DataPreparation_Phase_(Section_2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"E:/my_courses/data mining/project/data_genre/\"\n",
    "path = \"C:/Users/User/Downloads/test2/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "book = pd.read_csv(path + \"book_no_null_language_merging.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_df = pd.read_json(path + 'goodreads_book_authors.json', lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2️⃣ Data Preparation Phase : Section 2\n",
    "<hr style=\"border:2px solid gray\"> </hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📑 Authors clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "با توجه به اینکه داخل دیتاست goodbook-10k اطلاعاتی از نویسندگان جر نام آن ها در دسترس نبود لذا از دیتاست اصلی goodreads اطلاعاتی درباره نویسندگان کتاب ها بدست آورده و تلاش می کنیم براساس این اطلاعات نویسندگان را خوشه بندی کنیم."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>name</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.98</td>\n",
       "      <td>604031</td>\n",
       "      <td>7</td>\n",
       "      <td>Ronald J. Fields</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.08</td>\n",
       "      <td>626222</td>\n",
       "      <td>28716</td>\n",
       "      <td>Anita Diamant</td>\n",
       "      <td>546796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.92</td>\n",
       "      <td>10333</td>\n",
       "      <td>5075</td>\n",
       "      <td>Barbara Hambly</td>\n",
       "      <td>122118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.68</td>\n",
       "      <td>9212</td>\n",
       "      <td>36262</td>\n",
       "      <td>Jennifer Weiner</td>\n",
       "      <td>888522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.82</td>\n",
       "      <td>149918</td>\n",
       "      <td>96</td>\n",
       "      <td>Nigel Pennick</td>\n",
       "      <td>1740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_rating  author_id  text_reviews_count              name  \\\n",
       "0            3.98     604031                   7  Ronald J. Fields   \n",
       "1            4.08     626222               28716     Anita Diamant   \n",
       "2            3.92      10333                5075    Barbara Hambly   \n",
       "3            3.68       9212               36262   Jennifer Weiner   \n",
       "4            3.82     149918                  96     Nigel Pennick   \n",
       "\n",
       "   ratings_count  \n",
       "0             49  \n",
       "1         546796  \n",
       "2         122118  \n",
       "3         888522  \n",
       "4           1740  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>829529.000000</td>\n",
       "      <td>8.295290e+05</td>\n",
       "      <td>829529.000000</td>\n",
       "      <td>8.295290e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.844779</td>\n",
       "      <td>5.751610e+06</td>\n",
       "      <td>106.865331</td>\n",
       "      <td>1.595326e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.603013</td>\n",
       "      <td>5.129977e+06</td>\n",
       "      <td>1770.225828</td>\n",
       "      <td>4.479669e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.580000</td>\n",
       "      <td>9.327180e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.900000</td>\n",
       "      <td>4.952564e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.170000</td>\n",
       "      <td>7.838936e+06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.310000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.734337e+07</td>\n",
       "      <td>448570.000000</td>\n",
       "      <td>1.853272e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_rating     author_id  text_reviews_count  ratings_count\n",
       "count   829529.000000  8.295290e+05       829529.000000   8.295290e+05\n",
       "mean         3.844779  5.751610e+06          106.865331   1.595326e+03\n",
       "std          0.603013  5.129977e+06         1770.225828   4.479669e+04\n",
       "min          0.000000  3.000000e+00            0.000000   0.000000e+00\n",
       "25%          3.580000  9.327180e+05            2.000000   8.000000e+00\n",
       "50%          3.900000  4.952564e+06            6.000000   3.100000e+01\n",
       "75%          4.170000  7.838936e+06           20.000000   1.310000e+02\n",
       "max          5.000000  1.734337e+07       448570.000000   1.853272e+07"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ✅ Finding authors who are present in book dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "با توجه به اینکه این دیتاست حاوی اطلاعات بالغ بر  800 هزار نویسنده است و ما درون دیتاست اصلی تنها حدود 5 هزار نویسنده منحصر به فرد داریم لذا ابتدا نویسندگانی که درون دیتاست اصلی هستند را جدا سازی می نماییم."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "unique_authors = set()\n",
    "for i in book['authors'].unique():\n",
    "    res = re.split(',', i)\n",
    "    for j in res:\n",
    "        unique_authors.add(j.lstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_in_book = pd.DataFrame(columns=authors_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in unique_authors:\n",
    "    authors_in_book = authors_in_book.append(authors_df[authors_df['name'] == i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>name</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113224</th>\n",
       "      <td>4.00</td>\n",
       "      <td>8143161</td>\n",
       "      <td>846</td>\n",
       "      <td>Titus Lucretius Carus</td>\n",
       "      <td>23970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111033</th>\n",
       "      <td>3.96</td>\n",
       "      <td>3406353</td>\n",
       "      <td>9173</td>\n",
       "      <td>Justin Halpern</td>\n",
       "      <td>134969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47586</th>\n",
       "      <td>4.21</td>\n",
       "      <td>170658</td>\n",
       "      <td>6468</td>\n",
       "      <td>L.A. Meyer</td>\n",
       "      <td>81221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19145</th>\n",
       "      <td>3.87</td>\n",
       "      <td>138825</td>\n",
       "      <td>43628</td>\n",
       "      <td>Tana French</td>\n",
       "      <td>481984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549338</th>\n",
       "      <td>3.63</td>\n",
       "      <td>14175791</td>\n",
       "      <td>8</td>\n",
       "      <td>Tana French</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        average_rating author_id text_reviews_count                   name  \\\n",
       "113224            4.00   8143161                846  Titus Lucretius Carus   \n",
       "111033            3.96   3406353               9173         Justin Halpern   \n",
       "47586             4.21    170658               6468             L.A. Meyer   \n",
       "19145             3.87    138825              43628            Tana French   \n",
       "549338            3.63  14175791                  8            Tana French   \n",
       "\n",
       "       ratings_count  \n",
       "113224         23970  \n",
       "111033        134969  \n",
       "47586          81221  \n",
       "19145         481984  \n",
       "549338            52  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_in_book.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5784, 5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_in_book.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_in_book.to_csv(path + 'authors.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ✅ Normalize authors columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "در این قسمت فیلد های عددی مربوطه را نرمالایز کرده و در ادامه برای خوشه بندی مناسب از آن ها استفاده می کنیم."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>name</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.00</td>\n",
       "      <td>8143161</td>\n",
       "      <td>846</td>\n",
       "      <td>Titus Lucretius Carus</td>\n",
       "      <td>23970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.96</td>\n",
       "      <td>3406353</td>\n",
       "      <td>9173</td>\n",
       "      <td>Justin Halpern</td>\n",
       "      <td>134969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.21</td>\n",
       "      <td>170658</td>\n",
       "      <td>6468</td>\n",
       "      <td>L.A. Meyer</td>\n",
       "      <td>81221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.87</td>\n",
       "      <td>138825</td>\n",
       "      <td>43628</td>\n",
       "      <td>Tana French</td>\n",
       "      <td>481984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.63</td>\n",
       "      <td>14175791</td>\n",
       "      <td>8</td>\n",
       "      <td>Tana French</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_rating  author_id  text_reviews_count                   name  \\\n",
       "0            4.00    8143161                 846  Titus Lucretius Carus   \n",
       "1            3.96    3406353                9173         Justin Halpern   \n",
       "2            4.21     170658                6468             L.A. Meyer   \n",
       "3            3.87     138825               43628            Tana French   \n",
       "4            3.63   14175791                   8            Tana French   \n",
       "\n",
       "   ratings_count  \n",
       "0          23970  \n",
       "1         134969  \n",
       "2          81221  \n",
       "3         481984  \n",
       "4             52  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_df = pd.read_csv(path + 'authors.csv')\n",
    "authors_df.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "authors_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# scaler = StandardScaler()\n",
    "# authors_std = scaler.fit_transform(authors_df.drop(['name'],axis=1))\n",
    "# authors_std = pd.DataFrame(authors_std, columns=authors_df.drop(['name'], axis=1).columns)\n",
    "# authors_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "authors_std = pd.DataFrame(normalize(authors_df.drop(['name'],axis=1)),\n",
    "                           columns = authors_df.drop(['name'], axis=1).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.912076e-07</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>1.038904e-04</td>\n",
       "      <td>0.002944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.161618e-06</td>\n",
       "      <td>0.999212</td>\n",
       "      <td>2.690788e-03</td>\n",
       "      <td>0.039592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.226210e-05</td>\n",
       "      <td>0.902424</td>\n",
       "      <td>3.420220e-02</td>\n",
       "      <td>0.429489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.686619e-06</td>\n",
       "      <td>0.275735</td>\n",
       "      <td>8.665422e-02</td>\n",
       "      <td>0.957320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.560704e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.643424e-07</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_rating  author_id  text_reviews_count  ratings_count\n",
       "0    4.912076e-07   0.999996        1.038904e-04       0.002944\n",
       "1    1.161618e-06   0.999212        2.690788e-03       0.039592\n",
       "2    2.226210e-05   0.902424        3.420220e-02       0.429489\n",
       "3    7.686619e-06   0.275735        8.665422e-02       0.957320\n",
       "4    2.560704e-07   1.000000        5.643424e-07       0.000004"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.subplots(figsize=(8,8))\n",
    "\n",
    "sns.heatmap(authors_std.corr(), annot = True, fmt='.2g',cmap= 'YlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ✅ 3D plot of authors' features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "    نمودار سه بعدی زیر توزیع نویسندگان را براساس ویژگی های آن ها نشان می دهد."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qJYYUis2BEj+KTUf57B5nAvFSicfjnDhxgl27drF7924GBgbquNrqqUd6zePxsGXLlmJxdj6fJxaLMTU1xYULF9B1fY4YWo9CobxmSEpJNpstiiHHsV7X9WI3mUKh2Fgo8aPYNMxX1LzUgmMpJUNDQwwODnLTTTcRiUSIx+N1K15eCvV+bq/Xy9atW9m6dStg19LEYrE5Vhyl06fXgxgqFTOVBi5alkUmkymKY0cMKcd6hWLjoMSPYlOw0OyepYgfwzA4efIkHo+Hpz3tacVIQj07t2plNTZln89He3v7VVYcIyMjxONx/H5/sV7IiaqsJxYSQw5KDCkU6x8lfhQbHseQdL7ZPZqm1VT3MzMzw6lTp9i7dy8dHR1zfreW4gfqH/lZjHIrjnQ6TSwWY2hoiGg0WqyhcYsVh5SypuiUEkMKxcZEiR/FhqXa2T3VCpZSJ/YjR47Q0NCw5GOtBGstvACCwSDBYJAdO3YwOjpKIpFA0zT6+/tJJpM0NDQUI0OhUGjdCYX5xFA6nZ7TaabEkELhbpT4UWxIapndU41oKHViv/322+cVUtW4um8WnMnLO3fuvMqKo6+vj1QqNWf6dDAYXOsl14xzbjnnQyUxVOpYr8SQQuEOlPhRbCic2T2lppj1dmKvZg1rgRsiPwtRasXR2dmJlLI4fbqnp4dsNkskEimKIb/fX/c1LNVGpFoqiSHnfCwVQ043mRJDCsXaoMSPYsNQOrunlknN89X8LMWJfSmRH7eLlpWikhVHPB4nGo1y+vRp8vn8hrDiqCSGDMMoPsYRQx6PRznWKxSrhBI/ig1B+eyeWjaQSuKj1In92LFjNRXJqsjP0tA0jaamJpqamti7d++KWHGs9ftTqWbINE0effRRbrvtNoQQc9JkSgwpFCuDEj+KdU15WmEpc2bKRcPExAQ9PT2LOrFXQtX81I9KVhzO9GnHisP5fS1WHG4SE6ViSNf1YpF+adpWiSGFov4o8aNYtyw0u6cWHMFSrRN7NetaC9Z75GcxdF2ntbWV1tZWYONZcUDlyFA+n1diSKGoM0r8KNYly0lzlSOEIJfL1cWJXUV+5rKS78V8VhyTk5NFKw4nReZYcax0wXO9KbXigMpiqNyXbD29PoVirVDiR7GucNIC3d3dHDp0qC52CpOTk0xNTXHs2LG6GJKqyI/Nam/C1VhxCCFoamrCsqx1YcVRTiUxlMvlyGazxZsARww5r1eJIYXiapT4UawbSmf3xOPxZR/PcWLPZDJs2bKlLsLHbQJkM1PJiqOnp4doNMrExETRiqOlpYVwOLxmImE558tCYgiuONY7rfVKDCkUNkr8KFxPpdk9Tnv6Uu/eS53Y9+7dS29vb13WupYbixJeC+P3+4lEIoRCIdrb20mn08Xi6UQiQTAYLIqh1bTiqGcqrtyxHuwIWC6XA2wxVDpnaD1GvxSKeqDEj8LVlFtUOJvEUmtrKjmxp9PpDSEa1B19bThWHB0dHUgpi2Ko3IrDmT69Uu/vStUhlXaROc8DSgwpFKDEj8LFLGRRIYSoyYwU5jqx33777cXW6KUcy61sBBG3kswnNIQQhEIhQqFQ0YojmUwSjUbp7e0lnU4TiUSK3WT1tOJYrc9MiSGF4gpK/ChcR3maq9IFuNbIj5ud2OuFivzUDyEE4XCYcDjMrl27VtyKYy0+u0piyKkZcjopY7EY27dvn9NNplBsBJT4UbiKai0qqo3WSCkZGBhgdHTUlU7szvPXi40g4txINVYcpdOna5kR5Zb2+/Lvm2ma9Pf309LSUiygdhzrdV0vdpMpFOsRJX4UrqGW2T31dmLfCGmvtRZx64F6CY1KVhwzMzPEYjGGhoZqsuJwi/gpR0qJpmlzIkOWZZHJZIqPccSQcqxXrDeU+FGsOeVFzdWE1hcTLLU4sSvRoFgumqYVU2ALWXE406dLrTjcLH7K6+zKp08rMaRYryjxo1hTlmpRMV/Nz1Kc2DeK+Nkor2MlWa33p5IVRywWIxqNcvHixTlWHH6/35UiYTFRpsSQYj2jxI9izXCiPUt1Yi+P/GQyGU6cOEFzc3NNTuxuEA1uvfvfiKzF++zxeGhra6OtrQ2Ya8URjUbJ5XJcvHiRlpYWGhsbXVFYXOscrfnEUDqdnlNcrcSQwg0o8aNYdZaS5iqnPPKzHCf2jXIBdoOIU1RHqRVHKpXi/PnzhEIhRkdH6enpwefz0dzcTGtrK+FweE3E0HIFuSOGnLUrMaRwE0r8KFaVhWb31IIT+Sl1Yj927NiyW44VGxu3Rth0XWfbtm1s27YNsK04otEow8PDxOPxNbHiqPd7VY0YKnWsV2JIsZIo8aNYFaqZ3VMLmqaRTqc5f/78sp3YNwpui/y4bT1upZLI8Pv9bN++ne3btwNcZcURCoWKYigUCq3Iub/S5q+VxJBpmhiGUXyMM3DR4/Eox3pFXVHiR7HiSCmZmprC4/EQCATqcgFLJpNcvnyZI0eO1MWQVKFYK6qJsJRbcaRSKWKxGBcvXlwxK47VjpJVqhkqFUNCiDmRISWGFMtBiR/FiuLM7hkeHmbLli3LtgVwnNiTySTXXnutEj4lqEjL4rgx7VXrmoQQNDQ00NDQUNGKI5PJEA6Hi2Komo7Heqyr3lQSQ4ZhkM/nGR0dLaYAlRhSLAUlfhQrQnlRs67ry96Y4/E4J0+epLOzk2Aw6IqOmPmoduMwTbNol+C0Rqu6pc1FPQqLy6044vE4sViMc+fOLdmKY6XTXrVSKoZmZmZobGwsiiHn9yoypKgWJX4UdafS7J7lTFGu5MTe39/v2iiHE4FZ7MKbTCbp7u5mx44dtLW1MTMzw+nTpzEMg8bGRlpbWxedDlzpeRXz48b3ZyUKixsbG2lsbKxoxeGcX4tZcax15GchLMu6ymvMscYpF0OlJq1ufT2K1UeJH0VdcYqay2f31GpE6rAendirESGjo6P09fVx+PBhwuEw+Xye5uZm9uzZg2mazM7OMj09XZwO7Ny1NzU1Fe0GFEtjs22A81lxRKPRohWHM3Cxubm5+B1zu/gpj0o5EWaHSmLIKZ52fMnc+voUK48SP4q6sNjsnqWIlYWc2N3sx7WQ+LEsi3PnzpHJZOjq6sLr9WKa5pzH6LpeFDtgC8BoNMrk5CQXLlzA4/EUfx+JRIrvtYr8rE9WW2SUWnEAc6w4BgYGilYc9UhVrxSO79hCVBJDuVyuaNKqaRper7cYGVJiaHOhxI9i2VQzu6cWsVKtE/t6Ez+pVIru7m62b9/O9ddfX/WF1uPxFAfiwZUZMCMjI8TjcQKBAC0tLeTz+ZrcxDcjboxmrPWa5rPicM6v2dnZolhqbGx0ReTRudbUQqkYcr6fjhhybtjK02SKjYsSP4olUz67Z6E7J03TropwVKIWJ3bneetFvTahSuJnfHyc8+fPc/jw4WV3qJXOgJFSFmfATE1NMTY2xvT0NK2trcvq9FGsHmstfspxrDgsy6KxsZGdO3cSi8WYmJigt7d3TmRyraw4lluMXTphGuaKoVwuB6DE0AZHiR/FknDy6aZpVhUurkas1OLEvtQaovmod8GpszbLsjh//jyJRIKurq66R2aEEIRCIUKhULEINBKJMD09zdmzZ8nlcjQ1NRU3q2qLpzcybhIa4D7x4+AIjFIrDrAFQjQanWPFUZqGXY3XUu9ONCWGNh9K/ChqxpndU4sh6UJiZalO7PVMeznHq0dI3xE/mUyG48ePs3XrVm677bZV2RSEEEQiESKRCHv27JlT3Do4OFis53CKW92QwlhN3FjD4lbxM9+6fD7fHCuOTCZTLJ5OJBLFNGxzc/OKWnGs5HtWSQw5NUOlYqjUl0yJofWFEj+KqilNc9VqSDqfWFmqE/tKRH7qdTwhBFNTU/T393Po0KFiYelKU+k1lBe3OvUc09PT9PX1FVMYra2tc4qnFauHGwUZVC/KAoEAO3bsYMeOHUXRv9pWHCtNpYGLUkqy2exVBdSl3WQK96LEj6IqKs3uqYVKBc/LdWKvd+SnHpuQZVkkk0mGhoZcabTq1HO0tbUBV1IYTnGr3+8v1gs1NDRsuAu4W6MsblzTUouKK1lxRKNR+vr6SKVSxenTzc3NdbPiWG0WEkNnzpwpDmJ1UmRKDLkPJX4Ui7KUNFc5peKnHk7s9Y781ON4ThRLCMHhw4dXXfgsRcCVpzDS6TTT09P09/eTTCbn2CQsxZrErVENN+FWQSalXHZatNSKo7Ozc8WsONaa0utiIpFA13UsyyKTyRQfo+v6nDSZGz/zzYQSP4p5WWx2Ty044sdp916uE/tK1fwslampKc6ePcv111/P0NBQzc/tFoLBIDt37ix6RiUSCaLRaNGCw5kM3NLSsmjxtptel4MbxZibxU+91zWfFUc0Gi0W6NdyjrmR+aZPKzHkLpT4UVSkmtk9taBpGslkkieffLIu7d5uqflxirWj0WixWHt4eHhNNtmVEIRO8bRjkzA7O0s0GmV4eBjLsmhqaqK1tZWmpqbiZGC347ZNxq3iZzW8vUqtOJwC/Xg8zvT0NCMjI3OsONZLt+J806fL02SWZZFOp+cUVysxtHqsj6uVYtVwipp7e3vZsWMHfr9/2V9C0zSL+f5nPOMZdbmAuaHmJ5vNcuLECZqamjh69OiGn7SsaRrNzc00Nzezb98+DMNgZmaG6elpLl68OKe4urGxca2Xu25wq/hZi3WVWnEARauXcisOJyLtVsG92PtW6nkISgytBe48cxRrQmmaa2Zmhvb29mV/4RKJBCdOnKCtra3orVMPVqLmpxYx5cwkOnjwYHH+icNaiZ/Vfl6Px8OWLVuKxeq5XI5YLFac/+IYS8bj8RVtea4FNwoNN64J3LGucqsX0zSJxWKMjY1x/PhxgKIgX8+jG6oRQ6WO9UoMLR8lfhTA1Wkup2BvqUgpGR4e5tKlS9x0003ous65c+fqtt61ivxIKbl48SITExPzziTaqJGfxfD5fLS3txcHVI6MjDAxMVFseW5oaCi21S+leHqj4tZzZSndXiuNruvFLrGjR4/OGd3gRB+dOVZuseJYCpXEUOmYEaA4cNHj8SjH+iWgxM8mp9yiwvmyLcc41DAMTp06ha7rRSf2TCZTV7GyFjU/jvVGOBymq6tr3nqIzRL5WQyfz0ckEmH//v1zunyc4ulIJFJsq1+Pha31xI0bVzXmoWtBaU1N+eiGfD5PNBplfHyc3t7eeU2A1xvziSHDMIqfU2maTImhxVHiZxOzkEVFtV5c5cznxL4SYmU1Iz+xWIxTp05VZb3hNhHiBsq7fEoLW4eHh4u1HM78l5Wq5XBDKqcct4oMN75XsHAhttfrnRN9dEyAL1++zLlz59bEimMlqFRA7Ygh5/elaTIlhq5GiZ9NymKze2qN/CzmxF5vsbKcyFQtxyt9XbfeeiuhUGjRY6nIz+KUFrbu27evWMsRjUbp7+9HCFHcpJqamlwpDurFehQZa0kt6yo1AYb5rTjqNdSzntekWqgkhgzDmGM6rcTQXJT42WRUO7unFnFRjRN7vcVKvTf6SsfL5/OcPHmSQCCwoMP8Sq+tWtaT+ClH1/U5xdOl6Yvz588X79hbW1uXVTztxvfHreLHretaTi1SuRVHOp0mGo0yMDBQrEtzIpBLseKolz/gcqkkhvL5PJOTk6RSKTo6Oq4yaXXjZ72SKPGziajFoqJasVKtE/tqRWqWSrlwcNJ3+/fvL941LvVY1f6N4grl6YtKflFOvVCtFglue6/dKjLcvK56RKSEEIRCIUKhUHGo53xWHNVOOHdrtMxpYim98c3n83MiQ+UmrW787OuJEj+bBOekr9aiYjFxUasT+0pMil2JyI+UksHBQYaHhyum79ZibW5/3tWg/I49lUoxPT1dtEiIRCLFTcptfmqL4VaR4cZuL1g5gVHJisOZcH7+/PnieeZEhipd80zTdKX4cTBNs9gmXxqhcm6MFzJpdeO5sByU+NngLNWiYiHxs1Qn9npS741e0zTy+TzHjx/H6/Vy++23Lzl8vZFFiBso3aRKi6ej0SinT5/GMAyampqKYqi0eNqNn4sb1wTuLcRerehKpQnnjhiaz4rDrZEfB0f8lLOQGHKEj9frLabJNoIYUuJnA7Mci4r5xM9ynNjrSb2/eLlcjp6eHg4cODCnS20pqMjPFVZjPaXF03v37sU0TWZmZoq1HEIImpubaW1tdW2UxY1rcut7tVYCQ9O0q6w4nOnTjhVHMBgsppPcaMVhmmZVnZSlYsj5DudyOXK5HA888AD9/f28+93vXtG1rjRK/GxA5pvdUwu6rpPL5Yr/rocTuxtxhjGOj4+zb9++ZQsfcKcIWQvWauPUdZ3W1lZaW1sBu3g6FosxMTFRHFmwZcsWWltbXdHurERGbbhlXaV2L2ALi8uXL3P58mW6u7uRUhYjkCs5vqEW5ov8LESp3QbA5OTkHIPW9crafxqKurLQ7J5aKI381MuJ3W0YhsHp06cRQrB79+66CToV+XEXXq+XrVu3snXrVtLpNPv27SOZTDI0NEQ8HicUChVTF0vp8FkubhU/al21oes6oVCI5uZmDhw4ULQJKh3f4NQLNTU1rUlXWD260RKJBOFwuE4rWjuU+NlALDa7pxYc8TM6OsqFCxfq4sTuJhKJBN3d3ezevZvOzk76+/vr1j1Wqwhx44V8I+P3+2lsbJxTPF3a4eMUT7e2tq5KhNOtm7lb1+WWyE8lSsVFufedE4GcmpriwoULRauO1ZxltZTITzmpVIqdO3fWaUVrhxI/G4ClFjUvxtjYGIlEgttvv92V+eulMjw8zMDAADfddBORSASob9RERX7cTemGXqnDp7x4urGxkdbWVpqbm1fke+BmkeHWdblZ/Cw0fdqJQMIVI+DVtOKol/hZShes21DiZ50jpSzWM+zevbsuF6tEIsG5c+fw+/0cOXKkrhfAtbzQm6bJmTNnME2z6DnmsBHEj2JxFvtchBBzilpN02R2dpbp6WkuXbqElHLO5Ol6pC7ceq5s9m6vpVBLq3u5EbBjxTEyMkI8Hsfv9xfPteUM9ixfn0p72Sjxs45xipodZ+M9e/Ys63ilTuzXXHMNsVisrkLFEQVrIX6SySTd3d10dnbS2dm5bDuPhVCRn42DruvFDQjsOrFoNMrk5CQXLlyo2926GyMs4M51WZbliuLhSiynpqbciiOdThOLxRgcHCQejxMMBpdtxVGvyI8SP4o1oTzN5fF4lr1xO07smqZx++23k0qlmJqaqtOKbRyBsdp3bZcvX+bixYvceOONNDY2VnxMPb3HlnIst6Y+NhrLfZ89Hs+c1EX53brjFdXa2lp18bT67GvDze9XPYVZMBgkGAxeZcXR399PMpmkoaFhzvTpat6TegxhdJ57vaPEzzqj0uye5UYtKjmx19s+YqWOudCF0LIszp49Szabpaura8F6jbVOe9XjYq4iP6tP6d166QZVao/g2HDMNwXdzZu5G3F72svn89X9uAtZcVy4cKFqK456dHslk8lireR6RomfdUL57J7Sbq6lioqFnNhXSvyshCVFpY3Dac/fsWMHN9xww6Kby1qLn3o9r2JhVvJzqbRBlU8ELp087YhxJX5qw83iZ7WMTRey4ujp6SGbzVa0fKnHuZZMJlXaS7E6LDa7R9d1TNOs6ZiLObGvhPipZ2oJ5hdTY2Nj9Pb2cuONN9LU1FT12ta7+AH3Fs+6idUSGpXsEZy5L4ODg0gpaW5uJpPJ1P27tpFxu/hZK7uf8nOttGsxn8/T1NSEYRjkcrllRadU5EexKlQzu6dWoVKNE/t6SHs5Ysq507Isi3PnzpFOp2tuz98oBc8K96Jp2lXF07FYjMnJSc6ePYvX6y3WC61Uq/NGQImfxSm3fHGsOCYmJjh58iSmac6ZPl3LtTKdTlflcO92lPhxKaVprsVm91S76ZU6sd92220LnsDrQfyURn7S6TTd3d20t7dz/fXX1ywEVORnc+CmFJPH46GtrY3R0VH27duH1+u9qtXZqRdaanfPRsStLfhQn26qlcCx4vD7/dx2221z/O+cEQ6lAxcXKtp28/tfC0r8uBDHUXcphqTzUasT+3oQP07kZ3x8nPPnz3Po0KHiXfVSjrXexY8bC57dth63IoTA5/Oxbds2tm3bBtiCfnp6utjdU01Baz1x62fn1uGL4J7ITyVKP89y/7tSK46LFy/Oa8Wx1HPi3nvv5atf/Srt7e2cPHmy4tre+c538vWvf51QKMS//uu/ctttty3puapFiR+XUU+LCoelOLHXuzh5JY4phODChQtkMhm6urqWlceud6u7WzeO1cStG5TbmC8aFQwG2blz51XF005Ba2NjY1EMrUSHkVtxs8Bw89oWikrNZ8XhzLP6/ve/z/j4OM973vPQNK3m7/brX/963va2t/G6172u4u+/8Y1vcP78ec6fP88jjzzCW97yFh555JHaXmCNKPHjElbCosJxYk8kEq5wYq+nwMhkMsRiMTo6OrjtttuWvdHWU5jVKn5M02RqaoqmpqZl2Sco0bU4bkp7OVSzpkoFrbOzs0SjUYaHhzFNk+bmZlpbWxdNW9RrTWvFehUYa00tayu34ujs7OSb3/wmn/nMZxgYGOCnfuqneO5zn8vznvc8jh49uuj59uxnP5v+/v55f/+lL32J173udQghuOOOO5wBuzuklJerfoE1osSPC1iJNJcbndjrlfaanJzk3LlzRCIRdu7c6boZObUcy/mcQqEQAwMDAHM2Mbde5BX1YylCw6nhaG5uZt++fZimSSwWY3p6mosXL84prm5sbKz5PHKziHaz+HHz2pYz4HDHjh28/vWv59WvfjWveMUr+OQnP8kDDzzAP/7jP/L4449z5MgR/u3f/m3JaxseHmbXrl3Ff3d2dtLT07MTUOJnI7LQ7J6lHk8I4Von9uWKHyklvb29xGIxjh07Rk9PjyvrdKo9lpOOPHToUHEasBNuduqYail6VZGfxXHj+1OPKIuu63PSFo5p5ujoKD09PTX7RLm5qNXNAsPNa6tHVMqZ7rxz505e+9rX8trXvhawb0iXwzzfyxX9sirxs0aUp7nqkbbJ5/OcP3+eXC7nSif25YifbDZLd3d3sWDbSQ3WazNbzVZ3R8TNzMwUJ0/ncjng6nBzedFrJBIpiqG1TmOuV9wQBS1lJVJM5aaZzuTpS5cukUgkitYIra2tFYun3Zz2UmtbGvUYwOicO+W0tbUt67idnZ0MDg4W/z00NAQwsqyDLoISP2tAJYuK5SKl5PHHH6ezs5Ndu3a58gu4VLEyNTXF2bNnue666+Z8ydxapLzQsXK5HN3d3TQ1NXH06NFFn7e86DUejzM9Pc3p06cxDKOYImtublaRn3XKanxmjk9UR0cHUkqSySTRaJTz58+TyWSK04BbW1vx+Xyu38TdGl0B94lrh3pGfurN3Xffzcc+9jHuueceHnnkEZqamljJeh9Q4mdVkVKSzWaZmJigra2tLl9gx4k9mUxyyy23LFuBryS1ihUpJX19fUxNTXH06NGrvJHcOphwvmPNzMxw8uTJBYdLLnbcxsZGGhsb2bt375w6j76+PoQQmKbJ7OwskUjEtRfhtcZt78tqCw0hBOFwmHA4zK5du4rTgKenp4sD8MLhcDEy7TYHdTcLMzdTL/GzFGuLX/iFX+B73/sek5OTdHZ28od/+IfFco83v/nNvPSlL+XrX/861157LaFQiE9+8pPLWmc1uOus3sA4FhWZTIbBwcElbX7llDqxOy7S9cQRK/W6y6pFrDgRksbGxnnnErl1Nk/5saSUDA0NMTQ0xK233lq3z6m8zmNmZoaenh6GhoaIx+OEQqHiLI+NMJF1I7OWm3npNGCneHpiYoJYLMZTTz2FEKJYL+SWInwlfmpnLcXPZz7zmQV/L4Tg4x//+FKXtSSU+FkFSmf3LMWHqxLlTuzHjx9fsaGE9RQ/1bx2x4/m4MGDxdqXhdZXr7WtRBTJNE1OnTqFEILbb799RdtgfT4fgUCAQ4cOFV2fp6en58yFceqF3FYPtplxWyRD13UaGxsJh8PcdNNN5PN5otFosQjf5/MVU2TVFE8r3MFyur0cVirttRYo8bOCVJrd4/F4liV+HCf2y5cvz3Fir5eoKmUl7CicUGclpJT09/czPj6+qP2Gczw3R35SqRTHjx8v1mGtNKWvodT12UltzM7OMj09XSwsVC317sBt4gfmdnt5vd45xdOZTGZO8bQTYXQmT7vttawWbq+3syxr2SnMRCKxIRzdQYmfFWO+2T3L2bBzuRwnT54kGAzytKc9bc6GtR7sKBZ67fl8vugy39XVVdVm7OaC53Q6zZNPPlm1s3y9ZhXNR+lcGGBZLfWK+uO293shQRYIBNixYwc7duyYE2Hs7e2dUzy92ToS3ShiSzFNc9mfRyqVUuJHMT9OtKeeFhWLObGvB/Ezn1iJxWKcOnWKa6+9tuhrVO366tnqXo9jOdGrVCrFnXfeueq2A9W+hmpa6p3UxmbawBQ21W7klSKMiURiTkei4x7e0tLiuuLpelKPtNJKUq+aH8cPbL2zcc/ENWAlLCqqdWJfibSXrut1j/yUHs9J4Y2Oji6pELjekZ/lHiuXy3H8+HHC4TBNTU2rLnyWI7Jraamv5QLq9lSAG3Dje7TUWj9N067qSHQMMwcGBoqGma2trTQ2NrrWCmIp1GOOzkqian7mosRPnViJ2T21OLGvh8hP6fHy+TwnT57E7/dz++23L+lL6aZuLyd6dfDgQSKRCKdOnarLumqlHu/HYi31pY7QC7XUuzkFoFiYeqVwyt3DnXTrxMQEvb29eDyeYoSxmvEMbhSKDm6fP1SPyE8ikSASidRpRWuLEj/LpNyiol4nf61O7OtJ/MzOznLy5En27dvHjh07lnW8ekW7lip+pJRcunSJy5cvFyNz2Wy25mPVY7NZqSGHlawTpqenVUt9nXCjSHRu4upNebo1m80Wz6VEIkEwGCymyBzLl9VYVz1we9qrHpEpVfOjAK7M7jFNs+Zoz3ybnWVZnD9/nng8XpMT+0p1e9XzmEII4vE4p06dmtOptlQ0TcMwjLqtrVacOUsej2dO9GqjT1r2+Xxs376d7du3L9hS79ZNym248VxZLW8vv99/VfF0NBqlr6+PVCp1Ve2Z2z3H3J72cuuE57VAiZ8lUjq7p1bh49TSlJ+IqVSKEydO0N7eXrQ+qJZ6CxXnmPWK/BiGQW9vL9lslmc+85l1uUjUs+anVpLJJN3d3ezevZudO3deta612NDW4nkXaqmfmJggn88Xh3Cqlvr1w1p0LpWeS52dncXaM2fuVz6fJxKJYBgG+XzedbOqNkPay2mG2Ago8VMj9ShqdoRK6Ym4XCd2XdcXnKGzFOrVARWPxzlx4gTbt2/H6/XW7e6ont1etTA6OkpfXx833ngjjY2NV/1+OSm09R4tKW2pb2lpYXx8nMbGRtVSvwBufA/ccC6W1p7t2bMHy7KKk6e7u7uRUs6ZPL3WURe3p73WcsKzG1Hipwbmm91TK6UpKtM0OXv27LKd2Fcq8rPcYw4NDXHp0iVuvvlmNE1jZmamTqtb/ciPZVn09PSQSqWKbuzzrasW8eOcS8sVcm5Mt2maplrq1yFurK3RNI1IJEIkEuHGG2/EMAxisRiTk5NcuHChWDzd0tJCJBJZdSHi9rRXPT5TFfnZhDhFzfWY3eOkkxKJBCdOnGDnzp3LdmJ3W8GzaZqcPn0aKSW33347Ho+HTCazoq3zK0k2m+X48eNs2bKFW2+9dcHPyo0ixC2sVEu9or64tbamNLXk8Xhoa2srmjlns1mi0SgjIyPE43ECgUBRWFcqnl7JtbmV5b4HmUxmw9ykKPGzCCsxu0fTNEZGRpiYmOCmm26qi5J2k72FI+p27drFzp076zLduhKrJTKcmoNqO+82U83PcqhXS30trKf3Zy1xY+QHFk7H+f3+OYX46XR6TvF0OBwuplwDgUDd17YexE892CivUYmfBViJ2T1OqDafzxcjIvVgpSI/tQqqkZER+vv7K4q6eqepVjry4wxhHBsb4+jRo1VfMN24aawHVEu9e1gPkZ+FEEIQCoUIhULFKGMikSAajRbLDEonT9ejeNrtNT/LxQ11YPVEiZ8KOLN7BgYGiqZ+9fjQHSf2hoYG9u7dW9dR7/WexgyLG5GW4tQuLSTqVsIuY6Xu5A3D4OTJk/h8vqq9xtaa9Rb5WYxqW+rXu0u9Gz+z9S5+yhFCFOuFdu/ejWVZxcnTg4ODSCmLRfpLTblalrXqU92rpV7CZSMJICV+yiid3eNEPZb7YZc7sY+MjLiyOLnSMasRK07b92K1Sys5MbqeJBIJuru72bt3Lx0dHXU/vqJ2lEv96uLWTa5eqSVN04pRH7gSkS9NuZZOnq7mOd2c9qpHGtOtqdClosRPCeWzezwez7KH6FVyYndTfc5yj+m0fR8+fHhR9/J6f3FWItJx+fJlLl68WLdarNVkI12YFqMal3pn83J7S70b1+bWjW6lBEZ58XQul5tTPF3NiAY3p73qNd15oww4BCV+gLkWFaVFzbquk81ml3zc+ZzYV0KorLaxqWVZnD17lmw2u2Db90pSz/fRsiwymQyjo6N1rcXa7KxWSqeSS300Gr2qpX6thmLOhxtTXuDetNdqRaR8Ph/btm1j27ZtwNUjGsLhcDFy5NSfubnVXU13vppNf4VfaHbPUgWF48Q+NTVV0Yl9vUd+UqkU3d3dbN++nRtuuGHN7hDrFfnJZDIcP34cIQRHjhxx5UV/PbKWkYNgMEgwGKSjo2NOS302m+Wxxx5zTUu9W8XPZov8LEb5iIZkMnlV/Vk6nS4auLoNJX6uZlOLn8UsKpZSR1PqxD5foexyI0qVWC3x46QVljqJup7U4zVPTU1x9uxZbrjhBs6dO1enlSncRGlL/fj4OLfddtuKt9TXuj634dbIjxvqaoQQhMNhwuFwsXh6dnaWnp4e+vr66Ovrm1N/5oYocr0c3ZX4WedUO7vH4/HUJH6qdWJfqc6set9Floo/Z7pxMpmkq6vLFV0Ny3nNUkr6+/uZmJgotrE7YmqtL66KlcVNLfVuLSx267rc+P106s9CoRD79+/H7/cTi8WIRqNcvHhxTnF1Y2Pjmqy/HvVIG8nRHTah+KnFoqLayE+tTuwr0Zm1EjhiwEkLbd26leuuu841F8Wlzg3K5/OcPHmSQCDAsWPHXOHGvtHa1NcTa9lS71aR4dZ1WZblikhKJRxhVklcx2IxRkdH6enpKRbjt7S0EA6HV+V9VpGfq3HnWbQClBY1A4sKH6iuNmcpTuwrUfOzEmiaRjqd5oknnuDQoUPFtlC3sJTIj2Oyun//frZv3z7nd0qAbHwW+34u1lLvmGnWq6XezSLDbREWcO+6YP7ois/no729vdj04hTjX7p0qSgonHNqpSKN9SjG3kimprBJxE95mqvai81iImWpTuwrkfaqN5Zl0d/fTyqV4lnPelZd01z1uuDXGvlxpk/ffPPNFb/Eq+kVplgfrHRLvVvFtltFmVvXBdULjPJi/GQySTQa5fz582QymTlmv/W67ipH96vZ8OJnORYV84mf5Tqxuz3tlclk6O7uprm5mXA4XFfh40RX6iV+qtk8nLZ85/OaL2yuIj+Kxai2pb4Wl3o3buZuFRlujvwsZW2lxdNOpNHpTDx58iSmac6ZPL3UlJ8SP1ezYcVPeZprKV+YSuKnHk7sbk57Od1P119/Pc3NzTz22GN1PX49i4qree/T6TTd3d1s27Zt0bZ8JX4UtTJfS32pS71T31Fp81Eiozbcui6H5X6WmqbR1NREU1MT+/btwzRNZmZmijOGhBDF86mWtKtpmsuulUomk3Pm1a13NqT4KbWoWI4haenfSSkZHh7m0qVLy57+u1LpFScNtJSLgzObKBqNFou2pZRrMjW6XkxOTnLu3Lmq65WU+FEsh/lc6p3IkKZpV7XUu1X8uHVdbhc/9aZ0DAPYaddoNFpMu/p8vmKkcaHiaTXn52o2nPhZbHbPUjAMg1OnTqFpWl2m/65U5GepUZVsNsuJEydoamri2LFjxfdsJS5+K9GSX46Ukr6+Pqanp6vqvluptbl1A1ktNvNrh+pa6iORiCvrzNx67rpZ/KzGjZNjtO1EYDKZzJziaWdMgzN52vkM61XwvN4sfxZiw4ifamf31Ippmjz66KN1NblcKfGzlEJqx4Lj4MGDxTqGlWSp7enVks/nOXHiBA0NDRw9erSm86Cea3PzXb1ibajUUj86OkoikeDRRx91lUu9W0WG+k7NJRAIsGPHDnbs2FE8p6LRKL29vXOKp3O5nKr5KWNDiJ9aZvfUcsyBgQEymQzPeMYz6hruW6n0Si2F1FJKLl68OGfI32qwkmmv2dlZTp48yTXXXFP05KmFen4uKoWmWAinpX779u1kMhluuOGGFW2prxW3igy3ijJY+0hn6ZiGzs5OLMsikUgwPT3N9PQ0s7OzRXHd0tJScwZDpb1chmEYTE9PMzw8XDefqVIn9lAoRCgUqsNKr7BSX5JqhUUul+PEiROEw+F5LThWipUSP0NDQwwODnLkyJElf0GV+Kk/6j1YGOf9KW+pNwxjTm3HarvUK/FTG248zzVNK9agxeNx9u3bV3Srv3TpEkDRhqOxsXHRyJBKe7mE0jSXpmnFdNdyKXdif+SRR+pSKb8aVJNOi8VinDp16iqn+dWi3uJHSllsCe3q6lrW51TPmp9axc/4+DjAku7IFOuX+USGx+Ope0t9LbhVZKh1LQ3LsvB6vYTD4TnF07FYjImJCXp7e/F4PMVzqpLH3VLsLe6//37e+c53Ypomb3zjG3nve9875/czMzO89rWv5dKlSxiGwW/+5m/yhje8YXkvtkrW5VW2fHZPrR5clXCKZMud2B1BsR42pIWEhZPGGxsbq+g0v1rUU2CkUilSqRS7du1a8tiBUlai5mcxLMsqtkX7fD4GBgYqdgUplo8b786hukhwpZb6aDRadUt9rajIT224dV0Olbq9ymdWZbPZYkF+IpEgGAwW99Wbb7655pof0zR561vfyre//W06Ozvp6uri7rvv5tChQ8XHfPzjH+fQoUN85StfYWJiguuuu45f/MVfXBXvSPfv6CXMN7tH13UMw1jycUud2Eu9npxju3UmTzmapnFiZJbBnjgtDT6ed7ANv1ef42W1lDRXPS+E9RIYjoms3++vi/Bx1raakR/HM2379u10dHQUxbzTFTQ4OEgikSjera3UXb5i7VjKd6u0pX7Pnj1VtdSvxrpWA7euqx6t5CtJNevz+/1ziqfT6TQ//vGP+chHPlK8KfvsZz/LC1/4Qjo7Oxd9zkcffZRrr72W/fv3A3DPPffwpS99aY74EUIQj8eRUpJIJGhtbV21QMO6ET8Lze5ZjkBZzIl9JcVPvb/IDw9l+OzpSTTNPsm/eWqM33v+LnrOnuaaa665ysuqGpxITb3Wudy0V+k8oq6uLp544olVnxhdj2M56VVnBlGpeC/vCnKKFkvv8rds2UJTU5OrL7huw42Rn3qcu9W61Le0tNRUv+hGkeHWCItb11VKLZ+nEIJQKMQLXvACXvCCF2AYBs961rMYHx/n3nvvZXJykqc//ek8//nP52Uve1nF69Dw8DC7du0q/ruzs5NHHnlkzmPe9ra3cffdd9PR0UE8Hudzn/vcqr2P60L8LDa7ZykbarVO7Cvdll6vzUtKyX+djhPy6oSDPixLcno4xpcfmuUXnnfbkou2nQ6yep2QyxE/TqF2JBIpziOqp2BZjZqf0vRjNV12QggikQiRSKR4lx+NRpmcnOTChQt4vd7iXf5G6sRYKdy2oa9EJGM+l3qn/dlNLfVLwW2fIawP8bMcdF3H4/Hw27/927znPe8hm83y0EMP8eMf/5hXvOIVFf+m0vWv/LP75je/yS233MJ3v/tdLly4wAtf+EKe9axn0djYuCKvoxRXi59qZ/fU+mVwnNi3bt26qBP7Sg4krGeoVErIWZKgoHDBS2JZsPea65bVrVbvAuWlHm9mZoaTJ09eVajtHK8e7+NK1/yYpsmpU6fQdb1i+rGajVDXddra2mhrawPs1Nn09DQXL14klUqRzWYZGxujtbV1XW5sm42VTuOstkv9ZsXtaa963NSVnqt+v5+77rqLu+66a97Hd3Z2Mjg4WPz30NDQVbPyPvnJT/Le974XIQTXXnst+/bt4+zZs9x+++3LXu9iuFb8rMTsHqjdiX0lIz/1PK6mCW7rCPH4UBJ/OgO6j8YGHzfubF7mcesvfmr5IkopGRoaYmhoiFtvvfUqIefW9vTyY6VSKY4fP05nZ+ecUPByCQQCdHR0FAthH3nkEVKpFENDQ8WNbcuWLTQ2Nm76jc2N9SKrnYqrtqXeNE1Xvl9uZaNHfpYS/e/q6uL8+fNcvHiRnTt38tnPfpb77rtvzmN2797Nd77zHZ71rGcxNjbGuXPnijVCK40rxY9T1FxPi4qlOrGvtBVFvZBS8qoDPrLJWUbNMFsjQd561z62NfpJZA3GZrO0hLy0NtRWRV/vddYSXTFNk9OnTwNw++23V7yzquf6Vkr8OB5j1Qru5Tynruvs27ePffv2FX2ARkdH6enpIRAIFFNk9Z5dpVgaay0w5mupz+VyPProo6rYvkrcLH7qcY4tZbqzx+PhYx/7GC960YswTZN7772Xw4cP8w//8A8AvPnNb+Z973sfr3/967npppuQUvKhD32oGNVeaVwnfpzCZqjdiX2+D3k5TuzrIfJjGAanT59G5jO8/Tl7ueaaa4q/6x6e4Y++do68aSElvOU5+3jJ4eqnH69V2qs0UtLZ2TnvZ1bvOp16CinLsopTtGvxGKsXpT5ATvfG1NQU58+fJ5vN0tTUVKz9WA+jHDYqboquOC31w8PDHDt2rGKxfT1b6jcK9ayLrDdraWr60pe+lJe+9KVzfvbmN7+5+L87Ojr41re+tay1LRXXXfGc2p5aN7RK83jq4cS+HJEyOJ3iO2cnsIDnX7eVPVuu3G3XS/wkEgm6u7vZs2cPuq6TTCaLv8ubFn/y9XNIKWkMeMmbFn/3YB+3dDaxo6k6O4uVED+LvW4nBH/jjTfS1NS04GPrKVjqKaSklJw7d45wOHzV+ISVptJNgNO9EQqFirUfMzMzTE9Pr9hsITdt6rD2UZZKuHFNDpWK7Su11Le0tNDY2Oja17Ea1LN5pd4oR/fKuE78wNLSD+Xip15O7LquFyNRC2GYFt87P8nEbJZr28NsCft493+eIJU3kZbks48P8d6fOshzDraha2JJJqTlDA8PMzAwUBR24+Pjc445mzZI5yyaQ3aKz6traEJweSazpuJnvvdTSsn58+eZnZ2lq6urqkFXazmVeT6SySSTk5Ps2bOHa6+9tg4rqz+aphXv4OHq9uiGhgZaW1vZsmWLSnesIG4WP+XM11I/PDzM2bNnl9xSXy1uHFXg4Oa0Vz2iUs68sY2EK8XPUiiNpMzMzHDq1Km6OLFXM0DRsiR/+LWzPHIxipR28fH2Rh8Zw6Q56KV/KkUqZ/KeL57iWddu4QMvP1STCWk5pmny45+c5Ks9CWSgkZHADK+8JXxVNKkp6CHo00hmDRr8HnKGhZSSjjLhY5gWjw3EmM3kuW5bhL1lEarVKHjO5XIcP36clpaWRTvwSql3qmq50ThnblRra+uq5a5LWaqbfHl7dDKZZGpqak66o7W1lebmZtfe4S6GGzfP9SR+ylntlno3v1du7vaqR1RKRX5cjMfjwTAM+vv7uXz58rIMLkupJj11dizOY/0xmoNehBAYluTUSIK2iJfx2QyZvIkmQBdwfHiG3//yafKZJHkZ49Z9aV51awfnxuL8zXcuMJsx6NrTzG++8ADhwNUfTzKZ5KEnjvOxbpPRpEkiO87XTo7z2ceG+cgrrpkjBDy6xh/89PX84VfPMpu2oy1vu2s/20vEj2FavP+rZ3lqaKYo3H73xQd4+n777m41Cp4dv7GDBw8WCy+rpZ6Rn+Ucy7FHmZ6epquri97e3pqO5aaLuhCCcDhMOByek+6Ynp6mr6/vqtlCC63dbYLDTe8zuO/9gaWtaTVa6t0cXbEsa1UsGZaCSntVxpXiZ6kXqDNnztDY2MjTnva0un1Jqol8pHIWunalK00XEPLpGCYkcrZwEgKagl7G4zm+cWoc07IvMD8ZSfH1k6MksyZ+j0bIp/Pjvmn+8tvnef/P3jDneS5fvszFixfJRnYynblEMmvh0QRSwpnRBJ96bJSf3TN3bYc7GvnkL9/GeDxLa4OPpuDcO7DHL8V4cnCGlpAt3NJ5k7/9bt+KiZ/S40kpGRwcZGRkpGIbezWshR9XOYZhcOLECUKhEEePHkXTtDVzdV+J5y1PdzizhUpNNh0x5NYNwK24MZpRjzUt1lLv8/mqFtD1XNdK4WZhVi/xs5Ec3cGl4qdWpqenmZiYYNeuXRw4cKCux64m7XWwvYGAV2M2kyfo1UlkDY50NvKym7fzF9/uZTSeZVvEZ/s2mfZG7dEABMmsyeVYBilge6MfkGgCvntukl+6I8k1WxuwLGtOm/73L0TJGiYgEcL+wmlCcvJykpd2Xl3L0+D3sM9f+aNOZAwEVwSn36MRS10ZM7Cc9FwlHPHjdKhpmkZXV9eSv5xrPefHKTjft28fO3bsWJF1uY3y2ULOHf6JEyfm3OG7baNy4+a5Wda0mEt9NS31bhYYbl6bivxUZl2LHyfVMDk5SUdHx4qMxK4m8tMY9PKXr7qRv/7fC4zMZLhjXyvvfsE1tIR8POPaNt735dOcuhwnmTEIeDWyhoVpAUIipCSRs9NPiYyBLLwuXdN4x+e6efdzdxOOD7Jjxw5aOvbxmSdGSGQMgl6NRNbAKIipBr/OtsZAzVGQ67aF0TVBKmcS8GhE03mO7m4qXvxWIvKTzWZ57LHH2LVrV1UGeYsdb626vcbGxrhw4ULFTsKNFPlZ7Pmamppoampi3759xTv8sbExotFocUilmi00P24TP85g2ZWk3KV+vpb65ubmYrOK2wXGRl5bMplcFcuJ1WTdih/Hib2pqYmuri4uXbq0pvN49rU18NF7br7q5yGfzl+88kbG4lmmElne8pnjJLMmEsDZoywLTUDelEjstNnu1iCaNPmr+8/y2TceI275eMtnjhPPGCDA69HZGvETzxgEvTrbGv38+rP3MDN8oabXt6s1xPt++jr+5jsXmEnn6drTzG+84EqHUr3Fj+NL1dXVVZcv01rM+ZFS0tvbW+xKq1TMuZEjPwtReoc/MzNTHG/vFMGu9WwhtwkNt0Z+VnMjX6il/uLFi+i6Xuwgc9t75eDmVvd6FTwvt3nIbbhS/Cx2gldyYq8mPbUUljKP5/GBGOfH4mxt9HPXgTY8usb2Rj/OXlhpS/RoYFi2R1fQp2Hms0QzJlLzkNMC/PcTQySyZnFC82wmz+17W/j5o53kTYsbtkfI5fOciOW4vtDdNR+JrMGXj19mIpHjtt3N3HlNK/9+77Hi70+NzPL9RwcJeHRu3SpprkMZh2MkG4vFaGtrq9tdRK01P3nT4j8eHeKx/ihbI35+7c69dDQHisdaTLDk83m6u7tpbGzktttum/dc3SyRn4UQQuDxeIqDKstnCwkhiu309ZottBBueV9Kcav4Wcs1zddSPzY2RiwW48SJEyvaUr8U3B6VWu6NxlImPLsdV4qf+VjIiV3XdbLZbN2f88Jkiu/2Z5gMjXLXwa2EfAsr6PseHeSTD13CKhQ0/9MP+rlue4Rzo3HG4zmmkpVn3ORMCHg0MoZFJm8xELMnMge8Jm/5zHEO74iglVyPPJpGOm9ybE8zAF966jIff7CPTCbNXzz+I/xeHb9HcP32CNdti/DsA1s43NFIJm/y9s8c5+JUCl0TfKV7lHufsZv/c7vtN/X4QJT3f/UsUkosCV/WJb/zrK3skZIHeyZ5amiG9oifu4/sIFwmsBIZg9mMQVvYh89z5UKQzWbp7u6mtbWVQ4cO0dfXV+vHMC+1Rn4+8t0LfOPUOH6PRs94ghPDM/zzL91Gc6Hge6FjxeNxTpw4wTXXXMO2bQtPyXaTCHEL1c4Wam1tXdTtfqOw2lGWaliNtFctOC31oVCIkZERdu3a5TqXerenvZY7qyuVSinxs1Ys5sTu8XhIpVJ1fc6HL07z+18+QzKV59vDvXz+iRE+/gtHKgqgRy5O85nHhniwZ5ItYR8tIR/9U0kuz2Q4P54gnbOIBHT8OqTmCVRkDPsXhf9CE7Ct0c9UIoe/ICZSObttPmOYJDImr/7HR9ka9nFuPEGDV2fWhHjeQMvkEZrg0nSaR/qj/PdTI/zJ3TdwfHCGR/qj9vE1QUeTn089PMg9xzrRNMG/PTyIVxOEA/ZF5HI0yWe6p/nADx6hfzpFwKsT8ul859wEH7/nCAGv/V58tXuUf/jBRcDuavuTu29gX1sD0WiU06dPc91119HW1kYqlaoqUjM6k+FvH7jAYDTNTR2NvPU5+yu2/tfkFWZJ7j89TmPAg6YJgujE03meGprhroNtCwopp9Pu5ptvruoioCI/i1NpttD09DRnzpxZkdlCax3RqIQbPys3CjK4El1xo0u9m9NequC5Mq4UP+UXqGqc2Fci7fXRB/rQNUHYJwgGPJwbS/CRBy7wpjv3Mp7IEc8YXLu1gd6JBP/fl85gWXbU5vJMFsOUJLMmFnYrvARmMtWlz5xXrwmYTOaIBLxsjfj5nRcf5N8fGSRvWiRjJo9cnEZKsAp/s6c1wGwWLCExJOhSomkCDYEuBH/34EWeHJzBLFxvTVMyFM2wvSlQTMXlTQutEGKSUjKVMvlGTwJZeA7Dkvh1weB0micuxXjmNVvom0zy99+/iN8jmEzkGI6luffffsJHf7qDZGyS2267jWAwaL+mKmqIklmDd/xnN1PJHAGPzrdOj3N5JsOHX33TVedGLTVJAtCEmJN2lIX3GSoLKSfamEwma5oUvpbiZz1SOlto9+7dV80W8ng8bNmypabW6PWAWwWZ29YElVNLK9FSX6+1uQU14bkyrhQ/DrU4sa+EAWkiY+DVBSlL0j+VImdY/M+TI3z5+GUCXh2fruH1aOxpCTCTyRPPGFjYE58vz15JwdW6BRbroCVkciZhv5c79rVy085GnnvdVs5cnuXV//SY/ZiSv+mfzsw5gCntg+iaQBNwaTpJ1pi7uZsSbuqIoBcUwEtu3Mbff+8i2bzJ6GyGVN6yW+ELh7UsyUQyDzLH733pNK+/Yzc7W4KA5FI0S7Yw0HFsNssHHhjhU298Bl7PlbuOatJUPeMJZtJ5mgrRJ58uODkySzSVv8qVvhaRoWmCnz+6k888NoSm2a9lR1OQY3taKh7LmTrd2trKrbfeWtOFcy0jMG6MJtTKSswWcuv74jah4ba0l0M1AqMeLfVLwe1pLzXn52pcK35qdWJfCfFz57Vb+MapMWazkrxpi4igz8N4PIuU0NzsJZk1eKQ/RiJrYNX52mpJ+z+5vMGnHhrgT152CCnhEz8cIG9W92SmhNl0jtkMGPP8zatu3Vn83y8+1M7nHx/msYFYUYRJQCv8t2n34iOA8XiOv/h2Lwe3hUnnLXKG3bVmWRKPBhNZjdF4jl0tweLxq0lTeXUNS165A5USJAKfPvfiksqZ9Exm0YVk505ZjFgtxL3P2M3O5gBPDMRob/Tz80d3FtOYpYJldnaWEydOLGnqdPmxqn18PXDTplVPAVg+WygejzM1NcXJkyexLKvqVIeb3h9wZ5TF7WmvWlispd7pPixtqV8Kbn3PoD4pOVXzs0qk02m6u7u58cYbq+4K8ng8dRc/73jufgA+88gAuibY0RQgnjEwLUkyZ2KakqBXxzDrX2hdylTK4Junxrhma5ipZI6fDM6gC6hS/5A2ZDFyU4lbd19xTv/iU5fpGU+iCXsqtRMoKpUrWuFglmW35p8fT/C0vS2MzWZA2rYaHc1+DAu8Wu1pqhu2R7ixo5HjQzPFFODLjuyYU/MzNpvhbZ/tZiqexrQkxy5k+eDLD88ptK6EEIIXH97Giw9fXbDsbNbDw8NcunSJW265Zcl5bhX5WTmEEDQ2NtLY2DhntpCT6ggEAsW7+2Aw6DpxUYpbxY/b1gTLTy1V21Lf2tq6JJd6N75nUL/IjxI/q0AoFOKOO+6o6URfiZofv1fnt37qAFp8lK9fEmRylj39GLs25lI0TUvQS2dLkPFEjlhqcff3pZKz4KMPXKAx4EECAa9GMrf8+Ttbw14iAS+XplNcmEjyw94ppLTD3rZuubKR6qIkJQfFdJhpSSZn4hzd5qUvLvB7NHIm3HlNK9sa54aWNU1jImnw9ZNjhHw6z9jfyoWJJN3DMzQGvTzvYBt+r86HXnGYL3dfZiia5tCOCC+4vn3OcT78nQtMJnMEvTqWafLEpRm+emKUV966vFkUsVgMy7Lo6upa1p2gKnhePcpTHfMZbLqxg8yNQsOtaa96v1fztdSPjIysikv9alEP8ZPP511pXSOEOAY8KaU0S34WklIu2v3kSvED1KzwVyLt5XDnLj8NbVv42IP96JqgwauRKaR5wgEPf/1zN/L2z3WvqPgBO9ITTS9N4M23HTYGvLzyHx7m3FiCYKFzy7AkpiUpfSYBtIV9WBImErniMbXC73Km4J/e/Ey+1H2ZvokUB7c1cPfNO666WJ0cifMXj2fx+M4D0BTwMJM2MKVEE4IvPjnCx+45gs+j8XO37WQ+BqZTBDwaoiDUhJQMTi+92y+bzXLq1Ck0TePIkSPLvshuRhHiFkKhEKFQqDhbaHZ2lqmpKfr7+8lms1y8eHHJd/f1xo3ix60pnJUuKl5tl/rVws31SHXgX6SU5dOFHxBC3CmlXHBDdqX4WcrFoN6TiEvxejz88u0dfKl7jIBXs2tSLEk0lectz9nLD85PkTFMtjZ4mEwaNRc4ryUXJq8IhnjWxKcLcmX5NAF4dEEsbbCnNUgmb5LOm0XxEwnoHN1vR2x+/ujCdhUf/k4vpinxFWp/zo0lCAd0coYtuGLpPN86Pc7P3Lx9weMc3tHId85OENQlprQAD9dvX1pBnuMqv2/fPkZHR+uyGdXTcLXW51Wi6wql3UA7duygt7e3OC/m7Nmzaz5byI2flRsFGdjiZ7WmgtfqUu9mlitm3Xg+CCHuBG4BmoUQrwCSQBx7SwosJnzApeJnKazkh+NEle6+eQf//eQIPo+FYUmagl5u39PK6x54gojfgyfkY2sjXJpO4SvUuvg8gtH4ykaE6km58AEKaT4JSHonknZbsleQzEksIZBS8Nrbd5EzLPKmteB06alEloQBsZkMQjgF2Sa6JhACMnmTf390cFHx847n7mc4lubM5VlMU/KyW7fxwhvaF/ybSgwNDTE4OMitt96Kpmlcvny55mNUQokQ9yGlRNd1tm3bxrZt2+bMFjp79iz5fJ6mpia2bNlSt9lC1azJbRuLW9Nea9lOvlhLfTqd5tKlS64dxbDc9bjpPBX2QrYBzwVywFuAYOE/SeAD1RzHteLHLZuHZUn+tz/Lnz92CoSGYVlMxPI0Bb28/2cOsCVs50GdlQoBLSEv73reNTy90893HzvFX/5EJ1bljB+3Y0nwC8lsFiIBD20NXjKGxW/81wmSORNLwtHdzfzx3TdcNQEaoD0SoGcsia45k4OuzNpxIkmD0TSGaeEp6+6SUvKt0+M80DNJY9DDe198kExilpnoFLcfOVDb67Aszpw5g2ma3H777ei6Ti6Xq1u0RtX8uJ9qZgs5UaFwOLxiF3+3bCoOmzXtVQuldWaWZfH444/j8XhWvKV+LTAMY018+OZD2he4LwBfEELcJqX8yVKO455X5FI+/8QwX+5JEvJ7GJ7JY1qSvVuCmJbkr/+3l0+/4Sivvm0nn370ErowiaYNsnmLD3+7hyc7Na7fvwu0AQIei4yxMTalbEEf5E2LoM9D3srTN5liT2sIXYOfXIrxt9+9wO+95Lri3+QMix9dmCIc0AuF0wIBNAY8zGYMLEui64KWiB+fRyvOHSrl8z8Z4e8fvIiugWnBjy9M89d37yfiq+2CmMlkeOwnT9GxfRv79+0tbj71FA5KhLiPxT6P8gLYbDZb9CFb6myhatbkRvHjtjWBe9flpONKRzGsVEv9WuDW6c5CCC9gCCHeS2EGL3YvzriU8tOL/f36+hSqoN5fkG+eGSega4Wt2o5QJLIm7RE/iazByEyG192xi9awl888OsRUMs8WP+hC8p0h+NalAeJpA3u7r33goZvJ5i1ypt0Bh4RUNs9EMo8l4d8eHmR0Js0HXn4jPl3wts910zueJGuYmBK2NfoIeTXG4jl7NhAgpG3s+o7n7q/4GX72sSECXq3Yzj6TyvPwwCxHW6qP1gyNTfK7/93NhbiO1zPErzxD5+W3dPBPP+zn/FiciJXh8JGFjWGrQUV+3Ekt1wa/38+OHTvYsWNHcbbQ9PR0zbOFFsKNG7pKe9VG+boqtdQ7hr79/f1Fj7vVKLqvx7UgkUi4UvwAu4D7gB8Cg0AYaAB0sNNjcoE3wLXiZykXcac2p57KOujVMQEvIJF2SFgITMs2/oz4bZ+ou2/ewbdPXmZYM2kIBohmLKaTWZDQ0uBlNmOAJauezbMesIDz48niv8cSc2ubvn1mkqDvLM8+0EbPWIKQV0PXPCTTeSbiWSIBL4Yp2dkcJGuYpPMW17Y38LM376j4fOVvnV2FVN15IqVkcHCQP//fi1xMemgKerAkfOJHA3zp+GWmkjm8ukYilec9XzzFR37+5qqGJs7HUs5fN244CpvS2UJ79+69qubD7/cXo0KhUKjqz9KN4kelvWpjsW4qXdeL5wbMbak/d+4cwWBwxVrq6yFk3Tbjp0TUbAHOSinfXOlxCwkfcLH4WQorIX7ufeYe3vNfMRI5g6BHJ12wb0hmDe7p6mRrxI+UkoGBAaz0LHnhZWQ2b3dDFd76RNZkZ1OArGExFs+iC4FZcE3fyEjg++enuHmn7SY/ncqRNyVC2lYTLzrUznfOTuD3aPg9GiGf5PJMpuKxvn5ylGgqx0Q8R8inEfZ7CPs9PGNfM+mpkQXXYZomp0+fRgjBcC5A0GsghEAXdjru0nSa7U1+BALTIzk7mmB4JjNnMnW15AyLf/7RAA+cHSWkW7ynYRs37Fi9sfAq8jM/9XxfKtkoTE9Pc+HChZraot0qfty2JnCv+Kl1gvJqttTXY7qz28RPCQYQEEK8ALgEpIEMkJBSphf74w0lflZiyvPR3c2873kdPDaUZPvWLXS2BEjlTDqbg9zc2UQ+n+fkyZN4fT78ja3MDk1glKmavGGRzNkdTU0BDy0NXiwJs4k001UMh9aB9VounczmiacNZtPGHB8ynyb4Xs+kXf9TuNhmDZMDW6/+kj18cZoPffM8Hk3QHPSSyBps3xrgg684zNaApHdy/k0tnU5z/Phxmtu2c9/pFBcnkximpL3Rb88JEgJNFP6+cL23o3tzjzOdzDEez7KjKUBTcP4L0se+18fXTozi0yQTeZPf/MJJ/t8v3kJnQUhNJ3P82Td76B6eZUdjgPe++CDXbXPlhWVDslKbejAYZOfOnezcuXNOW/SlS5cQQhTv/CORyJwN3I1C1a0O5W4WP0tdV60t9bU+Tz0GHLo47ZUH2oGPAj3YV/Bm4H+BPxJCaFLKeWsiXCt+lnKRWqlBhwfaG9jd5GHfvl1zfj4dm+E7j55g585OtKYWTl0+SWNAZzo1dxChBTQFvbzujl385FKMxwdiaJrAsK7M0DFMOSet49VtTyvDkkhxxSaiWk8vt5Az4W8e6MMjKEa6BBSKliXPvKaVR/vt9yPs9/C7LzmIlJKvdI/y5e5RfB6B36PbXWZeHZ9Hw6sLdE2wd0uIVCo1b4fW1NQUZ8+e5dChQ/zFgyP86MI0bQ0+Ls9mGZ3NsKXBz40dEby6xunROJqAtAHPuqaFjqYrM1++fnKUD3/ngj3RWgj+8Geu5479rcXfZ/Imn3r4Ej1jCX58YZq2sB8NEw2LrGHx5OAMnS1BpJS894unODuWIOTT6Z9K8q7/7OY/7j1Gc7A+X0UV+Vl7Stui9+/fTz6fL6Y5Zmdni5ODt2zZ4sooi1vTXm58r6C+oqzeLvX1srZwk/gpSWedBu7C3lIaAT92zU+i8LgFi0FdK36WwkpYXDjHLRVVUkp6+gb4429dZCLnRTs3QlNwAhAVU1kCuO9XjtEU9PKKWzr4/BPDfOn4ZWYTaYS4WtDoAjQhsAqfnSbAkhIpC5YSJULCOb7bt7vSRjcJZPIWB9oDvOt513B+IknIp3PdtgiRgIcvH7/M33z3Al5dYFmSWNpA1wSZvMl4PIthSiYTWf7z8SHuvrHtqs3eSUOOjY1x9OhRAoEAD/WdIuzX0YRgZ3OAkViGVM5gMpnjt194gDOjcS5MJPGlJnjv3TcghODsaJzHBmL8v+9fJBLQ8Xl0sobF+792lv9+09MI+XQsyxY0Tw3OoGuC2YxB1rDobLK7gQTgLxRoxzMG58YSRPw6Qgg8fg+ZvD3o8Wl7m+tycXfT5uA2IbZWm6fX650zWyiVShWF+ezsLFJKtm3bRktLiysiLptBZNSTegiM+ShPr2YymWLhdDUt9fWY7uy2tFdJzc+1wG3AZewBh7PAdOE/i7LhxM9KRH5Kj2sYBqdOneJrvWnGsj5aQnYKZCKeJWNcPZtGE9DZHCymSnwejdlMnsuzGUI+nby0SOYlQoBP1/DrgkTOxDBt4RPyalzb3kD/VLo4RNDv0cka9nos6X7hUwlT2sMgX/PPjyOAg+1hPvzqmwD4SvcoXl0Q8GjMpvOkcyamtCdqO+QteN9XzvKTS+3c1pjjNue4pmmnIb1eurq6il/8Br9OzrDwewSTiSymlDT4Pcyk87zvy2f41OuP0tEc4Mc//jE+j8a/PzLI3z/Yh2lJZjMGecvLtohdm5TKmkwlc4R8QUZmMnQPzxIuCJq2Bi8TyRzTKQOBxd6tfp5xjR0lCnj14mBHTyHdZ1lyjqt8PXCT4FDMpTTNsXv3bk6ePElLSwuxWIyLFy+u2myhhVDdXrWxmusKBAI1tdTXQ5i50NHdud8/CnwQmAEi2AXQEeBPgfcJIfRSz69yXCt+lvLlW4maH7gifuLxOCdOnGDv3r2kB2J49ZniOoNenf1bGxDA8cEYs1kTIWBbxM8nfunW4rEuTaf4xI8GyBqW3dcthD2P26Ph9WjMlHh3BTx2Ye/T97fyqV/ex4Pnp/iz+3uYzeTs5xWsX/UDTKcMQl6LrRE/Z0bjfPR7ffzOiw+SM0yGY2nyVXyUXzw+zveDgkzDJX7uSBvHjx9n165ddHbOtdl49/Ou4U/v7yGdypPOW/g9GhG/ByEgnTM5PTpLR7Od6nqwZ4I//ca5oqO8Je2ojZOa8uqCtoa5wy0dIgEPpoTnXdtEi57jjT91S3HYo8+j8avP3MsnfjRAwrLwaIJje1q4qaMRkORyuWUPRHPjpuUm3Pj+NDc309FhG/I6s4UuXbpEIpEgHA6zZcuWus4WWgy3pr2U+JlLNS31jmXLcqJ5iUSi2KnmBqSUVqGe5zPAZ5yfFyY/vws4UfiRSnvV47izs7OcPHmSm2++mXA4zPXbTB6+GC3eZedMi649Lfzas/YCYJoWGeNqq4e/+Nb5wiRjgRSyaCeRylt2OKOEjGGRT+b4xI8u8c1T43zsF47wjGta+capMdt5XdoTkddrMTRAOm+Rzpt4dI0TwzN86P5zPNwfq+kYpiX5uwf7aEsP8MyjN8/x2jEtyfnxBFvCfv7qVTdybizBRx7oK0Rq7IuClLbBK9gpyPd/9RxwJW3jPC6Vs9/pPa0h3vflM7zu6bu4cUcjN3Y0cnxoBo8mMCzJ4Y5G3vGc3YxdHiESmPv5/+LTdnFwW5hzY3HaIwGed10bpmlw/PhxcrlcscBxy5YtS54foyI/lXHj+1K+KZXPFkokEkxNTXHy5ElM0ywWvzY3N6/YhqvSXrWxkmmvWqjUUt/f308sFuPRRx8lFAoVz59gMFj1Z5xMJtm9e/dKLr1mCgIoAgSwC5+FlDIqhNgLZLGLnhesCNlw4qfekR/TNIvtq3feeWexjf7VR3dybizBo/1RJHDLrmZed8eVgmhd12jQr/6inp9IEPF7mDJyVUU2KGy80VSO93/1LJ9+/VFu3NnIxx/oQ2gwncyjFwqBzHXYO2/X/5hIafLUTIYnLs3UfIx4DqQ0+PLlRl4UaSz+PGdY/OYXTvLU0AyaEDQFPfy//3MLTUEvf/6t82QNA00Iju5p5tjuZgBmcxJLSry6hmFaRQHU2uDjnmM7+ezjwwxG0wxMp/jJYIy/+4Uj/NnLD/GBb/ZwYTzJ4R2NvPsF12BkkvNutl17W+ja2wLYd1Xd3d1cc801NDc3Y1nWnALHQCBQvPMPBhdvvXdbnY1iYRb6rErv7J3ZQrFYjMnJSXp7e5c8W6iaNblV/Lh1XW4UZT6fj0gkQjAYpLOzk1QqRTQarbml3sU1P88HbgdiQFoIsQU4AtxfzXFcK36W2u2VzVbRO14liUSCEydOsH379qschX0ejT/82euZSNh36+0R/6Jr/vwTw4zN5sgZVtVCpVD6w0zGpHcswWwmz+7WIM852MaJkVmCXp10ziSWrn/Ea7WYXYbvmcDOHnp0wbmxJD+8MMVzDrQB8OXjl3liIErAq6EJmEzk+NC3zvPhV9/EvrYQpy/HaW3wcWNHhM89PkQyZxLMSXwejaaAh9msQS5voWmCP/7ZG/i3hy+ha4KA177Li2cM/v7BPvom0wzH0oT9Hi7PZnnGNa0c6/AvKkLGx8fp7e3lpptuIhwOk8vl0HWdtrY22trs1+DMAOnp6SGbzdLc3LyqxpsbCTdu6rWkmDwez5xzw5kt1NfXRyqVorGxkS1btix7PoxbN3PAletaTbf5WnGiUqW1Zp2dnTW11C+l5uf+++/nne98J6Zp8sY3vpH3vve9Vz3me9/7Hu9617vI5/O0tbXx4IMP1vryLGwj0xx2rU8CeJOU8hxssm4vj8dDMplc/IFVMDIyQn9/PzfddBOBQICJiYni7yxLcn7Ctmq4dmu4WLBaznQyy3fPTaIJ6NrTwj/9sJ+OpgCjsxkS2do3/FjG4I2ffpLBaJpE1p4b1BLy0hb2oWuC8UQOT6Gg1pLg18DvFcxmN24kQBN2d5w9JdoimswVf/fU0AzRdB6ZsjvkmgIeBqZSAFy/PcL12yNMJ3P88r8+wVQyZ3fQmQZvff4BPvXQIK26hiUl73nRAZ53/Vb+/dHBOc+dyZs80DNFzrAQwk59toV9/On95/jve2+eV/xIKenr6yMajXLs2DF8Pl8h/Xb15hwKhQiFQnR2dhZz+lNTU/T19eH1eoteVE4YW0V+Ng/zzRYaHLTPUyd9Wj5baDHcKBLdjGmarjUvnW/o72It9blcjgceeIAXv/jFNc/5MU2Tt771rXz729+ms7OTrq4u7r77bg4dOlR8TCwW49d//de5//772b17N+Pj41UfX0opCzU/XxZCfA/oxBZA00C02uNsKPFTj7SXaZqcOXMGwzC4/fbb8Xg8WJZVPG7etPj9r5zhqUE7ldIS8vKXr7qR7SVzYQAuTia55xOPkciamJYsOpf7PRoeTRD0amTzVrH7x8GZhzOfZO2dSGBYV+bkxDMGOcOia08z3z03CQI0JGG/B9OSpI36uJS7Ea8GAY8gX2h99+kambzFEwNRPv/EEF/uHsOU9vuOhFja4On7Q1ycSPAbXzjJpekMPh2ypp3WAphN5Ln/1DhfesvTGJ3N0hz08IPeKT7y3Qvc2NFI73iCZNZASrtdP+TTMSzbt820JDnDQhOCrCErihDDMDh58iSBQIDbbrvtKk+ghSjP6WcyGaamprhw4QLpdJqmpiay2eycmifFFdwoCuslNMo3s3w+TzQavWq2UDXpU7eml9yKmyNl1Q6sLG+pn5yc5Mknn+SDH/wgp06dYmZmhp/7uZ/jBS94QbE4fz4effRRrr32Wvbv3w/APffcw5e+9KU54ue+++7jla98ZbGWqL29vabXVaj5uQN4C3ALEMIWPn8EfLWaYyjxU0IymaS7u5udO3eya9eu4gVA07TihfObp8Z5YiBGS8iLEIKpZI6Pfa+PP3nZoTnH+t3/OT1H+IAtatKFomYN0DRo8AhmcvYjvBpsb/QzPJOdt0wrZ0g0zZ5/Y1hg5k0yeZMTI7O0hX2YlgXSRGgahinJVFVYtD7JW5AvvHcZ0wRM/uhrZ7GkHQ1yRKUlrwxW7GgK8NKPP3zVPCYpc4U5QJDKmTT4PezdovN/P3+CxweiGBI8muBp+1oIeHT8Ho0Hz0+SM+3p3c7hsnmT67ZH8Hk0Pn0izm9+70cEPBpvf+413Lk3zPHjx9m9ezfbd3Twxacu0zMe55qtYV5+ZHvNrz8QCMy585+ZmeHChQtcuHCBoaGh4iC9aoahbRbc9j6sVJTF6/XS3t5Oe3v7HAuF0vTpfC7jbu32cituFj9LLcZua2vj7W9/O29/+9t51atexVve8hZOnDjB61//eqanp7nzzjv5gz/4A1paWq762+HhYXbtulL/2tnZySOPPDLnMT09PeTzee666y7i8TjvfOc7ed3rXlfV2komN/8R8Bkp5S8Xfn4I+IQQokdK2bPYcVwrfla71X10dJS+vj4OHz684J3zUMweTOisL+DVuTR9tY3IyEzG7hKa5zgW0Bb0kDctIn6NeNYkb8FgLIvAGWxY9voK7udhv060MEVaSJACRmezNAY8bGnwIkzJnvYI3+uZQrI+hiDWC0fwGOXiBjAs+OcfD1QcRJnMmaTzJn4NXnC9ffdzdjTOTwZj+L06ASGwpOThvijfePvTaQp6+fsHL/Lvjw4S8eu2cS2wt62Bv3zVTfzjjy7y/UtZgn4f6ZzJ+79ymjfdqPGyZ9rdaL/7pdP84PwUVmHze7hvmg/87IElb4SOU3RLSwvNzc2Ew+E5w9DqVQ+iqC+rkWIqt1AobYl2Zgs5KbJwOKzSXjXilm6vStRjbel0mqc//em85CUv4bd/+7dJp9P88Ic/nDcVVinCWn4+GYbBE088wXe+853i8e+44w4OHjxYy9IiwKMlz3taCCGxPb8WxbXiZykspdXdsizOnj1LNpulq6tr0Y3hum1hkHaKw4kSPG3v1er3QHuYyb7KgyY1YUd9JODTBamsnBOpkMDOpgCGJRmPZ4uRC4kdfRDSqXWxTygLQMrCFGSLl18T4PM98U0nfKphvjpzXRMgJT4dfvXO3Uwnc3zg/h4mEzl0TdAc8uLT7cLpTN6iKQi/9qy9hHw63z47QVNA54137uXWXc0APHghWpzMncvbU5+jvl00NzczHEvzw95pAl6tWKPz+ECMgek0+9qWNkY+kTUYmckSS5k0NcmrWqZnZ2eZmpoq1oM4UaFIJLJpNjo3buprsaby9Gn5bCHDMJicnMTj8bi2lsVNbMTITynl9hbBYJAXvvCF8z6+s7OzeJ0BGBoauipV1tnZSVtbW1GUP/vZz+b48eNViZ+SQub7gTcLIb4GjAJ3YA88rKruZ8OJn1oiP6lUiu7ubrZv384NN9xQ1UXoOQfaODMa58vHRxECrt8e5q137b/qcR98+SH+z788zlA0fdWGKwQgBbfvaeThizFMy8Lr0dAtO6UV8mrkTXvyb1PQSyZv4vfoBH0aAY/G9qYAnc1BvvjU5Tkmqn6vjiUlXz2fZCJh/3zjVvzUl6aAB8OS+DUDXQh+6wsn6Z1IIgDDlEwlcjQGPFy3LcLWsF0fpGuCX376bn756VfPwAj7dYamJbHMlQLsJwbjSOnUBc19vF0wvTSZ2j08y2//z1km4lkMS7ItPMXH7wlyoL2hcGxBU1NTMaKZy+WYnp5maGiIeDy+JoP0FDZuqEMqF8pPPvkk+Xy+ODW4tAvIrRGOtcTt4me5a5uvaHo+urq6OH/+PBcvXmTnzp189rOf5b777pvzmJe97GW87W1vwzAMcrkcjzzyCO9+97trWpeU8g+FEO8D/gQIA08B90op17f4Wem0l1PVfvjw4WKR4GLYuXDBrz9nP7/0tN3kDIuWkBetfCcDGgMe3nbXfs6PJzg3Guehvmny1pXW7Ou3h+geSTCTsTAkWNLCq2u0R/zoQvChVx5G1wQPnJvgc48Pk8qZpBIGXl0jEvDy+EAUq+zCORHP4tUKAxOp391kaVRqI6JrEE3nQUJWt1vkTwzP0uDX8et+ZtJ58pbFgfYwf/uamyp+3uX8ytM6eMcXzhUL3QVwYTLFubEE125toLM5SP9UEo+uYViSnc0B9m1ZfI5POYYl+f2v9jAet0c8aMB4Is9vfOE0H3nNYU6NJAh4NBr8Oo/0xwj7dV5+ZDvbt9v/KR+kZ1nWnKiQWy/qGwW3RaOEEOi6zq5du/D7/as2W2gx3CAS58PN4qfaguf5WMr56fF4+NjHPsaLXvQiTNPk3nvv5fDhw/zDP/wDAG9+85u54YYbePGLX8zNN9+Mpmm88Y1v5MYbb6zpeYQQN0sp/xj445KfhapeZ03P5nKEEPM6fDtYlkVPTw/JZJKurq6q73SdomfnRCif3FtKzrD47f8+xbmxBIZlMR7P4dU1OlsD+HTBVCLHmdHknIiQJSHss/2nbtnVxImhGe4+soPnHdzKP/6g364NQZA1TCYTWUDYaS8pi9GdvCmxLb/qd0Fqj/hIZw0SOWvDpc80AR4NDNNJRQqypuS9XzoLwGzGwK8LGvw6QtN56137aQn5SGYNZjMGW8O+OV5u958a47OPD2MaBtc3JGnwgu7xILC7+yQQTeXx6Bofvedm/vp/e+kZS3DN1gZ+4wXX4i27RuVN2wJjoYtPPGMQzxqF1yOQmj2xOpbJc++nu5FSks2bpA1J0KuhCcH/dI/xqV86QmuDr+IgvenpaS5fvsy5c+fmOJAvJQXipk3LbULDwW1rKu32Wq3ZQtWsya0CY6PX/Czle/PSl76Ul770pXN+9uY3v3nOv3/rt36L3/qt31rSmoQQGvARIcTLsWf9WEAr8HfAq6s5hqvFT60zSxb7gNLpNN3d3bS3t3PdddfV9IE6KTVHBD3YM8mDvVM0Bjy85mhn0RcK4PGBKKdH48ym88QzRjFqMhJN4/dqVw31c+pyNE2Qzhk8dGGan1yK8sXjl7nz2i00h7wYpj15uCnoJW9aWFJilggfh3psNU5LvpT2MEcsjbxlVxDlTIug124pt+TVDvPrCUsWhI8GDT4PM5m59WISyJiSTMrAownCfp3/fHyID33rPJYliQS8/OnLbiCdt3jyUowvPDmCrkl0y+SS10vQY2EIW/jkTIlXFxxst4eFtTb45nQISml7ewEMRtO853/Ocmk6TWPQwx/9zMHiBOpyGgMeGnw6U4mcXf1eWHcyaxL0QsinE0sbdnpUQNCnE03l+ebpCX6ha+dVx/N4PHO6hJLJ5BzjxFqsN9y2qbsRNwqyhbq9ymcLxePxObVkToqssbGxrmLFzeLHzWtzszBbJiFgq5Qy5vxACBEFqq6YdrX4qScTExP09PRw6NChiu15i+GIH6/Xy1dOjPL3D14sejn9+MI0H/+FI7RH7Dvj8XiOsdms7dpdIgxMUzJbIS3nPGQikcOr2VGmRNZiMpHn8kyGmbQdZdgS9jEezxLPGOxpDRVTHaWUFzjrGoR9HtJ52xndrKIISMPeVO+6bitPXooR1CQvua6JA7va+dvvXGAicaWOBWkbffp0Da9mEcssXwmtZpG2BVgWVwmfcgxLcs8nHseybMEpgFQ+y6/++1NEAp5it5cAGnw6AUuys0EgAiEuTiYJ+3XufeYePPrCG50lJf/3C6cZm80S9NoO8u/9n7Pc94Zbi+dXKbom+NVn7uZ9Xz1XtIaL+ARBn7d44+AMW3CColJWN/9JCEE4HCYcDrN79+5iCmSp1htrjZuiUG6mWkGmadqcWjJnttDo6Cg9PT0Eg8GqZwtVsya3Cgw3i5/liutcLufWOkAduCSE+ClsI9M8cDMwC3MsMOZlw4sfy7Lo7e1ldna2pjRXOZqmFeuJ/vPxYUI+vWhzMJXI8aPeKV5xa6GiXVx5zyV2dERg33XH55ns7BF2e7aTRjELHV5NAQ+aEIzHs8xkDLJ5k+agl2gqZ5vClz4Pc//t92j8/LGdfPv0ONub/Bim5MJkat7XKLDre3TdtnB4z08dIOTTedd9j/E/p6Nkj09RyYki6BFc0x7m3Gic5cqWRr+OR9eIpfKuK9YuLS6XJf89WyKcJJDImSRyMJ2E3/6prTw15OfxSzH+8QcDfPKhS/zjL97KNVsrd3VNJ/NMJHLFc8vnsVvseyeSFcXPTDrP331/gEa/h5xhkcpbpPOSHc0exuM5dE3i1zXSloWuCbKGhc+jc+c1tbs0l6dAUqkUU1NT9PT0kMvl5syOcevdptuiLG5bDyx9w1zObKHFcPPgRTcLs+VS3unlFqSUM0KIjwC/D/wQcHy93l34/aIbkavFz1JH9Ttf3kwmQ3d3N1u2bOHo0aPL+vKUFlM7QqP4fGWP3d4YYFujn1gqj2EZRVEhkfNGNQJejawpyZu2izjYoikc8NIY9JDJGaTy9gam64JsIUXi1W1DU7OQgtre6Gc6kWXXlgY++pqbuDCR5BsnRplM5PB7Kn9BReH/CQlCE1jS3oR7x5OcujzLt8/PIqmc3pLAbNbi+OAMmma/Tlh6gfRs1sSrW1iF9yzo1Ujk3CaDqsMCPvStXiIBD0GvhhCQyBi8/6tn+PQbjhUf992zE/zP8cv4dLjnaIfdYWZJPJp9/puWpCVUWbT3TabIFwYtOt1ihoShaIZIwI74hXw6XXuaGYtnafDpvPU5eznYvvwLmmO9UTo7ptR6IxKJYBiGK1M7bsGN0ah6fF7ls4UsyyIWixVnT2maVowKVTNuwc3RFTez3M+xVmuL1URK+Q0hxOPYBqdx4J1SyhSAEEIH/M6/K+Fq8bMUnPTUzMwMZ8+e5frrr2fLli3LPm5p5OeVt+zgEz8aIGdKDNOiwa/z9P1X7qRv29XM9dvC9E4kaQl5iaXyXLO1geagl1MjM4wl8nOOHfZpPOdgG98+M2HX8RS6wpqDHkzL4txEkmxhap807UiTJa9EldojfqKpPFJKPLqGT4d4Os/P/+Pj5C2LoFdnJpMvzgUqRxb+nwSkKfFokMqbnB9P8IkfDRSfayF0zZ64XBiXsyzyhU3ckqxb4VMagUvlDEI+O2rj1TWGY5ni475xcpQ/+to5TMuu6Xr4Yoxf7NrJfzw2bNdZSXjRoa1cv63yBagp6CWVM4vvmUPOtMgZki+9qYvWBu+Ki4/y2THpdJrR0VESiQSPPfYYTU1NRQfptTKBVCKseur9PpWKHbBnC0WjUQYHB0kkEoTD4eLvKxXWK/GzNrjN0b2UQmprAvhsyc+c6c+3AT8HvGe+v9+Q4qe3t5d4PM6xY8fqNqSrdIbQK2/toMHv4cGeSRJZ21vrQ986z6tu3cGd17bh82j8+Stv5Junx5hI5DjS2cTte1sYjqV546efREvmi1EUDcgYFg/1Rblma0NxVosmBDdsD3NiJI5pFSJH0hloSHEKtGlJplJ52sM+fvH2TpJZk/sevkgk4GUymUJKSdCrs7c1RCydZzqZX1DIOJOQG/wahmUxEc8sKnz8HjuqYRSEVjK3fEsNJ0JWbf2P24Y5lq7FsCBrmPh0jbxpcaTzygTxf31o0Dah9epkcgaTiRz/9ONL3Lm/leccaGVnc5DbdjXOuxnt2xLkmq0hnhqKz/m5rZtkcZDiYhiW5KG+KN88PY6mC376cHvF4Z3VEgwGaW9vJ5VKccMNNxQnCg8MDBSFkrLe2Lz4/f6rxi2UF9aXzhZS4qd26hFVTKVSbo78SLiqvsf57wbsLrB5cbX4qfWimM1miyZ+x44dq+tFtVT8CCF48eFttIa8/NHXzuH1aJDI8cH7z/O+l2rcsb+VoE/n5bfMnWq5sznIb7zgWn7ni6dI5Uz8Xg3LtDC44ifV4Lc/ktl0nt//mRt4zxdPMRJLM53MoQs7uuJEfJqDnuJrvOdYJ48PzHB8aIakAc2WLNYaZQwLj64R9nvY3hjgwkSSrGEhsMWUE61xzhpdg9aQl2+fnVi0QFoASDtdF/Bq/O5LDvKPP+hnoILlRy04a2kJeZhOLT6126sL9rWFiKcNRgtTsd2CBsRSBs0hD/vaGnjfSw8yOpNhIpElkTUASd60iGdtjzBpSR4diNHa4OXumxf2/BJC8NZn7+XdXzhNKmsW66R0TfCzN20rnk8LMZPO86b7TnBmLIGU9t9+r2ea97/0AM+7rm25L3+O9QZcmSjc399PKpUiEolsWusNJfyYM25hz549mKZJNBplcnKSCxcu4PV6CYVCmKaponc1UI9OLzenvRzmqe+JYE97nhdXi59amJ6e5syZM0QiETo7O+v+Bak0Pfr+0+NomiDsCBYrz/2nx7hj//zFpMf2tNDS4CNjZBBCYErbHwxhC55wwEM8bdAc8rKj0U9nk5/hyVk8miBTYlhlSZhJG3h0jZagh6+csKc9+z2CaRPG4hn8ukYqbxIA4pk8rQ0+Pvzqm/jzb53n1EgcASRyBqmcicBON0ns9bzpWfv4m+/2Lli7o2GLlJwpaQl5+ehrbuLY3lYevRjl0nS6OOAPbJG0b0uQvsl0TYXM0ZSB32O7pC8U3bl+WwMf+4VbeP6Hf+Qq4QNXpmx3NAV55/P288r/9yixVB5T2sXiWfPKaxMCgj4PAvhB7xV7lJl0nn97ZJixeJan7W3mZ25sL57jd+xr4W3P2cvf/2CAdM6kya/xq8/ay2uOLuy+7PDxB/vpnUzaBfQCTGkb4n7y4aG6iJ9yFrPecDrI6m294caN0401P2uNrutXzRYaGhpidnaWRx99lMbGxmKKbK3Fsps/v3qJH7emvebBuZRufPEjpeTixYtMTk5y9OhR+vr6luXsPh+VxI/fo82ZsmxKScBz5WTLmxb/9vAlvnV6nKDXw9ueu49je1r4tWft5Y+/do5k1kTDTjnc2NGIRxdMxHPs3RLivS8+SCoxy12tMwxMhfCmbO+mUkwJ0rAwC5GXiN9DOmsUPMesYkTHAm7YHuH/e+n1dDQH+Ng9R7AsyXfOTfCBb/QQ8GhMp/Lomi18vvH2p/OxBy4yUVabVI4zmyjk1bipo5G/+t8LBLz9bG/y4/UIzELxtiULc4Ny6ZrzU3ohtef3aOiaIF3ioA5XDnd2LMnL/v6Rwjwid3JhIsmv/ftTc2ws0hUcWO0UoiwO0kzlTF7/6eOMzWaxgO+dn+LSdJq3Pmdv8c9+sWsnrznaQf/AICG/5yovnYU4N56cOxZT2u+5Uc1chGWirDcUi+G0zGuaxv79+5mdnS2eI1LKohCq92yhanBzF1o9rC3cXPNTihAiCOSllE6aIAxUNtcs4Grxs9hJlcvlOHHiBOFwmGPHjqFpWs3+XtWi6zr5/Fwx8MpbO3i4L2oPmMMeCPjKW69sOv/yowH++cf23TjAGz8d413P389/PjFCR1OAyzMZsgWLjIlElrDfwwuv38qJkVn+7CvdPHeHyQ3XH+JYfpJHLk5fJX7A3vhnMgYzaburJl+yXwW8Op1NARJ5k3Te4rGBaf75vkuk8ybPvnYL73jefvZuCXLqcrwgIgTXbm3gK92jfOPU6KLvicTeJONZkwd7p4o2Dl5dsL81RN9UCsOyTVt/dr/OgyMe/B6DTL76adFNIS8NPg9vfOYe/uJ/z9Mc8pLI5skXPmJNs7vdcqYkl1pYrK01mSpm60hgKmm/jq1hHw/1TZPMmkwlc3Z6FXsW0H2Pj/DmZ+2xDVkLeDRB0KfXfDd6oL2BnvEkOdMspj91TfCqW3fUdJx64PP5Vsx6w4136W7dON2GU/NTKpb37dt31Wyh1Z49tVz7iJWkHmtLpVKuEj9OfY8Q4g4gKKV8QAhxC/BK4IwQ4r+klHng0yxibelq8bMQ0WiU06dPc+DAAdrb24s/93g8NTu7V4Ou62QymTk/O9Ae5q9efSP/e3ocC3jhDe1z5rf8z/HLpHNm0aIga1j8/YP9bG8KEArqDMbs1NB0Mkdj0MdUIsXYbJYG3SSek/xgAPRHjmNJaG+sfNcrgfawn9HZLOV7a8YwsYQ9sLBnLMFHvttHMmeSyOT5zONDPNw3TWuDj2ze7hbSBYzOZPjEDwdIzjOPaCEc93nDkuQsi0+89gh/+OWTxNIGD47AVCp/VVfSYnh1DdOyJ1uH/R48moZPF4zFbcFpujjSs1ScdOKFiRTv+K/THOloLHTQ2RE+5zVbUqLXwcrk7c/ZS89YkotTKZI5kwafzjvv2serblm43milmc96Y2RkhHg8viTrDbeJDTcKMjcyX8Fz+WyhdDpdnD2VzWZXvMvQzYXY9Up7le6vLkADTOA1wATwAPAO7DTXDUAQ+BcgW+L+XpF1J36klAwMDDA2NsZtt912lbpfychPpePub2vg1569b561OnUc9gVXExQLjSfj2aIQyBiSXMIu0pWmSaTRT9LIY0nbp0kT9twdvw7lmkQTts9YNJXDyNnpIcuyyFv2RF8pKW6YqZxBMmvi0e103UA0zXgiR96ya30MCaPxHA0+bclzepxusaxh8dFvnmA2J2gOBxmYTl8lzsqplBGbTub4rRdey217mpESMnkTS4K30Fq/ESl9WYZpcXY8gY4d4XMyUV5d8JUTY7z4UDsh35ULXDX+dg6xVJ6ZjEFHk59/+aUj9E+l0DXBntYgWkGsj8ykCXg0Pv3oMN3Dcfa0BnjXc/extcLAxZVmudYbSmisX6oRGUKIObOnLMua02VY62yhaqhHammlqIf4cXHaywNMCiFeCUxLKe8VQrwf2/ai6gO4lvKTM5/Pc+LECYLBIF1dXRVPutUWPwvxtH0t9D+ewjBNezihZtfeTCSyjM7OTWE5AYyUCSOz+eJARKBoo7G7NUTvRKpowmkWUkoXp1LkC6IKZFFkSGB0NkNrg48X39jOV7uvpLKkBCEl6axxldBJ1mG2jsfMcinpJRL0ksmbiwofgHDAQyJjIIGwX6MpYHuadTQHaY/4eetd+/jDr57DKrMNmQ+NK0LCba3w1WJakMmZHOmM8NjALAA+XSCQvP9r5/ngty7Q0eTnr195aN6p0ZX4l4cG+ecfD6Jrggafzv/p6sAwJQfaG9jbGuT05Tjv+q/TZA2L2YyBR7PTun2TSU5dTnDfG26dI7rmY6UEx1KtN9wW+XHbetzKUorVy7sMy+vJGhoaiufIUkeiuDnttUFrfpxL+lPAYeB64IuFn+0E+qo9kKvFTykzMzOcOnWK/fv3s337/OF4j8dzVXqqHtQqfs6PJ3i8P0rYr5PK2S2aOxqDfPjVN3H/qTE+9r0+LMu8SngIsCMxlqTBr2NaVlGMxNIGzz6whUvTaUzLIpbOM5sxyVtWsdbGlHYUx6dBZ0uQeNbkZTdv5zXHOnm4L8pgNIVlSMrrbMtZrlgYTQmkMJjNmET81V0crIKiEUDekHg9GlJIUjmT3vEEPzg/RVvYi1fXGIzO/xnr4srkayFt8ajrGqllzh9aCwElgawpebQgfIA5BdOZvEXfZJpX/dMT/PWrbuCaBom2iOB4amiWf/nxoD1JXEouz2T4y//tI+jV8Xk07jm6g6+eHCeeNYrC27TA7xV4dUEsnefU5Thde5oXfJ7V3Nirsd4QQqzZgMX5cFs0ym3rcahHeqm8niyZTDI1NVWMHC7FnmWjp73cVvNTqPcRUsp/FkI8D/imlPKbhV9/G3ii8LhFb7fddSWogJSSS5cucfnyZW655RZCoYWjWrqur1jNTy3i5/vnp8hLybXtDWQNi7HZLJPJHG//3HGO7GyiKeglZ1qYZSokHPCAhI7mAFPJLNNJ+zk9mj0p+FW37uDmzmZ6xuO8/bMnin/nFB+3R/x4MGlp8IOmMRrP8qmHB/mf45f5+ds6+H7vFE8Oziy6i/s9WlUFumCn3qxCek4v/O+AT6c56GUommaiyvyUMxxRAIaUDEUzbG/0c/+pMT7y3QuMF1KDTYHKX2hds93u8taVtv2WkIcGv5eZdL64zuUS9AiCPg+ZQiF5yKfVJVq2HEwJ7/qvMzQGNN79jDZ27Zr/sRcmknanHnYRtqOlAh4NCfzHo8OY8ko3oyNhnEgjCDza/MImb1r8y0OD/PjCFEFy/P6uDB1Ngbq8zmopt96IxWIMDg6STCaZmZkp1goFg0EVfSnBjeMAwBYZ9RSupZFDZ7aQY7/h2LM4KbKFBnG6WfzUIyrlNm+vkoLnZ2IPMUwXCp5jwLeAq7uC5sHV4scwDI4fP47X66Wrq6uqD9ItaS+fbeaFEIJE1mA2Y+DVBT1jSU5fThD2a+RNOccOwqOBLgRNIS9/+rJD/N33+njoYpScaRVrMP70Gz1FS4NsQZxooiB+JDQGPSTSJpa0GInmyOYt8MDobJaPPHCRNz/bLm5N5myBOJ++aYv4yOUtJhK5BXVSa8jL1rCXi45hqtDwCMmWBh/egklrLZOawTZ5FcI29Az5dB4fiCKxBY0lYTJpzElpAWxr8PCnrzjMu//rFDJnYElbiMUzJn5dRxPQ2Rzg0gIRo8UIeDXShU616VS++JrWWviUkjclf/XDCfZ0bGdPa7Bibc6uFjsNNJOee5PgfE6aJvDrGpm8iVfX8OhgmIX2dyHY3xbkxo7IvGv4wP29fOvsJKZlYZoWb/j0cf7zV26jMeDhv568zGceH0EIwS8/rZO7b95Wz5dfEV3X2bJlC7lcDsMwaGtrY3p6mt7eXjKZDE1NTWzZsmVJhpvLwY1Cw62t2ystMpxzxLFCymQyxUGcyWRy3tlC9YiurBSmaS77fHZh2su57L4R+GngIraj+zYgBZwXQnxQSvnEYgdytfiRUhbDlNVSakBaT2oVPy+8oZ3P/2SYyzMZYoUWbF0IDCSakEjLojnoIW8Y+D0aiZzEsCQdzQF+8wXXcrijkQa/h6xh4dVFoZAVJhI5GoPeOfOESiPVv/KMPfzXoxcZT1kksga6JshbtsgyLMk//qAfU8qKosdJneUtyXA0gxAlU5/FXLNS5/Lo0yCRTNEY8JAxJWG/B8O08GgCq2C4qgvbrV5KuyXdqwv8QpKYJ0BnAl7sepehaNpujRe2KAI7emMBW8N2G/xkPE3GtHjrZ7vJFQYGNvjtQYHJrIHfq9HcYEd/OlsCTMVzpCu8AZUsNZz3xLAkrQ0+xmazxWGTsuzvKuHVhW1Wu8Bj6oawhXAiJ3nTZ7rJm9Aa8vC869oYiGZoCnj49WfvoWtPE9sbfcxmjDm9YsmcgU/X2NUS5LdesJ/f+uIZJBDyejiyN0Ik4GXPliC//LTOorAtx7Ak95+ZwKMJNF1Dw05bPnQxRs6w+JsHLhbPvQ99+wINPo3nX791pd+ZOQSDQXbu3MnOnTvnFMX29/evqvWGG8WPWx3KVzvCEggE6OjooKOjoziIc3p6muHhYSzLKhbXu73gebn2Tslkkkhk/hudNcC5jF4A/j/gq9hRn5/B9vO6APyZEOLXpJQXFzqQq8WPk6OthbVIe0kp+c7ZCX7UN01z0Ms9x3bSPTxDNm8VO60ift3u7rLsyEHQ7yPg1cjnYTpl32G3hDy0hHzcssse+PbyW3bw9VNjGKZ9NyaEsDdRKQn49OLGCvZgvB0RPz978w4ORTJMZgQff2SSJwdnCvN3NEwp7cGD87xGu1NLogEej1aMLPkK9TNOlMqj2REjCUWTViEMIgEPVmHuTyqfRheCgMf2swKKAyF9uqDRC0G/h4nkldk8TlrKknadiybseTOSgsDTBBoQ8Nqv26vrTKfyJPMgy1JryYLwQ9iFuhJbfN66u5nZdJ4HeqYqvgetQR1L2u7yAFsafPa8mZzJbDqPWVYTIYDmoI5X1xgvGQrp1SCow617Wnj80ow9xRvJTDpfVfH3Ugh4tKIRrFPeNJUy+PyTowjs2qcnLs3wH2+4he2NAYaiGXTdPqcSWXvGz5HORv7wpw/SHvHzP286xsWpNG1hL53N1c1Mcc4tWfK/BXZK8isnxwqfm71Z5E2LL58YXzXxU0lsrKX1hhvra9woyGBt11VptlAsFmN0dJTp6Wk0TSumyRYry1hNNmi3l3Mf+fPAi6SUlws//08hxLuBPwB+ETurvyCuFj9LYaXSXpqmzdtC/IWfjPDPPx7AowsMU/L9nklSeZNwwENr2Mds2sOl6TQeYUdCPLog6NUxLItM3mL/Fj8Bf4BM3uSJSzFe/NGHkNI2F20KeMmbdvQnnjWxLIv+qRSRgJedzQFGYlka/DoH2hv465+7CU0T+Dw68UyOiUQe0wID0MSVtWtlUZxSnFk9Xl1gWXbEQxZa7p0jlOoMWfI/gh47LZQzLMJ+Hb9Hx8IkZ1IUUmCniVI5aPBbRPwaGUMWU1pgR3gkAiEk6bxVTDeZlsSva4R8Hm7uaORnj2znj7927qrX4Gy8Hk0Q8OhcnsmSLrTIHzYsfuqG9oriRxN2sbkuBAGvTtYwEQK7qNywyJakKOdchoXg3mfu5T8eHWQ8nsWn20avwjL5mSM7OD4cx7AsROH/vLotQOvg/zqH9AK1VRL7tc0UhF/XniaOD83i3LRG/B7ufXon9z5jd/FvmoJebum8esM/P57k9796jsuzWQ62h3n/S6+lwe+hMeCxhyPesoMvHh8lb1lIS9Ia9nLH3ha+dmJ8TvRLQlUdY6vJQtYbQoi6tkq7UWi4tYbFTevyer1s3bqVrVu3Mjw8XGywKU2jruRsoWqpR1QqlUq5StCVFDI/CLxDCPFd7EnOzoXLufwnFjuWq8XPUi4MKyV+FlrL538yQtjvwVeYwHs5lsGUkuaQvXEEdEmLT/LyWzv40skpJhM5hmJpdrcEMT0aUtr1FP3TKdvgMmOQMy2SWY22iI9MXhD0auQMyfaIj5GZDDPpPFJKPvDyQxxsb6CzJVg0sUwZ8BffH0H3eNndGmQwmp5T6LvYDB8h7C4iKSW6ZtfeWCyc3pFgiy15JSWUyptk8uZV9TnO7xNZ21Psmde08PjADAJJ2rCfS9fAkvYzBjw62xv9zGZMPLpgd0uQP7r7BrZG/HzgGz0V19Me8fOJ197Km/7jSUbjOTRsd/vu4VkmpqIV/+Y5B9rI5zJcnM5yuLOZtz5nP5aU/NK/PI6pCTQxt9MKbJF418GtJHMGz7p2C4/2x7g8k6E15OOXDlj87E3byeYt/uEHFzFMSXskxMhMhqBPJ5rMXRUF0gV4NUG2EIFb6ryl+TClLeBee3snA1Np7j8zAcBP3bCVX3pa57x/l86b/PhClGg6z0e/1086b6JpgicuxXjJ3z1Gg09nR1OAj7z6MP/3+fvobAnwo/OTBGSa9959hEjAw68+cxdPDM6QKYi0oE/nDU9foDK7ztQaaVnMeiMSiRTF0FKtN9wmftwoyMBd4qcUKSXBYJCOjg46OzsrzhZyUmT19qpbjHoUPNe70LyO/C7w58DvAAHsup83Ym9THwYqh/ZLcOWrKkUIUdNFa6UmPC+EVbY+T6ECOZ0zMXJZRmbz5KXgq6enQEpu6ogAkumUQdin25u6kSdvWHaayLI3fMOyaPB58OoWzUEvo7NZBmOZogFlOmfyZ9/soSngwe/V+dArDnNwW5jRhD0ML+y1T3yfRyNvWIW6n8VrVewpMhJNE0QCHuIZA2FJdjX7uBjNLfBOXDlaKmfa84hE4Vnm+QiFgEf6Y2xv9OPRBMOxDIZpFzrnTQvDhOdfv5Xfecl1SGkfty1sp6LiGYPnHNjCfz91eU7dkyZgb2uQLzw5Qtiv06kH7MJdDcZnUiS9Pvy6bSjqEPJp3NgR4XOPx0jlLP73zAQ/ujDF7XtakAgkFkJoeHWKDvavObaTV9zawZv+/SkSWQMp7aLof/g/t3D7vhYeeughAF59dCevProTsDut3vBvPyGVNfF7dKRhEvZ5SORMLEsWWvTtmK2ugV/TSNV5muOnHh7Eqwl+/6UHeO+LrgXszq756BlL8Nb/PMlsxkRKi0xeEvTpIK+IQU3Yn91vffEM973hVu452sHdNzTT19dHU9DLB7/Zy1dOjGFJuGFbA0f3NHP3zdvYt2V17yqXs/lUa73R2NhY1fO4UWi4cU3gXvFTLg6qmS1U61TypbLctJdbzwUAKWUceIsQotH+p4wLIUJSyizwP9Ucw/Xip1ZqFUv14BW37ODTjwziMzTylkVTyMu9d3Tw4W/3MJmW5KRgW8RHPGOSyptsCfuZSGSJpfJoQnJtq5+OLQ3Es3k6mgKcn0hiFaJ7QthRIccHzIngyMLGE0/bf5PIGrz/q2e471e68OgasYzBbDZFyO/BNC10TbCvrYGpZI5YKocQAq8mKm6s7REvpoTfe8l1JLMmH7z/HNI0SWby8womx1fKMK1iisuS9ga+EKLwOMOya0G2hv1cns2QM2xjz9958UFeeuOVuq9IwMPXTozyl9/uxbQknS0Bdjb67OiO0LCkhWHZgurRgRgeTdAe9uHXIZlM4/f5uHXvVsZTYzQA0rLHST73ujb+68nL5C1JMmdhAYmsxXdL0mOWZQtIv0fjT152iJ+5aTsf+mYPsXTeFqTYEbN/+MFFbt/XQsqAh/umCQe8HN4RQdME12xt4L57j3H/qTEA7jq4hVja4NxYgg/d31MsphYCLNMWZUbBu6xejCfyfOBbF/h+7xT72xrom0pxeEcEjwbfOTdFyKfztufspWtPMz/um+Ydnz99VWdhJm8WI52isF6fLuibtP3cSlvh//WhQb5yYqxYkH1hKs3Lb9mx6sKnnpRbbzgeUyMjI5w7d64q6w03bi6btdtrqSyWWqo0W6h0KvlSZgvVsrblHtON5yiAEOKFwIuwp5s4a8xgR4KqYkOKn9XmnmOdNAW9/LB3iuaQl58+GGZm5CKffO1N/MKnzzKTzjGVzGNYEsOUDEfTJHMGQgga/R5iGZOX7W3htt3NfPGpy0T8HmLpPI0BD8msyTP2t/DFsugGXKnjkFLS4NMZm80yk87zT4+M49cFiZxFxsgBgrDfg1cXbGnwEvF7+L2XHODD3+nj1OX4nGN6NIGmadx1oI2bdzby5vuewjRNMgZkCrNzygl6BCG/B5+u4fPYJq0g8AjbXDVrWle1VZe+hv+fvfOOk6wss//3valiV+fcPTnnSBQEBBEkmEDMObum1dXVdXcNa3Z1zTkHFFDBQFKCIAwzA5Nz6JnOqaq7crjh/f3xVtV0T/fkARt+nM9nPmJ31e1b996677nPc55zFjWFiaZt+hI5coWiINzSVDBrVYBMweU3G7vZeHiUWDrPnoEUlQGToKXRPZKlOawzrTJINC/YM5BGFCtOKuhVki442LaHaflZ3hLhw1fOJewzuOWJHhCC58yu4bxZNaw/NEKh2Hab7DPmXUld2Md7LpvFNUsVIdvRmyBne+NIYW88x4GhNP/5jwxC34EnJQubKnjD+dOY3RCmrTrAm58zY9z2V7ZX8aV79uEX6unR1ATJvEPWVllbouDieJLlbRFm1wb5w7aBU85JOxoPHxxl3aFRdE3jsY4RHE+J2U1d44O/28V3XrGUj/1x7zi9VkkTBpRT34WAvO2haxDxmxM8gB46EMOTEkM7InR++ECMl5xBdtiGw6N0DGeYURvknBlVJ/WeJ/NGfnTG1NhFznVdqqqqJkRvTMWFZSpPe021YwWn1lo62pX8aG8hwzDKjtNnY9LwbFR+piKEEGHgO8B3gU4Uj7E4QZDp0Zjy5OefUck5Fkq5SUffHDRN8MKlTVy9pJGOjg6Gh7pZs2YNPp+PrL0DhEAXAluqhX00p4iAqUNd2CKbL7CzL8lnX7SINdOr6U9kSWQdEKpq8bN1h3GlRNcm+vJ4Eg4Mp6kNWtRX+PjD5l56E3naIwaaFSBvu6QKLivbKtnUHSds6Xzw+bP5zt8Ps7M/SdGOSOVl6Uqs+pw5dTxvQT0/fGg/w/EMbVV+oqk8WU8jUAzeSNtqOsinC15z3jSuWtzIZ+7ay9Ye5URc6depC1ssa61kIJlj3cEY2aMMHQ1Npch/91Ur+MI9+7i7WA3RhdIDBU2dL9y7D0MTbO6KjxP0FtwCLZV+AqbG9oEsFZaG0PTyZJhW9AoSnuT8FovrzpmLpgn2DKT42n0HOH92Lf9y6SxcT7Vwtvcmyu+bcN6BgGUQFPCGC6bxslWt5d/1J/ITqmHdsQzvv2UbWVsS1JVe5h8HYmztSWDqGp+4ZgFXLJoYFiiEwFe0NQAIWTo+QyPveoR8Bp+6Zh4Xz61lW0+Cu3cP43kTo0lOFY4Hzhghv+2B7SnR+n17h8s2DWMhgWUtFXSP5hjJ2HgSso6HqQm+eu28Ca+vC1vs7h/7E0F9+PQ0MgD/d38HNz/eW27/3rCymQ88b9ZJvfepWEBPNnrjyR6lPx1MRUIGU5uUne5+nchb6Ew1ZWcqeC4UCvj9T6056UnCD2yTUn7hTDYy5cnPVEJJTF26oBzXI5q2Cfl0fJpk27ZtBINB1qxZU35NXciiN5Ejax+JsigJgAWSaNrGFJK6kMWPH+kkkbM5f1YNVyxU5m+9ozn6EwVm1oY4OJxmsqZTtuAxLAvousZPHu1iKGWTyMKi1hA+Q4WU/vtV88jbHvUVPj539142dY1CcYJLCEVEpIRHDsbY3pfCcNJ0dfdimSamZRLxFagyfdSELT57/SI6ohnqwxYLmo6I+ObUh3iiaxQNwUjWJmO7LG2N8MnrFvHy769Hy9plAuPX4DdvXcPshggCeHh/lIIjy2aN0vVwPY/+eI5U3p0wyWS7spg8rybRfIbAMnVGs2oxlsX8L03A6y9bzOz6MK/98eP0JXIg4Y6t/bz70lnctEaJfBc3V/CCRY38blP3hONbanV5Ujlvj0XQ0ieQUk3AoWgGn6ZaliVjRNeT6Jrkv/+0m+fMqVW6mdK1pQmuW9rAn3cMFSsqgsqAwc1vWImmqcqdXoyamN8Y5h0XTeMbDx4uu2KDqtr4DcG180LsT+rs6E2q6qLfYCCRP6XHIkfCjx7p4lhirT0DKTQhsHRRJFASR0oSuYkVvvdeMpNNnXHyRZuF6oDJmy84PaFzfyLHrzf2KudpobykfvtEHzetaTmhi/Ro1uXgaJbZIsmipvBTtsiPjd4Ymzx+8OBBkskk+/bte9JaH6eKqdpe+v9hv472Fkomk+M0ZdXV1dTU1BwzuPdonClhTKVSU8rdeQwMoFII8QHgMZTTcwYYlVIOnspGnpF4Mp5gSuTHNE364jn+4/adDCbzeJ7HJc0Or3nO/Am+RM+ZU8uDe4foS+SVvkSCZWoUivqdVMFlXrXBpu44qZyDrgv+umuIS+fXsaytkpZKHwXXozpg0hTxTepQHLR0BBLb9agNWSRzBVIFj85YlpClM7s+zE0/2IiUkuqgxWAyh6kL8kWygVRxEoYmqA5ZpDNZPnvvQea21DCcjhHLOkRMiU94vGhZM+01Qdprxus1pJTcvrUf1wO3uGDarsvvNisbBkPXaK0KlKt4I6lseUFP5x3iWafs81PSAaUKHudMj/CPA7FJz4fteVi6RsRvqNFyIWis8DGQyCMlBEyN/3jhfJa3VfGX7f0MpfKELHXJO67H9x86xE1r2nA9ycdu38lD+6KkxpCJUkXHbyqfoAtm19AzmuMTf9rN0tYI1y9v5qa1bXzmzr3FUXZFJP2mgaYJCgXl60Tx54Aan0fQF88xa0wQqZSSN6+poTagsbE7TWPEx7ufO53q0JEnvp+u6+IHj3ThSVgzrZJ59UE29RxpWzoeVPh0rpgd5qNL5jGUzCOEanXuG0zz4T/sYv9wdtJjORlcV3KsuUl17chx5pdSwod+t4v/fangsvkqY6s36fDxW3eQyLv4DMENq1p4/XmqTXw6GMk4GLrALu6YECpvbCRjH5f8bOlJ8M7buwDwGOSSubV85rr5T3mVY2zyeG1tLfv376empqbc+rAs658avTFVKz9Tlfw8WQ7PQggikQiRSISZM2fiOA4jIyPjqoelqtDxrpMzOZdTmPwI1HTX64AXo+oJlcBu4GVCCO0Zke11OiiRlLM9ojd2jP4zd+1lIJknpEvShRz39/m5lhBHqxjec9lsMgWHP24bQBOqTq+hdBUhS+fyebXUkOBPh3JUBkwsTbB/JMsPHj4MUB4x7xZZKgKTfx7XU2aKek4ZDbZVBegeydBeHeDS+fX8an0XflMZ8Q2n8sQyNs2VfrpGsuXpLwG0V/vIZzP4DZ3epKCnKPYtOJKYCx+8oJmXrmqZdB+29ybI2ROXSp+hcf+eYVwpMTUTXRMUipNnfkPw9fsP8LfdQ4BECqVeKy2muYKjjPg0MaG15Dc0PnD5HM6bUc17f7O5WNlwsV2XtU0633rN2nHmXIUxYnFQmqCSiPhvu9VkVzKvfJHGIuzTAcFnrl/Ijx/t5KH9UTxPctumXj715920VPm5ZF4t9+wcKrfcMrbLvz5vDrduOMhQVu24rqlJNYkiQv/5x1386LWrsAzlH7Vr1y5yuRznV0tWBnJUVfkx7TSua5LIe/xx2wA/eKQLQxMIAU90xSfVUQ2kHD5y7yDGAyNE0zbVQZMvvWQhy1sjfOKa+bz6J5tP2mn6eIYRpW0c3XZzJXztgUNcNr8OT8IXH0sRL5Scw+HWTX28cs3k19DJYEZtAEvXyBZsdE3gSgia2gnF0/9++25ytlc2vnxwX5QH98W4ZF7tae/L2YCmaeNaH9ls9p8avfEs+Tk1PFX7ZRhG2VsIlP/O0dfJ0d5CZyoXmYIGhwAUjQ2fP/ZnQgidIp85GeIDTwPyczpfxFLExZNBfjxP+d8cGErhlza2B1WVlYxmbTpjWRY0jbcCr/Ab/M+LFnP+rBq+8UAHQ6k8OdvDZ2isnV5FZVDnp+sz5BwllJ0seFOi2hCelPh0dfMutYhAtRx8hkotPxxVGp2gKXjHc2cWtUOUXXUrAwaxjF2M0giQytmEfSZSumhOHl8oiCs1MnYeSxcq58lQJoWpokh7MhyKZjF15eY89jPUhizyjse8+jAd0Qy6UGP0r10S5Af/6OTOncMETE1Nw0vl/VJKX5cI9g+l0DVRjphAgmUIPveSRVy1WFHNL18/h2/cf4D+tMfMCo0Pv2g1t24dZOPhAzRX+lnYFOa+3cNkCi6O6+E3dRxPctVi1Vrsi2cpON4EAbEE0nlldPidvx/icCxDwNSIpW0KjocQ6vf37xkeRyhcT/KTdZ3U++C/rp7LgWiObz54CFAkM+IzODCU5h8Holw4s5ItW7ZQV1fHnDlq7FxKyejoKNFolD+u38uPdjrkHMg6yik8YOlo2uQNKU/CYMajlO/Xn8jz7t9s57dvWsX7bt1ZdgV/MlV0eUedv2jaJlGQGMUnY6NY2fvLjkGuX9ZERzRTzPmCV65tZXlr5ITbDpg6375pCR/6/S5643laIj6+9JKFJzRLHE4VynouIZRjeU/89HPezgYmIxqTRW9Eo1E6OjrOuiB2MkxVYfGzpGw8StXDybyFSmacruue0XHLZDJTqvIjhFgAzAMeAd6CEjungQRQAHpQWV8nhSlPfk4HpYiLs+2jUNpuLpcjIPPkPJ2aSLDU2aAxov6e43r89vEeHu2IUR2weOOF07lmWTPTa0Ns64kjhOC8mdX8dmMPt23upeByJMLhOEhmXVqrA8SzBRz3iIao1NrqHMkymrEZzTpcNdPivJk1bC4muHue8u3J2pJZdSEunlPDQ/ujBE0NQ3iMJAvEXB1bc3E8VVGwXQnFmAlQ2zgWqoMm9WGLWKZAtjixZWkCy1AtvhcsbmTN9CoGknlm1ATpPbCTj/0jSthvYGiCabVBOmPZcaJjgRKTSwnVIZMbVrXiMzTOn1XDjr4EP/rHYZ4zp5Yav86Lp9k0Nzczd+5c/ufOvdy9cwAhBOsOxviV7VEbNvEbgqzt0RTxc9n8Oq5d3sz/3LmH/YNpXE9OKq4vnZftfUn8poZPCHLFylVpJ0sVpFILyJMwlMqTygo+ced+rGLlClTVx/GUeWQ0kWbjxn3MmTOHhoYGbNsu30xramoIR6p4551RhKajCWUImcy76ELpmerDJkOpiYJkjuwaEkjmHB47NIrjKtJt6cppPGt7BEytnLuWybunpAsyhCLlR6O10s/jnXEePxRV15Dw0DUVlGq7ki/9rYMv/a1Dnd9iu/LBfTG+ddMSVhWjXY6H+Y1hPn3tfPriOeY2hJlVd+KR+Vl1QfYNppXAv2jeuaDxn/tUe6KF6Z8RvTFVhcUw9QwhYWoEmx7LW8i2bdavX1/2FqqpqTklAfMUbHsZxX9+4ELgAiAM+IAG4A7gA0IIXUp5QqfjZyz5ebLCTUdGRujr6+OjV83n8/d3k8w5OJ7khUsaWVZ8cv3hPw5z+5Y+gj6Dw9Es//a77XzjpuUsbY2wtPiaguPx4P5oceFRi+KJyI+HSlHvi2fHvTaRczkUzTC3IURHNI0G3H0wT+zWbbzxfDWJdeeOAXRNoGuC9102m+8+1MFgMk80XcDzFMkAFzQNXVNTRwX3iADZ0uD586sZSuZ5cO8wedfjglk1zKxTX47zZ9Vw4exa1h+KkS24DCRVGnzvaI6V7ZVct6wJn6mTyrt89q699EeTpHKgCQdD10jlXQxNMK8pxN7+JFlbaUrwwFds2b14ZQshS+d1P32CaEpVNn7y6GFeN0+ytCnAvHnzKDged+0YUNURIRjNKCdsTQhqQj7iWZvrlzfzwqWNvP6nT5DKO2WSYGhgSyb1ZMwUXAKmqq4py0OlBXKOGgMvwdQ0LB3StktBCIKWTtZWLDdrK/3L/oOHSbW2YmVN6qXksY4RBpN5FjSFmVMfYqRYoQM1Zn+k0gd+A961RPDTnRodickpy9jWVHu1H1spwRXJKzL2rO2hOR7mMSpJl86rYTBZYGdf6qSrRU90xXnDz7dQKsbkbI+AwaSVtdK1lXc8frKu+6TIz//+7SC3bx0oa8Q+dMUsrlly/HT4L79kEW/6+ROMZpWH01svnMbqaSf+W08mTvWpfGz0hud5ZUHs2YzemKoVlqmKqdiOsyyLhoYGuru7Wb16ddl2Yffu3di2fdLeQqlUakq1vaSU24HtQohm4BVFo8MyRPHCPRniA08D8jNVIi6klMTjcWzbLo+x/6C1nsOxLBG/wfQxmoO7dw5SFTQxdI2gpRNLFdjSHeeKhUfGm7Wi4VvY0hkuEp/SJz3WIqNrgmWtETpjGRI5B0uUvH4glXfoGM4QTRe1EK7kzh2DPLw/xsKmMJ+6bhGGLphZG+SuHQNqdD1fUBobVFXHNDQEktGMQ2uVn9GsTSbvgoCXLghhaPDum7cwmnUQSH67sYfPvngRi5oj6Jrgv69ZwOOdo3z6L3uKREujUGyBaJrgwFCaD9yyDUdKcjmXkZw37rNaumB3f6rclikRvJztIjBprPDxi8e6iKYKVPhNHMchlSvw1/4QC1osPv2XPewZSJIqJrlrk107xWN2/55hUjmHiqLwVtM0EtkCFT6DoO7Sk/Qm6FmWtlZSHTTZ3pugdzSHz9SIZY9UXsa+3JOyPAGmCUFjxMdQMk+m4GLqgpaAxx87dfSuXvT1fdSHfXSOZIsfWvCRK2ezoi1CIudMMDhc2FTBF168kNqQSbihlw/dceC4xKQ6aLJ6WhWvOaeNX27owfU8co6yTvA8RSCkEAQtjUzRwkCN92u86+IZzKwNctOPNtERzRTJmLp2J6v6wJHsN4lAFxJd05jXGGJbb/K4oa6Oq0T7W3oSOK5kWWtkQjtr72C6THxKXk5fvPcgl8+vK4bHTo7WKj/fuqaFtKczu71lSmSKnYkmQ9O0JyV6Yyou5lMZU/V4lSpSJ+MtVBLYH91KPR3Nz1133cV73/teXNflzW9+Mx/5yEcmfd2GDRs477zz+M1vfsPLXvayk9r2GCHz9cAC4H2lnwkh/gWYAfzr/9eVn7MdcVEoFNi2bRsAM2bMKLfTIgGTpa0Ty82GLsZrd4QiLuNfo/GSFS3c8kQPQR1Sjlo8Iz69nNo+tqVh6oLqoEnAMspj81Kq7fgNtcDEc2rMW8iiWNWT5GyXgWSe32/u5XMvXgxALJkhl8thGQYFV42LI0o3YyWo7SvqIUSRLORcuGvXMPGcQ13RoyWRtfnpo118/iVqu5omqAqqINb6ClVelVLSG8/RH8/xwN5hco5HVdBkKJGbdMG2XYn0jiS8q+0qMrG9N0EsU1DmhbaN67oE/D5yDvzvujhDuSSWoVpMA4k8NUELy1ABrVJCMu8QNHWuWFjPIwdiZbYppSSayuO4kojPIJ6Xk/rnuJ7HZ65fxHcfOsQv13eRLjjoQlAZNIhn7XHvKbiSggtVAYPaoEk8Z+M3NSzNozlssHPYRgjVUqwJmmzrTVAZUFNirif53D0HaIlMXLQChmBTd4I3/GILVy9u4K0XTuO1Azl+vr5HXQ8a+HUo2kQRsnS+dcMCAN558XQumFXFHVsH+MuOoXLrTkpFOkqCbNQVQcGRtFX5sQyNn752Odd+ZwOjGRtT18pBu8dD8XLCk5I59WG2H2WoOfZYCVSr8IYfPM5AsoBA7fvVixs4HMuyqKWCN57XRjRdKAvgQX2vsrbLrzb0sKi5gnNnVB3zgUnXBA1Ba0oQHzi7LaazFb3xbOXn1DEVj9ex2nFHewuNbaWWvIW2bdvGc57znFMmP67r8q53vYt7772XtrY21q5dy3XXXceiRYsmvO7DH/4wV1555al+rBYhxEXAy4C8EOIawC+E6AIWA12nsrFnJPk5m5WfeDzO9u3bmTNnDvl8/qSe1l59TjvffegQOdslkXMI+wzqxowsb+9NsKsvSXu1n/deNps/P7aT/WkfIxmbTMHB8aDgOZh6yexOJZm3VgVYf3iESMDATqnF1na9MuGwiyJoZwzzyjse2WJafO9oDpmOUmsPEQwECApIFrJjktlVXpYnZdn40NJgem2QTf15HGGNMwE0dK0sTi7BX/TDKd1ESxUcy1BZXa4n8bzJyYXtKl2SBHQh1HQcAkNTZOZDv9uOLgSe65CTAp9lYnuSFe0V3LtzkEjQRAhBQ4XFcMqmMmDQELZorg7geZLakMVrz2tnWk0QQ9P48aOdJLI2judRcNXY9kBKZZcdPV0mBCxtiXDbpl5+sb4LU9ewdJ1sQbWm6it8DKfyuJ4iPDnHw3Y8lreEORBVXkVCejxvToQHOzLlVqfrSWJFI8HSkq4CVD164nl8hobtKb2PlJTjL2Jpm1+s70EDPnj5bG5Y1cKdOwbpiaWoNfKEKmuZUWXQHrDJDh1iY/8BqqurmVlXx+UL6rh713BZ3Kom0DRCPkE675an/4yirxAo4f6K1giPHhrFZ2jjrrGjUTp2WlGbZJka1y5toDpg8L1HJt6fShYBewfTypup+P5k3uVH65Tv0t/2RrnliV5+9toVxWvFw9BEuTL2nYc70YTg2qUNfOwFcyfdr6lilvpk43jRG7t37yYUCpWF00frIp8lP88MnKwWaWwrVUpJIpHg5ptv5pvf/CbJZJIFCxawdOlSLrjgghNWENevX8+cOXOYNUsZjt50003cfvvtE8jP17/+dV760peyYcOGU/1YAmgCQihH5zcCFcV/DwO/gGfbXmeF/HR3d9PV1cWKFSsIhUJ0d3efVEXp2mVNdI9k+dljXQRMjYCl8d9/2s3/3rCUPf1Jvv7AQTxPRS8UHI9U1sUjR2OFT2lUYNzItUBw0ZxaXraqhY//cTcz68K0VrkMJvOk8y5hn07QtDgcy5I7qq8ggb54npCl84Yfr+Ndq8LccPl5zJgb56frugj7TCr8Ot0jufIkWntNkL54DoHEMnRqQhbd0QJBUyA9WQ4tzRZcLpuvjNtSeRXD0FYd4KK5tTy4d7hIfCQXzallw6EYtz7RQyJnM5q1meysKtKjFssjpoaqfRQwBD5dYySVpb3KhzAscrbLVUsaee6sSu7ZobytbNejL56n4Er2DqYRwO7BFK1Vfv7n+nOoCqpKXUuVn3+9fDYf+f3O8kLuSrXge54if0FLx/U8BIKQz+CBvVH2D6WQEmrDFgFLJ5GzyRRU2jwITENplHymTiKT5/HuJBrg1yQeOvd3pEGWhNzq7zquLBoYqgXddiUtlX4GEjksQ0O3vXFkI2TpZcPDv+wY5OWrW6gJmrz9oul0D8bo6u1n+aK2MRWOGTiOQywWo7e3FxGPM6dSsG9EVWU0TXDj6mZu29RP0NLK5wLEuFbSey6dybZfbiXverjO5N+vtiqLyoBFZcBUjtbC48NXzmFxcwUDyTw3rWri3j1R8o7H7PoQ+wfTyvtIHCFax+qM9ScK3L51gM9eP5+P/XEv6bwiPlpRRC6lxx1bB3jFmtZjiqCn0sL+VBGN40VvOI5TTh2vrKycstNez+LUcDruzkIIKisr+eQnPwnAJz/5SYQQ3HLLLbz//e+nra2NK6+8khtvvJGGhokO9T09PbS3HzEvbWtr47HHHpvwmt///vfcd999p0x+pJRdwFeEED8H0lLKkzctmwRTnvycDkqj7qcL13XZuXMnUkrOOeecMoM2DINC4Xip5gpCCHb2J2mt8hPyqUM8nMpz3+5B/rRtoGzut7s/Sd7xcF3w8OiITX4ugz6D/kSeoGWQyKoxa7+pUxkwCVg6lX6TrO3SWu3n4FBm0nZSpuAS1zTuH7C4zjA4f3Yt588e73Hyw4cP8Yv1XYQsHUvXyDkunpTs7k+RKTjcdyDOnIYKdF21Zl6+upVlbZW89idPMJDIYeiCDzxvDh+5ch7nzKhme2+Ce3cO8vvNffxqQw+WLmivDjCasckVbBATE8sLrsSYpKoQsnRy2Swhv4kwLG556znl32VzOeZW6xxMuYxmCuOEtRJVUeqMZfnNxm7edvHM8u++8UAHgWKERM+oavOpqS8IGhqvWNvKcLKAz9R45ECMoVQes1jtiqYLNFT4qPAZVPgNGiN+Wip9/ONAtCxstnSBh0RDYhgGouhx5EllRpguuDiu+nvVQZNYulD2dfI8yZUL6/nrnih+U8OTkoCpk84fCRT1PI/BVIGrv7UeCVy1qIF/uaCBar82obVjGMa4BXDhwiR/2dJNTzTJjEpBRUWeP+kwUlAtKL+p8/rzWselvc+qC3LzG1ey7tAof9o2wCMdoxPO0yvWtPHac5VrdjabZf/+/cxfUM8bfr6Fg9EMrqfE51940QJm1gV56fcfL79XTCI0Pxq7+lO8/aLp3Pvuc9jZn+KNP9+iqmzFc25oEE0XTmoC7J+Nf0aV5UTRG1LKsl4oEAg8pft2LPz/UrE7mzgbU2i2bXPllVdy1VVXAXDgwAHuueceEonEpORnsvN09PX9vve9j89//vOntW/iyMZywAuEEOeizA5LxeKfSin3nez2npHkR9f1kyIpkyGTybBlyxZaW1tpb28fd/I0TTtpUjVBbCtLSeweIZ9OzvZUe0keMRk81lc8b7vEszYfvX0nBUdVNkrvaa708Zpz2vnJo10YupoqShcm7qMEUrakcyRHKu9gaGKCQPTSBfXc8kQvyZxDVdBgMOnheR55F6r9OpV+nc6RLG+7aAYvX9OG43q89ieP0xfPEU0XkBLe+9utXLusiQq/ybqDsaKRovpkBVfSPZJlbkOYeFrykqV1/HqLIgup/BESVBLSWoaG6yrhcTLvkBICN2dTXxEoj+4D6JrGqxf5eTxdzS1P9GJoHunCeFLlSdjWk1BO1Fv6eHDfMIeiGaqDRrnF43qyPPrv85lcvqCBVdOq2NQ1yp3bBygUy3FaKbai4FIVMvnxa1fRWqUWinUdMf68rR+frjFLj/K/G7NoponQhKqyAM9bUM/9e4ep9CstUFPET4XfYCRTQHpQEzQYShV4+OAI//GCOXSN5mip9LG8NcLrfraFeE6lyKu4DEWyAO7eNcSMKoO11cdfLIQQVFVGeOXFqhz9xOEY7711F9mCi/RUC+qVy6t5w7njzQht12M067C4uYLhVJ5HO0YnXLPfeaiT+Y0hzp1RXf7Z3buGOBjNYDuqqmB7Hp+8cx/3/su5XDirmkcOjpBzPHxF5/Nj5ZUJYHFLuHwtfe2BjgnkOed4dMWyrJ1eNeH9U62lMxX25+jojQMHDpDP59m7dy+FQuFJTR0/WUyF4/R0w6kErh4L6XR63Kj77Nmzecc73nHM17e1tdHVdaSt3d3dTUvL+HvIxo0buemmmwAYHh7mL3/5C4Zh8KIXvehkdkkUxc0fRLW/rgQeBKqBucBtoEiSPAnGPOXJz1PZ9io9/SxZsqQ8RXG6233Zyha+cM8+HM8uh2devrCezd1xtvYkqPDpKgrCk5hjRqwnQyxto2mCCp+BaWjjRKl98Tx37xjkA5fP5qt/OwgUtzdJ78DzJCPpPDd+bz0geNGKZt584fQyiZhVF+LLL1vCjx45TDrv8taL6rh9cx9DqQKWKC78Ag4MpdV+ZWx6R7P0Jwpl52Ik3L6ln8YKH4PJvBplHmOs53qSRE5NpK1pr+DPexKMjnEqHkcCpSwLnwuu0pD4DI3eeI5fPNbFa8+fBsDBaJb/WZdCNx2l3znGSNGsuiA/+MdhfrauC1Hcl8FkgaaIn0jAYDRj47d0pCu5anEDy1sjxNIFbt7QTSrvjjs9Pl3wL5fM4srFjdSOCek8b2YN50yvYufOnYyOSt598TS+t65PkV8PXn/BdN7ynBn85vFudvYmeHDvMEFLL7pjq+koD4HPONL+umKhcnVN5hz+/crZrD80StDSuXf3MEOpQvk7Yjse2/pSrK0+ta/1r58YIO9KSnzRlXDXnhHOr9kKQG1tLUawko/85RC98TyelCxpDtNQYTGQPPKQYemCguuxrmN0HPkZydjFCpcon+NkXrW6vviSRdy2qY/d/SnmNaob7Vfu6yDveOMm/nRNsKQ5zOvOVWX1n67rYltvqpyTV4IGPNIxwktWNp/SMfhnYKot6kIIDMMgEonQ0NAwYTLonxW9MVUnqqZyRepsVH5OVfC8du1a9u3bR0dHB62trdx888386le/Gveajo6O8n+//vWv55prrjlZ4gNHBqIvBW5EBQJ8V0q5UQjxQ5T/z0ljypOf08Gpkh8pJfv37ycej7N27dpjCrtOpZ22cloVL1jcwBNdcdqrArz2/Hbu2NrPwaE0BcejK6OiB5I5Z9yUzWSYXuNnOK2mbGKp8RUtCWzsHOXrNy3nM3ftLYZMTr4dT8JgqsCCxjCmrvH7zb3MrgvyvDEj+ItbInz5ZUvL//9wNMudOwYQSIbSNgUPtnTHGUjk0IRgOFUoiluPTGfpxem20si9J9XPnFKVSwjeeU4t06osvvDSJbz5508wknHK48ulSpHjKj8dTUCFX6c+7FPxGK7krp2DZfLzmbv2k3OgJqBRHTDpTeQnfHa/ofGKc9p59Y83YhkCQ9No0HwMJHMkcipwNezTEQKumGFxw6oWrvv2Y4xmbDVhNsnxDPj0ccQH1GTg5s2baWhoQNd15tbWcNGCFjpiGdqqAsxrCPNvv9/BYx0j5B2XeNZhKGWXfZ5E8fh5UonCR7I2d+8awvUk/3d/B7YrcT3JZfNrmdsQYrBIPkrGfe1VfqQ8tvHhZLBdj1wxfLV0d+lJuoTb5jOz2iIWi/H5e/dyYFC5fuuaxtaeJNcua+QPWwaUtYChzBI9T1IdHD8Bubq9El0T2G4p/0ywqv2I35UulFXDvbuHmVbt513PncF9e4bxmzpvPL8Nn6FhahoLmsLlqcndA+ly9a9kvilQ111V8PQM/waTeQ7HsrRU+mmtevLTrKfi4jmWkB09GfTPit6YquRnqu4XnD3yU1FRceIXFmEYBt/4xje48sorcV2XN77xjSxevJjvfOc7ALz97W8/o/3hyHNxBxABssDFQohDqNH3U6p4PCPJz6mMuhcKBbZu3UplZSWrV68+oePqyZCfeNbmX2/dzmAyBwiSOYeO4Sz37hyiJmxRV+HDdj1SOYfrlzfzw4cPoWvKyA6K0y+oJ9r6sEVbTYickyoLPI+GKE4HjWZs7KPDqYowNfVCARyOZZnfGEYTgs3dcSIBE09KFrdECPuOXBLZgsvzF9azozfB452juJ6kwm8QSxf44G07+OgL5lITshhMFsaN9jsedI/mxvx/VcEJmhoXz63l09ctIjrQg+d5LGmN8Nu3nMOLv/MYmbyLI5XmxBKqmasDfkMnmXMxNJt03sX2PJI5h2iqQG3Yoi+ew9LUDmRst6hZ0cjbytDOpwt+/obVNFf6OVKiUm21mqCFQE0zRfwmBcflb4eyPHrzVpJ5h4CpHzNy5G+7h3jxiiNl3WQyybZt25g7dy6uVcGH79xAV2KAyqDFf75wAQuaKtjaE2fDoRECpkZ6jMFi6X/VdJdEF4IFjSE+dscehIDRjIOuQVXARBdw354oH7hsJrv7U6TyLh6CmbV+blrZwHB/z7EuzUlxzeIG/rYnWv5cpWPTEVXXSVNTE1GnH5+liIrneaTzDr9a36OsBKQiqqYuaYr4eMmKIwl3UkoWt1TwX1fP5bP3HCBTcFnVXsnnrl9ApuDyyh9v4nAsUybsGw7HCZgat79tjTpfx8C8hhDrDo3gN5VBJsXTGvYbvOn8yRPjj1dpuWfXEP/xxz1KcO5J3nvJTF59TuspHcdTxVSr/MDx9+lY0RuHDh0qE6UnI3pjqrpOn46o+KnC2di30/H5ufrqq7n66qvH/exYpOcnP/nJqe5S6fakAybwLeArqKmvx4ADACfT8oKnAfl5Mtteo6Oj7Nixg3nz5pUD487Gdu/ZOchAIkdDhRojjWdtbnm8G00TZS1QKdHddl2qfRAO+cjZklimgO0cafckczZ7B5P4dY05DZU8ciA6QROxqKmCmpBFpV+nP+mVyROoxdRvajRU+IimCyRzDjnbxfU8HNfjz9sH+OX6biTQVGHxpgungxDUhCy+/9AhknmHTN7BpwvqKgzCQT9CCIZTeRxPEvaZpAseqfyxyWbQ1GiM+Hj/8+Zw+cIGCo7H/QdTxPMul8owy9sq+fLLlvDBW7dTcD2MokA4IKAmZGG7kq6RLNG0qmhoxSrJR2/fyXdftYJ5jWEe78gSLh5XBFQFTRWAabssaKxgWZtqY750VQu/3tCDLVQbMGjp5B2PiF9VCyxDQwrVqqnwq6+HpYsJpNOTsvx7UC3T/fv3s2zZMsLhMK/58eN0xh3CPrX9j92+k1+8YQ3pvIsmBEIIcs74il/A1LhkTjWr2iupDRl86q6DCKGuGVdKPFcRSUMTOK5HuuDy2zetYmtvEkMTrGiLUMhlGTrFisLzFtRR6TdI5hyEUPonXcCMmiOC1/mNITqiGQxDQ2hq/N7U1ffTp0lsKamy4LJZQXRKSfZHvrtXLW7gqsUN4xbX3z7eS18iP6FSmbU93vKrrdz+trUT/LE6Y1k+/qc9dMayyuQQScRvEDQ1XraqmZcsb6K+4tRibdJ5h4/esZu8I8vfm/+7v4NL5tXQVvXkin6nGvk52WmvyaI3SkTobEdvTNUJtLOhq3my4HneKZlbToapGmyKGnUflVL2AlcJIUJSyvSpbmTKk5/TwYlIipSSrq4uent7WblyJcHgyU2GTLbd9R0xfrm+m4LrcdWSRq5d2kQy54wTPFu6hocK5EzlHTxPcmA4gy7g1xt6yNsQt/P4DEGl3wAE6YKDZysn3lyiQIVf562LGhhO5sgUHLpH83hSVXQ+cc0COjs7eW4L3LZPCWtLIt5IwEQTalFvqPCVfVySOZXjNJjIKx2RlHREs3z6zr3UhCyi6QJVAZOWKj+GJhhI5rE9tWh7UrVeGip8fPCKObzvlm2YoqhbOgoC5QfUXhPkysWN5G2X9/x2K3v7EnhS8qc9KT5w+Wzu2NKPT4eIKTAtlTjvSkgXVOxFSduhC/WE7zME+wZVNew/XzifN/0oSrronlzhM3A9SV56aEJw09ojT/Bvv2gmdWEff983TG3I4g0XTOMdv9pC3nHxGUqHJWUxhdz1MIsu3YWjEtSDlsFFs2u5eUM3XmaU2YEMa9aswbIs0nmHQ9E0AaNIDnRVhdo9kGRleyWWoZHO2+VMOHVNKrHuivZqXr6mma5Yukh81PVq6kK1AaVqIxqaYHZ9kJDP4PyZR/Q1k8n841mb7z7cycHhDAubwrzlwmnjpsGEEHzj5Yt57y07lV+UJ3nl2lYWNB258b37uTPYN5jmcCxbJmClqTNbFvPMMh43b46ys3uEt60IUllZieM44wjP2EWsazRH3p78ezqQKLB/KM38MflbmYLL63++hdGMDciip5OPr7x0EXMaQhja8RfIYz0Qfu6eA+SLKvtSFc7UBT2juSeV/EzVys/pVAx8Ph8tLS20tLSMi97o7OwsZ9XV1tYSDodP+TNP1fbSVN0vODttr2w2O2Um/mBcRacAfFQI8RdgCEgKIWwp5YFT2d7TgvxMFjh5PByP/Liuy44dO9A0jbVr157SBXL0drf3JvjUX/ZiGerp/Lt/P4QuBGumV3HrEz3s7k+Rd1w0TfDmC2dw8dxavnDPPp7oHCVgKuPCkUwBQ1cVGuV3ouF6ygzQ0ASGruG4Er+hc9fOAYI+k86RnHKNFoKQX+ffb9vE82cFWTx3JptHe9g/qEhwwNKoC5lUB9V7LEPQVq30RwsbK/jEn3bTX9yOUzQ2tF2PqoDJYDLPaNampcqP39QImjqpgoR0AU0TXLO0icaIn8aIn6WtEQ4OpRkpehSNhQQSOYd9A0le/9PHKTgeewfS1AY1DE1VEb75wEEsWUAX4Pf7GUgqYlca+c4WvVx0QNdUW204VSBo6cpVOmDyliUmK1euoiZksXcwxS8e68LxJC9e0cyl849U9ZSnTSs3rj5CiP7zhfP5rz/uVgaTruT50wwuWr2I//7TbiVI1zVWtEU4HMtiux4tlX6uWtLIZ+7aS65gownBubNqWbNGPeH6TR2z2CI1UAuKh6QmaFEdtPj6y5fxsdt3qkrWmAOmATPqghiGQVttBWGfzmjWwdIFfkMjUzQ7dDzJq9a2jBMVl1D6rmQKLl+7v4NN3Ql648pN29Q1dvWn2DeU5ms3LB5H0Je2RFjWGmZjZwK/rnHrpj4unVvL4hbV868MmPzg1cvpHsniepK3/GobqZxTdHBW58pv6QhgWwxaZ84lG4+SyWTYsGEDVVVVZX2Iruvct2eY3zzee8zJLkMXE7Lu9gykyDtesWupPudIxiZo6SckPmOPz1is6xjhrl1D434mAduTTK95csflpyr5OdN9Ghu9MWvWrHL0RldX12lFb0xVkjHV215nSn6klFO1shVDhZquROl8TKBCCLFGSpk77jvH4GlBfk4Vx9L8pNNptm7dSnt7O21tbae83aPJz9/3DgOq9QMgkdyzc5AvvnQJAVOnL5FHE4rYPNE5yhsumMYPX7OS67+1juqQRcdwBlCGhtmCiyPB8Y7oQFQFQlU6dAH7BtNYhobjeUipqh+Vhsf+mGQwmyVTOIAnJfMbwhyOpkjmXbpHcwynCjRV+nnPpbOYVh0gXmxvtFX5i15D7hGfFF0DoVo92YLH9p4EoII0Xzw/THtrC+01AS6ac8Qj6P3Pm8ObfvbEcUXbfYkCfYlC2c234AhaKgwsXRJLZnnlqgZ+s22UgqtGyIWAsE/HcSHnuAQMDYkkYx8Zmw9IeM/NW+gezZHKFIjs2cKnrl3IqmlVrJpWddLn9fxZtbx0ZSs/XXcYTQg2DXm8qzXCzW9ew4GhNA0VPhY0VZDKOdieR4XP4JL/fZhUrkDOUWf+b3uG+ePWPp47r45///1Oknm7aPyoro9L5texvE2JfOc1hvn+q1dw1dcfxXE9ksVqnCdVaOfqaVUELZ2vv3wZH7h1O0OpAhV+g6/esJimCouQpRHx6di2Xb5BCSHG3Yg//IddPN6VwBtjStkQ8SF0wc6+FAOJ/DhNzV/3DLOpK6kqTUKFr/7nn/fyg1ctY1d/koCps6w1woxiht3Xb1jMe2/dQSKnrtewTy9nbQnAtHxEGhu5b88wt251SeWHWdOU4KUzPQJ+i4/ekzoy6TjO0FNVSFur/MyuH08+/KaO68lxC7Qn5RnFVRwYzpQF02PxX1fNoSlyau2zU8VUJT9ne0E/OnojmUwSi8XYvn07UsqyyeKxojemKvmZym2vMyU/U/HaLEFK+c7Sfxe9fwzAOBXiA89Q8qNp2oRK0cDAAAcOHGDJkiVEIpHT2u7RF0PAGi+GdV1lRNc9msUDVo5JqB5I5umP52ivCbJyWhVPdI5iGaI8ZVNC6QnWKboM5x0XU9fQNFUZaooo7Q542K5H2jVxpYOhKzGzJgQDqTyZYlZYKu+QyKlKyQdv20GymP9l6oI59UEsUx8XUeF6sujHotpMJa2L64ErJdevaGZ3f4qtPQkWNVdg6hqr2iupDhkkss4Ef52jUWorOJ4kkXeROZtlbVVcvGQaoYoIf9zaT++oemG24CGRVAVMKgIGXWNMIDWhjs1D+2PUhkwCmorN+I87dvHbt6wtC7cd11NZUMf5Ij/eOcqvNnRR4VctwoFkgU/8eTffuGl52b8HlJgWYCg2SjxdoDD23HuSbz7Ywd/3RdnRl6A2ZOHTCuQ9yVsvmsFLV7ZwKJopejP5CRiCZa0VPLAvBsVj4jc0BpN57to5yEtWNDO3IcQd7ziHjO0SNPVxn8HzPKSUuK6LV+yfua6L67ok8w6Pd6aVNqgYF+JKFRFSHTQRTDweA4k8tifLkSq6JuiL53jtzzYXjRklS5pVoKqpayxuqeCefzmXvnied/1mG4PJQnGaS7B6WiVVAYMnDsX59qY0rlR5cf/ozlNV1cA7LmjG8TaphHmUIN3UBYubK0jbHnPrQ/zr82Zi6uMXvAWNIVZPq2RjZ5y87eI3da5cVF/OmjsRJqsez6wNKl3RmBJUU8THNUubJrz2bGMqLjBPtr5GCEEkEiESiZx09MZUJj9Tcb/g7FV+ptr1eTSKrTC7+O+U8LQgP6fa9hoLz/PYt28fqVSKtWvXnrH4biyuXtLI3TsHGUrlEagb+CvPbcNv6EVthnKz9Yp5ViVTwfddNpv3/nYrhzpVe2rsmLhbfJ1lqGpPQ8RPVcDkkrl13LKpF4CgAaO2LBrGFadcfIp8aAIyeadMqErb9YDRbClDSk3n7OpXlaSmiIUuNAxd0J/Iq+khqTK1dE0rkhWPew9m2PnLzcQzdtms8aY17VyxsJ7GCj8Rn1p4B5NKxKpr42M6SvCbOrZTXLR1g45Ynvffsp1zZ1TzjufO4r/+uEvtq1R760l4/sIGfrOxm0zBoxR5Ucq5GsnYOCa0VCgiN5DIk/a5fOz2newZSFLhM7hpbSuNET9z6sPMrg+N25/9gync4qg4gE+H3f2pSc/54OAgj23bO+lMZSpns6UnTqBIVPymhmt7dI1kec4X/668bYC6sMVl82vZ0Zcqn3uJ0vw4UjKSOaLcEUIQsiZ+TUs33dINzvM8HMdhcHCQgM+HJI1Ekh9TVvGk8oy6aHY1jRXjCcPCprCK9igSEs9TRDxdjC2RUrKlJ8ndu4a4Zkmj2gehKjQ/eNVyvv5AB50jOVa0RXjbc6YhhOCRQ3FsjzKhcjyP+/ZG+Y8XzKEx4qM/kS+Oqnu4HlzXmmVGbZDa2hA+UapmH4EQgv+7YTG3b+nnYDTDtJoAbZV+NncnWNZaMdFYdBIcfTO/YFY1L17exG2b+zF11b7+2g2LT7ids4GpPur+VGCy6I1oNDoueuNMhbtPFqZ62+tM9m0qXptnG08L8nO6yOfzbN26lZqaGlatWnXWv9SNET//d+My/rp7ENuRPGdODXMawkgpuWx+HX/dNYQHxIuePj9d18nrzpvG7zf3EU3bRAJmsW0g8TzVbvCkajloQjCrLshnXrSIRNZmOFVgfkOILZ1RLEEx/Vu1vvyGScjSCfl0RjM2PkNDOMf2Dhrrp1hwPFI5l1n1/rI3z39cPZ+P/mEnwyn3SPI3EM95eCNZfIbGQEKloH/h7r38bF0n73/ebL563wGClkFblUZzpY8nOuNMuheeS31QozFsEi1ohP0GUkoe7Yip5HNDY0ZNkGxxbF3XBBV+A7+hYbuyLE6FI7YAKVs5YUspqQtbvP+WbewfTFPhMxhMFvjcXfuoD1vousaHnz+XKxc3lrfRUOFDL7VshKDgQntkYuDjrr0H+NmGPjpzfkyjgDuGWAhUZagu5KMvniNQJAwagl881lWuoEmU19Jtm/qpC5sYmhj3O8eVRFOn/BADwN69ezEMg+WLF3BdzwH+vH2wnJguiq1MTQgum1834buwdnoVb76gne/9oxOEYFq1j0zBLRdEhFAO2AOTeCjVhS0+cc38CT8P+3T0MX9GSjX5J4TgWzct5R03b2M4VcDUdf7r6rlctbiBTCZDNBpl9+7d2LY9LndK0zQMTfDSlc3sHUzzpl9swZNKF7a8LcI3Xr7kpLU/Y/HvV87h1ee0MpqxmVUXLEfSPNmYik/W/8zJqrHRG9OnTy9Hb/T09JBIJMhms+Wq0FQQ4k7ltteZ7lsulzvpQaCnK56x5MdxHDZu3Mj8+fOpq6s7q9vO2S6/39zH4ViG+Q1hblzdOq5EL4TgXy6Zzapp1fz4H4fIFlwCpsZD+6Ns700wnCpQHTTxmxrpQhrblfgNjZzrlSsBPl3Qn8hx4/c3qMA5v0aF7rGwMczOoQJB06UmbOF5ML8xzGAqT0OFj3OmV1MX9nHr452czBoasDTSBZe9AymCls7chhAr2iq5ekkjv3isC9s94rSbd6CQyiuiVnx/1nY5FM3ws8e6+P6rVrB/OEOFT+fL9+7HOUrToaGqWZom0HSTw6M2lqUyrUKWgeepKSpdqFZNhd8gkXM4Z3oV1y1r5o4t/UgKuJ6k4Eoq/DpBUyeaVj5DtuvxoefPxW9o7BtMU+HTlYbIVhoiTRNYuuAL9+7jsgX15XN20ZxaFjSG2dydwDI0TA3+4+oji3nBdti2YztfXpekMymRZCk43pGEewlCg09ft4iwz+B9v91KNFUgXXDwG9qk3kyelMUR7/FELmCKcs7YyaLkVdXQ0MC0acr48aNXzWNBUwWfv2c/SElFQMV42K4kZAls20bTtHFaoTec384r1rSQLrjUBE3+8097+ceBEXRLUxNmumBh08mbnr1wUR2/eKybpK0qmpah8cHLZ9MZy/Ktvx+iOeLjxcsaed357VjFc6GZPn69u8CGwy4NFX7esiaAW3ReDwQCZdO9/7hjt5qqLAbEbuqK88etA7x4xbHbVccjG+3VAdqrn/oFdaqRn6nkqVOK3vA8j0gkQmNjI9FodMpEbzyT216pVOpZ8jMVcCo3CCklnZ2d5PN5LrzwwrN+Al1P8t9/2s3OviQ+Q+PRAzH2Dqb4t+fPPSoHTLB2ehVfvMdmWk0ATQjCQCyVL1v3hywDn6FyvhxNUheyGEoWMHRB2vaOdDGlZCTj4qswOZxwCfsNqoOB8v7sHUxx+zvOK48eA1hOih8+HjvmNA2A31Ap7ZpQ1RUJrJpWRchn8P7nzcHUBb/f1MdIVkV0uDAhcrv0vl19SQZTBS6aU8tjHTH2DKbQNYGpa+ieRBTrTe1VAcJBi0zOpjeeh7wShQ+LAtVBiwtn17C8rZKv/m0/iZzDmulVfOTKeUQCJj94zUru2NpHLG1zz85BEjmbdMElZBksrvb4wLXL+NX6Ln66rpOs7eIzVHVMWScLdKH2J+e4pPMuVUF1vL52/0F2D6TwkERTeQwN3n/LNj7zokXcvb2f323qAQS2hLqQpW54UhLL2FQGTDQh+NR1Czh3Zg0A1y5r5hfruwhbqv05GYLFcfyjY9g8KWg5gbtwKu/wtfsPsqs/RVvE5NK6FKsWzR1H8jUheNmqFioDBp++cx85x8MQGrPqglw6rx5NO6IbKhEg1arTy+3ZD14+i1hmD7uKLcDXndvKBbMmTpgdCxG/wacvCnPAqyeVd7hwVjVNER83/mgT6aIv1K7+FOmCy/sumwXAx/64h0cOjiCl8vTZPZDmljetYt48s1wV2rlzJ52xoq2HVPeHvOPRNXpGIc9POaYS0ShhqlajdF0nGAwSDAZpb2+fEtEbz3TyM0U9fs4anhbk52ThOA47duzAMAwqKiqelF7xcF6wpz9Jfdgqa5EePThCLG1PiDooaRBkUeWrRp6hIWyxpz+JEEIt0rqq7CTzyntHHsVYSq2d4bRNq2kwVgcqRGkyTDKUzNOfyFFwPZa3BDl/uMCWvixZ20NDMq02RDxrk8o7FFyPsM8gnrXxmTpz6kPomuC+PcO897LZWIbGuy+ZzUP7o0hgKDXRQabUTpFSGfQNJfN4nuSBvcM4rhrX14VEeh55T2mbuhMFal2JLE7N+Q0Nx1PeNUFL45wZ1QghWD2tCtv1qCseZ1BC4bddNJN41ub+PUPlWBCfLpheIfjkn3cTTRdU1IKm9rkyYCKBgKHhM5TbdktVgMqAuvQPRzPcvqUPyxBE02p7BVflaL3tF5uwbbfoY6PiJuK6Q3XQIuQz0DTBJ69dyNrp1WUxNMB9e4YJ+wyEdPE8STI/XtSuCfjcixawvmOUX2/sKcd5SKmE4K8/79iTiJ6UvO+W7YqQSI89fS57BoPcfEHNpK+/YmEDrVUB5eTtN7liYR0+44hOSEqJ53nlf47joGlKYF8ZMPnGjYtJFZPkxya8nwyUXknj1cuO2ArctqmPvO2qqcLi57l1Uz/vu2wWBcfj4f0xdE0U8+YEBcfj8a44VyyoJxQKEQqFmDZtGkt2bOGJrrgilx6YBrT4HQqFwjG/91NNxzAVicZUJGSTkYypEL3huu6TGutxpjiTa+voUNNnIqbumTtFpFIptm7dyvTp02ltbeXxxx/HcZyzfnEKTafUqpBSEksXGEjk+dEjh3ntedNoHKMVsQyNa5c28YctfVi6csXN2WpKqzJgMJgoYHsSoSs9TSn8czKURqHnN4QYytgMJ/P4LZ287XH5wnoe2h/l6/cfZCCRI2N71AY0plVZfPr6hWhCoy+e5Rfru2mosMjaLtKh7Jhc49M4MJxGSsblIo1mbZI5d1LiU4ImBLUhE8vQmFkX4hsPHOTP2wbIFXtduWKVS3kWKWfigUQev6kmgKbVBNA1QbbgYhgaqbzDV/52gPv2DAGCVe2V/M/1iwiMGWf+7cYeukdzYybj4I8dHrUVNpEiCWms8DGSsXnPpbPIFlxu3thNMucys6ijKt0Y4lm7KPRVx1cTAg+JhhoRdzzV7tGFmuZL5hz8po7nqdiGi+fWTXAhtgyB66nzmS9GWJTsC0qalUvn1TOcUh41VlHLlCk42K7kxd/dwJrpVXzm+oXj4kZABdnuGUih4+FJj5DPJJpx2DuYYknL5FOMi5orWNAU5uH9MW7fMsDM2gBrZ1SjaRqZgkvHcI6QT6e9yleeICv9g5JO55iXwCmhRNbHbU4c+d3YG7YsVuzMSRbjz71oIW/+5VZ64zk8CS9dVsfqRoPt27fjeR41NTXU1dVRUVExbptTiWxMRfIzFd2UT+Y4HSt6o6OjA8MwnpTojalc+TlTZDKZZys/Twf09/dz8OBBli5dWg5iO91k9xOhNWLSXq3ROZIjnXfpT+QI+Qz+vm+YrT1x/u/GZdSE1JPnzr4Ed+0YYCCRQyK4bF4tO/tThCyNA8kMTvFJ1HbB1IpfcCkxdHDdIx2mEiGqDpo4EkazaqLKk0psOrM2yJfu3Uc675K1PSxdMJrzsJI2O/tSfODyOXzvoQ7ytsr/yjte2THYlTCQtMthpDnb5R8HYjxnTi3VQZPh1ESBKzBOCO0zdN572SzqwxZ3bO2jJmhi6DAQVy2t6qBJwNKLNgA5bNfD8aDSp0JMh1IF4lmHsM/gum8/hu145crM452jfO7uvRQcj0zB5aoljezoS6jE9+KNRyLJO6qKIKXEKY7r60Jw5aJGKvwGrz63HduV41qDADPrQpi6RiKnWnsl5Gy7HFNfDnoUEkMXLG+tZF5jiDddOGMC8QF40wXtfOh3Oym4smzUFzQFFZaG7brcub2PVy0Jcu60SgKmrly/JRQcScin4zc1Hu+M899/2sOLljdRFTRZ3KwWcV2A47joQmJZFlKqvzHZfpQgpeSzd+/j/j3DqhqnabxibQuXL6jnrb/cSrbg4Ep43vw6/vua+RiGMa4qVPoeua5bbpGd7k3/uXNr+ebfD5PMOWXjxZtWq3w09d/N3LKpn4Krzl9zhY9zZ1ZN2E5d2OJ3b13NcKpAwNTLUSMzZ87Etm1isRjd3d3jTPVKlgBTBVOtEgVTl5CdykPsUxW98UwmP6lU6tnKz1TAsb6Mnuexd+9eMpnMhDH2J4v8+EyDf7+8nTt2jvKzdV00VPhoq1aanqFUnvWHRnjB4kYSWZuP/mEHB4YyuFLNMt+za4hIwKAzZpdNBUERiIJXXFwBSwPLUllLKtJB4DcFjudxKJrB9STZoj9QfyLHx+/YVT5GagHX0IRyiD4cyyClZHd/ili6UPYPKlUixu5DVdAkYOo8vD/KeTOr+WVR8DwZLF0rk43vvWoF02qDRaIk8KSL4RWYWeun4AlWtFXyRNcoPlNnVl2QkXSB82dWsTCU4dYDkMxlqQoa1Id9dBbjE4QQjGSUuPmWx3toiPgwdY1tPYkyEXClRKDGs2sDggWNYdYdGiWTdwBBhV+nI5pmWWslQggsY+J1VOE3+N+XLeHVP368/DMBpApw4ZwaHj0YK5vg6ZpgYVOEr9+0bNJjUiILjRU+gpaB5Sp/nGwxpqQqaGIYBlnbZTCR44t/7CCdcym4auH3GVp5Efekx107B7lr5yASmF4T4FevW07fwd2sbPazddAmZysysqi5grkNx75RHRzO8MDeqHLELk5t/XpDL/ftGWY0W8DSdUTRqPGiObVcsbB+3Ci9aZplElSqDJVMRCczWDz6mIxFbcji569bwXce6iSaLvDcuTXcsLK5/Pv3XjqTWfUhNhwapbnSz2vOaSVgTq5d0IrxFkfDNE0aGxtpbGwsm+pFo1GGh4dJJBI0NDScdtTC2cRUbTFNRfJzJsfpyYremMqj7meKKZzrddbwtCA/kyGXy7F161bq6uqYP3/+hIvWMIwnhfzouk7AELzt4pn8fX8UJJP6i+zsS9IZy2K7Uk2kIMk7HomcowjK0ewD5bNmCqgN+3jJyjZ+t6mHkaxNruCRKupGCk6urBHRhPrbBU8ipKpquJ76Oz4DhjM2gZEsX7//ILv6E0VvlRyON+FP40nllxPDZu9Aks/fvY+7dgyo6Iviq8e2bjRNIKRqY8VzxfZZ0GJ6lcHuvhSRkJ+sK6kNmbz14hl88NbtjKRV+yzsN3ndee0kevbzqtpmvvXgQaqCqloWsHRi6QLRVL5czXAl5GyPyoBJznYZTOWZURukZzSLJyURn8mbl1ssmd/GY4dGCVo6IZ+BqWt8/I5d/OHt5x73ptYQ8VEdNKnxeUjNIJe3MS0fbzh/GgLJzj4l+A1ZOh96/txJtzFWO5O1Pfymhuk38DxJPpFX5pGuR8H18BsaH79viNGMJOK38HseibyDlGpyy9CVZ5NEeSUh4XAsy43fW8ey1kqWz2xi0TSX3nie2fVBXrW2jXUHR/jSXw+QyjtcNKeWd18yk8qAgaGpVqIujoTq6prKDOuMZTGKN28hBHnbpTOWmfTzja32HMtg8WSrQi2Vfj55zbxJfyeE4LqljVy3tHHS358qxprqFQoF6urqsG2bzs5OUqkUkUik3BJ5qvUbU7XyM9UW9LNZYTlW9EbpejiV6I2pOup+Nq6rZ8nPFEUsFmPXrl0sWLCgLHg7GrquTxpxcaYYW1G6blkTP3+sC7+hY7sq9mBVeyXffOAgf9k+MM7tuCSO1oWgMmwpvY2gTGRK0DTBYMrmglnV/HpDF6nceKecrO0p8TRH6SZQo96iZJonBXkHOkey/OiRwwhgUXOE+gqLA4MpEnm1b7omjrR7pMQydHb2Jdl4eLT8d3RNmd5pAJoomyQiJWG/QW1IaUUOHjzAa+cLHqxrZvdAmvbqAO973hyaK/1879UreWjfMBK4cHYtdUGdTV0e7fUBdE3D8xRJtAyVY5V3VISHVmwFZscEYPpMnW+9YgV37VTaogtm1ZDp3cuPHu0inVehsrZn0xzxMZqxydneOM3Q0fDyWfL5POGAD79lIFwHdEFd2MfXX76chw/EyBRclrRUMLNuYoWlRARKLYMFTWH8hhKwW8VqjpSSsM9QonAdYqkCjisxdCU89xfNAD0pKThHTCop8mRPQncKevbEuWtvnJqgxXdeuYxFzRXsGUjx0dt3K4NCAbdt6uV3m/sIWwbveu4MrlveVLRVcPAbygiytUr5Ou3sS2EZ6tos6bZOhMkMFktEaKxW6Ml4+DhTlPQfpaiFRCIxrgpQEtGeTW3I8TDVqiww9fbpyWwvTRa9EY1G2bZtG1LKclVosuiNqdr2OhsVqWcFz1MEpYtOSsmhQ4cYHBxk9erV+P3HHgl+stpeY7d74+pWKvwGjxyIURU0uWlNGzv7kty9c5CGiI+ukQx5RyWgayiiMb8xRDznEPIZHIpOfMp2JYQMjaF0QeUsHfV7IdRiWPpkYw3ypFQExdIFkYBOMuNQcJQjctaWHI5lEAIyY11/x5Cvlko/kYDJ7oEUhiYQCDQNbM8j5De4oNGjpbWN323uw3Y9fIbGdcuaqQ/pbN68mWAwyMXnreW5k9w8Gyp8vHTVkamf0mJ57oxqXrKymdue6MXQBI0Vfm5c1crPH+siaOnomqB7JIuUkCrqRC5fUM+Hf7+d3tEc8xrDXLesiT8OOmzuLvrjCEXO+hN55jSE8ZtHbgQFx+MH/zjExsOjNEX8vHxxmPRQN/9y6Sy+/2gvqbxDzoXrljUwt0EtgM9bUM+xMJb4lG44Eb/JN16+jM/evY+e0RwrZ0X49yvn8sC+KF+7/yBhn0HO8Uh7Dum8Q6jYjnr9edOY2xAia3t8/p599MXz5WuihNI1kcjZfOOBDr71imVsODyK7SqCN5Kxi67aEk2Db/39EOm8w6y6INt6k+QcjyUtFXzsqnnYrsfbf7WV0ayNKyXXLWvi0nmTP0wcD8eqCsXjcTRNm9RT6FjHcjJd1tnC0XoWIcS4KkA+nycWi3Ho0CHS6XR5Yqi6urpcFZLFNmagaNZ4NvfnWUyOp6oVN7ZKWNKOHS96Y6q2vc5GRSqdTtPe3n6W9mhq4mlBfkCNsW/btg2/38/atWtPeNE9FeRHCMHVS5q4eskRY7V7dg6iFVsM02tDdAyn8TwVvthWHeDLL1vGD/9xiMcOjWDpgoIjy8nYAIamiIKhaZNOWfkMjaaIn+fOq+XO7QMMJQvjHJulgJqQRTSZx5aokg0qET2RtXElBC2ddF6595ZijXQBkaCF66pYjqBpkCo42K5KuBa4bB6SvOKSWtbOqKYrlqWtJsDqlgAbN25k+vTptLS0TNhf2/UUkTrq5qVpWvmm9s7nzuKGVa2k8g6tVQFcT/J4Z5yO4TSuB82VAdZMq8Rn6lw4u4ZvPNBBOu8QsHS2dCf44G3bmRtW56Q27COWLqgFWAo++6JF5GyPhw9EGc3YrD8UY13HKKYGu3rjrN/fz81vPY8LIkGWT6/jwFCa4e4DvPKymSe84ZYI3GSL+sy6IN971fLxn3nM5mqCJnnbxfWUQHtleyWvOqetvPDPqg1w4w82liM8YEylTypt11A8TS6XU07KxY2XTClLbt2JrMN3Hj5c3m6l3+TjV88v52Hd9ra1dMWyhH3GuEnF00XpOPT19dHf38/SpUvL53qseLp0cy69fl3HCB+9Yw+pvENLpZ+vvmxROUT1qYLP56O5uZnm5mY8zytXhQ4fPoyu6yS0Cj7xwBAjGQe/qfHllyw6Jd+jo/Es+Tk5/LNacSeK3nAcp2wGOJVI0NnI9Xq28jNFkMlk2LBhAzNnzqS5ufnEb+DYye5nihORqvbqAJ70cFyXSp9OU8RHVcDkqiWNXL+8mbqwj/+4ej579u7lU/fl2TsKecej4HjK4dfSmVUXotJvMJkwyNQFH7tqHhfNraM2ZPGNBzqQnocjKbeJfLoy5BsLDwj5DOorVI7XgeE0mpSELIO6sEXPaJZYMk80o5LXS8nvrpRYumBWfYhYPMl/3LGb/7txKXMbwph2is2bNjFz3kIilZXj/t5QMs+n/rKHvQMpwn6Df7tiDufMrEFKyb7BNMm8Q2pMMmh9hY/6MeLVb71iOY8dGiFvuyxrqywLWzd1jZKz3bKvToVfp2c0y6oaE11zCPt1wr4AyZzDkpYIdWGLF31nHV0jWUUaJFQHDRxbTby5msmOgQzPjQRZ0FTBgqYKNuS6jts3H6vvEeL4oaljcdGcWn70SFeRhCpzybaaAK88p42b1rSWNTme55EbOswPrq2jV9ayuTfJH7f0kyr6GkmUP9K5bQF27NhBdc6m1g9DmSPXZdin4jUKrkewOGkHqmL0191D3LRGVeEsXZuQdXYmkFKyf/9+stksK1euHHcTHusnNLY1NpS2+dDvd1FwVb5abzzHu36znTvevva4U2yns28ne640TaOqqoqqqioAUukMV31nE6M5td/pvMv7btnO7W9bTXPV6ZG0Z8nPyWEqtJcmi97YtGlTWS/k9/unTPTG2SI/pcnpZyqeFuTH7/ezbNmyUxJg6bpOoXBsf5rTxYnIz6Xz6/jt4z2sPzwCKJHs/924jMVFD5ZSFEGoopLVc1vp3zZAJGAwEk/hCJOXrmrl7RfPZN9QipBPJ5VzxrU9In6DP2zpo70mSMRvYBkaeVsii1NcnpR0xyePR2ip9LF/KKN0Ja7Sh1iGCmStDlpkbAfbA5+uY+iCTEFla82sC9EZy1CwwclmeMUPN1Ab1JkdkdTU1PLEpj0g4cpFDbznstnomuCTf9nDgaEUQUujP5Hjvbds41PXLuDOHYM83jmqcrryOaYtSLLgqMiE/YMphtMF5jWEx1UjPE+SyjlFQbemQjGLpO/cNj/DboCHOxLomqAxokjmV/+2n65YVi2iRa+ekYyDXvSUCfvcCXlQxwvSPV3iA2rS6XuvWsY3H+zgL9sH0TWN0ZzDd/5+mEq/yQuXNmLbdlnIP23aNFYKwdVLm2irCvD9hw+RyLkEfBqvWNPKuy+ZiaFpOI7D9BlD/GlzD/uH0qzrl+hIPCmw9PHmhBKVdP9kwHVdduzYQSAQYOnSpZNW+45uj3mex96BUZXhVny5oQslvk8XxhHifyZGbUHeleOqd0LAXY9uZWWzv6wVOhVH+cmusUzB5Qv3HmBTd5y2qgAfvXIOrSdw/H6mYyqQn6NhGAa6rjN37lw1wZnNEo1G2bNnD7ZtU1VVVc6ke6pF0c9Wfk4OTwvyo+v6KSvPn8y21/FIVUc0w3Aqz6LmCjSUUeAX7tnH5168mJAo8OjjW7l3MMBDHb24UmJqgkTOpsaC9142lxetaEYIwdz6MHMbwmzviSMdWfb8yRUctnUn+OgfdvDuS2bRWuXHdT2GUwVGsw7GJDohUG2Q0pRXacxeSoimCmXBs65p6MJVI+QeTK8N0juaLaa0S9zi+wu2h+PAlmEdLRqjvSaABO7aOcCs+hAvWNTAvgFFfEqj/p4nec9vtgFK1B32GZhS8pm79vKz168u7+e3HjzIbZt60YUS/y5priBV8JhRGyCattnaHSeVcxjN2EQCBpau8ZpzpxHxp/jAJc28+WIfWdtlRm0Qv6nTMZwtk5SjfV48qYwMFzePJ19CCHb1J+lLjrK8LUJLVaB4vMYLm49e3LtHsty8sYfhdIEFjWFuWN0yIZG9KeKnKeLHZ2jlAM2C4/GLDd1cOruCrVu3MmvWLBoaGsbtz+vOa+d1503swUsp2diVpGtEcs7i2bxrehUd/THW7e3DzibZHZU80K322ZPgN3Qumnvqup5jYSRT4L/+tIct3QkqdIcPXdLGsrmzT/i+saLppqoQrlR+TQKBV7Q6CBjjp8jOFGcyBVMdMHE9WUpKQblXCC5YtYRpEZ1oNMq+ffvI5/PlMNaqqqoTapyOvobec8sOtnQnsF2PzliOV/9kM7e/fU3ZvPP/R0xF8gNHtDVCiEmjN6LRKAcOHBgXvfFU5GWdLfLz7LTX0xRPheZnMnSPZNE0QYXPoGtEEYfukRxv+/lGrm13eHAowI7+eFnYaUtJY8TPyuoCL16pNDPJnMOX7t3H3sHUuARzgGjGJVnIk7aV7uAtF87gp+s6MQ2HoKXjNzRimYmJpq4nORTNEPHrjGY8dF2jYHu4EmJpG0NTZoUI5bxsux452+UFixu5a8cAtuOVRdZSALpFwbaxdCX8FKgR+J29Ca5d2kTA0ukayZArtvPK02kCDKHyqSpN6B9Tpdrdn+S2J/oIWuqG0hXLcO/uYaZV+9nWEydre8ysDRD26QylCsypC/HCZU1cMq+Ooa40UkqmH6UTWT2tko2HR/CKERqgtDdBn4GpCVU5O+oYf/uJNI/0bC4vchfPreW6Zc08b34tWrFidPSilcjZfP2BDiSqlfh4Z5xMweOdz50x6XVy9DLs2A5btmxhyZIl5XJzz2iOWLrAtJoAnpTcvXOInO3ynDm1zCm2qr5y30F+t7kPz1Mto5evVhWhWc1F2/9cnuYH9/HA/hF8wuPVKyoJumlc13fGN0gpJf/ym+3sGUjiuR5JIfj4X/v5/Zx2akMnHy2zqDnM1YsbuHPHIAgQUvCBy2YSsIzTGqU/Hk63zRTyGXzw8ll85b4O5QIuJS9a1sj8RrVAtLW10dbWVl74hoeH2b9//wnbIWP3J5612dQVx/WOkKKc47KpK85zzyJhfbphqpKfY7UtTzZ6o7q6+kmpCp2taa9n215TAKdzw/pnaX4aKnxICcm8cmEWgF8HQzrcfFBnKJXG9TxVRSm2bzwpiWY9tvXE+dK9+9nTn8R2PTx51Cg6JdNhSTLnksq7XLOsiauWNPLFe/bxi8e6SB+dlDkGtusxkvHQNfV0PbYO4nggHI/KoFl2371iQT2vPKeNv+8bViP2RQrjeIq0eIAmXPYNpqgJmgynbR47NMK9uwZ5+0UzeN8t28b9fQkICaB8j7IOrCwuHp4nGUzm0TVVGUrmHPKuUriYhg7YKjLCU6JvS9fY3B2nO57j2w928NL5QV7eMPHJ/u0Xz+TRfX1s6VeTUwIlCK/w6WRtj5qQVRb/Aty9Y4CHu5WIvMRQ7t8bZVd/inUdUf7z6omeUgDdIzlytktTpWpRNFf62DWQpOB4E6aXXrCogds29ZEpuGhC3dwvbICVK49MMP7wkcP8Yn2PasnJYpXKdpESfvxoF1+9YQkNFT5+v7lP+S4VTSdv3tjDjatbyhqpgN/He65cwnuuVH+ntDCXnkjr6+upq6s7LZ1CMu+weyCJdD11jorGk1u7E1w6v65cbTqRbkcIwcevnsdVixvoS+SZ3xgukwo4Mko/NnpDSnlCg8WzjVesaWVVeyV7B9O0VvlZ1V454TVHL3ylMNZSO6RUFaqsrJyweBrFkOASSpUqUz89wvZMwVT0HirhZNampzp642xUfp6Nt3ga48ms/BzPJn9RcwXXL2/m1xu6iiPuHtNr/NRGQhw8NIIuQCv6ubiexHE9HE/i1yWv+tHjOJ6H5x0JDdU1gcsR6XMxx5EKS6eluNAOp/Lcv2cI2zt+Wd/QBXlHTvAWMnWB60pcqQwSPamysR45OMK+oRTZglOcNDpyc/Y44j+Tybskcy5+Q6Vrf+mv+3nl2jbqwxaxtI0o+hl5Uglxc7aLRFATUJWKV/1wI0OpPE2VPmxXkkrmiWedMumLpfP4DI1kTh0A11V5Y0FLJZA7rsfN2+NcMC/LWNsn13XZtWMb/31JPZHG6YzmbEbSNp+6cw/pvENTxM+/PX8u7755C9t6Eli6UARjkmPnMwQPHxihezRHe/V4oiClZFd/ku6RHEJAY8SP7aqW5mQL/+z6EN+6aRk/frSToZE45zVbvOn5q8s3rN39KX75WA+Wod4/nMyTLnhl/VPOdvnqfQf58PPnjJuk04TA0CCedSZ1Pi652dbUqBDUbDbL8PAwu3btolAolPOwTtSuKSEeG8ZzPQxDg6JOSkoI+XRu29TL1+7voOB4nD+rmk9du6Dc5psMQgjWzph8cupEBotjw1hP1GY6UxxNzE6Eo9shIyMjDA4Osm/fvnL2oN/vx+fzEfIZvHBJA3fvHCpG0Gg0VvhYM63qjPf76Yyp6Dp9ujhe9EY6nT4rpptnY9T92VT3KYTjiVAnw5NJfo5XURJC8LrzpzGnWufjf9xNbThIVdhPtOhurAnIFGR5asfxJImszZ0xb1zsRKny4NdFWaOjCRWDEPapG/y/3rqdmpBFMmczmJw8g2ssXCkJmBr1FRZdsVx5kbeL4udKv4Gha+hCaZXiOZt9g6nxbasxKL1fCIGOpLkyQNinYjm+/9AhEnmHUuXGlBLbk1QGTCIBk6sWN7DEGODLf92P60mVI5YsEDAFg0lFtnRN/c1YxqYuZFEbtugayeK4EimOhLAauoYmBP2JI1qsXC7H5s2by+0IgBYUabnz3eeXM9Be99Mn2DeQJF3wJpDCsfAZOp7tknfGE18pJV+89wB37hwklXM4GE3TXhVgWm2AV65tPWbVY259gFfOsgkG65gzZ864m3tvPIfQjlRMtOL/elKWbRRSeYfptQF8hk4iZ+MzNHKOR3XQpL365ASygUCA9vb28sIci8UYGBhgz549hEIh6urqqKurKzvdHhhKs7k7QdinM8uXJhkf4a3Pmc5PHuspV7gWNlUgpWrHCQQ+Q+PRjhE+c/c+/ue6hSe1X8fDyRosHstX6J+5iOq6Xj6mUkq2bduG53nl0emamhred0EDCxpDPNGVYFp1gDdd0P6k+R4djanoOA1Tt+11NvBkRG+cjbT5XC53XB+9ZwKeNuTnVPFkxlucKCCxu7sbbaSLT1+/mG8+3M1wykbXBCGfTjrvKOfkoiGhEGrKpezTM2Y7WtH/J+I3WNAcxnYkeweSJLIuFIlQznbpT+RPmLqtFRlMfdiirSrAUKJAvugI7RY7WpctqOPunUMqWwvIF/PDTF2UKzdjUfqTSqNQjGIA+hI57GJ4qu0pQW9bdYB/f/5c5jdV4DM1qoMWP//LAI4ry2PrkYDJcKpAU8RH0KdjCEE865DMOzx/USP37hok5DMIWxp9iQJ98RwzaoNq34DGsNpOPB5n+/btLFy4sFzlGLffQhC0dAYSOfpGs2QKSpekAZOd2Sq/QSrv0BzxM+2oqs/hWJa7dw4SMDWCpo+s7RDN2Hzi2nl0j+R59Y+fQAh47bltXLFQiZjz+Txbt26lpaWF1tbWCX9vWk0AKRUxNjRRjKZQU1rpgkvBUULtbz5wiK/duISP/3E3XSM5ZtQE+cz1C/EfIwvreNB1nfr6eurr65FSkkqlGB4eZsuWLepz5oN8dV1MVR49j/aIzs/edB6rLINFLZVs703QGPFz7bJGfvJoJ7bjESyKvU1NY8Oh0VPep5PBycRulIjQseAW9VJPJYQQGIZBc3Mz4XAYx3EYGRlhaHCAOV6c5fNC1Nb6scRTF8b6LPn55+JsRW+4rovPd2ZTkk9lO/mfhWcs+Xkq4i3GIpYu8IvHOtndOcDsSp13Xr2agM9iVmMVf9szxC0bumkI+0iZOr3xnHqC18DSBInc5CTt4rm1vOXCGSxri2AZOh/5/Q7SeYeu0RyGUO0NLWhScD2Kk9wTHaFRGpm846EDQ6kCOdvDlZL6sIVl6Mo1WEpm14Wx3UFcV/kGlRDxGyqc03YJWxppW7XmSq0vTajqS972yBbU9vVi307X1N9/32Wzee788U7JYUvDlZJswaUvkSNfdJ72pMRLSPyGRlXIoqnSz0P7hhlK5kFAIgchUydtuyq3ShO8flUNjWGDvr4+Dh06xMqVK084WRGyjHIbUTvq2JWOpU8XtNf4mV0f5v2XzZrwFJ7MOcWsLLV4Bi0DTXjs7Evz/YcPl6s2/3PXPvymzopGix07djBv3rxJiRnAnPoQ73ruDL754CFcT1ITsnjThdP48aNd2K4kaGmEfDp3bO2nvSbAb9685rif81QhhKCiooKKioqy0+3Hv70ex3HR8BAIupOCe3cNcu3yFi6eW8vFYwS5tSEfhq6VNS1OsbJ3JpBS0j2aI+94TK8JYOoTb8xjq0JjyU+pKlQoFHAcp7yYbjg8yod+v4vRrMO0aj9fu2HxU2qsOFbzYxjGOPJZMtTbvn07nucdN2bhbO7PVF3wniltr1PB8aI3gLJ+7Ohr4kwFz6p9PTWJ8NnE04b8nGrbS9O0J+UETkaqMgWXD922ja6hOCGfRU8GzEe6uHFVKx+4dTs9xXFxKcFvacUKisRzwXEkhi4mEBefofHac9uJ5xz64nmm1wbpGE6TzNkUHA+pq6rQYDKHJ5lQlSkfB6HIhGloyOKLRrK2GnNP2wRMl9aqAHnXY1FzhKAJI0dxxnTBZWZdiGQ6y4cvn8GdexM8enAEU1eLvuNJ/uWyWfSN5tjdn6I/mSt752hC5ZE1TeIe3BzWuWJBDT9+tOvIMZFFQbMQ5BxJJu+ysq2SDZ2jZU8fgETeRQNuWNXCK89pZ3Sgh/7+fgDWrl1bLvt6nmT94REGk3lm1YVYUvRbAgj7Dd544XS+/Nf9KkusdI4FhH0GjudxUbuPG2Y5VFS4eJlRnEDtuJLyrLogPlMjlXcJmBrZgkd9hcWjB2MgKHvsZAou331gPyOpLIbl51o3yRvOrz5mxeGlK1u4fEE9o1mbpogPn6GzrmOUnf1JtU0JGdfh95v7OHdGFVLCox0j+A2N5y+qJ+I/M7IxFqZpkrYlAolhmAghyNku2/cepMXpK7dySr4gL1zSwB+29Kn4lqJJ5kdfMHkg7MnA8Tz+9bYdPHpwBE0IGit8/PA1K8YJ1Y9G6eZfug/s2bMHn89HMBjEdV0G4lnec8sOsrbysuoayfHWX23jzned85RVgY51fzraUK8Us9DT08Pu3bsJh8NlUbVpnr3z/Kzp4snjqdYhnUr0xtkKXH2mXwtPG/IzVVCy6h+L9ft66RoapaW6AtMy8aTk/t3DWLpGNJ0nkXPwmzq5gku2GHaqCYoGhR6lYoISiyrdTWuln6/edxAhlJD17RdNp2skRyJrA5KczbjF2j0G+TH1YtVHk5i6RnOln+6RLLUhi4ztkrVdDsUyfOjyOTQwgikmbsjzJO3Vfs6f72Nla5g1c1v46B92cnA4g5SSG5Y3c+OqVoQQ/H3fMI91xMZMhynX6snIj67rXDK3lju2DWJoSnsUTeVxPZhZH8Ismt3lHQ/HmVj+Nw3BX3cP87bnTGd7Tw8Bv5/Vq1ePy4L7/D37+NvuofKN/e0XzeBlq4+0ml5z3jRm14f43kOHOBTNMJq1CZg6pi6o8Ft86JrlNEV8JBIJhoeHy1EHYxf8r7xsCZ/88x56RnPMbQjxXy+cz//dd3AcIc0WHLb3F6gMWugFl5+t68LUtUm9e0qoDJhUBo4sbs0RH9v7kiAhmrGxHY/9Qyle8+NNuLKkCxP87LEufvb6VVQFzs7CmEwmmRV22TOqo2kajiexTJ3rn7OcWdUmw8PD7Nu3j1wuR3V1NXV1dXz/lct4+OAIWdtlVXslrVWn73p7yxO9PHJgpCiAl3SNZPnUX/bwfzcuPeF7S8aLwWCQ+fPnl/2e9keTZf1UCfGszUAiV/Z1eipwMgvM0TELpZbk1q1bAcpC9VPRhUyGZ8nPyeOf3Yo7XvRGKpXCtpXdSWVl5Snv5zNJYH48PEt+ThFjK0pSSrq6uuju7CIYDGJaZvHngFBP+6m8S84uZYGpjC2AlqoAiayN4yq9yZJ6k7rKMD0Jh4jfKLrbWhyKZknlHf711u0UXKVtKemFXCBgHNHjlNbasdoViSwKq0FKj8PRDJ6ErOPSFPHhN5UOqdEbolAIYlk+zHxOtbWK2xRFU8KVLSae51EXtPjmTcsZThfwGRqVAZOC45Gzj0xoGboap3clOI7LW36xmQ9cPodLx7S+hBAETA1DE4SLo+elvdaEiv2oCBhcOLuGdR0j5RwyoOzQ3BVLc+mXHsBFo7VS8LnpGWbXh+iP59jSk+Cvuwap8Buq/eJ6fOehQ1y9tIlgMeVdSsm5M6pYO20ZQghiGZtHDsYQCC6cXU11UFUXSr342bNnk8vlJiz4X71uxrgpqdef384TXaMkcw6Oq46L3zLKIatSety9c/C45OdovPO5M3m8K85QMo9dJIMqPd7Gk5QrIYPJArdv6T+lbR8LJfO+L9ywgs/+rYv1h0YI+Qz+7YrZLCqaQ471uBkZGWFoaIi9e/dSHwwqkhg4s0ViV1+KguthlLQ9UrJ3MH3C99m2zZYtW2hsbBwX0qhpGvUV/qJpoSxXlT0pCJkC27afklH60yEbR7ckJ9OF1NXVUV1dfcpVoX/2gv50wtmqrpwNHF0p3LZtG5FIpDxVGAgEym3TkxExZ7PZZ7y7MzyNyM9UY6Ku67Jr1y6klLz40rWsv303nbEMpq5RcDyuW9ZUJjgqc0sREkOAaWiEfTrDqTymodFaFaA/naM3naK2wk9PPMdwMs9AMo/jqYU+VfDQAL+pBJ624yGkRGgaSA9NyGJoqYbnqVaBlMVJrrH7XSQPyaxKfG+r8pPNZpnW1Mb8WdNofGQjEugdVdNgmoCWiI9NXXEubKokXOGSt118pl4ep77tiV6+/fcORjM2EtXmqAmZ9Cfy+HSBrmn4TJ2v/O0Aq6ZVlasZmqYxvdrHhbNreHh/VBEEU4V0DiZVxaylys+WngTTagLs7Euq9xWF4DnbIwckAUv3GEjZfOi27Vy9RPnoFByPwWQBSxekCy7pgvLJOTiUYklr5aRRFbUhi2uXNnE8+P3+CQv+0VNSc+rq+PoNi/jlgzsJBiPgC/OX7YNjzoM87uj3hOvNk9y+pR/H88aN4ydyLsjxLVNPSuLZiUaXp4qenh56e3tZtWoVlmXx1RsmetOMxdHTTJlMhqGhIbZv347rutTU1FBfX08kEmF7b5KOaIbpNQGWt030yxmLeY1hLF0rE2sBJ8wjy+VybNmyhZkzZ45zyy5hUXOYKxfVc/fOofKxe++lM6kKByYVTZ8Ng8WjcTYqLUfrQkphrKVpoVJ77GQ8ZJ6t/Jw8pmqiO6jzWFdXR1tbW/l7GIvF2L1790lFb5TCWp/peNqQn9PFk/GF9jyPjRs30tzcTHt7O0II/uf6Rfx+cy8DiTxLWyM8f2EDD+2P0ljhI5opkLc9rOLI1dWLG3lgXxRPQlOFj+qgyUA8g4dkYYWPSr/JYCJHuuBS4TPKlSMPpZ8pTYhVBSwsQ2MknScnwdSORAMYuobresV8MBdDV6PQUPTnKW6rYzjDDSsaaCoGxv7rFXP4rz/uKuuQKgMmVUGLRM7h4cNpvvFYDE90sKq9khcsbuTAcJqfr+tiJKNiMjxZmp7RyqJUUxN0RpXb85fu3cdHXzCfgKWXq2j/9cIF3LdniJ7RHLPqghwYTvP9hw5TX+FjIJ7n9mgfYb9OxG/gScomjGNRcNV01EjG5tcbeqgJWUhLGSceimXRik/3uib46O27+OnrVlHh08ZdH+s6RtjVl6Qx4uPyhfVYk4hqj8bRC346nWZoaIhNmzaRTqd5+ZJ6ZsyYQUaa/ONAjHjWwQN8usY7Lpp+0tfczx7r4ufrlXdUqetamuDzKHo1eRJPSixd44JZk4upTwZSSg4cOEA6nWbVqlXlG6Ttevz40S42HBqlpcrHu547c1I/IVAPK6FQiFAoxIwZM7Btm1gsRnd3N7/dGuXOwx6aJhBovOqcVt59yaxj7s/LV7fwyIEYGztH0YWgOmTyn1fPO+brk8lkedqvFEw62f596toFXL24ke7RHAuawixrPaIHO3qU/skwWDzb9yYhxIRpobEeMmOdhScbhf7/pd1xNjCVq2RjTQ7Hfg/HRm+UHMh9Pt+E6I3Tiba46667eO9734vrurz5zW/mIx/5yLjf//KXv+Tzn/88AOFwmG9/+9ssX778LHza08czmvyUJrPO1PNgLGKxGJlMhrVr15aNqgAq/AavPW8aoEwHP/Hn3WzuipPIOyxsqsD1JH2jOXRNcP3yZlZOq+Qnj3bhuB5behLkbYkmJNmCQ8AyaKzw0xvPYrue8rEpCnItTeBIVSH54ksX840HDhK0dNJ5m3TeJe96+HSN9poAlpshi4/d2cw4zcxY4uBKuHvvCLuGN/Ollyxm9bQqvveqlXzk9zs4HMtQHTCJZ20cDx7pzJDIq+rJH6IZ7to5QMhSLTpdE5jFCZ8CEM/Z6EJD1yHneMUbPfzjQIyv3Lefj75gflk/pWmCyxceeTr/2+5h/KaGEFAo/t6TguaIj8FkHr8O2UkG5OJZlypNJ2jpRdGqYHpNgH1DGQTqmDVU+EjlHNZ3RLlsfl35BvbL9d387DElvBbAX3cP8YWXLCq3Wk4GpfKz4zj09/ezbNkybNumo6ODdDrNxy6oYEtMR7f8XDKvnnmnYJZ3764hdCFAK7l8HxF/Gxpct6yJfxyI4Tc03n3JTNZMrzrpbY9FyXfGNE2WXoc2tAABAABJREFULVs2bjH89J17uXfXEFLCtl7YcHiU37xpDRUnkTtlmiaNjY0Y4Wru/nNMHXcpcV2Hnz56mLV1LotnNE9aoTB1jW/etJSDwxnyjsfs+qCKYpkEsViMvXv3smzZshOW7oUQXDD7+CTxbBksToYnu9JiWRbNzc00NzfjeV65KlTSrY0NYy21/qbqgj7VMJXJz/H27XjRG9/61rewbZsVK1ackseP67q8613v4t5776WtrY21a9dy3XXXsWjRovJrZs6cyYMPPkh1dTV33nknb33rW3nsscfO7IOeIZ425OdMIi7OBvmRUnL48GEGBgYIBoMTnii9oteNJ+FTf95DTzxHXchkNFNgV1+y7HJcH/bxqb/s4c0XTsenC/YNZsvbcCVs7ooTNHVGsmrkKu945cVcFwJNE5gClrdVcsHsWs6bWc1n7trHXTsHVFI7Kum8MmDyloWVfG/7xCrJuM8F2I5HImvz+bv38dUbl9JS5efT1y/kDT99nAPDaXRNsKSlgq3dGTXWrRXjKQoedWEdMqq9ZhYn0AKmRl2Fj/dfNovP37OfgUQeUxe0VPrxmTp/3xfloy849kRe2K/jSYmQoryTmlD/8rajXI1dOeFzecC5M6p4cG+UeNamPuzDkxKfodFa5UcXomggKYufQ90gCq7Hzx/rUmJzTRQdm1Ns70myYpIIg+Ohr6+Prq4uVq5cWb6BlBaf0dFR6kLDxGIDpHtH6bZVtMTJ3GjCPgO3WNUpLVQBU8Nv6mUH5TNFKVG+vr6eadOmjftdwfW4a+cQPv2Io3Qy57Dh8CiXza876b8xkrGLMQ5qG7quowFpR+PAgQNkMpmyaHps9pEQ4oStrv7+fjo7O1m5cuUZ+5xMhjM1WDwaT2WbSdM0qqqqyvetkrPwwYMHyWazVFZWTkmdx1QduT4bERJPFk6FmI2N3vjqV7/K3/72N2699VYefvhhrrjiCl7wghfwghe8gEWLFh3zWl2/fj1z5sxh1ixVvb3pppu4/fbbx5GfCy64oPzf5513Ht3d3WfwCc8Onjbk53RwtlyeSxMjuq6zdu1a1q9fj+d57BvK0BFNc//uYQ4Mp/EbOs+ZXc2jB2MIAXG/yZz6EAejaUYzDrqmMZq1SeUdvnjvfuLZienwOUeSK47SC6DgSnyexNRFcdRbTW1duVhVSnb1p7h7xwCjWRtLV9UXx5P0juboiJts7k2VNT/Huo1kbY+e0Sx98Rzv+PUWPnP9Ir5+/0GGUnZ5GmbvQApHgjlGdKxpqqVV6TcYKWqIhIBIwOJVa9t4zpw69g1m+PWGbhoiPnRNxV8EzCML2mSGkTetaePBvVFGs3a5EhPx6QzHM8ytCzCcldjpPLkxZkRhS6PCEmztSWAZGomcqoTNrA3yijVt3LVzoBjHIWmM+Dhn5pGqXTlHrXigRNFUMGuf+NoZSub5xoMdHIpmaPJ7XDtDcN7qVRMIt6ZpBCsq+eMTI2w4ZFAVgFctzTEwsGOCHmaym8w7L57B+2/dTtZ2CVk6tuvRWuXngtk1vOfSY7eMThbZbLacKF9fXz/payZz+j7VpbutSiXaJ3JO2TwzaOmsXTCdCr9RJolDQ0PlsnyprXi8/LHDhw8TjUZZtWrisX+ycHRVaOy/ErE5HhH6Zy7sRzsLx+Nx+vr6iMVibN68eVxV6J+JqVphmar7VcLpkGq/388LX/hCLMti+vTpfOADH+Duu+/mE5/4BLt37+bjH/84N9xww4T39fT0jBsoaGtrO25V54c//CFXXXXVKe/f2caz5OcEyGQybNmyhba2tvIJ1nWd3z3Rw81P9DGYyJPIO7RW+vEbGt99+DCeVMLkaKZAwfUoOB62JwlZOkJApuCQzDlFTcyxHVxF0bgw53iq/ZSzKbgS2/HY3Bnn4jm1fOJPuxnNFnA9yLoSYYnyipR1j7g0T9g2R8hQSaNTFTDpG83x2bv3cNeOQVyvFKkhyNgSUyhtDcUJMtdTT/9hn8Ga6dU0RfwIDc6bUcOy1ghv/9UWhpI50gWHg0MONSETU9d4zxXK82WsbUDB8Xhof5RkzmZxS4QfvGYF9+8ZpjeeY+PBKMOJFM9bUM+HXrCQnz/WxS1P9DKSKeBJsHShjBs9D78naI4EqC3GfixsDlMXVvYDyZxDU8THq9e2EhzjghyyDJa3RdjSnSDo08nbHgFLZ2HzsVONd/Qm+fbfO9hweBRNCCKmR6cnSMkqLjhHbdvxvOL+qZvkZ+/axwP7opi6oC8h+Z9Ynp+/fiXVAZ1oNEp3dzeJRIJIJEJdXR21tUc8hVa0V/L9V6/gof1RtnbH2XA4Tixjs6M3SSrvlAnl6SAej7Nz504WL15MJBKZ9DWWrnHtskb+vG0Au7hoN1RYnDOj6pT+lt/U+dYrlvGBW7czkCxQGzL50ksWl1tnR+ePZTKZcv6Ybdvlse7SCK+Ukr1795bL9f+sBWmy9thYMuQ4joqB0fXy66aKwLiUN6VpGrquM23atHEp5CWBbFVV1VNe7ZiqJGOq7tfZQEnz09rayhvf+Ebe+MY34jgOmUxm0tdPRuKPdV3ff//9/PCHP+Thhx8+q/t8OnjakJ/TbXudCfkZHh5mz549LF68eFybK+cJfr2pl5qQRc9oDr+pMZgqYBkaUkJVwCSZd8jZLpm8S9DSimaAXlF4q3KbStlVx3r+K/nEaFJiex5uMe7A0AQP7BsmYKmQ0IYKP/2JHI5U4/Uhv0FrlZ97D6QmeACVnKB1TeAUR+eFgAqfQUtVAE9KHtoXLYergmppaUJwzZJa1h2KM5B2UVmWgmi6QMhnUHA8zptVw/MWqKrBl+7dx2AyR3XQIuI36YlnWdBUwTueO5Np1UHu3DFAV3eWiwI5whGPD962nZ19ybIo+WNXzeemtW309vZyfmiIFSsuKD/5v/OSWbzpwum4Er794EH+sKWfhpBgIKUmxHSRI5FzcD2VyL6uY4RMwaXgeByOZfnM3fvZ0Bnn3c+dSW88x6829JC1PebUB4llHGbUBHnfZbOO6ZPTNZLlY3fsIlfM+vKkxNB0mioDHBzO0B/PcfvWfn6zsRcPuHReLR9+/hzu3xslULwWzKIW6omuOFcuaqCxsZG6+gYe64ixdyRJvH+EQ4cOlZ1/6+rqmFMfYjRj8+NHu7AMDU3AweEMn/jzXr7x8hN73kyGwcFBOjo6WLFixQmT3T/y/LlMrwmy/tAILZV+3vKc6ac0sVbC/MYwf37XeRRc74Si8mAwyLRp05g2bRqO4xCLxejr6ysbu2WzWaqqqli8ePGUIBIweXtsbGusdE+aak66JTI2thXium45hfzAgQP4fL5yVehE18vZwFQlGVN52utMMZng2TCMYz4YtbW10dXVVf7/3d3dtLS0THjd1q1befOb38ydd95Z1hz9M/G0IT+ng9ONuJBScujQIYaGhlizZs0E/UDB09BQkQ5mseogOOJAXRsySeWdst8PxbTroKmmlUxDIKQy//OkioMo3QInI0OltpIAHCkpuBJXeqw7NFIefxfF9yLgwlk1vP78dt7w0ycY21UyNMGi5jDdI1kVjaGBUdSP1FVY6JognrZxPVmOxADKlayXLW+gxnS4q6NQFA7bHBzO4HiSoVSeL9+7D0sXXDS3jt7RXHlh0zRBZcCiudJPS6Wfd/xqM9F0Adu2uWNvhtde4LGrL0mFX03Q5B2Pr/xtP00yWhaXH93K8BUrHbv6U0T8BqYmqfJpZGyXaNou+gBB3nbJFBQRKiGedfj95n7+vjdaFo9rQlWyPvz82eUMrmNhU1ecguMRMFXlQReQtlU1zJOSh/bH+M3jvfiKou0H9kapDhjK+6ioXypeaGUH6HjO5g0/3UTXSA7L0AiYGp9/8SIWN/jLJDyfz/PosIXtuPh8ZtlBumQBcKro7OxkaGiIVatWnZQnjK4JXn1OG68+p+20/t7ROJlpurEwDKNs7FYoFNi0aROWZZFIJNi4cSO1tbXU19efsdnf2UapKmQYRrkqFI1Gy5Njtm0/KaP0p4rJKlG6rk+oxEWjUfbs2TNubHqsx9XZxFQlP1PJ52cszgaZTqfTp6T/Wrt2Lfv27aOjo4PW1lZuvvlmfvWrX417TWdnJy95yUv4+c9/zrx5x57UfCrxjCc/p1r5cRyH7du34/P5WLNmzaRfvJqQSVXAJZYu0F4TYHdfCiGUhLO50k8sY5NW/SEMUapaqUU2YOksaq5g9bRKvnTv/iLRUAuv400kPn4DMraHqQkKnixP+HieEo/qQi3mlqFjAk0RH50jWX766GGyYzQxOkqfM5pRIasljcuc+iBDyQKHhjOEfLpq0bkSUxPFSpYaX3/VOW1MrwkQNgWWrqpPoxl107Z0QdAyyDs2v32ih9XTq1neVsmOvgQBqUxoHFeyrDXCbZt6iaYL1IQscjmPtO3x520DwJHqnqlBLJEBqlmxYoUyH0wX2DuYIuwzWNRUUc7Mqg1ZHI5lMDWBzxBU+AyytkvQUqPxw6kCBfuIP4ykdIwlIzmbgiMJ+zwawj4Ekt9t7j8m+Sn552iC4hO8TdhnkCq4CE+11Z6/sIG9gylV3ROC0axDImvz43XdzG0IM5TMkysuMtOqA5w3s5qc7fLqHz3BwWgGJORsF8fT+ezd+7j1LWvHeQr1bDyEkElyuRyaruF4graaU3sCH9sqWrly5ZRcXI6HyTx8SmPdpcm6ysrKcutwKi1SmqYxMDDA4cOHWb16NaZpPmmj9KeKkyEawWCQYDBYHpsumVru37+fQCBQrgqdLcH5VGkNHo2pSsrOxvFKp9OTemMdC4Zh8I1vfIMrr7wS13V54xvfyOLFi/nOd74DwNvf/nY++clPEo1Geec731l+z8aNG89oP88Uz5KfMUin02zdupXp06dPWrYrwW8a/Osl7fxo4zAd0QznzKjiioUNTK8NErZ03varLervCzWOrIJEKeoAJJV+g56RHO3VAUUCHI9Nh0cQ4sj4cgk5BwQe1UETYbvl8E+fqTOnLkh3TOUneRKaIn6aIn46oynuOqoa4KLaLD2juXIbzPVg/1BaBXyi9DuqMiHJOqrFVhe2OG9mDR943hwSiTgrm3xsGNboiyvBsZSSpko/w6k83aM5hlJ5Xv3jjXzihQvojed4YO8wANcsbeS6Zc188d596Jpq/eUdWTSuE+iaIFtwMTWIJrKsnV7J/PnzAdjdn+RDt+0oCpMl582s4b+vWYCmCd528Qze99ttJPM2ti1pqgyQyNmELbV4WKZOpnjMjiaWdpEc5myvOJ1nMZIu8PqfbcJ2Pa5Z0sjL17SiCWW6+OHf76RrJIvjuOi4CMtHxK/crNfOqOby+XU8f1EDP/xHp7qeCi7JnA1C6ZKGknmWt0WYWRukLmzxkhXN+E2d+/cMM5QqqJDYIqlL511G0uONCnVd50VrZ7GuJ8f6Q6MIJKbwuGGGy4YNG6irq6O+vv64hnau67Jt2zYqKiqYN2/elFxYjodUKsW2bdsmePgcPdYdj8cZHh6mo6MD0zTLoul/toC3u7ubgYEBVq5cWa62nSiV/qmqCp3qwjmZqWUpYsFxnHFhrKe771OVZLiue1Zz1c4WzsYU2qlWfgCuvvpqrr766nE/e/vb317+7x/84Af84Ac/OKP9Ott42pCfMxl1PxmULPmXLFlCZeXxx5t1Xac6qPPFly6ZcMN4YO8wFT6dUVO5MI/V24xkClSHLO7eOUhVwKQ6ZCGEYCCew56k6lOCT4d0QQmLXU+NOy9ujhBLFxhI2XjFZNP+RA7pOQwmJ06Rgdr+0RlgjgsjWQer2JJxPYkrVGyC7UpetLyFf7l0FlpxNDxgwFduWMZD+4c5HM3wx639xDMFBpMFNAENFT5sx+NTd+7hN29ey/ufN1slyxfbVBfMquFvu4c4HM2Qztt4gGVmef/z5vw/9t48Po67vv9/zrW37tM6bMuWb0s+EueEcIUrhQQINAffpoUvtNCW4we0hUIvWkqBfmkpgdKWcp+lHCmJSQhHIJDEjhNbsmXZlizJuqXd1bH3zvn7YzTjlSzJOlYXzevxoI2lndHs7Ox8XvN+v96vF59/vItwLMXzdpTxF6/a7x7jx3/cQdYwCHllLMviia4xHu+M8oKd5WwrD/L53znEExeGiYyO8IZbmvnnn3Xxi84xW6wt2m7TiigQy+ikNdsp2yZclltt002TRFYjo9tVI1EQ+PwTvUiiwG9fU8uHj17g0lgaDzqCYGGIHn5rfxVFfoU91SE8sjjlcwR3X1vLYxciXBhNYmG3i4oDCpYFkYTKv8zIpMroJoqj/3KiU4Dmuit77JIo8LHX7uX0QIxE1mB3dYiyoAdVVYlEInR1dZFMJmcdF89ms7S2tlJbWzsvuV+vWKiHjyPgdXy40un0tNZhSUkJFRUVK9aqmQ2WZdHd3U08HufgwYOzLlD5HqVfyjEulQznmuk5+qzx8XGGh4e5cOECgUDArQp5PHMH0s7EeiU/67XtlQ/yk0qlKCiYe9jjNwUbhvwsBZIkoaqzEwEHlmXR1dXF2NgYR44cWdAXM7eiNPNm4ZUFwgl11qT1hGqS1jIoskhSNRBEW6AcSagoIqhzDH5trwiS0kxKg166I0l006R3LEUkab83jyKS0UzSmsmlcRVFErjSBedKPZHTAnIE0OpUhUS0TwyGZfH1p/swLIs/ekGDO51V4JO5bb8dAdFUW8SffPeMHYUhigxPJdBPpnWSWYPQDAO85+8o5/mNEb7z7CCiKFDqsz2MHm7t5b0HBQ4evPkK35vhyQx+xRZ3j8azZDSTrx/v47qtJfg9tnHhS3eX06oOIpoaf/6KHbxqIM54SmN7RYBvnRjg5xeihLwypmVXmwYms4hAVaGHrGa3+vZsKqAnmnYnpyzL4uGzo7zmwCaOd4+T0Q1EASoKfAimRX2pnxsbSnnHt08zkbFjTPbXFPCx1+zl879zkE/8uIOjbWEKfRKyJJLMGrMGvB6uLyLgVdBMi2TWsKt4RV7+6rZds14PoiBcEQnh8XimjS7njov7fD4KCwsZHh5m9+7drn5jI2E5Hj5+v5/6+nq3VTM2NubGkQSc/LHy8hXxBgLcVHnTNK8wjpwPCzFYzGd7LJ8Oz45Qv6KiYlrw5pkzZzBNc1pVaL6/uZ7Jz3o8rnwIsROJxLr0fMo3fuPJz3xtL13XOX36NIFAgGuuuWbBF83M/SYyOj1jKfyKxLmhOJIo4BHBFEC3ZoyVWyAYFopo0hNNk9EMRKCiwIOqakxkLfd1AH4ZesczFPtlwKI0qJDRTFJZHdO08Mh2lliunsWYMlyc2UKbSYecbSTRFm3nIq1bKKItSn20fRRJFHjrDZuu8OX56blRSoMe0pqBJAhopsloLMOmYp8bHjoTtcV+6kp8hGS7bZbRdC5FVK59w/Nm9WjZV1PIiUvjRJPqlHDZ4uxQnI890sFfv3q37X2kKFRXV7v5NWVlZTRUV1BY6OdPXtrI6w7VkMzqbCsPIksC//Z4Dz88PQIW+BWJppogzbWFdEcuj3MaUyL1f/pJh+v5YwIjsSylAYWKkJd//tlFwkmVgqmqVEt/jB+eHubOQzW876U76I6muTSWxrBMCn0y737x9iveX1Whl0+9YT//9LOLROJZrttawrtevA3fEsfXZ46LDw4O0tnZieTxcvb8BXqyQTKCj6bNZVyzpZjxpEaRX8Ejr7+bOeTXw0eSpCsW5UgkwunTpzFNk7KyMsrLy6+6KC8Upmly5swZAoEA27dvX/I+56sKOf+d+/ulLIAr5fA8M3hT0zTGx8cZHBzk3LlzhEIhysrKKC0tveLhc72SjPV8XPloey023mIjYsOQn3y3vRKJBK2trTQ0NLBpKtdqocglP71jKf7mwXOkVIOsbtA7liajm8iiQMArE5slh8pe8KGxMsDQZIaMZr++qlDCk7Enj4YnsxhAWgfJMFBEO5yzIuSlbzxNWjMwpvREM/dvWLbA2aFn4hQRmq2tJgHO0I2TgOFMJCmySFWhF0USeKprjD+4yR59be2f5NxInOPdExzvGcMjCdQW2YGspmnXnP7mVXtc/cpMNFYGsSwwDAtVzZA2ZW7eVzPnwvb+l+/krV87yUgsiyQKlIW8FPkVHu+MoOk6TN20c0eio9EofX19xONxCgsLqaioYGt1qfs33vWibRyuL6alf5KqQi+vaqomntF59FyYybQ+FdAqcvfBUv7maAcVIYVoSgfLDiWtL/Vx47YSPvvLbrxTJ1CYsjG4NGa7dgc8Ev927wGe7ZtEM0yaagvnHJ/fXR3i3+7Nf9bN0NAQl3r7OKVv4hft4wxMZtH0jD1x9ut+xCldlCJL/N2rd3HT9rUfQXVgWRYdHR2oqroiHj65i7KTPzbzupnpt7QY6LpOa2sr5eXlVzhmLxdzGSw69yWnArCYqtBqiYsVRXGn9izLIpFIEI1GOX36NIBbFSooKFi3guf16vD8XNtr4dgw5GcpmKvyMzIywsWLF2lqalrShyxJEtlsFoB//UU3Gd2gOKDwzKUk8az99zTDIq1d2cdybkOiCCUBD35F4sJogrGUjmCYCKJEWjfJPWrDwk7ytizGUipjKQ2PLGKYBrP8CXsbbGLjOvLmVIIk0a52ZKdCUu32nGALaCUB05rSwlj2VFnWMqks8CEIAv99LsWzvz7DwEQG3bRwbBpL/TI7KgLEsiYfeuWOeQ0Cn99YRuvecr77TC+SJLG/voy3v6BhzteXhTy8+8Xb+euHzlHgldxUe1kSyKo6oigQSWoU+GQKfQqyLFNVVUVVVRWWZTE5OUk4HHbFr45vzi07yrhlx+XFPuCR+Pc3HuCRs6Oousm+UhAm+ykrDDKe1qkvUVB1k7Rm8PpDNYiCwN7qAn5yPjLVarTH6/dWX35q8sgiN+S4Sa8WHI3J5OQkA0otj3UM4lNE0pqJaVoU+KaIuWVRpphksjp/+t3TfOaOzeyor15zYbBTMfH7/avm4eNUD52EdEc0nZuFVVFR4WZhzQdVVTl16hSbN2+murp6RY/7agaLCxVNr0U1QxAECgoKKCgouCIANx6PoygKiqKgadq6Ehiv18rPWgmeNyI2FPlxMo0Wipnkx7IsOjs7icViHDlyZMlfptz9DsUyhKbGq5PqlVUmj2SLiuWpTCRZsBfEooAHjyTglRXqS/0UeGUOFKb41ahCNp69Yj8Z3WJwIoso2DliqmlR4IHSkJ+BySwZ3bxCY2QyRX6uKD3ZFaNCn0x9iZ8zg7Fpbs9gV34ymklnOEFtsZ8/fEED3dEMx4d0rKkEcaewowgwkdGpFyXee2sDz99R4Z7vibSGgECRX3YXjImJCQ75wtxy+xaymsk1+3bOWSVycH1DCQ3lAbrCKcDAtCwKfRKv/tzTjKcut23ecvNmXn9oupj3qQGVX3bqlATKuLO5HCsbcx2DnQXNaXNUFnj5P9fV0dPTw/j4OE2HD/Ouijh/+eB5ElkdURDYWhbg1j32e3zHixoYmMhwfiSBBdy2v5KX7Z1/TDSSUBmYSFNZ4GVT0cIDBBcK0zRpb29HkiQOHjzI/xy9gF+R7HYo9kRZVjexLJusybKC1yOgGyZDcR1zShjsRG44bsqrhfkyxlYLgiBMy8LKZDJEIhE6OjpIp9OzCsodpNNpWlpa2LFjx6qbuS1HNL0egk2dAFznwaWnp4eJiQlaWlrcdm55efm8E42rgd908vNc5WeDI9fh2bmhFhYWcvjw4WV9cXLJz+6qAp7tmyToEacZ6Tko9MoUBrwU+2U8kkBloY+7rqnh/se6aRmI4ZFE6kp8/N3te7hw5hS/GrXzuWZqdjySQElQJhJXwTIpDYhsLi9iIq1z56EaftERpXc8Pe1vO5v7ZBFjijDZk1cihmEhigIpzcCriGQ1ExO7yuSXbctEryLikSVetreCA3VFHO8eQxQspmxz7Kws7IXCIwr8/vO2uOnsqm7ytw+d48fn7CpKQ1mQf3vjQbKTYfr7+zl8+DCJRILx8fGrEh+wp8U+fdcBHjo9xMB4mh+0DDE8mbUNH02L8ZRGXYmP//x1L001heyaSkz/9jOD/OcTva7T9ZNd4/z7Gw9weI72WHl5OaOjo8iy7LZabtxWymfuauKZvgmCHpmX7C4n6LG/OoU+hfvvbiKaUFFkcc62loPHO6N8+OgFBGyi+Ycv2Mqdh/I3eaVpGqdPn6asrIzNmzcjCALVRV5OD8RcAmoYJtJUkJk0lWNmmLZdQlPjZmqLfa4weHh42NVlOMLglXwCdzx8tm7dSlVV1Yr9ncXC5/O5fkumaTI+Pk4kEnEF5c65cXSEe/fuverU6GrgaqLp3KrQemsxCYLgntv6+nrXy6mnp4dkMul+X0tKSlYtz83Bb3LbK5vNLmoib6PiN5r8OA7P8Xic06dPs3379rzcUHPJz9tuaeAfHrlAVziJIgmoM2bJvYpM0Cvx1udt4ctP9fFM7wSPd0YRgJoiLxnNxCMJ/LIjyjNdGltKQ2Q0A2lKLA02yRAEgWhCRdVNvLKAIHmIZw02lwb4oxdtQzNM+p5Jz6rrMbGwpso/RX6JQr8HASgOyAxOZN0KgLOxbtlTa1vKAiQyOtWFdnVie0UIAQh4bHLmvFXVsJBFi8881kV9aYBdVSG+daKfB8+MwFRcRcdogt/9z6d457VBvt/jpfvYs1QGZe5rCrBQv0+/IvJb+yt557fP2KGrgGraDssiAsbUAV0aS+WQnwH8ijiVowaTaY0nusa4vbn6ivZYJBLh3LlzAASDQQYGBtxAzZ1VIXZWzS4CFAWBioLpk0KWZdEZTpLIGmyvCFDoU0hrBn87RXw8sh1A+9lf9HDjtlJq8lABmos43HW4ltb+GOGESn2xj/BUFEt9qYfRWNa1OHjr82ziA1cKgxOJBOFwmFOnTgEsyFNosXA8fHbv3u2Oqa9HiKLojm0Drmi6paWFRCLBpk2b3OiK9UQm5qoKOYQok8kQCASmEaK1Rm6FZaaXUywWIxqNTmtLOmGsK33e13PlZ7nHtV4++5XGhiI/S2l7pdNpTp8+TXNzc94U7Lnkpzig8NHX7GUyrfPtE/18+rEudOPyoPlEWuWN19XyD490MDSZQcv5nUcW2VkV4uxgnJFYH7Jp4smmaSjzU+CTuBhOklbNqRwnC8G08Coim0v8CILAx167j5piH9mpjKiZcPQ42hQpkATYVhGi0KcwEsug6zCWVO0psZzTqogCFSEviaxOUUBxqzllIQ+/u8/D0UEvyazJWFJFNy38ikhjRRDTgn97vJtPvr6JZ3onMKeOFwsELEYSOv951iSRVSkJKESSKp96aoybDtoeRvPBeVo9N5ygdzxtx1eIAoJp2QJvy55wM03YVLg4ImGYFiNjMXo6O9m9ezcVFRWk02nC4fCc7bH5YFoWH/lRB7/sjCIJ4JEl/t+d+wh4JDcqBOy4EVMUGIlll01+YrEYbW1tV5j/gX2NfuJ1+zg/kkDAFlc7k2QDExl6x1JsKvKxtWx2nU+uLmPbtm2up9DFixdJpVLztoAWivHxcc6dO0dTU9OGmzQJBoNu6OP1119PKpViYGCA9vb2VauYLQW5VaGBgQHi8TibN2+eNkrvvGatFsO5ppdEUZzWlsxms0SjUbq6ukin0xQVFVFWVrasa/Jqx7UeCYJpmsuq2qy3vLmVxIYiP4uBaZp0dHSgaRo333xzXsuiM7VEgmCb2G0q8lLglRlPXXbmTaomH3/0IopoOyrnjr2PxLJTAlqTLaV+rKxKSaGP9uE4BT6Z67aWMJnWaB2IuYtmkU+hstCO0LgwkuDPHziLppt0R5L4ctpXcDkvXhQEAh7JTj9XDTTdJOZke0kC2ZwYDFmEkqDCS3ZXsKnIx8v2VlKZU9Uo9YnUl/hJqTrFAYWMqlNbYi+aWd1gYuq91xX7bK8j08Q07FF8j2KLbEsC9iIQ8kpEEyp/8t0zDE1m2FoW4E9f1siTXRMMTaZpqi3i+Y2lroBTEAR3jL+6yMdwLIsk2lNqPkXCMOF1hzaxv+Zyv/rOQ5v48pP9qJKFYViEvDI35giQT/ZN8hf/c5Z4KktZgZ+/b/BTge0Nc7XpsbmiE359cYxfdEQITrlMJ7M6H32kg3+9pwmPLJDRDHyKNOVYbZ+r5SAcDnPx4kUOHDgwp1A54JE4VH9lG6a22OdWexaKmZ5Cs7WAKioqFuyb48Q9HDp06AqPp42AwcFBBgcH3Yy0UCjkTjLF43HC4TAnT55EEASXCK2n/LG+vj43422tDBbnwkJ1SF6vd9o16YSxOkMOuVWhfGC9Bpvmqx23Xq7NlcRvJPlRVZWWlhZKS0vx+/157wfPNUWmSHYrYzaPndmmsiwgkdWRRIECn8xI0iI1kWYypVEa9GBYFt3hhCtYtiyomcoOKw0o/OsvuzEty23pZHXbgDCR1d2WlCiAIgnopkVNsY+/u2MvpmXxwQfaKfbLTKQ0svrl92JadqXo1c3V7K6eLnozTIsvtWVJCTEKfQqRRJaRuEpxwINPkYildWqKfNz7nydIqwY+BVIZE1Gyc8Jub67ml51R9Kl0esOwGE2ajHZEQbC4MJLg5+fDlIe86KbFN57u58U7y/mL23YgTd10d1cXUBbwMBrPUlngIaUa7KkK8c4XNRD0yvyiM8qffv8s9SV+7ruhnjceqaPQp/Cri2MU+2Xuu77ebVFNpDX+/PtnUDWN8sIAad3kzx9o55v/9zBe+fINZK7psa6uLjwej9sechbukVjWJrpTNxCfIjE4mcErS3z0jj184IF2OwxXgD9/ReMVLbPFoK+vj5GREQ4fPrwmffq5WkBnzpzBMIyr+uY44aq5cQ8bCY4w/tChQ1csOoIgUFhYSGFhIdu3b3crZk7+WHFxMeXl5ZSWlq6JfmSm63TuYr4QrdBqEKGlVFhmOnxnMhmi0SidnZ1kMplpYazLOe+/ieRnvWqZVgIbivwshI3GYjFOnz7Nzp07qaioYGRkJO/HMRf5ub6hFJ8sEsssfF+lAYXqIh+942kGJ00QbdFyVzhBf8QirtkEprbQx2RGp3ssxYt2lrN3UwH/7ycXEcgJO7UgoerTIizMqckuQbDFrdsrgqSmQlcvh3xehijYrtObSwPE0hpPdo+hGxaH6ouRJYFo2qKm3F5kqwp9GKZFRjfQTIv9NQW0DMTwySIZVcUnWFyzo5SGihBHtpTwol3lNJwY4EtP2tlXWd3OElMkkEQJHZOMbpFUdVKqPab7/ZZhPLLIn79iB2BXMP7pDfv5j19don8iTVNNIb97Yz0+WeRvj17gie5xPJLAmcE4rQMx/uZVu7kYTiIA28qDVBfZRMOyLJ5svYCq6RQXBBEEe9/JrMFITGXzHGGhuVNAO3bscNtjZ8+eddtjlX4/kog7EZfI6iiSyD3/+Qy1xT7uv6sJWRQpCyoEr9LumwvO5GI6nZ514V0rOBEHjpndbBUzZ7Hv6Oggm81uyHDVXA+iAwcOLOj4Z3PhdtqHDol2NGardfyaptHU1DTv8edqhXLJTz4NFudCPtpLPp+P2tpaamtr3fMejUa5ePEiXq/XJe+rcd5XGsslL6lU6n/FmDtsMPJzNQwMDNDb28vBgwdX9APMJT+aYfK1Y338/HwEryLyuoPVfO5XvQvel2HBn7y0kb/70QV8MgSnhLFjKR3Ha9i0YDKjs708SFWRl7+4bTd3ff44adXI8emx4ZFERBnbz8WpGGFXjUr8sv3/Awov2FHGj9pGbGKELXgWpv7W1rIAWd3g3f91ms7RBAnVQBYF3vOSbQDohoksiZiWRcAj8anfbqahPMi//bKbJ7vHGRxPYVj2ezvRN8nL9lXzwp3lJLIGiYxOaUDBsCz2VBbwXydHEJlOauMZA0USEEUBw7LbSOdHkuye8s8pD3n4wBQZchDLaDzZPU6x7/JI/eBkhnd8u5WUZuIRBVr6YwxNZnjnC7dy5swZSvxeFI8X07Jsd+qpcb3S4MIrELO1x0rDYW6pMvhpv2Z7QukWAgKJrE7rQIz3f7+dL9x3cMnExzAM2tra8Pv9NDU1rdsS9Wy+OY7fUiaTIRQKsWfPng1HfEzT5OzZs3g8niV7EM104Xbyx9rb21FV1R3pXon8McuyOHv2LLIss3fv3kUdv3MsMw0Wc1tj+awK5VtbM/O8p1IpNzNOVdVpVaGNdl3C8ttxiURizT2+Vgu/EeTHNE03tPDIkSNXtLnyPXXhjIUCfOeZAR48PUxZ0INuWHyvZZjqAg/Dc4SLXnHslsVPzkWoLPCSTqcIpzVU/XKPTMDx3DEYiWV4dXM1//GrHnqj6SuEyvLUyHhavdL1GaB1MM4//7TTbbMcrivicTVKWrWJkm6aWKZNoL55vJ/O0QRJ1UARRTTT5J9/1s0LNkm0J3QMw0Q1LK7dWsymqbwqwTKJxLNTVQ9bc5TMGvzbL7vRDZMft4/yzKUJ0lOu1K39MUQBVMNEsmwyJQpgTVWzTAtCXhlZsokD2J/lyb5JBiYz1BT5OFxfNO2ztbC1R2nVIJ7VSUuiO63mn8rquj4YYevmempqaniTNsAXn+hFFO1g13e9qOGq4uu5kNse27fP4v+Gx2jrGebvHhvFg4Fgegh57LZkx2hyVg3O1aCqKq2trVRXV1NXV7ek41wLOBWzYDBILBajrq4Oj8fjfm+d9thqewotFoZh0NraSklJCVu3bs3bfufLHwsGg65WaLmtTcc8MhQK0dDQsOz7otMek2V5msFiLhFaTir9SguLA4EAgUCAuro6DMNwq3GOfs2pCm0ULdpy4y3+t0RbwAYjP7N9UbPZLC0tLVRUVLB79+4rXuN4/ayUD8ST3eMU+RQUSUSRIKuZVBR4KNYNJtPGnEntYGtxdlWFONE7wc3bSnm2J+q6LYNNBCRRwDRtL5sD9YX89rW13PUfT2OYV4qIBCxEp4Qzyx/WdYPvnhykptiPLAqouklZwIsYgkvRFFhQ5JdJafbrUlMVH1EAeUpsXOQT+ZMbG/n4ox0IlkX7UJz3fLeNv355A0p8EEUSyBoWJo7eSESRRb53aoiRWAbVuEzMTGwDxrICDxnNpNjv4Q9v2cI//aybybROkU+i0C+jSCIVIQ/3P9bNYx0RRmJZAh4ZUYDXHtzE7z9vC4U+hVsaS/nvk0Nu5IcT2Br0SBT4FAzdIJvJULxpL8fDJmXJKK89sInrt5YwNJmhvsRPXcncpW/Lsjg/kmQ8pbKtPEjVLCGl7mchCNRWllFUVITvqRheWcA0DVLpFBkdRocGmCxkURlSqVSK1tZWGhsbKS8vX9A26wmZTIbW1la2bNnijuLnLvZDQ0Or6im0WGiaxqlTp6itraWmJn/eTDMxm82AM0oPuESxoKBgUeTFMAxaWlpWJG4DprfHFEW5YpTe+e/FhLGupvFi7ri8ZVluVai9vR1d1ykpKaGsrGxd+DfNheW2vZ4jPxsEExMTtLW1sWvXrjkXA8frJ9/kZzhp8mj7KFnNIKMb+KdCPBXZFheXBb2k1RTZGdIg51YlS1BbZHuuDIynGZtM4JchbUrIEliaYWdtGRaiaIeB/tnLd6FIIrIkYM1yz9NMQDcpCShIosDojOpT2rDwi1ARsp8eU5qBgD3xMzCRoaLAQ02RD1G0J5IswW7rCVMtoYBHxicLPNUVRUSgpshna09GYvzrw89y9/P3cX/Laayp6TELmwAJ2CaNlsUVRpAWcKC2iE/99n53ouum7eV8/NFOzg3HqSrw8o4XbePDRy/QN55mJJ51g8oKfDLfPjHAq5uqqCr0TgXFXiZXDnHsG8+wqVAno+oc3FzK//n6OQzLri7trgrypd89POeYt3uclsW/PNbNj86MTmWhCfz5yxupKvJR5JepCM1OhEJemdcd3MR3Tw1hWgKi6OXItgKaN5cueHoM7Gv93Llz7Nu3b8XcVyMJlZRqUFPsRc7zgjOfh8/MxT4ejxOJRDh16tS0Cam1dPV1PJS2bdtGRUXFqv3dXJuBhoYGNE1zIzcSicQ0HdV89zhN02hpaXE1R6uBxRgszkVw8pk0vxgIguDq1+rr69F1nfHxcUZGRrhw4QLZbJahoSFKS0sXPNW4Glgu+Xmu7bUB0N/fT39/P4cOHZr3w7pasvtScKJnnM+fyRLs7iGt6kSTGuMBDd20qAx5KfArpLJXBprC5ckr3bADMC0gqEBNcYChWBYJiYxquCGjTFVP9lQXUD1VaXjzTVv4k++escXOM/bvk0UKfQpDk1eqrkWg2C+T1U16oikSWR2vLPHOF23n6Z4JVMNgJJ6lPKTglSV+57p6vvhkL7puC6Y1w6R9TCCoZWz/HiCTUUlnVSblcp4eTBPyKehGlqxhcxQBm/T8wS0NfPLRToZi06M7LKC22Ove5ARBoDzk4eOv3QvY4uu/+J92numdQJpq60miwFhKI6HqmCb8wyMdvPG6Os4Ox+34j6mTYpgWHknAIwnUBSxeeuN2/vnnPeimiTKlWTo3kuTrx/p46/O3zvuZtw3F+dGZUYJeCVEQiKU1/vi/Trui77uvreH/3rRl1m3/4Plb2LOpgLNDMepL/Lx8byWKJLJphhZmzumxqVHwgwcPrkj53bIsPvnTi/ygZRhRFKgq8HL/XU3TLA6Wg8V4+OROSG3bts31b8n1FKqoqKCkpGTVKgLJZNIlbjM9lFYbiqK4Rn+5+WPd3d3IsjyNKDpwcsa2bt1KZeX80SsrhasZLM5VFVovfjqyLLvfS13XefbZZ1FVlba2NkzTdMNYF1PJXQk8V/lZODYU+REEwc0tMgyDI0eOXPWDzo24yBf+/Vc9+ESBypCH7qhOUtVJawaWBd5iHxnVoLEiyFhKI5JQp0q3ArXFProjqSsS1nUDTFEh5NEIpwwsQXA7V4ooUF3o5VT/BP/w8AVesLOcl++tpH98O//00070GZWUjGGRzGpTFRt7J7JoE5HigN2eOzsUwzDtjKcCn8T7f9CGapgksjpRU6N/PEXQI/PYhTAhjzQVFipS6JO4NJHgRTUeOiMp9GyagZhO1hQ4NRCndTCOXxHZVFNEWtUZT2mEvDL/+Pr97K4uIOiReMtXT5JUDUzLDlj1CHC40iaos7U4PvuLbk71x1yDS92w0Lks4pYlgfMjCZ7sHp+2nXN+RSwqgxKf/b2bmEzrfOzRLiRHtCkIGFh0RVPudsbUiP3Pz0cIeGV+/3lbaK4tJJpQEQR7G4DolMGjTxYxLfjWiUGu3VzMgborS+ITaY3vnRyidWCSgEci4JF48S67ejBzeiyVShGJRGhra3MrloZhrOgo+C86ojzQMoIiiYiCwMBEhg8/dIH7725a9r5HRkbo6elZsofPTP8Wx1PowoULBAIBd7FfqafvyclJzp49uy7NF3OvncbGxmn5Y5lMhpKSEgoLC+np6WHnzp2rnjM2H65WFXIMFtejn45lWSiKwpYtW9iyZQu6rjM2Nsbg4KDbti0rK6O0tHRN7CeWQ77+t+R6wQYjP5lMhhMnTlBdXe3mFl0NTtsrn4hndDwyTGY0hmNZNzrdr4hMZHTKCrz87HyEnVVBYhkNSbD9f8aT2hXhowAGAn3jabyCiWFadujk1O+yhkV3NI0IPH1pnBO9E/zejVt4y/O20htN8d+nBqcmlS67OGd1yyUXDjfSLYhldGIZHc2wKA0qVBR48SsiLf0xfIqIPBV4CfZ0WSKjY2ITjK2FQde/aCKVxVAz9GcssqbApkIv9SV+Lo2lGEvplAQ8hLwyqmHxqhy/oD3VBWyrCJLRTHyKSDKrI2JR4TV49tlnpyWuO2Onx3omqCjwkMzqZA3LNTUUsfO+aot8JLM6T/eMM5meEkVPvWcRqC5U+Mc3HMAjSxT5bbPHWEZDnKr8AFyz+TJh+cqxPr7x9AA+WUSPZfnAD87y6bua2FZhP0mrumnrmnTbeVsQBCQBsOz22mzk52+PXqB1IEaBT0YzLD76cCf1JQF2VF45kRgIBNi8eTN1dXW0t7eTyWTw+/2cOHFiQe2xpaAjnEQ1TDxTbROPJNIRTix7v46Hj2P+t1zkego5moxwOMzp06cxTdN14V6sFmYuRKNROjo6OHjw4IYYg87NHzMMg6GhIc6fP48sy/T395PJZFaUKC4Vc1WFVFUllUphWRaapq26weJcmFmNkmWZyspK19gykUgQjUY5ffo0gFsVytd1uZJ4btR9ncI0TXbs2OGOKS4EK9H2um5rCY+0xomnUqS1qX1PhYbGMgaqHiehGuiGhSKKTKRt0iMLxqxaZMO0SGV1KosVEqp2RT4YXK4CybLE53/dTVozUGSbeIzEsq6+RhTtdG5ZFFxRs4GtuQl4JCwLNENH1U1KfDKtg3GbkFmQ0qZPmTmi5fGUTmWBrW0ay5g83R2lIOBD0XQKfAKbS+22Y3nIQ8hrMZnRsSyL6xtKeNONl1tBXkXiw6/ew1/9sJ1YWiPolfngK3fSXFsIwEQ8SWf/CP3DZxFN3fbekCGlWWwrDzASV4lndSpCHnTTotivoJsWmmkxFMuyucTHUCyLZljIgsW/3LGV6/ZswTNlAumRRT7+2j2857tnXa+jl+2pmBYs+vDZUQKKhEe2txlPqTzVPc69R+r4s5c18olHL5LRDAIeyR1VN0w7HK2+ZPbKRkt/jJDPLud7ZAHN0Gkfjs9KfgA3HLO4uNgdRZ7ZHvN6va6T8nJbYZtL/CiS4E5FaobJ9oqlVzkcD6JMJrNiHj65moytW7e6nkK9vb3E43GKioooLy9fMlEcHh6mr69vzcwjl4tkMklfXx/XXHMNoVDIJYqO+WRpaemC41pWG6Ioous6bW1tNDY24vf73ZF65xpdSyI0X2spV6PlXJdjY2P09/cTj8cpKChwq0LrSczvIJFIPFf5WY8IBoOLvmBWgvz8wS0NtPcMcHzIQBQE/B6RVNaw/WQkAQsIKCItU7EUDvRZqj4OBAHqihSSOoxnslf83gLahhJoUzv86x+2I0v2BJYsgDmldVE1E48sEfSK1BT7GR6PkzUl28dHEBAE8MoiGc3g1ECM9JTPT0a/cjzewt6nb8qh2SOaxGSoKQni8XgwrSwDkxnXzC+tWbzmwCb21RTwbO8EFQVejJxZfMuy2FER4Cu/e5B41qDQp7g6npb+Sf726AWyhoksCLzjhVvRsxluqobvnk0giBIeWeKmhhI+8Iod/MMjnbQPJ5BEgZftqeDxi2MUeGX8ikAymUITPdywd8sVwt2btpfx8Dtu4FI0RUXIQ03x9Cd6ryySnFKpJ6dadw+dHmF3VYgX7izneY2lpFSDSFzlT75vkyjdtLjrmppZqz5gtxsTWR2fIk1ZJAhuxMdMOBNRmzdvprq6Ouf6mL89tpjssZm4dXcFv+yM8njnGJJoH+9f3rZrUftwYJombW1teL1e9u/fv2oL63yeQrNVFOdDb28vkUiEQ4cOrXpaeD7giONz405mEkVnQY7FYhQUFLhEcT0syNlsllOnTtHY2DitVZc7Su/8T9d1u/oqSatGhBajQ1IUZZo7fDwed40/Hc+hsrKydRN3kkwmV00Qv9YQrhJitq4SzizLQlUX5p/jwEn8zbcnypcfOc6DPSYGAhdH7baBadmtL68sklYNsrNUcOaCUxHySlwxITbX6x0/naoCD9Gk6kZoyCLs21RAwCvTPzqB5LErIj7Z9rIxLVsLk9JMFnKIRT6Zv7m1mouDEX7QqVFVFECWZTTDZHAiQ3FAwbLg+oYSmmoL+dwvewDbw6i+xM8//3YzfkW0NRvJLN85OUQ4rnKovohX7Ksko5n8zpeexTIt/B6JeNZgYCJNTZEPQYB91QUc3OTFyCSpV5IUBP1UVFQQKiolGPAyOJHlD7/VimDqWLqGLnnZVVXAp357P2BXZh4+O8r5kQRbSv28urnarQbNxC86Ivz9wx2kNYPxlI4kQlXIiyQJfPSOPdMITlozGJjIUOiT5xUHH+8Z50P/c871ZDq0uYiP3rHHJX4O4vG4O724mFRzZzELh8NLrnpYlkVXJEVySq8W8Cy+WqLrOq2trSs2Sr1UOAaC4XAYTdPcqkdR0QyPKMtyhdX79+9f8/bKUuA4Rh84cGBBFUHLsojFYkQiEaLRKKIorul0XSaT4dSpU+zcufOqFf5cg8XcdWylq0KxWIzBwUF27969rP2oqsrY2BjRaNSd3HOqQksh3ZZlceLECY4cObLkY/rbv/1bbr75Zu64444l7yOPWNGLb+M91iwSK1H5Aagv9gAZogkVRbSnmtK6harbTydXIz6yYDsgW9g6HadClDXsf8+2eW7LzAJX7DwSV6cLqE1oG4wjyyK7i0WaG0r47qkRYlPOyVWFXqIJFY8kks5RTM/2d0UBApLFzy9E+dPbr+XR3uOMpTQCXjs24//evIXfvqYW3bQIemXu+o+nCXolNxtrYCLDU11j3NJYQjKr877vtTMcyyCJIr/uGmNoMsNL91SiGSYFU22ksaRqT2rJoq1JGozx0r2NvHjXdsB+OgmHw3Sca8OyLMrLy7l3t4evtGSwZC87K0N86LbLDtD/7yed/PR8xD5vlq0j+ofX7HHFy7l4wY5yiv0KHz56HtOwqCr0IksisYzOfz0zyGMdUYYnsxzeXMTrDm6iseLq/fHrtpbwn79zkPbhOIU+hSNbiq8gPo6+pKmpadE995lPl45R22LaY8JU9MlSMZuHz3rBTAPBaDTK4OCgm7rujIp3dnYiiuK6ds2eD7kBsQtt1QmCQFFREUVFRWzfvp1sNusSKGe6rry8fMXS0XPh+FgtdKpupQ0W50K+8q88Ho9brTRN07V46O3tdT2HSktLF0xC8yEOTyaTz2l+flMgSdKiq0ULQXWBh3sPFfB3P+lDEkWCCmR0O1fLmK+/hd3iys3Vmkk4FBFMwx6Lt0mRhW7OXYZzfi7k/Dvok9lbHeLc4CTjHWM0lPnpGE1gAqms3aoBWxQMtr6nwCuRyBqYM3Y+mTWp21RNeYGPP7m5nB9fUkkaErc0lvHq5k2IooBT98jotjGiE4EBkFE1LMvi9GCc0USWkoB9YzZMi++3DPOGwzUIgi0i9soiac2O7VAkwf3SD0yk3UPKLeGn02l70fXq/Nm1MhME2VRZQpHvMpH66fkIBT4ZcUo7c3ogxsVwak7NzYG6IvZuKiStTrjvwTBNfnVxDI8kIEsCJ3on6B9P8/+9ZPscn8p01Jf4qZ/DQHFgYMBNBV+uvkQQBDfUcWZ7bCFBo0uB4+Gza9euRenx1gKSJE0Tp8bjcUZHR2lvb0eWZerr60mlUgQCgQ1FgAYGBhgeHubw4cPLatV5vd4rcrDC4TCdnZ151ZnNRDKZpLW1lX379lFYWLjo7VfCYHEurMT4vSiKLgkFXIuH7u5u0uk0RUVFlJWVzUtCl+vuDM+Nuq9bLOVmtBKj7mB/yeqKvRT5JLyyyHhKW1CP0KnyzBLy7iJj2ERGM+zsLI8Ekxlj1kkxp1rjuBk7pAbLdllO6xYlkkDfeAbTsn8fTWnA5UqSY0IY8EokVAPBmk7IDMA0LX50Zpj+iMozfQlESSKrm9y0vcyNyxiJZUmrBkOTGduvxSdRGfLSXFeIKIqusNqB82kGvBLvu3U7//joRTTDQJFEfIqINOUqLQoCW0r9fOPpfo51j1Ma8PB7N9VTHZQ4c+YMtbW1eIoqeM9/nyEaj6PpE9QFL/KO60vwFJTY2WXO35zSPWkz3RZn4PWHNvFM7wSxtOZW2SRRoGhKq2NaFkfbRvmjFzbM2UK7Gpw2SzKZ5PDhwyvyZO1Mj23evNltjznmissVBYOtL2lvb1+Xo+BXgyAI+P1+Jicn2bFjBxUVFW60QSqVcvO1VtNTaClwkuUPHjyY12tothysSCTihvg652e5kSTxeJwzZ87k9RrKh8HiXFgN76GZFg+Tk5MuGVIUZVoYq7Mu5qMilUgkNtz3eKnYUOQHcCdfFoqVGHUHSOnw2WP9mJbddnKmh64Gw7IrO9r8a6/LEZKqQRJsvY4JkiS4ImVH8+O8Xs9hR9pU68u0QLAs0pphuxpzmfSIgl2F8kgi28oDBLwy0aRGNqdyJQC6YfGz8xGOto0QTWTZWuIl6Ffojab4xKMdfPx1++kcTfC2r58iklSRBAETi0TW4F0vqmVTkV3xaK4tpDigMJ7UpmIwTF6xtxJZFHl+Yxl7NxUwNJlhJJblL394jjODcWRJ4A2Ha2gbjPPDMyP4FYmuSIrTAxO8dbfJtftt/5K/fug80aRGUdB2nR5Ka5xLh7gulKVC0eidyOL3yKQN8CsSneEkjRVBd6prJg7UFfGx1+zlh6eHEQSBhrIAX36qb0Gf8UKQG47Z3Ny8KlWGfLTHcjE6Okp3d/eSPXzWGk40Tm6rLrfqMT4+TjgcXjVPocXCIc/pdHrByfLLQS6RdrxtZkaSlJWVLap6OTk5SXt7O83NzSvWbplrlH5mIOtCtUKr7T0kiqJbzQW7xRyNRt2JSieMVVGUZZOfVCr13LTXbwpWSvNzLqoxkdbZt6mAZ3onSC5i25nGhAtBRrcnqgoUCdMCTTcxTJuc1BX7UA2DaFJDEoWpSoSAYZqEPLYAGxzH5csJ7qVBhZKAhxftKuf3btzC6//tOEFFQNPtF1qAhC2gDnhFDNMkq9uBpiFBoMivcG44wSNtI/zZ99tIz2B0AhZHT49wy45yykMeTMtia1mAvvEosijwukN2LpeDsqAHvyLxkR91UB7yUFssktZMWgdiJLI6xX57OkwRTMKxNFrRdncapH887b5PQRAQBYFoBnY0NvKv9Vv5zM8v8tiFMIm0hqFr/PNPLvDjM0N88reb8ciz3zCaagtpmhrDT6o6Pzw9zGhMRZ5y6X753oolVX00TaO1tZXKykrq6+sXvX0+sNz2WF9fH6Ojo3nz8FltOPqSuYS1Mz2FkskkkUjE9RRyiNBaebdYlsX58+exLGtVp+oczOZtEw6H3fwx5/zMN8WUO5W2mj5KCzFYnK89lo/20nLg8/muaE1Go1Gi0SiGYdDf3+9WhRaL5zQ/v0FYKfJjYVeguqIpsoZFgUcivsDqz1JH6LyyCILA1tIAFhZ9Y2lKggq1xT4CHomOcBLLgs2lAdt/yIJUOsVfvXwLX3t2jJ9diKDqxlT1SSDklYlndcaTKn/8zVOMxjN2e0iwqyMWoOkGBgLnh5MYloVhwmRaQ7UEsAQqCz184tFOl1jlvjcLeKJngtf9+3He/aIGnuyeoH0oTnWhl7Rm8njnGP/nujoKfZdvMIOTGdKaQaHfXlC9ikQ8o0+lvFvoWRVN1fD5fARzvtz7NhXwyNkwXll0E+F3V9vl25BX5k9evpPHuybYUu5BFgU0zfba+fojT3J9Q6krep3rphb0yPzLbzfx5af6GJrMcLi+iDdcs/iRUEejtNoZUVfDzPaYM447sz0miiKdnZ2k0+kV8/BZaThtloXqSwRBIBQKEQqF3FHx3HytoqKiq14/+YRTNfR6vTQ2Nq65NinX22bbtm2oquq2aJLJ5Kzt1bGxMS5cuLBikS0LxXxVoVzxtPN7URTXTeQGTG9NlpWVMTIyAsCFCxdQVdWtChUXFy/omJ/T/KxjLLbtJcvyirS99lQFiCRHmczaMQeLOKSrwiMJsxod+hWRXVUhQl4Fy7LojqYYnMwyOGn7AknY+plYRkcRIZrSaC73Ulvo4e9fu49/fLSDJ7vGGI1nEQSBkbiKXxH5xYUI4ymNgEdENwUEw8xJdBcwLAtRmPLXAMIpA1JpO6bDMjFNi/nuv/GMzmd/eQlJFKkMKbbZnyQSz+h0jiY5vLnYfW2RX8aYSrF3NEymZfH6Q9V87alLCICseKgu8HJk6+Xtfv95WxiczHBmMA7Aq5squXX3ZXJhmPYkXkCR7b/vUfBaAg07GqmplAmHw1y8eBGv1+tm+Mxsb5QFPbxngQLn2eBEJSxV1LlamOmZk9seU1WVQCDAvn371s0CsBg4i26uB85ikZuv5egxnPPj8XjcqsdKVDMMw3ANMLdu3Zr3/ecDHo9n1vPj6FUcndWhQ4fWTQvRwcyq0MxxekEQ0HV93R032Mfr9XqnuXw7393Ozk58Pp9bzZyLcK7X97YS2HDkZ7FYicrPxXCSj/9imHjWQBIEdGvp1ZzZMBvxAdtp+WI4iSgITKQ1ZkxLY2K3ttqHYpjYIadtlsjD56L8zvMr+MArdjKR1hCAb58Y5MHTw1SEZLpHJgCQRAlJhKxuT1tVFnpJZnU0w0SWJBRZRM/oWCbIsogswmAsiyKCV7YF0DNJoICtSzKmxEmGZY/5W5aFYYFPmf6kXBHy8sbr6vjysT6ymgmWxe9eX0uTN8pbry2lX/VTFvRwx4FqQt7Ll2/QK/Px1+5lIq2hSOK034EdDnvtlmJOXJog5LWPVZFE9m0qoLjA65oH5rY3nDH6q5XvFwJHH7NRohIcOO2xgoIC4vG4q+k4e/Ysuq6752c9OgXPxOjoqJszlq8b/Ew9RjqdJhwO097ePq+n0FKg6zotLS1UVVXl3bdspTDz/PT19XHp0iX8fj8nT550RdMLrUysJmZrjzlVrYaGBjRNW5FR+qVipuDZGZd3WrfpdJpoNMq5c+fQNI2SkhLKysqWJVh/+OGHede73oVhGLzlLW/h/e9//7TfW5bFu971Lo4ePUogEOBLX/oShw8fXtb7zBeeIz+LxGRa4yM/Ok98yolQNa5MVl8pWMBoXKXAJyNNOSrP/H1qKmC1wCdhmJDR4TstEW5tSlPgUwh5ZTpGE5wdijGRyhBPGiAqqKbGWEpzU9G3lQfYXBrgUjRF/0Sa0oDtTpzI6CCCTxHtgD+LqagHCc2wXOH3dD8ii4m0zptu2syj7WE7dBU4srXYbU3l4ubtpXzj6X6yWIgiPPDMJW549XZ++/D8N3xBENwx+tnwwVfs4F9+3s2zfZPUl/h494u3uZNqDpwx+i1btrjtDad8v9RE8XxnXK02HGFwruu0c35mtsdWs/2zGPT39zMyMrKiAbFgewrNFAUPDAzQ3t6+LCdlVVVpaWmhvr5+mvP3RsLQ0BAjIyNcf/31KIqCYRiMjY0xMjLC+fPnCQQCbnbdeqs+5EZubN68mbKysmWJplcCV4vdCAQCBAIB1+9qfHyc0dFRvvSlL/HYY49x6623LsomwTAM/uiP/ohHH32Uuro6jhw5wu23387evXvd1/zoRz+io6ODjo4Ojh07xtvf/naOHTu27PeaD/zGkx9RFBfVJrsaLkVTZHUTwTJQDRBFpuldHPKwUpBFyOomcy0tlmUfgySKCFikNZOQ1+KvHzrHSCxLIqtjGCbprMZ41kIETDR3e+fYxxJZRuNZMqqBbsFIPAvWZaJnmha6aeGVRbKaYZ8TwWJLiY/xlMpkdrr4WTMsfnx2hPG0Hawa9NjtJ1U3r6j+fPHJXiwLqkIKqVSKlOXh14Mmu7Yu79wFvTIfeMWOq79wCjPbG7nTP8Fg0I1MmGshsyyLCxcuoGnahtXHJJNJTp8+PaswOLc95rQ3ZrYPy8vL11TTYVkWPT09xGKxvI+CXw0zRcHxeJxwOOya2OU6Kc+HTCZDS0vLutOJLQaOD9GhQ4fcz0CSJLfFPJuofCU8qZYKTdM4deoUmzdvdicDV2qUfqkwDGPB5CX3+tuxYwcveMELePDBB+nt7eXGG2/kpS99Ka985Su5/vrr5/zOHD9+nMbGRrZt2wbA3XffzQMPPDCN/DzwwAPcd999CILADTfcwMTEBENDQ2zatGn5b3iZ2HDkZ62/BH5FJJFIEk/r+GXQLAFRsPteFsyr/ZGwPXMcOOPqi5n+0kwQTZP5bBt9iogxpZXxiAKRpI4gZqgs8HAxnECb8hGSZhA1ceqgTAsmMtNDWD2yyPYyP+MpDU3XmcxaKKJAIqtjmnbLTQBMUyXklSBrElDsL71umGgmJLImKdUELIIekacvTfLVY/28NWfiC2A8qSFYBqmURjAYwswajCXzb1S5GMyc/nGmW06ePOneSCoqKlwdiaPNKCgoYOfOnWt+3S4Fi/HwmdnecII0V9Jc8WpwJqJM06SpqWlNyacgCBQWFlJYWDjNSbmjo4NMJjPNSTn3OJ2ptMVGnqwn9Pb2Eo1G5yWfs4nKc6uKhYWFbtVstfPWZiM+uZhLNO0QIkdzmg+DxflgGMaSKmaiKHL48GGampr42c9+xo9//GN++tOf8sUvfpG3v/3t3HnnnfzlX/7lFdsNDAxMm1atq6u7oqoz22sGBgaeIz8bDaqqMn6pnSP1IR7tSmJkbcFwVaGPS2NpBCws7GrGbBxoZvMtN6JiNsyWAH+134W8EpUFPoZjGQAqgjIZ3aQ44KErkkI1Lo+7O54/ToI7wvQwldz/TqkGAxNZ4qpObRBec10Np4dT/Ori+LT3k9JM1Kk3pRomXllCt2ySJwgOwRKIZw3KQx5OD8amnxPLYnuBTkuvRkVREGNqyqskoPDlp/ooDSjcuqcC/4xqkWVZdIwmSaoGDeUBiv0r19qYOd2SzWYJh8OcP3+ebDZLcXEx4+PjbN68mdra2hU7jpXEcj18AoEAW7ZsWbP2mBOw6vf72b59+7ojn7lOyk4LItdTqKKiAr/fz7lz59a9QH4+9PT0MDk5uWgfotmCap0Ju9yqxUo7cTvEZ8uWLVRWVi5om4WM0juvyXfsxnL254y5FxYW8trXvpbXvva1WJbF2NjYrK+fraMy87NYyGvWCs+RnwUiHo+7viAfPFLONacH+NRPOkgaEv0TmaksKnsSLV9dr7n2I0sCkiBMy+UCm/g0Vgb5wMt3ktFMqoq8SJlJ/vJHPZwdiblp5a4eZ6pa5UyXWTOcp03sFpozOj6Z1VBEgaEk/OeTA3NWuRyPRN0ExbIo9iuEvBKiIDCZ1rEsC1m04yxqiy4vrM4I74s2e5EDm3n4bBhJFDhcX8T3Tg5NeQ8JPHouzCdet9fNDzMti4//uJNfXRxDEuwE+o++Zs9Vc7cymsGTXeNkdIPm2kJqi5cmRM6dsJicnKS1tZVgMEhvby8TExOujmG96WDmQr49fFa7PbZeA1bnQu5i7rR/+vv7aW9vJxgMEolE3MrIelk4rgbLsujq6iKVSi276iYIAsXFxW7eVyaTmVY1Ky4uXpIW72rQNI2TJ0/S0NCw5HZjvg0W58Ny/YcSicQVLVhBEFwvtZmoq6ujr++y8Wt/f/8VifALec1aYcORn6V8+QVBWJY3w/DwMF1dXRw4cMAt/79kdyXJ0T4+f9aiqsg7NWUVX1BK+nKhGhbCDGokYpOAe4/Uc6C+2P15OJzi+k0SzwwayJJtVMgUofHJIiGfTMArYZp2eyqaVPEpAvGs6ZIeAZsEFXglNGOqVSZYZOaxqZZF+3UFXpl3vKiB750cYjKjI4kChmlPeZWHPLz5ZntxUlWV1tZWqqqqqK+vZx/wtlsasCyLO//9BIV+GUWy9VvdkRTP9k5y4zZbg/JU9ziPd45R7Ld1RPGMzj/+pJPP3XNgzuNLawbv+e82eqIpwM4R+8jte1xTw6XAGaM+dOgQoVDIfWINh8N0d3fj8XjWhQ5mLuSmmq+URmkh7bGKioolmwc6wuC6urp1UVpfLARBQFVVJiYmuOmmm5BlmUgkQk9Pz5p4Ci0FlmXR0dGBrusrYsDo8/nchw1Hi+eQIZ/P5xLJ5XzHVFXl1KlTyyI+s2EhsRtLJULLjbdYrMfPkSNH6OjooLu7m9raWr71rW/xjW98Y9prbr/9du6//37uvvtujh07RlFR0br5Xm448rMUOBNfi72YLMuis7OTWCzGkSNHpj0FS5KELFiUBhWK/QqnB2OrNvUF06tCXtnOwNpTXcBN20pRdROPbBOF0dFR/GaKxooQPkViJJ5lIqViWHCovpi/f81eKkIezg7FGZjI8C8/v0g8oxPwCLa+xzAp8smUBBSK/ArnRxIokkhSNZCEy1WeXAjY5EoSbJfnZ3on+Ze7mjhxaQLdNCn2KwQ8MruqQgQ8kiuqbWxspLy8/Ir3qRkmAY99qTrZXNmcqtepvkkmUhqmZVHkVwh4JEZi2XnP3yNto3RFkhT7bd+hZFbn/l9082/3zk2Y5sPQ0BB9fX3TxqhnPrHOdFF2dELr4YneqbopirKqqeaztceWah6YTqdpaWmZ9TraKMhtNzrX0UzPnNyq2UoFjS4Vjs4KYM+ePSt+HeVq8QBXNN3W1oau667WbDFWA6qqcvLkSbZv376i19FSDBbnw2qTH1mWuf/++3n5y1+OYRi8+c1vZt++fXzuc58D4G1vexu33XYbR48epbGxkUAgwBe/+MUlH1++8b+K/CymhO+UzoPBIIcPH77iiyOKIsUei0KfQlckSVq1BcKKKKDlqIidttFKEiNVt6gs8DAwnubt32whldUp9Muk02nbswcRVTCwgJoiHwGvxOH6Yj7wip2YFnz7xAB942l2VAYpCShEE/Y4fZFPRhDgUH0Rt+4u598fv4SF7QMkSyIiYExVfyzs6okxZfgoSwIeETA1YmMR4mE/z9tSMa2smszqXOgdJtzfxYHm5lm/eKIg8LzGUn7ZMeb683hliX01dv7Mfz0zwFee6iOhGiSyGtFElvKpMNXZEMtofORHHfzqYpR4xr65lAQ8eGSRybQ26zbzwbIsuru7mZycvGqi9mwuys4T/VLH6PMB51ovKytjy5YtV99ghbCc9piTLL937143GXujYWhoiP7+/jnbjbNVzXKDRpey0OcTlmW5eXVr5Tyda1Wh6zrRaNS1GnDyx+ab0Mxms5w6dYrGxsY52z0rhfkMFuHqVaHlan4SicSijT9vu+02brvttmk/e9vb3ub+tyAIfOYzn1nyMa0kNhz5WcoXarFeP6lUilOnTrF169Y5+5O2S7HAh27bxR99swULu5WjT3nYzNTVrBTsUXWIJlRqS/1Igh0R0TduIooCFgI1QZOQV5rK1vLTVLuJ1xzchCjABx9op304jkcWeaIryoG6IiZTGqMJlYAisrUswBsOb+LGbWU0lAf59cUxHmgZJqnqjCU1Npf6kEWRaFLllh1lbCr08a0T/RhT/j/lIQ+//5LtKFLa7dGXlZXxxIjAt0+OoGsa26qKqd4BJ4ejKJLIwbrCaePv73rRNkJemROXJqgv9fO252+lIuS1zSYfvYhumIjYGiNLtwh6Jd53a+Os5+tfft5NS/8k5UEPyWya4cmMS1hfsXdhgkYHpmnS3t6OJEkcPHhwUdfmzIV+YmJi2hi9c5NeTEjkUjCbh896wGLaY5OTk5w7d25FwzFXGn19fa4X1EKf3mcLGs31FHKqZqvhLeUIzIPBIA0NDWteyQS7MpEb5OtYDZw8eRJBEK4wMHWIz44dO2bNe1tNzNYem0mEZo7Sr3blZ6Njw5GfpWAxEReRSITz58+zf//+BT1B1hT5uOvaWv7jVz3EM5pNOHKEP85/Cdg6mKuluc+Fuaa7nN0ZQKFXJpLIgmUiCiKiICAJMJ42kbQsiaxBccDDG68vxCOLdIwmuDCSoDzkIakajKU0ftQ2wh/dspWtZX6SWZPGyiA7Ku0vxJ7qAvZUF3DXNbX0jCV56PQoT3WPIwoCf/zCBu65thZBELi9uYrHOqIIAjx/exnbp4THVZs2cWE4zhOdQ3zl+BB+GYqmKmf3ffkU5UEFC6gr9vPx1+11XZp9isQfvaDhivf+5af6yOoGIgIeRcQ0LSRJ4I3X1VEemp00tA7EKPDZ+qGaIh+DkxliGZ3f2l/F22/ZuuDPQ9M0Tp8+TVlZGZs3b17WzT43n8cRvDohkc5NuqKiIu8L+3wePusNc7XHJiYmMAyDHTt2rJvWz2Iw04doqU/uMz2FYrHYrNNRK0EOTdPk9OnTFBUVrdvIjZlWA6qqTjMwDYVCTE5Osnv37nX3XchtjymKcsUo/UxCtFSkUqnnyM9vGhZS+bEsi0uXLjE6Osq11167IL8E07L4p59d5OnuMcIJFcO0EOe4+AIekcxS4tyd42N2AiQKIIsCsiRwKZokKFkIgl0aNS2LrGZiWCBkNSxsUfb7v9/GV37vGnTDVjOnNYPO0QRYYFgW//XsEPddX8dvXzP7mHaBT6appoimmiKMqVyv3PfdUB6koTzo/g7syaq/evA854bjjMbSxFUoCSjIgkwimyGhWlT7LRRFoWcsxQ9bR7jnyNxj4hfDSX52PoJlgW5Z6KqFLAl4BZHDOYLvmagIeegdS6NIIkV+GQsf7711+7QcsKvBMZ3bunXrrL4fy0Gu30lDQ8MVfjBOXEJxcfGybnSOh8/+/fspKCjI4ztYeThVM9M0SafTbN26lYmJCZ5++ul1Y664EDjCYE3T8upDJAgCRUVFFBUVsX379iumo5wWaz4iJQzDcCfrcv1c1js8Hg81NTXU1NSQSqV49tlnKSwspLOzk97eXvcaWo9RNLNVhcbHx11CtNTYjf9Nie6wAcnPSrS9DMOgra0NSZK49tprF3zBtI+ZPDEYpX88jTZV7THnmP/WDBNj6dwHmL3yU+RX2F4eJJFK0TOuUlsSJDaWocArE0mqriDZwiZJFnBpLMXfHj3HxUiKwYk0lmW7NYtA0CsRVCQePReeRn4M0+LbzwzwaLudnP4719dx8/YyJNEmWd2RFJphsqXMjyQKfOHXfRw9O4KIwO0Hqgh6JM4MxjDVDCnNwrRgPKWRzNpPKwFFwu/3o2kaejZLy4VubqzQ5qx4PHh6BL9HJKXaE2y6HQPGO17UwObSuW9Y737xdv7sB2eJZ3RMy+JgXSEv2LHw3n4sFqOtrY09e/a4QuaVxEw/mLGxMYaGhjh37pzb2lis8dtGzRnLxaVLlxgbG+Oaa65BkiTXgyXf02MrBcuy3Jbp3r17V/TYcqejcmMNzp8/v6wWq5M1Vl1dvWH9rDKZDK2trezfv9/9Pufms6mquu7zxyYnJ+ns7HRF8jOrQpZlLchgMZFIrLuq10piw5GfpWA+8pPJZDh16hQ1NTWL8gQ5OxTjaJdK56TqEp/5kK8R+FwBtSzaba1EMslkxqCmxE9p0MurmjaR1Q2+9+wQfo9IRziFJICFhSjYAulj3eNsLQ+wuypE21DcNTRMqyad4STlIQ+6aSJPfVn+69kBvnVikNKAQiJr8MmfdlHkV9hVFeIfHungZF8MUYCKAi83NJTw0JkRSgK21uD7J4epKZTRshlUZKypOA3Tgoxu4pUEioIygigiKx68PpH926v4+1+GGZ7sZUsI3nxdFQ21Va6Y0zAtgopMqFQmklDRDJP9NQXce2T+/K8dlUH+440HOD+SIKBINNUWIs1MiJ0Djvh2OYngy8HMOIBYLEY4HKanpwdFUdzfzVfx6O/vZ3h4eMPmjDkTmNlsdlbjvHxOj60UTNPkzJkzBINBtm3btqqkbDZPIafFCiw4yNcx/9uolgJweTpw9+7d0x5kcvPZnAeO4eHhZZPFlUAsFqO9vZ0DBw64DzJLNVhMJpMbwhcrX/hfQX7m0vxMTEy4T/GLYbxdkSQffbiDtG6Hdl6N1wiAtcyqjyNsdvYHNvlJZTQ6UrZ7skfLYlnw0Olh7jxUQ1FAoSQg0x1J2eTLsj2CDNMiltFpG4xT7FeoKfYxOJFBNUxkQUAU7QrWz89HeOmeSn56PsynftaFapiMp2S2VwSxVDhxaYK+8TQnLk1SHrJHxodjGb777CB+j+SSCsEyGJtI4fF6yWYNdPOyBkoUBHyywIt2lfPri2OIosBrD1TzwzNhdBMKQ0F60jpfak3y+9hizsLCQo5sKuAXHXabsbLAi2aa/N+bFzapVBb0cNO2xT3h9PX1MTIywuHDh9fFTS+3tdHY2Og+rc5V8Zjp4bNePWLmgyMwl2WZffv2XZU0rMfsMcMwaGlpWRcGjDNbrE5iuaODKS4upry8/Aqy6HjgbN26dcGux+sNDvHZs2fPvNrOmQ8ciUSCSCRCS0sLlmW5RGgtKovxeJy2trZpxCcXizVYfE7z8xuI2So//f399PX1cfjw4UWX/p+5NIFpmlQGRS7F59cSOTqd2QiSAHjlhWmBTMAnC/g9MpNpDRGoDlgMJnGJjR879d0ri3zpyUtsKvbTMZpy/74IGKaJRxaQRQFFsr18TNNiU6GXjG4iCgJFfgXVMOkbz3AxnOSzv+hB1a2p/2mcHohRW+wj4LHdre3oCtu9OZHRiSRVBKDQp1AZEEhnNV55zWZUU+B7p4Zc00RZFOwk9pCX372hnve9dDuiIPDMpQkePDNKkd++PMuCCt0TGlt3NBNQJCYnJ1FGR3lDg86vh8Dj83Hn4c2u6WE+4VQaMpnMuiYNuU+rTsWjt7fXjZNIp9MEAoFV9fDJJ5ystOLiYrZs2bLo9zBzeizXD2a12mOaptHS0uJqTdYbPB7PNE+hiYkJIpHINLJYWFhIe3v7invgrCScvLSrEZ+ZyI21ySWLTmWxsLDQrSyudP5YPB7nzJkzi6pCX81gsaOjg5tvvnnFjnm9YcORn6VqflTVDsY0TdPNYDpy5MiSLlKPLGJaoIgSpnUVIfUcP5en0uB9ikh2jiywmcjqFvXFCmlVJyRbDCYs1BzeFJ/6R1Y3UGQP8bRGWjOo9EMWhWRWm0pUtxfwjGZiWhZeWSDolRmKJSn0yXhkgbRuIQsC//lELwMTacBy88DSmkn/eIavHe+nxK+QUg1imRRjSZWsbtl+R7LAeEolnoHDm0u469p6igMKrz+0id//egsTaRWvJOGdykYrD3ncFlvAI2NYFpZlIQiCrUcSBLyyOM04cOfOnbx2qmwfDvfy9NO97lNaPoR7jhbM7/eviFPtSiG34uE8pQuCwMTEBK2trW7FYz1UsBYChzRs2rQpb9qSXD+Y1WiPbbRqSe4EItiEYWhoiBMnTuD1epmYmEBRlHWRuL4YpFIpWlpa8pKXlksWZ7q5y7K8YhN2iUSCM2fO0NzcvOT2e25VyDRNHnvsMdrb22lsnN0i5DcRwmzBYzlYTdPiBUNV1VkD0+ZCOBxmfHycrVu30tLSQmlp6bJ67dGEyof+5yx9o+N0jC/cP2gmZMEO/MwuYhd+WUDCImPOHYoqCXDtlhIUSeCZ3glKPSYxQ8YrCcQyGoYJkmg7Jau6hUcWkQQLRbJNBBVJ5EU7S+kMp8hoJj1jKQRs356sbqAZUBr00Fju5+xwgqxuohkW0hShcwiLTxYoDij8xxsPuuPuAO3DcT72SCfjKY1NRV4+8IodbCm9/CU2LYu/+9EFnr40gWXZrbG33LyZ25vn96FxJqPC4bDrJ1RRUbEk0zcnbqO6upq6uvl1ROsVjodPfX29e4N2Kh7hcBjAJYsrHRC5VDiTdQ0NDatCGnIrHmNjY27Fo6KiYkmJ2XC5xbJjx45VN87LF3L1MaFQiGg0SiQSIRaLrWni+mKQTCZdcfNKTzg6E3bhcJhsNktJSQnl5eXLNjF1zDzz6Wn1+OOP84EPfICHHnpovem3VvSG9L+C/IyNjdHX10cymaSxsTEvN9FoQuWfHnyG77QnF72tPHXtB70S8bTB1ZpeuSPuAnbGVkI1MOc4BV5Z4PmN5eiGyan+SUxDxxIlBARM0yKtGWCBJVzep1cWkUSBnVUhklmdkoCCblj4FZFn+ydJZE33taIAm0v9qLrJWFLDsEwkAQzzso+RIIBXEkAQeOBt11FXMr21aFmWm/o+G3TT5NcXxxlLqmyvCNK8yMwtwzCIRqOEw2Fisdi0p/kzQwnOjyQoDXq4ZUcZHulK4d9ccRsbBQvx8HG8TsLhMOl0et1NtTjvYdeuXW67ai2OwTlHpmkuuj3mLLirNR24EnDew2zVklzh/djYmCuodgj1esFqEp+ZcCbsIpEI4+PjBAIBtyq0GELtvIempqa8aXOeeuop3vOe9/Dggw+ux4e858jPTCyW/HR1ddHd3c3111+ft4tmaCLNvZ9/isGraH5mQgA8sj2lJYkiCdWYNR19Nk8fAbtSVBJQiCa1OcmPLEJTbREZzSDklWgbmCRj2CGjpQGFoViG0oCHyYyOYZpkdHsKTAB2VgYRRIGgYguWJ1Ia3dEU2alxNXHqQOqKfQzFsq5XEDOcrEXBDk4t8Ml89p5m9lSvnZeMU5IeHR3loTOjPNhtIEgSsiTRXFvER+7YjTJFgDay/42DyclJzp49u6j34Nygw+EwExMTSx6jzxccS4H19Dk47bFwOLyg9pijy1hP72GxcCoNC11wZ1Y81gOhXux7WEnkVl8jkQimabreXfO1EFeC+Jw4cYJ3vOMd/M///M+axtrMg+fIz0xomuaKtOaDM+ESjUZRFIXDhw8v+29nNIMPPXCWR8+F5001nwuSMNU+MiwUAZJz7MP51B1SYmELm72ygABkZksUxc4W21kV4uX7Knnm0gTDsSz90QQp3d6HRwKfLLGjKkT7UJyUZuJMelsW+D0SdzRXs7s6yDefHqBvPIOq2xNaAUXEsCz8Holk1rAn3Sy7ypNLxESgpthLgU/BNC3+7o7dTKZ0vIpIU23hFZWW1YJpWbzmc8dRRMA0UFWVlA7vfl4VL96/mUQiwaVLl2hubl73BnlzIRwO09XVRXNz85I9fHKjAKLR6LSJl9XwBYpGo3R0dMw5xbIeMLM95qSJO+2xiYkJN3JjPVVAFgOHgC61xeKMiUciESYmJggGgy6hXi292XoiPrNB0zTGxsYIh8PE43EKCgrcFqJjReHolPJJok+dOsXb3vY2vve9761nnc+Kkp/126BdJnRd5/Tp0wQCAZqbmzl79mxe9vvXD7bzs/MRzLnKLleBIAikp4TG6jy78HtE0qpJmR98Xh9pzWQypeKTReJTIiGPBKZ5OVldESHokdheHmD/pkIeaRtF0w3SxuWryLKgqtBLWjUIeiQyuolPFm3fHK+MVxZ530u34ZUlRODvHu5wtUVZw9YHFflkfLKELApMpDUSWTvU1a+I1BZ76R3PIgp2i+35jWV89OFOsrrdpttZGeJvXrULj5w/AmRZFk91j9M6EKM85OGV+6oIeK58EtdNC82wCHlkBEHG5/VipTU0U6SlpYVMJkNtbS2ZTAav17suNTDzIV8ePjOjADKZjGv65gRoXu1JdakYHh6mt7d33VgKzIWZgmDnaf7MmTNks1l0XWfv3r3rlrxdDQ55W44R5nxj4rBwT6Glwqm8refMN0VRpuWPObEkvb29iKJIYWEh4XCYpqamvBGfM2fO8La3vY3vfOc765n4rDh+I8mPw5S3bNlCTU0NmqYtKth0LliWbQ7oV0TbLHAJ+zRMexpqvpqRAKSmJrcSmohq6eiGhVeRUHUT07LF0o6rsaMDSusGmmkxGMvymV90kdEM4ln98k6nWlP9YyluawzwWzvL+W7bBF5FJOiRUQ2TipAXv2JfFjuqCgh5ZVLoaKZ97BnNIOj1c+ueUn5xIUq9pNKlg4lAVYGH0qAX3YT7rq+nqbaQLz3Zi2aYlAQ8WJbFuZE4v+yMLipOYiYujaX43C8vMRLP0lxbgGFaHG0bxSvbeWa/6Ijyj6/bdwXB8kgihzcX8WzvJAVeiYxmosgi1T6NYm8xO3bsYHx83A2HXC+meFeDZVl0dXWRSCRWZBzf5/NRX19PfX29m5Td19fnjtHn6xz19fUxOjrK4cOH17VwdjY402M+n4+enh42b97M0NAQHR0dG+Y6cjA2NsaFCxc4ePBg3iqgs42J52ZrFRcXU1FRQUlJSV7O0UYgPjMxM5ZkcnKS1tZW/H4/bW1t00TTSz1H7e3tvOUtb+Gb3/wmu3btyvM72FjYWHeYKcz3lBCNRjl37ty0YNLFprrP93f9HomUahD0yiSzVxcrz4QsgHaVopGT4yUAWd1Etyw0fbqZom5N1wWphmVXgTC5FE1RWeilqtDDZHq6Nkg3ARHOT8LZaJTDJSbPRiwsQyHg89BUU8C//6qH2iIf0ZRGRchL1qcwkVaxTPB5RP759fsJKRZdvYP0phSqikQSWR2/RyKW0fm9GzfzhsO2j8lYSnMT2oWpWfloUl3kWbuMiZTGB37QTlo18Hkk/uvEALGsgVcSEUSBLSU+LkXTnOqf5LqtV4pkP/DyHdz/WDcn+yapK/Hx8hqNTcVBtm7diiAI08IhnaT1ixcv4vP5qKysXHcj4rnJ8s3NzSterZqZlD3zHDlj9IsRcjrkLZlMcujQoXUhtl4KBgYGGB4e5pprrkGWZerq6q7wy5nZHltvcI7TiUpYKeRmaznnKBwO09nZ6Z6jpRpQOu26tXJizwfS6bTr3FxYWIhpmq5ourOzE6/X615HCz1HFy5c4E1vehNf+9rX2Ldv3wq/g/WPDUl+ZoMTTDoyMnJFMKkoiosSSM+Htz2/gY/86DyJrI40Q+uyECw05sIhNgJg5rhI5xKe3F0Zlh1gahgWkYTKWEqjKqSwqchLJKlN27csClQWBphIqxRXFfMfLy/nxy29fPZYhNb+GIZlC6urC7zoFjSU+akr8TGeVNlSFqBQ1mk708bf3L4X02P30SURhiZVSoIKNUWXv4yH6ov4ybkw5UEPmmH79uyuWnrv/fxIgrRmUDwVs5HWbMG4JNrno28iQ02Rb87IkZBX5v0v3+Fm+mzevJnq6itH6AVBuMIUb3R01C3Z59NPaKlwWrslJSVLMv5bLmY7R+FwmNOnT7vut845muvYLMvi3LlzABvWgBEuZ40dPHhw2lP5bO0x5xwtZXpsJTE6OkpPTw+HDh1aVYI/VwvRMaBciCDYweTkpEsaNirxcewd9uzZ407XiaJIWVmZa5WQSqWIRCKcPXsWTdNcYXlRUdGsDw/d3d3cd999fOlLX6K5uXlV3896xW8E+TFNk7a2NgRB4MiRIyv65Hj7gU1UF3n5n5ZhjndFGEuqxLWrb+ce6yJfZ1jgk2zfnJlwiJAogJZT2LKwF5WMbtE/kSXkEdEtUHUTy7JF0XbOl52RVV1ayPfOp9ERsbAF0IYJ48ksQa9ETyRJSrfwyiKv319EW1sbTU1NVyz8pUEvl8ZS/MMjHcQyOjc2lPC7N9STyOoc655AkQTe+rzNHKhbuKvqTHinDCYty0IzbEdqUbDQLZCwyBoWfkVi36a5++OOLfxiRqiDwSANDQ3TktYvXLhANptdlp/QUuGY5jkePusBTutn69atblvDidTITaPPdZldq4yrfMEZqkin07Nmjc1E7jnSNI1IJOKaK84VJ7EaGB4epq+vj0OHDq155ttMA0rHqiQej8/rKZRLfDaq1srJmty9e/e87tOBQMB1dNd1fVrgcSgUIpFI0NDQQHV1Nb29vdx77718/vOfz8vQz28KNjz5cVhydXU1mzdvXpUbaMgrc2EkTla3KPaJyDKMp5cZ3jUHRME2+XPqPLkUSJHslHZ9liqHk5ouiwIhn4RkgixYJFQL1bB4tncSRRK4eVsJ4bhKStXtvQuCPc6OhSWI+GSRjGYQEEwUBL7w1CB/e8f+WSsev+iI8P4ftKMZFoVeibNDcVKawZ+9bAe6aSIJwrI/n/21BW4Yq25YGBZUF3oRBBhP6VQWePj46/ZSHJj9Bu5MEs1G3haKmUnr0Wh0VXVCjj3/ejbNm9nWyE0SD4VClJaWMjg4SHV1NfX19Wt9uEuCZVmcP38ey7KW5ACuKMqccRKr2R5z2nWHDh1ad1qrmYLgyclJIpEIPT09yLLstllVVeXcuXMbmvhks1mX+CzGE0qW5Wnt+ng8zte+9jXe/e53A/Ya+cEPfpAjR46szIFvUGzIUXfDMNB13Q0m3b1791UXgSeeeIKbbrpp2X/7V51RPvuLbs4MTKIZ1qI1P4uBAFSEFMpDHroiKTJT0REAIa+ERxaJZ3QM05q3nSaLNnlyAkVDXsk2OgQCikRZyEORX+HcSALdsDCnrokSv4KFnbyOZWfB+GWRG6oFfu9wqXvjkSSJvvE0b/naKcJxFa8soJlQ4pepLvLxZy9rRJFEGiuCC05Qnw+qbvKz82HCCRVVN/lR2yiaYbGlzM9fvHInFQWzLxYDAwMMDg5y4MCBFSnr52pgnPHnfOuEluLhs55gWRbj4+OcOXMGSZJcndBqjdHnC6ZpcvbsWbxeL42NjXl/6HLaY44XzEoFaPb19RGJRGhubt4QYuxcOJ5Cg4ODxONxNm3aRHV19box6VwMstksJ0+ezKuh5/DwMPfccw8veclL6O7u5uzZs9xwww381m/9Fi95yUs2ghD8OZ+fmTAMg0uXLtHb27vg3u6TTz7J9ddfv+wvxTu+1YpmmPyqM4q2xHH3hUIWBd5763ZO9E7Q0jfJ1rIAJQEFTTcZSarEUjplQZmgR6ZtKEZaNaeJqR2PIIcYiUzpiARQJBHNMPFIdjWmvsSHIol0hlNoholfFjlYX8TFSIrxRBafIiJK9vj9q5oqee8tNYyOjhKNRvF6vZxP+vmPE2PEMjoeyU4Rd0bjqwttMtJcW8gHX7Ezr2PuYPv3pDWDgCLNujA4rYlkMsn+/ftX7Sbv6IQikQiwfJ2QIyzeyE+3M6Meck3xVFVd0TH6fCE3ZHXr1q0r/vec9lgkEslre6ynp4fJyUmampo2HFlwMD4+zvnz52lqanJ1MGvlKbRUqKrKyZMn2bFjx5xu7IvF6Ogod955Jx/72Me49dZbAVsj+OSTT/LQQw+xd+9e7rvvvrz8rRXEc+RnJgYGBujr66OpqWnBZdrjx4/npZ/9+187iSKLPN4RITuH0WC+0FgR4E9fthNBgP/3aAeyKDKeVokmNSTBHnMv8tuVoeHJDGMpDcuarityGmYBj4hpgmaaGOZlYuRTRCRRpNAn85Hbd7OzKoQs2SGiWjbL7Z99iglNtKMxLLtltr+mgLuvreG39lcjiQLJZJKjpy7x78dGmchYmNixFqpuj5JLgn2whX6F9710O6/cV7Wi5y0XzhO6x+Nhx44da7agOjqh0dHRJemEHA+fAwcOrLkmY6lwxo/nCpWcGUnipGSXlZWtm6qEruu0tLRQVVW1JnEAuZNR4+PjS5qwc6brUqkU+/bt27DEZ66RfMdTyDHpBBYkvl8LrATxiUajvO51r+PDH/4wr3zlK/OyzzXCcyaHM1FdXU1ZWdmiLmJn3H25C8cLd5Xzg5NDs0ZS5BNBj0hxwMOmIh+f+UUXfeMZ0qrhstGAR8SriAzHsgzFsnbwqCKQmjFH7/xLEsCjiOhTGV2mZcdgXE5St+Msivz2+XFiHg5uKaUjkkXVTYZiGUQRNMPkK0/1M5nW+Z3r6wkGg9x+/S6eGrY4NxxnMqVimCaI9s1aViRAZCyl0dofWzXyo2kara2tVFZWrrmuZKk6oZX28FktOE/o8/muSJI0TbvgpGR3dXXlJWB0udA0zRWZzzYhuBq42vSYs8jPZRxoWRadnZ2oqrokndJ6wdjYGB0dHbOO5Od6Cm3bts0V3zt2Cvnwy8kHHOLT2NiYN+IzPj7O61//ev7iL/5ioxOfFceGrPyYpommLWLECtvOu7GxcdkW57ph8m+P9/DZx7pYycJPXbGXhvIgfWMpYhnbojmrGaRUE58s2JNaUxNdjieQKNj+P+JUVQjsD3BTocJE2rB/b1qUBhRKgx56oik8kkRJUGFbeYCbt5fQP56lVFbZ4Y1x7aEDxDWBv/vRBS6MJhmNZWkoD1BV6EU3TBKqwdd+77B7A81oBk90jZPM6uzZVMCbv3KSRFZHsCw3nuOeQxX86Sv3klAN4hmDygKPm6uVT6TTaVpbW9m2bRsVFUs3VFxpzKcTkmWZc+fOIYoiu3bt2rAL1ejoKN3d3Rw4cGDJpnmpVMrVwBiGcdVFPt9wBivW8/XktMfC4bBrHJjbHnME2sCGvp6i0SidnZ0cPHhw0UR4tsrZcjyFlgpN0zh58iTbtm3LW3jy5OQkd955J+9973u5884787LPNcZzba+ZWAr5OXPmDPX19fOODy4Uj7SN8O7vnF60x89SIAq29sc0LSRJcFttIpcztUTRruzoxuWWlyIKiKK97WfvbuaxjijnhuM0lAV494u3EfTK/PR8mDODccpDXjpHE7QNJbD0LGnV4KadVfz5K3YiTI3DP9I2whee6KOqyL7ZqIZJRjP5yu8emvMm+uavnuSZSxMYlk3GPJLAB24u5lIkzs8HLBRFpqIwwN+8aje1xfm78Tii4LnaK+sZjk7IWcCKiorYtWvXRhAnzoqBgQGGhoby2q6buciXlJS47sAr0cJxdEprmS6/WMy2yGuaRigUYvfu3RuW+OSaMOZDy5MbMmoYBmVlZZSXl6+o5swhPg0NDXkj0vF4nDe84Q28/e1v55577snLPtcBnmt7zcRSLsp8uTwDnB2KrQrxAZvcqFOKZT2n1GQCWHaelwWoM96aZloIJlSUeDlYX8iRWdyOb91dya27KxmYyPDD1mF8VgbJI1FVHOL0YJyhWJaaIh+SKHDLznIePDPKaFxFkWw9z/+5vm7ez6Ku2MepPgGPKLihqWGhkCciKqUFAqau0R+e4EP/fYK/fcXWvLQ0nCrDcjKJ1hLBYJDa2loikQjbt29HkqQ19RNaKizLcgW1+W7XzRwRd9LoL1y44Apdy8vL80K2nGDMjUakc9tjpmnS0tKCKIokk0mefvrpVa+c5QNO6yqfJoxX8xRy2tH5sgBYCeKTTCa5++67ectb3vKbRHxWHBuS/CwF+SQ/naPJvOwnH5gZCu+0wAQBykMK5UEPac1EmWfxUVWVVDJJsNBHIBBw3bBzi4Ihr8zf37GHH54eZiypcbi+iOc1zt+nTqkmu6pCIAhIokBW1emKpBAEAZ9HAY+Cz+8nmsi6bsWWZS15Kqq3t5dwOLzsYM+1xGwePmvlJ7RUWJbFhQsX0HWd5ubmFRXU5jrf5gpdT548iSiK7rW0FLdfp4K4kfKhZsI0TdcF3JlMcypnK5WrtRIIh8N0d3evqAnjXJ5C3d3dKIriEsalPlQ5mrGtW7fmjfik02nuuece3vjGN26E6a11hQ3Z9rIsC1VdXD5UV1cXfr9/2W64qm7y4k8+Tji5uLbb1SABC6FmufEWc8FxcN5S6kNA5KbtpRT6Za7fWsJPzoUZiWVpqi3knmtr0bNpWk+f5oGBAN0TGn7Fzi7bt6mAv/ytnVMGi0vD5399iaNnRikP2X5BkYTGb+2v5KEzIxT7FSRRIJbWqCjw8pm7bct1VVUJh8OEw2EymQxlZWVUVlbOW4Z2FltN09i7d++GnV5ZaLtuNfyEloqV9r9ZDLLZrHstLbZy5kwSbWRbAcMwaG1tpaysjM2bN8/6mnxMj600cmM31uqhJp1Ou+0x51qaL05iJnRd5+TJk2zevJmqqvwMfGQyGd74xjfy6le/mre//e0bpoK3CDyn+ZkN2Wx2Ua+/dOkSkiQtezz1x+2jvO87rWTzU0RaMYiC43wssKnIS1Yz6RtPU1/ipzigMJnWaK7y8cKyOF1WFT++MEEkoVJd5OPFO8u483ANfmV5T4Ep1eATj3ZyeiAGwIt3l/MHz9vCV4/180DrCLIo4FdE/uZVu2kov/LJ3Kl2jI6OugnilZWVlJaWujccx3PFmezYqDeA5Xj45NtPaKlwFtvS0lK2bNmyqn/7ajAMg7GxMcLhMJOTk/O2NJwqw4EDB9YNAVgsDMPg1KlTVFdXU1tbu+DtZjNXXMv22OjoKJcuXeLgwYPrppo781oKhUKuaHq2Y9R13Z0SzBfxUVWV3/md3+HWW2/lne9854a9710Fz5Gf2bBY8tPf349hGMu+KX/jeB//+MgFkivs8TMXXKNC7P8zm/ZIBJrrCvFOeewEvBITKY2zQ3G2lQeoKfaTSiUZnszwf5+/na+fGKYkYFdnJlIa73xxA8/bnp/YBMuymEhrSKJAoe/yjWFwMkMio1Nb4iPouXr3NfcJdWxsjGAwSElJCUNDQ9TV1VFTU5OX410L5NN5erl+QkuFqqq0tLRQV1e3brLG5oJlWcRiMdcHxuPxuNWO8fFx+vv719Viu1homkZLSwu1tbXL+ixmmx5bzfbYyMgIvb296/qzyG21RiIRRFF0iVAwGMQ0TU6ePEldXV3e7BE0TeNNb3oTN954I+973/t+U4kPPEd+ZoeqqotKah8eHiaZTLJ9+/Zl/d3TA5Pc98VnSM0U26wCJGBHdRAs0AyLjKYzMDm9/ScAHlngs3c18V8nh4hndAIeiVha58xgjIbyAIWSjqobWIqfHZUhuiMpCnw2ARlPaRzZUsy7Xrxt1d/fQmFZFqOjo5w7dw5FUaZFJKzmuOpyYVkW3d3dxGIxmpqa8r6gzDQNXCmdkDMGvn379ryN7a4m0uk04XCYvr4+VFV1n9A3khjYgSOo3bJlS96qDLD67bHh4WGXhK63vLH5kM1m3e9cKpVC0zSqq6tpbGzMSzte13Xe+ta30tTUxAc/+MENd30uEiv65jamOGIJyJfgeX9NIYq0NhecASiY/M2rdvHl3z3EofpiZjsUWRRprAxxe1M18YzORFojq5uEvBKJZIqJrIkm+Xn94RpKAgpZ4zKR0wxzzlDQ9YLx8XG6u7u55ppruOmmm9izZw+WZdHW1sbx48fp7u4mkUgsihyvNkzTpL29nWw2y4EDB1bkSdoxDdy3bx833HADmzZtYnx8nKeffppTp04xODi4aO3cTCQSCTeTaCMSHwCfz4dhGIRCIW666SZCoRA9PT089dRTtLe3uy2g9Q7HNK+hoSGvxAcuT4/t2rWL66+/nh07dqBpGqdPn+b48eN0dXURj8fz8p0bGhrakMQHbDPTmpoa9u/fj6IoVFdXY1kWx48fp6WlhYGBgUV3LRwYhsEf/uEfsmvXriuIz8MPP8yuXbtobGzkH/7hH67Y1rIs3vnOd9LY2EhzczPPPvvsVbf9kz/5E3bv3k1zczOvfe1rmZiYcH/30Y9+lMbGRnbt2sUjjzzi/vyZZ56hqamJxsZG3vnOd67re/D/msrP2NgYIyMj7NmzZ1l/9/GOCH/wtVMLEifnGyKwu0xiMm0gSCJxVSCpGehT92Vnyivkkfjcvc0cqCviqe5xfnVxDK9osVmIoPrK0JUAu6pCHK4vYiiW5S9/eI54Rgcg5JP4nevqqSnysaNyfVnBg31j7Ovrm1OPoWmaK3JNp9OUlpZSWVm5rsbDHW2Mkw21FseVqxMSBGGa/f9C4Qi0m5qalm0eulawLIuOjg40TWPPnj3Tns5nVjv8fr9b7VhrYflMZDIZTp06xc6dO/PmFrxQ5LM9Njg4yNDQEAcPHly3k2dXg2EYbgSKo7eyLGuaUadpmm5LeiFhtaZp8s53vpOKigo++tGPTrtODcNg586dPProo9TV1XHkyBG++c1vsnfvXvc1R48e5dOf/jRHjx7l2LFjvOtd7+LYsWPzbvvjH/+YF7/4xciyzJ/92Z8B8LGPfYyzZ89yzz33cPz4cQYHB7n11lu5cOECkiRx3XXX8alPfYobbriB2267jXe+853LcZp+zudnNgiCsCjyk6/Kz+OdUbyyQGoNND8N5T764hqJLIA5KzP1yiK1JX4ujCY5WF/MjdtK2VsmcfbsWfbs2UNxcfG019cU+fj4a/fS0h9jJJbhoTOj/PuvLmFaFjc0lPLOFzUsa+IrX3BaRJOTkxw+fHjOJ0JFUaipqaGmpsYVJq6n8XBHG1NbW7umOqVgMEhDQwMNDQ2uTmgxfkKRSMR12d2o01CWZdHe3o4kSezdu/eK95rrlWNZlisGbmlpWTJhXAk4Joy7d+++4vu9Gpjpu+QQxs7OzkW1x34TiI9pmm6kTq7QXBAE11No69ataJpGNBqlt7fXHeYoLy+fNcfONE3e+973UlhYeAXxATu3srGxkW3bbKnC3XffzQMPPDCN/DzwwAPcd999CILADTfcwMTEBENDQ/T09My57cte9jJ3+xtuuIH//u//dvd199134/V6aWhooLGxkePHj7N161ZisRg33ngjAPfddx8/+MEP1m3MxoYlP4uFLMvour7s/QQ8EuIacIHKoEz/hEpWn15+zx19F4BtBSaZrIqqamiGyVgkTE9Pz7yLVGnQw4t2lfPe/25DFKA44MGyLJ7sGuN520u5dkvxSr61q8JpEUmSxMGDBxdcKZEkydUC5Y6HX7x4Eb/f746Hr5aYcjYPn/WAxeaOOW2Jw4cPr7sKyEJhmiZnzpwhGAwuaEpQEARCoRChUIiGhgbXkqGjo4NMJkNpaalLGFfTaiGZTHL69Gn27NmTF/f65WImYXSqHY6Hl0OsZ+qpBgYGGBkZ2fDEp6WlhYqKiqtOFTstMact5uTYdXd3I8syjz32GK961avYsWMHH/jAB5AkiU9+8pOzXlsDAwPTsgvr6uo4duzYVV8zMDCwoG0BvvCFL3DXXXe5+7rhhhuu2JeiKNPet/Pz9Yr/NeQnX5WfzcVeEtrqV32iSR1TuNLnx8IOKJUEAVGAiKaQ1XS+c6KXn7b2cNdOiRsONS3o6Xw0kaXAa18Szo1pIp1fP6PFwtEVOF4lS20RCYJASUkJJSUl7lP86OgoJ0+enEaSVqqKEYvFaGtrW/dOwTPDRXMJo8/nQ5Ikstkshw8f3rCL1EL8b64Gj8czjTCOjY0xNDTEuXPnKCgocNPoV1Kz4rhP79+/n4KCghX7O0vFbNWO2cwVU6kUkUhkxbRvqwGn4lNeXr5oOxVBECguLnardvF4nJ/85Cf84R/+IUNDQxQVFfHpT38a0zRnJT+zdUBm3ifnes1Ctv3IRz6CLMu88Y1vXPa+1hM2LPlZ7EnNB/lJp9N8/3jnsvaxVBhgx1lIArphTSdAFngVkeu2ltAzlmJfTQGpRILxjMHTsQI2XbrEuXPnrmoYuKe6gNaBScqDHjTDAgG2lK5dS8OZItq6dWteBZy5T/Hbtm0jk8kQDoc5e/bsioRmbtQW0UzCePbsWWKxGJIkcfLkyXXT9lkMnDFwpzWaD8ysMDpj9D09PSiKsiKTiA6Z3kju07O1x7q6uojFYpSWljI8PLyuzBUXCsdFu7S0dFoVZakoKCjgT//0T1FVlZ6eHu666y6+9rWv8Ud/9Ec0Nzfzqle9ijvuuMN1La+rq6Ovr8/dvr+//4pre67XqKo677Zf/vKXefDBB/npT3/q3gvn2lddXR39/f3zHsd6woYlP4vFcsnP+Pg4Z8+exVICQDx/B7YMCIBfsZ8EyoIKxQGZYEIiPjmJ1+ulqiRIJCuyZ98+JlNZ9FTMza0pLi6msrJyWhjkH96ylf/3k4t0jCaQRIG33LSZHZVrI2R1bu6z6ZTyDZ/PR319PfX19Vc8neYKppfSzhgcHGRgYGDDt4ja29uRZZkbbrgBQRCWpBNaa6iq6sYLVFZWrsjfEASBoqIiioqKaGxsdJ2Bz549i67rixK5zoWJiQnOnTvHgQMHlhTbsR4giiKJRAJZlnnBC17gPoBcrT223uAQn+Li4iVXEWfCsiw+8YlPcOnSJb761a8iyzKvec1rsCyLkydP8tBDD5FOp93P/siRI3R0dNDd3U1tbS3f+ta3+MY3vjFtn7fffjv3338/d999N8eOHaOoqIhNmzZRUVEx57YPP/wwH/vYx/jFL34x7Tq7/fbbuffee3nPe97D4OAgHR0dXHfddUiSREFBAU899RTXX389X/nKV3jHO96Rl3OyEtiw0166ri+azDzxxBPcdNNNi/5bAwMDrtnWx396iW883X/1jVYIsgCGBT5FJOiV3WrQpiIvO8p9PN0VprY0hN/nI5LIsqnIRzihYpgWJQGFP31pIzVFXjcMcnx83C3Tl5eXI4oiKc3AK4vIaxQT4bRZmpub1/TmbpomY2NjjI6OTnMFnk2UOBMr7eGzWnActIuKiuacTFstP6HlwJmGWku9la7r7nlyHkDKy8sXdZ6c2I2DBw9uKE+rmbh06RLj4+OzZr+pquqep9z2WK6z+3qAoxsrLCx0c9OWC8uy+NSnPsXJkyf5xje+sWA94tGjR3n3u9+NYRi8+c1v5oMf/CCf+9znAHjb296GZVn88R//MQ8//DCBQIAvfvGLXHvttXNuC9DY2Og+2IAtenb2+ZGPfIQvfOELyLLMP//zP7ui5hMnTvB7v/d7pNNpXvnKV/LpT396OeT1OZPD2WAYxqIFzIslP05mVCqVoqmpCVmWebRthD/+r9OLPdy8YVuZn7GUhmFaGJaFRxLZVh4gq+ncUa8z7qngqd44AgKlQYVIIkuRX8Gn2C7PhT6Ff37DPveCdMr0o6OjRKPRNc+J6uvrY2RkhObm5nVVKckVJV7tPJmmyblz5wDYvXv3urphLwaaptHa2kpVVdWCdQzrMXfMEQWv1TTUbDBN072exsbGFjRGH4lEuHjxIgcPHtxwraFc9PT0MDk5SVNT01W/G7PZDTjt1rW8P1iWxZkzZ1wBfL72+a//+q88/vjjfOc731lX9781wnPkZzasNPnRdZ3W1lYKCgqmBTQ+cTHKW75yck18fpw2V12xj6pCL71jacIJFa8scNsWiffdcS0ej4dIwp4KG45l+Zefd1EWuvwlGo2r/Nu9zYS8s3c8HSFwOBx2tQyVlZUr/pTp+K1kMhn27du3bioGc2GmT45znjweD6dPn6awsJCGhoZ1XbKfD9ls1tVbLadFlA8/oeUgHo9z5syZdSsKduCM0YfDYYBp50kQBDfc8+DBgxt6Uezu7iYej7N///5FPxQ4gwpOwOhatcccQ9VAIOCOiOdjn//5n//JI488wve+970NTW7ziOd8flYb6XSaU6dOsWXLlisEW8UBZU2ID9hMNKub9IylkUWRspCX6oBFIqNzSQ2StUS8gkBFgf3FMS0wLAvdtJBFgaRqEFDEeQNLc/1fnD58W1ubKwSurKx0b8j5gmEY7s2kqalpQxCGmT45jmB6cnKS0tLSDet2DJdH8vNhmLdcP6HlYCNpY3KnolRVdas86XQar9dLOp3mmmuu+V9LfGD6oIJznqLRqKvPKykpcduIK1VtdYiP3+/PG/EB+MpXvsKDDz7IAw888BzxWSX8r6r8PPnkk1x//fXzfjEcYfO+fftmLZHf/7OLfPoX3Ys93LxAFu3JrtKATFqz2FcmoCgKoVCISFLlz166g301059uv39qiO+eHEIUBSQB3vOSbTTVLt4PZKZzcr4WLlVVaW1tpbq6etEjousJjtFcQ0MDlmURDodJJBKUlJRQWVlJcXHxhmh/OULzla6UrLROKHfCbiNrY/r7++nr66OwsJBYLEYoFHJ1Z+s17HM2XLx4kVQqxb59+1bke2CaJuPj40QiEcbGxggEAnlvjzkTj16vl8bGxrzsE+DrX/863/zmN3nwwQfXPUlfZTzX9poNpmmiaYvzoDl+/DiHDh2a86YxMDBAX1/fvDfM137uSc4OJRd9vEuF4+vjEB9FEvBIAgnV5HBNgPKiEKZlEUmo/N3tu9laduWXZ3Ayw0RKo6bIl5fcLmfhGh0dnXNybCFwtBiNjY0bulIyl4ePc0MeHR1lYmJimrB8Pbb1HDHtagvN860TGhkZ4dKlSxu+RdTX10c4HHb9byzLIh6Pu7qz1fCnWi4sy6Krq4t0Os2+fftWpaqb2x7LbSOWl5cvuT3muIErijJNBrFcfOc73+ELX/gCDz300IaNiFlBPEd+ZsNSyM8zzzzDvn37riA2swmb58LNH/sZkdTKhxxKAngkAUkSUSQR3TCwLAG/RySd1RBFkZKgl7Kg7cb8gp3lvPXmpZsALhWOIHF0dPSKybH5FviJiQna29vXvRbjaohGo3R0dFyVMOT6v0QiEbxeL5WVlWsu3HTgEIa5MtNWE8vRCQ0MDDA8PMyBAwc2XChmLuabhnLgtKXD4TCaprnV2Ll8vFYblmVx8eJFstnsrPEhq4WZ02OLbY9ZlsW5c+eQZTmvxOcHP/gBn/3sZ3nooYfWhTv3OsRz5Gc2LIX8tLS0sH379mkMW9d1WlpaKCwsXNCFffPHfk4ktfKqH78iEvRIlIc8+BQJw7QYnkyj6ToVhX4KfR4Oby7iYF0RpUGF5tq1v+HNXODneoJ3Ftrm5uYN3ZJwPHwOHDiwaAIzU+DqCKbXouzd39/PyMjIuiQMjp4qHA5fVSd06dIlxsbGaG5uXpeVtYWiq6uLRCKxKG3MzDH6tbYbsCyLzs5ONzB2re9NDmZrj803ZWdZFufPn0cQBHbu3Jm39/HQQw/xyU9+koceemjVg2g3EJ4jP7PBsixUVV3UNmfOnKG+vt5l2alUyp1o2bRp04L2ce3f/YT4Cic+iAJUFXg5VF+IaliMJzWay0Ue756krrwIC4GEavCBl+9g76b1WzXJfYIXRZHy8nJUVSUej9Pc3LzuFtqFwrIsenp6mJiYyMtC6wiBR0dHyWazbqVjpZ/gnZaEs9Cud8Iwl06opKSEnp4et7WyEbRVs8EhDKqqLqtSMpstw0LDRfMBZ3JT1/V1RXxm4mrtMYALFy4A5JX4/PjHP+bv//7vOXr06IZu968CniM/s2Ep5Ke9vZ2qqipKS0sZGxtz2y6LKTnu+qufLPZQFwVRAI8IhzYXoxkWiiTywk0mB6s9dGql/LwjikcSecPhTVzfsHGeGFKpFG1tbe70ilPpyPfk2ErDKYFblrUiHj6zGeEtRU91NThPtKZprusFai7k6oQGBweRJIlt27atmzbiYuG03k3TZPfu3Xn9PJwqozMePnOMPp9Yyfex0nCm7CKRCMlkEkEQ8Hq9ea0k/vznP+ev/uqvOHr06Iq5jP8G4TnyMxuWQn46OjooKipCVVX6+/sXPQliWRa7//qniz3URcErCWwpC/CPd+6jpkCmve0MZWVlbNmyZUPdSHKh67prAb9161Z0XXcrHfmcHFtpOG7Hq+XhM1NP5Uz6lJeXL6tqtthE8/UK0zQ5e/YsHo+Hmpoad4FfCz+h5cAR00qSlNcKw2zI1b+kUilKSkqoqKjIyzSiQ6gBdu3atWGvK6dylUwm8fv9jI+PX7U9thA8/vjjfOADH+Chhx66otPw8MMP8653vQvDMHjLW97C+9///iuO6V3vehdHjx4lEAjwpS99icOHD8+77Xe+8x3++q//mvb2do4fP+46On/961/nE5/4hLvv1tZWnn32WQ4ePMgLX/hChoaGXAH9j3/847Ukac+Rn7mQzWYX9fqLFy8yMTGBJElLihxIZnUO//1ji9pmMZBFuKa+mP9782YO1/hpbW1l27ZtG/oJIZPJ0NrayubNm6murr7i9/maHFtpqKrqBmLW1tau+t/PnfSJRCIoiuIKphfTynDMO8vLy/OWRbQWyI3dmOmwuxid0FrDIXA+n4/t27ev6vE5+hfHPTkYDLoL/GLH6J2KqCiKK07gVhKOSFtVVbciOpu54mKnx5588kne97738cMf/vAKSw/DMNi5cyePPvoodXV1HDlyhG9+85vs3bvXfc3Ro0f59Kc/zdGjRzl27Bjvete7OHbs2Lzbtre3I4oif/AHf8A//uM/uuQnF6dPn+aOO+6gq6sLgBe+8IVzvnYN8JzJYT6g6zpDQ0MEAgEOHDiwpC9nVlucr9B8qCn0EE6omFP0sjzooaLAy2fuaSKbjNPS0nLF6PRGQzwep62tjV27dlFSUjLrayRJorKyksrKymmVjgsXLhAKhVzB9FrqURwPn7UcyRcEgcLCQgoLC9m+fTvpdJrR0VE3CDLXgHIuOMGecxHRjQJnSGGu2A2v10tdXR11dXUuuR4YGKC9vX3NhcC5cCpwBQUFeYtIWAxEUaSsrIyysjIsyyKRSBAOhzl58qQ7Rl9eXn5VEb5TuZJlmR07dmxY4gO22HzmdNps5oq54cdO9WyuB7YTJ07wnve8h//5n/+Z9Xo9fvw4jY2Nrmni3XffzQMPPDCN/DzwwAPcd999CILADTfcwMTEBENDQ/T09My57Z49e676fr/5zW9yzz33LOlcbXRsaPLjsPKrwRE2l5SULLnPbVkWF8P58ffZUebh3r0+/vWEit8jU13kw+/1okgiE5FR+vv7OXTo0IaehHJGwJuamhbcehBFkdLSUkpLS6dNjnV3d6/ZaLjj4bN37951NY7q9/vZsmULW7ZscW/GTjzIbJUOh8CtZbBnPqBpGqdOnaKurm5BQwq55DpXJ3Tx4sUF5WmtFAzDoLW1lbKysnVRgRMEgYKCAgoKCti2bRuZTIZIJML58+dRVZXS0tJZq2cr5X+zFnActa/mR+S0WWtqaqZNj124cIFAIMDQ0BBNTU3U1tZy6tQp3vGOd/C9732PLVu2zLq/gYEB6uvr3X/X1dVx7Nixq75mYGBgQdvOh29/+9s88MAD0372pje9CUmSuPPOO/nQhz60oT/T+bChyc9CkCtsTqfTJJOLJzCWZWEYBmcGxpd9PKIAO6qLuOtF+wmVD/GNY32k0lkyqRSv36nQ35/a8OZsAwMDDA4Ocvjw4SW/D0EQKCoqoqioiMbGRndyrKWlBVEUV8XczSFw6z0eIfdmPFulIxQK0d/fz759+9YVgVssstksp06dcoXNi4UgCJSUlFBSUuK2MsLhMC0tLauqEzIMg5aWFiorK9etq7nP55uzelZYWOhWz86fP4/X6131ll2+sVQjxpnVs2QyyU9+8hM+/OEPo+s68Xic+++/f94ojNke4Gcew1yvWci2c+HYsWMEAgH279/v/uzrX/86tbW1xONx7rzzTr761a9y3333LWh/Gw2/0eSnr6+PgYEBrrnmGnw+H6qqYhiL8+ixLMuN0WjpHVvW8XgkAZ8i8Xs32E96v9W0iRu2lTGezBLtu4hPFvB4PDzzzDMUFhZSWVm5LsrzC4XTL08mkxw+fDivxz1b5lh7ezu6rq9I5pjj4bMcArcWmNlG7O/vp6urC0VRuHTp0pI1HWsNp3I1Xwt1MchtZeTms6107piu65w6dYra2toF22usNWZWz5wx+rNnz6Ioilt9XGtzzKXCaV/t379/WZ+1c029973v5bbbbuP3f//3uffee/nqV7/Khz70IW655RZe/epX84IXvGDauaqrq6Ovr8/9d39//xWZknO9RlXVq247F771rW9d0fJy9IwFBQXce++9HD9+/Dnysx4xF/M1TdMt1x45csRdhCVJWhT5MQwD0zQRBAFBEPjR+diSjrMsIFNT4sc0TN54fT37ay/reEKyRdelc9Ru2uQ+BTo3mNHRUTo7OwkGg672Zb1645imSVtbmzsaupJPgT6fj/r6eurr69E0bVoIZGlpKZWVlUtetHI9fPJN4FYb0WiUoaEhbrjhBrxe7xWaDqeNuN7bq4lEgtOnT6+oBm41dEJOy27z5s1UVVXl8ehXD472rK+vj/r6ejZt2kQ4HObMmTNu+PFqp6wvBz09PW7Yar6O98KFC7zpTW/ia1/7Gs3NzYCtt/vlL3/Jgw8+SDgc5o1vfKP7+iNHjtDR0UF3dze1tbV861vf4hvf+Ma0fd5+++3cf//93H333Rw7doyioiI2bdpERUXFVbedDaZp8p3vfIdf/vKX7s90XWdiYoLy8nI0TePBBx/k1ltvzcs5WY/Y0NNemqZhmuYVP2ttbaW4uPiKMd7JyUn6+vqmlflmg2VZmKY5jfgA7P3wzxd9jEGPyJfuOwQIlAQVaoouLzSJRIIzZ87Mq8NwhIiOWaDH41lXsQhw+ZxXVlZO6z+vNmaa4C12cizX+2YlPHxWE4ODgwwODnLgwIFZqzzpdNqdiHIWrfXouzQ5OcnZs2dpampak+yjmbljS9UJOWLzhoaGJbXs1gvmE2k7DyK5MRLzCYHXGpcuXWJiYoKmpqa8HV93dzf33HPPtFH0heDo0aO8+93vxjAM3vzmN/PBD36Qz33ucwC87W1vw7Is/viP/5iHH36YQCDAF7/4RXcia7ZtAb7//e/zjne8g3A4THFxMQcPHuSRRx4B4LHHHuP9738/Tz31lHsMyWSSW265BU3TMAyDW2+9lU9+8pNr+QD43Kj7XJhJfpLJJC0tLWzbtm3WaZZkMklHRwcHDx6cc58O8TEMA1EUpy0ECyU/PllAFMEjSfx/L9nGGw5fORrtpE4vRhDsvAdn0RJFcc2f3tPptDuSv55u6rN55FRWVlJWVjZr9cwwDM6cOUMoFNrQ3jdgP806uVALuXE5i5bju7Tc6lm+4AStHjhwYF0Ed+bqhBbjJ5TJZNxpwY0sNl/MdJrz/XNI43LG6FcCvb29jI+P55X49Pb2ctddd/H5z3+eI0eO5GWf/8vxHPmZC7quu22saDTKuXPnaGpqmrM0nslkaGtr45prrpn1946w2bKsaRUfB1cjP797pJo/fcVuOkaTDExm2F4eYHPplULZ3t5eRkdHaW5uXlb1JpPJMDo66j69O3351RLnOk/l630k3/HIcapnMyfHNE2jpaWFTZs2rYmHT77gmLM58QhLuakbhsHY2BjhcJjJyck1056Fw2G6uro4ePDgutWSLMRPKN9apbWCaZqur9LWrVsXte1spNEhQmthQtnX10c0Gp03NHaxGBgY4A1veAOf+cxnuPnmm/Oyz+fwHPmZEw756e3tZWho6Ko3Sk3TOHnyJNddd90Vv7sa8XEwGwHaHYTv/H8vRBLn/6xM0+TChQvour7kxWkuqKpKOBxmdHQUVVXdNsZK9d5HR0fp7u6mubl5XTyVLwa51TPLsshkMmzfvn1DE59ct+N8ea3kas9yWz4VFRUr+vQ+NDTkOrCvhyrBQjBb7pijjVlvNgmLhWmatLa2UlJSMue49mLgZNmFw+E5rRlWCv39/YTDYQ4cOJC3++/w8DCvf/3r+eQnP8kLX/jCvOzzOQDPkZ+5oaoqbW1taJq2oGBG0zR56qmnuOmmm6b9fKaw+Wr47Gd/zv0R+OgBuOOOFy3oWJ2IB8eRdiW/5LnxEalUKu9tjN7eXsLhMM3NzRtmcZoN8Xic1tZWKioqSCQSKzY5ttJwPGNKSkoW/VS+UOQ+vYfDYdcEL992A319fe61tV7F/VeDZVkMDQ1x4cIFFEWZFkuyXnR6C4VDfEpLS1fEj2i2SqMjLs/35z8wMOBW3PNVxRwdHeXOO+/kYx/72G+0OHiN8Bz5mQtOcvBiyMQTTzzhkp+5hM35hqOL2bJly6o76zo3l9HR0SWJgHPhhBZqmpb3ytVqw/HwaW5udtuE61X7Mh+cCaLa2toFj7jmA47dQDgcRtO0ZVcanSm7WCyWVx3GWiAej3PmzBlXz7cUndB6gEOqy8vLV2WQIdfYNBqNoiiKS7CXq2kcHBxkeHiYAwcO5I34RCIR7rzzTj784Q/zyle+Mi/7fA7T8Bz5mQu5mp+FwiE/q0V8HF3Meih9zxQBO09ZZWVlV70hOFlKjgPseiUDC4HTVjlw4MCcT+KzTY45T6TrZWF2hLRrLTbXNM3NZ0smk64b8ELDMh2tkqZp7NmzZ92c36VgcnKS9vb2aaQ6Fxsld2w9GDHOnEh0ztX/396Zx0VV7///OezKvoOgoiKGyOKWWqamuaEsZi5ZLlles1Lr2i29lnlb1G67W9lNU8s0AREXxKWy0lJLAVEUUBYFkWGTHQZm5veHv3O+7LIMzKDn+Xjcx7XhnDPnnJk553U+n/f79TI3N2/Wubp165ZYFqEp4ZOfn8+TTz7JypUrCQwM1Mg2JeogiZ+GUCqVogFhU/njjz8YNmxYk+p7Wsvt27dJS0vTybqY6vUcubm5jXoJVVRUcPHixXYfXdA0arWatLQ0scujqcPqze0caw9KSkqIi4vjoYcewsrKSiv7UB8qlUocaaw+jdGQwG7PRPO2Jj8/n4SEhCZ3p9VXJ6QLuWOC8HF0dNSZOjhBYGdnZ1NcXNzkh5HMzExu3bqlUeFTUFDA1KlTWbZsGVOnTtXINiXqRRI/DaFSqaisrGzWOqdPn2bIkCFtKnzUajUpKSkUFBQ06yarLRrzElIoFFy6dAkPDw9sbGy0vastRvDwUSqVrRpdqN45lpubqxXfJW173zQVYRpDOFcmJiaiwDYyMhJbp01NTTv8aGJubi7Xrl3D19e3RVM0mvITai1KpZKYmBicnZ119kGneht9fn5+g+fq9u3bYk6ipoRPUVERTz31FC+//DIzZ87UyDYlGkQSPw3RXPEjXGzLyspwdHRsE38coevGwMAADw+PDjmEX1pailwu59atW5SXl4tuyrruBNwQbenhU70IWCaTiUKorUb6queN6dpo4r2ofq7g7tO8g4MD7u7uWt6z1iGE72oqk6+lfkKtpSMIn9o0dK5kMhnZ2dn4+flp7OGzpKSE6dOn89xzz923kQ86hiR+GqKp4qd2fU9FRQVyuRy5XI5arRb9cVp7M1EoFFy8eBFHR0etOh1rgszMTG7evMlDDz0kTo8plUrs7e3FbqiOgODh4+Tk1Oa1C9WLgNuic6wptUodAcFyonPnzigUCiorK7G1tcXBwaHZ9RzaJisrixs3brRpW3571Al1xMyx+lAoFFy/fp2srCyMjY1rnKvWPIiWlZUxY8YMnn76aZ5//vkaf4uKimLp0qUolUpeeOEFli9fXuPvarWapUuXEhkZSefOnWu4Pze0bkhICKtXr+bKlSucO3dOdHNOTU3F09OTPn36ADB06FDRCfr8+fPMmzePsrIy/P39+eKLLzrUb6keJPHTEE0RP/cqbBYuLHK5nKqqqhbf3IUaDHd3d+zs7Jp9LLqCMGUndN1UHy5WKBTk5OSQlZXVLl5CrUWb7tOa7hy7ceMGOTk5HboFHP4v5qF79+5ivlVVVZVYMF1cXIy1tTUODg5NLpjWFkKEiCZHF+5FW9QJCcLH1dW13btRNY1cLictLQ0/Pz/09PRqtNGbm5uL9WfN+bzKy8uZNWsWQUFBvPjiizV+v0qlEg8PD44fP46rqyuDBw9m9+7d9O3bV1wmMjKSDRs2EBkZydmzZ1m6dClnz55tdN0rV66gp6fHwoUL+fjjj2uIn8mTJ3Pp0qU6+/nwww/zxRdfMHToUPz9/VmyZElH70Jr05tKx72Kwj1vIk3p6KoeaFhZWSkmOzfn5i5MRfTr10+nazDuhUqlEotPfX196xyzkZERXbp0oUuXLqKXkJCILDy560rXitBurK0uO0NDQ5ydnXF2dhbtBqoHZQquyfe6uavVaq5fv05paal4Qe+olJeXExMTUyfLzsDAAEdHRxwdHVGpVOTn5yOXy0lISGjxDautSU9PRy6Xa7SepCnUTlgXal+uX7/eojqh+yFsVSA7O5vU1FT69+8vjsIJrfJCrV52djZpaWkYGBiI56qxEX+FQsGcOXOYOHFiHeEDcO7cOdzd3enZsycAM2fOJCIioob4iYiIYM6cOchkMoYOHcqdO3fIzMwkNTW1wXU9PT2bdeyZmZkUFhYybNgwAObMmcP+/fs7uvhpU3TnaqJhmurYXB1DQ8M6N/fk5GTKysrEm7uFhUWNbaWnp5OZmUn//v111oa/KVRWVhIXF4etrS3dunW75/kyMDDAyckJJyenOjf31ngJaQIhE8rHx0cnpueqGwJWL9ZMSkpqtHNM6ITS09PD29tbJ0RlS2lqd5qenh62trbY2trW8H1JTU3FyMhIPI/a/K0JHYOa9IxpCTKZDGtra6ytrWvUvsTGxjapTkgQPt27d8fBwaGd916zCHVX1YVPdYQ0egsLC3r16kVZWRk5OTlcuXJF9Kmys7OrcX2vrKxk/vz5jBw5kiVLltT7+8vIyKhR4uDq6srZs2fvuUxGRkaT1q0P4TgtLCx4//33eeyxx8jIyKgxrS+8h0TD3JfiR61Wiy3wLb351r655+bmcuPGDYqLi8UpDLlcTkVFBQMGDNDqRbC1CH4xbm5uLXr6q+/mLpfLSUxMxNzcXLy5t8c5EmqVdFWM6unpYWNjg42NTY3OMeHmLhRM6+vr1wiR7MjCRxiF69evH+bm5k1eTyaTYWlpiaWlJe7u7mIhflxcHGq1WvzOtafATUlJoaioSKO5UJpAJpNhZmaGmZkZPXr0EKfzExMT660TEuqu3NzcOrzwEUagm1N31alTJ7GRQ5h2vXnzJu+88w6dOnVi0qRJHDlyhIEDB/L66683+Purr2yk9rINLdOUdWvj7OzMjRs3sLW15fz58wQHB3P58uUWbetBp0OLn/q+ZG1hXFh9qFmlUpGdnc2lS5dQKpU4OjpSUFCg8/UJDVFYWMjly5fx9PTUiF9M7Zu70OqcnJxMp06dxJu7pqcwBA+fvLw8BgwYoFNTJA1R/WnU3d1dfHKPiYkRpxKdnZ079EXszp07XL16VSOjcJ07d8bNzQ03Nzcxy064uQujHLVHZjWFMP1YXl5Ov379dP63Xn06X3h4E0ZmzczMKCwsxN3dvcMLn9zcXK5fv07//v1b3ARQfdp19+7dnDhxgvXr15OUlIRSqeTbb79l0qRJ9T4Yurq6cvPmTfG/09PT63TKNbSMQqG457q1MTY2Fh/qBg4cSK9evUhMTMTV1ZX09PRmbetBR/fvEPdAUNDt5disUChIS0vD3d0dR0dH8vPzycrKIiEhQUzAtrW11fmLIyDWCvj6+rZJEnztJ/eSkhKysrI4f/68OIXh4ODQ6s4lIXajqqqqQ9fFmJqaYmBggFwup3fv3uK0lybiI7SB4H3j5+encZsEIyMjXFxccHFxqfHkXlRUpPFpV+H7pVQq8fLy6jDnX6D6w1tFRQXnz5/H1NSUlJQUbt++3WFzx/Ly8rh27VqrhE9t9PX1OXz4MI888ggnT54kKSmJAwcOMH36dFQqFW+99Rbjx48Xlx88eDBJSUmkpKTg4uLCnj17+OGHH2psMzAwkI0bNzJz5kzOnj2LpaUlzs7O2Nvb33Pd2mRnZ4vF7cnJySQlJdGzZ09sbGwwNzfnzJkzDBkyhJ07d7J48WKNnJP7lQ7d7QV3xYggetpa+AijJA899BDW1tY1/iYUHwoJ2GZmZjg6OrbbdE9zuXnzJllZWfj4+GjloidMYVT3x3FwcGj2TVKpVHL58mU6d+5Mr169OtyNqTqlpaVcvHixjqFkR8wcy8rKErtu2vP7VZ8bt3Bzb8looFqt5urVq+jp6XV4B2qFQkF0dDS9evXCzs6uQY8cBweHNnkY0iR5eXkkJSXh5+enseltlUrFP//5T0xNTfnkk0/qCOecnBzKy8vrWGZERkby6quvolQqmT9/PitXrhTbz1988UXUajWvvPIKUVFRdO7cmW+//Vbs3qpvXYDw8HAWL15MdnY2VlZW+Pn5cfToUcLCwli1ahUGBgbo6+vzn//8h4CAAAD+/vtvsdV94sSJbNiwoUN/X5Fa3RunoqKCqqqqNo+qEKZuGsrsqU5tZ9tOnTrh6OjY4guwJhFylMrLy/Hy8tIJYSb44zTXS6g9PXzaGqEuxsvLCwsLiwaXqx1U25zOsfYiIyODzMxMfH1928z7pinUdi43NDQUp12bcsMUDEtNTEw6vLCuqKggJiYGd3f3Gp12tZfpCLljQoyIJuv6VCoVy5cvR61Ws2HDBp35LT3gSOKnITIyMnj33XcJCgpi+PDhbSIsqteSeHt7N/tiXv0CnJ2djbGxsegu3d43ho4wSiJ4CcnlcsrLy8Un0drmd0KRdo8ePTp83UL17rTmPHFX7xwTRhtbM8qhCYTfio+Pj04I6+oIQZmCuWl1E8raCG7wgit4R0YQPr17925yRI2u5o4JNWSanEpVqVS88847FBYWsmXLFkn46A6S+GmIyspKTpw4QVhYmDjXGRwczGOPPaaRoXbB90Ymk/HQQw9p5EdRUlIiCiEDAwNxuqetpwYE9+mONEoi1HJkZWWJaeGOjo7o6elptEhbmwjTQ76+vq16itV25lh1P6KOUBAsiOzs7GzRykIY5VCpVMTFxWFtbU337t21vautQvBWak02n67kjrWF8FGr1bz//vvcunWLbdu26Zxgf8CRxE9TqKqq4tdffyUkJIRTp04xYMAAgoKCGD16dItuKpWVlVy8eBE7O7sm+d60hLKyMjFmozV1L/fifnCfVqlUYlFrfn4+9vb2uLi4aM1LSBOkp6eLdVeaHgWsXVMlTCW2RR6YEBqrUqnw9PTUyRHFxqg+ylFQUEBVVRUODg707t27Q98MBeHTp0+fOjWKLUVbdUIFBQVcuXJF48Lnv//9L9euXWPHjh1aL0mQqIMkfpqLUqnk1KlThIWF8csvv9CvXz+CgoJ44oknmvQDFQpPe/bs2W5TKtXrXlQqFfb29jg6Orb6ZnXnzh2uXLnSbI8VXeT27dvcuHEDHx8f8eaen5/f7l5CrUWIECkqKqJfv35tvs/VI1w03Tkm1MUYGxvj7u7e4YRPdaqqqoiOjsbKygqVSkVeXh6mpqbiKIc265eaiyB87mUq2Vrao06osLCQ+Ph4jYb5qtVqvvjiC6Kjo/nhhx861Gf7ACGJn9agUqk4c+YMYWFhHD9+nN69ezNlyhTGjRtXbxRFbm4uiYmJWhULCoVCHBGqHpDZ3OgMYUrFx8enwyayC6SlpZGbm1sn26q+4nIHBwedvVlVHyXR1FRqcxA6x7Kzs1sdS6JUKomLi8PS0pIePXq00R63D/XFPAijHELBtNAybm9vr9O/p7KyMmJjY9tc+NSmLeqE2kr4fPnll5w6dYq9e/d2uBb/BwhJ/GgKlUrFhQsXCA0NJSoqiu7duxMUFMTEiROxtLRk48aNnD59mm+//VZnLm5C3lj1AmBHR8dGn9qrF2l39CBMwWOlsrKSvn37NioWqt+ssrOzxe6e9qipagoqlYrLly/TqVMnnSg4b03nWFVVFbGxsTg4ONSw6O+ICGGrPXr0aDQAVyiYzs7ORqlU1iiY1vZnKSAIH09PT61k2glook5I6IDUpA+ZWq1m69atHD16lH379umkC7yEiCR+2gKhmyMkJITDhw+jUqkwNjZm+/btOlvkKOSNyeVy8and0dGxhqutSqUiISEBtVqtlZEFTSJ8Ri3tTqvPS8je3r5N6l7uRVVVVY0aMl1DpVJRUFAg+lSZmpqKI2i1xbMwSuLq6oqzs7OW9lgzNKUFvD7q816yt7fHyspKa0JImK7XtvCpTUvqhNpC+ABs376diIgI9u/fr5XrgESzkMRPW1JaWsrcuXOxtLTE1dWVI0eOYGlpSVBQEJMnT8bOzk5nnuqqIwwxy+VyioqKsLa2xs7Ojhs3bmBtbY2bm5tO7ndTEQrONTWyUFFRIU4lNsdLSBMoFApiY2M7jFior3NMOF9qtZqYmBh69uzZ6ChJR0AYJWltQbAwgiYUTAtO7+3ZFl5aWkpsbOw9faJ0gXvVCRUXFxMXF6fxYOJdu3axZ88eDh48qPMmjhKAJH7ajtu3b/PUU08xf/585s+fD9y98F+7do3Q0FAOHjyIiYkJAQEBBAUF4ejoqJOCQqVScfv2bRITE9HT0xOnxjpqJ1Rbe/jUN5VYn5eQJhBusB25004YQRMsB5ydnXFzc+vQT85tNUqiVqtrjKAJ0z1t6etVUlLCxYsXO2RTQ+06oc6dO1NYWIivr69GRVxISAjbtm3j8OHDza6dlNAakvhpK+bNm8e8efMYNWpUvX8XamfCwsIIDw9HT0+PyZMnExwcjIuLi84IIWGI+KGHHsLS0rKGvX9HyxsTnvray8NHqVSK0xfFxcVidIQmpi+EY+nbt69OTUO0BOFYevfuTXl5eZt0jrUX7SUWqk/3ZGdno6+vLwohTQnHjix8alNcXExMTAxWVlYUFxdrzE9o//79bN68mcOHD9f5HUZFRbF06VKUSiUvvPACy5cvr/F3tVrN0qVLiYyMpHPnzmzfvp0BAwY0um5ISAirV6/mypUrnDt3ToyyOH78OMuXL0ehUGBkZMRHH33E6NGjARg1ahSZmZni9+LYsWMd3rxVA0jip60QIjGauuytW7cICwtj3759VFRUiCNC2pxiys3NJSkpCW9v7zpDxNWfQnNzczEzMxPrOHSxJVxwOq7vWNoDob1ZLpdTUFDQqugIwZBNW8eiSQoKCoiPj8fb27vGU7MmO8faC+FBofaxtAeCnUV2drZGhKMgSLVxLJpGEHHCsWjKT+jw4cN89tlnHD58uM7UplKpxMPDg+PHj+Pq6srgwYPZvXs3ffv2FZeJjIxkw4YNREZGcvbsWZYuXcrZs2cbXffKlSvo6emxcOFCPv74Y1H8REdH4+joSJcuXbh06RLjx48nIyMDuCt+qi8rAbSx+Om4bUAaoDkXHJlMhouLC0uWLGHx4sVkZWURHh7Oq6++SkFBAZMmTSIoKIjevXu324U/IyODW7duMWDAgHqfjGQyGVZWVlhZWdWo40hJSRFbwu3t7XWiG0zw8NFkXk9zEaYMhdDH/Px85HI5SUlJzfISys7OJjk5uU3SzNsbQZD6+fnVGa0wNDTE2dkZZ2dnse4lIyODK1eu6GTmmGCUp+lakqZiYmJC165d6dq1K5WVleTm5pKSkiK6lwsF0005X/eT8BGmIPv16ycei0wmw8zMDDMzM3r06CHWCSUkJDTZT+jYsWN8/PHHREZG1lvTde7cOdzd3cX4kpkzZxIREVFD/ERERDBnzhxkMhlDhw7lzp07ZGZmkpqa2uC6np6e9e5P//79xX97eXlRXl5ORUWF1HGmJbR/1+uAyGQynJycWLRoEYsWLSInJ4f9+/ezYsUKsrOzmTBhAsHBwW3mditECZSUlDBgwIAmjeLIZDIsLCywsLCgV69elJSUkJWVxfnz59s9CqE2gofPgAEDdEKIwd3zZWNjg42NTQ0voeTk5Ea9hG7dukVGRgYDBgzQSZ+h5iCIuKYI0upTOkKbsyAcG+scay+EMExN+sW0BkNDQ5ycnHBychJHHLOyskhISLin0BZGr7Ql4jRJWVlZk6btjI2NcXV1xdXVVawTqi607ezs6Ny5syiefvnlFz744AMOHz7cYBdfRkZGjWYKV1dXzp49e89lMjIymrRuY4SFhdX5XT333HPo6+szdepUVq5cKYrg5sxQSDQd3bjTdHDs7Ox44YUXeOGFF8jPz+fAgQO899573Lhxg3HjxhEcHIy3t7dGnoAFrxhjY2N8fHxa9KOo/lTVq1cvSktLycrKIiYmRjRyc3BwaPMnEiFhXqFQ4OfnpzMjBLWRyWRYWlpiaWmJu7u76CV04cKFGknhmZmZ5OfnN1mQ6jKZmZmkp6e3SMTJZDKsra2xtrauEeyblpZWo3OsvYR2bm4u165d09mRuNojjtWFtomJiSgqjYyM7jvhExsbS9++fZtVr1T9GiUI7fj4eF566SW6dOnCgAED+Pnnnzl69GijdTP1lXzUvp42tExT1m2Iy5cv8+abb3Ls2DHxtV27duHi4kJRURFTp07l+++/Z86cOZSVldGpUydUKpXOXh87KpL40TDW1tbMnTuXuXPnUlhYyKFDh/jkk09ISkpizJgxBAcHM2DAgBZ9kTXd/i3QuXNnevToQY8ePcS8sbi4ONRqtXiR0fTTsuDh06lTJ7y8vDrMk0114dizZ0/KysrIysri7NmzqNVqunfvjkKh0InRhZZy8+ZNsrOz6d+/f6tHamQyGebm5pibm4tCWy6XExsb2+aZY3B39ColJYX+/fvrhNHlvagutHv37i3WvcTGxqJUKlEoFPTr1+++ET6enp6t6uoShPajjz5KdHQ0P/74I+vXr8fExISZM2cyefJksRyhNq6urty8eVP87/T0dLp06dKkZRQKxT3XrY/09HSmTJnCzp076dWrl/i6i4sLAKampsyaNYtz585hYGBAaGgo33zzDTY2NpIA0jAPdMFze1JaWkpkZCShoaFcvnyZUaNGERwczMMPP9ykUQJheLg9/VXq88ZxdHRstUdGW4k4baBSqbhy5QoGBgZ0795d7BxrTSyJNklJSaGwsFBjI5WN0ZaZY3A33uXGjRv4+fl1+CnIgoICLl++jJOTEwUFBSgUCrHAvC0sGtoSIXdM0zYDf//9N0uWLOHAgQN069aN27dvc+jQISIiIrh16xYRERG4urqKy1dVVeHh4cFPP/2Ei4sLgwcP5ocffsDLy0tc5vDhw2zcuFEseF6yZAnnzp1r0rq1i5jv3LnDyJEjWbVqFVOnTq2xH3fu3MHOzo7KykqefvppqqqqsLa2JiYmBl9fX9asWUOXLl0eNAEkdXvdb5SXl3Ps2DFCQkKIjo5m+PDhBAcH88gjj9T7pC2Ek2rTwEyhUIg3KoVCIXoJNdfav609fNoTIdvKysqK7t271zgP7eklpAkEf6uKiop7xoi0BbXdy1trOZCZmUlGRgZ+fn46U0fWUoRC7er1SlVVVaLJaXFxMdbW1tjb2+u8t1dbBa7GxMSwaNEiwsPDxSLk6pSUlNCpU6c65yYyMpJXX30VpVLJ/PnzWblyJV999RUAL774Imq1mldeeYWoqCg6d+7Mt99+K4qZ+tYFCA8PZ/HixWRnZ2NlZYWfnx9Hjx7l/fffZ+3atTVGoY4dO4apqSkjRoygsrISpVKJq6sr+fn5/PLLL5w5c4bffvuNxMREPvvsM5ycnB6kGiBJ/NzPKBQKfvrpJ0JCQjh79izDhg0jKCiIESNGYGhoyPfff8/x48f56quvdGYqpba1f1Nv7MXFxaIfUXsGLrYFlZWVxMbG4uzsLA5ZN0RbeglpArVaLbbn9unTR+v7VF/mmL29fZO9qtLT05HL5fj6+nb42ivBMqGxQm2VSkV+fj7Z2dnk5+eLlha2trY6JfwqKiqIjo5utaN2bS5dusQLL7xAaGgoHh4eGttue1FbzGzfvp2EhATWrl2LQqEgPj6eV155hV69erF27domTa/dJ0ji50GhsrKSX3/9ldDQUE6dOoWDgwN5eXmEhobq7Be+9o29Ia8XodumejtrR0UYvWrJFKQmvYQ0gVB7ZWpqSs+ePbUufGpTvXPsXpljADdu3CA3NxcfH5/7Rvg0p1C7oWgSe3t7rbZUCxlqHh4eGhU+V65c4bnnnmPPnj01WtQ7CtWFT2VlJYaGhly+fJkZM2bw7rvv8uSTTwKwbNkyioqK8Pb2ZvHixQ/K6I8kfh40lEolr732GomJifTq1Ytff/0VHx8fgoKCeOKJJ3RmBKg2tZ/Yra2tcXBwQKFQcOPGDXx9fTu8p0VJSQlxcXEaeXqtfWNvbxNKpVLJxYsXsbW11cmw1dpU7xzLycmpc2Nvz3qltkZ4WGhth1ppaalorKhWq8Xz1Z4F0wqFgujoaHr37o2NjY3GtpuYmMicOXPYtWsX3t7eGttue1FdwGzatImTJ08yadIkJkyYwN9//82nn37K6NGjxd/p+PHjuXbtGv/973+1vOfthiR+HiRKSkp49tln6d+/P2+//TYymQylUsmZM2cICwvjxIkTeHh4MGXKFMaNG6ezXR/CUPz169cpLi7GwcEBJycnnTK9ay6C03FbRAlUf2LPycnBxMREbKFvi2JdYdquS5cuOjuqeC+EzrHs7GzKy8sxNDS8Lxy1BWNJTRt+CnV7wvkSRmktLCzabBRBED7u7u4N+u20hJSUFJ5++ml27NhRwzywIyCIHqF4OTQ0lC1btjB9+nSioqIYNmwYY8eOBeCrr77CwsKCDz/8kLfffpvc3Fw2b96s5SNoNyTx8yAxd+5cxowZw5w5c+r9u0ql4sKFC4SEhHD06FHc3NwICgpi4sSJOpXmLHj4CAW0hYWFZGVlkZ+f3yy3ZF1BiBFpL5O86iMcBgYGYku4Jm6GCoWCmJgYunfvjqOjowb2VntU/55ZW1t36MwxuCt8kpKS8PPza9NRUsEoUC6XU1RUhJWVFfb29hp9OBG+Z7169dKo8Llx4wYzZszgm2++YfDgwRrbbnuRmJgo1iadPXuWBQsW8OWXX/Loo49y5swZvvvuO7p06cIzzzyDm5sbVVVVbNq0icjISI4eParlvW9XJPFTH20RSJeXl8eMGTNITU3Fzc2NvXv3anR+uikI875NQaVSERcXR0hICJGRkTg7OxMUFMSkSZPafb9r79fly5cxMTHB3d29xs1HMHHLysoiNzdXJ9x/74UQveHn56cVrxjBe0mYumiN95LQbdO7d2+N3pC0gVqt5urVq8hkshqF2pruHGsvBDPG9vYkUqlU3Llzh+zs7CbVVTWFyspKoqOj6dmzJ3Z2dhrb14yMDKZNm8bmzZt55JFHNLbd9kKtVvPUU0/x+eef07VrV5KSkliwYAGdOnUiPDwcExMToqOj+eyzz/Dz82PJkiWoVCpOnDiBv7+/tne/vZHET23aKpDujTfewMbGhuXLl7Nu3Try8/P58MMPtXikTUetVhMfH09oaKgY4hcYGMjkyZPbzRcI/s/Dx97e/p51JEINR1ZWVrtM9bSEmzdvip1DuiDOqnvjVPcSaorlgBAe6enp2eG77YTvu5GRUR2BXR2VSkVubi7Z2dligXlzOsfai5ycHDEPTptmjNXrqnJzc8VRR3t7+ybXHgnCp0ePHhq99ty+fZunnnqKzz77jJEjR2psu9rgwoULvPvuu+zfv5+MjAzee+89qqqq+PzzzzEzMyM+Ph5HR8cO/4DSSiTxU5s///yT1atXi0OAa9euBWDFihXiMgsXLmTUqFE8/fTTAPTp04eTJ0+Smpra4LrCMs7OzmRmZjJq1CgSEhLa89A0gjAVEBoaysGDB+nUqROBgYEEBgbi6OjYZk+/QheUm5tbi6ZTak/1CCMc2rgZqNVqkpOTKSkpoV+/fjp1oxSobTnQWA2HEIvQFvVK7Y3QoSaEXjb1+9zczrH2QnCh1rbwqY+ysjKxTkilUmFnZycWTNd33isrK8UpVU36eMnlcp588kk++ugjxowZo7HttheFhYUYGRlhYmIiBtL27duX/v37s2vXLtLT0/nwww+5ffs2W7duFUsYHpCuroaQUt1r01aBdFlZWTg7OwPg7OyMXC5vy8NoM2QyGR4eHvz73/9mxYoVpKamEhYWxuzZs9HX1ycgIIDg4GC6dOmisR+W4OHTmi6o6rER1WMQ9PT0xBGh9shmEqZTALy9vXX24lM7VT03N5ebN29SVFQkdtpZW1tTUFDA1atX74s8KJVKxcWLF7G2tqZ79+7NWrexzLHqGW3t2ZFYPX5DV0Y7q9OpUye6detGt27dUCgU5OTkcP36dVFsV09Wr6qqahPhk5OTw7Rp01izZk2HFD4A58+f54MPPuCZZ55h27ZtHD16lPj4eB5++GGmTp1KWFgYy5cv57PPPqO4uFgUP7p67bkf6JDiR1uBdB0RmUxGjx49eP3111m2bBkZGRmEhYWxYMECFAoFAQEBBAUF1XEobg5t4eHTuXNn3NzccHNzo7y8HLlczqVLl9o0bwz+r45KEGEd5btRPexR8BK6ffs2ly9fFqd6ddUioakolUpiY2Oxt7dvdSxKfZlj2dnZYqadUGDe2iiXxhCEl64Kn9oYGRmJ3YGCrYWQrG5ubk5RURFubm4aFT75+flMmzaNVatWMWHCBI1tt715/PHH2bRpEwsWLODgwYPi9+rcuXOMGDGCkSNH8uuvv/Lhhx+ir6//oI/4tAu6N5bfBFoTSNfYuo6OjmRmZgJ37fE7evxCbWQyGa6urixdupRffvmFffv2YWlpyZIlSxg9ejQff/wxSUlJ9QrEhsjKyhK7U9rKvNDExIRu3boxaNAg0bX3ypUrnDt3jpSUFEpKSjTyPlVVVURHR2NjY0OvXr067MVHSAm3tbXFyMgILy8vioqKOHv2LHFxcWRlZVFVVaXt3WwWwqiCk5NTm+TBde7cme7duzNo0CB8fHwwMDAgISGBs2fPcv36dYqKipr1u7gXWVlZpKWlddjcMX19fezt7fHy8mLQoEEUFxdjYmJCWloasbGxZGZmUllZ2ar3KCgoYNq0abz55psEBATU+XtUVBR9+vTB3d2ddevW1fm7Wq1myZIluLu74+Pjw4ULF+65bkhICF5eXujp6fH333/X2N7atWtxd3enT58+Nbquzp8/j7e3N+7u7ixZsqTB70lQUBBz585l4cKFJCcni6//9ttvWFlZkZiYKHa/dtRrT0eiQ9b8tFUg3b/+9S9sbW3Fgue8vLwHxlAqOzub/fv3s2/fPrKzs/H39ycwMBBPT88Gf4g3btwgOzsbHx8frVzAhfysrKwsKioqxKf1lrQ3V1RUEBsbe1+0f8Pdad/MzEx8fX3Fz6a9vYQ0hVBH0rVrV5ycnNr1vduic+x+ClxVKpXExMTQpUsXnJ2dUavVlJSUiN8xQSQ5ODg0a8q6qKiIp556ildeeYUZM2bU+75t0fQixLwsXLiwRihpfHw8Tz/9NOfOnePWrVs88cQTolh5+OGH+eKLLxg6dCj+/v4sWbKEiRMniqM3iYmJlJWV4ePjg0wmY/Xq1WzZsoUrV64QHx9PdHQ0L7/8MvDA1/jURqr5qY2BgQEbN25k/PjxYqicl5dXjUA6f39/IiMjcXd3FwPpGlsXYPny5UyfPp2tW7fSrVs3QkJCtHaM7Y29vT0LFixgwYIF5OXlceDAAd59911u3rzJuHHjmDJlilj4q1QqWb58Of7+/owcOVJrxcCGhobiMLxwk0pJSaG0tLRZBm5lZWXExsbi4eGhUQdabZGWlkZeXh79+/ev4aMkk8mwsLDAwsICd3d38SYVHR1dY9pMl1y4Ba8YTU+nNBUDAwOcnJxwcnISpxMzMzO5evUqFhYWYjRJU/2qbt++TXp6Ov3799eJ7sHWUFv4wN3vWPXavfLycrKzs8Xp16Z0J5aUlDBz5kz+8Y9/1Ct84O50kbu7uxhiOnPmTCIiImqIn4iICObMmYNMJmPo0KHcuXOHzMxMUlNTG1zX09Oz3veLiIhg5syZGBsb06NHD9zd3Tl37hxubm4UFhYybNgwAObMmcP+/fuZMGECMpmMX375hZdffpmHH36YpKQkNmzYwOrVq9HX12fQoEGYmpry2Wefie8jCZ/2o8P++vz9/ev4Hrz44oviv2UyGZs2bWryugC2trb89NNPmt3RDoiNjQ3z5s1j3rx5FBQUcOjQIT766COuXbvG448/zsWLF+natSsjRozQmS6o6jep2sW/jT2tC11QXl5eOmUS2RKqd6j5+vre87MxNTWlR48e9OjRQ+zqac+al3sh5EH16tVLo14xLUWYTrSzs6vROXbt2rUmdY7dT0nzQv2VUHDfECYmJnTt2pWuXbuK3YnCd7S+32VZWRkzZ87k2WefZfbs2Q1ut62aXhp7v6FDh9bZlqGhIa6urnVeF0Z8XnvtNQ4cOEBycjIzZsxg9erVrFixgrfffpsnn3ySTp060bNnT2nERwt07F+gRJtjaWnJM888wzPPPMOtW7fw9/fH1NSUCxcusHLlSoKDgxk8eLBOOTXXV/x769Ytrl69ipWVVY0uqISEhPuiC0qtVpOQkIBKpWpRh1r1rh7BS+jq1auiW7Kjo2OTvIQ0hWDGqKujcc3tHLt16xaZmZl1RuM6IoLwcXR0bFY0Su3uxPz8fDIzM5k2bRp2dnb4+/sTHh7OtGnTmD9/fqPbau+ml6ZuSxAxN27cwMPDg6+++oq8vDzefPNNLl26xJo1a8RYjupeRZLwaX8k8SPRJG7dusWTTz7JW2+9xVNPPUVZWRnHjh3j22+/ZcmSJTz22GMEBwczbNgwnXqqrf60LjjZZmVlER8ff990QalUKuLj4zE2Nq7hdNxSjI2NcXV1xdXVVXxar97e3NZ5UKWlpVy8eJGHHnqoQ5gx3qtzzNjYmPLycgYOHNjhhY9gNeDg4ICLi0uLt6Ovry/+Lo8ePcrx48f59NNPSUtLw9TUlE6dOjF58uQGTf5a0/SiUCjuuW5T38/V1ZX09HTgrvDJyMjAzMyM0aNHExoaytChQ9m4cSOPP/44Li4uTJ8+nZiYGK068EvcRTfmLCR0mtTUVCZNmsR///tfnnrqKeDuSEFQUBDfffcd58+fJyAggB9//JFHHnmEpUuX8vPPP7e620PT6OnpYWNjg4WFBSYmJvTr169GF5RcLkepVGp7N5uFUqkkLi4OU1NTevfurXFBIjyt+/r6MnjwYCwtLbl58yZnzpzh6tWr5OXloVKpNPZ+JSUlxMbG0rdv3w4hfOqjeueYvb09xcXFGBgY8Pfff7dJ51h7IQgfOzu7GlM9mtjud999R3BwMDdu3GDNmjVkZmYSFBTE6NGjOX78eJ11Bg8eTFJSEikpKSgUCvbs2UNgYGCNZQIDA9m5cydqtZozZ85gaWmJs7Nzk9atTWBgIHv27KGiooKUlBSSkpJ4+OGHcXZ2xtzcnDNnzgDwzTffkJqayttvv42fnx8Avr6+XLhwgbfffpsVK1bw7rvv4uPjo5mTJ9FiOmS3l0T7Ul5eLg7j3ovKykpOnjxJWFgYv//+O4MHDyYoKIhRo0ZpvZBWrVaTmppKQUEB3t7e4lO4kDcmWPp36tQJR0dHrTv/3ouqqipiY2NxcHBok/bvxlCpVOTn5yOXy7lz545Y/Nua2Aih/srb27vNbBPak/T0dDEaRV9fv8NmjsH/CR8bG5t7xtY0h6qqKp5//nn69+/PihUr6pyHW7duUVFRQY8ePeqsGxkZyauvvio2rqxcubJG04tareaVV14hKipKbHoRurfqWxcgPDycxYsXk52djZWVFX5+fmJb+wcffMC2bdswMDDg888/Z+LEiQD8/fffzJs3j7KyMh5++GEyMjIA+OWXX9DX1yc3N5c///yTw4cPM2bMGPEBUuKeSPEWHYWWhq3evHmTOXPmcPv2bfT09PjHP/7B0qVLAVi9ejX/+9//xIycNWvWdJiAu6qqKk6dOkVoaCgnT57Ex8eH4OBgxowZ0+5TTWq1msTERKqqqvD09GzwBl29fiMnJwcjIyMcHR11rh1caP92dXVttOC0PVCr1RQUFJCVlSXGRgi5RE0VjwUFBcTHx98X9VdwNxMuJycHHx+feqe6hFo0uVxOQUFBizrH2gvB+NPKyqrZrtqNoVQqefHFF3F3d2f16tU6LwCbgnCdWbduHaampnzyySf1PvRJBc5NQhI/HYHW+E5kZmaSmZnJgAEDKCoqYuDAgezfv5++ffuyevVqzMzMeP3117V4dK1HqVTy559/EhYWxokTJ3jooYcIDg5m3LhxbX6zayxl/l4I7eDZ2dli3lh7RyDURuiC6tmzZ7uG1jaFhryE7OzsGsytunPnDlevXsXX17fD11/BXf+rvLw8fHx8mjQKJohHYeTR1NQUe3t77OzstC64hRw1CwsL3NzcNLZdpVLJkiVLcHJyYs2aNfeVEBBCd7/66itKSkrYtGnTffG91gKSz09HoDW+E9XbRc3NzfH09CQjI6PGuh0dfX19hg8fzvDhw1GpVJw/f56QkBD++9//0rNnT4KCgpgwYYLG282VSqU4XN+Sp9ba7eByuZyLFy8ik8nEjrL2yBsT0HVPooa8hGJiYsQuvOoZbXl5eSQmJuLn59eu57GtSEtLIz8/v8nCB+6eMysrK6ysrGqMPEZHR2tVcLeV8FGpVCxbtgxra2s++OCD+0r4wN3Ps2/fvixatIiPP/6Ys2fPMmrUKG3vlkQtJPGjIVrjO1F92iI1NZXo6GiGDBkivrZx40Z27tzJoEGD+OSTTzp8p4Cenh6DBw9m8ODBrFu3josXLxISEsL69evp0qULQUFBTJo0qdUFrwqFgtjYWI1NDXXq1Inu3bvTvXt3KioqkMvlXL58GZVK1S6+OMXFxcTFxdG3b18sLS3b7H00SX1eQkJGW+fOnSksLGTAgAFarwfTBEI9WXOET21qd44Jgru9/ZfUajWXL1/G3Nxc48Jn+fLlGBkZ8fHHH+uMT1hzUCqV95yalMlkeHp63hfX6/uVjvfN01Fa4zshUFxczNSpU/n888/FEZBFixZx/fp1YmJicHZ2ZtmyZRrec+2ip6eHn58fH3zwAefPn+fDDz/k1q1bBAUFMWXKFLZv305OTk6zt1teXk50dDQ9evRok5oYY2NjunbtysCBA8UIiatXr3L27FmSk5MpLi7W6PsVFBQQFxeHt7d3hxE+tRG8hAYNGkSXLl3Iz8/HyMiImJiYDt0FBZCSkkJhYSHe3t4avaELglvItaudOVZYWKjxcyYIH0G4agqVSsWqVatQKBSsX7++QwufgoICtmzZwq5du8jNza33M1Cr1TWET0f9bt+vSCM/GqI1vhNwt4B16tSpPPPMMzz55JPiMtVzphYsWMDkyZPb6hC0jkwmw8vLCy8vL1atWkVSUhKhoaFMnz6dzp07ExQUREBAAI6Ojo0OlRcXF3Pp0qV284kxMjLCxcUFFxcX0Rfn2rVrlJeXiwaBLckbE6g+NXQ/1A5kZmZy69YthgwZgqGhYQ3n3/byEtIkgmOxEP/SVhgZGYn+S0LnWGpqqkY7x4R6FcF5WFOo1Wref/998vLy2Lp1a4cUPnB3+r60tJQnnniCOXPm8Omnn3L06FE2bNhQ46Gk+ujQokWL+OCDD3RymvpBRip41hCtCVtVq9XMnTsXGxsbPv/88xrbFWqCAD777DPOnj3Lnj172vPQtI5arSYlJYWwsDD279+PgYEBAQEBBAcH4+zsXONi//fff1NRUYGvr6/W26WrqqrIzc0lKyuL0tJSbGxscHR0bNZNPTs7m+TkZPz8/O6LqaGMjAxu374tjmLURogmkcvlFBUVYW1tLd7UdfGGKZg/9u3bV2v7p6nOMbVazZUrVzAyMqJXr14aE55qtZoPP/yQ69evs2PHDp22j2gMlUqFnp4eX3zxBVVVVSxbtoxBgwaxYsUKpk6dikKhwMjIiKqqKvEYn3/+efr168drr72m5b3vkEjdXh2FlvpOnDp1iscee6zGkLnQ0j579mxiYmKQyWS4ubmxZcsWrbc2axO1Wk16ejphYWGEh4dTVVVFQEAAgYGB/PXXX6xZs4ZDhw61yn22LWjopm5tbd3gTSYzM5P09PT7Iv0b7nZB5ebmNtj+XZuGvIR0oR1cyFErKyvDy8tLZ0aoWto5plaruXr1KgYGBs3uiLzX/nz++efExMTwww8/dMjvsSB6BKKiojh69Ci//vorr7zyCvPnzyc+Pp6ffvqJxYsXi8v94x//oG/fvrz66qta2Ov7Akn8SEjUh1qt5vbt2+zbt4/NmzdTVFTE7NmzmTFjhkafXDVN7Sd1S0tLHB0dsba2Fi+yN2/eJDs7Gx8fnw77pFyd6uaSLRkhqe+mfq8g0bZCrVZz/fp1Kioq6Nu3r85+z6p3juXm5jbYOSbkwunp6WnUJVytVrN582ZOnz7N3r17G7Q60GWqT1+98sorrFixgpKSEp577jnGjh3L6tWrARg7dixDhgzh/fffB2DFihUYGxuLf5doEZL4kZBojC+++IKjR4+yefNmjh07xr59+8jNzcXf35/AwEAeeughnb5BCaMb+fn5mJubA3enzKq7UHdUhBGS0tJSvLy8NDI1VJ8RpXBTb+sbrFqt5tq1a1RWVuLp6amz36v6EDrHsrOzxc4xe3t7bt68iUwmw8PDQ6PC55tvvuH48eOEhYV1yCnb6iM+mzdvZsOGDbi4uLBnzx7+/PNPfvjhB4qKiqioqMDT05P169cDdwVTSkoK7u7u2tz9+wFJ/EhI1IdarWblypWkpaXx7bff1rjx5eXlceDAAcLCwsjIyGDcuHFMmTJFYzfgtkAwYywqKkImk4lOyXZ2dh1SBKnVapKSkqisrGzTEZLqRpT1eQlpCuF4lEqlTgvqpqBQKJDL5aSkpKBUKnF1dcXBwQFzc3ONHNf27duJiIggIiKizufQUif8xtbNy8tjxowZpKam4ubmxt69e7G2tmbXrl189NFH4rYvXrzIhQsX8PPzY9SoUWRmZopNBMeOHcPBwaHOsUyYMAFvb29GjRrFsWPH+Ouvv8TaQ0HYjxgxAqg7RSbRKiTxIyFRHykpKWzZsoU1a9Y0esEpKCjg0KFDhIWFcf36dcaOHUtwcDB+fn46c6ESik319PTo06cPQA2n5E6dOonTPB2hbkKYSgE0kjTfVMrLy5HL5cjlco364gixBWq1ul2Pp62oLuTc3d3Fadji4mJsbGywt7dvtB6tMXbt2sWePXs4ePBgnfPeGif8xtZ94403sLGxYfny5axbt478/Hw+/PDDGu8dFxdHUFAQycnJAIwaNYqPP/5YzPuqj+LiYp5//nl+/PFH4K64Wbx4MTExMWzfvp3evXuLURWS8NE4bfojkz6pB4CoqCj69OmDu7s769atq/N3tVrNkiVLcHd3x8fHhwsXLoh/c3Nzw9vbGz8/vxoXiby8PMaOHUvv3r0ZO3Ys+fn57XIs1enRowfr1q275wXH0tKSZ555hn379nH69Gkefvhh1q9fz7Bhw1ixYgVnzpzRapq7kJ1kbGws3lgFp2R3d3eGDh0qGt5duHCB6OhoMjIyUCgUWtvnxhDapfX19dtdKJiYmIheQoL/UnVfnJZ4CWlLyLUVwtRdVVUVDz30EIaGhjg6OuLt7c2QIUOwtbXl9u3bnDlzhsuXL5Odnd3k38fevXvZtWsXERER9QrO6k74RkZGohN+dRpywm9s3YiICObOnQvA3Llz2b9/f5333r17N08//XSj+1/fcSYkJPDpp58Cd33JJk6ciI2NDcuWLSMrK0v8PkjCp2MhfVr3OUqlkpdffpkjR44QHx/P7t27iY+Pr7HMkSNHSEpKIikpia+//ppFixbV+Psvv/xCTEwMf//9t/jaunXrGDNmDElJSYwZM6ZeUaWLmJmZMX36dPbu3cu5c+d4/PHH2bZtG8OGDeP111/n999/p6qqqt32R6lUEhsbi6WlZaNF2mZmZvTs2ZMhQ4bQp08fMdj0/Pnz3Lx5k4qKinbb58YQIhFakqOmaQT/pf79+zNw4EBMTU1JSUnhzJkzJCYmcufOnXsKIaELStM1MdpCKNZuqGZJT08POzs7+vbty9ChQ3FxcSE/P5+//vqL2NhYMjMzqaysrHfb+/fvZ+vWrURERDRoM9GQy31Tlmls3aysLLEL1tnZGblcXue9f/zxxzri57nnnsPPz4/33nuvRnHz999/z7lz5zA0NGTXrl1s27aNtWvXUllZSUhICOPGjcPFxYXS0tJ6j1NC9+n4bSQSjdLazLGGiIiI4OTJk8DdJ61Ro0bVGWbWdTp16kRwcDDBwcFUVFRw4sQJ9uzZw7Jlyxg2bBjBwcEMHz68zaaZKisriY2NxdnZuVmt+Z07d8bNzQ03NzdxmicuLg5AnObRhhmiMIJlaWmp0UgETWBgYICTkxNOTk6i7UBGRgZXrlzByspKtB2o/vQuTEUaGBhotAtKmyQnJze5S+1emWPR0dE88cQTdO/eXfQwO3z4cKMO5K1xwm/Kug1x9uxZOnfuTL9+/cTXdu3ahYuLC0VFRUydOpVdu3YxZ84cZs6ciYGBAYcOHcLKyooXX3yR8PBwZs+eTXR0NA4ODixevJjhw4eTnp6uURdsifZDEj/3Oa3NHJPJZIwbNw6ZTMbChQv5xz/+ATTtSasjYWxszKRJk5g0aRKVlZX88ssvhIWF8cYbbzB48GCCg4MZNWqUxrqJFAoFMTExdO/evYaLd3MRpnm6desm5o3Fx8ejVCpFIWRqaqqRfW4MIUDWzs6uxndJFxGKoh0cHGp4CSUmJopeQtbW1iQmJmJoaKj1ESxN0RpfotqZY6WlpURGRjJr1iwqKiooKSkhNDT0njlWrXHCVygUDa7r6OgoPrBlZmbWKVzes2dPnVEf4YHD1NSUWbNmce7cOQoLC3FwcGD9+vWMGDGCkpIStmzZwrJly8Tp8YqKCgICAhg+fDiPPfZYM86ihC4hTXvd57Q2c+z06dNcuHCBI0eOsGnTJn777be22VEdwtDQkHHjxrFlyxZiY2OZN28ex48fZ/jw4SxYsIDDhw9TXl7e4u2Xl5dz4cIFevXq1SrhU5vqeWN+fn4YGRmRmJgo1rsUFxe3Sb5QVVUVMTExODg46LzwqY2enh62trZ4enrWmOb5/fffuXPnDmZmZlqtB9MUKSkplJSUaMyQsXPnzrz11lu89957WFhY8Nprr/Huu+8yePBg3n77bS5cuFDvd23w4MEkJSWRkpKCQqFgz549BAYG1lgmMDCQnTt3olarOXPmDJaWljg7Oze6bmBgIDt27ABgx44dBAUFidtTqVSEhIQwc+ZM8TUhHkStVqNUKjl06BCmpqZ4e3vz3nvvsWjRIgYPHsy2bdv466+/eP311/nrr7/Q19cnJiaGgICADjPVL1E/0sjPfU5rM8eE/3dwcGDKlCmcO3eOESNG3PNJ637BwMCAUaNGMWrUKJRKJX/++SehoaG8++67eHp6EhwczNixY5s8ulJSUkJcXFyb545VzxurqqoiOztbjGKwtbXF0dFRI23NQu2Rq6trh3cel8lkWFpakp6eTteuXXF0dEQul3P+/Pl29RLSNKmpqRQVFdGvXz+NjmD99ttvvPPOOxw+fBgnJydeffVVioqKiIqK4pNPPmHMmDHMnz+/xjoGBgZs3LiR8ePHi074Xl5eNZzw/f39iYyMxN3dXXTCb2xdgOXLlzN9+nS2bt1Kt27dCAkJqbGfrq6uNbLKKioqGD9+PJWVlSiVSpycnMjMzGTkyJGUlpZy584dVq1ahb29Pd7e3gwcOJDBgwcDMGzYMB555BGNnUcJ7SC1ut/ntCZzrKSkBJVKhbm5OSUlJYwdO5ZVq1YxYcIE/vWvf2Frayu2lubl5fHf//5Xi0favqhUKv7++29CQkI4duwYvXr1IjAwkIkTJ4pGhbUpKiri0qVL9OvXr8Fl2hqlUklOTk6NtmZHR0csLS2bfWMUpu7c3NzuC/Er+CyZmprWCfUsKSkhOzsbuVyOvr6+OKWoaS8hTZOWlkZBQYHGQ1f//PNPXn/9dZ2Mkmku//73v4mNjWXz5s10794dQIwhevTRR8nNzeXQoUNATcdniTZH8vmRaB0tzRxLTk5mypQpwF0RNWvWLFauXAlAbm4u06dP58aNG+KT1oOaWqxSqYiNjSUkJISoqChcXFwIDAxk0qRJ4ujOiRMnOH78OKtWrWqXGpymoFKpxLyxwsLCZoWIVlRUEBMTQ69evbCzs2unPW47hC41c3PzexawCkXmQgt4e9ZWNYcbN26Qn5/f4kiRhvjrr79YunQpBw4coFu3bhrbbntR24/n4MGDBAUF8cUXX9TI5tq+fTv5+fksWbIEfX19ycen/ZHEj4RER0GtVnP58mVCQ0M5dOgQtra2eHl5cfjwYUJDQ+nVq5e2d7FeaoeIWlpaiiGitS/45eXlxMTE4OHhcV8IXkH4WFhYNLtLTaFQiCNCFRUVohAyMzPTapH0zZs3xRBZTd6wo6Ojeemll9i/f3+H7HKqLmB+/fVXunbtSs+ePTl16hTjxo3jm2++YdasWXXWk0Z8tIIkfiQkOiJqtZqNGzfy8ccf4+rqiomJCUFBQQQEBODg4KCzHURqtZo7d+4gl8vJy8vD3NwcBwcHbG1tUSgUxMbGtnnNUnshtOdbWVmJUx4tRSiilcvllJSUYGtri4ODQ4umFFvDzZs3ycnJwdfXV6PCJy4ujgULFhAaGoqHh4fGtqsN3nrrLS5cuICrqyuVlZV8++23/PLLL0ybNo3//Oc/vPzyy9reRQlJ/EhIdEy2b9/Ozp07CQ8Px8LCguTkZMLCwti/fz9GRkYEBAQQFBQkWgroImq1msLCQnGap6KiAjc3N7p27drh0+ZVKhUXL17ExsZG49M3SqVSjIwoLCxs0EtI06Snp5Odna1x4RMfH8/8+fP58ccf8fT01Nh2tcHPP//M559/zoEDB5gyZQpOTk58+eWXwN3i6DfffJPff/8dfX19nf1dPiBI4kdCoqOxceNGoqKi2Lt3bx2bf7VaTXp6OqGhoYSHh6NUKgkICCA4OJiuXbvq5AW3uLiYixcv0rNnT4qLi8nJycHExETsgOoIeWPVEeq0bG1t27xuRaVSiSNp+fn5NUbSNDmVkpGRQVZWFr6+vhrdbmJiInPmzGHXrl14e3trbLva4vz58/z444+YmJhw5coVsTMsKiqKCRMmiFNjQmaXhNaQxI+EREfj/PnzeHt737MtWq1Wk5mZyb59+wgPD6ekpIRJkyYRFBTUaNxFe1JYWMjly5fx8fGpUdRbPU3dwMBANA7U9VZwbRoyqtVqCgoKyM7OJicnh86dO4sCsjUjabdu3eL27dsaFz7JycnMmjWLHTt20L9/f41tVxv8+uuvnD17ljlz5jBjxgwqKyv5448/AHj//fc5c+YMYWFhGBsba3lPJf4/kviRuD+Jiopi6dKlKJVKXnjhBZYvX17j72q1mqVLlxIZGUnnzp3Zvn07AwYMICEhgRkzZojLJScn8+677/Lqq6+yevVq/ve//2Fvbw/AmjVr8Pf3b9fjag1yuZzw8HD27dtHXl4e/v7+BAUFaS1Q886dO1y9ehUfH59Gk9HLysrIysoiOzsbPT09nW0FF7LUHBwccHV11eq+VI+MyMnJabGXUGZmJrdu3cLPz0+jwictLY2ZM2fyzTffiB43HRW1Wk1ISAi///47GzZsYNu2bZw/f57y8nLc3Nw4cOAAERERdTzQJLSKJH4k7j+USiUeHh4cP34cV1dXBg8ezO7du2tkjkVGRrJhwwbRf2jp0qV1ojmUSiUuLi6cPXuW7t27s3r1aszMzHj99dfb+5A0Tl5eHhEREYSFhXHr1i3Gjx/PlClT6Nu3b7u03Obl5ZGYmIifn1+zREz1VnCVSoW9vT2Ojo5ayRurjiB8HB0dddKbprS0VDxvMplMHElr7Ny3lfDJyMhg2rRpfPnllwwbNkxj29UGxcXFmJmZUVRUxPjx43nqqad47bXXuHz5MhEREVhbWxMYGIirq6vU1aVbtKn46dgVixIdFk0Frv7000/06tWr1Z06uoiNjQ3PPfcczz33HAUFBRw8eJC1a9eSkpLC2LFjCQoKws/Pr02EUE5ODtevX6d///7NngaonjemUCiQy+VcuXKFqqoqrXniKJVKYmJicHZ21tmn+9qBtdnZ2Vy+fLlBL6Hbt2+TkZGhceGTmZnJ9OnTWb9+fYcXPtHR0WzZsoVJkyYREBDA119/zZYtW7h58yb9+vWrEXSqUqkk4fMAIYkfCa3Q2sBVgfoCCzdu3MjOnTsZNGgQn3zyyT3DFjsClpaWPPvsszz77LMUFRURGRnJF198QUJCAqNHjyYoKIjBgwdrRAjJ5XJSU1Pp379/q+t3jIyMcHV1FVuKs7OzSUpKoqKiAjs7u3bxxKmqqiI2NpYuXbp0mAgOExMTunbtSteuXVEoFOTk5JCYmCieNwMDA7Kzs+nfv79Gu+6ysrKYNm0an3zyCSNGjNDYdrVBWloaV65cYcqUKbz++utcvnwZPT09ysrKuHr1ap1Cd8nA8MFC+rQltEJrA1fhrsHcgQMHmDZtmvjaokWLuH79uviUv2zZMg3utW5gbm7OjBkzCAkJ4ezZs4wcOZKtW7cydOhQ/vWvf3Hq1KkWh3Hevn2btLQ0jQif2hgaGtKlSxf8/PwYOHAgpqampKSkcPbsWZKSkigoKNB48KoQutqRhE9tjIyM6NKlC/3792fQoEGoVCpSU1OprKzk+vXr3LlzRyPnLScnh2nTprFmzRpGjx5d7zJRUVH06dMHd3f3eoM91Wo1S5Yswd3dHR8fHy5cuHDPdfPy8hg7diy9e/dm7Nix5OfnA3czyTp16oSfnx9+fn68+OKL4jpCQ4G7uztLliypc/y7du3imWeeYfv27Vy5coWPP/4YOzs70tLS2LZtG//+979RKBRtEvQr0TGQxI9Ek6iqqtLo9lobuApw5MgRBgwYUCMZ3dHREX19ffT09FiwYAHnzp3T6H7rGp06dWLKlCl8//33nD9/nokTJ/LDDz8wbNgwli5dysmTJ6msrGzStjIyMsjIyKB///5t3rpuYGCAk5MTPj4+DB48GEtLS27evMmZM2dISEggPz+/1TemqqoqoqOj74vQVYH8/HwKCgp49NFHGTJkCDY2NmRkZHDmzBmuXLlCbm4uKpWqRdudNm0aq1evZsKECfUuo1Qqefnllzly5Ajx8fHs3r2b+Pj4GsscOXKEpKQkkpKS+Prrr1m0aNE91123bh1jxowhKSmJMWPG1BBGvXr1IiYmhpiYGDGSB+4+5Hz99dfie0VFRYl/+/nnn1m/fj1RUVGMGTOG//znP/zyyy+MGzeOTZs2sW3bNnbt2oWRkZFOdFNKaAdJ/Eg0ic2bN5OXl6ex7Q0ePJikpCRSUlJQKBTs2bOHwMDAGssEBgayc+dO1Go1Z86cwdLSssZNbPfu3XWmvDIzM8V/h4eH15jTv98xNjZm8uTJbN++nQsXLvDUU0+xf/9+HnnkEV5++WWOHz+OQqGod92UlBSysrLw8/Nrd/NCfX19HBwc6NevH0OGDMHW1pZbt2616oZeWVlJdHQ03bp1w8nJqY32vH3Jzs4mJSUFPz8/DA0NxYBVLy8vhgwZgqOjI9nZ2Zw9e5ZLly4hl8ubNAJYUFDAtGnTWL58OZMnT25wuep1ekZGRmKdXnUaqtNrbN2IiAjmzp0LwNy5c9m/f3+j+5uZmUlhYSHDhg1DJpMxZ86cGuvY2NiwceNGTpw4wcGDBzlz5gw///wzL774IqmpqcybN48+ffq0SCRK3D9INT8SjVJZWcknn3xCeHg4S5YsEV9XqVSo1eoWFwgaGBiwceNGxo8fLwauenl51Qhc9ff3JzIyEnd3dzFwVaC0tJTjx4+zZcuWGtt94403iImJQSaT4ebmVufvDwpGRkaMHz+e8ePHU1VVxe+//05ISAj//ve/6d+/P8HBwYwePRoTExNWr15Nbm4uX3zxhdbrHvT09LCzs8POzq6GOWBiYiIWFhaiOWBj+1lZWUlMTAzdu3e/L9Lm4e6UVEpKSoOjcnp6etjY2GBjY1PDlTs5OZlOnTrh4OCAnZ1dnXWLioqYPn06S5cuFUOMG6I1dXqNrZuVlSU+1Dg7OyOXy8XlhGO2sLDg/fff57HHHiMjI6OGTYHwHgJ+fn6oVCq+++47Fi5cSJ8+fXj22WfZt29fDWGv7e+6hHaRxI9EoxQUFPD999+TkZHBv//9b5599ln69Omjka4If3//Oh481ef1ZTIZmzZtqnfdzp07k5ubW+f17777rtX7db9hYGDA448/zuOPP45SqeSPP/4gNDSU1atXY2FhgZ6eHj/++KPO3Qxq39ALCgrIysri2rVrmJmZiTf06t9FYcTHzc3tvhE+ubm5JCcniyM+90Imk2FpaYmlpSXu7u6UlJSQlZXFhQsXSEtLIyMjg+nTp2NlZcWMGTNYuHBhDd+shmhNnV5T1q2Ns7MzN27cwNbWlvPnzxMcHMzly5ebtC09PT28vLz47rvvyM3N5cCBA3zxxRe4urpKzs0SgCR+JO6BnZ0d5ubmREZGkpWVRUhICFevXiUnJ4d//etfjBs3Ttu7KNEM9PX1eeyxxxg+fDgrVqzg0qVLeHh44O/vj7u7O4GBgUyYMAFzc3Nt72oNZDIZVlZWWFlZoVarKSoqIisri5SUFHFkw9LSkkuXLtGjRw/R5LKjk5eXx7Vr11pcgC6TyTAzM8PMzIxevXphb29PfHw8U6dOFWuHRo4c2aRttaZOT6FQNLiuo6OjaGGRmZkpilZjY2PRZmHgwIH06tWLxMREXF1dSU9Pb3Q/AKZOnQrA4cOHWblyJb6+vpLwkRDRrUc9CZ3j+PHjyGQyhg0bRlZWFufPn+d///sf69at4/vvv6ewsJDk5GSOHj2KSqWqURhdVFREWloaFRUVWjwCidqoVCpeffVViouLOXDgAJ9++inR0dGsXLmShIQEJkyYwMyZM/nhhx+4c+eOtne3DjKZDAsLC3r37s2QIUPo1asXRUVFnDlzBrVaTWVlZZOLvHWZvLw8kpKSNNp517NnT5YvX46zszMvvfQSw4cP5/nnn+eRRx5h7dq1JCQkNLhua+r0Gls3MDCQHTt2ALBjxw6CgoKAuzVOQs1ScnIySUlJ9OzZE2dnZ8zNzcXPe+fOneI61bGzs2PhwoWEhoYyZswYSfhI1EByeJZolFdeeQVHR0dmz57N0qVLyczMxMzMjH79+nH69Gl27NiBiYkJTz75JH/++SempqZim+mFCxc4deoUM2bMqNGRJaFdVq9eTUlJCf/973/rvRmo1WouXbpEaGgohw8fxs7OjuDgYCZNmoStra0W9rhxFAoF0dHRuLu7Y2JiUidvzN7evsPlNeXn54vu2prc94qKCmbPns348eN55ZVXxM8/Ly+PQ4cOsW/fPrZu3drg5xwZGcmrr74q1umtXLmyRp2eWq3mlVdeISoqSqzTGzRoUIPrwt1pvenTp3Pjxg26detGSEgINjY2hIWFsWrVKgwMDNDX1+c///kPAQEBAPz999/MmzePsrIyJk6cyIYNGxoUNpLo6bBI8RYS2uPxxx9n+fLlPProoyxdupS1a9eiUCjYvXs3mZmZzJ49m/79+7N8+XLs7e1JTEwkLS2NLVu2MG7cOOzs7Ni7d69Oxgk0xr1yx65evcpzzz3HhQsX+OCDD2rEaTS0bl5eHjNmzCA1NRU3Nzf27t2rFQPGgoICLCwsmnRDUKvVJCQkEBoaysGDB7GwsCAwMJCAgADs7e21flOpqKggJiYGd3f3OjfssrIy5HI5crm8yXERuoCQp9YSd+3GqKysZO7cuTz22GP885//1PpnJyFxDyTxI6EdVCoVn332Gd999x0BAQGcO3eOdevW1Uh3FrJw5s+fz9GjR/nPf/7DCy+8gFwuZ+HChcjlcmxsbFi1alWD4YgqlQqZTKYzF+Om5I7J5XLS0tLYv38/1tbWovhpbN033ngDGxsbli9fzrp168jPz+fDDz/U1mE2G7VazfXr1wkLCyMiIgIjIyMCAwMJCgrCycmp3T8/Qfj07t0bGxubey4rCCGlUikKocbCWrWBIHyam6d2L6qqqpg/fz4DBw5k+fLlOvNbk5BoBEn8SGiftLQ0YmJiePPNN+nSpQsTJkzgueeew97ennfeeYfffvuN27dvc+HCBTp16sTJkycJDw/ntddew83NjdLS0jo3moZCBLU9TP3nn3+yevVqjh49CsDatWsBWLFiRZ1lawepNrZunz59OHnypFjYOWrUqEZrLHQZtVrNjRs3CAsLY//+/ahUKiZPnsyUKVNwdXVt88+vvLycmJgYPDw87il8aqNQKMjOzkYul6NQKMTcLDMzszba26ZRUFDAlStXNC58lEolCxcuxMPDg3feeUcSPhIdBSnYVEI7qNVqMeyve/fudO/enaCgII4ePUpRURG3b99m5cqVFBUV8dVXX/HWW28hl8vp2rUr169fp6qqCjs7O4AawkcQN5GRkcTHx9OzZ0/8/Pzo3bs38H9tqyqVSivt103xM2nJuo35mXQ0ZDIZ3bt355///CevvfYamZmZhIWFsWjRIsrKypg0aRJBQUH07NlT4zdbQfj06dOnRdOGRkZGuLi44OLiQmVlpRjiWlZWJuaNmZubt6tIEISPr6+vxoXP4sWL6d69uyR8JCSqIYkfiQaRyWQ1RmYEMTJ+/Hjxtddff53OnTvj6uqKnZ0dW7du5e233yYlJQVfX1/MzMzqiBjhApySksKOHTuYMGEC33//PRs3biQ9PR0rKys8PT1rrNOeQqglniSaWLejIpPJ6NKlC4sXL2bx4sXI5XLCw8NZtmwZ+fn5+Pv7ExwcjIeHR6vPhSB8HnroIaysrFq974aGhjg7O+Ps7IxSqSQnJ4fU1FRKSkqwtbUVW+jb8jMsLCwUhU+nTp00tl2VSsU///lPbG1t+eCDD+7776GERHOQxI9EkxHER3Uh4uHhIf590aJF3LlzB0NDQ7KyskhISODpp5+u1zMmIyODlJQUnnrqKd59910A3nrrLczMzDh58iT5+fn88MMP9OrVq8Z7Q8PTZZqiKX4mLVm3IT+T+w0HBwcWLlzIwoULyc3NJSIigrfeeovbt28zfvx4pkyZUkfcNoWysjJiY2M1Jnxqo6+vj6OjI46OjiiVSvLy8khPT+fKlStYW1vj4OCAtbW1RkVEUVERly9fbhPh8+abb2JsbMxHH32kcwaWEhLaRvpFSDSbhi6kPj4+jBgxAoB//vOfdO/enb1799bw+RFGRi5duoRarRZNEvfu3cuXX36Jo6MjUVFRrFy5koiICIqLi3n44YdrZBQJwufgwYPs2LGD4uJijR5fU/xMWrJuQ34m9zO2trbMnz+fw4cP89NPP+Hp6ckHH3zA8OHDeeedd4iOjm5SxpIgfDw9PdtE+NRGyM0S8sbs7e25ffs2Z86cIT4+vsUBotWpLnw0WXitUqlYtWoVVVVVrF+/XhI+EhL1IBU8S2iMhgqVq9cOCaxbt47y8nJeeuklHBwcePzxx/H29katVnP27FkSExN58skn+eijj1i0aBFbtmzB2tqa69evs3v3blasWIG+vj63bt3C2dkZmUwm3ow0cbG/l5/J7du3GTRoEIWFhejp6WFmZkZ8fDwWFhbN9jN5ECkqKuLw4cOEhYWRkJDA6NGjCQ4OZtCgQXU+P8HJuW/fvlhYWGhpj++iVqvJz89HLpeTn5+Pubk5jo6O2NjYNGs0sri4mLi4OHx8fDA1NdXo/r333ntkZWXxzTfftOkIqYREGyN1e0l0LAQRlJeXh4WFRY0wQSGaYPPmzVhYWPDSSy+hVCqZMWMGS5cu5bHHHgPg559/xtXVla5du7Jw4UJef/113N3def311zE2Nubll19m+/btzJo1q0YLukTHo7S0lCNHjrBv3z4uXrzIiBEjCA4OZujQocTHx/Pss8+yf/9+evTooe1drYGQNyaXy8nNzcXU1BRHR8c6eWO1aUvh8+GHH5KcnMyOHTsk4SPR0ZHEj0THJC4ujtdeew1ra2umT5/O5MmTa9Q1lJeXi50tUVFRbN68mVWrVjFo0KAadUXu7u5cvXqVd999l/Lycv7973/z22+/ceTIEVasWIGVlRXff/89sbGxTJ06lXHjxmmtU0yidZSXl3P8+HFCQ0M5ffo0FRUVvPHGG8ydO7eGiNY1BFEvl8vJycnBxMREFELVw0hLSkq4ePEi3t7eGm2tV6vVfPbZZ1y8eJEffvhBp8+VhEQTkVrdJTom3t7eHD9+nLS0NEJCQhg1ahTjxo1j5cqVmJiY1Gjpfeyxx7h27RpLly4F4I033iAoKIiEhASGDBnCwYMHCQ0NFSM0zp8/T8+ePenWrRsjRoxg5cqVdO3alQ0bNnDnzh2mT5/OxYsX8fHxobKysklp2BLax8TEhICAAHr37s2MGTNYtmwZFy5cYNiwYQwZMoTg4GBGjBihsawrTSHkjVlYWODu7k5xcTFyuZzo6GgMDAxwdHTE1NSUK1eutInw2bRpE+fPn+fHH3+UhI+ERBOQRn4k2pU7d+7cs2A1KysLExMTLC0tKSwsZMyYMSgUCpYuXcr8+fOJjY3lm2++ISAgAAsLCx555BHGjh3LtGnTcHBwYPv27ezbtw9XV1d++eUXevfuzYkTJxgzZgxQs/Vc24aKEnWJj4/n6aef5ocffsDLywu461D822+/ERISwm+//caAAQMIDg7m8ccf1/m4itLSUtLT00lPT8fU1JQuXbrg4OCgkegKtVrN//73P06cOEFYWFi927xXVItarWbp0qVERkbSuXNntm/fzoABAxpdt6GoluPHj7N8+XIUCgVGRkZ89NFHjB49GoBRo0aRmZkpjv4eO3bsvu14lNAIbXphluYFJNqVhoSPWq1GqVSiVqtxdHTE0tISAHNzc+bMmcPrr7/OzJkzATh//jx6eno8/PDD7N27lzfeeIMdO3Zw6dIlVq1aRadOnSgrK2PEiBFYWlpy4sQJxo0bx+nTp5HJZPzxxx9i11l1Q0UJ7VNcXMzs2bPZs2ePKHwADAwMGD16NF9++SUXL15kwYIF/Prrr4wcOZL58+cTERFBaWmpFve8cXJzcxk8eDC+vr6o1Wri4uL466+/SEtLo6ysrEXbVKvVbN++naioKEJDQ+sVPkqlkpdffpkjR44QHx/P7t27iY+Pr7HMkSNHSEpKIikpia+//ppFixbdc91169YxZswYkpKSGDNmDOvWrQPuJqkfPHiQuLg4duzYwezZs2u8165du4iJiSEmJkYSPhJaRRI/EjqBYKhYexRGJpOxePFiZs+eLbYD6+np4ejoiIWFBba2tlhaWuLk5MTnn39OTEwMO3bs4MaNG/Tr14+QkBDOnz+Pv78/hoaGnD59mpdeeom8vDwuX77M7t27KS0t7VD1QVFRUfTp0wd3d3fxplOdq1evMmzYMIyNjfn444/F12/evMnjjz+Op6cnXl5efPHFF+LfVq9ejYuLC35+fvj5+REZGdkux1IbMzMzfv/9dzw9PRtcRl9fnxEjRrB+/XpiY2N59dVX+fvvvxk9ejSzZ88mLCxM4/YHLaWsrIyLFy/i5eWFubk5JiYmdOvWjUGDBuHj44O+vj5Xrlzh3LlzpKSkUFJS0uRt79q1i/DwcPbt29fg6Ne5c+dwd3enZ8+eGBkZMXPmTCIiImosExERwZw5c5DJZAwdOpQ7d+6QmZnZ6LoRERHMnTsXgLlz57J//34A+vfvL/paeXl5UV5eXsPqQkJCV5AmhyV0ntqmhvPmzaOqqgo9PT2ee+45Zs6cSWxsLJMnT+ahhx5i4MCB2NracuPGDWJjY9m0aRMJCQmkpaVx7tw5lixZQlxcHHv27CEvL4///Oc/TJs2jffee0+LR9k0hKfx6sGpgYGBNTrebGxsWL9+vXhDEjAwMOCTTz5hwIABFBUVMXDgQMaOHSuu+9prr9VIp9cWzfG80dPTY+jQoQwdOhSVSkV0dDShoaF8+umndO3alaCgIPz9/cWRxPakujdRfS36xsbGuLq64urqSmVlJdnZ2SQmJqJQKLCzsxPrhOqblt27dy8//PADhw8fbvR8NSWqpb5lMjIyWh3VEhYWVieZ/rnnnkNfX5+pU6fy1ltvSVPOElpDEj8SOk/tll21Wo2BgQFqtZouXbpw5MgRwsLC+PnnnzE0NGTQoEEUFBSwe/duRo4cyYABA0hJSSE9PR19fX2Cg4M5cuQIBQUF/O9//0NfX5/U1FRx2w1dkOvzK2pvqj+NA+LTeHXxIySWHz58uMa6QowD3J1O9PT0JCMj476xCtDT02PgwIEMHDiQNWvWcOnSJUJCQggICMDe3p6goCAmT57cLt5K5eXlovBpivAyNDSkS5cudOnShaqqKrKzs8W8MQsLC3Jychg+fDh6enqEh4ezbds2Dh06dM9W+abErTS0TGuiWi5fvsybb77JsWPHxNd27dqFi4sLRUVFTJ06le+++445c+Y0aXsSEpqm44z1S0j8f4QLsHCBNjU1Zc6cOWzbto0ZM2YAkJqaipeXF3PnzqWsrIyKigpOnjxJUFAQNjY2TJ48GWdnZz766COUSiX9+vWrsW34v5tCSUkJBQUFdbLOqi/TXjT0lN5cUlNTiY6OZsiQIeJrGzduxMfHh/nz55Ofn6+R/dUWMpkMb29v3n33Xf766y8+/fRTsrOzmTp1KkFBQWzbtg25XN4mn1/1/LGWjDgZGBjg7OyMr68vgwcPBuCzzz7D19eXGTNmsHbtWsLDw5tk+NiUqJaGlmlKVAtQJ6olPT2dKVOmsHPnTjGeBsDFxQW4K7xnzZrFuXPnmnxOJCQ0jSR+JDo0glgRiqUFxowZw6lTpwgMDKSiooLi4mI8PT155JFHyMrKwtzcnE8//ZT8/HyeffbZBp9yU1JS+Ne//sWECRMYNWoUe/fupaqqqsYyarW63USQJoJTi4uLmTp1Kp9//rl4A120aBHXr18nJiYGZ2dnli1bppH91QVkMhmenp68/fbbnDlzhi+//JLi4mJmzZrF5MmT+eqrr8jMzNTIZ1hRUaHR4FV9fX369u1LeHg4a9euJTs7G19fXx5//HFefvllfv755xrfx9o0JaolMDCQnTt3olarOXPmDJaWljg7O7coquXOnTtMmjSJtWvX8uijj4rvUVVVRU5ODgCVlZUcOnRIfOCQkNAG0rSXxH1B7REZweRQT08PKysrLl68iEKhAODHH3/k6NGjzJ07ly5duqBSqSgsLBSf0oWpr9TUVDZs2IBSqeTPP//kp59+4tq1axgYGFBYWEhERASPPfYYbm5uNd67LdvnWxO6CndvPFOnTuWZZ57hySefFF93dHQU/71gwQImT56smR3WMWQyGe7u7ixfvpw333yTtLQ0wsLCmDdvHgCTJ08mODgYV1fXZn+GFRUVREdH06dPH43nj/3888989NFHREZGYm9vT2VlJb/++ishISEsXbqUL774Qmwpr46BgQEbN25k/PjxYtyKl5dXjagWf39/IiMjcXd3p3Pnznz77beNrguwfPlypk+fztatW8WoFrg7enjt2jXee+89sYbu2LFjmJqaMn78eCorK1EqlTzxxBMsWLBAo+dIQqI5SD4/Evc9ghipLkqOHTvG3r17KSsrY9WqVXh4eIh/E5bbvXs3ly5d4vnnnxdrbABOnz7N2rVrsbCwqNPqWxtNJ9BXVVXh4eHBTz/9hIuLC4MHD67hh1Od1atXY2ZmJhYxq9Vq5s6di42NDZ9//nmNZYW0ebg7xXL27Fn27Nmjsf3WddRqNbdu3SIsLIzw8HDKy8uZPHkyQUFB9OjR455CSKFQEB0dTe/evTVeU/Tbb7+xcuVKDh8+jJOTU52/K5VKqqqqNOIbJCGhQ0jxFhISbU1VVVUdZ9wTJ07w9ddf8+2332Jqaio6RT/99NP07t2bd999l4SEBN5//302b95MdHQ033//PZ988gnm5uZ13kOlUiGTyVo9KtTS0NWLFy/y2GOP4e3tLbb2r1mzBn9/f2bPnk1MTAwymQw3Nze2bNkiiqEHDbVajVwuF9vI79y5g7+/P0FBQTVEskBRURHx8fFtInz++OMP/vWvf3Ho0CGxZkZC4gFBEj8SEppGqVQik8nq+PuoVCpUKhUGBgakp6fzwgsvsGHDBnr37i3+vXfv3pw+fRonJyfKy8sJCAhg06ZN7N+/n8zMTD744AM6d+7Mpk2bsLe3Z/r06XXeX3KW7jjk5uayf/9+wsLCyMrKYsKECQQHB+Pp6UlWVhYBAQF8++23eHt7a/R9//rrL5YuXcqBAwfo1q2bRrctIdEBkByeJSQ0jb6+Pnp6emRkZLBixQqOHDkC3G2XFkaAnJ2deeqppwgMDGTy5Mn89ddfJCYmYmZmJk4/XL9+naysLDw8PPj1118ZO3asaN//zTffYGxsTGpqKm+//TZbtmzh/PnzwP8VKV+/fp2ff/65vQ9fohnY2try/PPPExkZyU8//USfPn14//33GTZsGOPGjWPevHn1Tju2hujoaJYsWUJ4eLgkfCQk2gBp5EfigUatVnPx4kV2797NiRMn8PX1ZcqUKXUKfgXR06VLF5YsWYKBgQEPP/wwR48epW/fvjz99NM8//zzfPPNN3Tt2pXk5GTGjx9PYmIiBw4c4OWXX54SPxUAAAk6SURBVGb27Nn89ttv9O3bl82bN2NoaMisWbMYNWoU//jHP6TRoA5Efn4+EyZMYOTIkSQnJ5OYmMiYMWMIDg5m4MCBrXIMj4uLY8GCBYSFhYkjjhISDyBSqruERFshk8nw9fXF19cXlUrFlStX+OOPP4C7U2Nwd5TIw8NDXOe1115j69at7NmzhwULFjBu3DhOnTpF165dcXV1BWD37t0MGjQImUxGTEwMU6ZMYe3atZSWluLn54dCoWDjxo2EhYWhp6fHI488IrX+dhDu3LnD5MmTeeuttwgICADuhpdGRkayZcsW4uLiGDlyJMHBwQwZMqRZBe/x8fEsWLCAvXv3SsJHQqINkUZ+JCQ0QFFRERMnTsTX15chQ4awatUqvv76a7y8vHjvvffw9/cnMDCQ8PBwtm/fTkREBGfOnGHatGm8/fbbpKamsmbNGm0fhsQ9UCqVjBkzhldffZXg4OB6lykvL+f48eNirtzw4cOZMmUKjzzySJ2i+uokJCQwd+5cfvjhB0kIS0hINT8SErqFkEBfHXNzc7777jv69etHfn4+ZWVljB49mtOnT6NSqUSn3pMnT4quyps3b+aZZ57hH//4x30jfFoaugrg5uaGt7c3fn5+DBo0SHw9Ly+PsWPH0rt3b8aOHatV92l9fX127drVoPABMDExISAggJ07dxIdHc2UKVMIDQ1l2LBhLF68mBMnToieUwLJycnMnTuXnTt3SsJHQqIdkEZ+JCTagLy8PGxsbNi6dStJSUmsW7eOkpISZs6cyWuvvcbo0aNxcHDg+PHj4pRbR0qWrw+lUomHh0eN0NXdu3fXyA6Ty+WkpaWxf/9+rK2tawSpurm58ffff2NnZ1dju2+88QY2NjYsX76cdevWkZ+fz4cffthux6UpqqqqRGPCU6dOMWDAAIKCgnB3d2fOnDls3bq1huiTkHjAkWp+JCQ6AtUfJAS/l+eff56KigoAYmJiMDQ0xMvLi7i4OMzMzPD19QXo8MIHWhe62hgRERGcPHkSgLlz5zJq1KgOKX4MDAwYM2YMY8aMQalUcurUKcLCwliwYAEHDhyQhI+ERDvS8a+4EhI6gmBgWLtjy9jYGLVazaOPPsquXbtwdHTE2NgYV1dXFixYQGJiopb2WLO0NnRVJpMxbtw4Bg4cyNdffy2+npWVJRouOjs7I5fLNbfTWkJfX5+RI0eyfv16cnNzGT58uLZ3SULigUISPxIS7YAQryF4AHl4eHDw4EEef/zx+yaWoLWhq6dPn+bChQscOXKETZs28dtvv2ly93SWhs7Rveqn1Go1S5Yswd3dHR8fHy5cuHDPdRurn1q7di3u7u706dOHo0ePiq+fP38eb29v3N3dWbJkSbuF+EpItCWS+JGQaCeq3+TUajWWlpbMmjWL7t27a3GvNEdrQ1eFZR0cHJgyZQrnzp0D7oauZmZmAnczyBwcHDS417qJUqnk5Zdf5siRI8THx7N7927i4+NrLHPkyBGSkpJISkri66+/ZtGiRfdcd926dYwZM6ZOJl18fDx79uzh8uXLREVF8dJLL4lF/YsWLeLrr78W3ysqKqodz4SERNsgiR8JCS1wP5oZDh48mKSkJFJSUlAoFOzZs4fAwMAmrVtSUkJRUZH472PHjoldT4GBgezYsQOAHTt2EBQU1DYHoENUr58yMjIS66eqExERwZw5c5DJZAwdOpQ7d+6QmZnZ6LoRERHMnTsXuFs/tX//fvH1mTNnYmxsTI8ePXB3d+fcuXNkZmZSWFjIsGHDkMlkzJkzR1xHQqIjIxU8S0hIaAQDAwM2btzI+PHjxdBVLy+vRkNXP//8c+Lj48nJyWHKlCnA3a6oWbNmMWHCBACWL1/O9OnT2bp1K926dSMkJERrx9he1Fc/dfbs2Xsuk5GR0ei6DdVPZWRkMHTo0DrbMjQ0FI07q78uIdHRkcSPhISExvD398ff37/Gay+++KL4bycnJ9LT0+usZ2FhQWxsbL3btLW15aefftLsjuo4TamfamiZltReaXJbEhIdAWnaS0JCQkLHaEr9VEPLNLZuQ/VTjW2rulhtbh2XhISuIokfCQkJCR2jKfVTgYGB7Ny5E7VazZkzZ7C0tMTZ2bnRdRuqnwoMDGTPnj1UVFSQkpJCUlISDz/8MM7Ozpibm3PmzBnUajU7d+58IGquJO5/pGkvCQkJCR2jKfVT/v7+REZG4u7uTufOnfn2228bXRcarp/y8vJi+vTp9O3bFwMDAzZt2iQGsn755ZfMmzePsrIyJk6cyMSJE7VwRiQkNIsUbyEhISEhISGha0jBphISEhKaoKXBqwkJCfj5+Yn/s7Cw4PPPPwdg9erVuLi4iH+LjIxsr8ORkJBoIdLIj4SExANBa4NXq2/HxcWFs2fP0r17d1avXo2ZmVm9y0pISLQYaeRHQkJCorU0xTjQwcGBwYMHY2ho2OB2fvrpJ3r16nXfOHNLSDyISOJHQkLigaC1wasCe/bs4emnn67x2saNG/Hx8WH+/Pk18rIkJCR0E0n8SEhIPBBowrBPoVBw4MABpk2bJr62aNEirl+/TkxMDM7OzixbtqzV+yohIdG2SOJHQkLigaC1watwN0x0wIABODo6iq85Ojqir6+Pnp4eCxYsEANZJSQkdBdJ/EhISDwQtCZ4VWD37t11prwEx2SA8PBwMZBVQkJCd5G6vSQkJB4YIiMjefXVV0Xzv5UrVzYavGpmZkZ8fDwWFhaUlpbStWtXkpOTsbS0FLc5e/ZsYmJikMlkuLm5sWXLFjE8VEJCosW0abeXJH4kJCQkJCQkdA2p1V1CQkJCQkJCQlNI4kdCQkJCQkLigUISPxISEhISEhIPFJL4kZCQkJCQkHigkMSPhISEhISExAOFJH4kJCQkJCQkHigk8SMhISEhISHxQCGJHwkJCQkJCYkHCkn8SEhISEhISDxQSOJHQkJCQkJC4oFCEj8SEhISEhISDxSS+JGQkJCQkJB4oJDEj4SEhISEhMQDhSR+JCQkJCQkJB4oJPEjISEhISEh8UBhcI+/y9plLyQkJCQkJCQk2glp5EdCQkJCQkLigUISPxISEhISEhIPFJL4kZCQkJCQkHigkMSPhISEhISExAOFJH4kJCQkJCQkHigk8SMhISEhISHxQPH/AKPVZNNBSUo3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Creating figure\n",
    "fig = plt.figure(figsize = (10, 10))\n",
    "ax = plt.axes(projection =\"3d\")\n",
    "\n",
    "# Creating plot\n",
    "ax.scatter3D(authors_std['text_reviews_count'], authors_std['average_rating'], authors_std['ratings_count'])\n",
    "plt.title(\"simple 3D scatter plot\")\n",
    " \n",
    "ax.set_ylabel('average_rating')\n",
    "ax.set_xlabel('text_reviews_count')\n",
    "ax.set_zlabel('ratings_count')\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ✅ How many clusters do we need"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "    حال تلاش می کنیم با استفاده از معیار های ارزیابی خوشه بندی مانند اینرسی و شاخص سیلوئت تعداد خوشه مناسب برای نویسندگان را ارزیابی نماییم با توجه به اعداد و نمودار های بدست آمده مشخص است که اینرسی بیشترین تغییر و به عبارت دیگر بیشتر کاهش را زمانی داشته که از 2 خوشه به 3 خوشه رفته ایم و اما شاخص سلوئت دائما با افزایش تعداد خوشه ها در حال کاهش بوده اما میزان کاهش آن در حد چند صدم است و چشمگیر نمی باشد با توجه به این موضوع ما 3 خوشه را انتخاب می کنیم چرا که میزان اینرسی آن به نسبت مناسب و شاخص سیلوئت نظیر آن نیز حدود 0.73 است که مقدار مناسبی ارزیابی می شود."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inertia for clusters number between 2 to 5\n",
      "[270.85330824540875, 106.85346383439068, 57.241666945285445, 36.743208425969954]\n",
      "_____________________________________________\n",
      "silhouette for clusters number between 2 to 5\n",
      "[0.7666373292173682, 0.7352857931161181, 0.7114222707452318, 0.6876575224948338]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "inertia = []\n",
    "silhouette = []\n",
    "for i in range(2, 6):\n",
    "    kme = KMeans(n_clusters=i)\n",
    "    labels = kme.fit_predict(authors_std)\n",
    "    iner = kme.inertia_\n",
    "    sil = silhouette_score(authors_std, labels)\n",
    "    silhouette.append(sil)\n",
    "    inertia.append(iner)\n",
    "\n",
    "print('inertia for clusters number between 2 to 5')\n",
    "print(inertia)\n",
    "print('_____________________________________________')\n",
    "print('silhouette for clusters number between 2 to 5')\n",
    "print(silhouette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(range(2, 6), inertia, c=\"#008fd5\", label=\"inertia\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"cluster_num\")\n",
    "plt.ylabel(\"inertia\")\n",
    "plt.legend()\n",
    "plt.title(\"Clustering Performance Based on Clusters Number\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.plot(range(2, 6), silhouette, c=\"#008fd5\", label=\"silhouette_score\")\n",
    "\n",
    "\n",
    "plt.xlabel(\"cluster_num\")\n",
    "plt.ylabel(\"silhouette_score\")\n",
    "plt.legend()\n",
    "plt.title(\"Clustering Performance Based on Clusters Number\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans01 = KMeans(n_clusters = 3)\n",
    "cluster = kmeans01.fit_predict(authors_std)\n",
    "centroids = kmeans01.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.51305025e-05, 7.24500009e-01, 4.10119836e-02, 6.58380417e-01],\n",
       "       [1.25187685e-05, 9.91187556e-01, 5.14689569e-03, 7.92019534e-02],\n",
       "       [5.14810538e-05, 1.35743079e-01, 5.22043229e-02, 9.79684151e-01]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_std['Class'] = cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "    با توجه به مقادیر زیر به نظر می رسد تعداد نویسندگانی که در خوشه 1 قرار گرفته اند حدودا 3 برابر و تعداد نویسندگانی که در خوشه 2 قرار گرفته اند حدودا 2 برابر تعداد نویسندگان در خوشه 0 است."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2971\n",
       "2    1865\n",
       "0     948\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_std['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>Class</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.912076e-07</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>1.038904e-04</td>\n",
       "      <td>0.002944</td>\n",
       "      <td>1</td>\n",
       "      <td>Titus Lucretius Carus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.161618e-06</td>\n",
       "      <td>0.999212</td>\n",
       "      <td>2.690788e-03</td>\n",
       "      <td>0.039592</td>\n",
       "      <td>1</td>\n",
       "      <td>Justin Halpern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.226210e-05</td>\n",
       "      <td>0.902424</td>\n",
       "      <td>3.420220e-02</td>\n",
       "      <td>0.429489</td>\n",
       "      <td>0</td>\n",
       "      <td>L.A. Meyer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.686619e-06</td>\n",
       "      <td>0.275735</td>\n",
       "      <td>8.665422e-02</td>\n",
       "      <td>0.957320</td>\n",
       "      <td>2</td>\n",
       "      <td>Tana French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.560704e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.643424e-07</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1</td>\n",
       "      <td>Tana French</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_rating  author_id  text_reviews_count  ratings_count  Class  \\\n",
       "0    4.912076e-07   0.999996        1.038904e-04       0.002944      1   \n",
       "1    1.161618e-06   0.999212        2.690788e-03       0.039592      1   \n",
       "2    2.226210e-05   0.902424        3.420220e-02       0.429489      0   \n",
       "3    7.686619e-06   0.275735        8.665422e-02       0.957320      2   \n",
       "4    2.560704e-07   1.000000        5.643424e-07       0.000004      1   \n",
       "\n",
       "                    name  \n",
       "0  Titus Lucretius Carus  \n",
       "1         Justin Halpern  \n",
       "2             L.A. Meyer  \n",
       "3            Tana French  \n",
       "4            Tana French  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# 3D scatter plot\n",
    "#========================= Clusters Which Is Made Using KMeans =========================#\n",
    "PLOT = go.Figure()\n",
    "\n",
    "for C in range(0,3):\n",
    "    \n",
    "    PLOT.add_trace(go.Scatter3d(x = authors_std[authors_std['Class'] == C]['text_reviews_count'],\n",
    "                                y = authors_std[authors_std['Class'] == C]['average_rating'],\n",
    "                                z = authors_std[authors_std['Class'] == C]['ratings_count'],\n",
    "                                mode = 'markers', marker_size = 8, marker_line_width = 1,\n",
    "                                name = 'Cluster ' + str(C)))\n",
    "\n",
    "            \n",
    "PLOT.add_trace(go.Scatter3d(x = (centroids[0][2],centroids[1][2],centroids[2][2]) , \n",
    "                            y = (centroids[0][0],centroids[1][0],centroids[2][0]), \n",
    "                            z = (centroids[0][3],centroids[1][3],centroids[2][3]),\n",
    "                            mode = 'markers', marker_size = 8, marker_line_width = 1,\n",
    "                                name = 'centroids'))\n",
    "\n",
    "PLOT.update_layout(width = 700, height = 700, autosize = True, showlegend = True,\n",
    "                   scene = dict(xaxis=dict(title = 'text_reviews_count', titlefont_color = 'black'),\n",
    "                                yaxis=dict(title = 'average_rating', titlefont_color = 'black'),\n",
    "                                zaxis=dict(title = 'ratings_count', titlefont_color = 'black')),\n",
    "                   font = dict(family = \"Gilroy\", color  = 'black', size = 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "    نمودار زیر نحوه دسته بندی نویسندگان در 3 خوشه را به خوبی نشان می دهد."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:/Users/User/Downloads/test2/Clusters.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.480000e+02</td>\n",
       "      <td>948.000000</td>\n",
       "      <td>948.000000</td>\n",
       "      <td>948.000000</td>\n",
       "      <td>948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.513050e-05</td>\n",
       "      <td>0.724500</td>\n",
       "      <td>0.041012</td>\n",
       "      <td>0.658380</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.226057e-05</td>\n",
       "      <td>0.134407</td>\n",
       "      <td>0.026948</td>\n",
       "      <td>0.145615</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.785359e-07</td>\n",
       "      <td>0.462616</td>\n",
       "      <td>0.001435</td>\n",
       "      <td>0.398791</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.347279e-05</td>\n",
       "      <td>0.607129</td>\n",
       "      <td>0.020269</td>\n",
       "      <td>0.529627</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.928053e-05</td>\n",
       "      <td>0.743342</td>\n",
       "      <td>0.035579</td>\n",
       "      <td>0.668265</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000753e-04</td>\n",
       "      <td>0.847379</td>\n",
       "      <td>0.058113</td>\n",
       "      <td>0.791559</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.352532e-03</td>\n",
       "      <td>0.916817</td>\n",
       "      <td>0.156044</td>\n",
       "      <td>0.884573</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_rating   author_id  text_reviews_count  ratings_count  Class\n",
       "count    9.480000e+02  948.000000          948.000000     948.000000  948.0\n",
       "mean     7.513050e-05    0.724500            0.041012       0.658380    0.0\n",
       "std      7.226057e-05    0.134407            0.026948       0.145615    0.0\n",
       "min      6.785359e-07    0.462616            0.001435       0.398791    0.0\n",
       "25%      3.347279e-05    0.607129            0.020269       0.529627    0.0\n",
       "50%      5.928053e-05    0.743342            0.035579       0.668265    0.0\n",
       "75%      1.000753e-04    0.847379            0.058113       0.791559    0.0\n",
       "max      1.352532e-03    0.916817            0.156044       0.884573    0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_std[cluster == 0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.971000e+03</td>\n",
       "      <td>2971.000000</td>\n",
       "      <td>2971.000000</td>\n",
       "      <td>2971.000000</td>\n",
       "      <td>2971.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.251877e-05</td>\n",
       "      <td>0.991188</td>\n",
       "      <td>0.005147</td>\n",
       "      <td>0.079202</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.739684e-05</td>\n",
       "      <td>0.017737</td>\n",
       "      <td>0.008699</td>\n",
       "      <td>0.104218</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.917816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.258719e-07</td>\n",
       "      <td>0.993246</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.005813</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.471344e-06</td>\n",
       "      <td>0.999619</td>\n",
       "      <td>0.001521</td>\n",
       "      <td>0.027522</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.192688e-05</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>0.005881</td>\n",
       "      <td>0.115390</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.016677e-03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074140</td>\n",
       "      <td>0.395323</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_rating    author_id  text_reviews_count  ratings_count   Class\n",
       "count    2.971000e+03  2971.000000         2971.000000    2971.000000  2971.0\n",
       "mean     1.251877e-05     0.991188            0.005147       0.079202     1.0\n",
       "std      4.739684e-05     0.017737            0.008699       0.104218     0.0\n",
       "min      0.000000e+00     0.917816            0.000000       0.000000     1.0\n",
       "25%      7.258719e-07     0.993246            0.000382       0.005813     1.0\n",
       "50%      1.471344e-06     0.999619            0.001521       0.027522     1.0\n",
       "75%      1.192688e-05     0.999983            0.005881       0.115390     1.0\n",
       "max      2.016677e-03     1.000000            0.074140       0.395323     1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_std[cluster == 1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>average_rating</th>\n",
       "      <th>author_id</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.865000e+03</td>\n",
       "      <td>1865.000000</td>\n",
       "      <td>1865.000000</td>\n",
       "      <td>1865.000000</td>\n",
       "      <td>1865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.148105e-05</td>\n",
       "      <td>0.135743</td>\n",
       "      <td>0.052204</td>\n",
       "      <td>0.979684</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.919066e-05</td>\n",
       "      <td>0.132006</td>\n",
       "      <td>0.028772</td>\n",
       "      <td>0.028711</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.391027e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>0.879263</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.328831e-05</td>\n",
       "      <td>0.024904</td>\n",
       "      <td>0.030823</td>\n",
       "      <td>0.972815</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.120830e-05</td>\n",
       "      <td>0.088802</td>\n",
       "      <td>0.046656</td>\n",
       "      <td>0.994142</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.758679e-05</td>\n",
       "      <td>0.221196</td>\n",
       "      <td>0.068408</td>\n",
       "      <td>0.998227</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.400610e-04</td>\n",
       "      <td>0.463144</td>\n",
       "      <td>0.173046</td>\n",
       "      <td>0.999958</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_rating    author_id  text_reviews_count  ratings_count   Class\n",
       "count    1.865000e+03  1865.000000         1865.000000    1865.000000  1865.0\n",
       "mean     5.148105e-05     0.135743            0.052204       0.979684     2.0\n",
       "std      5.919066e-05     0.132006            0.028772       0.028711     0.0\n",
       "min      2.391027e-07     0.000002            0.001050       0.879263     2.0\n",
       "25%      1.328831e-05     0.024904            0.030823       0.972815     2.0\n",
       "50%      3.120830e-05     0.088802            0.046656       0.994142     2.0\n",
       "75%      6.758679e-05     0.221196            0.068408       0.998227     2.0\n",
       "max      4.400610e-04     0.463144            0.173046       0.999958     2.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_std[cluster == 2].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_class = pd.DataFrame(np.zeros((10000,3)),columns=['class_0','class_1','class_2'],index=[i for i in range(1,10001)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_std['name'] = authors_df['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "    حال با توجه به خوشه های بدست آمده لیبل مربوط به خوشه هر نویسنده را درون دیتاست کتاب جایگذاری می کنیم."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "unique_authors = set()\n",
    "for i in range(len(book)):\n",
    "    res = re.split(',', book.loc[i,'authors'])\n",
    "    for j in res:\n",
    "        author_class.iloc[i, authors_std['Class'][authors_std['name'] == j]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15320"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>class_0</th>\n",
       "      <th>class_1</th>\n",
       "      <th>class_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class_0  class_1  class_2\n",
       "1          0.0      0.0      1.0\n",
       "2          0.0      0.0      1.0\n",
       "3          0.0      0.0      1.0\n",
       "4          0.0      0.0      1.0\n",
       "5          0.0      0.0      1.0\n",
       "...        ...      ...      ...\n",
       "9996       0.0      0.0      1.0\n",
       "9997       0.0      0.0      1.0\n",
       "9998       0.0      0.0      1.0\n",
       "9999       1.0      0.0      0.0\n",
       "10000      0.0      1.0      0.0\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ✅ Check if there is a book not in 4 class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl\", align=\"justify\"><h3>\n",
    "    <span style='font-family:\"B Nazanin\"'>\n",
    "    با توجه به اینکه دیتاست حاوی اطلاعات نویسندگان فاقد اطلاعات برخی از آن ها بوده است لذا به نظر میرسد 229 کتاب از نویسنده یا نویسندگان آن ها هیچ اطلاعاتی در دست نیست و لذا خوشه جدیدی ایجاد میکنیم و این کتاب ها در خوشه جدید قرار خواهند گرفت. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>class_0</th>\n",
       "      <th>class_1</th>\n",
       "      <th>class_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9809</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9829</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9858</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9891</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9929</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class_0  class_1  class_2\n",
       "43        0.0      0.0      0.0\n",
       "63        0.0      0.0      0.0\n",
       "80        0.0      0.0      0.0\n",
       "94        0.0      0.0      0.0\n",
       "233       0.0      0.0      0.0\n",
       "...       ...      ...      ...\n",
       "9809      0.0      0.0      0.0\n",
       "9829      0.0      0.0      0.0\n",
       "9858      0.0      0.0      0.0\n",
       "9891      0.0      0.0      0.0\n",
       "9929      0.0      0.0      0.0\n",
       "\n",
       "[229 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_class[author_class.sum(axis=1) == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ✅ So we add a fifth column for class named others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_class['class_others'] = np.zeros((10000,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_class.class_others[author_class.drop(['class_others'],axis=1).sum(axis=1)==0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>class_0</th>\n",
       "      <th>class_1</th>\n",
       "      <th>class_2</th>\n",
       "      <th>class_others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class_0  class_1  class_2  class_others\n",
       "1      0.0      0.0      1.0           0.0\n",
       "2      0.0      0.0      1.0           0.0\n",
       "3      0.0      0.0      1.0           0.0\n",
       "4      0.0      0.0      1.0           0.0\n",
       "5      0.0      0.0      1.0           0.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_class.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "author_class.to_csv(path + \"author_class.csv\", index=False)"
   ]
  }
 ],
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