2. BotB Active Materials.ipynb

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    "# __Back of the Battery Calculator__\n",
    "---\n",
    "# __2. Active Materials__\n",
    "\n",
    "#### Copyright © Intercalation Ltd. All rights reserved.\n",
    "\n",
    "<div>\n",
    "<img src=\"images/fig_botb.png\" width=\"800\">\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "In the [first instalment](https://intercalationstation.substack.com/p/botb-1-introduction-and-capacity) of our BotB series, we introduced the calculation of theoretical capacities of different electrode materials based on their molar masses.\n",
    "\n",
    "We also covered some existing cell models, and since then, [Matt Lacey](http://lacey.se/) has shared his very useful [CellModel](https://github.com/mjlacey/cellmodels) along with some very interesting case studies.\n",
    "\n",
    "<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">For my interest: how many out there are interested in doing this sort of basic cell property modelling themselves? The code is not secret, so I could e.g. share it on Github with a bunch of Jupyter Notebook examples. I find it a useful reality check at least. Comment below... <a href=\"https://t.co/ySd7jj16zy\">https://t.co/ySd7jj16zy</a></p>&mdash; Matt Lacey (@mjlacey) <a href=\"https://twitter.com/mjlacey/status/1427545083063250946?ref_src=twsrc%5Etfw\">August 17, 2021</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n",
    "\n",
    "\n",
    "<blockquote class=\"twitter-tweet\"><p lang=\"en\" dir=\"ltr\">Thanks to those who positively responded! Happy therefore to share my cell model code here: <a href=\"https://t.co/kGwdzZF30b\">https://t.co/kGwdzZF30b</a><br><br>So far I have made 1 &#39;quickstart&#39; example of how to build a model similar to that nested in these quote tweets, and more to come later. To quickly summarise: 1/ <a href=\"https://t.co/MW1qrCRX4e\">https://t.co/MW1qrCRX4e</a></p>&mdash; Matt Lacey (@mjlacey) <a href=\"https://twitter.com/mjlacey/status/1427686001510653957?ref_src=twsrc%5Etfw\">August 17, 2021</a></blockquote> <script async src=\"https://platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>\n",
    "\n",
    "Based on Matt's flexible CellModel in Julia, we'll use BotB to deconstruct the properties and makeup of each component of a cell before building up our model in Python. Instead of using [Colab](https://colab.research.google.com/) we'll assemble the backend of the model and add an interactive UI for it very soon, so make sure you support Intercalation Station by hitting that ***[subscribe!](https://intercalationstation.substack.com/subscribe)***\n",
    "\n",
    "---\n",
    "## BotB Plan\n",
    "1. Theoretical capacities [[link]](https://intercalationstation.substack.com/p/botb-1-introduction-and-capacity)\n",
    "2. Active materials\n",
    "3. Cell sandwich layers\n",
    "4. Winding a cylindrical cell\n",
    "5. Stacking a pouch cell\n",
    "6. Prismatic cells\n",
    "7. tbd\n",
    "---\n",
    "\n",
    "## Outline for BotB 2:\n",
    "* A) Comparing practical and theoretical capacities of active materials\n",
    "* B) Active material average discharge voltages\n",
    "* C) Visualizing voltage and capacity together\n",
    "* D) Defining an active material\n",
    "* E) Defining an electrode composite\n",
    "---"
   ]
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    "#Import standard packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "#Unit & uncertainty management\n",
    "from pint import UnitRegistry\n",
    "unit = UnitRegistry()\n",
    "from uncertainties import ufloat\n",
    "\n",
    "#Dictionaries with dot notation\n",
    "from dotmap import DotMap\n",
    "\n",
    "#Functions created for assembling electrode structures\n",
    "from BotB_functions.fn_cellstack import make_active\n",
    "from BotB_functions.fn_cellstack import make_composite\n",
    "from BotB_functions.fn_cellstack import print_struct"
   ]
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    "## A) Theoretical vs practical capacities\n",
    "\n",
    "Not all of the lithium is extracted without damaging the material, and so the true reversible capacity achievable is considerably lower. For energy density, a higher value is better for both anodes and cathodes.\n",
    "\n",
    "Active materials in this model can have these properties:\n",
    "\n",
    "* Practical specific capacity [mAh/g]\n",
    "* Theoretical specific capacity [mAh/g]\n",
    "* Average voltage [V]\n",
    "* Crystallographic density [g/cm3]\n",
    "\n",
    "Again we can first import the active materials database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>name_long</th>\n",
       "      <th>formula</th>\n",
       "      <th>type</th>\n",
       "      <th>cap</th>\n",
       "      <th>V</th>\n",
       "      <th>n</th>\n",
       "      <th>density</th>\n",
       "      <th>theorycap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NCA</td>\n",
       "      <td>lithium nickel cobalt aluminium oxide</td>\n",
       "      <td>Li(Ni0.8Co0.15Al0.05)O2</td>\n",
       "      <td>cathode</td>\n",
       "      <td>210</td>\n",
       "      <td>3.860</td>\n",
       "      <td>1</td>\n",
       "      <td>4.850</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LCO</td>\n",
       "      <td>lithium cobalt oxide</td>\n",
       "      <td>LiCoO2</td>\n",
       "      <td>cathode</td>\n",
       "      <td>165</td>\n",
       "      <td>3.860</td>\n",
       "      <td>1</td>\n",
       "      <td>5.100</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LFP</td>\n",
       "      <td>lithium iron phosphate</td>\n",
       "      <td>LiFePO4</td>\n",
       "      <td>cathode</td>\n",
       "      <td>160</td>\n",
       "      <td>3.375</td>\n",
       "      <td>1</td>\n",
       "      <td>3.600</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMC622</td>\n",
       "      <td>lithium nickel manganese colbalt oxide</td>\n",
       "      <td>Li(Ni0.6Mn0.2Co0.2)O2</td>\n",
       "      <td>cathode</td>\n",
       "      <td>181</td>\n",
       "      <td>3.860</td>\n",
       "      <td>1</td>\n",
       "      <td>4.700</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NMC811</td>\n",
       "      <td>lithium nickel manganese colbalt oxide</td>\n",
       "      <td>Li(Ni0.8Mn0.1Co0.1)O2</td>\n",
       "      <td>cathode</td>\n",
       "      <td>195</td>\n",
       "      <td>3.860</td>\n",
       "      <td>1</td>\n",
       "      <td>4.700</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LTO</td>\n",
       "      <td>lithium titanate</td>\n",
       "      <td>Ti5O12</td>\n",
       "      <td>anode</td>\n",
       "      <td>167</td>\n",
       "      <td>1.550</td>\n",
       "      <td>3</td>\n",
       "      <td>3.430</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gr</td>\n",
       "      <td>graphite</td>\n",
       "      <td>C6</td>\n",
       "      <td>anode</td>\n",
       "      <td>344</td>\n",
       "      <td>0.170</td>\n",
       "      <td>1</td>\n",
       "      <td>2.240</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Li</td>\n",
       "      <td>lithium metal</td>\n",
       "      <td>Li</td>\n",
       "      <td>anode</td>\n",
       "      <td>3862</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.534</td>\n",
       "      <td>3861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Si</td>\n",
       "      <td>silicon</td>\n",
       "      <td>Si</td>\n",
       "      <td>anode</td>\n",
       "      <td>2200</td>\n",
       "      <td>0.400</td>\n",
       "      <td>4</td>\n",
       "      <td>2.300</td>\n",
       "      <td>3817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     name                               name_long                  formula  \\\n",
       "0     NCA   lithium nickel cobalt aluminium oxide  Li(Ni0.8Co0.15Al0.05)O2   \n",
       "1     LCO                    lithium cobalt oxide                   LiCoO2   \n",
       "2     LFP                  lithium iron phosphate                  LiFePO4   \n",
       "3  NMC622  lithium nickel manganese colbalt oxide    Li(Ni0.6Mn0.2Co0.2)O2   \n",
       "4  NMC811  lithium nickel manganese colbalt oxide    Li(Ni0.8Mn0.1Co0.1)O2   \n",
       "5     LTO                        lithium titanate                   Ti5O12   \n",
       "6      Gr                                graphite                       C6   \n",
       "7      Li                           lithium metal                       Li   \n",
       "8      Si                                 silicon                       Si   \n",
       "\n",
       "      type   cap      V  n  density  theorycap  \n",
       "0  cathode   210  3.860  1    4.850        279  \n",
       "1  cathode   165  3.860  1    5.100        274  \n",
       "2  cathode   160  3.375  1    3.600        170  \n",
       "3  cathode   181  3.860  1    4.700        277  \n",
       "4  cathode   195  3.860  1    4.700        276  \n",
       "5    anode   167  1.550  3    3.430        186  \n",
       "6    anode   344  0.170  1    2.240        372  \n",
       "7    anode  3862  0.000  1    0.534       3861  \n",
       "8    anode  2200  0.400  4    2.300       3817  "
      ]
     },
     "execution_count": 23,
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   "source": [
    "df = pd.read_csv('activesDB.csv')\n",
    "df"
   ]
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   "metadata": {},
   "source": [
    "Comparing the theoretical vs practical capacities for cathode active materials:"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Practical capacity [mAh.g-1]</th>\n",
       "      <th>Theoretical capacity [mAh.g-1]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NCA</td>\n",
       "      <td>210</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LCO</td>\n",
       "      <td>165</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LFP</td>\n",
       "      <td>160</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NMC622</td>\n",
       "      <td>181</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NMC811</td>\n",
       "      <td>195</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "     Name  Practical capacity [mAh.g-1]  Theoretical capacity [mAh.g-1]\n",
       "0     NCA                           210                             279\n",
       "1     LCO                           165                             274\n",
       "2     LFP                           160                             170\n",
       "3  NMC622                           181                             277\n",
       "4  NMC811                           195                             276"
      ]
     },
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   ],
   "source": [
    "capdf = df[df.type=='cathode'][['name','cap','theorycap']]\n",
    "capdf.columns = ['Name', 'Practical capacity [mAh.g-1]','Theoretical capacity [mAh.g-1]']\n",
    "capdf"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B) Active material voltages\n",
    "\n",
    "Average voltage also matters because the cell voltage is given by the difference in potential between the cathode and anode voltage. This voltage is used to calculate how much energy is provided and not just charge in a battery.\n",
    "\n",
    "We plot anodes below, where we want lower voltages so that the overall cell voltage is higher:"
   ]
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   "execution_count": 27,
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       "      <th>Name</th>\n",
       "      <th>Average voltage [V]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LTO</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gr</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Li</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Si</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name  Average voltage [V]\n",
       "5  LTO                 1.55\n",
       "6   Gr                 0.17\n",
       "7   Li                 0.00\n",
       "8   Si                 0.40"
      ]
     },
     "execution_count": 27,
     "metadata": {},
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    }
   ],
   "source": [
    "Vdf = df[df.type=='anode'][['name','V']]\n",
    "Vdf.columns = ['Name', 'Average voltage [V]']\n",
    "Vdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C) Visualizing capacity and voltage together\n",
    "\n",
    "* For cathodes we want to choose one towards the top right of the plot to maximize cell energy\n",
    "* For anodes we want to choose one towards the bottom right to maximize cell energy\n",
    "* Other considerations come into play when actually pairing active materials for a cell, like rate capability and safety etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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   "outputs": [
    {
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+bZX1f9YCO1VyrLb6kIsC74PDyu8JPFDTQsxskJlFmtkVHOmnNVdFItB0SJF6yDn358AvvZHAl2b2GX4R1U34X3yD8GtFhS6s+hRwATAhsAbTcmBP4FjgNfx6TeE+AIYBr5vZVPxC67845/4ZIW9N6v2Zmf0FP/Xv+0A9CvBPqvbEd9L+GpJ/k5ldgl8r4ePAop8r8E/39sQv8joo7BrzAp2054HZZvY2vgPYCB9MOgz/9HWPWNoQhy/xndXTAp/XJ/jP6jh8B3Z5hHP+h+/AXBsYJRVcb+LxwIK2dwN7AZfhdyv6EL9wbHv8538IcBsQ71D4R/HBrk/M7DX8dIEB+M9hIjC08lPLdAG+MbNZ+G3ElwDN8cP2O+J3wtpYxfkiIiJ1npkNxi90Pss5V9Vi6f/A/46+wMxGRhglXi3nnDOz8/HrYb1qZm/gR3vtjp+OuRE4L2wXxlvxS2pcGwh8fYJ/oHYmMBU4KcKlhuPXsPqHmV0NfIEfjd4V6I/vkx0ErI62DXF6BHgG37+YhA9gHYIPgAU34wn3AXBWYF22GYFzpjvnptdiH/JN/Cj46wIPCL8JlH8C8G9q/vD5MaCLmX2KD6xtBfbDL5vyCzC+8lNF0peCYCL1lHPuLjObAFyBH+5+AX7HvrXATPyTpJdC8n9nZkcA9wDH4//9f4vfVWYDkYNgfwd2xu9m+H+Bc/4LxBQEC9TjJjP7BrgSOA/fsViAX3fqIefc1rD8EwOj2kYCZ+AXkp+O72zdTFgQLHDOS2b2LX6h0COAo/HBtuX4oM2rsdY/VoFdj07C3//fAlfjA1Z/D6RVCFQ559ab2en4to/Aj5oC/7nmOee2mdkp+DW1RuA7Tzn4DtrPwJ+IsN15DHV/28xOxH9GZ+Knaebi721PahYEWxRox+DAeW2BdfgA4M2ooyYiIqkhOArs71Vlcs4tMrP38YvNnwj8K5aLOee+COxkeDswJFDWGuAV4G7n3A9h+dcEdlT8cyDvAPzv4svxv6srBMGcc0vNbD/gKvyC/ufgpxSuxPdfHgdmxVL/eDjnnjWzIvzmQufjH9Z+jO8Tn07kINg1+LVRf4PvjzXAb9YzPVBm0vuQzrkCMzsSuB/fLzoMv2nS3cDDRO6TR/Jn/HqzA/CffSl+J8w/A48659bHW1eRVGTRreEnIiIiIiIiIiJS/2hNMBERERERERERSXkKgomIiIiIiIiISMpTEExERERERERERFJevQqCmdltZubM7Psa5u9iZq+Z2QYzyzezNwJbz4qIiIikDTPb38yeMLPZZlZgZosDfaTdanh+SzMbbWa/Bs7/yMz2rSTvSWY2w8y2BK5zZ2BHYxEREZEdqt4sjG9mXfE7lzhgkXNuz2ry5+C3vW0BPITf/vaPgAF7O+fWJrfGIiIiInWDmU0EDgEmAN8BHfG79OYABzrnKn3AaGYN8Duu7QX8Fb/z3BXATsB+zrmfQvIeB/wbmIbfna4f8AdgtHPu8oQ3TERERCQK9SkINh5oh9+Ot20NgmD/BzwADHTOfRlI2wP4HviLc+7WJFdZREREpE4ws4OBr5xzW0PSdgVmAROdc+dWce4ZwKvAMOfcxEBaO+BH4D/OueEheWfjHzwOcM4VB9LuAW4F+jjn5iW8cSIiIiI1VC+mQ5rZIGAocG0Upw0FvgwGwAACHa8PgDMSWkERERGROsw591loACyQ9hMwG+hdzelDgVXA6yHn/gq8BpxsZpkAZtYH6IMf9VUccv5T+JH4Q+Nth4iIiEg86nwQzMwygMeBvzvnZtXwnAZAf+CrCIdzgV5m1ixxtRQRERGpX8zMgA746Y1V2QeY4ZwrDUvPBZoCu4Xkg7D+l3NuObA05LiIiIjIDlEfFim9DNgZGBLFOa2BTGBFhGPBtM74NcYqMLP2+KmXoXLwnbzvga0VThIREZH6rjF+nav/OufydnRlasE5QBfgjmrydQKmR0gP7VPNCuQLTQ/P27m6CqkPJiIikpZqrQ9Wp4NgZtYGuAu4OzDsvqayAu9FEY5tCcsTyRXAyCiuJyIiIqnjZGDKjq5EMgXWSX0S+B/wYjXZs6hZn6q6/lfzGlRNfTAREZH0lfQ+WJ0OggH3AOvw0yGjURh4z4xwrElYnkiewu+eFGoPYOLLL79Mz549o6xO1QoLC5k1axb9+vUjK6uq2JwE6Z7FRvcterpnsdF9i57uWWwSed8WLlzI8OHDAZYkpHJ1lJl1xO/gmAcMdc6VVHNKITXrU1XX/6qq7xVUaR/s+eefZ9OmTWn3bySd/29I17ar3enVbkjftqdruyF9215Zu2uzD1Zng2CBHYsuwS+G39kvWwH4TlQjM+sO5Dvn1kU4fR3+KWSnCMeCacsru7ZzbjWwOqw+APTv35++ffvWtBk1kp+fT15eHvvuuy/Nm9fkIanonsVG9y16umex0X2Lnu5ZbBJ533JycoJfpuyUOzNrAfwHaAkcFlivqzorqFmfakVIengnthN+DbEqVdUH69OnDytXrky7fyPp/H9DurZd7U6vdkP6tj1d2w3p2/bK2l2bfbC6vDB+F3z9HgN+DnkdgF8X4mcqWcMisHDrLGBAhMMHAAudcxuTUGcRERGROsnMmgBv4vtRJzjn5tTw1JnAvoGNh0IdAGwGfgzJB2H9LzPrDHQNOS4iIiKyQ9TlINj3wKkRXrOBxYGv/wFgZt0Ca1uEmgjsb2ZlHTEz2x04korD7EVERERSVmC37VeBg4Bhzrn/VZKvk5ntYWaNQpIn4neRPC0kX1tgGPCmc64IwDk3G5gHXBK4XtDlgAuUIyIiIrLD1NnpkM65NcDk8HQzuzZwPPTYWOBwwELSngIuBv5tZg8C24DrgFXAQ8mos4iIiEgd9RBwEn4kWGszOzf0oHPupcCX9wHnAz2ARYG0icDnwBgz6wOswS9gn0HFRexvxC9o+66ZjQf2BK4E/u6cm5vgNomIiIhEpc4GweLlnNtoZoOBR4Db8aPepgF/jHKnSREREZH6bu/A+4mBV7iXIqQB4JwrMbPfAn8FrsbvAvklMMI590NY3rfM7DR8cOxx4Ffgz/jdvkVERER2qHoXBHPODa5JWiB9KX6ovoiIiEjaqqyvFCHfCGBEhPT1wEWBV3VlTCbCaH4REakfnHNs3LiR/Px8tm3bhnMuKdfZtm0brVu3ZsWKFaxZsyYp16ir0qHtDRo0IDMzkw4dOtCgQd1ZiaveBcFEREREREREJPGKi4tZtmwZmzdvBqBhw4Y0aNCgbKfeRGrYsCHt2rWjYcP0C0uketudc2zdupXCwkKKioro1q1bnQmEpeYdFxEREREREZGorF+/ns2bN9OiRQvat2+f1CBNSUkJGzdupFmzZmRkZFR/QgpJh7Y751i9ejXr1q1j1apVdOrUaUdXCajbu0OKiIiIiIiISC3ZtGkTGRkZdOrUKWVHKUntMDPat29PRkYGRUVFO7o6ZfRTLSIiIiIiIiI452jYsGFSpj8CFBdDaSk0aABJuoTUIWZGRkYGpaWlO7oqZRQEExEREREREZGEKy6GtWshPx/y8qCwcHsQLDMTMjIa0KkTtG8PGniWmpIVUI2VfsxEREREREREJGFKSmDZMv9atw4KCiAjwwe+GjTwgbDCQti0qQHLl0PbttCli3+l6BJZUkcoCCYiIiIiIiIiCZGfDwsWwNKlsHUrtG4NHTpUnP5YUgIFBY6SEli+HNas8aPGevWC5s13TN0l9WlhfBERERERERGJ27p1MGsW/PQTNG0K3bv7gFZlM+LM/PHu3X3+n37y569bV5u19lP2rrzyytq9aC0YPHgwgwcPTlr53bt3Z8SIEUkrPxkUBBMRERERERGRuOTnw9y5flRX167QsmV057ds6c9bvtyXk5+f+Dp+9tlnjBo1ig0bNiS+8B1kzpw5jBo1ikWLFu3oqtQLCoKJiIiIiIiISMxKSvwUyGXL/LpemZmxlZOZ6c9ftsyXV1KS2Hp+9tln3HnnnSkXBLvzzjsjBsHeffdd3n333dqvVB2mIJiIiIiIiIiIxGzZMr8GWJs2sQfAgjIzfTlLl/py001xcTFbt25NSFmNGzemcePGCSkrVSgIJiIiIiIiIiIxKS72waqtW6OfAlmZli19ecuW+fITYdSoUdx4440A9OjRAzPDzMqNoJo8eTJ77rknmZmZ9O3bl7fffrtCOcuWLePCCy+kQ4cOZfmef/75CvlWr17N73//ezp06ECTJk3Ya6+9ePHFF8vlWbRoEWbGgw8+yKOPPkqvXr3IzMxkzpw5AMybN4+hQ4fSunVrmjRpwoABA5gyZUrZ+S+88ALDhg0D4Igjjihr07Rp04DIa4Jt2bKFUaNGsdtuu9GkSRM6derEaaedxoIFC8ryPPjggxx88MG0adOGrKws9ttvPyZOnFjzm12HaXdIEREREREREYnJ2rV+IfvWrRNbbqtWsH69L79Dh/jLO+200/jxxx955ZVXeOSRR2jbti0A7dq1A+CTTz7h9ddf54orrqBZs2Y89thjnH766SxevJg2bdoAsGrVKg488MCyhfTbtWvHf/7zH37/+9+Tn5/PtddeC0BhYSGDBw9m/vz5XHnllfTo0YMJEyYwYsQINmzYUGER/jFjxrBlyxYuueQSMjMzad26NbNnz+aQQw6hS5cu3HzzzWRnZ/Paa69xyimnMGnSJE499VQGDRrE1VdfzWOPPcatt95K7969Acrew5WUlHDCCSfwwQcfcNZZZ3HNNdewceNG3nvvPb7//nt69eoFwN/+9jdOOukkzjnnHLZu3cr48eMZNmwYb731Fscff3z8H8YOpCCYiIiIiIiIiMQkPx8KChITqArVvDmsWePLT0TZ/fv3Z9999+WVV17hlFNOoXv37uWOz507lzlz5pQFgo444gj22msvXnnllbKg1W233UZJSQmzZs0qC4xddtllnH322YwaNYpLL72UrKwsRo8ezdy5c3nppZc455xzyvIdfvjh3H777Zx//vnlrr106VLmz59fFpADGDJkCN26dePLL78kMzDH9IorruDQQw/lpptu4tRTT6Vnz54cdthhPPbYYxx11FHV7gQ5duxYPvjgAx5++GH++Mc/lqXffPPNOOfKvv/xxx/Jysoq+/7KK69k33335eGHH673QTBNhxQRERERERGRmOTlQUYGmCW2XDNfbl5eYsutzJAhQ8oCYOCDZs2bN2fhwoUAOOeYNGkSJ554Is451qxZU/Y65phjyMvLY8aMGQBMnTqVjh07cvbZZ5eV16hRI66++mo2bdrEf//733LXPv3008sFwNatW8eHH37IGWecwcaNG8uus3btWo455hh++uknlsWwYNqkSZNo27YtV111VYVjFvIBhgbA1q9fT15eHocddlhZ++ozjQQTERERERERkagVF0NhYfyL4VcmM9OXX1wMDZMcvejWrVuFtFatWrF+/XoAfv31VzZs2MDo0aMZPXp0xDJWr14NwC+//MKuu+5Kgwblxx0FpykuXry4XHqPHj3KfT9//nycc/zpT3/iT3/6U6XX6tKlSw1att2CBQvYfffdaVjNzXzrrbe45557mDlzJkVFRWXpluhI5w6gIJiIiIiIiIiIRK201L8aJGmOmdn2ayRbRkZGxPTgNMHSQCXOPffcCtMZg/r37x/TtUNHXoVe64YbbuCYY46JeM4uu+wS07Wq8/HHH3PSSScxaNAgnnrqKTp16kSjRo0YM2YML7/8clKuWZsUBBMRERERERGRqDVo4F/JClI5t/0aiRDPSKZ27drRrFkzSkpKGDJkSJV5d955Z7777jtKS0vLjQabN28eEHnUWaiePXsCfgplddeKpk29evXiiy++YNu2bTRq1ChinkmTJtGkSRPeeeedsrXIwC/enwq0JpiIiIiIiIiIRK1hQ8jKgpAZcwlVVOTLT9RUyOzsbAA2bNgQ9bkZGRmcfvrpTJo0ie+//77C8V9//bXs69/+9resXLmSV199tSytuLiYxx9/nJycHA4//PAqr9W+fXsGDx7Ms88+y4oVK6q8VjRtOv3001mzZg1PPPFEhWPBEW8ZGRmYGSUlJWXHFi1axOTJk6stvz7QSDARERERERERiUmLFlBS4kdtJXLJKOd8uS1aJK7M/fbbD/C7PJ511lk0atSIE088scbn33///Xz00UcccMABXHzxxfTp04d169YxY8YM3n//fdatWwfAJZdcwrPPPsuIESP4+uuv6d69OxMnTuTTTz/l0UcfpVmzZmzcuLHKaz355JMceuih9OvXj4svvpiePXuyatUq/ve//7F06VK+/fZbAPbee28yMjJ44IEHyMvLIzMzkyOPPJL27dtXKPO8885j7NixXHfddeTm5nLYYYdRUFDA+++/zxVXXMHJJ5/M8ccfz8MPP8yxxx7L8OHDWb16NU8++SS77LIL3333XY3vVV2lIJiIiIiIiIiIxKR5c8jOho0b/deJkp8POTmJLXP//ffn7rvv5plnnuHtt9+mtLSUn3/+ucbnd+jQgdzcXO666y5ef/11nnrqKdq0aUPfvn154IEHyvJlZWUxbdo0br75Zl588UXy8/PZfffdGTNmDCNGjCg3yqoyffr04auvvuLOO+/khRdeYO3atbRv35599tmHO+64oyxfx44deeaZZ7jvvvv4/e9/T0lJCR999FHEIFhGRgZTp07l3nvv5eWXX2bSpEm0adOmLNgGcOSRR/KPf/yD+++/n2uvvZYePXrwwAMPsGjRIgXBRERERERERCR9tWkDrVvD8uWJDVitXw+dO/vyE+n222/n9ttvL5cWnAoYbtGiRRXS2rdvzxNPPBFxSmF4vueff77KPN27d6/02uDXBnvxxRerLAPgoosu4qKLLqqQPm3atAppWVlZ3HPPPdxzzz2VlnfhhRdy4YUXVkgfNWpUue8j3Z+6TmuCiYiIiIiIiEhMGjaELl2gcWOIYamtiDZs8OV16ZK49cBEQEEwEREREREREYlDly7QtSusXRv/IvlFRb6crl19uSKJpCCYiIiIiIiIiMQsIwN69fJBq2XLYg+EFRX587t08eVlZCS2niIaWCgiIiIiIiIicWneHHr39l8vXerX8mrZsubnb9jgR4B16eLLSeT6YiJBCoKJiIiIiIiISNxat4Z+/aBpUx8I27ABWrXyAS2zivmdg7w8vxNk48aw665+BJgCYJIsCoKJiIiIiIiISEI0bw79+/uRYMuW+V0e16zxUxszM30wzDnYvBkKCox27fzor86d/bumQEoyKQgmIiIiIiIiIgmTkQHduvnA1tq1fqRXXh4UFkJpKTRoAO3bQ0ZGKZ06+a+1C6TUBv2YiYiIiIiIiEjCNWwIHTr4F0Bx8fYgmBls3FhKs2Ya/SW1R0EwEREREREREUkq5/zujyUl26dGitQ2BcFEREREREREJOE2b4ZvvoEFC+CXX/zUyOBIsNatjfbtG9GnDwwY4BfTF0k2BcFEREREREREJGG2bIH334dPP4UlS2DdOj81MjvbjwIrKfGBsS1bGjN9utG9Oxx8MAwZAk2a7OjaSypTEExEREREREREEmLBApgwAWbO9Avhd+kCPXv60V+hSkthy5YS8vMzmD0bFi6E77+HYcOgV68dUvU6Y9q0aRxxxBF89NFHDB48OOHlv/DCC1xwwQX8/PPPdO/ePeHl12UKgomIiIiIiIhI3GbNgrFjYc4c6NoVdt/dL4BfmeAukR06wMqV8PHHfsrkeedBv361V+8d5emnnyYnJ4cRI0bs6KqkjQbVZxERERERERERqdyCBdsDYH37QqdOVQfAQpn5/H37+vPHjvXlpbpnnnmGF154oUL6oEGDKCwsZNCgQbVfqRSnIJiIiIiIiIiIxGzLFj8FMhgAi3WR+6ZNtwfCJkzw5dYFpaWlbKnFyjRo0IAmTZrQIHwOqcRNd1REREREREREYvb++34NsK5d49/lsWlTX87Mmb7cRBo1ahRmxrx58zjjjDNo3rw5bdq04ZprrikX5DIzrrzySsaNG0ffvn3JzMzk7bffBuDBBx/k4IMPpk2bNmRlZbHffvsxceLEiNd76aWXGDhwIE2bNqVVq1YMGjSId999F4D+/fsze/Zs/vvf/2JmmFnZ+l/Tpk3DzJg2bVq58r744gt++9vf0qpVK7Kzs+nfvz9/+9vfyo5/9913jBgxgp49e9KkSRM6duzIhRdeyNq1axN4F+s3rQkmIiIiIiIiIjHZvNnvAllY6NcAS4SOHf0aYZ99BkceGX9gLdwZZ5xB9+7due+++/j888957LHHWL9+PWPHji3L8+GHH/Laa69x5ZVX0rZt27IF5P/2t79x0kkncc4557B161bGjx/PsGHDeOuttzj++OPLzr/zzjsZNWoUBx98MHfddReNGzfmiy++4MMPP+Q3v/kN9913HzfffDM5OTncdtttAHTo0KHSOr/33nuccMIJdOrUiWuuuYaOHTsyd+5c3nrrLa655pqyPAsXLuSCCy6gY8eOzJ49m9GjRzN79mw+//xzrKbzU1OYgmAiIiIiIiIiEpNvvoElS/wukImKsZj58hYv9uUfckhiyg3q0aMHb7zxBgB/+MMfaN68OU899RQ33HAD/fv3B+CHH35g1qxZ9OnTp9y5P/74I1lZWWXfX3nlley77748/PDDZUGw+fPnc9ddd3HqqacyceLEctManXOUlpZy/PHHc99999G2bVvOPffcKutbUlLCpZdeSqdOnZg5cyYtW7YsV17QFVdcwfXXX1/u3AMPPJCzzz6bTz75hMMOOyyKu5SaNB1SREREJA2YWY6Z3Wlmb5vZOjNzZjaihudOC+SP9NoWlndRJfmeSUrDRERkh1qwANatg7ZtE1tu27awfn1yFsj/wx/+UO77q666CoCpU6eWpR1++OEVAmBAuQDY+vXrycvL47DDDmPGjBll6ZMnT6a0tJQ77rijwrpesYzG+uabb/j555+59tprywXAwssLrduWLVtYs2YNBx54IEC5+qUzjQQTERERSQ9tgTuAxcC3wOAozr0X+HtYWjbwDPBuhPwzgYfC0n6M4noiIlIPOAe//AING0Ki13Bv0AAyMnz5ziVulBnArrvuWu77Xr160aBBAxYtWlSW1qNHj4jnvvXWW9xzzz3MnDmToqKisvTQYNSCBQto0KBBxCBaLBYEIoF77rlnlfnWrVvHnXfeyfjx41m9enW5Y3l5eQmpS32nIJiIiIhIelgBdHLOrTSzAcCXNT3ROfdeeJqZBedujItwyjLn3EuxVVNEROqLzZth7VrIzk5O+dnZvvzNm5N3DYg8Oit0VFXQxx9/zEknncSgQYN46qmn6NSpE40aNWLMmDG8/PLLyatgDZ1xxhl89tln3Hjjjey9997k5ORQWlrKscceS2lp6Y6uXp2gIJiIiIhIGnDOFQErE1jkcKAAeCPSQTNrDDRyzhUk8JoiIlKHlJRAaakfsZUMGRm+/JKSxJb7008/lRvpNX/+fEpLS8sWv6/MpEmTaNKkCe+88w6ZmZll6WPGjCmXr1evXpSWljJnzhz23nvvSsur6dTIXr16AfD9998zZMiQiHnWr1/PBx98wJ133skdd9xRlv7TTz/V6BrpQmuCpbAXXngBM+Orr76qMl9+fj533nkne+21Fzk5OWRlZbHnnnty0003sXz58gr533rrLY499ljatGlDkyZN2G233bjhhhvqzLarwXY3adKEZcuWVTg+ePDgcsNIu3fvjplV+p/Jc889V7ZlbaR7OXPmTM4991x22mknMjMzad26NUOGDGHMmDGUhP1vvXHjRv7v//6PHj16kJmZSZcuXRg6dCibN28uy/PBBx9w4YUXsttuu9G0aVN69uzJRRddxKOPPlquXZs3b+bJJ5/k6KOPplOnTmRkZJCVlcXTTz9NSUlJrbVry5Yt3HffffTp04emTZvSpUsXhg0bxuzZs8uVt2LFCm6++WaOOOIImjVrFnHLX9j++WVkZLDHHnuQkZFR7pdR+OcHfqHIMWPGMHjwYFq3bk1mZibdu3fnggsuqPTn/6mnnsLMOOCAAyIeFxGRyplZO+AoYHIlQa4jgc3ApsAaYdfUagVFRKRWZGT4aYuJDlIFlZRsnxaZSE8++WS57x9//HEAjjvuuCrPy8jIwMzK/T20aNEiJk+eXC7fKaecQoMGDbjrrrsqjMAKXci+adOmbNiwodr67rvvvvTo0YNHH320Qv5geRmBmxRaPsCjjz5abfnpRCPB0tzChQsZMmQIixcvZtiwYVxyySU0btyY7777jn/84x/861//4scfty/hccMNN/DQQw+x1157cdNNN9G6dWtmzJjBE088wfjx4/nggw/YPVH74sapqKiI+++/v+w/tKo0adKEjz76iJUrV9KxY8dyx8aNG0eTJk3YsmVLhfP+/ve/c9lll9GhQwd+97vfseuuu7Jx40Y++OADfv/737NixQpuvfVWwM/BPvzww1m6dCmXXHIJu+yyC7/++isff/wxRUVFNA3s+3vTTTexbt06hg0bxq677srChQt54oknmDBhQrl2XXrppVx11VX85je/4brrruO5555j5cqVXHHFFXz++ee11q5zzjmHKVOmcPHFF7PvvvuyfPlynnzySQ466CBmzZrFzjvvDPjdVR544AF23XVX+vXrx//+978qP5PS0lLy8vLo3LlzlfkKCws57bTTePvttxk0aBC33norrVu3ZtGiRbz22mu8+OKLLF68mK5du1Zof/fu3cnNzWX+/PnssssuVV5HRETKORPfj4w0FfI74BPgB6ANMAJ41Mw6O+duqqpQM2sPtAtL7gX+/3uAgoL0GlgWbG+6tRvSt+1qd3q1G+pW27dt20bDhg0rPMyvTGYmtG5tLFjgR2xFIxisCe6WGMmmTdC7N2RmuoQE2oLX+fnnnznxxBM55phj+Pzzzxk3bhxnn302e+65Z1nbnXMV7sOxxx7Lww8/zDHHHMPZZ5/N6tWrefrpp9lll1347rvvyvL36NGDW265hXvvvZdDDz2UU089lczMTL766is6derE3XffDcA+++zD6NGjueuuu9hll11o164dRx55ZFk5JSUlZV8/8cQTnHLKKey9996cf/75dOrUiXnz5jFnzhz+85//kJ2dzWGHHcZf/vIXioqK6NKlC++9917ZOmelpaVlZQXvQ2j5yeKco7i4mPz8/Ep/1jdt2pTUOoRSECyNFRcXc9ppp7Fq1SqmTZvGoYceWu74vffeywMPPFD2/SuvvMJDDz3EmWeeybhx48oizQAjRozgiCOOYNiwYcyYMYOGDXf8j9bee+/Nc889xy233FJtIOWQQw7hyy+/5NVXX+Waa7Y/rF66dCkff/wxp556KpMmTSp3zueff85ll13GQQcdxNSpU2nWrFnZsWuvvZavvvqK77//viztlltu4ZdffmHGjBnlht7edFP5vwcefvhhDj300HK7iBx77LEcfvjh5dp16aWXMmvWLPr27QvAv//9bxo3bszAgQMZM2YMXbp0qbRdy5YtS0i7li1bxuuvv84NN9zAX//617J8hx12GEceeSSvv/46f/zjHwHYb7/9WLt2La1bt2bixIkMGzasys9kr732Yt68eRx66KHMnz+/0nw33ngjb7/9No888gjXXnttuWMjR47kkUceqXDOzz//zGeffcbrr7/OpZdeyrhx4xg5cmSV9RERkXKGA78CFdYKc86dFPq9mY0B/gNcZ2aPO+eWVlHuFUDE/5BnzZpFt27dyM3Njb3W9Vi6thvSt+1qd/qpC21v3bo17dq1Y+PGjTU+p337RmzZ0pgtW0piWhx/27ZtEdNLS6GoKIP27beyaVPkPNHaunUr4GfF3Hfffdx6661kZGRw8cUXc9ddd5Vr99atWyvch/3335/HH3+cRx99lOuuu46dd96ZkSNHsnjxYr777rty+W+44QY6derE6NGj+dOf/kRWVhZ9+/bltNNOK5sJdN111/Hzzz/z4IMPsnHjRg455BD233//sgc/hYWFZWUefPDBTJkyhb/85S888sgjZdM3zzvvvLI8zzzzDDfddBNPPfUUzjmOPPJIxo8fT+/evcu1JzgQoqCgIKrPOhbbtm3j119/Zd68eWVp4T/rixcvTmodQu34SIXsMJMmTeLbb78ti06Ha968Offee2/Z93feeSetWrVi9OjR5QJgAAMHDuSmm25i5MiRTJw4kbPOOivp9a/OrbfeyvDhw7n//vt57LHHqszbpEkTTjvtNF5++eVywaJXXnmFVq1accwxx1QIFt15552YGePGjSsXKAoaMGAAAwYMAGDDhg2MGTOGq6++mh49erB161acc+XmkQcNGjQoYlp2djYFBQVl7Ro9enTEdp166qmMGTOGbdu2VdquiRMnJqRdwf8wO3ToUC5Pp06dgPKLSUYqqyq33XYbw4cPL9sJJZKlS5fy7LPPctRRR1UIgIEfEnzDDTdUSB83bhytWrXi+OOPZ+jQoQqCiYhEwcx6AgcBTzjniqvL75xzZvYIcAx+R8qqFsx/CpgQltYLeKNfv37k5eUxcOBAspO5OnIdU1BQQG5ubtq1G9K37Wp3erUb6lbbV6xYQcOGDaPqu/fpA9OnG/n5GbRvX/NrOefYtm0bjRo1irg21urV0K4d9OnThGbNmtS84Co0btwY8EvihP8dFKq4uPJfb5dffjmXX355hfTQv52DLrvsMi677LIK6SUlJWzevJnu3bszderUCsePO+64iHU46qijOOqooyqt2+67715haiZUbM+ll17KpZdeWmk5ibR27Vrat2/PXnvtVenP+ty5c2ulLqAgWFqbMmUKAL/73e+qzfvTTz/xww8/MGLECJo3bx4xz3nnncfIkSN566236kQQrEePHpx33nk899xz3HzzzdWOBhs+fDhHH300CxYsKFt48OWXX2bo0KE0atSoXN7NmzfzwQcfMGjQILp161ZtXT755BO2bNnCLrvswtChQ5k8eTKlpaUcdNBBPPnkk1Uulgh+eGgwWl9du1au9GseB0eShbarXTs/w2TChAkJaVevXr3o2rUrDz30ELvvvjv77LMPy5cvL1v3LJ6fg2A7X3jhhUo/u//85z8UFxfX6Gc41Lhx4zjttNNo3LgxZ599Nk8//TRffvkl+++/f8z1FRFJI8MD75GmQlZmSeC9dVWZnHOrgXJ7ugf/MMrKyiIvL4/s7OxK+yKpLF3bDenbdrU7/dSFtq9ZswagwqCHqgwYAN27w+zZ0KED1HCt97IpeWZWbhYMgHOwYgX07evLT9SaYMHrZGRkRNXGZKkr9UgmM6NRo0blfrbDf9ZzcnJqrT5aGD+NzZ07lxYtWrDTTjtVm3fOnDmAn6JWme7du9O8efNajeJW57bbbqO4uLjctM7KHHnkkXTs2JFXXnkF8Pdn5syZDB8+vELe+fPns23bNvr161ejegR35LjllltYsmQJY8eO5cknn2TBggUceeSRrFixosrzH3300XJztStrl3OORx99tGzh/UjtWrJkCbNmzUpIuxo1asSkSZPIzs7mpJNOYqedduKAAw5g06ZNfPbZZ7Rs2bJG5VTmtttuwzlHXl5exOPBn7Wa1hfg66+/Zt68eWUBukMPPZSuXbsyblw0f8uJiKS14cAC59znUZzTM/D+axLqIyIiO1DTpnDwwZCVBSsTtAfxypW+vIMP9uWLJIqCYGksPz+/xsNcg9PeqsvfrFkz8vPz465bovTs2ZPf/e53jB49utpAU0ZGBmeccUZZsGjcuHHstNNOHHbYYRXyBttY0/sXXOjPzPjggw8YPnw4l19+OZMnT2b9+vUVdicJNX36dO68885yo5Qqa9eKFSuYM2cOTzzxRKXtmj59Ol27dk1IuwBatWrF3nvvzc0338zkyZN58MEHWbRoEcOGDYu46H40evbsSZcuXdi4cWPEzy+W+o4bN44OHTpwxBFHAP4zOfPMMxk/fnzSF4UUEakPzKyTme1hZo0iHNsH6A28XMm5rc0sIyytEXAzsBX4KAlVFhGRHWzIENh7b1i6FEI2vo/J5s2+nH328eWKJFKdDYKZWV8zm2BmC81ss5mtMbPpZnZiDc8/ysw+CZy73swmmln3JFe7XmnevHmNF8ELBhmqy79x48ao135Ktttvv53i4mLuv//+avMOHz6cOXPm8O233/Lyyy9z1llnRZyfHhy6WdP7F1wb68QTTyw31PPAAw+kR48efPbZZxHPmzdvHqeeeip77rknF154YZXtWrJkCevXr+fuu+/mt7/9bcR2zZo1i+nTp3PaaaclpF15eXkcdthhHHTQQdx3332cfPLJXH/99UyaNIlPPvmEMWPG1Kicquy6664AET+/aOtbUlLC+PHjOeKII/j555+ZP38+8+fP54ADDmDVqlV88MEHcddXRKQuM7Mrzex2IPhL5UQzuz3wahFIuw+YC3SJUMQ5gffKhs+eBPxgZveb2aVmdgswAzgEGOWcS9AYARERqUuaNIFhw/z6YLNnxx4I27zZn9+nDwwd6stNpFGjRuGco23btoktWOqNOhsEA3YGmgEvAtcAdwfSp5jZJVWdaGYnAG8Dmfgnjw8BhwOfmFn4tttpa4899iAvL48lS5ZUm7d3794AfPfdd5Xm+eWXX8jPz6dPnz4Jq2Mi9OzZk3PPPbdGo8EOOOAAevXqxbXXXsvPP/8cccogwC677ELDhg2ZNWtWjeoQXNMqfAF5gPbt27N+/foK6UuWLOHoo4+mRYsWTJ06lSZhvwFC2/Xoo4+ycOFCWrVqxe23315pu2655RZWrVpV6c6M0bZr0qRJrFq1ipNOKrcRGIcffjjNmzfn008/rVE5VWnatCnZ2dkRP7899tgDoMb1/fDDD1mxYgXjx49n1113LXudccYZAJoSKSLp4AZ8nyq4ou9pge/vBlpVdaKZNQDOAmY4536oJNssYA5wLvAYcCuwATjDOXdfvJUXEZG6q1cvOO+87YGwFSv82l41EVwDLBgAO+88X55IotXZIJhzbqpz7ljn3J3Oueecc38DjgC+Ba6r5vQHgIXAIc65x5xz9wBDgE74oJjgRyUBvPRSVZs0ebvtthu77bYbkydPrnTUzdixYwE44YQTElfJBAmOmqrJ2mBnn30206ZNo3fv3pUuWN+0aVOOPPJIpk+fXqMg4n777QfAsmXLKhxbvnx52YL1QWvXruXoo4+mqKiId955p2y3xXC3334727Zt47rrrqNt27ZVLv5/9tln8/HHH9O1a1f69++fkHatWrUKoMI0QuccJSUlVe6qEo2WLVtG/PyOO+44MjIyavQzDD7I1b59eyZMmFDhdfbZZ/Ovf/2rbDtiEZFU5Jzr7pyzSl6LAnlGhH4fcm6pc66rc26/Ksr/2jl3UiBfpnOumXPuMOdc+I6PIiKSgvr1g8sug8MOg7w8mDnT7/IYWAO/gtJSf3zmTJ9/0CB/fhRL/kod52oaCa0ldTYIFolzrgS/u1DLyvKYWWugD/Av59zWkHO/xQ/t3/HbFtYRQ4cOpV+/ftx7773873//q3B848aN3HbbbWXf33HHHaxfv57LLrusQtDj66+/5oEHHmDPPffk9NNPT3rdo9WrVy/OPfdcnn322bLdEytz0UUXMXLkSB566KEq840cORLnHL/73e/K1vwK9fXXX/Piiy8CfqvavfbaizfeeKNsxxWAd999lyVLlpTb5ragoIDf/va3LFu2jKlTp5ZNB4wkGFQzM1q3rnLDLS666CJuvvlmLrjggoS1a7fddgNg/Pjx5fJMmTKFgoIC9tlnnyqvVVMNGzaM+PnttNNOXHzxxbz77rs8/vjjFc4rLS3loYceYunSpRQWFvL6669zwgknMHTo0AqvK6+8ko0bN5btmioiIiIikm7MrGzXxlj16gXXXgvnnut3d8zPh6+/hhkz4IcfYP58//7NNzBzZgYbN/p8554L11yjEWCpprS0NOJSPDtKwx1dgeqYWTaQBbTArzNxHPBqFadkBt4jDefYDPQ1s45VrUlhZu2B8GmTvcAvcJ7ohd8LCgrKvSdKcETLM888E3Ek0WWXXcaLL77IKaecwqBBgzj11FM54IADaNSoEfPmzWPChAm0bNmSm266CfAjxy6//HKefvppZs2axRlnnEHLli359ttveemll2jdujUvvPAChYWFSR9NU9U9C147/LO6+uqr+ec//8kPP/xA7969y4455yguLi77vlWrVlx3nR9sGEyLVOaee+7Jgw8+yPXXX8/uu+/OmWeeSa9evdi0aROffPIJU6dO5fbbby/Lf88993DKKadw8MEHc8EFF5Cfn8+TTz7JLrvswjnnnFOWb/jw4eTm5nLuuecyY8YMZsyYAVC2bliwDosXL+akk06iYcOGbNu2jR9//JHOnTvz3HPPAVBUVFShXVdffTW5ubll9y3edh1++OH07t2bu+66i/nz5zNgwAAWLlzIc889R8eOHTnjjDPKfQZ//etfge27Ov7jH/8oW4frxhtvLFenp59+ms6dO/Pjjz+yYcMGmjZtSlFRUYXPb+TIkfzwww9cffXVTJgwgWOOOYaWLVuydOlSJk+ezI8//sjxxx/Pe++9x8aNGxkyZEjEf8N9+vShbdu2vPjiixx33HE1+lmTyum+RU/3LDaJvG+RAv8iIiLpJDMzk7y8PLZu3Urjxo1jLqdJEzjhBDjySB/sWrAAfvkF1q71o78aNIDevaF9+6306dOEAQO0C2QqKi4upri4uGyN7LqgzgfB8Ot5XRr4uhR4Hbiyivyr8GtPHBKaaGZt8CPEwC/0WtVwoCuAkZEO5ObmVjuSKFa5ubkJLS800BBJ9+7dadu2LX/+85958803+fzzz5kyZQrOOTp27Mjhhx/OCSecwEcfbd/IKRhgmDp1Kg888ABFRUW0bduWY445htNPP52lS5eydOnShLajKpHuWbDdX3/9NXl5eeWODRo0iI8++oiCgoKydhUWFrJ27dpy7axpmT169OCvf/0rb7zxBi+++CL5+fk0adKEnj17ctVVV7H33nuXK/eOO+7g5ZdfZtSoUWRmZrLffvtx/vnn8+WXX1Zo00svvRRxml+wDrNmzarQvuXLl3PJJX7JvKZNm1baruA1EtGu2267jddee42PPvqI1157jaysLPbff3/OPffcCmvI3XPPPeW+D23fgAEDytXp+eefL5f3qaeeKvs69PMDuPLKK+nTpw8fffQRf/7zn9m6dSutWrWif//+XHLJJfzwww88/fTTNG7cmIyMjEo/6/79+/Pee+/xxhtvlC26H37PJDq6b9HTPYtNIu7b4sWLE1ATERGR+qt58+bk5eWxYsUKOnXqFFcgDHxg65BD/Ms5v/B9SQlkZEBmpmPTpm00a9aEjIzqy5L6xTnH6tWrASr8bbUjWV2bnxnOzPYAugKdgTPw22tf7pxbVcU59wM3AfcDzwPNgb8AhwGNgMOcc59UcX5lI8He+Pzzz8sWiU+UgoICcnNzGThwINnZ2QktO1XpnsVG9y16umex0X2Lnu5ZbBJ53+bOncuBBx4IsKdzbnZCKihxM7O+wPeff/45K1eu5Igjjkh4Z/qFF17gggsu4Msvvyx7KFPTqRsfffQRgwcPBvyanvfffz9Tpkzhl19+oWnTpuy///5cddVVMa+Zmp+fz0cffZSUdtd16dp2tTu92g11q+3BwMW6desAaNSoEWaWlOlszjm2bdtWdo10kuptD64RXVJSQtOmTenWrRtmVunP+uzZs9lzzz2hFvpgdX4kmHNuHjAv8O1YM3sXeNPMDnCVR/DuANoC/8f2hfDfBf4BXAZUOd/BObcaWB2aFvzBzMnJSdp/TNnZ2Tv8P736RvcsNrpv0dM9i43uW/R0z2KTiPuWk5OToNpIKvjnP/9Z7vuxY8fy3nvvVUgPPhz94Ycf+M1vfsOvv/7KBRdcwIABA9iwYQPjxo3jxBNP5IYbbihbFkBEpK4yM9q3b092djb5+fkUFRUlbWHz4uJifv31V9q3b0+jRo2Sco26KtXbbmY0atSIVq1a0apVqzoV6KvzQbAIJgLPArsBEbfnDiyIf5GZ3RbIt8o596OZvYyfUjm/tiorIiIiIvXPueeeW+77zz//nPfee69COsC2bdsYOnQo69evZ/r06RxwwAFlx/74xz9yzjnn8OCDDzJgwADOPPPMpNddRCQeZkZOTk7SHw7l5+czb9489tprr7R7AJjObd/R6tXukAHBFdVaVJfRObfKOfdxIACWAQwGvnDOaeVbEREREUmISZMm8f3333PzzTeXC4ABZGRk8Oyzz9KyZUtGjRq1YyooIiIiQB0OggXW5QpPawSch9/5cU4grZOZ7RE4VpUbgE74hfZFRERERBLizTffBOC8886LeLxFixacfPLJzJs3j/nzNSFBRERkR6nL0yGfNbPmwHRgGdAROAfYA7g+ZDTXfcD5QA9gEYCZnQucHjh3EzAEv6j+351zk2qxDSIiIiKS4ubMmUOLFi3YeeedK82z1157AX4Dhl122aW2qiYiIiIh6nIQ7FXg98DlQBtgI/A1cJNzbko15/4ItAb+hJ8++QN+QfzRSautiIiIiKSljRs30qxZsyrzBI/n5+fXRpVEREQkgjobBHPOjQfG1yDfCGBEWFoucHhSKiYiIiIiEqJZs2asWbOmyjwbN24syysiIiI7Rp1dE0xEREREpD7o3bs3eXl5LF68uNI83333HQB9+vSprWqJiIhIGAXBRERERETicMIJJwAwduzYiMfz8/N544032GOPPbQemIiIyA6kIJiIiIiISByGDh1Knz59uP/++/nqq6/KHSstLeXyyy9n/fr1jBw5cgfVUERERKAOrwkmIiIiIlKbnn/+ed5+++0K6ddcc02V5zVu3JiJEyfym9/8hkMPPZQLLriAAQMGsGHDBl5++WVmzJjB9ddfz1lnnZWsqouIiEgNKAiWSpyDzZuhpAQyMqBpUzDb0bVKvlRudyq3LVS6tFMkXRUXQ2kpNGgADRtWfayqvNWVJRKnp59+OmL6iBEjqj23d+/efPvtt9x///1MmTKFMWPGkJWVxYABA5gyZQonnnhigmsrIiIi0VLvsb7bvBm++QYWLIBffoG1a7f/cdCmDey8M/TqBfvs4wMLqWLLFv8+ZQr8/HNqtTtdPtN0aadIOiou9v+m8/MhLw8KC7f/+27UyAe+wX9fWAgbNvj/E4qLfWArKwtatoRmzSA7e3uAvKCgfFlZWZCZ6csqKdlRrZUUMGLEiGoDXU888QRPPPFElXnatWvHQw89xEMPPZTA2omIiEiiKAhWX23ZAu+/D59+CkuWwLp1/g+H7Gz/h0JJCSxcCP/7H7RuDd26wcEHw5Ah0KTJjq597ILtnjED9toL/vMf2LgxNdqdLp9purRTJB2VlMCyZf61bp0PWmVk+ECVc/Drr7BypQ+OlZb6cxo18v8HbNmyPbjVtKn/915aCtu2+feGDaFtW+jUyZ9TWgqrV/v3xo3h++998LxLF39NEREREZEwCoLVRwsWwIQJMHOmfyLepQv07On/cAhXWgpr1sDs2T6w8P33MGyYH2FT34S2OzPTB8H69oWtWyvmrW/tTpfPNF3aKZKO8vP9v/GlS/3/y61bQ4cOfmrzpk0+PS/PB7Dat/f/vles8Od27uz/L8jO9sGyNWtg0SIfRGvaFDp29EGwwkJfVteukJPjz9261QfDVq/2I8rWrvX/TzRvvqPuhIiIiIjUUQqC1TezZsHYsTBnjv8jYPfdq147qUED/8dGu3b+6fvHH/s/EM47D/r1q716xyu83d27+/RIwZNgen1pd7p8punSTpF0tG4dzJ3rR4C1aeODWkF5eT6gtXo1tGjh/90vX+5H8Xbs6PPk5/sgWZcu28srLd0+FXLLFl+mc34EaVGR/z0QLA/8uQUF8NNPfmpl794+ECciIiIiElBJBEHqpAULtgcR+vb1U0Jquni4mc/ft68/f+xYX159kMrtTuW2hUqXdoqko/x8HwBbvtwHuFu23H5s06btAbD27f00xeXLYf16aNXKj/Jq2tR/vWGDX+Px55/9161a+emPrVv7/MuX+9Fg7dv78hYt8uWHatnS12H5cl+n/PzaugsiIiIiUg8oCFZfbNnip5EFgwixLhTetOn2YMKECdsXmK+rUrndqdy2UOnSTpF0VFLig9LLlvmRWMFF6oPHli4tHwBbvXp7AKxRo+15GzXyAaxFi/yrZcvtxxs18vnXrYNVq8oHwpYurbggfmamr8uyZb5uWjBfRERERAIUBKsv3n/fr6PUtWv8O+U1berLmTnTl1uXpXK7U7ltodKlnSLpaNkyH4hq06Z8AAz8IvjBKZCNG/vRXevW+bW8QgNgQYWFPmBVUuK/DtWokT9v3Tr/atzYlxsMqoXLzPR1WrrU11EkAuf8DNrgJqZFRX6DUhEREUldWhOsPti82e+kV1jo11FKhI4d/TpLn30GRx4Zf3AiGVK53anctlDp0k6RdFRc7ANMW7eWXwMseGz1av/erJkPbK1b598j/ZstLfVRiIYN/RTovDx/Xui6j9nZ/v+S4EiyZs38dMh163xALFzLlj7wtmyZr19DdXnE/1r65hv48UeYN88PLiws9D+CzZv7XzE9e8L++/t9Hdq00Y+OiIhIKtGv9frgm2/8QsBdutR8HaXqmPnyFi/25R9ySGLKTaRUbncqty1UurRTJB2tXesDUJEWn8/L88Nrgjs0btrkh9xkZ0cua/NmP8U5K8t/v2WLTwvuABnUtKlP37TJB76CgbBIQTDwwbL1631dO3SIrZ2SErZs8QOIP/kEfvjBB79KSnyAKyPD/2pZsgS+/db/mL31FvTvD0cc4YNiXbr4fCIiIlK/aTpkfbBggf9Do23bxJbbtq3/46CuLjKeyu1O5baFSpd2iqSj/Hwf2GrWrOKxggI/vCY46quw0M81a9IkcllFRX5EWWamf23d6tPCZWX5aEZwumR2dtXrAzZv7oNkWiA/rS1YAI8+CmPGwOef+x+ZLl1gjz38UpN77OEHK/fvD3vv7ZecW7fOb0r897/D22/Dd9/px0hERCQVaCRYXecc/PKLf1TZIMExywYN/GPNX37x10nUSJ1ESOV2p3LbQqVLO0XSVV7e9iE04TZtKn9s82b/77ayf6tbtvhjweNmkYNbwTybN2//vqr/X8x8PfLyat4uSSmzZvlNhWfO9DHZtm39e2Xx2AYN/ODGVq38AMKff/Yx2fx8/2PXu3fkwY8iIiJSPygIVtdt3ux7YZVNIYlXdrYvf/Pm5F0jFqnc7lRuW6h0aadIOiou9qOxwhfDDx4rKtq++H1JCWzbVvnCSqWl/pzQ4w0b+rTS0opBrkaNfHklJT7AFbxOcXHkBfczM31dw68hKW/BAh8A+/ZbaNfO/9g0b16zaY1mPmCWkwMLF8KMGdt/3Pv12z7TV0REROoX9QbrupIS/0dAshaiyMjw5de1LeRTud2p3LZQ6dJOkXRUWho5QAV+dGbosfDvK8sfOkrMzKc5VzG/mU8PHqtuJGiwrNLS6tslKWPLFpgwAWbPhk6d/POSmgbAQjVp4tcEW7DALz3XqpUfSda/v9YIExERqY+0Jlhdl5Hh/3BI1h/6JSXbp5bVJanc7lRuW6h0aadIOmrQwL8iBZaCUxSDx8K/ryx/aMDLucqnTwanQAePRQqUhecP1lfSxvvv+ymQrVr56YxZWbH/umjSxO+rsGCBn4W/dKnfdFRERETqH/UI67qmTf3+3AUFySm/oMCXH2nL+h0pldudym0LlS7tFElHDRv6qEKkxesbNvTzxrZt898HpywWF0cuq0GD7dMfg4JTFyMFrrZt8+UFIxrB61Q21bGoyNdVUyHTxubN8Omn/j0z0z8zqWwNsJpq08b/KM2fDxs3+iBYZT/SIiIiUncpCFbXmcHOO29fGyWRglPJdt657i0snsrtTuW2hUqXdoqkqxYt/L/DSCOxcnLKH2vatPLpjeAjFMEpjsFXpKhF8Fgw+B2cSlkZ53w9WrSIrm1Sr33zDSxZ4hew37LFx0DjZebXFVu1Ctas8RsUr10bf7kiIiJSuxQEqw969fI9uTVrElvumjV+nkCvXoktN1FSud2p3LZQ6dJOkXTUvLnflGLjxorHsrN95CG4i2NWlh+SE2nHR/DHGjf2Q22KivzXkRbdLyz0wbFgVKOgoOohPvn5PiCnVczTyoIFsG6d/zHcti3yj1IsWrb0P1Lr1/sNUPPzE1OuiIiI1B4FweqDffaBbt382Pvq1j6pKed8ed26+fLrolRudyq3LVS6tFMkHbVp44Pc69ZVPNaihQ88BaMEOTk+IlHZ9OimTX0wq7Bwe6Ar0lTnggKfnpPjvw8GuSqzfr0PmLdpE13bpN5yzq/b1bChD4AlcrBwcGm5lSv9e15e4soWERGR2qEgWH3QtCkcfLB/8r1yZWLKXLnSl3fwwXV3TaVUbncqty1UurRTJB01bAhduvhRWxs2VDzWvr1/37jRr9/VurV/D44OC9WggQ+cFRf7V4sWFdcDKyjw5bVq5cvZuNGvDda6deT6bdjg69ali9YDSyObN/tpik2b+iBYoj/6rKztwa/CQq0LJiIiUt8oCFZfDBkCe+/ttySK9AdENDZv9uXss48vty5L5XancttCpUs7RdJRly7QtauPOoQvkt+unQ+E5eX57flatvQBq02bti9mHyq4fV9GRsVFnLZt8+e1bu1fW7f6ctu390GxcEVFvk5du/o6StooKfHLxAU3HE30spHBjU6Dy9EleslLERERSS4FweqLJk1g2DDo0wdmz449mLB5sz+/Tx8YOjT+7ZKSLZXbncptC5Uu7RRJRxkZfm2+Ll38NOXQQFhGhg9CtW8Pq1f76EQwaLV+fflA2LZtfuRW9+7+tWHD9uPbtvn8rVtDhw5+6M3q1b6srl237xIZVFTk69Kli69b+HFJaRkZ2wNVZombiR8UDLCZbZ8eKSIiIvWHfnXXJ716wXnnbQ8mrFhR896dcz5/MIhw3nn1Z1HxVG53KrctVLq0UyQdNW8OvXtD585+pGbo1MicHB/UCg2Ede68PRC2ebN/rV/vR4r16OFfLVv6tDVr/JpjrVr580IDYN27V1wPbMMGX4fOnX2dtCB+2mna1C8Bt3mzny2b6OmKhYXbNxvNytJMWxERkfpGQbD6pl8/uOwyOOwwPxVk5kz/B0Fl4/FLS/3xmTN9/kGD/Pn9+tVmreMX3u5583x6KrQ7XT7TdGmnSDpq3dr/29x1Vx99WLTI/7t1zkcMevWCnXbyu0Nu3OiDXDk5fo2/lSuhWTM/qiu4gH7r1n6ITUGBf8/M9Ivgb9niy+nVa3skIhhQX7bMX3vXXX1dKlsrTFKaGey8sw9+NWqU2JFgwemPHTv69+CPoIiIiNQfen5VH/XqBddeC++/D599BosX+62QMjL8Hw8ZGf5pe0GBf2/VCvr29QuJDxlSf6eRhbb7m2982pw5/g+j+t7udPlM06WdIumoeXPo398Pw1m2bPtIrowMH8Rq1sxPVVy50o/uCk6PbNjQvxYu9GkZGX44T5cu2xfKLy720YzmzX2gbNMmH0wrKvLRiMaN/VTJbt38eZoCmdZ69fIx0IICHwgrKvI/gvHasMH/CLZq5X8MNdBQRESk/lEQrL5q0gROOAGOPNIHhBYs8MGEtWu3L1jRpo1/HNqrl19IPBV20gu2e+BA+N//4Nhj4eefU6Pd6fKZpks7RdJRRoYPRHXu7P9N5+f7EWGFhf7fd6tWflpzcHhOgwb+2IYNfhRXcbEPiGVl+dFizZr5AHkwOFZQsL2sBg18vsxMH0Dr2zfyIvlSxsxygBuBA4CBQCvgAufcCzU4dwQwppLDnZxz5bYANrOTgFFAH2B14Ny7nXNJ309xn338j+H330Pbtj5mGm8QzDn49Vfo2dOX2aqV/1UlIiIi9YuCYPVd06ZwyCH+5Zz/IyL0SXqit0WqK4Ijgk46yf+RlErtTpfPNF3aKZKOGjb0I7M6dPDfFxdvD1yFL6IUfqyqvJHy5+f7IJhGf9VEW+AOYDHwLTA4hjLuAH4OS9sQ+o2ZHQdMBqYBVwH9gNuB9sDlMVwzKk2b+gHECxf6UWAZGX4mbTyDideu9YG0XXbx3Y4uXbQemIiISH2kX9+pxMw/MU83qdzuVG5bqHRpp0i6qipaEH6susiCIg/xWEFg1JaZDQC+jKGM/zjnvqomz4PAd8DRwZFfZpYP3Gpmf3POzYvhulEZMsSPBJs+Hdq1275Qfiyx0i1bYNUq2GsvP0i5a1cfBBMREZH6Rwvji4iIiKQB51xR+LTFWJhZMzOLGE4ysz74KZCjw6Y+PgUYMDTe69dEkyYwbJifJbtihQ+A5ef7AcfR2LLFjyjbeWe/4WiPHn6mvgYeioiI1E8KgomIiIhITX0E5AObzWyKme0adnyfwHu50WLOueXA0pDjSderF5x3nh/B9euvsHWrX6Juy5bqz3XO7+uwYIFf4m7fff2rd28tiC8iIlKfaU6BiIiIiFRnM/AC24Ng+wHXAZ+Z2b7OuSWBfJ0C7ysilLEC6FzVRcysPdAuLLkXQGFhIQAFBQU1rvTOO8OIEfDOO356ZF7e9mXlmjTxG4uGKi31G4+uX+9n6u+1F+y3H+y5p19sP7gMXW0KtjeadqeKdG272p1e7Yb0bXu6thvSt+2VtXvTpk21VgcFwURERESkSs6514DXQpImm9k7wHTgNuCyQHpW4L0oQjFbgOrGUV0BjIx0YNasWXTr1o3c3Nwa1zuoVy//itXKlf61I8XS7lSRrm1Xu9NPurY9XdsN6dv28HYvXry41q6tIJiIiIiIRM0594mZfQEMCUkuDLxnRjilScjxyjwFTAhL6wW80a9fP/Ly8hg4cCDZMW6msmULzJ0Lv/zi1/pau9bvIFlaCjk50LatX/h+zz2hTRto2XLHr/9VUFBAbm5uXO2ur9K17Wp3erUb0rft6dpuSN+2V9buuXPn1lodFAQTERERkVgtAXYP+T44DbJT4FioTkCVj7ydc6uB1aFpZgZAVlYWeXl5ZGdn0zzGhbmaN4f27YPX8rtGlpT4r5s08QGvuroBaTztru/Ste1qd/pJ17ana7shfdse3u6cnJxau7YWxhcRERGRWPUEfg35fmbgfUBoJjPrDHQNOb7DmUF2tg+MtWgBmZl1NwAmIiIiiaEgmIiIiIiUMbNOZraHmTUKSQtfrB4z+y1+gfy3g2nOudnAPOASMwudSHg54ICJSau4iIiISDX0vEtEREQkTZjZlUBLtu/SeKKZdQ18/bhzLg+4Dzgf6AEsChz7zMy+Ab4C8oB9gQvxUx7/HHaZG4EpwLtmNh7YE7gS+LtzrvYW/RAREREJoyCYiIiISPq4Adg55PvTAi+Al/ABrkheBY4Hjgaa4tf+eg640zm3KjSjc+4tMzsNv8vj4/jpkn8G7kpQG0RERERioiCYiIiISJpwznWvQZ4RwIiwtNuB26O4zmRgcjR1ExEREUk2rQkmIiIiIiIiIiIpT0EwERERERERERFJeQqCiYiIiIiIiIhIylMQTEREREREREREUp6CYCIiIiIiIiIikvIUBBMRERERERERkZSnIJiIiIiIiIiIiKS8OhsEM7O+ZjbBzBaa2WYzW2Nm083sxBqev5+ZvWVmK81sk5l9Z2ZXm1lGsusuIiIiIiIiIiJ1S8MdXYEq7Aw0A14ElgNNgdOBKWZ2qXNudGUnmtl+wGfAT8ADwGbgOOBvQC/gmuRWXURERERERERE6pI6GwRzzk0FpoammdkTwNfAdUClQTDg0sD7IOfcusDXz5rZf4ERKAgmIiIiIiIiIpJW6ux0yEiccyXAEqBlNVmbA1uADWHpK4DChFdMRERERERERETqtDofBDOzbDNra2a9zOyP+GmNH1Rz2jR8IOxZM+ttZjub2WXAacB9ya2xiIiIiIiIiIjUNXV2OmSIh9g+vbEUeB24sppzngP6Bs67KJBWAlzpnHumuguaWXugXVhyL4BNmzaRn59fs5rXUEFBQbl3qZ7uWWx036KnexYb3bfo6Z7FJpH3bdOmTXGXISIiIiJ1V30Igj0KTAQ6A2cAGUDjqk5wzpWY2QLgHWACfmrk2cDjZrbSOTe5mmteAYyMdCA3N5eVK1dGU/8ay83NTUq5qUz3LDa6b9HTPYuN7lv0dM9ik4j7tnjx4gTURERERETqqjofBHPOzQPmBb4da2bvAm+a2QHOORfpHDO7Gb/4/a7OueBj3dfM7CPgSTN7yzlXXMVln8IHz0L1At4YOHAgvXv3jrk9kRQUFJCbm8vAgQPJzs5OaNmpSvcsNrpv0dM9i43uW/R0z2KTyPs2d+7cBNVKREREROqiOh8Ei2Ai8CywG/BDJXmuAD4MCYAFTQEeBroD8yu7gHNuNbA6NM3MAMjJyaF58+ax1Lta2dnZSSs7VemexUb3LXq6Z7HRfYue7llsEnHfcnJyElQbEREREamL6vzC+BFkBd5bVJGnA37aZLhGgff6GPwTEREREREREZEY1dkgWGBx+vC0RsB5QCEwJ5DWycz2CBwL+hE4yszahJybgV9TbCOwIJl1FxERERERERGRuqUuj4h61syaA9OBZUBH4BxgD+D6kKmO9wHnAz2ARYG0+4GXgC/MbDQ+aHY2sB9wu3NuW201QkREREREREREdry6HAR7Ffg9cDnQBj+C62vgJufclKpOdM6NM7M1wC3AjUBz/Pphlznnnk1qrUVEREREREREpM6ps0Ew59x4YHwN8o0ARkRIfwd4J+EVExERERERERGReqfOrgkmIiIiIiIiIiKSKAqCiYiIiIiIiIhIylMQTEREREREREREUp6CYCIiIiIiIiIikvIUBBMRERERERERkZSnIJiIiIiIiIiIiKQ8BcFERERERERERCTlKQgmIiIiIiIiIiIpT0EwERERERERERFJeQ2jyWxmU+K83m3OuVlxliEiIiJS76lfJSIiIlK7ogqCAScAa4CCKM9rAHQFHo3yPBEREZFUpX6ViIiISC2KNggGcK1z7uVoTjCztsDqGK4lIiIiksrUrxIRERGpJdGuCfYtsC6G62wLnLsxhnNFREREUpH6VSIiIiK1KKqRYM65fWK5iHMuD4jpXBEREZFUpH6ViIiISO2KendIMzvBzDKSURkRERGRdKJ+lYiIiEjtiToIBkwBVpjZE2Z2UKIrJCIiIpJG1K8SERERqSWxBMEuBWYDlwGfmNlCM7vbzHontmoiIiIiKU/9KhEREZFaEnUQzDn3nHPuCKAb8H/4BV1vA743sxlmdp2ZdU5wPUVERERSjvpVIiIiIrUnlpFgADjnljvnHnLODQD2AO4BsoEHgcVm9oGZXWBmzRNUVxEREZGUpH6ViIiISPLFHAQL5Zz70Tk30jm3O3AA8Bi+A/d3YEUiriEiIiKSDpLVrzKzHDO708zeNrN1ZubMbEQNz/2NmT1vZj+a2ebAtM2/m1mnCHmnBcoOf70da91FREREEqFhEspcBCwElgKdgCZJuIaIiIhIOlhE4vpVbYE7gMXAt8DgKM59AGgNTAB+AnoCVwInmNnezrmVYfmXAreEpS2Poc4iIiIiCZOQIJiZ5QCnAcOBIwPlLgL+DIxLxDVERERE0kES+1UrgE7OuZVmNgD4MopzrwM+cc6VhtTzbeC/+GDY7WH585xzL8VRVxEREZGEizkIZmaNgOPxHbTjgSz8Yq5/B8Y55z5NSA1FREREUlxt9Kucc0VA+Iitmp47PVKama0DIu5kaWYNgSbOuU2xXFNEREQk0aIOgpnZkfgO2mlAC6AIeBN4CfiPc644oTUUERERSVH1uV8VGLGWA6yJcHg3oABobGargOeAu5xz26opsz3QLiy5F0BhYSEABQUF8VW8ngm2N93aDenbdrU7vdoN6dv2dG03pG/bK2v3pk2197wslpFg7wOlwEf4DtrrzrmNCa2ViIiISHqoz/2qa4HGwKth6Qvw7ZmF3+FyKH665G7AmdWUeQUwMtKBWbNm0a1bN3Jzc+Oocv2Vru2G9G272p1+0rXt6dpuSN+2h7d78eLFtXbtWIJg1wPjnXPa9VFEREQkPvWyX2Vmg/DBqteccx+GHnPO/T4s+z/NbDRwsZk94pz7vIqin8Ivvh+qF/BGv379yMvLY+DAgWRnZ8fbhHqjoKCA3NzctGs3pG/b1e70ajekb9vTtd2Qvm2vrN1z586ttTpEHQRzzj2SjIqIiIiIpJv62K8ysz2AfwHfAxfV8LSHgIuBIUClQTDn3Gpgddj1AMjKyiIvL4/s7GyaN28eQ83rt3RtN6Rv29Xu9JOubU/XdkP6tj283Tk5ObV27QbRZDazx8xsv2gvYmZNA+fuGu25IiIiIqmoPvarzGwn4F0gD/htFFM3lwTeWyelYiIiIiI1EFUQDL8F9m4xXCcL+AOwUwznioiIiKSietWvMrM2+ABYJnBMlFM4ewbef014xURERERqKJY1wS41s6OiPCczhuuIiIiIpLo6168ys074nSoXBHdzNLNsYCrQBTjCOfdTJec2B4qcc0UhaYZfGB/gnWTWXURERKQq0QbBFgM7B17RWgwUxnCeiIiISCqq9X6VmV0JtAQ6B5JONLOuga8fd87lAfcB5wM9gEWBY+OAgcDzQG8z6x1S7Cbn3OTA1/sCr5jZK8B8/Ki1U4FDgNHOuRnR1llEREQkUaIKgjnnuiepHiIiIiJpZQf1q26gfNDttMAL4CX8Wl+R7B14vzDwCvULMDnk64/xga+OQCkwF7gMGB17tUVERETiF8t0SBERERGph2oSeHPOjQBGRHteIN/PwBnR10xEREQk+aJdGF9ERERERERERKTeURBMRERERERERERSnoJgIiIiIiIiIiKS8hQEExERERERERGRlKcgmIiIiIiIiIiIpLyE7A5pZgcCRwDtgaeccz+ZWVNgD+BH59ymRFxHREREJNWpXyUiIiKSHHGNBDOzxmb2OvApcC9wNbBT4HAp8C5wTVw1FBEREUkD6leJiIiIJFe80yHvBk4ALgd2Byx4wDm3BZgAnBznNURERETSgfpVIiIiIkkUbxDsbOBp59xoYF2E43OBnnFeQ0RERCQdqF8lIiIikkTxBsHaA7OqOF4CNI3zGiIiIiLpQP0qERERkSSKNwi2BL9Ia2UOAebHeQ0RERGRdKB+lYiIiEgSxRsEexm41MwOCklzAGZ2MXAGMDbOa4iIiIikA/WrRERERJKoYZzn3wscCEzHr1PhgEfMrDXQFZgKPBLnNURERETSgfpVIiIiIkkU10gw59xW4FjgAmAhMA/IBL4DRgAnOudK4qyjiIiISMpTv0pEREQkueIdCYZzzgEvBV4iIiIiEiP1q0RERESSJ941wUREREREREREROq8uEaCmdmH1WRxwBZgKfARMNE5VxzPNUVERERSkfpVIiIiIskV70iwBsBOwGBgL6BF4LVXIG0noD1wOn7Ho6/MrG2c1xQRERFJRepXiYiIiCRRvEGw24FWwPlAe+fcfs65/fAdtAsCx64C2gEXAn2B+2pSsJn1NbMJZrbQzDab2Rozm25mJ9bg3Glm5ip5bYuxrSIiIiLJlLR+lYiIiIjEvzD+g8AY59w/QxMDOxe9aGZ7Ao845w4CXjCzg4Bqg1gBOwPNgBeB5UBT/JPPKWZ2qXNudBXn3gv8PSwtG3gGeLeG1xcRERGpTcnsV4mIiIikvXiDYP2Bf1ZxfBHwh5Dvv8Y/3ayWc24qMDU0zcyeCJRxHVBpEMw59154mpmdG/hyXE2uLyIiIlLLktavEhEREZH4p0OuAIaaWYVyAmlnACtDktsA62K9WOBJ6BKgZQynDwcKgDdivb6IiIhIEtVqv0pEREQk3cQ7Euxh4HHgUzN7DlgQSN8FuBjYH7g6JP8wIDeaC5hZNpCFXxj2JOA44NUoy2gHHAW86pwriOZcERERkVqS9H6ViIiISDqLKwjmnHvSzEqBu/BrcLnAIQPWAlc7554EMLNM4I/4ofzReAi4NPB1KfA6cGWUZZyJb2uNpkKaWXv8orOhegFs2rSJ/Pz8KC9ftYKCgnLvUj3ds9jovkVP9yw2um/R0z2LTSLv26ZNm+IuIx611K8SERERSVvxjgTDOfe0mf0dGIBfzB7gF+Ar59y2kHxFwH9juMSjwESgM34aQAbQOMoyhgO/AhXWCqvEFcDISAdyc3NZuXJlpENxy83Vw9xo6Z7FRvcterpnsdF9i57uWWwScd8WL16cgJrEpxb6VSIiIiJpK+4gGECgU/a/wCuhnHPzgHmBb8ea2bvAm2Z2gHPOVXEqAGbWEzgIeMI5V1zDyz4FTAhL6wW8MXDgQHr37l3DYmqmoKCA3NxcBg4cSHZ2dkLLTlW6Z7HRfYue7llsdN+ip3sWm0Tet7lz5yaoVvFJZr9KREREJJ0lJAhmZo2APfDrdlVYzNU5Nz0R1wmYCDwL7Ab8UIP8wwPvNd4V0jm3GlgdmmZmAOTk5NC8efOaFhWV7OzspJWdqnTPYqP7Fj3ds9jovkVP9yw2ibhvOTk5CapNfGq5XyUiIiKSNuIKggV2KroPP32waRVZM+K5TpiswHuLGuYfDixwzn2ewDqIiIiIJNQO6leJiIiIpI0KTxejdCtwI/AScB5+4dabgcuA74BvgWNiKTiwOH14WqPAdQqBOYG0Tma2R+BYeP59gN7Ay7HUQURERKQWJa1fJSIiIiLxT4ccAbzmnLvczNoE0r52zn1oZi/i17I4Eng/hrKfNbPmwHRgGdAROAc/PeB651xwC6f7gPOBHlTcIemcwHuNp0KKiIiI7CAjSF6/SkRERCTtxTsSrCvwYeDrosB7EwDn3Fb8k8zfxVj2q0ApcDnwNHAdsBQ42Tn3cHUnB6YUnAXMcM7VZO0wERERkR0pmf0qERERkbQX70iwtUAOgHNuk5nlAz3D8rSKpWDn3HhgfA3yjcA/OQ1PL8V3JkVERETqg6T1q0REREQk/iDYN8D+Id9/BFxrZt/gR5ldjV+/QkRERESqpn6ViIiISBLFOx1yNJBpZpmB728DWuLX8fov0By4Ps5riIiIiKQD9atEREREkiiukWDOuSnAlJDv55hZL2AwUAJ85pxbF1cNRURERNKA+lUiIiIiyRXXSDAzG2Rm7ULTnHN5zrk3nHNvAQ3MbFBcNRQRERFJA+pXiYiIiCRXvNMhPwKOquL4bwJ5RERERKRq6leJiIiIJFG8QTCr5ngmfvi+iIiIiFRN/SoRERGRJIp6TTAz6wZ0D0nao5Kh+S2BS4FfYqqZiIiISIpTv0pERESk9sSyMP4FwEjABV63BV7hDP+08tKYayciIiKS2tSvEhEREaklsQTBXgO+x3fGXgMeAz4Oy+OAAmCmc25VXDUUERERSV211q8ysxzgRuAAYCDQCrjAOfdCDc9vCfwFOBVoCuQC1zvnZkTIexIwCugDrAbGAHc754pjrb+IiIhIvKIOgjnn5gJzAczsAmC6c+7nRFdMREREJNXVcr+qLXAHsBj4Fhhc0xPNrAHwb2Av4K/AGuAKYJqZ7eec+ykk73HAZGAacBXQD7gdaA9cHn8zRERERGITy0iwMs65FxNVEREREZF0Vgv9qhVAJ+fcSjMbAHwZxblDgYOBYc65iQBm9hrwI3AnMDwk74PAd8DRwZFfZpYP3Gpmf3POzYu/KSIiIiLRiyoIZmbPx3AN55z7fQzniYiIiKSs2u5XOeeKgJWxnIsPgq0CXg8p79dAIOxcM8t0zhWZWR/8FMg/hE19fAq/1tlQ4J4Y6yAiIiISl2hHgh2JX5ciGtHmFxEREUkH9alftQ8wwzlXGpaeC1wC7AbMCuQD+Co0k3NuuZktDTkekZm1B9qFJfcCKCwsBKCgoCCG6tdfwfamW7shfduudqdXuyF9256u7Yb0bXtl7d60aVOt1SGqIJhzrnuS6iEiIiKSVupZv6oTMD1C+orAe2d8EKxTWHp43s7VXOcK/G6ZFcyaNYtu3bqRm5tbfW1TULq2G9K37Wp3+knXtqdruyF92x7e7sWLF9fateNaE0xERERE0kIWUBQhfUvI8dD3yvI2r+Y6TwETwtJ6AW/069ePvLw8Bg4cSHZ2dg2qnBoKCgrIzc1Nu3ZD+rZd7U6vdkP6tj1d2w3p2/bK2j137txaq0NCgmBm1gM4Dtg5kPQL8B/tGikiIiISnTraryoEMiOkNwk5HvpeWd7CCOllnHOrgdWhaWYGQFZWFnl5eWRnZ9O8eXWxtNSTru2G9G272p1+0rXt6dpuSN+2h7c7Jyen1q4ddxDMzB4CrgEahB0qNbNHnXM3xHsNERERkXRQh/tVK9g+1TFUMG15SL5g+pIIedNz3oeIiIjUCeEdrKiY2fXAH/E7BR0EtAy8DgImAn80sz/GV0URERGR1FfH+1UzgX3NLLzveACwGfgxJB/AgNBMZtYZ6BpyXERERKTWxRUEAy4GpjjnznDOfeGcyw+8vnDOnQW8CVwafzVFREREUl6d6FeZWScz28PMGoUkTwQ6AKeF5GsLDAPedM4VATjnZgPzgEvMLCPk/MvxO1tOTHb9RURERCoT73TI7sDfqjj+DnBsnNcQERERSQfdSXK/ysyuxI8uC+7SeKKZdQ18/bhzLg+4Dzgf6AEsChybCHwOjDGzPsAa/E6OGVTczfFGYArwrpmNB/YErgT+7pyrvZVvRURERMLEGwRbDexVxfG9gF/jvIaIiIhIOqiNftUNbF9wH/zIruDorpeAvEgnOedKzOy3wF+Bq/G7QH4JjHDO/RCW9y0zOw0fHHs8UOc/A3fFWXcRERGRuMQbBJsAXGNmi/BPDwsAzCwb/8TvIuDROK8hIiIikg6S3q9yznWvQZ4RwIgI6esDdbioBmVMBiZHWT0RERGRpIo3CPYnYG8CT/fMLLgzUOdA2R8Bd8R5DREREZF0oH6ViIiISBLFFQRzzm0GfmNmJwPHsX14/dvAVPxCqS6+KoqIiIikPvWrRERERJIr3pFgADjn3gDeSERZIiIiIulM/SoRERGR5GgQz8lm9pqZnWpmmYmqkIiIiEg6Ur9KREREJLniCoIBhwCTgNVm9k8zO8HMGiWgXiIiIiLpRv0qERERkSSKNwjWFRiM31L7KGAKsMrM/mFmR5tZRpzli4iIiKQL9atEREREkiiuIJjzpjvn/oDfuego/PbeJ+IXcV1pZs/EX00RERGR1KZ+lYiIiEhyxTsSrIxzrtQ594Fz7lKgE3Ap0Bi4OFHXEBEREUkH6leJiIiIJF5CdocMMrNOwDDgTODAQPJnibyGiIiISDpQv0pEREQkseIOgplZe2AovoN2CH50WS5wA/Cac25ZvNcQERERSQfqV4mIiIgkT1xBMDP7ABgEZAAzgduAV51zi+KumYiIiEgaUb9KREREJLniHQnWHrgT30H7KQH1EREREUlX6leJiIiIJFFcQTDnXL9EVUREREQknalfJSIiIpJcCdsdUkREREREREREpK5SEExERERERERERFKegmAiIiIiIiIiIpLyFAQTEREREREREZGUpyCYiIiIiIiIiIikvLh2hwwys0xgX/zW3p8659YkolwRERGRdKN+lYiIiEhyxD0SzMyuBlYAnwCvA/0D6W3NbI2ZXRjvNURERETSgfpVIiIiIskTVxDMzC4AHgXeBn4PWPBY4Knlh8BZ8VxDREREJB2oXyUiIiKSXPGOBLseeMM5Nxx4M8Lxr4G+cV5DREREJB2oXyUiIiKSRPEGwXYB/lPF8XVAmzivISIiIpIO1K8SERERSaJ4g2AbgLZVHO8DrIzzGiIiIiLpYAPqV4mIiIgkTbxBsKnAJWbWMvyAmfUFLgamxHkNERERkXSgfpWIiIhIEsUbBLsdyAC+B+4BHHC+mb0EfAWsBu6K8xoiIiIi6UD9KhEREZEkiisI5pxbDuyH38XoTPwuRr8DTgReAQ4M7GYkIiIiIlVQv0pEREQkuRrGW4BzbjVwEXCRmbXDB9Z+dc6Vxlu2iIiISDpRv0pEREQkeeIOgoVyzv2ayPJERERE0pX6VSIiIiKJFVcQzMzuqCaLA7YAS4HpzrllUZTdFxiFnxbQEdgMzAH+6px7s4ZlDAFuDZTRAPgR+Itz7tWa1kNERESkNiSzXyUiIiIi8Y8EG4XvkIFftyJUeHqJmT0HXFnDIf07A82AF4HlQFPgdGCKmV3qnBtd1clmdgHwD+A9fCCsBNgd2KkG1xYRERGpbaNIXr9KREREJO3FGwTrCvwb+AZ4HJgfSN8VuAroj1/YNQe4FrgUH9C6p7qCnXNT8VuFlzGzJ4CvgeuASoNgZtYdeBJ43Dl3TRTtEREREdlRktavEhEREZE4d4cEngLmOecudM5945zbGHjNcM5dAPwE3O+cm+mcGwG8A5wX68WccyXAEqBlNVkvw28xfgeAmeWYWfgTVREREZG6pFb7VSIiIiLpJt4g2JHAf6s4/l/gqJDvpwLdormAmWWbWVsz62VmfwSOAz6o5rQhwDzgt2a2FNgIrDWzu80s3jaLiIiIJEPS+1UiIiIi6Sze6ZBFwAHAM5UcPxDYGna9TVFe4yH8cH+AUuB14MpqztkVvwbYGOAvwLfAacDtgTrcUtXJZtYeaBeW3Atg06ZN5OfnR1H96hUUFJR7l+rpnsVG9y16umex0X2Lnu5ZbBJ53zZtiraLknC10a8SERERSVvxBsFeAf5gZmuBp4GfA+k9gCuAc/FrcwUdgd/hMRqPAhOBzsAZ+GmOjas5Jwc/yu1m59wDgbRJZtYauMbM/uyc21jF+VcAIyMdyM3NZeXKlVFUv+Zyc3OTUm4q0z2Lje5b9HTPYqP7Fj3ds9gk4r4tXrw4ATWJS230q0RERETSVrxBsP8DOuAXqv8jfqQW+ACUAZMCeTCzJvhF7T+L5gLOuXn4qY0AY83sXeBNMzvAOecqOa0QyMZ3JkO9AhwL7ANMr+KyTwETwtJ6AW8MHDiQ3r17R9OEahUUFJCbm8vAgQPJzs5OaNmpSvcsNrpv0dM9i43uW/R0z2KTyPs2d+7cBNUqZknvV4mIiIiks7iCYM65LcCZZnY/Pri0c+DQL8A7zrkZYXnviud6AROBZ4HdgB8qybMcPyVyVVj66sB7q6ou4JxbHZIXgOC6+jk5OTRv3jy6GtdQdnZ20spOVbpnsdF9i57uWWx036KnexabRNy3nJycBNUmNjuoXyUiIiKSNuIdCQaAc+4b/HbetSEr8N6iijxf44NgXYCFIemdA++/JqFeIiIiInGr5X6ViIiISNqoszslBhanD09rhN8KvJDAGhhm1snM9ggcC3o18P77kHMbABcA6/BBMhERERERERERSRNxB8HM7Dgze8/M1ppZsZmVhL9iLPpZM/vAzEaa2UVmdjvwHbAvcLtzLrgb0n3AXPyor6A3gA+AW8zsWTO7AngbOBS4xTlXFGOdRERERJImif0qzCzTzB4ws+VmVmhmX5jZUTU4b5GZuUpeP4XlrSzfzbHWW0RERCRR4poOaWanA68Bs4HxwOXAy/jFW08GfgImx1j8q/iRXJcDbYCN+BFcNznnplR1onPOmdkpwD3AmcAI/Pph5zrnxsVYHxEREZGkSXK/CuAFYCh+5+2f8P2jqWZ2hHPukyrOuxa/83aonfH9rHcj5H8PGBuWpumdIiIissPFuybYLUAufoRVK3xn7Xnn3Idm1h34nO3be0fFOTce3wGsLt8IfCcuPH0TvtN2bSzXFxEREallSetXmdlA4CzgRufcg4G0scD3wF+Agys71zk3OUJ5twe+jPRw8Ufn3Eux1FNEREQkmeKdDtkHGO+cKwGKA2mNAJxzi4CngJvivIaIiIhIOkhmv2ooUAKMDiYEdpj8B3CQme0UZXnDgZ+dc59FOmhmWWbWJMa6ioiIiCRFvCPBNgNbAZxzG8ysCOgUcnwV0CPOa4iIiIikg2T2q/bBj9DKD0vPDbzvDSypSUFmtg/QG7i3kiwjgCt8VpsL3OOce7mGZbcH2oUl9wIoLCwEoKCgoCZFpYxge9Ot3ZC+bVe706vdkL5tT9d2Q/q2vbJ2b9q0KVL2pIg3CPYD/qll0Ezgd2b2UqDs4cDiOK8hIiIikg6S2a/qBKyIkB5M6xxFWecE3iNNhfwMv67Zz4Ey/wCMM7MWzrmna1D2FcDISAdmzZpFt27dyM3NjXQ45aVruyF92652p590bXu6thvSt+3h7V68uPbCRvEGwf4FXG1mNwR2XLwXvzPjBsAB2cCFcV5DREREJB0ks1+VBUTaHXtLyPFqmVkD/Npi3zjn5oYfd84dEpb/efzGRn82sxecc4XVXOIpYEJYWi/gjX79+pGXl8fAgQPJzs6uSXVTQkFBAbm5uWnXbkjftqvd6dVuSN+2p2u7IX3bXlm7586t0KVImriCYIGFVR8M+f4tMxsMnIZfd+LfzrmP4rmGiIiISDpIcr+qEMiMkN4k5HhNHA50AR6pSWbn3FYzewJ4BtgPqGoXSpxzq4HVoWlmBkBWVhZ5eXlkZ2fTvHnzGlY3daRruyF92652p590bXu6thvSt+3h7c7JCd+EOnliDoKZWSZwDLDIOfddMN059zHwcQLqJiIiIpIWaqFftQIfvAoXXHNseQ3LOQcoBV6J4trBtcZaR3GOiIiISMLFszvkVvxw9Uq31BYRERGRGkl2v2omsJuZhT9uPiDkeJUCgbrTgWnOuZoGzQB6Bt5/jeIcERERkYSLOQjmnHPAT0DbxFVHREREJP3UQr9qIpABXBJMCAS1LgC+cM4tCaR1M7M9Kinjt0BLIi+Ij5mF7+qImTUDrgXW4NcGExEREdlh4l0Y/8/Aw2Y2wTn3QyIqJCIiIpKmktavcs59YWYTgPvMrD0wHzgf6A78PiTrWPy6XxahmHPwi+tPquQyfzCzU4A38btYdsIv5N8N+J1zbmv8LRERERGJXbxBsAOBtcD3ZjYNWETFhVWdc+6aOK8jIiIikuqS3a86D7gb+B3QCvgOOME5N726EwPTKI/HL86fV0m2T/HTOS8C2gAFQC5woXPuwxjrLCIiIpIw8QbBrgz5+jeV5HGAgmAiIiIiVUtqv8o5twW4MfCqLM/gStLzgaxqyn8PeC+WuomIiIjUhriCYM65eBbWFxEREZEA9atEREREkkudLRERERERERERSXnxTocEwMwOBI4A2gNPOed+MrOmwB7Aj865TYm4joiIiEiqU79KREREJDniGglmZo3N7HX8Qqj3AlcDOwUOlwLvovXARERERKqlfpWIiIhIcsU7HfJu4ATgcmB3QrbTDiy+OgE4Oc5riIiIiKQD9atEREREkijeINjZwNPOudHAugjH5wI947yGiIiISDpQv0pEREQkieINgrUHZlVxvARoGuc1RERERNKB+lUiIiIiSRRvEGwJfpHWyhwCzI/zGiIiIiLpQP0qERERkSSKNwj2MnCpmR0UkuYAzOxi4AxgbJzXEBEREUkH6leJiIiIJFHDOM+/FzgQmI5fp8IBj5hZa6ArMBV4JM5riIiIiKQD9atEREREkiiukWDOua3AscAFwEJgHpAJfAeMAE50zpXEWUcRERGRlKd+lYiIiEhyxTsSDOecA14KvEREREQkRupXiYiIiCRPXCPBzOwvZrZPoiojIiIikq7UrxIRERFJrngXxr8K+MrMfjKzu82sXyIqJSIiIpKG1K8SERERSaJ4g2Dt8etW/Aj8HzDTzGab2Z/MbPe4ayciIiKSPtSvEhEREUmieBfG3+icG+ucOx7oAFwCLAX+BMwxs5lmdnMC6ikiIiKS0tSvEhEREUmueEeClXHObXDO/cM5dwzQCbge6IHf7ltEREREakj9KhEREZHEi3t3yFBm1gg4DjgTOBHIAZYk8hoiIiIi6UD9KhEREZHEijsIZmYNgaPxHbSTgebACmAM8Kpz7rN4ryEiIiKSDtSvEhEREUmeuIJgZvYP4BSgFbAGeAUYD0x3zrm4ayciIiKSJtSvEhEREUmueEeCnQL8C3gV+NA5VxKewcxaOefWx3kdERERkVR3CupXiYiIiCRNvEGwDs654vBEM8sETgLOAY4FmsR5HREREZFUp36ViIiISBLFFQQL7aiZmQG/wXfQTsWvYfEr8HI81xARERFJB+pXiYiIiCRXIhbG3w/fQTsL6Ag4/PoVTwCfaw0LERERkZpRv0pEREQkeWIKgplZT3wH7RxgV2AZMA7Ixa9jMck5979EVVJEREQkValfJSIiIlI7og6Cmdn/gIH4XYsmAhc55z4JHOuV2OqJiIiIpC71q0RERERqTywjwQ4AfgauA/4daQFXEREREakR9atEREREakmDGM65EliB38J7pZk9a2ZHBBZwFREREZGaU79KREREpJZEHQRzzj3lnDsU6AU8ChwGfIBfv+Iu/AKuWrRVREREpBrqV4mIiIjUnlhGggHgnPvZOXePc64PsD9+56LBgAFPmdloMzvBzJokpqoiIiIiqUn9KhEREZHkizkIFso597Vz7jpgJ+Bo4B3gTGAKfqFXEREREakB9atEREREkiMhQbAg51ypc+5959wIoANwNn5Iv4iIiIhEQf0qERERkcRKaBAslHNui3PuVefcycm6hoiIiEg6UL9KREREJH5JC4KJiIiIiIiIiIjUFQqCiYiIiIiIiITp3r07I0aM2NHVEJEEUhBMRERERERE0sqsWbMYOnQoO++8M02aNKFLly4cddRRPP744zu6aiKSRA13dAVEREREREREastnn33GEUccQbdu3bj44ovp2LEjS5Ys4fPPP+dvf/sbV111FQA//PADDRpo3IhIKlEQTERERERERNLGvffeS4sWLfjyyy9p2bJluWOrV68u+zozM7OWayYiyaawtvx/e3ceH1V193H880uAbBCWsAdBjBsgmwtaVEQfWq1brYrVuhS1LuWxaluttlrFpbX6VKtWsdpWkVqrAtZaa9WiuFSsWBVlrRpW2cOSQBaW5Dx/nDthMkySSZhJMjPf9+s1ryT3nnvmnDOTmXN/95xzRURERERE0kZxcTFDhgzZIwAG0LNnz9rftSaYSOpREExERERERETSxoABA/jwww+ZP39+axdFRFpYmw2CmdkQM5tmZkvMrMLMSszsbTM7LYZjJ5iZq+fRuyXKLyIiItKWmFmWmd1tZqvNrNLM3jezr8Zw3KR6+lRV9aS/1MwWmVmVmX1uZt+Pf21ERJrvuuuuo6KighEjRjB69GhuuOEGXnvtNXbu3NnaRRORBGvLa4INADoBTwKrgVzgLOBFM7vCOfdYDHncAiyN2LYlnoUUERERSRJTgLOB+4HPgQnAy2Z2vHPuXzEc/z1gW9jf1ZEJzOwK4LfADOA+4FjgQTPLdc7dvTeFFxGJl69+9au899573HXXXbz66qu899573HPPPfTo0YPf//73nH766a1dRBFJkDYbBHPOvQy8HL7NzB4CPgR+CMQSBPuHc+4/CSieiIiISNIws1HAucD1zrlfBdumAvOBe4DRMWQz3TlX0sBz5AA/B/7unDs72Pw7M8sAfmZmjznnNu9NPURE4uWII47g+eefZ8eOHXzyySf85S9/4de//jVnn302c+fOZfDgwa1dRBFJgDY7HTIa51w1sBLoEusxZtbJzDITVigRERGRtu9s/Mit2ouIzrkq4A/AV8xsnxjyMDPLNzOrZ//xQAEwOWL7w0AecEqTSy0ikmAdOnTgiCOO4Be/+AWPPPIIO3fuZNq0aa1dLBFJkDY7EizEzPKAHKAzcDrwdeDZGA+fBXQEdpjZq8CPnHOfx/CcPYEeEZuLALZt20ZZWVmMTx+b8vLyOj+lcWqz5lG7NZ3arHnUbk2nNmueeLbbtm3bGk+UvEYCnznnIjsxc4KfI/AXGhuyBN+vKjezF/D9qnURzwEQOQr/Q6Am2P9UQ0/QUB+ssrISSL//kXT+bEjXuqverVfvgw8+GIDly5dTVlaGc46dO3fG/fwvUluoe2tI13pD+ta9vnq3ZB+szQfBgHuBK4Lfa4DngasaOaYCv+7FLKAMOAw/hXK2mR3qnGuskzcRuDXajjlz5rB27drYSt5Ec+bMaTyR1KE2ax61W9OpzZpH7dZ0arPmiUe7rVixIg4labP6AGuibA9t69vAsZuBh4D3gO34db7+FxhlZoeHBdb6ANXOufXhBzvndpjZxkaeI6TePti8efPo379/2v6PpGu9IX3rrnonzrx58zjkkEOIHNj6/PPPA2BmzJo1i8rKStasWcOsWbMSXibQa56O0rXukfVuyT5YMgTB7gem4ztO5wCZQIeGDnDOPQc8F7bphWAk2NvATcCVjTznZCByDGwR8NdRo0YxaNCgmAsfi/LycubMmcOoUaPIy8uLa96pSm3WPGq3plObNY/arenUZs0Tz3ZbtGhRnErVJuXgA1iRqsL2R+WceyBi0wwzmwP8CR+0+mVYHjvqyaaqoecIU28fbOjQoZSWlqbd/0g6fzaka91V78TX+yc/+QkVFRWcdtppHHDAAezcuZP333+f559/nv79+3PrrbfSpUsXcnJy6NOnD8cff3xCy6PXPL3qDelb9/rq3ZJ9sDYfBHPOLQYWB39ONbPXgL+Z2ZHOOdeEfP5lZu8D42JIux6ocxUzdJWgY8eO5Ofnx/q0TZKXl5ewvFOV2qx51G5NpzZrHrVb06nNmice7daxY8c4laZNqgSyomzPDtsfM+fc02Z2L75fFQqCVVL/hcrsWJ6joT5YTk4OpaWlafs/kq71hvStu+qdOPfddx/Tpk1j5syZTJkyhR07dtC/f38mTpzIzTffTM+ePQH/+dO+ffsWex30mqefdK17ZL1bsg/W5oNgUUwHHgUOBP7bxGNXAgfFvUQiIiIibdsaoDDK9j7Bz9XNyHMl0C3iOTLNrGf4lEgz64BfML85zyEiEncnnXQSJ510UqPpli1blvjCiEiLSqq7QwZCQ+k7N+PY/YANcSyLiIiISDKYCxxoZpGXm48M2x+z4A6R+1K3XxXK4/CI5Ifj+5xNeg4RERGReGuzQbDg7kCR29oDF+GH0y8MtvUxs4ODfaF0kXcVwsxOxi+Q/0rCCi0iIiLSNk3Hr6t6eWiDmWUBFwPvh24aZGb9zezg8AOj9auA7+Hv4hjer3oD2BTsi0xbAfx9L+sgIiIislfa8nTIR4OrlW8Dq4DewPnAwfhbcofuoXkX8B1gILAs2DbbzD7G36K7FDgUuAQ/bP8XLVUBERERkbbAOfe+mU0D7gouNH6B7z/tC1walnQqcBwQfsu05Wb2LDAPv8D9McC5+JFdj4Y9R6WZ/Qx4OHiuV/F3krwAuMk5tykxtRMRERGJTVsOgj2L75R9D7+OxFbgQ+AG59yLMRx7CvA1IBe/RsXvgNucc+sSVmIRERGRtusi4A7gQqAr8ClwqnPu7UaO+xMwGjgLv8D9cuAe4OfOuYrwhM65yWa2E/gRcDr+AuQPgMg7TIqIiIi0uDYbBHPOPQM8E0O6CcCEiG03AzcnpGAiIiIiScg5VwVcHzzqSzM2yrbLmvg8v8NffBQRERFpU9psEExEREREREQkHpyDigqorobMTMjNBbPGjxNJN7t2QU0NZGRAuxSMGKVglURERERERCTdVVTAxx9DcTEsXw4bN+4+uS8ogAEDoKgIRo70QTGRdLRrl//fKCuD0lKorNz9f5KTA507Q36+/59JhaBYClRBRERERERExKuqgpkz4d13YeVK2LTJn7zn5flRYNXVsGQJvPcedOsG/fvD6NEwbhxkZ7d26UVaRnU1rFrlH5s2QXm5///IyvIBsJoaWL/ep8vL8/8rhYX+kZnZ2qVvPgXBREREREREJCUUF8O0aTB3rh/RUlgI++3nT+oj1dRASQksWOCDYvPnw/jxfnSYSCorK/P/K19+CTt2+ABXr17Rpwg7B1u3wurV/v9l40b/P5Kf3/LljgcFwURERERERCTpzZsHU6fCwoXQrx8cdFDD635lZEDPntCjB6xdC++840/wL7oIhg5tuXKLtKRNm2DRIj8CrKAA+vZtOL2ZD3jl58OWLfD5536q8aBBPniWbKLEw0VERERERESSR3Hx7gDYkCHQp0/sC9+b+fRDhvjjp071+YmkmrIyHwBbvdoHirt0adrxXbr441av9vmUlSWilImlIJiIiIiIiIgkraoqPwUyFABr7iL3ubm7A2HTpvl8RVJFdbUP7q5a5acJZ2U1L5+sLH/8qlU+v+rq+JYz0RQEExERERERkaQ1c6ZfA6xfv72/y2Nurs9n7lyfr0iqWLXKrwFWUND8AFhIVpbP58svfb7JREEwERERERERSUoVFf4ukJWV0Lt3fPLs3dvnN3u2z18k2e3a5YNVO3Y0fQpkfbp08fmtWuXzTxYKgomIiIiIiEhS+vhjWLnST8+KdQ2wxpj5/Fas8PmLJLuNG/2C+PFeyL5rV9i82eefLBQEExERERERkaRUXOxP7rt3j2++3bv7k3stkC+poKwMysuhU6f45pufD9u2JdcC+QqCiYiIiIiISNJxDpYvh3btICPOZ7YZGZCZ6fN3Lr55i7S00lL/fo7XaMkQM59vaWl8800kBcFEREREREQk6VRU+GlYeXmJyT8vz+evdcEkme3a5de429vF8OuTleXzT5Z1wRQEExERERERkaRTXQ01NX4kSiJkZvr8q6sTk79IS6ip8Y94j5YMMdv9HMlAQTARERERERFJOpmZ/sQ+UUGq6urd0yJFklVGhn8kKkjl3O7nSAZJUkwRERERERGR3XJzoaDAL/idCOXlPv/c3MTkL9IS2rWDnBzYvj0x+W/f7vNv1y4x+cebgmAiIiIiIiKSdMxgwAC/FlG8R7mEpkEOGBD/xcRFWlrnzv79HO+bPDjn8+3cOb75JpKCYCIiIiIiIpKUioqgWzcoKYlvviUl0LWrz18k2eXn+xs9bN0a33zLyqBjR59/slAQTERERERERJLSyJHQvz+sWhW/US7O+fz69/f5iyS7ggIfLN60Kb75bt7sg8UFBfHNN5EUBBMREREREZGklJsLo0f7NYnWro1PnmvX+vxGj9Z6YJIa2rWDwkLo0AG2bIlPnlu2+PwKC5NnPTBQEExERERERESS2LhxMGIEfPklVFTsXV4VFT6fkSN9viKporAQ+vWDjRv3fpH87dt9Pv36+XyTiYJgIiIiIiIikrSys2H8eBg8GBYsaH4grKLCHz94MJx9ts9XJFVkZvo17goL/XTf5gbCtm/3xxcW+vwyM+NbzkRTEExERERERESSWlERXHTR7kDYmjWxrxHmnE8fCoBddJEWxJfUlJ8PgwZB375+xGNTp0Zu2eKP69vX55NMC+KHJNHMTREREREREZHohg6FK6+EadNg7ly/tldhIXTvDhlRhn/U1Pi7QK5a5dcAGzPGjwBTAExSWbdu/n8lN3d3IKxrVx/QMtszvXP+LpCbN/s1wA44wP+PJGMADBQEExERERERkRRRVATXXgszZ8Ls2bBiBSxf7qds5eX5n9XVUF7uf3btCkOG+EXwx43TFEhJD/n5MGyYv6vjqlU+wFVS4v8/srJ8MMw5P/Wxuho6dvQB5b59/c9kmwIZTkEwERERERERSRnZ2XDqqXDCCfDxx1Bc7ANhGzf60V8ZGf7kf8AAHzQbOVJ3gZT0k5kJ/fv7wNbGjX60V2kpVFbu/j/JyYHOnX3QrKAgue4CWZ8UqIKIiIiIiIhIXbm5cPTR/uGcX/i+utqf/OfmRp/6JZJu2rWDXr38A2DXrt1BsFQIekVKwSqJiIiIiIiI7Gbmp0OKSMNSMfAVTneHFBERERERERGRlKcgmIiIiIiIiIiIpDwFwUREREREEmzp0qVcddVVHHjggeTm5pKbm8vgwYP53//9Xz799NPWLp6IiEhaSPHZniIiIiIireull17iW9/6Fu3ateP8889n+PDhZGRksHjxYp5//nkeeeQRli5dyoABA1q7qCIiIilNQTARERERkQQpLi7m3HPPZcCAAbz++uv06dOnzv67776byZMnk5FR/wSNqqqqRBdTREQkLWg6pIiIiIhIgtxzzz2Ul5fzxBNP7BEAA2jXrh1XX301++yzDwATJkygY8eOFBcXc/LJJ1NYWMh9993X0sUWERFJSQqCtTFTpkzBzPjPf/4Tdf+yZcsws6iPo446qjbdhAkT6uzLz89n+PDh3HvvvWzfvr2lqiMiIiKS1l566SX2339/jjzyyJiP2bVrFyeeeCI9e/bkjjvu4Ctf+UoCSygiIpI+NB0ySZ133nmcfPLJdbb16NGjzt9ZWVn8/ve/B2DLli3MmDGD6667jg8++IBnnnmmxcoqIiIiko7KyspYvXo1Z5xxxh77tmzZwq5du2r/zsvLIycnB4Dt27czfvx47rrrLsrKypg1a1ZLFVlERCSlKQiWpA499FAuuOCCBtO0a9euTpqJEydy5JFH8uyzz3LffffRt2/fRBdTREREJG2VlZUB0LFjxz32jR07lk8++aT27//7v//juuuuq/37e9/7XuILKCIikmY0HTKNZGRkMHbsWMBPqxQRERGRxOnUqRMA27Zt22Pfo48+yj//+U+eeuqpPfa1a9eOfv36Jbx8IiIi6UYjwZJURUUFJSUldbZ17tyZ9u3bN3hccXExAAUFBQkrm4iIiIj4vlmfPn2YP3/+HvtCa4RFuzCZlZXV4N0iRUREpHn07Zqkbr31Vnr06FHn8e677+6RrqSkhJKSEoqLi7nrrrt44YUXGDZsGAcddFArlFpEREQkvZxyyil88cUXzJkzp7WLIiIikvY0EixJXX755YwfP77OtuHDh9f5u7y8fI/F8kePHs0f//jHhJdPRERE2hYzywJuBy4EugKfAjc75/7ZyHFnAt8CjgB6AyuBl4A7nHNbItIuAwZEyeZR59yVe1mFpPTjH/+Yp59+mksuuYTXX3+dXr161dnvnGulkomIiKQfBcGS1AEHHMC4ceMaTJOdnc3f/vY3wA+rHzhwoNaXEBERSV9TgLOB+4HPgQnAy2Z2vHPuXw0c9xiwGngKWAEMBa4CTjazQ51zlRHp5wL3Rmz7bC/LnrQOOOAAnn76ac477zwOOuggzj//fIYPH45zjqVLl/L000+TkZGhPpqIiEgLUBAshWVmZjYaKBMREZHUZ2ajgHOB651zvwq2TQXmA/cAoxs4/Gzn3JsR+X0IPAmcD/w+Iv0q59yeq72nsW984xvMmzePe++9l9dee43HH38cM2PAgAGccsopXHnllXuM6BcREZH4UxBMREREJPWdDVTjR3UB4JyrMrM/AL8ws32ccyujHRgZAAv8BR8EGxTtGDPrALR3zpXvbcFTRVFREZMnT2403ZQpU5gyZUriCyQiIpKGFAQTERERSX0jgc+cc2UR20OrtY/Ar/UVq97Bz5Io+04AKoBMM1sO/No590AsmZpZT6BHxOYigMpKP+uyvDy94mqh+qZbvSF96656p1e9IX3rnq71hvSte3313rZtW4uVQUGwNurxxx/nlVde2WP7N77xjVYojYiIiCS5PsCaKNtD2/o2Mb8b8CPLpkds/xT4F/BfoAC/7tj9ZtbXOXdDDPlOBG6NtmPevHn0798/be+ymK71hvStu+qdftK17ulab0jfukfWe8WKFS323AqCtVGPPPJI1O1jx45t2YKIiIhIKsgBtkfZXhW2PyZm9m3gUuAe59zn4fucc6dHpH0C+AfwQzP7jXPuy0aynwxMi9hWBPx16NChlJaWMmrUKPLy8mItbtIrLy9nzpw5aVdvSN+6q97pVW9I37qna70hfeteX70XLVrUYmVQEKyNmTBhAhMmTGgwTSy30tZ6EiIiIhKmEsiKsj07bH+jzOxY4A/Aq8BNjaV3zjkz+zVwIjAWf4fJhtKvB9ZHPCcAOTk5lJaWkpeXR35+fizFTSnpWm9I37qr3uknXeuervWG9K17ZL07duzYYs+d0WLPJCIiIiKtZQ1+SmSk0LbVjWVgZsOBF/F3lDzbObcrxucOrTXWLcb0IiIiIgmhIJiIiIhI6psLHGhmkZebjwzbXy8zKwJewY/SOtk515QVbPcLfm5owjFJyzkoL4eyMv9z507YsQN2xRoyFBERkYRps9MhzWwIMAk4DH8HogpgIfB/zrm/NTGv3wHfBf7unDs1zkUVERERaeumA9cBlwO/AjCzLOBi4H3n3MpgW38g1zm3OHSgmfUGXgNqgBOdc1GDWWbWDSh1zlWHbWsP3AjsAGYloF5tQkUFfPwxFBfD0qWwahVUVfkAWF4e9OwJ/frBsGHQqxfk50NBAbRrsz1xERGR1NSWv3oHAJ2AJ/FD9HOBs4AXzewK59xjsWRiZofj70xU1UhSERERkZTknHvfzKYBd5lZT+AL4DvAvvhF7kOmAscBFrbtFfxornuAY8zsmLB965xz/wx+Px242cymA0vx0x+/DRwC/NQ5tzbuFWtlVVUwcya8+y4sXw5r1vgRXxkZUFPjR4Xt3AnV1dCxo097wAFw1FHQuzcUFvpHZmZr10RERCQ9tNkgmHPuZeDl8G1m9hDwIfBDoNEgmPmVVB/Ed+j+JwHFFBEREUkWFwF3ABcCXYFPgVOdc283ctzw4OePo+x7CwgFwebhR+1fAPTAj/6aC5zjnIu842PSKy6GadNg7lzYuhVyc/2IL+cgJweywm5DUFMDW7bAhg2wbRts3gzDh0NJCWzcCEVFfnSYiIiIJFabDYJF45yrNrOVwBExHnIh/urjmSgIJiIiImnMOVcFXB886kszNso2i5I02rEf4keDpbx582DqVFi4EHr08EGvbdt84Cs7e8/0GRnQrRt07eqDXgsWQGUldOjgf1ZUwKBBPo2IiIgkTpsPgplZHpADdMZ3rL4OPBvDcZ2Au4FfOOfWhm6vLSIiyaOmpoZ169axfft2ampqWrs4CbVz5066devGmjVrKCkpae3iJI3G2s3MaN++Pfn5+XTq1An1B2RvFRfvDoDtv78fBbZ1qx/J1di0RjPo3t1PjVyyxG877TRYHdybc+hQjQgTERFJpDYfBAPuBa4Ifq8BngeuiuG4W4BK4NdNfcJgrYweEZuLALZt20ZZWVlTs2xQeXl5nZ/SOLVZ86jdmk5t1jzxaLeamho2bNjAjh07aNeuHZmZmSkdwMjMzKRHjx5kZmbinGvt4iSNhtrNOUdNTQ2VlZWUlpbSoUMHunfvTmY9kYpt25pyw0NJR1VVfgrkwoV+5FZTAmDhsrNhv/18QO2dd+DMM/1i+rm5fvF8rREmIiKSGMkQBLsff0ejvsA5QCbQoaEDzOxA4BrgPOfc9mY850Tg1mg75syZw9q1iVnXdc6cOQnJN5WpzZpH7dZ0arPm2Zt269SpE126dKFbt2507tw5pQNgkljV1dWUlpayZcsWli9fXm9wdsWKFS1cMkk2M2f6NcD69fOL3ZeV+amQzQlaZWf7O0X+97/wySd+FNiXX/q7RvbvH/eii4iICEkQBAtu0R26TfdUM3sN+JuZHenqv1T+ADDbOTejmU87GYhcwLUI+OuoUaMYNGhQM7ONrry8nDlz5jBq1Cjy8vLimneqUps1j9qt6dRmzROPdlu3bh01NTX069cvLQJg1dXVVFRUkJubW+9IJdlTrO3WuXNndu7cSceOHendu3fUNIsWLUpUMSUFVFT4u0BWVvo7PK5e7QNhnTo1P8+CAr9G2CefwBFH+MXzV62Cvn2hXZvvpYuIiCSfZPx6nQ48ChwI/Ddyp5mdAJwEnGlm+4btagfkBNs2OefqndPonFsPrI/IF4COHTuSn6DFGvLy8hKWd6pSmzWP2q3p1GbNszftVlJSQkZGBu0ScSa4a5e/XVtGRps708zMzFQQrBliabd27dphZvW+Jzt27JiIokmK+PhjWLkSCgt9IKyy0o8C2xtmfmH9detg8WIYONDfOXLjRj9KTEREROKrbfX8YxPqbnSuZ39oAPnzUfYVAkuBH+CnWbY+53wvCvzPTp18j6itaMMniiKS+uI2AmzXLn9WWVYGpaX+8zb02ZaTA507+0V9Cgr0WZfC0mFEoSROcTFs2uTX8tq8GXbu9Avc760uXWDtWj8VcvhwKCnxH1UKgomIiMRfm+3pm1nPYERW+Lb2wEX4Be8XBtv64ANixc65ncAbwDejZPkYsBz4OTAvgUVvXEWFv5xYXAzLl/uTsSOPhAce8CdjAwZAURGMHOlXSG1JOlEUkVRSXe3nFq1a5c9ey8v94j1ZWf5zraYG1q/36fLyoFs3P8yjsFArU4tILed8l61dO//RsX17/K5ZZmT4R2jJ2cxM3wUTERGR+GvLUYxHzSwfeBtYBfQGzgcOBn7knAvdwuku4DvAQGCZc24FsMfKtmZ2P7DOOfdC4otej6oqv6Lqu+/68fSbNvneVJ8+fv/GjbBmDbz3nj8R698fRo+GceP86qmJpBNFEUk1ZWX+YsOXX8KOHf5zq1ev6GeuzvlbvK1e7YdhbNzoL0ZoCqyI4K9fbtzou0A1NX4UWDyvBebk+MBXVZXvelVW+uuSut4oIiISX235q/VZ4FLge0ABsBX4ELjBOfdiaxasWYqL/T215871PZvCQj+ePiNjd4Br4EAfEKup8SdhCxbAkiUwfz6MH+9PyBJBJ4oikmo2bYJFi3xgv6DArzLdEDP/OZaf71em/vxzf9Y7aJD/TGwhXbt2ZeLEiTz88MMt9pwtYezYsQC8+eabCcl/2LBhHH/88Tz55JMJyV+kutp3zzIzfVfIufiuXhG63lhd7fOtqfEPERERia82GwRzzj0DPBNDugnAhBjS7bvXhWquefNg6lRYuNDfU/uggxruOWVkQM+efqXUtWvhnXd8sOmii/z9s+MpSU8URUTqVVbmP9dWr/afuVlZTTu+Sxc/LGPVKv/30KFxD/TPnj2b1157jWuvvZYuXbrENe/WsnDhQp577jkmTJjAvvvu29rFEYmrzEzfPQsFqcx8ICxeQitPhIJsoSmSIiIiEl/6ek204uLdAbAhQ/xIr1gvHZr59EOG+OOnTvX5xUvkiWJTT8S6dPHHrV7t8ymr94abIiIto7raf06uWuVH3DY1ABaSleWPX7XK51ddHddizp49m9tuu40tW7bENd/WtHDhQm677TaWLVu2x77XXnuN1157reULJRInubn+WmF5uQ9OtW/vpyvGS2WlX3Y1O9uvN5aTo6mQIiIiiaAgWCJVVfkpkKEAWHMXuc/N3R0ImzbN57u3kuREUUSkSVat8lO7Cwqa/7kWkpXl8/nyy92jwtLIrl272LFjR1zy6tChAx06dIhLXiKtwczftyh00+ysrPiNBAtNfezd2/9dXe0DYiIiIhJ/CoIl0syZfg2wfv32/i6Pubk+n7lzfb57SyeKIpJqdu3yn0E7djR9ZGt9unTx+a1aFbdhH5MmTeL6668HYODAgZgZZlZnBNULL7zAIYccQlZWFkOGDOGVV17ZI59Vq1ZxySWX0KtXr9p0jz/++B7p1q9fz6WXXkqvXr3Izs5m+PDhe6ydtWzZMsyMX/3qV9x///0UFRWRlZXFwoULAVi8eDFnn3023bp1Izs7m8MPP5wXX9y9POeUKVMYP348AMcff3xtnUJrgI0dO7Z2XbCQqqoqJk2axIEHHkh2djZ9+vThzDPPpDhsxPOvfvUrRo8eTUFBATk5ORx22GFMnz499sYWiaOiIr/yQ0mJ7/q0b+9Hbe2tLVv8jOt+/fyg+o4dtdSqiIgkr1C/csqUKa1dlKg00DpRKir8XSArK/0aYPHQu7dfI2z2bDjhhOYH1sJPFBtbAyxWXbr4XtyqVT5PjeEXkZa2caNf5zDe6xN27QqbN/v8e/Xa6+zOPPNMPvvsM/785z/z61//mu7duwPQo0cPAN59913+8pe/MHHiRDp16sSDDz7IWWedxYoVKygoKABg3bp1HHXUUZgZV111FT169OAf//gHl156KWVlZVx77bUAVFZWMnbsWL744guuuuoqBg4cyLRp05gwYQJbtmzhmmuuqVO2J554gqqqKi6//HKysrLo1q0bCxYs4Oijj6awsJAbb7yRvLw8nnvuOc444wxmzJjBN7/5TcaMGcPVV1/Ngw8+yE9/+lMGDRoEUPszUnV1Naeeeiqvv/465557Ltdccw1bt27ln//8J/Pnz6couBHMAw88wOmnn87555/Pjh07eOaZZxg/fjwvvfQSp5xyyl6/FiJNMXKkv3H3ggUwbJifsrh1695dS3QONmzw90o6+GDfzevb119bFBERaYumTJnCxRdfzAcffMDhhx/e2sVpMkUqEuXjj2HlSj9VMF63DzLz+a1Y4fM/+ujm5ZMkJ4oiIk1SVuYX7In3509+vh/6UVYWl7yHDRvGoYceyp///GfOOOOM2kXkq4Pp5IsWLWLhwoW1gaDjjz+e4cOH8+c//5mrrroKgJtuuonq6mrmzZtXGxi78sorOe+885g0aRJXXHEFOTk5PPbYYyxatIinnnqK888/vzbdcccdx80338wll1xCp06dasv25Zdf8sUXX9QG5ADGjRtH//79+eCDD8gKzvYnTpzIMcccww033MA3v/lN9ttvP4499lgefPBBvvrVr+4x6ivS1KlTef3117nvvvv4wQ9+ULv9xhtvxIXNMfvss8/Iycmp/fuqq67i0EMP5b777lMQTFpcbi6MHu1v3L1+vf9oqKjwq1SEbvTdVBs3+iDa8OE+nw4dfFdP1xJFRCRZDRgwgMrKStq3b9/aRYlK0yETpbjYB5qCK/xx0727DzTtzQL5oRPFsBOfuMjPh23btEC+iLSO0lJ/a7V4XXgIMfP5lpbGN996/M///E9tAAx80Cw/P58lS5YA4JxjxowZnHbaaTjnKCkpqX2ceOKJlJaW8tFHHwHw8ssv07t3b84777za/Nq3b8/VV1/Ntm3beOutt+o891lnnVUnALZp0ybeeOMNzjnnHLZu3Vr7PBs3buTEE0/k888/Z1UzpsHPmDGD7t278/3vf3+PfRb2+oUHwDZv3kxpaSnHHntsbf1EWtq4cTBihF8BIjPTd30qK5u3JGpVFaxb50eADR/uA2L9+vkgmIiISLIyM7Kzs8nMzGztokSl60yJ4BwsX+4v48X7/tah+2cvX+6fpzkneylyoigiUmvXLn8murdrHNYnK8vnv2tXwodo9O/ff49tXbt2ZfPmzQBs2LCBLVu28Nhjj/HYY49FzWP9+vUALF++nAMOOICMiO+i0DTF5cuX19k+cODAOn9/8cUXOOf42c9+xs9+9rN6n6uwiWftxcXFHHTQQbRrpC1feukl7rzzTubOncv2sMWXLN7fXyIxys6G8eN9wGrhQth/f39NsazMB8Ri7e9XVfkRZfvtB8cc4webFhb6dcfa6DmDiIhITJYtW8bAgQN54oknmDBhQmsXZw8KgiVCRYXvHeXlJSb/vDyff0VF058jhU4URURqhW6vFu8LDyFmu58jweq7ahaaJlgTlOGCCy7gO9/5TtS0w4YNa9Zzh4+8Cn+u6667jhNPPDHqMfvvv3+znqsx77zzDqeffjpjxoxh8uTJ9OnTh/bt2/PEE0/w9NNPJ+Q5RWJRVAQXXQRTp/pAWI8efjH7sjK/TlhDUyOd8124det8AGzMGL+9b18YNEgL4ouIiCSaohSJUF3tT5QSdSkvM9Pn35yx9yl0oigiUisjwz8S9dnj3O7niIO9GcnUo0cPOnXqRHV1NePGjWsw7YABA/j000+pqampMxps8eLFtfsbst9++wF+CmVjz9WUOhUVFfH++++zc+fOeteLmDFjBtnZ2bz66qu1a5GBX7xfpLUNHQpXXgnTpvkbd2/d6tcM277dj/LKyal7vbGmxt8/aMMGv33IED8FsndvPwWyqEgBMBERkZagNcESITPTnyg1J0gVi+rq3dMimyrJThRFRGLSrp0/6wybMhdX27f7/OM0wjUvGMW7ZcuWJh+bmZnJWWedxYwZM5g/f/4e+zds2FD7+8knn8zatWt59tlna7ft2rWL3/zmN3Ts2JHjjjuuwefq2bMnY8eO5dFHH2XNmjUNPldT6nTWWWdRUlLCQw89tMe+0Ii3zMxMzKz2hgHgh9e/8MILjeYv0hKKiuDaa+GCC3xAq0MHH+RaswaWLYNFi/xIsU8+8Y+NG/1dH0eOhJNPhsMO88cNG6YAmIiISEvRSLBEyM31vZxgEeO4Ky/3+efmNv3Y0IlisF5M3MX5RFFEJGadO/uLBM1dL7E+zvl8O3eOW5aHHXYY4O/yeO6559K+fXtOPvnkmI//5S9/yaxZszjyyCO57LLLGDx4MJs2beKjjz5i5syZbNq0CYDLL7+cRx99lAkTJvDhhx+y7777Mn36dN59913uv//+OneGrM/DDz/MMcccw9ChQ7nsssvYb7/9WLduHe+99x5ffvkln3zyCQAjRowgMzOTu+++m9LSUrKysjjhhBPo2bPnHnledNFFTJ06lR/+8IfMmTOHY489lvLycmbOnMnEiRP5xje+wSmnnMJ9993HSSedxLe//W3Wr1/Pww8/zP7778+nn34ac1uJJFJ2Npx6Kpxwgr9xd3ExLF0Kq1b5EWE7d/qVK3r29CO+hg3zN5nNz/ddOXWXREREWpa+ehPBDAYMgPfei//Uw9A0yAEDmn+Sl0QniiIiMcvP92ebW7fGd1hFWZlf8CeOeR5xxBHccccd/Pa3v+WVV16hpqaGL774Iubje/XqxZw5c7j99tt5/vnnmTx5MgUFBQwZMoS77767Nl1OTg5vvvkmN954I08++SRlZWUcdNBBTVqodPDgwfznP//htttuY8qUKWzcuJGePXsycuRIbrnlltp0vXv35re//S133XUXl156KdXV1cyaNStqECwzM5OXX36Zn//85zz99NPMmDGDgoKC2mAbwAknnMAf/vAHfvnLX3LttdcycOBA7r77bpYtW6YgmLQ5ublw9NH+4ZxftrW62g/a79Bh90B5Bb1ERERal76KE6WoCLp187f7iXIC0GwlJdC1q8+/uZLoRFFEJGYFBf5zd/Xq+H4Obd7sV60uKIhfnsDNN9/MzTffXPt3dXU1mzdvjjo6a9myZXts69mzJw899FDUKYWR6R5//PEG0+y777610xCj2W+//XjyyScbzAPgu9/9Lt/97nf32P7mm2/usS0nJ4c777yTO++8s978LrnkEi655JI9tk+aNKnO359++mlMo9pEWoJZ4u6NJCIiIntHCzclysiR0L+/Hw/fwIlFkzjn8+vf3+ffXKETxWC6TNxs3uwDdHE+URQRiUm7dlBY6IddNGOtrai2bPH5FRZqCIeIiIiISJJTjz5RcnNh9Gi/LtjatdCnz97nuXatX29r9OjmrQcWEjpRLCnxJ3hduux92XSiKCJtQWGhX33688/3vD1bU23f7vM64ACfr4iIiIiIAPD444/zyiuv7LH9G9/4RiuUJnaKViTSuHEwfz68845fJ2tvAlcVFfDllzBmjM93b+lEUURSUWamny5eUeFHzhYWNu/zbfv23ccXFTXvbrwiIiIiIinqkUceibp97NixLVuQJlIQLJGys2H8eB8gWrAAhgxpXiCsosIfP3gwnH22z3dv6URRRFJVfj4MGuR///JLP0W7KSNet2zxn9uFhT4frXMoIiIiIgLAhAkTGr3BUkNrzbY2rQmWaEVFcNFFPoC1YAGsWRP7GmHO+fShANhFF+3dgviRQieKffv6E8WmrqGzZYs/rm9fnSiKSNvSrRsMHepHqFZUwLJlUFpa/+evc37/smU+/QEH+OO7dWvJUouIiIiISAJpJFhLGDoUrrwSpk2DuXP92l6FhdC9u79fdqSaGr9e16pVfqrimDF+BFg8A2AhoRPF3NzdgbCuXX1Ay2zP9M75u0Bu3uzXADvgAF8uBcBEpK3Jz4dhw/xIsFWr/OdWSYkfsZqV5T/jnPMjWqur/d1tCwt9YL+wUCNbRURERERSjIJgLaWoCK69FmbOhNmzYcUKWL7cn2T16eNHUi1d6kd+VVf7QNSQIX4R/HHj4jMFsj46URSRVJWZ6e+o27evn+JYVuZHfFVW+gsOGRn+YkPnzv6zsKBAN/cQEREREUlR6um3pOxsOPVUOOEE+PhjKC72gbDKSr+/oAD69YMBA3zQbOTIvVtMvyl0oigiqaxdO+jVyz9CI1p37oT27esf+SoiIiIiIilFUYzWkJsLRx/tH87B+vXw73/DNddAz56tezIWfqIIsGvX7iCYgl4ikqwqKupefNi4cfdnW0FB61x8EBERERGRFqWoRmsz8yOswP9sa6MRFPgSkWRWVeWnob/7LqxcCZs2+c+1vDw/Ara6GpYsgffe82sk9u/fMtPQRURERESSRCpNpFCEQ0REUlNx8e4bklRW+jUM99uv4RuSLFjgg2Lz58P48Ym5IYmIiIiISBtXVgZvvQWLFvnly0tK/PXjzEx/j7+BA/3S5scdl1z3yVMQTEREUs+8eTB1Kixc6NdaPOighi9XZWT46eg9evg7+L7zjp8yedFF/g66aerNN9/k+OOPZ9asWYwdOzbu+U+ZMoWLL76YpUuXsu+++8Y9fxERERFpmvJyeO45mDXLT6TYssVPpMjN9V3mmhpYtsyv6NSlC0yfDmPHwjnn+MkWbZ2CYCIiklqKi3cHwIYMadoaX2b+jr2dO/tRYVOnwpVXpvyIsMmTJ5Obm8uECRNauygiIiIi0krmzoXHH/dL6VZV+W5xUVH9EynWr/dd7iVL/DGXXAIjRrR0qZtGQTAREUkdVVV+CmRzAmDhcnP98QsW+PyuvTal1wibPHky3bt33yMINmbMGCorK+nQoUPrFExEREREWsS//gWTJ/vpj4WFcPDBjU+k6N3b31NvzRo/kaKkBCZOhGOOablyN1WUeJ6IiEiSmjnTX8Lq12/v7/KYm+vzmTvX59sG1NTUUFVV1WLPl5GRQXZ2NhnRLv+JiIiISEqYO9cHwBYvhsGDoW/f2Be+N/PpBw/2x0+e7PNrq9SrFRGR1FBR4e8CWVnpL0vFQ+/ePr/Zs33+cTJp0iTMjMWLF3POOeeQn59Pz549ufHGG+sEucyMq666ij/96U8MGTKErKwsXnnlFQB+9atfMXr0aAoKCsjJyeGwww5j+vTpUZ/vqaeeYtSoUeTm5tK1a1fGjBnDa6+9BsC+++7LggULeOuttzAzzKx2/a8333wTM+PNN9+sk9/777/PySefTNeuXcnLy2PYsGE88MADtfs//fRTJkyYwH777Ud2dja9e/fmkksuYePGjXFrQxERERHZe+XlfgrkokV+ofvmruuVl+ePX7TI51deHt9yxouCYCIikho+/tiv3llYGL97Npv5/Fas8PnH2TnnnENVVRV33XUXX//613n00Ue58sor66R54403+MEPfsC3vvUtHnjggdoF5B944AFGjhzJ7bffzi9+8QvatWvH+PHj+fvf/17n+Ntuu40LL7yQ9u3bc/vtt3Pbbbexzz778MYbbwBw//33069fPw4++GD++Mc/8sc//pGbbrqp3jL/85//ZMyYMSxcuJBrrrmGe++9l+OPP56XXnqpTpolS5Zw8cUX85vf/IZzzz2XZ555hpNPPhnnXJxaT5rKzLLM7G4zW21mlWb2vpl9NcZjC83sOTPbYmZlZvZXM9uvnrSXmtkiM6sys8/N7PvxrYmIiIjEy3PP+W5uYeHeL2yfl+dHhX38sc+3LdKaYCIikhqKi2HTJtgv6nl583XvDsuX+/yPPjquWQ8cOJC//vWvAFx55ZVkZ2fzhz/8geuvv55hw4YB8N///pd58+YxePDgOsd+9tln5OTk1P591VVXceihh3LfffdxyimnAPDFF19w++23881vfpPp06fXmdYYCkadccYZ3HzzzXTv3p0LLrigwfJWV1dzxRVX0KdPH+bOnUuXLl32yA9g4sSJ/OhHP6pz7FFHHcV5553Hv/71L4499thYm0jiawpwNnA/8DkwAXjZzI53zv2rvoPMrCMwC+gM/ALYCfwAeMvMRjjnNoalvQL4LTADuA84FnjQzHKdc3cnoE4iIiLSTGVl8MYbflndgw+OT559+/oF8998E846C/Lz45NvvGgkmIiIJD/nfKCqXbvot6/ZGxkZkJnp84/zKKb//d//rfP35ZdfDsDLL79cu+24447bIwAG1AmAbd68mdLSUo499lg++uij2u0vvPACNTU13HLLLXus62XNGC338ccfs3TpUq699to6AbDI/MLLVlVVRUlJCUcddRRAnfJJyzGzUcC5wE+cc9c75x4DTgCWA/c0cvhE4ADgVOfcPc65XwNfA/oAtdFOM8sBfg783Tl3tnPud865i4A/AT8zs65xr5iIiIg021tvwZdf+rtAxnMiRZ8+fiLFW2/FJ894UhBMRESSX0UFbNy492O465OX5/OP47pgAAcccECdvwcOHEhGRgbLli2rsy2al156iaOOOors7Gy6detGjx49eOSRRygtLa1NU1xcTEZGRtQgWnMUFxcDcMghhzSYbtOmTVxzzTX06tWLnJwcevToUVuP8PJJizobqAYeC21wzlUBfwC+Ymb7NHLsB865D8KOXQy8DpwTlu54oACYHHH8w0AecMreVEBERETia9Ei2LIFevaMb749e/p8Fy2Kb77xoOmQIiKS/KqroabGj9hKhMxMn391dWLyD0QbnRU+qirknXfe4fTTT2fMmDFMnjyZPn360L59e5544gmefvrphJYxFueccw6zZ8/m+uuvZ8SIEXTs2JGamhpOOukkampqWrt46Wok8Jlzrixi+5zg5whgZeRBZpYBDAMej5LnHOBrZtbJObc1eA6A/0Sk+xCoCfY/1azSi4iISFw5B0uXJm4iRbt2Pn/n4jfKLB4UBBMRkeSXmem/bRMVpKqu3j0tMo4+//zzOiO9lixZQk1NTe3i9/WZMWMG2dnZvPrqq2RlZdVuf+KJJ+qkKyoqoqamhoULFzJixIh684t1amRRUREA8+fPZ9y4cVHTbN68mddff53bbruNW265pXb7559/HtNzSML0AdZE2R7a1ree47oBWTEc+9/gOaqdc+vDEznndpjZxgaeo5aZ9QR6RGwuAqisrASgvK3ebipBQvVNt3pD+tZd9U6vekP61j1d6w1to+7btsHOnbDPPtCxY/zz32cfn/+aNbvzr6/e27Zti38B6qEgmIiIJL/cXCgogCVLEpN/ebnPPzc3rtk+/PDDfO1rX6v9+7HH/Ey1r3/96w0el5mZiZlRHRb0W7ZsGS+88EKddGeccQY33HADt99+e9SF8UPBr7y8PLZs2dJoeQ899FAGDhzI/fffz4QJE/ZYGN/MyAwChZF3gbz//vsbzV8SKgfYHmV7Vdj++o4jxmNzgB315FPVwHOEmwjcGm3HvHnz6N+/P3PmzIm2O+Wla70hfeuueqefdK17utYbWr/up52WuLyPO87//OCDPfdF1nvFihWJK0gEBcFERCT5mcGAAfDee37aYjzHdIemQQ4YEPex3EuXLuX000/npJNOYvbs2fzpT3/ivPPOY/jw4Q0ed8opp3Dfffdx0kkn8e1vf5v169fz8MMPs//++/Ppp5/Wptt///256aabuOOOOzj22GM588wzycrK4oMPPqBv377cddddABx22GE88sgj3Hnnney///707NmTE044YY/nzcjI4JFHHuG0005jxIgRXHzxxfTp04fFixezYMECXn31VfLz8xkzZgz33HMPO3fupLCwkNdee42lS5fGte2kySrxI7oiZYftr+84Yjy2EuhQTz7ZDTxHuMnAtIhtRcBfhw4dSmlpKaNGjSIvUev/tUHl5eXMmTMn7eoN6Vt31Tu96g3pW/d0rTe0jbpv2wa33OJvrt7IJIRmWbYMunWD22+vOxIsWr0XteDiYQqCiYhIaigq8t+0JSXxXd2zpAS6dvX5x9mzzz7LLbfcwo033ki7du247LLL+PWvf93ocSeccAJ/+MMf+OUvf8m1117LwIEDufvuu1m2bFmdIBjA7bffzsCBA/nNb37DTTfdRG5uLsOGDePCCy+sTXPLLbewfPly7rnnHrZu3cpxxx0XNQgGcOKJJzJr1ixuu+027r33XmpqaigqKuKyyy6rTfP000/z/e9/n4cffhjnHF/72tf4xz/+Qd++jc6Gk8RZAxRG2d4n+Lm6nuM24UeB9YmyL/LYNUCmmfUMnxJpZh3wC+bX9xy1guPqTKcMjVjMycmhtLSUvLw88tva/dZbQLrWG9K37qp3+knXuqdrvaF1696pE7RvDytXQvfu8c9/5Uro1Sv6nScj690xEfMx66EgmIiIpIaRI6F/f1iwAHr0iM+oLedg1SoYMsTnH2c9evRg2jQ/6KW6upqtW7eSnZ1duz9ySmG4Sy65hEsuuWSP7ZMmTdpj28UXX8zFF19cb169evXipZde2mP72LFjo5bh6KOP5rXXXqs3v8LCQp5//vk9tkfmNWHCBCZMmFBvPhJXc4HjzSw/YnH8I8P278E5V2Nm84DDo+w+ElgSLIofnsfhwMth6Q7H35E86nOIiIhIyzODgQPh3/9OzESKXbt8/m1pUXzwHRIREZHkl5sLo0dDTg6sXRufPNeu9fmNHh339cBEWth0IBO4PLTBzLKAi4H3nXMrg239zezgKMceYWaHhx17EHACdacuvoEfOfa9iOO/B1QAf49PVURERCQeBg2CLl1g/fpGkzbJ+vU+30GD4ptvPCgIJiIiqWPcOBgxAr78Eioq9i6vigqfz8iRPl+RJOacex8fsLrLzO4xs8vxQat9gR+HJZ0KRC7MMRkoBv5uZteb2bXAP4F1wL1hz1EJ/Aw41cymmdl3zexJ4ALg5865TQmpnIiIiDTLccf5iRRr1vgJEPHgnM+vf//di+O3JQqCiYhI6sjOhvHjYfBgPy2yuYGwigp//ODBcPbZPl+R5HcRcD9wIfAg0B441Tn3dkMHBdMdxwJvAzcDdwCfAMc55zZEpJ2MH202FHgYOBr4AXBXHOshIiIicZCfD2PH+q7umjXxyXP1ap/f2LE+/7ZGQTAREUktRUVw0UW7A2FNubQVunQVCoBddFFCFsSfNGkSzjm6J2IVUpF6OOeqnHPXO+f6OOeynXOjnHOvRqQZ65zbY/UO59yXzrnxzrnOzrlOzrnTnHNf1PM8v3POHeycy3LO7e+cu981tMCdiIiItJpzzvETH1atgvLyvcurvNwHwQ491OfbFikIJiIiqWfoULjySjj2WCgthblz/eIENTXR09fU+P1z5/r0Y8b444cObclSi4iIiIi0qLw8uOQSv37XokXND4SVl/vjBw2Ciy/2+bZFujukiIi0WXs1eKSoCK69FmbOhNmzYcUKWL4cMjP9t3JmJlRX+2/s6mro2tXfBXL0aL8GmKZAphznHNbWblEkIiIi0spGjICJE2HyZFi4EAoLoU+f2O7s6Jwf/bV6tQ+ATZzo82urFAQTEZE2KSMjgx07duxd4CI7G049FU44AT7+GIqLfSBs48bd94IuKIABA3zQbORI3QUyRTnnqK6upn379q1dFBEREZE255hjoGNHePxx321et84Hwnr29F3mSKGJFGvW+C73mDF+BFhbDoCBgmAiItJGZWVlUVlZyfr16+nZs+fejeDJzYWjj/YP5/zC99XVfjRYbm5sl7kkae3atYv169dTXV1N165dW7s4IiIiIm3SiBFw113w3HPw5pt+IsXKldCu3e4uc6grvWsXdOnil9EdO9avAdZWp0CGUxBMRETapF69erF9+3Y2bdpEaWkpmZmZKT2VzTnHzp072bhxY0rXM94aajfnHDU1NezatQuA3NxcBcFEREREGpCX50d0nXUWvPWWX+dr6VIoKdk9kaJ7dxg40E9/PO64tnkXyPooCCYiIm1SRkYG/fv3Z926dWzfvp2a+ha1TxG7du1iw4YN9OzZU1P2mqChdjMz2rVrR05ODvn5+XTq1EkBRhEREZEY5OfDaaf5h3NQVgY7d0L79n5fsnapFAQTEZE2KyMjgz59+rR2MVpEWVkZixcvZvjw4eQn0+W0VqZ2ExEREUksM+jcubVLER9RljcTERERERERERFJLW02CGZmQ8xsmpktMbMKMysxs7fN7LQYjh1jZi+a2UozqzKztWb2ipkd3RJlFxERERERERGRtqUtT4ccAHQCngRWA7nAWcCLZnaFc+6xBo49EKgBfgusBboCFwBvm9kpzrlXElpyERERERERERFpU9psEMw59zLwcvg2M3sI+BD4IVBvEMw593vg9xHHTgaWANcCCoKJiIiIiIiIiKSRNjsdMhrnXDWwEujSjGMrgA3NOVZERERERERERJJbmx0JFmJmeUAO0Bk4Hfg68GyMx+YDHYDuwEXAIcAvElNSERERERERERFpq9p8EAy4F7gi+L0GeB64KsZjnwNODH7fATwK3NHYQWbWE+gRsflggE8//ZRt27bF+PSxqaysZMWKFXz00Ufk5OTENe9UpTZrHrVb06nNmkft1nRqs+aJZ7stWbIk9GuHvS6YxFMH8K9PSUkJixYtomPHjq1dphazbds2VqxYkXb1hvStu+qdXvWG9K17utYb0rfu9dX7iy++CP2a8D6YOecS/Rx7xcwOBvoBfYFz8MGs7znn1sVw7Ah8MGsf4DtAMXC1c67BKJaZTQJu3auCi4iISLL6hnPuxdYuhHhmdjrw19Yuh4iIiCRcwvtgbT4IFsnMXsOv63Wka0LhzawD8BGw2Dl3diNpo40E64i/6+R8fCAunorwnbtv4AN10ji1WfOo3ZpObdY8aremU5s1TzzbrQP+wtlbzrnSvS2YxIeZdQaOA9oD00m//5F0/mxI17qr3ulVb0jfuqdrvSF9615fvVusD5YM0yEjTcdPazwQ+G+sBznndpjZi8CNZpbjnKtsIO16YH2UXe83tbCxMLPQr8XOuQWJeI5UozZrHrVb06nNmkft1nRqs+ZJQLt9HIc8JI6CzvCLZjYk2JRW/yPp/NmQrnVXvdOr3pC+dU/XekP61r2RerdIHyyp7g4ZCC340bmZxxrQKX7FERERERERERGRtq7NBsGCKYmR29rj7/JYCSwMtvUxs4ODfQ0d2wU4C1gZjPQSEREREREREZE00ZanQz5qZvnA28AqoDdwPv4ujT8KW9z+Lvyi9wOBZcG2f5jZl/jpi+uB/sDF+MX1v9VSFRARERERERERkbahLQfBngUuBb4HFABbgQ+BG2K4W8DjwLnAD/CL6G8G/g182zn3TqIKvBc2ALcFPyU2arPmUbs1ndqsedRuTac2ax61W/pI19c6XesN6Vt31Tv9pGvd07XekL51b/V6J93dIUVERERERERERJqqza4JJiIiIiIiIiIiEi8KgomIiIiIiIiISMpTEExERERERERERFKegmAiIiIiIiIiIpLyFAQTEREREREREZGUpyBYnJlZRzO7zcxeMbNNZubMbEI9aTPM7HtmNtfMKs1so5m9YWbDo6T7sZktNbMqM/vUzM5rkQq1gCa22Tlm9m8z2xK011tmdkqUdKneZkeY2UNmtsDMys1shZk9Z2YHRkk7KGjbbUH7/tHMekRJl9JtBrG1W9AOE8zsRTNbGaSbb2Y3m1l2PfleamaLgnb73My+33K1SqymvNfCjmlvZguD/+XrouzXe61uWn0X0OQ203dBGjGzLDO728xWB/8j75vZV1u7XM1hZmODz8Zoj6Mi0o42s3+ZWYWZrTWzB82sY5Q821T7WNP6dXHvo8SaZyLEWnczm1LPe2BxlLRtvu5N/PxOmdc81nqn4Os9xMymmdmS4POpxMzeNrPTmlvGZKh38Nwx1T3VXvN6ynJTUKf5UfbF/fsr1jxj4pzTI44PYF/AAcuBWcHvE+pJOwXYCfwB+C5wTbDtqxHp7gryeQy4DHgp+Pvc1q5vS7YZ8P1g30vAlcC1wNxg25lp1mbTgTXAg8F752ZgLbANOCQsXT9gA/AFcDXwU2BT0G4d0qnNYm03oGNQ7/eAm4K2eByoDt6fFpHnFUH66UHaqcHfN7R2fVvyvRZxzA+D/Q64Lsp+vdfqpp2Cvgua8rmm74I0ewB/Dv5H/g+4HJgd/H1Ma5etGXUZG7wHHwAuiHh0D0s3AqgEPgre53cCVcA/2nr7EHu/Lu59lKbk2cp1nxK8npHvgdOipG3zdacV+6VJUu9Ue71PBl4Bbg3KeA3wdlDOy1P19W5i3VPqNY9S5n5AefBenx+xbwRx/v5qSp4xlb8lGysdHkAW0Dv4/XDq//I7J9j3zUbyKwR2AA+FbbPgn20lkNnadW7BNvsMmENYEALIB7YCf02zNhsd+WEHHBB8GDwVtm0yUAH0D9s2LsoHdcq3WaztBnQARkc59pag3caFbcsBSoCXItI+FXwpdG3tOrfUey1sX09gC/AzogTB9F7b439U3wVNbzN9F6TRAxgV+VkCZONPAma3dvmaUZ+xQX3ObiTdy8BqID9s23eDY7/WltuH2Pt1ce+jxJpnG6j7FGBbDPklRd2b8PmdUq95E+qdUq93PWXPxAdjFqfq693Euqf0aw48A7wOvMmeQbC4f3/FmmfM5W+NN0q6PGj4y+/fwPvB7xlAXj15TAzyGByx/bxge9JdBd2LNltLRLAh2L4GeCZd2yyijh8CH4b9vQ54Lkq6/wIz1WbR262eNEODtvh+2LaTg20nR6T9SrD9gtauW0u3GX7U3PvAwMgvtmC/3mt1/0f1XdD0NtN3QRo9gHuAXeEd32D7T4LXcZ/WLmMT6zM2KPfZQCegXZQ0+fgr4fdEbO+AD/b+Plnah4b7dXHvo8SaZxuo+xT8xbLMyNcuIl1S1j3suRPeL02SeqfL6/03YG26vd711D1lX3NgDP57ZygRQTAS8P3VlDxjfWhNsFZgZvn4yOcHZvYLoBTYFswtPici+Uj8UMNFEdvnhO1PF28CJ5nZ981sXzM72MweBjrjpxWEpGWbmZkBvfAjkzCzQvyonP9EST6Huu2Qlm0Ge7ZbA3oHP8PThdolso0/BGpI0Xarr83MbBTwHfz0NFfP4Xqv7f4f1XdBI+p5r72JvgvSyUjgM+dcWcT20Os4omWLEzdPAGVAlZnNMrPDw/YNBdoR8d3inNuBH20Q+f2ddO2TiD5KE/NsC3Lx74HSYG2fh6Osb5O0dW+Jfmky1DtMyr3eZpZnZt3NrMjMfgB8HT86KOVf74bqHiYVX/NM4Df4wNO8KEkS8f3VlDxj0q6pB0hcFOGHOZ6Lj37+GH/ycw3wjJmVOedeCdL2Ada5INwZZk3ws28LlLetuBrojp93/2CwrQT4H+fce2Hp0rXNzscPqb0l+LtP8HNNlLRrgG5mluWc2076thns2W71+TH+i+wfYdv6ANXOufXhCZ1zO8xsI6nbbnu0WdDp+w3wrHPuPTPbt55j9V7b3W76LmhctP9PfReklz7U/z0Gyfc67gBm4Kd2lACDgeuAd8xstHPuYxr//j427O9kbZ9E9FGakmdrW4MfBfERfhTwSfgRIcPNbKxzbleQLpnr3hL90mSod6gsqfh634tfGxf8xd/ngauCv1P99W6o7qHypOJrfiUwAD8NM5pEfH81Jc+YKAjWOkIR4ALgKOfc+wBm9iKwFL+oYujEJweI9mauCtufLirwwz2/xC8Y2An4AfC8mR3rnPsiSJd2bWZmBwMP4xdzfzLYHKpnY22xnTRsM6i33aKl+yn+w36ic25L2K4c/AlNNFWkYLs10GYT8Fdqzm4kC73Xdrebvgsa0MB7Td8F6SWlXkfn3Gz8wr8hL5rZdOBT/OLIJ9H493d4nZO1fRLRR2lKnq3KOfeTiE3PmNlnwM/x36PPBNuTsu4t2C9Nhnqn8ut9P/7mAH3xa5xm4qeohZ6fesqQCq/3/dRf95R8zc2sALgduMM5t6GeZIn4/mpKnjHRdMjWURn8XBo66QFwzm3DzyceZWbtwtJmRckjOyKvdDANvxDgBOfcdOfcE/i1NTrgP1BC0qrNzKw38Hf8CJKznXPVwa5QPWNpi7RqM2iw3SLTfQt/B5I/OOceidhdSdgXXoRsUqzd6muzYFrfXcD/OedWNpKN3mt7/o/quyBCI/+f+i5ILyn/OgaB278CxwdTTRr7/g6vc7K2TyL6KE3Jsy36NX5ESfgIi6Srewv3S5Oh3vVJ+tfbObfYOTfTOTfVOXcq/uLe34LZASn9ejdS9/ok+2t+J/5ulL9pIE0ivr+akmdMFARrHauDn+ui7FsPtAfygr/XAL2j/EOFhgWuJg2Y2X74q6Mvhm93zm0C/gUcHbY5bdrMzDrjp+d1AU5yzoXXLTRktE/kccG2TWFDZtOmzaDRdgtP91VgKr5Tc2WUJGuATDPrGXFcB/zonpRpt0ba7Dp8AOLZYI2mffG3TgboGmwLBQv1XttN3wVRNNRm+i5IS2uo/3sMUud1XIn/HM2j8e/vyO/6ZGyfRPRRmpJnm+OcqwQ2At3CNidV3VuhX5oM9Y4qFV7vKKYDRwAHksKvdz3C6x5VMr/mZnYAcDl+GYq+Yf39bKB98He3GMrZnO+vpuQZEwXBWkHwwbgWP188Ul/8sL6twd9z8YvqDYpId2TY/nTQK/iZGWVfe+pO7Z1LGrSZmWXjR4scCJzqnFsYvt85twrYgL87UaRR1G2HuaRBm0Hj7RaW7kjgL/hFGM8Jm7sfbm7wM7KND8d/vs4lBcTQZv2BrsAC/DS+pcA7wb6fBn8PDv6ei95rgL4LoonhvabvgvQzFzgwGHEaLtVex/3w//PbgPn4dQLrfLcEFxNGsOf3d9K1TyL6KE3Ms80xs0749Q7DpxnNJUnq3hr90mSodwPHJfXrXY/QtLTOqfp6N6C27vUlSPLXvBB/bvMgu/v6S4NyHhj8fguJ+f5qSp6xca10G9F0eNDwrZHvD/Z9NWxbd/wQ2r+HbeuHX3PoobBtBryNXw8ls7Xr2RJtBvQAqoFZgEW0z1bgH+nUZvgTwL/ibxd7cgPpHsGvn7NP2Lb/Cdr4ynRqsya22yD8gsXzga4NpMvBX9H5W8T2P+Lv9NKttevcEm0GHAqcEfG4PHifPRH83Vnvtajp9F3QtPeavgvS7IHvDDvgurBtWcDnwL9bu3zNqE+PKNuGB+/Vv4Zt+wf+6nansG2XBm1xUrK0Dw33hePeR4k1z9asO37kRKco6e8J0n8z2eoey+d3Kr7msdQ7RV/vnlG2tcffHb0C6JiKr3esdU/R17w7e/b1z8CfKy0Pfh8apI3791esecZcn0S/UdLxgb8zxM3A5OCFmRH8fTO7TwR7BS9kGTAJv6jvf4M39vCI/EL/MI8C38UvBOyAb7d2XVu4zX4X7HsjSP8T/PSBXcCYdGozdp84vwhcEPkIS7cPPpjzBfD9oM024RfgzUqnNou13fCLbK/An2jfECXdVyLynBjkOS1otyeDv3/a2vVtyfdalOP2JeKLTe+1qP+j+i5oepvpuyDNHsBz+JPMe/AB9neDv8e0dtmaUZc38FPsbwIuw68RUw5sAQaFpTsUPzLsI/x0/Dvx6568mgztQ2z9urj3UZqSZ2vVHf/9uDnYf3Xw+HuQ9h9ARrLVnVbsl7b1eqfo6/0X4HXg1qCMNwOLgnL+MFVf71jrnoqveQPt8SYwP2Jb3L+/mpJnTOVujcZK9QewLHjjRnvsG5ZuP/ztVEvxJzyvA0dEyS8jeJMvw98VYT5wfmvXs6XbDD/N5SrgY/wV/634zuTx6dZmwQdOfe3lItIOAV7Fd7I3A08BvdKtzWJtN3YHb+p7TImS72XA4qDdvgCuJWyUSjI/mvJeizgu1I7RgmB6r9VNq++CJrSZvgvS74G/qv5/+HVBqoA5wImtXa5m1uVq4H38KOKd+CD4H4H9o6Q9Bn9CUIlfJ/Ahoo8uaHPtQ+x94bj3UWLNs7Xqjl836o/40Q7lwWs2P6hf+2Sse6yf36n2msdS7xR9vc8F/olf0mEnPhDzT+D05pYxGeoda91T8TVv5H9gfpTtcf/+ijXPWB4WZCgiIiIiIiIiIpKytDC+iIiIiIiIiIikPAXBREREREREREQk5SkIJiIiIiIiIiIiKU9BMBERERERERERSXkKgomIiIiIiIiISMpTEExERERERERERFKegmAiIiIiIiIiIpLyFAQTEREREREREZGUpyCYiIiIiIiIiIikPAXBRNKYmTkzmxSx7Qgzm21m5cH+EWY2ycxcKxWzzWmp9gg9T/DYlujna6AcY4MynN1aZYiVmV0b1mbOzLq3dplEREQiqQ/WPOqDtV3qg0myUBBMJIHMbKiZTTez5WZWZWarzOyfZvb91i5bNGbWHpgGdAN+AFwILG/VQiUJM/upmZ2RoOwvBC5NUN6Y2T1BZ+XZRD3H3jKzb5nZU2b2eVDWN+tJ+gq+vf7ScqUTEZG2Rn2w9KE+WGKpDyapxpzThQWRRDCz0cAsYAXwJLAW2Ac4Cihyzu3fisUDwMyygV3OuV3B3wcDi4DLnHO/D0vXDmjnnKtqnZK2LdHaI7hKON05NyGOzzMJuNU5Z/HKM8pzGP49ugvoBfRyzm2NSDMW/14e75ybnqiyNCTocB0GfACMAD51zo1tIP0k4Fagh3OuJPElFBGRtkJ9sNSlPljLUx9MUk271i6ASAq7CSgFjnDObQnfYWY9W6VEEaJ0qELl2hKRbhf+C1pIufYYC/QDTgBeBc7EnzC0NRcCq5xzNWY2v7ULIyIibZr6YCkqxdpjLOqDibQ4TYcUSZwiYEFk5wvAObc+/O9gaPFDZna+mf03GLb/oZmNiTzWzArN7HEzW2dm281sgZldEiVddrCewWdBfmvM7HkzK4p43knB71OAt4Jd08KHO9e3/oKZXWBmc8yswsw2m9nbZva1xhrGzA42s+fMbIOZVQZ1/nnY/gFmNjnYXmlmG81smpntG5HPhKCcY8zs0SBdmZlNNbOuEWm/YWZ/N7PVQbsVm9nPzCwzSvmONLOXgzqVm9mnZnZN2P467RH8ngd8x3avgzDFzI4Pfv9mlOf4drDvK421Vz1tuMzMXjK/VsR/gnaaF1wxxMzODP4OvZdG1pPV+cBC59wsYGbwd30yzOwmM/syyPd1M4vpanpYOauCtr+ivvdVNM65lc65mljSiohI2lMfrB7qg6kPpj6YpDuNBBNJnOXAV8zsEOdcLFdNjgO+BTwIbAcmAq+Y2ajQ8WbWC/g34ICHgA3A14E/mFm+c+7+IF0m8BLwP8AzwANAJ+CrwCFAcZTnfxRYBfw0KMMHwLr6CmtmtwKTgNnALcAO4Ej81azXGjhuGPAOsBN4DFiG76yehr9yC3AEMDoo+5fAvsD3gDfNbLBzriIi24fwV04nAQcFaQeY2Vi3e873BGAbcF/w8wTgdiAfuD6sfF/Ft90afLutBQYBpwZ/R3Mh8HtgTlAn8G38b2AlvlMTuT7C+UCxc+69evKMxf7A0/jX7ingOuBvZnYl8AtgcpDuJ8BzZnZQeCfGzLKAs4B7g01/Bp4ws97OubVRnu9GoAb4FdAZ+DHwJ/zrXq+g8/cKvk1vBTLx75kNTa2wiIhIDNQHi36c+mCe+mDqg0k6c87poYceCXjgOzuhIduzgbuBrwHto6R1weOwsG39gUrg+bBtvwdWAwURx/8Z3wHJCf6+OMjvB1GeyyKed1LY32ODbWdHHDPJf1zU/r0/UA08D2TUl3897fIWUAb0b6BcOVGOOyoo24Vh2yYE2/4T3q74DpUDTm8kz98C5UBW8HcmsATfKezSQPnqtEewbRswJcpz/AKoAjqHbeuB74BOikzfULtH7FsW1PErYdu+FmyrCG9f4PJg+9iIPM4Ktu8f/N0peM9dG5Eu9L5YCHQI2351sP2QRurxYtDOfSPeQzvrq18j+c0H3oyl7YDuTc1fDz300EOP5H6gPlh97aI+mPpgofeQ+mB6pO1D0yFFEsQ590/gK/gvn+H4KzavAqvM7PQoh7znnPsw7PgVwF+BE80s08wM/4X5N/xamt1DjyDfzsChweFnASXAb6KUK6ahz404Az+d+nYXMTy6ofzNrAcwBng8qF/U45xzlWHHtDezAuALfCfzUPb0mHNuZ9jfj+A7vifXk2enoN3eAXKBg4NdI4GBwP0uYgrFXrTbVCALCL+19bfwI3GfamaeIQtd3auY7wc/34ho39D2/SKOPx/4j3PuCwDnF2P9O/UPx3/CObcj7O936sm3VnBFfBzwgnNudWh78Jz/qO84ERGR5lIfbE/qg9VSH0x9MElzCoKJJJBz7gPn3JlAV2AUcBf+Ss90MxsckfzzKFl8hu8g9AgeXfBXlDZEPJ4I0ocWVS0C/uuCOw4lQBF+SPbCJh4X+qJucGqCmeWY2e1mthI/LaEEX88u+I5mpDpt55zbhh/2vW9YnkPM7C9mVoq/CrqB3R2gUJ6htTrituinc24xflpDeKfmfODfoY7PXojsxJYGv66MSBfaXrtGh5l1wXdQ3zKz/UMP4F3gcDM7sLHnAzZH5htFTyAH34GOVGebmXUzs95hj2ivtYiISKPUB9uD+mCe+mCe+mCStrQmmEgLCK7cfAB8YGaf4TtM44HbmpBNKGj9FPXfOebTZheybfkNfjrB/cB7+A6Ew69P0eTgfdDZCE0BuAW/VkQV/orm3c3Js4mmAg+YWT/8FcmjgKvikG91E7eH3+Z7fFCWHwWPSOfj145oar5743n8uiwhT+KnW4iIiDSL+mBNpj5YbNQHE0lSCoKJtLz/BD/7RGw/IEraA/FrC4QWr9wKZDrnZjbyHMXAkWbWPmKIerwU4zstg4G5TThuSfDzkEbSnQ086Zyr7RiYWTb+KmQ0BwCzwtJ2xLfvy8GmsUABcKZz7u2wdAMj8gktVnsI/i49TdHQUP1n8IvBnoe/IrcTeLaJ+cfb+firrdFOAq4Avs2eHbDmWI/v7Ea7g1Hkth9R94rmakREROJHfTD1wdQH89QHk7Sl6ZAiCWL+1szRrs6E1kj4b8T2r5hZ7VoLZrYP8A3gNedctXOuGpgBnGVme3RggrUeQmYA3YlypaueMjXVC/ih+LeYWZ3PkYbyd85tAN4GLjGz/g0cV82eV7a+j180NZrLzax92N/fwwf5Q+sdhK6e1eZpZh3wd38K9xGwFLg2uHJZX/miKaeeDqJzriQoywX4js8rwbZWEby3xgDPOeemRz7wV8n3N7MG7zhUT97dzd9+PRcgeN/OBM4ws75h6fbH31WrlnPuQ+fczLBHU6d6iIiIqA8Whfpg6oOFpVMfTNKaRoKJJM5vgFwz+wuwGOiAv+X0t/B3lXkiIv184FUzC789N9S9EnQjcDzwvpn9Dr8eRDf8kPJxwe/gh35fBNxnZqPwi2fmBWkm4xd7bTbn3Bdm9nPgZ8A7ZvZ8UOYj8FeOftLA4VcD/wI+MrPH8B2efYFTgBFBmpeAC4O1IxbiF7cdB2ysJ88OwOtm9hz+9twTg+d4Mdg/G792wpNB+zr8LbXrdKqcczVm9j38wrdzzewJ/LoWBwNDgBMbqNeHwDgz+2HQBkudc++H7Z8KTA9+/1kD+bSEb+Pr/mI9+1/GL2p7PrsXdI3VVfj37PHAm8G2Sfi7Jr1rZo/gO9JX4d/zI2LJ1MzG4DuN4NdmyTOzm4O/3w6/uiwiImlPfbDo1AdTH0x9MEl7CoKJJM51+Dn/J+MXUu2AX9RyMnBn5J1v8OslvIf/8uqP73hMcM7VrjHhnFsXdKhuAc7EdzQ2AguAG8LSVZvZycBN+C/bs4J0/wLmxaNyzrlbzGwp/urgz/FTBj4F/tjIcZ+Y2VHAHfirhdnAcuC5sGTX4K8cnh/sfxffAXu1nmyvCtLeDrTH36786tDdhJxzG83sVOBe4E58Z+wp4PXIPJ1zr5rZ8fjX4Uf4EbPFwO8abhF+CDwW5J+DX0shvPPyt+B5M6i/49NSzgdWOOc+ibbTObfFzP4FfCvoUO4V59yHZvZ14Ff4130l/j08iN13hWrMCew5NeCO4Odt+KvbIiIioD5YfcepD6Y+mPpgkvYsPnfqFZG9YWYOeNg5F4+FOtOGmU3AX809wjn3n0aStyoza4e/Ovk359ylMR4zCd/p6IG/Q3h9V2GTkpm9AAxxzkVbi6W5eWYDHYEfA9cDPVpz2oOIiLRt6oM1j/pgyU19MElnWhNMRKRlnIHvSE1txrEb8Fdqk5aZ5UT8fQD+Cv2bcX6qK/HtdX2c8xUREZHkdAbqg4X/rT6YpDVNhxQRSaBgYdNh+DUoPnbOvdWEw6fip0+AXx8imS0xsyn4u1MNwE/D2AHcE+fnmYFf5yKkNM75i4iISBJQH6yW+mAiYRQEExFJrO/h70g0F5jQlAOdc0vYfUvzZPcK/vbkvfEL+L4H/NQ593k8n8Q5txK/3oWIiIikN/XBPPXBRMJoTTAREREREREREUl5WhNMRERERERERERSnoJgIiIiIiIiIiKS8hQEExERERERERGRlKcgmIiIiIiIiIiIpDwFwUREREREREREJOUpCCYiIiIiIiIiIilPQTAREREREREREUl5CoKJiIiIiIiIiEjKUxBMRERERERERERSnoJgIiIiIiIiIiKS8hQEExERERERERGRlPf/Vi6a6ohLmGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot settings\n",
    "fontsize = 10\n",
    "figsz = (12,5)\n",
    "dpi = 120\n",
    "figure(figsize=figsz, dpi=dpi)\n",
    "df = pd.read_csv('activesDB.csv')\n",
    "\n",
    "#Plot cathode actives\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "ax.scatter(df[df.type == 'cathode'].theorycap,df[df.type == 'cathode'].V,200,alpha = 0.2,color = 'r',label='theoretical')\n",
    "ax.scatter(df[df.type == 'cathode'].cap,df[df.type == 'cathode'].V,200,alpha = 0.5,color = 'r',label='practical')\n",
    "for i, txt in enumerate(df[df.type == 'cathode'].name):\n",
    "    ax.annotate(txt, (df[df.type == 'cathode'].cap[i], 0.04+df[df.type == 'cathode'].V[i]))\n",
    "plt.title('Cathode materials')\n",
    "plt.xlabel('Specific capacity [mAh.g-1]',fontsize=fontsize)\n",
    "plt.ylabel('Average voltage [V]',fontsize=fontsize)\n",
    "plt.xticks(fontsize=fontsize)\n",
    "plt.yticks(fontsize=fontsize)\n",
    "# plt.xlim([150,400])\n",
    "plt.ylim([3.3,4])\n",
    "plt.legend(fontsize=fontsize,loc='lower right')\n",
    "plt.grid(True)\n",
    "\n",
    "#Plot anode actives\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "ax.scatter(df[df.type == 'anode'].theorycap,df[df.type == 'anode'].V,200,alpha = 0.2,color = 'b',label='theoretical')\n",
    "ax.scatter(df[df.type == 'anode'].cap,df[df.type == 'anode'].V,200,alpha = 0.5,color = 'b',label='practical')\n",
    "for i, txt in enumerate(df[df.type == 'anode'].name):\n",
    "    ax.annotate(txt,(df[df.type == 'anode'].cap.to_numpy()[i], 0.09+df[df.type == 'anode'].V.to_numpy()[i]))\n",
    "plt.title('Anode materials')\n",
    "plt.xlabel('Specific capacity [mAh.g-1]',fontsize=fontsize)\n",
    "plt.ylabel('Average voltage [V]',fontsize=fontsize)\n",
    "plt.xticks(fontsize=fontsize)\n",
    "plt.yticks(fontsize=fontsize)\n",
    "# plt.xlim([150,400])\n",
    "plt.ylim([-0.1,2])\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "# Active materials & electrode composites\n",
    "\n",
    "For our actual cell model, our components will come together following the same structure used by Matt Lacey, where (+/-) denotes the positive or negative electrode:\n",
    "\n",
    "```\n",
    "Cell\n",
    "    - Electrode (+)\n",
    "        - Electrode composite (+)\n",
    "            - Active material (+)\n",
    "        - Current collector (+)\n",
    "    - Electrode (-)\n",
    "        - Electrode composite (-)\n",
    "            - Active material (-)\n",
    "        - Current collector (-)\n",
    "    - Separator\n",
    "    - Electrolyte\n",
    "```\n",
    "\n",
    "As positive and negative electrodes both have a capacity, the limiting capacity is the overall capacity of the cell. The cell voltage also comes from the potential difference between the positive and negative active material average voltages. To make assembling the cell components easier, we will make each component into its own structure. \n",
    "\n",
    "The active material refers to an actual crystal of the chemistry that is storing the lithium. Active materials have these following properties:\n",
    "\n",
    "* **Active material**\n",
    "    * name\n",
    "    * specific capacity [mAh/g]\n",
    "    * average voltage [V]\n",
    "    * crystallographic density [g/cm3]\n",
    "\n",
    "The electrode composite is the actual powdered coating of active materials. Because it is a powdered coating it has porosity and non-active binders etc that reduce the overall density and capacity of the actual coating. The electrode composite has these properties:\n",
    "\n",
    "* **Electrode composite**\n",
    "    * active material\n",
    "    * thickness [um]\n",
    "    * areal capacity [mAh/cm2]\n",
    "    * active loading [g/cm2]\n",
    "    * active fraction\n",
    "    * porosity\n",
    "    * composite_density [g/cm3]\n",
    "\n",
    "Adrian Yao's substack post on [The Li(ttle) ion that could](https://honestenergy.substack.com/p/the-little-ion-that-could) has a great sketch that we can highlight to see what is considered an **\"active material\"** and what is the overall electrode **\"composite\"**:\n",
    "\n",
    "<div>\n",
    "<img src=\"images/fig1_active_composite.png\" width=\"800\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Lets look at the properties for an NCA active material:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>name</th>\n",
       "      <th>name_long</th>\n",
       "      <th>formula</th>\n",
       "      <th>type</th>\n",
       "      <th>cap</th>\n",
       "      <th>V</th>\n",
       "      <th>n</th>\n",
       "      <th>density</th>\n",
       "      <th>theorycap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NCA</td>\n",
       "      <td>lithium nickel cobalt aluminium oxide</td>\n",
       "      <td>Li(Ni0.8Co0.15Al0.05)O2</td>\n",
       "      <td>cathode</td>\n",
       "      <td>210</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1</td>\n",
       "      <td>4.85</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name                              name_long                  formula  \\\n",
       "0  NCA  lithium nickel cobalt aluminium oxide  Li(Ni0.8Co0.15Al0.05)O2   \n",
       "\n",
       "      type  cap     V  n  density  theorycap  \n",
       "0  cathode  210  3.86  1     4.85        279  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.name=='NCA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "The `make_active()` function makes a dotmap type dictionary that allows us to assign the properties for the active material. \n",
    "\n",
    "It also uses the `pint` package which allows us to embed units and the `uncertainties` package lets us propagate standard deviations.\n",
    "\n",
    "We can input the \"NCA\" active material by using the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "     \"name\": \"NCA\",\n",
      "     \"speccap\": \"210.0 hour * milliampere / gram\",\n",
      "     \"avgE\": \"3.86 volt\",\n",
      "     \"density\": \"4.85 gram / centimeter ** 3\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "#Make active struct\n",
    "NCA = make_active(name='NCA',\n",
    "                 speccap=210*unit.mA*unit.hr/unit.g, \n",
    "                 avgE=3.86*unit.V, \n",
    "                 density=4.85*unit.g/unit.cm**3,\n",
    "                 unit=unit)\n",
    "\n",
    "#See active struct\n",
    "print_struct(NCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "The `make_composite()` function makes a dotmap type dictionary that allows us to assign the properties for the electrode composite. \n",
    "\n",
    "It also uses the `pint` package which allows us to embed units and the `uncertainties` package lets us propagate standard deviations.\n",
    "\n",
    "* `make_composite()` also lets us \"fill the gaps\", for example, if we know the areal capacity **arealcap** and composite density **density**, the other properties can automatically be calculated:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "     \"active\": {\n",
      "          \"name\": \"NCA\",\n",
      "          \"speccap\": \"210.0 hour * milliampere / gram\",\n",
      "          \"avgE\": \"3.86 volt\",\n",
      "          \"density\": \"4.85 gram / centimeter ** 3\"\n",
      "     },\n",
      "     \"arealcap\": \"3.5 hour * milliampere / centimeter ** 2\",\n",
      "     \"thick\": \"(50.5 +/- 1.5) micrometer\",\n",
      "     \"arealload\": \"(0.016666666666666666 +/- 0) gram / centimeter ** 2\",\n",
      "     \"activefrac\": 0.95,\n",
      "     \"porosity\": \"(0.284 +/- 0.022) dimensionless\",\n",
      "     \"density\": \"(3.30 +/- 0.10) gram / centimeter ** 3\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "#Make NCA composite\n",
    "composite = make_composite(active=NCA, \n",
    "                           arealcap = (3.5*unit.mA*unit.hr/unit.cm**2), \n",
    "                           density = (3.3*unit.g/unit.cm**3).plus_minus(0.1),\n",
    "                           unit=unit)\n",
    "print_struct(composite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "* or if we fill `make_composite()` with the coating thickness **thick**, active fraction **activefrac**, and mass loading **arealload**, the other properties can automatically be calculated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "     \"active\": {\n",
      "          \"name\": \"NCA\",\n",
      "          \"speccap\": \"210.0 hour * milliampere / gram\",\n",
      "          \"avgE\": \"3.86 volt\",\n",
      "          \"density\": \"4.85 gram / centimeter ** 3\"\n",
      "     },\n",
      "     \"arealcap\": \"(3.57 +/- 0.20) hour * milliampere / centimeter ** 2\",\n",
      "     \"thick\": \"(50.0 +/- 2.0) micrometer\",\n",
      "     \"arealload\": \"0.017 gram / centimeter ** 2\",\n",
      "     \"activefrac\": 0.95,\n",
      "     \"porosity\": \"(0.262 +/- 0.030) dimensionless\",\n",
      "     \"density\": \"(3.40 +/- 0.24) gram / centimeter ** 3\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "#Make NCA composite\n",
    "composite = make_composite(active=NCA, \n",
    "                           thick = (50*unit.um).plus_minus(2), \n",
    "                           activefrac = 0.95,\n",
    "                           arealload = (0.017*unit.g/unit.cm**2),\n",
    "                           unit=unit)\n",
    "print_struct(composite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "* if we fill `make_composite()` with properties that cannot fill the gaps or clash, an error will be thrown:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Conflicting defined electrode composite properties.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-157b43386ad1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m                            \u001b[0marealcap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhr\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                            \u001b[0marealload\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m                            unit=unit) #works with & without error\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprint_struct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomposite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/Intercalation Station/BotB Project/BotB/BotB_functions/fn.py\u001b[0m in \u001b[0;36mmake_composite\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0munit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomposite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mkeylist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     \u001b[0mcomposite\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplete_composite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomposite\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkeylist\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0munit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'missing'\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcomposite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m#Check if any are unspecified\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/Intercalation Station/BotB Project/BotB/BotB_functions/fn.py\u001b[0m in \u001b[0;36mcomplete_composite\u001b[0;34m(composite, keylist, unit)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unspecified electrode composite properties.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;32melif\u001b[0m  \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m#Different values calculated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Conflicting defined electrode composite properties.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m             \u001b[0munits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Conflicting defined electrode composite properties."
     ]
    }
   ],
   "source": [
    "#Make NCA composite\n",
    "composite = make_composite(active=NCA, \n",
    "                           thick = (50*unit.um).plus_minus(1), \n",
    "                           arealcap = (3*unit.mA*unit.hr/unit.cm**2),\n",
    "                           arealload = (0.1*unit.g/unit.cm**2),\n",
    "                           unit=unit) #works with & without error\n",
    "print_struct(composite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Conclusion\n",
    "\n",
    "* Hopefully, by playing around with the `make_composite()` function we can develop some intuition in how electrode composite properties relate to each other. For example, porosity is related to the ratio between crystal density and coating density. \n",
    "\n",
    "* Next time, we'll construct the rest of the components like separator, current collectors, and electrolyte that make up the rest of the cell sandwich.\n",
    "\n",
    "**Please consider supporting Intercalation and BotB with a subscription. We'll work through some interesting case studies in the near future!**\n",
    "***[Subscribe Now!](https://intercalationstation.substack.com/publish/post/%%checkout_url%%)***\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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