{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa71ed11",
   "metadata": {},
   "source": [
    "\n",
    "# Поиск аномалий в сетевой активности\n",
    "\n",
    "Задача:\n",
    "- выбрать только **числовые непрерывные переменные**\n",
    "- решить задачу поиска аномалий **максимально полно**\n",
    "- сравнить несколько методов\n",
    "- выделить наиболее аномальные объекты\n",
    "- дать их краткую характеристику\n",
    "\n",
    "Ноутбук сделан без финального содержательного вывода.  \n",
    "После запуска можно будет добавить короткий итог по реальным результатам.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c04629",
   "metadata": {},
   "source": [
    "## 1. Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3073e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor, NearestNeighbors\n",
    "from sklearn.svm import OneClassSVM\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "from sklearn.decomposition import PCA\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bb2c5c",
   "metadata": {},
   "source": [
    "## 2. Загрузка данных и проверка столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a84b7e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер датасета: (86845, 43)\n",
      "\n",
      "Столбцы:\n",
      "- duration\n",
      "- protocoltype\n",
      "- service\n",
      "- flag\n",
      "- srcbytes\n",
      "- dstbytes\n",
      "- land\n",
      "- wrongfragment\n",
      "- urgent\n",
      "- hot\n",
      "- numfailedlogins\n",
      "- loggedin\n",
      "- numcompromised\n",
      "- rootshell\n",
      "- suattempted\n",
      "- numroot\n",
      "- numfilecreations\n",
      "- numshells\n",
      "- numaccessfiles\n",
      "- numoutboundcmds\n",
      "- ishostlogin\n",
      "- isguestlogin\n",
      "- count\n",
      "- srvcount\n",
      "- serrorrate\n",
      "- srvserrorrate\n",
      "- rerrorrate\n",
      "- srvrerrorrate\n",
      "- samesrvrate\n",
      "- diffsrvrate\n",
      "- srvdiffhostrate\n",
      "- dsthostcount\n",
      "- dsthostsrvcount\n",
      "- dsthostsamesrvrate\n",
      "- dsthostdiffsrvrate\n",
      "- dsthostsamesrcportrate\n",
      "- dsthostsrvdiffhostrate\n",
      "- dsthostserrorrate\n",
      "- dsthostsrvserrorrate\n",
      "- dsthostrerrorrate\n",
      "- dsthostsrvrerrorrate\n",
      "- lastflag\n",
      "- attack\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>duration</th>\n",
       "      <th>protocoltype</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrongfragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dsthostsamesrvrate</th>\n",
       "      <th>dsthostdiffsrvrate</th>\n",
       "      <th>dsthostsamesrcportrate</th>\n",
       "      <th>dsthostsrvdiffhostrate</th>\n",
       "      <th>dsthostserrorrate</th>\n",
       "      <th>dsthostsrvserrorrate</th>\n",
       "      <th>dsthostrerrorrate</th>\n",
       "      <th>dsthostsrvrerrorrate</th>\n",
       "      <th>lastflag</th>\n",
       "      <th>attack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>netbios_dgm</td>\n",
       "      <td>REJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>smtp</td>\n",
       "      <td>SF</td>\n",
       "      <td>1239</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>222</td>\n",
       "      <td>945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1380</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>uucp_path</td>\n",
       "      <td>REJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocoltype      service flag  srcbytes  dstbytes  land  \\\n",
       "0         0          tcp  netbios_dgm  REJ         0         0     0   \n",
       "1         0          tcp         smtp   SF      1239       400     0   \n",
       "2         0          tcp         http   SF       222       945     0   \n",
       "3         0          tcp         http   SF       235      1380     0   \n",
       "4         0          tcp    uucp_path  REJ         0         0     0   \n",
       "\n",
       "   wrongfragment  urgent  hot  ...  dsthostsamesrvrate  dsthostdiffsrvrate  \\\n",
       "0              0       0    0  ...                0.06                0.06   \n",
       "1              0       0    0  ...                0.45                0.04   \n",
       "2              0       0    0  ...                1.00                0.00   \n",
       "3              0       0    0  ...                1.00                0.00   \n",
       "4              0       0    0  ...                0.01                0.08   \n",
       "\n",
       "   dsthostsamesrcportrate  dsthostsrvdiffhostrate  dsthostserrorrate  \\\n",
       "0                    0.00                    0.00               0.00   \n",
       "1                    0.00                    0.00               0.11   \n",
       "2                    0.02                    0.03               0.00   \n",
       "3                    0.00                    0.00               0.00   \n",
       "4                    0.00                    0.00               0.00   \n",
       "\n",
       "   dsthostsrvserrorrate  dsthostrerrorrate  dsthostsrvrerrorrate  lastflag  \\\n",
       "0                   0.0               1.00                   1.0        21   \n",
       "1                   0.0               0.02                   0.0        18   \n",
       "2                   0.0               0.00                   0.0        21   \n",
       "3                   0.0               0.00                   0.0        21   \n",
       "4                   0.0               1.00                   1.0        19   \n",
       "\n",
       "   attack  \n",
       "0       1  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv(\"Training_Set1.csv\")\n",
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "print(\"Размер датасета:\", df.shape)\n",
    "print(\"\\nСтолбцы:\")\n",
    "for col in df.columns:\n",
    "    print(\"-\", col)\n",
    "\n",
    "display(df.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991baec4",
   "metadata": {},
   "source": [
    "\n",
    "## 3. Отбор числовых непрерывных переменных\n",
    "\n",
    "Сначала берём все числовые признаки, затем исключаем почти константные и бинарные столбцы.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acd94c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Числовые признаки: ['duration', 'srcbytes', 'dstbytes', 'land', 'wrongfragment', 'urgent', 'hot', 'numfailedlogins', 'loggedin', 'numcompromised', 'rootshell', 'suattempted', 'numroot', 'numfilecreations', 'numshells', 'numaccessfiles', 'numoutboundcmds', 'ishostlogin', 'isguestlogin', 'count', 'srvcount', 'serrorrate', 'srvserrorrate', 'rerrorrate', 'srvrerrorrate', 'samesrvrate', 'diffsrvrate', 'srvdiffhostrate', 'dsthostcount', 'dsthostsrvcount', 'dsthostsamesrvrate', 'dsthostdiffsrvrate', 'dsthostsamesrcportrate', 'dsthostsrvdiffhostrate', 'dsthostserrorrate', 'dsthostsrvserrorrate', 'dsthostrerrorrate', 'dsthostsrvrerrorrate', 'lastflag', 'attack']\n",
      "\n",
      "Оставлены как непрерывные:\n",
      "- duration\n",
      "- srcbytes\n",
      "- dstbytes\n",
      "- urgent\n",
      "- hot\n",
      "- numfailedlogins\n",
      "- numcompromised\n",
      "- suattempted\n",
      "- numroot\n",
      "- numfilecreations\n",
      "- numshells\n",
      "- numaccessfiles\n",
      "- count\n",
      "- srvcount\n",
      "- serrorrate\n",
      "- srvserrorrate\n",
      "- rerrorrate\n",
      "- srvrerrorrate\n",
      "- samesrvrate\n",
      "- diffsrvrate\n",
      "- srvdiffhostrate\n",
      "- dsthostcount\n",
      "- dsthostsrvcount\n",
      "- dsthostsamesrvrate\n",
      "- dsthostdiffsrvrate\n",
      "- dsthostsamesrcportrate\n",
      "- dsthostsrvdiffhostrate\n",
      "- dsthostserrorrate\n",
      "- dsthostsrvserrorrate\n",
      "- dsthostrerrorrate\n",
      "- dsthostsrvrerrorrate\n",
      "- lastflag\n",
      "\n",
      "Исключены как бинарные/почти дискретные:\n",
      "- land\n",
      "- wrongfragment\n",
      "- loggedin\n",
      "- rootshell\n",
      "- numoutboundcmds\n",
      "- ishostlogin\n",
      "- isguestlogin\n",
      "- attack\n"
     ]
    }
   ],
   "source": [
    "\n",
    "numeric_cols = df.select_dtypes(include=[\"number\"]).columns.tolist()\n",
    "\n",
    "continuous_cols = []\n",
    "excluded_cols = []\n",
    "\n",
    "for col in numeric_cols:\n",
    "    nunique = df[col].nunique(dropna=True)\n",
    "    if nunique <= 2:\n",
    "        excluded_cols.append(col)\n",
    "    else:\n",
    "        continuous_cols.append(col)\n",
    "\n",
    "print(\"Числовые признаки:\", numeric_cols)\n",
    "print(\"\\nОставлены как непрерывные:\")\n",
    "for col in continuous_cols:\n",
    "    print(\"-\", col)\n",
    "\n",
    "if excluded_cols:\n",
    "    print(\"\\nИсключены как бинарные/почти дискретные:\")\n",
    "    for col in excluded_cols:\n",
    "        print(\"-\", col)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a04fe8",
   "metadata": {},
   "source": [
    "## 4. Подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74e81ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер рабочей выборки: (86845, 32)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>104.166872</td>\n",
       "      <td>1038.273538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>40504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>srcbytes</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>8455.707191</td>\n",
       "      <td>358214.055880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>272.00</td>\n",
       "      <td>89581520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dstbytes</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>2732.094720</td>\n",
       "      <td>55430.411862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>768.00</td>\n",
       "      <td>7028652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urgent</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hot</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.144971</td>\n",
       "      <td>1.837958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numfailedlogins</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.000771</td>\n",
       "      <td>0.037009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numcompromised</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.320583</td>\n",
       "      <td>27.993474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suattempted</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>0.048450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numroot</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.356382</td>\n",
       "      <td>28.415210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numfilecreations</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.014370</td>\n",
       "      <td>0.528799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numshells</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.020072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numaccessfiles</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.004548</td>\n",
       "      <td>0.104986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>79.584939</td>\n",
       "      <td>98.068071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>143.00</td>\n",
       "      <td>511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>srvcount</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>21.308838</td>\n",
       "      <td>48.212191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.324545</td>\n",
       "      <td>0.464948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>srvserrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.323482</td>\n",
       "      <td>0.464469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rerrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.091022</td>\n",
       "      <td>0.286221</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>srvrerrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.091223</td>\n",
       "      <td>0.285844</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samesrvrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.639490</td>\n",
       "      <td>0.444652</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diffsrvrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.045231</td>\n",
       "      <td>0.122168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>srvdiffhostrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.078523</td>\n",
       "      <td>0.222239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostcount</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>186.012067</td>\n",
       "      <td>95.946960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.00</td>\n",
       "      <td>255.00</td>\n",
       "      <td>255.00</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostsrvcount</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>123.338269</td>\n",
       "      <td>112.704010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>77.00</td>\n",
       "      <td>255.00</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostsamesrvrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.526457</td>\n",
       "      <td>0.447353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostdiffsrvrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.050186</td>\n",
       "      <td>0.103323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostsamesrcportrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.075810</td>\n",
       "      <td>0.208388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostsrvdiffhostrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.016246</td>\n",
       "      <td>0.055271</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostserrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.324666</td>\n",
       "      <td>0.464291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostsrvserrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.318934</td>\n",
       "      <td>0.463506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostrerrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.092391</td>\n",
       "      <td>0.282768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dsthostsrvrerrorrate</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>0.091045</td>\n",
       "      <td>0.281452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lastflag</th>\n",
       "      <td>86845.0</td>\n",
       "      <td>20.060522</td>\n",
       "      <td>1.421425</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count         mean            std  min    25%  \\\n",
       "duration                86845.0   104.166872    1038.273538  0.0   0.00   \n",
       "srcbytes                86845.0  8455.707191  358214.055880  0.0   0.00   \n",
       "dstbytes                86845.0  2732.094720   55430.411862  0.0   0.00   \n",
       "urgent                  86845.0     0.000035       0.007588  0.0   0.00   \n",
       "hot                     86845.0     0.144971       1.837958  0.0   0.00   \n",
       "numfailedlogins         86845.0     0.000771       0.037009  0.0   0.00   \n",
       "numcompromised          86845.0     0.320583      27.993474  0.0   0.00   \n",
       "suattempted             86845.0     0.001267       0.048450  0.0   0.00   \n",
       "numroot                 86845.0     0.356382      28.415210  0.0   0.00   \n",
       "numfilecreations        86845.0     0.014370       0.528799  0.0   0.00   \n",
       "numshells               86845.0     0.000357       0.020072  0.0   0.00   \n",
       "numaccessfiles          86845.0     0.004548       0.104986  0.0   0.00   \n",
       "count                   86845.0    79.584939      98.068071  0.0   2.00   \n",
       "srvcount                86845.0    21.308838      48.212191  0.0   2.00   \n",
       "serrorrate              86845.0     0.324545       0.464948  0.0   0.00   \n",
       "srvserrorrate           86845.0     0.323482       0.464469  0.0   0.00   \n",
       "rerrorrate              86845.0     0.091022       0.286221  0.0   0.00   \n",
       "srvrerrorrate           86845.0     0.091223       0.285844  0.0   0.00   \n",
       "samesrvrate             86845.0     0.639490       0.444652  0.0   0.09   \n",
       "diffsrvrate             86845.0     0.045231       0.122168  0.0   0.00   \n",
       "srvdiffhostrate         86845.0     0.078523       0.222239  0.0   0.00   \n",
       "dsthostcount            86845.0   186.012067      95.946960  0.0  93.00   \n",
       "dsthostsrvcount         86845.0   123.338269     112.704010  0.0  12.00   \n",
       "dsthostsamesrvrate      86845.0     0.526457       0.447353  0.0   0.05   \n",
       "dsthostdiffsrvrate      86845.0     0.050186       0.103323  0.0   0.00   \n",
       "dsthostsamesrcportrate  86845.0     0.075810       0.208388  0.0   0.00   \n",
       "dsthostsrvdiffhostrate  86845.0     0.016246       0.055271  0.0   0.00   \n",
       "dsthostserrorrate       86845.0     0.324666       0.464291  0.0   0.00   \n",
       "dsthostsrvserrorrate    86845.0     0.318934       0.463506  0.0   0.00   \n",
       "dsthostrerrorrate       86845.0     0.092391       0.282768  0.0   0.00   \n",
       "dsthostsrvrerrorrate    86845.0     0.091045       0.281452  0.0   0.00   \n",
       "lastflag                86845.0    20.060522       1.421425  1.0  19.00   \n",
       "\n",
       "                           50%     75%         max  \n",
       "duration                  0.00    0.00     40504.0  \n",
       "srcbytes                 46.00  272.00  89581520.0  \n",
       "dstbytes                 44.00  768.00   7028652.0  \n",
       "urgent                    0.00    0.00         2.0  \n",
       "hot                       0.00    0.00        77.0  \n",
       "numfailedlogins           0.00    0.00         4.0  \n",
       "numcompromised            0.00    0.00      7479.0  \n",
       "suattempted               0.00    0.00         2.0  \n",
       "numroot                   0.00    0.00      7468.0  \n",
       "numfilecreations          0.00    0.00        43.0  \n",
       "numshells                 0.00    0.00         2.0  \n",
       "numaccessfiles            0.00    0.00         9.0  \n",
       "count                    16.00  143.00       511.0  \n",
       "srvcount                  8.00   17.00       511.0  \n",
       "serrorrate                0.00    1.00         1.0  \n",
       "srvserrorrate             0.00    1.00         1.0  \n",
       "rerrorrate                0.00    0.00         1.0  \n",
       "srvrerrorrate             0.00    0.00         1.0  \n",
       "samesrvrate               1.00    1.00         1.0  \n",
       "diffsrvrate               0.00    0.06         1.0  \n",
       "srvdiffhostrate           0.00    0.00         1.0  \n",
       "dsthostcount            255.00  255.00       255.0  \n",
       "dsthostsrvcount          77.00  255.00       255.0  \n",
       "dsthostsamesrvrate        0.55    1.00         1.0  \n",
       "dsthostdiffsrvrate        0.03    0.07         1.0  \n",
       "dsthostsamesrcportrate    0.00    0.02         1.0  \n",
       "dsthostsrvdiffhostrate    0.00    0.01         1.0  \n",
       "dsthostserrorrate         0.00    1.00         1.0  \n",
       "dsthostsrvserrorrate      0.00    1.00         1.0  \n",
       "dsthostrerrorrate         0.00    0.00         1.0  \n",
       "dsthostsrvrerrorrate      0.00    0.00         1.0  \n",
       "lastflag                 21.00   21.00        21.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = df[continuous_cols].dropna().copy()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(data)\n",
    "\n",
    "print(\"Размер рабочей выборки:\", data.shape)\n",
    "display(data.describe().T)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690f544a",
   "metadata": {},
   "source": [
    "\n",
    "## 5. Поиск аномалий несколькими методами\n",
    "\n",
    "Используются:\n",
    "1. **Isolation Forest**\n",
    "2. **Local Outlier Factor**\n",
    "3. **One-Class SVM**\n",
    "4. **Elliptic Envelope**\n",
    "5. **KNN Distance**\n",
    "6. **PCA Reconstruction Error**\n",
    "\n",
    "Далее формируется консенсусная оценка аномальности.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e633d354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Методы выполнены: ['Isolation Forest', 'LOF', 'One-Class SVM', 'Elliptic Envelope', 'KNN Distance', 'PCA Reconstruction']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predictions = {}\n",
    "scores = {}\n",
    "\n",
    "# 1. Isolation Forest\n",
    "iso = IsolationForest(contamination=0.05, random_state=42)\n",
    "predictions[\"Isolation Forest\"] = (iso.fit_predict(X) == -1).astype(int)\n",
    "scores[\"Isolation Forest\"] = -iso.score_samples(X)\n",
    "\n",
    "# 2. LOF\n",
    "lof = LocalOutlierFactor(n_neighbors=20, contamination=0.05)\n",
    "predictions[\"LOF\"] = (lof.fit_predict(X) == -1).astype(int)\n",
    "scores[\"LOF\"] = -lof.negative_outlier_factor_\n",
    "\n",
    "# 3. One-Class SVM\n",
    "ocsvm = OneClassSVM(kernel=\"rbf\", gamma=\"scale\", nu=0.05)\n",
    "predictions[\"One-Class SVM\"] = (ocsvm.fit_predict(X) == -1).astype(int)\n",
    "scores[\"One-Class SVM\"] = -ocsvm.score_samples(X)\n",
    "\n",
    "# 4. Elliptic Envelope\n",
    "ee = EllipticEnvelope(contamination=0.05, random_state=42)\n",
    "predictions[\"Elliptic Envelope\"] = (ee.fit_predict(X) == -1).astype(int)\n",
    "scores[\"Elliptic Envelope\"] = -ee.score_samples(X)\n",
    "\n",
    "# 5. KNN Distance\n",
    "knn = NearestNeighbors(n_neighbors=5)\n",
    "knn.fit(X)\n",
    "distances, _ = knn.kneighbors(X)\n",
    "knn_score = distances[:, -1]\n",
    "knn_threshold = np.percentile(knn_score, 95)\n",
    "predictions[\"KNN Distance\"] = (knn_score >= knn_threshold).astype(int)\n",
    "scores[\"KNN Distance\"] = knn_score\n",
    "\n",
    "# 6. PCA Reconstruction Error\n",
    "pca = PCA(n_components=min(5, X.shape[1]))\n",
    "X_pca = pca.fit_transform(X)\n",
    "X_rec = pca.inverse_transform(X_pca)\n",
    "pca_score = np.mean((X - X_rec) ** 2, axis=1)\n",
    "pca_threshold = np.percentile(pca_score, 95)\n",
    "predictions[\"PCA Reconstruction\"] = (pca_score >= pca_threshold).astype(int)\n",
    "scores[\"PCA Reconstruction\"] = pca_score\n",
    "\n",
    "print(\"Методы выполнены:\", list(predictions.keys()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d164820",
   "metadata": {},
   "source": [
    "## 6. Сводная таблица по методам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9e0d3e1",
   "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>Метод</th>\n",
       "      <th>Найдено аномалий</th>\n",
       "      <th>Доля аномалий</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Isolation Forest</td>\n",
       "      <td>4343</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LOF</td>\n",
       "      <td>4343</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Elliptic Envelope</td>\n",
       "      <td>4343</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN Distance</td>\n",
       "      <td>4343</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PCA Reconstruction</td>\n",
       "      <td>4343</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>One-Class SVM</td>\n",
       "      <td>4342</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Метод  Найдено аномалий  Доля аномалий\n",
       "0    Isolation Forest              4343           0.05\n",
       "1                 LOF              4343           0.05\n",
       "2   Elliptic Envelope              4343           0.05\n",
       "3        KNN Distance              4343           0.05\n",
       "4  PCA Reconstruction              4343           0.05\n",
       "5       One-Class SVM              4342           0.05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "summary = []\n",
    "\n",
    "for method in predictions:\n",
    "    summary.append({\n",
    "        \"Метод\": method,\n",
    "        \"Найдено аномалий\": int(predictions[method].sum()),\n",
    "        \"Доля аномалий\": round(float(predictions[method].mean()), 4)\n",
    "    })\n",
    "\n",
    "summary_df = pd.DataFrame(summary).sort_values(\"Найдено аномалий\", ascending=False).reset_index(drop=True)\n",
    "display(summary_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4176ca",
   "metadata": {},
   "source": [
    "\n",
    "## 7. Консенсусное решение\n",
    "\n",
    "Объект считается консенсусной аномалией, если его отметили как аномальный\n",
    "**не менее 3 из 6 методов**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c90a0855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество консенсусных аномалий: 2968\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>numfailedlogins</th>\n",
       "      <th>numcompromised</th>\n",
       "      <th>suattempted</th>\n",
       "      <th>numroot</th>\n",
       "      <th>numfilecreations</th>\n",
       "      <th>...</th>\n",
       "      <th>dsthostdiffsrvrate</th>\n",
       "      <th>dsthostsamesrcportrate</th>\n",
       "      <th>dsthostsrvdiffhostrate</th>\n",
       "      <th>dsthostserrorrate</th>\n",
       "      <th>dsthostsrvserrorrate</th>\n",
       "      <th>dsthostrerrorrate</th>\n",
       "      <th>dsthostsrvrerrorrate</th>\n",
       "      <th>lastflag</th>\n",
       "      <th>votes</th>\n",
       "      <th>consensus_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7930</td>\n",
       "      <td>718</td>\n",
       "      <td>8185</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>40</td>\n",
       "      <td>786</td>\n",
       "      <td>517948</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>13164</td>\n",
       "      <td>1204</td>\n",
       "      <td>11403</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.57</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2414</td>\n",
       "      <td>146</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2</td>\n",
       "      <td>2194619</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0</td>\n",
       "      <td>762</td>\n",
       "      <td>389</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>7800</td>\n",
       "      <td>1315</td>\n",
       "      <td>5076</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.00</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>5930</td>\n",
       "      <td>197</td>\n",
       "      <td>5339</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.25</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>2832</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.04</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>10485</td>\n",
       "      <td>145</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>0</td>\n",
       "      <td>3069</td>\n",
       "      <td>639</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>2552</td>\n",
       "      <td>589</td>\n",
       "      <td>28390</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>1557</td>\n",
       "      <td>329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration  srcbytes  dstbytes  urgent  hot  numfailedlogins  \\\n",
       "12       7930       718      8185       0    0                0   \n",
       "38         40       786    517948       0    0                0   \n",
       "54      13164      1204     11403       0    0                0   \n",
       "106      2414       146       105       0    0                0   \n",
       "183         2   2194619         0       0    0                0   \n",
       "186         0       762       389       0    0                0   \n",
       "188      7800      1315      5076       0    0                0   \n",
       "220      5930       197      5339       0    0                1   \n",
       "238         0        93      2832       0    0                0   \n",
       "247     10485       145       105       0    0                0   \n",
       "327         0       125       240       0    0                0   \n",
       "366         0      3069       639       0    0                0   \n",
       "396      2552       589     28390       0    0                0   \n",
       "410         0        71         0       0    0                0   \n",
       "439         1      1557       329       0    0                0   \n",
       "\n",
       "     numcompromised  suattempted  numroot  numfilecreations  ...  \\\n",
       "12                0            0        0                 0  ...   \n",
       "38                0            0        0                 0  ...   \n",
       "54                0            0        0                 0  ...   \n",
       "106               0            0        0                 0  ...   \n",
       "183               0            0        0                 0  ...   \n",
       "186               0            0        0                 0  ...   \n",
       "188               0            0        0                 0  ...   \n",
       "220               2            2        6                 0  ...   \n",
       "238               0            0        0                 0  ...   \n",
       "247               0            0        0                 0  ...   \n",
       "327               0            0        0                 0  ...   \n",
       "366               0            0        0                 0  ...   \n",
       "396              21            1       17                 0  ...   \n",
       "410               0            0        0                 0  ...   \n",
       "439               0            0        0                 0  ...   \n",
       "\n",
       "     dsthostdiffsrvrate  dsthostsamesrcportrate  dsthostsrvdiffhostrate  \\\n",
       "12                 0.50                    0.25                    0.00   \n",
       "38                 0.02                    0.01                    0.00   \n",
       "54                 0.03                    0.02                    0.00   \n",
       "106                0.85                    1.00                    0.00   \n",
       "183                0.02                    0.39                    0.00   \n",
       "186                0.01                    0.00                    0.01   \n",
       "188                0.02                    0.01                    0.00   \n",
       "220                0.08                    0.01                    0.00   \n",
       "238                0.51                    0.01                    0.00   \n",
       "247                0.39                    0.80                    0.00   \n",
       "327                0.69                    0.01                    0.02   \n",
       "366                0.01                    0.00                    0.01   \n",
       "396                0.02                    0.00                    0.00   \n",
       "410                0.05                    0.25                    0.00   \n",
       "439                0.27                    0.09                    0.01   \n",
       "\n",
       "     dsthostserrorrate  dsthostsrvserrorrate  dsthostrerrorrate  \\\n",
       "12                0.00                  0.00               0.25   \n",
       "38                0.00                  0.00               0.00   \n",
       "54                0.00                  0.00               0.14   \n",
       "106               0.00                  0.00               0.00   \n",
       "183               0.00                  0.00               0.00   \n",
       "186               0.88                  0.75               0.00   \n",
       "188               0.00                  0.00               0.09   \n",
       "220               0.03                  0.25               0.65   \n",
       "238               0.06                  0.36               0.47   \n",
       "247               0.00                  0.00               0.00   \n",
       "327               0.00                  0.00               0.64   \n",
       "366               0.99                  0.69               0.00   \n",
       "396               0.00                  0.00               0.00   \n",
       "410               0.22                  0.00               0.02   \n",
       "439               0.00                  0.00               0.00   \n",
       "\n",
       "     dsthostsrvrerrorrate  lastflag  votes  consensus_anomaly  \n",
       "12                   1.00        20      5                  1  \n",
       "38                   0.00        21      3                  1  \n",
       "54                   0.57        20      5                  1  \n",
       "106                  0.00        21      4                  1  \n",
       "183                  0.00        19      3                  1  \n",
       "186                  0.00        14      3                  1  \n",
       "188                  1.00        17      5                  1  \n",
       "220                  0.25        18      5                  1  \n",
       "238                  0.04         9      3                  1  \n",
       "247                  0.00        21      4                  1  \n",
       "327                  0.00        20      4                  1  \n",
       "366                  0.00        14      3                  1  \n",
       "396                  0.00        20      6                  1  \n",
       "410                  0.00        16      4                  1  \n",
       "439                  0.00        21      5                  1  \n",
       "\n",
       "[15 rows x 34 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pred_df = pd.DataFrame({method: predictions[method] for method in predictions}, index=data.index)\n",
    "pred_df[\"votes\"] = pred_df.sum(axis=1)\n",
    "\n",
    "consensus_threshold = 3\n",
    "consensus_flag = (pred_df[\"votes\"] >= consensus_threshold).astype(int)\n",
    "\n",
    "anomaly_df = data.copy()\n",
    "anomaly_df[\"votes\"] = pred_df[\"votes\"]\n",
    "anomaly_df[\"consensus_anomaly\"] = consensus_flag\n",
    "\n",
    "print(\"Количество консенсусных аномалий:\", int(consensus_flag.sum()))\n",
    "display(anomaly_df[anomaly_df[\"consensus_anomaly\"] == 1].head(15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18516298",
   "metadata": {},
   "source": [
    "## 8. Интегральный ранг аномальности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "775ad732",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .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>duration</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>numfailedlogins</th>\n",
       "      <th>numcompromised</th>\n",
       "      <th>suattempted</th>\n",
       "      <th>numroot</th>\n",
       "      <th>numfilecreations</th>\n",
       "      <th>...</th>\n",
       "      <th>dsthostsamesrcportrate</th>\n",
       "      <th>dsthostsrvdiffhostrate</th>\n",
       "      <th>dsthostserrorrate</th>\n",
       "      <th>dsthostsrvserrorrate</th>\n",
       "      <th>dsthostrerrorrate</th>\n",
       "      <th>dsthostsrvrerrorrate</th>\n",
       "      <th>lastflag</th>\n",
       "      <th>votes</th>\n",
       "      <th>consensus_anomaly</th>\n",
       "      <th>mean_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36194</th>\n",
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       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84214</th>\n",
       "      <td>9390</td>\n",
       "      <td>2099</td>\n",
       "      <td>74057</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86721</th>\n",
       "      <td>5158</td>\n",
       "      <td>89581520</td>\n",
       "      <td>7028652</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>18677</td>\n",
       "      <td>1544</td>\n",
       "      <td>32120</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46847</th>\n",
       "      <td>7510</td>\n",
       "      <td>7847476</td>\n",
       "      <td>474404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11961</th>\n",
       "      <td>3173</td>\n",
       "      <td>13904636</td>\n",
       "      <td>626184</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57078</th>\n",
       "      <td>5328</td>\n",
       "      <td>1604</td>\n",
       "      <td>920608</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7479</td>\n",
       "      <td>0</td>\n",
       "      <td>7468</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36947</th>\n",
       "      <td>60</td>\n",
       "      <td>7670332</td>\n",
       "      <td>117808</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64055</th>\n",
       "      <td>11014</td>\n",
       "      <td>1093</td>\n",
       "      <td>18740</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29265</th>\n",
       "      <td>7805</td>\n",
       "      <td>1828</td>\n",
       "      <td>148722</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29810</th>\n",
       "      <td>3138</td>\n",
       "      <td>329</td>\n",
       "      <td>205950</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.36</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67528</th>\n",
       "      <td>17903</td>\n",
       "      <td>5830</td>\n",
       "      <td>122343</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.11</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>31</td>\n",
       "      <td>18828976</td>\n",
       "      <td>33116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46880</th>\n",
       "      <td>4675</td>\n",
       "      <td>459</td>\n",
       "      <td>81673</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52766</th>\n",
       "      <td>15883</td>\n",
       "      <td>2109</td>\n",
       "      <td>144715</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51369</th>\n",
       "      <td>5632</td>\n",
       "      <td>3053</td>\n",
       "      <td>439262</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>191</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50490</th>\n",
       "      <td>2514</td>\n",
       "      <td>1937</td>\n",
       "      <td>84751</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350</th>\n",
       "      <td>2670</td>\n",
       "      <td>176</td>\n",
       "      <td>13070</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33173</th>\n",
       "      <td>12096</td>\n",
       "      <td>99</td>\n",
       "      <td>2822</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67404</th>\n",
       "      <td>47</td>\n",
       "      <td>5664648</td>\n",
       "      <td>96140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.996065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration  srcbytes  dstbytes  urgent  hot  numfailedlogins  \\\n",
       "36194     11045      1836    129701       0    0                2   \n",
       "84214      9390      2099     74057       0   44                0   \n",
       "86721      5158  89581520   7028652       0    0                0   \n",
       "2984      18677      1544     32120       0    3                1   \n",
       "46847      7510   7847476    474404       0    0                0   \n",
       "11961      3173  13904636    626184       0    0                0   \n",
       "57078      5328      1604    920608       0    2                0   \n",
       "36947        60   7670332    117808       0    0                0   \n",
       "64055     11014      1093     18740       0   33                0   \n",
       "29265      7805      1828    148722       0    1                2   \n",
       "29810      3138       329    205950       0    0                1   \n",
       "67528     17903      5830    122343       0   77                0   \n",
       "3060         31  18828976     33116       0    0                0   \n",
       "46880      4675       459     81673       0    2                1   \n",
       "52766     15883      2109    144715       0    0                0   \n",
       "51369      5632      3053    439262       0    2                0   \n",
       "50490      2514      1937     84751       2    1                0   \n",
       "12350      2670       176     13070       0    0                0   \n",
       "33173     12096        99      2822       0    0                0   \n",
       "67404        47   5664648     96140       0    0                0   \n",
       "\n",
       "       numcompromised  suattempted  numroot  numfilecreations  ...  \\\n",
       "36194              75            0       74                 2  ...   \n",
       "84214               2            0        1                 8  ...   \n",
       "86721               0            0        0                 0  ...   \n",
       "2984               78            0       71                 0  ...   \n",
       "46847               0            0        0                 0  ...   \n",
       "11961               0            0        0                 0  ...   \n",
       "57078            7479            0     7468                 2  ...   \n",
       "36947               0            0        0                 0  ...   \n",
       "64055               0            0        0                 6  ...   \n",
       "29265              19            1       10                 8  ...   \n",
       "29810               4            1        3                 0  ...   \n",
       "67528               6            0        1                19  ...   \n",
       "3060                0            0        0                 0  ...   \n",
       "46880               2            0        0                 1  ...   \n",
       "52766              37            0       36                 2  ...   \n",
       "51369             193            1      191                 1  ...   \n",
       "50490              74            2       77                 9  ...   \n",
       "12350               2            1        2                 0  ...   \n",
       "33173               0            0        0                 0  ...   \n",
       "67404               0            0        0                 0  ...   \n",
       "\n",
       "       dsthostsamesrcportrate  dsthostsrvdiffhostrate  dsthostserrorrate  \\\n",
       "36194                    0.00                     0.0               0.51   \n",
       "84214                    0.00                     0.0               0.84   \n",
       "86721                    0.01                     0.0               0.00   \n",
       "2984                     0.01                     0.0               0.01   \n",
       "46847                    0.06                     0.0               0.00   \n",
       "11961                    0.03                     0.0               0.00   \n",
       "57078                    0.05                     0.0               0.00   \n",
       "36947                    0.06                     0.0               0.06   \n",
       "64055                    0.01                     0.0               0.01   \n",
       "29265                    0.02                     0.0               0.00   \n",
       "29810                    0.01                     0.0               0.01   \n",
       "67528                    0.00                     0.0               0.56   \n",
       "3060                     0.03                     0.0               0.03   \n",
       "46880                    0.00                     0.0               0.95   \n",
       "52766                    0.00                     0.0               0.42   \n",
       "51369                    0.01                     0.0               0.01   \n",
       "50490                    0.00                     0.0               0.00   \n",
       "12350                    0.00                     0.0               0.99   \n",
       "33173                    0.33                     1.0               0.00   \n",
       "67404                    0.03                     0.0               0.03   \n",
       "\n",
       "       dsthostsrvserrorrate  dsthostrerrorrate  dsthostsrvrerrorrate  \\\n",
       "36194                  0.62               0.00                  0.02   \n",
       "84214                  0.76               0.01                  0.10   \n",
       "86721                  0.00               0.00                  0.00   \n",
       "2984                   0.04               0.01                  0.07   \n",
       "46847                  0.00               0.00                  0.00   \n",
       "11961                  0.00               0.00                  0.00   \n",
       "57078                  0.00               0.00                  0.00   \n",
       "36947                  0.07               0.06                  0.07   \n",
       "64055                  0.05               0.00                  0.00   \n",
       "29265                  0.00               0.00                  0.00   \n",
       "29810                  0.05               0.09                  0.36   \n",
       "67528                  0.29               0.04                  0.11   \n",
       "3060                   0.04               0.03                  0.04   \n",
       "46880                  0.00               0.00                  0.00   \n",
       "52766                  0.07               0.02                  0.15   \n",
       "51369                  0.04               0.02                  0.08   \n",
       "50490                  0.00               0.00                  0.00   \n",
       "12350                  0.98               0.00                  0.00   \n",
       "33173                  0.00               0.00                  0.00   \n",
       "67404                  0.05               0.03                  0.05   \n",
       "\n",
       "       lastflag  votes  consensus_anomaly  mean_rank  \n",
       "36194        12      6                  1   0.999042  \n",
       "84214        13      6                  1   0.998843  \n",
       "86721        18      6                  1   0.998734  \n",
       "2984         19      6                  1   0.998724  \n",
       "46847        19      6                  1   0.998194  \n",
       "11961        20      6                  1   0.998102  \n",
       "57078        20      6                  1   0.997919  \n",
       "36947        20      6                  1   0.997864  \n",
       "64055        20      6                  1   0.997829  \n",
       "29265        20      6                  1   0.997806  \n",
       "29810        17      6                  1   0.997471  \n",
       "67528        15      6                  1   0.997183  \n",
       "3060         20      6                  1   0.996757  \n",
       "46880        20      6                  1   0.996690  \n",
       "52766        15      6                  1   0.996605  \n",
       "51369        20      6                  1   0.996494  \n",
       "50490        20      6                  1   0.996222  \n",
       "12350        13      6                  1   0.996148  \n",
       "33173        20      6                  1   0.996116  \n",
       "67404        20      6                  1   0.996065  \n",
       "\n",
       "[20 rows x 35 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "score_df = pd.DataFrame(index=data.index)\n",
    "\n",
    "for method, sc in scores.items():\n",
    "    score_df[method] = pd.Series(sc, index=data.index).rank(pct=True)\n",
    "\n",
    "score_df[\"mean_rank\"] = score_df.mean(axis=1)\n",
    "\n",
    "anomaly_ranked = anomaly_df.copy()\n",
    "anomaly_ranked[\"mean_rank\"] = score_df[\"mean_rank\"]\n",
    "anomaly_ranked = anomaly_ranked.sort_values([\"votes\", \"mean_rank\"], ascending=[False, False])\n",
    "\n",
    "display(anomaly_ranked.head(20))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83272485",
   "metadata": {},
   "source": [
    "## 9. Наиболее аномальные объекты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e401d47e",
   "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>duration</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>numfailedlogins</th>\n",
       "      <th>numcompromised</th>\n",
       "      <th>suattempted</th>\n",
       "      <th>numroot</th>\n",
       "      <th>numfilecreations</th>\n",
       "      <th>...</th>\n",
       "      <th>dsthostsamesrcportrate</th>\n",
       "      <th>dsthostsrvdiffhostrate</th>\n",
       "      <th>dsthostserrorrate</th>\n",
       "      <th>dsthostsrvserrorrate</th>\n",
       "      <th>dsthostrerrorrate</th>\n",
       "      <th>dsthostsrvrerrorrate</th>\n",
       "      <th>lastflag</th>\n",
       "      <th>votes</th>\n",
       "      <th>consensus_anomaly</th>\n",
       "      <th>mean_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36194</th>\n",
       "      <td>11045</td>\n",
       "      <td>1836</td>\n",
       "      <td>129701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84214</th>\n",
       "      <td>9390</td>\n",
       "      <td>2099</td>\n",
       "      <td>74057</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86721</th>\n",
       "      <td>5158</td>\n",
       "      <td>89581520</td>\n",
       "      <td>7028652</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>18677</td>\n",
       "      <td>1544</td>\n",
       "      <td>32120</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46847</th>\n",
       "      <td>7510</td>\n",
       "      <td>7847476</td>\n",
       "      <td>474404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11961</th>\n",
       "      <td>3173</td>\n",
       "      <td>13904636</td>\n",
       "      <td>626184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57078</th>\n",
       "      <td>5328</td>\n",
       "      <td>1604</td>\n",
       "      <td>920608</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7479</td>\n",
       "      <td>0</td>\n",
       "      <td>7468</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36947</th>\n",
       "      <td>60</td>\n",
       "      <td>7670332</td>\n",
       "      <td>117808</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64055</th>\n",
       "      <td>11014</td>\n",
       "      <td>1093</td>\n",
       "      <td>18740</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29265</th>\n",
       "      <td>7805</td>\n",
       "      <td>1828</td>\n",
       "      <td>148722</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration  srcbytes  dstbytes  urgent  hot  numfailedlogins  \\\n",
       "36194     11045      1836    129701       0    0                2   \n",
       "84214      9390      2099     74057       0   44                0   \n",
       "86721      5158  89581520   7028652       0    0                0   \n",
       "2984      18677      1544     32120       0    3                1   \n",
       "46847      7510   7847476    474404       0    0                0   \n",
       "11961      3173  13904636    626184       0    0                0   \n",
       "57078      5328      1604    920608       0    2                0   \n",
       "36947        60   7670332    117808       0    0                0   \n",
       "64055     11014      1093     18740       0   33                0   \n",
       "29265      7805      1828    148722       0    1                2   \n",
       "\n",
       "       numcompromised  suattempted  numroot  numfilecreations  ...  \\\n",
       "36194              75            0       74                 2  ...   \n",
       "84214               2            0        1                 8  ...   \n",
       "86721               0            0        0                 0  ...   \n",
       "2984               78            0       71                 0  ...   \n",
       "46847               0            0        0                 0  ...   \n",
       "11961               0            0        0                 0  ...   \n",
       "57078            7479            0     7468                 2  ...   \n",
       "36947               0            0        0                 0  ...   \n",
       "64055               0            0        0                 6  ...   \n",
       "29265              19            1       10                 8  ...   \n",
       "\n",
       "       dsthostsamesrcportrate  dsthostsrvdiffhostrate  dsthostserrorrate  \\\n",
       "36194                    0.00                     0.0               0.51   \n",
       "84214                    0.00                     0.0               0.84   \n",
       "86721                    0.01                     0.0               0.00   \n",
       "2984                     0.01                     0.0               0.01   \n",
       "46847                    0.06                     0.0               0.00   \n",
       "11961                    0.03                     0.0               0.00   \n",
       "57078                    0.05                     0.0               0.00   \n",
       "36947                    0.06                     0.0               0.06   \n",
       "64055                    0.01                     0.0               0.01   \n",
       "29265                    0.02                     0.0               0.00   \n",
       "\n",
       "       dsthostsrvserrorrate  dsthostrerrorrate  dsthostsrvrerrorrate  \\\n",
       "36194                  0.62               0.00                  0.02   \n",
       "84214                  0.76               0.01                  0.10   \n",
       "86721                  0.00               0.00                  0.00   \n",
       "2984                   0.04               0.01                  0.07   \n",
       "46847                  0.00               0.00                  0.00   \n",
       "11961                  0.00               0.00                  0.00   \n",
       "57078                  0.00               0.00                  0.00   \n",
       "36947                  0.07               0.06                  0.07   \n",
       "64055                  0.05               0.00                  0.00   \n",
       "29265                  0.00               0.00                  0.00   \n",
       "\n",
       "       lastflag  votes  consensus_anomaly  mean_rank  \n",
       "36194        12      6                  1   0.999042  \n",
       "84214        13      6                  1   0.998843  \n",
       "86721        18      6                  1   0.998734  \n",
       "2984         19      6                  1   0.998724  \n",
       "46847        19      6                  1   0.998194  \n",
       "11961        20      6                  1   0.998102  \n",
       "57078        20      6                  1   0.997919  \n",
       "36947        20      6                  1   0.997864  \n",
       "64055        20      6                  1   0.997829  \n",
       "29265        20      6                  1   0.997806  \n",
       "\n",
       "[10 rows x 35 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "top_anomalies = anomaly_ranked[anomaly_ranked[\"consensus_anomaly\"] == 1].head(10).copy()\n",
    "display(top_anomalies)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81b504b",
   "metadata": {},
   "source": [
    "## 10. Характеристика наиболее аномальных объектов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a03c9b3d",
   "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>index</th>\n",
       "      <th>votes</th>\n",
       "      <th>mean_rank</th>\n",
       "      <th>Описание</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36194</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>numfailedlogins: выше среднего; duration: выше...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84214</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9988</td>\n",
       "      <td>hot: выше среднего; numfilecreations: выше сре...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86721</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9987</td>\n",
       "      <td>srcbytes: выше среднего; dstbytes: выше средне...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2984</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9987</td>\n",
       "      <td>numfailedlogins: выше среднего; duration: выше...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46847</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9982</td>\n",
       "      <td>srcbytes: выше среднего; dstbytes: выше средне...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11961</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9981</td>\n",
       "      <td>srcbytes: выше среднего; dstbytes: выше средне...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57078</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9979</td>\n",
       "      <td>numcompromised: выше среднего; numroot: выше с...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36947</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9979</td>\n",
       "      <td>srcbytes: выше среднего; dstbytes: выше средне...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64055</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>hot: выше среднего; numfilecreations: выше сре...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29265</td>\n",
       "      <td>6</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>numfailedlogins: выше среднего; suattempted: в...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  votes  mean_rank                                           Описание\n",
       "0  36194      6     0.9990  numfailedlogins: выше среднего; duration: выше...\n",
       "1  84214      6     0.9988  hot: выше среднего; numfilecreations: выше сре...\n",
       "2  86721      6     0.9987  srcbytes: выше среднего; dstbytes: выше средне...\n",
       "3   2984      6     0.9987  numfailedlogins: выше среднего; duration: выше...\n",
       "4  46847      6     0.9982  srcbytes: выше среднего; dstbytes: выше средне...\n",
       "5  11961      6     0.9981  srcbytes: выше среднего; dstbytes: выше средне...\n",
       "6  57078      6     0.9979  numcompromised: выше среднего; numroot: выше с...\n",
       "7  36947      6     0.9979  srcbytes: выше среднего; dstbytes: выше средне...\n",
       "8  64055      6     0.9978  hot: выше среднего; numfilecreations: выше сре...\n",
       "9  29265      6     0.9978  numfailedlogins: выше среднего; suattempted: в..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "z_df = pd.DataFrame(np.abs(X), columns=continuous_cols, index=data.index)\n",
    "\n",
    "def describe_row(idx, top_n=3):\n",
    "    z = z_df.loc[idx].sort_values(ascending=False).head(top_n)\n",
    "    parts = []\n",
    "    for col, val in z.items():\n",
    "        direction = \"выше среднего\" if data.loc[idx, col] > data[col].mean() else \"ниже среднего\"\n",
    "        parts.append(f\"{col}: {direction}\")\n",
    "    return \"; \".join(parts)\n",
    "\n",
    "if len(top_anomalies) > 0:\n",
    "    desc = []\n",
    "    for idx, row in top_anomalies.iterrows():\n",
    "        desc.append({\n",
    "            \"index\": idx,\n",
    "            \"votes\": int(row[\"votes\"]),\n",
    "            \"mean_rank\": round(float(row[\"mean_rank\"]), 4),\n",
    "            \"Описание\": describe_row(idx)\n",
    "        })\n",
    "    desc_df = pd.DataFrame(desc)\n",
    "    display(desc_df)\n",
    "else:\n",
    "    print(\"Консенсусные аномалии не найдены.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f02f78b",
   "metadata": {},
   "source": [
    "## 11. Визуализация через PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f14f352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kMzOTq666ijlz5hAfH6+3ueWWW1i9ejUTJkxg/PjxmEwmOnTowMsvv0zjxo31drGxsfz+++9MnjyZzz77jHnz5hEZGcmFF15Ya+C5n58f7777LjfeeCNz5sxh5MiR+r4XX3zR5zHr1q1j3759XoUPX3rpJQwGQ63X7u/vXyeXthMhICCAb775hkWLFvHBBx8wZcoUSktLadCgATfddBNLliwhISGh3p81d5EsEAjODyS1qi1eIBAIBIIzgEOHDtG4cWNeffVVnnrqqdM9nONi0aJFTJw4sdqK6gKBQHAuIWIsBAKBQCAQCAQCwQkjhIVAIBAIBKeIhISEaoP8BQKB4FxDxFgIBAKBQHCK6NSpU7UxCQKBQHCuIWIsBAKBQCAQCAQCwQkjXKEEAoFAIBAIBALBCSOEhUAgEAgEAoFAIDhhzqsYC0VROHbsGMHBwSelyqxAIBAIBAKBQHAuo6oqRUVFNGjQwKMwqC/OK2Fx7NgxkpKSTvcwBAKBQCAQCASCs4qjR4+SmJhYY5vzSlgEBwcD2o2pS1VagWblycrKIjo6ulaVKhCI50VQH8TzIqgr4lkR1AfxvJxcCgsLSUpK0ufRNXFeCQuX+1NISIgQFnVEURTKy8sJCQkRH05BrYjnRVAfxPMiqCviWRHUB/G8nBrqEkYg7rZAIBAIBAKBQCA4YYSwEAgEAoFAIBAIBCeMEBYCgUAgEAgEAoHghDmvYiwEAoFAIBCcfpxOJ3a7vc7tFUXBbrdTXl4ufOYFtSKel/phMpkwGAwnpS8hLAQCgUAgEPwnqKpKeno6+fn59T5OURSKiopEHSpBrYjnpf6EhYURFxd3wvdLCAuBQCAQCAT/CS5RERMTQ0BAQJ0nMaqq4nA4MBqNYqIoqBXxvNQdVVUpLS0lMzMTgPj4+BPqTwgLgUAgEAgEpxyn06mLisjIyHodKyaKgvognpf64e/vD0BmZiYxMTEn5BYlHM8EAoFAIBCcclwxFQEBAad5JAKBoCquz2V9Yp98IYSFQCAQCASC/wyxgiwQnHmcrM+lEBYCgUAgEAgEp4gTXQEWCM4mhLAQCAQCgUAgOEls3bqVBx98kObNmxMeHk5ISAgFBQWne1gCwX+CEBYCgUAgEAgEtXD06FEGDhxIgwYNMJvNJCcnM2rUKHJycvQ269ev57rrriMuLo5ly5axefNm9u/fT2ho6GkcuUDw3yGyQgkEAoFAIDirUBSV1PwySmwOAs1GEsL8keVTF7vx77//0r59e5o3b87HH39M48aN+eeff3j66af55ptv+O233wgPD2fIkCG8+eabDB48+JSNRSA4kxEWC4FAIBAIThVOB3zzHHz5GBSk1N6+IAVWPqYd43Sc+vGdhezPLGL++gPMWLuXWd/tY8bavcxff4D9mUWn7JzDhw/HbDazZs0aOnToQMOGDenatSvr1q0jNTWVF154gd27d3P48GH2799PcnIyfn5+tGvXjl9++QWA7OxsJEnSf7p3717jOX/55Reuv/56/P39SUpKYuTIkZSUlOj7GzVqxJtvvqm/Hjt2LImJiRw6dIj169d7nKvqD8CiRYsICwvzee6tW7ciSRKHDh06kdsmOA8RwkIgEAgEgpON0wGrnoHXmsGm+fDXh/B+5+rFRUEKLO0JM9vClg+1Y9aO/0+HfDawP7OYhRsO8fexAsICTDSJCiIswMTfxwpYuOHQKREXubm5fPvttwwbNkzP9+8iLi6OPn368Mknn5CZmYndbmfx4sXMnz+fv/76i7Zt29KlSxfS0tKIjIwkLS2NtLQ07r///hrPeeDAAbp06cK9997L9u3b+eSTT/jll18YMWKEz/avv/46b7/9NmvXrqVRo0Zcc801+rlWrFgBoL9OS0s7OTdGIPCBcIUSCAQCgeBk4XRogsBWpAkEdwpS4P3OKANWc1SJ4GB2CZaSNC7a/xZBh9chlWRWtg1NhPbD/tuxn+Eoisq3/6STW2KjWUyQvvIe7GciyGJkX2Yxa/7JoElU0El1i9q3bx+qqtKqVSuf+1u1akVeXh4ZGRkAvPrqq3Tr1g2AefPm8f333zN37lymTp1KXFwcoBUks1qt1Z5z2rRp9OnTh8cffxyAZs2aMWvWLDp06MD8+fPx8/PT2y5YsIDJkyfz/fff62M0m836uSIiIgD01wLBqUQIC4FAIBAIThSXoLAWadaJ0EQIjIEKsaCqIElAQQrpb3bgOeVx7pW/5zppO8HkePYVGA0Dv9X6EOgcKyjnQFYx8aF+Xjn3JUkiPtSP/ZnFpOaXkRRx8ovwqapap3bXXnut/n9ZlrnmmmvYuXOnV7uvvvqKoKAgjEYjSUlJPPLIIwwfPhyAbdu2sX37dpYsWeJxfkVROHjwoC4gvvzyS3766SeaN2/ORRddVO9rKigoICgoCFmWiY2N5a677mLatGn17kcgcCGEhUAgEAgEJ8ra8fDbXEr84ikiiriCFFKUSMyEEiMXIEmV4qKBlMNieQJGSfHqxu4XhWnoeiEqfFBidWC1KwSYfU9d/M0GMgrLKbGd3NiUpk2bIkkSu3bt4u677/bav2vXLsLDw2nRokW1ffgqPnbjjTcyf/58HA4H69evZ/jw4bRs2ZKbb76Z4uJiHn74YUaOHOl1XMOGDfX/b9iwgU8++YTx48czceLEeouC4OBgtmzZgqqq7Ny5kwcffJC4uDhuueWWevUjELgQMRYCgUAgEADYy+GtGyBlc52aOw79Tt6Mq7nouc9pv74lh5UYAsvTcCiQokSSKOdgw0imoqUadYkLwKeoyCaUpRd/gBKccNIu6Vwi0GLEYpIprUY4lNmcWIwGAqsRHsdLZGQknTp1Yt68eZSVlXnsS09PZ8mSJTzwwANccMEFGI1GNmzYoO9XFIVff/2V1q1be19PYCBNmzalZcuWPPLIIzRu3Ji//voLgMsuu4ydO3fStGlTrx+z2az38dxzz3HfffexaNEiZsyYwebNdXt2XciyTNOmTWnWrBl33XUXnTp1YuvWrfXqQyBwRwgLgUAgEJy5OB2w+nnIO6T9ri2zUkGK1i73sPa7usxKrn4LUir//05HSN8G73fxFhcFKdi/epZH319L++c+5MuxXVAX3kp4wW4+Nk8kjUh62sZxWIkhUc4GpGrFhS8ylBCGB7zGrzl+pOaX+W50ntMg1I8LooNIKyj3cktSVZW0gnKaxgSREOZfTQ/Hz5w5c7BarXTu3JmffvqJo0ePsnr1ajp16kRCQgIvvvgiQUFBDBkyhKeffppVq1axa9cuhg0bxrFjxxg2zDtexmq1kp6eTkpKCkuXLuXQoUO0adMGgGeffZZff/2VESNGsHXrVvbt28eXX37pFbztip+46qqrePzxx3nooYew2Wz1urby8nLKysr4888/+eWXX47LpUogcCFcoQQCgUBwZuGKV7hiELx/K5Rmw+Z3wWmDPV/DgK89XYUKUmDjPGjYDlYM0tq52gN0ecn7HBWuS+z5GhrdoMVFBMeDZADFgfpeF2YlvsKMfXHEk8My8xSS5UxucOxhvHkr8XIeAHZVZqxtIIAuLlxtU5QoXVykKWE4VQmD5NtP/1H7E6TYw2lhc550V55zBVmW6HxhHGkF5ezL1GIt/M0GymxO0grKiQg0c+uFsaeknkWzZs34448/mDBhAvfffz+5ubnExcXRvXt3JkyYoE/wX3vtNSRJ4sEHH6SwsJDLLruMb7/9lvj4eK8+V69eTXx8PEajkYYNGzJ9+nQ6d+4MwMUXX8yPP/7ICy+8wPXXX4+qqlxwwQU88MAD1Y5x0qRJrFy5kokTJ/LSSz6eeR8UFBTg7++PJEnExsZy991388QTT/iMCREI6oKk1jUa6RygsLCQ0NBQCgoKCAkJOd3DOStQFIXMzExiYmKQZWHgEtSMeF4EdcbpQFkznszmvYlp3Fp7XlyCorwAtn4EsgkUu9ehangj0rp/SrHNQdjvMwhL/QlzaRoq4DGltITAsI2+4xUKUmDRbZB3CDU0EYdTxVScSpoSTpSUj0lSsasyj1hHM968WBcKJuzEygUA2FWJ+6yT2EZTj67dhUiKEoURJ3EVQqQ6UpRIhhqnktS4OWNva31Kgo9PN+Xl5Rw8eJDGjRt7ZDWqC6qq4nA4MBqNHMgq5tu/MziQVYzVobk/NY0J4tYLY2kaE3yKRi84m3B/XnzFtwi8qenzWZ/5s7BYCAQCgeC/Z+14rVbD0f0QGwc3PAGf9IP07WCqmFS7iQqXaFABKe8QpoWdSKIEfyrdPiS3dshG6PcZBMVpbk7th2kCw+mANWPBVkJhl3mUfvIQcQUpZChRgGZdKFEtoNowSQoLLK8jSVCs+mHGTkwtogK8LRc14QroTpRzeE8Zx5rID06JK8+5RNOYYJp0DPpPK28LBIK6IZYUBQKBQHBqccUwuOIe8g5pdR6CG0BxBvy1GOZfq4kKAHsp7qZ0D1FRsS2aPA9R4dFOMsLA1ZB4pe7ypC66jT17/2bFtH56wbrCJf14uHS4HhdhQMGuSgRKVuyYKlPEAkFSeZ1EhYs0Ihltr74OhbPiAt0DuuPJpvfOochFqbXf0/McWZZIigigZVwISREBQlQIBGcIQlgIBAKB4OTiHhgNlfEMb1+n/Z59hVY8zr0gnK3YQ0y4hITr/+6/faGq2n4HBlZcsgClwRU4HAq/RN9HKjFIeYfw++huPixuR4oSBWhWgrnmmYy0jSBFiSJezsMkqThUiQDJ5jPQui6iAjR3qFmmOdXuz1TDPLNFVWw3FafWXKFbIBAIzmCEsBAIBALBycUlJBZ2g5WPQes7tXgHa6G23+XipDiqFROu13VFkrRA6gFMZPwfZpo8v4qmY7+h7/I07ivXsjUly5nMMs9huG2kh7h4y/wGBpx6X8ZqAqwBxtsfqpOoWG6eVJEdyhOnKpGmhBEv52PDSJYa6n2tFRW6hbgQCARnG0JYCAQCgeDk0n4YhDeC/MOaZeKje8FaiFoxfa5JPEhUugbVlx+cl7CxvBGlVZIquaeC9SUu4uV84uU83YJQE5NNi7iE/dXur0lUOCqyQjkxcqwiniMqNFir0F0VV6YrgUAgOIsQwkIgEAgE9aOqq5P79m+egx9fhnsXVGZjshWjIiGhVsZBVBEP7q+PN4nLrca/mGp8z+c+X+Jisr0fDrXyazBSKqq27xw1GLsqYZIUPrVM9CkuqhMVThW+dVzKvdZJpEmxJMrZNAgNgNBEpMIUMJo9xYV/BFz9KHSaXM87IBAIBKcXkRVKIBAIBLXjSgXbfpi2ku6qAeGqKeF0wLs3aQXmAPatRU1qj9Vqw68800tUuMcVQGUQ84lmhrxe/pt4ckgj0mtf1WxN71hmAJolwSipGHxUw1ZVyFRDiZULtFS05Ovi4j7rRN0typVi1iUqUpQIflUu4hL5XzJvfIVWF3fk8/AA5KJ79TS3hCZp964gBS7tD/YSyNoHg9eCqX7pWAUCgeBMQAgLgUAgENSOK27ir8Vw91uaqMg7pE2SG92giYb0bagYkHBCURrSzs+QVVn3d3IXFb6or6io2peqQqKczTLzFHraxlUrLkbbh/GZZaK+rYAgIvG2Vrj6t6smt0J3vsXFIOMqkuVMDisx/Kq05suQXrzQszMtGobTwr3T0ERNjLnExWX9wRysWScM4itZIBCc3QhXKIFAIBB4U9Xdqf2wygDs//WHzi+hhjfSJsd/fUjxjv+jXA5AwulhiTBLCsWqmXQlDDhxi4Q7VUWFy+qRLGeyzDyFeHK8joknhxkmz9gFXy5QqgpPW4foqWhBIkWJJF7OI1sN83KLmubozafG29nc8QPaj1rC0qceoE3DcN8Dd4mLdsPhthlaZXAhKgQCwTmAEBYCgUAgqIyP+PIxTUy4LBSLbtNehyZqBedko5bN6ZN+HFZiKJO0Ym7Bjjz8lFIUJK8YilLVH0e9cjzVjnv/7qKiJnHhWRE7Us/I5AtJgsfNnzHSNqJWcbEs4RP2vXQH941dwn03XUOj6KDa6yqEJgpBUVecDvj2eSisY32PghRNFDsdtbcVCAQnFSEsBAKB4HzF3SrhqoT914daqtOL7tYyO7ncnQpSUALjKLngNhTJgKQ6aVTwO/5qGYrbJF9G9ZjgA8TIBSTKeSd16O79VxUVkgSKKpEsZzLIuArwFBWHlRj+VJoTLRV49OlQZQ4pMWRUZIdyr3PhEhdlDa9HDWuk1bxocw/EXYL/I9+LAm2nkrXjkX6bh3HxXbWn4C1I0Z7X3+Zqz/RJYsCAAXTv3t1jW1ZWFhdddBFXX301BQUFvg8UCM4zhLAQCASC8w2XoPh6dKVV4qK7tWBi0CZnyweg3P0u9pBkyDuEfdZVFM67mcB9X2JVDXpXqgqy5DurkySBo56pY+uTalbrX9L/r6iV22RJZYfSiGmO3l6iYqRtBJfLe/V+0pRQilQ/jJKCBAy1PelR5+Lz6HdIHroU2g2n2UPvIT1U4cZ09zvwyE8i0PpU034YangjpPxD8MHt1YsLl6jIO6SJ4vbVVz4/UbKysrjpppvw9/dnzZo1hIbWnqpYIDgfEMJCIBAIzjdcbk4HvtfERN4hWDEYeixEdaWILUih4MO+vOjohwMZk7OEMHsGDiT8sQGewdNVYydcE35jPRfyfcVg1CQ2jJJKqWoG4Cvn1exQGnGz9TUWOLrS3TaFGPK9RMVc8ywSZc1FKkWJ5B7bVG61vsqRilS0y6LeJW7gx7rQkos0oUX7YZrrknBj+m8JTYQHv0INa4TkZkHzoKqocGUrOwVkZ2dz8803Y7FYWLt2rYeoOHLkCHfddRdBQUGEhIRw//33k5GRoe+fOHEibdu29ehv/fr1SJJEfn6+vm3Dhg107NiRgIAAwsPD6dy5M3l5mtVPURSmTZtG48aN8ff355JLLuHTTz+tsT8ASZL44osv9NcpKSn06tWLiIgIAgMDueKKK9i0aROHDh1ClmX++OMPj+PffPNNkpOTURQte9o///zD7bffTkhICMHBwVx//fUcOHAA8G3hWbRoEWFhYTXeCxdffPEFUpU/Bl9++SWXXXYZfn5+NGnShEmTJuFwVO/u9scff3DrrbcSFRVFaGgoHTp0YMuWLTXeE4COHTvy+OOP66/z8vLo378/4eHhBAQE0LVrV/bt2+dxXZIkceedd3r0M3PmTCRJYsCAAfq2xYsXc8UVVxAcHExcXBy9e/cmMzPTa+yNGjVCkiSPH9c4fb2//fr182hz6NAhJEli69ateptx48YhSRJvvvlmtffsZCCExamguhzv1SH8QQUCwX+Jq4BdQQqg6uKibNlDvBs7gSyDVlMh3JHB2NIXMVKZhtVYEZpdW2rYmqpX1xd3t6eqv9GugI8dNzLaMYI7bC9xmHimOvrhxOCRramnbRx3GDe6pYSN5J+uS/l5al82Tu9Pwyd/gPBG+Bcfxbj7Cxi42tOKIwrWnT5CE3H0+7IyYYC7uPgPRUVOTg633HILRqORtWvXekyUFUXhrrvuIjc3lx9//JG1a9fy77//8sADD9TrHFu3buXmm2+mdevWbNy4kV9++YU77rgDp1OrDj9t2jQ+/PBD3nrrLf755x9Gjx5N3759+fHHH+t8juLiYjp06EBqaiorV65k27ZtPPPMMyiKQqNGjbjllltYuHChxzELFy5kwIAByLJMamoqN9xwAxaLhe+//54///yTgQMH1jjRPxF+/vln+vfvz6hRo9i5cydvv/02ixYt4sUXX6z2mKKiIvr3788vv/zCb7/9RrNmzejWrRtFRdXXq/HFgAED+OOPP1i5ciUbN25EVVW6deuG3W7X2wQEBLBx40ZSUyvjgN555x0SEhI8+rLb7UyZMoVt27bxxRdfcOjQIQ/h4UJVVSZPnkxaWhppaWk1ju/PP/9k5cqVNbZJSUnhzTffxN/fvw5XfGKI5ZZTgWs10D3He3W4/0EEbRVMIBCcG7jXfqjLRMc1eXUVRnM/1tXX1UNh0zvefbofazBWvr7qYfj9bc90plVSntqDEigxxxJWfJRue8cymlG8pr5GnJSnCwknEga3fE/V1Z2Ya7uDh01f+RQWJ1Knwv18rroTrteBkq1ijAav46Y5egPwnqMbaUQyk95cER/MRdEWEm9+jkT3e+i6L+73ceBqreCfKVAUrDvdhCTAg19p7lAucXH3O/D50P9EVOTl5XHLLbewc+dOLr/8ckJCQjz2f/fdd+zYsYODBw+SlKQJ0g8//JALL7yQzZs3c+WVV9bpPK+88gpXXHEF8+ZVCtkLL7wQAKvVyksvvcS6deto3749AE2aNOGXX37h7bffpkOHDnU6x9KlS8nKymLz5s1EREQA0LRpU33/4MGDeeSRR3jjjTewWCxs2bKFHTt28OWXXwIwd+5cQkNDWbZsGSaTCYDmzZvX6dzHw6RJk3juued48MEHAe2ap0yZwjPPPMOECRN8HnPjjTdiNBp1y8c777xDWFgYP/74I7fffnudzrtv3z5WrlzJhg0buOaaawBYsmQJSUlJfPHFF/To0QMAk8lEr169eP/99xk3bhy//PILBoOBK664wqO/gQMH6v9v0qQJs2bN4sorr6S4uJigoCB9n91uJyIigri4uFrH+MQTT/D0008zbty4atu88MILPPDAA6xbt65O130iCGFxKmg/zDPH+4CvUYITSM0vo8TmINBsJCHMH7ko9T/zBxUIBKeBE1lkAM9jXUXp/lqspXzd8iE0v7Vysut+bPthla9d7Q98D006wq1TK915BnyN/b1umAoPY8KPUiwkksErzEDGs1icAW+h4EtcDDV9hRMZI846ta+OqsHYAD85LyRCLmGE7TE+NL9MspyJVZWxSArXVFMYz4mBJYEDeKfPVVyYGF4RZH2n9wlduNyc3F/fObv2AQv+G6rWAXn/Vm37KRYVAD/99BMXX3wxW7dupUOHDrzyyiuMGTNG379r1y6SkpJ0UQHQunVrwsLC2LVrly4sduzY4TGJdFkiXGzdulWfsFZl//79lJaW0qlTJ4/tNpuNSy+91GNbYmL192Lr1q1ceumluqioSvfu3Rk+fDiff/45PXv2ZNGiRdx44400atRIP/7666/XRYUvvvrqK4/rdDgc+Pl5xiO57oXBYKBBgwY8+OCDPPfcc159bdu2jQ0bNnhYKJxOJ+Xl5ZSWlhIQEOB1TEZGBhMnTuTHH38kMzMTp9NJaWkpR44c8WjXq1cvDIbKRYmysjLdRWvXrl0YjUauvvpqfX9kZCQtWrRg165dHv0MHTqU22+/nRdeeIF33nmHIUOG8N1333m0+fPPP5k4cSLbtm0jLy9Pdys7cuQIrVu31tsVFhYSGBjodU1V+eKLL/j333958sknqxUWW7Zs4fPPP2fPnj1CWJy1VF0NfK8bS1vOY1tREOUOJ35GA5cEF9N79zBMhYf/kz+Ix4XTAWvGQ/PeQEzt7auumAoE5zsnusjgOnZWW2h4LWpoIlJBCioSkq0I/l6B89+fcEomzCXHsIU0pDCgMZEz2yIpdpAMmqiQDJC1S/uRZH3irAQnsLTlPHpsvp9AtQTQLBMNyKau2WGrZmQySSr4EBXu7evbr1IRIN5IzqSnbbxeQXuQcRWLHJ1ZYn5JTy/rKozXKNTIG70vo21SlMjYdK4RmqhZKlyiArTXp/g7tEmTJnz33XdERUUxb948+vbty2233cbFF19cr35atGjh4bqyadMm+vbtq7+uyV2luLgYgK+//trLzcZisXi8/vnnnwkODtZfN2vWrE7nADCbzfTv35+FCxdyzz33sHTpUmbOnFnn40GzGMyfP19//dlnn/HSS55eGa574XQ6+e233xgyZAhNmzbFaPScQxQXFzNp0iTuuecer/NUFSsuBg0aRG5uLjNnziQ5ORmLxUL79u2x2Wwe7WbMmMEtt9yiv+7Tp0+t1+aLiy66iAYNGrBs2TK++uorZs2a5SEsSkpK6Ny5M507d2bJkiVER0dz5MgROnfu7DGmwsJCSkpKaNCgQY3ns9vtPPPMM7z44os1vh9PPvkkTz31FPHx8cd1XfVFzP5OFVVWA7ttGUxeq3k4whIwFqfSbcswTI407CHJmM5EUQGV6SeP7ocer0N4ku92Tgd8NRr+/QEKjmrbanLpEgJEcL5woosMA76GWZeC0wYH11NmDKXcGEuEI0OrdA0YSrMwAOlEsajsDp79/qlKTaA6NVGhVkz0Q5M8LKOp+WVsKwoit+GrPHZ4BEYUn5YJd3xVu37aOoTHzZ/pAdEuChU/gqRyfM3rVdcF+MChakHfkgROFR6xjmaseYmXeJjq6AdAT9s4VgRMJZkMfrlhJ4Zu02u8BsFZTkGK5v7kzudDT/kCXZs2bYiK0rKF9ejRg88++4z+/fvz+++/YzabadWqFUePHuXo0aO61WLnzp3k5+d7rEabzWYPt6OUFM94zIsvvpjvvvuOSZMmeY2hdevWWCwWjhw5UqvbU+PGjT1iQKqeY8GCBeTm5lZrtRg8eDAXXXQR8+bNw+FweEzqL774Yj744APsdnu1VovAwECP64yJ8V6gdL8XLVq0YM6cOWzdutXLheiyyy5jz549Hv3Vxq+//srcuXPp1q0bAEePHiU7O9urXVxcnEe/7pP0Vq1a4XA42LRpk+4KlZOTw549ezzeUxcPP/wwjzzyCN27d/e697t37yYnJ4fp06frz0fVAHmAzZs3I0lStYHtLubPn09QUBD9+vWrts3KlSvZu3cvX3/9dY19nUxE8PYpxLUamGWMJ9qRxoB9I0gs3s6AfSOIdqSRZYzn41bzUIITau/sdOAK8CzOgA/v1PLYKypHc0vZnV7I0dxSFEXVRMVfH2qiosrExYtTlGNcIDhjcS0yhCTriwwNjbk0iQqioTGXblsGYyo8rKV1rToxCk2EHov0qX6AowCzWk6eMdarCJ0/pTxrm+sxV3cieYqKgas9+i+xOSh3OMkLv4QX/Z+t9hKcauVXha9q14+bP8Nfsnkd5y9ZmW7r6bPPmgO/tZoSAAYJbjL+RU/bOA67sjZVFL67rkkISwddxc9T+9Jg1PfQbjiGzlOr71hw9lM1UHvgGq96K/8Vc+fOJTMzUxcAt9xyC23atKFPnz5s2bKF33//nf79+9OhQweviXJNjBkzhs2bNzNs2DC2b9/O7t27mT9/PtnZ2QQHB/PUU08xevRoPvjgAw4cOMCWLVuYPXs2H3zwQZ3P0atXL+Li4ujevTsbNmzg33//ZcWKFWzcuFFv06pVK9q1a8ezzz5Lr169PCbcI0aMoLCwkJ49e/LHH3+wb98+Fi9ezJ49e+o8BtCClMvLyykpKeH7779n586dXHTRRV7txo8fz4cffsikSZP4559/2LVrF8uWLWPs2LHV9t20aVM++ugjdu3axaZNm+jTp0+9g5ebNWvGXXfdxZAhQ/jll1/Ytm0bffv2JSEhgbvuusur/f33388LL7zg4SLnomHDhpjNZmbPns2///7LypUrmTJlikebH374geHDh9OtWzefQsydV155hddff90re1bVNlOnTvXpKnaqEMLiFOJaDVzSah75fgmElafSc8dgwspTyfdLYEmreWwtDCI1v+x0D9U3oYnQfyUExeqrrYtXb2DG2r3M+m4fM9buZfHqDdj3ufsQajMdnwLkP8wxLhCcSZzIIoPSvBtft3pdFxdBzgIkezF2VfLIlhRKqcdk3a5WxkXYgxK8RAVAoNmIn9GAoTiVoWXvVjt+g6R4FMEDzZLwtHUIKUokiXIOkZJnphWXW9Rz5mV1ukcO1fPryCgp+rZr5J1IwLSoaeT7JZAsZ7Khwy4+Gno91zSLxmiURQrY84HC1MrAbZd1r+HV2u/TIC4iIiJ49913efnll9m0aROSJPHll18SHh7ODTfcwC233EKTJk345JNP6tVv8+bNWbNmDdu2beOqq66iffv2fPnll7p70JQpUxg3bhzTpk2jVatWdOnSha+//prGjRvX+Rxms5k1a9YQExNDt27daNOmDdOnT/eINQDNnchms3kEHYMWZ/D999/r2aUuv/xy3n333RpjLnyxfft2/P39CQkJYcCAATz55JP07Om9GNG5c2e++uor1qxZw5VXXkm7du2YMWMGycnJ1fb99ttvk5eXx2WXXUa/fv0YOXJkrZN1XyxcuJDLL7+c22+/nfbt26OqKqtWrfJ5rf7+/jz77LO0atXKa190dDSLFi1i+fLltG7dmunTp/Paa695tBk4cCDXX389H330Ua3juvHGG7nxxhtrbNO0aVM94P2/QlLV+pQjOrspLCwkNDSUgoICr2wOp4Ld6YXM+m4fTaKCSCzeTs8dg/V9y9os4GhQGw5ll/DYzc1oGXfqx3M8KIpC5sGdhH/ZF0vhQbKM8SxpNQ9HkObS1WfXMKIdadiDEjAZJChIwR6SzMctZtPwwGLWhtxLmX987TElwj3qnEBRFDIzM4mJiUGWxbqFi6O5pcxYu5eGxlwG7BtBWHllSsJ8vwQWNZvDEUcEozs1JykiwOexV1p/o+e/z3pYJOyqVBHT4InLlQggXYrm2ysX0q/LdV6xBoqiMvOz73ng76FaXAXgVCUMdUwVm6JEslNpxK3GPyuOhedtgxlmWkmynOnhNpWjBhNBkf5aVUGVZI6FXMKfuf687+jCXPNML3cqBQMyTq2GwUMV5nzxt+KspLy8nIMHD9K4ceNq/eKrQ80/Cotu14rkVfcd8h+lnD2fmDJlCsuXL2f79u2neyj1QlVVHA6HR1YoQc3U9Pmsz/xZfPOfQlyrgcbiVLrs80yH1mXfBEzFx7AYDQSaz+wvR8U/kmUtZte82nrhOygDVuvuHvf90YuOuct5Ou1JWit7a3b3OBH3KFEzRHAW4HI5cgQlsLqZp9/06maTsAc1wOpwUmLzfi5dxx6N7sgrxkc99lkx+zyfS1QcI4rh5pf4Ndvf2zKashnbzMsY+ndvXVQ4VFkXFb6WnOyqxLeOy0hTwgGtKvVNhi36PoMEzQypjLSN0C0qAJlKKBZsHhYVqcLdyYjKFaaDXH9Za6x9vsYW5BmwKOMES4g2odw4T1gmzkcKUuADTVSo1YkGVzzTaXKLOtcoLi7m77//Zs6cOTz22GOneziCswghLE4hCWH+XBJcTJ9dw3T3p2VtFuhuUX12DaNtSDEJYae+YMmJkFNiY1tx7S5dKUqE7u4RoJbglAyEWVN5aO8jugB5u/EsdpYEnzz3KFc6z7p8iYj4DsFp4kQWGQJlB2OOPEzw4TX0tn/msS9IstZ43uelx8k2RFFqd3iKlt1foy64Bb+CfwmkHIAi1Q+jpKU+TFEiGWMb6NWfSVJpKafwiG20Li6Mkkqh6s991kkscHRlkaMzc80zPSwp/pJVH2upaiaTsIoLiEYNjCJezeCptKe4IC4Y85C1npPGuIvhkZ+h3XBRR+J8ZeM8pLxDqGGNtDoW1VkiqooLUdDwuBkxYgSXX345HTt29HKDEghqQgiLU4hclErv3cP0SfWiZnM4GtSGRc3m6Kv/vXYN01JNnsFYHU6sdVht/Te7xCOmxFARNGpQnTgx8FrQ03yyV+GVb/dUxme81+3ETNeuAHO3FSoR3yE40zjuRQang8RFV5Jk3ccj6eNIkjI4osaw1HGL7xNV4TX1VcLsGeQU2fhxTxYHs4tRdn6Fuqy3h0tVmhJON+t03nN04WNHR4bbRjHC5F3J1aFKJMuZ9DR+rxejK1T96Wadxjaa8p6jG0vML+nuTOlKODmGGIKlcuyGQDIa3kbuQxuIGvULhDfCWJKhjaOi8rdukRj4LVzaH65+FIZo1bCFleI8ptNk1HbDcPT7svbvCJe4EEL0hFi0aBFWq5VPPvnEK+5CIKgJISxOFRUTWZf7z6rLFnDEEcGh7BKOOCL45vIFutvQmW6ytRgNWOqw2gpU6+5hwMmogleII5u4EL/as+HUFdeXSFiyFmD+7q16gPmcdbs58NFIVnz5hbeAAW+XKOEmJThFHPciw9rxSKXZempZVYWV9nb0MtStyFEUBbxjfZaC9EPMWLuH2XNnIf2vjy4qnEisdLTnHttkUohhqqM/sxz3Mtc8i0RZc4/KVEL1IGqjpFKkWpBQSZSzOazE0Nn6CikVdW4GGVeRLGdyRI3m56CulPb/lsiRmjAwOUuIbdCIxEbNkcOTKleWS7K1wn3uE8HQRLhrNnSdLsSEQHsGOr+kVd6uC8JdTiA4bYjg7VOBjyCyOhXFOgODzRRFIT09g7V/7afbX0OIdqSR76cJhy77JhBWnkqWIY6s6HaEdHmBNzaV+gxQdWDAiJNjchw/JY+i+5Gp+DlLyDLG883lC+jb+doTK2K18jGtEjGQY4hhceu3uS77f1yR9jFODBhwakJu0Cqtveu+txuufQG5v2eubYJ6I4K3feD2bNlDkvU6FlaHE4vRQNuQYnrtqiaxQcpmeL8LKA4PceEr5WtNZKqhzFQeYKr8jt62HDOjLFNYX9wQq1P7GkiQcvjENEkXFSlKJD1sE4khj48sLxEslevbf1baMMtxL+lEEhVgoGlMIEnhfgws+4DADo+RkNy88jNdTXIGJe8omb9+TMytjyObfMeLCM4dTih4WwTjCuqBeF7qz8kK3hbC4lSw+nnNj78uYuEMn9BWlxXKHtQAU/ExPSsUgBqayKeNptLxnzG6AFnRcDxd90+ggZKuiwsX5YZA3mr9kXc2HKdDi4FoP6xuQqsgBXX9yxT/8y3BtgxtkyWW9clPcvveMRjQXLE+u2QBd99wBXxwO8bCwzhCkpEf+lqb/JzhAu9sQQiLKpzIIgNUbpONHuLChV0FU5XvTPeMUO64C5BMQhkZ8BpZcgwFZTZyS+zEksNys7eoSCMSgPgq+zOkaP68ZSm3XHk5ZvPxuUqI5+X8wjVxSU5OrndefTFRFNQH8bzUn9LSUg4fPiyERX34z4TFcUyMT1r6xJN8biXvKJnLnyTm2FqcIUm+V1v/GYqpWLNOqJIBSXVqgdpNZpHijCAz5QBzbONooGZ49L2yxSvsiejgnXK3rsKsSsXv30Jvo2XZH4TZPM/jEjRZRBAsleOnlpJljONA4OUcbdSD7gcnaKvFlhDo9xkkXln7fRLpcX0iJopVOJFFBqg89ronUf/vMQ9RUZPlojpxAZCtBvOAMg17UAKyLOFUVPxKj7GICSRI3qLCYoAAsxGzUeaS0BJmFD9HYLm2mKDHQxynEBfPy/mFoijs27cPg8FAdHQ0ZrO5zpM+MVEU1AfxvNQdVVWx2WxkZWXhdDpp1qyZ19/j+syfxYzoVGAw1s/y4PIHPRm4siTt+bp+ExnwHkNBilZx2xmi+UgP+D/6BSdwU9XV1mvWwIJboCgNSXWiSAbmhD/H90eNlNnzsFoD2W9sSDwZHhOjGw7P5F9zMyzGCC0bjksUNboW/lpcGZBd3SrvZ0PhnxWAVgDs/8L7se2CR+i14yFC7Nn6ed4IHcNDBfOIJhdUKJcCSIu4mnaZX3LlttWaRcMcjMFaCCsG15wfHTTRVtN9EwhcuGIG6iL0XfFCLsHqotG1qP/r5ykq8BYV70l3M5AvkFErKlf7Fheb1Is4RhSNLUaigi0czC5hkHE1Cc5KUXG/bSJFlhi6t4rhvsuTiAq2uFlXrtDcswqOVgps8RkQ1AFZlmncuDFpaWkcO3asXseqqoqiKMiyLCaKgloRz0v9CQgIoGHDhie8yCOExblG+2GaqKhtUl7V9cJXlqSN87T9DTpBj48gNBEZvAp4ASBXukLIqpMRedMxNZlFkTmWrnvGcoN9M4DuyuGUDHo2nG8uX0BCiAnevQnSt8Hvb4Pi0CwIFRW/P24xm+T9H2BwlvG/mIe4zHSE23Z9pk+2JLQg8xKrnXKHhLueHlIwi2CK9dclkj8Hw67lwsz/q3CTklnddCLdjs1GyjuE7b2uZN/7GTEJF5CZeoCoFfdgLjyCGpqIVJwOC7tB/mGRXUpQOye6yNDlJXjrBiRFcyHMVENwSmbiyfY4bK6hN+/RnfXK5SxiPEaUai0Wl0u7iFOziQqKYMgNTfhhVyYf/TsYqVDBopbzkX9PLmrahL7tGnJd02jv2KfQRK2K948vgynw1GbeOZ3WX8EpwWw207BhQxwOB06ns/YDKlAUhZycHCIjI4V1S1Ar4nmpHwaD4aRZd4Qr1LlINcGi5Q4nfkZD7VWwXTgdKGvGk9m8NzGNW1f/4axw91BDE9lrak1E/g49xuLHRqO4ffdzGNDy4+fJ4UhGM2G2DEqlQALUEsqCkvBrdiPSXx9W9ikb4f4Psa0ag7nwMCVSIIFqCQDFhlACnQVaICsSUnAsFKWTb46l3OYkjmwcyBgrzunCgUy+FEaUmut1CblSBIo5CNlRToQzk0xDHPvNLWlu20mUM5NcYyxmWSHIlqUdEJoIF9wEt80QrlFuCNeWU4C9nPK3bmJzcSSTyh+gg7ydcerbHk0cGOinTmKfqSXdwlN4NGsqcWp2ta5S6YSz+qoP6d/legCO5pVyMFv7fDWJCiQxPODEkinUkVqfl3MoXk1wYoi/LYL6IJ6Xk4uovH2+U+FS4Upn223LYBoac2kSFVR9mldfFawNRug8FQKjvM/hnpq102RoN5y0e77g7egXPArp3bX7GQwoOJHJkSMJV/KwO1RWm29lWPBMMgzx+BcfpfifVaiSWwCo4sC26jkmGB4nhVgC1RIcFY9rkC4qYPkF09lwwxLshkDCbBmaqFC9RQVAIcG8HzrcY1uJKQKACDWXKOsRApVCyqUAYpzpXFO2nihnJoVyKEacuqhwGgO0697yIXw2VMtItbCbNgF6vQXYyyvvT+5hz98ila2gvpj8yOqzjhWNJnGp6QhjFU9RoQJGnHzIeDoFH+HKKAdxVIqKHDWIY2oUklRZTTuOPPr8PRi5KBVZlkiODKRjixg6toihYWTgfyIq6oSoUyMQCARnFcJicY6iKCqLV2+g25bBvlPEVk3zWs3KoE/VX83K4O70QmZ9t48mUUE0y1vPXbuf0cfzZctXOObfgru3P0ycM51MYzxLWs4lwGzigR2DCbNrAdeu4G9FMiCrTo6oMTwrjeRVZpOIZ1B2PiH0YDrDpRV0V7/zugdVM+gAOJAwUvnIW7FgxK5bVPRj3VZ33ftRqKrGXRLHDb9w8A/V7o8lBKyFmsuIvQQu6QP+YeesZUOsEp0aFEVl1fL3uG3Xk3rK2anywzykfkEiGfrzqkoyqIr+vGYRyj22qSgKLLVMIVnK9PxcBMfB4O9OWxa0Oj0vJ8sCKzirEX9bBPVBPC8nF2GxEJCaX+ZRBTusPJWeOwbrVX+XtJrH1sIgUvPLtAOqWRlMySvVf2pbGQw0G/HTC+l5+l13PPg65XYnzwRNI9MYT4wjjYf2P0aoPcNjdfTfsGtxmoORVScODDSUMnmV2Xwh3+x1jWEUslh9gWvULT7vgfuU32XtcImKcrSc+RasXqICPANj3cWJ9wdGdfu34nd5HuQdQpWNmqiQDJqoANjxiSbg1oz1OWaBwBfy3lWVogJ4xvQs/1Nuor9zHEfUGE1UAJKbqCg2hTP3gndJSG5GVGIT3mo0k7KgJE+xXZQOC24+owt0HpcFViAQCASnBSEszlFKbI5qq2CvbjYJe1ADrA4nJbYK1xxXRpoKcWF/rxuLV2/gzXX7+L9tx3hz3T4Wr95QWcHaEgL3LvD4Ek8I8+eS4GL67HwUi7MYqxzA/nDNhzvUmkG/nQ8jobKkpafYCbGmYzUEsCP0JkKL92GwFVFMAEacKEgkksEIZanHNbgqAcdJecSQh8PLNqGRRzDpariXa5QRT5ckB5oFpCbcbXtV7XyuCV9lRWMZSXGgSDKobgGKSsV5bSU1nksg0Nn9NSzrrT9jX7d6HfsFXbgwIZTohAtY2mou9qAEr09AwN1zGHTb9Uy860Lm9L6MFx/siv+Q1dpn3J2idC0+6AxGCU5gact5epXyAftGkFi8nQH7RujVzD9uNQ8luI6VmQUCgUBwShDC4hzF03owwWNfl30TMBUfw2I0aGleXbhWBoMbYio8zO1/PkRDYy5xIX4eK4NOc7C2Er9isLbSWRFPIOcfoffuYUQ70wEoMwazLfpOnGixE1FKFq8VPstNGYtYHT/CY0wWZynNijYSZU8j3RDHU5YJlEgByFXcjN7nLlLVSIySoosLwMO9yZ0Iiogi32u7t9CAIIpqvKcuH3WlmkrHuruUCga08cmqdh6P0YUmQsdnazyXQABon63lA/SXUs+ldOsxiCc7t2TinRfy2v1teeb+TpiaeVv05J9eISkigJZxISRFVARjuy8gAPhHwFUPn9rMTieBeltgBQKBQHBaEMLiHEW3Huwapn/5LmuzQP9S7rNrGG1DikkI8/c4TglOYG/AZQBEOjN5cO9wIssP8eC+UfrK4OetZ6O63Kbe7wLvd4bf5qK8dS2mwsOUB8RTaIkjzJbB3XufwYATpUJcRKtZXJL1JfcdfMFrzH5KGWX482LkdA76teJrczevNv35P0yqtupvlLxdmHxhlLRpvdOH9nDfZET14RTliSRBbXGtsgQOVdLHp6qVoqPMEsN313zI77n+OBxVzuYrgL4m3APo/wvO9PGdi2ycB04bGMzQcym0vA1ZlrwFw+0z4NL+EJqkHRcQBYPW+O7TJS7aDYen9kG3V874eJ96W2AFAoFAcFoQwuIcRS5K1awHFWJgUbM5HA1qw6Jmc3R3gl67hmn1LNxIzS9jRXAf8s2xgObCdMOh2YSWp5JrbsDc5JmsLUri2F3LtUlMRZEsJzKyrYgsUzyvxs8gLfhiVEBGRQW+azqGbEOMfh4jimdcQgX+lNHLupzbHWvoYf3UY2xqxXExcgHZSrCHxcIdpZp0BL5aV9UIJ+sD4RIzHik+1XC6FI/nsa8zGfnxXzy0aDPf7XILSF8zVou/mHs1pGyu+QQFKbqg490b/5vJu6v4YkUMTs3jq6iT8ttc7TjB8VGRcY2Rf0HL26pvZzDCXbO1+hLthsOTe8DkV317V72MM1xQuDguC6xAIBAI/nOEsDgXqQiwdgU0rrpsAUccERzKLuGII4JvLl+gB0JWnSSW2BykS5F8ctECis3RHt1OMT/Oz5lm/k4t4JPNKdidlSvuBhTyTbF81uYdbstfQovsNR5xBzftn8bH/j31IGrwjktwcXHhekaUzNGz3+CjXaRUVK3FQpbA7kN0nI7im65zpinh3G2dzBFnBDaHQn6pjR2pBUz7ZneluHAFeNuKNdGQsrn61Jrvd65839K3/zeT97qm/izJ1iq2i9SfJ46rwF5dA5LPMsFQV47XAisQCASC/5Zz69tH4JW1ydT/S/r/9jZp7QZSaImtrLx9zarKdhUVuglNJMSawf0589lmeQCn5Dk5H1PyKs+FvUqp4qDvnnGYHGnkm2JRVJUIRyayLBFmO8aFeZWpXwvkcIKVAgw4GVE8SxcLrgm3+1zfQUWsg1oZ2CxJ3ili1WpiHNwx1eImla/4Eyb/N/7Y2Uow65VLyCRMF0gOp4oVB+bibOxfL8JxwdsYOzwL+9ZCURooTtT3OvPpxQtYV5hEbpkdsyzRLqqMR/4dgbnYzdIUmvTfTN5dLjSu1J/vdfNO/RlUxM1HZ1WKCpGlR3AScFlgTRUW2CXN5mAPasCiZnPos2tYpQX2mlXieRMIBILTiLBYnGtsnOc5qdv0DtKmeTT44j5a+hf6DuLMO6QdV5BC/Bf30TF3OUP+eZBQawbZcgyFUjAAMWoWr+aNYnb588Q40sg0xjMqYBoftHybfL8EQqzpPPD3w1iUUhRkrJIfYUoexYZQD4tDdaKgjEAPtyhX4sz6iIqq2Zqqq9ISIpVX30kdjq9Pu3CpmF7G9Ywxapmt7Ao4VAhzZPOWcwJdij8j8/NntPdk8DoIjgdAUp3cs20QZQc3sTutkIyU/dy3bYi3qBi4+r+bTNWS+rPrXw9jLM0UqT/PBc6UmJoTsMAKBAKB4L9FCItzDZdPtmtSV537SnYhud/NIP2WOahXD9Mywyy6DSnvEKpkJEAt0YSD/1QWBg6kDAsAkRQQ7cyk0BLHvOSZ7C8PI88Y4xVQKaNgUcspxp9QZ141yWA1XHPyYEo8ak9UzQhVF1FRdb+v9qoKsqTWSTTU1X2qpnYGScWhyvyfo72+LZ4clpmn0FDK5LASw+OHr2HZ5iMowQkoA9dSYNSqnRtRWMg4uhg285E8kQZStt6HPSjhvxUVFdSU+jPKkUaBIYJPWs4WqT/PduoVU5NyamJqqlpgB62iX5drGd2pOY/d3IzRnZrTt/O1mAat8vo7JxAIBIL/HiEszjWq+mRXU5/iwNIniNi+AOMXQ/m0/Crsi+7QvpRlI5LqwB6SzEct53LEGY7VoVCOZyCorFgJNMs4FIWSrEPcusf3ZKJY9SNDDa92uO4Zk1xIgN3Ho1nbJN99f02WC1fa2JMdc1GTUDFKCm+ZZxBPji4qkmVNVPS0jWNLfiBT/m8nTy/4mn8+m8bDxqmko903IwqvKa8RT6WoOKZG8VaTuadl8l5T6s8CvwTWJz3K1iKR+vOsp64xNTUUzTxhqlpgQxN9Z8XyZYEVCAQCwX+OEBbnAz7cV/ZH30q+JYEo+zHu2TYIU+FhVMkIigNbSEOy7l3BPR2v5voYK4/aFxNOgUeXQfY8Hto1lAvsuxmdOpoI2zFy5EgKK1baQSsSFyflEUFetUNzVQyuiqnWxK8148ty4Z5F6kREhS8B4S5UfKW1BYiX8/jSPJbl5okeoiKNSBQFQm2ZjEwZTZujS+hU/CV9rGN9BqEfU6Poo0zk1yw//jicWznJO/I7vHUD2Ovg5uVyc8k7VDd3F5ebS+5hAtePw2a3+Uz9+W2zCTj9IrCJ1J9nP9UsSsxYu5dZ3+1jxtq9nkUzT0VMTVULbF3G2274GV+XQyAQCM5VJFWtqxf52U9hYSGhoaEUFBQQElJzleVzDUVRWbx6A922DCbakUa+XwI/JY/itj3PYUBBQVOZ2aYGzEh4gzL/eC4OLuKurUOw+sUSU7idIks0mxIGccu/03Q3pTL88EebyDqQMeKybqj4YfWZ9ckX1bWrr2WhuvZ2VcIkVf+on6zz1LWvFCWSHraJpBEJ4NOKsdD8Mi1l7wn/I9bRfKteSbi/kZbxIYQGmGhh38tjR0ZgUJ0Qdwk88lPNA1j9vOa2YgnRih3WNCl0X5GuaL8+ogdbE3ozYN8IwsorYz7y/JL4X/M32GOPZnSnFiRFBNQ8DsGZT0EK9ve6YSo8rAVOt5qHIygBY3GqHjhtD0nW3JHqKSoURSEzM5OYmBhkWaxzCapHPCuC+iCel5NLfebP4m6fJ/hyX7lzzzMYKiwDMlCCH0sTxxEe34SGxlzu2DKQUJuWCjXXEMPyRlO5MvUDZLdCcv6UU2wIxVkhKgD8KPcQFXVRrtXNw+sy2a/q5uSLkykqnLW0lyQoUv340tGOdCXMZ5s/leY1igoDThpK6T6PnWOeycXsJ7fMwb/ZJUTk7eCxw5qocGLg28ZPczinRHNT8Ri4A755Dr58DC66WxMT1kKQjZB3CHXRbRw7vI+daQVs3J/Np9//ym8v30npm1dq+2UjWAtRwxuRlXSbz9SfoeWpdDw6n7bBpyD155kSTHyeUVNMjatOzset5omYGoFAIBAIYXG+UFPl2nxZ8+UPpJx+KRNJKNrOgN2PEOnIBKDYGMb3F02j+4HxhFtTKcVCoRSqHx/kLMCAgrOiuraL6upUnGwb2YnGStTneLsqYZDAXss1DLQ9zSjHSO62TSHdR4yJVCG3fIkKCVhteYYAyXNC7LpvRklhhWUil7KHMaWvMSl7NAacAKxSruKJDSYeWvg7z67Yxt70osoOvh4Nm+bDXx/C8gFw7wJNXCgOVMmIlHcI4+I7eH7hNzy1YBXXru9Nu7IfCVBLUVQJSXGQIcfhvOtduh+c4LP4YrYxnlBnLg/sfsyr+OIJcyYEE5+H1BRTk++XwJJW89haKGJqBAKBQCCExXlDTZVry1Uj2ZK2eh5uz6DX34MJs2uWigyi+cTUne47RxHlSAMgACthagGlxlCPftZd8Cx5pliPbb7m7KejUF19qEn4mCS1wq2qcptdlSlRTR7tXjO9TTw5AD4rhHczbKKTtNlLVACsMI8jSLLpbR2q1ocr6Bxc4mISdxk36lW+05QwXrH3xuZwkppfyv9tT2Poh5v5ZPMRlLyjcOD7ygEUpMDyASh3v0tZUBKS6sCBgRhHGnPLX+Az81ji5Tz9fsiSymElhqGlw0h9v0+1qT9XX/Y2joCYU5P683QEEwsrSY2LEqubTcIe1ACriKkRCAQCAWeZsEhNTaVv375ERkbi7+9PmzZt+OOPP073sM4Kqqtcm2NuQJyahUOVyZPCPI7JkSN4PfQZepd9jNFejFpFJvg5PAO6b/j3TXaryThreKz+64ie6s7na3u2Eoziw83JpkKJatZfmyQVW4VYUFWtGF+gZCdFiWSt4zIKVT+S5UyWmyex3DyRRDlHFxfFFf0YJZV3LTO8RMUX5heIl/P1czlUCaMEWWqIl7iQ3cbpUGUesT3BUTUSrSC6hMOpcDSvjAVf/UTuvFuhIAU1JBFbQIX4K0ihcHFfHi19lCNqDEacOFSZBDmbWLlAvz5JgkLVn8m2fswyz9HHvKnDh16pP3vfeg3G60ZqsRgnO/Xn6QgmFlaSGhcluuybgKn4GBajgUCzqLcqEAgE5ztnjbDIy8vj2muvxWQy8c0337Bz505ef/11wsOrT2UqqMRVubaq+8r8RjNJJZY4sghWCz2PUVWeLnoFf6w4MSChoro9MjKaq9Pr9KJQDcBfLaW943c9bsMX/6W1oqbYCV/bw6Rij8l6iWqhSLVglsCMnQyl0kKz3nmJPtEHzVow3DaK5vJRQqRySlQLiXI2ibJmtTBKCilKFH2sY8lUPC09e5QEMgljovF9YuXK9yBNCede6yQOKzHEy/ke4qLqdRolhVnmOcSTgwLYHCpOBWLUHBaok4iyHyOFWO4uG8d99smkoWXvCrNn8KLzdZ6XRpKqRmGsUrHcJSpCpDJmWOZ5CKEZG/JQFNUz9WfRMfh1tha74RIXJzP1Zy0F+rr9qWU4swcl1E1U1GZhOBNSrp5mqluUcLlF9dk1jLYhpyCmRiAQCARnHWeNsHj55ZdJSkpi4cKFXHXVVTRu3Jhbb72VCy644HQP7cynhsq1OXI070Q/jwMDRhQcSJRXFMMLV/OIVrIokMNY1Hwu2aYGSCgewdgSMFr9mBetD1A1VrgmTrXloq4B2a7gam1y7jmo/3O2o6/1BeyqjElSiZAKdXHRWj5Mjhqstw2RyphrnkmynAWABRveaP3b8HSbutX4FwuN0+lk2Kpvy1BCucc2mW00padtnC4uCtRAr15dk/9kOZNl5im6C1ZclfiN+8vHsq0omL2lwfRxTiRV0cRFgpTD67yOH1af9+gp2yMcVmIIkcooVP0ZaRtBGpH8eayUz7emek6uP7wTijO0yXW/z05J6s8ag4mdWsB7ud3pHbxelbpYGM6ElKunmeoWJRY1m6O/B712DTv5MTUCgUAgOOs4a9LNtm7dms6dO5OSksKPP/5IQkICw4YNY8iQIXXu47xMN1t1JXXA1yjBCaTml1FicxBizSBqxT2YC4/o6WLdUZA5FH4daxo9SWHWUZ5KHYWsOr2Csk9FwbnjpepYHKrstRLvoli1IKMSIHkKgQwllO62qaQRySXs51PLREySgqJCthpKTIWrUIYSSoRUjElyVrwOIVIqxigpHuPQXJrUCpGikKaEEy3lYfRxzw4p0fSyjdezRoEW5P2ZeZyHmxSAosJXzqt5xdGLJeaXdBEx2j6MGaZ5XvUy3Iknh+XmSSTK2dTEYSWGkbYRHm5Qrv4uaxhKo8ggLgkupvfuYRgKj5LZoBMxPV5HDk+qsd/j5WhuKTPW7qWhMdcr3W2BJRZVhTBbBo6QZIyDVnk874FmIwlh/tokuMrnokYxcApTrp7RuP39sIcks7TlPLYVBWF1OLEYDbQNKabXrmFaTM1xiCqRElJQV8SzIqgP4nk5udRn/nzWCAs/P63y8xNPPEGPHj3YvHkzo0aN4q233uLBBx/0eYzVasVqrVyFLSwsJCkpiby8vDNXWDgdsG4itHsUQuuQvrEgFX6bD7dM1KpuV+XbsVomoPBG0H+lZ58FqdoKc94hyoKS+N3aiA72nz0OV5DJCmmDpSQNJxDpzKywbjjPKDHhwn1MBYqFYoJIkHNQ1MqYhKrjdr12qOgTfYcqc791PNvRLGI3sYW3LTM8qnYfUyKRcRLnNtkvUv0Ilsr1Nu7nco3BocpkqyHEyfke5wTIUYK4y/Yi6UR4XNfFHOB/lskeAsklmOyqzAPW8WQSzlLzizSUM/U2R5QYette8OrPxaXSPpZbvC0KLgHk+n1EieFx2zDeNM+joZyp99uubRsamnLpvesxohxpWEMak3/nB0Q3anXK/pjvyShkzvf7aRwZRGLJDu7fMVTf978275BvjOaev4cRZdcm+8tazGZbcRB2u417cheQndSNOw9OqpwMV/1cuHD7bCmSgSXf/krXvx4mypFGgV8C3zabQOd9kwgtT6VUCmT1JbPpfvudWiXomqjtM3um4Pb3wXWflOAGHCuoFGkNQv0197cq7er0twvtyz8rK4vo6Gjx5S+oEfGsCOqDeF5OLoWFhYSHh59bwsJsNnPFFVfw66+/6ttGjhzJ5s2b2bhxo89jJk6cyKRJk7y27927l+DgYB9HnAH8+SHs+RqCYuHm8RAYVX3bkmz4brLmetLiNri8v+d+xQl/fQSOcrjoXs++3I8NikW5aRx//LWFK46+r8dOSIADI0UBDQktPYSMQh4hfG+8gXscX9VY+K6uhfFOJSowx34Xdxs3kChl69t8pcF1vc5VgwiVSjGgUIqFCfYBADxt/IQYKV9vZ0emRPUnTCqBiuMUZKKkyhgJlwXIAbimj05kPQYlXw0kWCrziElxIjPN3ouDxOvbGpPGGNPHXrEr7mN1IDPd3gsZledNS/U2L9l7c4AGPu9PBEWMMS4hQir22O5EZq79LnoafyBGytevI1MN4x3HbQw1fk2MlE8WYexK7sdlaUsJsuVQYIjgzwtG0LZJPGFhYafsj3lWkZWVW1OJMhRz87G3CLLl6PuKzZF81+ARyqxO7s5/D0tZFgWGCNYnPUrrwl9olvsjCjIyCo6AGIydJvj+jFX5bGU1f6Dac9pkP8xKOYXGSBwdxxIR4/t+++rX6zN7JnEy/xZVg6IoFBQUEBoaKr78BTUinhVBfRDPy8mlqKiI5s2bn1vCIjk5mU6dOrFgwQJ92/z585k6dSqpqb59e89Ki8XJXCV0WSssIdB3OSRc4b3PdWxxGur7tyGpDn3y7ArYTg+5hLjCbcgo/MpFXMUujBV1E850FFUiUw2tsA7IHi5Kvn4/bB1NNqEssrxMiFRGuhJGoFROsFTOESWGqbY+vG6ZT7BUrp8jVYniAds44qUcj9X/R62jGGv6iAS5chJa1ULhIkfRRILLAjHcOop2xl2sclzJMsuLuqUiXQnjMftIXje9RUM5k3QljCipsOI4iRw1jLiKNLFQvcUijlw+NU/U21a14qQqkTxme0y3UFRnuXBR4JfAB81mcsQRzoBLI2jdJPGU/TFXFLVG60G2MZ5vLn0bFZVufz2it/mp0WN02/OCXkjwy7bvcme3O0gvKq/1s7WnPLhaK8n/tZjODYdmE2pNxRbSEPNDX530lf3Twsm2nvpArCoK6op4VgT1QTwvJ5dz0mLRu3dvjh49ys8/V7rqjB49mk2bNnlYMWrirImxqOLX/HGL2TQ8sJi1IfdS5h+v+7ObCg9roqHfZ5B4pefxG+fBFYNgfjtw2rTqyvd/iHrwF1KuHEOJzUHC79MI7DASuSQd3u+iFUpDExWlUiABaglW1Uhe6EVEFW7HiPek/EzGfYyuibGiSsiSWq07lF2Vuc86kUzCPWIailQ/brW+CsDn5vEek/d7rBNJUyP1QGkXh5UYtioXcJfR06JWVVxkKqHcZZtKDHlusRzaON1duNKUMO6xTSGNSI/CemlKGNFSgVs9i3CG20dVG2OhxWuM96hTIUmQokRhxKlfW4oSyXDbKD22wnUPDysxvB8wkEnl0/VrWNZmAUeD2nA4u5gBl0VwWYtGp+6PuY94B3tQA0zFx/R4h7KgJF6Ne4MQf6NXHIZTMmBQnWQa4/mk9XwO2sMpdzjxMxo8P1tuMQM1xXXk+yXwafJEuv87gSj7MY9YhNr6Pd8RftCCuiKeFUF9EM/LyaU+8+ez5m6PHj2a3377jZdeeon9+/ezdOlS3nnnHYYPH366h3byqZJS874/etExdzlPpz1Ja2Uv3bYMxlR4GKc5WEvruWJwZY5990w3nz6kiQrQRMOy3kib5nFgyRNM/3Y/j+f34INvN6K+p4kKqHATMgezN6QdaURpk1wk/UE5W0QFoGevclbUmrCrMnJFgbuq4//ZeZE+cV5smcZF0r9ES5V1OopVPy6S/mWN5WkPUQEwyzSb5eZJ+iT+HutEDisxJMuZtJUPsMZxqUf7qhYLV5aobTTlEetoD/HjS1QApBHpkS3K3bGrgEC2qM31/e7ZolwB2y5R4arbcViJoYdtAnfbJuvZohLlHOaaZzLSNoLDSgwmSdGzT42xz/a4Blc9A7PRgMXoWYG9ztSlGJ17hrOgBDIjrybFHqIX6Pvm8gXYQ5LxLz7Ko4dH4m8yehV1+6rFNHJM8cQ40uj591DPdLUVny17SLLH5D9BymFQ7gz67HzUZ8rVuw6MZ/2FL2ELaeg7DW41/QoEAoFAcC5x1lgsAL766ivGjBnDvn37aNy4MU888cQ5mxVKUVQWr95Aty2DiXak6ausrt9Zxni+bjaF3imTMRcdQQ1vhHTvAtQVg5HyDuE0B5Nzy5tEfzcayVroEVswJXAs36uXE+7MYrH1cYIodYs5kChpdieB+74kXYpGkmTUoHhiCrcjV1Of4kwWGu6WCFOFO1GmEkq4VIipSnrZbx2X0d6wixCpzGN7sWohSPJMxZqiRDHSPoJZptl6rYoUJYoetgleFgX3c1clXQ0nTsrzyrzknskqQwmhu+1Fr6xOgEfGKocqc0iNoZttOja0Qnzu40hRIjGg6KKiSPWjv/VZbjP+znuObqQRiUHSal98ap5EQkW2qFQ1kieUx7nT+Bs/G9vzmv0lgigl30+rxNxl3wTCylPJMsbzzWXvckvbpsTFxdZ/lWj185ogrm5F3z3DWWgiIEHBUYraDiW13TiPjE+O97phLDxMvikWWZYIsabr3eRbEtjOBdxg/Unr1hLLN81f9LyOyxfQt/O1WiB2QYpm0Ss4ClCtlcQWksyXjSfR8Z8xRDvSfN8f934FYlVRUGfEsyKoD+J5ObmckxYLgNtvv50dO3ZQXl7Orl276iUqzjZS88vYVhTEklbzyPdLwKBqMQ0u//DXgp7mvUORPB88lTI5ECnvEMp7nZHyDlEmB2KwFRG1arCXqJCAscUvclnpz8y2jvMQFdo0W8Vu8CfbEEOcmoVR8VWPwZMzVVRA5dhMkkKKEkmaEk6MXIBJUnFUkdRt5f1MtfXx2JaphjLZ1s9jW7YazM9KGzLVMLxDwDXSiGSkbYQuKhyqTJnqWb8CNOGTokSRLGfyqWWibvX42nmV3uZ75XKfoiKeHGaZ5+jWGKOk8JNyKYps1j/Y7paNRDlHFxUpSiS3Wl/lL1ow1dGPNCKRK6xRaURyn20CKW51Lm6Tf+Mz811McM4hiFKOSXEsbDrbq57BA7sfQy7L8RqrT6paKKoUo1PyjnI4p4T1ezJZvyeTom9f1PaFJOASFYQ3IvjGUZUF+mQJQhORH/qaQkssYfYMQqzpnhYGayrNrTvJkbS4k1BrBj13DCasPJVccwPmJs9kQ5YfR/NKvUSFPSjBow6Mu5XEXHiYG3c+z5cXTNYtGa5+8/0SWNJqHlsLg0jNL/N1N6q/L7VRW5E/gUAgEAj+I84qYXE+UWJzUO5w4ghK8HLlMOBkVMErNFAzeDx3Kv5KCSogq04UJNYmjKhwX1K9RIXmXqPyBm+QSIb3foOZfxvei8OprZaHU8C5wmR7f5wej7zn4x8rF/KyeYHHNlWFl8zve2wLo4Rexh9YZRlDopxNihJFihJJopzj4XLkmvS7rA/+kh3AQ9DEy3mYJTsOt/SuI20jeMIxnI8dHfnYcSNjHQN9Xs8g4ypdiNxnncgCR1emO3tjNMj4mw0YZQmj5C4uogHYrSTSwzbRS6xIoNuk0oikh20CHztu5H1HZ96zd+LN8nHEK+lkGeN5LmQau8tCvSbXpsLDWnaggjoUS1s7XrNQVFS0rlqMLn/+rYz78Fue/3wHM1f8QPHu7wBwluXrosLDsuE2IZdlCX+TFkBctahbhiGeOLIxGmRsksVjSFPMj7Mhw8iNh9/k22+/wv7urbqoIDQR05A19OtyLaM7Neexm5sxulNz+rZLxtS0A7bgJKLsx7jv0ER+bDTKo9/VzSZhD2qA1eGkxFaLAKh6X2qiLkX+BAKBQCD4jzirXKFOlLPJFaqmYFFXHYliArwsDtrksHpR4cu6oAdkA3+1eILme97S+lVBlWQyQy4mpnA7qAqn2oPjZLhVVdeHa4KvFacr8Cpi506OGoxDlYmVK4VVthpMGCUYJQWnCoaK2ISetnEAHi5HIJEoZ1OkWrCqZqLkIkBzP+prfZ655llehelc46uuoJ07EuBvUHjO+DHvO7ty1BmhiUtJIszfSOsGoVySFMbPezPZnlqIomoWjkHGVUxz9MaJZxyES2L5cthyd6c6rMbwbMCLlPrHcd+VSTSMCKRJVCCJ4QHIRakoi+4g0xlCjKEQecD/1Vp0zlfxxs3bt9Po/x4g1plGqhTLjJBneLLoVeKVdP3ZLwtKwjLoG88ifC5XKjc3KV9F3ZqY8+n19xAinZleQ8qUovnLfCmdrWtwYsDgyn4WmggDv63RPav4wt6U711PlP0YVkMQFmdlGt98vwQWNZvDEUcEozs1JykioN735YSL/J2BCHcFQV0Rz4qgPojn5eRyThbIOxmcTcKiaoxFvl8CKxqOp+v+CTRQ0r2qZNdWn6Eu2ZwK8CcQq0f2J6tq4N+Q9rQo+pVMJZRgqZjAipX3U8WJiIvKInS+07o6VZlMNYR4Ob/Ge1KuGjDj1IWUQ4UsNZx4OU93bypU/elnHcM2mgKuStYT9ZiLQtXfI16jSPWjq3U6KcQQTw6fW8YTJ1UGgruqcwMscHRlqsPTBcuday6IICEsgJwSK6U2J/5GA8H+Bi5JCqdNQiiXJYVjNMocyi5m+NItHMgsweFUkCRwKp4Cwihr9w3Vt7AYa1zMYOM3HoJHAiIDTVwQE8QliWHcd3kSzeOCUfKOkrn8SWKOrUVu9yh0ecnHm+DQVtfbD9Neu2VAW9JyLp/sVXHmHWQFzxEslbrVA9F+H1Vj+MfSlvS2I7nuirY0jQnWJuM/vgz7v4PCChEemoQy4BvSCspQNs7j37bP0CgmlARyKH/7JoJsWdpwkCkgmIgK61wOoYRRhAEFJwb2R93MBb1eJ02KqnFir/T/ihXr/+C2HcMJUEpOLMaimorX51qWKfHlL6gr4lkR1AfxvJxc6jN/PoPLvp7fyEWp2sTBkaYFizabQzqRrLZMYWb5WBqoGR7tfYkKfbvbxLk6i0WRaiZULtNfS5I22RxufRyroylTlP00ktO9Dz4FnIjFwlXZ2igpZCnBRElFHv0ZJIV4ybumRVX8JG2lWlGhDDOBko14KY8UJYrhtpF6kPUs8xx9sp1GJD8rF9NL/oHDSgyTbf14y/IGpopMVH2tz5NCjH4Ohyp7vGFGScWhSnztbMc0R2+f12eUoGV8MGNvb03L2BDvVewqE9bEsAAiAiykW8qxGE2U2RScqoqiKJTYFFRAUbR7UF1lEtdYXAHeoD1r2SV2cg/msfNYERsPZPN4pxbc2CJBK6a2tyl08q7oDVS6+uz5WpsQD/haTx/bbcsQvjJO5i7pW4IrrHEuAW1EIYVYfpcu5F7bGjK37GBR0RzuvyKJRl/dr03wY1rpwsLuVPhi/R/cuPN5ouzHOJBVzPqmA+i9c6iHqDCg8IelHW3tW4hRsoisEBhKhcUiPH8H8388wEF7bo0TexnofnACJqVE/8zagxqwqNkcPcC7165hyNesql0EuDLD6fdlMHmt5uEIS8BYnEq3LdrfBntIMqaaRIW7iKuL8HClqu40+cyuCi4QCASCMxIh485E3FNqhiTrwaIZheWkE8XMsGerxApoVZ5dIsKd2lb/XfuDpcogbVd7WYKx5iUATLP38jzfGWznMkoKqUokd9peYrD1Sa974mrj695Uva637HeQp7qrc5VMwn2mcgUY6xjIAkdXetrG0d64s0JUyJgklVnmOXr8xTLzFBLlHNKUcErUSj9/o6RSip+XqxJAgAk6tIiieWwIwRYTsiyRFBHgGbhchbTCciwmmehgC/4mA3GhFmJDLMSE+BHqb9QCtqmwWFSDE4Me4F0VBSiyOvgnrYgpX+1kX2ahVqG589TqJ6ZVg7QVlSUt55Jp1FLAziwfyxZa6c+0OxMMj/EW95JKLDGONAbte5SI/90BeYdwhCSz/5YFFF/YG1tQAqbiVO7ZNogo+zHyLQnsj76V27c8hKm4QngEJfBe87dZZriD10xDmRbwtMe5Sk0h5Jm089z/z6M1p48Fn59ZnzEodYmdAJTgBJa2nKcHxg/YN4LE4u0M2DeC6IoFh49bzUMJrqF4nYjXEAgEAsF/iHCFOtOowb+6yGrnu41b6LnzUS0FbcVqq4uTWbxOVSFTDSFaLmZ38DU0LfwNs+QZdGpXwXSGZoSyqnC/dTLbaMp9/MCrlnfrdE+qu3dahiSVRDlHd1Nyjz3w5bpkwMkY41L+z9Fet3C4x1+49+meCvawEk1P23iPibzFAF0uisfmVGmTEMojHS6oU8rS3emFzPpuH2H+JnYeKyQlvwyr3YkK+JkMBFqM5BVbKXco2Jwn9qdAAq5vGs60ro2Ij4+r2fzs9pyXBSXxsGESNofC66UvkFDFGufOETWGPvZxWEwySw0TiVE0y0O+OZZZDeeQLkViMciE5GxjXNYTGNFSNH/dfBodD72hp50ttMQS9Mh3pCgRvLhqFw3I4bGjjxNhO+ZxvmwicCATR3b1rk3tkpE/vP2kx0TUVpSvTjEbZ3i8hnBXENQV8awI6oN4Xk4u52y62fOCjfO8vtxdK9OtA4p45NAofbVyYYu3KTDH6oeeTFEhSWDHTJYSSoRUjLEiu1GmUvlAea8nHx81rZYfLxYJPrWMpxO/M97yUb1EhapCoeKZLWiSvR89bFrmJZdrkCvbkvs2d1wr/dtoSk/bOD1zVKKcTboajktUaMXpJnKPbXKFFSTLwwriZ5S4sEEoVodCeICJixND2ZtZxNHcUpRabl6g2Yif0UC53YlDUbWJZLi/FnAd5g8qhAeauaVVDIYTfW6AX/fnsujXgxzIKqq5sVsRSP/io7xUMIYAs4F5YU97NXWo2p8puyrTUMpkiWkKDU2FKG4BIQ6nSoi/kSZRQUQqWQzNekkTFWh1X+7c84wuKvJNsbyZNIdUNZLE8ACuiSxn2OFRRNiO6WlpCy1xAESRy15La/It1aePLflxVrWf2appcN2zXrFxXo23qKbMcHXOMlXlnPb3urF49QZmrN3LrO/2MWPtXhav3oD9vW5ndRC4QCAQCM4MhLA40+g0GdoN9/5y9+EetVNuztvR4yihchJ8MmpKlGEmRYkiUc4m2i0rkopCjFyIvWIua5DOPGOXqlYKFZME71jeJEQq8+kOVfU4d2EWJHnW75hvnkkMeUx19PNwU0oj0mtb9VS+OXFSni4q3GM0qrpYNSCHsAAzEUEWEsL88TMZ+GxLqj4pnL/+APszq5/EJ4RpIuLvY4WU2Z3EhlgI9TfjbzZiNsoYZbAYDcSH+eN3nAWz3VGAv47mM3PdvhrHBZWuPpnGeBLJYHrBMzxRMM2jjV2VeMQ2Wq/87RIX7zjGEkcWx4jimBpFlDNTdxV69NAoEsjgmBTLm+HPe/S3O6oTy9q8S4YUSYnNoccyucS6Ky3t+83nk23Q4mEuUvfzY+Pq08emXjXG92fWF66Jfrvh1cegVOAShcbiVLrsm+Cxz1Xp3GI0EGiuJRbCTcSJquACgUAgOJUIYXGmYTBqmXR8iArXiqJp0Cr6dbmWp9oF8ljBywSiVYV2ngQLgqpCgGRjp5Lksd1R4fakngL3J5dHT32c8ny1dYmCUix6MTp3sVBT/1XbyZKKQ5XJVoIALSbjU8tELmF/3QfpxiDjKjdLRSWj7cM8XJ6qiouRgWsYeUszHrymEWV2hbSCcsICTDSJCiIswMTfxwpYuOFQtZN4WZZo2zAMu0PB6VSwORUUVcXqcJJbYiPAYuSihBBScsvws3gX8Dseyu0KG/bnsPS3wzVaVPQikC3nkmWI0QKnlcrieg602JRxpsWMso3giBqjVzA34sSBgSeUxxkkTyHX3EC3KETajnFMjuOVoKfpVfCuxznjindSbtfSzoZYM2qMi1h20btkGuOJsB3j9j1jPPrxmNj7+Xl/ZmsiNFFrX0twdEKYP5cEF9Nn1zDdSqIX+StPpc+uYbQNKSYhzL/WU56UeA2BQCAQCGpBCIuzgWrcoxrsWoh/8VFyDDHkSWEY8JzEuSbSdZ2wu0+sbzJs89hnrKOb1YlE7FSXsaqubSUJnCoESVZMbjmO6iMu7Kqs/zZKCuX48a3jUj3F7PGICwkts9LHjht1tx4XM0zzdJcnF2lE0ts2jo/l21gV9yjxIX58tzOTlLxSYoLMWB0KJVYHQRYjzWKCyC2xseafjGon8VHBFpIiAogL9afcrpBfaqPcrhAT4kfbpDDiw/wptTsJ8Ts5wkIFisrtfL0jjZ/3Z1XbzuXqE2A2YZE8E93myBE8G/IKR9EE1lt+s3jb8pBHGyNOXpVnE2A2srzhOI99y0IH80zxazRQ0skxN/CakF9jOUj8F/d5iXX3wneP3HEDP144TXenckoGvmz5ynFN7I+H6qwp7pXOe+0apsVH1IIu4lrNO7Gq4AKBQCAQ1IAQFmcD1blHdZpMUduhfB43ihC10Ouwuk6owdsVyBVT8Yh1FDkVWZHqIirc29RH0LhwqpWZmeoTL1KimrUAaAmsqlEvYufr2qobn2aNUfRK1oeVGBLlbI4Sx33Wibq4WGyZ5iUGXJhlzaLjb3Ay3rSYRsZcQvyMxEn5XCP/o7s/3VPRv8vl6RL2M9a4WC/IdoxIptj7cazQzsINh1j19zF2pBawcnsa3+/O4Kd9WWw+lEteqY34UD/2ZxZXOykMNBuJCrLQKj6E9k0iuapxJO2bRHJFcjgRgRbKbE4CzAaigy2YTzTQogJFhZwSG2+u28vedN/WlECzkTg1hwd2DCbEUbVYoIFUZxjbjW0BiCObybZXPdtgIIkMBjr/R48jUzz2jc6bSgM1nVRimd9opteE/O6djyHVEhdhLDlG94MTMLjFaiQU/HVcE/t6U01muOPNMnVS4jUEAoFAIKgFISzOBny5R1VsD7ysBw+ljvPIDuVOXQO6fU68K/qsiz6oeqxdleosCtzbZarhPGx9kiLVr87HZyqhBFbERHzsuJE+1uewqgYMUs2ioqrgkipqYIy0jdADrl2B2dtoyn3WiXrRO/fganfsiiaMnpKWMtDwDUuMU7gl+AjLLFO1ytUVMRVb1OYeLk+fWiYy2PgNY4xLMaDd8zK7QqnNiSyp5JfaKCi1k19qJ7PQSmZhOXvSi/jt31zK7c4aJ4UJYf5cEB1EemE5QRYDZoOMzalQVO5AUTT3qosTwriuaST+ZgOy5F0P5XiQZUjNK2fuD/t9umolSDk8fnQEYXYtC1ShOZbdkbeQb4olVs1ilnUcJRf2xCppMUQGFBRkPmo4lWxTA4w4KcGPK5x/EWE7hlPSgkTcK9C/E/M8OXK014TcYCsCSwjcu8C3C1OVif1nl7zH+ogefBA48LjTx9aZalwf3a0pfTtfi2nQKo+0vTWN4aTFawgEAoFAUAMi3ezZTEEKzL0abMX6JieSl0uUU6VeGX+yCSVULcQkqSjIZIZcTHjBDiwVRePS1XAcqkSinItVlUhTo2kkZwLaJN9fKidYsuor/C4UFdLVUOKkAnxlSk1Xw4mT8jisxLBbSaCz8S+P/XVNBQsw2PiN3t6hyuQTSJTke+W8ar8pSiQ9bBN91m2oKcWs+9ASpBw+tkwliQwcGDDi5Cix9HGM5YgjUk9Fu9HRmrcsM/TA5PutE9lKU31yHBlkoqiknImG9wGVsY5BeqB4gEnCaDCQGO5Pi9hgnri1hZZ21EeRs/2ZRby5bh9704twqhVltpEwSBLN44J5/JZmALzw+Q7+OJTH8WaelVFpFa6yK0/C3yQTGWQhItBCYoQ/VzQKJ8BkJCk8gNAAEw1/eY6gnUsByDHGsLjV29iDGmAqPqYXlLMHJSA5bRjLKl2qfvHrwGy5L2+WjiEezdLhSr1ctQK9LaQh2fd+RqEltu6pVU93itbVz2u1JOrSp/tY2w33XekcUBSVxas30G3LYKIdaSdWFfwkIlJCCuqKeFYE9UE8LycXkW72fGH9yx6iwioHYEClFM9UqbWJinLVc5VSQmK6PMRjm0lSSVEiSVGiiJPyAJkUJRKLpCKhsszRgS8d7bFh9BAVharmf25XZWQJ7FgYYn2SYtVzjKAJgBQlimQ5k/aG3fo2qF5U2FWtJgRIfOzoyDRHb6Y5epOthuiiIkcNrlZUAEy33e9huUiUc1hunujTIlFTilnXdB0gVY3kcfsIXVQ4MDDVMppcORqTDGOMSxls/MZDVJgkhTfNc4irOK8KZBfbmWh4n17GH+hlXM9U43v6+UrtKja7g6O5pUQHa1mjai1yJlX5j9s9bRoTzJTuF9EiLviEU88C2BRIL7Tyd2oBX29PZ9LKXTy3Ygf93t/E0wtWUbr3BwDKAhrw1WULvV19KorcGcuycPhH68kJ2pX/TGzpXo9zadYM7XKcGFjV+nXsIcmYC4/Q4Iv7aOlf6J32NSxZm5Cvf9lz4NWlfDbk0nLrdJJCzceVPrbOVOf66Is6Zpk6mfEaZzROhybM6mpBKkjR2juFC5hAIBCcDISwOFspSIFDP2n/D0mEy/pz+IE1rA6+h1nNP2RPyLW1duFQtYmaA01YuCbFkeTzhPqBR1u7KjHcNooetgl67MHPysV63YX28i5K8SNRztEnyYeVGLpZp7HA0VWPWUiWM5lk/oAitTLgdaWjXUWfORhwUqT6ESKVUaj685x1kJdblaqiZ33SKltr4uI2wybiycGJgWuss9ihNOI7Z1tiK1LmutLkVrXR9TatZ7D1SXYriaQoEYAmLqpzd6otxaxrrF2lXzHiRJE0cTHeNoOW/kXEh/rzk/E6/T7ZVZnh9tH6/RlkXKX3FU8O18vb9dfXy397jKncCeV2Jw0jA7xX0NsPA7TV6m//zsCpqHRuHcsNzaJp1ySSG5pF07l1LE5F1YO/m8eG8OYDl3JV4/ATdoeyO1VsThWHWim6FCBayWGeYzwxjjTS5Fh2WC7n+ubRnq4+7ZIxGSr/PBnMFla1ehUHMkYUZvIq8WR7VKCXAbshgKweX9L1vkGerkLz2kPK5srBhSZCkw7a/w/95DkRvXooxF0C/VZWTuyrE2z1SB/rQU0T4Ooyw1U3Aa4ty9RJjtc4oxGVxgUCgeC0IoTF2Yr7quqgb+HO2fhHX8CaxFH4WczElfvOXOQRU4BEJhEESeWUG4L4tslYfVU4ECvuUxiTpDLLPAcJ6OsYz/vOrox1DPSIE7he3kaR6qeLip62caQQw1RHP7bTlD62caRW1MeIl/NJU8L42HEjox3DKwrIRREv5xEsleviYrrlPUxV6mVIEvhLdt2aYZJUVBVCpDJWWcYQTw42zAy1PcmF8iG3a/DM+gTa72Q5k/HmxTxke5Yetkl87LiRw0q01yS/rigVE+lpjt685+jKCMtLpEqxJKgZzLGP4yrjfl5kloel4gXjYkbaRnhYQlxuV67K3K7aIlUFj12BjX9tr7bIWWp+GQeyiokP9UOWZUL8TUQFWQjxNyHLslfwd/O4YCbccRExweZ6X3tdGGRcpceb/GxvzVV5/0fkp3eTIOVUFpTbNB8KjkJoEoQmIhWkEHXsBwpkLV2vS/QYUHC4/Rn7O/wWYlpd52lRsISAtRAW3+M52ezwrFeMgpJ3FMfCOyF9G0U/zdEybVV1jaoQbDp1TB/rwX81AT4F8RpnNO2Heb+nisrR3FJ2pxdWFpWs7T0VCAQCwXEhhMXZig93CVfe+347HybUluHzMElCD/M2SAqR5FNkisbPWczVR9+lWA7T27qmSeWYSFUitAxGlimYDPAqD+LEoLsGpaiRJMq5BEvlHkXfQJsEWowSshFwEwlOjMxy3OO28l+5r1S14KhI+VoVe4WlRctcJenX5RIXrixLy80TSZRzAUhXwilSLV5Zn1yTe1d2JoAxjiH0tI2vtqJ2XXFV3v62IIlHDJNJN8QRp6TzWtHTJJHBETWG+90sObPMc3jP0Q0nBo9YDldlbpe1yDVWl7iIJ4fn0p+qtshZZVpX3xNff7PBK/i7ZXwIU7q3IdjPiHSSgrldTHP0ZoGjKz1t45jpuIcUYgktT0VZ6DYRvOI5ci8ezLF7PkcZsJr8lr24oPhPIpUc8gj16M+IQqYczVeGW5gXOMwzO1ZoIvT7rFJczGsPeYe0czgj2H/bJzhCNJco+7u3UvzWzRgLD5NlimdGcadTV5X6v5oAV+fWdYJVwc9YRKVxgUAgOK2I4O1ziYIU7O/eiqnY0086n2DC8IwxcCBhrJjIO5EpIIQI8oGK4G2KMeHEjoGckDasz42gvfw3yXImR9QY+tjHcVSpDG6eZnyXXsYfvESFC/eJcqoaCUgkSNm85+jKFEc/xhoXM9j4DSmKtk+Lm6hEVeE522CGmVaSLGdiVyUvSwZAkWohWLLqvwHSlHCcyF6Vrt3H5O6+5Wv8x4MsafEtdgUiA010DT3C1Nwn9f191cn8oTQj3J7tISJG24cxwzTPI4uUazxVBYd721QplnVXvke/Ltd5BOAezS1lxtq9hAWYCPZRq6KoXMs2NbpTcy34243FGw8x54f95JXYcFbUyTDKEv5mA2aDRKnNSbGtUvy5B28rdZAjBgmSjbl8bH6RWGca9pBklracx7aiIModTvyMBi4JLub+nY/gX3yUdCkaWYIYxbM+xpzAx1hp7ERCmD/Pdm1Jy7gqn++UzfB+F1AcOM3BfNR2mX6OODWHkUdGEFYhxvNNsXzSZgFldkdlEHlIsraqfzInoG6iobrr7r17mOaidLwTYKdDs3K0H1a3Y30E/p9KTlmAZUEK9ve6YSo8TJYxniWt5uEISsBYnHpq31PBKUME4wrqg3heTi4iePt8xOVHXSEqXL7nmYRjlfw8mqqAERVHxcTPgEJYhahIJxIFg15gzoBKgSGCd6R76OsYzxE1hoZSJh8ZPd1xJikD9VVoX5Nyl/vLETWG+20TecA2gQ+5jdfUPshS5Sq2a2W+WK0cs1IRuD3MtJKRthEVlgYV93pwLsvF18527FYSaxUV4Fnl2hVoXtUacCJoFby1nwhHJiPyPeswTGMu8eR4Vdv+zDLRp6ioOmb3tkfUGB4zT+HXbH+vehaudLNpBeVUXUdQVZW0gnKaxgT5LPTWr30jPhh4JfdelsjlyeG0SQzlmgsiuP/KJD4a3J6lQ9vRKj6YEIvveJPakCVIV8P423wx5QHxmAoP023LYBoac2kSFURDYy7dtgzGv/goeaZYUFVilCwv0TK8ZDYd2Uyov4nCMrv3qv+KwaA4UJEw2Io8zhHsZ6TcXRzJEqH2DAbuffTUVqWuWF13xTf4uu7qrFB1prpU1TWNqb5uXWcgotK4QCAQnB6EsDgXcHeXCEuGmFYYUMg3xyIbTMSqWVjlAEqMmguJKx2nu7iQgRLVgoRCDLl61/agBN429iFLiiKDCPrYx3kEGbumdw5Fc/txnwRLaCvSRjfh8IB1HKlqJClqJJOsfXBiQKbSbch1fL6qrZwXqVoWKXd3oRftffQsUw5VJk0JxyipFKkWgijHH01UpChRrFcu8SkqXLhnebrDOlUXF3WJrYgnx6OoXVVUwOaERDmX95hEnJLOMTmO+x2TOKLGkCRl8IE8mQbkkE4ko+2ebi6j7cN8irQ0H22ftA8jVYmgxGb3qmchyxKdL4olItDM3owijuWXklFYxrH8UvZmFBERaObWC2OrTTPaMi6UF+9uw+v3t2X6vRczufvFPNelFc3jggkPsNAg1A9/s+G4Mkk5FXhaWsrNZd9ic6rkGGJ8TgRzjDFIaIXyVDTLSIkUwJzAx/TUsmMKppCUuZ6PNx3x6faiWkKQUHFi8DjHg3uHE0cW6USRZYghxJpOzx2DCbGmA3A09IpTVpVaTIBPDaLSuEAgEJwehLA4F3D3o35oFTz8C1zaX6u47MykVA7EopTikP0pNFQGvyoV7lCu1f5AyUoseXq3ZZI/8xrP5ECJBavdCZJEhhRFr4qJ+MvO3pgN2kNkqJhVSlT640to1gaH6i0coCJTkKrqkRUS0EAPWM6lUPUnWLIyyfyBbqlIljN5xy1Fq1FScKKJi2DJyu3GTSTLWRVxCRMY6xhUoyUFKrM89TOu07NRLXJ0rvGWu1ySXEXtamr3kXEyDSXNVenZoBfZLrXwsP4ss0zhMnk/M0yefu0zTHOZZnzXy3oST45X29eM80gu30X3jHlkFZR4jaNpTDA3tYyhxOrkt39zWb8ni9/+zaXE5uSmljE0jQmu8Xp9+uVTaQ2RJQnTcSgLFVgi305ZYBIh1nQMBokCS6znRNAUi0GSCLNn6CLCgYEnzRNYaezEtNBx+vYncidylW2Tz1X/jDuXkm1qoFfSdp0jwnaMVCmWp4Jf4SW/Jz3GV2SO4vfkIaesKrWYAJ8aRKVxgUAgOD0IYXEuUDWQuzgdDv2EqTgVe0gyX7eZS7apAaG2dBySWRcXMmpFgLR3rIIDmdHmiXy6V8GpgtEgYZAlnIrKsYqJuF01YNP0BmZj5aPk6k3BPRzbG6MMDgWPYmwD3TIG/eC8BIBEOZu55llMtffxOH64bZSepjZU8pxMu1b7fQma6njP0Y3DSgwhUhlLzC9V6w5VNc7hPUe3att9YplCQ0lzVXo68EWOKZFYDBLH1Aj6OMZzlFhNXJgq3Z/u0QO6s+hl/IHl5kkegdru577HLfh7qWEi99lXIq2d4FXpen9mEd/vziTQYqR9k0g6toihfZNIAs1Gvt+d6bMydl2QZYn7Lk8iJsQPi1Gud5C3ySARGJ3M1puXkGWKJ8yWgaR4WoDsDruHqFAlI4tbvoU9/jISwvzJjL+ZNyIm6vt7/vssF2d+4bHqv7TlXHYZmjMl6mVdXLgz2jaM9IJSninxdFdTJSPlducpq0otJsCnBlFpXCAQCE4PQlicC7j7UftIL3nvXd2x9V2JLaQhEY4MFNlMJpq4MEqKV10HFZgS8DwbyhtRanVgkmUcTpVyu+JVkVlFEwZFVqf+GsCAk7HGxTXGKticle3jyeEF42JecTygWxhecfapCObWxMV880yP418wLWGyrR8lqoUAyeaxb4ZpXr3jJKrGL/iKtag6sa/OEuJqlyRlcpRYHjVMIqlxc5rGBtM0NpjIQAulfnFMMD7uUc9ipG0EW9TmFel3K699uXkSl7Df69xb1OaMtI3QrDcVK/EbLNfrdSmgso5FTrGNuBALJqOMv8lAXKgfzWODyC2xebSvL83jghl5czMaRQURaK491sKAJgCCLQZuahlDYrg/1sB45ifPItcYS4jdM3A/Ws3VRYMiGZAGrebB+3sw8c6LeLZrS3pf3ZCUmI4sa/Ky3u7WAy/pq/7zG83kk32wcMMhNmb7M9I+DIfqOc43DLP4iHF6Je9CYxSFljhCrOn02TWMtiHFPmNQThQxAT41uDLk9dk1TH8OlrVZoFuFTuV7KhAIBOczQlica1STXrJBcjPMg76hLCiJMHsGTgxkqhUxF1WWmSVgaOm7xKjZ+JkMWEwyzoraDHVdkXZVlq5LILS7W9Ezxk90C8MxNZIetomkKWGAK72szBBrZTG5ty0zCKwI1LarEkPd9h1PEHZN4qKuogIqg9WPEsvTgVPJMcZwQ/Noxt7WuiJe4RKGX2ZhivNNj3oWdxg36uPoYZvoIS5WVARqF6r+jLSN0DNbzbbMwSQpODFgwMmQrJfITv1Xc58pSKFk5TNsO5JNVlE5vx3MZdPBHDb+m8Mfh/LIK7V51bE4Hm5uFcurPS6m51XJNAr3J8gsY5S1PzASEGiWuSAqgAuiA4kKsdA4KoA7L4mnQZg/FqOBxlGBNIzwx+asDKLOlSPJlbWChZrrHqxq+QpKgys016xQMy23TifCmYXVqZAaeyNrL3jeY1zLE59nU24AuSVWGhtzGae8xXR1JkbJ6VbLRCJRziFO1twAU9VIRge/ynvN5p3yqtRiAnxqOG8qjQsEAsEZhhAW5xo+6lu4UIK11dsUYoknmwjJt/uLXZVJQAssbmjM94iBqOuatsutqLYJfk1uRSqau5STytVao6QwwbSYF+19POpc2FW4zzqJNeqVtVodasOXuLhM2ltnUQEw3dGbD9XbGCJNZHepJuCigyx6nMJ1MVb67RlBgppBCrH0ZzKLlG68pvbRxZtLXLgLK0WVCJHKmGWewyXs112tcs0N+OTid8n3SyDCdoxHD4/EeXQzLLqN4K3vcPPROeSX2vAzyYQHmPEzyWQWlbP1aD7ldudJcbdpHhvCM11a8MiNzVjycHve7NmW57u1ok1CCCF+JiQJTAaZC6KDuLZpFJFBfnpGqiQ5lwd2PkqcqgVRZ8rRRCg5RCiViQRk4Jbd48jc9Yu2oaLIXKOV9xOn5mAsTuWqVM+K8QP2jyKwJIWmlnxGpYziDuc6GkpZei2TFCXSI22xXZUYaR/Fxmx//sgPPOVVqcUE+BRwPlUaFwgEgjMMUcfiPOJobilTv96J8difzCx7DiOexeey1FBsGEmQcnBgwIiTY3IDxgWM55fcYFQkzXKhgnfZOm9qW+Gvab8ENDTmstigBT6nKFEYcBAv5/s8V6YSyl22qdXWe3CvXTHIuIppjt5uhflqH78LX9dRVXCZZAlZhjB/M7KkkltqJz7UnzWjbsDschVa/Tz8NpdMYzzDDJPYURKMqoLDqXrdW1eNEBeu90Z/j6Q4JkS8THRCE6KVbB7cO5wI2zEUczCyrYg8SwIPqRMgJAGLqfKaVVUlt8RGaICJhuEBPHFrC686FvVFzx0eGYH83cT/Z++849uo7zf+/t6dhi3Zsi3b8oxDFmHvUkpLacuGUsreBcIMSdgjITuBhL0SoIEkQNhlb+ii/RUoUGbDyoA4nvKWbdmWdHff3x8nnSWv2CFhFD2vF68gSzrdnU7S5/l+Ps/zwD6TWdPtY+nf19EcjlDsy6Agy0VPzKAu1EOex8k5OzsZ/byVih1UipiffyPOrjpu7bzS3m6HmkuWYXUUpFARk17D9BRhrjwcrb2SkLMI3TDxGw125ybRYQvjJqJ4yDMtglltWoQN4BnXbIpEa8oxbJSFnMUcRo/ZlpmHbccorRXlwSPsTqB5+ovUSD/hqI7HqVGakzGoo9aQGCTHIqJbmo5dszs56fNvmGPxPccW95rvGyh4xkuYWaXUtHWnvl8dNf0e9792bv/XkM4lSGMkSF8vWxbpHIs0BkQ4quPXG5kbuakfqWjCR4EIAYJ68u15/SKznouVxwjQgqIIXA4FZZiRBd9krKiYZh7WrNX4hMPT0dEF1Ju5Ka+RkAUUKqEUkfNAr53QKGzKySl5/4djAZvtVknUlQIwTImUkqhu0N5j4HaoFPvcBDsjvU86cD4du57LXaNuo1krIGZIYgOQCoCZ+lk8qv+KF8x9qBUBtLjwOEEuZqoX8U5zBm+ub+LVKpU52iX0KB6UaAd6dgV3lN2KmlNGZ0RPybEQQuBxqdS19VCQ7dqy4zZ/mQv/Xgr3H86EjBBTfzOOfcbkY5iSyuYwbV0xdir1pZAKgK+z9mBcoZdZ0dtSNic1V6+jmTSQyw/mqTf+w7UFN9KkFuKL1uM3GjCx9D06quUeJRU89NikoiYxXoefadrT/UhFTCqMEg2sYB7BqvUsevUL7v4wwoYjnrDTnD95clFKkvPdb6wfufh9AC3UaYfsyyUHTmDqb8ZzyYETOPXgfa0Atz4J3WkMgR9b0ngaaaSRxvcMaWLxI4LHqfHbtlUUmA397ovioEb6KRVNuBwqMW+pbctZQBtnOV7FkBDTTfSBYxsGxOaOFZ2lvUwZQSpln/v7LAy3kmWPCpUpTUzTnu732tVmPhVKA6tcizbp5JSMgWxdBxKFd0Z6T4jE6uZEDWjt1pFIdinLIc/jTB01UjXqfjqb9dFcWrtimHLwMTMDlen6OVxhTuPx8pkp92kYzIvdRhFNODWVrEiQq7tvwm2GiWVXsOHIJwgq+WxblI3boRJsj9DWFaU7qtMTM+jsMdBUwZ6j8zZv1X0gmAZEO8FXbhfE496ewXm7ODnxJ+UcsUsJJ/6knHN/MYbRq5fYxbLMLqNq9HGc/sV55EVr6VE89vFnR+qJoREVLsAiF8d8fCZ5wX+hJ4nOlfj51zBoJI8IvUnjJnBxbApBYXWufqasBqwORsJdK6F3GSUa+IN4iQyHwrsbmrnh3508v9u9/Cv/BO51n0FOpoMx+V5yMh2srg2x8s0NIyMX6QJ462CIUdB+SJzbn15oPS+NNNJII41vjDSx+BGhVDSzU/e79m0DhXmZM6gVAUpoQlUUWrUAubEgDlUBXxkqBj3CzTPaYThVgVAEqhjZhTOSZOkEEoF6J0d6R5iedM7rt8LsFx2AsMnFz5TV/Qr/hLVotugelj4ChrZ17avbMCQMZqgUiZl81dhJRDfxODVMU1LV0sXrn9Wz9O/rQEjyPM5hieJz9SZOqr425W86KuUiyHI5j4nRz1gam02pDNrBao7cctyaSk9MR1UE4ahOTVs3XzWFqWnrxuVQmBDIYruibDB0a0RruKvioWrr8UYfbcaHD8OHqwDZSy4+fJCuZQfw6pvv88LHtTz2bhUPv/4WsbV/sZ8myvfmqPXX4Nct4tvhsN4ji+Aq+I1GnDJCVLgwEShIpnTeQZFsTCFlChBSc8mjDY+I0EXi8bDCcSO7iYS7lqW1OD4613biSpCLTjJ5UjuMjS3d1Id6eH9DCwv+2c686Cnket1kuR2oiiDL7WB84WY4a6UL4K2DH2nSeBpppJHG9wVpYvFjQaga5f5D8EYbAYtUrJhwD8aEw1k25k4atGKKZCNetxa3ra0CrBVTt+zhoux/MMrvwa1Zc1B9naQ2heGOFYGV1K0oKouN0wg5CyilmT8551GqWFagdWYO9bJ3JKpYaQUE1aafCqXRLvyL488rjrv9VJv+zSIVCVvXkYrCBRbpCLb30BMzCEdj3P3Gem55/Utueu1L3tvQgq5LRvs3rWtI7FORWc9GWchJhpXenRiHKhdB7uy+moBRlxKsJoCcDAfvbWilM6JTmpPBmHwPpTmZZDpV2rtjVORlWmNQcTH0sEZuQtXI+w+Hfy+l5bmrqGrp6i2qJx5mrbCHqgGJ7ikCIDsSZMqGKYxxtFGmNHH4+2fi6KwFQGYVwca3cHRaIuV2V4CHi6bTrBUCoGJixr+unDJCq/QiZWoYYzJ8RisqVnhil/BYmS2oZIku7ncspkJpoNr085a5A0rcGrmBHE6KzqJGBPDSxU3dc3CGa8lwquR6XERjESaF76Wmch0t4d6xNiFEqrNWgqBVvzcw8YL+FtGDPc4+oHQBnEYaaaSRxvcfaWLxY8Eb19vFouHw8vQuK/hc3ZbmcIR2Z4BXE04pnTUkCAWhKhjzK9j2cEqPW0y+x2XZz2oKboelKxiIXwyUYTHUWFExzczUVqFioACZLg1FESgYzBHLeMo1m7I4qag2/RwdXcDvI/OpNvN7t6+04kSnQ7qoUBr4k3Muz/R5XmK2figMpf0YTs5FMhJr1zETWsIR7vr7et7d0ExnREc3THIyHDR2RviivnNIt62++3RqbBbv6uM5ITKLyji5SEZysFpnRO99k6RECHA5VDKcCg5FIITofe19Jveb5090WL6ob+8lD6FqYssPQ7RuoMlRws3tB9hag/WNHeDJh9Ofh5wKy+42otMoLNvYPD3I5M9O4g+fnkW+YXUmGskjHDGgo87aj+xSvOf/lWN+dxQdv70P02GNRClxogBWpyrEpjUh3Wo2+bLFSj1XLqODTLJEF524URQ4Sfs7L7um27qb7owAl2VcSzUByghyW89MSkQLqiK4lIc50XiBa0PTaa37OkWvkuFU6YkZrG/spOW5q+DfS5ErDrGI2p9nD76DCa3Fph6XRhqbg1gP3LOfRXKHg+r3rMfHerbufqWRRhr/s0gTix8DQtWw4Z/W//vKUae8wzG/O2pwoWiiW7Hb6XDYTbDH6WS4XOR7nfxyQj7jA1l43RqqALdDIcPRexkJYEZShkVJnDgMNlb0J+dc/uScZxd2EogZJqYpmacu5zjxN3v8qdr0c0KcHNTh54ToHGpkL7koVEJkiQhh6bByCZKeNxCpSCY0iduDkYoEWQKGRS76bruurZuPq9oIhnr4or6Dho4Ioe4YGQ6Vtq5ov+cnb2egfVIVQRA/0yJT7DyGBJKD1TojOm1dMfYanUthtpuOHp1gew8dPTqB7Az2Gp1LW1fMWmnvM88fW34Yq159M0WovOrVN+ladgiO9koatWIemriU3OIxttbg/rcqqWvrBl8pjPmldcnpTUgpaVasc5lJBD8hAOpFAZ+oO9idNADc2ZbeQG1h9BvTUGJhekRG/H2wskwAckT/3I2+Hndeo41WNZ+bi27iYO96suhCR8FLDyVY7mfZopuNspAHzMPwujSi3hIuzVxIvVJEkVHPYeGnMaXkIXE4dUoRJbKeGY1XEG6qpLGzh5q2Lr6oa2dtsJPH3v6KdV+tx0BFmDqmUKkvPxRdNwckaKw42CJyORUWsfsxwjTgtZnffAQvGVtqrO/7hu42WBiA9x8c3uPv2B3qP4b7DoAvXhr6sdXvwYpDrMffusP3/1ykkUYa30ukicWPAclC0bNeHZ5QNFQFrix79CIc1YkYJoHsDHYu85Hl1kAIDFOiG2bKSMqKpAyLP7nm8WQfTUVysnSZ0kyZ0kS1mc9y/TCLWMRMdBM89I6b6ChcYl5MyFmAUxWoCjSrfqa6r00ZiwLIIJZye1psaj9SsQvreM11ZYpDVCLULrGfDeTYnZfkwD9IJReTtJeBXjJRRkM/96nOqIFhStxOjdxMBw5VoTNi6R30vnHmcQxEKurxoygCj1NlG2eLHY6nx61zDaGmBKt53Ro9uuVMBUBCJC5BInE71NQMi/g1kPD5P+yDsxmltTAm38sorYXDPjibzHAVQbWYZ3ZZhswuS9EatIajfLixDdOUmL+4kjZHAIBCWvGYqeJmCaxUjmYH4zP7bxElExo+h/t+A0v3tq1Yr/YuoB6LRCaySwaCEAOQC72ZprqN3N31Gzpx245oOlZaeQ0BZmQvYsKEiYwp9LJtkZcWrZC5/ht40fN7HvJOIhoziGQUc0nGQotcmPWctXYKH/53NS9/Usc/1jRRE+ri6Lqb+Unn3+POVAqKNBBPn8Nl973EvBc+TSFosXsP6i18x/zym9ud/lCL6Q8fhnfuHngEr+8xbarDkzim12eOaKzvB9M1unlb0Hvghanwr9uHfuz7D0Jy/sljJ8MXL6HrJu9+3cwrq+t49+tmdN3sJRVm/Froavr+n4s00kjje4k0sfgxYAsIRT1ODbem0hXVyfO42KnUh9elWdkL8TpPFaCpUBsfGaox8ykVTZSKZmrkQPqG5EGq3mowUeY0C1/8toKGya3aEkqUFlyaSm6GA0VRaOuK2eMxCfQ1N+rr5LQL63jSNZds0U27zLAdohKC8cR+JpOJF/R9UroUYJGL+/RDWaSfnJIe/rJrej/3KUUIirLd9hiZx6miKIKIbqCbEkH/sbK+RCeIH00BgSDfbOQBJW7HKws5R11Ai7MEVRp0CY8drJYTbSCqm3ywsZXGjghZGRpF2W6yMjQaOyK8X9lqC8sTMLNKeWRib+r0GWunUNb5CWesnUKBXkcNAWblLqbTFUjZXxE/xrpQN7Whbmqkn8UltxEUBQC4Se3MCGC68UeK4u9NvShgqjaHkCMfOuotZymnl3d/+SBv94zm0qzFtOBjU0jW/0gJDiFZIWdynz4DL9aIR+KaShCk2acexPTDtqMsN5Path6khFqZx32ZZ9PUZZDpcrDrqFya1UJOM2axURZSKoMsM+ZSYDYBkjKlhZ0iH9iv3SZyqCefgFHHFfWXEar/Gk1RKFdbOOL9M209Cb5y+OVVmzyuAZFceG9KI2Po8NxUeH5q77jbUMX0liQeQ5GehCYneQSvtYqOZ6+g5dkrrbGy+w+3it/k7Il9JqduN5kgRMMDjvXVVq6l5enLqGpq7+0a9d3m9xmH3tj7/3+ZDf+6nZ4enQfe+orrXvqMB976ip4e3SIVL0zt93T52MncftftXPbEx8x6djXTHv2QWUvvx1x+cC+pgB/GuUgjjTS+l0gTix8DtoBTSokvg7EFXupCPUgpKc/NZGJRFjmZDjQFVEWgqQK3Q8WpCTQFEMlLx73VXqIIT3QqEp2L5LEiVYEb5Kk8JA7nD3IO7TKTMoIsZx47eELkeV2Mc4d4QF1AqWimcYCCs17mxgXdvSNLCVKRsBU9LTLdJjsGKgv10+zbyenhdziXMC06pR+5WKifRiFtdmchJpUB3aey3A7bFlUIQZ7HhUNV7LDBBBlKjFyV0YDA5FH9V/Z2FAUcmkKp0sRy5lFGkFry+be5A02mj4+1nflH7jE8tdejdseh+Jlj8XTX09oVIzfTgUtTUYSwyFmmg7buGFHdpDjbbZ+3mrZuPu7w8vB2d9HmLiWnp4YT/3s2OT01NDtLuChjIZWxXDp6+hecGU6VmGESjuqEozohR4A78q7B6EObQjJVH9FCDrPzbkIR4I31pm3HNA9/+6KB9h4d0VFPTnyEKoGh4j3bpdMmGZqQ+GkDUknFEzvezfm7ZTLhmSMYl+vgzH1Hs9foPFyaQkN7hJ6oQWG2m13LcxjttwTvddLPGeZsNsoA5SLIKm0+v3B9xXLmUUwTtTKfevLJly1IrOyMMoLc3DWTznX/4tTPzsMf15d0uIowz3hl87sVyWRix98PqZHpfGoqfPggfPAg3L3v0MX0ll7FT97P1g2pJCOhyUmM4N17EJ33/Iasj5bxWWUdLVoA0boBeZ81NiZzR1N71JN80Z1NxwvTre2uOKR3rCx3NOx/Vb+xvqeeexbnQ0eS98l9rH/kUqtrtPywkYXkfdddoT1OhwN6F3zkX2Zz5+JpzH/hc+79v6+Z9/znLFw0HdmXVHgK7cDIS1vmslfP25imyTbRz5nfchmKNHqXdtKBgWmkkcY3QJpYpDEsKIrg4B0D5HmcrG3opDOis02+hyy3hhofzSnPySA304kiBOc4XrU7FTXS6lwMlGFxXHQOx0V7NRfnOF4mQ7MKxoihcKv4AydkfEC2sGbjywlyS9d0Cto+YmlsNuUEqSMfOUCKdpFoxSfCVJt5ls2tczZPuebESYXg2MhcPmbcoMdch59p0Sm0y4xByUUidC9BKhzC7EcqHIqVArqxtYtwJEYkZiCR5GU68bjiI0zxX/VEl+Rl13Qmaa/xM+VTe380RVAimrlfWGnkdeSDhBPUv/M4V/KrrlfZvcLPKYfsZ+tlRNsGrmm6gvGuNlq7YkR0A1NKIrpBa1eMnEwnTk2hrr1XrBmO6vToBrq3lFfHz0s5J8+OnkO7M0BEN4ka/UeSuqMGDlXB49TwODWKaeay0CLUPvL0bFL1EdkyREXr2yyJzLDdnEJaPo6uIOd9PY0D1fd5QM7u94U1lDtZlhhYu6JhUqsUce/YOzDa6lAfONSaK19+EOMKs5i8/zhmHLYdu1fkEvC52TbgJTtDoy7UTWNHhMIsF1FvCTOyF1GrFDFKNLBSzqScoJXaLRZwlrKQWlFEMU2AoI58RokgK4yZdo5Mixbg1vI7qZFDGwoMiWTB/VNnwzH3IeO3u5YdwtJn/8Etr3/JqlffJLrmr73Pi+eMmKe/SJWR11/7sSVW8ZOL8OT9vOcXdheibuM6qlu7qDZzMQ+/Awk4OmvIjgRpcwTYMOpYe/RNYGAKlSdHzeWmf4f5+7P3Iz55jA6twBrdDFWDr6z3mLqzqT3qSaLZo3C0V3L0x5PIj9XS5iplXcFBHPbB2TjaK4llVwy/kO7bFRqKaAxEzrYE0fj5RXDAfJsoXC4f5kz5AgI4hr+zgGUISCEK+ll/5Za8ufZzbpY3cBbPsgrr+zDhsBbNHpUmFWmkkcY3gpByqDW//y2MJJL8RwdDt3789pmc8qNimiYNDQ0UFhaiKArrGjp4bXWQ5tqv2LfpcR7POZsvG3ro6IkBEt20xNeaMLhKeYR7oocipeQRh1V8J9C3+C6mmUnay1xvnIyqOnCogohusm2RlxLRwqymKykj2G+3a2U+KgaBuFBbR6DFf1KltArPDummW7ooVHpXu+vMXI6Ozh/SJSpZ49AuM+xOxLToFO5wLkkhE4ORCoC8TKvIbg5HMUyJy6GgKlaJbBomEcMkakikhDKlmYccC6gQ/YlKPX5mO1ZxpvpKvBgVlIomdFQ0DGTuaERyURCqJrr8UJztG/lX/gksdZ5Fa1cU3TTRFIU8j5MKfybt3TGm/mY8E4usz0RVSxe3/nkNo7QWzlg7hZye3jntNlcpV3qvY3VnFgdtH8CX6bTvk1KyrqGDPQtVTv/1zigdtYTvOYCsSD0AQXLRpB7PHolfdtIaoUu5FFFZMeFu/i/o4KbwDAr1Oju3ove1UkmFIQVqvEPWLp1kER2SdEzLXMyEohwu+OpCS2CvaJb+qGwv+zGJa319YycR3aAnZlLd2sXYAg9rgp3kepyM6/qERe29Y0zHx+bxlXt7DAlFNPFHYw6jRAMN5FFIUicGlcsKlqH7tmHaAb3nfrOQRARi2RU8u808fvnpdArjY2t35FzFlV034Y/WYqCgxjUmMW8pj2y/jI87vPToBpmq5MzWO5kQfs8a09rUynWo2tJvHTh/YBvcV2dYRXViO2DvpxQaQuo0Osq4f/QNdJkqs9afmPIedzgLEGAL+xOdpkatmLdKzuDIjYsQkHJt9D0ml6qQ3fwxsxovRcPAECovTVjEfpW3k9NTQ6NWzCt73MepB+87vIDIvqRr9H7w4YPo8TBKV94oSnMyUDpqUh/X5/j56YVWV3gz0dOjc+fiaVwuH7Z1Ra8be3KQ+h/7thAQzRqF8+xXeLclg8ue+Ji9et7mZnlDvyFUAWyUhTyy3VKuPP7ALReWuQXR97cojTSGQvp62bIYSf2cPttpWBhmhsG4wiwu2M3FjIbL+U3bk9yc8zTXHL4d2RkOhBAEsl1sk+9BVR3Mi51KrcwjKPxcbgydYVGHn0XGaZioxEyTnliiNS/4oiubk6IzUxygEnAQSyIVChqSBumzf1ilhCzRk0IqdKlQrLTymutKdmHdgMfZVzh9SWRySufi2tgpduEPDEoqACK6pCEcIWaYxExJJGYipURKiQH4Mp2oikAIqDL9nBjpDWqLScXujhTRzMLYyTyq74+UqaSiy1OeSioAfGU0HfM0b+Qdx99HXcheo3PZZ4yfvbfxs88YP3tW5JLhUHFpaorGojQng12yOjnl88nk9NTQ5i7lsZ3us8aiIjUsaLua7TLbqW+3CKVumnT0xFjbYBXbu43KQemoRbn/UJtU1JHPDdkzMYWDVAhCMjXHY7nvQioztmN9xMfDpbMwUFMKTh2Fbpwpz1GFRJcKQZlNthiaVADMCC/mgq8mo2IgRX9SAfFrff+xtnvaeb8cw/bF2XhcGpqq4Is2MLMjNbDuDu028o0mkFBj5nFKbBa1Mj+FVAA4MLi4aR6GlCnnfrPQR3C//6fTudVzKZ1kUkqQa9uuiJMKFRWTZq2QNkcAR2eNLc7fJSvMFRvOYYfgszg6a4h5SzdNKjY1KtXXvhgwT3+Rbm85QuoYqPhjtfy+eRkXfj0llTgCWdFGm1Q0KoVMdS+imgAFep1NKiSgIGlWC2lzph7TmHwv+WYj5zZeZ5EKVFRpcOSXV9rXdSLvpaatv8PYUOfaHrFa+1faXQG09kp8jx/Fypf/xYOv/ovuew+B1g3o2RWYpz5n2X2vOGR4XaBhdDUe/2Aj90QO59rISfZ33cFaKqmoNAuZkXMdZlYpzeEo3TGDN8SeLFVOTtmWAKoJcIY5mw/bRnAu0kgjjTQGQJpYpGFhkAyD6tYu+7/EmITy4BFo7ZWQOxrPL6exNthJeW4mEwJZqIpC1DDJ9TjJzXQgJRSazdysDZxhAZauYJa2ihKlBSHANK3sBwH4vU7GFngICj/TotNsq9EECoRFGGJSoGFSI/OJoVk/sPQflWkwfWjCJCI1skU3T7rmppCLYppZpC1LIRXTolOY7Vxli70rlAaWuW61SUUCgwX+xXSDmC7R4+MGpgSvW8OX4WB0XiZ+rxOkRMFava/Hz0nR/uTicdcCduRrfqZ8RpnSRExarkb1ahFLRt3Gukh/nUlR+Tg+3fFqatutIiU7w0G+10V2hlXg14V6GFfotQLy4lA6ajj5i8kU6HU0asXcP34JVd6duH/8Ehq1YgJGHXdGZ7GPv4e2rhgbmsK0dcXYqdTHGT+roDgnA/55Y9y2GHoyi7k+62quaF9MQZ8CWxUSn+hK+dtZoaVkNn5EodnEmbXzbMveBEJkkSmi6DL1zdWEiZ/OfucggURvNiahSLTFR65U5Jmv9JKKPkVdsnvanhV5jC/I4ODqO9hDW8/81qvwyHCKeUCRaOUBOYMC2UB31KCQVnx09N0VALyyk20zQinnfnPRV3B/TWg2XrqQYOecqBi0uUp5cOI9TMtcTEP8sWeuOZ9T//sHAj1f2dvriRnIvy/GbP6ajmevYN26L4YelUqct5bK3vM3gH3xg29v4Dx1PtUEbOcsr95Gnml9FzRJr72CnowLeibzSls5d0SOTLlfAG3uUh7c7h6mZSyyjylhOHD+hosoJUitKOK23Bkp20zOe7Fd0YaDZCLXWYMj2k5Iy6cgVseUDVM49L0zyei0nNOuK7yJz5+YY2lbQlWQVQzb7AfeooG3PUxtS01rD6aEFfyW1409U+4Twuo+nBidxcdtWdS0deP3ONEUwbbmGs43H++3vZXiKBqVAoQiRnYu0kgjjTT6IE0s0rAwSIbBbX9Zywsf13LbX9YOKHaskX7WN3YyPuBlr9F5/GwbH3Odj3DkaMmvty1kjLOVR52We1ENAc4QC/ppFJ51zmKS9goPafMJmM124eBQBXWt3axtCFNgNnOLYwlafB44GWbc+adG5iOlpFQ0U2kWckPsxH6HGcXBo/r+nBS5xu44JMiFldQ9l5O0N1JIRWLsqdIs5LLo+YOewr7uU8mQ0vpPweBq5UG83UFKczLZrSKXirxMzLgNrKYIXA5BqyOfU2KWJW9CEzJKNNjWvToqDmFSKwIsyL+Rr6K5vP5psDf9Oo6+2pi+HYY8j5ODdgj0jj7EC5vE7PnLu9/HRj2PDU1hNup5vBIPUswMV3H62gu5/KceOwvl/F+OZWxBFoSb4OtEbkoZzhMfZH70RoqwVp/rRQHTc26khayUfV2rjrc7MFfWXsyV5nJyYv3H3/yEaFX9NIucfvdpwiT5FPS9VtrMTBxJVWur8GHGk8E3VdQpiuDktns4IPQU85supUTWUyuKuCzrBhrJsx8XEK08aM5kf/Nd/uSYjUdEUrYjgWZ8FNLCBRsussZmEq+fvFI9AqFwTVs3lS1dVPn2pNlRjDeuYelboK8qnsH6aA5fdmVxV8VthFwBfJEgnrhgPqK4aXMGyIrUIz5ahXnnHmR9tMxejR9Q8Ay9523pnqnnr5998TlEdYM5udcTFAW29S9Ynahrxfk097kuAG5x3MVx/J3rnff2O6ZXx8+jVQuwPpLDXRW3pxgO+KO11CpF3OC9nJNC96Y8LznvpV/XaBPnPkHkuoSHDNlNlt5CBBd5epCAbKReFDA//way3BpFzW/1PjHcAB88iHx91sC5JsPUtpTmum1NxUHqf/rd/3x0b+rwo2kK4ajO7uW5/MqzkQflbGtsss/jZ5p/5BDHBxRlub55By2NNNL4USNNLNLoxSAZBkXZbjvDwNFeSSxrlLXqRq/QN9OpIYTg8OA9/Lzpcc5cO4WTq+fzsDKHCqWBDpnJeWIW/46Ns1fjKxSrUN5J2UCHdFMhGng07t6UKLJjpqSEJh5JEkj37UIowhpvUjEoUyxSMT96GpdqT6Q8ztIwNPEz5TPqyOfYyFybXDzlmstzzpmUKRYxqDbz+5GKadEpzHQ8nLLNmFQ4J3LJoIF5Cpbrk4inlE9XH2aS9gr3M5s988LkeVxkua0xskQRrAoFET/IROHliOsHEl0SDYNqAlyVvYj1kRyy3BrrGjoHHGMYV5jFmfuOZscSX78Ow5n7jmZcYbyQ61PYOCa9zGmH7DtokKJo3UDJs8cyMaO9NwsF4IuXoa0ynpvyGsqHq8iOWAShSS3k4sxFlnsW4ZT9LDWrWeiZbpOLvfTeginkCvCF/0D7tsvoIkArIWeADjU1xySxG8njcMTfgxyltzuiI8iXLegrB7EyHQA5tf9nvUeYGAgWZ17Gf2JjmJJ5PU1Kb7eqSGllmeu2ftbHCdKsCydtjgAZnVXW6yZeP7koH854YrwANqre44LKaeze/AJ1nu0Hfixw7IY5/HrNtXR091Dd2o1ipArcXxs/n8d2vJcmtRDALkILYnVMq5zC4e+fiaO9ks7MctYc+hi1bd2WFWzrhvj+RMGVnXL+krsphXodt3TNpEg0o4nUTpSGya3cRH68w5N436SEUaKBG1z3DphTcsjaOSgdNcj4a/U1HHjUdw5Xdt5IiVlPs7Okd6wvKe+lX9doE+c+4Zz2yITbMFFRMHEl5e4IgFANp315IX69gVbVj4kKpjV692TPT/oFT47EoeqE3UdxsuuNQc/Jhc4XOE99kfH5mXicGlr9+1zbdnmKULuaAAvEefbn5Ab9eg5UP9giHbQ00kjjx4s0sfih4FuyOeyXYbBmCv6eDfxh7UX2aMzazN0sy8oVh5AdCeLWVLTOGvb7+lY+LD7ensXfue0vFNGELhWyRBfHGi8S0yVB4eei6BS7qNelQpe0ZuZHKQ229iEcNYi1VnNj1zV9xMwF/FnfPWW/NWFSJFqpMfNZED2Ne+KjSjGpsChyIqYU9g9wggA0kMuxkbnoUkETpq3DqDbzuTA6rR+pSBZsA/b+zHQ83M8tKkEuhADDtLoqioCMeI5CEY2c+eUFeCP1dPbEUAU4HRb7MEyTfKOJxx3zKFJaB32vZoqptKiFGKaJGs/EGGyMoa9eINFhsEkFpAYpxgubTQYptm6wnpeM3U6BvS/oXc2Op77HvKW8uMcKipQ2FoeuREu4PwlLCJYpuzk/fA9L8mZgKg57hKdV8bNywj28OH4uH/h/S7NaSCbddAkPL5VeQlT2/xpLjMGZUnChfgntpOo4WvGgIYlJBXfnRuTyQzZd1IWqrXyEOFQkN0QXcugog6inhDNYmBLW2JcASwmXcxn1ahEB2YjboSJ9ZdbrDjR/P8h4YvJKt3x9Fvx7KRXPHk1BrI42Z4DyrtX99x3QpaCYJo7mryxmKQubLyFLT72+9qu8ne6ozqXexbSoFlFKaBlyYkHyjQbqZS6vhMcz++mP0Fb91rKCTWxA0eC0p1POX7J9cYuzhBJZz7Utl+E3U8fiklFv5nJl5JwUkX6yfmCjLOTG0jtsgjC58iLGuULk6kEOWTsnZVsXt15HiQxSQ4C7R9+eMtaXyHtRkoPkhnHu1zd20h3TMTxFdDlShYzN+AjIRpb2XE1+rJZGpRApNJS4q5WQOvt/Or1f8ORIHKrcnz7CArGs3zlZEvmtfftqxyMcH3uW0s7VsOIQ21I28djTjVk8Yf6aKx1X2YT3sM8uQ1nz8pCvnUYaaaQxFNLE4oeCYYqrgW/kQZ8oAqp81txudqSe/TbciS9J7NjeE1/lDFVR/PRR7OP8mlM+n8wetY8wsfJR/jRqrrU6F0fCLjLD7EIoUCKauT2RGB0v6gNKO3VmDp3SbWsffs37PKxZ1qrJDknTolOZoAx8DtyiJ4VUHBuZyzJ5JL+PzLO7HcnkAqBFpo5eLNWPHJJUJPbj2MjcIXMuimm2R5zAckBaoh9j24vmxoIc9/HZNFR/hSlB1yXSlBTIJh7V5lKqNAGWfmQgLJR3kmc0oCqK5TY10EhHHKYpqWnrJhzV8Tg1y7mm73L6FghSBEBR4eCF1v8nd0DOeZ3Td3RxS+dVtph2xYR7eGTXh2jVrLC9IpqY2n4zitmbnq45HLR363zdHOGhwst5ac+VRLMryJRhTvh6Bn6z//iZwBqtOdGcT6vmJ5OelPtzCNOID4cwMRGWmNiZNfSxv30XdNRBZq+JgDsWYvKaMwjVf82nXT7Oi1xCn2m03n0SsJfxPlOdCwiqxbi7agl3RyyrZFO3VvqTX3+Q8cTkle4ne35iOyyZKLijIXzRIEb885esSdKScmUOU/9NUdz0IDGMZKe2f3EhIHhgu3vpcBbY5zMBP20cJ/7Gsu5LKNTr7KJ0MBF8sn3xaxNSr5VGclmpHI3e56fIQOUtdmaZfni/cxiTChdFp/ARE2yCUKjXcUfX1Zz+xfm2MPu5iTdgCDWu5VBZVjCDZqWg31ifo72y//fqJs79o+9uJNy4keM+ORtvLJWc5fTR1Aipk6cHCarF3Df+7kGDJxu1Yh7d7i7MrFKGRDz8LkH4EkLtEyKzuEmexGL9ZPs92XfDnSgrDrTD7xKWso9MXEpB6Vh2KPURG3sIL29/c692JZ7QnUYaaaSxOUjbzf5Q0Hf+9oyXMLNK+xeLA9kcjsCT/Iv6du7461p2yQpz8uoz8URbaMjemcL2T3h8x2V8rm3Hr9Yu4FfhV+3nmEJFkdZozhTnAk41n+dY/cV+Iszn9H1YrJ9sC6NrZD4qOkWiDYDH9V/yhPkbHnNYAXamFChC9iEVvUV+gpQMhJhUOD46lw/lOFTAgJRwvMSqXkwKe8yoL4YiFQn3p77uUX3Hp/q6RKkCimQzT2XMp0hamoNa8jkxNodqw0+AZp50zkshFY6kc5AYE0r8WysCXFt4E4a3lJ3LfJz/y7H9CEOybWqPbuDWVMYWeDl4x0Bqx2ILIMXi7/WZ/e1G79oHIu3WOMjO9/F2dBsiukFANnNx1RR7ZMqGYo2PtO96Lh9sdwUAY/I9lIU/Raw8BCF7x2laRQ65ss2+3aHlsVCcy8LoDfFzpxAmE19c4G1K6BFuMumhS/Hw5J6Pcuoh+w1utZlkyWx+8iTir3Ps67tFejg7egV3O24hoLQPen5qZD63lN3GhICPE1afTU7UOl7D4UGd/JZ1rvoiVE1s+WE42itp1Ip5eLu70L2laJ3WKE+BXocUqn0uku1DL4pM4S7nLRQrbf23G8ff2ZNdnLXk2c5RBkG1mFXbLsUbqef89b1jTcnjZcn/6qg8u+tyjj7yqH7nL9m++Kw1F5AddwoDCJJPS/ZEtm1/CyVOcRKWuA0ymwLaU5PU48dWKQtZ4L+B7MA27JrdyUmfnmsnmTdrhTwz9lqO3TCXnEiNfUzR7AqajnmKdldg+N+Xg5x7taOGk1afa+uG2hwBFhincb15S4puJIF6UcAlmYvJCoxme097fxtndyn3j1/CRj2PSw6cQHleZr9tAAMmakezRjHDdx0fh7LQNIXx+ZkcH3uWfTfc2U+PMuRvx5qXLVIBoDph2offqzyLtH1oGiNB+nrZskjbzf4vYhirl/3mdE9/zlplHcH4VOm/F5CpSrpjurUCmYQDPp/BmrWfc7t+NM1aof13RVruLlcxlTXd2cioVbj1/VHbR/2Mp9zzqFAaqJX5gKRItNmrlVHFzZGOfzNFv5SYVFCSdAVDkYp6M4dG2euIJCVczKVszJhIlktllD+T3EwH/xXj+IOYj45qFysJUlFt5jM9elbK/t4V6+1ctMuMAS1l6/BzYpJmpG/nYpL2MorA7t+YEhoUP3Pzb6YeazW4hCYe1eaxh7KGPw1BKmoIcAbz2CgDNrkokUFmN13JGGdrqgg7jnUNHax8cwOra0PkZDoYk+8lJ9PB6toQK9/cwLqGgR2LtgiSOyBgFXCRdnBlIya9yjG/O8oezTrzsJ/jPf+vULBd7/PjpMJwZvGs4wie/6iWJ/5Txf2vvEn44dNSSEUlJURJtbLN0ltYHFtsE7I/MJ9T1BtpIsfavIBMeuhwFrBsh4f4qMM3tNVmUoL9xu3O5nZOtWfb80SYp11zByUVUkKj9FEqmpjecCW+WDClYP7SfyCmr2LA5w40npjb/CGnfXkhBXodzVoh7Uk6E4HVqZgWmUKQXAwGF+PqUrBU/x0zfItpcpSgxsd1AkYdp352Hkd9lTpWlDyWlPhXlypXZV3Pmz2jBzx/Cfvi0z4/zyYV7Vo+zYqfAlrIjRO9ZpFHqyOAiklMQqFot93doJdUSAkVooHFxf9n6X5+WoFD7f0pc6iCHeufJSdSQ5OjhGd2XU4suwJne2V/TdAmxvr6nft4l+GstefbpCIoCriz4k4+ZyxNSd9DKZASKU2igwRPDtuh6pUrUm/njsZ59ivccNYRrDjzJ9x6wq5ccej2/Oz0BYgD+nQSE8c62IjjxMPhxEcsUmFE+484ppFGGmkMA2li8UPCIOLqQed031k24vGprI+WcWbrnZzy+WSyI/W0u4roUi12WmA0cFvXdEp9GTwzZh5mEnXQMLmR29hDXcN+yscpm07YcRaKECU00UAuQkCpaEaXqmUTKwJkKjH+IF5mpraKOfrgRX7fTsUb5q78XfZqLoSAmeJ+CmQzvgwn+V4nLk1BUwQfGeO4NvPKlG3Xy1wujE7jfO3FlL8vci63Ow+HRRZxn37ogDkVfcnFb7W3OTE6i/v0Q1mkn4wpsQ1TFQH7jPFz/K/35hLPYuqxxmpKRRN/cs6lbBBSUacUcWXWdaxzbscF6jyqCdjjRAGjjktqLmWcK5SyX6YpeW11kJZwlPGFXrLcDlRFkOV2ML7QS0s4OqCT1BZDUiGeot+Y/DaU7dW/uMkth/P/BbudDr5ym1So0Q4O/fA8O29h2sYpdj5GLKOAL/MPIkM1CchGWpwlPLrjfXQ6LJemxLjIXZ7JHGi+xcaYj0u4LGU3M6PN+GXLiGxH39vQyh+NI1gUPamfcDaB5L8LATmEaCSP/FgtJ/73bHyRICFXgA/8v2VFztRBSU1fjUJOpIZJa87DH60lKArQDYlPb6JDuu3naMLkHtetPBG/purM3H72vBJrPOoox7/Zd/ddiJ76PNHsUfZCQRGNFMlGgjI3RTvSF5eLi3nfGEtbd2zA86d01HDyZ+fi162AzDZHgOcmXo+aFKrXo2Ty4KiF3FR6OyElN8W5S2DloMyhV2gsgfztf0W52oLy4BGWlauvHHxlZEeC7DC6mJadzyZ66vMcfeRRtuFAsmbCxhBjfcnnPtltyhfvrLVoAa7Ivp5gR4Rl5lyKRCuNpJKLZnIooonbIrPINxvROmv66UCGdKhKwNBh11NAxJcpNqWF2ukYKP9p7/PH/nrTHYiJh1udioFGHNNII400hoH0KNQPDKYpWfXqmxz2wdkU6HW0ua3Vr0PWzumfJDtAm3+T41O+MmKGxNFpbeuhiUsItYc5b+PlFGMVBu2OfFxGFy6zq18C8kBoJZtc+q/kJormKhngcs+16IbktshMygmmhM9B78hFQgSdQI30c7IxjzY1j/nKcvY2P7Bnx4Pk8oFzT251XoCqCE7vXM5L5j7cxG32aiNYKdwGiu0otTB2Cnc7b49blwrOiVzKX9ljk+9NIj18kX6yPeOeDAH4PQ6uO3pnnJrCjGf+y1hnGze3X0YhvXPauhRoySNgspAZWddx6iH74ve4yHSp6C1VbP/ayTg7NiIVDWHq/UY5EiMovnhmRdQwcaoKWW7LwaujJ0ZbV2zo0YsRYtD28yDJ7oMiVI18aymrzEM49MPz7Gu93rM9E5v/bD+sw5mP2+myr9eHt7sLv2zh5NXn2AnTAKaioZg6//Acyg7Rj8mP1aa8nIHCnRVLOeZ3Rw3rXKx6ewMLXvwMv9HEa84ryFZSNRyJ67XazMcpYhTG81YMBGqS2edjO91HlXcnNjSFU9LPk5EYT8zJcBLb8DY3tveuWv/T+Qv2i/4flWYhp0ZnMNX5HPuJj+zPAECdmQMIigcwAuiUbs733sG1Zx1BeW4mq159kyPePxO/0ZDyGK/oGXRssIoAF7nmk1s8hrlH7ph6/pJHOIF2V4AVRXM5pXa+JTZ3FdOZsz0lwb9jZpfTsOsUiv95hb1k0Y2TDKLUyHxOM+eyd0YN10UX9S5pTDgU1rySOm43WBp48r4MM/k6ce7H5Hsp6/yEE/97du95cRbw0I7LqWruYnrj5Xi7qmlWCzGxFmESMFDoUPPIMZoIOQPohsRvNAz93T3QOF4iyTynAsb8En551aYDDFs3WETdlTV4QvoPBOnRljRGgvT1smWRHoX6H8ZgK2gDJsmOdHzKVwYSO3n35d3v48tuH191ubnUcx2NijX+lB1rwmV2YaDYxf5AqJe5NEjfgKQCrBGndpnJJGbiKRiFllfGdKbZBXVMKlwVORsjibz0JRVnsAAjq5S9xhTy0ujpTM28IT5mBQFaOTT6Zy6P/ZEr1Yc5yXyR+5llk4pGkUe9zKVYaaVMaabatDoPq+UYGmV2/PUk97huHTShOxl1+Fmon2aTir6lgapAhlNjYpGlaxASDGn285TXhKRDunAIk42ykFOis3i/zcOf/lNNnsfJjiU57LrjTjjPtgoqkRD+9hnlCEd1mjojfF7XzttfNfPO1828/VUz/9nQSks4QoZTHXk42OYiuXsxHPjKqN57Fh91+lKu9WRSYaCQFW2yr9eXdr+Xz7uy+fmXi+J2sAq6KwcAxdSJCBc7Rt4nP1Zrp4knxMkqJqc23DRsq83xAS+lSjNPO2f1IxWQIBV+jovO4feRhQTJjb9O6rs9nJVqj1PDpSo01aznko6bU+6boK/lMX1/TozOooZC5nEel8lLUh5TIEIppKJLOu0OhFf0sCQ6i3Klxc7CUNXUK9ciFQoOIft1ZyRQTpA7I7PZ1RdOPX99dWG7nY73/L8yKfdDCmJ16NkVeM97He2g+ZA7Gq29kpIkUsGEQ1Env0PYU06paOK+bd9j3qWXICYc2vsaa16xyEWCUPvKrOssUUAnu+MNZTgwCDxOzXa969tlMISCFg5ySe2leLuqaXcFQFikosVZwkMV19pp5y6zi05nAb5oEL/RQLNa2C94clCHqgQSTlVtlXZOTF+XsAHzMPa/KvWcpJFGGmlsRaSJxQ8MyQ4rw5rTHe74lLcUENBujQg4VMFp+1Rwwl6jCPjcjB8/kb9sn7rCF1ayiEmBkjQHnYAhFW6OHJtCKow+l5sEskUX57v+gkNVKaaZ68UdNqlwCJPJjue5po/2AawZ8inRiwjipzuqE47o5HldHP6LPXmkYj5dorfA6Y7pPNC+R1zwbK1iN5LHNOOSPknegkJaLUcnpY06Mwc9vh+rXIsGDb8bDMnnRFNAFZYlrCklY/I9jHW3cXPH1QTov5Iclpk8qu/P6eZsmtQChBCsb+jktdX1vaNLyYXS+f/Xr2Bq6ohQ1dJFfagbt0MhN9OJ26HQ0NHDR1Vt1LV1Dz168R1jqGs97MhL6UhIJO09Oh3dOtNzb6LSOY6/jZuBGunVkDiJ4dcbUtLEl2+7jFbVIqI+LTp4UdcHe/q6eFydPaQo2iu60ARkB0bxzPjFKde/gaDDWTB0lkIcpTkZjHGGmNN8JSWynnq1mFn+W6hXiyky69lH+czasoBC2cQisTTl+cluUNWmnwMiN3N0ZL5NwHNiQZT7D7GzMHKiQTrjhCuBZMMDHYUWLOKdGDUrJch5X1+Uev762hf/7k6U3HKyfrsIfnoh2qSXUXLKwJMPpz8PIumzeOIjcPJjOAvH4Dn3VfjphYw56Racb8yzyIQnaf+q3xvcHrivO15f4rEJJPQhp3w+2Vq8cZXwhf9AO1jw5LUXkx+rRfrKyHBo9vX14LZLqSs5gJXb3kOX4iFDdtkhhAD1+T8bMHhyQIeqBDZHZzdC84400kgjjW+KNLH4gWGoFbTBVj8HEyAm2xyu9e6VMqdMqBrlwSOY4A7hc2m4umo5aE2qdW22GbJXMfutzguTG1z34hASXYp4gF2qW0rCEjQ05rfM2i+bG8IzKJVBapUiTozNszULC5z3pzxPSmuG/HbnEsrVFiIxg8qWLv65ppH/e+9jzg5eS6bsplbm8yf5a5YaR7HAuC0ueLYueUOa3Mjtdqei2synTGmyk60rzUKOji7gmMhc2mUG2aK7X/jdppB8TnQTTENncmQFH63+lDKlhdvC0ynG0lRUm36Ojsyl2rT0G0VKK79SP2ayeAqnauLSFCTwSXUodRY/USjljk4pmExT8tHGNjRVIAHdMInp1ihUnsdJV0RndU07Ywu839tArKGudc3s7RJIwNlZyzGfnEesdSOtEYW7Mifzq3WLEBhIFBAKQlrXX4N/75Si7oU9H7CKuo6qYeuRtPsPpJDUHIa+q/k5opsXXFdzYE4Nf6hbaHdRwOpcdAnvwCvVfTJrlI4aJq2fQilBakWAmb5FfOHYjpm+RVQToEJp4BXXlezOlzygzGeUCFIt/TQMICS+ODaVoOKn1VHAH8cujS8oWMc06sWT7CwMQ/RfBEhYvZ4u53O8XGR3YRLkwtm3KB7Mvnig7pWv1NLeaG447iFr1t++L4kMJFbtw40WucjIw5z20TdKsR4KSkcNJ38x2f6evH/CUl6YsJCVE+6hUSsm0wxjODwI07A6Z33S6j9TJvDyzkvjlsCGZUiw4zFsd859gwZPDqgDST4XI9HZfRek4lvKXEojjTS+n0hrLH5gGJHGIj43VNXUzvpHLmVdwUEcWzl3QJvD6lg2c9yPk/Wriyyv/lVHQ6SdnuzR3OW7kuOrFlBGHbVKEY/6zuHi1mtRMTepsZASGvHZM+YDocdViNvtglAVIXcpc3IX81HIQ1nXZ6yUswfUWiT+rSLAOWIuzrxRFJgNzGu9mjKCVBPgYtcC3E6VG8PXUGzWW/ab0Snc7lzCKGHNQFeb+RwXnUMhrbYVbSL/4mPGAaRYyt6nH8pC/bQRvWcC0ARM11ZxlvoKTWohPreCI2wJkGvJ57jIHGqkZV9ribh7CcyT/Jpb3VNwaQqluRlcc/j2A87iJ6OqpYt5L3xKbVs3tW09xAwTh6rgcapkuR10xwwUIZh/1A78YnzBkNsaCTY51zoCrYVpSp567lkO/++FZJrhftd6Dy6cRFDodQyqV4u413UW07uut4lkxJGLJxY/nw4P5vlvUiMCm2fTHKqG+34DHdZ7l+xUlMgTeNHYm8naC/bnwkSgIIllV/DsNvMY+9Uq8iOVLNnmLnbMiXHS55OtlerE6759V69N7zH3wVNnQ+sGGrRiZvoWs9HIi4ciKmxvfMn17Vem2DNvlIV8ZIzlSO3tfrsfFAUsKLyFmKeEaw7fjlFaqxXOF6oCoMVdgYx14zcarAyIRKha/Fiv983mS9/P6dYNSkULZ3Xfz/Ytf0OQlKI9gpXyzZqDTiINsewKHpl4Fx93eG0r5V2yOjn5iz7ndHMK7EFeJ6IbuDS1n80tvnI469V+OraydxYg3rnLCg8cQAs12GsOpgPZnN+AbxUJLchwzv0IdC/pmfk0RoL09bJlkdZY/A+j3wraMOZ0Pf+cx/4tf+LYDXP5x+iLUraXGJ/qNgQ1P51l/fGpsyHSjuHMwtm+kRNC91FCkFpRxA2eyzmpbZlFKkglFaljRRaEsNyg+gZg6Si0xt1T3JEGCFWhZ5Vye9ltCF85xUoLNypLUkgFwDX62XbQnSkF5QRZzlx2ll9wbfsMygiyURZyhpyNyCnj2NgLFJv1VBHgpMgsPpTjOCEyi0ozUUxLikUzdziXpIxg/TapKEu4PiVcnkb0fgGaCqoKK43DqJF+8o0Gm1S0aAFm591E2B2wX+vC6EUpoXj78RHbOFsRQpDp0IY1uvR5fTtrgh1EYiZFPjc5mQ6EgLbuGPXtPfgyHZTnZVCQ5RrR8XxjjCDoUan9D8d+cjaZZpguxdPvWnf3IRUSKDLqmdV1nUUqpMpbYvdeUiFUiIVR/vQHyn3OkaWJJ/D6bJtUkPS6iTTj04xZ3KWczJ3qafYonIIE1YVj0ssc87ujKDjzIbrO+jtTD95p4JXqHX/fezueyC1zRvP49ncTdhexQ0kWe22Tx8FlOrOjt9pjSoqQ6KhcxTR6cNr7WC9zaY2naAdkI/OaL2Pfgh7KcjOt4z7rVasoBnxGC36jgS7FgyoNKzww6Rxfat7PnVn3s/DXeVx8zK/ZbsqfEGe/ZhXNiTOSfP62xmr0t7Fq36fj4Zj0Mqcdsm//LsOE3yQ9yXrH+zo0iYMWWEXzWa9u+voahg5kRDq7LYGRdiB2/H2v5muQxPgt2VVKI400vl9IE4sfEuJfxIkfzeSW+1Bzuj17nE+Tw7KpPOLL6SmbPPDL2USaN+JUFSucLP5FL3NH88rYubb40EBlpXcSV3beRIkMoqOkjvqgsECczWDQMG1ykdA6mJorZZbblJKIblCqtHBz10yKzXr0Pu5K52vPc2H0Etplhl1IlcogC1suo8ioo1YUcXJ0FkHy6ejRuV4/mYc4nLOZQx3+uHWlnxOjs6mUhZQpzTydNP50bGTugASirzB7uDCBmAE9OtSYfj6U26acs5t906k28vBlOMhyqZTaJEfa5KKQFk7qegxVEexc5tvk6JJpSv7zdQu6IfG6VXwZDkpzMqjIy2SbfA++DA1NAb/H+e3rKxKjLJsqOqrfgxWHIKSVp/LSTkv7Xes9mcUAKYVvMq4Tk9jG3AiAiQLSsArg+o8HTqQfjri3eW2/PwmskLK63J+wY6mPncpz+WrCJGqzd+19kBGBuo97i86MdsrfXYgijf6kZvUz1m1XNpjW8T9ZMZePO7xUtXbx9voWmqq/4vQ1F5ITqbFzWWJSQcPgWPE3fqp+BlgGBzeVL+HxXVbS5rDIq19v4KRPz+1dfPCVwXErwZWNGusgll3Bk3s+SpVrPAqSbsXDy9vfbGVBdGzE++kjjHvhWMvqVRFW0vZZr1r7i4SiXazzN5DGYQthOOOdazJ2G76Vcl8C1FcfMpil6+G3wu6n2+OjAxKGxOhX2V7DE49vQgcyYp3dN8UIFgMIVdsLUwlykdaCpJHGjwvpUagfCr5B8raZVcpTzz3L0R9PQo1nH9yWO4OTQvdSYtZTQ4BlBTO4JnIzzvaNkDua+gOWoD17Lnmxeuqyd6W4/SM7GTdMBh56V8MSSbmJNOihUK8WsTjzcq7ouJFSgsS8pThG/xSq/wNtlbztO5xtuz8kL1prb69eLeJ696VcGr6ZcoJ2WN6drt6RpgSmZizm5bZRZDgUPG4HUd0kw6EQ0U1awzEkvaLq3cUannbNtZ97dGQuH8gJ3/SdGhKzHQ9wlvoaOoqV30GAOXk3EHIW4grXcF37DEaJBjbKQq6SU7lVvYMi2cgTyuH8c9zlXHzA+E2mZVc2h7n2xc+obrWujcIsV0oruCdm0NgR4be7lHDVIRO36LjEsNrPmxhl2cf5Ncd+cjZC6hYROOtVzJI9+13rX331JXmPHEaeObDupW9auW3Lm1UM7hw49w1wuAd87qCI9cD1FaAnOUHljobR+8GHD6JnV7DhyCdw5Y2iNNuB8uBvYWO8+5VIM4aBxz8ShWnCFrR1A8bdP0eNdthWug2igKaa9cxpvpJSgnQJD5kyTEdGGTd6L2eP5hfYw1xNGUHq1SKWjb6DNmdg4HTzZIvWgb5bWjrw/HMePXucT1H5OOu7ZcXBvcVln9GfSMtGAquX4zniOpRwcFijZZs7rpCc5j1QivVXnt3ZvfkF9OwKtEkvj3wcZzPskQe1ud3C2NSxDyvBeyTY3N+eY+4j9sSkIRPjY9kVVsdua43NpfGjRfp62bIYSf2cJhY/FHyTudV9JhNbfhiO9kq7yKoVAW7wXhHvQNTbf0980bf89VbyPrmPWqWER/Mmc0nTbJtYvKXsyc/M/wAQ9ZbyevmlHPr5lagYdCse7im7npM2zqUoLkxOxrTMxXzh2J5Cs5Fl5lwyw1X2j5D879N8XN3KrjWPxjslBrWiiNm5i6mTfiItG1nBPLvwvkX8gZu4JYXM1IgAp+izqJN+MhwqboeKqghiuklLVxTDtIhFsm4igb6p2lsDTmEwN+NxXjZ/ynXyTkaJIHVKEbd6L+OijpsoldYo14mRWTQpfiZkhJikvcqnO1zGiT8ds0lSsa6hg4f+XcnrnwVR41kVihAUZrvIcjuIGSYd3TFipmTm4dtz0A5FW/T4hv1lHqq2r8m+Rce5q08lU4YtweukV62V3gFQ1dLFipf+j2vWn4DWz5fMQq+TUZz0ZhVDZ4PVvSjaBc7/58gPMtYDt+4AXU2bLs7buuHLlyn78/kIM2atbn/9z2Gt1A46S79mDjmRGoJqMU/usJQLMv6C8rML0XUTfeXhuDs30uMdhXbmSyi55akFoGhG+ecNsCFpH+KkaNjfLUnk4ptqHDb3x3+ofInHdrqPNkchx/z3PApidSNeiPm+r5x/JxqLzdC1mFmlW3Q/04ViGiNB+nrZskgTi0HwgyYWm7uC9pPzYNWR0LqBaHYF17ou5dzG6yyHGVuIfZ3dyXhm1+UcfeRRVDW388F9U3mNn3FRxqts2/aPpI5FJh66CCoFmMesIP+1KTjaK+kSHu7d4QGipuD0z88nIBv77Vazs4S7R99Os1LAFft4KXn22JQf9Q3BZsoe+TUaOk2OEublXMcv257mnujB1Mp89vF3s7hzOgWxOmJSxSEMomhcrM5ipnk3JbKeTjI52ryeagrwujQ0RcEwTVq7YsQMSVESqag0C7kkNplbHXfZt7cmuXAooKkKbofKBHcbN4ZnMkoE7furCXCmnE2t9FOY5SY/y8WJe5Xzu11K0bShvxzXNXSw8s0NVLd2Ud3ajcelEtVNGjoimCb4MjQynBoel0puppM5v91hiwXjJTDcL/NNFUddwsPLuyzl6COPGrToMPUY6+45hbFNrw05oJYgFxFXPq5InOzGOyGDkZZNYqDP4xDFV5Fs5piOh5nQ9QGOjo3DKmJHvDK9uYsPe19gWb2O5LvljeuJrX8DR/vGb7QavbU6FvePX0J7t86MhsvR4qOjW03k/W1jAFIe85bg6KzdrE7AN3ndod7zLd1ZSReKaYwE6etlyyIt3v5fxGYEjLHPZJtUkDuapmOeot2/CysnLKHZWUKJWc9lrQssUiGsDsH+n86gvmodQtF4xv07ZkZvI9dsJSw83JQ7i3q1GA9ddJLJPRnn2aQill3Bn/Z6gkqjgH2qlhOQjSmC7ahwEVEy8UdrOe+raezmC1NUPi51tvwf1zP6qSPQ0DFR2eieyO8jz3Ns7HkeVObzu9EmO2y3PX/ezgqecggDKcGJzk/1d7nJezk6Kl66eEa9mhLRTCRm0h0ziBqSDKfKGGcrj7sWpJCID+QETozOsu1tR2orO1wIrDBBU0LMMFnbk8N0mSpYnKVMpd0ZQFMVpLS0B8v/tYF7/rGedQ0dA28Yq1B/bXWQlnCUnUt9BLLcdEUMst0OxuZ78GU4yMl0svfoXAq8bnYflfud2sxuSoC6bMeHeCuyzZACVOXZ85gQJxW6HDio0e5YSIEzQSoQcPyDlpZhc0XFA1qmDi4qzs7QKGx+B0fHxmGLioeapa/3bo/b0SfgcDB714EE1Ml6koMWjvi7xfztHTwy8e4hNQ6PbncXZlbp8LY5QvTLl4gHHiaupVM+n8xofybKmd9za9aRYjN1dlsCw9G1JL/n37oWJI000vheIE0s/pfRR4DY7goQMUzc/gr+vG2qePClCYtoc5WSH6vF/f49RFuruLHzGkpkkG5ceGSYU9vv5Wbv5dSKIrx0cU14kb3al3BNufynHn5iWnPkGibNWiFtjgBOGbHTulOcq5ILHIfHEv0pKgoGu3f8jT0KdLo95ZTIIJfXX0ZW08cc9uUsVAzbjaeTTP6q7svV3Tdbs/RCwyPDPKwtpFxrxetSyct08ovCCI+7rmWU6N+ZCMadn7Y2uVCE9Z8pLTeq65VUsedCeSd5RiOGKcl0aRRmu4noBu9VtrDyzQ2Dkouatm7WN3ZS7HOjKApjCz1kOFVawlFipsSX6aAzorOhpQu/18lBOwS+GyvKOL5x0VH9Hnz6rH0zJpwMdDgJa2JNSFvkzW/vgNdmfGei4uEW3EPleExs+jMn/vccArK5V4A/ENkZSkA9wrC4ZHzrzkR9MFx3PGCLvR/fOYbrVDWcPIzNwEjf883JXEojjTR++EgTi/9l9FnBHOqLfr/K23ly9FzeyDuOnt3OZvTzxxMw6mh2lPBk9unUKUUUGfVc0nEjd/mvplvxoGFgCpX6A5ZgZpWidNRQ8vRROMN1AMS8pby4+0ruqFhCs1YIYNnUCg1HeyXy/sOprVzLF93ZVP1kJuaBC2C308FTaO9XZvB9HiufQ40I4I/Wcuaa83udcLBIxZysudwg7qRQr0Pmjrbm8nNHEzDquF/MYzdfmF+VRFnQdjX5sVqCajFTXAtw+cvwuTUyHAqZToU2RwFnyNlbjVwowrKidGsq27pD3GPMse1xJ6kLqSJAqQzyR2MuE9whHKqCbpj0xAwyNJV1DR2sensDlc3hfm43iUI9M/4jnedxsWt5DoVZbnpiJuFIjJ6YwZh8L2fuO3qTWo2tjW9UdISq4cHfWzoJFIzMAjKIDvpayZbIH6m7IP/v5k1bXG5myFdNS8eQxdcjE+9k1Nr7qa9at8lNluZksI/za8799NSUVfl2l6WLyYkFubhqCqWi+Vu38/xOV6NHsGpvrjycypau74wA2dgSoXHDdaoarnXyCDHS93w4XaWhEufTSCONHybSGosfEYYj+vvHjos4pnIuonUDIXcp1wduojxQSE9XiNPXTMEfraXZWcI89SKu656Hly6aHCX8ffvrOGr9NYOGRUVaNlL0we14at9CtG2IJ9FaOopbS2+hO6M4debZV0bMkDg6rf26yXsFs9pm4qXLHm+pU4q4JesKLgrdQBlBotkVOBNzxUlF1Uelp9DWHWX/lj/R5Cjhpd2WUWnkUdvWg0sTfFTVRkvYKkw9LhV3Vz0PKPM3OxBvKCgCtnO18DhX4qWLjbKQ083ZdLmL8OuN/NGcwyjRQDUBJjGHr2M5FJjNnK29wi2cAorGzmU5/GxsPgfvGLAJQmKeOSfTQZbbYb+elJKOHp3WrijdMYNrDtuOUX7PFjseG3HNgbn3BTREHJvWWLRW8fljMylsfmfkws6ElgDAU4hUFERStsRQCOPGQ8+mZ+o3U6/QsvPZzOw+eVBR8bimv7Jn3aNEs0fhnPTK0Nutfg+53LLc7VI8LNvhIXuW/rTPz8OvW8YDMW8pj2y/7FvVD2yp+fkRz0FvhkNRo6OYp3dahi8W7Pd+VHl3YkNTmKm/Gb/J0MlvhC0RGvcdO1WN+D3fwlqQ9Mx8GiNB+nrZskhrLNIYEMMZH/j9Z1MR8R/t1uOeQeSUURfqocMZ4E873kOTowR/tJarem7hvoqb7PGpwz+5sB+pSF5RGzduIt7j70bEZ56F1DFQyY/V8vvIc/1mns0zXuWR7ZfZ+3VV100sUCZjSGG7/NymnMYl7TdSRpCgWsxjySMNSSNWO595O2NPvoWWnc8meurznHbIzzl571FoquDz+g6iukmPbmkxmjtjBEU+p8Y2LxBvUwjIZh6VV+ClCx2V2drFtDkChKMGNTKPU2KzqCJAGUGWyblsb6zjEccCzlRf5mL5EFHDpKatm39/1ZwyGlWak8HYAi91oR6S1wqEEGS5NSK6yS5lOVYo2kD4piuqCa/7B4+EcH83sL7PVe4/hB2Cz44o6NHGgfOtzlZWMYQbEB31GMP8KvPQQ6e7aNPF3XDzNvoUuj17nD9kJ+aj7F/T5CixbJ2/QY7HS3usJOa1rnVHZw1HvH/mt6of+M5Wo0e4aq9nV1AQq2OvymXf7TjOZl5PKV2mzdHZbeao20AY0Xv+HWpB0kgjje8W6Y7FjwWDONZEdAOXprJrdicnfR5f4XRlw2lPQ9lerA2G+OfHX/FpK/TETDobN7C4YwYlZj1t7lL+WXERB62bh9sI06gW0VjwUyaesBAlt3zA3ejbNYmoXp7d7mYOXjffXqlu8O+N79BruOWdrgFXx/rmZXQrHpZtfz+VRuGAK6SmKftnIDR1cttf1rKmvoOumEFrOAJSokvQFIEEuqKpqd/fBG5NUCSbeEC1hOOJjIVGRzF3jbqd/2tw0dYVo60rSqnSzIPaAipE7+MSFrT1+CnyuanwZ+LLcLBzWQ7n/3IsiiJsV6iWcJRin5sMp0p31KAu1EOexznkCJR8ZTrinbuIZo+i6ZinrdyCwTQYA62oxv9mtm6koeRACo+7GXxlA68kJ9uVJq22D3gtDrXC+9xUyyY1jkSeyqbwbu4R7Dn1oU1rTLaSxWZyV/Cb5HgQqiZ8zwFkRaxuTcgV4JUJ125d29Gkc7MlVqNHvKo4wlV7uzPW9G8KjPqBO2O7/5HTxKuIn1245TsByfsLm76ePjvXWqD5PrpUDfc9P365FZK3GZlLW8NBLI0fJ9LXy5ZF2m52EPxoicU3Ctcrob4+iO7K4qumLh59ZyMTM0OcuW7qZo0/DDfYqttbzsL8G8ktHtNvpOQuz4VMDi9N2e5/ik9ipfecfiMN6xo6eG11kPWNnfYP+JgCDy2dUeraexhX4KEu1MPbXzUTjlir71KCS1MIdceIGFvm4+HWBFcrqzhDeZlKs5CLolO4Ix7w1+Is4QrPdbzd7KY7aiKBXVjHk665OIRJu8zg8NgN1Eo/EsjLdJCd4WBsgRdFCGYcvh0V8fGm5ONNFOrjCr0ctENgUFKxrqGDN//zMYd/eA75sdr4uNi97LP7LmQ41eEXA6FqzPt/S4ORTS4hHpu4ZPDCCfqNy4246GiphHt+BtFO+0+t0kuu6GQwmMAdo+7kmKOOHZ7VbnIxpRZRlbMX71WcS3dMH7iATnp8s1pIZc7e/G381Wjhhn7F12DhYSPJ8Vj58r+YtnEKOdFgyn1bJSgt6ZwMe5FiK+VYjGRfN1UMG84s1GjHNxtTGgx9R6BgUMvW5PE2djsdfnfnljgDWwYjXZiKtG/x85kuFNMYCdLXy5ZFmlgMgh8tsfgG873mQQvtD+eahs4hQ6mGM688kmCrJkcJz46Zx7FfzyEnWms/rm/HwhAqKyfcw2fKBKuIUlvg7btYt+sVrHy72l7Bz3RqdEV11jd2srG5i91G5VCe58EwTJ76sJq2rhhZLg0hBLopyXZrBNt76Ip9886FUxW4VcmlPMRy/VCqTT9FNPMn90LKCFJNgItdC1gTySE7EuRhh9XZaJcZHBlbRBWFiLivkSIEDlUh1+NESsmB2wc49acVNnEYqEMz2Ip1cpdj24wQZ6yZQk6khrDwMCd7Pm15u+DUlKHn9pNWcM32Omofv5SS+r/QrAUG9LoHUsblBsWmio74dZ0IUwTL9WlTa/M9wk31Kf9g3LiJQ7/223dhHjCPVa+/Y3cgADqdBRhCwRcJpnYFkshQzFtKT8wgK1LPX3OO5aXiqSnFl8wdzZOj5rL/p9M3O8cj8VnaJSvMLyvvYGLzn+37tpp+4BssUmzJ5O2R7usmi2FFA1Pf8mF6fc6XefqLPPj2Bg774BwK9TpanCW8NmE+h6+dSXa889TuCuA9/6+Ddn2/dWzOe57U9R7W9ofRAUoXimmMBOnrZcsiTSwGwY+WWHwD0Z8pFPvDWdPWMyLx3kAFbk1b97CCraY3XJ6SFA7QhA+fbMchZL9EZbvA+2kFyoNH2KLtP2ZMYnyhF5FkDdTY0cPfvmhgtN/DuEIPn9V18GV9BzHD6hY4VYEiBCU5GXT0xAi2R/imjYtMh4IaLxANKYnqJroJ5aKZR10WuahTirhcn8x1LEnJ2WgQflQlri2J74jHpVKSk0HMkJTlZlCWmzlityfTlNz9xnpW14YYX+gFQLTXcN5np5JphtFRuSb3RvK33RdHeJARlz7Fv37AAh5/9R8c+NEUCvWa/qMnwxiXS8FQRYehE71nf5yN/yUkssmW7SmkInGNDITw+N/hOeXB/neEquEf18NX/4C2Sjp2PZc5kVMYpbVw5prz8UV6OwMhV4AV4+/miy4fZ+zoYK83TkOLGw+AgFAVenYFG458AlfeqH7Fl02cK+dule7fVulYbAkRchK22o//5hTDcXKxxcP0kval21vOeeo8orrJLV0zKZGppgNtjgB3VCzhzMN+vsXDKzcbW/g931ykC8U0RoL09bJlkRZvp5GKLST6G4l4b11DB3e/sZ5b/7yGO/66llv/vIa731hPd9QYVrCVeux9mMIiDToKzeSQT6gfqbhau5waApbY99NzUe4/FFo3oGdX8HzGURT73CmkQkid39YvYbSjlZq2bt7b0EpjRwRNFWRnaDg1hWhcyN0VNdgus4MF7oftFfHNgQL2qnNiRV0AmoCgks91BTdSpxRRbNbzsDLbIhWykJPiORtCgEOxuihSWtvKdGrEdJNAtpudS320hKO8/mmwnw3tUEjOvmjtivKfDa28VqVynpyBLq1zf23rFZQH/8YZayzf/6BazD3b3I7uKRlQaFob6mZDt5tHtrtzYHvP7e/mvtxLqJHDTDYfSoCqanz1++f5OPNn/UgFWNeIKXvPezIy172AufHdVPFsaxWsOAQ+eBDarAIyuOMk22LzlQnXpm7fNFjb0Mnq2hBtf7sNrb2SdleAmCEhVAW5o9Emvcy4cRP7iYqj2aPIj9Wya/vfNtuu9TsRUA8WwjcQkjNqDpw/9GO3NDbHmtXUMZxZWz5MLyk4MaOziutC08l0qizNvSLlYe1aPo/tdC9B4f9+hcb9UN7zNNJI43uBNLFIY9gYbijVxq/XsPLNDayuDZGT6WBMvpecTAera0M8+493OO7T8ynQ6+hSPDxZMbffNo775GzMP52BIo14R8LEJ0P2fggBMSnQMJgWW8nlcioNSoE1ux+qAl85G458gnrht3MdhNTZ7+tb+fX6xezT8Di39czEFa4lHNHJ8zjRFAVVUch2qWQ4NbZxtjHLvIcbO6dzsnyJaxyPbPZ5MwFdNzFMSUQ36Y6axMx4sSthbU82i9wXpzzn8uhk6vFbad0m9Ogmpmkdu0O1RqEyXRpjCzwoikKxz826hs4R+fEnfOl7YgYfVbXR0NGDogg+UyYwSV1gd4NO/Ho6OZEaapUiLnTO54k1kruf/yex5Yf1K97CUZ2oYaJ7vp18g+xYMzt0vdsbfpcEXaosVM5DR+l3v5AmjQ+fy5cPTOHBV/7Fypf/RfieA6zrB8DphdOfw5U3ynZ5OnTNNX1eu4nZjZdRrrbwztiL+MD/W9siecgC1FdG0zFP80becbzpP7qfW9FBX84m3FCJbkoyHeqgxz7cz+OAzlqbi+/YmWjY2IxiWO49mYd3eXirhOklghMbtGLKCLK47Uoua0tdzY9Ihe6o/v0LjfuhvOdppJHG9wJpYpHG8BCqGbZ9YO4TR3HoV9exmy9MltuBqgiy3A5284U5c+0UMsNVGM4sMs0wR301B1n1HttXPsR1/utpUgvJ6K5D66ghKApY6JmOjoImUsvGZnKoln5GiQbuEwtRzVjSvVZB5tZUuuIF7C823MketY+wTetbRJRMiox6Voj5FJhNSCnRFAj36ISjJqPUFh4Sczgo8hp5sTo2ykKW64dtcnZ/KPQYkq6YaY8yOVRBhlNFCokZquaq7ltTHn+T4y6KaLaLYSXedVEVQW6mk/K8THYtzyHP4wIgw6mOuGD3ODVcqsKX9R10Rw3yPE5URWBKWOuYyHTl0pTH3++dhMwupYgmjv/0/AFXcD1ODaeqoIW/hbTdUDXFzx6Lhm53ggysQtxARRMGk+QzvKvuCfH7k68iT6SWA0JPcUnVRUzbcIHtriRRLUH423dR9sQh/Jr/cNpn59ljUF/4D6RRsUIci2Qjd/bMYFtjLeM7/o1fbxhWAVpUPo6NY0/jnE/PsLsNy8bfTVAtJi9ay6T1U2mu+YoH397AmmC7fby2xW/aznNobEYxXL33LD7q9G2VMD07tXriUhqVQgplI3mmFb7ZrPhpUAooMBo45YsL06FxaaSRxg8aaWKRxqYRbrLyCeKr045JL3PaIftyyYETmPqb8Vxy4AROPXhfHJNeRs+uwBep5ecdL3Pi6nPwxos1b6Se4+OdiiZHCY2/exSZO5r8WC2X11zEQe1PcXjPc6zL6hX76abJXp1/Q+tjIVovcykSrQgEYVx46MFPG21KLu2ugFVwfrYiJdfhg5KTaHcVkRVtxGV2EUNllAiyVJ9NpLmK9h5r9d4XbWCZMZOAbASglnwuci0k5CxkS0AR4HIo5GU6yM1wsF1mByvEfEplkEqzkGMic+3k70edCyhXW/C6VLYryaLCn8nPx+Vz0PZF7FmRa5MKgO6oMeKCvTQng4IsF3WhHjwuFSEEqrD0JXlGA9PMVA3Cle2L2bPrTW6PzCJgDLyCW+LLYHRGDyd/PnXrj+e8fReiNR62CLaIu81Vyspt76FRK6aUILs5q9AzCgCLXJhxiuglgo6gwGwgR7eyNwwUBAbRrFFE172BqP+Ywz+/DL9hufW0OQO8EDifqRnXUU8+AAVGAyetPpusqHXNoGiMWns/NS0dg+660lHDKR+fQqYMY6Byf+AaHq8v4RR9JhtlIeUEuaX7Gt5470OmPvIh/3r/I4sg/HspvHRJygjaUJ/HvtkJaQyOrZkmnth2ptOBS039PrslZwZX+26kmgCFW6PLNBC2RBJ4GmmkkcYASBOLNDaNL14e9rzyhiOfoFmzivDsSD3HrT4fb6Se3WseIaenhmZnCYsKb6Qld2fEMfchFc0eefq74xfMl+fwknYAteRTKpr5Je/32x0dhRqZT6lowkPE/nuWGeKJ/Gm073ou1XtNZ3yRF6eqsCbYSTiipwTHOTAI46bErOdeOYcS0USZ0sJjjrmUYBWZ1aafSWIBDUo+bm3zPiqiz/9nuTRG+z14XRrj3O3co8+mQvQKtd+XEzgxOssmF49oC9jO087Vh07k2D3KcDlUstwaCgb7fX0r3kg9UkrqQj2MK/QOXrAPUBgoimDPbfLQVEFnj0FPTMeQJgVGA3fFZlNOkCoCXMzl9ljU9I5rrQwT18AruEpHLb+svpv8b2M85yfngSvbClt0ZqFi0OQo4cbim/lMmWCv2md016E6XYQdedY+Iu1APS2phyEBFZMaEeDq7EU8mHF6isuUicKKotm8GdQ4IvwMi7OuIqTlp+ySgcAte9i/5U94/jlv4HMf7zao0Q6k0FAxOH7jfPTWKjbEcjlVn00VAUaJBlaK+fhaPmbMSyf0fgYlI9cPtG6wdAdpDAqPUxsy3PCbdNs8To0i2cwJq88mO5YaIHlh6410RXVm+BbT7S3/drpMiUDL4bxOQkv176XW89JII400hkCaWKSxaex2Cux9wbDmlV15o1g6eikf+H9Lm8tapT7mk/N4wDiYp12/4zx1Hu+2ZPLXf39I7IlJCNNK4NYwuLB5MSVqiD/6LuZKLqFTuvEKizjUynxO0OdRKQspE82oMlVMrcfD0c6omc1jXXtxy1/W88g7lQTbeyBUzcmfT8YXDaYkNHeSSU18nOpBMYfH1FmUil5ScVx0Lmt6fIS6Y0hDZ5bjIUpoHtYpK6aZWdoqnIphF6YSa+UyEjPYztPBDeHplMQ7FSfGhdoAdfhtclEugtzcNRM1VMshOxaR53GytqGTn667jT1qH+GYT86jqeYr8jxODtohMLA1aesGuGufAQuD7YqymRDIwuVQqGzpJlRfyX1yFqNEA5WygJOjM/kbe3Fd5lUpz/vHNhf1X8ENVcODR6J1NWyd8ZzkVdZQNaw60vbLVy/4F3LvyfFkdWv1/tSfVuAY+0vIHY0IVRORDloU6xwPFKQnsAwBFjgvpcDr4tiGO1KIoYLJH6pn84fmOziVl7iifTGGHu2zDUFWrJEmRzE9e5zfvyjrI3gXk14lll1BiaxnlTqfUqWFIHmcps+yOxcPK3MoMetpdZVinv4iHHFrWky7FbA1xfCloplLqqbaeSMhV4BHd7yPFmcJJbKeOyKz+M22BbgmvfLtdJm2RBJ4GmmkkcYASNvNpjEkRmrZlmxhupsvzLH/PY+8aC21oohZOYvYEMtjrLuNa9uutgTcnnKuYhrTu26mRNZTrxZzs+dSLmm/3u4c1Jj5/EHMo9rIw6838bhzLmVKaoHfpuWTpbegYlo2qTk38pEcR3Y0yK3dMykjiClUFGnQ7goQ1SX5RgN15COkQZFotbdVI/0cG5lLPX40BVyaytXKg5zKS2yUhZwcnUX1EK5GxTTzmNPKoViuH8p1xmmWm5OwxNdHjZVc03AlOZEaKuOJ2nVxoTb06gCSt9PoKCZ84nPoWSW8tjpIc+1XXFA5jYJYHSF3Ka3HPcOobSYMbKd51z5WAR5PcU72ljdNycIXP+Pl1XV4I/U8iNW9AXjc+BVXxc6hXGnmMddCSmWv1eqA2SHDTd4eaRZAAsm2l9vsZ7k3DbaN5IJot9OJrn8DZ/tGXnMdxK497xFIer8TSLiN1SpFNPt2YKfWv/beR2/nopkcYlKkXDN98zO+zD+I8afcYlsf2/v59l39rDvf/uBjyp8/jjKCbJSFnCnnEBT57CjXskrMjockZjKj6I9cdcIB3x8b0i2A75Ul5BZKEx8Qz0+1rlegWS1k1fZ/HHzbsNUsW1OwGcny32US+PfqWknje4/09bJlkbabTeM7g6IIDt4xQJ7HyQdtHq7OWkStCFAi61nQNp3dlTUsbp9uu6zcWX4rH5njuDr7OurVYoqMOm5sv8ImFbXkc0JsDjWmH6emoCjEg+IsBMmlQSkgR2+iReRYQm8Mrm27gv3Nd7kzOosyguhYpKLbW85t5UtYMnoJNSJAMU0pBSLARbGpdvdAVRUMKXlU+S0bZSGjRAOPOBdQTDMK/cPYkslApSxkpXkYDk3B7VDj9qdwUOhpciI11BDglHinQhX9xcWJzkWVDFAQq6Ptb7czJt/LBfuP5czDfk7ohGctTUtPDaXPHceqV99Msfdd9eqblnNTglSYOjx1dr/VyfaeGN5oPQ8w2yYVdeSzXD2WUWoLDzsWUCqDtLlKeW7iDRhCRZUGZ355Pj9zfW2t4Cbbe/5mNvhKt/x4TvIq61f/gN1Pxzz9RaqMvKFXWfe/iqZjnuYvvmN4QDk2xX44gZgUcbcxhRKz3iYVIS2fFi2Q8t74aaOANvu5UkKb9KZeB6EPMZYf3J9ADeBW1OMp4nRjln193S/msytfcqO40yYVR8auIyjyv182pP9L2Jpi+FC1db0CMW8pL+6xcuhtw7fTZUqywd3iFrtppJHGjxbpjkUaQ2JzWf+6hg6eeK+KV1bXU0QTt0dmUWL2hkElArw+affydVMYRcA+zvUsbLnMfkwDuZzGQupFAV6nht9s5G59NqUySK3MRxFQRBMtWgBTWh2IJuEnR7amCL4T+oAaAqyYsIRa049DFYS+/BcPMKvfvidGk4L4cTsUTAlZbhVfrIEHmE2paKbSLODE6GybgEAfUmEWcnJsFm1aAR63NZPdGo5iSjhg2zxO7VjBotZf8XmXz17tHuyDOM7VxgWuV3mt5EJmHblz6or1AKusfZOuY9kVOI5fbpGKPquTLV1R1tW1siJ6GROwrFZryedsZSFuh8qd0ZmUSisZ/P4JS3D5R1EWfIMTvrra0io4s1Av/Dd4i+DPszH3voCGiGPT18sw03YHfN5mrLKapuS2p/7OSZ+eQzFN/TbbKH1E0SgVqZ2waZmLqTHyuLVnJqNEQ7/ORN8QvgZycWqqLQaPeUtxnPN6v4Ty5GP+91dNnHP/f8jRG3jYsYBRosG+r0oGOEWfRb3MY++xfq77/c7pjsWWxhZOE//Wtr0FYJqSVa++aSfL9wu0TE6WHyQB/tvC9+JaSeMHg/T1smWR7lik8Z1jXGEWR+1eythCD2PHTeSVcQO7rEgkAZ+bYqWZya03pjxGRyVmACYEaGJpzCIVVTLAVPcirvLdYNlz6kEqc/amXi0iXzbTIVIveg2DerWIizMW8nlnFoYp6W6sZDG3pzyuSfqokX4qlAYecy6gVGlGjwfOGSbEDImMB65VKI08Fu9cQH9ScWJ0FmFXIdkZDgSCnqiBqgi8bg0TlTsdZ/J1NAdFYHcrBoIAZFYxdzknUdkaoSMSS7k/4Y+/Se/9kj0HXJ0synZzpb7MJhVNIo9rcm6kLNvBquhFlMogNSLAJe6FfB3Noa3uaw6suh0Fiak4UKMdVkFU96G1Q97AcC6Pzfe638xVVqWjhslfX2iTCr3PV1+BCOESer+/z4jchgROisyiVvr7vU/JpEJH4e7M80H0Zk90RQ103RxSAJvtdpDrcdIg8rncuDDlviuNydTJPDRVZUz+EOL8NDYfmxOmN9xu29bc9haAbYO7FSx200gjjR8n0sQija2GLJeDvEwXRTRxzMbUln7CZSUnw8m++T3cHJ5JiaynVgT4u/Zz6kUBJTTxgDKf7VnDndFZlBOkTilifv71lI4ex6TDf0HHSc/Sseu55J+4lJuKb6FeFJAr2/rtywrPJELOAE5NoVxtYUHL5b1FZpws5IsQKibVpkUuHnYsoFhYWRLennoeVOdTpjRRbebbj3nMuYDdxZp+pKIOPzFT0hnRaeuK0h0zMBH4PU4q8j0UZ7spyskg3+tEVZVBuxVuh0JHxAQkXVGDzp7UUZiRFAYDkZDxkc/YW35sb0+XCsVKG7eGpuKRXeiozHdeSk7xaKbumcGclivJj9Uic0ejnJUkNF1xiFU0/2XuZl0rI8GwyVTCBjdUDSsOwd1Vax0jChomtaKIqZmLCQrLijafEBqmTS50VIqMeu6MzkJVBZ8aowfdJylBw2RO1yJyYkHqyaeefHzRev776DWpYYJ9BLBZbgcTirLY3tvBTerSlPuuV5dSrraS63FwwPaDiPO/7/i+W5tuzWTp73lq9da02E0jjTR+nEgTizS2GobjsrKvewPnbbjIzkVYMX4Jf564gErfTwiKAspFkBXqIkplkBZnCS/sdi/bbbs9MUOyuqadMWO2JetXFzHqP4sZneeyKrwBcGX7YnZT1rGNo41Lq6ZRFCcV9aKAc7RrqYmLsYtEK5ow7fC9Vep8dmEtDzkWWLawspDjo3M4LjqXSmlZwj7tmmuTitONWTQofhRAVRR6YgYx0xqgcShW0N1b65t5f2MrY/Izyc5woiIHJBaaAK9LI2aYtHbFyHAoeF2pK/wjKQwGIiGT1pxHQDZSRz61Mp8impjRco1NKjQMZvbcQnHnp2z/2sk42zcSzR5F3VFPWl2QY+6z9RumM4v1406jsSNiaR22Eka0yproFMQTtWU8yb1BK2bZmDvInfALnh9/bUqnol1k06AUoGHQSSalMsiTjtnsr1rWxwNdYn1lG1dyEWeI+TzFrylufmfIWfXSnAx+URDhHn0Oo0QDVQQ42ZzHRmlZzq7S5nP8eMG+Y1OtbX8w+L5bm27NZOnveWr11rTYTSONNH6cSBOLNLYalI4aTv5isr2KPFCmwTEfn40zSTDZohbym+ql7N32EgAtWoAMM0yP6uHViQsJu4sQQlDsc7OuoZP6qnVw/+GId+7igi/OoIgmu0isVwJcl32NXSBf23o5l2+8AE9PHQANooDLs65nY+b2THUvoi4eeFYkLI1GrcxnlGjgYXVO3H61kDOM2bQ5CmgQfi6Lpa48X2VeSNhdhCYEbk2gG5bOw+vUyMnUyHBq1Lf3UNncRUNHhC/rO1EEOB0qWp/CVGAJ4btjBqYpiRkmBVlustyOlMeNpDAYioQszryC05nHRlmIly46ZCYXyUuoIkC5CDK78TKcHRtp0IqZl3cDN/07bInDn5gEpk5E9XJt+TJuezfM8x/V8Md/fMW6hsED4r4JwlGdaCzKz5ue4J8VF6Xc12+VNTGKklUMigOBQSy7gld2v492ZwCjrYrffz0XDRMzft3kyTb+q0zkadfvmF54F21aPoW04BAQk9BC1ib38TqWkC9b+Yn8lCIZJKgOnsad+JwkyPX945eQN3E/llbcRlAtplQGmVx58dYPTdtaGMLa9Mtgey8RTVubfuvYmha7aaSRxo8TaWKRxtbBJlxW/rHjIisxWeqgaDiOX27nD1QccQXd3nICshFVgXZXEW4jzCFfzCTWspH27hhuh0JGdx35Tx1tFSKKhhrtwBTWinQ1Ac7X5vNncy9m5Nxgj794YpYmot0V4NZRdzBu7Lb8dEw+O2+/AwsLb7HTlItEKxmKNXNvOfNk8AdzNiKnDKemUEIzN2upc9A3qEvJ7K5HNyVCEeimSaZTxe0Q9ERNuqM6Lk2xbGeB5nCUulAPuRkOPC6NTIeCUxVoSu8KeFQ3cTsUXJrKhEBWvx/4kRQGQ5GQOfrtlPgyOE2fzUZZSJboYrpYxUNZZ9s5IzoqN2ddQU7RNil6hkZHCfPK7kPmVLCN34vHpfFpXYiVb27YKuTC49Q4tmUZe9Y9yuFrpqfcd9iamamrrAfOhx2Pgc4GMGPgyraSqvepYL74I3Oae0e7Go57nhbN0ojsKtbx37JTyTba8Oot9vYVFPwi9Zj6djBiUlBGkJVyFuXC0qjMyl3MhyFP/1n1AT4nrVohUkpinhJe2/MbZn981zDi5O6Y+yCnAlo3YCz5CU899yy3/nkNS/62juc/quHh197qHRdzZcNpz1vPf24qvHJ1OvF5K2E4iz/fShJ4Gmmk8T+DNLFIY8ujz8qjY9LLNmmY+pvxXHLgBI52vYuQOlJYYzTyqbNROmooz8tk3LiJtB73DE2OEnzRIKYpaVQLyYvWMnbtSnb69Hqav3iLi6ouxtm+ERTVslKNp3i3OEu40nMtNaYfb0+Qfbr/yRc5v0rZxc5fLULJKSfYEcGlWc5Pn3RkcToLqI2Ti1zZhoZJJ5mcn3krHc4A3VGDPKOBh5I0FUdH5lJpFsbHVhawjaMVhyowTAj1GDR0xuiIGoSjJm1dMUsQLgSGaRIzTLpjBpoqyHBqaIpAIHBrCtluzSIiiiDLrfGb7Qr7zdiPpDAYioTkRWu5qesaSnLcLBl1G02OEsoIMr3jWtQ4qdAwuCh0A3ktH9l6hlqliItdC/CXbEOW24GqCDIcKuMKvLSEo7z+aXCLj0WV5mTQWH44BpbtrSFU1uX+ArDS3k/97DzGONsoznZDZz2s/TNIwxrZOu1p67w9eATezx7B2bERfGWIM16icLuf88LuK2jUivHH6ti/6k7mNV8W110IDASqsLpQ9eRzjfMqdCwb4WRy4RAy/q+VqTKdaazpzqatO5Y6qz6Mz8mpB+9rZRt8G6FpWwOJMahVR0Ph9gCosTBHf3wWOxifcUbncsplLYd+eB6O9krr+yDSDo+eAMsPgg8fhHfuhtdnfvvai/91bE2L3TTSSONHizSxSGPLYxNOKBHd4B7nH3gj7zhuLLWKWNG6gba/3WZvoqh8HC/vdg9dwkNOLIhhSF53HYzLqXJ05DmubbuCQiNuX2saSGGRi27Fw81FN7DttttzwgSFlWIev488RzcuDK3XprPk7xdzzs5Odizx0dYV49O6EM3hKE1KPjd6r0w5nIvVa6iSATIdKrl6I/eL+ZbeIi7U/kBOsJOyK5QGVirzyexuwBignjYkdMcsQqEgkHFRtqYq6KaJqgicmoJTU+LaDHA7VH41sZCf9Z2xH2FhoNT+Z0gSUqjXcVvPLMYWZPGXiani0RuzrqZeLaLErGfSmvPI6amhxVnCpRkLqRf5dEZSk9CTx9W2qKOMoaO8OI2j1l+DimGTC3/nGprIAyDfaOCk1efw9AvP0vPHAyHSjunMwjzjFWskKlHMuxIjTRZZUxTBvnvuwopxd/KU83cURavinRqFDrJQ40qYTtzcmjODy1hld3L6kosENAyulXfgCtchBKmz6t9zx6B+2BwRdrTDOs+RduS614niBKzk8zPXTGaPusc4oHop+XqddR6lbn2WGz+H9qRV8nDDt6+9+F/Gj4HUppFGGt8J0sQijS2PIZxQ1jV0sPLNDfy3Lsw/t7kEpfwnPDRxKX/xHcvN8hR7dEZRBL/teZ5MGbbceWhkZ/1jPtF2tFfPDRRCmiW6FtJAR5Bhhjmn+hoyGz/krHVTyI/V0u4sYkzHf1D1LmRWETi9EGln9OolTO64nbnOVYzJc+F1aeyW3clV3bem7PM88w6yo0F8sQaWmb16i4T7kwrU4+fkmBVyVk6QR5OsaAeCKUFRBT6XRsyQCCQCgUNTKMvNoDwvk0ynSn6Wm3228XPy3qNSuxWbUxisOGRQEvLq7n9Ed3gJGHWcsPpsDl1zTcr+ntm1nBWeSSl/q87YjmaRh0ASNUz6IsOpbnlHmZcugQ8exNFZQ8xbytO7LKdBKyY3FkRHoUlY5MJvNHDMx5NsJ6h3MvZj1epoqjvTac9Y/4aq7KJpTL6X8RO2Y6nzTC50L2aNMoaP1J3JpR0AA5U7sq9gWuh68qK1NDlKWDZ2KdUELHLRZ3d1VMoJcq+cywR3KHWU7XvuGNQPmyPC/uBBGH8QKBrCNFDRSVDQhBeaghl31TLQpYKQqSSVrGKoeietvdiS+KGR2jTSSOMHg3RAXhpDYkuGzJim5O431rO6NsT4Qm9KArKUkrUNnexU6uP8X45FUQS1lWvRVv2WwvhqpkZvwWGgomJQRz4CnaKkJGSIF+4CGtVCHIogJxakyVFC9NTnKcnJgDeuh/V/g3arQKp2jWO262oWdFxDqQxSrxZxq/cyLu64mWKznjryUYQgIBuplIWcFLFIhRAWO9elte5dQjOPDGA9OxAyHAp+r4uYblKel4Hf46KtO0pTZ5RwRCcj3qk4ee9RjCvsIxh+dYZV5A0nTKv6PcsO1tTBlY15/ltUmXl83RQGYEy+h/J3FyLeuQsTFSV+nkOuAE9WzOfQdXMoMev7vQcAL2kHckvGFPYbX0B2hgOkJNPopEv10hHRaeuKccmBE4Yf6mboVgG7z+T+xxSqhhUH9xa1vnL036/gvy/eQXHLexSZ9bQ5AqhmlCyjN029Wfh5evwijvpqTm9g4KSXe0Pr+gTu/bPBxeraEB6Hyu7aeq4PXWl3Rx7f+V7GN7zOXvWP0eUpp+34Z2hVC7nnkce5tetqu8PxltidsXIjpTQQkyoOYbBh/B8YfcodwzsP30d8k7C3jjrM5QejSGtBAExUwEShIXtnCts/QUizn7tWp7MAl9OJo7PmWw2O+5/HUJ+zgbC5gZZbEOnAszRGgvT1smWRDshL43uJmrZu1jd2Uuxzp5AKGHh0pt0V4PayW2lzlfYraG9xnUeVDFBME/my3f57giYr8dXjLKOVnFiQBrWImTmL+bI725r5/+rvNqkAKIus4872KZTKIBtlASdHZ/Jy2yj+YFpi5mKaCMhGqk0/J0ZmUYsfGX+9hIRAAjX4U8aiJmkvD3o+FKA7qlOam8GuZT500yTDoTImP5Ofjy/gnP3G8IefjWZMvrf/k0ey2r36GZtUbDjuVe7+MMLtf1nLE/+p4vmPannh4zoqJ5wBvnKbVACs8/6EDZk7cH3m5SldorAjz37MHsZHFMkmvC415SWllNSFehhXOMJQt6FWxd++y/qbrwx85RCqQnngEHZreoH3lZ2oFUXkxIIppAJgjTqWYzbMHTjjYoDAvW2creRkOCjTWpgbmpVCKoJZO/FqyWTeyDuOtuOfoaRiPLlGA7Oit9hjURomGwlwJnOpkgEcwiAsPDh/9gNfae+zeh1bfhirXn2TW/+8hjv+upZb/7zGcgpL7golks9L9uSBbf+IjoqKidJnOatvgjlAI3lEDGF1pwax6k1jM/E9t8FNI400frhIE4s0erGVg6wSdqeZg3ii9x2d8Tg1ujOKeXL0XAxSC9dLI/dwZ/RIYlJBEya6VGiUvpTiRABurKTqdyKj+UuNxs1P/o22pb+xcw3sQ0fBQw8Abxk78lUsj46IwbqeHE6IWEShd6u9kEDfIaC6OLm4Tz+URfrJAx6rAAwgw6ES6oqxsaUbQ0p6YiZtXZ3kNrQAAQAASURBVDptXRH+ta6J2/+ylrveWMf/rW3ki/p2qlq6LGI0ksIgTkI2HP8X7v2vyeraEDmZDsbke8nJdLC6NsQT/6kiljTOFFEyuTN2JKs//4wrOm+0V+JVTDyxFtodBTSrhRTJRm6LzKK59ms6emLopiVGX9fYSZ7HyUE7jDDUbQhr0i92vpyOXc/FPONVOG4lUlhifR2Vv3kOZmnuFSmb0uPv1T76u+RFawdNEu4buDel8mL2dqxjcfsMvHTRSSZz8m+m3rsjUkpq23U+2+lqisrHQaia4mePpVCvI6gWM8d/M085f8fKjDOJekqY57+eoFqMR4Ypfv6EH/6M+mYmn9e0dfOxHMt07fIBSUTf2w3SRwwFv9HQnwymkUYaaaTxvUWaWKTRi60cZJWwO+0aZOa+O2qkhDElXIx+t3627UyUgIrJ9c57cQjTJhcFIjToa/9afZ8dzHXcE51Jnh5Muc+IF8wANTKfuzgGdzxYwqSXKFSahZQpTTy2Cf0E8ecs1E/rR4iSkeXScDkUwlGdDKdGtttJfXsPXzeHqWrpJifDAUie/6iW2c+u5rqXPufWP6/h7jfWj8zGVdUwD7qWlypVWsJRxhd6bQenLLeD3Xxhzlw7BUdnDdJXRsxbisvs4qbwNSw3ZlFGkBYtQKvIsTepS4W/73QDsewKCmK1XBW8DEI1VDaHCUd0dizxcea+o/uPcG0KQ62K//1r5kROYdXblUSfmISQum2Fe1X79VzWdl3KpkL4Um4PliTcL3AvUsP8pkvjSfBFTPYt4d3YGOpCPawPhjil7Y8cNsqwR35EfIzqgQlLaM7ZmX+Pv4y9xgbYa3QeBVluKrP3JJY9CjESAez3OK16xMnnWIsKSmcNFxv39yMRA6FQhCihiSZHyYBkMI000kgjje8n0sQijV4Mslrc2BHhy2DSavlmBlmV5mQwtsBLXaiHvtKegUZnLCvVCyjQ66gnn8uzrqdBKbCfk3DiuTl2/CZf2yNiPO2aS5nSlPL3blw2qaiV+Zyoz6VR5BPTU/evbgQjTpuCALLcGiU+N7ohyXCobGwO89b6Jho7I5imSWNHhA82trEm2IlhmpjSyrTwZWisrrUyItbUd1gr+cmdjEEw1BjaHnWPUqDX0eQooeb3z/DI9sto1grJNxrI04N0OPPRVEGBbKFJLaTNESBPD3KM611bHO7rqWFGw+Vc9BMPR+5ayrn7jRk5qUhgGKvizvZKGrRiVky4m0alkELZSJ5pkb0GcmkgD38f3c1gScJDBQfe47+SevJp747REo5wTs/9/LzpcUa/eDz84/oU8fzRv9qbnUp9GKakoydGViTI1cHL+EnrCzjG7j8yAez3OK16RMnncWRHglwdvIJSGUxZINgUVhXPGJAMppFGGmmk8f1Emlik0YsBVosffu0tnv+ohiV/WzfkDPVwoCiCg3cMkOdxsrah0x6d6eiJsbYhPjqznR/l9WsswfH9h+No32g9WQiayGWh+7KUbQoBVzkeG/Q1k/lLIoMggQbTR6u09Av1ooCLMhfRqORjmrKPosPCcEachgNVgUynSnVbN90xg66YQXM4gmFI3JqKEIKoYfJ1Uyct4Sh+r4tcj4O27hggGF/oZWNLFwte/IxbXv/Snm8fqpMx1Bja/42eyn+KT+LW0ltYF8nh4w4vlb697fuzok1kR+ppc5fy0PZ/5I6KJXTsei7ioAUp14zWXsnYdasoyHKNbPxpAGxqVTyoFnNpxkIMTxEuNfV9vUU7F132frW1O/IJuQKDJgkPFRx4acfN7BeIsEOJj/N+OZadj53eSxC++gfsfjrm6S9SZeShm5Ijdinm4gPGc8neHmY0XI6vJy463v+qkbk6DTUSVv/NSf43wVBEbMCuUNK4WEKrow/zp+cP9dcNSAa/Mb7HHaE00kgjjR8y0q5QafRHqJrY8sNwtFfSoJXyzPa30ZE5Ci1cyymfT+7vrDNCrGvo4LXVQdY3dhLRrfGncYVeDtohwLgPFlkrr4oVnIevHN0w0TpraHVYqci5sWC/bUppaRa0pHo2JsExSH3bLLPolk7KlGaqCXCuMhctt5zq1m5ihklHZCBq8c3hdgi28XuI6ibVbd0IIcjNcOB1a1S1dGOpNgRSSqKGJMutMirPgyKgK2qw9xg/ioB3vmqmrTvGruU5FGVnoCpQ3x4hz+MccASpqqWLW/+8hpxMB1luR7/96uiJ0dYV48hdS3jiP1WM9bs5fN0cJjb92X7MYzvdR5V3JzY0hZn6m/FMLEr6DMVdY8zfzKWhueUbO3Ek9neU1sIZa6eQ09ObadDmLmXJqNv4sLKN+8yZ/a6HRPFaK/NxORT8egNtzgCGIfEbDf2uXdOUrHr1TQ774GwK9Dra3FbBfMjaOeT01NCoFfPKHvdx6sH7WoRpACepjzu89OgGbk1ll6xOTv5ispUf8k2cjL6t1xkhNvXe3D9+CRv1vF43sLiDmRQaQlpp9lq8SziUK5SJtfLVrBby0p4re8//YBiJc9FIXNWSydtPL7S0TWl860i7/KQxEqSvly2LH4Ur1OLFixFCcPHFF3/Xu/I/h+TV4ny9jt/U3kNZ+L9DzlCPBOMKs7hg/7EpmQvn/3KsVQzvMxlc2XaSNsethDNfJeQMkBsLkhsL0qgUcqNzcko3QohUUgHQIn0pK9fJ8IsOypRmNspCLtDmUWXkETNMMp1qvLTfPIik/zQBOW4VZ3wXXJpCcXZGPPhOkOnUEEDMlDR3RjGkRAiBpgrLcQro6DHY0BxmY0u3ldwc0flvdYjGzihdUYMv6jp4d0MLX9R34vc4B027Hu4Y2ph8D25NRQ0HKer8LOVxg40SAVvcNWZTq+J+j4u7ojNsUhFyBXhu2xtszYWOyj2FM1lSsYRGRzE50aAVOugt7ZckPJL0cgzdKl6PuW94AuZj7rMeH+sZ+Qr523fB6c+NWCi9RTHAyv5QCe45PTWc++mp7JqVlNmx97ngykZIHVOoNqlIoF1m2IJuKbGDJROfXL/RwEmfnmud/8Ew0nGw73FHaLOQ7sCkkUYa3xP8IInFe++9xx//+Ed23nnn73pX/ieRPEMdcpfijTZz/H/PHXKGeqQYMIwJrOLovH/Z5CL2xCSe+X/2zjvMjfLaw+809ZW0vdrrjg2mEwghAVLoBAg11NCL6YRAIKETeqjGlACmQyg3QOhJaAklAUwzNu5lvU1b1cu0+8es5NWuttlre4F5n4fn3lgrzWj0aXTOd875/d77FLWXjbVppDk/c++AbschI0CzUUylGEYW+hu39eYa5VxS7mpMoDupEvAoyKLQz+hsuDhlEaciIopWoKSb1s6rxyFS7XeyfX0xO00s5SdTyxhXbAVe8bSGqhu4ZMsyTNcNtJ7EwARrxgITwzD5bHUXy9rjVhVHFAl6FJyySFN3gv+u6MQwDJa0Rvt9NsNqQ9uikrpiz6BBY6FWog3BYO1Jey+5gh+vvJNKsw2wdrSfnXAtu628IycNK6Pzx/StnLLrJMJHvIDmr6co3YIy7ef5AWWu5W547uW8cr61+/78ybww8apBB5hfnHQV5vMnW8Hug3uu08xE5N27uWvc7YRGMCg9qhSY9RgoEXuu3lJv8xhxjv78aCsRCK+BRw+EdARECbGP+V2nUIIuOHLmgoIACGK/VkQl1ggP7YXR1TA6wf96SOeOScbwTI6Njc33i29dYhGLxTj66KP5y1/+QnFx8aY+ne8kvXeL35iaH9QNpKwzqpTUw6wPyfjHo0RW8asvTqJMD9EqlBMiQAVhZAx0s38AAtnqReGEom8icr32Z35ckaLS70ISRDpiGXxOGa9TyvtyZI3w+h0LcEgCHkVEEQV0w0A3TGRBIOCW8TgkHLJItd8FgoBDFinzOQl4HEyu8CGJAhnNQBIFXIqIAKR1E9MEqSfX0nRQRJGaoIvuhEpK1REF8LlkTBM6YmliaY2m7iSfrOrm6+YwC1si/c51SkURJ+wygZk1AboTKivb43QnVLasXavgNKLd+w3IULvim3e9BUDGV8PL289lStubBNONtCs1/G2bB1H99Tgiq6l54VCmlPuQT3rVamPZ77Z8J+HHDh6Ze/mytyAwDqFrJT9dcCkvTr6abmefAWZnLS9Ovprdv77UUoIqngD7/3nEO+RhVy3Xdf2U55eanOO8hlaput+g9JPT72L8kodpaVg6vAs70p3qvjv7az7G7EnEEt5xPL35PaxSi+luXsFBy69AQscUZKRM1Pr7xw+1nivKYOR/WzVvJT6vi2LCqP56mne9GRPBElOQPZh9v3HhNcTu/TlzX/3P6AT/6yidOyb5rlVgbGxsvrV862YsfvOb31BSUsJtt93G7rvvzjbbbMPtt98+rOfaMxbDo3cP9W+WnEPaUUpF5EtEjMI91BuAxS1Rnnj+eS5ruyDX2vJ78QKuMmbjZeSVkqSpIKOhCGY/Hf0usZSXdniEiZOn89Y3IZrDSRIZlcWhOIm0jm4YaLqJbpoYpjV87XVabUxpzcAhiRS5JELRTK6yIgrgdUo4ZYlExkoEBEFgSrmPLWr9lHidmKbJy182sbIjgVeREEQBwzBJqjqSKGCY1rmKgkBtsRu/S2ZFe5xERqfIpVBR5KQ7qaLqBk5ZxDBN0qqBU5HYeVIpZ/98SkFlJsMw+7slDzI7kJ2D2cYf48iFg/f0j1pfa685nza5midmzEH11VDc+SUnLDo9F8AKJ72OUbMDjZ1RvO9dRWr706kaN6Ww+3Pvcw2vgaeOgpYvRt5nv+1xZJa9gyOymg5HDXO9J3Fh1zW5P72l+DJOiD9IaaaJjH88jpNeG9Dle6CZiQ5HDX+uvpWku4ovG7vxORXqE19xZ+L3ueM8veUDTGn/Fzs0P5V/nOG8h5HMCvR6XnZOol2p4bbaW0m4qpjkCHPSsrPwxBswiycgHPIAPH+ydRwAUQFDta6z5IL2b6zznPQzjM8eJ1SzBxX7XIj4f6dYz5EcoGdg5iGw+A3IxNBlL5JmOcXPK/0lb027DDnWuN4zXyOerxnLjNGZnNHC7pm3GQn2ehldRhI/f6sSi6effpo//elPfPzxx7hcriETi3Q6TTqdzv3vSCTCuHHj6OrqshOLQTAMkydf/zcHf3oMLjPJirLd+bT61+y15CoCqUba5Wpe3+4+jtrzR2t/bMON8NE98Isr17vPfllblLvfWsYPl97G4frLuWHcLEmcuEnnPSdEMYJZ2MsiYTroMn3Uip1ETDc3ZH7Nn5xz8+YoVE8lyin/wCiqoSlsBd1t0QxfrO5mflM3jV1JuuJpZEmi0u+k2OvApch0JzIsb4vTEUujGgYiAkUuGZdDIpJUEQBJFEhrBl6nTJFLxuuU2XpckGK3g3cWhVgSiiJA7lqqmoEiCTgVCY9TJpnRCbgdaIZBVyyDZhj43QqKJJLI6FalQ7ASkWhSY3K5l+qghy1rA5y666ThBUThRnj0gLWB+HEv5V0Lr0OmJuBGjDb1+zsCa9twDMOgra2N8vLydb+Z9zoX1V/P05vdxRcxHxlN55CO+9it87lcgFvoHAq+p53OgL2uzX9c1+CfV8IPzyj8/EKv17PGmxtXIj9+EOW9lI6yZP93m1yNdswLVI+fkvca6txfokRW0S5X8+SMu9C8tcjxRo5aeDZlWjNdjhr+XH0zZTWTiKY0PlrRQZ3YydXhS6nUW9a+lKuW5+sv58DlV1GqNq3X5zac967fswtSJoqOxCObzaG7eKu881b99Sgn/N163XBPYpc1onT64ZhnMSq3Jv7qHwnNOI6KBQ/jEVTaJx9K+VvnI3Ytt87t4Ptg/gvWvSTWivnhHP6W3p59vjwHt5kk7KzljWlXDH4/GiZruhLc/s8lPZso51oqXr2u7yNT72C1VsJ5v5hKXfGG2UQZVYaxvvI+p28Ro3JvsfneYK+X0SUSiVBcXPzdSiwaGhrYYYcd+Mc//pGbrRgqsbjyyiu56qqr+v374sWLKSpaR4397wPxdoxXLkTUkmjIvDd+Ft3u8UjpLnZvuIeA3onmqUDe4wrwlkG8Hf51NcRaYbP9YPvj1vnQhmHy2vwWvmmJ0B1PcoDxDmuUen4VWyspG8GLn3juf+uI3MbR/ND8gh8LX+a/HgJhvBQTo40gdwtHknEUM1ldxInG8/lD2t4y+MVV1v/tdT4LWyK8t7iNlKpT5XfjUkQ642lWtidIaTq6aaLpJj6HZLVyiAIpVSel6miGac1rmFaC4XFISKJA0KPgkCW64ipJVSWR1jF6KiImVpWi1KsgiSIBt0JtsZtYWmN5WxwTE1kUCCdVFElEFq1qhaoZIMBWdQF8ToV4WuOAbWopL3IOfeE/fRQWvQK+Svj55XnXoB+DfN6GYRAOhwkEAut2M+/92j3nYrhL6YhnrMqJBGVLnkWo/xF8cFfe3xU853g7fPMqbHs0iMP3T7DejA6fPQHT9+332rpu8NfX3+aI8ANIGOiI/Mf9U36SfAsREx2RvwZP5oi9fookrb0OhmHy9ryF7LDsLgJ6JzFHKfNqjmK7pifxZTrolkqY6zqWqqpaPA4ZE5OW5kYOD8+lxOyiSyzmeedBHKG+RJHWQVgqYX79cfwo9BRCrBXNU8G7dWewMukio1vVtAnuFLutuQc5ERr68x3gPRuGyTuffs0uS2/GaaZy571942N41W7CYjGfTj2H3bedsTa4j7fD/OehZT7EQ3nntkPny2wdf5+4UkrSW0dJ91eIvvKC59YZakJ+51r8WgcZ0YXDSOUeizlK+VfN6bTrvuGv9V6s6Urw9y+aqPK7KE2tZNeVd+Uee2/C2XQ4xzFpzYtU7Hgw1dXD2N1fn/U2Cgy1vsJSCZ9OOTv/c/qWsN73FpvvFfZ6GV2i0SjTpk37biUWL7zwAr/61a+QpLU3a13XEQQBURRJp9N5j4FdsVgneu3ymoKMaRosK9mdR/0nknRVM8kZ5vil565tezj4Pvi/09Z9J7QP2R1EWRT4qjHcs1P7Byr15oJ/n3XNbqSS3zmv4J70JQToX7VopYxLAjdQUj2RzoQ1R3Fa1WI2//eZ+clFn/egaQa3vLmYhS1hJpf78LsUupIZ/ru8g45YhpSqoxsmLkVEEsVcRWJ1R4KMboBpIkkiFUUu0rpOPGX1tnscEuNLPRgmhBMqbdE0pmkiSyLJjEZGN1FEgboSDz+cXEaJx4FhGLy5sBVMmFLp5aNlnbnTzg6yjy/18JMp5eimyaqOOGf+bAqbVQ5jra/H7n3vCtV67xK98Uf47z3DW0tDVSTWl0HOpXn10gErFgYCIiYJwUv0kCep3PzHuccG2yFPSV5uqb+PN5qc/Hx6JcUeB750Cwd/NYviTBPNQhWXB//EKq2YH1ekmbXqgrU70Ifdj/rsqeu/Uz3Aex7svAE+K92fJyouKLyzX2AX3S3LHP71aRRl2mjzb0nQ6EQ58WWazJJ+lZbM3P1xRFYTdtby3oSz+eWite1gz2x5Pw3emSNb670YqmKx3Lst23a8jOavRx7q2m3o9TgMvnMVmF7YO9A2I8FeL6PLSCoWo+g4tGH5+c9/zldffZX3byeccALTp0/n4osv7pdUADidTpzO/jtYoijaC60Q4TXw6P5rk4RDHkB//lQCWjuXtF3C3Ml3sCwd5PryGzk/cwFlXcsxH9pnbUvK8X9f757dhGqQ0gwmlnqZ7FzNpaGLqTZb6RBLMU2TMnNtMN0hlPJ/U6/noOVXME5r5iH5T0T2fRDj1ZNZ5tqc+vDHuHpaphYp0zH8dSCI7DixzPLMqNgBNpsKL54NtdvByv9A13IrsNr7OpaGojzzcQOvfd2CJAq0xVSKPQpd8QwtkQySKKDIEum0hiRJqIZJW0yFnmTXKVgKUZpuUORWqFBcpFWdjriKIJh4nQqRpIaJwLhSb0/1QkeWZTRDI2OC2dNaFU1rNIdTTO0JnKIplaDHgSJLmKZJStXxuxRm1gYRRJFkSsUhy/icjuGtddEB+4xAn7943IB/n0321+k7tufV1kT8zrOGXkvF46w1l/UuGO3v9I9mweJXrDXx6P5w/CsYRbW0NCyl+NmD8WiNdDhqeM59GKeF78g97S+Bczku+gA+I4r3+YMQgm9A3Q+AtetbC9bxxtQr+PVXJ+ee59Gj7BH5G29wJJGUxji5i8O+PoNgppFORw3XF9/IyoSflKrTbJbx+vb3c+TCWTgjKzD/7xRemnglu399CRVaI8cvOXvtrIDWMyswYzbHBOoG36ke4D0vb08QiqXxlVby+pQrOHL+2vOOOcr5uP5k0lGDhGr0+9yNQB1PT5/NvvNOzjs3UTAQMRAwSWUy/PWjVXwZ7ew3E+CKrKJdqeGFCZdz6KorEXvJ1e695Aoenjobh1wy/LXei7piL1sXxdl33pkU95mxKE41MEFNE3dWUBRZkXc9+s0oRRvz750/mjX663EYDLa+3ph6BaqvlnR7vODn9G1gve4tNt877PUyeozkGn5rrnZRUREzZ87M+8/r9VJaWsrMmTM39el9N/hwTv6wa90PEI57Cc1TgSfewK8XnEG90kVx9SRemHQVOhKCqaE7ikZtEDArM6rEm7i662JqTMunIGB0UWZ2EmdtomgIIsvSRby6nSUJ6ok3UPXi4cjpTqZN34rO37yLrngB2G7zaVy634x8zwywAr4zP4KD5sAJr+ackZeGosx9fyULmiNIIpT6HLgUicauJCs6EpimiVsRccjWVyiR0cmoOmnNoLErSSKjIUnW3IQoikiCgCAIuBwyfrdMRjfpTqjE0ho+l4zHIVMdcFJR5KTS76S2xI3PIRFNayxsjuTUm877xVTO+8VUflBfgrNnxgOgttjDNuODuaHwrC/FhpaFHXUk2RoqHomyzyj6Z/R77T6SpM+/+AKOxw/AE2+gUajkPtdJnByenfe0I1LPsapoWwAEU7ekZtd8jGGYRJIqaVUn3bGKvRdf0e+QU6L/pdglIkTWcNhXp+fUn57f6j4mTppGXbGbPTav5NL9ZuQpVuWpVLlq+6lHDSkRnfVBgIIyrE/9bzUrQnEWL1nILxZcmv9UQSSl6gO6Y/eWr+59boF0K2FnJVEpSCDTyr7zTimoypTx1/P25tdx4LLLR10CeSgVtFI9hEuRIDDuWyFLO5RM8wZxMbexsbHpxbcmsbDZCOxxtRVY9/phNIpqeLfuDNr76OgfuupKJHQSopcntnli1HT0e8uMlmSaCCtlOadeDYmzhct4SfwFbWI55UYb56+5AMM0aPnF7LVu3aKMsOXB1EycgXTW/+CHZ+I/4Mb+nhl96QlSDUHijfmtdMYzTCn34VJkdMPEKYu4FBHNMDBMy+RONyzlJlU3EEUBhyxgmGCYEEtpJDM6bkXKJSCmaZJWdbwOCUUSSWs6iiSSzOg0h1OEomnaYxm6ExqyJFHld3H4D8bnJURTKoqY9dMpXLLvdLarL6Yq4GKzSh9+t9LPl+Lb1kc95ugjSbrfl2dSpjbR7azlkaJTuShyXc4748XpN1nBbrqR8thiVLEnCTYN9IcP4NHX/8MD7y0n3LqS3yw+i2C6EZ38Smt5ejVXSw9ySPoFgulGupVKnt78HprNUpa2xakr9nDMD+upL/Van23P+WV8NZSpTWwT/mdBQ8EhJaJ7+yBAPxnW6e4I2wTi3B7/PeVGKPe0lOgmkG4dNLgf3OzwGp4oOpFGKqkYwKfjpUlXceCKy0dfArlHRWlID5NYI2DmzBXHsiztUDLNG8uHxsbG5vvLtzqxeOedd4YtNWszDArsFjeFk6xMunhyxl0Fd0Lv3+JxPo8G1sssrzd9dxCX+Xe2PCt6+tivMe/iUccRnO2+niahikq9mX0/ORHPSyevdes2NJj/N+sF12FHu7E7ybK2GNUBF363QonHQSylYfY4YwsIaLqJZhjEMzqyKKBIIoZhDU8KArkEI60Z+JyWm3da0+mMZ5AlkYllXiaV+8hoJpFkhpZIinhGR5EEXLKIYZjohkk4qeJzyv0SIlEU+MnUci7YYxo7TiglnNQK+lLYrD+9neg9ZpyU5OV/tb/h4sh1OSnkS4tvZlFwt7ydblxBEKxbrKQl2O9/x6E0f8odyT9SRyuaKSL1zGbovW7FW6bnsWW5lXAYpiX/PNTnKvboJ4tqfN12qvv4IBiGyRPT784Z853wzWnc2nYKVbTnnhISK3hsyh2Fg/tefhmD7aLvufhKIimVi7zX0Omo6Xd/yfqBOCKrh2dgOBLn6V5yxEN6mITXkFQ1OuSKTWNUOEzGig+NjY3N95dvdWJhs+GJZzQyuoHmLbTbuAHM8j6ck7eD+GT5efwreCh31t9Fi1RFLa3cmb4Mn1Ph6rKbCInlVJptlGqtdDpqMI5/LdfOtK5kd1g9DhlBEJhc4cXtkOiMZxAFAUUS0AyTRFrHNMHnUihyKbl/FwUBl2KZ47kUEVU36U5kSKkG5UVOKvwutq8v4fidJ1Dmc7CmO9XTV245b6c0A4csUuSUcEgiXzaELXOrAkypKOKM3SfnBUR5rV6jQEGjre8RfVt5XHqcPZevrVT8IXgzH6QmsrA5kh/sJloxfRW5pKGcTu5I/Z7anqQia+Ko9QgQRJ1V1q54rJFA8weYgTpKtFau6LyIC3/o5dSfTMIpSwUNz+RoIxFnJfXh/63bTnWftq/0g/vw1qI2y5hPKCeohXCaa9WYWijnNOVaPtEm9w/ue9zMs87Og+2iF2eaODH1GKVeB29My//OvjvhXA5deSVlquUHMiwDw+EmF33bPntmT8aVePIrmz3XRfPX40+3siqw07q1mm0MhluBGWkSZmNjYzMCvjWqUKOBbZA3clZ3xHjsrS/xikmOX3I2wV4qIxvELE/XrLaMnWflDUl2JzLMeeFdro9eQq3ZSotUzd3B33F21w1UGCEaqeCSwI1cc9xe1Jd61+sUsgaBQY+VMAB0xtMsC8XpiKdp6k6SyOi4FREEa7jaMCGd0dBMqx1hRpWfxaEonfEMW9UFKfE60A2TaEqj1OfI7Tz/9ePVXP/qN2R0qyVKEgWcsogiigQ8ClMrfICwQc0IB2NpKMob81tZ1hbLDdVOLvex18zKAcz3xpgpUa/1NKwWlfCatcPgPVWub1oi3PmvJUwq8zGt8x0OWHRR7s9f2uwmvinelYXNEQ7/wXhm1gTWDvP27Ii3UUIJ3ZajdB+yCUbMU4fnlNetYDa7kx4YB1jJw5CGZ4E6VN1EiTXmGQoqsaaRmcj1MiZcQyW3By7ikuifKDXWiiYYiPxl6r38O1nPgdvUskWNn6AaovqFQy2n8WzVMBu0Q0GzQyXWxJELz8L0lKMkWlEkE396rU9HWvLh1GO0KzVkjnmJmvqpg39uIzH/G+G6WLr0G1a9fDOfT7+A2viCvKHop7d8gAbflqxsj3P2z6cyvWoT/Lb0ddQebMh8MOPIMcyYu7fYjGns9TK6jCR+tq+2zaDUBNxMcKc4auHZG6dnt1c7Vu8dxJRqsDQT5PLgjbRI1VTpzVzTcQEVRogWqZrLim9iWTrAivb40McYgtqgm8nlPprDKbJ5d4nXyQ4Titl5UilTK33Ul3kp87kAiKc10qqBKIlUB9zsOLGU+jIv06uKKPE6EQRLxUk3TLaqy29n2XpckC3r/EyrLKLE48Dvkgm4FOpKPGwzLkh10D26FaERkB1gn98UJuhRmFTmI+hRmN8UZu77K1kaim70cxoxvWcHhtqhzQZnPTvtWXq38uy5NL9qt+uqO3DEmyn2OJlZE+i30530jaOcThqo7Hc4HSupWG1WcFvtbdZzelcOwg0w+Wd5sw4Fe/t9tYCAEmtc753qbNtXSK6mjlauDv8hL6kAEDE4bMVliNEm/rmwldlvL+WWj+I8N/5KTKEnqXD6c0nFYLvor297HzE5SLHWij/dQsRZlbu/OPUYCdHLO1tcR1XtBKu1aqBzz163bLWyVytWQUYoEuAsGc8r1WcjxVsHbzWTGfw8+zLUeQ6XEVZgchWeD+es33FtbGxs+mBLQ9gMihhtYrc191DW07P7xNTZqL4aHp46O7cTeuTCWYg/GmIndBQQTGiTyrg7+Duu6bgg9+93B39Hu1AGZEblOKIosNfMSprCSZaErFkLt0MimdFpjaaZUR3gZ9MrWNAU4aXPG+lMZAi4FSr9LqZUFFHidWCaJknVYL8tq/jl1jUkVH3trmGvWQmvQ6bc52JKuVUZyZqaFbmsNqxoSt0kKi6GYeYG2KdW+BB6eviLXAo+p8ySUIw3v25lUplvbA+I7zzLMv7LtskMdyd351m5l8i28uw7bxYuPY4myLw18SJ2bHokl1y/tv0D/ZPrQB0Ldr4V7z8u4mLO5ml+j7vXGpUwaKaME8wrqEwU0didtKpS2eDvwzkYv7iKJ9/8L/vOOznX279WRraZDqkCh6pbA8bFE1COf4Vji2r5Wd/396NX176/nutQ6PuabfvqnD6HYxacljeo3VvyuURr5ebIRTw57n70olrkWCO7f31JTiVOOu0/1pN6zzEUOLdq2mlZvXaA3TBMupWKvPvLgcuvQHz1LZjXY+I40C57dp6q9+49DF29GAa910Cwjyxt7zVQ9/H18N85g59nltE8z2zr53AqML3W1/q0jNrY2NgUwq5Y2AxMeA08egByIrTJe3YnlXkJeBScsWbO7L4p77Ezu2/CEWsm6FaYVLZ+bVBZplQUccIuE5hZE6A7ofYbjP75jErO/OkUrjhgC3aaVNqzM1iE3y3nKTPtNbOK8aXefopU2bmFaFqlzOegOZyiyCVT5nPidysIgrBJZWN7D7Bnk4osgiBQHXCxNBTbtD3lw6GAZOxI5UJ7D8QmBC+yqbFj4yM8V3/l4AOx4TVs9d8Lmc4KnjAvzUsqssiouBRrliavKtUTJDdG1IJSrdnq4argThSlW9B6KRKtz0517/kip5TfunVr8FJ+F7iFJtNyxq402zhpyRl5Q8wJ0cvfNr8LIzh+6F30xNeI9/0EOdmB6qsl4qwkqLYydfmj/e8vy97KSb5m7zUFZ3/6tgT1ShDXh+EORQtbHtxv3mOjnOdYkmm2sbH5XmPPWNgUpueHz+haTahmDyoO+zME6jZZz65hmNz5t7c5bP7p1oyFWMVdwd9xdvfNVBktNAqVPDvzXs751U9HdQfdMMz+77nP6/eeQ0hrlp7/lApfjwlf/zmEvnMLGc2gLZrGIYvUBt34XQqSCC2RNCVexyZReOo9VyAVuJ6aYRTsKR+zfa29Zgeyff6az9ppH3QGoVcAqPrreWHiVfx0waWUqU20KzW8s8V1HLD8ChyRVWT842k/5P+oGjcl73uhI+XUn7LSyb1pE0qYM+lejt/3x4iCkLfWFoeiuc+hLvZlv97+Nd4ZbPvNbdTv/zumTJk+rOvQd4akNw2dCea++h/OWX0WwUxr3mNNQhXnOK8hklJ5SrrSUr7qRb+ZiLqJA88xrPkYHtrbWi+lP6TimPsBiL97J407XoLX5ep/fwnUAQKEG4aeORnNe1GfNZA9bva7vo0/xpELex33kAfg+ZP7/f06necozAh9Vxiz9xabMYm9XkaXkcTPdmJhU5jXL4WP7sYonkRo/8eomLj5wF/OkQ5Orgu9AsMmoYoLPNfSJpZRbrRza+KP1JgtwxtOXQ8GSzIKPQb0+7fl7TEe+s9KGrsTlHgcFLkUommVeSu76EpYUrSKJFLqdfDDyaUctdP4TSIbW2iAvTfRlEp3Qu03VD5Wb+aGYfLY6+/nWor6trK0ydW8tv0DHLPXLmsTxwEGYlsallL2/ME4IqvJ+Mfz4sSr85KNtze/joNWXGEFjqIERk9S0TOo3UAlt3IsN3NrLskIK2X8bduH+TJalDckv9W4AH+b18h4uZPjl5y1wcUTjK4G4vf+gqKeIeqws5JXp/6JvRZfTkmmiRapit+6/8RPppZz9NcnUpRZKz/74vSb2G3FHQTTjWT843Gc9Fp+25kMdR9fb+3qP3YwpCMYooPQwf9HxWY7If7ryvwAOhsk73QqPHqg9TlsexzqsnetmQ1kHp02m67S7YaXIK5LwL2uQ9GHPID6zEkjT2T70nMfHlaitDHuw5uQsXpvsRmb2OtldLETiwGwE4sR0LNTZux0BqG0MvSXc0PulPXZMXxi+t182OEmkdHxOCR+VJraMDuVPRiGyfvL2vnXwlaawylEQcCtDK6OVEhNaVKZl2XtMb5ujCAIoJuWV0UkoaLqBhndQBZFfC4ZlyKyRU2A8/eYtkkSC8MwueedZcxvCufNWIBl8rckFGPL2gCn7zY5r4IzVm/m2URpRAH6YEFdnwrIi5Ov5tCVVxJMN5IQvXiMOKYgWc7bgCFIiKbOGio5y3ENEUcFO8jLuL77dzm1qC6plEc2fwDNV0sio9EcTlHscVBhtHF0TxvOsBKi9eGls61ZBqBDquCxze/rpy4Vkqt5afLVHL7ij/h7VTW6XbU8V38lBy2/gjK1qd9u/XGhP/PD8CuYgoxgWgPexjHPE1LGU/HZnYj/vTtPSSovSN55Vm7m5Pm/v8whX5yAiImBwN+n38huK+8YXoI40oB7HQN7c6dZPGruN7JEdojX/K6qPQ2XsXpvsRmb2OtldLETiwGwE4uRs8m/nJv4h3VpKMqTH63m7UUhkqqO1ylT7nNSE3SRVI2CrUpZNaXOeIbqgAuPQyaR0fhqTZiFLRH8boWKIieKKLKiPUZXUgUTRAFMwOOQkSUBhySx35ZV/GG/zTfJgHTf95EdYG8OpwZs0drk62UAerd2FWopKigXOkgbSqEKyLsTzmXvJVfj1GMkBTdus2f+pKdqkfGP574Jd/F5xIthGgTdDn7kXMHBX5yca5X6pmxPXtvsT4CVwLU3LufC5gsoyTStn4zscAivgbn7QrelNPXk5vdbSYGqoRsw3RPh9JXn4I415LV3ZUQ3KdmPP9NKm1zNuzOv54Dll+OIrM6ds1uR+fVXJxBQrQqHKcgIJ72OVrUdC1eswUWCiX8/ArlHOjfb8qT561l5wDM4S8ZTG3TT0rAUx+MHUKY2YVp/laPbWcvs+ttZkQly2m6T2aG+ZP3vC+vYitSww++57V/LR6fSNEAr1gZvARtjjNV7i83YxF4vo4stN2vz3WETyiguDUV56D8r+M+ydgwT6ordFLlk2mNploRilHoVOuMZ3vy6NWca11dNqcilIIkCPqdMWtdRdQPBNHFIIhlNJ5rWME3LYdmEHhdvAU0ziCRV/rmwlVWd6y+huy4MNcD+bXL2Hsz9eUBn6kEGYvua5gVTjRz4zUU49RjdrloWFv8MAFPoaYUqnoDjpNc486DduPKALbh4nxlcsOdm7LTrXtxVPxsdSxlpYtf7+HrakIoyrbmkIukbt2HFE7LBa7cVnCqnvMmxe+/Cr7arpcLvAgEWpwLcV3ppLqnQEQkrZTiMJIZJzpV6v6WX8XDVZWtduxefztHzT8wlFToigqmReeZEnnjjA176vJHb/xfnuopbiDgrrXMJNxB1VnFdxS3c+t94bsg++MyvKFOb6HbW8ubkS/PewoOeE3mv1cnXTRHue3f5kEP5w2Idh6LjGqQ0Hc03CsaiPfe2IWWHew3w29jY2Gwq7MTCZmyzx9VW+8JwfjD7atmvB9kEobE7iQgUex1IoohTlijxOkhmdJa3J6jyO/PUkQZSU4qmNGIpa9gznjHIaAZJzUDVrYxCFoVcgpFSDTK6SUbTaexOMvtfSzaZZ8TGcPbeGAzm/rwufixZ9aSBAse3pl3KaudUqxXK6bcGegskxQlVZ7FjM56a+Zee4ec4Byy8kKJUI4fNP92qVCjVNB703Oi5TheiQAK/vD3G6/NbaI2kqQ262cYf57jma3KO4xIGCzw70aZUE1RbkQSBbkcl7lgD+y3+I1cp59MmVhBIt+LLtAHWzMbcze4jJFfjiKxmn89Oo0yKMbHUh98tW9+HHjKagd8t5wXQnngDIbma5yZcyU6Nj+S9hXO7rmNzYzEBt8JER9cmDbjXKZEdhKy/SFaB6vglZ+WpcbXJ1Tw1Yw5GUe2GeDs2NjY2w8ZOLGzGNptIRjGbIBR7HGiGiSKtTRIEQcDnkumMZ9AN8nYee8t19iajGwiY+JwSqmGgGQaqbmCaYOUfJoYJhmmiGQaSKCBLVrKxOBQb0pCuoKTlKFGwQvQtY7hyof0kYwdgqMBRjoe4c+K9ZMq3hHTEUgnqHfTrGrx+Kf50Ky5ZYrlzOs9vcSdpyUdlfBG//OZigqlGOh013FN/Jx5FQnzzD4wLODZMpa5PAt+38lYtdHDEAivR6XbWckvtHXxeezQ1x9xD+IgXSPrGEVRbcWYidMtl1NLKFembkYx03mFem/YnuoKb84mwJZ1iKWVaMz9vvJe6+Fccv+QsSrUQrUI5LZRTqoc4fslZTO16hzMWHJv77O4u/j0HLrvcShCdtfy5+DI0JGR0bolezJ7ix5y16rxNGnCPdiJbqELWW3b4iRlz+DziG/vyzzY2Nt957MTCxqYA8YxGUtUQEdAMk1hao/c4kiKJ6IZBpI+BXTbgTPRpcXBIIoos4VYkFFEkmrL60wXIVSqs/y2gSCKCYCUZkigwodTTr+WqN0tDUe55Z1meN8M97yz7djhjD4eeIHyd3Yx72nwGcn9el5ai4QSOWwVSyEc9VdDXIPr3S+Cjuyl//hC2KoqSal/Nvosuy7VS/X36jXxafSS3VN/KhFIP1S8c2s8RPMdoVOr6JPB9K2/bNz5BMNVIxFnFs1veS7pqOx71n4ooKUyatBnPbH4vCcGLmyRFWifdgp8Ko40SwnmH2XvJFfxk0XXsq75JwOgCwKd2cPhXp+YSqXM913Oe93o6HTV5LWZpyceLk6/mzK4bcknD7PrbecP4ARcW3ZhLLn7bdQ3B9DoE3Ou7znoxokR2GMftXSF7d8K5eY+NuLXKxsbGZgNiJxY2NgVoi6ZZ1ZHkizVdRJIqDZ0JmrqTJDNWQqDqBqIg0JXI5BnY1QbdTC730RxO5SUiRS6ZYo9CLKMzvsRNiVchoxoIojVbofdULpSeKoWqGZgIeJwypV7ngIZ02QHr+U1hgh6FSWU+gh6F+U3hIasc3xr+cbkVVA8n6O9cBXN2XhuE9xn+V056Na+l6MIfejmWV1CO//uIWoqGHTiKQmGDvtgetCnVKJFVHPTZSfy28VyC6UY6lBruqb+DRakS/uI5haBHsYzXhjJSG2XDs76Vt+xKNnv+P7dDygWy2d30J6fdji5YLVJBM5J7rVahnLNcN9CuWInC1PBHaIhIGOh9foIeqvg9TWYpbUIZr1afmffYuxPO4dCVV1KhNZPwWjMnKzJBIkmVheI07ijOn7l4r/7ckQXcI1ln2XVVKNkbaSL7yvlDHje7YRHs/IL9F12S99i6tFbZ2NjYbCjsxMLGpg9LQ1Fe+6oFTTfQDZPqoAunLNGVUGnsThJLq3TFM5gm1AU97LlFZa49SBQF9ppZSYnXwZJQjGhKRTMMYmkNuWdGI5zSiCRVJFHAJUsIAsiiVb3Qe9qkBFHAKYtMKPXgdyt5gVyWgQbFi1wKUyt8g1Y5hsOGbK8aETvPGr6b8X0/tlqPRBlm/mrw4X93hJoXDkX47xz4333DbykaaeAI/YZv/W6Z/9vyfrodltt0qdZKSCjnVPEqXlwh8NHyDpzxJs5afd4mUfvpW3mbV3s03a5aAulWDpt/OkqsCYckEkmqzG8K05nI0FW8Ff+cdHHe63RSxHHmVbydmMCp4lVWi5PZgdyTVEh9zAKPbPwTjngT1bGvOXJVfsD+i2U3Ekw30q7U0H343zh27104bbfJbFET4CeVaU6J/yXv7/dbfAnFnV8OP+AeyTobyDV7iES24GzMMFzFq/0udnas4IRFpyOZOrog8eL0m9a5tWrUGMUqj42NzXcDe3vDxqYX2WC9K5Fhx4nFfLEmTDKjU17kIJJUCSc1VnUkKPM6+PHUsoIGdlk1payPRWskhVOWmFbpQxQFmruT6KaJQ4YSr0JnwpIWNU2QBAFRFJBFgVKfky1rgwiCQDKt9QuQBhoUB2sOpHeVo6+k5VCO4oV8OAbz7digZFt9spKbD+47sORmNqkwNGuu4bgXMU2T5s1PJJL049UTheWJs3Kix7+y1o+lEH0Dx+Nf4diiWn7W91r+6NW1f/fwfhjHvcyT0+fk5GmPX3IWr0+9it4fmwlsMz7IVFcFvnQLpy3/HZ6snOxGHj7OVt7mN4XxOWViziqenXkvh80/nWCqkVPmH8NNZdfx1H+hK5lhWShOZfgrftF9Y97r+IkzjeUcJbzCK/zSer8F8tN2SkAUqDVaeUL4I6VmGEkw0JFIKQG8aidijxLV25tfx46+GhaHolT6XfykIsUv552JX7OcwGNKMR41gmTqHLX4PJ7b8ZnhBdwjWWcDJXuFElnoLynb51hsexysfG/A4+7sWMEhX57Ucw0k5k67l67irVihTM3JDh+5cJa17gJ1G8+xO1vlWfTKyAz84Dtn4GdjY2Nh+1jYDMp3TQt6qIC6r+N0ZzzNslCc7niSExMP8bT4S7qUCs762RQO3KZ24EHm8BrMD+5mzQ8uIa6BW5H4+xdNfN0UYUq5l1haJ6MbluysrvPf5Z10JTKYpknQ46CiyEl1wIPHKaGIAi2RFFvVBfMM6Xp7M0gFzkMzDFa0xTn8B+OoCrjy3L8HSxoG8uEYzL9i7fXdgOuljyndgG7Ghz9oJRXro/s/UGA2kGFaocCsVyAV3eZUrkgfXdDXoE0ox2PG8JKk01HDm5tdPbQj+EiCwHUMMpdu8zvmfrgmz8dEiTVxyvxj8JpxNCQe3swKcNu/eZ8/dV+YcxLvFgL4zCgyRs5vIiV6cRlxuqRS/Ho3EjoZZJb7d2Za5H3ahVJkVErMbgA0RP5QfAsTJkzhyK9OJtiTOHQrldxZP5sWoZQqs4NzVp1FUG3NPfb0ln9Bjrdy1OLz8Jjxkft8DHedFXrN9QnoYy0Fjxvs/MKqVKBjCBLPb/UgH2YmktYslblt/DGOXNhnPX84Z+M4dm9En6Hv2m+RzYbFXi+ji22QNwB2YjFyvktfzuHswhcK1k3TZOelf2bn0F/pdNTw5+pbOXafXdYaqfXB6GrAmLsfcmQV0W1OxXvATTR2J/MSlr5EkhkWh2IokkAspZLMmMQyKrpuYpgm5UUuzvn5VH4+ozL3nL5JUF8aOuN80xJlXLEHSbLaroIehVA0bbV4FUgafvOjev7xdWjEjtu5974B10shU7oB3YyjjeseHMLACUShwHGwwKwncPxmqwu58+0VBQ363lF+wu7qv3PDx1kKGqmtSxA4XPdoXbP6/Ze9ZR3nh2eydLtLct+ZtKbjkET8HV9wWdsFyOgYCPxr4sX8bMXNOcO8Nko413cT08yVXBG/Ns/ELuysxDQhmGnFECQwTV4p/g3bdb9BrdmUdzodYimnKjfgr5rARKU7P4FwVPLytOvZf/ElBDNrk4o762fTKpQOHHAPI5gdzjoLle7EjF9fi1g8bujrP8xEsNBx36s/l/0WX2K1PyHxt20e5KD9D6Q5kho8cD/kgVxyvcGNRTeSgd936bfIZsNjr5fRxTbIs7Hpw3CHnAupOgmCwPzxx9DtqqUk08T5TRfgT7cW7L9euWwR0fv2Qo6sok2p5rbYHtzzzjIWNkcKytBm8Thlgm6FLWoCrOlKs7w9RiiSJpxUQbCkZ9/6JpQ3jD3QoDhARyzFxyu7SGR03IpImddJwKXwnyXtfNHQTanXUXAm4/lPG1kaig6rvWpjMxLJzRHp/hfqEx+o3z6cYemU44i+fQeGpg7ebw+5oWqvyzWgPO109RtCYnleUgHw/PjLCZkB9lpzB8mOVUMfCwr3sA93duCV82Heo9ZrBOpg51n9fEyO3Gk8kdKteXbSdZiAiMkeK25AQkdD5FV24VDtalrCKU5J/IW+qadsqAQzrbQp1Xy0+5P8K3AIS8Yfztvj8we140oJpUYH9+iXc9wWMsfvswsvbPsgHVIFYCUmR80/OZdUdEgVvLjtgxy/zy7r7fMx1DprCOzAFq0vYMxdzyHvYRz3gEUX5WYq5m52Lx+kJ9IcSQ1tEDr/b4VFA3opx42KgSDYBn42NjZ52ImFzXeekQw5DxSsx5xVPLPFvbTJ1ZSpTZQ9f3C/H+o7//Y23qcPItAjm/l/W94PgVrmN4V55atmMprRT4Y2SzKjk9Z03loYwjRNJpR6mFTuY1yJB7cikVZ1Vncm8oaxBxoUjyQz/HtJB5GkCsD85gj/W9nJF43dZHQdURBY3h7Pe3+9k4aupIrHIWOaJt3xDAuaw3y5pos1XXFccv8h8o3FUKZ0vRWARqT7X0gNqE+glg3M5r76HwJ/PYiiz+9n4V9OHnZgNpA8baejhirawND6KSTts/QKdl54PXtF/4+KZw9E/cue0LUSs3gCTQc9x4JEEf9b0cGCprCVHHQ1FA5iB3gv/YLMxf/qdXQrJejbOuhzyXhSLezTeGe/pOEe35k8UHEpfpfMI9I11JqtOUfxLF61k4hSzj31dyKP/wGv1ZyJ14xx8Job8v5OExW6HZVUaM3s0PQUgiDwZbSIxza/j7DTqtqJPW1XYWclj21+H19EixAEYb19PoZaZ/+rP4U2pRq5lzzxiIe8R3jcV6ZdT1fJVoN/9/rKDm/EgN828LOxscliJxY233lGMuQ8ULAeTal8FvYyd+psEt5xOCKr836ox0mdHD7/DMrVJjodNTy/1X0k3FW55CWt6qQ1g6bu/tUF0zRp6k4RTmh0J6x+dr/bgc8pU+RSKPU6SKlWUrKkNZpXLcgOis+sCdCdUFnZHmdxKEY4qeJ1Wq1PxR4HLkWkLZqmM67iVEQ64xmiqfwAxe2QMEwDSYCm7gRvLmjh2U8bePubEO8tbuflL5p59tPVdMYzm0TWciRuxiNJQgbc0ddLWLrfX0n6xqFEVrH/pydwzuqzKFeb6XZUUtX+wbADs4HkaR+ZNpsWyqmgq0d+1QrENSRqjBZ2ND6nXSghmGlFiTWS9NTwyNTZ/O4fHZzx+Kec+/RnnPf059z5t7eJ3rfX4JWToYLMWCOqr9ZSKAo3FExA/vXhPM5dcz7BdCNGn9TijNgcfpD8gPuMKxlHKxoSEjrdzlpenH5TLnFStBgTSj1sN66YrX0xdm+4B5ceJyV5eWmzm3LqU7pusqDqILz7X5f3eb427U95x31t2p8Gl5Udoc/HUOsspercU38nmr9+VKsBgx1311V3DE9Sto/s8MYK+G0DPxsbmyx2YmHznWcgN+wsfaVcCwXr3QmVLWsDHLTbTjzbU7no/UP9m8VnUmO20ChUcnnxjUQda2chBEGgJujGKYs4ZbFfwrIkFMOpiEiigFORUOT8r2XW6Tua0uhOqv2Cp97tKmf+dApVRS4ccvaYEqIg4JQlSrwODNMkksyg6QYZPV/qM5nRCbodlHgdvLeknWWhOBnNQBYFnIq1P9wRV1nQHOWblggbm5G4GY8kCRlsR//PH8U5TbqKFsFygg5mWok5yhEEKNVDwwvMBpGnjaZ0XI61n3cXAVoFqy1KQ6LSbKPYXGsyF06qvPxFM1+s7iYUSRNLaYiRNZy35nwCqUY6HDWs3P+ZnHt27110zVszdJC5xf0Yx782YALy6wVnUKE1oyEhYhLDw12+s3PzIZdE/0SN0YLe878bqWR2/e0sLt6NuZvdR0L04jaTHLlwFnLLp/x60dkE9E7a5Wru3fxxFpXslvMCKdVDTE18hhhvHdnnWYhswA1DyqP2XWdpycdfZ96Xt84mlHoQTxjdasBou3XDxgv4R5TI29jYfKexEwub7zwDuWFnSWb0fkFJ397y8/eYxum7TcbtkAr+UJdkmmgSqrgseCNL08GC1QCnLLLf1tUFE5b9tqrG5bASD1Xvr6egSCIZTUcUKBg8Zb0ZilwKsbRGkUtBy88bcMoiRU6ZSErDME0c0tqvv2maNIdTTKnw4VasJEY3TJyKiCyJCAiIgCwKZDSdRz9Yidb3ABuYkbgZjzhI81XBhF1RfbV5gWK5z0mmz/v0ZdoIpFtJSV6enH7X4IHZIL4G5+/k5dLQhQQzrai+WrodlZTRiWmaueQCsNSAkGijhCqzjVtTf6BYa0M3DEr1Nu43r6TWbKVZrOKW6lt5dbXE4tZIPzf2m95YxL/bnEMGmWuMkkETkGwS0SpVc4L7Dt7x7sPNRb/Pe9sSOknRyw3e37Ig7ufLNWE+1ibx7A7PrPX4eGhvlMgqNE8F727xJ7ZpfJLVbZGCXiC1QsfoBN3DMMHrvc50JJx6jMkd7/RbZ8CoVgNG5NY9TDZWwL/eiZ+Njc13BjuxsPnOM9iQc++Aum9Qkg3Wp/UoRi0ORVnWFiOpagV/qG/2XkC7VIZmFK4GOGWJGVX+ggnLjCo/xT3tT7GU2u88M5pORjOZXOA8exPPaIgilPuc/V5HEARKvA5ME3TDBMy8qkmJ18HW44Ks7IgjCuBSRAwTtB5lKocsUeRSUCSRpaEY8xq6hv0ZrDcjNKUTmz4ZWZD2j8vhs0dJqhodckUuUKyNfcmtiT9SZbbRLpTknZJLj7NLx/ODB2YDGfRJnUx55QirT794AsopbxI69EVCcjVVtBMKbpX3MiI6GVNkjVHKeCHE4/LVbCss4VHpasYLIVabFZxgXoHmq2be6i7ufntZP6GCBc1hFrdECQllgwaZy9vjA+5y64KVVITkauZOuYsWyhAjjfwm/mDe65mA24hzafJWKow2y7HbhKizkpZfzF7rNSLKyDscx8Grr2b3zme5yvPXgoPX4sN7c9SCUwf9PI/+7NeITZ8Mvo56t73N2Rk6V+VVdppWLcHsWWemICOh067U8Dfngf3WmTF3P1Z1JkanGjBS08VhGtJtrIC/UCL/15n3kZK8w0/8bPM8G5vvBPb2gc13nuzcRFM4yZJQLKfJn8zoOZnV3u7ZvekrUasbJg2dSarpYO81+T/Uv0/expna1WSclQWrAVvWBnK+GX0Ns2qDbqZUFNEey5DWDDrjGXwuuadSYdASSVPtd3HoduMG9s7ACiTciow7KBHP6Hmvo+oGsbROidfBjhNLCSc1QtE0Tlliy9oAe25RiWaYxNIaAuB3K5imidYzLC6LAqIgoBuQ0Q064pn1+FRGwLqY0j20N4qhWUHa9DmsjvpIt8dxylaQlpMhfXg/K+DfeRbagpfxR1bRrVQScVYRTDVy2tIzAGgVy/sZu+mCxOf+n+EUBwnMsj39fX0NPphtnWdgXC7hcOsJ7p1wJ/t3Pcqk2Ly8l9EQqRXaeVrfjZ1ZSL0Y4q+itf5WmxUco11OC0EmpTWWtcUIuhW2rgvic8oIgiVUMKXcx/K2GM2rlvLbZL5C0d5LruDhqbNxylbylNJ0tKCVPPeWxpVMa2bijuo/c9RPdsT7v3kc/vXlVBotpCUfTj2W860wgWqjhcs7LuLZmffRgo+PV3Uy46tHGGdomNnk4sM5CD2fbcnPz6ck0PPdyLaoPbQXhNegwICf59Gf/RopE4WH9oYTX4e6HxT+PAJ1lhTrQ3tDOoJ+7y48vs3TfBH14U42c37jBQhqE6YgI5gaZvEEMgc9x7HOyn7rTO5ayRnJc/i/Le/vd51yiVp7fOhqwDqaLg6nxSob8O87bxbBAtK5Ry+cxWvbP7Dejt3ZaovSk/g9MXU2P25/BpceR0fqb+A32DUA2zzPxuZbjF2xsPleMNjcxECGb4UkamsCborVVk5adna/loxKvZk70n/Er7bSGU/TGU8TSWZy1YCBkhdYm/yML/VQ7HEQcCskMjpt0TRt0TRVfhdn/3wq06oGd73OVmeSqsHWdQHKi1ykVIOuRIZURsepiOy9RRXX/2rLflWTKRVFlvKPU0YUBWsnPq0TS2u5+Y5wUkUzrDaqUq9jVD6bIRlo17+Q5OYhD6zdDXf681qPBpUhBVYe8AxtSrXlldB311TXqDTb0HqGq3VBQjJ1Dlx2+eA7sZJsBUkDBoBrs5XaoJv6Eg/jwp8QTFtr66mZD9AmVuSM5wTgfDV/MPtCbRYhoQzdMPmioZvWcIq2aJqPVnTy8couOnsSQM0wqTDbuar7YkozTTSJVVxRdiudjpq8XeWJZd4Bd7l1QeK5CVeSdFdTrIc4feW5VOpWMHnvjLl8Un0kT028AQ0JATAQKMk0cdj80wg1Lqc1nOKy+OH8zXkgL292A7qjCNTEEAPOPd+ZPq1kvT9P6bi/rf3cH9ob1nw8sFLT8yeDoVkViUw01/b2q/SLlKlN6EgIppWUCse/Qk391IJKU5q/nnK1mR+sun/9qgG91/eEXQEGXt+9Fa7evXHIHf4N0V7VjwGqLX9zHki7UoOEjinIVhXo4f1oWrVkvdSzbGxsxja2QZ7NoHzXTGaGct7u/Xf3vLOsn1GcL93CIV+eZs1UiFX8dfM5aEW1KLEmjlwwi0q9mSahijOVqwmJZQTcDnaeVMKRO40f0K26N9kKydJQlO5kBlEQmVLh45Dta5lWObw129s5u8rvRDNMoimNrkSG2qCbE388cRDnbJO7317Kg/9ZQSSpIgi9Ql+rmwVRgK3HBXjm1B8h9xk03yDrZSRuxlkzOKcfTv83FE8Y+DPvYzbXsOMfmfvqfzhr5VmUaK39Xlo1BRTBpF2p4YVJV3HgssuHNtobiEKOxYZJ+sF9cMcaCMnVPDH9bnQDjl14GpVmGwAtRjEaInViR+6lVhkVHK1eRpNZitcpIYkCk8t96CbEUipuh8zEMi/R1pVcG76YWrOVRqGSS/zX06iXUC108OfEH6nUrfcinfAKj324Ks+o7Y0pl3PQwt/i1GO0ydW8O/N6Dll1JUIfU7S0pqPpJp62z7gufgVuw9qxltBpEqq4vPhGQmIpnmQLd6Yvo0xrpanqF9QccWths7m+5n6+qoHXwpqPraTC0NAdRTy+zdOs6kywS/tfea3qDLb0p3JGbWbxBJ4bfyW7f31J3ns8cOEFuPR4YcfzPhhdDSx8+o9UtH9Eud4yuGHjIFXG3PpOR+GzR4dWkQqvsZKK5e9C96qBjRIHMK4b1LF7PczxChnx+dOtVL9wKELXylwVqF2p4bbaW0m6q0dknvdd+y2y2bDY62V0sZ23B8BOLEbO9/XLWcjV2pdu4bD5pxNMNdKu1HCB51rcpfXIkkBaMzC713Bd5BIq9WY6HTXcNe52lqtBaoMeTvxx4apIIYab/AxG7xaubCAxpcLHnltUDnkei1uinDD3fzSGUwUfFwX4+fRy7jv2B/3Oa5Ovlz5JyJBu671ckQ1B4tHX/8M+H5+QC+Q1xFy1AKDJLOMi/w2U105h20B8/QKz3oGfr5akagVirVI1v/NdB5jcFLuUKr2FZsoQTJ0qwZprWWOUcq56Nrcqc6gXQ7nkQi6pQxAEAm4FpyxhmiYd8QzBTIi7Mn+kpiepuLLkJhr0EjTdIJHR2bEkwU3xP+CIrIJAHapuosQac67lqq8GJdaUcy3PBol9g8loWuXrxggvft7INv44Jy87m2C6MTfw3SJVc1fgQs7supkas4WQXMs/t53N4XvthixL/a9R36RyKBfxNR+jP/orpEyUDqkCSRIIZlqZV/pLxoU/ySWCbYc8zy0fxRkvd3L8krMIptbu2Bd0PB/g8+vr7t73Og2YdA7l4N7nuqY7V1M5/0G8+1+HGG8d2i17iIB/1Jy3h/o8dA1ePh91yb9QYo25JPOT6iP5T9nh/a8TDOhSvsnvLTbfKuz1MrrYzts2NutJX4na3klFt6uW57a8F1/5BI7caTxn/mwKk8q8KCXjeXGb+3MO3ec2ns8u5Wm6Epk8Y7uhKNgGMUIGUrUaTnLjVEREEeQChxUBhyyyvD1BQ1dixOe1wenVejQst/Veuv9itJEjFpxBpdlGGyX9kgqwrk1VwM2RO41fZ2fnHFlvCV8tSqwRf7qVbqWSF7a+n23HBbgl/geq9BZaxCqucf0OU1gbeAuCQLNZytGZS4mYburFEE85r2XfcQbVfjexlIZpmgiCQK3Qwe1pK6looZxF7m2ZMXUKO08qZadJpewypRQhUEf7Ic9b5xRek/O0KDRErDuKrKRClK3Ws57WtLSm8/bCNv65sJUVbXFea5D4ne86GoVKZHTigpcqvZk/df6WGrOFTkcNT864ixUJF03hAQac+7aSDeEivsq9OY9t8xQdUkWePPDk2P/ylJq6lYr1U0ta32HrDW3KOJL2wREaCOaxx9VWxWSg8+gjiiChk5Z8LCvdvb96lmEO26XcxsZm7GIPb9vYFKC3RG2RS2G7pqdyScWzM+8lZJbiUlQml/sAaI9lqAm6iLuKeHbmvbkkZPvmp2ipPitnwDfg7ucGoNCQ+HBY3h6jO6HicUq4ZImUZmCYJooo4FIkEmmdUCTF8vYY9aXeDXDm609ft/VsK1uRS8HnlFkSivHm161MKvPltUW5Yw20StXMd2zFz5Nv5L1mRCmjVA1xceuFhPUXEMXStYFZdtf3wzkjGjw1impZ7P0BW8SsHXNRFAiorRyx9AqCPUHXrd7fcWn4Rqppp4kyTBNqhXZOVl7FNMEvJFFNkXFCK6euPIe/TLqT99LO3OD+EekXGUerpeAkws8SrxNYXcJ7E88HQDMMVrbHSag69DK9U6b+nGP33qX/EPFO78N9P4Z0xJpXOP4VlqYDufa7moCbcEKlJZzkm4Sf480rONP5Bp95f8xVnb/Lvf4b065G9dagdncMX+60z/VWH9w31+KT0nR03STZEeKAXkaYvoxVfep21fLE1NmsjviYntYGnCM5aMG53DfjEZxyeeH5iNEYtt55Fix6Je8xo6iWRr2E9H5/pfaFQ3H3mDJKkkBQbV1ryqiHrKRlsOrCQKIBg13TbKVgJGQTv4EoIIrgT7dwxPzTAOh01PD4tNlEOxIYc/dDzFb/7DkLG5tvLXbFwmb00LUhzafyGMPygn0lav894Ww+rTmKZ2feS9RRmSdR27e6EXNW8ezMe/m05ij+PeHsfgZ8Y52OaAbNMBEFSKo6Gc0goxkkVcNSjBItpaiO6EZShVoHRuK23jtQ1Pz13DfxTr6adDIpMT9pMkWJbkcl5WozE146vP9O8zCdnfue59zis5lX+stc0NVbsvTFyVfz29jN1GG1MF3kv5GzXNfxGPtxK8fwlPxLmoQqFMFy7Q6mGjll2TnsEEzgdcpEkhq3m0fxjPFTRFGgwmij21XLvJojc+eQlUKunP8ghBsspaptj4P9byu8y11SD7M+zO1ymx/cnZfE+d0KUyt9FLkVBEyajFIeMvbl9O4/5733fZZegRJvQpHEkcmdDuIiXi10cF34Ekq0ViJKWd7T3qs/N1eJ8LnkfvKoL212E7og4dLjnPL1bwYeyh+NasCGNmUcUjSgwDXt5dg9agTqBhVFuMZxHktDcU5bcU5OenmdZj1sbGzGDHZiYTN6DMN8Kkc2mPvobnjzj2MuIcmqNJV4HSwJxYikTd6qP5dms7SfylMhA76Ys4r3Jp6PKcgFDfjGMqU+B6IgEE3pJDI6Gd1AM0wyPf340ZSOKAiU+jaSKtQ6MCK39V6BonjCK9SXeDh60Tm4jHie6lcg3Yqum0SdVVYQ1DdQXIfALJ7RSOgCb027jFenXZv32OtTr2KbyFtU6s20KzVc5L2ONqGcqKOSh4tOZXJlkJnTZ3DnuNtolaqR0K3kIt3IlmueIJ7WAJMKMcJP5IVWUuG0Km4xZxWQ7+Pi3f86Kzk68XU48K7B30uvZGrNDy7pl8SVeJ1sMy5IbbGHcVInszOXUW200CpVc//Ue3K+D0ctPJuJnhQ1gZHJnRpFtQXN6c5pOI86WmkRyknr+T9v+y2+hOLOL3FIIpmO1Rz29em5Vpzn6q9k15V3IJnWNfSY8YHVkoZq/xngOvVLOgdIkAYzZRxNt+yNhbNkPPfU30mnXIlfa8977JLEzVwWOp9KvZmkb5ydVNjYfAewEwub0WOI/ucB5QXV+LolJBu4D3e4ErXrasA3VplY5kUWBXQTdBOMXv9l/00WBSaWjc02KBih23qvQFEUhUHlOUv1EC5Fgu2OG3nbyCDnOZCB2fulB/NOyWEs2+9p6idtRlXAxczaALtMLmO78UHSmolUPI4l+z5NW4+0ZwwPr7oPwOOUKNXauCP9R6qNFtqUGm6q+jPNZmk/Y8Q9t6hElJV12uWOaxRM4kq8Tn5arfK4fA3jhRAtUhVPzribcNl2uetZpjWza8M9iNGmEV23xu5kQRO/kkwTbVIFmCblRohOR02uEiGZOscvOp260DvUvXg4nrilvjW7+PccsOxygmlLlOFv2zw4+HzEKFYDCiVIg5kyjqZb9saiNuhmfIkbtZdpaIdYSqdYSoXRRhXttAjlPLP5PcN2KbexsRm72ImFzegxSHn/zn8t4bZ/LOax19/vP3y428XrlpBshD7c4QxB961uRFNq4cBtHYawNwUm/bzgBvy7sUqhZM80TSJJlbZoimVtMSaX9yR7vfvEhzOQG2uEFe9BrGVUzrNvS062QhJMNXLUN+ewasrxVI2bwu7Ty9mssggB6IinCSe1XJL7o2235onpd9MkVOEjwdXhPzA5tYDZ6uXUmq2WatH0u6kZP2XYPi7DZaAkzpdu4fCvT6dMbSIkV/PsFvfRoJf2u55yIgSPHjCiwfdsRarQ8LVTMKiinTVUcue421lUshtzp92bU6a6oPNKytQmup21vDT5as7quoEKrZmEdxyZY17i4AMOWr+h/BFQKEE6bekZlmKWWI5BvlLWaLplbyx6iyK0COWEhHJKjQ5KjLWSyQ5JZFVn8ltThbGxsRkYW27WZlDWSbKtgAyj5qtFjjUOLMM4gOZ6ViJ0JHrnm4rBJF4nlfnWW0J2Y/HWN63MemIeKdUY8G/cisjdR2/Hz6ZX5v37WJL46+3n4VZEmrpTtMXSxNMabkXmp5uVc9QPx6+Vnd0Y8px9GYZkaaejhtnj76BFKMUpiZQXudhhQjEzqv25dZSVRx4ndfKbxWdSkllbAegtn3reL6YiCMKorsNCni+9VdSyfg5H7fEjmiOpvGMTXkPo2d9S0fQPxOLxw76e2fdbSC4WoFOu5DzvDbjLxiOLAg5JxN/xBVe0nYeIiQn8Y/Kl7Nj4yMCeE318TjaEG/Q3LRHu/NcSJpX5qIt9mefe3UYJ5XTSJFTx98lXcMSaa0fmjzEW6HUNW6Vqri69CUeimdtiF+X+JKKU4VfbaVOqCR/xAlOmTC/4UmPp3mIz9rHXy+hiy83abFIG6n/uJy/Yu+w9yEDmeLmTfeednNtJ3hRJRcEKSh8Gqm4YpskNr33DlS99zQ2vLeTWNxdxzzvLLLnTMUhHNN2vx7svac2gI5reSGe0bmRb2aoDLj5b3c3KjjgAE0q9bD/Oy4+W3cr/vf1f63MYaiBX6kR88w+WQdv6yHP2ZgjJ0idmzKFNqaEk08RZq89l66I4xV4HDV0J3l3cRlrTc4FldgdfL6rlzc3yW7R6t88kVH29pYz7Uqhit23jk7kgeO7U2fxo+62RZbHAgHMt/PzyEV/PvpWeiLOKqGPtsLaJyc83K+cP+87g7J9P5cidxhMp3Zq50+7BQEAA9lx2Xa5KVHBuYT2G8ofLYK1w5XTSQhkXeK5lzWi7ZW8MCogiTKnwclnm9rw/G1AUwcbG5luJnVjYjDoD9T8P+iPOOiYkG4GloSj3vLMsr6VroMSgrzrM24tC/O7ZL3n5yyaWtcVo6k6xJBTjX9+0cNe/lrK4ZewlF5ppMpTlhmFafzfWmVTmo8TjYHyph59Nr2DXqeXsOLGEIyMP8Yvwc5yw5Cw++PQLjF9cNfBAbt+5nvUIOLMJ6tKl36D1aglUTnqVY/feJZeUnveLqcjBcdxWcyvdTssX5YgFp1MtdDC1wkdnPN8bZahZjQ3dPtN3HukR74m8U3IYr23/AAf/dKfBW628ZXDcSyO6nmK0MW8WZqnvBxRl2ulWKumQKyjVQhz1zZmMl7uYXuXH71ZI6wZdpdvx9+k35r3WoHMLG0otqYehEiRBEHDIEh2x9PD8McYSBUQRjvnGqqYNWxTBxsbmW8e3o0nT5ltFrv85aPU/9y7v537E2+P9fsSzCUnXjDm59obsc3tr0P90I/pB9G6nqQ648DjcJDIa85vCNIWTg/anL26Jcue/ltASSVHtd6EZBu0xlXhaRRAEGjqTdMYzXPbLGUyrHDuteV2J4cnIDvfvNiWN3UmWt8eZXO7LOagDzKs5ksmd71KeamTfeafQsvlLVO35J6v1qSUycOtTdq4nG3COgN6tcvs138WUyCrCrlq69n+GCYE6RMit64bOBMvb4wTLxvNs+VpflO2anuK9iefnyeWOK/HkAtR9580iqDXT7bK+e3svuYJgympBfG37BzaogMCUiiIm7d675e9Odh1uq1WgdvjXs2+lZ/ocvoq46NKdvF92BBNKPWsd0Xs8IryOklzitdvKO/Jebu8lV/Dw1Nk45ZKNPreQTZAUrdkykDNMgqqVIOmmSaUW4j79chp/+BzOkvFD+2OMJXp5aYiQe59tcjVPTJ2N6qvh4amz17bHumthi9ERRbCxsdl02BULm1FnXXdPBxvI3BRqKH1N1opcCpIoUORSCu4a933uc5820BZNU+V3YpgmoWiatKbjdkiIooAgWMHm3W+PrbYodYg2qJH+3aZkINnZrNdIt7OWMrWJ4DO/ygkNzP7nNyx7/Byef/GF4bkcD0P+uK8L+OfTL+CjiiO4sfLP/OXLTL/Pv/d59/VFAfp5o/Tdwe+rZrWx2mdGwzV+UPqa0/VUes7dc3Nqf30bJ+z744KO6LVCx6BD8kcvnDWwb8WGok+C1FL6I4JqK21KNXfWz+aV7eei+utxxxqY8soRVjveUP4YY8lLaBOKItjY2Gw67MTCZtQZSulmoB/xQROSxVeQ7lhNStWJJNWCwfxoMyKTtQGe65AFHJJEZ1xF1U3cioQiiThlEd0wccgia7oSPP7RKlZ1xDfK+xqKar9rVP9uozBAQDWYYtF2TU/xyNQ7CMnVeOINubme4+MPsXvnsxz8xUlDz/UMQ/64UIIqSgofTr2QstpJBRPUvufd2xcF+sjlDjGr8a1qnxmKdTSnE/9905hIvHIUSJBmnPIA0W1OJXzECwMmSEOaMq6rl9CGku4eIBHsPYc26Pu0sbH51mEnFjajzrrung6UkHQ6agimGzlh8VmkO1bz1H9Xb5Th5xGZrJE/4L2sLYZumDhliXhGI6nqOGSRbH4iCdYAdDip0hZN8+aCVq57ZeGYGOquKXEznI1mYywJ0gwQUBWSnc0qFm3f9CRbNDzNn8puzpvrafJva/ke9BjOvTjpKjRvzTrLH69LgjpsbxSh4/sVuK2LOd12x8Hyd8dW4lUoQZIVig66mSlTpg/fvbvv/Me6egltKOnu0XApt7Gx+VZhz1jYjC4F+p9XR32k2+M4ZetHvG//M4E60DXEl8/nqCX/yuvDDQllvOy8hhsyl1JHK3ekL+Mx5W4aV7XQsuAmOPRGplQVj86565oVoO48CwJ1ebvGvfvzsyixJg7reBKvfGte/3xK09F1k1AkjSQKRFMahmkgCWu/bmnVIKXqeB0yAbdMImPgdkjDmt3Y0Ki6iUMWSKkDV08E4OPlnRy+/fixIXm58yxY9EouoDKOe5k1RgnL2+MUexUUSWBxa4zpnjCHLT6LYNpSLPp32eHEDUuBKTvXc8AiSwoza6q26/xLuMcIskItXif547UJauE2G7dDorVHhjVLVmmpKZxkSchKStwOiWRGpzmcWuuN8t/r+wdu0H8GKRu4ZYPJD+dsEPnUDU7v9prhsuI96LY+J+X4Vzi2qJaf9ZUR3thzC73mD4adIH04Z+j5gz6fs/rgvptWuntDvU8bG5sxi+1jYTMoI9KCXh8vgHduhM8eBUD11fLk5vfzecTL180RYimNbYNxrum6mJJMExFnFaZpEsi0EvJOp+zk5xCLxw39ZsJr1v5oFVJ5ef1Sa9e717n31efP4k218KsvTqVca6Zrq5O5RTi+14C3TDyt8v6yDiJJFUkQ6EqquGQRpyKi6yZdSRURmFrhQ5ZFUqrBzpNKKXLJLAnF2LI2wOm7Td4kQfs7i0L89pnP6YqrDDRFIYmwZW2Au47cLi+I3aTa4b3WX6ejhvPc17I0GUAzTRySxBRnNzfFL6VCa6ZdqeHV7f5CVf0UXvissaCPwEub3cQuK26nNNNEq1TNU5sPw4+lAFnPhaBHKZigRlMq3QmV8/eY1i8hGMwbZUpFUb9keFjXaLDvwEZmg6+XPt/pQa/RYL4V37brvC5eQmMc25fAZiTY62V0sX0sbDYN61r2fudGWP527mUUSeDYnes5YsdxlHmdzKz1U14ziee2vI+Iswp/uoVAppWU4KYi/g3G3FHqJ+7TRiBGG9lj80ocksi81V00dSdRdQMhsiaXVGT89bzk+lW/AW+/28FmlUUkVZ2uRAZN1+lOqHRE00RSVrJRU+zG45SJpTRKvA6KXHKuNWZJa5RPVnUO6puxoZhU5sXvUhAE6wYh9PwnAooIsgiSIBBOqETT6kY7ryEJ1LFy/2dy3g/Xhy+h1Gwno5ko8Uau7r6YCq2ZqLuOzDEvccxeuzCjyj/gXM+uK+/gJs+FNFJJpb7u8sfDbmsqMDg8pPN7dgd/uMHhBpZPHXOsS+tUIdnbsTa7MARjVbrbxsbmu4+dWNiMHuv6I+7wQrgBAuOsfw+vQZ+7Hx/M+4KVnXGWtcX5aEUnX6zpzguwl5f8mDal2tI+H41+4j4Jj/rgvnw47wviGY1QNM1Hyzv44uv5HNVrx6/9kOf5Iurr1z/fGc+wsiOBQxIxAEkUQQDVANMEr0OkyKVYrtAOmcnlaysiSVVnQXOE+95dNqRvxoagrtjDjGprR0ISwaWIuBWr2iIKAqIg4HXKaIZJLLXhFbqyn+mCpjD/W9HBguZwwWTLMExeXiVynvMaGoVKamlljno5P5AW85h0DeOFEA1mJee5riXmrEIUhcGFBtKNnNN9I3cWX0y3c/h+LH0pZCCnGQbRlMqSUGxtW9MA1akNrrT0XWa0Eq+xNrswBOvqJWRjY2OzvnxPtq1sNgoj7X/O/ojrGghi7kc4W8I/5psz+Z/zGhLOKiqMDv4Qupig2Uq3UklDyQ95ofa3mJFWLg1diDxa/cRZB/Cec9h33sl0zZjDZpU1pDtXc8qyP1KhN5Pxj8dx0qtEkn5S2pK8/nnTNFkaihHu8XmQBIFKvxNRgHBCI5xSiaR03LE040u9TC73UeJ1YJomDZ1JPlvdSULV2ao2QGVg+L4ZQzLMdg5RFDhwm1oWL1nIUcbL3GIejYGEiYAgCngVCa9DwuOQ8Dk37C0k2wr0WUMXqzsSJFVLrnd8iYdtxxWz18zK3PVo7E7yVWOYZko5Q7mauzOXM45W/qL9AYA1VHKycAVdsSKem9fA7/eekecj0Fdf/8gFs6jVm7kofgvvTjqPA7+5KHdeg/mxFCJrIJdta2qNpHDKElvWBta2NdmMXcba7MIQrKuXkI2Njc36YlcsbDY9vXYVe5fwK7Rm7khdxoTEfK4NX0yN2UKTUMUfS27hzUmX0hTRKKudhHjCKzlVl6xk6KQyH+PlTvadd/LQkqF9KNRGMC7+FWetPo9K3QpAn55xD0ZRbUE502hKozOeJmMYZHQDpyzidymU+lxMLPcyudyLQxYBkyllHgzTZHVHnP+t6ODfS9tojqQwTFjTnSScVIflmzEsRtDOsWVRlCfkazlReo2LpCeRJatqEXQr+JwyHqfMuBJPwZmBftez0M7uMMh6P3y0ooM1XQk03UASIJxQWdwS5YNl7cx9f2WukhPPaCQyGpph0KiXcLV8Tt7rXa2cS0gsRxZhWShGS8PSQWVan515LyG5mtJME/svuiTvtdbFzXrItiabsU1202EU7zUbik3txG5jY/P9xb6r2IwpGruTfBVxsWVgBxyx/1GZbubOxO8BaBGr+GPgBlYl/EQbw9QVe9hzi0qgm8XubalIZHKJQM51uGcn+rUZczimqHZYmXRjd5LPI14aJt3JGSvPpXQQB/Bs//z8pjA+pzUjkdEtxaeMaoAJHpfck0hY8qI+l4LXqRJOary+oBVZFIlnNEzTJKMZeB0SZT4HbdE0sbTONuOClHgd/dyWR0wf5aTBhuurXzgUQW+mVarmP4HDKU87MTFxyRLFXgeyJLLd+OIhDcX6qmW5ZInJ5b68SkMhst4PHbEMWo8sbzylkdIMKzERBMKJDElV482vW5lU5sPrkPE4ZAxToFRv40ruzHvNy9U7OFm4koxcTVANUfb8SRBZPaBaULXfxd+4noO/OAnJ1NEFiZc3u57dVt6xzm7W2bYmm28n2U2HfeedPCr3mg3FWHBit7Gx+X5iVyxsxhTxjMY+LfewXcffkcx8TaIbPRewSguSUnUmlfmstiBnGGPufmzR+gINgR1GpZ94YUuEr5sj/LvVyVV9dr37OoAX6p8XBdBNSKk6DlmgxKMgCAKmaZJWddqjaRJpFVkUKPU4yGgGas9/pgmlPicBt4MSr4NkRmNZWwzTNPv5ZoyYAjMkWbfp7CzHY6+/j/rgvghdK1H99TwybTZy8Xh2mFDCDyeVsWVdkIDbwfgSz6BzAbC24vBVYxhZFPC7FGRR4KvGcK7SMFA1I+v94HfJNHQl6IxnSKoGkijgUiQkEWIZnSWtMf69pI3GniRvy9oA1bTzgHkltbSyhkpOVa6jkUrqaOUv5pVMcXXzy8QLOHqSioGEBuR4EwetuCLnZyGZOrXhzzadqdr3gY3pHL0Ox4q/dBFfRVxjfnZhrDix29jYfP+wEwubMYXXIfN+2RF0OyrxZdryHvtD+na28ceZXO7jNzvXM8UZhof3Q46sok2p5uP6U3l96lV5z+mbCAzF0lCUV75oJpbSqKSdS9O35z2+56LL+7URZPvnZ9YELOWnWBqXJCL0DDmLgkAio9HUnaKhK8GariRpzQqS60o8BNwK40s9TCjzocgisbRVvbCqGzKd8QzRlJbvtryujKCdQznpVQ7+6U5sWRtAN0yiKRXdMNmqLsBvdp6AU5YGbG/KVhxWdyYIJzJ82Rjmk1WdfNkYJpzIsLozwVP/Xc2cd5bmJTbZIfVsjzgCdMQzGAY4ZRFZFBAEUEQBWRLQdJMlrTHCyQyiKLBNIM4dqcsYL4RYZVZwlPpH3k9P5jfm5TRQyThauab7ElZPPRZzp1lDOmpnr8X/bf0g75QcxiPeE797btZjiY2pvrQOxyr6/H72abkHzVe73veaDcb3yYndxsZmzDGiCCWZTPLpp59SUlLC5ptvnvdYKpXimWee4bjjjhvVE7T5flEbdFNf4kFfsTZQjShlpHWRcrWJ8xsv4NLADbzzcYajvzkTJbIKzV/PPRW34Fc1jijQT/zw1Nk45ZIhA/JsMJzWDLYNxPlD26VUmS20SNXMDlzIrK6bqck0FWwjmFJRxKTdrZ3KhS0R3pzfTPvCDM3hFB2xDLoJopCVbjWRJBFVN1ncEsFEoNjrRNWtVqNwUqOiSMelyCiSlWikNZ2uhMqWtYH1bl8YSTvHlIDAhF29zGvooiOeodTrwO9S+MeC/PamSWVethkfpNSnoMXSpOQEnzV00RZNoekmPpeMIsmoukFbLI1mmCxvizGlwsfkch8eR/6Q+t4zq3DJEqFoClU3kMW1ruUAJiAKAookkshorOyI40u1MuONIyk1W2gSKvmNdhmNRgmmrrNaKuGi4LXcnrqcSr2ZI785G2EgDf8+yj7K8a9wSFEtjd17ceamNFX7PjCCdr31Vl9ah2Np/nreLzsC/wCzC8O912wwCqzdMWEIaGNj871h2AZ5ixcvZs8992T16tUIgsCPf/xjnn76aaqrqwFobW2lpqYGXdc36AmvD7ZB3sjZ6CYzvYydOqQKBEGgRGulRSgH06SKdjrlSkxMSrUQqr8e6YRXeOzDVblAuW8/cZtczWvbP8Axe+0yaOtO1shsvNzJcYvOpCTTRJNQxeXFNxB2VOJONnN9+BJqaR3QXCrb/tMZz6DqOl82hOlKqui6iSyCS5FI9LT0OCQBwzTRTfA5LNUlVddJZKw5i+qgC4csEU9r1BW7qSv2jIojd+/3mXWbztLtquXhqbNZrZVw/h7TSGt63oxERjNoi6bxu62Bco9Dpqk7wfymCKpmML7EzTS/QdpRxLuL2pFFq7WrtxSvYRgsbYuj6ga/3KqaCn++otaSUIyZNQFM0+Sf37QyvzGMJAgosojQ8zeaaVUtREAHbt+njB+8cxyBVCPdzlqemXkvC2JFfNMaJZZSSak6FX4XR0wVh1buGS1TNZshKXh/6XVNVX/9hlVfGuGxjONeHpV7zQbjO7x2bcMzm5Fgr5fRZYMY5F188cXMnDmTUCjEokWLKCoqYpdddmH16tXrfcI2NkC/Ev7ft3+I87zXs4ZKqsw2JEkkIpdRorVSqoXokCt4asYcgFHpJ45nNNzJZo5ffBYlmSY6HTVcV3Eza4xSuhMZWijj4qLrSHjHFWwjyFY8smZ5W9QE+dHkMnxOGa9TQhRFUpqBKIDbIVHUM3OQVg26EiqmaeBSZDwOCQRo7k7S1JXEKUvsOKFkVJKK7PtMafqQ7RwLmyPMfX8l85vCBD0KE0u9dMUztERSdMXTqLo1VL0kFEc3TGv4XDfwOGSWtERpj6UB8pIKAFU30Xtap9QCClE+p8y81Z3UFLsp8zmRRAETE1Uz0AwTzeip/AgCgijic8pMWvoogVQjnY4ant3yXuKuKurLvOy5eSV7bF7Fj6eWM67Yw+47bWslhFlPgg/n9L9Ao2WqZrNubEz1pREeSxSFsT27YK/dDcvGnAGysfmWMuzE4oMPPuD666+nrKyMKVOm8Pe//5299tqLn/zkJyxfvnxDnqPN94G+JfyTXuWnO22Hu7SeBybfRaejhnI9hF9rzz1FEgQ6W1ZjzB2dfmJ/upXzGy8gmLYGMZ/f6j4mTd6MnSeVsuPEUrauC1JeO5nuw//WzywL1g4c9zbL87pkSjwKE8o8+JwSsiRQ7FHIhtq60dMiJUA8o5NIqbgVkfHFbhRZotTn5Pf7bMYZu08ZNVnS4UhROiSRT1Z25TmKJzI68YxOtd9FSjVYGoqxNBQlmdEo9ToIeh10Jywn7gnlHgQBuhIqhpE/hK8ZVmLhkIScWhZAZzzNJyu7+GJNN183RXjly2bqgm5KvQ7Amq3QDRMEcMgSRU4JSRSYVllEYrfL+VfwUJ6deR8xZ1XuNQVBwO9WqC12I0sCSVUfOqDqa6o2VDDR11TNDibWm43pHD3sYxnm2J9dsJ3YNyzfMgd2G5tNwbATi2QyiSyvvfkIgsA999zDL3/5S3bbbTcWL168QU7Q5nvCh3PW9kv37LYlVR1JEvBVTODNzfIDwKijjKDayqkNF1rO2z3JyLF775LnE3DMXrvk71AP8oNQveAhytQm2uRqntniXmLOqlxgWup1EEtrTK0somrclDx1peyud7YS4OnVX+2QRBRZQtfBABRJxO92oEgC8bSGqhuIopUkZTSTpGYQTWs0R9JU+J2MK3Ezscw3qm0Vg7pN90hRTnJ20xZNUR1wARBJqrRGU6QyOrJkDZW3RtKEIml8Lkv1SpFEdMNAMwxckkyxR0HVDULRNGlNxzBN0ppuKWeJ1mC7U5IAK6n4vKGbUDSFLAoE3AolXoXWaJopFUWU+Zx4HDI1QRcTSt1UFDnQDCjxOvjNjyYQ8Hp4pfpsWoXSgu+53+D7SAIqO5jY6GxM5+jhHGtlRwJjbv7Gx/rca2y+pXzLHNhtbDYFw04spk+fzieffNLv32fPns2BBx7IAQccMKonZvM9o0AJf7CddVOQaXVNwmPE0Xq1RPSVDBVFoZ/MasH2F0DY8xq6tz6FuVNn81nYSzSlohkG0ZTKklCMEq9jrcRqgV3vQmZ5RS4rwO5OqKRVHQHwOCSq/C6csjXAbZhgmOCQoK7YzfgSD16HjGBCRjNGXWFmOFKUJy09G0+qhZRq8PHKLj5c3sHXjWE6ExnWdFkD2ZphoOoGimQlPapuIIkikihgmEYuwXIpIsmMTlciQ0o1qA64KPE4cCsyPqeEaZosC8VJZnSKPQoZzaDU56Q64GZqhQ+vU+aHk0rZvNqPbkB3QiOlGmxWVcTv95nOz2dU5vxEmsMp+o6NmaZJczjFlArfug2+28HERme47Xqj8d0YzrF2af9rbgNjNO41Nt9SRiDZ3XejzMbm+8Kw65+/+tWveOqppzj22GP7PTZ79mwMw+Dee+8d1ZOz+R6RLeH3YiiTp7RUxYKqg5h+xLXD7yf+cM7A/cSSTPBXt3BwL1O31kgKpyyxZW2APbfoY+qW3fXudb59zfK6EhmSGZ1oRiOpGmi6SXs0RZFLwaWIKJKAJFqu1ghQ7HHgVCT8LpPWSBqTDG5FWter2p++UpTT57A66iPdHscpW+0cRy6chSeyinPT53OB+1raKMPnUvA5JdK6QTSpktE0PD0VB1U3cUgQS2kUuSSWt8X5b0uYWNoa9l7TlbSShEofPqdMNKVRVmRVQpa2xfE5ZdrjaRyySFdCxe2QmVzuy7WTVQdcdCdUrj1oJqFYOqdOtd24YuSeVqqsn0hTOMmSkNWO5nZIJDM6zeFUflI4UrJrJzvk++C+G3ag2GbIdr3RVF8azrFeqzqDHSeUUPTTc0fnXmPz7SU7l9MjMrLvvJPpmjEHLViLHGtk33mzULRmqy3Ovg/YfA8ZtirUdwFbFWrkbFJlhV4KUW1yNU/MmIPqq0GJWZKv5dmb90CyoYOha1arys6zCj7XMMx82UmhA/G/91iBwiDtM71VodyKyKLWKLGURkYziKQ0DNNEFKyWqOqAm5ZwCsM0EEURr1Ompmc+wzRNGrsSuBSZ6w6eyQ8mlK5/O1TfHfUC8prVfhehxmWUPXcwjuhq1lDJtWU30aVUAvQE6kkiKZUSj8L4Ei8tkTSyaAX3qm5QLqVYHBbQTKsVzMRyIw+4ZbaoCbDd+OIex3R4Y34r81Z38nVThIBbodTnZHK5jxKvI3fammGwsj3O2T+fyvSqwb+3vZ2+05rV/jSlwtc/KVzH69d3PWo+K5hY7/X4PWWg+4thmDz2+vsbRX1pYx7LZt0ZSyo/9poZ+4yl9fJdYCTxs51Y2AzKJvtyDiADmQ0Wt/HHOHLhyHaIeycLtR9dQ9Hn928QWcaloSivz2/h1a+a6YxnCLoVSnxOSr0OFrVErRYr06TEoxBNaURTVjtHmc+BLEkkMxqRlAqmiUOW2XpckO3GF7PXzPUMjoeQouwdlBNu4IKm31JHK09L+/Oo/7RcwpM165MlgfElHtqiaWRRQJEEmsJJ6t0qC7oETEHAKYt4HRIGAk5JYJ8tq/n93jNylQbDMPlkVSf3vbucEq9CdcDdT0UqmlLpTqicv8c0xpV4hnyb/ZLCoHtkP+4DJJ0DBRP7LP4DgXQrbVIVr+3woB1MjIAB7y8bclOhLxvzWDbrzFgKFEci2T2ce5bN6DOW1st3gZHEz7YUhM3YYwOYPPUOmlOaTpW5B+e6XiGwAYy4plQU8cutRb5aE2ZaZRHFHgdFLpmuhIpLEWmLGaQyOuFEBockUux1kuxx5lYNE9O0VKKKnBI+l0yJV8kZx62X5Gy2LaNAlaZvpWVJOshvjMs4hle5IXMERjKKJArIoojPJTOztggQOGLH8ficMu8tbuOFzxqJJjU0F+gmmCbEMwbxjIEkWM7Z7yxqY/fNKvjx1HLAqnLsUF/Cx+O7mN8U7nfK2fmIkRgDZnvf15nssPaiV/LWVHbIt2vGnFww8euvTs49rSH4Az6P+Phpd9IOJtaHYbbr5dSXRsHHYqMcy+Y7Q24uJ2htLvS+D+RmgNrjm9aB3cZmE2EnFjZjjwIKUSL0D9b69L7z4ZyC1YTeQXN1wNXj8uziRvXP/Lb5Ako3QN98VtGqrtiDJAp0xjN8tLyDjlga0wRFFjA1AQN6lJBEHLKIoBvohokgCMQzBrKk4ZQlqgNuloRivPl1K5PWVSWqwBwL5PtvlHoVvlgTJpJU6RIruEE9Fh3TMqbr+ftoSuPTVd1U+F34nDI/mVpOidvB85+uwcTymBAF0HrVQnUT0ppBeyzNk/9dRVXAlUuQNuh8xLowgCPzsrYYoVgKX2kFr025iiPnrw0mYo5y/ld/Cuno6AwUf2/ZmM7Rtku1zTqyMWeAbGy+bdj1IZuxxyiaPPU1rStyKUiiQJFLoax2En+uvpVOR82oG3H1VogyTZMv13TTGkmhGSaSaM1YuGQJEchoJmlVtzwdBAFZEhEEELCUl5a1xQBriHlpKDYqEpu9yfpvVPldLAvFiSRVfE4Zo8dvwlJ5sjwkTAH8LgnNMAknVF79spmloSiRtEosbQXUJpbKVV90E5IZjQXNEZ7672pLSamHKRVFnLDLBGbWBOhOqKxsj9OdUNmyNjBqxoDDZgDll6f+t5oVoTiLlyzkFwsuzX9vgkhK7SNpazNyCm0qbCj1pY15LJvvFMOR7N7GH1s3FTobm285o/YLaBgGr776Kvvvv/9ovaTN95UBdtYHpI86U28KmdZlEQQBZ+k4zuu+lluMP1DRY46VG8LrkWN9bcYcjimqhRH07vdWiKrwOVjVmSCtGQiYZDQB3TRxymJud9/EkpbVdRNZEnDIEg5JRDdMQpE00ZSGxynRGkmN+o54rqwfN1nSFkM3TEseV9N7zsvEBByidY4xE9yKhM8l0RRO8ubXrWi6jqqbDFVT0Azoiqu8Pr+F3adX8JOeliiwkotJu/vWbz5itCig/NI5fQ5yIM5lod9TjmXUGFHKMEWJQLqVoxfO4rXtH7CDifVhkHa9fqyv+tLGPJbNd4qsZLfS8xvxxNTZqL4aHp46OzeXc+TCWVa1y65u2XzPWO/EYunSpTz00EM8/PDDtLW1oarqaJyXjc2osNa0rnCwpxuwLB1kTv0dnNd4fl7ffLerliemzmZ1xMekZe180RDOzWi4ZInJ5b4BB6p7t/d8sqqLaEpFAkRJxDRNZFHANCGlGgiCNY/gc8qIgo4iizgkq5iYyGiohkFGNxAybJAdca9DJqMZLGrt4mx1Ls8pB9AilpBSBRBA163KgqoDmGQEE48kUqa3c0L0SZ5cdTLNETXXBjUYTllEEAQ6Exme/3QNu0wuy0sc1ns+YhTJOjJnh7VPWnIG6Br+nqSiRSjn6uJbmVrh5ZhvzrSDidFgFDcVxtSxbL472HM5NjaDsk6tUMlkkkcffZRdd92VzTbbjA8++IDLL7+cNWtsl1GbsUUh07reRFJWQGwUDWyO1R5L8+T/VjO/KUzQozCpzEfQYw1Uz31/JUtD0YKvPaWiiN/8qB6wkogsiixR5JJxySK6ae3i6yZEUhqaYZKyInh00wrVFVFEEYX1MnkraOjWQ7XfRVo1ODE5l98Ir3CPdjlVdCKKAkLPiQuAIoIiCRQ5ZQJqK7el/sjuXc+yV9McS8VqGDhlCYcsIAoCXzeFaehKjPi9bCx6OzJHnFX40y34NSupaJMqOM9zPV9EvHyTCPDa9g9YykHZYMJ2XLax+e7Rdy7HdmC3senHiBKLjz/+mNNOO42qqipuv/12DjzwQARBYM6cOZx++ulUVlZuqPO0sVknhnJk7kpkCLodFGutBYfw5Ggj7bE08bTWb0ZjaoWPzniGN79uzQvUe+NWZGqCTopcMg5Fwu+SKXJaFYekZuTt8Gc0nbSmk8jotMfSxFIqggABj0xzOIlDFplSYbUKDXS8QiwNRbnnnWV57rD3vLMslxA1R1I4FZHXfL+igQrqaOV+7Q/UCO2oRq/rBTgkgSqhnQfNqxhHK61COYoWQ0EfslohAJIIqm7ic8nE0zor2uPDfh8bm2y1y63ICGZ+YvrPGdcxbeoMJpX7OHKn8XYwYWPzfcCey7GxGZJhJxZbbbUVhx12GKWlpXzwwQfMmzeP3/72t/361m1sxhLZlqQSr4MloZjlIWEYRFMqS0Ix6oIe9qxVOeabMwsO4Z329bFM0xYxqcxbcEajOuBiSWuUT1Z1snjJQlqeOZ8FazpyVYF4RkOWRCaUehEFgbRmoBkGyYyOblhtUGAF3YJgeUGIWMF3SjWQRQGnLJFQDeJpjRc+b+yXGAxGVhFrsGpLPKPhkEXGT5zGQue2AFTTzqPCFVTTAVhJhWqAP9PG3ZnLGSe00mSWohsmBxj/4jT1UTwOCVEYuB0qm1QokkDALTPWbx3ZatcPVt1PUaY977F9ll5BiR6ivMfQzw4mbGy+B4yisIiNzXeVYScWixYtYtddd+WnP/0pm2+++YY8JxubUWUwxaFTtnZw5urzKO8Zwnt46mwafFvy8NTZJEQvHjPOLdGLmZxZVPC1k6rOguYIj7z2H3xPHUTVgodY+Oj5XPX3r7nnnWW0R9O4FZnJ5V4q/S5EUSCZ0Umqeq7qIAoQcMtIooBmAD0JRlmRg63qApQXOfE6ZMaXeJhU5iPgVvjfyg5u/cdi/r2kbcDqxWCKWL2rLR5FwiVLuBSJlTPPolO2Ko+1QgfPOK7MJRfVdPCU4xrqxRBrjFJMBGqEdhqo5HH2I+hRcCsS0gDJhSSKFLlkKntar4JuhUll3vX+fDcUWeWXceFPAIg4qwZXftE1K5k45IHhBRPhNZZpoW7L09rYfCvIzuUMd2YiO5cj2UpxNt8fhp1YLF++nM0224wzzjiDuro6LrzwQj777DO7YmHzrWBKRRFn7D45rxf29G2cTHj58LVDeNs9wGqthJXtcVZrJby61d0YgoSMzpHzT6Ey+hWmaRJJqrTH0qzuSPDpyk6UWBN/aLuIGqOFZrGKZ+X9WdOV4KMVHbz6VTNBj0JSNdhpYgnjij0IgiXfmv3PJYuMK/YwrcLHpDIvE8s8VAdc7DShhOZwmnBSZVqllRiEkxkWtURpCaeYt6qL61/9hjlvLy1YvRhKESsrX2tCrl0s5qzkua0foE2qAKBO7OBZx5VsJyzm6V5JBQjUCu2sNis4U76aVZkiLjAeZoIjjFORCLhkFGnt8RRRoCboosznJJHWKNPbuUx5nLqAYwN+6utHVvklm3Q+NO2eXNLZJlevHdaO9rjuZo31nj/ZUhqSZCtpeP3S/m1R2V7tj+62ntf73+1kw8bGxsbmW8qwE4va2lr+8Ic/sHTpUh577DFaWlrYZZdd0DSNhx9+mMWLF2/I87SxWW/yemGlTsRH94eulWj+elYd8Aw/3Wlbzv3F1FzicfABB8EJr6MLEhI6R3x5Ch2LP+DD5R38d3kH/17aht7dwL36FdSYLbRIVVxTdjOGvw5NN1E1nTVdSbriGRRJZGV7nGRGw+eQ8CjWELMsgiyJtEbTmAgEPQ68Thm3Q0YSRcLJDMVuhWhKY1lblP+t6KQ1ksTtkKjwu0hrOh+v6iw4RL5WEavwbpnbIZHWrOpJ73axJakA53mup0WwpGDrxA7+z3llXlJRJ7azyqjgaPUyMt5qfis8wSGZl7jXuIpKOY7Q49ItiwKyKOBRREQgmtaoooN7jSv4YeiviP+8ouC5bXL6Kr/0SToLDmvvPKvfjIX55mXw0d1kHtyHplVLrOpSnwHQnJv7QMlGloGSlMHeg52k2NjY2NhsRNZJFepnP/sZjz/+OM3NzcyePZu33nqL6dOns9VWW432+dnYbBh6hvDCrlquq7iFW/8b5/Z/LuHlL5qRexIQURQQx+9I069eQMdKLi5pv5RaoQNFEvBnWpgrXEMdrTSJVVxdejMdUjmCICBLAkvb4qzsiPO/FZ20x9I0dCXpSqi4HSIuh4RTlvE4JNyKiKobdCYyGIZBLKVR7FGIpTUSGZ3FoRgfLGvng6UdNHYnSWsGhpGVbrVadgoNkQ+liJXMrDV0690u1hlPsywd5FjzKlrM4rznyIJBndhOxHRzTuYsmiklntF4K3goTWIV1UYLp2uPM8UVZkKph0llXnYsiXOt9ylqAw5+UJJgjnY55WpTflA9llhX5RfoZ6z3XGpH2pUaHJHVOB4/gOdffIHMg/tA10oy/vE0HfQcRlHtwMlGb7IVkeEMhg+VpNjY2NjY2GwA1st5OxAIMGvWLD755BPmzZvH7rvvPkqnZWOzYVm6ze/4T9kR3Fj5ZwjUDiohWzvzJ8zd7F5iePCR4Jru3zM5tYBHxWsYL7Sy2qzkTOVq2sUywArYO2IZEmlLKUmWBHwOCUNTuUh4lJ3LM/xkShl1xW4kSSSlGiiiQCyl0hROoeoG3fEMDSsWc7Y6lzUdUTrjGXTDwKVIxDM6LZEUkWQGw4B4WsfnlFjSGs1z5R5KEauvfG22Xey03SYzqdyLQwCtzy2iSugiarrwC0nudMymmg5SqoEYHEfD/s/Q7ayl1Ozm9vQVlBvtVAvt3Ja8jF8mXuDc1D1c0XERgVRjnqrKmGN9lF+yxno91Yzdv76EFyZdRbezljK1iV99cRKOyGpCcjVXldzELR/Feez191Ef3LffMftRoCJSUEZ4OEmKjY2NjY3NBmDYiUUymeSll14iGu3fyx2JRFi9ejU333zzqJ6cjc2GwDBM3ljQwRPB0yirnTSkhGxjd5IF4jT+MvMJOh011JgtzE79njpaaRarOEW4kpVqMRnNwDRNWqMpIimVjGbJxrZF0yxqjXKG+jCHqS9x9urzmOGNsu34YsaXeBAEiKY04mmdSFKlLZom0bGa+80rOVF6lQuFJ0ipOknVkqd1KyIpVWdVZ5JISuXrpjBfrAmzoDnCwpZI7n0OpYhV4nWw5xaV/QzqdqgvYYfiBPeaV1EndNBi5FctkqaTNWYp9WKIJ5RrKDfamFzuo6p+Cm0HP0PSWU6l3sKt8d9zR+ISKvVmuh2VTOj+L+5YA6q/Ho570QrEx2Jbz3oqv2SN9bJzGIeuupJ3J56LjjWvoyHx56LfEayayHi5k33nnZxruRr0mH0SGfXBfXns9ffzZISHnaTY2NjY2NhsAIadWNx///3ccccdFBX1dxn2+/3ceeedPPDAA6N6cjY2G4LhDjVnd/+zswp6US1vbpav9HOd6zxizgoymoFmWIPd3fEMmmYiCJDWDNKawZquJLOTe7ParKBSb+bgL09jvNzJT6aUsXmNH1kSEQQTE5NqoZ0n5GsYL4RooJKnxP0RBQHDNElkNNKqQUrV0XWTgEuh2OtAFgWiKY1XvmjOq7YMpoh1wi4TCruGRxs5Y+W5jKOVRrMUHSnv8QoxjGwarDFKGS+EeMC8CneiiTv+uYQ7Po5zr3wUrZRTpocIqq3EHOUIApTqIdrkap6aMQfzo/vGblvPeiq/9DbWyypIHfjNRUg9SYWMzrnhmyjp/Jzjl5yVGw5/asYcqy1qqGP1qojsO+9kxsudTCrzjSxJsbGxsbGx2QAMO7F44oknOO+88wZ8/LzzzuORRx4ZjXOysdmgDHeoOd4zm5CdVZBjjf1M9C5N3U652Y4oQDipEoqkyOgmptCj+mRYcrIOWaCZUo5MX8Zqs6KnLeY0li9bzOKWGBldx6VI1AqdPCZZcxsNZiUncDltYjlep4QsiiQzBrGMhigKuBQRj1NCADKaQX2ph7Rm9Ju1KKiItdvkgklFNoh3xxroViqRBIHankHtg9NX0mhY7V5VYhcyBi2UUWO2cMyiMxkvd1Lmc6HqRp7erC/TRiDdSrerlidmzOHziI/mzU8cc209g7mTj4Ts+tJ8/d3cby66hBapihqjhZMWn5bzTslel96tbAOeZ5+KyPFLzqIu9uXIkxQbGxsbG5tRZtiJxZIlS9h6660HfHyrrbZiyZIlo3JSNjYbkpEMNcNaP4OjF87qZ6JXbbQwR72CLXxRJNEKKi2zOyuhkCUBRRLRexysGynliF7JxaWhC/GrrXgdMtPdEf5iXkWN2coaKjlau4zVmWIiKRXDMKkKOHEqIoIg4JYlFFlEN0w64xncDokpFT5qgvnVliwFZwT60juID9ThdcpU0U6zWMVp0lV8JW7G4eoVecmFW4YOqSIX4NbGvuLk9ONUmm20CyV5L//61KtQfTWkNZ2Is3JMtfUM5U4+EgZLRE9IPMBD3pPy/q33dYkPsCZ7U6gi8uuvTl6nJKUftvKUjY2Njc16MOzEQtM02traBny8ra0NTbN/XGzGPiMdau7rZ9DbRK9NrqbGbGGucBWX7RpgenURfreMSxaRRAFJFMjoJlqv3e/mnuRilVFBLa08Il7Nz7wruCN9Wc8wuCXj2iGVkbWnTqgG0ZSG3yXjlEUyujUYbhgmFX4X24wLUuJ19qu2jIjs0HKgDhBQYo2o/nre3OFBpk+bznbjg5TVTuTuiXeR8tQAENDaaSraKhfgnrL0TErMLtrEcow+LVR7L7kCJda0NmkbI209w3EnHwl9E9GU5OXpidfTJFZRpbdwUeSGvL/vfV386dYhA/XBKiIjTVL6YStP2djY2NisB8NOLLbYYgv++c9/Dvj4m2++yRZbbDEqJ2VjsyEZ0VDzMP0M3LEGdnv/BKa6ongdcq5KoekGhmFimpBNLWQB2oRSjspcRoNZyThauSV6EdVGC6vNCo5IX0ajUYIsSYiigAk4JMux2wQ8ikSl38XOk8vYeXIZO9QXU+J1Av2rLSNij6th2+MAAcINeVKrv91rOlcdOJPZR23Ptb/ZB9dp/0D1Wa0245ILeK/+3LyXEgydCrONJqGK+6bcM6Bb9aZu6xnKnbw7liT24sUYXQ3De8HwGsSXz+GohWdQrjWTELy49Dh7Nc3mYd8puRkLHYmXNrsp77r8yLmC6hcOHTJQH6wi0i95Gym28pSNjY2NzXow7MTixBNP5JprruHll1/u99jf//53/vSnP3HiiSeO6snZ2GwohjXUPEI/Azmyij+0/Y5ys50SrwNJFNAMMFibVEgCSKKAKECbVMpFen5A9jt9Fq2UopuQUjUkQcApiyRVHVEUEIC6Eje1xZZHhN+t5AbQC1VbRoQkg7Mol1QMJbXadtiLtCs1BNNN7Lk0f+e8jE5aKOMCz7U0+rYa0K16fdp6RmMmYqhB/mOjD7BN4xMYc4e5g//Q3jDvUZToalR/Pa9sdTftSg3F6UYujlyXU4WS0Nllxe08Wv3H3HU55IuTEYYRqNcG3WzjC3Pq/GP6teYVSt5y5zaclqVhKE89/tp76Hf/cPgtana7lI2Njc33hmFvaZ166qm89957HHDAAUyfPp3NNtsMgG+++YbFixdz+OGHc+qpp26wE7WxGW2mVBQxaXcraI1nNLwOmdqge+38QSE/A2BciSf/hbLB2MP7Udy1khNcr3Jd+licskC6QCwligKabjJO7ORG8e68x26W5nAcl7NaK0HTTTANqgIuSn1OKv1OUqrOETuO59+L21kSsgJit0MimdFpDqcKSsiOiB7JVHae1S9YNAwz71pV107mb5tfx35fnolHjxNxVmGaRu7vhZ6kqD2WJqZY1Z0jF85a61Z9/CvEM36rrSdotfX8+quTc8/PtfW0x/u19SwNRXljfivL2mKkNB2XLDG53MdeMysLD6UPwNpB/vxETDA1frLyLhZU7Mnkrvco73XORlFt/zXT9Ak8ehBkYtYLBOpQTnyVQ4pqCU0rw3zuICRTwxAk7ii+jF933UNtpolDVl7Jo8VncH7XNYimBqIMhzwwaKAuRhs5+oujkcw4OhLP1V9JV09r3tELZ61N3n70qvU6vRNksBSsBiPbovbgvrkWta4Zc9CCtcixRg795BgkM44pyAhDnOuIj21jY2Nj861GMPs2mQ/BM888wxNPPMHSpUsxTZNp06Zx1FFHcfjhh2+ocxw1IpEIgUCAcDiM3+/f1KfzrcAwDEKhEBUVFYjievkpfvvQNaslpUCQXZDwGvhwDm+NO5Mb31zKyo44aW3t1ysb60uiQJ3YwSPi1TlJ2Vs8F/C7xK3UYc1Y/Ma4nJizCocssvOkUmqCbmJpje6Eyvl7TCOt6bnAOq1Z7U9TKnzsucXIAuvhUiiQ37ooxmFfn44n3kCHXIEkCPjVNpaX7Eow8g1lWgtJ3zgaD3oOZ8l4KwCPNuZVgZoOeo5bPoozXu7k+CVnEUw15o7Z7arl4amzWa2VcP4e03IJXXYmojOeoTrgwuOQSWS0XGI1kIxuIRo6E9z2j8UEPQpFLiX377uuuI3tm56k01HDo9WXcVbXDcg97XBPTp/DF1Ff7jrs7FjBoV+ejGD2JD+BOjjxjX5BvSnICKZmtXeNu5yjG6+hNNNEQvTiMeK5xwetAhR4vXalhttqbyXhqmKSs5uTlp6NJ96AWTzBCvyfP3nEA/CGYfLY6++z77yTKdea6XZZid/eS64gmGrMOdGbxRMQBkq2+nzWhY79vb6/2IwIe63YjAR7vYwuI4mfR5xYfJuxE4uRY385R45hmFzz8te8/f/snXd8W+XZ/r9naFrytuWZnUAg7PZXRl+gLTO0pS3QsgmFlkDCCGUFyGYXCDOMBkLZFCh0sEr7vpRCgdJSdiCLxNuyZVtbOuv5/XEs2fKKnQEEdH0+/dDY0tGRzqPj+3ru+7quTzvoiKZIaFbWKUqRoVbus5RtEJX8QlqEq2w8FVYHi7svo552WuQqlpRez3qtmG9NLKOsVw+yW20Rsw+ajCxLgzoIOd2WbYihCnk11pzdHU8U1POZbx92bX+WoKOWRyfdRGmRjxM+mWN3JwYWlf2L42+dw4PiqGEL2A61mhf2WcnJhx+Qfc93vbKeD1vCTK305YwvCSEGfUajuVZDHc+XbuO4D2ZTnG4m7K7Ff+IDGE/ZqdntSjW/mXwbrtJxlHS/z+mfzraLbElF2uVoOGxp33t98XJ4805EyQSeGreYgz+an32ff59wPkesXYrLjNGhVvP3GddyzKbF9jjUvnMG7+4P1DUcsxLxtD0+lSio577Jd7BBK8KbamNe84WU6y2jIytDIEO4hiN8T41fzI82LKJcbxmSbO3hj3HiJ+cMff1zPv/8/SWP0SG/VvIYC/LrZdtiLPXzqD9ty7K4/vrrOeCAA/jmN7/JZZddRjK5BXaGeeSxPfAlssls7kkSThp8Z6cKDt2lihKvA4cqUehWmeaJZElFMwFmiUUE5UoSmkmzKGdJ6fW0SHbOweKuSwmITjTDHDIpe1QWsluJocTNRXp7jrj6qRl3s9MZvyG65y8J//RpvveNXTjxsP2z2pP+QmAgJ61a+tbsER23htJkjCXccHMYTsjfKsq4oeomQs4ailLNGE+dydWuX9FMgIDZygVrTqNw9SNZUmGi8PQeK7GOuS+3gO4VxMdr9uetdP2g0DyXGctqSf6Znkjrj57KSfHOwcDRvLpvIvW6annjjRz/8WzGO7opqZ7Es5OWYKIgCQPT6R+zq9bmnKe6S3e3uyQF9fmgvjzyyCOPPLIYNbG4+uqrufzyy/H5fNTW1nLrrbcyZ86c7XlueeQxemxjm8ytEQZnirICl4PxZQV8e2o5xR4nZVYHdxmL7PA7AiwqvR5/5QSmVPr4fxNL2W9SGbvsvAuPTL+TdqWaaquNX8evwJdqHzEpe3tiqEJ+75bHBomrW2MG/h/9msmTd6LC78oKvPsLgXljRd+Bi+rsEbOHfrhZx62sJiPcNOZww9FgOCF/3YSpRH/2LHrheJyRTfyy4xp+7b+YuOTFR4KzwrdlScWqne7mn+mJgwlNrA02vorvo0c5su2uzVrERlyBnBTvHBy61CYd/Qr14Vy1jt20GAWThFzAI3s+MmZXrdE4TyXcVdw3+fZ8UF8eeeSRRx5ZjFq8/eCDD7JixQrOOussAP76179y1FFHsXLlynybKY8vHvudA58+17c7Ptq57yHcd/rrCZK6gWVBdZGHQ3apZP/J5ZvtDPQP4PO7HUws91HodvD/1jxEfbKdZinABc6r+MaUnTlhYin/+0mwd8xIwRSCpKeaW+qW86uWC6nRW7jQ/1cKDjpsu3QkNoehxM3/mHAuAO/UnIDuqBxSXJ1Fhly8sSJ3F36g49as5zjFX8t3B16v/Z/ve9wDR1H4o6dwqwrxXlW8Zlo4FRm/W0WSpC222x1OyA/w0M4rOOI/Z1JrtnJZ4kaeLfgpJ8UeyD73vqI5dBfPoLstykdNIYpfXUTBQefZ16v33I3C8bxe/jMKhyjUf/zxBdw9fRUutXLk81bUQeNRGVet7ukrsiNLGQF8VqMSLeI7PcnBpgMjIJPFMfOdcygeYkTtpNXn8MSud7M+Xcwjw7z2I1PvoCHiG/Nr55FHHnnkseNi1IygoaGBmTNnZv99yCGHIEkSLS0t2+XE8shjTBiFTeZokpz7h6WBoDuus64jxgsftbLg2Q+56rmPNxuYNlQAX5nPxfo9L+GNip9xfeBG9tljBhcfthPfmx7I7pZ3xzU+bomwKRTHHxhP5GfPwr5z8P/g2i+EVEAuSRJCEEnqdMRN/lw1l6gzMLpCvqhu8C78UI5bw9ja9r+u1R/fT7HXwevrOvnn+k7e+izEGxtC/HtjN6FYaqvsdod6/UzhvnLybdmAu/6kAuDn4Ttp//h11gXj8PIi/O/eS+zu76H/5rDse5RPf47xpd6c9PY/7HwDpqTgMmP84uNZuRaxoxztyxA/j0OlzbdLzu+2Jixvc6GQFUYrP183F2+qbfsE9eXx5caXaPQ0jzzy+HJhTMnbbrc752cOhwNd17f5SeWRxxZhK5Oc++sJbLF0nM5YGr/bQX2JByEEr63t5P7XRk5jzsztl3idvNfUw6ZQnO64RiQteLDoLPyBifz0G/Woqv31m1Lp59BdK6nwu5ElCVMIOiJpXmhQWLf3/KHHYrYxhhv9ypCkte0x3t7YxRsbQry1wS7m397Yxdr22JYV8kOM9QyLfpqM9XtdQjCaJqmbpHQLr1PFqUo09yT4+5pOFFnaOrvdAcgU7s7ScTxW9Iuc3z3im5UNvLshein7uz9j45TT6FErKUy328nlvlqsU/9MW0+cE/97fJ8uZfxiDvrsVhRhYiLjteKc8PHZWS0Jz80b1WhfgVOlSoT42QdnsnPnyzm/2+KwvFGGQnrjjcxrvpDirve2fVBfHl9u5BPa88gjj2Ew6ju+EIJZs2bhcrmyP0ulUsyePZuCgoLsz37/+99v2zPMI48xIDNznnEZmrV2bt8IR29R98L0FZzsrx3EqjN6gqpCF5+0xUhqBqUFzqy2oLjASVIzae5J8JeP2plU7huxgHWrMh1RjbXBGBJQ5HGw76Qyjv9/9bhUhU/aIhQ4VZK6wW//uYlQzCY0GfejD5rDtIST20xbMZyL1OYyIXau9vPMu81EUzplBU6KvA6SmsmGzjh+t4OdqvxjL+SHGOsZEUV1WIddzUuvrMe0BAdNq2B9R5zuhIZhWXgcChYQ8LuYVO4b27mMgEzHpqT7fU7vzj3f7yVf5BLpV1xv3YhDMrmm+2L+HLgWj4hlH5NIpXnplbf5wQdzcVh27sSDhWdxyvqFFButdKkBnLKFT+vAEW2wC7BjVsL6/7UPsJnRvlopxAWNcynU2wGIuKp4ftpVOSNLL+yzcvTEbwwjauKBoyjv3pgVsA81LjWm185jx8E2HD3NI488vloYtd3s6aefPqoDrlq1aqtOaHsibzc7duxolm2bs8kcKhchg0/aItz2t7WUF7j418Yu3A4Fl9r3ni0h6Elo7FpThCxJQx4Dcu1ZqwpdmBZEUjrdCQ2fSyVQ6KYnoZMyTFyKTGdMI6EbOGWZ7qTtSqTKMiUeB2pvjsVo7VOHw3DkYedqfz+Nx+BMiNP2m8DLH7fz5mchDMOiO6ljWhaKLFPidaAquee3PdfLwMwJIQTRlJHVWYAgnDSGvS5bAssSPP2HZ/nJe2dkhdq3lFzOCeHfUGO10SAqud35c67Tf42C2fc8FOTef5vIKFjZ7IfMf5OeamRZwhVvwfDXosgSUrjJDsmzjN5OjgThxuEtXT/+JY6YvcZDaiUPTb8H3VeDI9aStQPWC8fbDl2jcWbqtccdlT1t09uI+45AEgYJuYB7d314zK+9o91f8uiHfiR0ayyHR4v8WsljLMivl22LsdTPo+5YfNGE4dprr+X3v/89n3zyCR6Ph/3335/rr78+mwCeRx7QzyZzjEnO0Lc7HU7qpDQTu1YVOFUZSZLQTbugLnQ7CMXTQx5joD1rpttRUuDE51L5+5oOPI4YB0wpp8blIRhJ8Wl7FM0wKXQ7KPU5cSgqumnREUujyBLvNHTTvBUC2ME5FB4SmsEHzT385eM2Cj0O9qovzp6r3+3A51JZG4zx1DuNdETSTK304XOpOYW8360SSxtZe9ftLdAdKCSXJIlCT1+onWFZBKNDX5cthdzybzv8rp/7k1myO/d37c6Z685lnNXGZdKD/G3ypRy2vq+j8fKkS9l740oqrCAKFgYyt7vP5tzUXaiYGMgk0hplVogORzV3BW5hekHcfi3LAFehHbIHwyZgf/8/Z+Ewg/ZjfLX8eZd7aYj6SHfGcalDJ51vtrAbIXk9B+EmePrMrJ3tU3s+QkO0aOteO48dC5tJaJ/5zjk4MuQyf/3zyONrgx2Gxv39739nzpw5vPnmm7z88svous5hhx1GPB7/ok8tjy8RRmOTOdzcd22xh2Kvg/ebegjF0zR1JWnsTtLSkyKhGcRS9miUIjPsMYbLWRBCsL4jjixJZH6syBKqIqEbJropsITAqcjIkoRLVSgtcGKYFo1dCaKpLdMyDZVDocgSfreDqkI3HdE0ibRBJKXTGUsTSeoIIbKZEOuDMbqTGl6nmi3ky30uCj0OJEnaInvXLUV/IflQ2FJHqGERboKHfoIkDISk8vs97uNjeRqbQnG6lEqe2u0egmo1ZVoL39twfc5TD9p4K4tdF9FGOQAqFvNSd2RJhYpFmRUipFby+93updCjcvBH8/tyJ2b/wx7/GsZO9vQ1synrJRVRVxXKGS9xyhEHMO/QaZz7vanMO3QaJx9+wPBZIsOJbzMjagOLwP7i2wHjUsqcNzn5iANH/9p5fGUw3PrMWw7nkcfXFzuMqu7FF1/M+fcDDzxAZWUl//nPfzjwwAO/oLPK48uG0dhkDjf3vaEzRjCaRjctZElGSAJZEkRTOpGUTqDQzaRyL22RNLvVFg15jKHsWYUQtPQkaQsn8ThlDFOgmRZg26WaApyqTFK30AwLl0MB7B15l0MhmjKIpbescB8pUE63BBKwMRSnJ6kjSaDKMqVeJ5MrCyj0OLAEKJKctc4diG1ezI+AjJD8w5YwPpc6iLi1hlPDXpcxI1M8pyPgKkQ65fccU/MN9u03Q15d6OYZrrXHpITd0Wgs2ofq2Ie4zTjzkzex0HkxV+vXUyG6+g5NIWX0AOA2IhSlW/jZ+qV9GqC9VnJy0XhkRraTBehxBrit/nZOF2XU97pa5SAjfM8QgTdW2MQhI7799LnNdxP6E4kMBjp6wehfO4+vDDZnd5y3HM4jj68fdpiOxUCEw2EASktLv+AzyePLhNHYZPZPcs4gs7NvWoKDd6pkQrkXGYmEbgECEFiWRUNXkhKvY1jnoYG76l1xjbc3dvPvTd0Eo2nawil6knr2905VRlUkhGV3LMx+kichBGndwutU8Lm3rHAfKVAukTaJaQYJzcQhS5R4nbgdMsFoincbe2jtSVLscQyyzu1/fltj7zpWDJeSHU3pQyaTbxX62+Ge8wbUfXOQHa0ab+FHny3K0U1MCP+LpOynU6mkjnaWaL+2l08/lNFDJ8UYyBSQ4viPzhoUOJgJ2hspAfuTskN5fMZvaJfKRu4YZQr8b50NwrKJwn7nDOomDHIG626EP5w7WHy7hY5eQ6aJ57FDY3MJ7XnL4Tzy+Pphh+lY9IdlWVxwwQUccMABzJgxY9jHpdNp0ul09t+RSCT7fMuytvt5fhVgWRZCiB3j8wo3w4M/RIk0ki6cyPM73U5DzIfWGcOplvDC3r/hZ5+ciyOyCeuBH8Cpf4Qiu0Xf1J1gfUeU6kIXfreDg6aW01Se4OPWCB3RNJph0hZOosgwpaIAIYZeQ9WFLiaXF/BRaxjNMHm/KUxSM/A6ZdyqhGFaCAnWtUfxuRRcskyxR6UnrmGYFqZlIYSFblrEUgYOVaa+xIPPqWzRNfA6ZNyqTCKt53QchBC0hRPIAhwyuFUJRQJFlXEVOAjF0nzUHOaHe9RyyK4VPPhGA+uCUaoK3XicCknNpC1iC7wP3aUCm3iJ7b5eJpUXMGv/cfzlwyDrO2MEIyZOVWG3mkIO3bWSSeUF2+a1v7fYJgT7ng3+Ghh4zAFr7Y8TF1G38Skmx9+hXG+jU66knXIC2J2KDkooI5x9eiExZMDqt7fz4tQl6AXVaKE4sbSG1R2i+s3b8Co/xRHdyNGrL8x5fCD+CSndwKXKeB0ylq7BXxfb5wzw5l1wyGJ7vMlfA8jw1l2w5iV77f/kN/DwcdDdgH7f93l8p9t5L+YjbZgERIjzGs+nMG27TVEyyX6Ov8b+92FX2f8dzWftrxn28TvU/SWPQcjcX9RYE4evXZKzPg9fu4TfTr0Vl1pqr8+tvMb5tZLHWJBfL9sWY/kcR+0K9WXC2WefzQsvvMBrr71GXd3wO2aLFy9myZIlg36+Zs0a/P6tt+/8OsCyLMLhMEVFRV9uZ4V4J/xtKcTaMb2VBL91KWpBOQJ73MilKraVazKUfRy+AHxvIRSU09Sd4E/vtVBVaGdJAETTBhs6YiQ1E4cqoRsWkyp86KbA71Y5ZHqA6iF26lt7krz8cTvrOmLohkWhRyWlW3THNSwElT4Xmiko9jqZXFHAumCMlp4kzl4XKksIFEnC51JRZImdqwo5YkbVFu3EW5bghQ/b2BSKU1PsRsI+RkIz+aQtQkIzsAR4HAoep4IiS5iWIJYykCSJk/Ydx641RbT2JHmnoYe2cBLdtHAoMtVFHvYaV5zzGXxe68WyBKG4Rtow+67t5xUi2G+tZdaQ5SkjFNdob2ti3H9votAIZd2ggJz/n4EA+p9xzFnG32pm02n6+PFObkrfvA5i7TQU70sg8i4uK4Ulybxdexq7Bv+ITwsRVkr5z5RzOXiv6cj/fcgebSqwiR7xTtjpKNjn1MHnXVBuv3q8A1P1oBhJepRS/l53Ni5V5tubbsdn9ABgeMpQD1va+5xtix3m/jIWWCb89xHYeeboPrN4J3zyPOx1EsjK9j+/bQjLEvzfO6v5xvrbKTK7iDnLeKfmRPZueXTw+tzK7+dXcq3ksd2QXy/bFtFolGnTpo3KFWqHIxZz587lD3/4A6+++ioTJ04c8bFDdSzq6+vp7u7O282OEpZl0dHRQUVFxZf7y/nSlfDWXYTdtdxedxPtUhkuVWFyuY/DZlQyuaIfkezdbaZ7oz0ecvhVNHUnuOWvayn29FmZ/ntTNx1Re1deMy1SusW+E8vwu1XWdcSYUVPELw+cNOQfzNfXdXLdC6uJpgwSmoFhCiQJLAGqIlHidaDIMrvXFdMeSdEWTlHpd1LUq2uQJTAtQZnfzaz9x+ee/xixviPKA//cRHdcy3YcWnqSvLEhRLnPyeQKHx1RLZsJocoyRR4HLlXmspk7s1PA/q5YlqAl3KcxqCnyDHrvO8x62Rr0rjVKJuR0vSxLcM/fNxDb8CYXNs9DwcRAAiGhShZx4cKFhir13XINZH4/YSmHtKygVGuhU63m1RlX8+NNS5G6N0JRHbopcMSas+NWYVctT09YyA/XL6bcaCVRUE/4uKcIFLqRf/t9CDfaBy+qwzrtOVpEad81k7qQf9snpI64qljivJALozdRSzudSiVOyaLQ6ASgS63k+b1XcuJh+28X4vaVXC/DrI8hMcS9aIdCuBl91Q9wRDbRqVbz6PTb0QtqcMRbOHH1uZRnXKFO/9PIn8Mo8JVcK3lsN+TXy7ZFJBKhpKRk29rNftEQQnDuuefyzDPP8Morr2yWVAC4XK6cQL8MZFnOL7QxQJKkL/1ntm6vi2lb38nzBT/GXVTHxN48hg9bI7REUrkhcyX1MOtP9hz9oUtBlqkrKWByhd8WBrsdRNMmXQmdArcTgUQ0ZVJZ6O79nYFTVXivKcy/G7op9jpzAucAKgrdlBa40c0UAgm/S8XrUoilTYKRFB0xHYcs0xXX2HdSOX63yl8+auf95mi2IzChvIDjdg4wNVC0VZ/N1EARpx8wMZtj0R5NY5iCsgIX0wJ+6ksLGF82dCaEz+XMXndZhnFlmw+f2xHWy1bhsKV2q2GAJWtzT4JQ62fM7bgGBzoGMk5MkCCBi6jkxU8yp1PhxOLgTcu5wnkRC7mZWqOZn7x3BrIwe48tcMXsHItnJy7hOx9fTnm6kR+uX8idJZcxp/s6KuObSD1yNM/scg0/Mg0cvZ0R3TR59M0G3ot25WYLmGb2MYZh0KoWs7jkWq4NX0yl2ZZ9PxFXFQ9Pu4uGqJ/vRNLbTXz7lVsv+58Da56D7g3w4PdHDo978Pt9+pX9z7G/ZDsKwk3w4PdxRewcixd2XmHbHYcSuNQyXtznXk5YfQ6uyGfZz2FrLWe/cmslj+2K/HrZdhjLZ7jDEIs5c+bw6KOP8oc//AG/309bm/0HsKioCI8nn+z6dYZlCV76OMSHxWflZEf0z2MYlJRdVJfjUJMRBreEk6wNxnCpMrpp4VQluuImHqdCuc/JfzZ105XQiKd1ehK2aLiuxEO5z5WTVu1xKL2jOlaOI1ORR8bnlGnsTuJQFI7eq5ZpAR8PvdFAgUtlv0ll2eTtSMrgfz8JMr7Mu9XJ25PKffxgD5kNnbY98/gyL8+938pHLZGsvWwmE0IIwdpgbEiHpeHSu79WGCY1PK4ZHND5BKVaC21SBQ7JtpQF8JLGSxpdKDikPstZgCrRwdHW37it5FKu7r4YVZgIFCTLhGhrNgH7GH8tbXvPIPG7H1MZb2Ru93X8YfJSjt24mPJ0czbET/fZO8OOWPOw2QI9jgCmJSg3g9ycuJI7XRcPGtV6bupVI+a+5DEMBrhh6ffN3O7hcZ87xpDQnn1cPs8kjzy+FthhiMVdd90FwMEHH5zz81WrVjFr1qzP/4Ty+NJgJEvVTB7DaELcplT6Of2ACbz0YTvvN/WQ0u3k5EChm3Kfk8864yQ1E1WRiKdNLCFI6yahuEa5z8kHzT2sCUY5ardqijwqQlhIAyyBkppJVzxNNG2gSCZ/freFpG4XdHuNK845/6reAn8QKRojhkvdnl5dSGs4xdqg/dllRNmZ1O2BDkvDHSdDpnZkbAvCVOBUeaHqbLwiyaTIvyizQnTJZZT2kgsAh2TS6ahhieMCFqZupMKysyi+zTscEH8PtZ/DFNFWKKrPsXStqp/CQ7vezcx3zqTCaOXYTYt5dcL5HLVmPoowSUgFPLnLPQjgqHd+kc0WyFouG60E1Wou8l7DXvVFnLZmLjVaC1d3/WrQ+zl8zUIe3OlOXGrp52In/JXCVz08rr9rWt5yOI888uiHHeavxQ4mBcnjc8RQ2RH94XEqtEdSo9p1nVLpZ9LBPhq7E6x6/TM+64yzW00R7zT0kNRMSgucNHcnSRsWJV4n1UVuuhM6GzrjuFWZhq4kn7RGqC3xkNQtVEWiK67hc6sYpm3PmtLtorzApYAksb4jht+t0p3QKS1wZs9lLKRoOAyXuv1hS5iWcJLv7lzJJ61Re0QqksKlKuxWW8Rhu+aShc0dJ2fUbAfDtiJMtcUeJlYWE2lyEhAdhOQyTHLFuAYKj9Reybvt1dw75U7mbppLUbqdQt3WNPS4a3lq/GJ+tH4h5UYrTPqOXZyZBry8kLbppw/KDfjhp5cAkFIK+M303/Jxp4cSM8juRd/AEXtnULbAitrlrGtWmaAGeGnaUk74sC8Xo0su46biy5nT/WtqtJYRc1/yGBmZ8LgMCRxI8DrUal6YvoKT/bU7nu/7aBPaoY9cZEZP88gjj680drj7WR55DMS2TmSWZYnxZQWcvO946kq8fNASpj2asjUSKTul2u1QKC1wIct2DkVjR4QfBVdQQ4hY2sASAs2wMC3wudTeTkASzTAp9jrYxRdhnvVbChwiG5q3LhglnNByErC3Jtl6pNTtqZU+uuIan7ZFOevASTmpybMPmpxTUI/mOH/5qB3L2vHIf4YwfdgSptjrYFK5j2Kvgw9bwqx6fSPrgtFRHyszTlfgstdZkdVNpRWkTa7iAs81xCUvKibHNy5jvNqFs3Qcr0zM7RS8OHUJ3aW7s7xuOV27nwnfX27/ojfQrvzpn+BJtg6ZG/DsLrei+etxJ1uY13Qhe4f+RJt/l0HHt/y1SNiZLzPXXpHze0PItIpSLvReRXCE3JctwnBp38Ohf9r3DohMeNwj01fQ467NErzhMkt2KAyX0D4cMqOnyg6zl5lHHnlsIfLEIo8dHplE5m0d4pYZjZpYXkBSN4mnTZK6iUOVs6NDQgi64xpzzIc43vwTN6euQAo3825DD5ZlEUkZeBwKM2oKKXQ7qC/xsrs/xg3RK/hJ+g/8tPs3OBQFJHvs6dW1nbz1WYg3NoT498ZuWnuSW5xsPdoRsdZIKif4beAI0FhGzbYXBoW3bQMSsz0I05RKP+MPOhUTBRULA4VlrgtpLd6bZfX30a5UEzBbuTlxBT9Y/Su+/8n8nOcfsXYRjlgLSU818YOX9RViM34MrkKckQbmNV9Icdd7HLFmUe5z1yyipOt9ruy4hIBp6yhqoh8MOn6J0c5kVw9zN9rdEoCIo5wOuZJK0cHS7stwqTIv7L3SHtWJbMqG6I0Z/clEJu17pGNlyERmJv/NO+3n7YDIh8flkUceX0fkiUUenw+2427l9kxktsnFRHarKWJGTSE7V/kpdKmYQiCEIJLU6Ypr/EY/kgZRyTgpyKPOZZRbHUTTJknd4JP2KMGohixJlFlBruy8mBrRRpezhg/qT7bTriNpkpqBQ+lLwG6PJHl7YzfFXscWjaKMlLoNjLobsq2Os6VYF4xy1yvrWf7yGm7721qWv7yGu15ZP6ZuwlDYLoQp3ET9/52LgoklKaiYLNSWo0SbeL3Dza96OwGVZhs7h1/Lpnb/YecbsrvaJ60+hz0LY33XPNwET58J6QjCVUi53sKsT2dTnG6mRa7iPO91tMhVFKebmfXpbCqMViKuAKYQFKbb6HHX8vhuK7PHP/WT2dwev4Ry09Z39DgDPLrb/Ty8y90E1WrqaOducxGn7DcexxnPD0roHhP6k4kZPx4y7bsjmubT9ggtm9YiMmTi7v/JTfveAZHppKqxZo5YO4AE9hLILd00yCOPPPL4siJPLPL4fDCa3coMMo4jY9itzHQXZtQU0ZPQ2dgZpyehs1tt0RbP/2d2yeNpg0K3ysetETZ0xomkDBq7ErT0JAmF41wqP4QMnC4WZsnFA9JSKqwO0rpFLG3wQXMPencj14UvoVa000wlC0uup8EoAdEbliZJueSnt9jdHB0abjd/4IhYhghlRq0SaWNUhc2WjJptqw7DthxVGohtTpgGOOXIZ7yEVjiOgNnK8uQCDq832GPXGfxt3AU5kv6EWkSrd2dWTbk9O3503Eez7eMNOKb047uzhMVA4YaCi2j2784NBRdhYP/cQsarSpQZQTrUah6YegeNvt14YOodhNRKyowgRb05FVFXFbeNu4MPoj4azTJe3MfuUnhijcgPft8+wVnP9RGCN1aM7UPe75y+5z59JhyzEtH778S9R3DXH//OH/7bxMMv/RPnwz9E6t6IkFRIR3ZMt6R+qC32sIc/xkmrz8mOP/UneIMIZB555JHHVwA7XEDe1iASiVBUVDSqgI88bFiWRTAYpLKycuu8oAcUSCN6uw943FgKi21lh9pf0NsZS7O+I0YsZVDgUvC7VTqiGgnN4GIe5Az1BRpEJaeaC5Flid/KS6mnnQZRyUnaAoJKOVMc3dxrLaBWsh2C/uI6nBuc56DIEmnDRDMEiizhdSqYQqDKMqUFTqoKXYDEvEOnDSneHkl4PKncx12vrOfDljBlBU7Wd8TtEDzTQpUlLOB/ppRzxVG7jPgZWZbIHqe/nS/kWtPOPmgyIHhvzSb+2WywvjO+VWLosbzullzjxq4Ey19eQ7HXDkUciGjKthQe7rMfhBcvt8lwv/X90IuvZ8W7Pe5aXh3f5+DUP88iqFTxjrI7L7kO47LETQTMVqKuKtwOBUes2T7mMSsRT5+J1L0xm+TdKge4zj2PS1O3UGO15djY6r5aHt3lXt6L+kgbJgER4oLGuRT2jj/h9GGd/AzNvhmb/w5Cn/h2rHPy/b77mTyOgz6aT6XRShPVPFx9BWeGrqNca8o6YumFvd2SHZRUABBuyrpCdajVPDJ9BbqvBkfMFsVXZFyhdvT3+Tlhm/0tyuNrgfx62bYYS/2cJxZ5jIht+uUcUGB8Wb3d+zsgVRW6Wd0aoS2cxBSgGRZel4JpCcIJDV/KHn0aLwdpFAHOVpeQ0AxWyUsZLwXZZFUyTz+H2523Z0lFsyjnwoJribqq6IilSaQNJpT72Ku+GIciZ0Pq/G575GpjZ5xzvzeVnasKhz1P26nJDgXM2MWefsAEAG7561rea+xBliSKvQ4EgkhCxxSwR30xFxwydbMF/8DXGmhNm+kKrW0P8+w/V/NZXKWqyDPkOY2WXGzzwn8Atjlx6XVuyjjlZM5/nNqVdXDKPlRS+N3Ea5gQepXd9PfwJewuXo8jwJ93upbvr5lPsWYTAN1Xi+P439o7/t0b6VIDKJjZrkMGYWcASVgU6h32D4rqsGa9SLMoI64Z1L65DP+79/Z+pyQ7oXu471n/jYB952y9ReiAIvtG38WcH76BKhGktXBPqiPvIpBQMG23pH1WcvLhB+y4GSnD3OvSht3d27Mwxgmrv/h73Y6EfKGYx1iQXy/bFmOpn/Ofdh6fHzLe7r2C0JnvnMk4tYtJ5T7GqV3MfOdMHJFN6IXjP5c/tEON62QEvaGYRsDvoiuh0RFLU9xrLetzq5R4nXx7SjmH7FKFKKrhVGshjQSol9q5y1hEUhccn17AJquS8XKQ37sWZ0lFiyjnp9oi/tNTwGedceJpA8MUFHtUynwuCj0Oynv/K0nSsI5WoxUeTygtoNLvwuNQcDtkEpqBZghqSrwcNK0c0xKjEiiPZtTMsgR/+TBINGUwpWLrxdDbW9uxzbU5A5xyRhLvPjftWpoCB/NQxYXcO+k2QmolAMV6O0evuSQnrO59195E/v07u0j11SIQFBmdRJ3lOcds9e9Ood5BSK1E89VAuAn5rbuywnz/D661ScLPX4KfvzjyeFPGInTfOdvEIjRjvdrRO+p1fuQGfus7EwMl+14VTHpcO7hbEgwOjzvjeU454oAc57WTDz9g6/UreeSRRx5fQuRVY3l8rviyeLuvC0Z58cM2PmgOk9AMvE6V3WqL2KO+mP82dtMd19gYshOHe+IaKY+DsgInfrdKorfYL/e5qC/18lGygnPUJdxlLKJeaudx5zKO1xYwTz+H37sWZ1+zTZTwM30xbZTikCW8DoUKv4vGniTrO+JMLPdR5nNlH59xtBoqAXu0wuN3GrvpSegcMMUuQvt3QyRJwqkqo87JyGR8DDdq1tyTZH1njHEFTsRWBBVm0F/bMVTHYjhtx1hG4fqHIm4uy2OsGEm8e+CmW9ngnIppFfJpsoiHpt/Dzz+dTaHWjl+zOxEhqZQwPq6OziQQnszcQJDJ0bcoM4L0OAKDkrKro+/R7p7EvbVXc8aBU6lZvSqXFAxMDN9ctsCAdPqtQcZ6tXv6Ck75dA41Wgvzo1dhDfiWPz1+4Y6f9p0Pj8sjjzy+xsgTizw+V/QvMDLjIf3Dux6ZegcNER/f2cJAuNFgXTDKLX9dy5q2KKZloVsCYQnWtEX5v087aOlO4HUq+D0OnIqTWMogljLQTYtKvwvDstBMC0mSqC320NSdpMMsZ5ZYwH0sZbwc5CnnEjs9uR8MIWNYAhOQLUE0bSDLEsVuFYHEW5+F2KOuGKdsckTrXbzg+wmlxfVD7pr3DwX0pdvYu+Ux/jHhXFv4Sl8oYCiukTJMalwelCEK7LGEB26uaI9rRu+oh0xqiOeP5bWgz0b4w5YwPpc6aFRpIOna0qC7zRGmLUVGvDvznXMo7tVYZEl0r3j34Z1XsCZVhOGrpaVwdwo7X84+3yPilNHFHfoSHrcWUhl6C7dh28gKy8Rv2gTkk/JDqY6+T1G6nSKCHBN/gqr6B2D8ZorUbUgcNods96a4lmcnLOKMNWcN+bgfbVzCQ64dPO07Hx6XRx55fI2RH4XK43PFF+3tblmCR99q4L3GHpK6QUIziacNImmDYCTF+409dMbSJHWTtG7hUiUKnCqyLKEbFp1xDVWScCoyQgiSusXhu1bx7akVtIkKTkwvoNkqp1bupEruBuxORZNVRp0c4nHnMqoJYQkwLEEkpWNYML7US09C5/V1nez28c0cGHqSC5ou5Mhx5pBFcf/d8OM+nM0+LY/yPxtvz/4+s5tfVuDcJuGBo7F8LXCquFSFtGENeYwtCSoc7ajS1rpHybI0YpbHlkCONnPiJ7ZId6A7U2YkaPbG86gmhBprHpQ54SWNgUyNaOP8TedSYbQSUiqRhElJL6nocQR4u+p4+ivlpsbe3jaBdtsQ/dfrMZtyC2gTmesK59MiV1HWm/a9Q7sl5cPj8sgjj68x8sQij88VX7S3e1N3gjc3hDCFIKVbpAwLVZFx9oqmdUugW9ASTvNJW5QPWyIosoRDkTEtQTRp4HEqgMgWtyd+axw///ZEZtQWUlnkwinndirmmRdwnLY4q7l43LmMSmGTC0uAaVms74wjyxK71RWzceosQo4aAmYr0186gY3rP83Rg2wKxTFNizq5ixP7WVm+U3MCkBsKuHd9yVaHB45UtN//2me8traDT9oiCCGYVFZAV1zbZkGFo9V2bNdk8C3JYPnjubBqZlYz9MJe97Bn86P0tH5Gg1HKC/1sXS9onMspq8+yMyccATqk0uyh1H76AwMJr9mTFW23SxUsdl/MYauvpFhrJ+qqsoXesV5Xp6a3vzTJ1TnWq+lmTBSArMbitOj93Fp0SZZwbbO07zzyyCOPPD5X5LdI8vhcMZrxkBf2Wbnddis39BamlmVhWAKPQyFlmIR7XZL6QwBJ3aKxK0G5z4GiyBiGhW4KGroSTK70ceze9Uyp9NPYlWC6N8rZbVdQQXfOca6T7+R4FnC8toDHex2kHncu4wRtAUFRRkI3kbAIFLqoLfbg9hTye/89HPfBbMrSzUhP/IgH97yX96N+OmNpOmMa5VaQG2KXU2m20SJXsbJuOR5HJcmUnnVgOmzXAKoqc/iMAC3hJGuDsSEdnUYSKA8s2jPjSH63A82w+NfGLt5r7GFcWQEeh0Kxx+7urOuIUVXkGdNrDYdRaTtGGXS3ReN1mQyWT5/bvKlAuAnuP8J2XAJbvDvrOU75551IXU+yv/EWncf8nqr6PZD3fx7uP5zCXsISUiu5fdztdLR8xq3J+VlSkYGKQEUDoFsu449TruHyDQupNFsxHD4KTnoIuaimb2b//iPA6iUV/Ueewk1bbh27OQxwxsog071xGK3ZvI1mKcAN4lQu4FXGi3Z+Ff01f59xLUdvWNSX9p13S8pjW2KY9Tkstud3JY88vqLIdyzy+FwxmvGQ7b1baVqClGHhVGUMyyKSHEwq+sMCgjEdGYFXlUGAblp0RNK8/HE764JRaqUQ85rOpVLYVp/tUgVz3dfRIPq6FADHa31uUY+7llFDiJRmYVoWfrcDv9v+4xVzVfHkbnfT6aihVGth5ju/oFhvJxTX8CZbuTYynyqzjaBazcXeq/m/VicfNIWHDAXcmvDA4Yr2rrjGe01h0r1dnwqfi2Kvg7aIra6o8ru3WVAhjDyqtN2TwfuHvPVLjB4UAJhxA8qQisz8fFEd0v5zoGQCzkgDNc8eixxtxrIERr8uSlvZfqQMk8uTN6NiMWJ/RZj89LMrqDRaSeJB1WOYT51pn8cxK0FWbVLhKuxLru7XSRlL+OSYMFQQZu/n4ohswuwlFS1SFde4f8Ul0sN4SBOXCqg0Wjlm02IcP70v75aUx/bBdg5qzSOPPPI5FnlsBp9HjsXn6e2+KRTn9FX/oi2cosjjIJKydRaj/RLIEngdCiVeBztV+3EoCqUFTi7R7qRo9WMAdCqVzC++gXe6C/Am23jYYXcpNlmVnKAtAOCx3s7FKvNIluin4FYl9p9SzvjSgqxjkxCC9es+ZX7wIupop12p5mrX+VyevoUqs80uzip/zYSJ0/igJczE8gJOP2Ai9SVDawQy4utoSieWNvC5Vfwux4hC5U/aItz2t7VMKvdlxd9CCN7e2E1HNEWx10E4qfP/JpZR7nMhLIuerk4ClQG+v2cNSd3cZmLo4bC98y6AsWewTDgQDr40d/32P4avlqRuUJhuJ6RWsqbgm6yZ9kt+unounlhjdle/f+AdkBOql0GHVIopZKrozAnVs5x+Nhz7Iq7ySdRKIeQHjszppGyXbsDAIMx+oX4puQC3FafHVcuT4xfzk42LKNHa2FD2HZ52/5jZoWspSvWFAWZyO7ZJjkYeOzy2yd+izymoNY8vHvkci22LfEDeMMgTi7FjR0veHvm9CJq6E1z15495dV0njkzqtbn552bgViV8LgdJ3SZD+08uxRFvYUHnJZRqLdm045eaVd5r7EE3LcrMzuwIVIOo5GRjIZYlOF15nhvME0kLBa9DpqbYg6rIlHqdTK4sQJVlXl3bQYXVwR3aQsr1lux5tCnVXFl0LU1WGftNKkOSGFXxPFbnpKGK9khS540NIdwOW2uS0i32m1RGoccBQiClwjQkHcw7dKft5uzVH9s7oTuLIZKUDV8taqx59EnK/Y4Btvj6id1WktSN7DEsSUEW9q7+b31nMD969bCnlEngbpcqcMmC4l5Rd5ca4J7AAr4Ze4U3yo/hgqZ5+NNt9pOK6uC05+Ff94w4EtLfBaww3U71x/cjHbZs8yMh/b7rptOPokXpUKs5W1rI8bzAf4u+y68iN1CqtdDtruePu97CJlEFkRYuD16EGtlkk4n9zsmPoeSRxfb4W/RlDmrNY+uQJxbbFvmAvDy+fBjK232o8ZbM+MhI4V1bgIyr0S1/XUvSsHDIMkndxBzawGhYCECRJQrdKkndYM3aT7i49VeUai10qNU8usu91EyYglOVcDtk6ko8hOQyTtLtEahxUpCH1aUoClxtnEJa2CJWt1OxQ+xUmWA0xbuNPQSjKeJpAwrreGrclTnncWfxxYSdAcxe69vRjPtsiXNSxvK1v/hbMy0M00KVIZYyKO3N98jAqcpo29rZawQB9VDuUZ5kK/utvZH17eEt1nYMfG3LEjkhb7PWzqUu9j6z1s7NjvY9Nn2FPY40UDQ9xDEy516kt2ePYWKTik6lknWOacyKrxzx1BQsTGQCooNCM5T9ua5rnN68mEPCT3HxZ2dkSYXuDdgBea/dOOJIyMb1n/L+fedwzZ8+YMUfXsHx0A+Q3lpB85MX8UlbhIZQnE2heO4oWAb9gjAVLUpCLuDh2gV0u6t5wfcT5oSuo1RroctZw1O73klKLbGtiKUyNv7wd32hfHm3pDy2B75kQa155PFVQ55Y5PH54NCldsGQuVGP5LQzVOpvuGmLHG4sS/CPtR3c/PIa/rUxhCLbxfLu9UW4VRkhxvYlsCyBwJ6NNy34mfEnakQ7LVKAFeNv5Y2Qhzv+dx2xlEG5z4VTkXGoCs2iLKuvGCcFOYXnswMuDhk03WJjKE5bJIXLIRNJ6nzcGsHjUNilIMJPG6/KOY85Pb+mSGtHkW1Hq81ZuW6pc9JQRbss2QSrM5rG41SYXFGQ0yXQDAvntnb22sxsdH8dCeFmjvngLPYNPsEvUg9slbaj/2tbq45iU1eCR6avoMddm81gybhyPTJ9BRtDCaxVQ8xlD3OMwnRb9himpKBg0qUGcMgWB2qvUm21E3LWcF3RlRgDVqrROxSVCcqTEQggJPwEpG6q6MRExm0lss9JmmCFW2D9/9o/GEI38ve3/4v3saPZs/lRvt/4ay5ru5gKo5UmAszdsB9XPvMBsx/+D2c//A7XPLd6SOvh/knbXivOaa1XsbtYw9Xhy6gR9hjfwpLriToD9nn1rl9X6bhtQya2xMnrS+Kglcf2x8Ak+GE3Cfy1X/Sp5pHHDoc8scjj88FAb/fNiej671YOFNGNsmhYF4yy4v/W8Zs//4MD1t3M6qYu/u/TDv65vpNQTGNSRQE+t4JDlUb9RTAFJNIm4aSOblpcY5zIb8VR/EJeQo+zkqpCFx3RNJYAt6rQEk6BBJIErdjkYqVxJNcaJ2aPKWFrN1wOhVjaoLEriSXszsg3SxPM3ng+pVoL7Uo153mvo02pospsZWn3ZUxx9eBzKZu1ch2Lc9JADBR/h2JpXKqCU7VJhSXs8SghBEIIuuIaUyrGZiu7WYxCQD2p3MfZe7m4PHgRFXorRuF4dj92/taRCtMALQpFdaiRTZy96Tw8DpUXpuRmsLwwZQluh8KcjXPsUZ7i8X2i6X7nP9IxFGESdlYhCYMivTd9W63kmYlL+EXyflQsDNF37VQECZGrKZGAUvoK/AzpCEml9DgCFKbbkVYdCeEmRFEdhr8WujeS/M0RPPjia9z/3D+Y8vzPqDRaaaGcb5jvUiPaaCLAadZCPk0VsaEjTjxtEE8bhOIaIAZ1vTJBmBkCVaq1sDx2CVVWG21KFQtLrmNduphoykAgaIuM3Yp4RORFunmMgIHrc6hNgncjviHvh3nkkcfIyPeY8/hisN85tn1nplAcreZiv3NGZf+ZGftJdGzk2shl1Ip2LCG40TqNJOB1KiQ0QWmBk6Ru4XMptPQkSeojS44sAbFeUYYECEnhBnEKLqEgtceYUF6AQ5WIpXRUWcYUAk03UWUJ3RS0UsZVxinZ4zkkuyuQMizcToVJ5V6CUQ3DsCg12rkwtphKy97hXV5zI+vjRcy1lnGrdSW1oo3FXZdys/tmSovrRhz36Z/UPRQ2l4o90PL1vcYeHn2rgX991o1DlXCpCn6XSoFLYbdSlUN3rdy2Yu1MFyszG33fzM3ORqvbYozh5YXwzoNQVI/hq6Ui1sxx75+JOaCzc8jHl+OQrKzGgUkH5b527/kb982kIrLJPsYAeVtScoORpsSy7Yp1Xy3/O/lqfvTx5ZTpLbRLFchoVBDOPsc5IN1dCJvEDsQFYh5VXi/Xhi9BFQZCUnlqwlWsjhdwVuI8ArFGjnz7dFRZokwEaRHlIEFAdNBEgDOlxSTcFZgJnVjaoq7EjYREV0JnYyjB/pNKWd+Z4C8ftTOp3JeTtP3i1CUc/8GZ2XO5xnUBQbkcXTfpSWqYiRQlBYVbN642EFtzf8njK4+R1mc2qLUzvt2CWvPI46uMfMcijy8GA7QU+n0zeejF13OSnR968XX0+2YOFnJvZve6IRTnxQ/aED1NXNl5CbWinUYCPO08mgKXimEJYmmT0gIniiThcSh8e3IF+08up8zbx7U3V+JkHHq8ToXaEg8p3aS1J4lTlggndXqSdkp3xuFpKMoiJFBlGQlbr5A2BLpp4Um3ssJYRJXVRqejhot9V/PvrgI8DpmEp5rLC6+lXammXG/h0vZf8YvdnSPuzGeCCbcmgTujiVFlifebwhS6HVQXuXGrCpph0RpOEUnq7F5XzOSKregSDIcvYjY6s9bCjSiKRFgtp9Rop8IK0iZVcEnhr2mXKqiwgllSIYrq4KBLhzx/+bQ/kJK99jHMIEnc2V97RIqiXlJh+GtpPvQufrT+Csr1FlKeahySlSUV8d5OhSpZmEImZNmf91CkAuB6cSsXha9CxcSSFCRhcPBH8/G7FZaW3UCbVEFAdFBmBmkTJUgS1NBJIwFOMRfQKVegmXZHyhSClp40TT329V4XjPHP9V14HHK26zVSEOYV6VvxJttI6SZJzWRCWQGz9h+/dZ2lIT7rLb6/5PGVxxcd1JpHHl9l5IlFHl8ctrRQ3EzRcPXzq/nX+x9wYcuFlGktNBJgtrKEDrkcSbLFxUnNRDMsXA4F0xLM3L2KxT+cwdIf78budYXsHPBRW+zG65BHJBiSBCVeJ16nis+t0hXXiKRMuhIa7ZE04ZSBbtpp3tnn0EdaDAsMYSHLoFuCUEyj3AzygLSMWtFOp6OG5bU3U1U7mboSD7vVFnPHiXtyztEH8fHhj5LyjaMo1cyEP/90xJGPoUTYGYwlFbu/VmOvccUcMKWc/SeXc8CUcg7bJUB1kZvm7uSWp1xvBp/7bHS/tSaFm/AZfeGHlhB0xNIg+siaiQx13xr2cNI/bszVPOAihSvnMSYyN/jn89lLK+wUbYBkF6WWLc5up4SI8GUfr0gWZfJg4T2AIWSCoogaqZNAb3Djm65vZz+/09bMxZlozflDUCV1U00nzVKAM1jEJqOUaEonmjLQTDAte8dXVSS8TgVJgo5Ymk/bo3TG0sQ1Izdpu3e85PHdVtLjriVgtnJbegHHTIHLZu7METOqtp6IDjUeOcL9ZRdrDce9/dO8SPdripHWZyaodc/C2HYLas0jj68y8sQijy8UW1wojlA01BDimvBllOstdDiq+aW0mGZRli2old4OgmFZpHULr1PB73FQX+pl99piplT42W9yOftOLqPE68TjkBluQsOyIBTXSGoGhikIxTUSmi2rzdTWA0tsMeBnaV2gmwLLEiR1k5PE84yT2mkiwAn6lTy7QeLlT4Ks74jxTkM3j/+rkSfebuLJtYIbqm4i7K7drIPWUCJsw7KIpnTWBmOjdk4aqNWQJIlCj4Nyn4sir5PqIg+t4SQt4e0zm7zVs9FbItItqoNjVmL1iqsNZNopoYZOVllXZgv2jPWr9NHTvenbA4heuAnx0TPZf5pIlBLGTTrnYQoWZ7YsZEbirezPMo/ppAghoFrups0qyXYuhoIQdkejUuobnTKRecCayT2TbsvRPlSKDkJyWc7zF8rn0mCUIoS9XvsvDVtPY4dHqrJMqddBLGXQHU1Q8+Yy5JZ/jxiEWWm0cm7DPMYlVyP/96HNX4/N6aoGaip6r7NVEBh0f5nW9Qqnr5mNV8RJSAV5ke7XEF+GoNY88viqIk8s8vhCsTWF4nCk5NzGC6ijnWYpwOWF15H0VqNIEkndwrBsQmEJiKZMVFWmvtSL32UXaJmd/bZIikTaoCuhoVtiUNci828LSGgGnbE07ZGU7YikyJR4nThG+e0S2J0LIcCwTG7iJFaZMznVXEC3WkmhW8WpynQndD5qifDaus6sXSxFtVwfuInXyn/Guj0vHvF1tiaBO4PRpFzrprXdZpOzs9E+eza6P14df749Gz2c1e2WinTDTYinz0QWZm9wnW1X3B8dUikLy24mpFT2PqcxSy4sS9CyaS3ayiOQ9Thm1s0pl3Kaou+YlXRRQTcDUSyiVEn2z9+ydqKHokGP6bJsQf3AsSgTCQWLhdrNKJI06PMbuMYXGbdSYdlJ8ropSBl952tagkTaQDMsPA4FpyoDEj9PrqLw3Xvh/iOy3YDn915Jg1HKxs44DUYpL+yzMrshwKqjbC3EXxcPeh85+MuV9nUbirBB7njk/UfAykPgzTsJPn0RrwZdNpFy2feXH356CYowMSWFR6fdkhfpfhUwFhewcFO/9Tlu5PWZT37PI48xIz9AmMcXiq0R0WVISff0FcxaOzdLSgDalWrOdywlapRQ4lWxLIEl7I5ASrdzH2qK3KiKzN7jSrIt78zO/uq2CP/6rBvNsFAku+jvD7n3Zxagm9AV1xBAsceJ16ngcSqkdAtLMzAtu2jbXGSGQ5VQZQnNFNwgnUqxx4Grd5s4sy9tWoKOaAqvQ8naxfpqJ/FI8Cx2Wx1iUmXxiF2HgSLssaZi99dqDJVyndRMHIq83WaTc2aj1+SSg6PWzCc47W5alKlEkjqftEVyRbr3H2EX/KMU6WYC4gpeWU5p90Y61GoWO+axKP1rKnsL7gwECutTPm6quZmr2s5C1uMQbsyKzCesvZ+aqJ16HZaKKBLhQcRCkWzXJ1UaeozM6u1AALRbhewjr6NW6sQQcvbnACVSHEkaLOSWEUSUEurMdmZvmMPAzKhSK0QbJUgCAlI346QOHlaXcby2gDbKssQjS4R1C6dDxudW6E7o+Nwq76nf5UcdzyFZBsgqjp/ex0lV+zC9sZtQXKOswMne9SWou91nXw/LAFmBGT8a+WJocfu/GcL28xcHiLFLqT31z7np4sCnje18pIVp8rhwe3/ORell2d89N+1aukt3z4t0vwoYhaEHkCUVmTXimHwwpxxxAN8deD/c//k+UX+v+D8/KpdHHqNDvmPxdcRYPd7jnfDSldvF431rRHQj7V7/YdJCkt4qwkmd0gIHfo8DWZJwKRJlPifTKn04VIn6Us+gEaBJ5T4qfS7cqoQiS5i9BEKi7wtj9Sq3ZcCpSAjA71bZe1wximKPCMm9hb8qb55USIBLkXEotpNUgVPJnpNuWkRSOqleoUZ3QueVNR10xe3xmM3ZxQ7EUMGEA61bh9NIbE6r0RZJUV3koaZoG84m91uvObPRaTuJPO4oxURBESazPp1NXccrJP54KXf89ZM+ke5vDusrOCccuNkiIROouPzlNSxM/Iy/FB7DBe6rSLorkKxcJ6aQXEal6ODG+BUck3jCJhVOH7qvJjum55dTAFjIlIqeQaQi83EPRSpMIREUhTmjSGVSjDq5E70fqchcjgyZyPy3U/hskgH4zW7CUhElejtF6XZ61HJbF9KLchEmIHXTLMppssoYLwd5wrWMiY4uvC4ZVQaHIvUSZYFTsTsVlYVudgr4ONh4DUnYpALLQP/dGTzyl3/y+L8a+dN7LTz+r0Ye+cs/0X93Ri+pUMEy4Z2HR7weHHxp3zULN6LfeygPvvga1/zpAz57+HzufPYVHnxjI7qu9X1uyLxX8UOKPA6mW2u4oPuanEMeuOnWvEj3q4JR2FFbloC/X993Hyiqg4Mv/VyDWvPI4+uA/N3064jR7u4AhJvhb0uh5WW7MjnimuEfuwXIFIoz3zmHYqOVHrdNEo5Yuygronthn5VDiuhGIiU/bbyKtrrlvCk8qLJMWYGTlGaS0k0M3WRTKEGR10Gg0MOGzjguVcnu3Df3JOlJ6nxzQgmvrw/RE9cwzT4XKIlejYSwOxeyLCELifFlBb1EQkYIgSxJqIqE363SnTCGdIVyKnaXQpKgyONAliXimkHasFBkGUsIYmkD3bCQJDtMz7CgNZxE/8zimxNKKPO5N2sXOxLWBaO89GE76ztiWevWyRU+Dp8RGDQelenotISTrA3aWguPUyGpmbSGU5QWONlrnH/bWs32W6/yMSttS1mjlZBSiaJIFGvthB3l+PQuVEwuDC1GwhbpvxU4nu+/cxYOI2gfq7eY2Nznser1jXTFNaqL3HidHl52nUd87afcFLuUCrpyHm8i00Y5dbRTnvyP/RrhJpJSARG1kgqjFUfk3/So5RQbndnn9e8ojPRxKZKgVMRynqdKFpaQcEgWzVYZxVKUAkkb9FxLwBX6bBwOuJ0bkYBCYWsughRTanSh9OZjSEiokoWBzAXm+WiG4CHXtYyTgjwgL2WWtZAOtQJTCNyKhGEJZtQUUl9agM+lsK4jzuoZF3FQugJpt5+g/+6MLLHqnr4Co7gWNdbMzHfs66cXjkcZ901oWAcb/2FvdAx3Lyqqg+MegPsPB8vEEW/hyLdPp0LZkyONl9k7/g/kBhNH77XJ6F1ObbkK3ftzzg9fg4qZ/Q6nlILN3l/y2IEwWjvqta/Y3d+iOjuBfqS/fZljvrGiL6g1jzzy2CzyHYuvI0a7uxNuggd/CLH27ebxLkebOaFXRNeuVHPHuFtoKBidiG5zzh5nbzyfn02Vufyo6Zy833h2rS1kek0hB0wu5xsTSnA7FF5f18lVf/6YJX/6KJseHO/VTDT1pEjpAq1fu8GiT3gtAW6Hgs+lUF3sZteaIiIpgxKPg7Rh4XHIaIaFLElDFo4SdghegUtFkSV6krbzjoSdXh1J6USSOoYlcDlkFFnCsOxiUTMsmnuSvLqmk1AsPSq72KGQKaI/bAlndRvFXsegwLP+GEmrMWv/8VRv6yJtwPy8I7IJ0+nnj1Ov5qrKmwmq1RTpnUTwZwtHAbwVq+Dk1WdR1ksqIs4A1qwX+4qJITp3wyWUT3VHuM+YTxU2MehQKpnnu4EWqYpKqwNJkuhxBHAnWgAJw1drh9EhEZQrKNbbc0kFNqkwhExIbF7bYtvKSjYB6B1zkiVBTLhxS1qWVAw1snen42Y6RRH/lveAfp9PGWFULHQh0UUhSm/nQ8ViuXwLdzpvo1BKEhEexklBVklL8WlBErpFTLPQLUF7JEU4qbGuI05pgZNDZ9QiHXktVs03RmfK8N1F4AuM7l709JlgmZgogJ2x8S3zP4TkMgKiI0v42ijnjgl3EnLWUJxu5lfdy3JIRYtcxb3jbsyLdL9qGI3LYLTBdgHbHKnof8zMZlo+yT2PPEYFSQycZ/gKIxKJUFRURDgcprCw8Is+nS8WGSFr90b0wvHDho0pkUaCNYdSedxNyCX12/wc9Ptm4ohsIqhWM89zFQ1GKR6nwrhSLwdWpjlhdV/g2aDuSr/nd6jVPDJ9hT1+EmvhpNU2WdELx6Oc/hx3/TfNhy1hplb66E5ovNvYQ1IzKXApxFImxQUOKnwuynwudqsr4p5X1pM2LKJpnWjKHPYtuFQJt0Nhl+pCzvnOFF78sI2GrgTtkRTRpE4sbXcfNHPw1yzT/VAUCWEJFFliSqWPcEIjlNCRsW1qvU4FVZboSRoYlsAhQ4nXgWbablK1JR4Cfjf7TS5j9kGTR90tsCzBXa+sz34u/RO5hRCsDcbYrbZo2GNmNAj9Z5NBEAwGqaysRB44xL81aHo7O5MvkJAQdDpquKbyBmRJZn7rBZSZNoHIFJAD8WbRUdSetpL6Uq/9gxcvtzshRXUw+btw1HIawxrLX15DsdeR1ZD40m0c//7P8Wu2riKOh+XTHkTz1iBHmzln0/lUGq3ovlocimSvS18tPQmdCitIOyVZ5yj7syVHAzFQI7E56ELCIYlBzxsuHA/AQCauFFFkdg/4uUS3KKSi1zmqnRKEEFRJPfbHbpUzRzuP25x3MF4OstI4kquMU7Jp8X6PgxKPg+/uXMkJ3xqX7XA1diVY/vIaxqldWf1TBj3uWh6YegcNRikXHDIFZ89GKv98CnL3hhHvRRkx+MXiXC6LXpcleQPf5/H6EkIlu/E/xpssTV2X8/sWuYrZyhIqaidzUGAz95c8vlSwLGuz9xbLEjz04uvMfOdMKobogHeo1bywz0pOPvyAsXVVM/eK0ayTfn9b2XfONu/y5zE6jGa95DF6jKV+zn/aX1eMJUPiewuhaBvbMQ4gBY9OX8Geu85g30mlVPpdFLhU9t17dxxnPD+ou5J5Pg8cNSrnGWvVUYRaNlBdZIeRrQ/GSWomJV6HPf6hSHTHdQJ+F6FYmufea0VVJJKanUGhDvP3RwJUCVRZwqHI7DexjNMPmMDUSh+mZY8waYaFMQSpALsAtsXfAlNAkdfBXuNK2HtCGR6HioWEaQk0wyShmVjCdqeSJIlwyiSpm6R1k8auBMFomkN2GVva9UDr2Jz3NgrdxpCzydsLHz5jkwpJRUJgolCut7Cw81J28sbQ+2lCNAaLyjuUSv5UfEruqNh+52THlnjnQXhuHtG0TlciTUIziSR1hBD8z8bbs6TCRGF+wVI+04oJxdNEnAFezKy1WDNgz2c7Ys38S96DFlGeQyqgj0xkyMVQpGKYJQNARPiIC+eg5/W/hN1KGWG1HLDXmYpFkdlNF/4cTYWKyJKKJquMs1LzMERf1+sf1gzeYwrHawtYaRzJtcaJgE2o/W4H40o91Jd6KC1w2S5lvRhJ/5Q1Zci4dxWUw6l/HNW96N8HP8S/9ElcUXLjIHtcExkVi5vU2/lW+i0Wpn494PcKT41fREXtJM46aBInH37A8PeXPHZIbLUd9XAYS5c/n+Sex9cceWLxNcZoMiSe2Pl2LE/Z5g82Roh/3pklFc/scS+isA5Vkakp9rL3uBI0w+KvHwdtf/mBIroBN2/HGc9zyhEHMO/QaZz7vanMO3RaTtGgRjZx9qbzCIgQ0ZRtIasqEq3hFI3dSYLRNMFoinebwggh2BiKU1PkwRBgmBZOxWKB+hDVhHLfA5A0BIZpp2W3RlJYQmB0N3KBuYrdq33UlnhwKKNL8S71Ou0RlQInB0wpo9jrAAlSuiBtWLgdMgUuBVmyxbMIkGRboxFL6wQj6ZFfZACGs44VQhBJ6iQ0g+5EmmhaH9NxtwsOXYr41jk8tftKOtRqFGy70OJ0Mz9fczZVooNmUU6bKMZF7vmayCx2/IpGq5S2cGqAOL3vyuhr/sbf3niH9cE4b24I8caGEKE1/2Rq5996j6Owaqe7Ueq/yVkHTR5yrRFuRDcF/y3/AbfpPxwkcM8gJPxZctEfGUdXZdDP+27VZfLQeooM2ihn5U73cvuku2ijPGftlRJFwR6rynmOVcIc7Xxudd5BndxJk1XOY8bBXGmcAUArZVxlnJIdQxJASYEDzRAECt2s78gloGM1ZbD8NaManUp6q5AECAZbQPfgp4VyxktBrtGvy44/ARjYGSQnNC7loIDGN8aX5kW6X0GMidCOBfkk9zzyGDXyxOJrjFHt7kR9hOLDFzFbiqZvzueV0uN4ZPoK4u6qnN8N2i3P3NT3nWOL6N5YMejmPZKzh1E4ngq9lT2aHkYzLRKaQSimEddMHIqEQ5FQZZmehM66Drub4XUq+HpHkC6WH+UM9QUedy4bRC4sYedYfNwS4fF/beLOZ/7OWRvO4weJZzmibQWhmIYiy4MKxaGwviPGq58GeWN9J809SfasK2JCaQEep0xpgZMit4osSfjdtq7Dqdqi9GkBu0Pyt9XBod2chnEB628dm0FXXGP9+k/Z7aPreXt9B+uCcZ59p3lIrcXnCkWl6VsLeEObmF2virBH1BRMdCFzlTWLoSicgsUVyV+TDm1i1eufsfiPH3Lns6+g3TfTdogpqrfHmGLNHP/x2Uz3RlAkqJNCzO+83CYxKNw37S7+bUxiasDPN8aXDrnWMp2LnoTOfdJV1MqhQecTFEUE5PCQnQppiFtyZuSpP7kYDoaQWey6mA1pW/8ysBOVgd5LEDKQsbjTeRvj5SCbrEqO0xYx3/hllkgM+kxliVjKJp+KLA0q1kabbJxxD2sJj26nucCpMNndwzU9l2STyDMoI4wqdAxk5F5KIQFtShUX+a+nmcDQmoqB95c8BmOsToJfkMZga1wGN4uxdPnzpCKPrzHyxOJrjNHs7miGSdoYXmOwxa9twO/KzsbwDT1i5XEqucVKRkSnqPYf/33njO7mXVSHfPpzvFt7Eg/5z0SVIKGZaKbVm6gtoZsCn1ul0m87R2mmBZKE16XicSg8ofyABlHJeDk4JLlQMDnfXMU7b/yVi9t+RS3tNBPgIY5CSLaLU//RlmpCXKk+hEru56pb0BnX6IylWdse5fX1XcQ1A1mSSekmXXGdtGERSRqEkzq6JfA4FQwLClwqLT3JoVv8A1OJezHQOrYrrtH42RouD17ET9J/4JfpB6j0u2joSg4r5N5qjKFgiWsGnmQr3+78HS9Nyc2wcEgWtys3ZcPjMjB6iUaN1MltycsxuxtxtL7D6e+fiDOyiURBPdasF3h0l3uzu+XXReezt7yWJT2X4iNBVHg5jaWs2lhGazjFzpUe5L9cMeicMx3Af/hnsofxPnW000bJIEJQSpihIARZAXW/LDpUyRZYq5I1SJw98PmqZLHEWM7O5loWdF5MQHTQJkowBnQo3JLR+zr2uVXK4Wyn4nhtAa2M3KX0ORU0Q5DQTAxTDCrWRp9s3GsZPMqd5hKzg9uS86kS9mhaUKrgitKbaJMq7Pch2YL0/rjWdQEfy9O41H8NiYL6oYPP+t9f8hiMYe4hQ2JLgyi3AUZLaLfUBWw0Xf58knseX3fkicXXGKPZ3XGqCi516F3LbfHaiWFa0iO6HCmqXQSMckdILqnHd/T1FPs8fBbqHYMRAtOCpG4HupV6nUiShCKDW5WJp3T8LgVTQJNZwkn6AjZZQ5OLi+RH+bnyAg+yMJv4fZpYSEipoMAhI0kiO5JRTYjHncs4U32By9RHB51rSrfoSRq9TlISKd2kwCmjmQLNFFiWnR5uCTBMQUtPio2hOD6XiiJDNK0PnvsdZj64uSfJ1CofTkXm07Yomz77lGXhy6gV7TSIAPebMzEtQbnPQVdc4y8ftQ+bb7HFGEPBUphuZ17zhXyj9TGOXn3hoN+r/Qx9g5TQQjkqAr23qA6IDm6PXcid6fk2YcDLuc5lvNbhytktL9Va+HXkYmpFO40iwI/5Ne+JKZT5nRR6HDj/b8mQ59zck2RTV4Jd0+9SqrXQ0ZvCPbAzMVCzkxQOTHLHokKiKIeQOCSRI84eSDD6C8IDZhtzNs6lKNVMsyjvJRxDX7cb9eMGfIab30RQpV5ntF57qWA0xZRKX1+xNgb9Ew/+EOKdo7oXBUSISX88hmKtHYA2KjjPey1v6pNZ5Lxo2I7OQuM2DgykqaidTM9Pn8lrKrYEO4jGYPSEdstcwLabhiOPPL5CyBOLrzFGtbvjj1FW4Nwurz1S0FpreECxspXIWKROKvehKhKWsHdJ3Q6ZSr8LWYL2SAqHKlPkcdAWTdMcTmGYFgndotEq43htMLlQJHjO3A9dyDgkC13InJOcw7p0Cd0JnZ6kPSoCfaQiM25ynzFz0HkKyAq/E5qOYVqUFDjxOBQU2f69Iku4VBmnYou7U5pJT0KjO6Hz7DvNOXO/d72ynnXpomHng5/9b7OdmxHaxFU9l1FPO40EON+9DKm4llja4L2mMB6HPOoAvjFhDAVL9bPHUq63YKLgNuOEnDUsdF+GOcT4068L5zPXdQ0NotJ2UOq91ZWIsK0xQOZK31Lej/p4/F8NJDVjyN3yO4ovwlU2jjKfi91ri9mrvpjnC35M2F076JzXd8T4ZttjlGotRJxVOCWLql7hdhsltImS3GstoFP48Ug6Sr9ddkNI2VGpTLFsDXB8sga8Z0nqs7AVwiYIBjKq0KmWu7PHHYhLHU/k/LtK7uZJ5+JBXbmBr5U2BIosAwK/29EXMjlG/RPdG+FvS6mRuka8F52y+iwubDgHtbcg1H21vPj/VlFUNZGaYi/Huv9ld3T6nWfcUUrEVUWZ1sJZG85jr6I4VfVTRtZU7CAjP587dgSNwVgI7RaSyu2m4cgjj68Q8nazX2eMwq41XTiR7qMfpnLiLtvcsm1gENnAoLXTD5gwKKBta9EQinP186sxTEF3QiORNkjotusSgIQgoVmUeJ24HBKhqEY0ZWT3cQeSg4vMc7hRWcF4OZglF5usykHjJAOfN/D3Cibz1Ue5z5hJG2W2Da0EsiJR5HFQ6FZp7E5iWgKHLBOgk1ny89zMyUiKigR4nCq71xZlg/YsSxBJGZT5ej9LV3jQ9TZ8tSjRJn760dnUinZapAALi68n4gogSVJ2RKrc76LU6+S8Q6ayc9Xw350tsvgbpfWxI7LJdoUSBh1qNfdMuo29P7uXmfrLgw7ZSjknmovRDcGjzmWMk4I5v/+H9A0ukC+19TQulRKPg/8JpDmvaV6ONWqbUs2VRdfSZJWx36QyCj0OIkmNZKiBRaFL8MQa0QrH89jOK/hnyM3qxi4ute5lX/MdyoRdnLdLFRyvL2KS8RkrXTflEARDSNluQqtVDEhZIgC2HqOAFAXS2IT5Ay13+1vTNlll3K7/mOucK7NE5DLtTM51PEud3Jl9zHHa4kEjURKgyuB3OyhwqfjdDubP3Jn/mWqPIo3VltN64AcEzUIqJ++Buf6VEe9FWfSGm1n+2qzdcWGyhcBjh6Bo0Ww4HsAHxYdQFfsoaz3tOOP5PiewTPBZ//GnvK3oyBjib4bhs4MP+1t8Zz/nbYgR7y0DOyWznstZHxk7bDnaPOhxYznP0doozzt0Wp+tdR5fCPJ2s9sWebvZPDaPsezu/G2pncC9jTFS0Nr2IBUAdSVedq8txqnKHDC5jOk1hThVmQKnQk2hC8MCh2LvxIZiGk6HTFWxG68qoUi2O07/zsWTjsVZsnBsevGQ41K5pKKCf1q7Djqv+eqjnKm+wOOuZUxQu1BkCVWRKTc7mZ26j65oAtMSOBWZWjnEQ+oyZsnPc5nyCIZpF1FpwyQYTfF+c5h/b+ri/eYw4YRGQ1eCv3zUjlEwtPPOaWvm9o4/VTLbsSxLKsAW0vvcKh3RNKYQg0bThuwujBWjFEVmSEVmvZqmYG/9v9nD9LdRraaTR5XFeF0y13PqoNTzCaKBKkK4HDIuRaJID3LG+nMpTjUTctZwnvc62pQqqsxWlnZfxhRXD363Slc8zerWKP9od3Gp/xqCajXOyCaO/M8Z/LLnFv7H38w+xn+zpKJLDXBp4fUUelSWu+7KdhYyUCWBgcSfzH05R78wRyytC1szkCEVCZHbOWwTxRybXkyzGKyH6E8qYsKVQyrmaOdzruPZLMGRJLjKeT+LtNNosmyL2jo5xHnq04OOW1Xo5Ds7VXL4rgEmVRRw1G5VHDC5vO8BY9Q/ceof7QyRz14d8V5kOnvvBbJiJ3D3N2zwRKj5489QtCh64Xh+v8f9dKkB+/OXiobeqR5OU7GDjPx8UfjSagzGaOixpS5g21vDkUceXwXkOxZfR4xhdye7o6hEkGf9abu0tocKWtuemQiZTkkoptERTdGT0PC5HfQkNMJJg6oiF91x3ballSXcqkJSN9EMKzuwso+0hqddi7PH/El6Me+IaTkkIiI8/EqbzZWOR7Lk45/Wrpyg/t+grsUerOMh17UUSkkaRCUnaguQJHjEYe+2P2DNZLF2MnVyiEd7f9YgKjmDRXQqlTgUiYRmUuJ1UuZz4lBkdNMilrJHsepLvfzywEk8/q/GIXfbWqQAZ6lL2agVM6m8AHc/AmGYFpu6EhwwpZwLD51GfYn9R3pdMMpLH7azviOW7S5MLi9g/1qVPaaNH9Mu0eaCrcxeu1BRMgGpd70mnjoH38e2TiWkVvL7SVfxo3VXUmH1dSe6pGIKrUh2TEYConjxk6CRAHOdSylyStyTmIfHitMiVXH/1Nt5td1FFZ1cF7mcGtFGSi7ggck381xXLa54K8ebf+KD6RfS2ryJhaFLqMOe+09LblwiBdi2r3Nc19Ihl3GOtorjzT8REV7uNo/mEvWxnPf/nLkvM+QNjJeCNFvlIAlqJZucNFgV/NeazLfkT6nq181oFuUcl15EtdzDE45Fw4bstYliqqSeLKm403lbtjPRbhVSJsVQJYuI8HBKen729wPXqEOGI2ZUUeF309KTwuWQOWr3aqZXFW7xd9bqbiT45K+obHkZuWTc8Peinga459uQjuTuNA9zL2trXIf7P3cTP3ARtaX+se1Uj6GD9nWzFf0id+xH3IE2DVuvlcmm2RyG61iN4nmjCWXdHh2bPMaGfMdi22Is9XOeWHwdMYZ2f84f/n3P/sq0+9cFo/zu7UZe+LANRbbTs12qTHdCw6PKbOxKZrUfDkVCNy16p6VyyEMG/YuwakK85LqEQik56PfAoJGo/j+LCA+FUpJmqxyBoE4ODfm4BlHJLGshQbmC2mI3beEUhiWYUunH7ejb9RZCEIqlURWZ8w6ZynPvtzKp3Edd7H2O/+DM7OPm+W7gY2Vn2sIpCj2OLDmJJnXaIml002KXGj/1JQVMrvCxc7Wf//0kmB1j8zpVEppBWzjJxAKDH+0/namBolFfj5EKlrTiw2XG6HTUoJ38R2rGT80t/ny1PLrLvfwz5CbYtJ670ldQRUfO8Q0hc6H0K/YyP+JJZSb3sIx62mmjgkJieEliovAr//UkKvZiYyhOLGWwV3Gcmzpm47biGCgscl3CHG0VNaKNf1b8jAXJE6mWOrkxdlnWqSiDPymHcFH6TEq9DnxOiVOi9/EW07mFm3EMQwIaRCXHpxcggLMcf8YSEn809ueOfmSggxI0oVArddJklSFJErVSp62pwBo0BgV9Cdr9SUVm3KmS7iyp3WRVcp42N5u0nVl7PWo5PrfKztVFuFSZtG7hcsg4VdkmlBU+Dp8RGHOX0XrhcoKrXx/dxsVQo0fba3TpCxz5+TLjk5Yumn93MesmnUaR3p5zD3l8t5U0+nZjY2ecc7/XOzK5pQX8EPjCC8VhCGfasI1G9iyM5ZPcv0T4wtfLVwx5YjEM8sSiF2PY3bEsi+BnH1O55lHkw7b+j8OXCR+3hrnhxU+oKvTgcSgIIXhzQ4jOmEY4qaMqEmnDQpUlFFkipVtUDdBKzNPPYbljxSDtxKHS2/zGtTz7Wr9Iz+Nl8U0A6uVQthPRZJUBUnaH+Dxtbk7x1yzKODa9GMglJKcYC2iTynGqMlV+F009KQrdKtXFHiwhUCQJpyojSRLRlE40ZXDpkTvxv6s7hize25Vq5qhLaZPK8Lkd6IaFblmEkzqGJRhf5uU70ypJ6iYtPUlaewnIXvXFOVkJwrLo6eqkKhBg9sFTRr2L/UlbhNv+tnZI0vPEjHuYHHqFZ1xHc8oRB7CzJzLkLvWraztY+doG9vTH+VHrcqZ0/yN7jE6KOM64mk1mKT6XwkRnD/doV2YJSKYj0qFWs27mE0ScAZ76TyMJzeBbzs+Y89lcu2OCXbR3OKqZoyzlvxEfQgj2ktbwpHNx9vUMZGaxlLe0SZT6HBgGTNI/4TFlMWpv7sa5xvksVh7I6ULMYhmva5PRe3lHNSGedC7JroeocHGOtIBWq5jfSouyXY0OSrlO+QXXGzeiSmaOgxTYJOI/1jSOVt/o/Xcpx2lLst2I/mR5pXEk9xkzs//+rTiK/+58EUnd4uCdKnjzsy7SuklNsSdLKLdUF2XpGsG/3ELl/icgl9Rv/gkDC9XttFO9uQ5ah1rNC/us5OTDD9i+ifNfMkSfvRj/u/fS4wwgSVCUbs/+blDHQunaphqUL7RQ/Jw0HHlsO+SJxbZFXmORx8gYo10rBeVw+FU7PqkY4Pjidzko9brwOhUKPQ4KPQ68LpWkbiJJgnKzgyuUh3BKFroxmFScbCzgA6YNcovag3Vc6Xgk56VXOG9lD9YB0GiV8bP0ApqsMurkUE5+QJAScqbwhUS1FOIJV26Xo00qR5YknIpMT1JHliFlWDR2JWjqTtLYnaSlJ0UirZPWLbxOhQllBbnzwa4aPik7lLArQMBsZXn6StzJVpJpHQtBUrNTICaUetlnXCnFRpCjWu+gxm9rLoayCpYkidICJ+s6xuYgNZLd6OHrlvJa+U9Jeqptjccw89STK3xU+twUmyEm9vwz5xjlhLnI8wdUWcIhS0SdAT7y7J39fVQtodthB6jt+b8n0dawDgF0xDSebKviCuclWVJhITE7cTb/6ilAtwQVIsRy9fac11OxuM5aTkCEcCoK0601PCbbpMIQCicYS1jNZEwp18r5Gm6laoA2pz+p8Etprha3UWx2078vUUI3Fxv3oEo2acmIssH+b50cYqbyRvbx/7D2yBFmZ7RDK40judY4Meffa2bMI20Kdq8rJhTT0AyLaQE/frcDRZbwux1MrfRtmSWxosI+p0LRKGfyB2ojxnovG2VexdfOVnSUblgFB51H1FVFsdZOUbqdsLOSx2aspMtZQ3GqmRNXn8Me/phNeL9KGpTPScORRx5fBeSJRR5fHwzITBhoeStJEjXFHhRZopouHlaXcYb6AvN4mMohXJ2arDIMMVjQ/ZSrT9D9i/S8rFvUU67FWXJho/9Op6BGyhSSIZqsMppFObVyJ793LWacFKRRVHKKuYA2ynAqMk7VHtGSJLtYNiyBKWyBtyJDNKWzqSuJJQT1pV5KjY4cj/cNvn3YOfQypmnrAepp5yFlGfVqN5puEknZxKG62EO90sVxH85mn5ZH+W7TCpyqTCxlEE0NJhdOVUYbo+VibbGHPX1hfvnhyZsXRWYEwsestP9w91p9ZoSVJ669AEWYmJLCH3a+gYjLTnbfy3ifKc6e7Fb+HQVzeMl1GO1SBcVGJ/8wd6NNrsITa2TmO2cywxfl0OkBDq83uMBclSUVMoKblTuoJtTbUVhMXW/CdpsoodWybWXr5BCPOxazZ/w1HhALUSWbVJzKEkJyCQ+qS6mVOmkW5bT1PqeGTh5Tl7AH63LW22PGwZycvoJNViXjpCCPqYuolToJUoqBhIqgSuqh1SpmdnoeEeHJkotM58LR+99Wq4TbjJ8MugatlHGVcUpWQN5KGSvdZ9AQsSgtcLJ7fREbOuNUF7kHJXpLkkR1kXv7WBJ/Afja2YqOMk9GliXcSh9xdOkxXgu6OMexlCYCVBqtHP3fn2Ped/hXa9d+rKYE+ST3PL7GyBOLPL4+GOD4IkebOXTXSpyqzDsN3bT0JCj2qEx1hXlIXZot6v5s7DeIVLRRluPu00oZ52lzc/IsztPm8rL4JsemF+eQi8Okt7M70U1WebZz8XQ/QnKctph5xtyc05/PXNqlClyqTE2Jh0q/GwlIaAZ6b+ifsARpw05DVmSbuqR0iz38caqfOTbHeefPJafSoVZTarQjSRI9jgDVVhvLk1dSLXchS6AZFq0N6zj63V9mC/63q47Hqcqkjd6U8gHQDAvncOGGw0CONnPSeyfhFXFMFJ4av3j4YCtFta/l02fmpPtmwrG8VpyEXMCqaXezpuQgbqm/jWYpQI1o5wFlKTN8UVKGxdqOJBenz+R8zzU85fghd/nmMNe1jGYpkHW8qY9/wNyGC6gy22iWAvw4taTPEcy5hGecC7Okoskq58fppfxEW5rjrnS7fHO2k3CitZigKOZBZRnjewX4x2uLOE5f2jsWB3VyJw+7rslZb/ONX/IeUzheW0BEeLJrbIF+Oh2iT8vil5IscT6Q1Utcqp3JQJijvO07ZJBkiUhS57s7V1Lhd5EyTLzDXFePU9nxi+3enfvCdPtmA/tcqkJhuv2rkWMxGjes7ka4/3AcCXv8yUTBLRLMD14EwLWVN9KlBijW23HEmtF9tV8NUgHbrTOWRx5fReSJRR5fXmzrsKohQp7e+M/7xNMGwUiaNzd08dn6T7ldW2DrHwhwirmQHzreYJyUSyoyX5z+idq3Oe/IFnwOyeIHvfPs7zElh1zc61rej0As4jz93JzTnKfbYwO/VnPb6NdwBzVSJ26HjCxBVzxN2rCQJYlynwunQ8HoHUPxuRQU2XaG8iRbOO6js5B6NpL01aOc/hynHHEAp8/8NutmPkGHWk2gV3jcLlVQYbRyc+JKys1OAqKTu81FBMxWWuUqfjPpdkRhLX6XimYIHAPmyzO5F1MqxhBu2Du/rGhRhKSiYPKjDYvoaf2MTXoJT+x6N4mCetty9oGjoOntwWMWA+yTn/zG7/hYnsZnHTHeDHm5pOAaupw1VBqtLE9eSY0IIYTAtATrtBKerjiHnWtKiDkDXOC6inalOmf8pctZw2xlSba4b7LKqZM7s/qIJquc47RFtFJGK2Ucpy0aFIg3RzufRr2Y+1hKPe00EeBsZQnNooxGYQupM+TCL6WICA/naXMHjSzNTF+bJRe/VldQ3dupiAkXPimd7Xidp83lPMczOeegC4k6OcQZ6vMjXhKnDIfPqObwXauoLnLzaVsUr0PBrSpDjsABJDVbxDoWQrlNsC3vE70799XPHst+zs9GtBXd3/UZ1c8em0Nud1hsJgBv1fOvEbv7e9nPWBTV8fQeKwmq1dTRzgp9Id+uTKMqffeDpG5smf10HnnksUMjTyzy+PJilO15oE9c9+adRP80f/hchSEyE2b4ohy6S4Aj6g1uTi6gRrTR4ajmAvdVdMjl3OM4jQesmZykL6CdMmSJrO2sxODwu+PSi7Oz6hm8xxTmaOfnnMrV+kkALHfkEojbnLfzpHNJ3462sYQGYY/APCgvY6Kjh+6ETjRtIssShmURTRl24rIskdJN2qMaKd3E41SZpbxAldlGEwFOMxdy1T8ibOiMUV/qpaRmIrfULSfkrKFYb8cSghbKGSe1c57jGX6r2EVwi1TFhZ6r+G+4ACEEXqdKhd9FWyRNNKX3noPOuo4YfrfKobtWjk7U2l8UWTweadejEUV1lOstXNFxMZOdncxoeJg7Si6l01GD1L0RsbJ3zMJfBRMPBMvoO0ZRPY7JB3HKYd9i3qHT+Ok3x7F7YZS53r/w+93seflSrYXb9AXs4Y9R7nfidShMq/ThdakYliDtq+Y6z7yc07y18CI26cXZf6vZyEQb5+lzB4XJZVKzM1jqXMVTriXZdXKqtYD16eLsWsqM1H1i1dFslVEoJbnNecegFOwmKjk8fQObrEoKpSRpobBYP40e4cs+RsHiHufynBGtJqsMhySICA8PGIePeFmO3aeOyRU+irxOaoo9rAvGEJAzOtgfQghawymmVripe2vZGIr8ZvjPg1u347+F94lBZMA0QItCUR1S90aOff/M7NjgHeNu4V2mcce4W7IdtGPeOwOpd93u8BoCGDFP5vvdD9rdGezU89afPMub2iQend73nTrhwzMpTLcRcVXR4whQmG4n/vfbvuA3lUceeXzeyBOLPEaPbd1B2Bz6t+dX7AdNb282rCrsruW+7r1ofnwet/7lY+56ZT3rgtGcww4V8lQf/4A5DRcQMO1C4tGdVzBh0jRkSaJHE1xtnkK3oxKXQ0YCnKqE2ls3n6E+nzO28i5TcmbVwSYfVwwQdN/kvIsnnX3jTz9J2zvWdZIt6G4R5ZyjLmGNcxfOUZfQSIB6qZ3btQW4E63IEvhcKi5VxaFIuFUJSwgsAQhBuc8JCG4SJ/GM62iWld9Au1TBa+s7uf+1z1gXjFLgVEm4q5jvv5ZmKUA1nRQR5x/SN9hP+sju1IhKfikvJu6uoj1iB/CNK/Ny7nensGtBhH3X3sQnzd10xzVm1BRxyPQAkytG6QzUXxQ56SD48GkkJHRfLd54Iz//8DQO7n6SX3Zcy8v152OiIGWK+ngHvPMgPHFqllSAgP8+iPzXRdSXeqlTuvhVy4XsF3yCvVp/x5Mz7qbDUUON1cbMxDOEE3ZeybtNYeIpHUuAP93ORfGbck7zrK4bKDVtEfV56tM5Tk5gk8OBgYiZzkF7b+eiSuqxNRW9Qv0msxTdzC3QZ6kvsbPchMDugoyXg0N2F/qP3rkkk3tct2Zfr9UqoVruzumm/Di9lOO0xVky8ojzmkGEJYMDp5RRUdjXbcqMOCV1k8NnBCgtcLI2GMshlGuDMUoLnJwQXon01orRF/kP/hA+fQ7+unjkx46EbRVq9/JCez1ZJsgKkjCwJIU7Si7jpUaVVz7t4KVGlTtKL8OSFCTRuw7rvvHVGPdh+AC8SbF3ADsv5rFd76XHUTmsBuX5aVfx+G6/4W/Fx9L8/+Z/EW8jjzzy+AKRJxZ5jB7bamdwtMi0512FkI4g7juCp//wbLY9v/zlNTz04uvo9x0J3RvpdgS4u2w+p7Yu43s9T3Fs17182BJm1esbc8hFW+M6xq97gEd2ui3rZjLQ8WWTUcrZ35nMQdMq8LtUZEmgyuBSZVwOBaW3ayGAa40TWWkcmRMm1h8DOxq/TM8jKtz4pVS2GDxeW0CrsK1nMxAIZFm2bW8Larii8BralCrqaOdhdRm7eqNMKPPic6mkdIuEZqJbAs20/xeMaiQ0E7/HzWMls+lxBCgpcCBju9785aN2qgvdVPjdfBjzc6nvGqJ4KSDJ/4h/U0c7jQQ4g0U0mKX0JDRiKYNij5Pd64po2riWE1afw2GRp/lZz2+o8Lv53i4VVI8ldba/KPKgS+2CL9xISjcJKZV4LVtzUZxu5qcbLkfBJIXLJm2WiZBUrCNuQOx1qp1AHm7CKByP9a2zIdzEhD/+lAq9lS5nDe/UnECDUcI89zJ+K47idvlUXKqCQ5HpiKR4Y0MIZ7yFayPzqRXttMlV3Dv1bkJOm4j8UbmY7/Ef9pc/BuyC/ScD0tZt0bWtz4kKF8drCzhVuppnpe/RIGzdhSSBIoFhgtm7Pq5UH8KJRgGprOYGBI8Z38npfPVfU3c6bx2Uh7FUP3VQhkWmmzLQZKB/OnwGu1b72Gt8ac7P+o84Tan0c/oBE5hRU0RPQmdjZ5yehM6MmkKOnFFF525nYhSOH1uR7wvAvmePfs0wIPHdLMU69c/DjvH03SdmjiwozljWRlt715aCLEzO7bqa/StSfHNCKYfXG5wXuhpZ9OtYNf7Lfj9bu5HyJcCwblhp+9740PR7eDfiI5Y2RtSgpHST56rPpcDt/oLeSR555PFFIZ9jkceIyPGCjrZ8MV7eTW8j7jsCSRiYKKza6W66S3Yn3dXAL9afR8BsBaBbLkNRHRRqbfS4a3lyxt1EnQHWBmPsVlvE7IMmI0eb0e47EmekgecKfsxfpf1YHrsk+1L3Tr2bcPlefNAUZlJFAU3dST5qCdMV1/E6FSp8TjTToqk7hWkJNvflGUgqMkF3/Z2EMuFlmVCyZlGOBNRInTQR4PrAr/nxd/ZlSoUPrbuR6meOpSDeSKtcxbLyX7MuVURDdwLTEsgS6IbAAmQJHLLE+PICSrxO+3oKQXfC7izIksS8Q6exujXCVX/+GBDsk/wny7kxe/4XKRezuvBAOmJpnIpM2rQYX+pFjjRzS3oBNVYbPa5a7hx/C6sThZQXODllzxL22mn0ydv9k9cL0+1UPH0MjsgmutQApiVykrQNZHqkEspFKJs90eMIAFCst9PhqOau8bcxvtSbTUcOu2u5PnATZTUT+c+mHjoicc5IrmKVeSRBuQKnKmOaFkV6kN/KS7OEap77KpLear7t3sivWi7AiZF1WRoYiJi5xhkdTQaPGQezmNm4FJkyK8hDyjLqaKdBVPKzdG7o4QfWBHaTN/YKv/vCEU/SLmeW+hL3GTOzr9d//fSHLiQcUu6qHJigPdSabKWMuiInP9lnXG4uiRC535/e8bb+16wjmua9xh42dMRJGSYBq5PzmuZRkm4m6avniV3u4v2of8jkaqtkEsHvP0TlxF02v156Mys2Tj2N5zYpuYnvFT6OGm9S+4fjsqF2f5i8lD0j/8vrZcdw4ifnji7U7o/n2l0LICW5cWdT1Cu43ncxlyduzK7HmLOCAo8LKdwEe58Kn726zTIbviiMlCfTPwBvznen8PZ/P/hccz6+0FyCzyvZO49thnyOxbZFPscij+2DzQj8Rr0zOBr0G7uyar7BU7uvzBaSp386G+nTP/PzdefaomLK6aCIEitEodZGWvHy3NRltFhlhOIaPpfK2vYobY3r4IGjcEYaaJYC/NHYl/mJX+e87A/WLybYtI7GrgSfdcapL/HyzfGlFLpVEmmDlp4UwUgaWQKvU8GhDP9Hs44gL7kuGTJlO9OpyIiAH3Jdm03ePsVYwEnWYhpEJXW0c1nwYgpSbYwrK2DKlJ1pPvpJWqQqqq02ZsaeJqEZOBUZl2J/nTNlrSpLuBwyCc3MzsTrpoUqy/jdatbBZ3p1IdOq/OzkifBRMJO+AAEAAElEQVQrHsp5D9eZN1Me/oDuuEYwmsYwBVZPky2Attpokaq4yHs1/wi6aQ0neXtjF0+83cDaYGRUl3ldMMpdr6zPrqEb34xz+7jltClVtluVyN39VbEoFyHaKOeuSXdmXWiK9XZSkoeXdrqaQo/KzHfOzAq5u497Bqm4jvebw7RHU8wxfsvx5p94iosIWO2YlkWZ1ZklFc1SgCWl1xN1BVCizcxquyqHVAgBS7VTsoX6QEew/ls1ByofMk7tQpKhQ6rgLHkxjQQYJwV50rnY1tL0ro8rtZ+zyarsza2QaLLKGC8Hed41nzPVF7IdkYGkotUq5ifpxUSFK4dU/NHYd3C+ivoQQYpzOhdnqM+zz7giDtwpMOyI02G7BnIKxIyHvypLvPhhGx+1RCj2Oij2OHk/6uN0sYgmAnhijRz27zMp1tuZVO5jnNqVc2049Y92Ts5o0Nsx9T/xI5o3raXY62BSuY9ir4MPW8Lc+57Gnf00EKevmc03Wh/jrNWnZbUSj01fgeUfITPjqOWw96mkvNW4RQqjt/9TRQfLY5dkSUWnUslVVbfR/ONnbFKx4e+943h1tk5jB+1ajJQn098Nq1gL5lhXPzD1juFd3L4K+Ly79XnksQMjTyzyGBtGEPgNKhq2plPR70be1riON7SJrNrp7iy5uKh7GTVWG21KFXe4fkGJ6Bt1cpkJDvvoMtau+4S3PgvxXlMPweb1FP/ux1kdxnWei1iq3UilZbshve4+kDbFLtbPb7yQGilEXYmHT1ojfNAcRgiBQBDTTHTDQkUghAWmwQL1t1yr/iZnrKSaEM+75lMoJbPWs62UcZ76dJZAHK8t4DhtUXbu3RIShVKSk6QXCUrlzLIWsklUUiva0V+7MzvO5SmtZV3BXrzgPIybxcnENROPQ6HApeJSZFyqhEuV8DhknL2WmCf13INkGcRSBqUFTpt0qApeh4IlBNO9YZaFL6Ou163oIvkiDBRUTFaaV7I76yh0q+zk7uHW1AJqaadFCnCaWMg74QJcqkRpgYtij4PmngTXvfAJ/1jbMaIrzLpglFWvb+TDlnBOkfhuuIDTzEW0UU656BryuQJwK7arUwZukeToTy/h52vOzikkx02cxukHTGBieQFJ3eRl6QAMFPwk+D0XM834lJXW4myn4ubam5m+03SOqDe5nyVUW232TH2/XIi7XctzMkl+oL6RJRWSZAu3W60SaqVO7mcJRVqQhGbyabKIE7Urc8IRM6Qz4zg1kFxk1pCdkbJoUKfCRMUUMnHhzf5MFxL3GTMH5aucqb7AfPXRnAC8u9VTufpHu/Pzb08cNOK0W23R4DTtXtJvdTfy0oftdMU1plb60E3BB81hwkkdCms5k0U0EqDGauPCdbMo6nibWWvnDijya4a/BwyA9a2zCbtrKdNauKTtV1RLIeTea1LidbChI8ZfmlSenbQEU1JQeseV3GZ89KF2ior1/dtYMXEFTQRQh+hJtosSTrGW8Gq7i9/9uxF93d+hZ1PvvU6yOx47aEGZyYIZjRtWf+vqBqOUjZ1xGoxSXthnZfbvw6gK8R0B20rHk0ceXwPkiUUeY8ZwAr/BRcMo03SHQr8befnTP8GTbKWreDd+XZQrBvxf7xEsSd+AKlkYyHRLxQBUWEFujl7KFFeYKtHJ9dHL8cYbiXnruT9wBUu0G7MWq0Gpgof8Z3Jl0XU0E6COdm5KXsGnn37CB81hmnsSdMV1tN6xal1AwoSELrhUfZQz1Jc4Qf0/nnQuyZKLM9TnswWhQ7K4zXkHe7CO/5E/AKBQSjJLfSlnt1vudey5z5iJJExO5c+8Z03mVd+RPFZ0pp1qbOjUPfV9Doy9wN7mB0xwRdFNixK9ncv0FVyhPEiFV6HY40RRZHbSP+EJ60KOTj3LsV334nGqTCovoC2Sptjr4I/vtnDHs6/ws4/OocpsY5NVyU9TV/JUYm+OSS3Knv/jjsUcof6Hm5MLqJfaaRS2FW+rVYoESEikdZNwUidtWHzQFOba5z9hxf+tGySeB3uUpn9R2j/FeWJZAaawsAY9y4aBTDWdnLn2bCqsIEGpgpDca9Oqd2adaR7e+U5e73Dz6toOXKrCaftP4MDKNEuMW7KuTl6S/Iar7fdEgNPFQt7t8bFhwxpOWzOHcr2FpOTNztT/1do7+5k85Lo2e70fMA7PhtLpQkaVLF6x9mSTVWkHD6pLs4nadkfD3gkfOKbUXwfRn1xkxqv6dyRareJsx+tp1+KsWDszDnWH6w5UGS7Qc/NV/mzsl32ta81TmHvozuxcU8iUSj9nHzyZeYdO49zvTWXeodOYfdDkXFIBWdJvrTqKUMsGqovsOfp1wRhJzSauAO1yBfM5DwMFHwl+sW7OoOTqlvDow/SaRRm31t1i66LSzRzz/llsWP8pb2wI8a+NXTR3J9C6G/jxZ4uzpCKDsYTaNfck+TRZyEUFV9MplQ5+gASGZVEuOjlp9Ry7wPbV2r8IN+7QBWUmC2akTsQx751pu2GVTMBxxvOccsQBOWvm5MMPsMfNBhTinzu2pdnI1nbrP2/jkzzy+AKRJxZ5jBnDCvwGFA1blcDb70bujDRwftM82j5+jZPCv8l52InRB1AxMVA423kNJ0nX0iZVAFApOriq8wKuj1yS3Ym/xDqXExsWUWrY1okdciUX+q9nfbqIJquMS/xX0yAqqTLbuCl5BYVaEM1k2CL3PmNmTrBZhlxkBN3H9hP39t9tbrLKuc+YmZN/EREeZqavpYUyLhAPM0t5kR8qb/D/En9nL3Uj64Ix4n++HKntPYSkEjBbuTZ6OQe4N/JbaRE/5m/8zHqOVcZ89p1QyPd8DfyWRfhJEMXLi/6fMKWigFBcQ5ElgtE0a9d+wgVN86ijnU1WJSdoC2jpLXLfYwrH9cvfuCp9HTXCJh8/S1/Jeq2EtG4R10y6ExptkRTxtIEiy1T43aQNk7c3dQ0Sz2fW0PqO2JApzgXpdn4rLaWGTjrIzYJopwR1wNW4teQybi6+PPfCmAZrgzE+bAlz32sbWP7yGv7+r/9ySduvKNdbCDsDRJ32OikgSQw3l3EuMWeAaqmTy4MXUaq10KUGSCq2javuq+Ve39kcl15MRHgolJI87lzG3tIaHnFekw2lO7bXbvhK4+d9HQMpyGPOZezBGh5xLMvpVAwU/A8kF6o0ePXpQmK2diFztPMweokM2GRjYdmNtClV1EtBHncs5g7nbTn5Krf2WtiqMsw+aBKzDpiUPW5mxGnnqkLqS71Dz8f3kn41somzN51HQISIJHWCkRSqIqEZFoYQBEQH13LbIGveLU2ujmsGbVIZT864hy5nDaVaC5e3X8SFyTuY4goz3tnN/SyhXG/JcWSDvjGegAhR++Yyu2AbptiLawayDGUFLoZSIAbo5iGu5G7tcgJmK21U2O8jQyp21FC4AVkwQ3UiTKffHk+UVTv5vqhu6DUzoBDnjRWbe/Vtj209vrQ13fr8KFUeXyPkxdt5jIihBFCjFfid+72p7Fy1lZ9zuCkrts6M5jQR4Fn5EOZaffatdxTM5Q/KYTR2J9jDH+WO1BU5ot92qYIb/ZewOLKQAmzC0+MI8LvdV9IqytBMi0Ta4L2mHsyeJh5SljJOCmaL/SYqhz3FgWLa/mFpAHuwjqdci7K7za1WCT/Rltqf1xAi2swxn3ItoVayLU5NZG6qvY0D9t6Nvf/vZDyxRqxe1xoDOVtsZ/7/a+6D2C/1Gkov6TpTWcYadSeKvS72nWjvwiZDm7igaR4Bs40GUcnPxSI2GaUYlkCiL/zvEOltfuNann2/s9Pn8Q1lLfcZM2mX7FwPhyKjyhJORWJGqURC8dKdNPh/E0oh3MxPjT+x++m3IqsOIHcNKf2KV1+6jWPeP4tSrYU2ypFlKTuuBhCklFJ6cshFUKoAiZzHAbRRzgUF1zFt6nRKzSAnre7diXXUsLzmZir8Tk76+Kzs81pEOfOl87mKO6innVZsEX0VnWiF4+j4ydMs+XuY//20gyrRySMO+9plMBxRqCbE4y47aXtzjx28rpb0di76kBm3iggPb5o7c5j63+zvWkQ5v5mygg2dca4PX0wV9nObRRn/Maexh7KO8VIHD3MU5mFXc9r+E4d9/RERbsK4byZqZBOdjhou9V3DW10eVFlCkWVq5RB36guppz37vc2gx13LA1PvoMEo5YJDpuDUY6MSWDZ2JVj+8hqKPA46mtdzefBiakQbAF1yGbqQCYiO7Ov1uGp5cVqfoDikVOJUZfzpNqjaHUwdOlYPIgONXQlWPf8aczfOzW5ADIdOqRQDhSrRQdJXj+cXL375ScVQQuSBIzz9jDnSXQ0EPryPgu9fgxxpgXu+DenI6EjUNhQxj1mMO8J72lKzEcsSPPTi62MXrW+Hc8ljZOTF29sWefF2HtsVoxX4bYsEXstfyx8mLs0WC4ZQWCUdzWzr8ZzHnR2/iwO9G3HIEh1yBcvcF+b8XpUsLojfkiUV7ZRwRemNxFwBCj0OygqcvTvuJjFXgPPT9vhIoZTkLuctI56jnbS8eMjORZ8taB9/N5GploZ25ul/zOP1RbT37tgrWFzYfB6P/e0tTtCupIkAsjAR0EcqhISKRTsl7Jv6R5ZU3D7+dsbtdhDfmlhGhd9JZzxNQ3eCI2O/z5KK06yFtFKOQKDIduEKUEWIKwfkb9zqvDMrJg6IEALQdAvNtFBkCZ/bDppTZZkKq5OLWi9kz+ZHiP+5r6uQWUP9U5x96TaO+3A2pVqLTRawyUKXs4a7J68gKFdQSRcq9tgb2ESqUnRQaXXQIVfwSdkh2bGoKjq5PXk5043VOWN6j+x8JzXjptAeSfOmtEf2c6+ROvktC7KkwidiVNFJm1zF4pIbmP+3bj5ujeJ3K0ScldmE9AzesyYRpHjI9XGRkfvYefo5I5KKDAbu9seFE0kCU9jjdBlS0WoV02yVUyN1ctZn51Gkt6P0/jFtpQxZkvih+gaa5OYl1/9n77zD3KiuNv67MyNpu7Zope02YEyvSSjJF0JCh4TeAthgMNU0Awbj3k0vNgYDNr33EmpCIIUaOsaAbbC9ve9Kqy2SZuZ+f4w0qru2wbRk3+fxY1saTbkazT3vPee874HsPn7xhknFBkpDlHEv0OmswBNpZE73FVQpnbg0lYoMpKJZKWNi3jW2vPPJX5zHrgVBKtwbL01cWZjNVqV5fNMeZPWAmxlFV9GqWIS/2OyIkgrFXnxY6LuOlep23LPVTfSTTYnRapEKZx40f2qRCqGm1c07mt7n8q/H2qTCGGKaLJLdlMk2mvCw0rUrZq5v48bvx0Sm1fNEP5loMKsogmq1k1EvnED+x3eg/G0muCthhyOtYPfHykRsLL4HsZFvna3/IYVPhjGMHxnDxGIYm4yNafDbtSBI5ab4GQyC5ro1/H7lFDQMDFQ0YTBd3mGXPy1Qz0GPNnRPaZ7IH/Lr+H15hBmRZMfXErODSiOuUCKFRkNXP03+frp6w3zRFODrtiBFOQ72LBngJle8POnc8MUbPM9M5OJp1wyeds6wMxlNZpHdtPuUa9agpCKGOrOE4yJzbIM1DZOb+iZTbHYymQvQpZLkWaAJSRtuSqQfDZOIVJnsvhpZ+UuKcp3UlOSyS1Uhnb1h6jr7eLnsXB5RDuNtY3tUIZBIu/TDlBapSCQ/Z0b9N1zCsJuJH3HOpUx2WJ4eUpLr1BBIevojjHJ1M271+RSHG2lzlNOy4xn2ucaCxEQX590bH6ZwoAG/01IgKqOdNq2c+7ZZAu4qtIRVp6BWTMDhScpc5Mtetu34G7pUaMXKypSarfx5RfLEX6sXc9RWkpmdl3O48Tc+c/0yreSqgB7yxQARqTDHOZGvw27WtgVpCfTj79Nxh1vTHNMP195hnnZX2vdYTgfXacnbJhrqJULFYJp2v63+lGrEZ6LQZBaSKkY2K3IqZznm0YCPMrOZRX2TKTVb6SMbVQjKaadfyWVr6viFuYJCI3NTvI2NLA25b/QSGoSPStnCMjmLbfUvuC1KKiLSIhWNwsf5zrk05u/MvaNTVYMahz6PBCiK4KAdfeS6NPz9EVqVUm4uvCJpGw2TFlHKODmDvzc5eO3LFrZdfSfZJAR6rjzIL7f+LQ2bXPTfeTB337UEz+NHkCWt7SUWqQfrN5EKFRMDBRWTX3Q8HyfPP+VylkyNyPvPpmfXs1hz2KOWL8hgjcgxE0EE7DbWykQkIrG8bGPG4PsmX5tZbKQ3rA9qDLjBEr8fSvhkGMP4kTFMLIaxydiYBr/NIjXor8fz5NF4Io10uyp5bcvkIGJhzhW8X/InznctsMnFDcErOP/rc/BEGmkUZUwtvp5OJTlo73F4KJNtLIlMp3btKl77soWP67vpD5tUiA7mdF3BCGEF0weFrhmyDCoWCJbTkUYuykSy+/HR4TnMiYxN+vyGVq5rjRKODM2hyYyTizv0qdzMdRlr70vxo0Xr6c9U5/IZo2n099MeDBHojwBQ7s6mL2zQHZIIoXCC9gZ3iXjjuWGmk4oTw9NZIbfEL3MB7Jr9EYrVO1BOB4YJHb0hmgMD5IRamdV5BYWhBjqdFdw2YhGu4hr7PGNBYqKL8+s1E/iw5E/opsRjtNKXW82Luy8j0K9zzGdnU6y30O3w0e3wUai3kxuxgmN/9LUs2YeOik+2IVSNHi2ZLDxZM4NwbgXZ/U2UPnUM2cE6y+9A2Q+pJGfXcgmhR3sSruy/AaOrHoemIE3w0cED0TKoerOEZhk/zm+VT9PUwR5xzrXvp2NSDPWqaLXvH4Ap2gOM117iCdeMpNK6o6N9HfliAJ/oJpIS5N7qvJlCo51LUzIjOfTjle00ijLu2fIGul2VeCKNeJ48+jsr2zR091NrFLN8q8W0qOVU0cK9chpV0UyFQxg0CB8XZ82nQ/Xi0hTqjJJk1aD7Dofe9oz7z4RR3nz+vEcNxbkucvqbmNB1bdo2ijQoznXy261KOaBS55fGx/E3hQo9zchQD0a2lRVDGhgoZAfrOL1uqk1WJXG7Sgl0k0MmqJh46aRVK6dt5GHw+Olw694/XfnZTKvnr77LzNDJ3PBu79Cr5zFS4q+Ddf+EYHPSPdTz/JUWkbjrYLjroKHvoR+IfG1OsZHvmq3/QYRPhjGMHxnDxGIYm4aNaPDbbFKDb9+KM1BLu6OClyovZL+1Vye9fcbAXag9jXyujOb+7ZYihUUuiswO2lUvl+TMo0kWo8uU21wIOjVrlXVRaDrXuZbzpxrJDrmW5Kon0kitHLxefpp2P2q0ROVK7SG7LChGLi6MXJB2KRdGzsdLF0ucNye9PtjKdeLxztBe5PjwTDuA1TApoRuwyp8yYZKYyIfmVrQEBvhgXRfvru3g7W86eH9dF5oqyHaq+Psj/LPkOGqllxrRyj1iNmV0ZCQVgrj/RoP00IQHhzDRpUpNtDHZJztQBfi0Pm7VZ+GJNNLprOC68hvwVG6ZlsFKdXHuaq6jJvABJXorkYIR5Jz1MmP2HsGU1ssojTShF4yg4LzXKBjzAFJoqJhIoZE/5gEKznsNvWCEndkqNVrJ0ZO9NA5bPY2OL9/kovqJOAO1yKKRPL3lbK7sv94quUohoN0in0bpoVq0sCg0DbWnAV9CCVu9WYJAUCa6qDc9djbKIgwd1GidSeN4ij6dD+XoJAnYRI+KcjrwYJ2zI/q1tppujgvP5BO5FW8b2yFl1PhQkNRYrAq4X07jRuVGUtFGMZfnL6C1YCfuGX0L7Y4KnIHa71yOEVu9zSkdyd+3m5v0npWpKON851y223Y75hy5A1MO2y6zatBrc8A/xCJESpP1b7bycPzWsCg0nQrZzIBikd1YeVwpndzdfyF7qF9yccNEPEYrHWopITUHpIFEIMJBRH+8J0clLhUMliN64i9LAMWib9BT1BHcyFgqnj0BPn/S6kFw5jGk/OyPWSr1bVfPN1TSEzyAHleZRTz89VBQiRy5D43d/YOTV3c1hKLk63sYk80pNvJds/U/iPDJMIbxI2OYWAxj45Gykvm9Sw0eMAe553m8M+pSjv9mCqo0MITKc9tcQ7erkgqzmVvCMzhhtMKY/fdC5JXaH3VqCmVKN3O6JuOVbbQpXgIOy4grP9yGoUdophSf0cze/heYWHsui0NTqZSWK/LJkRm0KxmacJ1zbS8AsFShUk3IFjkWp13KUseN0QZuazW01Syg3vTYn8tELhKPd5r2ChfpF6VtowlJq3SnvX4pD+AOtxLWTbIcKjkODSklDd19fLC+i5JcJ578LALOMi7Kmket9FItWnnUkWzadmJ4OqqAh11WgFwrvZzjmMe5zgXU4UMTVt/LCKWVR7Pm8WvXWs43H6RCWkZzF7jmgrsyzWAthkSJ00sL/oYn0oiM3lu4q1DevQ0tsN5SITrjRZSiapSVz9jKNELqKCufQSmqRjvjRWTRSFQMTES0x0ShE2t8ymQb87sn4dWbCBfUII5ZxphGywm7WZSmEVAPflRh0IiHCtnCPczkMeesJFJRqbRTK70cH57JcWHL1HCE0spjWbN5VJtlj9mp+hTGqS9SFiWfMXKR6FHxiHMuWQwknUOx8OOji3naXRykfUiigFbiv2OEo1zpxkghmxEUCrMd5LlUvup38+LudxIuqBk6oMyvgS32Sfu+EpG4envo6qlJ7xmoPFIzA2/lVpz+f1vw261LB1cNCrbAO7cNfqCUngClxwrgvHoTHaqX27e/hzdLT+Ci7KtojZa05ch+Tvz8bDtgayzYBZfRZ/mRIJFYk19i4idxPGOaUqnVT4MpnWhI5utX207dEgWkaQXYrgLY4+zkoPmHKpUaQuY00+r56M43OGflKUOvng9BSgqyNbSwP36Mvi7ER/fhfOBwm4AkkVd3NSDho/vgLxO/lzH5TuVLKfiu2frNeS7DGMZPFcPEYhgbj8Ea/L4vqUFVQ/x6Aod8PQs1uhJ99+ilfFX8O+6J1mr7jCbO/vo8tPsOhZ5m69juagpCzVw3YJmetWrlPLD9Uh7a6S66nVZzZSmdZKnSlhzNj7RTarTShIeTwtNppphsp4ojGmGUp6ziL9cPBZKlQVMlZZtlEc3REiav4schJLoUNJmFeJUAIAclF6nHe17fmxu0WzIOUzHJK/MmgmpaeMAxl5FaF4GBCA3d/bT2hOgZiNDY3U/EMDn3d1uxf0WEc12vcLEz6vQdNW2rNz2cHJmOSxM8GC3lqZVexkamcGzkefrCOmeJWdRJi1z0kEM1LSwOTaXI7KJJlHFpznw0RTAhfBejSgbvt4ndQ8VHXA17TUAkrpAeMAf2mpC8ahp77fSXrb9jdd7uKsQxliSmgow2DpuEcPA6v7BkWaO9OX+tugj55Hiyg3V0OXwgJV7ZRqMo44Lsq2jCIqE+ulClQbO0zO4qRUc0c2SRivXSy+nMpMA3grKqLfhkvwfp1HxU0E457TSLUsboMzhVfZXT1eTMVuy+SSwr+4P6CRDPRmgCnnDN4gD1fXu8EjMVUkK/dCQHxVGhAFNCi1lABe3M7Licjsa1FOc62Wu3XXh429uSAsqtu97g3JVj7IBpde7u1kp7bGEgQ4AaW70ds/Js3CGr0fmrkv0IqXmoGJxTN4nfeCz3+oxwV1nO29scBvvPSn8/dswdj4o/S+46GJYfiKOnNnqt8It1ywn3dpMXbqMo5bcA8F7FqYz2v2Xda9LAJJ6NECnjmYpUKpw5P2gheTI1IdJnEZFQgMCT5yNv3s0Kml+YmFxqtuNR8NwF8OwEeGnyd1utjwzA0n2g/j/W/4eQOW3o7qerpRZTy6bbWU7hQAOHf3U5WUYvA2rukKvnQ5X0ZMt+u+Fd0fswUPBEGpnUcBHVfZ9z8AcWee3PqUA3TOu83FXwzevpGYzNgM0mNrIZsvU/pPDJMIbxY2FYbnYYQyJJsk2a6TKFQ2FDUoOZZA9TPx+dgA1nPi9tNYu8lnd4svgsHA4nuxYE+fMX5+IIWEEG7mor2OxpgvuPhlCASMEIHtr2Vj7pySOkG2wd/opz6y7FZVplDUFnKXnhRDnTIo4OzaFBlljqSECp2cHDQyg4weCSstuzjmWu6+3Ar9V0c2b4UhY5b7FXvkEk+RpAsgztheHzWeJcZMuO6lKgieSfrS4FFxkTuUpbSj59dllHrfQxXsyk2+FFIBjQTQzTJNelceWv8znswzNxBNbTvuMZ3NSzP2evv4hqWmkWpdyQfzkTg9dTbjZTh49T9OmcJl5knPoi9fg4R5tNcEDnLOfLtFbuz8V1F2Oi0FqwM896z6bH6ePUVRMo1ZssAnDwgg3fM98FCfdLi1rO5fIC5hk3Uy3ikqExlaKYg3uPq4yQbuIxWqnHx5SChRSUjSTUXsuczssoj8q19koXuSKUdDgrszUdPa+C34zyYEo4avdKtL9cxN7+FwB4kj8wwzyLYr2dB7Q5afdQIoEMSRWXMJLcu1P7aBLLdRL/DZZSVGpTd4NZQoEIki9C+LMq6TruadSiam786ypqtE5OW30+hQPx1dWQmsPnhfvxQuFJXNk22c4WMXIf+Og+9IIR1O+3mJK1fyH3txMw7j4UR9D6fLfTxyM73onW28K4r86xFgOc+agT3hlCvnMISciXp1hBcdFIOOp2eOAYCAfj1xttmrbHBmyZ5MRhiP1/ABdOwigZ8g6pY/ldkHr8xNcMoWFml+Doa7HGpGwn+PofoCeUWX2X38rSfaD5E8tn4vSXrSb1QWRO6z77J3u+fhJqVJq3MBS/D57b5hq+Kv7doLLhMenfTPdQd1Ylj9fM5PDVU20T0hhictiN0oMiLOW2gMtHtkOz7qNYBsNfnzYO31Y+9FtLxCZiM8nFbpZzGcZG4WcpN7uhmCgVm1HOeUMYlpsdxvcDVbMe9BurVuGusrYf7IYfyjQo5UGujn2aw5oXs2/n48zOeTRedrXVvvHPmLpFKp4cb+usJ5ZrTdwzl/O7rsJl9tHo3IJuhzeJVAB46eIRh+VJUZClMTorYHkQREta/jxI30UmSdkdxTcsdd2ISKiH9yp+ljgXcWH4/KgBWgeJmYvHnbN53DlrUFIRiZKKFtOdttLaQhHnMIWIVKxjAjWihbuYTWGklZBu4FAE2Q6F7P4mfvWPMTgC6/FnVfL1qFPpzS5nbsm1NIoyymQb1wQmUW4206yWM7NwIT1OH28UH0ebZjXqLpezqSjMYvXIsYxtuSbpXE6om2s5Y+tWb8T37kSccL+EC2q4JGce9Tk7cIv7sqTNbiycQpCcaAZMQdcjVg2+s4I5JdewJlTIp/V+glllTCu6zs5c5IoQ683SpH1dbkygVfEQ0k2a/P0U5Th46bNm7ig4n+fU/Xma/ZjHmeimSb1ZzEnh6XapVGrmYpl+CCeHpmBEA9yIVJgaPj2thyYx+DVTwtdUUmFKQaXSQb4IYaLgHmig6tnjWLPmSzr7wkRyK/jHyOTyOl042b3jeX7d8STrDn8sXku/+jUCLh9aYD3VTx9F/sd3EFmyt00qAo5Sbq5eTENHDyevvhgVq5dBDfck/75Ta+j9DfDBfdBVm/y6oVur1u5q6xnw4HFJpCJRrckem4S/ZcrrEgYlFanj+l2RaVexc4hIYZEKQAaa4auXkkmFM++7/Vb+eL1FKkzdyu70NKGf8jwDeTXQtY7g7Qdz78v/5slnn7FJhYFCfqgpaTf7rL95yNXzDZX01OXswDnO+bSrycIXMaloBd1SfVO8hHWJI9gQdS+PkorN6F6+WcRGNlO2/gcTPhnGzxP/JUaKw8RiGD8eMskeRhVGOl+7EbrWIYtGWg6vT45HRB/sxftNjD/I970iTnR6mpDPXmBJR+ZV8/Zv72O9bpUixfTYtcB6ZNFI3txuJkZCXPJlyQG0KlbgWKV08IhjFr9UV3OHnElNtAxojD6DDtVDVsI8m2qOlygpe6frRrvMZXzo0iQp2kzkolkWUaW0U6V0DEoqHELSZBZRInqSCIsmJA+rM8h3Obis4Bp0VDuYqaKFJ7mM7YxV9A4MMCl8Kw8ps6iULXQ4Krjadz33fq7T0RvGU7kVd+XGZWEBVqmjqIsUkpel8ecD9yYy5nnCBTX4jCZuDU3lnLUTKA5bKlyX5V2FXymkINRMYaSFgMuHMu4HkE5MmPhXHvgQtXoxlWoHE3tvSNpsTM8yZuTOJEgWKiZFRgfdWZU8tfPtbLvNdhy4vY+a4hx2qnIz6fj9uGXEYupl3OciEdeoSxihddIfMch1wAF1N4O/noqiPK51TmC6PAsTFaeqoijQ4fBwij7DLpt7xXU5VbTSRAnL9UO53nk7apRUOITJ1a5laNHyuUR0y1x0qdglT4nQpcIVofEEZDaKkHbfiBKVRdUC6wn9azFfNQVoWPEv/vjllUmfz9W78Ts8/LvkeDrVUtYc9ij9edU4gg0YuhHNElj+KbGsX+y4NY4AMxrPIdvstRqkkVaQ27XOUkn68oX4RPjCRGtivO9w+OoFuHXP5Nfv/INVd29GQMuBUMDqWyA5MzEYEkucpLT+Pxip+MEgIYtI/JxkSqmPUGDsM9/tt1L1KytTESUX5vKDmPvI3zk5Mp16fOT11XHoe2O4YP0Em1RIZFQ2N9q/thGNyBsq6VF6GmjBw+SCa2iLSj/HoGFSJrpplkVETCtb2CJK6Q3pcVKxsXKrQ/SQAGnlS2/ssJBdGx6iti2waWIjmUoyB0OMXCSWaWY4l+9V+GQYP08MERN9G+W+HwvDpVDDGBLfezox4QeSWLYUjoQ5tvMO2qoP48i1M62H7WATjr/ekjb019OplfGesjO3cywtiofCbCcHVIY5Z91FZAfr0AtGoBy7DOMJq863Q/WyvnBPnqu6jC9Wr+LGviupIDmAbMDH6cxgTaiIXJdGVWEW6zv7CUUMJqv3M157CYg7bpcLy6cihjNDE/mr/FUaCVmmH8Jy/VC7FCaG9WYpF4YvyEgqGqWHAoLkRT0Wzg1NZJ7rLsqEJWtroLB89FI+Wd/B4tCUaNhgBVY9Moc35C78SXkbgGZRyn3bLkUrqqZu3Sr26XicjhEHM27V+bbqVQx/zTqQR8svZ+Zh21D9/lWw41FEHjnVXrFuFqVMKbwWgeQy4262CfwbBZNIjg/HtgfBYTd+f6laQ4dXp0G4F/a9gpW9+cx/6K9c2zuFCtlCs1rOksJJTOi+ljKjiWY89EkHW4omul2VPL7TUoKuMnt3PQMRuvsiTDxgNP7+MDff+zC3haeiYfWSnGVcwdXKEmpEK3XSxznqbK6r/jfbrX+AdkcFk3IX8HXYTW/IwDAt9hoM6QjAqSpsq6/iEecs2yfltPAV3Oi41c5SzY+czB0JTucdZj4lSk/aZWcqlYqV4KkYvOi6kgLRn1QyNCCyudRzK4G2Ru6S03EIi3AMqLnkGtYxDBTmeG8kULwLA7pJb9s6rg1cYZe1mMRXpDooxFSclJqtdnmZFFZTvf13lGTYZAPQc32omgPpb6S1YBe8gU9QMCHXC5rT+k07ciCSrMSkI9A2gSDEZrdMGYlMJUs/BDKVXUkE4sQHYd2b8KvT4ZFToPKX8KebNvy76VoHj46F4++H4hHWa/X/wVx+EIq0eoouzbuK9eECloYm288KE2FllTDRUZladC0l2/wGZ2+j7VIfKRhhCykkwtQjfHHneMo63qJEb00r6WnVyrk4ay6GKVk8MMV2t8+EFqUUaUrKaE92L08p88g4FyWWy6XODf56uPtQ6F5vvX7cPcjoAlXnzuPp3XduevmSuwq2+sPmf14NO2//4PhZlkLBoDHRgG6Qpanskh/kpC/PGzom+h6wKfHzMLEYxpD4QX6c/noiyw/FEVhvOSNvdyt6XiVasGGDE1wM677+itxHjqQ00kiD8DG98Co6VC8i0MDisOWm3KKW83DVDMa1zMM90JD0o20Lhvi6Lch2WQGu9l+CR8YNxMYylw/laFQhGPebkexaU8RD76xHVQSf13dwbuhuHHKA60NHA2QgCsk19RdqT9JHFgv0kzFQ2V2sSiIiR4dmcaj6rk1YYqRivVlKPy62VeoJyGxO16fwubI1PtnOI+p0O2DoIwe/00d5eK0dxMSCqMRgtBEPt2+xhEqHnzFrJpJt9mIiUJCYqPSqBeQb1j47NC8v/OJuxvAC4t1bke4qevtDdilZm+JlQc5lXNZ7E2q+F9nTRI5m4I5ESdruY+HwdLWszYKU4KKxux/t/j/h1ZtoFGXMKLqKHoeHM7oXs2voP3ixvttm15Y8vPX1tKulOFWF/CwNIQS6adLdtJZLC/7GwG7j0R48Cq/eRJAczsu7iS5XBYWRVq7quZJKaYkD1P1+ETWvX0hp9JhzPddQbxbT1hOiN2TYDuNbOrtYzmxqRKudmUi8Ty4Mn89i1y3UiNZMV5qEbjOHQiUeePdKJ7kizHrTywAOtlEa7Jr2RHLRgRs3PVZZilTwi1xKsEhF7PvXUblnm6XUZm/PqtVfcFPvZMpSCDdAqyjltuwzmdZ3te30fnPJdE4N3IEn0hgnG0TvPxT85FOCpRykCwft+TvgDXxqEYsEJBKRH4sE/BCQwBtbXsq+dbchUogUu54CWe7MNdf+enh1Bnz5PBhhS31qzFOw4mn0P8xi7p0PMb3tEluw4GpxBlfIO9OImYHKlMJreSu0BaO8eRTlxPrXhghenr3AyihhPRvu3+52InkVOIJxUtKp+QjpBuW0p/XDJKJdFOORndTj4/Edl3LR0b9PDrC928EW+2LuP4fWjs7kuSgxYI9ev1nxSxq6+8l9YzrFny6ztssvt7I4/jrreo5ZBiuejtemJyxOAZu/J2woApSKREK021g4YiOemz9grf3PBT9bYgGbJSba3BgmFoNgmFhsOjL+ODdzg9F3bWgzTcmtb6zh7Q8/SVqlviH3Ei4MXEcVLdThY2nxZC4NXktxuJEOZwU9JzxDzRajaeju5+u2IA+/W8u2OX7GfXUuhZF4w28zHia7r0UtqmLW4TtSWZjNbW98zWcNfvx9YdqCIbI0hf7OWu6ScanWiZHzklaiB/PFyERETg5PYZz2Mr9RVrCtUs9608tb5g78WXudgMxmgpjGB+YoTFOiKOAx27lcPMiB6ns4o9mGwZpZdanQTgFlopu+aEOrlpDZ0BEE1RIKjXa6HT4MKW1vCcfxy+HxcdYEDfg1D1Ko9niZKHxd/Hve8JzAMd9Mp1iPjqO7Ck5/xb5fTFOmr9Z922bFFE18iUT462lWy5iSv5D1RjHnDizn2MhzSQFOqyjlkvyraZYlaKpCUY6TUd48SoxWTvlygiV96ypAhAK0qOXM8VzD16FCdNNEUxS2cnUzo/1yfIb1kL8ubxKnNs2jQjbTpJRxtjqb9XoRpikJ6SZes52HnHOpiZrlLYiczO0JmYmzQhOZ6niQEUorQZlFnohLzxpS0EkBpcKfevVJ6JNOckQ46bXYNacGdwYKfnIpJp4Ned+1J7uG3rca3IXKI9UzOaL2KvLoo41iSokT7sTMhbU/i0QEyeHukddwUuMCSsKNafdhjOxY+1BYU7A3xYGVeIi7jKea08VKmzZnL8RPAanEPw3RrI0sGknTkU8QcPnI1aDq31MQq1+B3hQCKlSQBk3bnc7RXx/GbsoaFg9cOWhQD/DsttfwVeE+fNEU4Phf1bBjhXvo1XN/PfKugxDRILzb6ePm6sW0CA+GlGyT7ee8byaQ1Wf1bQxFKmJoFqWc71pAcfkWzNq3kIpnjrWOq6hgWs8zc88JtO52YXqg6K+3Su1CAaTQeGLnZbwd3oJwJMzJbTexU/978V46dzXmMXdhPjEeLbCenl3PIvfwa6xrvevgOPHYmFXgTZkHDd2S0xVsXDbkuQssVbaNJSKx7+mHEMn4meDnTCx+ik3+w83bw/h+sZkbjL6raVBDdz+fNfhpFh5mFF1Ns1pOmdHENYFJVNFCPT7OFLPYted1isOWi/f15Tfw0noJr0xB7WmgNNfFFo5OTvpiAoWRFvxOH37Natwto52rg1fyf9nrqHx3Loo0OGhHHyV5TjRNQVUErr4m7ibZ/yHVDG1DkrJHJ7gyP+hcwDL9MA4LL2SZfggnhqezSD/a9j6YL29mS2cXDlVgmJIGo5hWCnFioKs5yGiPBaQHLFbGQmBIQQ4hi1TI+HZhHBQa7bSo5SyuuZkWz6+J5FVaq5dPjofqPex9hUzBXGNM0v5bKeCodXMo1lushszEsgZgTWsPt73xNXe/+G8aHpnIza+u5LY3vmZNa3q5T0ak1lXHaprd1eCvQ/jr6Xb4uNA5l8/7CigKt1IquqOBr1VL3koxXtnGtcEpVCodKEBrzwB1a1dx0spzLVKhqIiosth9W9/MUQPP8H/eAX45ooidKgsw8iq4d/Qt9tic3Taf6cr5BMmh3GzmgfDFFIWbCOsmPrOdBx1xUnFx+HymOh9MuqxbnTdHlcI89OFKek8VklLhp9V0o6fIzXaacTnXbJJJBWBfc2pwp2ImkYp+XDzhu5h7Ry+xfECkwcm1M8jDkgzNozfp86mTx9Kcc9FRyaOPM2svpx5vMinACqC1qBldDHn0pvU/JPZI/LeSCsDukxr00iJ9mCiIrnWo9/2R5vvPpOvuExAf32+TiqSRkwZSqDzY+wtcfU3sN/AK/hTH8NSVxN+tuxlnbxNFOS52rHAP3YgcXUkV/no6NR8dmpfCcAvn116MV7aDhPaeEH3h+L0Wu++aKaJD5mceB1PH3x+mtWEN+Y8cmUYqcFfBXudmHiN3FYx5yi69O/qTM9jeXMUITwGv+U5jwIiPbiQSpvfBMWiB9bQ5yrkxeEDcV2NTSAVs2jwYbLacygczS0zF7674r6i1H8a3w8/dSHE4YzGMIZGR9W/metEvmwMsem01W3ryqAp+yomfjbffe2SnZdTl7TSo7GHs8/NfWEljdz/FuS62jXzB3I5L7PfPcsznP8Zo3C7BvNzHWTniFFYPFLDvups4rPdpWrVy5jov4Yq+eHbjAucchBCcEnqEX8nPqBFttlRp9y5nUnjUdaxp7eGVFS2sW/slF9ZdQjUtrJdeTolMp86MZyZSCUQmSdlM8qOZshyJ79fh4zRzht2gXql08qhjNmUZauETESutGgqtWjkPbLuE8drLFHx8R7IMpCufVt9vMWvfo4x2e1ysY1pys97Ap7RrPvQxz1NRmG1nrNZ09HP3m+uQ3fVc1nQJxeFG3vGewL0FZ1Oc62Tcb0Yyyps/aEbDNCW9z11O/sd3WP0y415AKaq2TjqlPGPJyFtoCYSY3noJZVhjEltVb1NK0U1JOe3cz2HcqJ5GlejgVn0mVcSzVbFMS/ffb6Lwkztpd1Rwc8W17NfzDLVbn8Y+o0sZ+dwxdgbnS7El28pv7EC4R2ZxafhcrnMupUD02+VOMbnh1HIoXSp0kodXpPsxQGZJWQC/6aJAhOKBaqaegiGCcwPBu9n78Lj7dK7oWUBZ71cZt9NRmOmcxCXhpXY5Uwxm1KQv9TiDrcZLCVLE75cIKg4iSffsf3MJFGw8YUqUmM6UzUlFh1JMxJB2eWQmdMh8hCOHYr0lbQU08fdXEGqhfOVdiAPn0v3cZAo/udMuz8h2aBzz2dl4oiWoj9fM5LTm+RSGGpJ7e3DRo7opNVrpkPkU05N23c1mEbpQqBId9u8UsH+DZn7FoCvQpil58tlnOPqTM2zPoxuLpnCS/04qzGaa8aAJ0y5x7Xb6eHTHZfRH9G9fWvJ99038RGvtbaT0tm3w2P56+MfV4MiFA+d97yVbP+eMxXeNib4PDJdCDYJhYpEBoSDctDO48uDY5ZaqSALSfpxfvgBPnwv5ZRDpB3/td37obUgT/Z6tb6FWL2biAaOpLk4326rr7GPeCytZ0xpkC0cX8/xXUmbE5RPr8TFezCKcW8EuVW6aAyGa/P2IQD33qXOppsUOjuvxcVJkOo1mMVKCQ4W9staxzJhu1yrfOepWDjjwMCsA7qqj786DyeurpzZqltaCh4GIQSRhgTiRENSbHkDa6k9DkYcNvV8rvZwmZ9AuSikx23hYnWXXwgdVN7mGPynwiEhwbCCQGRDZXLPFXTQILzN+W0Dls8dZk2J+ubVKaho0ZY/msv6x3CNn4BCWhGQvOeTTR2vBzpT6P+Xm4qlctHU7LTucEdWqV3n+k0Ya16/h8uZLKQxZqy+P77iUHqeP9oZvOF5/npzD5vPXLztY09pDV38EVcBWpXn8ckQx76/vpLPpGy5vvgyv3mR7M9RsMZq6jgDtj0xgRPe7eIxWAq4yhKmTH+3zaFe9PL/1fA5bMwOv3kSzKOVddma+OJNCs5PlcjbVooU21UtRtoYWbLS9UUxTYtxtKbrojjy0SBBZUIVhSrRgA3p+JWzxBy78cjtuDk1LIgsxBGQ2Y0NXcnMKqcjUhL0hGNJqh9YG8bQYLOA0JQTIplD0J20b1IrI17voFzlkyz671yITWihCSkmZ6E57b1MzC4lENLXH4n8Vgy0IpJLKNB+TDZQcpXqgTDAvwzt6DyasvygpsF4TcvPKiha+bgvaz/OtSvM4YHsff/u8gdGfXss3o06lN6sMKSVff/0VU1snUSGb6RW55MreJGIwILKIOAvJDzXTLEoxTUmFaB/0XkkkUZG8ShxnvgruqiEDxdgcsr25yvZQiaFZLWNh9qVc0XO1rezmd/l4afT8b1daklgCBRsO/leeZYlcFI6AcZtYE/8TrLW3EesbgbRS1zT46+OlZvCDlGz9nInFd42Jvg8Ml0L9L2ND8nupuGkn6O+wGsWWHRh3bM0Ac+VfkI+cBCE/tH9FL1mEC2pwBNZz6IfjqdE62dKTR43WyaEfjrdl9Ta0khJz8D35i/PsVN8jOy3bKNnD2Od3qnRTJtuZ03WFpfyjlDFOmUc9Pqpo4U45iy0cXaxuDdLaM0BfSKdVeJiuXEREqjZpuFxeQINZjCQq1ap1sVAust/XMDh23Sze+uATTFMSeONmm1Scq86hN6sMTVEwUmKymF9BvelJkpTN1HeR6uadWkKV+H6NaOUeMYdtIl9wrzLH1oYPOErJSyEVsGFSAZAl+zlh7VQ+Xd/ODe/28c0fH7W+v54mMA1CSg4zwmNZKBfFA2NM3Fg+AxIrYLi4ax7qe7fhfOBw7n/5TRa88AXvffIplzRekkQqgq4y8sMtXNZ0Cbs2PMg3D1/KG6taWd/ZR1N3P+ta/dS8P4+Fj/6NJz6o5/2uHC7JnkezUoZ7oIHcR45k0VOvs+Cl1VzQO46ZzsvoF9kUhJptUtHjLOWB7W/nX/0jOd8xh0bho0y2safyOb/Pr+NeZQ7VooVmUcp/lF355o9RDwd/HZHlh3L/2+tZ6L2ONkc5WiSIgYoI1KMFG+jQvEx2X8NZ6//A5NBN9pikYl745DRS0SOzbFIRSbhnDCnSZGYTEUZFE9j3WSZSkSnEVARJpILotvl6FyYK2dJqHo6piSUidk0+uigT3RnPb7BMyTA2jNg4KWQes9RMVZKniWTQMqPYvlONFRepN9LWsI5Htr+NcFTm1FiyF0///S1WNPopzHGwpSePwhwHKxr9LHl9DR/W9/DWqEvozbJU1HoGdL4OFVolqEoZudIql4sF9n6Hhz7VTX6oGT2/EneWRoVopx4fM8VZGa8zRiqa8fDQ9rdj5lducOxivhrdhdvzkeuXSe89l3sck/uup0K004SHNsWLO9Ril5YMqLk8tO3ijS8tSSyBAjjtBVsmNnUe/OMH42zlPLb83SYH/0M5nMe8MB7e7taNGqPNjr3Pi2aysZvfza461nf08sZXrbzxVSu1Hb2YXXXJpMJdNVyytQF815jox8Ywsfhvw8bUfcbIR/1/rDpWGyYsO5Dgqre46MH3+eXcV9lj3qs89Np/8N91HOKxk+1AVZeCs/xjOX5gGs1K2Xd66H1X0yBFEfxxhMlNoelUyBYahOWgvFLbjjHGdOrwUU0L8/2TyQ81k+tU6YsY5Dg19pdv4hBx0nAVi6hSOjGl5X1xF7MpN5tpFGVcmn81jdFrrVl9D3VdfTxUMJ7nc47kdGbSRDHBAWuCyzRhNlHCv8wdAUtSNhOpSNw2kVycob046Ps1opXHnbOoEa00ijLmZF1GXyThW93EwE4C28hvODt8L69/1cqMZ1bQG4qvAPabDuYb11vHo4QukgOaeyMHEpGKFZxK8EQauaTxErYxvmKB/0o8kUbaHOXcPeoWepw+8kLNHPPp2RSHG6mVPq4L/IEvGv2sa+9FCLjAuJ9T5Avco8zGHWkh16kRzCpjYvY8GkUZpZFGjl95LmW0U6N2MjN0LdkyNUCQZDlU+iI6tUYxkwsWRntxmrnabxkBtileBHBo5FUKPr0rLWAoyNZ4aqc76NR8KXK8grbG9czqvsLuoZgcOiPtHljgvCuJVNSbJfhlLgD1ZgnHh+ZQb1qa/6qQGT0sYsgWOn7pSnuAW90zVsnXYAFqDLokiQAlZgzSGv6xsiqJu9MSfDJSkXjcRL+VYQyOmNt67N8bO2YyWoLmFf5B75cYqWgyizjPuMx+3t08cCU97XU8u8VspNBQwz1M+Gocu7l7yc9yoCqC/CwHW3vz6OgNUdfZR7YjPmeEDRPdMPE7vSwunJR0zKCzlHVFv7F7rTRFkN3fRF9uNZdkz+NJ4w9M1ceTCb24OM+1kLfaszcq2I/5avzfVwv4ZejdpPfOCiym3GymTvq4vOBqFuQkm2ZmGb0c0HQ7F38znt6+uAnjoIt0KV4Dpil5cNsltKteSvUmxn55HpU9nzJu1TmUGFYfTI+rDPO3l2/wOlLxk661d1dZnikJ5CJw635Mv+8Vpjz9GVOf+oxp971M4Lb9kknFUJmNYQA/fyPFYWLx34aNMFiRr063yMddB0NvG+QmOgqbZD14KOs+e5P23gjOvlYObb4Vd/3f46QCwXhtPh/LrfmsJ49TjOm0Oyq+3UNvc5gG+esZ+ZfjKY000ums4MqChXw14EZKSZfmY2L2PNq0csrMZm7on4Yj2IQiBO5sBzcxhoeVP3KKnE2ttIL0+7U5/EKs4n5tjm2Od5I+jX/11nAGM/mb+xjuzzudf65q45OGIG+MmEiH4qVnQCdiSBQxeF34NP2MaDP2jEFJRQyJrswL9ZMyvj8xkrzy86D7TCb3X08ZbbSrXppFKZsqGhGTpX3B3JuiSAvzA1eSO9BkOz0X4scbVfBxy2CaUtGh2ntMDY2LK95IKAk3ctH6C6iihVrpZawxg1fqVL7++iuO+sQiFXXSx0nhadRGihjQJX1hg5bAAHfqh1iO1aKVe5U5qMFGHIqgL6uMU+UM6qSPCrOZi2ov5Ja+yzNq5ueH2znxszOpFJ1kO1TWRYq4Lmdi0jYKOj7ZRqNSRmC3szKuFhaEWzASmJqOQonewjJjuk0q5oTHsNC1PC041ITVtBwjFSDimavILL7UtubP+mzbSBHgfbEjkUGCRXe0pyIGU1oP9EjBCP4+6ko7czT49yzsc0p/L3Z9Kq0UWQ3XpBvTacKkTSY3lEN6YBw7D2OILMwwrPGM3V4bSy4SKa42RO9Uu5nP0eE5vM4vOFubZ5OLKxovZv8VlyKkjoFKjuzlF00PJ31WCEG5O5u+sEFrT1ytzKkqaKqCO9zKBd3XJp+XUHjDO4Z33IdZ91PU/K77+KfJ841kS5efcx3PZzzXXrJRBPRFdHrDesZtEhFb4d0qMHjG/QYxBglMSjHNNBBs2/EaNaHVbPns0fE3BlukS2lqDy0/hI+/+NJ+LhRHmvjzivG4Q1avVrfTx43Vi2mQQz/vM2FDDueRvApCurFRY7RR2NSKB4Atf08krwKAwkgL1/gnsV12gG1y/FwTuILCsDUOkbzKYVKxMfgvMFIc7rH4b8QGmr72dq7l2E/HW86vimbVkAda0jISZ4cuYb5zORSOtGugdSk4x7WQL5TRhHWD7r4IINg/by23hafYp7BRDUabo/ktZR/m2L9QbxbzTbuVklcEfFrnZ93aVVxcP5EqWmhWyrgway5BVxktASs46w3plBptPBBV7olhfTQIbhUeshwqoYiBy6Hi0hSqi3No6O6nyp3FNx191lhIq4QktRTq+0A5HTzqSj7fWI1zrfRy64ibCHXUcWPvFd9q/82yCF2qVCnt9ip7rEE3FfWmhwsj53ODYylZhWUU+VfgEnpaHbWOyhg5m6/U0eyYF+SqnilUyGZqpZdTjRk0yBIEEkVRkBIMUyKEda0xolcnfUxwzqFOLyYY0imjnUeds9OMDVuVUhbmTGJm6HpbDrdD9XKeaz6dfRGWy9nUiJakzzRTylzvDZx6yG/wFWRx899WZ6xzbcSDlJJKES9Ri0iFGeHTmO+8G0VIIlLhnNBEZjvvsU0RwSIAfTjJE2HqzRL+be7MDY6z0YVKd38En0w2Uox9ZiiCGKtLb1LK6Dx4CVu9dDJZcmDwD0QxaKN39G9LHjbenL+pSOv9SPn/cI9FZmzo+/420KXCMaFZfMoo8rM0dmY19zDdlv5tUcuZn3UJY/M/5N9bXogUyc21EcPkb1+04M13sXtNEUKItB6LFrWcv283l0PWxHsX2jx7sn3zM0nP+CXPvMFRn55NFS1J8sOJ+Iu2P8/XXMm0P25PdXHO0DXz/noiyw/BEaillSKcGBSSLICgo9AuLZnt2D2d2M9ioiBGH4QoGmk1Fwebh5yfCkIteJ48Bmdgvb2/DqWEEjP+uw04PDy001181pP3rRpt6zr7uPnVlZzUdhNbBd+zyQoMUWv/XTwtNtVvIzo+n/uOwNP6lm2i2aGUIIDi6Fg0U8ore9zDmIN/OHnUn2WPxU/YSHG4x+J/HdEVlcHqPvf9/ErbGRdTJ/LYGSxblcP4gUvtFTJNSJa5rserxB/OuhQcE5rNZ+ZWAChCQVEEZaKDKaGbk07h4NUzcQQbcWkquc5BHm5v35r2w1AUQXVxDtuWFQwtezjIPpSiampKctl3Gy/7buNln9Fezvv9KI7fby9uqrqBDkcFZWYzp4mXCOkm2Q6FsG5impJWxcOVckLSKU7Sz6Nd8SAlhHUTBPSFrIDZl+/CoQgaugcIRUxL+lVuHlKRSfknBkXEG7hjGZXjI7Opx0eu7CVIDpfLC4joJtP6r/nW51AmuqhS2tETSnf6U2RQwSIgx4Vn8qEczcXh89Cxto9Iwftym6RtNQyuFYvZTv+Sq4JxUjE2MpXx2oss0O6kjE5URaCpVupHNyTNooRTzRnUSi/VooWlkakslDcxVb0HDQNVJge+7aKYye5red8YxQXZC2nGkg4uMVpZ1Hc5T3MpNaKFRjw0yyL7cyaStuAAD71by91vrqU9GCLbodGct33S/i/VL2CifkHSaw5hstB1F4qwSsDOCU3kNX7BhZHk7RQBeSKMLi3X4xO117lQ3k9f2Cqha6KE2ZGxaZ8ZCpqQ1JslXKJPYMTfzrFJxYZuxaEzGtF9Y9CjFg2+YRQZMx+D9AX87yxnbTrkZiAVmXsXTJ50zWJ3ZQ0uTaGVIjpwA9DpKOfOrRbxkTmKx0rOTSMVAAMRg5riHEryXKxuDdIzECFnoJnZnVdQIZtpED5u3+Jm6vOTSza27v3QMsiMPeN7Gjh73UUWqZBxUtGOGyMhJNlD/4Bfe/o3XEdur/DWAqAKhTBq0iYxmWOrN0iJ9syJpABIAGLVS/DubVa2ImXeiSw/lPtffpMb/7qKRa+t5tq3g8x3XWKTCgMFIZMJUshQ6A/rQ8+DQ6CyMJtxXYvZveN53KEW/C7f0LX2GyHvPiQ2ouIhVeZWLxjB/a4TuShnIa2KVf1QYnbYpKJVlHJx7kLe6sj6ycqj/mSwOWKinwCGicUPjcgALN3H6m/YmLRj/X+s7SMD1nYvT7E+twFsTNPXk7sss5uvD/v4HFYykvGhOLlIWmmOkopPGEUgZB1fVQTVSicPalbja5ujfNMajA6YY6lDjHnO1kgfFLEf0l4TYM+z4uMQ20eU2ac9ALF6MH45opgtttyW6ytu4IPyP/PuqAvJcarR1RNJxJT4ZAcLRfIP9DrtVjxmO4ZpopumvYIeDOl81uCnN2zQMxAhbBhEdHPQEqhNxWB9EZoClSJFNSo0nf8YW3NSZDq10ksefdwgbubKxvPt1bONDeQy1WhrwqTJLMIteskVobR9xf5fTgc3OW9Fw7QlbX+lJMuW6qhU0cKDygwqzGYa8DEmPIUljhs5mRc5VvydF7TLKDObEYCmCBQMJov70Q0YG5lCr3RSQTsH8yanqa/wvGMyvhRZzRzZT0/vAI7eJo7peZAVzl3tSc9HF3n0Y0QJUJnookGW0GB6qKCdG/unMdD6Db/88jpyWj/i2E/OYNv2vybt/zp1MTdqgzviCgEznPezC2u40ZH5oa8JSbnSRYP0sMw4BNO0gpJdWMOtzpvTtg8N0s8Qg0cEWMTVthnYt5FqlZKMJU+5Rnfydhk+K8TG9/P8N3pSbC58156UxKyQLgVt0m2/pwmTRx2z+D/9He5iNj66LHf64mtwFFWR41Rp9PeTWsggpaTJP8DuNUVM+P1W7FjhBn+DLTXb6azg8R2X0u3wpZds9NTC2n9aO4oGpM7Aekyh2sIFDbKEk1nIeG0+ejQs8dLFSSvOGLqOPMUck/wySmQHXrqiRMUaiMTbTRMmusR2IDdQkCiIWNYkvzzeXDzEIl2J2cZZbQtsUqFiUiyt51CnUkKrKKXUbOXkLyd860ZbpaeB0b3x8i4podvh5e5Ri2mNzu3HfX6ONQ6pLuQ7HrVxB0mMKzZApm7866q470c0AF53+GPUy2LalFJuLpyctvubiybTpnjoC2/Gkq3/ViTEMxvMQCTGRAfM+WHObyMxXAr1Q2PpPtD8iVWCtP0RsOLJwVNZ9f+x+iBMHUq3A71/aHfNBBm8OqN4g3Jl/r4Ih/vvp6r7fUr1Jtst+m7HNbgVSx0mVqpwc/sveNjcDwBNBV9+Fl7ZzuLQdLtuvrbgF6zY+lwGIsbgUnixc9zxKFjxtPWDiNWyZhqH1LTuIC6jMU+JVInEg3b0Mcqbb29z95vr6OwNU+7OYiBi8HFdN+vaeymKtPGQay4johmAy40JXKveSrWwrm2MMYMWShBC4FAFQgiqinIwTJO6jj76IuYGV4c3Byro4OHoea43vfw5PJ1mUQLSmjzLRAePOWcllegMJl+Zilg5TabymKB0kRclFfGgBVvu1CqbElQo3XyZ/2tK/J/jU+LBfpt0ExGOtHKld3N+x1fBbMYqLyfJZYalyhhzFh+bWzFJ3M947SVqpZcvjSoO1D4c9Bo6ceMmiIpBL1n0kWO7Rb/u2Id9Iv9Ok+RskB6OD81EVeBh1zwqZQu9ZJNLf5IUbCMepqsXM0e/icqobGWTWUStWcoe6qqkMYuVjm2MpGyTWcjR4bk0UcIurOEJ1ywcwkwjBj+kSVzisVPPY6gyHUMK/mNszZ4p4zEY/ldLoTb3dznY/lrMAt4xd+CXyioqE0rrYmhWy5nmXki9WcJOlQV09UXIdWqEDZNydxbZTpX+sEGTfyDZZ6arDvPuw9AC620/GdxVQ5dsuKsAYTXyJpjfdWk+znHOY12kCFUR7OFcy/U9V8QFEvLLYPxrlo9FcxPeT5ag/Drqdp1YunPMMnh8nN0o3EwpD3Agl8oHBx1rXSp0i3w8iZ4sKQ3Gg7kgH/DVDErCjbSIUhwyQjHd9i6mFl9PkyxmTtdkqmj5dpKwiSXNeZUMRAzyQ1a52aS8+YDg2uAUfEZTVM5bsxSoXAUQCnw31+5NkLmtM4qZ95eV+FvWsqj/yrQet1ZRyoU5C3GXbcG0w7b/weRRf5alUD9hDJdC/ZTxx+stUmHq8Pkz1upI1zoG7jyE19/7kHe+aWdde5B1n/wDuTxKKoQKA90bdtdMaDYLddYO2fSV5VA5d/1F7Nb+PHXuX9KklDFCaeUp1yybVCRituMedmENAA5F4JXt3JJAKj5Wd+b/el7imM/OJtCvZ24wMnS48w/xxvFYunaQ9Gvz5//CWLIXvLPEajgfxGU0RhgySSTe/eY629F5lDefcb8ZyY4Vbrr7ItR39dMXNtghr4cnc+czQrTSgI8Jzrms1LZlYvY8GoSPGtHKY675jNC6cKkKTtVytTZNSUGWg8qi7G9FKlQMpmn3J0nJDoVdWMPLrsttUnFSZDrtqgdNEbgcApdDie43Wd2nK0GGstV0D54NERJzkAAlT4TQpZL0XggXTWYhECubsgJ4t+ijVLEm6piEaqnwo2Haq5Ex7Nn3D0qVAI3Sk3TeTmHwgDKDbY01LNcPtdWvDlAHJxUmVkZExUBHIZcBm1R0ax6217/IqPOvYqCpgkZZwpv6tgDk0o+U2GSgFxdj5WzWhguThFhLRRd7aquSVpljDdqJf8f2kykjVK5087hzFvvxQZxUyHgw3y2zNykQ3RxLRWKQf0uSSUXqsVQhbVIxWGZrMPzvLHFtfqWs1P1JCW1mHj4lwBHa27xvjqZRetI+t6RwEn6nD90wEzISo+xn5Lr2Xrr7IuxU6bZJBYDy7m1oUY8i7YwXUYqqN1yy4a9PIxW4q3BPeI1rzziMq4/dhQVH78zE005CnP5yXLGwpxmW7GlJon/0oFWqFJtTYiu8xyyDJ8db+3dX059dThltXMJDg/5upIRucm1SEcnxWecbW8yKYjBlphipUKSRRCoAJnRdS1/YYKp7If151ZveaJsy1znOfJXcc/5Gf141PqOJ63qnslu1m2d2uYNuh4+CUAuOYIPVID3mqe/s2r0pMreVhdns7enn5r44qehQSuhUrGZ1r2zjpt4r+XXJwE9WHnUYmxfDxOKHRtWvLIk2RQNpYPa00KmUkBWsZesXT2T6fa8w69Z7qXr6qKhKh4KeU2p5CGxoBSIhQB/53PGUyQ7UnnoO/Cq51vLQVdM48bMz8epNtGrl/GfEWfxt9My03RkyPllpQvKkaya7sIYapYPFoelURknFJdnzKTp0OnrBCEojTUxpvZQxoUdQj12GXjDCqsNcfijy+YutbA1YhEnRYMejME1JsOLXhPOroWsd4eWH8OLjyyl9/AjUcA/9Si5PDOyRlH6NjYNpSl5Z0UJnb5itvXlpEomdvWFe/bzFLosa5c3n3H234uL9t2ZLTx67uYPcac6izGim01nBAt91hHPKMSV81V/AgtLr6HRW4DOauFPOpshoJTAQYSBi0tozQGP3ACH92620Xqk9xHjtpTSfikwop4P7XQspEP0EZDYnR6bTm+VDUQRCWHXFHrOdh51zbbfdmPRoibCIVbMsIiI02x05E4aq7dYS/Bl0qZArQoiUWVvKOLFpMgs5NjSHR419aZQleOmMkotk7MYXKAnNwbE6a4eQPO2awfasY354TJoWf+pxFcBLJ82yEH+CBK6Bwjc5u+CTbUk13DGUiS4eUacxV70zo5IRQI/MIT/Uwd1iDlWiw3IJTsjYmBImhc4kEiVfiYpYsf20m3m0ycKMY1uldLDMdX08UxH9TBe5uEX/Jq1uf59ZjVSSkSlAHixoHuq8fshszE8FqU3tG8KGtkncnxBQlLBA9Et1Fbe4zkwj9hd0LbQkYMMGJXkuDtzBx+gy6xk58YDRXLDf1kw8YDTn/G4rm1QA365kY89zoWznOKkQKhx3T1pf3IiSXJSaPaysgcOSYyYchMdOg20PTQ6ag82YB86n5/3H7Hp/feyLPDJiDjqqLXk92Hh5Ys9GPDy0412Yp72cVloylDKTi4i9eNGheVk+eimdzgoqZDOLQtP5wzY+XGe8lBbobxAZau1xV/HY9ktp08rx6k2MW3MB7khLUjP0qtxfYVb8cpPLmVK/x02RuVV6Gjh55dl243YzHia5r+cy93U0C6v8tIw2Tlp51k9WHnUYmxfDpVA/Fur/g7n8IBRpra52iUJKZSctsohiunEIiYFCQC2myGjf+FRqwipEt8NHSDctCU1RxrW5lzCl/zpKo9ra3U4fN1cvxp3j4IwvTyc/Ei9dkRLGhy5FAHO9r+FLUIV6Se7Fn5S3qcPHPM81HL/f3uy3nS95BQQIuHzcVTaLkxvnUBppojNrBEWhekRCo20kr5LesE5huIU3cg9h+9DHePUmu/zCQOWxLRewf+1NGUurYg6VhTkO8rMcacPRMxChuy8SV82IlmI1bjeOZf9ay/m1F1EcbkxygO4Z0Knt7OWr5h5+WdTL2PBjVPuj5WJR9aLerDIURRDWTXpDEcLfQjRnQw7bmbYLyGzGhK7kczGK/CwHuS6VXJdKKGIyrncZp4oXqJVeVjh3xaP42SOq6d6hlBAyBRW0p5XpbKoCTb3pYUL4QpY4F1GlJJc2JZa2NJrFnGzOojTXxZLQlfiiZVCJQWSim3ArxZgolNGOIRXU6Cq/lGAiUIeQ0kw6h4TriR2riRLcBMkhBGAr0aSWKP1V353fqx/Zsp1SWmVcXsVvj1eD6cHJAKVKXPO+Vbo5IjSP7VnHMtf1QwbJ9WYJThHBKwKDb4SlSFYjWtOD+Y0ahR8GmQjUpuC/rRRqqHEYVIFrE7NRGyJo7xmj2V1dbc8hnRRSSic95JBPX5IrNkADPq4tv57zjvwdo33f87xo6PD8xbDyaYssbEj55q6DrHnFmY959r9pjWThdUVQ7vtjkurhZ4EsDmm+jTc9J1CU4+CElecmzSNDoUGWMMG1AF/1qIylOkO5IMf34eH8rAX0ZpUxUutibvdkfEbCfAWZS46GGqdYNj9lrst0HgFXGWvyfsVDpRdz0YHRa/gOrt1fNgdY9NpqtvTkURX8lBM/i/uNJCo+Ttwzl1HPH2uXn3U7fFyYcxVfh6y+nlGubm7uv9KWnP0hfSyGS6E2L4ZLoX4G0Mt+wWzPDVFFCZMi2U0X+fhEF46o8VQnhRQZ7ZvmrhlrNsurpDDSgk+20SxKmVa4kIDDi2HEJ+9cp8b2eX2ctmJcGqmINZ+uZCSLI0clZS4OU95hre8g2o55giXnHG6RipRjAxSEWjitcRavbDOflqwtKR5Yj5AGprBS3KZQcQQbKAy30IyHZeIYruXUpMmgzv0LDmlcNKjhXmw1KWcQxY1sp5qs8x0tF/M8eTR/aL07iVQEXWUIISjIdrBteQG/LOpjRscV7N7xPO+yo+2lcLr2EooiUIUVFEe+nRLnBh22IZ18HBS6hk8ZhSkhx6mQ69Lo7tfp7I2wIHIiy/RDOCkynZtzzqdNLQcsUhFJIRXrTS/HhmbxsP57WjKsoqeW7CQuP6h2zmHwQL9POqlS2nlEm8Utg5EKRJL6lSlUpjkuoUF6bFIBRDMymY+V6osgM5AKKa1xjJGKBlnCieFZNMiSNB+HA7QPk7wAhLDOs0dm2b0SxaLLJhVSWqTCK/zc4byeX2srNhgkLjBP5d/iF0Nu0yVzqaQ1LShKbar+sRFr2B7qmlM9Lv6bMaTK1iZkcjJtl3g/D3WM3dU11hyCiorJx8r2BKOkokUt5+5tlvJJ6RG84z6EFqWMSlq4pncqo7OGJrobxMaIkagaHHkLTHjXmi+61hFZdsjgK+pR7wsmvANFNdY+3JVpDdVVjgAfbXc5Bdkax39ukQoDNe33k8lH5XaOocfpHbS5OJML8nPbXIMRVZ7SUZjpvJTerDLCusmKYD5TChbSl5tQAgWb1mirahb5SAjAh8qcvDh6Hq+Nnka/Iexr+C6u3THDQS3YwMGrk6sZYoqPPtnByGePTjK/KzjvNeaOPYj5R+3E/KN2Yu7Ygyk497U0h+6fovfCMDYfhonFj4QP67r4e7CGy/KvjpMLeuz3NWFSSiftjopNdtc0TclAQrSrCoE70sbc7smU0U4zpXQ7fTiCDRz7yTgKZXf8swjur5lHPT4r2M1agKdqK/qOuMfeRkGyRctf2b0oZK32JkwmZn4lD21/Bx2aF7AMcw7/8nK0cLw5LiQ1/JoHJSFzoSiC0fIbFkauSZoMRvrfG9JwL/YA7BtEbaI/bCRL/UXLxZyBWrbvfZ8PSv7EXVst5rOePFbVt3Hs+yeR2/YRWk8D09ovpzjcSCi3gnyXxqzC+Tyq/onlrtOQus700PU8bFxCjhKOX4fIvEJWTgfTtPtxJBTjCIYmF4NlNCRWmVODP8Tq1iDtPSH6IzoRU2WePoZmWUJTYICr9ZN4RuxHyBSUJZCKBnycKmfwCaPoJYtypTuJSFg9AZKIFLRJa2UiUfGnXOnmAdd8qpQOWs3MKxdOIvRJJ+W0U0Y7nWZO2mqrGg2Rg2TRLAspk23MCV+f4mo9NFIJR2o5SOoxm0w3x4Zm0WAWoUSlITcUrJUr3QSli17pQhMm2cI6PynhzNClnBW6lKDMYidlHflYpWpDYZF6E3l695DbFNJrl1kl4sfKWAw2Pok9F4OZ32W6jv9FbGxmIpGo2X07xH6DVuljULoyErZYX09AZjNWzuYZsR87yNXk0UdfbjUv/2IZK5XR3FF4EY9VTOaVXy0nUjAC5+Yw2xrMUG5QWBfp6Km11Za2KsnilNbr+OMH42yDsEzlVrGguSPqdn3aqgl20OwzmuxeKyPl15LpNp4t7mCvyH/IcWaWhE11QX5ixCx+u+7maC+XNXdfIxZzSLXBb0Z5OHB7H47iap7YcSkysQQKLLKwqd4SUWxMsJ84130X1+5MZCpV8fHY4INosdKmaCZCKapmREJZW01JLkpRdZpD909NHnUYmxfDxOJHQkdvmIhhUpezPUtzzhl0u3t8kzfNXdNfj3n3YeSHmul2+vC7fJSardwYvJwKafURPLzj7dxTPgspNETCo9ZAcF3VYj7O+y1/3/MuIgUjqKKFk3rvJ3eL3eHEhxIOZMJfLk2bTGIPs/u3u52AqwyAAr2dErODLlGEjkI2IfL1ZNUir9nGjN4FdllKV0KNPMRdRrP7m8h/dSK8NBkMncrCbLYqzaPJPzCoROIob168acxdBSP3AaDYaKWy8x1e+byFl1c0M2HteVSHVnP6qnM4ecUZeCKNSHcVQsDvel/idMdfeaDwbDSHg6vVJRzCW2xNHU9p0wDrx6SK9LKiGEEYr73E087pVCodCMChCpxKZnKxu1jF485ZUVJROmiZlCmjAX9CxGJICPTr1AZ02iPOpExFrfRypjILZ1EN+S6Ve/WDCMhsNCFpMd1Jq6IOISlNKNdRRDyTkS9CNJmFhHFmugvRhCRHxAlXsdI3aFCVxwC61GgyC6kQHZSJrkEJy6Yi9Zj5YgAfXTzhnE15VLVKjwZriUhtcC9X/OSKUNJr/WgU5anc6lpEnrB8Iw5R36VA9NOEh14ZL80zJfRLa8LXhMkf1I8zNnIPdt6A3dD9QxevDhUQJ76sbCCXktpcfHdovyGv5b+pSDeTUWAm9EhnUsbtWvMETIT9vceMF/NEKOPkHSMf5zGZoMPD/2lfUCFbkEUjyTnrZcYc/Bsm7rclc7Mf4rK9cjnloN9YpTBD9QBsrMz5pnog+OvocZXFycHq8zls9Ux273ieEqOVDtU76Ip6Q3c/6zv7UKMpz8JQox00G1FfCR1hL14YKLbqHUCHzE+Sop0XWsCfnB+nNxdncEEe1fYqRaEGGoSPqUXX0umsoDjcyLg15zPS0YU7x0lFYTYfB/JoOvKJzeY1sDHBfqKs7Xdx7U4lU/dsfQt1eRn8SXY7BXYbu+HyJneVRS52H2v12vzE5FGHsXkxTCx+JJTkOnGoCtV9Kzmnb+mg253WctWGjeYS8fataIH1tDnKeXTHZbw0en7S269uM4dsp8bJjXMs5+3ow1U682k77nkO/+MRTDxgdPKEE2yB+w6H8l0scuFyg3cHqNnLko1NmExCnbX0R3Q6VS8PVSU3jbtlFxpWc6qCVe7VK11pN2En+WlGbAevnklR56dcWn8RhV8+YpsYKYrgoB19FOc6bdMm3TTpGYiwujVIca6TA3fwJbt97nuFXa5VJtu4S07HJ9uZZZxhGzZ56aJbFBLRTZzBRprVMl7KPZqdK93snbWO30b+Hb8uEaQqmmWIeujZSO2P2FFZx8POeWzp6iTXpVGc68yYuXjKFXdefsvcMSOpiEEASIkjujScGLMs1E/iM3OkXf50OjNplCUEBiJUqZ086FpAgein3vQQQUtaFU1ETAs/sUyoVASoUtpZb3o5KzTRbuxORGqA3i2z017TpaBKabdJTJNZSLEIMhS+bdCZJ0I84ZpFZbQ3pMV0k2kdUxEQGSLwB8gROtfo11ARlZ6NSEG+CNEgPQipkysi9rkqArKFTp+0iJgmpC3tu7H4Lr0M3wWDHS+xI2JjzitxFV4IOM352gZLh4bCT514DCYEkOn/MWQTv2eEgInK48yUcVEAQ8aVxgYTYVCE5EbHUpawEE+kEYpGIhLNtt6/iuJPl1HxzLFWH4O7ylJVchWkB8CbYri2iR4IesEIbqxezP3b324Hx9t2xP1iVFWwrqOPuq4+6jr7+KolQFtPCNOUhDprOXf9hRSGWwg6S5NOI5apSPSp6FXdSc+uiHCxIG9qErk47ItLUVa9mH7tMWWmM15kzMG/YauTbmDt1qdyfcUNeLb5DU/uHD//41acQ16o2S7BDbh8m81rYGOC/T9/cZ7dIJ2rwfEdt1HY+clGZThsAhmdyxPJVK1enNmfZN2/Yd8rNq5nwl0Fhy+GQ6761lmbYfw8MEwsfiTsXl3EH/Jqua7niujqipK0Sq9LhTaK8UQaN2w0l4gD5tCz61ncNmIR/RE94wNlj/V3UhppshSbxv8V9jwXsdsplFVtkS4VOPY5yPPFJ5xtD4Pz3rIUnd673ZL4O2aZPZlUPnMsvW21rFr9BX/6JnmVJLEEKBJtmh3IsOJdIHupoJ16fFxVfjOdzgoKBxoY99XZFOuxJrBqWx4vVUZ2MInEGMz8Sh7c/naasSakStHOI47ZIA3aiK+Uu2U3zt5GGoSPU/TpPPUNtH35JrPaL7Ubf5vMQspENw865+KjA0m8pjyRVNTh4zwxlXp8VNHCfcpctsnqZsfKAjx51sp2EyVMjCRLCTfLIpboRwwpS2sCERPC0QPHyq6sCVblyPBclumHWFkPWULElHT0hjk2/Jzl3G16+Ze5I1VKB7XSywXyUgIyuYHRiJLAGGISqgGZzYnh6bwqf8XZoYlpgU5q9qaA/rTXYgG2pTolKBUBO3M1mIv5xtalx6BLhTYzP3o8a9/90kGJ8KeV6gSli3qzJNrrlH4gXUKnaanVCPs1yxCwWRahYlAmujOeaz+upIB8Y0jCUAHqj4WYN0qbdFvqY5twXp0yN4m8hofYdkP9BD9lDFViN9jrsRLEy6MKYw5hMlMsZ7o+noDMtnuSUpXHEmGgUqo3Ua2vx3TkWs/nWODnr4dwDxSOsJ7pt+4Nte8gnxwPoQADJTvQ0x/C7KobUo7URmo2YwhDuRqtk0M/HG8HrOsOf4xmUZJxRT3oLKUw3MLZay/kydff4ca/ruKWv6/huY8bePCVt6h85lhKI010O30YIjmMMRMyFSYKQdVNgWFlJztEMc2UUkY7p/feybzcqYSE9VwTAI+dGs/YDOaC7ClAO+QqKKiiP2IQdJXx2A5L7Xlqx9oH6AsluG27q75TCZQ9zrFgP68yKdhvDOeiOHOIZHmtno5l+4G/nqo3p7Jv5+OM++qcpAxHwOmhcKCBUz87nclrx9HT20Pj+tXIGIFc+ts0MpWoELZRWa5h/E9jWBXqx8L3pQrF4IY+B6+eSeFAA21qGW2le7HtCfOs+sdEk6GUelbTNGlduxLvqodQDpwDweZkd8+YEc8xywg/dgbOwHqasfTSy2inhSJKsDwMEjGRy7hCLrelUVOhS4UTIrP4StuGXZU13MP0+D7yy2H839Lrbk2ZriySQe6orrOPeX9ZSUfjNywauDLNtK1POskmbE/a07Km8FHO3hR3fcLdcoatqHRsaBaALQOb2AeRSiqmuReyqt9NzkAz9ypzqKKFOnycKWbxTchN2LQIwePOeKYCLAWhf5k78WftjSGVo2JIPO4y/RDm6WPStnGqAsOUIA2u1B5iuX4orRQyRXuIV8SvuU5ZbBEO6WUFW3GoeBuAZrOQAZxUiXa76fnM0MX8jT3sc69QugZV+dmQ+lTqqnem7TdGFWdwMyyRtGqZilbpJiw1qpQOemQWfdKFT/Fn3Haw40QkODaSLPzUA+PBEFPyapZFlNA15PVuSCVJCoWWgp1t1bn/BWQqi0odo3vMQ/mn87fcqU9DxSAiFW40judy7ZHkfRFXz1tdsBfbBt4ESDKbJL8Mtj4Q8zeXYN57BFpgPb2jjyBnzQsIU0ciEEjaHRV8mb0r/xd4MdlwbTCZ80HM1ez554MzKDWa8bt8vDR6fnz+0cp56RfL2HeP3bjpb6szqhz5XT4MA4r1Flq1ch7a7lb03Ary+2o5auXFePUGi1QYkhKjle6sSv454iIOW3UlqrQyFj2uCtxaBNFrqSDqeZUw7mWaewYoeeJosoN1RApGoB67DOXRkyG6na1clFeWpswUg2lKbnvja1Y0+inJdfJ1Wy9qsIEj+5/hruxx6ELlt6M8TD1s+4xz0AaRqAoFSWqLOPMwT3mahrwd6Q3rbPnkoTjbPkv+fK7Xujt626L3g8rd2yzFqSmc/PlZSX1szY4qNEw8kUYMZz5quOe7mev9RDCsCrV5MawK9VNH1FFbkQYmCgGliFLZST0+ThHzOVObbyt6FBid6Lllm2Sws8GUqdHM1n0fxR94Q9TGftUSoM3MxzxgbjKpKBqZZMQTfux05rsuoRkPZdGG3XbcNqlIDeemcielJAdtidtowmSxYxG7KKu5Wt6YTExixkmp151o0KR2orw6NWNdcG9Ypzei00IJf47MolkWJb2fI8JJK4KzQ1dzpPEq9zAziVS0UsQi5y22t0Rif0QsuK+VXs4Ws2gwS8hyqPidPs4Ss6iVXqpp4Q5zFqVmRxqpaJZF1JslVCkd/FZZQb1ZwgillVdcl9tGhTE4CfO8cwr78UFSs/fz+t525iIR2xmrecYxBRWDefoYmijBQGWZfijXijipOEWfzmJlDA3SIjJlSrdNKmIrzre5FnGA+E/SuQdlchmb/f0kzK+ZyqZSg63k7Ukz6MuEwXwugCFJRZNZxJGheZwYnkVA5pAvBvAmkIpMXg2QXuqVGGQnZltSm0h/rqTClHBcaCb/dO5DCd1J17shxaPU9/txJBkB/q/0W4iUe0SIZBU2Q8Lbrt9Qn7MDl+ZdbWcuJqWSiujYxRqVywfW4HdYizpq4jO3pxk+vA9z8S/RAuvp0LyEvnknSipAIDFQeWbL2Xw+6tw0wzVz7F+oM4o32lwt1mdXV/grANyhloxNwxKSegaMqFrggJqLO9RC2DBpEaV4Y4pGvZ+xX+NSPHqTJQ4iocRotee4isBHUVKhoiLJlwGbVOCuQjvzVbSSGqpGjib7zJetFfnAepSVz8BZr29Sc3GsBFdVBP9Y1UZjdz8uVUFTBaGIQX/YoKUnxDftQ5d0Wl94BjWtWO/i3YfCA8dGncurwZkH4SDKXQdSHfyU0d58uve4xP6u7e+8t9UmFdb9YHDkqis46fMzUTGS5trSSAOeSCNtWjk3bLmc7l3O3DR/ks1Q5jWM/y4ME4sfGlFSEXPUVvJ9FJsdDOTVsPrQR5g79iBmnXsq9Uc9jRQaKiZaX5vt0L1BcpGh2SxjfWQiURmiNjYx/ZxmqFP1Kzvt7QzUcm7b3KTgzRMjFTKmgOShSy2x30uUFIX0xtQK0c59zKBSxDMKBkpC8199fEyX7gORgaQxGKwuONepkevQMKTElNKWDUxFm3RHey4MzgosRouuHMZIRWIQPyZ0ZVJ/ROz1U/QZNCsevAUu3DkOslTB6pCbE0PTqZVeakQLj7lm87Rrhh2Y15sejgrN4bjwLNab3qhXhCV5WiD6ecI1K4lcPOmcxU6K5Z8QO+6F4fNZ5LyF8dpLXKnFm+53YQ1PuKztn3TOsl9PVaA6ITSdWqOEr8NFnBiZTWM0CxUzybs8NN4OeO503Wife7uZn9bknAhdWkF8qsxrIjKt6HbJ/CS/iQ3BCtY2/vE2IXIRLaKEds3DooJLktSXYucTzkSGhthnrGylV7rs0oyfOxQBp2qvsHv4fRwJPSJDSaEOSNVWOkrcPkdEMqp4ZcLPgYhJCQEzs5jBYNurAtabXo4JzabJtBY4VAFLItPZTV3DO6GRXKFclqYGJomP171ZY2h3VOAOtxA2FLqj5ZyClMUadPpxoQhBsd5iy7HGiMmx62dZhmsJB+oN6dz39rqhzdWOWWYF4oYOhk7uG9PJ7m/iPyPOSut/eGnUbDrUUtqCAzSuX8OfV55JaVQaVpUG3a5KHhh1E61aOWWyDYeqEHCVUTjQwPGfnUVeuIOAqwxVCAojLfTnVdtz3L25p/NG8XE8vauldKWGe8CVDzsek95cnBoU55XBlr+3to01Fw+lcuWvZ6sPFuDLU8l2qFSrHcztnsyRA89yqfIQvxvtwTBlkjkrhg4vXg637mXNWZEBa956arx1nCV7Wq9DNEtSbbmNt30BWg7mbycRySqJfqcSeddBvPT4nSivTrW/60w/kyZZSKt045Gd9iKfleVS7AZ3A5VXt5vPN7qHhwrP2Thpe7DGDaxFx43BxgoBDONnjWFi8UPjL5fGXad3ONJ21M468yV+v8fu7LWlh5GePEbu8jvEGXGHbrIKN6wuMUiz2UbVRw5SG7tFSR4eNcghH52dUf7P1spWvJTRjkd2EBDJaTIhoEH4uDB7Ic9UXJI02RnAW+oe9v/bcCe9H0vnd4lCmim1VuKEBl3rkG8tofnzfyGXHwzNnyCXH7hRdcGVhdnsVOmmgg4edMylUrTTkpK10KWCV/HjJzfp9RmR09JIxYnh6XzCqLT+iEnGBFpEMbphsq69l2b/AIqqkOty0KV5GGvOoEF6qBTtdklYg/RwXHgmTZQkNXRXKe0EZTZ6NJhPJBc3hY9JCuwWh49gkfMW+/yW64cCcVIRy7pMC58ODG3UFzYkHVop//7t/QSdll+JJkwudj7N1OjnY+iW2ZSIHnty6zaz0wJFTYCCSSSa8dgYXX8hoFSxpJh75cYHbhHUIZWXErHIuZhtsvzsUdLP+QN3Jk3QJoJXjd2T3LwHO9dM6CWLvgSFqJ87/qi+Rx59dtYqkQhm+k6zhIFfZm2UmV5mraOfNmLXKwR2w/7GQERJRez5cbKcSxvFAGiYLOi6jN8Y7zFJ3Deon4kQcPrAPbxUdBLNahmlZituAmnbxeAkQlGkxTbJa8BSNup2xeVIC6JmZgYKhZEWDv3wzEH7JDhmmdVnF1vE+esMij9dxsSGS1B7m9P6H/ZfOYXVq78k2LKe0S8cizPYCFgr6u2OCq4tv56Vymge3PZWWtRyivWWqMhIwrVLncJIC22OchqOfMKe4ybsvy1bnbKIow8/Mj7HhXqg4YPMX0Bi78NfZ8BH91nb/vp867VBMvmN61cTXn4I4t1b+e3axRxSY7AoNN1WXazfdhyeXI1TA7fT3vBNXM71rzOsvsTWLywvh6W/geZP4PNnrPfDQVh+kFUmbUr0UIIMrN6H+MuFOALr7XtBAId+MQlPpJFmUUp3hueTlHCPfgjFCVL2MVLRJYrQkDax3Lb9r5S7s1jTGkyToM2o7hW7po2VGN4UIYBh/Kwx3GPxQyMyAMsPhD9eD+W7DVrDaaP+PxYZOeNV6Gu3SMUBczI3gg3RK5GGDPWRg/Vm/LLpYbZsf50OzcdLv1jGKQf9xi6jirmBHt90DXt1v5DxUDoql+dfzfqwmwfCF5Itkx9anc4KVqmjqelfQQXtBMijgOQU8r9dv+V6eQq36jMpN5sxnPm8vNUMDv5ist2k/NJ213BQwyIrG7OBMVj39VfkPHwEXr2JBukBJJUiuTlal+ka/LpUaJMFlCvdg/ZUxFArvYyTM2iWHrb25bF9hZvekMG7azvo7o/gNdt5TJ1OmRLvMzk+Mov/GKOTgoHEfTeZRZQKP1qUHJwXvohpjgcZobSmBW6J55dKKo4NzeITRgEwTbuf8dpLGXs4NAVK812cvEcNpr+B4z4dT3m0J6VXupKyE7Ga+daCnVG612Kg2JKumRCJNjwHZZYt15oJsevRpUK7LKBM6d7kHoXB+jsSr8EA/sNO7IVVrxzChUYElU13Jx/sGv7bYEqBkpCl3FQn7pjzdqn/07QM5ubG9/kdfJt961LwutyNBunl5YoJjCh1kx9u4YKvz8IdbTYOo+FET89YSHg88luOc/zLHu8l+hFM0J7NOP4mIBP7LoAOZwXnqLMJOH0c72vijFVn2+/1ODzkR9qjPg1WJuHJETM4ct1sSsJW2cw/dlzIMetnIRKz2IC85zBE1zqbvPidPqRpUKhbz41WilGFoETGn7eRvErajnuWgMtHrlNDSsk9L73JhevPpzDSEr2GuEt7wFHKohG3MO7Q/0tzyrZ2OAB37AsD3dbi3YZ6AL58AR4/DYxwkht4c90aPE8ejTNQS7ighme3mMPvV07BE2mkVStnrvMSZuk3UpJitLrP2hv5ReNDtDnK8Z/wDKNGbWvNucv2s0rTABBIoSCkkfT9migERT4F0irFHCwTYf/WAL8ooFAGkl8f5O9ENKvlXJ93GXsP/JMV219KdpaLde29XLDf1mxbZsVIa1p7eGVFC1+3BRnQDbI0la1K8zhoRx+jXP7khbyhXNRTtvu+3beHeyw2L4Z7LH7KcGTBOf+0yogyuGumoepX1vaOrA2rSxwwx3qAfsv6yEyGOrH0s38QQ52YVva/t5nC+6490w4Tm5iu6Lue2TmP2aQikuVhlecg2rVyisONbBX5igU5V9CMJ41UAGwz8AltwTAnhKZRjw813MOhX0yyScVjWy5gz9XXDWmqZMNfz8i/HI9Xb6JNKSVGKmqllxP0xLKf+Ee6yMfAUrIqV7ppMgszkor1ppejQ1YJU41o5W4xhzKlgxEluZTmZ6EI6A0bGIbkfOXJJFIBcK16K2Up6k/tSgl/jmYuypUu1klfNCi3ypBix50UOjNpEpkTthq3b9YW84RrZhqpiF3eQv2kuGpUlFSoAvJdlqqJQ1V4b20XK4J5zCi53u5JiQXknTI3adIK4kJHS/KJyIROWcDD+r6MC0/K+H6XzE3KbGjCpCxq5jdU7X5mCc7013QpuDB0frwEBdhTWqSiUXo4SL+ec8MTo8Fz/DMhuenqLv+NpMJIIBWtZgEhqSZlLjYFP8T4fJdjJJrRSWlJIn/XfWtCcoDyIaepL3NV10T6BkKYJgjNyspJlCRSoaPypOtoO1NxnONf3BM5wP7t2aQiw/kokEQqAB6qmIrf6aMg3MKR62YnvWeg0kwpWvT5Whhq4IxVZ1MSbqRRKeO6vEnss+LKZFLhrgJ3FeKYZdEyXstAbn725ZxkzqNRWs9VL51JpKJD8/LwDndQVj3KViWsKsphRHEORsKP+SvP/gQd1vPJkJKRJTnpSomxfoU79rVKiIKtVnnTYD0A/np44nR4dIxFKlRnUjnwde/0Mrv4Glq1cpyBWo76xPI36nZV8kj1DCb3XUdJuJFOZ4VNKgA+rPgznc4KSiNNjHzueOhcb2XYD15GOMcXPbjETCAVsUtVMG1SoUuLvKdCJyFbCDapiAySRYz9nVoeuqRwEj3OUkwpiRhmmqHsmtYe7n5zHSsa/RTmONjSk0dhjoMVjX7ufnMda0LuTZIY/qFIxTB+XAwTi/8mbAxRSUQKURnKUOeVrWdmNNSJuYEWdq9kt9D7SZ/RUZmXeyUN+PAZTezQ/Xr0uNU4zv0Ho857lPCY5wkX1FCqN3FO/r+TJj8zwb6vVAR42jGNomyNG8SpcbMw4LUtr+CQxkV2s3pGU6UUje5YM5w7x0ml6KBZKeO68usp3e63vFR1cVLGQAeK6Ek6t1IRwEtXCqko5S1zB5pkCWOMGdEeilbuFXOoVruQUtIcGEAI8Mp2fqN8CkCDWWKTkRFKK4+45lIRJRdZmqCiMItOzcOJ4el8Zo5klNJEhOTSmqX6H7nAmbxaudR1A39xTeEI7W0cUWOtxExF7BoNVLuJG6xxzVJNrlTvp1x0YEoIhiP0hXXKC7LSghYXetJreYRswlRvlnBMaHbULyIZluKS4BbHorT3AIpEb/S8kwOlJD16aZldJWJjgzxNSBY6l5FYLBIbv5v1o4jokhnavUkr8pqQuMQPUx/8U29mjrmeN5lFhHHgEsYGvT82hJ/CdaVCpmQuhQC36Nts5W0SGBlew1ztLqa0XkZBqIWQmouIPm8EVkB4UfYCHig4nWnmWXEvEMdfeVXukRxIbuRxT2qcz25iDTf1T7NX3G3DtUgLEkmLKE0Tf6hXRzCxeyFevYm+3OrkQNFfD0+OR0gdU1iLStP904noIcaJObRGS71i6HGWcv92t6ctWMUESEr0Vjo0Lx+W/IkXtprBa5Xn0K6VU6K3Jnk22IiV5gx0g1CtMuKVz0LTR+i6yXtrO3hpRRPvre1A76iFZfvDiiet7RQNjrsnrRy4sGwLrs+bZC+SGaj8c+RFjG2cRyUt1ONjetHV9Dh9Cdfl47ryG/BnVaIF1mMs/Q3i3VvRnjmLm4qn06lEF3CIk4pMZYSW6Wr6j0JD0itd+MlLet0hJAGZPegz0C+TS3sv7LqKOe2Xcmz4OQ5tuo0m/wC7FgSpencuph7hlRUtdPaG2dqbR36WA1UR5Gc52NqbR2dv2Oohya/caInhYVLxv4FhYjEMGzGSoAUb0vwvDlo9O6NRX2VhNns71zLuq3Ps7MH1hVMJkoOGwZm9d3KndwqGM9+qlVU0OO5uWxO8YsTWOM94CXYbyza9/6GUTgB0FBQkf5e/sB+2pcLP/fplXCOvS5pED/xmAYUDDQRcZRmzKkm1nY+OtUiFMw+kiTPYQKRgBK/8cjlaUQ01/Ss5rX5mUm1y6vq0gUATJk+6ZvKUc7qdMXjL3JE/a6/ziGsuqoCzlVk0RH0rzvtiDHStozkwQLls5zHnbLv06p/mTnwoR8cN8kQrDzvnUkkHpinp6I3YJnpnhS+lR2YluVoDLHTeZZ/H+NCldplRiYjX1r5o7MEnjMKhDB18KAIuVx/mJPMv3ClnUSbbURCY3fWct3YCPpKzLLkilKaOBNCOmz/rVqN7ToZSpx7p4gDlP0mSrnqG/YhBHlO6tFSYPEoPraY7LTMyWJDaYhbY23oVP+VKN6Ho/2MT/FXanTzjnBJtnGezOYFvCoZSuPqpZED6pYaBQpXSwXrTy7Gh2Wmr+ZuCH+q6hhIOSL2XY+ekS2H7l+SIMDkikvG+39BxEl+LrVabqOTW/t1SbXJWcJdnEmbCr7RDuPmyN4+vW/t4OLIvU4yz7MzEQcp7Gz1uOioLCqbSqJRREm5kfvckKmlJUw9s1coppx0VEz3l97dH5F3KaKMZD8tHLY4v4iSU18qikby47TUYqOTTx9PKlexS0Iua+lOWkiyHmrxglSJA8pfd7+YB72Ws6wzRbuTx8u63pwuQxBDri+hpgjyvRS5MHXP5QVy5+G4uevgjZj33OVc/8hqBJfta24G13ekvY44+1OoZjBrOnbb6fIo7P+aiwDVRvymrvOvwry6nMNRAp7OCyfkL+Kg7lyZ/P129YdZ39PJJfTfCXUXXcU/bjeQGKp5II2e1LeTW3HOJJNwHJplLlYYqv8wVIQozGIk+oO8/6P1dovTQLAs5PjKLZjyUmq14ZRutSinP5RzJFo4u/vzFeYh3b6X3L1P4ui1IuTsLkXJiQoikfgy71zJh3KqCn3LaqgmU6k1DuqgnYbi5+78CPztisWTJEkaOHElWVhZ77rkn77333o99Sv81qCzMTpL/686q5LGd7iDoLME90JDRqE9pfJ9jPx1vk4q7t1mKse2fuGn0vbSo5VTSwpSB61FPecLyvTB1q9kvtdHrm7/jiDbydTt9XJZ/DQ+KP3JO+GLO0S+zg/wCgpY5HYLOlGY1YerohqQtGOLrtmBmWcRDrrUl+wg0JDW5X75jD5MaLrbk+ITGitxfE1CL0sZJJ+6eXK5000oxk+QFCCS10iIG92lzCYYMzh+YQEQKcmQvZ35xKmWBFdydoHRVb5awSD8GSHfffsg5l1LZQTBkMKBLO8wIyiz7XIyUYHp+5GRWMpJAykpWh5nPVfrJAHgLsih3uwadtCSwXD/YlsS9OTSNkaEvuCE4mbJof0WzLEpqeFeElZ5PRCEB5jnv5YmseeSLEL1k0Rp18NajLtUeJT4xtpn5HBOaQ73psV8zZVyJKpV0JBIOA8Gbxo7J72csf4J3ze34u7FL0uTrEpIe6UrK+MQaxltM95A9IN8nfuq9GQ70KKko5cLw+fxJe5sJ4Ys2umk+EZtqJvddMNiYCjF4MKcJiVv0JZGJDfXdZDpOImGMLWAoGDj6WvBrHh4tOpezWuaiIDFRCTg9+OjiAW0uezi/YZr2AB9rO/E5Ww557O6U1WkdS+HutJ7l3JN/JkZ0Bd4UKv/YYR67NjxEd9NaSz1w9zvpcvjwyI7o8zY9VHATpLunj/qoM3bnazfapKJl/1vYa8319ryQRx/XBCZRYnYm7SM/0s4e6++ML1gNIUAy4Q+jOHzXSk468NfpAiSd662AFOKlOT1N6DmlGCgo0mBB92VsGf6Cctq5IzSJYtNa2DFR4IxXoOpXGcuBz1h1NhVmM81qGdcUTE46/1e3mUP1FqNxagofrO/itS9beG9dJ209YVyaQji3wg64VQwMYZWVTe5ZiENgkwtVbPh3Plg2MPX3ca72fJoCWyI8BCjVkgmJIgSjc4KcX3sxjsB66zvc8QwGdIMcZ+bSz5i7eG9YzzhuJ342nsJQtDlfEaxsCvDP1W3Jzd+JGG7u/q/Bz6p5+9FHH2Xs2LEsXbqUPffck5tuuonHH3+cr776Cq/Xu8HP/ySat3/K8NcTWX4ojsB62rRyHtzuViK55RT01dmmRElGff56y7k1FEAKjSd2Xsbb4S0I6Vad5q4FQf78xXnxZuqYgkiioY6/3lLIiBENdzXmaS9Rbxbz6Pt1PPjueqoKszkx9ASn9N5rn2oPOfThSls9b6WYs1xX4a3Ykp3yeznj6/PJ6a1DFo1EJB4fLIJx7pvWuYEl/df8CSgactvDESufSjKa+qxoPyp6PqdET5bWC6PRRT4+unhC/oE9WEGNaKVelpBFGE9C1qBfOsiOKsc04uFUOZtvwkUIaTA5alYH8LhzNlVKe1JD9ZaOTu5R5lAjWmkxCygVgbTAJiIFfpmHR+lJel2XCsdFy6BKC1zkuTQEENINmv0DRBLKrzVAKOCVHTzsnEuNaKWPLHIYsPd1TGgWqoDHnLOSZGBNFFbl782WgXdwingJRatWzjhzJj0hnYfUWXYmIBH1ZgnHhWclXT8MbayX+F7MnG5TgvHEbVtNN2Esg7zE0gQTYZcjbMy+vy8y8FMmGWvMchzojFDa6JFZ5G8kEUtsyP2xDfISx1dK6EMjdzOWvWX6/mIO5jH0kU2PqwxfaC0GKo/ufCd9jmJO+ehkssxeuxwn8fc41P4TTSHbKMZAoYx2u7FaCg0hdaSrABEKEC6oof2YpygryCJ0xwFk91sr+l2ikCLZnXZN/SKb27Z/iHqzmHAkzLGdd9BWfRiHrZlOTm8d3a5K/jHiIo5Ydbn9mU6lhOsLp3Bh11X4ZJttmHfKQb+xvIfeWWItQo15yuoxjI1VajNuIgnJ8VjiJkUjYcT/gd5PZN07OIINtFBMsezCISQ6Cr3k4I728ZnAR6692G2XXVEOms+XbX0sem01W3ryqAp+yomfjbePvzB/KuP6llNmxJ//3VmVLKq6kb83OvC5syh3Z1OQ5UBVoDkQwqkq9IZ1dszrSTMBjEiF6ZHTucq5LPMNkwH1ZglZQscj/Emv6yjcafyRc5TnNtiwDfHXm/GgKAKv2WbfE+2OCl7Y7U622nobnvqwgcIcB/lZ6WV/PQMRuvsiTDxgNL1h3R636p6POGFFXAgg4PBQEGmnQfi4puw6FHc1pfkufrlFMduVFVApOlD+dQ188w9LXncz9WEMN29vXvzXNm/fcMMNnHnmmYwbN47tt9+epUuXkpOTw1133fVjn9rPH4P4X6zv6M2cfq7/j/V3KACuAsQZL3PMEUcOLW375HiLXCQa6rx9axKp4PSXUYqqqSnJ5XejS8l3ORgV+YoTex9IOt18+vDRRWOCNwZYjYF3hiZT2PkJR396Fjm9dbRq5TxRM4vIY2dEeyuq4lmLd++I7/SMV6FsFzj9ZcSvz7N9RAwU/unch8v0CXQb6XKnTnR8dNFKEbfJozkxNN0ytxMdNqmIZRZipCIsVc53LcTIr0QCk7WHGK+9xCPOuZTTjjv6uRFKK2doL1JOh00q6s0STNSkYNuUVhDhEDKJVHSY+ZYXhzB5zDWLXcQaTClRFcFITw6F2Y60sigd0E1oER5ONWfQiCeJVGjC5FbXIhY5bk7zowhKF/fpB9Ip4w8eE1igj2GdXoRDBRfJkpwDUqPJLKJKsUwCL9Se4sTwNDtzkUoq1ple6qMN14oAI9qQuCFSkWmRLHFlz6v4+Ze5M+tNb9KkvCmkIrbPzY2hfCJ+bJgSRilNjFDaMKWwScW3yVr8GEiUi43935KOtUhFaibu2yLTfZE4AUsgh37cUbnXHq2Ir/vz2XbdQ2SZvUhAi5qbpZKK2P6lTM5iakLShpt21UspnWQ5VXQt1yIVioY44T4oGokIBUDRcAZqqXjqSJR7DiG7v4k2xUu7KMlIKgCyZT9jvzybGq2TkaVuPq48iX0/vzL+3B05i33XX5/0GV0qNMliJmbPp0/kUqo3xfslDphjPYNDgfgiUMw8zt8AH9wXL5NxV1nziavAIhWuAmv7jx+AFU8SCvXTqpTio5NOURTN2Jg2qZBAgAJ+EXoH5b2l8Oq0IcuBJ/VcRZnRTKNSxvLRt9sr8+O/uZBKpZM9RhYzoiSXolwnBdlOtvbm0dEboq6zj0huRVrfokOYzHVsPKmQkuhCld8+f/t7xoyTCjKTisRnR2y7O/POYV72JLvEK2aU+E5nNi9+1kRhjoMm/wCp689SSpr8A4zy5lFZmJ00bkd8cUnStv26oFmUUilbmN5+Bb1t6/nLp03M+8tKFj39Or1L94cP77NIReEI2GKfjR6T4dKpnyZ+NhmLcDhMTk4OTzzxBEceeaT9+qmnnkp3dzfPPvts2mdCoRChUFwOMxAIUF1dTVdX13DGIhH+Brjv8Hi50NjnMPMraPT3EwxF0IN+th1ZidbbHN/OVWA9/KPb4x6idjJx/3ueCwfNi79n6FbaM9IH+0xK2o+um8y/8wGmtl1m17c+l3UkRw08aTe8TXZMoq9oB2a0XWL3Z4AVDGhIGoWP63MvZXL/DZQazRhaDmq22/YPyXju/ga451Dw19s10A34OJPpFMtO7hGzkp3AY8eUCieGZtBMEc84p9kBvpTwLP/HEfzbfuDPYDxfFh/E/O4rmNw/llaKeMg5nxql1ZbwjEjBy8aeXKcfz/3Oq6hRWglJhW7c+KK+F+1mvuUdkSFoaTfzOTI8Dy9dPOqag0OY6FJlgnMusmJ3vmzuobM3TMQwcWoCVQj6dYsoZDkUKguzKYq0cV3fVCqipVttWAF9aTRTFOvliBEOE4Xmgp0pi65AxzIKEakw1pzJCeobHMnraefabBYCwm76flTfl0f1fXnMNSfNGK/B9HBB+HwWOxdTqXSk7evbIChdvGjsySz9NJ52zmAbZXBN9k4zhyLR95PNHvxYSMwexVbKE1fMM34GhbaCnSgNfPajZCykhL+EfsVhrv9kzIptqnzuxiJNqhmBQGIiUKLeAhoGPeQwi7O4Si7GIZJlSQc7V0jOhJhCpfGApZS9Mw8tsN4KyPu7rYWVopFw9O3w1Nnx3rOwFXgHXGXMdl7CpJ5rKKOdNuHBqYDbsJ4FOvH+s4CrjJdHz+Gg1bNxDzTQrpWzpOhyLuhcSLFhSXAHNA9hU8FjttKAjxXOnTko/Fc7a4K72pasTZ1npLuKiCHoUorJrt6B/NwCxF5nwf1Hx+etA+dakrFmPFPajpuw6aAiQ4bUTzZuEnrxdhuDediNPPjKWxzy0dl49Cb8WZX8c+QFHPrlVFvlanrR1ZSM3htnXxN//uICq4fAUcEzO99qq0LF0Ojv591vOjiwWmdC7aW4EzIWmQL/je+VsUhSYkYd4j07ifdJNwWYapb9PSTtRyp0CTeldNn33IflJ/LPkRexpi1IRb7G7+pu46XcI8gqqSHbqdIfNmgODFCU6+S0X49gq9J8TFPy4CtvcdhH4ynWW+3sR2yfLXgQ0cxIo/Axy72A3rDBzQNT8ZiWQ3gkrxLH1n+Aj+7/7nEFVsaira2N0tLS4YzFZkAgEKCoqGijMhY/G2LR2NhIZWUlb731Fnvvvbf9+uWXX84//vEP3n333bTPzJo1i9mzZ6e9vmrVKvLz081k/mfxwX3w1QuQ54P9ZkBuQo27aeL3+3G73daPs7cdXpsDwRYo2gL2uSxp+0HR2w5fvgi7nQxKZqfrNLSvxvzrDBRp1fgu4wiO4XVK6U5yD71VG0O/VsA54XspMOPmUCaCuzmSw6OfSUJuKew/yzr3xHPra4cXrwA9PuEM4CKLEN3kcXfOGRQrvYwJ3pUxEDJQ6JcqedHMRGogEPt/G4VEcFBBGzoKHxhb8665Pec5nk1yR22VhXxpVrOP+lnavrpkHgYKHhEgE/pxMiMyjk7yGSWauFx72M7ALFFPoU5UEjEl4ag2edg0MQwwMVGEQkGWxhHm3/m1/g4dUTffEtKP1S4LWKr/ifO0ZykUffhzRuLuW0enzOMhfT/7mgwUesihkCADOFkWOYQTtdft80+cJDtlHkJAUYL0cKfMw4xeb6ss5FH9d1zgSF9QSERi8JMJIVRcUdWbdlnASrOGfdQVgwZwYRSCModiERwyyBvGhsceLGIRu1+UDAHSD4FMwZgk1vuw8Z/ZFARkNgUixYQsSipiv4N4Y7f1+lDH0gETB85oNtBE0J67NaV9axDStJ65/zcRPrzfenZv9Qdo+dz69zaHwbaHWs9AvQ++toh/UClACoV8o5su4QYpKSJAWLhwylDSOSci6CzhLe9J7NV4PwVmt/362sI9+dS9H7+rX0qh0UnYVYxDUxC98aBf5nro2nsqIcPE++7VqH2tUa8Ha9GiJXd7cgcaKTA60dVsNKMfmedD/PoCeGsxBFuQltTEkGOdikhWCY6DrCy6/tfZaH2t+NVi3i0/mV+3PERepMP+XrrVYp73jCfsLMKjBPld3W0Ump0EnSW8OeI8BrR475luStbWrmd86AEKzU4CWgnPK7/nxPCTqAwdeqXej6n/9qvFvFp4PEd13JFxoSu23QBO/lVyPAd0PJC2TQydShHPZh/NNuHPqa05kmynk/6IwQ4tz7JTz7/pdZbwfMmZdIt8HKpCuTub3WoKKY/1W/a2o786A63fWuhZl7srJaE68vX4uHVG55BiAnRRgCokBdJafAsohby/9UXsu00pyt/nWvdlNB4xs0vo6A3bJdYluU6U/o54HJIhboEMscswvhN6enoYPXr0MLEYzlhsJAwd/jYL9jo3bYUgI+v3N8A7t1mB+WCeGt8VDe8j7zoMIXV0VKZql3BB5D6qRAt10ssi7VSuNm9AkdaqyN1bL0HDZNzqdKdtgHaKKaI7vrqTX4Y57hWa/P0UP3kc2cE6AruOJ7/h34i2L+3PdWk++k2NCtNaaeoQJSwqvpILOhbgSciQZIKUcG94P05zvWa/dlXoBE5yvE6N0kqrLKJYdKNFJ5jYqn8k6q4d+7vZLMQrutNWVHuki3wRsj+XiD7pJEeEqTW9nBSeSjPF7Cq+5mHnXBzCICIVTorM4DO5FSZW86AENFVBFQLdlORpcIX6EEa4l1v0o6hWO3lASyfqDaaHiyPncavjJkqUoL0CfVJ4Gu+bW7MzXydlHhplCafJmXwTLqKMTp5wzrIzFZYJnjvN36PBLOGEsNXQF8vsxMZnMOhS0CULKE1QnUr8bmIr0V0yh2KlL2nchvHt0G7mI1Eyjnkm/FgZi++0Whz9vX3XLEYn+Zg4kp4jsZXoRPOzWHZ2sGP146SHPLx0oms5qPoAAtMqdTr0enjxUks0w1UApzwOK56xnt3BlrTnuKlH+HL52VS0/ZvCaHNzwOEhbMQzDTfmX8r0getxR43rQsKJS8Z/M49tOZ8DG26lMBRfne/SvKzM+QWve09lZHEOJ3x5gVVW666yMgwxdSbgXfehPF94Egd3PsgOfR9QFF1pN1FoKdgFX+ATTAQaBm1aOf/cYT6Hr51tNx0/VTODP3w2iSIzczbTkCJJwtWvFJF73utoqmKvgEcKRvDcFjPZd+U0SiKNdDgq+McO8/jjN7NxBtYTLqih4+jH0fPKufflt5hQewlF4Ub8WZU8scMSO3MheurtrEa7Vs4j1dM4Ye30pOz6UJDRGyAT8W048E6+Lv4tr7/5NtPqzkQjuRzo+epJ/L5uCXn00StyyJV99nsRVBwJMsITnPNpL9qZ7v4Ie2xRgifXhW6adDevZXrnlTgDtegFI1j/p4dxFlUhgf6IQa5To0J0otz7R/DXARBw+VhUfTOdvQNc2nQFlbTYmYtmPCgYeBN6I7sdPu7bdgm1ejEX7781VUpX0vfwyDaL+SSYZxOLXfKCnPjVBfHezUEyG8MZi82LTclY/GxG2+PxoKoqLS0tSa+3tLRQVlaW8TMul4uCgoKkPwCKogz/SfzjcKIcsgClqDrj+0KI5NeKqq3tHc7v53waP0DcfQiqDCMRPL7T7ZxQWkuNaKLD4eO+rRcTHHEgz+xyJ0IoOIkwfvW5HLN2Ggpmxj+qYuJAj5YImCg9jfTe+nuc9x9GbnA9TZQy6+vRBLta7c8IIVhSMplVyhb2a6Wyjbkdl+ClHUPG9y9k+jEbjQLGOf+a9NrJjte4JHIe9dJDmehAkdJ+zyl0TCn5h7ELjWYRLqFjSqhQOtFE8r4NKXGLfgwpcQo97dhZhOiTDkYqzdzuvB4fnXwkR3FCaDqGBJfQma3dhaIqSARhUxAxBcV6O5dyH0iD8+UDHGs8z6/Vz/GKLhaqS5KO0SsdtJhuqpVWnnTNwqd0W2OBdU3XOm7FRyctFNFJfnyskAyEpe1TYq16xsegUASSjmNKk/PDF9BICY2UcFH4PPsaTAmf6iMzjr9TGPiULqtEJ+W9HulCSBNVmJSIIF1mFgomeWJg0Hvoh/qT6Vp+LsfMF33kieAmHSd2v/yQf1Qx+P8HG4vY610yh5BU0/axoT8yZb8l+FmmHU8rxfZrTnT7vlQwUTfi/FyEKaOdDs3HI794FHH6SyiKgmqGUd5fhnL6iyiuPJRQN8rnz8Sf3Rme401Bg6fyT0LRFHv/hZFWvGYz3c4ylm91M72lu7H28Mfo1kpRMMmWyb+ZY7+ZRnGoLprxMRHuClyj/8BvAy8wpXUSp+w9EtcZf0EpqkHx16IoAjPXa39+u563mVh3Mfv0vIBL7064T0xUDAQmTiI0Cy835F/Kvp9fiSuwFqOgmpYjH+Pt8Bbcu93SpGdObNyklDiEkfS62+zC2b4C5b4/onR9g1JUg+uMv3Bs1nuURurRC6qInPIMRx9+JFnR884KrKPy2eOoVrspqdiK68uvI+Aqp2igjt2bHgEhyA21cPIXE/DpDegFVfxjh/mMrZ2Oj/aka0q9T3oUNwOOQvt7V+1rj2+vYlL5+oVskx0gmDeS+7ZZQuJzVMHkd60PcEvBRHrJIl8GaVG8nKHMoYViXESStp0avons/hZURcWpqiAE/RGT/uwKOo95AqWoBmdgLSOfP4E33vuERa99zeK/f809L/0/e+cdJjd1tfHflTRle5ntu/YaY0xvIQktCZCEZkIz1YAJYFNtDKbbuKwLmF6M6aaHFkogIdQQCIRAQujdGHDZOtv7FEn3+0MjrTRlvQYnIfn2fR6wPSNpVK6k895zzvv+jcHb90XpXmttq6CK/DNe4uRJP+XQvXfj4pwlNIlS/MQxEVQRppROz2//btxC4rnVRHWTgbhpjcuT/oCRP4ZAzzdMeu9UxmqdbFaSx1itk0nvnepcb+WkP2SMW9LGLqP/faf/Ror/GmLh9/vZZZddePnlodlf0zR5+eWXPRmMUfwP4JnzEabuyNeG87fnr5vN4p2q43hqxzvILR9HdVEWf4tuRsuRv8NM1HMW6m00igpmc0FKw2XI7KCREo43F9GUcNYu0NsoNy0N7+sKLuSSvisp0K20vBQqQhpMb13GHbH9aJaFKbvpE5YDry6VtLOJY7QuZ1b8ouh0R0b2et8KLjdOtGYnk2bcNSHZT3sHFZMWsyDle7DVj6TT32CjySzk1Ohs4om69lwRJSI1tlfW8Ih/CRW08z4TODJax0fmOI6M1aEogoCmIIAdWM2z/os4SXmW88WD3Bk7gHrKGUMLj/oWUSvCrJVlHBW3zPzyRDS18VsqrIgfyrqEH8dj/joeDyyiQnTSIEM0UkK1aOOhwBJ+IFbxiH8JNUobDWaIPhkAIFt4m7s1Abf5r6OSdippZ7l/BT5hOlK0O2lfDztznNxgDlCoDHocw4vFQPqV/41IbiL+d/3ed/nN5POaJeLkJF0/97L/DX0p6WWKrXs8LgXlSo8jwrAxSH7ZCmCOfgt3a8fQyFAZx4bOkRCWH4UNq8xQ5enNF/Nhd5D+dx+Do++3mqCnvWgpK531plc0w0ZS82t/TGfPtkcpjLXQ5y/1LPrilovJKatFUwXBklpe3vZKDNdR/d33Y/pFttNgrmISy69FnPICuftfCkXj0HrWotz/KwDME59Bz62G7no+Cu5Cu2YpOxaa7ZSaYXQUckWEFmk1XtunRWAd/3250zin+yqPMWqXv4yIbmBKkVJq5BZiAEv0QU8E4zxyfIoztNhvCew2A/+056iq3QJFEVaGxeU0rfz9VvbfrhxRWMOVFdfwWugoniieTn/rGg7/4DRK9Sbi+bX4pz3HkcF/UGgMlX3pqB6/JBtS83NH1WUeUZIWpRwjsbx97dV4P1nv3MaOeX0c+tWChHmfdT0MFAqjDVzUczl5DLCeci7MuYwurRThylQYKDRTRA0tLOudw4RAF3lBzdOcXTFmQkYDvF913k9e1FLJiudWwykvoBSNYUxxNuNLcunyV3BOYClNSgVa4neTHeAPX7s4xSMr2RfjxC9mkNf6Did+MWN4I9xRfC/wX0MsAM477zzuvPNO7rvvPj777DPOPPNM+vv7Ofnkk//TuzaKTYlpLxIp2Y5ra26kq3hHAKTQeG2z2U6K2dbQ7guUYWRbL+VWpZwrcy5gtnwADZlCLnzEiQbLuIBz0eXQ0PfJOJf2LqNCWk1kzZRwScFVNIpyqsxmruEGLo1O8xgagRVsfGiOZ3ADDry3xg/mt/LnzIrNpEdmUauEWaFel1IX61YtqlQ6KUuSFHT2V9jmcEMr1JshJseW8LEcT5uLBAWFji4Vy9Hbv4RK2vmACRwcu5wofnRDIgT8QF3N44E68sUgPTKLe/RJdPnLWOw/z0pjJ8qnZkVn8rZhmfnVm6GUkqU2mc9aKjgudilrzTJqlHaqRRtNlPC6sT0Xci7rZTljRZgnA3WWqZ8s43VzW3JFNCVQtRVuKpUunvbP4zG/tY4uRcZm23RIp6gUUvqJJroAvg8B779b9WlT/J4Q1ti7ODp9g2ZxmRRq/lXYlL9xZuwc6s2Q557b2P2wz3cvQ/4SFrm4md+yr+eZtCFczimef6sYHLmmjsMaryXv/Tvgd2fAMfeDL+F5U1BjyXu7S1fT+Abk+DWeqziTd0MHYwjv/hzw5UInACyMhTn0mwUeA71acx2faNs6x2Wg8Pvxi6zgLykgj981iSde/Sc9EYvQfNkluDLnYg9R0TAJU5Rw8E6agMFgTu9lVJnNtPurHGPUvohOhWzntC+mUZSmH8yGLhVUYdJBESYKTqHR/pcPyZyqmnXOkmVP7WNJELUJZXn8fKsy6o0Qlw4cx59XdbLFV/dTmnAmt+XZxW6ng8/yF4pnl/PwDx9nfWALbi+vY1CxxkREZFMYa+HEpiU8s9VVvOb/GZ+zGWfk38qdE26hMWsiN2z5G94JHczL+ZP5Q/AQpnx+lhNs37Pl7XQFqp0eHVv05GJ5Ns09EW6OXUpFogxJx+p9MFFpkCGqZQuLOi9G6W3gy3AfxTl+9tu23MooZTDAG9/3DoBlgLftHZ5AfzBuUJLrZzC7ksuD53pOYZgipqlLaRDlFMcaUzyybF+MW8fdSItaSXGskdNXn0lxrJEWtZJbx92YaoQ7iu8N/mt6LGysWLGCq6++mubmZnbaaSeWL1/OrrvuOqJ1R30sNh6bRAvaVn7a/ayRaVN319P7yo0sihxDfk5WRg1tuhuYG74ALSGR+9Rmi/jZx3MoN5pokCX4hElZUi1rOwVEpY8q0ebUM7vRRAlHxxbSIkpYotzBMarVyLgxddXp6rcvjJ7K2f6nqXWpPqXb1reZ1W0yC3nV3Jmb9UN50H85tUqYJrOIEtGJz1Hpsfbf9sVoIYSJFQDsLFbzqL/O6es4MlrHl/6J/KhogCt651JpNjv9DPb6AE/55zmu2VJCWBZQqvTycd7POKP1CCTweHAJ1YTpJ0AOUfpkkGXGVC7T7nT2f4q+iCk8zyHam2nPpdtHwjoWK4uRbtlumU2hkjn7YJ+HAamRnZATNaTVY/Lfhv9kBsD9201mkeO+vbHrmnw/fCw2dC7rzRC5DA47tjJtIyJVgi5PFxNokwVpJw5Gek3tmnUbhlBRpUGXWkKB2YmQhtVTcdabmHnVNHQN0h/TyfFrVBdmWdKubuPQxCy9aUoeeP4NJr07nVK9ia5gNc9vsYgDvlxIYaSBVq2Sv2y3jCPW1iE61xDWKrk290LO6bmKKrM5ZT87tHIGjnuagg/vpmW7aWT7VEqemIy/Z52jHNTur+JScQ7zY9dRLVtStmEfr4JMO1bumng7HaGdWNPWzzk/yqbyicOcWXQpoYscikS/d7/II0KAKtqIZpUTiLSBNKwszxmvbfgCuLA63Ms9b6yhvS9GflCzMhtGnF823MK7VVOYvM+uTAh0p5xv93XJjTTT9tL1PK4exAUtQ67es4JL6fWX0TkQB2lNqBlSoikKY9UOlnZfQlkiK/LQVrfwQW8uFb0fcX79OZakMAkREFGKlLrj+xRWSlmafTGX9F9DlWymWZSiKYISI0ybr4pnf3Ane+yyIxPKLJGb9R0DXP/SKsZqHSl+HF3Bau7dYgXr9GJm7zuRMcXZnnUK4y2c+c1MSl2qVE2UcEHeFRgmLI/Od47BJmGfN/dw+R8/o70/xuaRj1k+MGRMOCv7Cr4Kbkcox8/cg7Zmq4r0sVym2MU0Zer9sCGny1FsVPz8X0csvgtGicXGY5MQi+fnWrNiIzG+cZkevV99PLdnTWOLslyE620rpaSt4WsubknI9yXM9+QT0xGda+jQyokZJhWylUZZgk/EKcX7Em+khOuUk7jGvMbz+eRoHe/KiQBU0s6T/vlUKl2eZdI1Sw/tW2YNcZGQXfW5CEpcCrrIpzRDdmJDaDVziRKkRmlz1E/s4L+MTh4PLHRmWd3kYkpsPs2E2CmJVByr1/GR2IJtg508oF9ALgPUU85sfQbXqisSPhol+IlT5iIVQljN5N0yH62wmkhXM8fHL6W6KMjy6HyPqVTyuWmQJfiIZ8zQGNJ6OY702d9m5lEoetOSD7CctMtEt2cfukwfBSL+vchc/LdASkuyszChuJPOoHBDgbIhFVoLRkYspLTUj3z/pmvkvkfTZSo25jiTlxmUPnwYGZ8jw26HoXKgq/Iu4azISgrjLV7pUUUDU/cEnBHdIFuVnNx5ExP738bX15D6TE4ySX1688Xs1PNn3ghN5rjPZ1GqNznysLH8sSwqvorCis0o7nifaauGTNH6fcXkxK1JnaiSTcAcoNVXyS1jb8TsaWJ+63lW6Y5QeWzcUn629kaqzOZhn63rqOS+/DO4uGepo34F0BWo5t6JVmC7WLmd3E8eAqwG5W6Z7TEodSNMAVL4KZetsN0R0LbaKh1TtBFPhNlErPqLe/hom/NAGZoEk1LyZbiP7asLODNyF+Lvt2R8BzpBeLaPStHOUR+fQWGkgcf9h3Bv7mk0dQ8S1U3GFGeTG9AoiLWwuPNiqmQLAzljCE5/jvVmMd+09TP+3csY+8W9DnFLlqRtpIRLC66mtGosB669mh1j71Gst9C37XH41/8Vf886y1DWtZ+fN/fwwPNvcHj0aVYX782xnwxd60e2u50J7a/yu8ChTD1gTyfQt8/Nr949mZBukYpOtQRTSkJmOw2UM7dgGT/fspzjPncZ6Z70R9bqRZz5m3fJHmxiRdz7/mhWK5jpW8JAViW3nvADakNeh3mbOPRFY+h93Wy9WQ2aZpWPrQ738sLHLXzV2kckoYa4eWku+29X7pCoUaTH/6xB3ij+S7H7WUMmefceBN31mKZkfccAnzf3sL5jANOUXifVonEU/vwcinP8fBnuozcSRzdNeiNxvgz3Man/dx5SQYJUUFBDXlCjQrbSrFZwZuAyLiy83pJLdCFAlAvNu1J2dbnvJioZmnk10ghmXho7BSMNHU8mFelq2O0AfohkSP5k7EyTWZS6wSToaX6zSPRTo7QRl8JDKpoI8QETODK6iN5E74ImTKcs6mH/YvZX3/aQiuONRTRkb0N5boArzWvIZQAdlQvNmbxjWI3f9WYJNUqbQyp0qXBh9FTiUiFPRMkSUdpkPmOVMA/7l9DSHWWWbzEtYqhe2z43K6KH0CuDVIs2h1QkT3PIRDZBpPnORqeZ5fmuSPSmPNjc39ukwv3Z95lUfF+nfoTAIRVgBfxG0tgfrvxpYzMuQoDyb3xlCTFkOumGmRAzde+7k4GRXoO65O3Z5yBLxC3fl6RlM11rz7Mk8W8Ng1N67yAWt4JsJ3jMr4FTnrd8AXrWMumdaU5N/HGtN7Bty1P4+hqsmvgkUuE2SX1122Uc9vVCftj0MDs2/pbndlmJ4c+zPCcUjY79b2EwqxJffyNHrPP2bmhGhG7VeqYFzAEiSjal8SZmrT2bM1uXJPoBrCzLsd/MsUiFq+dMT7rOOgo3iKkcxitOeQ+AgUphtIHjPzuLPQLfkNPwBmCVGg1oRR5S0SyLaGPoPVBGN1mqjtz5RDj8DitT4QtapOKtm5131XBoXr+ag947lV92P87P1q7wfCeEoLIgyOpwH/U/mmOVTmWYWOuP6QzGDXRDsiZexD0TVvBm2THcHTyZ9r4ohinxqQqaIlCE4LDo01TJFkulq/o6bnkvwo1/+pLf/nM9y2LH8bvAoSyrvJ4mpcJDKvrJYqq5iL93ZrP3upvZZ+B58oIa7HwiuZNvwj/tOcswsXMN8m83O+/oSNtaZjecxw+bHubgT70GeAd/Yn0+u+E88qNDGSelt4HjPj3NIRVdvnKe2fpK1EQ5XjUt3GYsZOrutV4j3XsPQuttoMQMc2N0HhVGM81qJfND19GsVlJhNHNjdB6lZjilx291uJdbX/2K619axYo/r+b37zdw+1++ZnW418ksfdzYTWG2j/EluRRm+/i4sZt73ljD6nB6AjqKjccosRjFvx5p6msfeP4Nrn9pFctf/pLrX1rFA8+/QfyuSZ5U8bjNt+TkPcexXVUBXQNx1rT10zUQZ/vqAiqOvHLoQf3x74YctRHWSzO/lse2vY0OtYz+qEEUr2N2iF4nLdxsFtEsrZeg7f68I6udxuLk2udF/nvSls7YgcPy6CEOeUgXKNglRUdG61ipH8jN+mEjKgNxz8Db9ey2+ZhPSOrNkEMqbHzABA6MXsnnZg3NZqHz4q5VWrnVd71DKo6O1fGeuTn9cZOQ2cZX6nj6yELD4ApuolxaZMtdfgEWyTrb/zQ+YRKVKkXC8p1oMEOMFWHuVxfTMRDnqvxL6CfLc65m+H/vuDRD+kCzSwadzzMFoYVi0HOuVZGa3UgXBCYHwP+JAH4kv/l9c9webl/URDCe7lolf/ZtiJz6LWb4M2EkQXw6cz+JdF6ckqHmWwmcLy7k6PjijP0S7mspJQkjTMX5d7prnWmiwkChglbK6PD0JiCsWdtI3LpXS41mpyZ+875/OItF4gamKTFNSePaL4nddSB0rkEWjcN39F0cua6OkngjsfyxTDz0Yk7Yf0/UM/9qlViZOuV/msnu/m84/rOzKIw00BWs5pHtVxJVcwmYA+QY3bQnAvmYlkevv4QivYUKrJ6ri5TZnuO0y1LjUqBhep6IGiZXyOsopYsGyrm06Gra/VWOW3Sp3sQR75+M6FoLBWPwqcIR4gBolCUcL5dwrLycFoYmcfL1TsTql6DPVca1ERNhJU9MpiTeSFegmnerpqRcb7sXsF8nfb9GAm29Uda29/PGV238/Zt2XqjXWK6dDKpGb1RPEArQDclATOeuwEk85juEC3Iv449rVV7/spWCLI3xJbkEAn6WmVP5fVsVS3zneH7nyqLFZJfUkuVTeadqCrJwnJW5WvMa9DVj5lXTeNjjrJl4ElcYU7juxS+4/7m/Uv3UUZTEG9FRyZH99JHNZbmX0qxUkCP70VEpiTdS+dSR1vnqXI9+1yRr20BUyeK+yvkc8MU8CuMt9ATKiedWk9W33mnmd8cIOW9dy9V9l1ItW2gUFcwrWMbnvq2ZV7CMRlFBtWzhqr5LiXWud44tmThsFsolJ6DxSVM3d//1Gx76+zo6+mNsUZZLXtCHqgjygj62KMuloz/Gi5+0WNd1FN8Zo6VQoxgWm6QUykZSmv3BrW9Bz61G67NmnEqT6iyH9mEDNZGGDs/Mhq9fsbS0E8REz6ni1t+/xtGfnEG50URXoJq/1/ya/b663JnpaDHzOSx2GQCP+RdRk3BotT0i7NKlerMEDSOlWTkd1ppl3Bw/hCv9KzO69v5F35Zf65cmyq0WUDmC7dpoM/McV283mmURh0cXe4gFwDztAaZrz2UsMzgjNpvnzR8BUCPaeci/hLEizCP6PuyufGI1WJtlvG9uzqFJfRDu8qpZsZnc6L+FYGEFelcjQhhUi3Z+a+zNnsrHVIs2GmSILBmhWLFqnjOVjrldnDcG37Xv4L9FuejbYFMe23A+EH0yQK7LVXo4uHsshDS/9f5l6rv5Nug2A/iEmaJOBpkN8ezP18lyjonOo4xOrvPfSq1ocvZLlwKJ9JSL2eWL6bKdnvPLhv0sOtQQmqaRH21xypUiSjZRLZeC2FB9e3egHCmhMNbCYO4YfrvNbdSuvpe9Ox6jzVfFK9tczmHfLPSUpngCYldm2fDnocZ6ned5PLeKoo4POfmLM5wynAF/iLxYq2dfm2URCJxGYhu2qeKQA7nCoJJHntmNiUJz/k78tfRYxrT9BdM02XLwfUr0Jtd1EQnT06Hfi+dWc+v4m3mrLUjcNBnv62J+yznkuvcprxKm/ykle2N7KbjLyYKayo55fU75Tpuvige3XM5P2p/gtdDRtKml+FWFvKBGX1SnayDO7F+MZ8w/rxgqr+quhzdvgX0Xs7p9kLv/+g3/XNtJNG5SmuenMBZmv94nWK5MpX3QAASqIvApAlNC3LDuFUUIYoZJZUGQ4pwAVYVZBDWFd9d2IHobuE9ZQg1DWYRmtZJ5Bcv4Jl7EhLJcFv6sgKqnjnSu5RNbL+fptiq+au3DlJIts7q5qu9ST5+dPWZ7yeYcMZcl3ES1bHFKr+K51UTiBnnRZtrUMnKMXrIYRPflosX70PNrUU7+I4oikPcehOhcQyx/LG1HPElFfhDltavQv3oVrWcdbb4q6oqvZHW0EMM0URWFCYEu6joutohOfi3atGcx86q59dWv+Lixe6h0WkqyjT76lRw+aOimrTfKjzcLkZ+VvmezayDu6REZhRejpVCj+F4ik7LESV/OHFZCTlEEY4qz2aoinzHF2amNVqoGgTwPqaCgBq2/kTPWzKLcsLb9+Lg6dlt/tycwsM3QmghxVGwh9aalMJUnophyKLtwVGwhl8ZOSZlNTJ6ZlBJqlTBXBVamBApu7K5+zo6sTiEVeoYyCjcKkxoRbVSITh7zL6KSdgRWoOVXBS+wB/EEATDSzKTO9T3IWMWScn3QZ5GKtWYZN+qTOTY235HJnaRaJpT1Zgkv6TsDQ+VVs2Iz+YAJHBe7lLAspEppQ5EmT+u78RPlI6qFRdjK6Uh4CFtwZw/c5/QfxsQNnod0GGlgOpx61P8qvivhyrQte4wbcohUxF3juFN6a6BHun+mhHY5fN2zvV/JpOK7TJcVKFECSWZjMNTrk6wOB1YzdoMMMVa0ME17ljBFBIh59ksTXlIRlwpX6VM85WNCWEG1VYalEJbWjL8gQS5EqjypjTfFD1jgvxBTqAipYyIImgM05e3oWe65iZfxyHZ30qJWktW3nknvTueDqmP4Z+UUnhq/iL0/meOUQ6Ut3SmoscpPA/mosV7i+bU8+4OVrNOLWdPWz6fKRO6eeBs6Kiom2TFvU3+rLKBCdKaQCrBIRUQEsRzILTWobLPP+V7B5Iiv5rJ79x/Zs/c5sjb7sWUE6CwhPaSCgjH4Tn2RmYftzZWTt+X2sic555cTyT7zFauvQiRke3ub4MUFKcdo+PNSJFa3MVdx1NtH40sY5f1x5zuZuOYhdml6mGM+PZOvv/qcN79u5+1vOvgy3MeEslxq3l42VF5V/7ajxiVfnM8LH7fQORDnx+OKraC3u4G6zos5Wv8D0yL3EtMlMcNENxJZHAFSgJHISqkC+qMGX7b08tqqVt5Z20lOLMw9YjE1tNCkVHBxwdU0KRVUGE0s7ryErbJ7UIWgJ1Buyf76LIJ45AenEGp4id6ITn6shcu651BpNqOjOu/C86JnWaWvDHCruZDz9TMTJVdWz4yvr4G8aDPtSgjdkBapQEWL9zGQM4bmyY/zWjjAw58b3FRzPa1aJf6edbz7yFIufuJDuj97Ga1nHd3Baq6ruo7Nxk9k9/EhfrxZiN3Hh9hs/ESuq7qO7mA1Ws9auPcgmtev5qvWPioLgp5+TLBK0oqy/XQNxDEyZCSczFIs9b4fDh09g0y57XUmzv0jE+b+kZ9d8SIPv/UNsZix4ZX/hzGasRjFsNiUGYtvoywxYiQrTyXNOD212SJ+8cnFFOvW7E2PVoSmR8hm0GlmbiSUkkHQpcIR0ToAngjUpcz4N8sCIjJAtQhnbCqNSZV2mU1lIstgBxDuDIIuIYqfnBG6PtvbaDBDqJieTEq9WcKx8YW0ayVU0s69ymLGinDajMVQRiYECGqUNk+fBsCOWFK0dtnUWbFzWOh7wMnuOL8Zm8dJ2ouUFuRxcP9jKJj0ywA5idlrdxai3cxGFdJTo+9GspLOcOdgFP8eDHe+3d/Z46zeDOFDd5TD0iGTKpSe8GLZEOzM4kj3MxMyreP+3JCCdvIoE0MypiaKs9/NZhGvmDtys35YooyyPbGeklLClewab/9OWKvkb9tfxr7vn02O7CeslIIZp4wuaznSZ0z+4vspdwWncnnvfGpoQSIQSKJKNhEtj4LY0Kx1V7CaeybcxHvru7mmfy5lGdSfnttlJSfsv2fqRE5SL1yywlGO3/JBePL3T3P22hkpvgXp1Pjcx2UKBbY+jE9qj2PLF0/Cb/RhCpUBNZ++7FpnrPT7QvgDQavcJpAPxz0OT51m7RdAwRg45fkhYpRORKT+bbj7QDDj4M+D7Q6HvS629ikxmz4gcsiW/XQFq3mt9hwOWjUHVRoMksU3FfvT9JPLWfn8m1zdN5caWmhWK7go93K+GCggy69y8p7j2Ldap/KpI60+wERjvSwaxwe/+A03/XOQUE6AioIgXc1rOO3rs6mhhfWUM52FrI4WIgTkB30U5/joixpEdRNVQHt/HFVAtl/Dpwl0U1KrdXJLfAE1tLBOlnGmupgmEaLEaOUuuYgxooWwVslt45azz64788H6Lj77x59ZPngRaoLgLhGncZJ8irEi7IzVdbKMc6IzucG/glol7Mm6TY3OYXnic1vswL7OtoLZOlnGr80FxLIrGdRNegbjSBOqlHZOVp7lMv045mgPMV17jvWUs7zmOgazqzBMSWVBkCy/ymDMoKk7QnGOn1N38DPumaOhcw0dO0xn3uBxjC/JRbXHayJjMaDm0jEQ48+fh/nxuGLGhlInOkaSsTBNyepwD1f//j1e+jr95J4NnyJYcPA2TN193LDL/TdhVBUqA0aJxcZjUxKLz5t7WP7yl4wvyaWm70OO/Wi6890j269kfe72rGnr5+xfbJFRQm5ESPPik3+72VLlAPTcasyTnuPWV7/i8I9Od2bobRlVd0kUwNP6bhyk/iOjWkm9WUJd7ARuD9yQ0nuhS4VWmU+l0uUJ3r3LCNplHuWKFbA0mCG+Miv4qfrJsAGSTXpaKeIc7QkrM5AIZurNEs6Nz+Ra/y3UijDNZhElossJ1tpkAVE0qkW7R/Wm3rTkdhsTpKKSdh7xL0m8MLzBUL1ZgoruqGb1ySDZIsbneXtQ2P0pVcqQ1G+bmYPA8o4AW0Y2Vea1RwbJd/VdJMOQVpp1lFCkx7+CbIUzSKMmwx3k15slLIz9mtsD1w+rfJROFarFLGBe/BTuDFyfsvy/kkz2yw0TexNvmt/AUnULJVTnWmQRUuIQ/XRk3pSCq+LHcIn/Ec/nnYFqrqm8luP334O33/uIA9+dTpnexBq1lmxzgDLpLSdyI0wROipVtDmBnImKkuiJSiYOYa2SC3MuY6cxRZy8eiMneUag8meakvuf/ysHvX0ipdJ6DkTwo6GnkAr3ubRN7QwUXvf9hAfZn+vil5HHADFUOvK3d8bKoMgiSw5a5bNH32X12u16Grx1m7XBPWZmLOFyE6LwZ3+l9OkpqLFEeWleJVJREd31hLVKHhmzgBMbl1IYbXDtq0K/L0R+vJWm7C25In8+umlyaetFVMlmGkQ5s/xLWGcUU5zjZ5vKfPYIrOHID6YhMJCoPL7jXTzfXcMnjd0UZPmYEOhmftuFVJjNNCkVzM1fRpMsYW1HHz5FwZCQHVAxDBNFURiM6fTHrHMZUAWKIqigjfsSE0lrZRnHRucTFiGy/Sp+TaFG7eCW2HyqCRMR2dw9/nr+Ht+MD+u72dP4OzeJazzE1Sl7kgFeMXZmR+UrapVWRzLd/tMuhbXJhf25ezsnRS9ikvYPlunHYabYF1pQMZijPcS95iQ6tTK2rsxj++oCGrsjDMQMsv0qO1QXDqk4JUrK1v/wEq5/+WsKs31D8vQuYtETifP3bzopzfOzY02htU+DcToH4kgkXQMxdt2shDP33txDpJs7+/jV9X+hbWTzfR4oAhYdsu3/DLkYLYUaxfcSOX6NoKai9TVwwJcLPd+5zZds981vjTdvSe+iuuuZsPOJ1B/+O+78MM5H/XmcpixySn2mac8yTXuWGqWNerOEp/XduUvfny2V9Z7goN4MMTlalyAKUKO0cYn/0ZQgWUqrVMgmFUfF6pgVn5myu1E0h1TUmyUsiJ3E7upnKY2eydCEyXL/CiRwsX4aR8W8+/R4oI5aYXtadKMl3LqbpfVvpKDZLEpSvZHO7KFFKhZTq4SpN4t51djJ8/uL4lOZHFvi/GZughAUiz78GB7DtBKln5DST6uZ6wSH6Rrgk0lF8nGrgv+g28H3H/+KoFsmSpyGgylh0LWMj7hDKtI1I9t/ZirtWeh7IOWzAennsugxaRXZ3Nv9NrBUltKPrLBZQJtplWTZL0wjEX6pSAropTXRpFwuOh1SYTvD22iWhcSkiiIkF/u8pCKi5PDEuDoGsyrJC/jY84c7cm/FpfSSzThjLav8W6fdN7t0sozOBKlQHAdmm1QMiizum3Aj63O3594tVtCqVVKmN3FV36Vk+zWe32KRZ5vPb7GIeG5V5tKQfRcPq3AEliLQMZ+eSansoJViBgkQJJbxHEeUHB7dbBmdSihxXk32jr/GMv066tSzEw3d1gXuVwvQ0ciSg5Yk7vhFyMenW2Tn9WvhwCus/9z9Es/PhdwKT4OwcfNu/Ob517jq4zwWVN1Jh1ZuLd/bhOiup00tYxoLeaq1klv8J3n311dIfryVQZFF5cAXzG+/EBDMzlpKPeVUyxZuiM5n84A1FkrMVn7+8UWO67XAYO9P5rCZv5OCLB/ltDG39QIqzGYalQqWlFzNQFYlEomqKFQUZpEX1BiIGgzGTeK6ieki2YqActqc7PR6yjkhbvkVaQn/v9yARiy7ive0HQAIygFO/+pMtudL4obJ2/7duUw53XOct8QOdiYMDtHeSksq4gmlweX+FVwWP54emeUhFQD5YpB7A1cxXXuOOdpDGcv5DFSW6lNpMEPEDJNPm3p466swLT0R2vujDER0TNM1hhLGj9XFeWxemktTdwQpLUGChu4BmroHqe/qp6krwu7ji6kuzOa9dV386bMWfv9BA89/0sSzHzXx7touPlob5pdXv8i4S/7o/Lfbld+OVIB1/9/+6ur/l2VRoxmLUQyLTZmxGIn5Usb0+8ZgGEM+Wzmioz9GZUGQSNzk488+4ZDBp1imHwfAHO0h7tInOaVAV2q3cYxmmSbZBKEpUTb1mL+OGqXd0fDPhHqzhBmxWdzsX+7JWLhLPuxZ3tsCQ2pNc6LTWBa4y+N9keyJ4S5dqkqUclV4+jasWVPb/C5MkZOF8FyfRKmSvb3p2jNM014A8JQ02XBvzz4PGzI8uzx6LBf5H/HUnusSIvjITdMsO4rvD5LHeJNZyJdmDXuqHzskMWwWYKA4pYQbKjGyMxal3R+mlAvZ49ZdQpfus++KDWVBwrKAXAY8zdydopCgHCALK+pIV97jRrMs4i3lB7wqd+R6eZ1DqO4omMVRg487jsKPbXcbZxz8M7T+RgbvPICsvvW0iFIrC4I3Y5GpYV1nKAiXQkVIg1eLj+Kx0JkENJWd8vs46pMzyOpbT5fPclbOjw6pIn3nslRXZqBFrWRx6CqO6ryDveOvpyzaJopRNR9F8RZatUpWFF3MzNbFlLqMTW0fhuRni4HKb8dfzr7rb6Qk3mgtXFADp7yQtgmb3WZYykz1byPvOgAhdQaUHO7Y9jfoudUUdnzAKV+c6pDHbrWI4+QyCsw27pML8SWVZvb4StENk2LTyhI3igrmFy6joz/O8uh8xgirnGlZ4FwuM2+gKG6Vo3UoIaRQCRlhugLVXJd3Pme0X0WVbGadLOec4BJ8RWMAaOmJ0B8zqCnMQlMF9Z2D6KaJaYJumgzGrAkcVRFcojzAKeqz9MgsTonP5T1zcxTFavCOGxJFgYCqsEWwm7uMuYQS+62jMcm8DhSVlXIRY8XQeyH53rCfAbapqP0OSs5mZyr1Sy6zHSlUAQFNQVMFAZ/KZqEcZu6zBbttVsz7DV2098cYjBm8/U0Hq8K9NHYO0BeJs3meyRfdgvysADN/PoGqwiyufO4zVoWHL2XaVBDATcftxK92qN7gst93jJZCZcAosdh4/KtVoeK5Vfj6GodVhdpUME2ZqhyBdYwvfdLMV+39GIZ3Rnxo1r7VQypsePsPBO2yMEU5qlkWUSE6PWURzWYREpwArMEMcVbsHB4ILCNfDBKXCmdEZ7PA/0Bat+7kB/tK/UCW6lMTZGdRSrmVKeGI6ELeY0sAfiBW8WSgLvUcJQK2lfqBCCTTtOdTyjncZMiUgtOj53Ck9hf2U99DiuGJRbogLp3ngXtfNtYg7/8b/hXlQQPST1jmUyJ6PQpPbnLxsL4Pc/RTE/eAZcRoN1yXKr0p++XuNWgxC8gRg2QLnZb8HSjPoAplE/Lb/Nc5JXdDxBri+B0Bhg0h2dAuikZQ6BmXGQliUkFFepzhk9FMEYqiUWa2EkfDh+5kBQ1Ursyfyyk9t1JBG2G1gr9sd4WjytQTqCCmm5QYYUd1x92PMNz+mkJFkQayaBxNhzxK8L07iexyBhVjJkB3PX23/cLxHegJVPDsxKXOJE+7WkZL6R5sNX0liuZS0Rlm0gbwBPJ6fi0rii7hp33PslnvPymONTmL6Sh0iSJKZDudvnJMUxIywrSqZdT5L2BR5ApKZIdn022yAL1gM8+zxT4nsdxqFEDra3BUhwDMew5C61mbVonIXteZ4Fq1kMJog+f8dpBHIZYvji4VukQeJYmyN5vwtIhSJFAhW2kRpfzF2I7bzcO4R73ME6QDdPrKma4sZefaQmauO9dTgtYgyjlJLqRVlFBVmEVMN8nyqaiKoKM/Rsww6Y/qIGFQN/CpFmHwqwpCCGKxGE/657O9siYlgBeJ/wI+Swa9VungPuZQljiWfhnAxBItWWuW8WD858zxP5JEKryeLvbzOZlEZFrekDA5upgPmJA6br4FBBDUwKeoBPwKQZ9GUDE5vncld8Qm0UIxWxdJPu0UmGk7k6z3+zTtWZbpx2EkvFE2NeYdtDXTfzr+X7LtfydGS6FG8f1DkvmSW0VknV7Mc7ustEhFQuVhQ8ZE3wYNXYNplSP6ogYoCluU5rFZSQ4TSrNdpUBLqFVaE8pQXlJRSTvL/StcMzYSiUyjFCU9wXmTWcThscW8au7kLFMgernNfz35YpAemeUhFZbnxQJ6ZBZCWM3gdpOdLf93r76/s781ShtNZpGjjw/WC+DoRNalknau992S9hwpAj43a7hKP4ZsIjSZRR5SYZV3WQGk9WKR3BG4gf2190YUkKUjFZk8Kmwikc6XYhRD2NSkYlD6yBYxqkUHuQl1NBs+YWUqHtb3YZ5+CoBjxGgkAo1SpZd4kpeFKS0PinqzhHozRLnSTTChvCRIdaWuN4sBeN3cjjBFHqNKO2vXIEvIFjHP/mWaJksWgxEC3jfGe1TYkolHlxn0rGO4VJrsbfqFiZKxsCPhVYFJmdmKBA+pMBGoGFzUcxnBRDakzGhm8gfTrOdkbjVZPtVDKroC1dxRucR5xmRspgcUaVhmZyf9kaov7qf4w5VUPXUkSuM/UV6/iixtKJBanfsj6nO25uucH9CphAgZYbZpfgrlWa8Zmsc8rv5tq8TIcJEzVxmqcuRKTmmuY5f2P1Aca8IQQ6Z2Gia6FLSIUoriLciAVWpWaoRZELmauwtmpBxTPqmzzGrCLG+VbxtWhObR6qtE61lLz62/oOeWX6D1rKVFrWRe4RU88LevMG7eDdG5hkElh3snrqArWE1hpIFjP5pOYbSBRqWCuuxLHYO+4gSpkBI6sUhFC4VWfxgmplBZlnsxMwOXE1ZKKZetHK28wunKU8yV3rLXHi3EbeNvpkEW0xeoSClBu734YnyKwjnGvUQjUcryg+w0tpCxoWx6InE6B2Jk+VVCeZYnUyQunXKogKZgKipn6eez1iylVgnzmL/OMXuVWBNm0biJYZj0RQ3icogw5ohoQl5dsDg2lRN9L6WMLdsrySkLFDjKienKHd2kwjY6Xe5f4TGg/S6QwKAOPTGDtr443f1RjuleyUnKczzsX5JQIMwM+31pl2f9KyCAioLhy0j/FzFKLEbxr0dS05xv2rNMPWBPZu87kbN/sQWz953ICfvvmeK+ubHkIq2JkQv9MZ2IbpCd1MMRM0x00yQroCIU2HV8CXtNLOF0/3NOYJ8ufTtNe9YV+Fv9DZVKl1N2BNasjf2ZjVfNHQlTyAB+ntZ3p1cGyBUxKpVOmswipkbneEiF9dsl/NHYlUf0vWiXuUBCmjLhuP2YfxGP+Rc560yOLeaY6HyPs+/eyvv8gnd4IXBRom+ihGaX47fjCkyMRdq9TNFepUR0eb63gzpFwCC+bz1bnilL8f8nf/rdkMn5PR1G+rn97wDxhOO0ZXpoZ42c30ZjuT7ZM8P3ARM4LXr+kPwr3qBCSajHzIjNYkbsHEf+WJeKI6Nqw0RhRuxcxzzSJsu9cijQFwLGKW30S7+HdGYymFPE0Hf297uqqzzKU+596JA5KV4cakKuucksBKxtbmj8C2E5PNtkwiEVio8nNluMiWUGVyitmWMJCf8HQSQWxdfXYHlFYNChlfNxYEe+MGs9DtLJx0riN+JqzlAfhNv07e794d378fU3Esmu4p3QwdxZcDY7f3Y9P2j/AwVm19AGv3zR+xz2bOcARzK1ce2XdDx5Put/cAFy17PgiJUoT5zsZEQMFFRpEaN7tryNVq2SCtrID2r0bXscxdOeQM+1ykXKZSvndy1NOTZNmBgodDIkQSyxTDu363yJXzfW8fzEpXRo5RTGWiiMt9BMCecEl7CmvZ8j3rYatCWQZfazY+9rKcH9FcFzeSdrN26Qx6Zcx1LRTbMsIkdGSLQtoEiDOf3XMNN4AGkOEax9xLtcI673bMOn92NIyRhaOWfViRz4xaWe78/vvZIHlAWcrDzLecqDbFluGblF2tZxAfcTCqr4VIWugbg17hLXeyBm0j0YRzENfq08y/umlRGwzV7dgbwJlBjt/Ma3hGrRxoD0e4Jvn5DcHrjOUTRLxu/ie1IgXPK/wvtegNR7QpcK06PnO72Mj/iXbDJyYUMCPTGTu/RJzu885L+MYtK7abtFSdaaZdylT9qk+2OjuiDAfltV/Eu2/X3GKLEYxb8eaZqp03pTJDl082b6WfV0WB3u5dZXv3LcvK978QuueO4zXvy02SEZdvP4QFJDol9V0BSFwZiBpij4VYUdxxTRvvtc/pR/BBdmX0aXryTlZlmmH8dK/UCOj83lWO3Pjqyim2i4Z210KXha352b9cN4yj+fadoL7KR8xbnRmY7uf4no4hTNS2gAHvEvYYr2KpPUf1CpdNNkFtIrg06TXKXS6ZGKBbjefyuKGMqgVCqdrAxcm1D5CAKSCqXTacy1Xw61Spi9lQ+cVLbNz5KNu3Iy9ESMhBsMr3Y1gg38P4eaJoDOdE5taeN0nye7OdtBuCYkvTLIftGr+Mgchyqs/qJ6s4QapY1p2rMp23uZXbgweuqQH4O0mr7tfqB8McjN/hu52b8cX4JUaCK1SCFHRLnZv5xnjV150H+5cy/8OnpxShN5OhWnTMZ9YPVLLIsdmyQj613fkIKQ0u+QDrf3S6XSyUfm5vRJf8pvJcNzbsFDLhQzzg/X3pmS7bA3pyLJi7Wh59einvlX5M4nkh/U+EnPs1zZMcMqrUzzm+796SWHVeFe6/mXV215TygqmFavQK+/hKuqbmRl0bmYqHxVvr9VPoUxtCe9zeh37sfq1Z8nbSchmSo0/tizOf7fHELxhyv56pGLuF8eRPyRXzuExPDloGLS5qvi6spr+VSZ6GSoswabyG38G4rmQ5n2Ar3+Euf4Adoo8GSVVEyKEsGijpLwFbG+L4q3cNBnF6HrQ88lCYxRO7h+cB65DHhK0L7M2okDVi30nL9LIjfws97nOZ/0M9gldJErIsSl4myn3Gxm7/hrlNNJf2J8lopuKoS3JDaLCGetPoMHYudSGf2GgliL45iuo1IQayGkh+nyl9O67TS6B3Va1q9mYcdFnCCfYWnub8kNaFavgSIc13eJNYYvTki17qKsoikxYZRMLiqxDFBrlTC6FGSLmENQbLjJttuPBmCm//f4EiIgduYi+b2QfP+dHp3Ny+zi8UT6V5ALsPyo7N8Zq4S5UHuUCrwldcmk4tv0fIwEAjh97wn4/f+aEqvvM0Z7LEYxLDZJj8WG6nKT4XIlRd2wQlRqQ7bBF829NHVH0FTBxPI8dh5TxL7blPPSpy0pPRZSSt7+poOv2/sZX5LDj8YVe777MtzHtpX5dPRF+PMXrQzEDfoiOgNxi0gs0+5givYqYNWEHxVbSBMhlmvLOUR7y7OvlgGfpEZpd6T81pplLI5NdZq2e2SQPxq7s1yfDJAi9+omD27PDVPCqdHz+ZRxngfnrNhM7vBf6/gJSAltiTp4+/vb/NenbbgdqZ+ADXeDpSnNjXJDHvWl2DBGco5SXJulNQMfSnI7t7HarCSHSIr7uz2WwxQ6ggZASk2yisFc7UGysRS9pmivOj1B7vHurrkelD4CxCHRkzPY1UyQaFoHenuMuoUPRiINCzAgfU7T9bcZX81mEWfGZrMicCPVItHsKkfu8p3uWghhSTNH8RESqTOqUREgIKMYqKw97EnGFWfDA4ejxPuQqIhERiO9YKcF28E6rFZw22Y3UVuc7ThF22jXynhg69vRc6vR+hqcPjfbtdtu/Ab4a/4knqi6iPH+LqZ9dTbZ/eud5exeg65ANY+Pq+Pw1ZcSMhK9BQVj4KRnkG/dTsvYSfi/eIr+ny2kujgPpbfBK/96xEriDx2Pb6AlcZ4V2kQBFXSiS4EihKd/6yz9PC5WfmMFyUnN882yEAPNMea0zoklw5v8Z6NSwRXBc7kkcgNVZrOHAHaTSyFDM/RgBdsz4ucx3/cAY0TY05MRpoAS2e3Jog3iZ5AsihP9DMmwz5+zn1oOnPEG4X6dwt8eTnb/ejoD1cwrvIJ3O7PpHIgR1WXK1XcHzPUJfyP7fqo3Q8yKn831vls8XhP2eb4mfnSK/LG74fr38d2Y6f+9QyKWRqdwsv/FjJkNN9zBu3sf7b5AAD8xnvDXMS92yoh6MHZkNUv9d3NErI4Y/pTvK2nnYf9lBAsriHQ1MyV2acrv/ytJBcCSQ/93pGZhtMdiFN83qJqlxjHShuyEhNxISIVpSl74uIWO/hhblOUSN0w+auimezBOaZ4fn6LQORDjo4Yu7ntzDVtV5lGc4+fLcB+9kTi6adIX1dE0hfygD01R6Ivq6KZJbyTOl+E+inP87Di2kK6Iwc+3LufYH43lmB+NZdJ25Wyb08NPlY9de2Q9rGsIs7f6vmdfdalQo7RRo7RTb4aYGp3jzOAs8t/Ln42dEmQjwh7KJ5TRmZFUNBGiiRCvmjs621cE3B64jif9C5wH5/GxuZyiPecJYESiDh6sl16YIibHFjuzXO6Z7I0hFcnY2HmaUVIxPDK5uNvf2bCa+wVzoqc464SUfjrNrLTb8BHnI3OcZ2bYHqsvBC5iO75hqT7VGXN36ZOYoz2EmpDOnKM9xDTteaZor/Ir9U0e0ffi8Kgl42wRgVRZ4ywRd2q0/25sxX6xq5gcW5wgIl7cEj/EQyqazEI6pffFlml6LFvE6Zd+9KSeD4BomiyOGy1mAYfHFvMeEzgmVudkSzaGLKfLCvXLALki4tyTybveaWbRJ4OoGBT94RS4e1+UuBXcCoxEff/QWoP4SIb95Cwzmjm87fYhp+icKroC1QwSJKSHOenLmdT0fchJX86kVG+iVavkyR1ux/DnIaThzKhvN/gO/uZ3mfzh6WT3ryesVfKOfxfAyiJIBK/XzuSoNfMcUtEbqMA86Tmrz2OPGVT8aSbFH65kzD+vSJ+hvv8wh1QYWNmsCjpplkV0uMqfbFyq3s95+kzWybI0ilyCK+SJSefEIhFzfBdiCNvrQ3BN9mwa8nbgjqzpHlLRIfMopI9WV18NWOVCdb77uNI8AR3VKm2TgrDMp4zulH6wLGLoUnoyX+5rbpOKdgosgqT3Y9yyB6WPHUp2/3rafFXcXHsDn/bl0TUYI2ZIS8zCsw2DadqzzIrNTNx37RgonszFk4G6FFLRJ4OcFZ/NVN9LKefXyjQKbo4fwhn+ZzyZiXmBh533WNhMX5YHOFK0dobCziis1A90VBgBnvDXsb2yhscDdezI6ozbgyHBlO2VNTzhr0u7TBMhjotdSlgWMjbx+z8Qq/4tpGKL0iCrFh/wP0UqNhajxGIU338YutUkmKbnwt2QDfBVuJ/cSDPnGveSrQnysjT6owYV+UE6+mN80dzLr/eoZbuqAroG4qxp66drIM7u40NccuBW7DY+5Hze2R9jbHE2e00sxTAlg3GdbL+GEIL8LB9blOdzafEr1ChtNMgSGmTIST0/F7iE/ETK/NTobOrNEk+fxevmDnzABI6NzafetNbbX3uXV4ydHLLxuPMiSCUVNubp03hY39t5uGtCOr0ax8bmM0N7mkO1N6365KRAql/62Uqp5zH/QmZpTzIvdsoGm0I3BqNEYdOiTeZlnHX/uzHRE8C2yzw+leMYcM3mFYpBpy/n1Oi5PKzvk2j0bGM/7T00IR1yYUm6Wv07vwlc5imlSG54vEuf5AQweSLKQepbhClyShJqlLYUpTQbc2OncIf5KwxUmggxIzYrpWzrCv9KD6kw0KhR2uiRWcCGMxE9ModLotNTyId/A44opuv1aEjolcPLrmYq4UsmF27ZZrtW3n3MFaKLAQK0ygKKjHannn9AKwS8L+0IGllklmmO4mPbrlfIlv0MiiwisTiF0QayiBBRc4YalyMNRNQcHtrqJkLrnkeN9SYaxq1zVBhv4bKuC6imhUZRwRVZ51Md/WroGJEcvGoOBYmeikb/ZlxWfj3rzeJUY7rdzxrawYIaq7TKnwsxizx1aOV8Uvhz1/nodNzODdfRV4s2lmvLWRyb6hGqsNe5Xtzg+UxH5Vrxa86J34MqLXUtBcmSvjq2636Nef3LPCVrIdFLsywiiLfXBqBKtLFCvc7JfChCUpyU2XBf8zLR45FTtn9naN8EMtHUbqAQMAfw9TUQz63m4a1v5Z8d2bT1RYm5Bpl79NqO1cv9K1LIRTpiZONlY2cWaPc62bjk/fYJyVWBlY5AyYXRUzETJF1KuCE22XOfuOGWorV9osAK+pfqUz09WvNipzjLD0cuvCqMCvNip6RdDqCZYq7Wj2Fd4n1qE6t/Fan44Zg8Ppz3S146/xf/L8uf3BglFqP4/sOtRJJELtwN2b0RHaWvgesH5/Grgd9xfO9KfKqCbprETUllQZDV4T6yfBpn7r25p3n8jL025xdblzufH7pzNaV5AcI9EZ56v4GH/7GOte2DNHYNeH7/T9Vn8aD4Fb9mEaeIJTTKEmqUdvJEBD3xoFzoewDVVRHdK4PcrB/q2spQVLSLsorL4sd7JPwykQqwanzn6KdxRny25/NS0cV24mt+qnzofJaaPLdQo3QwRXvFMTRLV48/iv8spLSyTOkC6AHp44fqak8AW6b08HhgITkiRr/0ezIVp0bP40/8mDn6qcyOe9V3OsjnaX131pplTv9OnojymN962WdueBwaW/byALPjriAyDc7yPeM0WNoqa8ljcKgsD0g41681yzgpehFRqSRJ2qaeoEqlkysCd6UtSZISnojvkXbfKpVOHvMvco7bIuyFnt+wz3dcimEzGXYWyQ3JkB/N6dHZjtEkQJnodmRNraMGVU9VRQrIdJ0Wru+Jo2BgoKKrWeTHLS+Mbn85L07w9hcEjX72bHsMEeulTS1DTbh329Aw0VFYmXUK5/ddTSVt6GlkPAcJcIF2Ca+HAzzxylvE75qU0mPnwce/c0hFPLeaa2tu5LdjLuU1/089i7XIIpbFp9DgOk/VShu3+q0S0l4ZoFkWDe2vWyI7UfZ0pX41NbQ4mQaAHAZZFLnCMRbs04qd9Urosp7lw4RKiwIXMiCyUhzW28nzZAEhte/G2jfLd6SEDnq0Egb8Q8f3V3N7PujNprF7kP6ogSGtLF8yJXY3LSeTi0J60u53k1nEj5XPPOVMcRdpSMZZsXN4nH04MrrAIRdXBe5MO2mgS6+/ha1caEPFYJ72AJW0o2JwsPYmZ0RnZyQXlbRzo3ZTQtbaxJBwdHT+BsumOsjj/PgZns9mx8/apKRizv5jWL30QB6f8TPyc///KUClw2gEMYrvP9xKJAlyYStANXdHMExJf1Qna7CJq3rnUmE206xW8mzOZOKG6TRkZ/lVx002bfM4oCiCqG7wly9aWd85SFGOn/EluVQVZKEbJm+v6aS9z5rBklLSPii5yXcyTWYIU3pD91aZT7MsokZpo1LposkspN4sIU9EeNB/uROw2E7fdubijkSvhRvDPQzTycdqQnJn4HpqlHaazMJEo6y1d2GzgCazKKVG3Q7oomy4BG1j8f+nk2vTIp16li4Vp3nYasCMO0pk06PnO9kDn5D0ygC/jF7L5OhColJFCFjgt17o6cZNmehmF+VLZsVm8rC+j8vJvZ3HAwtTZvxsk0jbY8ImBDWKZdS43HdTxmOLS8VpsNyBr1z14SV0yNTSF01YwX69WcKs2EyW+1cQcN0nlk+Gd6ANlfRZy3UllYMJQUoflHus1ihtPJGY6bTOhfD8hhBWFqVNFmY8TmfZNI3adgC2wP8As2Iz6XdJgNr72J5okg0kZSbcx+FuQE93r/WRRZ5uNbGGlVLuq67jZ2tu9CxjCJVAtJ29+p8HLPJhu3fb0DBZMHA51bLF44btRhZRfm08wRi1gxM+n+FIjGd06t53Mex6JvzgRFqPepqBYAXhxnVMjH3u3T8UusnlmNgCHtb35vf6rp5Z8ROil3J6dHbaiZG/JEpGNWE6SlItopQuUehZLuIrJE/vcDIjmpDEUelgqPQu+fzOiNxORHqfma0UUCJ6U0pJ0wXtna5t5+tt5MVa6fKV86xvPxbFT+LE7jvYs2Rw2KycXWJUb4YS5OImJ5PjS1POqkuBguHctw1miM/NGnxCstYsY1ns2JR1Fvrup5J23mVLTo2el2JSqUvFlTk3PeQiXwzyoP9yaggzT3uApdrdTNee4xH/EpZqdzFde44F/gdSyMUveIdl2h38zr+AQ7U3LUl3aYlXHKT9I/MJSaCYXq713eb57HrfLd+5cXxiPrw79+esueIgTt9nBzRtNJR2Y7R5exTDYpMa5H0XuNLp8fxaHtrqFj7ozWUwrrO2fZCieAs3RudTEm+kWalgccnVtCkldPTHKMsP8sPaIvqiOl0DcY+brGlKGroG6Y/p5Pg1KvOD3P7a1ykN3gDtfRH+sqqNLL/KtpV5NHZHaOqO0NITpVK0cb+ymLEi7GlYdcM2+1ruX5G2xKlStKc1rYPMjqXehr0SNIyUGaR2mefpsRiJO/K3baR2N2+nMzwbhRcjOc/uF7i9fLpr/bS+G1foxzsu6ACP6HtxiX464B0rD+v7sIfyiUMUZsfPYrnvJmc9u3EbvAIBcSk4MrqID5jgcZ6310nnLm9IbzBuSEFYFlKpdBKVGp0F21HU/TEBoVNvlqCiOwFPOrTJPOJS9SwTNgsoEt2OeV+7zKOAvpTAzh2ML41O4ZLAo07wc3XsaMcULPm69MsAPTLbOQ9RKQi4ml+1hJb/xo73sFlADI0apZ1eGSBPpJbddMocisRQtkKXVv/ShhSp0n3fIENcIs7lFpaRx4Djj2EIFVUatKtlaKpCQayZHl8pOfG2YZvEPb/J0Cx8BD+9SgGlZiutWiXP7bKSE/bf05nAyQRdN7n6sZc56YszqMQaQy2yCB2FStHJx3k/48zWIxACHvIvYawYeo66n7vJx98sixDolLv6Wq72z+Sk2IOU4X1eupupTYSj3NUsCymhZ0TXuk0Upxj8DYdWCih1ZagWlFzHXwfHc1pkJccaz1BPOcdE5tEwzEx7sjlqJuO6ZCSLNLypb+MIiaQuG2JG7Bxu9t/oeb/pUuGIaB1hijzPBFv8w525yBeDDkm3J9Xse36tWcri2InO72cSPxhJOVMV7dxW+gTb9b7GerOE2fGznOb1jS2HqsyG2ftvy2E7j/l/W+Y02rw9iv89JBr9bBO9Se9OZ6zWwealeewWGuCq/rmUxBtpUio4N7iEBrOYjv4YWX6NzUst34em7ggTynKpLrRqs5Mlaq9/aRVXvfAF763vTDHRAyjOCbBNVT6DUYM3v+5gbfsAmiIoyvZxkniWscJ6YB0dW8is+Nmede3MxXL/Ck+pU4/MGpKHTZo9tvsz0kn0qRgs0+7weFccFVvI6bHZxJPiAJtU2LOaZUo3lUqnZzZ8pNKlG4NMKfVRDGEk59ntI2HLuCb3LcQlPGPsnsiAWU2VD+v7cKk+3VnGntW0SMXHnhfsu3IiR8XqXBmKNh7zL6JStHvq2n1CcrP/Rn4gVqWQiqNiC/mACSl9EjapkNIKNFQhKRXdNJlFVvAAjvysj7iHMDSbRY6Ep40S0ZuGVPTgSzSbNplFhEQvsTRNzfb5uyR+Kif6X/aUaxzv+7NHLteNHBH1KOz8Ino9T+u7eYwvRzLe7e/t81OmdFvuzWZ+WlIBeEgFWJmbdONGlwodZk7GfZESbpFHcGuCVEis50i7v4p7JlreEiEjTJZfoc9fSn68dcSkQk/Ir9pLB4lRarbSHSjnwa1v4f2eXBq6BlNX7K5HPjeH9W09vPhJM7f+/jVOXj3TIRUNZglH60uYKhexXpZRJrp4JLCYR/2LnOftccYiGmSJI4xhH7+3b6WTkMtgTwAXRlc4pMI9xoZIheKQijBFSKE5mUG7sdlzfoEl4nTCSulGkQrAkdC1cXr7laj9jdw8uD/rZBk1tPDQMBKtyeaodrYgeQzoUjAg/Z5/v5PwvTBQ+YO+uxPUx6XCxdHpnuOsUdr5baAuhVQcFZ3PwdqbqBj8zdzWeV7ZfVtuUtEjs6hR2ikQvTSbhdazLOGXVKu0ssD/AHOi074TqbBkdS+jTHSxzvWM2xjJ239csjdrrjiINVccxJsLDuLoXcf9vyUVG4tRYjGK/xqYedU8tNUttGqVlOpNjqLJrPrZVMsWGpUKzstaSoMsobU3SkG2j+2q8/GpwlF32m/bchRFsDrcy91//YZ/rGnHlJKSnAAFWRqfNnWzqrmXSNxbAtDRH+Wfazr5prWPzsEYMd2kLC/AuJIcQHKFfhx36QdybGw+klSSoEuFerOEWiXslDr1yCwmRZcBXklZGJptmud70KmXdT8Ml2p3MUV71ZnxscmJ5QSevpFUFd6SCfdDe1NnFjIZ4I3i20F11egnT/paL264M3C9i2TWMUc/1SMJO097gDI6E5mK1pQXdBOhFHLxZCKIqDdDKQozyaTCLo2y+ySScUlsOkdEFznmeKWi2/K5wBovmjAdSWRruyEOjy1m/+iVHnM8G/3SzxtyO4pFT0JbX+FU9TJOUS9nNWPITiNHa0o4IzqbGb6nPeaW9v21lVbvkIt0aDYLed3cgRA97KR8nXKcyQF9OsdvUwqWRY9xjqlSGWpOdiP5Hn4g/vOU7bmhCZNixRU8pwnKlip3ePwcdFQW+WbzMRN4ZNtbGcgZg7+vkZzYyANjm1zZ5MINVZoEfUMlqB501xO/axLi77fw2f3ncsfvX2Pyh6dRYTSzXpbxqLEPR8UW0mgWMJVnuUTOpJVCqkU71aKNRhniTXNbmvRCXjO2S+zL0B60yTwPYdCEiYmgg2zP+Wkyi5gVnekhIhLLMR2s7IOBSiVtrJNlTDEWsU6WpThLC2CafAJcRnkj6VfTpZLoX1H5Te1lhLVKqmULK806DBOm6gtYKzMHw8kyswZq2qxKPJE96JC5znnRhORQ7S0e81tlR+7G6COjdTwqf86R0UX0JpaXEk/5Ya8MMiU6h4cClzNde44/B87naO01j8+OJiT9MsBJ0YtYqR/ITdFDkNLqxSpPEAr7vmkyC6lVwiwLrEx5d2wMqXjEv4SxSpiwLOS4hNQseH0u0p3PHAX+fN4erLniIMoKczZ47UaRHqOlUKMYFt+bUihgfccA17+0irFaByd9OZPCSIPzXVewmnsm3MTnAwXstWUpa9oGaO2NEDNMAprKhLJc9tu2nAlleZimZOkzn/LXr9oQQDRulewU5fgZU5TF22s6qSwMsufmJQghaO+P8vY3HQzEDBQBHf0xyvOCDMQNegYtyVqfqqKbJoXxVk+Dqzv92mwWeWaZJ0fraJKhtJKydg15rRKmR2ZxUvQirvffmlGjfEbsHGf5tWYZq8wq9tXe95w/+0WTrMO/KfwjHFM0FFryd6A8oTU/ilQMd76NRP1wJrhnyG0kl7pNjtbxrpzoWWae9gDTteecsqp60yIR6V7QO7KaJwJ1nt85MjqfBlnG7wILPMZfzbKIw6OLCVPIUu1ufqp8SI3SnnY/7d8sozPRhCmJSYWmgl0Y0/OOZ7wk71+6fXLDDoQ+YAJjRDsPJspkwkopd+WcxoW9y6zgTQpaKaBSdKX0ikzTnuVefX/HlC/ddeqTAXJF1Jl9zTSrOij9BIillLAlL+f22fAeD05Z16ZCiiNyoqG5Ra3k3s1vYNfWx/kssAOnNdehYnhKmzaEdNfb+U5oLKy5m31/sjubl1oZY6Xxnxj3H44a66VVq+Sa3As5p/tKqmQLDaKcKbH5tIoQQsAF3M8p6nOsp5Ib8i/g7O6rGCda6JNBckWEdbKM2dEzuSdwFflicNh9AVJ6A8KyAEMqaT1UetQidOGnWG8hrFUyXamjXi9it+ib3OK73llunj6NM9WnHd+MNgoooteS4nWdd11a/iPJ48re57BWyXn6TC4zb6RWCbNOlnGiuYC4Li1jOxFmrVnK38ztUnyO1ppl/M3clinaKx5JWRtnRM9hju/htO8QGBof7nvJhluNyX0eT42ez5WBOylxEWP38br//rlZw92x/bkySUjBvVyTWUSp6Eorcy4lTI+ez8vskvKdDTfJWmNWMD9nIW905pNsw+leroEyAtP+SMmYiRm2OgrYuPh5lFiMYlh8n4jF5809LH/5S8aX5FLT9yHHfjRU5vHI9itZn7s9a9r6OfsXWzCxLM/TO1FdmOXU977+ZSsLnvqYqG5iSojpBoYpMaUk26+RG1AZjJvst005uil5bVUrHQMxNEUQ0yWGaZIfVOmLmUTiVimHKqBKtPMbLVUnO7kW3UaTWYSBkjAPS5WUPZJXuDpwJ0JAj8xianSOQx4yra9L+KcxkR+qq9DS1IpLmZiN+xY9FRtaRkqQQvGYWI1i49Bm5hFHHbbHwEYmk7h0s3o7sprfBRY6Sk9NZiF/MXdEorBcn+wsm1ynbaNXBvibsS0/UT/xyKU2m0UcHlvMNO1ZpmvPAd4g05TQyRDxsUmwbcjo7smxx0syqXAHAZmCxlOjs3lJ/shrgCXL+LWxgB5/GT9Qv+a2+KVoiWbkBhniqGgdja5zNJzBmPtcJ/dUJP+ZDl1mFgXC24BrJma53Z/9XZ/IWCXsuf7JEwHJ5DPdtpORHEwDXBM4i18bv6NUb6Jf5JAj+52eC5tUmGx8WUOfrxiESm6s1fmsmzwumfAUWT6N3f3fcOSH0xFSZ0DkcNs2DzBu9X1Mjj5NgyjnZLmA1dEiSwlLQEXiuVoj2vgk/2csbv8F12o3MVaE6RfZ5MgBTxO33W/hI+7JgMGGzT8tAYCh8TVAgGyi1FPOFeXX8HpLgAnRz3nE7w2y680QPsWkPFFaZQgNVeoecua+jsljpU/66SGPKtHOOlnGefrZXJc4xnWynCnReZgMkQiwSL3lN9Pu3PMqBs8FLk5bVpf8DnGPcfcYTg7e3fdF8jjqlUEWRac676l05znT+Xej1czFEL4Ut/Lk9dKRHjfsCZS1ZhlTYvMpKiris06RQix+VJXNnUeMofDxIyxRmN1mWN5Zo8iI0R6LUfxPIsevEdRUtL4GDvhyoee7A75ciK+vkYCmkuPXMqo+mabk5c9a6I3oxHSDqG7g0xRyAhpZfpW+qE7nQBxTSj5s6OZvX7XR3h/FpyogBJoqMExJ16BB3BhqTi6X6UnFEIYebDaVr1Q6E02b/sTLMOSs9wvecR7WUkK+GHRkBOtNSw7xL+b2zIid47xMZeLFtZuWnlSA9W/lW/ZUbGiZ/689FRt7zMMtX6L08qq5s1OOZPfLJJfA1JshZkXP9tQ/h2WBowrjTvHb5UmKkM52KpUujtX+whTtFauXIqES5SYVulOWJ8gTUfbX3vWQCoAKpZPH/HX8Qd/d2Wc78O+VAQ6PLuZX0csd4ztbXapS6UzR17eOqzgjqVhrlnFEtM4pyXJjoe+BFDnc42PzaaaEvqjB32ObMbfwavpFogwGgV8xWKDdxzLtTs+69WYxBaLfUaB6WN+b/aNX0WQWOseXiVSk62toMQsoVAZpk7me7xXhndkFaKGQUtfsrylTDfnUpHUKlcGMv+3+reQxdEb0XpZlnU8D5eTIfkxEEqkQKDCs1Go66Ph4rfo0T3eGRGG8r4uagU+Y/MG0hGO3ykMTb6BdK+MmZSoPil8xjTo6tHJ8qkBRrB1vkiGOj89nvSynlC6u893ChfJsHhQHcYJ6Db1kO88/e3JmRmwW8aQem+Tn4Z+NnVL2XU0irdlE+UqMZTp1vNeVQ5Hewj3+ZQ6JOTUhE1yjtFNOZ8LkLpVUAEQZKssykt4HuSJGPn00yBLGijAHa28yJTafdbKMscLqrwAc5Sew+kbsMkW7FPZB/+UeUjF0Dyued4jb56JXBj1j2FaNg9T779To+Z7yrjwR4Vz/71LKBzO9K9JlKlrMfEqVvoykAqwyypH4XCzTj2NloiS5mSHZ4HGFKm9dvJfTM/HYrH0orJ5gKZXtNsNSJhvFJsMosRjFfw2qC7PYMa+P4z87i8JIA13Bah7ZfiVdwWoKIw0c/9lZ7JTf5zRnp0ND1yBNXREMKYkbJn5VIKXEMCWaopAb0IjoBn5VoAjojVj1sgJJUBVoivWS1k2JbloPxkraeTiDo6e7qS5sFqQNQIYqk60X+x3atawMXOssd2H0VKcm9Fjtz9i3bT/ZHKv92XnJJW/3PxHoZ3qh/C8TjpGe5zYz1xPc2KShySzgRX0nOmQOv9V/xjz9FI6KWXX/dkNy8mzzTfHDuT5wS6K3wPqyTFiNwG5ykRxsHx5d7PFLgEQvhX8+LwUucEiFmeh5qDdDdEtvrXGLWcCp0dlOj0CN0s6Dgcvwu+RQDQknRi/hAyYkejcWemRwdSlSNP57ZYAZsXMzkopjY/MJU+Q0k/fKAM2JYN8tC7tOlnFcfD7NhNAUKMv1owhBV9H2/HXf39OfPYYq0cYf/HM5RXuBKdorzroNMkShMkBewtxyZmwWc/TTEn4OQ/ubKVPRnjAxdC9notBkFlKq9GUcKytih/Cwvjd7qx85/Qp6mutuQ0q4O7pvxmBuqOxRoS0hV6sIKwPxQO7J9JFNLgOc13s1y4supitQ7TQqD5EKiSFJ42qdGf0yQGG8hQO/uczpnQEopJuTPjuFWWtnoiZM5WYELqezaAd8qqA3DlfJE+nxl+NXFXyKAlJQENQI+lR6A+XcVnst3Wox1bRwvXoz32xxEltuvT331l6FiXBmtJfEp7Lcv4IapS2tH4q9n9sr36Q9hmaziKNjdTRKiwwHiZIdUAkMNHKfssRpQD4yWsdL8ke8bu7grBskiobu6mFR+Ds70EeQHBGl3gzRK4NoQjKQ5C+TK6KA5FF9H64yjqNJhjgmOtQPME17liZCKcIgNpGfpj3rOGvDUGYtuY8omVz80fgxH5njmBxd6Ok9SOdU/TK7cES0ztPzVKO0pSUXMPxz0c6OnBk/L/NCCZzl+/2wPhc2DFSW6lPJygnx9Nm7c+fUH7L68km8eskBVBTlpm64oMbKVKibXmL9/zNGS6FGMSy+T6VQdrOfr2ctrVolD259C/HcKnx9jRz/2VmU6k2WatS0Z9PrpWOVU13x7Ge8v66LwbiJogy9hDVFENQU+mIGpXkBtizPxa+qvLe+i7hhEjdM+mMGSEnc9a51p1/TkQr3g3kb1nhIw1Ba3noJDEi/03TqTktX0s452mNMUt8mXwyy1izlfXMCuyirqFHa6ZZWOUQ6fJceio1dN11py/cdmY4xncxrJkSkIOgqqxiUPrLS1M6nQ4MZ4jVze6Zor46ohM69P3Z5g1v+cUMldsNt133culSIo6YcR4uZTxw/NUqb03OQbt96ZYBnjT1YaJ5CkdmV9jebzSIuz5nDef3XMU5pdvZTgIesL45N5UL/o2QR9TSeA0nylgpHRet4L1EqIRiarVcEBDSFkNnKb1Rr2zGp4heGs26rLKBS6XT6D+opZ1nwfOpi11BqhlOvQ+I3DAldrpKvlH6GNGU3brivWa8MMiADKWU8yec3UwlW8ufJ5WM6Couz53JWZCUVZjPt/ire2OxcDvniorTHZq8zHMGwf6tVFlBCt/X7wOXamZwcf4wqMVRap6MwTSzhn8YEfjqhhIrCIM982ERcN8kJaAgBkbhBX1THr1rvHL+msvfEYkTHGs4NX0qNbKIrWM3zWyzigFULKYw2pJRD2X82yyLPbHg6+WP3v5vMIibHrBls+/ndRAmGiWPO6H7Oqxie/qKoVAkII2U/3OVKK/3XkMsANUoHphQoQjp9O3YZTzMhTHD6f5bpx1FGl6ccyobl7TKDexN9Jpnue/f7aFZsJgdrb7JMP84ReXAvYyNdBj5dyWSzLMIno4QUr4lsJlwUnc7rcseU54J7PCeXey2OTfWoVrnLoub/ajN+vdtWjqfE9yp2+R/AaCnUKP73kPCxsM2Wnv3BStbpxaxp62edXsxzu6x0pGjTOXTbyPFrRHSTmGkikZimRFFAEYKYYdI1GEdVBHlBja5BHVNKYoaZaNy23tZ2bbQNd/p1OFLRRIiX2YXp0fMd59Ih5QwTQwqPkk2rLOBTxjn/tklFXCp8YY7lUO1NxwDvLWOrjKcuOejYGPyvqjrZ5yFTCQtYgWiTWWQpFyXOgym969sIJgWNWSJOvVnC5GidM2Ns4y/6tjS7ynmqlXZ+qnycUsbURIh3TG9DoT0jbu/z7OhZfMAEj5pTpdLJZ2atJ7CZFZuZUf0p3XFbgbCZlhyVKz2OrGWvzE753pbizBNRjtFeYXngVh4NWDK4LWaBZxa5RHRjIjg+dqmjfPPbwFIu0R5M3DulLI5N5fbA9Wyl1KeoWTUR4phYnVMeqAmTG/wrnFIOiRX0238OxE3WG1bpyMdmLX5hOJkTTZiJ8qcQR0UWOjKfN0Yu8ZCKQYK8pf3YcW82EagCTwN9qyzwyD5bspve8yQljjysXRbZZBbSJ7M8pCJ5rHXIbE/wla7cUUdJZEus66ij0IE1DjVM6gaW0p23OWG1glCskYO+mOPdN4aecYYUHlKR7l4Rwjq/paJ7iNQAU+NPcItxqGfZP+s78kZkM6K6yYcNXUTjBtl+Fb+mMBi3SkwNCXkBH5oqUBSBQNLcHaNfLeDuzW9wstTHfjSdwmgDjUoFdcGLPd4Ndq9FsjKTmqTmpDp+JMK5Fo/5FwFDpUeVtHkU+NxBtoHKHP1UJ8sYEAY9MoszorPpkVkp96CBShYxapSORBZxESv1Azk4upQemUWtEuYU7VnnjDcRYqk+1UMq1pplnBad7ag11Sht/DawJCOpsLfjVkRa7l/BXfokh1TYy8yOn+U5X+nMWe0spF3iCAlJ3wSpGMl7ZpbvKZ70z09LKmzZaIsQCufZaJvomULFJ0yeGveEU9407SfbjBrVfU8wehVG8b2H2bke/a5J0LkGPb8W9eQ/MvWAPZm970TO/sUWzN53Iifsv6eVqUhy6E5GZX4QwzAxTcgNqPg1FSlBN02kKZEIVAX6B+N809rHa1+20jsYxzBMYolmb/ASCwOVy/SpzsNXMJSWTjfb8zK7cHhCdtMd1Hpn0RTKlG5POYv7pbGP+q6zbKno4XF9rxS9/2QMN+v+78pbbkyq/F8JeyZXCBxH6rhUPPtjuyDbM/L1ZojDo4tZqR/IEdGFTjmAex3331V0DKkQwXtd9lQ/QziNysXUmyWJmT+RUsa0i/KFs16TWYSfWNp66GSysK/2rme29GDtTc8+pCup8JyfpH/rkhSCVCy6qFQ6aTaLPPX7cSk8L5b95N8YK8KJdbqdzJydRZjre5A/Bi7hI7EVYVHCGNHCpISr7jdKLbcHr3dm3JvNIqYaC+gNlJHtE/hVQZtawtGxRTys753R82WeNlQ3bh//R+Z4YEhr34YfnaO1P3O+fqYjBerG+9oO1Ohr0TDokdmcZ57j+b5ZFlGmdNNJgefz5F4JISCK3+ndACgTPR6lHrv+vUcG+b2+G/VmiJAysEHC/5G2Pf0iGwWZyL6YDGy2P7GgFQgqwJbdb7BOG4eeaNjWUblcPT1l4sT9XNITEyI23Nfd3VD+Cruw1ixjrAizVLvLs29bKg2UynYMU9I5EOeTxh58qkJRjo+ApjAYMyg1W7lYuZ9tynPYbbMQO44p5PCdqynNC5JdMo7nt1jk2eYVwXPZTf0sxdRNw3Bmu09L48ptH0u9GeKI6KIUHxfAU+oEmR9Y7sA9Xwyyu/Ypk6LLHHKx3L8ibXnRB0xgqT6VqdqfnDKre/X9PdtOl2241PcgeSLqkAvNRagyybImk4tp2rMpv5MslZ7JqdomF80ytedpJEIgNUqbI1LQbBZ6nsM+ITFQnGejIgR9gQpqlTB3VT6BcswDULEjYtqLw//QKP4jGCUWo/heY3W4lw8fX4bWs5ZWXyWXl13Dre9F+bqtL7U5O2Gi55CLN29J2V5TT4T8LB+5QY2oLsn2q2T5FBQhkInXac+gQWNPlO5InP6YSdSQxAzJYNzAlNbL1P16UYVXKUOSPovhxgdM4MhonUMu3GiXeaiJoLBWCfN4ov57rVnmqjOVxOVQmcMdgRvIEdGUunU3hnvYb0xm4ruQgXSzq/8quLMSbrhnqHtlkGOj8xL9DCbNsnCo4RGfR9L3qFidEwS8y5acEJ2L4coe6Im/uxukHw8sdGY6w2aB87vlSnfC/2ERR8UWJuqdveTCWtfyE7BLZfISUqfTo+enBNHpyMKM2Dms1A9kmX6c53MrgLg547lTkzIwmoBC0ef5LJAIyAWGZ/z7hExZf+g7S+b0n9tcwmBWZcIvwBIn+BWvkWNas/4aJibwM95xAvtefykLy2+gYsx4frlVGVWF2WiKQJeSFhFijn5a2qBpjvYQ07XnUjTrl+uTnRlXN7koU7qZor3KvdqyVGlfChinf00NLaw1y5gavYTZPORZRpcKTWYRZSJ9KRMMkbRKpROBoD1BQtzNw30yyGHmldzPQRwpruNSdTbHG4tokMUp20se4xP1L8iRA9RTznXVN1K/5clU/Woefp+3lnzn6D/QEqRCw+Bs/b6MfR3ucq4ms5B6syRh3uhdISp9XGacyGXx41O2YT/T7GthmFavW0BV6BnUKcjysVtJhId8Szkq/ntm8xsURbBLbTE/2qyIgKag9aeKd1wSuYFXlN3pw5tBq0hkoGbFZrLA90BaRbEms9C5t9OZRN6sH8rD+j5Ok3ZyMO7ZViJwt++5esrYP3qVMyafdD3Lk98Nd+mTHFLyoP9yZ6zWEOaFwEUeUuGWFj8heqknAwrwgTmeMIUj2kcbyeRlsqsvY1gzuY14H9jj1FsmaHX3BITh6QepUdrJz1KJ5lRRJdrIDfqsd3znGnhhLkx5CHyp/jaj+M9jlFiM4nuL1eFe7nljDXcGT+KtsmN4cvs7oKCajxu7ueeNNawO96auZJOLDEoP/TGdgE9l182KyQ1q9Ebi9EYNdFPiS7j6SCBuSkwzIQeJ9T+7pMJ+8drf2Wl/jyRkoolsOCOfD5jAdfHJKZ8PSr8ll+iagdKlwtL48Szy35uYibaCtHaZ4ymNsV/8Iwn+vy1B2BRkIPJvyFRkqj+3gkjr0ZcnIhyk/cMJSCuVLlplPqvNSroTGQk78EiuMV7uX4GaIBJCWNc/JlWOis73NCrHpWBGbBanxs73ED81IX3qnkWsUdpQE34Ltg59WBY49fdrTStQeZldUoyedmR1ymzjpb4HU8odhgIISw50OBOvAel3guB0PQKakJQrqeZumSCxZpF/+tliciJNnplxKXFUp2zCoSAxhQrbHUHejFe58KhfsNfEchRFoaowi8rCLPKDPnyaJYGQLmiyA7bkACm5nEMT0nNPpJPyDdFNJW3UU87s+Exu9tt+A6WcIBdb5VNKO2XDKNwAxNGcTEWF0kkILwmRwLq9rueuc45k7JQbmDtlX56auSdH7FKDTx26lu0yd6is0rV+DhF6ZYALzLN5qq2Suc0/pee2/aG3GYB+XzEGCiomBirXaqfRK7PJExHAuu7xpMttX/96M8Tk2BKnIT+ZRAZEnMu4hZv9N3qP2VWaV6uEeSSwhErRTm8kzmE/qOYHY4vYJqeHK3vnUBpvpMNfxZP+Qx1j0+rCbMZlRTjus7O94h2BaqrMZq7qm0suAzSYJUmBtuAU7bkUtbN0SGcS+aD/cpbrkzkqVpeWpKfbxlJ9qnPPbUx5Ubp7+tnAHCdbvTg21UMqbEED3XV/AxyivcVS7e4R72O60t0NOVXbvVoVafw/MiG57NR6HptUKl3UmyFe2/Nmnlh6NrXnvwJF48iPthDQFCgYA93rYfOfDztxOIrvB0abt0cxLP5TDVCmKbn11a/4uLGbLcpyEa7oUErJl+E+tq8u4Iy9NnekZEcC22SvMNtHNK7zyuetdA3G0RSBKSX9MTNlAsY+aslGTc4AVvA4R3uIu/RJKS+SX/CO08g9dGzWw7fZLKRiGC+DuFTxCSNlvZEiU+PnhpbfEEbavP1dGspHCvdvtJh5lIlezzH3yiB/NHblUn16wll36OX6kTmO7ZU1gNdZGlJfwu+bm3Ooq9ToYX0fp5HTRrNpvfxtpRp75jRTc2U6ZGqitNdxN3+6jRkz/UaTaUmbWipEVh37cOZ8Iz3XG/q3++/2eCnt/tCZrTfACZPiUjAzeAWzT57CVhXWrL5pSsejJsunIvU40efnsbR9H1YN5tPZHyOWNPSSPSpeN3dgnn6Kc92f9C9Ia5CWCReqF3GufhfVwrrGj+j7sFg5nb3kP7lFucbJXCnDnJMmswhNGB55WTciJdsRPPMvQ4o1LvGKdq0MVZoUGolgOZFxsMmCjWZRytX5F3Nh9xVUYC0bzSqnPw7FeguGUFHl0HOkSZQTkQqb0WRtN6npXJcKRyQaZi3TtAWOkV9EagSF7jnWuFSYETuHS30PekxA7T/XyTKm6guorp3Aj4sHOXHVDIpjjbT6Krm1djkl1eOHjE0719P46HlUNf+Jdq3cEe8o6viQk744I5F5UWg1851AFQQ1Spvze70ySJ6IUG+WoGKkZCTd9/gs7Qn2UD6lVgmzUj+QpfrUEY8PN0baEJ1uefueztQIDq4Gc7OI0kSpYbpjGsn+begZM5xoQjq3b0ifpfA8D0hM3hWMgVOeHxJeSfRV0rkGdj4RAnnWZGFfs0Uq9l08rJrTaPP2psVo8/Yo/uvR0DXIV619VBYEPaQCQAhBZUGQ1eE+GrrSKyFlQnVhFuNLc/iqtY/eqEHQr1IbyqEox09ETyUVYEk0CjG8I3ImZCrDcJOK5EZiYFhSYUrwCWODuuHD9TNsDKnItP1vg+SXTKb+BBum/PbZF1uZ6BF9Lw6LXc706PlO6ZIpBXkiwu7KZ5TRBXhnu4+I1fGwvrdT4+u+fsn9M1frxzj9Fg1myCEV9WbIyVxUKJ2OoswR0bq0/QDpZjbdyDTLOSs200MqMs02er0aQhhoaMIknggevy2psM/1hv6dLsCARGDh+swmFToKJ4ulvDZQy7UvrHIylG6PmtpQDuPeu4otv3mAK/vmkh1pJpgob3T/jH1t7XKWKdornhldI2nG1w1dps5yLzOucUhFgyzhRn0yW8VXsVy5zjnW5PmO5NnaSqWTYuHNukpgfdZWDIocgm0fw0sLrC9c4hWGP49XtlnmrNOqVrCk9DrCWmUi2zW0rxWylSu7L3JIRYso5eOsH1Ost9AVqOaZLZe5f54HC08jyyUbHMXv/N1MNPQv96/gF7yTcE9PnF+ziGOiC+gwcz3HOic6jRflj5yxmKzaNFaE+Y22hG3llxz/+VkUxxoZzB1D9zFPcfKkn3DGXpszoSzPOv77D0EbCHvEO7qavuGwrxcmSJXVS2KTiqNidZafhev3TopezN3GgRwVW8jk2GKPv0pyNssurRtJliITvk15UXKvRo/M4tLYKSliDEDS/aw65Z32OB+2hCnN/o2kL+Mxfx1P+heMiFQAtMm8YX0unL92r/dmIdzVB7+6fkgWdlQi9nuP0YzFKIbFf4r1u1221TQZCd00HZftrSpGdi113eQPHzby+/cb+ay5h6huENMlflXQHdFTDKQ2BUYiOXth9FTO9T+ZUf4z3QPblihM56g7HL5NpiDZ6Xc4bIzcbKZZ7JEiOeuS7lwkZxt2ZDUPBJY5L+x8MTjsbGR6L4VCJwsFeL7/m7mNRza2UrTzZKDO2d7kaB3vyokp216pH8hd+qTvlLHY0PdDrsRDM7n1ZggfuqNCpEuFdpm7UeVNbmwoc5EM93iR0vSMsyu103lK2Y+eiM7E8lz23aYibYbS7FxP7+37UxBpYL0s52QW0CpKGIgannKeZInMetPqSbEzOxuShE0XQDXLIk5gKYV6Bw9rC53yRa+UNHTIAsoS53hD58SewZVF4xAn/dH60J65VTQwdaQ/FxGzel5ioS1RTv8rH6/6gsrfHUW50ZRWHrZdCTEj60qCRdWc0LuSNRUHcOTaOgojDUPHmMh8NEhrjFSLNvpkkGZZyEWx07nefyu1SthzDG551hcDF5Anop6MhS0Jmm4s6lJFEwYRJYeg2U+rVslzu6zkhP33HLrOHWvh9p9gRvsIV+1L2VHXQkENzetXU/LEZPw964jn13JF9vmc3LyUGsL0yiC/jl7s7K87Q3Je8DLe68nBkHZm4kn2UD5OURxzQ7Dx2epM9+ZIgvnk9W24ncVBOhMY9v2cLpMx3G9kkkrPdDzJ0rAbIhU2BrOr6N/tPEJ/viBJGMJ1ZrNL4PwvNhlhGM1YbFqMZixG8V8P22V7IKan/X4wZjgu2yPBy5+1cMwdbzLvqY/529dt9AzGiesmMd2gczAzqUinKDMcKmlnnvZA2vr5WiXM0/55HlIxPXo+b7BDigoPZJ7hBeiQ1otxJKQiXaZiY/BdZrIzwRt4CS6OTd/obdiyhO4ZYiNxbPVmEb0ySI3S5mm2/IAJTI3O4SNzHAdHlzqzkcnXzUa6uucyuhwikvzynqdPc5r2gWEVVtwZknv1/V2zj94a8WZZlNFRe6SzjUOylwIV0yEVGqaHVGjCRKJiZKhDN5LGTvJYGi5TsSEkj7Pz9ZVM1L9AVYbPUDYYBbyjbE8jlqLU3SwiJxJOIRW2UWWTMzbanWba+AZIBUAHefS7pIcBTFSqlW7u0Sw3ZiNhM+cmFZqAv4gf0JAwXEvuh7ARwTdEKoCm7WewTi+i55UboXMNUqhg6uAiFSgq/vYv0P5cR7CklsvKrqJRVKT1nPjcty2tSgk6Ch+OOZ5Dv1pg9SoEqrm2aL5DKnRULhbncYpYxDpZRq6I4MOkhRIWx6Z67t2wWeDxfLDFBS6MnppiZua+l+yZdy1Rzhk0++kKVvPg1rfwfk+u9zr/dipEeyxStcfZUFCNogiqPrsHf886KBrHNwc/ygtdNZwTO5u4VMgTEe4IXO/cG0dF61grLZWq43mGvCzrvWFlJk7l2NiCjEpJdi/dxmC4ezPdMyVT5iI5g3lW7Bwni1qjtCeeE15SYcswj+Q3NiQykrw/bvlr+3mRrBQWV7OJ5I5BZpc6n2UNNFLy3grEwTclbVVCaAKU7wCzPxnNQvyPYJRYjOJ7ierCLDYvzaWpO0JyUk1KSVN3hAllucO6bNt4+bMWlj37GZ819WJKSY7PkrUbiJseo7t0yFTKlA72y2S69hxztCGlGPsh32RaMpRuUvEp43jEv4TqpGzFhmaASpS+EQf8ycHdSDMD/6pcZvKxvWzszCzfU55lkpVmeZNEKgAAaHNJREFU3PvTZBZ4mqPt5mmZyKzEpcI75lbkiUiKdKPddL29soap2p88+vDJ181Gupd0OtlIW6M+HenIVALRRIj7zEk8FLjcIRUgLTUbGaJBlqRoudvrDydpnLz/v3JkL6Xj1aAJ02m8bKKESwquopkSKpROp98heQjYY86+PiMhDhuTidKl4NLYKU6Qu9KYxw/U1eimpKGrn1XhHj5v7mF9xwBmYjbAeHE+Px94HiQ0UsJYEebhDATMLhmxm5Rt+DZAKsByNg8w1NCto1Al2lhpzidPDNBPFjF8niC0QxTzBD9niTyVX7PIkeZMd0qCxB1SIYDK1y7k7rtvYdZXP2JAZCESvRBmPLHvigqmYTWz7n4WOX6NaFYVlwXPY4BUtZwt4p9RarZRo3Zw5ppzKNWbaNUquW+LGzh28FGPOtTVyk1k+TVOMIYC7of8S5jrf9B775o700TIMxb3j17F4+zjqN75hMlSv1V2lkwu3GjO3YagTyWqG/S7J5SOuR8C+Rap+ttN0N1gZakGo3RsP43Gwx6nXSklONDEdb6b8AmTHpnFMXoddxsHcnx8Pu8zgeNi83nCfyjPlJ2JT1HwKUMBkL1fdxsHcj1W2ZMCFGdr+DUFRQFNAZ8i8CkQ1ETG4OnblBele7+kk36d53uQd8wtnH+7SyyTf2dD0rIwMpERN9oS6mWWe7hJPWXctcNviPmLMSfsDz84Ed+stwle8DHi9Fetvgmwrl/nGmj9HI59CIJFsMMU2PVMOOvvcObrowpP/0MYLYUaxbD4T6YTV4d7ufuv39DQNUhRtp+8oIamCJp7ohTn+Dl5z3FW/e0w0HWTY+54k8+aehmMG5ssne1GsvNqpuWe91/EVorlrTEg/bxs7MTOytfUKG1OaROkL+kZCTKRkX9Ho7SNjXXeXm1WECTupNfDZgHFotdpQEwud7JLK8IUpTi/upd3lwoMVx6Q7rNML9mNcaUdaQmEAB4JLGGsCNNgliBd+3yCPh9VEdynLqZWhFOO6fjYXE7SXkgrDKAICGoKAwnmHNQEpWYbp6jPIBBkEeUY9RUAminlTP9lBDTBzYMXE5Idnm31ywB+ok49PaS6OafDSMZd8nixy2oqRSeP+uucme3jjEW8b25OftDHZqU5jC3OYfPSXPbdtowX3niHwz88nRpaaJAlSDl0jtxN7OmaedMhTBECQSkdab/XEXRQRJnre8uArpAyOoioOazJ2ZlQ3ypKzTANopw5ectQFcGyrvOoILVJ3GlgxcqMFNHrkIx28ighuRfDaranaJxVh15Qg2lK7n/+rxzw9slUSEvxq0MJIYGQad1frRQTDPjJizYTz6/loa1uoXb1vezd8RgDIodbiy5kas/tlOlNNIhyfm3MJyegsSK+kDG0ePbhC7Oav5rbsVL/lac80D0Wd2Q1S/13c0SsjhC9joP0dnzDg8EryMXr0tzlK2d57QpOnvQTxhS7pGO76zHvPZiwkU+R2U4sHicv2szLhUfyx8qzKdLD/HrVDMaKMOtkOaewgGZKkNKaiIobJiD44bgiztxrAne8/hXvru0kZpgIBJoqyPIp+FSFvqiBEIKqggAVBVkUZmlE4iZxw6Q/ZlCW5yfcG+W9dV1pJ6Y2trzIXQ5pT0okPyu8YgylrDLHsK825GXkLrFM9xv2eR+ul2gkUDG4puC3VOx7Dru2Pomyx4yhZut06K63+iZ+fDr84/YNNlxvSoyWQm1abEz8PEosRjEs/tPE4qG/r+Otr9vpHoiDgIIsP7uPL2bKrmMtpRCXQkyOX6M8N8D7DV2EeyO090X5srmXR/5Zjz5MDDScchMkP+RLed+cwAAB5unTMFBZpt3JFM0K0tKpi9gPdRWDZ/2XME6EPUGZrVTybQkFfD9IBWwcseiXfnplttOoXm9aqfZDk8zc7PNiH4ubFDwdmJfiF2D3hDys78MeyicpQeW3IRU2fiBWZeyZgOGJaDIB9fY6DJGG9ZRzulKHIiTH6M+wMrYfD/gvT0s+0hJdgSXRKCCumwR8KsXZfuKmSSRuogpBUDU5M3YPQaI8WziVrkGd2+JzKTVbvecShU7yKEnIoUYIoBFHw3Jz7iaHUFLQuzFwjxdTSjQhneMqo5PHA0PkYoq+iE+ULQj6VHapLcSnWm7N/REd0dPA/PYLGZtEwGzUmyGEsHoGhhSCsskT3sC2hSLe1nbhV/qfaJQlqMKgPA0ReJ7dOYA3Uz4Pa5V0Hf07KKjmykdeZlHnxdTQQrNawZfqFvw09nrGcyGBLplDkegnLAsopTv9PY1FQgx/HuoZb0BxrfVFdz3xO/fD12f1TDSLUuYWXg1ILu+80GngBiCnDE57BTOv2tOrIAvH0bLvCoqfPwt/7zpaKaabXBbpU3lAu8xZvdks4hVzJ6Zor2x0AP2wvg8/UT9ljGihK1jN81ssYtKqeeRHLSncnkA5uWe8DAU1nmd7JW00P3ouVS2voGDS5S/n0e1WMhjXOfbTM6kwmlknyzg+voB2rcTyJpJgmBKJJSV88I5VXH3kjqxu7eWK5z7ng/VdxHUTRbGWjZsSnyLYaWwhJ+4+js+bevmqtY+obpXeTijLpSjHx7JnP6cnkr5Md0Pvk3Tnxh34j2RiIpkYj/T59W0xY59xHPGDsYwL5W6UCuN/EqPEYtNilFhkwCix2Hj8p25O28OivS9Kjk+lqSdCX0xHSMmY4hwO2amaoE/h9VVtrGnt4YiO23ncdwif9OfRF9EZjBnoGUZ28oN8JDNM6WarH9b3Zrl+hKehzd0sPNLmXOslvSN/0nfhzsC135pcbAp8VyKysRkLG/VmiBmxc1juv4lapdXx6QAri5ElIuSJqEducXFsKrcFrvOUsdikokdmMSm6DAM17fWBjScVG8pYpAsImgk5WTJ7nDXIBDmwVYXMEl4zt2OK9irrZBlTYvOREh5OGjuPJjIbj8uf82M+ZqwIc49xIEviU8kJaOQHNRRhXT9FUSjK8tHeHyPgU9hjfAhdWqUcADHDpL5zkK0q8jhkpyrivzubndt+D0BEZBGUgynSpb2+UqRQyY81YyTcmiMiSFAOlRVtqAE6GSYKLfk7UJ4YL/GEf0c6cqGjcrq2lLdi4wnl+jlkh0rer++mtTfKluV5fPz5J9zDYmqVMM2yKFFCZsH+t7uJd66Ywe1cTg5RZ7kuXzlXVV7LHm1PcKd+AFtm97Os60JP780AAXpFHuVyKFCPqjl8UrgPzxT9mpMn/QSARX/4BKWngbrOi6kymz3H3SyL+FyMZ2/e8XwupWWQWaKkJ2s2qbBLluSuZyEOXGbNDN+9v/Vn4jhmZV/BV1GrdGVCoIvbe2cSNF1E6tiHYKuDrL+7pD3j+bU8tdkifvnxBRQl5GxjUsXvkreuN0uYEZuV4qmwoWenm/S1apWOZKyvr5Gpn51OSLfurXhuNQ9tcwcf9OYS0Q1yFZ0L151FX7CS2s43UDDpDpTz3MTLOODLhRQmGvdPkgtoNIvZVq6mTr2L48zF6Iof04SgT+XGKTux18QyYGji6s2v2ujojyGB/KCfn20R4rjdatNOXFXmB7nkyY94+v0G4v8CtY8NZcgtid8hsu2W8t3U5GLvCYXccMwuFOb9d5YojRKLTYtRYpEBo8Ri4/GfuDlNU3LLK6v5y6pWwn1RWnoixHVLecIerFpiVwSWCdivxR83+GC1Zozv4qfKx9QobU7qebgZIjcB2ZkveDywyKkzb5UFRKWWVpljVswyz7JmlkuZFTvbeQmnK8NoMgs5I3Yet/mvo3IYqdlvi39X5mIkxGJQ+sgSQ5KW9rG7gxT3+bOWKXJM4mw1J3d51Fmxc5iXpJVvlxAs963IqAT0bUhFJp+IadqzHoLaqoQsaWBFUJHnJyvSwh2yzjWrblKjdPCwvjfz9GnplaZkGcdGrf2bEOhkZvBF7s+dRr7eziGRp7iR4zGExvbVBUgEeUENVREYpqQ3oqMlBqthSioLgmT5VQZjBk3dkaFywkA38ZUH4utdR7taxgNb3cwWax7CP9jKvuZfAYgo2awp/glbtb1Iq1bJPyZewAFfLkSNW03EEYJ0kEMV7Rm17NONv3TjZThy8bkYzwnqVcR0k/22KUcIeOvrDrauzOfjhm5ETwOP+lJL5GCofGu9LGO6WMQZPM7hvAxYJUN52UF8fQ3o+bV8/atHeeHjFo799AxK9SZMVJSkxv5WtYw/bX05B662Alu3otGqcC/LX/6SzUI5FHe8z7RVpw+tRxEXqxeySL+RMcJbXjTcuXKOI0Eqwlol+tQ/UFWY5SEVFIzBPOk51pvFfNPWD8D4khxqCKPcujvorsboYx9Cn3Ag767vpL91LT969URyB9bTqYTINztRSVLBcvmxpHOBHq4s0E0q1skyFhddCUVjCGgKAU1lp/w+pnxympNxaVfLeGCb29Fzq5n67jGURtcSzt+BYP96hKZREB06d12BamYFl/DPzhz28H/Nbfo8NAw+lZtxNFegKoJfblXG1Uft5JlxN01JfecAXyfO02YlOYwpys44K7++Y4CzH3qXD+q7N7qsdkP4NgpvI7kGI0V5Fpw/aVsO23EMfv93K5n6PmCUWGxajKpCjeJ7DV03+cc37Tz3cRN//7qNb9r6PA2Zb3zVxu/eb+Djxm7WtQ8QjVsvN5Mhk7q4af0XM+G26AEbVL8AWKrdxRTtVUcZxg7i0jXS7cwXvBC4iOnaczzln08NYe4LXIXqalYtFd3UKO30ygALYyfxurk9a81SapUwjwcWOkHx++YE58Gvu14Ik6N1jv9BpdLF44EFVCpdwzrDfhs0m4V0yOxhl8mU3fmuSOdToac8dgTHan/2vBg/YILHAbdS6eQdcyIr9QM5L3q6S2JWcGS0jpfSaOXXKmGeDNQ5pUa2trutBPRtSUUmn4h79f0dhZUWQuimVcYVNSRru6J8HinkmOiQwzYoDqlI1/S9TpZxQnwBbUoJmoC1ejFXyBNRNI0ufxm3+E8hGAyyVWU+x+9Wy/bVBQlCEccwJTvUFHDuL7fg3F9uwXZVBXQNxFnT1k/XQJztqwscUsG9B+HrtSQ7n9nlHtaZZTxafCZXBGfzrG8/OrRyguYAZd0f8de8STw+ro791l3nkIoufzlXjL+P+7RjMgbFI/FZsRWnbLdyu+H0AyZwZLSOT2QtH7Il1aIdw5QMxA1K8wJk+1U6+qMENAWfKiBDxkRLSO1OE3X4isdQXFQIQKevnJs2u5XWo56GonFoPWuZ+McjOePrs5wG57u3vI0eX6lne6oQdPvLuHeLFbRqlZTqTUz57CyU3gZH1c7X38hRaxd611MULjeuY4xooc1XxU21N9NIybDnynMcGDQqFVxZfjU9gXKrht1FKjjleZSiMdSGcth7yzL23rKMsaEclNBmcPY/IWfoOOQjx7Pk9vs5/7cfcMlL7UwemEMLRRSZ7RapcO1PvRni8NhijootdMb9cv8KZsVmpn3+1hDmhcBFKaRirVnGMdH5vN4apHsgxqE7VTN734mcsFst2oSf0xuoACBkhDl51RnU9H1Ituxz9jnb6Oat6mmec/JA1Vyqxk5gT/+X3KHPdRrRbwicQUlugJ9MKOHMfSakkIrm9avJ/csCNg8F+dkWpdSGcoYt9emNxukciGT8/rtgODGG5GfQkS4xiIO1N0ekAJUOhX54/Kwf8fXlk/j7woM4+kfj/idIxSj+sxjNWIxiWGxq1v/yZy3c+8Ya1rT30xfVicatWcAcv0pJboAJZdk09cb5pL6bqMuwbkODNNNsj13v+gd9d272L3dmMt0Osu5t2GUzdkmNHSglO6Amw54NfVrfnUnqW055TlgWEEtkNdwNrw/rezNHP40dWc0TgbqUZuVN1RwrpWVQVJqptMIj+2pJY34XuGeghTSHHJYTfRL2+fvcrKGAfo/z7evm9izXj0h5odrXZK1ZymXxE7jZfyM+YSZIxQLeZUvP8unKzSZH6wCG7Y9Ih2/jSmt/n0n7fqTN3cfF59MkQwjF6pkwDIlPUyjJ9RP0aRRl+9BUhd3Hhzhjr80BPGUb1YVZTpBkmpL1nQPeGWylA+X+X1lqLYkmYDOv2tlGW2+U99d10dH0DWeunUVpvImeQDk+VSVroBGANrWMFbU3MTa+mpPXX7rRspzJGYs+GSQ3odjUI7M4KLqM9VilKwt8v+EU9VnqKedkFrLjNtuSn+VjXccA2X6VxrWruW7wUsaKMK1mAaVKd8rvNZglXFxwFZttviWhbIWfrrmJtyuO5aPeXMsTJ6vHM/tvNziv7RhgxpoZhPQw3b5yJFAYb3EcoseFspny2Vn4etZC0TjME5/hgTfXMund6ZTqlot1r78EEOTFrD6WZlHKw9veztexIj769BMeVuaNKFspgWUF82mt/AXn7bclYwr88OI8iPfDXhcP20wbiej84W//YIcPLmOL7jdQkOioXJB3Jav9W5Lf/gH3ywUpz550vWPusZo8az47fhb3+q90npnPGrtyqPZmyj3iU2CvLUu545BKZyy+WXAQWw/+k8KYN5vT5SunP6uayp73EZiesbaecpZrJ3GlfhUKEglcF1rIqoKfsUNNAftvV+ER+lgd7uWNf37AQe+dSkm8kVeLj+KT7S5h/+3K0wqC6LrJu+s7efbDJh76+9oUZ/dNgUy9GSPNqI/UH6MsAEuO3IlfbF2Jpv3vzi2PZiw2LUZLoTJglFhsPDblzfnyZy0se+5zOvtjmFLSNRDfpOnkdA9WuzzFDmhjUkGBhOPwkHmTvf6T/gVOsJusSDRU1399RnLRLvMcX4Dk7+yXtfslbf3m/LQBxXcN9DdkVmaTp+/SNJ6MTKVQbuM1b/+D17DMHbzYyGQUdWQSMbThrkO2ka6hdziVL5uMjqTEwG26tylLq5oIoWAFkjl+FQnkBjR2HltIXtBHb0QnlDsydbTV4V5e+LiFr1r7iOgGQU3lxJ472KnhwRRloeSa8qaeCNGOdYz7/dFoPWsBaKCcd9Xt+U3gGHyawj09p+JLKn2LShUVE01Iz9hzGy6aKDTl70hlzwd8alZzZux8ZmhPs3vCsMytlFMj2nkksNRSfxLlPLPTHbzXk0dtsY8TWm8ku/41QrrVX1FCtyc47pNB+siiQnTS5StnXfFu/HnzS5BCozcSp2sgzux9JzJG7YC7D7BcgG2yZUrMew5C61mLnl+LcrJlWpf8maIIp08hmlNFJG5QEGshrFawvvBHfFp5GAd/MZfCuBUw2wpIH/XlEq7/ikfV+Z6+kHRwJh5Q+c3Wt3PCEUeMODi89sUvuP9va+mLxjEl7OC6T3RUPta2Yzv9IzRSDc+OTJBw+4xaxnJPsIfyaVpyYWOoZKeUv5nbsVyfnEK8a7UOni+8kqy+9cTyx7Ko+CrGFuew19rlbNX+krOth7dbSUd7G2c0zXVKtJYqp3OyfIoaWoae14ltx/LH0nbEk5RVb05TT8QZ04Nxnade/QcnfzmTUr2JrkA1905cwReDBSlqg6Ypeegfa7jzL1/T3BMlmmzk8m/Ad1WZCmXBQTvUcNF+W5Ob48+47v8aRonFpsUosciAUWKx8dhUN6eum5x879t82tSDQNI5EE8x29oUSA7YlsaP55bEDPdQTT4IFA+5SJYvTX6xxqXCGdHZLPA/4KlxzZQ1CMuCFLUiSA2e7ZdGnwyQK6Ipy38XRKRGUOie47Eb/hb4HqBGadtk5MIpTUpDLNxlS7akrluZyX3eM7lgJ6sxnRadzYvyRynLpatDtlW3YGQ9FvY1sbNUI21MHYmTd6Z1baQoSSWuTW5QRVMUgj6FrSryKcr2M6Esl/22TT/D6oYthNDRH6OyIEi2X2MgptPS1c+U7pXUHHg+4zbfMi352Lw015nF/Xr1F3z82FIG4gbP5E7mm2gRudEW7pbzqBYuN17gT+zJNeYUcmIdDulKN8ZMFN7N24f3OwMs06dgoKIA1aKdX6upEpm1age/8S1hDC20qJXMzV/GifFH2avvOQA6KCSfnoRqlcp83wWcbdxHldlMEyX4FelIr35Yfih/2vxSvgz3sX11geXq/eKl8NbNHrLF83NTPwNPwzO7zYADLuev77zPln880lHXWifLONFcQHG2n1v1hZQbVtYny6c5/Rzv/fgaxrx8liMROxzCMp9i+ixjMlQe2Op29txnfyaWD/8+u/bFL7jtL1+hmxK/YoX0USM9CXdL39pYK8uYkuj1qXCN26f13dhH/YB8McjdxoH8WezGb5Sh0q/TorOHbS6uFu085LO2JYvG0XTY41zzVj9jtQ5O+nKmxxW8K1BFa/ZENu98zcmG6qjcphzDDPMhh6jcWTibU/kdomuNk3Gym8ADqoLZVc/c1gspN5roClbz2Ha30esvp2cwzurWPrapyuei/bZiTUc/5//2fT6o/3Yu9JsKG6sytZloZ0HFq2gHLGH3zf+3sxLDYZRYbFqMEosMGCUWG49NdXP+45t2zv/tB0TjBpG4QU/0/9q77zCpyuuB4997507ZNtv7LiDFCoIVE6PB2LBF7GIDFRKlxBrFQscKihEFIaio0VijJipYYvtF0WgUK1Xc3uvMtpm5c+/vj9mZndnGrru4rJzP8+Qx7M7OvDP73r333Pc95/h3/UM/UVd3uLsOLhRqcYYCga4q23SsSLS80+DCbypUmAmhlY+gUiOBs72LI04OwZPG6/qRPGW/u0Nw0R9J1+ErL8GmfOGVksIv+PtCNxXe8R/KsEQ7+7o/iVixCI7BbTqoN2PIUasjkuf/pP2DJmzcqV/Sodb6T+0f8SfvLC7U3uUY9ZteVYVqHyxc6rml05WRzl7vDG1jr+rF76p8rULgYtxqgYz4aE46MINJh2QR57BGbHXqimGYrHr/B74tqWdUWixK2GQyTTN0UX3CAek8sbFj8BFM8p7y66G8+HkRr31dSpzDQm2Tjs9vcIO5jkvMwB18t2mn3EzkVnMmeY4D8Bsm1Y2+Lren5RmpxChebo5ewv/VJeELNt0DrBYF0zQjykRnUs0MxwY+Sj6HhfW3kaaXUm7JpMJ5EGNq3wm8J9qqJt2b9RdKYg8i3aziDzv/RLq/NKLS1RfJZ/DXxOsi71L7dXh7HvxqRlsA0dnXgoJ1+k9cxL+3VbPq1Q/4S/McspVqCsxULvbNo8hI5nbtKa7U1tMcm4v9yvURqxumakExIv8ednbcV5txJCtuyswEUpS24GlO/L2cMvEMjj8gvdM50NKiM/7ud3F5fERZAEXB62/7bCer73BXawO7cE2mjfmWP3Gt+RTZZjn5RhrX+mbxF9tDrQUI2gpW1Jix/J9yOIfzPdlEHqddJRdnUc3f7YsZqlRQombAlNfIyB3JUxs+Cm0hCy9HG+upoMJ5MHp9CferU7nXvA8tLKE++LsvVtKpO2Ul+/7neqyu/FD1KT02G091PlO3zSKbcmpsWbx08GoK9ER+qGikpslLs0/HMOCo4Ul8U1jLloqmDp/Lnmjq0VlcNn4kw1IGTznY3U0Ci/4lgUUXJLDovf46ONd/W8rcV77FaK2l37Srltd91P6CbbrnulDVoMjgIjKI6CqoaH/HP3gXrrtciDIzEUwzrE9DWynacJF32dvKrIarMaJxKi3d5l10F4QE7xYXGSlY0MlU6ygykrBgdgiAevJ8XSkxkrkzZg63N94Z6uoc5DbtnOK5Bz+WHjds6umWoc62wUFb8NDbPhZ9ya3oqZ4GTFYVLKrCkfskM++MA3e5QhGusKaJ5W9vIyHaSpzD2uH77hYftY1eUuPsFNY2dxl8DEmKZnuFm50VDfgMkyavH01VaPF6ud58EofpCW1zsShgt6ooKDR6Axd+7Y/Hf+njqSCBy7W3+TbuWK6qPIeSsH33dqsFTVWwa4HwNEupZrU+nwyjjOox03hGOSNUsanOnkWdI4eh9f8N3bW+xXYL/1GPQDdMrBaV38UWML/qelTTjx8L/4s9ludzbmd4ekKPVn26Etw6Vt/iZcGr33F66UNMVd+IqOYFgRsIt2rPULjfVOZdfHLg4q++CB4eD96GiOdsW+GLXOExgUriSaOe963H8hvfR2j4cRPNjPiHuO2CE9k/q+28FswLeOl/hTz/eXFgW6UCutGW+9PZikW4Ruz82T+bOeqTDFHaVgGLzcDxlK1UdShLnG+kcr1vJveHHaftg4vrfTNYblvJEKX1uI5awpKpEzkw2o3v0VMjAgKH1cKF30zH6aukwnkwhquE+cn3ckjLZ8xofDj0uk/HTuX45g1k+MtojMnljZGLmfDdLREByolb55HsLaFEyeDOtKUkZAzjy4I6qho8mAQquOmGSX2jl5bde4rqk31T7Ez97XDOHTtUkq27IIFF/5KqUGKPkxxjQ1MVfIbZ72X62sukmuXWlRFfu936dKh6SfDuvVUBS7vRhAcVpUYi0z3X4TPVsJ8xWGNfHqp0UmPGdjmODKWWDLWOIiOZIiOFHLWqQ8WOyKBC6TSo0E243DuHhz2nd6ikEy44xs7UmHGUGonkqFWhfI7/Gfvh7+JPQG+CivDXzFBruUr7F1d5r6PKiLxYe83/a4pIo5RkluiX9jqo6Koa01h27LKj9nneBRHVbJ61LQbosppKZ5XCgt9rP66PjQN79kG1/uzt2lPkUBHxHGeHVXlpP0fsVgvZidFMP3Z4ry+AG706LbqfaFvn3W6jbBbqmr38UNlAZrwjIqgAUBSFzHgHOyoaaPL6URWFJq8fq0XB4/PjM1XuNqZwiz49dBFtmuD3m+iGEXrP7Y/Hg9Uf+ad+NAVGGmlKHX+338FIey02S+D14h1WYuwaCdE2jkxqZo1/ARlGGWbiMDyHX8WPvgSePmAldY5sEjwlDAsLKhRgtvcxfpvuYcJ+aZycqzOr9m5U04+hBPpvjFN+4PpfBbY/9fYzNQyTwpom3vqujLvXb+G+N7dw+8vf8FVhHXd5z+cbYxh/8syijOTAihPgx8Ji/VKe327ynx1hW560yB4BOlroBoaqQCkp1BE4iStAqlnPO8ahzDFmMt2yGDfRxNHE72peZPazX/D+1nIMw+Tfm8u59NFPuHLdZ7z4eXGgkp4ZqKTXWVChmyo1dPwcYvDwoHo/q32nB0onKwY6CjbV7DSoAPjYGI1uqviw4DLtnVaPesm+IBRUTDHnUamk4K0uCFQoc+Xjcw7ljUPXUqAnMWrnkyT4ynHZ0/HYk8igirtqbuAPjasiXveChqd4xDGNYiWdmMZCUgteZ83wB6m2ZZHQUsyF30wLBBVqBnOcd7KjJZ53t5TzXambcreXCreX4noP5e49L6hIjtZYctb+bFs0kby7T+OtG0/goiOGS1Ah9kiyYiG61Z85FpP/+gnfFNejKSYNvt0z7drfBe6u7np3So1EzvYu6pD8Ha7CiCetk0Tt9s72LKDUTO724tdt2olr3QbV2UV9pRFHiuLu0cV+V0GB31SwtAZOgb4Qlk7r/YfrLvci/HXa51h8WhPFYeo2ctRqvKaFl/1Hc6s+vcdbhHqzYtBZLkRXCY+dJTd29rWuxtF+xeRj40Ama+/3OrGyszG3f61p6nxqLWkMTYnhd/ulcfWEkb3e5tCTFYv86ib8psnorHgsnTy/bhh8X1KPoiiU1rUEqjDZLTS06Kitd9abw1YgLQpoFgXDMEkx2t5TgZnGJv9wDlV3kKNW4TE1bvNczpS07Rzk+pBiUrnUP5dGewY2TSWLaqYZL3CI/2vS/GUYqhV15ids8aXx4L+3MzwllpyGr7nwm2mh114T/ydOdz1HlllOtS2Lt/dbxElb55HkLaGYdO6JuZF53uWk+EqoGzudhLOWAXRIWu9qm1kwD+XLwlq2lblp9gWCLJ/fpLLBGzHvLvLNjQi2TAJBxlmHZLP0vLGoL10J373U9uTxuRhn/RXj6XPRfA2UWTJYO2IFKjB96zRSW7dqGiac5VnEV4wkq7Uy0J36RRhYiLFZGJ4Ww/byhojfSXuHKDt43rYgVF2t0nSSrtZTQRJJZl2HldHO/qZ0FlQUGSnM905htX156Dk8pgW74g/lvP3Vvjz0+Av1heRFHcRIRz2PsRCbu6BjhbKWFrL/exdRx86mvKoex7Nnkdyaw6Kj8phzJle4VoZKzD6YPJezkn7k6rJJmKqF/Xzf82DTnNBrzku5n/cbh1HmasG7+3bk/mR2FXKTojl9bCaTxuUESgXL9qZekxWL/iVboboggUXv9efB+c73Zdz80je4W3x4d0PmdlfNmNpvg7nDdzFrwk5u7QVzEf7NYaEciI36gV1Wgyo1ElEgtPWn/cV4MGEb6HR8+UYaG40DuFD7IOIEXmE4A2UEu9iqFBxrVz0C2pd6bRtPCv9njGGy9l6PksbdpgPdVEhU2xprdfa6hgkokcnbPd0mFJ6gCLvuit2+Kk37XIjuEh7bl2ns6mvhj+9sy9LF3luZqb0SariYb6RxqT6XMiW5wwVLoGxuIEk9PFdnsncu5UqgO7dhQjbVPOtYQi7llKoZXK0tYujwfZn9u5E/abtOT3IshiRFU+FqITHGtsvtUpsK69ha5sJvgM9voKqBudDS2gglWKLZbH3Pf2+3sjNZe58iI5k0pQ6b4sdvqrwXdzpHut/BqTRRTDoLku+h2etnmftmMmgtpkBrQnHaATSkHsL/Sjxsyr6QKTuujUjwLVEzuDfmRm5qXBbR7bpECdylLieFY9M9jC9/jg+GzmLKbwLzpbuk9aBgEnx1g5dKdwsVrhaafAYtPj8WVcHdokckNoeXDDbD3sMhOfE8dEY6Wc/8DjyBxGAzLoPyiWtJenMGNlcBXucQ/r7/Kr5yx/K//Bq8NUW8Yrs9VHHOZUZxsufeiApLCvSo1334SkX4cVxspvBHbQkp1PCo//YOQcOuVjE7CyqCWzu7KtddRDpz4u7kMuUNTnK91DFBPoxhGFR8/xEpL05CI1CU4n3rsaxImkNu0/csc9+MRmBVavmQFbxQkkayv5LVxgJyaCtdW6ykc27z7f3Wobqv4m0K+2c5OfGgDI7cJ5nEaHuP8qdE9ySw6F8SWHRBAove6++D86mNeTz83g7KXZ5eb4myW6CrnO/e7LHvLi8iPJciWM40/Lnb/6xuqrzuP5IztU8oNRJJVQJlLoNbC4IJ4Z0FF9C2p96Cn3/bb8Su6K1134/kbv1iDiSPtfb7Ik7oRUYSOirZSi1WxR+qetR+bJ2tNgTHUUECt2jPEEsTF2ofdJlbAnCn50JusT0bUXo3qNyII0bxEqt4+Kd+FBMtn1ETPyYUWPSkVwREllT82DiIydp73QYVwc/w7/qEUHDR02pMP0VnSdanWj5lmrY+Ym4VmGlc7JtLGcloFpVmnxERVATlG2n84+BHUOOz+eiHGmKsFlr8Bs1enVhPOfc23EaWWUa5JZPGya8yfOR+HQfVQ+2rQrXvvj3l10N5+7uKboOP0VnxjM5x8uxnhXxTWE9tk4dmb1ufGaV1H1J4OdLujsdyw0ma4sIMC0QNQMOgWktDNf0k+gPbwYIX5KZqQYlKhsbAsdOkxhBtNHa6f/7vCdO5oXZx6H3cnnQfW20HUtfk5ch9kkmOsbG9ooGseAfNPoPaps6T1oNJ3eEBWnqcnY07q6lv9gVWLFQFr27Q5PWjm7vubTLO6eJp6x1ENxaC3YkrdwJvxpzNcd/fSoqvhCprFq8dsoZ9Ru5Hs0fnmue+xKMHnneO9gzHWTb1qrRxuPCgIli0IdjpvExJZf0Rj/FpdTT761uZnT+rQ3DRle6CinwjjcXeS1nVemOmgWhusN3OfP1BsozAHH/rsNVcomxA+fXMLvtwGIX/peKlOaTVfQnAJ1HHcqs5ixZDwWpROT6ugHmVgTwaHQtXtVzL3NYqfqHVRttKhip961DdV0MSbCw862Ay46O6XR0TfSOBRf+SwKILElj03u44OLeUunji4x95/vOiXpWcTY+1Ehtlpby+hYawDkXdncjDv1dqJJCquDo0owvXvhRteInZfCONLUYOJ2tfAG3VlPKNNDYZI0Jbf4J35QKJ0v7QikP4BdY07XXAZK1+OkDEFpngnfdMqnnTfhNOpbnLgOdK7Q3W6SfztO3OHiWTn+VZ1GlVqlTqOFPb2OnnEl5VaolnMtfaXw6Vbw0GNcFtZqtsf0FNyO31ikXk77BjzfvuftdAxGpDb8szdrda0f41g9pvqys2k7GoChlmILi4yrKQ5qgMRtjruKP2z6SbbUGF256O9cq3qdRS2Vzq4vVvSvH4/GTGO/D5DSrcHhRXMXfU30KKrwT3uD9QfNTc0EUIhk7ja7dSPvpK7ElDdn1hUl9E3bsP8IxzGtsrm6lr9qIqKiPTYjnnsGxGpsbx8Q9VPPPfAho9OsNTYoi2azR7/ZTXNXJO9Wq+zrmYAn8SVQ0eNhdVU9ncduAqQJzDQpPXT6rRsb9BV8djueEkRWmgMr5that9gBsMKvxYMKMS0ZpbVzAUC4rpDyX4+mKz8FQXcHlrxZ/gPAgqs2Rye/xdFBnJ/Gp4Ms4oK65mL5/+WEtqnI2xOQldVsy66rcjKK5rDm0p8+gG720pp7bJR+D0qWACut8I/T3rbJ6WkUyupYZnrIvJoRyfcyjFZ77AX7/2clzeA5xQ/yJ19mwezF3OfyocNHp1mrx+apt8Xc7H3gbT/7Ldyhg1r8vy2fWObK6yLKTBo/NX3y277KsBUG7Ec6t3WpdBRfhx0kA0t6Q+jCN9OPHeCv6480+k6qX4nEOxXvlG1839ij7DeOxUKmIPJK3he9Qr3kDPOIwvCmupbvSSHGPj0NxEqrZ+RMoLZ6Lhj+g/1F2Rh90ZXCTHWMlJjMIZpZGbGMOUo4cyKtUpgcTPQAKL/iWBRRcksOi93XVwBvc0X/boJ/xY3bzLxx+UFcfRI1KodHto0Q0qXM1sLnHRpJu7bCAU3r25M+0b4YUHF+Enp/ATpNt0MMVzM8ttqyJOzO0fV2Sk8D9jFOPUHQxVKylorQdf3E31oeaodI4/II0rGtdyUP7faFJiWJ14HX+sW0600UiNLYulmfdhS8zlh6oGPsurJYNqnrIsYohS0drVuqFD072uGtBBIMC4X1vJaZZPI953+Ge0xDOZy2z/bg3SEolVmiOCigdtD5GrVrEl7tfY6ndiw8cQtbLHJ/AcKnjDfkuXd2N7Wt51rvYEV2pvdvt+O5sbj+onc4c+hSibire163uq0X2OxZ+8s1hhD5TfLDaTURSFLKooVtK523Ed87z3k+oPBCQuWzpxB51M/uhZvJ5vCW298ep+yuo91DV5adH9KCgkRFk5NLGRE+peYrV9KqaiEm3XSIy2c27VSo6tfj7U9Tkpax/G5iaQHGOjwaMT69CIs7eWo3UXh8qa1h48nVX2K/ihsgG/CQlR1lB+QLPPT5PXT3WDB1BIibWREmvn/OqVHFXxXOgC/oHPuj5W2wdgnd2tVxTIUat5SguUGS03EzDih5HerqFiON1UqVPiSKE1n0m1gOGP6FHg0f3YLCrO6q+YW3k9GoHqTwsdf2aGbx0Z/rJQJaDhI/ZDURRqGj28u6WCI4clMSQ5psPrhjfOa/TqobyO4rom3vm+nGZf4DVVVcEwTbyt5Zb0zoILM40/6zO5z7oy1IPjuYNWocRn832pm31THRyb/xDvJpzDh+V26hq91Db78PkNPHrHU/SuguGu2PDykm0Bt3uv6LAa21bxKQWAbKWKGiOGRKWx2y1QeuvfS8sugorwAEtVAs0eD41vZIVvLrFNRd1ug+KRYzHKvqEi4RDSzrkbNffITsfyRWE1Kx9+mL+2rvJ6TI0JnuX9XtGtKxqQGK3xq5FJXHjEMI7cJ3mv7SMx0CSw6F8SWHRBAove+zkOzhpXM7Of/YKPd9Z1uj3qt/um8MQV4zvtCryzooEnP9nOuM3LebDxJAqNtosYq6bg0CxEt5R32IoS5DPhKs8NoTt34cGFRltjt7M8C5lk3cjllvUUK+ksy1hGfMYwzKL/MbfyhtCFzNKsB/jKHEGKUc3iujkkeIp523kOH6Wcx1V515JhlFFMOpczn3w9kZt4kiu19RSYafzJtphhw/fl3MNz+fWIFFTTj/nWXEoPvAKXPR2np5zUl87B6srnk7QL2DjqRqrcHl7dVEyzz0+upYZLeJ11+sm8aFsYUe61zEgkQ63t0faizhLVoa17ePv8kPCLhzwjg7kx8/mo1kk6NR1O4OUkY9UUbKqCRzeIsmv4dAMFk+vMJyMS5bu70xj+mu3v2t6l/ZXJ2ntA98FU+4DzVfV4bmyZFtgPb5pkmNU808lKUlZY7kChmc69MTdyc9MyciinjBQ0xSDFrImc41o6Dw17iOPGH8KGb8tC25JafH42Fdaxs6oRn9/EqoKmKmD6udb8G4+1W3UJXpg/a7uDLLOMSi2Ta+yLafL5udD/Gg+ql2K32xieEsOxaV4u2jIjVGVnefb9/OhLjHjdvOomTNMkJdZGdkI0mQkOahq9xNg1Ljwil507tnLaF9NJ1Ut73Cgw30zlM8aw3HNWKIAOsihg11SS/ZX8TVtMrlrFNudRjKz/pNOVNtOEagKFC6BtpaJDgq9Xx+kpJ+Wls7G5CkIrFqGci4ZlZJllod4FDfYM8qsb+ezHGo4/IJ3ETjoS64ZBXlUjs48fRYxNY/nb24iPsrKlzMX3pa5QdSyLomCYYJgmFgVafEanwUVQsZLONY4lFPuTiLFrjMl2khBtx6P72VLqpq7Zi1c3cLcEeoXs7tLcHSvTBQZfbcaRhLvTLZBd2VVQ0dXcec6+mCFKBZVaJrbLXyc+e0S7J27BePRkKn6zhLQDj+7yXLT8pXc4+6urGapW4DU1TvLeQ76Z2eG80p/BhQqMyYnnD8cM5+DcBNnatIeQwKJ/SWDRBQkseu/nPjhdDR4Wrf+eopoWcpIczDvlQJyx9h79rNfr583NZWwpc+N0WDnhwDT06iLiX5hEhr+MShJJpe1iW0fhMnMxGz3DybVU8zctcHILJYoCBgoqJm/Hn8P2g2/iItdaEn53beiuWt3LN5Lw1V9DFzLvJ53H92PmBGrj2+sxP36YVbYpfFPayCHOBs799iqSvCVsTL2A9Vmz2FxSxx+963AcO5vcYfuSmxjd7Ulpx44t5L+2lE37X4+ianyWV0teVQM1jV78hkmaWc3znazOFJspmGZbMNBdz4b2lVvC6aZKpekkU63r9OJhsncuiYmJbK5VMFAinrvQTOfejGVsaY6nyaPT4NHRLAp+I9AQzdFUyt+0xR1WgDpbJejugqV9TkNnwUX7oKKMFKawmGIzCY/PT4ZZzd+snVdwqlCSGWat5Ql1ETmtSdY36DO5W1nBEKWiQ7WcSpKYn/YXzLhM0uOjKHd5GJUWS22Tly8LaimsaabJ58c0TDSLim4YzFG7X4XLoppnWy/GSswUTEyylWqeNE/jXvMyhmq1PEqgRGswEfiTmqiI1y2pa0E3TVQTouwWom0a0TYLY3PiqW70kZsURaXLw1BrLZdumUGyr/Pgov38ucg7F7cjHXeL3pYf0fpYiwI2LbAilG5W83f7HdjjMyI6tQe1v5htJIoYmjETh6G0v7Md1gXb5xzKK/ssDOUsPGs5g/9LOZ/FtTeT5C2hzpHN8wc9wkeVdqrcHo7cJ7A1qr3wFYvshChWvf8D/82rpqy+BTApqm3B6zfQ1MDNB601yPDpBn7TwOsPXHQeZtnGC9YFoee9LvYeCmLGUFrXQqNXx66pxNg1TANqmrzYNRWPz8Cimuh+cP8MpYuy6PzvBnRe/a07wTkSrKjX24ppa/VT2JA9mxevPjriMT05F73/4DQm1LzQoSpXeP5PV6/Z0y1lmgJJ0RrDUmP4/bhsfjMylSFJUrlpTyOBRf+SwKILElj03mA/ON2v/Jm4TWuos6ZjMX3E6W13kt22VP466hE21cdx4kHpjIlzM/adS1Dq8kKPMVFxjTgD1ykPkZ0U1/Hk4dcx35pL+ZBTsW19hcZj53d4XHjy7H5R9fym6nnWZ15NiUuP7PrbA+HlQ00TNu6sxmG10OTVMeoKWWfOC10cFJspXKvP4j5tZetWnRQUBbII5AFc4OnY76Gn5XiLjGRmeq/pcIFfThIHJJpsr1NQLYELpnSjikeM+QxRKqi2ZfHYyIf4oi6a7eUNuD0+omwadkugbr+1sYSnLJ2vnHQXVGgKnDkuE2eUjXUf55PRTXDRPqgoJYXzvfMpNpLbqhm1dgTON9O4xhPoNhwsmfpHdQH1tnRiPeU8xkJyKKfATGOJ7zJW2u5Ha3cJU6Gm8qeou7AlDiHKbiEn3sqppat43DiVr92xVLo9pBlVXKG9wRLvZHympV2Fs2T+zziY2/UrIra9ZFLNi/aFZCtVrb/vZP6o3YGiKDzsm8cQJbDl5oXRj/CDJ57EGBuxdo3P82oprmuiwaNjtait+QEmOYlRNHp00pwO9kuPpaAmWIY2gVff/zQUaPVkz7rTbqHB48eg88CipfUu/BHaNh5MeqXTwCKcjsp1jjs40/45B559C1lDR7V9MyyoCF/JKCvcgfejh/gLl9DsV9k/up6p22aR4CmmUsvksZEr8MRkUVrf0m1X8qt+OwJVVdhR4eb+t7fxRX4taU4HTR6d4rpmvLqBqkC0zYLDquFu0Ym2W3BoKtlqNctb5kZUqCpW0pmXcA/bWuKpawrcEHBG20iKtlJQ04RPNyKq5gWb5e1OgcCi81XdoOCKZXd5XBBoRvq2eUSfc50OH5oQEVz05Fz0l7e/JeaDJTyqnxrqIxKcgJ2NuDdbyhTgovG5/OHYEbu8ASQG3mC/dtnTSIM8IVrFnH4n36VPwm+YoaDCj4rblkqct5JLt87ihGwfF48fyrjRY1Aufx2OmglXvAmqhoJBfGMeuSldJNxZNJRT7iLjoGNIOvu+Th83Mi2Oy48exuiseAr0JB6PnU5ts8mY7PheBRUA2QlRjEiNpbS+BY/uR/cbWC0KI+31PGNZEBFU/NGymG3WA/mTfQmFZjrZShUqUKGkMkSp4Fn7Yq7R/tHlSsAfWpsDduYLYxS/1zaGLrYv0edSrSbjdGjE2jVi7RacUYF9/oYzmyuYTxHpJHtLOK72BfbPiGOf1BjS4uzEWC04rCpZCQ6aHBlMbm1M13471hLfxR1WR2osycTZLZwxLpul5x3CBUcO4TejknHbUjnPO79121Zg+9ALtgUcqmyLCCpKzBQu8C2gxExGVQNVtKZpb4SCiin+eWyxjmKmbTFFpDNEqWC1sRBrQyk/ehOY7LudfDONIUoF99se6hBU+FFJMypZ1ngb+9jqUBWFU0pXcVjp37m18s9kUkWKUcVT2iKmKG9ws+UZoK1BX5GRTI5azWTtPZZoj3X4PUTeF1JIp4pH/IGgotBM5+bYO/miLpraZh/RtsBFb02TF4fVEqoaFtjKY2KYJrEOrXX1K3Axa1FUmrw6hWbHhoGHKts6DSoUWrcFtU6d8KDCYbXgNwL5K1lKNQ9okY3zuqJhcIvnL/wrehIue3rkNzeujAgqiM9BVRWyho5i2EV/YeYJ+zMmO3DsLc28jyprFql6KX+0v8lF44eQ1Fohyt3iQzcM3C0+tlc0kBRj46SD0kPH88i0OCYfOYSkGDuNHh0UyHA6SHPaSXM6SIyxY9dUrBaFw3ITGBvfyPLmQFBRRDrTtDsoIp1ss5xFdTeTZlShWZRA41DdT4vPwOc30FubiJoEfz89+oh+MgW4QnuDHLWKarPzv0XBYCLfSGOO58puG3WutC1nLDvwY2GJfmmPtxi1b5z5eX4dde6WXr2X6Ufvx1JzCqW0lfgNlj/uyWuGC37sdk3hrEMy2LpoInecdTBDpa+EEN2SFQvRrUEf9dcX4Xv0VKyufCq1TAyLg7dG3U61ksTFm2d0X5Gk6DN47Qa48i2wOjp//l7oaROuXQmugBTVNlFU20ysXWOG+y+c7HkLgCpLGpcri9npTQBM0p0Ociw13OW6hWyznCpLGn7DJN2s5FnlVDyGwj99R3G/tiJ0kXiNt+0ufWd3KEtI5hr7HUyxvIn66xk8s9ngq6J6TMPPmGSFIo+NxGgbDquF6kYvumEyNtbN2d5/8lLyH7BabYxMiyU51sYTH+fhab3r2+zzU+HyMNrYznOtDbyCwrdHXeSdS5WWQkqsnXG5iVx34ihGpsWFVnTio6z8L6+KypL8LvNris0ULvDOp9hMJtqqoqgKut/ApprcbHmav+qn0ByVSW6CgwOzE2iuyufawuvIpjwUdFQoKRys7GCtsoRYpfOLIB0VDYOG6ByWpC4lSrMws+BaUnwllCup6IZBttL5FrXuVl3CVzQURQmtXEBga9dlLMITk0lmvIMYm8bQ5Bg8usGnP1YTbbWg1xbxB16g2bSzXJlCVlIsdovJebVr+Db3EuqtaaTGOSisbeKtb8vw03nOgMuM4lTPXRSRBoBVVUiIttLs89Po8Ycu6hLtJjeoz7DWdwp+A56zLSCDGiqcB5Na/3Uo+Vc3A9tNOvsMK7VMfJf+K3LFwq/D2/PgVzO6LlUaduw5PeVkfv8YykmLwaKFmt79UNmAR/dj1yyMTIsNbGdsF/QbhsnK93bwWX4N2QlR2DULsa2rMx7dz7ZyN65mH2ePUDj/u8C2x2IlncvN+bht6aQYVaHVpGIlnZvj7mJrk5Mmrx+H1UJDiw/dMEPVpWwWBX/Yv3cHhUDxhvu0lZxu2dhlINNZSdmutkn5TDi3tYlfX5x8YAqrLxsP9PxcdNvL3/D3/xZgmG39PcL7iAxNiqbRq1PbFPisw8XaVKaMH8J+OQlUuDxkxNs5af8M6XA9CA36a5c9jGyF6oIEFr03qA/OdvuuwyvI2DUL45wNTN4cSG7ttiLJHmhHhZsN35TxxrdlaA3FrNbnkWmWU2dN54Wxa8nzJvDRD9VYFIXcpGgSo62MczZy/vdXEdVQSEt0Fl/bD+PxhNkkGTVcW3wdqXophaRzE7NZygpyKKfYTEE19VCFqfAgo1JNY/0Rj3PJyUezo6KBuzdsZnNJPaPi/NQTjamo1Df58BsG+2bEccHhQ0iMsUZULdpW4eYv72wnNdaO3zSxqgo//LCVO+rnMETpuB0qWG73W2Uk8VFWzjkshwuOyA1dAIb3G7BaFP79fRmZSjV/U+ZGlM4sNxO5hCVs9ySiKWC3WkJdpA3TxOmw4jdNWnx+Ds1NZPFZo1GAF/79CRdvnUGGv4wKLZPF1muY67mPtNZOwBC4qL9FvZY7jAfIam3wFszBKVUz+KNlIVFWlQeabiGTzvNAumr2mG+kcYPvjzxmWxaR95GlVPNSWJ+NKpx8yBH8zX4B+6TEcL7+L56MvZJUZzSf/FhDtlLFHbV/DjWg+6d6AqojDsMw+H3LqzQpMbwx9mEO/tXxPPFxPpt2lvJdZaDsafueHgDfGMOY5F0caOgYbyfWrtHs1XG3+PH4DVTgJuVJpqpvUEoKVotKir8CA5Vy58Gku75u3e5jhIKK9vv5g8GFOX4Gyil39fKI6V5vgv7u+oLYNBVbYwm3lN9IkreEGlsWN8fdxafVUSgKqIpCllLN48pC0vRSyiwZ3BhzB983OPH6DUzTjOgPApF32y0qGEb/b4+K7G/Rsf8NQLnhJFlp6FBSNvzx/R1cDE+J5t0bjwN6dy667eVvePHzIjz+tr8ddk3l3MNyWHzmaAqqG/k8vxa3x0ddk4dom5XsxCgJIn5BBvW1yx5IAosuSGDRe4P24Oxi33WHi4ewcpyDLbgwDJOPfqjC+/ocjq97kRpbFs+PfoQKJYXS+hYSo62cOiaTlDh7p++3bux0XrVP4rQvp4cac70yfCFn/zifJG8JlWoqfsMkgypKSUFRFDLMyojkZF9sNtbpb0F8Djsq3DzzST6lZWV8UwMmgapcCdFWEmNs2DS1Q1fj8JyROIeVWE8Zk776I6m+EgrMNLb6szlR+zLifReYaVzomUtzVAb3nDuGkw7KDHyjvgg2rmTHuD/z+MYitpS52FLqZh9rDY/5byUtLHG/ggQ+NA/lPs9Z1GrJgIpFVTBNE39rYGFRobbJx8SDMlk+MYXGDx5kYcsF5FhdTN0+K6Ljc1CJmcKchHup09KJ9ZRyX8OcUPAQ/NxK1QwWWK9jvmdpKPAoMlI4zzu/y7wFoMNKgd+Ed/yHslKf1GVeTAVJWDWNRL2CEksO3zgO5x/2M1hUO4d0MxAMlZGIomikm5X8y3ICv/N/FEiSVjSUKzewTdufxz76kWc/K+x0xSJ4MblWP4WnYq7gNvuzvBl3NnXWNEakxTJpbDYuj4/ivB1M/GwqMc2lgbljT0H11FDpPJgk1zfUEUtasKRs8Llp25IC4LfGYJnxceBYHUBdrXJMHKKT/MLZxLcUU2fP5oUxj+CypvHRjmoq3C2oqkJ2QhRHJDZx4eYZZBmBMrhzE+5mhycezaJS7mrBNE08uoEetlCoAFFWFX9wq5RpEiwYFZ7H0lvhQYXPVLnIN5eJ6qf8Vv2KkWrr76o1iAhqX/3pCm19qHFfeKDR1+DikFwnL888Buj9uaipyceaj38IFQL5w69HEB3dMUlf/DIN2muXPZQEFl2QwKL3Bu3BueFW+OThngUL4UHIUTNh4p0/1yj7xY6yWhr+dRv/jJpEuZLc7VYOIHQBzomLMN+ai/LpSrzOIdScvJL0d2ah1Oahx2VjmCa2hhKaY3NZNfRBKtwt3FByPal6KV5LNDZ/U+D54nMCOSnxOei6n807C6kyoiitb+GTndV4dZOshM67Gg9PiQ2tMBwS38j5311FQksxxUo6X5sjOIWPI4YeXMEoMNOYrizEkTKEpecdzL4OV8TvcMeht/DcZ4W8/9mXPGEG7uh3pthIYQoLyNeT8bf+KcykmunW9dzHxRho3HdyMhM/nwa1efw74Vy+POAmchq+5sJvpkU8V7mSyuXKIpqjMtEsgRKk2Wo1f2m8hUS9HAADCyp+GpUYYsxGis1A1+5sparTCljhW6M6WymAtlWkAjONH5RcjuHLDrke4YLVlQCasNOgxJFmVlFEOnfYr+du/1Li9WAjOo0XD17Lf5qHUJi/g+XNt0ck1oc3bvyz817m7bOFpK/X4nUOoeqcf5CRO7Ltzn99ETx2cuC/BLenBDpvO11biW4dU3jgWkU8ibgjO0CHzbeB1Okqx1u3wScPU23L4r7M+3GkDCHKZqG0rpnP8gKB7RHDEsmId7Bj+1bmVd9EDuW8n3QedxuXoSpQVt/Sunqn0uTx4TMDeRYmCtFWCw0evXX1A1pa69paFLrcLmUBbNZA5apYmwWLGvjcY2wWDvBvY4XnVjQC3aov8C7gK0ZgGGBV/SxQH+MY9euIalGdNZ6DtkToN/Qjed6+OLSy4TKjONlz708q5/rYpYfyu9YbB4P2XCQGhMyX/iWBRRcksOi9QXtw9mDfdYSwi20s2u4fXz/7yfkb4Z/TxpWBYCw+FzADn0m71R5PTQHp3z5KzDEzUJ88A+oLA88TelwWFRUVpKSksvrDH/m2pH6XFXd2VjXwj/c+5fLts0jVS6m1Z3N31A1cW3dn6G5/mZmIHwvZSlXoorZUzeAqy0JOPDCDmQXXoLRbddKrC3CvOiF0Ue9HZVn8rVxW/0joeSGQa3Ghbz6lZjJpZluPikf1U/gg+XyeUBai1OWhO4dyZ9oynFFahxULPypz4pfyDaMYnROPzaJSXNfMvulx6DWFXFMwmwRfecRHX2XN4nLm4fEZPM5Csmn7fnclgds+x7ZtJ7qpst5/BGdon1JhxJOsuEM5C+HCfyb8Ar5Sy+SV4Yu4pGQRUQ2FEJeB2VCJYgYuOO913soU9xqyzfLQ5x/8b3CLktc5FNv5j8JL0zpdKYx5fy5JX6/FjM8BVxmKqWOgUuYcR4ZrEypGaEwt0VmAiaOplIZRZxLzw2soRljZ1T0kuOig9XjKGzUl1AQxuKKREG0FE+qafXh0Px7dwKwr4lzfP/ls1DV8X9ZMaX0zbo+O7jfJdNopd3tp9vnBNLFpKigKXp8/tETh8XcdWARXMayqgjNKw6Mb+HSD5Fgbac4ospRqllZeRYzZiI6FG+Pu4c26bAwz0EslyqZhGCYxzWX8zbqQoWpVaDViV8HCWHZwn20VaUoNTsXT6w7hAHF2C1/OPSnUYG7QnovEgJD50r8ksOiCBBa9JwfnXiQYZHjc8OWTu17tqS+CD+6BnR9AXT4cNRPjpCVUVFTgtcbywDs7Qluc2gvvEZCbFB3qB1JlzeKx9FuZWnYHaXopJQSqOmVRRQkpmGagI7COBQ0/L3E8v7F8T7q/NHK87e6OBy9+S5R0FlivY4F3WWgbEgSCi1meP/FAsOqUmcb1vtmsil5Jmh54buOy13hqYz6nfjGNVL0UjyUWu78hNJZiJZ0705aRlj2cMpeHpBgbv90vlVe+LGZsXCMXfXs5cd6211w9chXPl2cCCqcnFnJdwazQ9872LOALc1+gY5+I63wzeNC6okPPgSbTRrTiDfwqzY7VhMK3FflNBUtrIzQfFl4esZhzCu/C4nWH3utL73/OWV9diYY/9LPB9xoMKiq0TD4cfRdn7pzflqt0ztpQcBGe2+T1eTm3Zg0Vuady8vc3E+8L5FhUOA8mzfU1JgoW/FRqmaw/bC2XHDUU9dNVgWC/9Ev452xodoG7NaDbw1cXOwv2gYivNXv9vP19YEtVVYOHwpqmQNJxaz6Gu9lLXbMPFAWHpmLTLK3duP34/WaoEZ8FaN/tIhhY2CytyfReP80+P8mxdoYkRTPFvYbTG1+mUYnhzqQ72Krtxw8Vblp0A6umogLRNg3TNEnSK1nlnxcoGY2Ta6KXojtzya9potzljVgfi7YpXHH0cGqbfHz42SamqK/3ukO4RYE1lx3O8Qe0VQCTc5HoDZkv/UsCiy5IYNF7cnDuhfqw2mMoKhUVFdSaDla8+wPDU2KxdLJyEt7VeP8MZ0Q/kKQ3Z2BzFdAcm8tUYx6VDR6eUBeTSznFZjKqopBJFQ1E4TKjyVKqaYrJJfoPGzoNKnyx2bw8YgnHfXcrqXop5ZZM7ov7M9fX3UUGbUnX4VuKbuZPLFUCCezNsblETd8QeK6wCmPP7P8gR1f/g//YjuH8wkVkGWWUWzJZvc+DpGQP56SD0rFrFpa/vY0hWg1XbLsap6etn0GVNYurLAuxqLBKD+S1BHWWWxEssatZ4AXbolCORERDxy4Sb7sS/NlgI0i/LQ7LzE8o9Cdx/1tbSS35N7e4FkfkOQSDiyLS+dv+D3PjucejNZZE5iqdsxbf81eGPqunD1iJHpuN1lDMRZtnkKaXUqOlo6gqHkd6qI9FnSObdaMeokBPCgWdHebaB/eANQZOWjIoVxfbCw9AqtweNhXUsamojsKaJuqbfTS06OimiUOzkOa0Ud/ko7rRG1p98neTzK0Asa3NDxu9esSKRZTF5GL3Wt6IOZtqSyoe3U91gwev38RvBAoXWFDQNAVdN0iliiu1DWwcfg2zjt+fKJuFRq+ORYWPtldRUtdCdqKDCw4dgsMR+L00NflY/dEOtpU10uzT2VHeQHG9p9t8kLQ4G3edfXBEUBH4nORcJHpO5kv/ksCiCxJY9J4cnKI3gvOltysWQIeEe+Oy17jrYzevfFlMqlHJamMBuZRTRgoWDFIJ9CUpI4UXxq5l5pkTAtu/Xp0dWHGBwLauKzaEGqYlPH8W0Y2FVGqZPJV9Oxflzw9VRoLARfOd0TfzB89jZPgD/QeeH72Ka4/fF/XJ07usMDY2zs35319NVEMhunMo6uWvoybmYhgmT234KLTKUWPL4gHnjVxVc28gcbe1aWEmVdQ5stkwaiHHb5lLqq+UYiMFlEBH7RIlnT+oC6hQU5ln/RunN71CvS0dn98kxV/RIcm5J8K7YisEKle9PO5Rzv79JLZVuLl7/WbOKH2Yc7yvdvjZMksGs22LcaYPY8HvRwd+h2G/P3P8DJ40Twu97+B7m7h9PgktxRSRzl3py/hNqofjCh4KBRbPjllLYeyYyKBzLxMMNNyeQFBR2+jjnc3lfFdcT32LD5/foNnrb12NUHE1+0IrF+3ZNYV4hxWv38BnmKRE27BqCiYKyTG20BZF0zSpbvBgUVWOHpUMBny4o4oKVwu6YaKpCmlOBxP2TWXy+CG96r0TTtcNPs2r5t0tFXh1gzHZceRXNfJtSQOxURYuPHQIvxqZEtr+FPm5yLlI9JzMl/7Vm+vnwX+7Rwixx8mKDzTy+7aknli71iHHorS+hTHZ8aHtIUCHRmdqfA7nHebmP9ur+KEqkatsC1ljzI/IRQD4xnEYOz0JFNc1By5wbTGBb7QGFcTnoAIZuSN56qBHQhe7U0vvYLl9GvM996JhYKCg4WdeU2B7TZmawY1RSxjm9mA8fhpq61Yf69TXuTQum9+1z2k5egOsOw2tNg+ePD3wHoCLtszA2rpScoN9MaUtKeTF3cm99TeR1dp7ooxU1uQsJyp2KE/vv5JLvv8j2QTyKaosafx12ApOyR7O2NwEUmIOw/3fNMoPuoJH3t/BjSU3kGVGfiY9oYT914+Fx/d7hO89+zC+rpkYm4ZFUXlBPZWJyjvEmI0RP/t49JXUq+nEmtDo1Vs/75zAVrSNKyk6fA5f/XsntQesDOWjBJPda2xZ3GhdBA0eTqpdgM+REnreidvns27UQ9i1JGJse+fpSVWVDis1Jx6YTmFtEz9WBX4PKgrvbS3n0x8DwbW7RQ811guyWQLdwJt9fnTDJC3OwbRj9uHTH2v4qrCOcpeH+GgrYFLf5MMwYWxWHBePH8rwlFgKa5vYWdVAtdtLcqyN4amxfe44rWkqR49M5eiRqT/5OYQQe7a98y+3EGK3UlWFk0enU1LfzPaKhg71/tt3NQYCe+khYgvWvhlxXHhkLve/tY08PYFb1Fk8ydzQj3xoO4YPRs3B22K0XeCetAQUtcNWruK6Zr5yx4YudpNailnM3QC4iWaJ5Wru8d8XevydjmshPocT69aitet1okLHbTrBC+vgqsv790Deh1hd+XidQ7gz+k521kYTbTMDXa0tasTG+B2VjcTSgMOaRHnKr0kpfyXwtFFWph87PLLC0qSl1NU0UW9tYJ52HY/4bkPrsMu+514YvoTapIPxVDXS6NXZNy2Occ5Gzs6bRwyNoe1PQX92301d4lK8UYdGBgDxOTDxThrLXLTofvSEwEpFeAWtN/ddRFKFjVsr/kwilex07sf7Y9aEVjMu3jyD9YetjQw693KqqjA0OYahyTGhr/1mVApFtU3srGrE8Ju4vF5+qGjkox1V5FU10ezz0+w1sGkqB2XFcfWEkRx/QDq/GpHMM58U8MmPNVQ3eABIiLIyfngyF4WtRrR/PSGE6AnZCiW6JcuJojfaz5fedDXuSmFNEwv/9S16TRF3uOaQHXZ3vsaWxZP7Pdz1nvwwW8pcPPjv7QxPie1QLvZabuRGniInbDWkVM3gr8MfxKUlM9/xHHHHXdPznJPWoCK4AlMy6UWWbmwINIYzKpn2w2ySvCW47BmYmMR7yqnUMtlx6nPkDBtFttOK+vr1sPO9QOWtThLpDcPk4Vc+4Kyv/0AO5aE8ie60bzoXVGfPZt2+YbkNlhq8j56CzVUQCirKLBk8Hn0lf3bfHSpP+krr1qn2d7GD/UmGaDUdKmi57BnofpMkvZxyLZtXDnwAV3Qu1sZSLt48g1S9FJ9zKNYr39jzqj4NAoZhUlDTyGd5tbT4/IxKj+XwIUkR24sMw4xYARmeEkNOH1cjdjc5F4nekPnSv3pz/SyfthBitxmZFsfVE0Zw3Yn7Mvv4UVx34r5c9dsRvdqjnZ0QxTGpXpY23kq2WU61LYtH911NnT2bJG8JF2+ewThnwy7vcMfYNByaBa2hmInb50d8bxnLyaGcQjOd2VF3U27JJNMo4+q8azg4voWY39/b84vc+Bywx0Vs63LZ0/H4DdKcDo6vf4kkbwl1jmyeG/NXnh+zljp7Nql6KfvlPUluUjSqZoUzVwS2ciUOCzzXxpURL6O6i/lj3mxyKEc31VBQoZtdXxx2FlToqCR4its+R6Ua1p2GzVWAobRWvCKd2bbFvKeO57bEpaFg45yvpqGWfN7hObMTohgb18DFm2eQ0FJMnSObZ8esxWXPwOkpI0kPdIlff8gjVPljya9upEBPYv1hawNBhSs/sPLTmoAvek5VFYalxHLe4blc+qthHDW8Y85CcAVkwn5pTNgvjSHJMXt0UCGEGDxkxUJ0S6J+0Ru7Zb7UF0VUY3r6gJX4YrOwNpT06g53+0TqOkc2Hw69htO23oKl9Q78DbH34Bx5FEl6BZdsmfnT7563q6wV3mHcaVc4Jm8FX2RNpsGeAYDiKmJc8TOMuOh+clPa/W3qrMdKWKK0oVhQzcA2Jd1U0FrLyHa1OtHpcFv7R/his7Fa1MAqiaqBoeN1DmX1sL+wyRWDYQa2zRztyOOcr6ahmHrgcVdsgJwjIsbc/nfmsFq48JvpoX4evthslCs2sLnaRIuNJ9Zu69AdvkcNLsVeQc5FojdkvvQvqQrVBQksek8OTtEb/T5fwi6gO6vGNM7ZwOTNM9p6KOyi70b4xe6rIxZxbt4CEjzFoTvwFVomf8lZTnNUZu+eexcMwwx1GN9Vw8Bd3jkOr56lWqC1cVyLEoXDbO1eHdanoivhQQgEumwrZmueSvB52zW5i0hWL/kcHpsIhg52J8zY2Fbut5Pf2WmlKzi+7kVc9nSirBrWhmKMxOFUnP4UafscGDlfwt/jHt6vQvw85FwkekPmS/+SwKILElj0nhycojf6db60Kz/b5QVuT+5wt7vYfWWfhRz3/a2k+Eqosmbx/kF38vud87C5CvA6h1B1zj8CydL9ePd8R4Wbxz/Ko6bR22ky++VHD+vZFrENtwY6pIcFFcTnYFz2GuWvLyHxx9dwmC1Ax1WL9v9uxs7OhN9wID+g1OWBLQ687tbnbKuq1aWiz+Cps8HjCgQAv5rR9e+spYXs/95FzG//FAie1p2GUVtARdaJpJ13H2pibuRzd7ZSI/Zaci4SvSHzpX9JjoUQYvBrV36W+JxQKc79M5yBXARVaavG1EUuQvsAxXrlG5zr+C8pvhK8ziF4L/knZ/9+ErYr10PiMGyuArJeOTcQVOzquXthZFoclx89jNFZ8dQ1+ciraqSuyceY7PieBxUQuNDOGBsRVHDFm6ialczqT0NBhUFrA7WwW0eKQkSX5Cg87KtvQTl3bSAwuPo/kDA08M3hx+06iMo5IrBScdTMwLi6+51lJRE3aWkggAj/XBvK4ZNVHZ+7tcKUBBVCCDF4yIqF6JZE/aI3+nW+9KEDeMTFaPAOf/iKQ1fP3dUWnH68ex7eaTm06tLbxFm/Dm/dDt5GmHBz4D38czZ8EWgMaCoWFNMfyr9owsEXljGUmGl87DiG+5puxRIeYoR/Nn15r738nRm1hVR8/HfSTroW1Wrr3WuJvYqci0RvyHzpX7IVqgsSWPSeHJyiN/bI+dJfAcqebv0c+HQVKBYw/aHkazNxGKWTXqTOmkaDRyfWoZFU8zXpL56JYob1vhiAROk9cr6IPZLMFdEbMl/6l2yFEkKIIIsWWHnoTbnYwbgF56QlgS1Spj+QTG3okDgMZerrZA0dxYFZ8Ry5TzIHZsaTcdAxKFe+GXgcBP7bx61eQgghxCA7cwohhOiURYPp7wZWZ8b/AT5d0/0qTTA/YuNKOPKP8N/Vbd3PhRBCiJ9AAgshhPilCK7OQM9KtAZXZ3r6eCGEEKIbshVKCCGEEEII0WcSWAghhBBCCCH6TAILIcTg49cDZWTri3r2+PqiwOP9+u4dlxBCCLEXkxwLIcTg8/a8QG+Kra/vukRqeG8KkFwCIYQQYjeRFQshxODzqxlt3bDXnQb1RRiGSWFNE1vKXBTWNGEYZoeu2/xqxsCOWwghhPgFkxULIcTgE58TWKloDRp8j57KM/uv5Ct3LC26H4dmYWxcAxdtmYHVlT8gzd+EEEKIvY2sWAghBqfW4MLnHIrVlc+pX0xjiFbD8JRYhmg1nPrFNKyufHzOoRJUCCGEED8DCSyEEIOWEZfNM/uvpFLLJFUvZer2WeQ0fM3U7bNI1Uup1DL5+wErMeKyB3qoQgghxC+eBBZCiEGruK6Zr9yxPH3ASuoc2SS0FHPhN9NIaCmmzpHN0wesZJMrluK65oEeqhBCCPGLJ4GFEGLQavTqtOh+9NhsNoxaGPG9DaMW4ovNwqP7afRKmVkhhBBid5PAQggxaMXYNByaBa2hmInb50d8b+L2+VgbSrBrFmJsUqdCCCGE2N0ksBBCDFrZCVGMjWvg4s0zQtufnh2zNrQt6uLNMxjnbCA7IWqghyqEEEL84klgIYQYtFR3MRdtmRFK1F436iEKY8ewbtRDoYTuyZtnoLqLB3qoQgghxC+eBBZCiMGptfldsKTsG4eupUBPIq+qkQI9ifWHrQ2Vog020RNCCCHE7jMoNh7n5eWxePFi3n33XcrKysjKyuKSSy7htttuw2azDfTwhBA/t3Ydta1TX+fSuGx+V9dMo1cnxqaRnRCF+us32h637jTpZyGEEELsRoNixWLLli0YhsHq1av57rvvWL58OY888gi33nrrQA9NDGZ+HTbc2vM72fVFgcf7pcLQgNu4MhRUBIMFVVXITYpm/wwnuUnRqKrS1qE7cVjg8RtXDuy4hRBCiF+wQbFiMXHiRCZOnBj69/Dhw9m6dSurVq1i2bJlAzgyMai9PQ8+eRi2vr7rO9nhd8gBJt75swxRdOHERYH//mrGrlcggsHFxpVtPyeEEEKIfjcoAovO1NfXk5SU1O1jPB4PHo8n9G+XywWAYRgYhrFbx/dLYRgGpmn+Mj+v8VfD1vWt22TOgMv+iRGXRUl923aarPgoVHcJPPl7qC2AxOGBn/slfh794GebL4oKJy0JvuiuHx+X1bvHi5/FL/rvi+hXMldEb8h86V+9+RwHZWCxY8cOVqxYscvVirvuuouFCxd2+HplZSUtLS27a3i/KIZhUF9fj2maqOqg2DnXC1Y4/Sn49yJoKEd/7no+yLmavGYHXr+BzaIyLKqF3xatQvM7IetEOH4eeKxQUTHQg98j/bLni+hvMl9ET8lcEb0h86V/ud3uHj9WMU3T3I1j6dacOXO45557un3M5s2b2X///UP/Li4u5re//S0TJkxg7dq13f5sZysWubm51NbW4nQ6+zb4vYRhGFRWVpKamvrLPTjri/E9fgZWVz5VWibPHLACPSYbrbGYizbPJkUvDVQXuvxfEJ890KPdo+0V80X0G5kvoqdkrojekPnSv1wuF4mJidTX1+/y+nlAVyxuuOEGpk6d2u1jhg8fHvr/JSUlHHfccfz6179mzZo1u3x+u92O3W7v8HVVVWWi9YKiKL/oz8yIz+HZ/R/i1C+mkaYXM3X7bDaMWsjE7fNJ0Iup1DJZf8BDXNKaICy690ufL6J/yXwRPSVzRfSGzJf+05vPcEADi9TUVFJTU3v02OLiYo477jgOO+wwHn/8cZkoot8U1zXzlTuW2gNWMnX7LBJairnwm2kA1DmyeXrUQxS4YjmurpncpOgBHq0QQgghxJ5pUFydFxcXM2HCBIYMGcKyZcuorKykrKyMsrKygR6a+AVo9Oq06H702Gw2jIrMydkwaiG+2Cw8up9Gr5SZFUIIIYToyqBI3n777bfZsWMHO3bsICcnsrTkAKaIiF+IGJuGQ7OgNRQzcfv8iO9N3D6fdaMewq4lEWMbFIeLEEIIIcSAGBQrFlOnTsU0zU7/J0RfZSdEMTaugYs3zyChpZg6RzbPjllLnSObhJZiLt48g3HOBrITogZ6qEIIIYQQe6xBEVgIsTup7mIu2jKDVL2USi2TdaMeojB2DOtGPUSllkmqXsrkzTNQ3cUDPVQhhBBCiD2WBBZi79baUdvqysfnHMobh66lQE8ir6qRAj2J9YetDZSadeUHOm/XFw30iIUQQggh9kiyaVzsvVqDCmrzIHEY1qmvc2lcNr+ra+u8nZ0QhfrrN9oet+40mPo6xOfs6tmFEEIIIfYqsmIh9l4bV4aCimCwoKoKuUnR7J/hJDcpOtC3Ij4n8P3EYYHHb1w5sOMWQgghhNgDyYqF2HuduCjw31/N2PUKRDC42Liy7eeEEEIIIUSIBBZi72XRYOKdPX98fE7vHi+EEEIIsReRrVBCCCGEEEKIPpPAQgghhBBCCNFnElgIIYQQQggh+kwCCyGEEEIIIUSfSWAhhBBCCCGE6DMJLIQQQgghhBB9JoGFEEIIIYQQos8ksBBCCCGEEEL0mQQWQgghhBBCiD6TwEIIIYQQQgjRZxJYCCGEEEIIIfpMAgshhBBCCCFEn0lgIYQQQgghhOgzbaAH8HMyTRMAl8s1wCMZPAzDwO1243A4UFWJQ0X3ZL6I3pD5InpK5oroDZkv/St43Ry8ju7OXhVYuN1uAHJzcwd4JEIIIYQQQgwebreb+Pj4bh+jmD0JP34hDMOgpKSEuLg4FEUZ6OEMCi6Xi9zcXAoLC3E6nQM9HLGHk/kiekPmi+gpmSuiN2S+9C/TNHG73WRlZe1yBWivWrFQVZWcnJyBHsag5HQ65eAUPSbzRfSGzBfRUzJXRG/IfOk/u1qpCJKNZ0IIIYQQQog+k8BCCCGEEEII0WcSWIhu2e125s+fj91uH+ihiEFA5ovoDZkvoqdkrojekPkycPaq5G0hhBBCCCHE7iErFkIIIYQQQog+k8BCCCGEEEII0WcSWAghhBBCCCH6TAIL0Wsej4dx48ahKAqbNm0a6OGIPVBeXh5XXnkl++yzD1FRUYwYMYL58+fj9XoHemhiD/Hwww8zbNgwHA4H48eP57///e9AD0nsge666y6OOOII4uLiSEtLY9KkSWzdunWghyUGibvvvhtFUbj22msHeih7DQksRK/ddNNNZGVlDfQwxB5sy5YtGIbB6tWr+e6771i+fDmPPPIIt95660APTewBnnvuOa6//nrmz5/PF198wdixYzn55JOpqKgY6KGJPcwHH3zAzJkz+eSTT3j77bfx+XycdNJJNDY2DvTQxB7us88+Y/Xq1Rx88MEDPZS9ilSFEr2yfv16rr/+el566SUOOuggvvzyS8aNGzfQwxKDwNKlS1m1ahU7d+4c6KGIATZ+/HiOOOIIHnroIQAMwyA3N5fZs2czZ86cAR6d2JNVVlaSlpbGBx98wLHHHjvQwxF7qIaGBg499FBWrlzJkiVLGDduHA888MBAD2uvICsWosfKy8uZPn06Tz31FNHR0QM9HDHI1NfXk5SUNNDDEAPM6/Xyv//9jxNOOCH0NVVVOeGEE9i4ceMAjkwMBvX19QDyt0R0a+bMmZx22mkRf2fEz0Mb6AGIwcE0TaZOncpVV13F4YcfTl5e3kAPSQwiO3bsYMWKFSxbtmyghyIGWFVVFX6/n/T09Iivp6ens2XLlgEalRgMDMPg2muv5eijj2b06NEDPRyxh3r22Wf54osv+OyzzwZ6KHslWbHYy82ZMwdFUbr935YtW1ixYgVut5tbbrlloIcsBlBP50u44uJiJk6cyHnnncf06dMHaORCiMFu5syZfPvttzz77LMDPRSxhyosLOSaa67h6aefxuFwDPRw9kqSY7GXq6yspLq6utvHDB8+nPPPP59//etfKIoS+rrf78disXDxxRfzxBNP7O6hij1AT+eLzWYDoKSkhAkTJnDUUUexbt06VFXuZeztvF4v0dHRvPjii0yaNCn09SlTplBXV8err746cIMTe6xZs2bx6quv8uGHH7LPPvsM9HDEHuqVV17hrLPOwmKxhL7m9/tRFAVVVfF4PBHfE/1PAgvRIwUFBbhcrtC/S0pKOPnkk3nxxRcZP348OTk5Azg6sScqLi7muOOO47DDDuNvf/ub/DEXIePHj+fII49kxYoVQGCLy5AhQ5g1a5Ykb4sIpmkye/ZsXn75Zd5//31GjRo10EMSezC3201+fn7E1y6//HL2339/br75ZtlC9zOQHAvRI0OGDIn4d2xsLAAjRoyQoEJ0UFxczIQJExg6dCjLli2jsrIy9L2MjIwBHJnYE1x//fVMmTKFww8/nCOPPJIHHniAxsZGLr/88oEemtjDzJw5k2eeeYZXX32VuLg4ysrKAIiPjycqKmqARyf2NHFxcR2Ch5iYGJKTkyWo+JlIYCGE6Hdvv/02O3bsYMeOHR0CT1kkFRdccAGVlZXMmzePsrIyxo0bx4YNGzokdAuxatUqACZMmBDx9ccff5ypU6f+/AMSQnRLtkIJIYQQQggh+kwyKYUQQgghhBB9JoGFEEIIIYQQos8ksBBCCCGEEEL0mQQWQgghhBBCiD6TwEIIIYQQQgjRZxJYCCGEEEIIIfpMAgshhBBCCCFEn0lgIYQQQgghhOgzCSyEEEIIIYQQfSaBhRBCiJ9k6tSpKIqCoijYbDZGjhzJokWL0HU99BjTNFmzZg3jx48nNjaWhIQEDj/8cB544AGampoinq+oqAibzcbo0aN79PoffvghZ5xxBllZWSiKwiuvvNKfb08IIUQvSWAhhBDiJ5s4cSKlpaVs376dG264gQULFrB06dLQ9y+99FKuvfZazjzzTN577z02bdrE3LlzefXVV3nrrbcinmvdunWcf/75uFwuPv30012+dmNjI2PHjuXhhx/u9/clhBCi9xTTNM2BHoQQQojBZ+rUqdTV1UWsFJx00km43W42btzI888/zwUXXMArr7zCmWeeGfGzpmnicrmIj48P/XvkyJGsXLmS9957j5qaGtasWdPjsSiKwssvv8ykSZP6460JIYT4CWTFQgghRL+JiorC6/UC8PTTT7Pffvt1CCogEAgEgwqA9957j6amJk444QQuueQSnn32WRobG3+2cQshhOg7CSyEEEL0mWmavPPOO7z55pv87ne/A2D79u3st99+Pfr5Rx99lAsvvBCLxcLo0aMZPnw4L7zwwu4cshBCiH6mDfQAhBBCDF6vvfYasbGx+Hw+DMPgoosuYsGCBUAg2OiJuro6/vGPf/Cf//wn9LVLLrmERx99lKlTp+6GUQshhNgdJLAQQgjxkx133HGsWrUKm81GVlYWmtZ2Wtl3333ZsmXLLp/jmWeeoaWlhfHjx4e+ZpomhmGwbds29t13390ydiGEEP1LtkIJIYT4yWJiYhg5ciRDhgyJCCoALrroIrZt28arr77a4edM06S+vh4IbIO64YYb2LRpU+h/X331FccccwyPPfbYz/I+hBBC9J0EFkIIIXaL888/nwsuuIDJkydz55138vnnn5Ofn89rr73GCSecECo/+8UXXzBt2jRGjx4d8b/JkyfzxBNPRPTFCNfQ0BAKRAB+/PFHNm3aREFBwc/4LoUQQgRJuVkhhBA/SWflZtszDIM1a9bw2GOP8d1336FpGqNGjeKyyy5j+vTp3HTTTbz77rt89913HX62rKyM7OxsXn75ZX7/+993+P7777/Pcccd1+HrU6ZMYd26dX15a0IIIX4CCSyEEEIIIYQQfSZboYQQQgghhBB9JoGFEEIIIYQQos8ksBBCCCGEEEL0mQQWQgghhBBCiD6TwEIIIYQQQgjRZxJYCCGEEEIIIfpMAgshhBBCCCFEn0lgIYQQQgghhOgzCSyEEEIIIYQQfSaBhRBCCCGEEKLPJLAQQgghhBBC9JkEFkIIIYQQQog++38gWxjI7bCB0AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca_vis = PCA(n_components=2)\n",
    "X_2d = pca_vis.fit_transform(X)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_2d[:, 0], X_2d[:, 1], alpha=0.5, label=\"Объекты\")\n",
    "idx = anomaly_ranked[anomaly_ranked[\"consensus_anomaly\"] == 1].index\n",
    "if len(idx) > 0:\n",
    "    pos = data.index.get_indexer(idx)\n",
    "    plt.scatter(X_2d[pos, 0], X_2d[pos, 1], marker=\"x\", s=90, label=\"Консенсусные аномалии\")\n",
    "plt.xlabel(\"PCA 1\")\n",
    "plt.ylabel(\"PCA 2\")\n",
    "plt.title(\"Аномалии в проекции PCA\")\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209f8712",
   "metadata": {},
   "source": [
    "## 12. Частота срабатывания методов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3944ad96",
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>votes</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count\n",
       "votes       \n",
       "0      72212\n",
       "1       8461\n",
       "2       3204\n",
       "3       1489\n",
       "4        787\n",
       "5        579\n",
       "6        113"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "votes_distribution = pred_df[\"votes\"].value_counts().sort_index()\n",
    "display(votes_distribution.to_frame(\"count\"))\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "votes_distribution.plot(kind=\"bar\")\n",
    "plt.title(\"Распределение числа голосов методов\")\n",
    "plt.xlabel(\"Число методов, отметивших объект как аномальный\")\n",
    "plt.ylabel(\"Количество объектов\")\n",
    "plt.grid(axis=\"y\", alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46557426",
   "metadata": {},
   "source": [
    "\n",
    "## 13. Итог\n",
    "\n",
    "Для анализа были выбраны **32 числовые непрерывные переменные**.  \n",
    "Из рассмотрения исключены бинарные и почти дискретные признаки, включая `land`, `wrongfragment`, `loggedin`, `rootshell`, `numoutboundcmds`, `ishostlogin`, `isguestlogin` и `attack`.\n",
    "\n",
    "### Поиск аномалий\n",
    "Аномалии искались **6 методами**:\n",
    "- Isolation Forest\n",
    "- LOF\n",
    "- One-Class SVM\n",
    "- Elliptic Envelope\n",
    "- KNN Distance\n",
    "- PCA Reconstruction Error\n",
    "\n",
    "Практически все методы выделили около **5% наблюдений**:\n",
    "- **4343** объектов у пяти методов\n",
    "- **4342** объектов у One-Class SVM\n",
    "\n",
    "По консенсусному правилу (не менее **3 из 6** методов) аномальными признаны **2968 объектов**.\n",
    "\n",
    "### Наиболее аномальные объекты\n",
    "Наиболее сильные аномалии получили **6 голосов из 6** и максимальный интегральный ранг аномальности.  \n",
    "Для них характерны такие признаки:\n",
    "\n",
    "- **аномально большие `srcbytes` и `dstbytes`**  \n",
    "  (например, `srcbytes = 89 581 520`, `dstbytes = 7 028 652`)\n",
    "- **очень большие `duration`**\n",
    "  (`duration` до 18 677)\n",
    "- **аномально высокие значения `numcompromised` и `numroot`**\n",
    "  (например, `7479` и `7468`)\n",
    "- **повышенные `hot`, `numfailedlogins`, `numfilecreations`**\n",
    "- выраженные отклонения в `dsthostserrorrate`, `dsthostsrvserrorrate`, `dsthostrerrorrate`\n",
    "\n",
    "### Характеристика аномалий\n",
    "Наиболее аномальные объекты можно интерпретировать как:\n",
    "- соединения с **экстремально большим сетевым трафиком**\n",
    "- наблюдения с признаками **компрометации системы**\n",
    "- записи с необычно высокой длительностью соединения\n",
    "- объекты с нетипичными профилями ошибок и активности на хосте\n",
    "\n",
    "### Вывод\n",
    "Результаты разных методов оказались хорошо согласованными, что говорит о **стабильности обнаружения аномалий**.  \n",
    "Наиболее подозрительные объекты выделяются прежде всего по **объёму трафика**, **длительности соединения** и признакам **компрометации / привилегированной активности**.  \n",
    "Именно такие наблюдения следует считать наиболее опасными и приоритетными для дальнейшего анализа сетевой безопасности.\n"
   ]
  }
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