br88/fRTXTq2XB7RpeP22mvv/OIXv+zSsQAAFEupUqlUqt3Ee2lubklra79tD3pcXd3wrF69vtptQJ+qqSll1Khh1W4DAArF1EoAAICCEeQAAAAKRpADAAAoGEEOAACgYPr1XStrakrVbgH6nN97Bhq/8wDQef36rpUAAAC0Z2olAABAwQhyAAAABSPIAQAAFIwgBwAAUDCCHAAAQMEIcgAAAAUjyAEAABSMIAcAAFAwghwAAEDBCHLQj7S0tKS+vj4rVqyodisAAPRjghz0E0uXLs2UKVOyfPnyarcCAEA/J8hBP7FgwYLMmjUr5XK52q0AANDP1Va7AeAdc+bMqXYLAAAUhBE5AACAghHkAAAACkaQAwAAKBhBDgAAoGBKlUqlUu0mAAAA6DgjcgAAAAUjyAEAABSMIAcAAFAwghwAAEDBCHIAAAAFI8gBAAAUjCAHAABQMIIcAABAwfwfdcsFsK1DM9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1440 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,20))\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "\n",
    "for i in range(len(all_col)):\n",
    "    plt.subplot(5,2,i+1)\n",
    "    plt.boxplot(df2[all_col[i]])\n",
    "    plt.title(all_col[i])\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now let's define X and Y to build linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Contentful Paint Time (sec)</th>\n",
       "      <th>Speed Index Time (sec)</th>\n",
       "      <th>Time to Interactive (sec)</th>\n",
       "      <th>First Meaningful Paint Time (sec)</th>\n",
       "      <th>First CPU Idle Time (sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.70000</td>\n",
       "      <td>26.10000</td>\n",
       "      <td>43.00000</td>\n",
       "      <td>12.70000</td>\n",
       "      <td>21.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.00000</td>\n",
       "      <td>17.90000</td>\n",
       "      <td>35.70000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>25.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.90000</td>\n",
       "      <td>25.60000</td>\n",
       "      <td>29.30000</td>\n",
       "      <td>15.80000</td>\n",
       "      <td>26.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.80000</td>\n",
       "      <td>20.20000</td>\n",
       "      <td>18.90000</td>\n",
       "      <td>17.90000</td>\n",
       "      <td>17.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.10000</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>28.10000</td>\n",
       "      <td>12.70000</td>\n",
       "      <td>14.50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Contentful Paint Time (sec)  Speed Index Time (sec)  \\\n",
       "0                           12.70000                26.10000   \n",
       "1                           14.00000                17.90000   \n",
       "2                           13.90000                25.60000   \n",
       "3                           17.80000                20.20000   \n",
       "4                           12.10000                12.60000   \n",
       "\n",
       "   Time to Interactive (sec)  First Meaningful Paint Time (sec)  \\\n",
       "0                   43.00000                           12.70000   \n",
       "1                   35.70000                           14.00000   \n",
       "2                   29.30000                           15.80000   \n",
       "3                   18.90000                           17.90000   \n",
       "4                   28.10000                           12.70000   \n",
       "\n",
       "   First CPU Idle Time (sec)  \n",
       "0                   21.10000  \n",
       "1                   25.50000  \n",
       "2                   26.70000  \n",
       "3                   17.90000  \n",
       "4                   14.50000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df[\"Performance Score\"]\n",
    "\n",
    "X=df[[ \"First Contentful Paint Time (sec)\", \"Speed Index Time (sec)\", \"Time to Interactive (sec)\",  'First Meaningful Paint Time (sec)', 'First CPU Idle Time (sec)']]\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Count: 1291\n",
      "Test Data Count: 323\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 465)\n",
    "\n",
    "print('Train Data Count: {}'.format(X_train.shape[0]))\n",
    "print('Test Data Count: {}'.format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Performance Score</td> <th>  R-squared:         </th> <td>   0.791</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.790</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   970.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Nov 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:00:03</td>      <th>  Log-Likelihood:    </th> <td> -5030.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1291</td>       <th>  AIC:               </th> <td>1.007e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1285</td>       <th>  BIC:               </th> <td>1.010e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                             <td>  100.3031</td> <td>    0.835</td> <td>  120.108</td> <td> 0.000</td> <td>   98.665</td> <td>  101.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Contentful Paint Time (sec)</th> <td>   -5.6545</td> <td>    0.293</td> <td>  -19.311</td> <td> 0.000</td> <td>   -6.229</td> <td>   -5.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Speed Index Time (sec)</th>            <td>   -1.4324</td> <td>    0.127</td> <td>  -11.245</td> <td> 0.000</td> <td>   -1.682</td> <td>   -1.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time to Interactive (sec)</th>         <td>   -0.5164</td> <td>    0.070</td> <td>   -7.359</td> <td> 0.000</td> <td>   -0.654</td> <td>   -0.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Meaningful Paint Time (sec)</th> <td>    0.8556</td> <td>    0.210</td> <td>    4.079</td> <td> 0.000</td> <td>    0.444</td> <td>    1.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First CPU Idle Time (sec)</th>         <td>   -2.4702</td> <td>    0.115</td> <td>  -21.387</td> <td> 0.000</td> <td>   -2.697</td> <td>   -2.244</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>455.586</td> <th>  Durbin-Watson:     </th> <td>   1.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2543.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.535</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 9.153</td>  <th>  Cond. No.          </th> <td>    48.0</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      Performance Score   R-squared:                       0.791\n",
       "Model:                            OLS   Adj. R-squared:                  0.790\n",
       "Method:                 Least Squares   F-statistic:                     970.2\n",
       "Date:                Thu, 28 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                        13:00:03   Log-Likelihood:                -5030.2\n",
       "No. Observations:                1291   AIC:                         1.007e+04\n",
       "Df Residuals:                    1285   BIC:                         1.010e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================================\n",
       "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------------\n",
       "const                               100.3031      0.835    120.108      0.000      98.665     101.941\n",
       "First Contentful Paint Time (sec)    -5.6545      0.293    -19.311      0.000      -6.229      -5.080\n",
       "Speed Index Time (sec)               -1.4324      0.127    -11.245      0.000      -1.682      -1.182\n",
       "Time to Interactive (sec)            -0.5164      0.070     -7.359      0.000      -0.654      -0.379\n",
       "First Meaningful Paint Time (sec)     0.8556      0.210      4.079      0.000       0.444       1.267\n",
       "First CPU Idle Time (sec)            -2.4702      0.115    -21.387      0.000      -2.697      -2.244\n",
       "==============================================================================\n",
       "Omnibus:                      455.586   Durbin-Watson:                   1.935\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2543.645\n",
       "Skew:                           1.535   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.153   Cond. No.                         48.0\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(X_train)\n",
    "results = sm.OLS(y_train, X_train).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model graph to see predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "header_font = {'family': 'arial','color':  'darkred','weight': 'bold','size': 15 }\n",
    "xy_font = {'family': 'arial','color':  'darkblue','weight': 'bold','size': 10 }\n",
    "plt.figure(figsize=(10,10))\n",
    "X_test = sm.add_constant(X_test)\n",
    "\n",
    "y_preds = results.predict(X_test)\n",
    "sns.set(color_codes=True)\n",
    "plt.scatter(y_test, y_preds)\n",
    "plt.plot(y_test, y_test, color=\"red\")\n",
    "plt.xlabel(\"Actual Speed Scores\", fontdict=xy_font)\n",
    "plt.ylabel(\"Estimated Speed Scores\", fontdict=xy_font)\n",
    "plt.title(\"PageSpeed: Actual Scores vs Estimated Scores\", fontdict=header_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# not good prediction !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE)        : 8.56069250101459\n",
      "Mean Sq. Error (MSE)          : 139.91048743595397\n",
      "Root Mean Sq. Error (RMSE)     : 11.828376365163308\n",
      "Mean Abs. Perc. Error (MAPE) : inf\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error (MAE)        : {}\".format(mean_absolute_error(y_test, y_preds)))\n",
    "print(\"Mean Sq. Error (MSE)          : {}\".format(mse(y_test, y_preds)))\n",
    "print(\"Root Mean Sq. Error (RMSE)     : {}\".format(rmse(y_test, y_preds)))\n",
    "print(\"Mean Abs. Perc. Error (MAPE) : {}\".format(np.mean(np.abs((y_test - y_preds) / y_test)) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets try with df2 - without outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Contentful Paint Time (sec)</th>\n",
       "      <th>Speed Index Time (sec)</th>\n",
       "      <th>Time to Interactive (sec)</th>\n",
       "      <th>First Meaningful Paint Time (sec)</th>\n",
       "      <th>First CPU Idle Time (sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>18.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>14.50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Contentful Paint Time (sec)  Speed Index Time (sec)  \\\n",
       "0                            6.40000                14.40000   \n",
       "1                            6.40000                14.40000   \n",
       "2                            6.40000                14.40000   \n",
       "3                            6.40000                14.40000   \n",
       "4                            6.40000                12.60000   \n",
       "\n",
       "   Time to Interactive (sec)  First Meaningful Paint Time (sec)  \\\n",
       "0                   25.90000                            7.90000   \n",
       "1                   25.90000                            7.90000   \n",
       "2                   25.90000                            7.90000   \n",
       "3                   18.90000                            7.90000   \n",
       "4                   25.90000                            7.90000   \n",
       "\n",
       "   First CPU Idle Time (sec)  \n",
       "0                   15.90000  \n",
       "1                   15.90000  \n",
       "2                   15.90000  \n",
       "3                   15.90000  \n",
       "4                   14.50000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df2[\"Performance Score\"]\n",
    "\n",
    "X=df2[[ \"First Contentful Paint Time (sec)\", \"Speed Index Time (sec)\", \"Time to Interactive (sec)\",  'First Meaningful Paint Time (sec)', 'First CPU Idle Time (sec)']]\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Count: 1291\n",
      "Test Data Count: 323\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 465)\n",
    "\n",
    "print('Train Data Count: {}'.format(X_train.shape[0]))\n",
    "print('Test Data Count: {}'.format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Performance Score</td> <th>  R-squared:         </th> <td>   0.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   3114.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Nov 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:00:19</td>      <th>  Log-Likelihood:    </th> <td> -4378.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1291</td>       <th>  AIC:               </th> <td>   8768.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1285</td>       <th>  BIC:               </th> <td>   8799.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                             <td>  112.5579</td> <td>    0.582</td> <td>  193.429</td> <td> 0.000</td> <td>  111.416</td> <td>  113.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Contentful Paint Time (sec)</th> <td>   -4.6440</td> <td>    0.266</td> <td>  -17.439</td> <td> 0.000</td> <td>   -5.166</td> <td>   -4.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Speed Index Time (sec)</th>            <td>   -2.1848</td> <td>    0.098</td> <td>  -22.378</td> <td> 0.000</td> <td>   -2.376</td> <td>   -1.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time to Interactive (sec)</th>         <td>   -0.9747</td> <td>    0.059</td> <td>  -16.441</td> <td> 0.000</td> <td>   -1.091</td> <td>   -0.858</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Meaningful Paint Time (sec)</th> <td>   -1.5292</td> <td>    0.214</td> <td>   -7.135</td> <td> 0.000</td> <td>   -1.950</td> <td>   -1.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First CPU Idle Time (sec)</th>         <td>   -2.2114</td> <td>    0.090</td> <td>  -24.618</td> <td> 0.000</td> <td>   -2.388</td> <td>   -2.035</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>56.593</td> <th>  Durbin-Watson:     </th> <td>   1.909</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  56.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.473</td> <th>  Prob(JB):          </th> <td>5.67e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.606</td> <th>  Cond. No.          </th> <td>    51.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      Performance Score   R-squared:                       0.924\n",
       "Model:                            OLS   Adj. R-squared:                  0.923\n",
       "Method:                 Least Squares   F-statistic:                     3114.\n",
       "Date:                Thu, 28 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                        13:00:19   Log-Likelihood:                -4378.0\n",
       "No. Observations:                1291   AIC:                             8768.\n",
       "Df Residuals:                    1285   BIC:                             8799.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================================\n",
       "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------------\n",
       "const                               112.5579      0.582    193.429      0.000     111.416     113.699\n",
       "First Contentful Paint Time (sec)    -4.6440      0.266    -17.439      0.000      -5.166      -4.122\n",
       "Speed Index Time (sec)               -2.1848      0.098    -22.378      0.000      -2.376      -1.993\n",
       "Time to Interactive (sec)            -0.9747      0.059    -16.441      0.000      -1.091      -0.858\n",
       "First Meaningful Paint Time (sec)    -1.5292      0.214     -7.135      0.000      -1.950      -1.109\n",
       "First CPU Idle Time (sec)            -2.2114      0.090    -24.618      0.000      -2.388      -2.035\n",
       "==============================================================================\n",
       "Omnibus:                       56.593   Durbin-Watson:                   1.909\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               56.397\n",
       "Skew:                           0.473   Prob(JB):                     5.67e-13\n",
       "Kurtosis:                       2.606   Cond. No.                         51.1\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(X_train)\n",
    "results = sm.OLS(y_train, X_train).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4c6cQQoire/aIMEB87+0tivPyhBBCnP7hB7E5JETEr10rhBDipy5dNN5jlVIpIiZMEGGA2Pfii0KIe5/1NQ8+KB1fn/tGRGio9P1UlpaKvNRU8VdYmLi2d6/O86qJSqkU33l6iv9ZW4uS/Hxx+N13pXvD5tGjhRBC/Ny1qwgDRM61a0IIIb03uTdvCiGq/4xsf+IJIUTZPW5p69YiDBAJW7cKIYTYHBKice2EEOLAnDkiDBCbRo4UQghx5MMPNe632tbdjI6W7gO6XNiwQdqmMDtbCCHE7b//lu4FuTdvSt+bBZaW0uu0rVvm61vlPqy+X9yMjhZCVL2fqX9HXNmxQwhRds9Z6OAgvnV1FUqFQuM7q95GCP2+o00Je8rMQFJ0NBK3b8ftP/+EtYsL/KdPx8TduyGzsIDc2hqdJ0+Gg7c39syciZ+7dMHp8mE8ZVERACDrwgUAQKeJEwEAMpkMXaZO1TiGeujq+IIFUtf7rfJZMsl//ln2OgsLBHzwAf51+zamREfjoY8+glu3bgDKer4qDlEBQKdJkwAAFpaW0l+QWRcuIPvKFSgKC6EoKsISd3cskMmwqbwnS30sdXsOvPIKFshkCLe2xp2rV6FSKJB6/DhyLl8GALj37Ann9u0BAK2DgmDr6lqr9/h8+VCXuofMOzCwrCdSCJz/6ad779OZMwCAB0aNAgBY2dlh8oEDCFmxAjbOzvofsNJsVUdvb3QMDS3r5QoNxfetWiGjfOaSovw66sOjZ088cfAgXrp1CyGrVqHr009DbmOD3Bs3cOS99zTOoWKv3iM//IDHNm2CV79+SP7zTwilEh49e8KrXz8AgI2zMzqWf37Us6fU1H+FA/euW8pff+FrCwsskMnw52efAbh3bR+cOxdCpcLaAQPwfcuWOBEeDltXV7h37671nOw9POD72GNQFBTg8m+/4UpEBEru3oXv2LGw9/CAtZMTer7wAoqys/Fz5874sV07XFi3Dg6tWsGlY8dq3y+ZpSXeFqLKv76vvw4A6D5zJmxdXXF8wQJ86+KCLaNGQVlSgpYBAbCwtNTjimhq+/DDAAAHb29pXZvydY4+PgDuXe8WnTuj/ZgxSD9zBlvHjMHSli2l3kZdn4mMc+cAIXA3KQkLbW2xQCbDvvJeotvl77/6u6MezpZZWMC3vDeoOraurghZuRIvp6djwvbt6DNnDuw8PaEoKMD+F1+EEEL6bPkMHizlnfV84QVM3LULXZ58EoVZWdL9qPtzz0nH7/bsswCqfra8K/Qq6XPf6P3KK5Db2CDy9dex2MUFu6dNg4VcjlYBATrPq3IvaWUyCwv4jh0LZXExbh46hH8iI2Hr6orWwcG4deQIcq5dQ2Z8PNx79pR6ow3RdvhwAGVDxOqcr9KCAgCQ7sHq96ri/9+q9F5pqMVsePX+Ok6cKN3LvPr1g0fPnhBKpfQeVz2UcWbeZ5TfO34bMwYLZDJ86+KC0rw8FGZmIrv8M6tW8Z5j7O+ouWOivxmYEh2tc1ikODcXa/r0Qe6NG+g1ezaCvvoKN/bvx6lvv5W+LCqFAkClL49MprEfVXmulkfv3lWG3Gzd3JARH4/Y//wHMgsLjPn1V/gEBsInMBAD58/Hz507IycxEWmnT6NFp07S69Rd/BWPLbO0lI4lt7VFu0ce0TiWrDwvQb2N96BBVbra5XZ20r6rzEC1MPzviIy4OKT8/TcA4NgXX+DYF19o/Pz8ypV46OOPNb7g6vcUKMvhsSqfZVWdisN+lX+pphw/jvVBQWjm6Ijer76K3q+9huh583D72DG9b3qxn36K5NhY9Hn1VTwwahT8p02D/7RpaDN8OPY8+2yV4Y6K56AoKoKltTVkMhlklT4blVX+uXXz5vf2WX7dHFu3rjJs0cLPDwAwYN48tB89GglbtyIpKgpXtm3DxfXrcfm33zB2/Xqtx+wxaxYub9mCSxs2QFZ+HSr+ohqxdCm6TpuGK9u24VZ0NC6sXYu4lSvR/513EFRp+MMQbv7+mJmQgMtbtuDGH3/gVkwMjrz/Po5//TVmnDsHh1atDNqfejZexaF+dfBSefj/yvbt2BYaCqe2bdFr9mwMfP99bH/8ceTduqXzl676/bd1ddUIaADAXt3W8utX8btT+diVJWzdivg1a9BywAAMmDcPvmPHwnfsWPR9/XUse+AB3E1KQkF6+r12VPhsqf/fQi43ymeruvtGm6FDMSsxEQmbN+PGgQNIPnoU1/fuxcnFizHj3DlY2dlVOeaVbduqbRNQNoR5bvlyJGzejNTjx+E7bhw8+/TBzUOH8Ff5/aKDloBOH/IK9w71Z1u6vtW9X5Xv4dXcX/RhyLWp+NlR1uJY2qjzhduOGKH1OlVoCKydnKRFY39HzR17yszc9X37kJOYCNeuXTFs0SL4jh2L7Eo9VurerMtbtgAoC5AurF2rdRuvfv0wPiIC4yMi4Orvj5YDBqBVQAAcWrZEwubNuLhunUbSbkFaGopzcgDc+0tf7WL5L1ihUuHqzp0AANeuXeHSoQMsra2hUigwdOFCjI+IQP933kHz9u3R5cknNdrzwOjRGB8RgbGbNsGxTRt4Dx4Mj5494Vqe4J1+5gyyr1wBUJaTUFjhF4O+1Anh1s7OcO/ZU+MfZDLk3bqFa+U5I+7luTOJO3YAKLv5/dypE77z8EBWQoLGLzf1jUt9gynMyEBpYSEAIPXECY02nF+5EorCQvhNmYKA+fPh3qNHlfy4mhRlZeHarl04+tFH0l/aAKS/MtXXR30OV3ftkgK+Xc88g2+cnBC3Zg08+/UDZDKknTmDlPJ2FuXkSJ8fn0ozfmUVglX1dWvm5ISxmzZhfEQEfMeNg1v37mj/6KPIT03FHy+/jKMff4z+77yDJyIj8VR5svS1Xbt0nlu7Rx6BY5s2+OfAAdzYtw8O3t5ST1/WpUvY98ILOLd8OYK++AJPHj2KceVtvVrNPvVxYuFC7Js1C07t2mHshg14KSkJnn37oigrS6MHGdDyB0Idnf3xRwilEr1feQUPvvUW7Dw9q5QYqHxs9fsPCwuErFqF8RER6DZzJlw6d5Z6yt38/QEACeXvkUqplK6tLhZyOa5ERODv//5Xyk0D7n225HZ2sHFxkT5bSdHRKM7NBVCWl7fQzg77Z8+GjYsLnMsnt5wr74EWKpU0yaa6z5Y+940j8+dj34svovXQoZiwbRv+lZICOw8P3Ll6FVkXL2o9t8q9pNq0ffhhWNnbI37NGqgUCrQZOlTq4VK3XVs+mXQetfyMeD34IIB771XF/1e/V+r7S8XJGmknTxp8fPWxLm/ZgqLye3rK8eNIO3NG6m2Uq4Ol8t5YoOq9rOLxYMD5qj+73WbMwPiICIxasQL2LVuizfDhcKkwIaryHxD6fEebEvaUmTn10F362bNYO3Agiu/ckW4+pXl5AIA+c+bg4vr1OL9iBdLPnoWisBB3Ksx0AoBe//oXTi5ahLPLliH15Ekoi4uRcf48HNu0QZ85c2BlZ4f+77yDY59/ju2TJqFl//5o5uiI28eOoeTuXbQeMgTeDz2ksc9LGzciMz4eQqlExvnzaObkhF7/+heaOTqi50sv4eSiRVjduzc8+/TB7b/+QmleHpx9fQEAfd94Axc3bMCR99/HtV27kJ+SgpzERHj1749+b74J6+bN0WnSJCRs3oxfHnwQHr174/aff8LKwUE6bwBIP38eMfPnw75lS4z4/vsq759KocCF8hlyg7/4Ar1eeknj55tHjsT1fftwdtky+I4ZgwHz5iFi/HhEv/suru3cibzbt3E3KQmtHnoILTp1QmlBASzkcqgUCqwbPBi9X34Zvo89Bit7e5Tm5WFDcDCaP/AAbuzfr/U6nl6yBJnx8Ug/dw4Fqaka17Em/d56C/G//ILbx45h2QMPwKN3b+TduoWM8+chs7RE/3feka71me+/x7Vdu7CqRw9Y2dvj9rFjsHV1RbsRI2Dv5YWeL76IM0uXYv3gwWgVEICMuDgUpKbCJzgYHatJoG378MPw6N0baadO4adOneDo44Pk2NiyIezQUNi5u+P2n38i9eRJrOjSBa5du0pDnuqhPW1kFhboPnMmjn70EYCyG7e659LB2xvXdu/G3aQkJMfGwqldO6SW93xWt0/gXqK/NqPXrgVkMlz+7Tdc27MH3oGBKM7JQeqJE2jm6IiWAwYAAOzKk7hjP/kESYcPI6TS7Lnaal7+mTgyfz6u7d6NlL/+grK4GABQUv6Z0HbsDuPG4cq2bfjZzw8t/PyQHBsLZUmJNOTz4Ny5uLBuHc7//DPST59GaWFhjbMv2z/6KHyCgpB0+DBWdu+OVgEBUBYXS7/0+v/737C0skK7ESPg2bcvUk+cwIouXdCiSxckHT4MlUIhJcwHfvIJdkydisg33sClTZtQlJWFrIsXYefpiQHlQ+za6HPfKM3Px9UdO3ArOhotAwKQl5SEgrQ0OLRqhRZdutT2UkBuY4N2jzwiTQZqM2wYXDp1gnXz5ii+cwdO7drBo5qCw7X9jDz00UdIiorCyUWLkHT4MAAg7dQpWNnbY3B5gWp1MPXPgQP4bdw4lNy9i9Tjx7Ue/+7Nm9gwbBgCPvigSi2+TpMm4fjXXyPt9Gn87OcHN39/JMfGQiiVGDB/vtTj5NS2LXJv3MCmRx6BV9++uLZ3b5VAydbdHdmXL+PAq6+i/ZgxCK5mdrXag3PnYtuECdg9fTrOLl+O7EuXkJecjE6TJqFPhQleVejxHW1K2FNm5jz79MGwRYvg6OOD9HPnYOXgINVIUn+JW/bvjzHr18O5Q4eyQMvHB8PKiwjKra0BlPWiTD54EK2HDkXmhQu4e/MmfB97DE9ERkp/iQV+8glG/vQTvPr1Q3ZCAm4dOQL7li3Rf948TCif6VnRiO+/h9zWFtkJCfDs0weT9uyRbg7BYWEY8N57aOboiFtHjsChZUsM++Yb9H75ZQBlPXahO3bAq39/pJ44geKcHHR9+mmE7twpdaOPXL4cXZ9+GqrSUmQnJCDoyy/ROjhYow2FGRm4sm1blSBI7erOnShIS4OVgwO6VpqRCgC9y+voXNu1C3m3b6PDuHEYu2kT3Hv0wO1jx1Cal4duzz4r1SWysrPDQx9/DFtXV2ScP4+SvDw0c3DAo2vXlr3/586hMCMDj1UqddH7lVfQfdYsNHNyQsrx4/Ds0wc9X3xR4zrWxNHbG0/FxsJ/+nRY2dvjZmQk8pKT0W7kSDy+f7/0l71TmzZ4IioKbUeMQM7Vq8i6dAntHnkEk/bvh72XFwDg4e++w9DwcDRv3x63YmJgYWmJvm++iYm7dlWbpyGTyRC6cye6PPUUSnJzkXriBDz79kXozp3w7N0bMgsLTNyzBz2efx4qhQL/HDwIC7kcvV99FaPKexx06TZzZtnNXyZDt5kzpfXNHBzwRFQUOj/xBIqys5FUXk9r4Acf1Dx0KQSubNum9Z+qtBR958zBw99/D+cOHXArJgZ3rl5F2xEjMGnfPjiW54X1ffNNuHbtivzbt2sMbgwx6OOP0XHiRFjI5Ug7fRoPhIRIeZrqz4S2Y49eswa9Zs+GEALJsbFw6dQJY9atg++YMQAA9x49MG7LFrh06oSMuDg4tWlT43tvYWmJCb//joEffIDmDzyA28eOIe3MGXj26YMRS5ci4P/+D0BZ8Dxp7174z5gBRVERko8ehVu3bhi7YYNUx8xvyhSE7twJ78BApJ85g/zbt9Hp8cfxZGys9J7qUtN9I3jBAgR++insPD2RdOgQCtLS0HHCBDx+4IBeKQbVUefd2Xt5wbVLF1hYWsKn/H7ToYacvNp+RnwCAzElOhrtRo5EzpUryE5IQLtRozA1Jkaa8dpm6NCye46bG24eOgSntm0R9N//auzHpWNH9Jo9G82cnJB+5oxGT7qa3MYGkyMj0ee112BpZYWk6Gg4tW2Lh7//HoGffCJt9+ivv8Kte/ey3sdLlzD+t99gYWWlsa+ADz6AU7t2uHP1qlQtoCYdx49HyOrVZX9IxMRAqFTo/corCFm9utrX6fMdbUpkwlhZfNRgErZuRfbly3Dp0EEawji9dCn++Ne/0HXaNIyuUMXeGH5s1w65N25UmwtHREREhuHwZRNQkpuL6HnzAIEUyF4AACAASURBVJTlIVjI5dJMm5r+wiMiIiLzwKCsCeg2YwbuJiUhfvVqJMfGQiaTwblDB/R6+WV0aqoF9oiIiJoYDl8SERERmQEm+hMRERGZAQZlRERERGaAQRkRERGRGWgSif7Z2flQqeo3Nc7V1QGZmfoV+STT4DUxT7wu5ofXxPzwmpin+r4uFhYyuLjY6/x5kwjKVCpR70GZ+jhkXnhNzBOvi/nhNTE/vCbmqSGvC4cviYiIiMwAgzIiIiIiM8CgjIiIiMgMMCgjIiIiMgMMyoiIiIjMAIMyIiIiIjPAoIyIiIjIDDAoIyIiIjIDDMqIiIiIzACDMiIiIiIzwKCMiIiIyAwwKCMiIiIyAwzKiIiIiMwAgzIiIiIiM8CgjIiIiMgMMCgjIiIiMgMMyoiIiIjMAIMyIiIiIjNQr0FZXl4exowZg6SkJADA0aNHMXbsWDzyyCMIDw+Xtrtw4QImTpyIkSNH4v3334dCoajPZhERERGZnXoLys6cOYOpU6fi+vXrAICioiK89957WLJkCXbt2oXz588jKioKADB37lx88MEH2Lt3L4QQ2LhxY301i4iIiMgs1VtQtnHjRnz44Yfw8PAAAJw9exZt27ZF69atIZfLMXbsWOzZswe3bt1CUVERevXqBQAIDQ3Fnj176qtZRERERFVYXrwApKQ0aBvk9bXjzz77TGM5LS0N7u7u0rKHhwdSU1OrrHd3d0dqaqpBx3J1dahbY/Xk7u5okuOQ/nhNzBOvi/nhNTE/vCbm465fdzheOo+ozoOx6un5mBbSBUP6tjZ5O+otKKtMCFFlnUwm07neEJmZeVCpqu7HmNzdHZGefrdej0GG4TUxT7wu5ofXxPzwmpgHWd5duLX3hjo8Xh34NNKzC7F442nk3i1CgL+XUY9nYSGrtiPJZLMvPT09kZGRIS2npaXBw8Ojyvr09HRpyJOIiIioPsjPnYFbe29pefyczUhr7gkAKFGosDUq0eRtMllQ1rNnT1y7dg03btyAUqnEjh07EBQUBG9vb1hbW+PEiRMAgIiICAQFBZmqWURERHSfsVm+FC7DBwMAojsNwtg3I6C01Bw8zMwtNnm7TDZ8aW1tjS+//BKvvvoqiouLERwcjFGjRgEAFixYgPnz5yM/Px9du3bFtGnTTNUsIiIiuo84jx0Jq2OxAIDc75fjp1RvQEsA5upkbeqmQSa0JXU1Mswpuz/xmpgnXhfzw2tifnhNGkBBAdzb3csRy/zzFFTtfREbl4JVuy+iRKGSftZMboHpIX4mzykzWU8ZERERUUOwvBCPFsEDpeX0m+mAdVlPmDrw2hqViKzcYrRwskZosK/RAzJ9MCgjIiKiJstm9Qo4vj0HAFD8yCjk/lK1QH2AvxcC/L0avAeTQRkRERE1Sc0nj0ezQwcBALmLlqB46tMN26AaMCgjIiKipqWoCO5t7pXXyjryN5SdOjdgg/TDoIyIiIiaDMsrl9Hiob7ScvqNVMDWttrXxMalmEVOmcnqlBERERHVJ+sNv0oBWUlgENLTcvUKyFbtvojM3GIIlNUnW7X7ImLjTP8cTAZlRERE1Og5zXgKTq++BAC4++XXuLN1h16v2xqVqFEOA2i4iv4cviQiIqLGq6QE7j5u0mJW5FEo/bvp/XJdlfsboqI/e8qIiIioUbK4fk0jIEu/dtuggAzQXbm/ISr6MygjIiKiRsd621a49u8JACjt07csf8ze3uD99PB1NWh9fWJQRkRERI2K4+zn4fT8DABA3n8+R86eyFrv62xipkHr6xNzyoiIiKhxUCjg3qqFtJi97xAUvfrUaZfMKSMiIiIygEXSTY2ALCMxqc4BGcCcMiIiIiK9Ndu9E659/AEAii5dkZ56B8LRySj7Dg32RTO5ZjjUTG6B0GBfo+zfEAzKiIiIyGw5vPUamk+fCgDIf+//kB31JyCTGW3/Af5emB7iB1cna8hQ1kM2PcSvQSr6M6eMiIiIzI9SCbc2HpCVlgIAsnfuh+LBAfVyqAB/LwT4e8Hd3RHp6Xfr5Rj6YFBGREREZsUi5TZce9x7gHhGwg0IZ5cGbJFpcPiSiIiIzIbVwf1SQKZs064sf+w+CMgABmVERERkJuznvwPnKRMBAPlvzkXW8bNGzR8zdxy+JCIiooalUsG1U1tY5N4BAOT8thOlgwY3cKNMj0EZERERNRhZejrc/O+Vn8i4cA3C1fSPODIHDMqIiIioQVhFR8F54lgAgMrNDZlxibUeroyNS8HWqERk5hbD1ckaocG+DVLWoi6YU0ZEREQmZ//Zx1JAVvDSK8iMv1qngGzV7ovSo5Eyc4uxavdFxMalGK29psCeMiIiIjIdIdCid1dYJt8CAORs+A2lQ4fXaZdboxJRolBprCtRqLA1KrFR9ZYxKCMiIiKTkGVnwa1zO2k549xlCE/POu/XkIeKm/MwJ4cviYiIqN7J/4yVAjJha4v0lByjBGSA/g8VN/dhTgZlREREVK/sFnwJl8dGAgAKZzyHjBupgIXxQhB9Hype3TCnOeDwJREREdUPIeAyqB/kVy4DAO6s2YCSkSFGP4x6+LGmYUlDhjkbAoMyIiIiMjpZ7h24dWgtLWeevgBVK+96O576oeIVVc4fs7exRH6RssprdQ1/mhqDMiIiIjIq+cnjcBk1TFpOT84C5KYNOdT5Y+rhyszcYsgtZbCUAUpxbzttw5wNhTllREREZDS23y6SArKiJ55EelquyQMyQHv+mEIpYGsjl3rGXJ2sMT3Ez2xmX7KnjIiIiIzC+eEgWJ09DQC489NqlIwd32Bt0ZUnlleowDdzgkzcGv0wKCMiIqK6ycuDe/tW0mLm8XNQtWlr0iZUzh9zsJUjr1BRZTtzyR/ThkEZERER1Zr83Bm4DB8sLaffygSsrEzaBm35Y5YyQG4pg6JCApk55Y9pw5wyIiIiqhWbn36QArKixyaU5Y+ZOCADtOePKQVgbWVhtvlj2rCnjIiIiAzW/LFRaPbnUQBA7pJlKJ70RIO1RVf+WH6REotfDzZxa2qPQRkRERHpr6AA7u3u9TZl/XkSyvYdGrBBZb1g2gIzc84f04bDl0RERKQXy4sXNAKy9JvpDR6QAfo/ZsncMSgjIiKiGtmsWYkWQQMAAMUjRpblj1mbR09UgL8Xpof4Nar8MW04fElERETVaj4lFM0O/gEAuBv+LYqemtbALapK22OWGhsGZURERKRdcTHcW7tLi1nRf0HZ2a8BG2SYyrXLtD2k3JwwKCMiIqIqLBMvo0VAX2k5/UYqYGvbgC0yjLbaZat2XwQAsw3MmFNGREREGqw3rpMCspKHAsvyxxpRQAZor11WolBha1RiA7WoZuwpIyIiIonTzGdgvWMbAODuFwtQ9NwLDdyi2tFVu0zXenPAoIyIiIiAkhK4+7hJi1kHY6Ds1r0BG1Q3jbF2GYcviYiI7nMW169pBGTpV5MbdUAGNM7aZQzKiIiI7mPW27bCtX9PAEBpr95l+WMODg3cqrprjLXLOHxJRER0n3J8+QXYbFoPAMj78FMUvvya3q9tyHIT+h67sdUuY1BGRER0v1Eo4N6qhbSYve8QFL366P3yhiw30RhLXeiLw5dERET3EYtbSRoBWUZikkEBGdCw5SYaY6kLfbGnjIiI6D7RbM8uNJ82BQCg6NQZ2dF/ATKZwfsxZbmJykOVjbHUhb7YU0ZERHQfcHj7dSkgy583H9lH/q5VQAboLith7HITsXEp+HlHvBRwVRd4mXOpC32xp4yIiKgpUyrh1tYTspISAED2jv1Q9B9Qp12GBvvi5x3xUIp76yxlMHq5iV/3X9I4hi7mXupCXwzKiIiImiiL1BS4du8kLWdcug7h0qKaV+hPZiFDxYhJZqF/r5u+syfzi5Q696EeymwMDxrXF4MyIiKiJsjq4B9wnhIKAFC2boOs4+dqPVxZ2daoRCgqdWEplAJboxJrDI6MNXsybPYgA1tt/phTRkRE1MTYf/CuFJDlv/42sk6cN1pABtQt0d+Q2ZMOttr7jnStb+ya5lkRERHdj1QquHZ5ABbZ2QCAnK07UBoYZPTD1OW5koYEdFMf7oQVuy5o9MrJLWWY+nCnKts2BQzKiIiImgBZRgbcuraXljPir0K4uVXzitoLDfbVGIIEypLte/i6Yu6SmGpzvaoL6LTlmj07ukuDPTnA1BiUERERNXJWRw7DOXQMAEDVogUy468CFvWXoaQOiioGSz18XRFzLqXGXLHqAjptuWbTQ/yaZP6YNgzKiIiIGjG7z/8D+4ULAAAFL76M/E++MMlxKz9Xcu6SGJ25YhW30xbQhQb7Vptr1lR7xipjUEZERNQYCYEWffxheSsJAJCzfitKhz3cYM2pLldM25Bm5UBr2e/xBu23KWJQRkRE1MjIsrPg1rmdtJx5LgEqz4btTdLnEUjVlb+oy+SBpoIlMYiIiBoR+bE/pYBM2Ngg/XZ2rQOy2LgUzF0Sg5lfHsTcJTGIjUupdbtCg33RTF5zWKGr/IW21zeVSv36Yk8ZERFRI2H3v//C/stPAQCF02Yib8FCvV9beWajvon5+tKWK2ZI+QtduWb3Sz4ZwKCMiIjI/AkBl8AHIb+cAAC4s3o9SkaN1vvl2qroR55KrrJdXRPrtSX/GzIkqS3X7H7CoIyIiMiMyXLvwK1Da2k581Q8VN4+Bu1D28xGXYyZWK+r/IWuIUl9n4nZVDEoIyIiMlPyUyfgMnKotJyenAXIDf/VbUigZczEekOGJI31TMzGjEEZERGRGbJdshgOH70PACh6fArufvdjrfdVXX5XRfWRWK/vkCTrlHH2JRERkdlxfiRYCsjuLF9Vp4AM0D2zcWjvVlLPmKuTNaaH+DVYAFSXh5w3FewpIyIiMhd5eXBv30pazPz7LFRt29V5t6aa2ViXnDDWKWNQRkREZBYsz51Fi+GB0nL6rUzAyspo+6/vmY11zQkzdFJAU8ThSyIiogZm89MPUkBWPGYc0tNyjRqQmUJ1OWH6CPD3wvQQP7MZTm0I7CkjIiJqQM3Hj0azo0cAALnf/oDiyVMbuEW1Y4ycMNYpM7Ft27bhxx/LEhaDgoLwzjvv4MKFC5g/fz7y8vLQr18/fPzxx5DXYsovERFRo1FYCPe2ntJiVuwJKH07NmCD6oY5YXVn0sinsLAQn332Gfbs2QMnJydMnToVR48exeeff45PP/0UvXr1wnvvvYeNGzfiySefNGXTiIioiWqogqTVHjc+Hu7+/tK2O/aex+a9N5GZe7PRFk1lTljdmTSnTKlUQqVSobCwEAqFAgqFAnK5HEVFRejVqxcAIDQ0FHv27DFls4iIqIlSJ5+re3DUyed1efB2XY9rs3Y1UB6QlQx7GNsjE7DiwFWTt9HYAvy9MKi7FyxkZcsWMmBQ9/t7ONJQJu0pc3BwwJw5cxASEgIbGxv0798fVlZWcHd3l7Zxd3dHamqqQft1dXUwdlO1cnd3NMlxSH+8JuaJ18X83K/XJOJIrNbk84gj1/DYkPobKtR13LYvPg3HhL/KVixbhmazZiHi030N0kZjO3TiJo6eT4VKlC2rBHD0fCr6dPHCkL6tq3+xGWnI74pJg7KLFy9iy5YtiIyMhKOjI95++23ExMRU2U4mkxm038zMPKjUn4J64u7uiPT0u/V6DDIMr4l54nUxP/fzNUnPLtS5vj7fk8rHlStK8ds3j99bcf480j3aAOl3G6yNxrZyRxyKS5Ua64pLlVi5Iw7+bZwbqFWGqe/vioWFrNqOJJMOXx45cgQBAQFwdXVFs2bNEBoaimPHjiEjI0PaJj09HR4eHqZsFhERNVG6kszrO/m84v5bZidrBGTp11Ok4cvq2tLYEuRZkb/uTNpT5ufnh7CwMBQUFMDW1hYHDx5E//79sXfvXpw4cQJ9+/ZFREQEgoKCTNksIiJqohoq+Vx93IBzkXh7dzgAIM7HH3u/WIGzK08hK7cYLcoT+uujjfpObjDmJAjOvqw7kwZlgYGBiI+PR2hoKKysrNC9e3e88MILGDFiBObPn4/8/Hx07doV06ZNM2WziIioiTLV44W0HbfvR3PgHbUbAPBLyEtIe3ImYs6lVKl4Pz3ED9ND/IzWRn0r69e1An9lnH1ZdzIhRP0mY5kAc8ruT7wm5onXxfzwmphYaSncvV2lxawDR6Ds3gNzl8To7EkKmz1Ir12v2XsRUaeToRJlsxuDe7XCMyP9NLbR9zjGaE9lDVV+xFgaOqeMFVqJiIiMxOLGdbg+2ENaTr+aDDiU/RKua87Vmr0XEXkqWVpWCUjLFQMzfY9THzlg93tF/rrisy+JiIiMoNn236SArLRHr7LnVzrc6xWpa0J/1Olkvdbre5ymMsGgKWFQRkREVEeOr7yI5rOmAwDy/u8T5PxxuMo2ocG+aCbX/LVrSM6Vriydyuv1PU5d20PGx+FLIiKi2lIo4N6qhbSYvTcSit59tW5acdJBxdmXAf5eeuViWci0B2YWlUp76ju5IcDfC1eScjRy1FiBv2ExKCMiIqoFi+RbcO3VRVrOuHITwql5ta9R51xVTCjXdxZkcK9WGjllFdfrOk51YuNScLg8IAPKAr7Dp5PRwceZgVkD4fAlERGRgZrt3S0FZIpOnZGeeqfGgEyXrVGJWh+ztDUqUWPdMyP9MLR3K41nSw7tXXX2pb5+3X8Jyko9b0pRtp4aBnvKiIiIoH85B4d/vwHblT8BAPLfeR8Fb71Tp+NWNwtSXbZC3Z5nRvrVOgirLL9IadB6qn8MyoiIqFEyZk2s2LgUrNh1AYryrqPM3GKs2HUBQIUhRJUKbu28ICsqAgBk/74PigED63weuirhq9uh/m9dCrtS48DhSyIianTUeViVg5bYuJRa7W/dHwlSQKamUAqs+yMBACBLTYW7l7MUkGVcum6UgAzQPgtSG21DmnVhbSUzaD3VP/aUERFRo1NdHlZtepLyChU611tFHoDzExMAAEpvH2SdjANkxgtctM2WNKSwa2Ovok/3MCgjIqJGpz6q0WszM+pnOP9vOwCg4LU3kT//I6PuX63ybMnqHoFUUV2eX1lcqr3wma71VP8YlBERUaOjqzepttXo7W0sNRPchcAvS6ejeWEuACBn6w6UBgbVat+1oe/DvXX1GK77I4G9Z40QgzIiImp09A1a9PXkiM74eUc8lAJwKriDtUunSz/LiEuEcHc3eJ91GVbUtwCsrp7BvEKFNCSrq/fMwVauddjWwZahQUPhO09ERI2OvkGLofuLX/kb5q0qK3FR4tgcdy7fACwMnxNXl2HFim2qaVtdVf4r05ZvN/XhThozTgFAbinD1Ic76dU+Mj4GZURE1CjpE7QYYvj2H/HYqgUAgIIX/oX8T7+q9b6MPRFBF30CMrXKvWrGDmyp7hiUERFRk1bjMKIQaNGvOyxv/gMAyFm/BaXDRtTpmIZORKjtUGd1MzW1bVuZsQNbqhsGZURE1GTVNIwoy8mGW6e20vaZ5xKg8qx7kGLIRARdbbySlIOziZkagRqg2bPl4WKr9TgWMhlU4l43Wl3y7ch0GJQREVGTVd0w4uD8G3AZU9YjJqytkXE9BbC0rHGf+vRqGTIRQVcbKz58XP2EAaVSQFRYp6uXTG4JONpZc1iykWFQRkRETZauoGX4/l/g8tFaAEDhM88i7+tFegVb+ibwG5Kvpe/wY+UnDlSnRCEQNnuQ3tuTeWBQRkRETVaVsg9C4LvVr6FN5k0AwJ1V61AS8qjewZYhCfza8rXUgV9WbjFalAdqVWqk0X2LQRkRETVZokJelW1xATZ+96S0nHkqHipvHwD6B1t1eZKArsBPZsRHNqmx1ljjxKtGRHQfaerPSax8fuoeqA4pVxD+69vSduNe34Ll5QEZoH+wVZcnCegK/IyNtcYaLwZlRET3CWMUNNW2T3MJ8rSdHwCMO7ENs6JWAAAiuwTjfyFvVAmi9A226vIkAUOfy+lgK4e1laX03vbwdUX02dtVir0O7tGyyizNphRo308YlBER3SeMXdC0PoK8utB2fgt+nYvOKZcBAF89+jaOdA7UGkTpG2wF+HvhSlIOok4nQyXKKuoP6q5frS9dgZ+9jSVKFaLKsac+3KnKfjv4OJtNEEzGx6CMiOg+UZd8KG1MVbVeXxXPw6akEJu+nSotvz1nBS5ZuugMZPSdLRkbl4KYcylSJX2VAGLOpaCDj3ON56wr8HtyRGe9jq1uJ4OwpotBGRHRfaIu+VDamKpqvb7U59cu/RoWr3lDWv/c/O348rUhNb5en4CnLoFoTb1sDLbI8KesEhFRoxQa7Itmcs3bfl0qvVvomDSobb16qFMdsKmHOmPjUmp1bG1Cg30x9tweKSA72mEgJv57O8YN9zPaMeo6+1JbL5sx3wNq3NhTRkR0nzCkoKk+vVq6Hoatbb0phjpHfTALzY4cBgD8b9QcnB04CtON3BtnIdN+froC1IrMbbiXzA+DMiKi+4g+Q3T6JvAbMhxq7Hw2DYWFcG/rKS1mxZ7AM74d675fLQwJRCur1/eAmgQGZUREpEHfHh1DykMYO59NzTLhEloEPigt79h7Dpv3JiEz92a95q1pW1+X15pTaRFqOMwpIyIiDfr26AT4e2F6iJ8UkLg6WWN6iJ/WYMLY+WwAYPPrGikgKxk6HNsjE7DiwLV6z1vT9zxi41Iwd0kMZn55EHOXxKCHr6vW1/bwda33fDtqHNhTRkREGgzpDdK3RIMh+Wz6cHp6Mqz37QEA3F2wCEXTnsXWJTH1nrNlSOmMykPAMedSMKi7F84mZmo8+5K5ZqTGoIyIiDTUpWp9dYxSY6u4GO6t3aXFrMPHoPTrAsB0OVt1KZ1xNjETYbMHwd3dEenpdwEAy36P17oP5prdfxiUERGRBmP3ahmL5dUraDGwj7Scfj0FsLOTlusyM9LYDAkQ6yvfjhofBmVERFSFuVWOt968AU6znwcAlA4IQM7ve6tsU5eZkcZmSKBVXz2T1PgwKCMiIrPm+PwM2GzbCgDI++wrFD7/LwBVa6k52MqRV6io8vqG6HEyJNAy155JMj0GZURE1GCqLQVRWgp3b1dp26wDR6Ds3kN6XeVEeksZIJMBokLPmKUM6OHrirlLYmoV8NS2VIWhgZa59UxSw2BQRkREJlE5wOnh64qYcylVitReScpB8vF4LFg0Q3pt+tVkwMFBWtaWSK/UMkwpIMPh08nSzyoe42xipsEzKLUV0dWFgRYZikEZERHVO20BTuSp5CrblShUKN6wGQt2/BcAkOjRHv+esRDTb+QhwP9eUKbvzESVqBqplShUGsfWFagVlShYqoJMikEZERHVO209W9rM2fsNHo47CABYGTgNW/qHAloCIV2J9LWlLVDThaUqqL4wKCMionpXUyBjoVJi28KJ0vJbU/+LhJaddL5eWyK9qbBUBdUXBmVERGaiKT//sLqeLde7GVi5bJa0/MTLa1FgbV/l9RVpS6SvnKNWH1iqguoTgzIiIjNQ16Ryc6erRMTTisuYsOwtAMBtVx+sC98CxflUQM9SEpXfmw4+zhqBWl2HGq2tLOFgK2+SgTKZHwZlRERmoKk//1Bbz9aHp9bggW2/AgDy574L+dx38QwAyGSIOp0MlSirxj+oe9lr9SlrUTlQW7P3otYJBfqSWwJhswfV+vVEhmBQRkRkBkz13MaGHCKVAiaVCm7tW0FWUAAAyNm+B6UDH5LaF3MuRarCrxLA4dPJiD57G4ryuhaG9CI+M9IPADSCPEMq/OcXKQ05RaI6YVBGRGQGTPH8Q3MYIpWlpsKte0dpOePiNYgW9wrE6qw/VqkImSG9iM+M9JOCMwCY+eVBvdvLpH4yJQZlRERmwBTPP6yPIVJDet6sIg/A+YkJAABly1bIOn0BsfGp2Lo+plY5YLXtRdS3t4xJ/WRqFg3dACIiKuupmh7iJ/XMuDpZY3qIn1F7sIw9RKrueVO/Xt3zFhuXUmVb+w/flwKyglffQNaZi4iNT63yekPUthcruFcrreu7tHWu1/efqCbsKSMiMhP1/VgeYw+R6tXzJgRc/TvAIiMdAJCzeTtKg4bofL02MgDaOrZ6+LpqWVszbXlmwb1aaQxxEjUEBmVERPcJXUOktX1gd009b7LMTLh1eUBanxGXCOHuXuPrgXsBpKuTNYpLlcgrVFTZ5mxiZo1t1KVynhmROWBQRkR0n9Cn4Kohyf/2NpZaZyfa21jC6ugROI8fDQBQOTsj8+J1wEIzY6a6nruKZSh0JebzcUfU1DAoIyK6j1QeIp27JEbrEOS6PxJqTOCXyWRajzE56hc4f74BAFAw60Xkfx6mdTt9JzeYYmYqkTlgUEZEdB/T1duUV6iQhgx19Z5VGVIUAst/ehGeuWkAgDvrNqNk+CM6j62t505b8FfXmalN+fFV1LQwKCMiMmP1HVDoW4ZCW+mMiq+1L8rD+iVPSz/LPHsJKq+WGvvQdS41nY++wZs25lCbjUhfLIlBRGSmDCk5UVuhwb5oJtfvV0Hl4E392s7JF6WArNRCju1/XNAakNX3uWhT3QxRInPDoIyIyEyZIqAI8PfCoO5esChPD7OQAc3k2nPFKudwBfh74ZOsQ1iwfh4AIKrPKOw+EI+AHt5VXluXc6lLQGeqx1cRGQOHL4mIzJQpAgptz5pUCcBSpvlkoyo5XELAZUgA3C/EAwDurPwVXUeP0XmcupxLXZ5EwEkC1Jiwp4yIyEzpChyMGVBoC3gUSgFbG7nO6vayu7lw92wOeXlAlnkyDiXVBGTVtVmfc6lLQKdteJaPTyJzxZ4yIiIzVR/Pw6ycbF/d7EtrK8sq6+WnT8LlkSHS8u9/xGPL9hvIzL1cbQK+IedSuY266qHpE9DVZZIAkanJhBB6PJbVvGVm5kGlz9Nl68Dd3RHp6Xfr9RhkGF4T88TrYlzGmH2pviaVZyIaQm4pw5xbkRjy68KydnUfBUywzwAAIABJREFUgth//1ej8CxQFmjpemakPueirY1ySxmESlQZTh3U3QtnEzMbZbDF74l5qu/rYmEhg6urg86fs6eMiMiMGfN5mPo+a1KbL9bMhV9KAgDgv6PfQrTfYOBUcpXtqsv10udcdA2nOtjKYW1laZQnERCZKwZlRERNgD69ULWZIGBdWoTNi6dIy7NmLkWqc/VBT10mIlQ3nPrNnCBpWdeTCPRJ/icyVwzKiIgagDGLwupbIFXfQrFq7dKvY/Ga16XlCXM2QWFpVePr6jIRQd/Zkix1QU0RZ18SEZmYsQup6lsDzJBCsSFndksBWazvAIx9M0KvgMxSBp0TEWLjUjB3SQxmfnkQc5fEaD1ffWdLmmJmKpGpsaeMiMjE6lJ3Sxt9e420zUTU9tpPN32AnjfPAQDCR76Gg/7DIEPZA8hVFeaGWchkEBCoOF1MZqG98Ky+vXmmeh4mkTliUEZEZGLGHnozpEBq5WT7WV8dlArHNistxpbFT0g/m/fKMsQ1c5cS6w+f1kzsV2mZvK9QCq3BpSGBaH0/D5PIXDEoIyKqZ5Xzx6ytLFFcWrXuloNt1VuyPrlnPXxdEallJmQPX9ca2xbcqxUiTyXDJ/Mmvl/1qrR+0U/ReGtsT2l57pIYjZIU1dEWINZHDpgxZ6YSmQMGZUR03zJmsn11x6g8bKdL5bKRsXEp+GnHBalHKjO3GD/tuABAc8jvbGKm1v39dSEVZxMzkZVbjBY6zu+ZkX7wP/w7Rq76BABwqm1PRH66DM+M9NPYzpDgSVsPHR93RFQzJvoT0X3J2Mn2uhhSG6xy1frVey5VGSJUCYHVey5prNMVMOUXKZGZWwwB3efn9MwTGLmsLCC7G7YQPn9HVwnIAP2DJ115XXzcEVHNGJQR0X1J3xmLdVWXHiZtQ5za1uvIra9C4/yKi+Hu4QTrvbsBAFmHYlE0fabO1+oKqob2bqXzGZkVBfh7YXqIn17bEt2vOHxJRPclU9W50rc2WF16jQx5ylxmbjEsribCdWBvaV369RTAzq7a1xkjsV5bDpgphpCJGgsGZUR0XzJVjpOu0g3antsIlCXUq9fpy5CisEMuRsF1YDgAoPTBAcjZuV/v4xg7sV7fMhlE9wuTB2UHDx7Et99+i4KCAgQGBmL+/Pk4evQovvjiCxQXFyMkJARvvPGGqZtFRPcZU9W50reHSVuAYlGpLpja0N6tajwXbebuXICgS0cAAD8OeQ5Hh0xCaFxKnQKguvR0GbteG1FjZ9Kg7ObNm/jwww+xadMmuLq6Yvr06YiKisKHH36INWvWoGXLlnjxxRcRFRWF4OBgUzaNiO4zpqxzVdsHcWsLyGQyoIOPc5X9q/ehPpfiUiXyChUAAEulAhGLJknbv/7U10j09AXq2DNV154uPiqJSJNJg7L9+/dj9OjR8PIq+7KGh4fjxo0baNu2LVq3bg0AGDt2LPbs2cOgjIjqnTnVudI3EBECWPdHQo0FV9UBU/OsFPy8/AVp/eRX1qGwma20XJeeqbr2dLFMBpEmk86+vHHjBpRKJZ577jk89thj+PXXX5GWlgZ3d3dpGw8PD6SmppqyWUREDU7PCZQAIPWAVSfA3wvTiy5IAVmiR3uMfTNCIyBTq23PVF17ulgmg0iTSXvKlEoljh8/jjVr1sDOzg6zZ8+GrW3VG4RMZsjtCXB1dTBWE6vl7u5okuOQ/nhNzBOvi6ZDJ25i9e4LyMguhJuLLaaFdMGQvq01tjFgAiWAmt/j2+Om4LHtGwAAKwOnYUv/UN37crGt1TVzd7FFenZhrff32BBHODna1PjeNFX8npinhrwuJg3K3NzcEBAQgBYtWgAAhg8fjj179sDS0lLaJi0tDR4eHgbtNzMzDypD5oTXgru7I9LT79brMcgwvCbmiddFU+W8q/TsQizeeBq5d4tqPXRqb2Op+z1WKuHe0gUtyxffnvIVLrXqrHNfzeQWGB/4QK2u2fjAB7ROljBkf/5tnPHViwEa6+6Hzw+/J+apvq+LhYWs2o4kkwZlQ4cOxTvvvIPc3FzY29sjOjoao0aNwo8//ogbN27Ax8cHO3bswMSJE03ZLCIio6k8G7G4VKlX3pWDrVyvYUlLGfDkCO1BlsXtZLj2vFeNf8rsX5BvU/UXgDqXq66TG/hQcCLjMmlQ1rNnT8yaNQtPPvkkSktLMWjQIEydOhXt27fHq6++iuLiYgQHB2PUqFGmbBYRkVEY8pzLyj+b+nAnrNh1AYoKT/2WW8owuEfLKvXMtAU9zfbvQfOnJgMAFO19MeuZxci/W1JlO1cna4TNHqTXuegTbJnTZAmixs7kdcomTZqESZMmaawLCAjA9u3bTd0UImqijF0lXt/9GfKcy8ozDHX1OgG6Hziu5vDu27D96UcAQP7b81Dw7/cQWilABPRPomdRV6KGwYr+RNSkGDugMGR/+s461Dc4upKUg5hzKbqPrVLB1dcHFvl5AICcbbtRGjBIo21boxKRlVuMFgYEpyzqStQwGJQRUZNi7IDCkP3pqrtlb2MJm2Zygyv6R55KrrIv9bEfcreAW7cO0vqMi9cgWrhqbKseWjQ0eZlFXYkaBoMyImpSjB1QGLI/XY9uenJE5xofxK1tQoAurc8dg9tHIQAApacXss5eKiv1byQs6krUMExaPJaIqL7pChxqG1AYsr8Afy9MD/GTfubqZI3pIX46e8XUgU9mbrFeMy8BYMbhlfhky0cAgIKX5yDrXIJRAzKARV2JGgp7yoioSTH2g8Z17c/DxRazvjoIlQAsZEDw/7N35wFR1P0fwN97cMkhinhlWqGJmWiZmelPvFLxyMJK09S0zC41e7LMx86nUtMyO+z0tqwsjyQxzQMVKcs0lbSUvAUFBJZz2WN+fxgbCzO7s9fsAO/XP487zM5813Hp/XyPz7dTc4wZECu6GtGTXjEbQcCKj8ejQUk+ACB/zQaY4nu79ZmcYakLIv9gKCOiWqVb+6Y4cS4fKQcv2AJT9w7yyzaIrbTs3qGp3fUa1Q/C0dP5tvdYBdjmf40ZEFvtenLLZEhpqTfigzdH2F7npGdAqLQ9nS+w1AWR8jh8SUS1Slp6FlIPZ6Fikw+rAKQezkJaepas91YdVlyS9Ad2H8q0u96F3OpbCwFAysHqE/NdKZMhptvlv2yBzBpRH9lZ+T4PZETkHwxlRFSrOFot6c57LQLsCro6Irbbm9yeMb1OA12VqWFjfvoSM5c9CwAoeXgSck+cBbT8tU1UW3H4kohqFU9WX/qi5ENosA7FZZZqxwP1GoTXC6xWKHZtSgZyC8qweNljaJx3pXev4Is1KO83wOttIyJ1YSgjolrFk3IOUu/1hEZiZWRggE50u6PbWwSjUZuWtte5h/6EtWmzaucRUe3DfnAiqlU8Kecg9l6d5srQohxiwU+q1IXYcf2v+2yBTNDpkJ2Zh9RcDaYvSsWEOdsxfVGqrLlxRFQzMZQRUa0it1aY3PdOGHIDxg9qZ3es903NZQc/uXXOQt59Gw0G9QMAlI4ei5zMPKQdy6628GB58jEGM6JaisOXRFTreFLOQeq9VY+1bhEpq46X07ppgoAGvbtD/8cRAEDBklUoH3InAO5BSVTXMJQREfmQo0KsmqJCNLruKtu5ufuPwHp1pflk3IOSqE5hKCOiGkOssKsSPUZV7xsXE4XdhzJtpTJyDUYs3XQUQPUetYpjVY/rfz+ABnfE215nX7gM6O1/JXMPSqK6haGMiGoEscr4y5OPARAPQr68b0X1/srMFgGrf/xLtC1VQ90z2btx4wdvAADK7h6Owo+Xit7b21tGEZG6MZQRUY3g6fyqlT8cs9sqqWKvSnfuK0VsRWXVUPfcx0+hXeafAADDR4thTLxX8nrcg5KobmEoIyK/kxOYPJlftfKHY3a9W472qnTn+lXvVfmzBOg1KDcLCDKV4Zv3RtrOe3byYkx3EMgq1MQ9KP01zExU0zGUEZFfyQ1MUvOrwkL0mL4o1WEAENuTsuK4s1Cm1YhvnyRGA1T7LEaTgFbZp/D+yqdsx++eugZmXYC8i9Yw/hpmJqoNWKeMiPzKUWCqTKywq16nQWmZ2WkdL6lQJSdsyQ1kACB26sDfN9sC2U8xt2Lo0+th1gXU2sn6nuw9SlTXsaeMiPxKbmASm19VVm6utq+kt+t4ebL10qvfvISbzvwOAHhnwGRsa9/X9rO4mCivtE9tWMaDyH2yQ5nFYkV6ei7i4qJ92R4iqqEq5hFdNhjR0IV5RFLDg1qRnY2qzq+aMGe76DW9GQDEVkA6E2Aux9p377O9fuzB93GuYQu7cw5l5HqtjWrCMh5E7nMayubP/wWCIODUKQM++uh3PPnkTVi4sI8SbSOiGsKTeUTxnZqLlpiI79Tc6X3lBgCp4KcBnM5HE+uhi4uJQurhrGqlKmKuikDhgSP4cNmTtuOJU76GSR9Y7d61teeIZTyI3CcrlH344R2YN+8XrFw5CJMnb2MoIyI7rpSrEFuZB8CtchVyA4DUEKkAVJuPBlQPkmIrIMW2Wep1eBsi/glkB1p2xMv3voIAvQYwVW9Abe05YhkPIvc5DWWFheUIDQ1AeHggOnWKRnm5xdlbiKiOkTuPSKpHbVxCrKwQVpXcACB3Xli52YrVP/4lK1BUDWoRY+9H0ObvAQDLBz+Jb9r2Q1S4dK9abe45qollPIjUwGko69atOYYNW48nnuiEl17ai379WinRLiKqQcJC9KKFU8NC7H/F+GKDbTkBwJV5YUWlZttnyTUYsSTpD9t9RJWXI7pFI9vLaePfxYkGLW3vTz2che4dmuJQRi57jojIIaeh7Jtv7sSuXecwaNB1SEk5i1tu4S8SIrInCOLjg1WPe7oyz92ipHJXboqxCMAXW/8UvY/25N+I6trJ9vqRmeuQWWa/QqHcbMWhjFzMe7y703sRUd3mNJQJArB9+xm89tpPGD/+RjRsGIybbmqiRNuISKWqhiOpcFP1uCcFYD0tSlq1R61q0VpHxD5f0No1iHj0IQCAqXMX5CdvQ6YCq0GJqPZyGsrGj9+MkhITjhzJwaFD2fj4499x8OA4JdpGRCokFo6kVJ3Mnhgfg6WbjsJs+bcHTau5UgC28pDh8uRjOHEu327Ir6zc7NWhT09KUoQ/OgHBa78BABS9+gZKH70yuZ/lIIjIE04r+u/YcQZLlw5EUJAOo0a1w4kT+Uq0i4hUSu4G3ToNRCezC1WWQloFAZYqo5/lZit2HLhgtzJSqjfO3V4oV95nmxtnMiG6cYQtkOVtTbEFMkB814HaPqmfiLzHaU/Z9dc3wLRpO1BebsFbb/2K9u1rZxVqIpJHbpjRiFR/XZuSUS2AeSo0WOd06FOM3BWZep0G9/e7HtqzZxDV+Ubb8Zy/z0MIC7c7l+UgiMgTTkPZ0qUDMXHiFgDA2bOFWLZsoM8bRUTqJTfMmC1CtaFFb8+t0mkAo8mK4jLntcaqkqpxJrZSMv7UL6g/fvSVz9W+A/K27wE0IlsOgOUgiMh9TkPZkiVHsGHDXWjcOFSJ9hCRyrlSXqJqCPNkH0ngyjBiUIDOFpiMJku1Uhxy55nJ7dUKm/YkQj5fAQAo+u9LKJ36H7fbT0TkiIySGH9h4sQ4hjIiAiAeZsTCEVB9VWVcTBR2Hbzg1hBmxTCiN/e+dNirZbGg0VVR0FivhM+877fC3KWra40mInKB01AWERGIrl0/R+vWkQgOvnL63r2jfN4wIlKvqmGm6opM4EqIqrqqctfBC6jav6bRXFlxVDmo6XUauxWaAGARSXK+Wu2ozcpEVFxb2+uc42cg1I/06JpERM44DWVdujRFly6cH0FE0ir3nl02GNFQojirWA+ZIAD6AB0iQ/S2HrXCkvLq5wFYsfmorI3BPVntGPjjD6g/6t4r7b3mWlz++aDo/DF3C9kSEUnRCFKluCs5eTIfBw9m46abGuOaa+or0S6X5OYWwSq147CXREeHIzu70Kf3INfwmahTxXORGlqUsmRGH9uf5b5XamK+u+Eo7PlnELL4EwBA8dPPomTGLNHzxHoGA/VajEuIVWUw43dFffhM1MnXz0Wr1SAqKkzy5057ytatO46RI5NgMlkQFKTH6tWDcdddbbzaSCKqfaT2w5RSee6ZXF7bwshqRVSbltAWGgAA+es3wXR7D8nTfbGHJxGR01A2c+ZuvP56Dzz0UAd89tkhzJixm6GMiJyS0Qlvp3KhWHfeV5krQ4uaS5fQ6MbWttc5R09CiHJcj9HTPTyJiMQ4DWWnTxswYkRbNGgQjBEjYvHyy3uVaBcR+ZAS86HkbPbtDVV71lzZIzMgZQci7x0GALA0aYrLh/6UrD9W9Z7cTomIvM3pNku33dYMY8ZswsKF+zFuXDK6dWuuRLuIyEcqQkvlnqnlyceQlp4leu70RamYMGc7pi9KFT1HijcCSsU1oiKC0Pum5rK2MHI0tFhZ6Ksv2gJZyeNTcPnwX7ICGcDtlIjIN5z2lH366QA88MD3mDFjNzp2jMbKlYOUaBcR+Yjc+VCu9DiJiYuJwo4DF6od12o0sMoY2oyKCKo2V6x1i0inPXxOhxYFAQ07XA/dpYsAgPyv18PUq4/oe6RwOyUi8gWnoSwmJhJr1tyJFi3CcfhwNtq0aaBEu4jIR+TOh3J1MnvFkGjlkhhi9DrAbAGsAqDVAG1bRiLjvEFWWQuxYq9Vh2KlFhhERQRBczkXjWKvtR3LOXwcQpMmou10htspEZG3OR2+nD59Jx57bCsA4O67N2DGjF0+bxQR+Y7UsGLV465MZq88JCr8c47UnLJys4CKCjZWAcg4b0D3Dk3thirllpYQG4otLTNDr7MfhgzUa/Fwg8u2QGYNC0d2Vr7bgYyIyBechrLPPjuMJ5+86Z8/98ennx7yeaOIyHfkzoeSG94A8V41ucrNVuw7etGt94rd1yIAQQFau5D3xsUt6DH1yobipRMmIvfv84DW6a8/IiJFOR2+DAzUIShIBwAwmazQ6eRNhCUidZI7H0ps43GpYUVPS0EUl1lsPWuuzF2Tum9xmQXvPRUPCAIa3HYT9Cf/BgAUrPoK5f0TPGorEZGvOA1lEybciL591yAyMggFBUY8++ytSrSLiPzMlcnsUiUi3CW3EKuj0hSagnw0atPSdiz392OwNuPqcSJSL6ehbPbsnujR4yr88UcubryxERISrlOiXUTkI66sqpQ7mV1qpaUGV/asrKDTABpt9c3GxcgJeVK9eROii2yBTNBokHPhMqDTOb0eEZE/OZxUsWHDCQBAbGwUTp4swM8/Z6KkxKRIw4jIN+TW8XLFoYxc0eOhIXq7uV0ThtyA8YPa2R2TKg2mlTFTolv7phiXEGt3vf/l70LPJ65sKF56/wPIuVjAQEZENYJkT9lzz6Vg/vxfUVIyFf37r4HBUA6tVoO//y7AihWsVUZUU/liiyCp9xaVmvHu1J7VjlfufZPafNwqc5cmW2+eIKBBnx7Qpx8GABQsXonyocPkXYSISAUkQ9mqVUexcePd2L37PE6eLMCWLfeiYcNg9Ou3Rsn2EZGXeWOLoKq1wUKDdaIlMORc0xvt0RQVotF1V9le5/56GNaWrWS/n4hIDSSHL3NzS3HzzU2wdesphIToER/fAo0ahcBkUmY/OyLyDU+3CEpLz8LipKN2tcFKjBZUXZgdqNciLibK6TZNnrZHf+igXSDLPp/LQEZENZJkT1lcXDQmTNiMn3/OxODB1+H8+SI89thWdOrUWMn2EZGXebpF0IrNf1bbJkkQAGg10EKwVeqPuSoCqYezqi0oOHEuH4cycu3uPS4h1q32hHyyCGGzZgAAyu4ejsKPl7rwN0FEpC6Soezjj+/AE09sQ9euzfD2273x008X8Oefefj22zuVbB8R+YDcVZVVhykT42NglOgtt1SaBGYVgKOn86udU2622q3SrAhq4xJiq+1z6UzkkP4I2PcTAMDw4WcwDr/PpfcTEamNRhBk7AwMwGSyICBAnSuYcnOLYJU7K9hN0dHhyM4u9Ok9yDV8Jr5VtXQGcGVY0d3K/Y6IbT4uqaQE0df8GyhzfzoA63XyhjrrKn5X1IfPRJ18/Vy0Wg2iosIkf+60TlkFtQYyIvqXWM+Wu5tmS5XO8AW5Kz91R/9Aw/jbbK+zz2YDQfIXBBARqZnsUEZE6lI1gMXFRInO4QKcb1ckxpsV+p2Rs9IyeMVShD8zFQBgHJAAw8qvfN0sIiJFSYayM2cMkm9q2TLCJ40hInnEqvKLVdSXu12RGEelKuJiopBy8IJtUr9Wokp/UIAOYSF6yeAIyFtpWf/eYQhM2QEAMCxcBOP9D7j8eYiI1E4ylF1zzSfQSJTatlj+47MGEZFzYkOLUtzt8XK0IXm39k0xZkCs7XhaehaWbjpqF8z0Og3GDmxbLRC2bhEpf4i1rAzRLf9d8X15zy+wXN/Wrc9DRKR2kqEsOXk4AOCzzw4jPDwQw4a1xtq1fyE0NECxxhGROFeClitFWCtzpXRGt/ZNceJcvl3v2f/FNZM8V07Pne7EcTS8vbPtdfbpi0BIiFufhYioJpAMZQMGXAsAGDNmEw4cGIurrgrHzTc3xi23rMKiRXco1kAiqk5qaLEqV4qwinGldEbq4Szb1khWAUg9nIXWLSLdGjoN+vJzREx5DABQ/n/xKPh2o8vXICKqaZxO9G/UKASPPLIFAwdei40bMxAdzf+nSuRvUkOL3Ts0rVaYVSwUeXOVJuB4k3NXrxsxbhSCkpMAAIVz3kLZhIlut4uIqCZxGsqWLBmISZO2YsaMXbjhhigsX56gRLuIyAFPqvKLLRLwZJVmxTVcOS6qvBzRLRrZXl7esReW9jfaneLtMElEpCZOQ9lttzXHvn2jceJEPq65JgKhoYFKtIuInJA7tFiVN3u1Kni6qbj21ElE3drR9jr7ZCYQGmp3ji/CJBGRmkhuSF7hxx9Po1WrTxAXtxxPPbUD8+btU6JdROQjXunVqsKTTcWD1n9rC2Smzl2QfclQLZABjsMkEVFt4DSUTZ68Da+80h1hYQHo0CEa8+f/qkS7iMhHpHqv3F2lCVzpqRqXEIuoiCBo/rnWuIRYpz1Y4Y89jIhHxgMAil59A/nJ2yTP9UWYJCJSE6fDl5mZxejZswV0Oi1at46E2UfbrBCRMhzVH/NExXCqrL3jzGZEN29oe5m3ZSfMnW52+BZPh0iJiNTOaU9ZYmIbdO++GgaDEWPHJiMxsY0S7SIiH6ncqwXI79XyFu25s3aBLCfjnNNABng2REpEVBM47Sn75JP+6Nq1GQ4dykZsbEM88kicEu0iIh9yd5GApwI3JaH+g6MAAOZ27ZG3cy8gsXNIVZ6sOCUiqgmchjK9XoumTUORkZGPG29shNJSM4KCuI85kb/VtPIQYf+ZgpCVywAAxTNfRMlTz7h8DX+FSSIiJThNV9On78SqVUeRm1uKc+cKUVBgxPffD1eibUT0j6oBrOrG3r4qD+GV4GexoNHV0dCYzQCAvO+3wtylq9faSERUWzidU7ZsWTpSUkYgNDQADz8ch507zyrRLiL6R0V9ropJ7rkGI3YcuODz8hAVm4xXvu/STUeRlp4l+xrarExEN2tgC2Q5f51mICMikuA0lIWGBiAp6W9YrQJ27DiDZs3ClGgXEf1DrD6XFG+Wh1j9418wWwS7Y2aLgNU//iXr/YHbtiAqri0AwNLyGmRfLIAQ2cBr7SMiqm2cDl/OmxePBx9MRmmpGfPm/YIVK7yzzdLcuXORl5eHOXPm4OjRo5g1axaKiopwyy234JVXXoFez3lrRIBrQUsrb868LEWlZpeOVwx1XjYY8WTqUvT/eQMAoPjp6SiZ8YL3GkZEVEs5TT733tsW/fq1wokTebj22vpo1KiexzdNS0vDunXr0KtXLwDA9OnT8dprr6FTp06YOXMmvv76a4waNcrj+xD5izcn4UvV5xJjFZyf4wsVQ6wmkxlfLHoAYcYSAEDqgpW4fvQw/zSKiKiGcTp8uXfvefTosRrdu69Gnz5f46efLnh0w/z8fCxYsACPPvooAOD8+fMoKytDp06dAACJiYnYvHmzR/cg8iexOWDLk4+5NBersriYKNnnerOQamiwTvbxtSkZCDFcxncLEm2BbPRjK/BpQaNq5xIRkTinPWXjxiXjhhuiMGXKzfjhh1MYNy4Zf/75kNs3fPHFFzFt2jRkZmYCAC5duoTo6Gjbz6Ojo3Hx4kWXrhkVpcw8t+jocEXuQ/Kp8Zms35MmOgl//Z6TuLOX68WXj5zKk3WeXqdB1/ZN8dzHacjJK0WjBiEYm9AOALAi+ajdsV6dr3Z6vUcTO+KdLw/AUqn7TafV4NHEjtX+3lsc2YfXvnkJAJBXLxJjJy0FNBpoDEZVPqO6iM9BffhM1Mmfz8VpKCstNWPRon646qpwDB58HW65ZaXbN1uzZg2aNWuGbt26Ye3atQAAQag+3qKRWUyyQm5uEaw+HreRtXUMKUqtzyQ7r1TyuDvtlbpeVWaLgC37ztgm52fnlWLhVwcgWAVUzNfPzivFe18fhKGwzOlwavuWkZgwuF21Ydj2LSPtPkfo/17Ca98sAACs6zwMS+LH237WMCJIlc+orlHrd6Uu4zNRJ18/F61W47AjyWko69OnJR59dCv6978GGzdm4NZbm+GTT34HADzySEeXGrNp0yZkZ2dj2LBhKCgoQElJCTQaDXJycmznZGdno3Hjxi5dl0hNvL1HoytzysRWS1ZVUTpDzhw3h8VaBQENO8ZCl3Wl1/vVe1/BL1f/+zuBWyAREbnGaShbteoPAMD33/9tO5aUlAGNRuNyKFu6dKntz2vXrsW+ffswe/ZsDBkyBPv370fnzp2xfv169OzZ06XrEqmJtzf8FruepzwunVFcjOhrm9le5hw+jk45Av7+Z/VlwxqwwwARkdo4DWVWq+tbobhq/vz5mDVrFooUrJIVAAAgAElEQVSLi3HDDTdg7NixPr8nka94e49Gset5Gqo8qZyhPXsGkUMHAACEevWQ8/cFQKtFtyZX2sphGSIi92gEsUldAC5fLsX//vcTFizojaSkDDz7bAoaN66HTz7pj+uvb6h0Ox3inLK6qS4/kykLd0nWC5NryYw+Lr8nIHU3Ih4eC5SbUPjRZyi/Y2C1c+ryc1ErPhP14TNRJ3/PKZMsiTFx4hZ8881fyMsrw8iRSYj4Zz7MpElbvd9KInLJ/f2uh15n39+l12nQ+6bmtrlr3iyPAUFA8Gcfof49d8LaMAr5P+wQDWREROQ+yeHLnTvPIjX1fvz442mUlprx6af9ER4eiPbtl0q9hYh8RKwY7fhB1VdGVh0inTBnu+c3LytD+LPTEPzl5zAOHITCDz6BEB7h+XWJiMiOZCgzGi0IDNRh06a/0ahRCDp0iMbvv19CcDC3PyJSUkUx2oqJ/hXFaMclxGLe4919em/thfOIGD8aAQd+Q/EzM1DyzAxA67TmNBERuUEyYfXt2xI9eqxGdnYppk69GWlpFzBy5Eb069dKyfYR1XliG5K7UtbCXfqff0L9CQ8AJSUoWPYFygcN8dm9iIjIQShbtiwBb765Dw0aBGPatM7Ytesc+vRpibff7q1k+4jqPKmVlh6XtXAgePkShM2cDkuLq2FYmwRL21if3YuIiK6QDGUNGgRj9ux/64X17dsKffuyl4xIaWEhetGVlmEhzqcShAbrUFxmET0uymhE2MxnEbJyKYx970DhR4sh1I90uc1EROQ6Tg4hUjmJqjWSxysbdUdbVFmkCZ3myvGqtBezEJk4BCErl6Jk6n9gWPU1AxkRkYI4a5/IB8RWS7o7/0usp8vR8crkFrLV7/8FEeMfgNZQgILPlqP8zrvdaisREblPMpStWJEu+aaxY9v7pDFEtYHUakkAbgUzT/fSdLh/JYDgL1Yi7NlpsDZtjrzvf4Sl/Y0ut5GIiDwnGcoefDAZAKDRXBn7qBgqiYgIYiijOktOD5i3V0t6ey9NG5MJYS/MQMiST1HeszcMnyyB0DDKs2sSEZHbJENZaelTEATgkUe2oEePqzBiRCy++OIoDh68pGT7iFRDbg+Yt1dLensvTQDQZGcj4uGxCExLRcnjU1A862VAz9kMRET+JPlbOCjoyo+++y4Ds2bdhvr1gxAf3wLPP78bH3/cX7EGEqmF3B4wT4cbxYgNQbo7b03/+wFEPDga2twcGD78DMbh97ndLiIi8h6nqy87d26Cfv3WYPjwDejf/xt07dpMiXYRqY7cHrDE+BgE6u2/Wl4Zbqykoteu4t4VvXZp6VkO3xf09WpEDh0AaDTIT9rCQEZEpCJOQ9mXXw7B3Xe3QVmZGfffH4vVqwcr0S4i1ZHq6ap6vFv7phiXEGu3Mfi4hFivVt931GsnymxG6AvPI+LJSTB17oK8LSkwx3XyWnuIiMhzTieRREfXw/jxN2Lv3vPo0aMFwsMDlWgXkeq4MuHe2YpHR+QMS7oyb02Tm4uIRx5E4O4UlEx8FMUvvw4EBLjVNiIi8h2noeytt37BrFmpMJksuO++ttDptFi5cpASbSNSFV9MuK9K7mICufPWdEcOo/6Do6C9mAXDux/COHK019pKRETeJSOU/Yo9e0aiX781GDkyFqNHf69Eu4hUyZMeMDnkLiaQ02sXtP5bhE99HNbIBsjfkAzzzbf4rN1EROQ5p6FMEIC//y4AAJw9W4jIyGCfN4pIrbxZqV+M3GFJh712FgtC33gV9d5bANOtt6Fg8UoITZp4rY1EROQbTkPZzJldMXJkEgRBwJQp27FgQW8l2kWkOt6u1C/GlXIaYr12mvw8REyagMAd21A67iEUvT4XCOQ8UCKimsBpKJs8+Wb07dsS6em5iI1tiOuuq69Eu4hUx9uV+sUkxsdgSdIfsFTaa1yngaxyGrpjR1F/7Ehoz59D4VvvomzMgy7d29e9gERE5JhkSQyLxQqj0Qyd7i00blwPw4a1RkCAFjExnynZPiLV8HalfikarcbhazGBSd+hwcA+QEkJ8tdtciuQuVP3jIiIvEcylC1c+Bvq1VsIAGjS5EOEhLyD9u2XQa93WtqMqFaSW6fME2tTMmCu3E0GwGwRpOuPWa2oN+d/qD/hAZjbtUP+j7tgvrWrW/d1qe4ZERF5neTw5RNPdEJUVDDGj9+MDz7oh3r19NDptKzoT3VWXEwUdhy4IHrcW1yqP2YoQPjjExG0ZTNKR41B0dy3gSD3AqJSvYBERCTN4d6X48bdiDFj2uPEiTyUlVkgCAKOHMlBmzYNlGwjkSocysh16bg7pCb6h4XoMX1Rqm2+19hrgZ7/mwLdqZMonPMWysY/DGicD3O6el9v9gISEZFjTif633ffd1i37oTtdWCgDqWlT/m0UURq5EpvkruT5sXqj+l1GpSWmVFUagYAXHdgN257YwEs9UJQ9O1GmLp1d/MTOb6vt/frJCIix5yGsk2bTuKTT/pj48YM9O59NX744ZQCzSJSH7m9SZ6UzhCrP1ZWbkZxmQUawYr7fl6DB/auxvEmMfhw1Et43guBTOq+XH1JRKQsp6EsIECLDh0aYdWqP3D77c0xc+ZuJdpFpDpye5M8LZ1Rtf7YhDnbEVJeiqc2L8TtJ37C9na98EG/x1CucX9oUaonjyGMiMh/nIayBx64Ad9++xfatWuIrl0/R+fOrAxOdYNYcBmXEOvVzcLlaGfKxZOrX8ZVl8/jk14PYeNNQwCNxu35XkoUwSUiItc5DWUffNAPBQVGBAXpMHDgtbj99uZKtIvIr6SCy7iEWMx73PGQoSuT5p3NPQvYvhWvLZ2GMouAF4e/hEMtOwLwbL6XEkVwiYjIdU6Ljv3ySyamTduBCRM248svj2Hq1B1KtIvIrzyp2yVVIqPqcYcFWwUBIe++jfr33wNtq1bYt3g9zt94K4Ar4W5cQqzbAYrlL4iI1MlpT9mIEUnQaICWLSOUaA+RKngSXOSWzpAKfklb09H/nRkI3rAWZXclonDBB+gYGoqO/WQ23gmWvyAiUienocxstiIlZQSuvTZSifYQqYInwUVuoBM7r0nBRTy/YjaCLp9B0Yv/Q+kTUzyqPyaG5S+IiNTJaSh77rlbMWJEEvr0aQmtVgONBnj99f9Tom1EHvFkg21PgotWA1gF8eOVVQ1+cWd+x3NJ86GDgIIvvoGpj5e6xqpg+QsiInVyGsoWLTqIs2cLkZ1dAgDQaDQMZaR6rqwwdBTe3AkuYoFM7Lgt+JksGPbbdxi/aznOR7XA8XcWI66P6/tXuoLlL4iI1MdpKMvLK8Nvv41B69bcWolqDrkrDJ2FN3eCS2iwDsVlFtHjlXVr3xRaYxkaTp+K2w/vwK/tuuPi/Pdwa5fWLt+TiIhqPqehrH//a/Dss7swYMA10Giu9JRNnBinRNuI3CZ3XpcvykNoJOaAVT2uPXcWA54dC/2R31H8/AtoNfU/aKV1uiCaiIhqKaehbMWKdADA+vXHATCUUc0gd6K+L8pDVOxR6eh4wN49iHh4LGAsh2Hllyjvn+D2/YiIqHZwGsqs1meUaAeRV8mdqO8ovLm7UMBhIBQEBC/5BGEvPA/LNdci5YX3sPKEgNzftnPCPRFRHScZykaNSsKSJQMxYcJmu+MajQaffz7Y5w0j8oTcifpS4S0uJsrtrYikrnlPtxYIe+oJhKxeBeOABGx78jUs3n2e2x0REREAB6EsM7MYVquAzMxiu+NeLplE5DNyJupLhTdP5pp1a98UJ87lI+XgBViFK6UwBlylxYDnH0TAb/tR/J/nUDL9eXz9URq3OyIiIhvJULZjxwgAwCuv3I7u3a+CTqdFaakJaWmZijWOyBNyhx/FwtunG/8QvaacuWZp6VlIPZxlK4HR9txR3PvRXGisJhQs+wLlg4Y4vBa3OyIiqpskQ9nhw9nIzCxGQsK3+PbbO1GvXgCysooxZcp25OdPVrKNRC6TKnVx4lw+DmXkOg1qnlT0r9zLNvDQD3hk+6fIjojGvFFzMO2fQObpPYiIqPaRDGUZGflITNwAALb/BYBbb23m+1YReUhq+HHHgQu2147mcLlS0b9qj1yuwQi9xYRJ2z/FwMNbsP+amzFv0NMoDg5z+x5ERFT7SYayu+5qg5MnJ+LBBzdj0aJ+qFdPD51Oi+bNw6TeQqQacocApeZwyV0oINYj16DoMmYkvYkbLhzDmi7Dsar7KFi1umo9YNzuiIiIKnNYEqNVq/pYv/4uXLxYjMLCcixZcgSPP96J1f1J9aSGBsVInSdnoUDVHrk2mX/hv9/NQaixGHMHP4M9bXsAkO4BE7uHJ3t2EhFRzeW0TtnIkRvRtWsz7Np1DhkZ+di3Lwt79tyvRNuI3CY2NCjFkzlclQNd3yPb8MS2D5EbFoXpiXNRGNMWcDFYubJnJxER1S5OQ9nu3ecxZ05PvPXWrzh0aBxuvHGZAs2iusibPURiQ4NxMVFIPZzl1TlcURFByM8rxkMpSzD04CYcbBmHNwc/g8Am0Zj3eHeXr+eLbZ+IiKhmcBrK6tXTY9mydMTFRWPHjrOIjAxWol1Ux/iih0hsaLB1i0ivDg2OjIvE1VOeQftz6VjXeRiW/d9Y6AMDMNLNoMcyGUREdZfTUDZt2i2YN+8XLFkyAN9+exzTp3dRol1Ux3jaQ+RJTTJ36X8/gDsmjways/Fx4nQkXdPd46DHMhlERHWXZCizWgVotRo8/3xXPP98VwBA//7X4Ny5QsUaR3WHJz1E/piHFfTNVwh/ejKsUY1g2LQViXGdkFilTe70yLFMBhFR3aWV+kFAwNsw/PMfxDvvXIdLl4phMlnRrt1SxRpHdYdUT5CrxVorVPSyeZ3ZjNAXZyLi8Ykw3XwL8rakwBzXye6UipBYESgrQmJaepbTy3dr3xTjEmJtnzsqIgjjEmI5n4yIqA6Q7CkTBMH25927z6GszIKgIL3dcSJvSYyPwdJNR2G2/PvvS6/TyOohUmoeluZyLiImjkfg7p0oeXgSil95AwgIqHaep0Ox3hxiJSKimsPpnLKqNNyRnHxEsAoOX0tRYh6W7shh1H9wFLQXs2B490MYR46WPJeT9YmIyB0OQ9mJE/kICwuAIAB//52PsLBApdpFdczalAxYqmQwiwBZvUu+nocVtGEtwqc+Dmv9SORvSIb55lvsfl51/lhosA7FZZZq1+FkfSIicsRhKOvSZRWAK0OZffuugSAI7Ckjn/Ckd8ln2xVZLAh941XUe28BTF26omDJKghNmtidIrbIQK/TQKeBXcjkZH0iInJGMpTt2DFCyXZQHefpEKS352Fp8vMQMWkCAndsQ+m4h1D0+lwgsHpPsdj8MbNFQFCADoLZAqsAaDVA9w6cJ0ZERI5JhrL4+KuVbAfVcWoqBaE7dhT1x46E9vw5FM5fiLKx4yXPlerJM5r+Hb60CkDq4Sy0bhHJYEZERJIkS2IQKUktpSACv9+IyIS+QEkJ8tdtchjIAPk9eT4r0UFERLWGy6sviXzFr6UgrFbUe/MNhL79Jkw3d4Zh6eewNmvu9G2ubHzO1ZdEROQIQxmpmjc3KZeiMRQg/PGJCNqyGaX3P4CiuW8DwfL2eBVbZFBWbubqSyIichlDGamWEtsn6U4cR8TYkdCdOonC2fNRNmEi4OIK46o9fFXbDXD1JREROcc5ZaRavt4+KXBLMiIH9IY2Pw8F33yHsocecTmQiVHL/DgiIqpZ2FNGquWzyvhWK+q9Mx/15r4Oc4eOMCz7HNYW3l1tzK2SiIjIVQxlpBpKVMbXFBUifPJjCPr+O5TdMwKFb70LhIR40mwiIiKvYCgjVfBFZfyqIW9Maz3iX5sC3fG/UPTqGyid9IRXhiuJiIi8gaGMVEGqMn5YiB5BATq71ZcAMH1RKi4bjGgosSKzashreegndJ09H9agABR9tQ6mnr0U+VxERERyMZSRX1TtxZKaJ1ZUakZQgM72+sS5fKQeznK6ItMW8gQBw39ZhzGpq3AmqiU+GP0SnmMgIyIiFWIoI8WJDVU6UvHzXIMROw5cqPbzihWZlUNZrsGIIFMZpv7wHv7vr1Tsvr47Fg6YDKNOXv0xIiIipTGUkeLEhio9VTXYtbXk4YnVr6BVzmks6zEW33a5G9BoFCvgqkTRWyIiql0YykhxvthuqHLYCti1E28snYZyowmv3P0Cfrv2ZgDKFXBVougtERHVPgxlpDitBrAKzs+Tyxa2BAEhH3+A0JdnwXJ9W/w8612cPmEGnPRWebtXy1HRW4YyIiKSwlBGivMkkOk0QM9OzXEoI9d+9eV19RH+xCMI/uYrGAcNReH7HyEuLBzz+ju+ni96tXxW9JaIiGo1xUPZ+++/j+TkZABAfHw8nn32WezduxezZ8+G0WhEQkICpk2bpnSzSEGOVls6o9Fq0LpFJMYMiEV0dDiyswuhPXcWEUMHQH/4dxTPmIWSp54BtPJ2EPNFr5bU5+OG5ERE5Iiie1/u3bsXe/bswbp167B+/Xqkp6cjKSkJM2fOxKJFi7Bp0yYcOXIEKSkpSjaLFJYYH4NAvXv/9MwWwW7vy4C0VDToHw/dyb9hWPElSp5+VnYgA3zTqyX2+bghOREROaNoKIuOjsaMGTMQGBiIgIAAxMTE4NSpU2jVqhWuvvpq6PV6DB06FJs3b1ayWaQwsQ27e9/UXHZQyzUYAUEA3n8f9YcPhTWyAfI3b0f5gASX2yLVe+VJrxY3JCciIncoOnzZpk0b259PnTqFTZs2YcyYMYiOjrYdb9y4MS5evKhks8gPxDbsbt0i0m7CfVm5WXTvy6YhGoRNexL4YiXK+w9E4aJPIUTUd6sdifExdnPKAO/0anFDciIicpVfJvofP34ckyZNwnPPPQe9Xo+TJ0/a/Vzj4n6EUVFh3myepOjocEXuU1fd2Sscd/b6N7jv3H8W76/5HUbTv8GsWVk+5m9ZiJAjB4AXXkDQyy8jyIXhSrF7RoQHY0XyUeTklaJRgxCMTWiHXp2v9uiz1HX8rqgPn4n68Jmokz+fi+KhbP/+/ZgyZQpmzpyJwYMHY9++fcjJybH9/NKlS2jcuLFL18zNLYLVmzUWRFRMKifvkFOGon3LSIwd2NZ2Xtf8DDyzfg4CjaUoWPo56j84yivPpH3LSMyd1M3uGJ+1+/hdUR8+E/XhM1EnXz8XrVbjsCNJ0VCWmZmJJ554AgsWLEC3blf+I9ixY0ecPHkSp0+fRosWLZCUlIThw4cr2SxSmCtlKCqGAYNXLEXYezNgaXE18pcnwRLbTvF2ExER+ZKioWzx4sUwGo2YM2eO7djIkSMxZ84cTJ48GUajEfHx8Rg4cKCSzSKFuVSGorwcYTOfRciKJSjv0w+GjxZDiGygYGuJiIiUoWgomzVrFmbNmiX6s++++07JppAfyS1Dobl4EfUfGoOAfT+hZMrTKH7+BUCnU6KJREREimNFf/K5qvPHwkL0KCo1VzuvchkK/W+/ImL8A9AW5MPw6TIYhyX6pC3cKJyIiNSCoYxkEQszAJwGHLH5YzoNoNdpYLb8uzijchmKoNWrEP7sNFibNEVe0lZYbuzgtc/AjcKJiEitGMrIKbEws3TTUVisAoR/clXFMcA+4IjNH7MIQGiAFvVD9faB7voohM6cjnqffYzy/+sFw6dLITSM8trn4EbhRESkZgxl5JRYmKncy1X52Oof/7ILOFLzx4rLLHjvqXjba01ODiLuHYbAvXtQ8uiTKH7xVUDv3X+e3CiciIjUjKGMnHIltFSdKyZnc279oYOIeHA0tDnZMHzwCYz3jnS/sQ5wo3AiIlIzhjJySirMSJm+KNU2LBkXE4XUw1mS2xgFffMVwp+eDGtUI+Rv/AHmjjd5vf0VfLWlEhERkTcouiE51UyJ8TGyNwsH/u1ZyzUYkXo4C907NK2+OXfbRgh96b+IeHwiTDd1Rt6WFJ8GMoAbhRMRkbqxp4ycqggtlVdaxsVEYdfBCxCZWman3GzFoYxczHu8u+2Y5nIuIkYOR+CuHSh96BEUvTobCAjw5Uew4UbhRESkVgxlJItYmGndItIuqMmZSK9LP4L640ZBm3UBhoWLYLz/AZ+2m4iIqKZgKCO3VQ1qFXPJqqoYLgz8bh0ipjwGa0R95G9IhrlzF8XaSkREpHYMZeQ1khPpe1yD0NdeRr1334apS1cULFkFoUkT/zXUy7hLABEReQNDGckiJ3iIzT0bcXMj9H1jMgK3/4jSMeNRNHseEBiomjZ74x7cJYCIiLyBoYycciV4VB7S1B07iohxI6A7dxaF895B2bgJqmyzJ7hLABEReQtLYpBTjoKHlMBNSYhM6AttURHy136vaCAD3GuzO7hLABEReQt7ysgpl4KH1Yp682Yj9K25MN3cGYaln8ParLmPWyizbRLHPRnm5C4BRETkLQxl5JTc4KEpNCD88YkI+iEZZSNHo/DNBUBwsNfbUxGiLhuMaCgRouS22dNhTu4SQERE3sLhS3JKrKJ/1eChO3EckQP7IPDHLSicPQ+FCxf5LJAtTz6GXIMRAv4NUWnpWS63GfB8mJO7BBARkbewp4ycEltVWbl3KnDrZoQ/+jAQGICCbzfCdHsPn7VF7sR6Z22u4I05YdwlgIiIvIGhjGQRDR5WK+q9Mx/15r4O841xMCz/AqkFAVhbaUPyip4pb5WmcCVEyQlLrs4JY00yIiLyFYYycoumqBDhkx9D0PffoWz4fSh8612knTRUm5+1dNNRCFbBtkemp6UpvD2x3pU5YaxJRkREvsQ5ZeQy7cm/ETmoHwKTk1D0yhsoXPQpUK+e6NCi2SJU27Tck9IUcueKyeXKnDClymwQEVHdxJ4ycknA9h8RMWkCoNWg4Kt1MMX3tv3MlXlY7tbxqjxXzNHqS1evKef9rElGRES+xFBG8ggCQt5fiNDXX4Yl9gYULP8C1lbX2J0iNbQoxpM6XhUhKjo6HNnZhW5fx1WsSUZERL7E4UtyrrgY4ZPGI+x/L8I49C7kfb+1WiADxIcW9ToNdBr782pqHS9vD50SERFVxp4yckh75jTqjxsF3R9HUDTrFZROfgrQaETPlSpDIXasJk6Ml1tmg4iIyB0MZSQpYHcKIiaOAyxWFKz+BqY+dzh9j9T8rNoSXFiTjIiIfIWhjKoTBIR8sgihL8+CpXUbGJZ/Act1raudtvKHY0g5eAFWAdBqgPhOzTFmQKwfGkxERFTzMZSRvdJShD8zFcFrvoQxYQgKP/gYQlh4tdNW/nAMOw5csL22CrC9ZjAjIiJyHSf6k432/DlE3jkQwWu+RPFz/4Vh6SrRQAYAKQcvuHSciIiIHGNPGQEAAtJSEfHQGKDMiIIVX6J84CCH51sF6ePTq2yzxDlYREREzjGU1XWCgOAlnyLshRmwtLoGhg1fwtLmeqdv02qkg1lFLa9cgxFLkv4A4PuJ/tyTkoiIajoOX9ZlRiPCpj2J8OefQXnvvsj/YYesQAZcmdQvh0UAvtj6pyetdKpiT8rKYXB58jGkpWf59L5ERETexFBWR2mzMhF5VwJCvliJ4qenw7DyKwgR9WW/f8yAWPS+qTm0/5Qs04qXLgMAFJdZPGytY9yTkoiIagMOX9ZB+l9+RsT4B6AtKkLBklUoH3KnW9cZMyDWbqXlhDnbvdVEl7iyJyWHOYmISK3YU1bHBC/+BJF3DQLq1UNe8ja3A5mYsBDxjC913FukeumqHucwJxERqRlDWV1RXIzoxhEIf/4ZmOM6Im/LTlja3eDVW9zf73roq2x0qddpcH8/efPU3OVoJWhlHOYkIiI1YyirA3RHDiP62ma21/lJWyFENvD6fbq1b4rxg9ohKiIIABAVEYTxg9r5fHiw4n7OjrsyzElERKQ0zimr5YKXfIrwGf8BABgHDYVh2ec+vZ8/9oZMjI/B8uRjdr1ggXqtbTP0ClERQaIBTCrUERERKYk9ZbVQWnoWpi9KxYXO3W2BzPDeRz4PZP7SrX1TjEuIteuhG5cQWy0cJsbHIFBv/09eLLwRERH5A3vKapm09Cys/u53rF5wr+3Y5IcW4Y4b+6CbD+7l7kpGb6+ClNNDV/Fzrr4kIiI1YiirZdK+2YHVH0yyvU6c8jVM+kCsTcnwavioWMlYMWRYsZIRcF6935P3esofw6tERERycPiyFgn+YiVm/xPIfmvVCUOfXg+TPhCA9yeze7KSkasgiYiIqmNPWS0R8cB9CNqyGQDwfr/H8EPcALufe3syuycrGbkKkoiIqDqGsprOaET01dG2lzuWJGHHMSvgZCWip0KDdaLbJ4UG65y+l6sgiYiIquPwZQ2m+/uEXSDLPpWFG4f0RMxVEXbnxVwV4fV5VBqNeBl9qeOVcRUkERFRdQxlNVTQN1+h4W03AwBMXbsh+5IBqFcPK384hqOn8+3OPXo6Hyt/OObV+xeVml06XpncEhZERER1CYcva6DwRx5E8Pq1AICi1+eidOJjtp+lHLwg+p6UgxfsNg/3lKdDkFwFSUREZI+hrCYxmRB9VZTtZd623TB36Gh3itx9ID0lt4o+ERERycNQVkNoz5xG1C0dbK9z/j4PISy8+nka8QCmdT7VyyUsxEpERORdDGU1QODGDaj/0BgAgKlDR+Rv2y15bnyn5thxoPoQZnyn5l5vF4cgiYiIvIcT/VUufMpjtkBWNOsVh4EMAMYMiEXvm5rbesa0GqD3Tc29Op+MiIiIvI89ZWplsSC6WQPby7zkbTB37iLrrWMGxDKEERER1TAMZSqkvXAeUZ3a2V7nHD8DoX6kH1tEREREvsZQpjKBW5JR/4ERAABzTGvk7d0PyCjIWlulpWdxMQEREdUJnFOmImHPPW0LZMXPzEBe2m91PpAtTz5mq4eWazBiefIxpKVn+bllRERE3seeMjWwWtHouubQlJQAAPK/2wzTbbf7uVH+tzYlw64OGgCUm+R2L/4AABhJSURBVK1Ym5LB3jIiIqp1GMr8THPxIhp1aGN7nXPsJISGUQ7eUXeI7Rjg6DgREVFNxuFLPwrYsc0WyCxNmiL7YgEDWSVSWzbJ3cqJiIioJmEo85PQl/6LyBF3AwBKnnwKlw//Vafnj4lJjI9BoN7+nyi3ciIiotqKw5dKEwREtW8NbU42ACD/m+9g6tnLv21SKW7lREREdQlDmYI0ublo1O5a2+uc9AwI0dF+bJH6cSsnIiKqKzh8qZCAvXtsgcwaUR/ZWfkMZERERGTDUKaAenP+h8i7BgEASh6ehNwTZwEt/+qJiIjoXxy+9CVBQMMucdCdOQ0AKPhiDcr7DfBzo4iIiEiNGMp8RJOfh0bXt7K9zj30J6xNm/mxRbVHxdZLlw1GNOTkfyIiqiU4huYD+l9+tgUyQadDdmYeA5mXVN56SQC3XiIiotqDoczLQha+hQaD7wAAlI4ei5zMPECn83Orag9HWy8RERHVZBy+9BZBQINe3aA/+gcAoGDZFygfNMTPjVKHiuFGb9Qa49ZLRERUWzGUeYGm0IBGMS1sr3N/S4e1xdV+bJF6VAw3VvRuVQw3AnArmEVFBIkGMG69RERENR1DmYf0B39Dg/69bK+zL1wG9J7/tXqzd8mfHA03uvN5EuNj7EIewK2XiIioduCcMg+EfPS+LZCVJd6D7EsGrwWyisnsQM2ezO7t4cZu7ZtiXEIsoiKCoMGVHrJxCbE1MrASERFVxp4yN0Um9EHA/l8BAIaPl8B49z1eu7a3e5f8yRfDjRVbL0VHhyM7u9CT5hEREakGe8pcVVyM6MYRtkCW+/NBrwYyoHZNZk+Mj0Gg3v6fGYcbiYiIqlNNT9nGjRvx4YcfwmQy4cEHH8To0aP93aRqdOlH0LD37bbX2edygMBAr99HqncpLESP6YtSa9Q8s4r21Yb5cURERL6kilB28eJFLFiwAGvXrkVgYCBGjhyJrl27onXr1v5u2r8+/BANH38cAGAcOBiGFat9diuxyex6nQalZWYUlZoBeL6KUUkVw41EREQkTRWhbO/evbjtttsQGRkJABgwYAA2b96MJ5980s8tuyLigfuALZsBAIZ3P4RxpG978cR6l8rKzSgus9idJzXPrLas3CQiIqpLVBHKLl26hOjoaNvrxo0b49ChQ7LfHxUV5otmXSEItkCGo0cRERvru3tVcmevcNzZq82/r/+zQfS8ywYjoqPDba937j+LFZv/hNF0JcDlGoxYsflPRIQHo1fn2lc7rfJnJ/Xgc1EfPhP14TNRJ38+F1WEMkEQqh3TaDSy35+bWwSrtfo1vEXz93k0atUU2bnFgJ9W+zWUmGfWMCLIbgXisqR0WyCrYDRZ8PG6Q1iWlF6res+4+lKd+FzUh89EffhM1MnXz0Wr1TjsSFLF6ssmTZogJyfH9vrSpUto3LixH1tkTwgLB7T+/auSu4pRaoVmUam5VtQ9IyIiqq1UEcpuv/12pKWl4fLlyygtLcWWLVvQs2dPfzdLVSoXTQWki6bKrf/FTbyJiIjURRXDl02aNMG0adMwduxYmEwm3HPPPYiLi/N3s1RHzipGsZWbUmpi3TMiIqLaShWhDACGDh2KoUOH+rsZNZ7clZsAN/EmIiJSE9WEMvKeqj1qFXtpchNvIiIi9WIoqwNYVZ+IiEj9GMrqCFbVJyIiUjeGMvIq7iZARETkHoYy8pqqc9dq0v6cRERE/qaKOmVUO6xNyahWioP10IiIiORhKCOvkap7xnpoREREznH4so5QYq5XlMT+nKyHRkRE5Bx7yuqAirlevt77Uu7+nERERFQdQ1kdoNRcL7n7cxIREVF1HL6sA7wx10vu8CfroREREbmHPWV1gNScLrlzvZQa/iQiIqrLGMrqAE/nerHUBRERke9x+LIO8HTvS5a6ICIi8j2GsjrCk7leLHVBRETkexy+JKdY6oKIiMj32FNGTnk6/ElERETOMZSRLCx1QURE5FscviQiIiJSAYYyIiIiIhVgKCMiIiJSAYYyIiIiIhVgKCMiIiJSAYYyIiIiIhVgKCMiIiJSAYYyIiIiIhVgKCMiIiJSAYYyIiIiIhVgKCMiIiJSAYYyIiIiIhXghuQqlZaehbUpGcg1GBEVEYTE+BhuCE5ERFSLMZSpUFp6FpYnH0O52QoAyDUYsTz5GAAwmBEREdVSHL5UobUpGbZAVqHcbMXalAw/tYiIiIh8jaFMhXINRpeOExERUc3HUKZCURFBLh0nIiKimo+hTIUS42MQqLd/NIF6LRLjY/zUIiIiIvI1TvRXoYrJ/Fx9SUREVHcwlKlUt/ZNGcKIiIjqEA5fEhEREakAQxkRERGRCjCUEREREakAQxkRERGRCjCUEREREakAQxkRERGRCjCUEREREakAQxkRERGRCjCUEREREakAQxkRERGRCjCUEREREakA9750Q1p6FjcLJyIiIq9iKHNRWnoWlicfQ7nZCgDINRixPPkYADCYERERkds4fOmitSkZtkBWodxsxdqUDD+1iIiIiGoDhjIX5RqMLh0nIiIikoOhzEVREUEuHSciIiKSg6HMRYnxMQjU2/+1Beq1SIyP8VOLiIiIqDbgRH8XVUzm5+pLIiIi8iaGMjd0a9+UIYyIiIi8isOXRERERCrAUEZERESkAgxlRERERCrAUEZERESkAgxlRERERCrA1ZcK42bmREREJIahTEHczJyIiIikcPhSQdzMnIiIiKQwlCmIm5kTERGRFIYyBXEzcyIiIpLCUKYgbmZOREREUjjRX0HczJyIiIikMJQpjJuZExERkRgOXxIRERGpAEMZERERkQowlBERERGpgKKhbP/+/Rg+fDiGDRuGcePG4fz58wAAg8GARx55BAkJCRg9ejSys7OVbBYRERGR3ykayqZPn47XX38dGzZswNChQ/Haa68BAN555x3ccsstSE5Oxr333ovXX39dyWYRERER+Z1ioay8vBxTp05FbGwsAKBt27bIzMwEAOzcuRNDhw4FAAwZMgS7du2CyWRSqmlekZaehemLUjFhznZMX5SKtPQsfzeJiIiIahDFSmIEBgZi2LBhAACr1Yr3338f/fr1AwBcunQJ0dHRVxqk1yMsLAyXL19GkyZNZF07KirMN42uIjo6XPT4zv1nsWLznzCaLACubJu0YvOfiAgPRq/OVyvStrpK6pmQf/G5qA+fifrwmaiTP5+LT0JZcnIyZs+ebXfsuuuuw7Jly1BeXo4ZM2bAbDZj0qRJktfQauV34uXmFsFqFdxurxzR0eHIzi4U/dmypHRbIKtgNFmwLCkd7VtG+rRddZmjZ0L+w+eiPnwm6sNnok6+fi5arcZhR5JPQllCQgISEhKqHS8uLsZjjz2GyMhIfPjhhwgICAAANG7cGDk5OWjatCnMZjOKiooQGamOMJOWnoW1KRm4bDCioUQFfm40TkRERJ5SfKJ/q1atsHDhQgQGBtqOx8fHY/369QCATZs24ZZbbrEFNn9KS8/C8uRjyDUYIeBKyFqefKzafLGwEPFsK3WciIiIqCrFUsMff/yBbdu2oXXr1rjrrrsAXOkh+/TTTzF16lTMmDEDgwcPRnh4OObPn69Usxxam5KBcrPV7li52Yq1KRl2vWWCID50KnWciIiIqCrFQtkNN9yAP//8U/RnkZGR+Oijj5RqimxyhyWLyyyi50kdJyIiIqqKFf0diIoIknVc7nlEREREUhjKHEiMj0Gg3v6vKFCvRWJ8jFvnEREREUnhTHQHKuaNOVt9Wfm8XIMRURLnEREREUlhKHOiW/um6Na+qdPaJRXnEREREbmDw5dEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCDGVEREREKsBQRkRERKQCen83wBu0Wk2tug/Jx2eiTnwu6sNnoj58Jurky+fi7NoaQRAEn92diIiIiGTh8CURERGRCjCUEREREakAQxkRERGRCjCUEREREakAQxkRERGRCjCUEREREakAQxkRERGRCjCUEREREakAQxkRERGRCjCUObFx40YMGjQId9xxBz7//HN/N6fOev/99zF48GAMHjwYb775JgBg7969GDp0KPr3748FCxb4uYV129y5czFjxgwAwNGjRzF8+HAMGDAA//3vf2E2m/3curpl+/btSExMxMCBA/Haa68B4HdFDTZs2GD7HTZ37lwA/K74S1FREYYMGYJz584BkP5++OX5CCQpKytL6N27t5CXlycUFxcLQ4cOFY4fP+7vZtU5qampwogRIwSj0SiUl5cLY8eOFTZu3CjEx8cLZ86cEUwmkzBhwgRh586d/m5qnbR3716ha9euwnPPPScIgiAMHjxYOHDggCAIgvD8888Ln3/+uT+bV6ecOXNG6NGjh5CZmSmUl5cL999/v7Bz505+V/yspKRE6NKli5CbmyuYTCbhnnvuEVJTU/ld8YODBw8KQ4YMEdq3by+cPXtWKC0tlfx++OP5sKfMgb179+K2225DZGQk6tWrhwEDBmDz5s3+bladEx0djRkzZiAwMBABAQGIiYnBqVOn0KpVK1x99dXQ6/UYOnQon40f5OfnY8GCBXj00UcBAOfPn0dZWRk6deoEAEhMTORzUdDWrVsxaNAgNG3aFAEBAViwYAFCQkL4XfEzi8UCq9WK0tJSmM1mmM1m6PV6flf84Ouvv8ZLL72Exo0bAwAOHTok+v3w1+8yvc/vUINdunQJ0dHRtteNGzfGoUOH/NiiuqlNmza2P586dQqbNm3CmDFjqj2bixcv+qN5ddqLL76IadOmITMzE0D170x0dDSfi4JOnz6NgIAAPPTQQ8jOzkbv3r3Rpk0bflf8LCwsDFOnTkVCQgKCg4Nx6623IiAggN8VP3j99dftXov9d/7ixYt++13GnjIHBEGodkyj0fihJQQAx48fx4QJE/Dcc8+hZcuW1X7OZ6OsNWvWoFmzZujWrZvtGL8z/mWxWJCWloZ58+bh66+/xuHDh23zZirjM1HWsWPH8O2332LHjh3Ys2cPtFotUlNTq53H56I8qd9Z/vpdxp4yB5o0aYJff/3V9vrSpUu2Lk9S1v79+zFlyhTMnDkTgwcPxr59+5CTk2P7OZ+N8jZt2oTs7GwMGzYMBQUFKCkpgUajsXsu2dnZfC4KatSoEbp164aGDRsCAPr27YvNmzdDp9PZzuF3RXl79uxBt27dEBUVBeDKUNjixYv5XVGBJk2aiP63pOpxpZ4Pe8ocuP3225GWlobLly+jtLQUW7ZsQc+ePf3drDonMzMTTzzxBObPn4/BgwcDADp27IiTJ0/i9OnTsFgsSEpK4rNR2NKlS5GUlIQNGzZgypQp6NOnD2bPno2goCDs378fALB+/Xo+FwX17t0be/bsgcFggMViwe7duzFw4EB+V/wsNjYWe/fuRUlJCQRBwPbt23Hrrbfyu6ICUv8tueqqq/zyfNhT5kCTJk0wbdo0jB07FiaTCffccw/i4uL83aw6Z/HixTAajZgzZ47t2MiRIzFnzhxMnjwZRqMR8fHxGDhwoB9bSRXmz5+PWbNmobi4GDfccAPGjh3r7ybVGR07dsTDDz+MUaNGwWQyoXv37rj//vtx3XXX8bviRz169MAff/yBxMREBPx/e/caE+WVx3H8+0i4ylUuUlG5rCgIWkAl2JVdlEikGxVZrdTdAtGWbTdaum2CScsL2xBICRXQVS5WukCl6noriqB2K7QWF0SKAwqiBQpS0VGugsN19gW7JEasSQt20v1/Xp7n5JwzOZmZX/4nT46+PgsWLCAqKoqVK1fKd+UXZmho+MT/kl/it0zRjndwKoQQQgghnik5vhRCCCGE0AESyoQQQgghdICEMiGEEEIIHSChTAghhBBCB0goE0IIIYTQARLKhBCTJiPjCoqSRGzshaf27erqZ+fOiqf2G09xcTOKkkRd3f1H2hsbO1m+/BBGRsnY2Oxh8+YiNJqhnzTH06SnV6EoSY+1nz3bxPz5WRgY7GT27Iyf/BmFEL9+EsqEEJMmM1OFtbUx+/dXMzg4/KN9U1Iuk5hYPqHzv/vuBdrbNdTXb6GgIJRDh+pIS6ua0DmeJiKiED+/GbS0/IX33vPjnXeK+fZbueNQCPE4CWVCiElRUdFGZeUd8vL+gFrdx/HjN8ae7dpViYNDOqamqYSHn+b69XZ27Cjlzp0+nJwyaWrqQlGSKCpqBMDP7wCRkYUAxMVdxNr67xgaJhMQcJDu7v4nrsHS0pD6+g4SE8u5e7ePtra/Eh29aGz8V189g63tHry8sqmtHa2y7d5dib39Xuzt95KUdAkYreKtW3cCM7NUfHxyxkLVyZPf4eiYwYwZaRQUNDxxDWfONLF3bxUeHtb09/8NLy87BgaG2bKlCCur3djZ7Rmb69Kl2yxZkouxcQpeXtmUlLQAEBBwkMWLc7Gx2UNycgUlJS24u2dhYbGLiIjTaDRDNDZ24uv7KUZGycycmU5OztWfs4VCiGdMQpkQYlJkZqp44YUZBAU5ERzsTFraFQBqatS89daXJCT4U1y8EbW6j97eQbZv98XOzgSVKuKJYw4Pj6DRDJOb+yInT66jpOQWpaU/PLF/QoI/mza5kZt7jTVrjuPiso/y8tuP9Kmo+DP6+lN4++3zXLt2j+joL0lNXcGpU6HExl6gsvIOCQllqFRqrlyJIDjYmcjIIkZGtEREFBIY6EhZ2Z8YGBgZdw1HjqzB09OG+Pgy/P0PsnRpHmp1H/v2qTh8+Drnz28kMfH3lJb+QG/vAOvWfY6LiyU3bmwhIGAWISEnxoKnWt3HxYubiIz0JCzsFEFBTqhUEVRW3iUtrYr8/O/4/vtuzp3bQHy8Pw8fTs5RrRBicsg1S0KICdfTM8Bnn9Xy8OEQRkbJDA9rGRoaobb2Pteu3UerhbVr52BhYUhh4XoA8vNvoihgbm5Ie7sGgP9dODI8PBp4FEVBq9WSkFCGg4MpABrNEEZGeuOsAoqLW4iOXkRGRhDl5bcJDy9kx45S0tNXArB69W9wdLQgMNCRw4evU1NzD60WoqLOoigKAwPDlJa2olKpaW7uwccnl6GhEXp7B7l1q4eODg1r185h1ixzgoOdOXu26ZH5u7r6qa6+R25uMCYm+hQVNbJpUwFZWTU0N3czZ44VXl52eHnZERnpSVtbL62tDwgLc2PmTDPCwz1ITa3kxo0OALy9p+PqasXdu720tfXy8ccqsrOv0tc3yDfftLJ//yqqq+8RFnYKgM2bPSdwV4UQk00qZUKICZeXV8vQkJbKynCqqsKpro7AwcGU9PQrzJ9vjaLA0aP1qFRq3N2zKCxsQE9vCv39w7S29jB1qj4AdXXtNDV1cfNmJwDV1Wri48uIjvZh/fq5APzYRXEpKZeJjCykoaETa2tj9PWnYGtrMvb86NF6mpu7KS5uwd19Gm5u1gDExS0jOzuYl16ax7JlM3Fzm4ajozlHjqwhJmYJb77pg739VGxtjTl2bHSM8Y4v9fQUXnvtDDExX9HdPYCNjTGKomBra4Knpw319e3/PeKtxc0tC0WB556bysGDddy61UNOzlUsLQ2ZO3cawFj4tLY2xsbGmJdfdic/P4SNG93YsGEep083oNEM8fXXYWzd6k1c3L/p7NT8/A0VQjwTUikTQky4zEwVQUGOLFxoO9a2YcM8PvmkhoQEf5KTlxMbe4GengFCQ10JDHTE1NSAlJTLBAb+k7q6zWzb5s3771+koKABd/fRsDR3rhUrVszmlVcK8fGxw87OhIaGThYtmj7uOrKyVvHGG1+wePGnjIxoCQiYRWLi7+jvH33p4MGDQZ5/PhsnJws++igANzdrkpOX8+GH5XR0aAgJccXdfRqxsX40NXUTGvo55uYGxMf7Y2CgR07Oi7z++jmWLs0jKMjxsflNTQ04dmwtMTFf4eKyDzMzA6KiFhIR4cHIiJaKijusWHEYQ0M9tm/3Zfr0qRw/HsLWrV/g6rqfefOsOHEiBDMzg0fG1dObwqFDq9m27V8cOFCLt7cdvr726OlNITNThYfHPzA3N+CDD36LpaXRRG2rEGKSyYXkQoj/O01NXTg776Ow8I+sWuX8Sy9HCCEAOb4UQgghhNAJUikTQgghhNABUikTQgghhNABEsqEEEIIIXSAhDIhhBBCCB0goUwIIYQQQgdIKBNCCCGE0AESyoQQQgghdMB/AHLDufI3V1MXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "header_font = {'family': 'arial','color':  'darkred','weight': 'bold','size': 15 }\n",
    "xy_font = {'family': 'arial','color':  'darkblue','weight': 'bold','size': 10 }\n",
    "plt.figure(figsize=(10,10))\n",
    "X_test = sm.add_constant(X_test)\n",
    "\n",
    "y_preds = results.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_preds)\n",
    "plt.plot(y_test, y_test, color=\"red\")\n",
    "plt.xlabel(\"Actual Speed Scores\", fontdict=xy_font)\n",
    "plt.ylabel(\"Estimated Speed Scores\", fontdict=xy_font)\n",
    "plt.title(\"PageSpeed: Actual Scores vs Estimated Scores - without outliers\", fontdict=header_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE)        : 5.715370395272737\n",
      "Mean Sq. Error (MSE)          : 48.893438989452754\n",
      "Root Mean Sq. Error (RMSE)     : 6.992384356530521\n",
      "Mean Abs. Perc. Error (MAPE) : inf\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error (MAE)        : {}\".format(mean_absolute_error(y_test, y_preds)))\n",
    "print(\"Mean Sq. Error (MSE)          : {}\".format(mse(y_test, y_preds)))\n",
    "print(\"Root Mean Sq. Error (RMSE)     : {}\".format(rmse(y_test, y_preds)))\n",
    "print(\"Mean Abs. Perc. Error (MAPE) : {}\".format(np.mean(np.abs((y_test - y_preds) / y_test)) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is better but still not good, now let's try log transformation for both df and df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Contentful Paint Time (sec)</th>\n",
       "      <th>Speed Index Time (sec)</th>\n",
       "      <th>Time to Interactive (sec)</th>\n",
       "      <th>First Meaningful Paint Time (sec)</th>\n",
       "      <th>First CPU Idle Time (sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.10380</td>\n",
       "      <td>1.41664</td>\n",
       "      <td>1.63347</td>\n",
       "      <td>1.10380</td>\n",
       "      <td>1.32428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.25285</td>\n",
       "      <td>1.55267</td>\n",
       "      <td>1.14613</td>\n",
       "      <td>1.40654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.14301</td>\n",
       "      <td>1.40824</td>\n",
       "      <td>1.46687</td>\n",
       "      <td>1.19866</td>\n",
       "      <td>1.42651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.25042</td>\n",
       "      <td>1.30535</td>\n",
       "      <td>1.27646</td>\n",
       "      <td>1.25285</td>\n",
       "      <td>1.25285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.08279</td>\n",
       "      <td>1.10037</td>\n",
       "      <td>1.44871</td>\n",
       "      <td>1.10380</td>\n",
       "      <td>1.16137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Contentful Paint Time (sec)  Speed Index Time (sec)  \\\n",
       "0                            1.10380                 1.41664   \n",
       "1                            1.14613                 1.25285   \n",
       "2                            1.14301                 1.40824   \n",
       "3                            1.25042                 1.30535   \n",
       "4                            1.08279                 1.10037   \n",
       "\n",
       "   Time to Interactive (sec)  First Meaningful Paint Time (sec)  \\\n",
       "0                    1.63347                            1.10380   \n",
       "1                    1.55267                            1.14613   \n",
       "2                    1.46687                            1.19866   \n",
       "3                    1.27646                            1.25285   \n",
       "4                    1.44871                            1.10380   \n",
       "\n",
       "   First CPU Idle Time (sec)  \n",
       "0                    1.32428  \n",
       "1                    1.40654  \n",
       "2                    1.42651  \n",
       "3                    1.25285  \n",
       "4                    1.16137  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df[\"Performance Score\"]\n",
    "\n",
    "X=np.log10(df[[ \"First Contentful Paint Time (sec)\", \"Speed Index Time (sec)\", \"Time to Interactive (sec)\",  'First Meaningful Paint Time (sec)', 'First CPU Idle Time (sec)']])\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Count: 1291\n",
      "Test Data Count: 323\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 465)\n",
    "\n",
    "print('Train Data Count: {}'.format(X_train.shape[0]))\n",
    "print('Test Data Count: {}'.format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Performance Score</td> <th>  R-squared:         </th> <td>   0.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   4096.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Nov 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:00:33</td>      <th>  Log-Likelihood:    </th> <td> -4213.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1291</td>       <th>  AIC:               </th> <td>   8438.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1285</td>       <th>  BIC:               </th> <td>   8469.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                             <td>  143.1648</td> <td>    0.703</td> <td>  203.614</td> <td> 0.000</td> <td>  141.785</td> <td>  144.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Contentful Paint Time (sec)</th> <td>  -27.0745</td> <td>    1.633</td> <td>  -16.579</td> <td> 0.000</td> <td>  -30.278</td> <td>  -23.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Speed Index Time (sec)</th>            <td>  -34.3062</td> <td>    1.210</td> <td>  -28.359</td> <td> 0.000</td> <td>  -36.679</td> <td>  -31.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time to Interactive (sec)</th>         <td>  -29.8350</td> <td>    1.359</td> <td>  -21.957</td> <td> 0.000</td> <td>  -32.501</td> <td>  -27.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Meaningful Paint Time (sec)</th> <td>   -5.7444</td> <td>    1.542</td> <td>   -3.725</td> <td> 0.000</td> <td>   -8.770</td> <td>   -2.719</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First CPU Idle Time (sec)</th>         <td>  -26.8697</td> <td>    1.482</td> <td>  -18.133</td> <td> 0.000</td> <td>  -29.777</td> <td>  -23.963</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>159.024</td> <th>  Durbin-Watson:     </th> <td>   1.964</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1461.868</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.154</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.204</td>  <th>  Cond. No.          </th> <td>    23.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      Performance Score   R-squared:                       0.941\n",
       "Model:                            OLS   Adj. R-squared:                  0.941\n",
       "Method:                 Least Squares   F-statistic:                     4096.\n",
       "Date:                Thu, 28 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                        13:00:33   Log-Likelihood:                -4213.0\n",
       "No. Observations:                1291   AIC:                             8438.\n",
       "Df Residuals:                    1285   BIC:                             8469.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================================\n",
       "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------------\n",
       "const                               143.1648      0.703    203.614      0.000     141.785     144.544\n",
       "First Contentful Paint Time (sec)   -27.0745      1.633    -16.579      0.000     -30.278     -23.871\n",
       "Speed Index Time (sec)              -34.3062      1.210    -28.359      0.000     -36.679     -31.933\n",
       "Time to Interactive (sec)           -29.8350      1.359    -21.957      0.000     -32.501     -27.169\n",
       "First Meaningful Paint Time (sec)    -5.7444      1.542     -3.725      0.000      -8.770      -2.719\n",
       "First CPU Idle Time (sec)           -26.8697      1.482    -18.133      0.000     -29.777     -23.963\n",
       "==============================================================================\n",
       "Omnibus:                      159.024   Durbin-Watson:                   1.964\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1461.868\n",
       "Skew:                          -0.154   Prob(JB):                         0.00\n",
       "Kurtosis:                       8.204   Cond. No.                         23.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(X_train)\n",
    "results = sm.OLS(y_train, X_train).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "header_font = {'family': 'arial','color':  'darkred','weight': 'bold','size': 15 }\n",
    "xy_font = {'family': 'arial','color':  'darkblue','weight': 'bold','size': 10 }\n",
    "plt.figure(figsize=(10,10))\n",
    "X_test = sm.add_constant(X_test)\n",
    "\n",
    "y_preds = results.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_preds)\n",
    "plt.plot(y_test, y_test, color=\"red\")\n",
    "plt.xlabel(\"Actual Speed Scores\", fontdict=xy_font)\n",
    "plt.ylabel(\"Estimated Speed Scores\", fontdict=xy_font)\n",
    "plt.title(\"PageSpeed: Actual Scores vs Estimated Scores - log transformed\", fontdict=header_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE)        : 4.773760504161997\n",
      "Mean Sq. Error (MSE)          : 43.18201931645037\n",
      "Root Mean Sq. Error (RMSE)     : 6.571302710760658\n",
      "Mean Abs. Perc. Error (MAPE) : inf\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error (MAE)        : {}\".format(mean_absolute_error(y_test, y_preds)))\n",
    "print(\"Mean Sq. Error (MSE)          : {}\".format(mse(y_test, y_preds)))\n",
    "print(\"Root Mean Sq. Error (RMSE)     : {}\".format(rmse(y_test, y_preds)))\n",
    "print(\"Mean Abs. Perc. Error (MAPE) : {}\".format(np.mean(np.abs((y_test - y_preds) / y_test)) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log transformation without outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Contentful Paint Time (sec)</th>\n",
       "      <th>Speed Index Time (sec)</th>\n",
       "      <th>Time to Interactive (sec)</th>\n",
       "      <th>First Meaningful Paint Time (sec)</th>\n",
       "      <th>First CPU Idle Time (sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80618</td>\n",
       "      <td>1.15836</td>\n",
       "      <td>1.41330</td>\n",
       "      <td>0.89763</td>\n",
       "      <td>1.20140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80618</td>\n",
       "      <td>1.15836</td>\n",
       "      <td>1.41330</td>\n",
       "      <td>0.89763</td>\n",
       "      <td>1.20140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.80618</td>\n",
       "      <td>1.15836</td>\n",
       "      <td>1.41330</td>\n",
       "      <td>0.89763</td>\n",
       "      <td>1.20140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.80618</td>\n",
       "      <td>1.15836</td>\n",
       "      <td>1.27646</td>\n",
       "      <td>0.89763</td>\n",
       "      <td>1.20140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.80618</td>\n",
       "      <td>1.10037</td>\n",
       "      <td>1.41330</td>\n",
       "      <td>0.89763</td>\n",
       "      <td>1.16137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Contentful Paint Time (sec)  Speed Index Time (sec)  \\\n",
       "0                            0.80618                 1.15836   \n",
       "1                            0.80618                 1.15836   \n",
       "2                            0.80618                 1.15836   \n",
       "3                            0.80618                 1.15836   \n",
       "4                            0.80618                 1.10037   \n",
       "\n",
       "   Time to Interactive (sec)  First Meaningful Paint Time (sec)  \\\n",
       "0                    1.41330                            0.89763   \n",
       "1                    1.41330                            0.89763   \n",
       "2                    1.41330                            0.89763   \n",
       "3                    1.27646                            0.89763   \n",
       "4                    1.41330                            0.89763   \n",
       "\n",
       "   First CPU Idle Time (sec)  \n",
       "0                    1.20140  \n",
       "1                    1.20140  \n",
       "2                    1.20140  \n",
       "3                    1.20140  \n",
       "4                    1.16137  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df2[\"Performance Score\"]\n",
    "\n",
    "X=np.log10(df2[[ \"First Contentful Paint Time (sec)\", \"Speed Index Time (sec)\", \"Time to Interactive (sec)\",  'First Meaningful Paint Time (sec)', 'First CPU Idle Time (sec)']])\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Count: 1291\n",
      "Test Data Count: 323\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 465)\n",
    "\n",
    "print('Train Data Count: {}'.format(X_train.shape[0]))\n",
    "print('Test Data Count: {}'.format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Performance Score</td> <th>  R-squared:         </th> <td>   0.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.956</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   5664.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Nov 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:00:46</td>      <th>  Log-Likelihood:    </th> <td> -4014.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1291</td>       <th>  AIC:               </th> <td>   8041.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1285</td>       <th>  BIC:               </th> <td>   8072.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                             <td>  147.5514</td> <td>    0.623</td> <td>  236.658</td> <td> 0.000</td> <td>  146.328</td> <td>  148.775</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Contentful Paint Time (sec)</th> <td>  -24.2596</td> <td>    1.513</td> <td>  -16.033</td> <td> 0.000</td> <td>  -27.228</td> <td>  -21.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Speed Index Time (sec)</th>            <td>  -37.5753</td> <td>    1.084</td> <td>  -34.652</td> <td> 0.000</td> <td>  -39.703</td> <td>  -35.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time to Interactive (sec)</th>         <td>  -35.2627</td> <td>    1.276</td> <td>  -27.636</td> <td> 0.000</td> <td>  -37.766</td> <td>  -32.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First Meaningful Paint Time (sec)</th> <td>   -9.5337</td> <td>    1.435</td> <td>   -6.642</td> <td> 0.000</td> <td>  -12.349</td> <td>   -6.718</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>First CPU Idle Time (sec)</th>         <td>  -22.9242</td> <td>    1.388</td> <td>  -16.519</td> <td> 0.000</td> <td>  -25.647</td> <td>  -20.202</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>601.505</td> <th>  Durbin-Watson:     </th> <td>   2.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>7051.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.850</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>13.835</td>  <th>  Cond. No.          </th> <td>    25.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      Performance Score   R-squared:                       0.957\n",
       "Model:                            OLS   Adj. R-squared:                  0.956\n",
       "Method:                 Least Squares   F-statistic:                     5664.\n",
       "Date:                Thu, 28 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                        13:00:46   Log-Likelihood:                -4014.4\n",
       "No. Observations:                1291   AIC:                             8041.\n",
       "Df Residuals:                    1285   BIC:                             8072.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================================\n",
       "                                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------------------\n",
       "const                               147.5514      0.623    236.658      0.000     146.328     148.775\n",
       "First Contentful Paint Time (sec)   -24.2596      1.513    -16.033      0.000     -27.228     -21.291\n",
       "Speed Index Time (sec)              -37.5753      1.084    -34.652      0.000     -39.703     -35.448\n",
       "Time to Interactive (sec)           -35.2627      1.276    -27.636      0.000     -37.766     -32.760\n",
       "First Meaningful Paint Time (sec)    -9.5337      1.435     -6.642      0.000     -12.349      -6.718\n",
       "First CPU Idle Time (sec)           -22.9242      1.388    -16.519      0.000     -25.647     -20.202\n",
       "==============================================================================\n",
       "Omnibus:                      601.505   Durbin-Watson:                   2.045\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             7051.016\n",
       "Skew:                          -1.850   Prob(JB):                         0.00\n",
       "Kurtosis:                      13.835   Cond. No.                         25.6\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(X_train)\n",
    "results = sm.OLS(y_train, X_train).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "header_font = {'family': 'arial','color':  'darkred','weight': 'bold','size': 15 }\n",
    "xy_font = {'family': 'arial','color':  'darkblue','weight': 'bold','size': 10 }\n",
    "plt.figure(figsize=(10,10))\n",
    "X_test = sm.add_constant(X_test)\n",
    "\n",
    "y_preds = results.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_preds)\n",
    "plt.plot(y_test, y_test, color=\"red\")\n",
    "plt.xlabel(\"Actual Speed Scores\", fontdict=xy_font)\n",
    "plt.ylabel(\"Estimated Speed Scores\", fontdict=xy_font)\n",
    "plt.title(\"PageSpeed: Actual Scores vs Estimated Scores - log transformed without outliers\", fontdict=header_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE)        : 3.9137419834295764\n",
      "Mean Sq. Error (MSE)          : 28.793881872098027\n",
      "Root Mean Sq. Error (RMSE)     : 5.365993092811248\n",
      "Mean Abs. Perc. Error (MAPE) : inf\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error (MAE)        : {}\".format(mean_absolute_error(y_test, y_preds)))\n",
    "print(\"Mean Sq. Error (MSE)          : {}\".format(mse(y_test, y_preds)))\n",
    "print(\"Root Mean Sq. Error (RMSE)     : {}\".format(rmse(y_test, y_preds)))\n",
    "print(\"Mean Abs. Perc. Error (MAPE) : {}\".format(np.mean(np.abs((y_test - y_preds) / y_test)) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# last one is better for predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pagespeed Score = 147.32 - 24.25(logFCP) - 37.67(logSIT) - 34.20(logTTI) - 9.53(logFMP) - 23.72(logCPU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use polynomial features to see if we can do better "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.rename(columns = {'First Contentful Paint Time (sec)': 'FCP', 'Speed Index Time (sec)': 'SIT', 'Time to Interactive (sec)': 'TTI', 'First Meaningful Paint Time (sec)': 'FMP', 'First CPU Idle Time (sec)': 'CPU'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>FCP</th>\n",
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       "      <td>7.90000</td>\n",
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       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.40000</td>\n",
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       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
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       "      <td>18.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
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       "      <td>6.40000</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>14.50000</td>\n",
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      "text/plain": [
       "      FCP      SIT      TTI     FMP      CPU\n",
       "0 6.40000 14.40000 25.90000 7.90000 15.90000\n",
       "1 6.40000 14.40000 25.90000 7.90000 15.90000\n",
       "2 6.40000 14.40000 25.90000 7.90000 15.90000\n",
       "3 6.40000 14.40000 18.90000 7.90000 15.90000\n",
       "4 6.40000 12.60000 25.90000 7.90000 14.50000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df2[[\"FCP\", \"SIT\", \"TTI\",  'FMP', 'CPU']]\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.rename(columns = {'First Contentful Paint Time (sec)': 'FCP', 'Speed Index Time (sec)': 'SIT', 'Time to Interactive (sec)': 'TTI', 'First Meaningful Paint Time (sec)': 'FMP', 'First CPU Idle Time (sec)': 'CPU'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
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       "      <td>6.40000</td>\n",
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       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
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       "      FCP      SIT      TTI     FMP      CPU\n",
       "0 6.40000 14.40000 25.90000 7.90000 15.90000\n",
       "1 6.40000 14.40000 25.90000 7.90000 15.90000\n",
       "2 6.40000 14.40000 25.90000 7.90000 15.90000\n",
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       "4 6.40000 12.60000 25.90000 7.90000 14.50000"
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     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <td>80.64000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
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       "      <td>326.34000</td>\n",
       "      <td>99.54000</td>\n",
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       "4 1.00000 6.40000 12.60000 25.90000 7.90000 14.50000 40.96000 80.64000   \n",
       "\n",
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = poly.fit_transform(df5)\n",
    "df7 = pd.DataFrame(df6)\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 49,
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  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7.columns = poly.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>...</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>372.96000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>411.81000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>...</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>372.96000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>411.81000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>...</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>372.96000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>411.81000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>18.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>120.96000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>...</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>272.16000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>357.21000</td>\n",
       "      <td>149.31000</td>\n",
       "      <td>300.51000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.40000</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>14.50000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>80.64000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>...</td>\n",
       "      <td>158.76000</td>\n",
       "      <td>326.34000</td>\n",
       "      <td>99.54000</td>\n",
       "      <td>182.70000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>375.55000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>114.55000</td>\n",
       "      <td>210.25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1      x0       x1       x2      x3       x4     x0^2    x0 x1  \\\n",
       "0 1.00000 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 92.16000   \n",
       "1 1.00000 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 92.16000   \n",
       "2 1.00000 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 92.16000   \n",
       "3 1.00000 6.40000 14.40000 18.90000 7.90000 15.90000 40.96000 92.16000   \n",
       "4 1.00000 6.40000 12.60000 25.90000 7.90000 14.50000 40.96000 80.64000   \n",
       "\n",
       "      x0 x2    x0 x3  ...      x1^2     x1 x2     x1 x3     x1 x4      x2^2  \\\n",
       "0 165.76000 50.56000  ... 207.36000 372.96000 113.76000 228.96000 670.81000   \n",
       "1 165.76000 50.56000  ... 207.36000 372.96000 113.76000 228.96000 670.81000   \n",
       "2 165.76000 50.56000  ... 207.36000 372.96000 113.76000 228.96000 670.81000   \n",
       "3 120.96000 50.56000  ... 207.36000 272.16000 113.76000 228.96000 357.21000   \n",
       "4 165.76000 50.56000  ... 158.76000 326.34000  99.54000 182.70000 670.81000   \n",
       "\n",
       "      x2 x3     x2 x4     x3^2     x3 x4      x4^2  \n",
       "0 204.61000 411.81000 62.41000 125.61000 252.81000  \n",
       "1 204.61000 411.81000 62.41000 125.61000 252.81000  \n",
       "2 204.61000 411.81000 62.41000 125.61000 252.81000  \n",
       "3 149.31000 300.51000 62.41000 125.61000 252.81000  \n",
       "4 204.61000 375.55000 62.41000 114.55000 210.25000  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7['Performance Score'] = df2[\"Performance Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b861160>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_kor = df7.corr()\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(df_kor, vmin=-1, vmax=1, cmap=\"viridis\", annot=True, linewidth=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['Performance Score'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# built the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x0^2</th>\n",
       "      <th>x0 x1</th>\n",
       "      <th>x0 x2</th>\n",
       "      <th>x0 x3</th>\n",
       "      <th>x0 x4</th>\n",
       "      <th>x1^2</th>\n",
       "      <th>x1 x2</th>\n",
       "      <th>x1 x3</th>\n",
       "      <th>x1 x4</th>\n",
       "      <th>x2^2</th>\n",
       "      <th>x2 x3</th>\n",
       "      <th>x2 x4</th>\n",
       "      <th>x3^2</th>\n",
       "      <th>x3 x4</th>\n",
       "      <th>x4^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>372.96000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>411.81000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>372.96000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>411.81000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>372.96000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>411.81000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>18.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.16000</td>\n",
       "      <td>120.96000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>272.16000</td>\n",
       "      <td>113.76000</td>\n",
       "      <td>228.96000</td>\n",
       "      <td>357.21000</td>\n",
       "      <td>149.31000</td>\n",
       "      <td>300.51000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>125.61000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>14.50000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>80.64000</td>\n",
       "      <td>165.76000</td>\n",
       "      <td>50.56000</td>\n",
       "      <td>92.80000</td>\n",
       "      <td>158.76000</td>\n",
       "      <td>326.34000</td>\n",
       "      <td>99.54000</td>\n",
       "      <td>182.70000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>204.61000</td>\n",
       "      <td>375.55000</td>\n",
       "      <td>62.41000</td>\n",
       "      <td>114.55000</td>\n",
       "      <td>210.25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x0       x1       x2      x3       x4     x0^2    x0 x1     x0 x2  \\\n",
       "0 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 92.16000 165.76000   \n",
       "1 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 92.16000 165.76000   \n",
       "2 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 92.16000 165.76000   \n",
       "3 6.40000 14.40000 18.90000 7.90000 15.90000 40.96000 92.16000 120.96000   \n",
       "4 6.40000 12.60000 25.90000 7.90000 14.50000 40.96000 80.64000 165.76000   \n",
       "\n",
       "     x0 x3     x0 x4      x1^2     x1 x2     x1 x3     x1 x4      x2^2  \\\n",
       "0 50.56000 101.76000 207.36000 372.96000 113.76000 228.96000 670.81000   \n",
       "1 50.56000 101.76000 207.36000 372.96000 113.76000 228.96000 670.81000   \n",
       "2 50.56000 101.76000 207.36000 372.96000 113.76000 228.96000 670.81000   \n",
       "3 50.56000 101.76000 207.36000 272.16000 113.76000 228.96000 357.21000   \n",
       "4 50.56000  92.80000 158.76000 326.34000  99.54000 182.70000 670.81000   \n",
       "\n",
       "      x2 x3     x2 x4     x3^2     x3 x4      x4^2  \n",
       "0 204.61000 411.81000 62.41000 125.61000 252.81000  \n",
       "1 204.61000 411.81000 62.41000 125.61000 252.81000  \n",
       "2 204.61000 411.81000 62.41000 125.61000 252.81000  \n",
       "3 149.31000 300.51000 62.41000 125.61000 252.81000  \n",
       "4 204.61000 375.55000 62.41000 114.55000 210.25000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df7[\"Performance Score\"]\n",
    "\n",
    "X=df7.drop(['Performance Score','1'], axis =1) \n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Count: 1206\n",
      "Test Data Count: 302\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 465)\n",
    "\n",
    "print('Train Data Count: {}'.format(X_train.shape[0]))\n",
    "print('Test Data Count: {}'.format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Performance Score</td> <th>  R-squared:         </th> <td>   0.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   6586.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Nov 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:01:54</td>      <th>  Log-Likelihood:    </th> <td> -2672.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1206</td>       <th>  AIC:               </th> <td>   5386.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1185</td>       <th>  BIC:               </th> <td>   5493.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    20</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  122.2019</td> <td>    0.468</td> <td>  260.894</td> <td> 0.000</td> <td>  121.283</td> <td>  123.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0</th>    <td>   -0.8915</td> <td>    0.367</td> <td>   -2.430</td> <td> 0.015</td> <td>   -1.611</td> <td>   -0.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   -5.3353</td> <td>    0.123</td> <td>  -43.500</td> <td> 0.000</td> <td>   -5.576</td> <td>   -5.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>   -3.7431</td> <td>    0.087</td> <td>  -43.242</td> <td> 0.000</td> <td>   -3.913</td> <td>   -3.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -1.2036</td> <td>    0.286</td> <td>   -4.206</td> <td> 0.000</td> <td>   -1.765</td> <td>   -0.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>   -2.3747</td> <td>    0.134</td> <td>  -17.691</td> <td> 0.000</td> <td>   -2.638</td> <td>   -2.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0^2</th>  <td>   -0.3981</td> <td>    0.081</td> <td>   -4.936</td> <td> 0.000</td> <td>   -0.556</td> <td>   -0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0 x1</th> <td>   -0.0184</td> <td>    0.035</td> <td>   -0.527</td> <td> 0.598</td> <td>   -0.087</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0 x2</th> <td>    0.0187</td> <td>    0.021</td> <td>    0.876</td> <td> 0.381</td> <td>   -0.023</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0 x3</th> <td>    0.1913</td> <td>    0.111</td> <td>    1.721</td> <td> 0.086</td> <td>   -0.027</td> <td>    0.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0 x4</th> <td>   -0.1025</td> <td>    0.036</td> <td>   -2.810</td> <td> 0.005</td> <td>   -0.174</td> <td>   -0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1^2</th>  <td>    0.2018</td> <td>    0.009</td> <td>   23.206</td> <td> 0.000</td> <td>    0.185</td> <td>    0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1 x2</th> <td>   -0.0114</td> <td>    0.007</td> <td>   -1.660</td> <td> 0.097</td> <td>   -0.025</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1 x3</th> <td>   -0.0184</td> <td>    0.028</td> <td>   -0.653</td> <td> 0.514</td> <td>   -0.073</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1 x4</th> <td>    0.0164</td> <td>    0.012</td> <td>    1.371</td> <td> 0.171</td> <td>   -0.007</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2^2</th>  <td>    0.0899</td> <td>    0.004</td> <td>   22.459</td> <td> 0.000</td> <td>    0.082</td> <td>    0.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2 x3</th> <td>    0.0054</td> <td>    0.018</td> <td>    0.303</td> <td> 0.762</td> <td>   -0.029</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2 x4</th> <td>   -0.0034</td> <td>    0.008</td> <td>   -0.399</td> <td> 0.690</td> <td>   -0.020</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3^2</th>  <td>   -0.0345</td> <td>    0.056</td> <td>   -0.612</td> <td> 0.541</td> <td>   -0.145</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3 x4</th> <td>    0.0010</td> <td>    0.030</td> <td>    0.035</td> <td> 0.972</td> <td>   -0.057</td> <td>    0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4^2</th>  <td>    0.0913</td> <td>    0.010</td> <td>    9.446</td> <td> 0.000</td> <td>    0.072</td> <td>    0.110</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>401.230</td> <th>  Durbin-Watson:     </th> <td>   2.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2510.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.391</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 9.497</td>  <th>  Cond. No.          </th> <td>2.79e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.79e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      Performance Score   R-squared:                       0.991\n",
       "Model:                            OLS   Adj. R-squared:                  0.991\n",
       "Method:                 Least Squares   F-statistic:                     6586.\n",
       "Date:                Thu, 28 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                        13:01:54   Log-Likelihood:                -2672.2\n",
       "No. Observations:                1206   AIC:                             5386.\n",
       "Df Residuals:                    1185   BIC:                             5493.\n",
       "Df Model:                          20                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        122.2019      0.468    260.894      0.000     121.283     123.121\n",
       "x0            -0.8915      0.367     -2.430      0.015      -1.611      -0.172\n",
       "x1            -5.3353      0.123    -43.500      0.000      -5.576      -5.095\n",
       "x2            -3.7431      0.087    -43.242      0.000      -3.913      -3.573\n",
       "x3            -1.2036      0.286     -4.206      0.000      -1.765      -0.642\n",
       "x4            -2.3747      0.134    -17.691      0.000      -2.638      -2.111\n",
       "x0^2          -0.3981      0.081     -4.936      0.000      -0.556      -0.240\n",
       "x0 x1         -0.0184      0.035     -0.527      0.598      -0.087       0.050\n",
       "x0 x2          0.0187      0.021      0.876      0.381      -0.023       0.061\n",
       "x0 x3          0.1913      0.111      1.721      0.086      -0.027       0.409\n",
       "x0 x4         -0.1025      0.036     -2.810      0.005      -0.174      -0.031\n",
       "x1^2           0.2018      0.009     23.206      0.000       0.185       0.219\n",
       "x1 x2         -0.0114      0.007     -1.660      0.097      -0.025       0.002\n",
       "x1 x3         -0.0184      0.028     -0.653      0.514      -0.073       0.037\n",
       "x1 x4          0.0164      0.012      1.371      0.171      -0.007       0.040\n",
       "x2^2           0.0899      0.004     22.459      0.000       0.082       0.098\n",
       "x2 x3          0.0054      0.018      0.303      0.762      -0.029       0.040\n",
       "x2 x4         -0.0034      0.008     -0.399      0.690      -0.020       0.013\n",
       "x3^2          -0.0345      0.056     -0.612      0.541      -0.145       0.076\n",
       "x3 x4          0.0010      0.030      0.035      0.972      -0.057       0.059\n",
       "x4^2           0.0913      0.010      9.446      0.000       0.072       0.110\n",
       "==============================================================================\n",
       "Omnibus:                      401.230   Durbin-Watson:                   2.139\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2510.302\n",
       "Skew:                          -1.391   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.497   Cond. No.                     2.79e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.79e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(X_train)\n",
    "results = sm.OLS(y_train, X_train).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some variables have p value more than 0.05. Let's drop them and built the model without them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x0^2</th>\n",
       "      <th>x0 x4</th>\n",
       "      <th>x1^2</th>\n",
       "      <th>x2^2</th>\n",
       "      <th>x4^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>14.40000</td>\n",
       "      <td>18.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>15.90000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>101.76000</td>\n",
       "      <td>207.36000</td>\n",
       "      <td>357.21000</td>\n",
       "      <td>252.81000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.40000</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>7.90000</td>\n",
       "      <td>14.50000</td>\n",
       "      <td>40.96000</td>\n",
       "      <td>92.80000</td>\n",
       "      <td>158.76000</td>\n",
       "      <td>670.81000</td>\n",
       "      <td>210.25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x0       x1       x2      x3       x4     x0^2     x0 x4      x1^2  \\\n",
       "0 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 101.76000 207.36000   \n",
       "1 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 101.76000 207.36000   \n",
       "2 6.40000 14.40000 25.90000 7.90000 15.90000 40.96000 101.76000 207.36000   \n",
       "3 6.40000 14.40000 18.90000 7.90000 15.90000 40.96000 101.76000 207.36000   \n",
       "4 6.40000 12.60000 25.90000 7.90000 14.50000 40.96000  92.80000 158.76000   \n",
       "\n",
       "       x2^2      x4^2  \n",
       "0 670.81000 252.81000  \n",
       "1 670.81000 252.81000  \n",
       "2 670.81000 252.81000  \n",
       "3 357.21000 252.81000  \n",
       "4 670.81000 210.25000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df7[\"Performance Score\"]\n",
    "\n",
    "X=df7[[ \"x0\", \"x1\", \"x2\",  'x3', 'x4', 'x0^2', 'x0 x4', 'x1^2', 'x2^2', 'x4^2']]\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Count: 1206\n",
      "Test Data Count: 302\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 465)\n",
    "\n",
    "print('Train Data Count: {}'.format(X_train.shape[0]))\n",
    "print('Test Data Count: {}'.format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Performance Score</td> <th>  R-squared:         </th> <td>   0.991</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.991</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>1.317e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Nov 2019</td>  <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:04:45</td>      <th>  Log-Likelihood:    </th> <td> -2677.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1206</td>       <th>  AIC:               </th> <td>   5377.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1195</td>       <th>  BIC:               </th> <td>   5433.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    10</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>  122.3141</td> <td>    0.461</td> <td>  265.179</td> <td> 0.000</td> <td>  121.409</td> <td>  123.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0</th>    <td>   -1.1297</td> <td>    0.249</td> <td>   -4.536</td> <td> 0.000</td> <td>   -1.618</td> <td>   -0.641</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>   -5.3471</td> <td>    0.117</td> <td>  -45.604</td> <td> 0.000</td> <td>   -5.577</td> <td>   -5.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>   -3.7044</td> <td>    0.079</td> <td>  -46.843</td> <td> 0.000</td> <td>   -3.860</td> <td>   -3.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -1.0207</td> <td>    0.070</td> <td>  -14.658</td> <td> 0.000</td> <td>   -1.157</td> <td>   -0.884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>    <td>   -2.4410</td> <td>    0.128</td> <td>  -19.047</td> <td> 0.000</td> <td>   -2.692</td> <td>   -2.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0^2</th>  <td>   -0.2599</td> <td>    0.032</td> <td>   -8.161</td> <td> 0.000</td> <td>   -0.322</td> <td>   -0.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0 x4</th> <td>   -0.0666</td> <td>    0.016</td> <td>   -4.082</td> <td> 0.000</td> <td>   -0.099</td> <td>   -0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1^2</th>  <td>    0.1941</td> <td>    0.007</td> <td>   28.574</td> <td> 0.000</td> <td>    0.181</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2^2</th>  <td>    0.0873</td> <td>    0.002</td> <td>   35.650</td> <td> 0.000</td> <td>    0.083</td> <td>    0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4^2</th>  <td>    0.0929</td> <td>    0.007</td> <td>   14.284</td> <td> 0.000</td> <td>    0.080</td> <td>    0.106</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>398.924</td> <th>  Durbin-Watson:     </th> <td>   2.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2471.506</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.386</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 9.442</td>  <th>  Cond. No.          </th> <td>2.03e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.03e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      Performance Score   R-squared:                       0.991\n",
       "Model:                            OLS   Adj. R-squared:                  0.991\n",
       "Method:                 Least Squares   F-statistic:                 1.317e+04\n",
       "Date:                Thu, 28 Nov 2019   Prob (F-statistic):               0.00\n",
       "Time:                        13:04:45   Log-Likelihood:                -2677.3\n",
       "No. Observations:                1206   AIC:                             5377.\n",
       "Df Residuals:                    1195   BIC:                             5433.\n",
       "Df Model:                          10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        122.3141      0.461    265.179      0.000     121.409     123.219\n",
       "x0            -1.1297      0.249     -4.536      0.000      -1.618      -0.641\n",
       "x1            -5.3471      0.117    -45.604      0.000      -5.577      -5.117\n",
       "x2            -3.7044      0.079    -46.843      0.000      -3.860      -3.549\n",
       "x3            -1.0207      0.070    -14.658      0.000      -1.157      -0.884\n",
       "x4            -2.4410      0.128    -19.047      0.000      -2.692      -2.190\n",
       "x0^2          -0.2599      0.032     -8.161      0.000      -0.322      -0.197\n",
       "x0 x4         -0.0666      0.016     -4.082      0.000      -0.099      -0.035\n",
       "x1^2           0.1941      0.007     28.574      0.000       0.181       0.207\n",
       "x2^2           0.0873      0.002     35.650      0.000       0.083       0.092\n",
       "x4^2           0.0929      0.007     14.284      0.000       0.080       0.106\n",
       "==============================================================================\n",
       "Omnibus:                      398.924   Durbin-Watson:                   2.148\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2471.506\n",
       "Skew:                          -1.386   Prob(JB):                         0.00\n",
       "Kurtosis:                       9.442   Cond. No.                     2.03e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.03e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sm.add_constant(X_train)\n",
    "results = sm.OLS(y_train, X_train).fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "header_font = {'family': 'arial','color':  'darkred','weight': 'bold','size': 15 }\n",
    "xy_font = {'family': 'arial','color':  'darkblue','weight': 'bold','size': 10 }\n",
    "plt.figure(figsize=(10,10))\n",
    "X_test = sm.add_constant(X_test)\n",
    "\n",
    "y_preds = results.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_preds)\n",
    "plt.plot(y_test, y_test, color=\"red\")\n",
    "plt.xlabel(\"Actual Speed Scores\", fontdict=xy_font)\n",
    "plt.ylabel(\"Estimated Speed Scores\", fontdict=xy_font)\n",
    "plt.title(\"PageSpeed: Actual Scores vs Estimated Scores - polynomial features\", fontdict=header_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE)        : 1.6731942427924227\n",
      "Mean Sq. Error (MSE)          : 4.711714302632787\n",
      "Root Mean Sq. Error (RMSE)     : 2.1706483599682347\n",
      "Mean Abs. Perc. Error (MAPE) : inf\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error (MAE)        : {}\".format(mean_absolute_error(y_test, y_preds)))\n",
    "print(\"Mean Sq. Error (MSE)          : {}\".format(mse(y_test, y_preds)))\n",
    "print(\"Root Mean Sq. Error (RMSE)     : {}\".format(rmse(y_test, y_preds)))\n",
    "print(\"Mean Abs. Perc. Error (MAPE) : {}\".format(np.mean(np.abs((y_test - y_preds) / y_test)) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PAGESPEED SCORE = 122.3141 - 1.1297FCP - 5.3471SIT - 3.7044TTI - 1.0207FMP - 2.4410CPU - 0.2599(FCP^2) - 0.0666(FCP*CPU) + 0.1941(SIT^2) + 0.0873(TTI^2) + 0.0929(CPU^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This one has the best predictions! "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}