Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2da764f9-629d-4a73-b46e-f96e8d80e371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: TableauScraper in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (0.1.29)\n",
      "Requirement already satisfied: beautifulsoup4>=4.0.0 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from TableauScraper) (4.12.2)\n",
      "Requirement already satisfied: pandas in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from TableauScraper) (2.1.2)\n",
      "Requirement already satisfied: requests>=2.14.0 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from TableauScraper) (2.31.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from beautifulsoup4>=4.0.0->TableauScraper) (2.5)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.14.0->TableauScraper) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.14.0->TableauScraper) (2.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.14.0->TableauScraper) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.14.0->TableauScraper) (2023.7.22)\n",
      "Requirement already satisfied: numpy<2,>=1.26.0 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas->TableauScraper) (1.26.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas->TableauScraper) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas->TableauScraper) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas->TableauScraper) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->TableauScraper) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install TableauScraper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b41cbb6-3846-4339-8ad0-38aed48f501a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests, time\n",
    "from math import sin, asin, cos, sqrt, radians\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import pylab\n",
    "import seaborn as sns\n",
    "from scipy.spatial.distance import pdist, squareform, cdist\n",
    "from sklearn.cluster import DBSCAN\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from geopy.distance import great_circle, geodesic\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0308443e-ac43-4632-92bf-3a7995799044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "worksheet name : Sheet 5\n",
      "   SUM(Вага, т)-alias\n",
      "0        898980.84521\n",
      "worksheet name : Sheet 5 (2)\n",
      "   CNT(Вага, т)-alias\n",
      "0              149981\n",
      "worksheet name : Отримувачі гуманітарки\n",
      "     COLLECT(Geometry)-alias         ADMIN_1-alias      ADMIN_3-alias  \\\n",
      "0        5709502431550126797       Сумська область  Зноб-Новгородська   \n",
      "1       11152669815422936989  Чернігівська область        Семенівська   \n",
      "2        2487719945875751117       Сумська область        Дружбівська   \n",
      "3       13582824748672333069       Сумська область         Ямпільська   \n",
      "4        8229574966191498674  Чернігівська область       Городнянська   \n",
      "...                      ...                   ...                ...   \n",
      "1068    17757481284078875793       Одеська область       Саф’янівська   \n",
      "1069     8353070472132886832       Одеська область         Вилківська   \n",
      "1070    11679755357849403194       Одеська область        Ізмаїльська   \n",
      "1071    11679755357849403194       Одеська область        Ізмаїльська   \n",
      "1072     1330239403984468210       Одеська область          Ренійська   \n",
      "\n",
      "     Transport type-value Transport type-alias adm_1_eng-value  \\\n",
      "0              Automobile           Automobile          Sumska   \n",
      "1              Automobile           Automobile    Chernihivska   \n",
      "2              Automobile           Automobile          Sumska   \n",
      "3              Automobile           Automobile          Sumska   \n",
      "4              Automobile           Automobile    Chernihivska   \n",
      "...                   ...                  ...             ...   \n",
      "1068           Automobile           Automobile          Odeska   \n",
      "1069           Automobile           Automobile          Odeska   \n",
      "1070           Automobile           Automobile          Odeska   \n",
      "1071             Maritime             Maritime          Odeska   \n",
      "1072           Automobile           Automobile          Odeska   \n",
      "\n",
      "     adm_1_eng-alias   adm_3_eng-value   adm_3_eng-alias  \\\n",
      "0             Sumska  Znob-Novgorodska  Znob-Novgorodska   \n",
      "1       Chernihivska        Semenivska        Semenivska   \n",
      "2             Sumska       Druzhbivska       Druzhbivska   \n",
      "3             Sumska         Yampilska         Yampilska   \n",
      "4       Chernihivska      Gorodnyanska      Gorodnyanska   \n",
      "...              ...               ...               ...   \n",
      "1068          Odeska       Safyanivska       Safyanivska   \n",
      "1069          Odeska         Vylkivska         Vylkivska   \n",
      "1070          Odeska         Izmailska         Izmailska   \n",
      "1071          Odeska         Izmailska         Izmailska   \n",
      "1072          Odeska          Reniyska          Reniyska   \n",
      "\n",
      "     SUM(Населення, тис.)-alias SUM(Тон допомоги на тисячу населення)-alias  \\\n",
      "0                         4.828                                    3.881762   \n",
      "1                        15.973                                    6.290533   \n",
      "2                         5.406                                    3.881762   \n",
      "3                         8.153                                    3.881762   \n",
      "4                        21.388                                    6.290533   \n",
      "...                         ...                                         ...   \n",
      "1068                     42.588                                    9.858124   \n",
      "1069                     12.723                                    9.858124   \n",
      "1070                     70.731                                    9.858124   \n",
      "1071                     70.731                                    9.858124   \n",
      "1072                     35.637                                    9.858124   \n",
      "\n",
      "     SUM(Вага, т)-alias Latitude (generated)-value Latitude (generated)-alias  \\\n",
      "0                   1.7                  52.244519                     52.245   \n",
      "1                 6.022                  52.142891                     52.143   \n",
      "2                 1.712                  52.040402                     52.040   \n",
      "3                  8.82                   51.96909                     51.969   \n",
      "4                 2.688                   51.95544                     51.955   \n",
      "...                 ...                        ...                        ...   \n",
      "1068           124.8844                  45.450398                     45.450   \n",
      "1069              5.471                  45.448624                     45.449   \n",
      "1070         640.560469                  45.358574                     45.359   \n",
      "1071              269.6                  45.358574                     45.359   \n",
      "1072          390.24385                  45.320032                     45.320   \n",
      "\n",
      "     Longitude (generated)-value Longitude (generated)-alias  \\\n",
      "0                      33.546371                       33.55   \n",
      "1                      32.621228                       32.62   \n",
      "2                      33.956035                       33.96   \n",
      "3                      33.741536                       33.74   \n",
      "4                      31.524671                       31.52   \n",
      "...                          ...                         ...   \n",
      "1068                   28.904314                       28.90   \n",
      "1069                    29.61591                       29.62   \n",
      "1070                   28.843599                       28.84   \n",
      "1071                   28.843599                       28.84   \n",
      "1072                   28.426151                       28.43   \n",
      "\n",
      "     ATTR(Тип транспорту)-alias AGG(К-сть вантажів)-alias  \n",
      "0                 Автомобільний                         1  \n",
      "1                 Автомобільний                         2  \n",
      "2                 Автомобільний                         1  \n",
      "3                 Автомобільний                         1  \n",
      "4                 Автомобільний                         2  \n",
      "...                         ...                       ...  \n",
      "1068              Автомобільний                       125  \n",
      "1069              Автомобільний                         2  \n",
      "1070              Автомобільний                       324  \n",
      "1071                   Морський                         2  \n",
      "1072              Автомобільний                       195  \n",
      "\n",
      "[1073 rows x 18 columns]\n",
      "worksheet name : Розподіл по днях - лінійний графік\n",
      "    Transport type-value Transport type-alias SUM(Вага, т)-value  \\\n",
      "0                Railway              Railway            581.904   \n",
      "1                Railway              Railway                301   \n",
      "2                Railway              Railway           1113.506   \n",
      "3                Railway              Railway             567.24   \n",
      "4                Railway              Railway            432.256   \n",
      "..                   ...                  ...                ...   \n",
      "406           Automobile           Automobile        2197.289139   \n",
      "407           Automobile           Automobile        2419.854842   \n",
      "408           Automobile           Automobile        1780.337232   \n",
      "409           Automobile           Automobile        1393.343478   \n",
      "410           Automobile           Automobile        1913.047521   \n",
      "\n",
      "    SUM(Вага, т)-alias      DAY(День)-value    DAY(День)-alias  \n",
      "0              581.904  2022-03-11 00:00:00      11 March 2022  \n",
      "1                  301  2022-03-12 00:00:00      12 March 2022  \n",
      "2             1113.506  2022-03-13 00:00:00      13 March 2022  \n",
      "3               567.24  2022-03-14 00:00:00      14 March 2022  \n",
      "4              432.256  2022-03-15 00:00:00      15 March 2022  \n",
      "..                 ...                  ...                ...  \n",
      "406        2197.289139  2022-09-23 00:00:00  23 September 2022  \n",
      "407        2419.854842  2022-09-24 00:00:00  24 September 2022  \n",
      "408        1780.337232  2022-09-25 00:00:00  25 September 2022  \n",
      "409        1393.343478  2022-09-26 00:00:00  26 September 2022  \n",
      "410        1913.047521  2022-09-27 00:00:00  27 September 2022  \n",
      "\n",
      "[411 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "from tableauscraper import TableauScraper as TS\n",
    "\n",
    "url = \"https://public.tableau.com/views/InternationalhumanitarianaidinUkraine/ENGdashboard?:showVizHome=no\"\n",
    "\n",
    "ts = TS()\n",
    "ts.loads(url)\n",
    "workbook = ts.getWorkbook()\n",
    "\n",
    "for t in workbook.worksheets:\n",
    "    print(f\"worksheet name : {t.name}\") #show worksheet name\n",
    "    print(t.data) #show dataframe for this worksheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0edb2d4-1edf-4b3b-bcb3-58a5d0c8710f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>COLLECT(Geometry)-alias</th>\n",
       "      <th>ADMIN_1-alias</th>\n",
       "      <th>ADMIN_3-alias</th>\n",
       "      <th>Transport type-value</th>\n",
       "      <th>Transport type-alias</th>\n",
       "      <th>adm_1_eng-value</th>\n",
       "      <th>adm_1_eng-alias</th>\n",
       "      <th>adm_3_eng-value</th>\n",
       "      <th>adm_3_eng-alias</th>\n",
       "      <th>SUM(Населення, тис.)-alias</th>\n",
       "      <th>SUM(Тон допомоги на тисячу населення)-alias</th>\n",
       "      <th>SUM(Вага, т)-alias</th>\n",
       "      <th>Latitude (generated)-value</th>\n",
       "      <th>Latitude (generated)-alias</th>\n",
       "      <th>Longitude (generated)-value</th>\n",
       "      <th>Longitude (generated)-alias</th>\n",
       "      <th>ATTR(Тип транспорту)-alias</th>\n",
       "      <th>AGG(К-сть вантажів)-alias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5709502431550126797</td>\n",
       "      <td>Сумська область</td>\n",
       "      <td>Зноб-Новгородська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Sumska</td>\n",
       "      <td>Sumska</td>\n",
       "      <td>Znob-Novgorodska</td>\n",
       "      <td>Znob-Novgorodska</td>\n",
       "      <td>4.828</td>\n",
       "      <td>3.881762</td>\n",
       "      <td>1.7</td>\n",
       "      <td>52.244519</td>\n",
       "      <td>52.245</td>\n",
       "      <td>33.546371</td>\n",
       "      <td>33.55</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11152669815422936989</td>\n",
       "      <td>Чернігівська область</td>\n",
       "      <td>Семенівська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Chernihivska</td>\n",
       "      <td>Chernihivska</td>\n",
       "      <td>Semenivska</td>\n",
       "      <td>Semenivska</td>\n",
       "      <td>15.973</td>\n",
       "      <td>6.290533</td>\n",
       "      <td>6.022</td>\n",
       "      <td>52.142891</td>\n",
       "      <td>52.143</td>\n",
       "      <td>32.621228</td>\n",
       "      <td>32.62</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2487719945875751117</td>\n",
       "      <td>Сумська область</td>\n",
       "      <td>Дружбівська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Sumska</td>\n",
       "      <td>Sumska</td>\n",
       "      <td>Druzhbivska</td>\n",
       "      <td>Druzhbivska</td>\n",
       "      <td>5.406</td>\n",
       "      <td>3.881762</td>\n",
       "      <td>1.712</td>\n",
       "      <td>52.040402</td>\n",
       "      <td>52.040</td>\n",
       "      <td>33.956035</td>\n",
       "      <td>33.96</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13582824748672333069</td>\n",
       "      <td>Сумська область</td>\n",
       "      <td>Ямпільська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Sumska</td>\n",
       "      <td>Sumska</td>\n",
       "      <td>Yampilska</td>\n",
       "      <td>Yampilska</td>\n",
       "      <td>8.153</td>\n",
       "      <td>3.881762</td>\n",
       "      <td>8.82</td>\n",
       "      <td>51.96909</td>\n",
       "      <td>51.969</td>\n",
       "      <td>33.741536</td>\n",
       "      <td>33.74</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8229574966191498674</td>\n",
       "      <td>Чернігівська область</td>\n",
       "      <td>Городнянська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Chernihivska</td>\n",
       "      <td>Chernihivska</td>\n",
       "      <td>Gorodnyanska</td>\n",
       "      <td>Gorodnyanska</td>\n",
       "      <td>21.388</td>\n",
       "      <td>6.290533</td>\n",
       "      <td>2.688</td>\n",
       "      <td>51.95544</td>\n",
       "      <td>51.955</td>\n",
       "      <td>31.524671</td>\n",
       "      <td>31.52</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>17757481284078875793</td>\n",
       "      <td>Одеська область</td>\n",
       "      <td>Саф’янівська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Safyanivska</td>\n",
       "      <td>Safyanivska</td>\n",
       "      <td>42.588</td>\n",
       "      <td>9.858124</td>\n",
       "      <td>124.8844</td>\n",
       "      <td>45.450398</td>\n",
       "      <td>45.450</td>\n",
       "      <td>28.904314</td>\n",
       "      <td>28.90</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>8353070472132886832</td>\n",
       "      <td>Одеська область</td>\n",
       "      <td>Вилківська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Vylkivska</td>\n",
       "      <td>Vylkivska</td>\n",
       "      <td>12.723</td>\n",
       "      <td>9.858124</td>\n",
       "      <td>5.471</td>\n",
       "      <td>45.448624</td>\n",
       "      <td>45.449</td>\n",
       "      <td>29.61591</td>\n",
       "      <td>29.62</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>11679755357849403194</td>\n",
       "      <td>Одеська область</td>\n",
       "      <td>Ізмаїльська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Izmailska</td>\n",
       "      <td>Izmailska</td>\n",
       "      <td>70.731</td>\n",
       "      <td>9.858124</td>\n",
       "      <td>640.560469</td>\n",
       "      <td>45.358574</td>\n",
       "      <td>45.359</td>\n",
       "      <td>28.843599</td>\n",
       "      <td>28.84</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>11679755357849403194</td>\n",
       "      <td>Одеська область</td>\n",
       "      <td>Ізмаїльська</td>\n",
       "      <td>Maritime</td>\n",
       "      <td>Maritime</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Izmailska</td>\n",
       "      <td>Izmailska</td>\n",
       "      <td>70.731</td>\n",
       "      <td>9.858124</td>\n",
       "      <td>269.6</td>\n",
       "      <td>45.358574</td>\n",
       "      <td>45.359</td>\n",
       "      <td>28.843599</td>\n",
       "      <td>28.84</td>\n",
       "      <td>Морський</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>1330239403984468210</td>\n",
       "      <td>Одеська область</td>\n",
       "      <td>Ренійська</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Reniyska</td>\n",
       "      <td>Reniyska</td>\n",
       "      <td>35.637</td>\n",
       "      <td>9.858124</td>\n",
       "      <td>390.24385</td>\n",
       "      <td>45.320032</td>\n",
       "      <td>45.320</td>\n",
       "      <td>28.426151</td>\n",
       "      <td>28.43</td>\n",
       "      <td>Автомобільний</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1073 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     COLLECT(Geometry)-alias         ADMIN_1-alias      ADMIN_3-alias  \\\n",
       "0        5709502431550126797       Сумська область  Зноб-Новгородська   \n",
       "1       11152669815422936989  Чернігівська область        Семенівська   \n",
       "2        2487719945875751117       Сумська область        Дружбівська   \n",
       "3       13582824748672333069       Сумська область         Ямпільська   \n",
       "4        8229574966191498674  Чернігівська область       Городнянська   \n",
       "...                      ...                   ...                ...   \n",
       "1068    17757481284078875793       Одеська область       Саф’янівська   \n",
       "1069     8353070472132886832       Одеська область         Вилківська   \n",
       "1070    11679755357849403194       Одеська область        Ізмаїльська   \n",
       "1071    11679755357849403194       Одеська область        Ізмаїльська   \n",
       "1072     1330239403984468210       Одеська область          Ренійська   \n",
       "\n",
       "     Transport type-value Transport type-alias adm_1_eng-value  \\\n",
       "0              Automobile           Automobile          Sumska   \n",
       "1              Automobile           Automobile    Chernihivska   \n",
       "2              Automobile           Automobile          Sumska   \n",
       "3              Automobile           Automobile          Sumska   \n",
       "4              Automobile           Automobile    Chernihivska   \n",
       "...                   ...                  ...             ...   \n",
       "1068           Automobile           Automobile          Odeska   \n",
       "1069           Automobile           Automobile          Odeska   \n",
       "1070           Automobile           Automobile          Odeska   \n",
       "1071             Maritime             Maritime          Odeska   \n",
       "1072           Automobile           Automobile          Odeska   \n",
       "\n",
       "     adm_1_eng-alias   adm_3_eng-value   adm_3_eng-alias  \\\n",
       "0             Sumska  Znob-Novgorodska  Znob-Novgorodska   \n",
       "1       Chernihivska        Semenivska        Semenivska   \n",
       "2             Sumska       Druzhbivska       Druzhbivska   \n",
       "3             Sumska         Yampilska         Yampilska   \n",
       "4       Chernihivska      Gorodnyanska      Gorodnyanska   \n",
       "...              ...               ...               ...   \n",
       "1068          Odeska       Safyanivska       Safyanivska   \n",
       "1069          Odeska         Vylkivska         Vylkivska   \n",
       "1070          Odeska         Izmailska         Izmailska   \n",
       "1071          Odeska         Izmailska         Izmailska   \n",
       "1072          Odeska          Reniyska          Reniyska   \n",
       "\n",
       "     SUM(Населення, тис.)-alias SUM(Тон допомоги на тисячу населення)-alias  \\\n",
       "0                         4.828                                    3.881762   \n",
       "1                        15.973                                    6.290533   \n",
       "2                         5.406                                    3.881762   \n",
       "3                         8.153                                    3.881762   \n",
       "4                        21.388                                    6.290533   \n",
       "...                         ...                                         ...   \n",
       "1068                     42.588                                    9.858124   \n",
       "1069                     12.723                                    9.858124   \n",
       "1070                     70.731                                    9.858124   \n",
       "1071                     70.731                                    9.858124   \n",
       "1072                     35.637                                    9.858124   \n",
       "\n",
       "     SUM(Вага, т)-alias Latitude (generated)-value Latitude (generated)-alias  \\\n",
       "0                   1.7                  52.244519                     52.245   \n",
       "1                 6.022                  52.142891                     52.143   \n",
       "2                 1.712                  52.040402                     52.040   \n",
       "3                  8.82                   51.96909                     51.969   \n",
       "4                 2.688                   51.95544                     51.955   \n",
       "...                 ...                        ...                        ...   \n",
       "1068           124.8844                  45.450398                     45.450   \n",
       "1069              5.471                  45.448624                     45.449   \n",
       "1070         640.560469                  45.358574                     45.359   \n",
       "1071              269.6                  45.358574                     45.359   \n",
       "1072          390.24385                  45.320032                     45.320   \n",
       "\n",
       "     Longitude (generated)-value Longitude (generated)-alias  \\\n",
       "0                      33.546371                       33.55   \n",
       "1                      32.621228                       32.62   \n",
       "2                      33.956035                       33.96   \n",
       "3                      33.741536                       33.74   \n",
       "4                      31.524671                       31.52   \n",
       "...                          ...                         ...   \n",
       "1068                   28.904314                       28.90   \n",
       "1069                    29.61591                       29.62   \n",
       "1070                   28.843599                       28.84   \n",
       "1071                   28.843599                       28.84   \n",
       "1072                   28.426151                       28.43   \n",
       "\n",
       "     ATTR(Тип транспорту)-alias AGG(К-сть вантажів)-alias  \n",
       "0                 Автомобільний                         1  \n",
       "1                 Автомобільний                         2  \n",
       "2                 Автомобільний                         1  \n",
       "3                 Автомобільний                         1  \n",
       "4                 Автомобільний                         2  \n",
       "...                         ...                       ...  \n",
       "1068              Автомобільний                       125  \n",
       "1069              Автомобільний                         2  \n",
       "1070              Автомобільний                       324  \n",
       "1071                   Морський                         2  \n",
       "1072              Автомобільний                       195  \n",
       "\n",
       "[1073 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show original data for worksheet\n",
    "ws = ts.getWorksheet(\"Отримувачі гуманітарки\")\n",
    "df = pd.DataFrame(ws.data)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07415522-138c-4723-bdcc-362c7b76494f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopy in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.4.1)\n",
      "Requirement already satisfied: geographiclib<3,>=1.52 in c:\\users\\administrator\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from geopy) (2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install geopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4356616-b797-4b88-a7dc-4cb94595f98c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def haversine(lonlat1, lonlat2):\n",
    "\n",
    "    lat1, lon1 = lonlat1\n",
    "    lat2, lon2 = lonlat2\n",
    "    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n",
    "\n",
    "    # haversine formula\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2\n",
    "    c = 2 * asin(sqrt(a))\n",
    "    r = 6371  # Radius of earth in kilometers. Use 3956 for miles\n",
    "    return c * r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57784979-db37-4997-b74e-f4ed82b55243",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_nearest_target_point(clusters_center, target_points):\n",
    "    target = {}\n",
    "    for label, center in clusters_center.items():\n",
    "        dmin = float('inf')\n",
    "        closest_point = None\n",
    "        for target_point in target_points:\n",
    "            distance = haversine(center, target_point)\n",
    "            if distance < dmin:\n",
    "                dmin = distance\n",
    "                closest_point = target_point\n",
    "\n",
    "        target[label] = closest_point\n",
    "\n",
    "    return target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a43456d-4c58-493d-99ee-b7c4afc96fd9",
   "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_ENG</th>\n",
       "      <th>Name_UKR</th>\n",
       "      <th>Country</th>\n",
       "      <th>Type</th>\n",
       "      <th>Status</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Status as of</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vilcha - Oleksandrivka</td>\n",
       "      <td>Вільча - Олександрівка</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Road</td>\n",
       "      <td>Closed</td>\n",
       "      <td>51.375612</td>\n",
       "      <td>29.409339</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vystupovychi - Nova Rudnia</td>\n",
       "      <td>Виступовичі - Нова Рудня</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Road</td>\n",
       "      <td>Closed</td>\n",
       "      <td>51.516881</td>\n",
       "      <td>28.985531</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maidan-Kopyshchanskyi - Hlushkevychi</td>\n",
       "      <td>Майдан-Копищанський - Глушкевичі</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Road</td>\n",
       "      <td>Closed</td>\n",
       "      <td>51.539146</td>\n",
       "      <td>27.845577</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Novi Yarylovychi - Nova Huta</td>\n",
       "      <td>Нові Яриловичі - Нова Гута</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Road</td>\n",
       "      <td>Closed</td>\n",
       "      <td>52.074702</td>\n",
       "      <td>30.969933</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Slavutych - Komaryn</td>\n",
       "      <td>Славутич - Комарин</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>Road</td>\n",
       "      <td>Closed</td>\n",
       "      <td>51.399221</td>\n",
       "      <td>30.657726</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Yuhanivka - Yelan</td>\n",
       "      <td>Юганівка - Єлань</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>Road</td>\n",
       "      <td>Closed</td>\n",
       "      <td>48.687488</td>\n",
       "      <td>39.717300</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Novoborovytsi - Alekseevo-Tuzlovka</td>\n",
       "      <td>Новоборовиці - Алєксєєво-Тузловка</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>Road</td>\n",
       "      <td>Closed</td>\n",
       "      <td>47.859815</td>\n",
       "      <td>39.478740</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Uzhhorod - Vyšné Nemecké</td>\n",
       "      <td>Ужгород - Вишнє Нємецьке</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Road</td>\n",
       "      <td>Operational</td>\n",
       "      <td>48.653606</td>\n",
       "      <td>22.265710</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Malyi Bereznyi - Ubľa</td>\n",
       "      <td>Малий Березний - Убля</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Road</td>\n",
       "      <td>Operational</td>\n",
       "      <td>48.883723</td>\n",
       "      <td>22.420635</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Mali Selmentsi - Veľké Slemence</td>\n",
       "      <td>Малі Сельменці - Вельке Слеменце</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Pedestrian</td>\n",
       "      <td>Operational</td>\n",
       "      <td>48.511129</td>\n",
       "      <td>22.154123</td>\n",
       "      <td>2023-05-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Name_ENG                           Name_UKR  \\\n",
       "0                  Vilcha - Oleksandrivka             Вільча - Олександрівка   \n",
       "1              Vystupovychi - Nova Rudnia           Виступовичі - Нова Рудня   \n",
       "2    Maidan-Kopyshchanskyi - Hlushkevychi   Майдан-Копищанський - Глушкевичі   \n",
       "3            Novi Yarylovychi - Nova Huta         Нові Яриловичі - Нова Гута   \n",
       "4                     Slavutych - Komaryn                 Славутич - Комарин   \n",
       "..                                    ...                                ...   \n",
       "123                     Yuhanivka - Yelan                   Юганівка - Єлань   \n",
       "124    Novoborovytsi - Alekseevo-Tuzlovka  Новоборовиці - Алєксєєво-Тузловка   \n",
       "125              Uzhhorod - Vyšné Nemecké           Ужгород - Вишнє Нємецьке   \n",
       "126                 Malyi Bereznyi - Ubľa              Малий Березний - Убля   \n",
       "127       Mali Selmentsi - Veľké Slemence   Малі Сельменці - Вельке Слеменце   \n",
       "\n",
       "                Country        Type       Status        Lat        Lon  \\\n",
       "0               Belarus        Road       Closed  51.375612  29.409339   \n",
       "1               Belarus        Road       Closed  51.516881  28.985531   \n",
       "2               Belarus        Road       Closed  51.539146  27.845577   \n",
       "3               Belarus        Road       Closed  52.074702  30.969933   \n",
       "4               Belarus        Road       Closed  51.399221  30.657726   \n",
       "..                  ...         ...          ...        ...        ...   \n",
       "123  Russian Federation        Road       Closed  48.687488  39.717300   \n",
       "124  Russian Federation        Road       Closed  47.859815  39.478740   \n",
       "125            Slovakia        Road  Operational  48.653606  22.265710   \n",
       "126            Slovakia        Road  Operational  48.883723  22.420635   \n",
       "127            Slovakia  Pedestrian  Operational  48.511129  22.154123   \n",
       "\n",
       "    Status as of  \n",
       "0     2023-05-30  \n",
       "1     2023-05-30  \n",
       "2     2023-05-30  \n",
       "3     2023-05-30  \n",
       "4     2023-05-30  \n",
       "..           ...  \n",
       "123   2023-05-30  \n",
       "124   2023-05-30  \n",
       "125   2023-05-30  \n",
       "126   2023-05-30  \n",
       "127   2023-05-30  \n",
       "\n",
       "[128 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boder point data source : https://data.humdata.org/dataset/ukraine-border-crossings\n",
    "border_df = pd.read_excel(r'E:\\paper\\data\\geojason\\ukr_border_crossings_20230530.xlsx', sheet_name='Border Crossings')\n",
    "display(border_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "962d0ef4-ed8d-4e25-9393-bbeb67d7416e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: [50.27338488, 23.58982299], 1: [51.399221, 30.657726], 2: [51.3756116, 29.4093392], 3: [50.29801682, 36.28183012], 4: [47.87771135, 29.27498873], 5: [48.44485856, 27.79135141], 6: [50.36827375, 35.75717961], 7: [46.38315714, 30.08739523]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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wYECVzrl0jkd6r1Y8l1mzZuH3+xk3bhyqqka0c7lc/PTTTwDh85oxYwbFxcUx+y61F/nuu+8itjeryh133BHx+pxzzol4XkyfPh2TycStt94aLlNVlbFjx0Ycd7SfrdL78IYbbiAlJSVc3qlTJ4YOHRp+thwv/H4/l19+OT/++CM///wzw4YNi9ku1vP7SL8DKlKV5/eR3ttV4YwVMj179mTIkCFRf+UfnEdLo0aNIl4nJiZitVpJS0uLKq/4gfn444/p1KkTVquV1NRU0tPT+emnnyL2k+ONUzr3o/mCg9D++RNPPBG2nUhLSyM9PZ28vLyY4zdt2rRK/a5bt45LLrmExMREXC4X6enp4Zu9Yr8NGjSI2rdPTk6u0jl9+eWXBAIBunbtytatW9m6dSs5OTn06tWLzz77rNJjd+3ahaqq4W22Ulq3bl2VU8TlclFYWFiltseCxWLhpZde4pdffqF27dr079+fl19+mYMHD1bp+C1btjB9+nTS09Mj/oYMGQKUGSOWEus9fvjhh3E6nfTs2ZOWLVsyduzYKm0DQPXe37t27QKgZcuWUcdW9X0DcDgc4c//eeedx9///ne+//57Nm3axIsvvghAZmYmxcXFMftt27Ytuq5HuWtXvHbx5msymSLEEYTep3Xr1kW9T61atQKq9j7FwuVyARzxvRrvXCpeD7PZTLNmzcL1TZs25f777+e///0vaWlpDB8+nLfffjvi/b7yyivp27cvt9xyC7Vr1+aqq67iq6++qpKosVqtUUK74vNi165d1K1bN2pbpEWLFhGvj/azFe9aQOjeyMrKqnTr/ViIZeM0YcIEpk2bxpQpUyq1wYl1zxzpd0BFqvL8PtJ7uyqcsTYyVSVe0LSKxmTlibVKEW/lQggR/v+nn37KDTfcwMUXX8xDDz1ErVq1MBgMTJgwgW3bth1Vn0fCPffcw4cffsi4cePo3bs3iYmJKIrCVVddFfOhUl69xyMvL48BAwbgcrl45plnaN68OVarlZUrV/Lwww9H9Xss51QqVsrbNZRn+/btUV8Y1UWbNm1YtWoVe/bsibB1qCpHcp+NGzeOUaNGMW3aNGbMmMHjjz/OhAkTmD17Nl27dq10HF3XGTp0KP/4xz9i1pc+TEqJ9R63bduWTZs28eOPPzJ9+nS++eYbJk2axBNPPMHTTz8dd+wTfX8fCaUGyRVXqI6Eqnw+4qHrOh07duS1116LWV/xHqvqWG3atAFg7dq1RxR+4VjOZeLEidxwww189913zJw5k3vvvZcJEyawePFiGjRogM1mY968efz+++/89NNPTJ8+nS+//JLBgwczc+bMSld9q9tD8lg+W9VNqVeQx+OJWV9cXBzTc2j48OFMnz6dl19+mYEDB8b1Lor1nh7pd0BFqvKZPdJ7uypIIXMYSn8FVrTEL1Xh1cmUKVNo1qwZ3377bcQX25NPPlmt48T70pwyZQpjxoyJ8Kbyer1R534kzJkzh+zsbL799tuwUTPAjh07jrrPWOzYsYOFCxdy9913hz0DStF1neuuu47PP/+cxx57LObxjRs3Rtd1tm3bFvHLatOmTVUaf9SoUfzvf//j008/Zfz48Uc8/yO9z5o3b84DDzzAAw88wJYtW+jSpQsTJ07k008/BeK/x82bN6eoqCi8AnO0OBwOrrzySq688kr8fj+XXnopzz//POPHj4/74Kzu+7s0kNiWLVui6qr6vlWGpmkUFRUBkJ6ejt1uj9nvxo0bUVX1sA/g8vMtXUaH0BL+jh076Ny5c7isefPmrFmzhnPPPbdaI3P369eP5ORk/ve///Hoo48etRAoPZdNmzZF/Djw+/3s2LEj6v7q2LEjHTt25LHHHmPhwoX07duXd955h+eeew4IbfWce+65nHvuubz22mu88MIL/POf/+T3338/5nu1cePG/P777xQXF0esymzdujVm+8N9tiq7FhXZuHEjaWlpR+WuXr7f0m3V8mzevDlm8Nazzz6bO+64gwsuuIDLL7+cqVOnVjnURE18B1SkJu7tM3Zrqaq4XC7S0tKifplNmjSp2scqfaiUV69Llixh0aJF1TqOw+GIeWMaDIaoX7tvvfVWpatPhyPWOfn9/mq/fqWrMf/4xz+47LLLIv6uuOIKBgwYUOn2UqlF/ZtvvhlRXtGLJx6XXXYZHTt25Pnnn4/5fhUWFoY9iGLRuHFjDAbDYe+z4uJivF5vRFnz5s1JSEiIcF2M9x5fccUVLFq0iBkzZkTV5eXlEQwG486xlOzs7IjXZrOZdu3aIYSo1Ouvuu/vunXr0qVLFz7++OMoV95SN/Gj5ffff6eoqCgsLgwGA8OGDeO7776LcEU9dOgQn3/+Of369Qtv28SjR48epKen88477+D3+8PlH330UdR7dcUVV7Bv3z7ee++9qH48Hs9Rb1XY7XYefvhhNmzYwMMPPxxzdevTTz9l6dKllfYzZMgQzGYzb775ZkQf77//Pvn5+WEPmYKCgqh7qmPHjqiqGr5fc3Jyovov9WA7Wnfc8gwfPpxAIBBxLXVd5+23345oV9XPVkXK34fl38e//vqLmTNncv755x/VvLt3706tWrX473//GzX+tGnT2LdvX1xPziFDhvDFF18wffp0rrvuuirbHtXEd0BFauLelisyVeCWW27hxRdf5JZbbqFHjx7MmzevRiK0XnDBBXz77bdccskljBw5kh07dvDOO+/Qrl278C/D6qB79+7MmjWL1157jXr16tG0aVN69erFBRdcwCeffEJiYiLt2rVj0aJFzJo1i9TU1KMeq0+fPiQnJzNmzBjuvfdeFEXhk08+qfbtgc8++4wuXbrE/VV84YUXcs8997By5Uq6desWVd+lSxeuvvpqJk2aRH5+Pn369OG3336L+6utIiaTiW+//ZYhQ4bQv39/rrjiCvr27YvJZGLdunV8/vnnJCcnx40lk5iYyOWXX85bb72Foig0b96cH3/8MWq/ePPmzZx77rlcccUVtGvXDqPRyNSpUzl06BBXXXVVuF337t2ZPHkyzz33HC1atKBWrVoMHjyYhx56iO+//54LLriAG264ge7du+N2u1m7di1Tpkxh586dUbZcFRk2bBh16tShb9++1K5dmw0bNvDvf/+bkSNHVmpEWhP394QJExg5ciT9+vXjpptuIicnJxzjpqp95ufnh39tB4PBsBuxzWaLiP783HPPheOd3HXXXRiNRt599118Pl+VYh2ZTCaee+45br/9dgYPHsyVV17Jjh07+PDDD6O2PK+77jq++uor7rjjDn7//Xf69u2Lpmls3LiRr776ihkzZtCjR48juFJlPPTQQ6xbt46JEyfy+++/c9lll1GnTh0OHjzItGnTWLp0KQsXLqy0j/T0dMaPH8/TTz/Neeedx4UXXsimTZuYNGkSZ511VtgGbvbs2dx9991cfvnltGrVimAwyCeffILBYGD06NEAPPPMM8ybN4+RI0fSuHFjMjIymDRpEg0aNIgZk+lIufjii+nZsycPPPAAW7dupU2bNnz//fdhAVW6KlDVz1YsXnnlFUaMGEHv3r25+eabw+7XiYmJR52nzWw28+qrrzJmzBjOOussrrzySlJTU1m1ahUffPABnTp1CofQiHfeH374Iddffz0ul4t33333sGPWxHdARWrk3j5iP6dTnMO5tA0YMCDKtbS4uFjcfPPNIjExUSQkJIgrrrhCZGRkxHW/ruhGOGbMGOFwOA47lq7r4oUXXhCNGzcWFotFdO3aVfz4449Rrrrl3ewqEm9O5dm4caPo37+/sNlsAgi7Yufm5oobb7xRpKWlCafTKYYPHy42btwoGjduHOGuXdk1jOV+vWDBAnH22WcLm80m6tWrJ/7xj3+IGTNmxHTfi+XWW/H8K7JixQoBiMcffzxum507dwpA3HfffXGvi8fjEffee69ITU0VDodDjBo1SuzZs6dK7tel5ObmiieeeEJ07NhR2O12YbVaRYcOHcT48ePFgQMHKj2nzMxMMXr0aGG320VycrK4/fbbxV9//RXhIpqVlSXGjh0r2rRpIxwOh0hMTBS9evUSX331VURfBw8eFCNHjhQJCQlRrr2FhYVi/PjxokWLFsJsNou0tDTRp08f8eqrrwq/3y+EqPwee/fdd0X//v1FamqqsFgsonnz5uKhhx4S+fn5lV6bmri/hQi517Zt21ZYLBbRrl078e233x72nimlovu1oigiJSVFXHjhhWLFihVR7VeuXCmGDx8unE6nsNvtYtCgQWLhwoURbQ73jJk0aZJo2rSpsFgsokePHmLevHliwIABUe7yfr9fvPTSS6J9+/bCYrGI5ORk0b17d/H0009HXGtAjB079rDnWpEpU6aIYcOGiZSUFGE0GkXdunXFlVdeKebMmVPlc/n3v/8t2rRpI0wmk6hdu7a48847RW5ubrh++/bt4qabbhLNmzcXVqtVpKSkiEGDBolZs2aF2/z222/ioosuEvXq1RNms1nUq1dPXH311REhAuK5X8d6rsb6bGdmZoprrrlGJCQkiMTERHHDDTeIBQsWCEB88cUXQoiqf7biMWvWLNG3b19hs9mEy+USo0aNEuvXr49ocyTu16X88ssvYtCgQcLlcgmTySSaNm0q7r///ojrLET8z82kSZMEIB588EEhROXvaVW/A+K5X1f1+V3Ve7uqKEIcB8s5iUQikUhOIqZNm8Yll1zC/Pnz4zoISE4NpJCRSCQSyWmNx+OJ8NLRNI1hw4axfPlyDh48eExeWZITj7SRkUgkEslpzT333IPH46F37974fD6+/fZbFi5cyAsvvCBFzGmAXJGRSCQSyWnN559/zsSJE9m6dSter5cWLVpw5513cvfdd5/oqUmqASlkJBKJRCKRnLLIODISiUQikUhOWaSQkUgkEolEcspy2hv76rrO/v37SUhIqNZQ3xKJRCKRSGoOIQSFhYXUq1cvIst6RU57IbN///6jSkIlkUgkEonkxLNnzx4aNGgQt/60FzKlIdP37Nlz2FwoEolEIpFITg4KCgpo2LBhpalP4AwQMqXbSS6XSwoZiUQikUhOMQ5nFiKNfSUSiUQikZyySCEjkUgkEonklEUKGYlEIpFIJKcsp72NjEQikUgkJyOaphEIBE70NE4YJpMJg8FwzP1IISORSCQSyXFECMHBgwfJy8s70VM54SQlJVGnTp1jivMmhYxEIpFIJMeRUhFTq1Yt7Hb7GRmsVQhBcXExGRkZANStW/eo+5JCRiKRSCSS44SmaWERk5qaeqKnc0Kx2WwAZGRkUKtWraPeZpLGvhKJRCKRHCdKbWLsdvsJnsnJQel1OBZbISlkJBKJRCI5zpyJ20mxqI7rILeWJBKJ5DRACA10L6CAapNflJIzBrkiI5FIJKc4IlgARUsh9zvI+wE86xCa+0RPS1KDKIrCtGnTTvQ0TgqkkJFIJJJTGKEVQO408KwFvQi0fChaCPm/SjFzCnPw4EHuuecemjVrhsVioWHDhowaNYrffvut2seaM2cOiqKcsu7gcmtJIpFITlGECIL7TxDe6MpgBgSzweAItdUDIHyEtp7OTJffU4WdO3fSt29fkpKSeOWVV+jYsSOBQIAZM2YwduxYNm7ceKKnGBMhBJqmYTQeX2khV2QkEonkVEX3gW97/HrvRoSuIYK5UDgPcr6B3O+heK1crTmJueuuu1AUhaVLlzJ69GhatWpF+/btuf/++1m8eHFU+1grKqtXr0ZRFHbu3AnArl27GDVqFMnJyTgcDtq3b8/PP//Mzp07GTRoEADJyckoisINN9wAgK7rTJgwgaZNm2Kz2ejcuTNTpkyJGveXX36he/fuWCwW5s+fz5o1axg0aBAJCQm4XC66d+/O8uXLa+x6yRUZiUQiqSGE0EEvDv0JLbQ6othQVFP1DaKoIOJVGkNj53wLaCWT8oF7Mfh3IVznohikG/DJRE5ODtOnT+f555/H4XBE1SclJR1Vv2PHjsXv9zNv3jwcDgfr16/H6XTSsGFDvvnmG0aPHs2mTZtwuVzh+C4TJkzg008/5Z133qFly5bMmzePa6+9lvT0dAYMGBDu+5FHHuHVV1+lWbNmJCcn079/f7p27crkyZMxGAysXr0ak6ka7/kKSCEjkUgkNYAQGgQOQv6ski0dABUc3RG2tiiq9dgHUW1gbQPFK2PX29qCeylhEVOewIGQPY0UMicVW7duRQhBmzZtqrXf3bt3M3r0aDp27AhAs2bNwnUpKSkA1KpVKyyUfD4fL7zwArNmzaJ3797hY+bPn8+7774bIWSeeeYZhg4dGjHWQw89FD6Hli1bVuu5VEQKGYlEIqkJtCLI+wXQyxXq4F4GhiSwNo1oLoQA3R3aLgJQrSiG6F/k5VEUFWFrE9pe0vIiKy0tQHVUvvXk244w1gJFQVGO3NJA6B4QAUABxVq9K01nKELEXV47Ju69917uvPNOZs6cyZAhQxg9ejSdOnWK237r1q0UFxdHCBQAv99P165dI8p69OgR8fr+++/nlltu4ZNPPmHIkCFcfvnlNG/evPpOpgLSRkYikUhqAt82IkVMOYpXhERACUIPgH8P5E6F3G9K/r5D+A+EVnYqQ7VB4lBw9gZzAzA3AddgsHcCDCFBY24CSgyRoRiheDkUzEb4DyL0GEbDMRB6EOE/FBJq2V+E/grnIbTCKh0viU/Lli1RFOWIDHpVNfRVXl4EVYyUe8stt7B9+3auu+461q5dS48ePXjrrbfi9llUVATATz/9xOrVq8N/69evj7CTAaK2wJ566inWrVvHyJEjmT17Nu3atWPq1KlVPp8jRQoZiUQiqWaE0EMeQ/HQCkM2M+HX+ZA/HcqJG/QiyPsp1LYytKKQDYxnI6hOUM1QuCgkivQiQITKEgaAvUvksaY6ULw2tGqT9z0UrUBoVRAzWm6ofTCr9IxDwi33R4RWdPjjJXFJSUlh+PDhvP3227jd0QbZsVyk09PTAThw4EC4bPXq1VHtGjZsyB133MG3337LAw88wHvvvQeA2WwGQnmgSmnXrh0Wi4Xdu3fTokWLiL+GDRse9jxatWrFfffdx8yZM7n00kv58MMPD3vM0SKFjEQikVQziqKCsXb8BoYkSnf2hR4AdxwbF3TwbIi7KiOEHhIwaCFx4d0I3s0gPICA4tWhlRjvZiiYBcIP9s6hg23twb+fiFUj7zrQKxdOQveFgu/FsjDWCyGQUenxksPz9ttvo2kaPXv25JtvvmHLli1s2LCBN998M2yvUp5ScfHUU0+xZcsWfvrpJyZOnBjRZty4ccyYMYMdO3awcuVKfv/9d9q2bQtA48aNURSFH3/8kczMTIqKikhISODBBx/kvvvu4+OPP2bbtm2sXLmSt956i48//jju3D0eD3fffTdz5sxh165dLFiwgGXLloXHqgmkkJFIJJKawNoktHUTC0dPFEOJsa8IgJYTv59gBohg7DoRhOChSo7NAYOr7LVnPZjrg2tYaG6etdHHeDfH7690voED8et9Oys/XnJYmjVrxsqVKxk0aBAPPPAAHTp0YOjQofz2229Mnjw5qr3JZOJ///sfGzdupFOnTrz00ks899xzEW00TWPs2LG0bduW8847j1atWjFp0iQA6tevz9NPP80jjzxC7dq1ufvuuwF49tlnefzxx5kwYUL4uJ9++ommTZtGzaEUg8FAdnY2119/Pa1ateKKK65gxIgRPP3009V4hSJRRE1ZFp0kFBQUkJiYSH5+Pi6X6/AHSCQSSTUQ3l4q+A20glChYg7ZsliaoKiWUDvdH/JsCuyN3ZGlNbj6oSiGGGNoUDgfvJtiH2uqC8Z08PxZVmbrGNqO8u8AzECQiFUZa2sU1wDiITR3ybZVcewG9i4ozp6lE4S5c2HAAJAB+ADwer3s2LGDpk2bYrVWg+faKU5l16Oq39/Sa0kikUhqAEVRwZSOSLqwJJmjDqq1JKpu2WK4opoRjq6QF0fI2DvEFDGhMQwIW4f4QsbeEfwHI8uEHxzdwNE15CWlmEExhLaLAvvB2qLyE1PtITHkXhK73lrO1Xb6dDj/fPjlFzjvvMr7lUiOEilkJBKJpAZRDPbDx2oxpoCzLxQtJhzzRTGCsx8IfygppMERW9AYEiBhUChybzhejBIy7A1kgCkNfM4Sw1/A0iy0xeTdUNaH6gh5Pnl2gCG58vNRFIS1Jfj3Ra8iJZwT6quUUu+WKVOkkJHUGFLISCQSSRURuje0peI/GBIaptqg2lBU8zH1q6gWhLU1mBuFtqFEABQBxeshsA8wQOIQhLk+SgW7G0U1IxQLJJ4Lup/Qyo8dvFtCnkTG9JCoKZoPxlqh1ZfyIgZCKzN5v0DyRSgGe2hbDBF/JchgR7gGhjyq/HtCfZrqAWaY9C7kl2yllRcypXYVSUlw552gShNNSfUghYxEIpFUAaF5QlFyI7ZxFHD2QVhbVoOYMSIUJ/h2lETjLR+DRoP8mZByBRgTow/2bQ39KabQnIS/rC6YBcbekHwxKPaQfUvME/RBMLvEE2ot6B6EpQmYG6AYnNHttYLQnAwJIVdy9zIo8sETT0BuXsgmplSsFBXB44+HbGZSUuD66yEh4Wguk0QShZTEEolEUhUC+2LYoggoWnD4WC9VRS8OuUzHDKQnwLcr9nHmeiFvJMUSKWIADE4IZkLedMAfp+8Sgnmh8b2bwL87tF2V+11oa6v8TDR3yIhZeEN9l3pdOS3w2+OI3r1Cr0vjkpT+26cPrF4tRYykWpFCRiKRSA6D0D3gXh2/gWddyXbMMY8UEgfx0CsICqGFRIYIgJoQ2kJynRtpp2JtVxJbxhsKlJd0ASRfCs4+0f0bUyGQFVmmu8G9IhTvJlzmDZXHor4Tfp0C9gp2QXY7zJkDVQimJpEcCVLISCQSyeEQemTU3Yro7lCbY0UxhMREPEz1y6YkNPAfgJyvoGhRKBhe0fyQwXBC/9DqjLVtaLXG3gWMaaFVJa0AcqeF/k2+qKxv1RHKzq3nE/XV4NtWQWAd5lyXroCKUWndbli6tPLjJJKj4IQKmcmTJ9OpUydcLhcul4vevXvzyy+/RLRZtGgRgwcPxuFw4HK56N+/Px5PJQ8UiUQiqW4Uc8iwNx7mhijqsZscKqoNnGfHrlQdYEove60Xh2xUKooK3Q3Ff0LS+aCoIZuYwvlg71oSbViEjvH8Bb7dYO8WMghOGgGaB1xDwDUQEs8r50pdYQzVFromMTHAz3NC/734Yti6FS4qEUzff1+1CyGRHAEn1Ni3QYMGvPjii7Rs2RIhBB9//DEXXXQRq1aton379ixatIjzzjuP8ePH89Zbb2E0GlmzZk04QZZEIpEcDxTVhHD2gJxdRIXmV6xgaVx9gxnTIXE4FC4oc5k2NwRn30ij22AuoWB2MQjsCx3rWRd6LXxQMDvUr1YuB5TnL0i+BGytQSuGwjnlVl6UUBoDR68Sz6RywkW1h9zFC3+PHtvZC0Y1RnRsBVdeFLLR+eYrlC+nQONqvE4SSQknXWTflJQUXnnlFW6++WbOPvtshg4dyrPPPnvU/cnIvhJJ9ZPr8VLoCyAE2ExGajltJ3pKNY4QWsh+pOiPUOh/CG31JPRFMSZV/3iau8Rw1wCqJeTurXsBEdo28u2CwtnxO3ANCeVXKo+zXyiir39fWVny6JC3U87XlMWhKX9MHzA1QDElRc5P94eug3t5KM+TwQX27qEElXk/lzM6NoCzJ1hbhaMZn8nIyL6RVEdk35NmaUPTNL744gvcbje9e/cmIyODJUuWUKtWLfr06UPt2rUZMGAA8+fPr7Qfn89HQUFBxJ9EIqk6uhBkF3vJdHso8Pmj6nfmFvDyH6u57bu53P79XJ75fTnrDuXgCQRi9Hb6oCgGFHNtSBoZcoNOuRIShxyTiKnsd6RicKAYk1GMrpAxb9FyyJkSEhyF88GUAoaU2Aer9tAxFdHyKtjyGEN2Ob6dxBQxEFrVUU3R81PNKOY6oUB6yZeGVnsMTsj9roLnlBay4Qlkxj1XyanN22+/TZMmTbBarfTq1Yulx9kW6oQLmbVr1+J0OrFYLNxxxx1MnTqVdu3asX37dgCeeuopbr31VqZPn063bt0499xz2bJlS9z+JkyYQGJiYvivKunGJRJJiJxiL9+u287dP/zB9VN+48lZS1mXkYMnENrC2JtfxD9mLGLVgTLPlq05+Tw8cxH7C+Pk3jnNUFQbijEJxZh4VCsMQvMiAhmIgnlQMAvh2xlafYnbvgjyfgLPmtAWkQiAb0tIMCT0I+Zj3N4tFL23IsbksrxPENpSEnoo91I8tAJiZrouQVEtKAZHaOvJsyF+2+IVoczZEIonM2dO6F9JteHLLSRv424yl2wgf9MefLnVFBagEr788kvuv/9+nnzySVauXEnnzp0ZPnw4GRnHLwv6CRcyrVu3ZvXq1SxZsoQ777yTMWPGsH79enQ99Kvh9ttv58Ybb6Rr167861//onXr1nzwwQdx+xs/fjz5+fnhvz179hyvU5FITmnyvT7+vXgt/12xgVyvDwFsyMrjwV8Wsj4jB13XWbD7IAW+6F/6QV3wxZ9bKIyxgiMpQ2heKF4V8hrybgwFv8ufCXm/hARLLAIHQcuP0Vkg5FbtOjdkpwOh9AKJw0J2MMEKKyBqidgodZu2NAVrayhaApZG8SdtTA6t2hz25PTKs3hrBWVZvKdPh0GDYMaMw/crqRJFezKYc9WzTG13Iz/2vptv297AnKufo2hPzQqK1157jVtvvZUbb7yRdu3a8c4772C32yv9nq5uTriQMZvNtGjRgu7duzNhwgQ6d+7MG2+8Qd26dQFo165dRPu2bduye/fuuP1ZLJawF1Tpn0QiOTzZxV4W7jkUVS6ASUvXkVXsZfWBrOgDS1ifmUeR//TeXjpm9IJQ1NyKaDng2RQVi0YIDbxb4/fn3xXyZEoZDalXh2LEmOqGXK2VcqtFptqQOCIUaybxvNBWkKVFSFDhD9m3KNHbRwA4eoa8qQ6HYgilQIiHISVk5wORqQskx4wvt5AFt7zK/l9XRJTvn7mcBbdOrLGVGb/fz4oVKxgyZEi4TFVVhgwZwqJFi2pkzFicdCkKdF3H5/PRpEkT6tWrx6ZNkZE0N2/ezIgRI07Q7CSS05f1mXlx6/YVuPEFNVJs8bdSkqxmjNKjsHI8G+PXedeDrU0olksYJb7AgJI6NZSYshyhvE0NQ1tRGCBYAHk/lngkmQlF+C3B0gJUJyRdGDIOLl39UUwhV3BTJeKk/FQUBWFtFXL9rmhvo+vwyZ8ohatDr2UOpmrFcyg3SsSUsn/mcjyHcrEkV3805aysLDRNo3btyNAEtWvXZuPGSu71auaECpnx48czYsQIGjVqRGFhIZ9//jlz5sxhxowZKIrCQw89xJNPPknnzp3p0qULH3/8MRs3bmSKVPESSbWTYI7/OFAVMBpULmjThN+274vZ5pJ2TUl3nP7eS0eLECK2AW64QZCK9iWKoiJs7UMB6WJhbReK6VIBRVFDhreE3LWFYgjlPhIQIWJUJ1gao6gqqKmIpFElnlE6qNZQQsyqbCuVYnCGVoUKfitzHVcsYOgCTw+CnFyZg6kGCOTHt7ECCBRUXn+qc0KFTEZGBtdffz0HDhwgMTGRTp06MWPGDIYOHQrAuHHj8Hq93HfffeTk5NC5c2d+/fVXmjdvfiKnLZGclrROS8agKGgxDDDPblCHRIsFq8HAjd3a8NHKjRFfuec2q0+n2pVEpJWUrFi0iC9KzE0it4NKMSaVpBmoYLxrrA3W5iiKAhBKIaB7Qh5DiglUa9gYWTEkIJIuCsWN8W4NiQlLK7C3i4hNoxjsUGF158jO0QDm2ojki8pcxVUrqHaUVavh6qth0aLIHEyKEsrB9MUXUsQcJaZER+X1rsrrj5a0tDQMBgOHDkVuSR86dIg6derUyJixOOniyFQ3Mo6MRBJNnseHLgQJFhMmQ+gXtz+osXRvBi/MW4Fe7qlQy2HjpeFnUzfBET423+fnzwNZ+DSdLnXTSLKZSbPL1ZjDITQ35M8IZaQuj2KC5EviunILzQN6YWhrSgTB1goMKeEtJaEVh+K5eDcRXtUxN4GEPhFCRQitRGAQEjpHstoCIa8j3RcaQzGjGI7wPff7Qysv5dMXOByQmwumSrbQTiNqIo6ML7eQOVc/x/6Zy6Pq6g3rwcD/PVYjW0sAvXr1omfPnrz11ltAyDykUaNG3H333TzyyCOHPb464sicdDYyEomk+nD7AxT4/OgCnGYjuhAs3H2I7zfuxBfU6Ne4Dhe0bkKdBDtmo4Ee9dP5z8UDWbDrIAeLiuleL53WaUkRW0ZJNgtJNguNk+Sv5yNFMTgQicNC3kae9SFRYm4Mjq4hg9u4x9nAYItpryL0ALiXRWfm9u+EAh8icSiKGvqCUBRDBRucqiOCeVC4EAJ7QwWGZERCPzCloyhV/CpZujR+Dqa+fY9qXhKwJCfQ970HWHDrxAgxU29YD/q992CNiRiA+++/nzFjxtCjRw969uzJ66+/jtvt5sYbb6yxMSsihYxEcpqyJ7+IyUv/YtX+LATQMjWR67u04o+d+9mVF/JimLJuO79u3cvrI/tSN8GB1WSkgcnJlR1bVPt8iv1B8rw+PMEgdpORZKsFq+nMewQpBifC3iXk+gyhlY0YAeeqjO4JCaNYBA6E6tVj++UvtELI/QFEuTx3Wm7IgDj5EjClVa2jH34I/XvxxfDqq/DAA/Ddd6EcTFLIHBPOhrUY+L/H8BzKJVDgxuRyYKudXKMiBuDKK68kMzOTJ554goMHD9KlSxemT58eZQBck5x5TxGJ5AzgUFExD/yyICLmy5bsfJ75fQVPDu7Bhqw8fMGQnUK+z883f23ntrPaYTYe2VZDVclye3h32XoW7D6ALsCgKAxt0YDru7QmxX7mhWkPGeNWk92CKNnqiYdeDCQf2xi+vZEipmxwcC9HuAajqPGSSJbjwguhc+eQrYyiwNSp8L//yRxM1YQlOaHGhUss7r77bu6+++7jPm4pUshIJKcZQggW7ooduC6g60zfsodBTesxfUtZsMh5u/ZzVeeWOIQg1+tjV24hRlWhYWICyTYLlkoETpbbw74CN3vyi2iY6KS+y0Faua2oAq+f1xf9yfJ9ZQHaNCGYvmUPuhDc0bM99jPEPqJGiJuFurT+GFdjhA7++LG7CBwq8caqgpDp2zdy5UVR4Jprjml+EokUMhLJaYYvqLF0X/xonhsyc7m8faTnn1FV0TSdqVu28+mazWFjX5Oqck/vjvRrXBd7jG2gvflFPDJzMVnF3nBZmt3Ki8POpkFiyMg03+ePEDHlmbVtH1d1bCmFzLGgWsHcKLbYMCTHdM8+MpQSV+5449tCbSSSE4SMPCSRnGYYDSpplWzXJFnNURF4h7VoSJ7Px/+t3hzhsRTQdV5bsIYDhdFxKHI9Xp6bsyJCxABkFXt5ds4Kcj2h8jyPL+5cdCFwn+bJJmsaRbWEci6ZKri7GpIhcXhUsLwj7l9RwNY2fgN752MeQyI5FqSQkUhOM4yqyqg2TeLWD2vRkDk7yoLa1UtwcF7LRry/fEPcY77bsIOgHhk+P9/rZ2de7NDnu/IKyS72caiomBS7BbMh/qPGYjCQ6S7GLdMbHDWKwRnKQp1yGSSeH0pBkDQylDm7OlCdkHAOUSsvlpZgkYl5JScWubUkkZyG1Euwc1uPdry3fH2EGejwFg3pVDuV1QeySHfYGNysPl3qpmFUFA65YxlzhjhQWIw/qGE0hwSJJxBEAFd2aM7sHfvIdHtjHONm3M+rGNi0Ps+e25Pn566IstvpVi+NzVl5fLt+B42SnFzarhl1Euy4LFWwt5BEoKi20DaPMaUG+jYjLC3AVK/EJiYI5jqg2MOu3RLJiUIKGYnkNMRpMXNeq0b0aliLtQdz8Gsaneqkkmq3kmAxM75/NzQhsJXYvXgDQVqlJnGoKLaY6VArBYvRgC+osa/Azf/+3MKmrDzS7Fau7dyKrGIvn6wucwFWFQWzwUBQF8zatpeNmbn8vXcnnp1Tlg+mTVoSF7dtxovzVlIcCLI9t4C5O/bzSP9u9GlYC5NRPp5OJhTVBGoiGBNP9FQkkgjkk0IiOU2xm4zYTU7qu6INNSu6WVtNRq7u1JIFuw+iVwj2bTEaGNqiIQZVZe2hLB79dXHYjibD7WF9Zi6XtmvKsBYNmbk15Al1bvP6LNlbFrZ8b4Ebs8HAOxf2J6vYS5LVwl+HcnipRMSUIoC3Fq+lZWo/6rnk40kikRweaSMjkZxhCCHIcnvYnVfIgUJ32DalvsvBC0N7UcdZZrjZJCmBV4f3prbTRnaxl9cX/hlhDFzKtA07wp5Nl7ZrSsfaqUzfEulFs+pAJg1cDnrUr4Vf03hn2Trc5URMKUX+AHne+AbCEolEUh75k0ciOYMo8vlZvj+T/yxbT47HhwL0bFCLO3t2oE6CnS5105g4og9F/gAK4LKYMaoKBwqLAcEl7Zoween6qH51Ab5gkIfO6cKMLXuYun5HVIg2q9HI3sJimiQlVBa+TSKRSI4IuSIjkZwElK6SbM3OZ1NWLoeKigmUZgiuRtYeyuHFeavIKXGJFsCSvRmM/3UxWSXGvql2K42TEqibYKfA5+edZet5YPpC/vnrUgp8Ad6/ZCANY2TTNRsN7MwtZPGeQ1FCRVWgTXoyE/9YRb7XR5LVgtMcO3aM02wiyRqdBTqo6RwqKmb1gSwW7jrA3vwiimIE/ZNIJGcWckVGIjnBBDWdjVl5vDB3RVhgWIwGbunWloHN6pFQTR48OcVe/rsitov1gcJiduYVRkTk3V9YzP0/Lwhv/+QDn63ZwuI9h3h8UA9u+25uuK1JVWmc6KRFSiIr9mXyV0ZOuE5VFMb26sAvm3exJaeAIn+QWnYLd5/dgZfmrYoQPQpw99kdSLVHChm/pvHXoRye/X05nmCZwDu/ZSOu69qaZFu08JFIJDXPvHnzeOWVV1ixYgUHDhxg6tSpXHzxxcd1DlLISCQnmAy3h/EzFxMoF6fFF9R4e+lf1HPZ6V4/OuNxLIQQ+DUNo6piUKMXW32azr6C6MB2pazLyKFHyVgFPh9frt0a04ZlW04BO/MK6deoDvN3HwTg7707kWyzYjEauLlHW7LcHrZk5+M0m2iSnMBPm3axZG8o2rACmIxGutVN542R/Ziybls4vcFl7ZtTx2nHUsFjKcvt5YnflhKsYKDz85bdNE9N5PxWjUKB2ySSMxitqBA9Pw/dXYzqcKAmJmJw1mzuJbfbTefOnbnpppu49NJLa3SseEghI5GcYObu2BchYsrz8erNtEhNJDHGVkspmi7IcBczb+d+/jyYQ70EOyNbN6a20x52rwYwqgp2kzHCS6g85Y18C7wBlu6Nn+Zg3s4DXNq+GU6LiQvbNKVOgj2cj8llMfPYr0uo53LgCQTZW048daydQoIltKXksppxWc38vXcniv0B7GYTjjjbTQt3H4wSMaV8sXYLvRvWPiOTT0okpQSzMsl5+w18a1aFyyxdupFy170Y09JrbNwRI0YwYsSIGuu/KkgbGYnkBBLUdTZl58et35dfRECLLXJK2ZlXwNgf/uDDlZtYsT+THzbt4s7v57Fk7yH85bZhkq0WLm7bNGYfJlWlc5208GtFodJEkRaDSoMEB1d2bEF9lz0iD1OyzcJVnVqyJTs/QsQ4TEbG9uoYtVXmMJtId9rjihiAPflFceuy3F40Ic2HJWcuWlFhlIgB8K1eSc6kN9GKYkfgPl2QQkYiOYEYVZXWqfEDjNVPdFYa3j/P62Pi/DVRqywCmDh/TdjmBkI5mC5o3ZieFbaqrEYDzw7pGWGXkm63MqRZ/bjjDm/ZkIdmLOK2aXP5v9WbyS03jt1kZETLRrx+fl8GNqlHh9opjOnSmn+POodGSZUkH6yEznVS49Y1S3FVeo0kktMdPT8vSsSU4lu9Ej0/7/hO6Dgjt5YkkhPMgKb1+fzPrTG3l27o0hpXJdtKhb4A23MLYtYFdJ09+UXUSSjbMkqxW3mgX2dyPD625eTjsphpnJRAqs2KsZwYMBuNnNeyEYv3ZrCrQj6loc0b4DCZ2FWySvLNuu1YDAau7tQCkyG0iuO0mGiTnsz9fV0EdB2r0YhBPXoblva1U0iymsnz+qPqbunettKtN4nkdEd3F1deX1x5/amOFDISyQmmlsPGi8PP5oW5K8kuySRtNRq4uXtbWqYlVXqsFsdupJRYLtyJVguJVgtNkytPKFjX5eDJQT3YlJXHvJ37sRoNDG/ZCKfZxL0//hHRdur67Qxv2ZDazsgsyGajATPxt6iqSm2nnVfO681r89ewISsPCG2V3dGz/WGvkURyuqM6Ks8+rtpP7+zkUshIJCcYo0GlXXoyb5zflzyvH03oJFktpNgs4RWOeCRYTNR22GImfFSAJocRK4ejnstBPZeDPg1rU+Dzsy/fDQheOq8PVqOBpXsP8X+rN+MJaniD1R/3pjwNExN4+tye5Pv8BHUdp9lEqt2KKr2VJGc4amISli7d8K1eGVVn6dINNTHp+E/qOCKFjERyEqAoCmkOW0Qcl6qQardyb++OPP7b0qjUAVd2bEGStXpi0FhMRrzFXj7/ayt/HswGwGY0cFmH5rw49Gwe/20plkpEV67Hx76CImZuCeViGtqiIQ0SnUcc/6XU00kikZRhcCaQcte95Ex6M0LMlHot1aQLdlFREVu3bg2/3rFjB6tXryYlJYVGjRrV2LjlUYQ4vc39CwoKSExMJD8/H5fr2H6dSiQnI94SF+dPVm9ic1Y+6Q4r13RuRdv0pGqzHTlQ6Oafvy5lf2F0HJrberQl1W7l7IZ1Yno65Xh8/HvxnyzcfSii/OyGtbn37I7SbVpyRuH1etmxYwdNmzbFaq3eez8cR6a4GNVuR01MqvE4MnPmzGHQoEFR5WPGjOGjjz467PGVXY+qfn/LFRmJ5BTHajLSIjWRh8/phicYwKSqlRoIHw37C9wxRQzAl39t47URfeK6a2/MzI0SMQCL9xxiSPMG9Gtct1rnKpGcqRicCTUuXCoycOBATvR6iPRZlEhOE+xmI6l2W7WLGAhF841HvtePPxg71o3bH2Dq+u1xj526frvMlySRSI4JKWQkEslhqZsQ3+vBajRgMsZ+lGhCVGoE7Alo6KLygH8SiURSGVLISCSSw9IsxRU3W/Xwlg1JjWO06zSbOKeSraP+TeriMEvjXYlEcvRIISORSA5LXaed54f0IqWCYDm7YS0ubdcMmym2yFEVhQFN65Eaw6A3xWZhULP6xxQoTyKRSKSxr0QiOSyqqtIy1cWr5/Uhq9hDvjdAfZedRKuZVHvlLuO1nXYmnteHb9dvZ/b2fQghGNSsPpe1bx4VQE8ikUiOFClkJBJJlVBVNRwg70ipk2Dnlh7tuKJDcyCUIdtcSVJKiUQiqSpSyEgkkuOC2aAeccA/iUQiORzSRkYikUgkEskpixQyEolEIpFITlmkkJFIJBKJRHLKIoWMRCKRSCSSI2bChAmcddZZJCQkUKtWLS6++GI2bdp03OdxQoXM5MmT6dSpEy6XC5fLRe/evfnll1+i2gkhGDFiBIqiMG3atOM/UYlEIpFITnKEz40o2I/I2oYoOIDwxc6PVl3MnTuXsWPHsnjxYn799VcCgQDDhg3D7a7ZcStyQr2WGjRowIsvvkjLli0RQvDxxx9z0UUXsWrVKtq3bx9u9/rrr6MoMmiWRCKRSCSxEO5s9CXvw8G/ygrrdETtdROKI7VGxpw+fXrE648++ohatWqxYsUK+vfvXyNjxuKErsiMGjWK888/n5YtW9KqVSuef/55nE4nixcvDrdZvXo1EydO5IMPPjiBM5VIJBKJ5ORE+NzRIgbg4Fr0JR/U+MpMKfn5+QCkpKQcl/FKOWniyGiaxtdff43b7aZ3794AFBcXc8011/D2229Tp06dKvXj8/nw+Xzh1wUF8bP2SiQSiURyyuPLjxYxpRxcG6q3HHkgyyNB13XGjRtH37596dChQ42OVZETLmTWrl1L79698Xq9OJ1Opk6dSrt27QC477776NOnDxdddFGV+5swYQJPP/10TU1XIpFIJJKTC7/n2OqrgbFjx/LXX38xf/78Gh+rIidcyLRu3ZrVq1eTn5/PlClTGDNmDHPnzmXr1q3Mnj2bVatWHVF/48eP5/777w+/LigooGHDhtU9bYlEIpFITg7Mh4mYfbj6Y+Tuu+/mxx9/ZN68eTRo0KBGx4rFCRcyZrOZFi1aANC9e3eWLVvGG2+8gc1mY9u2bSQlJUW0Hz16NOeccw5z5syJ2Z/FYsFiscSsk0gkEonktMOSCHU6hraRKlKnY6i+BhBCcM899zB16lTmzJlD06ZNa2Scw3HChUxFdF3H5/Px9NNPc8stt0TUdezYkX/961+MGjXqBM1OIpFIJJKTC8XiQO11E/qSDyLFTJ2OqL1uRqkh+5ixY8fy+eef891335GQkMDBgwcBSExMxGY7fnnVTqiQGT9+PCNGjKBRo0YUFhby+eefM2fOHGbMmEGdOnViGvg2atTohKk+iUQikUhORhRHKmrfu0KGvX5PaDvJklhjIgZCseAABg4cGFH+4YcfcsMNN9TYuBU5oUImIyOD66+/ngMHDpCYmEinTp2YMWMGQ4cOPZHTkkgkEonklEOxOGrcO6k8QojjNlZlnFAh8/777x9R+5PlokkkEolEIjk5kLmWJBKJRCKRnLJIISORSCQSieSURQoZiUQikUgkpyxSyEgkEolEIjllkUJGIpFIJBLJKYsUMhKJRCKRSE5ZpJCRSCQSiURyyiKFjEQikUgkklMWKWQkEolEIpGcskghI5FIJBKJ5IiZPHkynTp1wuVy4XK56N27N7/88stxn4cUMhKJRCKRnAYI3YsI5iECGaF/dW+NjtegQQNefPFFVqxYwfLlyxk8eDAXXXQR69atq9FxK3JCcy1JJBKJRCI5doRWBAVzIbCvrNBUH+EagGJw1siYo0aNinj9/PPPM3nyZBYvXkz79u1rZMxYyBUZiUQikUhOYYTujRYxEHpdMLfGV2YANE3jiy++wO1207t37xofrzxyRUYikUgkklMZ3RstYkoJ7AvVq9YaGXrt2rX07t0br9eL0+lk6tSptGvXrkbGiodckZFIJBKJ5FRG+I+t/hho3bo1q1evZsmSJdx5552MGTOG9evX19h4sZArMhKJRCKRnMoo5mOrPwbMZjMtWrQAoHv37ixbtow33niDd999t8bGrIhckZFIJBKJ5FRGtYKpfuw6U/0a21aKha7r+Hy+4zYeyBUZiUQikUhOaRTVinANiOm1hGsASg0JmfHjxzNixAgaNWpEYWEhn3/+OXPmzGHGjBk1Ml48pJCRSCQSieQURzE4EYnnhgx7hT+0naRaa0zEAGRkZHD99ddz4MABEhMT6dSpEzNmzGDo0KE1NmYspJCRSCQSieQ0QFGtx3Ub6f333z9uY1WGtJGRSCQSiURyyiKFjEQikUgkklMWKWQkEolEIpGcskghI5FIJBKJ5JRFGvtKJBJJDIQIouNFiCAKJlTFiqIYTvS0JBJJBaSQkUgkkgroopji4J/49e2ADpiwGtphNbREVarmFaILD7pwExR5GBQHKi5UxY6iKDU6d4nkTEMKGYlEIimHLnwUBRYTFPvLlQbwamsAHZuhw2FXZjThpigwB03khssULCSYzsVAshQzEkk1Im1kJBKJpBy68FQQMWV4tfXoeA5zvJ/i4PIIEQMg8FEYmI0uiqttrhKJRAoZiUQiiUAXRZXUaojDZBIWwkdA3xu7Di867mOYnUQiqYgUMhKJRFKOqtjA6CL+qowgCIijOlYikRw5UshIJBJJOVTFjoo9Zp1RqYtf30lRYB6aHnuLSMGEgilu/wbFVS3zlEgqogsfmp5PUM9C0/PRxfHNQv3iiy+iKArjxo07ruNKY1+JRCIph6rYcZoGUxj4DVHOHsagJGE1tqEoMB8IEBC7UUXrKMNdVbFhNXTAo62K6tuo1EZVbDV9CpIzEE24cQcWExQHwmVGpS4O09kYFEeNj79s2TLeffddOnXqVONjVUSuyEgkkjMGITR0UYwuPAgRf/vHqCbhMp2Hw9gfm6ErTuM5WAwtcQcWAAEAvNomAvp+gnpOxC9fRTFgMTTHZuiOgrmkVMWsNsdh6huxdaWLAJruRtOLEUKriVOWnAHowhclYgCC4gDuwOIaX5kpKirib3/7G++99x7Jyck1OlYs5IqMRCI5I9D0InzaZvz6LlAMWA2tMKuNUJXY20iqYsWv7UTTc0o8lSKFRihgXiFFgd+xqC2xGjoBGgKBihWroTVmtREQAMWIghVVMZYcK9BFIcXaGgL6HsCARW2G1dgWg+Ks0esgOf0QwhslYkoJigMI4QXFUmPjjx07lpEjRzJkyBCee+65GhsnHlLISCSSU5LQ6oqHoDiEJgoxKukYlCQMavQyuqYXURCYjsBbcjAUB5fjU3aQYBoQU8woigGz2hCvKMSqNkVBIaBnEBQHATCpdQnqWQD49C2Y1Lp4guvQ8WBW62M1tMWgxraH0UURBYFfECWrO6Dj0zcR8O8lwTzsuGwFSE4fyu6jo6s/Fr744gtWrlzJsmXLamyMw3FCt5YmT55Mp06dcLlcuFwuevfuzS+//AJATk4O99xzD61bt8Zms9GoUSPuvfde8vPzT+SUJRLJSYAQOkE9i/zAD7iDi/Bqf1EU/J3CwEw0vbBCWw2vtrFMxJRDE9kE9ey44xiVNMxqUwL6Pvz6LoxqMk7jIFQSsRia4Nd3h9v69K0Y1RQExfj0LRQEZqDpBTHmruHVNsT8ctFxE9Qjf1nruhdNFKMfxu1bcuZSmXF5VeqPlj179vD3v/+dzz77DKu1ahGva4ITuiLToEEDXnzxRVq2bIkQgo8//piLLrqIVatWIYRg//79vPrqq7Rr145du3Zxxx13sH//fqZMmXIipy2RSE4wOsUUBudQcbtHx407uASnqT+qYi4p84W2k+Lg07dhUuujKJG/63RRjDu4kKDIDJdpWj4Ku0gwDcYdnE8ofUFpez8GNTH8WuDDq23ARjdU1RRRHi/ODIBP24lJbQIECeqZeLS16KIYo5qKzdAZVUlAVWrmi0lyaqIoVoxK3ZjbS0alLkoV02ocKStWrCAjI4Nu3bqFyzRNY968efz73//G5/NhMNR8frITKmRGjRoV8fr5559n8uTJLF68mJtvvplvvvkmXNe8eXOef/55rr32WoLBIEaj3BWTSM5UdL0Q4iyXB8XBEpuAkJBRAAW1ksguKkLoIQNgvICColjRtIIIEVOKoBifthlVcaKJvHC5Sa2DEKFUBIKQcaVf34vV0A4ifhErKIopbqgZRTGD0PHqG/Fqf4XLA/o+Avp+nMZBmA314p6N5MxDVSw4TGfH9VpSa8g+5txzz2Xt2rURZTfeeCNt2rTh4YcfPi4iBk4iGxlN0/j6669xu9307t07Zpv8/HxcLlelIsbn8+HzlVloFxREL+1KJJJTG53KvTBEuZUSBStmQ4uSXEnRWNRWBMQ+ioOLw9s9CjYcxrMwKnXCNjHl8et7sRnbhVdWFCyY1Hr4tC3Yjd0RBCgOrkBBJSSlylAVGxZDG4qDS2LOx2poXbKa81eMWkFxcAlGdXhcI2XJmYlBceA09UMIL4JAKJ6RYq0xEQOQkJBAhw4dIsocDgepqalR5TXJCRcya9eupXfv3ni9XpxOJ1OnTqVdu3ZR7bKysnj22We57bbbKu1vwoQJPP300zU1XYlEchJgUJLi1ilYI2wCFEXFYmiGX9+JLiJt7ExqI1TFSkFgVkS5wENRcD5O00CKAoeouHyioIAQgIJJbYjV0AJ3cBG6KMSv78Co1MFh7IUu3CgxguuZ1fr4Y4gki9oSg5JIUM+Ie346bnThl0JGEoWqWGrUO+lkRRGVBVM4Dvj9fnbv3k1+fj5Tpkzhv//9L3Pnzo0QMwUFBQwdOpSUlBS+//57TKb4+8OxVmQaNmwYXs2RSCSnPrrw4Q4uLnFdjsRh7INZbRoVqE7T3QRFBj5tG4piwKq2QVVcuINL4iaJtKit0HET0PdFlFsNHbGoLRH48Gmb8ek7gGBEG7uhBya1YUwvqtA5eNBEAT5tBwpGLIamqDhQVSt+bR9Fwd/jnn+iaVSEPY7k1MHr9bJjxw6aNm16Qg1kTxYqux4FBQUkJiYe9vv7hK/ImM1mWrRoAUD37t1ZtmwZb7zxBu+++y4AhYWFnHfeeSQkJDB16tRKRQyAxWLBYjnzFKlEciahKhYcxp54tWR82kYEflQlAbuhK0a1doSIEUIghC/kTq00waQ2QEFBUYwl3kB5ccfRRAFWQzsC+iFKhYqKE4uhBQomigOLCMSJ3+HTt2M2NK3kHGyoig2TWjuqLiRSVMobE4frlGSUM/BXt0QSjxMuZCqi63p4RaWgoIDhw4djsVj4/vvvpXqVSCRhVMWGzdABi9ocFB0FQ9R2iy7c+PU9+LRtgMCsNsdsaIRaEqdFwYiquNBF7LxJBiUBv76fBONAPNqfmA1NMan1MCgOdOEvSRAZm1BdtBCp0rlhw2HsU+IZVR4TDmPvKiW2lEjOFE6okBk/fjwjRoygUaNGFBYW8vnnnzNnzhxmzJhBQUEBw4YNo7i4mE8//ZSCgoKw4W56evpxs4aWSCQnL4qixg0ep+luioK/R3gWebQV+PQtJJjOxaA4UBUzNmNHCgPRBr2gYDI0oCgwBwjiNA5AVS3lazEbGhMMRns2AZjVRuVSFITQhQddeAGBghlVsaEo0c8yRTFgUuvjMl2AT9uCLgowqrUxq41RZeRfiSSCEypkMjIyuP766zlw4ACJiYl06tSJGTNmMHToUObMmcOSJSGr/tKtp1J27NhBkyZNTsCMJRLJqUJQHIgQMaXoogC/tgerIZTw0aAkYzf2oji4nNK4NAoW7Mbu+LTNgMCvb8dm7ACUEzJKyNBXZT06kSs6ChYsaouwSBFCoIlcioJ/oIvCkjYmbMZuJWkSoreKVMWEqiRhULoTWtkxRNn9nGiCOdnoebnoxcUYUtIwJCWi2mVUYsnx5YQKmffffz9u3cCBAytN6iaRSCTx0IWvZDspNn59Gxa1SYl7qhmL2gyDKQUhigAFgY5X24gmskp7BKFX9KTGoDhIMA3Dq23Er29HIDCrjbEZ2mNQy1ZOdOGmMPBrRDTfkIv2ElSjo9K4MKFAfSdffl//rp1kvfA0WmaJh5WiYB94LknX3YAhKTpxYDA3By0jg+CB/Rhr18ZQqzbG1LTjPGvJ6chJZyMjkUgkx87hVi4UKLe6oSgGEDpFwT9itlaxoyixH5cG1Yld6YqNtggRMkSu2DYg9sXNd+PRVmFUU04pu5dgViaZT45HLx+nSwiKf5+FIS2NxMuvRikX7yt46CCZzz1JcF9ZRGNDejrpTz6HqV6D4zl1yWnIySfzJRKJ5BhRFTMWQ6u49RZDy6jtHFVxYlBSYra3GbtWGrdFUQyoigOD6ogpeIJ6bDsaAE3kI4QWt/5Eo7mLCOzbi3v+PDzLlxA8dJBgVmakiClH0Y/foeXmlB1fWED2G69GiBgALTOTrAnPouXm1uj8Jac/ckVGIpGc0uiiGF34CBnQWlAVO4qiYFRrY1DSym0PhTAoyZiU6K0cg2LDaeyPR1tdkgxSR8GOzdAOFSdCBOOuysSelx8hAqAoGJUG+NkZs52qOKPyPJ0saPl5FHz1P4p++bGs0GQi5c57sfXph2dhRa8qEB4PIlC2+qTn5+PfuCFm/8F9e9HyczEkR29FVRdaYSFabja+9etRrRbMbdphSEpGlV6wpw1SyEgkklMSITSCIht3YAE6biAU1ddh7IVRrYNBseM09SeoZ+DTtgACi6EFRqUOBjX26kpQZAEGnMZ+gEAQwKttQRMrSDRdgEE5fBA6ITQ0kU9xcCVBcQgFExZDK5zGARQFF1AxcJ7N0AlVsR3bxaghvGtWR4oYgECAnLdeI+3RJ/EsWQRa5GqSmuBCMZd5awlvdNbx8uju2K7v1YGWl0fe/71P8ZzZZYWqSvId92Dv0w/VLqMjnw5IISORSE5JQga0sygfq0XgpSg4F5dpBKqSikGxYzA0waSGVmBKM2LH7s+DR/sz5NWkRxsK+/Qd2NUuh52XJvIoCEynNK2BwI9X+wuDkordeBbFwUUlLRWshg6Y1DpVPeUaR/f50AvyQ+kXVJWiWdNjNxQC76oVWDt2xrt6ZURVwugrMCSXbdGpTicYDFGCp5SaXI3xrloeKWIAdJ3cSW9gbtkKc+MmNTa25Phxcq5nSo4rgYBGVkYRB/blk5PlRtelt5jk5EYIHa+2hXgB5zzaX+iibHtDVcwxRYwu/Gh6AQH9AJoowmbogKrEDoUesmWp/LOhCx/FwRXESm2tiWxU7LhMI0gwDSXRdCFWQ7uTxsg3mJlB3n/f4cDYWzlwx01kvzqBhAsuxtq1e8z2WnYWphYtywpMJhIuuwrHgEEo5eJ8qcnJOIafH7MPW+9+KLaaWY3S8nIp+PbruPXuX6efdp6xPs1Hvj+fLE8W+f58fFrlyVWPlaeeegpFUSL+2rRpU6NjxkKuyJzh5OYU8/O365gzcwt+v4Yr0col13TmrN6NSHCdHA9YiaQigiCayI5br+k5hLZw4qc00YWH4uAq/Pr2cFko63UvPNrqqBg0JrXOYeO4CAIERUZJX2YUxYoQnrDHUkDfj8MUWxicSILZWWQ+9U+CB8pyTvk3byL75edJe/gx/Nu2RBn3Wjp3xd5vAI7+gxA+H6rTiZqcgmqOFIyqxYrrwktQVBX3rBmhrSaTCceAQVg7d8W/aSPGs/tU+zkJTUPLy4tbH8zMDK0SGU+Pr0F3wM2iQ4s5UFwW3LGuvQ69a5+Nw1RzsX3at2/PrFllSVeNJ+B6nh7voOSoKCzw8uHbi1izouzhVZDv5ePJSwj4NIaMbI3BIBftJCcfoXQELhCxs0SrSgIQP/p3aEVna4SIgVDWa3dwAQ5jb4qC88qNZ8ak1j/8xISCiqskeJ6CLtwlkXg1ioOrYwa+Oxnwb90SIWLC6DqFP36HY/BQCqd9Ey5WnQnYuvXA4HRicB4+0nBw/z60rExSxo4DNSQGPUsWkf2vVzCk18LSuk3EdlR1oNpsWNq0xbtiWcx6W/ceES7ipzI+zRclYgAOFB9k0aHFnFO3HxZDzdx7RqOROnVO7Pbo6fEuSo6IQEDDYFDJz/NGiJjyTPtyDT16NyI1XUbplJx8KIoBq6ENfn1rzHqbseNh7WF8WmxPGkEAHS8qDnTcGJRUHMbeKMKKJtygiJIcTdErlqpixWE6G3dgfkS0XxUnTlNvVE7O9AKepYvi1vnW/4XzvJHh1+bWbUm5614M6bWq3L9v0wY8ixfiWbwwqk47dBDh8x/ZhKuAaneQeM11eFetAD1yC1JNTIq7ZQagF7vRPR5AwZCUFLFVdjLi1bxRIqaUA8UH8WreGhMyW7ZsoV69elitVnr37s2ECRNo1KhRjYwVDylkzhCEEGRluFm5ZDfr/jxI7ToJ9B7QlLP7N2HxvJ1R7YvdAYqL/aQihYzk5ERVnDiM/XEHF1LmCaRiN3TDoCRVfrCiI4j/5akLH07TuSiIkkzTAq/2Fz5tE4IABiUFu7E7BiUFVSnbvhL4cQcXR6Us0CmiOLgGp3HAUZ1rTWNITo1bpya4MDVtRp03JoPBgJqQgCEhth1RPIy14/9iV+x2MNaMUDDWa0CtZ18k9923CezeBYqCpUs3km++HWMMISaCQQL79pL/2cd4V69EtdlxnHc+zmEjTuooxAEtdrDFqtYfLb169eKjjz6idevWHDhwgKeffppzzjmHv/76i4SEhBoZMxZSyJwh7NuTz/PjZ1DsLnt4//rTRq695Sw87gBrVuyLaK8oYDaf3L9CJGc2qmLEqKSTYBqELjyE4siYCOgZJfFb4v8CVTCgYEPgiVlvVFIwqqEva114KAr8EbZ9AdBEDoWBX0kwDkE1lH1J68KLLmIHitNEdol4OjEuv1pBAcHMQ3j+mIsIBrH37Y+xbj0MSUk4Bgyi8NuvYh6XMPJCjOm1jnpVQgSDmFu1IfWh8SgGI/4d2yn65YewzY1z5IUxUxpUB6rFgqVte9KffRG9qAgUBTXBhcER+wdaYN9eDv1jHJTEwdELCyj8+gu8K5eTNv5JjCnVu/1VXZgM8W3BqlJ/tIwYMSL8/06dOtGrVy8aN27MV199xc0331wjY8ZCCpkzgMICL/99c2GEiIGQh+X/PlzB3Q8PiBIyHbvWQ9ME+XkeEpNOzhgXkjMbIYJ4tbX49M2UOWCGthCCYj8JpnMj4rMIoSHQwiLGZuxEcXBJVL8q9ogVHV24I0RMeYq1ZSSoQ8LjxEtDUEbwMPU1g5afT/4Xn+Ke8XO4rOjnH7D26EnynfdgSE0j+fax5L77dsRx5g6dcAwagl5UhF5YgNA0VGcChpSUKiWw1AoLKJ77O/n/+wThCYlGc+u2pPz9QXInv4WpaTOc542ssq2K7vej5+US2L0L3efF3KQZalISBkf0lp3QNLScbAK7dhLMysTctBnGWrXjihjd7Sb/04/CIqY8gW1bCe7eedIKGavBSl17nZjbS3XtdbAajo/jRlJSEq1atWLr1thbvjWFFDJnAEWFPoQQtOtUhwP7CsjNLlv2DgZ18nM9JCRaKcwPBa6q28DF0Ava8Mw/fqF23QTufWSgtJWRnHToePCFbWQibSA0kYcuilEVW0mAuiJ82iY0kYtBScZiaIVJaYDN4MejraVUYBiUVJzGvhjUsvs9oMe2PQiNk48uQp8bVbGhUtkXhoLCiTH2DezeGSFiSvEuX4rvzzU4BgzC3n8glo6d8K5Yju52Y+3aDUOt2mgFBeS8MZHAzpBhdKnosXToiGqt/EeOZ8Uy8j74T0SZf9MGcie9SfpTz4dWR1xV26bSvV68q5aT/fqrEWLDef4oXJdfhSExKVwmdB3/9m1kPv0YotgdLjc1aUba+MdjbivpHk9UTJzyFC9agLVLtyrN9XhjMVjoXfvsuF5LNWUfU5GioiK2bdvGddddd1zGK0UKmdOcQEBD1wVdzmpAfq6HXv2aYLEa+d8Hy8nPCz2ALVYjt4/ry9ZNmdSp58Lv1/jP6wvwFAfYuS2H/3t3Cbff1w+7I77xpERyvBEiSLw4MhBaSREihYB+iKLg75TGdgmKTHz6FpzGgVjU1pgNjRHCDxhQFUuUEa9SaZwXFV0UUBxcisN0Dopixaw2i/KGArCorQ/TV82g+3wU/vR93PrCH6Zi7dodg8uFarNHJHEMHDpIxj8fQhSX/fjRsrPImvAMtV6ciKVl67j9BnOyKfjs/2LWadlZBA8dwFa/6gkjtZycKBEDoZUlc6vWOPoPipzjs49HiBiAwM7t5H38Piljx6FWjF+jgGK1IdxFMcdXq+CddSJxmBycU7cfXs1LQAtgMpiwGqw1KmIefPBBRo0aRePGjdm/fz9PPvkkBoOBq6++usbGjIUUMqcxAb/Guj8P8OaEObiSbJhMKvNmbSUlzcEt9/bh7Zfn4fMFadoiFZ83wPJFu8nJclPsjnxQrFmxj4J8rxQykpOKUN4jlXhiRlXs6KIYd3AB0QHqBO7gQlym8zEozkqTZZuU2oQaRAdPM6uN8Ov7CIpMvMF12I3dsBu7ogatePVNgAYYsRraYjW0ijAMPm5oGsLtjlstiovjRt31LF4YIWLKDhLkf/4JqQ+Oj7tVg9+Plp0FBgOWDp1QnU6Ce/cQ2LUzVL1lC+bWbdFzckri0CSgJiVF5EASuo6Wkx1KVHlgP2kPjieweycFX3+B8JdtlRdM+RJrp64YkpKAkLu3XhRbkHgWL0S79oYoIWNITMI5bASFU2MH0bP3OQehaSe1B5PFYDluqy8Ae/fu5eqrryY7O5v09HT69evH4sWLSU9PP25zAClkTmvycj3MnbmFex4ZSHZGET6/Rv2GiWzZkMns6ZvpN7g5BqOC0ajiVxRSUu3s250X1Y8Q4CkOcOhAIUajiivJisl08n6YJWcGCraS1Y/o/XhVcaEoDoQoRhA7uqnAh8ALh/HMUxUbDmM/3MH5lBczquLCbGhKUSAUb8anb8Uq2mJQndiMnbGIVoSEjAFVsaEoJ+Yzo9hs2Hr3xbdubcx6a49eKDFWG/RAAN9ff8btN7B9G8LrgXhCxmjCNngojt598a5agZabi73/IIz16pP/8fsY69Yl961/4Vm6ONTeYMAx/Hxcl16B8IfeMyF0ct9+E//6v8LdWjp2JuW+h8h+9cWwANNyshFamf1RpRm1dT2mHYxiNOI8/wK8q5YT2Lkjoi7hkstwz5uDXQtibtm6SvZBZwJffPHFiZ4CIIXMaU12ZhHtu9TjrZfmogXLfrX26teELj0a0LhZCnt35fLAbVOxO8ycc25z+g9pwbuvLyDgL/uFZjIbyM/18NSDP2OxGhl8XiuGX9iW5BSZcE1y4lAVI3ZjJ0TQT0DfHS43KMk4jf0xKDaC4nAJCQ8fol5RjJjVehhNo/Dru9GFG6OaCqi4AwsJiRVC/yp6yTGG0ErPCUb3hQSBrVcfCqd+jZYdGQ1ZsTtwnn8Bqil6pUgxGDA1b4EhNQ3VZsO/fRu+tWvC9Ya0NJQYx4WPdziwtm1H1gtPh34NAZ5F81ETk0j5+4OgUCZiADQN988/oFosBLOz8PwxF3PrtiT+bQzedWsp/OJTAHxr12CsVRvb2X3xLAiJSHPzliiWspUcU/MWuC6/GtVhJ7B7F8Xz/wiLIzXBFTctgjE1jdSHH8O37i98a1ej2uxYu3TD++dqin7+geL5c6n98r9OalfsMxEZtvU0xmQ28ul7SyNEDMCS+TtxF/nweQP8961QIKxit58Z329g1s+buOxvXSLaDxzakoVzQ79QfN4gv0xbz3/fXBg2DpZIThSqYsdhPJtE04UkmIbjMl1AgmkwBjUUw0LBghI3TYEJpYpZpxXFhEF1YVLroolCioNrcAcXRqz2qDg4WX4bBnNyKF68kOyXnyf75efxb9lE+rMv4RhxQSgztcGArW//0Jdyrdox+9CLCrG2aYeWlYlvwzrMzZqT9tjTGEvsWlyXX43BFT8buCjIJ/fdtzE2aIi1S7fwcXp+HoXTphDYvTPmcUW//IS9R08QAv/G9WQ+OR5r23aYW5XZ4xT/MQd7aVoDRSHxb2PCEYa1vDyCe3bjXbWcwh+/R/f5SH34n1jadwQg8ZrrKo0iLDzFFJR4WQUO7Cdr4osU/fxDaO65Oej5+XGPlZwYTo5PneSIOXSggGJ3AFVVsDtNpNeKDj60YvFu4uVEmzNzKy3bRO9jbvzrEEMvaIPFGro1Bg1vSZ16Ln6dvDGi3V+rD5CbU0xCoszHJDmxqIoZFHPMhASqYsNu7FWyLRSJw9jzMF5GsfsTeGPGn7EZu2NQTvwqpZaTTfbEl/BvWBcu865agbltO1LvexjXJZcBIePVeF5HWmEhBVO+pOjH78Jl/s2bcM+eRcq4B/Ft3oilXftK5xHYv5+0hx4lsHcPwYMHQmKmTl3yP/8/fH+uxjliZMzjhNcDarl3U9fJ+78PSPzbGLKeeTzUxu8HVcGQXovk28ZiKokkqxUWkv/FJ7hnlmXt9mRl4lm6mLQHx+MYOgJr166V2rmIoIaWnYUnOyt2fYxtqfBUA36E14titqBaTs50FKcjRy1kgsEgc+bMYdu2bVxzzTUkJCSwf/9+XC4XzpPcuvtkRwtq5OZ4cBf5MZkNJLgs4QSOBfketm/J5pP/LCMrI2TM1qhpMjfedTYNmySHbVeEEGQeKow7Rl6uh4L82LYDGQcKefa1kWi6zpRPVzP9u9ih3Hdtz6FR05MzroJEAqEtHpNaH5fpPDzaWjQ9H4PiwmrsiEFJOmK7FVWx4zQNwhNchV/fBQhU7NiM3TCpsVc2jjfetX9GiJhS/BvW41u3FseAQTGOikTLyY4QMaXohQUUzZxOyp13V74aIwSK2UTWKy+EhEkJisNJ6rgHyX1vclTagHAbc7QACGzbGuGmrSYmYWrUmFoTXsWYUhaVWMvNiRAxZR0EKPj6C9Iee+qwUYkNLheKxYLwxXg+mkxhg+Ly6AE/2sGDFP4wDf/2rRjr1sN18WiMdeuj2mtA3AoBRUXgdIail57hHJWQ2bVrF+eddx67d+/G5/MxdOhQEhISeOmll/D5fLzzzjvVPc/TGneRj9wcD0vn7yQ7y03bjnVo1DSZjyYvZvuWbJq1TOP2+/pSp56LzENFvP7CHIRettSye0cuLz0xiydfGUG9BqGHi6Io9OzbhPV/HqKoMPoD2bRFCrt35pKSZic/zxux/eRKslK7nouMg4WsXLo37rxdcjVGcgqgKiZUJQ2H0o9QvBjjMXkPGRQHdmMvbHQBoaEoJtSTYCUGQHMXUTT9p7j1Rb/8iLX7WYdN9OhZFh0osBTvssWIm26tfB45OeS8/kqEiAEQ7iLyPvgPriuuRi+OHVXZPuhcihdVWEGrYIuTeNXfMNatj6JGWkdUZpzs37oZ3e2uVIABqMkpJP5tTFT8G4DEK65BTY6MQiyEwL9xA5nPPB42Pg5s34ZnwR+k3PsAtr7nxLRBOiYKCmDLFmjZEhIrP58zgaOykfn73/9Ojx49yM3NxVbOaOqSSy7ht99+q7bJnU54iv3k5RZTXOyPKv9z5X7eeW0+iqpQr2ESa5bv440Jc7jp7t6kptvZviWLFx+bScahQr7/am2EiCnF6wmw+I8daJpGXk4xmzdkUFjg5Zqbe3Dn/f2oU6/sV4iiwOhrutCgYSLDR7Xlnn/057Jru2AyqRgMCq3ahoJFJbgsdOsZO86DxWqkfqOk6rtAEkkNExI0tmpxgVYVEwbFiUFNPGlEDBBa5dDiRw8WWhBEJbF3vB6Chw5iadue5LF/j7BLKetEHNZGWs/PizIsLiV4YD+mho2wdOiIsX7DsgpFwdanH5a27fEsWhBxjL3fAHwbNqA4nCTddBu23n2jRAxQqfExioKilB2jB/wEMw7h27wplP07KzMUudhkwj5gMKmPPI6pUWMwGDA2aEjqg+NxDD0PtcKKkZaTQ86bE2O6sOe+82/03Jz4czpaSr2yKvPOOoM4qhWZP/74g4ULF2I2R8YVadKkCfv27Ytz1JmDzxsgGNSx2Uz4/RoH9hUw7cs17N2VR606CVx8ZScaNEnG4TCTl+shGNTpO6gZC+dsx1McoG3HOtx459nMnr6JK67vxuSJ88nN8bBtUxbDRrVl84bMqHQDANs2ZVGQ7+Wlx2dxYF9ZvhdXopXb7+vLx+8sBeBvt/Rg3m9bmT+7LGhXu051uP3+fiAgKTkkTm12M9fc1IP9e/Ij+jOZDdz/+GCSpNeSRHJcELoe84u7IqozAfvAc/Fv3RKz3jFoCKozdjK/YGYGeR+/H8pQresYUlJJuOQyzK3bUvTDtHA7a7ceqCUu14FDB0PiyaBiqlWWc6rUQyg+Cqbadaj1zAS0gjx0dzEGpxNd1xGeYuq8Pgn30sUUfvoRxvoNcF12JcLvp85rb2FISQ3buOg+L6gqqin0XWRp3zH0Sy2GcaClUxeUkkSGWlERngXzyPvov+EtJDXBRer9/8Dcrj2GhATsPc/G0qoNIhhAMcbeUoLQdls80Sb8PrSszLgG1VVGCMjMLBNL5YVMqS2OwQDp6WfkVtNRCRld19FiqM+9e/ce14yXJxuFBV727Mpj+nfrKSrw0blHA7r1bMB/Xp/P/r0hIZCV4Wb9nwcZc0dPzhncHHeRn8XzdvDX6gPhfjIPbWXZwl2Mfag/rsQy9b9tcxbFbj+3jevDwf2FWK1Gdm7LYdG8Hfi8QVLTHRzYVxghOgAK8r28/+9FPPzMUIxGlbdf+YPtWyIN2db/eZAGjZO49JpOmC1lt0VaLScPPzuUg/sK2LIxk/RaTlq0SSc51Yau62RnetB1gc1mwumSxm0SSXUSzMzAt24tniWLMaSm4hgyHGN6rbCQqIiiKNh6nk3RT98TPLA/os5Ytx62nmfHjIESzMkm87knCe4pc2PXcrLJe/9dkm+7C1PTZgR2bEex2Ui6/iZ0rxf3H3Mo/Pbr0Bd1nbq4rrgGS8dOGFPTMCQlozidoUB8FUSFYrZgKNkOMSQnIxAEtm8n6/nJ4XxMqtNJ0h33UOuNyagmM6Y6kdmzg1mZeP9cTfEfc1BtdpwjL8TUoBGGpGQSr7uR/P/7IKK96kwg+ebbw8H7Aju3R+WV0gsLyHzuSeq8Pgm1xMMqnng5EkQ8j4sjQddh/34IVlht0zQoXTwwGiE1NSRozjCOSsgMGzaM119/nf/8J7SHqCgKRUVFPPnkk5x//vnVOsFThaJCH99/tZaZP5Z592zbnMXMHzZw+319efuVP/B6yqzdP/9gBR271cdTHIgQMaV4igPMnr6Zy67tEi5LS3cwe+U+2rSvxdqV+9m1PYc2HWpzz8MD+O7LPxl8XitefWZWzPnlZBWzf28+abUcHNgX231wzsytDL+wLTZbpCBJTrGTnGKnbceyh0lWppvvv1rLwjnbCAR0mrdK42+3nEWjJkmYzNIZTiI5VoIHD5Dx+COh6LglFP38A0m33olj4GBUW+wVUWNaOulPv0Dx3N9xz/4VAMfgodgHDMKYFjviavDA/ggRU56Cad+QePW1+LdvxzliJIrDSeE3X1L0/dSIuea8ORHX367Hee5w9OJiUu64B8VsJrBnNwXffBmOEOy68uoIOxPt4EFy3notYky9qIic116i9kv/wtSwUeRcMzPIeGI82qGynEKexQtxDDuPxGuuxzFkOJYOHSn86Xv07Gys3Xpg79MPQ0l+Ja2oiPwvPot5rmga7t9mkvi3MVWO4KsmuDCkpKLlRK/KKGZLzLxOR4zBAG3bwo4dISPfijid0KzZGSli4ChtZCZOnMiCBQto164dXq+Xa665Jryt9NJLL1X3HE8JcnOKI0RMKUWFPmb/spm+A5tGlAf8GjmZboJBjQFDW2C1Rn/5r162N7wXbbUaqV03gUP7C1kyfxfptZ0UFfpYvmg3b0yYw1U3dsfhNFOYH73lVEphQUhsnXNu85j1fl+QYCD+/nkpOdnFvPLkLOb+uoVASfttm7N47pHp7NsjYyxIJMeK7vWQ98lHESKmlLz/vhM3cq0QgmB2FnphIdZuPUh74lnSX3iVhItHxxUxAP5N0c+uUrSMQ5ibtyTp2jGY6tRF5OdRFCd3U+GUL9Hycjn04L1kvzqBrBeexrN0Man3/QNjoyYk3XYXhlp1COzYjlZYiJafS8G0KXEugh6KA1MuRYIeCFD4w7QIEVOKe+Z0gocOojocWFq0InXsONIefYKEiy7FWKt22UqU30fwQHwTCP+O7YiKKx+VYEhJIeWe+yDG1l/SrXdGGQcfNRYLtGoVPY6qhsrNJyaFzL59+7j22mtJTU3FZrPRsWNHli9fflzncFRCpkGDBqxZs4ZHH32U++67j65du/Liiy+yatUqatWqBvV5CrKqEu+e1Sv2RaxmlOJ2+/niwxUUFfq488Fz6FrBsNZgUMjJDsVque2+fnz3VSjEuMlkQNPKBEfArzH1izUoioLZHF+RJyXbWLF4N23ax96vTU61Y7EcfjVl1/YcDu4viCrXdcEXH6/EXXS4/XGJRFIZekEBniULY1cKgffP1dHH+P34/lxNxiP3c+iBezj0wD1kPvYPgjsP/8VsqGTVQLFYwGBAlKYDyM2Jm5tJ+HzoFZIu+jdtoPDnH0h78BEIBsmd/BYZjzxA4XffohW5Q/FlmjVHjZEFO7h/L3q5PFF6QT7u2bFXnQHcs2ZS+NP3+DZvRC8uRrXaorfSLFZMDRrF7oCSKMFH4GWkKArmNu2o/dpb2Aedi6lxE6y9+lDrpdew9+4Ttt+pFtzuaLd1XYcSsVfo87Mnv4iNmbnsyS+i0Bf/h211kJubS9++fTGZTPzyyy+sX7+eiRMnklxd4q2KHPUegNFo5Nprr63OuZzS6DE8icIIQe26CYy4qB3Tf9iA0AU2u4lgQOfg/kIO7g+5Od92bx+yDhWxZ1ceAGf3b4rZYuCaG7sz9fPV4fI+A5vi9QZJS3ewcN4ODu0vZN3qAwjg/EvaM+3LaBfEzj3qs3VTJroOqhrbGOyya7uQlHL4SKerlu6JW7fpr0P4vEEcTmkvI5EcNboeN84KEDORo3bwAJnPPhFxnJadTeazT1D7tX9jbtQ4bn+WVq1RzOaIRIyl2AcMpuD7qeAuIuGyq+IaC5eiGKNFgG/VCoLnjcSzfCmpDzxMzpuvUfjtV1jatMW7aiUpt99NcN8eFJOJ/C8+I7gv9MPQ2KAhmIzoxW5Ue4ldUCWiTAT8+Nb+Sf4H/8F53khcV/0tyt3a4HDguupvZJZLtxDGaMQxeEiVDKvLo1osmBs1Ifn2sQiPF8ViiUh+WW2URhVOSoIGDWDvXsjLg/x8MhUD/1q4hpX7y1bxutdLY1yfzqQ7qhbB+kh56aWXaNiwIR9++GG4rGnTppUcUTNU+d36/vvvq/x3JtL1rPjp6Nt3rsv837eTl+vhhjt6oagKV93QnVk/ly3nCl0w5bPVDBnZBgitjpwzuDmvPTubd19fEBYx3Xo1wGBQmfLJKlYt28t5o9py5ZhuGI0qqqpw7vmtuOy6LuFM1SazgQFDW9BvUHN+/nYdteo4qdcwkS5nNQgbtzsTLNxwZy+69KhfpWRopV5NsXA4zTKhmkRyjCh2O+YWLePWWzt3jXit+/0U/DA1tvjRdQp/mIoeQ6SUYkhNI+3xZ1AqfPla2rXH2rU7xbNmUDx/HoceuAfd7cbcNnZUX2P9BigOB8m33YWhQj4iEQjg+3M1ef99h6TrbgTAt24temEBhdO+wb95E7n/fYekG2/FWKcuqCoJIy/kwP13h7bZ8vJQnU5sZ/cGwNy2PfaBgzHWrV92XTp1xb859Fwtmv4TwQPR9ocA5kZNSLn3ARR7mdG0ISWV9CefA5OJYJyovodDLTFkrhERAyEB07QpNG8OVmvo36ZNKUxMihIxACv2Z/H6wjU1tjLz/fff06NHDy6//HJq1apF165dee+992pkrMqo8orMxRdfHPFaUZQoa+zSL7BYHk2nO6lpDs45tzl//LYtotxqMzF8VFve+dd83EV+GjdLZvxzQ/n1x41sWpcR0TY7001Kmp3rb+9Jx671KCjwcvl13VizYi9Go4HuvRqGV1Py87zk53nZuW0pA4e1ZMydZ5PgsmA2Gxk0rCVNmqbg9QYRApYv2s07r/2Bpgmuu60nteu6uGNcXwoKvAQCITfx5BQbqiG2rvX7g+TnenEX+TBbjPQZ2IzZ0zdTWBC9hTT0gja4kmSgPInkWDC4Ekm65U4y/vlQ1DaOrefZGCrYuwivl8C2yGdPeQLbtiK8XjCb0X1e9Lw8hM+HYrViSE5BMZmwtG5LndcnEdi1Ey03BzUxkeDBg+T865WyOWgaue++TepDj5DxyAOhPktQHE5S7r2f3ElvoBcUkHzXveS+82+0zAxQ1fB2TfDAfjAYUJ0JiGAQxWDAs2Qhaf/4J0XTfyL3nX+T+LfrUaxW/Ht2Q14e7hk/Y6xTl4QLLsL1tzEkXHwZ/h3b0DIOYe9zDoakJAp++g7h9aDn54XnVDTzZ8wtW0WtsKgOB/Z+/bG074BekA+KimKxUDRvDp55v2NISMB5/igs7TtWj7FudeF0hv5KURRITSUvvyhKxJSyYn8WeV4/CZbqt6HZvn07kydP5v777+fRRx9l2bJl3HvvvZjNZsaMGVPt48WjykJGL6f0Z82axcMPP8wLL7xA794hdbxo0SIee+wxXnjhheqf5SmA02Xhiuu60q1XQ36euo6iQh+t29XmrD6N+PLjlbiLQop45o8bGTW6A8sWxvYQSEm107FrPXZszebZf0yndr0EWrerja7rfPXJKvJzPYy8tD1tOtRm41+HAJgzcwvDLmiDucRbyJlgpWHTFBbN3c4v09aTn+elaYtUrr6xO42bhVIK2BxmbI7D39gF+V5++3kjP01dH86I3axlGg8+eS6vvzCH3OyyJe5W7WsxYEgLDHEEkUQiqTqmJk2p/crr5P/vU3zr1qImuEi4aDT2XmeH3ZdLUSwWjHXrEdi5PWZfxrr1UCxmgtnZ5H/xCcVzf4dgEMVqJeHCS3CeNxJDUjLGWrUx1qpN8fIl5Lz6YsytpuC+PaCo1H7lTbx/riKwayfmZs0xt2xN/pef4d8YSmmSO+lNEv92PTlvvoa93wAC5dzBA3t3Y6hVC0u79rh/+xV0PWzHU+rO7VmzOpzxGqBw6tfY+/RDyzhE1vNPRYgoY916pD36BJnPPRUxV1FcHDOmDIBiNIZESnot/Du2ceihv4f71A4dJOfN17D16UfSDbdUaih9MuD2x8//VJX6o0XXdXr06BH+3u/atSt//fUX77zzzskpZMozbtw43nnnHfr16xcuGz58OHa7ndtuu40NG2Ln5jndcSXZ6NazIV5PgIyDRezekcNrz85G08o+SLk5nrjxVurWd+Esyak0Z2YooNWh/YUc2h+ZM2nRvB2cf0n7sJAB2L0zNyLSblKyjeEXtuPs/k3RdYHZbAjna6oquqazaO52pn25NqJ8+5Ys3nppLo88O5Q1y/dRVOSjY9d61K6bQGJSzezFSiRnGqrZjLlJM1L//iC6pxhUA8Y4RpSqxULi1dei5WYT2L4tSoC4Lrkc4feTM+kNfKtWhMuF10vBV/9DBIO4rrgGtdTzJRCIKWJKUQwGTPXqg8OOpUMn9KIiPMuWECgXiE/LzgJVxTH0PKw9euLftIG0R58kZ9KbGFPTMDVqTPDgwVAaA6MRQ7lzC+7bS9G0byLG1AsKEF4vWS8+FyFiILTKk/fBe6SMe4jM8Q+Ey+0Dzz2sG3UwL5e8jz+I6hPAs3A+CRdeetILGYe5cuPkw9UfLXXr1qVdu3YRZW3btuWbb76Jc0TNcFRCZtu2bSTFCBSUmJjIzp07j3FKpwECpv4vhiEZ0LhpMsmp0fEfTGYDt97bh6TkUKC50hUchKBN9kY2prYJR2z0egJR3kmhYJYiwj5FVRWSDxN9V9d1dE1gNEV/2HNzPXz39doYR4UC++VkuRl+YdtK+5dIJFVDK8hHy87Ct/ZPsFiwduyMITkZ1W6vNPGgVhJZ1rNsMZaOnUm4+DIC27ZQMOVLFJOZpDvGYqxXn2B2VoSIKU/Rj9/hHHoeau2Qd6WpafOQW28Mmxtzi5aoDif+nTvIffdt/JtCP1zNLVuTfPtYCqZ8iX/LJgB0jwc1MZHsl58HTaO4dh1S7roXNTERxWwmZ/JbANjPGYgQocB1elEhamJSVJ4mQ2oqKAqGhASC7uhYKt7VK0m87kZqvfI6GeMfxFS/AeYWrQ573UVxMb5Yhr8leJYuwhIrVcNJRJLVTPd6aayIsb3UvV4aSdaacc3u27cvmzZtiijbvHkzjRvHNyyvCY5KyJx11lncf//9fPLJJ9SuHXLlPXToEA899BA9e/as1gmebASDGtmZxaxauofdO3Np2SadDl3qkZruCNuvtG5fC1eilYL8aIV/5ZhuNGySzDOvjeTXHzdy8EABrdrUov/QFqTVCu19qqpK7/5NWb5oN50y/uSBxRN59ewHWVu7EwAdutRj26ayG9ZgVHE4zeTleGKKpFgUFXo5tL+Q36Zvxl3kp1ffxrTpUJuUtDLjN79Pw10Y/1fZ3l15tOtUt0rjSSSS+Gh5ueS+/y6eBX+UFSoKSTffjmPA4LiRfLX8fAq+/JSi6T9HlNvPHUbt1/6NarOhJiejmsxoGzNi9gEg/H704jI3Z0NSMsm33hkV/Vax2Ui+6150n4eMRx+MWMXwb9lE9msvkfbwY2Q+/xRoGgaHg/yPPwjb2GiHDhLYsxtti4eir78AVcXefyDWLt0onPYNtj590QuLCMQIzue84GJyP3yPpBtvJWvCMzFOQiB8Xop++Yn051/BmJyCMTU1ul0s4qQ1CF2Mkz/AZ4LFzLg+nXl94ZoIMVPqtVQT9jEA9913H3369OGFF17giiuuYOnSpfznP/8JB8s9XhzVO/TBBx9wySWX0KhRIxo2DCX92rNnDy1btmTatGnVOb+TCl3T2bYpi1eemhUOBLfg9+3Y7CYefX4YjZqG7E9S0508+sIw3v/3IrZsyARCWz1/u+UsmrRIxWo10bhZCjfc2Qu/X8NiNUbZlTRrlUb9Ron0WLUMgLP2L2Nt7U5YrEYGDG3BGxPmhttecV1XfvtlM6P/1qVKQqao0MdP367j56nrw2Wrl+2lVh0nDz8zNCyoTGYVq9WI1xvb3bF8IkqJRHL0eJYvixQxAEKQ9993sLRrj9nRLOZxgR3bokQMQPFvM7H37ou5W49wmXqYrM+KpWzrWbVasZ8zAHPLVhT+9D1aRgaWjp1w9B+EmppGwf8+ibkVI3w+ihfOx3ZWLwK7dqJ7vVErK8V/zCX5zruxNG6KYjLhXbWCnDcnYmrcBMeAQaAqqAmJYXGhWCw4L7gIggF8q1Zg7dKN9KdeQPe40bKzKfrxO4IHD6AmuMBgpHje7yRcejnGtLSo+cVCTUjAdlYvPEsWxay39+pdpX5ONOkOG4/070ae14/bH8BhNpFkNdeYiIHQosbUqVMZP348zzzzDE2bNuX111/nb3/7W42NGYujEjItWrTgzz//5Ndff2XjxpCrW9u2bRkyZMgRud5OnjyZyZMnh7ej2rdvzxNPPMGIESMA8Hq9PPDAA3zxxRf4fD6GDx/OpEmTwqtAx5vcXA9vvjg3LGJK8RQHmPTqH4x/bhiJJa7JdesnMu7RQRQVeAkGdexOM8kp9ojrYzQZord0dB0mTyYlL4/HnH5MGaGl4LMOLMXctgVtmtfm0Lvrad2mP0lpDrqf3YhlC3exauleLr8u0iUzHtmZ7ggRU0rGwSKmf7eBK2/ohslkICnZxtAL2vDDlL+i2ia4LDL7tURSDWh5uRTGi24LFM2cTvKtd0Y9W3WPh4Lvvo17XOG0b7C0bhtezTGkpGCoVRst41BUW0vnrigVI8NabSh2BwkXXIxiMqEmJmFISEArLIgZkK8U/5ZNOIaPwHXFNQT37sHSsXPE1o1iNOBZuoTCb76MOM7cqg2BzAwKPv2IhItGk/78yyWZoxWK5/0eFhq+NasI7t6F+7eZGOvVJ3HMzRRO+wZ733Nwz/sdgOD+fZXGzSmPIcFF4t/G4Nu4IcLjCcB5wUWo1ZBv6XiRYKlZ4RKLCy64gAsuuOC4jlmRo14zUxSFYcOGMWzYsKMevEGDBrz44ou0bNkSIQQff/wxF110EatWraJ9+/bcd999/PTTT3z99dckJiZy9913c+mll7JgwYLDd14D5OV4KCqMHbX2wL4CCgt8YSEDofgszoTDB4ZzF/nx+4JYrEbsug+eeAJycrArCqLEbdCm+zl75gcoMwRWawK1nh1AXpGft18OCav6DRNJSKiaMe+iP3bErZv321bOv7QdKakOjEYDQ0a2ITvLzaK5O8Irr6npDu57bBCp6bGXuyUSSdURmhZyAY6Dlp0V2poxRj6udU8xrotHow87D8Vswbd+HUU/fR/OPK0XFiC0stVUY0oq6f98isxnHo9IfWBq0oyECy8he+LLpD74MMbUNII5OXhXLafgf5+GcggZDNh69yXxmusxJCVjSE4hQGx3bzUpGUOtOmQ++Sgo4Bx+Pva+/cl9998gBLbe/Sj+Y27EMYrZjO2sXqEtI13H++cqIOSpFNW/KzG8DRbcv4+cf71CrRdeRsvOJu+D0JaG6nBGHVcZpgYNqfX8yxQvnI9v9UrUBBfOESMx1m+AMaWK21OSE8ZRCZlnnomxP1mOJ554okr9jBo1KuL1888/z+TJk1m8eDENGjTg/fff5/PPP2fw4MEAfPjhh7Rt25bFixdz9tlnH83Uj4mAv/Iw38Hg4fMUlcdd5GP3jlymfrGGQ/sLqdcwkUuv6ULDhUuw3jQGFi1CKdlbVjQNoSgUdujOk/WuJXt+WaAni9XIrX/vW+X4Lf44W0UAwYAWzu8EoS2x627tyUVXdCI3uxib3URisu2wRsQSiaRqqHYH5vYd8cbZ2rCd1QulgogJZmWSM+lNfKtXhsus3XqQev8/yH7tZYTfh7X7WWXRcEswNWxE2mNPEdi9Gz03B0N6LbTcHHL+9TJ6URHF8+eRMOpifGtWkfv2G2UHahqe+fMI7t1L2iOPkXDxaLwrlsWcr2PAIHLffh29MJTGpODrL3AMHopj2AgC27ZhO6sXxXNnl82pUWNcV18bWqEpccFWnQlRW1Lh69GrNzmvvxJ+Lfw+3PP/ILBja+jYBBeGKm4rRVybevVJuHg0zqHngdmEIUZSTt3vL/Pskpw0HJWQmTp1asTrQCDAjh07MBqNNG/evMpCpjyapvH111/jdrvp3bs3K1asIBAIMGTIkHCbNm3a0KhRIxYtWhRXyPh8Pny+slWT/2fvrMOjuNo+fM+sS9wFgkNwdysuReruXqpUKaUtLZRSL9W3RpV6gUKhuLu7Ewgh7sm6zPfHkE2W3Q3S9msLc19XL5pzZs6c2Wx2nn3k95SXB/YEOl+iY02oVIJfOXUVBqMGc/jZv8FdLg8bVh/jy482+sZKS2zs3fkH9zzagy6Ll6CKi5V7a5xCMBrRrF3F2BIHS/84SO7Jcpo0j6d7n/rEnoN3pFOPNJbMPxh0rm3HVAxG/1I9o0mL0aRVcmIUFP4GRIOBiGtvxL55Y4D4nRgVHaDi66mooPi9t3GcFt6xb5Ub9ZmHj6BywXzMA4cEGEBel4vyH7/Dvm0rotmMt7zMr8zasmAehs5dKZvxVdC9uo4dxZ2Xh6ZefcKuvp6KH2f4zZuHjcCdn4/3tKaWluVLSHj1bcSrImXP0Asv4y7Mx1NYiCc/l9Lpn/qFvMJGXoZt0wb/iwsC4Vdei2PXDiSHv2fcdfQw6sQknPr9xDz5DB6bDenIYcSoqHPyqIhqNZym0eOx2/Dk52NdvgTXySy0DRpi7NEbVUIi4jn0ZFL4+zgvQ2bbtm0BY+Xl5dx6661cdtll57TWrl276NatG3a7HbPZzMyZM2nevDnbt29Hq9UGlHknJCSQmxvY+bSKKVOmMHHixHPaw9kSHqFn+OUt+O2nwJyRa2/tQFQt0v2nU1Zi47vPg5dCfv3xJlqURxNRw4gBwGJBv3MrqT16cMMdHXG7PGi06pC9k0KRnBJBs1YJ7N/lHyvX6dVceWNbDEblG4eCwv8n6qRk4l9+jZKPP8B15DCIIvpOXYi8+fYAZVlvWWmAEVOFfetmwkZfgemSAcEbQUoSktOJrkVLjL0vQVCrQALb+rVY165CcrmQPJ6gXbercB46gLZpU8yXDMDUuy/uwgI8BQVoGzbCsugPymuI2FVv2otkt/uMClVkJAhy/o918QK/Q81DLkXboBHaRo0xDxoq59dotGhSUrAsX4p1WWDTSHVCIobOXTEPG0HF7F99x6hi44h95nk0afXOq3WK1+XCuWcXha9M8hmZ9k0bqJj1C3HPT0LXrPkZVlD4/+AvqysLDw9n4sSJjBgxgptuuumsz2vatCnbt2+nrKyMn3/+mVtuuYUVK1ac+cQQjBs3jrFjx/p+Li8v91VW/Vn0Bg2DRqSTXCeSmd/voDCvkuQ6kVx1UzsaNY1Fpa5deKkmpcU2nM7grRysFifS3DnyD6NHw+uvw2OPwezZ8Ntv0KMHKpV43gq6EVEG7nu0J5vWZbJo7n6sVhdtOqQw4oqWxCeeW2xZQUHhzyNqtegaNyVuwot4rVYEUUQ0hwXVj/FWBmqo1ETQ6tCkBv/ME7VaIq69Eeva1ZT8730kqwVBq8PY5xJiHn0Sx5HDctKvRgOu4GqwqphYyn/+gYqZP4MgYOzRm/Brrgevl8r5c0Pv67T+Q6qISCJvupWwYSNk74sgYOjUGTEiCtRqRJ0OVb1wtPXkJoS27VuDGjEIAubhI3EeO0LhpOf9pjyFBRQ8/wwJr09DHXfuonaewgKK3n4jwFMmORwUT3uTuBcmoY5PPOd1Ff5a/tIC+bKyMsrKQietBUOr1dKoUSMAOnTowKZNm3jnnXe45pprcDqdlJaW+nll8vLySEwM/cbR6XTodH9f5+WwcD3deteneetE3G4vGo1IeMS5q9mKqtq/HdgHDIU+XeG66+QyxJkz4bvv4C8SGoqMNjJgWFM6d0/D65UwmrXodP9+vQQFhQsZVXhEQLfm0xHNtYeRQ2nOgJzjYV232i+JVnI6sCz6A09pKZG334VoNmPs1Rfr0kUB5ws6HZq0ehS/87pvzLpiKfbtW4mf8jqatHq4jh8LvK+4uKDVP6qwcFRhsrHitdtx52RT9uVnuHOy0TZthnnwMNTxCQhqNdqGjYm48VbKvvvaZ1gIOh3RD45FNBop/fjD4PdcUY7r2NHzM2RKipGCiO8BuHNz8FZUgGLI/OOc15Nr2rRpfj9LkkROTg5ff/21r3T6fPF6vTgcDjp06IBGo2HJkiVcccUVABw4cIDMzExff6d/kj8rxR8RacBk1lYr+NYgKsaIrl8fqKkJIwhw/fV/6pqnIwiCX5WVgoLCvxNJkvAUFyFZLCAIRN33EGUzvgooF9a374gYERlyHW9JMRVzZgWds29aDzffhspoIuKqa3FnZeI8WK3aKuj1xDz1LBXzAr0u3rJSbOvWEPP0BPLHPY63tDpHRjCZiX36uVpzVbxOJ9b1ayiZ9qZvzHlwP5V//E78i1PQNU33NXI09uiFK/skgkaDOj4BVVQ07oJ8ua9SCFzZWRjoEnI+FJKr9q7R0kXYIPnfyHkZMm+99Zbfz6IoEhcXxy233MK4cePOep1x48YxdOhQ6tatS0VFBTNmzGD58uUsWLCAiIgI7rjjDsaOHUt0dDTh4eE8+OCDdOvW7R+pWPqriYw2cN9jPXnzpWV4vdXJw2q1yH1jexIZrRgYCgoK4LVasO/YTsmnH53SVQFts3RiHnuKkg/flbtJA7q27Ym690FU5tDhYa+lMmTICORSb01KKuqERGIee1pumXDoAKroGLT1GlCxfCnWxX8EPde2YS2mQUNIePUtXBlHcR49gqZuGtqGjYLn69TcV2kJJafaFfjhclE87U3iJ01FFRWNqNcj6hNRJ/h7QQStDjEiAm+IiIC2XnBBwTOhjo2Xy97dgZWeotksi/Ap/OOclyGTkRFah+RcyM/P5+abbyYnJ4eIiAhat27NggULGDhwICAbTKIocsUVV/gJ4l0IqFQizVokMHnaCFYsPsSJjFLqN46hV7+GxMabzisxTUFB4cLDefQIRa+97D+2fx9Fb7xC/CS5bFo0mXyCdbVRU703GDX1V9Rx8YgmM6qkZFlhV6XGtTew0KHmuYJKhSo2DnVsHIZOZ+8BcedkhzSw3DnZeCoqUEVFhzxfFR1N+FXXUfrpR4FzsXGoE8+vlYoYEUH4FVdT/sOMgLmIm+9A9S9vJnmxcF6GzO23384777xD2Gl/NBaLhQcffJDPP//8rNb57LPPap3X6/W8//77vP/++7Ue919Fo1WTnBrBNTe1x+XyoNGoEM8zgVdBQeHCw1NeTtlXwT9PvWVlsopu3/5nvZ4YHoG2eUucQQwSVVw84qkO1JLHgzv7JKXffIF962YErRZT/0FE3XM/Bc+NwxtE1iJsxGhE/fl5kqUgzSn9DwjRB+kUgihi6NELT1kplbNn+kQBtY2ayBo1C+YRfvlVqMy1G3qnozKHYRowGE3depT/8iPuvFw0deoQcc0NaOrWU8qv/yWc11Pzyy+/xGYLFCuy2Wx89VVw/QGF0IgqEZ1eoxgxCgoKfkhOB86MoyHn7TtDd20OhiosjJgHH0WdnOI3LkZFETf+BV8eizsnm7wnH8G+aQN4PEg2G5VzZ1M0dTIxY58MWNfYfxCa+g2Q3G48xUVyPk+QcEwoNMkpAcrFvj3HxSGewdMkb9qNp7SU6EceJ+aJccSOew5D564UT3uDylm/hAw7nQl1TCzGbj2IfXoCCa++Tczj49C3aYfqlNF3MVOvnlzWfvp/Y8aM+X/dxzl5ZMrLy5EkCUmSqKioQF+jnM7j8TBv3jzi42uPhSooKCgonB2CSoUqOgZPQfDO1ZqU1HNeUwwLJ/bZiUgOB+6TJxHNZtTJyahPhUm8djvlP30fIDoHp8I85RUkvPke1uVLQSVg7N4bVVw8kt1G2axfsKxcjiAIGPv2k6uOzpAfAyBGRBJ58+2+FgPVEyLR9z98VqJ2ktWCddEfWBcFz+FxHcs4r9erirNtQvlPYql0UF5qx2p1YTRpCI/QYzL/fVW8mzZtwlMj4Xn37t0MHDiQq6666m+7ZjDOyZCJjIz0WVxNmjQJmBcE4W8To1NQUFC42FBFRRN+5TXBE2FP9T86W7wOB+7sk5R9/w3O/ftQRUURdtlVaOqmyQJ1VcdZKrFv2xxyHdualRgfH0fkrXf4xtwF+eSPf9JPSK/i15+wrl5J/EuvnNGYEfV6jH37oanfgPJffsSTl4u2UWPCLr/67PNbVLU/zgTDBVBAIUlQWQlms1zJWoPiQgufvbeO3dur29e0apfE7WO6ER379/TFizutpP2VV16hYcOG9OnT52+5XijOyZBZtmwZkiTRr18/fvnlF6Kjq5OvtFotaWlpJCcn/+WbVFBQULhYMXTqgvPwISw1PA2CTkfME8+gOgdtFNeRw+Q/9zScykfxVpRT/M7rGPsPIvLm26sThUURwWCUH5hBEMPCQawOg0teL9ZVK4KqAXvy87BtXI952IgzFjCozGGoWrRCW78BktOJYDAgniE5+fR9aZs1x7l/b8BcbSKB/ynKy+HQIWjc2K+VgqXSEWDEAOzalsPn76/jvsd6/a2eGQCn08k333zD2LFj/9+LVc7JkKmysjIyMqhbt65SWaOgoKDwN6OKjCLi5tsIG3kZruPHEI1G1MkpqKJjAnophcJTWkrx/97zGTE1sS5ZSPjIy3yGjCoiEvOwEZR9GbwYwzRoqN9nv7eyAuvqlSGvbV2xDGOffrWWhddENJrAeO4eBFVYGNFjHib/2af89XVEkZgnxtVa9fSfoaqHVUmJnyFTXmoPMGKq2LUth/JS+99uyMyaNYvS0lJuvfXWv/U6wThrQ2bnzp20bNkSURQpKytj165dIY9t3br1X7I5BYULDkmCFSugT58A17CCQihUJjMqk/m8czy8lkrcJzJDzjv27UVTpy4gVwCZevXBtnEdzn3+3o2wK64O0HBBFBG0Gr+faxpMglaLcI794KqQ3G4kjwfxLNXaNSmpJLz6Fo49u7Hv3I4mJRVDtx6oYuMQ/osVRpIEBQXVLRJqGjJVr4lKhdVa++trtYbWDvqr+Oyzzxg6dOg/EpU5a0Ombdu25ObmEh8fT9u2bREEASlISZwgCH7JPwoKCjX44w8YNgzmz4chQ/7p3ShcLKhq7wMn6P0NBVV0DLGPj8OVdQLr2lWIeiPG3n1RxcUHeFZU5jDMw0fhbHoQfeu2eK1WRL0eT1Eh5b/8iPnSUbKX5RzwVFTgzj5Jxbw5SBXlGLr1RN+23VklDqvj4lH37Yepb79zuua/Eq8XsrMDBfk8Hjh5Uv5/tRpjbP1alzEa/14j7vjx4yxevJhff/31b71OKM7akMnIyPAl9vxVgngKChcdP/9c/a9iyPztOD1OLG4LR8szsLntpIXVJUYXjVET2IzxQkY0h6Fr1UbuJB0wKaJt0ixgWBUVjSoqGn2rNmdcX9e0GbaN6yl8eaJP80WdnEL0I0+gST03L5KnsoKK2b9Q8Wt1Pyj79q2oYmKJnzQ10CN0IaNSQXo6ZGQEz1kym6FBA8KdEq3aJbFrW2B4qVW7JMIjzz7X6HyYPn068fHxDB8+/G+9TijO2pBJq9GsMO0valyooHDB4/XChx9Caan8c01Dpv6pb1GRkXDffX4JlAp/HqfHyeGyI2wp3Ooby6jIIFwbzoCUfpg0f08lx78RldlM1N33kT/+yQAxu6h7H0AVef6aKF6Xk8r5c7Gt8c+TcWefpPjtV4l/5c0QZwbHU1ToZ8TUHC/76Xui7r4PUfv35nv8q9DpoEkT2L7dP8dJFOVxUcSkhdvHdOPz99f5GTNVVUt/Z36M1+tl+vTp3HLLLajPMmfrr+a8rvrbb78FHRcEAb1eT6NGjahfv3ZXl4LCRYHFAs89B8XFck5MlbFSWQkTJsjfXqOj4eab4WxEvxTOGqvb5mfEVFHuLGdP8V46xLVHJdYecrmQ0KTUIeHVt7Ft2YR962ZUcfGYBw9FHZeAqD//b+zekhIq5/8edM5TVIQ7Jxt1zNlrsFjXrgk9t3IZEdfeiBh7ERkyIH+OnJ6o7fWC1Sp7ZYDoWBP3PdarWkfGqCE88u/VkQFYvHgxmZmZ3H777X/rdWrjvAyZ0aNHB82RqRoTBIGePXsya9YsohT1Q4WLmbAw2LYNrrsO1q2rTtrzeGTDpnt3+P57xYj5G8isDJ3cerj8CC2im2MSLx6vDIA6PoGwoZdiGjAYPHaEilykk+vxmuMQwpPBGB20GlVyu0AUEMTAR4bkdhN++WVoUpLwFBVTsWCRr5ElgDsnB1qeQwGIs5aO0x4PSGdoZ3AhUqVKHBkJqamQlSV7eUtLfYYMgMms+9sNl9MZNGhQ0HzZ/0/Oy5e9aNEiOnXqxKJFiygrK6OsrIxFixbRpUsX5s6dy8qVKykqKuLxxx//q/eroPDfo25dWLYMjKflZRiNsHw51LkA9C3+hTg9oR+IHil4QYLVbSPHmsPm/C3sLtpDmaMMl/fvr/j4/0ZwlCItnox30UtIm75AWvYa3j8mQGmm30NJshbjzViLd/U7eNf+D2/+ASR7RfW804pKKMMYmYE2+ycM6p3E3XcNkbfe7DumthwZyetBshQhFR9HKs1Cspdj6Nk75PH69h39GlteNERGyqHohg1Br5f/rV9fHlc4P4/Mww8/zMcff0z37t19Y/3790ev13P33XezZ88e3n777X/U1aSg8K9i40bZPVwTi0Ue73H26qwKZ0+qOZV9pfuDziUaEtCI/pUcFpeVZSeXU+Is8Y1tK9pO14Qu1AtLCzj+v4rktODd9AWUZ/tPOCrxLnsdcchEMEYjWYrwLp0KFbnV52ZugMb9ofXloDEhZe9EWvsBPh9OaSZC6VcYGg7B1b8/jj17UYVIzpUcFqSsLUhbZ4DLKg9GpKDteg+mocOxnBauEnQ6Im66DfH0LwQXA2azn+cFQYCYM7dtuFg4L4/MkSNHCA8PDxgPDw/n6FG5wVnjxo0pLAxUelRQuCiZM0f+d/RoOHwYRo2Sfw6Rb3Yx4va6qXBWkmPNJdeaR6WrEo/3/KUcIrThxOkDczNERDrEtUer0vrGPF4P+0r2+RkxVazP24DVHdgk9z+LvQJyQuiA2cvAUoTkcSMdXORnxFQhHVoClflgK0XaErxJsHB0EWFDBhL73Esh+yRJRUeQNnxabcQAlJ3Eu/QVoq6+nKj7H0Zdpy6q6BiM/QeR8Ma7f6pXksKFy3l5ZDp06MATTzzBV1995SvJLigo4Mknn6RTp04AHDp0iDqKy1xBQWbkSGjTRs6VEQSYORO++w6UCkBADgNlVBxjc8EWvKdyIFSCiq4JnaljrnNe3hCD2kDvpF4cKT/C/tKDOL1OEg2JtI9rS7jG/4uY3WPnUNnhkGtlVWYREd38nPfwr6RmyM0YA/pwsBSCQw4ZSY4KBEcF0pHQar3SkVUI6cPAEbyNAZIHlVFADNEnSbKXI23/Mfi5LitSwT7MAwZh6NRZFsQzmc9aFE/h4uO8DJnPPvuMUaNGkZqa6jNWTpw4QYMGDZg9ezYAlZWVPPvss3/dThUU/sv06OEfQhIEuP76f24//zJKnWVszN/kN+aRPKzJXcewupHE6EPLy7u9HuweO5LkRS2qMairmwMaNUZaRLegYURDJElCI2r8PDFVSEi4JXfAeBW2f8gjI9nLwV6O5LIh6MJAH4ag/ZMJylojxDZGTB+GZCsGSzFEpIAgIG37HsGcAEhQW26Q23FmuQBRhVScAbow0EcgqGoYo143lOdAbCMQNVCaCc4aodf8g9DoElQRkX/mThUuEs7LkGnatCl79+5l4cKFHDx40Dc2cOBAxFNv7tGjR/9lm1RQULhwcXlc7C7aHXJ+X8k+uiZ0QR2kYsbisrK7eDdHyo/ikTyEa8LpGN+BOH0c2lMPTlEQMaprz6vQiBpi9bEU2oOHw1PNKX4/2912JCS0ovZvK+GWKvPxrnoXSo7LPwPU6YTY4UYEY3U1qOR1gyT5Gwq1rasLR2x9Od6V74DbXj1hjkfs+SAYIkClgZT2cHxd8EXqdZWN8cg6UHoicF5jBHs53iVTQK1DaH0l1O+BoDP77kXs8yhS7h55D436gtOCtO172WMUdeF78//pSp9/C3/F63De6jWiKDJkyBCGKOqkCgoKfwK35KHCHSJEAVS4KvBIHtSnfVzZ3DZWZK+gyFHsGyt3lbP05DL6pfQlxZRy+lIh0al0dIrrwB8nFiLh/8EarYsmQhvhu2aONZd9JftxeV2kmJJpFtkUs8b8lzbRlWyleJe/GZiQe2ITktYIHW4Etx2p9CTSocXgcSE06IUQ29jPyAmG4KjAu+Z9fyMGoDIf7+6ZiD0fQFDrEFtdhjd7h38OC8heFGsx3r2/I/Z8EO+C58FVw2MlqBA63oR0YIH8s9uBtPVbMMYg1O2I5LRC5ia8W7+tPufQEtlL1O0evGs/REjt4P96uOwgCAjq/354SXOq55PVasVgMJzh6Asfq1V+f2n+RC+s8zZklixZwpIlS8jPz8d7mlDP559/ft4bUlBQuLjQiGqiddGUO8uDzsfoYlALgR9VFa5KPyOmJpvytxBdJ9ovzARgc9txeOx4JA86lR6DSu/zqETqIhlSZxCbC7ZQYC9ELahpEtGY9KhmGNQGbG4b6/M2kmXJ8q23v/QAR8qPMrTuECK0gQUQ5421JNCIOYWUsRohfRjeXTPh+Prq8ewdSJF1EfuOBbVeNlREFYI+wv98S+Gp3BYBTKcScS1FgAS5e+RkYK0JwuIRh0xE2jcP6eQ2UOsR6nWD8GSk9Z+Ax4l0dBXC8Jfh+AakgoMI4ckI8c3wHlwIhf45R9LOn5DiG8uhsppGTBWFh5CKmsCg5+TcHeTybyl/P9KRVfK9NBmAEF0fwRAReP5/BJVKRWRkJPn5+QAYjca/1Aj+ryBJElarlfz8fCIjI1GdoR9YbZyXITNx4kRefPFFOnbsSFJS0kX5S1BQUPhrUItqWkY153jF8QBviIhI06gmQcM3eda8kGtWuCpwe6tzXiRJosxZxqqc1ZQ6ZXExtaCmbWwb6ofVR6/WoRbVxBpi6ZvcF7fkRkBAr9L5rl3hqvQzYqpweV1sL9xO98Ruf1mJtmQtCj3p9cj5JDWMGB+lmUgZa5HsZXB0FRgiEVqMQEhqhaA/ZWg5KmTvTd3OSOWynL0QnoSUuRHp6CrwyLkxgiBCWAJScluEiFTZcDmxGXbNrN7nocWIjfsjpA9DajoYqTwbvB7ElpchtRiFtP8POHEq96k8BzxupIzQyr1SxmrEJgMRVGpZw2b5m3L+TNV8zk6k5LaIXW5HMESe3Yv5LyQxUS5JrzJmLmYiIyN9r8f5cl6GzEcffcQXX3zBTTfd9KcurqCgoAAQpg2jX8olrMtbj9Utu5rNahPdE7tjVgcXQKst70UURL8vWBa3lYUnFuPwOnxjbsnN5oItGNUG0sKqq8f0ah0QGMLIrAitFHyiMgunx/mXGTKCKZaQmQOiGtyhxf6kI8sRGvdDclnlCqB1/zul/XIlgs4EEalQeEQ2Ek5dRUJASB+G0HK0nAxck4IDSPvmBb+YywZV4oLluUibv0HK3yf/bIpFaH89xDWRPTDmeBBVYCsLfeNO+XcvSV6k4xv8jBgf2duRio8jpESGXudfjiAIJCUlER8fj8t14Qkuni0ajeZPeWKqOC9Dxul0+onhKSgoKPwZ1KKaJGMiw+sOxeaxAQI6UVtrl+p4YzwiIl4CJesbhjdAr6ruH1RgK/AzYmqyrXA7cYZ4jOra8xVEIXSVTiivtMPjxO62UWgvQiWKxOhi0Kv0aM6UmGuICplIKzTsg1R8LPS5HpdsMNRAOrQEoclA0JnAVoK07/TeSBLSvt8R+jwGp3s6EltAKEMmsg6odUgVeXiXviLr0FRhKURaNQ2x31NIUfUQmg1GMEQi1e0sJyYnNpd7jeXuQTq+Xk7yTWgOGj3YK5COLA95i9KhJUgJ6Qhq/wo0yeMCa7EcCqvMh8SWCNH1EIyhq97+SVQq1V/yIL/YOS9D5s4772TGjBlMmDDhr96PgoLCRYbT48TmtpFtzcEreUk2JWFQGU55RkJjVBnom9yb5dkr/YyZaF00raJb+VU5hapGAjlkVKVdU+mqpNJVyfGKTARBoF5YPUxqEyaNkbSwNPaU7A26Rv2w+uhU/vu1u+3sLNrFgbKDvjEBgS7xnUkLS/NVVQVDMEQg9noI77qPofDQqUEBoV53hOaXIhUdCX1uSluk/AMB41LxMTDFImWHEMNDDhUJ8U39DCEhIhVajACNARyVSMfWgU0WDhQ63ICgD8ebtdXfiKmBd+evCJ1vRTDJmmNCWAJepwXW/U9+RVI7IPZ+GO+WbxDbXoWgNSLZSuUQWii8bjjNZyV5XEi5u5FWvlPdj+ngYiRTLGL/pxHM8aHXU/hPc16GjN1u5+OPP2bx4sW0bt06INv4zTfPrW27goLCxYnD7WBf6X52FVeXX28t3EbD8Aa0i20bkKxbE5WoItGYyMh6I8i35WF120gwxBOmDQs4L1IXGXIdo9qAiEClq5KN+Zs5aTnpmztQepAGYfVpG9MGk9pEk4jGHCw75He+QW2gVUzLgPLwPFu+nxEDsl7N+vwNxBhiiFaFri6SPG6k/ANybkuLS2Uvi0ojtwQ4shIa9oHIuoGhF10YQp2OSMuDfAYLAtLeuQgRyQhDJ+Hd/hPk7PA/xloiX0sje7MktxOclUgVeVCUAaZohLZXyQnBkSkIUfVP3ey+kPdC8VEErQlBHyaXlC+e5CekJ2VuQMrfh9j/GTDHVd9HWlekPcGVr4WGfQIrmGylSKveC2wqaSnEu+VbxG73ImiVKqELkfMyZHbu3Enbtm0B2L3bX/9BSfxVUFA4W8pc5X5GTBVHyo+SakqhbljdgDm7247d48AredCqtBjVBhpGNKz1OomGRNSCOqjoXfOo5qgEFdmWHD8jpoqjFRmkhaWRak6hTUxr0sLS2F+yH4fXSZq5LqnmVMwaf5E6u8fB7iD3VcXBkoN0TugUOlxlL0Xa8o1cYn36nKBCbNADoe9YpGPrkA4vk8uv63ZGSO2Ad8On1XkrVYhqBI0e7+5ZAEhaM+Ilj+N1Was9PiB7YzTVITmp8DDSslerjQNLgWxgtb5CLvXWnDIMTIGtIHwYokAQ5QaRxzcGVwO2lyNlbUFIHy7vQ1RBw75y8rHttLYRUWkIcY2r9+hxyeuXZoUW8Tu5HRzloBgyFyTnZcgsW7bsr96HgoLCRYbb62ZfSehv8ntK9pJgTPAL2ZQ7y1mds8ZXdq0W1LSOaUXDCDknRpIkLG4LedZ8ihzFxOqiiTfK+S/9Ui5hZc4q7B5ZP0VAoFFEIwRBoMRZQkbFsZB7OVB2kGhdFHaPHbWgkns1iVq0Km3QL29eyVOrGnClWw5nhTRkHJWBOi9VSB4kawlifFNIH4pQv4eca6Izy3khp+u+ICB0uAnvoaXVQ85KvGs/QuhyJ9LiSfKYSiOXN58KeUm2UqQNnwV6OABp10yEtG5yuIlTeTs5O33ifX5XbzoYyVIMh5dDeBJC9/uQtn0HtlL/NU9sRmh0iXwfgGCORRw0AenISqTj62R9mkb9EOp2RDjV1FLK24uUuRFBZ0ao3xOaj0DaOyfYi1Z7qErhP81568hUkZUllyOm1tKqXUFBQeF0vJLXZ1QEw+Fx+HJXACwuCwuzFvsZCG7JzdbCbehUOhqGN6DEUcLCrMW4Tn0zP4Cs2jswdQAnK0/SPq4dGkGNW/KgU2k5UZnFpvzNpJpSiNPFkmsNbJIIsvpwob2IFTly/yGVoKJtTBsahjdAFySXRyNqiDXEcaIyiOotkGRMQiXUkuR5BrXgKmNDEES/5FxJH4HY+1H5AV98DMEQiZDcVi5rP701Q0UugijKHp+oNMTOt1WHdkA2piwFwTcgeZHKsuRE3/JsyN2DUL8HQpsr8R5eBllb5ePqdoG4xkgLXqg+1xSL2PUuvKvf9ze6NEa5IqvmfZpioeUohMb9ZSVhXRiCICBVFsiqwRY590kCpIw1CE0GIjQbIpd918QUq3hjLmDOy5Dxer1MmjSJN954g8pK2U0YFhbGY489xvjx431tChQUFBRCoRE1pJhSyLcFf1gmGhP9yplLHKUhvRw7inaQYIhnefZKnxFThcvrYmXOKjrEtmdFzkoEBARB8DOSbG4bbWPbYPPYOFwemEibakom11Jt5HgkD1sKtxKmDaOOOfBLnEbU0CamFVmVWQHaOFpRS1pY3drD8LowCEsM2n26qndRMKSc3UhrP4CoeggRyUiV+Ugr3wa8iH3Gykm5NcNOXg/ipVNBa6rWmanijGkCAt6170Pe/urrCwJC17uRGvRBNEUjWYqRFr3of5qlEO+++bIXZ//86tWaDUGoEdbyjYsquW1C1TXcTqQ9c3xGjN/9H1wktz44uPhUQvCpNTrchGCoXfFY4b/LeVkc48eP57333uOVV15h27ZtbNu2jZdffpl3331XqWRSUFA4K+SqoDR0YqBHQyWoaB6V7pdAW+wILRJnddtwSx4sbkvQ+UpXJXq1jl6JPdCIGj8jBiDWEMu2wh0Igkin+I5+cwaVgVRzKg0jA/NwthfuwBYiBBSmCWdAan8/xd84fRyDUgdgUtfe+FEwRCL2HOML3fhQaRB7PwxB2hBI9jKkPXLTXkqOIR1bCzm7ZMNFkpCyd8glz1WIatCHI4QnBRoxIId4IkK0eRDVct+kGkaMvAkJaf3HiBHJSLYypJVvgTcwNEXuHoS4JtX3W68HQkz94Nc6HWdF7aJ6BYcgqZX8Q1QaQv+nERKand3aCv9Jzssj8+WXX/Lpp58ycuRI31jr1q1JSUnh/vvvZ/LkyX/ZBhUUFC5cTGoTg+sOZGv+NrKscqJtvCGejnEd0Ir+oZBwbWhZeq2o5Uz+A5vbzu7iPXRN6MLKnFW+cbWgJtWUwtKTy5GQaBfblnh9HEWOYtLMdWke3Zz1eRtpEF6POqY6nLBUh4sqXBV4T0+sPYVH8lBsL6ZxRGOMalmGvthezKrc1fRN7ku4Nqz2DUfWQRw6CSl3NxQclL0sKW3lnkXBcmu8HrnqKASStRhBF+bzDwnNhiDpI0K+boI+ArHr3XgXT5Y1XmrOdboF7+nhG9+FJKSc3bUnACOBxoDQbChCWhc5IdhpxVt8TB43RoMhCiGYd1/Cz9sSgNeD2OVOec8qTXAjTeGC4rwMmeLiYpo1C7RwmzVrRnFx8N4nCgoKCqcjCAIR2gh6JHXH5rFjd9vJteay9OQy9Co9neI7EKOPOdWdOgaNqMYV5CHWIro5GkGDKIgB3haQxexEQaTEWUq5s5xYfQyF9iLi9HG0jG7BtsIdvhDQ/pL9DEgdgEfyUGAr4I8TC3B73XgkN21i2vgZMuGaMMQQuS42t5UthVuDzm0r3HbGlgaCIMo5Kw36QJ1OsmdF1CKoQnxsq3UQnRayFFqIqoeUvV02EJpfipDaHuF0FV/kJF+5skgCfQTipVORDi+Vq5XM8XLyrtYIGz4LuXfsZQhJrUKrE+sjIDwRMbG57Ena8espAbxTSsNaM2KfR5BiGiCc3vVcY4Tk1pC9I2BZAKFORwT9GYxEhQuK8zJk2rRpw3vvvce0adP8xt977z1at279l2xMQUHh4sEjeViXu46CGsJ1do+dRVlL6J/Sj2RTEia1iYGpA1h2cjm2GknCjcIb0jC8ARpRQ8uoFuwsDhR8axbZlGOnqpKOVRynR2J3Kt2VFNuLWZe33i/p2OaxU+Ys8/PagFxldXpeS5vYNhjUgXkdACctwZs+gtzSwOFxnLGlgWQtQTq6Us75cNkgsQVim6sgLDHAoBG0JsQ2V+Fd+BKni8WhMSKkdUGo2xlENZIpNsDbIXncUHIM77r/QcWpPlbGaIQud0DzSxHTh4PqlCFlL0cKoTwMyGrAWgOkdoCsLQHTQrtrwRDpa0UgHTmtEtZZiXfpq4jDX5ZbG/jdpwGx7TV48/YFeIpIbAVhivDdxcZ5GTKvvvoqw4cPZ/HixXTr1g2AdevWceLECebNCyFlraCgoBCCSlelnxFTk80FmxmoG4BBbSBaF82wukOxuq04vS7MGhN6lR7tqYqcppFNMGlM7CzahcVtwaQ2kR7VFI/kZe+pUm8JLx7JzYrslUGvZ1QbcXgC2xmkmFIosMl7VAkqXwgqFJ4QISd5DyF9FdXH2Erxrn7Xv4v0yW14c3YjDn4BouoEnhSRitDnUaRNX0JV48no+ohd75KNn1OGWNBwkqUQ7+Ip/los1mKk5a8jDnkJIapa00fQhyN0uBFpySsEGE3R9RDCEhB0YYidbkGKrIt0YIFcoRSWgNDuWoS4ZgiCKBtqe4KVSyM3qczbF1yRNzwJcehLSHvmyGXfGqPcAiG1Q0C3b4ULn/MyZPr06cPBgwd5//332b9fTva6/PLLufvuu5k0aRK9evX6SzepoKBw4eGVvD6DodxREfK4Mme5r5O1IAgYNcaQPZj0aj2NIhoSoQ3H6rZi9zg4XHaYYkd17kj9sPoYVUaitVEUOwNzStIjm3G47LDfmE6lo3FkIzxeD6mmZPRqPQaVIWhX7ipSTSnsKNoZdC7JkBiQA3Q6Ulm2vxFThdeFd/v3iD3GBISGBI1eblEQlSZ3yBZVoDWfMdQieT2ysF4wQTlJwrtnDmLXO/3UdIWY+tD/aVm4r/SE7K1p2Aeh+XBfZ2rBEAktRyI07AOSWz6mZi8nrydkawMAygIFCuFUJVN4EnS6BcFpBUFEMCgGzMXKeevIJCcnByT17tixg88++4yPP/74T29MQUHhwqXSZeFI2REyKjIQBJGG4Q3ok9ybdbnrcXr9wwVyJ+tzK7A0qU1syNtIibPUb9ysMROli2Jx9lJ6JHZnV9Eusk6p+aoEFa2iW5JqTqXSVUmFqxIJibqmVFpEtyDiVLKxxWWhwlVBjjWXCG0EZo05aMNJo8ZIvbB6vpBWFWpBRYf4Dj4vUkiyNoeey9kth5qC5LgACMaooJVNIXE7kIIZTVUUZ4DLLufhVF1DrUdISEfq9xS4HSCIchXUaT2kBFEFphBNG1VqCEuoDmWdTmzj4OO+Pej89qRwcfKnBfH+DFOmTOHXX39l//79GAwGunfvztSpU2natKnvmNzcXJ544gkWLVpERUUFTZs2Zfz48VxxxRX/4M4VFBTOl0qXhQUnFmJ1V4uhbSvcTqQ2ks7xnVid619a2yCsPnqVDrfXjcPjRED2vNTWjdqoMXJJyiWcqDzBobLDSHhJNaUSb4hnfd56bB47i7OWMKzuUNrGtsUjedCrdOhVBtSiHDZKj5ILGnQqnc/oKHWUsThryakO3TLhmjD6pfYjTGP224NepadjXHvqmFLZU7IXp9dJkiGRZlFNUQlqnB5n7caMppYSbbX2LHRezgGVFiEsAanwUPB5c1yAoJ5kr5BzVARRrqQ6D/0wwRCJ0OZqpNXvBk7qIxBiGpzzmgoXH/+oIbNixQrGjBlDp06dcLvdPPPMMwwaNIi9e/diMsl/xDfffDOlpaX89ttvxMbGMmPGDK6++mo2b95Mu3bt/sntKygonCNeycuRsiN+RkwVpc5S7B47EdpwypzlAERqI2gd0wqr28quot2csGShElQ0jmhE44hGmGp52Js0RuqFpSEATq+LHGuuX/dql9dFjjWHppFNAs7VqDRoTvMsWN1WlmUv9zNiAMpdFazNXUufpD4BHbsNagP1wtOIM8RS7qwgoyKD3zPny12+jcl0SeiE+TQDqAohrUu1Lszpcw37ysJ4fxGCSg1NByNlrA4+32Kkr+Gi5LQhlRyX2wwUZ8hqu82GQoOe/mGjs712Qjp0uhVp+4/VSr/R9eUmj6aY870lhYsIQZKkM2ednSU7duygffv2eDzn19OioKCA+Ph4VqxYQe/evQEwm818+OGH3HTTTb7jYmJimDp1KnfeeecZ1ywvLyciIoKysjLCwxU9AQWFfxKb28aCE4uocAXPiUk0JtIiqjl5tjwSjYlEaMNxe93My/wjQLE3QhtO/5T+mILky3glL1a3DafHQaXbQo4lhyPlRwMScOuF1aNXUo+z2nuRvZh5mfNDzo9MG0GELvAzRpIkDpYdYmP+poC5cG04A1MHBA1NSU4r0qElSDt+Ou2kZMRLnvjLH/KSy4aUtQ1p42dyB2wAUYXQ9lqEBj0RtLLR6M3egbT8jcAFUtohdrkzZD6O5HKAvUwOYbltCLFNwBiFoDMjeT1yc0inBUSNbBwpJdQXPWf7/D4nj8zll19e63xpaem5LBdAWZmc9BUdXR1P7d69Oz/88APDhw8nMjKSH3/8EbvdTt++fYOu4XA4cDiqKw7Ky8v/1J4UFBT+WmoLCYmIxBviSDYlAXLJ847CnQFGDMhJwIX2Qkwa/w7ZdredI+VH2VW8G5fXhYBAHXMqvZN6sTp3tZ8OTZQu8qz37fQGVjLVJFhnbZA9OTtDJP2WO8updFYENWQErREa90NIbiN3gXaUQ90uCNH1ZMG4vxhBY4C6nRDiGiNV5oPkRQhLkEM8pzxNkq0UafPXwRc4uU02RoIYILKRtBVp/Se+JpQSQFpXxPY3yIm6ptgziOgpKATnnAyZiIjas8IjIiK4+eabz2sjXq+XRx55hB49etCyZUvf+I8//sg111xDTEwMarUao9HIzJkzadSoUdB1pkyZwsSJE89rDwoKCqchSbBiBfTp85fkZOhVehpHNGJzQaC2CMjl0zXbEjg9Tl8ybjCOlmdQx5Tq6+/mlbwcKT/K1sJt1beARGblCSxuK21i2viuLQoidc11g64bDGMtbQVEQUQXIt/FLXmwBynnrqLYUUK8Mbj2iaA1yX2QOtxw1vv8MwgqDZjjEMwhyspdNrnDdgikwiN+Zdo+LEVI6/4XOH58PVJ8M4TG/c5zxwoK52jITJ8+/e/aB2PGjGH37t2sXu0fo50wYQKlpaUsXryY2NhYZs2axdVXX82qVato1apVwDrjxo1j7Nixvp/Ly8upUyeI3oKCgsKZ+eMPGDYM5s+HIUP+9HKCIFDXXJcjZUcCKoqSjUnE6KNPPwF1LV2itaLGTxTF6raxq3h30GOL7EW0jG6BKIioBTV9kntjUgev+gmGXqUjzVyX45WZAXPpkc3Qq4IL46kEEZWgCqkrY64tqfffhqCSDdpQGQkhqqhC5d4ASPvmIaW2P6/8GgUF+IeTfat44IEHmDt3LitXriQ1tbqT7JEjR3jvvffYvXs3LVq0AGRV4VWrVvH+++/z0UcfBayl0+nQ6ZRyPAWFv4Sff67+9y8wZEBOwu2Xcgn5tgIOlx9GEESaRjQhRh+N4bQQi0Glp0lkEz8PS00aRzb2C1W5va6gYagqnB4nQ+oMRq/SYVAbag1znY5OpaNjXAcMagOHyg7jkTxoRA3No9JpHNHIz5Pkfw8GGkc0Yn/pgYA5raglUvcf6sqsC4PktnIY6XREddAqI0nyIlUG73AOyDoyQdpKKCicLf+oISNJEg8++CAzZ85k+fLl1K/v3/3UapUz2MXTyvpUKhXeYB1VFRQU/hxeL3z4IVTlu9U0ZKr+PiMj4b774DzKbaswaozU06SRYk5GQAhpBAiCQP2wehyvyKTotO7XTSIaBzReVAkqBISQyrlmjTnQ63OO+24X2470qGa4vR7UohqDWo+qFq+RSlTRIro55a4Ksmu0LdCpdPRPueScvEL/NILWgNjhBrylJ8BSQ4lZEBF6PABBROkEQZRF+k4EJjsDENsIQrR5UFA4G/7SqqVz5f7772fGjBnMnj3bTzsmIiICg8GAy+WiefPmJCUl8frrrxMTE8OsWbN44oknmDt3LsOGDTvjNZSqJQWFc6CiAurVg+JiOYQgiuDxgEolGzmSBNHRcOwYhP3/VZVY3VZKHKUcLTuKWlTTOKIRZm0YepUu4LgyRzlOrxOHx8GB0oOUngph6VU6htUdWmvJ9t+J3ePA7rZR7ixHp9Jj1ph8XbH/a0jWYqSiDMjdLefUpLSXK5BCiNNJlkK8C18EW6n/hCAgDnpBVglWUDiNs31+/6OGTKg/4OnTp3PrrbcCcOjQIZ5++mlWr15NZWUljRo14vHHH/crx64NxZBRUDhHMjPhuutg3Tr/XAhBgG7d4Pvv4R/KO5MkKejnhiRJlDrLWJu7jmJHMQBmtYk2sW04UZlFvi2P/in9idJF/icNhwsBqSIP75ZvIHsnIEFECkLHWxBi6oc0gBQubv4Thsz/B4oho6BwHjidsufFYqkeM5mgpAQ0tXds/ieodFUy9/i8oPkxg+sMwqQ2/me9HxcSktMKjko5J0ZrUBo8KtTK2T6/zz/IraCgcOGycaO/EQPyzxs3/jP7OQOZlSdCJvnuKtqFRtQoRsy/AEFrRAiLRwhPVIwYhb8MxZBRUFAIZM4c+d/Ro+HwYRg1Sv75t9/+sS2FwuP1kGvNDTlfZC/2dc9WUFC48PhXlF8rKCj8yxg5Etq0kXNlBAFmzoTvvoO0tH96ZwGIgkiYJnTisVFjPKcyawUFhf8WiiGjoKAQSI8e8n9VCAJcf/0/t59aEAQhpE4LQKvolujPorzXbbVjyy2h7OAJEAQimtTBkBiF2qAkoioo/JtRDBkFBYX/PCaNiV5JPVmTuxZvDXG15lHpJBiCy//XxFFSweGvF7Hp8Y+Q3LICr6jV0O2Dh6l3ZW+04f8h9V0FhYsMpWpJQUHhgsDt9WD32Ch1lOLxeojURKAXdeh0gQ0ZTydv9S7m9X4k6NyITR8S26HJX7xbBQWFM6FULSkoKPzrseYWU7Y/k/LDJ3GUVPyptdSiCr1bgznLS/7LC1jZ71k23f8exbuO4rbaQ57nrLCy4+UZIef3vPkTblvtna8VFBT+OZTQkoKCwv87bquDgo37WXffW5QdOAFAQu/WdP/wESKa1T2vUmnJ6yVv1U4WDX8GySOHlwo37OPwlwu55OfnqXNpV0R14Eeex+ag8ljoqqfywyfx2J1KroyCwr8UxSOjoKAQgC2vGEtWAc5yy5kPPg/KDp5gwYDHfUYMQN7Knczr9QiW43nntab1ZCErb57qM2KqkLxeVt/2KtbsoqDnacKMxHVuFnLduG4tUJsDk4VtRWWU7DnG1ue/YOUtr3Bw+nzKDmWd194VFBTOH8Ujo6Cg4MOWV0LW/A3sevUHbHklJPRsSfsXbyO8aSpq/V/jkXCWW9j23HSkII1fHcXlZM5ZR/MHLzvnde0FZdjzS4Jfs8yCLa8Ec92EgDm1QUfLJ67hyIwlvkTfKlQ6Den3jUR1mpqxvbSC3KXbWXH9JJ/hdOTrRRgSoxm86DWiWtQ75/0rKCicH4pHRkFBAQB7UTmbnviI1be/Rtn+TJwlFZyYs47fOt5L0dbDf9l1XBU28tftDTl/csEmPI7gKr21caa6Ba/DTeWJfLweT8BcWMMkBi98FXO9RN9YeONUhix9A3P9xIDjHXmlrLp5SoD3x5ZbzIaH36Uyq+Cc96/w57AXlVG07TBbn5vO1uemU7T1EPbCsn96Wwr/DygeGQUFBQCs2UUc+WZxwLjk8bLu/rcZvOg1DHGR57Sm1+PB6/Kg1mt9Y6JWjTEpBkdRedBzwhokIWpU53QdAH1cJLrocBzFgeuqzQZsucUsGT2Bdi/eSv2r+6KPrZbIV+t1JPVty/A103AUVYAAuphwjInRfutIXi+CKFK8OyOksZWzdDuu0kpIjTvne1A4P2wFpWwd/zkHP/3dN7Zj0jc0umUwHafehSE+6h/cncLfjeKRUVBQACBv5Y6QcyU7j+IsrTzrtZzlFoq3H2H9A9NYdsXz7H13JhWnEmoNcZG0fuaGkOc2vetSBPHcP5qMyTF0/2SsLN53Gh0m386Bj+fiKC5n/QPTOPbTiqCeGWNSDFEt6xHVop6fEWPNLiRz7jqWXz+ZNXe/gS7STJsJNwW9FoDHee4epQsZe2EZpXuPU7BhH2UHT+A4h/fS2VC05aCfEVPF4S8XULgpuFCiwoWD4pFRUFAAQG2qRW9FEBBVZ+clcVXaOPrdUtbd97ZvLGv+Rra/9DXDVr5NZLO6JPVrS9P7RnLgw+reTYJaRY+PH8NcLzCP5WwQ1SpSBnZk5OaP2PHyt5TuziCsUQpNbh9K1oKN5Czb5jt26/NfUOfSrpjqnFksz3KygCUjJ1C07ZBv7OCn82h8+1DaTriR7S9+7Xe8OS0BTZjxvO7hQqTyeB7Lr59EQY1wYtplPeky7UFMKbF/en1nhZXdb/4ccn7Xaz+Q0LMl2gjzn76Wwr8TxZBRUFAAIKFnSwRRDJqEmzKkE7rYsxOUtOWVsH7MtIBxR2EZGx56j74/PochPooOk26n+QOjKdi4H7VBR2zHJugTo9EY9Xg9HqxZhRRtPURFRg4x7ZsQ0SQFY3LtDz6VQUdMu0b0+uIpirYcpPxQFiqjjsRerUkZ2BGP3cmeN3+maNshXJU2v3PthWVYTxaSv34vukgzsZ2aoU+M5ND0BX5GTBWHPp/PJT8+jybMiKvC6hvv+No9RDROPavX6kLHllfC0iuep2ir/+t3fOZqVAYd3T96FI35zIKFtSG53DiDhBOrcJZU4nUFet8ULhwUQ0ZBQQEAQ2I03T54mLX3vuU/nhBF59fuoXTvcUSNGkNCFMaU2JBaL/lr9wQ1hgCyF2/BUVSOLtKMLioMXVQYken+jSglr5eirYdYMOAJPwMhvEkqg+a/Qlj9pIB1KzPzyV2xg+MzV2FMjqHJncPRx0Vy/NdVrL3vbV81ki42gm7vPcT+/81BVSNvx5pbzLr73yFz1mrfmKhR0/ubcVRm5IR8zU7M30C9q/ty+Is/iOnQhPYv3U7kRV6x5CipwOtwoYk0Y8srDjBiqsj4YRntJt76pw0ZTYSJOiO6hbxOnRHd0Eb+OW+My2rHmlVI5m9rsGQWkDKkE9FtGv4lHiWFP49iyCgoKACgMRuof+0lxHVrzsFPfseSmU/KsC5Et2nIksufp/ygrJFiSIym368Tie3YFFEdGG7ynEEFVwqSm1ITS1Yhi4aO8zNiAMoPZrFuzDT6fDceXY0wQcXRHOZfMhbLiXzf2P4PfqPj1LtxVVj9SqodhWWsuuUVBi9+HX2CnAAqeb0cnbHEz4gB8LrcrLhuMgN+f5nDXy4Mapy5Kqx0efsBWj95DaJWjTktsMLpYsGWX0L+mj3sevV77IVlpAzsQNN7RhDbuRmFG/cHHC95vLjKrUFWOjdElYqGNw5k37uzAhK9tVFhNLxxQND36dnitjnImrueFddP9r0H9r03k/DGqXKlW9r5hUIV/jqUZF8FBQUf2nAT0a0a0OWdB7jkp+cxpcbxe7cHfEYMyCXGCwY8gSUzP+ga8T1ahlw/qlUDtJFhte6h4mh20MojkEuzHQXVJbUui40t4z/zM2Kq2PzUx9S7ondA4rDH4aJg/V40RlnkzppbzO7Xfwx6PVkteBdx3ZoHnW9040BMyTGEN069YI0Ya64sjmgLodEDcun+1vGfsfSK5ynYsI+KI9ns/2gOv/d4iHbP3Yw+SLWboBLRhP9FuUSiQJ9vn6HuqB4IKhFBFKkzoht9Z4xn+6SvsYeokDsbrNlFrLhhcoAhW34oi60TPsdVS/sLhf8fFENGQUEhAEEQsBeVs+Ghd4POu612Ti7YFHTOmBRN03suJb5HS5Iuaetz6wtqFd0+fBhDfGTwNW0OKjPz0EaYaPv8zcHzYSQJj93p+9FRWM6xX1aGvI/8DfuIbtMwYLx459HqHzxebHmhH9K2vBKMyTEB41GtGxBzATeTtBWUcvibRczr9TA/pl3HH/0fJ3POuqA9sawnCzn42fyAcbfVzq7XfqDxHUMD5hpc3x9Dwl9TFm05nkfWws1EtWlAnxnP0ue7ZzHViWfZ1S+SMWMpzj/Rxytv5c4AvaAqMn5Yjj2/9LzXVvhrUEJLCgoKQZFcHiqOhs4PKQySAAuAKNDs3pFk/LwCV5mFpveOQBBFwpvUIaJxStBTKo/nsX3S1xz5ejFep4v4bs3p9v5D7PvwN7IXbvYdp40KQxNuqt6jxxugxlsTj9WOqA38mIvv3sL3/yqjnrgu6RSsDy7Slzq4EzEdGqPSaznx21pURj3N7rmUxrcPvWBzJFyVNva8/TO7pnznGyvdc4wlo56l24eP0Pj2oag01a9rKKMWIHfFDlo9dS2FG/ahj4uk7GAW4Y2S6TDlzj+dH2M5WUDuip0c/OR3BFEg7fJeeKwO1o15x79RaO1aibViLwotqud1uWt9/4Vcs6AUV6UNQa3CkBCFSqs580kKIVEMGQUFBUD+Bu4oKMNjd6KLDkNl0BHRrC5l+zODHh/fNTDc4iipYN97s9j2/Be+sX3vzSJ5YAd6ffGUX4JtFZasAhYMfILywyd9Y/nr9rL0qon0n/kiRZsP+kJN7V+8FWNKDNbsQlwWO4Ig0Oa5m9j1yvd4g2i3xHdrwf6P5viNacJNpAzq5PtZHxNOp9fuYV7vR+A0dWBTnXjiuqRjqhNHtw8focPkOxBEEX1C5FmXo/8XseWVsPvVH4LObX7qE1KHdvZr9yDUloMiCJjTEojv3pKygydoes+lJA/ogOkMFWhnwnKykCWjJvgl+eYs237KCH6YVbdNBSC2czO0Ueef7JvYu03IucjmaedUau+y2CnaepAND79P8fbDqI16Gt85lFaPX4NJEVA8b5TQkoLCRYTbaseaXYTtNHd42cETLBr6NDNb3s5vHe/l1xaygFzvr8cFXUcbYSKpX7uA8Yqj2X5GTBXZi7Zw/NdVQdsIVGTk0GHKnVzy4/P0n/kinV6/F2NKLJLbw95pv9Lo1sEYkmLo+fkT1L2sJ5mz1vB7j4f4tektzGp1B7bsIvr9/HzAA6XOiG7oEyIRahgcEU3rMHTFW5jT/PVjots0YOjyN31hqKoci6FL38BUR37AaIx6TKlxGJNjLmgjBsCaVRAynOIqt+Ao9M85SR3cKeixAKnDu3Dg49/ZMfkbjv20gnX3vc3crmMo3Xv8T+3xxJx1QSuV8tftxVlhIbxJqlzi/eEjfirO54q5XgJJl7QNnBAEuk57MCA8Jv+NFQbNKSracpD5fcdSvP2w79h902ayZPQErDnBm5oqnBlBOlODkv845eXlREREUFZWRnj42elgKChcaHjdbsoPZ7NzygyyF29FFx1Gy8evJmVwJySXh7ndHsCaXRhwXtf3HkYfG87qO17DbZFd9eGNU+n74wSiWzXwS6SVvF7W3PMWhz6bF3QPYQ2TGbbqHT/FXHtROQc++o2dr3znWz+qZX06Tr2bDY++j+V4HqN2fIrapMOYHMvxX1ay7OoXA9aO7dKM1k9ex+rbX0UXE07Lx66m7mU90cWEY8spxlFYhqhVo4uLwJjg33bAmlNE5fE8yg+dxFQnDmNyLKJegy7SjLZGGOtiomDjfuZ2HRNyfvTOT4lqWd/3s6O0kt2v/8jOl7/1O04XE06fb8ez/NqXApSh47o2Z8Ccyehjzv1z2V5YxoJBT/oMgtNJ7NOGpncPJ6ZDU8IaJCKqQwcfbPkleF1uNGZDSNE8a3YRBz6Zy95pM3GWVBDbqSmd37yP6DaNfOExr9tNxZEcdk6ZwcmFm9FGhdFy7JWkDuuCMSkGe0GpvOcdR4JeY+iyN0nsE9r7czFyts9vJbSkoHARULrnOHO7PeBLlLXlFLH6tlepd0VvWj9zfVAjBmDHS19x6cYPuGz359gLShF1GvRxkQE9iEDOV3EUlGJMjsVUJw5rdpFfNZGzpDKg9Dp74Wa2TpjuN1ayO4OVt7xCj48eZd0D09CY9RiTY7FkF7Lx8Y+C7rNww370iVGM3v05gkr025+5bjzmusEVfCsyclg0/Bm/8JmpTjyDFkxFG6RT9sWCISkaXWwEjiBNFyOa1UUfF4kluwjL8VwqjuYQ1jCZpncNp+7I7nL5dX4pqcO7UGd4V5Zd/WLQ9hYF6/fiKCw7L0MGSQrpMQL5vZg8qFOta9vyS8letJmdU2ZgzS4itks6HSbdTkR6XV9FWxXG5BjajL+RJncMQ/JKqI26AC9P2f4TzOkyxic/YMstZs1db1Dn0q70+PQJ3BZ7SCMGIHvxVsWQOU8UQ0ZB4QLHUVLBxrEf+FX7VHHsl5U0u38UapPe5xGpiS2vBGdJBRHpaWfUyxA1ato8dzNFWw9SfjCLsAZJ6OMi2frcF5TtO07yoI5+wmTW3CK2jP8s+J4Ly7CcLKDtC7f49F7clfaQJd8ARZsOkNCtRcj507EXlbPylqkBOUCWE/ksGTWBoSveCmqwnS3Ocgv2/FJc5VY0ESYMCVF/Ork1FB6nC6/Lg8akP/PBZ4EpJZb+v05kwaAn/d432kgzfb9/FrfNwYIBj/slg4c3SWXQ71Po/fU4vE43mjADx35dFTLHqmrf54MuJpxGtwxiUwjDtvEdQ2s1YhwlFWx7bjoHPp7rG8tesImcRVsYtPBVkoOETUW1KmQei7Osko2PfRhUQ+nE3PVUHsvBkByL2qj3T0KugSGxOkTlqrRiyyvFmlWA2qTHkBiNMTnmvHqQXQwohoyCwgWMq9KGLbeYZveNovkjV5K3cgf7P5zj92Gas3Qb0W0akr92T8D52kgzJbsy5MTfM8julx3KYs9bP+MoLCNv1S7cVju66HB6f/U0G5/4H20n3ISmRj8nr91F5alGkkHXO5BFu4m3+PJRRK0aUasJmtQLYAhSIl0b9oJS8lfvCjpXfigLe27JeRsylpMFbBz7Icd/WSV3zFaJNLiuPx1fuStoKff5Yi8qo2z/Cfa+OxNncQX1rupNyuDOIT1QZ0KSJLwuNyqthriu6Vy26zMyf19PyY4jxHdvSVL/dqjDDCzo93hARVv5wSyWXzeJAb+/7OuSHt2qQchrmVLjsJwoQGM2BFVrrg1BFKl3VR/2f/gbFUey/eaiWjUguX/7Ws+35Rb7GTFVSF4v6+57m6Er3sSYePa/J2ephezFW0POH5+1hnYv3ELjO4ay792ZAfOCKPoS0O0Fpex+4yd2v/Gjz+tkSIii38wXQ4pQXuwohoyCwgWKNVv2eBz5ZpH8gSgIpA7tTJ/vnmXF9ZN8Hhi1WY/KoAu6RrN7R3Lkm8UUbT1I2xduRfJ40UaYAtoTVB7Po3jbYVRaNeZ6CTS9azhF2w+zY/K3rHtgGgPnTyG8YTKA3EfpZBH24nIGzJlM8Y4j7Ht3ZoCWS0z7xuhjqt33hoQoGt7Qn0PT/wjYp0qvJfYcNV2CeaBq4ig7vw7NjpIK1t77Nlm/r/eNSR4vR75ZhNftoftHj/wluTeO4nJ2TP6WvW//4hvLXrwFU514hq54i7B6Zy/Q57Y5qDyWx6Hp8yndl0li79bUu6I35vqJtHjocr9ji3dlULLraNB1CjcfwFFQ5jNk9AlRNLt/FPs/mB1wbNvnb2bbc9Ox55cybPW0cza+tBEmBvw2CevJQiSPl4pjuQgqkdQhnc9YAVRbR+zyQ1k4SyznZMggCIhqFV6XO+i0SqvBWWah9dPXkb9mt1+SsiCK9Pn2GQzJstGcOWcdu1793u98W14JCwY8weidnxLW4NyMvosBxZBRULgAcVXKireHv1xQPShJZM3bgKvSRqunrmXbc18AUHdkd1IGdmDJ6OewZBXIxwoCDW7oT+rwLoQ3TuHAx3M58u1ijn6zmPrXXELdUT18Dx5rThFr7nqD7MVbfJc68L+5NLplMO1fvJWtE6bjsTkR1WocJRUcn7maTU/8zydSFtuxKT0/f5L1D73r+3atNukDKkXUBh1tnr2J4h1H/B4EKr2Wfr9MRBd/bpUpuqgwRI065MPHEBdJ+eGTiGoV+vhI1MazC9vY80v9jJiaZPywjHYv3IKz1CLnn4gC+tiIWntXhaIyM9/PiKnCciKfnVNm0OWdB1AHKXc/HY/TxclFm1l2+Qs+9dqs39ezY9I3DF3xFjFtG/kd77bYgqxSTc1mnLpIM22fu5nYTk3Z+fIMLFkFxLRvTIuHryBzzlrf7zFn2TYa3zI45JrOcgu2vBK51YEgENO+MTlLtrL+ofd8JfOxnZrS97sJQY0YZ7kFZ0kliAK6mPAzKgqL6nML4ehjw2lwXT8Of7Uw6Hxsx6b83uthBs19mQG/v0z5/hOcXLwFY2I0KYM7YUiKRmMyYM0pYvuLXwVdo0qEMu2K3uhjw5UwUw0UQ0ZB4QLEllfMkW8WBZ3LW7mTthNuAqDdxFsxJsUgaNVc8stErFkFuC12dNFhnFy4mfl9HqXuZT1pevel5K3ZTf7aPeSv3cPuN35k2Iq3MaclkPX7Bj8jporDXy6g9zfPoIsO93k/8lbvZs2dr/sdV7j5AKtue5XuHzzM0itfwJAYTf9ZL2EK8g09d8UOmtw5DF1UGCW7M9DHR2JOS2Tnq9/Ro+FjaJucvaaHIVH2Fux9J9AYSLu8FyfmbWDzk/9D1KhpcF0/2k289az66tQmoIYkYc0uZNmVE33aOIbEaHp/PY6EHi2D6uyEIuOH5SHnjny9iLYTbkJ9FtoktpxiVl7/coAEv6vCyqqbX2Hw4tcwxFfnb+hiwkEQAjR3QPYu6KL9W1AY4iNJHdoZd6UNTZiRsoMnWP/we9hqlBtnzlxNw+v7I2oCH0mO4nL2fTCbbc9/WX1NQaDVE9fQ6olrfN6Lwk0HWDxyPAPmTEZQqdCEG9GEGSjbf4JNT/6Pk39sQlCJ1L/mEto+ewNhjVKoqKFdVEVinzbozrJc2+vx4KqwImo0tJt4KzlLt1V/GThF+oOXkb1kCxUHs1h29YsM+mMqiX3aBE3s9bo8teaBFW4+QN7aPcS0a0SD6/phTPrrwpT/ZRRDRkHhAsRZaqm1qsNtsXP5/i/Rx0agjTTLD9erXsCaVYioVfsleB7/ZSWtnryGTU/+zzdmycxn77RfaPX0Dex9fxZpl/UksmV9nKWVHPtpBbbcYvncX1eRemlXDAlR2PKK2fz0J0H3Y88vwWW1M2rHJ6iNeioyclh739uY6yZQ/8reGFPjcFVY2f7iV1Qey0UTbiKsQRLO0kpfns2xn1bQZvwNZ/0aqY16Wj99HRqznj1v/4LbYkel09D49qEk9m3LyhtfBmT11sNfLSR//V6GLHnjjGq+oUp4q3BV2Px6Sdlyi1k49GlG7/gkoBN4bXjsoZtzel3uoJo9wSg/kh0yAbVkdwaOwnI/Q8YQH0nDG/pz5JvFAcc3un1I8LYDksTeaTMpP5QVOIdszHm9XizHcvFYHXKCa3IMKo2a4p1Hfd7DmuvtevV7Bi95nbqje6DSaSjeeZT1Y6ZRvP0Iy6+fTGKvVnSYcidbxn9K9kLZ0JbcHo5+u5icpdsYNG8Ks9vf42eQ6WLC6fbBw0geibKDWQiCrCYdTIem4lguR2cs4cScdehiI2gx9kqGrnybkws2kTlrDdooM/Uu703h1oPsekVWSC7efhh7fknINh0qnZrwJql+vc1qEtk8jYOf/8HRbxeTu3w7PT97Imgfq4sNxZBRUPgP4rY5sOUWYzlZiEqjxpAcgzEpxpcIeEa1UUFg/YPT6P2VLHjnLLP4vgkGq24q2ZVBr+lPcvDTeWT+thaAw18tIn3MZXR+/V4yflxO5qw16OMj6TjlLixZ+WydMB1XuZW6o3tgSIjCUVJJ2b7QImiFG/aTPLAD83o85JdIuu35L+jzzTgSerXCelIuE3eVWwI0RMoOhK6OCYUhIYo2E26iyZ3DcFXKhsyhLxbITQJPk54vP5hFya6jZzRkDPGRITs+J/ZtS+GmIJ2g3R72fzSHTq/fg0pzdnL19a7qw953fg06V2dEd78KsdoI9vuuife010EbYabj1LvRRoVx8OO5eBwuVAYdze4fRcvHrgr63jMkRNNi7JWsu+/toNdocucwtj07nX3vz8Jjd6IJN9HyiWtofNtgDk1fQOtnrkcfF0n5oZMc/XYxzjILAHve/BlTaiwHP51H/WsvYfjad8lbvwdthInsxVvIXbmT/jNfpGD9flzlFt/1bDlF5K7cyeUHv+LA+7OoOJpD8qCOpA7rgrvSxsLBT1J0qgVHbKemdP/4MaJa1PP9fZUdzOL3Hg/iqNGMMuv39TR/+HKa3juCwk0HcFvsrL7zdb/rAiGNxqrXqcPLd7LsyhcC5rRRYYQ3TvX9DZ2Yux5LVoFiyKAo+yoo/OdwFJez/8Pf+DX9Nub3foS53R5gdtu7yVm6zfdQMsRHkjq8a9Dzo9s0pPzwSbIXbaH8qJyTUuXSF7UawhulBDRs9LrcbHj0A8Kb1qHrew8BcgKrx+FkyagJHPzkd0p2HSVnyVZW3TYVa3YR6Q9eRp0R3Ujo3oKTi7bgLC6n85v3hwyfRKTX5dD0BaQ/MJp+v75I3x+eo+8Pz5EyqCMrb34Ft9VZa5PG5AEdzu2FPIVKq8Gclig/qDRqdr78bcj+OSdr9H0KhT4ukr7fTyCmXWO/8fhuzWnzzPUhjY+irYfwWEN7WU4nvGEKqUM6B4yrzQY6vHwH2rOUzo9okhoy38KQFIMuJrBbuTEpho5T7+ayvdMZveszLtvzOe1fuq3WKq+6o3rQ9L6RRLVu4Pce6Pjq3RRuPcTuN370vX9d5Ra2TficvdNm0ujmgeQs3c7u13+k8ngevb4aR9rlvQCwZRehiw5H8ng5+u0Sdk75lsQerXy6NV6ni30fzKbRzQMD9nN85mqMCVF0euM+LvnpedLvH4XX4WJO5/t9RgzIIat5PR+i8nievLcKK1vGfepnxFSx951f8VgcHJ2xlIwflgUYMYJKRBdTe9gqqW8bukx7EHWNUv2I9DT6fD0uQK4gZ9n2Wte6WFA8MgoK/0JclTZclTbUBm1AqKJw04EA/QxnSQWLLn2G0Ts+QTzVgK7bew+xQaMmc9Zq33HRbRrS64un8Ho8qI16MmevIaFbC3Sx4fT45DH08VGU7j2GJtyIKSWOfe/NIm/VToxJMVQcyWb3az/Q8rGr5ITDuAiOfrc06DfM/R/+xqD5ryDqNfzc6CbfePKgDvT55hmWXzfJL8lWpdOQ3L89FUez2fTkx5TuOQbIfZFaPXWtXB6+Zhdd3nmAuV3uD7iePi6ShN6tsZwsRBAF9PHn1wtJUIloI0y+b/ync7ZNIsPqJTJw/hRseSVy9+zEaLRRYWx45H1cFdag50S1rI/KoMOaWyw/JCUJXXR4yHJtQ0IUPT57nKw/NrL3rV9wlllIHdaFlmOvwtzg7CuWDAlRtB53PTsmfxMw1+2Dh4N3IQfUeu1Zl027LDbcFjuxHZoQ3jCZmPaNQZI7pQtqFb80uTnoefve+YX4Gg09rScLOTl/Iz2nP0n5oZPE92xJfM+WmOrEYzmRz7GfVtL6mRv9SvRzl22n6d3j2ffeLL+1dbHhiBoVgiAgaNR4HE6OzVxFu4m3ojJoKdy4n2M/rcDjcOG22Dn4ye+0e+k2HMUVZM5eE/JeT8xbT5vnbmTrM4EaSU3uGHbGjt+66HCa3j2cuiO6YcstxpZXTEVGLmvvfzsgf0b3J3pIXUgohoyCwr8Il8VG+cGT7Jj0DUXbDxPWIIm2z95IVOsG6KLCsBeXs+3Fr0jo1Qq1UU/xjiO+fBTJ7WH//+ZStj+T7IWbSejVii5vj6HF2CupPJqDNiqMiiMn+WPgE8R0bMLA+a+Qu2I7IGu6ZM3fyPFfV/n2otJp6Pr+wzR/5HL2vl3tRdj/4Rx6f/sMkc3qMqvNXSHvpezACY7P8v/Az164BXPdBOpd1YejM5YAslZN/9kvIWrUrLhxiq+aCeRv5lvHf0b3j8ciIRCZXpd+M19k/QPTfGGm+J6t6P7Bwxya/gcH/jcXtUFL03supeGNA8+5EZ8+IYr0By9jx6TAh7ogikS3a8ThbxaR3K9dyAd8FYb4KDm3pFX1WMuxV5I5czXhTesgCPJrJHm8CKJI+phRFG09xKqbX/E10DTXS6TnZ48T1605an1gibwxKYYmtw2lzojuSC432qiws6pUqsJeUIa9qIx6V/Wh7mU92TX1O3KWbCO6bUPaT7qdyBb1zrma6nRclVaOz1zD6ttf9cvbqju6B93ef4TSfcdCnutxuAJCW5LXy9YJn9N2wk2YUuNY/9C79Ph4LGvueRNLZr7f+wdAE2EivFEKEel1KdtXHX5s8fAVqHTVr5UttwQBOPT5fJzlVpL6taP/7ElsGfcpRdsOkbNsG60qr5VVhWvJP5K8Es3uuhRXaSX73puN22pHbdKT/uBltHjocj9RxMoT+ZTszqBsfyZRLesTmZ6GKTXulJcwAV1cBAWbD3Dyj02+93sVgiiSUEtDy4sJpdeSgsK/BEmSyJq/kcUjxgdUhHR55wEa3zEUR1E5pXuOkbNkK45SCwk9WiJJXjY++iGucvkbuT4+ksNfyGXXarOBQfNfYeHQcXLZbI11B859GU24EVGvoexAFqtumhKwJ0ElMmTJ68y/5DG/c0ds+gBdbCQ/178+5P20f+k2jn63NKA5oEqnYeS2j6k4ko0m3Ii5bgKCWoXleJ5PNv7wlwv9PD0R6Wn0+/l5ItPTkCQJa3YRztJKRI0aQa2iePthirYe5ND0Bb5qmKg2DRk492V0UWZsuSWUH81GVImY6ydhTIr2e4jVxJpdyIobp5C7fLvf69Bl2gPkLN7K8ZmrSezblj7fPnPOVSO2/BIsmfmcXLgZyeslpl1j8lbvIr5bCyJb1GNmi9sDBP8ElciobR/79TYCWU3WlldK6b7jqI16IpqkYEiKQaU9c46N5PVSsjuD1be95gujmOsl0v2jR4lsXheN2RiQY2MrKMVrdyKoVed036X7jjOz5R1Bq5y6vD2GpEEdmdX8tpDn95/1EktGTwgYH772XTaO/ZCC9Xsx10+izTPXs+auNxi5+SN+63iv77hWT16Ly2onZVBHlox8FoAWY6+k6V2XojbpMSbHYM8vZcUNkwNCNWqzgX4/Pc+yq18kqX87+nzzDF63hxXXTyZr3oag+x255X/EtGuEx+HClluE2+JAbdIF/G5KdmfwR//HsReU+saMKbEMWfIG5vqJWI7ncWTGEgo37COskRxG3PvuTE7+sREEgV5fPEna5b38RCYvNP4TvZamTJnCr7/+yv79+zEYDHTv3p2pU6fStGlTv+PWrVvH+PHj2bBhAyqVirZt27JgwQIMhgv3F6hw8WHNLmTNXW8E/cDf9MT/SB3amYqMXPZ/8BtZ8zcgebwc+mweUa0b0PvrcSy9/Dli2jcmoXcbKjNyyV2xA3eljWM/r6TvD8+y+NLxfmsembGERrcOZsnAJxgwZ3JQ1VzJ4yV70RbiOjejYMM+eVAQ8NicuEoraP/yHUFd6AAx7RrLJbOn4XHI16gzvCv2ojKO/7KKLc9+jqOwDEElUndkdy758TlW3PiyL9ehbN9xn+EhCAKmlFhEjYqs+Rv99Em6TnuAzNlrOPLNYkp2HKFgwz6cFVbW3f2mL5SlMujo+enjpF7aNWgeiTE5lr7fPUvl8Vyy5m9EpdcS3aoBBz6e60t0zl2+neLtRwIe6F6PB3elHZVeE2AoOYrLOfDRHLa94P+atHj0SmI7N2PnKzOCqhZLHi+7X/+Rbh8+gvqUcKEtv4Qdk7+VwyWn3i9qk56+PzxHUr+2Qb03Nak8lse8Xo/4hbkqj+WyaNg4Rmz6AFNqdem7o7SSgvV72fzUJ5TszsBcL5G2z95I6vAuftVMoTg6Y0nQ9zTA7td/pM6IbiQN7EDF4WwqM/zVghN6tsJVaUMfF+n3wAfZm1QVcqrMyEEbYSZ5YEcsNfqGxXVJJ7ZTU5ZdNZEmtw+lzYSbiO/WgpylW/k1/VYMCVF0fe8hzA2SguabuCtt7P/wNxreNJAG1/fzaQl1eu0e8lbvDsiBaXTLYF+3dJVOzr0KhjW7iCWjJwTck/VkIcuvfYneX49jTqf7/BKx93/4G31mjKfOiK4k9W2LqU7cBW3EnAv/aLLvihUrGDNmDOvXr2fRokW4XC4GDRqExVL95li3bh1Dhgxh0KBBbNy4kU2bNvHAAw8gKmJAChcYjsJyP22NmnidLoq2H2bnlBmY6sQxcO7LxHdrDkDJzqNkzd9A3ct7Ed+tBSuum0TaZT1pdt9IAPJW7cRtsVP/qj4B6xZvP4LH4eLwN4tJu7xn0Gtbs4v8tEFSh3bm2K+rmN3uHjQmA43vHBZwTqunruXEvA0B2iQgl7iqTXokr5fM2WtZe+9bvuaEksfL8Zmr2TL+MzpMudN3jjktAZWx2jBwlFay7YUvWX3bq5QfysJjc5C/ZjfLrppI4iVtfYm2R75aSO7SbX75OB6bgxU3vkzlaRL7APbicioycnDbHJhS4ynedZTDXy5k0aXP+IyYKg58MhePU17X6/ZQfvgk2577gkWXPsOaO9+gcOtBv1ybkj3HAowYgD1v/UzxtkNUHAncTxUFG/f7GR1Z8zfJUvc1DAS3xc6SUc9iyazWMXGUVFCRkUPFsVycp86XJImMn5YHzdWRvF62vfAlzlMPaK/HQ+as1SwaNk5W85UkKjNyWH3Ha+ya+r3vuNpQmw3Uv+YSooK0K7Dll+Jxukgb1YMWD1/OgDmTqTOiGwARTevQYcqdnFy0mU6v30unN+5DUMmf+1GtGlB+8ITfWm6bg24fPEzhloM0uWs4vb8eR6NbBrHqlqnyvXm8lOzKYMnoCex+/Uf5+nklLLtqIrbsopChwhPzNtDwxgFENqsujY9oWoeRWz6ixdiriGhWl/geLek/60U6Tr0raKn26djzSwNaPFRRvP0wlsz8gGoyye1h3b1vU+fSbkSmp6Exn71m0oXOP+qR+eMPf6nxL774gvj4eLZs2ULv3r0BePTRR3nooYd4+umnfced7rFRULggEAVMqXE0uWs4kelpeOxOMn5aTtbvskHgdbopWL+X3OXbOfTFAvrOGM+GRz+g8lguR75ZzLAVb7P+oXdxFJWx4ZH36f3NM5yYsw59fBR5q3dR76o+ZPy43He5htf3Z8NjHwJQuHE/6fePIuP7ZQHbSrykHdtflB/AMe0b03bCTSy7+kUANjz8HqO2fYxKp6Fg3V708VE0vL4/YU1SgrYSAGgz4SaMyTFYs4vYeqoKQ6XXojLoZA+MJFG84wi6qDA0YUZcFVbaPnezn2S8Pa+EAx/NIbFvWxpcewmaMCPFO45waPofbH/hK9o+dxNr7noDlVGH1xlEufeUrkm3Dx9BpVXjcboo2Z3BhofeI3/tHtkzNLoH6feNkr1kQRBEkar0kZJdR5nX6xFfOCx/zW6OfLuY7h89SoMb+oMgsPuNn0L+6ne99iMNbxoYUhE4rH6ir42ENbeYnUESc0F+WGf8uIzWT19P6d7jbHz0fXKWbUcQReqM6EbHqXdjqhtfa7VL4aYDuCptaMNNWE8WsfGx4I0Z977zK83uGxmy3YKr0oYlMx97YRlet4eGN/QnvMmtbHriI5+Cc1y3dA5/udCnsyKoVfT431haPH41FQezWHrFC9jzSzjy5ULqjupBp9fuYeuE6bR/6TY2PPye/2vUMJkdU2ZQcSgLlUHHhkc/8BnIxpRYBFGg8W1DOLlgU8Bed778LY1vG8yOyd8GzKl0GrkBZY3vzoIoEt4wmQ4v30GrJ69B1GrQnWW5u/zaBE/4riJU+wxHcTm23GLMdc6vl9aFyr/KrVFWJr/poqPlEr78/Hw2bNhAfHw83bt3JyEhgT59+rB69eqQazgcDsrLy/3+U1D4L6CLDqfLtAc4MXcdy66eyLox72BOS+CSn55HHxeB2qDzfUvz2Bxsfe4L0seMAmQXuCWrgPw1u33r7Z32K41vH0LD6/uT8d0yqOHdTxnaGXWYkfID8rdaY3IMCb1rZKWewpQahzEpmp6fP8mAOS/T9L6R7Hz1e7q88wANbxwAwPFZq+Wmd4M7EVY/kY2Pfci8ng/T8pEr5RLwU097TbiJjlPvpuF1/RBVKtyVNvQJUfT+ehy9pj9Jx6l3MXDOZFo+dhWCKFK6PxNzvQRaPnENdS71LyUv2Z1B3++eJa5rOtte+JKVt7xC4ab99PjkMaJaN/BVeqVd1ovsRYGqwwBlB0/4uhVXHDrJ790f8jXOlDxejv+yipU3TaHLW4FVUgCNbx+KvbAcW1EZq29/LWj11roHpmHPK8XrcPmSsoNhyyshrnOzkPOtn77eFwaT3G4sJ0Krv5buOY4tv5Tfuz/oM1hk79cafu/5ELbcYsIbpYQ835gSi0on53I4i8sDkmerkLzekF4Ft83BiTnrmNnqDva88RPHf1nJ5qc/Ye29b9H13QfRRcsS+y0fuZJDn1cbvJLbw+o7X8djsbP23rew51f338qcvQZzWiL9Z7/Ejsnf+MqhAeK6Nqdwwz4a3zyIvFW7yF642WfEAHScejd73v6FQ5/No+ldwwP2W7zjKGENg78m9a+5hC3Pfs7ya17yuybIpfuG+KhzMmJAFgAMVfIuajWI2tA+Bq8ruDTAxcy/pmrJ6/XyyCOP0KNHD1q2bAnA0aNyY7IXXniB119/nbZt2/LVV1/Rv39/du/eTePGjQPWmTJlChMnTvx/3buCwl9BxdFsll7xgi9c4Kqwsu+9WRRs3E+/mS+x/bSwRMmuoyT0bkNcl3REncaXL1BF6Z5jdP/oUTJnr8FRVI4m3EBCr1Y0uftSIhqn8Ee/x33HNrp5EEgS5rQE+cNaEEge2IEWj1zBqlun0v2jR1l50xRfzkrmzNV0/9+jlB/Oxm2xU7BhX4AAXMGGffSZ8Qz2vFLcNgfacJNPrRVAZTbQ7vmbWXPPW34PnfpX96XHp4+jMRtodPMg9DHhASJrYQ2S2fjYh34JuTnLtpO7cid9v5+A2mSg0a2DkbxePxXdmsR1SUdt0uOy2Ng+6Zug+Sm23GJK958gpn1jv/5OSf3bU3k8l7X3vEmPTx/HVCee4h1HAs6X3B4Ktx4kvHEqXd66n12v/UDmrMDS3aRL2mJMiaXHp4+zfsw7vjwiUaOm46v3ENmqOtFXZdAT3a5xwO+7em/tOPT5/KCGlaOwjGM/r6TFI1ew/8PfguautHn2Rl+zTvEMicNqU/D+U7acYlbe8krA+vb8Ena/9gOtnr6WsHqJHPpygZ+xAoAkcXLBJhL7tCFn6Ta/qcPfLKLOiG6U7DzVtFIQqDO8K03vuZQV10+m74/P0fX9h9k19TusJwuJadeYdhNvpfJEPke+XoQgivT75QX2TvPX8jHVjUcfG5hMaq6fRL0re7Nk1AS8LjeLRz3LoD+mnndX9Cr08ZE0vmMoBz/5PWCuxdgrORHCM6fSa5W2BEH41xgyY8aMYffu3X7eFu+p+Po999zDbbfJWe3t2rVjyZIlfP7550yZElhlMW7cOMaOHev7uby8nDp16vzNu1dQ+HPYCkrZUKMBXk0KN+7H63ShjwuMvVuy8mn11LVoIsysuG6S35y5XiIeu5PDXyygzqXdCGucSpe3x7DpyY/JWbJVPkgQaP7QZVRk5KBPiKL1+BvRRZoRVCJ5q3ex4rpJOMssFGzcT1jDZIq2HPStv/XZ6XR+4z5UBi37P5oTeE+5xbILPsS3f6/TLQvdneZGz/hxOeYGyTR/cHTID223zeFnxFQhebzsfOU7en/zDK2fvg7JK2uxnG7MqHQaUod2xl5QhuT11hpqyVu5g/aT72D7C1+i0mupd3UfRI2aDQ++i8fhYvGlzzDgt8lkL94SVCXXllPExkc+wFlaSfNHriC2QxO2TphevReDjuYPXY4u0kyD6/qRdElbyg+dRPJ6CWuYgiAK5K7YgTZCbstgTI6h4yt3Mr/v2IBr6aLDSerblgWDnwp5PyfmrqPRrYPp/dXTrL7j9WoDThBo9eQ1JPRoWb1eXARRrRtUGw410EaaMYUIcRRuORBSVDBn2Xa6vvsQy6+fhD42grTLelKy55ifLL8trwRtVKAQn8fmxOtw0efb8UheCVGjImf5dpZf85JsuHm9xPdoQZ9vnkFt0mPLK2H7xK98ieqS14suNoKu7z7IgU9+991Xmwk3Et+zJcPWTGP/+7NxllSQ1L895roJrL79NV+OVcnOo1gy88/KkPF6vSFzObXhJtpNvBVTahx73vwJZ5kFXUw4rcddT8MbBnBy8ZagRk6HKXdhSDxzgvXFxr/CkHnggQeYO3cuK1euJDU11TeelCSLLTVv3tzv+PT0dDIzg8uR63Q6dLraM/YVFP5tuCtsQb/RV3FijvzwKdp+xCdRHt44FevJQna+PIPu/xsb8M222f0j2fvuTJo/cgXhjVJYOOgpen/1FC3HXknqsC7g9RKZnkbmb2vZ/s5XjNr+CZse+zCgXBpAG26k85v3seL6yT49C3tBKdqYMMr2HsddGdgROaxBMsd+XU29y3v6qiukU00T3VYHrnILKYM7kTl7TUBfqIOfzKXZfSNCvh4F6/f6kkLzVu/2C38UbTlI+cEslox6lriuzRmy9A2WXz+JslP3FdYwmY5T7mLHpG/o8vYYDEnR6GLCAz0DpzAkx5DQuzXNHhhN0aYD7HnzZ1+OB8jG05FvF5N2RW+Ofuvff0gQReK7t8Rt/RK31c7Ol7+l67sP+jw8cV2b03bCjVQczUEXE44+Jpyw+kmE1U/Cll/KlvGfceizeb71NOEm+s96kei2jbjkp+dZ/+C7vpBVbMem9Jz+JNqYcPSx4UQ2T6PxrYNBAlGjovJ4Hrvf/Al9fCQas4G0K3oR370FpXuP47E7iW7dAH1ClF/OiyEukj7fjmd+n0f9jEFRq6HfrxNRhxsp3Xeck4u2gNdL8sCOGJNicFXU3iFb8nrp/MZ9nJi7Dnt+Ken3j8KYHMvGsR9gySogsU8bItPTKN5+2O+1bnTTAA58/Du5K3YErCmoVYQ3rYPb4mBe70eCXlfUarDnl7LnrZ9p8dhVWE8W4nG4SBnQAV1kGAndWmBMimHHpG84/MUCSnZnBKxRdvBEyDCg1+PBcjyfzN/WkLdqF1GtG9Dguv6Y0+IDqtiMidG0Hnc9jW4ZjMfhRK3XYkiOQVSpqHtpN4aueIttz31B6d7jhDVMpt3EW4nt2MRXuaZQzT9qyEiSxIMPPsjMmTNZvnw59ev76yTUq1eP5ORkDhw44Dd+8OBBhg4d+v+5VQWFvxVBJaLSaXwhhdMx1Y1n/8dzSR8zivUPTENQq2j/0m1se/4L+UEmSb6OxIJKJP3By3CVWSjeeogmtw1l6RXP46qwcvTH5VQezaFkVwYIgtxwUZJIGdoZy8kC+nzzDOVHsjn2y0q5u/KpdSPT01h58yt0/+hRFo981uc5MsRFsXT88wH7je/eAmtOIRsf/ZC4jk0Jb5yMs8xC5m/r2Prs59hyilCb9DS6ZTB9ZjzLypum+IV2HEXlSCFyAWz5JUS2qOd7yHR7/2GsJwvYPO5TJLcHUaP25b4UrN8rh3/+N1b2EAkC1rxitk74nLIDJ3BZbETHR9HqiatZfftrQa/X9K5L8Vjt7Hnjp4D+TlWU7M6g7qgeAePNH7mCzNlr6PPteJZd8yLuShu7X/+RS35+HsvxfIp3HWX1nW9gyymiyzsP0PTeEag0army6IdlfkYMyAKBC4c8zeV7Pift8l7EdU3HUVyJSquWDaFTFTNd3nmA7MVbWHXbqz4jMyI9je4fPII+MconmldlNNXEXliK9WSRr7t4RJM6jNz+MTmLt8gP55b1qTOyO5oIE7unfs+uqd/5nZ8+ZhTpD10e9HUC2QBHgAWDnvS9j45+txRTnXh6fPIY6x9+H0NcJMuufpHeXz3Nksuew2NzENmyPrFd0olu24jyQyfZ+sKXlNT4fbR/6XYMCVG4rQ4SerUib9WugGs3unkgx2etpuJoDuvHTKPruw/S8KaB/gnLksSRrxf5VbrVxFw3dAf0kp1HmdfnUd9rfnzmanZM/paBv08h6ZI2iGr/R66oVmEO0uVdG2EisVdr+v06EbfVgUqvlROOFYLyjwri3X///cyYMYPZs2f7VSJFRET4NGLefvttnn/+eT777DPatm3Ll19+yeuvv87u3btp2LDhGa+hCOIp/Bdw2xxsePh9Dn4a6E4WRJFBC1/FkplPRLM6ZM3fSHy35ux951dfBcagBVNxllrw2BzoYsI59tMKDn+1kEa3DsZjdfiqlUypcXR9/yGWXTkRr8uNoBKpd1Uf2jxzA7/3fIh2E2/l4KfzSOzdmtiOTVlz1xu0mXAjlRm5HP5qIS2fuIbibYfJXrwFc71EBv4+BbfdwfoH36Vg7R40YUYa3TqYZveOYOmVEynbd5yOU+8mqV9byg6eZOUNkwPuL2VIZ+K6NGP7xK98Y+a0BAYtfBV3hQ1tpBl9YhQaox5bbjHrHpjmp0AMUOfSbqQO68y6+9+h/jWXoDbq/Kqmhix5nQWDnvTz/AiiyOX7vyC8UQq23GLWP/wex35a4bdu63HXo4uNIG10D7a/9A2HvwheiZV2eS86TrmTLRM+p2jLIYwpsTS5bSgVx3LYPvErUoZ0JrJ5GnvelCuXLvnxeZZd7Z/Lpwk3MXrHJ5jTErBmF/Jbp/tDluN3ffdB0seMDjoHkPHzCpafqiyriTYqjBEbPyC8YXLQ86zZRay+83VZdK3GvgbNm0Js52a+pokAOSt28MclgeEtgAFzJpO9ZCt73/7Ff0IQ6P3V05QeOIHkdLPr1e/9puuM6Ebb525m2ZUvUHk8j/rX9MWUGoc20kzdkT1Y/8h7FG8/QmTzNNo+d9OpvmNzaPPsjcS2b4zabMBRXIHH5mDf+7PY89YvIEmIWg2Nbh5IQs9WrL79NZ8sgDbCxKgdn/gZJ26rnQ2PvM/BT/2NSJA1hi5d/15QxWhrbjHz+z4atHN1sOsonJn/hCDehx/KpZ99+/b1G58+fTq33norAI888gh2u51HH32U4uJi2rRpw6JFi87KiFFQ+K+gNuho8+yN5K3Z7d8hWhDo/NZ97P9gNkn92+G22NGYDVQey/UZMcKph8uRb5cguVxkL96K1+VGbdTT4Np+slJw1XIqEWNyLKN2fILk8SKqRYq2H2Zu94dwV1opO3ACQ0IU+z/8jWb3j2Toqrc5+u0SDn+1EIATv62l4U0DyVu9i06v3cPSK55HG2Gi6/sPUXksTzaaflrOgoFP0vnN+1h+7Uu4LTY2jv2Q9AcuC3rvJ//YSPr9oxBUos/QaPXUtay4dhJF2w4hatQ0vftSWo+/gYL1ewOMGJDzPlKHdyGxT2sa3zqYJZf7e4lcFjv9Z7/Eypte8YWhGt82BH18JCBXkXR5ZwxN7xrOyUVbUOm1xHdJ5/is1eycMoPDXy2k73fjQxoyrZ66FmelFbVRT5M7h+EoLGPL+M+wnhJnO7lgE83uHcGeN3/CkBiNs6wyYA1XuQVHcQXmtAS8bm9IIwagbP+JkHO2vGK2Pvt50DlnSQX5a3cHNWQ8Dhe73/rJz4ip2teCwU8yetdnhNWTBd5cFjt73vgx5B52vfYDvaY/SXTbhuyc9C3W3GLiOjUl/cHLOfLtYo7/spL+sycFiDBm/b6BxrcN8VUHHf91NZft/Zy8NbuZ1aZaVyh/zW4WDn6KHp8+Rv/ZL6ENN1GRkcP2Sd+QOWsNGrOB9Acv44qDX2ErKMGeU0Lm7DV+RgzIXd8dxRV+BobaqKft87dgzS4ma1510q05LYGBv08J2fbCUVgW1Iipuo4lq1AxZP4m/vHQ0tnw9NNP++nIKChciIg6De1euAV3pY2CjfvQxYST0KMVh774g+O/rsJVaSN9zCg2P/UxTe+5lMa3D+XQ5/Np9eQ1aCLM6GPD0YQZaHrvSEr3HiOqRX22Pf+Fn4u80W1DyFu7h20TpuOqsJLUtw0txl5FXJdm5CzZSnijFHJPJb4e/HQ+KYM6sf+D2dV71KiJTE+j/8wX2TFlBmX75Vy1bROmE5Fe7XEAWULfmBJLTPsm7Jr6ve+bcbDqoMrjuejjIvHYHLR88hpsucU+6Xyvy82+92chatRoIkKLgB39bikdJt/Bsitf8IWWqhAEgc1PfkyXt8ew/oFpNH/kCtJP00DxWJ0su/pFwpukIrk87Jz8re+hV7LjCG6Lg+Hr3sWaVYjk9pD521pO/L6BLm/fT0TTOuSt3OlrDRGAJPn0bJrdP5JDIY5T6eUqIUEUGDD3Zbx2J0XbD3Pwk9+x5VXn8CT0CiyV992H0x3ygQqQv24fjW4aFDBuyyvmwIeBSdsg65oUbtrvM2S8Tpfffk7HUVjGsV9WEdWiHq0n3AQeL6X7jrP2njd9arZ5q3YS096/+kryepHc1YaGJElIHom1d78V9DqbnviYxN5tcBSUMafLGL9cqbX3vElS//Z0eXsM87o/RHSbhtS9rCf2vBLy1uz2hbWCtaowpcTS8/PHseUUU3b4JNoIE/q4yKCdwKs4vSfU6XjsgZ3NnWUW7PkllO7PRBNmJKx+EsaUmIAQlELtKK+WgsK/BK/LJfc+0qiod2UfclfuZNGI8XDqYSqIoi/uf/DTeQz8fQoJPeUKk9+7jvGts/edX2k/+XZyV+6sbisAhDdJJaFHS7/chJxl28lbvZt+v0yk4mgO4Q2TKTulLeN1ugLE5BreNIDsZds48MFvft9ss/7YRNN7R/oZMmX7T9Dg+n4U75TVg12VNlS64IZMRLO6DPzjFVRqNRsf+zCoaNn+D2dzya+hpRXcFTay5m0IeMAm9m1L4ZaDlO49jsas57K9n6OPj/KVgVfhqrDiLK0MKCOvovzwSfa8+TOFm/aj0mtpdMsgRm39H8bkGFR6LeZ6oTtOqww6VAYd6Q9dhjE51k/vp4qYDk3QxUZQsjuD9Q++60toje/Rkh6fPM62iV9StOUghoQo4rqmh7yWqFYR0awOib3bkDygAwAVx3I48NEcKo7mEN1GVtj1uNzYcorx2B2y2J4gBC3ZrqIio1pDRRNuJGVoFwo3HQh6bHz3FhRs2MeOSd/Qa/qTLL0iMI9K8kqc3o8yskU9TKlxpA7rQtb8jdS7ojcuiy3oewZkD5O9sIwDH80JqneTs2QrttxiBi96lcJNByjeeZT4Hi1oPe569rzzC86SiqDVgG6bg7yVO1l+3WQ0Zj1uqwOvy01EehqD5k8J6lnRxYQHrZAD2Wt6+vvDll/K9pe+Yv8H1WXwmjAj/X6ZSELvVmfVM0tB5l8liKegcLFScSyXlTe8zKKhT7P08udZNGwc9rwSevzvUZ+gXL0re/tyOCSPF5Vei8tqD5qkunX859S7sjcpQzuT0Ls1PT59nH4/v8Dy6yYFlHh7XW72TPuFfr+8wLYaeSqAnzBXdLvGpAztgtqgC2w9IEl4T0tUjmrVgLjOzdn+4tcgCIQ1TA4qi6+LCSe8SSoxrRtSeiAzqBEDcuijtoqNtCt6yfut8XRMGdyJlmOv8knSH/tlFaaUuAAjBkAdZvCF6YKh0qh9lWUeu5MD/5vLihsn+8JEutgIkvq3C3pu03tGENkijVaPXUNcl2Z0eXsMEU2rZSEMp4QB3RY7v/d4yK8qJ3/NbpZf9xIdJt1OfM+WDF3+Vq0hCmNSDH1mPIu9sIzl173EsqsncuSbxXR4+U7qXd2XlIEdseUWs+Olr5nV6g5+bXYrs9vezdEZS+jx2RMh161ZqSOqVDS+eRDaiEBVX7VJT7NToUJ3pQ1rTlHQMu3EXq0o2ladrCuIIm3G38i6MW8T3iSVS36YQLsXb8VjCfRk1ERUq6gzqjvdPniY2CDVREe+Wcyh6QvY8sxnZHy/jF1Tv2fJ6Ak0unkQfWY8G7SlgDW7iOXXTULyeHCWWXxezbJ9x9k28SvctsA9GZNi6PLOA0H32Gb8DQF9qTLnrGX/+7P9/h5dFVYWDR+HJTO04KFCIIpHRkHhH8aaU8Si4c/45cZ4XW4OfDwXlUFL2mU9seeXyn1uaiiLqow6tj07PciKMpmz19Dv5xfwutxow02svf9tP+G5muQu3Y6gEv10UFKGdMaaU0RS//bUHdkNXXQ483o+TP1r+tLuxVvZ9twXvmNFrcbPCNBGmjEmRbNwqBwSbnrvCAyJ0XR67R42P/2JLxdGE2Zk4Lwp6GIjqDiWizE5lv6zXiJ/7R72f/ibn+EjqEQMidEYEqICvC6GxGgaXN8fQa0irnMzXJV21AYdeWtlI6BKq0YbacJZZgn6ADYkRNPoxoEcCpIHE9GsLrb8kgDPQMH6fVQey8OQEI3H7qD5g5djiI/i2M8r5Twls4Fm947AXD+Rom2H2fT4R1Rm5BCRnkb7l+/A6/Kg0qiJ6dgEQ2I02577Iqix57bYyfpjIz0+fgxBiTSXdQAAm3FJREFULeIoq0QXEVxN1nIin6WXPy9XpJ2iZOdRVlw/maEr3kIbF86mRz/k0OfzffPOkgq2jPuUlo9dRcMbB3DkG/8y8oimdYhokuo3Zq6fyPA177Lh0fd96skJvVvT+slrWf/gu0S3aUivr56m9FT1U0014sa3DkZlNqCPjcCWX0pc13RaPHIlR75eSNG2wxRtO0zlsVx69G0LCGgjzT4xxpqENUjC6/Gy7IoXMKXG0fS+kTS6ZRDrH3jXZyAIKhFXkf9r6nW5WXf/O4ze8UnQ1zB3xY4ASYAqjn67hHbP3Yw5zd+YFNUq6lzalSHL3mTLM59SsiuDsPpJtHvhFhJ6tUJjrm7waM0pYmeQdghVe8ucvZaWj10VdF4hEMWQUVD4h7Fk5vsn+Nbg4KfzGLLkdUr3ZrJuzDu+cVNqHLqosKAPvSrsheWyB+OUF0MfFxnyWE2YgZLdx2g74SZW3DCZmI5N6fTaPRz4eC4RTVLZ/8FvvpDTwU9+p/fX49DFRvgMo0Y3DyRz9hrf3rp//BibnvwYY3IszcaMQhdlZmbz26h/dR9G7/yUE79vILxhMjHtG6Myatk5+Vv2vPmTz5BKHtCBvt9PkDtgnwoZNLi+P6Y6cQxf8y47p37H0e+W+sZbPykrxXo9Hg5/8YefkVWT5AEdKd2fSXyXwNCMxqSn3aTbcJZb/BKKo9s0pOfnT2I5kU+PTx/n8BcLyFtdXdqbt3Y3cV3S8djkHJuGN/Sn73fPInklJI+Ho98tZffrP9Lh5TsQTzU9LNt3nGVXvEDfH56j7ugeCIKAvaic7EWbETVqYjs3Q6VVU7TtsO8BnrN0O9oIE7umfk+nN+8n7YpeGOOjcJRWYM8vw1FUhibchMfmwBVE10fyetn67Of0+OzxkH2w9r47i+FrppH1xyb5dysI1BnRjS5vj8GY7C9OKAgCkc3T6PHZ41QezcVeUErR1kOsuu1V7AWl5K/ZjT2/hFZPXYegVeO2OtDHhNPyCdkrpY+LZMjyNynacoiibYcCWxLMWkP7l24nrFEKvb8ex5LLnvMT2VMZdPT8/Em2v/Q1AJasAraO/4zGtw0h/f6R7Htfzu1KHdKZ1Xe+HnCvrnILlcfzAgwSIKArdU28TlfI0mxthImkPm0YMGcyHqsDUafBEOTvTvJ4a/W6lOw5FnJOIRDFkFFQ+IcpOxQ6MdNtsWMvKGP17a/6xjThJi75dSK6mHBShnUha866oOfWu8K/m3X9q/uy49SH/uk0vmMYGd8vpendlzJq+yfoosysvO1Vck+TiK8i48fl1B3ZnaPfLqbpvSNo/uDlWE7kk/7gaAwJ0XjdbtpOkMtjD03/w5erk/HDcmLaNabFY1chqlR4XC52vfI9O1/2/3aavXgLtrwS2k+6jfVjppE6rAvtX7oNZ3EFok5D57fup+2EmwA5pFOliyKqVDS8YSAnfltH4Wb//I2Wj19N7sodOEsrievcDOH0BA3AlBxLj08eo8PkO7AXlaExGXCWW1h58xRK9xzDkBBF+gOjqXdVbzY8/D6AT31YE26i67sPYkiIwmN3ojbpyZq3gRNz5cqXsIYpWE+rRNo49gPiuzeXw106DY1uHkRYg2RyV+7AbXXQ5K5LsReUsPnJj9HHR8ol9g4X68e8Q3SbhuCVWHff2z4jEiCmXWP6fD2OlbdMDRD5y1uzWzYWQxRaeJ0ukCQuXf8ernIrKp0GQ2IUuqjQpa+S08OCwU+R0L05YQ2TieuSzskFm/C63Bz7ZRXtJt5Km2duIP3+0ai0arQ1+hJ5bE72vPkTXpcbR1Ggt7B0zzGiWtQjsXdrRm39H4e+Wkj5/hNEt2tIvSv6ULB5P80fGE3xloM+L92hLxYwcM5k9r0/m0a3DabsQCauEF263Zbgwn2JfdqEvN/IFvUCWmacjj46HGoR/1XpNUS3aehLaD+dpL6hr68QiGLIKCj8w5jTakkS1WkIb5RC3x+eo3DzASKa1sGUGsfWZz/HmBJLx8l34CypJH+1v/hXdNtGRLVs4DdmTImh49S72fzUxwHHpo3uQeZva7EXlFJneFdsecVYa/nG6CyppNPUe2gz/ga0kWYcxeWo9FpEjQZRrSJn2TZW3fxK0HN3vf4jDW7ojyklDlt2Mbte+yHocSW7jhLVsj6j90xHEGDNXW+SvXgL2ggT6WNG0ezekRhTYgPOE3Vqmo0ZhSAI5CzfjsZsIHlgR3KWbGXvtF+pd6WcQKo1B38Y6aLC0EWFYbLGsePlGX5Gli2vhK0TppM+ZhSNbhlMxvdLiTvl3XGWVrL79R8pP2WYCqJIwxsH0OOTx9gz7VccRWUB7RisJwux55Wij4/C63LjLK1k8cjqcvn9H8wmsU8bek5/Co/dyZZT3cIB9rz5E03uvtTPiAEo2naIjY9/RNvnbmL9A9P85vSxEQjCGVIjBYHlV79I0dZDqE16mt5zKS0evRJTSvCyY5fNQf+ZE8lZspXiXRmE1Uuk/6yXOPjZPI7/ugpbfimR6WkYTpW6V2EvLMNtsZHQuzUqnYb2L95G5m9rOfDx3OrfxSkROE2YEVGjRq3XkDq8CyW7M5jd9i5ADi91mfYgy685pZsjSThKKhi64i1MqXHM7fZg8NsURVmcLwhh9ZOI79kq4O8KoOs7D2BIiEKSpKDG8Nmgj42kwyt3sXDwkwFzupjwWg0phUAUQ0ZB4R8mrEESpjrxQTsaN7ptiNzQLi6Ssv2Z7Jr6ve9BCZDx/TKGrXybXa9+z/FfVvnKr9PHjELUqinafpjiHUcwJEQR3jAFVCID5kwmZ+k2nGUWEnu1xuv1sOjS8fSdMR7dqcRHbWQYKUM6Uf7eyaB7ThnaGWOdWFwVNraM/4yDn/zuyymIbFGPHp88hqlufFD3uaOwzNfB11X5f+ydd3hUZbe37+k1vfdCQiD03nvvHQQVUUQp0hRFioAiCoo0RREEsQEiRXrvvfcEQkgjJCG9ZybTvj+GDBlmgp7vHN/D+565r4vrIrs8s2dPMs/a61nr9yuza29QQXHyYyTOKnY1HmsZvzy/mBsLfiPtyFU6bvvExvdG5uZE+vHrpO2/hEej6pSkZHJv9R5LfYt/18ZosgqqDGQqKMswGxza497qPXTcOo/wER1RBnhS8jCTA52nWdXumIxG4n8+iMLfg5arpnKohx0JCYGA4pTHCGUSTHoD154xBgVzvYZ/10Yo/D2ttGWKkzLsFp2COZOh9PNAKBFbLYNETx5oNg9tW5fHJ2/anOfRMJK0w1csBpn6Eg13lmzh8enbdPpzvl2PIX1hCccGf2zV8RT3w15arJqCNrcIqbPtfS57nMv5SbYChHU+eIla7w7hzpI/kDircI40+3Tpy7TcXLiR+J9s29aLEtIpfZSFa3SIxV5DFext/t02GGi2dBwnXv7M5rzoSQNBJLTriaTwcaP9ptnErvyTu9/sQFdUikeDSJouHY9ThD9pR69yf/0Bc9HzGz1wiQq0Keb9K7yaRtH215lcnPqtZSnLo1F12v70od3lLgdV4whkHDj4X0YV4Em3g19wuO9sqyAlZGAb6s9+BbFCRkFsCtfmrrc511Cm5fL01bT9dSZNF48FoQC5txvarHyOD//0qTkk5qfa1mvf58bCDQhFIsRKOZdnrLF49RSnZuHeuDpgzgRFTxrI/fUHbAINubcbHvWqUZz0mMQtJ0jafAKpm5OlXib/ThLHh35Co8/e5ORIW2NXlxrBlu4jsVJmM9la3ZtALy5/uNpu4WXW+VgK41JtJlexQkad94by8M8zpO69YLXPo2EkQrGY0odZOD8jzf8smuz8Kq/LqNMjdXPCs0kUIqmEvJjkKnVV7n23k8DuTREr5TYFqwHdmpBx8ibxPx8iZEBru+cDxK3ZS/Rka9l/j4aRFMXbDzTBbOUgcVJa2oH9OjVEHeLLnlaT6LxzAadHf0HOladLG6pgb5p8OZYTIz61GSv74l0K7z+yudelGbmcGvWFTdu2yWjk4rvf0W7DLBR2gp/U/ZdsghiAW19sosMfc4n3cqXj1nko/c0Zt/LcIlL3XbQ5voK0I9fwbFKD/JhkpG5OCMViNLmFyN2dCezZjG6Hv+TKhz+QezMBdYiP2cdLIGBvq0k0/Wocfp0a2mSMVP6eNJg3iprj+pm7BJUyMJk4OXIhaQcvW46L/+kAoUPbWzI1z1KakUtxUga51+NRBnrhXiccZaAnUhc14S91wLdNHbR5xQilEuSezna7qBw8H0cg48DBC4BLVBA9ji+h7HEe5fnFKP08kHu7InviAPzwmQm5MulHr2EoK8cpzLxEZdDquLV4s1UQA+bWzlOjFtH215kcHTjHZhxVgCfph64S0LURci9XxM5Kuuz5jKKEdMRKOSajkdJH2bjVCefc+GWEDGxNyMC2qAO9MOoNqAK9SNx8nAe/HKIkNQuEAqRuTjb6Ho0XvmnWBknVo/Bxo9rILtxfu8/mehR+HiAQUGvSIDCaLJ0xlUnZeRahVIzS3wNVkLcl1S+US2n/+xwe/HaYR/svIVbJCR/eEc/GUZwatYieJ5Y9/wMBRPLnm/NJ1AqL1keFMKA9ygtK0JdqaPTZaEoeZXP1yfKQOtSXWpMHcmzYfEx6A1FjelY9Rl6RVUAglIip+c4A9lVhEQDg0bA6dWeNQJNVgFeTGhQ+eMSpUYswlus4MmAO3Q9+QUH8IwrjHuIaHYo6xIf9naZVGZClH7mK7zNCfNrsAqvguzL64jLECpmNg3lZZp6lHd4eKbvO0f/a98i9XS2WCAKxEKmzskpjT6mzEn2pBpFMQsvvpnBh8jfUnzuSoF7Nkbqo8e/YEPWGWeRciaMkLYf76w9YnK9PvLyAboe+RNGpoc24IonYSsk3/tfDVkFMBUmbjxP5WlcCezSz2l78MJOjA+ZYMlxgrqXqdnARHo2qIxSJUAV5V+ki7uDv4QhkHDj4h9GXajCU65E4KRCKqtYpUfp5oPTzwGQ0Ingm1S2S26qPViAQCREIn67Vl2XkErfa1rOp4lpKHj5GHepr1Z4rViswaHWcfPUzGi54g9rvDsFYrkebU8jdlTvIvnwPkVxK+IiOuEaHUJ5XTHFSBjErtpO48YjlOmpPG0q9WS9zY8FvlOcVEdyvlUXWX+7tRoOPXyP9xE2O9J+DwseNBvNfp/5Hr1KWnmuVPVEFe9Pyu6mceXMxJSmZtFk/HW12oU1xpEgm4cKUlRTFp9Ht8Je416uGQCBA5qrm9PyfUYf6UX/eSIzlepK3m+0GnCMCkPu4Vnk/wawoK3FS0Gnnp5Rl5BK//gCZZ+9Y9jtV87fYG4B5Oa0qzNmqQk6NWkTNd/rT9teZGMrMInSnXv8CXWEJCj8PZB5VP4n7dqiPWCWn3YbZ3Ph8A02/Gosq2IvQIW3tftbuDSKRuamJW7sPsVxK7IrtVlmT4sR0tHlFBHZrAt2amLelZD63W0fm6Ux5YanVUlFVLcqW/XaKio16A9ocW9G4CjSP85C6O1mp28q93ag5aQAXJn1j95zwlzuTeyuBiJFdub14M9mX73Ft7nq8mtW0ZDhSdp3j0nvf2T3/2pwfca0ZjMLPg5KUTHJvPKAgLhX3+tVwrRmMKsALTU4hsSu2VXndd5ZtxadNXUubta64jMsfrLYKYuCJ8We36VbeS5rsAkpSMnl08BJCmZTA7k1Q+nsgraLF3oE1jkDGgYN/CE12Afkxydxe8gfarHwCe7UgfHhHS+akMiajkeLkTB7uOUfGiZu41Q4lfHgn1MHeiORSgno24/IH39t9naA+LTBodZbiQ6PBQNhLHVD6e1CUmEHytlNWkv1lGXnI3J0sgYxQIqblqqkWVd6bCzYQPrwTZY/zODb4Y4v4nUFTzv11+8k8G0PTJeMoy8zjwc+Hnr4Hg5FbizbR5qcPUfh54Fw9iLCXO1F3xnAMpVqKkzK49eXvloCg7HEeZ99aQotVU2nzywxKH2aSfTkOqYtZ6+XcOysoTkwH4MLUb2ny5duces26gNi7ZS1uLdqEyWjkUM8Z9Ln4LapAL+SeLrRZ/yEHu31Awm9PNVEUfh502DIPqVvVE4QmK5+UXee4Nnc9pY+yUfh5UPOd/oQNa8+FySuRuTvTcdvHVpkG1xrBKAM8KX2UbTNejbF9iP/VfJ9iv/mT4H6tOP3mYkoqaQJFv9MfVbAP6jA/y3uuQCSTEPVWb44N/QTX6BA6bfvY4pXUYO5rGDU6Hvx62PI5+bSpQ9ufZ6ArLKEgxn5bf8X9r4zc25XI17tz97udNscKhEJca4Zw+o0vqP3eEHMw6OWKzMPZrq4PmNVsncNtPZ2kLmqqj+mJOtgHqYsak8FA8vbTJG07hUlvILhfK8TPZMMEAgGhg9shVsqQuTljMhjR5hYSu3IHPq1rU5aeQ/IfJ7g64wfLOUUJ6ZZ2fqPBYBWIPkvujQT0ZeXk3njA/k7TrLKIqmBvuh9ejMRFha6kauVjfYnGyqZAk5lP0hbb5TMwZ+kK7j5EHexDaUYuF6euNLvNP+HSe9/R8NM3qDGuryUr66BqHIGMAwf/ANq8Im4t2sTtSsZ6mediuLP0D3qdWYFL9SCr4/NuJ7K37VRLm2jy1pPcWPAbnXctwL9TAxT+7tSbM5Ibn1gr7yp83Kjxdh/2d5lGr5PLzBLp2QWU5xWRcyUO1+gQOv4xl3urd5Oy8ywAfp0a4FY3jLSDV1AFeeHVtCZ3lm21fNHrSzVosvK5NG0VJqNZQVikkJnrO0wmCu6mYDKZcKsdTt7tRJv3fn/dPqLe7oMmu4DSh9m41wkj9/oDjgywXc4CuDp7HYE9mmIyGLk0bZV5QnimNkWTmYcqyAvXWqHkP9HYaPTZaBJ/P2aZwMsycil5mGVZCnCu5k/Xg1+QezWegnsp5qUnkZBjQ+bR6PM3CXyiUlwZfZmW2O92cr1S0W1Zeg5XZ60leuogep5aZncpQBXoRfcjizk29BPLkoVQIiZydA9Uwd7c/HyD5djHp28hdVFR0RDs1aIWEa91RenrRo+jX3FjwW88+OUgBq0Ovw71qfXuEG5+tgF9cRnZF++SfSXOEsgo/Txo9vVE6s1+hfK8YsROChTersjcnSlKzEAkl1qJHFbm2YJSsVxK3Q+H8/jMbct7AHMQ02zFO9z9dgfJ20+TvO0UrVa/ZwmWW3w3haOD5tm0dDf4ZBRyOzUjBk056hBfrs1ZT2laNiKFjGovd6LDpo+4OG0VgT2a2pxjMpnQ5hRyb/Uei4WEOsSHZismos0ptJInqMC1VihipfnzFYpEuEQFIVLIbHy4wBysABzuM8tmKbQkJZOTIxfS6c/5hA5sw40F9oPD8OEdkbqoMBmNlGXkYtTpqfPBS9xft89uoFfRip92+IpVEFPB1dnr8O/SCK8mtmrF/xOUFxRT9jif/DtJiNUKXKoHoPDz+Le0RnAEMg4c/AOUPsq2CmIq0OYUcnn6Gtr8NN1iWFiWmceJlz+z0bow6Q0cHzaf/jfNKejqo3vgUa8aCZuOos0pxKdNHTwbRXFuwnJKH2bx+PRtJE4qjvSfbRkj98YDEjcfNwu6pWahL9Wi9PNA4etOWVouCb8fM1sIPINIJkVXWErbX2ciFAkpLypFFeBF+rFr3Fm6hazzsVVOkMUpmQT1asbRQXNRh/rQdNk7lCY/ptOf87m/bp8loLLck+wC9EWlGA1Gu+qtFegKS4mePMjccm00kvTHCRsF2spLFgatjrvf7uD+2n0ofN3RZBVYCl+PD5vPwNj1OEcEWJ1flpHLrUpBR2Viv/6T6AkDqrQHcKkeRLeDX1CalkNRfBpCqZjk7ac5N3651XEydyeixvSi4O5DQga1wSUqyJLdUYf40PDT1wnq1RyjTk/25Xucev0LK0XmuNW7CerZzLKEIXVSIrWja6Lwcyd60gBufWHbeeXXsQESZxW5Nx6gzS9G6euO3NsVVZA3XfcuJP9eCmn7LyFSyvBqUoO4dftI3n7acv6FKSvxaVsXl+qB+HduRO/z33D1ox/Ju/EAdagv9ee9hmfjKCQqudXrGg0Gkred5OzbT00gDWVa4n7YS1FCOt0OfWm3Y6c4+TF7206xCjKKkx9zpP9HdNr+iZU4YwUNP30DmbszhnIdZem5+HVsgEtUEDI3J+J/OWhVbFx72lBLDZg9ss7HoM0tJHJ0D+6t2WNTq6MK9iaoTwvKMnJJ2HCU20v+QJOZh3fLWrT4birxPx8g5U/rNnn3OuGUZeVz+8uq64XurvwTjx+m/Y+bSJZl5nP9k5+tDGHFKjntN8/Br0N9m4zYi44jkHHg4B/gecW5D3ede9KWag5ktNkFlizDs1jUR4N9wGji9BtfENi7Bc6RATzaf4nrlbyRsi7epTgxw2YMk8HI5Q/X0Gr1uwhEQvOEUFBCxMiu1Bjbl9OX7lk9TXs0jETiqqLh/Nc5/eZiqwkidEg7Wq99n8IHj8g4dsPmtQA8GkXyYMMRSlIyzf8S0jk6eB5CqYSG819H4etupRUiEAkRyqUoVArEaoXddmyxSo5Jb+DsW18RPWkgek058T8ftDnOqdrTTqSyzDzufb8bfYmG8oJngkSDkUcHL9sEMprsAgxa+waFJr2Bsif1G+V5RZhMJqSuarP42RMU3m4IJWIuvvsdGcev2x3Hv3MjXGuG2N1XwZVZP1haiW34m9olYrmUWlOHIBCJiFm+DX2pBoFISNiwDjT4eBRH+s22eEeBuUuu+Yp3UPp7ovQ312udfOUzc6D7TLZFX6qhJCUTl+qBSNQKvJrUoMPvH6Er0SCWS5G52xfQK03L4cqsdXb3pR+9VqVA3cNd5+yaQmIyEbvyT6InDeTaHLNdh9RVTbPl7+BRvxr6Ug2PDlzm5GsLLb9XQomYOh8Op84HL3Hry9+pMa4vAV3Nis/PQ19Uhkv1QHqfXcGtxZtJ3HQMgUhIxMiuRE8aiEgl5/SoL0jdc95yzuNTt8g8c4d2v80k72YCRQnmZUPvVrVRBnph0unRVGEbAlCanouxXP+3Ahl9mRbjk1q8Z2vsniV1z3mrIAbMS2NH+n3EgDvrcKlCX+dFxRHIOHDwD/C8IkiT0QiV5oUKTZWq0JeaU+FCiRiJk9Kq5qMybrXDSDt81e6+svQcRDIJRwbMsYiy3V+3D4mTkuB+LS1Pi0p/T9ptmIVQIOTEK5/ZBBVJf5zAKcyPyDd7cmeZbeGjQCQkakwvjg395On7fTIJGst1XJ6+mo5b5pGw6ZglAxU6uK05AJCKabZsAmfsyMnXnzOSez/sBcx1Jp13LSCuUjAEEDygtdVShklvsBGgq0xpWo7NtucVVYO5e+b4sE/M99lkwrtVbZp/PRG32qGWyUbm5kSzZRPY22ayjYVE9OSBf60K6+lC5Bs9SNh4lJrj+yFxUiIQiyi8n0rs19upMbaPlW/P81D4uFF/zqtUH9MLXVEpYpUckVzK/o7TKIx7aHVs8rZTyDydabZ0AmKFDF1RqU2hamVMWAc3Uhc1Bo3O7BStz7dpZwZzVq0qvy+AvNtJuNetZrXNqNOTdti2Y62C7MtxtPh2CsF9W2LSG5B5uqD090AoFpF3O5Gjg+dZBWJGnZ4b83+h885PiXijOzIXNQofN8vfmT3ESjmlGTlkfnOHsGEdaLpkPPVmvgwCAXIvF0RSCdlX4qyCmApMRiM3PttA1Ng+XJ39I9VGdqHBnJEovF3RlWrw79yQB78csvOqENy3JWKl3O6+CjQ5hRTcTeHO0i2UPc4juE8LQoe2x6kKN/bSjFxufGbf58mkN5C0+QT1Zr383Nd80XAEMg4c/AME9mhqabN9Fv8ujZBVKjaVuTvbTY3DE/XRJ1kDhZ879eeO5MyYr2yOE6sVeLesxdm3l1R5TeUFJTYTe9zavfQ8sRTPRtVxq1sNZYAHjw5cxinMl+YrJnLp/VU2HSZxP+wl/OVO9Dq9nKQtJy22B+pQX5otm0BpRh7tf5tF6oFL5Fy+R96tBKvzH2w6Std9C3l04CKPz9yh8aK3LRNz6MA2OEcEcHXeegpjU3COCqTuhyOQuqiReTpT8jCT/DtJ6EvKUIf7UfykNTxqbB9qvzfEKjsiVitwqxtuVe9h9TnYabeVe7niEhVk8ZWqjDrUl/zbSVZt4JlnbrOn5UT6XVtt5WatKyqhwx9zebjrLJnnYpB7uVLt5c4UJqSRcyXOqqX3WQRCIaFD2iHzcObqrHWUppmXO9zrR9Bq3fu41wmzOt6gLafkYRYPd5+jIC4V3/b18W4ejfpJ3YdIJrWa1B6fvWMTxFQQv/4gdacPxynMD5m7M0p/T8vrV0akkFnGB3MmK/3oNa7O+ZGi+DSco4JotOANfNrURe7x9DMRyaUIhEJb9/Qn2PMDE4hFNpmzyqgCPJE4Ky11QxUY9QburtpVpR3DrcWb6fTnfGRPLBPk3q52DTMBar7Tn/ifDpK87RT5MUk0XvS2zWdY2bH8WfJuJRDYoynhIzohc3Oy1GZJlHLqfjiCpD9O2CzVKnzcCOrdosoxAbT5xdxZusVKfTrzzG1uffm73Vo8AJPOQMnDrCrHrDIT+ALzF1rVDhw4+P9BFeRFxKjuNtvFagVNl4y38puRuqnpun8hHTbPpd1vs6yKHWtPH2Z5shUIBAT3bUmdGcOtnKaVAZ50278IkUxCrfeG2k0rK/w80BXZpu31xWUIZRL8uzXmwuSv2dV4HBcmf8PhvrO5tWgjbX+ZgfiZOgdtbiGF91LZUf8tjDo9A++up/uRxdSZ/hJXZq3l9GsLOdR7JiaDkWYrJnJvzV6r84sT0knZdZa0w1dp+tU4VEFPJwSpqxrftnVp/cM02m2YhU+bupwevZg9rSZybc56Gsx7De9WtZF5udLzxFIGxq5nwJ11NFow2kavROHlSrNl79j7eHCrE45ThG1HjUStoOP2T6jz4XCrSVXqoqLNTx9aFe1WYNCUc3vpFvRPJiJdcRnX5//KoZ4zKEp6jH/XxjiF+3Fx2iquz/uJ21/9gfY5tUBgrrE6/foXVkFE7vV4jg/52CpzYNDpyThxk23Rr3Px3e+4t2oXJ16az+7mE6r08CpJeWx3O5izZhXBrjrMl5arpiAQ2f4+NVs63nK/9aUa7n2/i+MvzacwLhWT0UhBbDJHB87lwW+HrZbq5F4uBPWxPzlLXdW4RNlf0qj5Tn9CBrW1mzGrO+tlu8aMBk15lWasYP49LE5+TPmTzKDMVU2TL96m3kevInmy7Cv3dqPhp28g93KxGIneW7OXMjt6NvYc1SsQiIQUJWaQey3eqrMJwCnCn15nv8anbV3LsaGD2tDz1PK/VPgtS8ux8SkDc63Ypfe/t7y3yoiUMtzrV7PZXoFf5wbPfc0XEUdGxoGDfwC5hwuNF75JSL+W3PpiE5qcQgK6NSF64gDUldqvSx5lc23ueh78csisFuuiMn9pD26HRCXHr0N9q6UIuZcr9Wa+TPXRPSlJyUJfXEpZVj6n31xMwb2HRIzsSuu173PqjS8sT6ICkZDGC8cQs3yrzXWKlXIwwYXJKylOsp7gCu495N7q3VR7tQv3Vu2ybFcFe6PJKQCTiVsLN+LdPJrzE7+2sVi4+625NZZnnr7d6oZTFP+IzLN32Nd+Kn2vrMLpGZXdwnupHOo1w2pb6aNsTr76OR23f4JTqK9d7x9diQbN4zxK03MQyaWogjzpsm8hl6atIv9OEiKZhLBhHQgd0o6CuFTUT7qPdMWlFNxL5canv5J7MwHnCH/a/jqT8vwiTAYjnk1rcO3jnymsQkk34+g1dAXFiOXuGMv15hoao5HUPedtlhu0eUWYqlAMBvNTdlXZvPKCElJ2nKHWlMEAlKVlc3TgXCtXaDAXLZ99awkdt31s077rZKclugKRQob4SXZMKBTi3aYOfS59x+0lf5B3IwGncD9qTxuKc2SA5feyLCOP6/N/tTve1ZlrCe7b0pIRkjqraLZsAgVxqVZBhlitoM366ZSl56LwdkMkl2IymShOzCBpywnSjlxFFexD+9/nkLLjDPfXmQUUa04agF9H+xOvSCHFq3k06ceu293vWjuM+2v34texAcH9zA7kCl936s1+hfCXOpB/JxldUSn31+/n8alKnksmE0XxaTZ1JL7t6lWZbQrq04LU3ee5t3o3PU8vx6dl7afXKRHjUT+CTts+pjy/xKzO7eH8l0uQAKn7q1Y7Tt1zwaoWrwK5hzONF73FfjtiijIPZ/w72mYqX3QcgYwDB/8QCm83gvu1wqdtXQyacow6PQmbjpFz9T4+rWoT0L0Jd5ZusXwpg3miurHgNxp++gbVpg212wopUSnQios4MfxTyh7nWu2L/+kAqiAvGn76Bkmbj+NeP4LoKYOIWbHNbr1DxKhu3PpiE3U/HMGRfrNt9j/cdY4Ov8+xCmRqTR1M3A9Psyy3F28m7KUOdn2J7q8/QOjgtsSuNBcWCiViwoa250h/cyt2eX4xSVtOUnvaUIsqryangKtPCjefxaApJ/vSXfw62U5emuwCYr/dwc0Fv1natxU+brRYNZU6M4YjkkjAaCRl1zmODpqHOtSHHseXIvdyIe3QVataiuLEdNIOXaHlqqlEvNYVk9GE4Tk1FDJPF4RPPiuJi5Lgfq3IvnTP7rFBfVsgca1ax0ZfUkbOtfgq96cfu07NCf0RSsTk331oYw9QQcaJG2iyCmwCGVWwt5UvUWVqvtPfqv5G5qJGVj+CFisnU55fjMRJgczVeryyx7kWHyub91KqQZtVYLW0pQ7xodOfn5B9+R4FMcko/D1Q+rhz49NfybkeT//ra8zXdyeJvW0mWxVq31+7lxbfTrGYjip8XKsUjROKRESM6sadpVtsO+wEAmq83ZtTb3xJyp9n8GxSA9UTA1KRRAwIODb0Y7vjgvkzfhaFnzutf/yAU6MWWS1nqUN8qPF2H/PvF1i6FsvSchCIRci9XFGHeJuXmKsokq4Ko96AUzV/pM4qCuMfWdVkmWvxnl5HZZNLjwYRdPhjLucnfm2xKPFsWoM26z74L/k8aXML0WSZzVClrmoUfu42kgb/ChyBjAMH/zASZyW5Nx5wsPuHli/8pM3HkTgpafvrTNIOXrZ0M1Rwc+FGqr3cqUpn7Lw7iTZBTAUxK7bT7/pqosb0QqxWIJZLqfPeELLOxz59ChYICBvaHu8W0Zx6bRGu0SG41Q6z0YUxGYyYjKYn70NF3Zkj8Gldh9xr8eRciQOgNC3bqg6iMprMfIs7tFO4H81XTEQgEdPiu8kkbjxG2pGrpB2+Qo0J/ZA8KWo0lJXbrVGpIOfqfQQCIaXpOWizCzGZjMg8XMi9lWCl/wJm0bcTwz+l45Z5HOo902pfYVwq+uIyynR6zrz1ld1aigtTvzUvDYX6UmvqIJK3nrR7TXWnv2QJGIQiEeEjOhGzfJuNUq7UzYmo0b2eTJb2EUolqIK8q2xFd60RjPDJ+boC26WDytjzilL6utN592ecem2hJdMgEJvNDz0bR3Gg6wfUnNCPwB5NLV5HVbV4Awhlzy+QFsqsg3GDtpzbizeT9McJ1CE+aHMKzZYWT7i9bAuNP3uTM28tsek2Azg/6WsG3f0Jp3DrLJ5Rr6fkYRaZZ+9QGP8Ir+bRuEWH0PPkMo6/NN/yN1ahKJ24+TjleUXmfwXFlkAGzEtgHo2qW37HKyNzd7ZrKSBRKQgZ0ArPxuu4/+M+StNz8W5eE7mXK6fe+NKyZJdz+R7ZF+9yYsQCy/W0+elDvFvVQqL6e0XcFfcxuE8LhBIx2uwC6tR7CU1WPpc/XIO+RINfh/oIZVKyLsQSt3YvuqIyIkZ2xb1eNZT+HoQMbINXs5po84oRScXIPP5rPk9FSRmcGf2lJeMllEqInjiA2tOG2vWc+idxBDIOHPzDlKXlcGzIxzZPrbqiUi699x3RUwbZSK/ri8vsfolX8DxvH11hCcZyvdWXksRFTY23e6MO9cWgKUeskpN26AqnRi3CZDSaDQEDvWwCGVWgF86RAXTYPBeTwcD9nw5y5cM1NFwwmohR3YhffwDPpjWqbF31aVOHiDd6WJbI7iz5g4RNx5C5OxH5eneqj+lF+rHrVpknkVyKS/XAKjtmXKoHUfoom/hfDhK7YjuarHyU/p60+HYyPY4vIWXHWe6v22e5fwZNOVmX7uLZOIrsy0+zJEKJGIFYhCa7oErJfEOZlpLkx8i9XHCtEUz9uSOtWt4Bot7ug3fLWlbbnEJ96XVmBVfnrjfrlZhMhAxoTcNPR1stLT5LWWYeJoOR5t9MZH+naTZLRgKh0Kr2yu2Zwt/KKPw8kLrar9twCvWl4/ZP0GbmU15Qgia7gOTtpzkx/FNMRiNnxnyFd+s6dPj9I5vaI5vX8XFF4edh5cxteZ1wP+Re1pOjrqiM7Ev3KM8vJtdOsJZ98S7avCKyzsfYfT2T3kDOtftWgYxRbyDrwl0OdP3ASvBOHeJD9yOL6bj9E4oepGMymDvZYr/dYRHWAyyBYQVyTxfa/jKD/R3etRKzEylkdN75KUp/+/dEolbiWjMY11qhFN5/xJ1lWyl6kGZ1jMLf0yrALXucx+G+s+l7ZRVuz1hdaPOK0GTmU15QjMRZhdzbFbm7MwZtOelHr3NkwByr7xXPpjVo99ssTo5cSNMl44lZvtUqU5r4+zE8m9Wg45aPUQV4ogr0shQtl2XmUZySiVAsROHnYcne2KM0I5fDfWZZyUYYy3Xc/mozYpWMujNf/pcK6zkCGQcO/mFK03OqnCgL4x+hCrRjGCcQPLft0q1OeJX7ZB7OiBVPn5LN2ijliBRSrs75kbxbiTbZB7faoWRdiLUZq+GC0Zx56yubZZKrs9bSedcCEn8/Tp33h3Gw+3Sbc8VqBUG9mkO5HqFExK6m4y2vq80p5Pbizfi2q0fLVVMs5oBgnkQazBvF4b6zbMYUyST4tqvHo/0X0ReX0eLbyaTsPMuDXw5xdOBcOv05n6LEdNptnM3Zt5ZYnvQL49PMQnqVApmwER1ReLs+V4Sv4v7dWrSJ6IkDqDVlEGHDOpB+9BpGnR7/Tg0tk1pRUgYCkdBsuCmX4hwRQKvV79F44RgEmLMxVbVNl6Zlk7LrHLFfb0dfoiGoX0t6nVnBqVFfWLJoYqWctr/OQBXy9PdF4etOtZFdeWBHU6fZsvGIlHJK0nKQuqhsxOnk7s5I1Aouf7iGmGW29VOZp2+Re/2BTSBjNBisPMOU/p503DaPA53et1rmkjgp6bB5rs35IqUMp2p+5F63v3zmHBGA4DmeZIDNUlFpWjaHe8+0Ue0tTn7M2bHLaLn6XU68vMCuqq9v+/p2MxGuNYLpfeFbsi7GknUuBpeaIfh3aoAqyPu5nmkA3s2jOTN6sd16magxPYn/2brd2liuI/6nAzRaOAbhk2L9kkdZnB27zKrGyq9TQ9qsex+j3sCR/h/ZZNyyL94ldf9F+l1fjTavyO5yb/aFuyT+foxaUwYhEArR5hWRefYOl6evJj8mGYWfB3VnjCBsSLsqMyslKY+r1L66vWQLka93rzKb/E/gCGQcOPiHMWjs1w9U8OxTN5iLA+V2dDgqcK0RjCrI26bAFqDerFdQ+HtQXlBCztU4Lk9fQ+7NBPNa/bg+AFx896l5nsRZRWDP5hQlZlCclIG+RINTNX/qTH8Jk8lI6KC2dus9Mk7coPf5b8i9mUDrdR9wa9EmHp82L1X4tKlDnQ9e4trHPxM9sT9yDxfkXq42iqgZJ26YCxyfwSU6mAYfj+Lmwo2WyUfh606z5e9wc9FGXKKCUPh5cGzIx7RcNZXCuFSyLsRy/8d9yDxcOPvWEpp8NY7jw8x6Nq41g0na8nRZyKVmCBGvdqE8rxi5l2uVPkkSZxUmo4kbn/6Kc2QgEa92QeqixrVGMAB6jZa8W4lcmLKSrHMxiORSIkd1o86HI1AHeyNRyW0CiGcpTc/h+PBPrQpKY1dsJ+HXI/Q8tZzCB6mIZDJcIgNQ+FtLyMvcnGiy6C08G0Zyc9EmytJzcK9XjUYLxyBWyTjY+X3KMvPwaV2HerNfwTkywOp8bXYBiRuPVnltcT/swb9LQxAKKYpPI3XfBR6fvo1TmC/VXu2CKtgbmYsaz0ZR9L/5Aw/3XSDnyn08G1fHu2UtEreeJGXnWcJe6oA62BuxUo5EKafO+y+RvPWU3des8/4wJCo5ztUDKYyz33nl2STK6ufC+EdVZjDTDl/BWK6nwx9zOdL/I6u/N4WvOy1XTa3Sz0gd7I062Juwwe1s9pWmZZMfm0L68euog7zx69QQVYCnucg82JsOW+ZyYvinVl1bYcM7IPNwtrtklXc7CU1WAUofN7T5xZwbv9ymUDz9yFVOjVpEg09et7tsCBD/k7mFPmapbXBaQezKHYQP74jc25WUXWc5PeqpxUNZeg4XJn1N/u0EGi18y9KeXpmCKj4XMGeTdcVV6zf9EzgCGQcO/mFUgV4IxCK7AYvURWXT3upeP4LmK96x6TZ4dszuhxdzfPh8yxKMSCah9vvDCB/REYDUvRc48fICyzmF91O5+O53RL3Vm6i3e3Pv+92oQ33puHUe2oJiSh5m0vL7dxFKxGgy84hdsZ2824m0+mGa3YleV1yGe51wsyJo/zlEjelJrSmDAMi+Gsfp0Yvx79KIh3suUJL8mKi3enHjU9vulvTj15G6OVGeX4zcywWFjxsCgYCixAza/TrT7AYuElGeX8yNBb+SdzOB4L4t8evYgHvf7eTa3PU0XvQWWRdiybuVSOQb3bmfmoUmMw+nav5oMvMJ6t0Ck9FotnVoWB2T0cjxofNpt3EW/p0b0fbXmRzs+oHV5CAQCmn61VhiV/4JwPWPf8K/c0OrDENBTAp7Wk2yfLYGTTl3V+0i/fgNuh360qruoirybidZd8U8QZtbyO2vNtP864nPLaAUiEWEDGxDUL+WCCUSMBq4+90uq1bxxN+PkbztFD1PLceraWXvHoFVK7/t2ObC1/xbiezvNM1i8QBwZ8kW2m6YRWDPpkjVSpzC/Yie0J/S9FxOvb6I8++ssBx7ff4vtN84m6A+LRArZLjUCKLVmvc4/84Ky0QvkklotmIiLjWCkbqoaPX9u+zvPM1GXDJ66mDk3k8zBeVFpcjcnWn760xS95wnaespm2VcY7kOv471GXhnHUnbT1N47yH+nRvh3apWlZYTz6M4JZODPT+0MuUUiEV02v4J/p0aIlbICOzRlAGx68m7lYiusAT3etVIP3adc+OW2x3TrXYocWv3Um1EJ4w6PQ93nbN7XPqx6zQUVr3sYyjTYtTpbSxPKqMv0WAymihLy+HSe6vsHnNv9R5qvTvUbiDzvIJgoVRi8bj6V+EIZBw4+IeR+7hR/6NXuTZ3vc2+JovH4dW8Jt2PLKY4JRPX6BDUwd4ofNz/clznyAC67FuINqsAfZkWmbuzuWtALqX4YSbnJ39j97y4H/bS7/pqaozti9zLFYmrmhMvfcLD3edt/GAAHvxyiLCh7bmzdIvV9uB+rQBwquaPX8f63FhgrWeh8HUn4pXOHOn/EQatzhLkPItAKODIgDkUxCYjlEqoO2M4Ncf3w6NhJEcHzbU5XqSQ4d+pIceGfEzzFe9wfPinFq0bdagvZRnmrE/25Tj8OjYgqHcLSy2Q1FVN4u/HLZmhtCNXcatbDa8W0fS/+QMxX28n71YCTuH+hA5uS/xPB3h88iZgdlOuHIxq84u49MH3dgPUgrsp5N548JeBjMlkIv7nA1XuT9pykgbzXkNsR0CvvKCYnGvxXJm1lvw7yTiFmT2OXKND7OrdGHV6zo1bRtcDiyxLKXJvVyJHdefGAvvt0zXG9qEsM48zby+xCmLA3BVz+vUv6Hfte6TVzYXA5YUlnBmzmLSDl599o5x45TNLka7UWUX4iE74dWxAUWI6GM32Egrfp10vnk1r0PfyKq59/DNZF2JR+ntYis1lrmqMBgOFcalcmbmWh7vPIZRKCBvSjk5/fsLp0YstNTsyD2ckLirEchnOkYHU/eClKu/330FXquHqR+tsnMVNegNHB85lYOx6nML9LEKElTu2StNy8GgYSVCfFgglIjJO3OTRwcsIJWKqvdyZXU3GU3T/EbU/GPbcazBoys1WFXYK1J2rByJWKwh/ubONr1kFwX1bInN3ojAutcplb4CCeyk4R/iDQGBVM+MU7o8q0MuqSLuCyDe6o/D96++v/0kcgYwDB/8wEpWcGuP64lozhGvz1lOUkI5rdAiNPnsTz6Y1kLmq7RZUGg0GytJyKM3IxaApN7eb+rpZ1c4ovFztioGV5xZVKQVvMhopeZhJYI9mAGhzi+y681agycpH8ozYl2vtMIvCrMLLlWZLJxAyoA33Vu2kvLAU/84N8W1Tl7Pjl1ueuE12vnQFQiFutcIsdSDGch3XP/4ZdYgPoYPbUnA3hXurdllqDWSeLrTf9BGx3+6gMP4RZY/zcKkRbBEBjHyjBxeeBHDOkQFEjurGwZ4zqlT3lTopOf7SfJotHY97vWoE9W2BQCSkNC2HY4PnWS0LuEQFIZQ+/crUF5WRcbxqNdfkP08T1LNZlfvBLHIoek7Xj1AqtuutZDQYSNl5jlOvLbRsy73xgKMD5lD/o1cJH9GJhA1HbM7LuXYfbV6RJZARikVUH9OLhE1HbYpSg/u3xjU6hLLMPKvC2MoYyrTk30m2KMhqsgpI3Wdf28SkN5B16a6lSFeskOEU5mejIVSBWCHDvV412v40HV1RKUKpxKqWpehBOruajrd0AxnKtMT/fJCMEzdovuIdjg0xt083XviWVXFuaVoO+XeTeXTwMko/D4J6NkPh7/G3O4Y0mfkkbjpmd59RpyfrQoxNR1UFztUDCRvRkfgfD2DQlhPYoym13h2CUCom/ueDGPV6EjYcoe6MEXg0iESTU0BJiu3ysdzTlbozR+BWKwyBwJylefDbYfQlGrNYoa873i2icYkOsQm4JM4q6nwwDLFChkj2/IJcoVTCqVGLEErEVB/TC+eIAOSeLqgCPOl68AsO95ll9XsT3L8V9We/+i9vwXYEMg4c/AuQe7oQOrgtPm3rYCzXI5JLn9vqaCjXkXn2DseGfGx5YhJKxNSfM5Kosb2Rezy/TfLZLoxnEVf60pY4Kwno2rhK3RO/Dg0wPpnQxUo5kaN7ED1xAFkX7yKUSXCtGYLS3wOPBhG41QlHpJCSdeEutxZtsozh0SCSssx864EFApotn0Du7USC+rR4YhponpSuzf2JgG5NqDN9OFFjelHyMBOxSoHEScmtLzdZnIvTjlwlqG9LNNkFNPhkFFkXY9Fk5SMQCgnu1wpdqZYmi97i/MSvbcTsBGIRHg0iufrRjxzo+gF9L6/CtXoQJ4bNt1tvUW/Oq0gqa5YIhUjd1FU+0f5Vt08F1Uf3sNISqkzk692Re9t+1qWPcrgwZaXdc24u3EjHrR/bDWQAm6VMdbA3PY5+ReqBSzz45RAihYzoSQPxbBSJwseNkiocoSvQVfLjMukNVVoCAOgKnl9YbQ+Jk9JGHE5fpuXWl5vsemkVJz+mOPkxgb2aE/FqF/y7NLIU5xY/zORwn1lWge2l91bR7reZBPZpYZEAsFxvqQZtdgEmowmpqwqZqxNGnb7K+hSgyoeC0rRsToz4lKzzT4vqY+JSSdx8gg6b5xCz3OxdZtTpMWh1BPVtidzTGWWAF7cWbbQU4/u2r49IJackOZPbX27GZDQS3LclnXd+ihHwalQdMC8/d9u3kHur93BvzR4MZVpCBrSm7qyXLYGWzNMFz6Y17AaqUhcVuoISi2XD/R/3EzW2Dw0/eR25p7mLr+eJZZRl5KLNK0IV4Incx63KeqN/Ekcg48DBvxCF99/TVyhJybTSnQHzF9zVj9bhGh1CyIDWzz1f5umMV8taeDaqju8T6fOca/eJ+2EfhjIt6tCna9xCsYjI17sT+82fNhO4WCUnetJApB5ORL3VG5PBQOqBS2yLft2ypCJSyGj/+0d4NY8GocAqgAHzU12LVVMsrZ6p+y6gCvQisGczcxv1zwfN7a4/fUj2lThufr6BkoeZGLQ6RHIpRwfNRVdYSnlxGcZnulUkzirChrTDZDTx6OAlYpZtRSgR03L1u1ybu57kbadwrRVKo8/eJGnLCRI3H7dcU8tVU4n5ejtgLnotevAIr5a16bRjPmfGLKHwicS/WCWn9vvDKIhNobRJ1lPvKx83ar7T36Ydu4LwYR2e+xlV4BwZQPTkQeaOJ09n8mNTSNpyEqW/BzUn9DML+T2DNqfQvhs05t+T8oJiRDKJjZO3f+dGCKUSK3E0AFWQN1Fv9iJsaHsEIpGlQNmoNyBxUtDpz/kUJ2Vwb/VuGyE9j4aRlv9LXFQ4RwZa7t2zeLWobXe7PUwmE+WFJQjFIptsSXleMY/2Xary3LRDV2i+cjJydyeLHYheU87NzzfYZOdMRiMnXv6MgXfXI6nk6VSUkM61eetJ/P04Rp0ev44NaPrVOOQ+bjhV87fJYFXg3cr+e8y+dM8qiKmgLD2HhI1HCezVjIe7zuHbrh4JG49wa+FGwPz712b9dG59sQmJk5KW30/lQNcPKE54+vrJ206RcfwGfS59a1VbV/H3W3PSAIxaHTJ3J6uMbsXf3b72U63bzGXmv4/bS/6wutZ7q3ZR7dUulocwpb9Hla3o/0ocgYwDBy8giZuPV6mWem3uerxb1XpuUKTwdqPVqqlcnr6auyt3WApdW697H6mb2ubLRx3qS8/TK7gwZSXpR8wO2r7t6tFs+Tuow3zN7dHuztxbu5fzE1ZYnWso03J0wBwG3F5HvRkj8O/YkJuf/0bZ4zx82tS1SNqLpBKC+7QguE8LyjLzONB9OnnXH1jGqXjiq/PhcBJ+PYxIKjEHCxMHcHHqt3bfZ/XRPbj0/vc8PnWTbge/pOOO+YhlUu4s3cKjA+aJLv9OEseGfUK3Q1/i26EBYoUUtzphPPjtiOUYgNL0XLQ5BZwbv4KaE/ujCvTGpDcgEAq4v/4AD3efQ+HrbglkKpZl0g5dIfPsHavrav71JMQquU2rsl0EAgJ6NOHWok2Upmbj2SSKHseXoPT3sGvDYH7t59vkKXzcMD5TJCvzcKbWu4M52O0Dak7oT+jgdii8XdHmFZntMdycrCbB0vQc7q7aReyKbZQXlOAcGUidD4aRezOB2CcBYNSYXsgqiSEqfd1psXISB7pNt8nMhA5uh9L/79VOFCc/JmnrSZK3nkTsrKLWlEF4NIh86jsmFiJxVoL9WAKpi4prH63DqZo/0ZMHIfdwRpOZR/x6+/VIJqORtMNXLJ9tccpj9rWfalUDkn70GrtbvEPfK9/T/JtJHOrxoc04Pm3q2C0eNmh1xK21n3UDeLjzLLWnDSXt4GVqvzeU06O/tOzTl2g4/caX9Ln4LTIPZ+J/OWgVxFSgzS0k7oe91J/3GprMfB7tv8S9NbvBaCJydA+CejW3K+ngEhVE7wsryTwXQ+bp2zhXD8QlKpCbn2+0m6m5990uvJrVtLSJvwg4AhkHDl4wTEYjpWnZuNUJpzD+kY32RVFCOsbyqlPbYNY02dfxPas6mcenbpF9OY5+V79HKLb+0zfXqoTS4Y+5lOcWASbETkokTgqLxktpeg43P7MtIgWzAnDiHyeoP/sVAro3wSU6hPw7iWSejeHSB99bJiK5pwtGvYHYr/+0CmIquLdqFx23foxLjWBLsBU2pD3J209bim4rqDG+H+lHr5Fx/DoAV2atpeWqKeyo/5adCzRxefpqaozry7W569EVl1FjbB9arprK2XHLwGTCpXogmsx8Gsx7DYFYxINfD1mMAisoTbMWfVP5e9JhyzyK4h/xcNc5hDIJ3s2jSfj9GNc+/ommS8YT1LuZjax/BeWFJcR+86dVVqfwfipJf5yg+7ElVoGM8UmdkFAoROblinNEgF3vJ6mbE6oQH/pc+o6YZVspfZSNV7MaeLeoxcV3v6Pgbgrn31lBYfwjQga24fL731NeWEpwnxZEPRFN1OYUcnbsUqvOmcL7qZwZ8xXNv55IyOC2BHZrgn/XxiifKez0ah5NrzMruPTBarIvxKLwcaP2B8MIG9zuL5dEAYoS09nbZoqVYWbagUtEvNaVJl+8jdzLFYW3G7WmDOLs2KV2xwgZ0Jqz45ZRnl+Mf5dGeLeshUAotLUqqETljETaoSt2C1kNmnJufPYbzZaNp+uBRVyc+i35McmI1QpqjO1D9ORB9rVXBCCQPK87TGTW49n+CTc/22CjCK0rKqUoMR25jxuJvx+vcpyUnWepPqYXRwfOtdLpyb58j9haoXTdt9DKtdtkNKLNKUSsUhA+rAPhwzpQlpnH9tqjq6yx0xWVmv3THIGMAwcO7GHQlJs7G55M+u51q1GUmM7V2essywQuNYPtugBXYDIaSdpywu4XkaFMy+0lf9Bs+TuI7Ywhc1VjKNWSdekucat3g0BA1Ng+eDaKMgdYz6mXqFAbzo9JZk/LiVbiaI/2XaT2e0OpO+tl9CUa7q3eXeU4jw5dptHnb1p+Vvp70H7TRxTcTSFhwxHEKjmhg9shlIh5dPASMndntLmFZJ2PsatJU0HOlTikziqKk83mmDcW/Ebk6J5EjelFXkwSuTcecGbMV4B5uSx68kAafvoGV2evs4zh38nWUE/p645Rpyf/3kPKMnK5+dkGS3HyqZGf03X/QgK6NrF7TZrHeVz/5Beb7UadnjNjvqLHUfP15MUkEbd6Dyajiepv9sStThjtNs5mX/upVnUiQomY5svf4fKMNTRfOoFGC0Zz4/PfSD923aarLPbr7fh3amipvbgVm8y9NXvofe4bDOW6Ktt/b3y2gR4nluJSaRmmMhK1Au/m0XT+cz76Ug0CkRCFr/tzlWIrMC//bLQKYiqI/+kgNcb1s7iSB/Vpgf/Wk6QdumJ1XPSkgZRm5FmEDm8v3kxg96Yog7zoc/FbDnT/kPJc27omr+bRaPOLEClkNgFsZdIOXcGg0RHQpTHdj36FvkSDUCxC7utmdxkQQCSVUGNsX1K2n7a7P/KN7ng2q8n26FF2637AHGgJJeLnumxLXVSUpGTaFRvMv5PEw93nqDG2L2DOOiVuOsaD344glIioMa4fAd2bIHN3IrhfS+5XkUGKGNnF5kHof5sX62ocOPg/jL5MS9qhK2Y7g0rFhL7t69Pm5xkcf2k+mEw0+uzN5xYK64rLeLj7fJX70w5fpTy/GLGdFsnStByODfuEzDO3LdtS917Ar0N9Wv/4AR4NI6uUjvfv0ghtQTHnxi+za2R4+6vNRL7RHYmTssov64rrlz1jBKj0dUcgFOBWN5zUvRctUvRezaNp99tMTo5ciK64DKFUjFAqMS+Z5Bejr1SIKnVRoX8muxW/fr95Miou49jQTyzbDWVabi3cSNMl4/BoGEnO1fu416uGS40gu9ecceIGD6todb08fY05MLXTXZZz7X6VxbEFsclosvK5MmutVVCR9Mdx/Ls2ptWaaXTdv4hH+y+SdycJp3B//Ds24M6KbaQdvEzooLZ4Nq6B3MPZbgGqyWBEV1RmpXFUnlfEtXk/ETWml91rAnNNh6mKZc/KyNydkLk7oS/VUPIw01ww66x8rjGiNruAB78drnL/g9+OWHRwlH4etPh2MjlX40k7fAWRTIJfx4bk3niAc4S/pVZHk5lPUUIa5yYsx6dtXbof/IKdjcdajevZOIripAx0haWEDGhtpVNj730Jn2RX/m7NG4B73XCC+rSwCRBdooKIfK07CMxF+FX9bbjXq4ZEJafW1ME2wVsFtaYOJvabP6u8hrgf9hI6uB36Ui372k21BPUAZ8Ysxqt5TTpumUfdD4aTvOWkTc2ca+0wPJvW/Jvv+F+HI5Bx4OAFofRRNkcHzbURAMs4fh2PBhEE929NUM9mNqqmzyKUSZ6rCiz3dK6yqyntyBWrIKaC9GPXyboQS7MV77C76XjbMb1c8WtfH212oU29iPV7uUG117oS2Ls5SU8Kb5+l2vBOaLLzKbj7kLgf94MJIl7rikFTzqX3VlkFeVnnY7jw7nc0nP86uTcTUAaYszfFyRnmwMFo4vqnv1AYl0rEa91I/N26bdZkMJr1PwbNs7vsELN8G3U+HE7ujQfUeX9YlZNwxombdrcD5N5MoLygBKm7k029jOAv0vMGTbndzEjawcs8PnmD2G92IBCCOsyP3Gv3iVm21ZINStp8gsyzMWSdj6H2e0PQFZdx+YPVVuNInJU2v2/JW09Sd8bwqi9KILA4fVdQXlhi7t4ymZC6qi33qSgxg+sf/0TCxqMYdXp82tSh6dLxuNUOs+/FY8K8bFEFJv3Tz16TXcCJEQsoepCOZ5MojDo9cT/sNUsVBHpRf95rnHlzMd4ta5H7pMD38cmbZF6Ipc70l7i1aBNitYJqr3QmsHtTTrzyGXIvV3xa16bG+L7k3nhAtZc7IXVzIj8mmfj1B9DmFlL7vSH/pQCmAoWPGy2/f5ect+8T+/V2DFodEa91xb9TQ1SBXpiMRhovGsPp17+wOdevQ32zxQbg0bA6Ea91I/4n63qf0KHt8WxW00bvyQqBAJPJxP0f91kFMRVknY8l60Iswf1a0efit9z4fAMpf55BpJAR9VYvqo/u+bdEHv/VOAIZBw5eEFL3XbSZVCq4/+N++lz6FnWIL5rMPAoy8xFKxWYBsWeWiMQyKbWmDCK5kiR/Zep88JJdt2ptbiGxK3dUeX2x3/xJhz/m0fHPT7jwzteWGgLvVrVpteY91CE+FFbRyWFBIECilNNg3muk7jlv8/Tp0TASlxrBnB27zCq9H79+PwHdmtB85STOvrXE6pyC2GTk3m7Ufm8IJ19bxONK2i4KHzda/TCN+F8P49u2bhVPq4IqayeKkx/j06YORQlpHOjxIYHdmxI9cQBO4X4IBAKMT0wIQ/q1JPH3Y1YZoArUwd6kHbmKR8PqeDaubrXE4tU8Grc6YWb/q2fwaBBJXhV+NmD+PIL6teTqzLVknrPNkkldVJSl55BzJY5zV+KoPqYXNd/pb7kHqkAvJE4KuuxaQNK2U09bwAUCxAoZYqXcbmYtqFczq0C5IC6VS++vInXPBUxGI14tomnx9STkPm7s7/iu1YT5+NQt9rSYaDZIrG1reCn1cCJ0SDtLy++zhL/cyfJ/TVa+RTKgctE2QElqFjI3c0YooGtj7lTykopbs4fWP0zDo2F1jOU6kraetDxAFBeXYSgrR+nnQfU3exKzYhtlGXl4Na1B63Xvk3srgcBeze1eW1UYynUIhAKEYjFKX3eUPZvh266eWaDRSUnJo2wyL8SgeZyHR/0Iep39mpMjF1IU/wiRQkb1N3tSd/pLKLxc0eYWIZSIaLpkHDXf6U/ipmOYjEbChrXHKcwPuZcrUW/1rvJhovqbPREIBFXeXzAr+vp3a4JzZCAtVk6m4SevIxAKkPu4/XXh+v8SjkDGgYMXhOLkjCr3mdf7BSRtOcnl6aspeZiJSCYh4o0e1JsxwqqAD8xeTPU+epUb863rLyJe64ZPmzp2X8NkND23iNig1SEUiwju0xLPRlGU5xUhlIqRebogf/IELnN3wqdtXZvC3Arc6oZRmpaNc0SAWbX1k595uOscErWCGuP7Evl6d3KvP7Bbo/DowCWCeje3296rKy7j3tp9yN2caLV2GkKxmIwTN0jYeJTToxfTZfcC9rabamPi5xTuh1hVtXiX0t+TzDO3uf3lZgBi41J58PNBep9fiUAkIn79PjJO3EQV6kPbX2bwaN9Fm/qfqLF9uL92H9c//pk+F79FFehFaXouhfdTST9+3Sw0Fu5PzNfbLROyWCmn5fdTSdx6ira/zkQklSAQiyhOSidm+TaKkx+jL9Xi2bjq7Fzk690RiISEj+jItTk/EbdmD112f8bdb3ciVitovnIyZ99aQsG9h9T5cDi13xvC7a/+MHvw+LrRedenHOwxw6p7Th3mR7PlT+0zipMz2NtmslVxata5GPa0mkjPU8tttYN4IiMwZz1tfpqO9Bl9GIlSTv05I0ndd9FGnye4fytLVxGAoazqwl0wiyu22zCbS+9/b7V8py/RIJRLuPnZb+TesC44l7qqQQBXP1pH/E9PjTjTDl8h/eg1uh78wqa4uSpKHmWRdS6G+F8OWYqBXWsEm9W0n7S3591J4lDPGVaeab4d6tPtwCKMeoO5c8/XDU1WAfdW7yZu7V4EQiFRb/XGv2tjmnz5ts3r+nVqiEej6jZ+Tm51wgnq1Ryj3vDcTKBA+FTFV6yQ2VWVftEQmOzJbf6L+Pzzz9m2bRt3795FoVDQsmVLFi1aRFSU7R+nyWSiZ8+e7N+/n+3bt9O/f/+/9RqFhYW4uLhQUFCAs3PVa7MOHPxv83DfBQ73mml3n3u9arT4bgp7Wk602efdujYd/5hn0y1Rnl9MaXoOjw5cwlCuJ7BbE5SBnnY7RzRZ+RgNRh6fvsXxYfPt1m20WDmZqLF9/rJoM+92IntaTTJ3N1Si5jv9MRlNFMal0va3mSi8XNGVlFGeV4xAKETu42rxbaroRLJ5ry1r4d2qto2rb48TSzGUaUnedvpJ67qewJ5Nqf5mLy5NW0WTr8ZxfsJyqy4fmYczPY4uQeKq5s/ab9hcL5jdv1N3n7PJeIS91AGZuxN3v91ptb3xojFkXbhrDsQEAqLe6kVA1yYWq4Xe51ei9HPncL+PrAoyBWIRbX6aTvaFu4hUMqq/3gOppwvJW05y5cM1FnsA1+gQGi98iyuz1xI+rAORo3ty/KX5NverzvThKAM8ufn5BlyqB1J3xgjKMnLRl2oRSEQovFy5Nu8nq2vosHkuV2atpcvez1GH+lCamo02twiT3kDxw0wkTgpco0MsnVQmk4mYFdu5ONW+MF/YSx0QyaV2W54lzioG3F5rE4BXjFuclMG9H/aQsv0MEmcltaYOxrddPasgoigpg+213rDraI1AQK/Tyzncd7ZNQFTr3cFIPZzxalidY0M/sfrcG3w8ivCXO7E14lW778mlRjDdj32F8i8sREpSszjUayZ5t6w1ayJf706jhWNQeLlSkprF7ubv2C1srvZKF1p8NxmJSkFJahYHe84g/7Z11s6jUXU6/Tnf7lJPyaMs0g5d4d7qPWAyF4gHdGuCKtALo8HA7cWbuTLjB7vX3nnnAoJ6/9eyTv8Uf3f+/l/NyJw4cYIJEybQpEkT9Ho9M2fOpGvXrsTExKBSWVdmL1u27G9VvTtw8O+Ke91qVbbUNlk8lotVmLtlnr5NSWqmVSCjKzEXcbrWDMG1ZkiVr1nyKJuHu85x99sdGLQ6woa2o9uBRZwZ85XVkoBzRACBvZv/rb9B1+gQ+lz8lthvd5B59g4Kb1fCh3eiMD7V0qFT+igbhZcrEpXCSuzMpDfYXZ6xvK/iMpuOLXWYHwofdw71mmElUpa0xdzR0v73OWiy8+l+fAk5V++TdzMB11qheDSIQBXkDSYT3Y9+xZH+H1m6sgQiITXG90OilttdtknZfppWa9+3CWSuzv6R3hdWEtC1Maogb9KOXiXvTiJ1Z47g5mcb0BWWcPPngzZdJSa9gdOjvqD/nXU4hZp1e9KOXuPsW19ZHZcfk8yJVz6jw+8f4VIjGIW3Kx1+/4j8uykkbTmFRC0nsGczkred4sKkrwFzcW7GiRu0+HYyrnXCuTrjB4tLeWUSt5yg+7GvECukxK3Zw9XZP6LNLUQgFBLUpwXNlk2wagfXl2p4uNt+ZxOYfy9rvTfE7j6Zu5ONwnAFAoEApzA/GswbRa3JgxCKRXZrk5R+7tT9cLhdD7Nqr3Qm7fBVmyBG7uVK5Os92NngbQJ6NKHaq124+615OTVsaHuqj+n13PdUcDcFXX4JPCeQMRoM3F+/3yaIAfMSceQbPVB4uVKUlGE3iAFI2HSUBvNGIg6Tk7LzrE0QA+YuvPRj14l4pbPNPlWAF5Gjupv90Ezm+12BUCSi2ojOxK8/QMG9h1bn+XVsgEejyGeHe+H5Xw1k9u/fb/Xz+vXr8fb25sqVK7Rt29ay/fr163z11VdcvnwZPz/7HhYOHPy7owrwpNuhL7k8fTVJW09iMhhxjgig6fIJqEJ8yDpXdRFtzpU4PBtFUZqWQ9aFWO6u2mlu1R3TC59Wte0+tZWm5XBsyDwrtdEbC34jbt1+Ou+Yz4mXF2Aymoh8vTvVXumCOsj7b70PgVCIyWCkKCEd/y6NKM8v5uJ731ktP+TeeIBH/Qibc6WuakKHtiP7sn27hKBezcmqFFi4RofQcfsn5FyLt6u0Wl5QQsKmo9T54CVU/p6o/D0J7t3imQsW4NEwkt7nV6LJzENfqkXh605+bDK6wlI6bpkHgK5EQ+zX28m+fM/c6WOnnsmo05N1PpaYFdvMk8STzFbL76fiEh2CMsCL+2v32n1vRp2ezLN3cIkIQJNTwJUZa+wepyssoeB+KhI3NfE/H8Q1OgTPxtWpP28k+hINd1fvslvweXn6GnpfWGk3iAEoy8hFolaQtPUU58Y/dWg2GY2k7DhDwf1Uuh/60mK9IJRInmvDIPN0sWv1AOZajbLMfEQKmV13ZQCRRPzcolqRTEqNsX2QuTlxff4vZk8wZxW1pgwkamxf9CVlaHOLSNhwBKPeQOjANkRPHsjFKSsx6vWk7r1Av2ur8etQH9eaISh83ZC5O/+l59Lz3MIBNI/ziVu9p8r9d7/fhVeLaErt6NRUYNIb0Jdq0eYUVtkGDRC3ejdBfZrbdPlVUJVdgCrIi26HviR1/0Xi1x9AKJVQc0I/vFvW+tvWGi8SL1SNTEGBWffC3f1ptFtaWsqIESNYuXIlvr6+VZ1qQavVotU+TTUWFlbt7OnAwYuGOsSHpkvHU3/OSMoLijHqDci93RCIhAikkirbXhW+HpRl5HJ8+Hwen3o6UaUfuYpnkxp03PaxTTCTdTG2Ssn0pC0n6HlqBSajEbmXy3+5yE8ok/Bo30VS99hvA6+qq0ogFBI2pD0xy7bZPK0qfNyIHN2DiNe6osk0G1nKvVyRuqi4MMm+0zeYdT/qf/R0qcBsxplLWWYeRp0eha87Cl83VAGelnukKy4j5+p9Lkz6xrKsI/d2o8kXb5G09SQyD+cq35tRr0dXWGq1PHdn6VYaLxyDSCmzsQ2oTFlattkktEyLyVj1qv/jU7d4fOqWxXNK6qqm/cbZxK7cgXv9avS/+QNn3l5iFfTpikqrFDkDCOzWBEOZlquz1trdXxCTTOGDNMtEJ5KKqTmxPw9+PWT3+NrvD7VbRO3XoT5OYX7sbPAWzVdOJnJUt/9vk0G5lys1xvcluH8r9GVaRDIpCj93RE+68gK6NSZ0SDsEQgFZF2LZ3XIS+mLzUpJQIjYHzoPaWo3p2bQGApHQbqDq37mR3UL5yphMJvTPEd4zlGjAaMQ5MrDKY8RqBRKnvzaxNJlM5k6vZyh7nEdZZh7luUXIvd2Q+7ha6tgqUAV6EfVmL0IHm+9PZVXnfzdemEDGaDQyZcoUWrVqRe3aT70qpk6dSsuWLenXr9/fGufzzz/n448//qcu04GDf5TS9BziftjLtXk/WU2E1d/sSe8zy9nVxLb1WayS4143nPQTN6yCmAqyL93l0YFLVH+jh2WbXlv+XMn0hA1HiZ406P/bR0Xu7Upwv1Ykb7ct2hWrFbg9Z7lLHeJDz1PLubP0D3N3hclE+PCO1H5/mMUpucJtGcBQrkf2nHZziVqBSCl7cqyOzDO3OTZsvmVSF8mlNFo4hohXu1ieYAvvp1p0eyrQZOZxevSXdNnzOQKpmEPdptu8lkAoxLlagE0QVnA3BWWAJ8UJaYQObUfS5hN2r9U5MpDtNUch93Gj9rtDKO3d3K6Xk2t0CJmVMnTl+cWcGfOVpeX4/rr9dD2wiJ2N3rYq4BaIRXY9mKSuasJe6kB5QQllGblV3sucK3H4tn5aLO4cEUCjz97kykzreotqr3bFv2NDRAopXk1rkLDR3NHl3ao2pY+yOPWkxfjSu98S2L1JlQ7YfweBUGi31gbMwe/ORmPt7qv2ShdkdvSYFH7utF73PqdeW2S1Xe7tRvOvJ1q8m6pC5uFMyMA2ZkFJO0S81g2hWIwq0AvPxlF2s4+1pw1F4e+BUCwm8o3u5Ey8b3esqLd622S0ihLSOTJwjpWnVGCPprRc/Z7dzGxVGbF/J14YjeEJEyZw+/ZtNm16aji3c+dOjh49yrJly/72ODNmzKCgoMDy7+HDh399kgMHLwiF8Y+4Nnc9UmclrrVCUTwpboz7YS9FD9KJeL271fFipZwuez9H4qq2rPXb4+63O9DkPH0aFwgEFlEvewjEIvhvlKRJnZQ0XTIOlxrBVttFChld9nyGMtD6C1VfpqUoMYP82GSKkx+jDPSkyZdv0//mD/S/tZYmS8bbnexK03LIv5NItZc60mXP59SZ/pKNRk7E691J/P04+bEp5sLJ7h9aZSYMmnIuTllpmVB0JRpuLtxot+DZZDCStPUkCm8X7PVJRE8eSJKdjit1qC9Z52M43Hc29Wa+YveeeTaOQpOVT3lBCYVxqZwduxST3mBjECqUiPHr0MDmfpSkZqEO9QWBgNK0bO5+t5P6c0da9it83JB7utD32vc0/vJtot7ujcLXHb9ODel1egXqUF9EMimi52RHlAGeZF+NoygpA4O2HJmbEzXG92VgzI80/2YSTb58m37X19B0yVgUPm5InVWY9GYfo5yr9zkz5isuvvudpRPKoNXZ1TP5n0Id4kPNSQNstqsCvag7Y4RddWuJUk5w/9b0v7WWWu8OJmRAa1qufpc+F77BJcq+IGJlxHIpdd4fhtTOso5Hw0g8G5vdqRU+bnTYMo+gvi3hSe2ZSCGj3uxXqDG2LyKJBIFAQHC/VrjWCrUdq1F1/DrWt9pW9jiXw31n2Rhjpu67yKVp31Fup6D9P4EXIiPzzjvvsHv3bk6ePElg4NN029GjR3nw4AGurq5Wxw8aNIg2bdpw/Phxm7FkMhky2f9fmtKBg/9NyovLiP/pAC1Xv4tEraTgnvkpXubmxI1Pf+XOsq203zib6qN7kH3xHsoATzwbVUcV7IWuWGNRZ7WHUW+wSkGLpBJqjOtHyp9n7B5f/c2eIBSSH5uMSC5F7uOG5BnDOZPJRFlGLia9AZFCZqM2rA7xofuRxRTEpZJ1PgZ1iA9ezWqiCvKykjgvTcvhxucbuP/DHgxaHRJnFXWmDzOLbz2n9TM/JplDfWZRnJhu2RbcrxVtf5lhru8xGPFuVRt1sA8nRnyKQCik7S8f4t2qtt2uqKsf/YhHo+qY9Aa7ui4V5N54gNzLjX5Xv+fmwo1knY9BGehF3enDyb4aZ7c+peY7/bn/4370JRqKEtPpfnQJ5yYspyA2GZFCRsTILoQMaMPRwfOszru9ZAvtNswi+Ym0vdRFRfOvJ3Fr8e+EDW7Hve+tn/qNOj0ihQxDqYakLSfpuNU8nlAipvX66YhVch78epiUP08jdXei9Y8f4FY71FLEq/B1o/qbPS2mkJWROCkRCoXsajwOkUJG08VjCRveAZmr2Wzy2aDVgkBg13zQsvsf9OyRuTtTf/arhA1uR8yKbWjzigkb0o6A7k3smjtWIHVSIq0VStPF4zDqDRa/sb+LU7gffS5+y50lf5C87RQipZwaY/sQPqITSv+nQbw62Ju2P3+I5nE++lINEhcVSj8PRLKnYoGqQC+67ltI6t4LxP2wF4QCarzdB/+ujW0yLKVpOTbu5BUk/XGShvPfsGl5/0/gfzWQMZlMTJw4ke3bt3P8+HHCwqwFkj788EPefPNNq2116tRh6dKl9OnT5195qQ4c/OMYyrREjOzGufHLrL6MpK5q2vz4AbGrdmEyGnEK98dkMHL/xwPE/3SAqDG98GxSg4jRPe122ACEDGyD4BnHZPe64QT1bWkjre9SI5jAns3YUW8Mmkyzv0v4iE40/OR1VEHmCa/scR5J205y6/ONlDzKxqNBBE2+fBuPRtWt1tqVfh4o/Tzwa1fP7nVpcws5N2E5KTueBlS6whKuzlqHoVRL3Vmv2H1qLknNYn+X9ylLtzZxTNlxBoW/B02XjEPqokZfprU4CZuMRs68tYQ266fbDWSK4tPQFZQg93bFpUaQxTvqWVxrhiB1VqLwcqXl6nfRFZYikkuRqOVIXdXk3khA5e+BNq+Ih7vPUW1EJwRCATlXzcsDmqx8Qvq1ouvezynPL6YsK5+cy3EUJqTR8tspnJ/0tcUnyFCmRe7pQu+L34LRiDa3iOsf/2w2fOzXyuq6BEIhMlc1TmG+5N9JMouwScVETxlE9dE9kbgo2dnwbSu7gtQ9F4ga24eG899A7uGMSCal7vSXKIhNIe3wUxl8qaua1us+4MaCXy3XdW7CcpyjgvDv2AB9mRZNZj5GvQGJWmHVQSf3dEYd4mM38yJWylGH/L0i8v9f5J4uyFvXMav/6g1/Wcz7LP/VIAbMGU/nav40+WocdWe+jEAoQOHjZjdokzqr/rI+RRXoRdRbvQkd3BYQWHUhVeZ5y4Imo9mS4j+R/9VAZsKECWzYsIEdO3bg5ORERoZZEMzFxQWFQoGvr6/dAt/g4GCboMeBg393BGIht77cZPNEVZ5fzOk3F9N+42xEciknX/2c9CNXLftT917Au3Vt2qz7AJeaIRTEWp/vFO6HR/0INI/zrVyYFT5utFw1ldy3exOz4olk+qiuuNUK40CX9y2tq0adnvifDlBwL4VO2+cjlIq5MuMH7q9/2nWYc/U++ztNo8OWeYQMaP23pRLKMvOtgpjK3F6yhcjRPXEKtf0OKLyfahPEVBC//gCddszn1Kuf23gM6Us0GMrK7arWutQI4uGucwT0aEr9uSPtZ6sEAmpNHYxIZg6uJEq5JVNlNBhQeLviWjOY9KPXUAZ60mn7fIoS061k5z0aRKLJyufaxz8Tv966c9OtTjit133A0YFzLNt0RaUc7TwHVYgPNcf3I3RIO4qTMkh+5vqqvdKZssw8S5Fq2EsdcI4IpPGiMRh1Bs6+tcSu59K9VbuIGtPbUsSq9Pek3YZZlD7KJu9OEiK5FJNOz/VPfrZpG782dz3O1fy5vuBXHvx8CGO5DtfoEJqteAevpjWQqJUo/T1p8/OHHOjygZW4HgIBrda8Z1k+/acRyaSI/sXJerFcivh/UNL/eT5VAMqAqjOYQokYict/XjYG/pdrZL777jsKCgpo3749fn5+ln+///77X5/swMF/GOU5RaTuvWh3nzanEJMJyrILLW7Flck8fZuM4zdo/vVE6s16GZeaIbhEBVFn+ks0W/YOZ99egq64lNL0HCu1VaWvO4E9mtFh6zw675hPQLcmHOo100Z/A8w+LMUPMynLzLcKYipzYdI3lKbZDzDs8bz6CEOZlvK8Irv7ihKrVkE2n1dsd9IGc8GvvfqgmhP6c+uL3znQ+X1krk60+WUGYvXTp3epi4qOW+bhVM3f7rj5McnsaPg2N+b/QuaZ2yT9fpx97adSnJRB+MtmrQ/fdvVQB3uTf/ehTRADkHcrgazzMfh1qA+YNXKKkx9j0OoojEvlwpSVFCWmEzW2ryWokHk4U3fmCHza1EEoFlNw76G5NmR8P2RuakQSCdqcQkuHkz2Stljvk3u64F6vGiEDW5Ow8SjHh39q11G58P4jMk7c4P4Pey1BSn5MMgc6TaPwhw2WOiOvZjXpf2MN0ZMH4tU8mojXutHv6vcE9WlhCQod/PdR+Lrj1cy+qWPE693+ZUHjv5r/9aWlf8U5Dhz8O2DQlFfphAyYHYT1Bnoc/YqcG/Hk3Uzkwa+HLZP9vTW78evUkMzTt6n2cicQCEg/fIVbizYhlEoQSiXsbvEOEicltaYMIrBnM0srbUVWoSQ1G02m/QAAIO/GAxT+VX8ZlqZlU55X9LeN5f6qlVWsktvdXpULNZhVYwVC+xkhgUhozhQ4qywaJxJnFfXnvErmuTuWbqP8mCT8OjVg0N2fzPLxAgEKXzeUTzpJnkWTU8DZt5faVQe+Mf9XOu9agMzDibrvv4TU3Zl73++q8voTNh2l9rtDyL4cR7Ol47k0zVoI8d53uwgb2p76c0cic3VCX6Ih/tdDiBQynKoH0XjhGEIGtLaS8xeIzP48Ch93BCIhWRdjiVuz19JabqyivkokleBaIwj7VRfgEh1s1w8qgFw8po6h3M8V6bDBiKQSXKKCaPzFW+hLtIgVUkcA8w+g8Hal/e8fcebNryxLgwKRkIiRXWkw9zWbOrf/FF6IYl8HDhyYJ1Spq9pSH/Esck8XDFodJ175jAZzX6MwLpU2P37Ag18PkbTlJAatDueIAG4t3GgjelZ9dA+0OUVoc4soScnkzJiv8OvciHY/f2j1lCaSmz19qiocVvhbFyLapYogoqrxqqqf8OvU0OxgbQenMD+cqwdSGJdqs6/2tCHIqxBSqzf7FdShvnQ7/CX5t5PM7tcmE3E/7CHt0NOakIyTN4ldtYvwYR0I6NrYppC5Al2phvLcIvSlWiRqhbn75Jlg1GQ0osnKp8mitxDJpBh1ehuzzMoYysrxaFSddr/N4trc9TZKz6YntTKlj7JJ+O0Icm9XaoztQ+a5GMQyCbXeG4KwUi2GrqSMgpgkYlZst3hU+XWoT7vfZnJuwnKKEtKf1F7YIhAKqTayK7cXb7arf1Nn2jDOPKlBqkwoT7yDtm2HYYMt20USCSLXv/j9cfDfQh3sQ/vfP0KTmY+uuAypqxqFj5v59/M/FEcg48DBC4LS34N6H73CJTtWBH4d6pN3JwmXSPPkbdTpybl6n6MD59J63fsU3n9E2PCOOIX5ETGqG4kbj2LQ6hCrFUSN6YVLzWDy7yQiUSssFgDph6+Qffke2Vfv49e+Hs7Vg5D7uFFtRCfifz5ocw1SFxVu0aFgMlXpjOzTuo655sUEbnZaRp9F5e9J592fcaDL+1aFii41Q2i15r0qlUmVfh503beQk68tIvNJ0CaSSYiePJCoMb0RKWX0u/kDJUkZTzq2TMi93HCpGWTWlZFJOf3GF1Uqz6pDfXnw62FSd52j4fw3qPXeEKuiY6PBQFF8Gtfn/0LqnvOIVQrCR3Sk5oR+nHxtEbpC63FNJpMlAyGUiIkY2bXK2qDgAa1RBnhxdfY6cq7Z1w8xlGrJPBeDW+1QtPklnH7jS7PgXW4hLb6bgrBStiM/JpkD3aZbBVjpx66TH5NMy1VTebDxqN06JMu9CPGh66EvOTH8U4uFg9RFRbMVE0FgLvzGZKIGj5Bi1qypCGQk+3bBggXmgVxdYdw4+Ae7lByYkbk5Vfm385+II5Bx4OAFQSgWEdyvNcZyPXeWbkWTmYdILiV8RCcCezTl0rRVNPjkdQBybybgVM0fTVY+V+f8SMMFo/FuHs3u5hMI7NGUtr/OBJP56T1h01HuLNtKpz/nW9kEACRtO4U2u5Dr837Cu2Ut2v8+h4bzXyf/bopVy6zEWUWX/YtQBnpiMhjp8MdcDvebbZW5Ufi6U2/Wy5x49XPifzxAjxPLUPr99Zq8W61Qel9YSVF8GkWJ6ThHBiKSS0ncdBTX6FBca4XibKcuxSnMj07bP0GTlY+hVIvUTY3C1x2xQkbZ4zxSd5/n1pe/U55XhMzdmbozhuNc3bzcovBzJ3rqYK7P+8lmXJm7M3IPF8ukfePTXwgf0QmnsKeTfVF8GjubjLMEheUFJdxevJm0w1dptmwCp9/4wmpM7xbRVj97Nq2Be/0Im7oTqZsToQPbsLvFBBrMHYlzZIC55bYSEmcVApGQtIOXSTt42WpfycMs9CUaxE8CGW1ekdkc0M6SZdnjPErTc2mxcpJdI9EKRFIJPq1q0/v8N2iyCjDpDci9XVH4eVCS9BiBUIjIUE4DEpCjx0SlTv/SUvjoI/Pru7vDyJHg9H9ngnXwr8ERyDhw8AKh9HVDFexD44VjkKgVmIxGkv88zfERC2ixcpJF9E7p526pbyhJycQtOhR1iA+NFr1FxrHrJG8/TfrRa5Ysh3/XxuRej7eRXRdKxJgM5mAk8+wd7q/dS91ZL9N5x6cUpzwm70YCigAP3KJDUQZ6mq0KRCL8OtSn37XVJGw4QumjbNwbRKAO8ubchOVoswvQZhegycr7W4EMgDrIG6WfOxIXFYf7zLLqSFIFe9N13yJca9rqlMg9nG3qbHTFZdxY8Cux3/xp2abNLeTS+99T9jiP+vPMtQI13upNSdJj7v90wDLRqwK9aPHdFC5PX20516DVocnKxynMF5PRiEFbzp3lW+2aW5rvsQFVkLe5tgao/cEwK8+g8qJSTHqD2ezx3kOuzlxLWWY+gT2aEj68IxemrETzOI9z45fT4Y+5pOw8Z6lbEkrEtPrhPe4ssdWqAfBuVQupy9NWXn2Jxq4NRQXpx64R3K8lOdfiESmkyL1tpezB3E6sCvCyMo0EUAZ60n7zRxwfNp+d+qa04zbeFFq6SAQGg3m5rWVL2LTJEcQ4+EcQmP7Dq2f/rg24AwcvCtrcQrKv3Of6Jz9TnJiBa+1QaoztS9LWkyT8dhiRXEqnP+dzsPtTifwBd35E6qoi5+p97v+4DwRCgnu3QF+mIf/uQ3xa1ebkq59bt78CHbfM4+RrCy01GzJPF/peWfW3DCKzLt3lwuSVyD1dKIh7aFOv0vPUciRqhdkvyssFpb/nczU5CuPT2NNqok3WCMC7VW06/P6RlZhYleM8eMS2GqPseuUIJWIGxq7HKdysilteUExZZj7FienoS8vR5hZya+FGm7qUvldWoc0vtpgBBvZqhq6wlEvvfWdTOxL2UgdkHs7k3Uig7owReDaJQu5pVgIuvJ/K5elreLjL7LAc2Ls5Dea+Rv69ZB7uPEfy1lMYdU8tBXw71qfezFeIW7MHtzphhA5qi0AkZFv06zZ1TFIXFb0vrLSybyjNyGVvm8l2DTUBar07hOLEDJK3n0IgFhEysA2NPhuNc7j9zix76Mu0lKblkHH8Opq0bGp/NhahplKQp1JBXh5IHLUxDv5r/N3525GRceDgBUPm7kxAl0Y4VfMn+4mx45k3F6PNLUSsktN63fuIFE9rINzrVUOslHHi5c+shN6St54ksFdzmq94hwNdP7AJYqq92oXcmw+sCk/L84oojEulLCMPl+oBSKtw1QVz0JNzJc5q4q1AIBSiKy5jb5vJgFlQrdmKiQT3aWGVMahMycNMu0EMQOaZ22hyCv9WIKPJKrAbxIBZE0eTU2AJZKQuaqQuaiRqOXtaTaI4ybboWB3qS3HyY44OnGvZlvj7Mfw7N6L5ysmceXOx1fFSFxUN578OJmv34eKkDHa3mGjVUv5w51ken7hB+9/nkLT5BCaj9XUX3EnGKdyPujOG82DDUc6MXUrYsPb0u7aac+OWWYq6vZrVpPnKSZb3VYHS1526Hw7nzJivbG+GQIB/xwYc7v8RYHZcTtp8nOKEdDr8MRd1SNXKt5URK2Q4V/M3L/+dPg1znslUlZTAxYvQqpX9ARw4+G/iCGQcOHgB0RWXcXXmWtwbVMOvQ30Ufu7IPMy1G7cX/45/l0Z4NatJfkwyrX/8gOzL9+yq1abuOU/emF70OLGUR/sukrDpGFJnFSEDW1PyMJMrs9ZZHe/brh6PDlzi9uLNNF8xkYhR3ay6HXTFZZQ9zqPoQRpCqZjuR7/i6kc/2rx2tZFdrIpZy/OLOTXyc3qeXIZPJdPBytjTrqmMoUyLrriM8oIShBKR1XJNZcTK56ueieW2+5V+nnTdu4j82GRMBiMGTTnxPx8k6+Jd2v46k9OjFtmck3b4CkF9W9h0T1Uf3dNKeBDMxcH3fzpoVxenvKCEh7vNQnzPOmo7VfPn8ZnbnHr1c8u2xyduoA7xodvBL9CXaBCIRUjd1DbLPhUE9W5BxGtdif/paQG3UCKm6dLxxP96yCazk335HiWPsv52IGPFridt5f37w+LF8N57sGMH7NzpCGQc/GM4AhkHDl5AygtKSD9+ncTNxxDJJCgDPNEVayy1EhIXNXU+HI5bnTCkbk6cfWtJlWPFLN9Kxz/nU310T8Je6oi+rJyTry4g7YB1oahALKLWlMGcet08aV+YupLAHk0tgYwmp5C73+3k+sc/WTIeYpWcVmveQxXkxYNfDiF1VVNzYn/UIb52swBX56ynwx9z7erHOD/HkE/irELiouL06C/JOHEThbcrtd8fRkDXxlZy+AByLzecIwMtrcaVca0VityOU3ZpWjZ3vt5K/Lr9GLQ6ZO7O1Jn+Ei1XTeX6p7/YLDVVkLjpGMF9W3J78WYAoqcORubpQkFcKiKF1Kw7IxKhKyghdfe5Kt9fxombBPdraRPI1Jv1MmffXmpzfHHyY2K/20njhWMQSZ+/ZKPwcaPpV+Oo/f5LZJ2PQeKkwK1WGJdnPF3iepbcGw/waVn7uePapW9fqFcPhg8318Zs3w4bN0JI1W7nDhz8d3EEMg4cvICIpGIUvm5oMvMwaHUUJaRb7VcFexPYqzm6gmJ0+cVUH9MLfZmWfDviZPqycgwlGvJSMknZcRZdUSn1Zr5M9KSBnHj5M3T5xfi0rkPtaUOJ+XqbJTNiMhjJvnrfomSbdT6Ga3N+tB67RMPJVz6n77XvqTf7FYRSMXE/7DUHMXbK7/LvJJF9+R7OEQE4hflaec/IPZ0JHdLOrgJt3RnDSdx0zLJPk5nHqdcWEv5yZ5otG2/VdaP0c6fT9o/Z13Galbifws+DDn/MtQl8NDkFnH7zKx7tf6qqrM0t5PL01ZgMxiqXwgD0pVpca4VS/c2eRI7ugTa7kB3130JXWGLplKr2alfETwppq0Lm7mQlSieSSWi06C20ecWUpGbZPSd+/QFCB7cj71YCfu3NWbuqPHtk7s7I3J1xizYHFPl3U0jdZ19FGniuWedzadXKOvMiEMCIEf9/Yzlw8DdxBDIOHLyAyL1cqfvhCE6M+NTu/hpv9ybzzC0uT19D3q1E1CE+1BjXB4NWx+UPVlsdW+3lzqQeuGTl93Nr0Ub8uzSi76XvyLl6n7ybDzj7tq0XT0VHkyangOsf/2z3WkxGI/HrD9Dky7fNPz/RbbGHc4Q/j0/d5MRL8+l9fiUuT7Iw5YUlZvXZpeNxrh7E3W+2U15QgjLAk3qzX8G1ZjD72r9rM17Cb4ep8/4wm/Zh1+hQ+lz8lrw7ieTfScatdihutcIsppeVKUvPtQpiKnPz8w10PbCImOXb7O4Pf6kD1V7pTOiQdlx+/3vufrfTsq+iU0qTU0i1VzpT+90h1BzfnwcbDpO46ZjVOLXeHYJXs5qE9G+FUWdAGeCJwseNc+OW2X1dMKvxFt1/ZDmm8RdvmZe1/oZ+iNzbjdDBbW2uA8wt4FU6WTtw8ALiCGQcOHhB8evYgOpjehG3Zo9lm0AopPnKSegKS9jf4T3L9oK7KVyYvJIa4/sRObon99eatUecwv3w79yQbTVH2YyfdugKD3efI+tCrN0JDYEAz0ZRABg1OoqTH+NaK5SIkV1R+LhRlJDG/R8PUPIwk/zYFHJuPKAkJZNqr3ShvKiUe9/utBkyamxfrs5aS3lBCVdnr6XZN5PIu5HAzUUbKcvIw699PaInDaT66B7oSzVmoz+VnM3+Q6q8T+nHruFeN9xmuzrYG3WwN0E9mlV5Lpj9gqpCV1SKUCK2aqeuQBngSdiwDghFIorTMri7yr7twJ0lW/BuVpMjA+YglEqo9e5g6s8daQkMw0d0pCwjl4QNhwkb2gGlv4fl3IjXutoVJwQI7tuS9GPXLD9f/mA1vu3q4dWkxnPfL4Dc3YlGn46mKCHdSi9I6uZEl92fWWnmOHDwouMIZBw4eEFReLvSeOEYak0exOPTtxApZXg3j0YoFbOz0Vi759xbtYvuRxbzaP9FIkd1o/qYXsSu3FHla8Ss2E6nHfNt2n7BLOcv93EFQKSW03TJOMrzi4n9difFSRm41Qmj8cIxZJy6idRZxaV3vyPjxA0QCIiePJBmy9/hwuRvzOfLpdT5cDh5txMtSyXJf54hevIgqzbygthk7v+4n16nV+DRMBKAosR0u9L/FYj/m/4xch83s+GiREz25ThrVV6BAKmzkh4nlhK78k8e/HIYTCaqvdKJ6IkDLQWxpRm5VV6fsVyHUWew/P/Wwo20+20mDT99A7daoWRfvc/ZsUvBZCJp22k6bJ6D8olthEvNEPy7NLKyTwCzUWTEyK4c6TfbanvsN3/ivmYaIulff7U7hfvR4fc5lKZlk3vjAcpAL1yignAK87XrJ+XAwYuK47fVgYMXGImzEoOmnMDezZF7OCOSScm98aDKDh+T0Ygmp5AeJ5dZtGA02fnUm/0KbnXCMZRpSfzjBI/2XcRkNFKeV4TUWUmfS99yc9Emci7HofT3IHJ0D9TBPpaiXqFQSO7NBG5/+dSZPvviXU68vIBmKybi2SiSWxX7TCZilm2l3cbZ9Dy9gtLULEQyCfE/HyR5+2nL+QKhkJLUbJv3YNCUc3b8Mrrs/gy5pwsyD2cCezazKYQ1DyLAt10963tgMqEvLkMoFf+lMaE2rwixQopH4yiMWh1Rb/WmJDWLKzN+wKjTE9C9CTJvV2QuahotGE2tqYPBBHIvF6siW6la+dzXET4TWNz64ndqTOjP8eGfms1Cn5B5+hYFscmWQEbp606b9dN5dPAyMcu3oSsqJah3cyJHdacoOQO5t5tVpqj0UTZGne5vBTJgth9Qh/jg3aLW3zregYMXEYfphQMHLyglqVnc/nIze1pNYlfjcVyctorChDREf9FebNTqONJnFqVpORh1eiJHdiP92HWOD/uEC5O/wSncj45b5yFWyfHv3BAQcLDHDIRiERGjuuHRqDpXPvyBfe2nknnmDmCWs7/z1R92X+/6xz+hySqwyUjc/GwDck9n8m4ncn7SN1ZBDEDokHakHbLunKog++JdtLnmVmWps4qmS8bZNYJs+tU4FJXUg4uTMohZtpXD/T7i1GuLyDx3x6KA/CyanEJuLtzIzkZjiVm6hbvf7uD4S/N5dPAyLb9/F9faYbT8dgoyFzWanEKKHqRTnPQYXWEJukJrl2u5jyvONYIJf7kz9eeOJGpsH6RPalU8GkRScDfF6viihHQEIoFVEFNBwsajVj8r/Tzwbh5N2ND2VH+zJ8WJGexsPJYL73xNq9XvWt2XwJ7N/tsZKgcO/t1wZGQcOHgBKXmUzaE+s8i78cCy7e7KHSRuOkavs1/b9emBpz48+THJ3P9xHyGD2rK/8zRLZqW8oITYr7eTdT6GpkvG4dU8moxTNylLz+HBL4dsxrs8Yw2eTaMoSki3EWurQJtTiLHcVhSvJOUxmediSDt8lYafvkHezQfcfhIMKXzcqD1tKHtaTaryHuiKStGVlCFRKXCJDKTPhZU83HOe1D3nUQR4UnNsX5zC/ZA6mbMhhfcfsafNZKtOpcTNx6k36xVqvTfYRtulIO6hVYapgrSDlwkd1JbuRxaj8HKl5GEmp17/gvSjT+tRfNrUoe0vM1AHm5eWZG5OtN8wi9tf/k7y1lOoQnxouWoquTfi8W1Tl1PP6NC41AymxI7jN4DEyTq7U5KWzYFuH1CSYl2jU5KaxdWPfiR68kCuzlqLzMOZkIFtEAj+vvu4Awf/CTgyMg4cvIBknY+xCmIq0OYUEvvNn7T//SObtmCBWETzrycSs3wrYG7Pzbl6367Kbfale6hDfMm9lVilAzSY26WNWv1fiswJ7FgPuDeIJD8mmazzMZx6bSFyL1dqvzeUJovH0vv8SiQuKgxlWrvjebesRcrOsxRVKsRVh/hQc3w/Omz9mJbfTcGjYSRSV7PysK6olMsfrrEKYiq4seBXytJyrLYZyvXEfv1nle8n9uvtYDIvPZ15a4lVEAPw+NQtTr76OZqcAgAyz91hV9PxJGw8St7tRFL3nOf4sE9QeLuRuOWkTTdY3Q+Hc//HA3Zfu9orXax+LsvItQliKsi+fA+36BACujel1+kVz3WxduDgPxVHIOPAwQuGoVxnpcL6LElbTiJWyelz5XsaLxxD6JB21H5/GJ13fErChiNknosBzLUi5fnFeLeqTUC3Jij8PKzGebj7PA9+3P/cLhd1qC8CiRBVsA+SKjRKXGuFWtq0LQgERE8cYPU+rn38M1Hj+lBryiDUIT7IPZxp8Mkom/HEagV1Z4wgbs0ebi/Zgv6Z5RexXGo2r6yENrfISkn4WZ7VTDHq9WifBCH20OYVYdQb0GTm8ejAJbvHPD51C01mPqVp2Zx6/QsbhVyASx+sJnRQG8vPUhcVLVZOxr1eBFI3W/uHGuP6oisuo7RS4KUrKLU5rjIKX3fab5xtaWV34OD/Go6lJQcOXjAEQoGVl9KziORSBAIhzuF+eDatQfaVOLIuxJrVZSvVqYQNbY9/54aUPspGm1tE2ND2CIRCLrz7rbnI10VJeUEJxnIdrde9z91Vu6xacQHqTH8JsUKG2FNOhz/mcrj3TKvuJqmLisaL3iL/dqKlS0kZ4EmDT0aRtO2UVYbEUKZFm11gMSSUOquIHNUd15ohxP90kLLMPLya1iCoV3OuzFpLWUYu+TFJGEo1iOXPL9o1GU1VLn0BNsaOEqWc0EFtbbqBKgjs0QyZm5rSR/bF6CooLyjBpDdWmTExluswaMrptONTjFodJqORuB/28mDDETptn0/muTskbT6O2ElJcN+W5N1KYF+7Kfi0rUv7jbNR+nmgDPSssmtLJJci93F7rmifAwf/6TgCGQcOXjCEYjE1xvezq3ALUGNsHxRP2qLFKgUSZxXa3CIUPm6UZeQCZuVf/84Nybl631IHcn/tXlxrhdLulxkcGTgX7xa1uPn5RrTZBVyd/SN1ZwzHu3k0MSu2IRAKqT6mJ0atjtjvdlJr0kB829ah3/U13F+/n+LEdNzqhOPRMJIrM38g72YCTZeMZ3DCbzw+fYuYZVvJuXrf5toFYjGa7AIMmnJEcikyT2ceHbyMwtcNl5rB5N9J4lCvmZagxL1+BGLVXxevCmViuu5bSHlhKWmHr5Dw2xH0pU/NMAN7NLU5J6B7U1SBXjbKuWKVnNrThiJWyJ5rmglmM0yTzjYTUxldQSnnJ32Nrsg6s1LyMJPygmLLEtuZMV9ZWr8fn7xJzrV4lH4eKLxdiRzV3exq/gx1PngJha+7zXYHDv4v4QhkHDh4AXGtGUK1V7vYFOC6148gfHhHs7t0qQaT0YhzNX/EChmRo7ohUkjJj0nBo34Ep0cvptnSCVbn599JImn7adptnMWD3w7jFO5HSWoWpWnZnJ/4Ne02zKZTxwYIxSKSd5zhwpSVCCViwod2sBybfycJv86N0BWUcGnaKgruPQTMKriqYG8UPm7kxyTbvCe5txtCkZCD3adTEJeKS2QgDT99g9rvDmF7rTdsMioCkZBaUwYjkkkxmUyUpmWjeZyPUadH7uOGwtcNgUBA3i1zV1TW+RgEYhHBfVvSces8zoz5ipLULMKGd0T1pBW9Mupgb3qcWMr1+b+QsOEoRp2eoF7NaPT5GIuLtNzbleB+rewuW/l3boTc2xWjRmd2yE7KsDlGJJMgdVPbBDEAKTvOUJqeY1+MELj3/S78OzdE6qKm0YI3UIV4E7N0C+UFJci9XKn30auED2v/l9kqBw7+0xGYTFWoOP2HUFhYiIuLCwUFBTg72xrVOXDwolKWmUfB3Yfc/W4H+lItkaO649m0BqoAT/RlWh7uOseJEQusAgCXGsG0+G4Kh7pPRxngSa2pgzk/8WurcUUKGZ13zOfY0E9o+8sMzk/6huJEs5eTU7gftacN5dz45VbndD+2BL929Ug7cgVNViEpO86gKy7Dv3NDlL7unJv4NdrsAtr/PoeUnWeQqBXc+3635XyhVELHrfO4Nmc9OdesMzXNlk3Ao3F1jg6YiyYrHzB3NbVePx3ftnURSkRkX4rj2JCPKU0z686I5FIafvoGQb2a82fdN23E/FRB3rRcPZXy/BKzD5GPfadsAP2TJS+TyYTUVW3jV1TyyBzkpew4Y1neCezZjJarplo8iTJO3uRAl/dtrqPxF2/z+NRNu+aMDT4eRc71eFKeaUuvILh/K9pvmmPRhDHqDZSm5WDQliNWyFD6e1h5VTlw8J/G352/HRkZBw5eUBTebii83fBuWQuT0WQlclb6KJsTLy+wyWIU3E0hbs0eQoe2x7tlLSvvnwoMZVokTkpar/uAyx+usQQxYNY3kT3jWwTmzIImp4Ckrae4V0mKP3XPeZyrB9Lmxw84+epCjOU6En8/Tv8ba1D4upN9OQ6P+tUIH96JS9NX2wQxAJdn/MCAO+voe2WVOZARCJB7ulgm6sKENPZ3nmbV4WTQlHNp2irkXi6oQ3xs3KlLHmaiycwn4tWuf3mfxQoZYjsZmwpUAZ60Xvc+mswx6ApKkLgokXu5WnkaeTWvSb/ra4j5ZjvZF2JxqhZA7WlDyI9JrtJhOmRQG4wmU5WBTI2xfaw+c6FYhDq46ut04OD/Ko5AxoGDFxyhndbm9OPX7bZVg7mrqeep5ST9cRy5lyv1547EqNOTsuMs+XeS8GoeTeaFGC5O+dbmXLFaYZNVkLqqUfp7UpSQbhXEVFAYl0rqvos0XjSGhE3HnnTvCKg/ZyTGcj1CqVn6364yL+bASpOZj1fTGnZdl5O3na6yTfv24j+IeL07V2ettT1v+2nCR3RGKPrvZy1krmpkrlXXy4ikElxrBtNs6Xh0RWWIFVLESjkKHzecqwdSGJdqdXyTL99GFeBF1OvdSfj5oI27uX+3JrjVrfbfvm4HDv4v4AhkHDj4N0SbXXXrsLFch9RZSWBPs1li8tZTiORSIl/vjtzbFaeIAE69+rndcyNf726lHCwQi2i3YRYSFxWxK+w7QAMk/n6MiNe6cnbsUhS+7ojkEgQCASKZWcZfKLENxiojqCLYMJlM5FyNI2RgGwK6NQaBgPQj10jeZvaGKryfajf4AVD6e/6PBDH/FURSCSKPp9YF6mAfuh9eTM6VOBL/OIHC152IkV1RB3sjdVEhdVHR49gSHu4+T/zPBxHJpURPGoBX81oWmwIHDhw8H0cg48DBvyG+7etXuc+tdhgCiYiTr35O6aOnXkbZl+/h274+bX6eTovvpnB0wBz0JU87e3zb18e3bV10+SXUGN8PqYsSv44NiVu7l9hv/iR68kCKH2ba7aYyaMopuPsQTCZqvz+MjFO3kLo5WbIYci9XlP6elhqXyij8PCjLyKXwfioipZzSR9mYDAYUfh4o/dypM20Yd7/fxZWZazEZTQT3a0nnXQs4O24ZUmelTddRBVFjev3d2/mPogr0QhXoRXC/Vvb3B3kTNbYPYS91QCAS2tToOHDg4Pk4in0dOPg3pOxxHkeHfEzm6VvWOwQCuh/+kswLd7k68we757bfPAeXyEC0hSWUpmSizS3EJSqY3FsJXPvoR5wjAmj5w3tcnbWOjGPXn9bhCAS0/mEasd/8aVPrEjqkHcpAL9RBXpRl5nNr4Ub6XVuNez3z8ojJZCLz3B0OdH7fyl9IJJfS5qcPufHpr2iyC2i5aqrF0VmkkNF0yThKUrO5ueBXq9dT+LrTeq25uPbhnvPErdljtb/p0vFEvtbNovzrwIGDfz/+7vztCGQcOPg3pSQtm9iVf3J35U50hSV4NIyk6dIJqMN82d10vEVT5llCBrRGrFYQNrQ9j8/eJu9mIsoAT0IHtOb6/F/Iv5NEl/2LKH2YhUFTTsLGI6TuvwQmE6pAL+rPe40zby62jCdxVtHz5FL0ZVoMGh1XZ68j88xtak0dTNOvxgFQmpaNNq8ITFCc/JikrSdR+Ljh27YeNxdu4PEpc0DWeNFbPPj1MHm3Eizjt984m/OTV9rYD9Sd+TK1pw3BqDNQkvKYR/svIXZSEtitCQo/d0dmw4GDf3McXUsOHPyHo/L3pMG8UdQc1w+TwYhYJUfu6UJpWvbz23KFQkx6AydHLqTFysnc+nwjAA9+Pkj7TR/xYOMRHu2/xPWPf0Lqqqb6mz0JHdKeM28upiQ1C+fIAJyq+aMvLsO/axOqvdyJkyMXknczAZFCRusfpiEQCCjLykdXpiXj6DXOjV9OyUOz+q13i2gafT6Ge9/v5lDvmVaKtZnn7uBWJ8wqkLm3Zg/VXu7EnaVbrN7Gw51nqTV5IAovVxRerng2ivofvLsOHDj4d8EhQuDAwb8xIokYVaCX2bvI09w2LfNyJWJUtyrPCe7TgvSj1yjPK0IgEloMHw2acq5/8jP1Zr6MT+tauEaHUp5fzO3Fm0k7dJmaE/sDIPdwocfxpbRa+z4mo5HDfWeTd9MceBjKtJwe/SW1pw0ldGAbyjLyONxnliWIAcg8F8PRwfMIG9rORnZf4e1GeX6x1bbC+Eco/a19osCswFtVkbADBw7+7+D4FnDg4D8MkURM1Fu9UIf52ezz79oYXVGpxY3ZoCm3au/OuXofTVY+Vz9aT5ufphPwRNo/YdMxAro0xq12GCKVHKO2nGsfrSPht8MYy619jAyackpSsxBJJSRtPka/62ssQVYF2uwC8u+m4FY33Gp7YK/mpB2+arXNtWYIxcmPbd5L9ORByNwdy8UOHPxfx7G05MDBfyDqYB96HF9Cyp9nSNh4FLFCSujQ9ggEAs5P+gYwt1bL3J2sim8BdPklZJ2P4WD36fQ4uoRH+y6CyYQ2v4imyydwYvinGMq0VBvZlahxfTk3bpmNpo2uuIxbizeTcfw6KTvP0mnnp+xpOdHqmJyr93GJCiLvZgICoZCGC94gZccZm8AoevJATr5i3S4e0K0Jvu3q/k/dLgcOHPwb4whkHDj4D0Ud5E3UuD6416tG0taT3Fq0ycoPKHriAJtWas/GUeTHmn2StDmFPDp0mfARnUjYcAR1sC8X3/uO7Etmh+zc974juG9LGnw8iquz11mN49Ewkvtr9wKQdS4GTWY+bnXDLUtQAM6RgXg0iiSwe1O8W9dGV1RK7Dd/WvbL3J1psWoK7nXD6bj9Y+J+2IvJYKT66B64Roeg8HHorDhw4MARyDhw8B+NSCzGvW44Rr2BrPPmgEId6kOdD16iNC3bqoBWrJRT76NXOTd+mWVb5pnb+Lavz+NTtyiIe2gJYipI2XmWiNe6IVbKLW7T7vWqUZ5fTMigttxaaC4kTtp6krBhHSyBjEAoJOKVzrjUCLaMZTKZ6H1+JdqsfIxGE3IvF5R+HgjFIpT+nvi0rmM+VyD4R+6VAwcO/j1xBDIOHPyHI3VV49+xAe57P8dQVo5QKkYklZB7K4Gg3s0pTcvBs0kUIf1bc23eT1YieuoQH3QFJbT4djIXp9paGsCTTqO64eRciSNkUBuqvdyZM2OX0uiT1y3HiJQyjE+WsEQyCe02zEYV4mM1jkAgQBXgiSrA0+7rOAIYBw4c2MMRyDhw8H8E+TNmkL5t6uLRMJLsS/eIWb6NQ71nWtW6CIRCqo3silgl5+jgeTbGjBVIXFQ0nP865XnFPNx7nmOD56EO80NTyUYh4pUulGXm0Wnnp7jVCkPp745IJv1n3qgDBw7+T/G/2rX0+eef06RJE5ycnPD29qZ///7cu3fPsj83N5eJEycSFRWFQqEgODiYSZMmUVBQtc+MAwcO/j4SlQKncD9kni5WQYxIJqHNT9NRh/ig9PPANSqoyjE86kdwsMeHHBv6MfHrD2DQ6oh6uzfxPx8EIOrt3qhDfAgd0Ibg3i1wCvN1BDEOHDj4H+N/NSNz4sQJJkyYQJMmTdDr9cycOZOuXbsSExODSqUiLS2NtLQ0Fi9eTHR0NMnJyYwdO5a0tDS2bNny1y/gwIGDv0Qd7EPD+a8TPXkgOZfuIXZS4F4nHIW/O1Inszpuo8/e5PGpW5a27QrqznqZtCP/r717D4qq/vsA/j67LhcXFlpYMRLcFAXLG1qDkhcSQ8rw8TLhmMlgzKgkJhReujjoWFKm5tgzjtUoguYYVCiZWQih2UNKmhM0ifcHGyA0hOUuwnn+8Mc+rtxX2XOOvF8z+8ee64f37I4fz/nu+Z75z4zXAAQBftEz0PcxdzwychDG/ffr0A32bHdiRyKi+yWrKQquX7+Ofv364dixY5g0aVKb26SlpeGVV15BTU0N+vTpvA/jFAVED0b1//6Da4dyUXTwf+Dg8QieiJkJp0GPoqmmHpXn/8btmjq4Pvk4HD0egaBRQ6XpA7WGd6+JyDqKnKKg5ZaRXt/+zypb/qD2mpiGhgY0NDSY35tMpgdbJFEv5TTQA36v/Rd8IkPvNCl2//kOGgAnY39piyOiXks2T/Ztbm5GbGwsnnnmGQwfPrzNbW7cuIH169dj0aJF7R4nMTERLi4u5peXV/v39omoewRBgEbr8P9NDBGRxGRzayk6Ohrff/89Tpw4gQEDBrRabzKZ8Nxzz0Gv1yMjIwMajabN47R1RcbLy4u3loiIiBREUbeWYmJicOjQIRw/frzNJqaqqgqhoaFwdnZGenp6u00MANjb28Pe3r4nyyUiIiKZkPTWkiiKiImJQXp6OrKzs/H444+32sZkMiEkJAR2dnbIyMiAg4ODBJUSERGRHEl6RWbp0qXYt28fDh48CGdnZ5SW3pkHxsXFBY6OjuYmpra2Fnv37oXJZDIP3jUYDFCr1R0dnoiIiB5yko6Rae+R40lJSYiMjEROTg6effbZNre5cuUKjEZjp+fgz6+JiIiURxFjZDrroYKCgjrdhoiIiHov2fz8moiIiKi72MgQERGRYrGRISIiIsViI0NERESKJYsH4vWklsHCnHOJiIhIOVr+3e7sRz8PfSNTVVUFAJxziYiISIGqqqrg4uLS7nrZzLXUU5qbm1FcXAxnZ+d2n1vTFS1zNl27do3Po+kG5tZ9zMw6zM06zM06zM063clNFEVUVVXB09MTKlX7I2Ee+isyKpWqzfmbrKXT6fihtQJz6z5mZh3mZh3mZh3mZp2u5tbRlZgWHOxLREREisVGhoiIiBSLjUwX2dvbIyEhAfb29lKXoijMrfuYmXWYm3WYm3WYm3V6IreHfrAvERERPbx4RYaIiIgUi40MERERKRYbGSIiIlIsNjJERESkWGxkOvDBBx9AEATExsZaLM/NzcWUKVOg1Wqh0+kwadIk1NXVSVOkDN2b29WrVyEIQpuvtLQ0aYuVibY+a6WlpViwYAH69+8PrVaLMWPG4Ouvv5auSBlqK7dLly5h1qxZMBgM0Ol0CA8Pxz///CNdkTKwdu3aVt89Pz8/8/r6+nosXboUbm5ucHJywpw5c3p9ZkDnuX322WcICgqCTqeDIAioqKiQrlgZ6Si38vJyLFu2DL6+vnB0dIS3tzdef/11VFZWWn0+NjLtyMvLw6effoqRI0daLM/NzUVoaChCQkJw6tQp5OXlISYmpsPHJ/cmbeXm5eWFkpISi9e6devg5OSE559/XsJq5aG9z1pERAQKCwuRkZGB/Px8zJ49G+Hh4fj9998lqlRe2sqtpqYGISEhEAQB2dnZ+OWXX3Dr1i2EhYWhublZwmql9+STT1p8B0+cOGFeFxcXh2+//RZpaWk4duwYiouLMXv2bAmrlY+OcqutrUVoaCjefvttCSuUp/ZyKy4uRnFxMTZt2oSCggLs3r0bR44cQVRUlPUnE6mVqqoqcciQIWJmZqY4efJkcfny5eZ1AQEB4rvvvitdcTLWUW73Gj16tPjqq6/arjiZ6igzrVYrpqSkWGyv1+vFzz//3MZVyk97uf3www+iSqUSKysrzdtWVFSIgiCImZmZElUrvYSEBHHUqFFtrquoqBA1Go2YlpZmXvbXX3+JAMTc3FwbVShPHeV2t59++kkEIN68ebPHa1KCrubWIjU1VbSzsxMbGxutOh8vI7Rh6dKlmD59OqZOnWqxvKysDCdPnkS/fv0QGBgIDw8PTJ482aJD783ay+1ep0+fxtmzZ++vA39IdJRZYGAgvvzyS5SXl6O5uRn79+9HfX09goKCbF+ozLSXW0NDAwRBsHjYloODA1QqVa//nl64cAGenp4YNGgQ5s+fj6KiIgB3vo+NjY0WWfr5+cHb2xu5ublSlSsb7eVGHetObpWVldDpdOjTx7rpHx/6SSO7a//+/Thz5gzy8vJarbt8+TKAO/f/Nm3ahNGjRyMlJQXBwcEoKCjAkCFDbF2ubHSU27127tyJYcOGITAw0AaVyVdnmaWmpmLu3Llwc3NDnz590LdvX6Snp8PHx8fGlcpLR7mNGzcOWq0Wq1atwoYNGyCKIlavXo2mpiaUlJRIUK08BAQEYPfu3fD19TXf2p04cSIKCgpQWloKOzs7uLq6Wuzj4eGB0tJSaQqWiY5yc3Z2lro82epObjdu3MD69euxaNEiq8/HRuYu165dw/Lly5GZmQkHB4dW61vusS9evBgLFy4EAPj7+yMrKwu7du1CYmKiTeuVi85yu1tdXR327duHNWvW2Kg6eepKZmvWrEFFRQWOHj0Kd3d3HDhwAOHh4fj5558xYsQIG1csD53lZjAYkJaWhujoaGzbtg0qlQrz5s3DmDFjevU4trvHoo0cORIBAQEYOHAgUlNT4ejoKGFl8tZRbryi3L6u5mYymTB9+nQ88cQTWLt2rfUntOqG1EMqPT1dBCCq1WrzC4AoCIKoVqvFixcvigDEPXv2WOwXHh4uvvzyyxJVLb3Ocrt9+7Z525SUFFGj0YhlZWUSViy9rn7WCgoKLPYLDg4WFy9eLFHV0uvOZ+369evmMQseHh7ixo0bJapanp566ilx9erVYlZWVpvjO7y9vcUtW7ZIU5yMteR2N46R6dy9uZlMJnH8+PFicHCwWFdXd1/H5hWZuwQHByM/P99i2cKFC+Hn54dVq1Zh0KBB8PT0RGFhocU258+f79W/vuksN7VabV6+c+dOzJgxAwaDwdZlykpnmdXW1gJAq6sIarW6V//6pjufNXd3dwBAdnY2ysrKMGPGDJvWKmfV1dW4dOkSFixYgLFjx0Kj0SArKwtz5swBABQWFqKoqAjjx4+XuFJ5uTs36rp7czOZTJg2bRrs7e2RkZHR6ZX8zrCRuYuzszOGDx9usUyr1cLNzc28fMWKFUhISMCoUaMwevRoJCcn49y5c/jqq6+kKFkWupIbAFy8eBHHjx/H4cOHbV2i7HSWWWNjI3x8fLB48WJs2rQJbm5uOHDgADIzM3Ho0CGJqpZeVz5rSUlJGDZsGAwGA3Jzc7F8+XLExcXB19dXipJlIT4+HmFhYRg4cCCKi4uRkJAAtVqNefPmwcXFBVFRUXjjjTeg1+uh0+mwbNkyjB8/HuPGjZO6dEl1lBtw51lPpaWluHjxIgAgPz8fzs7O8Pb2hl6vl7J0SXWUm8lkQkhICGpra7F3716YTCaYTCYAd24N3/2fka5iI9NNsbGxqK+vR1xcHMrLyzFq1ChkZmZi8ODBUpcme7t27cKAAQMQEhIidSmyp9FocPjwYaxevRphYWGorq6Gj48PkpOT8cILL0hdnqwVFhbirbfeQnl5OYxGI9555x3ExcVJXZak/v77b8ybNw///vsvDAYDJkyYgF9//dV8ZfTjjz+GSqXCnDlz0NDQgGnTpmH79u0SVy29znLbsWMH1q1bZ95+0qRJAO4005GRkVKULAsd5ZaTk4OTJ08CQKsfLly5cgVGo7Hb5xNEURQfROFEREREttZ7h/ETERGR4rGRISIiIsViI0NERESKxUaGiIiIFIuNDBERESkWGxkiIiJSLDYyREREpFhsZIhIEYxGI7Zu3dojxw4KCkJsbGyPHJuIehYbGSJ64CIjIzFz5kyr9t29ezdcXV1bLc/Ly8OiRYvM7wVBwIEDB6wrkIgeGpyigIgUobdPNEpEbeMVGSKyqS1btmDEiBHQarXw8vLCa6+9hurqagBATk4OFi5ciMrKSgiCAEEQsHbtWgCWt5Za5mOZNWsWBEEwv2/rSlBsbCyCgoLM72tqahAREQEnJyc8+uij2Lx5c6saGxoaEB8fj8ceewxarRYBAQHIycl5gCkQ0YPCRoaIbEqlUmHbtm34888/kZycjOzsbKxcuRIAEBgYiK1bt0Kn06GkpAQlJSWIj49vdYy8vDwAdybnKykpMb/vihUrVuDYsWM4ePAgfvzxR+Tk5ODMmTMW28TExCA3Nxf79+/HH3/8gZdeegmhoaG4cOHCffzlRNQTeGuJiGzq7kG1RqMR7733HpYsWYLt27fDzs4OLi4uEAQB/fv3b/cYLbeZXF1dO9zuXtXV1di5cyf27t2L4OBgAEBycjIGDBhg3qaoqAhJSUkoKiqCp6cnACA+Ph5HjhxBUlISNmzY0J0/l4h6GBsZIrKpo0ePIjExEefOnYPJZMLt27dRX1+P2tpa9O3bt0fPfenSJdy6dQsBAQHmZXq9Hr6+vub3+fn5aGpqwtChQy32bWhogJubW4/WR0Tdx0aGiGzm6tWrePHFFxEdHY33338fer0eJ06cQFRUFG7dunXfjYxKpYIoihbLGhsbu3WM6upqqNVqnD59Gmq12mKdk5PTfdVHRA8eGxkispnTp0+jubkZmzdvhkp1Z4heamqqxTZ2dnZoamrq9FgajabVdgaDAQUFBRbLzp49C41GAwAYPHgwNBoNTp48CW9vbwDAzZs3cf78eUyePBkA4O/vj6amJpSVlWHixInW/aFEZDMc7EtEPaKyshJnz561eLm7u6OxsRGffPIJLl++jD179mDHjh0W+xmNRlRXVyMrKws3btxAbW1tm8c3Go3IyspCaWkpbt68CQCYMmUKfvvtN6SkpODChQtISEiwaGycnJwQFRWFFStWIDs7GwUFBYiMjDQ3VQAwdOhQzJ8/HxEREfjmm29w5coVnDp1ComJifjuu+96ICkiuh9sZIioR+Tk5MDf39/itWfPHmzZsgUffvghhg8fji+++AKJiYkW+wUGBmLJkiWYO3cuDAYDNm7c2ObxN2/ejMzMTHh5ecHf3x8AMG3aNKxZswYrV67E008/jaqqKkRERFjs99FHH2HixIkICwvD1KlTMWHCBIwdO9Zim6SkJERERODNN9+Er68vZs6ciby8PPNVHCKSD0G894YyERERkULwigwREREpFhsZIiIiUiw2MkRERKRYbGSIiIhIsdjIEBERkWKxkSEiIiLFYiNDREREisVGhoiIiBSLjQwREREpFhsZIiIiUiw2MkRERKRYbGSIiIhIsf4PygLHZnILAlQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    # Get latitude and longitude points from dataset\n",
    "    latitude_column = 'Latitude (generated)-value'\n",
    "    longitude_column = 'Longitude (generated)-value'\n",
    "    X = df[[latitude_column, longitude_column]].values.tolist()\n",
    "    city = df[['adm_1_eng-value','adm_3_eng-value',latitude_column, longitude_column]].values.tolist()\n",
    "    border = border_df[['Lat', 'Lon']].values.tolist()\n",
    "    clusters = []\n",
    "    city_out = []\n",
    "\n",
    "    # Set up DBSCAN clustering\n",
    "    kms_per_radian = 6371.0088\n",
    "    db = DBSCAN(eps=40/ kms_per_radian, min_samples=15, algorithm='ball_tree', metric='haversine').fit(np.radians(X))\n",
    "    cluster_labels = db.labels_\n",
    "    n_clusters = len(set(cluster_labels)) - (1 if -1 in cluster_labels else 0)\n",
    "\n",
    "    # Assign points to clusters\n",
    "    for i in range(len(cluster_labels)):\n",
    "        for j in range(n_clusters):\n",
    "            if cluster_labels[i] == j:\n",
    "                X[i].append(j)\n",
    "                clusters.append(X[i])\n",
    "                city[i].append(j)\n",
    "                city_out.append(city[i])\n",
    "\n",
    "    # Organize clusters by label\n",
    "    each_cluster = defaultdict(list)\n",
    "    w0 = each_cluster[0]\n",
    "    w1 = each_cluster[1]\n",
    "    for point in clusters:\n",
    "        label = point[2]\n",
    "        each_cluster[label].append(point[:2])\n",
    "\n",
    "    # Calculate cluster centers\n",
    "    centers = {}\n",
    "    for label, points in each_cluster.items():\n",
    "        if points:  # If the tag has corresponding points\n",
    "            mean_longitude = sum(p[0] for p in points) / len(points)\n",
    "            mean_latitude = sum(p[1] for p in points) / len(points)\n",
    "            centers[label] = [mean_longitude, mean_latitude]\n",
    "        \n",
    "    # Find the nearest border point for each cluster\n",
    "    nearest_point = find_nearest_target_point(centers, border)\n",
    "    print(nearest_point)  #Output the coordinates of each cluster's corresponding checkpoint\n",
    "\n",
    "    # Visualize\n",
    "    df_out= pd.DataFrame(clusters, columns=['Latitude', 'Longitude', 'label'])\n",
    "    p=sns.scatterplot(data=df_out,x='Latitude',y='Longitude',hue='label', legend = \"full\", palette = \"Spectral\")\n",
    "    sns.move_legend(p,'upper right', bbox_to_anchor=(1.0, 1.0), title = 'Clusters')\n",
    "\n",
    "    for label, coordinates in nearest_point.items():\n",
    "        latitude, longitude = coordinates\n",
    "        plt.scatter(latitude, longitude, marker='*', color='red')\n",
    "   \n",
    "    plt.title('Humanitarian Aid Clusters and Border Crossings of Ukraine')\n",
    "    plt.ylabel('Longitude')\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00a26332-73a7-4166-9060-2515f75bc586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   label   Latitude  Longitude\n",
      "0      0  50.273385  23.589823\n",
      "1      1  51.399221  30.657726\n",
      "2      2  51.375612  29.409339\n",
      "3      3  50.298017  36.281830\n",
      "4      4  47.877711  29.274989\n",
      "5      5  48.444859  27.791351\n",
      "6      6  50.368274  35.757180\n",
      "7      7  46.383157  30.087395\n",
      "      Latitude  Longitude  label\n",
      "0    51.854924  24.728774      0\n",
      "1    51.746036  24.443565      0\n",
      "2    51.678087  24.631711      0\n",
      "3    51.643766  24.233541      0\n",
      "4    51.642622  25.040106      0\n",
      "..         ...        ...    ...\n",
      "684  46.277007  30.396477      7\n",
      "685  46.214615  30.570521      7\n",
      "686  46.184180  30.334643      7\n",
      "687  46.146412  30.523266      7\n",
      "688  46.059092  30.083300      7\n",
      "\n",
      "[689 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Convert nearest_point to DataFrame\n",
    "nearest_point_df = pd.DataFrame(list(nearest_point.items()), columns=['label', 'Coordinates'])\n",
    "nearest_point_df[['Latitude', 'Longitude']] = pd.DataFrame(nearest_point_df['Coordinates'].tolist(), index=nearest_point_df.index)\n",
    "nearest_point_df.drop(columns=['Coordinates'], inplace=True)\n",
    "\n",
    "print(nearest_point_df)\n",
    "\n",
    "print(df_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ba41dfe-5d79-4452-89f9-97041c9baf7a",
   "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>Hromadas_Latitude</th>\n",
       "      <th>Hromadas_Longitude</th>\n",
       "      <th>label</th>\n",
       "      <th>Boder_Latitude</th>\n",
       "      <th>Boder_Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.854924</td>\n",
       "      <td>24.728774</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.746036</td>\n",
       "      <td>24.443565</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.678087</td>\n",
       "      <td>24.631711</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.643766</td>\n",
       "      <td>24.233541</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.642622</td>\n",
       "      <td>25.040106</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>46.277007</td>\n",
       "      <td>30.396477</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>46.214615</td>\n",
       "      <td>30.570521</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>46.184180</td>\n",
       "      <td>30.334643</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>46.146412</td>\n",
       "      <td>30.523266</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>46.059092</td>\n",
       "      <td>30.083300</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>689 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Hromadas_Latitude  Hromadas_Longitude  label  Boder_Latitude  \\\n",
       "0            51.854924           24.728774      0       50.273385   \n",
       "1            51.746036           24.443565      0       50.273385   \n",
       "2            51.678087           24.631711      0       50.273385   \n",
       "3            51.643766           24.233541      0       50.273385   \n",
       "4            51.642622           25.040106      0       50.273385   \n",
       "..                 ...                 ...    ...             ...   \n",
       "684          46.277007           30.396477      7       46.383157   \n",
       "685          46.214615           30.570521      7       46.383157   \n",
       "686          46.184180           30.334643      7       46.383157   \n",
       "687          46.146412           30.523266      7       46.383157   \n",
       "688          46.059092           30.083300      7       46.383157   \n",
       "\n",
       "     Boder_Longitude  \n",
       "0          23.589823  \n",
       "1          23.589823  \n",
       "2          23.589823  \n",
       "3          23.589823  \n",
       "4          23.589823  \n",
       "..               ...  \n",
       "684        30.087395  \n",
       "685        30.087395  \n",
       "686        30.087395  \n",
       "687        30.087395  \n",
       "688        30.087395  \n",
       "\n",
       "[689 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clustering_result_df = df_out.merge(nearest_point_df, on=['label'])\n",
    "clustering_result_df = clustering_result_df.rename(columns={\n",
    "    'Latitude_x': 'Hromadas_Latitude',\n",
    "    'Longitude_x': 'Hromadas_Longitude',\n",
    "    'Latitude_y': 'Boder_Latitude',\n",
    "    'Longitude_y': 'Boder_Longitude'\n",
    "})\n",
    "display(clustering_result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d0e8ca5a-884c-4c0d-b495-15d780c7d8ba",
   "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>Hromadas_Latitude</th>\n",
       "      <th>Hromadas_Longitude</th>\n",
       "      <th>label</th>\n",
       "      <th>Boder_Latitude</th>\n",
       "      <th>Boder_Longitude</th>\n",
       "      <th>Transport type-value</th>\n",
       "      <th>Oblast</th>\n",
       "      <th>Hromadas</th>\n",
       "      <th>Weight,tone</th>\n",
       "      <th>Number of supplies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.854924</td>\n",
       "      <td>24.728774</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Volynska</td>\n",
       "      <td>Samarivska</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.746036</td>\n",
       "      <td>24.443565</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Volynska</td>\n",
       "      <td>Ratnivska</td>\n",
       "      <td>66.4862</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.678087</td>\n",
       "      <td>24.631711</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Volynska</td>\n",
       "      <td>Zabrodivska</td>\n",
       "      <td>47.686</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.643766</td>\n",
       "      <td>24.233541</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Volynska</td>\n",
       "      <td>Zabolottivska</td>\n",
       "      <td>120.868</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.642622</td>\n",
       "      <td>25.040106</td>\n",
       "      <td>0</td>\n",
       "      <td>50.273385</td>\n",
       "      <td>23.589823</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Volynska</td>\n",
       "      <td>Kamin-Kashyrska</td>\n",
       "      <td>302.606501</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>46.277007</td>\n",
       "      <td>30.396477</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Ovidiopolska</td>\n",
       "      <td>3.1865</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>46.214615</td>\n",
       "      <td>30.570521</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Dalnytska</td>\n",
       "      <td>7.679</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>46.18418</td>\n",
       "      <td>30.334643</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Bilgorod-Dnistrovska</td>\n",
       "      <td>469.36038</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>46.146412</td>\n",
       "      <td>30.523266</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Karolino-Bugazka</td>\n",
       "      <td>1.658</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>46.059092</td>\n",
       "      <td>30.0833</td>\n",
       "      <td>7</td>\n",
       "      <td>46.383157</td>\n",
       "      <td>30.087395</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Odeska</td>\n",
       "      <td>Marazliivska</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>789 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hromadas_Latitude Hromadas_Longitude  label  Boder_Latitude  \\\n",
       "0           51.854924          24.728774      0       50.273385   \n",
       "1           51.746036          24.443565      0       50.273385   \n",
       "2           51.678087          24.631711      0       50.273385   \n",
       "3           51.643766          24.233541      0       50.273385   \n",
       "4           51.642622          25.040106      0       50.273385   \n",
       "..                ...                ...    ...             ...   \n",
       "784         46.277007          30.396477      7       46.383157   \n",
       "785         46.214615          30.570521      7       46.383157   \n",
       "786          46.18418          30.334643      7       46.383157   \n",
       "787         46.146412          30.523266      7       46.383157   \n",
       "788         46.059092            30.0833      7       46.383157   \n",
       "\n",
       "     Boder_Longitude Transport type-value    Oblast              Hromadas  \\\n",
       "0          23.589823           Automobile  Volynska            Samarivska   \n",
       "1          23.589823           Automobile  Volynska             Ratnivska   \n",
       "2          23.589823           Automobile  Volynska           Zabrodivska   \n",
       "3          23.589823           Automobile  Volynska         Zabolottivska   \n",
       "4          23.589823           Automobile  Volynska       Kamin-Kashyrska   \n",
       "..               ...                  ...       ...                   ...   \n",
       "784        30.087395           Automobile    Odeska          Ovidiopolska   \n",
       "785        30.087395           Automobile    Odeska             Dalnytska   \n",
       "786        30.087395           Automobile    Odeska  Bilgorod-Dnistrovska   \n",
       "787        30.087395           Automobile    Odeska      Karolino-Bugazka   \n",
       "788        30.087395           Automobile    Odeska          Marazliivska   \n",
       "\n",
       "    Weight,tone Number of supplies  \n",
       "0          0.64                  1  \n",
       "1       66.4862                 19  \n",
       "2        47.686                 24  \n",
       "3       120.868                 14  \n",
       "4    302.606501                209  \n",
       "..          ...                ...  \n",
       "784      3.1865                  2  \n",
       "785       7.679                  8  \n",
       "786   469.36038                 84  \n",
       "787       1.658                  1  \n",
       "788        1.33                  1  \n",
       "\n",
       "[789 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_copy = df.copy().rename(columns={\n",
    "    'Latitude (generated)-value': 'Hromadas_Latitude',\n",
    "    'Longitude (generated)-value': 'Hromadas_Longitude',\n",
    "})\n",
    "\n",
    "\n",
    "# merge the dataset\n",
    "merge_result_df = clustering_result_df.merge(df_copy, on=['Hromadas_Latitude', 'Hromadas_Longitude'], how='left')\n",
    "merge_result_df = merge_result_df.drop(columns=[\n",
    "    'COLLECT(Geometry)-alias','ADMIN_1-alias','ADMIN_3-alias','Transport type-alias',\n",
    "    'adm_3_eng-alias','adm_1_eng-alias','Latitude (generated)-alias','Longitude (generated)-alias',\n",
    "    'ATTR(Тип транспорту)-alias','SUM(Тон допомоги на тисячу населення)-alias','SUM(Населення, тис.)-alias'\n",
    "])\n",
    "\n",
    "merge_result_df = merge_result_df.rename(columns={\n",
    "    'adm_1_eng-value': 'Oblast',\n",
    "    'adm_3_eng-value': 'Hromadas',\n",
    "    'SUM(Вага, т)-alias': 'Weight,tone',\n",
    "    'AGG(К-сть вантажів)-alias': 'Number of supplies'\n",
    "})\n",
    "\n",
    "display(merge_result_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf0e8cfd-8317-4885-8556-eae450ed15fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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