02_Resample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bae17de3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import sklearn.metrics as mt\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80257015",
   "metadata": {},
   "source": [
    "# 1. 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f65b0e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"departure_data.csv\", encoding='euc-kr')\n",
    "df = pd.DataFrame(data)\n",
    "df['최저운고'].fillna(0, inplace=True)\n",
    "df['강수량(mm)'].fillna(0, inplace=True)\n",
    "df['현황'].fillna('출발', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c0c6281",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: 사유, dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "# 취소 데이터 확인하기\n",
    "df.loc[(df['현황'] == '취소') & (df['사유'] == '태풍'), '현황'] = '기상 취소'\n",
    "cancel_rows = df[df['현황'] == '취소']\n",
    "reason_counts = cancel_rows['사유'].value_counts()\n",
    "print(reason_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ee3fabb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: 사유, dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "# 지연 데이터 확인하기\n",
    "delay_rows = df[df['현황'] == '지연']\n",
    "reason_counts = cancel_rows['사유'].value_counts()\n",
    "print(reason_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d2634e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 기상 취소, 기상 지연의 이유가 아닌 행들을 삭제하는 코드\n",
    "# 취소에 사유가 없는 열 삭제\n",
    "df = df[(df['현황'] != '취소')]\n",
    "# 지연에 사유가 없는 열 삭제\n",
    "df = df[(df['현황'] != '지연') | (df['사유'].notna())]\n",
    "# 회항, 기상회항 삭제\n",
    "df = df[(df['현황'] != '회항')]\n",
    "df = df[(df['현황'] != '기상 회항')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dc58921",
   "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>날짜</th>\n",
       "      <th>항공사</th>\n",
       "      <th>편명</th>\n",
       "      <th>출발공항코드</th>\n",
       "      <th>출발공항명</th>\n",
       "      <th>도착공항코드</th>\n",
       "      <th>도착공항명</th>\n",
       "      <th>계획시간</th>\n",
       "      <th>예상시간</th>\n",
       "      <th>출발시간</th>\n",
       "      <th>시간</th>\n",
       "      <th>온도</th>\n",
       "      <th>시정</th>\n",
       "      <th>전운량</th>\n",
       "      <th>최저운고</th>\n",
       "      <th>풍속</th>\n",
       "      <th>강수량(mm)</th>\n",
       "      <th>구분</th>\n",
       "      <th>현황</th>\n",
       "      <th>사유</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>아시아나항공</td>\n",
       "      <td>OZ286</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>SFO</td>\n",
       "      <td>샌프란시스코</td>\n",
       "      <td>00:05</td>\n",
       "      <td>23:40</td>\n",
       "      <td>00:07</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>화물</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>에미레이트항공</td>\n",
       "      <td>EK323</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>DXB</td>\n",
       "      <td>두바이</td>\n",
       "      <td>00:10</td>\n",
       "      <td>23:50</td>\n",
       "      <td>00:40</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>아시아나항공</td>\n",
       "      <td>OZ2041</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>LAX</td>\n",
       "      <td>로스앤젤레스</td>\n",
       "      <td>00:10</td>\n",
       "      <td>23:50</td>\n",
       "      <td>00:28</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>싱가폴항공</td>\n",
       "      <td>SQ007</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>SIN</td>\n",
       "      <td>싱가포르</td>\n",
       "      <td>00:25</td>\n",
       "      <td>00:36</td>\n",
       "      <td>00:35</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>터키항공</td>\n",
       "      <td>TK91</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>IST</td>\n",
       "      <td>아타튀르크</td>\n",
       "      <td>00:40</td>\n",
       "      <td>00:40</td>\n",
       "      <td>01:01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0.2</td>\n",
       "      <td>450</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585021</th>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>아시아나항공</td>\n",
       "      <td>OZ286</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>SFO</td>\n",
       "      <td>샌프란시스코</td>\n",
       "      <td>23:30</td>\n",
       "      <td>23:30</td>\n",
       "      <td>23:42</td>\n",
       "      <td>23:00</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>화물</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585022</th>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>대한항공</td>\n",
       "      <td>KE319</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>CAN</td>\n",
       "      <td>광저우</td>\n",
       "      <td>23:35</td>\n",
       "      <td>23:35</td>\n",
       "      <td>23:33</td>\n",
       "      <td>23:00</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>화물</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585023</th>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>필리핀에어아시아</td>\n",
       "      <td>Z27047</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>CEB</td>\n",
       "      <td>세부</td>\n",
       "      <td>23:50</td>\n",
       "      <td>23:50</td>\n",
       "      <td>23:54</td>\n",
       "      <td>23:00</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585024</th>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>산동항공</td>\n",
       "      <td>SC4722</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TAO</td>\n",
       "      <td>청도</td>\n",
       "      <td>23:50</td>\n",
       "      <td>23:50</td>\n",
       "      <td>23:56</td>\n",
       "      <td>23:00</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585025</th>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>에미레이트항공</td>\n",
       "      <td>EK323</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>DXB</td>\n",
       "      <td>두바이</td>\n",
       "      <td>23:50</td>\n",
       "      <td>23:50</td>\n",
       "      <td>23:51</td>\n",
       "      <td>23:00</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>538124 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         날짜       항공사      편명 출발공항코드 출발공항명 도착공항코드   도착공항명  \\\n",
       "0       2017-01-01 00:00:00    아시아나항공   OZ286    ICN    인천    SFO  샌프란시스코   \n",
       "1       2017-01-01 00:00:00   에미레이트항공   EK323    ICN    인천    DXB     두바이   \n",
       "2       2017-01-01 00:00:00    아시아나항공  OZ2041    ICN    인천    LAX  로스앤젤레스   \n",
       "3       2017-01-01 00:00:00     싱가폴항공   SQ007    ICN    인천    SIN    싱가포르   \n",
       "4       2017-01-01 00:00:00      터키항공    TK91    ICN    인천    IST   아타튀르크   \n",
       "...                     ...       ...     ...    ...   ...    ...     ...   \n",
       "585021  2019-12-31 00:00:00    아시아나항공   OZ286    ICN    인천    SFO  샌프란시스코   \n",
       "585022  2019-12-31 00:00:00      대한항공   KE319    ICN    인천    CAN     광저우   \n",
       "585023  2019-12-31 00:00:00  필리핀에어아시아  Z27047    ICN    인천    CEB      세부   \n",
       "585024  2019-12-31 00:00:00      산동항공  SC4722    ICN    인천    TAO      청도   \n",
       "585025  2019-12-31 00:00:00   에미레이트항공   EK323    ICN    인천    DXB     두바이   \n",
       "\n",
       "         계획시간   예상시간   출발시간     시간   온도    시정  전운량  최저운고  풍속  강수량(mm)  구분  현황  \\\n",
       "0       00:05  23:40  00:07  00:00  0.2   450    2  30.0   2      0.0  화물  출발   \n",
       "1       00:10  23:50  00:40  00:00  0.2   450    2  30.0   2      0.0  여객  출발   \n",
       "2       00:10  23:50  00:28  00:00  0.2   450    2  30.0   2      0.0  여객  출발   \n",
       "3       00:25  00:36  00:35  00:00  0.2   450    2  30.0   2      0.0  여객  출발   \n",
       "4       00:40  00:40  01:01  00:00  0.2   450    2  30.0   2      0.0  여객  출발   \n",
       "...       ...    ...    ...    ...  ...   ...  ...   ...  ..      ...  ..  ..   \n",
       "585021  23:30  23:30  23:42  23:00 -4.1  1000    8  30.0   3      0.0  화물  출발   \n",
       "585022  23:35  23:35  23:33  23:00 -4.1  1000    8  30.0   3      0.0  화물  출발   \n",
       "585023  23:50  23:50  23:54  23:00 -4.1  1000    8  30.0   3      0.0  여객  출발   \n",
       "585024  23:50  23:50  23:56  23:00 -4.1  1000    8  30.0   3      0.0  여객  출발   \n",
       "585025  23:50  23:50  23:51  23:00 -4.1  1000    8  30.0   3      0.0  여객  출발   \n",
       "\n",
       "         사유  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  \n",
       "...     ...  \n",
       "585021  NaN  \n",
       "585022  NaN  \n",
       "585023  NaN  \n",
       "585024  NaN  \n",
       "585025  NaN  \n",
       "\n",
       "[538124 rows x 20 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0335475a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "현황 현황\n",
      " 출발       532733\n",
      "기상 지연      4918\n",
      "기상 취소       473\n",
      "Name: 현황, dtype: int64\n",
      "\n",
      "사유 현황\n",
      " 제방빙작업    2522\n",
      "태풍        797\n",
      "시정        796\n",
      "강수(눈)     422\n",
      "뇌전        304\n",
      "바람        216\n",
      "기상-항로     179\n",
      "기상-기타     102\n",
      "운고         33\n",
      "낙뢰         14\n",
      "황사          5\n",
      "기상          1\n",
      "Name: 사유, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 데이터 값 확인하기\n",
    "print(\"현황 현황\\n\",df['현황'].value_counts())\n",
    "print()\n",
    "print(\"사유 현황\\n\", df[\"사유\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77f26d6",
   "metadata": {},
   "source": [
    "### 증폭 전 데이터 분포 시각화 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "21673057",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_9256\\663433293.py:8: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  plt.savefig('/Users/user/Documents/DataMining/termproject.png')\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_9256\\663433293.py:8: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  plt.savefig('/Users/user/Documents/DataMining/termproject.png')\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 사용할 칼럼들을 추출 (기상 요건들과 운송 현황)\n",
    "selected = ['온도','시정', '전운량', '최저운고','풍속', '강수량(mm)', '현황']\n",
    "sdf = df[selected]\n",
    "\n",
    "sns.scatterplot(data=sdf, x=sdf.iloc[:, 0], y=sdf.iloc[:, 1], hue='현황')\n",
    "plt.title(\"증폭 전 데이터 분포\")\n",
    "plt.savefig('/Users/user/Documents/DataMining/termproject.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993af5d1",
   "metadata": {},
   "source": [
    "# 2. resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a355f436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원본 데이터셋의 각 카테고리별 개수:\n",
      "출발       532733\n",
      "기상 지연      4918\n",
      "기상 취소       473\n",
      "Name: 현황, dtype: int64\n",
      "\n",
      "새로운 데이터셋의 각 카테고리별 개수:\n",
      "출발       532733\n",
      "기상 지연    532733\n",
      "기상 취소    532733\n",
      "Name: 현황, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 각 카테고리별 데이터 개수 출력\n",
    "print(\"원본 데이터셋의 각 카테고리별 개수:\")\n",
    "print(df['현황'].value_counts())\n",
    "\n",
    "# 출발 이외의 데이터를 추출\n",
    "non_departure_data = df[df['현황'] != '출발']\n",
    "\n",
    "# 출발 데이터만 따로 추출\n",
    "departure_data = df[df['현황'] == '출발']\n",
    "\n",
    "# 각 카테고리별 데이터 개수에 맞게 샘플링\n",
    "from sklearn.utils import resample\n",
    "resampled_data = pd.concat([\n",
    "    resample(df[df['현황'] == category], replace=True, n_samples=df['현황'].value_counts().max(), random_state=42)\n",
    "    for category in df['현황'].unique()\n",
    "])\n",
    "\n",
    "# 새로운 데이터셋의 각 카테고리별 개수 출력\n",
    "print(\"\\n새로운 데이터셋의 각 카테고리별 개수:\")\n",
    "print(resampled_data['현황'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89aaeea6",
   "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>날짜</th>\n",
       "      <th>항공사</th>\n",
       "      <th>편명</th>\n",
       "      <th>출발공항코드</th>\n",
       "      <th>출발공항명</th>\n",
       "      <th>도착공항코드</th>\n",
       "      <th>도착공항명</th>\n",
       "      <th>계획시간</th>\n",
       "      <th>예상시간</th>\n",
       "      <th>출발시간</th>\n",
       "      <th>시간</th>\n",
       "      <th>온도</th>\n",
       "      <th>시정</th>\n",
       "      <th>전운량</th>\n",
       "      <th>최저운고</th>\n",
       "      <th>풍속</th>\n",
       "      <th>강수량(mm)</th>\n",
       "      <th>구분</th>\n",
       "      <th>현황</th>\n",
       "      <th>사유</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>135407</th>\n",
       "      <td>2017-09-23 00:00:00</td>\n",
       "      <td>진에어</td>\n",
       "      <td>LJ001</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>BKK</td>\n",
       "      <td>수안나폼(방콕)</td>\n",
       "      <td>19:55</td>\n",
       "      <td>19:55</td>\n",
       "      <td>20:33</td>\n",
       "      <td>19:00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146105</th>\n",
       "      <td>2017-10-13 00:00:00</td>\n",
       "      <td>대한항공</td>\n",
       "      <td>KE847</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TNA</td>\n",
       "      <td>지난</td>\n",
       "      <td>13:10</td>\n",
       "      <td>13:10</td>\n",
       "      <td>13:35</td>\n",
       "      <td>13:00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>180.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406101</th>\n",
       "      <td>2019-02-01 00:00:00</td>\n",
       "      <td>에어인디아</td>\n",
       "      <td>AI317</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>BOM</td>\n",
       "      <td>뭄바이</td>\n",
       "      <td>13:50</td>\n",
       "      <td>13:50</td>\n",
       "      <td>14:14</td>\n",
       "      <td>13:00</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288823</th>\n",
       "      <td>2018-07-10 00:00:00</td>\n",
       "      <td>에어아스타나항공</td>\n",
       "      <td>KC210</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TSE</td>\n",
       "      <td>누르술탄 나자르바예프</td>\n",
       "      <td>12:55</td>\n",
       "      <td>12:55</td>\n",
       "      <td>13:37</td>\n",
       "      <td>12:00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122682</th>\n",
       "      <td>2017-08-24 00:00:00</td>\n",
       "      <td>산동항공</td>\n",
       "      <td>SC4988</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TNA</td>\n",
       "      <td>지난</td>\n",
       "      <td>22:40</td>\n",
       "      <td>22:40</td>\n",
       "      <td>23:24</td>\n",
       "      <td>22:00</td>\n",
       "      <td>24.2</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524907</th>\n",
       "      <td>2019-09-04 00:00:00</td>\n",
       "      <td>중국동방항공</td>\n",
       "      <td>MU218</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>YNZ</td>\n",
       "      <td>옌청</td>\n",
       "      <td>12:15</td>\n",
       "      <td>14:15</td>\n",
       "      <td>:</td>\n",
       "      <td>12:00</td>\n",
       "      <td>21.7</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>뇌전</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126404</th>\n",
       "      <td>2017-09-11 00:00:00</td>\n",
       "      <td>대한항공</td>\n",
       "      <td>KE035</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>ATL</td>\n",
       "      <td>애틀랜타</td>\n",
       "      <td>09:20</td>\n",
       "      <td>09:20</td>\n",
       "      <td>:</td>\n",
       "      <td>09:00</td>\n",
       "      <td>19.8</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>태풍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515131</th>\n",
       "      <td>2019-08-09 00:00:00</td>\n",
       "      <td>아시아나항공</td>\n",
       "      <td>OZ367</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>PVG</td>\n",
       "      <td>푸동</td>\n",
       "      <td>20:00</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>20:00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>태풍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109664</th>\n",
       "      <td>2017-08-23 00:00:00</td>\n",
       "      <td>캐세이퍼시픽항공</td>\n",
       "      <td>CX415</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>HKG</td>\n",
       "      <td>홍콩</td>\n",
       "      <td>08:40</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>08:00</td>\n",
       "      <td>25.9</td>\n",
       "      <td>500</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>태풍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295846</th>\n",
       "      <td>2018-07-04 00:00:00</td>\n",
       "      <td>제주항공</td>\n",
       "      <td>7C3106</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>GUM</td>\n",
       "      <td>괌</td>\n",
       "      <td>20:20</td>\n",
       "      <td>20:20</td>\n",
       "      <td>:</td>\n",
       "      <td>20:00</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>바람</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1598199 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         날짜       항공사      편명 출발공항코드 출발공항명 도착공항코드  \\\n",
       "135407  2017-09-23 00:00:00       진에어   LJ001    ICN    인천    BKK   \n",
       "146105  2017-10-13 00:00:00      대한항공   KE847    ICN    인천    TNA   \n",
       "406101  2019-02-01 00:00:00     에어인디아   AI317    ICN    인천    BOM   \n",
       "288823  2018-07-10 00:00:00  에어아스타나항공   KC210    ICN    인천    TSE   \n",
       "122682  2017-08-24 00:00:00      산동항공  SC4988    ICN    인천    TNA   \n",
       "...                     ...       ...     ...    ...   ...    ...   \n",
       "524907  2019-09-04 00:00:00    중국동방항공   MU218    ICN    인천    YNZ   \n",
       "126404  2017-09-11 00:00:00      대한항공   KE035    ICN    인천    ATL   \n",
       "515131  2019-08-09 00:00:00    아시아나항공   OZ367    ICN    인천    PVG   \n",
       "109664  2017-08-23 00:00:00  캐세이퍼시픽항공   CX415    ICN    인천    HKG   \n",
       "295846  2018-07-04 00:00:00      제주항공  7C3106    ICN    인천    GUM   \n",
       "\n",
       "              도착공항명   계획시간   예상시간   출발시간     시간    온도    시정  전운량   최저운고  풍속  \\\n",
       "135407     수안나폼(방콕)  19:55  19:55  20:33  19:00  21.0  1000    1    7.0   6   \n",
       "146105           지난  13:10  13:10  13:35  13:00  17.0  1000    5  180.0   9   \n",
       "406101          뭄바이  13:50  13:50  14:14  13:00   2.2  1000    4   45.0   8   \n",
       "288823  누르술탄 나자르바예프  12:55  12:55  13:37  12:00  26.0  1000    8   40.0  10   \n",
       "122682           지난  22:40  22:40  23:24  22:00  24.2  1000    1  180.0   1   \n",
       "...             ...    ...    ...    ...    ...   ...   ...  ...    ...  ..   \n",
       "524907           옌청  12:15  14:15      :  12:00  21.7  1000    8   10.0   8   \n",
       "126404         애틀랜타  09:20  09:20      :  09:00  19.8  1000    8   30.0  17   \n",
       "515131           푸동  20:00      :      :  20:00  27.0  1000    1  200.0   3   \n",
       "109664           홍콩  08:40      :      :  08:00  25.9   500    8    5.0   6   \n",
       "295846            괌  20:20  20:20      :  20:00  22.1  1000    6  100.0   6   \n",
       "\n",
       "        강수량(mm)  구분     현황   사유  \n",
       "135407      0.0  여객     출발  NaN  \n",
       "146105      0.0  여객     출발  NaN  \n",
       "406101      0.0  여객     출발  NaN  \n",
       "288823      0.0  여객     출발  NaN  \n",
       "122682      0.0  여객     출발  NaN  \n",
       "...         ...  ..    ...  ...  \n",
       "524907     16.0  여객  기상 취소   뇌전  \n",
       "126404      0.0  여객  기상 취소   태풍  \n",
       "515131      0.0  여객  기상 취소   태풍  \n",
       "109664      0.0  여객  기상 취소   태풍  \n",
       "295846      0.0  여객  기상 취소   바람  \n",
       "\n",
       "[1598199 rows x 20 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64c55fb",
   "metadata": {},
   "source": [
    "### 증폭 후 데이터 분포 시각화 하기 (resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a7f392f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_9256\\2829732093.py:7: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  plt.savefig('/Users/user/Documents/DataMining/termproject.png')\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_9256\\2829732093.py:7: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  plt.savefig('/Users/user/Documents/DataMining/termproject.png')\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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kSdx1110cc8wxLF26lDFjxnDssccyffp07rnnHgYMGEBlZSUff/wx06ZNY+rUqTz33HOEw2EOPfRQZFnmjTfeYNSoUZhMJgYPHsyOHTt46qmnmDRpEosWLeL111//XX02m80cffTR3HvvvaSmpjJ79mzef/99vvrqq3aPufnmmxk3bhzp6emcccYZWK1WVq9ezXfffccbb7xxUPXKsnzAL+wqioKqqu1+1Xjvt14OxL/yReN9kSSJM844gzPOOOPfOv6/RVFR0UH/sxkHwuFw/Eei9V+J3flPycjI4NRTT/3Nr1t/8sknvxn79fTTT7dpP5jYL0HHRQgfgeA3mD59OqNHj+a6665jyZIlvP322y2v6DY2NlJYWMiMGTMYOXIkoH8597rrruOmm27C7XYzZcoUPvroIwCOOOII7r//fu6//35uuOEGJk+ezIsvvsiwYcN+N39Hjx7N9OnTOe+886iurmbAgAHMnTv3gF+eHj58OF9//TV///vfeeGFF3C73QwYMIAbbrjhoOudNWsWY8eO5eyzz+b888+nR48eOBwOGhoaWLZsGffeey+DBg0SX9jtgITDYerq6gB9W/Wpp56ioaEhIY/NZkuI/zr66KN/cwWwPfaPjxMI9kXS/ptRhQKB4P+U3/sfH/1X2b59Ow8//DALFy5MCm6eMWMGl19++X/0zZXZs2dz9913/0sxPv8KkiTx008/HdQ/qvp7bHX9FrNmzaK5uZnXXnut5evTzz333L9V1sKFC5k6dWqbwcIHy4QJE5g6dSo33njjQR9z5pln8tZbb/1mvgsvvPDfbptA8K8gVnwEAsHvRvfu3f+rk9exxx7L2LFj/2vlT5s27aC/fTN06NADvub/e7DvW0uDBw8+qA8ttkd6evp/HGczevTopNfmf4vZs2cze/bs/6hegeD3RKz4CAT/Q/zRKz4CgUDwZ0f8W10CgUAgEAg6DGLFRyAQCAQCQYdBrPgIBAKBQCDoMAjhIxAIBAKBoMMg3uraD1VVqaiowOVyIUnSH+2OQCAQCASCg0DTNPx+P3l5eQf8QKYQPvtRUVFBQUHBH+2GQCAQCASCf4PS0lLy8/PbTRfCZz9cLhegd5zb7f6DvREIBAKBQHAw+Hw+CgoKWubx9hDCZz/2bm+53W4hfAQCgUAg+P+M3wpTEcHNAoFAIBAIOgxC+AgEAoFAIOgwCOEjEAgEAoGgwyCEj0AgEAgEgg6DED4CgUAgEAg6DEL4CAQCgUAg6DAI4SMQCAQCgaDDIISPQCAQCASCDoMQPgKBQCAQCDoMf+iXmzVN48033+SZZ57h559/brGvWrWKiy++mMrKShwOB48//jhHHnlkS/rjjz/OU089RSgUYvjw4bz00ktkZGQAUF9fz0UXXcSyZcuQJInLLruMa6+99v+8bftT2hAkpqjYTAZyU2zU+sIYZfBHFOKq1mIHqPaGSbMbqPBFcZkNNIXjSECuy4g5WI2kxtBMdjDYkML1IEnE3YU0heI0RxU0DZwWA3EF4qpKntNIebgKTVNJtaTgxIpqNCKpqm5Hw2NOxSZZMBNDCtSCpqBZPUhxBYiDEgdZBtmE12SlKeZFxkCBI4+yYCWKFsdpcpCuAmE/uPNAkwlqUWoj9QBkW7JpiNZjM9rxx/wAOE1O/DEfRslIJ2s2+EtBMoLJChEfuDsTVhVqIjVoaKSaPbglGxXRemJaDJtswWKw0RRrQkamwJEHvnKwp0G4CYypVGk+ImoUk2wiz5iGVwvRFPMhSzIF9jzwlwMSXouTZiWIJEkoqoJBMpBnTINAOZgdgAzWVErDVWiahsVgIapEyTSnYVXCur+aAlYPxKJgy9jjZxSbbCVLtYMUAiSIB1HsuZRHqtE0DbfFTVyNE1NimAwmwvEwKqre3mAjWN1gcOq+aBpYsqmWmgkrEcyymVxjKl4tREiNEFEiWA1WMsxpVISqkCUZk8FEKB7CIBmwGWykyw4I+KizQkAJtvZ/oEI/15ZUMDla/TfY9vgvUS0FCCthzLKRXGM6RHzUG2WalQBGyYjL7CIcD2GWLHjjXiyyBUmSCCthLAYLmqrhMNlwB+pANqLZs5EClXva5YFYM0gSaIDRCkoE1BhY3BANoDg7URGqRN1nPEuROqR4BMx2iEcBTT82GtDrMNnQsCGrQVBCYLCCEgZNBbOTekmmWWnGIBnJt2YTVMPURhr0cWvOpiHWQEyLYjVYyFadEG8ELab3U8wPSgzN4kFSYqDFwWSDaICgLZ3aaAMmyYTJYCIYD+IyuggqQb0fLA78UT8G2UCmJZPqsD4e0sxphNQQoXgQk2QmzZRGdbQagExLGvZgA6ARtKVTF21EQyPdkk5zrJmIGsFsMGPDRooShXgAjBYUSzqV0VoUTcFmsCFJEmkGN4aYF4wWCIaotxhpVgK4TC4iaoSoEsVisGCSTATiASxGC6qmElWiuEwuokoUJJCQiCgRDLIBu2QnVTVSIwcJKWGsBisamn6M0UVYDaNoCrIkY5SMRNQIEhIm2URMiyEjY5SNGFQDmqRhNBgJxAPE1TgWgwWbZCOoBDHIBmJaDFVVcZlcLfcUs8GMoinYjXa8US8Ogw3DnrKjShRJdxib0UYgFgANPGYPvpgPFRWTZEJTNYxGI+F4GKNkxG6y44v6WvrZG/Ni33Mf0zQNt8lNRIno/spGQvEQEhJuoxOPZKNBDRJUgi1+xeIx0ixpNMWbiKpRDJIBA4aWa99kMBFWwhgkA3aDnWBcP9ZmtJFmSaMiWIGqqdgMNlRNxWwwo6K21GuSTaiaSkyNYZSMpJvTaYg1oKGhaApWg5W4FiemxjBJJuJqnHRLKvbmajCYCVvT8KtBgvEgBslArjWbmmhdy3iwy3ZCSpCwGsYkm7EZbfiiXlJMHlyaiYqIjIaEpmo4LQZ84ThIUJBipcIbIaZquCxGstzW/5sJeD8kTdO0P6LiefPmcd111xEMBjGZTGzevBkAv99Pnz59eO2115g8eTKLFi3i2GOPZfPmzeTk5PD+++9z//33M3/+fDweD7NmzaK6upo5c+YAcPTRRzNy5Ehuu+02KisrGTNmDE8++STHHHPMQfnl8/nweDx4vd7f5Z+sKG0IsKnSz3M/7KSiKUTfXDezJnUn221mQ0WrvU+ui8sm9SDTZUbToKIpRI7HxjsrSvhyXSU//K0IqXoD0tLHwWhDm3AjrPsQadvXYHaiDT0Xek6lPGji1NlbOaJfNpeNySVoCvBtybd8su0TwkqYw/IP46w+ZyHJEt8WJ9pP73M6XW2d4JVJEKxHKxyLNP5KqN4EPz1J8KzPqYrW8+bGN/m56mcePvRhtjRu4e1Nb9MUaWJo9lD+2v+vZFrTSJUcFEdrmLN9Dt/s/gZJkjihxwlM6TyFb0u+ZUfjDqZ1m8acrXNYXbuadGs6Z/Q9g0OyDiG/ZiuUGagd0pcw4QT/D80/lDP6nMGysmXEtTij80cze+Nsfqz4EbvRzkk9T2JCwQQKmo1UOjQqgpU8v+55djbtZFqXaczoMYM3N76ZkH9iwUTiSpzS5lJsRhsvrHuBnU076eTsxF8H/JVuniI6NZZSlpLPiuqVvL35bRrDjRySfQgz+8+k3FdOz7SeFBrS4flhUDiG8il3sLVxG6+sf4XKQCU9U3tywcALyLZmkhsxU2oJM79kPh9v/5hwPMx1w67DZrShobGxfiNf7PqCcLz1vHQyZmFe/TKsepPKsz+hMlTNC2tfYHvT9hY/u7q78sSqJ9hcv5nrh1/PLt8uwvEw4/PH88r6V1hds5o0axpn9DmDwZmDCSthXlz3YoJ9aNYh5K9+j/Ihp7KtaQevrH+FiuYKeqT04NZRt1IVrGqpN8+Zx98G/I3O7s48uerJlnLO7HMmQ7KGMHvjbDIdmeQ6cnlvy3st5Vww8AIMkgGn2Um+KRvT2leRfnkdYiE44g4wOWDJY9BUAtn94dBroWYL5E+hzB5lfvF3zNk+R29bp3Gc0edM8k3ZmCt/hl2LIB6Bogmw4iWo2QAphWjjrobUrkjlv0JmT6hYBb++TsMZH1EbreeldS+zqmZVQv9c/t3lXDviWhRNSeiHCwZeQLY9m7zXZ8DQcyCtO3x9M9qwvyIVDAeDGVJ6UhKv45PtnxCMBxmdO5q52+dy/qDz+anyJ+Zsm0MoHmJcp3FM6TKFdza9w8SCiQzJHkJTuAlFU3hh7Qtsa9pGnjOP8/qfRzdPNy6YfwFHdT6Kk3qdRFyN88n2T/hq91cATOs6jRG5I3ho+UOk29K5YOAFZNoycchWAlqY70u+Z862OQTjQcZ3Gs8Zfc7ALJkxGAxYNCN1sUZeXf8qp/c5nd3e3czeNJtxncZxROcjeG3Da4zMHUlMifHRto9oCDcwNGso5/Y/l1A8xOO/Pk5FcwXdUrpxzbBrCMaCvLD2BbY2biXXkct5/c8jzZZGKBbizY1vUh2s5rrh17GyaiVzd80F4PCCwxmfP57y5nLcZjfp1nQybZns9O3k5XUvU95cTveU7nr/27JZ37CeH0p/4G8D/saXu77ki11fJJTjjXjpmdKTRWWLGJw9mOfXPM+Wxi3kOnL524C/EVEiPLLyES4efDESEm9vepv6cD1n9jmTwVmDeWHtC9QEa7hu+HX8Uv0Lc3fORdM0pnaZynE9jmN55XJmb5pNMB5kXN44zup7FoqmsKhsEZ/t+IxgPMhhnQ7jnP7nsKpmFW9tfIv6cD2DMgdx/sDzQYX7VtxHeXM5RSlFnN33bHIdufgjfh7/9XHKmsta7B6zh7k753JGnzP4oewHPtz6YUu9Z/Q5A4NsIKpEuW3JbfTL6MeULlN4c+ObbG7YTI4jh3P7nUu+K59X173K8NzhaJrGh1s/bPHnpJ4nUeYvY1TeKMyymbpQHXO2zWFx+WKsBisn9DiBbindeHDFgxzf43iGZg/lxbUvtpR/Xv/z6O7pzqsbXuWsvmeRY05HksxMfGwphxSm8peRBaTZzWyt9vPykl3U+aMcUpjCpZO6k+E0k+22/cdzLRz8/P2HCZ8PP/wQi8WCy+XioosuahE+L7zwAl999RUff/xxS94ZM2YwadIkrrjiCsaMGcMNN9zAjBkzAKirqyMvL4+qqirq6uoYO3YslZWVGI36YtZjjz3GokWLEso7EL+n8KloCvH28mKe+n5Hgl2S4Km/DOGZhTvYUOFLsC+8ZgL3f72Z66f05qyXl1HWGGLnjYOQfn0NadFDYLLDqbPhw5kQ9iaUq3UeCzOeouuDmwBYdPMAblh8A+vr1ifk+3TGp/x9yd+T7KmWVF6b+hpFFWF4f7RuNFrh3C/BlM92s5+zvzobf8zPM4c/w8fbP+bb4m8TyjDLZl6b+hoei4fzvzmfikBFS9o7097hnmX3UB+q54YRN3DtD9cSV+MJx8/oNoOLBl4EaKhobfqfYknh9amvo2pqiz/7MjRrKPeMv4fVNau5cfGNgP5E98qUV5j59Ux8UV9S/ptH3sy2pm0t+fflykOu5OguR/PIL4/wdfHXSe19ecrLvLzuZa4bfh2Fjw6g6ppNfLzjU55Z80xCXgmJRyY8wuCMwVy58ErW1q0F9Akrx5FDgauAOdvmtNj34rF4eGPqGxTVqUQKcvm+YhHXL7o+yc8rDrmCQmch/pifOdvmoKFxzbBrOP+b84mpsYS804umc2TnI7l8weVJ9ksHX8rcHXN5es3TLfZXj3yV2nBtm/VePuRyTLKJR355JKGck3qexJLyJby47sWkfnj4sIcp9ZUysXAiRV4DvDoQ+p8IqV1h8cOJFUgSnPQaJQWHcPOPN7Omdk1Cstvs5o2j3qCb1wjfzIRDzoG5V+grSPsy4WZdEC17DjbMgRuqWO3fwnlfn5fUP9O6TuPCgRfyTfE3PLX6qST/Hzz0QSbkjsd6fx4UjIAxl+urS1YPrP+IkiNu5cL5F5FqSeXEHidyx093MPvo2Ty44sE2/X/osIe4/PvLuW7odaTYUrjuh+vQSPT/0sGXcmTnI5nx6QzmHDuHyxdcTpm/LCFPvjOf64dfz+ULLm/xc1DmIK5fdD2ra1cn1fva1NcIRAMYZAPnzjuXhw57iG+Lv2Xuzrn0TevLjSNv5G9f/42z+51NeXM5X+36KqEMk2zi4cMe5t5l91IdrOaYrscwoXAC1/5wbYL//dL7cW6/c7l+0fVoaDwx8QkeXvkwpf7SZP9HXM/cHXM5sceJbGzYyBO/PsH+PHjog0RiEfpm9OWqhVdR4i9JSO/k7MQNI25g3q55nNnnTM748oyk/vxr/7/SzdONJRVLWkRT3/S+nNbrNG5fejsaGo9PfJxHVz7abvmXf996/fxj9D/4YucXrKhe0WJ7fMLjLChdwKc7Pk043igbefGIF3n818cTxsOto26lu6c7N/54I5WBygT76NzR3LrkVn6p+SWhLJfJxWtHvYYv6sMX9uGP+bl1ya1J7T2v/3l0T+nO0oqlzN05N8mfhw97GKfRCRJc+8O1NEWaEvL0z+jPzH4zCcVDbZZ/4cALOaLzEcycN1O/HlUXXR9aCcDFhxXRGIzx7orE820ySMz+20hGdk3n9+Bg5+8/LMbnpJNOanMV5qeffmLs2LEJthEjRrB69Wri8TgrV65MSM/IyKBz586sW7eOn376iZEjR7aInn2P/SMIROI8u3Bnkl3T4O4vNnH26C4J9sJUKw3BKMcOzOXT1eWUNYYAkGIBpB8f1TMNOBl+fT1J9ABIxUugfgcbbp3A5n8czuaGzUmiYVLBJLY3bU+yAzRGGnlj4xs0dclpNcbD8M3N1Dolnlv7HP6YH7fZjcvsShI9AFE1yqqaVXy568sE0TMgYwDlzeWsr1vPqb1O5fk1zyeJHoBPd3xKc6wZi8HK1satbfrZFGni9Q2vE4gFkkQPwC81v1DsLeaFNS+02K4ceiUvrXspSfTszV8bquXFtS8mpQE8vfppgkowSfTsbe/DKx/m5F4ns6B0AdxST1AJ88LaF5Lyamjct+w+6iP1CeLmqK5H8dWur5AkKUn0AHgjXl7d8Cr1ealUhmu5f/n9bfr5zOpn6JHWA1mSWVu3lsuGXMaDyx9MmtQB5u6cS6Y9E6vRmmT3R/18sfOLBHuGPaPdep9d8yyj80YnlWMz2Hhv83tt98Py+5hQOIGX171MQ4ZDTxh4Cix5PLkCTYOiKWxv2p4kGgB8UR+vrHtFL2fE+fD9XcmiB2DxQ2B26qIHKIvVcf/y+9vsny92fUFICfHy+pfb9P/+5fdTHanTDaXL9a3dH+4HRybx6U/zbfF8yvxlnNrrVJ5c9SS9UntRF6pr1/9Pt3/KlC5T6JfZj/uW3Zc0qQA8v/Z5AE7ofgKLyxYniR6AsuYyNtZvZHDm4BY/vVFvkujZW+/L617GbXFz//L7kWWZTFtmy6R42ZDLeGjFQ6ioDMoclCR6AGJqjOfXPs8pvU4B4PS+p3Pf8mT//9L7Lzyw4gE0NAZkDGBLw5Yk0bPX/w11G6gN1eKxeHhp7UtJeQDuX34/YzqNYWnF0iRRArTca6oCVTSEGzDJpqQ8b216i64pXVtEz14/n1z1JBoa/TP6s61x2wHLPyTrEADSrekYZWOC6LEarWQ7spNED0BcjfPgige5dPClCfanVj1FU7SppT9bfN34FiX+kiTRA+CP+Xlp7UvIyOQ6c/nnqn+2OX5e3/A6PVN7Jomevf48v+Z5XBYXn+/4PEn0AKyvW4+Gxnub32uz/JfWvYRJNuGP+Xlu7XPUW2W23zERoywxrEtakugBiCkad3y2kZL6QFLaf5M/XXBzRUUF2dnZCbasrCzq6+upra1FUZSWeJ790w90bHtEIhF8Pl/Cz+/F9ppmFLXtBbVKbxi3LTHE6pJJPViwpYZuWS6+WlcFwNqbx0PtFlAVPVOXsbA1eQJuYcMcrFYryCpf7voyKfnknie3eQPby/cl39MU209UlfxMIB7kh9IfAJhYMJGlFUvbLaOzu3OSKJpWNI15u+YBUOguZFPDpnaPX1a1DIfJ0ab/LX6Wfo/dZG83/YtdX9DF06Xl795pvVlYtrD9/Du/oLO7c5tpMTVGqb8Us2xuM31N7RoybBl8vftraoON7PbtJq4lizqA2lAt9aHW8eg0OWmMNDIwcyBLy9vv0+9Lvscf8+OL+mgIN7TrZ2VzJcsqlwH6E/3Gho3tlrmschljc8cm2X+q/IlbR92aYPNGvQestzpQjcfsSbD/XPkzI/NGtnlMXaiOQCzAwrKF+OLNuiAJNkAbYhhANZkOOG4XlC7QyzE5IFDbdiYlBo279FVTIKxE2FC/od0yl1Uuo9BV2GZafbg+UUSHmiB3MOz6gapIDd8UfwOA3WSnPlzPtKJpfL27/et2UdkihmUPI6bGqA+3fb+Kq3FK/aUcXXR0S/ntlpUzrMXPhlDb5w1ouSbW1q1lfKfxLK9a3pKWZktjXd06ijxFB+ynjfUb6erp2uJjXaguKY/dZG+xD88ZzuLyxe2W90PZDwzLHsZPlT9R6G67/xvCDQTjwYPqh9W1q+me0j0pvcBVwC/ViULCYXJQG9LHz/Ds4SwuO4CfpT8wPGc4AIOyBvFz5c8J6WNyx7CiakVbhwKwqWETLrMrwdYYaUTRlKR7URdPl98c/zajjVA8RE2wps08iqaw27cbl8nVZvqmhk1ISCwqW9RuPfN2zaPAXdBu+Tu9OxmXO46FpQtpjgeQ/ZV0yXAk7Gzsz8ZKH4Go0m76f4M/nfBRFIX9d98URdEDThW9cw6U3l5ae9x33314PJ6Wn4KCtk/qv4PJeODuNeznVySmYDbKoGmYjHpaPB7XYwb2oiqJfycVqj/By8htTtSKqmA+wPF62n79JclI0PLUFFNj7YoA0M/P/k9YitJarywduF8sBgsG2XDAOg7UBtCftvYVH235dKD8+7M3WLAtZElGQtJ9kn7bN4NkaPld0RSMkhFFUzAZ2vfvYPvOJJswysYEv9rDYrAQVaNt2iNKpF2f28IoG5NWTswGsx782g4GybDnnGh6UPgB2q9p2m+OBwn0bbEDYTDpAc0cXP8oWvs3ZMO+50I2gBIFgwUJuWWs7T1fcTV+wPFnkk0omvKb/bx3HB6wLIMJRW312yC3X+beciQkYmoMi8HSkibt+U/RlAPWtzcftD8+97X/Vl+YZT04+bf6X5bk3+5TVfe9rWs7rsWTxtT+1+ZvXZN7V6331rMvUSX6L90L9tLWuIxr8YRz05YvGtpBjZ8D9akkSb/Z5n3HVlvpgXgAs7znepRlFFXDbPjX5sL/Nn864ZOWlkZdXeITQ21tLTk5OaSmpqJpGo2NjW2mH+jY9rjpppvwer0tP6Wlyctx/y5d0x1Y2hE/PbKclDYGE2wPfr2VSb2yWLytjlOG6gLskAd/gvQiPdYGYOs86Hd8+5UOPJWmpiai0TjH9TguKfn5tc8zo/uMdg+fXjSdTMt++609p5Ji9nB00dEAzC+ez5hOY9otY6d3J8d3T/Tx3a3vcmy3YwFYW7uWETkj2jxWQmJY9jAaQg2/6acv0v5TxPSi6VQ0t261LataxtFdj243/zFFx1DZXNlmmtPkJNeZ264wGpc3jp3enZzQ/QQyLR46OTthM7YdrNfV3ZU0a1rL36F4CJvJxtratRyaf2j77ek6nSxLOm6Ti3xnfpt5HCYH6bZ0xnbSV3HKmssYmdv2iouExPCc4SwpX5JkH5EzgpsX3Zxgd5lc5LvarzfNlkYw3jqeJSRG5o5kZdXKNo/p7O6M2WBmWtE0MsxpemCz0brn7blkDMHgAcfDtKJppJtToblajxNqC7MTPJ307VvAZrAmbdHty/Cc4QljaF8KXYU4TM5Wg8UNddug66F0smZyYo8TAagN1lLgKuD9Le9zTLf2X7CY0mUKP5T9gKIq7a4y2Yw2cp25vLnxzaTra/+yFpXrT+0FrgJSrant5p1WNA2AcZ3GsahUXyHZO+kW+4oZmzeWnU076ZPWp12ROCp3VMtWWlyNt7lyWhOsabEvLlvMEZ2POLD/ZYsYmTMyIc5lX/Kd+Zhl82/2ww9lPzAkawjbm7YnpVcHqhmcNTihXZWBSrq4uwD6itHB+Amwsnol4zqNS0hfWrGUYdnD2u23kTkjKW8uT7B1cnZC0zTW1iZueVc2VzK9aHq7vhzd9WjCMf2tyb2rb/tjM9rId+YTiofaTB+RM4K4EmdKlynt1jO9aHq758RqsFLgLGBV7SqOLjqaFLMHxZXP7voAvXNd7T6TjC5Kx245sGD7vfnTCZ+hQ4eydGnikv+SJUsYPXo0DoeDXr16JaRXVlZSXV3NoEGDGDp0KMuWLUNV1aRj28NiseB2uxN+fi/cNiP3Hj8g6YTbTAbuOb4/ry/dnWBXFP3Vv8ZQlMN6ZjK4IAUA1exGO+YJ/Wl28xfQ5xhI7ZJUnzbkTHDlMPShn+l/9w8UOgs4svORCXnW1a8j05aZZAfo6unKiT1OwPHAo61GRyYcfhue4p2c3fds8p35RNUo2xq3ccHAC5LKSLemc1jBYYzpNIaBGQNb7KX+UoyykSmdpzBn2xxm9p+Jx+JJOv6KQ67AYbChagr5znymdE6+CLu6u3JijxPJtGe2ORnP6DaDbHs2N4+8ueWJ7tnVz3Ja79PazW+Ujdww4oakJ0BZkrljzB04TU4uGnRR0rFp1jQuP+Ryvi/5niFZQ+DeTNxGN7ePuj3phmc1WLljzD9Is6ZxVJejWuzvbn6XCwZeQEVzRZvt7eLuwim9Tsb++TwKbbncNfaupKe/vX4uLl/cUs5jvzzG1UOvJsWSklTm5UMuZ0PdBlQSV7EuG3IZdpOdm0bflOD/mpo13DWm7Xr/MfoffLwt8eWBWUNmsdu7m2uGXdNOP9zB3J1zObXXqTjn7tmuWP4iHHlP8qqNyQZNG8hz5DGt67SktnR2d24t5+dn4ci7Wh8U9iLJcPRDEAnCeP3zFrmqg2uHXUuqJVkYzBo8i5gS4/bRtyetYlgMFu4Ycwedbbm6of+JULkajrwbKlbDT08wLHuY/mbbptlcM+waGiINKKrS5uRV6Crk0PxDWVS2iIrmCu4ceydWQ6L/e8+vP+Jncfli+mX0a4kv2ZfBmYNJsaSwo2kHFoOFO8fcicvkarPeAlcBp/Y8lfpQPVcPvRqPxcPaWj02DODRlY9y5dArSbGk8E3xN21e7ymWFM7ud3bL+X929bP8Y/Q/kvx/a+Nb3D76dqwGK7t8u3CZXQzNHppU3qDMQaRZ0zgs/zC2NGxpt//vHHsnH277kB6pPRiWPazNctKt6UztMpVUS2rSdS0hcfWwq1lSsYQLB13Y6uemt7hq6FVYDVZ2+3bjNDnb9TPdms62pm0ABGIBqoPVLYIXQEVlVc0qrjjkiqTjPRYP1wy7hsd/ebzFZpbNXDfsOjLtmXy8/eME+6WDLyXTlslx3Y5LKivflc9fev+FqBplXf06rjrkqqQHLwmJa4ddy6KyRVw86OI2/ZnZfyb14XomFkykqztZPE0unExFcwV/G/C3Nsu/bfRt1IXryHfmc3bfs3CHmuhx6zdoGnyzsYq/H90nqcwUu4nbpvclP7X9sIX/Bn/YW117WbhwYcJbXWVlZQwYMICPPvqISZMm8eWXX3LJJZewYcMGHA4Hjz32GG+//TbffvstdrudCy64gNTUVB577DE0TWPIkCGceuqp3HDDDezevZuJEycyZ84chg5NHrxt8Xu/zl7RFKQ+EOPtn4spaQwyKD+FEw7Jx2WVqPHHePvnkn3snXDbZEJR8AbDuG1W1pY1MWdVOa8cl4MUaoCVryJFvGijLobaLUgbPkYzu2DYTEjtyp1f1LEjqnDs4Dy6pDvITmtmt6+YD7d9SDgeZkqXKQzLHookyez27k6yF8jpSF9fCYE6tG6TkHofjVaxGmndB2jHvUpJvJafK37mu9LvOL//+ciyzPtb3qcx3MiovFFMKphEF2suBH2UGEJsqN/A5zs+119n73YCPdJ6UOwrZnHZYo7sciSralaxomoFGbYMTul1ClnWTPIadkPJz5QNPgUN2O3b389h+h5ytJlp3abxc+XPfFfyHU6jk5N6nkihq5D8YBNN7jzqoo18uu1TNjduZlj2MKZ2ncqyymXML5nfmt/dGUVTqApUkWpJ5fMdn7O5cTOFrkJO7nUy6aZUMoP1lNvcVIdqeG/LezSGGxmZO5IJBRNoDDeSZc+iMGCCby+GosOoHngaDbEm3t/6PmX+Mvql9+OYbseQaUrFXbeF0tR8iv3FfLD1A8LxMOf0PYc0axpVwSqiSpQvd31JKB5qaW+hnAZVP8PKV2k69p/UR5v4dPunbGrY1OKn2+Tmi11fsLJqJSf3Ohm70c66unVM7jyZBSULWF61nHRbOif3PJlMWyYqKt/u/rbFfkrPU8iyZdLp19lUDTuXxkgjH2z9gFJ/KX3T+3JGnzPwR/1J9bpMLr7a9VVrOb1OIcOWwcqqlVQFqhiaM5Rvdn9Dsa+Yvul9ObbbsYRiIdwWF4WGDLTKn+GX15CiARh5ITiy9AD+xl1o+cOR+p8EOxeAK4eygmHs9hfz4dYPCcVDHFF4BCNyh1NgyEAqXai/WVW5FnofhbbtW6SKVZBWpH/uwepGKl8N6d30OKA178IxT7M7VMH84vksr1pOmi2NU3ueSqollQdXPsgxRcfQxdOFD7d+SIm/hD5pfZjRfQaZ5jTcn1+BNvQciDYj/fI6Wv+TkPKH6i8eGAsodcTY1LCJtbXrmFg4kaVlSzm04NCWt+4CsQCHFx5OgauA70q+Y1rXaXgsHir9leS4cvhsx2dsrN9IgauAk3uejM1o48EVD3Jst2MZkD4AFZVNDZv4dMenaJrG8T2Ox2lyMnvTbLp5ujGj+wycJifZplTKIjWU+Er4YOsHBONBJhdOZnjOcCSkPd9wymR3qILvSr6jV2ovHCYH7299nyxbFjO6z2BB6YKW1YQvd31JbbCWMXljGJ8/HjR4d8u77PbtpldqL07qeRKqpvLZjs/YUL+BAlcBJ/Y4kcZII+nWdL4r+Y5N9Zs4o88ZBONBPt7+MZqmcWSXI8mwZoAEoViIopQi7Ab9WzwfbP2AEn8JvVN7c1yP43CZXZQ3l/Nt8bfM6DaDEn8Jn2z/pKWcTFsmLpMLo2xkS+MWBmUO4stdX7Kmdg35rnxO6nESDeEGZm+azeEFh9MjtQcfbvuQ2mAtR3U9ioGZA5m7cy5bG7Zyep/TCSth/U1JTePYbsfSJ70P5c3lfLjlQ5rjzUwunMyInBHE1BglvhLm7pxLIB5gSucpjMwZSW24lve3vE99qJ7hOcM5ovMRyMjM3jybXd5d9EztyVFdj8JldqGoCu9teY+d3p30TO3J1K5TkZH5ZPsnnNnnTL3erXq9hxcczojcEUiaRESN8OLaF0m3pXNc9+OYXzKftbVrKXQVcnyP4zFIBhaXL8ZutFPkKeLLXV9SE6xhSNYQhucMJxQL0cXTBZNswhf18WvNrywoXYDdaOfEnicSiUf4YOsHHNnlSAZnDuarXV+xunY1+a58Tu55MlaDlRXVKxiVO4pCYyZ1gQBXfbqT0d3SGNY5DdAwGQy8+XMxtf4II4vSmDYgl3yPGbP5wFuCB8uf/nX2vewvfAC+/vprrrjiChoaGujevTvPP/88AwYMAEBVVa6//nreeOMNjEYjM2bM4PHHH8di0Z9Ed+7cyXnnnceGDRtITU3lnnvu4eSTTz5of35v4bOXQCRGIKKQYjVhNhuIx+N4wwoWo0ww2moHiMYVAuE4JqOMpqpENTBIIEvgII4U86NZ01AlCUOkCWQzQcmGqoGChrpnT1WSIBRTcZgk4ugfI0sze4gqGsGoSopZpVmLEdVieEweQjENhxRD0uKghlEtaciRJjDa9QnFYte/kWJ20xDzYpHMOJAJShphLUKK7ELW4noQKUYwSoCRJqUZWTbgxkpEUggpEYwGI1ElSorJgzfapH8UC1n/qJxk3FOn3raQQf/AVkyLkWbyQKSRiMlBczyIx+TEKBlpiHuxSBYccQn2blGrKihRokYbfiWI02jHosbA6NDzo/uvx2bILXabZCOkhnAYrFg0Vf8gnsGk+6VECBqMRPfEB8TVOG7JCvGgHuehRsGSon+ET1WImGytfmro7ZPN+kf5sNBsVIjGo6SYPXjjPqyylYgSwSgZiWpRvb2xZsAAmgHkuH4OrKlElSh+NYjTYMeixsFop1kN6bEakgW7JtFMnPieD5aF1TCyJGORTFg0BeJRVJMLr9KMRTZh1wxAa/nEQ0RkA83xIC6DHbMaA4ORKEb8il+vV9MgHka17DmPRhuReASHwYrRYKYp5sUsGTHJJprjQewGOyElRIrZgxxpBNmEJpmR1Jjed9YUiHj1OLZ4FMwuiPsBeU98DoRUC4ocIqrGSTN7iKkqRjWOFA/oq0OaBmpEH0OxoP4BQ9mGajBgUFWIefVYOFXRY4tMdlQMeOPNWAwm7EocjA58iv6midvgIEK8tR+UsO6fEgHJAmh7rpdU5KhfX13SFJBNIFvwqSHQVKwGO/64D6fRQQyldfzHfVhkC5qioRn0D/15TB4UVWnJb8HYUo7b4NA/SogGRic+LYSq7vmYHSohJYRFtmCTzMhaTPdTNoEqEzBqLR+4lJCwKYCk6B8njUdQjU68ajMWyQTIRLQIJsmExWChOd6MRbYQ1+Komorb6CKohvQPC8oW/YN7sgGnwYEcbiJqsuNX9XMeU2MoKLiNbkKKPkatBmvLBws1ND0WU6OlPCQ9VjGoBrHKVoLxIHajnbgWR0LCgP4BQw0Ni2xp+R3AgAFJ0j+q6DHoW5IBVf8oqCzJKJqCw+ho2Z41y2biWlz/uJ9sJbznw4shJaTH6WigoKBqKg6Dg4gaQVM1JFkiqkVxGp1omkZzvBmbwdYS7+YxOvfcr+xE9vSbQ3YQUgJ7xlWMkBJticMyG8yE42HMsrnlg5AOgx1/vFlPl/RYHg2NqKZ/TFBR9TjBSDyCyaDHMxlkA0bJSCAWwGm0Y9YMBKUYmqahoGDAgCzJRNWo3ndqDLfBBuFG/QUByURE1vR4HYMZZ9xA0KDXaZSN2GUbqhbHG2vGabQT1xQie+aXuCbjDcewmQ2EogqpdiNNoTgmowGPzURzOE4oGifVbkx4A/v34P8b4fNn478lfAQCgUAgEPz3+NN/x0cgEAgEAoHg/xohfAQCgUAgEHQYhPARCAQCgUDQYRDCRyAQCAQCQYdBCB+BQCAQCAQdBiF8BAKBQCAQdBiE8BEIBAKBQNBhEMJHIBAIBAJBh0EIH4FAIBAIBB0GIXwEAoFAIBB0GITwEQgEAoFA0GEQwkcgEAgEAkGHQQgfgUAgEAgEHQYhfAQCgUAgEHQYhPARCAQCgUDQYRDCRyAQCAQCQYdBCB+BQCAQCAQdBiF8BAKBQCAQdBiE8BEIBAKBQNBhEMJHIBAIBAJBh0EIH4FAIBAIBB0GIXwEAoFAIBB0GITwEQgEAoFA0GEw/tEOCAQHQ2lDkGA0Dkg4zAby0+xt5qvxhfGF48QUFatRpmumsyVNbSpDijaDFgeTg6gjD1OkESniBSUKRjtxTyG7G8I4LUYkwB+Jo6gaNrMBt9WIPxInElPpnGrB5C+HWAAMJjSzCzQVyeyCUD3EgnvsTtAMSNGmlnpjzk6UeaNE4ioWo0yuy0xEVWkIxFts+R4zpqgPwo0QD4PBgmZNpTzmIBiNYzHJmCSJXGMAKdIESgRMduLuQuRwM3KkTj/OaENN6UJpoAyzbCaiRogqUQqdhdSGawnHwxhkAw6jE5tipykmE1FU0CDTo9IUbSCqRLEYLGTbsrH6yiEe2scfO8GoAkjYzTKaBsGogt1sIMttJRCNEgoq5JkDSFFvgp876kI4zEYy7BJWg4GycBWheAiDbMRhdGBTnAQ1mbiqEVU0YnEVk0HGYpRRFBWbxYDHaqLSFyYUUzEbZOwWmUBEQVHBbJCQJAnQkJCwmg349xkbVpNMTNGwmw2kGuPIgco9fWZFtWVQFjIT1zQURcVuNhJV1Jbzk2o3EY7EkQ0SbrOENVwD0YA+0MwOwtYsmmMQiKpE4gqSJOG0GMkx+JDCe/vBhuLpgi+s4AtHsZuNBPfmR8JhMWCQJPyRGAZZJtdjw2Y2AFDrD+E0sk+/6efRbrBjVx00xGScZiONwRh2CfKswX36X6+3uCGMySCRYZOwRWohGgSjBdAgHgGDCdWSQUnERLbLSrUvTCSuYjLKWA0SioZ+buIqRoOE1WjAZJDw2Ix4Q3ECUQVZAkUFSQJZhgw71EbqiKoRzAYzKeY0mgJGNE3DZjJgNIAvrBCNq5gNEk6LibiqEldUNCDPbdavu3gAnJ0gVNd6zpw57PCCUZYAsJtk4hoEIwoaGlajAUmWMBvAYTJQH4wRjqnYzQYUVSOqqBhlGaNBQtPAaTEQjqvEFBVNA0XVkCUJo0EiGlexmQxIkj7eNQ2MBklPl/V+0TTIdBhpDKvYjAa84RhRRcVqMpDjlqkOVRNRIphlMymWNFJtnn/pnlhcHyAUVTDIEg6LkbwUW1KeOl9kn3plClKsGI1i2pc0TdP+aCf+TPh8PjweD16vF7fb/Ue70+FpCEQoqQ9y+2cbWFPmBWBo51T+cUxfumY4cVpbL+Li+gAPfb2FeeuriKsa+ak2rp3Si6P6ZGD27oR5NyHtWgiaBll90I56CEpXIC28B9Q4pBSiTboVLX8Ef/20hhuP6sPNH6+jqinA+xeNodYf46Ufd/LEUZnIdVuQvr4J6raCJKH1OBKOegipbhvsa+82Gabcg/T9XbDpc73eqfejpnWn2/1rSHOYee+CUXyzsZoXF++kKRhjxw0DkGMhpIqV8P094CvXRVS/E2DCTTRJKciyjDteDwvuQdr0KSgx8BSgHXkXKDGkz6+AWJDyqzewqmY1vdJ78dHWj5izfQ4fH/sxxb5iHljxADu9O5GQGNdpHNcMu4Z8Wz47duzAXeBm9sbZzNk+h1A8RIolhZn9ZnJklyPJf6Sf7k/f42DizfgM6Qy670cOKUzh1ul9efOn3SzYUsv7F44my6riVurh+3uRNn2yx898vZ8LRqNanNSpEXb6dif4MzZvLNcOv5Z8Wz5fbKjl4a+3UOHVJ+ppA3K5dGJ3fOE4vxQ38OzCHfoEbzZw8rAChhamcPX7axiY7+HSSd35tbiRKf1y2Frt59FvtraUc8ygPGZN7E5XQz1snIO09J8QbACzA23I2TDqYjZVBqiWM5jzaxnzNlQRUzTyPFZum96HPnkeOlnCGLy74avrkcpWAqDlD4ejHkBJ6cIpr2/m15Im1tw0DrfSoJ+vjfv3wyhKtQzKGsM88s0Wfi1pAmBIQQq3H9uPNLuJf3y+gWGd05gxJI+4otHJEqdBDbKjWe+3HU07kJAYkzeG64ZfR74tn+cWF3Pu8Gy9/xfcm1jvxFvQCkehmtMweHfCV9ch9TsOIs2w/PnWfhh8Foy+hE2Vcc74sLWfTx1ewAmH5DPrrV8pbghiMkhM7ZfDX8d1JcNp5tuNNRRlOfjol3KicYXpA/MYXKTybfG3vLr+VRojjdiMNo7vfjxn9T0LXykY0jy8uHgHn66uIKZo5HqsXHtkLzw2IxaTgTGZEeT67Ug//hOOfQyWPQ+/vqYLTlsq2ujLoP+JHP9OOXce0xdNkrjj8w0t/Tm4IIXbpvdlU6WXsd0zeWTeZib3z2FVSRPvrywlGFVIsZs4e3RnumY46Jfn4bPV5RzSOY2nvt/OryWNLeXccnQfLCaZf3y2kV/2sV82qTurS5s4om82dc0R/OEYgwtSeWz+Nr5Yq7frx78PYn7xfF5Z/0pSP+S78n/znljlC7KzJsg/Pt/A1upmJAnGd8/glul96ZntSrgfPj5/G1+srSSqqOR6rFx9RE9GdUunILXtB8f/3znY+VsIn/0QwufPxdZqP8c+9SPhmJpgd5gNfDprHN2z9BWd4voA57+xkq3VzUll7Lq2F9JrR0FzTWKCbIDT3oaPL4JQY4tZO/k14j2nM/6hRbwyczhOk4Gttc08NG8Lcy4Yir1+LdKrR+kCai/DL4ABJ8D+dgBXLpzzOTw1rKVe7bxvaPb04t3VNdQ3x3juhx0AbLh1AvZQFVLZMvjk4qS2aDkD4ZQ3AA3ePQOpZmNypx3zT1j+AvxtIZ+XzMNpcvLx9o9ZULqAm4ffTK/0Xsz8eiaqltinWfYsXjnyFYyykQdWPMCC0gVJRV848ELO6n0Gnge67OPP63R9cBMAdrOBOZeMYfG2WhoDMa4bbob3z0Sq3pDclhNfgaKprA5s5dx55yb5k2nL5NUpr3LYfeuTju2b6+bW6X34y4vLktKm9MumKMPJsz/swG428Mq5wymuC3DDnHUJ+frluflsZm/kZc8i/fhIsn99jkU78h6mvbGbTZX+hLSPLxmDb9d6Du2bifTCRH2Fb19MdrQLFvBrSYATP6hi13V94IOzkaoSfQDQTniReJ/jGXjnd4RiSkKazWTgk0vHoGkw9YnFfH3leKq2rOHQoUNYE9zOOfPOSeq3DFsGr015lcPu28Cu6/rCB2e1U+8rkNMP6aVJ0G0SpBXBkieS8/WehjblPooeSDwPR/TNpkeWk2cW7mix9c5x8Y9j+rGjtpm3l5dQ0RTioZMHMbSLmbc2v8Xza59PKn9iwURuGH4Dry/y8sLiXUnpj54yiCN6eHB6NyO9fCRcvga+vRU2fpLs69ir0EZfwrZmK8c/s3TPamQrVpPMx5eM5foP1/LPvwzmwXlb+Gp9VVI5b/51BA/O28x1U3pz0exfEsqRJXh95ggu3M++t/wXzhrGA19t4qojeuGwGLn3y42sK/cB8Ovto3l3y7s8u+bZpDon5E/ghhE3ku/qlJS2L6tLmjjxuaUoauJ9JtNp4f2LRtM1w0FxfYBL3/6V9Xvq3ZeHTx7IcYNy/ydXfg52/hYxPoI/LY2BCK8u2ZUkegACUYV3lhfTHIoBUFwfbFP0bLvrSNjxfbLoAVAVWPYCDPpLgln67k6MvhKqfGHWlTURiik0h+NsqfZji9bBd3cli5vRl7RtB/BXQslPMHRma70L7sER9zK2WwavLmm92duidRDxwYJ72uwTqWoteMvAW9a26AH48TEYei7FwQqeXvU02Y7sFhFzaMGhPLnqyaTJEqAmWMOK6hVYZEubogfgjY1v0BBpFYlS1VpoKmXNTeMAfdn/3eWlTOiZxYWjc6GpuE3RAyDtXkyl6uWpVU+16U9tqJYVVSv0c7gfGyt91DVHcVuTb95fb6hmRNe0Fn++Xl/FqtLGpHx/GVGIHG5EWvZM2/5t+gwp6k8SPZ1SrDRH4owb1g+Wv5wsekC3LX+Jwb276n3jLW1TfABI39+F0VucJHoAQjGFd5aXEozG+cuwAuqao4wb2o9KzcvTq59us9/qQnX8XLVM7zdvSfv1ejrBylf0FZMBJ8PyF9vOt/kLpEgzM/fbSfl2YzXDu6QhSa22zVV+GgIRUuwmNlT4OHFoPq8t2U1jpJE3Nr7RZvkLShfQHGvmkM5pbaY/8s1WHPEmWHAvaCpE/W2KHgBp2bNIoUbeXVGaJEoAwjGVN3/ezbQBuZQ3hvhpZ31SHqfFiDcUI8tt5duN1UnlTOiVxTdt2PeW/8W6SjpnOIjEFdw2I+srWsVHU6SJ1za81qbvC8sW0hxLvoftS0VTiMfmb00SPQC1zRF+2KLf5yq94TZFD+j9WdIYPmA9/+sI4SP40+INxVm5O3nC2suK3Y00hqJ7fm9oM49BierbW+1RvhKy+iTaGnbqMT/A5ko/9YEoGyv1m4ikxpDKVyaXoyl6We2xcyH0bJ3ApbKVSEqY5oge19NiV2JIaLq4adfnX6Bue/vpjbvAkUlYCaNqKmX+1rJiaox1dW1PhAA/V/6MyWBqNz0UD+GPJQoBylZgt7cuna/Y3UBzJI7D4YDS5BWZFhwZRNUoa2vXHtAfad+ZdR82V/kpaCfWq645gmNPPMzy3Q2kO61JedIcZgg3QSzUvo+Nu/n1+tEJpqGdU/m1uBEp3IRU+lO7h0qlPyOFm/S+OVA/NJXosTftsHxXA3FVY0r/bFaXNiJFmoiqsQP227LKZbogKVvRfr1WN1Lpz61/tyXg9tK4kyuvHJdkrvaHcVkSxWe1L8KWPQ8hPbNdrC5twh/zE4q338+l/lJGFyTHqACUN4WQlDBS2QroNQ28pe37GQ9D2Nvu/QDgl91N9Mx28ktxI53bGD8FaTa2VPnpmaX7vj+9stu272V1SRM9s11sqvRT3xzBbW29nppjzQfshxJfSbtpAOGYwqqS9u+JS3fUE47GWV3Svn+V3jDheLJg7kgI4SP402IxymS4LO2mZzotWI365JbjSZ7YADSTFc2V234ljkwIexNtJpu+DQY4rUYcFgMZzr1+SODIaqOg9ux7cOWBb58ldWcmSBI2kyG5HNm4J8i0Hdx5cKBASJMNNAWzwazH51hT9ildIt2a3u6h2fZsNA68+2017DdBufNQ1dYbaabTgs1kQFEU3df2iAZ1f2wH8MeR3a7wyXCa8e5Z8dsfl9XUIigzXBaCkXhSHlXTwPQbsQ62NHbtStx+qfKFyfXYwGRDO8A51xxZYLLpfXOgfjBaQW5fbGa6zEhIVPki5Lj1emUk0mxtr5AAZNmykCRZH3ftOqihOTL03w8gdgGwpVFeXp5kdltNSStVdouBDKcZAF8oRobTjG3/MbMfKZYUamvbXoWwGGWQZP1aLV0N+4znNjHZ97lek8lwmQlE4+Sm2NocP75QnAynBV84RvqediSkh2O/Wb43FCPDZcFuNhLep3+shrbvU3tJs6YeMF2WpAPWne22YDUb270fApgNMmZDx576O3brBX9qclNs/HVc13bTZ47tQqZbv8BHdU1v82LetWsXDDmr/UoOORvWfZBg0gb9BdWSjiTBlH45uK0memU79beJnPloIy9ILsdfA23ZQX+lZdCp8PXNrXWMvIi4u5CGQJRDCltvdoorH02JQf+T2i7LZIPcIZA9oH1xNOBk2DQXj9HFxIKJ2I12suz6BG2VrZze5/S23UTimG7H0BRpasm/P0Ozh+I0ORL96TSMXrfPbzGdM6YLjcEop7/yCxSMatdPTZLpZMnmjD5nHNCf1atXJ6VZjDI9s12UNSY/Ped6rPjCMeJ7tgP+MrygzfK/2VCNZnahFY5qMx13J3BkcvrHiduky3c1MiDfQ9jogVHJcVgtjLqYsNGj903+CF3gtIE24GQ0W/sT3syxXXHbjNw5dwP9O7kJGdwUODpxZu+2+w1gRvcZbNmyBfKHtV/vrh9g1CX6H+W/QucxbRfmygVnNke/kSgAs90WApE4MaVVKFuMMgWpdnI9NqwmmU9Wl3Pq8AIcJgfDsoe1WXyWPYsMWwZflbS9NXPckDzi7kK0kRdDsFwXQJ62g4C1/OFgcXLumC5ttwWYOaYrL/+4i0MKUxN830t5U4iiTAffb67h+CHJ8TZfra/iuCHtC8oThuTz7YZqijLs2M0G5H2Eu8PoYHjO8DaPy7BlkGnLbLdcgC4ZDmaO7dJu+inDCwEYmO9p46FK55hBuXjsvyF0/8cRwkfwp6ZXlovz2rjQL5nQjc7prRNwqt3Mc2cN1Z8O9+H6b2rQbOloRz+iPzXug9b/ZLClQeWaVlvBSBhzOau27ub+Ewbw8856lu2oIcdj5cGTBtLvru+h97FovY5OdOiNadD7WNjfLhv0YOOmEv01cEDrezz0mEKPW7/hH59t4L4TBlCQpj8Rd799PtjT0cZeCZ0OSSzLZEc77W0USwqqORXt1NlJk5pWMAp6T4dNn5FW2cT5A8/ns+2f8diEx0ixpDB5zmQOLzycwwsPTzjOIBm4ffTtuE1u8mx5PDHxCVIsKQl5ClwF3D7qNvK0PW+OmGxop76FYm1dfbrw0CI0TePGOeu474QBKKYUtFPf0gXSvn7mD4fhf8UQqmJSwUQmF05uxx8Pdy9LXAmwmmSeP2soHpuRwv22KtIcZu49fgDPLNC3AmeO7cL2mmZOHV7AkILE9izYUk3AlKafn7SixL62p6Gd+hYxdyEPnTQQqylx7CzcUsOK0ma09B5oYy5nf7Qxl0NGD/reqcdKKVZP2/3QaSiMv4bNtXKbE9rFh3Uj223lp531ZDitOMxGfi0PovnKOCx/Qpv9dtuo20gxublobjWKNRXttLfbrrfXUWhpPdDGXAErXoLx17TdD6e9TcxdmGBOtZt4+ORBCYHNFqPMIycPItdj5dNVZTx80iC21zST6jDj0lzcNvo2Cl2J5XgsHh6f8Dh5tjz657mT+nlwgYdThhXw9ooy6Hmkfu00lcKpb8He1aoWp7rAjKcJmDLw2ExcMqFbUn/+dVwXzEaJyw/vyS/FDdx9XH/SHYmrOgVpNuxmA3fO6MeuukDSw1dDIIrZKHP5pO5J5Z81qjNVvhC3TO+D1WRg7toKXj5nWIsIcWpObh11K53dnZP64Z8T/0mu7QCr03s4rFcW0wcm5pMluHNGP1L3CJoUu4kXzx6aJH4GdPJw+eE9yDzAqlFHQLzVtR/ira4/H2UNQZojcX7aWY8swaiiDBwWA/n7vZJZ74/QFI6xttRLTXOYQwpTyXJZ6JzuQGksRY4HofgniDZDl3FotjSkeBgqVkFzNeQPB1cONTE7fsmBxSixeFs9gWic4wflEIhpBCJxQpV+Dukah1ADFC8Bq0df2ZCN+upOqHEf+0gw2vQA6731WlP5psLM7vog/XLddM90ElFUiusDbKryMzzDwSG5MdCi4K/SY3pcuZA3GMWcwec7AvTJcdHdFsEQa0r035MP8SjUboSGHdD7LMpMwT3fxzGwy7uLOn8dYwvH4ov6WFG1ApfZxZCsIVgNdhp9NlaVNHHCoExqYjXsaNpBsb+Y3qm9yXflU2DMgFUvgStnjz9pzF7nR5IkRhaloaoaS7bX0zfPTbdMO+GYRr4lgCHshcpVenvyh4Erl5qYndq4lb52LxUGCW/Uy8rqX3CYHBySdQhWg51Q2ElzOE4gEmdzlZ8Mp4XeuS5icQXXntiJkoYgGyt9dE530C3TwcYKH95QXF+ViSl7bv4abquJSm+YDRU+clOs9MlxIcuQbTdgC1XpsVHV6yCtG2T2IWbLZWVliPpAlKJMB7tqA1T6wgzK95DlsuC0yHhDCl2NDUixZtj9I6BBl/FoJiclSjrNUYWVuxswGmRO6+vAEG2AitWt/eDMpY4sqkJRXFYzvnCMX4sbMcgyI7qmYjXKlDeFiasahWl2umQ4aAxGaAzE6GJooMIo6eexemVLv9kNNrZVGcnz2FhZ3MCJvSwYot7Eel251ClOamNmelu9SDG/vuqT2UuP9aneCCkFLf1QEYKyxiBbqv0UpNopynAQiin4w/p5SXOa6ZnlxGEx4DDK+KIqTcEoBlmmuD5AutPCkDw7dfE6Sv1lbGncTKGrM91TupFpzGRbQ5RUuxlF1dhY6aOiKUy/PDd5HitxVaMpGMNikulv9yKFG8GRrX/Lp2YLNGyD7P6QWkTYnsuW2jChmEKO20pUUfl5ZwOapjGyazqqpmE1yTjMBmoDMTZV+uid46LOH2FXfYCuGU5S7CZsRhm31YQvEqcpGMVmNrC+3IcEDClMocIbIt9jQ5ZlVhY3oKgaAzp5iCoqHpsZTVOJxFRSHGasBmiOaqyv8FLlC3Pm0BzqlHrK/WVsbthMobuQbindybd3wmQ6uJWY0oYgTaEYP++sx2E2MLxrGnaTTKfU1ofBhkCExmCM9eV6vYPzU8jxWBMeGP/XEK+z/5sI4SMQCAQCwf9/iNfZBQKBQCAQCPZDCB+BQCAQCAQdBiF8BAKBQCAQdBiE8BEIBAKBQNBhEMJHIBAIBAJBh0EIH4FAIBAIBB0GIXwEAoFAIBB0GITwEQgEAoFA0GEQwkcgEAgEAkGHQQgfgUAgEAgEHQYhfAQCgUAgEHQYhPARCAQCgUDQYRDCRyAQCAQCQYdBCB+BQCAQCAQdBiF8BAKBQCAQdBiE8BEIBAKBQNBhEMJHIBAIBAJBh0EIH4FAIBAIBB0GIXwEAoFAIBB0GITwEQgEAoFA0GEQwkcgEAgEAkGHQQgfgUAgEAgEHQYhfAQCgUAgEHQYhPARCAQCgUDQYTD+0Q60R3l5ORdddBG//vorFouFmTNncuuttwKwatUqLr74YiorK3E4HDz++OMceeSRLcc+/vjjPPXUU4RCIYYPH85LL71ERkbGH9UUQRs0BaPUN0ep8oawmAxkOC04LAaaw3Fq/BFUDYYWuDD5SyDYCBE/uPPQTE5W+Ryk2k2EYgr1zVH6ZNjJkOog4oPmGrCng9WDZjAjRXzgqwSrC6xpRF35VPhjxFWNyqYwY/KsGMLVEKgFJQKuPDSTm+KYE284hkGSsJkN1PgimIwy6Q4zne0R5FADKAqYrRBphubqfeo1IUX8CfUiG0GSIBZIsGu2VKRgLfgrwWgFewbEgmB2gKqAvxwMVvAUQjyU4CcWF0SbIdQAsRC4cvV6Gov18m1pYDBD426wuMGeCqFGUGL6scF6/WQ4MvU61ChRZz7VzXHSrWAP10CkCYIN4MxGM7uRJBniQd2HWFj/3Zmr+65EIB7RfYkFwZWDZnQSs2dgbi7d5zzmojkKKVfq8UX9NEWayLRl4DA6kANumoCGQJQUm4k+zhBS1Av+Kr0OWwqEmvT+MTvAVwGSDI5M4pZMGqQwgXiAmmANZoOZNGsaHnMq3oABiwrZxnoko1k/D6FGCPshtTOocfCW6/1iTyPqzKfCGwMZAhGFxmCUglQ7iqrREAiT7rASUVRq/RFS7WbcNiOaCjX+MJkuC4qm0dUaRY406H7HmlvPuy0VnAVowSqqw07S0pxUhqvwRpoIxANk2bKwG23kNpSAIxPFmoGshJCifvBXgMECjgwUay7lEYk6fwS7xUhRmgWzv0wfD3v72exiR9SDxSATiiqYjDIxRaWuOYrVJJPhsOjDUlEBiRp/GIMsk2o3YTHqz8XV/ggykO400xxR8IVipDrMSBJEYwppDgtNoRhNwRhjMmNIsX39zESx5rCqLko0ppCXYiPPbdL9DDfo/e/ORbMXUKl58UV9NEWa8Fg8OE1OjJIRf8xPXaiOFEsKLrOLAkM6FUoTzfHmJDuBUmiu0se+0QrhJoiH9etKU8FoocmailfxUxuqQ9M0smyZuExu0qrXgSNLPy5YD7ZUmh3p1Ee9NIQbiCgRsu3ZOA12NlcaSHWYcVmMGMNR8uw+pGizXrc9DawpaCYHUqheP+8W/bzX2t00R5upCdZgMphIt6bjMXsIxUOElBC1wdoWe4Y5A39EprQpRJbTSjiuUOePUJhmJ65q1Pgjuv9uK0ZJI6qA1WTAKIPLCI5INUS8elucWWB2Uac48WPFZTWSJvmRw03grwY0cOWgWlIwuDL/r6eD/3MkTdO0P9qJtjj88MMZOXIk99xzD42NjUyaNIkrr7ySE088kT59+vDaa68xefJkFi1axLHHHsvmzZvJycnh/fff5/7772f+/Pl4PB5mzZpFdXU1c+bMOah6fT4fHo8Hr9eL2+3+L7eyY1LZFOLNn4t5cfFOYoo+/BZdN4Fluxq47dMNhGIK226biLFxM9L7Z4G3TD9QktEGnwGHXsewp7Zyx4x+DM12kWOqg8+vQNr1w558Epz6FtrmL5DWvA17h3hqF7RT3iCW3odet3/L9puGIlevR5rzN30SBDCY0cZfizbwNJ5do9/MX1u6q8XPndf3Q9q9CKlsFYy7HD6/AlrqleHCxWhL/4m07v196u0KZ34Iix6Cte8l+nPya0gL74OtX+s2Rwac9w2sfhuWPqGLlNGXQ9F4+PhCXYTs8ZPxV0P+KJh9XGv9Q86EwlHwySWQ0hlOfg3WfwxDz4EPzoG8IZDdD76/C6IB/TizE45+CDJ6ockGYhl9Mfl2I30wE6rXt5w3rdskmPYoUsNOmHslNJXok8TJr8GmudBvBnxxjW7fcx608ddDn2lI753Zar+ugl2xaq5bdD1bGre0lD8mbwy3jLoFySsx7qk17Lq+H6yejbTkcb0fQBeXRz8Mv7wKkgGm3gevTAXZQNlFi/hkxye8vP5l4mpc72JLKvePv58enh5kRAOweylSTh94/2zwlsKMp2HnQlj3Qet5SSlEO+VNYp4enPfOen7cXsfdx/XDbDDw6LdbeOOvI7nr840s3l7X4nvfXDePnTqYaCyONxxnTEYIqXgxUuE4+OF+WPtOQvmc8gaauysxycT2UDFXLbyKikCF3m1IHFN0DJcMvphOz76MdsI4iMeRPjhjn35IQzvhJdTMAfxcKzE8147Juw3p/bOhqbi1/weeBhNu4t2tMKCThy/WVfHSj63XXardxAcXjebLdVU8+f22BPvdx/WnIM2OoqgEYir3fbmJDRW+ljZP7Z/N9VN6c9k7q9hQ4WPXDf1gzTtIix8FJdrq5/EvomYPYPgTa/n5hsMw1W3Sr+u9fl5bTrFSz3WLrmNTwyZ9SMpmnpn8DK9teI0fy39sqbNnak8ePuxhnl39LF/t/irB/tChD1FkzoH3T4bhf4Mvr4XAnnNkMMHIi6gcdTEra3/l7p/vJhgPAmAz2rhx+A2Myh1F3iN9oethMP0x6s0utvl3csPiG2gI69edUTYys99MTuh+POPuXcsPF/en0KXCF9cg7fiuxR+y+6Gd9CrS3GuheBEA5Vdv4POdc3lh3QvEVP08ntzzZP7S6y/ML5nPi+tebLF7LB7uG3cffVP7EgxZue3zDSzYUstLZw/FF45z6yfrCUQV3X+Tgdum96VLhp2oojI0z44jXIX00V+hcnXr9dvlUDj2n1RFrGTb4kjFS5G+uFp/eAIw2dGm3IfWbRJyaiH/P3Kw8/efdqtr1apVnH766UiSRFpaGtOnT2flypW88847DB8+nMmTJwNw6KGHcthhh/Hee+8B+mrP7bffTlpaGgaDgbvuuou5c+fS0NDwRzZHsA9Ld9TzzMIdLTfZM0fkU9sc5boP1xKK6RezMViONPuEVtEDoKlIq96ENe+w/LqRXPP+GrItTTDvxlbRA9BzKuz+EWn1W62TDUDjbqTZJ2Dyl3DnISCHG5HePa1V9AAoUaSF9yJVruJvYwoSxNmam8YhNVchfXaZLjrm3dQqegAm3AjrPkDaV9wAZPaCla/CmneT/XnrJJh4S6utzwwoWwmLH9YnOUmGQafAu6e3ip49frLwfgg36jfqPf3Dr29AQzF0P1yfWN4+GYbPhPfO0IVHzykw78ZW0QP6je+TiwEN6eubMfnLkN47K0H0AEg7vkeafwfsWtQqYobN1OvscYQuzPbaATQNKbsv0pvHJdhLlHqu/uGaBNEDsLRiKY+tfAxTipGtdx4B5SuRFj3UOtmD/vT68YUw5nLYtRAW3AvTHoYrN7O2bi3Pr32+RfQANEYamfX9LHxxH3zzOlLhcJh9gu5P98lQsxnWvp94XppKkGYfjylUyY/b68h2W+id4+b6j9by92l9eOSbLQmiB2BjpY/L31mF3WKkb7pBHyc9joLVb8K+4ntP+cw+ASlcR0W8lovmX9QiegA0ND7b+RkfbZtD8PKrkd45DSklT18BbOmHBqR3TkOO1AMSplClfr3sFRN7+3/NO/DL65wyIIVATGH2z8Ut4xmgfycPW6r8PPrt1gR7YzDGVe+toSkYw2iUueeLjQmiB2BEl3QufftXNlT49pyvX5EW3t8qevb6+e5pyOEGZk3sjslXgvRWop+lagPX/HBNi+gBOLHniby7+d0E0QOwtXErVy28iiHZQ5LsV/9wNaXxOhh7pT5GAvucIyUGQ6+iNlLP33/8e4voAQjFQ9z+0z+oCdXAVSX6Nf313/Eqfi797tIW0QMQV+O8uO5FVtWuZuudR1DgMcJ3dySKHoDqDbq4m/ag/vdNNWxo2MjTa55uETc2o41jio5hp28nz6x5psUO4I14uXzB5TRGGyn3hlmwpZaCVBsZLivXfLCmRfQAhGIKN328DoMs88/523BEa5E+Oi9B9ABIuxfBvBvJtilIgRqkTy5sFT0AsSDS3CuQ/OX8r/OnFT6nn346Tz31FNFolOLiYj799FNOOukkfvrpJ8aOHZuQd8SIEaxevZp4PM7KlSsT0jMyMujcuTPr1q1rs55IJILP50v4Efz3KGsM8szCHQm2WZN68sIPrbbN/zgcKlYnCpJ9kJY9h9xcTSSuIkX8SNu+TszQ/0RY9WbbDgTqoGYTp58wVZ/w9p1U963jx0cxN1cm2FxSEJb8U/8j6odt8xIP6nEkrHw5ubABJ8Gvr7fvT+NuyOqr/z1sJvz4aGv6yIv0FZt2/OTHx2DsFYm2FS/AgFP038M+fYupdjP0Ox7WvNN2OQA/PQVH3qeLi9rNbefZ9CkUjmz9u+uhULlG314J7vdw4c5Ltk/8DG+kie1N29ss/rvS72iONWPylyItfqRtH5QoFC+BglGweS5k9KIkVMXL69voeyCmxpi3ex7SSX+H6g2t/gw4uf3zEmyA6vVsun0SVx7eg1d+3AVAUYaTbzdWt3nIlmo/vnCcFFNM78tQPSx/of3yHZ3Y1riNxkjb4/zdze9SE6nT27vxUxh+fnI/rP+IkV1SoGZD69blfkgrX0IO1vDKj7s4emBuQtr544t4esGONo+LKio/bq/Tt/oCieNPlqBrhoNNlX4AXSzvO24T/IzBug85Z3Qh1GxMFCTS1fii/iQRPDpvNN+Xft9mcTuadpBtz8YoGZPsDZFG8KXpW6770WiFNze+iUbbmxyvrH+VOuOe406ZzbfF84mq0TbzvrTuJapCVUhRH9LGT9tud+0WfcvR7KQ0Us1L615KSJ5cOBlJknhl3SttHh5X48zdOZfhXVLolGLjzhn9eOXHnbS3R/Pioh1cO6UXhL36NdkG0rav9e3/n56l3YJ+fALVV9V22v8If1rhc9ddd/Hdd9+RkpJC165dmThxIhMmTKCiooLs7OyEvFlZWdTX11NbW4uiKEnxPHvT2+K+++7D4/G0/BQUFPzX2iQARdUoaQgk2MJxhZ11rTaj0ajfNNpjb5wK6DeW/S9gg1m3t0fdNiQ1jlR3gDoadoGWeLOXYkGkxp36H9Hm5Ho1te16f9Ofrfqq0N68jbta01K7woH8bNwFVk+iLdSob0EBOHP0+CMAV47ervZo2Akmqx5v1B6amijClBg4s/W4ov1x5iTbBw+mOljTbvGqptIc29O3jQfydZfeHk2FaAANlVJ/abvZdzbtJI6aOK6MVn2SaI/arRiNRrLcVnbX6ysEgaiCeoDggBp/BKIBfTtQiR64fKORnd6d7Sb7Y36ie/u6biukdU3KI9VuQUY78PUS9kI8QnF9kCyXJSHJYzNRXB9o50AoaQjSFIySud9xNpMBb2ifcaCpBxxbUt0WJFXV27Evl1ySsKKyl5gSQ9XUdstrCDdgN9mT7DWBaujevc1jAkqQEl9Jm2kApf5SAvHQHoeldsX53rwaqh7fdwA/8VVAWhGKljw+s+xZmGTTAcftLu8uojE9bizFbm4Zh22xuz5IF7tRjwNsD02DiB+poW2xCyA17kSKtj8m/hf4UwofRVE48sgjufjii/F6vZSXl7NmzRqeeOIJFEVh/7AkRVGQJAlF0Zf/2ktvi5tuugmv19vyU1ra/iAU/OeYDBLds5wJNqvJQO+c1v3YWCwGuQPaL8SZrQsE0Cd92ZCYHgvpAaTtkd0PVTKg5QxsN4uW2RNNNifaTC60zN76HxZXcr1IbdcbC/6mP1Tt2VaKRyCjV2tazUY4gJ9k9Ex+0ndk6itSoAdbuvc85TeVtAqstsjso2+BefLbzyMbE9stG3WhlN7GZOMrS7Z/8w25jtzkvHswSkacZpcev5NxIF976e2RjWB2YJCMdPUkC4O99EnvgxEZcvq3GqMBPWaoPXL6E41GKW8M0TNbH7MOiwGj3Pa9BCDXYwWzUx8nBrMes9Ue0Sg9U3u2m5xqScW8d5znDNTHwn5ouYNRkRLbtT/2dDBa6JntpLwplJDUEIwmXY/70j3LSZrDTJU3nGAPxhQ8NlOrH7IBMttvi5YzEFWS9LG+L888Q6YtOZjWKBuTVnT2Jd2WTiCWPDnnOvNg9eo2j3EZnXRL6dZumd1TuuM0OfY4rNE3vW+7ebuldEOWDGB16/FD7eHJh7ptGCUjRZ6ihKTy5nKiapSilKJ2Dobeab0xGS1UNIWoa47QM9vVbt6e2U62+6N6IHN7SDJY3WhZfdrNomX2QbP8b8e3/imFz/fff080GuXqq6/GZDKRm5vLY489xoMPPkhaWhp1dYn767W1teTk5JCamoqmaTQ2NraZ3hYWiwW3253wI/jvkZdi58rJiTfIWz5eywWHdWXvfNLnju8hu7/+RN8G2tgrUVx5OC1GNKsHre8JiRnWvqcHN7aFpwDSu9P9lq/1rZ82nhoBOOxGovbElcX6iARjrtBvHhYP9Nuv3g2fwOhZyWWteQ9GXNiOP/n621j12/S/l/5TjxXayy+vQp/pB/DzBlj4QKJtzGWwes+WljMLrCnQaZi+XTLoL7r/+yMbYPQl8NUNYEtF63RI2/UNOh2Kf2r9e/t86DIOTDa9HfvSXJNs3/A3PGY3AzLaFrbTi6bhMbmIOvPQ9u2HfTE7IX8YlP+ib+lVriHfksnFgy5uM7vNaOPwwsPRPp+lCyZ3np6w5h0YcUHbdbjzILMX/e5ayKPztzBzrD4+N1R4mTE4r81DDilMwWE2UN2swOjLwJqqxyK1V364kiJPEdn7jbO9nNPvHLLNGfrba72O0mOpEvrBAX2OYdnuJl0kuju1WY425jJURy4zx3blq3WJWxj/nL816Xrci91sYESXNKwmA4VpieNP02BLlZ+hnXVBH7Xnok24qe22mh3QdwYv/rhbF+r7CmvtUZwmB4dkJo63haULObro6DaL65/RnxJfCYqmJNk9ZjfkhvUxsh+ehgBn9z0Lg7T/AwvIksy5/c4l3b+nzE8vYmLBBBx7hdB+XDzoYrIsWWhmN9rAv7Td7rxD9PEfD5Fvzkgan9+XfA8aXDiw7XuDzWhjSpcprCzxUuOPcOOcdcwc2wVDG8JbluCCQ7vx8DdbweJBKxzdZplavxPQzG59Cz3pwQ393jD2CuT/8Te7/pTCJxqN6tsd+yDLMtFolKFDh7J06dKEtCVLljB69GgcDge9evVKSK+srKS6uppBgwb9n/gu+G0G53u4/Zi+OC36OZ6/uY4Uq5HnzhxKhlN/wo0689HOnAM5+0yQRivauGug1zQm/3MZj506mM1+G0y6Ba3/Sa0T+o7v0bL7o427Coz7LNHnDUE740Mijk5YjDKKPRPtrDmQ2qU1j9WDNv1xtPSePPLdLm6Y2qvFz+GPLEe1Z6Gd/DrMvUYPSt633qVPoHWfjDbmssR6Q40w8FQYe3miPXcw2ukfwDf7BDeX/6JPDkc9qL+CDrD0aTjzoyQ/mfao/hp2xS8t/cP4a/VVkOIlkDsY/vIefH8PnPgiFIzQY59mPKWvCu3FmQ2nzIZIEG3GP4nYc+GEl9D2Bk0DyAa0gaeijb8auk1uXYX69Q3oPV0PyJ7xdOLqlNGK5q9CO+vjBHu+4uT+8fczKndUi80gGZheNJ0LB11E8eZSet0+X3/yPPqh1n4ASCuCE17QA7sHngKjL9Xf3nl+FD1SunPziJtxmlonvXxXPi8c8QKp5lQ47Ea03UvhzDm6P7sW6f046pL9zssgtDM+IuLoxLGD8vCHFRZtreHZM4fyxPzt/HVcV44b0ilhAhrbPZ0HThxIYyBGSciMas9AK1umB6uPvTKx/JyBcOYcNLOHQks2z05+lj5prU/gZtnMzH4zmdplCrYXxqGd+TFac21iP6R2RTtzDootm1BUIeLohHbmR5C7z33OaEEbczn0PZ6vtvowyhJXH9ETl6X13lofiJGfauXe4/vjtrbaC9PsPHX6ENKdZkJRhcsO78647q2rV7IEO2r8PHLyIA7tkUHvf3yHlt4TbdojiVuvqV3RzvwIxZ7D0wt26H6e8aE+Nveeo7iDu8fdzdi81tjMuTvnckzRMczoNiNBqIzMGckD4x+g2Fvchv1+CjSPHvB+4ov6WNmLxQW7PyDdksbjEx8nw9balnRrOo8c9ggZljR4rrceIzjhZtLNKTw/+XkKXK3hDw6TgxuH30ivlB70un0+i1cXw/hr0Ab9JUFIaF0PRTvxJfh0z4PQ/dkUubtw26jbcJn0VZuoGuXj7R+T48jhllG3tNgB8p35PDf5OTJMGWS5zJwyLJ+mYIwtVX6eP2towtZjhtPM06cfQkMgwk1H96FBS4UZz6B1O7y1/ZKM1u9EmHQrmyu8qPZMtNPeSXy4dGSinfQaajtC/H+JP+Xr7F6vl759+/LQQw9x+umn4/f7Oe200+jSpQs33XQTAwYM4KOPPmLSpEl8+eWXXHLJJWzYsAGHw8Fjjz3G22+/zbfffovdbueCCy4gNTWVxx577KDqFq+z/98QjipU+sI0BKKYDRIpdjOZdjPVgQhNwRiqptE704o1tOdbFLGwvhJhS2Oj14TTYiCqaPhCMfI8VnKkJv17L2EfWJz6U43RghRu1L/7YrKBxU3Qkkl9WI81qg9EGZjnxOQv07/3ocTAno5qzaIsYiAYjSPLEiZZ1t9uMUi4rSbyPQYM3jIIR8GZAkn1mpHCTfvU6wJVBYtDj7fYxx/VkoEcqtSDXY0WfXJTIvoTazys2w1mfYUg2qwfr8T2fCskTe+biE+PJ7GngWzSv3ljsukTkAYEa/QybOl6Xk3R64rseaPD4tZXsGLNBC1ZNEbALkukaHX694iizbogtOjlSfFmXXApMb1eqwdMTogH9thiehusqWhmD2GjA1u4Wo9z2rMNqVkyqCREIB4gEA/gNrlwmVw0BfSVhUAkjsNipJtTQQ7VtvaP2aFvHRoseh/5qsAggzWFmCufoBKkMdqEN9KEUTbiNrnJseVQ5Y8RU1W6mvxImgJaXO+LWBhc2Xp7AvUt5yVkzaImqGKQZALROIFInGyPhZii0RSMkeYwE4mr+EIxnFYjLouRiKLgC8ZIsZtRNSh0gaG5Cswu/TyFGlvHgzUTLepjnd9Oj3Qz9fEGmmN+wkqYFIsHt8lDWmNxS7sMsQByqF7f1jSYWuyV/hjeYAy72UCey9BGP6eyK2zDapIJx1WMskQ0ruILxzAbDKTa9W0aVVNRVP1tLoMs4bQasRpkVA0aghFkScJtMxGOKQSjCi6rUQ8XicVJcZgJRFSaIzEGZxiRQzVJfm6uCRJTVNKdZjKtEvZITaKf1nRqpBjNsWaaY83YjXbsJjsW2YIv6sMX8+EwOnAYHeSqNmqNcZpj/gR7XtwGSp1+LVvc+gNALADxaOvWtCQTt+dREa3GG9VfZPGY3WRbMrDUbtK/E2UwQaBB/93ZidJIFb6on7gaJ8WSQro5hS3VKi6rsSXWqZ8rhBTx6tenxYlmdoHJiRSq2+e8uwnYMqiL1tG0d3ya3eRac2iINRKMB/FGvC32HGsOtQH9u2bpTrN+3kJxct1mwnGNpj0xVh6bCYtJIhzTsBpkTAYJCYl0tRYp6tOvc6sbzeJhs9eGzW4i12nFYNAweov1N0M1wJaK4szHaLX+H80Cvz8HO3//KYUPwPr167n66qvZunUrsixzwgkncNddd2Gz2fj666+54ooraGhooHv37jz//PMMGKCvDKiqyvXXX88bb7yB0WhkxowZPP7441gslt+oUUcIH4FAIBAI/v/j/3vh80chhI9AIBAIBP//8f/9BwwFAoFAIBAIfm+E8BEIBAKBQNBhEMJHIBAIBAJBh0EIH4FAIBAIBB0GIXwEAoFAIBB0GITwEQgEAoFA0GEQwkcgEAgEAkGHQQgfgUAgEAgEHQYhfAQCgUAgEHQYhPARCAQCgUDQYRDCRyAQCAQCQYdBCB+BQCAQCAQdBiF8BAKBQCAQdBiE8BEIBAKBQNBhEMJHIBAIBAJBh0EIH4FAIBAIBB0GIXwEAoFAIBB0GITwEQgEAoFA0GEQwkcgEAgEAkGHQQgfgUAgEAgEHQYhfAQCgUAgEHQYhPARCAQCgUDQYRDCRyAQCAQCQYfB+Ec7IBC0R31zhIZAlPmbqqnxRRjXI4PuWU46pzuS8sYDDRiCdbDrB6jfDp2GQf5QkAxQ8hNUrILMXtB1gn5A1RoINULuYChZCt4KKJoA7jzY+DFYPNDjSFSbB9mSDv4SKFupH5veHXYuhGgAuk8GowU2fAzp3aBwDMSCYE1BNVmRw17Y+jWoKvQ9Fuq26j4WTQBPPmz+EiI+6DkFPAWweyXl3UdRH27g+9LvAZhcOJlUSwqdgk2w6XOIhfX81hS9rpKl0FwDXQ/TfQj7dJ+2fQP+Sr2ujF7UmB34434WlS2iJlTDqNxRFHmKKJBSYe2r0OMIMDlAjaI4sjA0V8GuxbrPvaaBpxP4KvSyK9dC4269j3MGEHUWUOaN8dPOeibkG+hkj0FTCWz7Gsxu6DMNzeKmREmnwhvmu03VuKwmpvTLQdM0glGFH7bWomoah/XMZJgngBT16u2NBvT2phRCLAJbv2ptV+4giDTD7j1+dhoKeYcQdRWwrS7MF2srOLRnJlaTge8316BpGof3ySYcU/h+cy3DOqdSlOlgR20zvxQ3ku22cljPTFRNo7wpzPJd9TgtRo4ekIssQYYVHNEaqNmsn8fUzlA0ESrWQPU6vQ/tmQBoJgf3/RxkZFE63TOdfL+5mnP7GZAiftjxve5rxK/77syCHlNQzU52RT3ISIRiCiuLGxlU4EHT4JuN1RgkOKxXFkM9fqRIACQNUrpTHK5kff161tetp6u7KyNyR5Bm9GA1OnhxSQmjitLJlBQKnc3gK9XHpNkBfY5BM7uYs8tAvzwPNpNMYzDGhgovp/fUkIL1+jkA6D0drG7QVFSzi7gtE3NzKZT/CuW/QEYP6Hooiqsz5eEqNtRvYG3dWrq4uzC+03gUTWGndyc/V/7MiJwRdHJ2YkHpAnwRH4fmH0qaLQ00cBjt5JWsgNyB+rW843t97HU/Qh93VWtg8FlgskK4CXb+oI//osMgqx8oUSheArVbIG8Q5AzUx+KOBVSOvRR/rJnvS77HH/VzSq9T0NBYUbWCnd6d9Evvx4CMAeTZ8lhZ4kPTNNIcFlaXNpLtsdI1w0EoqrBydyNn9kbvn82fExh7JbXRRn6t+ZVdTbvontKdQnchsiSTbk2jU9yEFK2HTXMh6oMeU/Trv/hHqN4IeUOgcDRoCpT9AmocMnrCzgX6farHEZDSmapYBl9uraUo04nZKPPjtlocFiOXDDYjRf2J5acUUhWx8sKKBgbkexhSmEKtL8JX66vIcluY3Ccbl8VITortv3L//jMjaZqm/dFO/Jnw+Xx4PB68Xi9ut/uPdqfD0tAcYdG2Oq5+fzXqPiO0R5aTl84ZliB+YiE/xqrVSG+fDLFQa2ZnNhz/PMw5HwK1YLLDqW/BV9fqN5UeR8IX14Cmth6T2RuOvBveOwPUONqlvyDFAvDmDOh/kl7Gj48mOttlPAw9Fz76qz6ZnP4BWsgL8QDSnAt0gTLzK73M+u0w8iI93+JH9ivnUMpOeJanVj/FF7u+SEg6rttxXDjwAvIf7a8bek6FPtPhs8uT/T/pFXjxcIi39kXNtVtYWfMrN/14E+o++Ys8RTw56UkKGzV4fQic8DLkDkbzlyO9dbIurFKL4MwP4ZtbYPDpMOcC3d7Sz1loZ31KyNWFvncuYNf1/eDTS5CKlyS0QTv8NrT+J1L0wIYW263T+7CxwsdHv5a32HZe3xdp3YdIC+5OPL7rYUhDz4UPZ+qGPjNgxPnw9ilt+PMJIVdXtjdEefnHXXy6uiKxPwd3YkC+m+6ZTu6cu4kdtc0taQZZ4rFTB7O1ysdTC3bw2CmD6J3rxqX46GSLIL1zmi6y9iIbYNpjsPlz2PYtdB4LRz2ItvUrtP4nU/TgBrplOvn23C5IGz7g/7F31vF2FOfjfmb3uNxz3SV+4+4CSSDBpbi7S3Er0lKKa3EoDsUpFA8BkkBCIEJC3PW6y/Fzduf3x97cm5NzbqD9tr/SZh8++XDPzOw7787O7rw7M++74pv74cTXYNbvkuTIY/6CXjKRrysFV7y5nOfPHMO7S3cmts91gxBr3kP0PQjSerExUsn5X5xPY7ixs4zT4uTJA55kkK8fYGXA7V8b1+XDyxDbvk1s12k3I4ccz9gnN/Hy2WO55PUfmXtuD/jmPsTy1xPKMuREmHwlsmkTZPVFvHIU+Gu78q+vZmOkkgtmX0BDqAGANFsarxzyClfPvZotrVs4ofwEMh2ZPP3T0wmiR+WO4nfjf8fX27/m8F6HU/zqMYbxsguhwGEPGUZOv5lQuwo+ugJ0ratMVh845H546xTjvjjxNfjiVqhfR8XVq5m1fRaP/PgIACf3O5lDeh3CRV9eRDDe1X8yHZk8O+NZSp2lXP/+WkaWZeBzWumX56GmLcIVbyxj1ZXl8O0DiGWvEbu+mlXt61PKuXPynby7/l2uHXMtxQ8OSmzL0vEw/hJ4+wxw58Axf4H3L4TyQ8GbD3PvTrxOpRPg6Kf5cIeVNxfvZOFm43pvuX4QYu3fEbNvS1m+533G/ZbltvHQCcO4/r0V1LZFUAQ8dMJwxvXMpOB/xPj5peO3udRl8qukMRjlmnd+SjB6ADbW+Xnsq420BKOdaZZADeLt0xONHjAeyHP+ZAyOAENPhKUvQuNmGH0OfHptotEAUL8OVr4Ng34DuoZAGg+jaAB6Tkk2esB4Y2/cCMVjjHLvnoXI6Yt4/0JD/sw7jIdY4yawOqHnfslGD8DJ77KmaU2S0QPwweYP2NiyCW6oMRLGnGs89FPpP/9hGHR0YlPEA9w8/+YEowdgS+sWnvrpKRqzXcYA8v75oMc62rPjIX78S/D1nUabfHRFopEB4K9D/P1SnOE6Nt0+DVa+nWT0AIiv/ogINjHvYsN4K/Q5cNstCYP6/MuGIYKNSUYPgNg6D+rWGAMGwJSr4Z0z96pPJKYnGT0AHyyvJMdjZ/aa2gSjB0DTJVe/tZzDhhaS5rAwoMDLtoYAhV4V5tydaKyA0W6fXA1jzjN+b18AGz5DtNUggo3Mv2wYL5/YGxFuRHx9Bww5Dn56I6Uc8bcLUKJt3PjeSnrneGgNRRPaZ97FgxHhJkRrFWz4nJ3xRm5ZcEuC0QMQioe4eu7V1EabcIRqjOuy+m9JRg+AmHMnItjIB2cN4Pr3VvD5ZeOhdnWy0QOw8i1o3ITQNcQHlyQaPUBFvJFbF9zaafQAXDHiCp5b+RxbWrdgU2xMKZqSZPQALK1bytfbv6Yl0kJTpAmO+XtiAanDp9cYM6eBRvj4qkSjB4x77MeXYcgJMOgYWPG2cU+ctpTWaGun0QNw4oATuWruVQnGCkBTuImb599MQ6yBj1ZUY7eoPPLlRiyKwk3vrWTJTftD/TrEstcAqIrVdSvn4aUPMzR3KB9u/pDQDZUJ+ez43piJ7jHFeEbN+ZMxu9PvoCSjB0DsWAg/vcHh5b5Oo+fL88oR4eYko6er/Ots/v3+RtMEotz92TrOmdQTAF3CNe/8hD8STzr2fx3T8DH5VfLdxga0Pa2eDj5aUU1joMvwoa3SeGCkomIJ5HXMkvSeDus/MWZFalcnPzR3sfoDY0YFINpuvFn2mmYsD3THT2/C4GOMv/110F7TlZc/FNZ9bPzdayps+DyliOpoE2+se6PbKt5Y9wZ1eqsxW1W7pnv91/y9S3+A39WzuGYJcZn6ATdr2yzaYu3GDy0GFYuMWapdWJ2GzqoVgo0pZVD1I0TbUdurEEtf6vYc+OkNiouLAThkSEGSUVJQUACpBtzO4980DDCbx1jS61afZRBpIxjt/qH+1uKd2CypH4FxXfLj9mb+cORA2sMar36/HRFtR6z9ILUwPQ71G4ylRoClLxkGzvLXKSgoSDyvPjNg7Yfdy9m+gIU37M8F+/Xk1e+3J2QXFxcbcoYcC0tfIhAPsKZxTUpRzZFm6kP1ENE6rsuL3bbFLj1XV7VhjzbD4ue6L7voWcgfZlzzPfDHA6xuXJ2QNiRnCLO2GffO2IKxzK+c363odza+w0E9DuL9je9Dbm5yAV0z7t2mTUZfTcW6j6HPAcY9sKbDeOrZk482f9RZxKk6aY20JhmMu1jfvB5/zDCI/768koMG59EYiFLvj2CPNMOivxgFp31IY7ipWzkbmjfQ09eTdze8S120IbnAT28Yz428wcazqsdk2PxV6vMCxNIXUQLVnb979uwJK97aS/mXUAJdz6J1Ne30yvF0/tZ0yfxNKfT6H8c0fEx+lTTsbtjsQSSuo2m7GUWh1r0L63xASuPBaXN3byiBsUcAYfwdjxj//7ljQs3GYNypZCuoNuNvPW78+xk5cT1GW7St2ypao61EtKhRz8/qvxuKQlO4qdviMT2GJnczogINhp670GOgWoy9NHsjFjb+vzfdAg3GfifAZVNpDSYOXkJq3Rszu2Tb3ODwGW28N+Jh4t0YzwAtoRgOq9ptfmMgSobThkTSGooZ17C7wRaM/Sa7+kCoGSw2CDYgpERICcHdBpi9yQk0IITAYVFpSWofabSPaoNwC5Fd/bMb2iPtYM/s0qkbRLABsUsnLYr4ub6upzYoo3v2PUCTGjHdkO2yuGjdy3Vri7RhVa20RFqIdtdGET+EWrrXT4+DlCBEwr3QFOm6B7w2b6dh0x1hzejPLcEYaXZr18zI7u3Trx+BWGCvcmJarOOcRXLmrv78C54PhlItxnntTuBn7pc9ise0xFnfpr08a/9XMQ0fk18lE3tnd5tXnufFYdut62b37V6QI93YMAjGTExmL2jYaGxq7o7cAdBWYfztzDAGs+qfoGxi98eUTTLKgLEXIaNn19KbFjHqBWNzZjdyMmw+xuWP67aKiQUTybSlG8tqhSO61yWnvzELtou2Nkbnj+62eC9fL2yqvSuhdAI0be36rdrAnQ3evOSH7i4cPnCmIy0OZNmk7nXrdzBax7bCVZVtjO2ZmZAd1zE2ZnZHj0lGG7ZVQkaP7vWxp4EzA4vSTT4wpW8Ojf7uDYexPTJ578cKbBbBpN7ZYHHsva8VDDOWUQHKJkPTFuh3MHFdJ67rXefVXr13OT0m09bWxrKdLUzuk3gfxHXdWApp2gplk/DZ0/BYPSnFCASlaaWgN/7sdZF9DyKOgsduQbqykL2mdq9fr2mAMK75HqTZvHit3oS0cDxML5/R/9c2rWVU3qhuRY/JH8POtp3sX7wftu6Gp9z+kDcodR4Ys26BOmjdaTg0AMTj7F+8f2eRulAdhe5CFJG6DpfFRbo9HYBxvTJZXtFCcYYTRYB0ZSJ7TTMKPtOfQs/e5QghGJM/BrtiSy5QNtl4buhx41lVs9JwkOgGWTYZqXTdq/F43OgP3ZXvMRm5273tsqlJt8zE3lndHv+/imn4mPwqKcpwMKIkPSldCPjdof0pzeyakZA2D3LoyakFTb4SOtbiWfI87Het4UUVbIDiboyBKdfAkhcM2Va3cUzDBvAWQnpZcnnVZuy52TXlPOIMUG3I4rHG7+8egxl/NP5u2ABpRcagvQeej7/g+PLjSbMlb8rz2X0c1eco3Pc+bHgCBeqgZGxq/afe2Kk/AI/2JN+Vz7CcYSmLXzPqGkod+caP0ongyUMOPLKrwOoPYMYdhnfNkBNSypD73YDuzKJdeGH6zcay2J5k9oLi0fS7bTYA8zbUMb1/Lj5nV9l+t82G0nGG4bgnqg3GnG8sd4Gx8XXYKan12f8GdFcWpZnuBPm78Dmt7N8vm3G9Uj/0x5RloCrw8coarKrKcaOLiXmLkTPuSFme0vGGx1vUb+g59UZY9AKUjKXfbbON8yoZa7TBkhdgv+tS6106AbyF3P11BU9/s5ljRhYlt0/xGKMv738DWbZMLh52cUpZR/U5CrfqRLdn0CrdMPV3XbOQu5PRE0rH0++22VwyrTd/emMdjDjNMPr3xJEOI89ALn0ZOe2WpOwsWwYXD0/U5/mVz3P1qKsB2Nm+E5/dR6m3NOlYi2Lh4mEX89b6txiRNxLuykmuv3gMKFZQLN2/iEy5FhY/D0teNP4GuDuXwdmDE+ptjjRzTN9jUoq4YOgFpFnTSHNYOHBAHu2hGEg4blQJf7rvWxh+MrgMo92tOruVc8agM/ho80dcNuIy8uUe3qiqFcZdaCxd/viq8axq3mYYlLuWTPcsP/0W/KoPe8cSbfnvv4TCkXspfysBNb0z6bwpPXlvaUXn7xEl6RT+j2xs/kcwDR+TXyWlmW4ePXkE50zqgdtmLEcMKkzjlXPGMqAg0TBQMkph2k3IA24zZiYAMnshj30eWTyu6y082IS0+5Cn/c0YPCdfBWMvAHvHG2r+UDjhVWM/i78eOfFy0KPIgb+Bo56EBY/C4Q8Zg3/HACJ7TIGTXodvHzL2wsz4I4w+G9leDcf+BTn+Etj6jbEJ9JR3jDfVT68zvICGndI1EPWYDBN7UWzN48WDXmT/4v1RhIIqVKaXTOelg16i0JIDyuNG+Q2z4MjHDA+x3fU/7T3j/31nJqQXh2zcM+VuTh9wOm6r8QAuzyjnmQOfoU96H3iwN0y8HH7zFLJuLex/I/KA3xveJnP+BJ586H0A9DsE9r/BSAfI6Ik85i/I/ofxlyXNHPDYInR3IfLsz5C7BiaLHTnsFOSp7xJ05DOuY5bHqip8v7mBty+cwIwBeSgCFAEhZwHytPeQw04xXOcBWTYJec4sZPNWoGP5asGjyCnXIg/8Q6I+Rz+N7H8EKxoET8zZyBOnjGDmwC75Mwfm8cQpI7j176tZVdXKC2eNZkCB0VZeu4ULpvTigROG8fS8LQDc+N4K3FaFzY1hZM4g5Knvde0bs6cZ13jib+Gr243B+KxPkHXrkUc/RshZwIACLy+fPcY4r1PfRRaNNlywj3sB8od0yPEix18GRz9NTcTHqeNKueKAfjw5ZyNvnD+eGQNzE9vnqCeQ9RvwNqxnesl07pp8F8VeY+9UliOLK0deyUVDLyIvHmPgvYv50+ztHdfl866ZH4sdOexk5GnvEnIWcOfRgzloYB45PTKIOTuuYf/DjBlMoUD5IXD2p8jatTD2PGTfg5C/eabLSHXn4FnyAlOLpybos7ZpLR6rh2cOfIbyjHLuXXQvvxv3O47uczS2jlmQUXmjePGgF9nWuo3bJ95OmSUHeeq7Rl/ubJ+LYfLV8MqRyLRi5My7YPzFxuweQN5g5OnvIx0+CDUZG50rlsCpxj1Rpmbz5IFPcnRvo97zZ53PmQPP5OpRV5PlMAzgIk8Rd0y8g4N6HMR1b2/hiVNHsq66jYum9mbR1iZOH19K5pR+Rvuc9Smy/2HkP/wXzht8LteMuoZsZ3annBvH3kiuM5dzh5xLviMH+fVNXf25dCLy9L8bRk+oGZq2IIvHIY99Hr5/Eg6+13g+dJafgDzrM3RvAb99dy1PnTaKCR1Ge9CRhzzlHeTw04xZyT3KD7nzG4oznDxw3FCyPXa+XFuHx27hnEk9ePTkESnDg/yvY7qz74Hpzv7rIhCJUdceQdfBblEoznR1W1aLRlHadoKMg2JFZBnT67JxszGVrKhovhKUYBMiFjT2+1jdxlKUHjceMhZnxzq6gu7OoS5qI9OhYFMUaNvRcYwdpAAtjHSkG7LiUVAUY40+HibqKmRna4yerjhKpGNfiyMdYgFjf4dQwekz6pI6qA7j2HALKJnUWcKEdGMZxqk6yJE2QDNi20gd7OmGN5OiGPsYJMZbsCuzw3XfaeTrOqhWou5CAjGJokRpiTWjSw2rYqXYngtt2w05ziwINRNPKyEQ1fFYNFR/tdE2wgJpxeCvMgZBPQYoICVxXym1/hhOq0JbKI4modCj4IjUI7QIIJD2DHZGHLjtKqGYTjQuUQQ4bSrRuIYQAk0HXUpURWBVBflqoPN8peogZMvGIeIowbqONjPOSyVRz7ivlLawjj8SR5PSuCyqQiSmgxDYLIJYXDeaTAgjL66hd2wL8dhVQlEjX9MlQgisCthVhYgmyXGr2PxVRhsIFenyIYItgG4YslJBWp3sjDjRpOEe77aptIXjqECJI4iItBozF8iO/qCgu3LZGVQ6NlwLdCmJxnVER93hmI4AbBaFuKZTYg8i4mGjXk8RO4JVaFJDFSr5jhwUxUpFSwQhBIoCUpdkOxWc0QaEFgahIG0+doad6IDdqqLrEoSxD8SnQLrSjoh37IWxOIwYPvYMdgaNl5Einw1L646Oe05F8xRS6ZcUptmo6tRHId+Ri6JYqQlVE9PjqEIhzeqjNdaKRGJVrAghcAo7GcEAKHGkpwARqAEtDoqCdGYigk2A0R90VzZqqBHiYaMd7WlEbRlGf2iv6loG7dDbKCdocmXQHmtHSh27aifLlkFNuL6z7QpdhexsiWBRjeMtQiEc11AUgV0VSCAS0/GpkK62Izr2CkW9RdSE65FIBAKrYkWik6FmUR/SKbF1XHd0pGpHd+WhtlcYS/GKhXiaMRtladth3LdWp3Fv63Gk1Y6SXkZ7KEZDIAJSYFGN047pEq/DSpZoQ4S75IftudQGtM4+2DPbzY7GIFFNN2JSeeykpZgN/W/ml47fpuGzB6bhY2JiYmJi8t+HGcfHxMTExMTExGQPTMPHxMTExMTEZJ/BNHxMTExMTExM9hlMw8fExMTExMRkn8E0fExMTExMTEz2GUzDx8TExMTExGSfwTR8TExMTExMTPYZTMPHxMTExMTEZJ/BNHxMTExMTExM9hlMw8fExMTExMRkn8E0fExMTExMTEz2GUzDx8TExMTExGSfwTR8TExMTExMTPYZTMPHxMTExMTEZJ/BNHxMTExMTExM9hlMw8fExMTExMRkn8E0fExMTExMTEz2GUzDx8TExMTExGSfwTR8TExMTExMTPYZTMPHxMTExMTEZJ/BNHxMTExMTExM9hlMw8fExMTExMRkn8E0fExMTExMTEz2GSz/aQX2xqJFi7j22mvZvn07sViMxx9/nGOOOYZly5Zx8cUXU11djdvt5pFHHmHmzJmdxz3yyCM8/vjjhEIhxowZw3PPPUd2dvZ/8ExM/n+gBxogHgMhIRZBenIQ/nqwWEG1g5Qo3lz8oSjtwRh5tjDE/aCooLrZGbGR47bREo6RYRe0aa1E9CiZ1nScqgN/KIZX+CEWBHsaRPyggOYpJB6PY4+1QiwEdjeoVjSLBzXU1Fm+MurE54zSEmvDodrJFl7QQ0jVhQjVGHo4syDcDIC0pSNC9aDawOKAqB8sdohFwJNLixbEHwvgUp1kxm2gRvCrtkT5WhvoOkgdhAUUBWIBsKVRFXWQ7rLiirVBrB0sTtCiSGsaCBChZrDZCVvSqQ9KvA4LupRIJMGojt0iyHJZUf2VgEDzFFLbHiPXY8ESrAU9jvTkIyKBDvlu2kmjPa7hsVsR8RgxS4igFsRr8RKI2ECHQkcIIh3lpYeYopIhQoh4G0jAnU9DrIWIFsFpceKz+lBDzaCHjbaKRpCuXFpkkEA8gMviIjNmhXgT0pWBCDaD1Tiv1ijYVJWopuMSsuP6BsCeRpvw0h6JowpQVYEqJA6rheZgDCEENlUgBFgUlXBMI6brFNliiEgzWOyErem0xxTsFoVgTCOu6WQ7LThijUb/dGRQE7OBgAybaqSrVoiHwOKBSCtY7USsmbRGJTlOEP4aUKzEXbnU+eN4HArhmE40LnHaVHQpUYVAILFZVFxasFOfiC2T9qhERxLrKB/VdOyqQkTT0XSJw6qi6zoWVSEU1UGAy6bSHo7jtikoikIgEsdhVUAIIlENtyNMezyAS3WQGbcjLZKGuB2rKlAVgUOFpmgjcTSyLRnYI83gyKJBa8eiWAlrYXSpk+fIIxaL49DajftIsRC0e2mKtWBXbOSoPtAD1MZcaJrE7YzSHm836tXtNEo7oZhGhttKNKbhsSlYw3UQjyNd+fijOl6roEG2E9YipFvT8IRbweoGNCKqD4tFRW2vAiHRvcU0h6KEYhK7RcGiCvxhDYdFwWuVOGKtEA8jHRlURaw4VIW4lGi6xGVVCMV0FEXBYTHaoS2iYVUEmiZRFMhPd6G31xn9zeqlQXcjEdgtCv5oDEUIrIogEpdkeeyEoxptkRhOq0pumuM/+aj9r0dIKeV/WolUrFu3junTp/PKK69w4IEHEo1GaWlpwel0MmDAAF566SUOPPBAvvnmG4488kjWrVtHfn4+b7/9Nvfccw9ffvklPp+Pyy67jNraWv72t7/9onrb2trw+Xy0traSlpb2bz5Lk38FWlsVir8erA5E02bk1vmIkWcgV72H2PA52Nww+hxk4UgItSC9+YhQE3z/JKJmJWT0QI45F9KK0a0+qpUYm1o28dd1f6Up3MTY/LEc3+94Cuw5OD64ANGyA5k/FDHhEnBmIbUI1K+HH55CBBqgx2QYez4y0AgL/oxo2U7L6R/SGG/hrXVvsbRuKVnOLE4fcDo903pStOTlTj3l6HMQpRMgHkGufBuRNxhcmciFTyBadkD+EOpn/IGGSDOvrHmFDc0bKPYUc9ags8h2ZfPamldZUrtL/mn0TOtFcUsFaDEQCnLh4x36D4Hxl4AjA+bcgahdjczogZhyHdLmgsV/QWz/DunJhXEXQXY5f/ouyEljSvj78krOH19AmtYIG2YhVr4NgBx+OvSaChWLEEtfhEHHIkvGGe1cuwqZ0QMmXo70FlClqtSF6nhp9UtU+ispzyjnjEFnkG3NIGv2bV3l97sObC5Y8hKizznszLWzqnEVb6x7g3A8zOSiycwom0G6PZ0CzQVfXUfdzD/SFGnmlTWvsr55PUWeIs4adBb5zjwK5j+K2D4f6c6B8RdDdjlL2tIYleZHtFfCd48hWrYj8wbDhEvRXVmc9MZ2fjOiiLw0By6bypJtzXy5tpbDhxawX79cNtS2c3AvJ5ZwHSx+HrF9gSF/3EWQM4CVQR/vLK3gj1O8UL8WfngaEahHlk2CMeeCIxPq14AnF4EFhA5LXoBtu/S8CLL7w09vIDZ8BlYnctTZUDKOzU0ataTz5Zpa8n0O9uuXTabLRqYSxxKugiUvILbNR7qzYdzFkDOA75vd2CwK7yypYFKfLEJRnXeW7iQY1bhkam/65nrZWNfOm4t30uCPMLF3FieNKaU5GOWF+dvY3hRgYEEaNx1SQEPU6Idd7Xwm+a48CsIBNGc+1XobKxpW8Pb6twnGg0wtnsrhvQ+nMdhIWAuzoXkDs7bPAuCIXkcwuWgyJaTjt2rUR5p5e8PbLK5ZTKYjk1MHnEofX28cwkZjrIWXV7/cWe+Zg84g31VAflyw3u9igLsZKhYjlrwA0QBy8jVUlo5ma9sWXl37Go2hRkbnjeKE8hPJs2fhDjSAroHFhnjvXJASOeR4KD+Ydks2t366mdPGleGwKgx2tUDDBuN+99chSyfC2PPQnPlc9v4mtjcFGFCQxrmTexKP68Sl5Llvt7KtMUD/fC/HjixhYlYAEaiBBY8imrchcwfAhMvRXbnc9GUdKytbKUp3cur4MrLdNjbV+SnLcvPY1xuxKILz9utFUbqT4gzXf+qx+6vkl47fv1rD59hjj2X06NHcdNNNCenPPvssn332Ge+//35n2lFHHcX06dO54oormDhxIjfccANHHXUUAA0NDRQWFlJTU0NmZubP1msaPv9dxGIxLFu/QjgzYfkbsPJtOHc2vHo0+GsTC/c7GA78A7JlO+LNU4wH3e4cdBcVg47kpdUv89aGtxKy3FY3Lx30Iv3tJfBAkZGoWOCi72Dxs7D4ua7C/Q+DkrEw+/fG7+urWR3cytmzziYUDyXIPX3A6Zza/2SKHhqceHzvAyDUZMwsfXZ91/neUMPChsX8ds5v0WSi/reOv5XaQC3Prnx2D/mnUFSzBr78PdSt6TpAscApb0P1CvjqD+DJhSMfh3fPhmggQbYcdwly3IUM/fN6Hjt5BFNz/fDWaYja1V2Fzv4M+e2DiE1fwsDfwPCTIEU711+7ga8q5nDnD3cmpFuEhcemP8bkvHFwZza4c+C4F+GNkyDqZ+fVq7hn0T18U/lNwnG5rlzunnw3+e58Sp0FfFv7PZd/fXlS+9wy7hamFe9P7gPlu53XRchJv0Ws/wzxyTUJ5VEsyJPfJN5jKn1v/YL9++UwtTyHmCb5YWsjk/tkM3d9PU+eOAhX60bES4cZs3K7t9vYC5HjLwYUxPdPIBY9k1iHzYM86xOE1QWr/w79ZsJLhybJYewFUDIe3junS3afA+GQ++nz4DoePH4Yz3yzmRtmljOm1IOrdRPi5cOMmbMEfS5Ajr+Miz6uY3yvbL7ZWM/c9fUATO+fy+njS/lqXT2vfb+985hp5blM6pPFnz5Z25m28Y6ZLKpbxGVfXUZcxhPq+N3Y33FgyXTCeoR7Ft/LNxWJ1yvHmcMzM57hpm9vYn3z+oS8vul9eWTaIwRiAc6edTaBWGI/PLn/yRzd+2hO/fTUpHpvGnsTM0sPJEsHPrrceJkA6DuDyiMe5q/r3uDVta8mHOO0OHnxoBcZ5OoJ/u3QuhMy+8BjI4z2yh0AJ/6VnvevQ1UEG68dhFj8DGLhE4nXx+ZGnvkJoYxyBv5xDgCqInjujNH87ccKPlpR3Vl08+9Go2z8DPHRFYkyFBV54uvES/en7+1fdybfcHA5g4t8PD1vMxfu15vzXl5CVNP5wxEDOXhQPvnpTkwMfun4/avc4xOJRPj4448555xzkvIWLlzIpEmTEtLGjh3L8uXLicfjLFmyJCE/OzubsrIyVq5c+W/X2+T/P5bWHYhZtxhLQEtfgOm3wcLHk40egA2fg78W8cOzyUYPgG8a7TF/ktEDEIgFeGDJg1TJtq5E1WZMU+9u9ACMOB3m3NX5szLexD2L7kkyegBeXfsqgXgIpn3YlbjuE3D4oPxwmH1rQvmqaC23f3970qAOcP/i+5nZY2ZCmiE/ANV2YwZid/Q4fHwlDDjC+D3yTJj/UJLRAyB+eBIR9fPIdAdT+mQaMz27Gz055RDxG0YPwMTL4OOrUrazXwty/+L7k9LjMs4fv/8j24NVRsKoM2Hu3YYRcF0VO9t3Jhk9AHXBOj7Z+gnb27azI1TN7Qu7aZ8l9xPQEq+B+OFpRDSImHVzUnn0OOLjK7G0GUbAvA31ZLhsvDB/K6eOLSXDZWPehnqc0QaYdVOysQKIRc8gon5EtC3Z6AGI+hGzfmf016HHdyuHRc9CVi9jyXOX7E1fQvNWVty8P/d+vo7LpvUhP92JM9poyNnD6DH0eRYRbePCsaVkeWydRg/AKWNLAJFg9ACcPLaEB75INFCqglXcvvD2JOMD4IElDxDUw1T4K5KMHoBMRyaLahYlGT0AG1s2Mr9yPu9ueDfJ6AF4Y90bNEeak9sHeHDJg7THAxCs7zJ6AEZdTiAeTDJ6AELxEPcuvpcqrRk2fw22NIiHwdcLAFG3FjZ8zqY/HcQLB7mNa7mn0QPGPfP5DTjCXe2p6ZIb/7aC86b0SiiqRNsQn9+YLEPXjP4WqExIfnj2RjRd8psRRfx9eSWHDMkH4K5P1xGIpXiOmfwsv0rDZ/369TidTr7++muGDh1Kr169uPDCC2lra6Oqqoq8vLyE8rm5uTQ2NlJfX4+maUn7eXblpyISidDW1pbwz+S/iHAzDDwKNn5h/C4ZA2s/7L78irdg5Omp8/r2ZX7l/G4P/aHmB8K7D5z9DzMelrtj80CoGeKRzqSQFmJ5/fJu5X5f/T1MnJiYuGk2xIMJcgDaou3UBetSyglrYepD9XgsnoT0hbvku1LMeLbsMPaSABQMgx3fd6snW+aw/8SJKG07ESveTswbfrox27YLXYO2qmQZF2+gor2CqB5NWUV1oJq2aMc9WDActi8AQLPZ+HBL99f1qx1fEYqHaI22UhtMYfQCES1Clb8KLlzXlejMhOZtxmCXitYKCLV0/pyzvo7hpelE4joLNjUAIGJBxPbvutWNTV8ZS43dsX2BcZ3joc7zTcmWuVB+SGLaT2/gsNmobg2T4bahKqJDn73I2fQlQ3tn8t3mrmei3aKgCMGKytaEonaLQiimEY7pCent0XaqA9WkIqpH8Uf9fLLl05T5Ewon8NnWz7pV7++b/06avfu39WV1y+ib0TdlvTvadoAlvytRtUHviXxf/cNe5QXjQVjxtrEXrnEzHNdlpIoVb6O07mDixImwdV63csTOHxCxYEJabVsETe9aVPnyvHJoqzT2MKWivRrCLYnnpek0+CP4nFY+X1XDfn1zOtO31Kcwkk1+ll+l4dPe3k48HueHH37ghx9+4KeffqK+vp4rrrgCTdPYc3VO0zSEEGiaYf12l5+Ku+++G5/P1/mvpKTk33NSJv9mUl/fZP6vXX7Pen5JvXsvk7pv/tLz2fOo5ONSpf1zdf3/eVx0d6/u7Tx+/hz/D4iu8/6n6hGCn23jX9SNUrW/SPHXzwr653X4xTX888J+7ti99oNu+s7PVyo6/nX8nSrvF8lJLfr/itj13x7C/unz3cf5VRo+2dnZRCIR7rvvPpxOJ16vl9tvv50PP/yQzMxMGhoaEsrX19eTn59PRkYGUkqam5tT5qfipptuorW1tfPfzp07/23nZfJvwJEOaz6EvjOM3zt/gAFHdV9+6PHwY/KUNwAbNzK5aHK3h47LH4dD3c2bYt0n0HtaYqGoH5zpxtJbB07VwfCc4XuVywcfJCb2ORAsrgQ5AGk2L3muxBnPXThUB1nOLPzxxLfA8QXj4LvvIJhi1jO9FOxe4+/q5VA6oVs96bUf8777Ds1bjBx6QmLe8ldhyG5pigppRckynupHsbcYu2pPzgMK3YV4rR36VC2HMuN6qJEIR/Q6olvVDig9AKfFSZo1rdv2sat2Cj2F8Ez/rsRQE6SXJSwhJeArAUfX7MPU8hyW72jGblGY1MeYWZZWl7FRuTt6Twd1Lw60ZZM7PPecsDc5Pfcz+tzuDDuJUCRCoc9BYyBKXNeRVieyR/f9mD4HsGJTExN7Z3UmReI6miYZWuRLKBqJ6zitquHFtRtem5dCd2FK8XbVjtvm5rBeh6bM/67qOw7peUjKPICj+hxF665ZyBQMyx3GxuaNSek2xUaJtwTiNV2JWhQ2fWfcA90wIncETtUBQ44HBGT2hHfO7syXQ05ATyvhgw++gJ77dytHloxDWt0JaXlp9gRb6MDn1hv3hbWbTcneAuOZtvt5qQqZHhvNwSgHDcpj3ob6zvSeWe4UQkx+jl+l4VNWVobD4SAY7Jo2FELgcDgYNWoU332XOK28YMECJkyYgNvtpry8PCG/urqa2tpahg0blrIuu91OWlpawj+T/x7ivjLkQX8ylirGnAdf/wkmXAqeFINfv0PAk4ccd6ExMO9J21zSrB5OKj8pKctj9XDt6GsoFLv1Dz1meIyNOT+x8I+vwrRbOn8WWTK5ceyNOC3JmxDPGHgGHosLVp/Xldj/cAi3wroPYcafEsoX2vK4bfxtqCJZ/+vHXs8X275ISDt9wOm4LS4oiMAPTyceoFjg8Ee6lgaXvgyTrzKW6/ZAjr8EafNw5ddhFmxphr4HIfMGdRWoXw92j7HhFmDBo3D4w0Yde+BRnVw3+rqkdIuwcOuEWylzdQyoP74MU2809HmgiBJvCfsXJw88ua5cDu15KGVpZZS5Cvn9hN9jEcn1Xj/merxK4kAhx12MtLmRB9+dVB7Fgjz8EeJpZQBM7ZdDUyDKuVN68doPO2gKRJnaL4eQLRsOujt1u429EGnzGv/GXZxch82DPOhO8OTDincMObsM0d0Ze6GxBKN1LRHKPjMgoyfD7/qG6w/uzxNzNlHbEiZozYKZd6WUI8degLSl8fSiHTT4I0wrz+3Me33RTkBy+viyhGNeX7SD6w4qT0grdBVy24TbUrbztaOvwaU4KPYUp7xezeFmxuaPpX9m/6S8fhn9mFQ4ieP7HY/bmjyon9L/FDLtqZ1Urh19LR7VBa4cZL/dDKulj+KxuDlj4BlJx7gsLm4YcwNFaqZhoEb9hlHSVgGAzB0I5QfT55ZZ3LhMIK0e5ITLkiu3ueHgewnbu7ZZqIrg3mOH8tz8LQlFdVsa8uB7kmUoKvKIR4i7E18YrprRF4sQfLC8kqOGF/H5KsOw+91h/fE6UjzHTH6WX61X1+WXX044HOapp55C0zROOeUU+vTpw+WXX86QIUN47733mD59Op9++imXXHIJq1evxu128/DDD/P6668ze/ZsXC4XF1xwARkZGTz88MO/qF7Tq+u/D62tBiVQBxY7omkrcus8xMgzkavfN9x/bR7Dnb1gGIRakb5CRLARvn8KUbMCMnoiR58DvmJ0WzrVhNnSupXX1r5Gc6SZMXljOL78ePJt2Tg+uBDR2uHOPv5icGUj4xFoWN/hptzhzj7mPGSwy53df+bH1EWaeGv9W/xY9yOZjkxOH3A6PdLKKFr6WqeecvQ5iJLxoIWRK99F5A4EdxZy4ZOIlu2QP5S6A2/rdNfe0LyBYm+HO7szm1fXvLqH/B4Ut1YZe0gUpVOOzBsCEy4BezrMvbPTfVxMuRZpdcGS5w139l3u1Fn9uGthkBNHG+7s540vIC3eCJu+RKx4E4RAjjjdeCOuWIJY+gIM+A2ybHyiO/sEw529usOd/eU1L1PRXkF5ZjlnDDyDLGs6WV/+fjd39uvB6oClLyNKTmVnsZfVjat5c/2bhGIhphRP4cDSA/HZfBToLvj6BhoO/CONsSbDnb1pPcXeYs4ceCZ5rjwKFjyK2LbAcO8efzFk9WNpWxojfe2I9mpY+LjhXtzRProzm5Pf3MYxI4rI8dpx2yws3tbEV+vqOGJoAZP7ZrOpzs/Mnh3u7Ete6nCX3yW/nJVBH+/9WMkfJruhcQN8v8udfSKMPtcIKdCwFtw5u7mzvwjbE/VkxVuI9Z+CzWW4sxePYW1NiBZ7Ll+uqaXA52RK3ywyXDYybRoWfxUsebFLn3GGW/z3zW7sFpV3l1Qwvk8m4ZjOu0srCEbjXDK1N31yvWyu8/PGoh00BqJM7J3FCaNLaA5GeXHBNrY3BhlUlMYNB+XTGGvh1TWvsq5pXYdb+ZkUOPPIj/jRnPnU6G2saFjJ2xveJhgLMq1kGof1OozGUCOheIhNLZuYtW0WEsmRvY9kYuFESqSPsE2hJlrPO+vfZXHtYjIcGZw24DR6pfXELmw0x1p5dc2rrG1a21lvviuXPN3C+nYHA1zNULm0w53db7izl4xkW9t2Xl37Kk3hJkbmjuTE8hPJtWfiDtQbm/0tTsTfOtzZh54IfWfQpmbxh8+3cMrYDnd2dws0bDSeH4F6ZOkEGHMumjOPy/++me0NQQYWpnHOpJ7ENA1Nh+fnb2FrQ5ABBV6OGVnMhMwQIlBthE9o3orMHWSET3DncfPsWlZUtFKU4eTUcaVkewx39tJMF499vQmrqnDelJ4UpTspMt3ZE/ivd2cPBAJcfPHFzJo1C6/Xy7HHHssdd9yBzWZj1qxZXHHFFTQ1NdGnTx+eeeYZhgwZAoCu61x//fW88sorWCwWjjrqKB555BHs9tRT63tiGj7/veiBRohHjXX2eBjpyjYMHMUKFhtIULw5+ENRWoJxCu1hiLaDqoLqYmfETrbbRls4Rrpd0K63EolHybD5cCgOAmGtI8Bdx3JWuB3EHgEM4yEjIJpqQ7O6jcB6MT/Y06mM2vE5Y7RGjQCDWYoX9AhSdSBCtSBUcGUYm2mFQNp8iFDDbgEMO4IMxkLgzqFVC9GuBXCrTjIiFrDG8Ku2RPnxdpBaRwBD1ZiBibWDLYPKqI10lwV33A/RNuNNNx5G2jNA6ohwM1gdhC0+GkISr9OCphmPi0BUw25RyXKpqP4qkALNW0CdP0aO24IlUAcyZgQwjAYh2gpWL626l4Cm4XFYEbE9AhiGbYCg0BbqKh93I20qPhFBRFuN83Dl06C1EolHcFldpFnSjHbWQkZbxSNITwEt8SCBWDtuq5uMiAKyBenONoxTi3FerVGwW1WicQ27FPhUv9HOdh8Bi5fWoBFIzqIIFEXgtKo0B6MIYSw1IEBVFKIxnZiuU+jQEOFGUB2ErT7aYwoum4I/EieuSbKcKo5Ys9HOzixqoypIQbpdxRFtAosKWgSsXmPjvsVB2JJOW4yuAIbCSsyVQ30gjsdhJRyLE41LXDYVTZcoikBFYrEouGXE0EdxELZlEIhJNCQxTeK0qMR0iU1VCMc1dGkENZRxidKxqVnBaJ9AJI7bpqIIgT8Sx2kzZhpCUY00Z5TWWDsuixFIU6qSRt2OVREoCjhUQVO0ibiMk+fIxhJsAkcGjfF2VNVKJB5BQ+sIYKjh0NqM+0ixELR5aY63YldsnQE/a2JONCnxOOK0a624FCeZ0kajbicc10h3WonGJR4bWEOGQSOdebSFdXx2QaPeTlhG8Vm8eMJtYHUCkrDiw2pVOgIYGvd1SyhOKKbjsKqoCvgjGk6rgluVOLWOAIa2DKqiVhxWlbgu0TQdt1UlENOxqEZAQlWBtrCGTVWIaTqqAvk+F7q/3uhvNh8NugsJ2C0qgXC8s4+F4zpZHjuhuEZ7KIbbppLtNQMYpuK/3vD5T2EaPiYmJiYmJv99/FfH8TExMTExMTEx+XdgGj4mJiYmJiYm+wym4WNiYmJiYmKyz2AaPiYmJiYmJib7DKbhY2JiYmJiYrLPYBo+JiYmJiYmJvsMpuFjYmJiYmJiss9gGj4mJiYmJiYm+wym4WNiYmJiYmKyz2AaPiYmJiYmJib7DKbhY2JiYmJiYrLPYBo+JiYmJiYmJvsMpuFjYmJiYmJiss9gGj4mJiYmJiYm+wym4WNiYmJiYmKyz2AaPiYmJiYmJib7DKbhY2JiYmJiYrLPYPm5Aq+++iqzZ89OmTd48GCuu+46fvvb3/Lpp59y5JFH8vDDD//LlTQxMTExMTEx+Vfws4ZPeXk5uq4DcOWVV/LII4905hUWFvLcc8+xdu1aXnjhBe644w6ee+45zjvvvH+bwiYmJiYmJiYm/yxCSil/aeHCwkKqqqoS0qZMmcILL7xA3759WbduHeeffz7ffvvtv1zR/1+0tbXh8/lobW0lLS3tP62OiYmJiYmJyS/gl47fv2iPT21tLQC720jLli0DoL6+nr59+wLQv3//zrImJiYmJiYmJr82fpHhM2LECADuvvtuAN5//32uueYagM5lsF1omvav1M/ExMTExMTE5F/Gz+7xga6ZnrPOOotFixZx4403dm54VlU1oZyimI5iJiYmJiYmJr9OfpHhE4vF+Oyzz5g9ezazZ8/mww8/pLS0FIB+/foxb9489t9/f+bNm8eAAQP+rQqb7OM0bQUtBooCnlywJ6/j6v46RLgNkEjVjpJRmlLU1gY/mg6qgMI0B3a7Za9yQrYc3G4nAMFgCEekHqFFQLWBEBCLGHrZPEgkIhoCPQZCQfMWEhNW6tqjxDWJz2klU7YiLFaItIMWASGQNh/NIo22cAxdgteukmXVUQLVIHVQVOJppexsiVDmUVAiTSA1I09qYHFCPAJIkALNW0RNUCMS1+nhASVYD6oV4uHO8wrbs6kPSlRFENclBV4LNn8l6HFQFHR7DkKxUq01EdViCCFIs6SREW4HIY1z16KAQNp9VEbdAKgqhGPGjHCmR6c12owmNayqlWJbLrRt75CfzdagBZtF4HNaaQrE0HSJVQGbVSUY1ShNt2Np2wG6BkJFTyukJiDJ9ViT0hvD4LCqNAdjFNtjKOEGo31UG1F3AVVtMaxAoT2IiLWB7GqHiK7S0tH2qgIKAlUIrBZBMKIRl2C3CCyKwGW30BSIdvahdLeVSExHCAhFNCyqIK5jtGegqrPf6vZsKiNWNCmxW1SicR1dShQhsKqCSFxHVQQCsKkCVRGE4jqaLhFC4LAohGMacR2sqiDNYaEpGMNrVwlGdSQSJFgsClJKVAROm0okrhGJd1zfQLVxzYSC7sxGaCFELACKzehHetzI8xYSiCs4rUpXOysqureQuFS6zqtTTgQRawcpQbUhrS4adQ+BqE6B10JdpI6YHkMRCum2TIJhC+G4jiLAY1cJRjSK7SFEtLVTBhYHRAMd/dFCPK2E6rYoTptCMKxR4gghIq1Gf7bY0Z3ZqP4q0HVwFVGttxDRoggh8Fo9iLCTdKUNoargyGBHsApNaqhCJdeWTWtUIRDVcFgU4rokrklUBdKdVlpDcexWhVBUQ5dgUQRpTgvhmE441pWWm+bAaVPZ0RjAYVMIhDW0jj7VM9vzix93Na0hglFjFcVpVShIdyWV2dYQIK5LVCHI9zloDESIxHUEkGa3kp1m/8X17Sv8os3NdrudwYMHs2bNGm688UZuu+02hBAAzJo1iwsvvJAzzzyTl156ieeee44ZM2b82xX/d2Fubv6V0rwd6tfBl7+HurWGwTPyDBh7PmT06CwmGzbBvHsRaz4ALYosmwgz7kDzlWLx5gKwsynIgk0NPD5nExXNIbI9Ns6d3IvDh+ZTkuneuxxPKUIBpWUrzL4VsfMHsNhh4NEw5HiYcxeMuwiZVoj4+yXQsgPcOcgJl8Gg37BTzybHpeAIVILFhmivgS9ugQ45ctAxsN91BB35fLqwkuNG2WDlO4jvn4JgI2T0QO5/A5ROhEgbQovC4udg1Xsw5RpwZcN3f+6qd9IVMOAIo3G2zEOUjIVvH4Q1f084L91bzMsrg5w10IJoWIf48vdQtwaKRtFw8htUBmt4cOmDLKtbhlWxcnCPg7lw6IWUWbLhrWNgx/fGQLdLf2cBF7y+koeO7EnUFeX1ta/zt41/IxgP0i+jH1eNvIreaT0peGIccsQZMO4CasIe5leG+PNXGzuvyynjSrlylB2xfT5i3n3Qst04r/EXw6BjIVX64OOoizrItYbgh6cRy16FqB/yBiEPvB1ZMAIRaYF59yFWv2+0Q+kEmHkHureUoQ8uIxDV6J/v5eKpvaluCTOqRwZIySV/XUaOx8aTp43k05U1PD9/K42BKMUZTq44oC/jembyxZpaBhf5eHfpTu47IAPRuMFoz9rVYPcih58G4y6iLubks80hnvt2CxXNIbLcxvkWZTj529IKLtivN+tr2jhkSAFPzNnM+8sqAJhWnstZk3pwx8drCEY1rj6wH6N6pPPpylqen7+FBr+hz7mTe2K3KAwsSMNpU3nu263ce0AGonF9lz4Fw5GHPgDzH0LkDQJ3Dix8oqM9s5HjLjb69fYFiHn3QvM2I/3QB8CRjvjiFqhdtZuchxEbPjP6W58DkZOvhrQiaixWtrRt4aGlD7GheQMui4vf9P0Npw04DWvQyoRHlrDylqm4wjXwzf2IVe8ZhlnJeDjgVvjhGVj7IaSXIve7HnpMIWjP6Sq/+m+gxZAXzIXNcxALH6fp4oVUhKp5aOlDLK1dilWxMrNsJhcNu4gyWx5V8QaW1P7I0z89TYW/ggx7BqcNPI1Dex5KZb2ThVsaee377Z3t+dsD+jKyNJ3Za2p5fv7WzvQrDuhLz2w3V729nJ1NITLdNs6f0otDB+fTFokxf2NDQvnLp/dhYu9sSjKTjZhdRKNRtjVFuG/WOr5eVwfA/v1yuP7g/pSlO3E5rexsCrJ4WxOPfLmRHU1BMt02zp7YgwMH5nHEY98ihODQIQVccUBfeuX8cmPrv5lfOn7/IsNnlzdXa2srf/jDH1i7di3vv/8+Tqfx9vvOO+/w6aefcvjhh3Psscf+687iP4Bp+PxKWfMhvH16cnrZZDjqccjsiWzcjHj1aGPQ3x3VhjzvK0TBUFqCEf76w07un7U+SdTJY0u4fHpfCrSq7uWcPwe0GOL5A4030N3J7AXTbob3zoUDboNtC2DzV53ZcuSZyCnXI/QIYvVH0Hs/eGGm8ca8hxx56rtIxYKYczdixRtJusqZdyGGnADPH2AMUv0OhsLhMPee5LJnfwY/voqYcAm8dZoxeO15Xud+STxnIJbNXyDePMVIFwpcuY3VsQpO++w04nucb4m3hKcOeIqyh4cmysvoiTztPV5cqzBjqIWb5t/ET/U/Jen12PTHmJo3Ee7KMQyPo5+i531rEspsvnkcyoq/Imbflnjwftcj7Z7kdECOOBX2vxHeOx+x8/vk/MuXIl47Dpq37tEOVuS5X9Lm7smwu+d3Jj94wjBeWbiNGw7qT1MwiqZL5m9s4J2lFUmybzi4nOHF6dzwt5V8eeUkrFu/QrxxUrIOJeON871/bVLeb0YUke6y8tr323n29NHcN2sdl07rw2WvL+ssk+a08PjJIznrxUXoEn53aH8Wbm5izvq6BFlXHdiXkWUZ3Pz+KkOfbV8jXj/RyBQCTn3P6K9FI6F0Anz9p0Rl+hyA7Ll/Yjtn94Up18L7F+4m51147zwINSce7/DB1ZuYV7uQy76+LOlch+UM4+7JdzPlrhVsva4/4q/HQdOWxEKqFU56Hf52Qad8ecDvYehJiJcP6yr/m2dg+3fw48twxTbW6jWc+umpxPTE+6vQXcjbh7/Nh1s+5L7F9yXpdHivw/nt8Mu49W9VfLm2qz1PH1+GpkteX7Qj6ZjfHtCHNVVtCeX/et5YPl5RzRuLdiaVv+6gck4dV0K6K/VszJZ6P8c89R0twUTdvXYL7186iVyvjXd/rOSPH61JOvbo4YXMGJjHpR39pTjDyavnjqNntjtlXf9L/Eu9unbZRj6fj4cffpiDDjqI4447rjP/+OOP58UXX/yvN3pMfqU0boYUAxwA2+eDv+Nhs21+srECxpvjvHvQW6toDMR4Ys6mlKLeWryTbJcK2xd0L6diMcy5M9noAeMBHG6BjJ6w4M8w4rSEbLHsVYQWMh7OQ441Bpk9jZ5dciqXInQtpdEDIL65FyIthtEDMPwU+O7x1GVtHkSgHqqWJxs9u85r3j1YArXGG/wu+hxItRLg8WWPJxk9ADvbd7KiYQXclDjY0rwVKhZz6phCaoO1KY0egIeWPsSOcI2h446F0F7D0uvGJ5RRIk2Ib+5PPnjA4anTAbH8dYgGUho9DD8VsWNRstEDxrWYcxde2Z6Q/MTXmzh6eBFfrq3Fqir0y/Py7o/JRg/AE3M247Jb2NEUxNq+M7E9d9dx5/fgr+HH6yck5X2wvJL9++UQ0yQvL9zG+F5Z1LdH6LXbwNUWijNnfR379zNmMR/9ahPHjSpOkjVrdS0N/mhqfXpPh02zDWNi+Gmw4NFkRYefhvjmgcS00ecYs4a76DUdNn2VbPQAhFvZETJmC1PxU/1P1AZrWX/7gVC5NNnoAeO6/PA0DDu5M0l8+yBE2xPL5w2CZa8AUGuN8OTyJ5OMHoCqQBXNkWaeWv5USp0+3vIx/niQY/doz+n9c3lzcYrnAvDct1s5ZmRi+UyXnbcWJxs9AE/M2URTIMW9DwRCUd5dWpFk9AC0R+K8/oMxC/XI7A0pj/9geRU9s93s2m5b0Rxi0dbGlGX3VX6R4XPiiScm/L7qqqvwer1UVlb+W5QyMUkgFko9UO1i50L0eBix8Ytui4htCxCxAC3BaOea+Z7oEizE9y7HnobYtpc4Vdu/g/whEG41Zkx2R+rQWoHYMsfY07N9QfdyNnwOkbbu88OthpG1O1F/cjlfidF2fQ6EvbbPtxAPJQ4khcOJ6lGW1i3t9rh5FfPAkmKr4IZZWFSVJbVLuj12a+tWIlqkK2H7guS3tFCLsQdqT6LB1Olg7A1p2mos2+zJgCNh46xudRLb5yPioYS0LQ0Bcr0OftjaRCgap6olRHfz5P5InOZg1PgRD0NjaiMbgK3z8Xq9KdWva4/gsVv4YUsTgwrTWLytiYGFiW3zw5YmBhWlddarpnia98x289Xa2i59GjZ2ZRYMh+0Ljb8VNXV/E0pyeloRNOw26BYMM/p9N0T0CFtbu79/F9UswmKxGH2+O7YtgILdZhajfgg2df12ZUFrJbsuTEgLs7S2+37bFm2jPdZN/wG2tW5jYknXUpTDqtAcjKJ3c933fKakuyxUt4b2Wr6zn+xBUyjGgk0N3eo2f1MDraEYbeEUL18dbGkIUORzdv7+am0dwWj35fc1fpHhs3u05l28+eabFBUV/av1MTFJRrWCspd9+K5shLAiXVndl3Gmg1CwW9TuywBSWJHOzO4LaDFwZuylngxjI+YuvffE7kU6fF1lu8OVY+wd2huWrgdbt+0TbjWWGyKtxuCwN70RiXIifkCQZut+yjjT0U1bubNBCDLs3Z+jVbFi2b0+d05SeAwsjtQH/1zbOHypDaNIm7EPqjt2tcPueqoCIcDnNK6nx76Xvgg4rR19TLGk7gO78GTT3U4Dj91CNK6T7rISiGj4nMb/d8fnshKMdA1mlhQetYFInBxPR1sp1kR9on7jvoDu9UyVvmvTcYKc7q+zRbFgVbpvh84+9HPXZdd9tQvrbv0/0g72LiPS2Njbfb+1q3vvP2n2NNrbuwzgmCZx2vb+7LCqXf0mGNVx/0w/cVhTy7NZlM6+lop0l63bY3fhc1hpj3TNGGW4bdhVsZcj9i1M33OTXz92H/Q/LHWeaoPisYaHxogUe4A6kKPPBV8ZaQ4LAwqS37IBcrx26loje5ejqsgxe/kkS58DYdu3UDTK2Dy6O55ccGTC8FPB5oFRZ3cvZ/jJxkDl6sawKBppyNhFw0bIH5pcLtJmDBo/vWXI7O68Rp+D9JYgBxzVlbj2I/IdORzf7/hujzuq95FQkWLZZ9jJVFQ0MCZ/DBaRegCYWTaTNGvHtVCtUDqe8t9/mVjI7jVmJvYk3Jo6HQyjy5uXejnyi1uQI07p9nzkqHPQvIUJaQcPymfO+jqOGl5EptuOy66S5balPH5IkQ9Xx4AnHenIgUenrkixQNlk+t2W/B3ELLeNaFwnqukcO6qYT1ZWM71/Ht9tTpwFOHp4EZ+vMpYKBxelsaE22dBrDUc5ZEiBoY/dZ2w+38Xaj2BIx5aF2jVQODJZz7q1yekbZ8HAI7t+r/u4S04K0qxeDu5xcMo8i7AwNn8sFRUVe+2fDD8FVr/f9Tt/aIKhgxY1DCGPsfRX5CjghPITuhXntrgZkj0kZV66PZ08Vx4fb+4ytDRdEoxq5HhTG0wDC9LYXN9VPhrXcdnUvZb3OlLfF3lpTs6Y0KNb3c+cUIbHbmFESXpq/V1WMtw2WoJd/f+kMSUJoWf2dX7W8Jk2bRrTp0//h/61t3c/hWhi8g+Tlg8H/B6y+iSmKxY49nmwpwMgPTnIqb9LOlz22h8GHsWq2gD1/giPnjSC08Ynuri7bSpPnTqSbJ8d6clLLafn/lA8FoYchyydmKzn1JtgwyzDWJl+Kyx6tivP5kGe8CpxVz7SlYNc9goMOxHKJiXXM+OPSEcGcXce8oRXwbqH94c3H3nUE4a77iH3wxGPQv4gOPKx5OWd0/6OtDiRRz8JNi8ceHtyfT32g4G/QY9HYNpNcO5s49/pH2APt3JYr8MYnTc66bgrR16Jz54OLyYaXPLA3yOdWdz9TQVO1cndU+5GFYkP3V6+Xlwy/BKymsKgqMhjnkO3pnfNTnSgO9KRx/wFTvwrnPRX41yz+8LXdxrn1DHQdbWzG3nCa+iODOM4JbFe6UwHRxbygBSbontMgSHH0uf3czrTeud4OG5UMQpQmO5gU3074ZjGU6eNwrXHDECOx869xw1h9uoarj+onKrWGEz9HTKnPLEiRUUe8xd0mzdpYHTZVO46ZgjPfLOZcT0z6Z/vZXKfbL7dWE8k3jUbdtTwQtrDMapaw+R47Nx/3DDe3GM/SY7HzlUHlrO2upWbDxtAVVsMpt7YpU9blbGUOPREWPQMTL8ZPHmJuv74CvKox8Cb35W26j0YejIyp3+XnGATDEvexM2Q48isD3DRsIvond47IUsVKndNuQuXxcX+T61COjKRM/6YLKNsomHobOvYcO7OQR79FNLqSiz/zf1w/Etg82C5O5eZZTMZlz8uSdylwy7Fpbr448Q/kuNMvF+cFicPT32YPFsWb+2xifnNRdt5+rSRuPe47tkeG/ceN5Se2S5GlhozX06rilURPHlq6vIPnjCM0kw3OxqDrKpsZfHWJjbVtVPbGmJDbTuZbitvXTCeqw7sS9puBtJRwwsZWpxOSaaLe48bSt4erupOq/Ece3pe1xLrlQf2JduT2lDfV/lZr66vvvpqb9kpmTp16n+tdWl6df2KadoCdeuMDc3eQug7Axxp4C3oLKI37zBieqz7xJga73cweAvZ3Kxx4HOGJ5dNVTh3ck8OHpzHhz9V0SfXw9geWeSlOfB0PGT0ph1GLJF1nxpT+X0PgrQiNje7cDhiFDmj0FYBm+cYOvSYYsy6qFbjIa1YoGY5VCyB7H5QNgnNUUBFWFLfHmWUL4CIxsAqDDkbvjDklB+GtHl4+qcYAjh/bA5qsNY454ZNxkxP4XA0TwlqpBmatyD+dj60VhheZTP+iIwGDFf0kWfC+s8Qc+/qWiaYdKWxsXrDF8hQC5QfDGmF6MKKUrUE8cnVXXsnXFlw+COQNZKdTp2aQC3zKubhsXmYXjINj9VDQaDJGDw3zDLewPsfirR5OfXdau44ejDfbmjgkOEeAvE2vqn4hoaQMQvUy9eLEpEBK1+CPjPQ7V6WNzvJ9NhYUdHKqso2bprgQqz7GDH3zi7900uRRz0JvmI0dz6qvxIql0HVMsMgKpuE5sxjwN0LWHftUJSY39jA214DPSZDdj/8tlzc8SZEpN3oJ5F26HcQ+IrZEcvm600N1LSGGVaSTobLRlTT8DmsZLiszF5bx0OzN3DI4DzOm9KbH7c3s6XBz9DidAYUpPHawm1M6ZeLqghyPDZ6+FQ80Tqjb2z71jAg+sxAt6bx0qoww0vTqW4Ns7KilV45bsrzvPy0s4Ue2W6sqkKm24bTqlLbHua7TY1IJNP75xGIxJmzro5hpekMKkyjvi1MhtvOsp0tbK7zU57vpSTThQK47RZsqiCqSYo8IlEfTx6UH2YshW6bDyVjob3aaNPMHlA2GS2tANVfa2yOr/wRsvtAj/2MPt6wDrZ2yOl/mDHDuP5TiMeM+9OTixQ2/NYMWmhha+tWFtUsItuZzX7F++FUPLz5fQsABwzIY4i7FRH1w/pPINxmXBdvPlQs7XCbHwqFI4l6S6hojdHT0phYfthJxizwju8gexoVGYLaYB3zKubhsrqMfmtxU/TUFOSV69kRrmFN4xpWNqykh68HY/LHkGnJZNaaVvrmedlU52djnZ9BhWkMLfIRjMaxqirLdjazqS5AWZaLLI+Nez5bR1soxu+PGESGy0pxpguvXRCNgz+i8eOOZjbX+RlU5GNQYRqZbhvNgRhXvLWMtdXGRIFVFZwytpR+eV5u/mAVAGPKMrjl8IHM21DHlL45ZHnslO7mBr+13s+qyjaWVzTTM8vDhN5Z6LrknaUVuOwqMwfm4XFYKM383/fogn+xO/u+hGn4/G/R3Oxn5hPfU++PJOXdevgAzhxfamys3AvxeJz29nb2+/Mi2js2FDqsCj9cOxG323ig/JyM7mVrQNctuGDBCs6fXcsJI4vpkevGKgTHjy7CZU9+Y5P16xHP7m9s/t6d9DLkmR9DzXLEWymW7expcPEC+O4xpGJBTrwC0V6FeG46Sbt2hQLnz4FZtyKPeQ2t43wXLFjAxIkTURSFSCSCzWbrTD/zM2OT9fBiH38+eQTHPb2Q+vYIG++Y2VkejPhguq6zozlMcYaL2WuqqWuPUpThZGqfLGxbvkC8dWqy/lYX8oK5nP9JK/ceNwyP1Wh/TdN4bsEO7tsjVMHu9Q78Y9dszsuHeJg0yZhxCwQCHPLkEp46bRQ3v7+yY0+EQksoxsrKVmKazgeXTuKiV5dS1RrulDGtPJe+uR6OH13MjIe/6UzvneOhwGdnQq8sTh5bgtdhwdrRR7bUtbO1MUBrMMYPW5soSnfSM9tNdWuQE8aUke765W/n/kiUuz9dz8Y6P9G4TqbbypQ+2Qwt8SGlIM9nI67B0U98xx1HDaRvvhdVCop9dux2C7qu0/fWL5h/2TDy87tmdYQQaJpGY1Ajx2PtTNsdKSU6oAqBBCpawtgUQa63S//5CxbQb9hYCtOd/FJaQ1Hc1sTFCF3XUVWVeDzOiwt3Eopq2KwqZVlOhhenk98x8xEIBKgJQp9cT+fLt2xpBo/X2Lm1YAHM6VqmkyPPRs68B8XhIBqNsq42yI5GPy67hVBMp649zJCidMrz3HidXbMrm+v83PLBSnY2h6ho7rr/hIAPLpnEDe+t4OnTRnH128v5cUcL+/fNpneuh5WVrSze1swRwwoozXTxxJzNSed/ydTerK5qY96GegAGFabxwlmjyUv75W24r/JLx+9/7mm9G3//+9+pra3lggsu+L+KMjH5l/NdRVtKowfgqbmbmVae+7PBvVZVt3P0E4leK+GYzrC752NRBJ9dMYW+ean3Df0cWxqDbG8McP4riR4or3VMs1sUweS+OVgtceKajt2iUJTR8ca3+v1kowegZbsRUXpecowSwHgrX/kuDPwN4pUjYNSZ8N2jyUYPGJ5oC5+AGX+A6gVESvZn0B1zjbzPvuAvZ4zmoteWoqVwX1le0UpTIEpDR/v3vTXRqyzTbeOdiyZwwEPf8MVV+3HFWyvQdMmxI4uYWRBBfNON/rEgrP2Yp0+7glVV7fzmqe+Q0oi385dvk92hd9V733FDyfHYO/vDmZ/54bNZ3HfcUO7/fD33HzeE577dwqqq1N50z3+7hbt+M5izXuryVJuzvo456+uwWxVGlqbz444WADbX+9lc72f+pkayvQ6GFKUxsNDHjsYAv31rOasqU9cRicPlB/RNfd4pqG6J8ObinQnt//W6+s6/XzlnLEu2NdEaivHbN7vCCrx74QR+2NbUGc9q8uM/AV35gwrTOHhwPrqUHD+qhIn3fJ1U91HDCjh8WCGfrKhBl5IPf6pKqeNhFWu4/ajBZHt+ZkM6sKMpyJqqVm5+fxWNgS6vp+fPHM3N76+ipi2cUD7Xa+feY4cy+b65AKiK4JVzxpLhjxLRpBEkMLgD8ch+KesTy16G8ReyI1DK5W8s46eK1pTlrjygL1fO6AdAsz/K43M2sXBLU1I5KeHZbzbz5KkjCETjnf1h3sYG5m3s2qP1yYpqnj0jefkY4LUftnP7kYM7DZ/VVW1Ut4ZNw+dfyP9pc/MPP/zAhRdeyJAhqTeJmZj8p1lX3f1+swa/8fmIn6OqJdxtXlyX+CP/vJtoMBKnvj21W+su+U3BKJf+9UeOe3oh1767gh+2NqKHg4jaVXuRLBJdl/ekZiVk9TY2AIfbEQ2pY4IA0LDe2Dhat6ZzZmcXMU1PafR0VtMaxtWNB0pTIEpMM/at+MOxTjlFGU5Dr73oJGpWoABVu8l3WIzPVHTHtoYAuSnC9+9Kz3DZEzao7snGugDp3Wxq3lTn73ZWY3tjgLawoZcmJVv2Usea6jbiu+3l+TnawvG9tn9TIEplS7JxXJrlZE03Bh4YhlthupO11e3EtdT6TB+Qy/qadgrTHWyuTxFKoVNWAH+4++uyO0ZdIsHoAbj1g1Xce+wQThpTQrrLis9p5fTxZbxw1hienLsJl01lUp8s3r1oApGYxsGPzueSv/5IfX09+Ku7r1DqEGknru/9uqytaet0B2+LxNhU1/35bqzzs6U+yMba7svo0tgAnYq2UBy7JXFormzu/hlk8o/zTxk+sViMhx9+mBNOOIEPPviACROSg3CZmPwa6LeXmZgMlxXLL3DxLPB141KN8Yb5c+7Ne8Nlt+x146GqCFqCxnJLUyDKws2NnPjM9+gWW9fm0pRIY89Pd+QOMIIfKio40pCZvbsvm9XHiDuUU04sljiAWVUFZS9NmO9zEIqljpuU7rJi6wg+47FbO+VUtxobnvemv8wdiI5xbXbJj2o6ac7ur0Vplov69uTZv13pbeEoPbO7/4xAz2wX7aHUA3jPbDc1rakHp5JMF2kOY7lIFYIeWd3vtyjP82Kx/PLHstdh2Wv7Z7pt5Kfov1UtYcrzu5/p7JHlprY1TL88b4Kb9u4s2NBA31wvtW3hvZ5Tz2w3Hvte3Pp3w6IIQJLuSixf1Rrm3JeX0BqK8dq54/jg0olceWAf6tuC3HPMUD6+fDJ/Omowf/hwNee8vISmQJSVla08+eSi5A3buyMUsHuxKgple7n2fXM9uGxG3/I6LAnBJFOd75dra7v12gJjSczWzXX22i2dLwS7KEzv/hlk8o/zi+6wV155hVdeeYXHHnuMyy67jB49erB06VIWL17M+PHjf16Aicl/iEGFaWS4Uj90z53ckwLfz08fZ7ptlHdjQB0+pKBzUPtn8NotSCm7lX/I4PzOKe/dufOeuTD4uG7j2UhdR065NnWlFocRyO/5GciBRyPtPph4eeqyQsCES+Hz30HuAOZubiVzt1mPBZsamDEwP+WhxiZOa7d7Vs6e2AO1Y9SWSA4aZMj5bGUNMU8Rcsp13es/6GiOenIhmW4bGR3y/76sklPHlqU8JNtjw2u3UreH4bN7+i0frOK8Kb0QKcZ5IeC8Kb245YPkWTa7RWHmwHyW7kiOXJzltpHrtXcO/KVZbi6dltrItFsUDhtakDKvO9IcFg4ZnLr9BxR42VzvZ0yPzKQZhKOf/I5DhhQkpe/ijAk9+PvySo4YWkBMkwwqTN4v8faPlfTL87BoaxO/GVmUst0Azp/Si+xu3Lr3pDTLTbrLxqnjkq9jXJd8v6URt13l8jeWMepPX3H2yz9ywEPzWLazhROe+T5pqerFEODIQHYT+kD2PwxpS6Mky8Vl0/qkLGNTFY4a3hWzLtNt55zJPbs932NHFvPJimq2NAQYUuRLWeaggfn8tLMlZd5JY0sTlg375XkS7jmT/zu/yPCZM2cOc+bMYe7cuXz77bdIKRkxYgSZmXsJ9GZi8iug2GfjlXPHJXhCqIrg1HGlHD286GeDkgGUZbl58rSRDC5KfPgfOCCXaw4qJ28vM0I/R0G6k0GFafzp6MFJD8kZA/I4fGghb6cIe/+iP4Juz0Ke/HaiC7vFgTzgNqTVAcWjDbf83Y0jTx6c+g6EWpH9Dobpt7Glzo90FyKPfjrxa/cOH/zmWdBBHnIvMW8pj321kefOGN3Znm8s2sFxo4qYVp7oFjys2MdjJ4+gLMvDq+eOpSwrsf1PHlvCMSOLqWwOkeOxc9aLi7npkP5MK8/BH4lzw/trkIUjkdNu2UP/XOQp76A5Mnj85BE0+SO8co4hf+6Genpkuzl+VHHCLEiPLBfPnTGGVZUtCellWS4ePH4YD3xh7HNpCsRw21QePH4Y3t1m8bx2Cw8dP4zcNDsnjS1NMBZyPHYeOXE4df4Qx40q7jTkdsl/9OQR9MvzULrb+Q8tTufamf2S5Lxw1piUS3F7I9/n5IZD+jO9f6Jb/9BiH0+eMpLlO1p4cu5mHjpheEKoALtFQQVePGtMUvql0/rQHo5x6+EDyU2zU5rl5vFTRjCsOLF/Tu2XQ5bbxjOnj2LBxgb+dNTgBNdrr93Cg8cPSznjtDeK0p1M7ZfDiWNKEtqzNNPFX88bR89sD4+dPILhu8WxcVrVbvfy+a1ZcOzzyKJRCemyz4Ew408o6YZRM6jQx/UHlydcl2yPjefPGk2WO/G65KbZ+POJwxPO12O3cOfRg/liTS3tkTiPf72Ja2f2S9ATjI+N3nzYAA4dkp/w/SxFwPGjiumX5+n8MOngojSeOm0UZXuZUTP5x/mnvLo2bdrEAw88wHfffcd7771H376/fDPerx3Tq+t/k20NflpCMUJRjWyPnTS7lbx/cPp4R1OAtlCc9nCMDLcNj91CcUb30+P/CNUtQUIxHX8kTjCqkemyGQHsnvouIX7L7nx02WT6Z9mwBCoh1GQEcXPnoTvS8Vt8+Jw2dH8dItQCgTpQ7eDKAKsXQg1IWxo7wz4+WVfHxyuq+ejcQSjhRgjUA8IIBGh1QTxCxF3IT1UBsr12st1WmoMxWkNxglEjMrDTptIeNj7XkOaw4nVaE4zNbQ0BWkMxgtE42R47XoeFfJ+TeFynsjVEUyCKRCfX48QfjdMciJLjtdPTHkSJtnbobwNnJnF3LtVBlXSbQntcYlMF/qhGW4c++T4Hui6pa4/gdVhx21VCUQ0hBKoiaA5EcdlU0l1W4pqk3h9BEYIsj63zzbrR37UpO9tjJ8tjw+e0Ud8eoj2s0eCPoCqCdJcNi2rIdFhUQ34wis2i4LFbcdkUCtOT+0h9e4i2sEZ9ewSbqpDhtpHvtuHcS8TevVHZHKQ9YrRbmsOKx2GhLMvdmR6OxvE4rPgjcSIxnWyvnXSHSprDQkVrhJZgjJgmSXdaAYlVVcn3JOpT0RTs+FRCjAyXjTSntXNf0/aGAJG4hoROz8dMt62z3f5RGtsjBKLGvdASMgzSTI+VovQuA2BHU5D2Dn28DiuHPza/W3krfjcFb7zRCF0QbgVnJtLuRclIjOfV0B6hNRz7RdelJRihKRCjwR8FJDleO68s3M6LC7Z1lvE5rVy4Xy/K873EdUmPLBcum6Xzy+zbGgK0hmMEI8Z94baptHVcR6/DSprDQqlp9Pxi/r+4s3/yySdcdtllvPfee4wcmSLq538hpuFj8mthS72fwx+bn/LbYr1zPLxw1uh/+E3wrcU7WPfpSl4MgaIYMRB38dAJw5I+tGhi8t/A9oYA57y8JOUma7dN5aPLJ/+s9+a/gveXVXDVW6k/ynvPMUM4aWxpyjyTfw3/3+L4fP755zzxxBN89NFH/xcxvxpMw+d/nKatnR/zlDavEaSvaTPY3OjePNQU3/fZ2hCg1BlDCdUbX/22Oo3ozO4c2kIxYsEIGWorItoG8YjxKQmHD6IBou4CmsM6+T4nkZhGVUuIcFzDa7cSimmEYzpuu0pRmoP2mEZbKEYwquGyqUbIe0uks16sTqQnBxGPGS7p0YCxtOVIR3NmYWnbYbzRqjakzQNWDyJQA3rMiBxt9yJCzYYLvM2F7spnc7ugjzOEiDSDUI3NnrEO7xabl6i7gKaQTkG602gHRwwlVGe4lFudxtJYoA5sHuJppexojuCwCoRQCEbjROM6bpuFuC4JxzRsVgW7qhDXJT5FEHf4aY+2E9EjuC1uch0FVLcabeC0qWS6rASiceKaRJOSAreCM9aIiPg7zssNVg9EA0ibk8aIl3YEaQ4Vh0Wlpj1KKKqR7rKgCIVwXEOXkljHJwXsFpWWUMxY3pAQjmvYLMaGbYGgMM2B3W7B7w/hitUbwfK0KNi8xH2lqMF6RKTN+ACozYPmLWRTcxyPXcWmQlQzlmoMDx4JCKSE0gw7VcFqgvEAFsWCy+ImXfFRE5B4HRb8kTgSiQA03djf4rAohu66xG1TyXVbsAWqja+US8DmIeoppCGoEYnrhGIaihBkOyxkqS2IcKKe21s1QjHj+tgtgpZQHGNjsY1wTCMY1XBYjVkspMRpVYjqkkhMJ6bpOKwqNotCoSWACDd19Cs3ujOHtS0Cm6qQ7rYQi0vaI3FimsRjU/E5raRrjXu0Wwnb22IEIxr9chyJ52VPQ9pciGBjov5tOpG4Tu9Me1f53frtjpYo4biO26bic1oIRo3zjcZ1PHYVgcCqCiJxHYsi0CSEYxqalHjsloSXip1NAWKaRJeSSFynLE2lVbYRjAeI63FcFheFrkK2NoWJxHVsquCrtXWdMaXG9czkvuOG7vVFZVuDH39Ew6IKPDYLxZn/mtnkfYn/rwEM4/H4Px3A7deGafj8j+Kvh8aN8Mk1RlRjML6iftiD4C6EZyYix18Mw09FZBgbK6taQqysaGVmYRh+fBmx6FnDaLLYjZD9U64iaMvBFaqGL25BbJxluMd6cmHqzVA6HhlpQ/P1pDLm4psN9by/rJJ7jh3KfZ+v4+t1degSitOdPHPGKF5duJ2//VhJVNM522nhtsvLE+sddjJy4m8R2xfAvHsg0ABCQZYfBtNvRvztAqhZAYAsmwSH3o9o3m5sTD7zA+Q3DyBWvGUM3HYvctzFMOI0+OZ+hNUFg46GT6/t+sZY/hDkoQ+ipffj650RZhSG4cdXEYueMQwsix2GngSTroB3zkZOvRGZP5S1AS8b6/w8+tVGrju4nK/W1vHh8iqimo7HbuH08aVcOqWERq2RB5Y8wDeV36BLnSxHFpcOv5QJBROYfNdP2C0Kx44s4rTxZXywvIprD+yNtdm4hmJHx1fFM3rAQXdBeg/kyrdh5BkE7Hl8tq4Ju8XCte/81FnvM6eP5PstTby4YBv+SBybqnD0iELOmNCDj1dU8dr3OzrTjxpeyMxB+TQFwvxmWEHKeuVBd0M8injvbOO6u3OQU29C9j6AE9+u4o6jBqMqgqXbm8jyOPhxexPje2fRJz/GhpYN3Lf4Pir8xnfORuaO5OZxN9PD04Mn5m1jcp8sQjGdVxZu6+wn2R4bF+zXm5ZglKsnZqO2bDYibe8Ka5A3CHnYg2gZvTnihXWsrW5n5c37GZGaZ9+KWP9Zh57ZHXoeSK/7Vnee7wED8gDJnHV1fLC8iki843pNKOPkMcU0B+M8NXcTX6ypRZfw5Xnl9M60wPyHECve7DT65dgLYeQZ/PG9Go4/bAC3f7SGH7YaMW+61Wf/G5F9ZqBbPKitW4zzqllpfGfuhFeQPzyLWP9J6vItmxGfXmOU36Md+ty5mCy3jatn9sNrt3Lduz8R6TCGzpvSk/365mC1KERiOg98sb5Tz+IMJ78/YiCDC31Ut4ZZsq0JVRU8PXcL86+bwo7gDu764S6W1BoxnQrdhVw/5nr6Z/Rn0l3LcdtUzpnck0OH5LOjMUR5vpce3XiCVTeHWFvTxh8+WsOOpiAAo8oy+NPRgxlQYI5B/whm5OZ/EtPw+R+ldjU8O9UY9HfH6jQiE79yMvi3IiddhT7xclR3Fgs3NzIuJ45Y8DDih6eSRMryQ+HgexCvHQONm5LyOfppiAWRZZP4caedY99Zw98vncRVby1nS0NXzJCrDuzL0u3NfLNbgLMtN48x6v3+SSPBkwcnvQk1P8HHVybXlTsAxl0MH/22K82dA2d/ZszkfHYdbPoy6TA58QrEqLMNw+r5A4zBa3csDuQFc5HODMSCRxHfP5Fcd79DYP8b4LlpyLM+JZg9jMF/mstvp/flp50tzE3hlfbN74Zw2deXsbV1a1LeHRPv4LAeh9H31i84clghOV4bz8/fxtZryxEvzIRgY+IBQsCZH4NiRX5yJZzwVx5aGiPLY+e9pRWsrGzlyGGFZLptvPTdtoRDd5e/J1PLcxhS5OPqkQrixYMNQ3PPes/4yDAW69d1temRj6MNPZmxd8/h1XPG8vnqGlZVtnHKuBImlnlZ276Wc2adgyTx0Zthz+CVQ15h6t2ruWBKL3QpeW5+cvu8d/xARpaEEc9ONWZAdsdiR54/l7WNcOgrW9l63QDEmycn6Nel52NoA0+gzx++6jzfw4YUcN27KxLKXTa9D8OL07nv83Vs2C1+zZYbBiM+uwGx4bNk2eMvQU66hrF/XtaxB8Zg6/UD4I2TEan0OeJR6HsI4tEhXed1xKPw/ZOp9T/iUegzA/HYiG7aYQ6b2+2dn6q57fCBfPRTFct286Z69dyx6Lrkmnd+StBzF29dMJ5nv9nC1PJcbv27YWDOvWkQZ31+Fo3hxqTyzx/0PEN8QxhwuxHw8dxJPbh0Wh8y9xK8ccm2Jo5/ZmFS/NAMl5X3Lp74/2WJ7n+FXzp+m19nN/nfx98IC59MNnrAmJ5f/AJcZBgFYtHTKMFGdjYHeeCL9YhIC2LJ8ynFivUd3/FKZfQAzL3L+KbR4ucY3t9HzywXjYFIgtGjCBhSnJ5g9ACIcHNivSNPNzblzLs3dV11a41lH0d6V1qgHrZ+A6olpdEDIBY/CzJufFB1T6MHjAFl0XOIcCtiyXOp697wmfGNMilhzl04o8a5DCtJT2n0rLx5P3a270xp9AA8+dOTVAUNd97Dhhbw2vc72HjHTNj8VbLRA0a9c+82ziezD7Rs5/zxBTw7bzMnjy3plPPGHh+d3F1+Kuaur+e303oZ32Pb0+jZvd7ptyYki3n3oLZsoykQZf6mBqb0zaGmLcSmOj+teitPLH8iyegBaI40s6BqARvvmMnLC7cxpmdqr9nh/dPhh2eTB3swruEPT1NeksdPN02Glm0pjQYAMfdu1EBFwvlmeexJbtrjemQSiesJRg+AiPhTGj0AYsnziHBjgjFh6LM9pdEDRrsRae46L4fP6NN70Z9QU/ft8P1T9MpP70x65pvNnNjRH8AIBaAKwbqa9pRGD8D9s9Zz3pSePPON8WmJjXfMZFHNopRGD8BjPz5GY7wr79Xvd9AU7D5AaWVzkIdmb0gZNL05GOv07jL512IaPib/+0RaoGpp9/mVS4ylGzAMoUgbcU2yqrLV8ABJZTDtorUSSrqJZdWyA2QMUbkEEW5ldM9MlnWEsN+F12Glti3FgzvUkmiIZPczpvnb9xKFtn4tpJckpm1fAFrqAIKAcb7h1r22j6hcAuGW1IbRLlp3QnoZono5QovhtVuoa08d0M/pdLKifkXKPIDqQDXRjjaPa8aeClVqxhJfd1QtN5besvtBxWKcTietoVinR5Gmy5Tecd2l70IVcu/1Vi+HtMLEtNaKzsH4p4oW4rpO7xzjO01RPcrqxtXdiltauxQpDJ26iyouwi3GNekGUbkUEW3B5XIZHxXtjraqpGta3x5JcOV321QicY1NdYkR0JdeNx5aUxuMgCE3nBhTx+VyQcXe9RHabgEi08uMPt0d7dUIvfuI0KJyaYIOtW0RvLsFUizOcNEWiRn3eTesrGzF67B2fo9LURQW1yzutvyaxjXE9a5I7lFNp7WboJcAkbi+1/oXb2siEP7nI8ObpMY0fEz+97E6E77gnoS3wHDb7izvYpd7akJ6KpwZULEsdZ7NDYoF2SF/Z1OQ4ozEgImhqIYvlQuzbY/9AMEm42vYlr244HvyIbRHED1fqTFTtDdsLuML2N1g6P8z3mOuLPDXGXKEQjCqkd6NG7OmaRR6ClPmATgtTiyqMfjuim4rVSsybS8eZ9584zMXoSZIL0XXdYQQ6B2v0lY1dRt0l74LKVSk72fqjQYS06wuw/UeyEtzYFMVmoNR8tIcCCHIdeWmEGRQ4C5AFUZsqe4i+2J1GNekO529BWBxomka+Eq6LYfVaczU7YbPaU2ItB2O61gtCnlpif1u1apVxjXfG3v0YU3Tkg3zPfSRu+sTajL69N7KK927/+9qh104rErCTFtzMIrTopKb1v09lZtmJ6YZG+F3UeQp6r58imvrtu09gvPe6i/0OXHvJQK0yT+HafiY/O+TVmhEH+6O8RfD8jcAkL2nI+1p9Mz2cO7knkibF1k2MfVxGT0N767uBqgRpxvGwPiL8ePk+y1NDC1Ox7Hbl6ejmhG7p3CPIG/SnmZsUN7FircMb6thJ6Wuy+aBtAJjtmEXQoEhxxsGQXcDYK9phlfUuEtS5wOMvxjpSEP2mJw6P6MH2L0QDyHHX0o8rYS4LmkNxZIMPYDy33/J0JyhOC3JeQBH9z6adFs6YMyYTOiVxfLly2H4KXQbLnf8JYYH0MbZUDSSv/1Uy1HDC/lslTFDtnynIWdPuksHY4Pr/KWVMOxkoy27q3fJiwlJcvgpSGc2QsChgwvYXO+noinEfn1zKHQWcvqA01OKEgiO7H0k8+fPZ2LvLFZVpZ4JCCpemLCX6zXhEoJqGv3/8BUUjUw2onfpOexkpKNrOa0o3Uk4rhHbbaZJ0yVVLWHyfY6Ewf/Mz/zgyjaufSrZPSYjbd6EgJH9//AVFI4w+mqqY4aenGCo0Fph9OlfWn5PJlxKyNoVdPGo4UV8sbq283d1axiLqjClb3a3n/24cL/efLaypjPMw9/+9jmH9ToMpZv+cPrA0yl2dxnK43pm4tmL4dIz28N5k3umzBMCjhtthpf4d2AaPib7Bll9Yb/rEgcwoRj7M3yl8PXvkXmD4NAHUNKNh82BA/JoV9PhiMcgpzxRXloR8sTX0NxFyJPfNAb+3em5P4y7CLljCTK9lPqIwvhembz83Tb+csbohO97Pf71Jh48YRi5u4X1b1d8xsbOXfVWLYP6jTD+UijbwwCxpyGPfwm+fbArTbXBMc8aszkr3oWTXk+e1ckdiDzsAZhzp7E0sf8NSe0jp92MzOhJu/DB4X+GPb8PllYIJ74Gc+9GDj8Fek/nx51tHDmskMfnbOTOoweTt0c04n55HnwWH49Nfwz3HjNJY/PHcsagM1ixyFgK+2xlNbcfNYgb5/mRjnTk0c90zqZ0MuwUKJsEG2chj34KzZnND1samd4/t3Oge3XhNn57QB/65iYOop+vMuT3y0tMz0uzc+fRg/lgSyO6PcOIar1HvXLoyVAwHFa/15XWYz+YeDnL1m/loROGke6ysnBzI388ahCzVlfz1tIqJhVO4sjeRybIsipW7pp8Fz6bjz8thWtm9mNwYVrCshPA+F6ZLNjuR6b3QE67Ofl6Tb0JmdmLoXfOA0BzZiJPeSsxIjcge0yBiVewbP12wPjK+T3HDqEgzU7+HjMQn6yooiTDxZOnjkzQJ+ox7oGkpb6ccjj8EdoVHw+dMLzze2wAmqMbfcomw6QrkEjk9Fu6zuvbB+E3T6cuP/kKpGpJLL+rHfa/AZnZm0F3zAVgQu8sDh6U32kIA/TJ9eC2WUh3WbnzN0OSPt9x7Mgi9u+Xg89lYVKfLCb2zuK6peC2uLl7yt3YlMT+cFjPw9iveD/63DILMGJt3X3MkJ8NcjqpTzYnjk58MbGpCvcfN5Qs9z//ORyT7jG9uvbA9Or6H6Z5J8T8xr4HIYwQ9lY3bJxlDOhpBYg9Poy5szlApg1c4Tpor4T69ZDR03B5z+rNtsYAxZYwaqzJ2IQZqIf8YUaE5LhEWuxs09LJ8zpoCkZo8EfxR2IU+FxsrvfT0B5hcJGPHK+duK6xozHEjqYgfXO9DMix4Y7UJ9RLZm9Ag0Aj1K40lgKy+yEtDkS4GSqWGINE/hBjJmfndxDxQ//DINIKzdugeXvH+RYS8xRhbd9hpDsyDEOpo30oHIm0etgWT8emCtJtskOfKuNcM3pAeim014AzA2lP47sGJ4XpToQwvjJd2RKiNNNFSzDKzmbj71yvnUA0TkmGQrvWzJbWLTSEGhiQOYB0Rwai1cncinZKM92UZjlREKyobEUgOLhMR4kFoHqlEbdl1zUM1IErG82RzYY248OxUkq2NQWpaApSnp9GtsfWGW16R1OQkgwXBT4H2xr8ZHjsxDTJlno/+T4n6U4rNouC26aS5lLxRptRou1GuICI3/gciD0NoUWNr9f7azuuexaVQRsRWxp2i0IgEsdmUQjHdGK6TjyuY7Oq+LztBOMBVjasxG110z+zPw7Fwc4GG3lpDgIRIwaSqghq2sI0B2OU53mJaTq1rSH6F/joaW1B7OrPSCgajbS62B7PIBjTWFvVhqoIjujnQQ037KbnUHBlUxn08k2ln7IsFwU+J+tr25FS0j8/jerWMDubgpR1RBrOcFmR0pih3NEUpCEQpX+el/65Tmz+CqNPNW81jJ60IgK2HL7Z2s6QojSiccma6jbawzGGl6TTL0126LMB/DWd7VYT8fLV9nZO6WdBxP1Q9aOxgbxkPAiMe8Bf06l/TcTLisYYMwrjRvld7VA8Cmnx8FW1hbr2CAML0shw2YjGNSpbwlS3huib66XA5yAa1wlE47jtFiJxnY21fkKxOEOL0/E4VEoy3FS2hAhF47SGYkhgS32A/cpthPQg65vW0x5tZ2jOUNwWNxurbFS3huiT6yE/zfGLoy7vbAoSiMRZtrMFl01lUKEPj10h32fG8vlHMN3Z/0lMw8fE5NdDZXOQm99fxdwN9TitKtleG63BGN9fPwVVNZZe7PZ/7PtWJv936lpCXPnOMuKaQFHgqeP743K5aGtr44r3N/Hnk0aS8w9+d6w7vt/SyEnPfs+qW/bHarVSU1PD66sCfLupnqtnlDO5bw4Oa/I39/yhGB8sr+TeWetpD8fJdNkYUpJGMBxnW1OINy8YT2/TVfx/CtPw+ScxDZ//TmTLzg5PGgEWO5ozF8s/MCDqrdWIWMB4s7S4IB4FqaGll6G0Vxlv9mC42EbakL4yFIsF2bTV2EODQHflsTOkIKWk2GfH0roD0AGBlt6jM8inbNhspCtZ1DlihGKGx4hLdZITsaA7bYioH6EFwO4zPM6kBkKgeUrQAFugKqHesFRwRhs79ZSODJDCkBGPgICop5CacD0SiVWxoksdTWo4VReWiIsMRxgRagUkUrUTtGbSGhM4pU660oaIG8HVpNVNczwNm9tGoOPbYkIYH6UMxzRAENMlIFGFIM1poTkYx6qKjv0jErfNQigWJ64bLv25HivOUB1CixizcbY0ajV3Z35cl53Rn7c2+FGFQo7XTrbHhkBQ7IobEa6lBMVquPDHIki7ISeqSVRFkOexUtUWRVEEcU1HEQIF0DAOdVgF4ZiOoGPjs4SwpqMIyHRZaAkZG391XSKEwGEVBKM6eS6BxWLB5q8ywgMIge7MoSpsQQhBNK5jsxgRq5ESIUAVClFNx2lViOs6aQ5DvtNqfFvMogqimt4R7xkyXVb8EY08rw21dZuhsFCIp5VS2RZFARRFENN17IpAlwJNNyJeCwEuq0q6w0p7VMPnUIxI37Kjf3oLaYupBKIaUU1HAdJdFlwWC/aO5a14JILq3wmKzYiaDUZbCwWpOkAIRKQFPMVUROrQpNYRHdlKXItR4iokqutUt8fJ8ggaI/VIqWNVrOQ586gKVqFLHUUo5DtyqA8a7RzXJe3hOB6HEW3Z2LAucFoVhIR8W8i4d7UoCJBWDy1xDzFVpS0cx2FRyE+zdd2PIhNpjRrHAKhO0OPojgx2BBUjirMOEmn8rUlcDhWnxYLXaWVbQ4ASl44SrAUkKBai7kKq2mM4LCqRmAaKwGqE/yYcM/pPjtuO55/8FpvJL8M0fP5JTMPnvwu9aQeieRNi9m1G5FZHOow4FTn4eHBlUiGzKcnsfro5FothadoAX99pxCQRAsoPhWk3w/rPkVYnePMQ755tfORz6Akw6UqkaoOG9Ygv/9BZrxx1Fow6G4lA7PgOMe9eY/rfm4+ccDkMOBy2L0TMu5foeQvYEa3i0WWP8U3FNwBMK5nGpcMvpbe7FCoWIuxpxpLSN/dB05bd5ByBeOtxqHkWHD7kBd9CsB5m32a4XvtKkKe8C2iIr++EjeVUXHUuX2z7grk753LhsAt5c/2bfFvxLQBTi6dy2YjL6O0uRdxXAvGwsQdkxh/R0nujBqrg6zsQGz4HQPY7BKbfQiy9NzX+GOe9tJhzp/QipknKsly8v6yST1ZUE9V0njp1JI3+KKW7pUfiOhN7Z3HBfr14au5mXj+pN4p/p6H/tvlG0MTBx8GUqwk68llSEeC+WetZXdVGmsPCyWNLOWZkMcc//R2zLhxKvj0CS55H/Piy4b5cMAwO+D00bELWrOiUs74xQnVzGKdd5duNDWxrCHDa+DL+8u0Wvt/ShN2icPSIIg4bUsDVby8nL83BZdP68PnqGuasq+OkMaWcNr6UOz9dw6zVddgtRsTjqw7sR55sQDRtNPpD9U/GdRl5Jow+l8qghXkVGg2BKK99v5369ghlWS7On9KLmtYwG2rbuWZmOe3hMBluB5XNYVqCMbY2BhLKX3lAX47uqcOaDxALHzeWrTJ7GftZSsYz4KG1HDGskEum9mb+xgYy3TaemreZ1VVteO0WThlXyinjSikR9YiKRUYcnKYtxhfvJ1wGA4/mpTU6t3+0Bq/daOfTJpRiRZAv6mHTbERmb1j5Nqz6m/GiUTYZZv4R6cyEbx+kZtpNbPPv4JGlj7CmaQ0eq4cjex/JpKJJ+KN+hmYPBQHvbHiHd9a/Q3usnetGX0e2M5snlj/BjvYdZDmyOGPgGczsMZPnX9rKi5VtDClM4/ajBvPc/C3M6ti39dPv9sMdb4H2KsSCPxvxpHbrn3FPGa8ureGsgQqiajFizl1w0Q/Qshk5504jDpeURgDO6bcg22ogqxc1ETsT/vwjdovCoUMKOHZkMZvr/Uwrz0H6WyhNi8GSFxFLXzLCPOQPQR7wB2RWX67/qpmjhhdx9Vs/keO1c9n0Pny5tpbZq2s5cUwJZ0zskfDxXpN/Labh809iGj7/Xcj1nyHeSOHp1GMKcsKlyNyhBB05eLpxrZYNGxDPzTAeYLvjyoRzvoAnxsOIU4w3210BBfvOgDHnwesnJsvrMQWOfAzx6PDEjGs2I1f81TDQBj3H1pmjOe3T02iLtiUUS7en8+ohr9Jj0zxDp9m3JSs96myYcg08MhhGnQejToPnZ8CuGCjHvwTZ5fDSoRBqpvrqNTy58mk+3PwhTxzwBDd8c0NSvT67j9cOeZUej7wC8iEj0eZGXjAX8fzMZDd5Zwby3Nn88b0GzjpuKE/O3cSUPjncN2t9Z9j9syb2oD0cY1p5Lvd/sZ7tjcEEETZVYfH1E0kLbEU8PyM5XlJmL+Sp79Hz/uRYLuN6ZnL1jH6MTW+DDy5B7PguuZ1OfhMWPwdNW5CnvscPLT7WVLfx2apq1te08+jJI7jglaVEtcQ4PmVZLq6dWc7lbxhhCh4+cTiPf72RzfUBxvbI4I6jB3PQI4bR2CfHzaeXT8S6bQ7i9ROSVJClE+GoJ7j+63beWVqRlH/NzH4s3dbMsp0tfPbbyaypaWfe+nrCMS2p/JYbhiC+fcAw8PasZ+Yd6MNPo/cfF1KW5eKJU0am/Fr55lsnoax4HTHrpmQZI05H7n8957xfw9z1RuDJMWUZvHVSiVFvzylGUM7GzYkHqjY45S0omcy3dT9wyVfJHmfDc4ZzUI+D0KVOQ6iBF1cbnnATCicwNn8sf/7xz0nHHN3naC4ZehET71rOS2eN4cq3ltMc7IqJs/W6ckRrBbx7thHuYXcc6cjzvkR3ZqOsfAvx+Q0gDoBL74UXZqYsz3mzkZ9cC0c8Ss/7umItlWQ6efSkESzf2cJZ/SV89FvE/2PvrMPsKNL9/+k+ruPumYm7u4fgBEhw18Xd3XaXXWBhFw8OwQPBNUBCQtw9mcxMxt2Oa/fvjxo7mXPC3r337o/lzvd58sBUVZd19elvV73v9z20qvf8nfkOocL5zHlyFXccM5ir3xGaRU+cNpIXfy7hQL2LMbkJPHXGyL6I6/9L6FNu7sPvHmpzCdK3d0XPPLQKSQkhBd00xFBlDXvbofOr7XB4WmD7u3D6m7B1CQw8pjuv/3z4pveLA0A6tArJ1QBZ4yMz/C1IK/8KgPOE+Sw9sLQX+QBo87fxWclnuIecAh3le2HLG93aMdNvgB//2E16koqEwfO2t7vIijvs4dODnzI1cyqrqlZFbbfd386nBz/DffuN3YmTroLNb/QmPSDStr3NPRcMpcnlZ2e1g0aXv4v0AMwckMLWyjYaXf5epAeEoaxNdQqvsmgikS2lUL2J/Q/O65W1vqyFojiEiF000gOCNE6/qaue0dlWMuKNbDzUyiljsnljTXkv0gNQ3uyhxR0gP0l8mT/zYzHnTBTx2zYcasXhDZGXKNyo/3TqcHTOKqTv7o7aBaliDbjquGNOdLfkxStLWTQum3ZvkDZvkD9/tZfZg1JZuqU3SZKCbqStb0ZvZ+VjyB2q1uXNHrZWtFIQJTaU7G0UCsnR6ti2BCng5rSx3R5GG8tbkYIupEMrxT0/nPSAuHfrnqPSV8fjmx6PWve2xm2kmlN5fffrDEse1pV+StEpvLrz1ajXfHrwU9whL1MLk1l1sCmC9Ox/cJ4QYCz+vjeJAfFMb3kTGQVpxZ9E2m2vC9mKWOU3v4E07UZwVLPltsldWZUtXrSyxHFFZnA3RiU9ANJ396B1VFDZ4qXB6aMwRcz/0z3Wz5aKVmraowt79uHfhz7i04f/XAS94qUWC3U7wNuM3h9dOVX2NCOV/BT7+pKfILFA2EF4mrvFAy2pR263/BeYdmNkmretKyp8a6CNdbXrYl7+S80vtAS7y/eCqkBbOfxhg7DfKe/xZZ85ClChdIX4+8YKDjnKUVEZmjS0K6hiNKypWUNLoId2TNY4pM56okAq+QnJ08TGslZm9k9hQ1n3C0UoN/sZmmGPSO9VR8gnjrdiYf9XaGMIMNrtdig7wrVNB7p1XvZ/hU6jYW+NIH3Ds+ysL4sedgBgQ1kLQzOFBkxJo5v0HjpLa0ubOX6EEBBMMOvFkU9Tcex+HFod8+vT6Q8hIURkFBXq2n20uP29QhhsvnWS8LyLtUHvd0QQ1DUlzQzLjNKmt62XonIXVBWaS5iS0z3WjTdPEGstfSR0BmiNhkOr8YcDlLbHfi72NO8hxZwSkaaRNDiDzqjlVVQOOQ7x7GmDWF8auYa0Wq0gPkdQ1ZZKfhCEpnO8nhYo/TH2GEp+BFsalK7sdb+a3QESExMhFskG8ZvQYa+3rrS5a/0cavYIMdQOrCmJEv6kD/9W9BGfPvznQqMDubc3RxeM8aA14NREL6PKOmGsHPP6OOhUetVZug06JUmoKMeCJVn8KPeEtvuHTyfrsOkP0/3pgTh9HIbDtWoOh8EGOz4R/es5hoBbdLkzra2tqy1PyINdH3v7126wo5d6jCvkPeL8qMY40BhIsOho8waw9zDc9IcULAZhLGv/NYNOU3zsPHNSTOVpVVXBcgT14J7rw5SEKknYjKIvnkAYuzF2v+wmLZ6AMGTWaSLV7ZIsehqdYocqrAjj1iOuB3MSR7Io6KxfI0uoqJh0veuqr6/vrRV1OHqoeseZdLgDUUKVaH/F4N8Yh8PRHcaioqIC9DZBwo/4rMSjkWS0R5gHm96GN+RF10NtWXcE5eXOa9ra2rCbotT7zzy/PZWgNfrIWHaHwxQPSGBJRlEidwINWlmkHUmtWtZ0tWfvoYCtkaUI3c0E86882334X0cf8enDfyxUvR118EnRMzU6YedisGONIRkvJ+SgTrgsdgMTLoNVT4I1VagmKx0vkpKf4Ejt5kyEr2+JTNfbIG0oABn6JM4cGEOBGThz0JmkynGQNix6AUuK2HVa+yfx4z/mwu680hWAImyQAF4bQZo5jXhDPMvLl3N8v+NjtzvwTNK08d0Ja5/71fnx6xMZm5fAin0NzB3ULdcfCCuEFZW9Ne0R6YdDsWeh9uz/4Rh1DlVVvY99tLJESbMP8qfFJh2DT4bqjnAio86mqqqVwlQrWlniyx21nDomduiBOYNSu77Mjx6a3mXzopUlxhckdtnfrDnY1LEOF0SvSNZC/jQm/2Vl1OyhmXZKGsXOnizBvEHpBMIKSZbIl+Nxb5aBNU2Q6mhIHxFBjI4fkcEvB6PsLBhsQnQxGsyJYEvn67LuEBynf1Qv2m3YI2zbYmHkWdj0VubnzY+arZE09E/oj17WU+4o70ovbitmSNKQqNfEGeJIM6dxxtsHWTAq8l5VVVUJzaDhi2J2SZ14hdCyyhwjEgwJ4pmOhQmXw4aXod9MBtz3fURWvFnHaS9tFM+2JjpZUwedgNpxD44anMaqYrFmjhqSxqoD4l5oZIlpRTHuYR/+bfjNGzdfccUVrFixgn37RITerVu3cuWVV1JbW4vFYuGpp55i/vzuh+2pp57imWeewev1Mn78eF5++WWSk//5hdZn3PyfBbW5BOntRd1HT2MuECEogh7UcAjMiSiWZDSmhIjrlPZqJL9DbIPrLUitFfD1bTDoOBi6ULxggm6xdW1KEPGXgh7xVR0OCjHAcEh8AUqy2B0JuFENVug6vEB4gpkSIRRA9dQjvbkAPM1U3bybf2z5B18fioxufVLhSVw58kqyq7ZBQh50lO+CzgznfgQpw+CvHbYY121HXfYHpMqO47NJV8Koc2H132DXRzhvP8TetmKu/uFqrh9zPdsatvFt+bcR7Z7Y70SuGnUl2T6P2DV640TUjJFw8nPww8NIu5bCWe8L0UJfm/B2M9jxGtMwhl1CPNHbKuI/6e14TWnsa/Rj0mvYWNZMncPPcysi7UMm9kvkoZOGMsDQAsuuQDrsKEWdfTfq8DOY+2oZZYdFtH/yjFGUNrq4bpwFuWot0seXiyPAorkw+nzxcrJngcGGaklD9TRTpSYRCKs0uwI4fCFSbQae+amYF0/JQfI5RGyoHuPa2+BjdLwPKdARrNZgQ9XbaFMTqPEKF2W3P0x2vIF0pU70P2Mk+BygM4LfhRr2oyQP4cdaHbuq2xmRHYe/w7V906EWpvRL4palO5hcmMQtRw3AH1Z49Ku9LBqXy43vb4uIm1V8/3S09TvFeg/2sJmyJKOe9ym+uEIGP/gj18/tz5xBqdz0wTZKGiPnbde9szE5y8Q6dDf2WFcm1LM/JJQ8jG9Lvby0qpSd1e38/czRHDPAirZhJ9LeL4TA5c+H2fHkTYH5j4CaRoVd4ZofrqHMUdajXZm7J97NlvotXDzsYqpd1dy44kbCaphkUzIPT32Ye1bfExHx3Kgx8ty8ZxkU1x+NasCkuMS8+h3i+bMUUaPz4Q67cQfc2PQ2bFoLaZWt8MFk1BFnwKy7UIxxyJ4mJE+LkDgwJcHKR4X93ok/Uj8wC1fIhcPvxKKzYNGZSQ9b+KnGj0GnwW7URezIFRldyNXrkT66pPtDCDoM8T+kIWTj1c0tZCWYSLToMWhlkq0G9tc5eXl1KVfNKmJyvyQy4o8QaqMP/zJ+F15dFRUVDB48mJycHPbt24fT6WTw4MG8/vrrzJs3j59//pmTTjqJffv2kZ6ezgcffMCjjz7K8uXLiYuL45prrqG+vp6PP/74n26zj/j850FtOSSiOEsaMFiRll0h7BJAhHOY9wBq4TzkxLzu8j88hLRnWYeOCaj9ZsPxT8Cez5CK5sInV0L9LlGH1ggTrxRxslrLBMn69i5IHQZnvIH6+Y1Ih4RLOrIGhp4Cg04QkdsDTiici2rLJmxORuOogKoNUK2jevpkmn3NLC9fjiRJzMubR6IhgSy/B5QgUmMxpA8TaszVW1BTBkC/mWBKFrYGZT+LWEYDTgGtKqLB7/9KKDcPXyR2g9yNsO9znNNupjnQxvra9aSYU4g3xrO6ejWKqjA3dy6JhgSyn3wVeAp1xJkw6w5QFao9OrLMQXG8t+ElpA2Luw2R00egnroYarYifX59r3S/vYDxf17JXccNYmhWHP6gwtrSJto8QcbkJtDkDvDnr/ay4upRpBt8Hf3/WuxKDDkJVW9n3mtl/P2MUVS3eVlX2kKa3cDcwWk0tPuwGLWEFZXR8V7kgEPEdmo+iPTTH4X2EUBCPuopiwknDuSJ1bW8uvpQVzT2oZl2vjg3Fza+hLRxcXek8vQRqKe8KGJc/fAw0u6PutdJwUw44Ukc2mSKW0OsK22hKNXK0VkB2PgK0oYXIus5dTGBuH4cbPLx56/3svqgeLnLkogddeGUPBRVHH+Ewgo6jYSKRGWLB7NBy67qNsqbPQzPimNUTjw5ZgWNtw5KV0LjARGLK2c8QVsuL6wqZ1pRMhaDhkBIwRsM0+j0s6WijVSbgRkDUqhodjGrfxJ6ZyVUb4KqzZDcH/rNImxOpujhtSRb9Tx40jAGpll5YWUJS7dUc/DeyWg8TdBeKch36Qpx/NV/viCYyBByg7WASqWZ0vYyNtRuIMmUxLi0cXhCHjItGeSaMmkKttMeaGdl1UoaPA3MyZ1DuiWdnY072dG4g4K4AiZlTCJJn4Re1mPw1CG56mDZFeL5u66MCq2LR9Y9wtratR3zKTM/bz7Xj76OLIcTTGZUCaSS5UjLHxSECSB+HOr5L4Onieq4DJ7d9ixfl31NWBUkZnz6eO6fdD/xxDPyz8J+bHCGjTuOGcSO6jYum5Ir+hNwwcHvwVEL+dNQk/uDwc5ul5VgOMzOagePfbMfp19EVs9OMPHUGaPISzSRYu8jPf9b+F0Qn4ULF5KRkcHy5cvZt28fixcv5uuvv2bZsmVdZRYsWMCcOXO4/vrrmTJlCrfffjsLFoht56amJjIzM6mrqxOGaf8E+ojPfy7Uxv1Ii2d2GRhG5J2zFKn/UUKo8Pt7xA7G4WVyJiKd+A94a4EIw3A45v8RkCFvErx1Clz0FXx6tYijdThGniVIUMFMWPkYnLkEVday159MYZIRrVaLoigEFAmjttsAQFVVNMXfIHldMPI0kaiERV1KGLW9nILHD7D3/jno9XpUVeWv3x1gR1U7L501DLPZjKqqbN9+APAycuRINGEfbH9X6JjcdEDYzIRC0CGoSEMDvBgZg0sdcwHqrDvp96ctlNw/E3nTi0g/PNR7nPYsOO8TeHZ8r3T1gs8oeGw/AK9dOA69LNHgCvDB5ir21Dho93YbnafbjXx/7YQe/d/Owg+778GwTDtTipI4akga93+yi1cvmsD2qnae++kg26va2f/gPPSHfowubaC3oF62goInIg2QS+6fibz5JaG9czgWvop68Huk7e/2ylKzx8Gpr/DCjhDHDcsg26LGrseWgXrhF5z6Xj1bK9t6ZS8am82Nc4tYWdzEU9/t54vrZ/DYd/v5+UAjRalWhmTYkSXYWe3AGwjzj7NGdelShcNhQqEQyLkJGAABAABJREFUS7fWUphi5dVfDlHZ6uGGuf0xaGVaPSEaXF68foWKVg+fbKvm0VOGs6GshaVbqtn/4Lyuddj/3u8i+iVL8PIF47n67S2Ru04Pz0eWhUin1GG4oqoq2w+2gLuSkSNHCsHGQKBrfXaWCwQC7KrzMs7ahPTSu3Db3YJQBwLwl3S4swFVqyXU0Z8R2XF8elYGUsgv5Bo6jP2rbtrFHavvZHvj9l7zeXzB8dw45kbitHEYKn5Geue03vdEZ6bp+h38Y9vTLCtZ1it7SNIQnpjxONP+tKMrLdVm4L4ThnB8thfpzZMEyR5xpjCGLvsZaraizr4HZcJVfF/i4IolW3rVa9Jp+PzaaRQdFi+uD/9z+Gff37/ZePdffPEFLS0tXHvttSxfvhyAtWvXMnXq1IhyEyZMYNu2bYRCITZt2hSRn5ycTF5eHjt37mTmzJn/1v734f8Ddi+LSnoAWPkoStIApJAHaXf0HUAp6BXxjKKRHoBfnoILPodf/i4EDkO+6KQHYNdSoaez6m8w7GQ48A0MOpFcu4YhDy0nHBZBEO84dhCbD7XhDqpcuWQLZbcNFsKHdTvgsz/0rveohzj4yNUs3VrDHR/tjMia84/13Dx/QET66y4XM4tsSF912Bz9sePY96z3xH+jEQVA2va2cGcHZHetEMyLBkc1NB+E3ElQsS4yvekAu++dxdCHV/Dk8mIeWzSCa96L1GLpRJ3Dx9oKN59vLyYv2cIzP0beg101DnbVOFhX2sL8YRm0eYNoZYntVcJjR++qEfMWDQE37PuCg49c1xVAsmtca56Ofk1yf6Rl0e1BpKpNqL5WkhubcAdSkGmIXY+zFhr38/ZFsxnyUG8PwmVbq7l8ej/uWiZ2F6vbvCzbUk1IUal3+PnlYKTnWasnSE7HN1x5i5fTXliLJMHtxwziuz1C2K8gxUpli4dbl24XStE9MDQrrqut25btQVXhs+2HGeIjPMw+3lLFccPT+WhLdVd6/3u/44QRGRi0Mh9tqUYjSyz9w2Te3FbNp9vq4MPez87xwzMw6kT5g48cDevehMDf4JG/RRb8cypS+gi0pwuX/XcuHAWuMjj4Q7eHo3QTjqAzKukB+ObQN1w24jJSAt6YbvsEPThCDj4v+zxq9p7mPbT523njWKuIQg80OP3MKrRDzQ5BegB2vBdxnbTuWeShJ/Piyoao9XqDYb7eWcu1c/tH71cf/m34TRo319TUcPXVV/PCCy/0Sk9LS4tIS01Npbm5mcbGRsLhcC97ns78WPD7/Tgcjoh/ffjPgxLwItXtiJkvNRUjKYGO8A+9tVsAEdizbmf0PBDHRkpYqCknFUFbb6PbLoSDomxTsYj+XrsdkDAYDKRZhffN/nonbn+IMXlJ7KvtWHeqIshXLNRsQ5IkynrYbnQiM87YK330qFHdP9Q9oargiv4DDYhwGJ0vm5A/uvZJJxp2Q87k3un1e9DrhZFuSYMLRSUq6enEvjonY/LiOdgQw40fONjgIivehC8QpqmnPpMaFkEsY0Cq3Y7MYfc9HIi0n+qJgCvShuNwtFczd+5Ywqp65HoA6nah00U3iA0rKs4ecgvt3mAvstITtT00YIJhhWZ3gDS7McIGKqyo+ENK1Ho8gXBXenaCucuwOhoONrjISuh9LNMzPayoqB1psVDS2F1eluWOZyEGmg50zbvBYBChY3qWv+oqGj2x121YDeMMOJGUkKgrBlxBNyElFDO/xl3D6NGjI9IMBgPU74ndd28rhPxHnIud1e14A7Hb7cO/B7854qMoCmeffTa33norAwcOjMgLh8O93ELD4TCSJBEOi4clVn4s/PnPfyYuLq7rX05OTsyyffjtQtabUJMHxi6QUACSDvRWiLUemg6IqOWxYEoQXjqJ/YQ9ij3jCB3SdJQtEF/9KYNAEiEy6l3i5ZWfZMGk13Cgrr1HsERZ9DUWUgejqmpU2fsGp79X+tZt28AWpZ+SdGRXcEkWNi4gXKCP5DacPEBE0u6V3p9gULzU85IsaCSwG2NvMhemWNhd4yTvCJL+eUlm6h1+jDoNiZYeZEKSxVzHgJoyCOXwnzuNPva49FZRZyzY0vnhh41oJPnX3aRTBhIKRX/ZyRJYDd1zYjfqkGP/XJHWI/CnTiNjN2lpdPrJ6TFnWlnCqJWj1mPWa7rS6x2+I4ZPyEsyU9fuP2K6JIl/eUm/fs9A/L4f8RlLLOia92AwKIzUe5Z/7jmSTLGdVSQkrLqOe3eE58iis6CRYkthpFnSKC6OPBoNBoPCHioWDHbQGsg9wlwMSLNiiuFl2od/H35zxOehhx7CZrNx9dVX98pLTEykqSnSRbOxsZH09HQSEhJQVZXW1tao+bFw55130t7e3vWvsrLyf2Ygffj3Y8TpsV1Np9+MlJQvXI/7HxO1DOGAMCY+zAOsCxOvgMp14r8/PSKMcGP9iA86UQiijb9UHHsNPgnUMBWOEGERb5Q/LhhKokmLjMSwrDjizTqCtmzU6TdHr1NrgCELKLrnWxKteuLNkWOtbfeRYIlMv/BrF5iTIXVwZF1lK0FReqd3QB1yMqpekALFnIw67tLofbIkQ8pgUd/h6alDu453rplTxKriJs6bnBe1mkSLnoJkCx9tqWJqUTJ6TfSfpvMm5fPZ9mosBg1hRWVgmnAfDtqyUafdFL2PGj0MPYU//jHSk00xp6JOuDz6NS2lqINOiJ6XNhRMiZTH5WPWSx31xHCTNidB2jBOXbwxavZRQ9Jw+gQ51Gtkkqx6jh0W/fdqYJotQgMmy65n811z+PaqscSbdCR2uMBvKW8loCjMH9q7ngP1To7pSP96Zy2njomuKA2wcEw2X+2sPWL6UYPT+HZXHaeMji0NsHBMNl/tqGVAmpVtVQ4YsiCmnpA67SaCNtGnk1/cKAypB5/QXV79G3F6OwMTon/kzMqZhVlrJmDNiv0cafTYtVbm5fVWBAcojC8kwZAQYWP2xrFWsWuXMQps0e+POu4SFHMa50/Oj5qv00icNDL2PPXh34ffHPF58cUXWblyJQkJCcTHx3PCCSdQXFxMfHw8Y8eOZc2aSOXMX375hcmTJ2OxWBg4cGBEfm1tLfX19YwcOTJmewaDAbvdHvGvD/+ZUCypqGe+J/RIOqE1oM6+BzVdrAE5IRuO+TNqv1mRF2eORl30Kuqer4S7eHxud54kCxfpYYuEh1f9bnHs9cVNIqRF+vDIuormwogzxEvP3QTHPoaqtxO05XL70h38fMtMvr9xJt/sqeeFnw/hDYPNIPHmxROY8+RqyJmIOuPWSBJnSUY9+0PCRkHKnvjuAE+dMYrsHkcRsgS7qtt49cLxEekeYyrq4f3c9BqqJRX1+Cd79V/tPx/mPUCly0x2golHHlsDYy5AHXl25C5IQj7quR+jVm3snX7OxwTtueg1MrcePZAD9U7+8u0+Fo7JZtHYrIjdiJxEEy+dPxaXP8QnV00FVeX5c8d0vchBkIKrZhXi8Aa5bm5/Xl1dyrBMO4+cPIyhmXahu5I9HnXmHYLoRMzbByiGJLzDciLafeSZzTD6PNRR5/Tuf0I/mP8wauHcyHubMRL1tDfwGNM4ekgaq4qb+HiPA0af27ue+DzUc8U83HnsYIYepqQ8o38ydx47mCuXbCHJoueHm2bgDYS5YlYRMwdE7moMzbTz+GkjcHiDhNsbURsPoN/zAdo1TxLftpNjMn0sv3oi+clm7vp0JwNTbVw6rYAZ/SPreWd9ObcfO4iZ/ZNxB8J8vauWhxYMjdh1sug1PLZoBGZ95I6IRa/h/hOH8P2eelz+ENOLkrn92EGsL2vhq511PLxgWK96OsvnJpl5+qwx3PfpbsKmRNSzPojUJNLoxZrPmciA+75nSIadR08dQdCei6o1CymFjvI5ciKPz3ycIYmR+j+TMiZx67jbUNoVBt6/HDVjNOrsuyPXgzkJ9cx3SVQ03DDmBqZlTouoY2DCQJ6c9SRJmu7d0NLbhjFjYBLajS/Ank9Qz1kKs3qEypEk4Qk57iJq/VqKUixcO6cIg7Z7LSRa9LxywXiSbH27Pb8F/Ka9ugBWrFjBFVdcwb59+6iqqmL48OF89NFHzJkzh6+++oqrrrqK3bt3Y7FYePLJJ3nnnXf4/vvvMZvNXH755SQkJPDkk0/+0+31eXX9ZyPkc6Fx1YGnSdim2DJQDXZke+RXmtJSgRRwCAJjSgSjHSmxQKSjCJVmX5swjLWmC8KjhEBnh2C7sOkI+SG+AAgLd1lfm6gLCUmSQQ1BOAzrnkMdcQbhlBE0SxZ2VDu5+u2tEXGiRmTH8doF43H4g7R5goyM84v+OWtF2+YkQpZMKl0KDU4/EpAeZ0RRVVrdQfyhMCk2A1pZotklVJQ9gTCeQIhkq4FMq4zZ3yD0aLytIiK33g6ShBTyCtsnvxMsKahGOzVeOx5JQqeRaPME8fjDTE72IwUdIiq43gbGeAK2bHSepqjpW6tdpNoMqKow1M2IN/LoN3s5ZkgGgzPsNLr8+IMKDU4fz68sobLFy/CsOK6f15+nfzjAhVP7kWzRo9PKJHTsYuk0Mr5gmDZvkAK7ES/gDYQJhhWcvhBT0xShu+OsFbsElmRC5jTKHKCTJYKKSqPTj8WgwWrQsvVAIwuH6pGCzu7+m+IJWLNpafGSZmxH8js71kkCGOy4Dansbw7g8IZw+oIiNIEEBdq2qPXsqHEjSZBo1eMNhGn3BEmw6LEaNNS3e0GSSbMZeHNdBS+tKsWok7lsej9G5yagKCqpdgO17T7u/HgnG68biVy/FemD87rd5gE1YxSc9joVSiptviDeQIiceDMBRcHjF/MVb9JhNWqpaXWTHm/GF1RodvlJsxvRyBJNTj+SJJFk1ePwBrEYNGg1Mk3OAIqqkmQVBKLR6cdu0mHRa9BrJDxBBU9AmCFYDDpa3cL2KsGiw+ULodXImPUa2j0B7CY9dpOWeK2K1lMN7mbhJGDLQNXb+aIsTFaCmUSLjmBYRVFUChIN6P3N4ln0tIjyqWOpVtpwBB20+9tJNCZi0VrRec24ZbH2Hd4gk1JDQq+rcz2Ykwkb06nxS8hhBdnkwBV00+xrJs4Qh01nI1GTSI1LocnlZ1KiGzYs7mXcr068AsZdLOq1pqHqbTRKyaTFGWn3BnB4g3gDog6jTkOCRUeK1Yj1CEe9ffjv43fhzg6RxAfg22+/5frrr6elpYWioiJefPFFhg8XX6yKonDbbbfx5ptvotVqWbBgAU899ZQwSvsn0Ud8+vCvQnE1IH13H9KuD4SmkBLqNqTWGlGvWE2pksGxf18VNTjmWRNyuH5Of9J/Y+JmzU4/i1fsY8mmWryBMB8uSmfhh3VY9RoUSeX5s8eSm2SJGhSzE6FQiFfXVPCnr0Sk9VcuGMe172zFE+xtQLxobDaBkMJn22uQJMiwG3n+3LGc/NwvqCr846yRvPbLIf52+ijOeHEdDU5/l8hcMCx+zt44NoF+w0d2uX53oqTBxZmL19DoijSyfuWCccR7qlj4YR1aWeoyANbIEovGZHHXcQP5YHMNT3y7H1+o+97pNBLBsMoX105jWNYR7KB+BT/tb+Ci13ofh2llib8sGkF2gonzXt7A/hsKkV6cFkF6OqGOOgd17gM4NHZeWrGfZ3+uYmZBPLedMIyFz6/p6nfnrpeiwr0nDuaCibki9hXCEPn4f6xCK0m4AmGun9uftSVNXa74wbCKVa/BF1JIsOj57JqpZMT9a+v11V/KqFy+h1mzur2nOuf+82umcd27G6ls9aOVZW48agCXTM2j2RNk39atbHQECcqJzC9KA1clT/7YysMXTuLcV9bj9oVwBcKcPT4Db1jmo81VvHaMlRUr3Lzlh5CiYtLLfH7N9F91LVeLlyO9vTB63lnvIQ089l8aex/+d/C7ic4+a9asLtIDcPTRR7Nv3z4aGhpYs2ZNF+kB4THw+OOP09DQQE1NDc8///x/ifT0oQ//HUjeViF2p4SFvVBP77GQD6o3kx2ni0p6AD7eUo07EOZQU2yvkP9J+P0hyhpd7Kpup7jeSX2MqNFt3iCvravB7RekZ/SAPMpuLmLnFRnsvraIaTmmLjuVWKhq8/Hm2kMA5CeZKWtyRyU9AJ9vr2H+UOG9qapQ0+7DFwxj6LD7WbqpmjHZiTQ4/DQ4BQEIhtUu0gNwwdetuPy9699U3tKL9OQlmTnU7O6y6ejpDRVWVD7ZVkOTK8Qbaw5FkJ7OdgE+ihJN/Z9FXbuX134pi5oXUlQ+2VpNQ7uPnffNEeKXUUgPgLRrKZKvDbu/kVvG6ii7PpvXT0tnQKo5ot+KKv4BvLb6EJVt3fd9S3krvqCCqyPO17AsOxsOtUbMr6vDM6zR6afuX4w0fqjJxRtrDvGaly7S0zlegA83V/LsueMJKeALKby5tpyqNj+t7iAXfN3KM7+4OG9KIU//coiFH9bRLOlodgeobvPR5gsRUlQm9Evjs201KKpo4zWv2lW/N6Cw4bAgtdWtHg7UOdld086hJjdKey2sfz72INY9j9JWHTu/D79Z9O279aEP/1MIB7vVi6PBUSPceWNAuCAr4pjsfxmVLW5WFzfzt+8P0Ojyo5Eljh6Sxm3HDCL/sJ2bYFghEFZYfulAChO1sOIRIewX8oExDnny1QwfcQYQH7M9VZVocokXts2oo9kVe578IQX5MM+7dm8Qm1GLzxWg2R1gcqGBtiO4xgP4ohCrOkdv0mAzan+1PyFFOWKZeocPRVGOeH+PVP+R6m52BQiEO+o+PPhtT4T8QvH7s5uRyjqUxBP7oT3mUUrvGE6/R3tLNTS5/KB2z3W9I5LIBEJHPhDoKUL5X4Gi8ivz6ces67YxanL5UVEjxBRDYYUmt7if0wcl9eqLLEtdKt3R2+ge64F6Jw98tps1JYIM5SWZ+eniAqQjyBRI7kYIRyehffht4ze/49OHPvzHQGeKNIo+HDkTcDqdMbPzk8wYtDJGXWw32/8JhEIh1pa2cOeynTR2kJGwovLVrjr+8NZmDjVHagEZdTLZCSb6Jerg69uQNr8mSA+Ar10oQm9+nbA79ktCr5UYkysMs8ub3QzJjL0NnZdkptEZ+ULJSjDR2PGiHJ2bwNbyliO6UJv1mqhR4cfn9fbYq2j2MDgjdn9yEk0YtBrGRrm2EzP6p/xLpAcgyaxnUr/YyvJjcuNJtOhxuVxCKDIWEvKRAu5u0gPQUor07hlI7VXsvndWlLoT0PdQDh+bF9kPSaKXkXNP5CTEvgdHglGnYWxufMz86f2TWV/arR01Ji8BvUYmrsO2CCCsKEwsEP1dvLo8wqAfhLxD/hHWyPh8cW1po4tzXl7fRXoAyps9qKYE1LypsS5HzZ2CYo69Jvrw20Uf8elDH/6HICUVos65N2qemjkG7Fk4VQNDYrxkbztmEPEmHZkdNj617V7q2mMoUf83UNnm42/fRRd321/vpLo1ss38ZCt3HDsIKeBAKv426nXS+heQj/B1nJVg5pb5A5ElcPhCOLzBXl5OnbhyViHvbqjo+vuEERmUN4ugnBa9hvlD0qhz+rAaNBw3PLqW0uUz+pFk7U18shJMvdrt7M+wGP25/WixC3bz/AFooojjpNuNTDwCcfk1WE06zpqQ2/VC7wmzXsOxwzMIKSqj/7IG7DnCkDkK1Dn3wdpno2So8OMjmAKRUiCyBLcePZCsHuQlM97IsKzuefhkazUXTsmP2t5JIzNJtuqj5v0aMuNN3Dh/YNT5TLMbmFqYxO0f7+zq5y3zB5CVYCbDbuCKWYUMy7SRYjVw0ZQC1t0xm0kF8Vj1Wk4amdlVzzvry7lyVlHU9odk2Mnu0DBaX9rci2gDfPzlahH0uEfU+y7ozDDhsl7Bj/vwn4E+4tOHPvwPQs2eiLrwFYjv0KvRGlBHnQuLXkVKLCAvycLz545h4ZisLq2a3EQzz549mmGZdoL+MOXNbj7cVMlNH2znxg+289HmKsqbeys1/6vwBxXqHLFtM7ZWtPZKm54XD62HYlca9AqPsSMgM8HAkksmMjjDxqNf7+OmowawcExWl9tvbqKZv585itIGF/vqnNgMWq6aVciVswq54f1tTCxI5NlzxrC5vIVnzhpDdqKFO48dxB9m9Otyo06xiphKC8dkE2/qbd+Xl2Th+XPGsuiwdpMsep49Z0yv9GfOGs2ojp2JjDjR/07iKkswb3AqSy6dSEHyfy/+UprNyAd/mByx8zOhIJGXzx9Hqs3Ag58LxeBWKQ5Oe124zndq2yTkoy58FUzxsOeTqPVLNVuQwsEuw+bBGTaWXDKRzPjIOcpLsvDc2WNYNDYbg1bmuz31ZMQZeeDEIaRYRVmrQcs1swu554TBxJn/NeIDwmg92ny+fenELpupzn5md3wMmAw6zhybzROnj+bx74u5/v1tPPrNAe44dghhVeXWowdwxcxCbAYtB+pdFNc7efqs0V1CjXqNzMIxWTx/7hjyk8SRbs+dnp64dTOErBmoF36Jmt/t9q7mTka98EvC9tgaSH34beM379X170afV1cf/iegNpeI4yCNHtWUgGyJ1FNpcHhx+sIEFQWDRibepOXEZ9fw9qUTufqdLeyqjgydMjonnqfOHEVeUmzPqX8WJQ0ujvvHqpj2D0+eMSqqIJ1avhbptRjij4B65VqktCEx8ztR0ezGG1TQyhKHmt0EwwqKKuw4dla1M6EgEYtBi1mvYXhmHG3eIIqqopElZEnCatCQbDN21efwBGl0+wmGFPRamSy7EYPhyOaLTU4f7b4QwbCCQSt3EZdY6dH6r5FFX9L/Ra+maKhu9QjXcMRL2mqUCYfB6Q8TVlQSzFocDdX0S7MKN+1wEDRGlIR85OX3I62NETMssR/qOUspCacRVlRMOpncI6ylFrefVk+wax7yEi3UO334ggp6jUyqXY9O8z9zJFve7MbXMZ8WvQaDBC3+MGFFwajT9Frz60qbueDVDb3W718XjWBm/ySMOpkmd4hgSFwfb9YKyYewgk4Wx2XJtm7C9+hXe3nh59KofctOMPHtNRMxBVqQguLjQ9WZkBOiC3H24f8vfjfu7P9u9BGfPvw70N7ezsGDB3lxd5gVBxv5y4JhjM22s6rMwcTkICkpKYTDYcb8dW3XNcsvHU5R0RFsiP5JtLkCSGEvgYCwmVn46g58QYV3ziwgJSWF1pCG/KTeL3y1qQTprZOixv5SC2agnvQccsJ/LeTLhrJmLn19E0jg9IfYfOtkPB4Pd39bxb0zCykqSvnXBvk7xv46Byc98wv+kILdKATzrhkWZvbAJOHqHuUnXT32r0gTowS9/Q9DVbMbixzgH/9Yww03TKOxsZFLllVQ3epFq5H5+obpFERZu0dCbbuDv3yxn4EZduqcblpdCh5fiKOz7MyfnEVc3L8uU9CHfy/6iM+/iD7i8/tDMBikxlNDg6eBtkAb+bY8LDoLmbbIXY02TwCDFMboqQV3I81JBThDbiocFUiSRK4tB4vGRLLPjWpKRwoEQG0Hb4sgA4mFoNVDSxlIQHy+UI111ohjoLhs4equN4PfLXR+zEl4Tek0eBSyzDJab42IDu9pEYFQ9RZUWYMU9IGrDjwuyBgsgmg2lwiVamsaaK0QcgmvspYSMCWBNY2gKYNqj4o7EGZggg6tp4Zmkx1n0EWlSxCYHFsOVo2J5KaDHe0Wourt1EmJ+IIqDU4/rZ4Ac/onom/ei/TOaZEBTpMHCGVojR5FZwVFQQ46obUMcmZQGWykxddCo7eRLEsWdoMNU8BGm6JFURRc/jAjbW6kkBuaignmzqY21EijtwlnwEm2LRtfyIc76Cbdkk6i1k6jW09FiwdZlsiIMyKhopFlatq8DEnSkECHoGRbhQgxYE7GZ86g0aPQ7gtR1+Yl0arHqNVg0ErotRocvhA1bV7mZinIQZfoP0BCAYrORoscR5LSihRwQctBYecRl0NYn8T3FT4KkixY9BraDqvfZtSQYtaIdeVpEvc3PheM8bSShNUgd4231d9Kni0Pq86CzxeH0xekwRkg1WZAlsGs0+DwhjDpZPQ6DXXtPlz+EDmJZgaaHEiHViF9fq3YCeqAOmwhzL0fRWNEDjjE+kSFxH4oWisbW/S0ewIMyrDjDYY51OQmM96I1aAjX9eOFHRB80FhvB+XS1gfz7p6lcGJMomSs3ueE/uJ47e2SiHlEJ+LorOxoUmH1aTDZtB2iEgqtLiD1Dt9DM2w4w8pZNk0mLx14pkAcNRSm1qEK+im3FlOvCGeFFMKWbpUtP7Gw9Z/OiFzJhp/E1LIByEvBH1CZDQuS4hJtpQIba34XBFDr24HJBaiGmwcCiV2eABKtHoCDIhTSda4u8dlTQNLCj5zBkZvfa90Kanf/8rvVh/+6+gjPv8i+ojP7wter5f9zv3c8NMNNPu6z/KPyT+GG8ZcT1ZHXKAGhw+bTsXoPIT0/jnUXPAZP1Wt5InNTxBUxEvEoDFw2/jbmJY5jQxPG5LOCB9fDlUbYcJlYM+Cn/7U7dKuNcLRfwJZB59fK9KGnAyDjhcqybIO1j6NesYSQtYctE27kN4/J5JUDDkFdd79SEjw/GS4ai18dy/s/ay7jC0dTl8iAmW+cbxQDu5IV894m1DiEJDCaJv2UhOfxcqqlTyx6QkCiuinXtZz2/jbmJ45lcy/DQVAHXoKzHuQDa1Wzli8rqupAw8dhc5RIUhBcwmSNVWM5YcHIeBCvXIdlCxH+u4euLmcEn8NN/x0A4cch7rqGJ06mj9O/SN6r55vS72cNxDYuBhp3fN4byvngKuMG366gSZvtzHuvNx5zMubx5/W/4k/TfsTQ+IHMf6Pm8R90crce8JgShrcXDU5lWSNE5ZdgVS5vnuOkvujnr4Eny2PwQ/+2JU8NMPGU2eO5s6Pd7KpvJXS24YhHfxW9L9TL0drQJ3/CPQ/Wqj4rnuuW6PJYEc9dTFK+igK/7yFY4elc/sxg5j/5ApCCiw+bxxTC+wYneVIH5wLTd2BL9WciXjPWspB9yGu/+n6iPEelXcUN429ibXfbOfWzSJtbF4CV88uIhgKo9NquP7drTj9gijIEpw7KY/7ZyQih11QvUXcl5wJqIZ4odB98Dukb+/qNS61/3z2eRN49qcSvtxZy8wBKZw5Podjsnyw6VWkdc92R6o32FBPWYySMQY57IFPrkSqWAdjLxRE/ceHI+s/6mHUAcdyySf1tPsC3Hv8ENo9QS5+YyNfXjeNZVtquGF2HiZ3JdKBb0XA22Xrqb76Ep7c/BTflncb0ycZk3hq9lMMtBVi+ksP+xprKuoZb4PejuSshm/uiIzMnj0eZtwKSy8WbmqnvAAaA7y9SMRjO/qPfHpIx32f7uLbP4wg3eCFT65CqujebSVrjLDd+/RqpPIeYZOSClHPeBspRsy7Pvx78bsRMOxDH/47qA/UceXyKyNID8A3h75hafFHeHxOAqEwr/5ShtFTg/Te2XDhSqo9tTy68dEu0gPgD/t5eN3D1HvqkXTp8NWtgvSYEiB/Oix/IFLHJ+SDL28SEZ07I6Tv+QRqt8OBryG5CPxOpA/OR+uuQVqyMJL0AOxZhrTlDVACcMsBWL84kvSA2EF45zShKdIzMKOzDuntRWg91WjddUhxg6h11/LnDX/uIj0AASXAI+sfod7bCNcfAkDavQw2vca4dJnZA1O7yg6473scuhQwxiFtXAyfXQufXye+rvNmIDlrkb66FUJ+KoKNvUgPwNaGrfx1419RzWHOGpcF5b8grX0WVIX6QAtXfn9lBAkAWF6xnN3NuxmdOpobVtxAe8jJRR2mNf6Qwj2f7GZivySSdD74+o5I0gPQVIz0wXkYPZE6OJOLkvnTV/vYVN7K9junITmru/rffR/9Is1Rg1S5IVKY0u9Aev9cZH8bD4+Fr3fV8c6GCh47bSRzBqXiD4kdROmD8yJID4BUuZ6GYDNXLu893u/Lv+f9fe9z9PHd7tSby1tZsv4QeckWLn9zUxfpAaGL8+bact7aG2CXLxX/wJMJjDyfLc2pODXxSM4apC9vjjouqb2GLAt8ubMWSYJLphUwd2ASVG1AWvOPbtIDHev1PGR/G3x3jyA9BjsUzYOeZLGz/q9vQ2qv5KlTB7Ctoo1//FCMJxjmwZOGsr/OxeJVpZi89UgfXAiFs+GDc3FdfwMfHlgaQXoAmn1irur9kXOFqwFpyUIkjQa+vSuS9IB4Rje8KEJM+J3wwQXiI0VrRNr3BaxfzIkDTDh8IdJM4Y5xrY2sY9LVSN/dE0l6QJD/985GbSqhD/856CM+ffhdY2fTLlzB6ErIH+z/gAZ/M43OACflqSKgaEspDZKH13e9HrPOt/a8RYtRgYPLRcLQU2HrktidWP88zLy9+++tb8Hw02D9CzDrDhh/GdRsEfG+omHTq+Jl66yDLW9EL+Nthab9kD5KfNX2TK/fDU3FNGp8vL479rje3PMmzYbul6m06RVkTxOXTMuPKGeTvLDqb+KoIdBjbo/7C6zpMK498UdafC29SE8nVlatxBVyo3VUIv3ylEi8o549zXtwBqNrHS0rXsaxBccSUkJ8XfY1d999dET+x1uqkPxOpIPfRR9g0wHwtvLGsd02IJP6JbHiQEPHuDyw5pno14KI13T833pnKCHY+SFnnyoMv99ZX8GQjDhOHJmJVacRpPDwlzHA7XXsa9mHIxD9vn9Y/CHNgVbKbh3EwQdFJPH8RAtf7qiNUJfuiSXryjnU7GFbjYuZT6xmnyuATfZ335doWPu0GDswIT+R9aXN6JxVSKtjxDhUQrBrKZK/494PWQDb34td/5qnsUnujrluxKiVmVqUzPMrSvjjWMDbDAXTYM+nEA7SFGjl/f3vR63KFXSxq2U33FEfmeF3QO0OEc8rGkp+hLwp3f3f8Z4IMAxIW95A9ggyJQWcSPu/7n19cn+Ilg7QUgqextjj78NvDn3Epw+/a5Q7ymPmOQIOQkoIfyhMTk6OCDgI+BQ/Ne7YCrnV7mrcIU93gjUF2itjd6K1PDISta9dHIO1loM5GbLGCRuKWPC1d4TBCEYSjcPRaVukO0y0raUU3I14Q15qXEcYl6saT7CHhk+H15D1cA+poAepvYJeUBXoTC8q6rWLEVEUFXfQ3XFNx9xptUe8X66gC50stHkOOQ4hE8nxqtu8Yn6OdHrvqo8IcxMIKd3Fg97o4+pEWznI0T2ZpOaDSB0VufwhgmEVrSwzPE0H7oao14jxxm7PHXQTVIJIb5yAZssrlN0+lKwEEyWNsddAVasHnUbCpNWSZNV3zYnUFrsdqa0cgmI9J1sNHGr2iPvSFntNS437ute0NfWI619qr4Cgl2SrAVWFQFhBRdyv+bMmiA+O+DxBpIGQEoz5sQJQ3l4O2ihee80HxbMYDaoauRvbXALWjl3YgKtbgTngitzR60TQGz29E8662Hl9+M2hj/j04XeNIUmx3avTzGloZS0mnYYtW7ZAh4uqVWNmYMLAmNcNShxEnM4ujCRBEI7UI7hxp4+I1MCxZQgykz5C2Mrs+RRiiNKJ8ukgaTuijR/ByyltmCBIh3/1po+AuByseisDE488Lpuuh0eMLR00eprch4UW0FtR04bTC4oCnenffUemNbN3mQ7oZB02vR1V1nbPnd/P4KTYthLJpuSuF+KIlBEowDkTu92KB6bbxLGLprdwYRficnj66e5jDJ1G6gpwisEWfVwdUNNHxNxRULPH0/laTLEa0Gok3IEQW+uDwqg9Gn5lvEnGJAwavThi+/ZOWPUkFw3VHlFBenC6nVBYxeELUNPmpTDVKuyQ0n9lXAbhuVTe4mZ4th1Vo0M9gjSBmjsF2jviVP3K+lfThoPeRkWLB60sodfKyIj7Nf6JDeLYqXYbZI0FQCvrSDHFXudDkoaAP0qoiMyRscmarO1+XgGyx0F9RwgPS7L4EIGO9RNFm0hnjp7eifi82Hl9+M2hj/j04XeNwrhC0i3pUfMuG34Z2ZZsUu1Gpk2bBsYE1JyJJDqCXDD0AjRS7697razlnEFnY3c0wYgzReLez2DkWZE/rJ3Q6GDCpbDi0e60SVfBtre70tXWMkgZCHExXMGn3yJ+dH3tMO3G6GUSCoQHS/nq3umJBZCQR2KzlwuGnI9W6t1PrazlnMHnEF/THSZAnX4zYVsOT30feUxT0x6AyVf3Hu/753Sn774Uu87OmNQxUbt7StEpxOntBMzpqLPvFomPZVIQV0CmJTphOm/weSwrXoZNZ2Nm9kwG3PstcweldvRf4qSRmaiGONSRZ0e9Xs2bCgY7r/XY1PpuTz0LxwhiUtnsgUlXRr+PslbkfXBB7zxjHAw4hqJ7hE3KVbMLWbm/ng83VRJWFDDERQ998HgW+fY8sqy9NZMALh1+KenGbvsqaesbSEE30/unYDdG1ym6aGo+A9KslDZ4aO2IZVbZ7D7yuCZfTU1HoNJd1Q4GptnxG1Nh1l1R28BghwFHdxOj/V/C8EVHrr8dtlW2sWhsNqoC766v4MZ5A0QZox3VWS9sfIzxZBszuGz4ZVGbzrBkUBjfDx47bI3E5UBSf4hlZDzidDjwTUf/bTDoBPj0KgDUqTehdIgRqoZ41DHn976+ckP0dISROqb46O324TeJPuLTh9818uLyeGHeC4xMGdmVZtFZuG70dUzLmkpxo4fHvtnHLUt30oodTnkR9acbSTIk8PScpyNIU5Y1i+fmPkuSIQFVVmDmbULSXgnBxpeEt0jPr/v4PDjrfdj1kdhCN9iErY9GDzNugz1foi56FUadDc0lqOd+JH5EO2GwwZx7od9s0UbrIeFZNOtO0PcQdcubAme9K75aN7zYlazmTUE9+wN85kx85kxUg5tEfTxPz3maDEt3qIcsaxbPznmWZEMiLBkrdnTm3Av9j2b5gRZ21QgbFItew3Vzivis2IdiTkI96/1IshYOiBdHR3p20MQjUx9mds5s4ZWG2Ok5fcDpXDTsIn7c5mR9hRM1sT/qyc+BOYk8XSrPz3ue0amju6o1a81cPuLyruOxl+a/RJo+UQS6dPvpn2rl1QvH8+3uOlZtOwDTb0Ide3H3zo8kow46HhY8S1PY0rXDI0siyOZFU/M5Z2IuRy3eg2JOEf3vGXMtPhf17A9QTcmoc+8Gc1J3XuoQ1PM+IWRJw6LXcNNRA5jUL5E/frWfzeWteIMKTeE4WPCs6ENnANq4HNQLvyJHl8pzc5+LIIhmrZmrR13NrJxZGP7cIySHqkJLKcm6IG9fOpGBad2hFBIteh5aMJS8jnhvyCrXzini6R+KKfebUYxJqOd8GDmuuBzUs95HMSVzyGvg5vkDsOg1/OWbfWypdqMm9kM9+cXIY9rUwajnf0LInApTr0cdd4no19rn4NSXInc+Ous3p3DVsv2cOzGXM8bnkGY3kmI3oqLwt9NH0hAwwkn/QD2wHM79CO0rs5ieNY3rRl+HWdt9bDsqZRTPz3ueXF0q6Lt3JtWcieLZ8bQL4j3ohO551uhg9HniGdr2NqQMgvM+gQPfC6mIWXfC4OP5udTJoHQbb3+7CSZfK8bVucMjSailP4r08ZdGpg84Fk55gXZjJsUNTp796SA3vr+N134po7TRRTD4rwVx7cP/Lvrc2Q9Dnzv77xPVzmpcQRe+kA+7wU6SLp7Pdzi4+5NdEeXW3ziOFEMIyd8Oif2p8NTgDDhBArvORo45E9VVg2JIRuOpFeTE1wY+h7DXkVTwtgESGG2gtYCvRXi76CzCKEXWiCMpjRaWXoxUs1U0Pv4ymHItqt8p7E1MceKanx+Dza/B1BvFdr45GdWWLtrRmQQJ0hq7onPjawOdGVVvxa1NodGrEFJUMq0S5kAzki2TCk81zoALJLB1jEtq3i9sGUzxKMYUHJLwdPEEwniDYeJMOkIhhYONbiwGDVP7JaBtrwB/53jjCNnFi7UrXc6k3gauoBt30I1VbyVeZ6fVbUQjS2glCW9IoZ9Ng8ZTI/oeP4gapQ1XSNi4WHQWQkqIoBLEprOSok9l0AM/APD+5ZNo8fh5e10lswamMCzLTiCkMi09jORrE148Biuq3k69347GqKXdF8LtD2HWa7tCOFgMWtyBMPXtPibkx0WOyxBHKC6XZ1eUcc3ULHHffW1iN8MYh9+cwZ4GL3EmHQkmDS2eEC6fqF+SwKTToJEgTWpBCjgh4AGjXXjxtZXDzVVUq+24Qu6u9ZmgjyP+LwW91rF6wReUmEah0UroZIl2X4hASMFq1GLRa1BUlSZXALtRh6KoKAivN4teQ068Aa2joiO0iArGBEL2HIobPfiCCulxBtz+MC5/CKtBi1Enk2EMo/E2iLWm0YHBTsCSSXGTj2BYYWR8EMnf2rH+k8T67qzfYCdkz6W4wYNGI+H2hVm8qoRvdtdz9NB0rpldhMWgEWZp/iCZVg+SGhIkP+DCGZdLc7ANR8CBUWPEqrWQKccDfvFMdax/VW/Do0/ChB/Z1yquVxVht2S0g9YkJB467hcaAziqxTo3JVHlM6AoKkFFJRhWCCsqw+x+JH/Hc22woOrjaPSbSTEFeqSLdeU2pLKjup2LX98YoSZtN2pZculERmTH/zd+ufrwX0Gfjs+/iD7i838DxfVO5j/1c1Q72BNGZHDfCUNItRt7Z3agvNnNJW9s4mBDbyPMBLOOj6+cQkGKlXBY4emfDvLU8khX5pIHZiGvfgJp9RORF0+7EZBgdRTvIYCLv4NPr0I9+30KHtvflayRJb65fjoqcMxTP6OocN6kPOocPr7fU9+rGo0s8e0N0ylKjRKA8TDUt3u599PdfBejnu9vnMHJz/2CwxvqlT84w8ajp44gPc5IWpT5/GxbNde9t63r70SLjhfPG8dpLwg7nDuPHcQn26rZW9vb0yvRouf+E4dwfY/r37pkApe+salXOANZgm9vnEH/I4x3S3krpz6/JmredXP7MzTTyh/e2tqVduywdB44cShpcbHXSTQo7bVI39yGdLgsAYidiitWw/NTeueZEuCS5UIGAWh0+njoiz18vr22V9Ffu7/FDU6OeWoV4SjeYUcPTeOhk4Yiy9IR63/5/HFc9PrGrrS/nzmKyf0SSbV3h/D4ckcNV7+ztdf1AI+cPIzFP5dS0SIMq+cPSePhBUNJizPR7PJz3isb2FPb2+PNbtSy7KoplDS6ufytzVHrvm5OERdNzWNNSQvP/lQSs55Prp5Kv5T/Xpy1siYXpz63putosSeKUq28csG4/5FQM334dfyz7+8jB7TpQx9+p/i5uCmm8883u+q4cd4AUns8N83tXjxhBbc/jDsQwqDVRCU9AK2eIA1OPwUpVhrdAZas6+2pJLtqkba+2fvigcfBe9FtVADY8T7qucsIGROA/UwtTOKmjqjn7d4g8SYdF07N59XVh5hSlMTVb2+JWk1YUVlX2oLdqMWg0xBnEtv3/mCY2nYfLe4AOo1MvEWHP6jww75Iz6S8JDOXTCsgO95IbbsvKukB2FvrRKuRUFWF3dXt+EJh4kx64o1aTBqVE3ODnHh9tlDaNSWgmhL59ICHy2f0Y/HPpbz2yyEeXTicmz7YTksPI2ujTuaPpwzjmR+7veFuOmoAP+1riBqDTFHBUdmMYnQh+drFrkTHF3tnmI0PN8X2THpvQwXvXjYpIu27PfXcPH8AaQji0+IK0OoJ0OYNYNRpsBl1XcExe0K4TH8ZvSFVQS3/BWnYIti1tDtda0Q97U1ChjSUYBiDToPHH+aGuQPYWdUuPLE6cOKIDK6f2x+XL8y2ilbizDqy4wzodN1G3+tKmrtIzxUzClg4NgdXhy6QzSh2wlJsRm46qnf9kgT3nTCEvbUOXjxvLGFFRaeRaPMEu+p0+0O0efwsWRfbm+yTrdUcPTSdl1aVkpNg4spZhbR5g1S1ebEZdbx8/hjCKniDCk5fEItBi92oI03jQfbX0c/WTNn12WCw4zWmccuyvSwYlUUorDJvYCJ6VzXHJbdz3KkGMA1AMaVS5dXw9voKFq8qw+EL0eD0oSgq7R312ww6shK6iVtViweHL4Q3GCLepCfZqu8VmLXB4e9FeiYXJnH2hFw0skS7N8jBBhcWvYaM+P+5uG59+NfRR3z68H8SDm/ss/eQohJWFHzBMEadhpp2D4Ggyt2f7OSXg0II8flzohvtdsIbFKJvqqp2vVB6IZpruqw9osu66mtHsWfS/+5vWTgmkzPH53HTB9so73gx2U1a7jhmEE+ePhJVFS/8WHD4gry5rpysODPT+yej18l8t7uev36zD4dP9Lkg2cIL546J2BkYmR3HNXP688iXewgEFR5YMPSIc1HV6qWu3cewDBsTH12NQSuz/Z7ZGByHkJZdITx6QBzXTbqak8dewLYqhVcuGMfakmZ2VLXx1iUT2FXdzpaKNnITzMwYkEyzO8DI7HhmD0zlmGHpmPUyc574OWofPjotnTH9gOUPIO36sFuUr3Au6nGPoybkd405Gtz+EEiRaWFF7ZqXyhYP722s4OVVZV3Ea3hWHE+cPpIBaYftuiihSFHAw+FtRz36j8LYt2qTUDMumkvYkMTYJ9aw4paZ7Kl1cNMH23F4g9xzwhACoTA7q9s5Zmg6/pDC2S+vp8EpPJ9SbAb+fMpwhmbau168Dp9Y/0+ePhK7Scc5h5X/0ynDGJYJBclWXrtoPPtqnawrbcZu0jEmNwFvMER1m5fr3t3aNd5hWXZG5sST6A/y8Bd7uXJmv9hrH3AHQhh1Mv2Szbx64QTu/2w3Kw8IPRyNLHHiyEyumV3IKc/+gjsg5qvstmGw+U2hJt0pvZAxCtMpL/DkomGMfORHdt41DW3jTqRll3e5yGOwI8+9l5yiozlmWAb5SSbu+mQPDQ4/VyzZQlsHcZlWlMwjJw8jP9lCcb2TWz7czvaqdlGFVuaiKfmcOymP7B6E1hOMvJfnTcqjINnC3Z/s7PogyEsy88CJQ/EFFQpS+nZ//n+jz7i5D/8nMa1/csy8oZl2DFoNlS0e6tt9eP1hrn9vaxfpAQirKrYYEcA1skR2gvhhtBu1zByQ2quMqrWg9pvd++Lmg9BvVuyODz6JYFD8mF42vZALXtvQRXoAHN4Qdy3bRbxZTygcpig19jb+5H5JLN1UyZ3LdnKw0UV9m497PtkVQQDKmtwYtTL9e9Rzw7wBXP/eVsqbPdQ6fOQmmrtsZQ5HvFmHPxjm8rc20+QJctPsfPwhBYO7GmnJqd2kByDkE0d/ez/n3qMLuGLJZnZWtzNzQCrnv7KeF1eWUtfu48udtZz4zC/c+P42Wj0BzpqQgyxBgyPAgLTo4x3dPxNW/AVpx3sRpEMq+QE++QNSewVHD02LOVfT+ycTOOwFNzDNhtWoJRAI89WuWp79qSRit2lndTsXvLqBQ02HucBrTUeWP+g3kwaPRHDo6SjHPUFo3GVUqlkU/Wk9Dl+IFk+Ic19eT1mTm2Z3gBvf38ZTy4txeAMkWPRc/tbmLhID0Oj084clm2nusWM2uV8yKVY9/dNs0cu/tblLxiCsqPy4r547jx1EVryJ51eWUNLoZvHPpRHj3VXt4PxXNlDj9PPexkpsRi1zBvVe+52YWpTMtso2njl7DLd8uL2L9HS2+cnWap5bUcJDHcT64IOzYf8XSKse7yY9ALXbkJacit5ViS+ooHXVIr11cjfpAaGw/dWtSA27GZpuZkK/ZGQJ7CZdF+kBWH2wqWNtu7no9Y1dpAeErdQLP5fy2fYaAqHutZCTYELT8QCk242Mz0/goS/2ROyCljd7uPqdLVS2enqvhz7829FHfPrwfxKpNgNTCpN6pcsS3H3cYNq8AV775RAOXxCXPxzxAwjw/sZKrp5TFLXuC6fkYdELV3iLQcddxw3i9mMGctdxgzl6aLrY/laNwjtLe5h9yPIHUGfe0TsdUFOHQPpwBj/4A2dNyOX7vfV4AtF3Dv7xQzGFqTbuOm5whMgfwEUmKHlgOqMszay9vB8l98/k78uL0Wh6s5dZA1PQyhL3nDAESYIxufFsqWiNaHd3TTvnTspj7/1zKL19OGW3DKD0jpFsv3Ma18wu4t0NlYQVlVdXl3HOlEL23DcbGveBI7qYovTLU2icNQTDKuvLWnh/YyXHDMugtMnNygONXfYarZ4gMwaIF9hLP5fy9I/F3H384KgkTPK1I+38IHp7lRvA28aI7PgIgtcJo07m+nkDuPGDbV1psgTXzS1CVVSq2r0sXlkate7adh/FDZH2SVJSAerRf+72POoBtXAOWFKZ+vfNrDzYQuHd33DrR7vQ6GR+unkmux6Yz/d76rp2QHq2c/KobF5eVRbVbiesqLy8qpQWtyA4yVY9z587hldWRy+vqHSVT7TosRl13PbRTiYUJHL2hFzeWHMo6njrHD6K611cO7uQMY/8wIJRmaTYDL3KJVv1TC9KZm1ZE4oKm8pbo9b36bYaBmWIM2eNs6Zb5ftwOGqgYS8HHzkair8VRu1RIK18FL2rmm9317P0isksj2K3tr2qnTZPMCIkSE+8tKqUqtZu4mXRa7loSj4Ai8Zl83qMufEEwqwrbabO4Y2a34d/H/qITx/+T2JbWROPLhzOjUf1J8VqQCNLTCxI5IM/TCYv0ciCZ9ews7qdkKJS3ebpdf2q4iaaXQEeWzSCwRk2ZElsZ/9l4XAunFLQdaRQ0exm+d56PtpSzZtrD5Fk0fPGxRN4Z2sjYUs26iXfo/Y/WnjMmBJQh5wsvLYu+R51wDEi3RiPOulqOPMdHNoM3r98MtOLkthVHSPEBVDc4CKsqHy/p47XLhzPxIIENLLEnvtmc981g5F/eQrp7dOQ3lmEvPbvfHxmBnlx3TtYWhk+unIyx4/I4PIlWyhrdPHeZZOYMSCZ4sNsm275cAcPTLdirPwZadnlSG8uQPryRuyO/Vw8TMvaUrFTtq/OidMfRq/XQ93O2DfH3QjhAOdNysNu0vLh5kqmFCXx10UjyEsSu0tDMuw8ecZI6h1+PthUxaJxOeyvd7KzysG7l01ifL4Yb6pNuGnjd3RH/o4GRzV22c/LF4zj4qn52E1adBqJeYNTWXbVVDz+IM3uIBpZYnx+Ai+cO5bPttfiCoQJhpWI3ZTDsSeKYbaSPAD1wq9Q86YILz9rmtAzOuEpSlpChBSVQEgQkv31TvwhhRSbHqNWYmd1e6/6QBh7H6iP/sIHMf+du3m5SRZSbMYjlt9f58TpC5FoMXDZ9H6cNSGHDzdVMSTDTpMr9nh31zg4aqiQgWjz+Hnv8kksGpOFSafBqJNZNDab1y6cwN9/KCYrzky9wxezrrCiimNGEMrLh8ey64naHUiSBDXRjakBQbhR2FHVRla8iSXro9sgVbd5GJMTHzWv1ROM2OnKiDdx4ZR8/rJwOANSrRTXxz6qPtjgoskZe+768O9Bn41PH/5Pwmgy8MKKUm4/ZiAnjRBiaDqNxLbKVhZ1eBT1S7YgS0T1RgLx5dcv2czLF4xHkiQkVPKTu3cMDjW5uemDbWypaOtKe2dDBV/vqmXplVNoVDSY7QOxHf8khL2AhGJK5OMv1zB//jRsxz0BYZ9IN6egMdmJAybEmWjzBNhd6+Db3dHHl5tops0T5N0NlSzf28DZE3J45OThmLy1wo26tayrrPTzX2HXUszndBvTvnjeON5YU85n28WuzO6aPQzKsPHCOWP4ZFsN3+zqlugvvWME0vb3kH56pLtORzUc/B4WvcHBB46m6IEfyEs0Y9bLBINBNMkDjnBz4kGjo87Rzr3HD0GvldlY1srF0/KZkD+BA/VOihtcPPl9cZdHUF6SmT+dMpzL39rMoAwbtxw1kPQ4I4GQwoF64XqMJMUOZ2FN49Mf1vJ2hZV3LpvIeZPyQVLRa2Ru+WAbqiRx47z+WA1a9te7uHvZLvyhMLcdI5Sw7UZtTBuhwig2HZq4TDy6eIwnvyBCkSCh2DMpvE9Ejpck0Gvlrnup18g4fSG0Zn2Hp1bvEAkuf4i8RDO7a6IT4twkc9dOJAgX/n+2fEa8CbNewzvrK9DIEnaTNqZBe1GqlY0dZPfU59cxoyiBJ04fzdWzi3D5Q2yrbOPz7dXcffxgDFpN1B2nTkgSmPUdr6mOjwO80XeHSBmIqqqQHFudnIR8QKJ/qpUdMQgkiGd+Z2Vb1DyrQSu0knogO9HMGYm5lDe5yE2KPac5iWbizUdQFu/DvwV9xKcP/ycxLDOOh77YzYwBKVyxpNsldsttkyl+eD7hcJiDzX5sRh0FiUYOPHQUkiQRDofRaDR4vV6MRqP4woQIj5lOZMXpeefisby/uZaNh5oZlGFnb40Tpy9ATZubsVkJmM16ypviufiNg5Q0ukixGVh9yzxkWSakil2j7/c1MbnQQGKPuuPNehaMzGLxz6UEw71fHFfMKuTjLVUMSrfR4PDxza56rp2RDxs+ETsfmeMgd4LQ/tn6Flz6C+h0HHwwh1p3gGAwyFWz+nH3cQP5eEs1f/n2AIVJBhLMOhaMzOTZnw52tSsFvSKAZ9pQoW/SGbdJVZG+uQ3NhcMAuHR6PyxaGa1WFvHJMkdH/TpXx1+KYk7l6dNz0Gg0OBwO3D4rBo3MxzuquWhSNrMHJHHmCBG6wW63Ew6HeWtDNRdMzqW23ceNH2zD6QsxKMPGE4tGouq1MPA4pH1RvKmyx0NcDucsGIFxWy07K9uYOTAZrVZLaaOLvfUu2jxBKlu82IxaWt0BRufGMXdwGmk2Aypw3uQ8nv2p26YkxWbgpBGZJFi0DM+K7lbrDsm8tT3E5opWdtc4qW7b15U3b3Aaa0tErLPLpvcjTJh5T/2MToKPr5jBR5ursJu01Lb7CCsqk/sl8um2aq6YWUhZs5vadl+E7QrAH2b0I8XWTeKTrQYund6Pr3b1JlEAN83rT3KP8nFmPQ+eNAS9VmLTnbORJImamhqu/KScqUXJ1Lf7mK7zcfTgZCRJ4vzJuSiKwrd7m7AYNCiAXfYzaFwWiqLw5a4GClOs2Mxaih+ej6qqDLjv+4g+HDcsnWa3n+wEE4opBXn8ZYKoHw5LCuRORlEUNEMWwKrH4dZK0GjA6YR/CE0kddpNBG05nDsphKvVG7XdwhQryRY9yuFnxMDkfok8tGAY2XG9j+8A8pKtXDOniCuX9Pam1GkkZg5IJrPPs+v/O/p0fA5Dn47Pfz6qWjw4/SHWljSj10qMz0/C4Quw8VArQzPt5CVayEsys6fWwSdbq0m1G1k4NJ4E2oWwWdVGsGVC1hixQ3Bwufj0LJjR8ff3IrZWYqHYfm8tE0c3yUWQMgRMidC4p6OeDMgcS9iQwMf7PfRPs+H0hdhf5yAn0cyANBs58QZKmz0UmfzIgTao3QquRkgfLsTWTHGoOgvfVulIjzOikSTizDo8/hC1Dj83vr+N9g4vNZ1G4oqZhSwYmcmqg034gmFG5ySgoDIp0YfcckD0SQlD2c8ER15IdbCB4tZiyp3lDEocRK4tl2RtMks21ZFo0TEsMw67UYs3GKam3UeLK4DNpOW2pTv49soxJAaqkVpKoWG3EHGMz4Ff/gEVYudMvfQHPq5PZ3x+AjlSo4hWXrkODDakuGzY9JoI+yFJqMNPhzmPQKgdqjeLwLHZE8CeSVi2owm1dKTXQc5EsTtU+kNHsNexhA2JfLTXxcAMO3EmHftrnaTaDWQYtKTrm+Hz65HKVoqFojXCOR+h6s1waLXYFcqbiqK1UhGOw6yVCCrCa6emzUdFqzj+CCuwvqwZq0HL+PxEtLKEyx/itTWH2FHVzmOLRtDk9FPS5KYwxUpekhmjTsOqAw2Y9FqGZNgx6GSaXQG2VLQSb9IzKieeNm+Ax787gEaWuHhqAbct3c5txwxiamEi4TDM/ttKbpxbyMmjc9hb52BvrZPRufFkxZvYU9NOQbKVeqePkkY3aXYDigp/+XofTl+I+08cwvQByWTFmylvdlPX7mNLRRtzB6ewo7Kd+z7b3WW3deGUPM6ZmEehvlUILpb9LNZ9wUwaLIm4w14212/CFXQzLm0cCYYE6ot9jO6nRwq6BfkNeaFulwgamjGasC4BjeIWtjhV68GaDlljqTGaafW1sbF+I2atmXHp47BoLcz56zb+snAEGXFGVhU3Em/Wk5NoZm66D1Y8irTj3e7du0Wvo6YM6OgntI08jbagk11Nu2jwNDAiZQTp5jSyQ1bAyc+VOmbkBsDTCodWCQHQvGmoegvHvFbC46eNFIbPRh3Xvb+NbRVtTMiP5/HTRlHn8LO5vJUUm4FROXFYdFrS47s/gEB4+H26rZq//1Dc9XEQZ9Lx8IJhDEi3Uphkivqh1If/PvoEDP9F9BGf/2xUtrh59ZdDvPbLoYj0S6YVYNJreObHg+QnmXnlwvEUplipa/fh8gUp1DXB0ou6VZRBqCIveE6EgahYJ9Km3gC5k+C7e+GMN+GdM4X6bifMSajnLEVa/gB0vmB1JtQz3yGcOpxZz+2MMIxMtOh5//KJ9DP6keu3IH1wPoR62Dxkj4PpN6NWbkAdcxHjnj3AE6eN5MNNlZwyJpvvd9dw2YwiWtwBfKEwmXEm/KEwC59fG2GHUHLneOSG7Ui2NNj+Hqx9lsAt1RzwHeKK5VfQ7u/e9s+x5fDs3GdJkpIY8adVGHUyi88bx5c7a3h/YxUgXNqfOmMU+dpmoT5dvemweXsWNr4M5WtQL1/B0ppkFhWG4Zu7IjVsZA3qCX8XMZb0FlRDElLDNqT3z4uYBzVrLCx8GWnxXKHa24mssTDjFvjgfJC1qKcvIZwygqNe3kP/VCs3HTWAj7dUMaFfEsMTIVXvFYavzjpIKoLv7kHa90Vkf457ArXfXHZ77PgCIXxhlUvf2MTSK6fwzA/FfNvDIFYjSzx00lA0EozLT8QbDHP+qxsidF1yEk08vmgkyVY9Jz6zmocXDGf53nq+7rHTIkvwx1OGMTjDjtWgpcnpJ8VmxKqX0Gs1FDe6SbYKZeXzX10fUf/kwkRuP2YQF722sVe7L58/Hp1GwqKTSYs3U9Hi5vp3t7G14xjn+rn9SbXrSbIYCSkKmXYDzoDC9GQ30qZXkNb8o6u+2pv3sqZ2LQ+ve5iw2m1cPSdnDreNv5XM9a8i5YyDVU8IN/xOGBNRL/8RPr4UqUd61c27eWLTEyyvWN5jHmTumnAXMzKncfWSQxHHxAatzOLzxzE91Y8UcCC110BiPmx5E375OwBtt5Wwp/UA1/90Pb5w9/oZmjSUx2Y+hr/eT2GSDr6/D2nPJ919lGTUYx9D7X8ME5/dR6PLz7zBqdx/4hAaHH5S7Qauf29br/48e84YCpMt5CdbIshPfbsPVyBEXbsPrSyRYNFj0snkJPa5sv9v4p99f/cZN/fhd4X9da5epAfgldVl9E+1kmDWcajZwz3LdlHV6iE9zkg/swdW/iWS9IBwmf30KphybXfaL0+BrIMTn4bPro0kPQCeZqQPzoO590XUI713NhpfawTpAWhxB9hX50IOtIjrQocZelZtgv1fI7WWI7UcZNVNU7h72U5mDkxl+d56UmwmvtpVx3mvbuC6DoXcw0kPgBxoQ1r3vPjiXvssADXhJq7/6foI0gNQ6azkwbUP4pSEnYIvqHDZm5uYN7jb3Xtgmo08owtWPRFJejrn7ZOrYPI1kFQIBjunDEuBvV/0Fu5TwkifXQMaPVsqPUiBNqT3z+01D1L1ZqTVT8HMWyOvr94M+76EwSeJeX7/HDTBNq6cVUhFs4eadi/1Tj/f7Kpj4pObKPjLbgqeqiCcMx32fRVJejr788UNSP5WNI4akmxGLn1jEyeMSGd9aXME6QFhfHv3J7sYlh1PWbOby97c3EvMrrLFy2Pf7mdfrZNHTx1BbbsvgvSA8KK68+NdOL1CHHNSYTKFqVbS4i0kWI1MKEhCkuDytzb1qv+U0dlcuWRL1HbvWraTz7bX8PnOOlpdAZ5fUdJFepKteqE3s2w3VyzZzI3vb8Oo1zIuy4zUcjCC9HDij7QH2nlw7YMRpAfgx8ofWV7xA+Fpt4l7UXXYeph5C9IvT0WQHu5pZmXlygjSI+ZB4ZH1j9AabOev8yPlBfwhhcvf3IQU9iO9fRpUbBTx6zpID0Br0NmL9ADsbt7N4u2LScxKhAPfRpIeAFVB+upmJG8T756ZD8DyvQ18vauO/qkWnl9ZEkF6Ovtz1ZIt+EIKdYcZaKfFGSlMsTK1KJmJ/ZLEzm4f6fnNoI/49OF3g9o2L6/8UhYz/5OtNRw7TAR9XFva3CWuJvnakXZ9FP2ioFeQhZ7BODcsBntG7x/4TrRXCYPVntGqg16o2cK+B+ZGFNVrZI4enCyOhUJ+omLnhzD4RFj/Aqawg5p2H/FmHZ9vr+aooel8uKmSQEghwaJnU3lrL9Jz4KGjRP0zboN1z4vEczfT5G2iwRPdS2Zz/WbcwW69EX9IoarVS3aHqu3CsdlIPofQxYmGkA/aq1BPfYmgLQeNuxppw+LoZQG2v8eoUaOgcn3sedjxvgjI2mt+lorAlJ3tVm1kwfBUrpzdj9d+OcRRQ9L4fHuk67zGVY208Uj9eZeBAwfS7AoQb9Ry2rhc3lh7KGbxj7dUkRFv7PUC7MSm8lYMOg2DM+x8cASF6BUHGqlqi+7u3OQKUNseWb9WlrAbdb3SO7G5vJVhWXG8vuYQLZ4AH2+p7so7ZlgGy7Z2/z0qJ57dNQ5MofbuddKVOYrPS79AJfoBwTt736HaXy/uxeHImybuXQ9UuKt5Z987UesC+OTgJxQU9I5V9txRZnA3iWds7Lmw/oXuzDvq2dW0qxfp6cRXZV/R7m9D2vBC1HwAti6JaPeNNeU0uYMR89YTgbDClvJWXEcQv+zDbw99xKcPvxv4QwrNrhgvTUQk7zhT99m6t1MLJRwQ/2LB3SiCG3b93XDk8iBCIvS8BsBRg0ajiUgy6mVkWYb26Jo2gCBNsgbJ1dC1E+LyhVBVsbXe2CE+ZzfpKG/uLY4my7Igb1qdGAtASgpt/rYjDyEcOZfN7kDX/Jn0GjEHsUgKiCCRkhan0ynsMdxHcEV21iCpqrCxioWQD6K9eEO+SE0cRzWyLGMz6Gh2BZCQooSxUI/sGu2oRVJV2jwBBmbYMOnkI7oh1zl8hENHthoIhEUfmo6wRmvavLh80VXF2zy92zdoZZwxyne1G1JodgUIKkqvIJo9+5Ie10HcQj6kznXSA/We3po3nWjxtQBq7x1LADUcKTgIKCg0+5p7l+1Ag6eBYLj3PA0bNAh8HV5d4YCwheuERkOdO7qhNkBACRBQgpHXHAbJVYcU7B5Dk8tPSFHxBXuHQelEvcNHKBw7vw+/PfQRnz78bhBv0jI2LyFm/qicePZ36JZY9BrsnSRIa4S47NgVpw6JPNLKmSTsWKKIDHbBnimMeHsie7wgAT3g9IXweDyQFxkHKgIJBeCqR82dhGqMB4RtUIrNgNMXZEyuGHN5s4fBGb3Ptd1uN+ROhNYKYRAM8NRT5NhyepXthElrwqaLDLUwKM1GRYdKdHWrVygQx+fG7nd8LmgN3PVVGapGj5o9PnbZglmEFEXYT8VCQn50opWQD56m7r9zJuLxeNhT62BsfgKtnkDXTlUnfrU//WYRVlVyksz8XNxMTbuP0bnxMYtPK0qOsPE4HEadjEEroyjqEeuZUphERlx0r5+cKHG/3IEwydboHkad7cqSxKjceAxamZzE7roP1DsZ3UOrZlNZK6NzElANcai5kyMrCoeZlB773gxPGY5O1ou1ejhCgV7pBtnIyJSRMeublDERjdTbAHjik5sgrmPNKWHo2U+n84h1ZlgyMGqMR7zvasFMwpruWFyjcuLRa2TyknrPfSfG5CWID4E+/Megj/j04XeDeIuBi6YWYNT1XtYWvYZ5g9NYsV985V8+ox9JVvHDKiUXoc6+J3qlGaMEgelUgtVbYcx5sOdzmHhF9GuGnAItkUduasYoiMtmzF/XRqar8MNBB8TnC2XmaJh2I2x/H8ZdjOR3cfDB2ViNGl46fywjUvW8ffZASm8fwea7ZnJivkzp3eOFOjIi1tZjP1aI+qs2wYTLQWcG9W9YtRbm5M6J2uQFQy4gwZjAwycPJSveyNBMO+3ebjXbx77bTyguF3XBc3Dyi+JfwczuCtJHoGpNqDozP+xtwKNLhNn3RFUrxpYBBdP5+0+lEJ+HmjYsap/UOff0PoIBYXC++XVRJn04xOVQ3BpmR1Ur50/KY9mWKq6cWRhxiUeXCHO6+xO4q476m/fRdnsZ3FgB+dO57r0dWHQahmTaeezb/dx01ICoqtCpNgMTC5Kod/o4YXhG1L6fPzkfo07Da2vKuHBKQcx68pLMpNq7iYwI9uml2e2nMMlE6R2jIu4vCA+z44anR2330un92Ffbzi3zB5AVZ+DWowdR/PB8Su8YxeJT87jr6EIum17AnEGpNLj8mPUaVm/eA2MvFt5OnfhTCuPSx5Fq7h2CQpZkrh19LZlyHEy7ITIzLgcCTtRTXoD86V3JGVi5etTVyFHWQ7IpmckZk9G4Gyl+eD5njs+hMMXCqWOyuGX+AFS9FXXIKfDy0TDuItHPxH6w5mEyLOkMThwcdS6uHX0tWZYsmH2XEI08HNY06Deb/vd+BwjS+NCCodiNGv66cERXWIqeGJhmI9VmIP6wwKV9+G2jz6vrMPR5df1nw+X1c7DJy4Of7e4y4hyfl8BVs4t4/Lv91LX7uGJWIccMTYswNlRay5Eq1yP98KCwH9DoUYefBiPPRHrvbEF8cifB0X+Ctc9D2U9w/hdw4GtY+zR4WkBvRR17IYy7CGnJadBaKuoZeirMup1W4nlzSxNvrDlEqyeIRa/hvhOHMD4/kSR9GHuoGVb8GWnPMmEjFJ8L024CnwM1c7Rw/zYnom5/D4adiqrRI7VVwIaXkDxNqAUzkYachLrnE8gYDSmDWLoPTh1qRvY3w5a3kMxJ0G8mfHMHTPkHVTl23t77Nh8Vf4Q35CXBkMDFwy5mXu5csg6thexxNATj8GmNbChr5snvi6lp97Hx5gkka1xQuR5p+3vC3X/EGZAyEDa/jjr6PLCksqW0kdOW1nH+5DzunZGI3LwP6Zs7hYKuJEPRPNTpt4hI6YZ4nJIVe7gZfjpsHubci5oyGNwNSEsvBl+bSJ9+M9TvFm0OPQVm3UGblMCBNgWjVkaWZBy+ILuq27EYtDy/ooTqNi86jcS+W0cS1sVTE2rkq7Kv2Fi3kURjImcNOosMSwY+nziqNGklnv6phNPGZeP2h3n4iz0cqHchSTBzQApXzCxkzcEm5g5Ow6iT+XhLNW+tK8cTCJNo0XPhlHxm9E/mULOHW5du55ajBzI0Q+hIddYze2AqN87rj0kvo6oSSRYt1W1+7CYdKSYJk7cWDi5H2vs5qs4E4y6GlEH8vLMCa+4wkmxG3lpXznsbKnAHwiSYdVw+ox9Z8WZCisKI7HgyrXKUei6ClCH8vNMF6al8sLGKG+f3J98cRtNeCt/cgVS5HgD1jnrKfNU8vukJfqn5BUVVKIwv5LZxt1FgKyDdWQdlPyMZ7bD6b3DUw2BORN30KpKrAbVgBlLOJPjmdmgpofHGXZQ4DvHXjX+luK0YCYmpmVO5efzNFJpykN5diDroeOg/H585k5Ulbbyx5hC3jE1iTD8DbFiMVHQMxKVD+RrY8wkc/wqVWhfPb3+Bbw59Q1AJkm5J59pR1zI6ZSz/+K6JR+clIbceFOuwYY+QUSicC/MfIRDXj4H3L+fiqfksGJXFexsrKGl0MyI7jgWjslj8cwmfb69Fp5E4YUQm18wuIs6oJTmGyGkf/r3oc2f/F9FHfH4fqGzx4AmEkYA4kwZfUMUTDGPQymTHmdBH25qu2gz1u8AUL/6u3QF5U1Bt6SBpQG8W7jcBJ+hMSFojSFoIOMQRjNaAakpC1ZmQnDXCrkGjRzXEU9kaYK9TS6JZh0aWMOk1tLj9JFmNnLF4LS5fiJ+vHkmWXQcBB1I4IF76vnahNbLlDWGTMutOGHEmAOrWN5FWPRE5BnMinPcpvHcOas54mPsAtJYhvb2o2y4pfYQgEnFZIGlw2NJo8bcRUAIYNAbS9MmY/pIl2sieAKcu5u9bgpw2NhtfMExIURmgb4aPLkGq3hzRfGd5xRDHnlaZFncAb1BhfVkzK/Y18O31U9G1HBC6P5Ik9HO2vdOxK7AYJW8WVU49uXaP8JALB4Xg4obFsPND1NxJQmIg6AWDTdwXX1uPefYy8/ldXDwtnzijjieXF5NiM3D2hFym908mzqQjrKjotDKpRg21wUou+vYiHIFIpd0/jPgDJxeezAlP7eH9yyeh18rc/MF2jDoNtx09EEmSqG33saGsmaWbq3D4Qhi0Mm9dMgG3P0R6nBEJCa1GQq+RaXYHMOo06DUSOq0GrSRs0vxhBZ0so9dKLNtSzbMrhAji6xeNJzPOxKHd+5kxLF4EdG09bBdxyCkw9342tNu58LUNzBiQwgkjMtHIEt5AGKtRy1try1l9sInHxsazaE56L9Xurnrm3c+gv+3j1QvHc6jJTYpNz7SCOIy+JqSQG1RQdWZ8xhScigNXyE1YCWPSGkkzpnOwyUt+vE6UlwBVgR3vIa34c+T6NCWgXvAFaI2oejtBQxwNvnp8YR+yrMGsMZGhWODJHkeo8Xmo5y1jy6E2Nrni+fPX+/jLWC2LjpuI7G+FZX/oImcAXLSD5mQzjqCToBLCoDWQZc5iZ42TmjYveUkW+icb0buqhd6QrEXVWXBKNlqDWsKKyv56J1e/syVC6FuvkXn5gnGAUMlucPqYOzCNnCMcg/Xh34s+4vMvoo/4/B9Fcwm8uaBbdbgnDDa47CdI7k+r209coA7p06uRDq3qXVaSUK9cw583KswYkMa5r2yI2twFU/K5YHIudy/b3RXLqvjh+Wi3vYFUNF+40ceonyvXCvuGF6ZGH0vRPBh1Diy9CPXCL4WmUNXG3uX0VtTLVxCKy0e7/U2kL26KWp160jMEh51BWYuPJatKue/koWi3v4X0xY1HLD/qkR954vSRXSq2kgSlNw9CWnJKbxkAEDo+l6/s6M+R6n+a0PCz6H/vd+Qmmvnb6SO7wozoNTI6jcQzZ4/hotejjBlhj3PtnEKyU/w8sPYB1tWui1ruoxM/YvsPxXzuTOGKmf04+yXxcn3h3LHc+P42vIdFawcYkGblgsn53P3Jrqjt3nTUAE59fg0AP9w0kxOfWR010Oz6G8fhdGkoSJORV/0Fqaf3Us+5uOBzHIkjGfnn1SK8g06DNxhGUcGk0/DUmaP4w1ubKblnEvKqv8au5/zPcCaP5vgXNnHlzCLuWraTu44bzEebq9hf7yQn0cT1c/qj08pc/962qHU8ecYo/vTlXm4b6GfRjHyk56N44CHsaNSTnuH+lQ7eWifWwfY7p2Fv2YH0xgnRrxl/Gcqsu/my2MN9n+2mzRPkwENHodv1vpBDiHbN8X8jNOp8hj+0nE+unsqxf18VNWLJ5MIkrpxZyOfbq1kwKovsBBPH/2N1r0CwANkJJq6aJeYHYMklE5jWPyVq+33496NPx6cPfYiCujYfBxuc/LivnrUlTZQ1uWj3+sTOSjTSA+KYqyOSeKsnKEI0RCMlIIx2yn7mlqMGsHRzVcx+fLWjlrBCF+kB0Dirkba9LbyhjlR/3S4oXRF7kCU/CP0cEGKFA46JXi7gAkc1Gkc10ta3Y1YnbVuC1l3LLyXN3HPyUDTOKtHPWOW3voXWXYsnECYUVjFoZbSyxF8WjhDBQqORHoCAG9qrRBTuX6lf4xL3o6LFg0aWGJ0TzzNnjeZvp4/kT6cOJ8GiY9HY6Abrv5Q0YTHo8IV9rK9dH7UMwLradZx88nxWFTcS6PCGMmhl/KFwL9KzaGw2r144nj/MLCQ/2cIzZ42OMBzubldIHOQlmqlp9/YiPWvvGcOPdwxmf3A/LeZSKsMOHDNuj9lHtr2D1WzmlvkDefn8cTxw0lBePG8sDy0YitWoxRsICwNnXwvSriiu5p3Y/i4Wk4nKFi8D0608c/ZoShqczBkkbHoqW7zYTDoO1Lu4dk4Rh9txXzw1n/JmN40uPwsWHCVUlGNAKluJ5Hfy0DQjB+6dBYDVau3l8h5xza6lyN5mftjXwLd/GEnZbUPRuaqRti2Jfc22JWicVfiCCjWtXky66AbIa0uaSbMb+HBzNcu2VtPoDEQlPQBVrV6SrN32PB9uqiIYhQD34beNvlhdffg/g8oWN2+ureDVX8q6AiPGmXT8/cxRzDSHiO2Tg9hhQexadP5/TISDSJIUNYZWJ4KKgnL456eqirrVX3GNVcNHdqdX1S45fykciNQTOhxKCNAeeUzhIKgKwZACnbMUPoJuiRIEVex+mPUa/nTKMEbkxGPSSnAEl+ju/si/0p+QOHLsgQum5HPXsp1dgUI1ssSFU/K57eiB/PXb/RFlO6ddVdWYujQAQSXYVb6zlCxJvYJq3nb0QJrdAS57c1NXnt2k5aGThmHdUsWq4qYe7Yp8gxZCh62PVXeN4L397/HG7je6RALtejt/nvZnht56kKTHiqLMhVgHq4obefy77nEWpVp54rSROL1BZEkCpF+Z0wCSJPrT4PRz18c7+eMpw/H4u++zoqo8+9NBPrxiEmPzEqhu9eLwBRmcYef7PfU8tbwYQHi3hY/sYi+1lMDXt6E7YwnF93UYPR/pmo68W6elkKrzwGfXIk28vGO9xLom1HWzg4raMQ/RUdYkvBVDYZXwrxyC9Hxug4qCeoR6+/DbRN+OTx/+z2BdaQsvrSqNeHG1e4Nc/uZmEV/LEmPLWmsQ8acAm14HOgtkjondUL9Z7NhRxYLRmTGLHDU4Db1WZkR2t9aPYs1EHXKy8Lo6Uv2pw6Bwduz83Eng6iAYw0+D4u+il9MaID4PxZqCOvTkmNWpwxai2HOYVpTMe+sPoVgyhSFxzPKLUOxZ3DhvAHmJZn7c18ALK0pwBxQRW8va2zMIAI0eEvJQLKm/Uv9CFIvwYkq1GbAZtdy6dHtEdPSwovLK6jJsRl2v6OhjcuPxBkIYNAZGJI+I2c6UzCkUFxczJjceXYdHjzcYxm7SodeIn87CFCs2o45XVpdFrCuHN8StS7dzybSCrt2RMbnxXWT4QIOHnI6o6yBEJnc07eDVXa9GKCM7Ag6uX3E9rcF2kKIcRQ4/nUAoxPqylojkgw0uHvpiD/nJFjyBMKrBjjrwuJhjZfhp+INhUmwGPIEwDl+Imz7YRm6SGVmiKx1gdXEzT/9wkIIUC8u2VHPx6xt5e31FV1XFxcXCgD4WssYK43ZnLdKShWg99fh8Phi+MOYl6qATUPV2Mq3A9/eKHdd9XwkPyljXDDsVxZqFTiORl2TuEiw9HCOz4yhuEF6bJ4zMIN1u6BV9vRPJVn23/hdwyugs9DHK9uG3i7471of/EzjU7OaFlSVR8wJhhbA1A457jF57+ABz7hN2PkCy3YDfnI56zKOCOBwGdfT5qHo7Cz/cw4BUa1RdoSSLntPGZfPiihLuP3FI149s0f3LYcjJYEqAY6PXz+jzRDBNYwLq8DN652uNwpvmh4eEYm5cjnBhjzIudfY9KDqLaHfwAuES3KuzRTDgWO5athu7Scv9X+ynyhkSISJilR94LEX3fEswrLC7pp21pS0sGJXF0z8eJGxNRz02+jyrc+5B0dmY8NefYdCJMeovhEHHUfTAD0gSPLxgGGsONsXcXVuyrpyFY7qPvAxamatnF5FqM5KsS+b2Cbejl3u7Ii8oXIBNZ+OUd8q5/8ShZMSbKEgWBOrjLVVcNVscJS4am8Vb6w5FbTsYVllT0syE/EQMWpl7ThjCY9+ICOxnjMtGllRumj8AgDpvHa/sfCVqPSElxFelX8E9kZILau4USB7IkId+jHrdwQYXze4AGlmitN4BU68Hc1Kvcmru5K56bj16IG+uOdTV/x/2NTCxXxIPLxhKdYvYFXlnQwVXzi7knfUVHD8y8/DNN45/qwzVEI86+tzendIahCfe5tfE3742qNkmhD0Ti1B7uLx3wZQA02+ixhFACjiR9nWEPdn+LvSbJdbc4UjsB4NPouj+5dwwbwC7qtpZOCarVzGDVuaaOUW8s76CcXkJ9E+zYTZouOXogb3KShLccvRA3lorjmrH5SUwIM3Wq1wffvvoM24+DH3Gzb9PlDW5OfrJn7vUcw/HiSMyePr4FHDUiiCLDXshIQ+m3ihetgl5XWUDgQCypw2NvxF++bvwKLEko068CrLG8tBrh/gZiatn92NsXiI/7WvgvY2VeINh5g5K5aghaaiqEJDLTjCSlWBh8c+lbCpv4ZurJ2L214POiuRpEFHOO+qno3515Qsw5Tyx81S1QRiseppRcycjTb4a9n4BliQR1fzNBXDGO6DVd4xrD2pCHky9ATWxiFkvl2IxaFh66VjMvjrY8QHS7o8ACXX4Ihh+Gm59KgdaApQ1eqhr95Fi03P8kCTRzx0fdNiORJYf9shKbj9mIIUpFow6Lfd/thuzXsOzZ48hV9uK5KyF1X9Dqt/d3Z+kIh5e7eLcSXkUplhRm0tg51KkXR/2qv/Gj/dy1awi2r0B3t9Y1Sv2VSfMeg1/O30Uf/xqD2NzEzhvcj7JVj15SRba3H5CkocWXyOv736drQ1bSTAmcO7gcxmRPIJXvm/hzKn5pNoN+AIhvEFVxL3aXsPCMVkMTLejqnDtu1ujGjoDHDssncn9EplQkMSmQ80s3VLDRVPzGZObgNsfwqiTKW/xkJfm4fQvTscbih6u4qi8o/jrtEfRPjcRdGbUMRdA/6Oo81uY/PfoRtwAjy8aQaMrwNxBqeQnGtA5DsHG15CKvzmsHiO7WmU+3VbNFztqu64/Zlg6t84fyPqyZgpTrFS2enl7XTlpcQYundaPihY3eq2Gt9dXUNniYUiGnStmFpJi05NNI1RvQlr/vAgzkT0ORp4Nqx4X7ucdUGfcTnjGbdTV1ZFlDgp3+82vQ8CN2n8+TLiUoCmLVRUu5lgrkF6Z1z3ApEI4fQns+0KEdkFFHboQRpyOx5jOlhoPWfEmnvvpIGPyEtDIEu9tqKTJ7WdcXiKLxmbz8ZYqhmfFMXtQKnlJgtxWtbgpbfLwwsoSKlo8DE63c9G0fL7bXc+60mbOHJ8TUb4Pvw30eXX9i+gjPr9PlDe7ufqdLeyqdkTNv+eEwVw6rWOHwVErDG11R1Z0dniDWIMtSAEnqkaHnJCHPxjuipuUaTeg12u72ldUFatBy+riJg42utlf52B4djxJFj0T8hPQaoTKrkknk6wH2dMMOh0EnaiyDszpSK5KVGM8lV4jSBJ5SRahQRQOoprikXxO4Q6/+knYvay7s9kT4Pi/oWr1qFozPkMSrpCMOxBCK0sYdRpcvhC5cTpkZxWoELZlsKLEycurS1lXKo5SHj11OLkJJj7cUsX1cweQY5eRncLQOGzNoOj+n7qafOWCcawva+blVWUoKiwak8Wdxw1Cp9GgBvzYcCIF3ag6Ez5dAvUehRSLAWuPsCLhQADZWQlIhK2Z1LoVQmEFWYZ9tU6KUm38uL+BP365N+o9Gp0Tz9NnjyYUVtFqwG7Udyl2N7n87K5xUGj2Y0ww4gq50MgaUvWpXLpkG386ZVivwJItHj/tniASEpl2I/VOH9e+t41tHZpRh+Pe4wdz+vhs/EEVpy+EViNFKDA3Of2oahg/rdy+6jZ2NO2IWs8NY27gwsEXILcdAllDyJbNoRYvkiRx7N9Xxdzx+uLaaeQkmmh1B9DIwjC7wBpG9jSBpCFkz+aT7XVsr2zj0201XQKVnbjruEFcMCmPGocPg1bmzbXlaGSZaUVJpNsNGHUaQmGxGxJSFEw6DUZZJoSwM7MaZPTuGqTi76FmM+z+pFdYC/XMd/DmzqbBo2LSy6TZTajNZaAqKKYkyj1aWj0BtLLMCGM90gvTetu4jb8MBh6HGp+DYslgQ42fvCQzmfFCqbrZ7aPdE8KoldHIMrIsoZMl2rwBpI7nKBqqW70EQmHMeg0WvZZGtx/5COX78P8XfcTnX0Qf8fn94oe99Vzyxib23Dcbg0EcI6mqiiRJhMNh9Pr/ffXVjWUt3LVsJ19fN7Wr3TNf3sSWilaKH56PLMsoikL/e7/j2tmFXDAlnzizDp0mukdKNKhNB5AWzxZeW53InQL9ZqKOOgcpPnaoCoCqVg/3froLX1Bhe0UrC8dmkxlnoskdoKbNyz/OGIEsy6iqylvrK/lpXwMvnTsKrVaLqqpUV1eTnS0Io8/nY+jDK9DKEl9cO41BUUJq/Ffh9AXYWNbKxFwrRmO3cJyqqhTd821E2TcvnsCMAdFtt9rcAR7/bj/lzW5mDkzh/Ik5aDSarjUB9IqtFg0r9jdw4WuRuy6yBFOLknls0QjSY4SgOBy/VP/CFct7q4FbdBbePf5dCuK6wz7srmmntNHFjKJkvt3bwBfba9ha0YbTH2L5pQO7Am36QmGMWg0godHIBIMhnvzhIBvKWpjePxmjVsOgTBsvrCzFoJXZVd1Ok0uQis56OucgEAggyzKSJKEoCk/9UMLMASnkJJqQkEi26tBquw3pg6EwL/xcypVTs9BoDcIs3ueDx3rYvg0+CfXUl5B0kQKAzQ4/n+6o5pzxWV3ratmnu1l4VBrSykeROo/KekDNmQCnvMQ5H9Vxxcx+zBgQw5asD79b9BGffxF9xOf3i+YWN25tM2XtZfxU+RNWnZVjC44lXm8ns/gHyJtG0J7LPZ/t5YwCE2PydeCsgV0fCZXhCZcLj6VdH4OrDgrnQsZIpKRCKls8tHoCfLmjFoc3yOUz+pGnbUXyO2B3j/KZY4Qnyr7PofkgZI2HQSeBr1m4sFdvhuSBMOBowsZkJCWMHPYJ0cTd/4+9s46zot7//3NO53Z3ALssSy3doSBIiqAgdmIrdgt67UDFblBEStIApDuWbra783TM749Zdvew53Dv9/5uqHdfjwcP3ZnPfGbOzJwzr/l8Xu/Xa4UkWu44Sso+OvKDpD1Km4RbFcA5ix6VUkGsxoGi7jzCyrtBrobJH0NjOaLaIKW0V+dImV2xfXH4xXOuwoJMJrD+RCmldVYGdQwhSK+ivN5KSrgf5Y1W3G4YFKun2FnOvtL9HK88TqJ/IsNihhGk8sM/awtkb4Yhj4GlCo6tkI45dTwEd6TSHUBuo8j6k6VY7C6uSI/AX6OgJruQpJ7+lJnL+S1XIi1jEq4gTBdGzLEc6BSBSxPCtkInBdVmusb4E6tzESJrgIYSkMmk8v7So9J56zgKlyaQ3u8e5skxqUTKBYYmOqW2J1dJFXMdRwEy8ItEVPvjVAaitBRD7g6KO15OjaOOX3N+xeQwMSJuBAl+CWhtgZgEAYdb5GRRHQfyahjfLarJqFCyJXj1l9PUmh2M6xrJjL6xHMyrobjWwrCUMBKCdSzPLKRPQhAJwVpUCgW5VWY2nChFr1YwoXsUIQEm9pbu5Z2D71BnqwMg0T+Rlwe9TKIhkdOlDlxuN5EBWqxOF6W1VkRg8+kKBAH6JwUxOsqGYKmB48sk64BuM8AQAmd+hcozkrA4YQgObRTP/nKekalhRAfoWHO0mBqznf5JwdgcbqZ3dHv2kzpBmlY6+xuUn/Dop8pdT4OjgfV56yk3l9M/sj9dgrsQqgyl2llNnb2WX3J+pdHRyIjYEST6JxLn0INYg2gql75fAF2uAmM0BYQRrAGdtQxKDkv2DIYI6HIVosofQXDD9rcRjiySqr0EAbHDKBj7BjX48/HuUiZ0iyJQp8AtCmRXmljfdJ6v6xdLo83FmiPFNFidjO4STnKoAbUgUGFx8PPREuosDm4cEIdCLmPT6XLOlzfSLTqAgR2CiQ1Qo1S2zRC7GGJVNlRnw6nVUsxN+tWIuhBkQfF/d9t2/PNoJz7/JNqJz18XBQ0FPLr1UU5WnfRYfsGlN+aToYg3rsLq3wGNpQzWP4Nweq3U6PIXJZHl2ofwcEELSkKcuYxSu44B70lGfX+bnMb0FBWy7I0Iax9s054r34IfZ0ruwwMfhPQpsHAyWGpa2ik0iNcthdB0hHNrYc0DXvp5E368HpxWxEkf404YRsZ7x6mzODgz53JUjeUgWhHWPwfdroGVszxDPnVBiDeswhrQkbS5mzy6jwvS8fHMDA4X1LL1bAXvX5NOtimbO9bf4eFyrJarmT9yPl0CUzCaayXR6q73PM6vGNMHpnxB4hsnPJb3ig/kvevjePvA22zM3+ixblT8KB7OeJjY93sjXvcjztCe/HC8hhdWnyD7sS7w6xMIfW6DFXd6OW8/4gztzg0/nOaHqZGw4XmEU6s8+qfzBEgYghgQB35RCAsmUXT3dpaeW86Xxz1Fxl1DuvLG0DfIK1MT4aehuNbK8sxCVh4u5tPre7HuWAnX9o4hMkCL3eniZEkDjyw94nE+Y4O0fDAjg1+PlzC9TxwP/dh2euyByzpw66BIah3V1NnrUApKDCoD4epwVh4to87ioGOYke/35fHgyI7M35zFbydatE3Zj3dByFyAsKPJzTuyu5Tz9tMsz+klbSDiDauwBaXw9u85fL492+M4sh/vguDNFTw6Q4pQWXKDdC9qAym7ezd7y/fz7M5nPawBog3RfHb5Z6zMWsnnxz736KZLcBfeGvYmMb9+BSfe8Vgnpk6A0S+B6EL4fppEHi5AEBAnvI87cTiCTIlgq5NImcqAqDZS5dDQ5+0Ww9B7hicTalQzZ430fX9veg/OlTUwf7NnkUO3GH/mz+jJiHe24HLBXUMTGNYpnFu+2e+RZu+vVfLd7f3oGu3PpSBW58JPd3q6SQPigPsRe9+GLNhLkGs7/iVoNzBsRztawWQ38dO5n9qQHoBPj35Kra0WrtuGsOpeNNYySTR8gfQAdBgFax+mjfVrdTZsfoVwXctXaUByKDJHI8LFJOlC+0MLIX2q9HevmyVC0vrhDeC0Iiy7GcFWDWse9NHPd1K5uigirL4XmaOBOovkd5LywkZEhRx+nyORnnWz2yabm6ulz2spbdN9frWZxQfy0SjlrD9ZRoW9gmd3PNsm2sHmsvHYtseosteBuaIN6QEQCvfD0R/JmusZiLro1l4crzzehvQAbMjbwKnqU/BoHsKyW1DYKpiz5iTnnx8GhxchJAyGjS96P29Lb0Zhq+TbmzKgOLMt6QE4tQYUagRtgDQydvNeKm3VbUgPwLHKY6zOWk33aC1Wh5v3Np7h9iFJxARqqTLZWX2kmJlf7mPk21uRy2Q8tuxom/NZUG3hi+3ZDEgKZuXhIq+aoIoGG8+tPM/wV08w6e1Crnwrh6GvHCPlhY08teIYgzuE8vqvpxmZEk5WhcmD9Ox+MAPBVNFCegAG3i/dsxdparDUIKy6G7WpuA3p2f1gBoK5si3pASjKlBzAL4TRWmowuS08v+v5Nn5IlZZKikxFbUgPwImqE/x0fiWW8Y+0WSecXiONehpiPUkPSPf5mgeROcwkvZpJ4jtZzPjFxVuH5SS9dtSD9AB8tCWLqAAtOpUcP42C2EBdG9IDcLSwjh8PFPDSRCkcd2rvOO774ZAH6QHJ+uKRJYfJqzK1PTdNcNpscHxZG9IDIOz+AMFU7nPbdvzn0E582vE/gUpLBSvOrfC5fnX2aoiMhJIjYGtAONDqAZg6TnKi9WEsKJxahWCXpiZiAjTEBqilaR9fRoSn1zZNtyC9sZZ7F+ZirpKmtrRB3tefWiNNn4FkTpe3i7NzR7Ucl70BIWsTILYlCBdQelQ6Bi/QKuT8eEBys25wNHCu9pzXdrW2WiosFVDuWw8jZH6LrKGYDQ8PZeU9g1h57yCq7dX8eMa3W+/iM4spddZIAbANpay5PgGZtQrBPwriB0rhpN5gqYGGUpTWCoT9X/jsn2NLQaGV+gkJYeW5lT6brji3gkp7JefKGnllSjfqLA7Gd41k9ZGi5jYz+sayJ7uqjcHhBfx6vJSoQC2L93l3CB/SMdRndZpbhP251UT6a0mLNPLjfs8+wsLCpKnPC5ArQab0fd3LTkhTkRchLCxMyk7zhaM/SlYGAE9XcLDsgIfv0AX0jejLhrwNPrtZcW4F5fZKr+uE/V8gWHwYXYpuyN7CuZdGAzAmPZKfMn07pG88VcagDiHcNDCBlYeLfLZbsr+Q3vGBUoZeo51qk3eD0LNljR5+URdD3lAgVaT5QuYCXK52p+f/NtqJTzv+JyACjY5Gn+vrWz/8nTYppuIC1H6S34gvXAjSBML81JIw9u+1b72vS8FuAr0PkebFrrWWmmZRbvN+BDnYzZfeh9P7j7y6qdILwH4pp2jA4rBAjO8KOGz1IIrc8OU+Jn+0k8kf7sQhOi55TUx2E44Ln9HeSMfEWIkM7p4Pdb4fdlJ7EzgdntexzTE1NFcHuUWRBi9E4AIurHOJbkIMKqwOKQS0odVDMECnbB5x8wanW8TlFn0a6V1o4wt1Fgd+WgVuERpsF+3H7fa85+QqKfrkUvB1713q3rXWg7JJrC0I1Nm9k2aNQnPpa+vwPWoiXZdLODJba5vvc41Sdsnz2WB1olXKMagV1Jl9X5t6q+RwbVDJvWantYbd6eOFBgDxkvecYKuTgnfb8V9FO/Fpx/8EtHIN/SL6+Vw/Mm4kOBySjkcTgJjcyiskaxMkDvXdeUTX5odBZn4ddXWWlukAbwhPh5pc6f91QRKx8gaZXPIPqmg7PdfcT22LYy4JQ6isbPUWrdSBMUL658tWXxsI2gB6ezFarGq0MTxFqogKUAdgVHo3a5MJMmKMMVC21fs+ADFpOKJKT2l9y7RLsCqQwVGDfW4zJGYIIcpASVgelIjCVIqwYCJUnpNGuDQ+tBaCDAITEPXBkujVFxKHgUoHmgBkLpd0D/jAgMgBaOVawowa5m08R1KInj3Z1QxMDmlus+pQMf2T2hoEXkCncAOIMKiD9zZFtRapjQ/0TwrmSEEtVoeLoRcFYzrdbkgZ17LAbpK8nwQfP/Eaf68jiU6nUxrh9IWkYdJUFEBlJX3Ce3ttdqLyxCW/b/0j+6OWeTHoBMSOo3y7qAMkDqOuThphPVpQx4BW1+BiDEwO5mhhLRtOlnFZmu8qr8EdQ6g226lotBMTqEUu8/59MaoVBOp8i5tFlQHxEq7VYup4ZCqNz/Xt+M+gnfi0438C4YYI7ut5n1eX3uSAZFKDUuC1cMTLnpfiEDJukEgJtMQ/eHOVFWSIo17Grm8p0S23ucAY4d2FVhAk59oDX0l/m6phxDNej1nsfTuiUg8JXkiXIMDQR5v7EZNHgi6kWWAN4DDGIF7xNzj7q+T47G0fI5/DrQ9jWm/PEndBgGGdwhjUQarwClIFcF9P7ynY01Omo5VrpEqfkI5tGyg0MOxJcHqe+7de38akDpMIVLclXUGaIMYnjUf7ejRin9sQtUHSFOGFt+nMBdJ59PaZ+tyGqNTx8uvboccMiQBcDH0IJA5FzN+DOPIZeDWM9OAuHiXjF6CWq7m7x90obAoC9SoW7MnHbHexO6eSoZ1CeH1KVzY8PJRvbu1LhJ+GwV6IjSDAc+PSyKpo4MHLOnqNRNh+toLnxqd55agDkoJxuUWm94nj823ZTO0Vw6OjO/HU2FSmZETT7aVNENUDMaRTy0anf4aMm7yfo5HPgUJD9hPpnJ07iqy5I8l5LBVVYyFE9kQMbetcjEIDfW5vCRP9NJUgTTADIge0aVpiKqFrSFeS/Nu6b6tkKu7pcQ8RDi/ERxcMPWYiFh3w7u6dMASMEfR6cw8AKw8XceOAeDTKtuczKURPkF5FbpWZA3k1JAbr6RjWlliqFTJmj+rE0yukxPWyeis39vdeffXw6E6EGnzbXsj8ImHo4y2jYq0RnCz5abXjv4524tOO/xmEqoJYMHYBg6IGIRNk6JV6rku9jg9GfEAcgYjXfo+YNIIOc7fi8ItHvOVXyQVWroJltyCOewdx6OPSj7MgIMb2Q7x5He6QTpQ2Orl5YAJ6lZzJn+yiSAyDSR8iDn3Ms/0tvyLKVS2BkeseQux0BeK0byE0VVoWEI847h0YcC+ILsRJH4BHP/0RZyyWyurtDYjDn4Lx88hyhPDxzIzmH/eR7+5ADE5FTJuMmDgcLp8jRVgAhHWWPm/yZSTP2UFSqPSQEATIiAvkk5m98NcqOVNazwczevL0T9kMjR7C28PeJjlAimuI0kfxTL9nuCHtBiLy9oLNBDOWQO/bpRJemVwShd+2HkEXjuD2zJP62gJR2ii+HvM1YxPHopQpUcqUXJl4JV9f8RUJmkjEcW9D//tBZUQoaiF1ZG+W3IAnzoewzk3nLU46b/3vZ0ORip1+Buk63vwzYrdrpOsoV0H61XD1l5IzdIdRiB2vQJy2gDh5MPNHzmd6ynR0Ch0yQcaQ6CEsHLuQYE0YDaKa+xdJx1BSZ+GZKzoRH6TH6nTzwOJD3LHgAN/syuVvV3Vl9qiOBDefzwC+vrkPv54o5fPtuThcLpbfPZDhnUKRywT0Kjk3DIjnufFpHMqrZtHt/ciIC0QQIEiv4oHLOjBnYhrFtWb0ajnPTkjjWFEdG0+Vs3BPHk6XyOc39qZRFQYzfkTse4d0/jO/RUwahjjhPQiIa/oSpCJe+x2ojQjfXIls7YMoq88i2/sRwg/XIvxwLZzbADMWI/a9U+pHkCF2GI1401o4+K1EPkNTEad9S7TTxfMDnuee7vcQpAlCQKB7aHe+uOIL/JR+zB/5AdelzkCv1CMTZAyOGszCKxcSrglHdFVK4nyFGuQqxPSpiLf8gsMvHndQMuLN6xBj+0kESBcsfZcmfUh+g6z5vE3tFUN8kJYVdw9kREqr89k/nq9v7kOncEPz8keXHuGT63tx04B4DGoFMgGGdQxh2ayBhOiVDO0UikGt4Mav9nH9gHhenpxOTKBEYDqEGfjwugxGdQ7DqL2035fLGIV463rJdVomB5UesfetiDOXIQR7iWFpx38c7eXsF6G9nP2vj5KGEuxuSdvhrzLib64BZAjBiRRUm3G63QgIBGiV+DlrEBwNEuFQGxHlOmRNCeOioPDw5agz26i1OHGLIgqZQKhBjdJlbdPe6XQir8tr2kqGwyhlCCkbCrmQ3i0EJ0uusS4XsQEa5LZ6sEpCVVGuko7XZQVRxK2PIHnOVmQCLLytL3FBOpxuKUddFEUiDHLUljIEmQJcVhAUUtq6MYZOz2/AT6Ng9X2Dm1+w5YKAGxGH002ATkmDxYlCLsPuchPjr6LUUoooSJHlUboonE4natGCsGs+Qv4+uHah5EsEINdA7m7Y9Q7i9O9JfKOtkHv3gxkojQpMLkmToldqCbbYARtOvzgEQUAQBGRb/ta22ii4gzSqEZqCGNwBl388jyw7ymWp4aiVcgwqBUkGB+E6EC7EQcgU4Hbi1oVRbpPe/YJ1iubzX68LotZRDyIoZWr0bj0/Hi7nrQ1nm/Udy2YNINSo5rGlR9iX6ykgDtQpWX3fIFwiOF1ufj5Wwvd78ymrb9HUPDgymam9Y3G4RAQBtEo5FocbpVxAq5BhdrgkvY8IBrWColoLNWY7FoebDSfL+OmQp1BXrZCx8La+WPKzGdw7GZmlGgQQ5RrchoiW+83tQFh6q+TFA3Dtd1J1XNV5z/Makop402qwNUr9KDS4dGEo6pumVgUBhyGGsgYHLlEkRC9Q1SRWlgtygpXBlJokch+ot1PXpJFSy1Ro3UZMooxwpRWZyw4OEyAiKoyUmd3c/1M282dm4K8CtaUCQZQ0MW5dFMUW6fy73FIdmUEpx+pwY1AKWNwiNpcICPipZZJDOCImm7N5eYBWgSCDGpMTEFHJ5UQ3kZtai43apuUymfT9rzbZEREQgISQ/5tbs7u2EMElXXO3Jgi5vu3IZjv+tWj38fkn0U582vFHR1Z5I2Pe2+Y1piDcT83iO/uTGGIgv9rE/YsOcaSwjk+u78Xjy49Qb2krBL13RDKjOocx+aPdzcu+uKk3s5ccpt7ipGu0P6FGNYfza7E4XDx+RQpju4Tz+vqzHMirRq+Sc1u0iqnDAhA+HeIp3m6COOULHKmTuOXbg2Tm13lkWy2bNYDHlx0hu1IiPn4aBeF+GjRKGR/NzMCgEtCrFCit1QjvpUv9y1XSaAHAsaWIkz7EkTq52XX5/Pkq7lx9iucmpHHXwoNez+OnN/Ti0SVHiPDX8NVNvYltFUNQUmfhxVUn+O1k2+qijmEGHrsiBblM4LZvD3jte2pGNA+P6sT9PxwiM7/Wa5svb+rN0yuO0SXan4oGGxO6R3LrwHiK6mzc8e0Bzlc08sWNvfn5eCnLDhailAt8cn0vn/vsGRvA5J7RdI02svFUBR9t8SzdznkiHVbcjlDQVPYdnQGdxsLmv3ntTxw1F1fvWQx8azvlDTZuHhDH1b1i2HW+kpsHSITU7oYHfjzAvcNTya00UWWSstMKqs0euXh+WgUr7h6EzeFi3Ac7AEiP8mP5XX0RBIH8GitOl8j6U+XcMTAGuVxOTU0NBSY5VpfInDUnqTbZqbqo2mpKz2ieGteZUIN3vVA7/rfQTnz+SbQTnz8fxKpsSYdTdFAS8kZ2x6UNRqH3UQb+D6DWbKOy0cHBvBrMdid9EoIIMaj+4fgBURQpqbNyrryB/GozqeF+dAnXobUWQ3Wu9MYdlAwhKQghydJnqGm9vCNYaqH0CMQPQlTqqFKEY3c4OV1m8vnwA1h17yC6xwZwprSeK+ZtByA1wsijV6Tw9IpjlDdIb6GCAJO6R/HI6BRGvrsZRyu+khJu5MOZUsbV6dIGKhptZMQFolfLycyrIVCnIjFEj8nmxC3C6dJ6ruuiQVZ6GGHFnWCVxKfIFIgD74eMW/jyhIvkUANRAVqOFdXwt3WneeyKFIZ2DOG7vfn8dKiIR0enEGpQ4a9TUV9jYUisHcFaC3m7pVT62L4IFyqW8nZJI3FxA0BpBJcFCvZJoxTxAxA1gWw/ZuKsTMPb689gdUgPYo1SxqOjUzhf3siJ4jren5FBVaONwwW1hBnVpEf7IwhgtruYu+Yke3Napug6hhl4b0ZPVDKBDzadZ9WRYq/XwE+jYPndAxn17jav67VKOV/d3Bu5TOBIQR2hRjWpkUb8NQo2n6mgzuIgNcKP2CAtUz7eRb3FSWqEkSu6RPDe795tBQBW3D2QGrOdDmEG3l5/ltWtji9ndhLCR/1bGg96CHK3Sf483hDZA/e0bykSQ3GLkF9jpkO4E4vbwuGKw5gdZnqG9cRP5Y/MGYjJ4eZEcR0VDTYSQwwcL6pj/ubzhBhUfHp9b86U1FJnc5FdYeJvl4UgczS0XK+4/jQExFPlrON01WkqLOV0CUknTBuGxqZh0ZEaIgO06JRy5v1+jvPlUsWYXiXnlweHYHG42ZdThQj0TQjC6nCiVshRyGXUWew4XFIocIhBRWqkH8F6JQG6drL0V8M/+vxW+FzTjnb8CSBW50hvsUWt3upVeuTTF+EM7/ZPkZ8ak41fjpfy3KoTHp4sE7tH8uTYzs3Bh5fC6dIGZn6xt9kP5NgzQ9Ga8iUNRWtjtuCOiNO/R/hhuudyfSjMXA5VWfDzozB4NsE9b+L5rfVcnhZ+yX0LAhRUm5G1EoeeLm3g1Z9P8dgVKRg1ShptkgtwqFHFS2tPeJAegL5JQRTVWpm18KDH6Eyv+EBmDUvi7u8yee3qbmw5U96c5v3cKjj/3ADkt2+SIiIcFqm6Sqkj6bUjzX0khuh57equrLh7IEq5jMFvbObGAfEsuLUv8zedY3SXSO7+fi8nZ6fChhcQTq5sOTCZAnH8PISgDtJ5AYSu0xDjByH8/EiLdgog7SqGjpqDrs7AslkDqTbZEQSI8NNQVm+lZ2wA9wxP5uEfj3Awv2W6SquU8+bUbizen8+UjGieurIz58sa8NcpKaq1ctOX+5jWOwalF4HyBSjkMgRBuhYXv1oq5QLvz+jB2+vPciDPc7+f3tCLBquD1389A8DPDw5GKZP243KLPquNQNpXeYONWd8dJNKoYcHtfZk1LIniWisBOiUIFZ4HJLokrx9fkCtAgIIaC8/+dJyFd3Zkb9l+5uyeg1NsGTkcFT+K2b1mc+VbRz3K8cd1jeS3h4YgivDF9iwqGh3cPDCe29NVCDmbENY82GzJUP94LidqT/Pg5gc9Euq7hXTj9SGvMSrKyZULjhKkV/H2tO7MXXuSnEoT78/oyY8HCvhoS5bHeb57WBL9EoOxu9x8ti3b4zxrlDI+ub4XXaP8CTaqcbvd2O2Xtmtoxx8DSqXyH8rP+3toJz7t+NPC3VCBsO1NT9IDYDchLL4O+Z1b4Z8gPsW1Vp7+6Xib5auPlNAvMZiZPio+LqCkzsLNX+/zMEEzOKtg9b1t3Wi7TkVYfX/b5aYKWHI9XP8T7PlI0rYkDOHJUX0oNUk/3hdGMFojIViHn0bBljPljEwNY0ByMLuzJL1NVoWJx5YdbUpjl7HkrgE8tuQof5vSlV+OezrKzuwXx6T5O9u41x7Mq+H3U+XcMSSR0jprM+m5gA4v7UYQYMNtfTD6q5j8xcHmtPoLyKk08e6Gs9zQPx6tUs6kHlEE6lQ889NxZg1P5t7vMznx4ig48Lkn6QFwOxHW3A93bQdNgKQP6TpNIpQXMQzh5E+I8QPp2etWus/d2PxQntwjGpfbzcx+cXy8NcuD9ABYHC4eXXaED2ZkADD9s93IBQGLw8WF5/p3e/N495oeLDvo3U9oYvco/NQKBncIYfs5T6O+semRbDhZ7vEwvrDfOxceYO39Q3j1F4n4/H6ynIndo/h6Vy5ZFY2XjEsYmBxMZtNnKWmwcuNX+5g9qhNv/Hqaey/rQEaaDpJGNJlaIomYM24ELy7DAGKP63EZonlvyQHeHGGk0dno1aV5Q94Geob15PTcGR4hseuOlZARH8Cp4nqWZRZhVMt55spUBHsZwur7PK5XlaO2DekBOFp5lM+Ofc6DPR8Ecqg22XlqxTEevaITH23JQimX8aEXN+aPt2YzuGMov50obXOerQ43dy08yM8PDMGoFsjJycHtvpQ3Tzv+SAgICCAiIsLTs+z/iHbi044/LQRrDcLxZd5X2k1QfMh7efXfwZIDvs3xvtqZw7BOocQE6Xy2Ka2zeghZAckNumBf28aR3WDLq947qs0HS7VURuy0wp6P0U3ozN4cKy9PTm8TjaBWyHj96m7sz63h6525XJYayvPj05j+2R4PYz2nW+T2IUkcKaxjZ3YV1lYjOgBDOoZwtKC2Dem5gJWHi1h8Z3/u/+GQ1/WiCD+crmRMekQb0nMBe7KruWNIEk63yKyhSWiUcrrF+BOkVzFrWDLy2lyEvZ94Py+iCEcWw5TPYe8ncH5D22GVJgh7P0be4XJMrUzpVh0p4q2p3TFqlazI9O7ma3W4qTbZkQl4JZj1FidZFY1M6hHFqsOe012xQVpuHpiAye7ijiGJHCuqo7aVed7otHAeXXbk4i6b93s4v4bx3SJYe7SUDzaf5+cHhvD76XLyq838dkJKHv9kqydR9tcquXNIEg/+eLh5WUmdFYNGQY3ZwVfbc5nSpQ/G0S/DN+MkR+eK01KlYEwfKYqi9SmO7gWJw9lyrpq9OdX0uH0M72a+24b0XMCiU4sYGt3WduGbXbl8e0tftp2r4PWp3UkI1sHGHz2v15NlHC/a0Ib0XMC67HXckn4LtwxKoF9iMG5RJDFEzyuT0/lsW47XbQAcLjcrD3ufirQ53RwuqMFcbUMlkxEVE41aqcDhcuNuYrcyQbjkqF47/rMQRRGz2Ux5ufSSFhkZ+U/31U582vHnhct2aefjet8W9b5gd7oorff+AwxSnpLr78jiarw5xDp89OnDNbkZlhqpBL3qHEJjGaLTyjMrT/D5Db1Yec8gFu7Jo6DaTHq0H9f2iaPebOexZUcxqhVYnSJGpcDyuwey5kgxe7KrCDGouL5/AjUmO/c0lWabHS6m9oqhoNpMlyg/7hyaxA8XRSK0htXhRiGTUdHg+9zXWeweD3tf/ejVchxukWeWHmkWAWfEBfJQz7AW/yRvqC8CXShoA6DxEvlHjeVtokOsDjcymYDDJfokdwDlDVbGdY3wuf71X8+w4eGhTOwexfd786XU+S7hDE8JIyFEz+mSeuasOcW8a3uwL6eazPwagvQqEkP1XsnUBRTVWYlvElrbnW7eWX+Gb2/tw45zVfx8rISuMZF8f3s/Fu/Lp7xB0l71Swri5XWn2pzzeovkXFzRYKPCBhpDAsrbNsDxFQi52xDPb4SJH0D5aYRDC0AUEXtcB9G9qXQbsDulqSi3202p2XucBkjZXN5IUUWDjcIaC0tmDSRU11QGfvH3Ui6n1OS7b7vbjsPlIKfSxLe7cnGLUnn5nAldSAzVwRnv27ncosc07cXIq2gk0GElKDScaitEapQ4XE4K66Tvqk6lICpAhUYhR3aJKcZ2/Oeg1Uoyg/LycsLCwv7paa924tOOPy8UWvCLgnrvb3VE9/o/d6lSyBmQFMxvJ7w/dHvEBqBXXfrLFudtNEgT0DJy0xoyufflF+AfA1WSmFWM7YeoCQDgjoUHCTWo+NvkrgTolQRqVfx+uoTXfpHado8NQKOUY7Y7sTtd1FvszOgbR73VwewfD1PS5KCsVUoBjncNSUQuF2i0Oll3pJBecb5Lb2MCtdRbHPSIDWBPdrXXNp3C/YgO8O1Qq1XK0ShlRPhruOrDXR4PqMz8GkR5DET3Rsjd7r2DxKGQs0nKOes+HU56CSJFGrkQ5Z4i1phALdUmOynhBmICtRTWeCel8UE6fjlWyhNjUpo1N60R6a9BFEW+3JHDfSOSiQnUEdeqMkytkGFzurj56/30SwwiNdKPBqsDm8N9yf1mxAXwt3Utbt3h/hpC9SpuGBBPRlwAC/bk8fn2bHrGBvL0lam8vf4sH29tO90DEGZU02h30j8xGKVMYOT8vRTWWDg/535kPa8HhZYGpxxNxySU8YNAdOHUBNPpeSln6+PrM9Aqpfu9b0Q/fsv9zet+0kPSUXrRC/WIDWB3VhWv/nKKz27sRYRChSxxGJxolZvX2Ej30O5e+wWI0EeglqvYcqaiedn58kZu+GovC27ty8pDxW2qvUCyc4gN0lJQ7f08dwrTIdosCHIFDTYnlkoTya1K1s12J9kVJjqEGdDI/v91Je3410Cnk35fHQ7HP0182qu6LkJ7VdefB06nE/mxHxFW3dNmnRjZHaZ9ixAkOfG6LA0IdhOiNgD5JSzj3fXlIFNiEVR0eWkLcUE6rs6IJipAy5T0EARHHaLCiEOuQS64kdtqJK8alxVRqUdw2bngxYNbxCJo6PLSFrKf7o2wZz7Crvc9d5h+NaJfFMKuD9oeTOdJMOIp+Kg/dJ6EOOE9RJcVVEG4ZDI6PrcekB5u2x8dgrLJK0VUGRGsNYgaIyaXEpPdyY4TpUzpFYFgq0dUGcgpt/NNZiGBejW3D05EJXfQ4GjET+mPytkIbnCojAx8YxuCACNSwlDKZRwuqOGJMSkYNArcbrjmsz1tZpn6JwXx8uR0RFHkh30FfLUzF4AQg6q5n8RQPZ0jjBwvrue1X04DcMeQREamhuFyi/SM0qGrOobw9di2Ya/GCLhpHcxvIrbTv5dSyC8e+RFkiLf8iimoC+kvS3EaChmsvHsQVpeLjmEGtpyt5OMtWUzsHkWoQcXhwjpWHS4mJlDLjQMSWHu0mL9NTuerHTmkRBgJMahotDk5kFfLiJRQQo3qZhF5QrAOg1ry43GLInaXiNnq5LNt2fRKCESjklNWbyO7opGecYHMXtJ2uis1wsgnTWRDKZNhdzlxukVigiRTyrwqE7O+O8ipEulaf9fk22SyO1m8L59vd+cTrFcxIjWM9Cg/EATe33iWnx8Ygkwm0Gh1MOa9bdhdEKBV8uHMHnQK98PtFgnSKZBbqpvufzVdXtrCmTmXo2wK4C1wW7jlt1ukQNrWpxmBb8Z8Q4ohhS4vSQGicksVyFSIcjWCvQFRbaTGIcPugkhnIcK346GhZZSnYPZxHt7yMGdq2hLMlwe9zJiEMTTYGlDJVThcNvxUAezPqyOn0kRUgBqVXI5BoySvysT2c5UU1JiZP6MHe3NquHdR2ynZTuEGZg2Ow+ioISwqBkEhjUZFB2gJ0CkRkKaEZYKA1eFCq5Ijl/15p73OnDmDw+EgPT3dY/mhQ4dIS0tDrf7zVLhZrVZycnJITExEo/H8Lf/Tl7Nv2rSJ5557jrKyMkRR5KGHHuL+++8HpIt19913U1JSgl6vZ968eYwePbp523nz5jF//nwsFgt9+vThiy++ICTEd55La7QTnz8X3NV5CLnbEDb/TaokkisRO0+GkU8jBCXhri1AMFfB/i8RanMRI3tCj+tw6cM8Kr7EyizI3oRweh2iSg+9boHgZI6bA0kzOpDZKiHzO4Syo4idxkCnMVB2HEEXgnh6LULZcSkuIG0yQu4O0AYihqUCgmSy57SCy4FwajXs/lAKglTqEDNulByajy1H2DmveTm9b4W+d8KnQ+GOzYiNpVLSuKkSkoYhdp4EfnGYHQ501jIozkQ48oNU3t1tOkJIJ8TDP0CP6bgNkcgsVU3HfwwxMBH63IpbE0KlXEaVo4YlZ5ZQ0FBAWlAakztOIlQVjDF7E0T2pNQewAsbs4kJ0DK9Tyx1FifLDhYS5qemW4w/r/58muxKE0o5LLlrILVmB4v2SVM/47tHkhxq4GxZA1qlnF+Ol0rLu0XSPymY1349RbXJzmtTurHzfCW/nShDp5Izo28cQ8MdyCpPIfz2pJTPJQiISSPgilcRzv4KG1+Qrl3qeBjxNKx/DiF7k6QfCemIeMVruIPTSHnrME63yD3DkpjUM5oVh4o4WlhH3/hAruoVw57sKtYeKUGnkjO5ZzTxwTpEEapNdvblVhOiV5EUqsctSlVTPzcJuqf3jSUp1MBnW7MorrMyrFMo47pFUmt28N2ePPKqzaRH+TOtdwwFVWa+3JlDiEHFzP7xRPlrOJhXy2u/nKa03opCJnBl10hmj+rE0cIalhwoQqeSM71PLAkhepRyGbFNI4l5VSaWH8zl6ox4jhXVsyyzEESk6IqYAM6UNrA8sxCz3cWTY1IQZALf7c4jt9XxuN0uFHI5xbVWekXr0FlLIWcrwsnViEot9LkNApOg5JB0X4ki4rRFZFsKeWP/G+wu3o2ISKJfIo/1eYwO/h3xkxnRWUsgZ1tLP71uQQiIR8z8HjJm4NKG4VIZUNXlwG9PIWRvlmIcpi6lQKjjg8Pz2ZC7AafoJFwXzr097iUjLINGRyPV1mqWnV2GxWlhZNxIBkQNIFQRSkG9C4fLzf7can4/Vc6sYZJm7Lu9eXSN9sdfq+Tt9WcpqZPO86i0cK7tE8ura47y6MAgD+Ljr1US6a+h1uKg0epEIRMINqibq+kUcgHFf5kAffLJJ8TGxjJunGem2sGDB/n444/54osv2mzz2muvUVtby2uvveaxPCEhgS1btpCQkPDvPOR/Kf7SxOeOO+5g9uzZdO7cmezsbIYMGcKXX37JoEGD6Ny5M9988w2XX34527ZtY+LEiZw+fZqIiAiWLFnCa6+9xsaNG/H39+e+++6jrKyMFStW/P2d0k58/oxwOp3Ia3PBaQG5GlHjj8wYjqu+HFnOZoSVd3mKKZVaxBt+QoiTMobEqiyE766GGk+hpNhtBoyfB0X7Eb6/WtITKbRw905Y9yj0vgVW3O6pMxJk0jZHf5Q0Kle8ihiUgPD9NVL/Q2ZD6kRpFEOhAb9oKDsmJY6nT5GWuV1wciWkTYbo3nD4e9jq+YOFLhhuXoeojoJl0xAursyJGwhjXoED3yB2ndpy/K2Os/KRc+wt28tTO57y0Gdo5Bo+ufwTegV1RfxuIkz6iO/PyUgONZBdYeLZVcebT2dyqJ6bBiSQER+IQa3gnQ1nWH3Es9IrIVjHW9O6c82nu2kdPp4QrOOLmyQvm5u/3k9elWea+Iy+scwZnyo5KjtMUim7yoBNHYLGUtaSPq7UY9WGobZVIdgbwe0ApR6HMYayRgfZlSa0CjkicONX+7A53agUMtbdP5g7Fhwg96L9TugWyTW9Y/n9dBkWh5tgvYrkMAOL9uZz8KIKoV7xgVzXL45HlhzhxYlpBOpUPPTj4Tai829u6csnW86ztanC67ErOqGQCfhpVfhrlQgCbD1TQWyQlvRof276qkVsPLF7FHcNS8JfoyQmSEdJrRm7S+TRJUfYf9HxZMQF8ubUblz2zlZemJBGsEHFQ4sPe5x3tULGglv7cqK4HmPJSaaO7CzdH60rCyd/jJi5ACG/ydTygaNwbAn0uJMSmRmzy4TT7UKr0BCriaTBasXoqERYNFWyYGiNLlOk+/6LyxBnLscR3Q+LxYLR3YAg2qGxDGHhVXDTASoD1NQ7G3C4HWjkGupsdTQ4GlibvZa12Ws9uo0xxvDp5Z+Sc6gCITKOX46XkB7lR43ZwVvrzza36xrtz00D4+kUbkQmCPxyvIQvd+QQrBF4cUSYB/EJMahxukRqLZ5TZ2F+Gvw0CtxuEYPmEhYA/wHcd999pKamct99ntl5W7Zs4dlnn2XHDskg8vLLL+f06dNe+4iNjWX37t3txOePjtmzZ6NQKOjQoQO//PILP/30U/O6SZMmMXLkSB588EEGDhzIE088waRJkwCorKwkKiqK0tJSgoL+fmlzO/H560CsPCc5CXsTFod0RJyxBLcuENmmvyHs/9x7H/dnSj/KtU2W/1O/hbwdEN4Fdrzbsrw11EaY9CEsuVESjpYeh32fej/I+zOhdf+toQ2Em9fBxwO9b9txNOK4dxE2vQRHF7ddP+lDiO0L308Dc3XTqFPLD3ruw4e5Zu21Xqtp4v3i+eiyj4h7pyvipI9wdJnGjuwa7v3+kFfBaP+kIB68rCMzPvdeGn3TwAQKa8z8fspzOuquoUnEBel4ZmVb+4A7hiRxprSebReVg4NkR/Prg0O4d+FBHhmTSudII8Pf2sriO/sT5a/G6XDz/uZzDOgQxhPLj7L87gE8/OMR8qslkvPIqE7UWhx8ucN7VdD863rir1HidIu88vMppveN5aW1beM2AJ4b35nVR4p5Z1oPJszfgdne9vzEB+t4e1p3pn7S4o79zS19uPWb/R6kBOC3h4Yw4YPttO7m+9v7EeGnIjnMD6vdzpojZTy2/KjX43ltSldOl9Zzff94Js3f6VHRdgFxQTruHJrEjG5+yLa/ibD7w5aVIZ2kEcdfn5T+Fvzg7vXw8QCv1XNi0gjESR8h7PkQYfd8r8fETWvgl6fA0Yh4/QrWFmgINqgYEFiPsGhaC1m66lMolV4Ech4+zIObHuTZ/s9y2/rbvHZ7Xep13N3tLnactRKoUxKoVzPug+1ei/wGdQjmstRw5DKBF1afINoob0N8kkL05FSZ8fZY7BhuJL/KREKwHrXyP6v56dKlC2Vlku7Q6XQiCEKzvmXQoEGsWrWqDfH5R/C/Snz+NJOWFRUV+Pv7s3v3bgYNGuSxrm/fvhw+fBin08mBAwc81oeEhBAfH8+xY8e89muz2aivr/f4146/CGpyfFdTVZ4DWz0yUxXCsaW++7DWeZKSsFQ4vkxK9/ZGVkAKcRTdIFdKIzjHlvzj/bdG4jA4/7vvbc9vQHCYoOtUmLUD7toGxpaUeA59B4ISrv0epn4leQLduRW6Xgt3n6WosdhnCXFefR6NTZoh4dBCFKYy6i1On1Uyw1NCWerD0wZg3dFiLksNa7N8eWahz5LhXvGBbD/flvSA9PzdmVXFtL6xyJsqtADigzTYXVBQb+O6/gn0ig/kgZHJyAShmfQADO4Y0ibvqjU2ny6n0eakuNbCyNQw1h31XXW07mgpr0/pRkmdxSvpAcirMrcxHzxaWEfHMGObtjvOVXLzQM+U+OJDhSSqGhDLT6HO28rU8GJyHkvl7NxRbbZfcqCAq3pGU1pn80p6APKrzZI+yVyNcPSi+7PTFdKI4wXMzoTsLb4tA3K2INgbEC6ktnvD4UXSdGRNLljr0KjkhBg10nel9QhRZHfp+3L9QcrN5YxLHse67HU+u12Xs446ez0bT5Vjsjs5V97g6zDZeb6KqAAtYX7qNtV6ApK+p8bs8Ep6ABqtDoL06r9b1fnvwIkTJ6isrGT//v18/fXXLFy4kJMnT1JZWcmqVd6F/QDLli1jwIAB9OjRgx49ejBgwACWLLnE79H/CP4UVV379u1j7dq1zJ07l1mzZjFy5EiP9WFhYezdu5eKigpcLlcbPU9YWBhVVVVe+3711VeZM2fOv+3Y2/FfxN8rFW9yjW09CtK2jZcHh8vu8yHQ0sYhTXsh/J3+22ZnNUOu9F3tBdIxuF3w/VTp76Th0gjR12OkabZx78Chb2HXBy370QXBVZ+DQoHDfulyc9cFUbHLDm4nTrfvnwuFIGsO8PQGu8vttSTY7nLjq1BYRLzkabY7XIxJj+BMWQNuUWTbY8NZuKeAz7ZlN5sVBuiUzLu2Bwa157ELgOMSx+twibjcIi5RbCJWl2rr5rcTZXSJvvQIseuioR2Hy+3VidnqdDOlZzQL9uRhdbi5RQtTLw+ATXMQji1puff8Y1Bes4Bzz4+g49zNLefFJSXUXuqYAdyiCIhts9UEuec9K1dc2jZCFCWif6l72WmTKhgBRBcut+TL0mYbEel4NBqcbicKQYHN5XvfDpcDEZr9d5z/wGf+eMt5Hrq8E1f3CEdvrybUqMag0yGTQVGtbysLtwhy2d//6v+78Oyzz/LTTz8xfvx4bDYbDz74IHPmzMHtdvP4449jt9tJS0trbl9UVMQDDzzAwYMHmz1vKisr6dOnDxkZGXTo0AGArVu3cvr0aUaOHIlKdenk+b8K/vAjPkuXLmXSpEksWLCAxMREXC5XG0bucrkQBAGXS3pI+VrvDU899RR1dXXN/woKfPuXtONPhpBOUhK3N/hFSQJktT9iypW++9AFSqM7F9BQAp2ulPRE+hDv28hVUq6U0wb2Rkgd570dSNNZvvo5vxk6XO5729i+kp7lArK3SILfqd9A79ug9Kg0Hdf64WKuhsUzwFFMnDEOheD9/IRqQ/FTSQ9yscsU3PpIQgxqFD78TLadq2Bi9yiv6wAuSw1nr5fS9zFdInyOkpwra6R7jG+n4kEdQ3l/03nCjBrCjGoy82v5aEuWR2xCrdnBnQskZ+/wVkGWx4rquCLdt0fP0E4h+OuURAVo2ZNdxWWd245WNX+2zmFsPVtBdIAWpdz7+Qk1qFFdNLLVPTagOXPK43N1COHDLVlc1zcOgGcfGwyZC6URlda/bXWFCN9djcLsaecwrmsku89XEht06eOx2F2Iar+293/udkm8fwE/3CiRal+IygCVHrHTWN9tuk6FfZ9JUSyaQBwuN2aHS7J5MLa6DrX5kHIlfNGFKEMUv+X8xpjEMT67HRE7Aj+lgWEpofhpVV5H0C6gW4w/OZUmxnaN5JOtWcxdfRKL3UWd2UlulQmTzYVO5ZvcGzUKakx2n9+Bfyeys7P5+uuv2b9/P6+//jrz5s1j06ZN3HfffcycOZPS0tI2OlalUonL5eL06dNYrVZsNhtnzpzBZrN5EJyVK1fy3XffYbH4Jn1/NfxhR3xcLhcPPPAAmzdvZv369XTt2hWAoKAgKis9h78rKiqIiIggMDAQURSpqanx0PNcWO8NarX6T1XK145/HKJSB0MfR9jyiucKQUAc+wZOvziUSiXi0Mfg/Eapoqr19vGDQBOGOOYNhBW3SQ+dlffADSvg16dh5POw9sG2r4BDHoHDP0ijK0FJiLH9EM5tgAdOUeSswtk0ihShCUVtrZG0OD9Mb9vP4AdAGwzpU6XptdZQqOGKV+DoUpjwnqQrKjsBh76H4U9JVTnLbm17UkaspqFvd6rttagFJXf3uJsPDnmW0gsIPNX3KaLV4RCUBClXsuFcDQE6JZ/e0Auz3YWf1cHgdCMySzUIICp01LkNZMQFNscmXECATsmMvrH0ChN4Z2y4JFRGQFT5UWDW4BSkNheb7x0vquPpKztzw5f7mpO+T80dSblF0gkpZCZeHt+JogYXdRYnH1+URn4BdpebNUdL+PKW3kyYvxNRhDd+O8OyWQPYeKqszX57xQcSYlCzP7catwgxgTrSIv2ID9a1EWDHB+voEuXHvI3n2HGugocu78Sbv3mWZAsCvDAxjYW7c5uXTe0Vw7HCWo8Ec4CrekahUcgY2TmchCAdcpmAzFzuU4OGpQZKj3LqhbF0nrOJb8ca6N43jDp7DSLVbH8qA61LS/dXW3QfggDPju9MVnkDYno8wuCH4ewvUl8ghf0OmS1d++psKNgmEZRuM+DoD577l6sQx7yGTROGevBDcO5XiVy3RkxfqbIxexPi1V/hMkTSKdxFUa0FV2gM8rFvIiy9Ubr/1z4Mt/4CScPQyTSMSRxDoDqQXmG9OFjuGU3jp/Ljjm53YK62EmZUsyenivQof6b2imbZQc9pTJVcxv0jO/DJ1izeuaYHkf4a1LhQu+twim4Q5FjsLqICNGRXmJpGw1oQqFMhFwQi/DUo5f/58QJBEBBF0eOl/kLMht1ux2az0djoSaLDwsJYvnw57733HjNnzsTtdjNo0CAWLVpEXFxcc7t33333T6Xx+VfgDytuvu+++zh//jzLli3DYDA0L3/99dfZv38/y5a1PAjGjBnD9OnTufnmm+ncuTNvvvkm48ePB6CkpISOHTtSXl7ebHx0KbSLm/9acNfkIZSfhJ3zEGoLEMPTYehjiMZIZAExgGSEpajNgb0fSwRFbUDMuAk6jaXIrCDKICLUF8G2NxHKTyH2vRtSroC8XQjGcNj/OZSfRgxMQOhzO1RnQX0JdLkKsbYQkkZQiotiSynvHXqPoxVHCdIEMSN1BmMTxxJTWg9GtzRaU34KAuNh8MOIwZ2gYD9CXB/I3SkJpM1VEDcABj0sTYOdWQeHFkrLY/vBgPsQAxMlsebHgzynyh4tosBdzXenvmdN1hosLgufX/45JqeJr45/RVFjEZ0CO3FXt7uI0IQRZq4EbRBmRQDvbs5jaKdQPvj9HF9d3xWDowL2fIJwdDE4zIjJl8HIZ3EZ4/gms4of9xdgcbgYmRrGYyPjMWAGax3C9rfg1GpAQEybBENmYzfGU1jn4JtduWw+U45WKWdKRjQjU8M4mFdDXLCeCGrRhGtYl72OxWcWU2OtoUdYDx7o+QARuiisVgPj3t/hU4N0ZdcI5k7sQk6lmY+2nOd0aQMTukVybd84Fu7OY+Opsub9Du0YSlmDFbVCzpIDBQxKDkYuE4jw17LtbAW/npD0PhO7R9IjNpAnlx+jolGajvnk+gwUchmfbcumoNpMSriR+y/rSFWjjRdXnyDIoOKWgYn0Swykwebiky1Z7M+tJsig4taBiaREGJn+2W4QBD69oRdHCmqZ1dmO8PEAn/e4OPwpXIMfxWw2U0sdP5xexOqs1ZidZgZGDuT+jPsJU4cx7v1DpEYYuXNoEqFiLcnBSji2BOH879J34vQ6hPMbJDuFXrdA4mA4ugROrQH84PpvoWCPFBNiqkCM7Q+DH8KtDefDA3Xc0z8KubkI9n+GcPY3qXqy540IKWMRt7wJfW5GNEazpUTJnHWnKKyx0DHMwM83JyE0FEuWDv3uQjzwJcKh78Bhouihw+Q3FBCiDWFvyV6Wn1uOyWFiaMxQZnaeSbQijPN1TpRygQW78th6toL3ZvQkt8rEVztyqWqUnK2n942juNZM38RgVh4q4qtdOQSq4OXLw0nrlIxcKfkxme1O/LVKKhrtmGxSOXugXoWANHoY4a9BJRdQKv7zhobPPfccS5cuZdy4cdjtdtasWcPcuXPRaDTMnTsXk8lEdHQ0O3bsoL6+np07d+J2u3E6nfz4449YLBYmTZqExWKhoaGB6667jsGDB/9Pipv/kMTHYrFgNBopKioiPNwzibqwsJCuXbuyfPlyRo4cyc8//8w999zDiRMn0Ov1vPvuuyxatIgNGzag0+m48847CQwM5N133/2H9t1OfP6acNcUILhsiEo9Mn/vGS8uU43kdyPIcPnHeQQuHn16CAZXA4LLgihXYdOEonJakdlrmrULokyBIMikKS5BDm4HLv843G6RfRV7uWfjPW1s/QdHDeaZfs8Qk7UFki4HRwPI1RIBWnazZHDY/17EwGSEhiIpUVupQTjzq0QgcrZ6fghBhnjjaghKQlh8HZQcbl6V//BR7t/8ANl1njlPGWEZvDzoZVxuFxq5mgi3Dt6Nk3yGbt+AOSCFQ/m13PDVPkQRch5LRVg8AyouMptTahFv20ilrgPVZimyIjO/hlldBQTRDV+PlcJXW8MQhnjzL2S5wwnUKjhcWM+pkgbWnyzlWFEdA5ODGZESxhXdVby0Zy67S3Z7bC4TZHx6+af0DO7JtM/3c7Swzuu1fWJMCp0j/Hhu1XEeGZ1CTKAWhUygzmwnPcaPeqsbEJuiPlzYnSIqmYBcIcNid2HUKLA73IiCpNVRK2TszaniwcVtTQjTo/x4fEwqMYFa9Co5brcLQSbH4nAhFwTCjEoO5NXhcrspqrWSGKInxKDmg9/PsbrJI+jJManszKpk+7lKch5NQfh6TNtz1wRx+iLMscOpclXx4OYHOV973mO9Rq7huyu/I0QM4f2dpSgEgWcHaeHnRxHOb2w6kQpIGQux/RATh0L+boRfnpAE9h0uk9pkbYLpSxEbi8DtQlQbKWt0s+K0iWvS/QlxlSGsewSm/wCWKuk7oAuCxlJEhZ4GeTCf7s73Gip65KnB+Dkr4cfrpRcVj4tXSrGzBqVcidVpxS26CVD5YXDLqXQqcbqlEb2EYD1Ol0h+tZmyeguj0iKarrMkzBFkArd9c4DTZZJo/0JVV3h0LB0jg2i0SVPCSrmATqXAbHdic7qpNTuwOVsIdWKIHuN/qaQ9Ozubw4cPo1Kp6NOnj8fzsXVVV2VlJR9++CEKhQKFQoFSqUSpVKLRaNBqtRgMBoYNG8awYcNYu3atxwjQHx3/CuLzh5zqupCW269fP4/lycnJ/P777yxevJh77rmH6upqOnTowJo1a9DrJavxBx98kKKiIjp16oRCoWDSpEltTJva8b8HWWAsgE8hLYBcH8j+CpFpn+wGPEuXu72ynbRIPz69oRexgTrcNidfZhZjUCvoEedPaZ2NuxYdYM9D/QkIkPQgcrkcBZBfn88b+9/wmmW0o3gH1bZqYkKHg6kUPhvm2eDQQoSSw3Dt9zj9YqivryfIZUb0j0G4mPQAiG6E355CvHYR4vAnEX6YLi1/qpxjRRvbkB6AzPJM3s18lyf7PEHYm51aVjjMsPVNtGNeY86as4ginJlzOZxZ05b0gFRBt+0Ngse8zpVfHOeqjGgeuSwZ6nIlXyNvD+7Gcji2hMT+D3C2xsEdCw54lHfvPF/FOxMSKbGWtCE9AG7RzZsH3mTe8Hk8dFlHbv32AMefHYZWq0UURQRBwO12k1VpIa/aTGWjnYd+PEyXKD9CDWryqs28Pa0bW86UccugJAwaBXuOVXHf4sMe+5HLBFbdO4hZCw9QWCuNov0+exh6lbxN5dTx4nqcbjexgRocbpEdZ2vw0yrpkxCEXC4nr8rES2tPcVXPaDacKkOtkJEe7d9MejRKydn6tV8lDxaXMQr5oAcR1j/b9vz5RUNoKgqFgpPlJ9uQHgCry8rHhz/mqb5P8e2uXHY/mAGmghbSA5IO7NQaOLUGIawz4jULJUKfs9WTXL8WAbdvolQIZ8BbB5oX392rJ/zyujQi9KZnRRoAV3+JutOENuGqF6BWq6HwWFvSA/B6BFEp4yBtIvx0l7RM4494+ybciij+tu40Px9vW3X37e583r22B50jjCQEqth0rqaZ9LSGKIqU1VsJNarJqmhELhNIDNZ7VAG2RnGtlYQQGer/wqjP0aNH8ff357LLLrtku5CQEF54QTL4/PXXX3n//fc5depUs95VJpORlpbGW2+99aciPf8q/CGJT1paWvP8pTdcccUVPo2ZZDIZb731Fm+99da/6/Da8RfGDi+eMRdwsqQei91FtclGYbWFikYb5Y02YoK0yGUCZx/uAg1nYd8qBEDschUYwrFiJ6fOd4r0gdL9dEu9EYp9COtLj4G9EUXlSYKcVvCPg/ITvj9EU3sxNA2ufBth01zsgsjv+b5L43cX78bspbRdyNoEdhPnmkS4SkGE02t89iNkbQa7ifkzM/j1eKkkrnXb4fx639uc/Q2h+3XkVavbeNqApOtb2bq8+iKcrTmLxWWhS3AA25/uypG6I2w5vgW9Us/ohNH4K/1IUZjpEBvEuG4RjE2P5GRJPYU1FoanhBLpr2Fyz1g2nCrncH4NyWEGNs4eRl5lI7ctOEiYUc0rU7pSY7I1kx4Af42C727vx+wlR8ipNAHgp1HwxJhUOoQa+HhrNmX1NoZ1koS3r/92BoNawRVdpEq0+ZvP88GMnpTUWfh+b35zv8mhBo4VtYxcdXhhMzlPTEC01Eh+OxemL6N6Ik7+GJs+GrlMdsnru6tkFxaXdH1DQkJg9yKfbSk/BW4H4vQfENY8KAn6AQzhiOPn4daFM/mjlvsv1KhGsJsQsrf47vPUapQp49tUtp14brjkGu2oa5pS84HsTTD00Za/rXVgqcaiDmeHD8sDgN9OlBIXpEWp1PObF3J0AY02JxH+0uiBQa2g0e67Qs3mdHGJx9O/FTt27CAiIsIr8Rk+fHgbD58TJ05w/fXXs2rVKgYMGICsyXXa5XKxa9cuJk2axI4dOzyqwf4X8IckPu1ox38LflrfQ9gKmYBcLjBn9UlWHWmppPliew45j6fBxhcRWj2ghb0fI3a9BsVlTyETZLgvzpxqglHlJwk7FT5KSWUKBKdVKks/8RNct0zSYfiCTAEyOUlvHCfruauRJQ1HKVdiVPqueNEr9d5Hw9QGEATkMgGXW0SUKxHUl5gCVkt6vGs/3QPAs1emIiADleHS28gU6H1U1IiiiFHl+9jlghyFoMChreOp7U9zpKJl+unL419yV7e7mJw8mejKk7w6pivpr+9pTmVPj/KjV3wgN329n+pWQZdvKM7w9c192PXECKxON1tOlzF3nefLVr/XNpH5zGV8fXMf6q0OHC43gToVotvN8Le2NJO47/fmEx+sY+7ELsz6LrPZRK/R5uTeRZm8NbWbR7m91eHCcNG5SHz9BNnP3gPdpksiZKUW1EbM6jAGv7GNA89cdslzpFe2hG+Kogha39VyyOQgKHDGDkVx81qw1ErLNQE49dGUWUV+uKM/9RZJFB6gU4JQKl1Hmw8fNLUfotzzu7X7wQx01lLJwHDo46C5xDGpDG1L3+UqEAT0agX1Vu9ExU+tQC6AzSnip/X9uJO1qvp1uUXkPqqAQRo1vsTqPxQuCKKdTqeHMFoURRwOxyUHGP7K+MOXs7ejHf9JDO0Ygq9q1bFdI6gz2T1IDzRN/xRlepCeCxCOLcGg1DEsZlibdSA9tHuF94KvLgNb29JmAFLHg6VeIj0Ai6ZC0ogmn6C2EDtPRGwiJ8kv7SLxrTNUVlczucNk7/0DV3e8mkhteJvlYsZNuI1RzeaD1dXV0PN6n/2IPW/ErW+poKyurpYIXZ/bfW/T5zYc2lCC9CqvD6dOz2+gX2Q/ZD4+78i4kfip/FiTtcaD9FzAp0c/pdpWjaBKQmGraiY9AE+P68wTy495kB4Am9PNvYsysTrdXP7O1jakB+Cdad3YmV3N8Le2MHH+Tq7+eDd5VWZGv7ejzchVXpWZHw8UMKlHFKdL6xmdJp1rs93FK7+c9rACyKowkRJhbOPxk/TyXhLfPsesrXKe2yOQ+MZJury0hRqzg/LyciYmT/J6fgCmdJhCiEqyTej96mZIGNLiq3MRxE5jEVUGOs7ZROKbZ0icXyb9e+sMHeds4kRxHXUWOzd+vQ+VQoZcJuA2RiP2vNHn/ul5A9XVFokkNSFcJ8DmVyQDw41zoPsM39tn3AinWxkZhqaC2p9Io4rpfWJ9bja1dywGrZLbFxxgSkaMz3ZBeiXmplEei8OFXq1A8DExbtQovfov/afw8ssvExMT4/NfaWnLyFZ6ejrfffcd8+bNIyUlheTkZJKSkkhJSeGDDz5g8eLF/3OjPdA+4tOOdnggUK/kpcnpPPOTZ4RCbJCW2aM68ZyXaAWlubSl3LjvXdBjBjisTQaEFkKtSh7u9TCnq09TYmrJsRIQeHHAi2hlasSJ80FjRLj9d+ltXqkDmxlW3CoN8QtyuGuH5B+k8QelQYrDWH1fSxn8iNVU9kmj3t5Ag62cjU+kYFAaCBVUyCyV2LQh3Jh2IwtOLvA4/vSQdCYkT0D1iqflgxidAd2msyu3kcfHpHKypJ4+b+8j+4kuMOC+NvEEYnQGdJ9B8pwWTUift/eR/XgaRPZASBknVaG13iZ1AkT2ZOpn++kUruf96T25c8FBjzJvf60SnVzP8/1fYM7uFz20UtGGaB7o+QBmh5nl55b7uqysOr+Kbn2fhoLd/HxjIlcukKYe9SoFJ0u8j1LUmB2U1VtZe89AZi8/xulSSR8SooOfHxyJxenmyeWSI3yfhEDuGppMSZ0Fl1skIy6Qx8ekoFHIcLhENEoZSw8U0D85hOdXHWfRHf05VlRHYY2FwhoLbhFGp4Wz/qQUS7B4fwHPjuvM3LUnPVwOYgK1zOwXz6zvPEu7B7yXyfanu3NLl1v4+sTXHuvSgtOY3PEq3v7mKCq5jA9mZOBWOpHdf1jKNrM3No22CLDkBrj8RWqcvi0+vtqRy8uT0/np7oEE6hUE6bXStewxA/H8eoSiTI/24oD7EPXhHC1rYP6Mntz27QFsTjeCvR7hVJPrcEOxZII46GHYeVEhSmQP6DqtJbpFE4A4+RNs+kgyC+u4qmc0m89UMDOslquv6INgqwFrPaj1iGoLFVa4YUA8gTolL4xPY85aTx2RWiEnUKfibHkDgiAQ12QjEB2opbDGU+ejksv+ayXtwD8l4xg7dixjx17CY+l/EH/Iqq7/JtqrutpRXmehyuxg1eEiyhskjUb3mAD8tApmfLaXMxcJJLMfS0W25AYYcL9kKLjxhRYvk/AuEkEJSiPPUc7RiqPsKdlDmDaMsYlj8VMYiCg5jCjXICy9SZoqEGRShc2gh8AQIZGd1fdDwd6WkNSd70nVY12vlowLNVdQlJHM58e+YFXWKpxuJzJBxvCY4Tza+1FiLSr4uBOFDx+nxl7Luux1NDoaGZ0wmgS/BGKcWgRzvlS+bG+AtEkQ0olKl4ER8zNZeld/rE43x4vqyMyv5e1RAVLq/YX2nSdK7d1G+ry9r805zX4sDQER6ovg2FJprqDrNWCMYtvxAuK69CKrvJE6q530qAA2nirjfHkjPWMDGNQhBJvTSYDRTIOjgV9yfqHCUsGAqAF0De5KmCqMMnsZ16y9xmcExxUJV/DGoNcQsjZQHdKXXm9KU3FLZw1oErN7x+c39mL98RIm9oyhqNbC8dxq7hzZkU+2ZRGsU7HpTAU9YgPoFG7k1xOl9IwNIKeykbuGJvP48qOcLZNG8YL0Kp4am0qXKD+ufH8HaZFG3p/RkxPF9Ww9W0Gkv4YpGTFUNdpYfbgYp1tkRt9Y1Ao5q48UU1ovpb/LZQIvrT1JWX1bN+OV9wwkOMBMra26+fpeHn85Sf5J6B16CkxyQo1KVDIZIa5SWP8swpmfm+NVxO4zYcjDWDXhTPviAMeLvBPCtEg/Luscxpc7crhraBJTMmII91NTV1NLiKwOqs7ByVWgMkK3aYi6EM7bg1ErZQTolFQ02PntRCn3pHkp07/1N+mF4cgSRFu9JGgOTZEqygr2Q3QvSB6BUxfNhM8O8PiYVD7dep7nx3chTVsDu95HOLRQcp4WBMSOY2DMK1SL/kz8/AivTulKuJ+axfsLwGlnYrKS5MRETG4ZckHAT6tEKReQy2Q4XG6cLpE6ix2HS8SgVqBTyf/jOV3t8MRftpz9v4l24tMOX3C63Ly94Wwbo7ys5wcjO/ojQlA8LLqm7YaaALh9I3w+AmZngULRFDa1E048CRWn4bofpTDR1kgcCn3vBGsDrLpbWnbDKsnM8NBC6W+ZHOIGUDHtaz44PJ+fzv/Exegb0Zc5A+YQ804XacHtJyAyEhGw2GzUOwSyyhuI19qbre1tNhuVZhff7y/kcH4tQzuF8uZvZ4gP1tEp3Ei9xcHb42I92n+0PR+HS+TTbd4rd3bc1725PUBjYyO3LjrRnHg+sXsEr1/dnSMFdZwprSfET8OBnGq+3pULwIsT07i+bywKhaJ5nzuza+lkdKDwl/O3fa+wpXCL132/NuQ1xkVfjliXQ+I7Lddv7f2DmfHZHhpsbTUiMgF+e2goo97dhkImsOb+wYQa1Ly94Qw/7CtgaMcQMuIDSQrR88Diw+hUct6+pjuJIXqmfbzba58Lbu3L2+vPcKSwjqWzBtAn4e8HJ7vdIqLoprDWyohW2qHWMKoVrLl/EAkhBgoLK4iMlPq12+10nrOpuc3K+waRJK+C1fcg5LYNsxQzbkIc9gyv7qjm8+3er+M9w5ObCRtIIa0zekfzyi9n6Bjux7QeYc2msCUlJQyef4SYQC3f3tIXtUKG3eXmm525zBmql2wRyi8KfzWEw8jnEFOvRNAFtxxbk1al43OSUP6Lm3oze8lh6i1Osp/ug7DtNYQDX7b9THH9YfInJL5xEoVMYNV9g0gNN2C1WsnLy0MdGEFMiH87ofmT4C9bzt6OdvwRUW91cHnnMH7Yl+/h9rti3Q6mXjZKckoWZNBxtBQn4bRKlSrlp6Q31gnzIXcT+EWCywllX0L5Seh1M5z+ue0Oc7ZJo0gR8S3LjOEQEAvXr5AyjQ59B6fXUO9oYE2W96qYfaX7aHQ0gjAbxHfgiyYCNHMZ7sgBrD9RyeCOITS61Nz67QG2nq0k8/HBxKjMPNlHiTggFrMigDd/O0NelbnZvXjw/GpkwhG+uaUPZ8oakckEZvSOZcmBAmouckMO0qtwaAK4/qs97M6qBaRqoEdHpzQTnwg/LXuyq7nlm/1eP8eHm7MY2jEUrVJGZICOc5VWcqpMDI81IjgU3NvjHnYV78Lu9tTrJPglkB6cjrj+CcQhj3isW3u0mIdGdfSavD69bxzqppgJp1tsHuFY1hTGuv18JY+PSeG5lVKFk9kuiZJ3nKv0SnoA5m08y0OXd+TTbdmEGz2nk+rMduwuN0aNEo1SjtnmxGR34nSJ3P3tPt6ekcHLk7tQa3bSYHWy5mgxhTXSCNes4ckIgkBBtZkHV58lM7+2zb4bbE5+P1lGUieTV9IDIBxZBAPuZXhKKMszC9tonwJ0SsakR2BQKwjWq5oFsxWNTu4d0YFJH+7ihdVtKw4LayxkVzaSEWMkSmlizhAtyOSIV3+N8NVoT1F0YxnikR8kHVIr4lNWb2Xi/J0AdInyo9Fq58ubeqNVKhBspdJIj7fPlL8H0VrHm73gsYMin2/P4ZmxKUz+YDvvj4smtuk62J0uaQrLaQdEkKsQmqqgbE4XiKBoGg1qx58b7VewHe34B9FgdfLR5vO8P70n47pGoJAJKGQCm+0R0giO0wIzl0rE5PAiOLseesyEyR9JKe2hHSUitPQWWP8MdJmCeP9BaTrs4Nfed2quaClfHvSwNMJjqYVfHoctr0rmcnduocHegFP0XYJbai6Fe+7xXFhyBLVazY7zldz6zX5eXnuaO4cmk/VEDwIt5xF+fhRh0TSE1fehLz8gaXtaITFEz9c392HbuUp+2JvPptPl7DhXyU/3DGRit8jm8zOuawQfXteTDzdlce/wFJbc1R+AigYbRo2CCD8NL0xI4/YhSRwtrPX5GSoabNhdbh5bdox9OVUU11p449czYGtEWDyeCG0YC8YuoH9kfwQEtAotUztN5cPLPiROHgyDH+a8LYD3ru1BhJ/0prjhZBmDk0P4YEZPkkOlyqcIPw3Pj0/jnmHJjHp/S/P+TxbXU291NifBi6IkWr6g/RmTHkGon7pNZEdrnCppIDJAy5tTuxMXLO2vqtHGxlNl3PrtAaZ9spsf9xdwtLCWx5YdZdonu3li+VFenNIVpxuOFtaz5EABe3OquWd4Mq9N6corV3Wl0erELYrYHK7m4/GGq9MDpOlGX3A5wFJLcoieL2/qzZWt7vPx3SJZdHs/vtiew7HCWq7rF4dGpWDh7jweXnKEg3m1fHBdT3zxgpHRboIaziL88jjC91MRVt8P1VmIt61HHPSQ1EgTgDjoYZj8EUJQgsf2Vqeb8gZpiu/T63sRatTy/u/nuW9RpqTpuVQYcE0eo0cPBuBkcR1mm4sfbuuLQi6jvMGGQgZKnNBYhlCTjVCTA6ZyRKcNl8tFfpWZ3CozZfU2rD7cwdvx50H7iE872vEPQqeUI4oCs747yOQe0XxzS18im4WOZTD2DVh6k2dWUclh6DJZGrn5fGQLianOhvw9kHEz9LjO9061gRJpMkZLoumvxkjxFBew9iFIHIZ+4tuXPPZgTTBs2XLRwmScTmezoDa3ysw313dHlrMbYcmNkvYDEKqzIXszXPEKWU9eRfJrh1ArZMyZ2IWHfjzsMSrwbNFxBncI5vWruzFreAeyKxqbiNUBLA4XyzILeWZcZ96f3oNnfjpOSoSRH+7sT2yAGoVCQUKIHl8wqhUoZQI7zldyILeaH+7sL1VoKTWIAXEEvJFEwCOFzB3wIja3AwEBf6UR//wcxFgDSa8fR6OUseSuASy5awBOtxuZIJAQoifUqKZrdG/colTabLLYGPzGZo/9xwRqMajlCEKLnrygxkJ0oBaNUsaozuHMWX2C9OgAn58hJlCLTikjNkiyI6g123nv93Ms2J0HSCMZBrWCyR/ubJ7SMtldVDU6uHfRXqwOd/O1ysyvYXqfWK7sGkGgTsmn27KZ0C2K6ABts+/Sxdi0r4KpXUN9Hh+CAGoj5ysaUcng6bGdeeyKFAAO5dUw9ZPd6FUKXru6K9d/2fZ4pvaK5pOZvbhzoaf4+twLI5Hlb5Wmti7YOlRnS94/w59CTBmD2Oc2cDlw6yOQa9reByq5DD+Ngl8eGMxvJ8uY21qkrI7A48JcDGME589L5o6xgToUChlnys24G2xgdCITndJ93iriRWgoAXM1suDk5igUW6OLWrOdpFADmvapsT8t2olPO9rxDyLUT8Odw5LYdKacRfvyWbSvxXQu+7n+sO0HhIsDGgHCukgjNK1zs5ogZH6D2Pd2BJVBqq65AP8YCE1FFBQItka4ZR1sec2T9FxAzlYMCj1DooewvWh7m9XJAcn4q/zhRKuScm0ghKcjl8s5/eJlXDl/D1UmO3JTIcKOeRDcUYrHsNVL7sqA8PscZB2v4PSLl/HUqtOsyCxqJj1+GgXdYwOoqLex43wVRbUWPtp8nh3nq3C6RQJ0StIi/bA6XXzw+zlW3DOQZ8Z1JtKgRtfKO6lXtD9n547C6XQy9sO9FNaaGdtFqjbLiJXCQ1+Z1JnPd+bjp1HSPcafBlGH38AH4MzP8HYMFweSiLf8SoNTekhZHW7mrDnJe9N7kBTc4i0UZFAT1BRM2iVMg8LPwI77uhMaGoooishkMmw2G1aUXNszkv4dw6hqsLH4QCE39I9Hp5Lz+q+nqWy0cdewZL7emeOREn8Bs4YlE9SqpLu8wdZMegBuHpjAG7+dbiY9ATols4Ym8dGWrGaS0RqL9xdwXd84gnSweF8B5fU2Zg1L5pGlbcv6FTKB9B5RoK6SBMNe3LfFDqMQ1UbWnyyj3uJkQvdILusczsniOmYvPQrAbYMT+bjpeNQKGbtn92vOU2xsbCTfJEOnkmG2txyvwlSEsO7hFtLTGtvfQojuhei0k/jWGZ6+Em4dGN+s5bqAUKOaWwYlYnG6eb3J1fro00PQaDSI5kbodCXCRVWDAAQngz6Eqz+Strm6VwzZFSYW7s7lujQdHcMMkiGil+8nLhtYa+kaFUp+jQUBqLM6qGiwEemvQfFfqu5qx/8f2olPO/6ScNfkIVhroWCflFwenYFbbURu9J7TBSBW50gP+eJDkg4nLA2XJozTdZCZV4NBo6B7TACbHhnK2Pd2NPvBqBUyBEsNwqnV3jsOTYXNf/N9sNlbEUc8g/DbU5BypaT5qS2QDN+MEYAoVYt5+1FvQqRVyZN9n6R+R72Hl02ifyJvD3ubOGVIS+MuX1A0ZgD19gYOZy3HoDLwxe1dMCh0YKqBUXOkB1TFmabSeS3seFdK7a7OQl18iHdGdset0LHtbAUfXZ+BXCZwrKiOSD8NHcMNZFeYGJYSxq2DkwjUqRAEyK4wUWO2kxppxOmSitK3nK8gJcKPEIMCP0sp0dZyyMpEaYxgyy1dcGnDWHOmgcRQA1aHi1WHi4kJ0vPVTb3RquS8MKELh0ttDAmNgSvflGIdnE0VTwo14ui/IfpFe6STH8yrwWJ3UVRtxuRwcSBXmpq6JtVAb0MV5JwAUyXRkd2hzgSNJVCVhdI/FkNQEnMnp7P4YDEBejUfX98LlVygqNbSPA3z9c5c3pjajRdWnWjW+sgEuG1IIr0TAtl2voqyehvdYwII1quID9KQVy09dI0aJWX1NhKCdcwe1QmHWyQhSMdL69pqkC5gV3YVoQbJ/HLT6XJuH5LIHUMS+WpnbrNTslGt4L3pPaiotxIfG472moUIP14PlWeb+xFj+8KY16lyaEiLFEmP9ufX4yUM6xjKnuwWQp8W5cfn27M5/uww9KIJbFWQux+qcwgM70ygfyz7nxhKl5e2ABBqUEvEot7T/6oZLocUZ6KX7tHVR4q5oksE8cGejyelXMb1/eM4VdLAsecvo8RSwuH6wxQUFtAhoAOR414jduLnCG+2eCIR3AHx2u+w6qJQKyR91Z7sKqpNdqIDpDJ8we1EsPienhQsNYiaAIwaBaIIYX4anC43LreITBCR/Rc9fVrjiSeeIDw8nNmzZ//TfcybN4+HHnroX3dQf1C0E592/OUg1uQibHwR4YLhH4BciWzih7gSBiP3j267TXUO/HQXQsHeloVqI/JrF5Eans74DyTBplIu8MbV3dj0yFDOlDUiIBAXrJOca2U+vk5/x+ZVkMkRu16LmDwSig5I4aJuZ4t9WtepMOJZycvHF3a+SNyYV3ltyKvU2xooMhURpgsjUB1IjCIEsSEHrv0OtEEUB8fzXub7/JL7S/PmCpmCuQPn0ie0FxHrHpKm4S5AZYBJH8Lu+Qi2Blh1DyAimzifAw8M5tofzrA/r+XBoVfJeXNad9YcKSYzv5YpGdH0TQxq9rwBGJAUxO1Dkrhr4UF+vDqcpKRgWDkLIX+3x37l0xcxoUMaV3x+mKwKU/OqUKOar27qzf7catKi/Og67wRHZ49DljRCmkZEgMAE3Eodya95jn4IgjSd9cMBKTBTFOH8032RVxxF+HGm58hb3AAYeJ80Yud2giEM5cxlXJuRSsoLG1HKBV6b0pWUiBbX5K1nK2iwOnn16q643SJWp5vuMQE4XC6ufG+7R7ZX34RAFt7Wn4kf7KDW6kQQJM+oFyd24bFlR6losPHp9b0uOYujkAkezsPXfb6XV69KZ+39g8mrMmFQKwjQqZi75gT7mkjeqRdGopnxozSC2FACAXGgCaAGP/q8LV0DnUrO+9N7Ynd5ascEYO+jA9G76hBMFVIlY2NZS4PgDuhmLObo00M4XGojNkgLtnPeD/4CZPJmQ0654Ms6EEKNGvxUAtmN2dy98W4qLS1xFQl+Cbw/8n0SZhdA4RbppUEXglUXyc+nqph/XQZLDxSw/mQZk3tE4xJbaXUu+R2V1l0QkgOEGdUE6lWY7E70KsV/hfzccMMNbNiwofnvmpoa5HI5b7zxRvOym266iddffx2AF1980eO/3pY9/PDD/xPEp32crh1/KYhOJ5xa50l6AFwOhJWzkFlr22zjNlXC9rc9SQ+ArQFh8XTkrX5cHS6R2UuP0GB1MTI1nBGpYSSHGhA0wdBliveDKj6EmDDY90EnDkNYcRe47Air72trzX9sGeRuh/53++6j+3RY9SAxX44jbfMJLg/oS1pAGkNfOUbS3M3M/SSX+qihuGL6sblgiwfpAXC6nTyz4xnqnA2Q/LRn3/ZGWHk3DJkNCrUkIm06n4K1lllpnhVcJruLR5YcYdawZABWZBZRa3bQJaqlvHR3djVbzlQwuks4PVPiYMc8T9LTtF/hh+nI7TUepAckofPd32cyOi2cOxYcwGR3kfzaYRLfOktlYAaVgT1JfPtcG9IDMDA5mLJ6K5tPVzSTCbmtBmHxdE/SA5C/G87+JvkUATSWIyyeiapRquxyuEQ2n6mg2uQgJlDbvFlmfg33LTrEY8uO8savp1HIBaZ9sqdNoOm+3Bo+2ZbFl7f0AaCywcYjo1J4duVxKppGkOosDgYlB+MLgzqE0CncM67iqZ+OM/a97by05iRKucCE+TuaSQ9Avze2sbZQQ2a5jProYSzbIyPxjRNkvNFyDcx2F/f9kElpvZ2BHVr232BxYnTXgbkSltzoSXoAqs7DmgcwOGu4/dsDUtinxh8CvYSXgjSiqAmUTDmBab1im4Xf3lBmLeWhzQ95kB6A3PpcXtz1IsXU05A4lsxyPYlvnqLXq1t5buVx7lhwoFnPNq13DKV10iibKFMgan3bCoi6IESZZ9xGeYMNu9NNo9WJw+Vl+u4/gIULF1JaWkppaSm///47Wq0WvV7Pzp07m5dfID3t8EQ78WnHnwZiVTZi2UnEijO463wMm9flIez7xEcHbji6tM1iwVyNcGyJ923sJig+wukXW0IBRZE2sRXoA2HgvZI25+LdmqoQxrzmPYtoyGOg0sN1ixCO/uj7tX7X+5Kpob8Xe/60ydLygGiozYMTtyMsvAxFfYsG6WsLdH91B0WmIr4/9b3XXYiIrMteB4O9kDSHWaqcOdlqOk8U4eiPDPfS3uJwUVJnbZ5O+HF/AZN7eo60LTtYyNj0SGma8MgP3j+3wwxFmVIsyEUorLFQZbK38bUJCAggRKgj5+EEch7pSPYzvdl4uyTQ9dMquGNIEq/9IqWjw4XIkQNSurw3HFsKnSe0/F1XAKYKlk+TtEdjukTw+q+neWpsZ1QXaT7sLjdzJnXhaGFts0D2YqzILCJAK01VLd6f3+QY3HIs/loFdw5N9hrncdvgRCobbeRVmbh3RHLz8r6JgSy6vR8Lb+/H1rOVbW6reouTohoLX5yCbbkmnj/k/ftkdbjJzKshyl/Nj3f256OZGXSO8kOwNyA4zNL95gVC3i4EewPr7h9MTIAOhzEGcdJ8iThfjMvnIOpDcfrH0TXanyGdQiirt3K+vIHzZQ2U1XtelxpbDUWN3ivTMsszMTnNFNdaCI5PIDZIi9nuwtyKcI5KCyPMqGZG31hkAlSbHaA2InrJvxOVWlD7cby4rZljRYONIIOKequT3VlVrDpcxO6sqjZBrP9OnD17lueee46xY8fy6aef8tJLLzFgwADmzJlDVlZWm/bz5s0jISGh+d+8efPatFm5ciVr1lwiMPYvgPaprnb84eGqLUZWfRbh16ck3xuZHDqNQbzsRYTQTm03aPCdwkydlwR0l71FF+JjG7ncc5qpsNrctl1wB7hxtSSyPb0OUaWH3rdBWBq4RcnE8PAPkLcT9GHQ7y6JKIki1Ob7fIg0f6aKMzD2dSg7IfkCqfTQ5SppiqC+uEkP1Kq9FyGpiEiFpcLnbopNPgglQE2eZLbYGnUFCD6mCcobbATolJL+pd5KwEUBsBaHS8o8cju8C0svoDa/OVX6YlSb7PhrFFQ0SiLr7MfSEc6sQfh9DtTkSg/a9KkkD32MzMcHcKzCyd9+PkVhTUtulEwmg1ov98UFOCxtc61MlSQmDgRKkckEThTX893ePD6/sRe/nSjjTFkDMYFaJnSPQqWQcbSg1mf3Vocbh8vNFV3C0atkzeGfF+AS4eV1J/lgek92ZlWRmVdDkF7FhO5RnCqpZ9f5Knacr+T+kR344Y5+lNZZCPfT8tK6kySF+A6HfXP9GZ4f35nEEJ1PUgZQ1mBj85lKXlp7kspGO59c34suQXbP6kWv583MZ3uyWX2kGJvTTdZTfZDduRUyv5ViLQLiETNuAL8o3EojK4+U8sF1PXG5nNy/7Cj7cqT+e8cH8vyENDpH+KFUyKi11V1yt2anmcnvbeem/nEsuLUvm0+X8+uJMrRKOdf1jSMxVM9VH+5kREd/7uzph1sUcYoyFIEJiPbG5gIFURcEKgMuvE8xO90i64+X8tK6U836LoBIf8meYUy6bz3h/y+OHz/OuHHjiImJ4eqrr+bIkSMEBgYCMH78eD799FOmTJlCeXk5+/btIzZWemF66KGHvE51tUZpaWkbYflfDX/tT9eOvwRk9QUIC69qeZC7XQin10HJEcQbVyEEd2huK8qVEJ2B0Fqj0hoJQ9ouU2jAL8q3+DK6F42NnlMgA31NPQQnQ9+7cadNAZkcm9IfjakIFkyAhjp49AT0uhWUajiwAHqlQHGmZFYY1VPy+fGGqAwoPSoltEf3ajJItMGml6HzeAjpDHktAl4xKgNR3vbtWilTkh6SzsGyg23WgeTyjK/E5uDktuQscQgul/eHZqdwAwv35ALQLSaA8xeVWMcEaqkx2UGuBr9o3/4ysX0xm70QTSA2SNdMes69NFoiPctuaWngtCEc/h6x7ASBU7/m5m9OIYrQPymo+XgsFgvG2L7e9w0SOb34IR+YwAcfSNNCtWZJKLs7q4r9OdWMSA2jR2wAFQ027luUydc39aFHbKDP7qP8NagVMvw0Svy0SqICtB7ra0x2zHYXN3+znwFJwXSPDaDe6uDZlcepszh4b3oPFu3L58XVJ1h1Xz+0SjlXf7Ibl1vE7YYpGdGsO1bSZr8ut8i6Y6UMTwkj5qJRptZIiTDyyJIj1DURsspGG4TrvY5uNkOhAU0ASw+2aHuSX93P8mkR9Bj2JILNJMVkqDRUWpW4RZjWW0tWeSOTPtxDYysDyAN5NUz/bA+r7h1Ex3AjkXrfhEIlU+Gn8gOK+HZPPt/uyWfFPQO4Ij0Ch9PNS2tP8vtpifiP6xZNvbUGt8lObRNvSY8KlKI2kKbASuptVDV6Py/7c6p5duVxLh7fKa2zcvd3mXx8fca/jfykp6eTm5vr9aUjJiaGl156iZdeegmXy+Xx0jZv3jy++eab5r9ra2vbaHpmzZr1bznmPxLap7ra8YeGuyYfNs31XgZbVwj5nrlQsoA4GPGcd7GiMQK8aG3cAQmIw5/yun8xPB0C4vBXOcl+YRDHnhlKqFHNlB4RYK4Bi5e3T4WCAlcA2eWNqGQuRG0A4r2Z8MhxSSfpdsJbHWHLS2CtBZkM9nwIYZ2bK1s8IMjgsucBGSQOk4jSno/hwFdStUzv26TPduaXlvYjn8GslHQL514aTfZzfcl+YRCB8kDu73m/1+TpEG2IRHz+5uUYwtKkaT9rq89rCIOEYc0RAq2REm7EbHdRb3EiE+DWwQksbXI8voD7R3Zg0b58XAEJNNy7h9rHc+Bpz9EoMSwN/OM8qrIuYFRaGBqljE7h0qiGoi5fGunxAqHkMNQXkvn4QAQBHh2dwuqm6cppXx6CwHjEsM4tGzxZRv0T+dQ/kQ93H4SD37QcU8qViGo/vm56Hv5yrLTZ68bpFtlwsowvd+Sw+kgxcUE6IgO0xARqyZo7kuxn+pA1ZwQ/39iid5k9uhPFdRaWHiwkLkBDmFHF1zf3JiNOIks/7M/nzqFJiCLsyqriyx05LD1QSJ3FQccwAyqFjDqLg9mjO2F3ynlnw9nm6ZYzZQ0khuilyqqLz4kAj41OQSETeXR0itfz1inMQK3Z3kx6AH7Yl4+oDUJ0uyBlnNftxL53IKr9yZozQvrMc0fyZq8wzrlCWXmygWKXPzJjGHK1H+H+WiL9tVgdLhbty/MgPRdgtrtYuCcPi82BQalnZNxIr/udnjpdsm5ohSkf7abB4sQtwmvjEsl+cRjZz/RhcGIAlos0V8eL6zlWauZEmQW7SyRYr/IQjjd/PhE+2HSuDekBmpfNWXPy3zLtVVBQQEREBJGRkURERDT/0+v1+Pn5eSyLjo4mIiICl8vFiy++SG1tLbm5uc3/amtrPUZ9HnnkEd87/guhPavrIrRndf2xIFael4IMfbiyil2nIVz9hccyd20BQtlxhN+ebqrwATFpOIx5HSEs1Ws/7ppchKwtCFtekcSaMgVi6ni47AWoyUfY8z6iyiAFh2oDEEqOwpFFgCC5M0d0haCWh5mrrgiZuQry9yN0GIFYegThyGJpZY+ZEJ4O2TsgsguUHYc1D0gjKmPfgB3zJDEzNC17E6pz4cxaabukYZJrs9slTX2d3wTdZ8D73SE4GXHM67hD0rCqAtBZSyF3B8KpVdLx97qZstBUztSd4/X9r1PQIE3x9Ivox5N9n6SDLhYOfSkdQ2OZFCvQeSIMewLhp7ugRBILi4nDYOzr2P2T2JVdy+MrjlHRYEMuExidFs61fWJ5YPEhgvVqnh+fxsmS+uYHcphRzSOjOyErb2TAUCN59fksP7sci8vCqPhR9A7rRZw8GPHne+CyF6hXhPDp7hK+25NHvdWJWiFjaq8Ybh+cyFMrjvLS5K68s+EsH12uRvhkkO97acQzOAc+zPasGtLCDRTX2zhSWMPwlHCCVS6Mjgqw6yjQOzhdfZrVWZKeaWLyRFKDUokzKREPvgmDHuBUiZkrF+QwIDmYFyek4adRsu1cJW+vP0N5q/PwxJhUYlQ25LYKOPIDQskRxMBE6HUzbk0wS8+5GJAUzNIDuUztHc/JkgaWZ0oEcUrPaKIDddz89V6u7xdPdICOdzee9ej/lkEJZObVMqRjCEF6JRaHmwkf7PAQUccF6XhpUhc+2ZbN7izJByohWMfjY1KJ8tegVMg4XlSHwyXy/u/nPPp/dHQKV3+yyyOiBSRt0bODdOByIuz5CA5/L01XavwR+90NPa+XSP3BbxCqsxEjukH3Gbg0Idy/Oo8ZfeOID9YR30rEXFJr4Y6FvsNRUyOMfHlTb6IDdRQ1FPLNiW9ZeX4lVpcVo9LIzM4zmdRhEtH5ByCiG+VOPf3ePQDAx9dncEW0HaGxHA58idBYhiXpCnL9BxKf1IHz1W1/X5JDDciaqumKai3N04FapZzCGovPaJXW+OGO/gy4hDD9X4mHHnqImJgYHn300Uu2i4m5xEgdUFhYeMn1/220h5T+G9BOfP5YECvPIyyY4HMaShz4AMLol7yvq8qWksPlKkSVEVnApb/wTocNeV0BOEzSUL3aiLD2UTjbyj/ngcOIP81CKLhoKi1hKEx8H4IScTVWIsvZgrDiDrg/E36aBRe1FxOGSKntdjNC1VlYerO0QhcEGTdJ01kKDWj8YPUDntoapQ5uWCmNgv32DOgCoc+dENoJRDeiMZJak40Adw3C91c3k7/mffeYCePeI99cjNlpRilTolPoiMII9nJJCCu6JPM2uRpRZcClC5XE0naTRApVRkyKALq/so13rulBl2g/bA43cplAo9VJncWO3SVS0WBj6YECesQGMCY9gmCDGp1KhkohwylU8dGRj1ibvdbj+BL9EvnwsvlEi0oqrGryzBJZcouSLkitkBGgUaJTCZQ1OLA4XfipFUS7ihA+HexTryVO+hB72jSWHyplYIcQDudXkxYVwFvrTzOyczgj4tTYtXae3/U8B8oOeGzbO7w3cwfOIVqmJ9+qwWR3opAJ6NVy/BUCKq0aOSL5NVbMdukYjRoFEf5axPzd0lRta/G0IEO8+kvccUPIs2mRywQeX3aUvTmeU2p9EwJ5Y2p3XG6RCH815Q02KhpsVJsc7DhXwfLMIiwOF8F6FYvu6I9KIePGr/ZSUO05PROgUzK9TywjUsKwOt3kVZlYuDuPc+WNTOweRadwI5tOlzG9bxyBOiWJIXoqG2wE6FWMmdfWFBMgIy6ApTenI3OawNYgER+lDrcuVLr/l93iOVKr0CDe8BOOyN50en4Dk3pEMXtUp2byU2u28+Diw83hpxdjUIdg3p/ek2CDmrJ6C0qFiTp7PVaXFY1cS6g6CMPrcdK1jhsAkz8m8Q3J3Tn78a4Ip35C2PB8c39WQyw5g+eR0KU3Kp2REyWeUR8dwgycL28kwl+Dv0bZLKKXCfDzsRIeXtK2YvBivDe9B5N6tLXP+FcgNzeXwd4KEZpQVFSEw+H4P+l1BEHgj04J2kNK2/GXh8sYi7zPHT6nMOjmJQ29CUJwUsv//wP7UijVECLphVyWamSbX/UkPdMWQvbWtqQHIHcbFOyFoERk1loph+jObZC9tQ3pARBytyMW7JU0R3KllEjdWCZpSXa8KzW69jtYcKNU2dQaDjOsvhf63A7FB2HKZ7DmPklY/fEI6HQ5/hPeh+2ftiE9gKR56TGT+KPLIPOrtuvVRsQ7NjPuxxoeHpXCHQsO8PLkdD7bli+VJl+EZ1ce58Op3TDYi6nWxnDHggNt2hwvrufytHA6R7b8GO0vPdGG9ADk1Ofw45kl3NP1LkrrXTy+9BBf39KXpNC2Jc6JmpYpHHdDIKRPRTjspWpNoYaYvqS8sJFHRyVzMK+GPolBPPfTcab3i2PO6hNMeXQo6/LWtSE9AAfKDnCwLJPYjpNJ8K0XJinUc6VYlYWw8p62FWOiG2H1/cju3EKMvz+rjpa1IT0glbvvy63mmt6SONVeY+Xaz/a0qdKqMtl5Zd1JPprZnZsGJPDyRYaHtWYHn2zNpndCEPd8l4m9VQn26iPFzL+uJ+fLG3l8meTOfMeQRHIqTbwwPo3e8YEcaOXTdAEFNRZWnmrwcIlefEc/+lEp2TJcPD3ttCKsvAfl9Sv4+cZErlyQw/Q+sc3EJ0Cn4tbBCezPrcLuFNs4X986KJHgpim7OrOThCA/As9uQFh5kSZlwiaENSMRc7Zx7qXreOWNrQhOM8LGF9p8BkQXNJYh03pWdOlUiuZpqtI6Kyq5DI1S3hxTEeGvbdOVN4QZNX+/0T+JhISES47O/NUFyv8/aD8z7fhDQ6FWI6ZfjZi3C+F8i1kXggxx3DuImoB/iNT8XyBW5yBz2RGOXVT6HpEOK273vhFA5rcQPwRqciRyojLCoQU+mwuZ3yLG9AFtMEyaD8vvkKYHQBrVcdnbkp4LqDwnCYL73gEXHKdztsMjRxFKj4H1EiX6AEcWS9N4XogPtgaoL+b9cdFUyxXcNjiBUKPaK+nJfioDwd4ADWfBXw26enKe6YNd5U/KCxub2902KJHkVqTA5XKx8txKn4e3NnstUztN5VSJjHkzehLtr/TZ9gJkxlDEoY8hlp2QND0XoFAjXrMQtyqQ9Q8Ppbzeil6twOkSKaqz4HC66RYTQLm1nBXnVvjsf/m55fQJ70NeuZpwPw1+GgWhfm0fbHlVJuot0qjXIH29V/IJSJ5BdYWUy6NZvN93VdnifQUMTA4mJlDHrqy2pekXsO18JcX1Dq7oEsGe7Co2nipvOTcCPDEmld+Ol3qQngvYdLqcAckh/HZCqog8VlTHa1O6UW1x8LerunL7gv0eo0h+WoUUkPqzZ5VfiFgridRtPoJSa3LAUk1no5ucRzqCRmwW4OZXmwnWq/nsht4EG9QoZQIrDhXyydZsbugfT1or0rw7u4oOagXChe+XMJvS2XfS6DRRYalAf+92/FV+xLrdPPXYUDj0jU+rCMFhkghQE1RyGdEBWo/7vb7JXPIC8embGESkv4bSOqtXnY8ARPhr6Jvo2x/o/xfnz5+nc+fOREd7H1GKiYnxKn4WBIGUFO+aLl/L/2poJz7t+MNDCIxHvPItRFOFVP2k8YeEIbiVBuQB/9phZLHirDREP/A+L1Mm4qUToJ023KITobmN+++2x2WH5bfBuHfg1l+hcL9Uhh2WJlU7XQqGcECQ9D4gpcMfXgwbn0O47yA4L7VvK1xUou8Blx29Qc+pchsahZxgvapNk5zHpVR5Yec7UuwASOLsqz5DFZTE2bmj+HZvIYM7hKBXybHYJTLgr1UhiiI2t28LAXvTeeuTGERcgBql8u8THwAhKBFx6jeI9UXSSJsxEmL64FYF8sRvRaw4VNQ8ZRETqOXlyenYHW4UcgEREbvb9zmzu+24cXPdF3tRyWXcPTyZqb1imgNHAbLKG3lg8SFONPm+5NwXfukDdtrQiiJ2p28TPLvL1Tz64C2v6wJEUSrIszhcPD8+jftGdmDX+Sr0agUDk4P5bHsWSw96r5yzO90o5dJDsn9SEHcMSeLqj3dRZbIT7qfmybGpaJUKcisb8dcpCdCpePO3M2RVeFbq6fV6aLzEfQcI9cWw5AbJLXrCe8iCOmBSBXPP95ke+p7BHYKZMymd6b3j0KjlHqMnNqcLRFnzd7Ro9m18evRjVmWtwt000hSlj+Kd4e/Qyb/Tpe0SmhDup0GrlKOUCxTVmqV9NJ9bkdYMRy4TeGFCGnd/l4mAx6rmF7EXJqRJdg3/RsTHxzeHr/5fcPr06b/f6C+M9qqudvwpIAQlIMT2QRj6CELf2xHCUpAH/otJT3UOwpLrJbFx3i7odIVnA2t9i4OvN3SZgqAJkvx8ZApQaC/ZXuxyFagDJC3ND9fCxhelqqnzG6WIBLnSdwyGMUJyyN33WcuyxGFSdRiAUgOdxvg+1vSrIXOh93VyFQTEUy/oeWTJET7ckkVhjcWD/JydO0qK19j2egvpATBVwuIZCHYTyvp8+sYHsvlsCVWNNhpsTqpM0oNKoVAwNnGsz8MbHjscf4WRaZ/spqDWN0HyBiE4ESFxMMLQRxF6zqTRmMi8vdUsyyzyMDssrLEwe8kRYoN1HC2sJUwdxqi4UT77HR0/mjB1GCAZE773+zkOt/Lnyasyce+izGbSA4AmAPQ+0tDlSghKwt9PzZVdI7y3Aa5M/3/tnXd4U2X7xz8nSZM06d57l5ZCy95DZCjKEBUUEfdGBdy4XhF9VVw4UFF+iPriFlEUVAQpyN57d0Mp3TNt0iTn98cpaUNShoKoPJ/rygU95zknz3kyzp17fO9wIn0VI7hPUuuJshlRvhi0KoxaNfd/vpWZSw9yZadIhrYLQZZlWlEdAKB/m2A25VYgSXDPgEQmfLqFsqYGtMeqzTzw5XaeWLCTngmBHDxWyz3ztrCvyNWrs6NcAr9Y5drcYQxSDHRQtKu+uRWpZB+GhmMuSc2rDpUx/ad9lNVZMGicjfReiUHYDSHIaaOom1LA91kLWXBogcPoAUWT6q6ld3HEdATi+7d+8Ro9SCo0KomjVQ0cLK51EjwE8NZr0Gqcb5dD24fz3vjOhPk6e/3CfPXntJS9Jbm5uU5VXCc+5s+f7/a4qKioVh8LFixwe8y/CWH4CATHqStt7li982vofKOz2vLsAYrB4BfjemxAgqKsvOE9ZEMwcr+HYUZq6+P94yFlGDYPI/LQlyC2D3S/UzFW6iuUHkq7voHe97seK0lK766Ns5u3pV+r5LHUNok3fnMb9H/ErVq0HN0DglOQfdx/McsXPYbsYSAh0JP9D7Yh55G2XBFrYdN9bch+vBObH+mJR00B0u4f4cH9cN9m5fHwAQjvpPwK3/cDWBtIDtIxKzMXH08tH67KoaK22Uhq45dMRlCGy/P7aH24Lf02qktrKa+zsN/NDfZMKK4x8/GaXLf7lPNXM65HLAt2HOOSuEuI8nJNgo/yimJI7BCn8B3AO8sPkVeqtNOoMFlcjAGbVzjyZS+7lVeQ+z2CrDUqRmD7cKWn1YnP6+/J5enhjnwNP4MHIztEuIzTqlU8PTyN6AAjkf4G/jOiHSsPldHrpd/o8eJy1CgGjW8LEcmDz11CziOp5DySwph4Gyse6kvnGH+25FU6GvCeuFZPfbeL63rEOJ3nOB2j/Qj30SN7eCFfNMVlP9D0vm1RhVlfAdWHobHeoYTdkl/3HkOrUXGs1tn41WtUPPL9XkgdTklDWatq5FXmKvaW7UUOSEXOGOtmhITsFYqs8lDCn240rAxaNRqVym0/rqHtw1n12EA+v6Mnb47tyOd39GTVYwP/EqMnKSkJq9XqaE/h7nH11Ve7HCfLMocPH271ceWVV57zuZ9vRKhLIDhOSwG9xnqlYmrMR0qvrKzfQOeFrNIi3fAdbP4Y9iwAJGg/WinfXfce1JehMgZg7zAOwjOQSg7CDd8hb/4YqWm83DTepA+loNpGm6huoPVSErhN5Uq+T3Ym7JqvVGuN/kjx5FTmK+XsFz+OnL9OCRmEpUOPe5Rmmu/1ap5/wTpFafm2X5HXvoN0cAlojchdbobUERQ3ehIc2QNu+QlWvIxUvEfRsun7IHJwWyTZimbnF0ir32h63nbQcwI0VBMQ20sx5gY/qWgsHe9KnzYKrp4NJQdg19eQdCkeHh5U1yv9jHLLTFjtMna7HZVKRbRPDNP7v8SveUv59uC31Fvr6RfVjxva3kCURyht3ssEcBE+PFMarXaqG1y1YY5TWNlAapg3NWYrQRp/Zg2ZxYKDC/gpR9FFuiz+Mq5Kvoogjau+UV6ZCXtToKOk2tUzlTQ1k+wp3ZrX+dhuZZ37TEYObY+qqWFuXJCR/93agy83FfBDk77Q8IxwxnaLIS6oOak7JsDIw5em0C85iLmrcymtNdM11p97ByYR4tPskUsJ9eL7+/ow87eDbMyt4NWlB5g6oi3z7+nNnFXZvHCxD+z6Cmn1DOV9EpKGdsDjfH1tKhN/al2R+WBxLWU1Zubf05sPV2fz294SDFo113SL5rL2YVz93hpKay1kPzoaQtshrXrdcX663Qb7FkGBs/YWFbkQ3JakpDTAWXVdlqHOYnUxOlYfLOWGnnHUe+qwmo9QbXFfAg+QVZWFPWYI6gFPIMf3R1r/HtSVIMcPBO9QUHuwq7CaQKOWpGAvSussVNc3opIk/Awe+Og1qFUSWo370LBaJf1lJeuCs4MoZz8BUc5+4SIf2Yw0+wRhNLWHEq6K7qF0T1frkL68Ba58pzlHxmqB9weAbEEe8hxSn4mOw+3leUg2C7IxBJWpBBmwe4Xw/PTfmVsPWU/1RrX3W6RFDzY/pyRB4iDocbfiSVoxXenF5RWseIoSBiGbih2l5eh8kWZ2Vn49n4hvAvIdP0NDDSBh84qkzbRl3N0/kX7JQaSH6jE0ViBZG5DVWiyGULT1JUjrZiKtd9PzbOhLcOBX5MtfRvroctcGld5hcPMi2PsDcvKlmLxi6f/6Gr66uxdv/3aIm3rG0CnWOeHTajZzpOEoMjJ+Hr4U5hZx+Sc5jv2zb+zCkLTWQ0GnIrukltGz1lJe5z735IMbuhAfZESrlpAkCZUEvgaZsgalCWagPpDXp690CBa2JD3Sl3fGdSYm0MDOw1WMmOkqtAiw66mLMFirkBpNjnVemVXJJe2cr6uuwUJxrQUZCPXSYtS75lYdJ6+sDptdxqjTEOomyRqgrNZMdX0jGpVEdKCRugYLnuYSpHXvIq2d6TJeHv4G9nZXkfis++toF+HDXf0T6JUUhAqZynoragmi/PTklddz7QfrKG3K8dk3dRBaUxGSvVHRsNr8ofs2FyPfRo7syi/7Srj7J2cjV62S+OG+Pug81E7J8asOljB+zgYGpobwnyuDufnnmylrKHM759cvep2+oX0prrMR7qPFo+YIyFbq0ZFdVI3NKwRJo8W3STVbAkdOlUoCj1YMHsH5QZSzCwRnE88A5IhOSIVbm7fZGmHXfGQPA3LaKFTeocgXPYD0Xg/X47VeLnk1qoBYoCnh0eBLflkdY15bS3E9LL4xHlVDKdKyac7nkWUlzyd7Ody7UTEmqo4oncKv+gB568eQOAgpKFmZoqUeVa/7kX474TwAshnMtUhBSWQV1zL8v5nY7fBuZhbvZjY3MZxxbUdCvXXc8vpy9k2MQ2qZO9SSlS/D2C9h++euRg8oPcJ2zldCdDVHGTpzPXf2T6C4qp5b+8ThpXX9ymmUNCzaauXln/e77Avz0ZMc4u2y/UwI9/Xkjn4JTP/ZNaEzzEdPfJCR0bPWOqkTg6JTM/vGrhyrbuRLmxpwTZKZNCiZmEAludnXoKFTtB9b3fTlenrRIR69tA29XtvW9LyH+PxO1/eQUa8l/iTGTktiT9LB/DiBXjpHCfjx81NTC+vfczteWvYsqrh+GLVql07yoJSUp4R5O5SgA5tskZLqBn7cXsgNveKY8esBAFKnLgMga9pAVIYARw8sJ7zDkI0h4GFwMXoARmSEkxiodynNTo/yZcKARD5clUO4PoOb29/Ma5teczk+2DOYNv7J9Hh5JQvv7UN+hZlpi0rIK6vDajEz9eIQQryUz2ewtw6Ppiazwtb5dyNyfASCJqSAeLh6jiIueByVGjnjWuj/CCpvpUJHDstQ8nJ0LW7IAQnIN3yLzds1/6IlsYFG/ndbd7IfaU9b/0bFS3O8hL0lxiAY9R4U71WSRbvfDm0uQ147s6kZaXMuglrrCelXI/e637n7dXgH5PHzkQKVrt1B3hrm3d7dKZfEW6fhycvbEu6jY+6aXNY91FMpv3bXIgSUX+weeqSDv7R+kfsXI9cVYzZG8n83dWVYehgqyYaXVkOYG8+Ep1bNiA4R3NkvAV2LBNJ2ET58fGt3p1DPH8FTq2Z4Rjh39Xc9/0e3dGNNVimvjslg2sh2jv39koOYOa4zgV46In31fHJrd2JaVG956zRMHZlGapgXFXUWdh2pwkOyMOPajk593FQSTUJ9ybzUpK1z/LriT9JA9JxSkauofrujvgLqy5lzUzen6/XSaZgyNIW4IAObciv4ZvNhsktqKW/KvakxW5mzOhcvnZrb+sY7rfOI9zZBynDk/g87vz/D0pGv/hDC0qlWB9M3qTmUqJJg/7ODmTHEG+2+Bah+noK8/n3kkgNYqyt4Y+lB1CqJr+7uxcpD5QyJGcxt7W9Dq2o2Gtv4t2HWkFlE6sOprrdSaWpkQ245iUFGbu0Tz9QR7fBQS6hVKmICDXi00ghX8O9DhLpOQIS6BPbKPKSGGkVnRe+HrPdFdUIisM1UrrSkMJUr4TC9n5Ng4knPX56HtO1TpSLqzkz4YIDzAA8DXPOJUtkVnAJJQ2Dxw87GSFAy8nVfOowaAHtNMVJDhXLz8jAoytMt2mgcJ7+sjqp6KxabDR+9B55aFY02mbJaC2GeHkTWbkL636jWL+Du1bD4IWi1EWxf7Fd+QMKL20gMNvLClelE+nkS1eJG6o6Smgaq6q1Umix4eqjx0mtOy6txurQ8v06jprqhkecW7WFfk2Jvj4QAXrwyHYAAoxY/g7PnJae0lup6K1a7HX+DlmAvLRabzPSf9/HVJkVIbstjA6i1Q53FRq3Ziq+nBza7zGPzd/DW2E40NFrx1J7d6zpjcn6Hj4e3ulu+cwXX/VDPy6MzHO8Tb70Hx6oaeHXJfrYfVvq1BRi1zLutO0FGLSaLjWtnr+NYtZmRHSIY2SGcAKMOuyyzKa+CD1flsO7+dCRLtfL+VGtB64Ws90PlpRg8RypM1DRYqTVbSQvR41mTo7wPa5v1iPDwRB73FdagdJL/uwaABRN6kxKkw4SJaks1VZYq9Go9Xh5eRHsEIR/diDU4ncwCM3d80tycNy1Ez0uXhBEXF4eXweA2eVnw90OEugSCc4DKL9bpb3dfh2pDgNJe4g8g1RUrRg+AzheC2kDpgeYBGdfAlk8Uz8tl0+Hzsa4emNKDsGI69sHPo/JRSqxV3iHgHXLK549p5aZr0DYw7YfdzLw0WqkGa9mQ9Dih7aBkP3LX25BaMXzkrrfSqFduZlkldcxbl8fwjHCCjVp0uta/coK99QT/uajWSTl+/m0FFW5zcdZnl7No51Hu7BPvdp7uPDTL9xU7jB6A8kY7Q9/43UV1GOD1JQd4ekRbgs+hmu9p4RMOnv7uc8KCU0Dvy/U9gwk0ehATaCS7pJYxs9Y6ytuPU15n4eGvd/DO9Z3w1mu4pms0b/92iIXbC9mYW86s8Z256p21jvEJLzQrYk8enMyE/vFotc0xpUh/Ayv2F3PT3I3kPNoO6ft7nY0egMZ6pK9uRHObImbaNdaf5fuL6eilxfDNrQQVbnG5JMnTH81tv3K4wvk1rapvpKLOQhwIo+cCQ/j2BIITKKpq4MCxGrbmV3CouIbSGhP7i2oorj61CNppkb0C7siEu1cpXqUr3wddi18niYNg/2LFIDq2u9WwhLT7WyRz5R+eRpWpkaySWrbmV7DvaDUmi5WJg5KweQYgXzXbVYtF7weDnkHe8QVEdUN2oxMkp1wOEV0cZd+r7uvAUyPCSYysY3/1HrIrsymvK/3Dcz6O2Wwlp7SWbQUV7C6sIr+87rSP/bqFoXIin63Pp7CmuTqr0mThULHyXigoryOvtJYdhyvZcbiS3NI6+iT4OcYue6Af67PL3Ro9AD/tPkpVfevVZeeSSpOF7JJa9hRWY/UMRr56juJ1aYneF3nULCzGCNIifDlUYiK7pJaSGrOL0XOcPUerMZltVNY3MqZrFA8OTmLhfX1YMKE387e4F0sEpcM72JDLspELtyEf2YJclkXvBH9WPjJAUX5umWvXkvoKqCkk575Ivr7Sh8ld9crnyI3R4xhfXcjAKNfbncUmYz8HHdTPNx999BHjx48/o2NkWeaNN944a3OIi4v7Q+KKffv2JTMz86zNwx3C4yMQtCC/rI7nF+9l6Z5j2GXQaVSM6x7DLX3jGP/+Wv47pgMdonxbLW09FXZLA6q0EfDjZEUkESCqh9Jn6/BGKFiHbAxGsluVZOn6ytZPZmsE+x+7kRaUm1i4vZD3MrOoNSvn6JUQyNSR7fhgQwl3de+K6q5VSql6yX6I6KQ0TrVbYeh0KvDF/9IXkXtOgB1fACrFU+UXw5Ycpbpm/QNdsRgsTN/0CssKlmGX7WhVWka3Gc2NaTcQ6X3yprGtcbjCxMaccv67eK+jgigt3IfpozNIDTGcVOXZarVTYWpdWbiqvtHRpLGg3MTs37P5cmMB3eMDuLZbNC//vN/RyiAmwMALV7Zn3ZQB9HwpE3+jttXKMYBGm4zVTbuIc01+uYnHvtnO2uxylj3YnwV7KrkquT2qu1fB7gWKtzGyCyRfgtUYSsozS5l9Yxfu+GQziUFGnhjW9qTnN1ttqFUSeg+J2CAvbvtoE3f2j6fiJGux5r4MVIfXIv0wSWljAeAfh2b4DCIDUxVRz5MgNVTDsufh8AZFA6uVRsUOGqrwi/Rzu+uvf0X+HAUFBXTr1s1pm91ux2QyUVt7etIPcXFxrFu3jrCw5qpCm83GAw88wOTJk095fK9evSgoUNqsFBYWEh4e7miPcaru7qtWrWLKlCnk5uaiUqm46aabeOaZZ/7S3mLC8BEImsgvN/HQ19vZmNscAjBb7cxdk4tdhrm39+DiV1fw06R+JIb8scRUqeYIfDoGKvOaNx5er4gjXvMJ8pWzlWqpwEQoOwgtSuNdCE5V1KHPELPZytK9x3jlF+cqqrXZZdz5v028NbYT87aX4+PpxYg+Dzr1+7FYLFzy9lpH36adT/bHMELpEF1XV8fN83ayJb8SgEZPC8+sfoaNxzY2H2+38Nm+z7DLdu5Ov5PA1lSNT0J2SZ1LZ+w9R6u5/v/W8d2EPiQEt274aDQqBrcNZfHOIrf7eyUG4qlVU1Fn5v2VWcxbl49Oo+L2vvHc/skmGm3N3oH8chO3fLSRhff14ZHBcbzx636Gd4xu9bkTg70cvZ7+KoqqGrhhznryyky8NjqdNVllvPjTPoxjOrDx20M8+eTDSJKE3W4n+eklwAESg40crlBe36zSOqL9DUiS+1ZXXjoNvgYterWKvDITk77YBsCyfcVc2y2GH3YcdTlm6e0pqEzFSJ9e7az6XZGL9OkYVHcsV0KtrYVbJQkCEhWjB5DyVkP10ZOPD0qmw4uu4U2VBGo34pJ/Z6Kjoykqcn7/btmy5Yw8PHV1dadtJLlj7drmEKaXlxe7d+/Gz8/vlMft3LmT6667jq+++opevXpRUVHBrbfeypAhQ0hMVPIV/4iX6EwRoS6BoImq+kYno6clX2zMx2SxYbHZ+WhtLhZrK1UxJ1JdCGVZzbkKpfubjZ7UYTD2cxj3NfR9AH56HCpylNL5Ic8p7v7aYxDtpnRekpAveR4psDl52WqzU1ZrprIVj4bJYqW01syR6gbeWe7+yyWvzERZnZkNOZXEB3kx8PXfSXzyZ7JKTWCuRW8uY+VtsWQ/0Zl9UweR/t+VJD75M4lP/syQmRu4ppty43+lC9Q01joZPS2Zf3A+VY2uonPlJgs5pbUUVroRzQEKyusc5dInUl1v5bd9xW73Wa1WcktryS2tZUCbIOICXROttWoVDwxuQ7ivJ2V1Fr7aqPxyvSQtlO+3FzoZPcdptMnMWZXL2J6J7C+sIjbQky/v7Mm713emU7SzavaTl6f+6Qq1MyWntJa8MsVD1TEmgLd/O4TJYqOwsp49YQEkPfULiU/+3GT0KEwalMy8dfmOv8vqzIzt5kZ9HLh/YBJ6jYQkwWstXpd12eW0CfFi39RBZE/JIOfhFLKf6sHiG+NJCAuA9bOcjZ7j2K2w9h1kz2DkAY+7v6iM61xz3jbNQb74KbfD5YzrkD3cr7t3kzjhn8JuUxLGd36j/Ntaxdw5ZMWKFfTu3fu0xubk5FBaWsqqVe61ms4Es9lMfX095eWti1625N133+XBBx+kVy9FbNXf35+5c+eyevVqrrnmGsaOHUtw8Jn/GDpThMdHIGiiwE338eOYrXZHSGhbfiUms+3k4a6KPKVseM1bTcrH6dB3EtRXK7kyN34Hhdvh91cUlejkoXDDfCSVBmnBHXDJ88jjv0XKfBH6TILcTrDtMzBXQ3hH5MHPIgcmOhKvC8pNfL2pgJ92FWHUabi1Txw9EwMJ8dZT32gjt7SO9zKz2F1YzTvjOjlCRO7YXVhNtzh/Pl2Xx3NXtKNnnC/auqNQXoK0+WMoWI9kDEbX+35yHm1H/Mu7AaWnU1yAER+9hksu6cnGSvdGD0CjvZEaS3N7h6r6Bo5VNzJ3dQ4bcioI8tJyS5942kX4ODUBtdpgb1HrKr1b8iscytDHySur46ddRSzcpigij+wYwce3due7rUd4NzMLs9VOr4RAplyWSnhTub1SzaTcXOOCjCzb696gAthTWE29xcZzozvxxtKDjvnf0T8Rf4MHzy/ay5TLUkn8i40ewKn/VaPNTnFT/tIrv+znjbEdSY/05atNBVQ3WGkX4cO9Fydhtdl5dGgKU+bvoMLUyPgP1/PLpP4kBBmY/XsOxTVmYgMNTB6UTOdYf6w2GZsss++o8+uS5lkBObsUpeTaYojpRdue9yDbzUhFO1qds1S0A7mhDDn5MjAGKZ+BsizwCoFe9ymCom93dT4o6zfodgfy6LlIy593jJd73QdpozgiB/HksLbMXplNcY2ZmAADDw+Kx1Nbh0b9J37/71kIPz+m/MA5jk8EDJ0OaSfp63cWkWWZjz76iDfffPO0xj/55JOMGjWKqVOncsUVV+Dv7++0PygoCL1ef8qQFUB2djZ2u50DBw6QkHDqqtaCggIuu8y5R5+fnx9+fn506NCB0NBQfH1d2+ycbYThIxA0EeLdejd0SQJjUwVKbIABnfYkX5ZVR2HvD7DkyeZtpcTleOMAAEbhSURBVAdg73cw9jO44TtY8h/IXdm8v3gvbP9MUT2uyId3uiNdOw+umo1st0J4R0XJ2W4FlQaLdwy6prBJfnkdV76zxikBdeIX2xiQEszrYzqw80gVt3y00dGg00MjYdCqXRoxHic2wMDGnHKCvHVEqqvR1tYglWfBVzcqRlrT9Uh5q5G730X2o/eS8PIuvHQa6httTB3ZjkOVNoI8XVs8ONYTCUOLX+G5pQ1c+8FaR/fxrBJYn1PO9T1iuPuiRIfxI0kQ6edJVon7ZOb4IKOT0ZNbWsftn2xyanux52g18zcfZvZNXRnZMRJZltFqVET5t9StUTvCO6W1ZiL89Ow56t7gahvmRWW9lTGz1lLfaHOa/3XdY3jv+i4OkcO/mtgWz+uhVuGl01BrtmK22pnw6RZ+f+RiusT6IwM5pXW8sHgvhyvqSQz24oWr0rln3hZsNhj8+ko2PTGQIWmh2GQlRBRo1LKnsJqxs9eT+fBFRPh5crBpnbMfTYe1byK16MsllR6AnV/BzYuQY/siHd1+4nQBkP1iwMPAgoM2RmaMQhPRBWQbICEd3QFvdXR/seZqrGlXownv0OQRkrB5R3DUJBMdYOCWXjEMbhuCXQa1BGFeGnJyctyf63TYs1D5THCCJ7D6qLL9mk/+EuPno48+wtfXlwEDBpxy7NNPP01hYSFLlixhzpw5XHzxxSxYsID4+GbPcWnp6RcffPPNNwQFBfH5558zdOhJmiI3kZaWxsaNGxk5snldcnJyqK+vd/Qda2x04wk8y4hQl0DQRJCXzkm0rSWDUkMw6BRD447+CXh6nOQ3g6UGlj3rut1uQ8p8CepKnI2e49Qeg7Xvwk1Nva/m3w42K/wwCVmWqdMGY/GOIavcRnGTcFxDo433lmdRVmdhzo2dmXNjZ8fpMveXUFJrJqu4locGJzu2h3pKbH3yYpbenuIyhb3PDOTStkEMDmhgUGoICaE+cHQ7rHi52ehpgbThfSRLLfumDuKarlHMW5/Ho9/soLreSoDOn1ifWJdjAPpF9sNLo+RJmc1mwrRmh9HTkk/X51Pd0PxFGBdk5K6LEp3GbHyoO/ufHcz2x/u6NPBcuveY215fB4tr+W3vMeKDjCQEezkZPQC+nh5clKy43BftPMpVnVtPxL6jfyLPLNztMHpa8vmGfGrM5/6LvDXSInzw0Svv1aKqeq7r3pyDdP/FSSzZe4x7Pt3ChE+38Mov+x25PSU1DRRXm+ke74+vwYNXx2Tg4QFxQV4kBnsRH+RFaa2Fm+YqXr0dh6u466IEgr11zB8ThmSpcjJ6HDTWw89ToOutrU+6133UYqRtuA/JTy9h2pp63t0JdkMgcpF7YwmdN0R0JvnpJcS/so/4Vw8Q/+p+kp5ZTpVJ8dRqNBrim+Yf92fFI+02xdNzotEDzdt+nnLOw147duzgiSee4P333z/puJqaGgYNGsSuXbtYtGgRWq2We+65h2nTpjF48GCOHGm9Aq81TCYTs2fP5ssvv2Tx4sWnZUQ+9NBDzJs3j/fff5/c3FxWrlzJ1VdfTVpaGtdeey2jR49m3z5XhfWzjfD4CARNxAUZmX1jV+74ZJOjcgeU1gVPDUvj8W928NLV6cQHnyJkUZ4FtlZCSf5xyDu+cKsNBCheoV4TlP9bG6CqAGnkW2AzYyxaDrXFJIZ3BFkDxFFhsjDh4gRu6RvP9qZWCb8+0B9PDxUyMg2NMt6eHnh7erD56b5UWytYX7ae0oZS0gLSWPVkB2zlNqL8NKga6+DQIjDX0j+lK7KuBsnciKTRKRVnrZGTic4vl6d7JCJ7hLEgO5xvNhfw6lVpvDHgDSYun0hBTYFjeEZQBlO6P0aYRYN8aCnaijxCglPJeSSNOl0w7Z9f4XT6VQdLaRfhS05pLfnlJrrH+XNz7zgeHhiL0VwCNQcgdx9a/zh8/OMwm3Ucq1Vudgu3F9Ia328rZEhaKNEBrq9nmK8nz17RjslfbGNrQSVrs8p49NIU3lh60BEC06pVTLk8FY1aYnOe+9wwgN+b5v9XUFrTQFWDlT1HqqkxN9Ix2o9F9/flhjlruO3jDSx5YAD55SZ+2X2MASnBTPtxr9PxMQEGHhjSBgnF0/XsyHZISGwtqCRzfzntI3zx1mkI8dVzuKLe0cV9/pYjPDMijccvSyUjPRQ2uunz1oRUsAHZ3oh82ctIS55q/qyoPZAHPYvsG83+ciufrMllcNsQrusWQ05pLctyzAzpeityeTbS8ca4AMYg5Gs/xaoPBg66PF/mgWLaR57l9c9b4xzeckFWmh7nrYH4ficZ98fZuHEjV155JbNmzaJt25NX3nl7e/PGG2+Qnp7utH3kyJEMGzYMtVqN3W5n2LBhp/38Dz74IMOGDWPgwIE8/fTTXH/99axcufKk1VmhoaGsW7eOl19+ma+//hofHx+effZZRowY4RjTt2/f057DH0UYPgJBC1LCvPnk1m6U1Fgoqm4gNtCAr6cHVbUNvHB1B4K8dXieqjJHOokjVZaRTiaNf+Kxeh+oOoL05fVKsvPx08RfhDziLRrlYL7ZUsjM3w46QlkqCZY9dBELtjZv3/J0Lw5U7WVy5mTqGpvDRD3CejC191RU2YeQFo52Ntg6jIP+D538WgHJUgfLn1PK3gc8zpXp19A+sg23/G8bH93YiVmDZ1FaX8ox0zGivaPx1foSZTUifdRL6e11nJC2GK+dx/bH+zpV4GjUKrJKarjug/WOHJXtj/fF2FCkrEtJi1+I3uFor/+aUP82HKuzojpJxY5Kkpwq1k4kNtDIW9d1orzOQn65iaRgLy5tF0Z2SS0ySpWWUaem5iSd35XnOfn6nS2OVtSzPrecR7/Z4TDOAK7qFMn/butNca2Z7OJanry8LZMHt0GnUTnNLcrfk+euaMej83dwrNrMc1e054OVOSzY2uwN0KpVvHR1Oj0TAhzHXtY+jH7JQQx943csNjsj0i/hdIIJ9pQRqBIuhrJDgAyBydg13uQ2eiHLFu4ZkIjeQ01xtZmHv95BjdnK/DFhdBryPFz0mBI+NgSCTyRWQzDJ09wn66pP9nn8o7jrU/dnxp0BJpOJGTNm8PbbbzN37lyXnJnWOG70FBQU8NZbb/HLL784Krt8fHwYMWIEc+fOPa1zvfDCC6xfv57Vq1cDMHHiRLZs2cKwYcP48ssvT1rhFRoaygsvvIBWqz3p5+9cIkJdAsEJxAV50S0+gBEdIsiI8iM20EhGbCDRAYZTGz0AAfGgaUWdtzIfOl7f+rHtR4O+KdlQa1RKdD8f62T0AEg5K2D9e0TqLby1rNnoARjQJpiC8nqn7VXWKiYun+hk9ACsL1rP//b8j+o2GS5eKmn7Z9BQg2y1KFoprRHRUTF6ZDvS8v8iVeYRYVQqe1KnLqP/CzvxqfGhX3A/zHlmohotSJ9c5Gz0gJLntOghvO2VTpv7JgVyy9yNDqMHwNtWqWghlZzgFq85ivTF9WhrD7M+u5yrO0e2Ou2ru0QR7NV66TtAdICBDtF+jOgQQdsIHxJDvBjSLoxL2oWRGOJFmK8nnlo1vRICWz1Hv+RzX6UCUNnQyINfbXMyegC+3XqEzAMldIkNYEDbUGIClTBSQrAXo7s0h/DuvTiJp77fxbFqM2nhPjRYbU5GD4DFZuehr7dTZbIS4eeJl07Dtd2ieeq7XY7n/e67JZA4oNV5ynF9kT28UPtFIAW3QUq9HCl1GFJwG9T+4SSGeNM9PpC0Ji/ZbR9vpKapsODqr4v48qDEqppQ6uIuocizDdtMgdz82d5Wn++iNq3nmv1hvELP7rgzYOnSpaxevZp169adttFznLKyMnr06EFQUBCZmZlkZ2eTnZ3Nzz//jMVioXfv3lgsrRc+AGRlZfHjjz+yePFiDIbmEPGcOXPo2LHjaVV4paSkkJWV5XbfddddR3R067IQZwNh+AgEZxudj9Jq4kTUWhg8FQISXLq4A+AXA93vhPcuVv6+8gMlxNTovtpM2vo/VKYSl+2PDk3l/37Pdvy9/9nBbC/ZTr3VfYn4gkMLqLC40T8BpB/uVRKr+z3UrC59zVrKHs0m54FtHJq0ifyQFHiiRdXTuncxys6G2uD/20/751dQCUqOU2U+7pCyM5EszTk5d12UgN0uk1/uPHfJUouU+7vbc1CZB3WlhOg19EsOJj3StWdPeqQv/ZKDWHewlPxyE9UmM9kltewvqia7pJaahtPPy4nyN/DU8LaOXJqW3Nk/AW83HelbYm6qujtwrIa8sjrs9lNL6tWaG8kpreXAsRpHNeKCrUdoTYR4zqocckub1zWvrI79RdX0TgwiI9IXtUoiwKh16DON6hTJ5+vdv0ayDN9sOUxsgCfvjOvEygMlTs/7yGaQPbyQ+z7gerDOBy59AZX/6d3YdhyucuoSr5Ig0EvLz7uKmPn7YXq9uYX/fL+LJy9Pc7v+t/WNx0d/cuP2DxHbW6neajVoLYFPpDLuLDNy5EgWL15MXFzcGR+7du1aAgICeOyxxwgIaG65ExYWxvTp06moqGDv3taNSIDExETWrFlDeLhz/0K1Ws306dNPq7rrZNx7770OTZ9zhQh1CQRnG+8wpe3EbUth3bvKjTi8A3K325HUOvj0Ghj3OXS4DjbPVQyb1BHIqcOQynOQ43sg9ZyL7J8AG2a1ng9kqXObPKmScPKOqNVqCmtaz0eot9bTaG/lRl+Rr+ReBCbBTQuR9LEc1tQxe9tbLMpehNlmJtYnlsmdJ5P24G4iXm+HVHMUudF9e4+uSUlQ6Cax+4TrGtI2hJt6xxEbaHRfTdV4ihYV9WW0D09iyLtreGdcJworG/hyk5JndGWnSNIjfflsfR7X94gF2c63247x7vIsimvM+HhquKFnLGO7xTiV0p+MeH8DCyb04evNBazPLifQS9s0f8NJm7MeqTDx2YZ8/rcuj+p6K8HeOu4dkMil7cMI93UvTllQbuLdzEN8u+UIZquduEADc2/qypEK94YtKFIDsgxFlSa2Ha7ipZ/2kVtmIsCoZea4TpTUmGloYWD4Gzw4dpIWLUcq67HJEh2jffl6s2vZc8LLu8h+9BaIvwg2fIBUV4wc0wc6j8dqjOZ0TZEjlc5Gv95DCS1+uj6fD27ownvjOzN/82FWHTjGggl9mL/lMGuzyggwKusfF3Ty9f/DqNRKyfpXN6IYPy0tzqZP7NCXlHF/I3r06EFJSQmzZs3ixhtvdHhsqqurmTlzJt7e3qSkuBY9/NsQho9AcC7wi1YeAa86urxTmQ/vdVH2v9UJxn0DI98DeyOodWA3Yw9KhqEvITdakdU6VFHdTvIcMaBSueTE1JitdIz2Y1+R4nVpaGggIySj1dNEGCPQqtzfiuSITsjYWZzvwdDUNIrMxTy04mH2lO9xjMmrzuOBzAeYMWAGEU+WIv/yKPLxcN0JPPvGKl67K671a/LwBL0fL14VQ1BTM8/j+klO6P2UcKK1lZuzbwxdXlGaqF43ez0jOoTzn+FtyS0z8d3WIzz+7U4A1maX88roDKYubL6e6nor7yzPoqC8nseGphDpf+obp8HTg0RPD+6/OJHre8biqVE55t8aJTUNTP95v1MCdkmNmak/7KG6wcodfePw1Dm/LocrTNz72RZ2HG720OWWmXh5yQF6JQayaKerUjJAepQvnloV2wqquHtec0+r8joL42av5/L2YUxqUfmXVVJLRpQfa7PL3J6vR3wAWo0KrUZHz4RAfnSj0Jzw8i52Ptkf4/B0ZKsJ2TMQtd542kYPQIcoP6e/TRYbAUalx9j8LYeJ8vckyEtHqcnKf3/cw/W9YriiYwR2u8yarDIyos5hUnnaSKVk3a2Oz0t/mY7PmRAcHMzq1at5/fXX6dq1K1arFUmS8PDwYOjQoaxZs8al4/m5onv37k7SEy25++67ef7558/ZcwvDRyA4F9RXKU0+jYHKA8Dsi5w4CClrmfL3Z6Mdw+U+DyD3mQh6fxrtdrRGFSpJQg5MhqBkpRv7ifSeCBs/xLv3/Y5NkgTvrzjE08PbUVprJnN/Ce2fX8HKJ9KJ94knp9q15HRCxwnEGtzkwkgS9J4Eah3Tf9lNv7guFJqOOhk9LZmxeQZt/JKJ7n4n2/a5l8Pf6e2FrPWB5CFIB3912S93uwPZM4Agr+YvX1+9hotTglm+v4QAg5Y7+scj632h2+1Ia2e6niP5UmSdc5v3H7Yf5dJ2YTw+f6cjXwSUUEpZrYVwHz1HT/BwLNxeyL0XJ7mcv67peKObDu5GvRajXrkxmxtt1Dfa8DNoXcYBlNc1tlp1NmtFFsMzwl3ab3ioJF66Mp36RisPfrWNvHJlzj/tKmLS4GQCjVqXhqKSBA8NaUOjTealn9yXCi/eVcTEQckMbhvC0r3FfL3pMC9elc6HN3RAh+INNONB22d/I8Co5aI2zXlLfZOCCPLSUlprIdRHx+8P90fTaAK1hrpGmUa9P/WNPnh5aGi02fFoRTCwztyIVqPGQ63CZLFitcnEBhpoG+5NqLeeYB8d04a1QUcj2c/0w4wHh6utvPLLPr7ZfBirXea3A0roV6dRsfC+vi5r39j0mnhopNY9qWdC2khFgT1vjZLI7BWqhLfOs6fnuuuuY/To0W73JSUl8e67757zOWzdurVVMcLc3Nxz/vwnQxg+AsHZpDxb+RLcu1BpMtr1VmSfSDi2C3YvhKEvIq95G2nHl0oysc4Huec90OF6smo9+HL5Xg4V19ItLoDhGeFEe4chjZwJq2bAwSWKMJtXCPSZrJS6b/8Mut9Bapg3Bq2aSYOSWZtdxtPf7yYpxIuP+8Qzd3UOAaoA3h74Nq9uepWVR1Zil+0E6gO5t+O9dAnpAnkrIKYX5Df14PGPhwFTkHbNB/84CsrrMRqNbMrZ1Oql59fkY7absenDCInX8fwV7Xlj2QFKay2oJBjcNpT/DE/jsGwn+vJXkVe+hrTzS7CalXXocTd0Go/KyzkZNcLfwLQr2pFfVo9eq+LLjQXc+k05c6+4E1njibRhlpL8rdEhZ4yFvg+SXeYauttTWE1MoIHdhc6hs015FXSL92fhdlevRX55HSlhihGl5MXUMH+LEtq5unMUKWHexAY6l8MXlJkorTPz+YZ8imvM9E0OYlBKCPHBztoxJ1MKN1mUjuctySmt5bd9xfx+sJRgLx1vjO2Mh1pi+NtKZc1zP+zh0zt68J/vd7MhR0kwjQ7w5OlhaUQFeFLXYCO3rPXn/GlXEVNHtCPUR8+dnX2I8a6HvM2KYjig7zCWnEfaccQSRFSLa4kLMvL5HT0JM4CXpQT2zEfa8x2yhwGvLjeBXxwvfV9MZFIQA1NDUEvgqdUQ0qSSnVtax2/7i1l3oIQnR7TjSFU9n2/Ip85sY+qINGZc2xFPcwUxXjbI+81pPkkh7fjPwHBu7BXP/9bl8cvuIlLDvJl2RXvCfZqNnvJaMyW1Zr7YWEBOaR3tI3wY1i4Y22nkU50Slfqclaz/UXQ6HTpd64KsfwUnKkL/nRCGj0BwtijPVhqQlrXog5U8BJa/iJTXFIra/yMMeU65ydutoDViN4SyPLeeO/+30pEkmrm/hHeWH2LnM0OQd3+P5BejuNU1nuDTlFRosyrNGiWJ/7uxK0XVDdwwZ4NDSC9zfwlzV+cya3xnjplk/NSBPNH9KSZZa7DYLRg0BsL1IeiWPAH7foAuN0OvexXjqq4UVr+pCM5JElq1CsluJ9jQeoWSVqVFI3nw5JIC7h2YxJjOkfROCqTOYsNTo8LX04Nas5WDxbXUSvWkDnwKet+n5DhpjdgNYagNronIoHgtdhVW8WILj0X8yyVkPXUPqoxrlHN4GJD1vmw+cJgxX7s2IQ300rotPQ/x1rEmy71ara+n4nHJK6tjyvydTqGfX3Yfo1dCIC9dne4wfg6X17F4V5HTPDP3l/B+Zjaf3tGDNqHeLudujZYVhAeP1TBu9npKaptzt77efJjHhqbyw/19GPH2ajbmVmDwUDHjmg7UmBWPiZdOQ1yQkbyyOo5W1aPTqBzaOyeSEuqFxWbnpl5xxGhL4YdJSvXgcfb9iBzbl8grZgLNr1NBuQlZlvFqLIMvxiKVKM1vJYDd3yJ3HMd/Rj5C/Ct7mbUii89u78kXG7K5rW8ClfWNXPd/6ympMZP58EW8tyKLLzYquVgf3tiVPUdruPezLRx6OBV+mNzqfPq/s513xnXm0UtT8FCrnJSyaxssbMytYMJnW7A1fcAy95eweFsBr18ejsVq468J7gj+LoiqLoHgbFBfCevfdzZ6gtuBqazZ6AHlBr34IaRZfZS2D0HJHDVrmPjFVpeKHJPFpvTLybgGNnwAub+Dp6+iCPteb5h7KXLhVpAk/LRKmfGJ6sE2u8yDX25HkiS6vLKCp7/N5+MVDbRFR9zHV6J7JQ7aXKoYOitfhS/HKwmbix6E4j3I3e7AZozksvQwVNV5dAnpgkcr+UDD4ofho/XhuZGpFFU1oNNpSAj2Ij3SFw+NijeWHWTom79z17zNPL+6ihVHJK6ZX0b8W4UkvHaQrOrWgw8ms52XfnYN0yQ+v47bfqzix5JgHlpuYsovRW6NHq1aRWyg0UmY8vj2jjF+rMt2LcEN8dYR3NTGZGNOudt8l7XZZWzMaT7WZHE/z5JaMy8u3svRFs1XQ3x0rbZJ6RTth79BWeejlSZeWLzXyeg5zsu/7EOrVjNhQCIjOoTjZ9AS6W8gNcyH9pG+jqaom3IrWLC1kOEZ4S7nABjVMQJ/o5bJX24jLkAH+eucjYwmpLxVkLfaaVud2Uqijwyb5jqMHqdjtn0G1YVsfaw3pbUW/rt4L9d2j6HUZObFn/dRUmOmV0IAJTUWh9EDEBtk5JGvt7N36mAo2ND6fHJXse/ZITz89XYkCZf2IMU1Fh7+ervD6DmO2Wqjoq4Rm12m0XYWPD+CfwzC8BEIzgamUtjxpVKyfvMimLAOLp+uhI8mrIPU4S6HSJs/wl55hMPl9a32zbrovV3IhgDkK96BjLEw93LIaaqKaqxH2jwX6dMxGM3HHF24T6TGbKWoqoG2IUYGtQ2lqKoeGqoVD5XVDPt/goFPK26VFsjJl0LqMNo9v5wpF0VAZQE+GiOvXvQqGpWzszg1IJU7Mu4gsLACj+o8ftrSXOmTV1bHTR9u4NP1+ZitdmQZ1mSVcfe8zdx3cRJeOg2yDKtb8boA/H6oBPkEw3DVfR3Ifqw9cy4zMNwnm9f6Skwf6MPOJ/s7jdOqVbx/QxeW7jnmsv3d6ztj1KqJ8HX+ze+j1/D+DV2I9NFyuKKOz1vckE/k840FHK5Q1n7VoVKXeR4n80AJtZZmj1O0vyfv39DFpQw7wlfPy6MzCPP1pL6+kTqLnRUHXGULQCktX3WohEFtQ3jokjb4eLrmExVWKqGjxTuPcnl6OO0inL1qySFejOoUybj/W8+Ow1VoTEVIWz5u9XqlLR9jL89z/F3d0IjKVIy044tWj2HbPHy8lPDYyoMlNNpk7HZYvk+RQZhxTQcnzaCpI1I4VtNAjdmKpu7YKeejqT1CncVGgZvKtmPVDU55XS2x2mVkcDGKBP9uRKhLIDgbyIDNBhPWwu8zlIajctOvyMBEGPMxaL1hx+fNx1gbwG5zEZw7zt5nBqLVapVC2fgBsOgBJHetMMoOQeFW9j87gpRnlro9l8Vm5/kr2+JrNGC12UFucSPd8rHiVRr3ldKXy2JCThkKxlC25EgMbR+GTucB1WaCXm1Dh0cOMn/EfDYf20yJqYQuoV0I9won2uYF89pDv4d46vLJ2Gw21Go1m/Mq3OaWmK125q3PZ1SnSOaty3Pbq+s4DSd4slbc055IQyMsegTpUHOStBzRGa+rZrPrqYuYvbaQMF8d3eICMGgl4oMSGZ4RzsbcCkJ9dPSID8RLJ7GvqJZP7+jBoeJadh6pIrHJSxXupeNItZIgbmklPKRch43jqSInzrMlsgz2FjdYlUpFeoQv39/Xhx2Hq8guqaV9pC9tQpW8oUqTmT2FNdSZba3q8wDUN9rYX1RDoNF9ErXVZsfS9Jj8xTamXJaKt17DnqPVBBh1DEwN4Z55m5sNNpsdbK7epeYTmpvf29BcyW092TENSJLsWAebLCvrISuNVCVJoq7JKJx9Y1eOVtU3r7lsPcW5zRyfvNnN+jfahFEjcEYYPgLB2UDnhTz2M6Rtn8O2ec77yrJg3lVw049Oho+cNgq7TwSxshm1SnL86tzyaC/85UrI/U3JvdF5Q/e7WhfsA9i/GE3alW53eaglov0NjPu/dbw3vgtPf7+b8Q+nKHL/pqbwzY6vYOfXENxWUYzuOI74l3cjScqNqO8bG9h9fwKoNAS+kkwgkPBECahUUFcHH3SFIc8BIB1aitTpBlSePthsdn49wdPSkrVZZfxnRBoAfZJaVz/ukxTEK78ccPwd7aeBXx53MnoApMIt8M3NGMf8j4woX/olBqJtISAYF2Sk7wlKyqG+SjgoPsiLIWlhju1HKkzc++lWDFo1A1KC2XnEvcjjZe3DCfVSjI4+yUHwi2u4ByAjyhfPE8QMNRoV8UFK088TKam1cMtHG7lvYBIZUb5OJewt6ZcUzN3zNtM23Ic4N/sjfHUMbRfGjsNV1JitPPndLnw8NcQHGqlpsNIvOdDRVR3A7hWO1HYk0pEtbs4GctuR2L0iHOECo06NrPWFNpcqSfvuaHeVw5BpH+mDXqPCZLfRIcqXxy9vy8Pf7OCKjhEcq26g0WbnP9/vZumD/fFQS9iNEU3z2dzKfEZg9wpDo9pHQrDrOkb4eeKhltwaQGpJCXucrLWJ4N+HCHUJBGcD7zDwCYONs93vry1WPDMxTUqufrGQOgyNRkOQl45Jg5Sy6Vs8wZ8q+OZmpC/HIW3/HGnDB0qiqM4H7j1EySMHyHtgO7kPbKduSgFID4JnACqbmZzH2pPzSCo5D6dwaOogQGlFYNCqmDy4DR+sUGTibYYQ5EtfcJ6jLCt5PUmDkZtabsgyfLf1CJe1D0P2MCL3mdw8/oVgeD4QZsTAxU82X7veD1RaTGYrpkabI1fFHT56DSaLjeEZ4fidJNk30KjjwLQhrHiiPZmPt+OoJCON/MD94KKd0FBB2a7tHK5qIK+sTgnvoYR9sktqySmtxWo9eY+tsjoLe45WsymvgowoP6L8mwUF900dRPaj7ch5OIUJGSp0TaXt/gYtl6e75tF4qCWeHp5GzBmI6S3acRSz1c5n6/O57+IkPNSuN+fL0sPwM3jwwJBkPLWuX+dHa45yuO4wl3f2YP+zgx3bq+utbD9chdlqx0Otdpx739RBqOqOQtsRMOw1CE51PqFvNLS7Ek1TxZC96jBpulIkiwX6Pai89icgR3SC0PY88+N+PNQS/xmexi+7jxHkpeM/I9LQa1QcKq4lyt/AtJHtWH2olOdHtQdZZuKgZOySpMzn3g1w5wpIadFI0zcK2l9N0jPLueuiBAxa1zJyX72G+wYqn6+O0X48MyKNV8dkMKZLNL4GpV+UVvPvuhV+9NFHjB8//oyOkWWZN95444yfKzMz021j0WeeeYZp06ad8fn+CoTHRyA4W1gboMH9r3JA6SuVOBA5qit0uRkpUJFlN+o03NAzjnYRvgyIN8DvryEd3e58rKWeyrvXUtJYznsb32P1kdUYPAxcmXQlVz54K1FWL6T8dbDmTSjYAF4hqLvfRc6jwzhMMBabTKiPjpxSJeSUNO13sh/tBTctghUvIRXvAf9Y5L4PIYems/yolotTQli+v5ic0jpGZITTY+Y+Ntx7A3JER6TfX1cUqUPbKW029i2Cw0qpu9z9Diz6QB6bv4MteeW8Oa4zn21wnyMzumsUkX56Bg1NPalKsuRRzs8FW/hw14cU1haS5JfEvR3vJfbB3US93s71gJqjDBrYk63H6rj/860se7AfW/MreG3JATbnVTiUlYe2C2v1eYuqmnV9nvpuJ9OvziBzfwn/6WtEOrwaafl/lf5ifjFKe4bonsQExDHlslT6JgXy0ZpcymotdIn1Z+KgZEK83Yei3GG32zlwTBGgPFrVwMdrc5l9Y1c+W5/P5rwKAoxabu8XT9twH0a9s5peCYF0iPKjrsGCUa+l2FRMUV0R72x7h63FW/HX+XNd6nWsenIw1QXV3PrdUYZnhHN9zxjqLTaGp4fz2iUBynVlvgDH9ijX1ft+pMYGWPUacruroOutSIGJ2GqOoqo6ovRmy1+ndEi/5jPk25bAmreRDv4CHkbkTuOh/dWUyz5clu7FvRcn0dBoY1hGOCE+erx0avYfq+WJy9vyfyuzeXJ4W4xaDW8uO8iLZiu7HmgL+xYgrZoBFXkQkgoDnoDeE5H3LYSut9FgiODd6w2kR/oS4eeqdh3so+eqjlFckhZG5v5iPlydQ2mNhSFt/Lg40hv1X9VF9ixRUFBAt27OwqZ2ux2TyeRoOnoq4uLiWLduHWFhzR5Om83GAw88wOTJk095/J49e1iyZAkAhw4dorCw0GE0hYaGct1112Gz2U7ZhLSkpIS4uDjq6k6hxH6WkWS5tVS8C5Pq6mp8fX2pqqrCx8d9aa1A4A659CDSnCFQX+F+/3VfQGh77Ho/1Hpv92PKcpDMVYoR5emvCBd+eT3cn80BqYzxP4136bmVGpDKaxe9RswbHV1aWMgpw+DSF2nwjiKvrI6Zvx1yUtnd/fQAPC1lSDYzssqDRmM4Kc8sRa2SmDmuE/9dtJeusf4MzwjnSGUDm/MqeOWqNLR1hVCyDylvNWz/XKkKA+T0a+DiJ2n3xj5Hwvbb13XiYHENby075DS3HvEBvDI6g5gTdHBOpKimiHn75vHxHtcE1+d6P8clUYMwTI9y2i7fmcm0OaW0v7wt/ZMDOFhs4oYPN7gksQ5uG8J/RrRz8sTkltZRVd+I1WZH76Hm++2FfLAyG5UEB57uj3r/90jf3+syF7n3JOTud6Hyi3Scxy7LGLVqQltpP3Ey3ll+iFdahM2MWjWjOkWSGuaDWgVeeg0TP9/m2G/Qqvn2nt6khvuwsWgjdyy5A5vs/H7oH9WfKd0ex9LgS53ZyqPfbOOT23oQoLGh3r8Q6fsJrtfVZzJ0uQW73h+1QRGkk3NWIf1vpOv7reP1MOgZMFcBKmzeESQ9s9yx/6Wr0hmeHoZXUxJ2o9XOjzsKeeCr7Sy8rw9PfLuTXU06S9lTOiBt/D+k1TNc5zTiLexpo8irVaNRSSd9D5nMVjbklvPp+jx+3dPcUy7SW82zF4fQqV0ygb7uP4//FLZs2cL48ePZs0cRF/3oo49YunQp8+bNczs+ODiYtWvXkpTULNBptVrx8PDgdEyCHTt2sHDhQsdzL126lEcffRSAiIgIbr31Vp566ik0Gg1Tp05t9TxLly5lyJAhHDx40GkuJ6OhoYGcnBzi4+NdVKZP9/4tPD4CwVnC7hWBquc9SMtfcN3pEwFBbZD8omlV09VmQ/KJgv92VP7W6KHnvTBhHcU6K+9tfM9to9F95fs4WHGAmEcL4aWmbtBPlIBajWS1IpfuxjMglpgAA3dflMiMMemoVCoaGhqQJAnZJxKb3c6aNWvo0yeKQ89fSl1dHeM+3sH1PWJoF+HLnf/bxPqHe3N9d8XAWLkzn/7pqcieAU03PxW0uwKMIXyzrNypSu3+z7cy45oOfH9vH37ZXUSd2col7cKUPlan0Q7CZDMxb6/7L/DXNr9Gx5COTrktcmRn0Pvj09PI/C2H6Rrnz3OL9rit3Fm6t5j7BiYRE2DA1GBl37EaanJ30KeP0o2+rq6OGH8Db47tyKQvtqE2HUP69Wm3c5HWzYRO1zv+Pl5K/kcZ2i6Mmb8dckgU1FlsfNrUOPTVMRlORhEo8gdvLTvIE1eEMn3DdBejB2Dl4ZXcmXEXITY1k747xMND0zBZZIItx5B+fcr9da19G7njOIbP3s7747sSralEWvKE2z5x0rZPkbvcQpEmil5vbgGUOa66r4PDu1BS0eAwfI5U1vPcor10iPJFDdw/MJk9R6t5c9lBpRHt2rfdz2npM6hi+5AQfOqbZWmtmUab3cnoOY6MUu7ubbT/4XCXzW5jS/EWSkwlBBuC6RzSGfVfrNy8YsUKevc+vYaoOTk5lJaWsmrVqtM2Nk4kIyODjIwMGhsbufTSS5EkiZSUFMaMGXPa56ivr+fRRx9l2LBhTJw4kR9//LHVFhZnG2H4CARnCbXeiJx+LbKpHGnTHLA1Ke+Gtke+ejY2ryg2ZZcR7qt3CN7JtbUctpdzqDKLjUUbCTWG0nfyFrzUBkJfS4VVr4EhEFO7YawuXN3qc/+at5RBURdT9UgO5dYqNmQvILc6l/SgdNoFtiOisRGdqYR2HtWw4ReI7InBOwhKD0DO76h8wunfdggc3Qp7vsenzaX8OD6KfJM/aurZOzkVyrfDuiWg86Z/6jBkVJQZ2xM8tIdjHker6pm601XH5oGvtmPQqnj2ivaM7hx1Shd4S/Jr8t3exAEqzZVUW5qVmOW4vjDiLarV/gQYK1iTVUa9xcbeozVujwdYfaiUjtH+HK1uoKNPNVJKoLLu5hp82lzC9SnR5Ndo6Brrr3jzTK6aP4BiCJRnQ/DZafIY5K3hk9u688CX2zjcVKZt1KqZNDiZA8dqHZ3UW7LiQAkPWH3YX+E+wRpgXeFabk27laeHabn1o418O6E3WE9xXWWH+PHmnqjM+WCqVqr/WiN7OUG9lc7st3jC0/e3QzIdg8wPATthqcOxlwehCoijst7CiIwwbu4dz44jVWwvqCQh2ItVD10ElWsVkU931FdAfSvzPYEKUyO7jrhpdNuExWrD/gcDH0vzlvLShpc4ZmpO4A81hDKl+xQGxw4+yZFnD1mW+eijj3jzzTdPa/yTTz7JqFGjmDp1KldccYWLwnJQUBB6vV7REDsJ+/fv57777qNNmzbMmjWLq666imXLlnHrrbfSqVOnkx5bWFjIuHHj6N27N2+99ZajxcacOXP+EsVnYfgIBGcRKSAOudf90PUWMFUoTTdVHkjLpqHuM5kuYRnc9OkOpl+dQZRURYG6kfuW3efUQ2vG5hm80v8VOj60n+DXUmDNG9BuGJ4aT7ceHwCDhwEbcLAmmwnLJjiNC/IMYvaQ2STqIpC2fQKmSkgNhs+vae4BlnyJksjqG6V4mRrrkVUaYkL1UFMJWi8k73BF3VlrhBmdYNAUAtOuBJo9GxJg0Gqoc6NLZLLY2VFQxZgu0We0pnr1yXV1NSoN8i0/g84bWetDlTaU5xbv5dstii6MRi2hkmi1JNyrKTE5Xl2KtGs+0rIWCZlr30aO60fMFTN5eXQGNB5yf5LjeJy9TuC+nnq6xen5323dqaq3YrXZCfLS8du+Y3ywMtvtMQatBrWkRi2pWzUWjR7K67XzSBV1Fht6lUrRnzoZWiOqI6uRvrkZRn+ktGlw4/EBlCrEJv5zfzv4/RWkrS08dqtmQMZY5AGP4+8ZzLgecVzzwTpKappL1uePCSMy4BThQfXptTvVqCU83SQ9H+ePZvgszVvKg5kPIuP8xio2FfNg5oO8PuD1v8T4+eijj/D19WXAgAGnHPv0009TWFjIkiVLmDNnDhdffDELFiwgPj7eMaa0tHU9reM89NBDLF26lGeeeYarrroKgE2bNjF79mymTZvG3Llz3R5XUFDAZ599xsyZM7n//vsd4bEvvviCadOm0a1bNyZOnMjYsWMJCQk5jav/Y/y7UtkFgvOMXJ6L9MVYeKcHzB0KH1wEs3rD/p+QvroBjamQNVllRHhJlGhVvLX1LZfGoTbZxqO/P0qtrR4kFVz0GCG6QEYmtN7teWTiSI7WHuWBzAdcjKPS+lKeXvM0R6xlsH8x9LgdfnteMXpUGrjtV2g7EpZNU8ruf30KGmuU0vytn4AkIW36P6Udx6ejleqt+1YjqbRIJ/zqDvLSMa5HTKvzvKZrVKv7WiPMGIaXh2uZMkCSXxLeHt7Ev1dO/Bt5bK3xprqhkV93N/8CN3ioGZjq/ktUJUHvxCCsVjtSQ7mz0dOElPs77JxPjI8K9L6te3T0vuDjptnrnyQ+yItgLy2Pf7uTG+asJ/2EjuUtGd01CqPWyEVRF7ndLyHRM7wnFfVWtuVXAhCtNynleyFt3Z9U5wM+kUjzb1WMnexMaDPU/VhJgoQBvPDCb+x9ZiAU73Y2eo4P2/EFFO0g1EvNo99sdzJ6AK7+ugi8Q91WiQFK417d6XVe9zdoaRvmTWs5zEadBs0ZJjjb7DZe2vCSi9EDOLZN3zAdW2vG4Vlix44dPPHEE7z//vsnHVdTU8OgQYPYtWsXixYtQqvVcs899zBt2jQGDx7MkSNHTnr8iUyfPp3t27c7jB4AvV7P/fffz48//khgoKs0hd1uZ+LEiRw+fJjMzEweffRRZFmmuLgYSZJ45plnWLx4MXv37j1pXtDZ4F9p+NTX13PnnXcSGxtLVFQUjzzyCPaz0YxOIDgV5mqlnNodtcVQV8LHl3mhqj5CbWMdy/KWuR1qtVvZWrIVphRBzu8Ypr/ONSnX0Ma/jcvY8W3HE6QPpNBUSKW50u35dpXuos5ap4QobFbY+72y49pPYd17sPA+KNoB1YWw6xuYMwQS+iNtnYf03d3gFQYVOcrj99cUAyjlUkWosQUatYqx3aNpG+6aLHpX/4STVm61RoAugJf6vYRGcnZQe3l4Ma33NEJ0ilFzU+9YIvz0hPnqeXNsR0eJ9u8Hj/H4ZW0J83H1HE0d2Q5PnRqNRgVbP211DtLmD1HXHMVijEAe9Z5iDLRE7YF81WxsngFnfH2nQ6S/gdeu6UClqRGNSuKmXrEuY9qGezO2WzShhlAmd5lMqCHUZcyU7lPw9PCkqNpMQoji+ZHMlUi/PA4j33a9LpUG+arZUJbdHLrd8SV0vc2tkSdf+hKyhxf9ru2EzlIBG1qRdwCkDe+jbShjeyv6RDbPAOSrZ7t6dnTeyKNmIQUmtHruloR46/D19ODxy10NO7VKIshLh6aVjvGtsaV4i1N460RkZIpMRWwpdq+FdDbYuHEjl19+ObNmzaJt21aM1ia8vb154403WLBgAUZjs4d25MiRHDhwgMjISFQqFcOGDTvJWRQ+/vhj2rVrR3JyMvHx8URHRxMeHk5YWBghISGOqq4TUalULFiwgLfffpvERKWitaqqioSE5texTZs2vPfee+e8e/y/MtT10EMPYbfbycrKoq6ujsGDBzNz5kwmTpx4vqcm+LdjbTj5fnMNbdqkA3VY7VascutaMuUN5YpA4J7vAIiWJzNjwAz2le9lSe6vGLVGRiWOItgziCjZh32W1vM6ABqOq9/arcpD5wPGQNj9rZvrMMOSJ5VWFp+Ohl73g1co1DZ92ZdnKyXsfR5xOTTc15O5t3Rn95Eqvtt6BB9PD67tFk1MgAE/w+mXdB/HR+9DWkAaX4/4mp9yfiKnOof0oHQGRA8gSBvEf37cz/x7ehHp50lYU/VUj4QAFk3sxw/bj5B5oJyh7cL5/M4erM8uZ+XBEkK89YzuEoWfwYMoPwN2mxXJdBIXf30FIPN2Zg6T+rdBc8dvihfvyCbkwGRIH41dH4TGeG4MH4C0MB8W3teHX/ceY2z3aEZ0iODrzYepM1u5PD2ctHAfR+5YvG88/3fJHLYUb2b1kdUEeQZzRdJI/LR+RHhFsCeviL5JQczKzFbeC3lrYPXbcOvPcGgpHNkMAUnQ/kow18HhDc0TsdTCwvth6EtQVQAFG5B9IiHjWmS9HwnTd/DjfX3AZkJqpcLRsaYnUYhOem4dWU90Q3XX77DzG6Syg8gRnSF1GFbvKE4v0KUY42kRPvgbtHSK8ePH7UcprmlgSEoAwXoTOo8zT0QuMblvIfJHx50JJpOJGTNm8PbbbzN37lwuu+yy0zouPT0dUMJNb731Fr/88ouj/N3Hx4cRI0a0GqJqydixYxkxYgQajQaNRsOWLVuYPHkymzZtchr31FPuk+X/DvzrDJ/a2lo+/vhj8vPz0Wg0+Pr68sQTTzBt2jRh+AjOPZ7+Sg6MxY0uhaQCv2h6vb6F7Ckd0KtsxHjHkF+T7/ZUnUM6Q2Vl84YZMcQAMY8XMzhqkKI2W14OryuVGbGTNrk9D4C3hze+x0MDKhUEJEBkF8jKbP1aDm8CY5PK8Z4FEN0d9v7QvH/PAkgd4fbQMB89YT56BqaGnFEic6tTKdOwcO5+nnpqAjbZhgoVY2dv4NJ2dtIjfYkJMDoaigIYdR60CfXgoUtSsdnsqNUqfI1K2Gh05yjFw9MClVqD3OZSh5F5InJsX2SNnmu6+fPYwoMs2HaEg89NQIUdWVKz52gtKX6erVfsnQU0GhXxwV7cGezluKaucQGO1iAnEucbS5xvLCPjR6LROH/Vh/vpyS8z8fo1HUBTD0FtFC/g3u+hw3UQ3ROq8uGDAYrieFR355NXH4GvblDCfgkXQ+/7ySquY/BbuwAYPnM1WVP7IycMQDq6ze31yAkXI3sG4q3TuO2lJUlwyKRHq4kmftDT2K1WVE3XcbpGz3G0GjWxQUZig4x0jvYHZCwWCzk5Oac81h3BhuBTDzqDcWfC0qVLWb16NevWrSMuLu6Mji0rK6NHjx5MmjSJzMxMAgIUQ72oqIgZM2bQu3dvdu/ejVbb+g8UnU6HTtf8WTvxvfVP4F8X6tq8eTPx8fFOMcbu3buza9euUyq1CgR/FrshELnfQ273yZ1uQPYwolWrqFX5EO0ZwYNdH3Q7tktIF4I9gyHra9edL4agOvgLfD8B3mkuRzVqDIxIcG+I3JVxF4FaP+WPjR8q7SVsjaBx3x0cUO48UtNXhIena4WNWtu8v9VTnB1xuGh/A7tC/Ul88mfaPPUrSU/9wqa8Cv67eB8xAQYno+dE1CeEMU40epqfpAf4x7tuV3vAxU9QI3kz/M1MHrwkmV8m92dNdgXbCuvIr6gnPcr3pDeLs03La3Jn9LTE3Y3J19MDg07Dd9uO0OgdjTykRW7T9s/hl8dh3XuKh8U7DLzDkGN6uZ68ZD9yXD+snkEM/j9nj+PvefVKeb+nmyodvR90vgmTypOJg5PdzvvKjpHoNCrCmhrIqs7SDValkv502XTnkM6EGkKRWkmNlpAIM4QpP17OMiNHjmTx4sVnbPQArF27loCAAB577DGH0QMQFhbG9OnTqaioYO/evWdxts1MmjSJsLAwp0dKSgomk8ll+/H5nCv+dYZPYWEhoaHOse2QkBCsVivV1a4ljWazmerqaqeHQPBHURsCoP3VyCNngl9Tkq9XCPLgZ6HfQzz31RG+uqsnVpUee305qX4pzBw0k2Q/5cvfy8OLG9Nu5Pm+zxFdLUG8+yRVlr8AHcc5bYr4eQ0TOk7g/k73E6BXvtSivKN4vs/zDIoZhPf0pryQjbNBo4WO10PCgNYvJnGw0mYDoO0VkOtcTi93ugEpwDXX5FwQ5K3j7es6c/dFCY4qrLbh3nx2ew86x56d8lcpMBF5/DfIGWMdVU5yTC/kW37Cbgxn0NsbmDi4LTqNhjah3vRvE0znGH+3fbb+7kQHGGkT6sX9A5N59Ns9yMHtkK+fD6HtlQE6b+Qe98BVs5EC4imSQuDKWcg972uu2gpthzzua+SwDsxYluV4XVLDvPn09h50jfXH5hOHfPNi5JTLFSNZUiG3uRT5lsXY/OLx1mu5NC2M18Z0ILqpiivQqOXhS9rwwJBk9B5qPD3+fh4FtUrNlO5TAFyMn+N/P9b9sb9cz+dU9OjRg5KSEmbNmoXJ1Nw4uLq6mhdeeAFvb29SUs6OHMOJvPnmmxQVFTk9jh07ht1ud9leVFTEY489dk7mAf/CUJfNZnNRnrTZlMx6d78+X3zxRZ599tm/ZG6CCwPJPw7845Bj+yheEpUam1c4edV2br8mmEiHaF8okUC4LpiEi+NptFtRSyoCtQEYzDXI+npQe8GAx5EyX3R+kroiZL8YaH810q75yrbdtxO1G25+oohLYy/BJtvRqNTEeEbAf4OcDpeL90GXWxQtlAFPKG0KWmIIhMFT4fOxioFTuEVJ3D5+fExviO1zNpftlIT56nnokjbc1CsOq13GoFUT6HUSj9UfQApMwn7p89D/EZDtyB56GrQhlDXY+fH+voR461H9w1octEaEnwE/Ty2PDG1DbqOdyJh+aK+dp3gCJRV2YxBqTz8Awv0NNDZGo+r/GKput4DdDmoPpIB4JOCBIXZu6BmL1S7jqVUT1PJ1CU3Dfvmrjia2socnKt9Ix80nJtBATKCBLrH+WO12VJJEoJcWX8+/zoP2RxgcO5jXB7zuVsfnse6P/WU6PmdCcHAwq1ev5vXXX6dr165YrVYkScLDw4OhQ4eyZs0aFzXkfyP/upYVixcvZsqUKezYscOxraCggJSUFGpra11cnGazGbO5OcGuurqa6Oho0bJC8LfBXp6HVFMIm+Yg1ZUiJ1wEbUfQ6B2DR22h4pXZPBepsR457QqI64dJH8qTPxzg5ava4VGdD3u/R8r5HdkrBLreiuwdwRcHZH7aVcTHV4Y1nf9DpLoSSLgI2lyGvOd7iOkJfnFwdAvSts9AkpA7jIPwDKSA06uqEQj+jpys9cGZ8HdQbj4Rs9lMY2MjXl7nzxt5PHH6bM/hbLSs+NcZPkVFRcTGxlJUVORQgPziiy+YNWsWmZmZpzxe9OoS/F2xN1SDtQFJH4DUlO9gtdmx2OzobXVgNaPybtarqW2wUm+x4m9QxqpM5aDVo9I3v69LaxrQaFT4eWqbzm8GjQGsdaBxHmuvK1POY3TV6BAI/mmcLcNH8NdyNgyff12OT1hYGEOHDuWJJ57AarVSWlrKCy+8cFodZwWCvzMqvQ8qrxCH0QNKqa5Bq0Hl6etk9IDSxDLYR+8oO1X5hDgZMgBB3nr8mkIKyvmDUemNqLxcx6qMgcLoEQgE/3j+dYYPwJw5cygsLCQ8PJyuXbty5513MmrUqPM9LYFAIBAIBOeZf11yMyhN1r7//vvzPQ2BQCAQ/M35l2V7/Os5G6/Xv9LjIxAIBALByTiuf2SxWM7zTARnwvEyfA+PM5WxbOZf6fERCAQCgeBkaDQaDAYDJSUleHh4/GlRQ8G5RZZlTCYTxcXF+Pn5nVK482QIw0cgEAgEFxySJBEeHk5OTg55eXnnezqC08TPz4+wsLA/dQ5h+AgEAoHggkSr1ZKcnCzCXf8QPDw8/pSn5zjC8BEIBALBBYtKpRI6PhcYIqgpEAgEAoHggkEYPgKBQCAQCC4YhOEjEAgEAoHggkHk+JzAcXGk6urqU4wUCAQCgUDwd+H4fftUIofC8DmBmpoaAKKjo8/zTAQCgUAgEJwpNTU1+Pr6trr/X9ed/c9it9spLCzE29sbSZLO93T+tlRXVxMdHU1BQYHoYn8WEet69hFrem4Q63puEOv6x5FlmZqaGiIiIk4qSCk8PiegUqmIioo639P4x+Dj4yM+nOcAsa5nH7Gm5waxrucGsa5/jJN5eo4jkpsFAoFAIBBcMAjDRyAQCAQCwQWDMHwEfwidTsczzzyDTqc731P5VyHW9ewj1vTcINb13CDW9dwjkpsFAoFAIBBcMAiPj0AgEAgEggsGYfgIBAKBQCC4YBCGj0AgEAgEggsGYfgInIiLi0OSJLcPq9XqGLdu3TrS0tIoKipyOr6srIwxY8YQExNDbGwsr7766l99Cf9I6uvrufPOO4mNjSUqKopHHnkEu91+vqf1j0SWZT755BN69uzptH3r1q307NmT2NhY0tLSWLJkyXma4T+P3377jT59+pCUlERiYiJvv/22Y59Y1z/Oyy+/TEpKCjExMaSnp7Nw4ULHPrGu5xBZIGhBbGysvH37drm+vt7pAciNjY3yoUOH5EsvvVROSEiQAfno0aNOx1922WXy1KlTZbvdLh85ckSOjY2VFy5ceJ6u5p/DPffcI992221yY2OjXFlZKXft2lV+8803z/e0/nH89NNPcvv27eWEhAQ5JSXFsb26ulqOjIyUf/31V1mWZXnFihWyr6+vy/tX4J7bb79d3rNnjyzLspyVlSVHRETIP/30k1jXP0lmZqZssVhkWVbWTq/Xy6WlpWJdzzGiqkvgRFxcHD///DOpqalO2yVJorGxkZ07d7JhwwZuvPFGDAYDR48eJSwsDIADBw7Qp08fjh49ikajiILPmDGDlStXsmDBgr/8Wv4p1NbWEhoaSn5+PoGBgQAsWLCAadOmsXXr1vM8u38W33zzDTqdDm9vb+6++2727dsHwAcffMBPP/3k9D684oorGDhwIJMmTTpf0/3H8uCDD6LRaEhKShLrehYJDAxk9erVrFy5UqzrOUSEugRnRKdOnbjrrrvw9PR02bd27Vp69OjhMHoAunfvzrZt2/7CGf7z2Lx5M/Hx8Q6jB5R127Vrl1N4UXBqRo8ezYgRI1y2r127lj59+jhtE+/NP05JSQm+vr5iXc8SDQ0NvPHGG3Tv3p3U1FSxrucYYfgIzhqFhYWEhoY6bQsJCaGsrOw8zeifQWvrZrVaqa6uPk+z+nch3ptnjw0bNvDjjz8ybtw4sa5/kqysLKKjozEYDHz22WfMnDkTEO/Xc40wfARnDZvNxomRU5vNJrrcn4LW1g0Qa3eWEO/Ns8PXX3/NFVdcwSeffEJ8fLxY1z9JYmIiBQUFmEwmJk+eTK9evTh48KBY13OM6M4uOGsEBASwYcMGp20lJSWOHCCBewICAigtLXXaVlJSgqen52l1GhacmtbWWLw3Tw+bzcbEiRNZvnw5S5YsIT09HRDrerbQ6/WMGzeOZcuW8fHHH4t1PccIj4/grNGlSxfWr1/vVIa9evVqevXqdR5n9fenc+fO7N+/n4qKCse21atX0717d1Qq8RE9G3Tp0oU1a9Y4bRPvzdNn0qRJZGVlsWHDBofRA2JdzzY6nQ6DwSDW9VxzHivKBH9DTlXO3hJOKGe32+1yhw4d5BdeeEG22WxyVlaWHBMTI2/atOmvvox/HCNHjpTvvvtuubGxUS4pKZHT09PlBQsWnO9p/WNZvny5Uzl7QUGB7OfnJy9btkyWZVletGiRHBsbK9fW1p6vKf5jMJlMslqtlouKilz2iXX94xw+fFj+7LPPHN+rK1askCMiIuSDBw+KdT3HiFCXwIUOHTr8oeMkSeLbb7/l1ltv5fXXX8ff359XX32VLl26nOUZ/vuYM2cOt912G+Hh4RiNRh5++GFGjRp1vqf1ryEqKoovvviCCRMmUF5eTlJSEj/88ANGo/F8T+1vT05ODna7nR49ejhtT0xMZNmyZWJd/yA6nY45c+YwadIkvL29SUxMZOHChSQlJQGIdT2HCB0fgUAgEAgEFwwigUAgEAgEAsEFgzB8BAKBQCAQXDAIw0cgEAgEAsEFgzB8BAKBQCAQXDAIw0cgEAgEAsEFgzB8BAKBQCAQXDAIHR+BQPCvID8/n1dffZWlS5ditVqRJAmtVsvw4cN5+OGHCQwMdBq/bds2unXr5rL9OKmpqWRmZv4FMxcIBH8lwvARCAT/eBoaGujbty8PPPAAGzdudAi9VVZWMn36dIYMGcKWLVtcjktJSWHXrl1/9XQFAsF5RAgYCgSCfzz79++nb9++lJSUuOwzmUx4e3tTXV3tpHy7bds2xo8fLwwfgeACQ+T4CASCfzzJycnExsYyefJk9u3bR0NDAyaTie3bt3PnnXcyfPhwIfcvEAgAYfgIBIJ/ASqVit9++43w8HDuu+8+IiIiSEhI4LHHHqNnz558+eWX53uKAoHgb4IIdQkEgn8dkydPJioqiocffrjVMadKbgbIzs7GYDCciykKBILzhDB8BALBP5bDhw/Ts2dPl+319fWoVCp0Op3Lvry8PNRqtcv2UaNGMX78eEaPHn1O5ioQCP4eiKougUDwjyUqKorDhw+f72kIBIJ/ECLHRyAQ/OMpKipiwoQJ53saAoHgH4AIdQkEgn88hw4dYvDgweTm5p5y7Nq1axkzZozL9rKyMgwGA56enk7bw8PD2bhx49maqkAgOM+IUJdAILig6NWrlwiPCQQXMMLwEQgE/woKCgoICwtrdf+8efMYPHjwXzgjgUDwd0SEugQCgUAgEFwwiORmgUAgEAgEFwzC8BEIBAKBQHDBIAwfgUAgEAgEFwzC8BEIBAKBQHDBIAwfgUAgEAgEFwzC8BEIBAKBQHDBIAwfgUAgEAgEFwzC8BEIBAKBQHDBIAwfgUAgEAgEFwzC8BEIBAKBQHDBIAwfgUAgEAgEFwz/D0mtpGPBBKk7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 사용할 칼럼들을 추출 (기상 요건들과 운송 현황)\n",
    "selected = ['온도','시정', '전운량', '최저운고','풍속', '강수량(mm)', '현황']\n",
    "sdf2 = resampled_data[selected]\n",
    "\n",
    "sns.scatterplot(data=sdf2, x=sdf2.iloc[:, 0], y=sdf2.iloc[:, 1], hue='현황')\n",
    "plt.title(\"Resample 증폭 이후 데이터 분포\")\n",
    "plt.savefig('/Users/user/Documents/DataMining/termproject.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593bf80d",
   "metadata": {},
   "source": [
    "# 3. train-test data set 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b89b7783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1118739, 6) (479460, 6) (1118739,) (479460,)\n"
     ]
    }
   ],
   "source": [
    "#Dataset 설정\n",
    "X = resampled_data[[\"온도\", '시정', '전운량', '최저운고', '풍속', '강수량(mm)']]\n",
    "y = resampled_data['현황']\n",
    "#test, training (7:3 분할)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 100)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d53e2f17",
   "metadata": {},
   "source": [
    "# 4. 모델링 - Decision Tree (원본 Resampled Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "787a542a",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning - GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "38e3d5a1",
   "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>max_depth</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>splitter</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0.946392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>best</td>\n",
       "      <td>0.946194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>best</td>\n",
       "      <td>0.945493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.944940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.942823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>best</td>\n",
       "      <td>0.942054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0.941997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>best</td>\n",
       "      <td>0.941409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.931948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.928605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.924569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.923950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0.923766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>best</td>\n",
       "      <td>0.923702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>best</td>\n",
       "      <td>0.923195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.920700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.913453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.909171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>best</td>\n",
       "      <td>0.867144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>best</td>\n",
       "      <td>0.866946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.862489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.854661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0.852221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.838454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>best</td>\n",
       "      <td>0.756966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>best</td>\n",
       "      <td>0.756962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.756436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.719787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.719117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0.717641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max_depth  min_samples_split splitter  mean_test_score\n",
       "25         30                 16   random         0.946392\n",
       "24         30                 16     best         0.946194\n",
       "26         30                 32     best         0.945493\n",
       "27         30                 32   random         0.944940\n",
       "21         25                 32   random         0.942823\n",
       "18         25                 16     best         0.942054\n",
       "19         25                 16   random         0.941997\n",
       "20         25                 32     best         0.941409\n",
       "28         30                 64     best         0.931948\n",
       "22         25                 64     best         0.928605\n",
       "15         20                 32   random         0.924569\n",
       "29         30                 64   random         0.923950\n",
       "13         20                 16   random         0.923766\n",
       "12         20                 16     best         0.923702\n",
       "14         20                 32     best         0.923195\n",
       "23         25                 64   random         0.920700\n",
       "16         20                 64     best         0.913453\n",
       "17         20                 64   random         0.909171\n",
       "6          15                 16     best         0.867144\n",
       "8          15                 32     best         0.866946\n",
       "10         15                 64     best         0.862489\n",
       "9          15                 32   random         0.854661\n",
       "7          15                 16   random         0.852221\n",
       "11         15                 64   random         0.838454\n",
       "0          10                 16     best         0.756966\n",
       "2          10                 32     best         0.756962\n",
       "4          10                 64     best         0.756436\n",
       "3          10                 32   random         0.719787\n",
       "5          10                 64   random         0.719117\n",
       "1          10                 16   random         0.717641"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#테스트하고자 하는 파라미터 값들을 사전 타입으로 정의\n",
    "dt = DecisionTreeClassifier(random_state = 33)\n",
    "parameters = {'max_depth': [10, 15, 20, 25, 30],\n",
    "              'min_samples_split': [16, 32, 64],\n",
    "              'splitter': ['best', 'random']}\n",
    "\n",
    "grid_dt = GridSearchCV(dt,\n",
    "                      param_grid = parameters, cv = 5\n",
    "                      )\n",
    "\n",
    "grid_dt.fit(X_test, y_test)\n",
    "\n",
    "result = pd.DataFrame(grid_dt.cv_results_['params'])\n",
    "result['mean_test_score'] = grid_dt.cv_results_['mean_test_score']\n",
    "result.sort_values(by='mean_test_score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3cbcf6d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=30, min_samples_split=16, splitter=&#x27;random&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=30, min_samples_split=16, splitter=&#x27;random&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=30, min_samples_split=16, splitter='random')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최적의 DT 만들기\n",
    "best_params = {'max_depth' : 30, 'min_samples_split': 16, 'splitter': 'random'}\n",
    "\n",
    "#최적 파라미터 조합으로 DecisionTreeClassifier 초기화\n",
    "best_dt = DecisionTreeClassifier(**best_params)\n",
    "\n",
    "#최적모델로 학습\n",
    "best_dt.fit(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee3944a0",
   "metadata": {},
   "source": [
    "### 교차검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9335a2c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차검증 평균:  0.9470065990092363\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "#각 폴드의 스코어\n",
    "scores = cross_val_score(best_dt, X, y, cv = 5)\n",
    "scores\n",
    "\n",
    "pd.DataFrame(cross_validate(best_dt, X, y, cv = 5))\n",
    "print('교차검증 평균: ', scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d62fb2",
   "metadata": {},
   "source": [
    "### 학습결과평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7dc10951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       기상 지연       0.92      0.94      0.93    159849\n",
      "       기상 취소       0.93      0.99      0.96    159881\n",
      "          출발       1.00      0.91      0.95    159730\n",
      "\n",
      "    accuracy                           0.95    479460\n",
      "   macro avg       0.95      0.95      0.95    479460\n",
      "weighted avg       0.95      0.95      0.95    479460\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predict_test = best_dt.predict(X_test)\n",
    "\n",
    "# 학습결과평가\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print('Test Data:\\n', classification_report(y_test, predict_test, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b77eacb",
   "metadata": {},
   "source": [
    "# 4. 모델링 - Random Forest (원본 Resampled Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07e1f27",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning - GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1294236a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>max_depth</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "      <td>0.947397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>0.947397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>130</td>\n",
       "      <td>0.947397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>130</td>\n",
       "      <td>0.947397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "      <td>0.947397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>0.947397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>140</td>\n",
       "      <td>0.947395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>0.947395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>0.947395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>0.947394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>0.947394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>140</td>\n",
       "      <td>0.947394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>0.947384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>120</td>\n",
       "      <td>0.947384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>120</td>\n",
       "      <td>0.947384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>0.947384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>120</td>\n",
       "      <td>0.947384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>120</td>\n",
       "      <td>0.947384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>0.945038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>140</td>\n",
       "      <td>0.945038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>0.945038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>120</td>\n",
       "      <td>0.945029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>120</td>\n",
       "      <td>0.945029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>0.945029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>130</td>\n",
       "      <td>0.945011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>0.945011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "      <td>0.945011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max_depth  min_samples_split  n_estimators  mean_test_score\n",
       "13         30                  8           130         0.947397\n",
       "10         30                  4           130         0.947397\n",
       "16         30                 16           130         0.947397\n",
       "25         40                 16           130         0.947397\n",
       "22         40                  8           130         0.947397\n",
       "19         40                  4           130         0.947397\n",
       "23         40                  8           140         0.947395\n",
       "20         40                  4           140         0.947395\n",
       "26         40                 16           140         0.947395\n",
       "17         30                 16           140         0.947394\n",
       "11         30                  4           140         0.947394\n",
       "14         30                  8           140         0.947394\n",
       "9          30                  4           120         0.947384\n",
       "24         40                 16           120         0.947384\n",
       "21         40                  8           120         0.947384\n",
       "18         40                  4           120         0.947384\n",
       "15         30                 16           120         0.947384\n",
       "12         30                  8           120         0.947384\n",
       "8          20                 16           140         0.945038\n",
       "5          20                  8           140         0.945038\n",
       "2          20                  4           140         0.945038\n",
       "6          20                 16           120         0.945029\n",
       "3          20                  8           120         0.945029\n",
       "0          20                  4           120         0.945029\n",
       "7          20                 16           130         0.945011\n",
       "1          20                  4           130         0.945011\n",
       "4          20                  8           130         0.945011"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#하이퍼 파라미터 튜닝(그리드 서치)\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#테스트하고자 하는 파라미터 값들을 사전 타입으로 정의\n",
    "rf = RandomForestClassifier(random_state=10)\n",
    "\n",
    "parameters = {'n_estimators': [120, 130, 140],\n",
    "              'max_depth': [20, 30, 40],\n",
    "              'min_samples_split': [4, 8, 16]}\n",
    "\n",
    "grid_rf = GridSearchCV(rf, # estimator 객체,\n",
    "                      param_grid = parameters, cv = 5\n",
    "                      # n_jobs = -1: 모든 cpu를 사용)\n",
    "                      )\n",
    "\n",
    "grid_rf.fit(X_train, y_train)\n",
    "\n",
    "result = pd.DataFrame(grid_rf.cv_results_['params'])\n",
    "result['mean_test_score'] = grid_rf.cv_results_['mean_test_score']\n",
    "result.sort_values(by='mean_test_score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4498b82d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Random Forest: {'max_depth': 30, 'min_samples_split': 4, 'n_estimators': 130}\n",
      "Accuracy of the Best Random Forest model: 0.9470320777541401\n"
     ]
    }
   ],
   "source": [
    "best_params_rf = grid_rf.best_params_\n",
    "print(\"Best Parameters for Random Forest:\", best_params_rf)\n",
    "\n",
    "#최적 파라미터 조합으로 RandomForestClassifier 초기화\n",
    "best_rf = RandomForestClassifier(**best_params_rf)\n",
    "\n",
    "#최적모델로 학습\n",
    "best_rf.fit(X_train, y_train)\n",
    "\n",
    "# 최적 모델 평가\n",
    "predict = best_rf.predict(X_test)\n",
    "print(\"Accuracy of the Best Random Forest model:\", accuracy_score(y_test,predict)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e12306",
   "metadata": {},
   "source": [
    "### 교차검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61b6d94e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차검증 평균:  0.9473269596104459\n"
     ]
    }
   ],
   "source": [
    "#교차검증: 검증데이터셋 나눠서 평가하기 (데이터셋: training, test, validation 으로 나눔)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "#각 폴드의 스코어\n",
    "scores = cross_val_score(best_rf, X, y, cv = 5)\n",
    "scores\n",
    "\n",
    "pd.DataFrame(cross_validate(best_rf, X, y, cv = 5))\n",
    "print('교차검증 평균: ', scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d0bbe8",
   "metadata": {},
   "source": [
    "### 학습 결과 평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "49f5c7cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       기상 지연       0.92      0.94      0.93    159849\n",
      "       기상 취소       0.93      0.99      0.96    159881\n",
      "          출발       1.00      0.91      0.95    159730\n",
      "\n",
      "    accuracy                           0.95    479460\n",
      "   macro avg       0.95      0.95      0.95    479460\n",
      "weighted avg       0.95      0.95      0.95    479460\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predict_test = best_rf.predict(X_test)\n",
    "\n",
    "# 학습결과평가\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print('Test Data:\\n', classification_report(y_test, predict_test, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c732d0da",
   "metadata": {},
   "source": [
    "### confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6b15f0db",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 시각화 (confusion matrix 시각화하기)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, predict_test)\n",
    "\n",
    "# 혼동 행렬을 DataFrame으로 변환\n",
    "cm_df = pd.DataFrame(cm, columns=y.unique(), index=y.unique())\n",
    "\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d5dd8a",
   "metadata": {},
   "source": [
    "# 4. 모델링 - Multinomial Logistic Regression (원본 Resampled Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "636524d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import statsmodels.api as sm\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "778bfc56",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = resampled_data[[\"온도\", '시정', '전운량', '최저운고', '풍속', '강수량(mm)']]\n",
    "y = resampled_data['현황']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072e7923",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning - GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d4b36b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'C': 0.001, 'penalty': 'l2'}\n",
      "GridSearchCV Results:\n",
      "         C penalty  mean_test_score\n",
      "0   0.001      l2         0.601481\n",
      "3   1.000      l2         0.600683\n",
      "4  10.000      l2         0.600683\n",
      "2   0.100      l2         0.600666\n",
      "1   0.010      l2         0.600657\n"
     ]
    }
   ],
   "source": [
    "# 테스트하고자 하는 파라미터 값들을 사전 타입으로 정의\n",
    "mlr = LogisticRegression(random_state=42, multi_class='multinomial', solver='saga')\n",
    "parameters = {'C': [0.001, 0.01, 0.1, 1, 10], 'penalty': ['l2']}\n",
    "\n",
    "# 테스트와 훈련 데이터 분할\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)\n",
    "\n",
    "# 데이터 스케일 조정\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# GridSearchCV를 사용하여 최적의 하이퍼파라미터 찾기\n",
    "grid_mlr = GridSearchCV(mlr, param_grid=parameters, cv=5)\n",
    "grid_mlr.fit(X_train_scaled, y_train)\n",
    "\n",
    "# 결과를 DataFrame으로 표시\n",
    "result = pd.DataFrame(grid_mlr.cv_results_['params'])\n",
    "result['mean_test_score'] = grid_mlr.cv_results_['mean_test_score']\n",
    "result.sort_values(by='mean_test_score', ascending=False, inplace=True)\n",
    "\n",
    "# 결과 출력\n",
    "print(\"Best Hyperparameters:\", grid_mlr.best_params_)\n",
    "print(\"GridSearchCV Results:\\n\", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f819c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.001, max_iter=1000, multi_class=&#x27;multinomial&#x27;,\n",
       "                   random_state=42, solver=&#x27;saga&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.001, max_iter=1000, multi_class=&#x27;multinomial&#x27;,\n",
       "                   random_state=42, solver=&#x27;saga&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.001, max_iter=1000, multi_class='multinomial',\n",
       "                   random_state=42, solver='saga')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최적의 하이퍼파라미터로 모델 생성\n",
    "best_mlr = LogisticRegression(random_state=42, multi_class='multinomial', solver='saga', max_iter = 1000, C=0.001, penalty='l2')\n",
    "best_mlr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1850d80b",
   "metadata": {},
   "source": [
    "### 교차검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "63b058bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차검증 평균:  0.600865097702283\n"
     ]
    },
    {
     "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>881.420461</td>\n",
       "      <td>0.405927</td>\n",
       "      <td>0.601158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>112.715948</td>\n",
       "      <td>0.387525</td>\n",
       "      <td>0.601739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111.968354</td>\n",
       "      <td>0.459771</td>\n",
       "      <td>0.601348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>111.578477</td>\n",
       "      <td>0.420325</td>\n",
       "      <td>0.600932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105.281230</td>\n",
       "      <td>0.449994</td>\n",
       "      <td>0.599148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     fit_time  score_time  test_score\n",
       "0  881.420461    0.405927    0.601158\n",
       "1  112.715948    0.387525    0.601739\n",
       "2  111.968354    0.459771    0.601348\n",
       "3  111.578477    0.420325    0.600932\n",
       "4  105.281230    0.449994    0.599148"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 교차검증\n",
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "# # 데이터 스케일 조정\n",
    "# scaler = StandardScaler()\n",
    "# X_scaled = scaler.fit_transform(X_train)\n",
    "\n",
    "# 교차 검증 수행\n",
    "scores = cross_val_score(best_mlr, X, y, cv=5)\n",
    "cv_results = cross_validate(best_mlr, X, y, cv=5)\n",
    "\n",
    "print('교차검증 평균: ', scores.mean())\n",
    "pd.DataFrame(cv_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c68a79",
   "metadata": {},
   "source": [
    "### 학습결과평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cb81fd01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       기상 지연       0.65      0.65      0.65    159849\n",
      "       기상 취소       0.57      0.60      0.59    159881\n",
      "          출발       0.58      0.55      0.57    159730\n",
      "\n",
      "    accuracy                           0.60    479460\n",
      "   macro avg       0.60      0.60      0.60    479460\n",
      "weighted avg       0.60      0.60      0.60    479460\n",
      "\n",
      "Accuracy of Multinomial Logistic Regression:  0.6014474617277771\n"
     ]
    }
   ],
   "source": [
    "# 학습 결과 평가\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "best_mlr.fit(X_test_scaled, y_test)\n",
    "predict_mlr = best_mlr.predict(X_test_scaled)\n",
    "print(classification_report(y_test, predict_mlr, zero_division=0))\n",
    "accuracy = accuracy_score(y_test, predict_mlr)\n",
    "print(\"Accuracy of Multinomial Logistic Regression: \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "14bff6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import sklearn.datasets as d\n",
    "import sklearn.model_selection as ms\n",
    "import sklearn.metrics as mt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "# 글씨체 보이기\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic'\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee664de2",
   "metadata": {},
   "source": [
    "## 2만개로 출발 데이터 줄이기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c5d721d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "출발       20000\n",
      "기상 지연     4918\n",
      "기상 취소      473\n",
      "Name: 현황, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 데이터프레임에서 \"출발\"인 값을 가진 행을 추출\n",
    "selected_data = df[df['현황'] == \"출발\"]\n",
    "\n",
    "# 랜덤하게 20,000개 추출\n",
    "random_selected_data = selected_data.sample(n=20000, random_state=42)\n",
    "\n",
    "# 지연 데이터 와 취소 데이터 추출하기\n",
    "delay_data = df[df['현황'] == \"기상 지연\"]\n",
    "cancel_data = df[df['현황'] == '기상 취소']\n",
    "\n",
    "new = [random_selected_data, delay_data, cancel_data]\n",
    "\n",
    "# 데이터프레임들을 수직 방향으로 합치기\n",
    "df = pd.concat(new, axis=0, ignore_index=True)\n",
    "df\n",
    "print(df['현황'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e55ccffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>항공사</th>\n",
       "      <th>편명</th>\n",
       "      <th>출발공항코드</th>\n",
       "      <th>출발공항명</th>\n",
       "      <th>도착공항코드</th>\n",
       "      <th>도착공항명</th>\n",
       "      <th>계획시간</th>\n",
       "      <th>예상시간</th>\n",
       "      <th>출발시간</th>\n",
       "      <th>시간</th>\n",
       "      <th>온도</th>\n",
       "      <th>시정</th>\n",
       "      <th>전운량</th>\n",
       "      <th>최저운고</th>\n",
       "      <th>풍속</th>\n",
       "      <th>강수량(mm)</th>\n",
       "      <th>구분</th>\n",
       "      <th>현황</th>\n",
       "      <th>사유</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-29 00:00:00</td>\n",
       "      <td>대한항공</td>\n",
       "      <td>KE8213</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>LAX</td>\n",
       "      <td>로스앤젤레스</td>\n",
       "      <td>01:20</td>\n",
       "      <td>01:20</td>\n",
       "      <td>01:27</td>\n",
       "      <td>01:00</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>화물</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-02 00:00:00</td>\n",
       "      <td>필리핀항공</td>\n",
       "      <td>PR487</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>KLO</td>\n",
       "      <td>칼리보</td>\n",
       "      <td>08:25</td>\n",
       "      <td>08:25</td>\n",
       "      <td>08:50</td>\n",
       "      <td>08:00</td>\n",
       "      <td>14.6</td>\n",
       "      <td>450</td>\n",
       "      <td>6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-31 00:00:00</td>\n",
       "      <td>대한항공</td>\n",
       "      <td>KE893</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>PVG</td>\n",
       "      <td>푸동</td>\n",
       "      <td>08:35</td>\n",
       "      <td>08:35</td>\n",
       "      <td>09:00</td>\n",
       "      <td>08:00</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-10 00:00:00</td>\n",
       "      <td>에어캐나다</td>\n",
       "      <td>AC064</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>YVR</td>\n",
       "      <td>밴쿠버</td>\n",
       "      <td>15:20</td>\n",
       "      <td>15:20</td>\n",
       "      <td>15:46</td>\n",
       "      <td>15:00</td>\n",
       "      <td>17.8</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-02 00:00:00</td>\n",
       "      <td>싱가폴항공</td>\n",
       "      <td>SQ008</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>LAX</td>\n",
       "      <td>로스앤젤레스</td>\n",
       "      <td>11:20</td>\n",
       "      <td>11:20</td>\n",
       "      <td>11:49</td>\n",
       "      <td>11:00</td>\n",
       "      <td>20.2</td>\n",
       "      <td>900</td>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25386</th>\n",
       "      <td>2019-12-02 00:00:00</td>\n",
       "      <td>진에어</td>\n",
       "      <td>LJ023</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>CRK</td>\n",
       "      <td>클라크 국제공항</td>\n",
       "      <td>21:35</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>21:00</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>태풍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25387</th>\n",
       "      <td>2019-12-09 00:00:00</td>\n",
       "      <td>산동항공</td>\n",
       "      <td>SC4988</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TNA</td>\n",
       "      <td>지난</td>\n",
       "      <td>22:05</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>22:00</td>\n",
       "      <td>8.8</td>\n",
       "      <td>600</td>\n",
       "      <td>8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>시정</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25388</th>\n",
       "      <td>2019-12-10 00:00:00</td>\n",
       "      <td>산동항공</td>\n",
       "      <td>SC4988</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TNA</td>\n",
       "      <td>지난</td>\n",
       "      <td>22:05</td>\n",
       "      <td>22:05</td>\n",
       "      <td>:</td>\n",
       "      <td>22:00</td>\n",
       "      <td>8.6</td>\n",
       "      <td>180</td>\n",
       "      <td>6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>시정</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25389</th>\n",
       "      <td>2019-12-09 00:00:00</td>\n",
       "      <td>산동항공</td>\n",
       "      <td>SC4722</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TAO</td>\n",
       "      <td>청도</td>\n",
       "      <td>23:50</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>23:00</td>\n",
       "      <td>9.8</td>\n",
       "      <td>600</td>\n",
       "      <td>8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>시정</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25390</th>\n",
       "      <td>2019-12-16 00:00:00</td>\n",
       "      <td>중국동방항공</td>\n",
       "      <td>MU5086</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>YNJ</td>\n",
       "      <td>연길</td>\n",
       "      <td>23:10</td>\n",
       "      <td>23:10</td>\n",
       "      <td>:</td>\n",
       "      <td>23:00</td>\n",
       "      <td>9.6</td>\n",
       "      <td>800</td>\n",
       "      <td>8</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>강수(눈)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25391 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        날짜     항공사      편명 출발공항코드 출발공항명 도착공항코드     도착공항명  \\\n",
       "0      2019-04-29 00:00:00    대한항공  KE8213    ICN    인천    LAX    로스앤젤레스   \n",
       "1      2018-04-02 00:00:00   필리핀항공   PR487    ICN    인천    KLO       칼리보   \n",
       "2      2019-01-31 00:00:00    대한항공   KE893    ICN    인천    PVG        푸동   \n",
       "3      2017-05-10 00:00:00   에어캐나다   AC064    ICN    인천    YVR       밴쿠버   \n",
       "4      2018-04-02 00:00:00   싱가폴항공   SQ008    ICN    인천    LAX    로스앤젤레스   \n",
       "...                    ...     ...     ...    ...   ...    ...       ...   \n",
       "25386  2019-12-02 00:00:00     진에어   LJ023    ICN    인천    CRK  클라크 국제공항   \n",
       "25387  2019-12-09 00:00:00    산동항공  SC4988    ICN    인천    TNA        지난   \n",
       "25388  2019-12-10 00:00:00    산동항공  SC4988    ICN    인천    TNA        지난   \n",
       "25389  2019-12-09 00:00:00    산동항공  SC4722    ICN    인천    TAO        청도   \n",
       "25390  2019-12-16 00:00:00  중국동방항공  MU5086    ICN    인천    YNJ        연길   \n",
       "\n",
       "        계획시간   예상시간   출발시간     시간    온도    시정  전운량   최저운고  풍속  강수량(mm)  구분  \\\n",
       "0      01:20  01:20  01:27  01:00   9.8  1000    7  200.0   2      0.0  화물   \n",
       "1      08:25  08:25  08:50  08:00  14.6   450    6   40.0   8      0.0  여객   \n",
       "2      08:35  08:35  09:00  08:00  -2.9  1000    8  100.0  13      0.0  여객   \n",
       "3      15:20  15:20  15:46  15:00  17.8  1000    0    0.0  15      0.0  여객   \n",
       "4      11:20  11:20  11:49  11:00  20.2   900    2   40.0   9      0.0  여객   \n",
       "...      ...    ...    ...    ...   ...   ...  ...    ...  ..      ...  ..   \n",
       "25386  21:35      :      :  21:00  -1.3  1000    8   30.0   3      0.0  여객   \n",
       "25387  22:05      :      :  22:00   8.8   600    8   20.0  16      0.0  여객   \n",
       "25388  22:05  22:05      :  22:00   8.6   180    6   20.0  11      0.0  여객   \n",
       "25389  23:50      :      :  23:00   9.8   600    8   20.0  13      0.0  여객   \n",
       "25390  23:10  23:10      :  23:00   9.6   800    8   40.0   9      0.0  여객   \n",
       "\n",
       "          현황     사유  \n",
       "0         출발    NaN  \n",
       "1         출발    NaN  \n",
       "2         출발    NaN  \n",
       "3         출발    NaN  \n",
       "4         출발    NaN  \n",
       "...      ...    ...  \n",
       "25386  기상 취소     태풍  \n",
       "25387  기상 취소     시정  \n",
       "25388  기상 취소     시정  \n",
       "25389  기상 취소     시정  \n",
       "25390  기상 취소  강수(눈)  \n",
       "\n",
       "[25391 rows x 20 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4ff367",
   "metadata": {},
   "source": [
    "### 2만개 데이터 증폭 전 분포"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "40b3b5f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0WUNVszB0BY/pR0uGMJCJah40PYJpGCgWB0YigimpGDK0mnEUIFu102ImSSajuCUFm6xhmiaGoSPZfBixFlrMBCgWvKZCUIaYqZIwEyiAV7UQTgTRAJ/mhngQ9ARoDsAEUyeg2mjVI6gmZGsOtESEgCQRw8SBhKxYadRDKMgUaR5IRDFVC1I8BKZOULUSQccqKXgSUeIWN34jgqLrZCtWsGVDoJK41Y1fj2BKCppiQdFjeEwZDB0UDZIRsLhoNuKYskLCSKIgkW2ayJICpp5qu9UNiSgoNuqJEzOTOGUrWYYDpDBIMiSjmMg0yWCYOnbNQSwRQZdlJEkB00AydDyaHUs8Akh722EFSQPZJGQahI0oViQ8spU40GTGMUwDt2rHJWk0JwIkZAVZUomZCWyyFZuh4zJN0BNEFI0gCTQkvJYszEgTkqGD1QWmSauZIGYkccgaTknGRCIoSUSNOFbTxGOCLtsIKhJhPQaSjIqBzZQwFSd+PYCKhFtxEEcnrseRTBOv5sQWC4BpkLT4kJMhJEPHVO2QDCHJGrqsIckqciKIaUJSsWMgIdl8BKM1GKaO2+IlhgMtGcaSDIIkIckKpmEgKQpyMoIhaYQVDwlksggj6RGQJNjbz5gQsdgJJoJokoLX4kFPRmkxoiCp+BQ7IT1CfO/x5kwmMC0O0OOYkoZpGpCMYVrdKLEAkgSmPRcp7keXrTSbccBAVewEkyFsih3VSJIwEmgWDyE9DJKEhkwCA8MwcFtcSHqS1mQIq2zBrblpjDcBkK960eKtIKvoEjSaOnF0HIodSVYIJsNImHhkB55EPHW8ySamadAoQRQDWVKwmJCl2FCSMTCSYJoENAsBI4Yma3g0F42xFkzAoTrQTR3dNIgbcWyKFYdkoSXZiqZYsCsOWhOt6KZOluLCoydplnRCZgK3xUtrohXZlLEqVoJ6CEVSyLHmUB+rxzRNnKqTqB4FQJVVFElGMRWSJDFMAx0D3dBxqg7iegzdNLCqNqJ6FAUFu2onlAxhmAZWxYoiKxiGTlyP4ZFtSLJEyEiiSApRI4aEhEWxpPa9HkGVVTRZwyJbCMT9mIDP4iOSjJAwE2iyhmEaSEgYpgGk3g4sSRIqKgk9jqpqxPQYXouXlngLpmmSpXnwSDINyQihvf1qk60YiThZFjst6MSNOHE9nspTbAQTQfLseTTFmkgaSbwWD63xVkzArblRMPAnw1hkC5IkEUlGUCQFt+okrIdRJBXJMDAliagRwyKpZFmy8cebMSUJAwPTNNBkLfWdk2QkPUmWbEExTdBjJC12Gk099T2XNfI0Lw2JwN7PVpw4iBEmbETRZAsWSSOSDJGtubFZPCiaheZQnLieuh5F4jqSBLkuK1XNYRK6iceukeOy/seuuYcimaZp/hArfuedd/j1r39NOBxG0zQ2btwIQGtrK/369ePxxx9n6tSpLF68mBNOOIGNGzdSWFjI888/z+23384HH3yA1+tl3rx51NbW8vLLLwMwY8YMRo8ezQ033EB1dTVHHnkkf/vb35g1a9ZhtSsQCOD1evH7/d/LP1lR2RRifXUrD368nT0tEfoXeZg3pSeFHitb6oLct3Are1oiDCz2cMXU3nTPcxKK6Xy9u4k8lxW7ReGpz3dz+Wg3Of51yJ/dC6odc9JvYc2LSFveBYsL/5CfEe8+jVYth4c/2c6iTfX0yndx59wufFDxEc9tfpGoHuWYLuM5o+fJ2B3ZvLPtTZ7b/mYqvevRnNFrDiUVX8GGN2DkzzDXvoS0bSFYnDDmlxjdjqTKjPPE1pdZVLUYh+rg1N5z6OIq4a5ldzE0dwA/6306pdZsVElhT8LPqzve5o3Kj5AkiZndZzKt2zQ+3fMpL29+mageZVLJJKZ0m8Iz65/h+NJJjIhGyF74J+h1DI1H/pIGM86iysUs2L6AqB7lNyN+g1XR+Neahylzl3HuwHN5esPTfLrnUxyqgzm95zClZBLFK58nFG1iW+/JPLDlBba0bKPEVcJFgy/EHwvwl6//gkN1cFbfM5iSN5KIDPM3Ps1He7drVvdZ9MnugzURoa+rFM+WhTS6cllps+ByF/NJ5Scs3L0QJDipfCazSiZRtOC30LQVysZTN+V3rA/s4JG1j1EdqqZ3Vm8uGnwRZZZsfLXrqbM6WBjcwTNbXyKajHLV8KuwqlYeWfNIW/nT+pxGQ7ie0e5uFL97I5JpwuiLCRUOYHsyyANr/snm5i2Myh/B+YMu4PnNL7CochHZ1myuG3Mdq+tX8/zm54kmo4zrMo7jyo9jZ2AnA3IG4JRtJBKtPLJ+PssbVpFty+anfc5gRDRGzsJbaTn1MdbrrTy47jH2BPfQx9eTK4deTtiI8eDq1HqLnV24uP+5FPq6s7phDYqk8Pzm51EllXlD5/H2jrdZVLkIh+pgbu+5DMobxC2f30IgFmBKyWTO6no0Je/9AQwdafTFkF0On94LFZ+DMw/G/ALy+qaCk1XPgdVJ3dAzWLjns7Z+m1IygbP6nEFx5Urkz+6DUD0cdxc4c2HxXVC3HnxdMY/6Nfi6IlV9BbIMyx+Flt0kxl/F7h7jeHjNoyyrX0G5t5xfj7iahbsX8vr2NwA4vvx4RhWO4q5ld5Fvz+GSXnPp3lSJo9t4qPoKlt4PoXrM0rFIw86BVc9A6Wiqe0/lte0LeG1Hqp5pXacxqmgUz258lunl09kT3EOJuwSn5uSRNY9QFayih68HZ/Q9g/pwPSMKRyCbMg3RBj6v/pw3d7wJwHHdpnNK71OQ4xFe3f0er+9Nn1E+g2ndpvHn5X9mh38Hw/OHc8GgC+iKlVY9zAcNK3lu03OEk2HGdRnHjO4zqA3WMjr/CNw1G9idU8Ij6x5na/NWbp9wO5/v+ZyXtrxEOBnmqOKjOLvf2by85WXWNaxj3rB5vLfzPT6q+AibauOUnqdQ5i3jrmV30TenLxcOupC19WsZlDeIz6s/x6W5GJA7gEfWPtJu/eOLx3NW37N4YfML9PD1YFDuIGyKjcZoI8FEkJc2v8RpfU9jec1y3t31LgDHdD2GccXjsMgWPqv+jDe37+2HshlMK5vGX5b/hW3+bQzLH8b5A8/HIltYUb+CFza9QGO0kWH5wzit72k8veFpnKqTnw3+Ga9te423d7zNST1Poru3O/PXz6cyWMm0btOY2WMmj697nK9rvybLlsUpvU4h155Lubec1ngrz218jouHXMziqsW8uPlFwskwd4y/g2Ay2LZ/u/u68/NBPyfXkYsmaayoX8ET656gMdrI4LzBnN3vbPLseQRjQV7a8hJn9DuDZzY9w+LKxdgUGyf2OpEppVNoibVgU208tvYxvqr9Cp/Vx5l9z6RfTj9sio1APMA/V/+Tzc2bKXQWcsHAC+jl60VlsJL56+e3pZ8/4HzCyTC59lyqW6sYn3sEumalMe7nlS2v8GnVp1w85GIUSeGpDU/RGG3kiPwj+OnAn/LOzndYsH1BW/1FziKe3/Q85/Q/h0LZxcUv7mBzTZBh3bI4Y1Qp2Q4Lm2tbeWTJDhpa4wzr6uOXU3pSmmXHY7f8n6+1cPjX7x8s8HnxxRexWq243W4uvvjitsDnX//6F2+//TavvPJKW9nZs2czZcoULr/8co488kiuueYaZs+eDUBDQwNdunShpqaGhoYGxo0bR3V1NaqaGsy65557WLx4cVp9h/J9Bj41LRGe/HIX93+4LS39lGHFlGQ5uG/hlrR0SYJHzh3B8l3NjCzLwm3TuODxZTx+ek+G7nkWafGdqZGQ056EF8+HqD9t+UTX8awfew9v7dD55+LtLPnNAK5Zeh2rGlanlcuyZnHPpL/ws/cvJGkk09KfGvtHSpM6vHBeev1nvcguh5szPv4VrYnWtPqG5Q/juPLjuPWLW7HIFp6a/hjeaIiffHETe0J70sp283TjimFXcOWiK9vSfFYfd0y4g3kL5zG76zFckbRjag7Wlo/h76sfYG3DWgBmdp9JviOfR9c+itfi5ZFjH+H8d87PaM/w/OHcMfIaVtet5FfLbsvYL5cMuYStLVt5f9f72BQbD059kMs+uoxAPJCxXTO6zyARqmd6yUTuXvc4x/acxe1f3k5VsCqtbFd3Vx4ZNI/Cp06nZcadPGPR+ceqB9L3LxJ/mXg3I7Bz6boHWdmwCkhdWAudhTyy9pGM8n848g+8uf1Nbup7HqVPnIJRNISPJl3OFUt+B4BNsXHflPv49ce/bmv/TWNv4uUtL7P6oP3utXq5c8KdPLT6IX476recseAMEkYirczJ3Y7j0rLjeb15Pfes/kdb+pC8IZzY80T+sPQPGf0574h5FDmLuG7JdVgVK3+d8te09uxzRN4RnNDjBG7+/Oa29jw99ja6zp8DyRhM+DU0boN1L+9faPDpMOBEkDXq9TBXb3uer/f22z4ei4dnjryNrvPnwoA50OtoeOmncPCpbdLvIKsbvPLz1OcuQ1l3wt2c8/6Fbf3w18l/5c7ld1LZWpm2aIm7hN+M+A2XfXQZEhL3jruNSbtWIH/y5/R1KBaY+zjVFjsXrLzrkPVcMuQSurq7cu2n16aVkZC4YewNvLvjXa4dfS1XLrqS7f7taWX+cfQ/uO3L2zLrd5Xwm5Gp+gE0WeOFmS9wy+e38FXdVxn9dtfEu3h49cNcN+Y6Tn3jVOJGnCePe5I/L/8zK+tXZpR/fPrjNEYauXrx1fhj6eefwbmDObnXydy09CY0WeORYx/htS2vUROu4ZIjLuGCdy74xvofm/4Y579zPmOKxnB2v7NZUbeCe76+h3sn38vdy++morUibZliVzHXj7meiz+4OCP9mlHXcNmH+/vh/in3c+PSG6kJ1bSV02SNP0/8M9293fnlwl+yu3U307pNo4evBw/s/f7m2fO4YewNXLXoKuJGPG0907tN59wB53LWW2fx8uyX+ePSP7b185XDUue4e76+h4PdcdQdxJNxrl96fVq6KqvcPfFuenh7kDASnP/u+bTEWtLKDMwdyO3jb+fk10/OaM+fjvwTdoudKxddiUn68X/x4IsZmDuQeR/Oy0jfE9rDlK5T6ObuRmO0kas/vpqWWAsXDLyAunBdW1B5YDvvmnAXdy27q+0c//PBP2dQ7iCu/eRa5h/3BGaTwdSHUte4SyZ2pzmc4Nll6ftPUySe/NloRpfnZPTRd3G41+8fbI7PnDlz2h2FWbp0KePGjUtLGzVqFCtXriSZTLJ8+fK0/NzcXLp168aaNWtYunQpo0ePbgt6Dlz2h9AaS/LAou0Z6TMHd+H+j7ZmpJsmXPfqWoaU+ghGdf792S4C0SRHZCeRPr07VWjQXPj63xlBD4C2+1PyYrsY3yuX4wYWsjWwJSPoAWiONfPqtteY2nVqRvoTlQuJ165Lr79sPOF4kAe2vpQRZAB8Xfc1Ls1Fti2buBHn5W1v8HbDioygB2BXYBfb/dsZkDOgLa0l1sJb299iarepvLjzLRp6TKSh21hqI/VtQQ/AceXH8e91/wbgiuFX8MiaR9ptz1d1X9EiS9yy+oGMPICH1zzMrB6z2up8ZuMzGRfpfdtlV+08veMtajGojjWyun51RtADsLt1N4tad0DxMJp7TOJfqx/KKGNictuXf6LZV9QW9By8XQeX/9uKv3FCjxN4bNfbxAacSN3Yn3PzV/svttPLp/PKllfa2l/gKECW5IygB8Af8/PGtjcodBayrWUbXqs3o8zLu96mwZ3PA+seTUs/rc9p/G3F3zLKA/xz9T/b6ppeNp1Xt77abn+urF+JRbGQa89ta8+jOxcQG3BSqsCSe2HwqekLrX4WZBVUK5ttjoygByAQD/DozrdS9Yz6Gbx9TWbQA/DJXWDPavvYPPVGbll+V1vQMyRvCOub1mcEEwCVrZVsaNrAkLwhmJjc/NWfqS/ok7kOPY656S0WBrd3WM/6pvUMyRvCQ2sewmPNPDmbmNy/4n5m9ZzFw2seZmDuwLT8IXlDWNOwpv36g5Wsb1zPEXlHAKngcmPzxoygB1L99urWV+ni6sKGxg34bD56Z/WmMdqYEZTsK7+sZhlv7ngzI+gBWN2wGlmSKXAUkDAS3P7l7Uwrn8blwy7nzi/vJG7E6eXrRVOsqcP6H1nzCFcMu4L3dr2HIiv8a/W/GJQ7iC3NWzKCHoCqYBUr6lYwNH9oRvrahrUMyx8GQMJIcN/X93Fq7/TjK2EkaAg1sKhiEbtbdwMws8dMHl7zcFuZU3qfwsNrHs4IMgDe2fUOzbFmxhSOoaq1Kq2fxxeP5/6V92csA3DHsjvondM7Iz1pJHlw1YOEEiGeWP9ERtADsLZhLZubN9MnO/P465fbj9u+vC0j6AF4aM1DlLhL2k2fXjadjQ0b2d26mze2vUFLrAVVUhleMDwj6NnXzn+t/hen9tnfnw+veZgSdwmtiVYeWP0gedlWxpRnocoSI8qyM4IegIRu8ofX11PRFM7I+0/60U1u3rNnDwUFBWlp+fn5NDY2Ul9fj67rbfN5Ds4/1LIdicViBAKBtL/vy9a6ILqRfgD6HBo1gWhG+j7V/iheu4ZVk3lvfQ39i9zQsCk1vwOgbBxsfrfDdeZtf5WWcIKfjSvhlR0LOiz3ccXHjCgckZH+bsVHtDgOuiB2G0fAYmVhxaIO6/tsz2cMyRsCgNfmY0HFwg7LLq5cnLHujys/ZkRBKm15y2YaJJ0le5a05bs1Nw2RBnQz1Q99s/uyqLLj9jRGmmiONbeblzASRBIRLLKFEYUjWFy1uMN6lu5ZyuSuk/ms+nNGFIxgcWXHZV+v/hR/n5nsDFaQNJPtlqmP1BOIB9s+uzQXzbHmQ5Z3aA7erVpMS/cJtFhdNEWb2vIPbtMR+UewpGpJe1UBqb4fXjCcxZWL2/bXwb6o+ZKunq5paVbFmrbeAyWMBIF4AJtiS/XnIfrosz2ftV2UAd7f8ykt3SekPuiJVMCt2dMXql6JaXHx+q73Oqz3vapPaOkxEfR46nZXe/QERJr2zh+DVlce6xrXtWV/0/498BhtjDbSYnO3W87vLuCN3e93WM/iisWMKBhB0kjSHG3GrtozyjRGG7EpNj6s+LBtnYfbzgO/X0fkHXFYZRdVLmJo3lBmdp/Jezs77meH6uCj3R91mL+kaknb/l3fuB6vxYsqq22B+MzuM3l3R8fnr0UVi+ib3ReAL6q/oKun62Htl5GFIzvctn3WN62nm6dbRrk+OX14f1dqf1lkC+FEOG0ktHdWb1bVZwbc+3xd+zU/HfxT3t25f7tcqouGSEPGiOo+TdEmoslou3kbmjagyAofVXTcz2/vfJsZ5TMy0kPJEHXhunaX0U2dHf4d9PT2zEhvjjbTL7cfcT3e1tfdvN3Y0LihwzZsaNqQdp7YV38fXx8WVSzCLxlM72GhLNfJuj0dX1fXVwcIxdo///2n/OgCH13XOfjum67rSJKErqcueofK7yivI3/605/wer1tf6Wlpd/TloCmZnavrptYlEN3uyyBYabKRRN7J7TuY+ip4fQO6KoNRZaI6yZWueNyFsWCvi+YOig949eyoSOZJpqsZZQ/cLl9t80MwzhkWU3WMtZtUSxtQY1VsSCbpNWhm3raZ/Mb2qPISod5+/INMzVh8pvamjSSWBUrSSOJphy6D2QjlurDb1j3Prqpo0qHfsZAkRQsigXJ0JEPOpZ1U09rk2Eah2yjJmtty+zr74Pt29aO2tweVVZTk1+/oT8P3M/72iMduC5FS83pSVu5FSQO2a8WxYKkJ1MTlg9F3l+/LElI7O9Pwzz0cWuR09uuSO1/j+VvOqYO6HtN0domyx5MkZWMdULmd6Hd+vd+v75pm/Yd3xbZQtJMfuMxbvAN9SlaWxAvISFLMrIkt/WzbupYlY4ntGqK1nYOtyrWvROqv+GYkts/lx18ntnXnoOZptm2zQYGSjvHUHvLta1fsZDQE2nHZ5IkqvwN3+tDfKckSfpO2/xN67QoFmJ6LHM5RW2rb19ffNN3GTL7xaJYiOpRLHuvPQldQjcO55rX8TX6P+FHF/hkZ2fT0NCQllZfX09hYSFZWVmYpklzc3O7+YdatiPXXnstfr+/7a+iInM47rsqz3FiPSj4aY0l8djVjPR9euW72NUYxjThpGHFbG8IY2Z3B9WWKrD5Hdh3a6AdjT1OxmVVuf2drczt2XG5Y8uO5ePKjzPSTy47jpzIQbePtrxLVlLnxHZ+YewzrngcX9WmhnkjyRCndZ/ZYdlpZdMyfsEdW3YsH1d8jITEUHcZBZLCxNKJbfnhZBiH5sCmpPrhi5ov2v3Fs0+2NZtiV3G7eS7NhYRE0kzyceXHHFt2bIf1jC8ez4e7P2Rs4Sg+qfrkkGXPKDka95qXKLHntfsrHqDcU47zgLxIMoJds3dYvsxTRnWompO7HkP2hjfxBWopce0frv64Ir39X1R/wYSSCR22cV8/H931aJbXLM/Il5AYWTA8bR4EQEOkod1hcgCn5kSTNRJG4hv786jio1hWs6zt80ldjyF7w96RSYsrdVsrecCJWZKgYABSqJFTyqZ3WO9Je/sH04Cs8vYLWVyp79HeX9rehq2M6zKmLfub2j6tbFrbd6aruyvelsxbngCecDNndj+hw3qOLTuWxZWLsat2rLK13QtRqbuU+nA9s3rMyvieflz5MdPKpmUsk1b/3lHML2u+ZGq3qYcuW7mYY7odw7KaZTy36Tlmde/4QZCEkWDmIb7bE0smtu3fMUVjqGitIBALML54PADPbXqO47sf3+Hyx5cfzxfVXwAwonAE1aFqFlcu/sbtbe9cdnD66KLR7Y7crKpfxUl7z5VJI4ksyTg1Z1v+V7VfMa7LuIzl9hmaP5Q7l92Z1i/RZBSPxZNWz4GKXcW0czcq1c7C0cSSsUP28wk9TuCZTc9kpGuyRrm3/ePfrtopcZVQEUy/xtkUG1bFyqr6VXgsnrbvwK7ALnpn9U77cXCgUYWjWF2/Oq2eYlcxu1p3MaP7DLIMiZc3htjZGKJvkZuOYpux3XNw2/+7D5j/6AKf4cOH89lnn6WlLVmyhLFjx+J0OunTp09afnV1NbW1tQwZMoThw4fzxRdfYBhGxrIdsVqteDyetL/vS5ZD47aTBmXs8OeWVXDHKYMz0u2awp/nDiEcS5LUDWYf0YXSbDtvbDcxZ92XughsXAD9ZkFWWcb6QoPPo0Er4pUVVayq9JNvKeL4suMyypV7y5na9WiW7lmakX5y9hCU/D7p9deswaLHOafrtLSL7j4zu89ka/NWwskwObYcTut5MuNc5RyROyij7IiCEdgUW9v9dEgFA6MLR/PZns/4zaCLyF3xHDlrXiFbc3NMt2P299vG57hqxFVISDyw8gFO73t6uxfj2T1m4ws3cfsRV7T98thHlmR+PfLXPL3xaSA1tD67x+x265nZfSbb/du5btDPydeTHNflKPLsee3eIhqdP5xhpgaNW8n7+hluHHNDxgnDptj4w5E3kVO1ihPK95/8n934LFePuLrd8lcMv4KPKz5mbv4o1K0LyV/yd24fe2Pbr+aPKz9mcslkSt2pkcpAPMCe4B6O7ZZ5AS/zlDGueBxei5dSV2m7vw6vHvAzciu+5tYx16e156n1T3H18Kszfq3Lksx1o69D2vu/xZWLmVgysa09B9r3VFkwkbrV183TjdMKxqBueT/1yPX0P8GXB82Nmnxd6r+qhW61mzmxneO5m6cbpxaMTtXz8R0w+/79PxT2kWSYcRcgpf4/4H7n91wz7EqyrKl5P9v92/FavRnzRSB1cfNavezw78CqWPnTyGvIS2TO+cCRA/1nM9LQGLF3fsnB9fisPnYGdnLtqGuRpMxRCKti5VfDf8WHuz/ktD6nETzg1ijADv8OSlzFGbfAIHVry2f1sa0l9UCFKquUeco4sceJGWVL3aVMKpmEx+Kh3FuGIinUhms7DG5K3aWMKBjBmKIx7d4yOrbsWCqDlQTiAXxWH1ePvJrV9au5eenN/Gr4r8i2ZbfVv2+O3cH1n9rnVB5c/SA/HfhT4nqcG8feyO7W3bg0F8MLhrfbn9083djakj5nckjeEHJsOW3pPquPS4deyitb0x9y8Vl99MjqweC8wW39+czGZ/jNyN+07ZfXtr7GWf3OItuWnbH+CwddSJY1i+pQNQ7Vwewes9vyXtryEjcfeXPmqIhs4eYjb6Y+nHlL1mv18rNBPyPLlsXsnrMp92QGMdPLplPiLiGSjGTkbW/Zzh+O/EPGDykJiRvG3MCKuhUZ6b8b/Tve3PYmY7qMIceew+TSyZR7yjExWbBjAZcMuaTddp4/4Hxe3vLy/vrH3sBXtV9R4irhvP7nsKkixIbqVkwT3ltfw3Uz+mXU43No3DCzP0Xe9n/4/af8YE917bNo0aK0p7oqKysZNGgQL730ElOmTOGtt97iF7/4BevWrcPpdHLPPffw9NNP8/777+NwOLjooovIysrinnvuwTRNhg4dymmnncY111zDzp07mTx5Mi+//DLDh2d+adrzfT/OXuePUBuM8/Tnu9jdHGZIiY+Th5WQ41Spa43z1Be72VYfZGRZNicNLabYZycYS7K7KUQ4ZpDl1FhR0cKQbIM+dj/S8keRYn7MMZdA/Sakda+gW9wEBl9AMrs3TaaLL3c08d76WnIcKr87Lo+doQqe2fwC4WSEE4onMjx/GBaLk62NG3hm5wLCySgnlB/P8NzBFDTuQtrwGvSbhdm0HWnjm5hWN9Lwn2K6cqlVZD5vXMdbu9/DrbqY23sOgXjq0csJ+cOYXDyeLkrqV06dHmFt0wZerPgASZI4pdfJdHEWUxGsYMH2BUSSEY4tO5ZCRyGLdn/IieXHUVq1Gvf6N6DPDPw9JtIgmWwP7OLNHW8STUb5Sf/zyLZl8eLmF0mYOuf2P5fltcv5YPcHuFQXc3qfQndXKYVVK0lWLqdqwExeq/6UNU0b6Ontzkm9TqY+XMe/N8zHrTo5o89p9LTmEFVUvqhdzoLd7+NSXRxXfhyarFKqeSi2+LDt/oKAHqGioA8tEtRHG3hv53tIksSp3U+gv6sreZ/9HfyV0HMqLX1nUG1EeH7zi1QGKxmQM4ATepxAsezAGqyhKVzPNquNp7e+RDgZ4ey+Z5Ntz+alLS9R0VrBwNyBjCseRzjWSh/VQ8En94K7EAaeQtJTxB7J4LVtr7O6YS2DsvpwQq+TWVG/ird3vp16NHfQzwjEAzy/6XnCyTCTSidR7i0nnoxT4MzHJdswYwE+3LOET+tXkO/I54yeJ1G6Zw2eda8Tmfw7quwOnt/0IjuCFRyR3Y8Te55MEp3Xt7/J6oa19HCVMrf8eKyOPNY1pSbHvrvjXVriLZzb/1x2+Hfwzs53cGpOTu51Ml6rl8fWPEYoGWJm2XGMdBRTuORvmM58GHgKkuaAbQthx2JwFcLwczGtHiTTgDXPQf5AmroMZluihWc2v0goGWZm12mMLBxOQXMl0tfzIVgLoy6C7O6YK59GqlkFWd0xR/4ULE6kPSvBkQ3rXoHmHTD4NKrKxvDhroV8UruMLo4Czh1wHrv8O3lha+qkfkqvk3FqTuavf5J+njJOKJ5Al9YG1Px+mME6+OoxpNZazO4TkUpGwvrXoM9x1Of2YF3LVl7Y+jKmaXJ89+Oxq3aW1SxjctfJfFXzFf1z+pNnz+PVra+yI7CDPll9mFQ6CX/UT8/snvijfkLJEKFEiFe2voJpmszucQKDcgagJWKsbd3Bi9texTRNZvWYRS9fL+avn091qJojuxzJlNLJdDVkmpJBtiT8PLf5eUKJEBNKJtDT1xNVUii35ZHdXMEuXyEfVH7Msupl/HLYLwnEAry4+UVCyRDHdD2GUUWjeHv722xu2cw5/c5hV+su3t7xNg7VwSm9TyGaiPLC5hcYWTiSqd2msqp2Fd183QglQuwO7GZsl7Es3L0wVf/QXxKI769/atepDMsfxpvb32RU4Sjy7HnYVBvVoWpcFhevbX2NUYWjiOkxFuxYgGmazCifgcviwq6m3sH08pZUP5/Q4wR6ZfXiyfVPUhWsYmyXsUwomYBFtlAZrOTN7W9SH65nbJexjCocxXObnsOqWDmn/zlsbNrIa9teY1DOIMaXjOeNbW+w3b+dsV3GMrFkIosqFvFF9Rfk2HM4rvw4oskoA3IGpNq1bQHHdT+O2nAtL21+iWAyyCWDL8Gu2Xlx84vsCuyib1ZfTuh5Qtuoc224luc3P099uJ6h+UOZ1HUSHs1DNBHlte2vMbvHbFY1rOLdne+mXhvQ6xRK3aXs9u+m1FPKospFfL7nc7JsWcztPReLbCHHlkPUiPLGtjdY17iOEncJc3vPxa258cf9vLvzXTY2baSruyuzesyiOlhNniOPZDJGd1s+uiwRQGdF3Qo+qviIKaVT6J3dm5c2v0RduI5RhaOY2m0qH1d8zKd7PqXEXcKc3nMIJ8LsaNnOmKIx5JoObv2whl1NYcb2yGFEtyzARFMU5n++i/rWGKO7Z3P8oCK6ZdtRlG+4RX2YfvSPs+9zcOAD8O6773L55ZfT1NREz549+ec//8mgQanRA8Mw+M1vfsMTTzyBqqrMnj2be++9F6s19Ut0+/btXHDBBaxbt46srCxuvfVW5s6de9jt+b4Dn33CsQShuI7PpqFp+3dyUjeI6wY2VUGW03/xJ3QDXTdBMkkkDXQT7JIOiTCq3YOiaSQiQeKGjKRZ0A0TXTexaTJx3USVJZBAkcDQIxjJGFbVRgwLcR1ccoKkGcOQrWiKlUjCwCIl0fQYpqwQl2xYpTgJXQLTQFEVEgkdiyYTNXVUQEHGMHUMycBqmEiKhmFKGHoSU7GgkSRmxEG1ougGuqQRMXTUvXNsXKpKPBlGNUFVrGAm974MzoWUjINhkFStxM04kmFgt7gh1oKu2onrcWyKhiSpBI0IKio2UwKSmIoV0wTZiGHICjFDx6JoKIkwWLKImgkUPYomWcCUgDjIqfvTspLqS4upo6gWSMZTt2CMJEgySUCXAQMkWcGix/e+DE8HScLUnEiJMBg6Cc1OJBnCpbmRExGQFZBU0GOAQUK1oCfjWDUXsUQQWbGiGwkUScXY+6I4DCNVNwbIqRf8YcvCSIaImQYWJBTFknohoSxhGgYWSUE1IUESXVJAgqSZGpWxYqKaOpgSpqwQMxOoyCiSio6BpKde2ChjYqATN5JYZRUJOfVySlklYcSxAAqQwAKKQjgZwqraMJJRLLIGqp1QvBVNllFI3QpTFZWknsCmuVASrWCaRGUHFjOOhI4h25CMGObelyVKioacjGCioksSBiqGKaPIUQw9gaa5iRqgYSLpMWRZwpRUMOLIigUSEVA04pKVhAkuBeSEHxRbarKzomEaCUzVQTwZQpUtKIaOqVhI6DFMWcYiqeimQcJIzeOQE7HUBGk9RlLSUvMT9Bim5kZJ+JEkBUPWUIzUcRMHTCOJrNoIJ0PYVCeSkcDQE1gsLgJ66iWFZjIJqkosGcNt8YARJ5yMpMqjENdDSIBDsYCug5HEUC3EMdGNBJpiQTdNEqaOBDhlG7IeBczUSy/1OAlVI2rqqeNAkpEMGUUykIwEoGCoGiEjhlVSkQ2ISQaGqWPVHEQTEayKhbAewS7bsUoyQT2CLKvIUuqlpKZp4JadyMkQSUUlpCewqTaSRgIDA5viJKgH0SQNWZLRzSS6aWBXbMT3vgDTNE00SUGSFSTDIGLGsMo2onoEl+IgYcQwJAlVshDVI3tHIU0M0yBpGiiygmJKSJgk9r48FKTUOULeOxdRAqtsI2EkSJpJrLKFuB7fOwctiWGCXdaIGUl0DFRJxcQAJEx0FEnd+9LB1EsEk2YSu2wlmAxiUxwkjAQmBl7VDbFmEpqDoJ56gaFVsmLqISySQkxWiO+dx6TuncsXS8bRJBUDnZiZwKt6CCUCIClYJA3FNIiQTPU7MlEjilW2IiXjSIqGjoFsSiiKRjgRxK46kEwVQ0pgGnrqXaiGgSpbMPTY3nNOEoey9zazJIGsEJUkonosdStMN4jJEDOTWGUbpqGiyakXbDpUF4aRIGnEcVo8qJql7fqV3Hs9iiR0VFnGosqE40miCQOvTfneAp59/r8JfH5s/lOBjyAIgiAI/zk/+vf4CIIgCIIg/LeJwEcQBEEQhE5DBD6CIAiCIHQaIvARBEEQBKHTEIGPIAiCIAidhgh8BEEQBEHoNETgIwiCIAhCpyECH0EQBEEQOg0R+AiCIAiC0GmIwEcQBEEQhE5DBD6CIAiCIHQaIvARBEEQBKHTEIGPIAiCIAidhgh8BEEQBEHoNETgIwiCIAhCpyECH0EQBEEQOg0R+AiCIAiC0GmIwEcQBEEQhE5DBD6CIAiCIHQaIvARBEEQBKHTEIGPIAiCIAidhgh8BEEQBEHoNETgIwiCIAhCp6H+0A0QhMPRHIrjjyQA8Dk0fA7Lt68kWAeRFjCSYHWDrxTiIQg1gB4Hiws8RYesoiEQJRTXMTDJwY/dCKGpFnDkEE0kkWXQos2QCIOiYVpcYGpIiabUejUnCVcxlf44saSBTZUpzrLRGEoQiCRwWGQUWcEwDQrNBpR4KygahuokKLsJmRqmKaEbBjLg1RJYYs2gx0koDoKWXFxyHHusHikZxVTt+C1FtBot6EYIWVZRJRkkFVVWCcT9KLKKTXZjGg7CUR2bpqDKMlaLQSDeRNyIY1VseC05WML1KMkwKBYilmxiWIgnDSIJHUWWUGQJ0zSxqgp5bistkTitER2nHCfL9KORIKk6qSOL1mgSiyLjtin4DD8BM0LQiCMrFhTJRziqYFEV7JpCJJ4kaUDCMLCqMhZZRlVlCjw2wrEEta0xYgkDm6bgtqm0RBLEkwYWRcZnV8lx22hojdEYiiFLElZVxmVVaI3pmKaJRQFvshFNj4Jmo0XOImoo6KZJLGGgKTJ2i0wwksRmUejis1PZHCGa0HFYFbpoEdR4gIRhEJbdRDQPEhCOG8STOpIk4bGpeAhjizeBHsNQHYQcJQSiCYIxHasqY1dlIkmDhG5iVSVyXFbcNi3jOPSH4wSjSbxymKjZStCIo8gaNtlFOOHAZVUxgHBcx9BNvNYErkQLZjJOXHEQ1HKRJZOkARYFtEQrtmQARQJZtaLrCSRZIaDm0BABMHFoCqoiE03ouGwKoZhONGGgKhJWVUGSTfJdNiyq0tbOQCROQzBOXE/tC5sGMbOFpBnHolhQTS/BqIkkSTg0BasqEYjqxPXUfi702HBYU5epxmCMYCxJDn60ZBDVlYMcaYJkFFQbUXshtREZ3TAxAadFRtdNEqaJKkskdZNoMlWvx6YSjOkAuG0yLRGd+N68HJdKIinhtMi0RJOEYjoJPfVdtWkSkiRT6LWn7Y9af5RQPImmyOQ4LcSSRtv5ymvXsFlkGltT/eC0qGQ7VfaE6ojpESyylXxHLg6L7VudzqpawgSjqe+d166S585cPpJI0hiME08aOCwqBR4rkiR9q/X8LxKBj/CjltQNNtW0ct2ra1hZ4QdgRFkWf5w9kF4FbhT5ML/EdRvhnd/CjkVgmpDfD6bfDsEGePXnqaDE1xWm/RHKJ4Hdm1FFVXOYPf4oX22t4uT8WjyLr4WGzSBJmL2mYZ1+J1LjVnh3fzo9psKxtyKtehY+vQfy+6Edezul2T3pdfcqLp3Sk67ZDv7y/mZGlWXxyym9qKjazRR3BdJ7v4P6TSBJKD2m4pl2C1ZLFrtjdgKRJEO8IZQPbkVe9xLoCazeUhzH3IJkJJDeuBwSYQITf8OXJQP488r7qQ3Xosoq07pN44QeJ7C5aTOyLHP38rsZXzyeK4dfRWssiyueW8k9Z/Zg/pZneGnL80SSEXxWHz8fcAEzEhLZr18Oiobafw6WCddy5vMVrKrwI0kwoVcePxtfzle7mpnUN49E0sCXrCdr5V+wbnwZ9AQWbwnO8TfyXHU5zXGZayc4WBXczR9X/4Pt/u1ISBxVPJ6f9b+S656t5vcz+7OyooV/Ld5OSziBw6Jw1uiujOuRQySu8+/PdvLc8grCcZ0sh8bPJ/bAY1P53Str0RSJWUO68ItJPbjqhVVp7bzs6J64rCrWeDPu7a/h/PIeCDeBxYlv6AU0Db6QU5/cxu7mCJoiMWNgEb+c0pPLn13BWWO6sa0uyOfb6vnHMXaUT66FymVogLtkFM7j7mZtsgs3L9jE17tbKPbZ+Oin5WiLb0Na/yroCWRvCe7J15MoOJI/flCHpkiceEQJd7yzkS11QSQJJvbO48ZZ/SnPde0/DlsiBEJRCuVaNkZT/batZRsSEuOLx3H1sKt4c7XMiLIcvtzRyBl9FSwf/gl57Yt7j5MS9PE34C8aj2zzoDRvwbPwGuSqL1MrKBmBMuHXsOSvOPOPYGvp2Zz93E4GdvFy4wn9cVpUnv6iguMGFnHyA0uIJAwm9Mzl2hn9+Gj9bo4dWES+x8bupjD3fbCZ11ftIaGb3DannJj1Kx5b9xDNsWbsqp0Tus9hqGc217+8i7+fOYwXv6psK1/ktXHVtN6MLMtCQuLNr7ZzTrdmXJ/+EU5+CD65G75+PPXjxZ6FbeyldB14Cm9WaryztgZVkbns6J58saOJhmCcf3+2k6ZQHLumMGdECWPLsinLdzL/8yqe+XI34biOz6Fx4VHdOWFIEYGoyV8/3MqC1fvb86tjepPnslITiNGv0EUsabJkawN/XLCBqpYIqiwxc3ARp4/sys+f/ArDMPnXuSN46esKXluZqufFXw7m7d0f8+i6f7X1w0k95nDegPPo4i74xlNZIJJgbZWfm95Yx+ba1HFyVM9cfj+zP70L3G3lqlsi3PPBZl5dsYe4blDktfHb4/oyqU8+XntmMN2ZSKZpmj90I35MAoEAXq8Xv9+Px+P5oZvT6e1oCHHcfYuJJoy0dKdF4a3Lj6JbjvObK2ncBo9NT434HEhW4Px34OULoXnn/vTTn4G+M9KK7moMsasxzK0LNvDv6QqFzx+fCqD2GXwGjDgPHjsuPR3AXQQ/WQB/G9a2XvOC93i+OpdYEm54bR0AC6+ayO9eXsMz00F+vP16zLNfpt5w4bYq2J6di1S3LnN7Z/0VvvwXyCrvjv85Vy//U0aRvtl9mdt7LnuCe9gZ2MnC3QvJd+TzwNEP0xRQeXLr3XxctTBjucv6/4Tztq/EsuF1APSCIXww7H5+/kpFW5l8t5WbZw/klRVV3HJ0LrmvnYlcuzajrsaZj+LpPoJN/i2c9clVGGb6Ps6z53H7mId4f02chz7ZnrH8n+cO5oMNtbyztjYj7+cTurOrKcw7a2sA6F/k4ewxXfndK/vbke+28u9zh9Bz88Non9yeUUesz4k86ruMOz6uaUvrV+TmthMHcdIDn3HbiQOZXBCi6OmjUyN8B9IcVJz2PpMf3U3SMPnyF73Ie+sCpJo1GesxTnqIdTnTaAzFueDxZRgH7fY8t5VXfzmOYp+dyuYw2+qD9LE00UQVZyz+VUa/5dpzeWTKI9z5djO3Tcsn65UzkWpWZ6y3ccbDOLsdge2Riang4aD2c9qT8PSpxHrO4PGcK/jTRzXYNYXX5o2joiHEc19VMnd4CRfO/6qtnfMvGMVfF27h2hn9uPjJr1i3JwDAsQNz6dP7Kx7b8EBGOyYWH83ZPa7iljd2tpU/0N1zhzCpdy5y3RqynpwG876ChX+A9a9mlDXHXUntoAtZ3ajwu1fWMHNwEdlOK395f3NG2ad/NoqnvqhgwZrqjLyLJ/bgqJ45nPXIlxl5f547hGU7Grlkck8217Ry0d7tP1DvAhcXjC8nqZs8u2w3a6tS23X1tB7IWYt5YPXfMpaZVDKF60ffSL4rOyPvQCsrWjjlgc/QDzpQ8lxWXrh4LGW5Tupbo/z08WWsrsrsz7+eMZRZg4v+J0d+Dvf6Leb4CD9a8aTOY0t2ZAQ9AKG4zrNfVpDQM/MybFuYGfQAGDosuh2OvjE9/b3roLUmLakxGGPdHj8n9XOSt/S2zKBk4q9h4S2Z6QCt1bBrCRx31/71fnQrc3sqPLhoGwAXHlXOki0NXDM+C+mjP3ZYj1S5DA8RNP+O9oMeSI0sDf8JdaN+yt0bHm+3yMamjXgsHl7b9hozylNBXl24jtX1X1HkU9oNegAe2vwc9UNPa/us1K5ioKMJj23/4HFda4yK5jC1gSiecEW7QQ9AzpKbicRbuG/T0xkXb4D6SD17YmtZur0hI0+SINtpbTfoAXhi6S5mH9Gl7fP66gBum5bRzlxa0D6/r906rJteZUb39EHxDdWttMaSFPtsrK9qInfjk5lBD0AiTPaG+RzbL4din43sRHW7QQ+A/NEtlFBHRVM4I+gBqG+N8dnWVB80tMaJxeLYQpu4b9Mz7fZbQ6SB1Q1fYdNk3JHKdoMegJzqxWgrHssMeva2nw2vQ5/jsG5+nellCpIEkYTO/KW76JJlZ3dTmCKfHVne385lO5vwOVSqWiJpQcxJI9w8u+XxdtvxcdVCXM4o66szL9IAf3l/M3IiiO+z28E0IBlpN+gBkL54gAIlyKNLdjB3eCmjynN48ONt7Zb1OiztBj0Ajy3ZQb6n/VtPf3lvE6eN7MqO+hC3vrWh3TKba4PYNYU8t6Ut6AGYMtDG4+sfbneZRZUf0hxrajdvn4ZgjHve35wR9ADUB2Ms2pQ6z1U2R9oNegD+9NYGagOxQ67nf50IfIQfrdZokqXbGjvMX7KtgVAseehKknHYvqjj/Krl4C1NT2vannExq/ZHWVnRwhEFGsqeZe1UZKbq6sj2RdBzattHqXI5kh5jjz8KwJE9cliyrZEePgmpsr3699rxMaoMUsUXHZdp3gHOPMLObKpD7Z/YAba0bCHHlpOW9nnN5xhmx30aSUZoldNPG/baFZRmO9LSVlW0MLZ7DlLVIbaleScRRWNlQ/sBAcCnVZ/Q7aC6AVwWlfrWjk/ekYSeETtuqA5ktJNoCyQiHdZjCVbisqYHPysrmhlRlk2ZS0fbtbjDZZ1VSxhWoDC8WxZy5SH2V8tu7FIcl63jmQcfbqzDMExqAxGUeJCoqhyy3xbXLGVyn1ykykMck648lF2fdpxfuTx1SxiwtFbg3tsPy3c1gQS9C9xUNIUpOiBAWLKtkWP6F7Jid3N6XUqESLLjfq5orcTdwfZXtUTQ9BBS5RdQMAj8Fe2WA1LzfaJ+VuxuoXdh6rZPOK5nFPM5VKpboh1WE0saBPbO0TnYHn8URZYwTJNdje0EvXttrG6lIRhPS4saoUP2w+5AZYd5AMFYMrNvD/DZtkYSSZ1VFS0dlqn2RwnHv+G8+T9OBD7Cj5ZFlclzWzvMz3fbsB4wkbJdqiV1q6kjzrzUL8gDaXaQ0++Bu20quS4rTVEDnPkd1NVBOoC7S2oS9T6uPNg7yRagJZKgwGMlnDC/sR7DBNNT0nEZzQ6mjsU0scgdTwLPseXQGm9FU/Zva4GjAFk6dJ/apPS+SbqK2iZy7pPrslLXGsU8VN9rdmTTJMee02GRYlcxgWjmSTqa1PHYDz1FUVPST295biuB6EEXMy19kurBDJuPaCL9wlnotVPtj9AclzCdHc/J0B351EegJhDFdHfpsByqDUPWiLUzsrlPSbYdWZZwWVWSsoZqJMm2d3xLpNhRSG1rHDyHWG8siHmoY82Vn3oYADDtWUT29kOuy4qERCCSwOew0Bzef3Ev9NioDUQp9KaPlmhSx99jgCyrj2i8/e23qjKmpKW+q827weY7ZF1oDvLcVvyRBJrS/u2cYDT5jfNc7Jb2vwdWVW6byG/XOv6u5LosOA7Ktyrf0A+2zLmFB1JliVxXx3UUeKxoqpLR/weyKDIWpXNf+jv31gs/am6bxiWTenSYf9GE8g5PTmmGndtx3uiL4esnDip/Xuqkf4Bin4Oj++Xz8IogzUMvyaynfiuMvqj9dUgSDDkNnpzTlmSOvphmazHHD04FBn94fT1zR5Ry3/Io5uiLO65n0BxiqBjFo0Dt4AQ4aC5seJOc9W8yu+zYdovYFBtFriK6uLqwtiF1K0pCYkb5TEJxg3xH+xfEEfnDyKo4YN6DZqfRO4DK5v3BoyTBhN55fLmjiUSXkR22MzrwTLINmQu6n9j+5iIxpWQG8WTmBTGhmySSBoUd3I4YWZbF6sqWts9WVaYky0FFU3qQG9WyMLuObbcOPMVsjbhIHnBbwarKDCr28uWOZiKmDf/wX7S/LFA35BJeWtPClzua9/ZD+201B81lZ8yNeoiJ+nOHp0Yli3x2nC4PqlbET7uf1GH548tn8vTnu0kWDe1wvRFTIzZ6Xod1MPi01C0ldxFbo24SeqofLhhXTiiWYEtdK3aLQviAgOXkocU8sGgbg0t82LT9l5eVO5MMzx/R7mryHfl4LLnIHVyNZg3pQsSSRXT0ZRD3pwIgb/uBv1kyEl1zMXdEKa+tqGJNlZ+x3TMDxKQBVk2m2Nd+4Dusq6/du80AswYX8dWuZrx2ldNHlrZbxqLIlOU6cdnUtOCoulFhZMGodpfJtedS4Chsf6V7lWQ5uGBceYf5p43sCsDAYm+HQdlJQ7uQ4/oOT8X+DxGBj/CjNqCLl0smds9Iv3Jqr7QnGA7JUwLH3w3SQYf7gJOgeDiseWF/WtexMO7yjIt1rstClsPC9AFFrPZMItprVnpdz58JfU+APumTopGV1GRjzNRJGzD7nwS9pjHmTx8xY1ARQ0q8tEQS1AciDCzOornsOMw+x2fWM/OvJBUbjVI2LWoOidOey7iomaVjoO9M2PA61rUvc1HRRIbkDkwrY1ft3DLuFhZsW8DPBv2MZzc+iyIp3DD2RsyElwc+qOeWMffis/rSlit1l3Jzv/PxfvFQKkGz4z/5aW7+uKWtjCJL/P74fnywvoZbTxpIjeGjYfZTGSMryeLRbO51IZv8MlNyBjO9ZFJaviIp3Dz2Zp77vJXLj+5F14NuUWU7LZTlOvnnOcPJcqT/cu+W4+DSKb14YukuAGyazN/OGJo2QVqRJa6f2Y8W00nj1Hsh+6BjzJFNwwlPcuNH++dc2DSZB88eztNf7uaIUh9je+byanUWraOv5GDJ8VezWy2nPpi6HXfX50H00zL7wSweTvzIX7GhSUc34dgB6SNIiixx15zBbRfoIo8Nt02lknwm5gziuJLJGf12y9ibWLcT7j3jCJ7bmCR5xguZ/d9lJHUDfkLQ25fW0b/KaD+jLkpN+DeS7Jk5nxs/St1e+dn4crrmOHh5xR7uP3MYd7+3qa2dt544kHV7/Dxw9nC6eGw8fO7ItuDniSV1XND3t3R1d01bjdfq5foRd/Pgwnr+PGdIWrAEMKTEy6VTehKM6XymjEQfOBfWvwanPQXO3PQ2Z5XB7L/zr1WJ1Bys6gDDu2bxm+l9KctJP358Do1E0uAfZw0jx5keBJRm27lzzmCiiWRG8DC4xMtFE3tQE4hS6LHz84k9GFmWlVbGqsrcOWcwD3+ygwc/3s5dcwa31XPja9u5duQNdPN0y+iHv07+O8WH8VTX5L55zBycPpIqS3DL7AEU7R3pKfTYePyCkRntP6LUxxXH9MZu6dwPdIunug4inur68QlEEtQHY3y5owlZgpFl2eS523+/SYdCTRBugN2fQTwI3canTpyGDtUrU5Ofi4en5vu48tqtoiUU2/tejyRqtJkuSgv26i+R7F6kkhEYKCiSkbo9sGsJ2LxQOho0J2z9ILXesvGYtize22NhZ2OYQV28lGTbaQjGWVnRwlE9s5EkmXwa8SSbYdenqXcOlY4hqTpoxEdDxMA0ocAp49UbkKtXowdqSBYNQ/KWYDOjyPXroWkbFB5BfU451bEm1jauJduWTam7FAMTl+ZiWc0yVFnliLyhyIaHtZUxin0OvDYF2RJgm38blcEK+mb3ppujiOzWBozdXyC5C6FoCC1qDvUhg692NWO3KPQr8hCJ6XgdGm6bSlw3aQ6E6Gbx42pahxquw+wylBZrEa9viZPtsjK1VCIhRaiPtbCicS1Oq5cBuYPZuEfBa3XSxWfHH4nTGIqzoyFEWY6TYp8dwzTo4rXTHEmypbaVHQ0hBnTxUOyzUx+MsbLCTxevjf5dPLisCnWtcZbtbMKqygwp8eGyqsR0nVAsSZk1iMW/A61hA3JOdwLuHlQZOUSTBqsrW8hxWhnQxUNVcxinTSPfbaWyOczaqgCTy6yU28IolUvRDZNI0Wii1mxCOAnEkqzY1YyqyBzTy0Oe2YC0ZwW01mCWjCTp6sK2uI9PtzTQPddF1xwHgWiCNVV+3FaV4d2yyfdYcRxwoYrEktS3xrAYITQ1SGO8hRUNa3BaPQzOHUI0akdV3YCZevxfMSi3taLUrCYZqCZRMBTdU0IzXkzAK0ewRhtQKr/AKhtIJSMxWqsxTUhk9+bdCoXmSJKx3XOwaTJ1gRi5biuYBh9uasBlVRneLYt4UsfrsFDssyNJEpFYkupAatJybSDGsK4+XM4wNaFKdrZupdRdSqmrnFU7IJwwOLJH6pbnmio/NYEoQ0uzKHBbcKgKFotCfTCOGW6iVPOjSKBa7VC3CZq2QMFAyOpOBQXsbgrTEIwzsNiDKkus2NVMeZ6LUFxnfXWAbtkOeuS52FQbwGFR6ZrtYGtdkO0NIfoVuuma48BlUUjoJuGEzro9AWoDUYaU+MhyakgS+OyWtvfmNARj7GmOsLKyhTyXlf5dUteNz7c3Ypowdu92baxppTYQZWS3LJzOMJXBCjY2bqKrpyu9fD0o9XRB7mjY6yC1/gj1wTifb2/EaVUZWZZFjtNClnP/D7akblATiLa1f1Cxl5IsxyGnD/z/7nCv3yLwOYgIfARBEATh/z/icXZBEARBEISDiMBHEARBEIROQwQ+giAIgiB0GiLwEQRBEASh0xCBjyAIgiAInYYIfARBEARB6DRE4CMIgiAIQqchAh9BEARBEDoNEfgIgiAIgtBpiMBHEARBEIROQwQ+giAIgiB0GiLwEQRBEASh0xCBjyAIgiAInYYIfARBEARB6DRE4CMIgiAIQqchAh9BEARBEDoNEfgIgiAIgtBpiMBHEARBEIROQwQ+giAIgiB0GiLwEQRBEASh0xCBjyAIgiAInYYIfARBEARB6DRE4CMIgiAIQqchAh9BEARBEDoN9YduQEeqqqq4+OKL+frrr7FarZx//vlcf/31AKxYsYJLLrmE6upqnE4n9957L9OmTWtb9t577+X+++8nEokwcuRIHn74YXJzc3+oTREOUzypUx+MEYomqQ/G8dk1ethascYakOIh8BSBMw+sbnY3hmgMxQnGknTx2eimBVCjzRCsA2cO2LPB5oFQA7TWoGsuQlo222Mu3FYNRYaK5gglXguFsh81XAd6nISjgHrTi2ZzUuixEU3qNATj1PgjWFWFPLeVQlsCOVgLegIsDogFMYO14MgBmxfTkoUUroZEFKwuiLRAPAS+0tQygT1gc4MtG9PiRoo1gyMfos0Q84OeTLU93Agm4MgCRyFEGyFUD0YCvF0hEYJgfaouqwdTsSElwtC6B6xudFs2umJD9u9G0uzINg9SrAXCTeDKB6sHEmEI1mFaPYStuWyPuLBbVOyaTK5ejzWRKm+6CgirHjRZwRJvhmAtOLIwbVlIegIzWIukWva2uxmsbgyLkwAeorIdd6IRLdoAsSCSp5CYJYcYQZoSAQIxP9n2XNyyA3/Mh6JIRJMGAOWOCNZ4C7RWg2IFZx5JXceUVWTMVNsVC2FLLnWmD0UzkdRW6iP1KJKKR8tGM32osoyum3jlEM5EE3KoDtnmwVQsmKoNU5JJNlciW10k7HkEtRxq/BFyXFZiuklrNEkwmiDXZUVTQJMV4oZBQjeJJnTCcZ18txVNkalsDuOyqeTZJbLNZqyqipSMgB5L7duYH9NVRFD1UZ9w0BJJ4PNECevNhBMhCmzZZCXiOONh4vY8dse9qEacUmsQKVgDsobuzMev5BBKQGMohteu4baq5Ok1qJFGiAcx3UUEcLMtYifbaSGWMGgMxclzW0noBpGETpHHBphEEyb1rTFMTAo8NjRZoqI5Qo7LgqrIGIZJ0jCxqhKmCU3hBLGETqHXhtOqEIrqxHWTLC2B12hBCdUgq1Z0Zz7NcjYtEQNFkbBrMrIk4U40YIk1oiRCGK4CEoqTuKLjJ04gHiSSjJBnz8elOgkl/DRFGvBoLnJUJ9mylRps+PUWmqJN5FhdZEsaWckkiiSljk3NBjYfKHZIBiHiB7sPMxknpus0uXzUx1vANMjX3OQmdDRVw7S4MeNBksFGVLuXsKeAxrifpmgTMT1GgaMAl+JgS62K166R7dCIJgysST85UitqpB7sWfhlH2HDQoESQA7WkFQdxG25hG0+wnoLdeE6NEUjx5ZDnqLhRyeiR6gP12PZm+5UckkkFXY3hXFYFDx2jdZoElUGTVVS+8s0yffYsKsSOxoj5Lqs5LutGKaOJ1aPGmvCDDchufJJWHxsiTjwOmzku63oiThSqBaptQYwwV1E0p6H0+n8YS4A/0U/2sDn3HPPZfTo0bz++us0NzczZcoUSktLOeWUU5g1axaPP/44U6dOZfHixZxwwgls3LiRwsJCnn/+eZ544gm+/PJLvF4v8+bN46KLLuLll1/+oTdJOITWaIIttUHeWlvNvz/bSXmuk3/PcGB7/ifgr0wVkmTM4efTOvoqznhkE1UtEXwOlaUX90R57UrY8fHechKc9hRsXACrngbTRAE8WWXkH/cY5zwf4IhSH2ePKKSo5Wtsr5wPkWYANMWCb+w1LFCn0qNrKbsaQ1z36loSugnA+xf2RvIvhe1LYMpv4fXLYMfHSPs2pGgInPIwUnMlOLNg/kkQqIQTH4CV82HtS2Cm6iKrDE56EMmaA5F6eOF86DIUCgbAh7ekgiUAiwuOvxsqvoBVz8Dpz8CCK2DbR/s7sGgI0vQ7UsHX8+dCLIDq64Y693FY8yyM/jk8fzbUrk31z+x/wPZFsOZ5ME0kwOXrRu85T3HLZyY3j7OhvHR+qjwgAc4eU2D6HUiPTYdYAKb8PhWQLr0fSU+k2uHIgRl/ho9uRVE0fMfdhRGtRnn+DGjZnSrjyqf2rOe4ctltbGre1LYJ47uM5YZRv+el5TGGlWUxwhtE++pJpCX3pgLGvfVr57wK616EJfe0pXsdOYTPfp1XK5fwrzUPkjSSqS62ZnHdyNspsvamryOK8ualKNs/bFunVDgYpt4EmxagdhkGT83D4i1FO/VpZG93djXHuOG1dayvDrQtc1SvXH41tRf+aJLfv7qWyuZI22E3e0gx43vlsGZlFVd224G955Hw2d+gz7HwwU1Qs7qtP909jkY+7m80qM384sOr2BPaszdP4uSy45iXPZTcZ06j2+yHMALVWN+5/IB+yEY68TFu+dzK3NE98FpVCsKbUV44F1p27W2PhHfwGZSPvppj/rGNP50ymMc/28nS7Y3MHlLMWaNLCcaSrK70c9Pr6wjF9VTVFoWbZg2gwGPl2Hs/4aieOdwwawCfbqmnT6GHK55bSVMoDkCfAhe3njSIt9ZUM3eAE+f6F3AsvRP0VL7myCb35MfZpffCVC147Rp5ke143jivrZ04c2k+7zV2xoLctPQmqoJVbf1wfPfjGVM0huuXXI+JSe+s3tw9/nYeXHs/C3YsaNsnvbN6c+/I31H61JkQrEklekvh5H/Bh7fBUVfCO9cQzu/PJ70ncOPi+wgnwwDYVTs3DJnHpOptuPqfAF8+jDJoDo2OcrY0b+KaT66hKdoEgCqrnD/gfE7ueRIvfNGEBEwp1ildfj3a9vfb2pNTMICs4+9FfuocCNagAtHpt7HAYeHvqx4gYew9bq1e7jjqDurD9dz8+c1p6beP/xM9XP15f101Z4wp45dPf82cYaW47So3vLq2bX/ZNYUbZvZnQBcPx967mHPHdOWm8Xbkl34K1Svbzk1a2QT6nfBXjntsNQ+f2Z/82k+wLrgM4sG9BRxw7J8J9JyOx5fD/7If7a2uFStWcOaZZyJJEtnZ2cycOZPly5fzzDPPMHLkSKZOnQrAhAkTmDhxIs899xyQGu258cYbyc7ORlEUbrnlFt58802ampp+yM0RvkFVc4Q1VX4e/mQHCd3kz9NyKHp17v6gB8A0kJY/grT6GXrm2QH450ldsX3wO6R9QQ9A7+mw81NY+dT+IAOgeSddXpvLncfk8MqKKsotzdiem9sW9ACgx/F+egtT3bv4/atryffY2oKePgUuemqNSK9fCkf/Ht65dn+wtU/1KqQXL4DcHvDUHPBXQK9joWYNrHkxoz3Sc2eDKsNz56TK9j4W3vnt/qAHUiemV34OvY6BURfBsofTg5696+Wtq6F+YypIgtSF5em5cOSl8MJP2oIYek2D2nWw+rn09rTswvb0bG4+yonywjn7y+8lbfsQ6aM/wvF/gdxeqVG1T/cHH0BqlOqVn8O4y2H7R0gL/4DSvHV/0AM0TP4tl315S1rQA/DpnqXc/fU9TO6poUgSWvVypMV3pddvz4Kq5bD4zoz09S1r+ceq+9uCHoDmWDPXfjYPSW1Gff93aUEPkApE3r8ecntDw2boORVadmN58gTciQaue2VtWtAD8MmWBsIJnV89v6ot6IFUV766soqdjWEuO0LGN+DYVLCb2z21jr1Bz/7+XEhreAe//vQXbUEPgInJSzvf4sVIJcniYdifPw2nLx8O2C7CTdiem8vNk3xE4jpd5TqUp07eH0zsbZC06ml865/igrHFzHt6BRdP7AHA66ur8DlTIwi/fnF120UUIBzX+c1Lq3HZVHrmOflkayN3v7uJCb3zuPCJ5W1BD8ClR/fiyudWcnS/fHKbVuD59I9tQc++dmrPnMKwrFCqrwJV5L08J62djRN/zaZYI9d8ck1b0LOvH97c/iabmzczoWQCAJubN3PFJ79mSP6QtL7c3LyZS7+8hYbJ1+xP9FfAs2fCsbfABzdC4zZ2HzGH3yy/vS3oAYgkI1z71V3s7DEu9ePjqF/BBzfh11v55cJftgU9AEkjyUNrHmJF/Up+ObGMaChA3zV3pQU9ANSuQ35jHkz8depzVjmrvHncu+KvbcENgD/mZ96H88i156Kbelr6pR9dRshs5swxZVz2zApq/FEGl3q5+oVVafsrktC59pU1RJMGw7p6uXqMC/mlC6B6ZVqTpJ2Lkd75Lc+e05uCZDXWV3+6P+gBSISxvvkLbP7t/K/70QY+Z555Jvfffz/xeJxdu3bx2muvMWfOHJYuXcq4cePSyo4aNYqVK1eSTCZZvnx5Wn5ubi7dunVjzZo17a4nFosRCATS/oT/rnA8yda6IE8sTZ0Mi312CkIb0wOSA7iX389FQx0ADM01kba8m15g4CmwYn77Kws1UJzYxbQBhajrXkq/eB4gf/ndnHuEm2U7mxnWNQuAe0/oCkv+mioQb4Ut77S/jpo1EPVDdO8FYNAc+PqJDttD/WaINEH/E1MjOh1Z9VwqiNq0oP382rWgaJDXZ39aNJC6tVW/cX/awEO0J9yI3NBx37PhjVT9R5wNyx5qv4weh12fQelo2PgmZJfvz5MVGrK6srVla7uLvl/xIS53goGOZqRP7s4sMPRs+DJzvY3Dz+UfW19ot86EkWBpzYfI4Yb221u7Dnxd4ev5MGhuKi3chFy3jpZIPKN4gcfKrsZIWgBwoFgkhG/FAxBpTAWpvq6pdRzMXciWUBXNsfb7+omtL1M/9PRUf+78BLoemV5Aj+PZ9gZdsx1YGtengs52yMsf5pz+KnHd4MsdTYwqy+bMkV3xh5M8tmRH+30C/Gvxdm6aPQCAPI+Vt9fWENt7CxIgz20lGEsyunsOZqiBvOXt7C8APYG09mXG98olO7g1dczvI0m0FPSjIdKQFmAc6OUtLzOjfEbb520t2yhwFKBK6TcstrVsoz6rBOQD0qMtkIxDzRpi/U/g3zvfwsSkPY/seINwzympY/9nH/H+rg+IG+3v44fXPExNpIaT+liwbHyl/e2u3wSuApBVmoefzd+3vthusaSRZGn1UkYUjMhIf3P7m8gStIQTXDalF48v2ZH2W+VADy3exh9PHITL8Kd+CLVD2vIuXiWG/Pnf6agiZck9hFr97a/kf8SPNvC55ZZbWLhwIT6fj/LyciZPnsykSZPYs2cPBQUFaWXz8/NpbGykvr4eXdcz5vPsy2/Pn/70J7xeb9tfaWnpf2ybhPZF4joWVWZ3U2qUI99txdq85RALNOPVUl9aKd6a+QVWLBBr7XBxS8s2eubYcTWv73gdTTsodcvsbgyR77ECUGhLIjXv/TUUD3Z44gAgVAcDZx1We2jYnDpBeoqgqeMLEc07wdAPvd5IEyRj+z+7ClPzYw6kWlO3qjpSv7c97TGN1GiUu/Ab2roD3EV7y+//dY3FRUMHF3oAwzQIJ0JYlL11HMxd2G560lNERWtFh/VuatmM0dE2QSpoMBKg2vanNWwmz23NKJrntlLZHM5I36fQbqA0bUkFLEayw4AEVyHbg3vazwNaE63E97WneWdq2w+i1a3BooDUsLnDeoj6sZipC/jW+iAFHhtlOU4M02RnY8fbsasxjNuqAdAt28mW2mBafr7bSkVThHy3FYdiHPJ40OrXYJFMLC3bDspwEDQSaSNeBwsmgqhyepDTFG3CoTkyytZHm1K3hg+on2AtABFPETsOGFE62M5gJVFfaaq8JHUYnANUtFZgYmDRw6ljvCOhBrC6iXu6HPL4rGytJN+Rn5G+w78Dw0jNyyrw2A65v3Y2hrHIcmoeYEdMEykRQW3q+PyqNG/FiHe8nv8FP8rAR9d1pk2bxiWXXILf76eqqopVq1Zx3333oes65kEnfl3XkSQJXU8N/3WU355rr70Wv9/f9ldR0fHBKfxnOKwKsaRBz/zUCWuPP0I0Z0DHC7gKaIyl9qdh9YCspOcnIqlbIh2IZfdhXU2QQO7QDssYuX3Y2qLTs8BN1d7bGbtDKmZe31QBqztzvQdyF8LaN/a2J3zI9lAwIDXhuWV3+mjNwfJ6p9Z5qPU6ctMv3sEa8Jakl0mEwZF96Pa0dnAhklWwOL+5rbm9U7czZDU1AXyfWCv5to7XrUoqTs1FJClDbjv1t+xuN93SvItyb3lm+b0G5wxCDnR80cORC5o9fei/YCA1/mhG0Rp/lPLcjieAVoVk9PyBqYBXtaXqbk+git7urh3Wk2XNwhbb257c3mm3C/eJF48imjQw8g/xfXHkEMUCQL8iN5XNYTbWBFBk6FXg7nCx3gVumkOpIHpzXSsDiz1p+dX+KOW5DiqbI7Qm1dTx2YF40UgiSZNYdt/0jEQYt2yh6yH6wWf1EdXT90OOPYdQIpRRttCWkxqNPaB+PEUAOJp30ddT1uF6+nrKcTRsA08XME365/TvsGwPXw9kSSEqO1KjrB1x5UMsgK1pJz0OcXyWe8vTbvO1tSm7L7JsYU9LhMrmML0Pub9cRJLJjn+0QGqepOYgnj+wwyKJvIHIVleH+f8LfpSBz4cffkg8HudXv/oVmqZRVFTEPffcw5133kl2djYNDelD1vX19RQWFpKVlYVpmjQ3N7eb3x6r1YrH40n7E/677JpKvyI3F4xLnRhqAzGqrN3b/YUL4B/9K/6+PHVBWFItY/Y/Ob3A6udg5M/aX5m3lJ0U89Gmesx+s1O/CNvRMPoanl0dZFCxlzVVqWHfea/sgCMvB0kGqxcGnNzusnQdk3piyrX3JLXqORh1YQftKYHs7pDVDda9CkPOSNV/MFlJ3YZZ/xoMOrX9ukpHp0Z79qzYn+bKB5sXioftT1v1LIzsoD2eYvTsHpi+Di5Eg09LzR1Y8SSMvrj9MhYXFA+Hqq9T5esPmMtjGuTWbmJITvsn3tnlx9PQorIx4sWc9NvMAiuegjGXZCRnfTWfy3uf0W6ddtXO0NzxGDkdXJhLRqZG3Ub8FFbuvdXo6YKe05vSrMzjoyEYp4vPTpHXlpEHkON10zzk52DLSs2vaticWsfBQvV0dxRS4Gj/QnVR71PJ/frJVKBZOhoql6UXsDhp6TqNyuYosaxe4Clutx7zyEt5YGUch0VhSImPr3e38OLXVfhsFi4YV4YiZ/4olCW48KhyfvdKap5XQjc4ul8BTsv+oLspFEeVZVZXtqC5cqgbdW2768fihH4zWbKtkSZ7WXogbppkVa3AZ/VS6Gz/+356n9N5Y9sbbZ8H5g5kd2B32pwYgEE5A8it35IaFd3HVQhIUDoay4Y3OafsOBQp84eDLMmcXz4T266lqR81TxzP5NJJOLX2A9xLhlxCvjWfZ9bHiAw8u/3t7jIsNTpp6Hi/fpLLep/ebjG7amdEwQhW1K3ISD+27FhMoFuOg3sXbuG8sd063F8XTejBVc+vpgU3Ztex7a7LHHAy9XENRl/S/g8oScYcdwVOV8cB1v+CH2XgE4/HUdX0oU1ZlonH4wwfPpzPPvssLW/JkiWMHTsWp9NJnz590vKrq6upra1lyJD0yXDCj0uxz06vAhfXTO+Dy6pyxdt1VJzwAhQO2l9ItcGka6HfbIKx1PDyvFe2Exx/LebAOfsDhm0fQsFAGH9l6rbOPl2GsmvmM/z2gwZ+dlQ5X/vdRM96PfV01T42L83T7uP12jz+ctoQogkdlzV1LFY0R/ja78ac+294dR5M/n1qvswBgYrZYwrm7H/AloVw5t72b/8o9at/9MXp7Sk6AvPU+dCwE05+JHVxXDEfTvhb6rH9fVwFcNrTqfk1yx+BfrNg8OnpAVKPKXDMLZDTMzXJeW/9nPEcvHc9zH4Ayiem0rcvSj19deSlGe2JnPkql73TTPKkhzH3lQeQFczBp2EeeRks+HVqNKd2LUy7NRXk7ZPdPfUkzaLbYfCpmON/hV44BAoH7y/yyV+4a+RvObJoTFuaIimcWH48Px/0c1ZWGximSSS7P+aMu9Lrl5VUnx53Z0Z6mbM3vx91Iy5t/6/VEncJfx73T2Qjm/iEa0kOOiiw7D4JJv4mNa/D5knNpSkaQvys12jV8rl+Vn/G9dz/hIsiS5wyrBibJnPHKYMZ0GV/G6yqzE/Hl+OwqvxhSYTm7V9Cv9mpW4MTr4Huk9P7c8gZ2Gxl3Drm7/TL7teWZZEtXNTnTGboGkpLBa2nvUJrMJS+vVnlRM96nWsXpn7k7TbySZ71Uuqpwn1UK8aRl1NVMpO31zfwwFnDuOf9zVhVmQvGlbG7OYTDovDAWcPSbunluaw8cNZwdjSEqAlEOfGILlwysScL19fy7wtG0S1nfzD494+28tczhvLe+hrq3X3xT7s3FWgf0M7E2a/zSb2dLj47krcLdSc9nzo29xX59D76WvO4/ajb6Z+9f5TFIls4r/95ZNmy+Lz6cwDGFI7iziNvoTJQmRbAjCkcw90jfkv24nv2r7twEJz+dOohhKOugm5HUvrZP/nHmJvIte8fhcux5fC30TdQumkhnD4fFt4C439FjsXHP6f+k1L3/ukPTs3Jb0f+lj6+Xtz6zhZ6FOWxpudFRAedlRZImOUT0WfcvX9OYKCKfjVbuGXsH3Br+4OKElcJ/5z6T8LJcEb6g1MfxEku97y3mTvnDGFYqY+FG+t48Oz0/ZXrsvD3M4cRiifYWh/k71+FMWc/gNnj6P19IcmYA07BmHIDZz25mRq5gNjpL6T/uHTmEZvzJDF3N/7XSebB94V+BPx+P/379+euu+7izDPPpLW1ldNPP52ysjKuvfZaBg0axEsvvcSUKVN46623+MUvfsG6detwOp3cc889PP3007z//vs4HA4uuugisrKyuOeee755xUAgEMDr9eL3+8Xoz3+ZYZg0BGMEogkCkSROq0JXawR7Yu+8FXsWuAtAtbGnJUJLOE4koZPlsFBqi6BFGlPvwbG4975Tx5O6Xx9pQVdstCpeqhMOHBYVSZKob42S67SSJzWjxZpBTxC3ZtMoZWOzWch320joBnWtMZpCcTRFIttpId+ugH83RGLg8kHcjxkNgNWFafGAakOKNkEiBlZn6vZJIppqu55ITTbW7GD1YFizkCN1IDtASqbKGkbqPSSxYGqOiM0D3nJorUhNmtaTqbqS0dRnzQ6aA1N1ICVaU+/R0RzELF50VLRIPZKiItvcyPHWVL02T+qXbTwE0QCGxUVQ9VEZsWO3qlgVGZ/RhEP3p95TZPMSUHyosoQz0Zxar9WFYfUgJWMQDSApSmoELR5KXXQtLpoMN0nZglP3Y403p/rB7iOk5YLUSkAPEkqEcGsenIqbpqgDiyqT1E0M06TIYeCM1aX6TNHA5iOBhImMZBpI0RZMWSWs+mgkC0UxQAkQiLcgo+JQvah4kCWJhG7gU2I4E80osRYkzY4pq6l3+UgqRrAONDtxSzZBxUNda5Q8t41Y0iAS14kmDDx2FRmwagrRpEFSN9ANk1jSwGvX0BSZutYodk3BY1XINpuwWaxI8UBq35sGJCOYtiwCSjYNCQuReBK3M07MCBDTo2RpTnISCSx6nKjFR03Si2QkKdYCyJFGkDWStmyCWjahuE5LOIHLpuJQFXLMBixxP1IijGnPolHOpiai4bWrxJMmrbEEbpuKbprEEwZ5LgtIEuG4jj+Smuif5bBgVSVqAjHcNg1VhoRuYmJiVWV0A4KxZKo/HRY8NoXWqI5umNhVkyy9CTXahKRq6LZsAko2LZEEiiRhtyhIkokjGcAeb0bWo+g2HxHFg6RECZgJQnqEuJHAo/lwK07CST+tiVYcipVs2YZbslMr2QjpLQQTrXg0O1nIuJI6igRSzL//lqxiS73vKhHCtHgwjSS6YdJos9Oih5FMA59sI88wkGUNQ3VgJCMYYT+KzYnk6UpVrIZAvJWkkcRn9ZFj8bGl3sRpUchxaARiOpoeJk8KoMYDmFYXLXgIm6l3hMnRZnTFRtTiQ7f5CCSbaIm1oMkqHs1LkWqjiThhPYw/5keVVXwWD261kFBMp641hkUx8dgtBOM6mgySJLftL69dw2VTqGqO4rVr5HusJJIm9ngDlngLUqwV0+YhZslhR9hKltNCgduGaejEWqpRIqk5aIY9G83bBVX70b7l5hsd7vX7Rxn4AKxdu5Zf/epXbN68GVmWOfnkk7nllluw2+28++67XH755TQ1NdGzZ0/++c9/MmhQamTAMAx+85vf8MQTT6CqKrNnz+bee+/Fas2cpNgeEfgIgiAIwv9//r8PfH4oIvARBEEQhP//HO71+0c5x0cQBEEQBOE/QQQ+giAIgiB0GiLwEQRBEASh0xCBjyAIgiAInYYIfARBEARB6DRE4CMIgiAIQqchAh9BEARBEDoNEfgIgiAIgtBpiMBHEARBEIROQwQ+giAIgiB0GiLwEQRBEASh0xCBjyAIgiAInYYIfARBEARB6DRE4CMIgiAIQqchAh9BEARBEDoNEfgIgiAIgtBpiMBHEARBEIROQwQ+giAIgiB0GiLwEQRBEASh0xCBjyAIgiAInYYIfARBEARB6DRE4CMIgiAIQqchAh9BEARBEDoN9YdugCB8L2JhaK2E7R9D41YoHgF9Z0PrbqhcDntWQF4fKDsKbHlQuQRy+4CRhM3vQGs1dJ8Eub1h1+cQaYRe08BVSFx1oO2rByCnJ2xfBPEQ9JwKrsK99bx9QD19iMs2LAk/bH4XCgdBXj+IB2HP15DXF2QFdn4G3SdA03bYsRi8pbT0m0F1rJlFVZ+CpDCldCIFFi++SADCjaBYUm22OKF8QqrOrHKQLVC/HnZ8At5iKBmV2m5vCUZuXzbFnWjWEIsrF1MfqeOoLqPpac8jf/VLYPNA6Whi0QhNli4ELbmUWwOo0RbYthBMCfrPhNYaUK1QvRqad5LoMpwGz0DqpFwkSeLttdX0ynVxQncDxb8Tacu7YPFAv5kE1WweXhEkFEsypW8+2U4LElDRHGFTTYDjB3dhe0OIJVvqOWewg67WINKGN1Pb13s6uq8MKRFC2vwO0t5+jucPRk2EkHd9Ag2boXg4Rpfh7DYLCCd0lu9spqo5zMzBXQgndD7aWEfvAjcDi70s2dpAtT/KkT1yKPBYeWdtDUNKfZTnONBNqGyOsGRrAz6HxsTe+bREYhR57Dji9bgDW3FUfgKeLtB9MlIijBRphJ2fYlo96L1nsDvhpTZmYd0eP8O7ZVPVEsFuhBiTl8C+413kqB96TAI9CQ0bMbpPZXvMjWL38tnWBuqDcWYNKSKWNHhvXQ1WTWFa/0LqAhG6Og08yQYc299BkWVqBhzPupZtrK5fRXdPGSMKR5KFg0bDQ2ssyVc7m6lsDnPeECf5yWrMTe9gWFzQdwbVug9sbkxTYnt9iCVbGyjw2jh1gANnrAEt2YoUaoCKLzA8xQRLj+b9SomcLB9jciKoNStRqpZj5PYmXjKOD6oUepdCZXAjq+pX0t1ZzIiCEdg0N1v8W/m8einDC0bSxdOV5lgzUT3G8prluC1OJheOIa92Cy5JRs7vi7n5XaR4GPrPxgTY8i5EAxhDzkGRDdj5CdRvgi5DU/ve6iUajWKvWIzUuBX6zwIk0BPQtIOgLFNf2JePar6gNR7g2OIJOK0+VjSsYYt/B/1zBjIgZwC1jXbe31BHntvCxN55bKgOEIwlObJHLmuq/Kzc3cKvxrpxxeph45vorgJqe0+lRY+gKBof7f6I1ngrE0omUOoqJhlSKaQFc+NbSDE/Uu9jUbxdMHZ9gVK/Dr1oKM3ZgzEljezmVSh6BCmnJ+b2RSQjAcxe0zB83dmTdLFwYx3V/ihju+fQt9BN1xwnQX8TlkgdxoYFqfr7TCfuKWPB9iQba1oZ1tXHoGIflS0RPtlcT6HXxuQ++RR4rNgtnS8MkEzTNH/oRvyYBAIBvF4vfr8fj8fzQzdHOByJGFR+CU/PhUQklfazj0DVYP6JEGrYX9bigrNfgtzBsPVNeOXnYBr78/P6whnPwFOnQ3AP5hVbkPxbU/UMnAOaAz79S/r6u0+CoefASz9tSzJPnQ96DOnli6D70TDrHog0w5Mnw9x/w/9j77zD4yjOP/7Zcnv9TqfebLnKvRfcAWMwYNN7r6GFGpJAEkIJoYYEAgZCrwm992pscMXduFfZsqwuna7fbfn9sbLk850MyY9U7+d5/Fg3Mzvz7mz77sy8727/Bhb9BS74CF45yxRrQPPR93B/dDNvV32S1sQpvY/nyt4nkfvJb8xt9+bgG6HvdHj7Emjc1JkuSjDzz7D2LeI5FXwz6Eiu//qX6Hvtb29fTx4dfi0lfz3dFG8zH4DGzWiDT0TavQI++BnY/XDBh/DF72D4mfDmJZCKdrbjKWTncW/w+4VJZg4t5cjyJPK7VyBUzUszU596M3M9R3L+q9sBGNcrl1uOGcRna2uZObSUnzy/hC0NEV48rQcT2j5EnP37zo2n/AKjoD/CW5eArplpA46DsT+Bv52aYY9+zjuc9HoLy3e2MuuMETy/sIrF25o5tF8hRwwq4qa3v0PTO299vQs83HfKUKJJDVkU+NNnG1m0rTnN/huP6s+JvQ0K3z7DFFl7OPkZWPq0KTj3InrIbTwbn8LgXt24/tWVnDLYx6W+efjn3pp+/ComwNhL4I2L0E94ghcbKxnVrzuz19eT1HQe/GIzBV47D54+nJcW7eCisXn02PEW/rm3gKeIbac8wQULb6Ep3tRRpVN28sTUWVQqhRz59DaqmqK8cmYPRiz9FUrVnLTmE4feSmTwWZz63Do214cBuO/oMo5ueg7X4KPh09+Y4mIPgkjLjCfRCgeR/+qxEK7rzMupYOs5r3HRF5fRGOu87pyyk3sm38PDKx5mWOEwch259PT35LUNr7GkbkmaPdcNupCTG+vwrX4dTviL+UJgc8JXd5sFxlwGg4+Hv55kvnzswZ0P57wDdh88fjDM+COsfRtGngdvX05owEzeKenFPasfA6BPTh+uHnE1N359I1G18/zJdeRyz4RHueaFGuraEogCPHT6cHoVejn36cU0hBKsuHYg/kX3ISx/EaNwAGuOup1tepyGaAP3L7s/bX9GFo7kzgm3UXb/sPTj3n0cjLsCXj230/4Tn4Ats82Xmq/uSiuudp9IeMZfGH7/6o60nvlu3rhwMO51r2L/7Ffp5btNYMVBf+Ssl6v4y9mjuOujdWysC3fkiwLMOnMkh/YvxGmT+F/ghz6/rakui/9+gjvh1XM6RQ+AMwfeuixd9IA5evDquRCtgbcvSxc9AA3rYc69cNYrMPgkhOhus55kBHpOzhQ9YI7+NG2C8jEdSUJ+JcIeUXXcgxCuh3euANEGsgJf3QlH3A5z7ukQPeR0Z7U3kCF6AF7b8jbrY7XQtCWz/Tl3Q7INWqrS03UNPrgOxl5Cw8CZ/OLrG9NED8CWtm08suNj4oNPai//MxhyMlK0ET643kw7/DaY8wcYdAK8d026yAAI19Nt9jVMKZco89uQv3s1Q/QAiF/+jkkFCbwO8w1z4dZmvlxfT6nfwZ+/2MSWhgg+h8y4/Fi66AEYdILZn3tED8Dkn8Fr52W1R3znCu49Ip+xPXNZXxdicbuIOWd8Bb/dR/QAbGkI8+6KGkQBvt7cmCF6AGoamslf/Id00VMxAerXZIgeANdXt3B2f4EHPt9IWzzFmf2ETNEDUDUf6r6DbgchvnUJpw+wccMbqzhyUDEPfmGeG+dP6MGdH67nxFHl5Kn1pugBWsZfzo2rH0kTPQAxNcZVc35OUI9y+ZgcxlTk0K/x8wzRA2CffSu07mRns9mPxT4Hk9zVuIQErHwpXfQAGDqBDy8hX61LFz1Ay5jz+fWCW9JEzx57bp5/MxcOvpDJZZNZWLOQbcFtGaIH4P41T1NTOdW8VmffBf2O6hQ9AGMuhNfOTRc9YF7rb19mng/HPWKOohYPgdm/h1gLuwce3SF6AC4YfAG3LbgtTfQANMebuW/5zVw4pRAA3QCPU+FXb66mIZRgUp88fMH1CMtfBKB+ys/45bL76eHrkSF6AJbVL+PdbR+QGndFesaOheaIbI/JnfZ//jvoPyND9ADIO+bhXPsqF0+q6Eirb0vgiu3OED0A8s759N39HtdM7c27K2vSRM+e/brqpeXUtcUztv1fxxI+Fv/9tFWboyl7kwybD5NshOsg0gC+gdnzv3vDHBrvfRgkQ2Y9vQ41p6y6YuXLMPhE8+/RF5lD8Hse0okQCALUrobpd0D7DZPiobDu3Y4qQoOO59mqj7ts4tlNbxAZeGz2zHXvmcJsX7QURJtYGq5CNdSsm36w43OaBxzdWb52FQSrzREggJLh5tSaZDOn2rJRs4zxxTDA2Yaw9Nku90Fa/RLnT+jR8ftvi3YwtDyHD1btBuCiST2RVv4tfaPeh5kPCX0v+xUPxNv2Y89yKlwJZg4t4c2l1QD0ynezuT6Mqmcf5JZEaAonO8rvy8n97Yhr3khPHHSCeey7wL32JfxOhfG988jb/FqX5VjxklmXriLtnE/ApRBLdYq8yiIPoXiKeEojb1NnPS3Fg1jbtDZrlS2JFupSbRxUInDBMBc5q57ssnnXmpeY2CcfgJMG+yn87klzqnev8zMNXYXdKyC/b3qbxUNY07Qm6yatiVacspMFNQs4sueRvLflvS7teaPmG+h5MDj9sPadzgzFY05Dh+uzb1i7GrQk5PWCZc9D4UDYtQx6Tubd2oUdxSRBwiW7MgTjHja0bKBvSefvgMvGip2tANx8WAnC4ifMDFGiwelnes/pfLD1gy7357WNb1A37OTMjJUvdd43AFwB81ruAvvSxzl/iLPj97kTKhBXvdJlef/Kpzipn40PV+/Omq/pBgu3dHEN/Q9jCR+L/37iwcw09XveYpJhqBiRPU9Ltj9kDVATZprizhRXexNrMW/KAJ7C9JEmLdVZj93b+bDW1bSHuWp305Zs67KJYKqNlN39/e3vSypG837qTekpVGGvW0GkEVJ79Z+ugiRDIpy58V6IWgJJZP/9FGnE5+hcUxCMpUAQOsSI3yFlihlXXubIncMPiSzHfS8ENY5bkWmNpcxq7BKt0WSX5R2KjCQKHeX3RUYzj+XeKG6ItXZZpxiuw20XcSsS9lgXD2uAeKtZFyBEGgm4bCTUztE5VTNw22WSqo491tCRntSz27qHcCKEZKh47eJ+j4sjXo9HMc8Bvx3EeIsp1vfd373Jcs6lDK2LwiZRNUo0FcUluwju5/g1pkLoitusf2+R48z53vMQNW6OtMZbO68vxUNDsrM9m2jLGOnJqGavvk3udSw8so6wpy8lhagaw6f4aE20dllXMBHEkJTMjFhLx3E37XR3LeoA4q3YZaHjp99hQ47up3ysBVkg7Vzal6Zw19fE/yqW8LH47yevb2aaM5B+Q9kbQQR/N1j5fPb8wgFgc5g3IGfAvPnuXmlOa3RFxUSzDMB3b0KPSZ15NqcpeBQPrP8AKqeb6VoCcnt1FPNWL2NyQRdiDDi0aCzeXSuyZ/aYbC44zoavjFE5lV3W28vfC3frzs6EbmPBX9r5W02Y6w+8RebDMBsOPy2Gm8aUglExscu29Moj+WpD54N7XK9c4kmNnvnmsZq9oRG97/T0jbbNMft3b9p2QaBH1/bYfSRtflbvCjK2Z65ZTUOEwWX+Lm1rDCUAg4N65mXN3xIkY4Tj+86LROWxbG+MsqamjcbuR3ZZbu/zR6+YxOrqIH6nrSNbMwwaQgl8ThuNFZ31+DUNjy274BUQKPd1o1m1M786Rap7lhHBdporjua7mhAA86tThCoOM/s4v+vzhtIRndO07fjUBF6bt0t7ch25DMo3R6lGFY3qsuoj8ocj1q4yp6sq9+q3YDXkdDOv4WwoHnDkmMKnYpIpfBx+2L2Kw/OHdxSLa3EC9gBiF/W4ZBeG5ur47bBJHYL96xodo9ehZkYqRrHi4+vqr5lUNilbVQCMKR6Du2lrZkbFpM77BphT2ZXTM8u1o1VMYW1T54jllxvqiPed0WV5tWIK28MilUVdvBQB43pnP9//l7GEj8V/P44cGHrGPmm5MPnn2cuPPM8URXutyUnj8Nth2zxY8hSGIxem/Nxc1+EthZyKzPKSAmMugj1Dzo0bwF2AUT7W/J1sf0OdfD0sfQa6jTMf2vMfgsN/11GNvOkzTi6ZjE/JXJTnt/s5pvcxSPsubAZTPJWOhJZtmXkVE6B5K+WtNYwqzC6qfjXoIvIWtQ/ddx8PybD5dtqt3f75f4Zpv4MtX8KQU7PW0Tr+V/xlaYTqhAtj6k3mtFgWO6OFI5nXPrSuSCJXH9aXp+Zt47czzWnHOZsaiRWPMb3U9hCuMxdqdx+fXl/DBhh2ZlZ7jINv4KnVSd5cVs35E3pgkwQiSY26tjijKwJZtzlsQBElfifnT6xAkTJvja+tT5KYts/ai5UvmwuTs73NB3qy0z2Y6YOL2d4UZYdrcJrQ7UBSzDpWvozRfTy1YgFnHNSdhKpRkWc+fF9fUs3Fk3uytSFCc2BYRz0FS57jmoHnZd2fk3sdiz8R55Fvw7y4rImGMT/v0k6xYhxbG801M3M2N1Pf6yRY/QZM+UXWutVu40k6C8xp3L0o+PY5rh52edZtZvSawdzqufjtflbUr+D0fqdjEzPPk3JPOSNsueZC/f4zwZ2X3m/RJhhxbtY2mPJzEEWYex8cdIk5hTjxWmjZxhDRRXdv946ic6vnclzv47JWc/HgK3h1UecI0dLtzVx7uCkCf/3ueozhZ4LLFNS56z6kr7uU3jm9qfBl3h9kUeaq4VeQ++nN6RmSDQ66FFbsNbU74BjzZSuvd6ZRkg310N9y00eda/kWb2vBKB3RZfmGg27ktk928NND+2R9RxhdEaB7rjMz438cS/hY/PfjL4VDfw2H3WyOTAA8PQ0GnQjHPdwpVjxFptCYdB188Wc48UnTq8Le/oZaPBTOeQt83eHTX5uu6qkwxsATzMWS8x6EmX8yH/7tDxCjx2S48FNo3OvNt3goRrQRTnoCY9wV8OwM8020/wzTnq/ugbNeN71PIg1w5qtQNAgMnbLPbufFQx9iavmhiIKIJEgc3v1wXpz+LCVb5sGpL3SOfsh2GHY6zPgThGtN24sGmXl2Hxx0ubl/371JINCHO8bfyTn9L8BtM0dX+gX68fTkPzB09fvm6NZBl8Gkn9HUGqLeVYlx1B9g7KWmF1moBnpNhcqj4OAbwF1gthPoScuMJ/jGMZkzxvfiT59t5N2dLowLPsbYMxIi2zGGnYl65uuc/motAAf1zOXlS8axqa6Nk0aWU98W5/kLxzKgxMvJL+8mecbrGMPONPcRML55AOP4RzHGXdl5vOY9iDH5eoxpt6XZYxz/GLHK4zBkB4os8tjcrTx+7mgO6pnLPR+v55IpvbhoUk88dvMNfmCJj0fOHMncjQ2omk6J38kzF4zhoPaRIrsscurocn56aB+W6n2JnfkOFA022zMMjOYtcP4HacdFHXEu0TPe4tFlUVKqzu+OG8TtX7ey6ci/ER96Tsd+UTEBTn0eFj+BPvwcYjMfZXmzmz4FHi59YSn3nzqc08Z0Y8HWJtriKvluhUYhj4YTXiM57Bzk7V9zVGsz9x30W8q95QDkOfK4YcQ1XDH4IpJSEYO75+OwSfzyqxj1p32IUTGpw87ksHOInfkWLXI+z10whoElPgwDrvu0mZYjZ6GH6+Hkp81FwgB2L9GxV/PduPv5aLeXxAlPd4pUdwGJ7ocw0DOeeyfdm2bPL4ZfxcWDLmBp7VLuWXwP1466lnXN63jgkAcYXTQaAEVUOLHXMTw16gaK596PMeN+c+Tm3avhhMcwRp5n9ttzx2JMvArj8N+Z08pgXuPHPYIx4Fj0SBNUfWM6KUy8xhzxmfEnir55iCdGXM/JvY5FERVe2fAKk0on8svRPyfPYY56lHnKuHPS3TgTY/hiXRMeu8yFE3tQGnDSPeDkgdOGUZ7j4jdfhdHP/xCj/ww8S57hUndftjSs5s+H/pkT+pyAIpr3h5GFI3nuyGcpV/JIVhzSeT53n4hxzjum6Im1QE4FsWP+QqzHNJh9Bxx5jynq28vr3SeSPP8z5rcGzOlhoH+xl2fOH0PKWUTyzLdIDj8fZIdZf8VEIud8wj1LdFbtamNZVQvPnj+GQaXmS5XHLnPZwb2YdeZICrwODjQsd/Z9sNzZ/4vRVGitMhcVSzbIbb8hN20xh7xFyZziSkQg0WqWd+ZBogV03XR/twcgUm+W9ZaAYr5xp1Ip5LYdZt02OwIiYJCUPNhSbWBzQzLYXo9Cm1JCUzRFd6eKlGhE0HXwlWEkI+aCad0Ah8ccDZLsnet9BBFsHiJanDZBx5AUvBo4HAFkLYKQCAMGCBLoKRBkMFTwlJqLPkWb6eElyGYfqHFU0U7EXkQ4qSILBnGC6IaOTbSTZwgoWgTB0NElO0kdWsVcRMHArQg4kkFkLYKhGyTsAWxqBEkAtCS6IJFAoUUIIIrmrFMsqSMAbruIT21B0aIYgkjSnkerphBXdXQdbJKAANgVkVhCRzUMvA6ZlKoTT+lIAhQ4UjiTzWDoaLKHZsGPIqj4kg0Iho4m2akX8rFLEEjVIegpdEEh6ChG1Q103SCe0tENU7zYJJFI0lzz4bZLxJI6umGgyCIYBknNQBIE7LKIYRgkdQNVMxBFUCQBTQebJJLSdAK0YTcSaIikFD8OLYRoaAi6RgqJhD1Aa1JEwFy/ZJdFVN1A0w18sorfaMPQdURRRDRUDMOg1VZAWJXw2iVaoiqiCG5FIqXqJLQ9a6BkIkkNQRBwkMSrB7GhgeKiSU+QMjRkQcQneWnpmKox+8EwwC4LBKQociqMIYhEpRxCqozDJmAgkEjpqLqBKJh9JGlRfEIcUU9h6DqaINMq5RJOmovBXYqEO9WMYiTRBJmglEckpaNIIoLchq7FkBHwCy7isp+U2kCKFCIiDlsOETWKgYZmaNhEGb8hYksmMEQbms2FM9mMIIAqOoiJblx6G4amExNdiA4Pntju9mtbRnXk0qI70TSNHK0FhSSCIIIoIWCesxgGCZudFgF0PYUHCZdop97QSOg6NlHBI+cQiqtohgGGOc0Vb19o7nPIhBMqKR0UGQrlGEr72qGUPYdWPYIg24mpcQzDwC7aUcQcwgkDj6ThVlsQMEhKbiSnHylaj6in0ESFRiEABuQapu26aMfQVQxdA7sXhy+feMoctdTaz6myQOd0XCwaRog1g66RlNykFF/7fpjnb7dcN02RBLGkhigIFHgVbNL/hhv7Hn7o89sSPvtgCR8LCwsLC4v/Pqw4PhYWFhYWFhYW+2AJHwsLCwsLC4sDBkv4WFhYWFhYWBwwWMLHwsLCwsLC4oDBEj4WFhYWFhYWBwyW8LGwsLCwsLA4YLCEj4WFhYWFhcUBgyV8LCwsLCwsLA4YLOFjYWFhYWFhccBgCR8LCwsLCwuLAwZL+FhYWFhYWFgcMFjCx8LCwsLCwuKAwRI+FhYWFhYWFgcMlvCxsLCwsLCwOGCwhI+FhYWFhYXFAYMlfCwsLCwsLCwOGCzhY2FhYWFhYXHAYAkfCwsLCwsLiwMGS/hYWFhYWFhYHDBYwsfCwsLCwsLigMESPhYWFhYWFhYHDJbwsbCwsLCwsDhgsISPhYWFhYWFxQGD/O82YH8sXryYn//851RVVZFKpZg1axYnnngiy5cv5/LLL2f37t243W4eeOABjjjiiI7tHnjgAWbNmkUsFmPMmDE8+eST5Ofn/xv3xOJfQqQRRDekWiCVAE8exEMgSGBzgmQDxdVZPhVHjQVBi6PacogJTvxOG6IomNWFa0mqCbz2HGS7F0QRPVQHqSgoPkQ9adbjKwFADTUgqhGQneiChOzJJxVqQEhG0O1+FE+ARKyNSKIFm2RHkZzYFTuabiDE20CNYth9GGoC3QDJ7keI1YMUADHR3q4HklFw5pDUk0TVGA7RhkOwgZ5CFSXCWgJFtKHYfEiJFgQtCXYvIGEYKkIyjKr4aUg58Dhk1EQMF3HsooGhJkgpflKCAvE2JJudkGEnoRp47DKxlIZLEWmLazhtIqIgoKTasEkiQdzouoHbISHE25AxiIheFEFF0WMkUIiiYJckoikVuyQiygmiWhSn6MYuOYmndGxGEpsWw7A5aFNtuBWZVCpBQIwhqhE0xU8rEkk9hizKuCQ/jlQQQQ2D4iWe0ohLXlRDBSmBIjiQUglEQ0NxeFETQVRDISk7cMg2IkkNVdfwyhJeow3UCCg+2gQvbfEUdllCN3RssojXLlHXZh73Aq9CXNNIpgySqoEsCvhkFTnRAjYHEclLNGGmOxWJ1mgKj13EY4QgGUd35NKmyficEi3RFAEpCVoCUZSQjBSCoYMokrDl0ZrQkAQDnxHGECRadCd2WSTf66C+LUYspeNRJJx2mZZIEkEAlyIjawmUZAua5CCp+IgmdWRJRNUMDAzsNgFVhYSq43PItMVVbJKIKIBuGEiCgNcuUR9JIYsCBW6FUEJFFAXCCRVJFHArSUJqG07RhVMTSAoOEgioGjgUEREBQ21GQMUrOxFTcXR7DqFUBFUU0TFI6SoeKQ9R0PBoYdCSYBjE7V7iWgxF13CJNsBBnWYDwC7pYARxGAaK6KRNcKMbEEvpOGQRn13AiLUg6Ck0ZwHNcY1cu4auhkkaGh6bx3wAxlrBZicoBZBEEUe8EQODqFJAIqWhA3ZJRJFFmqMpFElEFAxyhAiiGidlz6EpIaJIIrIkEE5o+B0yCU3HJookVA2bJKIbkFR1FFkkz2NHEgXzWk7FaNMVIppMvtdONKnSFkvhssnohoGqG+npikyex/6vuKP+zyIYhmH8u43Ixvr165k6dSrPP/8806ZNI5lM0traitPpZMCAATz77LNMmzaNuXPncuyxx7J+/XqKi4t59dVXufvuu/n888/x+/1ceeWV1NXV8eabb/6gdtva2vD7/QSDQXw+3z95Ly1+FNpqINIAdj80rINFfzFFUI8pMPoCcOfD5s+hcADoKuT0QI+1IIRqEBY8DK07MIqHwvgr2JjIozBHZV1wC8+s/xvN8WYmFY3h5H6nUmoPIL59BUJ7eWH8FSBIGACtO2DRowiRRugxCQ66DCO0G+Y/hNC6A7X/TGqGn8Irm95gYe1iCpx5XND/bCr9vchJRBAWPwlFAzDsfoTlz0OkEaPnZITRF0I8hPH1fQitO6B4CInJP2OnaPDcmudZ27KeCm93Lh94ATZB4PUtb7OgYQUFzgIu6Hc6lc07CSx6AvrNgCEnYSx9HmHLFxjFQ2DcFaj2HOS5dyHsXoUR6IEw7goMuxdWvoywbQ6Gp5CW4ZczL1LK8gaBM8Z2592Vu1i4tZl7Di+ge9PXyCtfACA48Gyk3odga1yLffEsKB6KPvhkWPYc4u6V6IFeNI+4ghXRfPr08NKYqOH5tc+yK7yLfoH+nDfoXEpw4PzqHmx1K9ECvdCn/BLRmYMYb0FY9BhNA2bQmNeDBbWL+aTqEwBO7HMCU1zdKfr0Fgj0ID75eqoFlefWv8Ta5rV093bjwl7H0bNqCW5NRaiYgL78r6SGnM52uRe//LCat84sR+g4XlUYRYNh/E/RXQX8/ONaWiJJfj1jIPM3N/LW8l0AnDCijEP6FbJ0RzM1TSEuHyohLX0KoWoehrsADrqcNn9/7lsU4uSR5VQ627A3rUNY9ChCpAGjYiKMuYiw4MXZugFp/p8RIg3QfRwMOgFatmP4SkFUMHwlRFe8iWfze2Bz0jr0YqrcQ0k6CjAMg/lbmxha7ufVb6upao5wzphSTu+rIyx5CmH7NxjufIyDLqfG0YdL39nNIZUFHD+yjIa2OI/O2UpjOMHYnrkcNbiEN5dWM6xbDsV+BzlOG/WhBLNmb8ZpkzhvQgVDy3P4fG0dQ7vJeL1Rnl/7PBtaNlDmKeP8QedRZsvBnzJokIqwyy2salrFCxtfI6pGmd5tKkf1PJqdLZt4ZtNrNMYaGVE4giN7HEl9tJ4h+YMpr92Avu5taqZcx2ub32be7gXk2nO5sM8J9Pf1wql42KVFeX7dC3zXtJZu7lIu7HsyPd0lJFMuLn5jO0+fUEJO4zKEJU9BMoIx6TpaiwexsW0bz2x+k4Z4E+OLx3Jq5cmURcOI8x+CQ2+ELV8irHwZDAN9yClofY9iRciH36Xw+do6Pli9m4ePKaZ7apt5HMP1GBUT0EdfzMZEPn/+ajtVzRH6FXm5eHIvatti1ATjjCgPsHZ3kNeXVhNNatxwaBkTAq1I8x9AaNpCsnAwrcMvY2mbn8LcHBrDSYKxFK8t2Uk0qTF9UBET++Qz68vNyKLAxVN6URFwUZzj/DfddP8z+aHP7/9Y4XPSSScxevRofvWrX6WlP/7443z00Ue89dZbHWnHHXccU6dO5ZprrmHChAnccMMNHHfccQA0NjZSWlpKbW0tubm539uuJXz+y0ilYNsXkN8f5v0Zlj6dnm/3wvkfQttuM2/KDRiSjLBjAXz0y/SyokzwJ7N5rOo9XtjwclqW2+bmxenP0GfjHPjkho7ynPkquPLg8YM7C5/4JMSaO+v3FLLx5Mc4e96NxNRYWr0XDbqAC1ta8Ok6JEKw7LlM+09+Bt66FKJNGMVDWXjYL7h8wc1ohgZAniOP2yfezvVzrs+sv/I0LgyG8S18FHylcPab8PghoMY77d/0GSx6FDyFcOwseP0CSEbS6gmNuYrHteN4ZmkzD50xgjy9icFzLkas+y6tnFE8BGHy9fD1H2HC1fD2ZaBraWUaz/+Uz2LbuHPxHWnpsiAza+qfmTjvCVj3LrgL4Lz3IbgDXjqdpqPuYVPpIP6w9I9sbNmYtm2/QD8erjyPotl3s2jajVy64Lcd/bOH3424jqO++wTH7pVw3MPwt1MJj7wMx8TLkDZ/gvDB9el9L8oYZ7xMVWA80aTGz15dyfraUFqR/sVebjyqP5M8NcjPzYBkOL0/xl5G1YBLqA8nGbPzWYTFf0lvY8I1GIaOsOCh9HTFA6c8Awsehul3wosnQWh3WpFkryP4pM9vUHzF1IcS/PYd81j0KXDz8ek5yM/PNM+pNHsuYVH5BZz+t20Ueu3cd8owLnruW1Ka+RhwKRKzzhzJDa+vYkCJl0P7F5JQdZZsb+bzdfXMOmMED3+1mWOHljK0so4rv7jSHFnbi1+P/TVHOctQcyq4fck9fLnr6468M/ufiW7ovLzP9eWUndx38H08+92z3Dr+ZpKJIGd/fimRVPp5+LOhlzGwYDiXfZnZ7i3Dr2Zm7lBUbyXuDy9D2PixmdH3cNomXcPTNXN4auMrGe2+OP0ZKlHg9QuhdlV6fxUOQD/tb0x6fBu7g3GeP60Hk+r+irjw4X2Olxv13PeZ+UaY9bvNPpdEgcfOHkWRT+FPn21m9oZ6AA6vDHBP/83kfnpVeh2iRONxf2UhQ3l35W4+XVuXll3gtfPHU4Zx8XNLSGo6tx4zkGOHl5LrtkZ/9vBDn9//kWt8EokE77//PhdeeGFG3oIFC5g4cWJa2tixY1mxYgWqqrJkyZK0/Pz8fCoqKli9evU/3W6LfwNtO2DV65AIZooeMG/8n95kjvZs/BhsCoIomWn7Igg0JFsyRA9AJBXh3iV/JDTomM5EXYX3r2ufRtqLkqHw2W87fgbHXMTv1z6TIUoAnlrzDE19p0LPKZmiZ4/98x+EkecCUD/hCm5a8ee0h/oplafwxOonste/8RWa+k8HUTJHxhY/CYf8ei/7r4VR55u/R54H3/wpQ/QAeL99iFP62YilND5fV0ef4LwM0QMg1K42R78OvhE+vyVD9ACEvTnct+QPGemqoXLbwt9TffDPzIRR54EgwnvXgjNAbWFfVjetyRA9ABtaNjBPbab+0Bv4zT79s4c7Vj1C09gLTQGx9m0YcAyeJbOQkm0In92SUR5dRXj/WiqEWhZta84QPQDra0MM9EaRPv1VhugBEBb/he7OKCPyUpmiR5Shx8RM0QNmXd/cD0NPh2+fyhA9AMrWTxntbsRhE/n9B2s70u87qhjp019niB7TnscZlafikEXqQwneW1XDkYOKO/KjSY1Hv9rMaWO7MXdTIzkuhefmb+e0Md0ZVOqjuiXGut0hjhpp47YFt2WID4D7ltxHS6AbVeFdaaJHEiQmlU3KED0AMTXGk6ufZGTRSObs+po1we0ZogfA6y7i5oW/y9ruXav+QpMawa02dYoegNE/ocnQMkTPnnbv+PZegtGGDNEDINSvQ9z4MeN756HIIuOLjEzRA5CMIH1yI388sqgjSdMNfvP2apyK3CF6AK4Z5yN39i8z69A18j+/jrH5yQzRA9AQSvDOil0cNcQ8Xnd+uJ6WaCqzHovv5T9S+GzYsAGn08mXX37J0KFD6dWrF5deeiltbW3U1NRQVFSUVr6wsJCmpiYaGhrQNC1jPc+e/GwkEgna2trS/ln8FxFrgWMehE2fdl1m2xzYIwqiQWjZbq4h2JfCgcxvWN5lNQtqFxHU9hEXrVUQD3b+DvSC5m2gJjqSgqVDWN6wost6v23dBC3b9mP/XCgdaTbnClAfrU/LrsytZHl913Yvbl4L+ZXmj7VvQsX4vezf0Sl0SobBjoVd1uPePZ8+BR66OxO4Vv+1a3vXvQveIlNo7Ysrj+poLUk9S/8DuyO7aRXaf5QMh0QbtO3CqJjI1mSQL3d+2WWzr1d9QrO3kLpo5kMDIKElqNHj5nqvde9B76lmxtavIFCRvdJgNcSCvLMiy7604xdiCFXzu8ynZQfytq8y0wv6QU3Xx42q+VBQCWu6nqYv2PQKTptEQtU70vrlGAhV87rcRtr6BdMGmPfQT76rZXJlQVr+t9tbGFzqB2D2+npGdM+hLZ7ikH4FfLq2FoBwMsTuSKYYA0jqSXaGdvLVrnQbevp7sqZpTZd2La9fTv/c/ry/9X165fTKWsZv91MTyX4sknqSnclW85zp2FkFnH6+bV6bdRuAZfXLCOqJLvOFVa9w/mA7U/rmI1V93XW5nYvo7U0X3HVtCdpinSJNkUTytAZIZb6kABDajRhr7rKNj7+rZUpf83glNZ2tDZli2+L7+Y8UPqFQCFVVWbRoEYsWLWLlypU0NDRwzTXXoGka+87OaZqGIAhomnnSdZWfjbvuugu/39/xr1u3bv+cnbL4J5P9+JpZ+8n7Mdv5B7f5Qeb9P/ZBSGs/Sz0/uO4fu1xXW3ex/ffY2eV2P6DF/Wf/f/fnB7TxPVvvN//vrXrv/fmB2/7dTXSxxfcdI0EQ/h/Hsb3+f+B4Cft7FAr/QB/vs/mPxb7Psa6eaxb75z9S+OTn55NIJLj33ntxOp14vV5uu+023n33XXJzc2lsbEwr39DQQHFxMYFAAMMwaGlpyZqfjV/96lcEg8GOfzt37vyn7ZfFPwFnDrx3DfQ9vOsyPQ8GuX0RoNMPORXmm+C+1K9lQsGILqsZX3wQfmmf+fScCnD4O3+3bIXcniB3lvPXrGREwfAu6x3t7wuBHvuxfwpULzGbi7ZQ5Eof8dzQvIERhV3bPSZ3ADS2Tw8NPAG27/UmntPdHAEB2L0Cuo/P2H4P4ZLxbG4IUxW1Ex18Vtf2DjgGQnXgK8vMizZR7irGvm8/tlPqLsW/58WlZoU5jegrQ9j+Db1sXg7rfliXzZ5UcQS5ofqM/tmDXbJTKtrNt+0Bx8DmL8yMnlOgeXv2Sv3dwOHj+OGlXbbbqjvNhcpdYAS6o/Y8ODOjYT2Udn3cqJgI9eth0IldFmnoeyrRpIpd7ryVr28VMHpM6nIbredUPl9njopNH1TM3I0Naflje+ayepc5inlo/0KWVbXidcjMXt/AEe3TYh7FS6k7e5/YJTvl3nIOKUvvk23BbQzIG9ClXSMLR7K2aS0ze81kc+vmrGWCiWCX7SqiQrniB2WvqWctCdFWxuQO3G+7PrFrB2djyKk8syrBVxvq0Somd12u20FsbpPS0op8djz2zrqTmk6TlA82176bm3hL0J1dr0WdPqiIOe3HS5FEeua5uyxr0TX/kcKnoqICh8NBNBrtSBMEAYfDwahRo5g/P31Yed68eYwfPx63202/fv3S8nfv3k1dXR3Dhg3L2pbdbsfn86X9s/gvwlcBQ082xceYizPz7T44/HaoXweVR4GawDA0OOL3WasrsOdwbr8zMtI9Ng+/HH093lVvdCZKNjjmAYjvMz1asyKtfv+3T3PTwAtwypkeGBcPupD8jZ+b01l71tqk2e81Fwkvfx6AwvkPc/vwq5GEzhvs6xtf5ydDfoJLzryZXtz3NPLWf2yutfGVwtiL4as7zUxRhpkPwNJnzN9Ln4NJ15kLa/chNOYqXtuQxGmTOGJQEZtyJqAXDc4oZxQPMcXCV3fB4beZbeyDJ9TKL0b/IiNdFmRuHncT5V/90UxY9hzoensfBymu38Sg3IFUBioztu0f6M9EOUDh7Lu5Y/g1yEJmuzcN/Sn5i58GbwkMPB7Wv09o9JVodj/G9CzngyhjzHyAKr2YsT1zGVDizSgyoMTLupAL7Yg7s/abMfYyquJuljXKGOMuT8/UNdj+DcaEqzK2Q/HApGth1Usw5iLz2O1Dsvd0vg3nk0zp/HZm54P9+o9q0Q6/I3PtGaCPvZQljRJxVafIZ+eYoaV8sqa2I9+tSFx2cG9e+XYHB1cW0BpNcsHEHrz87U7W7m6jLMfJwBIfHy1LcfP4m7P2889HX0+geQcVnlIOK5vSka4ZGvN2zeOM/pnXl0t2cdGQi1hRv4IpZZMY4u+F25b5UA9H6rl9/C1Z2/31sMvJk92E5VyMyqM6M5Y8QT4SF1eenrXd34z5JX5XoTnVuw9G4UD0yiNZuLUJVYcFtQL6+CszyqG40abfzc8/6pxmlUSBO08YQiypMrV/YUf6AwvbaJ56b2YdokTDtAdY3KgwfVDmi3qB185xw8v4+DvzeP16Rn9y3bbMeiy+l/9Yr66rrrqKeDzOo48+iqZpnHnmmfTp04errrqKIUOG8MYbbzB16lQ+/PBDrrjiCtasWYPb7eb+++/nb3/7G5999hkul4tLLrmEQCDA/fff/4Patby6/gsJ1UK4Hhw+8w150V8g2gQ9J5tiwlXQ7s7e33zY5FSgx1oRwjUI8x+GYLs7+7ifsjGZS6FfZUPbVp5e91daEi1MLBzNSf1OaXdn/ylCe3lh3OUgyBgC7e7sf+l0Zx97qenOvmAWQmsVar8Z1Aw/hdc2vcnCum/Jd+RywYCz6evrabqzf/sUFPbHcOQgLH+h0519lOlhZcz9A0JrFRQPJT7pWqpFgxfWvsia5nVUeLtx+aALsCHw+pZ3WNi4knxHPhf0P53Kph3kLHoS+s+EwSdgLHvBdGcvGgLjr0BV/Mhf34uwewVGTg+Egy7HcPhg1Svp7uzhYlY2ipw+thvvLN/Fom0t3HV4HhXNC5BXPAeCYLqz95qCrWm96c5eOBh96Kmw9DnE2hXp7uwVXpoTNTy/7jmqQ9X0y+3PuQPPoURw4vjqHpS6lag5vTCm/BLR6UdMtCIs/AtNA46mMa8nC+u+5ZPtnyAgcEKfE5jsLqfo01sh0JPE5J9RjcrzG15iTdNaKrzduKDXcfSoWoxb1xG6j0df8TdSQ06nSu7JLz/cxZt73NkXzEJo2d7RP7qzkF98spumUILfzBzIwq1NvLnMdGc/aWQZk/sWsHxnC9WNbaY7+7JnEara3cfHXUGbt5I/LQpx8qhy+jpC2JvXISz6S7s7+wSM0RcRETw4WzciLXwIIVxvurMPPB5aqjC8xSA7MLxFRFa+jXfTu6C4THd212BUZwG6YTBvSyPDuwV45dudVDVFOWtsGWf0URGWPNNpz0GXs8vei8vf3c0hlYUcP6KUhlCCR7/aQlMkyUE9czliUDFvLatmeLcAhT47AVenO7tLkTlvfAWDy/x8sa6eIWUSPn+MF9a+wPrm9ZR5yjhv0HmU2/z4UgaNUjGKrZnVjat5YdNrRFNRjuw+lek9jmJXy2ae2fQ6jfFGhhcMZ3qP6Xu5s2/EWPcWu6Zcxxtb3mXe7gXk2QOc3/sE+vl64rS5qdHjvLDuRVY3raGbp4wL+5xED3cJiZSTS9+s4snji8lpWo6w5GlIhjEm/ozWkoFsCpru7I2JZsYVjeGUypMpi4QQFz4Eh9wIW+cgrPxbuzv7aWh9Dmd5yE+OS+HLdfW8v7qGWTNL6K5uR1j4iHkcu49HH30RG5L5PDRnO1WNUfoXe7lock92t8bY3RZneHkO62pDvLG0mnBCbXdnDyIteBChaRPJwqG0DruEpaEcCgM+GkIJQnGV19vLHzW4mPG985j15WZsksjFk3vSPddFsd9yZ9+b/3p39kgkwuWXX84nn3yC1+vlpJNO4vbbb0dRFD755BOuueYampub6dOnD4899hhDhgwBQNd1fvnLX/L8888jyzLHHXccDzzwAHb7D3P5s4TPfzGRJhBdkGo13bVduZCImB5NNoc5QrP3ELMaR422mf87cogZjvQAhqFaUloCj92PbPe1BzCsh1QYHDmIasKc+/eaAQxT4QakVBRkx14BDBsRkmEMZw42Vw6JWJBIohVFsmOTHNgVJ5qutwcwjJkxdFJJNAMkhw8h1rBXAMMwKH7zRu7MIdURwFDBgQxGClWQiOhJbKJsBjBMBhHUuDny1RHAMISm5NCgOnArImoqgcuIYZfMAIaqPUBSlyAZNgMY6gpJzcDrkIkkNFw2mbZECqdNQhJAUduQRTOAoWEYuO0yxEPIokZE8KAIGooWJSE4iOk2HIpEJKliF0UEOUFUjeKU3DglJzFVw6arSFoEweaiTZXxOGQSySQBKYaYDKcFMLSJCk7JiyPViqBGOgIYJm1eUpqKISZQJAdSIolICrvLhxoNkUQmJblw2MxgiinVwK1I+I02SIVA8RORvLRGUyiyhG4YKLKIxy5SHzIXZud7FBKqTlLVSaoGNknAI2tmAEPZQVj0EE8ayDK4FZmmSAqPIuIxwpCKozsDtKkSXodMMJoiR0qClkSUJCRDRTA0EAQSci7BhI4kGvgIoyHTqjvSAhjG9wQwdMi0RFIIgFMRkY0USqIVTVRIKn5iSR1JFNAMA8MARRbQdINESsfjkAnFVZSOaTMDSRBxKyINkRQ2SSTPbSMc1xBFzACGkoDbliKUasMpO3GqIklBIWGIaDrYbWYAQ/RmMFR8NjdCMo5u9xFKRtBkCc3QSekqXjkP0PBqYdCToBvE7T4SWgybnsIl2kGwU6vaEAWwiwY6QZyGjiK6CBouDAHiSR37ngCG0RYEQ0Vz5tMS1wjYdXS1jZSh45Y9yAIQbwXZQVD0I0sy9o4AhvkkUjqaYeCSRSRZoCVqesSBQY4QNQMYKn6akiIOSUQQRSJJFZ/TRlLVsEkCiZR5bhiGOdVlk/YKYJgyAxiGdDthTSLfYyee1GiNpXAqEsaeAIZ7pbvtkuXC3gX/9cLn34UlfCwsLCwsLP77+K+O42NhYWFhYWFh8c/AEj4WFhYWFhYWBwyW8LGwsLCwsLA4YLCEj4WFhYWFhcUBgyV8LCwsLCwsLA4YLOFjYWFhYWFhccBgCR8LCwsLCwuLAwZL+FhYWFhYWFgcMFjCx8LCwsLCwuKAwRI+FhYWFhYWFgcMlvCxsLCwsLCwOGCwhI+FhYWFhYXFAYMlfCwsLCwsLCwOGCzhY2FhYWFhYXHAYAkfCwsLCwsLiwMGS/hYWFhYWFhYHDBYwsfCwsLCwsLigEH+vgIvvPACn332Wda8wYMH84tf/IKrr76aDz/8kGOPPZb777//RzfSwsLCwsLCwuLH4HuFT79+/dB1HYBrr72WBx54oCOvtLSUJ598knXr1vH0009z++238+STT3LxxRf/0wy2sLCwsLCwsPhHEQzDMH5o4dLSUmpqatLSJk+ezNNPP03fvn1Zv349P/nJT/j6669/dEP/VbS1teH3+wkGg/h8vn+3ORYWFhYWFhY/gB/6/P5Ba3zq6uoA2FsjLV++HICGhgb69u0LQP/+/TvKWlhYWFhYWFj8p/GDhM+IESMAuOuuuwB46623uP766wE6psH2oGnaj2mfhYWFhYWFhcWPxveu8YHOkZ7zzz+fxYsXc+ONN3YseJYkKa2cKFqOYhYWFhYWFhb/mfwg4ZNKpfjoo4/47LPP+Oyzz3j33Xfp3r07AJWVlcyZM4eDDz6YOXPmMGDAgH+qwRYHOM3bQEuBKIKnCOzejCJ6uB4h3gYYGJIdMdD9H2pKizQhxloAA112IuWUpxdoq4VUGAMBZAckYyAKaO5CZENFV5OkElFUZCK2PAr9LgDq2+IkVZ1cOYFTMhCSYdASGIKAofhp0D1EkhoG4JRFihwCYmQXGDqIEilfd+rDKRQRcowgshpBwADJbpYxVAzFSyKZRHXlE4obxFSdbh6wRZvAZodk2NwvyU6TlE9KE0ioGoYBLkXCp7ciaXEQJZJKHpKRJKGHiaJiIOMSvXjVEOgqsiia7RsqcVuA+oQdQRTwOGTaYilUHVwK6GIbKU1Dkezk6CClIgiiTJuUQ1QTEQVIaQaiIOCwicRTGroBkggOWcKvNSNoSVTB7E8EgaRmkKM1o5BCRSblKkAURVKqTjCm4pB1crRWJDSSopO4LYdQXMUwwK+oeLU2dEMjLrgxnH5CcY2UqiMI4LRJCALoBqQ0HVUzkEQBr0MiltIRELBJps1JzcAwDBRZRNcNNN3AJomohoFTFnFrrchaHEOQiCu5RFQzD8M8lfX28jZRIKHqJHUDEXDbJTQdEpqOphvIooAkCGZf6QaqZiCKEHDaCCVUEqqBJIBDkdB1g4SqY5dFvHa5PV9HFgVyhRCKHsMQJMJSAIeo4ky1gihhGBqGbqCLCiElj7a4hluRcKeakfUkmigTteUTU3UCRhs2I46OREgO4JFUlGQLoqGjS3baRB8x3UZSMxAFEOUImpFAFiVkw4eui+Z+6AaSIKDIAsVSxLwmBAMMHV1QQBAQ1ARIMs22YtriKgCKLOASVDx6EHSdmOhCs3nISdaCroHsoE2SiOhxRERckhcND0YqgtcIIStOavQYqqEhChK5SgGNUfNYeu0ykYRKSgdJgGKfnYZwkqRm9rHPYUPVNZLtx18AnIoEhkGhz4kkCub9SDeoD8VJaQZ2WaTQ5/jB96DmSIJgTAUMfA4beR77927TGk0STqiIgkDAbcNp+0GP+QOKH9QjoVCIm266ibVr13LjjTdSWVnZkXfFFVdw3nnncd555/Hss8/y5JNP/tOMtTiAadkJDWvh81ugfh3YfTDyXBj7Ewj06ChmNG5GmHMPwtq3QUtCxQSMw29H83dH9hb+4OaMxs2Ie9Uj7qknty+yJEH9WoxPb0LYuQhBtsOgE2HiNfDBr5GHnoLR7SDE5i3Y5/0Ze8MG9DHXsKvPcYSkHO75eD2PzQhgE2SEYAN8ehPsVU/hlF/w6Hc6j8/ZyPJrBsG3ryEsfBSiTRDoge3gGymrmIARbkFc8QKsfNkUMkWDYepNsO5dBMPAOeUXqNu+ZK3Wm7HlDti6CqFoIHx2O6x9p2O/Cg6/nU1qISc8vZaTh+RwzYA23HNvgfq1YPciTPsdW7qP4J6lf2JZwwpsoo1jes3g0u5HUfryuaYAnXQtbP4Sh6HTbcovuPrjIIf0LySa0CjOS7K4+R3e2/o6UTVKZaCSXw66mMGr3sG9/n08wy/ENvJirni3hvlbmlAkkZlDSzh+RBnXv7qSm6YWMcO1Dvmr30NrFXZ3AdroqxAGHYdn50JcX98JrVUo7gIS465BG3Qyn27XGOCOULD5eZwrn4VkGKVoENJhd/BaVS6HV9hwr34IZe3roCWxV0wkcujt3Ds3xftrmygPOLlsSi8m9Mnn07V1PDF3K02RZHt6b+rDcf7y1VaOHV7CGWO7c9XfllMTjDOmIsA10yqxSQJ/XbSDs0bkMlzaiv3z30DdGrB7UUZeSGvfc5n57GZiKY2DKwu5YGIPVlW3ctTgEm559zu+2dxEqd/BrDNH8tLiHbyzooakpjO6IsCvju7P2po27vl4A+GEyi0zB5LjsvHHzzZS3RIjz61w9rgKDq4s4OtNDWxvivDTQ/vwyOwteMUEP+0XxDPvVtOekuEoMx+AuX8wzw13ASx4GFqrwJ2Pf/w1SH2OxbFzMY65d0DLdnDnIx10Nd4+07C/exnUrgLFg33UBQiV0xFeOh1SUcQ+0whM+hm6rZgnlrYwpl+Ep9c/wMaWjbhkF6dUnsoJvU/nlrd28s3mJi6Y2IObJzhg5ZsIxUNg3gOwYwGSpMDAY2HYGTD7TnJHXUCDewwXvrGLN8/qRs7yBxG/exW0FLZLvoK1sxEWzCI15CQ2jziDe5c9wJL6pdhEG0dXHM7lQy+ndOVLNHQbyUJiPLryMarD1QTsAc4ecDYzex7NEfetZGyvAi6Y2JPfv7+OSFLlqql9GFrm56gHv8GtSNx94hC65bq486P1LN7WjCKJHD2kmJ8e2od5y6o5uF8hggDvrqzhkdmbaQyb58/1R1RySGUhAbfS5f1H13U2N0S49+P1fLm+HoCDKwu44cj+9CnwIMuZMytJVWNTfZjfvbeWRduasUkCxw4r5dpplXTLdf3ge9+BwA/y6trjzRUMBrn11ltZt24db731Fk6nE4DXXnuNDz/8kJkzZ3LSSSf9043+Z2J5df2HsvZdePWczPSKSXDcLMjtidG0BeGF46F1R3oZScG4+AuEkqE/qKn91nPJXAQ1Bk8dDrqanp/bC055Fh6bAtPvhPIxkGiD186HRIjosAtYM+A6huWqKN+9Bv2OgKePMEew9qnHOOt1DFFGmH0XwqqX0vPHXYHR82CEr++D6m8zd+DkZ2H2783Rn7NeQxdtCF/djTDlenjxJPPhte9+XfQ5V81WuXVgLfnvnNmZ58hhw+nPcvrXP0PdZ3+7ebvx9IBLKH7lPDPhxMdh7h9AU1HPfJ0+f9zAy5cPYtZ3t7CycVmGmY+Nv50Jn/4eGjeR6jaBD/rfxbXv7erI757r4o8nDmRw7es4P/91xvbGiHMRFCcseiwtPTXifLb0v5w+X1+HXD0/Y7vkJfNQXjsbWrbt0w82ak77hCP/1tgxonDDkf1YtK2ZrzY0pBW97vBKlu9o4asNDXTPdfHLI/tx5d9Mh4+HzxzBPR9vYFKffG4ZVIv9pZMzbFDLJ/DRwLu56t1qAHxOmVlnjOShLzdx9JASbntvLbPOHMG9H29gR3M0bVubJPD4OaO55pXljOwWYHzvPO76aH1GGyeMKOOowcV47DI3vrkamyTy8rQYBW+dZhYQBDj7LXj9AigbCd3Hw5e/T6+kz2HmufbZzRn1M/RUcOXBwkc708rHwJBT4KNfmr8dfjjrdeYaCX46+4rMKvKHM73gBm5+s4ptv+iP8Nr5cNjN8PKZ5ovL3uRUwOG/g9fOQz/sFuq6H0PJO6dC89b2HX4MqubDsufAV8amc17htI/PI6WnX1+l7lKeOfxxvqiew71L/pBh08xeM7ly8E+ZeM9KfA6ZWWeO5PxnFqMb8Mvp/Sj1O7jr4/U8ee5oTnp0AUktfZ1recDJE+eM4ul525ncN5+rX16R0cZNMwZw7vgKFFnKyAPY1hDmhEfn0xpNt91rl3n7pxPpXejJ2GZjXYiZD36T1Z5XLx1PaY4za1v/S/yoXl17tJHf7+f+++9n+vTpnHxy58V8yimn8Mwzz/zXix6L/1CatkC2Gy9A1TcQNt+I2P5NplgB8wY65270YE1mXtY653VdT6Qe48vfZ4oeMG/ADeuhx2SYcw84A/DtkzDUfNC4Vj1Hd0cE264FMOJM8yGzr+jZU8+upQjJSKboAeh7uCm+sokegDl3wegLO+oRkxEEfxnsWJgpevbs15y7+eMRAfK/viktKzziTB7c9HKG6AHYGdrJKiFpCj6AufeZ7bZsQ6pZwuljymlL1WcVPQB3r32aprE/AcC2cz6jc8K4lc4HwY7mKD0cIZxf35V1e2HFC9Dn8Ix029o36aO0ZBU95PVGqV6QKXoAtBQFi+/lnJF5HUmPzN7CyaPKM4o+MXdrR/qO5iiN4SQ98930zHfTEE6yoznK9RNysH96Y1bb5er5jPSH8NjNQfe2mMrsDfW47TJFPgeDSn00ttezLynN4LkF2zlpZDknjy5n1pebs7bx9opd2G0ibrvMjuYo14zzUbD38e09FbZ8CbEWGH42zHsws5LhZyPMvS9r/ax+DXoflp5W/W36FHQ8SFOyjfuWZQoMgFWNKygMRLjnpMGwayl0G2teM/uKHjBHoUK7Ib8S8bs3KAqt7hQ9AEWDYPnzAEQOv5WHVz2eIXoAaiI1LGtcxYfbPspq0/tb3ydGDJsN2uIqn6+r49D+5mjxw7M3M7jczw1H9ufBLzdliAyA6pYYq3YFaYokyXHZEITMNv702UbqQ4ms7SdTGq8tq84QPQChhMpfF1URS6Vfj5GEyv2fbezSnsXbmrO2daDyg4TPaaedlvb7uuuuw+v1smvXri62sLD4EUnFsj+o9rBzAboaR9j0aZdFhO3zEFKR721KVxP7r8eRg1A1r+sKNn4KfQ+HeBBireYbaMkwM8/Q8cR3I2z50ryx76+eZByat2SmO3LM/qhZ0fW2DRvAV2b+HWkx6+k3EzZ+0vV+bf8aRY+nP0iASNkIvq1f3uV2n9cthuL2kbTGjeAtNf/e+AknDC9jTXPXdm4LbiOy17op1+5vqchzp5Uxoi2QCGWvwDAgXAdK+jbk9kLYuSD7NsXDTIHcBbYdXzOuzNbxO5RQEch8cu1ZQ7GHxduaGFjiY1Cpj2/bHzIuYtCUXZQAuGoWUZHXOQWxaGszg8v8fLcryKQ+eR31ZGPR1mYGlfqQBIFQIosIx+ye2mCCtrj5AO3lAxo3dRYoGQ7b5pp/i5I5Orkvgpg9fU8DoVpz2nlvdq+AvD4dPyOCeay7Ym3Lcg4fUAQbPzZtqsoiWPdQNR+Kh0Beb4QNH3amu/IguMu0CQjn92Vx3dIuq/l8xxd093W99m97cDsTepgCeNHWZgaX+s19SWoEoyl65rtZtLXr4/Pl+nqm9M2nri2B15G5oiSa1LIKG4CWWJJ5mxq7rPubzY20RNK3DcVV5m3pepsPV+9G1TNF0YHKDxI+e0dr3sPLL79MWVnZj22PhUUmkg3E/SxHc+WDYMNw5XVdxplj3sS/B0GQMZy5XRfQU+ZITle4CyAWNP+2OUyhkgx3bm7zYjj87Tbtp55kyJwm2Bc1bi5i3t+20l5rB9SoaYMaMR8OXeEMmH28Tz8LyQg+pesh4wJ7buf+7d2uO59gXMWn5HS5rU20IdM5064684kk0x/iou17FnPavaDu8+acDJvnRDaS4f33nTNAKJH+gFDkLK/sgE3qPJ9ynAqRpEo4oZLjMoWTIcrmudsFqjOPaLIz/IffZSOaUPE7bQRjnfVkI8dlI5LQkKXstu3BY5ext9upIqXbkwyb1wV0bed+7AfA7gFtn/53BtLOeVmUsYld1+NTAsRSmnnMvvf45EAyYpZz77VmLxFKc3QQ1QS+fQXZXuQ784iqmaNpHTbZfexsiQHtfb3XeWm3SSRUHf9+jk+uS6EtnsJtl0mksgsOe5Z1OgCKJOF37u/YK9j2Oe6iaJ6DXVHgtSNlG3o6QLF8zy3+83HkQP8Z2fMkBcrHIkoSjMiyBqgdY/RFGP6K721KkCQYuZ96EiGMURd0XcGQU2DxY1A2CmxuGH4GrHnLzPMUsiPlwxh+lmn3/urpMcEcPXHtI8LUOMRbzLfersTg4JPMt2eAXoeCtwQ+uQlGnN31fo2+kNVtLpL9T0hLz1/5Gmf3nNnldscVj4Pt7ZHaBx4Lm9pHlYadwa/eWs3AwHBkIbudR3c7jNy17W/tko3WvOFUNaU/jOJKwBwFyIa7wOyPfafh4q0kysZm759tc9EHnZCZ3k7r0J/w7KpOG4aU+dlYF84oZ6Z3jkQd2r+QBVuaWLCliUP6mQ/ktUGF1MDM9T0AiDKtBaPZ1tg5Cnn88DI+W1tH3yIvby3f1TG9ko2TRpXzwerdbKwLM7Q8i0AG8twKiizgVMx+eGN9gviAvexZ9x4Mb1/PVbcWSkdmVlK/Lns6mEJaS6ULT1GGgv5pI0u5OszoeXTWKmRBpo93KOc/vaTzWhl2epf7TeV02DYHoq2oQ07tTNeSYHOCx+yz/BV/45y+XfQ9cEKfEwh1MZKYY8+h2FXE1kbzPDh+RBkfra4FYGCJD0kUeHv5Ls4+qOv7ycmjy3lzaTUCkFAzhc/AEh+5nuxCJeBWOG9Cjy7rPn9CBQXedM+wAo+diyf37HKbM8d2R7CETwffK3wOPfRQpk6d+nf9C4W6GJq2sPhH8BbBYbekDZ8D5k32pKfAaY5kGJ4CjEOyLILtdTAMPI5NjTFW7mxlZ3OUWDL79ACA7inNXk/Pg81plOFnQPcJmRtOuw0a1plvwcfOMj2n8vrCzsWgeGiY+TzOQBkr27zoq16GYadBxcTMdg7/HbojlzURL8apL4BtH4+MZS+ao1sz/mhOUexNwQBz0emaN2HareDKw3DlYkz/vTlSNDV9DQ+A0WMKxsATuOuLHahTboD8vh15YtU8Znh6cVDxmIztbhhxLSVrPzAffAX9YOjp8N2bGIfdwta4x3RLF3O4acxdSEK6nb19PflpxVE4vnsDRIngMU9z7/z0KZVrDuvLtpCEfuITcNpf4fS/wjEPmvYpbrRTX4Rlz6cbpbhJnfYyjy1L0DTjyYz+0QM9aFJKCU65LWN/UhUHs6HgCBZvbzW70mvn3pOH8sq3O9O72GPn50dU8uz87QBcfVgf5m5sIKHqJFSdrQ1hbjyyP5e/sg79kF+hF/RPb0iUaJrxJPfND3YkHT+8jHAixTXT+vL8gu0kVJ2vNjRwzWF92ZfxvfMYUOxl8bZmnp23nRuP6k+BN31kzKVI3H3SEHJcCh+squHnR1Ty6spGqoZeg57fbk9bjbk+bsS5plif+htzfc7eLHse49hZ4C1OT7e5MI57GOb9OW2/mPFHWPJUZ9qQkxGROa33hfTyp1+/kiBx80F38fw3LWxqCJO0BzD6z4D8Sug5JWO/mfxzc2pO8aAe8yAvrBcIHrzXYuy5fzCdCxQPwtLnOKJkAhOLx2ZUc93QK+iWTHHT2BspcBak5TllJ/cfcj9LN5li5dhhpcSSGrtaY+R7FP546jDu/nAdL3+7kymV+UzskzmKet3hfalpjfHH04bTO9/Nzw7vy1PnjeaRs0by1HmjuerQ3jx4xnDy3HZqWqOs2RVkyfZmtjSEaQzF2d4UoXuui7MOypyKO354KcO75WSkC4LAkYOLObiyICPvhiP7UZ77v7+w+e/he726vvjii7+70kMOOSQtsOF/E5ZX138wzVuhfr25oNlbaq6lcQRgLzd1vWUHQiII6z8wh8QrjwRvKX/6NspDX20HQJFEfjK5JxdO6tllXAwtuAsx1gzrPzSH1SunY/jKEXN7mAVad0BLFcbGT8Dhg35Hme7mkUYoHQ6ighFtQlv7Homc3ujl4wg7irDJNpZWNdPHFaOXS0ewidBWba4Ncvig3wwMxcNDyxJEEhrXTSnCEW8097lxs+l9UzqciKMMZ7wOMdGKsPUrs+2eB4Pdg7FzMfSeiqF4ENa/j/DVnXDOe+DwggCoSVj/AUYiBP2OxPCVsSvlIb9+IU49aj7k2nZBzTJzVKXPETS6/FSHdzG3dhFeu58pZQfjq9tK/q6FUDHOHN3atQz6HEa95mFZs53KYi87m6LsagsxrAJWNC2iPlrPQUWj6e3II2/1OwiuXCLdDmaXHiCYFJmzsQG7LDFtYBEFcpScLe/i+Oo281gC5HTHOO5R4t5uNAt5yNFaPE2rcdSvQMvrh9F9PO9sA6/TgUtUGeQN46meiy1Si1YxiWSgkjk1Eooe4aCCJK5tn5GKtBKpmIa9oBefVmmsrw3TI89FwK3wzopdnDamO3VtcbY1Rhha7qey0MuX6+vRdIMp/QqQBJi9oYFwXOXgfgXYZZEdzVEq8txsrG1jRk8BqWkjwra5GL5SxD6HUaPn8NbqZjTDYGLvPFKaTsBtx++QqQnG+WJdPR67zIyhxaiawRfr6wlGU0zsk0dZjhNVN1i4rYldLXEm9s6jIt/FupoQy3e00C3PxegKc7rom02NjKwIkO9VaI2kWLK9mcO6GZQkdyDvmAveUrRe07Cl2hB2zDfPr7YaqFmBkV9JstsE5tbamVCUQq5bhbBrCUJBP/Ru42hOSnjbNuPa9Q2apwSj5yHYBB3WvoWgJqHv4WjuIuo1Lx9tijC4QiAh7GZp3WIKnIWMK52ArPupakzxzeYmclw2LhtmQ0xFINLQvg5uvjmF1etgcxRJENFLR/LqFhuNkSTH9vdSKLSgbP4UIx4kOeAkHHYbwo4F0FpF8/AzqE62Mrd6Dm7ZxSHdp1KoqnhaqjBCDezsM4k1LZtY3bianv4ejCkeQyrq4d3VzUwbUEQ8pfLZ2joGl+UwrDyHaFLlreW7KPDamda/EEkSqG1L8OW6elx2iSMGFqFqOqoBpX47mi7wi9dXMn9LE2A60h09uISbjxlIWyzF1S8vZ93uELIo8MDpw/l0TS3vr9qNAVx+cG/G9cpjWVULSU3niEFFFPscFPu7FjGN4QQ7m6N8sc5cKH/4wEIKvQ58+5k6+1/ihz6//66PlB4IWMLnf4uW1hhHzJpHQzjTg+LWYwdx7rgKRPHfOwQcjCZQdYNIQuPOD1ZR1Zxk5tBSnIpEQyjOFYf0wZtt/r5hA7xxsSm0kmFIxMzpgkQb9D8Wo2oOwiv7TNud/jIUDgBPMWz8yPSYG3c5tGyFT38L5aNM7yxPofnmHQ9C7WoI9GDjse8guvO49+MNfLG+nv7FXrwOGQGDwwcWM6xbDkPLc9LWvjSH42xtjLKqupU3lu3C65DZUh8hz61w0zEDOKhHLjZZIhRL8ecvN7Kquo2kquNWJJ6aFMTxyqlkYHORumQej6xMceKIbmyoa6O2LUYspROKqzz4hbmguDzgpDzgxO+0IQoCJ48u56Jnl1AecHLH8UN44ustKLLIRZN6cfmLS0lpBm9cPp7b31/LzpYY1e1rPAD6Fnp47JxRLKtqJpzQ2VDXxrrdISIJlQKvgsduI6XpHFJZQEnAyXe7gtS0xogmNYaV53D2Qd1wO8xjuLk+zCOzN3W4zPscMoossnpXG7POGEHPgkxX5X8GDW1xbv9gLfWhBPWhBMPL/Rw3vBS3IpHrcdDrX2THvmi6QTieJJ7SWLytie9qwkgiuBUbK3a28LNpffA4FJKazosLtzO2Ry5HDC7tCBi4L3qkBaF1mymGvrgd1JgZcLRkGEbPQ9HHX4Hk9BNPJpmzsZFlO4JEUxot4STnTahgcJkfWRSwZXE9jyc1Plu7m+IcJ7pu8OHq3czb0szm+jB3nziE15buZGlVa8Z2xw4rpVuuk4dnmw4Mp47uhqrrvLks3WHILoscM6yUm2YMIMfV9RoeC5Mf+vz+f4d0fOedd6irq+OSSy75/1ZlYfGjs2x3W1bRAzDry01MH1REyX7eoP4VbG+KcdzD6R5e62o3AiCLAieN6pZd+Kx5ywweV7uqM21Tu4tuj4kIc+7N3Obl9vUTh90Coy+CNy/GGHYawrw/m1NvH//KzA/Xd4YJAGjZTk7rOua1DuHTteaHiNfUdE5NLdjaQq5b4c3LJ9Ajv9PLavH2Fi57MdOdvSGc4OqXVvD6ZePpVeChti3OM/Oq0HTzPey9iwbg+PKXWfuLVBRt3fuEE9OYv7WJG94w9/+Ppw7jkdmdnnDV+4iXQ/sXUuCxU90S4w+frufwgcXc/9lGjhxc0j7KlM8jX21hQRZvnU31YR78YhNeh8yUykJufW/NXnmd5eZuauQvZ4/sEF8AH66uZUplAQNKFGpaY1z7ynK+25XdU+r91bu5amrm9NY/g1eXVvPuyt0dv7c2RHhzuRnyYVCpjyfOHUVpzr8+8F19KM7MB7+hKZLFpR2YOqCI+9vdwQu9di6a3LtL0QMgBHcgPHFoeqIaN4OGVn+LOPh4WnQn5z+zmJXVwbRi7682++eG6f24/NB9ptrbbb3u1VWoeuf4gcsGb18xHpskceObq7Pa9P6qGh4/d3TH72kDCrnir5nXSULVeX1pNRdO7GEJnx+R/9fi5kWLFnHppZcyZMiQH8seC4sflXW7u3DFBRrDSZJZFh7+q6lpjXWZp+oG4WzuyskY1H3XdaWGnu66vC+1q003cF0lEQ2ZUZptLjOmSxc4WjbQGs3+MAJojmT257rdXa/3a44kSbXHHQkn1A7RA1DkxHSP78qW2qUMKvGxfa/FwQ5ZoqULF2GA7Y0RCn3m1ObWhghlOY609LIcJ1sbug55sLEuTIHHTst++kDTDVJa5iD6npgtKU3fbxtra9rQssRi+bHRdZ21NV1fG1sawiTVf89kQCKldyl6AKqaIvQt9HDu+AreuHwC5YHvEWfh3V3nGTokQsRVbb/HZUV1a1Z38FBCTRM9p48p58NrDuGztfVsqs9cFL8H3SDtWlHbP9vRFbuD8a73weLv5h8SPqlUivvvv59TTz2Vt99+m/Hjx//YdllY/Cj0K8r8ltceAi4bShcupf9KSvxdf7tHEoWOIHdpKE7Te6ZLxM7AgtkoHGC6uosSQc1GKrevuUjZkdPlJolAH/z7cZnNydKflcVdT5fkuGwo7dNiHrvM3i/tjXH2a3+8cDgb60J03ysOTlLT8Tm7HsTunueioV2A9MhzUxtMpKXXBuP0yOv6Idq7wE1TJEFgP2/eopDu5r6HwvbFx4ok0GOfWEV706/Yi5Rl+x8bURTpt59j0yPP3XFs/tXYZXG/rvwjuwe4/7Rh3DRj4A/7FMO+C7b3RhDB7sUuS1Tkd13X4FI/cpYPcHvsctpo06UH9+acpxfxyJwtWeP3dDQrkHat2CRhv6NWRX/H970svp8fdGY///zzPP/88zz00ENceeWV9OjRg6VLl/Ltt98ybty4f7aNFhb/MANL/QS6uIlecUgfCr3//htKgdfepUCbMbiE/K4+TDj4ZJC7yHMVYEz+efY82QEDjoW7upHsfyIvrgzRMPRyWP0qjDov+zbuAnZIFThsErldfGPo4kk9Kd1HxA0s8XVZ/oIJPTpEX55HYfqgTs+hu+Y0EJv0qy7tFwcdy46WmPnhxvb631m+i7PGZncxzvcoeO22jpGXcydU8NrSnWnpH67ezYWTe2aNtCsIcOGknuQ47exsjjKwJPv6gemDivlmc3oguQElXgraj2FJjosrs0yZgPnAnzm0JPs+/xM4ekhpl7Fkrpzah9LAv2cKuNBr57KDe2fNC7hsDC7zU+hz/uCXFsORi9FFSASj/ww0ew65boXrD++XtYwimetsspHvsXPccDPvyXNG8umaOnY2xzAM2NoYYUhZ9lADRw4qZuXO1o7fX21o4OjBxVnLVhZ5KPJ9/8dJLX44P+jMmT17NrNnz+arr77i66+/xjAMRowYQW7ufgK9WVj8B1Ca4+DlS8bRfa83Q0kUOH9CD04YWbbft6x/FWUBF4+ePTLjJnnYgEJ+cWS/ruf2vSVwxqum59UeZIf5raNEC5SPNt3y9xZHniI46zWw55DqezRrBl3PYwvqeGQ1BCtPMUeRhp+VHuwxtxfVx73Gzz9t4qEvNvHEuaMy+vOccd05cWQ5ii19AWjPfA8vXDQ2LUKxJAqcMbYbJ40qx9EeYybXbefXRw/g0H7mvny9qZF1tkHED711H/sLSZ71FqK/nJtmDGT+lkb+eOowKvJcfLWxgR75bk4ZVZ42etQjz8V9pwzjvk83YJdFrprap2N6ce/0yw/pTZnfyawzRuDda5TNaze/o9Ut4GRyZT5VTRFuOLJfRvycaQMKOW9CD15e3Pm5k6Hlfh4/ZzT5e7mbj6rI4edHVKaJjgKPnWfOH0PZv3BNTZnfwbMXjOkQZWCKr+sPr2RMj3/fvV2SRE4eVc75E3qkXZ/dc128fMnf/80pMbcCTnoKo2xUWrrRZxoc/ntkvyk2R3TP4bczB6Qdl3yPwgsXjaU0kP0FyW2X+eX0/kwfVEzvQi8ff1fbkTfry838/IjKDPfzqf0LuOWYQcwYUkLP9vVwry2pZsbQUqYNSI/dNLjMx5PnjcmI22Px/+Mf8uravHkz9913H/Pnz+eNN96gb99/zWK8fwWWV9f/JvVtcRrDpqdInkch32PHnW0K6d9ITWuMlmiSUCxFwK0QcCvfPyKVSkJwB8SaTfdfT5EZ18htPrj0cD1CrBUi9WYcH1cA1VlM9c7NfLxV44F59R0B1i6e0I0bJ+cgp6IYho4Ra0WzeUjY86hKekioOgUeO3ZZJBRXCcVTJFSdPI9Cntu+369NVzVFaI2miCZV8j12cly2rDfz+rY4LdEkLZEkXqeNUreAO9VkLrSWFAx3PjZ/qRmwEgjGkgSjKWIpjbiqE09qFPvtpDSDxnAStyLhcchE4hrRlEqe2048paEbBh6HTDShEU6oFHrt5HvseJ2md1Z9W5yGsLnOpMBrp9BrxyaJxFMaDaE4sZSO3r4uY88+5bUHpGsKJ2mJJvG2j0ZlC5kQiqVoCCdoCCVQZJFct0KZ35n1q9v/THRdZ2dLrGN9VoHXTkF7P/y7iSRUGsMJmsJJHIpEvluh8P8x5aM1VyEmgqanojMXze5HDqR/hy2R0qjfc1wkkXyPeQ1+n+dnMJYiEk9y3aurWLTXp0b8ThuXTulFv2Ivqm7QI89Fkc/R8TKzszlKSzRJNKlR4FFw22WiSY2WaBKP3Ua+J/v5Y5Gdf4k7+wcffMCVV17JG2+8wciRXUT3/C/DEj4W/8u8s3wX1766gmxX/SNnjeToIf+6qRYLi/813lpezXWvrMyad/eJQzh9bNffB7P4//MvcWefMWMGkiRxyy238N577/1/qrKw+NfQvK3zO0KK1wy737bL9HDyFnf9baJ4CMK1ZiA9m9P8rpA7j5Sm0dSWwCHEcCVbkLQYguxAl2xEdDthyU+Oy4bbnl5vfVuc1liKRErH45Ao9zux2SSawgmaI0kSqo7HIVPu0pEine3G7XmIgoCSaEbYY4u7AFy5aE3bEBJtINnQHHkkDAFbohVDVzEUPzaXl2PKIxxzVRmGzU3QXsSC7UEKHQJDcpKIWjNqYxRDkNBScZI2PxE5B69DxtNuv5pKoAV3I6SiSDYFXbChJSPoih/JV0R9KIlhGBiYb+wg4HVIKLJEJKEiiQKxpIYB5DhlfC6D5kQzSS2JTXBiqD4SqoFNEtrfuO3Ut8URBYG4qqEZUOGIougJczG2GgXRhm5zE8RLbVxCkUXcdokSf+a0USSRojaYIJrUcCkSXodES1RFkQQMA+KqhiJLHe0X+cy3/ZZIAuKtOFNBBENFtfmIKvlIahRnqhlRS4DdR1DOQzUMokmDeMpso8TnIJRQiSRVIgkNRRbxOyRUoY1QKowkSEiGB1F3IcuQVA2cNomkphNNatgkAUEAEQED02vM67BRmuMk3lqHEDc98QxHDrozj4awef5427/0LooCiUgrYrQJQ02Qkt00CLloOoiCgMchp42+NUcStEZT6AbtnnYGDpuE2ybSltCIJjXsstgewwl8QgRHosn8eK7iJmYvZGubuTYmxyVT6DOnprY3hgknNBSbQHcljiPVYrqVK17injJ2taWIJjS8DokiKYJdDYJhEJF8xAw7OUYLgpogKbtpEnPRDZGkavZnnhBCSQURBVDtOUTEnA6PK5skkNIMREFAloQO+502CUEwPzyqaQYOm0gspZHSDDx2OS0sQ2s0SXMkSSypYZNE/C4ZzYgQ0ULoaATsPvxKHiO7B5jUJ49vNjelnXcH9cxlfO/9fCvP4l/KjxLAUFVVZPk/a9rgH8Ua8fkfJdwATZvgg+tN120wv3c144/gyIUnp8Kka2HkuelrZgBatsPS52Dx46Zoku0w9AyMydfxzjaRo7rryJ/9GmnjR6Z7rKcQJl4LhkZt7lj+ut3L6Qf1pKzd7XZbY4Q7PljLl+vr0Q1zfcfPjqhkQu88bn/fTBdEWHvdQJQVzyPs1a5x7nsItathzl1mlGhBxOh3FBz+O4R3rzEjPOf1wZj5AMy+wwzaBhDoiTHtVoR178N3r4Hdiz7+Shh2BtrcP2Fb9TdzqszuNeP75HSDpc8SP+oB3qoJMKV/CflCG8LSZ1AWP2x+FFK2m98m63sEfHk7iWl3soL+CHY3N7+zhvW1piv7wBIftx03CEUSuOGN1ayvDRFw2Xj0/F68tHkWX1XPRjd08hx5nD/gSmpre7FsW4JfHdWf2mCcsoCTT9bUsrkuzCNH+hATQYSW7fDl7dBaZe5fxUSMabdSpeZy0ZvVnHVQBSO751CR5+6YgtvZHOWVb3fw7PwqwgkVRRI5fkQp547vwfuranhx4Y6O9OOGl3LEoGLiSZUR3QM42rYS+OIXSDvbvxxeMhz9hL9gfHEH0sYPzOPuLkA77Fa+kcbyk1e3kNR0PHaZiyb1ZELvPC56bgnhhMoJI/OZPirOn1fcTXW4GoARBaP4ycBfsnC9wMmju/Peyhoen7u1w55jh5cyfVAxO5qiNEUSRGNxfjNaw/b+1Z1hDYoGkTj6z9yzQuHpBdXkuRV+O3MgMys0pE9/g7Dh/XY782md8Bs+18fgyy1g2Y4WThvTnZ75bna099FBPfOYNXszi7c1o0gij5w9ki/X1/PG0mpTlNtlnjp3FGMCYYRv/oSw6mVTiCoejLGXoo04l/73r2FEWYD7ThnGpoYwv3tvLYIAn57XDeXLmxE2fNRhj3Hwr/jON4nbZzfw4KE2ir/6uRlyoX2/jGm3IXzzgHl+u/NpHf9rvmAs3UoK6aNvJ/eL69PKx456gGqlN+vq49z36UZ2NJvf3RpdEeDKqX34w8cbmDqgkEl983li7lZ+dng/bntvTcc0VXnAyW9nDGR4Nz+tsRQb68L84ZMN7RG5XfzhjFIe/e4eltR9C0Cpu5RfjP4V367L4YwxfdhcH+G1pdWAwYkjyulX7E0TUhb/HKzIzf8glvD5H6VuDTx+iPlw3xubE34yG549CaK74OAbYNL1sOer4OFG+Po+WPRoRpVGv6Mxpt+J+NeToWlzRj5H3werX2XHwX/i8g9beeb8McRSGhc88y1bGzNjhtxx/GD+tngHa2raeP28AYyqegJh4SOdBYadBd3GwPvXZrZVOMD8PtiTh8EZL8E7P4XoPoH4BAFOexHeu8YUTYfciFG9DGHzp5n1jbscQnWw4UNiF8/l8ZUJrhTeQJr/QGbZyummiPz6j6Qu+Iwpf23LiDtil0Xe/ulETnh4HnFV5w+n9eCFql+xLbgto7obR93OxwtLOGd8BdUtUWpa4zz5zTbWX9sXR7zBXM/06jlkzNe58zFOeJyvWgu44PWd3HrsIMZUBBhU5icYTTLry8088U16e8cOK6XAq/DUN9sz7DikXwGXHdybfK2OPm8fY/ZZx4azYMFDZvTsfWg+8hHOX1LBqr2C4Z0/oQfNkSTztzRy5xl+fv7NxRik2x+wB7hl1OP89ZsQg8t9aUEQAQ6pLGBoNz+5boWjSiIU/fUwc8Rkb2Q7sYu+ZtSjW4kmdZ4+qRtTF/8EGtZn2jn9YS5Z0ZOrD6vkjg/W8fi5o7j9/bWcNqYbv3pzNY3ta5yunNqHtTVtfLm+M1LjzKEl3D0tD/fnNyBs/CijbmPcFWwddCWXvrqJO04YwulPLMQwYMlP+5L37jkIWezRj3mQltJDyXtqTNb94vSX4KXTzA+jAs1HPkyqaARFf52atXz8orkMe3hbxkdCAy4b9548jJ88v4RzxlVwzvgKznxiYcf+7s1rl44nnFS58NlvO063Jy7szR3LL6Mp3pRR/sEpT3HD39o4alARF07qiVOR8DkVHLbMqM8WPz4/9Pn97w9iYmHxzybcBAseyRQ9YA7Pf/s0XPaV+Xv+gxCp68yPNaV/dHEvhJ2LEJq3ZRc9e+oaejp5616kIkehtv17T9lED8Cjc7ZwZvsagJF5KYR92x1zAcy5J3tb9esg3gZ9p5tf2t5X9IApFBb+pd1rS4DSkdlFD8DSZ2HQCaDGEZc8zVkD7UiLM8UfABs/gW4HgWEgffk7zh6W6cKbUHX+urCKqw/rS47Lht3VnFX0ADy3/hGumFbAk99sY1yvPF5YWMUxw0pQWrdAqAYWPpIpegAijQgNGxjki+N32nh8zhaqmqPUt8VoDCd5fmFVxiYzhpbw4sIdmXUBy3e0YpMEArXz00WPM2A+iLOIHoDcBXdyzdj08AQvLd7BjKElnDI2n79tfDRD9AC0JFrYGl5ObVucEd0CGW71X21sYHi3AFt3t5C79rnMhz2AmkD69jF+cVgvfE6ZUd6WrKLHtPMOrhjt4b1VNfQuNEd7grEUWxoiaSJgVPdAmugBmDm0FLcezip6AIQlT9HLEeYX0/vxwOebMAwoCzjISezKKnoAxDl3EyDY5X7x3RvQf2an/bULyFv3fJfl5W8f4+iBmd5pLdEUG2pDDCr10RxO8O225qyiB2D1riBPfb2143SrLPJQHVudVfQAPL1uFqcdlMdzC3dw8H1zmHTPVx2xoyz+c7CEj8X/PolWqFnadf6uJZBqFyOpmOn1sYd4MLtgAgj0gF3fdl1v6w5w5+PevYihhRLBaIplO7qOjFzdEuuYlhHirebNfm8ECUL7iUJbswx6T4XdK7ous3uF+XVzux/CdV2XS8Wg/eFs37UQjx7KtGdvIg2guBF3L2doUfZ1Ust3ttKn0EP3XBdbgmu7NjGyG7dTZ1V1K+GERkLVGdPdhxhvBckBNfvZv+rFuOL1lAec1ATjCEA0qROMpTLe/MFcw5ItHaBbrpPGUJy83V+nZ+RUmEKzK4LVFDu1tKSEqqPpBn2LFNY2r+liQ1gX/JbeBS4awgk8SubygYZQgj45AradC7usQ9llnm/dAi6cDau6LEdbDcVOne92Beld4GHFzlZGVQRYvddIlVuRaIpkHndFFk1vwq5QExAPUuR3snqXWd/I7gHEmszPMuxtj2hkiVK+h90rzO/H7cFbhLyffpCrFzKsMPtIy+pdQfoUehjWPYeFW7OLGACvQ2blzs7+6F3gYW1r19f82qY19C7q9MJKaua5Z/GfhSV8LP73sTnNmDdd4S0xy3SUd2X/e19iLeDfj5eG4gYtheoppS4Cdpu43/D6LkVC3fO5AiXLegBRMuP0dIW/3FyP5M0eCA0w86ItptDbT5RmoGOht+YtRZW+J76Mw2++eXuLaYhkFxLFPgfBWIrmSJICZ9c2OmUnui5R5HPgbJ8i2B1SMUSbKUL3t3/+bqRkD8FYCqdNQhIFZFHApWR/AGaLsryH1mgKt91GxLNPUMRY8/5tsLmIG5mixSaJNEdUCl2FWTYyybeX0xJV8TttxFJaRr7faaM2aqD7sgfUA/N4NcRM+zVv2X7sdBLXJYq8DlqiKcoDTnYH42nB8uKqjteRRcjqhukYsD8UN7Gk2lFfdXMUw99tv/YY4n7c6L3FZt93GNeGsZ9+0L3mdZeNIp+d1miKumCC8v1Ef46ntDQX+tZoigJHeZflC12FtETSxZuzi3PP4t+HJXws/vfxlcL4n3adP+5yWNg+jdN7mumxtQe7DyomZN/OMKB8dLpo2pthZ8Dat2kYeimfbAxSmuNkVEUAhy37ZXfiyHI+WG0GQGvSXBgVE9MLbJ8Hw07P3pbigaLB8O0T0OdwsoYfBhh5Hqx6xVwnkQhCVw+iXofAzsUAJA+6kk+qNIwek7OXDfQwp4J0jeT463hiefbvc10wsQezZm+muiVGsaMSp5y932b2OJEPloc5Y2x36kNxxvfK45lvtqEVDjQF1qjzs9shiNDrUGrIo7olxokjy7DJAuW5LnxOG+N7ZT6oV+xszZq+B79LobnPiekBHVt3mCIzmzgFYkPP4YXV6dMv43vnsXxHC28taePEXudmNx+BicXTO2K77PvtpvKAk9ZYkoRgJzii6/M5edBV3Du7ml2tMbbb+3VpZ3TI2bzwndlPX66rY1h5Dit3tjKlsqAjAKSmGzRHknTLTT9WS3a0oDryzGOfBaPHJKKCh78u3MGFE3sCsHxnkGTRMPNczbbN0DOIGPuJWTPsDPPDvO3EBCfBEZd3WTwx7mpeXZU5wioIcEi/QuZtbmRtbZBjh5bQVZgen0Pm/Ik9On4v2tbEuMJpiEL2a/j0yvN4a0nnN9AO6pnbEVnc4j8HS/hYHBjk9YUpv0h/gAkiTP2tOWoz/wFTOBxzPzj3WqMS6A7HPAQF+4Sz95VhnPYi37Z6SZ7+uukNtTc9D4aeUwgWjua9Gi+zzhpJsc9Bic/OU+eNyfj+1qQ+eZw/oUfHsPvZr+3EOObB9Ha/uA0OugwqJqW3ZfdhnPEyLJgFugorX4aj/gDSPjfcoaebIm2PF9Cat83t9h29KBwIE66CJc8QP+RWViVK6N29HPWYWZnfB/OVmp5xX/+RxLBziVUcyhEDi9IeJKIAPzu8ErcisbPdu+aRzxq5/aA/47alP5RHFx7EQXkn8uKCXRzUM5f5Wxq5/bhB3HbsIGr1HAzFDaUjTG+yvZEUOO4RglION882xcxxw0s7PgVSmuPkjhMG07cw/aH78Xe7ue24QVQWpacX+ezccfxgPluzm6S7hOajH0vvz2/+hHHKs6Yw3gu916Fs6XsR76/pXBPUt9DD1VP78OLCKtbUtGFPDmJmjxPStrOJNn49+k4+WRHjL+eM4sPV6VOae+yZv7mRsT3zeG2Hm+TUWzPO5/jBN7MqWdbxwc1XNqhoZ72ZYWeqYgob+1xEWZ6fLQ0R7jtlGMU+O4+cPZKPv6vl98cP7vhW18OzN/P74wZTvNfIxwsLqtiiFmCc9qJ5DuxNQT+Y+WcuebeGT9fWMa5XHiePMkeebp0bQjvjlQx7jIpJRMdexYsr2whOviXzOp14rTm92L7WKtV9Mht7nMlatZT4obdllFcP/S1BXyVT+6ePrimSyK3HDOKtZdX0yHdz9dRKvt7cyP2nDc/4fMdxw0sZWZFL/yIvJ4007dcN+Ov8IDeNuQtFTL++jul1HM7U0I5F7b0LPNx3yjDrq+r/gVheXftgeXX9D9OyE1Jh2LXMfO0rGwU2N2yfa64d8HcDbxcfNGzeZsb7adgAgZ4QqIC83tS3xYnHExQKLUhNG5CiDQj5fTFEiYjkp1b14PLkUOx3dITfjyVS7G5LsKk+TGMoweAyP3luhQKvQk0wwaa6EI3hBGN75NJLbkQIdbZrBHqCICBEGqBuNYKnGAoqUW1epGgDVC8BuxejdCS6YZDa/R0kwsjdRiHZFITmrdBShVHQH81TSqujGE+iHrFlG1JwJ1J+H3RJIdlcjVg8kHrdh2j3UORzIEsiiZYahGA1QtNGpEA3DEcOqaYqxIJKYvY81rWIuBQRp01mxc5WBAGGleeAYVAXSlDgtbO2pg0dGF3hQ7SF2BHaTmOsid7+viQTXupaJLrnutANg4BLQdUNrnt1Bct3tHLDoWX8ZJQPyVBNF//a1eYoUEF/ooKTb3bL5Pqc5LkV3IpEkT99pGJHU4TdwTib6kP0yHNTluNke1OYgNuM9ry1IUyx30nAZcOpSOS6FDx2idZQGFeyEal+LTYtgl4ygqQ9D5JhpKZNyLEGhOIhJJyF1Kseqltj7GyO0rfI0/E9sto289gW+xz0LBJRaWNN01ockpNunt4k4268DieGYZ6ewWiKzQ1hinwOAi4FRRaJJlVqg3H6l/gosqewJ5rRdi03F5aXjyQi57CyQaMpnKRfsZdinwO/Q8QI1iA0bcII15PMG0CjkEtYyiGW0sh1K5T47DjbYzVtawzTEklik0R2NEeJJTVGVOQgCQK7WuNUNUXoXeBBkQQKvHZKjVqElipo2QYF/Ui5S1kR8tESVelb5KHAayccV2mNpVixs5X++XaG+UIITZsQQrVQMpSUs5BVbS7W1YYYW2qnhyOCXLcK3TDQioaSEmTsrVsQwvUk8gbQKOYStwWoa4tT7NDo7ogg1a5EwEAvHk5QCrA9CCldJ8elsG53CKci0avAzc6mKF6njUKvHVEQqA8l0A2DfI/C+t0h2uIphnfLIcdlozTHZUaQDiUIxlOsqWnD55AZXO5GFYJsbN5IXI8yOH8QbimHnY2wsyVGrwI35TnO/1ekaYu/H8ud/R/EEj4WFv85tMVSXPvKigyvov4lXqYPLOKCCT3IcVsh/f/V1AXjHP/IPJrCSSrynfTO9+Bz2FiwtYm6tgRfXH/wD/tyuoXFj4glfP5BLOHz34neUoWgJQEB3e5HcAYQ/56gmm27zQW/Nrfp0WRo5vC5vwIitZBqX7Ph8IKum9NDggAtVeaCW0EEbykonSMM0aZdiEYKHQlHoPP7UkbTFjNwmygSdhYQTrYCOj5DxKWp6M4ChGQYQY+b5QTJtMfuR7N5ke124q11iGoMDYmokouOQK7WZEYQBpL2AGFVJiC0IagJECDpyKNWDWMYOjbJhm4YGAgIuhMZOw5BxaW2Yhg6qs1Ds+pAax99CEhJHFoIQRAI4SFs2Ml3K4QTKpGkhiCAxy6TSGkkNANRAIckIkgiPrtMKK4SVzV0A2RRwGkTiSQ1VN1AFiHHIUMihF2PACIpe4CILhFN6qyvDbG6OsirS3bSFEn3sHvnpxMpCzjRUkm8WguSAHabjVQqiWYIhCQ/umQjmjQQML2UECGR0lEkkYSqk9J0nDYR3aD9nxnFVzdAwEzb84X55ogZmdrjkPE706cwWsIJ5EQLNiOOgUjElksoZUaobo2qCO2Rg1VNRxQE7DaRWFJHN8z+ynfbCSVUEqqOKELAqdAaS5LSDWyi2CEkDF0nFazB0FSSyERseUiCuYg7GE+hAw5JwEAg2X4s7LKAgEiB12wjmkjhVVtQhBSqIRFS8hARias6SU1HFCDgkvE57B3fqaptjeJVm1BQEUURXRBpaYtSE1J5a0OcaQMLGZmvI0sSrZJI3NARBB2HKCFoKXIEBxEpgBm32yCktmCgIWGnxB2gPlpDytCRBJEiRzERFUJxjZSu45DFdh9DAwxQdTAwcNkk7EIKt9qGpCcwBJGI5KdZU5Aw+zulGYiiQI5m7i+iDVlWEGwOiDaAroEgoDsL2R4RkQQBpyLitok0RMwI1pJgfjXeZbfREEog6CperRlFMIih0Kh70dpHKPee2qpujpJo788irwPXf9j3Af/X+Jd8ssLC4t+NGtyNVP8d4he3tU975CCNOh9j1Pmogowc2I8XCUAqBc0b4cs7YNR5EG2EOX8wh+29xTD+KugxCZ441FzjMfRUmHCNOfXUVg2f39rRLqPOh9EXEhacyNvn4prz+4564uN/hjJgOkLVAoQ592DIClXH/Ik/r3iQ2dVzAJhWNoWr+p1J92QY5t6LsOFD08bKo2DMxVC7GmnAcRi7tuH49LdQuwocfpQxlyAOOx0+/DnC1tkgO1AOu4Xc3ocgfPF72PQpuy/5nI9r5jJ751dcOuxSXt7wMl9Xm27aU7sdxlXDLqdw+ctICx4CNY7e82CiE27mD0sNrh3jwPPtvWaEYiCn30yMcTdQp1Vw98fr+aJ9NObwAUWcPa6CW979Dk03uPTg3gwt97O8qoWb31lDQzhBjzwXd544hN2tcR78chM2SeQvZwzBEduM7fObELd/bdo/9Ayiw3/KWX/dwe5gnHG9crn35KE8Pndr2kcgWyIJSsQWPKuew9W8BsZcBPMfwrb9G2yyA9vQM2gY/lPO3KueKw7pg0sReWnxTt5ftZuLJvWke66LR+dsoaopSoHXztnjKujVHml3ybZmjh9ZxmtLdvLGsl0kVJ3xvXK5aeZA+hZ6iKc0ktEQ3pY12L+4CXavBIcfccRF2EZcwNxNIg7FxpV/W8bMoSUcM6yUBz7fyHkTetAaSXHb+2sZWurjtuMH8/jcrXy61gwzMG1AIeeM78Et76xB1XWunVbJkT0klA3voHzzRwjXYc/thTb+V0TKJrGkWeDJr7fy66MHEE/pzPpyMwu2NmGXRU4YUcZpY7pRE4xRXVPDeHEtnoV3QvNWFE8h0vjrSPY7note2sKqXW147TJnjO3O2eO7IwJirAnvlvdxL34A8nrDpOuR5v+Zwm1zKZTtDBtyGnjOJbbkVVb3PYQ/rHuetc1r8dg8HNv7WCaWTSQRrmdMYABJZw4vb3yT1za+RCgV4i/T/sLq5lYeWfEIO0I7yHPkce7AczmqxxHc8UoVx07qxbIdLWypD3HuhJ488fVWllW18Oz5oxmVl8QW2oUw/8/QHlPIW3k0/8feeYfJUZxb/9c9Oe/M5pyUc85CWSJICCSiyFkgMGCSCQYbcw22yZgkMgiBECInCREkJKGcc1xtzmFy7P7+6NXuzs6MzOdr+wLe8zx6YKtqqqure6ZPV73vOYGRv+PG5U6m9MlgWqEGY/UGzD/+DzQeBXMa8thbEIomw0fXQ9U20FkRh1xK0fBr+J81Ti4aU8SnO6pYuPoota4AeQ4jt07tTkGKCbWnhoLj76Hb/gr4mzFm9Ec/5g88f9CCS9KxYHJ3LDoVB2rcPPzlPna3zuf5w3O5bExB10rYzwBdKz6d0LXi88uCvP9zhHfnxVYUnIJ8xmMEtUnobInTh6k/CC9PU4KGNQZY+UBsm6FXQu4I+Gi+8vfEuxWl4jjHlQvGw8wnEf4+NLri+vXIh1cgfH0/CCLll33E+T/egzPojGqWpEvi3dEPkf367GiRPqMDznldcVlfdnXsGPPHQs/TYMV9IKrhutXw+hnga6L+vNd5qmkrnxz5hGenPMtdq++KOa5NZ+PdkX8i582z24+r1hG+8hvUSy6ElrLo4xnsHJvzBZNfPRY1zCSjhqfOH8TlrUq3d8zoyYBsGw98soej9R4m9khlSL6dx78+iCjA21ePZLC+Cv2rk2P1khxFbJr4FucuVoQHtSqRhZcO5abF23AFlJThrbcOwvr5tahrd8HcV5Rr8hP6eeXyYdywaCtD8u0MK7Dz2IqDMVN6ztAcjFoVE3qk8vq6En44VB9Vr1WJLL9lHAatGnvNOnSL58T0Ec4bR8mkZ/iuDJq8QZ77/gj5yUbumN6TG9/Zxm3Te7C1pInLxxZw85LtNHujNV9sBg1PX6DM5/yRafxW9S6aLS/HHKd54p/ZmzUHvVaHOxjh6jc2E4xEywrkOYw8OrcPPSo+JOnbu2L6CAy6gjUFN3LVu+3CjFeNK+D07hZ673sS4/ZXlKDkc16FJRfF6joVTmT9pN9yzXc3xvQ9KHUQMwpm0MtWzAu7X2FD9QYAruh3BamGVP666a8xnzmr21ncPGA+CxYraubPzhvMtW9tIRCWuPeM3szrrcbkPArvXxEr2KlP4vg5X/LAqmae670H44rbY/pnwPnK92p9uzCnnDsSzl7IfatcvL0hVqfoi6t60WPtb1GXfB9TVzd7MfO+M5Ni0XLDxG5c+urGGJ3NIXl2nrpgUBf5+TehS7m5C796SA1HEVbcF7+yZDWCvxlNwBm/HhRxwi2vg78Zep0Bq/8Wv93W1xWicwJ9ZisEIw6Ekh8QvA1KunNHqFQIq5Qf93C3qSyrWhNDPgCaA818Wr2BSOHE6ApvIxxbDYe/iT/G42sVYT2NEcb/FrYvVnSGgOb0Xnx8+GPGZo3lh/If4h63JdDCZ7UbiRRNai8MB1CtelixpOgMXxPJR5ZxSrdoZdxmb4jVh+oZ302RBHhx1RH84QgXjVL0js4ZlsPC1UcBOKVHKkmqAJrvH4ovEtl4lHzfXvKTlYdEMCLx1vrjnN2aYTOpVyoadznq0jVKmv/GhT+5n9fWlnD2kGzOHZbDwlVH407psq3lTOqZxp+/2MeknrHkORiR8AQljKFGdCtiiQSAunQN2dSzeMNxzuivaEkdb/DS4AlSkGxk4aqjXDuhiFUH62JID0CLL8Sqg3VM6J7Kub11aLbGVxFP+vERkiKNqNUir68riSE9AKWNXgq0LpLW/TluH7odrzM6Pfpzg3PtJAstGHe8qhQMOA82vRxXzLKh16k8vPXxuH1vr9tOujEddyTQRnoATi04lee3x1cE//jwx7SEfWw41shZg7N5a/1xAmEJg0bF8AI7muZSOPR1fJVyfzMZh5dw33g7xtV/its/u95TBD87QCjbAM4KRGLnz6BRYQvVxiU9AKk/3Meto21M7pXOnz7bG1dcfGtpExXNvvjj6cJ/DF3Epwu/WAghr7J0nQjH1wKxInBt8DbAke+U/w+6213bO0OWoKW8XfBPCv3j4/aeHV3ma27r35k7lNV1iRVsv6vbgitncGzFkW9OLpxXvRMcRZA1FI5+r5TZcjnuKkdGpm9yXzbXbE583NqtuLKHRJUJJashc1Dc9tbjKxmTHSs4t/5oA/2yFUkApz9MKCKT0erQLQoC7tbVmv7ZNlK1QVQlqxOOKeX4F/TNan9zW39E6fuM/plcd0ox4WNrlIrMQa3XO3E//Tr0s+FoA/2zbYiC0LZ61BmyDPXuANUtfjJs8bNzdGoRbcQL9YcSHpvj6+ifY8MbbL8XNxxTzsMVCKNVi1Hbd52x/mgjg/OTMHgq4lt1AAScGCNO1KLAxpP0hb8pWpm8I2QZmo6R2SETSasW0XU8btaghPPssedxtCXx92Jf4z5qvdFB6v6wH1covu6TjEyJs4RXLhlE/2wbG44q51WQYsSsU6N2l5/0muuOfU2q2nPy83VWxQp5HlvFgkk9YpoXpBjRVyf+/tB4lEKLRHaSgYM1CX5LgHVH6hPWdeE/gy7i04VfLlQaRc04EYwpCImE/ABEjZIKDbGaN52hM0PI2/458SThcaYUZUuqI9TtmUeagAeLtpPuTwfYNFY0gTiSs3pbq5VEAuhtyhjDvvbz8rdgaRWM84a9WLWJl3+tWguaQKeHkD6p3c6jEySdDWecBRabQRP1kNeohLj/7w1GCEmAISnhmAKG1Ki+bAYN/bKsXD42nz98soeIvnXFKehpP+c4CBpS8XTox9o6xo7jiQejVh3XV+sEJFlW7oWT3A+yMZkWb0ixeehwHp6AMh6VIGA1JFYsthk0uHxhRcPoJJBVOmQU0b1EENQnz4AT9FbcwXYiKED0cYOJFb/VsoT6JPNg0VrQdvqedf473mfWHarFGwxjMypz5A1GiMggdfz+xoPehiT+hO91Z68vYwrN3tgb2xOIENHbE/clqgijRpZp00CKB3uXrs//ObqITxd+sZD0duSeZ8SvVGkgdwSScBJilJQLI65R/j/kg/S+8duZ05QtpBPbKJ46ZWss4XFHwq6l0eVaS1v/lj0fcVneqQmHdVn+dEx7P4mtGHJpYuKj0igeXI1HYcMLSjA0QMBJhsZKki6JlcdXckZRgnEDl+XNwLTv06iyyPCro9RyO6K2/zUs2x37Nj17UDZf7VYUqPtn2/CHInx/QCGCR2o99MlUyNdXu6upCFnwD7k24Zgaup3Hj0favZTmDM2h2Rvioc/2sb/ahT9zpEI69n6kKPsmQF23c6P6mTs0h893VXG41h21otQRDpOWYERiep8Mvj9QF7eNLxSmRbQR6h0b3wOAqEbOG029J4jb376VNalnGuuO1NM3y8quihZmD0psvXDmoCy+2FVFaShJIdXxkDGAypCJcERiztDElgp+jT3hCh5GBx5dOi5/O/E5VOumQUhuP+6ejxKqh9v3f8XpBXG2RQGVoKI4qZh0UzpqoZ0cOYNO+iT3ifsZm85GhjGdNzZW8fnOKua0bnEeb/BS0+IlktoH+p+T8FybBs1nVQWQNSR+A4NdWc3tSHxEFRRN4JJXY/24Shu9+NIGt1m5dEawx5l8dDDIt/trOHNgfIsclSgwrluCa9iF/xh+9sHN8+fP5/vvv2f/fsXRd9u2bVx//fVUVVVhMpl48sknmT59elv7J598kr///e/4fD6GDx/Oyy+/TErKT7/RuoKbf1mQGw4jvH1u+9bTtauVtzh/M3IkDEYHgilF+ZHrAKmlAiHgVJbBdRYEvUNJbX3rLLjoA9BbIehSiIbBDrokCHtBbVCIT7jVzHTHO4prNICoQp7zMuSORgi2KNtbGoOSjl61AzlzAMKbs8HbQMMZf+MJ934+Lv06alxzi2fzm9SRON65OPpE+58L2cOgYDwsvQQajrTXiSrkWc8g7P1QiXkAuOJLJRZj9zJCfeewfdx8bvjmRm4ecjPba7ez/PjyqO7nFJ3JzdpsHF/e3VYWzBuPc8ZTGNf+FePed6PnfdDFSKfcydYWMzohRKHOjTbUDBojLtFGs2DjxE9LvSvA5a9vIhCWSLPoePTcgdy6ZDsNniAPzOrD3B5qjJ9eh7o0etuiZfz9vOQZz9/XKaRjeIGd26f3JNms5WCNG4NGpF+6Dlv592g/uALOeQ1yR0OwRYn7UGlBbyOoMvDJ7iYMVgeCoKS05ycbWbJJydJ658pBSC3V6MNOIiodG2tFGiULU/tmoJe8pAgtqIJOXLKe9/cFeGVzA0/PG0yySU9EkkCWKdQ2o108BxoOt5+AIBKc8xpHksbgldQ4fWEqm314g2G0ahXpVh3JZh0tvhBJBg1vrT/Ox9sro+Zg5oBMhuTZefCzvYwutPPmqSo0i85qX30EMKVQPedDSshmX7WTXhkWHv/6IJtKou0abp/eA4dJy3h7M7kfz1Xu4xPQGPBf+CF3rdfw8U4lq2xgjo2H5/THoBYwN+wk9YNzleOe+7oST3V8XfQ9esodVPWZyXU/3s8x57H221MQuXfkvWyt2coNPS/kkK+W21bfQUSO0MPeg4fGPsT1K6+PcjzXq/Q8N/VZ+lm689VBD0UWiQH2UKtpcAhMyTRpjLQEW/B6a7GEgyQf+BrT5lchEiLS/wI+z7yBVaVB/jrFhqr+oLJ85a5TFM6dFTDnVTyiQENyPq6QB6PaiENQYVYnsaVJw+WvbYpabXSYtLx12UCyG9aR9OmVShp8W2URh099m1lvlXL9xCLmDslhX5WLkCShEgTq3QFeXXuMGyZ2Y2LPVBxd2lP/FvwqdHxKS0vp3bs3ubm57N+/H5fLRe/evXn99deZOnUqq1ev5swzz2T//v1kZGTw3nvv8cgjj7By5UpsNhs33ngjNTU1fPDBBz/5mF3E55cHufEo1OxRiEHTMYQP50OzksGDzgrT/qB4cNkVs0m5qQRWPqgQBVkJYpSHXA0T70DQ2aH5MHxwbbu1g1oPE++BHtOVjKq2ch2MvhE5bxRCzV7ocarSduNCxTPrxApR5kDks18kYu+OylmieGBVbKe5/1lU6c2sLP8eUZaYkj6CjGCQJGsu+JsR9n+mfL5oIkhhxZBRZ0OWwtQf24W9Zi2yNQe5+zS0Gp2SOn/gC+Wc+5yprDK5a+Dw1wQGnE+1KLO+ZgupxlSSdHbWVKxBJcDUpD5kBP0kiVoo3YAshZB7nEaVOps3dng4u5eBXrpG2PshghRW0vurdsLB5cjnvEpg01voNz3bfr4ZAyid8iyv7RO5ZHQBalFgzeEGDte66J1ppWe6BYNWxd5KJ1tLm5g1MJOeZj+alhLEg18i66yIfWbRICazZGczzb4Q47ulkOswsuFYI/d/vBtREHj6wsF8tbsaIezjlhFm8lOtCIeWI3z7JzixZWcvQD57IR5bT0Y9vqEtvijXYeDJ8wfRyxJAs3kh2o3PtgfsZgzANWshx1wiPXc9hm7v0vb7pGgSoTOe5vGNHsYUp/CXr/azp9LJOxd1Z6S1HrHxqKIMbkqG/HH4TNnMXlzRFvOR6zDw+HmDqHH6+N2y3W3jyU828NIlw/GGwnyxqxqVKDCjbzq+YISvdlczMDeJIXl2cpK0yM3lCCU/INfuw58xBClrGC3aNMJhmXVH6kmz6ihINlPR7GP1oTpsBg0Te6biC0YoafCSadPTQ9+CrXEn+uothJN7Es4bxwGvlSp3iC2lTQzOScKoU/O7Zbsw6lQ8cU4/uulb0JWvRdt4SAnwD/vg4ApknQWp50xQ6Ygc+JLmgmEclgOsq95EssHB0PShBPwt5BjTSFeZaVRb8IZq+aHka6r99czInoDVls+uht3srNtFoa2Q0ZmjSNcm8c1hH9NzZLSeCuU72XQMdBaqL1zEHw4uZm3Vj4BCrk7Pm8GtPc7DERHZ6rQgqNQM8axBteIeOJHkkJSPPPvvYMujXq3i6e3P8mnJV0RkhcSMzBjOfcN+z9LNfs4alMOGo40crHUxICeJoXlJGDUqbKFK9K7W+CJPPWQNRnIUcZQcgtokVKLAmsP1PPn1obb4sRy7cr8VpZq6SM+/Eb8K4jN37lwyMzNZuXIl+/fvZ+HChXz55Zd8+GH70vvs2bOZPHkyN998M2PGjOGuu+5i9mwlsLS+vp6srCyqq6txOByJDhOFLuLzC0bdAVg4If520EXvQ/dpSM4qhBX3Iex+P7r+lr3IKx9AmPQ7JQ3cVR1df85rsPzu2HKA6X+GoVeAq1LZcvk2ThaJLQf50k8RkoviDj0SUX54ZVmmaddXpDbthgl3RDeSJA4dPci010pIt+rolmZm/inFLNtSRl6Kic0ljVw5tgh3IMwfPt6NOxjhopG53DsxDc2SCxAqt0KvWZBcpBDFsF/JanFWwKZXlC2jnGGQ3h95xHW8sivA90fdXH9KPmPq3kM4tEIZR8UWZY6LJkLuKFj1SOwJWbNZP3kJSw9GuHhUHiatisdWHOTiUQUMK7Bj0LZvd+wub+a7A3UcrnMxLN/ByEIHt763g6vGFvL+1nJ6Z1qY3iedO97fSWmjcm3njcjDEwy3rZAsumoEYyObEN6Ns92lNSFf8z2Fj0UHIE/umcJLPbegWnF37GfG3ozkaUDcviimSsoZQfmMVzjvrUNUO/2MLbbzXLfN2Fb9HiyZileV36now5gz2DB1Kee/054abdSqeHbeEK54PXo7xahV8dGCMby7qZzj9R7WHWlgfPdkeqRbuWR0Puk/0f7gaJ2b0gYPR+o8VLb4OFLn4fLRBVz5xiYkWYkZenbeYNYfbSQiSRyt87C8VTsozaLjD7P6kOswMvvZtXT0Sc1zGJkzOItzhuawr9pJsknH8j01bDnehCcYJttq4OoJhUgSvLuplAybHp1a5JTuKaRYdOwub6HJF2ZWbhD7m5OUrVm9TfneuqqgYDzyGY8TTirk+dUlPP71QZZdVMDQDI2io9WaHNA0/UFubtrEtvodMed+RuGZZIYv4EhNiAf61ZO87NyYNmiMtNywmaf2vMDSIx/FVPd19OF3A/+Hs547wKc3jqV/TlJbXaCxDN0bpyrJDlmDlfHXHwRnJeGpD1Lb5yo2Hm/hliXbY/o1aFR8dct48pNPHqvVhX8ev3gBw88++4zGxkZuuukmVq5cCcCPP/7I2LHRjtUjRoxg+/bthMNhNm/eHFWfkpJCfn4+u3btYsKECf/R8Xfh/wB7PkwcA7PqEUjpgRDyIuyJswIYdCKMvkkhBJ3JjSVDWUGIR3oA1j6hpHzLYWUZPR5ayqHhoEI64mD14QaueG0T/zMjk4sO/EXJ0Fr9cEy7lAkPMSBnMDvLW6hxBvAEItxzei+ufWsLzd4Q645EZ/Qs2lDGnYMjaCtbs8j2R8fwULoezntDIT5SWPm7dD3C4IsZmyHy0IoGXj0zBeGjJ2PThgdeAMvviX++zgrypTJ+OKTlynGFqFQqlu+tZfneWt66cgTje6QCUNbgobzZx6trj9HkDVHW6KPGFWBG3wxejNM9KwABAABJREFUXXeM3RVODtW46ZlhbSM9oKSyz1/Unhk31uFGWBarBQMoAbn7P2PTPTcw/M/tcgBXDdSjWvlo/M/kjkR875K4VWL5RhxyM7UuJTbk+iEmbN88qVS6qpR/J+CqIj9cSqpZR51bWVHyBiNsLW1iSF4SW0ub25p6gxGW76nBGwi3iUKu2FvLir212Iwarhkf/97piCZPkFuWbOfhs/tz9ZtbCEsyZw3K5oNtFW0kRi0KVLX4+ft3h2M+X+sKsOZwPYGwTCdzeEobvTz5zWEO13m4cmwhTd4gL65uz+LaV+XivBG53PD21ihn+We+bd+WXTp/FOLhxQqJqdwWfYCSHxCWXUXpqYt4Y10JDpOWvmk6OPBZe8alINCQ2Y9th2K1jAC+Kvmcx8ZchCUSJHl9HEIOEPLilD18eOyzuNV7Gvei1fnQqkXuWraLRVeNwGFuXaWpP6R8lyFm/Oq1T6AtnMkb6+L/TvhCEb7YVcX1E7vFH1cX/mP4WQY3V1ZWsmDBAl544YWY8vT0aBPJtLQ0GhoaqKurIxKJxMTznKhPhEAggNPpjPrXhV8ggj6FLCRC/SFlKybgatu2iILfCWm9FBXmzrBmR8dudIanTklxDwfbtHPiomZPwqp9lcp9V5SkhvoDCdvZ6raSndRui3G41o1aJcbVgAEw6VRILZVx6wCF7ETipHO7qjCLSrlKCsTXSlEb4pe3Qte4n1SLTsneCrfP+f7q9syxE8VNrePPsRs5XOsm227gcK3ysMuw6TlWF51ZForIRDo+meWIsnKQAELVDpIM0T93dq2kSBrEQyQUHcPRCZKzEmPrqpVDd5J+AF3jPtJt0dsbh2vdUdfxBPZWOunZySkeYOvxJqTOTCQO/OEIh2vdeEORNvKRbTdwpK49vTrDpudYffxMPQC7Scf+6sS/g4dr3YpbS5zMp7AkR5GezohIYDmJlAP1B0nSSjR4gmTa9KhVSnxcGzRG6gLNifuXI4RkL93samUlJgG8ITdhKb6MAUCNp5J0i44jdW4CHe5dqWZf4rH7mtDKwbb7Nh62lTYTluL8/nThP4qfHfGRJIl58+Zxxx130LNnz6i6SCRC5525SCSCIAhRWwXx6hPh4Ycfxmaztf3Lzf0HFgdd+HlCa4CUnonr7YVKGrrWrJhPdYbeAs2lkNorts5dA/aCxH0b7Erfap0SX5MIKbHaICfQLU152JU7w8pYE8CV3I8aZ7t4XH6ykYgkY0ngAeQJRBCtJ9H+EcT4WSrmNLyykhEnidr4acOR4MlTyJO6Ue8OYNKq0HRI7y1Oa1/qF0UQBKEtBbva6SffYaSmxU++Q2lX6/KT64gmCRqViNjxMgoiOBLPm5zaC3cw+rfBFVIlHr9aq/SZAKIlHW9r2rczpEqY4g3KPNS5ogX/8pONVDv9MW27p5spafTGlPfLsrV5Zp0MWpVIfrIRo1bVNj81Tj95HZSC61yBkyoHt3iDbXYd8aAIQcpRnlQnoFEJnGyYKlHA7eiXuIGjEFdIwGbQUOsMKCSh43cy5CX5JJIMAgJawchxZ+Sk3yOD2ozqJBmfqcZ06lqtKjQdZAjElJOs1uishAQtecmJ57Z3phW1+LN77P7X4Wd3BR588EEsFgsLFiyIqXM4HNTXR4s/1dXVkZGRgd1uV+Ijmpri1ifC3XffTUtLS9u/srKyhG278DPHgPMSppoy/jZwFCDrrcjd46SSayyw8n7IHBiTAUZLOZhSY8tPYNiVrem+guIVFQ+mVEjrnXDofbOsJBk1vLDZRf3wOPL6AGodDbnT2Vrafo9fPCqf97eUccGIvLgfSTJo8OpSEh+79yw4+l10WUZ/ZK2Zr44pLxNflamRh8Wxydi1VEmxjwdTCpW6Qobm2fGHIkr2E5Bs0tIro/3BlZ9swqJXt41/U0kjo4uT+Xh7JZeNUYLRa5wBkoxa7Mb2a7vuSD3T+rSv/u722pHH/Tb+WFRa6HsWYx9dGVX82k4fkVGx9goAlG1E6ixCeQLpfXGqHG0P/oXbPLiGzI/f1phMpb44iqxqVSIjC5PZfDz6t0qrEjmtXyabjjbSK8OCqpVBaFUiZw1OnO7eEclmHb+d1oNDNW5O7av87n25q4o5Q9pT3GtdASx6dZvxamfYDFrOHZb4BXDukBy+2V9HOCLROzNaj+qHQ/XM6Bv/97ZHupm9lU7CPc6I0rXqiND43/H8phYuGplHnTvAnmqvEkh9or0sk1x/hJ72+C85p2RPYu1BPy9vaaE20fdIpcUo65mRNyVudXFSMXLYjD8sceu0HqSY28cqZg1IKCIaGDYfvz6VS0cXxK3XqATOGpwdf0xd+I/iZ0d8XnzxRVatWoXdbicpKYmZM2dy6NAhkpKSGDp0KOvWRadQrl27ltGjR2MymejZs2dUfVVVFTU1NQwcODDh8XQ6HVarNepfF36hMKXBBe8q/jsnoNbBpPsUQgOIthw49WHkoonRn934CvKMP4M+FS75EJI6EAlBVLKYLv04tnzQPOg2DXwtyrGGXKboyXRcVbIXIl+8DCFZeVv0BkIcqXPz3uZSnvn2EOuPNiAK8M41owiEI3zl7oZzzN3RJM6UQv3Z7/H77xTdHK1K5IaJxVS1+HhvczlD85M4b1hO1Nt2rsPAwkuHcsxnpOr016NtNwC5+3SF0Gx9s70wcxDyOa+Bwc6UAcXk2A3c8sFeAoMuQx44L3oVpP4Q8pDLCQ68NLrcXkDl7KW8d0Di9hk9SbXomf7kDxSkGHlv/mhqXQGe/e4wb/5YwqEaF7l2I9P6pHP24GwE4MXVR7lpcjdc/jDXTyhGqxJ5bMVBHj9vEDl2ZeVn8YZSzhmawyk9lK3tWc+ug5zhyBN+Fy1GaUpBnvceQV0y4WD7GJNNWi4aU0RwwCUEB10WM35njzkcHnwPoaJp0fdJ5kBC577NK9vcPH7eQHLsBr471Mgmxyx8AzrNQ1I+rgs+5J5v2glOsknL61cOp9btRyNGj+eVy4ehFQVm9MvgtH4ZvHTpMB4/dwCf3DiWNYfreWXNMfZUtuB0e6DpOOx6H9Y9A2UblFXJVgwvcNDgDnDLtB5M6J6CJxjhy91VPDi7L+bWlcEnvj7IE+cNjFpJEwU4e3A2g/JsmPVqHjt3YFt7UGQAHpjVh43HGhmWbyfdqufB2f2idJCWbCrjktH5TOqZGjVtfbOs/PHMvhyrdyNZcgjO+yBak0ilJXjKvbgyRjG9TwbFqWbOGZrDnV834FcZYd57be2Tv/8LTw28iT6OaP2fUZljODtvAYvW1XC8wct3ngLCUx6Mvh+MyXjOe481JRLX9/8NE7LGRfXR096TJ8Y/xl3LKrltWg9GFyXjdzUSqt6Pf91CpD0fI1/0vpLleQKCQHDAxQjDr8RuNtI9zcxNk7uh67BS5DBpef2KEWQn/bQA9S78e/GzzuoC+P7775k/fz779++nvLyc/v37s2zZMiZPnswXX3zBDTfcwJ49ezCZTDzxxBMsXryYr7/+GqPRyLXXXovdbueJJ574ycfryur6hcPvBne14rIeDihZNgYrmKPf0qSmUkXHx1OnECWdFcFRCE2lYEwHVzn4G5XAWHO6skUmSUiCgOCtV2KFjMmgMSFo9MiISltRAG8jgtYA7lrlc1qTEv+T3B0vGtYeaWTB21uj/JQG5Nh48eKhiKKi+WEgQLbGjeiuQVZpkUxpNIoOKlsCBMMS6TY9oqBsDVl0GjQqAYNWRSAkUecOYNapMWhU6NQC4QiEZIl00YU+2IQq0AKmFIJaOzo5iCrYrMyDwa7MgyEFjMq9X1LvockbxBuIMDglgj7UCO4aBK2FsN5BqZSCJuwlQ+1C9NYiqU24VEk0qRxoVCJqUaCyxY9Jq8aiV/OHT/fwzb52VWtRgAdn92Nan3T8wQjeUIRGT5AUsxZREPAFI+g0Io2eEGadCrNOjdMfxh0Ik27RoVIJHG/w4vSFMOnUTMqSEIIuJcBYrQNTCiFjJo0BAXdQotbpR6sWcZi0OExqdGo1fo8TY6gB3LUIWjNBvQOXOhlfUEIXdpIstKD2NyIYkgjpk9EnZVDe5CUQihCMSATDMr5QhG42GWukGdldg6A1E9A5cKkdeEMSda4Aeo2KNKueTKsetz9EnSdIjdOPrnU8O0qbuOW99ji1c4bm0C/bxoOf7mkLNB5XZOO5sT6sH3YyCc0aDOe/DTZlRcEXDNPoDhKISATCEg3uAOlWPSqVQEtrPFVmqw1HoyeEOxDCbtQiClDW5OO6t7YwNN/OxaOUVTeNSqAo1UwkIiHJoFELGDVqIpKEPyzhDUZw+8M4zFr0KhEZJZi3yRvCZtCgUYloRGULK9miQ4pECDZX4m2sJOD34dQk8+o2D+/tbOBPs/sxpjiZUERClsEbDNPXIaENNCuxVGE/WLOp0xhpDLtxBp1YtUlosRIJG2jwBNFrRGwGDQfL68jXubGFG5BVWjwaBzp7Nt6QjEoAk8aNHxdN/kYsOhsWlYU6t4Ukk1a5B/1NCD8+i+7H6GdIZOQNMOwKIq46BHMakjEVnTkJgFBEud7uQJgGd+t1t+jIsBnaVvG68O/BryKdHaKJD8Dy5cu5+eabaWxspFu3brz44ov076+8yUqSxJ133smbb76JWq1m9uzZPPnkk+h0P103oYv4dOGfht8JX9wBO1vF/rTm9mwUtR7mr+WInMFpT/4Q10TywhG53HtGb8y6xPYF/1do9AS56o1NbCttxqxV4W4VdhNFyLMZeXf+6IR+VqB8N19ZU8L/fBE/OPTzm8bx0Bd72VXhpHuykRum9OCaNzcjCIq/l0oUCEsSkgSvXzGciT3TOFbv5vwX11PrCqBRCYQi7T9lM3rbuf/MQWTbf5oL9tE6N3OeW0uzT4nb0atF/GEJs17Nmf0yuHtmHyz6f991WXu4notebjfv1KgEXrxkGFd2Snlffnk+Pd+fHNcklCGXw2mPKKKZ/yTKGr1MfXxVVEDviWf1+cNyeeDMvug1J1FD///A4g3HufcjRZMp0ikgevkt4+mZ8b/7/V28oZR7PlSSFVSigCTLbZZjP7X/wOFV6BadGb/uwmXoek79X42xC/9a/OLT2U9g4sSJbaQHYMaMGVF/d4Qoijz66KM8+miCFNUudOHfCXcNdEyV72h6GvZDxWaOqCfGJT0AH2yt4NpTin6WxKfJE2wjPS9c2I8hyUGEsB9JbWRFqRKAfDLiU9Hs540fS+LWDcmzI8tw7+l9iEgyRq2Kv3+rZNHJMkTk6AyuV9ccY0COjTpXgNrWoOGOpAdg+b4mbpke5qdGVGw+3thGegD8rQ9+tz/Msu2VXD2h+N9GfDyBEC+uPhJVNroome/2R/u95ToMOJp2xic9ADvfUbSftGbwNSqZafoksKTHbx8H28uao0gP0Lba9MG2ChZM7kbOTySTJ0Ody8+Lq4+2Xd/OWLKpjPtnJbCQ+cn9t89pZ2IVr/+qFh8uX5iQJGHWqsk0SWjWJ5CnADTrnyaQPRSd+ST+XV34WeJnT3y60IVfDCLBdvXieHBVEbQkTpEOhCXCkZ/nAqwvFCHJoGHdjX3QrHsSzbK3FDKnt3H6qFsIGS8EkhJ+XpJl6t2xD+x5I3KZMySHuz7YyZ7WlP5+2VZun96TsiZvlM7NCTR4ggRDUlsKfCL4Q4nnujOqWxKQCVqvy09IJf9nEQjLNLij7xuLXkODJ3pMVr0GlSeBlhSAJQs54EL4aAEc+14pcxTBGY9D7ghly/UfoCZOpln7OKUYgvnPIizFnnNHVDT7iUgSqn8yAyosyTT+f/R/sMbFHz7Zw7pWP7f8ZCNvXNCNAm98jzYA0VOHfLLvexd+tvjZBTd3oQu/WGhN0cHPnZEzgjRL4m2IgmQjeu2/ZhvhX40ko4ZPruqD9qvb0Wx5qd3Y0d+C7vs/otn6Kl5PYm0Yo1bFkDx7pzKRi0cVcNHLG9pID8DuCifzF23hlqk9UMeJiRhR6CDJpG1Nq058vJM5nnfGsILEb+25DgMGzb/vp9KiVzO2k3HlgRpXzHyVNnjxpQ9N3NHpf0V488x20gOKh92is6H2JPozHTA4L/E85NgNGP9F96dZp2b4SeZ8Wp/0f5r0/JT+p/ZOa+v/WJ2bi17e0EZ6QDFC/Z9vKggXTkzYRyDvFFSGk7jDd+Fniy7i04Uu/KtgL4Ap98evyxoCSblk2fVt7uSdcdepvcj9F2wj/DuQataRoXahPvRl3HrdhmcQvbVx6wBSLXpun94zKuvsN5N7sGhDSczWCoA/JPHl7iqm9E6LKjdpVVw0Mh+9RoXNoOH0/vFdsK89pYgMc/x07XjIcxgTurTfdWovch3/PpsBjUrkopGKrccJHK51k2M3ktHBpsIVCFOrykDKHBzbSVofpOZyJaC+M2QZVj6gmOb+A+Q5DAzIjj8P953R+yfbZvwjWPQabp/RM26wb7pVx+ii5P91/7cl6D/NomNMcTvRXH+0IUZnCeCbAw3UdTsfdJaYOjRGxBHXoNF2ZWn9EtFFfLrQhX8lCk+Bua9AkpINg1oHgy9WHK2T8sixG3nh4qHMHZKNtlXUL89h5Nl5Q0666vB/DZ1GhdRYmrhBqNWt/iTolmZm0dUj27RfuqeZ2RZnK+sEdpa3ML7DSsjIQgdLrhtNfqv4XqbNwN2n9eK6U4ra0q5TzTrun9mHc4bkYDL8dOKTYzfy/EVDOGdIdlsacp7DyN8vHMyowv/dQ/inHn/ZDWMYVdQuxfDxjgreuHIEM/qmtxHG0oCJihkvK6nzJ7Rt7AU4pz+BULI68QEqtihZh/8AqRY9L146jHkj8trmIddh4MVLhkaRhX8FilPNLLl2VNuLgCjAjL4ZvHfdaLLt/3yAdsf+37tudBuhFQWY3ic9pv+OKz0dIclwwxf1hC5fjlQwvr08byyhy5cjOgr+12Pswv8NfvZZXf9pdGV1deFfgqYShQyoNGBKV5ShO8DpC1LvDhKKyBi14r91ReFfhcDxTeheS5zFErh+E7r0xOrUJ1DZ5MUdjGDSqrjnw92sOhg/jmJyzzT+PKcfbn8YBOUtPt6KgycQosappPnr1CI5SQY0/2Tmkcsfos4VaL0uqpMqHP870OwN0ugJEpFk9BoVDpMGGWjyhIhIMqkWLc98e5imFhcX9tWjFSKUugVWVYrcZ1iGacOT8Tt2FMEVX/3kQGd/KEK9u30e/lUrPfHQ4A7g8odRiQJ2k+ZfHtzfsf8koyYmSP2RL/bxQgfPsY7IsRt455qRpGr8iP5mZEDW2dBb/7UksAv/Gvxqsrq60IVfJE5mcQFYDVqs/x8rEj8HREwZYMtpN2nsAKlwAiGdg58iHJHVYTvvlsnd2FfZjEatosrpZ2heEg3uIEfrvcwfX0CGzQD/IIzCpNNQlPqveVha9LEPxv8kkozauFYQHcnA2YOzOfPva1myXdkiNGpVqESB6y88G9PGpyDeu+zYW/6/srv0GtW/JHvrpyDZrCPZ/NMlR/7V/Z81OJuFPxyNMWUVBZh/SlHrS4kJLP/+lb8u/GfQteLTCV0rPr8+REIh6jwV1PhqcQZayLXmk6QykmTLid++sQTRXYPfYKdRo6bMXYWATI4+mZRQEK3WTEifQSgYxCi3KKnDLeWKlL0xBQwZULkOzJkgRRBUanBWKZYZokoxMm0uU8QVjclUiRnsrnQyLt+KQXJBsAWayyHsg+RuyGoTrmAYg68GMeBHTCtACLqh4YiiZmtKA18zkq8RISkPKeACSSZkyqBaduAORhAAly9EX4sHteSiUaOmwq/Yv2Sbs0lBg665RDEdTS5G1lrZ57VS2eLHYdJg0akx6dSk+o6iXXx2dCxJSg+k894irDLgFJMw4UMfdkLTMVDrqXfkUxdy0uCpIcOchV1tpdFv5XijnySjBqteTb7OjS7kgoZDRNL6U6GWqfPV0xRoIt+Sj06to9ZdQ5opDbtopTGgw+UPIQoClS0+rHoNDpOWJKOaVIsBT2MN2kAdcnMFEWMKAUMaTaKdUETieL2XVKsOi15DrdOPPxQh227E5QsRkWV62sAiNSI0HgNAshdSK1kJqMykaINo/Y1IDUcQtEZkWy61so29NT4KU0zYjVqSTRpKm/zUOP00eYMUJpuU8YWr0fnrwF2DbMvFp7FTIziwaESCUj11/lqcgWZyrfkYRQu+sIVGT4hal5/sJAMWvZpAWKK00YtJqyYsSyALba7vgzO05Df8gOqj65RU9lZIAy7EO+F+miU9GaITdctxAGR7AbWSjWMtkbZxGrQqSuo9WAwaMm16MvRhBE89NB4lrNIRMOWw26nHaDCQaVCTTB2EvIpitSAq4qEBFyTlI2tMNAsWDrcIuPxhcuwGQhEZtz9EikVHU6sRqS8kUdHso5/VT5I6hIoIOCtxGWw06E2Uuisxa21k6u0kOxvQWFLa73+jA8wZBI1Z+PweTKFG1M1HFc88nRlEHbJai9R0HFQaREsm4YAXqaUCwVGAU0zCiYk9lU5uW7qjbYXr/snpTMsTsAZrwJSKR5vKXpeBvkkh9IEGcJYjWtJQWTMRrfHjzLrwn8evRsDwP40u4vPrQijk52DjPhZ8fysN/va9/Jn5M7htyC2kWKPJj1y7H2HJPFxnPsNX7hIe3voYIUl5iOhUOu4bsICpNUcwDbkVQWiBD66F8g4icyk94PxFYMqCx7vB5PvAVQ3dpoI9P257+fxFNGkzseNCqNoOH16nPDwARBXyqBsRhl0JCyfAdatgxe9h3yftfVgyYNZTsPKPirP78GsUUrXheXznvstft2vYcKyBV2foMFV8xfKUbB7e+QJBSUnF1Ypa7ht2O1MDEpaPblDmoc/ZMO2P9HxiN+EQvHDxUD7eXsneKid/mZZMb3UVJtdRRHOaMtZv/ghBN9L8dQhHv0VYcR8YHJSes5AFW/9GibOkbbhDUwfz8NiHmPvcISpb/By+oy+qzQsR1j9PaNbf2Zvbj1u+u4V6X7sv37T8adw85GYu/fJSHhzzIAOtvXhqVT2vrWvvN9Wi4/FzBzIyxY/mo2sQSn/sMM/dqT7jDQ5H0nlzXQmn9svggY/34Aoo2j2iAJeMLuDKgSZyq79G/Predr0ctQ5p2kP4Cqej3voaug1Pg9wakK2zEpjzKl+0FHHrB/u5+9QejCxK4Zq3trQFzPbJtPDJBWmol14M9Yfa77XckQTOeokjkRZu/P7mqPM9LW8atw7+LRP+toOwBI+fN4gVe6r5YreSzj5vRB6FKSYeXXGgLThcpxb54OrB5GhdBMq2IgQ9+FIH8XWZTJ9sB8O8P6D56vao82qZ9DAv1PXn+fXKduNpfdO5aUp3zvz7Gh6bmctM78eo1j3R7lSvs1B/2kIWV+dw0wgzfP8IwtArwN+s3NuB1uw8QYRhVyL3m8vqBhuXvadcpxl90zmjfyb3fbSbz24ax6L1pXyys5Il56aTX/Ot8v358DrqZz7Go7Vr+bys3V8tWZ/Mc5OfptfWpYg/Pt1+bc3pyJd/Bqv+irBraYfyNJj1NOz5CPrNgfevVKxkTvsr7P0IDi4n1OssnBP/xJoqFT0zLBypdTMy1Y9j+Y2Ix9e295VcTOjctxG+fxj1/o+jyuUL30M4mXlpF/5j6CI+/yS6iM+vC5XNx5j7xTzcIXdM3Q39ruaqvlej1SvxNZHGElSLzgadlR1n/o2LV8Q3HH13/KP0tfeFz2+GwytjG6T2ggvegWdas28ueFtJJy7dCIdXxG0vX/AOQtgPL44HKRzbZs5L0HMmfPcQrH82tt5gV8jPe62moWc8BhteAE8928/4FLUo0G/Fheyc9TcuWnNH3PN6Z8Zr9Ft8KTgrAJDH3krVgBv4w/JSspIMvN5KMqwGNSvOt5Hx9Q3gqmkXaswdDVN+D6+fDkDdmU9y5fEPokjPCUzOmcj8Hrexo0rgfM0ahI8VwlVy6w7mfT4PV8gV85kr+l5BD3sPfr/u97w/cykHyk3c8PbWqDZ3T87i2vq/IByMk32W0oPNE99Cl5TB2c+ti9HmyU7S88OFRsTX4pjYpvZCHn8bwgfXxNaJaoLX/Ui/pw/x8Y3jOX/hjzg7iCGuv747GZ9eBPUHYz5aecVnnLvudziDzpi6a/pcRpHmHD7cUUdBsolX1igrUFa9mr+eM5D5i7ZEtbfq1fzt3IFc99YWMm16tGqRiiYfRq2K5edZyHzvtNixA1Xnfs6M9z1tY75mXCH5yUZOVW0k5Yv45yvPXwvfPogw4ALF4f7tuVGrTG047S9EskdyytuNVDQpEgiXjylgXLdkXIEIty7ZzrOzczhj63yYuxBemkS4eDILi4fx/P63Yroza8wsG/Z7shZf2F7YfTqk9IQfn4k9vs4KZz0HW16DtL6w7mkQ1XDhu7D4PJAlAqNu5g3dPEb3yMDT0sTQHb9Hc+DT2L6Si2H0TfDZLdHljiK44suE5qVd+M/hpz6/u7K6uvCrxu76XXFJD8Cig+/R4O/gG+WuhcajeE/9My/veT1hn6+XfImfUHzSA1C3H/xNIBQrf+9YAnlj4pOeju13LI1PegDWPgmuCtj6Rvx6X5OyTWVpXXbf/CoMnAe+JrIDR8gIlODrPo2Xj8X5QT9xXvvexj/pd21/C5tfIVPVwswBmSzdXNZWfk7/ZJK3PKVsNXRUpz7jUcU0E0BUU2fLikt6AL4rX4XBFOKcIglh7ZNK4Zib2duwNy7pAVh6cCm9k3sTlsJ8cexLxhY7otK9AabkigiHvop/gvUHyVS1JBQk/P3UbIR1cR6eAIPmIfyQQBFeCiPvWsYT5w/iYI0rivRo1SIpQktc0oM1iwOe8rikB2DxofcZki/x0JQUDlS1tzmtfyYfbI2NszqtfybLtijlVS1+jjd4CUsyZ/dPJmXH8/HHDqTseI65/duzyd7ZVMbpxVpSNj+e8HzZ/T6CSquQiJI18UkPwKZXEJuO8sCUdg3tpZvLKEo188L3RxAFGJjkh8JxsPdjiISoH3Q+bx35MG537pCbvaFGsHVwj+9/Lmx7M257Ak7Ft63+MOSPaR//kW+VDExAt/UV5vbQUO8KkqpyoTn4efy+TmwtqzrFgDUeVY7RhV8MuohPF37VOO48nrDOGXQS6kA05NYfr4DOTLkn8Q9ZqbcKfyLbgBPw1MOFTyn/31ya+MHQ1r4OgvEf+FF9BOOTOACc5e2O182lYFYcsrUtJej89fgsaSc/L3cFflMHV+2AEyIh1CoRT7BdBTnXApp48yqqoKU15V1rojGYOL1dRsYX8iASgZZWUpUz/KTXyx1yE2ndcilxlqBViSR30upRR7zxg3tPDNFbjy+BonOuWUI4Mf7OMKUqcVkJoGvcR4HDREl9dMq43agBTwJ9I1Max0+ixOwJeQjV7SPzvdN5psd2nj8rF0FQtvQqmn0x7VPM8ctzzKBpKUl4HE1LCbmWdr0bdyCMhtBJz1eo268E8Ae90HAoYTuaSxHUWnJM7VpNnqBi7lrR7EOvUaHyNSryDw2KxURQrU/4sgJQ4qluu7cBJa3/ZFIKLeVgSo5WVW8uVa4pQNCNRg4iyTJiyNu+jRkP3gbFEqQz4ukndeFniy7i04VfNfok90lYl25MRye2JzYKdkV7x+iqpa89cVp2f1s3jGq98rabCNYsWDyz9UB9QaX+B+2zwZaVuD69b6vbeGriNsnd2rapSO+rpNQD/uS+eI05mOqP0jcpcSxCf3svjA0d0notGaDS4g2GSelAMPY1SPhTB8Z2EPBCev/W/3eRoU+cBaMRNZg1FkJoIK31Gu37mN7JvRN+JsWQgigoP1kDUwfgDUWoaom2WPCJptg38g4ImzMxaeNfh90NAvKJ8XdG0zHk9MT3kj9nDJtKG+mbFZ2CVucOIFsTOIa1lNHbWpCwz2R9MvqAE5yV2L+7iwnVr3P9yDRKG7z0zIgV1Stt8MQt39cg4UsblHjsaYPY19D+sE816/DJOqS0xF5Zct4YqN6jyDRkD0vYjvS+yOEge5vaiVWKWYtOJdIzw4I3GCFkSoeq7ZCtqFLrfM2kGhLf532sBdGkzN/SvtIZDyndwVkd/f1L76sE3wOYUvChRRQEQmoTqE6SbWlOb49j6ogEiRJd+Hmii/h04VeNYlsxGab4e+8L+l1Nirl9yVwypCDnjkT3yW+4rM+lqIRYLRi1qGZe3jS0Kh0MuCD+QQsngLb1AaTSKIGVR79XhAzjQC6coKjD9pqpxCTEw8S7lYDpsbfGr7cXKisd3kbl75HzYdsisBdyKJLBUTkdXfkWLis4HbUQ++BXi2ou6nU+2u/+3D6u8bexzWVj6eZyrhhb2Fb+8e4GGgZcG0vkll0Ooxco5bJEctVuhqXGURkGzut2FpUNKh7f4EGedK9SuGspRbZCskzxCeA1/a9hVdkqLBoLE3ImsOpgPY2eaK+kr0okpMGXxf18OG8cFSELoqjEw3TGQ1+XIo+6IT5B3fEuTLovbr/obQjdZ/CHT/aRbtOT00EcT5KgxGtAzh8b+zlvAwX6VLLN8YnRgp7zSN3UvrVp3PEq5/TWsXxPNWcOzIqx81ixt4bZcco/39tAY/+r4p+XqKah/zV8srs98H/+xCLe3N5M3cjfxbYH5R7tMQM5KUfJaEzvp8T5xMOo64mk9eHBFe0raVeMLeS7/TXcOlV5uVhdIRB21kLxJNAnkbb5dRb0nBe3u0xTJsURQVkhPYFtb8HoG+If35YLKh0UT4SDrVugOgvkDIPyzQD4x97JS1u95Nj1VEds+AZdEbcrOXcU1B1oD/Q+UZ43RiFEXfjFoIv4dOFXjYykAl6Z8gKDUtpXKEwaE7cPuomJWWM5WOflkS/3ceuS7XxXpUY+eyFy5kByy3fw4qSno0hTtjmbl0Y/RPb+FcgBF0y4E4Zc1r7CIIjQe5YSZFxfpizfn70QDn8D+WMV0jLk8qj28on2m19DNmciX/qREhzdNtgUOPtFSCqARzKg1xkKCepoOJk/Rglm/uaPSvszHoMj3yE7inGeu5SdLQbuWVlPyWlvkH3ga14Y/ScyTe1vyNnmbBZOepqc7UuVrTStGXny75G7T2fOixvZVNJIvsPIHTN6YNSqCIQlHlzrpXnue9GxFghENBbkC5eALRf793/l4Z6XMCVnIgLKw1gjariox7lc1utyLnl9Dy/+cAyPrTvyWc+BMZm81c/w/NTnGZzWTpiMaiM3DrqRoelD+fzY57w0/SXsKgfNnmCUzcPwAjvj++QRHHsb0vBro+Y51GMWtVOfol4ycd+Hu3nygsH0SG/fsnCYtNw+vSer6s1IFy6J9lxLyiNyxpOUa4sJnPUSGDusZKX1IXjJZzy6wY1Jq2JPRTNvXDEiyifqqg8qCM96FrnXGco9AqDSIg+9glRLNi+N+TND04ZEne+tfa9iqteP2DEzTZYxecrQqEReW1vCM/MGR5GsFLMOs17N61eMiCmvFNIJXvRh9HnZcmme+x5/XOslEJYwaVXcOq07vTOs/P27IyyvTyEy99X27VOAtN5UzfmAS5ZVwNibkWsPgMYA85a2r9yBMken/Q3JXsTLOwI4/WFMWhULJnWjINlIjwwrWpXA4+cN5PmNjewf9RdCB1fCxcsQAm4mu13c3u9ajOp2LaHBqYN4eeoLpLvqo7abZATkHmcQmfZQ9DZU7kjlu1W1E4omwfa3le/WnJfgmz+B1kRgwv2UZ82gf56D3RVOLGYL1f3n4x1ybfvKjyAg9ZqJd+Zz+JwNUeVyr5kIc1+OnqMu/OzRldXVCV1ZXb9ONDvLaQq5CYT9WHVWHJpklu1q5t6Pdke1G5Zv460Lu6MLNoLGSL0g0xzxIcgSSYKWVFkAtQanmILor8do0CP4m8HvBJ0ZWWsBHwhyPWgMCK2rHwRcSr3errwlt7YHAWHxuUrMAcDdFcjuWgg4EaSQkq1lzoKHOyzl37oXOeRVvJe0RtCakIIBpLAfUW9GCocBgYDOzvY65cEnigKBsES21odF9NNIiBYpAIKIRW0mXaVH8NQqatOGJCL6VEp9alz+MHqNCr1G5KXVRxldnILNoEGrFgmGIwy2+1H5m5SHg96OR5uMWhBJ8pdDoBkEEY8xmUYieAItmLUWLKokan1afMEIBq0KtSjg0AtYA1XKXOodVGm1uMIe/BE/Vq0VnUpHS6AZm8ZMTbOZK17fzPACB+cNyyHVokevFtFrRHRqkUtf3cjYAjPzh5ixij7QmvCoHfgFAyFJbn0Ii2hUIr6QRDgiYdSpCUUkXL4wLl+QMSketKEWQCCiS6JGlY5GFLAb1EScVQj+JgRRTUjvoAkr9e4gSUYNGRYdBp2GyiYvLf5wm6u9QaPCGGnGEmlBCLhAb0Wo3AYfLwCdhZZR19GYM5SAJQ1rUxmp2xaj2f9FzH0cunw5e1W90KlF0qw6nL4wTn8IWQabQU26TUejO0QwLOEOhpEkMOlUaFQiGpVAGs2oA02ADMZkqqUkmrwhfKEINr0Gs15FvTuoqEYbtdgNKkWOwddIWFDjVdnY26LF6Q9T2+jhwoEWxBOxXKJauc8jQdCZiajNNKuSOVTnwRuMkGbVIQD7q13c8f5OZBnmjcjlghF5hCIS2YYIyRo/KimIEHQTVuuo0+hoCvvQiDqSRD02vxu1OQXR36jc/xoDstZCWSQZXzBAvtaJLtiMoNIomlkqLZKoQ/I2KOPTWpGlIPhdyPokmlR23CFlbmQgHJEwadWopACWcCPqkAtBa6JFTKLKryHDCJZwA2LQhaAzo7Gkgb7rOfFzQVc6+z+JLuLz34HDtS6mPbE6bhzsrAGZ/OmsfnEVdE+gzhXgwpfWc7g2NgjTbtTw+W/Gk5WU2G8o6PehWfM3hDWPxW9w01Z47xKo2RNbZ0pBuvJrxOSixP2HIjz57SGe++5I3PqPFoxhUO5P8wbzBMLcvnQHX+6uxqhV8fh5g2JSqU/gN1O6selYEz8ebd86mTUgk7/MHYBR99OE4ndXtDDzmTVRZV/ePJ4znv4hRl0X4PR+Gfxxdl+MWjV3vr+Tz3fFBnCLAnx3+0TykxNbg8Q77gncMaMn8ycUxzW9/P+Fy9mM4cubUe/7KLZy6h9h9zKo3hlbZ7DD/DX/p/EkX+yq5Ia3t8Wte+qCQcweFL1tt3JfDVe/sbnt79cuH87Vb24mEudCntYvg0fPHYjpJ9wnZQ0e9lW7uPat+PfhLVO7c9OkbjT7Qlzyykb2VsXG5Vj1ar685RSyT/I97cIvC12WFV3owkmw+mA9KSYdV44roFuamWBYRq0S+Hh7BV/urubWaT1iiE+LL0iDO4gnGEEUiEt6AJq8IWqc/pMSH9FdhZAoBRdg76cw+3lYdFZ73A4ob7jnvBG1xVTr8tPoCRKOyDhMWtIsOqqdfpZuik15PoH3t5T/ZOLT6AmyYm8NAGO7pfDN/hrGFCfz22k9UIkCoYiMSaviw23lvLepnIfP7svdp/UiGJFAAI0o4gmE4hKfSNNxRH8zhPxgsBMxppHrMHLbtB489rWSAj6pZxobjjbEJT0Ay/fW8NvpPQlHZCb1SmXWQCVGyB0I8draEvZUOpFk2FjSSKoujMpb17biFtQ7sNiUbYp3NyU2YX1jXQkz+mbQ4gthN2pIs+piPKUC4Qi1zgDNvhB6jUiySYvDFGuVoAk0oo6nEwOw4QWk899GXHyukkF0Amq9oj3TIYi31uWnyaP4vdlNWtItOtQqEUmSKGvy0ewNIaMQ8Vy7AVGMH9kgyzI1Tn9bvJTdpCXDqkcQoklegzvAWz8mnqNF648zpjiZVIsiMeDyh3jlh2Nt9YUpJg7WuOKSHlBilBo9QUw6NeGIRI0rQLMngF6jJizJeINhMqw6VHIIR6SBCZYm9v+mkB+rYcleH9edUoQMaEQBq0GDu7ESa6iZj+aYCWhzqJGTaAnA4ysOsPZIA05/mIom77+F+IQjEpXNPho8QURBwKRTY9KqyOwiWT8LdBGfLvyXQuax8wbyl6/2s6dSeRvUa0QuG13Anaf2ivlxrmz2cdeynfxwSFHXff6iITE9doQ/Qcr0CQjIJ09Nr9kFjgK49FPFWbtym6LyXDwRyV6MSq0hIsnsqWzhpne2cbzBCyjigg/M7MuIQjvuQAJNIIjSmvlHiEhy23zo1SJ9Mi2cPyyX25buiDruXTN68ei5AwiGJa56c3ObcnGaRcfDc/ozUqvG3MEHS67dh+rD+UpGD4Baj2rUAixDL+eyMQXM6JvB57uqyEs2UNkUnb3VeXySJLOv2slfvjoQddy7T+/N5zsr+f5AHWPTI2hX3I1697ttAarq4mkET38cjSOXZm9iyQFPIMy20ibueH8nOrXIVeMKuXhUfhu5bXAHWLT+OM99f6RNSbl/to2nLxxEYUqn9GcpEhMg2wZXFcHKXdSe+yWG6s1Y67YgpvdB3X0Kgi0XRBURSWZflZOb3tnGsdb0eatezX0z+zCpRyq7Klr43Qe7qG2dh9TW+R9R4MBq6ETWQhE2ljRy23s7oto/es5ARhQ5MHQwew1FpJPeU55ABHcgTGprXH8kIke1d5xkBRWU6xiWZJy+EF/vreHRFfv5n7MH8Ma6g6w6WMcT5w3EJjtJO/A2pk1PK9uywKTMQYw8cyFzluxkf7WbflkWXj1Vj235/LYUea3OSnj8A6wJDOOWqT3IsB1n2dZKvMGTf0//Gbh8IZbvreHBz/a0fc/yk438YVZf/CGJwtSfvyHxrx1dwc1d+K/EuO6p3PH+jjbSA+APSby4+iiyLJPU4QHR4A5ww9tb2kgPQESWsSRYkleJwj98iwxrrMhFkxI36D0TfnwWXhirCLsZk6FsA0LdIVQaZRWhosnLBQvXt5EPUAjNbUt3UNroY9bAxCm+p/b76VkoJp2K7mnKw3tHeQvjuqVy6asbY45770e7ichw/yd72sgHQK0rwLVvbaG0sb291HAEYdGcdtIDEPYjrHkMYd8nGDUyPTIs3DqtB3OH5DKmW+LU+F4ZFlQiXPPmlpjj3r50BxePyueKEWmkb3kM9c63o0iH6sjXqD66Bk9zLWcOTCwnMK57CtvLmgEIhCWe+/4In++sJBKRkCSZT3dW8cTKQ22kB2BXRQvzXtpAVUu0tk5YbYkOBO4EOb0/Z71Vynlrs7m2/gIu2NKbY5FURRIBqGj2cf6LP7aRHgCnP8yd7+9kd5WTP3y6t43EgLIte12n+T+B4w1eLn9tU0z7K17fyPGGaE2iZLOOyb3SEo57TLdknlhxkKpWLSGLQcNp/dqTA2b0TadHemy6/Ql0TzNj0avZXdHCbUt3cPGoAp777jCrDtYxMMdGvsOAo+QLzOv+0kZ6AKjajvGds3hwkiLC+Ph0B2nL5rSRHgACTuwrb+N0RyX3fLiLa8YXIwpQeJKtz38WuytbuH3pjqiXi+MNXhYs3kpZkzdG66kL/3l0EZ8u/FeitNFLjTO+COHLa44R6RD8U+sKsL0sWiBtyaYyFkyOr4lz3SlFUW7QsixT7w5Q6/QTDCsPXZ0tFSbeo2xhdIKc1kdJTy/fqBQc+QZ+eFRR/81o15lZvqcm4RvroysOMH9CMTp17Fe8Z7qZYbk2cNco/xKtPrRCJQrcfXpvBAHGFCezYm/842ZY9dTV1fDynFyuH52O1dBODCOSzCtrjuEOKKsqQt1+cFbGPZ6w9knE5ugtlVSzjvHdYzNnRAHun9mHdzaWxt1CiUgyH26rYP5QC6odi+OfX9l6tL46hufb6ZUeK06n14hcNDKfj7ZVRJW/sOooZU0+alx+nvkmvohfVYufQzXRK3vm5AwCM/7WnuHVAaGiqZSGHTR6gxyr97DqYB1bSpv5unWrEeCbfTVRgpId8fiKg8wdEpseH5FkXv7hKG5/+6pWIBzh5TVH486bJMNLq48S6LByqVGJnD04m9Q4TucpZi3juqXw6a4qDtYqQpwqUeDMQVmkWnQIAhSnmdlf7WRCj1iNHkGAB2b1QasS+NuKAwiCQmg3H28C4OYp3RHc1SRtSqAm7aykWC7llO4ppFSvave664S0jY9w6SALK/bW8PQFg3CYT74K9f+LZm+Qvy0/ELfOG4yw/mgD1c5Ykcku/GfRRXy68F+JA1WJVZLrXIGoN/fOb+wAPxyqp8Ed5G/nDKB3pgVRUJaznzh/IFePL2wL0Kxx+lm0/jgXLFzPnOfX8devDlDa6EWWZfzWQuSrViJ3n6GkXhvsyKMWIF+wGFmlQ+5xqlKuT4JRC+CCxdAa0ByRpLaHQjwcqnFT2ujl5cuGMaV3GhqVgNWg5uYp3fj00kLSdjwLr56q/FvzhKJuGwc1Tj+f7Khk1YFaXrpkKKf0SGFXRaxK7u8mpvPx6SHm7L+DXp/O5jbXX1lxvo3zB7ZbIeyvduE68RZcvSvh2PHUIUSi9Xnykk38+ez+3DatB6kWHSpRYEShgyXXjSbPYWTtkYYEnSlzYRV8ie1AgHBLJUs2l/PCpcO494xeWA1qNCqBab3TWXjJMJ74+mAM2WjwBAmEJQIhiYZOekIdsS9OYK0/tT/By1cg5Y9TFK/N6fgn/YGWaU8w541Ye4vtZc1EIhKyLLOppDGmvu1ca13kOIxx6/ZXu3B2ID7eQCRqxbMz9lQ68QSj56wgxcQ7147i7MHZGFqz/WYPyuLx8wbxwCd7kGXY26HPHLuRZdeP4dJR+TR5gzz+9UHmDMnmpsndSGu9jsPy7bxw8VDSrXq8QYlDNW4MGhVN3vY5dZi06IXwSRWS9Q176JttJakufsAzAHX76W5Xs6eyhVN6pmLRJxa7/GfgD0ViiG5HHK51U+9KfK904T+DrhifLvxXolucN/sTsBk0aFTt7wQngjU746UfjlKUYuTVy0egU4uoVUJU2xqnnwVvb40iKC+vOcayreV8tGAs+clmMPXHc9ozaCNuEARq5CS+2dvCjnI/903+K0nTQwiCgGBOb01/V6ASlVib5XviWx7kOYzsrnCycPUR5gzJ4bHzBtE7w0KxrgXxjZntqrUA3/5J0Ti57NOojKEap58bF29lU4ky/i92V/OXOf3p1ilG4fyBDi7UrMb20R/bytTOCjKOrOCOWW+wr8HBznIn+Q5je8ZOSmJlbPRJyCoNnfOnch1G5p9SxMwBmSCAViWSbTfS4g2S7zCyLwGZzU8xImtNyrJCgiRWwZTKoyv2886mUt68YgSzBmQjyzKVLT4uf3VTm4t7R1j1arRqAa1axKpX4/THJ1bFqbH3ms1qA+twXGe9jibiIyDBO3v8PPJUfELYPd2CqvWe7Jtl44tdia97XYKVzPxkY5RqtV4rUphiSkh+ilJM6DWxIp5GrUJ4/jxHWX384VAd17y5ue1loVun881zGLn79N4crnUTisjc/O52RhQ6uHlKdyx6NQdr3fz+490suXY0WpVAnsPIvmonNkP7aozTHyYJtZLZ5otP+IP2bhzd48GV2Qcby+K2wV5AhStCcaoZq/5fu9oDijdbXrIx4ZzmOowkGf+1ZKsL///oWvHpwn8l+mXZorZiTuDtK4ew+Z5JpFval/N7phrY+8dpXDQ0gwUTi7n39F6c2iuZT24YzScLxpJt05KZZIghSPUuf9xMpCZviIWrjhJo3fZqFCyc+mY53R/dx7jHfuT0ARk8PLcfsiaFBm02i3dDcyT2R/qswVn0z7aSatZRmGKiR7oZbevD8eJR+by/pQynP8w7G0t59tvDfLq9AnnvR8rKR0Z/yBoMqT2VLReNEWoPRBGDw7UuJVi1dWvD6Qvxt68OcMaALDSqdlpy1WALtk1PQd5oKJ4K5tY4EFkm5bs7uXOMoup7zSlF7cG1GQOUh1gcyMOvRk6gZqzRqChMNVOYYibbrqxs2Ixa7jsjsdXFNeOKCOpSCPeYFb9BznDchixGFyVzvMHL8r3VpFmUa+ow6VCpoimYTi3SI93MTVO6kWUzkGbR8YdZfZjeJw1LqyJ0r0wLN04q5trxhfTNTJxWa7Gnok/Jo1qyYzHEbiEBaFQCswa0x2ud0T+z7Tp3xo2TurF0S3yPrWtPKcLWIcDYoFEzf4JipDt3SDZ3TO/JsAI7/bIsXDO+kLtO7Ykxjr1HmkXH3CE5imTC1wf5YGtFG+lJNWsYVhB7XfUaFRlWPZN6pnL1uEKSTVru/Wg3v3l3OzUNLfxwxwSyrVocZh33ndGb/tk2fMEQZ/TPYEC2jeV7qvDqUnANmR/dsTlNUY5O7k4gYzh9Mq005s0Aaw4UT1HuSV17XFH9sNvY06Ll8jH5cefofwuHScctU7rHrdOoBCb0SDlptmcX/jPo0vHphC4dn/8OyLLM3ionV7+xmaoWP29fOYQxKX7F66p8E1iyIHsIaK0Ie5Yhp/VVvLRq9kDmIMVQ9NgPyipC4SmgMSFIYUXVVQC5uQy5fCOyOZNgxmD+us7Naxvb39KTjBq+uvkUMmwKWdpZ3kxfSxAx2KxkcbmrFeVZcwZIJmR1kFW1OnqmW8i06alx+jla76HFGyLXYWRHeTMN7iB9Wh+ye6udPL3yEL+Z0p3emRb2VDo5p5eRnMBBZVvL36L4KOmsVJvsHGo5SmlLCT2Se5NnyUcgma2lLeyrdpJtN9AjzUKzN8TuihZGFtrxhiRuWbIdjSiw+spsDEJI6bf+oOIZpjbAinug7gDHz1vBRm8WQ/OSSBLcaDzV6EMtqI0WhPcubd9mEwTk/ufBpHsVEbyKLYrrdc4IsGaxoUFPH7MXY/1OkCRUOUPAVanEQlkyIXso2xq1zHlVEaU0aFT86ay+9Mm0Uu/2MzIliPrTG1Ed+145nloPFy1D1hoVh3GdhUjuGFaWSvQuysMXDGPRa2jxBbn6jS3UugLcNKU7fTKtHKp1kWrWMSzfDgL8eKQBlz/M5J4pqFQqDlS7ONbgoXeGlfwUIwb1yVOZ3YEwB6qcLN9bw2trjxGKyG33yVMXDGJ4vh1ja/p8MBJhS0kT17+9tS0TTaMSuHFSN84blssPh+v5wyd72uKwDBoVD8zqw7Q+6SSbddS7ApQ3edlW1szU3mmtmVRhtpQ2oRYERhQ5EAUIelrI07owVG5AFGSEgnF4tBaaJS/bajbjCXkYkjEchzqJ057Zx3uXdKPQ6IdjqxUCXThO+a9aT0ifgirsRmwpRy7fAOYM5Oxh1OgNNPqb2VSzCaPayLCMYdjVJg7VaRAFgV3lLRi1KnplWDDrNah9deRsexx1xQZFwTzgAmMysi0XyjaCFCHcbQq1osye+l3UBZrp5+hNdkMJSY3lePpfwqrjESbnRjAFG6DkB0WpPH8sAZ0Dg+0kXng/EU3eIG+vP85T3xxqu442g4Y/ze5Hjwwz3VKMqNVdmy3/DnQJGP6T6CI+/12obvHjdgcoNjTC+1coaronoDEocTUZg6HpKLw9F679Hta/CBuei+5o7C0w9HJoLkXetRRh21tR/UTOX8yfd9t4Zb1CflItOj68fgw5DiOSJCG76hFrtilEINwhdTtnOMx5GfnYd8iFk5n66jEWXjqUy1/bxKiiZHqkW3jky31RK0un9ctgUq80VILAF7ur+GZfLdeOzeHOXvWol8xr619O7sbBs57iqrW/oyXQHreTa8nlhUnPMv/NMvZWuXjs3IF8ubuKlfva4yvOGZLF/AndyFK3YHAdR/j4hjZTVACMDpj7Gnx+K945b1KmzkflrSNn/R/QH/xEaTPgQhh3C3LAqejqWLMJacxoGvYhLLkkah7k7KEw9xWEN8+FkBuu+hKWXQkVW6PmWT5vEY2OAfxYHkatEnln43HGd09lT6WT8kYPL5/XDUOoEclVgzY5H2HFfQj7P2vvQ1Qhn/4Y7qLTqQwaWLzhOFePL0aWZUKSzGPLD/DF7nYCqxIF7j6tF3urnNS5Atw2vQdXvLaJpg6p8bkOA69cNhyzTkVWUvz4G4BgOEKtK4AnEKbWGUCvVZFm1pGVZEDTKUg9IinaO7VOP4GwRKZNT4pFh1GrxhsIUeMMUO30I6MEnadbdZh0Gqpb/CxYvJUtx5v44jfjcPnDfLWnmtfWlkT1v/Syngwofwfd2kfbyrwDL+Cb3pP5/caHicjt8U7Tcydx97A7SV6/EGHdU9EnNXoBdJuObM+DD65BKG8XM6y4bQ+Pbn6MlaUr26dfELlnxD1MyhrHiD9v71AOd5/em4k9Utl3vIrTsr1o3p4Dw65UZCHWPw9AOG802065iRvW/wF/pP3+GZDSn4fH/o0zntzNxht7of/2foS9H7WPUxCRT/sb/p6zMNj+975b3mCY6hY/VS1+1KKA3aTFoBHJdXSlsv870UV8/kl0EZ//PkiuGoSVDyDseCe2UmOA69bA66crD+r8UfBOAnPSiz+E/Z9Dzxnw9rkx/fivWkWvJw8DcMXYAs4ZmkPfLBvHGzzkSeUIL46HcJz4jKFXwqR7kat3sM8whPs+3svuCifPXTwkShW3I+45vRcWvYa7P1BiRnbf0hPzy2Oi+q+Zu5B5+xdS640NGB2WNpTfDXyQ331YxqReaTz+dWzA7SOzunOubS+qDc8qb9udYc2GaQ9SZhtKTcRK0fElOL6/O7adSkvVRd8w/uVyDt3aDeHFcfHnYchlit9ZJAjrnobti2LbqPXI1/1AjycPt71tA7xy2TCue2sLg/OS+NOZfUGW6XH8bcQV98SZPZCv+4EfWjKwGjW8u6mM38/szYfbKrmvk8XJCbx6+XCSTVque2sL1c5YzaFh+XbuPaM3RSmmqO2m/yQC4QgPfbaPt9YfJ8WoYdG1o9hf5eKWJduj2iWbtHx2JmR+eE57oajmyCVLOXv1LcjEPjLuHXYn5697DaEiTmDxvCVQuQO+bzfA5b563j74Ho9seiTuWJfMXMJrK9ws3dFuRjprQCY2g4apuRITf2gl8DMehg+uaWtTcclSzvrx3ijScwJnF89hmOUKZvlXIHx5R9zjyteuQsgaFLeuCz9//NTnd1eMTxf+6yEEWhB2JwiG1JiVbSd3DQy/qu3NMi42vABjb4aAu5N5JxDyoanexpA8O0UpJsZ3T+WJrw/iDYbJtGig9Mf4D3uAne+Avwk2vEAvS5Ctpc2M6ZbM9/sTZ7gs3lDKiXeascXJiOWbovtXaakxWOOSHoDNtVvQ6HycOSiL9zbHjxmZUSgqmkLxSA+AswLZlotoSSPsqsGx/YX47SJBUg4v44WLB0PZhpPMwxKQQ8rr/+6l8duE/VC+iQUTo+08Vh2sY3RxMptKmjDo1KTItYibFsbvA2DHO2QkGUCAL3dVUesM8OLq+PYfAD8crCMYluKSHoDNx5tQiwL1J8n++nej3hVou5YvXz6cg9UuPuyUog8wt7+dtJ0vRhcWjOfT6h/jkh6A1/Yton7whfEPvP4FSImOeynzVLJ4f3x5AYCPDn/EI+cNjSqb0TeDD7dX0N3oUbZHe8+GXe+1N7DlssdXE5f0AHx+7DMG50kIGxPchwDb4pDpLvzq0EV8utCFcFBZRYgHayZ4WoULI0HwJCYbeGqVwGFXNehtMdWCq4obJhVz2/Se3LF0BzXOAMGwpFgJJNC0ARSxNimE4K6FsJJab9VrqHMnfog2uINtAbDpNj2Cq9MDTmOgJZQ4pR9QDF0NGurd8YmIKIUTz1srwn43gbCEXiWfdO7ULcfJsRuVGKvEnUEkDJFQYnIE4KygOC1aKK/BHcTWGlgdjkjoRE6aGo2zEpUg4w9G0GtVSHDSNOQWX5AWf2LlZ1AEMkMdZBL+0whF5LYgZLtJQzAsxb22qQZQdSbEehsVgcTyCY3+RqQ49zygXHdN9BZfBIkGf2IJglpvLeFI9NhEUUCSQAw428aEu31FCL2NmpP0GZSChKVw9Gc6QXBXEw4mVgnvwq8DXcSnC11Q6xMbP9bsgWQl8wVBhNzRifvJHaU8lNP7QvPxmGo5exiv/nCMBYu30uAJMrY4GZNOjdfrVbJPEsFeCCodct4oZL0NnVrkYI2LwXlJCT8yOD+pzS5gw7EG5JyR0Q0CTrL1sYKAJ2BQGzBpLBysdjE4gaeXS9KCSqdsByZA2JKDgEBLWEske0TCdr7CaSzZVAZ5oxK2wV6gBI+rdJB0kqyc3JEs6eS7NSgviQPVLgwaFWpRpDGkRc4ZnriPool4ghI2oyJtoFUJcbOVTqBXpo0sW3zZA1BEEE06NXrN/91PrlGrItehXKvVB+tJMWsZnJsU025LdRh39rjowrp9nOJIrDY9MHUAxuo4hrqgfC866UQZRT0DUwcm7G9U5khkotO+690B0qw6AsbWDLfavZAzrL1B83H62+KLigJkmjIRZN1Jr7tcOAG1NvF17MKvA13Epwv/9RBSuiFPuk/5I38W3FkGt+yBO0pg5ougs0LP0+H12TDyupi3VwC0Zhh2BXxxGzQcjlGOlTMH4TFmk59s5LIxBZw/LId5o/LQqER8shbs+YpiczxMuR+sWTDqBvZWuHnzyqGMKHQwNM/O1N6prLppMFtv7sdH14/g9fOLee6cbtw0qRuFKSZEASqa/LToc5HT+3UYkExyyVqm50yIe8hr+l7BsVotS7eUc+W4AuKZkr+41UPIkAIjr4/bh9x3LlVhM49/fYCczEzqR97drlasT4Wbd8Mtu+G3B6h0jOS1dcchKT96nAMuhMu/gMu/QD77Jag/pPiWTb4v/jEz+iPbcllzWBH5s+rVXDY6n6F5dpLNWi4dnU+ty49sTEGadF/beKSCcbhHXkdg4IWQ0h2p4BREUbEauHNGT3IdJu6c0SvuPKRZdOQnGylv8nFa34zYBsDV4wpBlkm3RqesS5KMyx/C7Q9T6/TjC8bXAnL5Q3gCIdzOJryek3i8dYA3GMbbob80q577zujD9ROLGJyuoV+GjjMHZWHURmv1fH2gkcZe80DbIRC37gDD9OmkGWMtK0RB5JbBN2HZ/UHsIJK7w/jbkPPHwMgb2sdSe5gFgxYgxlGvTjGkMDpzDBUV5SyYVNxW/uXuau6Y0ZPPjoTx954Lh79WvpcnxhlwkeOqo4+9V9z5+M2gW6l3m2DSPYpoZGeY05GLJkcVhSIR3P4w4cj/3UpdF/716Apu7oSu4Ob/TkhNxxFEA4K/Hra8DjW7kO1FMOIaBGMKRAKwYSH0mglaA3z1OyXtHZRVilMfAQkw2pHLNyF88wflLVelRe47h9D4u7hleRPDCx10SzPz5a4q5g7NwaxTIQgigiTRTdsA3/0ZYd9HyspRUh5MeQCyhiG7ymHjywieWvyFU5H6nIVao0PTfBQ2voTgrUcunIDQbw5y5TZknY1Iam9WlKsZli6QJrRA0IWw/gXY97HSv6OIugve5K1jX/Du4WX4wj7sOjvze1/CqWo7EY/MXlUvPjkc4fQBmTzy5X4O1boRBZg5IJOHp6WhrdyARkDJelv/nOIkrzUjD7+aqt5X4Nel8OWuKiRZ5sw+DixNu3AkZwAybH0Dqne2zXPIkMyNn1bx4swUWL8QYURr1taOd5VU94EXQtYQhMXnK9l21dvhmz9BS1nrPJ8NE3/HHStbeH9rJfMnFDGswMGH2yqodwUYXZzMzAGZGDUiNoMOv6uRJO9RqoUIX9WsZ03DblL0di7udRFldUkcrokwc0AmDqMGh1mPLxRhZ3kzv/9oNwdr3AiC4hx/3SlF3LZ0B/WuAB/fOJb3NpfxzsYyvMEIDpOWa08pYmqvNL4/WMfW0iYuG11AYYqRQFimstmLXqNm0frjHG/00jfTygUj8shOMmDWq6lo9lHT4qWPyYN8eCWG/R8gaYwEh11LJLUvJkesH1uN08/W400s3liKLMO8kXkMzbeTbtUTaChFOqT0I2uMRIZfR5O1Jzd8VNomVDk0z85Ds3uT5j+G4/u7EcrWAyD3nkXZ9Af425YnWF25FkmWKE4q5t6hd2AXCiimDr66E6FsgzKQiz4AjR5500sI7lrkgnEIfc+GlX+EIytpumU3B1wl/HXTXznUfAgBgbFZY7lt+G0U7/oE4ci3yL3OQOo2jcc2hxhVlII/FCEUkUgXnfQqXYy5agOM/y388JgSH6azUH3REp4r/ZLPjn1BSAqRYcrgt0NuJeLpwZvr6rhhVCoTLBUIX/1OWTUSBOTiKTD9fxDSFNLkDYYpa/Ty5rrjHKpzMyjHxgUj8shxGNCq4pCmLvws0JXV9U+ii/j8F+PoKnj7nOi4FUGAOS9D3inIKgH8zUrAsxRECHkBQdnqURmU+B6NXkmrdlcjB72EBDVfHpP4/VeluPzRTs23L93Oy5cN57LXNuL0hblubC53TUhXgq0jQUULx5YHax+D7x+OHqvRgXzhEoS3z1XG1KGcyz6Dz25DtmUTnvk06srNCG+fo6xu9JsLPWYAINtykKyFeKQIbtlNsKkEg7uWlC1voSr5AQApdxQNpy1kV4uOPlk2Wnwh1KJIlqoZ7QeXo6poJX+FE2DQhcjGZKSkIu78ppkPd9RgNWh47zpFkVerErEZ1Bir1iMsmhszz/LZCwnlT6bKryNPUw/LrorJEpJzRsCchRypacGUkk+60IAQ9oNKi0dlZVs9HKjxMjA3iXWH63liZbSHVpJRw9/nDSHfYQAZagNl/GbVVTiD0Uq7l/e+nobK4Xyxs4kl146iZ0b7b0Gt00+1009ls5/tZQpZGNS6HZjnMFDj9JHrMBGKyOjUIk2eIA98uofdFe3HuHVqd7qlmwiGZH67dEeUoLROLfLGFSMoTjXxyJd7+fPUZHSLZkerbQPBPucQnPo/mB3tq0w1Tj83vr2VTZ3sTIblJ7H4vFy0i86M6SfS71w8k/5EVciEKAgYtCKSBAICP+49TD+HjAD8WBXhq0N+/jonF5U2QFgKo5b0PP19C0u3lNMr3cIb5+WRpvEr25G7liB0vm8NduTLPkPWmPCpraj1Zur8NfgifkRRhSUYIO39qxRC0nbR8pEv+ZDNzUkUpBlJtRgoqfcQDgXIVzeidlcjGO2KZpCkxID5NHoadUaCsoSMkTveLWFLaXNbl6OLHbx0ZgZGfCCq8YgWwho7dqueYCTCd/vrmL9oS9R10apE3r56JMML221YuvDzQhfx+SfRRXz+S9FwBN6crawedIbOAtd8156Z4muG9y5RhNo6QxBgwWZI6cbaQ3Vc9Er8jKfLxhTQK8PCJ9sr+fGoEpDZO9PCoqGHSe47BT5eAKXr4MJ3Y1PjT6DbFEjpqay0RJVPhWl/gudHI9+0FeGdC6E+jnGi1ox87fectqiKRcOOkrLy5riHqZvxHBf+mMPCS4ZSlGpmf5WTvPJPMH6+IG5736wXWbCrmG9bs84m9kjl8fMG8OQ3h/njWAPCorPjxkChNSFfu4p3Dmu4UP0twme3xu1fPvMZHigbzJs/lsbU/f3Cwdz70S6ePH8wV7y+Ke7nx3VL4aHZfdldU8NHFX9hQ/WPcds9M/5drnyphLHFKTx+/sA2Ze7yJi/THl+NLxSJ+Uyew8j8CcXc8+EunrtoCIs3lLLmcH3c/lf+9hTO/PvauIavPdLNPHPBYI7UNDOlaiG6Dc/E7SNw6Vfoitrjwz7bUclv3t0Woxh+au9knkn/DM36+P1Uzv2I0z4IYTWoefvqkWTa9Dy24iAvrDoa0/aqcYUcqnWx+mD88/ry5vH0FMsRn48ftyYXTiB81ossL5F4b3M5qw/V0zPdzFdniQhvzIz/meHXsLv3rfgFA8MLHRytc3Ptm1t4d1w1KV/NBzl2K6ph6pN4+5zPJa9soKQh1plerxH57MbxTH1iFQ/N7sdFo/IQBIGKJi/Tn1gd1wg2x25g2fVjSLd2xQH9HNGVzt6FLsSBJxDmeIOHtYfr2XK8kcpmn7J/72+JT3pAidfpmHXla4pPekB56zy+lkhEYumW+MafAF/srKJPprWN9ADM7mkked9bShZXyQ+Q2ltRL06EI9/GD4o+8g2odXDOa8q4x98K896DgZ3SjYNucFZw6UA7KfveTHiY1H1vMrunifVHG4hEIgj+Zow7XkvY3rD9VW4Yldz296pDdTR6w3yyoxICzvikByCopCmfmichbH87Yf/CtreYPzh+QPV3B2qZMziHnXGMVE9g7ZF6wrKMXhdgY/X6hO32NW+le5qFNUfqafG1Z2xVNPnikh6A0kYvfTItfHbTOHqkm1l7JD45sOjVVDX7Y0jPOcNSef2aIhacBk4OM7BARtWwL+EY1TveJBKJ0OgJsr/aSViSefGSoTw4uy+pHWxXzumtR7Mrjk5VK9IOLmF0sYOyRh/N3hAN7iDvJ7h/hxc4+OFQ/PMCON7gRkj0/QCEY6tQB5o5NdtPtk0Z401TuityBYk+s/t9+lh8rdt3MhFPA8vmJpFiEOD8RXD6o2COFh5M3vcmulBLXNIDSpZdWZOXy0bnM6NfBoKgBHBVtfjjkh6A8iYfjf+HkgRd+NegSze7C/81aPQEeWXNUV5YdZSI1C4l//zFQxitDceYYkZB6vBDGOftMrqt8pDsKKDXGSFJovNiq1oUlOOc6F9UKenbiSDLEE9XpXgyNB6Fz25B8LcSAFGtBGZP/r1iSto21jAGPdHn1xmREGpROR9ZFhCQTz4uKUSe3UCPdDMHa9zKDoQsE4nIIJ/kOK3jUQn/aDytbeIgFJFRq8STBqPK8ol/ckJdGoCwFEYlCm3t2w7/DxbJq50B7ly2g6cvGMy4bilxSYJKhHCnZZk7T8/Bpf2eW9a+1qaMbNVaeXTk7xiKjPbwNzH9CJEgnmCEBYu38mMHh/puaWYeO3cgty3dQZ0rgFoQTjqnohRE3TqnyrCEhPevjJzI67X98/9A5kAIB1C/czZ/Pn8RZm2ucu9HTiIHEAm1eszKhJsrKP7+JsSSVe31KT3grOfh4xsUOQlovUdPfq3CksQtU3tgN7WLSv6j69u1SfLLR9eKTxf+a7DmUB3PfnekjfQAtPhCXP7qJjA4wJTAp0etg6QOgoT6JMgakvhA+eNQqUTOGpyVsMm03umUNXkZkNOuffLlET9N3ecqWWNZQ6B2X3S6bmfkjYKaTkrCgqioG79zgbKKdQJSGH58VokBSu7W4bzyeWubUzluAjT2OI+vjvgZXZyMWi0S0drw9T0/YXtf3wv5+/p6bpmqOLAPybPT6A4wrU+6Mnfm2MwgQElVtxfwTblaCVROALnfXJYciP8Qn9Azlc93VjIwTpr2CQzJS0KjEgkEtfRPSZxS3c8xlEO1LobkJbWbqwK5dmNCk9A0iw53IITTF+aaNzdzw8RihDgkrdkbJtdhaOunONWMI7mcRQdejrKDcAad3LD+fqrG/YZ4HQX7z+Opbw9FkR6Aw7VuHvxsLzdNVq71F0cChHqdmfBca4vPZWNJM6kWHXajhiSjhpn9YwOnAfZXuRialzi1P99uQi6Kny0IQPZQ8DaAqwph0VzuGm3k1bXHoH/ie1DuNZMyv56rRqQiLL8nmvSA4hH31V1wyp1tRU09ziWkTSIjwbaURiVQlGKOIj0AWTYDOnX865ti1sa078IvD13Epwv/Fah3BXjqm0Nx64IRiYg5E07/W9yHC5Pvj3J4xpQMMx9XiENnDL8GLMqSe68Mq2Ji2QnJJi2zBmbxt+UHuXNGz7Yf2S3HmynPmKYYZp72iDKWis1KQHJnqPXIE++BLW9ElxdPhmOrFKITD5tehkHzAJAn3YcbI1tLmynPmAKOotj2yd0oSxlPv2wbaa1bJ0a9hkiPMxK2DxZN580fS2n2Bsm1G7hhUjH3fLSb+ROKKQk7kE+LP8/y5PsIa8z0zLZBr1kJ+i+GXqcrqe+dMLzAji8YobJFyWo6a1As8dSpRRZM6oZagL4ZWVzX5za0YuyD7IyCs9l0JIIoCPx+Zp+omI4Us5bfnR6bMi0IcPuMnrz5ozK2UERmw7FGxhbH6iWdPyyHqhY/v52uxI2dMyKJ9w6/Enu+KCtPX9Zugvyx0eV544ik9OKVNSVxP3e41k2GVY9KFNhW6YUxN4MxOaZdKHcMO0NZ1LkDPDi7L9k2A3qNiusmFOGI85DfW9nC/bP6xCUHcwZnE5FlqsIW5MEXxw5KrYMZ/9O+6uhvRqzajk2vIWArRi4YH/sZgx3G/5aHv6siV+tBfeDTuOdL/SGFVItqcBRRnjGV5bureHB237gyBLdM7YFFF7vpkWrRce8ZvWPKBQEenjOAdEtXfM8vHV3BzZ3QFdz860RVi48Jf/2eYIItkDMHZvL06angrFJSY2v3gT1fWT1JLlb+vyPCQcWUc+1TcHwNmFJg3G8VR3VT+4OurNHLt/treGdjGb5QhCm90pjaO51F648zrnsqWTYdqVY9L/9wjM3HGylOMfPU6amYNCLqsAvWPgMF45Qtoq1vgLeRYN54pFE3otEZEcvXI2x4AbwNyHmjESbeo7ii70vwcNBZkM96Qck8c3Rj4stHMelUXDGmgFmFMsKupej3vAsIOHtfQF3BTEpDSQzISSLZ3E70Kps8JEsNyDuXot/zDiDg6zePUO+zOWtRCUfrPNw6rTsTe6Ty4Gf72HK8iYtG5HLl+CLy1I2o3TWw5nGEmj3I9nwYewtycneOhRzkJxtRq0TkhiOw632E3UsBAbn/OdD/XARHEUfr3CzeWMrXe2swalVcMiqfvlk27v94N82+EJN7pXH56AL2VDlZuPooDZ4AQ/PsXDK6gFSzlrxkRfvleKOTxkAVSw6+xY76Ldj1duYUXULEl8/GQ0GunVBEnt2IodMDssUXYl+Vk2e+OcTxRi89MyxcMDyPj7ZV8PmuqrZ2p/XL4L4zevPYioNsOt5IsknHlWMLGFZgxxeUaHAH8IYiRIRm7t50Mb5WZe7OmJY/jUdsI9D+8BfQGPEPuQa6T+NYwMxpT62Jf62BR88ZgNMX5tT+GWQlGQjWH0Xe9Bq6Q5+BxkjLgKs4bBvNK9u9XHNKEYUpJpI6eImVNXp5e8NxvtxdjVGr4rIxBYwpTqGmxYtFr+WlH46ysUQ5ryvGFjAk144nFObV1Ue5f2ISxpotiBueU9TPc0fC6BuV+3jTy23HkE65i+VpV/DCqiMsOz8b1dGVCFteh6AHuft0GHE1GxuTqPaGGKguo+D9GQnPl9l/x+9upixjGo+ud3PZmAIKHSZqXH6e+/4Ie6uc5NgNzD+lmG5pZnIc8U1jnb4g+6pcPPPtIUoavPTJtHLTlO4Up5gwxiFLXfh5oCur659EF/H5daLRE+DSVzdGpRR3xENn9ePiUa3kxlmlBNpqTqLofAJBHwSdyjaNIfHyf2Wzj4gkY9SqWH2wnsN1bj7dUUlpoxerXs0dM3oyujgZi06DRiWALKENNmHUqBBCLmS1gbCoRQqHkXQW6rwCoiiQ6zBCcylEQkj6JKRwEHH3+4hfJxD4yxmBfM4riEl5+EJh6l1BVKJAkkGNJxjBrFNhCDYRlsCtsoEgRD0IO6K00cN7G49zerFCiJbu9fJah0yrVy8fzl+/2s/+akXM8YLhuTw4ux8RWcLrDWKnBSHkQVYb8GtTEDVq9JpojZRIONiq+isgW7NRa5Sx/HiknlfXljA0z04gIrFiTzVFqSYuGZVPqkVHmkVxIwdo9gZp8YXQqkSSTBoMmvYH1+qDdWhVkGXXEMFHvTvM9uMhUkw6RhU5yExKrEp94rquPVzPjrJmPt5eiSsQvdJ292m9uG5CMS5/iGZvCK1KIN3W3qc/FMETDOMPu7hzzc3srN8Z9zi3DL6dC7qdj+RtAFGFpTWFvbTBy5THv08Yj/PFb8bRK8OK2GHJIxAIEHTVI6pUBLV23IEwZp0m4RZOKCLR7A2hEsFhUq61JMk0eoNIEQlPSEKjEhTLkVY0e4M0e0NY9WqSglWIznKo3a+s9HSUXwCkCxbTkDUJT1DGpFWRatUTaShBkiLUC0l8sKeRL3ZUs6fSyReX5tJn2ZTEMUTz1+Cz98QdlNCpVVFblDUtfrzBMDqNiqx/cF1PwOUP4Q9FMGrVmLoIz88eP/X53XUlu/BfAYdJx12n9uKSVzay9/5J6HTKD/gPB46xvxZm9OmQEWKNH9sQF1qD8u8f4MQP7Y6yZm59bzsH/jSD307thjsQYeCDX/P7j/dw6E/TEUURVZtA2ol+0xGAjo+l/I4vqkl5gLJvLQLhXjMRVz2iZG11gjzlfsTW9gaNmlxH+0+AsZUooE1DDST9g3PKthmwmXSc/sp+cuwGJvZI5boJRYQCAe46oz9qEfzBIm5YvAO1KHD1uEK0amWUBpsaUE5CaPu/WKjUWkiO3vKqdwe4/+M9FKQYGZBrw27QMLLATlWLn3s+3M0T5w+iMKX9gZdk1MYlb2WNXv702V6avEEm9kylT6aVBk+QDUcb0KpV9Mv+xy8+WUkG0iw6Fm2ITa0369RKXBNg0Wuw6DUxbfQaVSvZ07Fg0G+4buXVMW1MGhOT8yZhMurBmB1Vl2bVMX9CMc98e7itTKsSGVpgZ0C2jQybPor0AOh0OnQ6pR8TYDfF2bLtAI1KjMoQA8U3K8Wc+HMd59wfyERXvQvh0Feg6jQHWUMQ8kaTajTSMcLOY8zi4S/30+xt5rR+GZw9OIvj9W5+rFHTfdBlaLa8FHvQ3FFgycSg02KIM7T0k1iKJEKi69aFXza6Vnw6oWvF59eLFl+QYLCKIy3HWFn2HRatmdMKTiNDY8Vy5Dvk/HFsddtZtrWCfIeBef1NmL3liLuXKUHDw69WTEJ3f6A4thdPgcyB7V5eHRCMRJCctegCjbCnvb2cOw4h2AQHvlCsLbKHQ9Ek5el/5BslfT2lJ/SYQcSUjlpUIXubIOiK6ofMgYrZ4tFvoc9sjnqNlAeN5DpMHKpuYZSpCtsX85W4B1AEDyffr6xKpXRHkqE2oGHZUYFBOUmkW/RIyPiCEZp9IVbsUbaQpvdNx6rX8OPRBiZ3c6DWNbKleiPb6nfT3VbI+NyJJEsaVJXbUB1ZiSopR7ERqNiqxCf1mgnJ3SmT7LjDIp/tqMTlD3Fav0y6pZtJs+hpbCml3FvDlyUrQBQ5LWciOe5GTPVliD2m4dKns7nUzXcH6shO0nNa/3TSBSdaVzlqUUao2Q3Vu5BSexPuNp16MYXs5PagcXdjFRpPNZqIF6G5DErXItvyCPc8k2NBG6LWSKpUh6psHRGDlprUYr6s+AFXyMOk3CmYhGw+3OSkIMXEuO4prDlUR3mTj1kDs8hzGJFl+GZ/LQ9/uY9mr5KZVJxq5rFzB9Avy0adJ8Du8ha+PVDH5WPyCYQlPt1RidsfZs6QbOwmLasP1mM1hhHN+3h8619pCSiB6YW2Qh4a8zC5xiLsZoUIewJhqp1+vthVRUWTj3HdU0g2ablt6Q7O6Wvh3O4CqSWfoQ258PU5D701GeHIt4h1e5XtpoJxYMvFG4pQ0ezjy11VNHtDnD04h/ImL6sP1TG7p4nBSR6E3R+gCjqRx96CKuKDg18pgcTZQ6FgPOVyGt8crmZQgcD62u+pdlcxJnssucYevL/RxdkDTWj0br46/jWuYAtTkwdS7HOTuvz3cMHbyAiwe5lyofqeDdZsBEchAP6GMqjagfrwCsKWLMS+Z3EsaEMV9lK491lU295UMsEEAbnbDIKn/o3lZSrWHK4ny2Zg5sAsWnxBPt1RRUSSmNI7newkA4IAH26toMUX4rT+mXRPM6PXqKh2+qh3BXH6Q5TUexhRlMy20iYOVrvpn2NjbHEy+clGxVT4HyDQVAkNhxD3foSstUL/uUjWbPSW2BirLvzr0LXV9U+ii/j8elHjLOPWVbezq3FvVPlN/a/lgqLZWBdOJHzxR5y5tJmbRiYx6eij6A9+rDQ69zXwu+Czm6Nzmx1FcNH7UeQnHJHwNFZjLf0aoWP72w9DzS5YfH77Ur0tB2Y9BcuuVvSBTkCtR563FFK6Ixz+Gj79TZzjLoWVD8H+j5BnP0995gSmvrCbFl+IXIeBP0xOZXK2jNBSBiEvbH4Njq9VojRnPw8qHeW6Qua+38CQPDu3TevB7e/vYHtZtAbONeOLuGBYNpKqnMu/viZK5Vin0vHimD8z6OuHUFXtUAoFAU77m6IzdOALxRRyzssU/jXaxHJYvp0X5+XwyJbH+aosOlX79NzJ3GntT/JX9xK4cBm/26jnwx01AOy/cyCar25HNfxK+ODamHkLXriMRscgMuxmPI1VaL77A9qB58Hnv1Xisk5AEAic8xZhaz6mxWfiHHIJb1tNPLc/WkOoX/JALu/2e657/TA6tchj5w3kxVVH2VXRwpkDM7l/Zl+sejWVLX6avEE0KpEko4Ycu5GyRi8XvbyB0kYvS68bzXcHannu+yNKv9lWrp/Qjd++t73NNX1kYRKXn+Igwy7R7I0QDBoIB014gkp8mFGn5us91dy8ZHvU7ZDrMPDxlf2w7noV9apWteTMgTDuVvhwvuJsfwIGO9LlX7Cy3sF1i7agV6t44eIh/P7jPZQ2erl1XBpXaL7G+uNflPZnLwRLBiw+L6Yf35XL+arpMA/8eHeUNEC2OZu/T3yBlSWf8Ozu6NWZASn9eXz8I6Qvf0Ah8x0g95oF0/9ESBbQvn22IsvQ8XrNfJ5tprGY9Ro0vjr0EQ/JDjvNQhJXLznYtq36wKw+bC9t5uMdlVH9T+uTzqWj81lzqJ4XVyt9D8pN4sHZfSlr9PHKmqMEwhILJnXj1iXt1wUU6Yu3rx5Jv+wELvStCDZVIH5wFeqyaGHMwOjfIo9egN6a2By4C/87dAkYdqELHRAJevjw0AcxpAfgmV0LqQq1wKnPoP70Rp4/K4eh4sF20gOQ2gc+u4UYAZPGo/Ddn8HTnk5c6wpglV0Indv7W2DpFdHxCaNugBX3RT+8AcJ+hPcvRwi64dOb4x/32z8r2V+yjPDJAlIEV5vQXlmjjzyHGVb/Fd6dpxCr42uVz8oyfLIAMvqS8+1v+O3YZFz+MJ/urIwhPQAv/XAUm76Ju9beF2Pt8P/YO+/wKMr9i39mtu+mbXrvtNB7r1IVlGIBxA5iBdu1198t6rX3rogogqgggqCI9F5C7+khvWd7md8fk7bZ3Yhe773qzXmePJqZd955d2bJnPmWc2wuG3ft+Stlw+9q3ihJcmtx3+sBEAr2wuFlnHrS0wDyxqFJHK445kV6ANbmb+SoPgAMEWi+vIZHR8o2Ae9f0wshcymK5KGw4Umf1029Yg6BzgrqbTYU5/eh1uhl/7WWpKdhnZqaHAzf3gJOG4Vpw71ID8DRikMcr/2JwWlGbE43j648yrzhclTim0NFZBZUo1YpSA430DvRSLe4YOKNesx2J8+tP0VepZnoIDVKhdBEegBuHp7KIyuPeDxcd2dXc+vHWTy8rIIzBQHMX3SaGouD1zeeodJkp7TWyt3LD3l9HcrqbEjVuc2kB2DInfDt3Z5kBcBShfjVXOori5EkuKxXLMv25ZNXaUarEpmR5m4mPQBxfWDF9T7nKbeU8dSuR7z0kMot5ZTZirxID8Dh8iOsOrcal6XSa59wcjUU7kcREO5JekC+X9/eRp9QO9d+cpQJH+fzwA7YVBbE0xvPN5Ge2GAtwTqVF+kB+OF4CXkVZoakhzeZs2bmV7P2SBH1VgcH8qqZNzyVR1ce9bgvIBe037M8k8Iq34KIAG6XC+nICi/SA6DZ+SKiP/HOdvxH0U582vE/gQpzGZ+f/drv/m/OrYauE6HoEHE6F5Et/2APnA9ZP/kXLjyxCizNxMfudHmP73uLrAzdqrATY5LcQeYL5gqoLWx2NPd13oaUCG4X5O1geu/mGpDUADvCqbW+j3W7IHcHRHalf4SbSd2i+WyPH+VqoNpRz5lq33IA1bZqStRauY245fy1RTBrGSQNRTjwMWqT54NoWDIsPr3c7zk/zv+e+owpYK5Eby1FrxYZHC2gCY6QFatHPgAzPpBTNy1hqUJZX4TbUo9271uQOgpOrfF9ktBUKDkGKSP46rz/7qg1uSt49NIE5g1PxeZwI4pCkwbP+1uzqLN6i+9V1NtZ29Dh9fDFGazY16yErFLIxemNqbHWOFFUR3KYXPm0KvM84zKiOVZYw96cSg8dqkYMTg3HcLyF8rFCBaLKmxg2ouQYvcNlvaARHSP4/lhJ0zzhZ75oHmeIgfoS2Xy2NcLSOFh1ykN3qBEDogfwQ+4Pvs8NLD2zgoru033uE/a+j8IHKQJAcuPK2kKfxBAAJnaLQaNSsP5YcdOQ8V2j/apOA6zMLKSi3saw9ObIy5f7C1EpRBSigFop+lVnPl1ST5WfewZgrylG46v+qAHigUVI7nan9/822oub2/E/ATcS9Q7vYt9GVLaMZLgdsk1FI7RG2Z/LH1wOD1VcCbwJTkI/n8XGbSogg3yMQuVbl8ft9FTjtVQRFtBcxCu4HW0rIFuqwBCO4HagVSmot/pfi+NnlHgtLtko1GOdtflwZDlkXCpHW1qFKZwuR5v3xGQ34QyQ284lez0zesegs5bAzteba5d0RpkAhXeCfc06OJLdhORquI+S5P86NKxXUumocpr8rqXOXseJ4hrOllp44+o+1JjtqBQCdhfUWZ04fXRVudxSkzqzQa2gpgU5UilErH5sLxrR2KlVb3OiUyuwOF2Y7b4fmjq1iNrWgiwo1HJ6sw0ILZSSG9epVbWaJyDc9/cWQKmj1s/90yq1bd9bhwlJ5acpwFbX5r8LwVrd1K2lVYm4W1znxm31Nv/H11mduCU8OghrrQ6UCgGlKPzsfbE72yAuktvzb0crKG1VuCU3QnvM4b+K9qvfjv8JBIgaBkcP8Lt/YsJocDhAZ8SuNFCX3EIr5PByaEuJNrq77KTeAKUgyG7lLbFyrqyY7CXcJ4HGTy5aVIAxVfbu8oWobqBs0amSPJzVhwqbfrWglaX8/SF5OJzdQLlLz6GCaoam+y+8DNGEEKgK9LlPFETi1EbvB21MLzh/EL57AClpKJI6wGO3WwjmohhPUb6WuCiqP4H5e+WIV0gSDw4JRFw8pZn0gEze1j0ISYMhqCHaJYiIxiSOV4A1/WKoLYAIb8FBeREu0IYgnM9kUkRfv2sZGD2EzBybXKPz01k6RgU2+TlN6BpNkNb7HTJAo6RjlPyZv84sZHyLzkGz3UWYQeNTWA8gSKdsskYYkhbOofxqOkUHMiDZt2TC4YIaqlKnNG+wm2Q9KX/RQm0wlZJMKgurLXSKku/tkcIaypJbGIWWHIGQZN/zVJ6jT2iGz+mPlR9jYPRA3+cGhsQMIiB/n899Uodx/lXUASFlBNvPlAFwOL8Gu8tN5+jm7+ahghpGdvR//ND0cAI1Sg4XVDdtG94hnFqrE5vTjVGvRuHnxgRqlG0qNwu6EBypY/zut3aejqhojzf8t9FOfNrxP4GAoFgW9rrTp0pvenAanUM7wTNRuMc8zgs7aihNu1y2dwCoygJtsEwUWkMQYfzfIDS5aVOIQY1dH+2tQqsKgD7Xe2478AkMv8fnmqV+c5HUBkge4eO8Aoz/e1MkSkobg10bTnFtc2Tm/7aZkCb83bcaddoYUOmo6jWfV/bUs/l0GQvHdvSpxpseGYCGYO7p5duN/Zq06YQeW+W5MXWUXKPRSIa2v4LJCcvmDSQ9Qn7gXvTKLqakX4ZR4/0wD9WGcklEXxRZm7D1u4XTZgPi6e/8v03vfht6Xw2Ard8tVAnBXLN4P2KvmXDsaxh5v8/r4CzMxHXRk1CVTTdBS0pwitcYjULDFWlz+Xq//LDdl1tFvc2JIMgq3NN6x6HwYWERHqjh/y7rhlYlolIo6B4XTFqETIQGpYbicLm5ZlCyz49z26h0Pt+bT6hBzdD0MFwuiYgALbEhOp8P9cJqC2JcH6TwTs0bT66FPtf5nF8a+xQarZ5HxkTz08lSnpzShWcnxnDnwBDKQ3riDm9JFAW5o7E1HBaiVQEMixvmtavIVET38O6kBXurb6tFNXf0vA3D6e+959SHQa+rsRVm+r5fyaOw6GMZlyHrGK3MLCQqSMMdY9Kbhu88V0G/ZCMRPtrtQw1qxnaJxOZ0NZmXapQiNw5NISXcgFYlsv5YMbMGJHqvDbhnfEdi2nBm1+gD5Qikr2hWWBpCQn+/x7bjP4f2rq5WaO/q+vPCUVdCtq2clw++yvaiXeiUOq5IvZSru8whWtAhFe7hmJDOlI/P0jshhEWXhRO46wWE41/LNhTXrobMT2Hfh2CphPgBMPZJOapi8OzUKK+3EWovRsj8BKFhvBQ/AaY9C2fWI+x4Ta75icxAuvRVqCtB2PhXKDsJIUlIQxdC+lgEQUCS3AgHlzSdV4ofAGOfQAhKgPdHI/Wfh6v7TJadkQg1aHnxh9OcKa0n3qhjxZx0ouy58ONTCIX75AdL/3nQ40oqCs7weUEYZ+sUXDs4mRCdinqbkxd/OM3m02XoVAqm9o7jyn4JFNVY6B/n4Ej1CV4+/Bbnqs8Ra4jllm43MSKiF6Ff345QuFeev/c1EN0Dvp7fXMitDmDvJet4J9PK7aPTsdichAVqSYswcL42m7cPv8O6vA0ATEq8iPnJk0nc+Cy2bldhTx3PlgInl5x8GE584/vmqg1w6WvYLPU4Usfz/sE6Xt5whpuHp3DfAA3i6XUojQmw41W5zV4Xin3Abbh7zKLYDCHlBwg58DrFo+7nk+LtrMheg9VlZXDMMGZ3uIVXvqtmf25zOvTZGT04lF/F/JFpJDWoQPtCrcVBYbWFd7ec43RJPU9emoFCENlwooT1x4q5ZWQaFruLd7dmUVBloUNkAHOHp5JdVk9BtYWZ/RM4XVrHiA4R2B1ukiMM1FmdfLEvn4+251BpttM7IYQFF3UgJUxPrFiJuOdtFAc+BocZ14wPwVqDYtsLstBlRGfcYx5FsplQbPoHhKXiHPs3OP09yiOfyWvufCXqHjMQD3+Oes+b4DAjLTgIZzcgbH+laR5p1EO4YnqxvsjGGfMmvs76jCprFT0ienBTlwVsPqbkhiFhLD+9hC+zVmNxWhgWM5gFvRaQEJiMrq4AYcs/5XsqSUhdLoWR9yNEdKKmphpd1WmUPz6GWLAbdKHY+t2Cq+fVjHjrOE9e2pVai4M3N50jMlDDE1MyqDQ5eP2nsxzIq6J7XDBPT+/Oh9uy+fZwEZIkd3TNHZ6CViVy8+L9FFZbGNkxggcmdSZEp+K7w0WkRgbw0Y4cRnaMwOmS+HhnDgVVFtIjA7h7bEf6JYcQFdS2bpfTYUcqP4u48SkU536QOw17zEEYcgeqUN+Eqh2/Ddrb2X8l2onPnx91tecxue0IQKg6CJW1Wi4EDUmgymzHYnehEATCAzUonJbmAlF9KCDIBccg11G0NC/1AbPZhNZSjAAILcZLFY3dPSKOwDjUajVSZVZTHYzQShtIstSCWY44SKIKmzYEVX0pEpDrCOVkuZXTJXVMyIgiQKNq6LGRncpPFdUyLMqGCieCJOHShmK12agWgnC6JQQEVAoBCXC53agUiqaOFp1axOWW1Xu1SpGoYB3l1Tk4BVBIcLBQw2OrjvHk2FjGRdWhrDgFR76A7C2eFyIyg88z3uDBdUUIArx9dV/GdoloEmu01JdS4zSDIBAsqFA4XbgFBVqj7LdVWGUmfP8raLY94/tCR2Zgv2opmbVyyuN0ST2PrpQNXJNCtbw/uzvxegcqyYkbcEkiJlW4bLoqSdidbgJc1ahx4lRqqcDJkcIaDuRY+GJvGbUWz5qRlbcPoWNUIHp122mLY4U1TH9rR9P1fOeavjzz3Umyy5vribrHBXPdkCT6J4eiVYkgQZ3NiSgI1FkdrD50nve35SAI8P61/egUFcgH27LoFBNEgEbJmZJ6Pt+bh93p5otbhhCikTiXnYNChC15Nvacd3Btdx0JwSrSQgT0K+dCaYO0wFVL5O64irOeCw9NxTlnFQ63gChIiLpAnMoAtHUNQo2CgBCaSm6FCbckoVaAW6zDJblRCWrMVg2iICIBIVoXdnc1EqBX6JGEIOpsTvSimzCFCcFhAiQkdSBiYJTHMuoqS1FJVlySQLEjCLNbQqtU4HJLVFtsxAbL9iYapfxTbXHgcLkRBQGNUiRYr6KszobLLaFWiBj1KpQKuXjZLUGgVtkkUGh3uqgy2XFJEi436FQiFocbtyShFAXijP5kNn3DVl8tq7ojIhrCUGkuTC26Hb8e7cTnV6Kd+LTj9468SjM3L95Lvc1FQZVn/U9UkIZVtw8lOlhHQZWZ2z89wKECufMrSKskKkhLhclOpcnOh9f3Z/4n+7h5RCoLxnRA08ouoi1U1Nv4+5oTfHVQJoGiABtvSCB52Ri52LsVqiZ/wPP5ndh0qpTCaitRQRqW3TyY5HD/0ZKWsNjtqGpyUb49uEG0ToQGoTsqs6m65D3m7olBFAXmj0hDKQosXJZJjcVBdJCWAK2SwioLFoeL20enceeYDl72GC1RZ3Vw3/JDrD9e4rWvQ2QA80emMrJjBBFtGFZWme3c9PFeDuRWo1MpGNM5gs4xQbzw/emmMTqVgjijjnqrk1tHpXHt4CTyq8yMfWGLT1+5wamhzBuRyo2L9hEVpCFQq2r6XAAz+sTx5JSufLA9m5c3eHbhPTE2hutyH0TM3y1viOsDHSfBT3/3uX7ruKfRDL4FoQ3BvpxyE1e/v4snpnTl5k/289zlPXj++1OU1Nq8xqoUAj/eO4pEP/5Y/rDjXDmz39vtc9+V/eJ56tKu6H6GgLbjfwPtlhXt+N9BZRbUlcD5/RAQLQu36cLAEPrfXpk3KrOgMkd+4w5Ng4hOsvhhi+1SaBqEd4CgJDi0GJKGIqn0iMYkqkx2Kk02LukRi16tICU8gM9257LhRCkAJbU2qswOooN1mGxODhXUEBGo4f4JnVApRHIrzcQGa9GqFORXmpg7LIXZA5J8kh6H001JnZVj52spq7PRIz6Y2BAd4QEaai0Ovm9BCtwS/HVrHc9cuoSI726WNYsARCWuYfdSFz2QJLOdf0zvTohOxV9WHKba4k2QcspN5FeZOVlUR2q4nmHRTtTWcrR5O5GMKUgzlyKc2whpo+U2dASkpCGI6ig+jrejKT6Is/YEisSB/Hhrd45UihRWW6kw2ekYFYBCgK6xwW2SHpCtCv4ysRNVFgd7spu7nDpEBvDilb0orbey9kgxMcFausQEER2kRdWqPqrW4uDE+Toen5xBTIgWtULklR9lMqJTKfjLhE7EhGg5XVJPqF5NcrieaoudwiqLXzPdkZ0iyMyt5t1r+lJaZ2v6XMU1Vp5bf4rvj5Ww4KIOXD0wifxKM18eaC52HxKnRNzWgkAkj4DT3/m9Btqjy7B2uwJtcHNNkdVppcRUxuGyw9TY6ukR0YOP53Zj2e4KHpvchUCt0ifpAblLLa/CRGKoHkd9FUpLGeTvBls9JA7CHJJEqbOGkxUnKbOU0jW8G1G6SKKD9MwZmOjTFmTN4SIWXNQBU6WFwwVVpEUEYnW4OFVSR0KontRwAxa7kzqbi5PFdYQb1GTEBhETpPUynm3H/w7a73w7/tiozJbF+Qr3N29TG2DmZxDV4/dFfspPy6rNLYXZDBEw50s4vAx2vgHI7hUYIuDqL6HHbHgmFobdg7v3dTy+voLVh5rdv5WiwKOTM9CpFKw+LG8XG6o8RUEgRK/ihSt68tiqo+RWNHddhQeoee/afkzspvVZs+BwutmXW8mNi/Y1RRMABiSH8uqsXkiAUuFZfPrjmWputQXx0MVrSNPUYFA4cQUl8NKOat55vdl8MyXcwNtz+iK5JcrrrIQ3RE3OldZz08d7m4pO993ZGfWG/0M4vrL5ulzyIpLagPDpFU3zCUDgsHsRFQrY/E8anZWM3a5ASr6LR1c2i8b1Tzby2qze/u6QB6pMdkZ0iGD+iFQqTXZC9CpignW8+P1pNp4qbRqnUyn46Ib+9E00epAfQYBXZ/XirU1ZHMir4uYRqShFEZVC8Njecp4Pru9HYBsP5HCDhpAINXcuPehxX/okhvDKzN48/JV8nSMCNTwxpSt3jE6nsNpCoFZFsrZSXlRjkF9yySlef1AoPQqMzQ4zG/N/4rFtj+KUmlN/4xImsnDovZhMOtpuBAeFKOKoK0d5dj3C6gVNcgKWe89yuOYUC39a6OFQ3yO8B88Of4aUUAV3je3gFcVaOm8QS/fksWRXLq/N6sN9Xxwiq0UaMdSg5tWZvXls1dGm9KJWJfLOnL70Tw5Fr1Hidrux29uWa2jH7wMqlaqFl+GvR3uqqxXaU11/IJjK4Icn5ILj1lAb4ObNcuTk94DqfPjyRsjf470vJBFmL4c3B3lvv+ZreE1us5auWcmaug7c8fkhj2GCAB9e1595i/cRb9Sx/JbBRAZqKaqxsPlUGV/sL2B/rreQXVyIjscnZ9ArMYSoVp0q+ZVmxr642Uu9FuCGIcncPS6d578/w+KdvpVo35zdmy4xQcx+fzdFNVav/YNSQ3luRg92ZFVwVf9ECirN3LUsk30N67y8TxzPJexEWP9g80EBkXIH3Vc3+zwnM96XW9tN5U2bKi96nusyu3CksFmR+trBSTx6SUaDYap/FFSZGffiFqxOF3qVgrEZUaiVIl+0ECJshFYl8sPdI0lokcapMtl4dt0pPt8rC0N2jArgyn4JHC6oQatSsHyft2CkViXy3YLhXPLaNsx2bxrx6dyB3PTxXqwO7/tyRb94+iYauax3rIcDfSMsdZWoV85DcU4uIieiM/S5FtY/7PPzWye/ibbf1U2/Z1VnMXXVVC+VZoA7ejzA0IgpRAZpuOKdnR4kuxE6lYIf7hlBnC0L4Z1hHrpO2XdnctW3V3mQnkZMS5/Gvb0X8uNJC0+vPUlZvRxR6p0Ywj3jOnLNB3u4fXQ6B3Kr2JlV4XV8dJCWv0zoxL1fNP+70ShFvls4nPhgNdnZ2bjbRQX/MAgJCSE6OhrBR9dfe6qrHX9+mKvg6Arf++wmWUPm90J8rNW+SQ/IXTK2OrlYuqVQYHWerJgb0weKDsCut7h4yiteh0sSbD1TxrAO4SwY04HIhghKTLCOHgkhPPjVEZ+nLay2IAhQbbZ7EZ+DeVU+SQ/A0r15XDM4iWsHJ/HTqVLyKz0fVhd3j6agykJYoMYn6QHYlVWJye7irU3nGN81ErvLzdzhqcwdDieKapnZUUJY+bbnQRmXwaFlPucD4NDnkDEV9r7ftCn04JvM7fMhC1sQn+X78pk/IvVni1UjAzW8cGVPbv/sACa7i3EZUdy7/JDPsVaHm8z8ag/iU21xsDKzOdV0uqSesAANU3vHctunB/zOc/R8LW/O7sMNH+/10HxMizBQVmf1SXoAvsk8z60j03ySHoAqlxb34CeIP79fLtgvOyl34cX3h4K9HmNdcf0R0kZ5bFudtdon6QFYmfUp8er+pESm8tKVvZj13i6P748gwHOX9yAmSAMbPvcUs7xlB0fLj/okPQBrstZwQ7cbCA8I4JO5A7A53FgdLoJ0Sp5bJ9dL9UoI4Y2fzvo8vrjWik6tQK0Qm1KINqebvTkVKCJBoVCQkJBwQeaj7fjvQZIkzGYzpaVytDUmJuZXz9VOfNrxx4XLBk7f9QRAc/fV7wH+RAgbYamUIxo1raIJliroPx++mY9QX4LU2i+pAWV1Np6d0YNQvadOkf1nVGjrrE6fSrWF1f7Xa3W4cbok7l9xiPev7ceurErWHS1Gp1ZwSfcYKs12Fm3L5lE/4naNMNldzOgTR16FladWH+NAXjUAfRKN3NErUrZKaAltiPe2lqgvlUUTW20L03k+0KwOd5MqcltQKxWM6hTBdwuHs3hHDgEapV8yCFBU43nN7E7Ji6Q88vURPp070C95aZxn7OCkpvOeKzPRKyGEawcnsfZIsd/jbE63z7fgluu5aVUlb09dQ2T+WoILNuE4sxHXxa/iKj2J4cgnIElYe12HkDAAjbHZ/kSSJArq/NtAlFvKUSrA5nDTPS6YdXeNYPnefA7kVZESbuC6IckkhepRKBRIrf9dBsdTXLrD/7rddhwuBx9tz2bz6TLckqwt9fJVPSmttzZ8trYjNjUWB1q1iN3SPK6sxoI5QCI2Nha9/pcVXLfjvwOdTk7Ll5aWEhkZ+avTXu3Epx1/XKh0EBQLtd5mhADE+Vfi/Y9DGyKrLPshLgQneJMekN3bP7sSAClhIJLWt3Lv6M6RXlEbkIt0NUrR7wM7PEBNgNa7zqNPku/zAMQbdWjVIlq1gomvbOWrW4dwtrQeu8vNM9/JqQhRxCP60Ro6lYIgrZKJ3WK49PXtHvUqB/KqOFoWRq+4fgg5W5sPKjsp39OSo74njesLZZ6+Z67Yfhws8WxFjzfq0Kkv7A+mXq2kc3QQT13WjdJaK/FGnVcnXSN6J3heM51K9Bpvtrs4dr62zXl6JRjRqRrP2xWrw41epUChEOnV4FHlC3EhOvRtFG3r1LJMwbiPchiUMpQ+MaOpsMJ37+ZjNBhZPOddYkK0aHXeCt2CIDA8bjjrctb5nLtbeHdcLhUBGiUqpUhKuIH7xnfE7HChVSlQtRR4TBnp6cq+fzE90/yrPEcbotEo1Px0qqxp29nSel7ZcIaBKWEcLaxFEOTvlMUP0Y8K0lLXypKlV3wQSDWo1f6VmP8sOHXqFA6Hg27dunlsP3jwIBkZGWg03mKPv1c0klSHw/Gric/vNra3ceNGhg4dSnp6Omlpabz22mtN+w4ePMigQYNISkoiIyOD77/3VAB9+eWXSU9PJy4ujqlTp1JeXt56+nb8GRCcDKMf9b0vpqdMJhphq4O6YrD7IR6NsJv8R5EcVnkOW3PxpLu+FLe5CrepEmxmMNdAfRluWz1uc4u6GkMkDPBdmyJ1uUxu0X6wCO4+AQ8UQP9boctlYIiCae/C3adh5AMIkpusxwYzoYX9QWSghiHp4XJUyWHB6bBjqyvHYbMQFaxh3vAURAHGd4nghanpPD01gzGdIpjULQqHy01UoAaT1UKNqZJ6qxW3pYYe4SLfLRxGv1YP2v7JRl6Z2YswvYoHJ8rqvhtOlHK+xsKyvflN9RduNxTXWBmXEenzM980LBmNQmDJrjwMGgVX9I1n9oDEJuuBv3xXhDTmUU+rhFPfyekulQ9CpdLLnmCnWzyYBZGygQ/y0X5Pw8uHJnXxiIzZHE5qLHbqLI6myEGtxU6VyY7N4cLmdKFSiATrlHxwXT+uHuCt3dQlJpCkcD0ul4uyOis1ZhsRgWrun9DJa+zSPXncPird53XpEhPYZE4KoFIoCNSqmpShk8L0ZMT4tg556OLORAXL5Ndkc1BjcSBJEiabE7PNgVoUePGKnogC7Mqu4s0dJSw7UEKt1YlOqSAgIFA2Xm31/Teb6jGZ6ugb3Y8InbdqtIDAtZ1uo2t0FGqlAofDibu+FMFWg1aUcFrqMFvtTRYcUvJwCIxunmDDo8QYYuhk9L5WAHf0uoMYQyx7HuvP8SdHs/+xAWQ+PpaNp8oY3zUKvVrBqszzXDckyefxYzpHcjCvyiO71jEqgHijDklqTt65JQmny43T5UaSJFxuCbdb/t39OyqFffvtt1mzxtt0d//+/cyd60NhG/j6669ZsmSJ1/Zp06ZRVFTk44jfL9qKal7wHL/X4uZ58+Zxzz330KVLF7Kyshg+fDgffPABQ4cOpUuXLixatIixY8eyZcsWLr30Uk6ePEl0dDTLly/nmWeeYcOGDQQHB3PHHXdQUlLCV1999fMnpb24+Q+HqlzI2QIb/w51RbKhZ5epMOZh2Xm7Jh9MFbD3A6jOgZje0Gu2TERadnzVFMC5n+Dol6AOkB3ZI7vIisz1FWAuhQNLoOQwRPdC6nst5G5HOLYSSa1H6D8PSRcKW55FsJsh41KklJFgqYXQZARLJSDINUk735BrflR6pD7XwqDbwGFGaJzfmAr9bwRtOBTsAn04ksOE4LTCkRWykm7XaZAynKe2mrl/WAj60kzI2wWdJyGdWodQdAgpNBX63UiNJhqVrQrNkaUoiw5gM6Zj6Xk9Fn0cSpWbamcZy04vJ7sun15hXbk0ZihxR79BjO2DM6YP2Y4wFn5+kEcmZ3Aov5od5yqICNRwzaAklAqB+784zJOXdeWHYyUs3ZuH1eEmRK/iztFpjO8azXtbs1m+L79p+7zhqUzoGoXLLZGZX41SIbLuaDEWu4vhHcJJCNXz+KqjPDU+jouN5+UC5/IzIAhI/edBz5kIa++DQrlOxh3XD/fEf8Khz1Huf0+uHwnvgHPSC3xaEMlf12fjdEtEB2l5YFJnnC4XO85VcsOQZPQaBVtOl7PxZCl6tYJrBicRqlfz6sYzmGwuJnaNYliHcMrq7HyyK4fyejtD0sIYlxHF09+dZNuZci7pEcPDF3fBZHOy9khR0/WZPTARp8tNboWZV388S3GtFaUoMKFrFA9O6sz+3Gqe+e5k0/aLu8dw3/iOJLahBA1wvtrCP9ef5NtDRU2f684x6XSJDSLcoCar3MRnu/Mw211c3D2GgamhfL47j6NFtfSIC2ZKz1he+/EM64+XoBQFLu8TzyMjglHkbkV/YoX8fR54K86gRJznD6E9vFhWVh5wC3kRKTyz7zm2n9+JhERKUAoPD3wYrSuFuJAAwu3FkL0Z4fg3SGo99JiJ4LRiKTyGvfssrIY4IkMCoOw0rH8QIesnWQF92vvkK+G1zNf5IecHnJKTKH0Ut/e6nb5Rffno2Efk1ebRObQz0zpMI1gdjNlppro6AAmRp1YfZ3LPGOqsThbtyKHa7ECrErmyXwJX9E3gtk/3k19lQSkKTOwWzX3jO3E8v5xARxWxCYnEhgXhdElUmOy4JYnwAA0qhUCFyY7d6UanVmDUq1ErRER/Bmv/Idxxxx107tyZO+64w2P7pk2bePTRR9m2bRsAY8eO5eTJkz7nSEhIYOfOnSQnJ7Np0yaSk5P/3cv+zWC1WsnOziYlJQWt1jPK/acTMLznnntQKpWkp6fz3Xff8fXXXzftu+yyyxgzZgwLFy5kyJAhPPDAA1x22WUAlJeXExsbS3FxMaGhP9/a3E58/oBwOmVS47SAQiP7agVGQW0pZP8EK+d7FlOqdHK3VOJg+ffqfPh4ClRle87b5zq46HEoPQGfzpDfhEUFzFoGa//iPb775bJLeKMgXFg6zP4CCQlhyQx5/GWvQ1x/uYhZqZEjOiVHmudvhCDCjA9kf7DMT+VC7Ya27iaEpSHNXoGw6nYwl8OYx+CruZ7zhHdCuvg5hM+u8Jq/ZtZKdivquW/bQx5Fq1qFlg+G/J0eax5EMkRivuQNSsRIpr+1k2qzp/bOfeM7MqZTJGfL6ll/rIRJ3aMRBdnh+pvM88wfmUZuRT3d4kJwuuUalGOFNQRolKSEG3hvazbfHPJMVSaH6XliSldW7M9n4diOBFiLCFVYQVSSWSHyzNZynroohii1lRCdGpsqkAqXAaPSisFZi9vtxKUKZG2Wk3151YzqFIEkyQ7c32aeZ96IVO5alsmLV/biqdXHmtrnGzGlRwydY4J4bv0pruqfQESAhtdbFc4a9SqWzhtEoE6JUacmv8rMzHd3UdXq+tw2Kg2NUiQqWEuYQUNymJ5qk50gvZr/W32UyT3jCNapEATYfKqMxFAdV/RLaFMYEaCk1sqp4lrqbS5qrQ6W7s5jXEY0p0tq+eaQ5xt8UpieJ6d0Ze7ifbjcEhqlyCc3DUSrEimsttArqJ6YlVd6yixMfQvpwGKEvJ2eJ04bQ92oB6i2VuJCIqC+nLCEIZSpoohwliN8djk0qZI3IGMqxPaCTU9TNn0Flqi+JIYZcNaWIDpMCKZShE+mQuepVE34K1XOOhxuB1qFFrvTzuVrLsctNadq1aKat8a9RZgmDJVCxbFTAqdrLIzoGIGArCmlUgicr7Fid8pmsEE6FUpRQBDAoFEy9+N91JnMPDk6ki4d03CLKqrNcmOBQaPEqJeFIVs+HEVBIDncQMB/Qf+na9eulJTI9W1OpxNBEJrSPEOHDmXVqlVexOdC8L9KfP4wNT5lZWV07tyZnTt3MnSop6PzgAEDyMzMxOl0sm/fPo/94eHhJCUlceTIEUaObMNhux1/XCiVEO4jdWCvgW8XepIekFNC39wJs5bLNTS73vImMQAHPoZeV8N39zeThk6XwMk1vscfWQFXLgZNoJxaqzgL2iCEzc80j1/l+ZbGnQdg1e3e6TXJDasXwLyfZJuLDU94n6/iHBxYBJoA6HEFfP+o9zyDbpH1UnzMbxKtPLrrKa9OHavLysOHXmXRkNsJX30P2vO7qAm9GJePguAXfjjNuIwoHvzqCGa7izVHPB+6ORUmbhiawuTXmv8Yv3NNX55ff5InL+3mRXrkY8xsOl3GHaPT+b/Vx9l21jtVPXVRDR9d35+nVhxj0Y0DSI00AIGAnIrJKqvnL19uBmDZXs+2cVEUuH9iZ348WepFegBWHy5iQrdoQnRKJnaN5oZFe73GVJkdPL32BM/M6EGd1cHf15zwIj0Ab246x0fX9+emj/filuCaQUlM6hbNP9YcZ/u5Srafq/Q6ZmyX6J8lPruyKrh7WSYqhVy/FaRVkhyu5/nvT3mNza0w8+PJUsZ0juSH4yXYnG7u++IQN49IZeupIoaFf+tJesI7grXGm/QAnNtIYPpFBB5cIr8QAFLqaMIv/wh2feBNekAm7BmXgVJHxA93sn/s54To0wkKipKFRz+7Tf43eWQpxvRRGIuPwM7Xyb47k2vWX+NBekAudn5i+xO8PfZtPj3xKbf2mM+tTx3m7c1ZvDyzF/M/2c/VAxMpr7ex/lhzMfzw9DDuHJPOVwcKOVlcR1ygAlEAtUKg2NTcTRluUFPQivSAnAorqDSTGmFArfzXtWR+CY4dky1GsrOzyczMRKVSMWDAACIjfaeSG7FixQpeeOEFLBa5nkyn03H33Xdz5ZVX/tvX/HvG77bGpyX27NnDt99+y+zZszl//jxRUZ5+LpGRkVRUVFBWVobL5SI8PNznfl+w2WzU1tZ6/LTjT4KqbP/dVOVnwFYrR0p86QA14vDnsp5OIzqO9468tMSZHyC5hVu1pVomRP5grYFq31o42Orkwu1qbw2eRgiHPodOk2TBQ1/zRGZAVY73dpWeQrfVbwtxbm0u1cFyV4/i4CcEu6sYlBbmNU6S5Nb0uBDfPkQ5FWaig5sf4lqViMXuYnqfeL484L9LaM3h84iiwPZzvuvzJAnOldUToFV6+GiZ7U6Kqs1sPl3m8ziArWfL6RYXzNoj/msbfjpZyrQ+8R76P62x5Ww5JruTOpuTrT7IWSOOFNbQIVKuy1l7pIiwAA1b2hi/9azvtTuri5BKTyCd+YFLQws4c29nlswdyEfX9+eBiZ3Z0sZnXnukiIu6ND8k8yrNJIcZuKKLjsDjn3sO7jih7e/4sZXQcWLTr0L2JgRzOcLhNqQGTq+D1JFQlUOYUNes2m2p9CRLMT3hyHLoeR2l5lJMDpPP6QrqC6hz1LEmew01dvlvtsXhwmx3oVMpWHOkiIu6eD4ntp6twGjQNNmsgKznY7I1F0QLQoNnnZ9EiN3lxun+7yRJHn30USZPnsyuXbvYsGEDgwcPZsmSJSxevJjo6GimT5/uMb6wsJAFCxbw1VdfkZmZSWZmJqtXr+aBBx7g7NnmCObmzZtZt27d/5SI4+8+4vPFF1+wYMECFi9eTEpKCi6Xi9bZOZfLhSAIuFzyF1iSJI8CqMb9vvD000/z1FNP/fs+QDv+e3D+zD9kt1N+grraGOewyumtRgiKtse77PKY1tvaWkNbcDlA10bKtfF8vv5QX7PSf6G2IOL8mXO7Gt95XXYE3E2K0K1hc7pQtFH30LIwVBQEnG43SlFoswXZ7nJjd7p9fqxGmO0uHr64S1NEoNJk55OdOWw4UcKoTv7fhCVJVnt2tHF+h0s2pnT4sY5onMfllsitMLe5TofL3XR9HA2Fs22NtzrclNVZPaI+7qpcFD/9HeHI8qZ7rQiOp9+Vi3kyU0mN1c2MvvF8sb/A59wOpxtFq/snCNAhwuDtrfaz33GH53e88YRtfZ9aHiO5cTeSB3erLiypYaw+4ue/n5ILh8vhEZlxNlxrX5+3cfrW97Tl8UKr333iv8B7srKy+Oijjzhz5kxTV9Pdd99Nz549qaio4Nprr21KdTVCpVLhcrk4efIkRqMRQRA4deoUNpvNo5Nt5cqVGAwGBg8e/D/R4Qa/44iPy+Xi9ttv54knnuD7779nypQpAISGhnp1aZWVlREdHY3RaESSJKqqqnzu94WHHnqImpqapp/8fG811Xb8QRHeEUQ/3D4oFnRG0Buhy6X+5+hxhadzde52j7ddL6SNkf2HGqEOgE4X+x+vC5ULqH1BoQZjIghtaBV1ngI5W8Fh9pyn40S5A62uSI4GtYa9ngR1CErB9/WJ0EUQYpGjHe6u06kRQzngQ/0ZYGhaOLkVvt/MIwM1mFu8UZvtLox6NWuOFDGlZ6zfjzUhI5r8Sgs944P9jukSE8TinbkYDfIf693ZFby04QyniuvpER/i97jeCSHkVZoZ08U/ORrRMZy1R4ranKdXQggltTZOFNfRK8H/uJ4JIZwtrQdgTOcoLA5nm+OHpofz19XHqWjokHNZzQgHFssRlZaspqYAYckMHh+qZ2XmeXZlVTCqo+/PNKZLJLtaqBpHBGgoqbVy3qbD1nGK5+CcrW1/x7tN97SIie0DmmCkjpP8H5M+BvJ2giGCWiGIIF2DhII+1LPDqzpP/vey83liA2JR+bHUCNOGEawOZnTCaIJUAYBs3xIWoKHe5uSiLlEenxegR3wwNoeLCV2bz2d3uTG0kDZwSxIKQfD7oqwURRSK/3xxsyAIDYS5+f43qk3b7Xaqq6upr6/3OCYyMpIvv/ySN998k9TUVJKSknjxxRf57LPPSExsjmK/9NJLLFmyhOBg///W/mz43RKfhQsXcu7cOfbs2UP37t2btvft25cdOzzFrrZv387gwYMxGAx06tTJY39RURElJSX07NnT53k0Gg1BQUEeP+34k0CthxH3e28XBJj0TwhKlNugR94v6+y0RvIIuVh55APNnkVHv4Tec3yPTxoit8ObGlIO+lCwOZCG3yeTrMBoakbdT/GNayi9cw/uOw/K5ObyD5vnTxkBU16Rt836HNTBgCSr67aGPhTGPw0jH5T3z90IE5+T9w26Dba/DHvfk4ueG+cXFTg6T6Zkxjsog+JY0OsOr2kFBB7vcRuRO9+B0FTs6ZMwaFWolSJ3jEnntVm9eXZaN5be2Iej93YnVVPDhjv6kh7R3I0UpFVy/ZBkPry+P3qNggldo7iiVxR7bu/IqIg6vpiZQP8YFcM7eKfPQvQq5o9IZfHOHO4Yky63WDdgcKqRp69I5PNbOxNjlMgpN2F3uimoMvPyD7KPk93l5lBBNZf6IFYapchto9N5eu0JZvSJJ0Tv/WDtm2TE7pQorLa2Oc9jk7vw3PpTLN2dx+2j09H4sMC4tGcsmfnV2F1yR9v0PnF8tb+Qxydn+Bw/tVcs5XU2Vh8poqKh7kSoL0TY+57XWAAsVYglRxjVMYLFO3KZMzCRcRnhPD8zmVfnpHDXuESemtKV20al0SM+mK6xQQgC3DuhI5/szGXV0TLEYXfJtWkj7oOAKJnURHWVuyJbw5gi6yU1pn8VaqSJz1BFEAy7C0KSZNmGGe/D1Deh8yWQMFDWN6gvpmL0P9GHxhHaQFYJjIEprzZ/P1feAkMXgj4UvajlDj/fzwcGPIBWoWVej3n8/Rs5xTt/ZBorDxYSrFNxed94j/oxtULk8ckZvL35LDePSMXYcN9dbrkY2tCiYLnKbCcy0LeuTWyD2ex/GikpKdx000307duXe++9lzvvvJPRo0fz6quvsnr1aoYNG8add97ZNL62tpbvvvuOmpoa5syZw6hRoxg8eDBTpkzhxIkT/POf/6SgwH+q+c+O32VXl8ViITAwkMLCQq96noKCArp3786XX37JmDFjWLt2LbfddhvHjh3DYDDw0ksv8dlnn/HDDz+g1+u5+eabMRqNvPTSSxd07vaurj8ZqnOh5LhMAqrzIaobjPiL/Ac3JF4eI0lyHczut+XCZU0ADLxFfusNjJZb4msKYctzckFn8nCkoQth/yKEU2vl8f3nIcX0RPj2brlVveNEGDAPqbYcorpjsZdztr6QF46+x6GyQ4RqQ7k+dSoXh3Qi3IX8kKnNh5ztcHAxmCuQEgbD6IcR3A45/F90SK5/cFhkNeeMKbJNw773wVwhP2AuegIpIAbBVg0fjJcFE7tfAV2nwpEVFPe/gU+LtvJl9losLgtPDHqcMF04Hxx5n4L6QroYO3JrhytJPv4dOk0Q7h6zKFZEcaKolmC9hme/O4nT5WbZrETY/Q6aw5+Aw4wzbTzu0Y/xTYGWdcfLmTs8lfe2ZrHldBnRwVpWXZOC8fAHCAcXy9GptDFIQ+9GCoxmbYGaVzacxeJwMbpTJNcMTqKgop7UyCC2ny0nKdzAin35XNzLwGnTZr489xlV1ip6R/bmzl53EaxIwOFUMu3NHU0CdqIAT0zpigSs2J9PZb2dgSmhzB6UxIdbs8ivtnDP2I7Eh+pZtD2bLWfK0ankdvae8SE8+vURqiwOxnSK5MbhKRzMq+KDbdlU1tvplxzKraPSOJhXxcNfy2KKA1JCuWN0Osv35XMgt4rQADVzh6Xicku88uNphqWHc2nPOErqrAxKDSNQoyS30szbm86xN6eS0AA11w9JJjnMwMx3d+J0w1e3DqFPkhGp5DjCW4P9fsWlUQ/xkn0aH+/M5ZuFXVl2ahmrsr7E7DQzLHYYszvewvPfVlJW5+TGocn0Tw7lg+3ZXNFJTdfydQRlvgOmMqT4/ghDF8K5TfL3MWUYHF4OJ1bLJ+p8iVy/tuN16DAOqegwDLsLty6KtKf38vE1PRgRXgMb/45w9gdZrqHnLIS+1+Pa+ip1Pa/HGpxGaKgRdUvhObsJys8gbX5WlmHoexNC1ymw510Kh99FVk02Hxz9gIK6AjqEdODmHjcTpY/C5rZhcIdzx7IjXD0oifPVFvIqzVw/JJnCKgsvbThDRb2N/imh3DwilYJKM51jgkgM1ZNfZeGrPVn0DHEQE59IYmQwZruLSpMdt1siOliLQhQorbNhd7rRqhREBmrQKEWU/wXi04isrCwyMzNRq9X079/f4/nYsqurvLycN954A6VSiVKpRKVSoVKp0Gq16HQ6AgICGDlyJCNHjuTbb7/1iAD93vGnbWc/fvw43bp187oZaWlp/Pjjj6xfv56FCxdSWVlJeno677zzTlNUyO12c//997N48WKUSiWXXXYZL7/88gUrU7YTnz8pqvPlehe1AYL8eLw4bXIxsiBCgI/0UE2hTDqUavmN11SFZKkAUQR9BAKC/ACRnHJEyF6H0PDWvKdoD3O/n+vVQTUqZgj/Z+iCMWEwbH4Gzm30PKcgIl27Gik0VQ6/O6xAQw3b6oWQvdl7/DUrcRtTUSyfA0WZ8nadkZLZS7l5/zNk1WR5HJIUmMSbF72Grq4EPQoMTge47XBuE47uMylTJ5JTZebq93cjSXDs3u4Yll8OZa06iFQ6HDdtIkuI4bLXdzTZMnw+M5FB22/yOZ6ZS5GCEzliDUOvUmDQKDA1tGifLa1nTOdI6qxOnEI9Lxx8iu3nPVt1RUHknbHvU10Vz5s/neNwgWcxcmq4gSk9Y+mfbCQjJgidRkGdxYlKIWI0qHG5JarNduptTpSiQKBWRZBORV6FCZckEaRVICESHqBpsU1FWICGtzad5dl1zZ9JqxKZ0iOWTtGBqJUiE7tGN9U3KUUBBIHwgOa/QyeKajiYV01KuAGLw8XH23PYfKa8afy6u4aTHhmIu/ws4kcTmyOJrSDN/IyrNhu5bngo7595mLPVng7mWoWWF4ct4pYP8zHZXcwekMj0Tlp67H8YdZan+CuCgH32Sig/jXr9X2SV5fSL5H3nNkL2ZqTuV8HoR5CUGhYfqOLJ786hVyvYdlM8oUvGycS2JcI74pj9Jc5A3+apTZ+jKheytyBUnIVTa2HyK3JRdL+bOK9SYXfb0YgaVJIKlUqB06GmxiahUytwutzUWp1IklwY//3xEi7pEUNaRAD9k4xIkuxYr26hbF1bbyIvN5ek5GQCDXpcbtmKBUAhykXOjY9HURD+q4SnEStXriQwMJCLLrrIa5+/dvZ169bx6quvcuLEiaZ6V1EUycjIYOHChYwfP/4/tfzfBH/advaMjIw23XInTJjgV5hJFEWef/55nn/++X/X8trxR0SIt9quF5QaWf/HH4LjPH83GBEMrawdtAE0VwDI5KnCUsHfdv/Np8HjpqIdlAy/HKO5wpv0AEhuhPUPUjD5M+Z/mUtZnZXdd/WCinMIrUlP4/jvH0GcvRzXyAdRfD6zYV0hHKnN8iI9ALl1ubx+4DWeNPZFv/I2z51VeTDqnzyx6hiSBJf1jEFRdNCbxIBMCrc8izj4GRSCwND0MCIDNXTnnP/x+z5A6n4F32al8O42z/o6lUJg6bxBhOrVZNcUeJEeALfk5rl9z/BQ75e4Y3Q6N3+yn57xwcwemEhahAGnS+J0ST12l8T5Givd4oI9Hr6KhrqQsADPFyNfIoKtt03oGs3rG89ianBRtzrcfLFfTh98dH1/In1YiLREiE7NlwcK2e+jdmpq77gmkuQOSUQYuhDhex8q5UFxuMI6cb46D0ld4EV6QJYn+OLc+0zrO4clO4tZmVnAY/2ivEkPgCSh/uFBTFM/Ri0qZGLd8nsmKjjf9WYuevGYh9/Y7N7hhOx5wZv0AJSfRlVyGFXoz0QVagsRDnwsp9okN9jrYNfrsOMVvJKN2mBs87YSFpFEfqWJ+Z8c4HiRZ0fuqz/KtXlf3DKY/sneGm5qpQKFKDTZaShEkd8Bt2kT27ZtIzo62ifxGTVqlBfpOXbsGHPmzGHVqlUMHjy4yYjV5XKxY8cOLrvsMrZt20ZGRtu+en82/M5vczva8QdCZQ7k7oTvHpR/8nZDVQ719nqya3zo/jRgf/UZ/y3tAMVHiFfWsPqKIPZcF4SgUEH2pjbHY6lmjy0J98UvgDYYd1wfvivd5/eQrUU7qYvy/uOnOvcDAZg401CcO713DOqT/lXQVec2EKO18fyVPUkND2BwSij601/7HU/WZkRJ4vYBIfRt4Q923ZBk3prTl02nyyissXC86qDfKU5XnUajdhATpGbHAz14dKpAjnMxe8oXExZUxNTkSkaHVaPDv/FqYZWZbzILeWLVUT7dlUtepRlnGx1dAAlGPUvmDiQl3LO26enp3ekYHdC0rbjGwsYTJTyx6ijvbD5HVlk9ZruTmBAdL13Zk5EdmovSFaLAjD5x3DW2AyENthpKpRqpy2VIw++V/d4aEdsbac6X3PVDDXeN7cCWwh/8rnVX8Xb6JMvHJocFIOZu9//BSk9QZbZhumKZnBJuREAUzpnLqNUnEKxrro2KCNRw26AwxLMb/M95ZLlc5+MD1op83KfXw+FlsmDn7GVyetZS6d311XRQDZhlDSSLw+1FelpiT7ZvKZP/BTQWRDudTo/CaEmScDgcbQYY/sz4XUZ82tGOPxwqc2SRwZb6J7vfgu5XorjoIURB9BJia0SgSi+rT/uDqERw2VHseA2OfQ237pLVqdsYj6ikThnEuYhpJNwwEpUgEXz2M7+HGFQGRLe3AB8a2RJAIQq43BL1DnBrQvy/MWkCcEkCty6R7STGdonkcqOxRRTMezySmxMlVuYOT8HqcDEoNQyFKDD3431olCL9Eo0YlAH+ZkAhKFCICkKDzDyw7SEOlB9q2vfWsQ+5s9vNzNTFk6oNA3swqD01h86U1HHVu7uobCFip1GKfDp3IL0TjX5b9VVKkaQwPY9c3Bm3BE63hNst8dmePPZmV/LwxV2wOl3MeX+3h1Dis+tO8uqs3lzUJYrEMAPPX9mLSpOcbgvWKQkzaJo61RohGhNxDl6AosdMsFaBUodLE0yuK4wFYyQ2nixBp/Lt4QXy/W2M0FgdLgRdiN+xiAq0Gh2vnY5jyKjlJGllwphn1bErS8Xc4cGsun0YlSa56yzUoMYoVMv30uaHgOhC5ZRwK9grctF+Ns1Ty2e7ABc/B34MeRshKOVrpBAElKLQhr6OQE65ieRw7yjeHxF/+9vfePnll/3u37dvX1MXc7du3ViyZAkvv/wyN910UxMREgSBHj168Pnnn//PRXugnfi0ox2/DQr3+xZ9O7Ic46BbGJ0wmh/zfvTarRAU9DYkgEEp1xb5IkedJ8ueX8caIidvDYLbdvsdL3W5FEkTyM2fNLcchweo+WjuZXxx5kufy5+dPp2wI96RGWvvuVQJwYzPiOK7o8U8s/YEY2ddi/LgIp/zWHvfxKozzQRix7kKKq6eRcSBj3yOp8dVuHRhPP99OefKcnjy0q7oVIqmtducbuptTroYe/slj2MTx4FTx5qCLz1ITyNeO/ouIyYupvOy62H2lx4q3xX1NhZ+nulBehrPO2/xPtYsGE6sH3FGgIN51cxdvN/nvmsHJ/HRjhwvdWi3BAs/z+Sne0eSGGYgIlBDhJ8uopZQ6kNAH9L8O5CG7N21aEcOf71yEl+d9S0ieEnS5azJlKN25SYbQtIQWZ/KR0TF0fES8u063t5ymrd9zNUrMZRxGVEewpS41dD/ZvjxSd+L73u91yZzfR2qn/7urfYsSfDdA3DzFrlz0eytbk1EZ9w6uSMwRKdkQtco1hwp9homCpARE8T8T/bz2byBXinNPxp+TRnHpEmTmDSpDamB/0G0p7ra0Y5/FdX5cts4wID5cPMmuGEdzP0Rrv8Wg0LLfb0XEmPwLKoWEPhHn3sJz1yGFBCJdOlr3nMbk2HoAtj5iud2lQ4ueam5DRhAVGAZupDCsY9yyFzBB3NTuGFYDMceHMTeW5JIUOiY2/VGr1P0DOvG5JRJiLve9NjuiuuHo9uVqJVKHrq4C/FGHflVFgqFKKyD7/aaxxXXD6nHTB5f3Vx/Z7a7WJGlwD3sHu/PFtcHKW0spcoY9uVWUWV2EKBRsumUZxHve1uzEN3B3Nf7CbmAvOUUAXHc3msBRq2NpWf9p+C+PvctdJ8J9Z4Px0qz3W+apMrsoLjG6nOfxeGkoMrM+1vlFGb/ZCMvX9WLN6/uwzvX9OXlq3oRpFNxcfcY3pjdh5ev6kX/5OYIhsstsTvbxwP9VyAqSMvLM3tzMEvk6o43ee3PCO1Gz5Bx7DhXiVoh8vqsPhAQiXTZW57fH4CQRGqGPcZrW72tRBrx/tYs6qytooOiAnrOlH3oWmPYvXKbeyuIlnJUJ/zcM7dL9qeb/Ipce9cS2hAc095HGyJHNcICtdw7vhNpETq+vLEr5+5JJ+v2SLLuTuXI/X2JCFTxj+ndqG295nb8z6I94tOOdvyrcDnkEP/Ut8HtgCXTm99So7rCpW8Qv2cZi7vdyWHRwdbSg8TpwpgQM4So+kp0Qxci2cwIoV1kb66jK8BUDgkD5BRB9laoL/U85ys9YOERmL9NtsQwlVI+9HbeOfs1K769HKfbiSiIXJQ4hjrr9Rg+mU6IqZzrh93FhNFv8G3JbursdUxMuIgQVTzf7qtm/FU/EJG9EpWjDnvnaTjDOmFShjZFPL6YP5jM/Gre2l3KvcPnY+w6DeHwMkR7Lc4u03CFd+b2Vd4CoM9uLkEYeQlz505BcXSF3O7fYTxSeCfyTArGv9f8xu9yS1gcnmm/QwU1vPZjLvdPGsLH47/gp4LvqbCVMix2OBlhXVm8pZqbhhuotfuv86h01CBpUhBsdR7b21JvBjDZvFOQpbVW3vjpLEFaJbVWB1cPTKRjVCD/9+1xKk12usQE8tCkLtyyZD+nS+QoS6hBzcKLOtAxKpBPd+cBeBm+/looRIE+SSHEBGvJqQplZPxFbDm/DpOznjEJ4wgUE/hiVw0PTOjMuIwoYkO0YCrGXVeMYvYXkLVJ7kZMGYE1djCZFQZK60r8nq/O6vStaB0UAzOXyIXsR1eAJhh6XiX74el8pK3cTm/V6BaQavIhqjvcsg3HyfUoSo/jiB+ImDYKldGzUDo1IoDvb0hH3PEywsFPGhTNBfQdJtJlwtOM+SAbpxv+Pq0bfZOMBGp9CyO2438Dv8t29v8m2tvZ2/GLYa2DzKUQmgSf+TD/04bA9Wvg7aGyPkpEZ0gaCj2uhM3Pwr4P4dad8OZAWXhQoYby07JmUFU2dBgHoWmyzlBrGBJh4R7MkpsXDjzP8tPevmCDovrxz4CuGDf8Vd6g1ELX6UgXPc6kj85wskgmAxqlyMCUUHrEB3Pt4CQig/yneKrNNv727TH6p4SjVytYuiefPokhOFwS72zx7hwDuOuiDswemMjenEoKqyx8vDOHwurmiIoowA/3jGRfTiUPfHnE5xy3j05j4UUdUCsV1FpszFu8n8IqC+tuSefR/c/zY8Emn8e9NPQfjN3+Lkx6DiI7N20vrLYw8aUt1PkgOKIAP907iqQWtSEWh5On155k8c5cRnQIp0+SkdRwAws+z2wa8+H1/Vm49KDPOV+b1Zt/rD1BUY2Vb+4Y2qYy9K/F8fM1LNmVR0mdlSMFNRj1ahLD9JhtTv46tRuxaguqr29AkbNVjtQkDgZNEJSdxJE8ivMDHmHJ/jLe2+r7Pi4Yk86Cizr8y+3d5spi9J9PbTI7bQ333B8R4/td0FxOUzWKn/4PYd8HXvukxEHUTHqbXq8cRykKrLpjKF1jg9tsi27H7xe/RTt7e6qrHe34V6ENhI7jYNMzvvdbqyF7C4z7h5wCG/d/sk2GwwaDb4d+N4Kq4eGa+aksEnd2Q7Oj+9kfocN432/NkgkK9lBhOs9XZ1b6PP2ukn1UxvVpTms4rXDoMyg5yqgWHUU2p5stZ8p5/adzsmqwzSRHmqzekZQQvYbrh6bx+Kpj3Lk0kx3nKnhz8zlm9I1vUsVtiVCDmst6x2JQKymptfGP7056kB6AWQMSUQigVys9OqUaoVHKOjmNztjFNXZ2Z1exaHZnDDvf4o4e81GL3l5DyUHJdA9KQQpNkx/wLRAVqOHBSZ29jgG4dnAyoQGe85XV2lm6R47YbD1bztgukXy0Padpf4/4YI4U1vgkPQAfbc/myn4JDEsPb7N26NeirNbK7uxK4o06DGol1WYHp0rq+OF4CdvPVfDjiRJU1nKZ9ICcUsrZJuvmVGahOvQJRncVozpFNKsrt0CIXsUV/RL+JdJjq6/AVZGFRnThnPyad7oNcCYNx6qP83G0b4jmUjnS4wNC3i6CpRruuagDHaICeG9rtneqrgGSJOFy2nE57V6ekO3486Cd+LSjHb8FJAlKj/nfX7gPel0FPz4lR4W+ngfn9yMJIgxZAK90k8dZa2SV6auWQOoo+aGg1ELuDrj+W8iY1tS1RdfpMGsZBMdTX52DU/LfGVZqq5LtOVqi6BCTe3jbMUzvZiTFmQ2rboUPJ8AX18lt+lZPccDkMD0rbhnMsPQwBAE0CgXbz5SxfP5gLu4WjVKUu20u6R7N0nkD+WJvPseKahnfNZJXZ/UitYHcRAdpeWJKBreNSmP9sRKe/OYYT0zJ4Mp+CWhVIoIAw9LDeXtOXw9rgaIaudsoRmtDyNlK8t5FLJ3wEUNiBiEgoFPqmNXhct4d+RKRhYdxD7ubLHuQR5u6UiFySY8Y3r2mL2kRzev5+9Ru3Dkm3SslUmd14GgQuZMkyK0wc7K4OX2WFKrnVLH/lNuJojqGpIfx4pU9PcQMfwtU1Ns4kF/Nt4eLWL4vH6VC4P3r+tEnUSbMSWF6+iYZMZW34UfoclBTVY5CkI+9uHvzfZzcI4avbh1CQqje//FtwFZfhVR4APWau1B8OgPFN7ehsNch3bYHKa4hsqMNwTb0flxT30Ef6kdo1BestW0aq0pVuZwureOyXnFM7BpFrcWb+LidNqT6UhSV51BUnkOqL8Xtz+S3HX9otNf4tKMdvwVEJQQneBqatkRYOmx5Xo7oAFRmQd4u6HM9DGtVKJyzDUqPQ+9r5NRXWJoceTl/EKK7QsalckdXZBcoOQEfT0Y/e0mbywvVBMuCgR5rSuNgrmeBbY/4IB7uWon2w0uaO8Yqs2RxxUtegF5Xy4XVyG7oCz/PZFxGFLMGJOFwufn+eDH7c6t54tKu3Du+E1aHi2Pna5j17m4qzXbe3JzFXy/rylX9EugVH4LTLSEKAglGLQqFguRwAxUmO/MW72NC12iendEDpShyqKCax1YeZfktzdYNjR06NU41+pBElHveo+OZDTw34W+Y+t2PIKoIFdWo7CZMaZPp9o+taFUiX946hK6xzXIAIXo147tG0yfRiM0lO8dHBmp8GlXqNUoEodkrNL/KQpxR12RCWlZvo3tciN/7EG/UER+i+1lxw1+KarOdV388w8c7m/WgcirMrDlcxBtX9+HJVcf462XdmP/Jfr6eEYzfJIAgYBN1zPlgN5N7xLBgTAf+MqEToiAQEaBBr/l1jwy304m6YBfCstke3yshaxOMfQr3tHdxCEpciCgDo1D9UpdwTQAeN6Y1AmPYdKqMb48UMyDZyItX9mq1PjtiZZYcDW2AWHcezBW4w9IRlf8bruX/K2iP+LSjHb8FNEEw9C7f+xQqObW15x3P7QGRCLlbkez1kH6J5z5zJex8A+nUOlDo4KNJsOoO+OkfkPmFXBR6bjMYk8BpJdRcw8i44T5PnxaSRlhFjmfru84IUd34YEcuMS3akh8bYST8x3vkOqOwNAho4fa9/uGmImurw8Wbm86RVW7inS1Z3P7ZAe5alsnaI8V8e6SIMyX13Prpfi55bRv3f3kENxL9Eo10jQ3iufWnKK2zkRhmIDUigORwA4oG76ZuscF0jwsiRKfm28NF3LUsk2fWneC7o0XM7J/g0fYdFaQhLcLAglW5ctQMoCqboM+vJubVfkS/3BP1i13AVM61Sw43rNvNE98co9rsHR0ID9QQF6IjKkjr1507IlDNpBbu3l/uL+CaQc0dS3uyKxmaHiZbVPjALaPSiDP+uohJWyits3mQnkbYnG7e/Oksf5nYiW8OnUepEDlnNkBEJ5/zONPGszbLhdMt8e3hItRKkZTwAJLCDL+a9ABINXkIa+/xLdfw098QXVbUKhW6sIRfTnoAty4SqePFvneGpWHVhlHfoLC9J6eK0yWeRe5YazxITxNcso3NHyHt9cADD/Diiy/+S3O0pQ/0Z0J7cXMrtBc3/0lQlSvX1uTvAU0gxPWR/xvYRvi8Mrs5shIUA5EZslu19gK+BxVn5WOPr4I97zb/gdcGw/T35FTR9gaj3IQB8oPaVC6bkSYOgrAOCJ9MlSM9ogKG3Y2UNAyq8xDcLgiOBUkJEelgq2n+XElDZZuAosMUx2Rw/95nOFiW2bSslOAUXh/wOImfzYb6hk6doFhqrl1FiSiwPf8galFHh5DORKgCSXIVI+Ztl73Iyk6BIULWUtn2kqxVNHs5OG04IrvzwPoSvjriWxV3Rp84THYXp4rreGxyFyQJ8ivNpIQbiA7WoRQFDuRVEWpQ0yk6kOggLUpLBVJtIc7CTKpEI/bQzpQSytFiE91ig7G73GSXmUgM05MabkCnVlBaZ+OWT/bz+ewkIgp+kG0dGtMTSg3S+L9TFjeOAa95piE33juSiEAN5fV2MvOrCNAo6RFjINhZDqUnUGgDUASEITgsSLY6hMpsHMFJVOtTqFCEc6q4lge/OoLV4eahSZ0pqbXy8c5cXG6JkR0jmNo7lsdXHmuq9REFuHFoCjcNSyHmV9b2VNTbKKqxUlBlJinMQH6lmUqTnW5xwZjtTq79cI+HjURLrFs4nOJaq6wpJElcmeZEv2K2XETfAFf8QE4Pf4UZn+aiEARendWbgSmhbROeumKkiiw5zRsUC8YU3JpgFCGe9TnuggOI74/2P8+spbLze2SXC74euTW55Nblkl+bT3pIOjEBMSTYHAhv9GkeFJaO+6ol3LC6tskHDWByjxievawzeXm5JCbEY7Cc9223AaDS4TKmIQkKHC43FocLpSiiVYmoFKJfkvzvxjXXXMMPPzSrdVdVVaFQKDyeW9dddx3PPvssAE8++aTHf31tEwThd0/y/rReXe1ox7+EqhzY8GSz4B/IUZdL35ALh1t7boFcSPz1fMjf3bxNEwgzP4OYXhdGfpbOgvF/h/lb5fSQ2iATB01Qc7dX4mAYcLN8LrsJAAGQUkYizVwKplIElQ62v4qwZarn+qe+LTtl/9Dg2XT9OrkV+du7wO0kWhPIy6MeoKLnbRS7bNidQcTqI4nXSkjXfC2vSRdKhTGBFw69zbfZq5umV4pKnh74OFHhPdCf+EZOwzVCHQCXvQE7XwdbHay8FRUSj018A5EEVhzx9poSBYEwg5q/Te3GXZ9nUlbfXCuRFhHA09O7EWpQM/+T/RjUCrbfkYFh9XyE3O2ogMiG82ou+xQxtiv3rjjEuTJT0xwRgRpemdmLf647xQ1DU6gWQ6mPnkTy/FEIVdmAgGRMJtusJavaM4IgCLJGT06FmZs+3kuCUc87V/cguCIT7fKZMOHvcGYNdJsO6x5EaCAHaiAyIBLnpcv46SSsWziCnAoT4QEaQnQqJnaLJqfCjFYpEhOi5YtbBlNeb8PicJESbiDUoCbU8OvqeoprrNyz/CBWh8Tc4Slc/taOJo8wgIEpRl6b1ZtblxzwUjC+pHsMJ0vq+MsXh5rqk94M1PDutCV00NdTV15IaGwqVWIIJ87DM9O7oxBF3t+ahV6joE+CEZXSR3KgKhc+uwKhpQ9bQCSKae8iuZ0IoS20e36OHAi/7FF0svIkt264lXJLM5lJDkrm1TGvknxPNlLBdgiIwqoN59bVZR6kB2S15+YlCT+zPnlfXqWZ+hZF66IgkBxuQK9WIP4XyM8nnzQXcx87doyhQ4eiUqnYvn07aWlp//H1/JHQTnza8eeC0wkn1niSHpBTQytvgVu2eRMfUzlsecGT9ID8kF86E27e/PPERxsmFxt/c7v8e1CcTGys1bDwCFLSUITc7TB0IXxxvVdYXcjejLT7LQR9uCzYdmS59/q/mitHXEQFaIygMcDqO5vrGmx1hK5/lFBBJP3S1/iofgh3/nCUl6/qxYIvcvjHlJ70Dw9lR/FaD9ID4HQ7uX/nE6wc9wGpBXs9z22vh5W3wuUfyF1ADUWkxrXzWTBzE18fq8bV6mF7aa9Yai0O/vLFIQ/SA3CurJ6XfjjD7IEJjO8ahU50otj+knx9Wp03YuUsTFdt9CA9AGV1Nu5fcZg7xqTz/pYsEkJ1XP+RnM5KMMoRlfwq2bDz4xv7kxphIKthjiFpYaw/VkzX2CCMejV3jEknXlGNdtkVstSApRqComHbix4REQDqS4ldcy1Th3/GRzuyeeRiWe7/H2uPs2hHLgEaJQ6XG1uDPlBkoJqvbx1K3K8sCAawO928s/kcO85V8t61fVmwNBOLw1NxeXd2FUlhpUzqFs3qw0VN25WiwJxBicx+f7dH+UtpnY2pi8/y4MROWB2BVBXa+XjnIa/178+r4oe7R3oXNFuqkb6505P0NFwfvrkDJj6LU1SibIj8uLVGRGNKc6diS6h08r8XbcgFXY+8mlzu+ukuD9IDkFObw5M7nuQfQ/9OaIdJvPzDKd7Z4rtV/op+8U2GnQqlErcuHNFu8jnWrQ+j1iZ5kB4AtySRU26iY1RAU6fhfxqnT5/mk08+4eOPP+add96hqqqKwYMHc/vttzNnzhwvAvTyyy+zaNGipt+rq6u56667PMasXLkShULBlClT/gOf4L+D9hqfdvxhIFVmIZUeRyo7hbvGj7JsTS7s8SW0j5x+OvyF93ZLpTfRaITdBOczf35xBiMMuV0WawOoLZRJDyCpDAgTn4HoHnLUxVctASAcWAxdp0L6RXDHPnioyHOAJMG5n+T01oz3IfMz38Wckhth+8tc01nCbHdRVGMlVKPlkZXHqXVXs+iob/sICYk1eT9C0jDvnQ6z3Dlz/BuP9YRlr2JgSigDU0J57vIevHl1H76+bTCJRh0dogL56IYBfHP7UAakeLbi78yqQK9WMqlbDHO66dAd+dTnmnCY0ZUfJt7onR4qqLI0EZeWLeWBWhWPjgxl7ZxYVl6dQFZRBQtGyw+AIJ2SecNTWb4vn68PFnJJ92iSQvUoig7Ixd/dr4ADH0NcX7nI3Bdq8umor+fL/QWU1Vkpq7Py+V65U6re5mwiDQCldXYOFlT7nucC0Th/vFFHYbXVi/Q04ptDhUxoUX8EMLpzBFvOlPut+f1sTz7jukaxfF+Bz/VbHW4y832s31SO0NK1vSVqChAkNwprDVaHi4IqM1XqaFmZvLUKM8Ck53CLCjm9fAGoslVRWF/oc9+B0gOYnGaKa2zMGpBMQqj392Z8RhQp4c3eb25Jkj3pGiUlWkBS6UETRGG1b4NbtyRRZ3Wy81wFqzIL2Xmuwusl4N+Bo0ePkpSUxA033IDRaOTQoUNcddVV3HLLLRw4cACn08n06dOJiYkhP7+5i++uu+4iJyen6ac16QEoLi6mpMS/gOWfAe0Rn3b87uGqPY9Yfhph3UPNNTAdJyKNfRIhvKP3AXXenj1NqPHRyuu0N9eFXOgxvhCWDtd+I2uinFyDpDZAv5vAVAzaUJj5KRzwrTUCyOSi7CQsmyMXFQ+7BxYcgld7No+pK5YLk/WhUFvgf666YhSC/AArrbMRoldzvsaKILgps5T5PSzPWu5bLwjk1EbZSY9N+rpsHp+cwU+nSnl23Ulm9k8g3hjNfSsOszdHToENSAnlscldWXOokLe3NL/xWx1uFKJAfKDSLxkECHOWEB4QT0GV98OnxuIg3KCmtE4+/rZBEdwUX0DY9jvklKdSQ5euV+Hoch93jEmnf5KRv689QbXZQWmtjd4JIUhICDUN11IXIl9jp//WaADRUgmosLskhIbP4g+FPtb9S+BwSVgcLoJ1Kkpr/V8nq8NNaoSBSd2iKa2z0S/JyKU9Y3hzk28hQoDSOisqUfRLpgAKqnzUvvirh2mEpQqcFv5v9XG+PFCAzenmlRmduPTmzbDvQ4SiQxCShDTwZiR9BGKQt6yCP1Tbatrcb3aamfriJpJC9bwyszdHC2r49kgROpWCqwcm0jU2iDijDqvVisstyQaxToG00CQUThOiWa5bc+vDQB2AE4VMjnxgx7kKPtyWTWld89+PmGBZnmFit1/Qjv8L0a1bN3JycnzWF8XHx/PXv/6Vv/71r7hcrqbGAbiwiM8tt9zy71r27wbtxKcdv3uI1fkIn0xrLhh2uxBOroGiQ0jXrkIIazadRKGSC5lb1qi0RLKPzielVi7MrPUTRYrre+GLDUuDAbfizpgOooJ8ux6DpYjwT8eBUwlXvAFb/un72JCk5gLk+lJY96BshXFfFjyf2rCWPnBwCeTtlOuVTqz2OZUU24d6l6xB0ykqgE925hBv1GG3K+ke3p19Jft8HjcstCscf8fnPsLSoNqzc8idNo6d58p5dt0pdGoFl/WK47I3tmNuUX+yJ7uSme/sZOXtQ/lwezZ2F2hVcnGoyeZEpQuQUx21vt/i68J6ctuoNA/T1UZEBGg4WVxLn0QjAoJMer69oXmA04bm0GLUZUcYOfgtrly0tyn60SM+mONFdQxIDm3WkSk7LV9jUSF/L/wQMkdAPMHaCvQqBQgQF6LzGxXolRji+3peIHRqRYNPmplO0f4d2GODtYQa1Nw/oROf7s7j6PkaNp8uY1rvONYcKfJ5TI/4ECpMNuKNOp/EEmjSAfKANlhOUbWWSGhEYAxuTSCf7TnTtGnhl6d4RK3ghwUPE660ICp1KHQBCIpfZh/R2vOuJdSimiB1EFBIbqWZ6W/u4PZRabw6sxc6tZJgXfO56q0Oai0OXPU2BKWaE6UWAjVqQg0JaBQiapUKURQQXG7UChF7K5uOHecqeOa7k7RGcY2VW5cc4K05ff4t5Cc/P5/+/b090erq6lAoFOj13mnVwsJCnnzySY/CZl+49957f6tl/q7Rnupqx+8a7uo82Ph/vttgawrk7qaWCEmE0Y/5LlYMjJbJQmuEJMOoh3wvIKqbPKfD6h0FMFeBxcfbp1JJuRjG1E/OsPbwec7bdEhXfAyWApk4+GklZuhC2L/Ic9v2V8FaBaMfhenvy6KG5adh3QOQdhEYwr3nEUQY8wh3rimhc3QgIXo1/7gkhU+u60FcUAgL+9zlZfYJEK4Lp39UX98WApEZDTVLLT5vQCSupGE8u16u8/jnjO58sivXg/Q0wmR3sXRPHs9eLkev5gxKQkAgSKeiwGHAfttObH/JgnvOeBwnRXblpD2cOB+prrFdItmXW8VrG88wZ1ASdw8KImz7U95rB4TzB4mjlAC1/K6nVcnChSeKaikz2bAGJeGOzJB1lgbdCtlbsF/6Gvbec5rTlw2wd5jM+lw3D13chahgLVFBWr/qz52iA0gJk1Mobpcdt6kcl+OXieI1zl9rcWJxuOgU5Zv8PHhxFwK1Kp5afZz3t2Wz/WwFJ4vrSAk3EOFDLFEQ4KZhKby+8SzzR/guhu0YFeBTRZvAaKRGCYHW6DAeyeVgb7mae8em8drlnbl1RApKEertLq744CCVYiiKAKP8ovILEaAyMCZxjM99MzvPJFgd7LHtjU3nMNlczaTH7QSHGbPN6fVdrbM5ya20Ulhrx+mWkCQJlUL0kHwA2VPOn6VHY2zoqdXH/y1pr4SEBIqLi71+5s2bx+OPP+5zX8uoT3x8vN+fzz///Ddf7+8R7cSnHb9rCC47Qmty0xLnNnpvMybBzKWyL1YjUkfBNasgPN17vFIJKSNkJ+iAKHmbqISMqXDVJ1BbDMtmw9c3Q8k5uU7n2EpYOV/edvwbuRW+BSKVFlZND+EW8zv02LkQTq+T/bgqsuXOr86T5agCyC32k/4JleeguJVH1Yj75bRC7XnZZuLo13KBdu85sP8TObXWMooVloY0ezln7GGkhgfw7Zx4htWtZfKpB0necg+hpbtIUYfx6ug3SQhMaDpscPRAFo14kVhBJQsVNl0HBa6MGUiXfwQtfJBcKaOovPIbcp2hjOwoa/0kGg3sacNxfHd2JanhBu4Ync7ErtEcyyqnR7QVKyd4aOeT3LXzUdYUbaX47kNw5afYO0+j6JKP+fK0k/PVFlLD5TdZjVLkuiHJ3DOuI18dKMBsd/PJrhyGJmrl9JYfaEv2kximp1tcEEvnDSLRqOPhSZ2JCtJS6g7BfuXnuFLGUKbSsLnLWO4p28I9AQKbJz5J2dTXQWvE2mcex3s/RkRkJMNa2H0M7xDOS1f1IrJBZ0jRoHT84fUDCFU5kMpOIWz8B+JX8xDXP4RUdBhX7YXXUQzvEMFLV/Xi/S1ZPHRxZyZ2i0bRoBUUEajhxSt7MqJDOLVWB3tzPO/B39ac4PkrejA4LaxpW3KYnkU39CevwsSu7EpyK008dWlXr/V/dH1/32KLSg1C/3lIY59sLkpWaqHPtUgjH8AZ3ZOewWbm2z5iyon7WKj6iiMLOnLtgDgKqiw+yfGFIjYwjvv7/YWZnWaiVchrC1QFckuPW5jVeRbO41v56PIEdKrmh31elVl+SSk6BKvvgs+vxnjuKyIDlKhbkAK1UiQ5zECARklBlblhrU70GiWJoXpUDVYdJ87XUlHfhlI0UFRjbfPfw7+KnJwcD9KyYsUKXn755abfBUHA6fRWcy8oKPD7U1joO+r6Z0O7jk8rtOv4/L4glZ9FWDzFbxpKGrIAYfxffR9ckQX2OlmMTx0IIfG+xzXCaYPqfHCY5D/imkD47mE40aJDbEEmfH0L5LdKpSWPgEtfhdAUOTJyap3chdUyUqXSwzUrQWsEUURCArcTQXLD6gVQ0Cr9NOgOiOsFX93sY56vZcIkquT1uhzym6xKh10TSoFFTbKiHPHT6TJRa3nNel5N7fBHMEv1WKrPoRREjDk7Cdz/MVhrkK79BikwFrfDikupo4ogXt9+njv7B1NTXY4LBRvzXLy3t4Jai4Onp/fgqwMF3DehIy9vOMP2s761fUZ2jOCZ6d2afJ4U7hJeOfQ2X2V/6zEuJSiFd8e8zvKNJdSo9JwpqefOMR2IDNLgcLnRqhREBGpQK0SKa62YbC6UokAcZajeHuC3Xss57X1yYyaiVytQKUQe+uow1wxOoaDSRJBezQMrDrN6YVf+vvcJ9pR4drYNiOrPPwY/ieQMBqWGqCBvZWdJkprWo1aKhBnUGDRKpLydcqq2ZVpIEJFmfIA7dTQKvZ+aqlZonN9sc6FVyZoyTreEQaMgukF0sbTWyoy3d5Bf6ZmCCtGrmNk/gTGdIwk1aAjSKYkM1GKyOakw2bE73QTrlDjdktf624TLibumAMFWCwo1kkqPSxuKO2szmhVzPL+3Si3WOd9w+yaRpy7rSvy/KORYaS6nxl6L1WVFq9ARoQkl4FnZtd2ZMIRdfZ5jzjI5NbtlYT8Sc7+WI6UNsAYkkD3sZeK69CXfJIIEiWF68istON2eEeaYhjSi2w0uSWLN4fPc+8Xhn13jKzN7cVmvC/cc+y2hVCqxWq0olRde0dKu49OOdvwOIIUkQP95CD/6TmHQw4cbeiPCUv3v8wWlpjki5LTDxr95kp4HC+HoCk/So1DLhCNni9wOH5oCdUVyS2/r9JzDLLe7T3lVVmKe9g7ED5DHVfsooO41Ez4Y72eeO5rnATlvcesOeGMg6k4Xk3jp6wib3/YiPQDCoU8J7H01wRuehALvaJrw+SzqbthCj1dymrb9bWo3rvg0i7xK76LWv357nOcv78GbP57lhqEpfonPDUOTiQlpftgdKirxIj0A2bXZLD+9jPkT57Mtx8L6oyVEBmlICvNOucQEt0iBOUToMQsOLPI+uVKDMnEAaUY5TbQqs5DEsADe35rF41MyuOz17QxICeNgeaYX6QHYU7KXHSWHmdZxss/PBvJDw2M9gFRxDmHlbd61MJIb4Zs7EW/eBBdIfHzN3xoRgRrmj0jj0ZVHPbZXmx28vTmLab3jSY9s7mgyaJQ/T27agkKJGJrcvEbAXp6L9pv53t9bpxXtqvk8c8UqAn8Dn7JQfTih2VtleYiWSByOMm8rnbscpGNUItVmB7HKOlj/oPckkguVuZRwfTwuRMrr7V6kB+ToTZBWhUalQAXEhlwYaYsM/Pe5vp89e5YuXboQF+ebWDVGfVpDEAQ6dfKdbve3/c+GduLTjt81RKUGqdsMpNwdCGebVUoRRKRLXkTShfioVvkXUV8qd/bE9oKrv5AJzY7XZb2fg4vlMROfheSh8jalRi70PPkdVBfJ6RZ/XS/lZ2SyBAgHPkYK7wghHeWOryWXN7XAow+T19DmPC3rIwSoLYIb1kJNAQpzGYK/Fn1AOPQ5dBzvk/hgq0OsO+9R0BkRqPFJep6bnMS0jioU9aeZME4D+lp2LOzJVZ+c9og63DQ0hfSI5geuy+VixdlVftf3VdYaZna4nJ9O1fPG1X2atHnahEoHo+6H4kOy+nYjlBqYtbypXbrG4uDjHbnMG5HC6kOFFNVYMdldXDkwjM/Ovep/TWeXMTh6CNFBoW0uo7zeRkW9jRqLg/6aWp/kE5D1kWoKILzDz3+2C4QgCEzoGsX2s+V8d7S5u1EU4O/TuhMXcmEPYqfTTUG1hQqTDZvTTVSQlnCDmmD9z9tJCLWFsgaWL1RlE+4uQygplbsHdWFNxO98tZlKk4Nqs10WhdSriG6L6FlrZZV0AMFI8T07qXeaKLOUYVAZCFYHsbpvDMX1ThS5X/r18RIcJoJCBOySgop6//VXdTYnmob02YCUUGKCtRTXWPE1qwBEB2sZkNL2d+VfRVJSEmfP+vEHbAMnT3oXZf8voZ34tON3D8GYhHTJ80imMsjaArpgSB6OWx2AwpcK87+Cymy5nbykxRtz+kWybg5uWbxvzteQux3eGy2nmEAuMp7+nvwX72daoXE35N2dNvn/n4uDB/Jh7gYoPgwV5yBhoJzCanOehjdTQYDLXoejX8p1QJKEMO+nttfhtDYRMF9QC050ahG7RT6HryLNdTem06ngS4R3XvS4DrHT3mXjjaksPQ1mh5tRHSNQKQSMhhbnkyRsbv8PGbvLjgTcMDSFlDB9k9jczyIoDmYvk1vv8/fIZCeun/zfhs/rliTsLrnGRKNUYG/QrVGKYHf7v2Z2tx0X/tvWQVb3vXXJfo6dlx3as++Ianu9/wb374hALX+f1p07x3RgV1YFBo2CgSlhRAZemMmoxe7kQF4VC5ZmUmGSr4dKIXDrqHRmD0hom4xAmy7pAELteVh+jdw0cMUicMSQ4wjmjqUHOFrY7Gw/LD2Mf0zvTmKoj+JqoKLeSljD9Su8ZxvvHH6LVedW4W6INMUaYnlx1Isk6lJwO6y0JTGoVoi4JdEniWlEyxSQQhR4YkoGty45ICuvt/x8Df99YkpGUx3Wvws5OTlER0f73f/GG28wY8YMr+3x8f7T/q+99hrTpk37Tdb3e0U78WnHHwKCMRmMyRDf3Mb5m2ul1pXA0qtkj6qWOPujXCM0eRgMvRtMpbD1ec8xpnJZ5Xn+VjldJiqbCU5LBEY3PYClrtNkPR5DODzbUGj8cJlMZEwVYKv8mXka/vl2GC93YjU6v4NMoDpOlFNzvtBtBux8zfc+hZpyZTSX9XKyuMH40uZ0E2ZQNz0IR3WKoKPtCMKWZ72vw+ezUM3dSP9QkbvXlWOxO3G6JC7uHkPXOLnjRqFUcmnyJL7LWe9zCeMSRhGkCmDSazv59s5hxP4Sf6uAKPknYYDP3SE6FdN6x3Ou1ITRoG4oWhX46UQ9Y1MncbziuM/jxideTLTBf1qqvM7GzYv3cbK4OdohaUMQDBFg8qGdpFB5FuD/hpDtMdRkxP7yOsXCags3LtrnIWTocEm8+uMZ0iMCuLRX2/dCCEmUP1sjGW4JQzg4GyKB1XmwZAZcv4b881UepAdg29kK/vbtCZ6e3p2wVqmx89UWHlqVzUvpM9Bcs5JVJ5fw9VlPtfbzpvPM3zCfJZOWYIsYgI/+RxlKLQqFEg0ierUSs93HvzcgQOPZgTaxWwxvzenDU6uPU1TTLHsQ/R/Q8QFIT0/3Wbz8c/i91/D8J9De1dWOdjSivtib9DTi5GrI2yGnv7a95HuM0yZ3eAXGwfD7vPcLAkx8Rg7PG1Og0yWUC2FIE5+Bh0rlwum6fKjNA0MYZG+F4T50NQRBTrXteU/+vdvlsP9jzzEbnoLhd8t6K60gJQzEHNIRR995Pj9GzdCHeXlHFTP6xPPq5FgO39WFqYk29t3egT0Le9A5OoDHhwchHvsW7jkFd+yXf+47DTG95etwcjWxOgcVJjsf7chhQrdo3tmShcXR/Ie6U3A6vcJ7eJ0/SB3ETd1u4tHVOVSa7JwsrvUa869AEAQmdYvmp1Ml3DkmndwKE3eO6cBnu/MZGX8R8QHeb8PxAfGMSxrr0RbcGqV1Ng/SA/B1log06Z8+5RWk4X/Brf4vN1DUl+KuKcRUVUpprRWTzckPx0o8SE9LvPHTWQp9CRq2QJVoxDryMd87Rz8Ke99v/t1SBaUn6Bjs9ulo/8OJkiay3RKnS+rYfKacwqhRlFkr+PSEb+XvGlsNJypOoIzoiLvXtT5GCBAgv0QoFSJxIVqfvlshejUqhff2id1i2PbAGJbOG8QrM3uxdN4gtj0w5t9Oetrxr6E94tOOdjSiLcVnt4umILYvv6FGlJ2QPbT6XgcxPWW/p+o8WQ9oxF9kDZ7AWJjzFZIxBVOVmbD4/pCzEWHT03LkxpgkR5Y6ToDyU3DVEtj+Sot57kNS6RHydkF0d7mg2taKHFgqYePf4YbvkHa/i3BmPagNuPtej7PjZL7PUxMf0IeEK9cQsfcFFGXHwJhEad97+LokkkGJSrpWrKfnwX/CT3kQ1RUG3UaEtZbvZg3GFRgNYx+RNZZONNhYZEyFGe/JIoBHv0CTPI7IQA3HztfKbhtl9VjtbhrlVCJCknhh+DNsyP2BpWe/xuK0MCZuONdkXEMgkXyZKdchnCs1Mca3TM6vRmyIjtdm9WHr6TLijDrGZ0TRJSaItzcU8dq4d/k2axXf5cjikJOSpzC9wzQSfyatWu6jPuTer04w4NZexN/wHWz+J0KJfJ2loXchRXdHEew/TfFvhalcNrjd/AxiVS6GyAzMAx9ioy2JvslR9EoI8WlVkVNhajI69YczlU5UYZPpMbMruu3PymnHyAzofxOcXOOtvVV2Co0hFb1GQa3FM4IhSVBv9Y5qNPqu3fhVEUtvT6LW7p8cn6s5x0WJ46gd+hAh6aPkFxdTGaSNkyOnqua6J41KQXpkAKV1Nkw2JwpRICJQQ4BG2dSN2BoKUfCQCmjH7x/txKcd7WhEW7L5ChWEd5TTVOGd5FSSL8T0ap4rKBZie8v1NEqd/F9DBHS/HNQGRCBZ70Q69iPCt/c0z1F+BlbdBkMWQP/58OUNsn2FNljuGFt5G1y1BPfVK5BEJQpBkAtFLa1c0k+thaxNSDdvxjX0Lhwu2Fqq5Z+LT5NdbuLtOX2Z/6WFa3o/QqcOIvl1Eou+q+aSTi6udnyFcsPrzXOdPwhfzZM9x9Y/imLC3+DbeyF/Z/OYg5/A2R/g+jUQ0xMrKoprqggzqJEkiYyYIPQaz4hJZFACV3a8mvHxo3EjEaQO5pbPTrLpXLOIYuc21Ir/FcSG6Li8bzyVJrmeKDJQTUZ0IDaXm2s638DUtBkIAkTpw9Cqf76oNyrId6fS8LdOMKJjOB9Ofx2F04yk0CAaE3/7ovwLha0edr0JW19o3laUScTKqxg2/hXu/akz94zryK1L9ns4wAOkRwag8eXU3gKRgVrGv7yHnvHBPDLmbfpEuFEe/lyWbDD70LWJ6oYFNSabt7aPQhQI0nk/pjpGyYXyZfU2lKKKMG0YFVbf3YSdjJ3IKqtHqwompNsMSBkFbjtIGigskQU/GyAKAlqVgrgQHW5JtiPxR3ja8cdF+x1tRzsaERAtExVf6HWNTGRCEmDkA77HqAOg0yTPbUExckQmKBpCk+X6H3WLYk1TKcKG//M93643wGmSO68+nwWLLoYvb4LAaITAaMTwdBShybKysK/UGoDOiKgJQBmWQiERLFx2iLOl9bjcEjkVJuKNOl7ZVsJt3xTx9E/FFNVYmdVVS8D+N33Pt+Wf0GsWbH4OLvZhvVFXDEe+hAHzOVktUmGyc/OIVL7JLGTu8FQ0PlysBZWaVcckBjx7gs5/3cWmc9VN+6KDtKRHBXgd81tBoRCJCNISGaQlNEBLXKie1IgAwgIMJBtjSAqJuSDSAxAeqKGPH3uK6CAtdl0UQngHRGPib/gJfgVMZbD9ZZ+7jFuf5LZ+Bj7dncu03t4RrrvGdiTmZ+qtwgPV9E0K4VBBDVcuPsk/dlqRdCG+SU9gNFJYGqeqRZ8F9FN6xBDpo/U9LTKgSU05UhvJ9d2u97mWCF0EHY0deHNTNupGwmYIkzWw1P5b0hWigEohtpOePyna72o72tGIgAi48hNZxbkRogJ6zYFRDzT/oYzvJ9fYaFpEIkJTZVHBkOQLOpW7Khep6BCSpaq5hd1rkEtOE1y9rmmTlDoaJr8kiyS2XGOPq2Ty09L9OqYnXPdNUyQrLljD4psGNDlWv/bjGe4b34khLcL0wToVYfbzvi1CQH54KTUI536U28d94dRa3PUlPLShnPkjUumbZGRkp0iSwnw/aBSiwNRecdwxOs0jmtA1NojP5g38We2aX4OiagvHCmt44ftTPLX6GNvOlJFX8TNddD+DMIOG12f3YXgLRWdRgBl94rhvfKcL6qj6j6A6ryF16wOWKsLFeraeKWdQi+9FgEbJk1My6BnvXTPWGqEGDa/Nar4OH+7IoyB2ItLQuz2/n9Hd4eovEQwRpCUnMSzd87rdODSZZ8eFoj7zLfY1D2Ld/RH28iycDjsxwTo+mzeQrrFBZDzxE+MSx3JTt5tQi80ktaOxI2+PexujKobxXaOo9lEr1I7/TbQrN7dCu3JzOzBXgblc1lnRBoMhEjStog4Oi2yqaa6Q01+6ULk25wLgrspFyPwUYfOzcPMmeHeU/8HXr5EjSdW58nny98jF0UExMHu5Z1eQwyKbnFqqZFKiD/fp5ZVTXk+NxYnT7SbUoEajVFBrdVBvdRKiU5FiPozi44v9r2nmp7D2L7IFyBveZokkD8M99V2y7EGIgkBWWT29EkMID2hbQ8bqcFFWZ6PabEerUhBqUHt18/wWOF9t5ov9Bbz0g6cv2IBkIy9c2YuE0H9NUbjabKfCZMdkcxKkUxEeoPbqCPqvIn+3LIzpB9lX/siVX1XzxS2DqbU6cLjchBo0xARp0aovvJeyxmKnot5Ovc1JkFZFrN6J2lwi//tSNSij68JkeQqgtNZKldlOndVJmEFNvLsQ1SeTZV2tRqh02Gd/hSJhAAqlkop6G2V18k+XeIFaey019hq0Ci0BqgASzPXU1VZw7Q9unpnR28PktS0F4Hb8ftGu3NyOdvw7oDf+vJquSgdh6fLPL4RQXyqTHgBNsFw7VH7ae6DOKLdlvznQ+w29/AxsegYmPdf04EClk1v+jcltnj853Dt1FEuLqEp1okz4WhqSNiKqK1RmI/Wag+BrzYDU70aOm/RMfm1L07ZHL+nCjUNTENvQNdGqFCSE6v9l4vFzKK2zeZEegD05VazMLOSW4amoVL9eLCFErybkAoT+/msIivNdEwYQ0YlDVUquHpRIglGHQuFbQ+dCEKxTE6xrdR30/iNGkQ0pRwBrTSmqZfM9SQ+Aw4J6+Wxsc7egCEvEqFezMvM8Wkc1wzfdQ/j5A17zBuqM/N+UtYQZfsf35DfGokWL2LBhA0uWLLngYyRJ4pVXXuGuu+76TdaQnJzMhg0bSE//ZX8jhw0bxt/+9jdGjRr1m6zDF9pTXe1ox38a2jBYeBhu2Q4BcTDtbdC0ejtRqGHGB6CN8J+WOPaVb32YC4TD5aKwyszRwhpOFtVyuqSO7LJ6TNpIuGKxt3O2NgQuegIpewv0mo108BOvOaVOF0NsXya/tgOAGb2iOfJIH8Z3c3Gm/DAFVeew/gtrboTbLVFUY+FkUS1nS+raVNxtjS/2Ffjd99nuPApbaLJYHS7yKs0cP19LboWJ4hoLp4vrOFVcR2mt1e88v2sERGOdtshbwFIbTNHYN1h12s703nEofkV9i8d9Kb2A+1KVg3Q+E6nwII7ybAqqzJTUWlFYKjzVt1sivh8qZz22gkM4y06jd9UxJEYCH6QHAEsVnXU1hAf+9tHD/wby8/OJjo72+ImMjCQg4MJr4ZKTkyku9uxidblc3H333Rd0/ODBg5vMUEVRJC4urun3n8O2bdsYNmwY8fHxJCYm8thjj/0qPaJ/Be0Rn3a04z8It9OKiANW3g65MjnggQKYtxHOfA+F+5HCOyFkXAaaUNjrp8gYGoxJfYjEXQCqTDZW7C/klR/PUG+T/+gMTg3j1lFp/HC8mKk9+sA1WwjPW4tQekIu+o7rCy470sRnWXLMzTUTnkYadBsc/hwQZd+0kEQuXiyLHt4wOJFbRgfz1L5/8kP+RtySG7WoZmaHGdyQcQ3hQQltrNA/6qwOtpwu54lvjlLe4JCdERPES1f1pGNUoE9/okY4nW6qzP5rPWosjiaBt7I6K29vPseSXXnYnG5EAcZ3jWZGn3gWLD1IRKCG56/oQa+EENQ+irZ/t1Ao2WxJpeNVG4kp/B5NxXHMMQOpjR/J4bpALunxc/rUvlFvdbD1bDmPrzxGWQPh6RITyItX9qJTVKBntM9ukk15Vy9EaJCHUBmTibnkJVaVxTI50c89Gv0ISG7EDyegaZBwuDJpGO4UP15+DVA6/Vho/AGRkJDgRVoOHDjAnDlzLngOk8lEfX39r17Dzp3N3ZwBAQEcO3aMkJCQnz3uyJEjzJo1i+XLlzN48GCqqqq48cYbGTduHGlpaQC/yoLjl6Kd+LSjHf9BiDWF8OkVcs1OI55teEt6pBxpgCiLl7w1AOxmmP4uNKbFWiOis1z/8wvhdkt8d7SYv6894bF9Z1YF52ssfHR9fxYsP8LunEr6Jg6ha+xEpifE4XJLnK2sZ+feSi7qEsnAt49z3ZBEbp78OqIA2WUmxv5za9N8C0YF85cdT7C7pNl13u62s/jUUtySm4Xdb0YbEPmL13/sfC23f+b5dn+8qJYr3tnJ2gXD23T9VipFxnaJYu0R35pNg9PCCNAqsTicvLnpHB9tz2na55Zg3dFiaswObhudxgvfn+bq93fz3cLhpEf+e1ru/12IDg1izJtH6JM4mLiQMRgrVBRn1XG4oAC9WsHnN//y+3KiuI5bl3jelxNFdVz1zk7WLBjumcKsyoFPZ3iqO1floPjsCi6d+xOnapR0bZ1uje8np3O/f9TjHIrcbShMxf7Ts4KAEJr2iz/PHwmbN29myJAhFzQ2Ozub8vJytm3b9ovTUK1hs9mwWCxUVlZeEPF58803ueeeexg8eDAARqORjz76iMjISB566CFEUWT37t3/0pouBO2prna049+J2vOy91ZjrUL5qWbS80i5rNZ8x354sABenQLV2UimUqTLF8l+XaIaEgZ5zysIMP5vcnv9BcJsd1Jeb+N8jYUXf/Bdn5Nfaabe5mRXdiWSBPtyq/h4Zy5XvLeDGL2bK1KcvDguhIs7BrHo+n48t/4MGU+sJ6/Sglql4N7xHYkN1hIfBBWOeg/S0xLLzn5FueOXv4VXmew8u863wWKtxclPJ0t97pMkiYoG89C+SUaSfXSYqRUid4/tSESglrI6O5/uyvM5186sCjJighAE2crhw205TX5ffxTEGXUMSQ1nf24V3xw6z8c7cll/rISiGitPTOlKxC9MC1Wbfd+X3gnBvDcjiQBrkdwsADKh3/WWb0sLtxPFrjcod+lxjH7cc1+f62DnG74XsO8D3KMf9b2vz/U+i/x/M7hdssr6kRXyf/2lpv9NkCSJRYsWXXDE55FHHmHq1Kk8+eSTVFV513mFh4dfUMoKICsrC7fbzenTvv+etEZ+fn5TZKcRISEhhISE0LNnT8aOHUtw8M93Dv6raI/4tKMd/w5U5cpvtTtebVBc7g7T3oXCgzDlPUjpD4c/gwOL5G6sjhPhutcRAhIQPpsKpnKk1FEIkks2IN2/CA4slhWaY3rBuKfkduALgMXhIqfcxFubznHsfC1PTMloShG1RoBWSV6FpyXB36Z25ep0Jxx8DeH0OlAbEPrdQJekYWTf342t5Xre35bNrqxKwgPUPDipMymhWorr/dRoAA63gzr7Lyc+VqeLE0X+VXp3ZVUwZ1CSR7qrqMbC+mMlLN+bD8C1Q5JYdMMA3tx0llWZ57E53QxODePBSZ1JaohK1FkcTc70vlBeb8egVlJvc5KZX029zUGo8o9TQxIeoOGlq3qyZFcui3bkUGt10jU2iMcmZ9A99pc/eCwOFyfOe96XjfM6kGQ7jWLP01BfipQ4BGHQrXIHpD8BUEAoPkxEFwuZQaPpdfliVD89Jb886MNkAU9fOLcRV//5uFqOD4jEPexexG4zfFq3/CY4/g2se0B+wWlEUKwsd5Fx6b/nnK2waNEigoODL6gY+LHHHuP8+fN8//33fPDBB4wePZqvv/6alJSUpjHl5eUXfO4VK1YQHh7O0qVLmThx4s+Oz8jIYO/evVx6afO1yc7OxmKx4HQ6KS4uxuH4den7X4J24tOOdvzWqCmCE6vh+0eat5Wfhoufh7A0OWT/zQLIae56kk1GP5Pb14MyIPd9uWsq81Ok69fC4AXQ7yYE3KDUyqKFFwBJktiTVcENi/bSqA9XY3GgVysw273fTM02F5EtFIiTQrVcnW5H+Ohiz0Lqwv0IqaOQLnmJYeFmrt0tR0fOlcHu7EoemZjM0C7+37IFBPSqX969pRRF4kJ0nCvzrbnTusanqMbCNR/s4Wxpcz3Dg18eISMmkA+u78eto9KRJAmdWuGhF6TXKBEEOevoC0a9CotDvn4JoTq0f6QanwZEBmlZcFEHZg1MxO2W0KoVhBl+HXlTKURiQ3ScabjOy2ankHLyHYQWvlxC+Wk4skz+jicNg6JDPueSQhIpNIuYRC39e1+GNW4AotuGQnKh0AR527M0oKqmlgdPJPP8laswm82cq7Bx1qxnhhhMyK/6VD+D49/A8muhtad7bZG8/crF/3byc/jwYR5++GE2btzY5ri6ujqmTp1KUFAQa9asQa1Wc+uttxIXF8fYsWPZsmULUVFRv+jcZrOZ9957j2XLlnHVVVc1tZi3hXvvvZdBgwYRHx/PhAkTyMvL46677iIjI4Orrrrq/9u77/CmyvaB49+T2aZN96alE8relKmAoILIEEEFcbyKqKgsB76OH4q+KA5A8FUUESfi5EUEFQGLskeZskdpS+neTZs2yfn9caAlJCkFWsp4PtfFdcFZeXKacu48474BOHDAeY9uXRJDXYJQ1yqKYbWTyZZvR0OTwUoQdHbQc0ZJJmz8AG55/qxrlSL//iJmNEj+Mcry+VoGPQAZReU8+8Nuzk6KuzU5j6dvboqHzvF7T6S/gRAvN2IDlGXMy8a0hHWzna8eO5aIlH8MSaWlY2P7b9T/+S2ZAL0PkV7Ocxv1atQTP82FL5UONOoZf1MTu20Bnjq6xvjRupG3Q7bh1fuz7IKeM/adKmbNgWyiAzyICfR0SJLo76GjT7zzeS4xAR5kFpmrMg0/1iv2yklOeIE0ahWh3u408jVcdNADSg/S+L5NCDTqiQn0oIO/xS7oqVJZBr89D50ecnktW9cneffvrKpkiW6+oej8o5C9IzB3Guv8JL2RTM/mrDmQzZgfU/jhqMQDP6Ty2oqDZBXVfsVfrdmsSk/PuUEPVG/77fl6HfbaunUrt912G/PmzaN58+Y1Hms0Gpk9ezZLlizBw6P6927w4MEcOnSIRo0aoVKpGDhwYK1ff/LkyQwcOJCbbrqJl19+mXvvvfe8q7OCg4PZtGkThw4dYsyYMcyePZtXX32VzZs3s27dOtatW0fr1rXryb4UV+dvqyBcyfKOgtXFqhSdBnn3t67rNO3/H3QbZ7dJdWIdenMO7N+gzBUKbafU/Dpd+iCzsIzCcgu7TheVbBfhg7e7BoNOQ3axmaxi5T/+EZ1CeKi3L2klx8kz57LoyeaYTAbGfnaEHpGePNvDh0bmo9iOL+XX0R34dr8Zo60Yaf8y1+919/cwcCbf9MggrW88C3eV8mWSUjNJloJ4v/csxv05gdTi1KpT2ga04cWE59FZdJiPrMWWn4o6sCmydwR63/NXte7ZJICxN8aw5kAmnw2PxN+aBTmHkHwjsdhySc6RMbppUUsS321LdXmd77amMrB1qNOcO17uWl4f2oonFyWRlFJQtT3K38Arg1vy7A+70KlVTB3cgrjA+iup0dAKyyrJKTaz+2QBBp2GFqFeBBr1uJ2T58hcaaV1I2/+PaAZBp0GzYnvXV80dYuyGnHA20qv6JnfFbUWW99X2V4awH/uCHaYpK7R6rB1HkNl3lG0+5dU7/AIIOP2L3nhD2W+SlJKAY/cWJ3Y8+/D2TSt63pvJzbYD285kJUEpyc2QPQNdfrSJpOJWbNmMXfuXBYuXMiAAQPOfxJUBRSpqanMmTOH33//vWpll5eXF4MGDWLhwoW1utb06dPZvHkz69evB2D8+PEkJSUxcOBAvv322xonOgcHBzN9+nR0Ol2NKzDrkwh8BKGuSTV0pMoyUk37ne2TJKScw/DtWZMXo3vB4DmkE8zibam8v+ZwVa+OSoJJNzelf8tg0guUXDP3JIQypGsF/1o5itLK6mGiLiFdWDH5FUKSt6L9dqhdwHZvhwdBNbHm9yNJSOYCdEseJkZS8UzPlwi5sRdv/5XJTzvS6dssiM/7fkhmWQ7ZpkzCjBEE6LzxsmjRLuyp1PY6I6g5lXcvRusf5fr1AH9PPU/dFMczCe7ovr0bss/qGjeGohn5E29vlHnohhi0NeSiUUlSjf/xhvm4M//+TmQVm0nLLyPQqMfHXcupojLeGt6GmABPAox63C8h2eGVLKfEzLsrD/LNlurgUadWMfPuttzULAjD6R7DsgoLaw9lM/6bnVRYbfRrHsQtTVQ1F2GVVEqZldg+yLlHQLZh829Cts2LILWRcB93NE6Koep8QjEPeJfi7s9SeuoQNncfUq3+TP0tjwOZ1Z9r6axXrylp5kUryazb4y7AqlWrWL9+PZs2bSIqKuqCzs3NzaVLly5MmDCBxMRE/Pz8AMjIyGDWrFl0796df/75B10N9emOHj3KL7/8wooVKzAYqoPTBQsW8MILL9RqhVd8fLzL5IYjR44kIuLiUl3UlhjqEoS65hetzMNxxlwK7Ua5PrfVcCWN/9mie0PqJvttx9fCpg/RU86c1YfthrJsMry78hDZJRVIyIR5u/GvXj5M+HO8XdADsDljM1/t/xJLcYpDL5Uq6TOklI3I3Z503d62I+H306tpZBvef09jWEQJPgYtzUK86P/eOjq/sZeZy8spLWrD/y0pxq1SQvvFLfZBD0DWftTLJ1Je7LzK9tm0FYVofnnKPugBKD6F23f38FA7dxauO87Efk2cXwC4t2sk3u41l5Lw99TTPNSLm1sE0y7Ch6gAD7rFBNCraRARfoZrNugBWHsw2y7oAaiw2njqmx2kF5RVbUsvLGfc10lVk8ETD2Zhjerl+sJRPZVkmO7eENAEKX4AUrOBqAObEhIcQlSAh9Og54xCPNhZHsSA3zy46bty7l6cahf0dI32Y3daQdW/b2gSeGFvvDY8azkfprbHXYDBgwezYsWKCw56QMm/4+fnx5QpU6qCHoCQkBBmzJhBfn4++/fvr+EKEBsby4YNGwgNte+dVavVzJgxg5iYGBdn1s4TTzzhsPKrronARxDqmt4LBjjJvaPWQdYe8ItVVnGdy6cxJIyFHyZXb3PzgW5PwKGVcMdH8OjfMHYt3Pc/yNqHj8V1FuRP1x0nwFPP2yPasi9vN2WWMqfHLTmyhKz4ftB5jFKk9a4voe//gXcErJsJLe9Qyhyc0fkZCp87RvKknRzxDSP1lleUpfmnBe2ex1uDY1m9P7MqIFtzuIAnF+9ie3IBUkmGstLNCdWxP1GZzh/4qMryUDmbJwVQcAI/Wz7fb0+jkY87T/SOQau2/9bfNtzbriimYC+n2Mx//3SeSE6W4Yft1dmv/7fjpF3gbbHBulNg6zHZ8WS9F9z6BhgvPiDIKjbz294MhnUIp7zSfuWdUa/h37c159vTK/jG9Y4lqD4yNkd2P13811VvkqT8zkTWLrfO5dKlSxeys7OZN28eJlP16s2ioiKmT5+O0WgkPj6+AVt4eYihLkGoa8YQiO0LD6+CTR8oeXtC2yJ3HoPkFghr3oX+byi9JdsXQqUJmg1CbjYQyRCGrC1DCu+ELbYvUtP+SBv/CyM+gz9fh6XjlAmTYe3hlteQVK57LDKKylGpJP45WYjF2/V8hDJLGZUqrbJU+IcHq6/f/w3YtZi0Egh/YBnsX4YU0580Tw/m75zD8mPLMVvNRHpFMrHDRFpP/ofgmS1Rl6Tjq5P5arNjcGPQqbGV1LxcVq5wvmLL/hhTzftNeejUEgcyiikst7DsqZ5MX7Gf8koboxIa0zXGnxBvUZjSlUqbjcwaSnIk55qw2WQkCY7nOP68/vX9CZY+cDetRt+IauvHSKVZENkD2t8HvjWv/DmfwrJKvt6cwrjescwd2Z6lO0+SW1JB+8Y+9I4PwlRhoX1jXx65IZqmIUa8ztOrd1FUamXJ+nf3owQ/Z09yPh0M9X9TOe4KEhgYyPr165k5cyadOnXCYrEgSRJarZb+/fuzYcOGy1awNSEhAZXKed/LY489xuuvv15vry0CH0GoDz4Ryh+/d05XefeBwhSYdXroZdc8eD4TGiUoEz0NwVBZCBV52IZ8QKVNQutuRHU8EXpOUrLcnj2ZMn0HfDEU6eE/aOTrxsl8x4dU+whfThaY+GZLCtNGtnHZ1DCPMNwqTHBguf31v38Q+d4f+e1QCf9Zc5K/Jv0LlXsRT699hn15+6oOPVF0gkmJk5jVexbBL+Zg++Mllu53vuS42GxB7VfDg0/rjuTu43r/aZK7tzKcaHHxcPYOR6tOp8BUwVebUth7sogPR3fAU6/B6HYFVUq/QnnoNLSN8GHDUee9b72aBFbNnenVNJBfdjvm1xny+VEGtw3l7cEfoLeZlfw9mksvFHomYP0g8Sg+Bi39W4YQE+jJwYxiFm3Zxm8TbmTOyHZVc5DqTYvBypJ1p3l83rxseXycGTlyJMOHD3e6Ly4ujg8+qKEUTh3ZsWOHy2SEycnJ9f76NRGBjyDUB3MJqDTg4a/8ASAS+blkpD3fw6/PwpvV3f1yj0nIPSYiGYNRA1XfE0Naw8EVzleQ2Czw53TeHvA6oxYdtdvlplXxQLdI7p2/icySCnw0LYj2iuZ40XGHyzzRbhzh651kxLVZYPMHVPr9G4BKG+SWnrILes42a/ss4n2aEJ7wCG2S9bDV8WEYF+hJpZs/2rj+aI785rC/ovM4pNoMg3gGU9H5MXQbZzvssjQZwL4iPWN7xVbNUdmZWsCpwnI6NPY9/7XriNUmY7HZ0Ndxjp9ScyU6jbrGiduXykOn5q3hbfh+Wyqn8kuQZfj7aAEZReX4eei4oWn1MGH32AACPHXklFQQ7KXnzTtb0zFYS5lVosiiQe9RuxVVZosVjUqF2slkZJtNptJmQ6dWEeKp5tXbm/LhXylkFJWzeGv1PKRHb4whxNvNYdXZ2SqtNiosVurkp9JiMDQbqKzeKslU5vREdm/wnh69Xo9e37AJNX19L9/v2oUSgY8g1KW8Y8p/gvt/Vupo9RgPem/I3Iu0azEyILcbhfTUDvhhDOQdRu76OLS7F5XBx/F6Bn846jo5mZS6mc63amkWYuRAhpIJuUWoF1MGNOOvQ9l89nAX9qcXMuHro8x/aA6zk97lr5N/YZNt+Lv582S7J+iqC4Q93zm//okNtGqqPGAjAz34fa/zEhQAKcUplNvM5Ooa0SNW4vUhrZi9+hA5JRWoJOjXPJgXbmuOl58HFbfPpHJtKNo9i8BiBr0XFV2eRO7wADr9+RMb6twMmLs+ToXGHd2W98FcDBo9lW3upaTLJPYesZBRVMaek9W1m7Yn51+WwKeorJLUfBNfbUrhVGEZ/ZoH0yc+iEa+7uc/uQbJOaWsOZDFX4ezCfTUM6pLY8J93Qk01t3QRFFZJekFZfyQlMaAKBVjwk7gmfslMpB1692k6uPwC4qwW2reyNed7x/tRm5JGR29iiF1DdKK/2HUGgjq+ADkRYFflMvXPJlvIvFQNn/syyTEy43RXSNp7GfAy11LeaWVk/ll/JiURt8IiOcEHnu+5H7gjkGj2FYeweNLT6JVqxhzQzSju0S6DHqyi8tJLyjn680pZBWX07eJL+186qDUiEpd50vWhfolAh9BqCt5x5QCpLlnTQq96WVY+gScUPJdSAAHfkGO7gUjPgGbFZsxFLXeRS4YtRY8aigYafBHq9HwyQOdKDBVcrKgjOM5pby4ZA9p+WWoVRIfjOrAoocT0GrUTOs+jXxzPmarGU+1G6GH/0RzyHnQo1w/gIJy5eFgs9kINLheIaNT6dBIWt5eeZRJNzfh7k7hdI/zp7TCiptGhY9BW/WQ1vk0ouLW/1DRY4IyX0fnido7FI229kMheu8QLDdOwtz2HiXRo9bAKauRnWnlLN+TwdZk+zpEwV71/w24xGzhpx1pvPJzda9Y4kElUPnusa5EB1xczp/DmcWMmr+5quo5wPfb03h+QDNGdAzH3/PS31uZ2cLm47k89lUSnwyLoPXWF9Al/wkon9uQA8sIju6FNOxjwP59eOg1RKlL4Jt7kLIPVp3DPz8pgf6NzymrHc+RnFPKiI82kl1c/b4Wb03l5dubM6JjONtPFDDmi23MvyOC1lv/D11y9ZcArwPL6B3Vi3Xj5rIpW0uYjxteBuePtLwSMz/vSue1X6pXLB1Oz+O1vkE0rrRymaa1CFcIsapLEOqCuQQ2f2Qf9Dy4Eo7/VRX0nE06vhYpbRtSQBPXQc8ZHe53va/zI6B2w6CVeWJREo9+uZ03fz1AWr6ygstqk3nm+12YKm34e+rxdfclxieG5jo/Ir4Yhub356HprS4vn9/uURbsVCavaotO0DGoI1oXE6oHRg/ET+dFRmEZqXllaLVqYgI9ad3ImybBRoeeCZ2bB7qAaPRhLdEHRF5Q0HOGRqtHHxCFPqwlsncEH61PZ9L3uxyCHp1aRbvL0NuTXWzm1WWOQ4HZJWb+s3w/xeUXXocot8TM9BX77YKeM2b8doDcUhfJMi+AyWwhJb+Myd/uorGfgVaWf6qCnrNJx9dC8jqH7Xq5HLYtrAp67M7ZuUhJ5neOknKL8r6KHd/X68v3k1NSwYTFO4jwdaeVbZ9d0HOGKnktnFjHe6sPc/+CLS6zNOeWVvD6csdl2lab0hNUWUNdNuHaIwIfQagLJZmw+1tlEvPEvTBuM3gHw44vXJ+z/TMocHwgOPAKU5YAn5tsr8mtENUDPr8NX3MGJ3Kdr3QqNlvIKDxnErApV+mhspjh4K9Kz9Q51zfH3cYe4w3sSiuka7QPFKQSoPHgnV7voFHZf7Nu5teMR9o8gve275h/uy8/JaVRH2w2mcyiMg5nFrMtOY+MzFNUZh6kMnkT6tyDvNQniK4xfnbn6NQqPnmgEyEX2eOTV1LBocxitp/I42h2CQUm14HGxqO5Lut7rT6QRb7pwgIfm02mwFTJ2kPO0xbIMqw7XPuikq7sSivgUGYxxWYLQ5t7ErjvM9cHb/kIyqoDy+LySoyV2Ui7F7s8Rd7xFVjtyzfkmypYtd95gr/bWoVQYbHxxrA2fHJPU4JqaE/gPwu5s7kHpRXWqoD/XDX9XEorrFXlR4TrgxjqEoS6YlXB439C4puwaxE8uV0JLFyxlINci1o+nkHQfBBE3wjHEqGiFGJuhMpy+PRW5TrpO/jmkV6MnL/F6SUcKo2f/bpJn0Obu2DUd0rhyAoTcvwAsqQQdh0sZ97oDnRo5A1Za/F6pymdnj3Mj4N+ZHvmdrJN2XQM7kiYZygRRQWw5nk0lU/TI+Z+bDaby+WqF6PSauNwRjE70gqYufIQn94Zjv/fU9Ee/b3qGE1YB74ctpC9pmZsOpZLiLcbHSP9CPHSo7uIScZpeSbGLUpid1r1XKF+zYN4fWhrp8vhyytd/zxlGWyunr5OVFps7EwroKC0kpqey2WVNddHOp+c071RD/VUhqL0asBa0+fWDHL158lqk5VhrfN91iX7N2GTZafv64k+cWhUEkP+ux6zxcaUXsHE1XhtM2dKpVVYnPfclLvYLlyfRI+PINQFN294aiMkfQE7v1IeDCoNNBvk+pwWQ8EY5nxf3nHYvxyWPAYrnlV6lCpKIWWT0lPzzSj4cmj1cu6DK+gU6ef0Ulq1RMQ5dY8seh9l4vQZu7+DRXfB3p8g+W+OlXsy7LNDrD6QRUygB0G+BiS/GFBp8ErdRsw39zEiegjjWo+li9qfiJmt4ZOeAKiOrOKGMOo06AE4VVDGycIyXlyyl7vb+tJi9wy7oAdAlZ6E9ofRNPcy83jvOO5oH05jP8NFBT05xWbGfrndLugBWLU/i+kr9lNiduy96Rbr77DtjDbh3ni51f67ZnphGaM/2cyBzCLahDtfFgyXnpm4xGxhb3oR3u5atGqJlcfM5Mfe4fqE1iPArXrY0NtdS7nGG7mGIVNaDlN+H85idNNWFSI9o7GfgcZ+Bt5bfRjz6WDlj+Pl5Me5bk9B3FBWHqtAo5KI8nc+Mb6mn4teo0bVQDWjhIYhAh9BqAseAWAugq3zq7e91xpaD3deTd0nUlkGq3HyIMxPhu8fgG9Hwa5vYMvH8ElfJc/ODU9T2OF+Uh75g9SntlIxaA6o3SCqF+rSDA4/05xjU1px5Ol4ZtzRElC+QfucM+kzBz9K+52TXVqWIWsfhVG38vG2QrJLzOxMLeSDP4+SW2LGpvdE7jFR6Q1w84bpgfC6P3zY0f46bj7o9Je2gsmZlDwTZlsRM0dGcVdnH7SnXKwwy9iDqjTrkl8vq8TMvlPO8xGdzC9FU5qJLfcYlrzqRI0hXm4MbecYzGrVEtOGtMLvAiqg/7onA7PFxqLNKTzZJ84h+zTAgNYhNWYmLq4oJqM0g2xTNrKL3iaNSkKrlli6M515ozvw7d2N8Gk7UPmMnss7AloOhTNBbXkRUlE6ss0CPScrQ73nkMPaQ0grh+1+HjpeG9rK7n3d2TGcLzYm2x1XWGZVhnWdtiecU+ED2HQ8n/F941xO8g7w1DG4rWMBXAkI9NKjq6FExtXos88+Y/To0ec/8CyyLDN79uwLfq3ExER69uzpsH3q1KlMmzbtgq93OYihLkGoK5ZyKLfvHeD4Wrj/Z9i2AP5ZAkjQ6k7o+CD4O6lHU1muzP05tcthl7k0kxSDBx/u+pL1J9dj0Bq4I+4Ohk3YRqMtC1HN66F8k2k+CFXrEdylPs5dz/UmXQrE37N6WMZitVFgtrK2rCV97lpGwJa3UWX9A76RZHV8mmW5YXy7qzoHz+GsEpbtSsffU8/ADvdBQSpSwiNOJ20DyAmP4O5TtzWK8sryyGMrnx79mPSSdOJ84ph4+xs0P/Qnxq0LHNtQfOnFIbNdZC5eOCKSG70yUP94P2TtR+XTGLnnJGyNe+LrG86LA1twQ9NAPlp7lNySChKi/ZjYrwnRAR61fm1Zltl9sgCAU4XlfL4xmfn3d2LR5hS2n8jHz0PHmBti6BnnT4i3Y5BZbinneOFx5iTNYXvWdnz1vjzY6kFubnwzAQb7Uh1+HjoGtQljWh9vPIr+QVoyHUpzYfBc5KNrlLxTyMht7kHq9JAS/FSWQ84hWP0qnNiAwSMA27CF8PBK2DAX6fDvoPVAbj8aWt3JlFUFDGybRYswL7tJ7s1CjCwffwPvrTrMpmO5xAZ42M1Vm94/jFsMh/Fd9RYMfFdJ7bBvKSBT1nwEp+JG8ta6YhY80IkOjX3x0Dt/pIV6u/N8/+b0iAtgwbrj5JZU0D3WR6k0f5UFPampqXTu3Nlum81mw2QyVVVbP5+oqCg2bdpESEhI1Tar1cqkSZOYOHHiec/ft28fK1euBODIkSOkp6dXBU3BwcGMHDkSq9V63urr2dnZREVFUVp6/mztdUkEPoJQVzRu4O5rN/GTXyYp34LHbYSO/wIkJcmZm4ukbkVpsOMrx+2ewZy4cSKjV4yuqrllspiYv2c+f5/8m1kJLxO+7l3l2KQv4MgqpNtnw+//plH/N4Dqb8vJuSaGfbABU4WVAE8dbw58l0C9le0nTXz6RyFp+faJB2MDPdmZWsj/dp4kd3BLgjxjGBAeDq3uRNr7o92xcuu7kMLaX9h9Ow9TpYmv9n/F/D3VvWl7cvbwcM4eZnR6jluzuqM+scHuHMkr5NzLXLAgL8c5PA91i6CXtBPV109Ub8w+gLTkUVTdJ1DZbTyBxgDu7BBOr6aBWKwyRjeNyweyK5Ik0aGxLyv2KIVc1x/JZWdKAUPbN2Jiv6aoVdC/ZTDeBucr4Q7kHeDB3x7EenouV5mljOmbp7MpfROvdIf++OEAABxeSURBVHsFX/fqoarcEjOv3haLx5GfkZaOq77Il0OR4vohD5pDpU80KRY/4nxOn5exGxb2V8qbABSkoPq0L3Lbe5H7TYUeE5CR2JxnYORbOwD4LimdN4e1Zlj7RuhO59rRadQ0DTby1vA2FJdbsMkyMYEe7E4rZHSHAAaV/4Ix8R3lNU5sgLi+0P0paNQRKaAVhgoVM+9S4etx/hWBYb7u3N25MT3jArDYZPRYyc86ibqOh2TrW0REBBkZ9gV+k5KSLqiHp7S0tNZBkjMWi6Xq/PT0dHJycqr+7eXlVevr7Nq1C5PJxJEjR5xWaq8vV9dPXBCuZF5h0PVxx+3lBcpQlaSCgDjXQU9+MkgapcTFOQpvmcaHuz92Wmj0QN4BDpZlQthZQ07Bp4cWvEKVFVynlZot7D1ZwJT+8SRE+7J4TGf6tImlRZM4pidmEuipJyHar2ouikqCoe0b8ds/p/By17DucA4eei3NZu4nv8fLyA//AV2fgG5PwsN/IPV7xfnQ3iXILc/l072fOt33xp6Pye4yxm6brVFHbDXlPqqlMG83usfaz5ua1FmPatXLTo+XNr2Ppjyv6t8BnnpCvN0uOOg545aWIXbV30srrHy9OYWXl+4lzMfdZdCTV57H65terwp6zrYmdQ0ZJuWhWWmxsSMln/sWbMHTnIn0x0v2B8s2OLwS6Zu70GLhqe/2kFtiVgL7HV/ZzxE7Tdr1NdaiU7Sae5SYdw4y8lMl6An01PHibc0wW6xknrvCECUPUIi3G2E+7jx7q1Ik84E2BoxbZp/Tnj/gt+dh0V24VeQR4u1Wq6DnbI18DUT6e+Bzgee5YrVZ2ZqxlRXHVrA1YytWWy0WLNSxtWvX0r177QqiHj9+nJycHNatc0xLUFtt2rThpZdeYsqUKRQUFCBJEvHx8bz00ks89NBDtbpGWVkZzz33HAMHDmT8+PHYbJdvArro8RGEuqLzgDZ3gylPGdqynp78GtwKedh8jloCyT2WS7ivO41OTzY2F2VRaDNxpCSFLWl/E+IVSY8HlxKY+A5uh1dWXbowuAXr98x0+dJ/pKym76DZmLd8xKme49mSvYPkgp20bt6XlgZvGgOY8jCUZDHE9DvED+H++GDI2Qg7/0YVdQOHn2oMh1dSWlxIUY++7CrxRW0MYMmONN4eEEZHYwFeqcvQZfiw8r4+/PhPESO6tcOnf0I93lRIK05z+hAHKDAXUKgzcKZ/xxZ1A9ZB/8XN+yKH2sqLoDgDDq3Au7yYL265hc0FMTz0fTJmiw23ykLl5+uMzYqcdwwpsOnFvfY5wrzd+GZsV55clFS1TNtDp+bftzWnXYSPy/NKKko4mO+YT+eMzRmbae7fnJR8E6M/2UxphRXK82t8X+Qe4aa4cLzK0uDYKqXmVr9XAAlWvmh3rnx0LXPuuYdnvt9FkbmSTY/HE0AhHPgaKmxQfjvkB4FvY6cv1ybchxnD2mA071fKpjhTdrq9dRxkX6hVJ1bx5pY3yTRVD60GG4J5PuF5+kX2uyxtkGWZzz77jPfee69Wx7/44osMHTqUV155hSFDhjiUlggICMDNzY20tJpTUhw8eJAnn3ySpk2bMm/ePIYNG8bq1at56KGHaN++5l7f9PR0Ro0aRffu3ZkzZ05VbbEFCxZcllIXIvARhLrkGwU9JkGnh5T/mLXuoPdCOrYGT88WjPutkgBPPW8Nb0OQVEiebOLRxEl2NbTUkprZXV6mm2xFf2Q1AJLNgrvG3WmPD4BBa8BqDGVvl3/x+G/32R0X4B7A/JvnE1dhRtq/DPT+INngm7sh5zAkjIWYvlCcBp3H4FlpxtNcSKgkYfUOxNeWT4dIP9QVBgi9D9Q6Iud0ZmTvl9DIEYDz1WR1Ra+ueUKwyieKyodWI+s8sRkCcPMKqPF4l8oKlRV5v79QtUnz9zt0j+nNzskfcKjUA406ucZLSNrzl9uoLY1aRbsIH358vDu5pWYsVhk/Dx1BxpqX5qslNWpJ7TJY9NQqCTNX7cuktMKKXqMC9Xl6P7QGHorIRPthn+rhLYCAJjBsPnw7GiqVz1yx7MaizSnMvrsdHXxK8Nz0LtLZw7frZkGbe6DPC+DrOGHZ213LsI5hSGlO6tOdTdWwj69VJ1YxOXEyMvaTxrNMWUxOnMzM3jMvS/Dz2Wef4e3tTe/evc977Msvv0x6ejorV65kwYIF9OnThyVLlhAdXZ1VOyfn/Hmhnn76aVatWsXUqVMZNmwYANu2bWP+/PlMmzaNhQsXOj0vNTWVRYsW8f777/PUU0/x3HPPAbB48WKmTZtG586dGT9+PPfccw9BQZfea+uKGOoShLpUmgOLhsN/E5Q5EB/3grntYfnThCx/gKm9fNlwNJcdKflUlpzkvV0fOBQOtcpWJm15nezezykPJHdfgoqzGRzrutrzkOjbOVWew8TESQ7BUU5ZDi9veJkMc4FS8NTTF7L3K1mmQ9tDtyfAYgH3EKW3I/E/8OUQpMUjUW/9iE6N3FGX5ijlOL4eDkmfwZMb8MSMW6ljIdK6FuoRWvWwPlecTxx+HkFoG3dCF9Ls4oMegMJUu6DnDOlYIu4H/0fbRl5Y3fwgMN75+W7eyF6NLv71XQj2cqNFqDdtwn0I9z3/0nwfNx9uirjJ6T4Jic4hnbHZbFXL9Ee09Qe9EYKaO7+g3gu8w/FfOso+6AElcN7xlbLEHUCSKArpxpqDWfyTXohn/n77oOeM3YudTuA/Q6tWo/EOc7pKDFACLsMl/KwvkdVm5c0tbzoEPUDVthlbZtT7sNfu3bt54YUX+Oijj2o8rri4mL59+7J3716WL1+OTqfj8ccfZ9q0afTr14+TJ2uRSPUsM2bMYNeuXVVBD4CbmxtPPfUUv/zyC/7+jsOgNpuN8ePHk5aWRmJiIs899xyyLJOVlYUkSUydOpUVK1awf/9+XnnllQtqz4W6JgOfsrIyxo4dS2RkJOHh4Tz77LOXdfxQuI6ZciBjj/N9JVlEuxWjVkn4WPMo0Bv4PcV5AVKLzcLuskx4Ygv863f0oe24O+4Omvo6DqOMbn4vocU5nKoopMBc4PR6e3P2UuTurTxsfnwY/vlJWX48YiGselXp7ShNgQU3w/aFUJgG2QeRfn8BacljoDNA/nHlz9/vKgFQqztg59cXe6dqLdAQyMzeM9FI9t/wPbWevHHDG/i7u87RckF2fOl636YPoDQLjV9j5KEfKsHA2dRa5GHzsbnKy3QZeWg9mNxpMsEGx+G+/+v2fwS6B6JSqWgSrASTw5u5Ie34Gob81/F9qTTIwz6mUtZUD92ea/8yiFN6NvJveouPkkqRZRgQrYEt852fA0oG6KIM1/uNoXDX50q9urPpjXDnAjDWX4/A+SRlJdkNb51LRibDlEFSVlK9tWHr1q3cdtttzJs3j+bNXQStpxmNRmbPns2SJUvw8KheXTh48GAOHTpEo0aNUKlUDBw48Lyv+/nnn9OyZUuaNGlCdHQ0ERERhIaGEhISQlBQUNWqrnOpVCqWLFnC3LlziY1VVrQWFhYSExNTdUzTpk358MMP+eCDD2p7Gy7KNTnU9fTTT2Oz2Th69CilpaX069eP999/n/Hjxzd004RrXU0ZZgGpogStWkKvBotsxSK7zrqbV5gMq95S8v14hxN+YDnvDXyXfXkHWHliFR46D+6IGURocTZBv0xmzx2zanzt8rOz8e75Af71lzJXYvB/lermf7yjTMQ+V+omZenyc2nw1uk5FXnH4MBy5IQnqO/UbxqVho7BHfnf0P/x2/HfOJh/kE7BnegV3otQT8fcLBfFZlN6u1wpy6/Kdi0Ht4FH1sDBX5FObkP2bwKth2M1hqOph/xFFyPcGM6Xt31JUmYSa1LWEOoRytC4oYR4hmA4PRw3sHUoH/x5FK1kg7/fAd9oeGQ1HPwNTm4D/zhoPQKbpEWdvtX1i9ks2Nz9OHnPGj7da2HxLmWoxKC2IJ29wvFcZflgdZ4yAAC1Bhp3V8q/7P0JMvdAZHdoOkBZUt+Ask3OS4hc7HEXwmQyMWvWLObOncvChQsZMGBArc5r3bo1oAw3zZkzh99//91uJdagQYNcDlGd7Z577mHQoEFoNBo0Gg1JSUlMnDiRbdvs82q99NJLLq7Q8K65wKekpITPP/+clJQUNBoN3t7evPDCC0ybNk0EPkL9c/dVJjlXOMlLIakocw+jvDKFjHIt0XIFjY2NSSlOcTwWaOcbD+EJSjmJ9xOg62OEV1YS/vc8+sXdjMpigi/vgVLlP9dIo+uJnkatEW/NOQ/lsBawcbYynCDblJpdruz9AeJutt+2bwlys0H1HvgA6NQ6Ir0iebTto9hkGyqpjjurVSpoMQT2/c/5/uheVb0hKo1WGWoJaILNZkWlUoaerrT/TEM9QhkYM5AB0QOc3q9wX3cWPNiJ1NxTtAxoCj+fXqJ/11fQ7HYoyYD5N6F+YBkENnH9Qv6xbCsJYPTiZLvSKDvztNwS0xvp1E7n58X0gfOtvtPolHxXvZ5VgtMrZOl5oKF22bJre9yFWLVqFevXr2fTpk1ERUVd0Lm5ubl06dKFCRMmkJiYiJ+fMj8vIyODWbNm0b17d/755x90OtdzvvR6PXp99bw7jbMkrFe4K+NTVIe2b99OdHS03RhjQkICe/fuxWK5tJo2gnBeniHQ+0Wnu0xtHuD7/eXo1CqaNg7DV2Pk+Q4TnR7bObgToX7x0GYEfNAN3Lwg9iZlCCr6RlQrX4A1r1cFPQC+pkIGxTovkfFom7GE7PrBcYfGrfphcu6QwrnHnUutA+nCS0FcqjoPes6I6KL0epxLrYWbXlKGWM5ti+ryv/8L5ep+ues0dI32p3V8HBU3v1G947vRyry0hQMgtA34NAavcIh0zM4LUNjrNV78I8uhHpzezYDU/l7ly8C53Hyg4wPKEGqt38iV87jqENSBYEMwkouwX0IixBBCh6AOdf7agwcPZsWKFRcc9ABs3LgRPz8/pkyZUhX0AISEhDBjxgzy8/PZv9+xin1dmDBhAiEhIXZ/4uPjMZlMDtvPtKe+XDmfpDqSnp5OcLD92HZQUBAWi4WiIsf082azmaKiIrs/gnDRNDpoN1JZ6eJzermuZxCFvV7jr7CH2ZFp4btHuxLpb0D2DKGtVwwf95lLEx/lG7Wn1pMxze/nzYSX8P/4JvhsoJK07c5P4Ld/K/W5mg1U5mOcSeHvEQj9XsHfEMxTrR5hfPun8HNT/lMLN4bznx6vMcAtDP3f71a3U5Ig94ASTHk2Anc/aH2X6/fVdiRkHbHbJLe/D5Wf8yXJVyXvRvDAz9D+vupVTpE9YMxq8K+hx+MqptWoaORrQBfVFR5YVp3/SW+EHhNh+EKlSK5noPIZ7Dm5OgAMbgn3L4XI7tzcIhjP0/mKmoUY+XpMFzpF+oJvLDy4AuJvU/JYSSpoeiv8a4Wy7yqlVql5PuF5AIfg58y/pyRMQX2FBcZdunQhOzubefPmYTJVZ8guKipi+vTpGI1G4uNdTN6/RO+99x4ZGRl2fzIzM7HZbA7bMzIymDJlSr20A6683tlLZrVaHWrSWK3K2Lyz9NlvvPEGr7766mVpm3CdMPgpw1PRN4LFTKWkwYQvTSttvNdcQ3BVRmANWn0sHfT+fNxrFmbZgkrS4OsehBsWKsckosECWfuRvn9QmYcDyoqxwXOR7/sf2CqRJBWc2g1z2xEKPDR0Hv37f45FtqGTVDRa+w4knTNxt99rYAxHriiC9e8itbkbuj4GR/5Q5u+cRW4zEskrDOa0q97WuLsSFFxrfBrDbW9D7+eVoRU3o/Mei2uN3lP5vN6/VBmmVWmUgOfsXkCvUGUJeucxSn4dnQE8AvEGJt3szX1dI7HYZNx1agLOrpkV3AJunwUVr4GMEjgZ67akSUPoF9mPmb1nOs3jMyVhymXL43MhAgMDWb9+PTNnzqRTp05YLBYkSUKr1dK/f382bNiAm5uT3t1rjCS7qlx3lVqxYgXPP/88u3fvrtqWmppKfHw8JSUlDhWjzWYzZnP1pM+ioiIiIiIoLCy8oNTbglBv8k5AcbqSFLE0B2J6QfNByuTTvGRlWfr2hUoelRZDIPoG8KteKUFBirLy5vAfSnbphEeU/W5KZWxbQQqSWQ+enkjlGUp9sf3LQOcJHR9E9ouBUzuRdi4CSUJuOwpC2yjV2gXhKlVeXs7x48eJjo6+pIe91WYlKSuJbFM2gYZAOgR1aPCeHrPZTGVlJZ6eztNAXA5nJk7XdRtq+rkVFRXh7e193uf3NRf4ZGRkEBkZSUZGRlUGyMWLFzNv3jwSExPPe35tb5wgXHblRUohVDc/x6ruZYXKijJXS3xlGSpNyhCOi7k8tvIikPRIKhVSeb4ybOfuU72/VCl9ofKoo+XjgtCA6irwES6vugh8rrk5PiEhIfTv358XXngBi8VCTk4O06dPr1XFWUG4orl5KcMPzlZRuHvXnNdEkpTVZjVMYFa5eaHS65G0WuVaZwU9oAQ8IugRBOFqd80FPgALFiwgPT2d0NBQOnXqxNixYxk6dGhDN0sQBEEQhAZ2zU1uBqXI2tKlSxu6GYIgCMIV7hqb7XHNq4uf1zXZ4yMIgiAINVGrlQnIFRUVDdwS4UKcWYav1daQd+w8rskeH0EQBEGoiUajwWAwkJ2djVardVjxK1xZZFnGZDKRlZWFj49PVeB6MUTgIwiCIFx3JEkiNDSU48ePc+LEiYZujlBLPj4+hISEXNI1ROAjCIIgXJd0Oh1NmjQRw11XCa1We0k9PWeIwEcQBEG4bqlUKpHH5zojBjUFQRAEQbhuiMBHEARBEITrhgh8BEEQBEG4bog5Puc4kxypqKiogVsiCIIgCEJtnXluny/JoQh8zlFcXAxAREREA7dEEARBEIQLVVxcjLe3t8v911x19ktls9lIT0/HaDQiSVJDN+eKVVRUREREBKmpqaKKfR0S97XuiXtaP8R9rR/ivl48WZYpLi4mLCysxoSUosfnHCqVivDw8IZuxlXDy8tL/HLWA3Ff6564p/VD3Nf6Ie7rxampp+cMMblZEARBEITrhgh8BEEQBEG4bojAR7goer2eqVOnotfrG7op1xRxX+ueuKf1Q9zX+iHua/0Tk5sFQRAEQbhuiB4fQRAEQRCuGyLwEQRBEAThuiECH0EQBEEQrhsi8BEuiMlk4t///jdPPPGEw75vvvmG5s2bEx4eTp8+fTh+/HgDtPDqVFZWxtixY4mMjCQ8PJxnn30Wm83W0M26KsmyzBdffEHXrl3ttu/YsYOuXbsSGRlJixYtWLlyZQO18OqzZs0aevToQVxcHLGxscydO7dqn7ivF++tt94iPj6exo0b07p1a37++eeqfeK+1iNZEGrp3XfflUNCQuSoqCj50Ucftdu3YcMGOSoqSj5x4oQsy7I8ffp0uUOHDg3RzKvS448/Lj/88MNyZWWlXFBQIHfq1El+7733GrpZV51ff/1VbtWqlRwTEyPHx8dXbS8qKpIbNWok//HHH7Isy/LatWtlb29v+dSpUw3V1KvKmDFj5H379smyLMtHjx6Vw8LC5F9//VXc10uUmJgoV1RUyLKs3Ds3Nzc5JydH3Nd6JlZ1CbU2e/ZsevXqxdKlS8nIyGDevHlV+0aNGkWXLl2YMGECABaLheDgYNasWUPbtm0bqslXhZKSEoKDg0lJScHf3x+AJUuWMG3aNHbs2NHArbu6/PDDD+j1eoxGI4899hgHDhwA4OOPP+bXX39lyZIlVccOGTKEm266qeozK9Te5MmT0Wg0xMXFiftah/z9/Vm/fj1//fWXuK/1SAx1CbU2ceJE2rdv73Tfxo0b6dGjR9W/NRoNHTp0YOfOnZepdVev7du3Ex0dXRX0ACQkJLB3714sFksDtuzqM3z4cAYNGuSw/dzPJyj3WHw+L052djbe3t7ivtaR8vJyZs+eTUJCAs2aNRP3tZ6JwEeoE+np6QQHB9ttCwoKIjc3t4FadPVwde8sFgtFRUUN1Kpri/h81p0tW7bwyy+/MGrUKHFfL9HRo0eJiIjAYDCwaNEi3n//fUB8XuubCHyEOmG1Wjl31NRqtYoK97Xg6t4B4v7VEfH5rBvff/89Q4YM4YsvviA6Olrc10sUGxtLamoqJpOJiRMn0q1bNw4fPizuaz0T1dkFB1FRUVV/b9Omjd1KA1f8/PzIycmhcePGVduys7MJCQmpjyZeU87cu7NlZ2fj7u5eq0rDwvm5usfi81k7VquV8ePH8+eff7Jy5Upat24NiPtaV9zc3Bg1ahSrV6/m888/F/e1nokeH8FBcnJy1Z/aBD0AHTt2ZMOGDVX/rqioYNu2bQ5LigVHHTp04ODBg+Tn51dtW79+PQkJCahU4le0Lpz7+QTlHnfr1q2BWnR1mTBhAkePHmXLli1VQQ+I+1rX9Ho9BoNB3Nf61oAryoSr1NSpUx2Ws//0009yVFSUnJqaKlssFvmll16Shw4d2kAtvPoMHjxYfuyxx+TKyko5Oztbbt26tbxkyZKGbtZV688//7Rbzp6amir7+PjIq1evlmVZlpcvXy5HRkbKJSUlDdXEq4bJZJLVarWckZHhsE/c14uXlpYmL1q0SK6srJRlWVmyHhYWJh8+fFjc13omhrqEOnHHHXdw5MgREhISsNls9O7dm08//bShm3XVWLBgAQ8//DChoaF4eHjwzDPPMHTo0IZu1jUjPDycxYsXM27cOPLy8oiLi2PZsmV4eHg0dNOueMePH8dms9GlSxe77bGxsaxevVrc14uk1+tZsGABEyZMwGg0Ehsby88//0xcXByAuK/1SOTxEQRBEAThuiEmEAiCIAiCcN0QgY8gCIIgCNcNEfgIgiAIgnDdEIGPIAiCIAjXDRH4CIIgCIJw3RCBjyAIgiAI1w2Rx0cQhGtCSkoK77zzDqtWrcJisSBJEjqdjttvv51nnnkGf39/u+N37txJ586dHbaf0axZMxITEy9DywVBuJxE4CMIwlWvvLycnj17MmnSJLZu3VqV6K2goIAZM2Zw8803k5SU5HBefHw8e/fuvdzNFQShAYkEhoIgXPUOHjxIz549yc7OdthnMpkwGo0UFRXZZb7duXMno0ePFoGPIFxnxBwfQRCuek2aNCEyMpKJEydy4MABysvLMZlM7Nq1i7Fjx3L77beLdP+CIAAi8BEE4RqgUqlYs2YNoaGhPPnkk4SFhRETE8OUKVPo2rUr3377bUM3URCEK4QY6hIE4ZozceJEwsPDeeaZZ1wec77JzQDHjh3DYDDURxMFQWggIvARBOGqlZaWRteuXR22l5WVoVKp0Ov1DvtOnDiBWq122D506FBGjx7N8OHD66WtgiBcGcSqLkEQrlrh4eGkpaU1dDMEQbiKiDk+giBc9TIyMhg3blxDN0MQhKuAGOoSBOGqd+TIEfr160dycvJ5j924cSMjRoxw2J6bm4vBYMDd3d1ue2hoKFu3bq2rpgqC0MDEUJcgCNeVbt26ieExQbiOicBHEIRrQmpqKiEhIS73f/XVV/Tr1+8ytkgQhCuRGOoSBEEQBOG6ISY3C4IgCIJw3RCBjyAIgiAI1w0R+AiCIAiCcN0QgY8gCIIgCNcNEfgIgiAIgnDdEIGPIAiCIAjXDRH4CIIgCIJw3RCBjyAIgiAI1w0R+AiCIAiCcN0QgY8gCIIgCNcNEfgIgiAIgnDd+H99kNWysYsl1gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 사용할 칼럼들을 추출 (기상 요건들과 운송 현황)\n",
    "selected = ['온도','시정', '전운량', '최저운고','풍속', '강수량(mm)', '현황']\n",
    "sdf3 = df[selected]\n",
    "\n",
    "sns.scatterplot(data=sdf3, x=sdf3.iloc[:, 0], y=sdf3.iloc[:, 1], hue='현황')\n",
    "plt.title(\"2만개 데이터 증폭 전 분포\")\n",
    "plt.savefig('/Users/user/Documents/DataMining/termproject.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed232e91",
   "metadata": {},
   "source": [
    "## 2만개 데이터 resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e3d5b8c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원본 데이터셋의 각 카테고리별 개수:\n",
      "출발       20000\n",
      "기상 지연     4918\n",
      "기상 취소      473\n",
      "Name: 현황, dtype: int64\n",
      "\n",
      "새로운 데이터셋의 각 카테고리별 개수:\n",
      "출발       20000\n",
      "기상 지연    20000\n",
      "기상 취소    20000\n",
      "Name: 현황, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 각 카테고리별 데이터 개수 출력\n",
    "print(\"원본 데이터셋의 각 카테고리별 개수:\")\n",
    "print(df['현황'].value_counts())\n",
    "\n",
    "# 출발 이외의 데이터를 추출\n",
    "non_departure_data = df[df['현황'] != '출발']\n",
    "\n",
    "# 출발 데이터만 따로 추출\n",
    "departure_data = df[df['현황'] == '출발']\n",
    "\n",
    "# 각 카테고리별 데이터 개수에 맞게 샘플링\n",
    "from sklearn.utils import resample\n",
    "resampled_data = pd.concat([\n",
    "    resample(df[df['현황'] == category], replace=True, n_samples=df['현황'].value_counts().max(), random_state=42)\n",
    "    for category in df['현황'].unique()\n",
    "])\n",
    "\n",
    "# 새로운 데이터셋의 각 카테고리별 개수 출력\n",
    "print(\"\\n새로운 데이터셋의 각 카테고리별 개수:\")\n",
    "print(resampled_data['현황'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "089a81c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>날짜</th>\n",
       "      <th>항공사</th>\n",
       "      <th>편명</th>\n",
       "      <th>출발공항코드</th>\n",
       "      <th>출발공항명</th>\n",
       "      <th>도착공항코드</th>\n",
       "      <th>도착공항명</th>\n",
       "      <th>계획시간</th>\n",
       "      <th>예상시간</th>\n",
       "      <th>출발시간</th>\n",
       "      <th>시간</th>\n",
       "      <th>온도</th>\n",
       "      <th>시정</th>\n",
       "      <th>전운량</th>\n",
       "      <th>최저운고</th>\n",
       "      <th>풍속</th>\n",
       "      <th>강수량(mm)</th>\n",
       "      <th>구분</th>\n",
       "      <th>현황</th>\n",
       "      <th>사유</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>2018-04-06 00:00:00</td>\n",
       "      <td>대한항공</td>\n",
       "      <td>KE847</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TNA</td>\n",
       "      <td>지난</td>\n",
       "      <td>13:10</td>\n",
       "      <td>13:10</td>\n",
       "      <td>13:50</td>\n",
       "      <td>13:00</td>\n",
       "      <td>8.4</td>\n",
       "      <td>500</td>\n",
       "      <td>6</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>2019-06-29 00:00:00</td>\n",
       "      <td>이스타항공</td>\n",
       "      <td>ZE611</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>KIX</td>\n",
       "      <td>간사이</td>\n",
       "      <td>08:45</td>\n",
       "      <td>08:45</td>\n",
       "      <td>08:55</td>\n",
       "      <td>08:00</td>\n",
       "      <td>20.4</td>\n",
       "      <td>600</td>\n",
       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>2019-08-11 00:00:00</td>\n",
       "      <td>티웨이항공</td>\n",
       "      <td>TW293</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>FUK</td>\n",
       "      <td>후쿠오카</td>\n",
       "      <td>18:25</td>\n",
       "      <td>18:25</td>\n",
       "      <td>18:40</td>\n",
       "      <td>18:00</td>\n",
       "      <td>31.4</td>\n",
       "      <td>1000</td>\n",
       "      <td>8</td>\n",
       "      <td>40.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>2019-09-19 00:00:00</td>\n",
       "      <td>아시아나항공</td>\n",
       "      <td>OZ3533</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>CKG</td>\n",
       "      <td>충칭</td>\n",
       "      <td>10:15</td>\n",
       "      <td>10:15</td>\n",
       "      <td>10:39</td>\n",
       "      <td>10:00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11284</th>\n",
       "      <td>2017-12-02 00:00:00</td>\n",
       "      <td>에어아시아 엑스</td>\n",
       "      <td>D7505</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>KUL</td>\n",
       "      <td>쿠알라룸푸르</td>\n",
       "      <td>15:55</td>\n",
       "      <td>15:55</td>\n",
       "      <td>16:39</td>\n",
       "      <td>15:00</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>출발</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25157</th>\n",
       "      <td>2018-09-15 00:00:00</td>\n",
       "      <td>유피에스화물항공</td>\n",
       "      <td>5X195</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>ANC</td>\n",
       "      <td>앵커리지</td>\n",
       "      <td>23:35</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>23:00</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>화물</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>태풍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24928</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>중국국제항공</td>\n",
       "      <td>CA802</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>TSN</td>\n",
       "      <td>천진</td>\n",
       "      <td>17:05</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>17:00</td>\n",
       "      <td>6.5</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>시정</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25055</th>\n",
       "      <td>2018-06-26 00:00:00</td>\n",
       "      <td>대한항공</td>\n",
       "      <td>KE1407</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>PUS</td>\n",
       "      <td>김해</td>\n",
       "      <td>19:35</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>19:00</td>\n",
       "      <td>21.3</td>\n",
       "      <td>250</td>\n",
       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>운고</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25084</th>\n",
       "      <td>2018-07-22 00:00:00</td>\n",
       "      <td>상해항공</td>\n",
       "      <td>FM828</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>PVG</td>\n",
       "      <td>푸동</td>\n",
       "      <td>18:25</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>18:00</td>\n",
       "      <td>31.3</td>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>태풍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25164</th>\n",
       "      <td>2018-10-24 00:00:00</td>\n",
       "      <td>티웨이항공</td>\n",
       "      <td>TW301</td>\n",
       "      <td>ICN</td>\n",
       "      <td>인천</td>\n",
       "      <td>GUM</td>\n",
       "      <td>괌</td>\n",
       "      <td>18:00</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>18:00</td>\n",
       "      <td>15.1</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>여객</td>\n",
       "      <td>기상 취소</td>\n",
       "      <td>태풍</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        날짜       항공사      편명 출발공항코드 출발공항명 도착공항코드   도착공항명  \\\n",
       "15795  2018-04-06 00:00:00      대한항공   KE847    ICN    인천    TNA      지난   \n",
       "860    2019-06-29 00:00:00     이스타항공   ZE611    ICN    인천    KIX     간사이   \n",
       "5390   2019-08-11 00:00:00     티웨이항공   TW293    ICN    인천    FUK    후쿠오카   \n",
       "11964  2019-09-19 00:00:00    아시아나항공  OZ3533    ICN    인천    CKG      충칭   \n",
       "11284  2017-12-02 00:00:00  에어아시아 엑스   D7505    ICN    인천    KUL  쿠알라룸푸르   \n",
       "...                    ...       ...     ...    ...   ...    ...     ...   \n",
       "25157  2018-09-15 00:00:00  유피에스화물항공   5X195    ICN    인천    ANC    앵커리지   \n",
       "24928  2017-01-01 00:00:00    중국국제항공   CA802    ICN    인천    TSN      천진   \n",
       "25055  2018-06-26 00:00:00      대한항공  KE1407    ICN    인천    PUS      김해   \n",
       "25084  2018-07-22 00:00:00      상해항공   FM828    ICN    인천    PVG      푸동   \n",
       "25164  2018-10-24 00:00:00     티웨이항공   TW301    ICN    인천    GUM       괌   \n",
       "\n",
       "        계획시간   예상시간   출발시간     시간    온도    시정  전운량   최저운고  풍속  강수량(mm)  구분  \\\n",
       "15795  13:10  13:10  13:50  13:00   8.4   500    6   30.0  20      0.0  여객   \n",
       "860    08:45  08:45  08:55  08:00  20.4   600    8    7.0   3      0.5  여객   \n",
       "5390   18:25  18:25  18:40  18:00  31.4  1000    8   40.0  14      0.0  여객   \n",
       "11964  10:15  10:15  10:39  10:00  21.0  1000    0    0.0   8      0.0  여객   \n",
       "11284  15:55  15:55  16:39  15:00   6.8  1000    4   30.0  10      0.0  여객   \n",
       "...      ...    ...    ...    ...   ...   ...  ...    ...  ..      ...  ..   \n",
       "25157  23:35      :      :  23:00  23.8  1000    7   40.0   6      0.0  화물   \n",
       "24928  17:05      :      :  17:00   6.5   500    4   30.0   5      0.0  여객   \n",
       "25055  19:35      :      :  19:00  21.3   250    8    7.0  17      0.0  여객   \n",
       "25084  18:25      :      :  18:00  31.3  1000    5  200.0   5      0.0  여객   \n",
       "25164  18:00      :      :  18:00  15.1  1000    0    0.0   1      0.0  여객   \n",
       "\n",
       "          현황   사유  \n",
       "15795     출발  NaN  \n",
       "860       출발  NaN  \n",
       "5390      출발  NaN  \n",
       "11964     출발  NaN  \n",
       "11284     출발  NaN  \n",
       "...      ...  ...  \n",
       "25157  기상 취소   태풍  \n",
       "24928  기상 취소   시정  \n",
       "25055  기상 취소   운고  \n",
       "25084  기상 취소   태풍  \n",
       "25164  기상 취소   태풍  \n",
       "\n",
       "[60000 rows x 20 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7312736e",
   "metadata": {},
   "source": [
    "### 2만개 데이터 증폭 후 분포 (Resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2ad783bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 사용할 칼럼들을 추출 (기상 요건들과 운송 현황)\n",
    "selected = ['온도','시정', '전운량', '최저운고','풍속', '강수량(mm)', '현황']\n",
    "sdf4 = resampled_data[selected]\n",
    "\n",
    "sns.scatterplot(data=sdf4, x=sdf4.iloc[:, 0], y=sdf4.iloc[:, 1], hue='현황')\n",
    "plt.title(\"2만개 데이터 resample 증폭 후 분포\")\n",
    "plt.savefig('/Users/user/Documents/DataMining/termproject.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f72600b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 6) (18000, 6) (42000,) (18000,)\n"
     ]
    }
   ],
   "source": [
    "X = resampled_data[[\"온도\", '시정', '전운량', '최저운고', '풍속', '강수량(mm)']]\n",
    "y = resampled_data['현황']\n",
    "#test, training (7:3 분할)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 100)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f995de71",
   "metadata": {},
   "source": [
    "# 4. 모델링 - Decision Tree (2만개 Resampled Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb116994",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning - GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "250308c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>splitter</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>24</th>\n",
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       "      <th>18</th>\n",
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       "      <td>0.836889</td>\n",
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       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
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       "      <th>13</th>\n",
       "      <td>20</td>\n",
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       "      <td>0.826111</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
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       "      <th>20</th>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>best</td>\n",
       "      <td>0.815444</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
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       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.785111</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.782944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0.781333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.777500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.774167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.774167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.773722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.769333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.757611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>best</td>\n",
       "      <td>0.746389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>best</td>\n",
       "      <td>0.737611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.735000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.735000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.732333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>best</td>\n",
       "      <td>0.721889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.719556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>random</td>\n",
       "      <td>0.685000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>random</td>\n",
       "      <td>0.684056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>random</td>\n",
       "      <td>0.671722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max_depth  min_samples_split splitter  mean_test_score\n",
       "24         30                 16     best         0.850611\n",
       "18         25                 16     best         0.850556\n",
       "12         20                 16     best         0.848833\n",
       "25         30                 16   random         0.837167\n",
       "19         25                 16   random         0.836889\n",
       "6          15                 16     best         0.828722\n",
       "13         20                 16   random         0.826111\n",
       "26         30                 32     best         0.815444\n",
       "20         25                 32     best         0.815444\n",
       "14         20                 32     best         0.814833\n",
       "8          15                 32     best         0.803333\n",
       "21         25                 32   random         0.785111\n",
       "27         30                 32   random         0.782944\n",
       "7          15                 16   random         0.781333\n",
       "15         20                 32   random         0.777500\n",
       "22         25                 64     best         0.774167\n",
       "28         30                 64     best         0.774167\n",
       "16         20                 64     best         0.773722\n",
       "10         15                 64     best         0.769333\n",
       "9          15                 32   random         0.757611\n",
       "0          10                 16     best         0.746389\n",
       "2          10                 32     best         0.737611\n",
       "23         25                 64   random         0.735000\n",
       "29         30                 64   random         0.735000\n",
       "17         20                 64   random         0.732333\n",
       "4          10                 64     best         0.721889\n",
       "11         15                 64   random         0.719556\n",
       "3          10                 32   random         0.685000\n",
       "1          10                 16   random         0.684056\n",
       "5          10                 64   random         0.671722"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#테스트하고자 하는 파라미터 값들을 사전 타입으로 정의\n",
    "dt = DecisionTreeClassifier(random_state = 33)\n",
    "parameters = {'max_depth': [10, 15, 20, 25, 30],\n",
    "              'min_samples_split': [16, 32, 64],\n",
    "              'splitter': ['best', 'random']}\n",
    "\n",
    "grid_dt = GridSearchCV(dt,\n",
    "                      param_grid = parameters, cv = 5\n",
    "                      )\n",
    "\n",
    "grid_dt.fit(X_test, y_test)\n",
    "\n",
    "result = pd.DataFrame(grid_dt.cv_results_['params'])\n",
    "result['mean_test_score'] = grid_dt.cv_results_['mean_test_score']\n",
    "result.sort_values(by='mean_test_score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "61b7cd1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=30, min_samples_split=16)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=30, min_samples_split=16)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=30, min_samples_split=16)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최적의 DT 만들기\n",
    "best_params = {'max_depth' : 30, 'min_samples_split': 16, 'splitter': 'best'}\n",
    "\n",
    "#최적 파라미터 조합으로 DecisionTreeClassifier 초기화\n",
    "best_dt = DecisionTreeClassifier(**best_params)\n",
    "\n",
    "#최적모델로 학습\n",
    "best_dt.fit(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ebec06",
   "metadata": {},
   "source": [
    "### 교차검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2ab72682",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차검증 평균:  0.9081333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "#각 폴드의 스코어\n",
    "scores = cross_val_score(best_dt, X, y, cv = 5)\n",
    "scores\n",
    "\n",
    "pd.DataFrame(cross_validate(best_dt, X, y, cv = 5))\n",
    "print('교차검증 평균: ', scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92fa2120",
   "metadata": {},
   "source": [
    "### 학습결과평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "06004565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       기상 지연       0.89      0.86      0.88      5845\n",
      "       기상 취소       0.93      0.99      0.96      6151\n",
      "          출발       0.91      0.88      0.90      6004\n",
      "\n",
      "    accuracy                           0.91     18000\n",
      "   macro avg       0.91      0.91      0.91     18000\n",
      "weighted avg       0.91      0.91      0.91     18000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predict_test = best_dt.predict(X_test)\n",
    "\n",
    "# 학습결과평가\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print('Test Data:\\n', classification_report(y_test, predict_test, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221e9fcd",
   "metadata": {},
   "source": [
    "# 4. 모델링 - Random Forest (2만개 Resampled Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c0d4d7d",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning - GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7c05e17a",
   "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",
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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>max_depth</th>\n",
       "      <th>min_samples_split</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>0.926524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>0.926476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>0.926452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>0.926452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>0.926405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>0.926357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>0.924619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>0.924524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>0.924476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>120</td>\n",
       "      <td>0.918381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>120</td>\n",
       "      <td>0.918357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "      <td>0.918214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>140</td>\n",
       "      <td>0.918095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "      <td>0.917976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>140</td>\n",
       "      <td>0.917952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>140</td>\n",
       "      <td>0.916452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "      <td>0.916357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>120</td>\n",
       "      <td>0.916190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>120</td>\n",
       "      <td>0.905762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>120</td>\n",
       "      <td>0.905667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>130</td>\n",
       "      <td>0.905619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>0.905571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>0.905571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>130</td>\n",
       "      <td>0.905524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>140</td>\n",
       "      <td>0.903952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>120</td>\n",
       "      <td>0.903571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>130</td>\n",
       "      <td>0.903524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max_depth  min_samples_split  n_estimators  mean_test_score\n",
       "18         40                  4           120         0.926524\n",
       "19         40                  4           130         0.926476\n",
       "20         40                  4           140         0.926452\n",
       "10         30                  4           130         0.926452\n",
       "11         30                  4           140         0.926405\n",
       "9          30                  4           120         0.926357\n",
       "0          20                  4           120         0.924619\n",
       "2          20                  4           140         0.924524\n",
       "1          20                  4           130         0.924476\n",
       "12         30                  8           120         0.918381\n",
       "21         40                  8           120         0.918357\n",
       "22         40                  8           130         0.918214\n",
       "23         40                  8           140         0.918095\n",
       "13         30                  8           130         0.917976\n",
       "14         30                  8           140         0.917952\n",
       "5          20                  8           140         0.916452\n",
       "4          20                  8           130         0.916357\n",
       "3          20                  8           120         0.916190\n",
       "24         40                 16           120         0.905762\n",
       "15         30                 16           120         0.905667\n",
       "25         40                 16           130         0.905619\n",
       "17         30                 16           140         0.905571\n",
       "26         40                 16           140         0.905571\n",
       "16         30                 16           130         0.905524\n",
       "8          20                 16           140         0.903952\n",
       "6          20                 16           120         0.903571\n",
       "7          20                 16           130         0.903524"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#하이퍼 파라미터 튜닝(그리드 서치)\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#테스트하고자 하는 파라미터 값들을 사전 타입으로 정의\n",
    "rf = RandomForestClassifier(random_state=10)\n",
    "\n",
    "parameters = {'n_estimators': [120, 130, 140],\n",
    "              'max_depth': [20, 30, 40],\n",
    "              'min_samples_split': [4, 8, 16]}\n",
    "\n",
    "grid_rf = GridSearchCV(rf, # estimator 객체,\n",
    "                      param_grid = parameters, cv = 5\n",
    "                      # n_jobs = -1: 모든 cpu를 사용)\n",
    "                      )\n",
    "\n",
    "grid_rf.fit(X_train, y_train)\n",
    "\n",
    "result = pd.DataFrame(grid_rf.cv_results_['params'])\n",
    "result['mean_test_score'] = grid_rf.cv_results_['mean_test_score']\n",
    "result.sort_values(by='mean_test_score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9e3119e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Random Forest: {'max_depth': 40, 'min_samples_split': 4, 'n_estimators': 120}\n",
      "Accuracy of the Best Random Forest model: 0.9356111111111111\n"
     ]
    }
   ],
   "source": [
    "best_params_rf = grid_rf.best_params_\n",
    "print(\"Best Parameters for Random Forest:\", best_params_rf)\n",
    "\n",
    "#최적 파라미터 조합으로 RandomForestClassifier 초기화\n",
    "best_rf = RandomForestClassifier(**best_params_rf)\n",
    "\n",
    "#최적모델로 학습\n",
    "best_rf.fit(X_train, y_train)\n",
    "\n",
    "# 최적 모델 평가\n",
    "predict = best_rf.predict(X_test)\n",
    "print(\"Accuracy of the Best Random Forest model:\", accuracy_score(y_test,predict)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa8a10a",
   "metadata": {},
   "source": [
    "### 교차검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d76fef6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차검증 평균:  0.9365166666666667\n"
     ]
    }
   ],
   "source": [
    "#교차검증: 검증데이터셋 나눠서 평가하기 (데이터셋: training, test, validation 으로 나눔)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "#각 폴드의 스코어\n",
    "scores = cross_val_score(best_rf, X, y, cv = 5)\n",
    "scores\n",
    "\n",
    "pd.DataFrame(cross_validate(best_rf, X, y, cv = 5))\n",
    "print('교차검증 평균: ', scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7039b4",
   "metadata": {},
   "source": [
    "### 학습결과평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "07865ccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "       기상 지연       0.91      0.92      0.91      5845\n",
      "       기상 취소       0.94      0.98      0.96      6151\n",
      "          출발       0.96      0.91      0.93      6004\n",
      "\n",
      "    accuracy                           0.94     18000\n",
      "   macro avg       0.94      0.94      0.94     18000\n",
      "weighted avg       0.94      0.94      0.94     18000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predict_test = best_rf.predict(X_test)\n",
    "\n",
    "# 학습결과평가\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print('Test Data:\\n', classification_report(y_test, predict_test, zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec4f895",
   "metadata": {},
   "source": [
    "### confusion map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3e397452",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap 시각화 (confusion matrix 시각화하기)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, predict)\n",
    "\n",
    "# 혼동 행렬을 DataFrame으로 변환\n",
    "cm_df = pd.DataFrame(cm, columns=y.unique(), index=y.unique())\n",
    "\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc07729f",
   "metadata": {},
   "source": [
    "# 4. 모델링 - Multinomial Logistic Regression (2만개 Resampled Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7186bf83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import statsmodels.api as sm\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e658cae",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning - GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6422293f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'C': 0.001, 'penalty': 'l2'}\n",
      "GridSearchCV Results:\n",
      "         C penalty  mean_test_score\n",
      "0   0.001      l2         0.605905\n",
      "1   0.010      l2         0.603786\n",
      "3   1.000      l2         0.603143\n",
      "4  10.000      l2         0.603143\n",
      "2   0.100      l2         0.603119\n"
     ]
    }
   ],
   "source": [
    "# 테스트하고자 하는 파라미터 값들을 사전 타입으로 정의\n",
    "mlr = LogisticRegression(random_state=42, multi_class='multinomial', solver='saga')\n",
    "parameters = {'C': [0.001, 0.01, 0.1, 1, 10], 'penalty': ['l2']}\n",
    "\n",
    "# 테스트와 훈련 데이터 분할\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)\n",
    "\n",
    "# 데이터 스케일 조정\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# GridSearchCV를 사용하여 최적의 하이퍼파라미터 찾기\n",
    "grid_mlr = GridSearchCV(mlr, param_grid=parameters, cv=5)\n",
    "grid_mlr.fit(X_train_scaled, y_train)\n",
    "\n",
    "# 결과를 DataFrame으로 표시\n",
    "result = pd.DataFrame(grid_mlr.cv_results_['params'])\n",
    "result['mean_test_score'] = grid_mlr.cv_results_['mean_test_score']\n",
    "result.sort_values(by='mean_test_score', ascending=False, inplace=True)\n",
    "\n",
    "# 결과 출력\n",
    "print(\"Best Hyperparameters:\", grid_mlr.best_params_)\n",
    "print(\"GridSearchCV Results:\\n\", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bfa76f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                   random_state=42, solver=&#x27;saga&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.001, max_iter=1000, multi_class=&#x27;multinomial&#x27;,\n",
       "                   random_state=42, solver=&#x27;saga&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.001, max_iter=1000, multi_class='multinomial',\n",
       "                   random_state=42, solver='saga')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최적의 하이퍼파라미터로 모델 생성\n",
    "best_mlr = LogisticRegression(random_state=42, multi_class='multinomial', solver='saga', max_iter = 1000, C=0.001, penalty='l2')\n",
    "best_mlr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5790fa7a",
   "metadata": {},
   "source": [
    "### 교차검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f33fa87b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차검증 평균:  0.5985333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.300400</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>0.603750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.660924</td>\n",
       "      <td>0.019701</td>\n",
       "      <td>0.597667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.673146</td>\n",
       "      <td>0.031246</td>\n",
       "      <td>0.600083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.452140</td>\n",
       "      <td>0.031245</td>\n",
       "      <td>0.599000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.914699</td>\n",
       "      <td>0.031240</td>\n",
       "      <td>0.592167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fit_time  score_time  test_score\n",
       "0  18.300400    0.015622    0.603750\n",
       "1  17.660924    0.019701    0.597667\n",
       "2  18.673146    0.031246    0.600083\n",
       "3  19.452140    0.031245    0.599000\n",
       "4  18.914699    0.031240    0.592167"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 교차검증\n",
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "# 교차 검증 수행\n",
    "scores = cross_val_score(best_mlr, X, y, cv=5)\n",
    "cv_results = cross_validate(best_mlr, X, y, cv=5)\n",
    "\n",
    "print('교차검증 평균: ', scores.mean())\n",
    "pd.DataFrame(cv_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affde825",
   "metadata": {},
   "source": [
    "### 학습결과평가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "604acca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       기상 지연       0.66      0.64      0.65      5845\n",
      "       기상 취소       0.58      0.62      0.60      6151\n",
      "          출발       0.58      0.55      0.57      6004\n",
      "\n",
      "    accuracy                           0.61     18000\n",
      "   macro avg       0.61      0.61      0.61     18000\n",
      "weighted avg       0.61      0.61      0.61     18000\n",
      "\n",
      "Accuracy of Multinomial Logistic Regression:  0.6057777777777777\n"
     ]
    }
   ],
   "source": [
    "# 학습 결과 평가\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "best_mlr.fit(X_test_scaled, y_test)\n",
    "predict_mlr = best_mlr.predict(X_test_scaled)\n",
    "print(classification_report(y_test, predict_mlr, zero_division=0))\n",
    "accuracy = accuracy_score(y_test, predict_mlr)\n",
    "print(\"Accuracy of Multinomial Logistic Regression: \", accuracy)"
   ]
  }
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