titanic_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "%matplotlib inline\n",
    "\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "train_data=pd.read_csv('train.csv')\n",
    "test_data=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据类型和缺失情况\n",
    "train_data.info(),test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 部分数据详情\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# 提取Name中称呼，如Mr，Mrs，Miss等\n",
    "def get_title(name):\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    # If the title exists, extract and return it.\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "#增加一个Title特征，对称呼分类\n",
    "train_data['Title']=train_data['Name'].apply(get_title)#名字只保留称呼\n",
    "\n",
    "#分为Rare尊贵称呼，以及女士、小姐、男士\n",
    "train_data['Title'] = train_data['Title'].replace(['Master','Don', 'Rev','Dr', 'Major','Lady',\n",
    "                                                 'Sir', 'Col', 'Capt', 'Countess', 'Jonkheer'], 'Rare')\n",
    "\n",
    "#分出小姐和夫人\n",
    "train_data['Title'] = train_data['Title'].replace('Mlle', 'Miss')\n",
    "train_data['Title'] = train_data['Title'].replace('Ms', 'Miss')\n",
    "train_data['Title'] = train_data['Title'].replace('Mme', 'Mrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'Title'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>考察各列数据</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2b0c4da48>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 总体存活情况\n",
    "sns.countplot(x='Survived',data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249ba743488>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 这样也行\n",
    "train_data.Survived.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2b07cedc8>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 等级情况\n",
    "sns.countplot(x='Pclass',data=train_data)\n",
    "# train_data.Pclass.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "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>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Pclass            \n",
       "1          80  136\n",
       "2          97   87\n",
       "3         372  119"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不同等级的存活情况\n",
    "pd.pivot_table(train_data,columns='Survived',index='Pclass',values='PassengerId',aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ae58b488>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同等级可视化\n",
    "sns.countplot(x='Pclass',hue='Survived',data=train_data)\n",
    "# 第二种方法，比较笨\n",
    "# a=train_data.groupby(by=['Pclass','Survived']).count().reset_index(1)# reset_index展开第二个index\n",
    "# sns.barplot(x=a.index,y='PassengerId',hue='Survived',data=a)# x不能用Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ab15a588>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 男女分布\n",
    "sns.countplot(x='Sex',data=train_data)\n",
    "# train_data.Sex.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>81</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>468</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Sex               \n",
       "female     81  233\n",
       "male      468  109"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 男女存活情况 透视表\n",
    "pd.pivot_table(train_data,index='Sex',columns='Survived',values='PassengerId',aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2abe20f88>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "sns.countplot(x='Sex',hue='Survived',data=train_data)\n",
    "# 第二种方法\n",
    "# a=train_data.groupby(by=['Sex','Survived']).count().reset_index(1)\n",
    "# sns.barplot(x=a.index,y='PassengerId',hue='Survived',data=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# 年龄有缺失，我们通过随机森林建模填补\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "def set_missing_ages(df):\n",
    "    \n",
    "    # 把已有的数值型特征取出来丢进Random Forest Regressor中\n",
    "    age_df = df[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "\n",
    "    # 乘客分成已知年龄和未知年龄两部分\n",
    "    known_age = age_df[age_df.Age.notnull()].values\n",
    "    unknown_age = age_df[age_df.Age.isnull()].values\n",
    "\n",
    "    # y即目标年龄\n",
    "    y = known_age[:, 0]\n",
    "\n",
    "    # X即特征属性值\n",
    "    X = known_age[:, 1:]\n",
    "\n",
    "    # fit到RandomForestRegressor之中训练\n",
    "    rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1)\n",
    "    rfr.fit(X, y)\n",
    "    \n",
    "    # 用得到的模型进行未知年龄结果预测\n",
    "    predictedAges = rfr.predict(unknown_age[:, 1:])\n",
    "    \n",
    "    # 用得到的预测结果填补原缺失数据\n",
    "    df.loc[ (df.Age.isnull()), 'Age' ] = predictedAges \n",
    "    \n",
    "    return df, rfr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     PassengerId  Survived  Pclass  \\\n",
       " 0              1         0       3   \n",
       " 1              2         1       1   \n",
       " 2              3         1       3   \n",
       " 3              4         1       1   \n",
       " 4              5         0       3   \n",
       " ..           ...       ...     ...   \n",
       " 886          887         0       2   \n",
       " 887          888         1       1   \n",
       " 888          889         0       3   \n",
       " 889          890         1       1   \n",
       " 890          891         0       3   \n",
       " \n",
       "                                                   Name     Sex       Age  \\\n",
       " 0                              Braund, Mr. Owen Harris    male  22.00000   \n",
       " 1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.00000   \n",
       " 2                               Heikkinen, Miss. Laina  female  26.00000   \n",
       " 3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.00000   \n",
       " 4                             Allen, Mr. William Henry    male  35.00000   \n",
       " ..                                                 ...     ...       ...   \n",
       " 886                              Montvila, Rev. Juozas    male  27.00000   \n",
       " 887                       Graham, Miss. Margaret Edith  female  19.00000   \n",
       " 888           Johnston, Miss. Catherine Helen \"Carrie\"  female  16.19395   \n",
       " 889                              Behr, Mr. Karl Howell    male  26.00000   \n",
       " 890                                Dooley, Mr. Patrick    male  32.00000   \n",
       " \n",
       "      SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       " 0        1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       " 1        1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       " 2        0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       " 3        1      0            113803  53.1000  C123        S   Mrs  \n",
       " 4        0      0            373450   8.0500   NaN        S    Mr  \n",
       " ..     ...    ...               ...      ...   ...      ...   ...  \n",
       " 886      0      0            211536  13.0000   NaN        S  Rare  \n",
       " 887      0      0            112053  30.0000   B42        S  Miss  \n",
       " 888      1      2        W./C. 6607  23.4500   NaN        S  Miss  \n",
       " 889      0      0            111369  30.0000  C148        C    Mr  \n",
       " 890      0      0            370376   7.7500   NaN        Q    Mr  \n",
       " \n",
       " [891 rows x 13 columns],\n",
       " RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                       max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
       "                       n_jobs=-1, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_missing_ages(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249cbf7ec48>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同年龄的存活率\n",
    "sur=train_data.Age[train_data.Survived==1].value_counts()\n",
    "die=train_data.Age[train_data.Survived==0].value_counts()\n",
    "per=sur/(sur+die)\n",
    "# help(per.plot)\n",
    "per.plot(kind='area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 年龄分块\n",
    "train_data['Age_bin']=pd.cut(train_data['Age'],bins=[0,10,45,90],labels=['children','mid','elder'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'Title', 'Age_bin'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2aee19dc8>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄分布，只有儿童存活大于死亡\n",
    "sns.countplot(x='Age_bin',hue='Survived',data=train_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新特征：家庭成员大小，同一家庭的应该会一样，统计有偏差\n",
    "train_data['FamilySize']=train_data['SibSp']+train_data['Parch']+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 家庭大小分区\n",
    "train_data['Family_bin']=pd.cut(train_data.FamilySize,bins=[0,1,4,7,12],labels=['little','small','medium','large'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ac27aac8>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看看不同家庭大小对存活率的影响\n",
    "fig=plt.figure(figsize=(10,10))\n",
    "# 只有一个人的时候，容易死亡，但是家庭人员多，其中之一死亡可能比一个人的时候死更容易\n",
    "# 可以看出，家庭成员为2,3,4时存活率最高\n",
    "ax1=fig.add_subplot(2,2,1)\n",
    "sns.countplot(x='FamilySize',hue='Survived',data=train_data)\n",
    "ax2=fig.add_subplot(2,2,2)\n",
    "sns.countplot(x='Family_bin',hue='Survived',data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA. 2343         7\n",
       "347082           7\n",
       "1601             7\n",
       "3101295          6\n",
       "CA 2144          6\n",
       "                ..\n",
       "S.O./P.P. 751    1\n",
       "PC 17597         1\n",
       "323592           1\n",
       "2662             1\n",
       "364850           1\n",
       "Name: Ticket, Length: 681, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ticket无明显特征，暂不考虑\n",
    "train_data.Ticket.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ad801748>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 费用\n",
    "sns.kdeplot(train_data.Fare,shade=True)\n",
    "# train_data.Fare.value_counts(sort=False).sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.Fare.fillna(0,inplace=True)# 少量缺失的以0填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 费用分区\n",
    "train_data['Fare_bin']=pd.cut(train_data.Fare,bins=[-1,100,300,600],labels=['little','mid','big'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2adb09f08>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不同费用的存活率，肯定是费用高，等级舱高，存活率大\n",
    "sns.countplot(x='Fare_bin',hue='Survived',data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>Age_bin</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Family_bin</th>\n",
       "      <th>Fare_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>Miss</td>\n",
       "      <td>mid</td>\n",
       "      <td>1</td>\n",
       "      <td>little</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>Mr</td>\n",
       "      <td>mid</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "      <td>Mr</td>\n",
       "      <td>mid</td>\n",
       "      <td>1</td>\n",
       "      <td>little</td>\n",
       "      <td>big</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                Name  \\\n",
       "258          259         1       1                    Ward, Miss. Anna   \n",
       "679          680         1       1  Cardeza, Mr. Thomas Drake Martinez   \n",
       "737          738         1       1              Lesurer, Mr. Gustave J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket      Fare        Cabin Embarked  \\\n",
       "258  female  35.0      0      0  PC 17755  512.3292          NaN        C   \n",
       "679    male  36.0      0      1  PC 17755  512.3292  B51 B53 B55        C   \n",
       "737    male  35.0      0      0  PC 17755  512.3292         B101        C   \n",
       "\n",
       "    Title Age_bin  FamilySize Family_bin Fare_bin  \n",
       "258  Miss     mid           1     little      big  \n",
       "679    Mr     mid           2      small      big  \n",
       "737    Mr     mid           1     little      big  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# big分区只有3个，可视化显示看不出，可以看到都存活了\n",
    "train_data[train_data['Fare_bin']=='big']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         204\n",
       "unique        147\n",
       "top       B96 B98\n",
       "freq            4\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.Cabin.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin缺失较多，根据Cabin存在与否分类\n",
    "# type(train_data['Cabin'][0])\n",
    "def replace_Cabin(name):\n",
    "    if type(name)==str:\n",
    "        return \"Yes\"\n",
    "    else:\n",
    "        return \"No\"\n",
    "train_data['Cabin']=train_data['Cabin'].apply(replace_Cabin)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2ae32ff88>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 港口，可见存活率与港口也有关系\n",
    "sns.countplot(x='Embarked',hue='Survived',data=train_data)\n",
    "# train_data.Embarked.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最后，处理缺失，以最常见的填充\n",
    "train_data['Embarked'].fillna('S', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 17 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   PassengerId  891 non-null    int64   \n",
      " 1   Survived     891 non-null    int64   \n",
      " 2   Pclass       891 non-null    int64   \n",
      " 3   Name         891 non-null    object  \n",
      " 4   Sex          891 non-null    object  \n",
      " 5   Age          891 non-null    float64 \n",
      " 6   SibSp        891 non-null    int64   \n",
      " 7   Parch        891 non-null    int64   \n",
      " 8   Ticket       891 non-null    object  \n",
      " 9   Fare         891 non-null    float64 \n",
      " 10  Cabin        891 non-null    object  \n",
      " 11  Embarked     891 non-null    object  \n",
      " 12  Title        891 non-null    object  \n",
      " 13  Age_bin      891 non-null    category\n",
      " 14  FamilySize   891 non-null    int64   \n",
      " 15  Family_bin   891 non-null    category\n",
      " 16  Fare_bin     891 non-null    category\n",
      "dtypes: category(3), float64(2), int64(6), object(6)\n",
      "memory usage: 100.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['Pclass','Sex','Cabin','Embarked','Title','Age_bin','Family_bin','Fare_bin']\n",
    "# 对类别型数据的每个类别单独作为一列\n",
    "X=pd.get_dummies(train_data,columns=cols)\n",
    "# 用于展示相关性\n",
    "X_show=X.drop(['PassengerId', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare','FamilySize','Name'],axis=1)\n",
    "# 用于训练\n",
    "X=X.drop(['PassengerId', 'Survived', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare','FamilySize','Name'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=train_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "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>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Cabin_No</th>\n",
       "      <th>Cabin_Yes</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>...</th>\n",
       "      <th>Age_bin_children</th>\n",
       "      <th>Age_bin_mid</th>\n",
       "      <th>Age_bin_elder</th>\n",
       "      <th>Family_bin_little</th>\n",
       "      <th>Family_bin_small</th>\n",
       "      <th>Family_bin_medium</th>\n",
       "      <th>Family_bin_large</th>\n",
       "      <th>Fare_bin_little</th>\n",
       "      <th>Fare_bin_mid</th>\n",
       "      <th>Fare_bin_big</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.242424</td>\n",
       "      <td>0.206510</td>\n",
       "      <td>0.551066</td>\n",
       "      <td>0.352413</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>0.771044</td>\n",
       "      <td>0.228956</td>\n",
       "      <td>0.188552</td>\n",
       "      <td>0.086420</td>\n",
       "      <td>0.725028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077441</td>\n",
       "      <td>0.797980</td>\n",
       "      <td>0.124579</td>\n",
       "      <td>0.602694</td>\n",
       "      <td>0.327722</td>\n",
       "      <td>0.054994</td>\n",
       "      <td>0.014590</td>\n",
       "      <td>0.940516</td>\n",
       "      <td>0.056117</td>\n",
       "      <td>0.003367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.428790</td>\n",
       "      <td>0.405028</td>\n",
       "      <td>0.497665</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>0.420397</td>\n",
       "      <td>0.420397</td>\n",
       "      <td>0.391372</td>\n",
       "      <td>0.281141</td>\n",
       "      <td>0.446751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.267440</td>\n",
       "      <td>0.401733</td>\n",
       "      <td>0.330427</td>\n",
       "      <td>0.489615</td>\n",
       "      <td>0.469646</td>\n",
       "      <td>0.228097</td>\n",
       "      <td>0.119973</td>\n",
       "      <td>0.236661</td>\n",
       "      <td>0.230276</td>\n",
       "      <td>0.057961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Pclass_1    Pclass_2    Pclass_3  Sex_female    Sex_male    Cabin_No  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.242424    0.206510    0.551066    0.352413    0.647587    0.771044   \n",
       "std      0.428790    0.405028    0.497665    0.477990    0.477990    0.420397   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
       "50%      0.000000    0.000000    1.000000    0.000000    1.000000    1.000000   \n",
       "75%      0.000000    0.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "        Cabin_Yes  Embarked_C  Embarked_Q  Embarked_S  ...  Age_bin_children  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  ...        891.000000   \n",
       "mean     0.228956    0.188552    0.086420    0.725028  ...          0.077441   \n",
       "std      0.420397    0.391372    0.281141    0.446751  ...          0.267440   \n",
       "min      0.000000    0.000000    0.000000    0.000000  ...          0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000  ...          0.000000   \n",
       "50%      0.000000    0.000000    0.000000    1.000000  ...          0.000000   \n",
       "75%      0.000000    0.000000    0.000000    1.000000  ...          0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000  ...          1.000000   \n",
       "\n",
       "       Age_bin_mid  Age_bin_elder  Family_bin_little  Family_bin_small  \\\n",
       "count   891.000000     891.000000         891.000000        891.000000   \n",
       "mean      0.797980       0.124579           0.602694          0.327722   \n",
       "std       0.401733       0.330427           0.489615          0.469646   \n",
       "min       0.000000       0.000000           0.000000          0.000000   \n",
       "25%       1.000000       0.000000           0.000000          0.000000   \n",
       "50%       1.000000       0.000000           1.000000          0.000000   \n",
       "75%       1.000000       0.000000           1.000000          1.000000   \n",
       "max       1.000000       1.000000           1.000000          1.000000   \n",
       "\n",
       "       Family_bin_medium  Family_bin_large  Fare_bin_little  Fare_bin_mid  \\\n",
       "count         891.000000        891.000000       891.000000    891.000000   \n",
       "mean            0.054994          0.014590         0.940516      0.056117   \n",
       "std             0.228097          0.119973         0.236661      0.230276   \n",
       "min             0.000000          0.000000         0.000000      0.000000   \n",
       "25%             0.000000          0.000000         1.000000      0.000000   \n",
       "50%             0.000000          0.000000         1.000000      0.000000   \n",
       "75%             0.000000          0.000000         1.000000      0.000000   \n",
       "max             1.000000          1.000000         1.000000      1.000000   \n",
       "\n",
       "       Fare_bin_big  \n",
       "count    891.000000  \n",
       "mean       0.003367  \n",
       "std        0.057961  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249dd677948>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t0oyD1ri6cfY6mr8QV2Xamp5cbKui0YNiwLZL8PFH0aA5xluXEfVFGO5a2oCcXw/i0bmFnYxH5xZk/7QPAO25a8g8NSgCfJC5u+LWtB7Z31sGyBiMmPMsX53V37qL/k4CMo0bxpR0jPeSwWgkf8d+hzbGlJmDLuY6otHxK7BFp2Mw5+QV/3br2JKUTZbZ3tzTN1B4alA5Oe8+beqR9stxAJK/P4B/96Zl+tK/W9Nizfwz11EG+aBPTEefkGaXzrNNfTJ+tQyo0jb9jo914G7MyKHg/E2HegEgU8iRq1UIchkKVxWFyVlUfCKKWz9Y2oO0M7dQeWlwdVKGhH3ni/9PP3cLtxDf4t/F/UCKtR84vA9V01L9wLUS/cB1Wz8AIMjlCCoXkMmRBQRj+MNSrpzTN1F4ujn1qW+buqT8Yrm4Sfz+IIHdm1h2iCIKd8uEiEKjxpCdj2g0E/RkM3LO3cas1ZN96jqiwUjoC20ddP1K6QZZdTU1wsg4ZLnbVXAzEdfwAFQBXsjdXQlsURO5i4rbPxzGbDCRcugSKi+N0zYgKyaOAmdtgCii1LgW223OzUNVs/pfbgdLonn2acTCQvTnL1r61m0Hce9sH/funVqQ89P9vvUaMg8N8gD7dsGtZUP08ckYE1OLtwXOGE7aB5+BXY2W+K/xPz+QB0KAdFEUdQCiKKaLopgoCEKsIAj+AIIgNBEEYb/1/9mCIKwWBGE38KUgCCcEQah7X0wQhP2CIEQJgjBQEIRlgiB4WbVk1v1ugiDcFQRBKQhCVUEQdgqCcFoQhEOCINSypokQBOGYIAinBEGY8//ZH8XIfAMwZ9g6GHNmGkKJBro0qugnMZw9CYDp7h0UtRsguHuCygVF3UhEk60xMqakowzys8uvCPLDkGw7njE5DUWgH/obsbg2qYfM2wNB7YKmXVMUwTY7/MYN4PVjS/CrFsqpz2zLXvKSM/EIcj6IefKD4Yz941P8qoXyx/rdDvsbvNQBc2wMgrsPYl5W8XYxPxvB3VFTFlwFdb83cXlmNIJvSPF2ZbsX0B/+0W4QBuAb7EtGoq3Bz0zOwDfIF2e8OLk/7+/8mFfeGoxCZbl29AnypekTzdnztf0yH49gH/ISMx7KBz0+GM4Yqw9OO/GBqkN3THdvI2aWigEff4e0iqjWuL//OW6T5qFd63jXR/APQl6pGsabV5AF+GNKtXUWptR05AFlx1VJSuZ17dIR3dkLDnmVwb7oS/hWn5SBMtgXVYgv+qQS25MzUJUYsIRNfZkGJ9fi17sdiYtst7hdg30pTMygwtMtuPvTMbRJmbiGPNrg2DeqGp33vEebDVORhVZC8PFHzLT5wOJXP4d8isatcX/vM9zGz0P7mc2vrn1fQ/v9GjDbx5VPqbjK+JO4emlyfxbu/JhXS8QVQI3GNVm44yOmf/GWxVZvf8wlYkDMSkfm7SQGGrVGM3cdbuPmUrTeZqv6pVEUbVqDoPEAvb54uzG5jDYgMc0ujSLYD2V4CKbMHEIXTiBi6yeEvDcWwdXF3gCVElOGra4aU9KLl2D9FRRB/ugSbHVJl5SBS4i9L5W+HhhzCxFNltv+ukT7NJ5RNWiy7wPqb5yBW80KuIT42mkKchk5R2PsNBW+HphyCsCqqU/KQBXsGBslMSRnErNyO31OLuHFs8vQ5xaSeDAGt2AfCkq0BwVJmbgFlx27gkJO1efakPD7heJtMt8AzOmP0A90svUDYmY6RVu/w2vl93it3WxZRnbGdvehKCkT9QN8WpRoSxO/bheaGmG0v7CClvs/4OqbX4AoIijkuEUEofRxR+aqQlAq0FQNcdA1lNK9f67yLscT/KTlYsmrUVXUFfxRh/jiVimQoow8Qjs1pMHkZ2m+aChyVxcKE//cj6W59vlveFYP5dmzy3hy33yyP1qGzN/vL7eDJVG3a40htsTdMSf1Shnkj7Fk35qSjjLIvm54Ptme3G37i3+7RzfHmJKB7qrjcrJ/A1E0/+t//1WkgTzsBsIFQbguCMJyQRDaP0SeKOAZURT7Ad8CLwAIghAChIqiePp+QlEUc4DzwH3dp4BdoigagNXAGFEUo4DJwP21GUuAFaIoNgXs7w3+24jOL8wVdSNxie6B9utVAJgT4in6+Rvc31qE+8yFmNOSHV4fJZbWEgSnx9Pfvkvm2k1UWDefsDVz0V29DSUuCjKWfMGnLceRn5JFrdKzYWXYu23KapY2G03GzURqP2U/K1ixZW0avtge/ZGfnOYtrWlOi0f7+UyKNs7FcH4/Lk+NAkAWUR9Rm4eY6nhXUnBSVmemfrvwKyZFv87Mpyfj7u3O0yOfBeDVWUPYuOBLxNLrBp3qOvfB9imrWfYnPlC1647h6F7HjE70jKePkD9tEIUfv436uYH2O13UaMbORrthORQVlnmeH4r7eRUK1G1aYbh81TGvU32AP/d5wsINXGg2lIyfDhI4qEcJPfCsHoZJqyf32j1rvoefoMq6GMv2puPY03kGN9ftwm3sO05tcTbnZTxzhPwZgylcOgt170EAKBo2x5yXbbvbUYKHjatvFn7FhOjXmWGNq2escXUn5havtxrO1O4T2Ll+O66j33HuTyfGGs8eoeDNIRQum21ZLw8oGjRHvG+rMxnHAjg9lKCQoa5bjawN27nz9FjM2iL8R/Z5sN4jnCdHWx5C7k/8nXfhDsejXuOP6CkkrNtBvfVT7UQFpQKFrye5B86XUnj0+iH30lDxicb80GIC3zUeg9LNhSrPtn7kutbyvYGknLhK6slrf2pOmf1AvUhcOvVA+5WlHxA07iibtibntZfIGfYcglyOMrJJKanS9dfZ4Sxp/Ds2JC8mjgMNRnEsehq15w9C7u6KqVBH4uYjNPl+Jk2+eQNTgRbRWLptLLsctz/5GYWXhlZ7F1BxSDfyLsYiGk0ICjm+9StTkJDB8YlrMBbqqDv6qT/1gTNCOtQn61IcPzYazfYuM/GeNBbBxcUx4aPGq0KBslYNy6z+n+n8iU8BUCpw79ScvB2WuzeC2gW/US+RvuSrR7NH4l/hf36NvCiK+YIgRAFtgY7Ad4IgTH9Atq2iKN6/F/898BswC8uA3vEpE/gOeBH4HXgJWC4IgjvQCthUovO9X7NbA89Z//8KeN+ZEYIgDAeGAyz/cC5DX+37ALMfDXNmmt0tUplvAGKm421DecUquI2cQv570xDzc4u36/dtR7/Pcuvf7fXpKEpoKYL8MaZm2ukYk9NRBgdw/ya9IjgAY5olTe7mXeRutsw++40fiDHFYodXv6fwer4bg80Kki/FUrlNvWI9j2Bf8lKzyyyfaBa5/MtxWox4koubDgIQUCucHu8P5fsBH/BK7wLE/CwED9vMi+DubXlotSR627ICc2wMdOwLag3ykKrIIxogr1wPQa4AtYY1578mPSGN2xdu4BdqmxHxDfYjq5Q/ALJTLTOMRr2R/Zv20XP4M4DlIdexSycD4B3ojax3e/JmZBB7+BIeobbZGI9gX/If4IMrvxyneSkfdH9/KIUfT0Vw90Ro3qE4vcw3ADE7oww1MF27iCwoFMHd0xILcjluY2ejP7oX4x+WTsKcmoY8MLA4jzzQH1P6wy1JuZ9X3bIZhms3EDSuDnkNSRmoSvhWFeKHISUTQalAFVJie7AfhmRHn2duOUitnxfg3bU51UQZmedvE9q9CXe3WNaru4b4UpRctk9LYyyxbCd533kE+atQVIjga/PBA/16/SKywBAEd0/k1euhjGyJskEzUKoQ3DxYZ42rW6Xiyu8R40pbwtZzv59GkA9DLCpE5ltiqYSPP+YH2RpgtbVaXRQNW+Jevxmo3RDcNIR+OJnESYtQBPtjSLHXMSanowwNQGudClEE+2NMyUAUwZCcjva8ZYCZt+MIfqUH8noDcj9bXVUE+WNMK9tOZ7g/1QlFxVAq/LAcXcx1XMJsdcklxA99qXgxZOSi8HRDkMsQTWZcQm1pTFZfhg56gtD+nVFX8Cf3tEVTdxq8oxshGowUXrNfyWnMzEXupQG5DExmVCF+6FMcz2FJvNo2IC8+DV1mHrUGdMa3XmUCm9UkfscfaEq0B5oQXwpTnMduwwm9Uft5sG/oZ3bbzRlpyPwfoh+oVAW3UVPIn2vrBxQNokClwv3tDy0+uReHvEYdYAcA6hBfdMlZdjqGjDw7n6pDbWlCX2rPnaVbAcugXunrQYvd75F17DL5l+O4Mc9yJy366hryrsQ76CrL0DXla4kZb3sJRPtTS/FtV58KfTsgmkVyryfgFupH/K8nqTv6KdxCy/ajM6q+2J5Ly34BID82BVNiMigVf7kdvI+6ZTOMsfHIvLyKtymC/TGUqvOG5HS7u9iW/tdWN9zbNUF36RamDEuZVBVDUFYIImLrp/eTVADOAM34tyYXpYddy0SakQdEUTSJorhfFMVZwGgsg2gjNv+oS2UpKJE3AcgQBKEBlsH6tziyFeguCIIvltn8fVbtbFEUI0v81S5p1kPYvVoUxSaiKDYp70E8gOnmNWQhFZAFBluu/FtHo//jqF0awT8QzZQ5FCx9D3PSPft9nt7FaRTV64BKjSIsCJQKPHu0p+D343bpC34/jucznQBQN6yFOa8Ak3UgL/e1NFSKkAA8urQmz3oLsPDIaeKffZ3PesxEl1uITCEHILRRVXR5hRQ4GcT6VAoq/r9650Zk3EoEwDPUj+dWjeeXCSvJtD4kZU6JQ/AORPD0A5kcRY2mmG5fsBd08yz+VxZU2TILVlSA4egWij57g6LPZ6LbsQ5z/GWGNezPGz0m8MfuE7R9rgMA1RrVoDCvoHhwVZKS65ubdm3O3WuWzmlcmxGMbTOcsW2Gc3TrYbaOWcbKNhO5sfs09Z5r80AfeJfwQbVSPnh21Xh+nbASc/I9TLevIg8OQwgIBrkCZYuOGM7Yx4AsMNT2f6XqIFcWd+SuQydjToxHv9P2dhD9lasowsOQh1jiyq1zNEWHHB+6dMb9vG5P9UC7d7/TvNm7T+L3fAcANI1rYMorwJCaRcH5G6gjQlCFByIoFfg+04bs3yxLAFwibLfhvbs2I//0NS4/MYE9XWaQuPM0ga3rcHfLMXwbV8OQp6XoTy6OSuMSYOtkfSKrWN5+dPkM8sAwBH+rX5t1wHD2z/xaDRQWv+p+WEfepL7kTelP4Yp5GC+dZkjD/kzrMYFTu0/QzhpX1f9CXHkF2Nb9Vm1YHQQZpstnkAXZ23r/wdb7CCVtrVjC1h8/I39KP/KnvYJ2xRwwGEj76CtQKvDq2Y78vfYPeebtOYF372gAXCNrYs4rwJiWhSk9C2NSGqqIMAA0rRqiu2k/UDMXFKIIDrC0MQoF7t07OLQxDyL/l70Y4xO59/xrFOw7SlAfy41Uz6jqGPMK0Ts571lHLhFgvaMV/EJ70nda3h6lsvoy8fNdXJu8Cn1qDqlbbJpBg3pgSMvB4OT85B6Jwa+nZZ1zQJ+OZO069ad26xPSCWhcDblaxdUv9pB87ArnFv9I/K7TVH3e0h4ENK6KPrcQrZMyVO/bgbAO9Tnw+qcOs7kO/UCbP+kHPrHvB8zpqcjcNOTNeJ28yUPBbEZuvSj0iqpWpk8zj1wm6CnL8yuhL7QjbecfABQlZODX1jJZk/LrCYx5Wk72fJvUHX9Q4WVL3AR2i0Lu6sLdLx3vJpbWTbHqKjzdEJSWvqNC/2gyj18hbuU2jrSfQsbZW6Sfv02V59sQ3LYu+twC9LmFj9QGFCSkE9zWsgJX7e+JolI4usPH/nI7eB/XLtEUbvnVpqNU4PlkO/L32sd9/r4TePW+37fWxJxfgCnNFneePduT++uB4t+667HcbNmPW9GDuBU9COAe0Jj/2goBCUCakUcQhJqAWRTF+/epI4E4wBXLoHsHttnxsvgWmAp4iaJ4sfRO66z/SSxLZn4VRdEE5AqCcEcQhD6iKG4SLNPyDURRPA8cwTJz/zXw8t8vJUyZtYBTZy+QnZ1Lp179eW3IKzz31BN/nslsonDdEtxnfgAyGfrfd2C+F4uqy9MA6H/biuvzAxDcPXEbNsGSx2Qib7rl1W2aye8i8/BENBopXPsxKJVUWDsPZDJyf9yN/mYcXi9aljDkfLedggMn0bRrSuVdn1lfP7m42C0W0RcAACAASURBVJSQJW8h9/YAo4mUOZ9izrU8WOU/cTCqiAoMMcrISUjn9oELjDz4IQatnm2TVxfnf2H9ZLZPXUt+Wg49F49A5e6KIEDqlXh2zlwPQOtxvVH7uPPEnIEAuPiD7tv56Pd/h0uvsdYB2FHEzCQU9S0PUhkvHkJRrTGKBu3AbEY06tHvWPvA83F232kiO0bx8cGV6LQ6Vk3+pHjf1PVvsWbqMrJSsxi9ZAIevl4IAsRdvsPaGX/+6tBb+85RpWNDRlh9sL2ED/qsn8yOMnywq4QPXH3c6TpnIO7uLyOaTGi/XIpmyvvFr0k0J8Shira+pnPfryiatkPVpguYjIh6PYWfWh7rkNeoh6pNV0zxt3Gfa7nVXrRpHdw8QfaHS/H/+H2QySn4dQfGO7G49bbcsi786Rdkvj4Efr4SQeMGZhH3F58jpe8gxMJCcj5Zie97s1BWjaBg63aMd2IJ6G+ZnU37ehc5+07jFR1FvcMrMRfpiJ1o9a3JTPxba6ixYRbI5GR8t4ei65bZ0ApvvIq6SiiiKKK/l0bcGyuK/WYqKMKQW0jb797ApNXzx4RVxftafz2F05PWUJSSTbUhT1DjtZ6oA73osncByXvPcXryWir0bEaVAZ0RjSZMRQYKV84FsxnthqVoJi2w+PXQTsyJcag6WP26/1cUTdqialXCryvmPlRcNeoYxZKDK4tfa3qf6evfYpU1rsYsmYCnNa5iL99hjTWuWvRoRZf+3TAbTeiL9GhXzQOzmaINy3CbMB9BJkN/eBfmxDiU7S22Gg78ijKqLcqWncFkQjTo0K50YqtoxpQQS8X1cxBkMrJ/+A3djXh8+nYHIOubHeTvP4V7hyZU27cWc5GOxGkfFWdPemcVYR9NQVAq0N9NJnHqxwB4dG1J8NsjUfh5Yi7SEb5lNab0THJ/2o3hVhyeLzwJQO7325D7+VDhu6XI3N0QzSLe/XsR/8xwxIJCAhdOx7VpA+TeXlTa8zWZy7+iKC6F5ieWYtLquTaueHaS+hve4NrElehTsrg992vqrJpAxPS+5F28Q9JGy8O6AU+1IHRAV0STCXORnssjPiL3j+v4dWpE5LEVuIT5c/nF2cWaNb+aye3JyzGkZBE/7yuqr5hI+NR+FMTcIfWbPQAoA7ypt+MD5B6uYBYJHtqTCx3Gkn/2BnHbTvL0rrmYjSYyL8VxfcPvmPVGwqIb8uyRDzFp9RyeaGsPOn85mSNT1qJNyablgkHk30vnya0We+K2n+L8x5ZX/GI2Ubh2Ce5vWfuBfTsw341F1dXaD+zeimufAQgepfqBaSMw3biC/tgBPBetAZMJ450bmJISaHNiCSatjkvjbO1Zow3TuDxxNbqULG7M3UiDVWOpNv1Fci/Gcm/j7wDcXvwjdT8ZRcv9CxEEgRtzNmLIzCN9z1nqfDicrne/RjSbuTrrK4w5lvm2qA3TiLHqXpu7kYarxlJ9+ovkldB1rxFG/aWvIZrM5F9PIKZEHf/jzS9ovmgomvAAAlvUpOBeBkfH2uzu8NVkTky2+LHmkK7UGWVpA3rsmU/ivvOcmLyWmI+30PLjETy5dz4IkPPpasxZ2X+rHRRcXFA3iyL7/Y8wZWTi//H7+KIg54fd6G/G4/2SpW/N/nY7BftP4d6+KVX2rMOs1ZH8hq1eCWoXNK0akfzWUsc6K/FYIDzKWs//i1iX1SwFvLHMwt/EslylNrAOSAFOAE1EUewgCMJsIF8UxUUlNIKABGCOKIrvWLcNtOYZbf39PJZlNx1EUTxg3RYBrMDywK0S+FYUxXet2zdiudDaDLz5oNdPGtJvl/uJzB81+MGJHpG0mNI3N8qHzYV//aG2shg38W+98dMpQxb/MxMaDSl/W0e1S3pwor9A/s3yb3OS7no+ONFfINakKXfNLt1Syl1z+L7yP/9gff1kOXNvv6rcNQFcXB3fRPN3uZv2z8SVWl72W0j+KlcFtwcnekSeaXHvwYn+AqcOB5e75j+18CJDXv7znR0qJZa7JkBexj/Tv9a6vv1hnnD5R9HfPf+vD1ZV4Q3/dT84439+Rt76YKrjS4XhEFDDSfrZTralUMqXoiiuB9aX+P0DpR45EUXxDtDNid4doOT7o/4zr7GUkJCQkJCQkJD4b/A/P5CXkJCQkJCQkJD4D2Mu/7tY/1eQHnaVkJCQkJCQkJCQeAyRBvISEhISEhISEhISjyHS0hoJCQkJCQkJCYn/Lv/hL6v+20gz8hISEhISEhISEhKPIdKMvISEhISEhISExH8X6cuuZSLNyEtISEhISEhISEg8hvzPfxDq/wpZfTqU+4l0X/FZeUuyrPHb5a4JsFPMKHfNTz3+mRmAmxk+5a45Qbxd7ppnN48pd02AomXlH1d7DoaWuybAP9E6VlPm/wOqkKJzLXfNKr4P/xn6h6Ww8J/5IFSsrvw/ipUv+2fmup7uXv4fBTv0a/l/FA8gskr523rt9j9j6z/BFtfy/wbQe33L/+NlACm//DNtS9WYXf/6h5D0t0/+64NVVZVm/7ofnCEtrZGQ+P/IPzGIl5D4JwbxEhL/xCBeQuKvIEoPu5aJtLRGQkJCQkJCQkJC4jFEmpGXkJCQkJCQkJD47yI97Fom0oy8hISEhISEhISExGOINJCXkJCQkJCQkJCQeAyRltZISEhISEhISEj8d5Eedi0TaUZeQkJCQkJCQkJC4jFEmpGXkJCQkJCQkJD472I2/dsW/GeRBvL/h1BENsNt0GiQydHt3YZuy0a7/ao2nXHp1dfyo0hL4ZqPMMXdAsClx3O4dOoJAuj2bEO3/YeHOuab7y3m4JGT+Pp4s+XrlY9kb4d3XiGiYyQGrY7dk1aTGhPrkKbbklEENaiC2Wgk+dxt9r7xGWajCRcvN7p+MByvSoGYdAauTP6AuGtxjHpnJM2im1Kk1fHhxA+5GXPL6bEHTh1A2yfbYDaZ+fWrbfz8+VZadm3Bq5NfRe3mgn+wP7LsXLI3/Ez22u8d8vvPGIVbu2aI2iJSZ3yI7spNALz698KzT3cQBHI37SDnq58AUNWqQuCssYRXCEPh7kpRYgbnh31M7kXHMrtWDKDhqnEovTXkXozlwuvLEA0mFF4a6n88ArfKQZh0BmLGryT/6j0ABozoy/MvP4MoiiiUCuQKOdpCLTPGvMvli9ccjvHV1tVo3N0A8PP34cLZy4wZMAVPLw/mLXmL8MphqMJ9MFw/xuGTp3n/m12YzSK920YypEdrO631O4+x/UQMAEaTmTtJ6ez/aCJe7q5s2HOSzQfPIiLyXNtG9O/S3On5UDRsiutAS+zq921D9/M39vubtMb1hUEgiogmE9ovlmG6FuNUq+GcVwnp1BCjVs8f41eR7cTHVQd1ofqwbrhHBLO17gj0mbYPqQS0rE3Dd19BUMrRZ+ax/9m5RJbQPPUnmjWsmj870Yy0asqysrn5wkw82jeiwuxhCHIZGd/+RsryzQ6aYe8Mw6tjFGatjrhJS9DG3EYZ4k+lj8ajDPBGFEUyNu7Ct0IF/Ds1wqzVcXHsir8dV+oQP+q8PwR1iA+m7FxMGdkow4PJWPoV2V9usZRpxig07ZoiFulInvEhusuWOuD9yjN4WetAzqYdxekB3NpEEbFgKko/L5KXfEvy4o0OdobNHoZnxyaYtTriJ39cXOaKH41HGeCDaLaUOf3zXyzlqhNBhXmvEaFywWwyUxCbgneDCExaPWfGrSTHiS8iBnelqvVcba8zAn1mHgDBT0RRe1ofMJsxm8ycnPU1aSevA9BkziuERUdi1Oo4NmE1mU50awzqQu2h3fCICGJTvZHorDGg8nKjxeLheFjbK9lPCxB8/FH3fQ1BkKE/tAP9ju/stBSRLXHpNRBEEcwmir5ZjunmJVsCQYbm7U8xZ6Wj/eQtu7x15g0gsFMkJq2e82XEQ6XBXYkY3h1NRDC7aw/HYPWBplooDZeMwLN+BPmr15G/8XtcWjTFa/xoBLmMgq3byf+qVP2sFI7PzKkoa1Ynd9Vn5G+0tJmKiuH4zLHZpggLwXD9Fj7egZi1Oi6PXU7+xTsOtqkrBlB31XiU3u7kXbzD5deXIhosAznvVnWoPmcggkKOITOPs71nA9D453fwbFwN0SySffQyFwd+gFln+Nu67Qd1p+VLnfAKtNS3nJQsNkxewb1Ljnbf57nZg2jRpwNT6g6w216xQVUm/jQX3bcfYoo5jrxGJC49B4NMhuHUXgwHfnKqJ6tQFddR8yn6ZjGmmOOgUOI6fA4olCCTY4o5hn7Pdw75XFs3wX/6SAS5nNzNO8heZ9+XKSPCCZwzEZc61cj45Aty1j9cvy/x30JaWvN/BZkMtyHjyJ83jdwJA1C1jkZWoZJdElNqEvmzxpE3eQjaH77EbcQkS9bwCFw69ST3jZHkTh6KMqolsuCwhzpsrx5dWLl47iObW7ljQ7wrB/N5u0nsmb6O6HkDnaa7uuUoX3Scwldd3kChVlHvpQ4ANHv9GdIux/H1EzPYOWElo2aPpGnHpoRFhDKo7RCWTPuEMe+NdqrZ9YUuBIT4M7TDcIZFj2D/1gMAnD18jte7jQYE3h06B3OBFo8eHVFWrWiX361dU5SVwojvNojUWUsImGX5AqqqWiU8+3Tn3otjudt7JJoOzVFWsnxx1H/SUAoOniT33G3ODvoQY66WOguHOrWvxpv9iF21jUMtJ2DIzqdCv2gAqo7rRW5MHEc6TuPi6OXUnmvxmUuwD/2HvsjzXQew6N2luHtoWPHhOmZNms/bC6c5PcYrTw/n2ej+PBvdn3N/XGTPtt8BGD5+IFdirtOrw8sYrh1BqNyI9zbsYPn4vvw0ZyQ7T17iVmKandbAbi35ftYwvp81jLHPdiSqZkW83F25kZDK5oNn2TBzMJtmDefghRvEpWQ6GiPIcB08joL508mbOBBV607Iwuxj13jxNHlTh5I3bRiFKxfiNmKK03IFRzfEo0owO1tN4syUdTReMMhpuoxT1zn4wnwK7tqXRenpRqMFgzgy8EN+6zCNY8M+ITi6Ie5VgtnRahKnH6B5oAzNxgsGcXjgh+zuMI3YUQtBJiN87ghuDXiHK51G4/N0W9TVw+3yeXaMQl05hMvtRhI//VPC540CQDSZSJj7GVc6jeb6M1MJHNEbj3qVOdRiPDGT15RLXNVZMJhTfeZys0lvzJk5JE9biKjVkb/nKACadk1RVQoltttgUmYtIfBtS11TVa+EV5/uxL8wjrheo+zqADIZQbPHUnTzLvp7qXh1a4lLqTJ7dIzCJSKUK+1HcPeNT6kw11bmxLmfcbXT69zoNQX/V3sU5w15YyDJS77h984zSN51hsB29dnTciLnJq+l4fuDnfoi8+Q1jr7wHoWlzlXaoRh+j57O751ncHb8KlossvgyNLohHhHB/Nx6EiemrqPZ/IFOddNOXWfPi/PJL6Vbb+wzZF2KY1vnGRwdtxJ139dwfXkMhR/NIP+toSibd0QWYt/OGK+cpWD2CAreGYn280WoB06026/q0htzYryDDQGdItFEBLO/xQQuTl5DvYVDnNqadfI6J/rMozDe3lZDdj6XZn7BnRW/WjbIZHhPGkfGxOmk9B2EW5doFJXt66c5N4/sj5YVD+CLyxB/l7QBwy1/g0YimswgmjneYixXJ6+mZhmxWvXN/txdtY3jLcdhzC4g1BqrCk83ai4YyoVX3+dk+0nEDFsMQNDzbfFoWIWD1Qdy9tl38GxUlcBerf62rqZWOC1f6sS2D78j/sItEi7HsWvpZl6Y59ynAOH1q+Dm6eawXZAJPD29H1cOnr+/AZenh6H9fB6FH41H0bANQmAFR0FBhqrbK5hunC/hWAPatbPRfjIJ7SeTkNeIRBZe3T6fTEbAm6+TNOpN4p8ehnuPjiir2MeYOSeX9AUryF7vOIkg8fggDeQBQRBMgiCcEwQhRhCETYIgONZCW9rZgiBM/oft+UwQhFRBEJxPOTpBXq0W5uQEzKlJYDRiOLIPVRP7mVPT9UuIBZYZItONy8j8Aix5wypivHEZ9DowmzBePoeyWduHOm6TyPp4eXo8rJnFVO0axZXNhwFIPnsLF08NmkBvh3Sxv9sar+Rzt3AP8QXAt3oY8Ucss1NZt5IICg+iw9Pt2LN5LwBXz15F4+mOb6Djl1R7vvIkG5ZsRBQtX3zOycgBoKiwiJqRNUiMTSQ/Nx9Ekfwd+3GPbmmXXxPdkryf9wCgu3AVmYcGub8vyqoVKTp/BbFIByYz2lMX0HSyngNRxLVJAxI3HUTp6UbBrUSUnm64OCmzX5u6pPxyAoDE7w8S1L2J5bg1wsg4ZAmJgpuJuIYHoArwAkCukKNWu9CpRwfSUtJJTUnn/OkYPL08CAj0K/M8uGncaN6mCXu2Wy5mqtWI4PihUxaTtblcvBFHeJA/FQJ8UCrkdGtWl/3nrpept/PkJbo3qwvAnaR0GlQJw9VFiUIuI6pGJfadueqQR16tFuaUREvsmozoj+5D2dQ+dtEVFf8ruKgB51/rDu0WRdymQwBknrmJ0tMNtRMfZ8fEUXgv3WF7eO9WJGw/hTYhw3LYjFwHTdUjalbs3Yp7JTSNGTm4RVZHF5uMPj4F0WAk65dDeHVtZpfPq2szMjdbLrAKz15H7qlBEeiDMTULbcxtAMwFWkSjiazjFr/mnL75t+NKUy0UbUI62rhUMBjJ3X4AnwHPYribhDEx1ZInuiW5P1vqWtH5q8g93ZEH+KKqUpGi81dL1IGLuHe2DKjUDWoiuKhImLUaRJGcXcfxKnWHxqtL8xJlvlZmmXU376EMssa1KCK/f3epRS3ybiQAkHWmbF/kxMRReNfxXJkKdcX/y93UltlwIPyJKO78YGmv0s/cQuWlwdWJblZMHAVOYsCrehjJhy3tVe7NJGRBFTBnpiKmJ4PJiOHkfhSNSg08S8d8iZAXfPxRNGiO/tAOh2MFdYsiwRqv2X8SD7kxsWid+ECfnkvOuduYrTPVqjq1MN5LwJRo6VsK9+xD3c7eVnNWNoYr1xCNZS9/cGnSGAwGCrZYLhByT99A4alB5cQ2nzZ1SfvlOABJ3+/Hv3tTS9mebUPa9hPorHXJkJ4LgG/Hhpi0OmRqFXnnbiGolJiLDH9b1616GHFnb1CnQyQnNh/g5onL+IUH4eqhwTPA0W5BJtBrRn9+nr/BYV/7gd05v+ME+db+RhZeDXNGMmJWCpiMGM8fRlG7qUM+ZavumGKOI+bn2O/QW+NDLgeZ4+IKl/o1McQnYryXDEYj+Tv2oynVl5kyc9DFXEc0Gh3y/+cQzf/+338UaSBvQSuKYqQoivUAPTDyX7ZnPdDtUTLIfAMwZ9hmVsyZaQjWgbozVNFPYjh7EgDT3TsoajdAcPcElQvKxi2Q+Qf+JcMfFvdgH/KSMop/5ydn4h7sOOi+j0whp/azbYg7cAGA9CvxVOtmbYQbViEoLJCgisGkJdo6pvSkdPyC/R20QiqF0P6p9izdtoS5X75LaOXQ4n2turWmVmRN5nzxLqlvLsaYnI480F5DEeiPMdnma2NKOoogP/Q3YnFtUh+ZlweC2gVNu6YoQiznIG3BStSN6lBrzgBqzurP9XnfUJSUiYv1wuQ+Sl8PDLmFlpkroCjRlibvcjzBT1oGe16NqqKu4I86xBddchafL/+avWe30uvFHmRlZnN0v2XAlpyYSmBI2eeyy5MdOH7oFAX5BQBcvXSDLk92BEBw9yM1K5fgANuFQKCPBylZeU61tDoDR2Ju0blxbQCqhQZy+kY82fmFaHUGDl+8SXJWrkM+ma8/5ozU4t/mjDRkPo7nTdm0DR6Lv0AzfT6FKxY6tcE12JfCRFtcaZMycQ0pO65K41E1GKWXhvabZ9Jp11wq9WnjoFn4iJruVYNRWTU775qL73MdUQX7oS8Rq/qkDNvA1Ioy2A99ki2NITkdZbB9GlWFQJQB3mQevVK87e/GlSrQC2NuYXFeY0o66sja5G3bX7xNEeSHoWQdSE5DEXi/DtRD5l2iDgRb6oAmuiWmrByKrsRaypOS6VAeZbAfhhJ3fAzJjn5RVQjEtW4VCs9ZlowlvLuW0BmD6Hp6KX7NanBzlW1wW/SI5wogpHsTOh1aRMuvp3Bs4hoAXIN9KCgRAwWJmbj+SXtVmqzL8VS0Dhr9IqsgePkiFhYU7xez0pF5O8a8olFrNHPX4TZuLkXrFxVvV780iqJNa5wOLtQhvsUXjWDxgbpUPDwKsgB/TKm2+mlKTUceUHbfUhauXTpizs7BlGLT0iVlOI1VY4lY1ZWIVbeqISi83Gn04yya7F5AcJ92ljxeGpI3H6LVmRW0vrAaU0ER2tjkv61bcPUuVZvVwrdCAPnpudTp2AjvED+ykzPwCnb0absB3bi45w9y07LttnsF+dDgiaYc3vBb8TbB0xcxx1a/xdxMBC/7WBc8fVHUaY7hxG5HhwoyXMcsQjPzM0w3z2O+e8NutyLQz7GfCnSMMYnHH2kg78ghoBqAIAivCoJwQRCE84IgfFU6oSAIwwRBOGXdv/n+TL4gCH2ss/vnBUE4aN1WVxCEk9aZ/wuCIFQvrXcfURQPAk7WIDwiovNZS0XdSFyie6D9ehUA5oR4in7+Bve3FuE+cyGm2Ftg+qcfLBEctohl2AsQPW8gCSevknDS0nmfWv4Lai8NL++YR6NBXbl56ZbTSVpnmkqVEr1Oz5gnx7Fj404mfTiheN/189c4tP0ws4e+i+/Y++sbS2k4mg6iiOH2XbLWfk/ouvmErp6H7todsM5Qeb3UE/2teM4OXszVt7+k3kcjivM9jDbA7U9+RuGlodXeBVQc0o28i7GIRssa5+hu7enSpBfHD55CpXbhqee7lchetl979O7Ktp9sncSaT77E08uDH/d9jSK0FmJRnkP5BWc2AgfOXyeyWjhe7q4AVAn1Z1C3loxYvJHXPt5IjfAgFDInTY5TQUebDacOkzdxAAWL3kL9ovMlE87892fld8gul+PTIILD/RdxqO8Cao/vjULj8rc0ZSU0D/ZdQNDYF1AEORlYOWg6LYxN101NxKppFN24i0mrKzNdWVJlxZU2Pg3MJfLLZSjDgsjbdaiEnnPb9Lfvkrl2ExXWzSdszVx0V2+DyYSgdsG9S2uKLt38y3beL3PlldNJeHct5nwtAP79u5MwZy27o8aQdyOBGmOe/tNDPIikHX+wt+1kTgxaTMOpz1uL++fn4kFcWvYLKm8NPX6bR83BXTGnJzvJ76hnPHuEgjeHULhstmW9PKBo0BwxLxtz3A2H9ADCI7atD+Rvlh0AhQJ1m1aYMpx0aw8VA9ZdcjkeDSM4338B51+aR+WJz+FaJQRBqcC7eW2ONX2dIw1HIMhk+HaM/Nu6hTcS2LNyK5UbVafXzFdIuBKH+X7fWMpuz0AfInu04OD6nQ6HefbtgWxdsBGxZL16QP0GcOk5CN3Or5zPBotmtEsnU7BgOLIK1ZEF2S9TK5fzJvFYID3sWgJBEBRAd2CnIAh1gZlAa1EU0wVBcDal8aMoimuseecCQ4ClwNvAE6IoJgiCcP/+20hgiSiKGwRBUAHycrB3ODAc4JsBfXiu05PF+2S+AYiZjrdN5RWr4DZyCvnvTUPMt82O6vdtR79vOwDqvkMRM9KQE/V3TbRDpvbk5R3zAEi5cBuPENvsg3uwLwUp2U7ztRjfG1dfD/ZM/8xmb76W3ZNX0/DVztTr2xGf6qHs/3k/AaG2GQf/EH8yUzIc9NKT0jm83XKb/MjOo0z6cKLdvoDQAGJOxKAMD0FZOQxTqr2GMSW9eJYRQBHkjzHV0kHl/biLvB93AeA7fhDKCsGE/7gcVfXK5P64G9cwP5J+Okq9xcPRpWSjS86y0zZk5KH0dEOQyxBNZtShvsVpTPlaYsbbHihuf2ophfFp1Hr3FTzqVmfdpmXEnL2M0WikUdMG/PLDToJDA0lLtl8Dex9vHy8aNKrLmIFTi7cV5Bcwc9wcAM5uHkNwaFWSS5Q/NSuPQG/nS6l2nrpM9+Z17bY927YRz7ZtBMAnP+4jyMcT0u/ZpTFnpCHzs901kPkFYM5yPG/3MV25gCwoFMHDEzEvF1XXXnR+qxcAmedv4xbqx/3criG+FCU7jytnaJMy0WfmUfnFdkS83BGVjzuF99LtNN0eUbMwKRNdZh4mrQ6TVkfBiUvINWpUJWJVFeKHIdV+kGNITkcV4s/9eVtlsD8G6zMG/oOeJHRKf0z5WnJ/P41rmB/3Lbp/l8ZO6xHiquP5Fcg9bKsL3Zo3xJicjinDVmZjcjrK4ADuL/5QBAdgTLPYlrt5F7mbLXXAb/xAjCnpKMNDkPt44tGtLXUO10cZ4k/I1FdI/2q7vZ1JGShDA4Ar1jKX8ItCTuWV08nacoCcnceK8/i91BX3ZnX5f+ydd3hUxfe439mS3nuBQIDQe5NOEjoWUAQFFUFFurRQbQgIYkF6E2yIvaBSpQqigiCBhN5CSe+bZDdb7++PuyZZsoBo+Kmf732fZx/CvTNnZ86ce2Z27pmZgHGDyU+8RLUB5WFZbuEBlN6giz+LT4PqhNxTj/v2LCD76AU8IwL540nyjAjAcBN/5QxzsYFfJq0t+3/NU0sQ7p5l/xf+QdgKbmHz55JQBYcjvHxQ12mEpll7vJq0Ba0Lws0Dj+eX0WmSGwCFiZdwjwzkj1o7s4c7wZaVjTqk/PlUhwRhzanct9wMzwH98BoyCKHVYk3PQB0aAsiz5a7hgU5tVVPBVl0jAjBmyDZgTM/FnFeETW8k/JFYtP5eNP/seQwpmViLDJhz5beFktWKe3ToX5Jr0xsp+PU0UeMewKdZbQpVcGzzL5z7JZmIelEUpOfRMLYFhZmO5a7WqCbBNcN48cclAGjdXXhx3xLmxk4gqmktnlz2HABe/j5opRaYD3yH8C33AcInAEnn6ANUkbVxGyz3T8LDG3W9lhhtNqynDpcnKtVjZMGAgAAAIABJREFUvZyMum4L/nhu4Cb9VPbNbexfj+3fG9ryT6PMyMu4CyESgSPAVWA9EA98KUlSDoAkSc5myBsLIQ4IIZKAx4A/RjEHgfeFECMoH7D/AswSQkwHakiSZPi7hZYkaa0kSa0lSWrdy5CLKrwaqpAw0GjQdozHdORnh/QiKATPqXMpWTYfW7rjYEr4+JWlcbmnC6aDu/9u8SphK9Wxsc/zbOzzPBd3HKXBgE4AhLWojalIT0lW5Y6x8aOx1OjShK3jVjjMJrj6eKDSqjn+4S6Of7iTH7/7kf2bD9B9QDcA6reoj76ohLysyh3Yzzt+oVlHebamabsmXL8sx9RG1Azn7PFzRNaM4J5ubRFaDZ5x7SnZ+6tD/pI9v+Ldr7tcjqb1sRXpsebI5qEOkGPWNeHBeHXvSPacZVx7aAzmlFTM19KIGNiFgM6NKc3Ix1ykx+ikznkHTxF6vxw7HDGoC5nbj8gyfTwQWtmcqj0eT96vp7EWG7j+0R5ysnIZcu/T7N72I/G9u3DxXArNWjWmSFdMdpZz593rgW7s2/kTJqOp7Jq3jxdarfz7Xh1Wh0bVgriamcv17HzMFivbD5+ka7O6lWQV6Us5evYKsc0d7+Xq5GFoem4hu38/WxY/XxHrxTOowiJRBYeBWoNLh3jMN9iuKrQ8/EkdHYPQaJCK5B+iph82savHLHb1mEXatiPUGCiv7whoWQdzkYFSJzq+GWk7jhJ0Tz0ubdjN3gdmU5qZz/Wtv1WJTKFWoXZ3waNFXQp3HcE1OhyX6iEIrQb/+ztTuPOwQ77CnYcJGCCHOXm0qIu1qASL3Z49m8aQ+9kuTrZ9isIdvxJhDwXwbVXnb9tVzoEkPGoE4x4VDFoNXnHt0G3a6SCrZO+v+PSTnzW3ZvWxFZVgza78DHj36EjRln2YzqdwqeMjWLLzuDD4BczpOZgzcinY9KODXN2uinWuh7VIX1bnqNfHY7xwnex13zrkMaVmkTpnHXu7z6LkanbZLiT+LetgKTI41cXN8KxZPvjLO3IeY24Rm+Nncn37UaIflv1VUMvamHR6DHcgV2v3VwB1hsRiOXUMVXAYIki2eW3bWCyJvzjkESHlNq+KqgMaLVKxDuPX71I8dQjF05/AsOZVLGcS0b86np+6zeSnbjPJ3HaESLu9+rWqg+Um9vBnMZ0+g6Z6JOpwuW/x6B5P6YFfbp/RTslX32I6eZrCt5dj2P8THn16AODTKgZrkR6Tk7IVHDxJ8P3tAAgfFEuO3Vaztx/Bt119hFpF+qd7MWbmc+KJheTu+h3vZrVQubvg0yoGoVFTfKLyrjJ/Rq7K3QWflnW4vnYrv3WbxsonXuXED7/RcXB3mvVuS3ZKOqVF+krhM6f2HuOFNiN5pdN4Xuk0HrPBxNzYCQC80nl82fXEbb9i/HYt5p++QxUUjvAPAbUGTbNOWE8fcZCpf2MM+tdHo399NJZkOZ/11GHw9AE3+49tjQua2k2xZac65DUmn0UbFYkmMhQ0Grz6xFbqyxT+N1Bm5GUMkiQ5vIcT8rvU272Heh/oL0nScSHEMCAWQJKkUUKIe4B7gUQhRHNJkj4WQhyyX9shhHhGkqQ9VVYDmxX9+iV4Pf8GqFSY9m7Ddj0Flx7ya2bTzu9wf/hJhJcPHiPsoSRWK0Uz5BAPz4Q5qLx9kCwW9OsWly2KvR1TX36N346doKBAR7f+jzPm6ScYcH+v2+a7vCeRmnHNGH7gLSwGEz8klM9W9X8/gZ3T11GSWUC3+cPRpebw6KbZAFzY/huHlmwioE4Evd6Wd0HIPZ/KjKmvU1xYTJv4Nrz307sYDaW8NeXtMplzP5jD29MWk5eZx2crP2f60mk89Ex/DCWlLJ66GIBOfTqV/RB4fvUsKCyieMd+TBeu4POI/LZD99kW9PsP49GlDTW2v4et1EjW82+VfU/YkpdQ+3kjma1kz1uOTSfrMevlxQTNHI0qPIxWG6dTmpbLqenlbxhabZxO8uS1GDPzOTvvY5qteY6YGY9QlJTC9Y/lxX9edSNpsmwMktVG8blUkifJoVGFv1/g9827+WrXBqwWK/oSA0+OHCxvP2mfXQdY8/HbvDDpVbIz5dm0vv178M7SDxzapXbdaF5b/jJWqw2Vvwrr+V+YOaQ3oxd/gs1mo3/H5tSJDObzfUcBGBQrv7XZc+ws7RvVwsPVxUHelFVfUlhsQKNWMeux3vh4ulPKDdhsGN5diucseTcX0z677Xa/HwDTru/R3tMFly69wGpBMhkpWTzHqV1l7E4krFtzev+yCKvBxBG7jgA6fjSVo1PeoTSzgDpP96LumPtwC/Glx+7XyNidyNGEdRSdTyNj7wl67HkNyWbj8sf7uPTBLnzrVaOPXeZvFWR2+mgqRyrIrGeX2XP3a6TfILOnXWbupzsoPZPC9RfXUnvDbHn7yc92U3ruGoGPyyFRuR9tR7fnKD5xrWl4YLW8/WTCMgA82zQgYEAchtMp1Nsm23hxaj5dDi3BajCSNKF8dv2v2pVf6xhafzoLrQsIrYaCjd/h+0hf2d4+20rJj4fx7NKGmjvetW8/uajsO8OXvIjazxssVjLnrih7BrDayJ63klofzkYbHkje6q8pPX+NwMfsdd64Hd2eI3jHtaLB/jX27SeXynVu3YCAAfFynbfKz2vaGxso2nuUa9OXEzl7BMEvabEazWTuO0GPX9/GYjBybGJ5W7XbOI3EyWspzSyg1tO9iBl7H64hfsTteY3M3YkkTnmHiPvaUn1gZySzBWupmQOjlwOQujuRiG7N6Pez7K8qzq7HbUjg14R1GDILqPd0TxqOvg/3EF/u3bWAtD3H+TVhHb4xEXRYMgrJZqPwXCqln8xHU7shHpMWIFQqTD/twJZ2BW3X+wAw/7gZbavOaNt3B6sVyWzEsPrP7Q6WtesYwd2aE3toMVaDkRMTynXQZuM0Tkx+B2NmPjWf6UWtsffjGuJHl70Lydp9jKTJ7+Aa7EvHH15F4+2OGhtejwyg4O0VBC1eCCo1JZu3YbmcgseD8vOp/+Z7VAH+hLy3GuHpATYJr0cGkDl4OJJej3B1xa1tKwoWvo1UUoJbh3tof2gpVoOJ0xNWlpWt6cYZnJm8BlNmPhfmbaTxmonUmvEoxUmXSftY7ir151PJ25NI271vIkk20jbuoeTMNUrOXCP0wY50Ofc+ks1G/k/JpG7Y9bflAjy9ajKe/t54BfpiNZt58IWhbJy6qqzcI9+bwSfT16BzMml0S2w2jN+tw/2pF0GoMB/Zgy3rGpq2PQGwHHYSF29H5e2P68BxINQgBJakn7GeOeqYyGojZ/4KwtfMR6hV6L75AfPFK/gMsvdln29BHehPtc+WofLyQLJJ+D3en6v9nkUq0Tv51n+Yf/Fi038a8bdi5/5HEEIUS5LkdcO1RsA3QHtJknKFEAGSJOUJIWYDxZIkvSmEyAEaAvnAViBVkqRhQojakiRdtMs5BgwHdMBlSZIkIcRiIEWSpMW3KFNNYLN9Ae5tyR8YW+UN6bXq3dsnukOWt3ypymUCbJeq/pXhCu+qdxwXcu9s4d2fZZJ0qcplHvtqfJXLBChdXvV2tWt/xO0T/QXuhneso/1zP5LvhEyje5XLBKgV8Ndncm+GXu9y+0R/gRSj1+0T3SHFztZ0VAEP9MmscpkHNlf9Qsbmtaq+nABnL/13Fl1ucr/JoqC/wfzBd2eXmMzvq963ANRO3lH1SrhDjMk7//HBqmvjHv+4HpyhhNbcBEmSTgKvAj8KIY4Di5wkexE4BOwEKu6r94YQIsm+feR+4DjwCJBsD+GpD3x4s+8WQnyCHIpTTwhxXQhx801rFRQUFBQUFBQU/k+ihNYAN87GV7j+AfDBDddmV/h7FbDqhmxIkvSQE3EL7J8/U57BfyadgoKCgoKCgsL/PMpi15uizMgrKCgoKCgoKCgo/AdRZuT/IYQQgYCzrWG6SdJdCPhWUFBQUFBQUPgPIkl3+2yb/y7KQP4fwj5Yb37bhAoKCgoKCgoKCgpOUEJrFBQUFBQUFBQUFP6DKDPyCgoKCgoKCgoK/16UfeRvijIjr6CgoKCgoKCgoPAfRJmRV1BQUFBQUFBQ+PeibD95U5SB/P8I2cluVS7z/btwCuu43+dUuUyAg60mVrlMs7nqT99z4e44o911PKtc5jePbK9ymQAetvAql6m9K2ewwj1106pcZsqFgCqXebderWpdqn6nCHXp3Wmrmq5Vf6plqfnudJHndlT98+ojzFUus7RIW+UyAUx3wWLduDu7mkz2y69ymXvfuzsn2waKqj/dGKD2XZGqUFUooTUKCgoKCgoKCgoK/0GUGXkFBQUFBQUFBYV/L8pi15uizMgrKCgoKCgoKCgo/AdRZuQVFBQUFBQUFBT+vdiUk11vhjIjr6CgoKCgoKCgoPAfRBnIKygoKCgoKCgoKPwHUUJrFBQUFBQUFBQU/r0oi11vijKQ/+/TG1hSc/u7FH65nfx1n1dKEDxrNJ5d2iCVGsmY9RbGUxcA8HuiH74D+4AQFH6xjYIPNwEQ+NxQvOLb85hFhSFXR1FaLtXaNcBsMPLDlLVkJadULsSS0YQ2rYXNYiEj8RK7Z76LzWLF1deDnm88i2+NEKxGM6i1YL31fscvzF/E/oOHCfD3Y9NHq+9IGU/NHkGLuNaYDEaWJyzmcvKlSmnGvjmBhu0ao9eVALAiYQkppy7zwMgH6dyvK+5e7gSGB6HRashZ/jE5SzdWkhH64ki8urbBZjCSPn0RpacuAqDy9iR8/gRcY2oAEukzFmNIPEPQ+MfwG9SL2loXNF7uGDPzSXrqLYqTLleS7RYVTKM1E9H6eVGUdJlTY5chmeX4QL8ODYmZOwyhUWPOK+LYg7MBCPnqEyS9HuHqhjo4CGt2Nvrvt1C84RMH2Zoa1fF7fjraujHo1qyn5BPZXtRR1QmYU35ugDoynHoLv+bsOnkv+ZZzhxIR3wyrwcSvk9aQn5RSqdwxw3tQ75neeEeH8VXjkZjy5H2964++l5oPdQRAqFX4xkSyrdFI6icMIKRbc6wGE8cmrKbQiczop3pSa0RvvKLD2NZwJKa8IgDCerWi/vSBYLMhWW0kvbiBvMNnAWgyb2iVybWufRvh7o7Pc+NApcawZQslGz92kKWOisJ3hqzTonXr0X/6Wdk9j4cH4H7ffSDAsHkL+i++pMbcp/GPb4nVYOTipOXokyrbqGv1EGJWTUbt54U++TIXxi9BMlsIfLALEWP7A2DTl3J5xlr0p1JouHgU4f3bg0qFMS2Xq+t3cPWdbQ4y3aOCabpmAho/T4qSUkgauxzJbEXj7U6TleNwiwxCqFWkrNpM2qc/AuAz5EG8B/RBeHigcnPBVqyn6JttFL77mYNsbc3qBM1JwLVBHfKWvYfuwy9l3YQGE/zqNNSBASDZKD2aRETrFqBSkf/ZTrJXf1mp7uEvP4t3bCtspUauJyyh9OTF8psqFXW+W4Q5I48rz8hnUlRfPAWf3h0AgSktmwuDZmHJynOQGTl7BD5xrbEZjFxNWIwh+RLa8CCi3p6INtgfySaR+/EOct77HgC3BjWpPn8Mwt0d4/UsLoxdTLXpQ/50u2nDA9F4e2DOKSTr450YU3Md2u3qzNUEDe6Ob3wrbAYjKZOWorf7Kp/YFkS98gyoVeR8spOMFV/LuvTzovbKBFyqh2C6lsXF0W9gLZR9mHuDGjT5aDZafy+wSRwf8DLFR8/T5OtXcAnxw1ZqAuD8lFXUXjACrZ8XxUmXODtuGZLZQkCvNtSc/iiS3e4vvfgeusNnAPB9vD8+A+R+QvflNkyXrxE0YxRCrUb31TYK1jv2Odro6oTMnYxrwzrkLv2Awvcd2zhw5mjCOrTDZjCS9NwqdE6eT/eoYJqtmYDWzxNdUgon/rBVX0+aLB6JR81QrEYzyRNXU3zmOgDNN72CT6sYsEmkvrudi69sqCTXLSqEhmsmovHzojjpMqfHyvUH2bfWmTu8zLcmPvgyrhGBhK59Hk21CDSB/thK9Og+/ILC9yrbf+Arsv3nLy+3f+GiJezdRQitFjRq9LsOULDqQ4e8jeY9SajdXyVOWOXUX9V8qie1RvTBMzqMHQ2fLfNXkQ91pM64BwCwlJRydcZq9KeuED33Kfy6tcRmMHFh4jJKnPQzrtVDqLt6Eho/b0qSLnF+/FIkswX/Xm2ImjbY7gOtXH7pvUp5Ff5dKKE1/23UwAqgT8r9z+JzbywutaMcEnh2aYNLjQhSej9F5stLCHlpHAAuMTXwHdiHq4MmcKX/aDxj70FbIwKA/PVfcqX/aDb2eZ6Cq1lEdW7Me12msGvGeuJfHea0IGc2/cwHcVPZ0GMmGjcXGj8aC0Dbsf3IPnWFj3rNYvuk1ag9b38QRv++PVi9aN4dK6NFXCvCoyMY33Ukq2eu4Nl5o2+adsP895jadyJT+04k5ZTs5L5b8w3T75sMwLoXV6M/lIR39/a41KnukNeza2tcakRysfszpL+4lLA548ruhb4wkpL9R7nUeySX7h+H8eK1snvF+49QlHiRfVGPcXLUEuq9/ozTstV+4XGurdnCr+0nYCkoIWJIPAAaHw/qvfYMJ4Yu5HDXKSSPWOSQL/e5KSAksh4bRtaQYbh374amZg2HNDZdEYVvL6P4E8fO13r1GtnDRsifp0YilRq5tu0IAOHxzfCODmNzxykcnrae1guGOy13zm/n2PvIAoqvZTtcP7NqC9t7zGJ7j1kcX/AZOb+cxr9VDJ61wtjdfjLHE9bRbOFTTmXmHT7Lz4Pmo79BZvaBZPbFz2Bf91kcm7iG5m+NACCkW/Mqles7bSo+kyaQP3U6OUOfxK1bPOoajjqVdDp0S5dS8qlj566Jjsb9vvvIHTmK3KeewbV9e9zv64t7dDiJHcdyedpqai141mn5op5/gvR3vud4p3FYCooJGdwNAOO1TE4NeJGk7pNJffsLar0+Cr/4lrhXD+Lw/S9z5MFXMBeWUH14TzzrRjrIjHlhCFfWbOFg+0mYC4qJtNtV9ad6UXw2lV/ip/PbQ3OoN/sJhFaNV/1qeA/oQ9oTE8BqxXwllYwJL+HZOw5tLUc/Y9UVkbtwBYUf3DAwt1rJe3MNqQ8+TdrQiXg/2Ie0l9dwvudYfB/ogusNz5Z3bCtca0ZwLm4kqTNXEHnDMxw0/H6MF66XX1Cp8GhZn3M9xpDU6BFU7q5EvvS0o8y4VrhGR3C660iuzVxBNbtMyWolbd67nOk2lvP9pxI0tC+uMXJ5ohaOJ+21DzjRbRJ52w4RvXDUn2+3dZuRjGbyf/iN9He+J7BfZ0BybLfVU3GLDie502iuTF9J1IJRZfWJmjeSc0/M4WTceAL6dcYtphoA4WMHoDt4guTOY9AdPEHY2AFyHrWK2utnUZqSwcGowZwctpDac8vt/uzYpRzrPpVj3adSbWx/0tZs5kiH8VgKSgiz20DBgSR+j5/Cse5TOTdxJTFvyTpyqVMDnwF9uD74Oa4NGIVH13sInj2B9NEvcPWBEXj1rWwLtkIdOa+touD9ryrpx6NzG1yiIjnQbiLJCe/Q8CY+sO4LQ0hZs4UDdlutZi9n7Qn90SVf4WDcdJLGraTBvGEAVH+yO15Novmp7jASH55D+OPdcY8OqyS31guPcX3NZg63fw5LQTHhFXxrzGsjSBq6kN+6TubkiLdkG7FYyV+0FiwW0oaMwaYrwqtfr8r2X1hE3usrKPzQ0f4lk5mMEVNJe2QUaY+Mwr1Da1ybNCi7H9KtOV61wtjTfhLHE96hyUJH2/2DvMPn+GXQq5X8lf5qFj8/OIcf46dz/u2vqf2G7A/caoVzrMM4Lk5dRa3XnNtqjReeIG3tZo51HIelsNzHFB5I4ni3yRzvkcCFSSup/dYYp/kVKiOE6C2EOCuEuCCEmOHkvq8Q4nshxHEhxEkhhPPO9A5RBvKAEMIqhEgUQiQLIb4QQnjcIu1sIUTCXSxLdSHEXiHEaXtDT7hF8rbABeASZgu6rT/iGd/eIYFnfHt03+4GoPT4GdQ+XqiDA3CpFUXp8TNIpUaw2jD8loRX9w4A2Er0ZflDGtck/1I6ABnHLuLq44lniF+lgqTsPV72d0biRbzC5dMrA2IiuXrwJAD5F9MRag0I9S110Lp5E3x9vG+ZxhltetzDvq/2AnD+2Fk8fDzxC/G/Ixl1mseQkZJO4w5N0X2/F92W/Xh3c9Spd/d2FG6y6zTxLCpvTzTB/qi83PFo05iCL3bICc0WbEUlZflcY2qQ8cV+AHRHz6Px8cTFiS79OzUi+/tfAUj/fB9BfdoAEPpQJ7K3HsKYmiuLz9E55NPWjcFyPQ1rWjpYLBh27cGtc0eHNLb8Asynz4Ll5qfWurZuiTU1DX1qDgDVerUi5csDAOT+fgEXXw/cnJQ7P/kKJddzbioXoEb/DqR+8zPhvVpx7XNZZv7vF9D6eODqRGZh8hUM1yrLtOqNZX+rPdxAkk8LrXK5LlqsqalY02Wdlu7eg1unG3RaUIDlzFmwOu6qoK4RhfnUKTAawWrFlJiI+733kf3lPgCKfz+H2tcTrRMb9enUhNzNvwCQ/cVe/Hu3lfMcOVs2C1v0+zlcwgPx79WW1I/2UJSUQuHRC2i83DFczcI1zPEE2YBOjcj8/hAAaZ/vJ6RPa/mGJKHxkk+G1ni6YS4oRrLY8IyJxHjiDC51amK+lobh5yN4dmlHyfZ9eMR2cNRBXgGmk+eQbrAra04epjPyG0CXWlFYi+SZRMlsofD7/fj0uMchvXePduR/vQcAQ+JZ1D7yswWgCQvEO64NeZ/9UJbeo1kMxstpmK9lIpktlJ69gku1EAeZvj3uIc/uF/TH7DJD/LFk5WOwz4LbSgwYL1xHGxoIgGutSEoOyX6rcP9xfONa/Ol2M6bmUJqSTsZ7W/Hv0Zrcb3/CNTK4Urvl2uWV/H4OjY8sz7N5DMaUdExX5frkffsTfj1lHfn1bEvuF3I9cr/Yi38v+bpv1xZgtZL+nvz2rGDfcTQ+Hmid2L1fx8Zk2+0q8/N9BNrtyqYvLUuj9nAte560taIoPXG6rJ+wXE9HMpqwXM8Ai4Xibfsq9TnWvEKMyZVtAcAjrj1F3+2S9Xr05s9n4A22Gmq3Vc+6keQeSJb1diEN9+rBuAT7EnpvW4oSL2IzmND9dhab2ULooK6V5Pp3alzmWzM+/7HMt4Y81ImcrYcw2n3eH77VlFWA0GqwXEvDfCEF86WrlB5Lrmz/+bL9O/OrkkHWrdBoQKNBkspPNg6r4K8KbuGvdMkpTv1V/pHzmO12lX/0Ai7hgQT0bkP2F/IbteLfz9ttq7JM306Ny3xM1uf7COhT2RZUFWzhH8dm++c/t0AIUTaxCjQEBgshGt6QbCxwSpKkZkAs8JYQwuXvqkYZyMsYJElqLklSY8AEjPoHy2IBpkiS1ABoB4x1Ygx/EAmUTflaMnPKOqI/0IQGYs4o/xVvychGExKI6XwK7q0bo/LzRri54tmlDZqw4LJ0gROe5JlflxBYJ4Jj63eUXS/OyMMr7OaDY5VGTYOHOnHlxxMA5Jy+Sp3e9oFos1qg0oDq1gP5v0pgWCC5aeV1zcvIJfAGffzB4ITHeWv7Uoa9+DQal/IIs4CwQPKz8mjetSW6HQcxZ+SgqaTTIMzpFXWagyY0CG31cKx5hYQvnET0t8sIf3UCwt21LJ1b/VpET3+E+otHo/H1xJiei2u442BLG+CNRadHsspOw5iWV5bGo3Y4Gl8vWnz9Mq1/eI2wgV3KM0oSPgkT0dari0e/+wCwZmejDr7zo8Ddu8ej37m7/P9hAZSk5Zb9X5+Wh8ctbOBmqN1dCI9tStqWw7iF+2NIKw9/MKTn4R5+ZzLD+7Qm/sCbtPtoKscmrQWocrmG7zdjzSpva2t2Nqrg4FvkLsdy+TIuzZoifHzA1RXXdu1QBfhjSivvkE1pubjcMODWBHjLgz67DZjSc3EJq2zHIYO7U7D3GC5hAZSmlrePKa8Ir4Y1KPz9Qtm1G+2qNC0PN7tdXV2/A8+6kXQ9sYr2+97gzAsfgCRRfOYabq2aoK1ZHWt2Lu6d2qIOC8aaJdv7neJSvzYqNzf0iXIIlDkjF+0N9dKGBmJOL9ePOb08TcRLI0h/7T2HDlUTJqcPTXiChr+sx61uFKVnrzjKDAvEXMEvmDNyK/lJl2ohuDeqVVa20nNXyn5kBNzXAY2X+59uN5cQf0xpuWXtZkrPxaXCcx4yuDuW/CJHeem5aMMCcAkPwFSh/qaM8ryaID/MWflyHbLy0QT6AuAaHYHKzZWIZ++jxQ+vU21sP0zpebiGy3Wsu3gMLXa9QY1ZQ7Doyu3KeEO5Avu0pdWBJTT6aCbnJq2Uv/9CCm6tmqDylfsJt9ZNwFL+g9WSmYMm5M/bgiY0CEuF/qg0Pc+pDzTfYKt/pCk6dZWwe+UBp2+L2rhVC8ItPAChUeMeHYbG3wuVuwsqrQb32uGV5Dr61twKvjUCja8nzb+eTasfFhJawbeqQ+QyayJCcalfB2PSGdR3UGdUKiI+W031PV9Q+uvvmJLPlN1yCw+gtIJvNaSXP5d3SvUhsRTskf2BsYJtye18Qx8W4I3lBh9T8Yd/QJ+2ND+wlAYbZnFh0oq/VJ7/g7QFLkiSdEmSJBPwKdDvhjQS4C2EEIAXkIc85vtbKAP5yhwA6gAIIYYKIU7YX4NUCrgTQowQQvxmv//VHzP5QoiB9tn940KI/fZrjYQQh+0z/yeEEDHOvlySpHRJkn63/10EnEYesDtDOMl/YyGdfQmmS9fIW/cF1dYvIPKdeRjPXHKYUcxd8gHr2k2gODOfmL5tbv0dFYh/dRiph8+Qao9X/m3l97j5evLYtldpMbwnksV407x/G6dVrVzWja9/yIT4MUx/YDJeft70HzWggghBUEQIZ4+cxlZY/IcQx69x8j1IEkKtxq1RHfK/mUVkAAAgAElEQVQ/3srlfuOxGUoJGjkIgPyPt6A/dILTE1ZiysynzitDncp2VgfsSYRajXezaI4//hrHH32VmpMH4F5L7qxyRo2naOVajIcO4/lQf1yaN71p/W+JRoNrpw6U7vnxlmW6Y7lAZI+W5Bw5h7mgxKkS71Rk+rYj7OmcwOHhi2gwfaC9rFUr161bfOUEf1Kg9cpVSj7+hIBFbxLw5utYLl4sa8tby3P+zFbEp0NjQgZ34+qrHzrUWe3hineDKK6s+h5rseE2ImWZQXHNKEq+wo9NR/NL/HQaLBiO2sudkvNpFLz3GX4jH8e9YxtM5y6VDeLutP2Fuxu+QwdiPHEaW4VyVfZXzsvpHd8GS04hpckXb0gvZ8h8cwOn2j9NydEzuNWveVuZFfWp8nCj5uoZpM5ZV1a2q1OXEjS0L423v4Hayx3J5qS+N2s34SSN/Z8/2q30YqoTec4LeztVC40KtZ8XVxd9wfF+LxDYpy0aP0+QJM6OWcLvcVM40e9FfFrVRe3lflPhudsOc7TzBE4Nf50a0x8FwHzpGgXvfk7EOwsIX/0q5vSsym12J7Zwm7a4XZpLS79F4+tJh92vEfV0b4qSUpAsVqx6I5lfHaDZ5y/S9JPnsZYYkCw3zKDewjfIvrUWJx5fwIlH51Fj8sNlvhUhQKMm+M2XyHtjFZLRdGd1ttlIe2QU13sNxqVxPbS1a96yTH9lBjywY0OiBsdx5dUNiD8j06kuytPkbTtMYufnOPvU63K8/L8ByfbPf26Nw8QqcJ3KY7flQAMgDUgCJkjS31/FqwzkKyCE0CC/FkkSQjQCngfi7a9BnIW4fC1JUhv7/dPAHwFuLwG97NcfsF8bBSyRJKk50Bq5kW9XnppAC+CQs/tjx45t9dNPPw0UQhz5rOCaPNtxwyIvS0YO2goz7ZqwYCzZchrdVzu4OmAc15+YirWwCNOVNAB8h9xP1NcreGzbq2Qlp1C9U+Oy/F5hAZRkFjgtb7uJD+Ie4M2Pc8oXh5qKDfyQsJaNfZ5n+8TVCJUabLde7HonqNx80PhF8sbWxeRn5hEYUV7XgLBA8m7QB0CBfVbLYrKw94tdxDSvW3YvNyOH6vWi+Ok7OQRGG1ZZp+aMHLThFXUahCUrF3NGDuaMHEqPyz9idNt/wrtHe6K/W0bUe3MxZ+biFhFI2ke78WlRG9fwQIwZ+Y6yc4vQ+Hgg1PKj6RoRgDFD/n5jei55e45j0xsx5xVR8OtpvBrJ8dq2nFx5ttjPj9L9B9A2qI86OBhbTi53glv7ezCfO4dbXFd675xP753zMWQW4BlRPqPjERGA4SY2cCsajn8AvwZRxO6aT2lGPu4R5TNA7uEBlN6giz+LT4PqBLSrR9y+hVUqN/fXMwhfX9QREWXXZJ3eOnyoIoYtWzFs2Yrw8MS1cydsuTm4RJTP5rlEBGLKdCyfJU+H2tcT7DbgEh6IKbPcBj0a1CBmjRzd1+DzVzBl5uEWGYjQqGn27mRsRhPpXx10kHmjXblFBJTZXsSjXcncclgub0omhqtZeMbIdS7+ZjvZMxdgOncJW2ER5qupqEOCsGbdgV1p1IQsehn9/kNIFWbTtWGBWDJvfLZy0YaX60cbLqfxaNUAn+5tqXdgHdWXTcOrQ1OqvT0ZS3qOQ3rTtUxcIh3fmJjTc9FW8AvasEDMfzzTGjU1V88gf9OPFG7/pSyN8WIqup2HEGoVQf06YdWV/Ol2M2Xm4xIRWNZuLuGBmDLy8GhQg1pvjuHs8AWYrmY6ygsPxJyZZ5+9r3A9LBCz/fm35BSUhfNoQ/yRzBYa7nibkGH3Yk7PRePljs1gIm/3MVzCAzFm5GGy57WWlJL5+T7U3h5lduUaHojJybOh+/U07jVD0QTI4Y1FX+/g+qBxpA1LwJqdh1CVDwI1oUFYsm9tCz6P3k+1L1dS7cuVWLPyHN78uoUHOPWB2pvYqrXYQPLE1fzcbQaFR8/j3agGTVeMw5iZT8mpKxztMZ3E/i+jcnOh5PSVG+TqbvCtgWX6kX1rYplvLazgW605eXh0voeSrXvQ7/kJTWgQ1tvU2Rm2ohJKjxzHb8yTdNm1gC67FlCakY9bBd/6V/yVd4MoWq2VhyeNPp+NKTMP1wq25RpeXs8/sOTq0FTyMc5s4RRuNUMB7vwV3P8gQohnhRBHKnwqLkC4xTRcGb2ARCACaA4sF0L4/N1yKQN5GXchRCJwBLgKrAfigS8lScoBkCSp8ogQGgshDgghkoDHgEb26weB94UQI5AXpAL8AswSQkwHakiSZKgsrhwhhBfwFTBRkiSdszQrVqx4oVOnTrmSJA18JDgan75dKdn7q0Oakr2/4tNPXsTi1qw+tqISrPaBvDpAfjWrCQ/Gu0dHirbsA0B/8ChXHxrLxj7PYyrSo9bIVQhrURtTkZ6SrMqDuMaPxlKjSxO2jlvh8Ovf1ccDlVbO33hwLJK5tEpj7mylOiwFqUztO5HDPxwidkAcADEt6qEv0pcN2itSMW6+Tc92XK3wKj7tUio+/j6knLwMWg0+93ahaLejTot3H8K3v12nzethKyrBkp2PNScfS3o2LtHyj3DP9s0p3n+Eyw+M59rTL1G86xfCBnYhuG9bTJn5WIv0mJzosuDgSYLvbwdA+KBYcrbLi06ztx/Bt119hFqFyt0Fn5Z10J9PReXhivBwx3z6DJrq1XDt2B7Llau4d4+n9Kef70if7j3iMezcg/7rTWULVFO3H6Hmw50BCGxZB7POQKmTct8Krbc7HhGBbOk6jX3dZ5Gx/QjVB8ky/VvWwVxkwHgHMj3lzgWAvCPnMeUWsTd2epXK9W1SEyFJqMNCUYeHgUaDW7d4jAf/vE5Vfn7ov9lE/sxZSEXFlHz5FcEPxwLg1bIuVp2+LFyiIrqDyQTeJ8ceBw+MI3/HbwC4RAZRd900zg5fwPGuz5HUYwr52w8TMbALjd4eibmwBMO1bKd2lXfwFKH3y+EiEYO6kG23q9LUXAI7yz/WXYJ98agdgeFKllz+AD+MJ8/iEh2FR68ulOw8gGfvWPQ//lJJ/s0Imj0F86Wr5L21Gm1UJNpqoQitBt/7u6DbddghbdGuQ/g/JL8FcW9eD2uRHkt2PplvfMiZDsM52/kZro1/neKfT3B90iL0J87jWqd6mUz/B2MxnHbcoUO36zABdr/g0cIu067zqNfHY7xwnex13zrk0QT6kvPhVpJ6JlCceIGcb/b/6XZzrRaEW3Q4ocP7kr/rKIH9OlF8/Dx1103jwnNLKL2UTsEPhwm0y/NsWRdrUQnmrHxKjp/HLTocl+ohCK2GgH6dKNgp66hg52ECB8r1CBwYR+6XeznVaxKnek8ClSD00ThQqwjo2RpLYYk8cLUPxoVGTWCPVujPXSfYblehg2LJtduVW83yhaGeTaLluHD7zihl/URYMG5N6iPc3NBEhoJGg1ef2Ep9TiWdfPo91x8ew/WHx1Cy52e8H+gOgG+rOpiL9E6fzxttNdNuqxofD4S9P7GZLaRv+pmDcdPI2naE8MdknxzYuw0qd1fSP9xVSW5+Bd8aNqgrOdvl+uds/w3fdg0q+VYA7wH3IjQa9HsOgkaDZ68/b/8qf19U3p4ACFcX3O9pSfGm7ezvPpP93Wc6+Cu/ljfXx81wjwykzbuT+O3JN9nXJYHjPRLI23aY4IHy+gCvljFYivSYncgsrOBjQgbFkr9dtjNntgDc+S+X/0EkSVorSVLrCp+1FW5fByqu3q+GPPNekeHIE8CSJEkXgMtA/b9bLvFXXpH/ryGEKJYkyeuGa88BIZIkvXDD9dlAsSRJbwohLgP9JUk6LoQYBsRKkjTMnu4e4F7khmsuSVKuEKK2/dpE4BlJkvbcpDxaYDOwQ5KkRc7SVKAvsNh0NS1G9/UP5K35FN9H+gJQ+NlWAEJeHItHp1b27ScXYTx5HoBqG95E7ecNFitZC9di+DURgPAlL+ASXQ2dRUVRag6G/GIi29bDYjDxQ8JaMk/IHWX/9xPYOX0dJZkFTLj0AbrUHEzF8kKZC9t/49CSTYS3rEOvt0chWW3knk+lZtvg276imvrya/x27AQFBToCA/wY8/QTDLi/1y3zDG41EYBn5o6kedeWGA1GViYs5WKSHCc86/2XWDVtOflZebz8yTx8AnwQQpBy6jJrZ62k1L7AJ/bheOIHdsMvJIAgjYqCL38gd9Vn+A2WdVrwiazT0JfH4NVF3joufcbblCbLOnVtUEuOjddqMF/LIG3G29h0xUS8kYBrg1qoggNRu2kpTc3l1NhlFB2XF9s13TiDM5PXYMrMx61GCI0rbJF2cuwyJJMcRhc15n7CH41DkmykbdzD9bVbcasRQpuNkwAQXl4IdzekkmL0m7dR/MFGPPrfD4B+0/eoAvwJfncNwtMDbBKSwUDWkGH2rStdCd30GZkPP4ZUUsKPKeUz0a3mDyM8tilWg4lDk9aQZ7eBrhumcjjhHQyZBdR9uhcNRt+HW4gvpTk60vckcjhhHQDRg7oQHteUn0cvx8MeptB0wTBC4pphNRg5NnENBcdlme02TiNx8lpKMwuo9XQv6oy9D9cQP0w5OjJ3J5I45R3qjLuf6gM7I5ktWEvNnJzzcdn2k1UpV3pvKcLTA5/x40ClwrB1GyUbPsL9AflFm+G771AFBBC41lGnOUOfRNLrCVi2FJWvD5LFQtHylZh+/x2emo5fbAts9m0MS07I4SL1NjzPpYSVmDPzcY0KJWbVZDR+XpQkX+bC+MVIJgu13hxDQN92GFPlOGPJYiW5zzSi35lB6L1tsZaaMFzPwVZq4sL8T6n+VC9OTV6LMTMf9xohNF3zHFo/L3R/bD9psuAa6k+jpaNxDfVDCMHlpd+S/tVPAHTZ9jwqXx+EqwtCq0EymSnatIPCdR/jPVBei1H0xWbUgf5EfLIClacHkl0H1x98Bpe60US8vxjTuUtINgmVpwdoXbAZTeR/sYvsFZ8TMKQ3AHkfy4s1I+aMwqtLSySDkevTlmBIKo/1B/C8pzFBIx4q236y1hcLcW8qRyqarmZwcciL+HSX46hzN8oyI+eOxKdrS/v2k0sxJF3As3UDYr5aiOF0SlncfdobGyjae5Sg4fcTNLQvkiTI2/Yr1+Z/RM35I/50u7mEB6L2csecW0jWJ7txiwolsH8nLAXFWPKLwGKl5Ng5fGJbYis1kjJ5KXq7PN/4VlSf/RSo1OR+tov0ZfatPP28qb16Ki6RQZhSc7g46nWsBXL4X8BDXan+yjOoPd2wFOk5OWQ++vPXafrNHNxrRWDKzCN/byJp67dRf9UE2bckp3B27BIkk4Vq4/oTMrArktmCrdTE5Tkb0B0+Q0RQEREfvIXazxvJYiX39TUIFy2B00ch1Cp03/xAwdpP8Bl0LwC6z7egDvSn2mfLUHnZbUFv4Gq/Z5HsmygEPT8Wbft7sBqMJE1Yjc7uA1ttnE5yBVttZrfVoqQUjttt1a91DE2WjUGy2ig+l0rypDVyrDcQd3wl2gAfJJuNCy+/T/r7OwFosnEmZyevLvOtDddMKtva9/TYpWW+tfqYBwh7NA4kG+kbd3N97VZ829anxfdzMaemowkOBAQlO/aR8+LreD9st/8vZfsP/1i2fyQJm95A6kPPoIkIJWjuNIRKBSpByQ/7KVz7EUkZ5RPcjRcML/NXiRPXUGjXR9uN0zg++R2MmflEP92L2mPvr+CvjnFiyjs0fWsE4fe2xWDfZEBttXCi93Si5z+Df1wLrAYjFyatoOS4bFsNPnqeC1PKbVXeflL2MefHybYQObY/wQNjy2whZe6HNPn2VWezzf9fKT248R8frLp1fOymerBHdJwDugGpwG/AEEmSTlZIswrIlCRpthAiFPgdaPbHhPFfRRnIc9OBfCPgG6C9fRAeIElS3g0D+Rzk1cn5wFYgVZKkYUKI2pIkXbTLOYY8mNcBlyVJkoQQi4EUSZIWOymLAD4A8iRJmvhn63CuQe8qb8gtJVX/Nm3c73OqXCaUD+Srkrkuf3sNSiXSdV63T/QXqF87+/aJ7pCKA/mqxMNZvPG/lHvq3jih8vdJufDXFrPdCp31b2984JSYsKqfiCvWuVW5TACrrerHGqXmu3PUikZV9YfbGCxVX9aIoKIqlwlwPvvOF8vfDjest0/0F6gZfudhhLej4kC+KgkUprsit0P6V8pAnlsP5AGEEH2BxciRGO9KkvSqEGIUgCRJq4UQEcD7QDhyKM5rkiR99HfLpRwIdRMkSTophHgV+FEIYQWOAcNuSPYicvz6FeSFC3/smfiGfTGrAHYDx4EZwONCCDOQAdxsRNsReAI5Tj/Rfm2WJElbq6RiCgoKCgoKCgr/JW6z/eO/Afs4besN11ZX+DsN6FnV36sM5IEbZ+MrXP8AeXa84rXZFf5eBaxyku8hJ+IW2D+3K8tPOF80oaCgoKCgoKCgoFCGsthVQUFBQUFBQUFB4T+IMiP/DyGECEQOu7mRbpIkKSvEFRQUFBQUFBQASbo7ayD+F1AG8v8Q9sF683+6HAoKCgoKCgoKCv9NlIG8goKCgoKCgoLCv5f/wGLXfwolRl5BQUFBQUFBQUHhP4gykFdQUFBQUFBQUFD4D6KE1igoKCgoKCgoKPx7uc2J8P+XUQby/yN8pa/6k+L23YXNcw7ehRNYAT45WumQ3L/NlsYvVLnMFpFZVS4TwO+RelUu87tFGVUuE2CAybPKZcY2v17lMgEuHg+scpl341TPBpFVf7IvwE+pYVUuM9x6d06fjAio+lNIQwPvzsmmH1+NrHKZA8Iyq1zmzpzQKpcJ0DOy6n2L1Xx3Agx8a5urXGZKrrrKZQJ4mu6OXIV/N8pAXkFBQUFBQUFB4d+Lstj1pigx8goKCgoKCgoKCgr/QZSBvIKCgoKCgoKCgsJ/ECW0RkFBQUFBQUFB4d+Lstj1pigz8goKCgoKCgoKCgr/QZQZeQUFBQUFBQUFhX8vymLXm6LMyCsoKCgoKCgoKCj8B1EG8goKCgoKCgoKCgr/QZTQmv8xesx+gtpxzTEbjGxOWEtmckqlNH1ff4awJtEIIci7nMHmKWsw64006t+BdqPuA8CkL+XqzMVcOn2Z0a+Mom18G0oNRt6a/BYXki86/e5h056k872dsFltbN6whW/f+472PdsxNGEoks2G1Wrlw1fWc+bIaQCemj2CFnGtMRmMLE9YzOXkS5Vkjn1zAg3bNUavKwFgRcISUk5d5oGRD9K5X1cA1Bo1msAoLHlXbrkg5oX5i9h/8DAB/n5s+mj1Hem1ybyhhHZrjtVg4vcJqylMqqzX6Kd6UntEb7yiw9jacCSmPMfDZPya1yJqywZyZszDZiglIGEMqFUUf7MN3fufOqTV1KxO0OypuNSvQ8GK99Bt+MLxy1Qqwj9aiSU7h+wJ5QdXqWo0xKXrIBAqLCcPYjmywzFbZF1c7x+NpMsBwHLhGJbDW8sTCIHbozORSgpg0Utll5+c/QzN41phMhhZlbCUFCdtNerN52jQrhF6nR6A1QlLuXLqctn9Wk3rMHfTQg6NXE7qlsM0mzuU8G7NsBhMHJm4hgInOq09vAcxdp1+12gkprxiAILbN6DD+5MpuSofhKQ9thvDxx+gbdUWz1HjESoVpdu3YPjiYwd5rnHdcR84BADJYKB4+SKslyvYs0qF39K12HKysWak06xpB2wGIxcnLUefVLnOrtVDqLNqMho/L0qSL3Nx/BJ8Ojah5mvP4hLiDxo11+Z/RMbqb8vyNPllLSpPN9Se7mCzcebBmejt+vSJbUHUK8+AWkXOJzvJWPE1AGo/L2qvTMClegima1lcHP0G1sISfDo3I3LmULQeAswW9Lv24flAH1CpMJ08g7Z2NEKtwvDTIQqXrUVTozoBL03DpX4dCle9S9FH5XYlvDwJeCEBbe2aIEnkzX0TUvPK7recO5SI+GZYDSZ+nbSGfCftFTO8B/We6Y13dBhfNS5vL623O+2Xj8EjIhCv8AAkqw1TdgFnnltBcdLlSnLcokJouGYiGj8vipMuc3rsMiSzBb8ODWn8wXRKr8oHq2VvOUTRsQvUmTcclwBPsNmwFugo/GIbBR9uKpMXPGs0nl3aIJUayZj1FsZTFwDwe6IfvgP7gBAOeQKfG4pXfHtUKiu2/Hz0W3fg/dRQUKnRb95CyUefOJRXHVUdv1nT0daNoeid9ZR88nnZPc9BD+N+/70gSVguXaJg/sKye3GvPEF0XHMsBiPbp6wly5m/XjKa0Ka1sFksZCReYufMd7FZrLj6etDrjWfxqxGCxWjGNvc1zBdTcO/QmoBpY0Al+5bC9z5zkKetWZ3AVxJwbVCH/OXvofvwS7n9XbSEvbsIodWCRo1+1wGYW+4/2s95gurxcll/nLSWXCdljVs2mqCmtbCZLWQnXuLAjHeRLFZq9GxJq6kPg03CZrFiXbIUY2Iybh3aVJkfzBgj+yv3jq0JnD4aoVah+3o7hetvqH90dYLnTsG1QR3ylr5P4QdfltvJnMl4dGmHNa+A6w89W16O5m3xeGocqNQYd2/B+I2jX3Hp3B3XBwfL/zEY0K99G+sV2a+43vcwrt3vBQmsVy9RsnyhQ94urzxBDbted01eS7YTvfZcOpoQuw1kJl5i7wzZBlqMvJd6D3YAwCsiADc/L0rOXefU2OU3ea6CabRmIlo/L4qSLnNq7DIksxUAvw4NiZk7DKFRY84r4tiDsytmVQNHgFTgvkqC/3+gLHa9KcqMPCCEsAohEoUQyUKIL4QQHrdIO1sIkXAXy+ImhDgshDguhDgphHjlz+atHdcM/+gwVnedwraZ6+k9b5jTdLvmbOTdPs+zvvcsdGm5tHqyJwAF17LZOGge63vP4uDSTUxY+Bxt4toQGR3B8M5Ps2T6UsbPH+dUZs9BPQgOD+KZ2GcZET+Sfd/9CMCxnxIZ3XMMY3qPY9GUtxm9cDwALeJaER4dwfiuI1k9cwXPzht903ptmP8eU/tOZGrfiaTYB4bfrfmm7NrGhR8imUtv+6D379uD1Yvm3TKNM0K7NcerVhi72k8mMWEdzRY+5TRd3uGz/DxoPvprTk7ZVAkavTCY0l+OgBAETB9P1vhZpA14Gs/ecWijoxyS2wqLyHt9ReWOy4734AcxX77qeFEIXGIHY9y0nNINr6Cp2wYREF4pry3tPKUfv0rpx686DuIBTfN4bPmOpy42j2tFWHQ4k7qO5p2ZK3l63iinZQLYOP99ZvadxMy+kxwG8UKlYsjMoRzfnwhAWHwzvGuFsb3DFH6fup6Wrw13Ki/3t3PsH7SAEic6zTl0ll09ZrGrxywMH38AKhVeYyeie3Ea+SOfxDW2G+qoGg55rBnpFE57joIxT6H/5EO8nnN8lN36PYzl6hWErx/qiGoc7ziWy9NWE73gWZxR/fknSH/ne453GoeloJjgId2pOX8E50e+xemBL2PNL0Ib5OuQR+XmguHUZX6vPZCzj7xI1AK7PlUqouaN5NwTczgZN56Afp1xi6kGQPjYAegOniC58xh0B08QNnYAAOY8HReGzyNz8Ajy5ryO75inyZ4wk6wRE/Ho1oX81xaT8cjTqAP8cW3TApuuiIK3ljsM4P/Af8o4Sn/5jYyBw8kY8izmy1fK7oXHN8M7OozNHadweNp6Wi9w3l45v51j7yMLKL6hvWKG9aDwXCrHF3xG8ckUhEbNuRnrqPv6CKdyar3wGNfXbP5/7J13eBRV9/g/szXJbnrdBAgJVXoLRVqA0ERFRBB9VVBUpAlIU7AjoK8VK1IURLDQbEAoAhaQKl16J71ns9m+8/tjlt1ssmDQ+NXX33yeh4fszL1n7j33zJkzZ+7cYU+nx3EUl2G4t6dnX8nu4+zrNZV9vaZy8a01NHh5JGee/ghnQRHOwmKynpiDLrUD6sR4AHTdUtAkxnOh30PkPDePmGclH6ZpkEjokP5cGjqBi3eM9qlTtHgVF+8YTf6Dj2D5ZTehUydROOVJ8u4bQWBaL1R1fe1KLDVS+tY7mD7/0me7IiqKoLvuJH/kKPIfeAgUSgJ7SX1J6tGS8LpxfNRtMpufXEza7BF+dXH8q5183GMqS3s/hSpAQ/NhqQB0GDuQvN8u8knfGaRPmu8J3iOeGk/O2Blk3Pmw5FuSfX2L0+1bSj5Z5bNdtNnJfmQqmXc/RubdjxF4czti2tQDoHbPloQmxfFll8n8PH0xXeb6b+uZtTtZ2X0qq9OeQhmgofE9Ulszfj7Gmt4zWNN3Jj9OWUjkM09Iba1pP6hQEDVzHNljZnJ54CPo+6f67X/B3PcpXrKKyhi/3kzW6Bm+GxUKgh6ZQNns6ZROHI6mS08UtSr5ldwsyp6ZgPGJkZhXfULQY5MBECKi0N4ymNJpoyid9CAoFGi6eG05sUdLwpLiWNZ1MlunLyZ1jn+9nly7k09Tp7IiTbKBJm69HvhwHZ/3m8kvr3yJ8XI+xTuOcWLSfBr992G/cuo9fR+XP1zHrk4TcBSbiHefV6qQIBq9/DCHH3iFPd0nc/SRNypXnQAc9ytU5m9HDuQlzKIothJFsRlgA64dqfz1WIGeoii2BFoB/QRB6Fidig16t+Xo6p8ByDxwFm2IDl1MWJVytjKz52+VVg2iCEDG/tNY3NnUzF/PEGWIolOfjmxZ/T0AJw6cQBeiJyImvIrMW+8fwPJ5KxDdskoKSgCwlFs8ZQKCAhCR9qf07sD21dsAOH3gJEEhOsL8yK0OXQZ2w2Ut+91y7Vo1JzQk+Iblx/Vty6UvfwKg6NczqEOC0PrRa8nRi5Rfzvcro97IvmSu24OzsBhVnQQcVzJxZGSBw4Fp43YCUzv7lHcVFWP77SSiw1lFljImisCuHSj7yjcIV8TWRSzJlbLtLieOU3tRJreodj8FfRjKpOY4ju7w2d62d3t+Wr0dgDMHTv2hseo3YgC7N/xCab5kF/H92nJxpaTTQprDi78AACAASURBVLdOA/zotPjoRcqv+NdpZVQNb8KZmYErW9Kr9YetaDp28SnjOH4MsUyyFceJYyiioj37FFHRaNp3xLrxOxThEVi+l7KRZb+eQhmqQ+2nzyFdmlP43S8A5K/cRtSQ7lguZFF+6Axl+09SfuISgY1q+9RRBGop/EY6T02/nkIVIsnWtWqA9UIWtks5iHYHhV//TFifDgCE9WlPwUrpfClYuY3wvtJ287Hz2HOKABACA8El4szNRxUbjSM7F20bafwte/YT1LNrBbty+LRJ0AWhbd0c09dum3I4EMtMnv21+rblwippvAp+PYMm1P94FR29iMnfeImg1gVQq29b8tP34iguo3TPSVQhOjR+5IR3aUbet7sAyP7yB6L6p1SVCYS0qY/5fDZKfSCWQyco/W47utSOmPceQZ8mZSp1PTtR+rXkwyyHTqAM0aOMjkCTXAfLoROIFis4XT51XKZyzzFUdWrjKjPhzJTsyrxlK9oulc7X4mLsJ6rqFUBQKhG0WlAqELRanPkFANTr05bf3P466zr++vy2Q56/sw6eRW+IACCyQQKXdhwDoPBsFqr4WAJubofjciaOjGyPbwlKvdm3rUXF2I6dAj9tFc2SvxZUKlCprl4aSOzTltOrpLbm/noWTYiOQD9tvbzV29a8g2fRudvqKLd69RmoBUQ0zRrVuB/UNm+E/VImjivu/m/4AV2PSv0vLMZ67BT4kWnZfwRXie+TVGX9xriyM3DlSO20/7wVTYpvO50njyGaJL/iPPUbikivXxGUSgSNFhRK0ATgKvSeH8l92nLcbQM5bhsI8qPXixVsIKeCDVSU47DayVm7g9L9p69zXjX1nFdZX273nFexd3Yhb/1urBmSbdrzSz11tNKxBgCLqgiU+UcgB/JV+QmoDyAIwgOCIBx2Z8eXVS4oCMIjgiDsde9ffTWTLwjCEHd2/5AgCD+6tzV1Z9oPumU28HdwUeJqVKp2/xOr0/DguHBKMws8v43ZhQTH+g+4Brz6KI/ve4/I+vHsW7Kpyv4Ww1LZu20fUXGR5GV6HU9+Vj6RcVFVyhsSDXS/rTvvrJvHS5+8SHzdeM++m/vdzKJtC5i19EXen/o2AJFxkRRkerN2hdkFRMZG+m3rPVPu4/X0txnxzEhUGt/ZYJoADa26t0G0mfzWrQkCDeGYM71TDCxZhQQaqh/IBsSFY7glhfNLtwCgCAnGkZ3r2e/MzUMZ47/v/gifMobieQsRXb5mIejDEY1Fnt9iWTGCvmo7FXHJBNz7NNqB43wy9upuQ7H9vMZzY3eViLgICirYQGF2ARGxvheSq9w95T5eSX+L+595yDNW4bERpPTtwJZPvY/pA+MiKK9gq+Yb1ClARNv6pG2ZQ5fl01DWqYsiKgpXnlevrvw8FJFVbfUqAX0HYN+32/NbN2ocpsXzwSUiaDS48r2ybJkFaOJ8+6yKCMZZYgKn9CTIllWAJjYCW4V+OY3lKIN1vgdWKogddQc3rX+dqP/0wZZVgDouAo0hAluWV8+27AI07gu2KioMe640tvbcIlSRvll+gMCeXXEWFYPdjv1yBoqQYNR164BSQWBqZ5SxMdfUhSrBgLO4hIjnphH76XzCZ05GCAjwyo6LwFShX+WZhQTFVX+8Tn28iZAGCSQN7Ury0//h9NMfgyhizSq4Gih4UEcE4ygtR3Tr1ZrpWyakbUPabX2V5itmENymAdbMAkwnLhPYrhkuYxnq+Bh03VJQxUnBlCo2Enu219c4svNQxURiO32BwHbNUIQFIwRofeoARE4YTszqL9De3BH7QW8g5crLQxl9bbuqiCs/n7LPvyRm9RfEfLUa0WTCtncfAPq4cIxZvv5afx2dKlRKmtzZhQs/HAYg7/gl6veTArG4lsmoDLFo6tXFUbGvOfkoY6rXVukgCuK/mE/trSux7PqVvAPS9BBdXDhlFcbflFWI7jptFVRKGgzuwpXthz3b6vZrx5Dt/6XvJ1PIf+E1VNFRNe4HVTFRlfqfh/Ia15XqooiIxpXvlekqzEOoEKhXRtNrAPYDewAQC/OxfPMFofO/JHTRasTyMhyH9nnKVtZrWdbv20CjO7twqYJeAfQJkUQ2rkXud1KQXr3zqtBTJqieAVWontZrnqPdppeJG9LNU6/BrBEA04C/d26Ly/X3//uHIgfyFRAEQQX0B44IgtAUmIk3Oz7BT5U1oiimuPcfB0a6tz8L9HVvv9297TFgniiKrYB2wJXrtEMpCMJBIBfYLIri7muUe1QQhH2CIOzbU3YaBKFqIdH/PcC6qQt4p/04Cs5kctNtvgn/Op1uouXd3Vk85yO/MkU/MtUaNTarjfEDJrBhRTqTX5/k2bczfScP93iU5x9+kWGT/+NuvL+mVpW7/L+fMKHnGKbf/gT6sGDueGywz/52ae05ue/4Xzt/zq8Oql+9+awHODbrM7h6wfE7TtWTFdi1A67CYmzHT1evQqWGuvIuYf54JpYVL2E/tB3tbdKUJkVSc0SzETH3UhURQjX7//l/lzG551hm3j4FfZie2x+7E4AHnhvJipc/QazoCKs5/tei6MgF1qdMYEvaDM4s3kjIs7P9C70G6hat0fYZgOmjD6Xf7TvhKi7GeebUNRtYtX3VHUffjab9J7g45V1O3/8iMcP7owzRuYv8cTtTJSei65+G9dBRqZ6xDNN3mwjo2I6YBfNwZuX4zWp6UCrRNGpA2apvyLnvMUSLheARw7z7/+R4GVJbUHTsItk/HePEhPdpMHckSn2gW06lwtexN+Ph8+xqO4Z9PaeSsXgDdcYNBKD8dAaFi1YS/vBQdD06YD1xDpzOa8pDFLGdu0zhopXUWjyXhIUv+dYBCuYtJXfw3dgOHUFZaSpNdQdGCNYT0OVm8obeQ+4ddyEEBBDYJ03aVy0b89Jr9giu7DlBxp6TAOx5/1sCQnXcv2E2rR/sg+3kGUR/fvBGnJXLRebdj3Gl7z1omjUivFEtd0eqf20B6DJnBFm7T5DtbivAhfR9rEydxuaRbxI2+sG/xg/6cwE30n9/3IBMVbNWaHvdgnmZ5FcEnR51SmdKxgyj5JHBCAGBaLr19oqu5vX1KqmzR5C5+wSZFfQKEBQdSsGJyziKKyS0Ksvx2w/3LqWS4JZJHLrvZQ4Nm03dJwYTmGwgsncbbNJT1P3XbJTM3478sqtEoDtwBikjvxgYBawSRTEfQBTFQj/1mgmC8BIQBuiBqynHHcASQRC+BNa4t/0CzBQEoRbSDcA1IzFRFJ1AK0EQwoC1giA0E0XxqJ9yC5Ay9o/kHLtI1uFzhMR7sw/BcREYc4uv2WnRJfLbt7voOGoAR1b+CEB049oMem885UVGXvl8LqcOnSI63pvRiTJEUZhTUEVWflY+P6+XHhHuSN/J5NefqFImqXFdbmrflDc2vcOpX08QGR/N1Wl3EXGRFOZWVXGxOwvpsDnYtnILtz86yGd/59u68vM3P9Ky1Z3X7OcfQREQQo8tcwAoOniOwHhvdiPAEIElu+haVasQ1jKJlA+ldwMCIvWIDjuOTG8mShkTjTOvqk79oW3ZjMDunUjo0h5Bo0HQBRH50pNwehViWRFCsDebI+jDpJdWK2LzTnVyXTgKPe6BAB1KQz2USS1Q1m2GoFRBgI6Fhz4lPyOPc4dPE1nBBiLiIin6nbHavnIrtz4qBVnJLerz+DtTCNAHogvRwaDu5O34jaD4SK72OtAQgSX72rZaGUeF6WHZWw+B6n5EczmKaG/WWREVjaug6jQPZd1k9BOnUvLMNESj9AhZ3aQZ2tReBPQd4Akwgh58FFY8D4AmPtIzhcXThsJSlKE6UCrA6UJjiMSWW4imwjmoDA7CaSz3qWe9kI0mPoqyvccpTt9N9PD+2HMKEdQqNAavnjVxkdizJT078otRx4Rjzy1CHROOwz11DUBtiCTqvy9Q8uESAnt09Wx3FRdj/HwNxiWfoRs0wJOJ84czNw9nbh62YycAKP/+R8KfnEC/zVJmruDgOXTxkVzVZlB8BOac6o1XgxG9afHUUKz5peTsPI6gELBcyiWoQQJaQyS2bF9bsheUogoJQlAqEJ0utPHeMs4K4174/QEQRQISYwEoXb0RZYQ0nUChC8SR436hOzsfdVw0Vy1fFReNI6/QU6d0teS2IyeO8NSpiGXbD4Q985TntyI62jM95vfQtmuLMysbV7E0Xi6TieDHx3H/4GKyD58j2ODrr03X0GmniYMIigjm6yc/8myzlZnZOGWB5/eEXa9hO3GWoI5tPdtUsVHV9i0VcRlNiGYL/ZdPx1JQSt6hc+jjI8lx79cZrt3WNpMGERARzE/TP/K7P3v3SdS1HsZVXo4qznu+/lk/GD13OqWff+PzVEUVG43Tj6+6EVwFeb5T8CKiEQv9+JXEZIJGT6XspemIZZJfUbVoiys3C7FUGn/7rh/R9L6VYWn3AZDr1utV9NfRa/uJgwiMDGar2waaD0+j6T09ANCGBHF+3ylPWa0hEmula5S9wFjpvIrA6j6vrFkF2AuNuMqtuMqtFO86jr5pIsEtkojq2w7gAhAAhACfAvdVU301xz84I/53I2fkJa7OkW8liuJ4URRtSPevv3crvwQYJ4pic+AFJENHFMXHgKeB2sBBQRAiRVFcgZSdNwMbBUHo6V+kF1EUi4HtQL/rFHsPaPXRLTM5tWk/zQZLc4LjW9fDaizH5CeQD3df+AAapLWm4GwmACHxkQz+cCKrHnmThb2mM6bfOHZu/IW0wb0AaNy6MeVGE4W5VYPYnRt/oWXnVgC06NicK+czpHbU9U7dOL7/OKWFpTzRZzx7Nu0mdbDkhBq0bkS5sdwTCFak4lzslD4duXTS+wJeUHAQTTo2Y+8mvw8s/hQuSynb0mawLW0GWen7qDNUCpDC29THYTRjvc4NUmU2t5/IppQJbEqZQPmWHymc/RYKvQ5VfByoVOj6pmL+YWe1ZBW/u5iM/veQcet95D01G8u+gxQ8/bLU5pyLCGExCCGRoFCiapiC85zvI1iCQjx/KmLrSkGrxYR951dYPnoKy8czsW5YjOvSbzzS8j6eumUS+zbtpuvgVADqt25IudFUjbHqwOWTUnZ/QpdRPN7lUR5tdT8/rdnO7sfe5fTCdBKHSDqNaFMfu9GM5QZ0qo32Ti0Jb5UMggL7gf0o42uhiJX0qu3eE9su3/n+iugYQp6ZhfHV2bgyvA/FypcspHDIrRTcnkbpjMk4zp6Gcilo1LdpiLO03DO1pSKlO44ScWsnAKKG9CB/9Y8EJBnQ1o5BUKsIbFQH8ynvcRSBWkp+PEjkXakoArWE9e+Is7gMe24RpkOnCUgyoHHXjRjYheLN0iP64s17iBwinS+RQ3pQvEnargzR0WDp05S8twjTNxtQ10lAedWu+qVh/nEnQrAe/V23e+e/+8FVUIQzJw9VopSBDUhpjWXnHtJ7zyC99wwy0vdR9y5pvCLb1MdeWv3xOr1kMxfX7OTCmh1kpO/D8J9eBNWLRxWmw2Esx+ZHTtGOY0S7nxTGDe1OfvpeADTR3nm/wa3rIzpcBNSOJqBODMqYCEJu6Y7l6EmCe3fGuG47AKZtuwgZKPmwgJaNcRlNON2BvDJCsiOVIdqnztWXXgFpSpIgoDRIeg1M64l1R/XOV2dOLuqmTUCr9Wwr+3gpy/rP5MzG/TRx+2vDdfx182Gp1O3WnHXj3vPJsmpDglColVKZe1Kx7D+Cdf8hVHUSfHxL+Q+/VKutivBQFO5pYIJWg0Kv4+fpi1nTdyYX0vfT4C6prTFt6mEzlmP209ZG96RSq3tztlZqa0hd7zUnslldUKux7P4VVe2EGvODeU+9gvXoSdSJCagS3DL7d8e0vXr9vxbOMydRGGqhiJFkqrv0xLbPt51CVAy6qbMwvT0HV5b3fHfl56Jq2AQ00virmrfBvnM7n/ebyef9ZnJu435ucttAbGtJr+V+9NpkWCp1ujcnvYJejyzdwuf9ZrJmyEsEhOsJT5autSFtG+C8xnlVXOG8MgxNJT9dmuaTl76P0I6NEZQKFIEaQtrUp/x0Budmf8bO1qMB6gLDgK38HUG8zHURbuTx6L8VQRDKRFHUV9rWFFgLdBJFsUAQhAhRFAsFQXgeKBNF8TVBEPKBJkARsB7IEEVxhCAI9URRPOuWcwB4ECgFzouiKAqC8BZwQRTFt/y0JRqwi6JYLAhCILAJeEUUxe+u14e5ifeJAH1mDSe5ewvsZhvrpiwg270E1dAlU1g/bRFleSXcv+oZNPpABAFyj18ifeYSbGVm+r/yMI36p1DqflmtxGll/IAJjH1pDO1S22E1W3h98pucPiw9TJi19EXenPYWhTmF6EJ0TH97GjEJ0ZhNFt556h3OHT/P0NFDSBvcC4fDgdViY8WcJZ7lJx+eNYpW3dtgNVt5f8rbnD0iLQk3Y8mzfDDtXYpyC3nus5cIiQhBEAQu/HaeBTPe97xAm3pXT1p3b8Ob41/js/1VVFmFqc+9zN4DhykuLiUyIowxI+9n8G19r1l+XTPvso4t5o4gtkdLHGYrByZ+SPEhSa8dl0/j4BMLsOQUkzyyLw3G3oo2JkzKPn5/kIOTF/rITFswHPNPu3BZrNKyawoFZd+kU7p4BfrB0qpeZau/QxEZjuHT91HogkAUcZWbybxrJGKFl/C0bVsS8sAQ8iY8TfTw+gAo6jZD022ItPzkbztx7N2AqrkUgDmO/ISqRSqqFt3A5UJ02LD/tApXlu+yioqEhqjbpnFfqnf5yQdnPUpL91h9OOVtzh2R5s5OW/IMC6e9S1FuEU9/9iLBEaEIAlz87TyLZszHWuFlZ5CWqNRsPELGuj20mjOCuB4tcJpt7Jv0IUVunXb+dCr7Jy/EklNM/ZF9aTjmVgJiQrHml5L9/UH2T1lEvQd7kzw8DdHhxGmxo1z+Bo7jx1CndED/6HhQKrBsWo/5808JuEWa3WZZ/w36CVPRdO6OK1damUd0OimZMMqnjermrQgcfDfO3ByE5p1wma2cm/QupsNSnxstm8m5Ke9jzylCWye20vKTbxHSpQV1Zz+MNiEa0enEZbGDKJLxxpcUbdpLw4+moY6LRKkPxFlSxunhL1Hulh3asy21n5dWNyn4YgtZ70grayjDgqk3fyqahChsGfmcfey/OIvLMDw+hLhxg3FekYIHQReE4HbpLrMZQalCEazHvGM3RbNfRxEZTuzSD3zsKvvuhxBN5agb1iNi5mRQq3FkZFH44n/56YTXLbadMwJDqjReuyd9SOFhaby6L5vKnikLMecU03BkX24aLY2XJb+UrK0H2TNlEYGxYXR46zECY8LQxYUhOlzY8ks4OeE9jIck+2u+/ClOPjEfW04RAYkxNPlwkmeZvONj30a0OUh4qB/xw/u49WrjzLNLUYXqqD9rBAEJUbhMJpx5RZh2H8J+/jIlX0g3LzHPjCWoS1v38pNvYD0m+bBay15DGRYMDie5ryzAvEt6MGuY9zSapFooBCfOnBzMW7cTPOJ+UCgwr9tA2SfLCRp4GwDlX3+LIiKcqEUfIuiCwCUims3k3TcCsbwc/UMjCOzVA9HpxH7qNCWvvMaKs1Imutes4dRNlfz1xikLyHHrdNCSKWyavghTTjGTzi2lNCMfW5l0Lp1O38uueV9haFOf/m8+huh0UXA6A+2cObiMZQR2aU/E1NGSb/l6IyWLVhB8l+RbjKu+QxkZjmHFez42kHHnw6jiY4maNQ1BoQCFgGnTj6yas9kz/je/NJzaqS1wWGz88MQC8t1t7fvJFH6auojynGJGXlhK2ZV87Caprec37OXAW1/RcsytNBjcBZfDicNiQ3zvPWn5yc7ta8wPepaf7JpC5DRp+Unj2o0UL/yM4CEDpP6vXIcyMpyEL95FoQtCdI/V5YGPIJrKiXnlKQJSWqAMC8VZWETRe8tQHfsGVZsOBD04TlrWdesGLKs/RdNH8iu2Td8QNHoq6o7dcOW5n1k4nRinS34l4O4RaDr3BKcTx/nTlL//Kp/u9N7YdH9pOIluG/h+8gJy3Xq9bekUtk6TbGDs+aUYK9jA2Q172TtPWiq18ZCuJKa2wFJURoPuzXGabRyf8L7nvGqx/ElOPPGh57xqVmFZ12Nj30G0SS891xlzG4ZhPRBFF5nLt3JlgffGv2fOlwKQCkzhb1p+0rzurb89WA0cMLH68zf/D5EDefwH8u7tw4GpgBM44A7Sn8cbyI9GegnkInAECHaXWQM0QMrqfw9MBJ5EupO1A9nAvf6m6wiC0AJYirRuqwL4UhTFF3+vD1cD+Zpku+vGH8n+HsEKdY3LBKoVyN8oFQP5mqJ1fO7vF/oDXA3ka5KRb2T/fqE/wGCb7vcL3SCpra75ysmf4uyhP/einD+Uipp/RBxrMP5+oT/AzxlxNS7T4LTVuEyA+Iia10FwpPX3C/0BVlxKqHGZgyNyfr/QDbI5P/b3C/0B+sTUvG9x2v+aCQbhDWveXj/dU6vGZQI0tf0155Y7kP9bMX/3xt8erAbe+sTfrgd/yHPkAX9BvHv7UqSguuK25yv8/QHwgZ96/iZsz3X/+722HAZa/145GRkZGRkZGRmZ/7+RA3kZGRkZGRkZGZl/LvLLrtdEDuT/JgRBiESadlOZXqIo1vycFhkZGRkZGRkZmX8VciD/N+EO1lv93e2QkZGRkZGRkZH530QO5GVkZGRkZGRkZP65/JUfffwfR15HXkZGRkZGRkZGRuZ/EDkjLyMjIyMjIyMj889Fftn1msgZeRkZGRkZGRkZGZn/QeSM/L+ECU/4XQr/TzHkw7wal2m3O2pcJvw1H28acPSlGpcZGN+1xmUCrJobU+MyX44vq3GZAJbymv/ITv7Zmv/IFECuS1vjMtM+qPnPRCQPX1LjMgHOzmpU4zK/nltS4zIBagk1/72YS+fCa1wmQOu/4KNYqwpr/uNND7W9XOMyAYrP1fx5VVwcWOMyAX7dGV3jMgfVyaxxmQBZV0L/Erky/2zkQF5GRkZGRkZGRuafi/yy6zWRp9bIyMjIyMjIyMjI/A8iZ+RlZGRkZGRkZGT+ucgvu14TOSMvIyMjIyMjIyMj8z+IHMjLyMjIyMjIyMjI/A8iT62RkZGRkZGRkZH55yJPrbkmckZeRkZGRkZGRkZG5n8QOSMvIyMjIyMjIyPzz0Ws+e9E/FuQA/l/EYrEJmi6DwVBgePYDhz7NvruT2iI9rbRiKX5ADjOHMCxZ723gCAQMOwpRJcDQRtEnUEuSldtoHjRl1WOFTVjNEHd2iOaLeTOeB3r8TMAhN53ByFD+oMgULpyAyXL1gKgaZxM3GtPoUqIA6B45UZyXvigitzYZ0ah756Cy2wla/obWH47K7U9WIdhzgS0DRIBkawn38J88ARR4/9D2NC+xOdLHy8qzygguGECTrONXyfMp+TIhSrHSHqoD/Ue6Yc+KY71TUZhKzT67A9rlUz3dS/iNOUh2kzX1fnTc97gxx17iAgP46tP51+37I3QqFE9Fi98k9atm/HMs6/wxpsfVrtu85ceILZXqz+tgzrrlpH/5Eu4zBYipowBpYKytRsoXfK5T1lV3dpEPT8VTeP6FL/3MaXLVvoeTKHA8On7uBx2lHo9oqCkZFU6hQsrlQNiZj6GrlsKosVK1lOvY3WPf/jwOwi9qx+IItbTF8h+6g1Em53Ix+8n5LaeqGIiwOWicOnX5L+xtKrcpyvY1ZNvYP3tLJqkBOLfetJTRl3bQP68ZRQt/dqzLXn0AJo+dx8XP/2eqM5NcZptHJzwgV+d1n2oD8mP9EeXFMfGJo96dJpwZ2fqj7sdAIfJglC2A7E0jx0nr/Dfr3fhEl0Mat+Ih3q0rCJz79ksXv1mFw6Xi/CgABaPHgDA8p+Psmb3SUTgzva+H22a9coMevXuhtlsZuKYGRw5dLyK3C7dOvLsrCkICgXlZSYmjJnJhfOXGD3+Ie4ceisAKqWSwMb1MC+axo5Tl3n1p1O4RJE7msTzUNu6PvL2XSli0vpDxIdIH+XpmRzNqPbJANyydAc6tRKFQkApCNyNt71tZj1AfM+WOM02dk36kCI/em3wYG8aPdyP4KQ4Vjcbha1QOtfVwYF0encMIQ0TCDJEIBrLKF665k/5K3XtOJ9ycS3bk/jscPY3G47DPZ7a2jHU/+AJVGF6TEfPc3b8PET3h+4SZ40krGcbXGYrZye9S/mRcwDEjhxAzH96gwCOIiOq2EhcZivGIxcIaVMf0e7AfCGH4xPex1FaTkCdaJp+OBF1mB7jkfP8NvYdRLsTgLCbm9Bg1ggElRJ7oZEDg54H4JEdb2IzWRCdLlxOJ5/e+qw0Fi/cT1KPVjjMVjZMXkDu0ao67vvfh4ltkYQgCBSdz2bDEx9ir/DhNnXr9gQ9Mh4UCqyb12FZvcKnvqZ7GgF33guAaDFT/sEbOC9I566g06MbNxVlnSQQwfTOK3BO0n9g53ZETh+NoFRQuiadksVf+MhVJ9UmetZktDfVp/DtJZQsXeXZFzd/DoEdW4PTReH7n1D8+jdV+lXrhUcI6dkW0WzlwhPzMB+VxiMktTW1nn8ElAoKPttMzvurpfY0SaLO3NEIWjWi08XlmfNhXxYx3ZrR/r0xqEOCEJ0ih55dxvlPt1Y5Xr0He9PA7Ve/aeq11ehON3HzkicwXZI+sijs3Ipx8TK0HVMIe2IcgkKB6Zv1GD/5zEeeKrE24c9MQ9OoASXzP6Jsude2Bb2OmCUfoIqLBVGkaO4bsGB3lTZpasdQ7/3JqML1lB85x7nHvfZa58WRhPZsi8ts5fykdyg/eg5NfCRJ8yagjg4Hl4u85ZsripsFDARcQC4wAvhrvm4lU23kqTX/FgQBTeo9WL96F8uyF1A1TEGIMFQp5so8jWXFbCwrZvsG8YCqVU9cRdkoIhOwfvUul257hOBbeqCuV8enXFC3FNSJCVzq9yC5txB3cwAAIABJREFUz80j+rnxAGjqJxIypD9X7n6cy4MeQ5faAXViPABRkx9GEazjXP/HuPL4XEIHpaGpX9tHrq57OzSJCZxNe5isZ94m7sVxnn2xT4/C9ON+zvUbxbnbxmE96/3iYOGSr9iWNoPf5n6JQq1kS6cnODhlES1fecivqgr3nGTn0DmUX/bz5VqFQNOn7yFn++HrKNvLHbf0Zv4bNf8F2MLCYiZOeuaGAniA2F6t0CfH1YgOLL/sA0EgYvp4csfPIHPwSHT9eqBO8rUHV4mRwv++VzWAdxN8zyDs5y+hqZdE7vgZnL91FMEDUtFUsitdtxTUifGc7zuS7GffJvY5afxVMZGE3T+Qi3c9zoXbR4NCQfCA7gAUfbwWRJHztzxG3hufEHZ3fzT1/NhV3QTO9X6Y7GfeJu4FSa7tfAYXBo6X/g2agGi2YNz8i6eeKi6K6G7NseSXEJQYy9ZOkzg0ZSHNXxl5DZ2e4pehs6votPxSLjsHvcgPPadz+s01aFr1xelyMXftTt4b2Yc1kweTfvAcZ3OKfOqVmq3MXbuTeSN6s2byYF69vycAZ7ILWbP7JJ+OH8iXEwfx0/HLKJRStqpn724kJydyc5t+TJ3wHC+//pzftr78xrOMfWQavbveyZpV65g4dRQAH7zzEb273knvrncy58U3cWWcxmk28fIPJ3n3tlasvrcj6adyOFtY9au/rQ1hfDGsA18M6+AJ4q+yYFAbvhjWgRV3t/dsM/RsSXBSHN91nsyeaYtpN/dBv23N33uKbXfPpaySXhuM6E3J6QwQYfPtLyCoVAQP+OP+KviO3mgbJXvKxbw0idBuLbBe8T1u7Zn3k7XwWw51GYejuIzoe3oBENqzDQFJBg51Hsv5afNJmvsoAIGN6hDzn94cGzCNS7OXEdiwNofumcOJKQsIaVOfPd0ns6fHVMrPZpH4+CAA6j19H5c/XMeuThNwFJuIv1cae1VIEI1efpjDD7zCnu6TOfrIGz5t+/Lu2XzSf6YniE/q0ZLwunEs7jaZTU8upvfsEX51vO3F5XzSbyZL+86gNKOA1iP6eHcqFASNmojxhWmUjBuOpmsvFLUTfeq7crIwznic0gkPYfniE3Rjp3j1//B47L/uoWTsA5RMfAjnlYseuVEzx5E9ZiaXBz6Cvn8q6mTfsXOWGCmY+z7FS1b5bEehQNOgLtkTnsd+MQN9/1QCGvie9yE92qJNMvBb18e4OP096swZ7alb+6VRnHngBY73HEf4wK6eugkzh5P15uec6DeJrNdWkDBjOADB9Q2UnrjC2sQR7J+8kNazh/vVY8HeU/w4dC4mP341f/dJtvSewZbeMzAuXgYKBeFTJ5A/8Umyhz1IYJ+eqJIq6bXUSPHr72JcXvXmNHLu84g2Oxld+pI3bgr6oYP8tqn2zAfIWfgtR7qMxVFiIqqCvWqT4jnSZQwXpn9A4lzJB4gOF5dfWMLR1PH8dtt0Ykb0B2jiFvcq0AJoBXwHPOv3oDL/p/yrAnlBEGYKgnBMEITDgiAcFAShQw3J7eqWe1AQhL/kO9CCIKQKgvDdH62viK2LWJIrZdtdThyn9qJMblH94+vDUCY1x5l5Duw2SY7dQdmG7eh7dvIpq+vZCePXWwCwHj6BIliHMioCdb06WA4dR7RYwenCvPcwul6dpfbpg3AWlGC/nI0yKAD7pSyCe/nKDU7rSMlX3wNgOXgSRbAOVXQ4Cn0gQSnNKF7pfsJgd+AyVs2Ux/Vty6UvfwKg6NczqEOC0MaEVSlXcvQi5Zfz/eqh3si+ZK7bgy2/ep+Rb9eqOaEhwdUqeyPk5RWwb/8h7Hb7DdWrSR04C4tR1UnAcSUTR0YWOByYNm4nMLWzT3lXUTG2304iOpxVZCljogjs2gHr4WOIFoskx+7AuP4H9L06+pTV9+pI6dfu8T90AmWIHmV0OACCUokQoAGlAkWgFkduIQCaerWwX8rEfjkbIUCD/WIm+rROVeSWrL0qV7Krq3KvEtSpJbZL2Tgycz3bYmY8yvFZK1AGaMj8Wgrwi6+j09KjFzD70WnRvtPYSyR7Ldp/BiEgmKOX86gdFUKtyBDUKiV9Wyaz/dgln3obDpylZ7NEDOF6ACL0kus5l1tCizoxBGpUqJQK2ibHodJKgXy/W3qy8nPpicKv+w4TEhpMTGxUlTaJoog+WJIbEhJMTlbVwOOOwbfgOL2Pozml1A4NpFZoIGqlgr4NYtl+zr/t3Ai1+rblwirJVgt+PYMmNIgAP3otOnoR0xU/xxMhODGWsgs52IrKcJYYKVv/wx/2Vzgc2HPyPeXUdeLJev+rKo/0Q7o0p/A7yR7yV24jvJ90cxLetz35q7YDUPbrKZShOtQx4QQ2SKDs11O4zDbCe6dQuuMo0be0p3T/aRAE1JEhAJTsP4U2PkKS1aUped/uAiDry+1E9U8BIPbOLuSt3401owAAe37pdXVcv09bjq3+WZJz4CzaEB06Pzq2lZk9f6sC1D59VjW4CVd2Bq4cyQfYftqKpn0Xn/qOE8cQTdLNnePkMRSR0dKOwCBUTVti3bzOXdDhKadt3gj7pUwcV7Il37LhB3Q9bvaR6yosxnrsFFTyLdrmjbCdvoD99AUATBt+ILRPe58yoX3aU7h6GwDlB06hDNGhiglH16oB1gvZ2C7lINodFH3zk7euCMrgIACUIUHYcyQ/E9q4NueXS7Iur90JSgVBtSKr6LH46EXK/dmqHzRNGuO4koEzU9KrefNWArtV6n9RMfbjJ6v0X9AFoWnaGOOS5QDYDh1FCApCHePr1wCCOzencN1OwG2vfaWwKKxvewpWSX0yVbBXe24R5e4nFy6TBfPpKwAJbnEVDU4naez/CJfr7//3D+VfE8gLgtAJuBVoI4piCyANuHz9WtXmP8Broii2EkXR/Lul/wYEfTii0ZvRE8uKEfRVT2pFXDIB9z6NduA4n4y9uttQbD+vQQjQITq9waMjOx9ljG8goIqJwpHtvfA7cvJRxUZiO32BwHbNUYQGIwRo0XVLQWWQHLpx/XbUdQzU/3EpMdNHUrxmC6pYX0eoio3CXiGgcGTno4qNQl3bgLOwBMMrk0j6+h0MsycgBGo95cLvu40eW18mtlcr7KXlnu2WrEICDVV1cC0C4sIx3JLC+aVbql3nn0agIRxzZqHn95/VgSIkGEe2N7h15uahjKl6AbsW4VPGUDxvIUJICKLV5tkujW3l8Y/EkeW9CNrd4+/ILaDwo9XU2/oJ9X5agctYTvmOX911olBGhlHvh6WE3pZKyVffo64kVx1b1V7VlYLbkAHdKV233fNb37MDjpwCSn+7hEKpwJLtPbfMWYUEGCKqrYOK1L43FWfueXJLyokL1Xm2x4YGkVvqe3N6Mb+UUrONkfPXcc+8r/h2/2kA6seGs/98NsUmC2abg59PXEahlOrEGWLIzMj2yMjKzMFgiK3SjimPP8unK+ez/9hW7rr7dt55a6HP/sDAAHqkdcV55gC5JguxwQHetuq15JmslUVyOLuEoZ/tZuw3Bzlb4M3YC8CYbw5y7xd7WH00w3uMuAhMmQWe3+WZhQTFVd9WT328ieB6BqI7NKL/1pfJn/MBjuy8P+yv1HXiETRqAIJ6dMRZWoazgj8BUEUE4ywxSYE/YMsqQBMn2ZsmLgJrptd+bZkFaOIiKD9xieAOTVCF69EmRBHYoBbaBKmONasArduW4u/tScH3B1FHBOMoLUd0H8OaWegpE1TPgCpUT+s1z9Fu08vEDenmbZwoctenT3Lfulm0uLcHAPq4cIxZXh0bswvRX0PH/V57lNH73yOiXjy/frzJs12IjMKZ7/UBroI8FJFVbw6vou09ANuv0hQPZVw8YkkxusefJOTNRQSNmwragGuMSx7K2Or5Fn911XG+dTVxkdgqjkdWPpq4SNSVttuzCjx1rzy/iISZI2i2ezEJTz9I5svLAMlWy922mjCgPfbScrTuG7DqEtG2Pmlb5tBl+TRUSXVRxkThzKnoW/NRRkdXr//xBnCJBPVLI+aTDwmfMRlnfgHqOF+/pAr3tVd7Vr6nr5J+vLYh6cG3vqZWNEHNkgAqztmZjRRb/Qc5I/+P4F8TyAMGIF8URSuAKIr5oihmCoLQVhCEHwRB2C8IwkZBEAyCIKgEQdgrCEIqgCAIcwVBmO1PqCAIDwNDgWcFQVju3jbVXf+wIAgvuLfVFQThhCAIiwRBOCoIwnJBENIEQdghCMJpQRDau8u1FwRhpyAIB9z/N/JzTJ0gCB+5j3FAEISB12jbo4Ig7BMEYd/WU1eqFqiUSXLlXcL88UwsK17Cfmg72tukR42KpOaIZiNi7qWqMiRBlQ7sr4iI/dxlihZ9SfziucQvmI315HlPJiGoc1ssh05wpttwcuYsJHxInyrtE64hV1AqCWhan6IV6zk/cDwus4WoUUMBKFqxjrO9RrKt11O4bHaSH+xTuXq1aT7rAY7N+gxc/8Mv1fhR4p/Sgb9Bqaa8wK4dcBUWYzt++ho2U3mDv2OJKEL06Ht15Fzag5zt9h+EQC0ht/XwFLEcPc3Z7sMp+XY7us6tq3bYr9gKZdQq9L06YNwgZS6FAC2Ro4eRP2/ZtTv3B168iuzchDr39MB+bLtfFQqVGup0uTiekc+7D/Xh/Yf7sWDLQS7mlZAcG8aDqS14bGE6Yxen09AQ6WmO4Hf8qx7t0TEPcN+Qx2jbtCefL1/L87On++zv3S+Vvbt/BWt5lbr+aBwTzPrhnfnyng4Ma1GLSeu9U9M+HtyOz+5uz7u3teKLI1e4oDRe7XC12notDKktMF3O5+LanaT3nkH002NBq+GP+iuXyQxOF0KAlohR9+C4ku2nPf5tVNrlX/eWMxlkvb+Wxp8/j651AywXshAdLp/6iRMHITqc5Kz+6brniqBUEtwyiUP3vcyhYbOp+8RgApOlhMyKwS+ybMDTrHngVVo9kEat9o2q2JRPeyuRPmUB81PGUXgmk8a3VXxaVn0foGreGm3aAMxL3VMClUqU9RpgSf+a0kkPg8VC4OB7rym22udVdepe61y4Tt2o+/tz5YXFHO0wkisvLKbOq+N9jhfSMIHmTw/DeCbzhmy16MgF1qdMYEvaDM4s3kjkqy/670R1ZSqVCDodlh93kPvAKFwWCypDbLV04LVXP3Ir1FcEBVB/4XQuP/cR+GbiZwK1geXAOGT+dv5NgfwmoLYgCKcEQXhfEITugiCogXeAu0RRbAt8BMwWRdGB9JLGB4Ig9Ab6AS/4EyqK4iLgG2CqKIr/EQShD9AAaI80T6ytIAhX0yL1gXlIc8gaA/cCXYApwAx3mRNAN1EUWyPdzc7xc9iZwFZRFFOAHsCrgiDoKhcSRXGBKIrtRFFs1yNehxDszbQI+jBEU7FvBZsF7FImzXXhKCiUEKBDaaiHMqkFAQ/ORt2qB4IuDE1fab6qKi4KZ26BjxhHTj6qOG/mQMqaSllg45qNXLlrHBkPTMFZYsR2UcrABbRtDk4pqDdu+Al1YrynzlXs2fmoDRXkxknZWHt2PvbsfCyHTgJQmv4zAU3rARDSrwtJX82jx+bZFB86R0gT7xzLAEOETyb19whrmUTKh+Pps3ce8bd2QKmPQtAEVbv+n2X0Y8PZt3cT+/Zu8ptFvV69Hlvm0GPLHCzZRQTGe7Mqf0YHQWnd0A/si+amhp79yphonHkF15HgRduyGYHdO5Hw3aeEDBuEMjqSyJekl0uvjm1FHDn5qAzeTJ/aXSaoUyvsV3JwFpWAw0nZ5p0EtG7iqXPVZkq/3U5gq5uw+7GrqvbqPba+Wzusx87iLJDOF00dA5p6tWmw6zMGXFqGQqum9bzH0EaHAhB4gzoFCL6pDi1ff5S9I14Du4XY0CCyS7wZ+JyScqJDfG0tNlTHzQ1rEahRE64LoG1yHCezpL4Nat+IzyfewUejb6V7/9tZl76WzT+tISc7l3j3C+UAhvhYsis8UQGIjAynSbNGHNgvBdvfrN1ASvvWPmXuGHwLX62S3qGJ0QWQY7R421pmJVqn9Smv16gI0khrJ3StG4XDJVJklp7AxOilshFBGiY9+hD3p79Bv81zMOcUo4v3ZlGD4iMw51TyWdch6e5uZG45QFB8JGUXcrBfyUZ7U70b8lcKrQZBoZCm/pUYcZWbUdc2oEqII6BVExp+9CQaQyTNNr6GOjoMR2EpylAdKKVLp8YQic09/cKWVYA23mu/mvhI7DlFxI7oR+xDt4BCoGi9NF3GfC4LAK0hktD2jYnq3ZZjY94GwF5gRBUShOA+hjY+Amu2dAxrVgGFWw/hKrdiLzRSvOs4+qbSvGqTW3cNB7RHFxXKre+Ooyy3iGCDV8fBcRGUXUfHokvkxLe7aHBLindbQR7KqBjPb0VkNK7CqtNHlInJ6MZOxThnBqJRivtc+Xm48vNwnpJeuLbt/AFlvYbXGJdonJXO3Wvhr+7VaTBXsWXlo6k4HoYo7DmF2LMKfLarDZGeupF39aB4gzRtShUZSvDNzUlz22pYk0Q6fTSJvY/PRxuux5JdfVt1lJlxul8ezt56CEGpwlVejjLWq1dlTBTO/OtPy9HdNZCYZQuIeHY6otmMq1yaIGDe+iOK8DDsld6zqWyvarcOJP0UoKlw/kl6kOoLKiX1F06jYO2PFG3Yda3mrAAGV1cHf5q/e1qNPLXmr0cUxTKgLfAokAd8AYwCmgGbBUE4CDwN1HKXPwYsA74FHhJF0eZPrh/6uP8dAH5FCtgbuPedF0XxiCiKLuAY8L0o3bYfAeq6y4QCKwVBOAq8CTS9xjGedLd5OxAA1PFTzoMr5yJCWAxCSCQolKgapuA8V+mFzSDvo0BFbF3pbt1iwr7zKywfPYXl45lY1y8Epx37L99Imcr+qZi2+Z7Ipq27CB6YBoC2RWNcxnKc+ZJzUEZIwY7KEI0+rTNl67cD4MzOQ9MwCXWtWIK6tAGXiPF7X7ll3+8m9A7pRZyAVo1wGU048opw5hfhyMpDkyRN09N1aoX1jPT0wLhpB+dvH8+2tBk4yiwoVJJJh7epj8NoxppbfWe7uf1ENqVMYFPKBDK/242zLB/RVr2MZE3wwfyltEvpQ7uUPmRl5dxQvW1pM9iWNoOs9H3UGdoV+PM6KN/yI4Wz30Kh16GKjwOVCl3fVMw/7KyWrOJ3F5PR/x4ybr2PvOmzEG12St5fAmoVwbd0p2xrpfHfuouQge7xb9kYp9GEM08a+8CWjRECpIAwqFMrbOekWXPOkjLUifGoa8US3LsTglZDWWW72rqb0EFX5TbCVSbJvUrIrd0p/e4Hz2/rqQucbnMXJ5sNZF2d+7EWGCn57RLWvBLC2tTHbiy/IZ0GJkSS8tEkDox7D9M5adpL01rRXMovJaPQiN3hZOOhc3Rv4nuKpzZJ5MCFbBxOF2abgyOXckmOkc6vQvec5qyiMl5/70PS3C+oblj3PUOGSQ/w2rRrgbHUSG6Ob3BQXFxKSEgwyfWkALBbj06cPnXWsz84RE/Hzimkr5dW5WgaG8ylknIySs3YnS42ns4hNcl3akW+yerJUB7NKUEURcIC1JjtTkw2aYUMs93J/I+X8lG/iaT3nkFG+j7q3iXZamSb+thLzVhuQK/lGQUExIYRnBRHRMskNEm1CGzX/Ib8VdnGH7l85xiyxj6HMiwEdXwMttMXyBr7HNbjZzmY8ii2rAKO9p2CPU9qW+mOo0TcKs3DjxrSg6KNeyW9btpL1F2pAOjbNMRZWo49t4icJemcGPYiR3tPxrj3BKFdW5KzdgchbaXLRsIDvTn8wCu4zN5LUPGOY0S7s+KGoankp+8DIC99H6EdGyMoFSgCNYS0qU/56QwUQVrUOmnKyrGVP1F8KZf0qQs5s3E/TQdL89kNrethNZZj8qPjsERv4qBeWmsKz3gXInGcPoHCUAtFjOQDNF17Yt+zw6e+IioG/VOzML01G1em9+mwWFyIKz8PRYL0Mqm6RRucly8AYD16EnVigrSSmUqFrn93TNt/oTpcrauMlYJ5Xf/ulGze41OmZPMeIgZLT+6CWjfEaTThyC3CdOg02roGNLVjENQqwm/v6qlrzylE37GZdIwzVzAfO8eW3jPI/eEIzWbczdG5XyA6XdiNN2arV5MAAOGtkkEhYN27H1XtBJQGqf+BvXti/vH6/Tet+prc+x8l596ROHNy0N8prYalu6UPokmyt8oYdx4lYoA09z5qSA+KNkl9Ld60l8i7JP3oKtgrQN3Xx2I+c4WcBVVWAmpQ4e/bkRKTMn8z/6rlJ0VRdCIFvtsFQTgCjAWOiaLY6RpVmgPFQPXTn9IDqbmiKPosJyIIQl2g4sRRV4XfLry6ngVsE0VxkLvO9mscY7Aoiier3SrRhW37F2jveFxafvK3nYiFWaiaSxdKx5GfUNVvg6pFN3C5EB02bBsW+ZEj4irIRHvH4yT2clK6dhO2MxcJuVta9q70i3WU/7iHoG4pJKZ/jMtiJXfm657qcfOeRRkWjGh3kvfSu7hKpbmyuc+9RczsySSnS2orXrkJ25lLhN1zi/T7s/WUbd+LrnsK9b5f7F4m8E2P3OxZ84l/fRqCWoX9cjaZ7n0x00aivSmZeKeC8st55Gw7TO9db+IwWzkw0TtEHZdP4+ATC7DkFJM8si8Nxt6KNiaMHltfJuf7gxyc7DtHuLpMfe5l9h44THFxKb3uuI8xI+9n8G19/5CsisTGRrP7lw2EhOhxuVw8Pv4RmrdMxWisulpIRXK2HCS2V6ua04FLpPCVd4h572VQKCj7Jh37uYvoB0tLFJat/g5FZDiGT99HoQsCUST43jvJvGskoqnCTZDLhe3sBWLee5loQUnJamn8Q++Wxr/ki/WYftiLrlsKSZs+QrRYyJohjbHl8EmMm34mcc074HBiOX6Wki82ABA9aTiCQkFS+gJwOila9q1kV8PcdvX5ekzb96LvnkLyFsmusp/y2pUQoEV3c2uyn3nnmjp1WWyYL+fRc9dbOM1WDlbQafvl0zj0xEKsOUUkjexLvbG3oY0Jo/vWV8j5/gCHJy+kwRN3og7X0/xlaQUhba0A+OETnhzYidGL0nG5RAamNKR+XDgrf5Eyl0M63URybBg3N6zF0DfX8v/YO+/wKIr/j7/m7nLpvV0SWkKT3qv0DoKCBRtNuvReFBSpgogKSFcRQRRRsICAgogg0lvohE56L3e5ur8/9khy5AKBb/zZ9vU8eZ7L7sx7Zz4z87nPzs3OCiHPwlewr2Edv243GXojGrWKqd2b8mK0vKZ59659tG3fgoMndmDQ5zJ2+Ot5ZV2/aQXjR00nIT6JCaPfYM26D7BJNjLSMxk7fFpeus5d2/HrngMY7LN9GpWKyS0qM+zbE9gkeKpqGOUDvfgqWg7Ynqteip9jEvkq+g5qIXDTqJjXsTpCCFL0JsbZl9lYJYnOlUIJviwHNbG7TxLWtjZdf1+E1WDi0Nh8u7b8bCKHJ6zGkJBOpQEdqfJqV9xCfOn889vE7TnJ4QlrOPv+Fhq9PxSEoN23byJlZZO9fe8j+6vE1xfi2e5xp+kqLBtHzOjFmBPSuDXnMyosH0fpSS+RE32NpI3y8yTpu4/h17YutX5fhs1g5OrYpXn5K66ZiIu/NzazlYz9p2mwax5Wgwm1uxaA2pum4142hKQfj3J+5FKuzN5A9ZVjiJryAtlnrhH7uXxTpb98h9Q9J2n4y0IkyUbshj3kXLiFW9kQXlw7EQCVRs35rb9z/VfZ7pGtazHwt3cxG0zsmLAqr0xPr53AzslryEnMoPN7Q9B6uSMEJJ67yc+vry0wAKzoV72P94yF8vaTu7djvXUd105yEGnc8R1uL/RFePviMWRsXp7M8fIuKPrVH+A1bhpoXLDFx5Kz+G05jdVG8tyl6FbMRahVZG3ZiTnmBt7PyW2X9dU21IH+RHy5FJWnB5JNwrd3D249NQgpR48lLpHwj98BtQpbjh7POpXxaijPiyWv30HmnmP4tqlPtf0rsBmM3Bi/JO+6t6avosL6GQi1ipQvd5N7SZ4YuDH5Q0rPGAgaNZLRzI0pywDwKh+G2s2FhsuGg01CfzsF10AfjCmZPL5+IsfGryY3IZ0KAzpSaZjcV9vvfpv43Sc5NmENpbo2JKpvOySLFWuumdRps8FqI33hEoIWz0eo1OR8/yOWa9fx7NENgJwt36MK8Cfk0xWyb7VJeL3wDAkvvIKUoyf1jbkELV5AxG87kUwmkidNz+9v66ZxfeKHmBPSuD1nHVHLxhMx6SX0Z6+RbO+vGbuP4dumHjUOLJe3nxwn28erQRWCnm2N/tx1qu3K2xWpC7AdeBuojBzT3ACG8v+F9PedEf+rEQ+zzuvvjH2tuU2SpMv2/2cDAciz270lSTpoX2pTSZKks0KIp5E74QjkbZQaSpLk9BZbCLEW+EGSpM32pTWzgLaSJGULISIAM+BhT1PdSZ5yd88JIbYA6yVJ+loIMQPoJ0lSOft6/QmSJHUVQswFfICRkiRJQog6kiSduF/99R8MLfGGjF15raQlMZvVJa4JcDGr8E4M/ytPRJf8tpLu4c1LXBNgc0DLEtesE5744ESPQK7epcQ1JZuzBZ//O5czfR+c6CFpt7LOgxM9JFF915a4JkDMrFYlrvntvOLtCPWwNPD733fSuZeU9EIrGkuEHGvJz6Edd9WWuGb/OiW1X4Qj6VddH5zoYTXT/5QN5bhq8SpxzcZl4kpcEyDudsn7K4AGd7b8OQ72ITCsf/0vD1bde835y+3gjH/N0hrAC/hUCHFOCHEaed/TN4BngflCiFPASaCpECII+c5ygCRJl4ClyGvbH4gkSbuQ14YdtM/6bwYeZv/BBcA8IcQBoKiodhbgApy2L8GZ9RD6CgoKCgoKCgoK/wH+NUtrJEk6BjR1cioZaOHkeN4TfJIkLX6Adr97/v8A54F/dWd5JEm6fvecJElk70mgAAAgAElEQVQHC14bmG4/vhf7Mhv7FpdD7lcmBQUFBQUFBYX/BH/jh03/av5NM/IKCgoKCgoKCgoK/xn+NTPyJYF9/XrkPYcnS5K0868oj4KCgoKCgoLCf55/yfOcfwZKIF8ASZJ6/NVlUFBQUFBQUFBQUCgOytIaBQUFBQUFBQUFhX8gyoy8goKCgoKCgoLC3xflYdciUWbkFRQUFBQUFBQUFP6BKDPy/xIGLIovcc2+uf4lrqnlz7mrrhNR8i8v+jNe3mSI/a3ENQFiOwwucc0TsSElrgngKVlLXPPPmpGoriv5lwy1G7ajxDWv7y/WazAemj5PfFjimu+V+3NeCHU65mFe0F08QrSGEtcECPPMLXHNgfVLvqzr95cucU2ASFPJ+4AgtfHBiR6BRqVK/rv14M2wEtcEiFTn/Cm6fwuUGfkiUWbkFRQUFBQUFBQUFP6BKIG8goKCgoKCgoKCwj8QZWmNgoKCgoKCgoLC3xdJWVpTFMqMvIKCgoKCgoKCgsI/EGVGXkFBQUFBQUFB4W+LZFPe7FoUyoy8goKCgoKCgoKCwj8QJZBXUFBQUFBQUFBQ+AeiLK1RUFBQUFBQUFD4+6LsI18kSiD/L6PvjIHUbl0Pk8HI8gmLuR59tVCaoQtHUaVxNfSZegBWTFjMjXPX8s5H1azA7G8XYErKxJSSwZlRy8k8c72QjnuZYGqtHI2LnyeZZ65zevhSJLMVja8nNd4fgke5UKxGM9FjVpB94TYAdb99C5+6FZBsEum/n+NMv3ewGc0Oum5lgqm2cgwufl5knbnGueFLkMzyC0T8mlal4qx+CI0ac2oWJ3rMACDih/VIEmiCAwBIX7GOzLVfOOhqypUmaMZEtI9VIP3DT8j87CvHCqlUhK1fhiUpmaTR04pt88qVy/PR6veoU6c609+Yz6L3VhY7b1FMm7uIfQcOE+Dvx9b1K4qdz61pAwImDAO1iuwtP/5PNuC5xXmHa8zuQ2jb2lgNJo6PXkGGk/4Q2b8D5Qd1witSx/aqQzClZjmc96sdRcvtszAlZWBKyeDsqOVknblWSMetTDA1V47Gxc+LzDPXiL7br7zdqb5sJG4RQQi1ihvLfyD2i72oXF1odnQpLr6eSJJE3KZ9XJy42qludbtu1plrnLXrlhnWDd0zzQAQGjWeFSPYV3UglnT55SruzRoSsuhNsNlIX7WejI+/dNB1KVeaoJkTcK1SgdQln5C5bjMA6tBggudMQh0YAJKNrM3byfx8i0PeMTNH0KRNI3INucwZu4BL0ZcLlRtg8OT+tO7aEpvVxpZ137H54y14+3ox9d1JRJQNw6VcEJY7Z/ntjyPM/3gzNpuNp9s2ZcDTHRx0snIMTP3gU+KT07BarfR9qi3d2zTh2p0EJi36OC/d7YQUhr3whEPefjMGUqd1PYx233LNiW95deEoqhbwLcvsvqVq4+pMXD2VxFuJBLtYMfz6G+Zz5/EdMwLUavTfbyP7s40OWpqypfF7fTIulSqSufIjcjZuyjvn+fyzeHR7ApAwx1zFlplF87qNsRmMj+yvvKuVxTXUH1NyJjfX7uLaku/kjCoVj217F3N8CsabCfi0qYdkMHJ93AcY7DbwaVWHUjMGgVpFysafSFj2tXzNKuUoPe9V1J5umG4lkrxpNxFT+qD2cgNJwpqRjWQ2kzj/Y/R/nAIgdPoQvFo2wGYwEjd5EbnnYuRieHsSNnc0rhXLAhJxU97HcPICQSNfxq9nR1S56QAYvlyD5eQh2Ya1GuDeZwSo1Jh+2Ybxu3tsXO9x3Hu+AjYJyWbFsG4p1ovRhWwH0Pyt3pRtUxuLwcjucatIii5s4/aLXyWkZhQ2i4WEk1fZO+VjbBbZd0c0rkKzGb1QadSQksUfPWYCUHVOX0LsvuVUEW1Xtn8HIgd3xjNSx64qgzHbfUutpcMI69YIldaF+DU/cHPGJ4XyupYOocLycWj8vMiJvkbMyA+QzBZZd9YA/NrUxWYwEjN2KfozcnvqBnUl+KV2IIF04wr6nbvxGz0Utb8fktWKNTkVy+1YUmcuQMrOQVO2NAFvTEL7WAUyln9M1vp8vxr27QZser0ciFqsJPQd5lC+2rP6ENa2FhaDiSNjVpLupP7lX2lPJbtv/bbaEEyp2YS2rkmdmb3ReLqBEJgyclClpXPx2fzvrtIzB+Lbph42g5HrYxejL9Bfy7w1ENQqkjf+RPyH3wBQalpffNs1QDJbMN6I5/q4JVgzcwAaAqvssgKYATg6M4W/DGVpzb+I2q3roYsMY2zLV1k9dRkDZg8tMu2GuWuZ2mUsU7uMdQjihUrF4PnDyUrL4txrnxA9YTVVFwx0qlFp2ktcX7mN35qMxZyeTamX2gBQfnR3MqNvcKD1ZM6MWEaV2f0ACHu2Gd61othXsR8nnn4LnzrlCenetJBu+Wm9uLVyG380GY0lPYdwu67Gx4PKbw/kdJ/5HG45nuhBixzyCQGxzwzgZrMn8ezUGpfIMg7nbRlZpC74sHDwasf7xR6Yr90s0mZFkZqazpix00skgL9L9y7tWbFo9sNlUqkImDySxJGvEfvMgBKzQWjb2nhF6fi5yThOTlhDrfn9neZNPXyR33vORX8ryUnZBHXeHYQpNYsLr33M+QmrqbJggFOditNe5sbK7RxoMgZLeg4R9vYv1b8j2Rdv80ebSRx9+i0qzeiNcFET0Kwa2edu8EuZXpx4bja6Z5rhU69iId0K017m1srtHGwyBnOBfnVz2fccbjuZw20nEzPnc9IOnssL4lGpCJ4zCcPvR8k9ekq2aZSjTa2ZWaTM/5CMTzc7XtBqJXXhSu70GEBsr1H4vPCkQ94mbRpRKjKC55v1ZsHkRUyYN8apPbr07ERIeAgvtejHy61e4edvfwGgz8iXuXz2Cn3bD8JyOxoRUoG5qzex/PVhbH1/Gj/uP0bMrTgHrS927KN8aR2bF03lo5mjWfjpFsxmC5ERoXz17lS+encqXyyYjJurC20b1srLd9e3jC6Gb1k/dy2Tu4xl8j2+5fyRc0zuMpakfoPI/nQ9vhNGkzJ+Cokv9cO9XVs05co66Ngys8h4bwnZBQJ4AFVQEJ7PPU1S/yEk9eqPOjgY11o1+K3xmEf3V2dvgATHey9Afz2BsB6P41kpAoCQAV3JvXILTaAvrpFhnGs+lBuTP6TM3FftBVJRevYQrvR5i/NtRuD/VHPcKspvRC3zzghi317H+fajSd91iLLvjORKn7e4NWwmtqwc7oyZR9ykRYS/Mx4Az5b10ZaNIKbdQOKmL0Y3c0Re+UOnDSFn3zGudhrC1W4jMMbcyjuXunYrWVMHkTV1UF4Qj1Dh/spocuZPIWtCP7RN26KKcLSxJfoYWZMHkjV1EPqVC/AYNNGp7cq2roVfpI71zcfzy+SPaDm3n9N0l7b8zoZWE9nYbioaNy1VX2wFgNbHg5Zz+rGt/yI2tpvC8UHvAxDctjaekTr2Nh7LmQmrqV6ET0g7fIlDz81BfzPftwS3rY2bzp+DT73F7U2/EvBEY6d5S7/em7jV33Oq2Qgs6dkEv9gWAN82dXGLDOPU48O5NmkFkfPkt2O76AIIHfAE0Z0ncabNGIRKRcC0CSSNnkry9LlYk1NIeX02lpu38en3EiD31fR3lzoE8AVJGjqehJeHFAridW1q4RWl48em4zk28SPqvv2K0/wpRy7xa8955Nz1rSpB3bn9ODhkCeYsA+ZMPQcHLyZmyDt5eXzb1MMtMozoZq9yY/Iyysyzj1mVijKzh3Cp90zOth5JwFPNcatYCoDMfac423YU59qPIfdqLLoRz9yViwbqA7WBTsBK/r8ngiXbX//3N+U/F8gLIV4XQpwVQpwWQpwUQjT6q8sEIITI/l816rVvyG9f7wXgyolLePh44hfi/1Aanfo9gSHHwO3L8gx6xrEruPh44BriVyhtYLNqJHwvf2nEbtpHaOf6AHhWiiDlN3lWJ+dKLO6lg9EG+xLUuhZWgxGVm5askzEIrQu2XHMhXf9m1Uj6/g8A4jbtJahzAwBCn25G0vZDGO+kAGBOzszP5OKC+U4cljtxYLGQs3Mv7q0ed9C1paVjOncRyVL49eDqkCDcmzcie+v2h7IXQFJSCkePncJsLlyXR6V+7Rr4+ng/VB5t9cpYbseWuA10Hetxc9NvAKQdL7o/ZETfQH8r2WnZyg/oiCU7l6y8fnUZjY8nWic6Ac2qkWhv/9hNvxJsb38k0Hi5y2X1dMOcno1ksRHcqQFxX+wFIOvMNYRGhYufVyFd/wK6cQV1CxDa43ESthzI+9+9eSNAyDPpkkTOjr14tHK8+bSlpmM6ewnJYnE4bk1OxXThilx0vQHT1ZuoQ4Lyzjfr2JQdm38C4Ozx83j7ehEYElCoTD36PMkn761DkuRdG9JT5JnXcpXKcmz/cVnfpOfMxRjKhIVQSheEi4uGTs3q8suR0w5aQkCOwYgkSehzjfh6eaBWO34NHDpzkdKhwYQXKEuD9g3ZZ/ctl09cwvMRfEtBXKo+huV2LNZYua8aft6DW/PCfdV8/iLcY1cAoVYjXF1BrUKtCyX3d7ldH9VfGRPS0V+LJ2VfNO6lgkj66TihnerjogvEp019kjf+hCbYj9Sv5Zso/YlLqH080YT441m7Isbr8ZhuJiCZLaR99xu+HRoC4BYVQfYfZwGwpGYhXNSYbiZgPH2ZjG9/wbttE4yXbyBctQitBu92jcnYuhuA3JMXUXl7ogn2R+XljkeD6qR/tVOujNmCLSvnvjZWV3gMW3wstsQ4sFowHdyDS31HG2PMzbepqxvgfGeQyA71uPD1fgASTsTg6uOJhxMb3/jlVN7nhJMxeIXJfahS96bE7DhCdqzsu0123x3aqR53vpJ9S/p92i4z+jqGe3xLaKd63NrwCxknr2K4lYzKVYuLkz7p06wGqT8cBCD5q1/w7yS3jX/HhiRv3gtA9vFLqH098/ILjRqVm1buX8FBWBISsd6Jw/j7YfS7fsG9ZVNM0edQh8rjOd+vFu6r9yO8Uz1u2OufevwKWh8P3JzUPz36Bvrb+fUPqFOe7OsJBDWoxO3tR7ixeT8RHethScnIS+PXoSEp9vrlHL+Exkeun9xf4/L6a+q3+/HrIIdBmftOgtVmz3MRbVjgXTk9cLdyRXcUhb+E/1QgL4RoAnQF6kqSVBNoB9y6f65/DgG6AFJi8wd7anwKAaGFAwOA5yf0Yv6O9+k9vT8arXxj7R8aQIOOjdBn6jHq8x18blwqrmGOOi4B3pgz9Uj2QZ8bm58m69xNdE/IztK3TnncSgXhFhaAi68n8V//RtPjy3n89CqsObkYrscX0rUU0DUW0PUoH4bG14s637xJ/V1vo3uuRV4+oVKhrRiFbsMyvJ5+AmtiEuqQQIqL/4RhpH+w+h+9xZUmOAhLfGLe/yVlA/cwfwyxqXn/58al4h5W/CDOTedPWJcGmDP1WPXGAjopuDnpV5Z7+tXdNLc+2oFnpQhanF5Bk70LuThtLUgSrmH+5Mal0nD3fJqfXY0pMQNTcsZ9dQv2q7uo3LUEtq5N4g+H8o75DXiB3ONn8tZnWhOT0YQG8bBowkNxfawCxjMX8o4F64JIjM1vr8S4JIJ1hbUjyoXR9snWfLR9OQs/m0epSHmm+Mq5GFp2aQ6AcPchISUdXYEbhdAAfxJTHO3wYueWXLsdT9uBr/PMuLlM7v8sKpXj18COA8fo3KyewzH/e3xLyn18ywsTerFgx/v0KeBbACrVrcyCH98j4N230VarijWhQF9NSkIdXDy72pKTyd64idAtXxL63dcIlQqjPZCHR/NXIR3qYohNyfNXNqMZV10ApWYM5M7cT8EmoXLVYipgA1NcMlpdIC66QIfj5rgUXHTyuDNcvJkX1Pt1aoTQuuSni09GExqId6fHMZ6LQTJZ0IQGYY7Ln3W2xMv9zaV0GNbUDMLmjyXy2yWEzRmNcHfNS+ffqxve89fgPmQSwlO+iVX5B2FLybexLSUJlX9hG7vUb4b3wk/xnDQP/coFTm3upfPPC8IBsuNS8dIV7QNUGjWVn27Gjb3yjaRfpA5XX096bHqdnttmEfGc3G/dwgIw3MnXzY1LLeQTiuLevJYsPVqdY15NgDfWjJy84NQUl4LW3jZaXQDGgu0Zm4JWF4A5PpW45d9S58hK6p78CEmyYb4Yk5fOmiD3Vc8nO5P7+5EHF1SSCF66gNB1y/Hs4bhczV0XgL6AXfXF9K3uugD0d1LwKq9D6+tJ2eeaUXFwZwKfaZWXxkUXcE9/TcFFF4A2LABTXIHj8Slondg86Pl2ZPxyvOChRsBZ4AwwlPzAXuEv5j8VyANhQLIkSUYASZKSJUmKFULUE0L8KoQ4JoTYKYQIE0JohBBHhBCtAIQQ84QQc4oSFkJcF0LMFUIcFEIcFULUtWvFCCGG2tN4CSF2CyGOCyHOCCGeKkJrov3ap4UQb93nmoPt1zp6Jfs6QohCaSQncekXCz5jfJvhvP7kBLz8vHhy6NMA9HlzAJ+/vQ4nMoWF7pPm6uJv0fh60nT325QZ0ImsM9eRLFZULhr8GlXhYIPhHKg1RP7JsnXtYujaT6nVeNeK5FSvtzn1whzKjXsG96gwANKXrUX/ywESR7yGd88n0ZQtXew5A/fmjeRZ1fPO1yf/Y3DWcCVhg2L2q6KoMasPZ2dtdNq2UjH61d00ga1rkRV9nX01h/JHm0k8Nq8/ai93OZNN4nDbyRyo/Soab3fcy4Y8UPfeSgR1qEf6kYt5y2oC29fFlp2DLT3znmwPd7Mn3N0IefcNUt5ZjpSjzz/u1K6FtV20WkxGEwO6vMr3n2/ntXfl5Q+fLd2It683a3etQh1YGsmkL5T33kscOHmeypGl2L1mDl8tnMrcNV+RrTfknTebLew9coYOTevco1O8PrBxwWeMbTOc1+y+5Sm7b7kWHcPwpoOZ1HksOZu34PXyC8WquzOEtxduzZuS+OyLJDz5LKjVaBvWv3/hHuCv1B5uhHZukO+vrDbcSwdhSUnHcCamSA1Jku6rfWPCYoL7duGxbe8i3LSFHthT+3oRMrE/cW8skS9RhJZQq3GrVoG0z7dz7amR2Ay5BA3pCUDa59uIaTuArCmDkNJScOtlX77hXKzQEfPR/WRN6EvOu9Nxe875sjnnPqDo9mo5px+xhy4Qd/giACqNipAakXzfdyHf9ZpPxXE98IzSIZwYr9j9oFh5nTaa/ZTz/GpfT/w7NuRko1c5UWcgQqtFUzrcIZ32sYpIFiv6H39+YDkTBo4mofdQkkZPxevZp3CtUyO/dMXxiU64m0+lVuNfM5KLy7cTu/MYYWN64hoZfh9xcGaTey8ZNvJZJKuV1G9+LXj4EFANaABMRZ6Z///DJv31f39T/msPu+4C3hBCXAJ+Br4EfgeWAE9JkpQkhHgemCNJUn8hRD9gsxBiFPK6sActw7klSVITIcR7wFrgceTOfhZYAeQCPSRJyhRCBAF/CCG+kwqMXCFEB6Ai8sMlAvhOCNFCkqR9915MkqRVgAsw6PrZa1w9fZnA8PwZlwBdIGmJqfdmIz0xDQCLycLer/bQdfBTtO/TmXrtG1K/fUPMJgtqjRqpZgUkqxW3sACM8WkOGuaULFx8PBBqFZLVhlt4fhprtoHoMfIDmmVe6UDYU02o+eEIjMkZWLMMmFPkB5UkqxX3yNBCupoCuq7hARjj5ToY41Iwp2Zh0xux6Y2k/3Eer2plMVyNwxRzFY92zbGlpaP/5QCu1SqTe8L5Q1v34lqrOu4tmxDRrCFCq0V4ehA4ewp071tknleH9mXAgJcB6PZkb+LiEop1rT8TS2ISGl1+AKsOCcaalHKfHPkUsoGPN10urMJwO5m0k1dxD8+fsXELCyD3nv5QFJGvtEfXsS5hHethM5kRGjX+taLs/SrQab/SFNGvwl9oxfUl3wJyUO8S4E2jXXNJO3get4hAMkCedbdJeFYuDfxRpK5reOE+Hdq9qcOyGr+GldFWqYCqXk3cWzRC5emBOiSInJ0OX273R6MmZNGbZG/fg373fryff5K1T3UG4PzJi4SE57dXSFgwyQmF2yspLom92+Th/+uPv/HaIjmQ12frmTtOnkHds3MqurAI4hPzZ3MTUtMIDvB10Pp2zx/079EeIQRlwoKJCAnk2p0EalQsB8D+E+eoElWaQD8fVAGlmL/9PQBi7vEtgQ/hWwAM2fk3C8aDh5AATUR+cKQODsaWXMy+Wr8eaF0JfH+hfK0bN9FWrwb8CPBI/uryvC+oMOFZzoz4kJZHliBcNGi83PBq3xCf1vVQuWrR+HkTMaUPl56ZCoA2LAhzQioqFw3aArZxCQvEnGD3WTF3uPLyDAD8uj6OT8u6+fWoWAbPZnW5/eoszDflXybN8cm4hAVz11oaXRCWxBQkST6Xe0oOjDN37CdoyHNy+e1LrZAkTHt+wHPSPABsqUmoAvP7lyowGFta0Ta2XjiNKjQc4e2DlJWJtn13np/WHYDEU1fxCs//dc8rLICchHSnOg3G9MA90JtfpuQ/PJ0dl0Zu6mksBiNVejbHxd+Lhl++RvK+M7hHBHK3tZy1XUE0Xm40/mY6ktVKxsmrDnk13h6YExzzWlIzUft6gloFVhvasEBM9rYxxaXgGh7E3TWt2vBAzAlp+DavifFWApZU+QY+9/fDeHbrlKfp1qIp6uAgkoY5f57gXu72a1taOoa9+/F68Rnaj5KflUk9dRWP8EDutopHWAC58c7tWhB9XCoeEYEkHjiHMTULtyAfcm4kkqW24lG1HMZrsZjjUhz6pdbeL4WLBm1YgeO6QMzx+WM58NnW+Larz6Xn3yjq8ueBHKA6cLRYRlD4U/lPzchLkpQN1AMGA0nIgfwQ5A75kxDiJDANKGVPfxb4DPge6C9JkukBl7Bvc8AZ4JAkSVmSJCUBuUIIP+TAfK4Q4jTyjUQEEHqPRgf73wngOPAYcmBfFB8Ctad2GcvRXYdobv9prUKdSuizcvK+WAtScG1rgw6NuHXxJj+t+5E+FZ+jd8XnWDJyIdlpWZyb/DHGxHTMWXqMiYWdS+qBc4R2k+9twnu2IGGHPKY1Ph4IFzUANrOFuK2/c6D1JJJ+PoF3rShU7lp86lVEaNRkny68a0n6gbMEd5MfXArr2Ypku27SjqP4Nn4MoVbJGnUroL98B5WHK+ZrN9GUjkATWRa3JvXRlCuD4dff72O2Atdb+hF3Or/Ina69SJo6h9yjJ0mZ9vZ98yxf8Sn1G3SgfoMOf4sgHsB09qJsg3AdaDR4dmz16DY4dIztjw3ml3avEbfjKGV6yj+F+9etgCXL4LQ/OOPaJz/xfZm+fFemD0eGLsGUls35yR9hSszAkqXH5EQn7cA5QuztH96zJUn29s+9k0xA8+oAJP5wCEuWgSNd3yB1fzThL7YGwK/JY6i0GjJPxtxXN6yALoDa2x3/JlUdjsXM2citdi9gTU4l9Z3l5B49BUKg//VgseoOEDRjPOarN8n8TN7FJOvL7+jXYTD9Ogxm3879dHq2PQDV6lYhOzOHFCfB8b4dB6j3uDxDXqdJLW5dlZ8z8PLxROMiz8Wo/COoFhnBjdhEbickYzZb2LH/OK3q13TQ0gX5c+iMHAympGdyIzaBUgWWCv24/2jeshpb6u28h1aP7DpEC7tvqfiQvgXANzh/3a9LlcfAYkGtC0UdJvdV93ZtyN1fvL5qTUhE5elJ8pARJPUbhGS15i3L8a1X4ZH8VWb0dTyidESOeorUwxcI7dKAc1M+IbrhAM42Hcy14QvRn4nBmiOH2B51KmHNysGSmEbOqcu4lgtDWzoE4aLB/8nmZPx0WNYOtN9ICYFvu4ZIZgva0iGo/H0J6PsUycu+wHD8XF4Zs3cfwre7/DCmW+3K2LJysCSlYU1OwxKXhNa+rMqzSW2MV2TbaoLzbe7SoDnWW7JPtcZcQKWLQBWsA7UGbZM2mI852lgVWuBmqlxFhEaDlCUHsKaftvJlp9f5stPrXN15jMfsOzuF1imPKUuP3omNq77QijIta7BzxIcO07zXdh0jrGFlhFrF+S/3YUxI50jvBST8eDRvmY1fvQpYimi7u1iyc/nj6VnsbzvVIa9bWAA2oxmzkz6ZeSCagK5NAAh6rjVpO+XlMOm7jhD0bCsAvOpWwpqpx5yYhvFOMl51K6Fy18r2DQ9DuLmiDtfh1qwxHs2bkDJ9LpLRWOha9yLc3BAe7nmf3RrXJ2fLNn5q/xo/tX+NOz8epay9DgF1K2DOMpBbDN+advIqXpE60k5fJajxY5Tu3oT4X0/jVbsihiu37fU7TKC9fp515f5qtvdXt8j8/hrwVDPS7f3Vp1UddMOe5sorc7HlOoQ7keRP/JYFKgPXH1hQhf8XxMP+TPxvQgjxLDAccJMkqUkRaTYCrYF+kiTtuI/WdaC+JEnJ9pn8+pIkjSh4Dnl9fmeglyRJZvvxVpIkXRdCZEuS5CWEeBe4JEnSQ22B8mLZ7hLAK7MGU6tlXYwGIysnLOaq/WfhSWuns3rSUtIS05i2cSbeAb4IATfOXWPNaysc1sQDLNy9lCAfL0wpmZwZvYLMU/K2VfU2TCZ63CqMCWm4lw2h1spR9u38rnNq+FIkkwW/+hWpsWQYktVG9qU7RI9diSVDXq7QdMdsvKqVQ7LZSNsfzZlXFiKZLNTcMIUL41ZiSkjDrWwI1VeOQePnRfaZa5wdvgTJJC/HKzOsG2EvtEaSbMRu2MPtVdtxKxtC/fVjEZ4eaIKDsBkMZK7fTOZHn+P1TFcAsr/+AVWgP2Hrl6Hy9ABJwqY3EPvsAIflDq71auHT5zmSRk+jfPT5Ytk+NDSYQwd/xMfHC5vNRna2nhq1WpGVVfj5ZUPsb8XSnPjm2xw5cZr09EwCA/wYNqA3z3TrWGT62A7yjgtujzeUt59Uqcj+bsf/ZINdBbafrDmvH8kINCAAACAASURBVKGta2ExGDkxZiXpp+RgofGGSZwct4rchHSiBnSk4vCuuIb4YUzOJGH3SU6Od9wGsu1v76D19cSUksm50cvz+lWdDVM4N25lXr+qkbdN5HXO2NvfNdSfaotfRRvqjxCCa4u3Ev/1fryqlqHe5ulovN2RbBJxX/7KxUlrAKi1YQrnHfpVvm7BfhX2fEsC29QmesgHDuWN0qXi3qwhQdPHoPL1Jn3NRjLWfI73c7JNs776AXWgP+EbP0Tl6YFkk5AMBm73GIi2UiTha9/HdOlq3nMHaUs+5pmt+TcC4+aMonGrhuQacpk7bgEXTl8CYOG6ebw9cSHJCSl4+Xjy5tLXCQ0PwaA38M6U97hy7irV6lVl+gdTsFltlAp2xXLnHL8dOcWCTzZjtUl0b9OYwc92YtNOuc/17NicxNR0pi9dT1JaBpIEA3q0p2tLeQ23wWiiw+BpbF/2Ft6ecvDR54kP88ra3+5b7m5te9e3TFk7nZV23zJ940x87L7l+rlrrLb7lo59u9C+VydsFithNgMZi5eh8vbCd/RwUKvQ//Aj2Z9uwKN7NwD0W79HFeBP8McrEZ4e8vaIBgOJL/VD0uvxHtAPt3atwWrFfOkykiEXUacRVoPxkf2VytUFF18vTKmZ3N6wh6vvb6Vq/5YA5F65TeiQ7pjuJOHTqg42g5Eb45egPy0/zOzTuh6lZgxAqFWkfLmb+CXy7iXB/bsS3LcLAOk//kH2obOUmjEAlwAfhIcbppibqP18ALjWfRTW1AxC3xyGVwt5y8C4Ke+Ra9+S1LVKlLw23kWD+VY8sVPew5aZTfg7E3CtEoWLuxVbUjz6NYuQ0uUbQk3tRrj3GQ4qFaa9P2LcugFtO9nGpp+/x7XbC2hbdASLBclkxPD5CoftJ9fvj8j73GJ2X8q2qonFYGL3+FUk2idhun46gV8mrSEnIZ1h1z4l604ypmz5++Tqj0c48sFWeYwPeYIqPVsgSTbi1//C9VXyLyjV5r1CcBt5I4TTo1eSYW+7BhsmcXrcaowJaZQb2JGo4d1wDfHDlJxJ4u4TnBm3mprvDyHiueYIIbAajNgy9ZxuNYoKy8dxdcIyzAlpuJYJvWf7yffzxn25uYPwtbfn1bFLyTkt9+mICc8T+OTjSBYb0tWL6Hfvw2/UEDQROmy5uVhj41H5+mC5E0/SkLGoAv0J/XS5g1+Nf74/Kj9fghbIq2OFRk3Ojt1kffI5f9zW5dm1ztx+6FrXxGowcWTsStLsvrXZ+okcHb+a3IR0KgzoSOVhXXEL8cWYnEnc7pPc2X6E2jN7o/X3QrLZMCZnYr18k6wDp0laLz8UXWb2YHxa1cWWa+T6uMXo7fXzbVOP0jP6g0pNypc/E7dE3nGr+v7lqLQuWNLkX82zj1/k5tQV1L+9tQ8wBTADNmAmsJX/R/RLhv3lwarHyGXO1qv95fynAnkhRGXAJknSZfv/s4EA5Bnw3pIkHRRCuACVJEk6K4R4GvmhjhHAD0BDSZKc3i4XM5B/GaggSdJIIURrYA8QeU8g3wGYBbSVJClbCBEBmCVJSnRy2TzuBvIlSd/ckl8Cp+XP2cKpfEThmcz/WbOYgfzDUNxA/mG5G8iXJCdiQx6c6BHwlArvmPO/8mf9tBilK/l+1Su55Ou/Z+fUEtcEx0C+pHgvIuPBiR6B0zH3/rj5vxOiNTw40SPg7lZyO1zdJax+yZe1YCBfkkSaSn4MBKkfPEP+KIT/Cf21YCBfkkSq77+T0aNS//bWvzyAVQL5ovmvrZH3ApbYl7lYgCvIy2xWAYuFEL7INnlfCJEAvI0cUN8SQiwFPgCKXjj9YDYA3wshjgIngQv3JpAkaZcQogpw0P6AWTbQC7hvIK+goKCgoKCg8K9EebNrkfynAnlJko4Bhd9ABMlACyfHKxXIu9jJ+YLa5Qp8Xov8sGuhc4DTJTySJHkV+PwB8k2DgoKCgoKCgoKCglP+Uw+7KigoKCgoKCgoKPxb+E/NyJcEQogtyE9wF2SyJEk7/4ryKCgoKCgoKCj8q/kPPc/5sCiB/EMiSVKPv7oMCgoKCgoKCgoKCkogr6CgoKCgoKCg8PdFedi1SJQ18goKCgoKCgoKCgr/QJRAXkFBQUFBQUFBQeEfiLK05l9CLbwenOghGStdKnHN3RU8S1wTwO/5yiWuuXleyb8Q6c94cRNA+K5VJa5ZPrx5iWsCrAluXeKarYITSlwTwGIq+bmOt62+Ja4Z32d+iWsC9DIGl7jm9WvqEtcEKOOVVeKaARF/zgt2vrxeqsQ1n7kaW+Kann/SaoZIr8wS1xSqP+dhSN/aJd9fL/xJb4UJznX5c4T/DtiUh12LQpmRV1BQUFBQUFBQUPgHoszIKygoKCgoKCgo/H2RlIddi0KZkVdQUFBQUFBQUFD4B6IE8goKCgoKCgoKCgr/QJSlNQoKCgoKCgoKCn9flIddi0SZkVdQUFBQUFBQUFD4B6LMyCsoKCgoKCgoKPxtkZQ3uxaJMiOvoKCgoKCgoKCg8A9EmZH/l9FuRm/Kt66N2WBk24RVJERfL5Sm84KBhNWIBCFIvRbPtvErMeuNVO3elMZDuwJg0ufyx4S3uHj2Mq/NGU+Ldk3JNeTy2siZnDtzsZDmZ9+twtPLA4DAIH9OnzjHyL4T8fH1Zs4H0yldLgJjrgnNooVYrl7HtVEDfMeMALUa/ffbyP5so4Oepmxp/F6fjEulimSu/IicjZsAUJcpTcDMN/LSqSPCyFr9CRCNqmxVtC17glBhOXsAy9GdDpqqiEq4dnsVKTMZAMuVE1gOb89PIARuL0xFyknH+N0yh7w1ZvchtG1trAYTx0evIONMYbtG9u9A+UGd8IrUsb3qEEypji+o8asdRZltn5E8ZTY2Qy4BE4aBWkX2lh/JXPuFY/3LlSZoxkS0j1Ug/cNPyPzsK8eLqVSErV+GJSmZpNHTCpXFGdPmLmLfgcME+Puxdf2KYuUpDpUrl+ej1e9Rp051pr8xn0XvrSxWvoYze1OqTW0sBiP7x64i1Ulfbb7kVYJqRWEzW0g+eZXfJ3+MZLHiWz6Mx98bTGD1chyf/xV88wkA7k3rEzBpGKhku2Z88qWDnku50gS+NQHXKhVIW/oJmes2AyC0Lug+XoRwcQGNGv3Pv5G+fF2h8ng0q0/Q1KGgVpO5+UfS12xy1I8sTeiccbhWrUDKB5+S/snmQhpRs/sT0LYONoOJi6OXknPmWqE0rmVCeGzFWFz8vMg+c5WLI5YgmS15571ql6f2trlcGPIeyT/8kXfc7fGGhCycgWSzkbF6fYn2q2qz++aNgZOjlzsdA+X6dyBqUGc8I3XsrDo4bwxEPP04FUY8Kdct0AssNiwZOVwZs8R5/UuHUGnFWDR+3uScucrlkYuRzBaCnm5OxPAeAFhzDFydsgptqD9Rbw9GqwvAmpaJNSUDl9I6kt5fT+rabwEIfWMI3q3qYzMYiZ30HrlnYwCo8OvH2HIMYLUhWa1c6z5Gvn6VKMJmDcfFSwNWK4ZdO/Ho3h1UagzbtpGz4XOH8qrLlMF3iuyvstZ8hP6L/H7n8ewzuHftCgIMP2xD/1V+n2j5Vm/KtZbHwK7xq0hyMgY6fvAqoTWjsFosJJy8yp6pH2OzWNF6u9Pxg1fxDg9EpVEjvvwc/Q87cG3cAL9xIxAqFTnfbSdrXWHf6j99EtrKFclY8THZG/L7sPDyxP/1CbhERYIkETz6E5KOXQGg0T3jNcVJWVsUGK9J94zXZu8NJrBGJKZMPeqcbNK+3EXKyq8KaRTVVipvT8LnjcK1UlmQIHbK+xhOXMC7czNCp/THJSIES3wyaet/IGWVE93pQ/Bq2QCbwUjc5EXknsvXDZs7GteKZQGJuCnvYzh5AQD/3t3wHNwVXLQIN3fIycL02w5MOxz9iqZWE1y79wVJAquV3C+XY71yFgCveeuQcg3y9olWKzlzRji274w+VGhdC7PBxHcTVhLvxK5dFwwivMB39rfjV2DWG/POh9WMYsC3MzElpWNOyeTCqA/JdjKu3MqEUHXlGDR+XmSfucb54fl+xa9pVSrMegWhUWNOzeJkjzcBqPz+qwCJ9r/qhUQV/nKUGfl/EVGta+EfqWNly/HsmPoRHWf3c5pu98wNfNz5dT7u9BqZsSnU69sBgIxbSWzoOZuPO73G74u38tbCqbRo25SyUaXp1OgZ3hw/jzcWTHaq2fvJwTzdphdPt+nFyaNn+HnbLwAMHtOP89GX6N7qZaaMmIHvmJGgUuE7YTQp46eQ+FI/3Nu1RVOurIOeLTOLjPeWkL3RMVCy3rxFUr9B8l//IUi5RnL37Qch0LZ6EePWpeR+9haaSg0QAWGFymmLvUzu53PI/XyOYxAPaGq3wZYWXyhPaNvaeEXp+LnJOE5OWEOt+f2d2iD18EV+7zkX/a2kwidVgmrTXiT34FEQgoDJI0kc+RqxzwzAs1NrXCLLOJYzI4vUBR8WDrTseL/YA/O1m07PFUX3Lu1ZsWj2Q+UpDqmp6YwZO73YATxARJta+ETq+KbZeA5O/ogm8/o5TXd1y+9saTGRb9tORe2mpdJLrQAwpudwaPpnRK8s0IYqFQFTR5Iw/DXuPD1QtmuUo12tdrtmrHMMsCWTmfhBE4l9fiixzw/FvWl9XGtUcSyMSkXwtOHEDpnGzW6D8O7SGpfy97ZbJklzl5P2yddO6+PRogHuUWEcbTKSyxNWUGG+8zf9Rk7rRezKHzjadCSW9Bx0L7VxKEfktF6k7T1VqHxBsyZjOHiU3GOnSrRfhdjHwJ4mYzk1YTU15g9wmj/18CUO9pxTaAzobybye4+ZnJ+zEXN8KqaEVGImLifqbef1LzutN7GrfuDE4yOwZGQT8mJbAIw3E4l+ejqn2o7j9vubKf/OUKLmDuLsc29xoeazWFMzuD1+IVKukaxdvwPg1ao+ruXCudJmEHGvLyFs5nCHa914eSpXu43MC+IBQie/QvKSz0kZMJCsTz7Be8gQ0iZOJrlPX9zatkFd1tFfSZmZZC5eTM4X9wR4kZG4d+1KypChpPQfiGuTJqhLRQBQrnUt/Mrp+LTFeHZP+Yg2c/o5tcXFrb+zrvVENrSfisZNS7UXWgFQq097Ui/f4fNOr/N1zzn4jRoKWi3+E0eTPGYK8S+8gnuHNmgiC/vW9HeXkrVhU6Fr+Y0bQe7BIyQ834+EXoPIuCy/LbaUfbx+3Ww8vz9gvH7TYiJb28pldRivb67HYjBy+fNfuNLxVXy7tUBbobRD/vu1le6NwWTvO0ZMh6HEdB2B8cotWfvKTYRKheHURW6PmodP15aFdD1b1kdbNoKYdgOJm74Y3cz8YDp02hBy9h3jaqchXO02AmOMrOvRqCbebRuTM3MYwmpF/95rZL8xCJeGrVCFOY4ry4UT5Lw1lJyZr2JY+y5ufcY5nNe/O5Gcma8WCuIrtK5FQKSOD1uOZ9vUj+gy+xWndt01cz2rOr/Gqk5TyYhNpoH9OxtAqARd5w9En5bFldc+5tKElVRaMMipTtS0l7m98gcONxmFJT2bMLtf0fh4UPHtQZzpM58jLcdxdtC7eXniv9gL0Mmp4P8nNumv//ub8p8M5IUQOiHEF0KIGCHEOSHEdiFEpSLSlhNCRBdxbo0QouojXH+GEEIvhAgpcCz7YXXupWL7ekR/vR+A2BMxuPp44hniVyidKduQ91nj6oIkyR30zrHLGDP18ufjV9CFh9Cmcwu+3SQHS6eORePj601wSGCRZfDw9KBRs/r8vP1XACpUiuSP344AcO3KDdRhobg2aoDldizW2DiwWDD8vAe35o876NjS0jGfvwgWS6Fr3MW1fl2sd2KxxiegCi2HlJEoz7bbrFguHUEdVfOBNruL8PJDHVkDS/SBQud0Hetxc9NvAKQdv4KLjweuTuyaEX0D/a1kp/rlB3QkdtthrKnpaMpEYLkdi+WOXP+cnXtxb1W4/qZzF5Es1kJa6pAg3Js3Invr9kLn7kf92jXw9fF+qDzFISkphaPHTmE2m4udp0zHesRslvtq0vEYtL6euDux6Z09+cFq8skYPMICAMhNySTl1FUkc759XKtXxnIrFsud+Dy7erRq6qBnS0vHdPaS034lGXIBEBoNaDR54+IubjUqY74Zi+V2PJgtZP+4F682TRzSWFMzMEY71wfwbNOExE17Acg6fhmNjwcuTurt93h1kn44CEDCpr0EdmqYdy58QGeStx3CnJzhkMe9eSMQgqyN34AklWi/0nWsxy37GEi/zxjIjL6OwckYSDt6GXNGDrqO9YhftwttWCDZxy+j8fF0Wn/fZtVJsdc/cdNeAjrL9c86ehFrRo78+dglXMuEYrgej/FmApgtZPywj8D+3THdjMMcK99MeLdrTPqWPQAYTl5E5eOJJti/0DUdkCRU9l8YXSpVwpaVjTVOHq+5u/fg1uweu6anY7lwEayOdlWXLYP53DkwGsFqxXTyJG7NmwMQ1aEe5+3+Ot7urz2c2OL6L/ljIOFkDF72MSAhofV0l8vo6YYtMwtt5YpYbt/J960/7cG9ReExIPtWx7IKTw9c69RE/529/S0WTPbvgzId63GlGOP1doHxmnQyBs8C41WoBMa0bIzpOXlt5d2usUP+otpK5eWOR4PqpG/aJSc0W7Blyf1A7eWB8cpNJKMJLFYyt+3Du22TQroZW3fLZTl5EZX3Pbpf7Syk6//SEySv+gp16ShsSbHYblwCqwXzkV/R1Ha0KcbcfDu6ugHFC/oqta/H6a/lcXXnxBXcfDzweuB3tlae+bfToF9HTDm5JF++A0DmMXlcaZ3o+DerTtL38i948Zt+JahzAwBCnm5G8vZDGO/IY9ecnJmXJ+OP8wCpxaqQwl/Cfy6QF0IIYAuwV5Kk8pIkVQVeA0IfVkuSpIGSJJ17xKIkA+MfMa9TvHX+ZMWm5P2fFZ+Kd6jzL6wu7wxm5NEPCawQzrG1uwqdr/VCK37bfZBQXQjxsQl5x+NjEwkJCymU/i7tn2jFH78dISdbdoYXzl6m/ROtAahRpyrqUB2aqEisCYl5eaxJSaiDgx6usoB7uzbof5Kds/DyR8pKyzsnZacjvArXXaWLwu2labg+NcJhxt6lRU9M+79xcJB51wnzxxCb78dy41JxD3tAIFAAN50/YV0acO3Tn+Uy+HhjiS9Q/8Qk1Pe5OboX/wnDSP9gNdLfeIbgQXjo/Mkp0Fdz4lLx0BVtU6FRU/6ZZtz55XSRadQhQVji82eCLQnJqEMeol+pVIR/uYLSe74i94/jmKIvOOqHBmIuqB//kPqAJiQIY4F6m+JScQ1zbHtNgDeWzBywyg93GeNS0NoDIq0ugKAuDYn7tPCY9XnlRYzHz+T1i5LsV25hAeQWKLchLhU3e5keBrewADyrRZK+5wRwt25O6p+RX39TXAquusLXCn2xLfpzNzDdyb9xsMQn417nMTK+/zVfLzQwL6i/m0ajs19TkiizdhaR336A3wv5k47xs1cTOqU/wZs34fHsM5hO5Qeo1qQkVMHBxaqv5do1tLVqInx8wNUV18aNUYXI/tNL5092XL5Ns+NT8brPGFBp1Dz2dDNu/CqPgVNrf8K/QjgDjy7l5V3zSH9vKergQEffmpiMuphl1YSHYUvLwH/6JELWrcT/tfFo3F2BRxuvFe4Zrx46f0xZ+QGpJT4Zl9B72r6ItnIpHYY1NYPwBWOJ/G4xYXNHIexl04QGYo7L7wPm+GQ0hXSDMMfdoxsalKcbNn8skd8uIWzO6DxdbWQ4HvWr4dZ/EqqIKFTl5Lk+KS0JlV/hcaWp8zieMz/CY9Qscte+63DOY8w8PKd9iEvzLg7HvXUBZBawa+Z9vrO7vTOYsUeXEVQhnMP272zvUH8e61gfY6beYamNMS4F13vGp0uAN5ZMPdJdvxKbn8ajfDgaX09qfzODervmE/pcC6dlUPh78p8L5IHWgFmSpLxFwpIknQROCCF2CyGOCyHOCCGeKpBHI4T4VAhxWgixWQjhASCE2CuEqG//nC2EmCOEOCWE+EMI8aAbg4+B54UQhb6hhBDjhBDR9r8xTvLeTTdYCHFUCHH0cPZlEKJQmntnFe+yfeIqljYcQcqVWKp0c5wVKdOkCjWfb8m7s5Y6kyxSE6BLjw5s25IfZKxevA4fX2++2bOeXgN7Yr58GZw8fX4/TadoNLg2a0runl+LTnOPpi3pJoZPXif389mYT+3FtdurAKgiayAZspASi1iq4tSuxS9qjVl9ODtrY/5Pc06NWjwt9+aNsKWmYzp/ufgF+DvivGMVmbzJ3H4kHLpA4uHCz2c8qmYhbDZinx/K7Y4voq1eGZfy5R6sX9yGy9NwJuGoIe5Tj6hZr3Bt1vpCY8i9eSOk7Bys6Rn35CtesR7Yr/5X29rRBnjj374eN+Z8VrROMfyYT9PqhLzUlqQt+xwTqtVoS4WSuX3/ffXu2uV6z4lce2o0N/u/QUCvJ/BoUA0A/5e7ED97NUnP9iR3x060Ne/5da+YdbfeuEnO5xsJWLSQgIULsMTEFJi1fzibtp7TjzuHLxBrHwNlW9Yg+dwN1tQfweedXsdvwihwc30oTQfUalwqVyTnm+9I7DMEW24uNUZ0+z/2zjJKiqNrwE+NrLsLuiwQ3N0W9yRADAgheII7BIi8AQLkDZIEgsYDccMhQAgJbou7w7rb7Gh9P3pYYWdhybv5Yv2cM+dMd1fdrrpdt/p21e1qe1Ef3l7jDp4nvoC9OmzX91LMtRI6DS41Ikldu5lrj47FZsgl4IUnS1w2x6YrEVqtInfdZq49NkaRO+Ip5bhWi9bbA+P3H2C9fh63EQXfGSlad8vxvWS/MoScZf/B+bGBefuz548ne84oct6eiVPbnmgr17p/dYvR64Ypq1jSeBRJl+9Qw37P7vTqAHbO/6JkTek+9zGh1eJZJ4KTz87j5DNzKD/xCVwjioam/qlI25//+4vyb3zZtSZw1MH+XKCXlDJDCBEAHBBCrLcfqwoMkVLuFUJ8AIwE3ronvztwQEo5UwjxJjAMuF9AchaKMz8OePXuTiFEA2AQ0ATFPA8KIX6RUh6/V4CUchWgB4bFn7lB7MmreIbljxR4hviRlZBWbAGkTXJuwwGajOjOqa+VG2LgI2V5fNkYclIz+eDbZZw+fpaQsPxnkpCwIBLjHMSAAz6+3tSuV4Mxz0/N25edlc3McbPztrudWI/54mWcGzfM26cNDMSWlMzD4NKsCeaLF7GlKqPwMisV4Zk/kiE8fJDZ99TdlD/9abt+Gtr2BRd3tKGV0FasjbZCTYRWB06uOD89nbZKWC6p0VdxDct/3nIJ9SM3LpWS4lOnIo1WjlHy+nsgLWYsMfmjZtqgQKyJJau/c52auLZpRnjLxggnJ4S7G/5zppe4LKXFiy8MZMiQ/gD0fHQAsbHxD8iRn6eczoOk6Ku4F2ir7qF+5MQ7bqt1JvTCxd+TXUM/uK98a3wiupD80UddcECJ9VoQW2Y2uUdO4NqiIYZz+Q931rgk9AXlhwRgTXiwfO++PfF6sisAuacu4lyg3k6hfhjjCs9am5Mz0Hm5g1YDVhvOof6Y7O3Ns04Ej6ycACgjbL7t6yMtVpzrlEP/SCTO9Wvj1qopwt0NbVAAOdvv86BbAEftKuTTpfhIFwDSoq/iUqDcrg9hAxUGdaRc/3ZoXZxwDvbh9ltfYklVIgmVuhWuvyU5A513fv2dQv0xxeefy61aeSIXvsjZ/nPQ+XoS8FjLvGPuzepgjk3CmpzflixxSejDAjHYe31dSACWeOW6WRKUc1uT08ncvh/XOlXJOXwGn97tiX99JX7hkLt7N259eufJU/orxyF0jjBs2oxhkxKu4vvfN9FVrEC/LY2JP3kVjwKzER4hfmQVYwNNxvfC1c+TndPzbaD6k204snwDAOk34rHExCG0OrTB+TOm2qAArCUsqzUhEWtCIqYz9pkom43qQ7pRtmO9h7LXusXYa3ZsCk6ernnbupAAzPGF7ae4ayWlMtJuOKE8GGRu2Yu/3ZG3xCWhD82fGdOHBORd17uY45LQhwZydz5AFxKAJSFfbq5dbsbW3wgYkS83c9s+nGvmIvROYLMhPLwRvoHY0oqPNLFeOoUmKAzh4YXMykCmK2llZhqW4/tw6tCLYd2VGP6Yk1fxKqBXrxLcs89sOECzET3QuzpTpWN9qnZsgMVkRqvTYqtdEWm1OrQrpV9xQ2g1SKsN57D8NMbYZMwpGdhyjNhyjKQfOIdHjfIYrsYWWxaVvw7/xhH54hDAG0KIk8AOIJz8cJtbUsq7wdOfAS0d5DcBG+3/jwIVSnDOd4CBQgivAvtaAt9LKbOllFnAd0Cr+8hYBtT9sNtMLm0/Ss0+StHC6lXCmJlDtoNOwad8vmMe2aEeyVeUF5q8wvzpvXI83w5bzJr20+jd7ll2bvmFx55SpgPrNKhJZkYWicU4L50fbc/un37DZDTl7fP08kCvV54Xn3z2MUzRJzFFR6MrE442NAR0Olw7tCP3t30PUFVhXDu2w/DTrrxtW/wNhE8QwssfNFp0VRphvXpPGIZbvpo1wRWUEYrcbMz7fiD3g5fI/XAmxi3vY7t9HuOX8/m5wwx+7jCD2K1HKPeUcgl860diyTRgvE9ney8/NR7P9kbj2N5oHDk79pAydwkaD3d0YUr93TtHYfilZPVPW/o+d7r25U6PZ0l8aS65R6JJnjW/xGUpLZav+JiGjTrRsFGnEjnxBfOs7zSTm9uOUukJpa0G1q+EKSMHgwOdVu4bRXhULX4ZteyBI4vGMxfQlQsvpNecX/aXqGwaX280nu4ACGcnXJvUx3ztVqE0uacvoC8fji48GPQ6PLpGkf3zAUfiCpH++QZu9R7Jrd4jyd65j6CnogDwrF8Za2YOZgf1Ttt3hsAeSpxv8FNRJG9T3jM53HgUhxuN5HCjkSRtPMCV2mvgFwAAIABJREFU6atJ3npYaRedn8aalELKwuXkHj2BEOJ/aldxA0azp8NL7OnwEnFbj1DWbgM+9SMxZ+aU2Aauf/gThwe+hdBquLjwO3za1AHAo35lLMXUP33vafzt9Q96KorUrYcAcAoPoOr7U7g05h1yr8aSFX0Z14qhOJcNAr0Oz/aNSftuZyFZmTsO4tNLeanPtW5VbJnZWBJTEa7OaOwx5sLVGfdW9cm9eAMAS3wKbk1q2Y+5gc2W11+5tG+HcW/J+yuNjxKrrAkKQhsSQtLzg1nXdSZXth2lmr2/DrH31zkOdFHjmSjKta7FltGFbSAzJomyLZQZBLcAL/TlypL72350ZQv0rR3bYdhTMhuwpaRiTUhAV05xMmVWDhc+25Vnr5H/o70mRV/F2ccDJx930Ovw7tGarJ0HC6Up7lpZk1KxxCbiVFF5Udi9eR2Ml5WHbMPJizhVCEc4KytOeXVvTebOwnaZtfMg3o8rIzMu95PbrG6e3MwdB3BrVgfr9QvKy61OzkhDNvpGbbCcKKxTERiW919TLhK0OmRWBji5gLP94cXJBW31+pj2bGZ1txms7jaDC9uPULuPYlfh9SLJzTQ4dOR9C9yzq3SoT/KVGI588hPzKj/PG5UH8t2YpeSkZnFp2hpMCWlYMnMwOZCTuvcMgfbR/JCn2pC0VelXkrYexrtpNYRWg8bVCa/6keTYY+7/MvzZL7r+hUNZ/40j8meAJxzs7w8EAg2klGYhxHXAxX7s3ivo6IqaZf6cmJUS6FZKmSaEWIcywn+XEsw/OubKrmgi2tZhxJ6FmA0mNk9elXfsyY8ms2XqGrIS0+mxaAROHq4IAQnnbrJt5kcAtBjXC1dfDzrNfh6AHuYcnuw0kNYdmrPt0Hfk5uQyo8Do+sp1i5k1YS6J8cqIT7fHO7L6nY8LlalSlYrMX/oqVquNKxevkb5kEVhtpC96B//Fb4JWQ87GLViuXcftcWUaN+eHDWj8fAn8YCXC3Q1sEo+nnyCh3/PInByEszPOjRqQtmBRAWXaMO3+EufHxyrLT57dh0yJRVdL6SQtp35FF1kfXe3WYLMhLSZMW9aUSK/xO6IJbl+XjgcWYzEYOT4+f3WWpmunEj1xFbnxaUQM6UzlUT1wDvKh7a75xO+MJnrS6qICbZKUBe8StGy+skzi+q2Yr97Ao4+y9GfWtxvR+PsS+tl7aNzdQEo8+/Um5okhyOycEpXZEVNenc/h4ydJS8ug/ePPMnLIAPr07Py75d0lODiQg/u34OXlgc1mY+yYYdSqE0VmZvHvb9/eGU14uzr03rsQq8HEbxPz22qHTyazd8oaDPFpNJs/iKzbSXRf/xoANzYf5sSSH3AN9KbHltnoPVzBZkMzoiN3eg8lZf5SgpfPU/T64zbMV27g+YSi18xvNqL19yV03bI8vXr1782d3kPRBvgRMHsqQqMBjSB7+x4Mvx6k0FiH1Ubi3GWErX4DodGQ8f12TJdv4PV0dwAyvtyENsCXsl+9i8bDDWmT+Ax4nBs9h+ddt5w9h6BJCxoeWIrNYOTi+PxlTmusncGlicsxxadyffanPLJyAuWnP0PW6evErSvsnDrEaiNlwbv4z5qAxsuT9A/WlVq7SthxnKD2dWl3YAlWg5HoAjbQeO1UTkxcjTE+lYpDOlNpVE+cg3xos2sB8TuPc3LSaipP7I3e14MyT7TELdSPpte/wHA1hssTluXJqfbZTC5Peg9zfCo35nxGlRUTKDetL9mnrxH/uVL/shOeRO/rScQ8ZVUOabVydcYaqn/+Mlq9BvQ6Uj/ZgG9fZQYk9fMtZO0+jEdUQyJ3rcGWayRm2mIAdAG+lF0+Uzm5VkvGhl/I3qMMBcfMeIeQV0agcxmKNJnIWL4C37f+CxoNhs1bsFy/juujynKahvXr0fj54b8qv79yf+IJkp4biMzJwWf262i8vZAWCxmLlyCzsgAfru+KpkLbOgz8dSEWg4mfCvTXj300mR3T1pAdn0a7NwaRcSeJp394DYDLWw9z6O0fOPTOD3RcOIL+2+eBgPRlq7ClppH21rsEvLMAodGSvUHpW917KX1r9vdK3xr08QqlDdgkHs/0If6ZQcjsHNLeehe/12eAToc1JpaTLypLsN7eGU2ZdnXoY7fXXwvYa8dPJvOb3V6b38dee26ZjcZJR61RPREje5C0/EuMl26W6FoBxP5nJeGLpyD0Oky34oiZugQAz/ZN0Hi44lo2mApf/BfTrThMl2/i01cZgEr7fDNZuw/j3qYRlXa+ryw/OT1fbtzsFYQtnIrQ6zDfiiPGfiztm+2EzRuPe68VSLMZoRd4vL4G095t2GJuoG+j2L35l03oG7RE36wDWK1IkxHDqrkACC8f3Ea+mtfGzAd/xnrmCIqrAZd3RRPZti6j9izCYl9+8i7PfDSFjVNXk5WYzmOLXsDZwxUExJ+7yeaZHxayz8u7ojFl51J53hBMyRlcGJdvV7XWvsSFiSswxadydc5nVF85gYrT+5J56hqx65TBsJxLd0jZFU3DnxeCtBG7difZ55WBjGorxgHsBwKA2ygRBO+j8pdBPHRs8t8c+8uuB4A1UsrV9n2NgG5AgJRyjBCiLbALqGjPdg1oLqXcL4RYDZyXUi4UQuwGJkspjwghsqSUHnZ5TwA9pJTPF1OG14AsKeVb9jCew0ColNJFCFEf+Ahoij20BhjgKLSmIPPLP1vqF/Jjw8XSFsnOSPdSlwng83TVUpe5fV7GgxM9JPXCEh6c6HcQtn3VgxM9JK5h95sI+v2sCWxb6jKjAks2K/CwWEylP2kZm1L6KweVCyv5DNHDcDKmZC9KPgz+wvTgRL8DH7fcByd6SPzCs0tdJsCX18uUusw+oTGlLvOn22EPTvQ7aOJS8tDEkiI0f4wvEx5V/Mppv5e3t5e+XQG0yv1jbCsq/uvfPcBYWmS/3v9Pd1bdX1n7p+vBEf+60Br7qHkvoKN9+ckzwGvAZqChEOIIyuh8wSUrzqGEwJwE/IDlpVieJJRVdJzt28dQHPlDKE78mgc58SoqKioqKioq/1hstj//9xfl3xhag5QyBnjKwaFmDvYBOFwrXkoZVeC/R4H/3wBFP+mYf/y1e7YnAhMLbC8CFqGioqKioqKioqJSDP9KR15FRUVFRUVFReVvwl/4ZdM/G9WR/wMRQswEnrxn99dSyrl/RnlUVFRUVFRUVFT+OaiO/B+I3WFXnXYVFRUVFRUVFZVSR3XkVVRUVFRUVFRU/rr8hb+s+mfzr1u1RkVFRUVFRUVFReWfgDoir6KioqKioqKi8tdFfdm1WFRH/h/Ci61jS13m+OFjSl3m909vLXWZAOsXxZW6zPlhxX+Z9PdyPCao1GUCVPoDPt5kiPm11GUCpDw5qNRlHjgXXuoyAZz/gOnccLfS/8jQH9Wu6gQnlrrM9DTXUpcJkJDlVuoyj132LXWZAM81u13qMnfsK30b6F79VqnLBDh7uvTbq+33fxT9vny9XV/qMsd3+GM+DHh7t9MfIlflr40aWqOioqKioqKioqLyN0QdkVdRUVFRUVFRUfnLIv/CX1b9s1FH5FVUVFRUVFRUVFT+hqgj8ioqKioqKioqKn9d1Jddi0UdkVdRUVFRUVFRUVH5G6I68ioqKioqKioqKip/Q9TQGhUVFRUVFRUVlb8uamhNsagj8ioqKioqKioqKip/Q9QR+X8QulqNcBkwCjQazLs3Y9z4ReHj9Zvj0mcQSBvSaiV37XtYL55G+AXiNmI6wtsXpMT08yZM278DYO/pKyz4fBs2m6RXq7oM6daikMyPtu5n88HTAFisNq7FJrF78US8PVxZu+MQ3+45jkTSp1U9nu3YpFDe+rOfI6xdHawGEwcmrCT11PUidao8qCNVh3bBs2II39YcgSlF+UjTIy92p0JvpSxCq+HpymEMrzeQJyY8Q922DTAZjCyf/A7XT18tIvOFt8ZSrWkNcjJyAFgx+R1unL2WdzyidiSzf1hA8vQ55Oz8FZfmjfCbPBK0GrK+30LGR/fotUJZAl6bgtMjkaQt+5CMT78ufEKNhtDP3sOSmARPvpO3u9ac5whuXxerwcSxcStId1D/ioM7UWlYFzwqhrC5+ghMKZmFjvvUjaDNptfp3f8FvvtuU5H891K1aiXeX72YevVq8vIrC1i0eOUD89yPWW8sYs/eQ/j5+vDDZytKnM+pUWM8R48BrQbDpk3kfL6u0HGXDh1we6YfANJgIHPJIixXrgDgNXUazk2bYUtLJXlw0Y9L1ZrzHEF2vR6/j14j7HrdUkCvIZ0b8Mi0J8FmQ1ptnJ/1MWmHLhTKW23uQALa18NmMHJq7HIyHMgvN7gz5Yd3xb1iCDurDcOckklA2zpUmzMQnQ5Sv/yJxBXfFMkX+upwPKMaYMs1cnvy2+SeuZJ/UKMhcv0izHEp3Bj6eqF8kS92p+ar/dlcfQRVJ/YqtXaVMHUOOTt+xbV5Q/ymjURoNGR+v4X0D74slF5foSwBr0/GuVokKe9+SMYnSt20wYEEzp2K1t8PpI3co6cIbVAPodWS/MV2EpZ/W6Rs4a8Nw6ttQ2wGIzcnL8Fw+ir60ADKLR6PPtAXaZMkr9tG0ocbACi/dAqezWuj8XADIci9mcChFuOKyHUpF0T1lePR+XiQdeoa50a9izRb8GlenZofTyP3pvKRnsRNB7mxSCn/I8O6ENkvCqREo9chtBosOUb2T1hFigO9VhnUkWpDu+BZMZiva76A0d5fOXm70XTRcDzLB2E1mtF8PAeNfyBug0eDRotx5yaM3xe2AadWHXDu1VfZMBjIWbUY6w2lPTj3eALnDt1BgvXmVbKXLiiUt+7s5whtXweLwcTh8StJc1DWSoM6UsXeBn6skd+3AgQ2q0bd1wfg4WHDlp5Ozudr8Rg1BjQacjdvIueLwmV1bt8B93vt9apSVs/J+faaMnQQHqPG0qReC6wGI+fHLiPr1DXupbhrBeDTvDqRswchdFrMKZlE93oVgMZ7FuEeGYrNbOVoj5eLkRtIjZXj0ft4kHnqGmdHvYs0W/PkVp79fJ7c471eU+rm5UbPBcOo2Lw6Tm4upMck8c2od4k7XVSnPd8cRmitigghSL4Wx4+TVmDOMVK+aTWeXj2RtFvKB9acLu3GtOEztDUb4tJ3JEJoMP26BdOWwnalq9sM58efBynBZiX38/ewXj6Tn0BocH9lGdJiRrh5EGnUkvrldpJX3nMPAoJfGYFnlGJXMVMX5/UtGk93wuaNxblKeZAQM30JhuPn8ezaksCx/XCOLAvQEDhSROj/J3/Ax/n+KaiO/D8FocFl4FiyF0xFpiTi8fp7mI/txxZzIy+J5cwxso7tA0BTNgK30S+TNW0QWK0Y1q3AduMSuLji8foKLKePYrXZeGPtFlZO7E+wrxf95rxPVN0qVAoLzJP5fJdmPN+lGQC7oy/y2Y6DeHu4culOAt/uOc7amYPR67SMXLKOVrUr5+ULbVcHz4ohbGwxCf/6kTScN4iferxapFpJhy8S89Nx2n07q9D+88s3cX654riGdaxHwIhOVK5flZCKoUxo8yKR9aowZM4LvPz4VIfqWvvGRxzavN+BGjX0e+k5TuyJpgyARoPftDEkjJyGJT6R0M+WYfhlH+ZrN/Py2NIzSXlzGW5tmzs8l2ffXpiv3UR45H95Mrh9XTwiQtjRbCK+9SOps2Awe7q9UiRvyqELxP90jJbfvVxUsEZQY1Zf4nefdHheR6SkpDF+wss89liXEue5H49360i/Po8yY/ZbJc+k0eA5bjxpUyZhTUzEb8VKjPv2Yr2R31atsbGkjh+LzMrCqXETvCZNJmXkiwAYtm4h5/vv8H5pRhHRQe3r4h4Rws4S6DXOgV4Tfz1N3LajAHhVK0ujVWP5reWkvOMB7eviVjGUX5uOx7tBJNXfHMqBroXbJkDqoQsk/nSMxt/Zz60RVJ8/mMNPzcU/7RaVflxExo6DGC/nfznTM6oBzhXCuNh2BK51qxI+50Wu9Jqcf+5BPTFevq04rAXQhwbg27oWObcTCWxds1TblQDQaPCfMYa4EdOwxCcRtm4pObv3Y76abwPWjEySFyzDvW3hB32sVlLeWonp/GWEhzvld3/DlcGvk73/NFXWLyR9xyGMlwrooG0DnCuGca7NCNzqVaXMnBe59PgUpNVKzJwPMJy+isbdlSobF5H5WzTGS7dI+XYXWk83jj09n2rLx+LdtHrROgERs/pze+VGEn7YR5U3hxHarx0xH28HIP3gOU49O79QeqcQPx4Z0okNUdMIbl6dZguHcXzel2RcjqHxvOfZ2uO1IudIPHyROz8dp+O3Mwvtrzn2MVLP3GDPkCV4RYbSdeloNEEhZL0+GVtyIp4LVmA+vBfb7QI2kBBL1svjkNlZ6Oo1xu2FSWS+NBLhF4Bztz5kjB8IJhPuk17FqWU7+OUUACHt6uAREcKW5pPwqx9J/fmD2NW9aN+afPgiv/x0nKjvCrdfvZcb9ecPYk+/BTT2PoHw88Pv7aWkTp2ELTER3/dWYtzvwF4n5Nur58TJpI5W7DV32xYMP36H17QZODVugrZMGfY3HYNXg8pUeXMYx7oWtePirpXOy43K84dxsu9cjHeS0Ad4AeDXvh7WLAOHO8+g5poJVH1zKEe7ziwit9KsZ7m1chMJP+yj6pvDCOvXjjsf/4TOy42q84cS3XcuxjvJeXIBurw6gMy4FO5EX+GLoQsp36Qa3ecM4v3Hi+p02+ufYcoyANDp5f40HtiJvcuVB86bhy/wxWClnxzfIQGEBtf+Y8heOA2ZmoT7y0uxRO/HFptvV5Zzx7FEK/cpTZmKuL4wi+xZQ/KOO3XshS3mJrp6zcl6ZRi3fswg4vvFZO48gKlA3+IR1RDnCmFcbjcM17pVCX19FNf6TFTayyvDydpzlNuj54Feh8bFGQDjxRvcHjmX0DmjcW9au0hdVYoihOgCvA1ogTVSyvnFpGsEHACellIWHdF5SP4VoTVCiBAhxBdCiCtCiLNCiM1CiCrFpK0ghDhdzLE1QgjHd4niz91JCLFfCCHs21ohRLQQwrHX9zvRVnoEW/wdZGIsWC2YD/yMvsE9pzDm5pfL2UV5ygdkeorixAPkGrDF3EDjF8DpazGUDfKjTKAvep2WLo1rsDv6YrFl2HroDF0b1wDgWmwStSPCcXXWo9NqaFClPLuOnc9LW6ZzA65/8ysAyccu4+TthkuQTxGZqadvkH076b51L/94c/b9+CsNOjbm1293A3D5+EXcvNzxCXq4T6x3eb47B7fsJyMpHQCnmlWx3I7BcicWLBayt+3GNaqws2JLTcN09gLSYi0iTxsUgGurJmT9sLnQ/pDODbj5lVL/1GOX0Xu54eyg/umnb5Bzy3H9Kw3pTMymQ5jsZS0JiYnJHDl6ArPZXOI896Nh3Vp4e3k+VB79I9WwxtzBGqvoNHfXLpxbtCyUxnzmDDJLGSE0nz2DJiD/4dF88iS2jMIjyHcJ7dyAWyXUq8GBXq05xrz/WjcXuCcsM7hLQ2K+3qPIOFq8/MzT1zHYR98AfOpHknMtDsONBKTZQvqGPXjdM0Pl2bEpqd/tAsAQfQGtlzu6QKX96kL88WzbiJQvtxet88tDOTN7HUgIblen1NuVc82qmG/FYLkTp9jA1t24RRXuW2wpaZjOXERaLIX2W5NSMJ2/DIBTRDmsmZlgA2m2kLrhV7zv0YF3xyakfPszADnH7ToI8sWSkIrBPrtmyzZgvHwbfbB/kTzeTaohzRacHNTZt2VNEjccACDuq18I6NrIYf0LInRatC5OlO3SgJyENAzxqSQdu4KTtzuuD9FfeVcOJ+43ZSQ143IsmvBy2JISsMUrNmD+bRdOjQr3K9YLZ5DZig1YL55F459vA0KrRTg5g0YLTi7YUvLPGdalATe+VtpAyrHLOHk57lvTTt8gx0FZy/Vqzu3NhzHcSQZAGxKK5c4dbHZ7Nf68C+fmhe3VcvYeew0sYK+n8u3VuUVLcrdvU/Rw9BI6L/eHulZBvVuStPkgxjtKuc1JGQAEdGnE7dWbsaRlYcs13UdujTy5sV/tzpMb3LsliZsPYrTX+a5crYcr5Zo8gs5Zz4lvf8VmtnLtt9M4e7nh4UD+XSceQOfshJTFx3VrI6piS4hBJsUp9+xDu9HVe9A9O/+Q8A1AV7sJlitnkaZcRY7ZQvrGPXh2aFpIjGeHpqR9n9+3aOx9i8bDFbdGNUn7yt6vmC3YMrOVuly5henanWLLr1IYIYQWWAZ0BaoDfR35i/Z0C4BtpXXuf7wjb3egvwd2SykrSSmrAzOA4IeVJaUcKqU8+5B5tgM3gLuP0WOAw1LKfQ97/vshfAOQKfmOgy0lEeEbUCSdrkELPBZ8iNukuRjWFB1FFQHBaMtHYrl8joTUTEJ880cmgnw9iU917EAZjGb2nr5Ch/rVAIgMC+LopZukZeVgMJr57dRl4lIz8tK7hviRHZOct50Tk4JbyMM53QBaVydCo2pzcMt+/EL8SI7JvzGlxCXjF+znMN/Tk59lwdYlDHh5MDonZWLKN9iPRp2bsOOzfPvSBQZgiUvI27YmJKIN8i9x+XwnjyTt7dXIe17UcQ31xRCTkredG5uCa2jJ6+8S4ktot0Zc+3hHifP8VdAEBGBLyNepLTERbUDRtnoX127dMR06WCLZLvfo1fCQegUI7dqQdr++RdPPpnB6QuFwIedQvzwHB5Tr5hzquI0Vyhfih6FAezfHJaMPKdyO9MH+mGPz2685Nj9N2CvDiJ3/IdzzdUPPDo0xxyWTcVYZxXMJ8in1dqUNCsAal9+3WBOS0AUXf72Kw+mRSmhcXMiJvmCvX1JRHYT4Y47JP5c5LjnPYc+TUyYI1xoReXLu5vFuWg1zYjq5NxOKXBO9nyeWjBykVdGfMSa5UBqvBlVouOu/1Fo3A7eqZQAwxaVwdvlmeh1+m4inWmFMySD2F2WMJzsmBdeH6K9Sz96knN1p9K8bgcbHL89JB3t/XcBRvxen9t0xHz8EgExJInf9l3iv+ArvNd8ic7KwnMiPenAN8SOnYN/6kG3Ao1IITt7utPl2Jr7LV+HSrgO2xML2qrmPvbp0Ld5eNQEBhWQZY5Mf6lq5VQpD5+1O3e9eo8H2BQQ/2RpQ7DL3TtJDyk0pIDcUnbcH9b57lYbb5xNil+taPoic5Ewi29alzcQ+9FgwFL2rM5lxKXgGO9bpo/8dzsQj7xEQGcahj/IfvMvUj2T4ljfo9/FUNGHlET4B2Arcs2VqEhofB/fsei1wn/M+buPmkPtR/j3b5ZkXyf16NcLdE0ymvP2WuKQiNqMLLmxXlrgkdCH+6MuGYk1JJ+zNCVRc/w6hb4xFuDo7rNefjk3++b/70xi4LKW8KqU0AV8AjzlINwb4FkhwcOx38Y935IG2gFlKmXdHllJGA8eFEDuFEMeEEKeEEAUVrhNCfCyEOCmE+EYI4QYghNgthGho/58lhJgrhDghhDgghLjfg8EE4CUhRA1gNDDNLiNYCPGdEOKIEOKQEKKpfX87u9xoe/ncH1hL4WCfg9EAy9G9ZE0bRM6SV3Dp83zhg84uuI99DcPa9yA3B3nvcCQgHJ0H+OXERepGlsXbwxWAiLAABnVpxohF6xi5ZB1Vygaj0xRobg7k3G/0ojjCO9Yn6chFstOzEA4K50jkF29+yqR2o5j56GQ8fDx49IXeADz36hDWzf+k8KegHVW4hMV0bdVEGak8d6nowRKWtThqzX6OM7M//3u+yf8QOtXXrYdrt+5kriphLP//qFeA2C1H2NVqMocGLSJy2lMPzlCSE5SkvReTxrNdIyxJ6eSevlI4uYszQaOeIn7x2gI7/4B25VDmwylVuLrg/dyTGE+ew1Zg1LJI4R7Qj2ncXKiwYjp3Xl+TL8eeJ6hXS+K//82h2PvpJfPkNQ40GMmRdlO48/4Wan6khOPpvN0p27k+PzSZQNyvZ9A6O1Gxd4uiAkrAmaUbcPJxp9tPc6k6uBO2hLii+YuRp6tZF+f23TB8qtiAcPdA36gF6SOfIX1YH4SLK06tO96vqg91vTRaLb61K/Lbs2+RNm0Kzq3bINzvuQXdz167didrdXH2WoL2eZ9rJbRaPOtEcPLZeZx8Zg7lJz6Ba0SoQ7kla1sF5VbkxLPzOfHMXCpM7INrRChCpyW0ZgUyYpLZMHkV5hwjLUb2LKbgCuunrGJx41EkXr5DjZ7KyHjs6eu83Xwcq7rO4NBH23Ad/Z9ibqYO7tnH95I9awg5S19T4uUBXe0myMw0ZSa9mHty4bo77nOFToNLjUhS127m2qNjsRlyCXjhyRIIVHFAOHCrwPZt+748hBDhQC+g5C+UlYB/Q4x8TeCog/25QC8pZYYQIgA4IIRYbz9WFRgipdwrhPgAGAncO3ztDhyQUs4UQrwJDAPmOCqAlDJWCLEE2A+MlVLeHTJ7B3hTSnlACFEB2Ggv7xRguJTyoBDCw17WIgghhgPDAb4Y8jS923TLO6bxC0SmJTvKBoD1wik0wWEIDy9kVgZotbiNfQ3Tvp1Yjig3w2Bfr0Kj6AmpmQT5OA6j2Hr4LF2b1Ci0r3erevRuVU+p6He7aN2xG1VHNgYgOfoq7mH+3B1DcQvzwxCfVmx5i6P6mEdx8nZn3ubFXD15Cf+w/BENvxB/UhNSiuRJS0gFwGKysPvrXfQYrjzDRdSOZOy7Skyyp58n+vb1SP/0G3QhQXl5tUGBWBOL12tBnOvUxLVNM8JbNkY4OSG8POl2fhWG20mkRl/FNSx/xMgl1I/cuNQS19unTkUarRwDgJOfJ0vb1sBisbB+fdHZuhdfGMiQIf0B6PnoAGJj40t8nj8CW2IimqB8nWoCA7EmF53i10VE4DV5CmnTpyIzMoocv4vr448T9W4vgCJ6dX1IvRYk+cB53CoEU3F0T0J7KQ5cevSaELeJAAAgAElEQVQVXMP9udtSXUL9MJZAvjE2Bdew/FEyfYg/lvjCbdMcl4w+NL/96kOVNN5dW+DVoTGebRsgnJ3QerhRZvFEEld8i1OZYKr+sppq7m4IvRYnX0+8HilDiv0F3f+1XYmOtUn/5Bu0IfmjxdqgAKwJJbMBAHRagha9Ss6eg+grli1QvwDM9+ogNhl9WCBwTkkT4o/5rg3rtFRYMZ3UH35BH+RL1c1LAMg5eQl9mSACuzfmaMdphA3oiCnuHrnJGei83BBaDdJqwznMPy+NtcCDRcrO42jmD0Xv50nErP741ihP+y+nkxx9FZvZSkDDylz7bi/uD9lfmbMM7J+wKm+7/Om3EW75zrHGLxCZUtQGtOUjcHtxCllzpin9NKCr3QBbQiwyQwl9Mh/Yg1PHHnRs/6xShxNXcQvz5+4Vcgv1Izeu5GXNiU3BmJKJ1WBEZqRjuXgebXj+ddMEBmJzYK/aiAi8Jk0h7aXC9ur62OO4PtoLbWgo5rNn0QQGAUof5Bzq/1DXyhibjDklA1uOkZCno9D7elD7y1mk7jmJS3gAxtiUPLn32qU5OfMeuX4YC8nNxJZjxJZjJO3AOcqNfhTvBlWQNknipTt4hflzbvMhWozsiWeIH5kJxetU2iRnNxyg2YgenPh6T6GQm8s/n0Bon0Pm5qDxKxAu5RuA7X737Iun0ASGIjy80EbWQFenGR61GoOLG8LNHZeh02D3YnQhAZjjC8uxxCWhDwvEYPeEdCEBWOKTkRLMcUkYTij9ReaWvfirjnyxFPS57KySUt417JI8mS0BpkkprY4GHn8v/4YR+eIQwBtCiJPADpQnp7uj6reklHvt/z8DWjrIb0JxvEF5UKjwgPMtA7RSyo8K7OsArBBCRAM/AL5CCFdgL7BECDEG8JJSFg2+BqSUq6SUDaWUDbuYk9GGhCMCQ0CrQ9+0LeZjhaN3NEFh+f/LVwatPu/m4Dp0MraYm5i25r93UaNCGDfjU7idmIrZYmXroTO0qVP01YLMnFyOXrhBVN3Cx5IzlFi72OR0dh67QIQmla0dZ7C14wzubD1ChSdaAeBfPxJzhoHc+3SMjtB7uuIW5s+mNlN5qdsEjmw/SKs+UQBE1qtCTmZ2ntNekIJx8406NeHWBSUsYVzLEYxtOZyxLYdzcPN+Uua9Q+ZnX6MrG44uLAR0Otw7R2H4pWRRUWlL3+dO177c6fEsiS/NJffgUTY/MpyfO8wgdusRyj2l1N+3fiSWTAPGh6j/T43Hs73ROLY3GkfMxoOMHjvDoRMPsHzFxzRs1ImGjTr96U48gPn8ebThZdCEKDp1adcO4769hdJogoLwfn02GfPmYr19+77yDD/8wO4OM9jdYQZxW49QtoBezQ+pV/cK+RNr3rUqoNHruLZ0A/vaT2df++kkbDlCmH3a3btBJObMnBLJTz9+BbeIEFzLBSL0Orx7tiZjx6FCaTJ3HMS3dzsAXOtWxZqZgyUxlfj/fsL55oO40Goot8a8Sda+k9yesAjjhRucazSAc/X7s77ccxhuJ3F88mpCuzTIq///2q6S5r5L+iffoC8Xji7cbgNdosj5peiL4sUR8NokzFdvkrJwBfpy4TiVDUbodfj2bEXGT4VDMDJ2HMKvT1sA3OrZdWC34XJvjsF4+TaJa34k6ZPNXOg2ngvdxpO+/SBBgx8l51IMzmH+WDJzMDmoc+reMwTaR0hDnmpD0tbDADgF5sc6e9aLVFb9Sskk5rMdGBLS2Pbof7i99ShlO9cn4/IdAupXwpSRg+Eh9Kr3ckOj1wIQ2S8Ky6mjaIJD0QQpOtW3bIfpSOF+RQQE4T5lNtnvvIEtNt8GbEkJ6KpUByclBEJXqz7mfbv5qeMMfuo4gztbjlD+ScUG/Ow28DB9a8y2owQ0qYrQasDZGW1IKBpvrzx7dW5bjL2+Npt0B/Zq+PEH0mZMwxpzB+PeX3Hp1BkArwaVH/paJW09jHfTagithrgvfsYUn8rpAfNJ2nI4LxxG4+KEtRi5aQXkhj4VRdJWJSQpcesRvJs+gtBq0Lg64VU/kturNnOozSTuRF8m5uRV6vRpRcUWNTCkZ2PMNJDlQL5v+fz+o0qH+iRfiQHAPdA7b39YnQgQGqxnj6EJDkcE2O/ZjaPyXmy9iyh4zy4XCTrlnm387gOypvQja9oADMtng9mM8YePQa/Du0drsnYWtqvMHQfx6ZXft9gys7EkpmJNSsUSm4hTRWXg2L15HYyXb/JXRNrkn/8r4HPZf6sKFPE2ULbAdhkg5p5qNAS+EEJcB54A3hNCPP6/6ubfMCJ/BkVh99IfCAQaSCnNdsW62I/d+xTlaA7NLPPnK608QJdSSpsQosgkItDYHk9VkDn22YHuwGEhRJSU0kF8RgFsNgyfvIv7lAXKjWjPFmx3buDUrgcApl0b0TVqjVPLjmC1IE0mcpbNBkBbpSZOLTthvXkVjznKlGju1++j1Wp4qV8XXlzyOTabjcdb1CUyPJCvdiuP9U9FKQ7DruMXaFYjAjdnp0JFmrT8G9KzDOi0Gmb074KXu2vesZid0YS2r0uPfYuwGkwcnJA/Fdvm0ykcmrwaQ3waVYZ0ptqLPXAJ8qbrjvnE7orm0OQ1AJTp2oi4PaewGoygh+O7jlK3bQOW7FmB0WBk5eT8pR6nfvQyq6cuJTUhldFvT8DTzxsh4MbZa6yZcZ9ZLquNlAXvErRsPmg0ZK3fivnqDTz6KHrN+nYjGn9fQj97D427G0iJZ7/exDwxBJmdU6zY+B3RBLevS8cDi7EYjBwfn1//pmunEj1xFbnxaUQM6UzlUT1wDvKh7a75xO+MJnrS6uLL+wCCgwM5uH8LXl4e2Gw2xo4ZRq06UWRmZj04swOmvDqfw8dPkpaWQfvHn2XkkAH06dn5/plsVjLfWYLvm28py9lt2Yz1+nVcez4KgGHDejyeG4jGyxvP8ROUPFYrKS+MAMB71ivo69ZF4+1NwFdfk/XRh3DuBJCv1w4HFmN9gF4jHeg1tEdjyj7ZCmm2YM01Ez387UJFT9xxnID2dWl98G2sBiOnxuW3nQZrp3F64iqM8amUH9qFiqN64hTkQ4ufF5C4M5qzL31Iwy9mKMtPfr0D46Wb+PVTVg9KWbeVzJ+P4Nm2IVV2r0IajNyeWvjcJSFxz2n8m1Qt3XZltZE8bykhy+eBRkPmD9swX7mB55OKDWR+vRGtvy9hny9D4+6GtEm8n+3N7V5DcapSEc+eHTFdvErYuvdAQuQ385FGEylf7SD30i38+ys6SF67lYxdR/Bs24Bqe1bal59UbNi9YTX8+rTDcO563kh8zH8/JfPno2TsOkLYzMG4lQmmysIXuDBuWV7Ra619iQsTV2CKT+XqnM+ovnICFaf3JfPUNWLXKS//BfZsStjATkirFVuuibMjFiv1OnaZm5sO0W3bHKTFijk7l0eGdc1bfvIubT+dzIHJazDEp1F1SCeqv9gD1yBvuu+YR8yuExyYvAbvymE0f/sFpM1G+sU7GN7/D9qqNfB4+b+g0WDatQXbres4dVJswLR9Pa5PDkR4euE2LN8GMqeNwHrpHKb9v+D11mqwWrFcu4Txp42AMssVZ+9bu+5X+tbDBfrWlp9N4cik1eTGpxE5pDNVRyp9a6ed84ndGc3RyWvIvBRD3M8n6bRrPm56I4bNm7DevoXPgrcQGg2GLZux3riOSw+lrLkb1+M+wG6v4/LLmjpSsVevma+gr6PYq+eESVhv36bJwXexGkwPfa1yLt0hZVc0DX9eCNJG7NqdZJ+/Rfb5W0TOfp5mB94BrQaTuwuh/doSu+5naq+dzvmJKzHFp3J5zlpqrhxPxPRnyDp1jZh75Db++S2ktBGzdhfZ55UoiS2vfkLPBUPxKRNIucZVSb+TxA8F3p3p+9EUNkxdTVZiOo8vegEnD1eEgPhzN9k080MAqndrTINnO2CzWLHkmjGsnAs2G7lrl+I2YR5Co8H02zZsMTfQt1HsyvzLRvQNWqFv1gGsVqTZiGGFg0l/acN65zpuE+YROVJL2jc/Ybx0E9++XQFI/XwLWbsP4xHVkMhda7DlGomZtjgve+x/VhK+eApCr8N0K46YqYp9eXZqRsgrL6D18wbYBEQDD+jc/9UcBioLISoCd4BngH4FE0gpK979L4T4CNgopfzhfz2x+D1xyX8n7C+7HkBZCmi1fV8joBsQIKUcI4RoC+wC7ir5GtBcSrlfCLEaOC+lXCiE2A1MllIeEUJkSSk97PKeAHpIKZ9/QFny8ti3vwL2SykX27frSimjhRCVpJRX7Ps2AiuklBsdS1VIH9C+1C+k8/DnS1sk3z+9tdRlAqzXZ5e6zPn+v8+5vR/HY4IenOh38ETKL6Uu0xDza6nLBEh5suja7/8rB86FPzjR78D5D1i7ONyt9NvqFcPDrRxUUuoEJz440UOSnub64ES/g7Tc0n9J77b2j3nxr3uz+88y/R527Ct9G2hTvfTLCXD2dOn3g7YSBYs/PL+56Etd5vgOpfaeYyFu73Z6cKLfQfUrm/4Y5T4EmWN7/OnOquc7G++rByFEN5TwGS3wgZRyrhDiBYCC72na036E4sj/z8tP/uNH5KWUUgjRCyVUZTpKvPl14DXgHSHEEZQnzfMFsp0DBgohVgKXgOV/UPFGAcuFEINQrsXP9n2ThRCtABtwEii65pyKioqKioqKispfAinlZmDzPfscTvk/aOD3YfjHO/IAUsoYwNHyE82KyeJwrXgpZVSB/x4F/n8DPPCpqmAe+3YiDsJ+pJQvPkiWioqKioqKiorKv5t/hSOvoqKioqKioqLyN8VW+mGO/xRUR74UEULMBO5du+lrKeXcP6M8KioqKioqKioq/1xUR74UsTvsqtOuoqKioqKiolJa/B0/fvj/xL95HXkVFRUVFRUVFRWVvy2qI6+ioqKioqKioqLyN0QNrVFRUVFRUVFRUfnroobWFIvqyP9DyLpc+o1cLv2g1GW62UJLXSZAH5N7qcvMzTGWukx3aS11mQBrAtuWusw/4sNNAH5ff1jqMq01Z5W6TIAJ8mqpy/xSBj840UPi+Qe1q9yc0v8YjtH8x9x29EU+nP2/EyENpS4TIDa69PsrT1vptwFTlrbUZQKYKH25TvwxNhBqLf1vIRmu3vsx99LB1ePBaVT+eaiOvIqKioqKioqKyl8WKdUR+eJQY+RVVFRUVFRUVFRU/oaojryKioqKioqKiorK3xA1tEZFRUVFRUVFReWvi/qya7GoI/IqKioqKioqKioqf0PUEXkVFRUVFRUVFZW/LuqIfLGoI/IqKioqKioqKioqf0NUR15FRUVFRUVFRUXlb4gaWvMPwrlpI7zHj0ZoNWSv30zWp58XOq4rXxbfmVPRV61MxsoPyFr3lbK/XFl8Z7+cny48lIzVH8H+b4qcQ1enEa7PjwaNFtOuTRh/vOccDVvg+tQgkBJptWL4eCnWC6cdlrfWnOcIal8Xq8HE8XErSD91vUiaioM7ETGsCx4VQ9hSfQSmlEwAQjo34JFpT4LNhrTaOP7KpyQfukid2c8R2r4OFoOJI+NXkuZAZqVBHalsl7m+xghMKVkABDarRvOPJmJMycQ11A8MBlLe/4aU1V8XkRE08wXcWzdC5hqJfWkhxrNXAPAd+DjeT3QBKTFeuk7cS4uQJjP+Ywfg2b4ZFQP80bo5Y7iVyOmRS8k8da2IbJdygdReOQ69jwcZp65xetRSpNmKztOVmu+NwSU8AKHVcGP5RmK+2I3GWU/3H/+D1lmH0Gqx5OTi7OOJxWDktwmrSDldVAet3n2RgDoR2MwWkqKvsm/aB0iLFe9KobRYPBz/mhXI+WA1OV99iVOjxniOHgNaDYZNm8j5fF3h8nbogNsz/QCQBgOZSxZhuaLow2vqNJybNsOWlkry4JJ/YGrWG4vYs/cQfr4+/PDZihLnA6g95zlC7O3q6LgVDttAxOBORNrbwMYC7SqgeTWafTSJ7JsJAIzctI33Fr7PjLmTaN2hObmGXGaMeZ2zpy4Ukfnp+lW4e7gB4B/gy8njZxkzcAoenu68+d7rRD4SQXBIICIzh6T3fyRxeVH7Cnt1OJ5tG2AzGLk9+W0MZ64gnPVU+nI+wlmP0GpJ37KX+MX518B/YA+qTeiP1s0ZS4aB6GfeKLZd1bS3q8xT1zhjb1flRvYkpE9LAIROi3vlcE6NWELkS31xKeOPNT0TW3oWGVt/I+mdtXnygl8egUebRtgMRmKnLSLXbgMaT3dC3xiHc+XygCR2+hK8erbBs2NztD6eGGOSsWYZuP7SKrKjL+fJcy4bROTyieh8PMg+fY0rY95Gmi0AlJ89BJ929bEZjFyZsJScU8qHuoKHdCeof0f0wb5gk5jiU7k0binYJJXeHI5w1oPVxpXpqzEnZ1B1xQRF/qmrXBz9LtJswa9zI8pNewZps4HVxtWXPyTz0HmcwvypvfEN9AHeIG3Evr+FG7M/KaJX57JBVFkxAZ2PJ9mnrnJpzDtIs4WA3q0IH9ULAGu2gavTV+EU7EvF1wej9/NAWm1YUzNI+3IbKR/9mK/XV0bgGdUQm8FIzNTF5J7J12vYvLE4VykPEmKmL8Fw/DyeXVsSOLYf1SqXIzcmBUt6FqfGLifDQbt3LRdInZXj0Pu4k3HqOifv9i3e7tRaMgK3CsFYjWZOj19B1vnbAIR8vw6QaAP8AchY/TGZnzi4t7w8FaeqlUlf8QFZa7/KOyY83PGdORl9REWQEvOVa7Ss9AhWg5HTY5eTWUw5a68ch87HncxT1zlVoJw17OW0Gc2cKVDORnsW4RYZhjRbOdZjFlkObSCI6ivHo/PxIOvUNc6NUtqAT/Pq1Px4Grl2u0/cdJAbixT7rDGkM4/0jcIt2AdskuyENH6ZsIpkB/1q23dfJKC20q8mRl/l1+lKv1q+U30aTHkCbBKbxYp2zSLMp0/9IX2ra/OG+E0bidBoyPx+C+kffFlIpr5CWQJen4xztUhS3v2QjE+UemqDAwmcOxWtvx9IG5nfbCZj3fdF6vhnIdXQmmJRR+T/KWg0+EwaR/LE6cT3HYRbx3boKpQvlMSWkUna4qV5DvxdLDdvkThwuPIb9AIy10juL78VPYfQ4Dp4HNnzppM58XmcWrRHE174HJZTR8mcOpTMacPIWfEmbiOmOCxuUPu6uEeEsLPZRE5MXkOdBYMdpks5dIF9T71Bzq3EQvsTfz3N7nbT2d1hBsfHr6TBwmGEtKuDZ0QIW5tP4tiU96k/37HjmHz4Inuemkf2PTIBkg4qDtr21lO43Ko/nt2jcKpUrlAa99aN0JcP41rnIcS98g7Br44GQBfkj8+Ax7jxxFiuP/oiaDR4dm8DQOr735K48EMyoq9wac46ci7FUO3NIQ7LV3lWf26s3MzeZuOxpGUT3q8dAGUGdybrwm0OtJvKkd7/ocprAxB6LTajmW1PvcH6jjM5Ou9LvCNC+XXse+yf9j7N5j3v8BxXv9/H962n8GP7l9C6OFGlXxQAxrRsDr78KadXblYSajR4jhtP2vSpJD8/EJf27dGWL3zNrbGxpI4fS8rQwWR/+glekybnHTNs3ULqNMdt4H483q0jKxbNeeh8we3r4hERwvZmEzk2eQ11i2lXyYcu8NtTbxTTBs6zq8MMdnWYwXsL36d1++aUjyhLlyZ9eHXSPF55c5pDmQMeHU7vds/Su92zRB85xY5NPwPQb/CTXL10HSkl/XoMRei0+DzaGufIsoXye0Y1wKliGBeiRnBnxjLC574IgDSaudpvJpe6juVit7F4tqmPW72qALg3q4XvUx3IOH6Fn8s+y5kxy6haTLuKnNWfWys3s7/ZeMxp2YTZ29XN9zZwqP00DrWfxpW560jdf5bIWf043vcNLjZ/FmtyOrcnLMCjdUNc6trP26YhTuXDudJhKLEvv0PI66Pzr8GsEWTvOcrVLiO42nM0upAAnMqHY7xyi4RFn2BNz+b2f7+g3KznCpWv7MwBxK7ewImWo7GkZRHYtz0A3u3q41Lx/9g77/CoivZh32dLdrObXjaNUEJHSui9JVQFFRUUkVfBhgJSpChiV+wFK0XsYOFVVFAQBKmK9BI6pEDKpm+ym+275/vjLJtNsqEovOrv2/u6cmX37Mwzz8w888ycOTNzEjjYezJZcxbR5IX7AAhu2RDduMHkvrKCqgOnqTqSzbnXV9L0pfto9Ph4zr62koODZnP25S9p/Ph4Gs+/g/zFa9jXaypOQxVxnvwbth3mQNrDHBw0m1PT36PZa1K5h/Vogy2vmJ2NbuXouOdJmDic4BYN6pRro/njyV+yhv29p+CsMKHz6G07W0TGTY9zMH0muW/+l6avTCJlwb1kPf4hzpJyXGUV5E57iZC0bgQ1TgQgZEAXVI0TOZ12LwWPvU3CM5O96cQ/cR+mrXs5M2QSZ0ZMwXb6nJTOyRzKPv4eh6GK/RNeI2PWUtq8fI9fG2gx/3ayF//Itp4zcBhMNPCUQdNpN1KZkcOOgXM5POU9Wj93V62YAvpbJ5A3cATBQ9JQNPHTt7z2DsblX1ObiJlTsP6+m8Jb76Li3SXIwkLZ3mM6Ry+gZ/P5t5Oz+Ed2ePQ87wNTpt2IMSOH3z16tvToGZOeistkZe/QR7Hpy2jx8r1+5abMH0fu4jXs6vkQToOJBI9cgIo/jrEnfTZ70md7B/HaVsm0GjuAPa/8l+KDmZQePcv+N1fRpx6/enrVb6zsP5tvBkl+tdXYAQDkbT/Ct4Pn8e3Qx9g6aylhs2dfHd8qkxE9byqFD84jd9Q9aIcNRJlSs/9yVRopfeldKj6pNZHgclH26mLyRt1N/h0PEXbb9XXiBvhn8q8cyAuC4BIE4YDP3yOXEXeAIAhr/mL6mwVB6PIn4140fUEQhguCsEcQhGOCIBwXBOHVi8kNatMKZ24ervwCcDox/7IJdb9eNcK4yw04jp1AdNb/KmtVl0448/Jx6Qvr/CZv1gp3YT7uogJwObH/tgll1941A9ms1flQqQH/d9EJQztz7uttAJTvO40yTINKF1EnXEVGDpZzJXWuu8y2ar00ahBFEod1JmelJLPMI1PtR6YhIwdzbl2ZAMpwDabsQqrOFoPDifGnLYSk96gRJiS9B5XfbwTAevA48rAQ5LGRUp7lcgR1EMhlyIJVOIvKAHBXmQlJ70HByq3INWpsxQYUYVqC/OgX1ecailbvBCD/6y3EDu8q/SCCIiRYyrNWjcNgQnS6AXB6yqPh0M7YKsyIIhTvO0NQuJZgP2nkbTro/Vxy4AyahCgpP6WVlB7MRHRINqJs1RpXfh6uAsmurJs2oerdp4Ysx5EjiCbpqYbj6BFkMbHVvx06hLvS6LesL0SX1HaEh4VedrzEoZ05W8uu/NlARUYOZj925Y+04f34/mvpxubg3gzCwkOJ1UXXG16j1dC9Txd++WkLIL2RMLlxEmezcjGUV+IyGDGs3kbYkO414oUN6YHh200AmPefQB6qReGxK7dZaleCQoGgUHjfchg97lqc+jIKvtoMQNmmA/XaVaSPXRX42pUPcaN6U7nvNJasQqw5RYiVJip/3ErooJ4ICrm3OYcO6kHFd542cOAEMo+uspBgNF3bYlj5sxTQ4UTbO1UKK4o4i8qQh2tRNYjFXlhWM/992lG25ncASlb+SuSwbpLeQ7tR8l8pf6Z9J5GHa1HqIglunoRp30nCB3SiZOWvVPx+FHWjOBRhGgSlHEWop62EarDrywjv3ZYSj/yirzcT5ZF/vmwB5BoVeMo2vEdr9B+uBaByRwaiy4WmVd2BTXiftpT6yh0uyTXuOYGrokr6vPckqoZxWLL1yEOCsew/QcUPmwlN64Z512FCh/T0lqthlWQDlgMnkIXVKtev13vL1W2UZNvPnCO4XXPsRQYAKvbW70+j+1xD4eo/AMj/eitxw6VuTNsiidJt0pPTqtP5BCfHEhQbLkVSKnHmF3j7FsuGTQTX07dQq28RtBpUHdtj/kFqP+rePahavdarpyJMU68P9NVT56NnmUdPs4+escO6kLv0R5wGE26r/QJtoC3Fnjag/3oLMX7agC+a5kkU7T9D8sAOnFq5nYKdxwltqCMozL9fPefjV4sPnEHr8atOn/5KEawC8er4VlXbljjO5ePM04PTSdW6zWgG1KqrMgP2IycRnc4a110lZdiPS0/IRLMFe+ZZ5LqYC5bP/xS3+Pf//UP5Vw7kAYsoiqk+fy/+rxIWBEF+leW3Bd4B7hBFsTXQFsi8WDxZbAyuoiLvd1dRCfLY2AvE8E/w4IFYNmzyn0ZUDO7S6jTcpcXIIus2dGXXPoS+/gnaR17A/P7LfmWpEyKx5Fd35JaCMoITIi9L14ThXUjb9io9Pp/NnhlLCI6Pwpxf+pdkhrduSGSHJvRZPoegZg1x6ktQxNUctCnionEWVA8CHfoSFHExOItKKfvwG5pu+pSm21bgNpox79jnDafp0YEWz95Jws19OPPy11gLSlF7HP15lFGhOCvNiC5pgG7NL/OGObdsHdoWSfQ7tIiem1/lxPyPvYMOQSZw/frnaTa6L0V7TlKyX3r8WlVQhia+/jIQFHKa3tyHvF8P+f1dFhOD28eu3MXFyGPqd+7B116Hfdcf9f5+tfFnV+rLtIGozs1J2/gCvVbMoVnLFOLidejzq29s9flF6BJ09cYffN0Adm7bTZVJGmgtX7aSJk0b0blHKt9vWUH+00txFJSgrGVXyrho7PnVdmXXl6KM94SRyWj+00La7P0M4/b9WA6cBECVkogqJZHGM26i06onCU1tiq2gFNVF7MqWX1YnjCw4iOiBqVSdzsN6vh3JZESMHU7s5LGYduzHelB6YqWIi8FRUP00w+lpA8rkBFxlFSS8NIMm379NwvPTUCbocBQUU/j8EuLmTkTVIJYGs27l3ILqZTqKqFBp0OvRz15QSpAn79nbjB8AACAASURBVEHxUdh8yyW/lKD4KMzHzxLavQ2qZB2Okgoi0zsSlBiNraAM/UfraPz4eLrsXUTjJ/9D3rvf4ayslm8rKCXIJ/9Rw7vRcdtCWn/+KKdnvCelmxDtTVfVIBZBLsfhGSz76u2spbcqvma5AsSNTcd8NAd7XgnmE2fRdGuLu7IKZZKOkP5dUCbEeso1Gkd+rXKNj/aWa+LLM2jyw1skLHgIIVhVrUdcNG6bw/vdWlC3fpVRoThq+ZbzYYxHzxJ/nXQDEt6xKeoGMV6/I8hlKJuloPtkEdobr7usvkWRmIC7vILIx+eg+3Qx6p7dcJdXl6G1oOyyfKDx6Fl0Hj3DPHqqEqJQJ0Rhy6v2/ZfWBmqGCevcgi6bXqHdinloWkpPXqqOnyOhe0tCk2OwlFSSnNaBkMRoqgrK0F7Erza/uQ+5m6v9auNhXRi9+WWGfjqLypdfuiq+Va6LwaWvth9XkdQuLxdFYhyqVs2wHT5+2XED/O/5tw7k/SIIQrYgCAsEQfjdM6PdSRCEnwVBOCMIwiSfoGGCIKwSBOGoIAiLBEGQeeK/74l3RBCEp2vJfUIQhO3AaJ/rMkEQPhEE4TnP9yGetPcJgrBSEIQQz/Vhnpn17cBNF8nGHOB5URSPA4ii6BRF8b1LyHzda+Jl3kEqFKj79MKycculp+Fnxt2xezvGmXdS9erjqG/1v7TBn6zLVbdg7R429Z3Frgmvc82c0eC3CC5daPnhbPY9+hH5P+3h9LKfSXrnCY+Q2iH9l7UsLISQ9B5kDprAmX7jEIJVhI0c6A1iz8rj0MTXKfhmO8kTh/nX7wJ5iB7YAWNGNlvbT2Jn2hxavTARuWeGXnSL/DDkMQp2HCW8eSIRLRv4Cqg3zz0X3EXhH8cp2lV3zbekjz+F/AdVpnYk+NrrMC5ZXG96VxvhMvT1h+FQNuu6PMSm9Ec5s2w973zycj1Nq36h144awo+r1nu/9xnYg7xzBfz4zc/clHYHSc9MQqZW1q0Xv83LE8bt5tS10zjWcwKaDi1QtZBmhgW5HEGh4PispZx+5nPaLZ1eM96lyPYQM6Qzht0ncJvt1Rfdborf+AzDNxsIbt/Cs+69PlcgIsjlqK9pRvmKn8i6YSpui5WgRgkARN5+LYULlmLcdZyCxatJef3BCyt4Xj+/vkLEejqPgvdWEdKpBQ2fvAvzkRzwPKGKvq4HWU9+zJ7Ok8h68mOaPOfHD/nkv2ztLvb3ncbxCS/TcO5tNVSSadS0XDYbS1YBLp/Z+wvp5ktYr7bobk+neNVWACyn8ihd/F+i77+F0PTuWI9nVT8lrcd+BYVMKtflP5F1/UO4LVZiJo2uDnMp/v8CNpD51vcowrX02vgiDe8ehvFwtlenisUfYt2ynZLpj6C95UYUDZMv3VnL5ShbNqfq2x8o+s/94HajGT6olgqX7gOz3voeZbiWHn70rCdrPnLr73OMh7LY2flB9qTNJm/ZWtp+PAcA86k8Dr63Bl3HZnR/4nbKjp7FfT69C5RBnwV3UfDHcfQ+fjV73R5WDpjDhrvfQDtx4tXxrZdgjxdDCFaje+0JSl95H7HKfFlxA/w9/FsH8sG1ltbc6vPbOVEUewLbgI+BW4AewDM+YboBDwPtgKZUD64fE0WxC9Ae6C8IQnufOFZRFPuIovil57sCWA6cFEVxviAIMcB8YJAoip2APcBMQRDUwFJgJNAXiL9I3toCey+lEARBuM9z47Hnp+PHkOuqZwnluhhcJZe2dOA86p7dcJw4hbu83O/v7tJiZNHVaciiY3GXl/oNC+A6dghZXCJCaBgAQUNuZMAvCxjwywKs+nKCE6tnQ4ITorDq/ad7IZpMGEy75/5DTI+W2A0mNInVs5ySTMMFYtfEabJQlV1IcFI0+k0HEZQKgho3wFlUM4/OwhIUCdWzHMp4aTZe0zMVR24hrvIKcLowbfiN8DHDabTqHRqtegdnUSnqpGj0324nbkR31AnR2Grl2VFqlJYGyKWmqU6M8oZJvG0ART/uAsCSXYjlbBHa5ok0mDCE69c/z/Xrn6cqtwRTThFJAyTT1SZEYS70XwYdZoxCHR3KrqeW+/0dpFkimY9dyWJjcZXWtStFSgphs2ZjmD8PsbKyXnlXA5k6jLRfFpD2ywIsf9GunCYLLrONlAmDuebRMSQ0SMBQXkl8Ypw3THyijmJ93bX1ABGR4bTveA1bNuzwXrtp7Ag2r99OfFIcZ7NysZ/To26dgqOo5tISh76UoMRquwqKj8ZRa/mJu7IK087DxM28g+Y/LUSZFIv1ZA7qpGgq959BdLtRN4i9qF2pfOzqPHE39qJw1Q7pSZFPO1LGx+DILcT8x2G0/Tp7dC3xziIDKDxtwKEvwaEvIbhtc5r88DYh/bsiBClRJsQSPmoQxp93EJQYTcnKzYSkNq8u97JK5OFa8OgXlBDtXXpjLyhF5VsuidGED0il7YbXiJt4LWWrd1Dw7iocBhOWrAJUCVFEDupE6Y/S7GXpD7+jbdsERVi1fFVCNHY/dqFp3YiwHq1J3fw6dn05qmQdLZfNpvjbbciUCuz6mvXhLK1EUUfv8hrymr32AMfvehHLqTyCkqR8GFaup/yLtZR9uhq3wYg9O1+Spy9BmVirXAtLcRRIZWvxPBExrt1B6JBepKx+m5TVb+MsKkWmUnrjqRPq1q+j1IiyHt/iMlnImL6I39If4fCUdwmKDsN8VrJx5+ks5HE63OUGrJu3o2zV/JL7FldRMW5TFRGPzET32RIcZ7JQNmt6UT3r84Euk4Uj0xexM/0RKvaeIvSaRrR7dwq2wnJUSdU2K9VvrfZVWlmrDVSHcZks3pu0so37kSnkJE+5gS4bX+GaCUPIXPMHh95bg9VQRUVWIdqEKKrq8audZoxCHRXKzqf9+1X9HydQJCbhNpuvuG91FRYjj6+2H7kuBldR/X103cTk6F5/EtNPmzBv9LNP7u/E/Q/4+4fybx3I115a47st+wfP/8PAH6IoGkVRLAasgiCcX9S2SxTFTFEUXcAXwPmFaWMEQdgH7AeuAdr4yK259RsWAxmiKD7v+d7DE36HIAgHgDuBRkArIEsUxVOidGv8+V/N/HlEUVwiimIXURS7pBWWoEhOQp4QDwoFmkFpWLf9flnyggen1busBsB15jiy+CRksfEgVxDUKw3Hnt9qhJHFJXo/y5s0l9bzGiXnY1//HZsHzWPzoHno1+0heUxfACI7NcNhtGAruvRBt7axNLDK+mgD+2cswV5q5Nx3O2k0WpIZ5ZFpvQyZqthwyg9kEtIknoShnUAQ0KZ1x7RpZ41wpk07CbtB2tCm7tAKl7EKV3E5zoJigju0QlBLj7w1PVMx/rydnFFTyJ++ANPG30kY3Y/YoV2wF1fgNJq961p9Kd9xFN1IaV1+4pj+FK/bA4A1r4Sovm0BCIoNR9M0EUtOEYU//M66W57nhyGPkbvpAAl9rqHiTD6xnZpirzRj8ZNG87EDSBrQji2T373gzJLj+HHkSQ2QxUt2pU5Lw/bbjhphZDod4c88S+ULz+PKzb3U4r5iuK2V3s2pBev20LCWXV2uDQBkfrSB/bOXUVJUytrvNnDDmGsB6NC5LcZKE8X1dI5Dr09n84bt2G3Vs9oFeYXExsfQKCWZtqmtUaU0IKRHWyo37KoRt3LDH0TcJG2+03RsictoxllcjjwqDFmYFgBBFURo71TKv97AqWunUfDCR7hMFuJH9yM4JQG5Rto7cTG7SvCxKwB5aDCRPdtQvG4Pxv1n0KTEE9q+CbLIcMKu64dp2160vVKxZ0r1a9r4B+E3etpAakvcxiqcxeW4SqR2UPXbfrKun0rFD79iOXyS8BvTcRaVEnH7dbgqzQS3aIA1q6Bm/ndkEDVCWiseM3og5T/vBsCwfjcxtwwAIKRTC1yVZgre/paMwQ9z/LZnKF+3C90dQ4i+tjuWzAKpXRWUEdbrGgDC+7TDmllAxW9HiPHI140ZQJlHvrpx9dyKcc8JHKWVHBgwk7J1u2j02B1YTuVi3H0cp9FcZ2kNQMWODKJ95Javk+o1KCmGlstmc2rqW1gzCzAdOE1wkwRUyTrkcVGEj+iH5dBJQof2omK19BTU+MsfRIySbCDYT7kGNUkCQNurA6Yte8gcOZXMkVMxrt/pXRMe3rkZDqPZrz8t23GUuJHS3ozEMf0o9NjA+X0FAA3uSKNs5zFcJguCWo0jOwdFchKKxo1Q9eiKslEylq2X1re4y8pxZmdT9tizFI2/D7HKjCwkxKtnfT6wtp7FfvR0O5zov/uN3wfOoWjtHuJGSwcLyNRBF/CtR4j1tIH4Mf0pWSfZQFBs9Xr30I7NQCbj3Dvfsyd9Nj/d/hLZ6/bSatxAmgzvQkVWAXajf7/acuwAGvRvx6YpNf1qWOPqiYDoto1BocCxd+8V9622IydQNkxCkSTJ1A4bgHnLpY8DYp56GEfmWSo/++aS4wT4+xEu97HLPwFBEEyiKIb4uZ4NdBFFsUQQhLs8n6f4/oY04/20KIr9PdcnIs3MvwVsALqKolguCMLHwGZRFD/2leuJsxk4BjQHRoiiaBUEYSRwuyiKY2vplAos9EnveuA+URRH1JO3z4BfRVH88HLKJK9nmqjq2Z2I6Q+CTE7VmrWYPlmOZtRIAMyrViOLikT30SIErQbcIqLFQuHYCYhmM4JKRfz3X6K/+Q7EKmltr7ZhXdtQpHYn+M7JIJNh37wW26rlBA2S0rD/shrV9bcR1G8ouJyIdhuWzxfVOH5yy5YE7+f2L9yFbmAHXBYb+6cvxnBQOi6sx/I5HJi5BGuhgZS7h9Js8ghUugjsJZUUbjzAgYeX0mzKSJJH90V0OHFZHRx8dgWlu06SuuAu4ge2x2Wxs2fGYso9Mnt/Ppu9Dy/FWmig2d1DafHgCNS6cGwlleg3HmDvrA9oOmEwKXcOQq5WEhwXibuiEsOKHylb/CXht0oDuYqvpE1buscfRNu3C6LVSsG8N7BlnAIgeuodhA7vB04X1mNnKJy/ENHhIPGtxwhq3AAhJhq5OghrXgkZk9+h8qC0/aHj8kc4OnMxtsJyghvpaOc9JjCbw5PfRrQ7UcVFcs1bDxAUF4kgCGS99R36b7YT0qYhLd6ejCCTIcgEnBYbqshQXBY722cuofSQVAaDPp3FjtkfYCk08J+cTzDlluCskmahcn7azcE3vyM4NpwRa59FGRKMQnBKR5698w4h99wLMhnWtT9RtfxzgkdeD4Bl9Q+EzZqNql9/XIV6qWJdLsom3Q9A+PwnUKamIgsPx11ehunjj9BM8FkSUA+zn3yR3fsPYTBUEh0VwYN3j+fmkUPrDb+67Xzv5w4v3EWcx672+thVr+Vz2Oexq6Z3D6WFx65sHrva9/BSUiYOIeXOQbidLtxWO5OfeJEDuw/z+Iuz6ZPWE6vZyrxpz3Lk4DEAFq94g/kznqe4UJpJ+2TV+yx96xO2/1p98xcbF8MLbz9B45SGxMbFIBjNlH60mqJ3vyZqnLTEqmz5OgASn5lEaH/pmMXc2QuxHD6NulVjkl+bDjIZgkyG4cftFL0lPRgUlAoavPwQ2gFdUGjU2PRlHL73TYweu+qw/BGOzVyMvbAcdSOdz/GT2Rzx2BVAwq39iU5LJeP+hZIdp6fS8qV7UMVF4Kow4iqvxJGrx7R5D4YvpDYQ9+SDhPSTjsoseOQNrJ42oGqdQsLz0xCUChzn9OQ/8gaxM/5D6OAeyMNDseWV4qowkfXoEpLnjCVz1ns4CstRNYyrdfzkm179Gi+4l/ABHXFbbGTOeIeqQ9IekNarnkMZGYoiJhzR7sRRbuT09PcQVEpSnp2AOiUBa7ae0w8vwmkw0XKx5/jJjGxOTl6IaHeSNOVGdKP743Y4cVvtZD/zGcZdxwnt1or2PzyH2+4At4gtt5isJz/GsGkfrT9/jNMPV+vd4vyxlhlZnJoiyW366gNEX9cDW640sy26XJx9+SuaPDMBVVIMbpMZZ3EZVTsPYT+TS/kX0ibQ+KcekMrVaiN/7htYD5/2lmviCw8hKBXYz+nJn/Mm7koToUN6Ev/EJBQ6ac22y2Jn103Pen1L5+VzyZi5xOtbOix+yGsDBye/g2h3EtGlOe3efhDR5cZ0Mo+MGYtxVlTRoZec6JefQdBoUMTG4LZYMK1YifHj5Wg9fUvV+b7lk0XIPH2L22Kh8LYJiFVmlM2bEvnYLFAocOUX4DJUIOvQBZfFxpFpi3x84FyO+ujZ3qNn5fnjJ+1Owrs0p+3bD4JHzyMePQH6/P46wQ3jQC7DUVJB5oIv0K/YRLvlj3Ji5iJvG2izeIb3CNZjk9+SbGDiMBLvHILoktr96Sc+oXKPtA+l1Q/PoYoMITgmDJfdic1gYsvMJZR4/OrQT2exbfYHmAsN3J0t+VWHx69mrd3N/je/o8ODI2h+cx/cThdOqx3lxwul4ye7dyd08tQr5lvLPv+F4D7diJ4jnZpm/O5nKj5YQehoabhhXLkGeXQkiV+8i0yrQfSMA3JH3UNQiyYkfvwm9pOZ3qMey9/+EMv2XTQ5uMHfwqz/KRXj0//2wWr4Zxv/9nLwx/+vA/m1SLPnOZ7PS4DTwKdARyAWOATMvcBAfhbQDxgIjAIikZbEpImieFoQBA3QADgLnAQGiqJ4RhCEL4DQCwzk2wPfAteKonjSs35/uiiKr1+oTPJ6pl3xivQ3kP+r+A7kryQOv4t2/xrtwssuHugyOVsedsVlAuQplBcPdJkMb3HuissEiFr50RWX6TuQv5I85j598UCXyVeauIsHukyKLZorLhMgMezyTxy6GJVV6isuE8DpvvIPmAXh6vSPERrrxQNdJmdNl3/K08Vo27jo4oH+BEez698s/mcJov7T2P4Kp5Sqiwe6TK5vfnV8q7ks6KrI/ScM5A3jrvwY53KJWL7pby8Hf/xbXwgV7Fm+cp51oihe8hGUwO/Ai0gz8VuBVaIougVB2A8cQTolZscF4gMgiuLrgiCEA58B44C7gC8EQTjf8ud7BuP3AT8KglACbEe6mahP5iFBEKZ75GiQtr/8eBl5CxAgQIAAAQIECPD/Af/Kgbwoin6PgBRFsbHP54+RNrvW/m2z589f/LsuJtfzfYDP5yd9ftoE1DmYVhTFdUhr5S8JURTXAH/prPsAAQIECBAgQID/E/yDz3H/u/m3bnYNECBAgAABAgQIEOD/a/6VM/L/FxAEYQIwrdblHaIoTvYXPkCAAAECBAgQIEAAXwID+b8JURQ/Aq78rr8AAQIECBAgQID/S/yDz3H/uwksrQkQIECAAAECBAgQ4F9IYEY+QIAAAQIECBAgwD8WMbDZtV4CM/IBAgQIECBAgAABAvwLCczI/x+h4NyVf9FQdrb2istUcnXuqgekXvz11ZdLyZkrn/+rdec8ILbwisvceSzpissEcF2FlzeNzHjuissEWNNlzhWXGZ1w5V80Vph5dV4Ida7yyvuVmCDLFZcJ4BKv/LtaosPNV1wmXJ0Xw6VEGa64zKvx4ia4OjZgcVz5l+IB9NVc+faacTT+issE0ModV0Vuk6siNcCVIjCQDxAgQIAAAQIECPDPJbDZtV4CS2sCBAgQIECAAAECBPgXEpiRDxAgQIAAAQIECPCPJbDZtX4CM/IBAgQIECBAgAABAvwLCQzkAwQIECBAgAABAgT4FxJYWhMgQIAAAQIECBDgn0tgs2u9BGbkAwQIECBAgAABAgT4FxKYkQ8QIECAAAECBAjwj0UMzMjXS2Ag/+9kGLAQkAMfAC/WDpD8zD2Ep3XGbbGRPeMtzBmZAIQN6EjDp+8BuYySLzagf/dbABJn3U7E0G7gFnGUVJA9cyHk20m+qRepz4xHERIMoogsSMEvQ+ZTcSSnRnpNJwym+b3DCGkSzw/X3I+9zOT9LbZnazo8Mx5BKcdRZmTHqGcBaPfcf9Clp+Ky2Nk/bREVh7PrZLTJxCGkeOSubXM/9jIjAPFDO9Nq7mhwuxFdbvjsdQR1MNpJUxFkMqzrfsSyckUNWaqBgwgefTsAosWC6Z3XcWWdqQ4gkxHx1hJEhwMhJIQwlwLDyp8pW7Kyjl66+fcT0r8rbouNgkdex3b0DEFNkkh88xFvGGVyAiULP6P8k++91zr/8DQR3VthOpHL0cnvYDycVUe2umEsbRdPQxkRgvFwFkcmv4PocNHwwZHE39wHAEEhR9s8ia1t7sFpqPLqn/j9RyiiI3GVlGFatZaKj76qIVvZOJnop2ehat2M8nc+ovLT/0rygpTEf/g6glIJCjnmX7bBk2u98a5kXR18/DNKd52g/XP/Id4jc++0RRj8yEyZOIRmHplrfGTG9GpNz48fpupskZT14DDclgu/EGf+gtfZumMXUZERfPf5oguGvRC3PzmRdgM7YrfYWTbrHc4eqVuHE1+dTMvubbAYpRcKLZv1LueO1syfqkdXwqdPQZDLqPrhJ0yffVHjd0WjZCIfm4OyZXMqF3+IacXX0vWGyUQ++3h1uKQENIfOooqPxGWxceSh9+u1q/Yeu6o8nEWGx64aPTiShFp2tdljVw3vv5YGtw+UflPKEeRyXGYrGQ+9j9FPfQV70lBEaDEezubw5HeI6tOWVgsmoIqLwGWy4CwoJmfW21hPnPXGa/D0vYSldUa02MieuRCLj79q8NS9IJdR+sUGCt/7RkqnTRMavvAAgkqJ6HJjyykguG0zZEEK3BY7bqsdAE2bRhwcMhvzkWxUyTpaLJqBIiKUqsOZnJr6FqLDScxNfUmaPAoAV5WFzEeWYD6aQ8TAVJq+PRV5qBZXpYmyJV9T/ul3NfKre2wS2n5dEa02Ch59DdtRyZ9E3nkj4bcMA1HEdiob/aOvI9odRE8ZR/joYSQWS/7RkltCSMsGf6reFKHBtH1vKtqWDVDHR+E2mSj/eBXlH3xdR0bsvAe8eurnvYbt6GkAIu4cVa3nyWwK572GaJdeKhQx7noGTvkPco0KZ6WFvbctuOQ6P69fu/emoE6KQZDLyH5/DbZCA61fuht1fCROQyXOkgpUDePJf20FxctWX9AWGr46lfD0LjhLKzg26CFv+gmzbidiSHfkukjkGjW2c8WcnLyQKj9lqWqoo+WiGSgiQqg6nMnJKW8jOpxEDe1Kw7m3Ibrd4HKT+fhHyLVqmr50L0HxUbgMlbhKDXV8+uX2A4lvPkJQkyRSdLEowjSILjd7RzxxRfqBlNmjaXDnYKk95BXjMlnIGF7zhXaqZB3N35+JPCIEc0YWp6cuRHQ4UTdLounrU9C2S+HcSysoWFTdZ8XfOwLgCCACh4EJgLWOwgH+NgJLa/59yIF3geFAG2Cs57+X8LTOqJskkNHnAXLmvkfDFyZJP8hkNHzufk6Of4YjA6cSdUNf1M0bAKBftIqjg6dzdOgMKjbuJmH6rQA4DFWUH8xiVeO72DNzKW6Hq84gHqB090m2jnmBqnPFNa4rwzR0fHECO+56jQ0D5rL73oUA6NJT0abEs7HnTA7O+oAOL030m9myXSf4bcwCzLXkFm/LYHPaI2weNI/90xcTMm02IZOnU/n4HMrvvxPVgHTkDRvViOPSF1Ax5yEMD07E/MWnhDw0q8bv6htuwXk2B0XjFCofn0PmtZMIG9GfoKbJNcJp+3chqHESmYPvQf/4W8Q/PQUAe1Ye2TdMlf5GTUO0WDFu+N0bL+zGNDTNErGcK+bUk5/S8uW7/ea52fxxnFv8E7/3nI7DUEXi7WkAnH1vNbvS57IrfS5nnl9B+e9HqwfxQNi4m1BER2LLOE7eTfegHTYQZUrDmmVQYaTs5Xep8AzgzyPaHejvnU3+rZPIv3USwb26ENmp2VWpq06v3UtceiohKfGs7zmTfbM+ILUemaW7TrB9zII6dgVQ8sdxNg2ax6ZB8y46iAe48drBLHr9r70Btt2AjsQ1SeDRAVP5ZN4i/vP8ffWG/XrBZzx17WyeunZ2nUE8MhkRD0+jdOYjFI6dgGZwGorGNe3VXWnE8MY73gH8eZxnz1F8533S34RJiC43otvNjh7TODZrKa3rsavm88eRs/gndvScjtNQRZLHrnLeW83O9LnsTJ/LKR+7UsVH0uieYewcOo+TzyxHERJM5uvfcnTWUtq8fE89adxOzuIf2dFzBg6DiaRx6bR+cSLlO46Q/f4a7CWVFLzxJQ2eqo4fNrAzqiYJHO07iZy579JwwQPeMkp+7n5O/+dpjqVNIfKGvqibS20x6bE7KXjjS44Pm0HF+l2E9Ullf68pnLjvNZyVVRwcPItTU9/Cdq4Y8xGp7BvNH0/+kjXs7z0FZ4UJ3dh0AGxni8i46XEOps8k983/0vSVSSCT0fSVB3BVmjjVayzO0nJChvdD2SjRq7e2X1eUjRLJGno3+ifeIu5JyQ8odNFEjL+BnFseIvv6B0AmI/S6/t545Z98x870uZxe8AUypfxP11uDiUMxncwFUWTXyMcR5ArCRgwkqGnNNq/t15WgRolkD5tI4ZML0T1RrWfkHTdw9pap5Fw/CUEmI/TaAQAEd2tP2M1Dqdh/hl+Sx5Mx9d1Lr3OPfskTh2I6kcfvaXPZfdMztHxqPK1fnMje0c9xsPVtOEsryZ72Bm6LjYp1Oy9sC0DZyo2cHv90nfQLF60i74VPqTqYSc5zy7GcyqPpS/7bZeP5d5C/eA37ek3FaagizqOrYdthDqQ9zMFBszk1/T2avfYAKS/cQ8YtT3Oy4824yirIn/VqDZ/+Z/qB/OkvUvz6J1QeOMO5D9ahX7n1ivQD4Z2bE9wkHru+jBMTX8RVUVVnEA/Q8LHxFCxdzcE+U3AaqtuAs9xE9uPLagzgAZTxUcTffR1AF6At0vjjNr8KB/jb+NcN5AVBcAmCcMDn75GLx/LGHSAIwpq/mP5mQRC6/Mm4F01fEIQbBUE4JAjCcUEQMgRBuKVWkG7AaSATsANfAjf4BogYnXlJbwAAIABJREFU0o3S/24GoGrfSRRhWpS6SLSpzbFlF2A/W4jocFL2/XYihnQHwG2qfmW2LFgNonRma+KwzuSs3AZAeKsGOM1W1LqIOnobMnIw55bUuZ48qhd5P+3GklcKgL2kEoCEoZ0597Ukt3zfaZRhGlR+5FZk5GA5V1euy2zzfpZr1KAMwpWfh1tfAE4nti2bCOrRp0Yc57EjiCZpJsx5/AiymNjqPMfEEtStB85jGYhWiyTH4aTyx62EDOpZQ05Ieg8qVm0EwHrwBLJQLfLYyBphND07YD+rx5lf5L0WM3UcZ99bA6KI8WAmijAtQX7yHNnnGopWSx1bwddbiB3etU6YuFG9KVy1o7oMdDFohw3AkXUW0WoDp5OqnzejGdCrRjx3uQH7kZPgdNaRKVqkSRZBoQCFwmsDV7quRFEkcWhnztaS6c+uKjJyMPuR+WfoktqO8LDQvySj45Cu/PbtZgAy959CE6ohPLau3hcjqE0rnLl5uPIlezX/sgl1v7p15Th2AtHpqleOqksncDjI++wXACr2nqrXrqJ87Cq/HruKH9UbvY9dCXI5MnUQuuFdsRUZsOnLqdh7GkWYpt40Clf/4UljK4lj+mLO0qOKj6Rsy2H03/1GcOvGqJJ1KGLCAQgf0o2yb34FwLz/JPIwLQqvv9J7/VX5D9sIH9JNSkgEeagGgJAe12A9fQ4A075THn8XQcyoPpR8t92rW3iftpSukQZURV9vJmq4JMu45wSuCumG2Lj3JEEJ0YR0bIbTYMSyJwPRVIXxxy2IZguhg6rrKCS9B5Xfn/cDx5GHhXj9gCCXI6iDQC5DFqzCWVRWp6xih3WlYOXWP19vImgax2POKsRRbsJVYaTyp81o02r6K21aTz96Rkk/+ugpBKtwFkl+OuK2ETgLS8j/agsAJZsOXnKd64Z7ukdRRBGiBkChVeOy2DBn6bHkFHnrU3fvDdhy9NjzpBv1+mwBwPTHUVwGE7VxmyyED+lG0debkWlU2EsMKMI0KP3oGt67LSW+NjBMsgG3uXqCWa5RIVMpsWbpsZ0t8vYDURNurOHT/2w/EJLeA/3KrcRd34Ocd1dfkX4gdlhX9B5bqjp0Bnm41OfXJqxPO28bKF75K5Ge/DtLK6g6eNqvrxEUcoBgpBUcGiC/TqD/Be5/wN8/lH/dQB6wiKKY6vNXZ1nJ1UIQBPlVlt8BeBW4QRTFVsBI4CVBEDr7BEsCzvl8z/Vc86KMj8KeXz34sReUooyPIighCnuBz3V9KUEJUdWC54yj/a4PiB7Vj/xXpcf8wfFRmPMl597g+h6YMgsJTqjrIOojtGk8ynAt/b95jPSfnyN5dF8A1AmRWPKrOzdLQdllyQVIGN6FtG2v0uPz2djWrcFdXO0s3SXFyKJj6o2rHnodjj1/eL9r759C1bJFCKFhiHa797pTX4IyLrpGXGVcDE599Qyxs7AEZVzNtMKu60/lj5u930PSugMChl3HvddsBaWofMofQBkVirPSLC0XAmz5ZXXCyIKDiB6YStGaav2jZj9A1fqtuMqqZ6adhSXIdfWXQR1kMhK/WkTyppVYd+6jfL+0TOBK19W+GUv8ylRfpsyozs1J2/gCvVbMAbnysuL+WSLjoinztAeAMn0ZkfHRfsPePGssT699jdsevwtFUM1VjLLYGFxF1fbqKipBHhtbW8RFCR48ELehAmtetU7WglLUF7Era35ZnTCy4CBiBqZS6LErm76c7PfX0G/fuyTe2g97qZHSLYc8adSN7y8NVXwk1vxSjEfPoruuG9b8MjRtmxKUpEOZINlmUHx0LX9VQlB8NMpa1x0FpSg9ZZ371AckPXYXbf9YRkiX1hR98IM3nK2glKCEaGKu703JKulmUREVirOiCjy62QtKUcXX1B8gbmw6hk37UcVHYcnSo+naFllEKM7SClQtGqNIqK4jRVw0Th9/6tCXoIiLwVlUStmH39B006c03bYCt9GMecc+b7jIcSPp8evLxKSn4qgw/+l6O7dsHZpmiUT2aEXPza9S/MIiv/5KEReNw9df6YtR6KJxFpVS/tF/Sdn4GSlbV+A2VmH+TdJT2TiJoMZJpMy4iS6rniAsNeWS6/x8mLPLfkbbIon+h96n5+ZXyF+5DatP23EUlKLt1JLy77d6r9VnCxcjtHd7mjw7gdib+3L25a+wFZShSqhVDlGhOCurbUCyk+r8RA3vRsdtC2n9+aMUfv5LDT2c+hKCU1vX8Ol/ph84H08REYq9uAJLlv6K9AOqhEiseaWIokjrL55EqYskbsLwOvl31WoDFytbh76Mgve/BzgLFAAVwPoLRgrwP+ffOJD3iyAI2YIgLBAE4XdBEPYIgtBJEISfBUE4IwjCJJ+gYYIgrBIE4aggCIsEQZB54r/viXdEEISna8l9QhCE7cBon+syQRA+EQThOc/3IZ609wmCsFIQhBDP9WGe2fXtwE0XycYsYIEoilkAnv8LgId9s+onXs1Xngl+goj+o4o+MfNeXs6hbvdQumorugnX1kgtqmNTXBY7LrMVUbz0N6wJcjmR7Zuw/Y5X2Tb2RVrMGIU2Jd6vjpchFoCCtXvY1HcWuya8TlD/tEuOp2zfEdWQ66j6cLH0vVtP3AYDrtMn8Vu8tRXzG8QnjFJBSHp3jGul2UBBrSL6gduwZ+f9Kdm1w8QM6Yxh9wnvsprowZ1wlRtw5usvLv9CuN3k3zqJ3KFjCWrbktBWDTw6Xdm6ajN3NEK9NnppGA5ls67LQ2xKf5Qzy9ajCIu/PIX+LBerew/fvLSceenTePaGuWgjQhg+6cZaci7Bzi6GQoG6Ty9cpXVne+vodAl6x9ayK0W4Ft2wzmzrOpXSbRnI1UrvWvpLTeN8nrLe+h5luJZmj45B3aoR5iOZcH72z699iReUFzN+OLlPLyOj+91YT50j7sGaDy41LZKlGeAT5y6chg9hvdqiuz2dnOc/A0HAVVFF2dKVJC9bQNTdN+Mqr6w1Y+m/DmVhIYSk9yBz0ATO9BuHEKwibKS0z8DwxY9kDp7IzrS5uO1OkicOvaBOF6q36IEdsJ4rQv/tDnamzUE3/0FkqiA/Mi6gZ1pPsgbfRWb/cciC1YSOlPyooJAjKBQcnbWUk88sp8PS6ZetX8zADhgzctjS/gF+T5tL0u0Dpad955HLUSXHUf5j9ROgS6knf9gy8zl+96sUf7ONhInD6tH1wm2ubO0u9vedxvEJLxNzY++a4eQylA3ivD5dkudPXP39gG+8qH5tKVz1m1896pN94X5AinDkhnkcHjoL85Fsokf0IrS776rby/c58nAtkUO7ATQBEgEtcMcFIwX4n/Nv3OwaLAjCAZ/vL4iieH5H3zlRFHsKgvAG8DHQG1AjbdQ4v7utG9Ka8hxgHdLg+r/AY6Iolnlm3TcKgtBeFMVDnjhWURT7AHhuChTAciBDFMXnBUGIAeYDg0RRrBIEYS4wUxCEl4GlQBrScpiaOw/rcg3SjLwve4CpPt9zgWSPLvctWLDgCQBjVTY3aRsD0kxHUGL1zEBQQjSOwjIEpYKgBJ/r8dE49HUHAYJCRvwDNzFocE/KDmaiSYwmqmNTzn33G41G98Wqv/h65PNYCsqwlxlxWWw0vrUfQZFaen31KEVbDhOcWD3DEJwQhVVffslyfSndeRxZWDhiQvX6VVlMLO7Suksy5I1TCJk+m4rH5yAapWU+yjZtCerRi6Cu3RGCNQjaEEJmP0bJpDdRxMfgqPVY3KEvQRHvOzMX430kDRDSrwu2I2dwlRqIGDeCyPEjCWqYiGi10f6jZigiQui24UXcDie2Wnl2lBpRhGkQ5DJElxtVYlSdMHE39qqxrCaiW0s0/XuiGdgbeXgYostFzPNzcZzJwVVcyuXiNlYhWqz0/PIR7CWVlB/IvCJ11WTCYBqNG0hIiyTyvt/5l2Q6fZaCFW70uANBdlWONkgbP4x+nrWkWQfPEJVYPYsVFR+FobBuG6ooltqI0+5k+8pfGXbv9TV+dxcVI9fpvN/luhhcJZe+hEh78w2E3D4GQanEVaBHnVStkzoh+qJ2pfZjV/E39qqxrKb5k3cQck0jOn/9GJUHzmBxuIjo2oKCb7ajTqgb328ahQbUidG4TBaOTF9Ek4duQCN3EDNuKLZzhYBn1jUxhvO7PYISYnAUliFTKmr4MaXHjwHEjB1MSPc2xE8bg/ngaaJGVa9BVyVEE9q1ZY1lNc7SShThWpDLwOUmKCEae2G1/prWjWj22gMcHfccznKTNFubFEPRs+up+GY9UfeNQdO7E46c6ptxZ2EJCh9/qoyX/ICmZyqO3EJc5RUAmDb8hrpjGypX/0ro0D7SZleXnIoDZ4js1upP11vibQMoXr8X3bXdsGQX4sjVo2qVgkNf046c+hKU8bHe3YmK+FicxWVoenbEkVetp/GXHYSPGU7khJtRJOiw7DmMOika/arfEN0iwQ1iLlO//mS9LT0psWQXYssvQdsswRs3tHc77AUlOEsqvNfqswV/xNx5LTFjBwNgPngaVWI0Jau20frzecgUcuy1+jZnaSWKsGobUCVEY/fjcyp3HkMRFYqqUfXkgLZHB5z6Elyl1X3f5fYDEWOkmzZrxmmihvbh5LyPAMle/0w/0HjajSjCtHTb+BKVB86gTorGtDcDAGVsOOUbdhPSsTnGP45K+S+rRF6nDfgv2/OE923vaaftzj96+BboBXx+wYhXgcCpNfXzb5yRr720xndwfP756mHgD1EUjaIoFgNWQRDOL0LbJYpipiiKLuAL4PwU0xhBEPYB+5EG1L63srUH4IvxDOI933t4wu/w3GTcCTQCWgFZoiieEqVb9YsZv0Ddecnat9G7geZAE1EUP3700UdLH3300aHnB/EAhvW7iL5lAADaTi1wGatwFJVTdfAU6iYJBCXrEJQKom7og2HDLgBUTaodrKvSjGHjHn4ZPI/8tXtoNLovSSO6Yzydj8NowVp06QP5/J/3EtO9pXRqwVdbsBYa2Dn+FfTr9pA8RlpmE9mpGQ6jBdtlyNU2jvN+Dm/XGEQRuS4eWVw8KBSo+qdh37mjRhxZrI6wx5/F+MrzuPNyvdfNHy+lfPxoyu+6DeOCp8DhwPzpMlAqCLuuH6aNO2vIMW36g/BR0sBO3aElblMVruJqJxs2oj+Va6S1pYbla8gadj8n2owkb+ZLVB7MxJZfyrFZS3AYTNj95Ll8x1F0I3sAkDCmP8Xr9nh/k4cGE9mzTY1rZ57/gtyht5M77HZcFZXYDh+j5MnX0A4dgHnL73Xk+0MWGY4sVAuAoApCFqLl4OxlbB4074rVVdZHG9g/Ywn2UiO53/9Ow1oyL8euVLHh3s+RHZsCwlXz9Js+W+fdtLp//S563TQAgJSOzTEbzd5Buy++6+Y7DelK3smzNX63HzuOIjkJeYJkr5pBaVi3XVpdAVR98z32I8eoeOMdLFu3kzC6n5Ru5+Y4jeaL2lViLbtSeOyqyOda3ucbsRca2DXiCYrW7kE3rAumU3mEd25WbxplO44SN7K7J41+FPx3G5qUeELaNESmCSL+xl4ISgWmP4569+VUbNhF1M3SjLWmo+SvnB5/pWpc7a8ir+9Lhcdf2XOLyH1qGceHzcB2TlpDDxDSScp/ZHpnSr6r2f4rdmQQPUJaP64bM4DydZKsoKQYWi6bzampb2HNLADAdOA0wU0SUF3TTPIDN6SjTNRR+eMWrzzTpp2E3XDeD7TCZZT8gLOgmOAOrRDUKilPPVOxZ0pPBowbdpAzago70+fiqrIgKGR/ut6seSWo4iLRpMQTlppCUJMGBHdtR9WvNf1V1a819XQbq3AVl+EsKELtq2ePVEzrtnH2psmUvPoB7ioziaP7oUlJQK5R1+uvatd5tX6lRPdtK5VxbDhBukhUugiCG8YiKBWED+pK6cpNNeuoHlvwR8knP3F82AyyJr2M4eed6MYMIGpoVxwlFTiNZhx+dK347QgxPjZQ9vNuqVwa+wza2zUBUUSdHIuqoU6aWU/rTsW3G2vIutx+4PwGWEd+EaJLxFZQRtgl1ru/fkCdGMPOfg+zK30uxWt3k3DbAGRaNSGdWuAyWQjt0hLz8Zp+p9KnDcSOHki5J//1Yc8rIaRTC5DWxgtAOnDsgpEC/M8RLmeZxD8BQRBMoiiG+LmeDXQRRbFEEIS7PJ+n+P6GtOv6aVEU+3uuTwTaAW8BG4CuoiiWC4LwMbBZFMWPfeV64mxGMuTmwAhRFK2CIIwEbhdFcWwtnVKBhT7pXQ/cJ4riiHry9jmwSRTFD32uTQR6iaLoe2TAtcCbSDvIPwSez3nkfRGg+POfAWj43H2EDeiE22oje+ZbmA9J653D0zqT/NREkMkp/eoXCt6WTi9pumQu6pRERFHEnltMzqPvcypP2qTY88MZxKd1wJSpZ8+MxZQflI7K6v35bPY+vBRroYFmdw+lxYMjUOvCsZVUot94gL2zPgCgxQPX0fi2/ohuN2eX/0rm0nUAtH/hLnQDO+Cy2Ng/fTEGj9wey+dwYOYSrIUGUu4eSrPJI1DpIrCXVFK48QAHHl5KsykjSR7dF9HhxGV1IPtyIYJGQ8h9U0Euw7r+Jyxffo76Wmkm1PrTD4RMm01Q7/64i6QlKKLLRcW0+2vUgbJdKpqJ9yMLCcXlVlDx3/WULvqKiNukpUaGL38CIO7JB9H2lY731D/6BtaMU1J9qVU02/IJZ9In4jaZqU3II9NIHD8IS1YBR6a8i/GgdLRah+WPcGzmYuyF5agb6XyOHcvmyOS3Ee3SQCXh1v5Ep6WScf/CGnKbJkgzK2ETxhBx7x24Sssxff8zFR+sIPQWydyM/12DPDqShBXvItNqQBRxmy3k3XQPisQ4Yp6dgyCTgUygav1Wtj7zs1f+layrw8+soHTXCTq8cBdxHpl7fWT2Wj6HfR6ZTe8eSguPTJtH5r6Hl5IycQgpdw7C7XThttoJa6pBdNq4ELOffJHd+w9hMFQSHRXBg3eP5+aRQy8Y5/4udU9+uOOZe2jbPxW7xcaHs98j+7DUtqZ/NI+P576Poaic2SueJDQqDASBc0ez+fSxJdg8G+qe9dSVqmd3IqY/CDI5VWvWYvpkOZpRIwEwr1qNLCoS3UeLELQacIuIFguFYycgms0IKhXx33+J/uY7EKuqcN87l+i0Drgsdo5Oe59Kj111XP4IR2cuxlZYTnAjHe187OpwLbuKSUvlcC27Spk9mvgbeiK63NJyC6Ucl9nGkWmLfNKYy9GZS7xptF/8kOeoROkowui+bWn9yj2odBE4K6ow7z6KafdR3BY7JZ9LviD5ufsJG9ARt8VGzsNvYz4kHY8YNrAzDZ66G0Euo/Srjejflo6C1XZtTfJT94BCjmhzYMsrQtO2GS6LjYIP1xJ3WxqHRzxK688f4/TD7+EoLEfVMM5z/GQIVRlZnJqyENHupOmrDxB9XQ9sudKko+hycWjYXCLSOtFq2SwEuQynwYh+9iveU2sqvpL8gO7xB9H27YJotVIw7w1sHj8QPfUOQof3A6cL67EzFM5fiOhwEP/SLNT/j73zDq+i6Br4b25NbzflJqElVJHeewkdLC/4WRGkgwLSi4iNIqgoigVQERUVFV6xUSxIESyI1IC0JBBI7/Xm1v3+2EuSm9wA0fAKur/nyZO7uzNnZs7MnD07e3b3lmgsNhUlFzOw5BQS2KnJn+o3fVggt656GM/ocDyMgTgKCsnd8AXZaz/G/97BFeo5Ga9ubZ2vn3wJ8wlnPac8iO+gnkh2O+Y/4khb+DKS1QpaDcYlM/Ho3h6NlwclqTkcG//yNfd5+frpwwIQQpCw6gusuYU0XvwQGq1AExLA8bajCbpTvgi92lio99osfDs1QxPkhzUzl5QXN5L1yfdErZ2HR/1INCFBqDy0mJOyODtlFYVH5Xl5y4cLiJu5Gktajvz6ybWXx8B5zkyWx0DklP8QendPHFYbjhIL5xdtQOPrSdSi0ah1KjTBAZzr+iB+t/UC/tp5IHz5DBzGCPQRBhzOfq+J80DT1yYTdmdncEhYUrNJ/3gnyav+S+MNjxM/u2wONFw9s3QOnJv6MpLFhjYkgGbbX0Dt6wkOCXtRCcd6PYq90ESt2fdSa+a9pwEb8kLnOODKxvY6kDmg59/urAZ/s8dd0NPfzr/Rkd9OWWjNduBN5LCX94HWQAhwDJh3BUd+NtAD6A0MBQKB34EYSZLOCSG8gFrID4icAXpLkhQnhNgI+F7BkW8FbAL6SZJ0XghRD/kuw92SJJ2+kl4O1vpPjXfkebt3TYtEe53GW9fWbuLP/yKZcTXf/qS8v/bWlKq47MjXJEdTq//w5bVQ9TtY/jy3x/6110pWhTtH/q+y+Dr01cn469NXkttg3b9GsM509UR/ghJbzUeKGvwrX4zXBIk5fjUus25Q3tUTVZPz2dV/I9O1cD3GgMl6fR54N/jV/Bi4XucBb7X1usjtlPzZ3+7AKo581fwTYuR3SJJ0za+gBH5G/oBSc2AvsEWSJIcQ4jByLH08sP8K+QGQJOklIYQ/sAEYDowCNgoh9M4kCyVJOiOEmABsFUJkAvuQLyaqknnEGV//lVNOPeSLgCs68QoKCgoKCgoKCv8+bjpHXpIkt6+AlCSpXrnf7yI/7Frx2G7nn7v8o64m17ndq9zvp8od+gGo9KJXSZJ2IMfKXxOSJH2G/EAJQojlwBIhxABJkixXzqmgoKCgoKCg8M9Dedi1am46R/7fRDXvNCgoKCgoKCgoKPyLUBz5vwEhxGhgWoXd+yVJmvx31EdBQUFBQUFB4UZFWZGvGsWR/xuQJGk9sP7vroeCgoKCgoKCgsLNy834HnkFBQUFBQUFBQWFfz3KiryCgoKCgoKCgsINixJaUzXKiryCgoKCgoKCgoLCTYiyIv8P4Xp8vOl6fH2hY6Pk6yAV4o4aalxmukN/9UTVpJkxs8ZlAtgsNX9Nrr9OSyAzpPgal/n1dfhwE8Dag8/XuMytzRbWuMwWoddnXGVl17xdcUjX55sqvh41/4berDyvGpcJEKCp+boWF+tqXKa4LmcB8PKs+Q8XXQ+ZAN7+Nf8R04L86/OhLav9hvxeUc1wnezGPwFlRV5BQUFBQUFBQUHhJkRx5BUUFBQUFBQUFBRuQpTQGgUFBQUFBQUFhRsW5WHXqlFW5BUUFBQUFBQUFBRuQpQVeQUFBQUFBQUFhRsWyaE87FoVyoq8goKCgoKCgoKCwk2I4sgrKCgoKCgoKCgo3IQooTUKCgoKCgoKCgo3LMrDrlWjOPL/MFouHkl4n5bYTBYOTl9L7vHzldLUH92PhuMH4hNl5MtbJ2LJLgQgpPMtdHl3JkWJGQBc2vYbf6zcQqtyMn+7gsxGTplflJN5WW6rRSMQWjWepkyyH52OrkMH/B6dAio1pq1bKfrwIxd56jp18J8/D22jhhS8vY7ijz8pPeb1f3fhedttIMD09VbU4eG0bNsFh8lM3IzXKD5e+YND+tqhNFg9E02AD0WxCcRNfQXJasOjQSTRL03Bu3k0F5/7iNQ1X5Tm6fPbKmyFJiS7A4+wQGyFJuwmC0emrSbPjQ7qjelP9PhBeEcZ+abpBCzZBQBEDutKgyl3AKCzFJC1dBXq4CCC5j2CUKko2LKdvHc+cZGlrVeb4EWz0d/SgOxX15P//mZZL2EhhCydi9oQBJKDgs3byP9oS6W6AHh1a0fwY5NArSZ/83Zy3/7UtYyo2oQtnYm+aQOyXnmP3PWb3cqpyC1LHyK4T2scJjPHH11Nvhtd1BkzgLoTZF3svGU8VqcuyrNg6Sx69O1CiamEBVMXcfL46UppNnz5Jt4+8kd5DMGBHDt8kqkPzcHH15vn31hEeC0jGrWaveu2s2/Trkr5H3hqDM17t8ZisrBu9msknkiolGbMisk07tgUU0ExAOtmv87Fk5XbVBULn32JvfsPEBQYwOcfrLnmfADNl4wkrE8r7CYLh6atcTuuosb0p75zbm1rOrF0XF0moFU0UVs3kD7nWRzFJgzzHkaoVeR/toO8dRXGVVRtQhbPksfVqnfJe6+sz0MWzcR7YG+EWoXl/EUyFq6A/SmV6qOrHUr9N2ahCfSh+Hg88Y/KcwmgzqKx+Me0xWEykzDjVYpj4zHc1YO6zz2CUKux5xWS8tpm0td9XSovdPQQImbdj8pLj72gmLPDn6E4Vp7Dfr1aU+eZcaBWkbnxO1Jf/wyAiNkPEDCgAzgkJLsDtZ8XAomiX2PxbBqN0GqQrDZSlq2n6OdjhD81Ad9ebXGUmLk0+xVKTsSVNUilosGXL2FNzebCuEUA1H51LvroSDRhBtR+3mB3cPyOBRQdrzx+9LVDabRmBpoAX4qOx3N26iokq43gYd2JnDwUAI3BF2wObHlFnJv+KoFdmhJyf18kCUynLpAw81W0oYHV0qvQa2ny36VognzRhRtwFJeQvuYz0lf/F4DgUUMIHjkEye5AMltQ+3rjMJlJnP0ypth4tOHB1Fk5HW1IIJJDIuujb8hc/xUAHrfUo/azj9C0XiRqDy2mxAxip7xOgZv2e9QJocXaaWgDfMg/nkDs5NeQrHY0vp40e2MqHpHBCLWKC6u/xpyWQ+Mlo9BqIXfzt/gN6o4tLYtLE58uGw8LJ+LTsz0Ok5mU+S9hPhmHLiqSiJfnl43j2uFkvrKBnPe+wHdgN0LmjUUbEYotNZOcD78m+81NlepZXbnBU4fjf88ApLxchJcXQqtFslgo+mIbBe997CJbU7c2QU/ORdekAXmr36Hgg7Lyw7/4EEdxMTgcYLOT9tAjLnn/ig0wDmjLLfPuBocDfVggWG1YcwurtMuedUJouXYa2gBv8o+f59jlvvL3pvnLE/GqF4bdbCV2+hoKT10qn1UNHASSgNsqCVb4W1FCa/5BGGNa4httZEeXWRyas442y0e7TZf12xn23rOMoosZlY5l/noQrBekAAAgAElEQVSa7/st4Pt+C/hj5RaMMS3xiTayvcssfr+KzD1uZGr9vGizfDT7Rr3It73mkfvk06BS4TdjGjlz5pE58iE8+sSgrlvXJZ+Un0/+qlUUfezqiGiiovC87TayJk4ia8w4PAcOQNugAUe7TiZh7hqilk1wW7/aj48g5a2vONptCrbcQkLu7wOALaeQC0+sI6WcA1+en+9awqlln5B3PIEfOs/g6Oy3aP7cWLdpsw+c4ed7llJcQQfFien8NHQRe2LmkfvmhxienI5hwVTSHlnApaHj8B7YG210HZc89vwCsp573cXRkg/YyV6xlqShY0l+8FH87rujUl4AVCpCFk4meeJCEm8fj+/g3mjru6Zz5OWT8exqctb/12173BHcpxVeUeH82Gk6sbPfounz49ymyzlwmoN3L8WUWHmMAfTo04W60bUZ2PEunpq1jCefn+c23Yg7JjAs5kGGxTzIkYPH+X6r7Kw/MOZu4s4kMLT3cEYOncQ9j49ErXVdl2jeqzVhUeE81msq7y1Yw8il7scGwKfPbuDpwXN4evCcajnxAP8Z3I81Ly2pVh6AsD6t8Ik28n3nmRyZ/TYtnxvjNl32gdP8dM+zlcYVACrBrQvvx/TT7yAEwY9PIfWRx7l453h8BvWqPK7yCsha9ga571a+aLMkXMRy6hzWC0lkPvMywQsfdVuf2o+PJO2trzjebTK2vCKCnXPJP6YN+qgIjnd7hPPzVlN32URQqYicM5y4SSs41Oh+rFl5hI27HY+GtQDw7dKM4Hv7UHTkLIei7yZh2ivUWTbJ2TYVdZZM5MyIRZzoPZWgO7uX5ktds4WT/aZzctAsdLVCKDx8hrP9J+Pd/laSl67j7KCpXJy9ktovzcS3V1v09SI403siSY+9TuSSh13aEzz6dsznXJwWLk59nrQX3qfwaByp67aRvmkP0cvdj5+6C0eQ/ObXHO46BVteIaFOfZgT04kd9gQXln6ANTUbS1o2cXNWU//FRwgbM4QTg+dwos80hFpF0J3dqqdXQDJbOX3fUyBBbMw0zOeTMdzbD33D2vh0bo5/v46cHvgoyc+ux55fxB89J3Lxsdep5Wy/ZLeTvOQdTvWZzNn/zCF45GD0DWsDUOe5qeR+8wv5h89xasF68g6f45bn3du9hguHc2HtNvZ3no4tt4jIB2IAqDVmAIWnL/FLzFwODnuGRk+PoMnysRx+YBnxgycR+ODtWNOyXGR592yHrl4k8f3GkfrEKozPTHGOzSTO3zlV/hs6DclUQsF3P8t6jktECEHJ0dMkTVuG32090dWv/ZflAuSs/5y0EQ8jgPSJM0i9Zwxe/WPQRLmerxz5BeS++JqLA1+ejEmzSBs+sZIT/1dtQMaPseyKmc/JZZ9SFJ+Krajkina50cIHOL92Kz92noE1t5Bazr6qP+0/5MdeYH/veRyf8ga3LBlVMes04A+3Qv9HSJL42/9uVG5aR14IYRdCHCn3N//quUrz9hJCfH31lFeUsVsI0e5P5r1i+UKIMCHE10KIo0KIk0KIbdciN2JgWy5s+hGA7EPn0Pp54RFa+VPQubEXKL50bZ90ryhTV02ZdYZ24dK23zAlyQbbkZuL9pYm2JOSsKekgM1Gyc4f8OjW1SWfIzcX26nTYLe77FfXrYP15Ekwm8FuR7LZsWfIxq3w0BnU/t5oQwMr1cOvW3Oyv5YNdOamXQQO7ACALSuPoqPnkGz2SnkuYxzQloufyjrIdepV70YH+bHnMV2srIOcg2ex5hUBYD72B5rIcKwXk7ElpYLNRtGO3Xj16uLa/uxcLCfOINlsLvvtmdlYTp0DQCo2YYlPRB0aXKlMj+aNsSYmY7uUClYbhdt34xPT2VVWdh7m2DNQoYwrETawHcmb9gKQ93vVuiiIPY/JnePpJGZQD774VB7WR3+Pxc/fl5BQQ5Xpvby96NitHd9v2wOAJEmlK/Ve3l4U5RbiqNCHrfu356fPdgMQf/gsXr5e+IfU/KfR27Vqjr+fb7XzGQe0JdE5rnKuMK7yYi9Q7GZcAdQfO4DkrQewZ+eirRNR1uc2G0Xb9+Ddu/K4Mp84A27Gu7Z2BIU7dgNgPnYKla/7ueTbtTnZW38CnHNpQEcAAgZ0IGuzfKFV5JyL/jFtKIlLIu/7g0hWG9lb9uIwWdAZ5b4OGTEIS2oWWZt+ACB/9yE0fnK53q0aYj6fgiUxTc77xT4C+stlOQpNAHi3aog9txBHQTGS1UbO5u/xbtNEbsOZRIRei9+AzuR8Jss3HTmN2s8bTYjcLo3RgG/v9mR/8m3ldvbrRMamPRhu70Ly6i+c9arcP/7dmpHltC3pn+4maJBsWwoOnsaeV0TQwPakvv8tunADhYfOovH1Qug0qDx0oFah8tRjTc2ull4v94tn4zqYz6dgTclCaDTk/3gE/34dMTw4iLQ3/otkseHfryNZH38HQPFhZ/tDA7Gl52By3vlwFJkwn7uENkzuF310JPo6RlI27SVrz3H82zRE4+eNzk37g7rdSvpXvwCQ/OkeQga1lw9IoPHxBEDt7YHdZKY4IRXThXQ0Qf5IJWbsGdkusnz6dCJvy04ASo6eRuXrjTrEdQx6dW6JJTEVW3I6ACpvL8xxF5HMFrDZyd+6F5++nf+y3Mvobm2C9WIS9iT5fFX83S48e1aYVzm5WE6ermSvr8ZftQH2YnOpnLQ9x0C6sl02dLuVtK9+BSD5072EDZJdGO9GkWT9GAtA0blkPGuHoAvxB0AfHgQwBHi7Wo1T+J9x0zrygEmSpFbl/pb/rwoWQqivcxGLgO8kSWopSVJT4JouUjyNQRQnl61wmFKy8QyvfCK+EkFtG9D3+2fp9uFc/BpFVpJZXE2ZPvWN6Py96fnfx+n7zRI8BvRHFRyCPb3MybNnZKAKCbkmebaEBHQtWyD8/ECvRxMZCVpt6XFLchY6Y5BLHk2QL/a8IrDLQXaWlKxSR+KKSBKdPn6MiP90xr9Z2QqMKSUbj/CgK2SsGp+hA7GeScCeWq796Zlowio741dDExGGvkkDzMdPVTqmDjNgLVeGLTXTrcNfXfThQaUXZQAlKdmXDX21CDOGkpqcVrqdmpxOaHholen7DenFLz/+RlGhfEH04bpNRDesx97j2/hiz0dsfGY9kiS55AkMM5Bdbuxmp2YTWEW/3zX7fp7Z/iL3PTEKje5/E3HoGR6IKbnMkSmp5tzyMAYSPrg9Ce99D4DK3w9b+T5Py0Addg3j3Ik61ODiWNnTMtFWnEuBrnPJmpKJ1qlTndGApZy+rSlZeDaqjSW5zAFxmK3oIoIpPHxGbkN0BB7RERin3U3jzUvwatkAS0oWWmMQuvAgLClleS2pWejKjbXIucNp8O7jaIJ8SV6xUS4zNau0Pn6DulByIh5NSBDWcnKsKWVpIp4cT8ry9XLYQwW0YQY0gT5YM3MpSUjBnJKFLtxVn5ogX2wVbIu+gs50xiC8b40i94fDAJRcTCf78320PPAmrQ6/gz2/iOLj8dXS6+V+0YUb8G7ZgFbH3qXgxyMUHzqF1mjAIyoCnw5Nafj5CwQM7oLKS1+WPzWr1GEvrWOtUDxvjab4iBzeVnLmAp7N6lOSlEXY7Z3wiDRQkpJVye5pg3yx5RcjOetdklxmGy+u24F3o0h6HFtD590rSNm0F7OzHaGPTyRvy07UAX4VdB5cYQxnoq1gG/2G9CR/626XfrKllvWvLTWzUvv+jFyAwAdvJ2jpQjQRRoSvDwD2tAzUIdWwpZJEyGvPE/b+aryHDnE59FdtAED4oHbUvqsrDScNIXbGmlI5Fe2yNsgXa4W+upym4GQixiHyBah/6/p41Aou7cdbFj8EMBdQotRvUG5mR94tQojzQohnhRA/CyEOCiHaCCG+EULECSEmlUvqJ4TY4lzxXiOEUDnzr3bmOyGEeKaC3CeFEPuAu8vtVwkh3hNCLHFu93eWfUgIsUkI4ePcP1AIccqZf9hVmhEOlN7rlSTp2LU1vvKuis7Nlcg5fp5t7afxfd8FnFv3DV3Wz0T8RZkqtZrAFlHse3AFe+9fjs9DI1EFu3EurlGm/UIiRR9tJOilFQSteB6puBgcrquLlevnthFXLWvf7U+zt/8Cco/EYxzSgaBOTapd3/IYujbFd+ggCrd976Y61ZMnPD0IffFJsl5YjVRU7CaBu9uA1a/zNfEndFHdcTV4aH+2bilbNe3WuxOnYs/So/lghsU8yPBFY/Fwrv6VFXJtZfz3uQ9Z0Gcai++ch3eAD4Mm/eea2/GXcKOE6qiy+eKRnFi8ERzOTG67/NoFCvedUjFR1Wnc3nkuy6/y8iBszBAKDpwsXVEXahVCqyFx3mouLXmP+qvnlMt2Zf0kPf8hiY+twXw+ldDRg8ulkdA3rINx3iiSHn+9ynHgG9MeW2YeJbFxlRM4i/fv3oLMLfvcVwCq6EPXNNogPwL7teXC0g1yFo0an05NOdZpEkfbjEXl5UHg7V0rybmiXsuVkbP9F462G4dXq4Zoww3yMY0atb8PZ/8zh5JzlzDOHF5lfpWXB/XWzCdp0dul/ZI4ZxW6WqE0XTkRjY8nDovNbduuNMcMvVtSEHuevS0m8UvMXCIeiEFo1AT3a4M9KxdrUhqVbNLV5qxWg0+fjhRsL9cn1zRuqy8356OtxPUdS96qtUglZgKmTyqfubLAKkgbN420EZPImPYYPv93J/rWza9Y9+qa05TtB8n8+Q9OLvuEBvPuqVrQFcZR/Kov0Ph702XncuqMHUjB8fNINjsh/dpgycwD+L16tap5JMff/3ejcjM/7OophDhSbnuZJEmXA6ovSpLUWQixEngX6Ap4ACeAy0+jdQCaAheAHcjO9WbgcUmSsp2r7juFEC3KOdIlkiR1A3BeFGiAD4FYSZKWCiGCgYVAX0mSioQQ84CZQojngbeAGOAc4Br4XZnXgU+EEFOA74H1kiQlV0wkhJgwf/78xx966KGQjt88hel4El4RBi6v3XiGB1GSmnuVosqwOY14/VH9iBreG69awWQdOusi06uaMotTsjFnF2A3mbGbzFiOHkV4eaIOLVuBV4eE4Mi8tlAfAKHTgVAhvLxxFOQjmUpKj+kiDFjTclzblZ2P2t8b1CqwO9CFG7CkZVcU60LYqIE0eaA/ALlH4rHmFxPQuj7Zv5xy6jXnivkr4ntLHVq+OIG0R+eh9vfDe2Dv0mPq0GDs6VlXyF0BjZrQl56icNsPFO/c5zaJPTUTrbFMxxpjNcsoR53R/an1oBxLmXckDs9IA5dHgEd4EOZr1MVlOZ9hIfbwSYwRYaXHjBGhZKS6D8UJCPSnRetbmTpqbum+Yfffxlur3gcgMeESmRfTCa8fSVSLBvRwxhYnHI0jKKLsojHIGESum37Py5BbY7PY2LdpFwPH33FN7fkzqDz86P39swDkHInHM6Js1cyjmuMqoGUU7ddOlfMafJCsNmzl7nJowkKwp195nPvddzu+d8lOsDn2NOqQsvqow4KvOpe04cFYnTq1pGShK6dvbbgB05lL+HVtgdCoafDWXExnL1J4oCzU1pKahWS1o4sIJvuLH5EcErpaIVjTshFaDbrwspVPndGANdW1PZaULBwlFgIHdSZr1YdojfJDn3XXLuDSrJVYElPlFehycrThBmxp2fgP6opf3w749m6L0OtQ+3hR//MXEc5nLUzHz+E/qCsJj8sRBfpwA5YK5duy8tFUsi05GEcNJGx4X1QeOrRhgVxc8Qm2HPkFAJ5R4RT8HIstOx+QHXGfNo2qpdfL/SIfC8aeX0zhz7F4d7iVogMnsaZkkbdDDvcpOZOIvl446iA/7Nn5aI0GrJfHhUZNvTXzyfl8D3k7fiZ45GAM98l2L//bX0jec4r8owkE92uNR7ih0ly3ZhWg8fNCqFXySwEiyuxBxH29OP+q/OyR6Xwa5uRMvBqEE9ChMT59OuF3W09QqxFqFeEvzCZlzgqsqZloytutsGBs5eyWT492mE/EYc8qOwfJecr6V2MMLmufS5rqyb38256WgaOoGP2t8kKOOiwEe+a121KHM60jJxfT7n343H8XvR+VY/j/ig2IGt2PesN7l8qx5BTiVS8MbZCvW7tszSpAW0Vf2QtNxE4ve1C/52+vUpyYQfjQLoQOaAtwHtmH8gM+AB68ZgUoXHdu5hX5iqE15Z3jL53/jwO/SpJUIElSBlAihLgcOHZAkqR4SZLswEagm3P/PUKIQ8Bh4FZkZ/8yFR3wtTideOd2J2f6/c6LjIeAukATIEGSpLOSvAzwwZUaJknSN0A0svPfBDgshKgUeyJJ0pvLli2r26RJE69fBzxD8vaD1L27OwBBbRpgLTBRkn7tTrfeGRMX9+53/D7nbcwZuVz8/Je/JDP5m98J7tgYoVah9tShvaUplp9+QV2rFupwI2g0ePSJwbz/p2uWWbJrN1ljx5Hz2AJU/v6ow+SQDJ82jbDnF2NNr2wI8/fHEnSbHDcZfHdvcr757YplZHyyi/13PsPevo+Rvusowd2aUXDqEgFtGmAtKMZcDR14Rhpo/84MDk95HduFJMwnTqOtE4kmUm6/98BeFO/5+eqCnAQ/PQtrfCL5G6p+SLUk9jTaupFoIsPk1aZBvSja9cs1l1GexPXf8lOf+fzUZz7p2w8ScXcPAPzbVk8Xl+UMi3mQndv3cOc9sgPZsm0zCvILyajiQmPAHX3Y/d0+LGZL6b6UpDQ69ZBjcQ0hQRijI8hITOOHDTtKH1o9/O0BugzrBUB064YUFxSXOu3lKR8336Z/e5LOJF5Te/4MjpJ8dvVdwK6+C0jZcZA698hzK7BNA2wFpmqNq+86TOfb9tP4tv00ir77kczFr6Dy9S4bV4N6UrT7yuMq/+OvSLr7YZLufpiiH37Cu69sBvUtmuAoLHI7lwp+iiVoiBwjHHx3b3K+PQBA7re/Yfg/2bHwds7FvF2H0EeFE716Fqb4JHThBnK/O1AqK3fHr9iLTBj+rxf6qAg5ljq3AGt6DkVHz+IRFY6udihCqyHozm6lefVR4QAUHT2LZ6PamJMzEVoNAXf2wq9/Z1Kff5/i3+ULhoLvfyVwmHwh6tmqMfaCYmwZOaS98D6nuozmdPdxXJz6PIU/HSPuP7M4N2Qa54ZMw5KUjmR3YEnJxqdNQ2wFxVjd9E/e/lgMTtsSek8vcnYcIPXdHfwxajmoVVx6aRMBPVsCyHLyivBqWk+OkQf8urXAdPZStfRqTc9BE+RHSXwS+qhw9A0i8e3eCo/6tcj/7lfyvv0Fny4tAPmCRO3rjT07H6/WzvY7+7XO81Mxn7tExtuyw535/jZOD55O3IinyPv2V8Lv6UnUjGFk7T6GraAYi5v25+w/SejtnQCIuKcnGTsOAlCSlElQ92YA6EL80YUGog8N5NKG74nrMwZrSgbpi9dQ/MsxUuasAKDwh1/xHypfiHu0bIyjsAh7RtkY9LutJ/lf73Epv+T4GXT1IhB6LWjU+A3pQeFOV1v3Z+RejqG3nDyFNroutuRU0Gjw6tcb095rO18JDw+El2fpb49O7SjasrVGbEDC+u84MO6VUjnRY/qj0mrwigqr0i5n7z9J2O3ysxcR9/QgzdlXGj8vhFaOGK71YAzZv/yBvdDEmaUfs7v1ZIB6wH3ADyhO/A2HqO4t/RsFIUShJEk+bvafB9pJkpQphBjl/D2l/DGgGfCMJEk9nfvHAM2BVcB3QHtJknKEEO8CuyVJere8XGee3chPcTcEbpMkqUQIcTvwgCRJ91eoUyvglXLl3QFMkCTpml7j5Hwwdr0kSVV6bpvDh0sArZ4dhbF3C+wmCwdnrCXnqPy6sK4fzOH3WW9RkpZLg7EDaPTIbXiE+mPOzCd15xF+n/029Uf3I/qhvvIDpCVWjj79AVkHz9K6nMzfysns9sEcDpaT2biczBSnTIBGDw8h6r6eSA4H4rsvKN60GV2njvhNnQIqFaZt2yna8AGed8groaYvv0QVFIThzbUIby/5FXMmE5kjH0IqLibo1VWo/P2QbDYKXnsDfc/uqFt3xmEyEz/jNYqOybfKG294nPjZb2BNy0FfJ6zC6ydfRrLY0IYE0Gz7C6h9PZEcEo6iEo71ehRNkB9R6+RHE1QaNbZCEzqDH3aTmSPT15J3VH5IrMOHczk68y3MaTlEjR1A/cm3ow8NwJKZT9rOwxyb9RYtXhxP+JAOmC5l4qGxg91OzhvvYZj7MKhUFHz+DXlvf4Tv3fJwKNj0NWpDIBEbX0fl7YXkbP+loePQNYoi4t2XsZyJR3KGVOS8+g6Fuw5WGhNePdoTPH8SQqUif8u35KzdiN+9coxm/idbUQcHUvvTV1H5OMsoNnHh9gmloTrnstzHat6ybDQhMa2wm8wcn7aGfKcu2n44j9iZb2JOy6HuuIFETb4dXWgAlsw8MnYe4cTMNwGYIcnpn1g+h24xnSkpLmHBtMWcOCo7Xms/WsnCGUvJSJPv0ry3ZTVvrXqPfeUuRELCgln26pOEhAYjhGDX2q/55fMfK9X1wUXjaNazFRaTmXfmvMH54/LYmL5+Ae/OW01ueg5zPnoK3yA/EIKLJ8/z/uNvYi6W7/KsPfi8Wx2UZ85Ty/nt8DFyc/MxBAXwyNgR3HX7gCrTb222sPR3i2WjCOvdEpvJzOHpa8l1zq1OH87lyMw3KUnLJXrsABpOvg19aADmzHzSdh7hyKy3XGT2WTuK4j2/4igpwTBXfv1kwZZvyH1rI753y31esGkrakMgkZ+85jKuLt45HqmomNDnHsOrd2eEhx5sdnLXfcK5xfLbbRq+v5Dzc14vnUvRb8xCE+BD8YkE4qeuRHKGXtRZOgH/XvKrSRNmvkrxsTjCJt1JnSdG4bBYseUUYMvMo/DASUynE8n8ZCf1XpyCf++2qLw8sKZlEzfpeYqdc9g/pi21nx4DKjVZn3xPyqtyfeq/OQ+P6AgkScJRYkEb5AdIWC6m4t3mFmx58uv57DkFJIx8krBp9+PTow2Sycylua9gOn7ORX/eHZsRPH5Y6esnAWq9MB1VhBF9hAG7ycy5Ga9TdFSu1y0fPM65WWW2RX79pGxbzk55Bclio/6KhzEM6YT5Uga6cANqH09M8cmcm/E6wf3bEnRHV3RGA3l7DhM/ZSU6o6FaevW8pS5RLz+K2s8bnTEIR3EJGW99TtprmwgeOZjAYTGovPRIVhuW5Aw8G9d1vn5yFabj5/BudwsN//scpj/Olz4jkPzCBgp2/U7w6NsJHjkYtUG+yC1JzuLktNWlc731h/M5OXMt5rQcPOuG0tz5+smC4+c5PvlVJIsNfVggt656GF1YIEIIElZ9ji23kEaLH0KrFeRt/hbToZOEPjGJ3A+3kvux/PB72FOP4N1dftVm6mMrKYk9C4Dw0NNgz3vE9RmDo7AsnNCnX2eMix9FHegHDgeWi6kk9B9PwH3yQsGflRv+wmz0TaJRqRxIJSWoA2VdFH65nYL1H+E9TLbXRZ99jcoQSNh7q1F5e4Ek4Sg2kXrvGFQB/gQ/L0foCo2aoh07KVj/Eb9fLLsb+VdsQMMpt1P77u5IVpv8cKvdIb9+sgq77Fk3lJZrHy3tq6OTX0Oy2Aho15Dmrz6CZHdQeCaJ2Blr5Wc/nAxM+1gAvYDZ/E2vn7zYvs/f7qzW/m3nDfnqmn+zI7+dstCa7cCbyGEv7wOtgRDgGDDvCo78bKAH0BsYCgQix5LFSJJ0TgjhBdQCEoEzQG9JkuKEEBsB36oceSFEDPCLJEnFQghf4AAwUpKkKpeRLzvyNcn1GBndGyZdB6lw4dyfe/j0SqQ79FdPVE2ahV17CFF1sFtr/uZaVY78X+WyI1+TdPZy8wrOGuBaHPnqUt6RrylahFyfcZWV7V3jMoW4PuccvbbqN0/9WQpKdDUuE0CrqvmAW62m5tufUeJ59UR/gtr+lb8rcaPi7W+ucZnlHfmaxOM6BXI7Hfm/FcWRr5p/Uoz8DkmSrvkVlMDPwHLklfi9wBZJkhxCiMPIsfTxwP6rCZEk6SUhhD+wARgOjAI2CiEue4ELJUk6I4SYAGwVQmQC+5AvJqqiLfCaEMKGHP709pWceAUFBQUFBQWFfyo36Zrz/4Sb1pGXJMntKyAlSapX7ve7yA+7Vjy22/nnLv+oq8l1bvcq9/upcod+ANq7yb8DOd79qkiS9ALwwrWkVVBQUFBQUFBQ+HdyMz/sqqCgoKCgoKCgoPCv5aZdkf8nIIQYjfzp4/LslyRp8t9RHwUFBQUFBQWFGw3JcUOGp98QKI7834gkSeuB9X93PRQUFBQUFBQUFG4+FEdeQUFBQUFBQUHhhkVZka8aJUZeQUFBQUFBQUFB4SZEceQVFBQUFBQUFBQUbkKU0Jp/CP0GptW4zPidNf8xmPPX4cNNAJrr8IGVvqtb17zMR3bUuEyA5Xb/GpcZ6VV09UR/gk+kmv8YiiE8u8ZlwvX5eNOQ2CU1LnNom6k1LhPg/6h5G+Bnuz4frel6HT42V3AquMZlAiTZa/5DS0F2a43L9FPXvEwAre46fLyroOY/4Adw+mJAjctsZcyocZkAOdle10XujYDyHvmqUVbkFRQUFBQUFBQUFG5ClBV5BQUFBQUFBQWFGxblYdeqUVbkFRQUFBQUFBQUFG5CFEdeQUFBQUFBQUFB4SZECa1RUFBQUFBQUFC4YZEkJbSmKpQVeQUFBQUFBQUFBYWbEGVFXkFBQUFBQUFB4YZFuj5vrf1HoKzIKygoKCgoKCgoKNyEKCvy/yA0zdrj8cAjoFJh3bsd87aPXY+37oLH0FEgOZDsdko2rsZ+NrYsgVDh89QbOHIyKX5F/hCOb8/W1Hp6PEKtIuvj70h747+Vyo18Zjz+vdviMJm5MOsVTLHxaMODqbtyOtqQACRJIuujb8h452vqLh5LYEwb7CYzcTNeo/h4fCV5+tqhNFw9E3WAD8WxCZyb+iMoXMkAACAASURBVAp+XZtTf+VkNIG+2HIKMV9MI2H+mxSfPI8uwkCDVY/i1bQeKi8PbNkFnBu1hOJYWbZfr9bUeWYcqFVkbvyO1Nc/A0Ad4EP9N2ajqx2K5WI6cQ+/gD2vCL/uLYl8bCT6en7gsLPr47d4bsNXOCQHw/p0Y9LsBSBU2NPisJ3cA8BvcSm88OUv2BwOAr08WPfwEAA+3BfLZ7+eRgKGdWjs0s7pi6bQOaYjJaYSls54njOxZ93264R5Y+h9W08cdgdb3v+Sze9swdffh8denEtk3XACgwPxttqx5xVzetprFB1PqKzTOqE0WTMDbYAPhcfjOT3lVSSrrfS4T6v6tNr6LKcmriTz618I7N2KhktHgVqNykOHKTaeC+MWucgMf2oCvr3a4igxc2n2K5SciCs7qFLR4MuXsKZml+bz6dGGiKfGow7wQxPkx4nWw7Hn5LvIjHhqAr7OsXRp9iuYTsQh9Frqf7Icodci1Grytu8nbeVHpXkMD91G6JjBYLdjTTiPtkF9hFpF0ZfbKNyw0UW+pm5tAh+fi7ZxQ/LXvkPhR5/K++vUJnDxE2XpIsPJf+tdWL4fgOZLRhLWpxV2k4VD09aQd/x8JR1HjelP/fED8Ykysq3pRCzZBS7HA1pF03PrIuxFGUiWK39sa+GzL7F3/wGCAgP4/IM1V0x7JSY8M5F2vdthNpl5edZK4mLj3KYbMWck3YZ0w2F3sO2DrXy1/qtKaTosGkGtmFbYTGb2zXiT7NjzldJ0f/VhgltG47DayDwSz0/z3kGy2fGvH07XlRMwNKvHyeWfcnb1VgBaLBmJ0anX36etIdeNXqPH9KeBU69fu9FrYKtoem1dRN6ipzHv3eNyTNe+A75TpoJahWnrVoo3fuRy3KNvX7zuewAAyWSi4OWXsMWV6Shq8RgC+rTBYbJwbvqr7udW7VAarZmBJsCXouPxnJ26CslqI3hYdyInDwXAXmQifv6bFJ+8AED/317BWmjCIzQAjY8nhXEp1R5XxgFtuWXe3eBw4LA7sCWm4dMsCrvJzJlpr1Popq4edUJpsmY62gAfCo4nuLUDrbctw5qZiyUzn4QZr+LXqSkhD/QFCYpPXSB+xmvoQgNpsHommgAfimITiJv6SqmcuovHEhDTBkcFGx82dgihw/uBgOIt28j7YAueXdsRPH8San8/JCQc2XkU7f2V7JfWAaCNqk3o4pnomzYga9V75L27ubSuIYtn4t2jI/bsXC4OnVi637dnGyKfGodQq8n6+FvSV7s5Zz09Hr/e7XCYzCTOfhlTbDxCr6XBp8tQ6bSgUZO3bT+pK8vsR/MlIwl1jtXDV+iraGdfba/QV02cfSXZHZhWrsJ85ASeXdoRNFc+Zxdu2U7e+k9c5Gnr1cbwzGz0tzQg57X15L8vt1/otBjfeQmhletqOR2HvmkjwtFU6zwNUOeFqfj1aYctK49T/R4tTe/ZNIrazz6M0GsBDgKPAAcqCVb421FW5P8pCBUeI6ZStHIBhY+PRduxN6qIOi5JbCcPUfjkBAqfmoTpnRV4jp7pclzXbyj2lMSyHSoVtZdMJO6hZ/ijzxQC7+iOR8PaLnn8erfFo144J3tMInH+69Re+jAAkt1O0pJ3+KPPFM7cOZfgkYMxDB+AZ1Q4R7pOJmHuGqKXTXDblDqPjyDlra842m0KttxCQh/oS9Sz40lY+DaH2o3HmplHxqe7iH5+klyWzUHu9l8oOnyGI81HINlt1Fv5aGkb6iyZyJkRizjReypBd3bHo2EtAMIn30X+/mPEdn+E/P3HME6+CwBrdj7nRi/BvGs9poNfs2zTLl4f25/P5g9nx+FznPxsNeZd7yD03qiC65BvMrNsy0+8Mqofn826ixdGxABwLjWbz349zQdT7+TT6UP58Y+L2IV8susc05FaUZHc220Ez897idnLprvVxeB7BhIaEcoDPUYxvNdovv9iFwAjpw7n7IlzrFn2NhfjL1ESn8rZ2Wto8Jx7nUYtfJDktV9zsMtUbLlFGB+IcennqIUPkrP7aOl2/WXjSBj1NNkfbkfloUfl7eEiz7dXW/T1IjjTeyJJj71O5JKHXY4Hj74d87lLLmVELJrEpbmrMJ2Iw2Gxoo+OrCRTFxXB6V4TSVrwOpGXx5LZSvwDj3N20KOcGfwovj3b4NVavijy7twcv34dSR8xjvQR49A1bULWzPmk3T8ar34xaOrVdSnDkV9A7srXSh34y9gSL5Lx0AT5b/QkpBIzJXv2ARDWpxU+0Ua+7zyTI7PfpuVzY9zqOPvAaX6651mKL7r5aqNKcOvC+0nbfcxt3or8Z3A/1rz0174A2653OyLqRTChx3hem/8qjyyd7DZd37v7EhIRzKTeE3m4zyT2frm3UprImJb4RRn5rNssfp63js7LRrmVFb/lJ7b0mMMXfR5D7aGj0QO9ADDnFvHrExuIXbutNO1lvX7beSaHZr9Nqyr0mnXgNPvueZai6upVpcJ32nRy588la9RDePTpg7qu63iwp6SQM/1RsseNoWjD+/jNml16TNexIx7R4RzuMoW4OauJXu5+btVdOILkN7/mcNcp2PIKCb2/j9zmxHRihz3B0T4zufTyZuq/MMkl35lXviD3aDxf1X3oT42rjB9j2RUzn119F5C4cTdBMa35rfNUzs5eS4PnxruVFbVwOElrv+a3Lo9iyy2sZAcavThJtn8L3uHs7LVEr3iEsLFDiB00l+Mx0xEqFYY7u1G7go0OcbbZP6YNHlHhHHXa+CinjfdsXIfQ4f04MWQux/vOxKtnR7T1IglZOJns1R9iPpOAPTOH1JlLXJx1R14+mctXk/tuZce04PNvSZ70uOtOlYpaiycS/9AznOo7mcA7eqCvcM7y7d0WfVQEf/ScyMXHXqfWkjI7E3f/Qk4PmsbpQdNc7Exon1Z4RxvZ2XkmR/9kX+2Omc/uvgs4PH0twU/NBJWKoMemkjZ5AUnDxuE9sDfaaNdztj2vgOznXyfv/c0u+yWLldTxc0i+dxLJ9z+Cd5/u5Kx6p9rnaYCsTTuJG/lMpbZELHiI1Jc/5vSgGQBPAs+7bfT/CIck/va/G5V/jSMvhDAIIY44/1KFEEnltn9ypqknhHigXJ5eQoiv/0RZvYQQkhBibLl9rZ37Zju3Fwkh+tZE2wDU0Y1xpCcjZaSA3Yb1wG60rbu6JjKXlNVR7+HyzWMRGIy2ZUcse8tOtF6tGmI+n4olMQ3JaiPnqx/x79/BRaR//w5k/1d2LosPn0Ht540mNBBbek7pFb+jyETJuUv4921PxubdABQeOoPa3xttaGCltvh1a07W1z8DkLFpF8F396TkfAo5W3/BlplP1hf70BkN6MINAFjTc/BsXIeszbtxFJVQHJuAxt8HbWgg3q0aYj6fUtqG7C/2EdC/IwAB/TuQtUmue9amXQQOkPebTiRgTcsB4PjJP6hTpw61QgLR+RsYFNON3UfOyO3KOI86ojHbD8cR06wu4YE+AAT5yJ9fj0/Po0WdUDx1GjRqFW2jjZg1JgC6DejCjs3fAXDi0B/4+vtgCA2qpIuhI+9g/cr3kZx9lZuVC0C9RnX5fd8hug3owpb3v0RfO4SSxHQ0fl5oQyt/UjygazMynDpN+3Q3hoFl/RgxdhCZW3/FmpkHgG/rBpQkpCJZbfj2bEve9v1ogl37ybdfJ3I++0HW15HTcr+HyGk0RgO+vduT/cm3pem9WjbEciGF4LF3kvLsOzhMJfj2auva7/07keuUWXz4NGrfMpmOYnnsCo0GodGU6sMwfDAZqzeD1YquaRNsFy5iT04Bm43i73/Ao0cXlzIcOblY/ziNZKv6E/H6dm2wJSVjT00D5NW0xE9/BCDn0Dm0fl7o3eg4L/YCxRcz3cqsP3YAyVsPYHHq+Gq0a9Ucfz/fa0pbFR37d+KH/8r6PH34NN5+3gS6mW+DRwxm48sbS3Wal1W5jnUGtCVus3xhk3EoDp2/N55udJD0w9HS35lH4vAKl8d0SVY+WUfjkaxleo9wo1ePP6lXsxu9apvcgj05CXuKPB5KfvgBfdduLmmsJ04gFRbKv0+eQBUcUnpM37UbGZvkFf7CQ2fR+Hm7nVv+3ZqV2qv0T3cTNEieWwUHT2PPk++8FPx+ptReXSYspuVfGlf2YnPp75CeLbDlO8ty1lVXpR34BYC0T/dgGNi+9Fjk2IHYC00Un00qlaP29ULoNKg8dKBWofLUY03Lxq9bc7Kdbc7ctItApz0JHNCBTDc23rNhJIWHzuAwWcDuoOTgMfwf+A/WxGR8YrqQ+/bHFG7bhXdMZ+zZZX1pz87DHHsGyVZ21+AyJb/H4shzvTvjddneXyx3zurX0SWNf7+O5c5Zp0vPWVDezqgRWk3pOTJ8QFsuXmNfma7SV2ovDyQJ9M0aY7uYjC0pFWw2ir7ZjVevyvbKcuIMuGm/ZJLrqm/RFMlqxZaeWe3zNEDRgZPYcwsryUcCla9XqQgguXIihRuBf40jL0lSliRJrSRJagWsAVZe3pYk6fLsqQc8UKWQ6nEcuLfc9n1A6VlOkqQnJUn6vobKQgQGI2Wnl247sjMQgYZK6TRtuuLz7Dt4TV+K6Z0Vpfs9738E06dvgaPMudcZDViSy4ySJSULbZirTK3RgCWlLI01NROt0TWNrlYoXrdGI9RqV3nJWeiMrs6rJshXPvnZHaVl6sKCsCRnudTDr1sLcncdLlePICzJmXJZzaIpuZCK1hiELjzIpX6W1Cx0TudCExyANV122K3pOWgM/pX0laHyxxjkDw47UlEO4bXrkV5kBSFQhzdEePpyITOffJOFsWu2cv8rn/PV73KITIOwQH5PSCW3qASTxca+UxdxCNmRCTEGk55c1l/pKRmEGIMrlR9ZL5w+d/Rm3bbVrNiwjFpR8ir2uZNx9BzcnRBjMJ7ennjUCkEfYcCSko2+gsOgCfKVT/JOnZpTynSgMwYRPLgDKe+VOd368CDMyZlEPDmelOXrsWXloZJvr5bpO8yAtXy/p2SV9vvlfDjKnk7SGA0gBNbULEr+OA92R6mTXl6my/hILZOJSkXDba/Q9PcNFOw7jMl5MaWPjsC7w62EvP06AQtmI1kspfnt6ZmoQ0KoLp79emP67oey7fBATMnZpdslKdl4hld2iKvCwxhI+OD2JLxXY9P9mjAYDWSmlK0MZqVmYjBWtgnGuuF0v70HK79+maffe4aIehGV0ngZAykqNweLUrLxMlatA6FRU/+ubiTtqvoOhEcFvZpSsvGopl4jBrcnvgq9qoKDcaSXs4kZGaiDK8+xy3gOHoLlwK+l2+rgYMzlxqM8b9zMrQr2Sm+sfEEedn8fcn8os1eSJBE+sB1NZt9F3QflVfHqjiuA8EHt6PPjCox9WnHhpbJV6/Jz3KWu+cWudS1nBwyDO2LLL3ZxOs0X08naso/Wv62lzZF12AuKKToeX9lGO8eVzhjkorPLNr74VCK+HZuiCfRB5anDq3t7NLWM2FIz0NaLxKNtM3yHDcT//jvQN2tULR2UR2usaJcqn4+0RgPW5LJ5YU0td15TqWi87WWaHdpAwY9HKHbaGXdj9c/0VcyPK+j0wRyynl6BOjQYW2pZPWxpmahDqx6flVCpiPhkDWGvL8WWnIYl9hTw58/TFbn0zNtELhjFrb+sA1gBPHbtlat5JEn87X83Kv8aR/5KCCEuX44uB7o7V+lnVEjjLYR4RwjxmxDisBDizquITQQ8hBBhQggBDAS2l5P3rhDi/5y/lwshTgohjgkhVjj33S2EiBVCHBVCVL7XXbkVlXdJlXfZDu2ncMEYil99Co+howHQtOyIoyAXx4UKMdruxq1UUai7csvSqLw8iFo7j0vPvI1kd/PY+TXJc930bFgLr0a1SFz6frlsAuGho/6b87j49Dr5JCO5l1epyCoQvgbUtZriyHKGiFjN2BJj0YRFo+/2AI7ifJAc2B0O/kjK5LUx/Xlj3EDe/P4IFzLyiA4LYHSvFkx6aweT1+2gUTknQB4SFetVuWJanQ6L2cLYwQ/z1UfbWPDiHAA2vLYRX39fWnVsQa8hPSiMTShbZa4gx11Zl9NELx5NwuIPXJxuhEAfGYwtM4+SKmKq3Xe7hG9Me7f5hE6HZ9Mo0lZ+WD7HVWWWtsXh4OzgafzReTReLRuhbyTfghZqNWo/HzLGTaZ4x3fo27Z2n/9a0Wjw6NYF085ysdZu++raRTZfPJITize6XCT/LxDXOPa1Oi1Ws4UZt03nm43fMG3FNDfCrjzPK9L52VGk/XqK9AOnq66fW5lVJq9Ei8Ujib2SXqshX9uqNZ6Dh1Dw5tqr5K84Zq8+j/26NCP0gT5cWLqhdN+Ptz9N1oHTxD7zIdGj+2Ho1MSt+KuRsv0gO7vPJvdYAsbhMa4HK1W1an3UXzxKtgMV0giNGt9OTTnS8WEOtx6HyktP4G1d3MiRLhfi5pBEybkkUt7YQpOPn6bxh09gPpNQmkeo1aj8fMhZ/QElh08QtuLxSjKunWvpMzfZytmZ04Onc7LTGLxaNcTDaWf+qg0Aua9+6D6bA6NfIuCRUdWeU5VwOEi+dxJZS15BFeiPtn69K8ipflnBIwZxadE6TnQaCzADWHftlVP4X6I87OrKfGC2JEm3gRwiU+7Y48APkiSNEUIEAAeEEN9LknSlp9Y2A3cDh4FDgLliAiFEEDAUaCJJkuSUDXJM2gBJkpLK7auYdwIwAeDj8fcxrOfg0mOqoBCk3Cx32QCwnzmOKjQc4eOHumEztK06o23RAbQ6hIcXnhPmY3nuW3QRZSsEunAD1vRsFznW1Ex04cFcVoLWGIw1zZlGo6bRVytQ+3phnHYvxcfOucqLMGBxhrBcxpadj9rfG9QqsDvQhRuwpGeji5CdYK9b6hI6oj/pH+/EllN2O9Calk3tJ0aTtekHcrf/Qq35I7CmZSO0GnTh5co0GrCmyvWzZeaiDQ3Emp6DNjQQW7mQAm24AV2HoQRvfpPUrLI6piYmEASYf/wQdd2WOCQHYf7eBHh54KnT4qnT0jbayOmUbOqG+DO0Q2OGOh9y/bHYwMh53VGh4o8jpwmNCC2VGxoeQmZa5f7KSMlg91b5Om7P9h9Z8NIchj10J3cMlx+m3fnlbn7ff5hWj02gJDEdXXgQ5tQKfZSVj8avTKf6cAOWVLlNvi2jabJWvmbVBvkS2KcNSau/xCMqHE3jWvj2bos6wA+h1/4/e+cdHkXV9uF7djd103uhBRKQTggdBBI6gjRRUOkdpIcioCJIURDpzYYKFrAgHSnSew+9JJT0Xnez2TLfH7sk2eyG8ho/8X3nvi4uMjNnfnPmnDPPefbMc85Q7rNJxE5cYqr3NGyKlauNvye6pHRcOzXHpW0jnMPDEOxskTs5Uu6zSeQcvoDMWUnIruUAyF2dce3UnKRPN6JLySzUtA3wQlW8rpLM78WQnUfuqSicW4Whuf0QbWIqWXtPYA8UXLgMg/ohc3PFkJmF3McLfar1kIzSsG/aCO2tOzhEtMTx1VcIL1CQcSkah4Ci0U17fw/yEzOeoGKOW90gGq4ba7wnD2fkDjboc0XEAtVTznx+Xun/Ch36dgTgzpXbePkXvZHw9PMi3UobS01I5fhu46Tek3tOMGHxhEKtV/sa21nqpWiUAUU/RJX+HqiSMq3moe7EHth7OnNw6FcWx7zqVcazZgXK92puUa4Oz1mu7nWDaGQqVzsPZ2Tt65Ct16M5bgwBMqSkIPMpesZk3t7o0yzbg6JyZVwip5A5fSr2ERE4vNIFAO3NW9gFePE4eMP43Ji3R11aNoqS9qqYTXOsXpHgT0dx/a2P0GXk4jewI75vtUWHQMalaBRKexJ2n8M9tMpzt6ugQe2o9FY4ABmXognoGGYcdU/PsZpXox1wNMurJjEd/0Ed8OzQAM8ODTAU6JAp5DjXrYyo02Mf5E/2yavo0o2T0jN2ncapflVLG216TgsS0rAL8OKxZbYN8CwMU0z54QApPxwAoOqHr1OQq8KuWhC6pFTy9h/Hvm51NDfvYVczBJm7K4aMZwtDM7vHxNQSdsnLwoZoE9KwCfAGbhjT+Fn2a/rsPAwqDZU3foh/Ss5fbqvFSTt1E0X5YRjyVCj8ip5Pha8X+pTS++zS0D2IRVTn49C8AZzd9fz9dCl49gon7oPPH29uAb547sxJ/L8gjcg/O+2B6YIgXAIOAfZAhSeeAZsxOvJ9gR9KSZMN5ANfCILQEwr9mOPABkEQhgFyayeKorheFMUGoig26KhPQ+4TiODlB3IFNo1ao714wiy9zKfolbmsYjAobBBzs9H8/CU5k/uSM+VtVGvmobtxCfX6hagu38EuyB/b8j4INgrcu75M1j7zSetZ+87g0cvYmTiGVkWfk4fOFK5ScdFYco5c5FqjwdzqNJGsvafwfq01AE71q6LPVhWGtpgVyPGreHZpCoB373BSfzmCfZA/Tg2rUfWLqegyc0n58aDZObYV/JA7O5L0+TaU9Y350CZnkHf5DvbF7sGjWwsyTfeQue8Mnr2NeffsHU7mH8b9chclId/MQnv9CNVd4GFqNnHpOWh1evZeiaFVjQpgY4ciqB76B1doXaMiF+8notMbUBfoiHqYTGUfY5hOeq4xJj4hI5dPV61jYPvhDGw/nCN7j9HxtXYA1KxfndzsPNKSLQ3rkT3HCWtuHGUObVqXR9Gx/PrN77zz2kSGvjKaI3uP0X/cW2SduoFj1XLoc1Roky0drMwT1/A2lanv661J23sWgLONxnC24WjONhxN6o5T3Jv+ObFrjSuW3Ht9OrcjRqJLy0R1/kahEw+Qs/807j2NI4AO9aqhz1GhS8kgadG33Gw2iFsvD+XR2E/IPXGF2IlLyNp+BF1iGtF9Z3IrfCSiXs/9wXMKnXiA7H2ncTNpOoYWaco9XJC5KAEQ7Gxxbl4PzT3jW5KsP07h1LQuAIacXASFDYKDAygUOLaNIP/oSYuyeBIO7SJQ7ztI3i+/kzJgOH+2nUHCnnNUeP1lANzrB6PLUaOxUsalsa/RBP5oOJ4/Go4nfsdp9Lmpf4sTD7Dz252M6zSWcZ3GcnLvKSJ6GcuzWmg1VDl5ZFh53k79cYq6zYxlWLtJbeJi4gq1trWfybb2M3m49zxVXjPGl3vXr0JBtgq1lTII6duawNa1OTxmldURv9RL0dxdt5uDVspVm6Mm/znKdW+jCextOJ69DccTt+M02Us/K3TiAbQ3byIPLIfMz8/4piUiAs2J42YaMh8fXOfMJXvBPPSxsai3biV92FDShw1Fc/wo3r1bAeBUPwRdKc9WVjF75fN6azL2GO2IbaAX1b6cwp2xy8mPTgAgccMeol6dydFuc0jYc46KfVrh3ao2Bp3+udtV8p9X+LPtDP5sO4PsG49QODmgS8/B2ZTXglLtQBMAfF9vRdresyR8vZdjFd7kWIU3uTlyKdqMHO5O/4KC5Ex0WXnG1cAcbAHj/CX13Viyj1/Fw3TPXr3DyTDZk8w/zuJVio1/HLpoG+iFsk1zMr/ejE2FQNQXruLQpD5OnVpTcCsawcbmP3LiAVOfFYBted/CPit732mzNNn7i/dZJjuTbLQz8mJ2Ru7sSOz0VRxqO4PEPecoX6KtPk9dKSv5Fv7tWrsSgo0N+acvoKgQiCLA2D6VHVqjOvxs9krm7orM2ZjXgrsxyH290Wfn/kf9dGlok9JxalLr8WYEYH1Ztf8nRIPwj/97URGsvc7/b0cQhNlAriiKj8NYckVRdDKNwJcckY8URbGLIAjngTdFUSz9XXGRfvHz9gPlgRrAe4+vKwjCBmCHKIo/C4JgB7TBGEdfThTFCJNOY+AVYBBQTxTFUn+uZw1qKyrqNMK+r2n5yaN70Oz4HtvWxtGlgkM7sO38BrbN2oFeh1hQQP7m9ebLTwLyanWx69gb1bJZRB9Q4hIeRuAHQ4zLT/50gKSVW/B82zjil7ZxDwDl5o7ApXWocVmryBWor9xF2bA6VX9ZiPrGfURT2EbCJxtRhjfEzZT23sSV5F0xhmBU+24m0ZGr0SZlYFfBl5BiS5vdHbsU1xZ1CF49EbnSAW1qJtrULOSuSuJX/Ir61kNqbp2PNj0buZMDGERiF3xL8pfGecquEWGUnz0YZHLSftpPwgrjCgByN2eqrJ2CbaAXBXGp3Bv5CfrMXPzH9cbvnV4IWmNncvj4SebPX4BBr6dn91cZNXo0P/78G4akaHpVN8ZJbjh0hW3n7iAI0KNRNd5+2WgAB63eQZZKg0IuY3KXxsz8rChmeNK8cTRp3Yh8dT7zJ33CzSvGeMzF3y5g4ZTFpCal4eSi5IOVM/EN8EGtUrNo+mfcvR5NzbAavLdsOga9AYWNAk9BgT5Pze0Jq8m9bCzTmptmcGfSGgqSMozLzq2biMLNidyr97k1ZhligfkEqqrLxpC+77xx+ck2oYR8NBBkMvJOXkHh7UHOQWPnkP69sd4D5ozEqWV9RLWG2KnLUEfdNdNTNq6F17CehctPOrcOw//9YSCTIXdz5lbrEbh2Nr6mT99UpOncyrh0XewUo6b9S5Uo/+kEkMkQZDIydx4jeblxaVXBRkG5T8bhVKcSok6H+sAhlF06gkxO3o7d5H6zCcceXQFQ/bYdmYc7Pl+vRVA6gkFEVKtJ6jsIUaVCsLPD7/cfSez1NmKecezqbIwfAHUWDMQ3vC46tYaLE9aRedm4tF+TTVO5NGk9+UmZVB7SgZAxXbDzcUOTmk3SgUtcmvw5xam/bAQBnV566vKTUz5YyNmLV8jMzMbTw43RQ/rRq2uHUtP3qD/W6v6Rc0cR1jrMuPxk5GfcvWKso9kbZrN82nLSk9JRuiiJXDYF70Bv8vPUrJqxipgbxvt7TV8UB9x43gACW9dBry7g2KT1pF0xpmn7bSTHp3yBOimT/g++ITc2FV2ecSLeg11nubx0Kw7ernTZWQvX8QAAIABJREFUPRcbJwcwGNDn5bOv5VRqznwD3/C66NUazhcr12abpnLBVK5VhnSgaolyvVCiXMOWjcD1+gHL5ScbN8Z5zFiQycjfvYu8TRtx6PoqAOrt23CJnIJdy1bokxKNJ+j1pI8sWsrQ0H867uGh6NUa7k5cRZ7p2aq+cSZ3JxfZK+Pyk0Z7decd47NVZfEoPF9pgibWGAct6vVc6TgNuwq+BH81DQAH01sOdWL6c7erkHe6Ur73y4haHfp8LfqkdJxqVsSgLuDWhFXkXjYuNlBr07vcnrTWzA7YuDmRezWGm2OWW9iBsKNLsXFVok0zLj/p1r4Bnq82x9bPk6xDF7n7zlJs/TxLLD+5tFCn0vxhuJpsfHQxG1/9t4+wcXfGoNWTtWQN6tOXcHy5IZ7TR6Hwcseg1qBPTkV9Lgrt/ViyN+9E7ulOuZ9WIHNyRDSIiCo1D7sNR8xT4fPJdBwa1kHu5oo+LYP4JT+S/tM+nMPDCHx/KIJcRvrm/cY+6y1Tn2WyM4FzR+BisjMPI5cX2pkKS4wr8yATyNxxjKTlP/FAY1zEoM6CgfiY2uqT6irYVFcFxeoquERdaVauMi4/2aIRHlNGGZef/H0vWV98j/Nrxj475+cdyD3d8f9+FTKlI4giBpWauJ5DUQT44jV3amFeNTfvYV/7JQzIn6ufBqi0YjJOTWuhcHdBm5pJwpIfSP9pP8qG1Sk327iMp0ONoDMYl588zz/Ezaqd/3Fn9aXbu15Ib15y5DFz5MOAJaIotjLtb02RQz4fcAHGmkJgQkVRvFiKfvHzmgE+oihuLX7dx448sAdwFEUx2RRmc1cURQ9BEKqIonjPpHcRGCSK4qXS7ilrUNsyr8joA8qylkSjs/py4S+jkJX9Z99qrrISC/oXaTt6T5lrAizUW06w+6u4OVhEgpUJf4fJ8fR/smP8n/LYkS9LXrn615aVtEZpjvxfpbgjX1a4WJsrUwY0rx5X5pr3bj7H5MPnIFm0K3NND7RlrmknK311p7+Cl0fZP685OWVfpkChI1+W1PWzspRqGZCR7vj0RP8BoQ9//8cdWMmRLx0pRt6cK4BOEITLwAaMse2PmQssBa6YJq/eB7o8TVAUxRNPSeIM/C4Igj3GGSmPJ9kuEgQhxLTvAMVWvJGQkJCQkJCQ+F/hf3DM+Zn5n3TkRVGcXWLbyfS/FmOIS3EOmY6pgRE8A6IoHnp8XmnXFUVxYLFDjayk7fks15KQkJCQkJCQkPjf5H/SkZeQkJCQkJCQkPh38CJPNv2nkRz5v4AgCB2Aj0vsjhFFscc/kR8JCQkJCQkJCYn/HSRH/i8giuJeYO8/nQ8JCQkJCQkJCYn/PSRHXkJCQkJCQkJC4oXFIEqhNaUhfRBKQkJCQkJCQkJC4l+INCIvISEhISEhISHxwiJKI/KlIjny/yUMP1j2H60YpLEvc82/6xVQ9cCy/8BG5QEbylwT4P6xZWWumdi/5Jzrv87FeJ8y1wRwFsv+IzNJ0X/Ph1Dq+KSWuebf9fGm3y6sKHPN0Q2mlblmZcXf8+Gea9HlylxzWEjZf2QKwPmRc5lrVupW5pIArP/Vtcw1PTLK/nmtqCsoc02A0PJJZa55Lta3zDUBKtn8PR/Gk3ixkUJrJCT+H/k7nHgJib/DiZeQ+DuceAkJibJFGpGXkJCQkJCQkJB4YZG+7Fo60oi8hISEhISEhISExL8QaUReQkJCQkJCQkLihUVafrJ0pBF5CQkJCQkJCQkJiX8hkiMvISEhISEhISEh8S9ECq2RkJCQkJCQkJB4YZHWkS8daUReQkJCQkJCQkJC4l+INCL/X8bA2UMJDQ9Do9awJnI5MVejLdKMWjyOGk1qospWAbA6cjkPrsdQo0ktpnz+LsmPkvHw88BeJkOTkE7UuDVkR9230HGo4E3ddeOxcVOSHXWfK2NWImr1KFyV1F46AsdKvug1Wq5OWEvuzViUVfxptP1DbJwcEEURUa/n7sLNPFy/20K3zrrxKNyU5ETdJ+qxrrMDtVe/g32gF4Jcxv01O4j/8TAATn164ty3F3IvTwwqFTkbN5PzzY9muoqK5fF4fyq2LwWTteYrcjZuKTwmOCnxmBWJTZVKIIqkz10MdxIKj8/9eAZt2rVErVYzYfQMoi7fsCiPFi2b8P7cSASZDFVuHuNHz+R+zENGjR1Mz9e7AGATXA7BTsnB71fx8Rc/YTAY6NmmGUN6tjfTyslT8+6yb0hMzUCv1zOgWxu6RzQlJi6JqUu+KkwXm5TG6D6v0M60bd+sIR6Ro0EuI/e33WRvKFEGlcrjNXsKti8Fk7nqa7K/22J2HJkM/42r0aWkQu/lhbtrf9Qf3zb10KsLuDB+LVlW2kPQ4PZUGdYRpyA/dtUYQUF6jtlxt3qVabVrLgUpWRSkZXFj3BpyomIsdOwreFNr3Xhs3JzIiYrhmqn+K4zuil+vFsb6UshRhgRypMZQDOoCWpxbiY2rElEUid98hJtTPreqW8ekmx0Vw1WTbsXRXfEvoXuoxlB0mXm49uuJy9s9UHh5IGq1ZH61mcz135vp2gSVx3vuZOyqB5O+fANZ3/xceMx7ziQcWzZBn55JbM/hFnkqzvAPR9AgvAEatYalkz/j3tV7VtP1m9KfFq+0wKA3sGvjTrZ/vf2JusWZNX8JR46fwcPdja0b1z7zeQB9PhhE7fD6FKg1fB25iofXLOvuMX1nD6ZZ73DG1uwHgIOzI0M+G4tHoBf2cgWn1+8iassR2s3uR5XwemjVGnZErifp6n0Lrc6fDMWvdhCCIJAek8iOyevQqjTU7N6MJiONz1WBKp9dM78m6cZDOszuT3B4XbTqArZFriPRimaXT4YRUDsITJq/T16LVqUpPO5fpzKDt35I1uwP0Rw+jG2jRriMewdkctQ7d5K3ybwNyCtUwHX6NGyqhpDzxZeofvyp8Jjja71w6NIFBCi4EoVdaD3cDQoyftpHytqfKYn/B8Nxbh2GIV9DbOQy8q8VawcyGcHblqBNTOfB0DkA+Izvi0efDsj0WQBodnyL/sZ5Y75eqo99z2EgyNCe2kfBAcvrAcjKh+A4cRH533yC7vIJq2nCP+xHUHg9dGoNeyavJ9laXS0bhW+dyhh0OhIvRbPv3a8w6PTYuTrSYdFw3Cr6oNNoOTXpczJvxQLQeE4/ykUYdY9NXE+aFd2WK0bhVbcyBq2OlEvRnJj2FaJOj2sVf1p8NhzPWpXIuXQPWx83DGoN18etJrcU21Jz3YRC23J9zApErfEDdW7NahAydyCCQo42PYeLPWbjWMUfv6/nIzg6ovD1AoUC9fHTpE6cZaarqFgezw+MfUvmavO+5XG9+X23Gn1yGikTZ5odqvNRf/xMtvX8+LVkWrGtlQe3J9hkW3cUs61ezarTdMNk8h4mA6DacwLV5duUmz0M5DLSfthH0upfLPTKfTgMl4gwRLWG+5OWoTb5CRUWj8W1TQN0aVncaDvO4rx/Emn5ydKRRuT/i6gXHoZfkD/jW43i83dXM+SjkaWm3Th/A9M6T2Ra54k8uF5k8G6cvc4PizZyL+ouB18axtXIz6nxyVCrGlVnvcn9dTs52nQi2sxcyr0ZAUCV8d3JvvqA4+HTiHpnNdU/GgiAQyVfci5Fs79CP871movM1obkXWctdENmvcmDdTs5btINNOmWH9yB3FtxnIyYxtmec6g2ux+CjRynl8rh1L0ziCIJfQajvXMPZZcOKIIqmukasnPI/HSlpZEF3Ce/Q/7JsyT2HkTim8PRxjwoPBbRriWVK1ekWf2OTBn/AQs//cBqeSxc8j5jhk2l3cs9+fXnnUyYMgKANSu+ot3LPWn3ck/0SXfQZacyf90PrJk5mq1LZ7H72HnuPUow0/pxzxGqlPfj5yXv8uWc8Sz+5je0Wh1Bgb5s+fRdtnz6Lj9+Mg17OxvaNKprPEkmw2PaWJLHziC+1xCUHcOxCapgXgZZOaR/ssrSgTfh3LcH2piHZvt829TDqbIf+5tO4lLkF9T9eLDVc9PP3OLE6/NRPbLylV2ZQOinwyhIz+H2jK+4Gfk51T4ZYlUneNZbPFq3i5NNJ6DNzCPAVP8PV2/nTJtpnGkzjXvzvifj5HV0mXm4t6hJ7vUHHKjwNud7f4R/rxa4hoVY6IbMeosH63ZxvOkEdJl5he3qwertnGozjVNtpnGnmK6dnzsub3YHg4FH3YagOn4O59c6Y1PZvEz1WTmkLVhN5gZLJynn930kjJph9T6L0yC8AQGVAhjechgrp69g9LwxVtO17d0W7wAvRoaPYFSbkRzZduSp2sXp3rkda5d89FznANRqHYpPkD8zW4/luxnreGvesFLTVqxdGQcXpdm+8H4dSLgby5xOU9j0xjzazHqT4LahuAf5sbbVZHa/+yUdTXaiJPvnbOKrTjP5suMMsuPTCBtg/NGb+SiFTa9/xJcdZ3B8+VZeWTCE4PC6eAT5sarVZHa++yWdPxpkVfOPORtZ32kG6zu+S1Z8Kg0HFP2QFmQCbd7tw70jV4w7ZDJcJo4nY8o0UvsPwL5NBPKK5rZFzM4me/ly8oo58ACKoCAcunQhbcRI0oYOx7FDB7KWLOFO+zG4vtoSu+DyZumdW4dhVymA2+EjiHt3FYEfjTI77jWoK5q7sRb3k/rV76gWjUe1aHyhE48gw/61kajWzSZv4RgU9Vsi8y1vcS6CDLuuA9DfvGi1rACCwuviXsmPr1pOZt/0L2k7b6DVdDe2nuDr8Cl80+5dFPa21O7TGoDGY7qRcv0B33aYwZ6Ja2k8x/gDr1xEXVyC/PilxWROTPuSpgus60b/doJfW05haxujbtU3jbqazDxOv/cdD/acw8bDmVNNxnEzcj3VSumzqsx6m0frdnKq6Xh0xWyLwsWRaguHcqX/x5xpNZmrw5YAoLqXQGK/USBA/OtDMKRnYlMuwGrfkrF4JdlW+hYA5749LewqFNnWP5pO4kLkF9QrxbamnbnFsdfnk2fFtqaevsnBtjM42HYGiSu2UP6jEdzt/yE3It7BvdvL2IeY17lLeBh2Qf5cf3kkD6atosL8ojaWvuUAd/t9aDUPEk9HEISOgiDcEgThriAI060cFwRBWG46fkUQhPplcV3JkS+GIAiegiBcMv1LFAQhrtj2CVOaSoIgvFnsnNaCIOz4D67VWhAEURCEIcX2hZr2Rf4n+W/YrhFHfjkEwJ2Lt1G6KHHzcf9LOlnn72Lj4oidj5tFOs8WNUnafhqA+M1H8O3UAABl1UDSjl4FIO9uPA7lvbH1dsW3YwPitxgdD4XSAVFnwFCgs9D1KKHrY9JFFFE42ZvOt0ebmYuoM6AMCUQbm4D2YSz6R/Fozl9GF5+IQ6tmZrqGjEwKrt9C1JlfU1A6Yhdam7zfdxl36HSIuUWfuu7YOYItP/4OwIVzV3BxdcbH18si36Io4uTsBICLizNJCZZGV+bqx6XTR6ng50U5Py9sbBR0bFGfP89eMc+TAHlqDaIoosrX4OrkiFxu/riejrpFeV9vAnw8ALCtVQ1dbDy6uATQ6cjbewiH1s1LKQO9Rd7kPl44vNyY3K27zPb7dQjj4eajAGRcKL09ZF19gOpRqsV+gCpDOqDLzSfnjtEJyT5/B4WLElsrOu4tapK8/RQACZsP492poUUa3x7NSfrtOADeHRsS/+MhAHKiYhAUMhRuThbneBTTjS9F169HcxJNugCCgx26uCR0CckIdraoT15AGV6iXaVnorl2G6yUaf75KAxZORb7S9K4fRMO/nIQgFsXb6F0UeJu5dnt3K8zPyz9AdE0PJWVlvVU7eI0qFcbVxfn5zoHoF77hpz61fj2K/riHRydlbh6W9adIJPx2ox+/LLgO7P9IiJ2SgcAbJX25GfmEdwmlKu/HAMg/uI97FyUKK20h4JcdeHfCjubwqG5uPN3yDe9VYy/cBdnfw+qtgvjyi/Gthp38S72Lo44PVXT1my4r+HADtzcfRZVajYANtVfQh8Xhz7B+FzlHziIfYsSz1VmJrqbt0Bv3gbkFSugvX4dNBpsqoagT03BttpLiFodWduP4NKusVl653ZNyPjV2A7Ul24hd1Gi8Da2A4WfJ87hDUn/6Q+L+7GGrGIIhtQExLQk0OvQXTyConZji3Q2Lbugu3ICMbf0tlSlfRjXTXWV8IS6ivnzcuHfCZfu4eRvtE2eIYE8PH4NgPR7CTiV88Ley4UKHcK4+7NRN+XCPWxdlThY0Y09WKSbcukeSpNuflo2qZejcankR/Zl46jy02xLSqFtOYSXyQb49mxByq7TaOLSANCa6h7AtuZL6B7FYRPojy4unryd+3AspW9BZ9mfyX28cGhuaVcBAqzYVvvntK3FUdYLQXM/kYKHSYhaHRnbjuLavpFZGtf2jUj/5U8AVBdvG9uYydbknr6OPjP3qdeRsEQQBDmwCugE1AD6CoJQo0SyTkCI6d9wYE1ZXFty5IshimKaKIr1RFGsB6wFPnu8LYri4ye3EvBmqSLPRxTwRrHtPsBlawkFQXhqGJS7nwdp8UUPe1piGh6+HlbT9ol8m0/2LKX/e4NR2BZJV61fjWavvkynwa/gVK0cAPkJ6dj5m+vYeDijzVYh6g3GNPFFaXKuP8TvFaPxcA2tgn05L+z9PbDz9yDfZCj9ejQlPy4Veyu6uhK6j9M8/HIvyqqBtLqyhqaHFnFz1jcgiuTefIRtjWoYMjIR7Oywb9YYZDLk3pbOtjUUgf7oM7Pw+GAqvhvX4j5zMoK9feFxP38f4uMSC7cT4pPw9/e10Ikc9z4bt6zl/LWDvPbGq6xYah7e4eBgj8zJi8RH0fh6FTlpvh7uJJdwyPp2akVMbCJths6k16T5TBv8GjKZ+eO65/h5OrUIK7oPby90icmF2/rkFOQ+ns9UBgDukaPJXPY5osH8HaaDvzvq+PTC7fyEdBz8n/0Hor2fO/6dG6LNVqEvFr6gSUiz2q6K178m3rLtyRxs8QyvR/IO4489O3938hPSaXLgY1pd+5yC5Cy0qVlP1C3erorreoXXI8mkq0nMQHXkNPZhtah48EcMuSryz15G7vvsZfqsePp5klrsh19aYiqefpbX8avoz8tdW/LZjqXM/uZDAioFlHlerOHu60F6fFrhdkZiGm5+lrYlYkBHLu8/R1ZKptn+g9/swT84kEVn1jN07wL2ffgdzr7uZBfTzElMx9nXert6ZdFwxp1bhWdwAOc2WDqydfq05t6hyzj7eZhpZj9Bs+ui4Uw8txqv4ADOmDSdfd15qUMDzm/cX5hO5uWNPrmobvQpKci8va1qlkQXE4Nt3ToILi7I/P0RHJXIfHwA0CamYVOijm18PdEmFNlwbUJRmoD3h5Gw8GswGCyu49n/FRynLse+7zhwML4Nkbl6Ysgo0jJkpiG4ml9PcPVAUbsp2uN7nngfTn7u5CSY15WTX+k2QKaQU6NnC+4fNg5QpNx4SHBHo9PsV7cyTuW8UPp74OjnTl6x+spLSMfxCbqCQk5wrxbE/Wk+8GHjZI8uu2jw5Xlti2MVfxSuToT++gEN/liIX++WhefJfbzQJ6Xg2CGcvL0H0SWnIPd5tr4FwH3yGDKWr7caG2JfwraqE9Kxfw7bCuARFkLEgQU0+34qjvWrUhBvvf08xtbP0yxNQUIqtlZszYuGQRT+8X9PoRFwVxTFaFEUC4AfgW4l0nQDvhWNnALcBEHw/6tlIznyz4ggCI9/pi4EXjaN0k8skUYpCMJXgiCcFQThoiAIJSuxJA8Be0EQfAVBEICOQGHAuCAIhwRBmC8IwmFg/DPk0WKftbiyHz75jokRY5jxaiRObk50G9kTgJir9xjTbDg3z17n9K5ThG6YXLqQtTZtShO9/HcUrkqaHVhIhSEdyYm6bzYCLNjI8W4fRkFqduHI4pN0H6fxCq9LztUHHK4zipMR06i+YBByJwfy7sSjPnwc+2aN8Fq+EO2de8bO7lmD6uRybKuFkPvzNpLeHomYn4/zwD5FWbJarpbaw0f35+3eIwmrGcGPm35j9rxpZsfbdWyNQZVpMWpnvIb59vFLN6gWVI4DX8xjy+J3mf/FFnJVRaOIWq2OQ2ejaN8stHQRgGcsAoeXG2NIz6Tgxh3Lg8/Yrkqj9tz+XJv7wxPbTNG1np7Gq30YmWdvoct83HELYBA51WYaR+uNQuHsgH1Fn6fqlqxD7xK6ClcldrWqkbvnMA/a9EXmYI99aM2/JVhTsJJBa5exsbVBqylgYpcJ7P1hL+MXP9UslA3P0AZcfdwJ69yUgxt2W6St2bIej67fZ0qj4XzVaSbt5/RHppBbXqeUst05ZT0rGr1D2t14qndtYnasQtPq1H2jFQcW/Gj9EShFc/uU9SxtNIbUu3HUNGm2/6AfBxb+aP5j9lnabSnoHzwk7/sf8FiyGKf+/RBzss2e/2e1f84RDdGlZpFvZd5E+qbd3Go1HNWi8RiyMrDv/vglr1Uxs027HsPQbN8AouWPA/NsPZsNfEybeQOJPXOTuDO3ADizejv2rkr67Z5H6KD2pF19gEFvKMVmla7bdP5AEk/fJMmkW5TBZ9CxWo+mQ3I5znWDuPz2Qi73mUelSb1wqFzMv5IJOLRshmr/kafmsTj2LZqgT89Ae9OKXcV63/KsNhsg88p99jQYx8E273Lvyz/wNfXl5noly+H56lLimQkEHhXbjjXte940z4002fX5mQ5EiqLYBYwhMsWOzQQOiqI4WBAEN+CMIAj7RVHMs6LzmJ+B3sBF4AKgKXHcTRTFVtZOFARh+PTp02cOGDDAe86Oj4m9+gDPgKKRAk8/TzKS0y3Oy0zOAEBXoOPQloN0Gd6N9v070aaPMU703pU7pCemIVMosPFwxt7fA01ihpmGNi0HGxdHBLkMUW/APqAojT5XzdUJxol0FQa1x79bU+qseofMC3ewD/TExsOJ7Kj7OJTzsqqrKEU3oE8rYlZsA0B9Pwn1w2SUIQFkX7yHatc+FIH+pI6bjuvoIci9PNCnpvEs6JNT0CenUHDtJgCqA0dwnz6efUd/BeDyhSgCAv0K0/sH+JJYbOQbwNPTnRq1qnHxvHGkaNtvu/n+5/Vmabr36owhKxFfTzeSUovuOyk9A28PV7O0vx88xeAe7RAEgQr+3gT6eBITl0TtkEoAHLt4neqVy+Pp5lJ4ji45BYVfkQMr9/FGn/JsZWBXtxYOrZoS2KIRgq0tgosznW+uRx2bSsalaBwCika37P09yC9Rb6URNKgdfh3q498hDEOBFkEhx71uZQx6PXb+nk+tf7sAy7bn270Z2vQcGh34GIDsS/ewDzSOKOmyVYgGEadq5UnmVKm69lZ0/bo3Mwur8WhZG11cEnJ3V9Dpydt/DJc3uqL68+Qz3fvTeKX/K3To2xGAO1du4+VfNMrr6edFepJl3aUmpHJ8tzGPJ/ecYMLiCWWSF2vI7F14f9ciAGIu38UjoGjUzt3Pk6wkc9tSoWYQPpX8mHd4BQC2DrbMO7SCma3H0rx3OHvW/Ebrfh1o26cdjh7OJF9/iEsxTWc/D3KSzUfyiyMaRK5vP0WTEa8QZQrR836pPD1WjUWVkcPb388g/kq0maaLnwe5T9G8tv0UTUd04fKWI/jXCaLnincAcPRwRt62Nnk/bkbuU1Q3cm9vDKlPD3N4jHrnLtQ7d2FTswau781CF2sML7Px80RXogy1iWnY+BfZcBt/YxrXTs1xadsI5/AwBDtb5E6OlPtsErETl6BLNd2fCNpTe3EY9j4AhqxUbNyLtGRunojZ5teTlw/BYcAUAASlC/LqYWAwoIs6hU2LzvQb1hmAxCvROPub11VekvVybTqhB44ezvw+vWhSfkGumr2RRntYr39bqnVpQsvlo0i5cBdlsfpS+nugKkW33sQe2Hs6c3CoUfelAW2p+la4sdxy1SiKzct4dttiLA9NQhra9BwMKg0GlYbMUzdwqlkRdXQC+uRUbKsFU3DzDob0DBTPZVdr4tCyGQ7NGxvtqpMjvt+sIkI0vvEtaVsdnsO2AuiKhYglHbhkvGaForfFNv6eaEu0sYKEVGwDvHjslNj6e1mkkbCOIAjDMYbEPGa9KIqPO/on/FQskniGNM+N5MiXLe2BV4vFuNsDFQDLJU6K2Az8BLwE/AA0K3H8J4szTJga0HqANyp2F0MjwugwoDMnth0lJLQqqpy8Qqe9OG4+7oX7G7ZvzKNbD/nj292c3n2SrJRMQiPC6PFOb5AJOAb5os1RobHSGaYfv45v18Ykbj1JwOstSdpzDjBOHNKrNYhaPQatjoStJ4gauxrvtqFUHNwBbY6KrAt3sXF1pOAZdFNMuvlxaXi+XIvM0zex9XbFsUoA6gdGh1oXn4hNhUBs69TEIbwFol6PevWXTyj2IgxpGeiTUlBULIfuQSz2DUPJP3GGdmONk37atG/J4GFvsfWXXdRvUIec7BySk8w78szMbFxcnKlcpSLR9x7QMrwpd24XjZ45uzjRpHlDDPHnqBlckQcJKcQmpeLr4caeYxdYOGGgmZ6flzuno24RViOYtMxsHsQnUa5YXP7uY+fMwmoACq7dQlE+EEWAH7rkVJQdWpM6Y/4zlUHmyi/JXGksL7uwurj0780fplVrfNvWo/Lg9sRtPYl7/WB0OWqr7cEaMV/vI+brfYU69RYN5e6sDRQkZ6HLUVmt/4zj1/Hp2oSkrSfwf71VYf0DyJ0dcG9ag2P1R2Mwhen49mhGQN9wEn87gVvTl5DZKsi+ZDlyWVw3oISuwqQbNWZl4b78uFRsyvkhONijCPTDoUl9FH7e5B0qG0d+57c72fntTgAaRDSky4AuHNl2mGqh1VDl5JFh5dk99ccp6jary77N+6jdpDZxMXFlkhdrGPKzmdPZ+Fapdnh9wgd05My241QODUGdo7IIn4n68wKRDYsmwa649h0zW48FID0+lZea12ZwZKtGAAAgAElEQVT70i2k7rrA4J0fcX3HKWr3epnr204SEFoFTY6KPCvtwb2iLxkPkgAIaRtK2r14AFwCPOm1bgI/D/uMuPN30CESHFGPhgPac23bSQJDg8nPUVt15ItrVm1bv1BzZYuil6yvLh5BhSsH0Bw7hmO3rsj9/dCnpGLfJoKsOc8+YVjm5oYhMxN9Wjpybx8Koq4i2Chw7dqSR+MXm6XN2X8az/5dyNp+BId61dDnqNClZJC06FuSFn0LgLJxLbyG9SR2onFCpsLbHV2Ksa0oajfFkGCcqG94eAeZVwCChy9iVhqK0Jbkf2d+vby5RZNC7d+cgO7aGXRRxh/A2mO7+G6S8UdjUEQ9Qge04+a2k/g/oa5q92lNpZa12dJ3gdlIsJ2LI1q1BoNWj16rI2bbSY6OX0e5NvWoPrAdMb+fxLt+FQqyVait6Ib0bU1g69rseaNI9+Y3+7n5jTEEquWK0fjVrQyAS1gI+lJsS+bxa3h3bULy1hP4v96aVJMNSNlzjqoLBiPIZQi2ClzqB/NonfHZLLh+E0X5cuTtOQgKBY7tw0mbNc9C2xpZq74ka1Uxu/r266RMnMm5WKOz7WeyrbEm26rNUZP/jLYVwM7bFU2KMYzQPbQK6PTYlvPBtrwP2sR03F99mftjPzXP074zeA98hYzfj+IYWhV9Th46K7bmReNFWEe+uM9lhVig+MzickD8f5DmuZEc+bJFAHqJonjrqSlNiKKYKAiCFmiHMXympCP/pNF8My4ePE9oeBjLjqylwLT85GOmb3iPdVNXkpGcwdhlE3HxcEUQ4P71GD6fYRw9b9K5Ge3e7ohBp8fV2w1BJ1Lr0+FEjS9api5s0zSuTlqPJimDWx99T9114wiZ/gY5UfeJ/d44gcapaiC1V4xG1BvIvR3H1YnrAEjZfxGfDmGUeyuCvLvxXB27ulA3dNM0rpt073z0PXXWjSN4+htkF9ONXvIrNZePoumhTxAEgTtzv0f7eBmuBe+DTI7PmsUYsrLJ2fI7uugHKHsal6fL+3UHMk93fL9Zg0zpCKKIU59eJL4xGDFPRcbiFXjOmQE2NujiEkif80lh3g78cYQ27Vpy8uIe1Kp8Jo4pWj5s4+a1TB73HkmJKUSOf58vvl2GQTSQlZnNxDFFS5R16tKWwweP06maDQq5nBlDX2fU3FXoDSLdI5oQXMGfzXuNk55e7/AyI3p35L2VG+k5cR6iCBPe7oa7i3ECp1pTwMnLN3lvRF/zBqA3kP7xCnxWLQSZjNxte9BGP8Cpl7EMcn8xloH/xtWFZeD8Zk/iXxuCmKcqtV0l7b+Eb5t6tDv1GTq1hosT1hUea7JpKpcmrSc/KZPKQzoQMqYLdj5uhB9cSNKBS1ya/LmZji5XTdUFg9GmZXN9fNE8n7qbpnNj0joKkjK4+9Emaq0bT2VTu4r//mBhOp/OjUg/fKXQiQfIuxWLc61KtHm0EdEgkvDTYVL3XTC1q+lcn7TO1K42UXvdeIJNunHFdL07NyKthG72hbvk7TuKU4+OlN/2pXH5yQ0/o733AOferwCQs2Unck93An9aiUzpiGgQce3Xg0fdhiHmqfD5+F3sG9ZB7uZKhf2byFj1HWw4YFHG5w6epUF4Az4/+oVx+cnIzwqPzd4wm+XTlpOelM7Pq7cQuWwK3YZ2Jz9PzYqpyy20nsSUDxZy9uIVMjOzadP9bUYP6Uevrh2eel7UnxeoHR7KvMMrKFAXsGHKqsJj475+l2+mrSXrCc7AjuU/M2jxGD7Y8yl2gsCfC3/ixrZTlG9YjZFHPkWrLmBnZFH/+PqGSHZN/YLclCy6LBmBrZMDggDJNx6yZ+YGAJqP74G9uxMd5g4EQK/X82XX9wgOr8eYI0vQmZaffEyfDVPYMfVzclOy6LZkJHZODiBA0o2H7Jr5dek3r9eTvXQZ7osXgUyGetdudPfv4/DqqwCot21D5uGB5/p1CEpHMIgoX3uN1P4DEFUq3ObOQebqgqjTkfvV17jPmY2LQUHGlv1o7jzE403jW5n07/eQ8+c5nMMbUPXQekS1htipy55aN37vDsKhehA2LiJiejL5m011YzCQ/8taHEd+CDIZ2tP7MSQ+xKaZ8XraE0+Oiy9OzMFLVA6vy5CjxrraW6yuemyI5I9pX5CXlEnb+YPIjkul79bZANzZc5ZTy7biERxAp89GIuoNpN2J47TJLsQeuES5iLr0Ov4penUBRycV6bb7NpJjU75AnZRJs4WDyI1N5ZVtRt0Hu85yeelWHLxd6bp7LjZODshtFbSO+wHVvXhujC1qn3U2TeemmW2ZQOXpfciNiim0Lao7caQfvESjPxcjigbiNx0k76YxAkJQ2Bjf8vXsglOvruRt223Vrvp9W9S3OPftRcLrg59oVwESTba1/anP0Ks1nC9mW5ttmsoFk22tMqQDVU22tY3Jtl6Y/DmBXRtTeUBbDDo9hvwCYsYsRu6iJHjjbAS5jLSfDpB/+xFebxvrPHXjHrIPnsc1ogE1j63FoNbwYPKKwmtWWjkZ5ya1UHi4UOvMlyR8+gNpP+0vmW0J65wFQgRBCALiMM55LDmfchvwjiAIPwKNgSxRFBP4iwhSbJR1BEGYDeSKorjYtJ0riqKTIAhhwJLH4S6m0JpIURS7CIIwH3ABxoqiKAqCECqKotU1vUqc1wzwEUVxa/HrCoJwyJTmnDWN4rxRsXuZV+SgfPunJ3pO/q5JGdUrWFny8C/S6M5ffr4suH/s6R3zf0Ji/4/LXPNivM/TE/0HOIuWcwT+Kk+O8P3PqeJT9iNV47Jsylzztwsrnp7oP2B0g2lPT/ScVBbtylwTQPfX31BbMKzi3/PGI+XR868c9DQqPW1G1n/A+l9dn57oP8Cj7E0AFXUFZS8KBJd/tjCa5+HxiHxZU8nmmcf9nov6j37/x4fDTwf0/Med1cbxvz6xHARB6AwsBeTAV6IozhMEYSSAKIprTXMhV2KcD6kCBj2Lf/c0pBH55+cKoBME4TKwAWNs+2PmYqzEK6YKuw90eZqgKIrWv8AhISEhISEhISHxwiOK4i5gV4l9a4v9LQLWPxLyF5Ac+VIQRXF2iW0n0/9aoE2J5IdMx9TAiGfUP/T4vNKuK4pi62fNr4SEhISEhISExP8WkiMvISEhISEhISHxwvKPx9W8wEiO/N+MIAgdgJIBzDGiKPb4J/IjISEhISEhISHx34HkyP/NiKK4F9j7T+dDQkJCQkJCQuLfyDN8WfV/FunLrhISEhISEhISEhL/QiRHXkJCQkJCQkJCQuJfiBRaIyEhISEhISEh8cLyInzZ9UVFGpGXkJCQkJCQkJCQ+Bcifdn1v4TsIe3KvCITT8jLWhIb27/hk37AqZSy/wppt3fL/quGgxbHl7kmwNsahzLXrOWbWuaaAPmqsv+y6aNslzLXBHBXaMpc8xrKMtc8blP2+QRYfa7svxj8R82ZZa4JIPwNC9QF2KvKXBNAIS/7bxFvNpS9vZrSOb3MNQHu7Sx7G5Cv/XsCDPL1Zd8P+rr8PV9gzcgt+34AoFnCL//4cPhRv9f+cWf15cSf//FysIY0Ii8hISEhISEhISHxL0Ry5CUkJCQkJCQkJCT+hUiTXSUkJCQkJCQkJF5YRF7IqJYXAmlEXkJCQkJCQkJCQuJfiDQiLyEhISEhISEh8cJi+Menur64SCPyEhISEhISEhISEv9CJEdeQkJCQkJCQkJC4l+IFFrzX4S8VgPs+45GEGQUHN1Nwe6fzI4r6jXFrvtAEEUw6Mn/YTX6u9eKEggylO+vQtRpERydqKSRk/XzHjK+2GxxLe8Zo1C2bIiYryFxxqdort8FwK1fN1x7dwJBIGvLbjK/3Wp2nkv/1/CcPIKkKXNxHzMQQSYj57fdZH1lnlebSuXxmhOJXfVg0ld8Tfa3Pxvv0dcb73lTkXt6gGgg5+ddZH//W+F59ef2JyCiLnp1AacmriMj6r5F3kMGtaPa0I44B/nxS60RFKTnGq/p7EDTlaNxDPBEppAjf3QQ/Y1THH+QxqKjtzGIIt1rBDA4rJKZ3rnYDCbuukyAi3EN34jK3oxoVBmAzt8cR2kjRyYTkAsCdpQrPG/g7KGEhoehUWtYE7mcmKvRFnkdtXgcNZrURJVtXM96deRyHlyPoUaTWkz5/F2SHyUDkL/zHHeW/ApAzY8G4NumHnp1AZfGryHLShlUGtyeysM6oQzyY2+N4RSk5wAQ2LM5we+8CoBtQQ5p85Yj9/LAY9rov1xX+eejsG9UDwQ5mZv3krZ+i0W+fN8bgVOrhhjUGhKmLSH/+j0AZM5K/OePxy6kIiCSMH0p6ks3sateGf857xCosEfU6bkx/Sv8X2uBd5tQ9GoNV8etIcfK/TtU8KbOuvEo3JTkRN0nasxKRK2e6ouG4t+rBYJchup+Eg9GL0Z966HZubblfaiyejIKdydUUdFEj1uGqNUBUGHOEFwjwjCoNcRMXIHqajS2AZ4ELRuPjbc7GAwYNh3mxpd7AWg0px+VuzfFxsmBvIR0Huw6y/l5P5pd7+UVo/CqWxmDVkfqpWhOTPsKUafHtYo/zT8bjmetSigX/8Afn28HoM8Hg6gdXp8CtYavI1fx8FqMxf0/pu/swTTrHc7Ymv2M5eLsyJDPxuIR6IVcLkews0PU5JZ6PsCs+Us4cvwMHu5ubN249olpS1Jj3gB8TG318rg1ZFupq4qD2xM03NhW/6g+HK2prSqDA6i7bAQutYO4veAnYtbsKNR8XP9XStXsQCWT5r7qw8w06ywbiUvtIOK2HMGnaTUEuZy0H/8gec0vFjqBs4fhEt4Ag1rDw8ilqK9GY+PvRYXPJmDj7Y5oEEn7fi+pXxvrxrNfJwJnDQGZgD49i4cj56O+cttM0//94Ti1boCYryF2ylLyrxmfgapHvsSQp0bUG0Cv5163iQD4Th+ES5tGjC4QSX+YxNYp64mY+Boh4XXRqgv4LXIdCdcsy6Dbx8MIrBMECKTFJPJb5FoKVMbvEVRqUp1O7/fD0Q3EvGwK9v6Efe+RIMjQnthDwR/mz66iThNsu/YHgwEMejQ/r0d/7xqCTyAOQ94tTCfzCUQsyKf6VHWZlal99UqUnz8a20oByOxt0TxM5u745aiiLO2pXXkfQtZMQu7mhOpqDHfHGp9dzx4tCRjTHQCDKp+Y6etRXb9PpfnD8OnbBlGEgoR0kn45yv1F5v2hfQUfaqybgMLNidyoGG6MWVFoD9ya1SB47iAEhRxteg6XenyAR3g9qn4+AZmjPbr0bO42e8sin89tB18KovyXc5G7OWPQaLnx9nxyztywev9V105E4eZMXlQ0d8YuR9Tq8Or5MoFjegCgz1MTPX09qusPip8qB84BcUAXC+H/BwzSZNdSkRz5/xYEGQ5vjSXv02mIGako31uJ7tJJDAlFTojuxkV0l04CICsXhMPIWeTNGlJ43LZdDwzxD1GENiP3/WEk7M6g4ubl5P15ioJ7RTrKlg2xrRjA/Y6Dsa/7Ej7vv8OjPhOwDamIa+9OPHx9PKJWS+Dn88g7fAbtA+NHkBR+Xjg0DUMXn4THhGEkDpuCLimVgO9Xojp0Em100TX02TmkfbwKZXhz8/vU60lfvI6Cm3cRHB0I/HE16lPnISUf/4i6OAf5saP5ZDzrB9NgwSD2dfnAoqhSz94mft9FIn6ZZbY/ZGA7sm7HcWTAp9h5ONPj3Kfk3jjDwsO3WNMtFF8nO97afJZWQV5U8XAyOzfU343lXetZrZr1Perj7mALFH0Qql54GH5B/oxvNYqQ0KoM+Wgks7pPtXr+xvkbOL3rpMX+G2ev88ngeUDRB6F82tTDqbIfB5tOxK1+MLU/HsKxzu9ZnJt+5jZJ+y7Q7Nf3zfarHiZzoscctFl5NOwZhOf7E5C7uZI4YtpfqysnJRUP/Uzi2JlkHrlB0C9LyTl4ioK7jwqTK1s1wLZiIPfaDsW+XjX85rzD/ddMzsqsEeQdOU/c2Plgo0Bmb2e836mDSVnxPTd33MarTT1qLhmBJiGdY00m4BoWTI1PhnK6k3k9A4TMepMH63aSuPUk1T8ZQuCbEeTHpuDZqg73V20n7dBlaiwaRoU5Q7j1hnkbKj+zP0mfbyd92zEqLhyJV982pHy7F9eI+tgFBRDVYjTK+lWpuGAEN7pOQ9QZePThBlRXo5Ep7XlpzxLij0ThVN4b7/rBpF9/yKUlv9Hwvb5cW7vLIq/Rv53g6Ng1ALRcNYaqb7bm1rcH0GTmcfq976jQMawwba3WofgE+TOz9Vgqh4bw1rxhLOg+w0IToGLtyji4mH+cKrxfBxLuxrJy6Mc4ebiw5Nx6dE9x5Lt3bsebvV5lxtzFT0xXEu829VAG+XGoyUTcwoKp9ckQTnSybKsZZ26TvO8CTUq0VW1mLtdmfoNfpwZmmo5B/hxuMsGkOZQTVuo/48wtkvddoLEVzeszN+DbuSEV+rXl7quT0CamUXXbp2TtP4PmTlF7dQ4Pwy4ogButRuAYWo1yH43iTvcpiHo98R99hfpqNDKlA1V3LCHn2CU09+IInDmYR7PWkLv9T0L2r8X/w5FE95hUqOnUugG2lQK4EzEch3rVCJg7muiekwuPx7w5A31Gtlme845dImnRN2zWOtNueh9eXTgUO0d7lrWeTLnQYLrOG8T67pZ2cM/cjWhy1QB0nPUWjQe05+ia7di7ONJl7iC+G/Axw+vdQXB2wzFyCarlMxAzU3GctgzdldMYEov1LbcuobtyCgBZYCXsh8xANWc4YnIcqgXvGBPJFDgt24p65az/Y++8w6Oousf/me3Jpm96aAkBRDqhSe8dFRUVLEiHF1BBqtheQey9UcRewQKKCCiIICo1AUIPgUB6dlM2ZbN1fn/MZpNNNggaf+r3nc/z7JPMzL1nzr33zJk7Z+69Q9qb6Q1Tp2cv0eTpuRRt2Yu+e3tMm38hsGtrEp6cTuroJXXK3GTZXeSs/QbTpr3EPzWDyPGDyHt/G9ZLeZy4+WGcJeWEDOhEwjMzyXxhPbomUey9Zgr61o1JXDGZsIEdKdyZjPnQWY/MhIfuIHP1ZvI3/kLLZ6YRM2Eg2e9tRxXkT4unpnF0/BNYs4yow4NAoaDFU1PIefhV7JdyafrxM2gSG/9pPxj3yoM4cvI50GEajRePp+WqeRzqPL1O+Zs+dBfZazZj2rSXhKenV5f/Yj6pN7nLP7ATzZ+dybFRS2tmvQ84Cfw1X96T+VP8zw6tEQTBIAhCivuXKwhCVo3tX9xpmgmCMKFGnv6CIGz+A+fqLwiCKAjClBr7Orn3LWiI8igTWuHKz0Y05oLTgX3/LlSdenonslZW66TVUfNDiEJoOKr23XGcO4Foq5Tk2B2Yt/yEfuB1XmL0A6/DvGkHAJVHTqEMCkAZEYYmoQmVR04hVlrB6cJy4BgBg6t1iFgyg6IX14JahSMrF0dWLjgclG/dhX9/b11dhcXYjp9BdDi89juNhdhOSdF/scKCLf0iyshwABoNS+LC53sAMB1OQxPsjy4ypE5dFaVmUJ7p46ulIqj1OgBUeh1iZTmpOUU0DvajUbAfaqWCYS2i2JX+57942nVIN3Z/sQuAs8ln0AfpCYkM/dNyo4clcWm9VAfFh9NQB/mj9VEH5tQLWC7VLUfRwbPYS6SvDlqPnkQVF4P9UvafbitNQhOcpaWSzdkdmL/dTeAgb7sKHNyDko1uu0o5jSJQjyoiFEWAH/5d21K8QYpiY3fgKnV/GVEUUQT4A6AK8kfhpyF7w24ASg6loQryR+Oj/GG925D3zT4AstfvJnJEFyKGd8FhLqdwTyolh9JQaNVomkajCvf+YmZgr3YUfvsLAMYNPxI6rDsAIcO6Yfr8RwDKD59BGaxHHRmKPb+ICvfbFld5JSVns/GPDqPJsCREl8ix178hf/9pNMF6BGVdl5y184jnf2PKOfxjwqQ6MpkxHUlHtFd/Lbnj0K789uVPAKQnn8U/UE9wRN3yCwoFtzx4F188+YHXfhERrV56KNT560D8/S+QdunYjuCgwN9NV5uo4UlkbXDb6qGrt1Wb0UxJSjquGuWPGt6FLHf7F7vbv36ZBfXK1IYHYyssxXYpD9HuoOibPQQP6e6VNnhIdwq/kNq7Ivk0yiA9qshQHPlFWDztbcGalok6yoB/xxa4LFZEixXR7qAi5TSC2juWFjS4O8Vf7QTAkuKWGXF5v1D2czI4pXbKTE4jpnVTUr7c49nWBfoT4MMGqjrxACqdhqqvvLe7vicntx6gJNsEgBAejasgG9Ek3Vsch35C1aFHLWHV9xY0OvDxlV11v1FgteA6d7zB6hRAmxCHtkk0BZ/vomT3EQKSWnquvdoE9W6HabMUFCnY8COhw7tJdXjwNE633ys9fAZNjIHQYd0o+HwXzopKzIfOogr2R1GjnqoI7d2Wgm+kh5jc9T8RPqIrAJE39ca4ZR/WLMl27UYzQZ0TsZzPpfTb3ThNxTjN5Q3iB9WNIil8dxMAeR9sR20IQu3D7oN7t/WUP3/9LsJGSOUvrVn+Q1L5q9BI/mYU8FYdgf8fERH+9t8/lf/ZjrwoiiZRFDuKotgRWAW8WLUtimJVT6UZMKFeIVfHMeC2Gtu3A0d8JRQE4arflAgh4bgKq29MYpERRUh4nXSqTr3Qr1iH/30rqHy3OoKmu30WlRvWIugDwWbz7HfkGT0O0yMjyoA9t/pcjtwCVJEGbGcv4NelLYqQQASdFn3frqiiIwDQD+iBI8+E7Uw6KBQ48qtvzM58I6qourr+HqrYKLTXJGI9dgoAv+gwyt03H4CK7EL8o6+8c3zmne0EtYjjxuTXGLHzKWx7Pie/vJKoQJ0nTVSAloJya528R3NLuPWTfcz+OoVzpuoIpgD85+sUJny2ny9Sszz7Q6PDMGVX14Ep10RYVJhPvW5fcCfPbH2Jux+ejEpTbRotO7fime9eZMl7DxPQShqyo4sJo7JGHVhyCtHF+Jb7ewSMHY79zHmcNdr6j7aV5prmKHQ6T1vZc42o6thVOPacmnYlnUvdOAZnYQkxT88jftOrxDxxH4KfFInKe2INUYsn0/fw67R89E4qM41UZlWXv9JH+dVhgTjMFdIQBaAyW0qjiwmj9NQlIkdJNzdHqQVtjMHrpqYKDZRueO689hwj6mjpuCbagK1G3dtzTKijvc+taRRBWNumGJPP4R8dii4sgKhurRj1zWPowoOI6du23joUVEqa39ybrB+P1psmNCqMwho6FOWaCImu2/4DJw7nyA8HKSko9tq/872txCTG8ez+NTy67XmcZX/+obU+dDFhWH6nrf6IzN9r/ytBFeCHwz2cDbzbuQp1tAF7drW92nNNdXylplEkfm0SqEg5jTraQNlvx4h9cBKtfn6HgD6dqTzpPexJFW3AnmP0kqmqOq8o0uy9x2m+6SVCbx/mU+/O4/phq6j0dMIBzLmFBNXjB298djqLDrxBRPNY9r27HYDwhGh0wXomfboM/yWvoOo6AFdRdTldRUaEYEMdWaoOPfF/ZA3+/3mcyg9erHu8/XU4a0TxG6JOASrPZODXtjm2bCNho3uijQ3Hlm1CU8vuVWHe164tx4Qmum45IscPpvjHZDTRYdiyjaBQ0GXHs/g1i6Ls2HlKD6dV61vLl1izTWjd9ubfPBZVsJ6OXz5G0vaniRrXF210GNYabSM6HA3iB8WKSlSxkQAYxvREUCi8/FZV+R21yq/14Ruixg+ieGeyZzv+8ckAi4Dff6qX+Vv4n+3IXw5BEKp6Yk8BfdxR+nm10ugFQXhbEIQDgiAkC4Jww++IvQjoBEGIEgRBAIYD39WQt0sQhJWCIPwE3CcIwjhBEFIFQTgiCMLuK1Dax866URFH8l7KH5pCxWuPSePlAVX77oilxbgyzuLrobN2BMLnuUQRW/olCt/aQKN1TxK3dgXWU+ngdCLotITNuB3Tq+/Xq36dc/wOgp+OyOcfwfTsm4jl7hvuleh+GWL6t6foeAYbO81h65AH0fS9FZSa3813TWQgWyb2Yv347tzevhHztlR3tN65uQuf3NaN18Z05LNjmZQJkq6Cjzr0peonz3zAvIGzefD6BQSEBHDDzJsAOJ96jtk9p7NoxDy2vruFru+4X8/X0zZXi6HXtQSOHUHZlh98iLv6tgq+exzWoyer28qHXr5NWERQKtG1SaTo4y2cv2EuLksl4TNuBSB0wkjyVq5ld+fZnH7kfQKvbfL7+l7GTnI2/Iw6WE+PHU+hizVQeS7Lc5OuV8kq+fXoX4XCX0fi2sXsf/RD7GUWSZZCQBOs59sxj1F6Po+kJbf6ECJx3cp7yNt3ivz9p+tN40u/2sUPjgwlaeR17Hz3uzpp2/TtyKUTF1jYbTqPj1yIMiC8nob58wg+KuxqbetKaDCZV2BHtdu72aolZD3+Fq4yCyCgTWxE1vK3ON17EsWbdqG/rr23yMvYV/q4RZy7/n4uTH6UsLtG49+1jVeyvrNvwOl0UppX5EOE7zrYuHANz3afTUFaFm3HSFF2hVJJbLt4Ppz0HJZXH0LdqReCn95n/po4jvxCxePTsax+HO2Yu70PKlUomiRKb3p9lM3DVdcpXFz4CppGkcQ/NxtlgB8um8O3bN8XvtdmUM+2RI4fxMUn3q+2e5eLg4MWUrLvFP6tGqG/pnENkfVfb4JSSWCHBI7e+SRHb19B0/m3oPH1QNUAfrDy1AUCh/Wk/bZnUOp1UntfwX27tl0E9WxL5IRBZDwhvakLHZyE3VgCcMiHVjL/EOQx8pdnCbBAFMXRIA2RqXFsGbBTFMXJgiCEAPsFQfhBFMXyy8j7HBgHJAOHgdqh3RBRFPu5z3UMGCaKYpZbfh0EQZgOTAf4dNrt3NxnRPWx0HBcxSZf2QBwnjmGIiIGISAIZWIbVB2uI6BdN9D5I/jr0U1dDL88hyoqHEd+oVdeR64RdXQEVUvYv4cAACAASURBVC9TVdEROAqkNOYvtmH+Qnr1Z7j/Himi3zgGdaNomm58ExBRBgehH9KHohfW4DQVoYwMx5lfv651UCmJfOFRyrbsRBkeRuxnqxjuVGFKSUcfa6AqnuUfG4Ylr/iyomoSf1tfTr4mTaAqu5CHaDYRHRtL3tELnjR5ZVYi9FqvfAE1ouR9moXz5E+nKbLYCPXTEBkgpQ3z1zBv+mSadhuKSlRx7uhZDLHVkW1DtIGiWvUMUJwv3ZQdNge7Nuxk9HTpedFS47V4ZONI/BtF0G/XMxQdPIsutjoS4xcTRmVu3Rv75Qhs3YQOz08n797FUlsNH+A59kfbqmL3PtTx1TdAdXRdu7LnGlHHRFBVMlV0OI58E6IoHas8InVgzVt/JnzGOACCxw7Glp5Fjx1PSXkC/dHFVZdfFxOGtVb57aZSVEH+CEoFje4eTJPpI9BGhFBy6CyasACO3y9N2Oy19wVUAf5YL+Z58joKzSiD9aBUgNOFOiYce55UDluOCU2NulfHGLC7O1WCSkni2kVYM/PpOG8sHeeNxZiSjj46jIvfHZTSB/rhcjjRhgVidU++rKLDvLHoDIHsnPp2nSoO75hAs7ZN6DG2L+ePpBFWQ4fQaAMled713KRNPJHNonnip1cB0PhpeGLXqyzrP5de4waw9U1p8nhBRi6i04Gg1CA66r6F+iModEH03vEkACUp6fjFGahqHV9tdSUEd2pOcNumxN3Sm+KUc7/b/leCo8yCKsjfs12znauw55hQx0YgDR12R5OrbFqlpNmqJRRt/ImSrdJQBnuuEV1CHCXf/YpKCY78QlSG4Foyjahjqv2COtqAw33equvFaSqhdPuv+HVoScUBabGCuOfm0XJ4bwoz8shMOUdwDRsIig6j9DJ+UHSJpG7+jV7TR5O8YTfm3EIqikqxW6yI5WacGWdRRMZ60itCwxFLLnNvSUtFER6DoA9CLJfG86vadMGVl4kQUF3eP1un4XePxHD7UADM23+jcM9xyo+eI3RQEppYA7ZaDzS1r11NjAFbjfP7t25Ki9ULcBSaab3+v5SlpKGJDQek8fCayFCM2w4QNqAj5aekMe12k9njS0SnC22sAVuuJNOaY8JeaMZVYcVVYaXkt5Mo9Tq0NdpGUKn+sB8UnS5CJ15PwMBuWI6epeK3I2R8uh9dQgyN5t7k0cNTfpMZVZ3yV9eRf+umJD4/ixN3rMBRJMUyA7tdQ+jQrgAXAB3SGPkPgTtrt/tfjfw6oH7kiPwfZyiwRBCEFGAXkpHXDQd6sx6pIz8e+MTH8ZrLgewF3hUEYRrSjPE6iKK4RhTFLqIodhkhFqKIikMIjwalCnW3/p6JrVUINZ1xk0RQqRHLzFi/fJuyhRMoW3wXljeXg92OdeN7oFYRNLIf5T/+5iWn/MffCLphEAC6DtfgKi3H6e7IK8MkR62KiSBwSC9Kv92F7ewF0nvfzvnBE8kceReOvAKcpmIEnRZUKvTD+1PxU93JnPUR/tgD2NMvYv7gC0o/+5rs22aydciDZG09SLNb+gBg6JyI3WyhMv/KO/IVWSai+khRLl14EEJoFK0D4WJJBVlmC3ani21n8+gf7z20xFhu9UQ2UvNKEEWREJ0ai91JuTs6ZLE7WfXOe8wdNYvFI+dxYPs++t7cH4AWnVpSUVru6bTXpOa4+a5Du3PJvYJKzXHP546kYc0v5qf+i8jdepDGt0p1ENI5EXtpBdarqAO/OANd355H8pzXcWRkYT1+GnWTOFRx0X+qrQqfX1UtR60iaFRfSnd421XZjn0E3+i2q46tcJWW4ygowmkswpFTgCY+DgD9dR2xpkn14Mg3YT2bwW+DlnDmsQ+pzCwgdlxfqY6SEnGUVmDzUf7CvSeIGtOdS+9sp3BPKmdXfEz+dweJGz8AQa0kOCkRQaOi9NdjnshfFaW/pBI2Shp9Fz5uAEXb9wNQvP0Ahlukhx5955Y4zRXY3W3a7PnZWNIyOTftGb4euoyvhy7j4rZDCCoF0b2uJaJzc5xWOwqlok4nvsX4/sT1b8dPs1/3+XbFmJLO929t5vGRC0nZfoAeN/UDIKFTCyylFXWGzxz78TALuk5jae/ZLO09G5vFxrL+c6V6yTZyTa92AASGByMo1YhOez2te/W4Ks38PGgpPw9aSt53B4kb57ZVd1tdja1WUZJ8jvOrtvDzoCVumX3/tMzKnCI0hkA0jaMQ1CpCx/TB/P0+rzTmH/YTdrPU3v6dWuEsrcDhbu8mz8zFmpZJwVubPOkrjpxFFCFkTB9J5q1DsWV4R6jNO/YRMnYgAH4d3TILihD8tCjccxcEPy0BvTthPSOtKhLQtzN+7VrwQu/7eGPEUk5tP0jHm6R6bdQpkcpSC2UFdesgrGmU5/9WgzpjPCdNxD+5/RBNu7ZCoVSAWosiPBpBH4RgiAKlClVSP8/E1iqEiBjP/4rGzUGl8nTiAVRd+mPfswVFZCyCoWHq1Pj+Fk6PvJ9zdz1KyfZ9RNzSn7j7xlG8O8Xr2vOSvzcVw2hpTHrEuAEUbTsAgCYunJZvLeL0pCc50u9ejg15gKKt+4kcPxhVkD9BSS1wlFkISmpJRVqWl8yivceJcL/NiL61H8atkkzj1gME92iNoFSg8NMQ1DkR0w+H8UuIQd0oClRKlEH6P+wHXWUVFH/6Heevn0vFvqNSHkGg2eOTseUVYfdh9yU1yh95a3+Ktu73lL/VuoWcnfsKlek5nvQXV37EoaTpIA0zvh3Yyd/QiZe5PMJf8Srz34YgCI8BZaIoPufeLhNFMcAdga8dkV8giuJoQRAOARNEUbzMe26P/Jr5fgAaA9cCD1edVxCEXe40B2vk6440yWQS0FEUxXrDIOYpQ0RVu25ob5+FoFBg+3kbtm8/Rt1PWinK/tNmNCNuQ33dYHA6Ee1WrOvXeC8/CShbtUd7yzQE/wCcNgXmL7dTuPpTgm8bCUDJZ9KqGpEPz8a/d5J7+ckXsB6XohaNPngOZUggOJzkP70Gy28pXvLVGieNtnxA4cvrCJs9ERQKSjduo+StjwkcJ+laumEzSkMosZ+8jkLvj+gSES0WMsdORdMynth3X8J2Jh3R/am3olff5sevLgCQtPIeYvq3x2mxsW/eagqPSmNQ+32wkP0L1mLJK6bllGG0njUaXWQwlUYzOTtT2L/gLfyiQuj+0kz8IkNAAL+LO3Ce3s+eC0ae23MGlwg3XBvD1C7xbEjNBGBc20Z8evQSG1KzUAoCOpWC+b1b0DEmhMwSC/Pdw2ycosiIllHs3VUdzZ+8fDod+knLBL654BXSj0lLjC1592FWL3qNovwiHv7kcYLCghEEuHDiPGsfXIW1opJhE0cy5M7huBxObJU28h75iKKDUhu0fXISkQM64LRYSbl/NSVHpEli3T5axJH5a7HmFRE/ZRjNZ49BGxmCzWgmb0cyRx9YS/vnpxEzqhuWTCM6lROcToreeA/Doll/uq0Uen8EjRqX1U7x59sxvfkZIeMluyr+RLKrqEf/Q0BfafnGnCUvUpkqlUnbOkEaE6pWYb+US/aSF3GZy/BLupaoh2bgEDS4rHZOLF5H3Pj+hA/siNNi5fh9qzC7y9/po8WcmL8Ga14Rfk0jab/6XtQhAZirlp+0Oeiwbh4Rw5LAJVJ04AwZ05/EWVJOi/cf4sLC17HnFaFtEkXCGw+gCgmg4vh50ue+iOh+YGvyxHSC+3eSlp+c/yoVR88R0LU1rTeupOLEBRBFKlFw6Kn1ZO08Qo8n76H5Tb1QqJSUZhr57cF3yd17gsHvL2Dvwrew5BVzd8Z7lGUacZRL78AythzgyEsb8YsIZvR3y1EH+OEQXVjLK3lkyDxuWjSBNv06YrPYeHfh62S4l+G7952lvLd4FSW1OjivHv/As/xkcGQok56bTXBkKIIAUbG6311+cuGjT3Eg+SjFxWYMYSH8Z8pd3DzG9xhugO1tlnn+b/PkJCIGSrZ69L5qW+360SKOum212dRhJNSw1fwdyRybvxZtRDC9tj+BKtAPXCKO8kr29FlAq2W3Ez6wIy6LlaP3rfLI7PLRYo6527/p1OE1ZJZQsCOFY/PXoIkIptf2lagC/RAUCpRaNbbsAgo/+5681zZguGM4AKaPtgIQt3wGQf06u5dKfAXLsTT0XVrT4ounsZy8IC3HCGQ/+wGlPx4iYvqNxCy4EwQBh6mYizNW4Ne+JQBFH0tDnWL+O5PAvkm4Kq1kLnqJymNpqBtH0WSVtPqOoFRQ8vVPFLwhLYPYYucaFBo1xmJp2Fpmchoup4sW/dpLy08uXE32MckP3vnOQjYtXktZQQlTNjyCNsAPBMg9eZHND73jmQDba/ooOo3rh0Fvx753G678LHS3TAeFEvuv27Ft/RR1H+nate/ZgmbIOFTdB4HTAXYb1q/W4TznvreotQQ88T5lj0xC2bwNulumY69UUrj+hwap0/BJYwi/eyQqQwggYss2cW7ea5Qflfxpqw+Wkb7gDc+12+LN+ahCAihPPU/a3JcQbQ4SnvsPYSN7YM2SxqaLDiepIxaR+OZ8woZ3RxRFbLmF5H72ExkvfE67j5Zyev4qbHlF6JpGcu3qeahDAig9dp6Ts1/x+IPG/7me6NsHgOgi56MdZK7ZQtigTrRZcz8K9/h2R0ER5b8ewZJ88g/7wdCJNxBx7x0IfjqcJeWcuPMJyo9I5W/94TLSHqguv7T8pFT+s3NeRrQ5aP7cLAyjemDNdJff6eTo8MWe67RnzhcC0B9YwN+0/OT3Ubf97Z3VIXmf/SNnvModeS7bkU8CXqgx3KU/1R3ylUivmeaKoigKgtBJFMXkeuTXzNcTiBRFcWPN89buyAuC0FwUxXPu/5OBSaIopviSD1JHvgGqwovcX3y+CPhTqDXO30/0B/itILLBZd6wNPj3E10lVctPNjRVy082JG2j/pqJjpUV6gaXecn816yKFqpqmCElNTnO7483vlr2qhteT4A3Dj7d4DJrduQbEsHHnKA/S6yu4vcT/QFUyoYfKLDe1fD+auHIusP9GoJz3za8D6i0/zUjhSudDX8fjAq63AjcP05RWcPfB8DTkf9b2R51+9/eWR2a9+nfXg++kMfIX56jgEMQhCPAu0hj26tYDrwEHHVPXr3AFTypiqL4yxWe+1lBEFogzdDZQT0r3MjIyMjIyMjIyPxvInfkAVEUH6u1HeD+awcG1Uq+y33MAsy4Qvm7qvLVd15RFPvXOnbTlciWkZGRkZGRkfm/jDzZtX7kya4yMjIyMjIyMjIy/0LkiHwDIgjCMKD2oNLzoiiO/Tv0kZGRkZGRkZGR+b+L3JFvQERR3AZs+7v1kJGRkZGRkZH5v4I8tKZ+5KE1MjIyMjIyMjIyMv9C5Ii8jIyMjIyMjIzMPxaRf+TKj/8I5Ii8jIyMjIyMjIyMzL8QuSMvIyMjIyMjIyMj8y9EHlrzf4TMXZoGl2lzNPwX7ZSVf83H2WKctgaXuenJkgaX+WKzhpcJcOF8w7dVSfFf85VA61/wBcZwjaXBZQK4xIZ/nRvkaPhpWwkqbYPLhL/mK6xDjz/R4DIBbKseaXCZT78V2OAyAW4XihtcZqK94W0164e/ZjhDTLy5wWWWmxr+HghQ/Bf4QXO5rsFlAiiEv/3jp38ZLnlkTb3IEXkZGRkZGRkZGRmZfyFyRF5GRkZGRkZGRuYfi0ue7FovckReRkZGRkZGRkZG5l+I3JGXkZGRkZGRkZGR+RciD62RkZGRkZGRkZH5x/J/dxrvn0eOyMvIyMjIyMjIyMj8C5Ej8jIyMjIyMjIyMv9YGn7R3v87yBF5GRkZGRkZGRkZmX8hckT+389w4OXEnWsp+mw7ptUb6iSIemQGgf274LJYyV70IpXHzwGgCNQT++S9aFs2BRGyl7yEJfkUEfPvxjDtJgS1CuN735L16Jo6MuMem0bQAEnmxQUvYUlNRx0TTpMX70cdEYroEjF9vA3jO98A4HdtPE1fXYCmcRQAhZ9tJ+fR1XXkxjw6ncD+SbgqrWQueNmjq6SwgsSvX8CeW0jG1Melss2/g7A7RqDQ++OyOzg+5XmKdh2pI1fXJJJrV9+PKiSAsmPnOTn7VUS7g5Ce19L2vcVUXswHoODbfWS88DkALacMo/kdAxAEAWtRGX5RITgtNn6bt5qiYxfqnKPFpCG0mjqcwPhovmg7A1thGQDqQD+ue+0/+McaCPATKfvkM1xGE8H3zwGlkopvvqXsg0+8ZKmaNiZk2WLULVtgXr2O8k/We47pb7sF/zGjABH7uXRc5lI6de6Oy2Ij7f5XKT92vo5u2saRtFw1D1VIIOXH0jk79xVEu4Pwm/oQN3ssAM5yC+lL1lBxIoPICYNIWDkVEHAWm1H4+5H7wscUvP31VduAtmk0QQO6oAwLwlFQhLPSjipIj8NcTuqQBzz6Jb45H1VIAOWp5zk392VEuwOApsunEDKwMy6LlXPzXkNtCKbp8smoQgIRnS4cRWZMn2ynYF21rSV+8CjK4ABwuchY+gZFX+wCIKh/Jxo9Ng2UCkyffE/eG1948sS//oDHPo2ffk/mQ2sIG9uX6JljPWX2u7YZtmwjTnM5F+a9QkVqukduk/9OBaUC4yffk/v6lwDELphAyLBu4BKxG0u4MP9lyLIS2bctbZaNx79xOCq9jsrcQvZNfZliH3aVMHkoidOGExAfzeZrZ2ArLPU6HtoxgcXfPs7GOa9yessBhjx2F80HdMRusbJ5wRryUuvKHPnMVKLbxSMIAoXnc9n8wGrsFVba3NiTHjNHA2CrqCR9wTpKT1wE4NonJhI5qCNOi40j976J2YeuTScPJX76CPTx0WxvPR27W1d9YiwdXp5BULt4cJhxWS7/YbSHVr7A7r37CQsNYeOHqy6btibKhPZoht0FggJHyi7sv3zjM50iJgHdpMewfvkqzlMHAFB1G466U38QRVwFmVi/XgNUf2hu5KN302JAB+wWG18tWE3O8brlv+HpacS1jwcETOdz+WrBKmwVVgCa9WjNjU9PIygmDIXLRcErn1yVv0786W1c5RZwuhCdTs7feD8AEfdOIOS2YUSbygFIfmo9WTurfWDXx+8ibmBHnBYre+etodCHPfR+dRaGDgm47A5MKen8uvhtYnq3IX75BASlguIN2yhcU1fXyIdmENCvKy6LlZwlL2A9cQ5NfByxLy3xpFE3jsH48gcUvbcJbesEov87B1WACpxOzC++iOPUKQA03boROEfyiZZvv6Xi44+9zqUbPBj/8eMBEC0WSl98Ece5c9TGr1cXDItnISgVmL/cSsm6z7yOq+MbE7H8AbStEyl85V1K3vvccyzi8fn49+2Bs7AY0/NrMCyeRayg8vIVNWn032kEDUxCtFi5MP9lLG5/0OS5uQQP6oLDVMLJwffWyRc98waaPjKRQ20nEjfvVi//VnEsvU76+vyjLjGOhBfmoG+XQFlKGprIEJy/I6fFm/NRhgRQkXqetBpymrvlXHr6Y3JWbfLkUQb5A3wOtEUaqj4Z+LWOcJm/jf+JiLwgCAZBEFLcv1xBELJqbP/iTtNMEIQJNfL0FwRh8x84V39BEEoEQUgWBOGUIAjPNWRZaqEEXgdGpA2bRfCYvmgSG3slCOjfBW2zWNIGTiNn2avEPD7bcyz6kemU7T7EuaEzOTd6Dta0S6BQEDJ2IBenPEr5vuME9uuMtoW3zMABSWjjYznZbwaXlr5OoxWzABCdTrJXvM2pQbM5e+NCwu8e6ckbs/QelEF6zgz5DxdnP03ozYPQ1tI1sH8S2maxnBkwg6ylrxPnlltF+KQxWNMyvfZZTp7HcjSN3U0mkPPhD7R+ba7Pikp46A4yV29m/3X34iguI2bCQM+xkn0nOThoIQcHLfR04vXXNKb5HQPYPuoRUp74lOCWcey68xn2L1pHlycn+TyH8cAZfrztScouFXjtb3HPEErOZLF1yIOY5txP8NxZBC+4H9MDS8ifcA9+gwehatbUK4/LXErJi69SVqMDD6AID0c/7iYKJs+g4M7JKCMi0HZoR3LPOZxb+CYJT033qVvTh+4ie81mknvNwVFSRuT4QQBYL+aTetPDHBk0n8yXPqf5szNBoaDR3Js4NWg2x9rchqPQjMtmp3hbte++UhuInDEWvzYJnOw3g/S7H8Pp7rwXfvsbRVt+88hrvOwuctZ+w5Hec3AUlxHh1i94YGd08TEc6TWb84tWEf/kdJqtnEbGw29jLyjGYSohbdYLBA/qirZZjFTW5+ZiyzGSkngLWc98SJPlM9yVp6Dxihmk3f1fTg6cQ+gNfdC57TNu2USUgXpODJxD+sxnMNwyEF2LxhR+tZsTw+ZxYtg8Cj7ajlhp41j3aWQsfoMmT870yG2yYgZn7nqc4wPmEnZDH3QtGgGQu+orTgy5nxPD5lGy4wAx998m1XthKWlrtlCUco4fhy1DqdfR8enJPtvOtP80P9+6kvJadiWdW6DNQ+M5v/soAM0HdCA0PppV/R7gu6XrGL7iHp8yf3j8I94esYx1wx/EnG0iaeJQAIovFfDRrStYN/xB9r6ykXbPTwMgYlBH9PHR7Ooxj2ML1tL2mSk+5RbtP8O+cU9QcdFbV3txGceXvcf5N6/Mpd44cgirXlhxRWk9CAKaEROp/OQZLKsWoWzTAyE81ne6QbfhTD9avSswFHW3oVjWPYxlzVIQFKja9PAcb9G/A4b4aF7u/wBfP7iOMU/49gFbl3/IGyMe5I0RSynJNtLdXa+6IH9GL5+EQqXk1UELOTtw6lX7a4CMO5aSPmaupxNfReE7m9g8dBmbhy7z6sTHDexAUHw0G3s/wK+L19H9yXt86n3+q1/Y1Hch3wxailKnocUdA+j+xEQypz1C+siZBI3uh6a5t676fl3QNIsjfchUch9+hej/zgHAdj6LCzfMlX5j70O0VFL6veQ7IhdOxvjaxxROnUrZ228TOLP6Ggq87z6KFy/GNHEiuoEDUTb19onOnByK7ruPwilTKH//fYIeeKBuQRQKwpfNIfc/y7h0wzQCRvRHndDEW05JKaYn36D43c/rZC/d9D05sx4E8Mip7SuqCBqQhDY+hhN9ZpKx+HWarKy+XxVu2EHaXf/1WdfqmHCC+7bHmllAUJ/2dfybL+rzj46iMjIeXkfh1n2oDYGkuOUk1COnSS05kTXkXHh4nVcHvopmj08B2ApcA3QATvoU/hfjEoS//fdP5X+iIy+KokkUxY6iKHYEVgEvVm2LotjTnawZMKFeIVfHHlEUOwGdgNGCIPS60oyCIFzNW5JuQBqQjt1ByebdBA7u4ZUgcHAPir/aCYAl5TSKID2qiFAUAX74d21L8frtUkK7A1dpOX4dWmJNu0jFL0dAFCndnUzwkO5eMoOHdKfwix8BqEg+jTJIjyoyFEd+kSci4Sq3YE3LRB1lAEAZ6I/DWIL9Uh4Kfx22jByCaskNHNKDoi+rdVW6dQVQRRsIHNCVws+2e+UJ6N3Jk8duMiNoVGgiQ+pUVGjvthR8I3Ucc9f/RPiIrpetWP8WcZgOp+G02Igb0om8vcdpPKIrpsNpaIL90fk4R1FqBuWZxrrCRFDrpU9yC35+uCqtODKzcGbngMOB5Yed6Pp4m4irqBj7ydPgcNQRJyiVCFotKBUoo6Oo/EUqV9nhs6iC9Kh96Bbcuy2mzdLNNH/9LsJGdAOg9OBpnCVSJK/00Bk0MQYCOiViuZCL7VIeot1BxdE0XBYr9qzqztmV2oDocFK2/4RXOnVkKGHX98S48WePvKDe7Sh062fc8COhwyX9Qod1w/j5Lnf5zqCODMWWbUQZoKPs8BlMX+0hdHAXyvalEjJcsn2VIZjyQ1KUz1FQJO2LDEXfsQXWC7nYLkrlKvp6D8FDpfMoA/2xm0qwXcxDqddhvZDjOVZF+IQhlPyUDED54TPuuq6Sm+ORW7jpZ0KGSrbtKrN48iv8dCBKay+UpGYQ3v0aLq7fg/lUJoJCgTpY79OuSlIzqLjkw66A5lOGkf3tfsqN0ifvWwxJIvULqV6zk8+hDdKj9yHTVkMvlVbt0Svr0FkqzRVS/sNp+MWEARA1PImsDXsAKD6UhjrIH60PuebUC1h86GozmilJScdld/osR226dGxHcFDgFaWtQhHbHFdhHmJxAbicOI//hqplUp10qq5DcZw8gFhuriVACSoNCApQaxDLijyHrhmaRMqXUvkzk9PQBfoTEFG3/Naa9arTILrrtd31PclKScN4LpuiSwU48wqvyl//URoPS+Lc55I9GA+fQxOsx89Hu9Xs/BtTzhHeMYHSC3nYL+WC3YH5290EDL7OK0/AoB6UfLUDgMojp1EE6lHW0tX/ug7YLubiyJbeeIqiiCLAHwCFXo/TKNmK+pprcGZl4cyRfGLlzp1oe3n7RPvx44hl0ltO+4kTKCIi6pRD264V9ovZODJzweGg/Luf0A/o6ZXGVViM9fgZcNS1xcpDx3CVlCLotB45tX1FFcFDu9XwgWc8PhCgbN8JnMVldeQDNHp0CpdWfACiSMiAzl7+TRks+ZTa1OcfHaYSyo+koWsWQ9mR9CuSU3UfKPAhR6xVJ8oAPwJ7XAuwzr3LBhT7LJjM38b/REf+cgiCUHW1PQX0cUfp59VKoxcE4W1BEA64I+03XIlsURQtQAoQ55bTTRCEX9wyfhEEoZV7/z2CIGwQBOEbYLt730L3+Y4KguD70V6Se6lqw5Fr9HScq1BFGbBnV3fAHLlGVNEG1I1jcBaWEPvMPOK/foWYlfci+Gml9DnVN2KHsRh1tLdMdbS3THuuqc55NY0i8WuTQEXKaQCKvt6Npmk0rfa+TcyDkyn6YkddubXObc8xedLEPjKNnKfeAZerTp7A/kn0OPwmUTf3ofzkRbTuzocnTVggDnMFolPKa802eaUJSmpJl53P0u7jB/FvJUVSy09dIqL7NWhCKYlamAAAIABJREFUA/CPMxDUIg7/WClPRXYh/tFXfnM98852glrEcWPya0R88DaWrdtx5uV7jjsLClBGhF+RLJfRSNkn64n66jOivv4CQaHA+kt1ZNuaY0ITU8sGwgJxlJSDu/y2HBPaaO86AogaP4jincloo8OwZVW3g6ZZDPasfK+0V2oD6ogQyvYd80oXMjgJe0Ex1vM5Hv2ctfTTuNtdEx2GNbuGPZZW4DCXU3HqIoHdr8VhLkPTKIKgAUmoY6U6tGbkEDqmN233rSPuoUlYTmWgiTagjjZgy/ZtX4Ub96BtGu3JU7hhZx371CXEUvRddV3bckyoo8PQxIRhq2G3tlwTmhr2FbfoDtrvfwvD2L5kP1c9hEoXE4olu5DY0d0oSc3Akm1CF3PldqWLDiV2ZFfS3/vBsy8wOhRztsmzXZpbSGCUb5mjnp3OvQdfx5AYy8F3t9c53v72/uTvTHHrGoYlq1puZU4hupi6NvR3IgSGIpoLPdtiaSFCYGidNKpWXXAc3uG1Xywtwv7rFvzvfRn/+18DawXO9FTP8aCoMEpq1Ks5t5CgenzAjc9OZ9GBN4hoHss+d72GJ0QTEBFC1DWNmfnNCoLHDrwqfy0pKdLk3eXEb3qZkNuHe+ULvWs0Y75fSc/np6EJ9vfs948OpaKG3hU5l/ddgkpJws29Kb2QT3l2dV360lUdFY4jt4aueUbUUd5+LGhUP8zf7vJs569cQ+SiyYSvX0/ArFmUrV0LgCIiAldBtSxXQQFKHx31KvxGjcK2f3+d/arI2joVoKyl9xWhUnrJqekrqtDU8ie2HKPHb9VH8JBu2HNNVJy4AIA6KtTLv9myTWhq+ebL+ccqlAF+OM3lf1pObbRNo3CYzADvAMnAW4D+spn+IsR/wO+fyv98R74GS5Ai6R1FUXyx1rFlwE5RFLsCA4BnBUH4XWMWBCEUaAHsdu86BfR1R+sfAVbWSH4dMFEUxYGCIAx15+sGdASSBEHoW1v+Cy+8MOSzzz67QRCEg+vNF+tTou4+EQSVAl2bRIo+2sL56+/FZakkfOa4etLXMmFfb5hqpFH462i2aglZj7/liUgG9u1ERfJpTveaTM6Ktwi7dagnWnU5uaIoEjiwKw5jCZWpdcdDIkDhJ9v4rfMs8r7YgzYmrI66vspUlab06Hl+S/oPBwcuJGvdd7R9dxEAFWezOPnGNwz4dAmGTomUXsjD5XDVyH/ll3VM//YUHc9gY6c5FEyciv/okQgq7xcvVypPCAxA16cn+beMJ+/6W0CpRNOti+/CeTL5Kr93mqCebYmcMIiMJz7wSi+oVfi3ae7pdFcf8KGcDxuoPHsJ0T1GuIrgfh0x1YjG19Pw9eqOCJVpWeS88RWxs28idGgXLCcueB7UNI0iyX/nW1K7TyHzv+vQtWgklfcyOgf160T54TOePIbxQ73Ko+/UAlwubBfz6ujiS3DN6s165iOOdpuK6avdRE4aWV1qQcC/SThtHxpP8sK3asi7Mtovv5vU5Z+Aq0amK7l+3Xy7cA2vdpuDKS2b1mO8I8NNrmtNh9v6cWq59OAh+CzjP+zWdgWvvjVD7sS289O6daLzR9WqMxWvzaPi5bmg1qJsWx0R9l2tvsu/ceEanu0+m4K0LNq661WhVBLaNJK03cd4/+6nCJ9zO6pwHx3qeuwd4MKtCzl/w31cnPwIYXeOwr9rGwAKP9pC2oCpfDN0GRX5xXR55I7Lyrtcu/VYeQ95+05hPpftq8C1dPWVpEYatYqAQd0p/a76Wg8ZP5L8lWsx3norpa+/TtCiRfXqUp/dqjt2xG/kSEpX151j9Xt+6U/xB/yqV3Kdhui548h+vnrs/5XZ1Z+/J1+xnNo5lEr07RIA3kQaYVCO1FeS+QchT3a9MoYC1wuCsMC9rQOaUP9YsT6CIBwFWgFPiaKY694fDLwnCEILJPesrpHne1EUq0IgQ92/ZPd2AN4PBADMnz//XaDZbbfdNuxE81GiKjoce56pZhIpkhIbgeWQtK2KDseRZ0IUwZ5rxHJEipiXfrcXw8xxlO06iDqmOqqiCg/Bnust055jQh0b4Sm+OtqAPd+tukpJs1VLKNr4EyVbq8dUB3RrQ/lBKX3Jtz/T6Pl5ODbt8paba/I6tzrGgCOvkOARvQga3I3AAUkIWg3K4ABap3yMPasAy9Gz7jzp5H35M80W3Iott9BbrsmMKsgfQalAdLrQxho8aZw1XoUX7khG8dRU1GGBRNzQk4i7hgBwacsBtIZASs9LzegfG4Yl78rfLsbf1peTr0mT7pxZ2Tjz81E1qR5vqYyIwGU01ZfdC22XJNBoMbwkTb1wZFxE07YNIE1C1cYY6pTfYTKjCtaDUgFOF5oYA7a86mED/q2bkvj8LE7csQJHUZkU1Y+T2iGwfxL2giKsGd4d+cvZQPg9o4lZeCfOMgulPx70ThcTju6aZlx87J1q/QrNKOvoJ8my5ZjQxoZT9dpMFeiPM1h6hi74ZAdqQ7AkN0iLLUeqQ3VUGLaLUlsVb95L/OsLsOcVolCr0MR625fdfZ6A7tdSfvCUJ0+zl+6n8KtdnrRh1/fBcjbLK7/GnV9Qq9DUsFtNtAF7rTYAKNy4m2s2PcXAIVLnrvRMFm0fnsC+qS9TnpGPX0wYlblFdfLVR2iHeLqtnosqQIc6SE+bsb3I+OUEQbHVUbbA6DBK8+u3VdElcuKb3+gxYxTHNkjuJeKaxox8eirHv9pL98+XAVCSko5fnIEq7XQxYVivQtf/H4jmQoSg6iikEBiGWOqtoyI2Hu1YaSy34B+IKrEDVpcLlEpcxQVQIU3OdZ46iLrTAGZtkR68so6kE1yjXoOiwyi9jA8QXSKpm3+j1/TRJG/YjTm3kIz9pwiOMVBRVEbF/uNo2zTHevqCV776/DWAw319OU0llG7/Fb8Orag4cBynya2HKHL2ox8ZselRRm9/AgBTSjr+NfT2j6nfd7WfNxatIZBfp75NeOfm6GPDqBp8pIoOr/bxbuy5RlTR1VFzVVQ4jvxqPxbQtwvW4+eq9QOCxw4mf8VqQmPBumsXQQsXAlIEvuZQGUVEhGfYTU1UCQkELVxI8eLFiGZzneOOvNo6ReDMr3st/i4Op5ecmr6iCluOEU1sOFVxcE1MeJ00NdE2i0HTOJI2e1ej1PshqFWowgLxb9WUsv3uCb+xBux53jZbn3+Mumc4EXdI9yhnmQVlUHVsURPr7eMvJ+dy2HJMkg9uHLnPvetz5I78Pw45In9lCMDNNcbVNxFF8XITPvaIotgeaAfMEgSho3v/cuBHURTbAmOQHgiqKK/xvwA8WeN8iaIorqMuB5A6+PGoVQSP7kvZjn1eCUp/2EfIWGlip1/HVrhKy6WVQ4xFOHIK0MTHAaDv2QFr2kUsR8+gaRaHulEUCAKBfTth/t5bpvmH/YTdPAAA/06tcJZW4MiXnEaTZ+ZiTcuk4C3vSTP2HCN+1zRD3SgKfZ9O4BIx/+D9arT0h32E3lStq7O0AkdBEXnPvs+pnpM43Wcql+Y+Q9meFE52nEDaqPuoOHLGk6fR1BE4K6zYfHRcivYeJ8IdHYu+tR/GrdJKFZoa41wDOyWCQoG9sJTsd7bx4+1PsXXIgxgPnCamT1syNv6CoXMidrOFyst0jmpTkWUiqo8UPVOEhqI0hKEwhKGMiQaVCr/BA6n8+ZcrkuXMy0eh12OcMYeCe6YhOp2eYTkBnVvgKK3A7kO3kr2pGEZLY1wjb+1P0Vap7jVx4bRat5Czc1+hMl3qrJelpOEXH4OmcRShN/RFoVVflQ34t0+k8LMfONF9EiXb93mlQwTLmUxPp9sjb28qYW79wscNoGib1D7F2w8Qfkt/d/laYi8oRhMXjrZxJOqoMMJu6E3ZkbOEDL+Ook1SR9RRUEzklOsBiJg4ElelDUd+EeVHznpupoJaRej1fSj5XqoHe7YR3TVN0TSOJLCvZJ9VxxAEQkf3pOCjrRjcuug7t8RZWo7dLVcXXy037IbeFLvzauNjPGUMGdqNskOn2Tn4QfbctILQpBZY84spPHCG0M6J2Euvzq62dbufbV3v49vWM0j98mc2zX2Ng+9so+3NvQGI7dQca2kF5T5khjaN8vzfYnAnTO4IbFCsgZtX388381ax58Uv+XnQUn4etJS87w4SN66PVI6kRBylFVivQtf/H7iy01GERSOERIBCibJNDxxnDnulsbw2H8tr87C8Ng/Hyf1Yv3sX55lDiCUmlHGJ0hh5QBHfBsfJfbw58kHeHPkgp7YfpONNUvkbdUqkstRCWUHd8ofVqNdWgzpjdNfrye2HCG0UgaFZNBHNY/Hr2BK/tolX7K8FPy0KvR8Agp8WfZ/OVJ7JAPAaQ99kRBfy9p3yTHy9uO0QzW+R7CG8c3Ps5gosPtotcXx/Yvu3Y8/s10EUMaWkExgfLd0H1CqCRvWlbMdvXnnKdu4jeKw0WVLXoRWusnKcBdWdx6DR/TBv/skrjyPfhH+3dgBoOnfGmSktXmA/fRplo0YooiWfqBs4EOsv3j5RERlJ8PLlmFeu9OSrjTX1NOqmcajiJDn6Ef0o33X1C6yIlVaPnNq+ooqS72v6QMkfVPlAX1SeyuBYp4kca38XB5rdhi2zgPSFbxI6TJqvFdC5JU5zBXYfMnz5x7x3t5I65AFShzxAZVoWAR0SrkhO1X0gooafrQ97QXHV0J9W7l2DgBOXzfQX4foH/P6pCP+416N/MYIgPAaUiaL4nHu7TBTFAEEQkoAXRFHs597fH1ggiuJoQRBWAkHAXFEURUEQOomimFyPfE8+9/Y8oJsoiuMFQfgK+FAUxS/cetwjimIzQRDuAbqIojjHnWcoUqd/kCiKZYIgxAF2URTzfZxyJPCSLSOnRfHn32N84zNCx48AoOiT7wCIfmwWAX2lJR2zF79I5bE0ALStE4h98l4EtQrbpVyyF72Ey1xGxLw7CZ95KygVuCoqKT94kpJtkhM3fbQVgLjlMwjq19m99OArWI6loe/SmhZfPI3l5AXPWPbsZz+g9MdD6Lu0pvHz96GJiQBECtd/T84jqwibII31LPxYkhv7+EwC+nZGtFjJXPQyFreuVei7tyV82k2e5SebvLEUfY92KPQ6XFY7x6e/RNFOqWnafbSU0/NXYcsrQtc0kmtXz0MdEkDpsfOcnP0Kos1B3OThxE4ciuh04qq0kfbIe5gPngHg2q+Xow0NxGV3UJ5pJLhVI5wWG/vmrabwqLTEY78PFrJ/wVosecW0nDKM1rNGo4sMptJoJmdnCvsXvIVfVAjdX5qJX2QIeq2Dsg8+xmUuJfi+2aBUULH5O8re+wj/G8cAULHxGxRhoUS8vRpB7w8uEdFiIX/CPYgVFQROuQfd4AHgdGI/cxbRUomyUzecFitp816n/Ig0BKn1h8tIe+AN7HlFaJtEuZeflJYvOzvnZUSbg+bPzcIwqgfWTGk8qOh0cnT4YkIGdqb5iklomkST9/p6cp//GMMdw/+QDdhyTegSG+GyWLFl5lP4/WHyP9hOqw+Wkb6gWj/v5dVeQrRJE32brZxGcP9OuCxW0ue9hio8mKb/nYw2LhxnWQW2vGLKfz1K5bksjB9uRd+1NQlrlkgRKqeLjCWvU/Sl1KEIGpBEo8emICgVmD7bQe6r0pJ6+q6tafr8vWhiJfs0fvoDmQ+txnDHcLQJsQR0uYZT1y+myYrpBPXvjKvSyoX5r1BxVKrr4IFJNH5sMiiUmD77gZxXpRUxmq9ZjC4hFlEUsWUWkLH0Tc5lVtLq/htpde/1OC02VIF+4BL5deJz5P8kjcvu+dEiDs9fQ2VeMc2nDKPl7NFoI0OwGs3k7Ujh8ANrva6LxFdnkrYzmdNbDjB0+UQS+rXHbrHx7YI15LqXI7313QVsWfQWZQUl3PX5w2gC/BAEyD95ka3L3sVWZmHE01NpNaIrZvekba3dyd5hUlS+zZOTiBjYAafFytH7VlPinlzX9aNFHJ2/FmteEc2mDiNh9hi0kSHYjGbydyRzbP5atBHB9Nr+BKpAP1R6LYgijuJL9b7aX/joUxxIPkpxsRlDWAj/mXIXN48Z5jNtFbZVj6Bs3gHN0DtBocCR8hP2vV+j6ix1jB2Hd3ql14yZjvNssmf5SXXfm1Bd2wNcTlx5GVg3v8XTq+2e9KMev4cW7nr9auFqst31euc7C9m0eC1lBSVM2fAI2gA/ECD35EU2P/SOZwJsr+mj6D5xKAERwVBmofCdjVfsr9WNo2n8ptQOKJX/j73zjo+i6P/4e66mXnoFQq8CAqFJh9AVwY4KUqQpvQvYHsGGClKkKYrYsUu1IUVREKSFHpIQ0nsu5XK5sr8/9lIuudAMj/h79v168eKyO/OZ787OfGd2ZnYW49a9ZK6Rt1UMf302bi0aYLKpKEjM5I/57zp11ju+OIpavVpjNZVwYNYGshy+q8/mOfw+9x1MabmMuPQ+hYmZWAqLAUjY8SdZJ+Lo/sIjoFaR98UPZK37DN/h8gxF7qc7AAh57kk8u0diN5lJXbCc4ugLAAg3PY32vs/FqLHYC4rKbHGPbEHIoomo3QSUlGB8802s52V/q+vUSd5+UqWieOdOCj/8EPe75Qdy03ffYZg7F32PHtjSHMvbbDayJ04s0y7Mkh/C3Lt3IGCevP1k/tffk/v2J3g/cCcA+Z9vRx3gR63PVqPy9EBy+NbLQ8cjFRYR/OoC3Dq0Ru3rg72gEMlmx1ZYXOYrAkfIPjDzQ9kH1lkyEYPDN12avYqiE3J7VW/1bLw7t0Tjb8CSmUvKG5+Q9Vn5uywWm5o2B9cRPWgutecMd/JvhQ6fci3+URvkS8udr6H2dkfotAi1CtPFZC5OX1mtTuMKOjEudLBL2AqLOdFrGrYCEx631aP1j8uOADogFhgD/Nen4z4Le/Qf76w+lPLRLbl1jdKRL+/Ia5G3WAoENiEvayntyLsDbwJdkEfL40s76i70e+HckXdH3lmmGxAKvA9kALuBka468o5404Fxjj8LgBGSJLlYJC5zuuGdNX4jS6zqmpZErbo55S2ryO3qga6TFLWuxjV71nOx9rQGiI+r+RcPPXSWqwe6AcyWml/Rp1Vf224o14tdqnm/nWD1uHqg6+Sc/uZMrrYurvl87X/qxRrXBLkjX9O8+s7N8VfD1TU/m3G4+MZ3t6mOjp43sCzlGvALL7p6oOuktCNf0+Tmute4psVW820r3LwXMjsnf/WPd2CVjnz1/M+tkZck6flKf3s5/rcgTxtVZI/jnAmYyDUgSdKe0ngV4tZy/BkHNKkQ/BlHmE3IDw8VdVYAK64lTQUFBQUFBQWF/6/Yb8ku9K2BskZeQUFBQUFBQUFB4V/I/9yIfE0hhBgAvFrpcJwkSfe4Cq+goKCgoKCgoHD92F3usakASkf+hpEk6Xvg+3/aDgUFBQUFBQUFhf9NlKU1CgoKCgoKCgoKCv9ClBF5BQUFBQUFBQWFW5Z/fMuaWxhlRF5BQUFBQUFBQUHhX4gyIq+goKCgoKCgoHDLomw/WT1KR/7/CXp3a41rns/0qXHNevqCGtcECPfPr3HN2qLmJ/NOXAy5eqAbIMKr5q8/vaDmP1wEoL0J+Wq7CR9uAvB2K6lxza6Nk2pc81Rs7RrXBBA3YUL7Zny4CUA36YUa14x+e0aNawLEF3jXvKZbzd+r3j7mGtcE8Gxf821L2hf2GtcE0KhrXtdk0da4JoBadXPyQOHWRllao6CgoKCgoKCgoPAvRBmRV1BQUFBQUFBQuGVR5hqqRxmRV1BQUFBQUFBQUPgXoozIKygoKCgoKCgo3LIo209WjzIir6CgoKCgoKCgoPAvROnIKygoKCgoKCgoKPwLUZbWKCgoKCgoKCgo3LIo+8hXjzIir6CgoKCgoKCgoPAvRBmR/3+Ee9f2BD41CaFWY/xyJ7kbtzid19avQ/DiWehbNCJr5fvkbfqi7FzQ4ll49uiELTuXy/dMdIrXasljhES1wWYq4a/p68g7GV8l7fpj+9Nw/EC86oeyo8VESrLlDxSFDoik+fwHwG5H5aFHr9cgWaxkffoD6Wu/rKJT6/nxGHq3x24ykzDnTUzRsWjDAolYPgNtkB+SXSLr4+/JfG8rAAEjBlHrmbEIlYqSyylcfmgG9sIiJ82ghU/g2aMDUrGZ1IVvYD4dA4DvyKH4PDAIhCDv853kbv5Gtvm1+Xj174pAYElOI2nUXGwZ2U6agQufwKNHRyRTMekL38B8Rtb0GTEMg0PT+PlO8j742ilex++ex79TM/LPJXJy8mqMLvLSPSKI29dPR+vrifFkPCcmr0ay2ND4eNLqzYl41AvBZrYQPWMdbmEBNF8yCq0Wcrb8iGFwN6xpWVwe/58yvZBnJ+LdS87T5HnLKT51EYBGe9/FXmgCmx3JZiNumPzxG33zBoQtnkyjiHA0Xu4UJ2dyevxyCk7GVbHVLSKYFutnoPH1ouBkHGcmr0KyWPHt0oKW78+nOCEdgIztB7m07AsavTiGkHu6ofZyw5ycRcGxi1yY8RaS2VKmqY8Ipum6mWh8vSg8Gcv5KbKm/4AORMwfjmS3g81O7DPvkX/oLLrwAFpvewltoA9IdlI27uTS4s1VbNXXCabJuplofL0pPBnLhakrkSxWAu/tTq3J9wBgKzQR+9QGdCF+1H9hLFp/LySbHVtOPtmffk/We9+V6YU9NwHvXpHYi80kzllRlq8AqFQ0+m4ZltRsLo2TP1RUZ9U89A1qodHZUHl5YS8oIHv8OAB0HTriPWUqqFWYtm+n6JOPnfO5b188hj8CgGQykf/mMqwXL+KKAc8/RqPet2MxlfDdnPWkRlctY3ctHU94q/ogBNlxqXw7ex2WovKP/4S1bsDYb/7DsQkrSN12EIAWL44iKKotNpOZE9PWuiy7dccOoN6EQXjWD+XH5uOxOPyAZ6NwWq+YhKFVfaz7P8f6xw7UDVqjGzAShArrsT1YDmx1eT2qsAa4jXke81ersJ39EwBNx4Fo2/YCScKekYj5uw0u41bm6ZeWse+3Q/j7+fLNh+uuKU4pjz8/nna922M2mVk9501io2OrhJny+nRu69ySImMhAKvmrCD+dBwe3h5Mf3MWQeFBqDRqct/aTuKne2ssT8+//BmH39tZFmfA84/R2FEGvq2mDAxZOp6wVvURQpBVoQzU7dych96eRe7lDPy1Fky//ErJqbP4zp4MKhWF3+4g//1PnbQ0devg/+w8dM0akbf2XfI//LzsnPDyxP/pOWgb1gNJInvx60ACAOqmbdEPHQ8qFZaDP2L5pWqbAKCq0wj3qUsp/vB1bCcOAKB/cCrqFu2RCvIo2foeTX8eB2oV2Z/9SMbaL6pohD83Ae/ekdhNcn01nbqI0Gtp+NkrCL0WoVaTt/M30pbLda/O8ln4DumBZLdjyzGSOH8V+XuO/C3NiNWyD1CHBKAxeCLZ7JwcsohCF771ev1g/SVj8Y9qi624BJVWTXFcKudGveSsWSeYxmtnofb1oig6jpipK5AsVtwa1aLhsil4tmrA5Vc/JmXdtwC4NQyn8brZAMccEg2AZ4E3Xd6om4iy/WT1KCPy/19QqQh6ejIpTzxNwt3j8RrcG22DCKcg9jwjma+sJXdTVWeZ/80PJE9aVOV4SFQbvBqE8tMdszg25x1uf3Wsy+SzD53jwIMvUXQ5w+l4xv5ofunzFL/0X4RGr0OyWDnbdzJ+d/dA37iOU1jv3pHo64dzpudELi94i9pLngBAstlIXvIuZ6Mmc2HYXAIfGyzHVamo9ezjXJqzggvt70Hl4U7gHGf7PHt0QFc3nPiBY0l7bgXBz04BQNe4Lj4PDCLhwelcGvYEnr06oa0bDioV7m2ac+nOCVzsMAyVuxuBT01y0vTo0QFt3VokDBxD+nMrCHpuqqzZqC6GBwaR+NA0Lt8zqVyz9PqG9sWrUTimyxmcfe4DWiwd5zIvmzz9CPHrt7P/jplYcguo/UgfABpOH4Yx+hK/9Z7PySlraL5kNC1eGcvhR14hZsAT+D92F9a0LCctr17t0dcLJ6bPeFIWrSLshclO5y89uoDYIVPLOvEAIfPHULDnT/KPXSR6zGvYjCaaLB3v0tYGTz9K4vptHLpjGtbcAsIctgLkHTzD4ai5HI6ay6VlX+Af1RbPZhHYCk2cGr4Ea04BQq0iaFhXJ816T48gef02/uoyFWtuISEOzdz9JznWZzbH+87lwow1NHpDLh+Gzi0wJ2XwR92HOP3oi4SNHYR7k6pfOq379EiSN2zjaNcpWPMKCH44CgBzQjrR9z7D8ahZJL75BQ1fm0SDl8YT98y7WDNysWblkTBtKYY+HdDVC5PvZa9I9PXCOd97IkkL3qKWo6yWEjhmCOaYRKdjl6cuJebO6WSPH0fxvn2Y9++XT6hUeE+fQe5T88gaPQq3qCjUdes6xbWlpJAzYxrZ48ZS+MFmDLPnuLwfjXrfjn/9UN7qOZvtCzYyeMkYl+F+eOFDNgxayIaBC8hLzqTDqP5l54RKELVgOBf3nSg7FhTVBo/6YeztPIPoOW/Tspqym3PoHIceeJGiBGc/YMkt4PSiTcSt3eZIRKAbNIriT5ZiWjcP9W2dEYHhVQWFQBf1ELbYcluEtx/ajv0xbXwG04YFIFRobuvs0p7KDBvcj3XLllxT2Iq06x1JWP1wJvecyLoFbzGh0v2uyOaX3mP24BnMHjyD+NNyB23QY3eSeOEyswZN59mHFtLs+ZEED2hXs3nqoFHv2wmoH8rqnrPZtmAjd1ZTBr53lIH1AxdgTM6kY4UykPDnOTYMXkjaoxMxvvsRfvOmkTF9AakPjsWjfx809Z3Lp92YT+4bq5068KX4zZ5C8e9/kvrAGFIfmYAl7pJ8QqjQ3zMR0zv/oei1KWjadkeE1KkSH6FCd+cobOeOOl//4Z8pflsesNDfM5G40c9zvt9kfO/ugb5RpbZ/09FwAAAgAElEQVSlVyS6+uGc6zWRpIVvUetFR9tithD7yCIuDJrG+cHT8O7ZDo+2TeU62bs96eu/4tRtD2LNNlKSmP73NIGEKUtJXbqZwuOxpGzcQcbne2n46gSX9+d6/KBfVFvcG4RxrOtkjL+dRONvcKkZsWgkKW9v5Xi3KVhzy32gNaeA+Gc2lnXgSym+mMzJfrMB2gCRQBHwNQq3FDe1Iy+EuEcIIQkhmtWw7h4hRHsXx+8WQjz1X0invRBipeP3aCHE6mriF9SULVdD36oploRkrImpYLVSsHMPnn3ucApjy87DHH0eyWqtEr/4SDT2vPwqx0MHRJKwRe5w5PwVg9bggT7Yt0q4vOhLFF3OrHLc5hjl82vbCFNqDpLVimSxkrN1Pz79OjmF9enXiewvfwGg6Og51AZPNMF+WNNzMDlGv+yFJswxiWhDAvBo01ge8dj6K1is5G/bjVc/506hZ587MH77s3yNx8+iNnihDvJH1yCC4uNnkYrNYLNj+vMkXn274Na6KSXxSVgSU8FixXwhHm14cBXN/G9/AsB84iwqb0/Ugf5oG0ZQfPxMBc0TeEaV2+P/5Aji1mwDCYzHY6vNy4But5G2VR4FTd6yj5BBchH0bFKLrP3RABTGJOPZKBxTUiamS+loAnyQis1YK80cePftTO7XuwEwHTuHyuCJJsivSppOSBIeHVuS9vleNAYPii4mozF4onNhq1+3lmRs/QOA1C17CRzUoVrZwIEdyNj6B0KtpuhMAhofT9Q+npSk5jiF8+naksxtvwOQvmUP/gM7AmAvKi4Lo/bQgyRvSObTuTmp78qjkcbfopFsNjyaOT/EAvh0a0lWRd1Bsm7+4XPY8uQR1Pwj59FHhGCKT0Xt5U7RsXPkfbcXQ58OFB6KxjBArlPe/TqT81V5vqor5KsmNADv3h3I/uyHavPCrVdvin+Wy5C2WXNsyUnYUlLAaqV49270Xbs5hbecOoVUILsTy+lTqAKDXOo26RfJiS/l+pp0NAY3gwdeLu5bSYGp7LdGryvLS4AOowdwduefFGUay46FDGxP0uf7AMg9EoOmmrJrjI7HVOlhHqAk00jesVgkiw0AVXhD7NlpSLkZYLdhO/UHmiaRVeJpOvTHeuZPpEKj8wmVGjQ6ECrQ6pAKcqrEdUX7Nq3wMXhfU9iKdOzXiT0O33T+6Dk8DZ74BV+lHlVAkiTcvdwBcPN0x5JbQHDfdjWap6U07RfJ8QplQH+NZUCSXG/wp7utGZbLSdiS5PJZ9OMvuPfs4hTGnpNLyelzVdoW4emBvm0rCr/dIR+wWpEK5LqmimiMPSsVKTsNbFasx/ajua1jlfS13e7EduJ3pII85zRjTyMVFYBWhz0rlZLLaUgWK7lb92Ho79y2GPp3JtdRX4uOnkPtXV5fS/2K0GgQGg2SJOHRpjG2vAJsufk1plkxXvqWPQQO6ULSmm/RGDzQurg/1+MH/Qd0IH3LHnRhAXg0jcBeXILQaatoGrq1KvOBGZ//gp9D05qVR+HxGCSrrUqcCkQBF4FLVwqk8N/nZo/IPwz8Cgy/yekAIEnSd5IkvfJfSOewJEnTbiSuEEJd0/YAaIIDsKaWO3trWiaa4MC/rese5ocpubxzWJySjXvYtTdgAGGD2tPxnen4tKxLwtyVAFhSMtGGBjiF04YGYEkuvwZLahbaEOcwutrBuN/WgKJj59CGBmDNzcfgeCDQhAajNng5hdeEBGCpmC+pGWiCAyi5EI97+5aofL0Rbno8e3RAExpUlo8B00dR9+cP0TWqiznG2W9pggOr5nVIqWYrVD4VNMPkDpdH784gBDmHzjnlpT7M3zkP/L2xGIuQbPJEYnFyeZj80wmE3ik7Xp+2DdEF+2A1ysuIQp+eQO5XP6P2cx6J0YQ456k1NRNNab5LEhGbFlP/2xX4Dh9YFiZ1ydt4RLag4eLRNHzuMWJf/AhzSpZLW60VbDUnO4cxRDah/e7XaPXxQjya1kYf5k/h6Uskrf2O9kfWoo8IBqud3L3Hy+3198ZqLIRSzZQsdBU0/Qd1pO3+FTT/cAExM9cAoAsLwJwsP0Tqawch1Gos6bnO+eDvjTWvXLckJQt9qPP1AIQ8HEXR6UuUJGVSdC4Bz463YTMWoq0VjHev9mjD5DqlDQnAklL+4GpJySorz+HPjifllffA7noyWNu6NfacbGxJSQCoAgOxp5eP9tkzMlAHVl933QffScmhgy7PeYf6Y0wun5UxpmbjHeK6vg55bQIzD68hsFE4hzbJDx3eIX40G9CeIx/+5BTWLcyf4qRy3eKUbNzCqubftSK8/ZCM5X5Fys9GePtVCaNp2h7rXz87HZfyc7D8vgOPaSvwmLEazEXYYqNv2JZrwT80gMwK9SgrNQv/Sr6plEfmjGDZrpWMeeZxNDp59eqO97dTq1FtNv65ieXfr+T00+/XeJ6WUrkM5F+hDNz92gRmVSoDALXbNWLCzpcIXPEyupbNsKWVX7stLQN10LW1LZpaYdhy8/B/bh4hH67Db9FshJsbAMInACm3vA5JuVkIH+c8FQZ/NC07Y/l9V/WJqDROOpaUqu2GNiSAkuTyMCWp5fUVlYrGO1bQ4sgH5P96FNOx82hDArDnFxI46k4a71yJd89IdHVC/5ZmxXgaPy8smXkUx6ViTslGH+Zs7/X6QdkHZlH3P2NJWLIZa2YeKjdtFU1bJR+oC3VdhqthOPDJ9USoSey3wL9blZvWkRdCeAFdgcdxdOSFECohxBohxCkhxDYhxA4hxP2Oc5FCiL1CiCNCiO+FEGFXSWKEEOKAECJaCNHRoVE2Oi6E2CSEWOkIE1uazhXsnSeEOCmEOC6EqPgw8IAQ4pAQ4rwQorsjbC8hxDYXGvWFEL8LIf4UQiyucLyXEOIXIcTHwEnHsREO3WNCiPWlHXwhRIEQ4kWHHX8IIUKuYPMEIcRhIcTh3wqzqwaoZoTluhBVXxW/XtmUnYc5+ewHZOw7SdjsR6sXcvVWeoUwKg836q17iqQX3sFeYAIEhQdPEfjYYCK+WIXQa5HslTVdiEoSJbGXyX7nc2pvfJlaby/BfDYWbLay8Fkr3udS1AiKj51G37jeNdlpib1MzjtbCN/4MuEbXsR8Lg6sNoSbHv+JD2OJT3QZ71rzIHblt2h8POny8ytEPD4QU0IG2CWC+rXDmpWHJTGt6lczXF6//F/8g3OJGzqdhLHP4j/iTjw63AaA36ODMcdc5vTYN4h5dhPNlj/h0tQrlY38E3H8Efkkh/vMJWnjTlpumgcI1F5u+A/swOGOkzEePINw0xJ0X/er2FuecPbOQxztPp2zY5YSMX+4U56pPNxounEuprgUbBVGraq31fmCDF1aEvxIFBlfy6OkpgtJZKz7kqBJ92Ho2xHTmTiw2p3SrKzn3acD1sw8iqNdr18HcOvTl+KfK3ROr3CPKqNt0xb3wXeSv2G9y/Ous8+12Na5G3iz42QyY5K4bYi8NKX/cyP5+ZVPq9YjF1Sne024MrQSun4jKNn9adWC5+aBpmk7ilbPpGjFVNDqUbfs6lqkhrjWfP1o6Wam9nmSeXfPwsvXm3sm3QdA255tiT8Vx+MdRjN70Axue3kMQlN1TOdv5ekVbK3OaX83dwPLO04mo0IZSImOZ0WX6WwYtJCCz77Ge6SLcbhrtVOtRte0MQVffEfaiElIxcV4j77CuF4lXf3QcZi3vw/SdXajrqdtsdu5MHg6Z+4Yg8ftTdA3iQAhKI5J5GyPCVwYPB2bsRDP9s3/nmaFeD49WpPx9a8Vol1bu1VKFT8owLtDUyyZeRSedLy7UeUWXVnzKuiAu4Gqa6cU/nFu5suuw4BdkiSdF0JkCyHaIb8oUQ9oBQQDZ4B3hRBaYBUwVJKkDCHEQ8CLgOsF2TKekiR1EUL0AN4FWroIEwZ0A5oB3wFV34ABhBCDHPZ2kiSpSAhRcVhEI0lSRyHEYOA5oO8VbFoBrJUkabMQYnKlcx2BlpIkxQkhmgMPAV0lSbIIIdYAjwKbAU/gD0mSFgkhlgLjAZeLOiVJ2gBsAEh8dIakCS2fbteEBGLNyHIV7aoYhg/BcP8gAq1qco7F4h5enh1uYf4Up17bNHZFTMnZCI0GXd0Q1H7eaMMCsaQ5P3xYUrLQhgchFwvHCH26I4xGTb11T5HzzV7ydslTg5bUTFRe7sSOfA4PjxIC543Hlu08/WpNzUQbGkRpt04TGlS2/MT45fcYv/wegIAZo7GmZcqj6xXy0ZqUhnv71s6alcJoQgKxOuzM/+p78r+SNf1njEFbO5Q6X7yFNiIcyWym3XtN0Ph60uXHl7FbrJgr5aUlKx+twQOhViHZ7LiF+5eFsRWYiJ5R/oJe7+NrUXt74NexCd5RnTAM6YFQaxBqQfgbc0ie/bp8/eFBmI6UXn9g2Tr6UpttWXnk//A7/mOGEfrsJPRN65L7xY/oawWQ/vVvNF02iZK0XEpSK92vLCOaCrbqwwPKwtgc0/bhYwYQPqIvbrUDMR45j1/vNpgT0rFmGdGH+pP83i68OzQlw7EUwJplRGPwBLUKbHb0YQFVlt4AeDSvi6Fzc9rsWUb+4fPo6wRTe+YDZHy1n9AR/arYas0yovEp19WFBVCSluOk1+iNJzj96BI0ft4EDpWXtuRs+RFNgA8AKk93LKly3llSs8pG5wG0YQFY07LxGdQVQ9+OePeOROh1qL08qL18Fokzl8kB1Sr03buTPbF8Xaw9IwNVcPnyLVVQELasqsvUNA0aYJgzl9yn5iEZy5eauA8bxvi+9wKQfCIWQ3j5KJsh1J+CSrMTFZHsEqe2/sEdE+/i+Of7CGtdn3tXye+ReAX7orqnK8XJWWTuO4lbrXJdtzD/KmX3epCM2QhDuV8R3v5I+c56qvD66O+RbREe3mga3Y7Zbge1GntuBhTJSwFtZw+jrt34hm2pDpWbgTd2yO/0xZy4QGAF3xQQGkBOetXBk5x0+RqsJVZ2f/4TQyfIL1H3eSCKr9Z8ycDHBtNveH90/t4YT12qsTz169CECQ/ID8SVy4B3qD/5VykDpyuUgYpLbooPHAJAU7t8XE0dEoQt89raFlt6Brb0DEpOnQWg6Od9GEYNx3YGpLwshG95HRK+AU6zNCC/5Oo2Qn4fRHgaUDePxGyzYTtVYUbKbnXS0YZVaDccWFKz0IUHUroNgi40oEr7YzcWUvDHSbx7RlJ05LS8ztwxq2a+mIhHmyY3rBkyawT6CHlMruhkDL6DuhK3cCMA+jB/1/7qKn4wdMxAQh6NwqNJbTK/PYChQ1O8WjfEL6odurAA7BYrDVdN5+LUFbJmthF1FR/oYgDQNYOAv4C0a42g8N/jZi6teRgofbX9U8ff3YDPJUmyS5KUCvziON8UuSP+oxDiGPA0UPVtNWc+AZAkaR9gEEJUXWQG3zjSOg1UO7KN3Dl/T5KkIodmxdL9leP/I8gPIVeiK+VTTx9UOndIkqTSV9OjkF8c+dNxvVHIDzkAJUDpaP+1pAmAOfoc2ohaaGqFgEaD16BeFP7yx7VErYLx060k3v8kv/RdSMquw0Q8KDcQfu0aYc03Yb5Co1AZz3pytuceu4ihWW2Emw57gQm/Id0x/ui8PMD40yH87+sNgEfbptjyi7A6GsaIpVMxxySS8U75yzhFxy/g1rA2ujohoNPic29/8rbscNIs/OUPDEPlF3rcbm+GPb+wbAcatb/cQdOEBeHdryv52/dQfPIcuoYRcj5qNXjf1YeSs867UxTu/gPvofLznL51M+z5Rdgyq2p69e1KxgurSLhrHBdbDyZ19svkHY/FnJxN9Jy3seQWuMzL7N9OEzJEXi4U/mAP0nYdljUNHgitPIpXe0QfMvefxKNuEJc/+JkLvR/HmpxB6n/WUvj7CZJnvw5A/k8H8b1HfknKvU1T7PmFWDNyEO56VJ7yml3hrsezeztyPt1F7JCplMQlU3IphZAHeuLbvSXm1Bys+UWUuLA157dTBDlG8kIf7EnmLnlXEV2QXB2T3/uec3PWU5KeR/o3BzC0bYR3ZBMMd7TAml+E9+0NMV1IctLMO3CKwLvktejBD/Yi+3tZ061e+dR2/uFzWLKMHOs1i+xdh6i7aASmC4nk/3kWa35RlaU1AHm/RRNQQTdnl9xJ0dUKpOnGuVyYupLi2BQKjsXgXj8MfZ1g1CH++AzpQdGJCxgGdiH3u71l+ep3b3m+2vKLsGbkkPbaZs52GcO57uO4PHUpBQdOlHfiAa+ubbBdTsCeWWEJ2dmzqGvVRhUaChoNbn36YD7wm5PtquBgfF5YjPHlF7ElOs/smL75hrcHL+TtwQs598NhWjtmOGq1bURxvsllR96vbrk7bNK3HVkXkwFY3W0mq7rNYFW3GZz67neOTVzJng7TSNt5mFoP9ADAN7IR1vyi6/IDlbEnx6LyD0X4BoFKjfq2zljP/+V8XatnYVo9E9PqmVjPHMK8cxO280eQ8rJQ12okr5EHVPVvw56Z5CqZv4W92Fj20uqhHw7Sy+GbmrRtSlF+UVmnvSIV18136t+ZhHPysryMpExad72dXZt38MJjz2PNN5Hy7e81lqc5f55nw+CFbHCUgdsrlAHzdZYBzyCfsuO6Fk3BZkUTFoI6XC6fHv16Y9p34JrssmflYEvLQFNXbs7dOrQte9nVfvkCqsAwhH8wqDVo2nTHduqQU/yilyaU/bOeOID5q/XOnXgASwmqwDC0tUMQWg2+Q3pg/NFZx/jjQXwd9bWsbcnIQe1vQGXwBEDodXh3bYP5YiJFxy+gb1i7TNPvnl6YKs2yXY9mzpYfuTB4OhcGT6ckKR3JZqckJRuvdo2r91dX8YOp7+0iZuYaLFlGMr/9DZVOy9H247nwxDKK41Iw/nqyrBNfZnMFHxj0QG9yHJrXwMP8g8tqACTxz/+7VbkpI/JCiACgD9BSCCEBauSJnuredhbAKUmS7qjmvCsqzwm5miMyV/h9pdsgqolfUcPGteVXdTqFldJ7X5KkBS7CWaTyebZrTRNsdjJfeouw9S8h1CqMX/+A5eIlDA/eCYBxy3bUAX7U/mwVKi8PJLuE74hhJAydgFRYRPDSp3Dv0Bq1rw91f/qQ7DUfwIbDpP10jJCoNvT7YzlWk5mjM8qn9Dt/NI9jszZQnJZLg8cH0HjyXeiDfem9+xXSfj7GsdlvE35XR+o80B3JYsVaYELtqaPZz2+RveUnii9cJuBReW121ke7MO4+jHfvSJrvW+/YflJeT+/Zvjn+9/XBdCaepo4RsuTXPiD/lyMY9/5Fs91rEQJMx06Tve4TfB4aDEDeZzso3HsIzx4dqPf9u47tJ8s7VWErnkHt6w1WG2mL38JudLxMmJJOvR3vIABLYgppT7+B4SFHPn62naJ9h/Do0YG6u97DXmwmfdEbZZqhK55F7euNZLGRsWR1mSZA0b5DaDt2I6BHK5q/MJITU9aUnYv8aD7RszZgTsvh3JKPuX39NBo/9RD5J+NJ/Fh+3vVqUotWq55EstkpOJ9E9Mz1+LZvTPtPF6LVQO4XP2JJSkcbHoTfw4PI+WQnBXv+xKtXexrtfgd7sZnk+csB0AT6UWetY5citRrj1r0U7pOH7ZMXriT02Ymow4Np/dECipOzuDD/7TJbW320gHOz1lGSlkPskg9psX4m9Z96mPyTcaR8LL/8FTSkM+Gj+iPZbNiLSzg9cTnGw+cJiGpL8L3duO3TZzCnZIEQpH7wI80/WsjFWWspScshfvEHNF0/k4inhlMYHU/ax/IylIC7OhP8QE/sFiv24hLOTZSvxWosQh8WQOioAYSO7I85MQPfPu3I3f0XzT9cRMzsNVjScri05EOarJtJxPyHKYyOI+0TWbfOzAfQ+nnT4GV5Zx7JZiN24Tu0+OQZ9LUCsRUUEbbocfL3HsH3ru5kf7yL/F8O4927PU32bEAymUmc59xgVofvkB7Oy2oA7DbyV76J39LXQaWieOcObPHxuA+5GwDT1u/wemwUKoMP3jNmynFsNrInTaQyMbuP0ah3GybvW4bVsf1kKcM3zWXbvLcpyMhj6LJJ6L3cQUDamQR2LHrvinZn/HSU4Kg29Dy4ArvJzInp5TND7T+az0lH2a07biANJg9BH+xL919eJePnY5yctQFdkA9df3gJjbc7Gq2EtuNASn74ALeH54FKhfXYXqTMJDTt5I6R9a/d1dpiT76I9cwh3MctAbsNe9olrEd/Qde+e7VxSpn73Cv8efQEublGooaN4MnHR3LfkAFXjXdk92Ha9Y5kzb71ju0nV5adW7TpWdbMW01OejYzVszG4G9ACEHc6TjWL5Tr+OcrP2PqG9NZ/v1KhBCcXfwxKV8fwL9j0xrJU+wSMyYOZk3feVxwlIEp+5aVbUFaysOb5rLVUQaGLZuEzssd4SgD2x1loMXgjkSO6IvdasPXVkjWoiWovLwIWvkqQq2i4LudWGMv4XnvXQAUfrUNVYAfIe+vReXpAZKE1/D7SH1oLFJhETmvryLghYWg1WJNSiH7haX4tgPsdsxfb8B9/PMgVFj+/Bl72mU0d8htgvVK6+IB/aOzUTdsifA0IJkKafT1a9iLisnZ8hPmCwn4O9qW7I/K62vTvRvkrSLnyvVVG+xPnTdmgEqFUKnI3f4r+bvlzq05PoWmu9cCUJKQSuK8lX9bE0AfEUbRmQTa/bEau8lMzIzyduBG/WD+obP4RbWjzYE12E1mUt7eil8/eeOBph8sInaO7AMTXvyAxmtnUWfeIxRGx5H+ieOF+yBfWu58DbWjLIWOu4sTvaZhKzChctcB9AOqOhyFWwJRE2vyqogKMRFoJ0nSxArH9gI/AZ2Q11qVzlNOQF72choYKUnS746lNk0kSTpVjf4e4KwkSZOEEN2Ql7O0EkKMBtpLkjRFCLEJ2CZJ0heOOAWSJHlVozcQeW/UvqVLayRJynakM0eSpMNCiEDgsCRJ9YQQvRzH76qU5nfAFkmSPhRCPAG8JkmSV8XwjvRaAN8iL61Jdyzl8ZYk6VJFOx3r+u+SJGn01fL8YssBNX4jT2Ze14sw10Q9/c3ZyMfDo6TGNVWi5utGTNb1vSh8rUR4Vd1x6O+SXuBR45oA2puQr+ImaAJ4u9V8uQquZ7x6oOtkQ+zVJjBvjMjiK+5icUP0nHpzVnTqJr1Q45oPRc64eqAbYFxxzdetw241v4/CmODUGtcE8Ovpsin+W8R+cSu/juhMfrH+puiqVTcnDzonf/WPj0evqTPi5jj56+DJyx/+4/ngipu1tOZhqo6+fwmEA4lANLAeOAjkSZJUAtwPvCqEOI788YEuXJkcIcQBYB3yC7U3jCRJu5AfJg47lrq43qT56kwHJgsh/gR8qgvkWOrzNPCDEOIE8CPyen4FBQUFBQUFBQWFa+KmDI1IktTLxbHSfde9JEkqcCy/OYRjFxdJko4BPW5U33F8E7DJ8Xt0pXNXHAJwbFv5SqVjvSr8zsSxXl2SpD3AHhdpxgEVlwe9Ujl8Bb3PgM9c2OFV4fcXVPOCroKCgoKCgoKCwv82N3PXmurY5ngxVQcsdrz0qqCgoKCgoKCgoFCFf8/Cqf8+//WOfHWj6a4QQryFvBNMRVZIknTlN7Oq12tF1d1kzJIkdXIVXkFBQUFBQUFBQeFW5Z8Ykb9mJEmqvBf739U7CbSpSU0FBQUFBQUFBYWbxz/+pustzM3cR15BQUFBQUFBQUFB4SahdOQVFBQUFBQUFBQU/oXc0ktrFBQUFBQUFBQU/rex35I7uN8aKB35/ydczjDUuGaBuuYnbIotN6fIhQTU/AeREmJr/uNNwTpTjWsC+NcqvHqg6+SvmJvz8aoGUs3nQYBPUY1rAmTl1fyHe/LPBta45vjGSTWuCZB6qeb9yqvveNe4JkD02zX/8abPjrxZ45oAHVuOrHHNXfW1Na559ExojWsCXP605tuBSFHzbQCAxVbzH9rSa6w1rgmgUSt7u/wvonTkFRQUFBQUFBQUblmUR5TqUdbIKygoKCgoKCgoKPwLUTryCgoKCgoKCgoKCv9ClKU1CgoKCgoKCgoKtyzK0prqUUbkFRQUFBQUFBQUFP6FKCPyCgoKCgoKCgoKtyzKl12rRxmRV1BQUFBQUFBQUPgXonTkFRQUFBQUFBQUFP6FKEtr/v0IYEWnP1ZhM5k5O+0tCk7GVQnkFhFMi/Uz0Ph6UXAyjjOTVyFZrPh2aUHL9+dTnJAOQMb2g1xa9gXuDcMZ/PYsADxC/dD7eWFKz2PPqDfIPhlfRb/JmH40HzcQ7/ohfN5yEubsAgB0Ph50XjYB77rB2MwWkmatwnQuAYC6ix/Hr087bCYzF2eupuhkbBVdfZ1gGq+dhdrXi6LoOGKmrkCyWAm4pwfhk4cBYC8qpnj166gD/DFMnwIqNUXbtlP44SdOWuqIOvgunI+2SWPy395I4Sdbys55Png/nqNGovJwRyouJmvOfIhNdWlPo7Wz0Ph6URgdx0WHPaXX49unHfYK1+PTqy0NV05FbfDEmldI2ltfkLFxKwC1Fo3Gp28HNP4GhF5HSWI68TOWY4qW88HQqy21nx8PahVZn/xI2povAXBvXo86Lz+B2tONksvpxE1bBuShax+JYeYM1CEhAJi+/wHja69XyoMIfJ5y5ME7Gyn69LOycx7334f7XXeBANO27fDyH2Xn2i8eSa0+bbCazPw+c8MNlwG3QAMqiwVrXhExM1ZR6KKs6usE02TdTDS+3hSejOXC1JVIFiuB93an1uR7ALAVmoh9agNFpy/h27sNDVdNRe3tic1YQPaGLeRs/sZJM3jRJDx7dEAqNpOy4A3Mpy8C4DdqGD73DwRJwnwhntQFy5BKLARMeRS/sffRWK0BSSJ+8Qekvrvjb9sKcNvXi/Fu1xjsEnkHTnF27KtIZsvf1g367FMkJNQBAQAUvLeJwo8+vqH7X3LiJPq2bfCza77W8+AAACAASURBVMn69AfS135Z5dprPT8eQ+/22E1mEua8iSk6Fm1YIBHLZ6AN8kOyS2R9/D2Z78nl3b1FfWq/+CRPuLlht9rY9sx73D6sG417347FVMLXc9aTcqpquRr66nhqta4PCLLiUvl6zjpKiswA1OvcnEHPjkStUZORncszDy3k8efH0653e8wmM6vnvElsdFW/MuX16dzWuSVFRvlDaqvmrCD+dBwe3h5Mf3MWQeFBqDRqhN4NyVxQJX5Fnn5pGft+O4S/ny/ffLjuimErM2/JDLpG3UGxqZjnpr/I2ZPnq4TZ+M0aPL3kD5P5B/oRffQ0s8YsILJLW5ZveoXkhBQAPA/tp3DzZqe4uo4d8Z4yBdRqTNu3U/Sxc3lw69sXj4cfBkAymchfvhzrxYtl55u/OIrAqLbYTWZOTluL0UW9d48I4vb109H6emI8Gc+JyauRLDY0Pp60enMiHvVCsJktRM9YR8HZRACG/74cS2Exks2Oe7AvlsJirCYze2duICu6ahq9Vz1BYOsG2C1WMo7Fsv+pd5GsNur2b0fk3PvReujxCvXDmm0kY9OOMl9Zkdr/GY+hTySSyUz8rBVlfjbi9an4RLXHmpXHmb7TysKpPd3lupCWA8Dllz8ib/dfwPW1A9oAH+ouHovG1xvJbseaZcR0Np742avK6n2dF8bhP6wHWn8D5x58mvwD0YDcBkT8ZxyoVWR+8iOpb30FgN+dXQifNRy3xrU5c9dcNP4GIv4zDpWnGyBhyy1AslhJfmkTBQdOANdfX92a16POS0+i8nAD2Ao8ChirZOx/CeXLrtWjjMj/+xkEND7YeSrn56ynydLxLgM1ePpREtdv49Ad07DmFhD2SJ+yc3kHz3A4ai6Ho+ZyadkXAJguJrOj3yKOvbyF7BNxFGcaObTgPTq+PNqlfsaf5/npoZcpuJzhdLzltKHknLrE9r4LOTB9HfVeGAuAb592uNcP41jXycTNW0eDlye41I1YNJKUt7dyvNsUrLkFBD8cBYD5chqn73uGk31nkbT8c3zmzcYwazrZc54iY8Ro3PtGoalX10lLMuZjfHMVhZ9ucTquCgzE89GHsZw9R2rv/pScu4Dfs0+7tKdOJXuCHPb49GmHW/0wjjuup/7LE0Clov5rT2DNK+Sv1qOxZOTiN6Qb+nphABj3HyNxyXsUHr9AxvvbKYq+SMRLTziMUlFnyURiHvsPZ/pMwW9od9wa15Hz5LUpJL+ymTP9ppP7/R+ETJI7dnajEaHVkjlyFFmTnsR90EDUdSvngRHjypUUVujAAWjq18f9rrvImjiJrLHj0N9xB9715QeC8D63410/lG+7zubgvI03XAaOvvQZxthUTLEpXJy7lgavuL7ndZ8eSfKGbRztOgVrXoV7npBO9L3PcDxqFolvfkHD1yaBSkXD157AZizgQpeHsWbl4DWoB9q64WV6nj06oK0bTtyAx0l9diUhz02Rrzk4AN+RQ7l0/zTi734CVCq87+wJgLZuONbUTP6oN5zo+54l6L7uf99WIOj+Hnjd3pBDTR8j+r5n8WrbiMCh3f62bkUyR44ibeBg3KL63Nj9HzcBjwEDyFu2jLN9J+N3dw/0jrJXinfvSPT1wznTcyKXF7xF7SVyuZVsNpKXvMvZqMlcGDaXwMcGl8UNWzCa1BWfsHbwQnYv+4Khr44noH4oK3rN5ruFGxny4hiXebxr8YesGbSQNYMWkJecSadR/QFwM3hw1+IxfDzuDVb3n8/rT75Ku96RhNUPZ3LPiaxb8BYTHHa5YvNL7zF78AxmD55B/Gn5gXLQY3eSeOEyswZN59mHFqL2DKg2finDBvdj3bIlVw1XmW5RdxDRoDZD73iIJXOWsvDVOS7DPT7sSYb3Hc3wvqM5cTia3Tv2lp07evB42bnKnXhUKrynTyd3/nyyRo3CrU/V8mBLSSFn+nSyH3+cws2bMcyeXXZO16kTHvXD2N95BtFz3qbF0nEu7Wvy9CPEr9/O/jtmYsktoLajbWk4fRjG6Ev81ns+J6esofmS0U7xtj3wIn++uoWs6Hi2dJvNr/M30q0a3xLz9QE+7zmXL/suQO2mo9nDvQBI+vUUXw9YBBLEjnsZe2Gxk68sxdA7En39ME53n8Sl+W+V+1kg+/OfiRn5nyrh8n46hGS2EN1vNtH9Zpd14uH62oF6L43n4oxV2ApNWDPziJn4OkKtxv/u7o44kbg3q0vRiYuUpGdT++kxZfcvYslEzo98gVO9p+I/tDtujWsDYDqXQMz4Vyg4eBpUoixc3ISXsBmLiJ/yGgmz3iRi+UzgxuprxKtTSX7lfc4NmAbwNTDX5c1R+Mf5V3bkhRD3CCEkIUSzGtbdI4Ro7+L43UKIp2oyrUr6k4QQj7k4Xk8IEX2V6EOBzQDGIxfQGDzRBftWCeTXrSUZW+UR1tQtewkc1OGabKszIJLs6HjyL6WT+P1f6Hw8cXehnxN9icLEzCrHfRrXIvXXUwAYY1LQ1wlGG+iD34COZHyxB4CCv86j9vFEG+xXJb6hWyuytv0OQMbnv+A3sKMc5/A5bHnyaFr+X+dRh4ViS0zGlpwCViumn3aj79bVScuem4vl7Dkka9XPYwt3d4p/2QtqFZjNCK2mWnuyHfZkVrDHb0BHMitdj2+fdlhz88k/eAab0UT2N79iLyzGd2Bn2e59x/Dp24HsL3+h8Oh5hEqF2uCJJtgPzzaNMcenUpKQhmSxkvPdfnz6y2m5NahFwR+OPN13HN9BXeRr0GqxJiRgS0nBGhODZLXi1rNHlTywnj0HNpvTcXXdCCynT4PZDDYbJceOUWeQXBXqDIgk7otf5Wv+6+INl4E6AyK5sPln3OoEY05IR2PwROtCx6dby7J7nr5lD/6D5OvOr3jPj5xHFxaAV9tGWHPzMR2ORiooJH/7XqQiE959u5TpeUV1xvjtzwAUHz+L2uCFOki+t0KtRrjpQK1C5a7Hmp4NgK5+HYpPXXDczws1YiuAb8822E1mVG46Co5fRKXTYjeX/G1dALRabCmp2FLkOlD8827cXNSBq91/bZPG2DIz0DVtJpe9rfvx6dfJ2b5+ncj+8hcAio6eKyu31vScspFOe6EJc0wi2hCHjZKE2jGy7GbwQOum49hX+wFIPBqDm7cHXkFV89hcYCr7rXHTIUnya2+t7u7CmV1/kpecBUBeVh4d+3Vij8Ou80fP4WnwxM9FPa4OSZJw93KXbfR0B+nqm961b9MKH4P3NadRSs8B3di2ZRcAJ/86hbfBm8Dg6h8cPDw96NCtHb/s3HdN+tpmzbAlJZWXh9270Xd1Lg+WU6eQCuQZB8vp06iCgsrO6bt2JflzOa28IzFoDR7oXdSBgG63kbb1IADJW/YR4vAbnk1qkbVfbr4KY5JxrxOELsjHKW7d/pFccPiW9L8uojO49i2Xdx8v+51x7CKeYf4AWIvMBLVpiDE+DZuxECTJyVeW4tO/Y4Xyer6svAIUHDyNLbegSjhLShbCTfe32gFtsB8lyZmUJGUiNGqydx3Eb1AnVO46LGmyr/Ht3xGVu57EF99HMltRe3ugLWsDUsragOxvf8W3v1wPi2MSMccmA+DeJKIsnOn4BXK+3oNPv04Un09ApdcidJobqq/6BrUoPHiq9JJ/BO6rkhH/Rey3wL9blX9lRx54GPgVGP7fSEySpO8kSXrlJuqvkyRp89VDuqQWcLn0D3NKFnqHkytF6++N1ViEZJOLojnZOYwhsgntd79Gq48X4tG0tlNc91A/fJtHEP+N7LQKk7NxD732hjHndAIRjoeGgDYN0NcOQhcWgC7Un5Lk8k5fSXIWulBnuzX+3nKnxWF3SUoWutCqDV3ww32xXozFlp5edsyekYE6KPCabLRnZmJLS8MwYyrB33yJVFiINSHxuuzRhfpjrnQ97k1qUxyXgnenFmj8vLBk5eHWrC7a8HK7dKEBlCRnEvhgFMZfjlCSkokuNACt43gplpQstI60TOcSyhoqv7u6oHPoqQKDsKXLo+H6nj2xJaeg8ne+huqwxsWhu701wmAAvR595854hMvpuYf6UejoLMGNlwH3UD80Hnq5DIQHYE7Jcu6IIuextVIe60OrXkPIw1Hk7j6KPtQfU1wqHh1aovL1xpqVh75JPTRh5R0STUgA1pQKeZmaiSYkEGt6FtnvfknD3ZtpuP9j7PlFFP0mj7qpvdzx7NyG239eRsNlT1KSkfu3bQVQ+3iS8eV+Ig+vo8Pxd7AVmiiOT/3burK4Cm3DBgS8vR73IXdhy8hw6phdiYr3XxUWhvDwRBUcLOdXSmZZ2StFGxqAJbl85sWSmlXeYXegqx2M+20NKDp2DoCkF94hfOEYZh9YyYCFj5CblFXWCQcwpmZjqKZcDXttAvP+XENQw3AObvoBgMAGobj5eDLm00VM2rqEXvf2xj80gMwKdmWlZuEf4rpz/MicESzbtZIxzzyORievMt3x/nZqNarNxj83sfz7ldgKqj6Y1hTBYUGkJpf7rLSUdILDqr9ffQb34NCvRygsKCo71jqyJZ/9vInVH7+Oul49p/CqoCDsGeV5IfvE6vXd77yTkkOHyv5WBwVhSiq/P8Up2S7bFkuFtqU4uTxM/ukEQu+U/ZRP24a41Q7ErTS+JDH446docHdnAlqWzxIUpmTjeQXfIjRqGt/XjcQ9J8qO1RvUnqC2DWn4/jNcmrPKyVeWoqvkT0v9bGUqh1N7utHiq8XUXzYZtY8ncH3tgDW/CKuxEEtqNilrvyX8yWGEz7gfW34Rxn3HAPBo2YCSpAxMZ+IBsKRlow31RxfmT0kFv1WSmoUurKof0AT4OIUrra8+g7tgOhWLVGK9ofpafP4ShvIH+AcA52kOhVuGf11HXgjhBXQFHsfRkRdCqIQQa4QQp4QQ24QQO4QQ9zvORQoh9gohjgghvhdChF0liRFCiANCiGghREeHxmghxGrH701CiJWOMLGl6VRjay9H2luEEOeFEK8IIR4VQhwSQpwUQjR0hHteCDGngr3HhRC/A5OvkhcT9uzZ023AgAHvbDWVrwOVKu/TJKouLisNk38ijj8in+Rwn7kkbdxJy03znKOqBUEdm5LgGHFxnUD1nFq9FZ2vJ4N/fJGmY/tTGB0nO30XNrkw/KphDF1aEvxwFEU//HQNeq4R3l6o/P3IffZ50ofdj3BzQxXgVzbyd032uMpjwGYsImXN1zT79HnCnhiKLdtY1uiVxgu4rzeSzU7213sdkpLLpErTujRnJUGjBtNs+xuoPN2RLBYn8zT16uE9aQKmnTuvOQ9slxIo/PgT/Je9jv/rS7FevIhktTlMvJZ7VT2lZSC4c1Pq3NlRLgMO7So6Lsuqi3v+SBSXXvwAhMCWV0j2259TZ+NL+D9+H7YcY7m+LOrSfpXBC6+ozsT2HcPFHo8i3PUYhvQGwJKcQfLMlznedzaW9Fzc6of9fVsBodVg6NSMI52e5HCb8Qi1Ct/ebf+2LkDBOxsp3v8rOXPn43HPMNQRETd0/70eG4mUb3Qeta9y7S5EKoRRebhRb91TJL3wDnbHiHrgiEEkLX6HN7pMY+fiDwltHnHV6yzlm7kbeK3TZDJikmg5RJ7RUqnVhLeqz4djXmfzY69w/7SHcJPX815V86Olm5na50nm3T0LL19v7pkkDza27dmW+FNxPN5hNLMHzUDtFejaV9UArupVddcPMPCevuz6utzPnT1xjsHt7+OhqNF8uvFLfJdcw/KeavS1bdrgPngw+evXX1/8K5SD2JXfovHx5P/YO+/wKKqugf/utvTeQ00gVOldMHQpggiI5RUVEUHpICiKFQR7oahgxQKKWEABkSJYQKVICyWUkEAgvW92s/V+f8yy6dT4Cu83v+fJk2Tmzplz+5kz5965cctL1H2wP0UHk9398vuhc/huwFNk7Uuifv/2RHZqfFEdAbrNH0XaX0dJ35noPpa1L4lT63eRNGY+UTPuqUbPSyzrMumyPvsR455Ejo9/E1tGHnWfHVV9pi8wDyCVB/igfh1JeW4Z2V/8jMbLk+Bh3dF4GvCIiSL7i82VrqnqPlUWTRW31AX4ED3rfs488U61aS7WX0/PXEjofQNptPYNAD+g8qtDlWuC63Gx623ABinlMSFErhCiLRAL1AdaAOHAEeAjIYQeWAQMkVJmCSHuBOYBoy8g30dKeaMQIh74CLihijRRQDegCfA98PUF5LUCmgK5QBLwgZSyoxBiCjAJmFoh/cfAJCnlL0KIV6uROQF4yDUQrerRo8fP2yJGrADwiArBmp5bLrEtpxCdvzdCq0E6nHhEl6ZxlHltnbtlL5qXxlBn4hAihnajpRCUZBdiSs2mJFtZ4+ITHYw5I/8C2S2PzWjmj2nvAdBoVB/q39qJhoumUPT3MbcnGcAQHeJeVHQee26h4gXRasDhxBAVgjWjNG/eTesR+9p4jo6cS+3moO1dGvevCQvDkZ3DhfAedhveg29B+PoiCwoQ3t7gcFDy62/4T5mE7TL0sabl4BEdyvllcYboEEqOpRLQrQVZM7aQ9cUWoicOI7BnKyynzpXq6eWBb5cbONJPaQaGqFBsGblo9Lpy5aOPCnG/irWcPMuJe54DwCMmmoDeyqtsZ1YW2tq1CJw3l4J5L2Jo1RJn9qV7FM3r1mNepyzoDHr1Fep2aEhE12bk7EvCJzqE8/6cy2kDjUb1oeE9inGc8v1fZGw/TPsnRmA5nVllW7XnFKKrVMal9eDdtB4NX3+Ew/e8gD3PqHj1a4WSOXcjBd9sJHjsHXh3bYst5WypzIxsdFFlyjJS8cZ7d2mNLTUDR16BUn5GE2FPjiNo9HBKDh5DFxECMomMzzcR/fCtV6xr5je/0vyr5wCw5RbhKDJjz1H6k7Q78aofeVVl4L4uKQnP7vE48/Ox/PY7+qZNsO0/wKVyvv71zZsR8PRT2FOVhYl6V5ssiy0tB310GMpQ6/LQu8KS0Gmpv2QWeat/oWDDH+5rgof3wpJ0lkfWzwfAw9eLgOhSr6B/ZDBFF2hX0ilJWPsnXccOYu+qXylMz8WUV0Sb2+Npd3dPfAP9yDqbRWgZvUIiQ8jLzK0kKy9TKU+71c7PqzYzZKyyzqTXiN5861oomZ6ShnTYEVoD0m651GK8IBpPfzSefny5eRmH9h0hMjrcfS4iKpys9Kr7a0CQP81bN2P6A0+6j5X1zP++5Q+EbgYiIABZ4GrPFd7IKGNiZfm62Fj8Z84k//HH8ezVS1nwDNiOHsWrVgjna8QzKhhLevkx0ZZThL7M3OIZXZrGYTSTMLV08W/3XYswnc6i7gM30+FeJaY8a38S1iITYa0bkP5XIj5RwRRX0wbaThuKZ7Afvz3+UbnjxWm5+EYFk/nXYTzqReIRG12pvVrTsjFEh1Ls+t9Qpk2H3j+QsPsG4lE3guI9ie509uwCDJEh2NJzyVy+icafzgYubx7Q+XnjCPAh4KaWWM5koPPzxpqWg2lfIr7tmmA+nIww6IlZOBWnyYIhKgR9ZDBOu13x9EeVf3trS6/clu3ZBRhubOn+37NRXXzj25D80Hysp5W3fVfSXy0nz5J077MAtE75/gvglior5r+Euo989Vx3HnmUsJovXX9/6fq/G7BKSumUUqYDW13nG6MY4puEEPuAp4DaXJgvAKSUvwL+QojKAXuw2nWvw0DEReTtklKmSSktwElgo+v4QZSHDzdCiAAgUEp5fjXTZ1TN20Br189q4D4A/3Zx2ItMWDMrD4R52w8R5vJkRd7RnewNuwAwlIlJ9WvTEDQazixew+7eM1nfdzZaD537aT60bQOshSbMVcivDr2/Nxq9FgCn1U7Omu0c6D2NvA07Cbu9BwC+bRvhKDRhy8yrdH3h9gRCBnUBIGxET/J+culdK5RGHzzGickLKElKw3b0KNo6tdBGRYJOh1efXli277igbqZvV5P9wEPkPzsHDB54DewPgGfvnjiLjdXqE+zSJ7SMPvkbdxFaIT/5W//GMyYK75YNEHodobd3x1ArjLw1Stypf482GOpFYj2XjSyx4t2mEY6iYuyZeRTvP45H/SgMdcIReh1Bt95EwSbltbcuxBVnKgSRk+8g+3MlztZ+JhVD8+YUf7UK29GjePa+eBmURROotAVNeDjayEjW9Z7F+r6zSd2wh5jblQWZl9sGji3bzKbh89gw8BlSN+zhhsm3UvjnYbwa1cZeZMJWhZyCMnUefkcP8jYo+TbUCqXxhzM5PmkhJUnKTh3GfSfwionCo3lD0OvwH9IbfXQ4hetKFwQaf/4T/yGK4eDZqgmOomIcWXnY07LwatUE4enhTpvz9gpShk7EtPug+5qosYNwmEquWNfU11ayv+8M9vedQd6WPfi2aoDGy4Bv2ziETovxwMkrknu+DACEpyf25BS0tWujrV8PQ8cO6OrWvaL6d+Tkog0Lx3owQWl7g2+icNNf5dIWbt5J8HDlAc27TWMcRSbsrv5S95VJWE6kkvXBmnLX2DJzKTl2mncHPslPLyyn4Fw2rYcpC/5qt2lISZEZY1blMg6uVzrENu7dluyTyoPwkY17qNehMbtXbOGDYc+Rl5nLXz/uoIdLr0ZtGmMqMrmN9rKUjZvvdHNnTicqO/9knc2mZddWAASEBiK0eqTDVun6K8VZUog9/yx39RnF1g2/MugOZcxp0bY5xiIj2ZlVOx/6Du7Fb5t3YC2zniIkrDTMonmbpiCE24gHsCUmoq1dG02kMiZ69uqFZUf59qAJDydg7lwK58/HkZqKefVqcseMIXfMGCy//070CGWNTUC7htiKTFiq6AO52w8TMVgJwYi+I56MDbsBFOeRa+yvPbIXuX8ewWE0c3blL/wwbC7f9pvNma0HqNWtOXmJqYS3bYC1qOqxpfHdPajdvQU/T3y7nCfZv34EWfuT8I+JxL93e1c8eEf3WHmegk1l22vpOAuQ/cl6Tt4/B0tKGvk//elO59+rPY6iYmyZeQQP6OTebQ0ufR6wZeVjqBWK0+7At20jQobeRN7GXfh1a0nJiVTMR1NIGvcKpgMnONhlLLacAsxHUyg5kkLx/uN4xpTOAcFDupFfIV8A5mNn3Ol0wf6EjR5MxqKvKN59pFTfK+ivZecZFNvp8rZkUvmvIS70Ku9aQwgRAqQCmSgPaFrX7++AfVLKj13pvgVWAInAe1LKLpcofxvwvJRyq+v/0yhe/qFAeynlRCHEMmCtlPJrVxqjlNK3Gnk9gBlSykFl5M+QUu4ue04I8RxgBD4A9ksp67nStwRWSCmreivgvg2w2JycPt5htpI45W2K9ithNi2WP0Hi9CVYM/LwrBdOs6XT0Af6UnTwFEcmLERa7dQa3Z/o+29GOhw4S6yceOYTCncrW6Cl+foxbNcCTv+4m8iuzbCbrcrWgweUHR56fjaDP2d8gDkjn8YP3kyzRwbhFR5ASXYh537ez58zPiC0XUNuXPAw0umk4NhZMh5d6F6sV3/+QwT2aOPepqvYZdA0/mw2STPewZaRh0fdCOLKbPN1YtJbSKud2NfGEzywM5azip9Yr7Vi/OBj/KdMAI0G87ofMX66HO8hgwEwrfkBTXAQoR8sRfh4g1MizWayRo5Cmkz4jh6Fz4hhCE9PZEkJuTNmcfKHtCr1Kb/tmKLP+fwEuPKT5MpPQK+2xL0/E6HVYs8rImXKG3jEKDuqhI+7DY1Bj/DUo/X1xllcwomRz2E6cAJQdlCo/dyDCK2GnJVbSF+0CoCw0YMIu38gAPk//sm5lz4lOqYAn/vuxefeka5X9gJnYSE5ox/EI16ZjM3ff48mOJiQ98qXQfZ99yNNJoIXLUQT4I+02yla/A7rvyqN3+0w/36ie7S86jag0WvRC3AUl3Bi2tsU71fqvOnnsznxaGkZK1svKmV8fOICpNVOg9ceIeSWzlhSlTqXDgcH+j9OYK+2NPlwBkKrwZ5fRPrMV9271hSsVN4whD89Hp+b2iNLSkh78k0sCcpC1pBJI/EbEA92ByVHTpLx1AKkzUbkyzPw7d4B4e2F02IjcexrFGzbf9W6ArT48WV8mtcHh5P87QdJHP0K0mq/Krlx8QYC581FeHujDQtDms0Ur/yK4s8+x+vWWy+7/ku2bMVrYH8cTh25X20mY/EqQu5RjM6c5cqDY6254/Dv3ta1nd1CzAdP4NO+KXHfvKzE+zqVELJzr35G0dY9+LRvSq3nHqJQp8dusfHDUx/T9o4exHVvqWw/OXMp51zbkY78eCZrHn8fY1YBD656Bg9fZSvA9COnWfvUx+4FsF3H3kKbEd2RTifrvvyJtR99z0Nzx9Gme1vX9pMLOXlQ6U+zlz3DO48tJi8zl+e/eAH/YH+EEJw6fIqlT75DiamEoPBgJr0+haDwIIQQREd7XXT7yZnPvsSuvQfIzy8kJDiQ8Q/ey/DB/S54Tccb7gVg1ovTubFnZ0rMJTw3dT6H9x8FYNHy15gz/SWyMhQP+vvfLuLjRZ+zY2vpA9Wdo4cz4v6hOOx2Skqs1P7oHWyHDpW7j6FTJ2X7SY2Gkh9/pPjz8u3Bf+ZMPOLjcWRkKBc4HOSOG+e+vuS+Jwjr1RqH2cLBKUsodM0t7ZY/TsL097Bk5OFVL5xWSye75pZk9k9YjLTaCWwfR4tF45EOJ8ZjZ0mYthR7QTFe9cK5YZmyO45Gq8VaXIJXsB/2Eiu/TH+PbNfY0u/TGfw28wNMGfk8mPwJxtRsbMUlAJz6cRd731pNq/GDiBveDa2nHt+IIOz5RWR/+iPpi1YROlJpr+cdHXVeGIe/a3xOeXSRe5ytv/hR/DrfgC7YH1t2Ppakcxhqh6EN9MOeV4jTZMWSmoXG24OTkxZc9jygCw2g3vOj0Yf4Ix0ObFkFOE0l5Hy7jaxlyvhU94Wx+PdoiyE6lOP3zqFou/ImLaBXO+o8Nxo0WnJWbiZtkfLyP7B/J+rOfQhdcACOwmKsp0VymgAAIABJREFUaTlofT3RBfuj8fbEcvwM2iBlAfaxQdOx5xRcdn8NfWAwofcp84xnbK2XgSf4Fx3jL9Yb+a8bq0+kfH5NboJ5vRny44C2UspxZY79AmwGOgG3AuffH41FCXs5DNwrpfzDFWrTSEp5qJJw3Ib2USnlw0KIbsC7UsoWQohR/BcMeSnla0KIA8B4KeXvQoiXgVsuYsgDsC1iRI1XZKrW4+KJLpOGmC6e6AqoF1vZ63a1nE669AWdl4pe67h4oisgOqbg4okuk80nLvby6sqIleaLJ7pMQgL+mXaVU+D9j8itaWLjLhxGdqWkp/jXuMxvxeXv8HIpJMiiGpe5cs9bNS4TSg35mmRDE32Ny9x7JPLiia6AM/qaj+ptJ2q+/gFsDm2Ny9Rq/pk9UHTaf0Zu65Tv/3UDdl69e/51Y3V2yvJ/vRyq4nqLkb8bqLh7zDcoMeipQAJwDPgLKJBSWl2LURe6wlZ0wFtAlYa8izwhxA7AnwvH0v9TPIAS328CfvoX7q+ioqKioqKionIdcF0Z8lLKHlUcWwjKbjZSSqMr/GYnSgw6Usp9QHzF6y5Vvuv4MmCZ6+9RFc5V6Y13ndsGbKtKftlzUsrnyhzfg7JA9jzPoaKioqKioqLy/5RreR/3f5vrypC/CGtdC1MNwFzXolcVFRUVFRUVFRWV/0n+Zwz56rzpVSGEeBtlL/qyLDi/WPZyEUK0oPIOMxYpZaeq0quoqKioqKioqKhcLf8zhvzlIKW84IeWrkDeQZStIFVUVFRUVFRUVGqQf32l6zXM9biPvIqKioqKioqKisr/e/5feuRVVFRUVFRUVFSuD9TFrtWjeuRVVFRUVFRUVFRUrkNUj/z/CJ7/wIeGbh2QUeMyj/3kU+MyAVacrlXjMts4rBdPdJlE+ZTUuEyAlck1//Gm+7qk1rhMgLR9Nd8GTufV/IeLAAJ1Nd8Gzjq8alym35l/5iNL/8QHZu4S+TUuEyDZWPNl8E98uAlgZ0LFvRGunqMdptS4zCBtzbd/AB+7vcZlFlPzH8QC0P8DH2/6pz7cVGJTTbr/j6i1rqKioqKioqKics3ivCa/qXptoIbWqKioqKioqKioqFyHqB55FRUVFRUVFRWVaxanugFltageeRUVFRUVFRUVFZXrENWQV1FRUVFRUVFRUbkOUUNrVFRUVFRUVFRUrlnUwJrqUT3yKioqKioqKioqKtchqkdeRUVFRUVFRUXlmkX9smv1qIb89Y8AFrTe/jYOs4WT0xZjOphUKZFHnXDi3p2ONtAXU8IpTkxagLTZ8WxYiwZvTMSnRSxnXl5B2pI17msMfYeh7zMMTUAQOOxY1q/Eum5FObm61l3wuG0USAlOB7a/fkbf/RaE0GD97UesG1bh88zbOPOyMS98GoA6c8YQ0KsdTrOF5GkLMSUo+vr3aEPd58eAVkP2F5tIf/tbALSBvjR4ZwaGOuFYz2Ry8pFXcRQUA+DVtB71XnqE+n6+SKckdedR6se3xCcsAHNuEZZCEwBfj3wZc04hAAMXPEJEy1icdjvp+5LY9MRHOO0OPAK86ffqWALrhWO32BAnz+HfNg6n2cLhye9gPHiqUrl61g2j+dKp6AN9KTp4isMTFiFtyse5Am9sRtzcUQidFltuESkLvyN2/v0Y6kZizyvCkZ2LtDtJHlb6IZeIp8fh270DTrOFtMffoOTwSQA0fj5EzZ+CR1w9QJI26y3M+44SOukeAu/oR2iOUh47XvmKej1aUr9na+xmCxsffY+shORKevdzlYHDbidjXxI/u8rA4OdFvwWP4Bcdgr+fk5I1K3Hm5eI9eiJotFi2rMPyXfk2YLipDx5D71b+MZsxvfcmjhRFb49Bt+PR5xaQ4CzMRxMcio9NS97KjeQsXVVJr4hnxuHXoz1Os4Vzj71JyaHS/Ee/OBmPRvVAwrlZb2HeexS/Ad0Im/wfmjasw87+syncn0TjeaMI7d0Gh9nCocnvUlRNvbVcOgV9oC+FB0+RMGEx0uag6StjiBpxE2g0WNJyyVi4iuyvfi6f3zrhNHjnUXRBvpgOJpE0WelLAHXnPOhu26emLcKUkITw0NPkm3loPHQIrRa/tbs4+uo3ALR44T5qD70Rna8X5rRc0tbt5NDcL8rdL2b0zTR4qD++MZGsbzYOa24RAJH92tH08RHovD3wigrCkVdIzifryFrydaX8Rj07Fr8e7XCWWEidscBdrkrhamj4/RvY0nNJGTMHgNpvTCdwcDzS6cSRV8jZJxZh3La7vMxnxuLboz2yxELqzLfcMhv9+iHOYjPS4QSHg5NDpil1O+sBAgbFowsJAIeD7Pe/JXth+bZ0oTbQ8JePcBabweFEOhycum0qAGGT/0Pgnf2onW0EwJyajW/j2jjMFg5MfpfCg8mV7lFvdD/qjx2AT0wkm5o+hM1Vpj4No2m54GH8W8Rw7MWVsORd9zWPvTCVrr27UGIu4dkp8zh68FgluR+ufgcfX28AgkODSNh7mOkPPEG7G9vw5rKXOHc6TSlyr0Cc5gt/GOup+W/w6/adBAcFsvrzJdWmE3ovtD4hNNq6lLyVm2qk/iOfeAC/3h1xWuyUpGSQNG0RtWfeTWCvtjgvMs80fHc6ukBfihNOcbLMPBPrmmeM+06gDw/EabaSOGUxxVX0T4+64TRZMg19oC/Gg0kkTlzk7mMAvq0b0HrdfI6Oe5PstX8C0PCVsYQPj0fotFjSc0kcv4CiPceuWq7W35vY1yYQ0O0GNN5eWM9mcezh1y9rng0ZGk/0hNuU+tJp0XobwO4k58uNZL77TSU5tZ57CP+eSh84PeMtzAlJ6KNCqfvmVPRhQUinJGfFT2R//IP7mgYr5+HTrik4neSs+Z2TUxdfkX5OUwmnZr2H6XAyhugQGiyYDHAExZZ+D1hQSbDKv4oaWnP9MwCI29d1AqceW0Lsi2OrTFR39r2kvf8D+7tNxJ5vJPzu3gDY84wkP/1hOQMeQB8ZjKH3bQjpxPjUg9gTdmPocQuaqLrl0tmP7KX4uXEUP/8w5mWv4zH8QUxvPonx6THoO/XEY/honOdOu9MH9GqHZ0wUCd0eIeXxd6j74sPKCY2Gui+M49i9czjUcxLBQ27CM075WmnUhOEUbj9Awk3jKdx+gMgJw5VrtBpiFk4jZdYSPukzi78WriGwbjgfxT9KXlIaNlMJnw2YzWcDZruNeIAjq3fwcc+ZfNL3CXSeBlrc1QOAThOGkHU4hU/7PUnCl9sI7tmaPztP5uiM92j8ypgqy7XBUyM5s3Qdf3aZgj2/mOj/9AJA5+9N45fGcOC+l9nZ/VESxr1J45ce5MyYZ7Cl5+DML+Ts9FfKGfE+3dtjqFeLk33GkPb0QiLnTHSfi3hqHMW/7iGp/ziSBk/EcvKM+1zustWsGDCbFQNmAxBYP5JP4h9ly6wP6TVvVJV6J67ewac9Z7LcVQbNXWXQ6r6+5B4/y4r+syl6Zipe94/H+6GpGOc9TuHU+zF064Wmdr1yshyZaRifnkLR9Acxf/0p3g8/CoAIDsVj4HAKHxtH4aMPomvYBMvmtZzo9wgBg+MxNKxTTo5vj/Z41I/mRK+HSJu9iKg5E9znIp8Zi/HXPZy8+WFODpqI5YSSf8uxFFLHzyPvjyMAhPZujXdMJNs7T+HIjPdp+sqDVeY/7ql7SFm6nu1dpmLPL6bWf3oR2rs1Qd2ac/rDn9gzbA72IhN1nhmF0Jf3d9SZfR8Z7//AwW4TsBcUE+rqSwG92uIRE83BbuNJfvxd6r04DgBpsZF4xzMc6judQzdPJ7xnK4LaNiSid2uC2jak4NBpdtwxH2tuEcffXVdJ19ydiey4Yz6mM1nljmf9lsDWPk8AkPLwfBzFJQTcGo9HhXL169EOj/rRHOs5jrNPvE2tFx4pdz70gcFYTpT5iq9Gg1/P9mS99y1HWozAnluILbX8V559e7THUD+a473GcvbJxUTPHV/u/Kn/PMnJQZPdRjxA8fb9YLdzst8j5C5fT/DIWy6rDQCk3PMESYMnuY14dxl9vIbfe88icf6XCL2OXzpPJWHG+9xQTb/N25nIzhHzMJ0uX6a2fCOHZy/j1Ltryx3v1rsLdWNrM6TLnbww4xWefHlGlXIfvG08d/UZxV19RnFgdwI/r//FfW7vX/vd5y5mxAPcNrAvS9544aLptL6h2AvTOX7zhJqpf8D4+z6O95vAwT7TKUk6R8xr4/GMiWK/a56JqWaeqVNhngkrM8+kPP0huRv+Qh/ix+4ukzg+YwkNX65aTsxTIzm3dC27b5yEPb+YSNe4CoBGQ8xTI8nbtt99KKh3G4L7tuPk0x9zYOgz2HOLMB2v/GXqy5ULUH/Og1jTczD+fZxdcf8hadblz7OWMxkcHv40B2+egS7AB0dBMUf7TCDo1ng84irUV892eMREc6T7OM488Ta1XfUlHQ7OvfARR3tP4PhtMwm9b6D72vBHhuPVqB674v7D4eHP4NWk/Bh9yfr1mc7ZN1cR+4oyL0u7k5Q5nwA0BToDE4BmVQpXuSBCiGAhxCYhxHHX76ALpNUKIfYKIdZWl6Ys16whL4QYKoSQQogmNSx3mxCifRXHbxVCzKrhexmrOb5MCHF7Dd1mCPApgPHvY2gDfNCHV24f/t1akLP2DwCyVm0lqH9HAOw5BRTvP4G0OypL9vDAmZ2OzM0EvQH7ob/RtbmxfBpLiftPbUxjsNuR2engsGM/uAtdu5uw/vajO03gzR3J+XobAMV/H0Pnr+jr0zoOS3Ia1tMZSJud3DW/E3hzp9JrVm0FIGfVVoL6KccDurfBfCQZ85FkAOp0bcbhr38HwGosweDjhU94YKVsndpaOlCn7TuJb1QwACFxtTi9/RAA4S1iwOFAHxZA4Z7j6Px9MFQhK6hbc7J+UDw3aV9tI3RABwAihnUja/1fWM7mAOBVNxzTqXRsZ9IBKNy4A7/eXcrJ8uvTmYLVWwAo2ZeIxs8HXVgQGl8vvDvcQP6qn5SENjvOouJKugDE3tyOI98oZZC+9yQe/j54V6F3cpkyyChTBhKJwccLAOHphbRYcKafxZmRBnY7tt9/xtChazlZjsRDyGKlqTuOHUYTEuY+J7RahMEDbaPmSIsFR/JJsNkpWPsrfn06V8p//neK99u8LxGNf4X8f7WxUv6tJ89gPXXWLSOsfwfSVv0KQMEF6i24W3MyXfV27qtfCBvQgbD+HSg8kIzO10u5NsAHe5GpUt/w69qC3HU7AMgu0x4D+3Uk52ulnRZX6ItOk9JPhE6LRqcFKYns1w6k5Pii78n5KxG9vzdCU3lILkhIwXQmu9Jxh8lCUJuGGE9l4CwwgpQU/PAr/n07lde3b2fyvi0tV62rXAF0kSH49exA7sqN7vTereJwFBhx5Bchz9dV3/J15d+nU7m6KiuzOhxGE5bkNGxn0jH/fQR7Rs4lt4FLJaJ/e8666j9/zwl0/t54VFH/hQnJmCs8GAFYswsp2Jfkfqt2nu79urH2qw0AHPz7EH7+foSGh1Srh7ePNx26tWXrj79esu4Vad+6BQH+fhdMI3QeSIcNnHalrmqg/gGMv+0FhxLMYNxzDO/mMWS7xu2LzTO5rnkmu4p5xrN+FMb9iie76O/j6Py90VdRP4FdbyDLJSfjq22EuOQARD84gOx1f2HLLnAfCx3UBaHXkbFiC0V/H0fr54XG03DVcrW+Xvh1bobGw0DW19uQNjuFvx287HnWuDsRR0Exvm0aYj6eii4kAGmzk/fDbwRUqK+Avp3I/UYZR0x7XfUVHoQ9Mw+z6+21s9iM5UQq+gilDYbcfTPZyzcgrXalfnw8r0g/gKK/j2GIUuTaMvPKvnkoQvHM16ok+L+AE/mv/1wls4AtUso4YIvr/+qYglLWl8Q1a8gDdwO/A3f9N24mpfxeSvnSf+Nel4sQ4kIhULUAt3vWei4HQ2RwuQS6YD+lk7oGZmtaDobI6ichAFt6LvYDO9HGtcD3jZVgLsaRuB9NYGiltLo2XfF54UM873wY+9F97uPaJq1xJB8DWRrdpo8Mxnqu1CixpuUo3v+oYKxpZY6n52BwGZe60EBsmXmKXpl5yqt5wCMmGiTEff4sI9e9QO2OjSlKy3HL8Azy5Y6Vs+k8+bYq86jRaWk2rBvJvxwAIOvIaRr2Vwzx0Ea10AX54enSwZKWg0dU+XLVB/thLzQpIQSA5VyuO413gyh0Ab60+fZZ2m98iYih3bCcc+kmJQFDehH8wG0E3tm/tBwjQrGllRoX9vRsdBGh6OtE4cgtIOrlacSsWUTUvCkILw93uqCRg7nnp/n0efUh/GuHYixTBsb0XHwjqzeENDotTYZ1I8VVBvuXbSKoYTRjdi/G/42Psf7yE87sTHd6Z24WooyhXhFD71uw7d2pZDM3m5LvVxKw5Ct8n3wJaSrGvn+3O2/nJ6HS/IdgO1ch/5Eh7vxHvzKNmO8XEjV/crn8l8UjKoiSs6X5L0nLcdfheSrWW8m5XDyjgvGICuLcFz/j06gW8QeW4Fk7lIwP1yphY+d1DCrfl2xp2ehdfckQGYL1XOm9ba62rRS0huYb36D1gWVk/nqQvL0n8YoKwhDkS0jnxsSvn4NHWADh3W+otmyrInpge4LaNqDeR89y9rEF2NJz3Pq48xsRgq1M31L0UtJEP/MQaS99DM7SPqqLDMFZVEzIfbfQcP0i/OLbYagTUU6mLrKCzPQcdOfvKyX1P5lDgzVvEXRXv1I9IkPc7Tvw9r6Y9iZechs4L7fusrnErFlA4F39y10XdO8gum19mbDerbEVlD7klqTlVqr/KyE8Koz0c6X9ICMtk/Co6vtBr4Hx7Px9D8VGk/tYy3Y3sHLLMhaveA20+qvWCQCNDpyloSE1Uf8VCbu7l2I4lh23r3Ce0fp64SgsrR9rWi4eUSGV5NgLS+VY0krnAkNkMKEDO5L2SfkHD6+YSOy5hTRaMIE2m15F6+uFV0zkVcv1rBeBPaeQwN5tqT3jTmJfG4/Gy+OK82+IDEHj7UnRtj1A+fHjPPrI8n3Alp5TqZ8Yaofj1TwW075E17390UcEccPal2j2zVwcRaYrtgPC7+5D/ta9lY4D9YE2wF9VnVS5KEOAT1x/fwJUaZgIIWoDtwAfXKrga9KQF0L4Al2BB3EZ8kIIjRDiHSHEISHEWiHE+vNebSFEOyHEL0KIPUKIn4QQURe5xUghxA4hRIIQoqNLxighxGLX38uEEAtdaZIu5j0XQswUQuwSQhwQQjxfxXkhhFgshDgshFgHhJc5V6XurjcH84UQv6A8nVV137Hbtm3r1q9fvw9Wm8rEGcqKT46i8sWV0pRHG+CDtn5jbLu2YXz0LvDwRNugGVVtAmXfu53ipx7EsmEV2npxAOhadoISExRVeIUsqtKlah0voiJCp8G3Q1NOTXqDL4fPwSc8UPGkA+snv0PavpP89NgH1OrYmGbDu1W6vve8UaTuPMrZncpguPOdH/AM8OHeH+fhXycMc1Ia0l5mgquoUBVZOV88QqvFr1UM+0e+xP675hE+uDNaPyV2NuWuGWS99RlFW/4i6J5BeHVQjLeqigYpEVotns0bkrdiPaeGTMJpLiF03B0A5K1Yx8neD7K8/2yKM/MJjqvCWXKBguw5bxRndx7lnKsM6nVvQfbhFD5oP5HCGWMw9L4FdBWeI6uRp7uhNR69B2L+bKmSHx9f9B26UjD+LkxLXwOtFkN832p1qa5tCJ1Gyf/y9Zy6dbKS/4dHVCeksohLqDcljSCgTUOKEpL5teXDFP59gqiHb0Pj63URHc9XehXqnD/ndHLo5unsbz+GoDYN8GtSG4RAaDXoA3z4deAzFJ9Kp9mTl+e3yNuXxLm1O0kZN4+I6SMvK79+vTpgzy6gJOFkhfQCy4lUEruP5cQtk3EUGvFu16xCkurLIWnEY5y8dSrJo58l+N5BeHdoXi5Z6Pg7kQ4H5r8PV5ZR7fgAyXfM5NSQKZwe/QzBI29xy81dvp4TPcfwe69ZOK126o3uV/7yiw0kl0BV+b2Q3P5D+7Dhu83u/48eSGRg++Hc2XsUX374DTr/yGqvvVquuv7LED15ONLuxJqed/H7XMo8c6E+cj7JBdpW7NwHODX388oPHkLgFRtN2rKN7O07E+lwEj6ix1XLFTotPi1isZ7NImn62zhMJURPHFZ13i4h/15N6uLVoBbnXvyk2jQXKyONtyf1l8zi7JwPcBrN5zOHxsebhEGzSJn7CV5N6l6Rfv433kD43b05Pe/Tiil9gW+AqUBhxZP/DeQ18COEGCuE2F3mp+oYq6qJkFKmAbh+h1eT7i3gMS5jfe+1utj1NmCDlPKYECJXCNEWiEV5ImyBUgBHgI+EEHpgETBESpklhLgTmAeMvoB8HynljUKIeOAjoCo3WBTQDWgCfA9UXkEECCFuBuKAjig95XshRLyUsuw71aFAY5fuEcDhS9Q9UErZvYrbTgAecg2kq3r06PHzn9HDVgAYokOwZpQfdO25hWgDfECrAYcTQ1QI1ozcCxQPBNzUEmd2Ohq/QHA4sO/5HX3Pwdj3/VHtNY7DexAD70L4+qNt2BxN/UZo6sWhax+P8PTGc8zjFJzMwRBd6tU3RIVgy8hF6HUYosocjwzBlq7oaM/ORx8ehC0zD314EPYc5dWnNS0HW3Y+jb6cQzQa8lMyiGzVAABjRh5+kcEUpGRwdPUOIlvFctgVcgLQZepQvIP9WDPrI/exZsO6Et5ciS08tXU/Tfq2xXxa8cJ5RIVgqTCZ2XKK0Pl7I7QapMOJR3QwFpfOlrQcbLlFOE0WnCYLhftP4h0bRS5gz8xFHxmK7XQa9vRsvFo2wrwrAVt6NvqoMFxDM7rIUOyZOUgJtvRsSvYrxnbhht8JHacYso6cfILuGcR/RtyCRq9D7+2BbxkPl29kMMaMquNxO00dilewH1vKlsGI7ux+V1lA5Uw/i8zJRBtd231eExyGzK0c5qGtF4v3IzMxvvA40qiM87qW7XBmpiELC3BmZSDNJrSNmwM70EWGYsvIKSfDnp6NPjoM854y+c8ozb/Zlf+iH7cTUsaQDxp5C/6tYrnh3Unk/XEEz1ql+fe8SL3Vuq8PdccOxCMskII9xwkf1InE2csA0Af5YTmTiVfD2hTvO67oWKEv6aNCsbn6kjUtB0N06b31USHYKvTFkKHd8YurxY1fziJj8z68ooJJW79Lya+vF9LuwBDihzWnqKoqq4T5XC5e0SFk7jyEoV4Uhpha2Cv0bVt6DvoyfUsfFYI9I5eAAV3x79MRv57tEB4GtL7e1H5zOrmfrkMb7K8YNUJiSUrFq3Xj8jLTssvLjAxx39eeqfx25BRQtPEPvFo1wrTrELb0HLxbN8YZV5eUe2cTPOrWS24DVcttjGnXIfwHdCXozv7Udmoo2JdEUMdSXT2jgivV/6US1KERX96+DIBD+44QGV0670ZEhZOVXrkfAAQE+dO8dTOmP/Ck+1hZz/zvW1xjqNCUe1t5RTjtilfeRdl6OM/l1n/qtDcAqP36NPz734glOR3j3uN4RIdyPlbUEF25bVc3z0SM6k/YPcoDvMNoRuvv477GEFU6Zrr1zSlE518qxyMqxP0g4dcqliZLlXUXhvBAwobHE3Mum4KdiXg1jKZo73G3HM+6YVckVx/sR1DvtgR2b4l/hyZIpxPTsVQM0aHkrv2D6InDrmie9W5aj/D/9MF0NAVHfpGrLkrHD7eeaTnoo8M4H1mhjwzB5mr76LTUXzKLvNW/oA8PovH6t5RyLTS51w0V7zuB0GpxWmyXrV/sa+M5OnIu9rzSqGCh04JixC8HvuX/MVLK91AW/FaJEGIzUNVT+uxLkS+EGARkSin3CCF6XKpe16RHHiWs5kvX31+6/u8GrJJSOqWU6cBW1/nGKIb4JiHEPuApoDYX5gsAl7HtL4SoHKQHq133OoxifFfHza6fvcDfKIZ/XIU08cAXUkqHlPIccH4bjIvpvrKae74NtHb9rAbuA/Bt2whHockdhlKWwu0JhAxSYrLDRvQk76ddF8gSWM9mowmLQhNZGxEaibZZWzTB4ZUMeREe7f5b2h2g1YGnN5Y1nyJzMjC9NA3z0nnYj+6j5IOXyd+4k5DbewDg07YRjqJibJl5FO8/jmdMFIY64Qi9juAh3cjfpIRo5G/aSciIngCEjOhJ/kbleOEvexF6HUeHPM7ng55Go9UQ3CAKodVQP74FliIT5twiYvu0IftY6cKnFnf1oH58C9ZNfLucR+LI6h0sv/UZPhswG3uJFafNgcNoxr9dHI4iE9bMygZx/vZDhA1W4nyj7uhB9gYldCRrw24COjdBaDVovAx41Q1HH+yHoWEdNAF++N8Sj3H73/h0a4PlWAoAxi1/EXCbsvjIs3VjnEXF2LPycGTnYU/LwhCjeNt9urTGckJZQKwLCyJv+VpWDJjNoS+2kn30DE1dbx8i2zTAUmTCVIXeze/qQd34FvxYoQyKzmVTp6vi6RQBQWgCgxGBIWjCI0GnQ9+tF9bdO8q3gdBwfGbOpXjhfJxppeXszM5E16gZGDxwnEhEGx6JzM8DvY6AQfEYt5R/Q1u0+S8ChyqLz7wulP8bW7nzD5D3+ToK9yeR8Mgisn7cRdSIeAAC2sVhr6be8rYfJnxwZ1I/3kjebwkcf2EFWT/uQh/kR/BNNxDQLg6H2YJHnXAsKenl9dyRQPAtylqR0BE9yXO1x/yNuwi5XWmnPmX6oi7YH62/8jYma+UWjEnp7Jv5IWkbdiO0GkK7NSeobUOcVjtoNJdsxPvUjyB/30l8YyPx7dUBodfh36cjhZt3VirXoGGl5eooMmHPyiPj1U85euMDJN40hjOTXsG44wCp097AdOA4Hg1qo68dgdDrCLytJ+YKXtvCLeXr6rxM4eWB5vwaCy8PfMu0b22AL4bY2qQ//y7S4bisNlBRrs9Kh8JxAAAgAElEQVRNbSlxyS36aQdJgyfxe+9Z2IvNCJ0yrQW2a4i9yISlivq/FPJ2HXMvTt264VcG3aGE87Ro2xxjkZHszJwqr+s7uBe/bd6B1WJ1HwsJKw1zaN6mKSCu3ogHpN2C0OpBo0PodQQMjr/q+gfwjW+LV4uG7Ov4MAf7TCdvw05CXeP2xeaZYNc8E+qaZzKWbSCh76Mk9H2UkhNn8W0VC4BfW2VctVU1ru44RJhLTsQdPchxzVe7Ok5gV4fx7OownqzV20l8+E12dZxA1urfEVotXg2i8Wsbh9AIiqvYretS5Gav/ZOTs97nxMz3+LvHdIx7j1O8/wRht/cg4KaW2AuNlz3PGmqF0uiDxzj+yBsYIoMx1FH6VtDgmyjcVL4PFG7eSfBwZRzxbuOqL9e96r4yCcuJVLI+WEP2p+tJHDiVxIFTKdj0F0FDlHEveHAXkBLzsTNU5GL6nZi8gJKktHLXxL4+AZSnijcqCVQph5Syj5Tyhip+1gAZZSIuooDMKkR0BW4VQiSj2L69hBCfX+y+oiZeO9YkQogQIBUlkxLQun5/B+yTUn7sSvctsAJIBN6TUnapWmIl+duA56WUW13/n0bxlA8F2kspJwohlgFrpZRfu9IYpZS+1ch7HTgmpVxaxTmjlNJXCPEWsP9ydHfpOUNKubviuYpJgcUlyenjz28LVnxAmXQbfzabpBnvYMvIw6NuBHFltgU7MektpNWOPiyQG358Fa2fFzgljuISDvSYjMNops2Soei69UPjHwR2G5YNq7D+8Dn67oMAsP2yFsOAO9F36QMOB9Jmwb7nN/TxtyA0Gqy//4R13Qo87hyHrllbip8dx7GffKj7wlj8e7TFWWIhefpCTC59A3q1o85zo0GjJWflZtIWKS9BtIF+NFgyE0OtUKxnszn58Cs48hWPQfCw7kRNGE4xglNb96P38qB+z1b4hAdiTMvFYbNz+vdDBMVE8tNj71Ockc+0pE8oPJuN1agsQDy+YRd/LlhNVNuGDHjzYaTDSc7xs/jkFhLUtTkOs5UjU96hyLVIq+XyWRydvhRrRh6e9cK5YelUdIG+GA+e4tCERUirayvC8YOJuqsnUjo5t/xnzCfTaPryA+giQnDkF+EsKMKSco7iX/eQ/8V6ACKeHY9vvLJ9YdqsNylJUDxMHk1jldh4vQ7bmXTOzXoTZ6GR6Fdn4NE0FqNTQ2FqNlue+IiOk4ZQr0dL7GYrm2a8R+YBJexqyLIZbH78A4oz8pnkKgObqwxObNjFzgWr8YkIpO/r4/AJDyTI207JdytwGgvxfmCiYmT+/CMl33yO4eZbAbBu/B7vR2ai7xyPM8u1s4nDQdHjyo4tnneOwtC1FzgcOAvy0ISE47Bpyf96E9nvrCTo7gEA5H2hLIiOfO4RJf8lFs49/iYlB0+48x/94mSEXof1TDrnHnsLZ6ERv5u7EPnMw2iDA7AVFlOUkIL5VDohvVrhMFs5POVdCl311mb5LA5PX4olIw+veuG0cG0/WXQwmYOuemu+4BEihnRRwkvSc0l//Utyvv2FuE+fInnm2+6+FPvOo+gCfTEdOkXSpDdL63zeWAJ6tFG2n5y+CNOBk3g1rUfMW5OVhawaDSnf/0XiG98B0Orl0dQZ3hWh02I6k8X+WR+Tvf0wnZc/xr7p71GSkU/sg/2ImzAIj/BALNmFZGzZx75H3ydu4mDqjLgJrYcer8ggHPlF5Hy+nqy3vyL4P4rRmbtCWaAZPedhfOPbIs0WUh9bgNlVrufx6XQDoQ8Nc28/WH/5C/i0V8JprKfTSb7nSfeC17wVSl1FPf8wfq66Sn3sLUoOnkBfJ4K6S55SBiathoLvfyHrna8AiPv5PbR+PkqokhCUHEkiedj0S2oD+jqR1HnX5dTSain84Rey31H8HNGvPYpns1isDoH5TBa2PCNBnZviNFs4MGUJBa76b7/8cQ5Ofw9LRh71xvQndsJgPMIDsWYXkLVlHwenv4chLICuG+ejc42HucXFDI+/h2KjiVkvTufGnp0pMZfw3NT5HN5/FIBFy19jzvSXyMpQPPTvf7uIjxd9zo6tpQbanaOHM+L+oTjsdkpKrDRrEIi0W7gQM599iV17D5CfX0hIcCDjH7yX4YP7VUon9F5ofUOwpuaQt2pzjdR/o61LEQY9Npdn1rjnGDid7raddIF5pvz2k5XnGWHQI7QaTCfPcWzyYoz7FTnNlz/J8envKuNq3XCaLJ2mjKsJySROWODuY+dptGACuZv2uLeJbLJ0mrJ4VUoK9xznyOhXsBcUX7XcgBZ1iX1tPB51wtF4GrCkZnFy8sLLmmdjXxtP8MDOWM5mofX1whAZjC09h9yvNpOxeBUh9yj1lbNcqa9ac8fh372ta/vJhZgPnsCnfVPivnlZ2dzBFQJ07tXPKNq6B6HX0XjjIgy1w8EpSX72IzI/23hF+oHilEsY8Bh+HZvQfPV8gIOUhno8Cay/YOP9B5hR/+5/3Vh9LfmLqgKfLgkhxKtAjpTyJdfGKsFSyscukL4Hig046KKyr0FDfhzQVko5rsyxX4DNQCfgVuD8e6exKGEvh4F7pZR/uMJVGkkpD1UjfxtwVEr5sBCiG/CulLKFEGIUV2bI3wzMBXpLKY1CiFqATUqZWcaQHwaMAwaihAUdBh66kO6XYcgD8Gf0sBqvyGYDLs0zeDkc+8nn4omugF80VVbPVdGmjEetpojyr3Ijo6tmo+XqF/RV5L4OlbdvqwnS9tV8GzhrrPn6BwjU1XwbOOvwuniiy6SBV833VQAhan5+0PwDMgGSjRfe4eVKmC0qezVrgp0Jn9W4zKMdqlxKdVWYLDW0KLcCVue1GgxQGb2m5j9F5Km3XzzRFVBi+2eipTuf+/aKDdia4n/AkA8BvgLqAqeBEVLKXCFENPCBlHJghfQ9uERD/lqMkb8bqLh7zDco+5imAgnAMZSV0wVSSqtrMepCIUQASp7eAqo05F3kCSF2AP5cOJb+okgpNwohmgJ/uBbTGIGRlH9t8h3QC+Wp9hjwi+vaK9FdRUVFRUVFRUXlOkFKmQP0ruL4ORQnb8Xj24BtlyL7mjPkpZQ9qji2EJTdbFxe7xBgJ4phjJRyH0oc+hXJdx1fBixz/T2qwrkLuvuklAuo4mtn56+TymuPiRXPu85VqXt1eqqoqKioqKio/H+iBvZx/5/lmjPkL8Ja18JUAzDXtehVRUVFRUVFRUVF5f8d15UhfzleaiHE2ygrgMuy4PyC08tFCNECqBjYaJFSdqoqvYqKioqKioqKytWj+uOr57oy5C8HKeWEGpZ3EGW7RxUVFRUVFRUVFZV/netn6biKioqKioqKioqKipv/WY+8ioqKioqKiorK9U/NbwL6v4PqkVdRUVFRUVFRUVG5DlE98v8jHBXeNS4zZ61Hjcv0F7YalwkwPDKjxmV+nRtR4zLHtDfXuEyA4Unnalzm5h21alwmgJ/TUeMyY4Mrf+a9JjCZDDUuM9hR832g/pAaFwnAq6sDalxmQ9s/822ZZM+aXw63Ieaf+SDSP/Hxpia7Ku2AfNXUjxtc4zIBpvm1qXGZ9W3/zHLITrVqfnO8P1Mja1wmQIy2+B+Rey0g1eWu1aJ65FVUVFRUVFRUVFSuQ1RDXkVFRUVFRUVFReU6RA2tUVFRUVFRUVFRuWZRF7tWj+qRV1FRUVFRUVFRUbkOUT3yKioqKioqKioq1yxOdbFrtageeRUVFRUVFRUVFZXrENWQV1FRUVFRUVFRUbkOUUNrVFRUVFRUVFRUrlnUwJrqUQ35/2E6zrmX2r1aYzdb+H3ae+QmJFdKc9OiRwhtFYvTZid7XxI7Hv8IaS//wZ5m8+4nvHdrHGYr+ye/S+HBynLqjb6ZmLED8ImJZGPTsdhyiwDwaRhNqwXj8G8Rw7EXV+JfO5jg3m1wmq0kTllM8cFTlWR51A2nyZJp6AN9MR5MInHiIqTNTnC/DtR//C6k04l0OEl6+mMKdx4FwO8/Q/EfOQxdaAjOYhOFn66i4OOV5eTq69ch5PkZeDRtSN7ijyn89GsAhEFP5EdvIPR60Gkxbf6N/Hc/rbZcez1/LzE9lXL98dH3yKyiXPu9MoaIljEIIcg7lc6P05diM1mqlKdr1QGv+yaCRot16zos339R/ny7rnjd8QA4JdLpwPzpYhyJCVXK8ujcgcDpExEaDcXfr6fo0wqy6tUh6OnHMDSOo2DJRxiXf+U+J3x9CJo9A31sDEhJ8KSPyN1zAoDWc+8jqncr7GYru6YuJb+KNtDggb40eqg/vjGRrGk+Dmuu0X0urEtTWs+5F69aweg8PTCdSuNgNW3Jq24YrZZOQR/oQ+HBZA5MWIy0OdAF+NDirXF414/AYbGRMHUJxqOphPZsRdxnM0FK7PmFODJzOD1icjmZYU8+gk98B2SJhfQnX8dyWMlX4P1DCbi9P0iJ5VgyGU++jrTa8O7WjsiXZqDx98ORbyT3m59Je3FZOZm1nnsI/57tcZotnJ7xFuaEJPRRodR9cyr6sCCkU5Kz4ieyP/4BAM+m9akzfzyG+rXQeOopOZ1J4qTFGKvoA551w2myZCr6QF+KDp5y94Hz+LZuQJt18zky7k2y1/4JgL77EPSdbwYkzrRkbHt+xXPIAyA02P7chHXL15XuA6CpE4f3tFcp+eQV7Pt3VJkGYOCz9xHXsxU2s5XvZiwl7VDluhvy8kPUahkDCHJOpfPdjCVYXe2+fuemDHjmXry0Wkpyi9h4+7xy13aYcy+1erXGYbawvZrxqtuiRwhxjVc5+5L4o4rxqt9zpXqumbGU9CrkDH7lIaJaKP0z51Q6ax5dgs1koV7nptz5/nTyz2QB4PPXFoo/LT8WGDp2xG/iRNBqMa9bh2nFinLnPfv0wfvuuwGQZjNFb76J/eTJctcGOnXkrdxE1pLKdRL17Fj8erTDWWIhdcYCSg6dLD2p0dDw+zewpeeSMmYOAJFPPIBf747oAsOQDjsOYxbIqvf4eGr+G/y6fSfBQYGs/nxJlWmqY85LT9Crbzxms5lp42eTcOBIpTTd4jvx1JwZaDQaiotNTBs/m+RTpwHo0rUDz784C51Oh3eBjZV3zKt0fU2Nra3KjFe7LzBexbnGq+/LjFdhXZpy47LpFJ9W2oDYvhXroaMEPjoBNBqK16yn6JMvy8nS1atD8DOPYWjSkIJ3P6Lo81Xuc1FrluM0mcDpBLuDjPvHl7v2SsfWiJ4taTPnXnQ+niD+j73zjo+qWB/3M9uyySab3mlJCB0CBBAQKYFQRUUsWCkioID0bgXFrtcOeO1YQVF676D0FjqEgOm97yZbzu+PsymbbCgK93q/v/Pks5/szpl5552Zd94zZ2bOOYLyghJUefmcve/ZynT154/GOy4Wu6mMpCnvU5qQCICxZzsavDQa1Cqyv99E+ke/ABD58XT0UfKLANVGA7bCEk71m1Ihrg2wGDAiP0SmI2CupazCfxxla83/UcLjYjBGhPBLt2n8Puszurw6wmW8xBV7WdF9Br/1noNar6PJwz2djgf2boshIoTtnadwYvqntHrjCZdy8vafY9/9r1DqcH4VWPKLOTnvKy59shpDdBjukaEc7DKR89MX0fj1MS5lRTz7KKmLV3Ow60Ss+SWEPBwHQP6uExyOm8aRPjM4N/ljot9+CgCPZvXxuncA2CVS7htN+blEDHf1RRvZwEmuraCI3Dc+ouBr55OnVG4h/ckZpD44jtQHx+HetQNurZu71q1XDL6NQvis+zQ2zv6M+Fdc1+u2+d/ydf95fNVvLoUpObQb0ddlPIQK95GTKHl9NkXTR6Dr2htVeEOnKNaEQxTNGk3RnCcpXfwGHk/OcC1LpcJ3xiSyJ88mfdhI3PvGoYlwlmUvLCL/7Q8pqjaAr8Bn6gTMvx8g48ERZDz6JEXn5bfFhsTF4BkZwrqu0zg04zPavzbSZfY5B86x44FXKfnT2Qa0Rg/avzaSc5+uI/dIIjs6TCBh+qe0eGO0SzlNnn2YpMVr2NVlCpb8Yuo52j9q0j0UJlxmT69ZnJjwMc1fHgEqQYvXRmHNyedi78ex5xaQPvstJ3mG7h3RNQwjqf8oMl54j6DnJwCgCfLH99G7uXLfRC7fNQ6hUuE1sCeoVIS8MpXyy6kkxDyMNSuPwk37nGR69YrFLSKM0z3G8uecj6j3smyLks1G6sufc6b3eM7fM4OAxwfiFl0fgAavTyR/wx8UHb3AhXmfU3jkAo1ff9JlHUQ8+wgpi1dzoOszWPOLK/sAACoVkc8+St72o5VBwtsPXffBlL4zhdLXJ4BQox82kdLFL1Ly2ng07bujCq5fOyOhwm3wcGxnjrjUo4LonjH4R4TwXs9prJz7GYNfcW0D6xcs5eMBc/l4wBwKUrO5bbhs93qjB3cuGMl3o99mZdxsdo79wCldhb/61eGvbqvDX11asZffus9glcNfRdfwV417yXp+2GMaq+d8xqCXXeu5Yf5SlgyYy+L+cyhMzabT8Kr+eeXAWZYMnMuSgXNrDeJRqfCaNIn8WbPIGT4cfVwc6obOfcyWlkbepEnkPvEEJV9/jXHatFppz/cdj/dd3XFr7NwmXj1jcWsUxrleY0mZ8xHhDruqIGDkYMouJDuFFe8+yvl+47HmpyDZLKjcfVyWGeCegfEseuflOo/XRVz8HURENaRb7ABmTX6RV99+3mW8V99+ngljZtG3+1B+Xb6GSdPHAmA0erHwrecY8fAE4rrezaqnPqiV9mb51pC4GLwiQ1jfdRqHr+GvdrrwVwDZ+86yOX4um+PnUvj5t/jOfIasSXNIf2AUHlfzq9UG8NXJGjeNjEfG1hrE/2XfqhK0XziC38d+gKXIhKWwlN/HvM/FsW9WpvGOi0UfEUpCt6e4POtjGrw6zpFWRYOXx3Lusfmc7DURv7vvQB9dD4DEp9/iVL8pnOo3hby1v5O37vcKcRpgKTAOaAn0BG7Na9rrwI70X//8U/mPDuSFEDYhxNFqn0Y3QeY4IcTjju9fCiHu+wsyiusIny+E6PMX5I0QQnzoQr8RQoiwavEmCyE8qv1OEkIE3Gh+rmjQL5aLy3cDkHX4IjpvA+5BtR18ytZjld+zj17EI9TP6Xhw/1hSlu0CIP/QBbRGD9xcyClMSML0Z3at8PLsQgqOJmK32PBqWp/Mn7YDUHT4PBqjB1oXsnxub0XWatmBZPy0Hf/+nQCwl1Zd/Ks93ECSO5ZHdD0syelYr6RgvZKK+eAxrKnpePTs6iTXnpdP+clzYLVSE8kkyxYaDWg0SJLrTtu4bywnf5brNe3IRdyMBgwuylBebKr8rtFrK3WtibpxM+zpqdgz08Bmpfz3rWg73O4cqayq3MJNT12LjLoWzbAmp2BLTQOrFdOmrbh3r10HltNnocYspjB44NauDaUr18oBViuWwlIAwvrHctlhA7mHL6AzeqB3Ueb8hMuUJte2gQZDupK89gD+HaK5vGyXbBNXsSX/bi3JWCUPnFN/2knwgA4AGJqEk7NLXokouZCKe/1AAuLaUnopHWw2sNooXLsDQ1wXJ3mGuC4U/rYFAPOxM6iNnqgDHXauViP0OlCrEO5uWDNz0LdpCkKQ++FS7CVm8lbtwtCppZNM7/jbyP15GwClR86iNhrQBPlizczD5Jj1speYKLuQjDbYHwC3yHDcGoSQ8dMO8nccxxjbBI3RgK7OPiDPtGf8tAP//h0rj4U/0Z+sNX9Qnl3onEilAq0OVCqEtx9SXjZSTgbYrFiP7ETT+rZa+Wi734n1+F6k4oJax6rTrG8sR3+RbSD5yAX0Xh54BtbWu8zJ7nWV/aj1XV05vf4ABak5AJhznHWvX81fZf8Nf9U0PpZjP8t6phy5gJvRA89r9U83XZ39vSbaZs2wpaRgS5P7mHnrVtxud+6vlpMnkYrlU4rl1ClUgYG10koWKwWrdmKMd24Tr/jO5P2yFQDTUYddBfrKeob449WrI7k/bnRKU7zrCNjkGXjJakao1HXq36Fta7yNXtdV1ur0GxjH8h9WAnD44HG8vb0ICq59qpIkCS8vg1wWoycZ6ZkADLl/EOtWbyY1OQ2A0hrtDzfPt9b0V9ob9Fc10bVshuXPFGwpcpuXbtqGew8X55ZTZ5FcnFuuxl/1rX7toihOyiCgYxOS1x7g8vLdhPeLxZpT1Y99+nYiZ/l2AEoOn0NjNKAN8sXQNpqypDTKr2QgWazk/rYbn761fYPf4NvJ/W1Xxc++wHGgogPmALZaiRT+K/ynt9aYJElqezMFSpJ0Y+uDNybb9bTDjcmort8IIAFIdfyejHyVW/p386mJR4gvJY6TJkBJWi4eIb6YMvNdxhcaNVFDu7Hv+W+cwvWhfphSquSY03LRh/pRVoecq6ExulNWTafytFzcQv2xVJOl8fPCWlhSeWIqS8tBV+1k7T+gE43mPoI2wMjJR1+Vy3bmCm4tR2DedwShd8O9WyekUhPqoBu4JlKpCPv+YzT1wyj6cSXlCWeA4FrRPEN8KUqrKkNRei6eIb6UuKiP/m+NIaJXDDnnU9i+4LtaxwFUvgHYczIrf9tzstA0rr0aoO3QDf2wJxHePpS8McelLHVQALaMKlm2zGx0LV2vLNREExaKPa8A3+dmoo2OwnLmHOopP2MzleEe4kdptXYrTcvFPdQX83XagGdUCCqNhnqDOhHYpTlJakHqsl2Y03Jxq2FLWj8vLIWlSI72N6fKcQCKTl0hZFAn8vefxbtdFPp6AXg1rYcpNQfv6GDqfbYQldETa0oGeZ9WbavSBPtjSa+adbOmZ6EJ8qfs5HnyvlhO5JZvsJeVUbrnMKV7D+PZtxtoNbjHtiR64gjUXgZMZ5KcyqQN8ceSWiXTkp6DNtgfa2ZeZZiuXhDuLSMpPXpWLsu5y7i3iqLsxz0EDO6CW5g/RUcuoAv1o7xWHyit7APlaTmVdaAL8cN/4G0cH/oSXm0bV6aRCnIp37YCzxc+R7KUY89IxpaSWHncnp+DumETpzIIbz80rbtg+mge6gbOx2piDParHIQDFKbnYgzxpTirtg3c8+YYmvRsS9aFFDa8/C0AAZGyDYz8YR5GD3dOf7aBRMfAHWR/VdPGruWvIod240ANf+UV4kdhqnP/9Ar2pdiFnLveHEPjXm3JvpDCRoeeAPXaN2bMuoUUZ+aj/vJdbElJlcdUgYHYs6ra3Z6VhbZFC5c6ArgPGkT5/v0u01rSc/Bo61zv2mB/LGlVAzZLWg7aEH+sWXmEPf8kaa99gdrgXmd+Kr0X9rKSOo//VUJCg0hNSa/8nZaaQUhoMJkZzgPh6ZOe55ufFmE2mSkqKmFwX3mLUWRUIzRaDctWfYGnp4Hkr3Zx6ufdTmlvlm+t6a9MN+ivAPxiG9Nn80LMGfnoju7GllHVbraMLHStrs+vAiBJBH74BkgSxStWU7JiTZ26Xq9vdQ/xozQlp9K3Bg3qiM7Xi7SMNHJ+3g6ANsSP8tSq9ilPy0Eb4if7m2o2Vp6eg2e7aCf5nre1wJKVT9mltIqgJsgzSBuAQOAH4I3rrwSFW8l/fWuNEKKREGKXEOKw49PVEd5TCLFDCPGTEOKcEOI1IcQjQoj9QogTQogoR7wXhRDTa8jsLYRYUe13vBDil2vo8bYj/y1CiEBHWOUMv2O2/CVHnBNCiGbXWb4XhRDTHXI6AN86ViMmAWHANiHENhfpHnWU9agQYrEQou5pFtcZ1w67yqxTl4UjyNh3hsz9Z53FUFvO9c5e1VLJhayaOolr6J2zbj+H7pjEqZFv0HDWMABM51Mo3bYX924dCf5oIZZziUh2+1XLWwu7ndQHx5Hc7yF0rZqijWr0l8tQwfrpS1jUcQK5F1JpNriz63xdldfFjLvl4G6Kpg+n5O3n0N8/qo5C3FibO6FWo20aTckvK8l8fCx2s5lmEwfXqeKN2IBKrca3TQR5xxI59tJSoqbei0dkqGv9XFaHHCfx/d/QeBvouuU1GjzRn6ITSXI7A1censqVoRPI+3w5bk0a4d6hVTWZrutFZfTEM64Ll+JHkNjjEVTuerwGx4EQCCFQGb04f88M8lbuxNij/XXrCaDy0NNo0WxS5v8bu2MG8cqM99HVC6LJO+NQe+qRyh2zdzWrwKW+8r+oBSO4tGCpvN+2Ou4GNK1uo2T+aEqeHw4aLaqA0Dr1A3Ab8iRlq76scz+1s04uVKrDBn6dsYQ3bxtP1oUUWjnsXqVWE9Y6gqUj32Lzw6/TZvI9eEWGXDWDq9lY57r81TXapTorZyzh3U6yni0deqYlJPFe10ksGTCX/V9uwOfl69iGUod8bdu2uA8cSNHixVdJem37lyQJr7iOWLMLMCdcrB3BgcrdBySQylwuMP8tXNmkq/Z58qnHeeyBcXRo1Zsfv1vBCy/PBECtUdMmpgWPP/g0Dw8dQ5dn7sE3IsQp7U3zrX/TX+WdSGJtx0ls7jOXC59twOuxYdetlysyRk8i47FxZE2ag+d9d+PWrnWVqn9R14p0Fb717CdrSd1wiNDJD+AWEXYV4eCqgmpm6Xf3HdVn40Ge9O0GPOL4PwTofU1FbyL2f8Dnn8p/ekbeXQhRsbHzkiRJQ4BMIF6SJLMQIhr4HnnACxADNAdygUTg35IkdXIMgiciz2i7YivwkRAiUJKkLGAk8MVV9DIAhyVJmiaEeB54AZjgIl62JEnthRBPA9MB15t8XSBJ0nIhxARguiRJBwGEEFOAXpIkOU1rCCGaAw8Ct0uSZBFCfIzcgb6uEW8MMAZguHcnxj39FE0e6SUrejQRQ5h/VQFD/SjNcH2VHzNlCHp/L7aO/hyAZsP70OSRXqgliYKjibiH+1Mxz6gP9aMsPc+lHFc0HBlP/UfjcAvywfRnFm7VdNKF+lGWnusU35JTiMZoALUKbHbcQv0pd5Ff4R+ncW8ULM9e5hZRsnoT2nqhZDw9B5+Jo1AF+PI5jCcAACAASURBVGHLyqmV7lrYi0owHzyG++0d4IDsyNo+3oc2D8n1mn48Ea/QqjJ4hfhRXEe9Akh2iTOr/qDjuEEkLNtZO7/cLFT+QZW/Vf6B2PPq1tt25jiq4DCElxGpyHl52paZhTq4SpY6KABb9rWXjivS2jKzKD95xqGYnejRAwiLb0/usUQ8wvyp0Moj1A9z+vXNbkWNiCd8UEcQgrRNf6L19CDvjzN4tWzg0pYsOUVojR4ItQrJZkcfVhXHVmwiYXLVAlePAx9QmHCZgB5tMGXJdqTyNGA+k4i+dVNMB+VtONb0bLQhgZV3ZWlCArFm5eLRpR2WlAxsefJydNHmPbi3a07hyq1IdjvFm/YAIFmsSFYraj8jtly5zi1pOWjDAgH5hj9tiD+WTIcta9Q0WjSbvF93ULD+dwIeH4j/MHkfb+HGP8jccZri44n49Yl12LerPuBR2Qd0of6V/cQrJormi2W3p/Uz4te7HZLVhqaJHXtuBlKJrJ/tzCG0nap2Bap8/JEKnfNR14/Gfbh8v4UwGFE3jwW7HesJx82z3Qby1JiBAKQcS8S7Wt81hvhRdA27T1j9B7ePuZMjy3ZSmJ5LaV4RFlMZZZZyMv44Q6un78S/TQQAOUdlG6vAI9QPUx3y20wZgpu/F787/FXT4X2IfqQX5UDq8USMYc79s+gqs5uSXeLUqj/oMvZOji3b6bRt48K2YwjN4whvb6QC2UbsWVmVW2VAnmV31cc0kZEYZ8wgf9YspMJCl2m1If5YM2q0fXoO2tCqlURtqBzHe8DtGPt0wqtXLMJNh9rTg3rvTiV5yjsA+Nwbh9B5YCtI42ah0hvZuPNnAI4eTiAsvGrgHRoWXLltpgI/f19atGrKkUMnAFi5Yj3fLpMvYtJSM8jNycNUasJUaiJ53xkCWzSg4R2tbopv1bjr6DBMllPTX7nfgL8CsFazgXTHVi5NvaqLYnVwILbs6z+32B1x7Xn5mLbvxvOhocQ/08ClrtfrW0vTcvEI9ydzzynKcovQBxgpuZxJkdqGR4tGlF1KxZKWgy6sypZ0of5YMnIRWg26ajamC/HHUt0HqVX4DujCqYHTqmeZDOwAKox9LdAe2HLdFaFwy/hPz8ibJElq6/gMcYRpgU+FECeAZUD1dcoDkiSlSZJUBlwEKjYHngAa1ZWJJF/SfgM8KoTwAboA666ilx2oWItfinzF6YqKWf1DV8v/JtAbiAUOOC58egORNSNJkrREkqQOkiR16GmI5sxXm1nZdx4r+87jyoZDRN0nFyOwfRTlhaUul6mjH+pJeM/W7Bj/UeVleYWc3b3nkLHuIOH33wGAT2xjrEWlN7St5vIXm9jdew5XvtpM8Zlkgh7oCYBX+2hsRaVO22oqyN97ksA75X3OwQ/0JGfDAQD0japOJIbWEQitBqvj6TiWlAw0DcLRxbTAEHc72vqhlO74vZZsV6h8vVE59nUKNx3ut7XHcunPyuNHv97M1wPm8fWAeVzYcIiWQ+V6DW0XRVlRqculX5+GVdtyovq0I/dCaq04ALaLZ1CFhKMKDAG1Bl2XOCyHnJ8cogquvK0CdaNohEZTaxAPUH76DJr64ahDQ0CjwT0+DtPO66sDe24etsxMNA3km++k4lISv9nKpvi5pKw7SEOHDfi1b4ylyHTdy9QXv9zEjvsXUng2mdQNh2j0YHe82zdGpVVjqcOWcvecIniwvGcz7IHuZKw/CIDG6IHQygtT9R6NI/eP0+TtPYVHVCjaxg1Bq8F4Z0/UPkbKzidVyivZ9gfGu+XJI31MM+xFJdiycrGmZaKPaYbQuwHg0bkt5Rf/xHxCnuX1jO+K0GrwGxqHZLFWDuIBCjfvx2+oPHDwaNcUW1Fp5baaBm9MpOxCMln//g2A7K/XcnbgZC4+9gIFG/cR/EAPGkwZSt6OY1iLSp221VQg9wF5pjH4gR6VfWB/p/Hs7yh/slb/wYXZ/yZn/QHs+VmoGzYDrVwWlW8w6NwQfsGg1qBp1x1rwn6nPEoWjJZn8OePxnpsL2XLP6kcxANYdq/lk4Fz+WTgXM5sPEjbe2UbqNeuMeYik8ttNX7V7L5p7/ZkX5Tt/vTGQzTs2BSVWoVaryOgXRSnP13P6r7zWF3DXwW0j8JSh79q/FBPwnq2Zlc1f3X2q82s7juPJQPncnbjQWKGynqGt2tMWZHJ5bYa32p6NunTnhyHnoZA78rwsJhIEKJyEA9gOXsWdb16qELkPqaPi6Nsb43+GhSE94IFFC5ciC052WVaodXgPbg7hZud26Ro8z5875VvbHZv67CrrDwy3vyaM11HcvaO0fw58Q2K9x6vHMR7dm9P4Lih2ArTuZkP6bObC+nbfSh9uw9lw9ot3DfsLgDad2hDYWFxrW01BfmFGI1eREbJN4J279mF8+fk7V0b1m7lti6xqNVq9O56QttFkXs+9ab51qNfb668OTX1b/grALdqNuDbNhJsVjShwajD5Db3iO+FaWfdT3eqjtDrER7uld/1nTtQsmINm+Ln/i3fmnc0Ec+IEPKOJxLQuRn17+lC+o7jeLaNxuS4GTp/43787+sJgKF9E2xFJVgy8yg5dh59RCi6+kGyf7u7G/mbquzQeEcM5ovJWNKcLlY2ID+1xgN5ArgHcOq6KuEmIf0D/v6p/BMePzkFyECefVfh/Dij6s+Uslf7befaun8BrHLIWyZJ0o3chVJXi1Xkb7uO/P8OAvhKkiTXm6Gvg+QtRwmPi+HePW9jM5Wze+qSymN9vp7Onhn/xpSRT5fXRlKcnM2glS8CcHntAY7969fKuJmbjxDYuy099/0Lm6mM45Oqlok7fjuT41M/pSwjj0aj+xE5fjBuQT503/Y6mVuOcGLqp7gFenP7xlfQeLmDXUKlUdNx/0fYSsycm/xxpayW387l/NRPKM/II2nBNzRbPIWGs4dRnJBE+nfyRX/AnZ0Jur8HksWK3VzOmbHvVqYPevNZUKsI/fRNbAVFFP+4EsvFy3jddycARctXo/b3JfS7j1AZPECSMD5yLyn3jkYd4EfAgpkIlQpUgpKNOzHt2oerPfKJW48S0SuG0bvexmIqZ/30qnq998vpbJj1b0oyCxjw7lh0nu4IAZmnrrB53peuG8pux/Tl+xjmvAEqFeXb12FPTkLXR97WUr55FdpO3dF17wdWK1J5GSXvz3cty2Yn/60PCHj/dYRKTcmqdVgvJWEYIssqWbEKlZ8vQV8tkuvALuE5bCgZw0YilZSS/9YH+M2fCxoNttQ0To/9CoD0LUcJ7d2WAb+/g81UzoEpVTbQbekMDk77FHNGPo2f6EfTp+9EH+RN3y2vkbblKIem/5ui86mkbztOm2cfwi3QiJAg8pl7ODGpanY99ttZJExdQllGHmdf/o6Yxc8QPftBik4kkfydvPPMs0k4rT94Gslmp/hcCglTFiPZ7Fx85xda/vwBAoGtqJj8b35DGx6M94MDKfhxLSU79mPo3pFGGz53PH5SHgCZj5+leMMuGv78IZLNRtnpixT8tA5sdjLmvUvIGzNp/eAgrHmFXJ70Dv6P9Acg59v1FG49iFevWJrvXOx4/OT7ABg6NMdvaBym00k0XfsvAFLf/IaibYfwuas7AY8PRO0v38xWnprD2ckfVdZBq2/ncG7qIsoz8ri0YCnNFk+h0eyHKE64RPp3W123eYUZXT6H9dgePKb/C+w27MmJmL97D49xL4FKhWXfZuzpV9B2lctg2bv+qvJqcm7bUaJ7tWXyjnfkx0/OqLKBR7+YwW+zPqU4q4B73x6Hm6c7CEg/fYXVz8oLodkXUzm/4zhPr38Nrc3Ohe+3k3+2apCb4vBXQ/a8jdVUzt5q/iru6+n87vBXnV8bSUlyNgMc/urK2gMcr+avzm89SuNebZmwU9Zz5fQqPR/6cgarZsp63vPOuMr+mXH6CmvmyXq2GNiJ2Ef7YLfasJotFMyv0ddsNoreew/fN98ElQrzunXYkpJwv0se5JpWrsRz+HBURiNeU6ZUpskdO9YprdGuIW/ZZsrOX8HvYblNcr9bT9G2g3j16kCT7UuQTGUkz3zvmm0T9tJYhE6LxtsIgN1Shr3E9UrcjBde48CR4+TnF9L7nkd5+onHGDq43zXz2LJxJ3Hx3dlzeB0mk5mp46secfj1T58w45nnyUjPYsakF1jy9b+Q7BL5+QVMm/AcABfOJbJty242716BXbJz/IftZJ9zfvrOzfKt6VuOEtK7Lf0d/upgNX91+9IZHKrmr5o4/FX8ltdId/irend2InJ4HySrDZvZQs68l1F5ehL4/usItYrileuwJl7GcK98bin5ZTUqf1+Cv/qk8tziOWwo6Q+OQuXjTcAbLwHyfR0l67dg/v0AEFKp61/1rUfmfkn7hSPR+Xoi2e10em8cpvNX8OrYHPPZKxRsPYR3XCytdi/Cbi4jaarso7DZufLcpzT59gVQqcn5cTPmc1UTV3533UHur07bagDygHeAA8jjo7XAmpqRFP47iL+63/kvZSZEsSRJnjXC3gWSJUl6WwgxEvhckiQhhOiJvA3lTke87Y7fB6sfE0K8CBRLkvSWEOJLYLUkScsdaVYhL//ES5JU59WjEEICHpIk6QchxLNAsCRJE6vLE0IkAR0kScoWQnQA3pIkqWcd8kY44k6ood8q4B1JkrY54p0A7pIk6ZLjdxLytqIg4DfkrTWZQgg/wEuSpMt1leHL8EdvekMGWm/+TelGcWueWFU/5OpP3fgrLM+tPZD/u4y+PeWmywQoTnS1Ofjv8fuV0GtH+gt42W++XUX63fjN19dDaanupsssLHO76TLbDbs1j3N+81fjTZfZ2HLzbRUgSXPzz2VjI25Nf828fONPjrkWzQ5ce9B/ozSKHnzTZQJM8Wp302U2styascxt9dKvHekG+SM55NqR/gIR6pt/ozNAh+Rfb02nvQFGN7rvvz4l/u+k5f/1enDFP2FG/mPgZyHE/cA24GZa4rdA4NUG8Q5KgJZCiENAAfL+9FvBl8AiIYQJebvPEmCdECJNkqReFZEkSTrluKDYKIRQIT+vdTxQ50BeQUFBQUFBQeH/Iv/km03/2/xHB/I1Z+MdYeeR915VMMcRvh3YXi1ez2rfK49JkvRitfARNcR3Az69Ab2eqxE+otr3RtW+H0R+IUJd8r5EHrTX1O9n4OdqUT9wfFzl8SNV+/YVFBQUFBQUFBQUnPgnzMjfEhyz6yXAtGvFVVBQUFBQUFBQUPhf4//sQF6SpNiaYUKIfUDNDaqPSZJ04q/k4djTP6lG8B5Jksb/FXkKCgoKCgoKCgrO/JOfGvPf5v/sQN4VkiTVfg/x35P3BVd/Pr2CgoKCgoKCgoLCLeH/q4G8goKCgoKCgoLC/xbKza51859+IZSCgoKCgoKCgoKCwk1AGcgrKCgoKCgoKCgo/A+ibK35P8LdnZOvHekGKU29+dd55iLtTZcJsCn75r+8aVTsn9eOdIMs3V3/pssEMNyCdcdBLW5++QHKi9U3XeappKCbLhNA3IIbrIzqm/9StCW/eF870l9gxp051450g6RsvjXvVOnlXXbtSDfIkdO35sU9vurymy7zVry8Ken8qpsuE+CX1s9dO9IN4m2/kZe3Xz+H/rz555Zb8ZIpgMz0m/+isX8K9v/gy0v/11Bm5BUUFBQUFBQUFBT+B1Fm5BUUFBQUFBQUFP6xKPPxdaPMyCsoKCgoKCgoKCj8D6IM5BUUFBQUFBQUFBT+B1G21igoKCgoKCgoKPxjsSuba+pEmZFXUFBQUFBQUFBQ+B9EmZFXUFBQUFBQUFD4xyIpM/J1oszIKygoKCgoKCgoKPwPoszI/x9C07YTHqMmgEpN2ZY1lK34zum47o4+uA15SP5hMlG65F1sly8C4Hbnfbj1GQQS2K4kUvLh64AVt84d8Z48AaFWUbJyLcXffO+cZ8P6+M6bibZpNIWLP6f4u5/k8Ab18V1Q9dIPTXgohZ9+ifnfK2vp7X57BwJmj0Oo1RT+vI78z35yOq6NqE/Qgqm4tWhMzvtfUfDl8jrroMv8x6gf1xarqYwdU5aQk5BUK06vD54ioE0kdouVrKOJ7Jr9OZLVRsO+7YmdcR/YJexWG5rv38F6+gTadp3weHIiqFSUbVqD+eca9dqjD/p7HwZAMpso/eQdbElyvQqDJ4YJM1A3iAAJQiZ8QfrhCwDc8dJjNHToumXqErJc6Br//lMEtYnEbrWScTSR7bM/x261ARDeuTndXnwUlUaNJbeIdfe9AsBt8x+jnkPu7jrqoPsHTxEQU1UHe2fJdeAdFUq3d8fg36oRpV98imnZj+g6dsJzvFx+89o1lP7gXH633n0wDHOU32Si6F/vYE2Uy+81fRZunbtgz88jd/TIqjSdO+IzdQJCJdtV0dcu7Oq5meiaRlOw6HOKv62yCeFpwHfedLSRESBJ5L38Jk2fHERg73bYTGUkPPMJRSdql9m9QSBtFk9C42Og6EQSJ8Z/iGSxofFyp/XHE9CHByDUKpI+WU3qDzto9eHThNzVBUmSMCdn4xbsw8U3lnFlydpKmXqHTK2PJ4UnLpFQTWarjyeiDw/ALdQPyWanPCufk898AqfOgUpFq/VvUJ6Wy7nhC3GrH0TjT6ai8fGkJOESFye+h2SRX3DTcMET+MS1x24q4+KUDyk9kQhA8BODCHokHgSU/rCDw59tAKDXS4/R7K4u6DzdKUrP5cK6A+xc+INTXQx87ymCHXaVfjSRTXNku3Lz9qDfm2PwaRiEtcyCatWb2NMuo24Ri/7+cSBUWPaup3zjMuf2atMZ3eDHwW4Hu42y5UuwXTyJCArH/Yk5lfFUQeE0LjZjLy4lf9kGcpc4ywEIenYsnj06YjeVkTb7HcpOXUQXEU7Yv2ZXxtHWDyX7vW/I++o33JpHEvLSBNQeGoSbG0KnQ7JaKfltLUVfOZdb07A+fs/PRNesMQWffE7R0qr8hacBv2eno41qBJJE7oK3aD52IAG922E3lXHimU8orMOuYhZPQutjoPBEEscrbMDbQOt/jcWjUTC2MgsJkxdRfEZ+cV/bfYuwFZvQBvmg9nTHfDHVqW2rU5dt6BuHE/nOBAytI/nz9e/g3Tcq08x/bQ5x8d0xmUxMeXoeCcdP15LbrfttPDt/OiqVipKSUqY8PY+kS1cA6HJ7R156dTYan3pIkg1bQVqt9NV5duE77NyzHz9fH35duuiqcWvSbsHjhPaOwWYqZ//kxeS5qOPGI+Np8mR/vCJCWNFyLOW5xQA0fWoQDe+9HQBDmB9aX09KziWTMP4jik5cqiXnevqrUKu4/MlqUn/YDkDfA+9hKTahD/JB4+lO8cU0Dk9aRIELPSNG9SXqyf54RoSwtsVYynOLnI77tI2kx5r5ZM9dgGnLTvRdOuI7fTyoVJT8upZCF/bq/4Jsr/kff47lYpJz/G9+IuSbj7Fl5pA1ZR4A9eePxjsuFrupjKQp71OaINuUsWc7Grw0GtQqsr/fRPpHvwCg9vEk6uPp6OoHUf5nJhefehNbQQkA7s0b0vC1pwBOAnagI2AGHgTmAWpgDTDzqo2scEtRZuT/r6BS4fHkJIpfmUXh5OHousWhqtfQKYotM43i5yZRNPUJTMu/xmPcNACEXwBuA4dSOHMshVNGgkqFrlscqFT4TJtEztTZZDw0Eo/4ODSNnGXaC4vIf/fDygF8BdYrf5I1fIz8GTkOyVyGecdul3oHPjuetKee5cpdT+I5sBfayAbOeRQUkv3aJ+R/+fNVq6B+XAzeESH81G0au2d9RrdXR7iMd2HFXpb1mMHPfeag1uto9lBPAFJ2n+SX+Ln80m8eO6d/imHCDLlex06m6KWZFEwYju6O3qjq16iDjDSK5j5D4aRRmH/8GsP46ZXHPEZPxHJ4PwXjH6dg8ihyL6QC0LBXDD4RISy9YxrbZn1Gj4WudT23Yi/f9pzB933moNHraOHQVWf0oMcrI1gz6h2+7zObbWM/AKBeXAzGiBB+7jaNvbM+o0sddZC4Yi+/dJ/Br71luU0eluWW5Zew77lvSFjsGKyqVHg9M5n8OTPJHTUct7jeqBvWsKu0NPKmPEPuk6MoWfo1XlOrym/esI78OTOcM1ep8J0xiezJs0kfNhL3vnFoIlzY1dsfUvSts10B+EydgPn3A2Q8OIKMR59EFRSAISKU3Z0nc2r6p7R4Y7TLMkc/+zCXF69hT5cpWPKLCX84DoD6o/pRfDaF3+NmceDe+TR98TEC+7VH52dkS4NHOXTvfKyFpdhM5WSu3V9D5iNcXryWPV0mY80vqZRZb1Q/is8mc+GV7yg6mYRKo+b07M9o/sYTAISMHoTpfNXbmOvPe4y0T1dxrNsErPnFBD7UGwDvuPboI0I5dvt4Ls1cRMSrYwBwb9qAoEfiOTloJif6TCWydzt8GgUT0SuG0LaNyTp9heWPvIY5t4gDi9dSk9O/7uWLXjP4Kl5u/9bDegJw2/i7yTp1ma/7zWX9lEW4OQbv+gfHU/rhc5QsGIumQ09UIc591Hr2KKWvPE3pqxMwL30Xt0cmASBlplD66gT58/pk0GhJnvAKiQPHYbyzB7oo5zcdG3p0QNconMT40aQ/9z4hL00AoPxSCkl3T5Q/QyYhmcwUbfodgKAZo8j+8DsyHnsKlcEDW3YO6Q+MwuNqdrW09gWE7zTZrtLvH0n6w2NQBwXgERHKrs6TSbiKXTV59mGSFq9hl8Ou6jlsIGrSPRQmXGZPr1mcmPAxzV8e4ZQu5f3llBy7yIGIYU5tW5O6bMOaV8zl5z4jbdFvTvHj4u8gIqoh3WIHMGvyi7z69vMu5b769vNMGDOLvt2H8uvyNUyaPhYAo9GLhW89x4iHJ2DNT8ZWmOEyfXXuGRjPondevma8moTGxeAVGcLartM4OOMzYl8b6TJe9oFzbH/gVUr+zHIKP/vJGjbGz+XEqz9i/jOLvD0nOTVlcWU/q8m1+usfcTM5eO9LNHnxMYS26g3U5977jfxjiaxqOJyj0/9NzOujXMrP3X+WvQ8spLSGngCoBC2ffYiM7ccdv1X4znqGzGfmkHb/KDz6ubbXvLc+pHDpMhCiVnzv8U9gcVx8AXjHxaKPCCWh21NcnvUxDV4dV5lXg5fHcu6x+ZzsNRG/u+9AH11PboPxQyncc5yEO56mcM9xQsYPldOoVUS8P4XLsxcBtAR6AhbAH3gT6O0ID3Z8v6XY/wGffyq3bCAvhLAJIY5W+zS6CTLHCSEed3z/Ughx31+QUVxH+HwhRJ+/q+PNQAgxQgjxoeP7i0KI6ddKo27cDHt6CvaMNLBasezeiq7j7U5xbGdPIpXIxbedO4XKP7AqT7UaoXMDlRp0euy52ehaNMOanIItVZZZunkr+u5dnWTa8/KxnD6L5JgldoVbh/ZYU1Kxpdc+Ibi1borlSirW5HSwWiletx1DXBdnvXMLKEs4h2S9+iu4G/aN5fxy+WIh8/BFdEYD7kE+teL9ufVY5fesoxcxhPoBYC2tesW7xt0NJNBEN3eq1/JdW9F16uYkz3qmql6tZ09W1au7B5qWMZRtWuOIaKW8sBSAiL6xnPlZ1jXjyEXcjAY8XOh6eVuVrhlHL+Lp0LXJPV25uP4Axak5AJhzCgFo0C+WC446yDp8EZ236zpIrqMOzDmFZB9LxG6R21PTrDnWlBTsaXL5y7Ztxa1rjfKfOolULJffcuokqsAqu7KcOI690HlWqqZdmTZtxb0Ou6KGXQmDB27t2lC6cm1lneo7xZK6bCcABYcuoDF6oHNRZr9uLclYtQ+A1J92EjSgg3xAktB46uXyGvRY8osJ6NO+mszzuIX4Yk7JxpycXUtm5qo/HDJ3EDigo0MmaDzdCezfkaz1B7HkF1Ow/xwaowGP1pH49I4l67vNlXKM3VqTu1oemGYv24Zv/04A+PbrRPby7QAUHz6H2tuANsgX9+hwig+fw24qB5ud5D/OEN2/A1F9Y5Ekif0fryJl/1ncjAZU6tpu/lI1u0qrZlf+0eFc2XMSgNyLaaj8g1G3iMWelYqUkw42K9ZDO9DEdHYWWGau+q7T4+r1Ldoeg6DMhPnQSbBYKVyzE88+zn3ds3dnClZsAcB87CwqLwPqQF+nOB5dYii/ko41NVOuaklC5emBrmUzbHn5WFMc/mrTNtx71Lar8lNna/kS2a5aU/Jb3XalNXrg5sKu/GvYVbDDrgxNwsnZlQBAyYVU3OsHogv0rkzn06u9y7atSV22Yc0poOTYhVq+t9/AOJb/IK98Hj54HG9vL4KCA2rJlSQJLy8DAF5GTzLS5foccv8g1q3eTGqyYxZeuvYQpkPb1ngbva4Zrybh/WNJWrYLgJzDch3rXdRxfsJlSmv0vZpybGXlpK/YQ8Gh82iMhjp9wNX6K4Da4QMka1W5g+NiuPKTrGfe4bptoSDhMqV/utYz6ol+pK7ZT3l2AQC6ls2w/pmCrcJeN27Dow57xWpFHRLkFN+0dz8ecXdQ/GvVhbpP307kOGyq5LDsb7RBvhjaRlOWlEb5lQwki5Xc33bj0/e2qjTLtgGQs2wbvv3kcO8e7TCdTsJ0OqlCfA5gAyKBc0DF1cpmYKjLQiv8R7iVW2tMkiS1vZkCJUm6sTW7G5PtetrifwSVXyD27KpZAHtuFuroFnXG1/UehOWIPLso5WZjXvkj3ot+Qiovw3LsANZjB1E17YktM7MyjS0zG13L5jesm3t8L0ybtro8pgnyx5pepbc1Ixt962Y3nAeAIcS3cmALUJKWiyHEF1Nmvsv4QqMmemg3fn/hm8qwRv070HH2A+gDjJQsnInwD8CWXVUH9pwsNE3qrgO3+EGUH5ZP6uqQMKSCfAzPzEYd0RjrxbNoxq/AairDs4auxWm5eIb4UlqHriqNmqb3dmPXi7KuPhEhqLRqhvw0D61Bz5nPNnBx+W48QnwpqVEHHteo5Gy7KwAAIABJREFUg8ZDu7Hv+W9cHlcHBGDPqlb+rCw0zesuv37AIMr376vzOIA6KABbxl+zK01YKPa8Anyfm4k2OgrLmXOoAwMxpxysjGNOy0Uf6kd5tTJr/bywFpYi2eSTszlVjgNw5bMNtPtmBj2Of4La053jY96j/oh4zClV9YhKRZ5jgHs9Mv/8bD1tv5mJd4doJLvEidHvgiRhTsuhwbOPc+Xlr1E7Bg4aPy95KdshpzwtB12IPwC6ED/KUqsGBuWpOehC/Cg9c4V6sx5B4+uJ3VxORK8YMo5fwjPEF3dfT+p1akq3GffjEWCkQbdWnP7FxWoYsl21uLcb216S2z/r9BUa9+9IyoFzhMREIvyCUIU2xJ5XzbfkZaNu1LR228R0RXf3CFRePpR+XNudatp0wZZeNXtoTc/GPcZZjjY4oJY/0AYHYMvKqwwzDupB4Zrtlb8zFy6h/mcLUHm4IdzcyJ76LAC2jCx0ra7TrsJDseUX4PfCTLTRkZSfPo86KABTyoHKOOa0XNxC/SirYVeWGjbg5rCBolNXCBnUifz9Z/FuF4W+XoBsl1kFSJKEb/9OeLZvgsrgTta3myrb1pJZVdar2UZdhIQGkZqSXvk7LTWDkNBgMjOcB5jTJz3PNz8twmwyU1RUwuC+8rbLyKhGaLQalq36Ao1PODZTAVKZy/mvv417iB+l1fyVKS0X91BfzHX4q7rwCPfHq1kDjq6WfY85LeeGfEBFf+1+fBFqT3dOjPkXSPLFqCRJhPbvgG/7xmg83bm8dCtmh55l16mnPsSX0IEd2T30ZXzbyisvsh+sZuuZWbhdxV5VBoNTfH3HdpSfPl+pJ4A2xI/y6v4iLQdtiB+6UD/K06qFp+fg2S4aAE2AT6XNWTLz0PjLF5tuEWEgQfTSFwAOAz8AbwAXgGZAIyAZuAfQXVdF/A2Ux0/WzX90a40QopEQYpcQ4rDj09UR3lMIsUMI8ZMQ4pwQ4jUhxCNCiP1CiBNCiChHvFqz00KI3kKIFdV+xwshfrmGHm878t8ihAh0hFXO8AshkoQQLzninBBC1DmyFEL0qLbqcEQI4XUD5RkshNjnSLdZCBH8V+sW4SJMcm34mlZtces9ENM3i+WkBk+0HW+n4OlhFDw5FKF3R9c9HoQLoXXIrBONBn23rpi27KhD75uQx1+U1W3hCNL2nSF9/9nKsKT1B1nWcyabnngX90dG4bJi6xCpad0Otz6DMH0l1ytqNeqoaMzrf6Nwymgwm4kdP7hOXaWr6NrjlRGk7jtDmkNXlUZFUOsIVg1/i5WPvk7byfdgjAy54TrosnAE6fvOkFGtDpy5/vJr27bDfcAgij9dXGd+dcu8zjZXq9E2jabkl5VkPj4Wu9mMun6YC3E15LnMUo4T0CuGooTL7GjzFL/HzaL5qyMRmqpldaFVo/P3ImfnieuW6d8rhqKEJHJ3JnBq0ic0e3UUak93dH5eWPOLa+yFvkp91GEn5gsppH28gmY/vEjTb58j6/QV7DYbAoFQC9y8DXx394vkJ2Vwx6wHast30PuVESTvP0OKo/33f7wKvbeBx9a9QruRfbEnX7yuGVkA67G9lM4fg2nxfNwGP+58UK1B1aAxUna6c/gNtBMAWg2evW+jaF3VhYnPQwPJXPgpea/9i/KEU/g9N7164uvSHbUaXdNoipevJOPRcUhmMxoXdnU9+lbESXz/NzTeBrpueY0GT/Sn6ERS5ez5qbvnUrT/DJfnf0XwiAF43daidlnryuAaZRLX6VuefOpxHntgHB1a9ebH71bwwsvyNme1Rk2bmBY8/uDTWAvSUHv4gkp71Tz/Mjdw3roa+kAfis5cwZpfUk3M9dtWRX/d2WYcf8TNrOyvALsGv0jO/rMkvPQtkSPj8e/c7IbVbL3gcU4u+B7s10h0NaHV9Nd364y9uAR7Xl6NOHX5a1c2cXVVhEaFZ8fmXJr4DkA3YAjyFpo84CngR2AXkARcfblc4ZZyK2fk3YUQRx3fL0mSNATIBOIlSTILIaKB7wHH+jYxQHMgF0gE/i1JUichxCRgIjC5jny2Ah8JIQIlScoCRgJfXEUvA3BYkqRpQojngReACS7iZUuS1F4I8TQwHXC9QVI+Nl6SpD1CCE/kG0Gutzy7gc6SJElCiNHIN4xMu4ruTgghxgBjAL4ffj9D+wyqPKbyC0TKrb3Ep24YicdTMyh+eRZSsbwdQ9MmFntmGlKhvORn+WMn6qYtsSdsRh0UVJU2KABbdt3Lm67Qd+mE5ez52g7HgTUjG01I1VYMTXAA1qwcl3FdYRw2GON9A7jXpiHrWCKeYf5UbOAxhPpRkuF6xqT9lCHo/bzYNetzl8fT951FFTIayVSKOqCqDlT+gdjrqFfD+BkUzZ+JVCTXqz07C3t2FrZz8o1mkt1OzKh+NOrTjkyHrhV4XkXXjpOH4O7vxbbZVboWp+Vhzj2O1VRG8wfuQOfjSd/vZpG6MwFDNbmGUD9K65DbdsoQ9P5ebB3tug4AbNlZqAKrlT8wEHuOi/JHRmKcNoP8OTORCgvrlAdgy8xCHfzX7MqWmYUtMwtt86b4zJ6KcNcjtFr04VVl1of6UZbubG+WnCI0Rg+EWoVks6MPq4oTNqwHlz5YSf2RfQl/NA6tnxe2k5fRh/tTAAT0bodksVJ8+s/rkllvZF+i5z1EeVYBuXtPIVQC05VMDNFhuIX54+ZvpO2+RQg3LWovDxq+NBK1twHUKrDZ0YX6U56RC8gzam5hAVTMh+rC/LFkyHpnfb+FrO+3EDyiP42nPIjdauPS9mN4hvpxfp28QqHzdMdus+Hu54Wpxo13XSYPwcPPi9+q2VV5sYkN05dU/m525G1syYlomrevDFP5BiAV1N1HbRcSUAWEIgxGpBKHj2nZAXtGMsKzamuJJiQAS2auc52mu/AHmVV5eXbvQNnJi9hyqmzae0gfMl9ejKZbFJLVhltreVCsDg7Eln19vqTCrnQtmuI7dwrC3R2h1eAe7k9FTnXZlbYOu7IVm0iYXLWI3OPAB/h1b03rD55GLSRKjl5A46knb/0+DO2indq2AmtuYZ22UR3PDk3ZuFO+h+jo4QTCwkMqj4WGBVdum6nAz9+XFq2acuSQfHG6csV6vl0mX4CnpWaQm5OHqdQEkh3JYkJodEjlluuqy2uh0hvpu2khALnHEvGo5q/cQ/0wpV/fLHfjEfFEPtILAK23B7n7z1Qe04f636AP6EnSB/K9BqakDExXMomcei/+PdpgB/KOJqIx6ElbdxDfdlHoQ/0wp7s+p7nCJyaCjosnAqDz80LEt6Fw6XLUwdVsPSgQ21XOffbiErQN5H3tbjEt0bVoiq55Ezx690B4euA/fw6laTnowqq2UelC/bFk5CK0GnSh1cJD/LGky3Zkzc5HG+SLJTMPbZAv1hx5HFCelkPRHyex5hUBlAJrgfbAFmCV4wPyGKTuvbUKt5xbOSNvkiSpreMzxBGmBT4VQpwAlgHV934ckCQpTZKkMuAisNERfgJ5CcclknxJ/Q3wqBDCB+gCrLuKXnbkK0mApchXmq6omNU/dLX8gT3AO0KIZwAfSZIqrkyvpzz1gA2O+piBfOPIdSNJ0hJJkjpIktShX0kOqtB6qIJCQKNB2y2O8oN7neKLgCAMMxZQ8v5C7GlVN9rZszPRNGkBOjcANK3bY0++TPnpM2jqh6MOlWV69InDvOv3G1ER9/i4OrfVAJQlnEXbIBxNeDBoNHgO6EnJtj+uW37hD6tIvu9pfuk3j6T1h4i+T27OoPZRlBeVutxS0vShntTr0ZqtEz5ympYwNqpaEPFv1Qih0WA9dsipXnV3xGHZv8dJniogCM85Cyj51yvYU6vqVcrPxZ6dhSrccUNfaQkJ327lx/7zSNxwiGZDZV2D28m6utpW02JYTxr0aM2GGrpe2niI0E5NEWoVp3/ciSkjj83D3+bKhkM0dtRBYPsoygtd10H0Qz0J79maHeM/uurUjPXMGTTh9VCFyOV36xVH2d4a5Q8KwvvFBRS8+gq25OQ6JFVR067c4+Mw7bw+u7Ln5mHLzKRs/2EyHxuDaf0Wyk+fJez+7gB4xzbGWlTqtKReQe6eUwQPlvd/hj3Qnaz18mDXnJKD/x2t+POLjRwe9irWIhMZv/1eKbP+qH6UZRW4lJm35xRBgzs7ZPYga/1Bkr/YSNrPu0n7eTdZ6w4Q/khvPKLC0Pp4YkrK4Ej70Ry9bRwXnnqHwt0nuDjxPQr3JOB3p7xfPOD+XuRtkLd05G88QMB9PQHwbN8EW2Fp5TJ4xRJ43qaDlBWU8FXf2VzYcAiVWkWDri0IbReFrdyCSqWqNYhvPawnjbq3Zk0Nu3IzeqBy3OTX+qGe2C6cwH7+OKqgMIR/MKg1aGJ7YD3u3EdFYGjld1X9KNBoKgfxAJoOPbHsWosqKAxtvWDQajAO6k7xFmc5xVv34T1Evm9OH9MUe3GJ87aaO3tQuNp5dc+amYNHp9aUnzqDLjoSa3qG7K/ie2Ha6ewD68Kek4ctIwvz/kNkPDKW0nWbKT/lbFeWolKXWylq2lWGw640Ro/KGybrPRpH7h+nubxoDfsGv8Cpe+aRt34/AQ/G4d0jBslqc2rb6tRlG071dvAsfbsPpW/3oWxYu4X7ht0FQPsObSgsLK61raYgvxCj0YvI/8feeYdHVawN/Ddb0ja9FwKEjoD0KiWEXiyABbygoCJKr4KCioKAXUGFoF5FRRHsgiBKEwtIJ6GTEEoaaZvNbjabLef742zKJhua4V693/nx5HmWOTPveeed98yZ8545Mw3ljyt7xnflzGn5LdGPP2ync9f2qNVqQCA0Xkj20muy47XgKDGwtd9TbO33FOmb91P/nh4AhLRrhLXIfM3Tas5++BNb+z3FjuGL8Az0xaeh7IMB7RvX2Ae4u14BStJzCe7REgCPsAB8GkaT9tZ3/Hn7M+y+83kyt+yn3shehPVqhcNmx1ZkvuZpNQA/dZrO1o7T2NpxGhkb95L/4nKK1q5HGxuDOtp5f+1/ZX+1Z10uz1+YuAZ7ZjaXH5lG7vzFWPYdJu+Zpei3/kmIs7/QtWuCvciE9XIBpiNn8IqLwiM2HKHVEHxnd/Q/yVNr9T/9Scg98gNRyD290W+V0w27DuHdvB4qLw+Qg769gONOdcoiMUHAROC9azbGDSL9Df79XRFXep3/lwQLYZQkybdK2kLAFznyrAJKJEnSCCHigdmSJA115tvp/P/+ysec5Y2SJL0ihPgQ2ChJ0hdCiGjkp8P3gDhJkmpcCkkIYQc8JUmyCSEaAF9KktS2irw0oIMkSblCiA7AK5IkxV9BZitgMHKkvS8QeY312Qm8JknSd870hZIkxQshxjrPP7lyna9k74IR8ZKmXWd8xk0GlYrS7Zsp+fITPPrLHXrp1u/weXwO2i49ceQ4Y9Z2O0Vz5ZUKvO4bi8dtCWC3Yzt3huJ3Xqb4gh3Prp0JnD4RVGpMGzdjXLMWn2Hy9JDir79HFRxE+AerEDofcEhIZjPZo8YhFRcjPD2J/HYdWSNGI5nkV54lRdVf0fr06EjI3McQahWGr7eiX/0Z/vfKbxcM6zehDgmizucrUPn6IDkkpGIzF+58FMlUXC5ju16ObHRb/CCx8bdiKyll18zV5B6VlyAb8NFsds95j+JsPQ+nrcF4KRerSX55cm7zPg698Q2tJw6l8YjuckddUor3hjfl5Sfbd8bnYefyk9t+oGTDJ3gOlO1q2fIdPpPn4NG1F47LzmkDDjuGWbJd1XGN5NVvNFocWRmseeRjLIWy3j0XP0i9+FuxmUvZNms1l526Dl0zmx1PvIcpW8/Ec2soSs+l1Cjrmrp5H/ve/AaAthOG0PzenkiSg7Of7uT4e/Lyg11eeJCY+Fuxm0vZPXM1eU65/T6aza9z3sOcrefB8642OP/DPo688Q3eYQHcvnkRWl9vNMKGZDZjfPstdI+MR6hUmDf/QPGnn+A1VK5/ycbv8Js1B88evbBnZ5X7VcFE5woY859B27oNqoAAHAX5mNZ8gGHDj3h160zAjIkIlRrT95sp+nAtOqdfmcr8as0qVE6/cpjNZI8ch2QqRtu4IUHzZ4NGgz0jk/xFL2G/fxKhCW2wmy0cm7YKwxF5UNJ27VyOz1yNJbsA73rh3Jo41bn0nHP5yVIbnhFBtFj+OJ4RgQghOLf8WzK//JVmS8cR2qcNXjGhHLx7EQV/nHDKnMfxmYnlMls5l7OTl7Rc4SLTIyIIz8ggJJuD0txCjk9bCcnyNJZbNi7DbjRzauRzeNaNqLLE4BtIpXJMoP6S8QTEy0sgps54C9NReWnP5l8vRhvkh8Nq54fFn5V/pNrnhbHcMuw2VFo1hRdz2bbgQy7+fpxhH85m61zZr2akrsFQya/ObNnHnje/IapdIwa9/hiS3UHemXQif34JzEbULTridfejoFJj/WMrpVvWoe0xGADr7h/w6HcPms59wG4DaymWr9/HnuL8pkDrie8LH2F8Zhzqhi3QDJ4AahWFX2wlb9XnBI6U5ejXyR/tRTw7EV0Pefm8rCdfpyT5DADCy5NGu9aQ0uchHMaKa9+7/S1EzJ+AykOF8NAiPD3Bbsf43WaKPvgU3fChsl99tRFVSBARa1bKfiVJOIrNZN33kOxXTRoSPH8WaLXY0jPJf/4l7KMnEub0q6RKftV+7VySK/lVa6dfFSWlccTpV4EdGtNqxUQkuwPj6XSSZyRiKzThXS+cDh/MBOQ3LAIozcp3adumH88ndfY7WLMLavQNbVggLTe/jNrPG8khkWMsIr7rHRiLTLzw8gLi+9yG2VzCzEkLOHpYbouP1q9kztRnyM7KYeCQPsx+ajKSQ0KvL2TW5Ke5cF5+EH9syjjuu38YjRvF4igx4Ci58lu2Oc8uY9+ho+j1BkKCA5n48BhG3D6gxvxftapYmrjdkrFE9Zb7wT9nJFJwRO6venwyh32z3qUkW0/jhwfQbOJQvMIDsOQayNx2mH2z5bFj/Xt7EtX7VkS+gZAEeRnL49NWVuoDru96lfuAb8j68lc5/wfyVC1v55sDc1Y+h6Ynonfq2WXtExyeuZqSbD0NHh5A40lD8QwPxJJrIHvbYQ7Petel7u3enID3wZ3y8pO3dSJo5iRQqzB9txnDvz/Fd4Tsr8YvZX+N/KjCXyWbHYfRCEKU5w+c8RhendqTNWo8l7P8qLv4Ufzj2+EosZA2cznFTp8KSGhP7MKHQKUm7/OfyVwhL+OsDvSj4ao5eMSEUpqeS8pjL2HXy+8Ag4f3ImrSCLyb1j2GHJEvG1t9hjzrAOB55PnzN5W7693xXx9Jf3H+O3eTwf7r/KcH8q8DlyRJelUIMQ74tzyr5K8N5J1lvkd+7dNPkqTj1IAQQgJGSZK0TgixAIiQJGnKjQ7khRANJUlKcf7+BvgQ0F9jfQ4Bj0iSdEAI8QHyQ8gND+SvdPxGKM6o/Rc27gbytUHZQL42GdH+4tUzXSefHoy9eqYbQHcT1sYa0qz26w9QalRfPdN1cjwt/OqZbgBxE6Iw/uramaJQmV81PrUuE+DRodc+ze1aSf/55twLdQGWq2e6To5dqP1+BSBIXXsR7jKGm2v6zuXGSTvz/dUz3QCVB/K1RbDj5kzTNona76/a1bn6kp43wuWs61856FrocOmb//oAVhnI18x/ekOod4AvhRD3ADsA01XyXw9rgbArDeKdmIAWQogDQCHyxgZ/helCiN7Ic8SOI0/r6XrlIuUsBDYIIdKBPUDcX9RFQUFBQUFBQeF/ir/zOu7/bW7aQL5qNN6Zdga4tVLSk870ncDOSvniK/0uPyZJ0sJK6WOriO8OvMtVqKTX01XSx1b6Xb/S7/3IGyHUJG+Km+RynZ154iv9Lj8mSdK3gOtuHnL6h8iRfZc6KygoKCgoKCgoKJTxn47I3xSc0XUT17Hii4KCgoKCgoKCwt+fmzUN/H+B/4mBvCRJ7aumCSH2Ap5VksdIkpRUNe+14JzTP61K8m+SJE26EXkKCgoKCgoKCgoKf4X/iYG8OyRJ6lzL8j7gyuvTKygoKCgoKCgoKPzH+J8dyCsoKCgoKCgoKPzzcfyN13H/b3MzN4RSUFBQUFBQUFBQULhJKBF5BQUFBQUFBQWFvy3K8pM1owzk/0fY92tkrcvU3IRLp/QmvQTqH5NV6zL1qVW/lf7rxJXaa10mQJzvlXdevBGOJ9+cTZZKqf0NVkI9zLUuE8DHu/Y3b9J61L4PBBfcnA2hUjbV/gZuUXG176sAug4BtS7z4rqbc4vU2Wp/86IZfm1rXebN2LgJYHjSolqXua/VnFqXCaB21P6UDlNh7d9bAOwOZZLF/0eUVldQUFBQUFBQUFD4B6JE5BUUFBQUFBQUFP62SMrHrjWiROQVFBQUFBQUFBQU/oEoEXkFBQUFBQUFBYW/LcrykzWjROQVFBQUFBQUFBQU/oEoA3kFBQUFBQUFBQWFfyDK1BoFBQUFBQUFBYW/LZKkTK2pCSUir6CgoKCgoKCgoPAPRInI//MRwJvd97yB3WwheepKipLSqmXyrhvGrYnT0ATqKEpKI2nSW0hWOxo/b1q9MxmvmFCEWkXayo1krNtF0xceJGrYbah1Xlgy87n07y1cWv2Di0yvumG0SJyONtCXoqRzHJ+0Askqb3YT2O0WGi8ai9CoseYXcWjYQgA6ffssge0aITkk8n8/zqGxr+KwWKvp2jpxGtpAHYakNI6W6Rqgo9UbE/CpH4HdYiV5+iqMJy8B4DdqGH7/GoEmNASHqRjDxxswfLjORa6mfiyhC+fg0awR+rc/wPDxBlcjqVREffIOtpxccqYtqNDntg6EzH0coVZh+GoLhe9/7lJMGxdL2KJZeDZvRP7yDylc80X5sbDnZ+LTswv2fD2Xhj/qUu6WFx4kvE8b7OZSjkxdicFNu9V7qD9xjw5CFxfJ1uaPYs0vAkDXKJrWb07A/9YG2AzFCJORgs+3kpe4oZqMiGcm4BffAYfZQsYTr1NyLEWurp+O6KVT8WxSDyTImPcG5kMn8RvUnbCp99O8cV0sGXnYCk2cnPo2xqRz1WR71Q3nlsTpaAJ9MSad48SkFUhWebObwG630GjRuHIfODzsWRq9MI7I++JReXtScjGHX7tMryazRl8N0NHC2f4Oi5VjzvYP6d2aW1dPRa3zwpZvIKnd2Goy6zw3Hv+E9khmC2kz38ScnApA3VemENCnA7a8Qk70nVqev/6KmQQO6gYCbBmXuTB6LrbL+S4ywxdMwLdXRxxmC5nzXsNyPAWPuBii35hXnkcbG0Xumx9TsOZb/AZ2J3TKv/BoGEv6qKmoAgMInfcYQq3G8OVm9O+vd5GvjYslfNFMPG9pRN7yNRR+WMmvFs1E17Mz9nw9F4dNcCnX+fkx1Elog81s4dcZq8lLTqtmj54rHie0dQMcVhs5h1P5fe6/kWx2AhpG0f31RwlpFUepoRiV0UTeuq1cXvllNRkxC8fj31v2qwuz38CcnIo2KpS6r09HGxaE5JDI+/RHcj/4XvaV5vWJXTIRj0APHFlZFC5ejFRcDIBHp074TZ4MajXmTZso/vRTl3N59e2Lz6hRAEhmM0Wvv44tJaWaTuqmbfG8czyoVFj3/oR1R3W9AVSxjfCe8hIln7yC/ejvAHjeOwX1LR2QjIWYX5larUzX58cQ67Trrhrs2nvF44TeWmHX3fNku9br3472c+4Gh4R3eCBqmw1bgZFT097C5Oa68qwbTrNVM9AG+mJMSuXU5IrrCsC3TUPabFrCyQmvk7txj1zG34cBLz1CSJM6IElsmfMumQfPkvDcGOJ6y3pvnrWay270HvDSI0TcGocQgoJzWWyemYi12OKSp+2iB4jq0xq7uZQ/pydS4Ka/ajSuH03GD8QvLpKvW0ygNN8IQNPHh1Bv+G2y7TUqNCF1sOWfB6nmTQcXLHmNX377k+CgQL75ZFWN+cqIW/QQgX3a4TCXcnb6Cvd2jQ2nyaoZaAL9MCWlcmbKciSrjdDhPYiZNAwAu8lM6rzVFB8/T9yihwgZ1qPW74OyIVQ03LoaTVgQtpwC9Ot/JG+1m/776Ur9zNzXKHH2MzFvuvYzJSdSUIeG4jBbSJnxFsVJqW7r32jlTDSBvpiSz5Ey5U0kqw2vRjE0eG0yulYNuPjip2St+rZqUTWwH0gHhl61MW4Cys6uNaNE5P/5DAIa/9plOsdnv8stLz3iNlPjBfdzPnETv3WdgVVvJOb+BABiHxqA8VQ6fyTMZd/w52m6cAxhA9rh3yqO0hw9R0a9gK3ASGi/dnjHue4e23DBaC4mbmJP12nY9CainTI1/j40XfYIRx94kT97zSJ5/GsARNzdg4DWDfi58UP8Ofx5Ato2JOqubtV0bbLgftISN7HbqWsdp9yG0+7CkHye33rPJWnyOzRfPBYA32Z18B02GCSJjHsfpvRMKr6390cbV9dFrqOwiPyX3q4+gHfiN2oY1nMXXBNVKkLnTyZr4nwu3jke30HxaBu4yrUXFpG39B30lQZaZRR9+xOZjz9VLT2sTxt0cZHs7DKDpNnv0vKlh93qVPDnafbe8wLFF3Jc0q16I8ee/ghHcQkX127n7IDHCbi9Jx6NYl3y+cZ3wLN+NGcTxpM5fwVRz08qPxb5zKMYfzlASv/HSBk6GcvZiwBYTp8n/8NvsemNHBv3MqdnJ9LkpfFu9Wuw4F9cStzIn12nYtMbiarkA42XjSfpgRfZ12smx8a/SnCftnjHRXFo9Escm7EKz8ggtzJr8tUG0+6iKPk8fzjbv+nisaASNF/2EBfnvc3Ju+ai9tPh1djVBv692+MZF8XxHo9xfu7b1F3yePmx/A3bODvmOVcFVCp07ZpxPGESp9uOQHh7Evak60OYrlcHPOrHkNrvEbKeXk7kc5MBKD2XTtqdU+S/YdOQzCUU/fSHbNcz50mfvJiSA0mUMsVwAAAgAElEQVQgBGELJpH5+AIu3DEe38G9q/mVo9BA7rKV6D+sPhgt+mYrGY/Nr5ZeJ6E1/nGRfNl9Fr/PfZ+uS8e6tXHq17/zVc85fNPnSTReHjS5P17WUW9i77OfYDNbOPPpDk72nUTQHT3xrGJTv97t8YyL5kSvCVx88m3qLJZtKtntZCz+Nyf7TOLMXXMIfWBwedm6L04hY9ka8h96iJLdu/EZObLc3n7TpqGfO5e8Bx/EKyEBdb16LuezZ2ZSMG0a+Q8/jOmjj/CfNat6pYQKz2ETML/3HMUvT0bTtgciItZtPo8hD2I/dcgl2bp/GyXvPlc9PxCb0JqAuEjWd5/Fr3Pfp3sNdj379e9s6DWHL/s+idrLg2ajZLum/3qMr/o9xb4X12M4l4XdWMKZ2ato9OKjbuXELRhNRuJG9nebgk1vItJ5DZTZK27BaAp2HnEpk7BwDOd2HuWDhCdYM/Ap8s9mENe7NUH1I3m/5yy2znuffi+413vH82v5aOB81gx4CkN6Hm3H9nc5HpXQGr8GkfzQbRb757xP+2Xj3MrJ3XeanfcuxXTRtb86tXITW/s9xdZ+T3F0yedI1pIrDuIB7hrcj1WvLb5injKE1huvBlEc6jaZlDkrabDMvV3rLRhDxuqNHLptMrZCI+Gj+gBguXCZ5OFPc6TPTC698QUNX36MwIR26Fo1qPX7YBnBY+9CHRpE8ZHTpAx6DP+hvar137peHfCoF0NK30fIfHo5kc9X9DPn7pgi/901Dex2cEgcuW0S555YRdxS9/WPnT+GzHe/50j3ydj0RsKc9bcVGDn/9PtkVh/AlzENOFHTQYX/LlcdyAsh7EKIw5X+6v/VkwohHhNCPOD8/aEQ4u4bkGGsIf15IUTfv6rjX0EIUV8Ikez83UEIsfwmnu5O4COAwgNn0fj74BEeWC1TcPcWZH+/F4CM9b8QPqiDfECS0Ph6AaDReWHVGwnt2w7DkVQMB86g//0Ean8fio6cI2xwJxeZQd1bkPO9HA3KXL+T0EEdAYgY3p2cH/ZiSc8DwJorb8ke3FuO5qi9PDAcTkXlocVeUlpN15AqukY4ddU1iSFvdzIAprMZeMeG4REWgK5xDNb0TGwX0rFdyMCy/zC2jCy8429zkeso0FN6/BSSzV7tnOrwULx7dMb4jWu0xbNVU6wXMrBdygKbDdPmXeh6uz58OPL1WI6dBjdySw4k4SgsqpYeMbA96Rt2A6A/cBatvw+ebtrNkJyG+WJutfTSXANCpaK0wIi1wAhWG4Ubf8GvbxeXfH59u6D/ejsA5sOnUPnr0IQFofL1xqdjS/Trt8oZrTYcRSZZdspFvFs1pvSyXtbhwBk0/jq3fhXUvWW5D2St31XuA+HDu5P7w14s6bLu1lwDoQM7kr1hFwV7TlLwx0mESnVdvqprEkO+s/2Lne0fmtCG4nNZFHz/G/bcQuxFJgL6u/ppQP9O5H+5Qy536DRqfx2acPkhwrj3OHa9a1eia9MYy7kMSi9kg9WG5fR5PGLCXfL49ulC4dfbACg5cgqVnw51mOuDiU/X1pReyMKWcbncrqXn0gHwaFTfxa+Mm3eiS+jqUt6eX4gl+TSSzUZVSg4ku/WrugPac/aLXwHIOZiCR4AObzc2vrS9YhCYczgFXVSwLDfPgFAJLAVGLHoTktVGwfe7CejX2dWm/TpXsumpcpvaLheUv+1wmMxYzl5CGxECgGeDGEx7j8m22L8fr549AdA2a4Y9PR17ZibYbJRs347nba7XrvXYMSSj3E7W48dRhYVVq5OqbmMceVlI+dlgt2E7vBtNi07V8mm7D8F+9A8kY6FLuiP1OFKx29sK9fq354zTrpcPpuDh796uF2uwq80Z3a7Xvz2XfkkCSaLo4Bk0/j5o3cgJvK0lORvlB8Ds9TsJGVhRj+iHB5G7aS/W3Ar91b7e1OnUlKR1O+W6WO1YDMU06t+eY1/KemceSsHTX4fOzflKjeby3xovLVSZjxwzsD1pzv4q76DcX3m5kaNPPk/xper9VWXq3tUNh8W9nSvToU0rAvz9rpoPQHjoyNmwCwDjQbm/cmfXgO4tyXPa9fL6nQQPku1atP8U9kK5/ys6cBqPqBCCB3bEeDSl1u+DAJrIEPyH9KQ05SJSiQWsNgybfsGvj2sf4Ne3C4XfOPuZw3I/o6nSz+i6tcZRakO/brOz/qdRB+jQhlcPlPh3b0W+s/65G3YQ5PQrW14hpiNn3d4bPaJCAIYA71U7qPC34Foi8mZJktpU+kv7qyeVJGmVJEkf/VU5Nch+RpKkn2+G7BtBkqT9kiRVf09be8QAF8v+U5KZj5fz5lGGNtgPm6EYyS5HQEoyKvJceP9HdE1i6HV0JV13vszJBWvwigqm8HAKgV2aownypTS7gJA+bfCMCalRpiUjH0+nTJ+GUWgCfGn71bN02LqMyHucN+wAHRlf/kqvg2/T++gq7KYSitOyq+lqraJrmdyi4xeIHCJ3PAFtG+JVJxSvqGCMJy/ieUtT7Pl6hJcn3t07g1qNOjyEayVo9kT0b76L5HC9gWnCQ7FlVUSXbNk5qCOuXW5NeEUFY3Z28OC+3a4qIzIIW1HFDdiWlVs+aCpDExGCNSPHJY8mMgRtbBT2/EKiX5pB3HfLiVoyFeHt6VLOYakYQFoy88rboYzqPpBXyQei0QToaPPVQtpvfZGIe3riGRVcflMDcNjs1+WrRccvEO5sf39n++ua1qEko0KmZLOjjXS1gUdkCKUZFYOL0sxcPCJrbkOtM3/0E6NpuGsNno3qYjlz3jVPRFW/yEUbEeqSx39ILwybdro9hzo4oFp5TXio27zXg09kEKZK9jBl5uNTw5sPAKFR02hEd9J3HHWRUVrJr6yZudVsqo109StrVl413/OoE453iwYUHz4FQMnp8/g7Hwi84uNRhcsPR6qwMBw5FbIcOTmo3QzUy/AeMoTSP/+sXpeAECR9RTtL+jxEgKtOwj8YTcsuWP/YUqN8d+gigzBWsavuKnZtPKI7l3ZW2LX+wA40GtaNVhMGc3rGOwCUZubjGVXlmg32w2YwQdl1lZmHh/Ma8IgMJnRwJzLXbHUp41UvguL8Iga++ihjflhM/xcfQevtiW9kEEWZFXoXZeXjW4PeA195lMcPvE1ww2gOfuAq3zsymOJK9Tdn5uMdVXP9a0Lt7UFk71uRSk3XXfZKCLUaS6VrXLaZG7sWVti1NDMPz8jqfW7EqD7otx/CIzIY4+GztX4fBIiYPwHD5l+x5enL06xZuWiq9d+hWDOr9N9u+hl7QaFLvtKMPDyq1E0T7Cc/rFSq/5X6wTLqPfcQwBP8l2e3SH+Df39XbmhqjTPivFsIcdD5182ZHi+E2CWEWC+EOC2EWCaE+JcQ4k8hRJIQoqEz30IhxOwqMvsIIb6u9P9+QoivrqLHq87zbxNChDnTyiP8Qog0IcRzzjxJQohmV5C1UAixRgix1VluuBDiJWe5LUIIrTNfe2cdDwghfhRCRFVKPyKE+AOYVEluvBBio7t6CyGSnbasL4Q4KYR4z5m2VgjRVwjxmxDijBCielipkupVE6p93V0tR0We0N6tKUo+z65bH+ePhLk0XyrPaS65mMv5t76l7foF+LasT3FKJpLNcUWZZX4u1Gr8WsdxZPQyjox8gfozR+DdIAqh1RDUuSm7Ok5hR+vH5WkrvVtfVdey6FDq8m/RBOjotm0ZdR8eSFFSGpLNjulMBuadv+F9W0fC31pK6ekUcDi41uvOu0dnHPl6Sk+cqX7wCvr8FYQbwdf9Vb5wp9w15JFAaFR4tWhEwdofOHfHVBzmEkIfu+eK5aqpd4U8sg804OjopRwduZh6M+9G5ePlJv+1++q55d+iDdDRpVL743Bzb6km8zpt7cye8dInpPR6EPOhE3g2rX/NegKg1eDbpzNFm3+t4RxuBdSs07VynXK7LhlL1t6TZP95qpKIa5BxletC5eNF/VXzSH/+PRzOaO+FOcsJfWAwwYmJCB8fsFrdCLmyzto2bfAePJiixMSay15Bjuedj2DZtOaq0zqqcZ127b5kLJl7T5JVya5pW/aTueck+5etp97ckTXKuZL9Gywax7lFn1Tze6FRE9GyPoc/3sbHgxdgNVvoNPF2t/1MTXpvmb2aVR0nk382g2a3u77Zq61+MLpfO3L3nb5++1+Va/HZq/cD/t1aEn5/H86/8DFCCCyXcmr9PhjSrx32PD3WS1lX1dltF1+1n0nojC2n4Kp1uyYbVSGwb/uyNz8HrphR4b/KtXzs6i2EOOz8fU6SpGHAZaCfJEklQojGwGeAc64GrYHmQD6QCrwnSVInIcQ0YApQ/es2me3A20KIMEmScoBxwAdX0EsHHJQkaZYQ4hngWWCym3y5kiS1E0JMBGYD7ieRyzQEegO3AH8AIyRJesL5gDFECLEJWAHcKUlSjhDiPuAF4CGnrlMkSdolhHj5CueoiUbAPcCjwD7gfqA7cAfwFHBXpbyTgPGXL18O2b59u9eOHTs+8janMNi7IV5RwViyXC9qa14RGn8fhFqFZHfgFV2RJ3pkL86t+I7Ycf2JGZ2ANtgP+7HzeMWEkPnpDjI/3UHn397AkpWPOTWzRpme0cFYsuSPAS2ZeVjzi3AUW4i6Lx5tkC9tPp+POS0bW5EZa55zSoDdjk9cRDVdtTXoajeaSZ5e8dFTr30ryueOGzf9hKZONJcnzSNw8kM4QkOw5+RxLXi2bol3r67EdO+E8PBA6HwIWTyPy3NfkiOlkRXRQU1EGPYqHz1eK/4jb6f7nbcDUHg4Fe+YEMpayl27XY2SzHw0ft4VukWGYs12rbMtKxdtdBjmAxV5bNl5SJIcATIfkQcaRZt/I3LRJPwS5IipOek0HtEVc0E9o0IozXKttzXPUMUHKvLIPmAg8r54okf3RRvki+VSjjOaJT8wqTTq6/JVu9HMsUrt32PfCgzJ5wnpdWt5mtCosWa76lmamYtHdChlMUCPqNBqeVx0yMzDI7oi6mW9lI1Px1auebKq+kUotssVtvft2QHLsRTslSJulbHn6fHuVPEQq4kIxXaN/loV/5G3c8cw2a9yD6eii66IsumiginOdq9DmxnD8ArxY/sj/3ZJN2Xm41HJr7Ru7GXNzEMbHUbZtFltZAjWsutCo6b+qnkUfLOLwi1/lJexpKSTOuZZouIMqOvUwbOLPFh05OS4TJVRhYVhz60+PUPToAH+c+agnzsXyWCodlwqzEMEVrSbCAxBMrjqrYpthNdoOZYidP6om7fHYrdjP7a3mjxtt8EMHzcQgJwjqfhGh1D2/lAXFYypBru2mzEMr2A/ds+V7XrLg31pdn/vcjkWvQnv+hFogv3wiKroN8uQrysdqFVgdzivPfka8GvdgGaJM2T9gv0I6tMOyWbHcOAMRZn5ZB1Ooc0DfWnQuzU+oQGc2rgHv0qRab/IYIw16A0gOSROfr+Hjo8NQePtQfuRst75R1LxqeRX3lHBmLNqllMTde/qwoVv/iC83aDrLlsVlZc/Ki956o1ks+AZHUrZZDN3/ZUtz4AmoMKuHlEhlGZX9D8+zevRdPUsrPkGWqxfiPHIWTyjQ7lUC/dBR7EF/Z4T+Laoh9+tcfj26YJvv26oA/3Abif6ldlYzlyo9kG9NSsXbVQYZe/HNJEV/UzQv4YS/PBwhIcW66VstFFhyEMu8IgOwZrt2rfa8g2oq9X/yvcyv47NCOrfESAN8AL8gU+A0VcseBNQdnatmeudWjPMmaYF3hVCJAEbkAe+ZeyTJClTkiQLkAKUvaNLAurXdBJJfnz8GBgthAgEugKbr6CXAyhbPuQT5EGvO8qi+geudH4nmyVJsjp1VQNl71/LdG8KtAR+cj7cLADqCCECgEBJknY58398lfO445wkSUmSJDmAY8A2p03c2e1toE14eHjsyJEjxyYmJp4a7N2QgPaNsBUVl89trkz+b8eJuF0epEXf25OcLfsBKEnPI6RHSy5+sJWDI5diKzKT/e0fRN/TE22oP/7tG+MoKSU4vjXZX//mIlP/2zHCnJGbqHvjyXXKzNmyn4AuzRBqFZnrdmDJLuDomBfJ+/kgAa0boPL2IKB9I4RGjeFo9ZUFquqa7ZSr8fdBaNUA1BmdQP6eE9id0T5bejaa2Bg8WrfAp3d3tHWiMO/6/ZoMr3/rfdIHjSJ96GhynnyBkv2HyVuwDABL8im09WLQxESCRoNuUC9MO/+4ikT3GNZ9z699nuTXPk+SvXk/Mff0ACDQ2W4WN+12JQoPpaAN8kUbqAOthoChPTFucx2QFP28l8Bh8sdX3m2a4igyYcspwJ5bgC0zB4+4GECeZ2nctZ/U26eQevsUirbuKZ+/7t++cY1+VVDJByLv7UXuln0A5G7ZR0CX5mR+9BMHh86nNLuAnE17ibinFwB+LesjORzX5auV2z9mdAIFe06g//04Pg0i8YgNR2jUqP10FP7kOu2i8Kc/CR4hD0h82jbBXmTCdrnmhybTkTN4Na6DR2w4aDX439mbklOufmrcvpeAYfKHYl6tm+IwmrBXior5D+2FYeMuaqI05TzaujFoYiJAo8F3UDymHXtqzH8lDOu+57v+8/mu/3wu/HiARnfLXWFYu4aUGooxu7Fx41HxxMS3Ytekt6tF5XIPp+IZ6ItHoE5+i3Z7Dww/ufqV4efKNm2Kvai43KZ1X5qC5ewlct5z/XBOExIg/xAC3ZgxFH/3HQDWU6dQ16mDKlK+xrwSErD87nrtqsLDCVi0CMOSJdgvXXJrB8fFM6hCoxDB4aDWoGnTA/sxV18oXvJo+Z/t6O9Yvkp0O4gHsP7+A18NmM9XA+aTtuUAjZ12DW/XkNIi93ZtOiqeOr1asX1yhV2Pr/mZnycsL5dzy9h+CK0G7/qR2IuKsbqRo//9GGFD5fnSEffGk/ejfF3t6zSJfR0nsq/jRHI37iFl3rvkbdmHNUdPUWY+QQ2iOPzRz5z45neOrt3O2R8P0GKErHdU24ZYiooxuTlfYL2KgErDvm3JP5vB4Y9+Lv9ANX3zfuo7+6uQdo2wFpkpuc7+SuvnTViX5qRvqZ3grqPEgE2fjk2fjsNiIszZt/i2k/srd3Yt/C2ZEKddw++Np2CL7B8eMaE0fX8OJ8cu43DPaRzpN5v8zX8Sdk+vWrkPqrw98G/XiOIz6aS+8BlnezzA2Z4PYNcbKD58kown38B/SE+Ktrn2AcZtewm4y9nPVOq/AQrWbsR85BTZixMx/vxHeT7fdk2wG4qxuunjDL8lE+ysf+g9vSlw+lVNXFy6lkMdxoM8BhmJHHD9jw/iFa7MjS4/OQPIRo6+q4CSSscqr1nlqPR/xzWc7wPge6e8DZIkVf/Kq2ZqelwrO7/9Gs5vAZAkySGEsEoV76bKdBfAMUmSXL5IcT54XMvjog3Xh6fKcw1u1G4/AIO7730Tu9nCsWkVEcu2a+dyfOZqLNkFnFn8KbcmTqXRvPswJKVx6VP5Q7XU176ixfLH6brzJYQQnFn0KVlf/05gp6Z0O/AOQgjMl3I4Pfc9bIUmbl07j5MzEynNLuDs4rW0TJxOg3kjMSadI+NT+aPK4jPp5G8/TKcdryBJDjLWbsd08iKmkxcJG3YbfU//G8nhIO/XY1z8WP6Qp/3auSQ7dT21+FNaJ06l8bz7KKqkq2+TGFqtmIhkd2A8nU7yjIrX62EvPQ1qFZGJL2MvLMK4/lusqefxHSGvlGX8ciOqkCCiPnkHlc4HJAm/+4eTcffDSKbimq1rd5C75C0iVy1BqFUUff0j1pTz+N0zBICiDZtQhwQR8/lbqHQ+SA6JgDHDuHjneCRTMeEvPolXx1tRBwZQ9+e1GJd9xcVPd3L550OE9WlD/F552dCj0yrq0nHtExyd+S6W7ALqPzKABpNuxzM8kJ47XuTytkMkzXwXz7AAbtv6AioPDQ2n3AmT7yB35edYzlwgaJQc7Sr4bDPGnfvwje9Ao+3v4SixkDH39fLzZD6XSMzrcxBaDaUXs8h44g0A/Pp3JfKZx9D4+9Bu8xJ5eczhC8vLtVr7JKdmrqI0u4DUxZ9wS+IM4uaNoijpHJlVfKDDjldBcpC5dhsZH25F1zSWhJQPUHl6gJDoeehtUl7+gvAhna7qq7omMbRcMRGc7X9sRiKS3cHJJz+g9dY3UXl5goBGnyyk6PckTPtPkvvJFgzbDxCQ0IEWv67CYbZwftaK8rrUf2sWfl1aogn2p+Wf75P56mfkff4zlvQcbtkpz2O2Xswi68nXCRw5GAD9uh8w7dyHb6+ONPj5fRxmC1lPVthVeHmi69aWrKcrzgPg268rEU8/jibYn8i3nsOWnUtUouxXhq+3Yk05j/+9sl8Z1st+VefzFah8Zb8KHH0XF+58VParl+bh7fSrej9/QuOXv+HMul1c2naYOgmtGfHbq9jNpeyeubr8/P0+ms2vc97DnK2n27JxGC/lMuQ7uV3P/7CPI298g3dYALdvXoTKQ0OrSbcjJg4l++0NlJy5SMi/5Oh03totGLbvx693e5r/kuhcflL+nl/XoTnBIxIwn0ij6Q+yP2W8/DFFOw4QeEdPQh8YjEbrwLJ7NyWbnXEau52iN98k6OWXQaWiZPNm7GlpeN9xBwDm777D98EHUfn74zdjRnmZ/Amuy27icGD5ejXe4xeCUGHdtw1H9kU0XWW9bVeZF+/5r1moG7ZE6PzxWfA+pVs/g3XyIP/i9sPEJrTmvl9fxVZSyq5Kdh3w0Wx2z3mP4mw93ZfKdr3zW9mu5zbv49Ab3xA3uCONR3THYbPjHRYAdgeNX32M09PfKZfTYu1TnJm5ktLsAtIWfUyzxBnUmzcSY3IaWZ9uu6LuANueWcOQ5Y+j1mrQX7jMltmrsRQWE9e7NY/sfhWruZQtsyv0Hv7hbH6c+x6my4UMen0CHr7eCAGXj1/g5/kfusjO3HaYqD5tGPLHa9jMpfxZqe/t8ckc9s16l5JsPY0fHkCziUPxCg9g4LZlZG47zL7Z8jeSMYM6kr0rCbvZdVnLmpjz7DL2HTqKXm+gz12jmfjwGEbcPsBtXslqpuR8Nu3+eBu72cLZGW+XH2v+yXzOznoHa3YB5xd/QpNVM6g7dxSm5HNkfybbNXbGPWiD/GiwVF6dS7LbOTpwLoF92tX6fbAcu4P8D78lbPL9NNySiP6LrZSevUDgKGc/89kPGHfuQ9erIw23ve9c5rZKP3Ob3M84jMXoenWk9e/v4DBbSJ3xVnm+ph/PJ3W2XP+LL3xMo5UziX3ifkzJ58j5TP6cUBsWSMvNL6P280ZySEQ9MpSj8VPLA2UKf2/E1eblCiGMkiT5Vkl7HbgkSdKrQohxwL8lSRJCiHhgtiRJQ535djr/v7/yMSHEQsAoSdIrQogPgY2SJH3hLPM90A556s7xK+glAaMkSVonhFgAREiSNKWyPCFEGtBBkqRcIUQH4BVJkuJrkFeuU9V6lx0DlgPHgTGSJP3hnDffRJKkY0KIo8BESZJ+FUK8CAyRJKlllXqPBoZKkjRSCNEOeQpNQ6cKGyVJauk8X+U61K98rCa2Roys9fdOmpvwbUvpTVrxtHnMlVdKuBHs1trX9cTlv/6hrDvifKtPNfirXDb61LpMgFLUtS4z1OPm3HB8vK8wj/sG0XpUXxnir/JLQc0fh/4V2mpq36+i4mpfJoCuQ0Cty/xs3bWtmnK9NLOVXD3TdbLXo/o3KH+VWOvNmc4wPGlRrcvc12pOrcsEKHHUfn8V5X/1VXtuBIOp9n0AoHPGV9fwQdbNpU+d/v/1uTXbLm39r9vBHTcakX8H+FIIcQ+wA6jNT9DXAmFXGsQ7MQEthBAHgELgvlrUwS2SJJU6P6Rd7pxOowHeQJ4KMw74txCiGPixBhFfAg84p+XsA07fbJ0VFBQUFBQUFBT+N7nqQL5qNN6Zdga4tVLSk870ncDOSvniK/0uPyZJ0sJK6WOriO8OvHsdej1dJX1spd/1K/3eD8RTA5V1qiK/qr6HgZ5UQZKkA8hTjcpY6EzfSUW9zUB/3FMeca9Sh7TKxxQUFBQUFBQUFBTgxiPyNwVndN0EuNm2T0FBQUFBQUFB4f8byqo1NfO3GshLktS+apoQYi/gWSV5jCRJSTdyDuec/mlVkn+TJGmSu/wKCgoKCgoKCgoKf0f+VgN5d0iS1Pnqua5L3gdceX16BQUFBQUFBQWFvwl/551V/9vcnCVEFBQUFBQUFBQUFBRuKspAXkFBQUFBQUFBQeEfyN9+ao2CgoKCgoKCgsL/XxxX2fPo/zPKQP5/hNrfuunm4EXtb4YDN2fzJr3eu9ZlhqqvbVfD60Woar+Tc3Bz9r7wuAk+YLZqa10m3JwNoYqKqn67/9epZyutdZkAJVLt3yJMeR61LhMg+4va7wXbi6Jalwlgovb9tf5N2LwpwHE9m6tfOzdj86aOSS/XukyAY+2n17rMAmPt31sAtKp/ykhAoTZRBvIKCgoKCgoKCgp/W5R4fM0oc+QVFBQUFBQUFBQU/oEoA3kFBQUFBQUFBQWFfyDK1BoFBQUFBQUFBYW/LcrOrjWjROQVFBQUFBQUFBQU/oEoEXkFBQUFBQUFBYW/LUpEvmaUiLyCgoKCgoKCgoLCPxBlIK+goKCgoKCgoKDwD0SZWvPPRwBv9tjzBg6zhaSpKzEkpVXL5F03jNaJ09AG6jAkpXF00ltIVjuaAB2t3piAT/0I7BYrydNXYTx5ieYvPEh4v3Zog3yx6k04Sko5MWMlhv1nymV61Q2jReJ0tIG+FCWd4/ikFUhWebOfwG630HjRWIRGjTW/iEPDFgLQ5pvn8G/fGBwS6f/eQspzH1fT1atuOLckTkcT6Isx6RwnJq1AstrK5TZaNK5c7uFhz+IZHUJU4nw0sdFoQoJwmIrRr/mCwvc/d5GrjYslbNEsPJs3In/5hxSu+aL8WOSqJc57oToAACAASURBVHh3aQt2B/nvfFStLECd58bjn9AeyWwhbeabmJNTAfCPb0udheNBrSLvs5/IfudL2ea3xFF36eN41ItE7etNaUYuZx57DU2QH3WfGo3QapCsNi4sWoPlwmUarZyJJtAXU/I5Uqa8WV7neoseJjChHQ6zhZQZb1GcJJ83cvxQIh4Zikd4EJLNRt7qDeS9s47wuQ/h27szktWG9UImtjw9um5tcJgtZM59jZLjKQCo/HRELZmGZ+N6gETmvDcwHz4JQNCY22k0dQxqH09shmIOj1yCMemcm7a6Ph9o/MI4Qqv41akZKzHsP33V9g/sdgst18yl5MJlAHI27eX8a3Ibtvz6OfzaNwFJIuP9LZx//qNqunrWDafpqhmyjZNSOT1Zlhs8oCN1545EcjjA7iD16Q9Q67xo+OJ4PCKDsesN2PP0aGOjyH3zYwrWfAtA+IIJ+PbqKNt13mtYjqfgERdD9BvzKnyuUpnQKf8i4N4B2HINABRs+Z3gu+IRajV567ZyeeWX1XSOWTge/94dcJgtXJj9BubkVISnlkbrl6Ly0IJGTeEPvxHiG0hIn7Y4zBaOT32nVtqq+VuTiLijG0gSlvQctOFBXHp5HVnvbaywaWw4jVfORB3oS3HyOc46/TZkWE+iJ90FgKO4hHPzVlN8PA3hqSX60+UIDy0qP19UPl44DEYMX225rus17PmZ+PTsgj1fT96rqwmZ+ziSUJP/+U/krPyCqkQ/+yh+vdvjMFu4NPtNzMdSEJ5aGn6+DOGpRajVFG7+jezXPwUgYvooQh4YikrnBQjyv/2FC7OWV5NbU59Q95UpBPTpgC2vkBN9p5bnj5oxkvAJdyE0GiRJ4tyij8l8f3M1uZ51w2m2agbaQF+MSamcmlzRBwL4tmlIm01LODnhdXI37pHt5e9D+1fH49+sDp6h/tiLLZTqTeyfnojezT2h4bh+NB4/EN+4SL5rMYHSfKNs267N6fbhTEwXcvCJCkKoBCWZ+RybupKiGvzq1sRpaAN9MSSdI7ns3uLnTct3puAVE4pQqzi/ciMZ63bS9IWx1HmwH9gdWC7lYDeZOTpwrmv9Y8NpsmoGmkA/TEmpnJmyHMlqI3R4D2ImDQPAbjKTOm81xcfPA6Dy8kfl5Q+Ao8SAo8RQTdcFS17jl9/+JDgokG8+WVXteGWE1hu1LoTmuxL/8vWZ9fpnAAQMvo3IGaPwalSHo4PnYTqSQtyihwjs0w6HuZSz01dgcmPjmuzR7KMnCezVGhwSJReyOf/ESoz7TriU9YgNp+E7s9AE+VKclErq1Ip7S93nHyYgQb4uzs1YQXFyKl4No2m4cnbFuetGAEwH3gDuARYCzYFOwP4rGrEWkZSdXWtEicj/8xkENN7dZTrJs9/llpcecZupyYL7SUvcxO6uM7DqjdS5PwGAhtPuwpB8nt96zyVp8js0XzyW0D5t8ImLQr/nJBc/+JHS3EL+TJhD8el0F5kNF4zmYuIm9nSdhk1vItopU+PvQ9Nlj3D0gRf5s9cskse/BkD0g/3wbRXHr03Gcvju54ka3RfvuMhqujZY8C8uJW7kz65TsemNRFWS23jZeJIeeJF9vWZybPyrAEg2O3mvvgs2G5fum4S9sAi/O/ujbVDXRa69sIi8pe+g/7DKjV6lwqNxfbKmLcR6Ph3fQfHVyvr3bo9nXBTHezzG+blvU3fJ4+VlYxdP4OwDz3EiYTJBd/bAq3EsADHzH6Rw+36Kj54ldfwy7MZi4pY+ii3fwKkHl5DUZwYp01bQcPk0YuePIfPd7znSfTI2vZGwUX0ACEhoh1dcFEdum8S5J1YRt/RRALSRwUQ8PAQcEkd7TcX0ywGC7huER6NYTL8dInXI45y7fRIOux3dbW1J6fsImU8vJ/L5yeV1ilgwAdMvB0gdOIHU2ydjSbkIgE/nWwm8uz+GQ2fZGfsvjk95m6Y1+NX1+EBIn7b4xEVSuOcE6R/8iDW3kP0Jcyg+fema2h+gcO8J9veZw/4+c8oH8dEP9kfXqgF7mz5I0t3PETm6L15u/Kr+gtFkJG7kYLcp2PQmIpxy9buTOJwwiyN953Bm+js0evVxGix9hOS7n+N02xHY8wvJmP0KkrmEop/+AEDXqwMe9WNI7fcIWU8vJ/I52a6l59JJu3OK/DdsmksZgIIPvuHU4OmcGjqTkLv7kPrgc5zsO4mgO3ri6fSbMvx6t8czLpoTvSZw8cm3qbNY9jnJYiVl1AJODZrGqUHTCLy9B/5tGrKny1ROzl5da23lEezPn3H3cfzuZ7AbinGYLeRv3usis24Vvw13+q3lYjbHRzxNUt+ZpL++gQYvPVaue+bDT5B+7ySwO7Bl5XJ5wStur7kar1eg6NufyHz8KQBC508ma+J8TvebROAdPfFsVMWO8e3xiIvmVPwE0p96m5gXKuyYev98zgyayunBU/Hr1Q6ftk3lQkKAgOMJkznSfCQ+LRuUX9dl1NgnAPkbtnF2zHPV9PaMi8Kamcdv9UaRNPxZwkf0dNtWcU5f3e/01chK1wAqFXELRlOw84hLmdaLxpC14whHn/2EgkMp/NR7HgfnvE+7ZePcniNv32l+uXcppos51Y7l7j1F8tLPMRxOYWezRzgx+12av/SwWzmNF/yL84k/8FvX6dj0JmKcutZ5aADGU5fYk/AE+4c/R5OFYwgd0B6fuEhKs/I5Me5FbAZTtUE8QL0FY8hYvZFDt03GVljJry5cJnn40xzpM5NLb3xBw5dlv/JpGovKyx+bPh2b/hLCwwdU1eOUdw3ux6rXFrutR1XUvqHYDFl/+fqs7Fclp8+TNmEphj3HAQhMaIdXgygOdZtMypyVNFj2qFtd3NkjMKEdKg8te+qNJHnEM0h2B/VfmVitbOz8B8h+93uSuk/CVmgitNK9xTMumqTuE0mbu5J6SyfIOqZkcKz/TPlv4GwcZgvA105xycBw4JdrMqLCf4SbPpAXQtiFEIcr/dWvBZmPCSEecP7+UAhx9w3IMNaQ/rwQou8NyBsrhHjresvVAncCHwEUHjiL1t8Hz/DAaplCurcg+3v5Jpyx/hciBnUAQNckhrzdyQCYzmbgHRtG1F3dyPp+D4Fdm3NuyTo0/jq0QX7YDMUuMoO6tyDnezkalLl+J6GDOgIQMbw7OT/sxZKeB4DVGYEMG9KZosMpOMylGPadwmG1EXFvr2q6BnVvWS43a/2ucrnhw7uT+8NeLOm5LnJLL+sRWg3WCxlYz6ZhPXeBkkPH0PXu5iLXka/Hcuw02Owu6Z6tmlJ6Jg3rmTTZDpt3VSsb0L8T+V/uAKD40GnU/jo04UHo2jTGkpZF6YVsJKuNgu92E9C/k1xIAt/OLcn/cgdqfx8sKemoA3RYL+uxZhcAYD51AeHpgX/3VuRvlAd8uRt2EDRQlhE0oBO5X+wEwHjwNOoAHdrwIABU3p5YLl7Gkp6D8PLA9PtB/Pp0xfTrIbDLW3WrfX2w5eQDUHL4FCo/HZqwIFS+3vh0bIl+w4+yrlYbjiKTfM77h2DNziPr810A5G8/jMZfh4cbv7oeHwgd2IHs7/cQ0PUWzi35zOlXvm78yn3710TokE6YDp/FYS7FuO8UktVG+L3x1fIF3NaSXKeNL6/fSbDTxo7ikvI8ah9PVJ5aSs5lYblwGaw2DJt+IXjcXZReyMKWIb8N8O3ThcKvt8l2PSLbVR0W5HI+n66tXcq4HGvTGEtaJqUXnX7z/W4C+nV21bdf50o+d6rc5yrrLDRq1EF+5P18EADDgTO11lZZG+R7tfHgabQRQZRm5FKa7jro8+/eijynTXMq+a1x/ynshbI/FR08jUdUSHkZyVyCZ6umWC9lgsMBNrvba66m6xWg5EASjsIihJcn1gsZ2C5lIVlt6L//Bf/+rnb0798F/VfbK+zovAZc7agpj5IDaCNDsOUb3F/XZe1TQ58AYNx7HLu++i3Gs2EdTElnnXY5g8bfB62btgq8rSU5Trtmr99JyMCKc0c/PIjcTXux5haWp6l9vQnr0oy0T3cSPbA959fvxmooJv+gfE/wcnMOffJ5ii/lVksvP8/A9mQ6faDwCn4V3L0Fl51+lbF+F2Fl16sEGl9vWT+dF1a9kbC+7cplmo6myH2AG5kB3VuW+9Xl9TsJHiTXv6iyXx2o8CvvxnWQbCWU7f8pWUtQeeqqye3QphUB/n411rkMofFEslvBYfvL16fQasDpV5azl7CkVgTEggd2JGeD3M8aD565LnsED+zI5XXbK8r6+YCq+pDO77ZW5G/6HXDeWwbI9Qgc0Im8L2T9TVXuLWX4d29FyfksgPPOpBPAqasa8CbgQPqv//1d+U9E5M2SJLWp9Jf2VwVKkrRKkqTq785rAUmSnpEk6eebIbsyQojamtYUA1ws+09JZj6eUcEuGbTBflgNxUjOwd3/sXfe4VEVXwN+Z1t6QnqjBUIHCaEICBKaIGIXBRuiIgIiHbuiqGABQbCADRHsFRUQqdI7SGjSW3ovu9k63x932eymQNDNz/LdlycPu3PPnDlz5tx7Z+fOzC1LK5cpPniGmOuUi2RIu8b41o3Ar34U0iGx5hbRYs4ofGJCafbqg2j8fTx02tx0mt10+jeORRcSSLtvn6PDyhnEDFJGnTQ6LX4JMehCA9H4GdDodfg1jq1kq6feXDe9cehCAkj6dirtV75C9KDy0SxdVAS2jGx0cdH4NE/E/PthtNHh1IQLeS9gy8yulNcQE44lrfymZ0nPwRATjr5CujU9F32Mkvfc1PcJ7NiculMfJP7pYaTN+ARLWi6GmPL2CbuuC6Yjp5Wbk7POlvRcDE4dhpgwzO7lOvNbM/IoWL2LoCtbkLz3AxzFpZRu3Y+ugt2+LRpRuqV85M6WkYMuOgJ9vVjseYXEvjKehB/mEvvSWISf0r6GhDgMDeNpMP4W2n03laCkxpjTc6uMq8uJAZ/YMHA4sOYW0XzOaAwxoTR9dfgl4sqz3OD2Temw5jXafPok/s3qAqDR6fBNiHXFldBr8W3kGVe6sCBsReU+NqfnYnDTG3ZtJ9ptmEOLxU+QuXiVR5vaMnLwS2pB0c/ryu2MrhgzOeijIzzKDL6uh0cegNC7r6fZijeJnXwPtpwCV7o1PccVN64yYsKxppWXYc3IRX+hfTUami2bTevdn+AoMpK/6YBLzlttVXa+3AdCq6Fwc2oln1YXt+5EDelDwdo95QkaDVGvPIFfxyswbd2Nef/hKs+5GqHTerSDNd3NRxfqHl3h3M0oP0fRaGiybA4td31C8cY9mPYqU7w0AX7oY8JpsXIO9V8fg72wpFL7VHdNuBjaQD+Cul5B8pqZNHljFNbsAnxiPfNcLFYNMWFEDOhE+scrPfL4NojGnFtMh9kjqHtjF+rd3BWt83w2pefhF+vZQbsUYe0TqXdTF+o9eC0BzvOsLD0X30vEVVlankvm7AcrCGgaz9W/v0uXda9z5OmF+MSGUnY+F6Sk5efPoo8OJfb+AZXrXyGufGI8ywWIHtKbgjVKXBmPnEHofUFoAFHtiHyN0ejAUT6V6a+cn8Ub9mLc+wdVUfH6rrR1FfFQhT8u5A27thNJG95EHx1K+jzP6T+6UM9z1L0eSvzmutUxF30FP4fd2J287zdU7yeVfwR/y9QaIURDIcQGIcRu519XZ3qKEGK9EOJLIcQfQogZQoi7hBDbhRD7hRCNnXJThRCTKujsLYT4zu17XyHEt5ewY6az/NVCiEhnmmuEXwhxSgjxvFNmvxCieQ3rd70QYpsQYo8QYpUQItrN7gVCiJXAIiGEv7OuvwshvnDm6eCUvUYIscVZ9ldCiMDqiquUUnEuWWUJl8yJN39AFxJA19UzqP9Af4r3nwIkQqshsE0C5z9eSdGeY9jLLDQYc9MldDoPabUEtU1g390z2Df4JRpOuBW/RrHYjWYyv9lA2y+f4YrPnsJeakLaHBWdV211FL2N+P3u6fw++EUaTLgNvwsdNqGMfkS/8Sw5r7yDNJsr+6E6LuKfi9slL5o34p5rKTt6lhMPzeDc8x9Q/7Ux5fkAv6b1qPfUPZx+4ePqy6+mXG1IAAFtE8lbuok97R5E4+eLX3ILD7vDR94BEko37q6kW2i1+LZKJP/TZZy8cQwOUxkRI25Xjmu1CL2WI5MWcOyFT2j93vhqfFJV3Z2HqogBrb8vQqshqE0C5z/+hWJnXNX3iKvq27/495NsbT+Knb0mc/6D5bReOAUAu9FM1je/0eqLZ2n56dPYS8ugBnHlXp+85dvZ030sh4e9SsRNV3nKaTXo60ZTvHzjRevuMYdTryOw95UeefI//ZnjfR7gyLVjsRWWlE/jqMKe6spwyTgcHBkwjoOd70cbEoBvg+g/oct5qJq2cmXV69CFBVO0fl8FBRf3KUBw19ZEDenNmZfcxl0cDvJmvUfJsrX4tG6GPrFh1Tb/WS7Tj0cHjOVQl2H4t22KT1Nlek/xhr0U/rSRQ/3GYcvKJ/TGq2t+TbgIlvPZnBz1Ort7T8KSmY9vQmwlveIisdpo2jBOTlusPMlwz6PTUqdNQ058vIrc7Uewmy00H3N9je1yJ3//KZZ1HEvOtiNk/byNpIXlt9pKei5yHoT3bEtx6il+u+JhtvaaQvPp9yN0Sud6/w1P8fs1kzEeOEX4wC4Ed27p7oBqdV4guGtrou7szemXlDVWpqPnsRsL0YXEog2JQdosNa5vjfmT56d/UhN8m9avQvjibe0mVIWIdOXNW76dvd0fxXjoNJH39Ltk3vJ7y0XsRznv61zTkbyfNldpu8o/h/9FR97PbVrNhY52FtBXSpkM3AG4ryJqC4wF2gD3AE2llJ2A94ExFylnDdDiQoccGAZ8dBH5AGC304b1wHPVyOU4Zd4BJlUjU5GNQGcpZTvgc2CK27H2wI1SyjuBUUC+lPIKYJrzGEKICOBpoI+z7J3ABDcdo4G9WVlZZz///PNOI0aMWLTMpCxg9I0Nw5yR72GMNbcYfbA/Qqs0t29cuYy9xETquHc5t3gNQS3qEdSqAcbTWWh0WsxpuRTtPoZPbDiZ328mqE2Ch06dm06fuDDMGcoUDnN6Lnlr9uEwmom6sSv60ECSvngKS2Y+pQdPs6vvY+y96Tk0vgZKD52uYGtRBb3hWDz07sVhNGPNK6Zw6yECWzUAwJaTh//VV1Ly8xqMqzehi47EnpVXo8ayZeagi4l0fa8qryU9B0Nc+YirITYCa2Ye1vRcj3R9bDiG+Eiar3iDyKHXYtx/AkNcBAU/bSIgqQmGuHCsmfkYYsNp8sFjHB/7JsZ9x9GGBICzzobYcCyZec5yc/FxL9eZP6T7FZjPZqELD0ba7BSv3IRfy0bYnHaH3NybwJ6dKF63HX2sW91iIrBl5WLNyMGakUPZPuUpadGKjQT17ULC0rno46IwHz2Db3wExXuOg8OBb93IKuPqUjEQe0cKSV89gz40EGl3IHQ6zGm5FDvjKuv7TQS1aVSj9reXmLA7H1n71o/Ct24EHdbNxJyZh/HgafZdM4XUm59V4uqwZ1zZcovQBZf72Cc2HEuF+gAUbT2ELiwInwblc+wDOrfFlpGDPddtBD2jYswofr1A4NUdMB847pHHnlugdMCkpOC7deijy0e/9M548vBvei76uPIy9DHhWJ3tG3HvAJotm03i5y9hOZdFWEpbl5xPbLhX2so3Xom7Or3aIa02jEfOeui05RVVG7cA/i0a0Oj1URwZNh1bvuc0E1tmDtrwUEw7fsf/qg6Xdb56KrJ7tIM+ttxHrrpneJ6jhpjwSr52FJVSsnU/QT3aKz45ehp9bDhISc6nK/FtHF8pT3XXhIpEDB1A8xVv0HzFG1jOZimj61KSsWQV+tAgVzu47L1IrAa1bUTz+ePpuONtIgZ2pvGM4YT370idq69AOiTJrz6AKbOA4j/OU6dNQwD8YsMoyyigJjS+ry8p3z1Dzx+ew5RZgDk9D6HTog8LwrcGceV+b4kbnELWz9upO+wa2n4wAX1YEI4yC77x4a6phfqIEPJW7iAwKdGl05ZbhK5SXJWX69+iAYkzR3L4vhkecSXNxdgKzmMvTAdpB7u1RnWuEofNY0T/cs/PC9iLSinZkkpQSrIrLeLeAQS2bUzTt8ZhyczzuL4rbe2pw90fMff1p9VXU/GpF1Upr9bfF0NkKLrQ8qlDFc9R93pY0nMxxJWP/utjy9sFIKRnMsb9J7C5TeH6O5H/gH//VP7XU2tudqbpgfeEEPuBrwC3n+PskFKmSynNwHHgwjPE/UDD6gqRyk/2T4C7hRB1gC5A5e0AynEAF7ZJWAx0q0buwqj+rouVX4G6wC/O+k0GWrkdWyqlNDk/d0Pp6COlTAV+d6Z3RvHJJiHEXmAo0MBNx1tAUlRUVL3BgwffN3/+/CMD/BoT0j4Ra7ERc1bli3bepoNEX6/MjYu7/WoyVyiLzXXB/gi9ljMfreTMRytJ/34zmUu3EtW/A+a0XKJu6Iy92Ehw6wRKKyxKLNh0gMjrOwMQe3sKOU6d2St2EtK5OUKrIf3ztZgz8/n9nlfIXr6d2LuUhTbh/Tui8fMhfVHlWUz5bnpjbu9BzoodAOSs2EFI5xYIrQaNn4Hg5ESMR5X5hsG3XYfQ6ShdvQl0OgKu7UHpui2VdFeFOfUI+gbxaKOVi3JVeQt/3U7YrT0B8G/XFHtxKbasfEr3HcWnYSyGelEIvY7QG7qT9sonHO4/HvOJNMynMwi7tSdBV12BNTNXWTRYZqbpoqc4O30xJTuUXWKKNqUSNrALABGDepL/i1LngpU7iLgtBYDA5KbYi4xYs/Ixn8/Bt0E0vo3i8KkXhX/XduhiIilevZWA7u0Jf2gQ5x5+npKVmwm5SfG5b1IzHMWl2LLzsefkY0vPxpAQr9S5SxIlv+3k5A1jyHr1QxwlRmIGXY1fo1g0/r7YCkqwVBFXl4qBtEW/smvg05gz88n+eRuR/TtQlpZL5A1dsBUbCaoirqprf0Nk+dzR4r3HsWQVsjNlIjnLdxB9l7KsJbSfElcZi36tZGvh5gNEOH0cdXsKeU4f+zZ067S3SQAp8a0XiU/9KGVkvdeVFH672kNXyZpthNzs9GvbZjhKSrFnl98Egwf2oOin9R553OfQG+pFu/4Xeh2h13en6FfPhaRFq9xjrhn2YiO2rHy0YcHkf7+eIwPG8cfNUxAGPQFN4pRy2zfBXmz0SltdmBIXPWwA1uxCrFlV/PDZlEq406eRbnFriI+g6ftTOPboHMpOpLvkdWHBaIICnOdcXfyv7oT1TNplna/uyDIz+gbx6OJjlBHE66+m6Nftnjb+uo06t/Ty9GO24kdNsDKPWvgYCLoqCfNxJRYtaTkYGsZhqBdFnQFdEQY9hRX0VndNqEjOx8s43H88h/uPp3jbAVeeuOEDsRvLsFbVVpsPEOn0a/TtKeQ6/bqj02h2dBzFjo6jyPlpK8cff4/cFTs4N/c78vccY+vDc0lbvpP6t1xF0R/nCUtOxFpsoqyKMqri+MJf2XDnK6zq+yRpy3dS74H+oNHgnxCDrZq4yt90kChnXMXd3oNsZ1yVnc8hrHtrzn20kt2DX8ZWbCLjh83EDk5BE+BLYHIT7KVlBHVohvHIGU/fusVV1O0p5K9QfG+Ij6DZB5M5OuZNj7gCnNNqAI0WjSEAh7nKZXA1QtrMCK0eNLo/dX5q3eOqW1vMx8qvcTmLllGy7zh/jJ5N3vLtRA5S1okFJjfBVmysMh4u+CNj4QoKN+7nzIufkLd8O9H3XuPKK212hFaDLb/YI2/x5lTCrlPWn0QM6kn+SsWXBSt3EH6bYn+A273lAmE3dVOn1fxLELW9pY8QokRKGVghbSoQiDJSrQHKpJQ6IUQKMElKOdApt875faf7MWf+Einl60KIhcBPUsqvhRBxwI8oo/cJUkr3kfCKdtkBHymlTQjRCPhGStmugr5TQAcpZY5zysvrUsqUavTd55R9xGn3LCnlUqfdU6WUKe52O/P8AMyWUq51ft8NPATEAndKKYfUxMXAPOOpzFF2k5n9Y9+laJ+yBVr7JY+ROmEB5sx8/BpE0Xb+o86t506xb/Q8pMVGnQ5NaDN3FNLuoOSP86SOn4+tsJQW04cR3b8D+tBAzOl5lB45x6Gxb9Py7TEcnjBfeSzcIIrWbtsEHhg9F2lxbms16npiB/dESgdpS9ZwbsEyAK7a9y76sGCkw8Gx5xaSvlDpcLVZ8gRHJrzr0tty/njXNnmHRr/p0ltv1A3EDO4J0kH6ktWcW7CMkE7NaffjNKzn09FGhiMQlKxYR/ZTrxE06DoAir/6GW14KPFfzEMT4I90SKTJxNkbhyNLjcR++Bq+7VqDVoOj1Ejeq/MxWZWFWjmLVyhlvziC4BRli7/TE+di/F1ZtBbcsz11pz6A0GrI/WI1GXO/AiCgYwvqTX0Qfd0otAG+WNJzOfbwLEJ6tiNuzC1IuwNLWg7SZufEpHdIeHm42/aTs111bvjycEKc5Z4YP4/S35WnL/GT7iBycG/0ESFIq428978mZ95nNNn6qbKN4IVFllodGl+Dc5vENyhLVbYQ9WnRSJkbr9dhPZtB2uNv4CgqAb2OuOnj8Lu6Izp/X8wZeaQOf4NiZ1xdseTxPx0DTac/QIRbXBmPnOXw2Ldp8fajl2z/+Pv7Ezf0GqTdjqPMwrFnP3ZtW9ll33z0YcHgcHDiuYVkOucQt1jyJMcnvIMlM1/ZfnK+c/vJ1FP8MXqOoveRm4ga1AOH1YajzMKpFz5BF+RHwgvD0Bo06CLqcOyquwkeqJz6BZ8rsRz93CgCuitbt2U8Ue5X4etD4vqPOd77fhwl5Qt5Y1+bhE/zRkgHWM5lkv/TRmLGDkZoNeR9uYrMeV8Rfld/AHKXKDEXP20EwT2SndvbvYlp/zF8mzek/qxxCI0GNIKCnzZiD40gvFdb7CYLh8a+7b226p2EXkpvuAAAIABJREFUT3wEB++YSvEWZR5+s0+e4sSkt7Fm5uNTP5ombtumHnPGbaPXRxE2oDNm5+JYabOTeu0U/Fs0oNm80aDVoAkIQBPgi6O4lOLvfqHgvc9qfL5GvfIEvh2vQFsnBEdJKdLuwF5aRv6Xq8h660vCnH7Mc/ox7oWHCXL68dzkOS4/1ps5DjQahEZDwc8byXrzc+VcnzUB/44t0UeFIe12sj/6mbQZi4i4u3+NrgkN500kqHNrdGHBWHMKSJ/5GblfrKLB7HGE9GqPJsAPh9nKoeEzKXDuPtNqyZMcdcaqb/0omjtjtST1FEecsepO0zmjyft1l2v7ybLWTWk/80E0eh26AF+EAFupmZ3j55O/T9nS8KrFk9k18T3KMgtIfKAfTUcNxDcqBHNOERmr97Jr0vs0HtaXRkP7IG12/CJDkHYH1oISDo59x3VvabfkcQ5OmO+6t7Rxbj9ZvP8U+51x5RMdSqs3R2KIDkUIwck3vyfjm420mjeamBu7gENiycgj8/M1nJ/zDS0WP8WxieVx1fTCVrGpJzn6iFL/xq+PJPy6zpjPOePKbnftetP5zGKE0AISe2ku0lpGRSY/N4Mde36noKCI8LA6jHrgHm69vl8lOXBuPxkYjuVc7l8+PzPfVMYMQ/p1Jv75h9CFhWArKqX0wCnKTqYT2rMddpOZY+PfonSfcn2viT/a/PgyAVc0BocD04k0zjy5gJIdh2iy6GlOTX7LlbfR2xPR1QnEeOAkJ8a8UX7ev/SQ695ycsJcjM57i8bXQNud7/N7l4exFxvpeP67CxNxbgbmApFAAbAXqNqBXqZDbPe/fUh8Z/qGqiYk/e38XR35N4BzUsqZQohhwIdSSvFXO/LOPD8CyShTdw5exC4JDJFSfi6EeBqIllKO8VJHfg/woJRylxDiI5QfFVV15CcDjaSUI4UQLYF9KE8STqM8AeglpTwmhPAH6kopq14xA6yIHuz1hjTguLTQZaKppcdT9aO8//ivoMDP6zqtdq3XdQIEB1S+af1V0ouqW5bx16iNGNCL2omr8GDjpYUukzKz91/fkVfme2mhP4G/1nZpocskKvzPj5RejJJin0sLXSY2e+08tC616r2uM1N4v/4hDu+3P0CAxvt6O+5/zes6AQ60H+d1nUaL99sfQK/x/j0bcO/I/22oHfnq+bv2kX8bGCqE2Ao0BUq9qHsJcPZinXgnpUArIcQuoBfwghdtmAp8JYTYAFS/v5fih0ghxO/AYyhTawqllNnAfcBnzmNbgRottFVRUVFRUVFRUfn/Qa2PyP+vce7lvkdK+cHfbculEMpzQL2Ussy5I89qlMW9l73kXh2RV0fkvY06Iq+OyKsj8uqIfG2gjsirI/KXS3Jst7+9s7o7fePf7oeq8P4d5W/EObpeCkz8u22pIf7AWiGEHmWu+8g/04lXUVFRUVFRUVH5/8d/qiMvpWxfMU0IsQ2oOFRxj5Ry/58pwzmnf2yF5E1SytGXq0tKWQx0+DN2qKioqKioqKio/P/mP9WRrwop5ZWXlrosfR9x8f3pVVRUVFRUVFRUvMR/bRq4N/m7FruqqKioqKioqKioqPwF/vMj8ioqKioqKioqKv9eHP/gN6v+3agj8ioqKioqKioqKir/QtQR+f8IuVrvN+U2g9XrOifUqfwKc28Q0tj7tu7eHOl1nVfWzfC6ToCQJO9va/nVytrZIi3W7v0dvLr753ldJ0BAiNnrOo+creN1ne3qZXpdJ8CJs2Fe11kb27oC6LTe33qvtraLrY1tAq+M9/61ZdfZaK/rBNA6vD+6WhvbRAK02jXb6zoD6/bwuk6AeRG1o7djrWhV8RZqR15FRUVFRUVFReUfi1Sn1lSLOrVGRUVFRUVFRUVF5V+IOiKvoqKioqKioqLyj8Whbj9ZLeqIvIqKioqKioqKisq/ELUjr6KioqKioqKiovIvRJ1ao6KioqKioqKi8o9FXexaPeqIvIqKioqKioqKisq/EHVEXkVFRUVFRUVF5R+Luti1etSO/H+M9tPuIb5XEjaTmS3jF5C//1QlmabD+tL8wf4EJUTzdeuHMeeVAKAP8qPrvJEExIUjdFp4/0e2fbWOW5+7j5Y922ExmVky6R3OHThZbfm3Th1G50EpTG411CO9/hWNmfDdi+Q89iLGVRvw69qBsCmjQKOh5LvlFH70hYe8vmE9wp+fhE+LRPLnfUTRoq8BEAY9MR/OQuj1oNNiXLWBgncWAaBL6oT//Y+ARot59c+Yv/vUQ6ehex98bh6ifDGZMC54A/vp4wD4DLwNnz7XgQT7mROUznvFI2/bafcS27stNpOFnePmU1CFXxsP60uT4f0JTIhhaasRWJx+jezSgq4LJ1B6Jht/gxXT2o1YDhymzsTRoNFQ+sMyij/+3EOXrkE9wp6dgqF5IoXvfEjx4q9cx2J/WILDaASHA2x2MoeOch3TtuqA7+CRCI0Gy4YVWFZ4+lXXtgs+Nw0FKcFup+yLd7AfOwBA4PRFyDITSAfY7bDyeVe+flPvpUnPtlhNFn6YNJ+M1Mr1v/7V4cS2SUAIQe7JDH6Y+C5Wo5kGnVtwx3sTKDibDcC55TvYM/t7urxwD/Wcsbp+/AJyq9DZc+5IIq5ohMNqI3vvCTY8/iHSZqfBNcm0n3wbOCRaPwMBvlqk1UbBV7+Qt+CrSnqinh5BYI+OOExm0h+fhfngcQwJ8cTNftwlo68XS86cT8j/+AfiZj+OISEejVaiDQ9DExSILTPbq20F0ObFe4nqnYTdZGHP2HcprCKuEu6/hkbOuFrecgSWvGIAYvq1p/ljg8DhwFdjpmDm2wh/P0InOePq+2UUVWFr+HOKrQVve9oKgEZDzCdvY8/KxXY+jSuv7IrdZObwo29Rsr/yee9bP4qW88ehqxNIyf6THBo9F2m1AVCna0sSpw1D6LRY84rZe/NzhPVMouWCcWgDfLHlFbE/+b5KOus+P5zgXu2RJjOnJszBlHoCgPqvjyGkdwdsuYUc6vOoSz44pR11pw5HFxqELiyYA+3uwp5f5KEz7rmHCOrZHofJzLlJczAdOI7w0dP4ixkIHz1Cq6Vw+Sa0wYEE9WyPLiwEa04+dpMVXXAAtqJSUvtOBMCnXhSJ70xAVyeQ0tSTHB8zx1XnBtMeoE6vZBwmM8fHz8O4X7E9ZvhAIu/sAxKMh09zfPw8pNlKg2kPENorGY2/D4boMHa2HorN2b4X8KkXRZN3JqCtE4gx9STHnOWF33w1caNvAsBhLKN01kysR0/g26Wj164t9Cq/BrR58V6inbG6+yKx2tgZq8vcYvUCdZIa0WPZNCzZBVhzizj46NvVxFUkreaPQ18nkOL9Jzk4ei7Sald0dG1Jk2n3ueJqz81TCevZluYvDUVotQg/A6bUE5y8f5qHzvipwwnu2QGHycyZSbMxpZ5A+OhJ/HI6GoNyPylctomMNz4DIGTAVcSMH4IuvB72wvNIm6WSnRd4+uVZ/LZpO2Ghdfh+8bvVylXFrJnP079/L4xGEw8On8DevamVZFJSujJj+tMYDAZ27/mdESMmY7fbmTB+BIMH3wyATqejRfMmLG47kuQJt3jl2uqw2dkydfFl1Uflf4/akf8PEderLcEJMSy9aiLhyY3pNP0+fhk4tZJc9o4/OP/rHvp885RHetP7+lL4x3nWD52FT1gQN218jdL8YiITYpiWMpaG7Zpw+0sPMOump6ssv16bRvgH+1dKFxrBDY/fyaHf9hEJoNEQ9sQYMh9+DFtmDnFL5mFcvwXriTOuPPbCYvJefQv/nld56JIWKxnDJyNNZaDTEvvRG5g27oCyVPyHj6XkhUk4crMJeuVdrDs24Th3ulxnVjolz4xFlpaga9cJ/4cnUvzEKERYBD4DbqVo3FCwWAiY+ByGbr1g/X4AYnq1JahRDCu6TiQsOZHkGcNYc91zleqZu+MP0n/dQ49vK/snZ9sRNt37uvJmV42G2G8+JuuRKdgzs4n++G1Mv23BdrLcVkdRMQUz5+HX46pKugCyH56Io9Czs4LQ4HfnI5S+8TgyP4eAp+Zi27cFR3q5X22H92B7fgsAmvgE/EY8TemzD7iOG2dORpZc0Ku82TaxZ1vCE2KY12Mi8e0Sue7FYXxwU+X6//LCYiwlJgCueeYuOg29hk3v/AjAmR1H+Pz+1wHlza71erUlJCGGL7tNJCq5Md2m38cP10+tpPPYd5tZO+YdAHrOG03zISkc+mQ15zce4PTK3QiNYPDW2ThsZk4OeJiG38ymZPVWLMfPunQE9OiAoWE8J/o+iG/bZsQ8/winB43HcvI8p24cowhpNCRuWETxr4pv0sbNUPKGWolf9R3FXy+lcO573msrIKp3EgGNYljdZQKhyYm0feV+fhvwbCW5vO1HyPh1N92+fcZT74ZUMn7ZBUCnlAAiZjwDOh1Zo5W4iln0NsYqbM1/fR5+KVXbGjTkFqwnz6CrG49Oo2Fb5zEEt29C01eHs/vaJyvJN3r6Ls7N/4ms7zfT9NXhxN7Zi7SPV6IL9qfJjOH8PuQlzOdz0EcEg0ZDkxkPcPbxtyg7m0Wzr17Gt0k9yo6Wt1Vwz/b4JMRysPvD+LdrSv2XR3LkhsmKH75aTfbCn2k42+0NnhoN9V4cwcmxs4mfOJiAK1vj0yge465yfweltMeQEMeRlBH4t2tG/EsjOXbTJKTZyok7n8JhVK4lTX+Zh72wxCUX99xwUgc8Qf1n78NeXOrSV++pe0h/70fyfthEwxkjiBzSm6xFvxDSKxnfhFj2XTWawOSmJEx/iAMDH0cfE0b0A9fxe8pYZJmFxHcnEnFjN6w5hfglxHJw0LM0WTAJXWhQlW1S31le7g+bSJgxgqghvclc9Avms5kcvPUZ7IWl1OnZjoQnJ5D1wKOETnnUi9cW5c2u0b2TCGwUw6oaxGpmFbGqtJWg3czhWPKK+ePJDzGn59Hs1QfZde1TlUQbP303Z+f/TNb3m2n26nDi7uzF+Y9/RRfsT7MZD7J3yEuYz+c640rQbMYDnLj7GUKu7Ur0I4PQBPh66Avq2R6fhDgO9VDatu6LIzl602Sk2crxIU+7YqDJ1zMoWrcb454jlP1xmlMjptNs5awq/eTOTQP6cuetN/DktNcvKetO/349SUxMoGWr7nTq1I65b75M96tv8JARQvD++29wbf/BHD12kmefncg999zGwoVfMOuN+cx6Yz4A1w3ow7yJk4hKTvTKtRUgrEU9er8z5rLqpFI1Qogw4AugIXAKuF1KWelV90KI8cCDgAT2A8OklGUX0/2PnSMvhLALIfa6/TWsxbJKqkl/QQjR50/ou08IMc/5+WEhxL1u6XFucuOEEP5u308JISIuvwYKdfu158TXGwHI3X0cQ0gAvlGVXwefn3qa0nM5lRVIiT5AeX26LsAXY0EJrXols/3b3wA4tecofkEBBEdW1ik0gpuevJsfpi+pdKzHfdeyb/k2SnILAfBp3Qzb2TRs5zPAZqP0l3X4p3T1yOPIL8By4A+w2SqbaVJiWuh0oNMhpUSb2BxHxnkcmelgs2HduAZDR88blf3IAWSp0tT2Pw6iCY8st1+rRRh8QKMFgy+OvHL/xPVvz+mvNgCQt/sY+mD/Kv1akHoaY1V+rYChVXOsZ89jP6/Yavx1LX49qqj/wSPIKupfHdqEZjiy05A5GWC3Yd2xHl2Sp17M5dcD4eMLNVhA1Kxve/Z9o9T//J5j+AT7E1hF/S904gF0PgbkRR6FNrimPUedsZq1+ziG4AD8qtB5ds0+1+fsvccJiA0DwGY0AxCZ1BhjRj5YbWC1UfTzbwT26eKhI7B3Zwq/Ww1A2b4jaIIC0EaGesj4d2mL5UwGtrQsj3RDq+Zg0FP69VKvthVAbL/2nP1S8Wu+M658qvBBYeppTGcrx5Xd6QMA4eeLMOixucfVyrX4V2NrVeeVNioCv6uupOT7ZWjDQyldthKAol1H0QUHYKjCttBurcn+cSsAGV+uJ+Ja5WXuUbd0I2fZNsznFbutOUUEJydiOplB/o+bsOcUYi8uJeSaTh76Qq7pRN43awEw7vkDbXAAuiilrUq2HcRe4HmpDkhqgvlUBtHDbyT95Q9xmMoISmnvIRN8TWcKvl3j1HkEbVAAOmf7O4zl1xJdaBBFa3d6yOmjQgm7oSs5328s19etDXk/KT/4cr5aS2h/pQ6h/TqR8/U6xdbdf6ANUfIr+rVofA2g1aDx88GSmUdov05kf72OBlPv5+SU+SAE+oiQSj4O7taGXGd52W7llew8gr1Q+YFRvPsPtFGRtXZtienXnjM1jFVjFbEK0PiBfthKyig+eg64VFy1csVV+pfrXHEVfUs3spdtw3w+FyiPK+PJDKTVRnBKMoXLN6OP8Dy/Q/pe6RZXRzziqjwGtAi9TnlaCZiPncN84nyN/NMhqQ0hwVX/ELsY119/DYuXfAPA9u17qFMnmJiYKA+Z8PBQLGYLR48pTy5Wr97AzTcNqKTr9jtu5NgPW7x2bQXQ+flc9Dr+v0T+A/79RR4HVkspmwCrnd89EELEA48CHaSUrQEtMPhSiv+xHXnAJKVMcvs7VZNMQgittwyQUj4rpVz1F3W8K6Vc5Px6HxDndngcUHkI+0/iHxOKMS3X9d2Ylod/TOhFcnhy5KNfCW4Sxy175nHdmul88/xCQqJDKXDTWZCRS0hMWKW8Vw/tz/5VOynKLvBID4kO5Yp+Hdm45FdXmjYqAltGtuu7LTMHbdRl/H7RaIj74l3qrfmKsq27saQeRhMWiSOnXKcjLxvh1lGviKH3dVj3bAdA5uVQtvQLQt79kpD3v0EaS7Dt2+mS9YsJ8/CrKT0Pv9ia+xUgrH0ifVa9TMSc6RhaN8eeWW6rPTMbbeRl1F9KIue9SvSidwi4+TpXsqgTgSOvXK/Mz0ZTJ7xSdl27qwh44QP8H51G2cKZHsf8x00n4Om30Hcvv1EExYRR5Fb/4ow8gqKrrv8Nrz3EhJ1vE5EYx/aFK13pdZMTeWj5y9z58RRCm8YTEBNKiZvO0vQ8Ai4Sq0Knpcmt3Ti37ndXWsP+Hegz/1HCWjUg/YnZANgyctBHe9ZZH1053vTRnv4Ovq4HRT+vq1Sub6dkpNGE7axyQ/dWWwH4xoZiSstzff8zcRV7bQd6bXidyNkvUfzdMo+4smVlX9Z5FTpxNPlvLgApEQYDdjefmdNz8Yn1PO/1YUHYioxIu0ORSSuX8W8chy4kgKRvp9J+5StED7oan5gwzG5tLm129DGebWWICceSVt4RtKTnYIipHMMuG2LCQQisGbmUHToFdoerk+6Sia6gMyO3vFyNhibL5tBy1yfYi42UbtnvIVenT3us2QWYT6YDoAsLUjrPzjpb0nNd9hliwjC7l5OWiyEmDGtGHunv/EC7HfNJ3vsB9mIjhev3YYgJwyc+EktGLsaDiu36CufVxcpzJ2pIH8o2b0cbGVEr1xa/CrFadpmx6hsTSuyAjliLjB4/QGsWV3lucRWLLiSQdt8+R4eVM4hxi6v45x4k7eWF2HIKED56T50x4VjTyv1izcgtv05oNDRbNpvWuz+heMNejHv/qHG9/ipxcTGcO5fm+n7+fDpxcTEeMjk5eej0OpKTrwDglpsHULdunIeMn58v1/RN4dSyHV67tg5a9yr9Fk3it4nv/aU6qri4EfjY+flj4KZq5HSAnxBCh9I/TKtGziPDvwbnqPwnQIAz6REp5WYhRArwHJAOJAEthRB3o/yyMQDbgFFSSvtFdM8EegL5wGApZbYQYiHwk5TyayHEKRTnXw/ogUFSysM1sHkqUILyKKUDsEQIYQI+QunUrxVC5Egpe1bId1n2OzNVTruMX9OxKW3IP3Ca1YNeJrBhNIO+eIws5w3sYjqDo0JJGtCZuYOfryR6y7P3sXTGp0iHW56/aCcOB2l3PIwmKIDIWVPRN24IVaisTqeudRI+vQdQ/JTyyFAEBKLveBWFowYjS0sImPQ8hqv7wuaDTnurUl1ze/P3n2JZx7HYjWZ63F6f0KcnUbZpW41srYrMB8fiyMlFE1qHyHmvYjt1BuTBqn1QxSiCbc8mbHs2oW3SBp8bh2J8QxkYKJ0xDlmYhwiqg//46dTvVMyZ7YerbK7q7F06eQFCI+j/wlBaXd+ZfV/9RnrqKeZ0HYvVaCaxZ1uu+2A8hScyaqwToNvL95G+7TAZ24+40k6t2InQamgyqDuR4+7h7H1PVa3nUu2n1xHY+0qyZy6sJGdo2xrb+QrnwF9tq7POUf8qHHu5g1/py3eSvnwnna+PInTKGCwHjngK1FChb7fO2PPysR4+ik/7ttX4rELCRewXWi1BbRux97YX0PoaaPfzS5z/YPmllVaps/o6CIMOv5YJnHj4FbdRqUu3v6tch4OjA8aiCQ6g+foFGOpFY9x50CUW0iOJXLfR+Is6phrbtSEBhPbrxN4rR2IvKiVxwSQibrkaodMQcVsPDtzgNmWpUlUv3RDBXVsTNaQ3eSPH4NMh6ZLyF6NivIZvLyF36+G/HKttpt3LgWmf0WzCzZe2r8r2ch7Saglqm8Ce26ah9TXQ/ucXOfvBCnziI7CdPY8p9Th1ru1SOf8lYuDIgHFogwNouOAJfJvWp+yPM1Vk8D6ihvF+zz2jee215/AxGFi1+jdsFZ6mXHddX7Zs2YG5oPSy76/VXVtPrdhJzJXN6DD5tsuoUe3xH1jsGi2lTAeQUqYLIaIqCkgpzwshXgfOACZgpZRyZUW5ivyTO/J+Qoi9zs8npZQ3A1lAXyllmRCiCfAZSucYoBPQWkp5UgjRArgDuEpKaRVCvA3cBSyiagKA3VLKiUKIZ1F+FDxShVyOlDJZCDEKmIQyj6lGOH8MPAJMklLuBNdcqJ5SSo9nkTW1Xwjx0OOPP/7U0KFDI7v/8iym38/jH1c+WuMfF4Yx03OE/GI0vqMHB+b9SNP7+tD4rp74hwVhPXSaOm4668SEU5jpOa2rbquGRDaM4Zn1cwDQ+xl4Zt0cpqWMpf4VjRg6V1mYFhgajK5XWwoXfY0upny0XBcdgT07l8vFUVxK2c59+F3VAUf2PjQR5To1YZHIvMqPeLUNGuE/cjIlLz7mmguuu6I9jqx0ZJEy9ce69TcMfQfSp/fdAOTtO4F/XDgXLPSLDaMso+Z+tblNOSnbrDwF0NWNLbcpOhJ7Ts3r73DKOvILMK3bqEz/SD2IzM9BE+Y2XSg0EkdBXnVqsB/djyYqDhEYjCwpQhYqsrK4AEd+DrfOG01pTjFpv58g2C0GgmLCKM6qvv7SITn441a6jBjIvq9+c0256XBvX5IH9yQwPoKsXccIjAsn05knIDaM0mpiNXn8zfiGBbHhsQ8rHStNz0Or06KvF4U2NBhdTATWLM86WzNyKsWbLavc34FXd8B84Dj23ArlazUYWjbDeqx8Md5fbavAIbeS8mg9APL3nsAvrnw0UomrSlMma4R5z340dULQxZfHlS4qssbnlU/bVvj360XgTQNcHYGQRx6En15QjseGY8mo4NfcInTB/gitBml34BNXLmNOz8WaV4TDaMZhNFO49RDaAF983OJI6LRYMz11WtJzMMRFcGFGuiE2opKMB0KgDfanxS+zEQK0IUGEXHsVmTMXY3M+HbRm5GKIi8B4QWdMuEtn+D0DCBvSTyn7XBZBVydT8N1aV9m+zRtyZupHruJseUVoQwJAqwG7A0NsOBanLkt6Lj5xEVyY/GOIC8eamU9I9yswn83ElldE9H39CWjdiKBOLSjacgBDbARXrFLmYAsfPY3nPEpq/8lYnbZfrDwA/xYNaPT6KA7fPY1wcxH2rBy00eWx/lfi1Z6VTYd3H8GSU1QpVn0vI1YThvUlpl8ysf3a47BYETotoW0bKTETG465gh5rbnGFuArD7BFXxa64Kth6CF2AL/4JMWibxdMypT3a0GCEj576sydwZpziW2t6Lvq4SOAQ4Byhr3CdsBeVUrIllaCU5FrtyGt8g9m+bQUAO3ft8xhdj4+PJT09s1Kebdt207v3rQD06XM1TRITPI4/NuURQkKC6fzLS2TvO+GVaytAxrYjBDeIAogALj1v9D+OEOIh4CG3pAVSygVux1cBMZUyQuWFIFXrD0UZuU8ACoCvhBB3SykvuuL43zK15sLPeD3wnhBiP/AV0NJNfruU8sIdtzfQHtjh/DHQG2h0kbIcKIsQABYD3aqR+9b5/y6UBQu1RY3sl1IumD59eoPmzZv7b+j3AmdX7KLRbYrp4cmNsRQZKbtIh6sipedziOneij8WrmLtkFcoKzax+8fNdLrlagAatmtCWbGx0vSZg2v38HTHETzfbQzPdxuD1WRhWspYAJ7vPsaVvnf5VnJfnkvRJ1+jqx+PLi4GdDoC+qVgXL+lRjZqQkPQBCkPZISPAb8rk7GePIv92BE0sXXRRCk69d16Ydm52SOviIgiYPI0St98GUf6OVe6IycLXdOWYPABQNcmGevmdazq+ySr+j5J2vKdNBjUHYCw5ESsxabL8qtPZPm8V0PLZmC3oYuNRuusv3/fnph+23wRDW518PVF+Pu5Pvt27oD1+CkA7KeOoImKR0TEgFaHvmMPbPs8/Soiy28amvqJoNUpP2gMvuDj5zTSF+EfyNLJ77FgwJMcWbmTtrcq9Y9vl4i52ERJFfUPbRDt+ty0TzK5x5UnggHO+u9c9Cs/PfE+puwCji/dShNnrEYlN8ZSbMRUhc5mQ1Ko26MNax55y2NUKbihUlb2vhOENquL8DVgLzESfN3VlKze6qGjZM02Qm7uDYBv22Y4SkqxZ5d3HoIH9qDop/WVyg7o2g7r8ZPoYiK91lal3/3Muj5Psq7Pk2Ss2Em92xW/hjrjynwZcRXQsNzf+mZNQOIZV9fU3NbCtz7gfK+bONulP1mjp2A5fAxpVH6ABbdvgq3YiKUK2/I3HSDQhVQnAAAgAElEQVTy+s4AxNzeg5wVOwDIWbGDkM4tEFoNGj8DwcmJ5K7ajV+jWAz1ohA6LdqgAAp/3e5px6/bCbtVeTjp364p9uJSbFnVdxjzl27Amp7L0Tue5kjPh5F2O6fuf8HViQco+nUbdW7p5dTZDHuxEVt2PtqwYPJ/WM/RAWM5dvNkNAY9Pol1XXISiemPc1jSPTvCRZtSCRuojPpGDOpJ/i9KnQtW7iDithQAApObYi8yYs3Kx3w+h8Dkpmj8DGQuXEHR5lTOzfqS7M/XULLzMHuufJijI2chLTb2953g6sS7lxfuLC/SrTxDfARN35/CsUfnUHZCeWpkOXgYff14r8SrJjiIPeMXsLbPk6Sv2El9t1i1XUasnvzoV36sP5Sl9e9lx8NzseSXcOSx97FkFWCvJq4K3OIq9vYUclYoUx2zV+wkpHNzj7jKWaUszDw26AkO9RyJLbeA0l2HXJ14gKJV7nHljIEsJQa0weX3k6BubTEfO0dt4igrotOV/el0ZX9+XPoLd9+ldNA7dWpHYWExGRlZlfJERjqnbxkMTJo4kvfeL+/XBQcHUbduHEntevFtv6c4tWKXV66tAOGtG6Ix6AAuf6TtP4iUcoGUsoPb34IKx/tIKVtX8fcDkCmEiAVw/l+5oaEPysB1tpTSitLn7FqFnAf/5BH5qhgPZAJtUX6EuK/kLXX7LICPpZRP/MlyqnuGc2Fyn53a9d2fsj9t9V7ie7flhs0zsZssbBlfHmMpn0xi26T3MWUW0OyBa2g5ciC+USEMWDWdtDX72DbpfVJnf0+X2SO4bvV0ELB0xhJ2/7iZxh2b8+z6OVhMFpZMfselc8RHj/PZY/MpusiNtkrsDvJmzCP6nenK9pM//IL1+GmCbhsIQPHXP6ENDyX207fQBPiDlATfdQvnb3kQbUQYEdOmIDQa0AhKV/6GacM2DI3tGN+fQ+Azr4FGg2XNchxnT2G4RtkBwLJyKX6DhiKCgvEfPt5ph53ix0ZgP3oIy5b1BL/+Htjt2E4exfzrT4Dy5Ctj9V5ieifRf8ss7CYLO8fPd1XlqsWT2TXxPcoyC0h8oB9NRyl+7bt6Bhmr97Jr0vvUHdiJRkP7IG12/Byl5D71IprAQCLffAWh1VCydDm2E6cJuEWpf+m3P6EJDyX643dc9Q8cfCsZd9yPpk4IEa8qU5iETkvpitWUbdmBT5IWHA7KPp2H/7iXEUKDZdMvONJOo++hzHW1rv8Zfftu6Lv0AbsdaTFjWvCSoiu4Dv6jnDvRaLVYt63l+HplzuTRNXtJ7JnEI7/NwmqysHRSef2HLJzMj1PeoyS7kJtmPYwh0A8hIPPQGX5+ShnJbDmgE+3v7oPDZsdWZmX1qLfI2nWUer3acsfGmdjKLKyfUB6r/RZNYsPk9zFmFtBt+jBKzuVw4w9TATjp3LoyYUBHmtzaDYfNjqXEhCHQQKPl8yn8eiWWY2eoM1iZ41/w+TJK1+0gsEdHGq36AIfJTMYTb7jKEr4+BHRtR8YzcyuFafB1V2P8ZQ32jCyvttWFnUAyV+0luncSfba+gd1kZs+4cr92XjKFvRMWUJZZQKMH+pE4eiA+UXXouWYGmav3snfie8QO7ES9Qd2RVhu+DiO5T0xDBPoTNfcV0GooXboc64nTBN6q2FryjWJrzKJyW4OG3Er67fcjS40edXfkF2BLz+TKbXOxmywcGfuW61ibJU9wZMK7WDLzOfHiYlrOH0/C40Mo3n+S9E+di0qPnidvzV46rJ0J0kH6ktWUHjjN0Sc+oNXKOWh8fUBA4uKpFG/eT+nOw+QsXkHRml2E9OpAq43v4jCZOT2xvF0azptIUOfW6MKCab39A9JnfkbuF6s4+8wCEhdPRWg1OExmzCfOE3ZXfwDylqygeO1Ognp2oNn6Bcr2k5OdTw6jwqg3cxxoNAiNhoKfN6KLqOOSM59MI8+5e0ezT57ixKS3sWbmc/alT0h8ZwL1ptxJaepJsj9TllEVrN5Fnd7JtN38Ng6TmRPj5ykxsucoeT9vofUvryNtDoypJ8havBJpsVGndzJJTnlbfvlWje7lnXnpE5q4lZflLK/u+NvRhQaRMF0ZINQLC5lDR5H/6lyvxWvW2t89YrXv1jewXSJWm1QRqxfIXLUXW4mJptMfwJpbxKGxb7uOXbHkcQ5PmI8lM59jLy6h9fxxNHp8MCX7T5JWIa46rX0dKR2kLVlD6YHT/PHEhzRbpMRAyZb96CJDCXfGQO6SFRSt2UlQz/a0+G2+c/vJN10xUH/WONf9pOCnjRStUX40hPTrTPzzDyF0vmiDY5A2C/aiKqYEApOfm8GOPb9TUFBE75vuZtQD93Dr9f2qlHVn+Yo19O/fi0MHN2I0mhj+0ETXsR++/5iHR04hPT2TCeMfZsCA3mg0GhYs+IR168p/nN14Y39WrfoNo9EE/nB2zV6vXVttZRZWj5zHDd89+7fPa/kPvNl1KTAUmOH8/4cqZM4AnZ2boJhQBnF3ViHngfinrEiuiBCiREoZWCHtDeCclHKmEGIY8KGUUjjnyE+SUg50yrVEcdJVUsos57Y/QVLK01SBEEICQ6SUnwshnkaZyzSmijnyHaSUOUKIDsDrUsqUavTd55R95MIceSnl60KIH4FZUsq1Trn9wA0XniRcKAOlB1lj+wGWxN3t9YbcZrB6WyUT6lzkMflfIKSx921dvTne6zqvrFv1jeCvEpLktTXeLmavrH6x8F8h1l7lZP6/RHf/2omrgBDzpYUuk91noy8tdJm0q1v5cbw3OHG28sL2v0qwofr9uP8KOq3D6zpNFv2lhf4EtXHXjY8v9LrOXbUQqwDB8vJ2eKoJYb4X3aHvT9Nq12yv6wys28PrOgHmRdSO3uHnFnv/on2ZNIls/7d3Vo9m7/rTfhBChANfAvVROuyDpJR5zp0M35dSDnDKPY8ytdoG7AEelFJe9Eb0bxuRfxv4RggxCFiL5yi8CynlQWeHfKUQQgNYgdFAdR3hUqCVEGIXUIjixNpgIfCuc7FrF2ABsFwIke6+2PVP2K+ioqKioqKiovIPREqZizLCXjE9DRjg9v05lHWaNeYf25GvOBrvTDsKXOGW9IQzfR2wroLsF5TPe69pWc9USL/P7XNDt887gZSL6FuI0mlHSjnVLf0b4Bs30bnOv6rKqLH9KioqKioqKir/Vf4Du9bUGv/kxa4qKioqKioqKioqKtXwjx2Rrw2EENsAnwrJ90gp91clXwN9w4CxFZI3SSlH/xl9KioqKioqKioqnvwHFrvWGv+vOvJSyiu9rO8jlBc7qaioqKioqKioqPxPUafWqKioqKioqKioqPwL+X81Iq+ioqKioqKiovLvQkrvby/7X0EdkVdRUVFRUVFRUVH5F6KOyP9HSGmQ5nWdN6aEel0nBLP2I4PXtZ7K9f4LkW6u732fAmw5E+t1nYeretnzX0QAY/t4X7HpRO28ECj1YIzXdRYX1fG6zqSYbK/rdNg07M7w/gu8WgRXfrW7Nygq9fW6TpPV+y9v8tF5/8VFUDsvr9p6zvvxj4Ar473/ErvSwop7TniH/BI/r+usjZc3lZxb73WdALuvmFQrev8JONTFrtWijsir/E+pjU78v4na6MTXFrXRia8taqMT/2+iNjrxtUVtdOJVaofa6MTXFrXRiVdR+TegduRVVFRUVFRUVFRU/oWoU2tUVFRUVFRUVFT+sUj1za7Voo7Iq6ioqKioqKioqPwLUUfkVVRUVFRUVFRU/rGoi12rRx2RV1FRUVFRUVFRUfkXonbkVVRUVFRUVFRUVP6FqFNrVFRUVFRUVFRU/rGoi12rR+3I/4fw6dyRkHGPILQaSpcuo+STzzyO6xrUI/SpKeibNaFo/oeUfPqlkl6/HqHTnimXi4+l6L2FkLkGbdMkfAbeDxoN1h2rsa7/rsqyNXUb4zdyOmWfzcKeuhV0evwemgY6PWi02FO3YFn1hUeeVi8OJbp3EnaThb1j36Fw/6lKehvefw2Nhl9LQEIMv7R8CEteMQDxt1xF4iM3AGArLaPkqYXkHDrD1c/fQ4NeSdhMZlZNWEB2amWd17w5kqgrGuGw2cjce4K1j3+Iw2an3YjraHZzV6U+Og1hiXGk9b8FQ8vm1JnwCEKj+LV4URV+fWYKhmZNKHz3Q0qWfOk6JgIDCH1qEvpGCSAlYWM+JG/XMQCSpt1LbO+22EwWdoybT0EV9W88rC9Nh/cnMCGGH1qNwJJX4joW2aUFSS/cg9Br6ZRfzKI7XgSg39R7SezZFuv/sXfe8VEVXQN+7vb0nmwSCCSEjpTQBIIEQi8qIgoqKgqC9F4UFRQVFZVXpNv7K3ZQitKVXgKhk4RQ0rOpm2y23u+Pu2yyZANE46d83z789sfu3DNnzpw5M5k7d+69BhM/zVpDtgsfDH59LBF3RIMgUHAxmx9nrsZcbnQcD28dg8+6TzCsfhnRaEAzcgKCIMO0ZxOmTc7tqGjbBfW9j4Mogs1KxZcrsaacqhQQZHg9vwJbYT6Gd6Q4U3XshM+kySCXYfj5Z8q//MJJp6Z3bzxHPASAaDBQuuwtLKmpAPjOmYv6zi7YigrRPTEagCYvP05QYjusBiNnpqyiNPlitTprokJotWYqSn9vSpMvcmriu4hmK1EThqAdFi+ZqpDj1TiS3S3GYCkqo++h/2DWG9CE+qPw9kCfmsXRqatdxmr0E31pZG+rX1qMc8Sqtl97ms8dDjYbNquNirffwZh0Co+uHQicMwFkMvTfb6L4Q2e/KhvWJ2jRLNTNYyl890NKPvlGslGlRPvBWwhKJSjklP+2B17Y5MjXevGjaO396sjU1S7jKuaJvsTabd1Yxdbgrs3p8tFMjAWleIQHgsGAbt236Naur6Yj7LlxePfoiM1gJGvuW1ScltpH5uNF+CtTUTduAIhkzVuGIeks6mbRaF+cBBpPjFdzSZ24jHpzH8K/Vxw2g5HU6e9SnpxWrRx1/VBiV81A4e9N2cmLpE7+D6LZgiY2kpi3JuF1Rwz6pBSUof7YDCYuTH2XMhftr44Kpenq6ZKe5DTOT1qOaLYQ2K8jUXNHIPNUo9YGYi4sJev9TWS8+4OUUSajxS9LMWXrMF7Owa9Xe2wGI+nT36H8pGSvb0I7ohaNAbmM/C9/JXvFdwAEDOpKxIwRaBrX48zg2SgCfYlaNAaZlwYQsRbpEc0WMl/5CP3eEwBELhyLb88O2AxGLs9ahuFkGsrwYKLenoYyJADRJqL7Ygv5H25w1C348UE0GjUEm9VG1m9JyNXKOhlbPD1EbEXFlHz0JQGzJoJMRtkPv1Dy8VdOuhQN6hP0whxUzWIpWvkBpZ9dFy8yGdpPV2LN1ZE3/VlHslf39oQtGIcgl1H09ZZbjjNVdCSR/5nnkFHWD+fy61+Rte5nAKJfegL/xDhsBhMp05a7jof6oTRZPR2Fvw9lyWlcmPwOotlCQL+ORM0ZCTYbotVK1+lz2Lv3EG+9uYj+/XtRXm5gzNgZJCWdrKYzIaErS15dgEql4uixE4wbNxur1cqM6eMYMWKo5CuFAkVQNJaCSyDW/GKwBa+8xe4/DhIY4M8Pn62uUc7hYq8g2vyx4i/3pSuvfUH26h+vzyoHDgMZwOCbGuPmfxX31pr/K8hk+M+cim7GPHJGjsazTy8UDRs4idhKSil6+13HBP4alstXyHvsKekzejxihZGKXb+DIEN991gMH75M+dvTULSJRwitV71sQYaq/yisF45XUWrG8N5CDO/MxPDOTORN2iKr39hxODSxLd4xWrZ3mc7xWeu447UnXVar4OB59j3wMuVXnN+GWX45l71DX2RXr7lcePs7er72BA16tsE/Wsun3Weyfe77JLzyuEud577fy2cJs/mi93wUGhUtRiYAcGzNz3zV/1m+6v8se5d8jfHYCUR9GQGzp5I/bR7ZI0bj0bcXimgXfn3zXUo//7paWf4zJlGx7xA5Dz5OziNjKb0gvS1W26sN3jFaNnWdyZHZ7xO3ZLRLW3WHzrPrgVcpu67+Sl9P4paM5vfH32Rrwly+mfAOALE92xAYrWVFj5n8PP99Bi52rXfri5+xdsAzrO0/n+LMfDo+1tdxTJAJJM4fgeXkERAEPB6eTPnbz6B/bgzKzj2RhUc56bKcOUbZwnGULRqP4cOlaB6f4XRc1WcotszLlQkyGT5Tp1E0bw66xx9Dk5iIvIGzT61ZWRROm0LBmCco+/QTfGdWvrHQsHkThXNnV+rv3BmPaC377pzK2VnraPq661iKXfAwV9b8wr4u0zAXlRHxUC8ALq/cwMHEuRxMnEvqy19QuO80lqIyR77z//mRouNpbGjwGEmz3qPNa0+41F9w8Bx7H3ilWqzm7TnJjl7z2NH7GY5NW0PwCzNAJiNw/mRyJj5Dxn1j8OrfE2WMs1+txaUUvL6CYvsE/hqiyUz22NlkPjiezAfH49G1AwFxsQCE2fvV1i4zODrrPdrWYKvu4Dl+f+CVanEFkH/gLAC/dp/N+a6P4Du4B6rY+k4yXj06oGoQSWrvMWQ99440QbcTtmAcZbuPkNZ/HGlDJmFMvQJA+MtTyVv6IcmJ0yncdICGr41HEx3O8W4TuThnNdGvPuXS1vrPjiJr3QaOx0/CUqQnZGQiAJZCPZeee5+CzQdQBvlwtMtkUmatptFrrvU0XPAImWs2crTrZCxFZYTZ279oTzJJvaV4OvPEG1j1FYQMjcejiTTWhT05GEPKVRRBfmiiwzkZ/zSX5q4k6tXxkmKZjKjF4zg/6kVO9ZxM4D3d0TSW8hrOXSZl7BL0B06DTHDIXXzqFawl5aRPeoPLM5YR9fZ0AHx6tkcdHcGZHuO4Mn8F9RY/LbW51Urm4g84mziRC/fOJvjRgagbS23i3eUO/Pp0ZmvifLYmzKXoZHqdjS3ZDz5J/jOLCZg7hdwp88ka/gSe/VyPgYVL36Xk+gm8HZ+R92G+eNk5USZDu3ACV8Y8T+qA8bWKM9PFDC7ePVn63DsV0VBBwaaDAPj3ikMTE86xrpNInb2KmCWu46HBglFkrt3IsW6TsBTrCbXHVfGeZI4nzuB4n1mkTF/J6lWv079fT2Jjo2nRsjsTJs5l+TuvVNMnCALvvfc2o0ZNJK59by5fzmDUqPsBeOvtNXTq3J9Onfvz3HNLEM0VN5zEA9w7sA+r31p8QxlH2UoPBLnyL/elrOoT+GtMBc7ckjF/EzZR/Mc//1Zui4m8IAhWQRCSqnwa/o1l6WtIf1EQhN5/Y7nvCYLQwkX644IgvHuz/KoWzbBczcCamQUWC+W/bUdzV1cnGVthEeYz5xAt1hr1qDvEYcnIxJqdg6x+LDZdNmJhDlgtWI7/jqJ5x2p5lF0HYD25H1Ff7HzAVCH9L5eDzPnij7Zfe658vQeAoqMpKH09UYf6V9NdcjIdw5X8aumFhy9gLpYmW4VHUvAODySmb3vOfPs7ADnHUlH7euHpQuelHZUnHDlJqXiHB1aTaXJPFwxbt1fzq+HX7XjU4Feu86vg5Ym6XWvKf/pFSrBYMJeUAxDRvz2X1kv1LziagsrXE40LW4tOXqL8avX6Rw3tytVfDmHI0AFQriuR7O7TnhPfSnozjqWg8fXE24Vek97g+K5Qq6TVdDsdH+/H2U2HEEuLkIVGYsvNRMzPBqsF88GdKNo51x9jRWWd1RqqPlxACAhG0bozpj2VK8bKZs2xZmZgzZJ8WrF9O+pu8U4qzadOIeqlrmg+fQpZcOWbS80nTmArKXX8VneLJ3v9bgBKjlxA4euFykWdA+JbkrthPwBZX+8iZED1WA4b2o2c7/9wTuvVhsv2WC28QawWn7xEuYtYtVa50iH31CCKoG7VFMuVTCwZ2WCxULZlJ54J1ePKdOo8WCzVdIoGyeeCQgEKhaP9Ivq1r2arq7iqyVYApZ8XZRdzKL+cC2YLJT/vxiexi5OMT+87Kf5hGwAVSeeQ+XihCAlA5u2BZ8dWFK3fIgmaLdhKpX6qiqlH+UFpFbN493H8e7Yj/5udAOiPnkfu54UyNKCaPb7xd1CwcR8A+et3ENC/EwAWXTFlx1PQNAxHfzzNrucCCl9PlC7q7NetFfl2Pblf7yTQrsdWXoFPu1gqLmZjLS4DUSTvhz8I7NcRZXgQfokdyP/iV5Qh/ujs9pYdPY/CV7LXq21jjOlZmC7nIJotFPz4O/59O0u+SbmKMU06efdoEuWQMxy/QOH3O/Hr05mK85eRqZUIKgV+fTpT8O0OAMqPnUPu64UiNABLbiEG++q/rcyAMeUqyrAgAIIeGUDOym+xmaQ4CenavE7HFkWEFsuVDKwZ9r8tW3fg2cNFrJ4+5zJW5aHBeHTrjP6HX5zSPVo3wXQpE/OV7FrHWVW8urbBdDkb41XphCSwf0fy1u8CrsWDl+t4iG+Frmo8DKiMh2vIPNWIosiQIX357PNvATh48Bj+/r5otaFO+oKCAjAZTVxIkVb/t23bw9B7B1Yr94EH78FmdDnNcKJD2zvw8/W5qRyAoPLCViGNiX+lL7maG6jCgwAGAe/dkjFu/te5LSbygEEUxbZVPum3kkkQBHldGSCK4vOiKP5WV/pc6B8jiuLpP5tfFhKMNTfX8duam488pPavbffo0xPDr9sBEHwDEYsrB3qxpADBL8hJXvANRNGiM+YDW6srE2R4TF6K17MfYE05ju3KBcchTXggFZk6x29DVgEaFxPqW6H+Qwlc2nECL20A+io69VkFeGurD2bXkCnkNL0vnss7TzilKzQqGiS0pnzHbuShwVhz/pxfFRHh2AqLCXhuDqGfrCHgmZnIPdQAeGgDKa9ia3lWAR7hNdt6Pd6NtKj8vOjx7bP03rKY1vdJE2EfbSAlVfSWZBfgE+Za75A3nmL64ZUEx0Zw8COp/XzCAmjWrwNHPpNCXfDywVZQuWInFuYj8w+uXtd23fBa/D6eUxdT8dFSR7pmxNNUrF/ntPokCw7GViVWbXl5yIOr67yGx8BBmA4eqPG4PDiYiozKOhuzdKiviyVloA+WknJEq2SHMbOgmozMQ0VQz7bkbqwsSxRFwvt3oNmsYTR4RFrBrahlWwGED+hA4p6ldPlsNrqFS5GHBmPJrvSrJScfeWjNPqiGTEbEf1dTf/t6KvYfpfCYtK1FEx6AIbPAISb1q9rZ6tc8Cv+20XT9Yg6q2CjM2fkowpz7vSIsGHNWFfuz81GEBaOsH461oJjw16YT/eNywl+eimCPeeP5dLwT7wQgcHBX5N4eGDMrxxdTpg6V1rlNFIE+0uTa3m6mLB0qrbMtcm8PrCWVV1CMWQWow4Oq6bGUVOoxZulQVWn/wIGd8IlrTPPP5pMyfSUmewzVX/gkV1/+GEQRmVqFqaq9WTqU2kBU4YGYsqqkZzvrdtgQ5OckZ87KR6kNwm9gVwyn0hBNFpTaIMyZlX41Z+scE/ZrqOqF4tEyhvKkcwBooiPw7tSCXj8vIuG7Bfg2qVdnY4v201V49e+FNadKW+fm1SpWA2ZOpPCdtU6LBQAKbRCWqv6oRZxVxXdQD0o27nT8VmkDneJKamsX8XBdXKmrxF7ggE603fMOzT99hqfGzSIiQsvVq5mO4xkZWUREaJ105ucXoFAqiItrDcB9QwdSr16Ek4yHh4a+fRIQTWXUJYJcDrbKk6g/25dc0WDREwBzgBtfQnDzj3G7TOSrIQhCQ0EQ9giCcNT+6WpPTxAEYYcgCF8Ayfa0RwRBOGhfzV9zswm+IAhv2nVuEwQhxJ72kSAI99u/pwuCsMgukywIQrMb6FooCMLHgiBstee7TxCE1+35NguCoLTL7RQEoYP9+2hBEM4LgrAL6HaLDqmeVttLQQoFmviuGLbtuqb0pjrVg0dj3Pyp68uEog3D8lmULXkKWb3GyMKqXDatC3uBoG4tiBrZk72vfIXgQueNbpBJePlxMg+cJfPgOaf06D7tyDp0HrGklFvxQY3I5SibNqbsu5/IfXQctooKmk0eAtRU/Vuvv0wuJ6B1NL8/spTdI5cQP2UogdHaWundMHstyzpNJD8lg5ZDpAlW3xdGsW3JV4g2ex4X+nDxPF/LsT8oW/Ak5e8ulPbLA4rWnRFLi7BduuAs7NJI1/VUtm2Hx8BBlK5d41qgRn3XKXRVj+tkgvu2p+jQOadtNXuGLER38BwnF31OzOg+BN3ZzKX6m5G16TDbus/iwOi38J/w+F+Pf5uNzAfHc7XfSFStmuLbTNrK4aoP1Obxy0Un0kma9wFZvxwm9f2t1F/1nEvbXBWDKCLI5WhaxlL4xS9cvGcyNkMFweMeACBr/jICHhlMq81vIPf2qIwxJxXXp/25tq2m5yb+1ielotu4j7OjXydq7ggA1PVCsOQXU56cWmM5km9djTsuZF3kV/h5ETHvMa7MX3mDMiqVyTw1NFw9j4wX38N27aqaQo7cz5vtg17g+ItfENC6oQsVf25syZ00F49edyF4e9Vo043QxN+JtaAQ89kLLo7evG1rijMHSgXevTpTsun3KnluJWZu/LeiYNNBkrpP4dwTr7PwhVm3/Ldl1KiJvPHGC/y+ZwOl+jIs112hGDSoD/v2Hbrptpracyv21X7M8e/dHnN+McCRP21aHSH+C/79W7ldbnb1EAQhyf79oiiKQ4FcoI8oihWCIDQGvgQ62GU6Aa1EUbwoCEJz4EGgmyiKZkEQVgIPA5/UUJYXcFQUxZmCIDwPvABMciGXL4pinCAIE4BZwJgb2N8I6Am0APYBw0RRnCMIwvdIl6x+uCYoCEI4sAhoDxQDO4BjrpQKgvAU8BTAJ/c9yPAhlZfx5KHBWPNdXzqvCU2XTpjPXcBWWAiAWKJD8Ktc/RB8AxFLCpzyyCIboRkp7YkWPH2QN43DaLNhPX2wUqiiHOvFkyj7jOCu3tIqRlFSGpqIytUAj/BAKrILa2WvT/Mo2q+diklXyr1fzSf3eM4EoX4AACAASURBVBreVXR6hwdSllPkMm+naUPxCPJh+7wPqh1rfHcXzv+0j5aANTcPeVjlJdTa+NWam4c1Nw/TKWnPMTYbjccMIKJPHAXH0/CMCOLauplneCAV2a5tdUV5VgHGglKsBiMNH7wLD39vHv50Hmm/n8S3ig98tYHoc2vWK9pETm3YT5dxgzm+fjfhraO5b7kU7soQb0SrGVFXuXouBARjK9LVpA7r+WRkIeEI3r7IY1uiaNMF7zs6gVKFoPFEM2Yu+s++QxZa6VNZSAhWXXWfKmJi8J01m6J5cxBLSpyOafr2RR4RSeC69zCfPYcmMohrG7vU4UEYr4sls64Uha8nglyGaLWhjgisJhN2b1fHtpp6o/sS8UgiNqAwKQ2Fl4asTYcJaNdIuppUy1i9hm7/WRT1x2IrK0ehrbyyowgLxppXs19rwlZahmiooNtX8zDml1CYlIZHROVKXG37lUVvoCw9l6j7u5OzLQleewxVdCSWXOd+b87ORxkewrUNWgptMJZcHaIoHas4Lp0cl2z+neBxwwEwpV1F/9t+fIYPIOieeKwlZagjgrm2yUAVEYQ5x9lWS0EJcj8vkMvAakMVHoQpp4Cwx/sT8nAfAKx6A3LfyommOjwQU7azvRZdCQrfSj3q8CBMVfxiytShigimZP8ZNA3D8GgUgdxbg3ffjvj1ao9MrUTu70Pk/FGcu+8Zyd7wIMw5BQhKBarwynFSpQ3CfF35AJb8YlRdWzt+a5pE4X1XO9LHvoLpcrbk1ywdyogQrm1HVmqDMF/zvUJOw9XzKPxhF8Wb91W2RZYObCJ9fpX2bYsWK75NItEdPA/8tbHFVlyC6cx5lFGRjuOK0JBbjlV1m5Z43NUVj26dEVQqBG9Pgl6cT9nUt6TV9Sp+U2qDbznOruF9VwcqTqfi2z+eyPsHAKA/noI6Iphrm++ktnYRD9Xiyjn2tI/3J+zh3jRpEsn69RucVtcjI8PJysqpVt8DB46SmDgMgN6976JxbLTT8QeG381/v/6Jvp2n39x5N0Gm8UWmkbbeiBaj0/bV2vSlG+HTsRkBfTsCpAMawBf4DHjkL1fATZ1xu6zIV91aM9SepgTWCYKQDKxHmiRf46AoitduU09EmhQfsp8MJAIxNyjLBlx7fMRnQHwNct/Z/z8CNLyJ/ZtEUTQjXSGQA5vt6cku8nYGdoqimCeKoqmKLdUQRXGtKIodRFHs0CsnH0X9SOThWlAo8Ozdi4o9+2rK6hKPPr0c22oAbFdTkAWHIwSEglyBok081jOHnfKUvzGB8tefpvz1p7Gc3I/xx7XSJN7LFzSekpBChaJRayyHt7O793x2955P9ubD1H+gOwD+cbGYS8sx3mDCWc3WyCA6fjCdQ48tZedds/iq/7OkbTlCc/vTR8LaNcJUWk65C50tRiQQ1eMONk9aUW1FQuXjQeSdzUjbchQA05mzTn716NMLw+5b86utoBBrbi6KKOlKhKgvJ+3T7fza5xkyNh2mwXCp/oFxsZhLDVTUov6ZW44Q3LkpglxG+n93oc8t5KsnlnJu62FaD5P0RraLpaLU4HIiH9AgzPG9Se84dKnSZeN346ezPH4ay+OnYT6yh4pP/oPg4YUQrAW5AmWnBCxJzvUXQiv/wMmiYkGhRNSXYPzuA/SzH0I/dxSGNS9jOZtExXuvYT57FnlkPWRayaeaXr0w7nXely4LDcXvxZcoefVlrFevVrO/YutWrJkZFIwdg/GPPWiH3wWAb/vGWErLMbmoc+Efpwm1X3kIf6AHeZsrY1nu40FAlxaOtKsfbuXwkOfZc8+LZG0+TIMRPQjpcQc2ixVLqaFWserVsNLXfnc0RFAqqThwFEVUJIoIyQde/RIo33VrcSUL8EPmI01cBbUKmbcXx2a/z/bez5C1+TBR9n4V8CfiSh3iR2FSKt4xWsL7t0eQCfj06kzptv1OcvptB/C7V7pRTtO2KbbSMix5hVjzC7Fk5aGKliZ+Xl3aYkyRbnKUB/pR+PlGTvadhT4phfzvdxN8fwIA3nFNsJaUY86tftJR8sdJAgdLe6eDh/ekcMshcj7azMk+MznZZyYVKRl4t4mx65Ha3+yizsV7TxFs1xP6QAIFWw5J9jfUUpqUgkdMOAF94hCUCgL7diB1/nuc6DiG5C5PkTbxTcqTU7GVSVNKr7gmWEvLMOcWUnb8AprocFT1Q6W898RT9OvBauUbzl9xyCkCfQl5Ygg5y7+m7HDlPYQlvx0kcFhPADzbNcVaWo7F7pOo1ydjTLlK3nvONyQWb92PVV/Or32eYd+4d7AaLUTa7//4q2OLoFajiNQi8/NDbo9Vz749Mezee0u6ile8T+agEWTe/TD5zy7GeCgJ3fOvSv5IPo+qYQTKemGgVOA76K5bjrNr+A7uQcnGXRR+vpHjfWZxvM8sCjYdJGR4D+Am8fDHSYKqxEPhZqnNNA2lxabsjzaTMmMFeXkFrF//E488LE3QO3VqR3FxKdnZudV0hoRIiygqlYpZM59m3XufVdrq60P37neyYcOWW/LdzbBVlGApysBSlIHNWOaY1Ne2L92IK69+zrEOY0Gap4wAtvMPTeJFUfzHP/9WbpcVeVdMB3KANkgnJBVVjlXdgCYAH4uiOP9PllNT6127g83Kzf1oBBBF0SYIglmsjAhbDXlrHzFWG0VvLid42Wsgk1O2cROWi+l4DpW2cpR/vwFZYAChH65G8PIEm4j3g8PIGTkasbwcQa1G06k9Ra+9XanTZsP403t4PPEcCDLMh7djy72CopP0hBPLQRf74u3IfAJQD58EghwEAUvyXqxnjwAqAHJ/O0ZoYlt67V+G1WAkaVrl1olOn8/h+Ix1GHMKiX6yH40mDkEd6k+P7a+Rs+0YJ2auo/GM+1AGeHPHEumpHJ1sNr4e9DwNerXh0d/fxGwwsW3mWofOIR/PYvuc9yjLKaLnq6Mpzchn+A8LAUjddIhD/5EuisT078Dl3clYDEYIsPt16XKC33kNQSanbIPkVy+7X8uu+fXj1ciu+XXEMHJGjEYsK6do6XICX3wGFAqsmVmcGfcxANnbkghPbMuAfW9hNZg4NL2y/vGfzebwzHVU5BQR+2Q/mk4YjCbUj77blpC1LYkjs96j9EIm2TtO0Hf7EkSbjf1f7STv/FXyzl8ltmdbJu5+C4v98ZPXGPHRbDbOWYc+r5h73hqP2tsDBMg5c5lfnv3QdUOKIhWfv4vn9FcRZDJMv2/BlnkJZQ/pCWTmXRtRtu+OsktvsFoRzUYMq2/ypAWbldJ3lhHw+lKQyajY9AvW9HQ8hkiPEzVs+AnvRx9D5uuHzzT7ypXVSsH4cQD4LXgeZdu2yPz8CP56PfqPPqTsUi5dDvwHm8HE6amrHEW1+XweZ2aswZRTSMriz2m1Ziox8x6kNDmdzC8qT1pDB3aiYNcJbFVuTFWF+BH3ofS0HA/7VY4GDyVwrEqs3vn5HJJmrKUip4iYJ/vReOJg1KH+9Ny+hJxtSSTNXEfE4E7UH94d0WzBWmEmb85isNooWPIuYatelR4/+eMWzKmX8Llf8mvpNxuRBwUQ/sUKKa5EEd+H7yPjvjHIgwMJfmkOgkwGMoGyrbvJ/lW6aJf9WxJhiW3pu/9trAYjR6rY2vXzORy129royX40sduaaLf16Mx1RA7pTMxjvRER6bR6MraiEko27cGUchn/kdIVv6Ivf0G/8xBePTrSaNv70mMB51WOG9kvrSbizTkISgXmK9lk2o/5Dkkg4OHB2GwChZv2c+WVz2j4ylja7F2JzWAkbXrlPf1NP32WtFkrMecUcuXlT4ldNYP6cx6i7ORF8r6U7t9QhvjTatMbyH08EFRKul79L4bUTC5MqdTT/PNnSJ2xClNOIekvfUrTNdOJmjeCspPp5Hwh3UQZNPhOQof3QBRFmq6bhaVQT/5PezGcu0q9x/sBUJGagUVXjOlqHq1+X42twkj6jHfssWnj8nPraPL5CyCTo/vvb1Scl57U49+/M1EvjUUR6EfjD5/FlKWjyecvoAj0BZmMoPsTCR17LwDnB8+gZPthfHq2p/nuNfbHT0pleHVoTuCwXhjOpNP0l2UAZL7xKaU7jlDw9W/Uf2MKfXcswWa2sH/8cuoN6lQnY4tGbqLsh18wX75K6PLXQC6j7KdNmNMu4T1MilX9txuRBQWg/WSVI1Z9Rg4j64EnEMvKqRGrjexFq6j/wWLp8ZPfbK1VnAkaNV7d2pH93HIntYXbjuKfGEfcvhVYDUZSpq+ojIfPniVlphRXlxZ/RpPV04maO5KykxfJ+dIeD4PuJGR4AqLZgq3CxN2jJrB37yH69+/FmdO/U15uYOxTMx06f/zhY8Y/PYesrBxmTB/PwIGJyGQy1q79lJ07K0947rmnP7/9tpvy8soHDdyI2S8s4dCxExQVlZB47yNMeHIUw4b0cykrmg2IKs866UuiTSR8zGBOJEzBqr81W938swj/5rOMawiCoBdF0fu6tLeBq6IovikIwmjgA1EUBUEQEoBZoigOtsu1AH5E2lqTKwhCIOAjiuKlGsoSgZGiKH4lCMICIEwUxcmCIHwEbBRF8RtBENKBDqIo5tv3tS8VRTGhBn0LAb0oikuvr0vVY4Ig7ETaopMB7AfigBKkM+Djoii62t7jIKNLrzpvSL+E2t0kdyvs+FBV5zoB0pV1dl+zg6ERmTcXqiX7LofXuU6As3+DW6f2rr7iVBcY0kx1rvPkae3Nhf4EpXV3v7yDttrqj3z8qxzNrv2N7bdCc59bX8m9VUrKNHWuE8Biq/sLzGpF9aew1AUKed3fN3jBdGtPOKkNnSOz61wnQFmxus51Fuo96lwnQM/C6ldX/ir6q7tuLvQnONp61s2F/gSdM79zebfU/yZhfs3+8clqTvHZf9wPrridV+RXAt8KgjAcaR+5y9vARVE8bZ+QbxUEQQaYgYmAy4m8XU9LQRCOIO1Rf7DOLb8Boihm2Sf4+4As4CjSdhw3bty4cePGjZv/d9j+xTeb/tPcFhP561fj7WkXgNZVkubb03cCO6+T/S832GteQ1nPXZf+eJXvDat8Pwwk3EDfwhr0Ox2ruqIviuKHQA17Hdy4cePGjRs3bty4uX1udnXjxo0bN27cuHHjxk0VbosV+b8DQRAOANdv1BslimLyn9Q3Guk1xlX5QxTFiX9Gnxs3bty4cePGjZvavQvh/xv/byfyoih2rmN97u0wbty4cePGjRs3bv7X+H87kXfjxo0bN27cuHHz78fmXpGvEfceeTdu3Lhx48aNGzdubkPcE3k3bty4cePGjRs3bm5D3Ftr3Lhx48aNGzdu3Pxrcd/sWjO3xZtd3dycs00G1nlDKlXWulZJjq7aKwHqhApb3b8zy0dhrnOd0ouD6x69WVnnOkO9b/B69b+Ah3fdv9k1J6/u32oJUGSt+1fmhqnr/rXnf9cwXmGp+7Ue2d/UB/4ONMq/582uFea696tCVvdvi/27sP4Nb+H9u+IqSfCqc52tbX/P2Bp3YunfolcZHPOPv9E0wDv2Hx84CvUp/7gfXOFekXfjxo0bN27cuHHzr8X9Zteace+Rd+PGjRs3bty4cePmNsQ9kXfjxo0bN27cuHHj5jbEvbXGjRs3bty4cePGzb8W9/2cNeNekXfjxo0bN27cuHHj5jbEvSLvxo0bN27cuHHj5l+L+82uNeNekXfjxo0bN27cuHHj5jbEvSJ/+9Mf+E/Mr+9RtH4LBWvXVxMIXTAO7x4dsRmMZM17C+PpVFTRkUQsm+eQUdYPJ/8/n1L48Y+ONL/H7yd41lguxg/HVlQCgEe3DgTPG48gl1Py7SaK3v/aqSxldH1CX5qBukUsunc+pvijb2o0PPqlJ/BPjMNmMJEybTllyReryajrh9Jk9XQU/j6UJadxYfI7iGYLAf06EjVnJNhsKMMCsJmtWIr0nJ6yEr0LPZqoEFqumYbS35vS5Iucnrgc0Sw9J9+/awsav/Q46ogg5B5qDBezOD1lJZw5DzIZLTa9gTm7gAuPvYyqfiiNVs5EEeBNeXIaaVP+g2iWnjUd9eKT+PVqj81g5OL05ZSfTCNo2F00eG0CglyOtVhP1rvfkPv+RgAiZj2Ef79OKEMDkHlqMF3N5eLktyk/mQaAb0I7ohaNAbmM/C9/JXvFdwDUW/AYfr07IpotGC9lY84vxqdbG0REsIrYKkwgQPob68nfdNBe/1BarJmGwt8bffJFzkxc7rDbv2sLYl8ajaCQYy4oJWnoCwT2bEvTddOQeWqwFJRw4c5Hqvk07Plx+CR0wGYwkjnnbSpOpQIg8/Ei4tUpqJs0ABEy5y3DcOwsPgPiCZnyEOrY+uheX4XviLsRZDJKv99E8Qf/dY6jhvUJfnEW6uaxFCz/kJJPpDiSh4UQ8vIc5EGBINoo/eYXSr74ngYvPUlArzisBiOp09+lPDnNZSw1XjUDub835ScvkjJZajtNbCSN3pqE1x0xXHntC7JWS32gwUtPEndvd+ReGiqyCihJSiV56mpsxsr3C3hEhdBmzVSU/l6UJKdzYuK7iGYrCj8v7lg2Ds+GYViNZk5OW40mPIjmix9DoRDQ/fc3/Ad1w5yjI230Yoe+yEVj8espxdClmf/BYI+FqDcm45vYAYuumLN9plSW3yKa+q88jaBWIlptFHy7g5BHB4Jchu7LX8lZ+W01P9RbNBbfXu0RDUbSZ1QpY+lk/OxlnOk9hXqLxuLTs/2f6p/B93UncuJQAKxlBtLmraX89CWHXwMS41BpA9EnpXL6vgV10lYBvTugCPDGUqxHrDCTOuNd9EfO/2mdISMTiX5lLCBgLSpB5ulB9ltfkPfBT5XttXAsvj2lPnB51jIMJ9NQhgcT9fY0lCEBiDYR3RdbyP9wAwCa5g1p9MlC5P4+2Ixmzo16mdKDZ265/kFD7yJi4r3IvTxQhflj1pWS8/Em8r/bTew7U1CG+INNJO+LragbaB3jUfr0d2ocV8pPXSRq0RiU2kCsegOW/GLkvl5YS8o43W86ANHLpxMwoAsIYMzUcbr3FESjucYxSu7vTaOVs1DVD8V0JZfUp9/AWlzmUldyr2mIRjOBd3cjcsowkMsoPXgGr5bRKPy9KTt5kdQqbRVTpa3UkcH494qrHHNPpzuN10Ctx2xNowgarZrlaIs2UVqOLP0GnwahxAzujMrPE7lSwQ+DXyD/hHOf6Ln8aYJbx2AzW8hLSmPPvA8QLVYa9I2j/ez7wSZis1iRXcnGq2U0tpuMV7GrZtzUB9mrf6yWV+EfiWizYC3JqXZswStvsfuPgwQG+PPDZ6urHXdz++Fekb+9kQMrgAFpA8fjO7gHqkb1nQS8enRA1TCStD5jyH7uHbSLJgFguphB+j2Tpc/QqYiGCkp/3efIp9AG49mlHebMKgOBTEbIgolkPb2Ay3ePxXtgT5QxUU7l2YpLyF+yiqKPqk8gquLfKw5NTDjHuk4idfYqYpY85VKuwYJRZK7dyLFuk7AU6wkdmQhA8Z5kjifO4NKrX1CRloW1rIKzs9bS9PUxLvU0WvAIV9b8zP4uU7EUlRHxUC+pnr6eNF0yhitrNlJ6LIW9HSY46QkbM5iKC1cdeuo/+yg56zaQHD8RS3EZwXZ7/HrFoY6OIDl+AulzV9Hg1XEgkxE5+2FSxy/laJORmHXFhI0ZgqZxPQCyV39PxqufUn4ihYxXP8GQcpWoV8c7fB21eBznR73IqZ6TCbynuyNfye7jnEqcwuk+0xAtVny7t+HAnZM5N2M1lpIyDifO5sSIl2my9CkEudTFYxY8zNU1GznYZQqWIj3hVerfeMlYkh99jUM9ZnBq7Jsgk9F4yZNkPbuci8NnIff1QhXrHFfeCR1QN4wgpddYsp5dTviLEx3HtM8/hX73EVL7jid18CSMKVcAMJ6/xNUJL1NxJBn/0Q+QM+EZrg4dg1f/6nFkLSlF99oKij++7kTQaqVg6Royhj5J5iNT8B1xNz73D8IjOpykbhO5OGc1Ma+6jqWoZ0eRtW4Dx+MnYSmqjCVLoZ705953TOBAik/PZlFYyio4POIVzIV6kMkIv7erk84mCx4ifc3P7OkyHXORnnp2vzaaei8lJy/xR8+5JE9aSfPFj9NiyRMcfmgJZxInEfzYQEw5Oiddvj3bo2kYzum7xnN53grqv/y045hu/TZSH11UrU4RzzxG9rKvONt/Ollvfknk/EdJeXQRZ3pNIuCe7mgaO7ebb8/2qKPDOd19PJfmriDqlcoyCtZvI2XUIie5P9s/jZdzOXnfcxxPnMHVZd/Q6I3xDr96RIeT/dFmSvafxrN5lEu9tW0rj+hwSg+cJvujzVjyijnRewaGKv22tjqRyYicPIyziRNJbvkgloISbCYzRVsqx0ifnu1RR0dwpsc4rsxfQb3Fki9Fq5XMxR9wNnEiF+6dTfCjA1Hb26HhyrmYMvI42PBBsj/4mcarZtSq/sYrOZwe/jyIIhfnrcWcW0DQPd1RR4Vx9cUPOdVzMmfunoN2/L14tozmZPzTXJq78objSsPXJnB+1IsktRqFRVdM2qQ3KfxlH4WbpLr69e6Af5+OnBkyh3PDF2ArLZcWQW4wRoVPHEbJHyc42X0CJX+cQDtxmJOuU4PncWbY8w5digBvop57lDMPLCS55zT8EtpRvPu4o/4hVdrqkr2tNLGRaKLDOW7v9w1eHVdtvIZajtlARWomp/rOkD79Z2ExGDGWlOMXreXnB15h59Q1mMuMLtst5fu9rO8xm297z0euUdFsZAIAGb+f4rs+z/Bdv2c5//Vu/HvGOeyOrmG8qn9dDLjygStkGj9ES80vM7x3YB9Wv7W4xuP/VsR/wb9/K7flRF4QBKsgCElVPg3/xrL0NaS/KAhC7z+hb6EgCLNcpEcIglDz8rVrOgEpQBpmCyU/78a7dxcnAe/EOyn+fhsAFcfPIfPxQh4S4CTj2aUNpsvZWDJzHWmhzzyF7q33nV4Zqb6jKebLmViuZoPFgn7TTrx6OZdnLSjGePI8ouXGb0QM7N+RvPW7ANAfvYDC1wtlqH81Ob/4Vug2Sn9Qcr/eSeCATgDYyisceop2HQdRpOSIpEflQk9AfEvyNuwHIOvrnQQP6AhA2H3x5P1yAL+OTclevxtzfolDj0erGPwT25P35W8OPT7d7qDg570A5K/fQUC/zgD49+uE7psdAJQdPY/czwu/XnFUpGZQ/NthRLOFgu93YzOYUGmDpDroDfj37YTum53IPDRY8orsfgjAq21jjOlZmC7nSHl//B3/vlJZJbuTwCq9xVHu44k5twCA4r2nHfWXaVRObRcQ38pR/+yvdznqH3pfPPm/HMCYkQ+AOb8E37hYDBezKfl5D9b8IqwlZfj0vtPJnz6976To++0AGJLOIfP1QhESgMzbA8+OrSj6eqskaLZgK5VW4kypVzBdzEDm5YklOw9LhhRHZZt34pngPEG2FRRhOlU9jqz5BZjOpgAglhswpV3Go0cX8r7ZCYDe7ntlqHOMA/jG3+GIpbz1OwjoL8WSRVdM2fEUREvlm4wD+nVCt3EfglyO/swVlH6eKP29qMgudNIZFN+SnA0HAMj8ejdhAzoA4NUkEt2ek1I8pGTiFRuBISMfw6VcFEF+iBUmLLnOuvz6dqLgWymGyo+dR+7rhcJej7KDp7EWuRiKRJD5eALg2ToGa3GZI2YKf9qDX99Ot1yG/kBlGVXl/kz/LD18zrECW3rkPKpwKeYD+3ek8LcjBCS2J3PF9wgKRd201Ya9+NzZgqtLPkfu54UiwAdrSfmf1undLpaK9CxMVyRflp9IwWYwYs7Iq6x7n85VfHnO4UtLbqHjKoetzIAx5SrKMKn+qnqh5H0grc7nfroVRZBvreqvP3wOj5gIKtKzKNhyEJU2CN2Pv+PToZljxd1WVoFosaI/eBqQxqOaxhX94bPYjOZq40zgkG4U/LgHgNBHB1B+Oh3DmXRpbPPSoAz2u+EY5d+3E7r1km90VcbJa7rKT6ejP3oemV2XOkpLRVomlgLpyq8ywAdFgC9gH2ddtJVn0/rkV+n3iiBfAvp3dhqvoXZj9vVt4Rt/ByWXcglr35gL3/xOUUomFzceQKaQoQ6o/pbyK9uPO77nJaXiFR4o2V1eOfGP7N4Ka0mZw+4bjVcF9hioyQfVkMkRVJ7YjCXVj9np0PYO/Hz/njdhu/lnuF231hhEUWxb20yCIMhFUXQR/bVHFMXn60JPFX2ZwP21zBYJXLn2w5Kdj0ebpk4CyrBgLNmVf3wsOfkow4Kx5lVOInwH9aDk552O3969OmPJ0WE653y5TxEaVE2X5o5mtTRZQqUNxJiZ7/htzNKhCg/CnFtUWV6gD5biMsek1ZSlQ60NdBwPHNCJkPvuQrTaSBr5skOPOjwQUxU9ykAfLCXliHY9xswC1PYB1rNROIJCQcjgO/Hv2gJBISd7/W6MWTrqP/coVxZ/jNzbQ7InwEeanNj1mLPyUdon5SptEKbMyhVWc5YOjyb1MVWpo81oRhURjP5Y5eV+n2534N+/M5b8Ys498ByamEiU2kBU4YGYsirzmrJ1eLdrXM2Pni2iyf18i+O3RV9Ouw0voQrxl7bPWG0u6q+rUv8IBIWctt8tRO7twdV1P2MzmDBWqYtosTgmIo62CQvCnFklFrLzUWiDEC02rAXFRLw+HXWzaCpOppD90hpEQ5UVLJUSa3blSaM1Nx/1n4gjRUQY6maxmNIuOfnZlKlDpQ3EXGWirAh0bjtTls5xQuUKlTaQ/G93kr5qIz2OrkCmVFCenotu1wmHjDLQB3MVv1ZUiavS05fRDupE0cFz+LVrhCrUj+IkaetR5MIx6L7ZjmerRk5lKrVBTm1uzpbi6/oJf1WuLnqP2E8XEvnsaAQPNaV7kirzZ+nwbNfkunoFOfsqKx+VizKul/sz/fMaYSMTKdp+zOFX7nC/qAAAIABJREFUj5gILr/4EXJvD2wVpjppK1EUsehKaPT2JFRhgUQveYqUicuw2eOu9jqd+7OqYTjmjFwnGaXWuQ+Ys3Uow5x9qaoXikfLGMqTzgHSAoQyMgSAwMFdEWSyWtf/mm2hI3tTtOMYpiwd3nGNncpUhPhTsu+UI82UpXM5rogWq9PJsilbR0D/zpjzijBezJL0RYZgytLR+LMXUAT5gkx20zFKEezvqJM5txBFkJ+TrqZfPIcyyM9R/4r0LDwa1UNVL8S+NVBAFRF4w7aS+3g6/Q2Re2nI+GwL2GyVvqzlmK28ri0C7+nO+R/3UT+hNfoqslaTBY9g32o2XUNQyGk8LJ59L3zqSGvYvwMd5z2AT4NQLi9YV+m3OhivKn0QhLVMhyC7Lddob4j7Ztea+T/T2oIgNBQEYY8gCEftn6729ARBEHYIgvAFkGxPe0QQhIP21fw1giDIb6L7TbvObYIghNjTPhIE4X7793RBEBbZZZIFQbjZrKSNIAjbBUG4IAjC2Cr2n7R/9xQE4WtBEE4IgvBfQRAOCILQwZVp1VKuD/bqEs7PY1Uq8E7sTOmm3yVxjZqgp0eQ/59Pq2cUXCqroYo3RrgVXS5kqtpesOkgJftOcXnJ58TMffAGelwYYBcR5HJ82kRTmpRKyguf0HDGMDxiwlEG+mAt1DvvXbyRza7KqHIpTuapIeyJQZQePI1Nb3CkV6Rlkjr2NXTf7yZ09MAq2W7etOGT7wdRpHRX5eTNWmrg1Ni3OdJvHlFThyJTK2vwY9X6x3DikVc5MWIxDWbcj0pbfXWoGi59AYJChqZlLIWf/8LFu6dgM1QQPH74TdXV9hnBgoeG0DefR/fGKscfu+sUXp/jFmSqigvIvDwI7d+eXR0nU3jwLDKNkvBh8TdUeU1n2js/ovDzouu2JUQ92R/D5TywiYT0icOSX4Tpau5ftxEIHjWAqy++z8nOT1LwzXa8O7W4cf6b9Kkbyf0ZXb5dWxH6UCKXXpbGE1WIP5ZiPWVV+1UdtJUgl+F1Rww5n2xBf+wCVoOJiEn3/QWdVb4qFXi2bOSY2N5IZVWdMk8NDVfPI+PF9xx9vuJsOv79u9Bq8xvIvT0kf9W2/gIoQwMIHZnI5Zc/sR+vLLPR2rlUnL/ifPLskLlOt4s2VEeHO1bjJaUCHs0bcHHyW5wbOh9FkC+erWNd2nnTbmzXlTppGafvfQZFkC9ebRphLS7j4vw1NF49k6afPGs/wajSr28So/692yOaLdW21dR6zK5SjqBU4N+3Ixc3HqhxvKuJ+FceJ+vAWbIPnnOkpW8+zPqEOeSfuEjIQ32uK/avjwX+vdsj2qxgNd1Qzs3/PW7XFXkPQRCuzV4uiqI4FMgF+oiiWCEIQmPgS+Da5LcT0EoUxYuCIDQHHgS6iaJoFgRhJfAw8EkNZXkBR0VRnCkIwvPAC8AkF3L5oijGCYIwAZgFuN6sLdEauNOu+5ggCD9fd3wCUCiKYmtBEFoBSdcrAJg4cWL7kSNHDu/evXvbRaEtGa8NdmyzuIY5Ox+FNsTxWxEWjCW3cmXB+64OGE+lYtVJK22qqHCU9cKI/mkFCCKKsBDqrV9BxogpWHJc6Mpz3ud7I3xHDEF77yAA9MdTUEcEU2o/pg4PwpTtbLtFV4LCzwvkMrDaUIUHYcopRPt4f8Ie7u3QYynU49FQizLQB3V4EMbrtj+YdaUofD0R5DJEqw11RCDG7AIiR/cjZFAnkMnQnTmCwseTov1n8G7ZAE1EEOpgH1rvX4NMrUTm40nUwieQV7FHGR6MOUey2ZSlQxVRuWKiDA/CcP4qvt1aIyjkxK6bg+HCFfTX3dhmztKhigim4IfdNP54AYJSgTmnAEGpQBUe7JBTaYMw2/0T8tgAtBPuQxHgQ+GGP1BFBCPtsKr0oym3CGt5BV7N6lN6PO26+lf62pilw1xQgq3ciK3cSPH+M8i9NKir1EVQKDBft5/bkp2PMiIEwxHpt0IbjCVHhyhKMWc4Lv0BK930B0HXT+RNZuRBlScL8tBgrLm3Hkco5ER88S4yby/8n3oE06lzdh/YfRUhxYmTvQUlTm0nxZJzvHm1jcWrZTTBw+5Cn5SCf0JbDJfzMOtK0WgDufTBFgI6NiHrW+mk16wrRVnFr5qIQEfsWfUGTk6rvJGs5/FVyH08CejUBL8+nQi4uzuCQoEgl9Fg2XQuTXsbc3Y+qvBgyux5lNrK+KqJoGE9yXhBWt0r2vgHoaMHO44pw4Oq5Tdl5aOKqCxDVSWGgx8bSMijA1FHhVF25JpPLwC165/X8GzegNg3nyb32920/HohAKIAvl1a0u7AagS1EkWANxFThnFh7Bt/qa0EhQJTlg79sQuoIoLI/uAXQh/uUyudzn6q7M8+Ce0x5xVivOQ8kTdn6VBGhABSn1ZqgyrHX4WchqvnUfjDLpShATT9ZRkA5ScuULo3mexv96KJCSdy0n21jlWZWolvt1Yk95mBpVAvHc8uQFDIabR2LgXf70LTKNK5T9hj4fpxRZDLEBSV0wBVRDDqemEUbPi90hfZBQj5JVgKpdFatFhRagMxnE6vcYyy5BehDA3AnFuIdtIwZB4qWmx5G3NOIeb8EiwFzroAin49TNGvhwHocOFzKtKzHLZfq3/Y4/0JebgPyhB/jFfzUEcEowd8OjZDGeJP7PvzkSnlyHw8iXlnGmlTltVqzDZXaYuol8YgUykZ8Plc8o6n4R0RxLU7xuQqBQad6+0rcdOHogn0Yc/cDxxpLR7rTbOHegKQdzyNgL5x0hWtglJUEc7l3koMuMKnYzNkKi9kKk/pxEOQIfcOwarPu2E+N7c/t+uKvEEUxbb2z1B7mhJYJwhCMrAeqLo0dVAUxWu3lycC7YFD9pOBRCDmBmXZgGuP1PgMiK9B7jv7/0eAhjex/0dRFA2iKOYDO5BONKoSD3wFIIriSeAELlixYsWC+Ph4nSiKwx8IjsF30F3ot+13ktFvP4DfUOkmGU2bptj0Zc7bagb3oGTjLsdv4/l0Uro8RGqv0Vzu9xiWnDyuDp+IVVeI8eQ5lFGRKCLDQKHAe0ACZTucy7sRJV9t4HifWRzvM4uCTQcJGd4DAO+4xlhKy50u21+j+I+TBA2W9uGHPpBA4eaDZH+0mXNjlzr0aJ8YgEypwCNai7W03GlbzTWK/jhFyBBpn3f4Awnkbz5MxodbOHb/S5Sdu0L+lsNoRyTgGxeLTCnHkJ7D8bgxnLhzHKkT3qT0j2TSpiyjdO9JAgdJ+7mDh/ekcKv0VJiirYcIul8aqL3immAtKad4x1HU0eHErJqJIS0DVXgQRb8edNikjg6naOtBgu5PwL9vJ0y6YqylZZhzCyk7fgFNdDiq+qEISgWB98Q78hovZWMrqyD5zqco/HkvQfcnSPYM7ITFXn91vWA8G0VQcUUaxAur1F/7QA/yNx8CIH/zIfzubI4glyHzUOEbF4vut6PSVYl6YaCQI/f1Qr/tgJM/S387gP9Q6cZOj7ZNsZWWYckrxJpfiCUrD1V0pOSLrm0wplx2ymsrK0ehDUURqQWFAq/+CZTv2setErxwJoa9R7jSZySZD46nbMcfhNh94G33vdnFdpSSKrEUMrwnhVsOOR0vS0oha+0GkvvMpHDzQbzbNcEvLpaArs0xl5bj1yYG/YUMpzwFf5wmbIi05zbigbvI2SxNRBS+nghK6UJfvUd6kb8nGc8GIVz5dBun48dhzszn6vNrKd17gkvT3gag+NeDBA6TYsizXROspWU33FYDYM4pwPvOVgDIvDwQrTZHzATc3Z3iKvF2szLyP/6F1Mde5H/aO+9wuapyjf/eNAIh9CZKl45CMEgRBdGLimKniVyaolKkiYrcKwgqwsVLCYI0A0JE4FJUFAHpBJEaEqr0IgEkJJBK2nv/WHuSOZPTZpfMKd/vec5zZvbMvHvNnj17vrXWu77v3RcnMuXGexc8r9nvJ8CQ967Ehhcdw9OHncUrp12x4Hv/8sm/Y/rDT/Pw1t/mX/97JfOmzmwTxOf9rFb41Fa8++qbrLDrdsx7ZwbDNluHmU+/3JRmPdPGPZN9/1Zl+S98jAFLDOadm9t+B975W/2x3JB5U2csOJZrnnoY7z7zCv++8A+8+du/8NQuR/DULkcw9e8T0msk1vrJgcx+Y3JT5+qQ967E+47ag3lvT8dz5qHBg1jxC9sz+ab7Weu0Q5n1zCu8fsEfF1xXILsedXBdWXrkRgwYOmTB/ZX3/CSznn2FORMXdqzf/N1NLLXZOgwYOoRhIzdKNrOH/tnpNWrKzfex4m7p2GDzxm/+zOOfOpJ/j7kxaS05hKVHbogGD2L6Q6mzWLPfDFx2GJ47f0GnYKW69//6xX/l0f84mjcuvYmZT73EStl7nHzj/Uyf8DzjP/zNNtdroKlrdv1nMXCZpXjpvy/kmk8dxwt/fZD1v5p++lfZcj3mz5vPu5MXXbOy4V478r4dPsCth/6qzQj6K3dM4JpPHcc1nzqOt558mUFLL8nct6Z2eb1aITsHVurifAV4+eQxzJ38EnMnv8y8qW/gOTP7VBBvu+V/PRX15MZ1hKRptpdu2HYCsDTwfVIHZZbtQZJ2BL5n+3PZ8w4DVrd9bDf3NQ9YwvZcSesCV9seIeli4Hrb/yfpBWCk7TczC8xptnfsQO8E0nE/Prv/W+Bq4JFMbzNJfwDOsH1b9pyHgINsP9CO5C7AGbNfmrj+2/93E5N+fQXL7ZksGlN+/xcAVj3+YIZ9NKXYeu3Y05n1aLpwaugSvP+OS3j2Ewcwf9qMRYQHD5nHmjdewit7HLYg/eRSH92KFX/wbTRwAO9cexNTzr+cZXZPo+zvXPlnBq64PO+7YhQDll4KzzeeMZOXvnAQnp70X5+08GNb5+ffYPmPj2DezHd55shfMf2R5CHe+LLjeOboc5jz+mSWWHPVLL1dSsH19KFn4tlzee8hX2Tl3XbEc+YyeJXl8DwzZ8o0njj8HKY+kqbtPzjmhzx51HnMfn0yQ9dahc3q0i8+dsgoPDtLQXbwrrxnz48zeJVlwWb261N44vBz4LE0qrzxn37BvGmz+OdeJ7DEmquy7jlHM2i5pZnx2PM8d9jpC3V+dhDL7jgipTI7ahQzxj/Lqt/+Amv+937Mnz2HuZOnMvfNt5l23+PMfOollvnYFgxdd3UGrbI8A4YOYfarb/L8d09nxvh0HJbd6UOsccIBMGAgk674GxNHpbXQm919LgOGDF4wQsagAWiJJdDgQXjOXOZNm4XnzwebCfucsuD9b3LekQvSbz5xyFkL2r3GwZ9ntT0/Dp7PxDG38Mr5f2GFT4xg0/OPYMCSSwAw943JTL93PDMffJzJl98AwGonfIelP/Yh5s96l1d/cDqzJmSzAhuvy+onfxcNHsTsl1/j1e+fwfx3pjF8521Z7cffZtCKyzB/1rto8GDmvfkWU6+7kbcv/B3Dd0ujyVOvup6BKy7P6pf/igHDsvNo5kxe+dI3GLLBOqx+8RnM/udzeH66dk0e9RvYanuWy479s0eezfTsGG546XE8972F59L6dencnjnsDDx7LoNXXo7NbvgfBg5fEuabedNnMX7H77LGj77Oil/8KAOWXIJZEycx5f6nefSo89hy9NE8etT5vPv6ZJZcaxU2P++72XF9gUcOORvPnstyI9fnA6MOxvPmM+2f/+LRI89juZHrs/FJ+zJoEEy64hamP/AE7zvxm/z7tzcw6bK/AvC+k77FMtn7ePF7o5g5Ph3TtUcdzdLbbsag5ZdhzptTmPi/l/PWFX9j2FYb874TvgEDB+J35zDp2jtYZb9d0MABTLriFl4bdRUrff3TALyZ7WONn9bt4+hRzKjt4+yjGb7NZgxaIe3j3edeZfB7V8n1/VzvtO+w4me34d1XUiDhefMY/+kfALDuyd9guR1HgMTs1ybx+Jf+q5TPavmdt2LQ8sOZPXESM//5Ms8eeTbvP/uI3JrDP7wx65x0AEPWXI3Xf3Ulr/3yd6y4dzqWk8akY/nek77FMjtsmaWfPIuZE55h2MiNWf/qU5j5xAsL/Nqv/s+lTL3tQVbaf1dWO3IvBiw1lHnvTOfJvX/a1Lm67mkHs8Iu2zD37WkLPNOvnH4lU+99jE2v+zmzJ05asGB09sQ3Gfr+NZg/611eOOqsDq8rMx57fsH9eVOn8+aYmxZco/99WVp/s/7lJzB8601hvnnr+rG8cMSZnV6jBi43nPV+fQxD3rsSs//1Js9++9QFC6nrtSb9aSzPHT4KgBEPXci86TNh3nxeu/RGVv7yDnWpFxf9rDzfKWXupLeZP33Wgmvu8G03Zb1fH8NjOx+54Fg2c80GGDB0CJs/cCHjt/02D05LNpftfrov6+yyVVrkanj37em89diLzJ8/n7uOuZAZr0/hwBcuYdorbzJnekrG8PwN9/PwGdex+cGfY/2vbM/8ufOYO2s2g1+fxFKbrM38me/yXCfXq7bpJ9s/BvOz83XetJlsOf40ADR4KAOWXLbd9JPHHP8L7n94PFOmvMOKKyzHwQfuw1d2/dQiz6tn8ErrtmseXZwMHbpmy4PVWbNeavlxaI++FMifDrxi+5eS9gd+Y1vtBPKbAH8gWWvekLQCMNz2ix3sy8Betn8v6b+AVW0fVjCQ/yJ11prs9hAWBvLHAOva/k7W3keAbTsI5AF4coNdSv8gBw8pZV1wG+oD+TKZNb/TZQ65GD6o4xReeUmnU/lMmzO4dM1Vll60c1cGSy5dvofz9X9Xk4VhyrwhpWuuusTMrp/UJFVdxmfNLd99OaCi70AVDB3cefatvMyaU/5xHTSgnbUiPZR588s3A1R1Xo3TsNI1Pzi/mmtrLZAvm54QyC8xdI2WXzjenfVyy49De/RWj3x7nANcLWk3kl1lentPsv14FpDfJGkAMAc4BGg3kM90NpX0IPA2yV9flPuAPwNrAifZfrUhheY5wCWSxpMC/fHZvoMgCIIgCIIA6KUj8n2dLIvO4Gzh7nrALcAGtjscyowR+RiRL5sYkY8R+RiRjxH5KogR+RiRb5YYke+YvjQi35dYCrhN0mBSHqrvdBbEB0EQBEEQ9FVi0LljIpDPkPQPYImGzfvYnpBTb3/g8IbNY20f0t7z67E9lYWpM4MgCIIgCIJgESKQz7C9dcl6o4HRZWoGQRAEQRAEQY0I5IMgCIIgCIIeS1hrOqa3FoQKgiAIgiAIgn5NjMgHQRAEQRAEPZYYj++YGJEPgiAIgiAIgt6I7fjrZ3/AQf1VM9oabe3v7z/a2ns0o63R1t70/uOvNX8xIt8/Oagfa1alG23tPW3t7++/Kt3+3tb+/v6r0u3vbe1N7z9oARHIB0EQBEEQBEEvJAL5IAiCIAiCIOiFRCDfPzm/H2tWpRtt7T1t7e/vvyrd/t7W/v7+q9Lt723tTe8/aAHKFj0EQRAEQRAEQdCLiBH5IAiCIAiCIOiFRCAfBEEQBEEQBL2QCOSDIAh6KJKWl/TBghoDJR1ZVps62EfhdgZBEATNE4F8EBRA0pKSNmx1O1pB9t6V3V5P0i6SBrW6Xa1A0oqSviTpQyVo3S5pGUkrAI8AoyX9b1492/OALxRtVyNlt7NBe3tJ+2e3V5a0Thm6PZ3F0ekKgqBvEYtd+zCSpgIdfsC2l6lgn/vbHl3g9RsB7wX+YXta3fZP2/5rTs0PA7Z9v6RNgE8DT9r+S952Zrq7AqcBQ2yvI2kL4ETbny+im2lvD6xve7SklYGlbT9fUHNz4KPZ3btsP1JQ7wHgY8CywP3Aw8Bk2/+ZU+9U4Dnbv27YfiSwmu0fFGjrCOB7wMbZpgeA/7H9tKRBtuc2qXc98EPbj0p6D/BQprkecL7tMwq09WHbIyR9A1jD9vGSxtvOPeIt6Wekz+kKYHptu+2HelI7M93jgZHAhrY3kLQ6cJXtjxTQXBn4JrA2sKCzafuAnHoT6PzaWuSzut32jnlf34W2gL2BdW2fKGlN0nfrvhxaR3X2uO1CnTpJI4HjgLVIn5mSbKFjuxRwNLCm7W9KWp90nl1fQLO0Y5rpjaLzc+u7+VoKkrZsZ/PbwIvNXgODnkO/HD3rL9geDiDpROA14FLSxXBvYHhFu/0JkCuQl/Rd4BDgCeAiSYfb/kP28M+BpgP5LCj4DDBI0s3A1sDtwA8ljbD9szxtzTgB+HCmh+1xktYuoAe0DWRIx3IwcBlQJJA5nBTIXJNtukzS+bZHFWjqANszJB0AnG37F5LGFdD7HLBZO9vPBMYDuQJ5SV8BTiGdQ6eQvgNbAldJ+g7wU+ATTcquY/vR7Pb+wM22/1PScGAskDuQJ52r7wF2JwUyZbBd9v/Eum0GdiqgWUU7Ab4EjCB1jrD9anZci/AH4C7gb8C8glqQzlVI1ytI11ZI19YZBbXHSjqbEjtddZwDzCd97icCU4Grga1yaFX1G1JjDHAMMIHU5jIYDTwIbJvdfwW4CsgdyFPuMYU0IADper8J6TwA2I3U9iKcQ7r2jSddBzfLbq8o6du2byqoH7QC2/HXx/9Io9tdbmtCb3wHfxOAdwvoTiCNPEMaOXsAODy7/3ABzYHAUsA7wDLZ9iWB8WUc1/q2FdXMNMaRLrKl6Wafz7C6+8NK0BxH+rG6B9isdrwL6D2W57Fuvve129m+NjAL+Hme9153+xZgz/Yey9ne3bI2n5PdXxe4uuh5VfYf8NUq2gncl/1/KPtfyrla0TEY251tTWre1s7frSW1t3ZM668tj7T6XOqgrXdXoPlA2e+/qmOafe6D6+4PBm4rqPl7YNO6+5uQOjfrVvUdib/q/2JEvn8wT9LepC+xgb0oNiq1KvApYHLDdpGCurwMdGansf2CpB2B/5O0Vqadh7lOHuEZkp61/U6mP1NS0VGeRyV9DRiYTdF+l2Lvv8Zs25ZkAEnDStAUbT/zeeQ/pjWOIs3A/NnJYrIuadQzLzMkrW/76fqN2bGdWUB3kO0XGjdm59iLtn+UQ/NlSYeRRvS2JJstkrQk6Qc3N7avIo0S1u4/B3wlj1ZV9gdJA0l2mgU2hyLtbOBKSecBy0n6JnAAcEFBzesl7eKCdrp2GCZpe9t3A0jajtTxyI3tj5fSsvaZk312tWvLyuQc7ZZ0VmePu4AFJON4SReSOsrv1ule0/FLumR29h2tvf/16rVzUtoxbWB10qzHW9n9pbNtRdjI9mO1O7Yfz2amn8uWOwW9kAjk+wdfI9kTziRdbMZm2/JyPWnkfBEbhaTbC+i+JmmLmq7taZI+B/wG+EBOzdmSlrI9A1iwEFHSshS/2B5GshS8C1wO3AicVFATqglkRgP/kHRtdv+LwEVFBG3fCtwqaYns/nPAwQUkfwzcIOmnLJxCHgkcCxxRQHeOpDVtv1S/Mesg5v0RP5A0jf5JYA/bU7Lt25DTWlbXrlNJdp+ZpA7C5sARti/LIXcaaebkBtJ7LeXX2vY8SZ8HTi9Dr0H7NEn/QZpB2xD4se2bC8oeDvxI0mxgNgv91kXXCR0I/Ca7ngBMIX1fcyNpVZINbHXbn8nW9Wxru9D3NeMs4FpglWzdxFeB/8qpVdTm0RX7AxuROsa1a7VZaA/Mw/Gk79QaksaQ7Cv7FdCDco9pPb8AHpZ0W3Z/B5KdswhPSTqXNKgHsAfwz+waPqegdtAiYrFrUBmSlrfdOGrf2fPfRxpBf62dxz5ie2yzupKWsL1IsCZpJeA9tifkaWvVZIHMzqSA48YSAhmUMqp8JNO80/bDBfU+TOoMLGt7zWwx7TdsH1ZAczOSL7bmlX8UOK32OeXU/CJwKik4epAUDGwF/BD4ge3r8mp3Y9+jmj0eksbZ3kLSl0gdriNJU+qb59j/FsCepAXeD5I6nLe4hAt/RQtoB5LO908Wbd/iRNIypN/Tt0vQuoHUGTzO9uZKmaAetp13MKNRfyPSmhCRzoUnytAtG0kTynrPDborkjrcAu61/WYJmpUcU0mrkdZ1QbJyLvLb2KTekqTBlu1Jbb2b5JufBSzlugQTQe8hAvl+gKQNgHOBVW1vppTv+fO2f1rxfh+y3d4q+R6n24ympD/ReVaBwllregOS7iWN6Fxne0S27VHb7S1YLXO/eYLjzUnZKjYl/YA9CvzSBTP3dGO/TZ+rkh6zvamkC0ie879KeiRPIN+gux3JVvdJUgfmjwX1bmtns20XWUCLpD8C+5QRFNdp1hb5r2P7JElrkDryeTOLfN32ZR1Zl/JaljLt+21vpSwrULZtnO0tCmiu0Nnjtt/q7PEutFcmLUTfBBhap1n0PLgAON3240V0GjRLy9pS1TGVtJHtJztoa6GOctA3CWtN/+AC0ijneQC2x0v6HWn6vkqqMt1VoduM5mkV7L+zdKG5bQANmqq7PYiUNrPINWCA7RcbvJVlZATpiqaz92QBe6dpMfN0ECriT5KeJFlrDs4CpVlFBDONESSL2ivAG0UbWaGXexYwQSnLVP1IfxHPdX1mkZOAacCvyJ9ZpOaDryJzy/Rs1Ljmud6GFGwWoTYTVfuyNl4T1i2gPYY0K/NZ4NvAvsC/C+jV2B7YV9LzLLSF2cXSm5aZtaWqY3oUcBDwy3Yey5VpStKVtndXB2lTCx7ToMVEIN8/WMr2fQ0B1+LIGVvVdE8Vut3WtH1HBfvHWbrQKjWV0vgdDHyL5OsswsuZvcaZJeIw4J8FNVtJ7vSeZWL7h5JOAd7JvOjTyVnQSamo0h6kkdL/A3a3XSiIr2oBbR1/zv7KZGvbW0p6GMD2ZElD8orZrg2K/KSsBtZxFPBHYD1JY4GVSb7r3NiusqDWirZr6YLvAO6QVMY18tMlaDTyAnBgbcFntv7gGFLn7hqg24F8VcfU9kHZ/zI7yodn/z/X6bOCXkkE8v2DN7PV+bURnq8CE1vbpN6PUjaVk1l0SrnI6Fa9/ioNui918vSutJYjLRj9T+B3wFa2JxVs4ndIC72L3jHLAAAgAElEQVTWBF4n5ej+TkHNvka3Z3ok7WT7VklfrttW/5Q8i/wuIqVgfYmUaWrnes2cNrBKFtDWtemSMvUyKsksolRx9jAWLTSV215n+yFJO5AW+gp4ynYpCxEl3WL7E11ta5Ja2yZK+izwKvC+Ano1qhiwKT1rS0XHtLZe5LMsem413VG2PTH7/2Lmu/8w6fjeX9R3H7SeCOT7B4cA5wMbSfoX8DzJL1o1fdVaU2M0KQvC6cDHSVkWCrctywbyS1KqsTdIlQ2fIPm7m9VaieQN34OU/WdEUe+xpG1s35uN7O5ZRCtvE1qwz7yc2cRzdwBuBXZt57G82TqqsL9sSfrcP0vJC2ihsg5yVZlFriN1lv5EwY5BfQeugQ0kFUq7KGkoyQ60kqTlWfgdWobiKQ1/qpS152hgVKZ5ZEFNSLMyNevKUGAd4ClyXAfr+GdZWVsqPqaQzqlZlFgQS6kK849J1xkBoySdaPs3ZegHrSEWu/YDJA3MpuiHkXzNU0vSXQ94xfa7SjnfPwj81lkqPkkr5FnwU4VuRZoP2v5QfXYFSXfZ/mgzOu3oPkLyQf7N9ghJHwf2qk25Nqk1neRXHU2qONiGPKM7mT3hPtKCySldPb9sJO1n++IKdBcsLuzGc/vUgmdJV9tuOgd82QtoM827WdhB3pWsg2z7+IK6pWcWkfQP21t3/cxuadXSlq5CqsR7a3b/48DttjsK9LujfThpRm514F8sDDrfAS6wfXZe7cVFtvjzW7a/VUCjtKwtVR9TSePL9q5LegrYrjYbm63FuMf2hmXuJ1i8RCDfD5D0Eil37hWkCoFljZyNI+X5XpuUQ/2PwIa2d+lpuhVpjgU+SvIe30q6mP+i6EVR0gO2R2YB/Qjb8yXdZ/vDObROoPOAs2mPr6QBpOJXBwMn2b60i5d0V7elwXEzHYTM+gDwZWA1oJbjfS/gBecoMrUYvOed7bvbnZi616wM7E6qRDsH+G/b95bQllI7yNn5Ot4VZFNSKgi3PslbXV+0qEgKzuuBb9bsEJLeA/yqSCBfp/1d22c1bGs3RW8TmqXbizrZV+6MZZlV5RLbXy+5TaUf00zjFFKHs5kFuF1p3gJ8xvbs7P4Q4C/uZeleg7aEtaZ/sCFpZOsQ4KLsh+L3zqoRFmC+7blK+a7PsD2qtpisB+pWoXkEsBQpqD2JNIq+b0FNgCmSlgbuBMZIeoOci5Ntn9Cd50k61vbJ3dScD5wh6Sbg75LOYeEUuJ2/yE4tG1C7wXFOzW53EJoZ5c8W9SHpJNsfq3voT5LuzNnUSr3nXdDtzr0qWEDbwKws+H5a0qGkDvIqecWyjvAjaqcoWAl8ANiH9N2vL1pUJPXi2rUgPuN1YIMCevXsR7IZ1fN3kl0qL6XZi+pp6NgOILUxdzacbFZ6ZUlDaoFsSexH+ccU4F7g2uy7MIdi2ctqx/JfpMKAfyCdp18gza4GvZgI5PsBtmcCV5Iqhi5P8u3eAQwsKD1H0l6k4LXm6y1Unr5C3dI1bd+f3ZxGmv4viy+QpnqPJK1lWJZURbRKdiP5kruFpANJBZWOI40WFp7lqSg4hoo6CBkrS1rXqaptbXRy5ZxalXrPS6SKBbT1VNFBfg/wmKT7aJvSsmhbvwSsW3JgeLukG0mfv0nnRHs5+7tNtsDxvcCSkkbQ1s+9VBFtYFbjiHRJ1GfcmkvyzF9dUPMFYKxSrYL68yCPxbDKYwppndS2wIQSrgG1Y/ls9lfjDwV1gx5ABPL9hMwKsAfwGeB+0pR4UfYn5Q3+me3nsyAmTxn5xaFbuqakkaRAdi3aTikX8jXanp7pL0Ma5VocNJNd5R7SD+JHK8p4UGZwXGUHAVJn63ZJz2X31yal9mwa2+NII/I/rPOej5JUive8C5oZ/a8qfzzQfgdZ0loFZatIEwnwCLAcJeTlr2H7UKWFrzUr0fm2i6aK/RRp5Ph9QH3QOhVo2gbWwJmSjqdEe1H2+io+s1ezvwEUrwFQ5TEFeBp4tKRBkm4dS/WcWhpBE4RHvh+gVFBjHGlU/o+1QLHkfSwPrGF7fE/XLUszWzh0DA1ZBWy/WFD3W6QR+JmZbm1KtZS0lh3ss5nKtv9h++ZuPK/bdp2G132alGWpTXBs+8ZmtRp0nwA+29BB+IvtjQvqLgFslN19sgRvbFXe8yWBNW0/1c5jO5fpxc00m15AK2lb0ijnnbbfUKpC/UNSp3GNMttXBpJuJy2cv5+2QWyPXOws6Su2i45qN2qeTLIXPUudvcg5K7tKOsP2ER1Z4nrasa3imGa6F5OKStWsdkDla2UqqcYeVEsE8v0AScvYfqcC3duBz5NGo8eR/It32O500V4rdCvSvNv29nlf34nu08C2tt8sW7uTfTa92LEbmkUWppUaHGeapXcQJC1FKuCzlu1vKqVO3ND29Tm0Gr3nV5blPZe0K8liNMT2OpK2AE6sMihq9pyS9D+kgjXjgPcD15MWVP8cOM9209Vt1XG1ZAAKrOeo6e/Q3nbnKBrXSVuLrj1B0tdtXybp6Pb2USQ4VKpC/MGy7EWSPmT7wTKPbZ32ysD3SSks61Ob5qmWWtkxzfTbzdJU0UxFbZ8RyPdCwlrTh5H0fdunAj+T1N6FpkjJc4Blbb+jlJt2tO3jJZUxcl6FbhWax0u6ELiFtiMmufM9ZzwLzCio0SxXVaCZa7Fme8GxpFzBcT22/5oF2mV2EEaT/OzbZvdfIR3LPG2t0nt+AqkIzO2Z1jhJaxfQ6w7NjhJ9lpSlaVY2a/YqKUB8OncDssrGkk4EXgMuJZ2Xe1PcWtFlUCnp77a37ew5dVqlV3auY1j2f+kKtEu1F9l+MPtfRQXtMaTsbZ8jWS33Jf8C2iqPaZcBe9hgghoRyPdtanmSH6hIf5BSarTdSV7xnqxbheb+pKBwMG0zVhQN5I8F7pH0D9p2EHJ3vLKRqG+yaIq4A7L/P8+r3Ql5p/vKDI4XUFEHYT3be2QLqbE9U8pZIrJa7/lc22/nb9piYWZt1N32ZElPFQniG/iU2+Z7Pzf7fp1akn5HDO36KYsiaXtgfdujlYq6Dbf9fN5G2D4v+1/FaO6qwJOSSrEXSZpA57MoRdYgrWj7IkmHZx2FOyTl6jBUfEy7w0cq0OzRF4igfSKQ78PYri2UHG+7jLSQjZxIysl+t+37Ja1LWqDTE3Wr0NzcWZ7rkjmPlJe+tIp+pOwEdwF/A+aVpNkVeX8UygyO66migzA7854bQKnwWK5R/u6OQObxngOPKuU8H5jNSnwXuKfZNjZJs5/ZekrZRGqsXX+/4IzEPEl7kyp6mrSQeHF8D5ruzGaWipGktMGjgSGkhfm5AzdJnWaVKTg7W6hQVzt8Lvt/SPa/Vqdib4rPVNaqt06U9FnSrM/78ghVfExbRTOVqIMeQnjk+wGSbiOlX7uKlD/+sRY3qU8g6QLgdNuPl6x7j+3tStYcZ3uLMjW7sc8f5RnpV8qK8wlgrO0ts+D4cucoiNWgWyu0tcC7LekR25sX0PwP4L+ATUhZOz4C7Gf79iJt7WKfeYo3LUWaidqZFGDfSCrm1bTvvEG3tAW0HXmiaxT0Rq9NClI+QgquxwJH2H4hr2Y399u051ipeN0I4KG687RQlU9JnabvtH1JAe1hpNmU+ZI2IM1S3mB7Thcv7Up3rO2PdLWtSc3PkQY01gBGkVJF/sQ5skJVeUy7uf8859YGpAQNjZnWitQ9CFpMBPL9BKWct7uTFtMtA1xh+6cFNYcCB7LowqEDeppuRZpPAOsBz7OwgI8LTv0i6WfAi6TUk/VT1W8V0PwpqRT3X4q0rUGzU7tOAd1KguMKOwgrAtuQPv97XfEi5Z6yIK0VC2iz/eaZkWgJOTtd99n+cO1zzgLlvxe9rnRz3037riU9SEqVuTypiNEDwAzbexdsyzjgUGeFC5VSsp6zuAckilKVlz3nufUI8GvSzOSCGanauoSgdxLWmn6CU77vs7LR+e8DPwYKBfKkKc8nSQvzTiRNfT7R6Stap1uF5qcLvr4jvpb9P7Zum0mpyPJyOPAjSe9SsEpgHZXYdWzfLOkhFgbHh5cUHB8P/BVYQ9IYsg5CEUFJJ9r+MalYDZIGSBpTNIgpC3Wzqm1OTmDxL6CFJr4HkkbR+fuv2v6wT47XXCnpPGA5Sd8EDgAuKLdZHZJntFu2ZygViRtl+9QsCC/KgcBvJC1L+gzfJh2L5hvY2vOgCi875LPBzLV9buktCVpKBPL9AEkbk0bivwpMIvlEjy5B+v22d5P0BduXSPodacq+J+qWrmn7xYZFaStTQgYD2+t09ri6mce9QbOKjBhL2f5B2aJVBccVdRDWVJYvXyll5lVAoUI43aAZ7/lpXT8lN61aQNvMNHJVC/0BUCrcdAqwCulzadNBtv1os5q2T8tmpd4h+eR/3Oz3fTEjpfz/e5OCbyheNbw2Sry5UmE82X67gFyl50EVdGWDsX1xDtk/SToYuJaSZnuD1hOBfP9gNKnc9862Xy1Rt+aBnCJpM1J6t7V7qG7pmu0sShtMwUVp3eQUoFs/7JI2sv2kpHatGC5WffF6SbuUadfJqCQ4rqiDsD8wRtKxpKwzN9g+vYS2dug9B7rdefLCqraH224zgifpcKBIir9WLKBtiqp9yqSsN7vaLmMmcgFZ4N7ud1xNpLRcTBxOmj281vZjSokEbisqKmlVUg2B1W1/RtImpPoaFzWr1d3zoCobTE6uItlgLqC8Gc+ar/+Yum1FZ3uDFhMe+T6OpIHAb6uY6lfKyX41qbLhaNJo9I9t/7qn6VakWfqitG7ut9veSEnn2z4os1Q14iKLnJQK2AwjjeyUZddBaYh3DClrT5nB8cXAU40dBNsn5NCq7xgNJmUaGkvKBV+og1SF97w9X30ej23D6ytZQNuN/TZz/ldaJbTo4suc+yy9eFuV2nmDY0k3kK7Vx9neXNIg4GFXkymsts/S15/kPaaSHrT9oTLbEvRNIpDvB0j6K/B5l1R5L0i0alFaT1nsWDZVBseZfmkdhA46RjWKdpAeBHYCbi/aQVRK4fk1YHvSeoYaw4F5tj+Zt51V0tmMhJrIhqMKq4Rm+mcCqwHXUW5RuM72Wdn3X9J+OS0bnWnmaq+k+21vpbZZpirNvlVRIJ/rmEo6gVRkq7ANRtJOtm/NrGCLUOX5GlRPWGv6By8CY5VyMk+vbXTOEtKSjurs8Z6kW1VbM1q5KK0plLL2HEwK6EwK6n6dZ/S0QrvOLxvuTyZlrvklqc25guOGdp7Jwg7CHZK2zNNe2x+XNADYzfYVedrVCWV6z+8BJgIr0fb4TgVyVTbuaHS7Rgmj3AtmJIBFZiS6G8Rnz62ySiikDGAzSLMSC3ZL8aJwlVCR77oqpitlhKrVaNiGtOC1R1HhMS3TBrMDqTbJru081mPP16B7RCDfP3g1+xtACWXJS9JYXLqla0pawva7LVyU9kKO1/yWFLyNyu7vRcrks1sOraOAg1g08IYCAXeFwXElHQSnvNmHkEq+l0lp3nPbL5I68mV6qqtcQAsVZMOR9JFMtxZs1WxghbzBtvcv8vqcFOnhVeG7roqjgD+SCoWNBVYmJWyokjzHtpJj6i6SHjSpdXz2vxXna1AxYa0Jgiaps9JcajtPermu9E8iFSmZm91fBjizyEVY7RQ+am9bT0DSnbY/VrJmJaPnkv4bmEkK5utnu4rk/C/Ney7pbtvbZ+sZ6i/2hdczdLSAtnFbDt1/2N66wVJRtCDSk8CRLJo/e1JOve87pVlsN62hK0xnKGkz58iGk712sfuui/juM1/8hqTz9SkXLDLVjf01bYMp+5hWaYPJZjiOZ+HM7N2k2a5c34OgZxAj8v2AzM/b3o9NoWpuki4hpfCbkt1fHvilixcEKl23ZM0hSlX9tmvvYluC33AQ8A9J+5P8t6NYOJKel4clbWP7XgBJW5PsJbkp067TwM2SvkeJwXGFo+e18+eQum2FskDYnkEK5I8r0K4awzLNKma79mXRXNb7tbOtWarIhvO27RsKatRTy1JTelpDVZDSso5WpB/MdT4oJWrYhYUF53aWVMgOWZENpuxjWqUN5vfAnUCtoNrepGtij1wrE3SPGJHvB0iqHy0YSvoSz7X9/YK6i4y0lJH1oArdMjWVcsfvTaqU21ja20U7Mtk+Pkmq7DoZ+JjtZ3LqTCBd/AeTRrZeyu6vBTxue7MCbbySZNe5LNu0F7C87Tx2nXrd59vZXNgGUcXoeZlU4T2vaPFepQtoS56RqL333Um5za+hbbBVSs7/bNbMtqeWoPUMFaS0zLRL/251FRwX0P0LMIu0OH1+ne5PCmiWXtm0qutVFbQ3eyDpAdsjW9WmoDgRyPdTJN1hu91MDk1oPALsaHtydn8F4A4XTA9WhW5Fmge6k5zGylG4KXvdx4BzSQHyB4AVgAOcowaApLU6ezzzUOeiN9l1oNIOwmYkz/3QOtHf5tDp9PuYZ8GmpFeADkcw84xuZufUOsDJwA/rHpoKjK9ZwnoCqjC7UKY/kpQicTip0zGF9F0tEhgu9pSWRagiOM50S0/l2wprUV6qsMFIOo00i3RltumrwKY1D33QOwlrTT8gC1prDCAVMVqtBOlfAn+XdBXpQrM78LMeqlu6ZmdBfEa3Czc1cBrJz/04LJhqvxXYqFmhxkBd0irUBZwFKd2uU6Os4LieMheP1VAqCrYjqa1/AT5D+sFtuq2upnjTQFLNhNLKr7qaBbSVzEjY/nihRnXNb4CDbd8FC2brRpPqVeTlAUlXUGJKyyp916TZ3XMLvL4jblATqUa7SWk2mIqPKZRog6lbIyPSIuJLs4cGAtNIHYaglxIj8v2AbCSy9kHPJWU9OdH23SVob0LK+iHgllrw2RN1q2prJ/vLa90ZaHtew7YVC47EfJ7UmVmdlJt4LeAJ25vm0KrMrpPptxsc2y6csaLsDkJ2LDYnFarZXKka5YW22/O3dleztOJNFVlrKllAW8WMRJ32z4FTG9bIHG37v/JqZjqLjJ4XHVGXNLqdzYUse5J+Yvv4irRPoKR85w26XyLNSg6gpIJzZc7KVXlMM/3FboORtKntx6rSD6ohAvk+jKStgJdtv5bd35fUu38BOCHvhTZb5Pht4P0k/+JFZUynV6FbVVu7ue+8hVBqpcnfa/vTKlCavE7zEVIn5m+2R0j6OLCX7YNyaFVm18n0Sw+OM93SOwhaWBTsQVKRqanAozk7SKV7z8tYs7I4NBv0S8+G08EamdydnDrv/T7AUsDlpE7NHsBk22UsVO4VVGhZew74IjDB/TBQaYUNpoqOf1A9Ya3p25xHNg2X+a5PBg4DtgDOJ39O3ktIIyR3kYKhjYEjija2It2q2lolF5OVJs/u/5M0pZo7kAfm2J4kaYCkAbZvk3RKHqGK7ToAM52yzMzNFhG+QYEsMHV8lYUdhP1rHYSCmg9IWo6UQ/pB0jT1fTm1Si/eBHwi5+s6o+qgqopsOAOV1X8AUKocu0QBvcbaBPXBVa7jo8WQ0rIK33UVlrWMp0md4sLnW5U2mLKPaYttMKVZ8ILFRwTyfZuBdaPuewDn274auFrSuAK6mzhbJCrpIvIHLotDt6q2docXcr5uJdtXSjoWwPZcSUULjUyRtDTJczlG0hskm1VuOrLrAE2PRjdQZnBcT+kdBNsHZzd/LemvwDK2cwXdVXjPi9obOmAVdVIx2fkrO9dmJNZRqkJdYzhQNM/1ZcAtmQ3CpLShl+QV6673XtK+tru7n8pSWtZRpu+6ao/4ROB2STfQ1rKT5/zqNSkd3c1UsRXZYPrdzEdfIAL5vs1ASYMyK8knSNU4axT57BcU5ciCzAJSletW1VbUReEm2+3+wHWD0kqTS3o/sCrwBVLqxSNJPzRrkWZninASsA0Ndp2CmqUGxw1U0kHIApn60bhcba3Ke14BpS+gzahiRgKAbKR7PCm4Eimd5Y1FNLvJ4XSzw2D7T9n/S2DB9cQuIaVlHSvYPqnu/k8lfTGnVpXBMcDz2d+Q7C83rrayaZnHtBkuBcIGE4RHvi8j6ThSQY03gTWBLW07C+4uybsgKxsdruXhFrAkMIPii91K162qrZn2yaRc120KN9k+O69mprtlprUZ8ChZafI8wayk64EfNb5WKW3e8UV857WFV5n/fkQ22n2f7Q/n1azTbhMc2762qGaD/tqU0EGQdA5p/cXl2aY9gGdtH9LxqzrUqtR7XhZ90Ucr6e+2S83Ck+k2/ZmqgpSWddp9Jv2gpFG2mxqMqMJa1KpjWtH6l3ttb1OmZlA9Ecj3cbLR3PcAN9menm3bAFjaJRVD6c+opMJNmdaCxclKpcm/RZqufRz4cR6bhKRH3UEWGUkTXCyP/t9Ii9FOJo2ivgFsZXu7vJqZbmnBcTvapXYQJD0GbFbz8UoaQFqcl2exa68IkKvqcLRyRqLC99T0Z5rNHBzitiktz3GBnOoNvuthLMz3PhCYVnBAo/TguJv7zXNsbybZYGpF7PYm1RcpmtKx1GPazf3nef8ived1bZ8oaU1gNduL03IalExYa/o4znJ8N2z7Zyva0tfIFhCfCZxIKtx0tqRchZsyFixOBrYjLXYtuji5s0WoS+bQq9quA2nKvj44voSUcagQ7XQQviXpkwU7CE+RZrtqC4DXIL8NpBLveQVUsYAWUjDUbY9wyVQ1opXHfjS1FsQD2L47CxpzU7HvulSPeMWUZoNpsZc9L+eQquTuRPrdmgpcDWzVykYFxYhAPgjyU1rhpowqFiffL+mbti+o3yjpQJJPPA9nkOw6NcvSfOCSzBJwAu17ZpuhzOC4ntI6CFpYvGhZ4AlJ92X3tyZ5vfNQlfe8VCpaQAt9c6FdtwukaWFKy/sknUfblJa3l9+0dsnju26VRzwPt0nak7Y2mD9XvM+qvOyzc7xma9tbSnoYwPZkSYXWHwStJwL5IMjPtq4r3GT7Gkm5i9ZQzeLkI4BrJe3NwsB9JGnx2Jdyaq7dnrfc9gOZ9zwXFQXH9ZTZQTithPY0MtH2iRXo9hZaOSORq/OkhTUfVrf9GTXUfLB9aBNypae0zEGe49CK4BiaaKt6YUrHrmwwOb3scyQNZGEihZVJAzFBLyYC+SDIz0pKFSPbFG4if773y4E7JL1JsqzUPLLvJ2fWGtuvA9tlGWVqXvk/2741ZxuhArtORhXBcSUdBDdUGc0yjBS9nvbokfjFQCtnJPbJ+bqLKanmg6tJadl0M7r7xBYHx9BEXYEW22DydsKqsMGcRarAu4qkn5E6XYWqGwetJxa7BkFOlPIbjwaOc6pAOohUbKjIAtIevzhZ0uXArR3YdXa2vUdJ+2kTHOe1dEjaobPHG4PyJrUPIqXhnEn60a0tzMxT8n2FCm0rPZ4qF/tmtrdTgFVIn1EpC2gl3W97q/rFspLG2d6icKM73meVx6l07bzBcXbdO4a09qb+OrBTic1r3GcV7z9vhe+HajaYunPrEdubF2zPRqQZXwG32H6ii5cEPZwYkQ+C/JReuKmXLE6uwq6zgI6CY3IWb6po9LzGMaRUc28WFerPQXxGlSPxpwK7VhC0lFbzoQmqPE55fNddkdcjfhXwa1Ldh6IF8bpLFcc27zEt1QaTZdQan2UxezKvTtDziEA+CPLTih/xllORXaee0oLjesruIGQ8S6pLEBSnqmw4AK9XNPJ4FPBHYD1JY8lqPlSwn3pyT6NLuhr4DXCD7UWCwopyiOcNjufaPrfUlnRN08e2Ii87lGyDcarz8YikNW2/lFcn6HmEtSYIcqISCzcFC1Gq5vpl26UGyJKeJi1ELK2DIGkEyV71D9qWkf9uWfsIiiPpTFLRtuto+zkVrT5KZqnbkBSwPmV7ThcvKbq/3Dnvlepe7E+qyHwVcLHtSkdnC1hLTiDVpriWtp9ZZTNXOXOzn0vmZbe9saTlSdbIwikdy7bBSLqV5LG/j4WFErH9+SK6QWuJEfkgaBItLNz0UOa/rhVuugl4paWN6xscC9wjqezguIrR8/NIKUcnENkfejLLkD77neu2GSgUyGfe+3o2kPQ2qSjYG0W0O6HbKS0bsf034G+SlgX2Am6W9DLJvnJZ1Z2QJtk3+39M3baiM2hd0SNSOlZog/lJiVpBDyEC+SBonioKNwULqSo4rqKDMNd2hykTg56B7f0rkj6QlKnqtuz+jsC9pID+RNuXdvTCjig5pWV7+isCXydl6nkYGEOqyrpv1v6yyeURt71O2Q2pyFpUekrHqmwwRRb2Bz2XCOSDoHmqKNwULKSq4LiKDsJtmff+Tyym6f+g+0j6vu1TJY2iHf9zCbM884GNs3UjtSD8XFJq0ztZmJKxGS6mpJSWjUi6hlSw7lLS4t+J2UNXSHogp2apwbGknWzf2s5sR02vyCzKuSRr0VmSyrIWVZXS8T3AY1m63FJsMNk6rlHAxqTkBAOB6UWzNwWtJQL5IGieKgo3BQupKjiuooPwtez/sXXbqp7+D7pPzVOcK0jtBmvXgviMN4ANbL8lKa9NpfRsWHWc3dGidNsjc2qWHRzvQOpwt1chupAdqgprke0xkh5koZf9iyUtrK7CBnM2sCdpfcRI4D+B9SvYT7AYicWuQdAkko4DdgHeJFUK3dK2s8JNl9j+SEsb2MuR9Hw7m3PlZm/Q/RmpqmuMnvdTstSjtj21JL1zSNeAq7JNXyGtkzkGuN7dLPLUoHl7pnNz5r3eBjjFdqf1ELrQbHd0u0ZJi35rwfFxQE/13Tdai15lobXoA7Z3bFKr3sve45H0gO2Rksbb/mC27R7b27W6bUF+IpAPghz0hsJNQVvK7CDULBvZ7d1sX1X32M9t/6hAU4OSkTSSZFcZTho1nQIcYPvBTl/Yta6AL5MCQYBJwHtsH1JAs/RsWJJGd/KwbR+QVzvTLy04btA8PtMxcDdwou1JBdpZby26uM5atCDIzaE5Bji27JSOVfCb3WoAAAmgSURBVNhgJN1JWt91IfAaMBHYr2iRqaC1RCAfBEGPoDcFx/Vp6hpT1lVZeTPIh6TxwCG278rubw+cUxuVLKi9BclitTvwPHC17bMLai7WlJZFqCI4zl57M2mdwWXZpr2BHW1/suNXdam5U0fWogKalaR0zNYsLGKDKXIdlLQW8DqpY3AksCzpe/BMkbYGrSUC+SAIegRVBcdVdBDUtmx6m7zeRfJ8B9UgaWyj5a29bU3obUAKsvYijcJfAXzP9loltLU9G0yhlJaSvm77MkntrhGx/b95dDPt0oPjTPdB2x9q2JZ31Lwya1GWgrg9zUIZYsq0wZSd/SboWcTCvCAIegrq4HZ795thT+DU7PaxLPQzA3wayDPC5Q5ut3c/aBGZTQXgPknnAZeTPp89gNsLSD8J3EXK/PJMtq8jC+jVU3pKS2BY9n948eYl6oPj9gLlEnz3t0naE7gyu/9V4M85tdpbOFuj6ALaqlI6zsjy0Y+TdCrJBjOsi9d0xHVAbZDkattfKamNQQ8gAvkgCHoKVQXHVXQQNpf0Tvb6JbPbNb2hOTWD8vllw/3j624XOae+Quog3qZUifj3FOts1lN6Skvb52X/y8yEUklwLGlq9noBR7Hw/Q4EptH2M+wWrq6OQJUpHfcBBgCHkmwwa5DOu1zNrLsdGbX6GBHIB0HQU6gqOC69g2B7YP7mBIuL7maNkbSv7Uua0L0WuFbSMOCLpEBrVUnnAtfavilXgxNVpLQEQNI6pOJ1a1P3+5/Hz11VcGy7W7MGkja1/Vg3n1uZtYiSUzrWbDC2X8w2zaJ4KsrOroFBLycC+SAIegQVBscxeh50xeFAtwP5GlnGqjHAGEkrALsBPwSKBPJ3Sbqetikt78w6DVMK6EKyWFxESsFaqChaxcFxd7iUzC7SDUq3FtVj+xlJA23PA0ZLuqeAXBU2mM6ugS5h9iBoIRHIB0HQp4nR86AbFLbFZPUIzsv+inAIbVNa3kdKaTkdaDovfQOzbJ9VUKNGpcFxN+j2Z1aRtahGmV52qMAGE9fAvk0E8kEQBEF/p8fYDbLics+SPPELUlqWJH+mpONJMwb1RdGarn1RcXDcrSY0+4IyrUV1lOllh7DBBE0SgXwQBEHQ3ylroWr+BrSf0lJ5qsN2wgdIgedOLLTWOLufi4qC46oo01pUhZcdwgYTNEkE8kEQBEF/Z2yrG0C1KS1rfAlY1/bsEjVLC46bJM97KNNaVElKx7DBBM0SgXwQBEHQp8lSOP4cWN32ZyRtAmxr+yIA24e2tIGJKlNa1ngEWI6UCacsygyOFyDpauA3wA22F+kg2N4mh2xp1iIipWPQQ4jKrkEQBEGfRtINwGjgONubSxoEPGz7Ay1u2iLUpbTci2R5uYTiKS1r2rcDHwTup20gm9sGI+lrpHSLZQTH9bqfBPYHtiFl8LnY9pMFNU8mWYuepc5aZLtpa1FnlaiDYHESgXwQBEHQp5F0v+2tJD1se0S2bZztLVrdts6oS2m5R55gsx29HdrbXqQ6aZnBcQf6y5I6NccBLwMXAJfZbjqnvqQngQ+WYS2SNA+YTuZlB2bUHiK87MFiJKw1QRAEQV9nuqQVybKAZNU4325tk7qmxJSWNb3cAXsnVOG7ByD7zL5O6ig8TMrZvz2wL7BjDsnSrEXhZQ96ChHIB0EQBH2do4A/AutJGgusDHy1tU1a/GQdmFHAxsAQYCAwveDocRW+eyRdA2xEKvy0q+2J2UNXSHogp+yqwJOSSrMWBUGrCWtNEARB0OfJfPEbkqwPT+WxZvR2sgB4T5LnfCTwn8D6tn9UQPN2SvbdZ7o72b61iEY7mqVbi4Kg1UQgHwRBEPRpJH25nc1vAxNslzqS3JOR9IDtkZLG2/5gtu0e29sV0Cw1OO7gs6rXvSaPbhD0VcJaEwRBEPR1DgS2BW7L7u8I3AtsIOlE25e2qmGLmRmShgDjJJ0KTASGFRGsYDR71852B+QO5CuyFgVBS4lAPgiCIOjrzAc2tv06LMgrfy6wNXAnyYfdH9gHGAAcChwJrEHKX5+bsoNj2/sXaU8XnE071qIK9xcElROBfBAEQdDXWbsWxGe8AWxg+y1Jfd4rL2lN2y/ZfjHbNAv4SUnypQbHkr5u+zJJR7X3uO3/zaudvf4ZSQNtzwNGS7qniF4QtJoI5IMgCIK+zl2SricFm5BGoe/Mii9NaV2zFhvXAbXiRVfbLjQK30jJwXHN6jO8hKY1Urq1KAhaTSx2DYIgCPo0kgR8mZSDHGAS8B7bh7SuVYuPhkJYC26XpH0n8EngQuA1UnC8n+3Ny9pHWUhaC3idZAE6ElgWOMf2My1tWBAUIEbkgyAIgj6NbUt6luSJ3x14Hri6ta1arLiD22VQuu8eQNI6wGHA2tTFKnnSWlZsLQqClhIj8kEQBEGfRNIGJP/2XqRR+CuA79leq6UNW8xImgdMJ+XQXxKYUXuI1M9pemFqLTgur5WL6D8CXARMIC1WBvJlyZH0kO3KrEVB0EpiRD4IgiDoqzwJ3EWqDPoMgKQjW9ukxY/tgRXIVuq7B2bZPqskLdXdXrckzSDoEUQgHwRBEPRVvkIakb9N0l+B39M2qAvyU3VwfKak44GbaFsx9qEcWlVai4KgpUQgHwRBEPRJbF8LXJtlp/kiycO9qqRzgWtt39TSBvZuqg6OP0Dy3+/EQmuNs/vNsrmkd8isRdltKGAtCoKeQnjkgyAIgn6DpBWA3YA9bOcJCgOq8d036D8JfND27EINDYI+TgTyQRAEQRD0KCRdARxm+41WtyUIejJhrQmCIAiCoKexKvCkpPtp65FvOv1kEPRlIpAPgiAIgqCncXyrGxAEvYGw1gRBEARBEARBL2RAqxsQBEEQBEFQj6RtJN0vaZqk2ZLm1WWbCYIgIwL5IAiCIAh6GmeTKvI+TcqK841sWxAEdYRHPgiCIAiCHoftZyQNtD0PGC3pnla3KQh6GhHIB0EQBEHQ05ghaQgwTtKpwERgWIvbFAQ9jrDWBEEQBEHQ09iHFKMcSio8tQbwlZa2KAh6IJG1JgiCIAiCHoGkNW2/1Op2BEFvIUbkgyAIgiDoKVxXuyHp6lY2JAh6AxHIB0EQBEHQU1Dd7XVb1oog6CVEIB8EQRAEQU/BHdwOgqAdwiMfBEEQBEGPQNI80uJWkfLHz6g9BNj2Mq1qWxD0RCKQD4IgCIIgCIJeSFhrgiAIgiAIgqAXEoF8EARBEARBEPRCIpAPgiAIgiAIgl5IBPJBEARBEARB0AuJQD4IgiAIgiAIeiH/D5paiZdXYHy0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相关系数矩阵的热图，可以得出若干结论：\n",
    "# 有舱且等级越高，女性，儿童，家庭成员数目较小，票价越高，则存活率越高\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(X_show.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>准备建模</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split #for split the data\n",
    "# 准备若干分类器\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV#模型微调"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分出验证集\n",
    "x_train,x_val,y_train,y_val=train_test_split(X,Y,test_size=0.2,random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建4个网格搜索，探索给定参数中的最佳组合\n",
    "xgb=XGBClassifier()\n",
    "n_est=range(100,1000,100)\n",
    "lr=[0.001,0.01,0.1,0.2,0.3]\n",
    "gamma=[1,0.1,0.01]\n",
    "params={'n_estimators':n_est,'learning_rate':lr,'gamma':gamma}\n",
    "m1=GridSearchCV(xgb,param_grid=params,cv=3,scoring=\"accuracy\", n_jobs= -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc=RandomForestClassifier()\n",
    "params={'n_estimators':range(100,501,100),'min_samples_split':range(5,21,5),'min_samples_leaf':range(2,10,2)}\n",
    "m2=GridSearchCV(rfc,param_grid=params,cv=3,scoring='accuracy',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc=DecisionTreeClassifier()\n",
    "params={'min_samples_split':range(5,21,5),'min_samples_leaf':range(2,10,2)}\n",
    "m3=GridSearchCV(dtc,param_grid=params,cv=3,scoring='accuracy',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc=SVC()\n",
    "params={'kernel':['rbf','sigmoid','poly'],'gamma':[1,0.1,0.01]}\n",
    "m4=GridSearchCV(svc,param_grid=params,cv=3,scoring='accuracy',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'gamma': [1, 0.1, 0.01],\n",
       "                         'kernel': ['rbf', 'sigmoid', 'poly']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 开始探索\n",
    "m1.fit(x_train,y_train)\n",
    "m2.fit(x_train,y_train)\n",
    "m3.fit(x_train,y_train)\n",
    "m4.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'gamma': 0.1, 'learning_rate': 0.01, 'n_estimators': 700},\n",
       " {'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 100},\n",
       " {'min_samples_leaf': 2, 'min_samples_split': 5},\n",
       " {'gamma': 0.1, 'kernel': 'poly'})"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最佳参数组合\n",
    "m1.best_params_,m2.best_params_,m3.best_params_,m4.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1.best_estimator_#最佳模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>看看在验证集上的精度</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8435754189944135"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,colsample_bynode=1,\n",
    "                    colsample_bytree=1, gamma=0.1,learning_rate=0.01, max_delta_step=0, max_depth=4,\n",
    "                    min_child_weight=3, missing=None, n_estimators=700, n_jobs=1,nthread=None, \n",
    "                    objective='binary:logistic', random_state=0,reg_alpha=0, reg_lambda=1, \n",
    "                    scale_pos_weight=1, seed=None,silent=None, subsample=1, verbosity=1)\n",
    "model.fit(x_train,y_train)\n",
    "pre_y=model.predict(x_val)\n",
    "accuracy_score(pre_y,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8603351955307262"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这个最好，但还没以前的好\n",
    "model2=RandomForestClassifier(min_samples_leaf=4, min_samples_split=10, n_estimators=100)\n",
    "model2.fit(x_train,y_train)\n",
    "pre_y=model2.predict(x_val)\n",
    "accuracy_score(pre_y,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8324022346368715"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3=DecisionTreeClassifier(min_samples_leaf=2, min_samples_split=5)\n",
    "model3.fit(x_train,y_train)\n",
    "pre_y=model3.predict(x_val)\n",
    "accuracy_score(pre_y,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8491620111731844"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4=SVC(kernel='poly',gamma=0.1)\n",
    "model4.fit(x_train,y_train)\n",
    "pre_y=model4.predict(x_val)\n",
    "accuracy_score(pre_y,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8659217877094972"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不使用Fare特征时，最佳验证结果优化了一点\n",
    "# 提交测试结果得分为0.79多\n",
    "model5=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,colsample_bynode=1,\n",
    "                    colsample_bytree=1, gamma=0.01,learning_rate=0.05, max_delta_step=0, max_depth=4,\n",
    "                    min_child_weight=3, missing=None, n_estimators=700, n_jobs=1,nthread=None, \n",
    "                    objective='binary:logistic', random_state=0,reg_alpha=0, reg_lambda=1, \n",
    "                    scale_pos_weight=1, seed=None,silent=None, subsample=1, verbosity=1)\n",
    "model5.fit(x_train,y_train)\n",
    "pre_y=model5.predict(x_val)\n",
    "accuracy_score(pre_y,y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>以上模型最后训练时应该使用全部训练数据，包括验证集</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>预测</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试集对数据执行与训练集相同的处理\n",
    "\n",
    "test_data['Title']=test_data['Name'].apply(get_title)\n",
    "# 测试数据多了个Dona\n",
    "test_data['Title'] = test_data['Title'].replace(['Master','Don','Dona','Rev','Dr', 'Major','Lady',\n",
    "                                                 'Sir', 'Col', 'Capt', 'Countess', 'Jonkheer'], 'Rare')\n",
    "\n",
    "#分出小姐和夫人\n",
    "test_data['Title'] = test_data['Title'].replace('Mlle', 'Miss')\n",
    "test_data['Title'] = test_data['Title'].replace('Ms', 'Miss')\n",
    "test_data['Title'] = test_data['Title'].replace('Mme', 'Mrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_missing_ages(test_data)\n",
    "test_data['FamilySize']=test_data['SibSp']+test_data['Parch']+1\n",
    "test_data['Age_bin']=pd.cut(test_data['Age'],bins=[0,10,45,90],labels=['children','mid','elder'])\n",
    "test_data['Family_bin']=pd.cut(test_data.FamilySize,bins=[0,1,4,7,12],labels=['little','small','medium','large'])\n",
    "test_data.Fare.fillna(0,inplace=True)\n",
    "test_data['Fare_bin']=pd.cut(test_data.Fare,bins=[-1,100,300,600],labels=['little','mid','big'])\n",
    "test_data['Cabin']=test_data['Cabin'].apply(replace_Cabin)\n",
    "cols=['Pclass','Sex','Cabin','Embarked','Title','Age_bin','Family_bin','Fare_bin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X=pd.get_dummies(test_data,columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_x=test_X.drop(['PassengerId', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare','Name',\n",
    "       'FamilySize'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 因为Cabin没有缺失，所以要自己补上一列\n",
    "test_x['Cabin_No']=0\n",
    "# 并重新排好列的顺序\n",
    "test_x=test_x.reindex(columns=['Pclass_1','Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male', 'Cabin_No', 'Cabin_Yes', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Title_Miss', 'Title_Mr', 'Title_Mrs', 'Title_Rare', 'Age_bin_children', 'Age_bin_mid', 'Age_bin_elder', 'Family_bin_little', 'Family_bin_small', 'Family_bin_medium', 'Family_bin_large', 'Fare_bin_little', 'Fare_bin_mid', 'Fare_bin_big'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将多个模型预测结果分别输出到文件中\n",
    "i=11\n",
    "for m in [model,model2,model3,model4]:\n",
    "    test_y=m.predict(test_x)\n",
    "    result=pd.DataFrame({'PassengerId':test_data['PassengerId'].values,'Survived':test_y.astype(np.int32)})\n",
    "    result.to_csv('result'+str(i)+'.csv',index=False)\n",
    "    i=i+1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>过拟合分析</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "        model, X, Y, cv=3, n_jobs=-1, train_sizes=np.linspace(.05, 1., 20),scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train_sizes在300以上表现为过拟合\n",
    "t_score=np.mean(train_scores,axis=1)\n",
    "v_score=np.mean(test_scores,axis=1)\n",
    "plt.plot(train_sizes,t_score,'ro:',label='train acc')\n",
    "plt.plot(train_sizes,v_score,'b^:',label='val acc')\n",
    "plt.legend()\n",
    "plt.title('learning curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V7XcubXTpAmvXVt2fRqNOGrusbKMvKLBafLjy8oa31Y8dO5apU6cybdo0zjvvPMBSCh9wwAHk5eXx+uuvs7qWR4fq0hlXl244Wmpi59LK979fdV+ajTpp7LIy0L//vtXiw5WWwnvvNey6ffv2Zfv27XTr1o0uXboAcNFFFzF//nwGDx7MlClTOPzww2u8RnXpjKtLNxwtNbFzaWXqVBtKmcajTho70epmdaXQ4MGDdf78+ZX2LVu2jN69e6eoRJnN/+xcQrz8MvTpAwcfnOqSOEBEFqjq4GjHsrJG75xLsD17LAfMDTekuiQuBjEFehEZLSLLRaRQRG6NcrytiLwkIotFZImIjIs4nisiC0Xk5XgV3DmXQs2awdtvw4MPprokLga1BnoRyQUmAacCfYALRKRPxGnXAEtVtT9wPHCviISvJHA9sKyhhU3HZqZ0539mLq5U4d137XXPnjbixqW9WGr0Q4BCVV2hqqXAVOCsiHMUaCM2DrA1sBkoAxCR7sDpwCMNKWjz5s3ZtGmTB646UFU2bdpE82ACJucaauZMOO44eOmlVJfE1UEs4+i7AWvCtouAoRHnTARmAmuBNsD5qhoc4PgX4ObA/mqJyBXAFQA9ooy/7d69O0VFRZSUlMRQZBfUvHlzunfvnvgPmjIl9ux/LnOdfjo8/HBo0Q2XEWIJ9NFm60RWq08BFgEnAocAs0TkbWA4sEFVF4jI8TV9iKpOBiaDjbqJPJ6Xl7dv1qhLM8FcJ8HFF4K5TsCDfbbYvt2GTrZuDT/9aapL4+oolqabIuCgsO3uWM093DhgRiDlQiGwEjgc+B7wAxFZhTX5nCgiTze41C69TJhQdYWd8DU3XeYbPx6GDas6QcVlhFgC/TzgUBHpFehgHYs104T7ChgJICIHAocBK1T1F6raXVV7Bt73X1X9cdxK79KD5zrJfj/9qQX7pk1rP9elnVoDvaqWAdcCr2IjZ55T1SUiMl5ExgdOKwCOFZFPgDnALaq6MVGFdmnghRdsweRdu6rPaZKMvgGXWHv32u8TTrAFsF1Gimkcvaq+oqrfVdVDVPWuwL4HVfXBwOu1qjpKVY9Q1X6qWqV5RlXfUNUz4lt8lzRlZTbSIriKS9u20L69rZ8ZbYUdsBWFXObautXWeX3qqVSXxDWQz4x1JtoKQRDKDrd6NfzgB6H/9CecALNnW16TaCvsPPww/Oxndu6LL4ZSibrMUVEB3/0ufOc7qS6Ja6CMyXXjEihy1AxYDb1PHxg0KDT78a23rEMuLy/2a8+dC0OHwsSJ/ujvXAJ5rhtXs+pGzXz+ORxySGjf8OF1C/IAQ4ZY8qvLL7ftLVtCK8K49PTBB3DuufZ35bKCB3pX/eiY7dvhppsafv3TT7fRGrt22azKYJOOSx/hTXdnnNHwnN4urXigb+xKS6Fr1+jH4r1CUNOm1p5/+unxva5rmMjFvTdtgm++gVdeSXXJXJx4oG/szjkHcnOrjppJxApBublw220wapRtP/SQNRt5R21q+YS3rOeBvrG79lq4776qo2aSsULQJ5/ARx9Zc0F1o35c4vmEt6zno27iJVOSepWVWa26b1+45JJUl8YWsJg2zTprd+0K7W/Z0pejS4aSEhg8OHpQz8+HVauSXiRXPz7qJtEi2ziDSb3StVY6bx58/HGqS2GaNbMvyPAgD9Z0cNttqSlTY7FlCwwcaMNok9F051LGa/Tx0LOnBfdI6VQjmjPHhjq2aWO16GbNUl2ikJyc6odcBvdXVNh5Lr7++EfrM1myJDOeSF21vEafaOnexvnVVzB6dKiGlk5BHqof3dO+fej1kCHeORgP33xjTXaff27bN98MAwZYUF+1yr5QV63yIJ9lPNDHQ3WBKt7DE+sqmFK2Rw9LQ/CrX6W2PNWJliunZUu4/357XVYG3/8+HH64be/ZA9/7XuVVjrwzNzbbtsFrr8GHH6a6JC6JPNDHw3XXVd2X6jbOBQssR8ncubZ92mnRE4+lg2i5csI7Yps0gT//GX7yE9tet86GajYJrJvzl79YLTVT+khS4e237fdBB8EXX4T+LF2j4IE+Hq66yv7jdO9ugSonB+65J7mPv5E12gULoF8/WxEoE9Sl6SA/3/LunHqqbd99N5SXVz4n08aBJ/KJ5IUXLH1F8AkoU/5NuPhR1bT7GTRokGasjz9Wvfhi1ZUrk/eZTz+t2rKlqtVn7adlS9vfGIhUvvfgj0iqSxabRP39VVTY77Iy1Ucesd8uawHztZqY6qNuGur++6FXLzjzzNSVIRNG/SRSpt9/deXv3t2yfo4YAfvvX/M1IudxjBkDb74Js2bZ2gEu6/mom0QpL7e86zNmVD1WXJy89TXTfdRPolXXmZsp48Cr+3v6+ms4+2xYvty2P/wQLrvM9kOouSraPI6//c3Gye/Ykfjyu7Tngb4hcnNh4ULrKAz31luWKOyNN5JTjnQd9ZMs1XXmjh2b6pLF5oADou/v3t1SBh95pG2vXm3t7MHhsQ8+aO+99daquWr27LGKRrduiSu3yxge6Otr926rUTVpUvWx+uij4Xe/Cw0HTLTf/tYCXLhMqtHGQ2RnbrducNhh6f9Uo2qdo9H+/u6+2xZtadHC9v3oR7Z0Y8eOtt27N5x3XqiGH2nNmsSV22UUD/T1de+9li9m+/aqx1q0gF/8Ink16nPOsREonTolNylZOsvPtxpxZGqFdCNitfbf/77uSeVOPBEeeMCf6FytvDO2vv71L3j9dRtGGU1ZmS3e0KuXjV12LtKqVaHg3hDVLQXZ2L/sGxnvjE2E00+vPsiDPWKPGBFaTDtRSkth0SJfnq86wfH0GzakuiSVbdxoTXw339zwa9U24cw1eh7o62rPHnjkEWujr0nXrvDqq5bvPZFefdUyEM6Zk9jPyVSrV1szW3i6hHTQvr2lpLj00vhcz3PVuBrEFOhFZLSILBeRQhG5NcrxtiLykogsFpElIjIusL+5iMwN2/+beN9A0r30kuVOf+ed2s8dNQr22y+x5Tn2WFupafjwxH5Opurd26b8X3ZZqksSEszEed11Vj7nEqzWQC8iucAk4FSgD3CBiPSJOO0aYKmq9geOB+4VkabAHuDEwP4BwGgROSaO5U++c8+F99+HkSNrP3fvXqv9//e/iStPhw7WPtu0aeI+I9MF+0gKC2Hr1tSW5csvrRM/mIPIuSSIpUY/BChU1RWqWgpMBc6KOEeBNiIiQGtgMxCcbx2csZEX+MncxmRVawM95pjYOtCaNIFf/xqmTk1Med5+G6ZPr5rnxVW1aZM1cf3yl6ktx7ff2nDcLl1SWw7XqDSJ4ZxuQPiA3CJgaMQ5E4GZwFqgDXC+qlbAvieCBcB3gEmqmpn5USsqbDjbxRfH3q4qAvPnQ+fOiSnTgw/a5KxzzknM9bNJhw7w97/b32EqHXmkjcZq6Egb5+oglhp9tH+RkbXyU4BFQFesiWaiiOwHoKrlqjoA6A4MEZF+UT9E5AoRmS8i80tKSmK+gaTZuhVatYLmzev2vi5dEvef+sknbfatr7wUmx//2DrJIflPQYsWwR13WHOeB3mXZLFEiCIgfCB4d6zmHm4cMCPQVFMIrAQqTQtV1a3AG8DoaB+iqpNVdbCqDu7UqVOMxU+i9u1t7PyFF9b9vb/+tS3ZFm9NmsAhh8T/utmsvBzOPx9uuSW5n/vCCzbkMdoEO+cSLJZAPw84VER6BTpYx2LNNOG+AkYCiMiBwGHAChHpJCL7B/a3AE4CPotX4ZNm+XIb91xfS5bAZ3G+7SuvtBq9q5vcXGtKC6YRSJY77oDFiysvj+hcktTaRq+qZSJyLfAqkAs8pqpLRGR84PiDQAHwhIh8gjX13KKqG0XkSODJQDt9DvCcqr6cqJtJmPHjYe1aC9b1eez+5z/j+7heWmpfHj7jtn7++tfkfdb8+XDggfZ3lY5Pqq5RyJ4UCJH5uOO5iv3SpZYg6pRTGnad4KideAmOx3b18+678Omn9nSUCBUV1vnasqWlGPa2eZdANaVAiGXUTfqLzPURXDMU4hPs+/Sxn4a4+WbrkHvttYaXZ+9eyMvzIN9Qf/87zJsH48YlZh5CTg7MnAnffONB3qVUdkSKCROq5uOOx5qhixbB1VdDPEYB9ehhaYsb+gS1bJk1Bcye3fAyNXZ/+5utrZuIIB/8N3PwwTBgQPyv71wdZEegT9QKS/PmwbRpVntuqGuvtWUHG1qzy8mxhGp9+za8TI1du3aWC7683L7U42X9ensCTMRIK+fqITsCfXV5t7t2tWaO+rr8cksQVdt6nXXR0KeDww6zjJg+szJ+JkyAIUMsf31Ojq3hOmVK/a+3//72byeV6wg7FyY7An11a4bm5MBZkdkaYhQcThl53Ya4/XYb917ftWS//tpG/7j46tbNnrS+/jq05uoVV9Qt2E+ZYl8QOTn2Zdy3rycsc2kjOzpjgx2ukaNu2rQJtb8GH88HDar9eqtWWXv6Qw/BJZfEr5ynn25rfJaV1a9d+J57rEwbN8b3C6ixu/feql++wT6ebdusKeY3gcSrH3xgv48J5OarqIBnn03sYADnGih7hlfW5vHHLUfNe+/BsGE1n7txowXVa65Jr7Hqn39unYcXXJDqkmSXnJzoneQilt545cpQ5/cpp1g6jA8DKZtOOsmGaUZbnyA/3yoNziVBTcMrG0+g377dHq+vvNL+A7/3nj1id+gQ38+pze7dlst+5Egfcpcueva0WnikaIG6sNBq7kceaduTJ1c/Dl/EavzOJYEvJQjWjDN+vP3n27sXxoyp3CwTbGMVsY7OhnTG1eTJJ+Hkk22YZF08/XSoFuniq7o+nrvuqnrud74TCvJgTTT5+dGv64tzuzSRHW30dZWXZ0vwBT3yiDXTBNtp161LXBvrOedYYKhLMrLycptwNWoUDI3MEO0arLo+nlj/7u+6K/ri3NG+KJxLgcbTdFOT9u1hy5aq+9OpjXX7dvsJptl16SWRKTici4G30demps64RLSxrl8Pzzxji5gku4/AOZeVvI2+NtW1pSaqjfWrr+DGG211qNp8841NvPH2eedcPXmgh7p1xsXDoEGwYkVsSwB++aVlWPR1YZ1z9dQ4O2MjNbQzrq5ycqBXr9jOHTjQvhScc66evEYfdNFF1vFaUWG/E92Rtn49XHWVjeevzt69oRz2PubeuRoVF8OIETZoLhM/P5Hl90CfKq1bW2bMmpYYfOgh+O53YdOm5JXLuQxVUGBzEQsKMvPzE1l+H3WTSuXltoZpdV55JbSotHOuWsXF1hq6Zw+0aGGtnZ07J/fzDz7YJr43a2aNAp0723/fww6z/HYVFbYOTe/etq+sDF5+2fLftW4den99y++jbtJVTUEe4LTTPMg7V4PSUguYBQX2G6z+lOxafUFBaCR2sDwA555rI6nBjp9zji0hHSz7OefA889Xfn8iyu81+lTavdsyWp55JvzsZ5WPLVtmncKtWqWmbM6ludWr4bjj4JZb4KabKueVS2at/sUXYezY6J+/bp0tCNeli3W3LV5sZerc2QL7xx/b2IyhQxtefq/Rp6vmzW3CVOvWVY/96Efwgx8kv0zOpbGNG+H99+31QQdZVpDZs6vOaywvr1p3SoTFi+Hss6uubxSslQ8YEFojSMS2g8E7J8e2H3wwevnjWav34ZWp9txzVfepwsSJnvnQuQg/+QksXWq13dxcePRRG4EcuZxAaSlMnw67dlntOFH697cH78hVS0tLax5QF+7996OXP9b3x8IDfTpQtTw2++1n2yI2zsq5Rm7FCvjDH+BPf7L/HnffbWv2hHdvLVxY9X2rV8Py5YkJ8uXlNuXmyiutAzhahuu6iFb+ePOmm3QwZEgop7kq/PnPDf/X41yGCR9HHuxY3bTJMnQHu+wGDLB112uTn2/NOgAvvQQPPxy/cn71lY2RePHF+F0z0WIK9CIyWkSWi0ihiNwa5XhbEXlJRBaLyBIRGRfYf5CIvC4iywL7r4/3DWSFceOsoQ8s3UGseXCcyyLBceSDB1uNGeDoo22Z5BNPrP91n3jCAn1kO3pdBZtXevWy5qNk9AHES61NNyKSC0wCTgaKgHkiMlNVl4addg2wVFXPFJFOwHIRmQKUAf+rqvTRw9kAABgLSURBVB+JSBtggYjMinivu/rq0OsjjrDavGe1dI1IcbGt9llRYZPGO3YMHWvbtmHXnjoVduywZSj27rVO0NpGNkcqKbFVI6+/3lYkTeYY/XiIpUY/BChU1RWqWgpMBc6KOEeBNiIiQGtgM1CmqsWq+hGAqm4HlgHd4lb6bLJtm3Xhgw+rdI3OhAmhvH05OfFdBiIvD9q1s1bRyy6zJZfrOs6hbVubpJ6pi4bFEui7AWvCtouoGqwnAr2BtcAnwPWqWumPUkR6AgOBqPl2ReQKEZkvIvNLSkpiKnxWGTHCGiBFbNxYopYydC7NFBfDU0+FmlZKS612H++cLyI2SqZ/f/syicWiRfY00LSpTXQ66aT4lilZYrndaNm0ImdZnQIsAroCA4CJIrLfvguItAamAz9T1W+ifYiqTlbVwao6uFOnTjEVPmtMmVI5501RkS1N58HeNQIFBVUDb6Jmt/7v/4ba/5cvr/nLZPNmq3/deGP8y5FssQT6IuCgsO3uWM093DhghppCYCVwOICI5GFBfoqqzmh4kbPQhAmWpCPczp2hf5HOZbFkjCOPVF5u6QfOOy/64nJgK4w+9hjceWfiypEssQT6ecChItJLRJoCY4GZEed8BYwEEJEDgcOAFYE2+0eBZap6X/yKnWUiZ1vUtt+5LHLhhfDaaxZww38SOb48N9eahyZODGUADw7vvPPO0Ozbc8/NvI7XaGoddaOqZSJyLfAqkAs8pqpLRGR84PiDQAHwhIh8gjX13KKqG0XkOOAnwCcisihwydtU9ZVE3EzG6tEj+rj5TO35cS5Ge/ZYsP3hD+Hkk5P72UOHhl5PmgSvvmrDOxctgjVrYNiw5JYnkTypWTqYMsXa5HfuDO1r2dJmZSR6ARTnUmzvXvun39BhlPW1Z4+lUfj8c2vSad4cCguhW4aND/SkZunuoossqOfn23Nkfr4HeZf1gk00eXmpC/Jg+eOHDQuNra+ogN/9LnXlSQQP9Oki2UsZOpdis2fbiOIvvkhtOYqLLWd8sEM4UcM7U8kDvXMuJUSgUyebNpJK4Yt+BKVi8ZJE8uyVzrmUOOmk9JiAlIrhncnmgd45l3SffWZrpDZtmuqSJCdNcKp5041zLqkqKmw55PPPT3VJGg+v0Tvnku6BB2wEsUsOD/TOuaTKyYHRo1NdisbFm25c1ghfocilp+JiuOce2LIl1SVpXDzQu6wRXKEom4bFZZv//AduuskW8nDJ4ykQXFYoLrYJxXv32oLQK1ZkRzKqbLRqFfTsmepSZB9PgeCy3s03hxauKC+3WmNhYWrL5KLzIJ98HuhdxisuhmnTQtulpfDss5aoKjxPnKtZovs4xo2DO+5IzLVdzTzQu4xWUQE33FB1CntuLgwfHhrC96tfWfu9q14i+ziCCczqulariw8P9C6jPfAA/N//RZ/CvjawDtrmzfDww/D227btAaeq4mJL5FVRkbj1Wp94IjtWa8pEHuhdRrvoIrjvPgtQ1a1Q1L49rFwJ119v2//9L/TpY2uGhmvMwzMvvRTKyuz13r3xrdWXl8PXX8fveq7uPNC7jLRzpwX3du2s6UaiLWEfpnnzUDNObi706hXqFPzsM9ixo/EOzywutqX8goG+rMzWSo3XF96rr9piacEnKpd8HuhdxlGFiy+Gs86qfmHnmhx/PPz737bghCpccIHtS2TTRTorKIAmEXPkKyri94XXrx/88peVl+5zyeWB3mWkk0+2FLe11eRrIwJ//zt07Rpqt9+1C8aMsdeqsHVrwz4j3VWXpvff/7bl9RqqRw8bbZMOmSobKw/0LuOIwJVXhtrcGyo/H2bNqhzs5s61Wv1XX1nz0FNP2f6dO61tP9qTRCa28X/5pd3Te+9V7t/YscO+8H75y4Zd/+WX7c/SpZYHepcxvv0WTjzROlPjKdoKQ8H9zZrBb38banZ45x04/HCYM8e2166FN9+0BaYzsY1/2zabQZyfX3l/q1b25ffYY/W/tqpNZLv99oaV0TWcB3qXMUpKYOPG+rXL16SmFYY6d4YJE+C737X9Rx4JjzwSCvwzZlj7/scfh9r4H3nEngQywVFH2RdV165Vj/XrZwG/tBT++tdQZ22sROCDD6xpzKWW57pxGaW83EbNpIstW+DDD+HFF632G/zCGD/eAtz06dakc+21qS1nNMuWWU2+trzwL74IZ58N//qXLRji0pPnunEZrbDQHv/37k2vIA/Wft+/v00GCn8qePJJa6ufMcNq+EG//a2l6Y0mmW38qnDeeXDmmbWfe9ZZsGBB3YL8mjUwapQ96bjUiynQi8hoEVkuIoUicmuU421F5CURWSwiS0RkXNixx0Rkg4h8Gs+Cu8bj+edtBuz69akuSXTR2vjLy23/lCmVx48vXFh5jdIxY0KBP9lt/A8+GHtn61FH2e8lS+C552o/f8UKG7HTpk39y+fip9amGxHJBT4HTgaKgHnABaq6NOyc24C2qnqLiHQClgOdVbVURIYDO4B/qGq/WArlTTcuUnExdOmS6lJEN3AgLFpUdf+AAdEXnla19utgrXrYMJvhe/DBsHt3eqdZPu88mDfPJpm1aFHzucH7dMnR0KabIUChqq5Q1VJgKnBWxDkKtBERAVoDm4EyAFV9K7DtXJ28+aYFPEjfIA8WzCPTL4SnYIgUDH4i1ob/859bLT6YZrmsLLG1+lWrbFz7pk11f++jj8Jbb9Uc5Ldu9SCfbmIJ9N2ANWHbRYF94SYCvYG1wCfA9apap7RRInKFiMwXkfklvvxMo1dWBpddZjlYsl0woVh5uW3v3ZvY2blz5sDvfmfj5OuqbVvrwFW15rRoo4t+/GMYObLh5XTxE0ugj/a9HNnecwqwCOgKDAAmish+dSmIqk5W1cGqOrhTp051eavLQk2awOzZlTsys1VNbfyJcNll1lnavXv9r7FuHdx2G0yaVPXY2LEW7F36aFL7KRQBB4Vtd8dq7uHGAb9Xa/AvFJGVwOGAz4lzdbZ0qWWXbCwrEVU3jv+11+L/WRUVkJMDBx7YsOt06WLDSg89tOoxD/LpJ5Ya/TzgUBHpJSJNgbHAzIhzvgJGAojIgcBhwIp4FtRlv+JiG93Rr581XTQWkW38O3bAz35mE7biSRWOPRZ+//v4XO+ww+xLY/NmGza6ejX07m0poV16qTXQq2oZcC3wKrAMeE5Vl4jIeBEZHzitADhWRD4B5gC3qOpGABF5FngfOExEikTkskTciMt8BQWweDEcc0woqVhj1KoV/PnPEO8WzF27bGZvQ5psopk+3f7uzj/fRuNcfXV8r+8azmfGupQrL7ca4e9+Z00W6Ty8MJnWrrUZtb//fSgFQzpStaePk05K/+Gh2cxnxrq0U1BgtVaw2a733BPKpZLIjshMkpNjY9Y/+aTh11q/3mYYJ4KITQwLdij731/68UDv4iZyCn/4w+JvflN5qOTcudZME3xfWVkoUJSWNr7FP6Lp3NnSCJ97bsOv9Ze/WPt5IkYuB4eHBjuU/e8v/Xigd3Hzq1+FpvDffbe1BweVl1fOfjhzpuWHgeQPL8wkwcU6Zs2CDRvqf53rr7ec+okYuex/f+nPA72Li0cftTHvwaX42reH444L1fLuvBP+8Y/Q+eGzJmtKE+ysrf6MM+CPf6z/NTp3tvHtieB/f+kvlnH0ztVqzpxQ/pbycstaGGse8upSBTjTtast63fssXV/b3Dxj/PPh8FRu+kazv/+0p/X6F29vfwy/PrX1kb7/POhNnlvo42/E0+E5s0tPcLu3bG/b80ae9ryYNy4eY3e1ducOfD66xboq2ujjTZF3tXPrl1Wqx81Cv7wh9je06MHFBXZCB7XeHmgd3Uyb57lGD/8cOtwFbEJTt5Gm3gtWsCpp8LRR8d2flmZ5QyqbQUpl/18wpSL2e7dljP96KNteTmX3m6/3VI9z5kTGr3jsldNE6a8Ru9qtW6dJcFq3tza4tN5lmZjoGrr04JloqzOIYfA9u0e5J13xrpafPKJBfannrLtoUNtnVSXWv/8J7zwQuVJaZHGjYO//jV5ZXLpy2v0LqrgCkF9+sAVV9iMV5ceROD//g/22y/6Kk6q1kk+YkT6LabuUsNr9G6fYAqDGTNg+HD45ptQHpr8/FSXzoVr29aC/LZt8NFHlY99+KGt8BQ+Qc01bl6jd/sUFFgKgw4dLIBs2GC1Rpe+LrzQmtcKC0Nt8YMHW9PO6NGpLZtLHz7qxgEWLAYPDqUJLiy0GZkuvS1aZJOoYh1y6bKXpyl2tRo7NjQWvrwc7rorteVxsRkwIBTk16yxjvOG5MRx2ckDvaO42BaKCPIUBpnnr3+1ZRi/+AIeeCDVpXHpxgN9I/bmm1aT/81vPM1spmvbFrZssdfr1/uXtKvMA30jVlhobfPvvOMpDDLd3LmhoZQVFf4l7SrzzthGaMMGOOAAe71nDzRrltryuIYpLrbUFOFZLX3d1sbHO2PdPhMn2pJywTZ5D/KZz1d4crXxcfSNzOmnw8qVcNBBqS6Jixdf4cnVxmv0jcDu3fD00/a6Vy+4917Iy0ttmVz8LFxoaQ8if3yxERcUU6AXkdEislxECkXk1ijH24rISyKyWESWiMi4WN/rEu+hh+AnP/H/+M41VrUGehHJBSYBpwJ9gAtEpE/EadcAS1W1P3A8cK+INI3xvS7Brr3WhlIOHJjqkjjnUiGWGv0QoFBVV6hqKTAVOCviHAXaiIgArYHNQFmM73UJ8PnncOaZNrY6N9eSlDnnGqdYAn03YE3YdlFgX7iJQG9gLfAJcL2qVsT4XgBE5AoRmS8i80tKSmIsvgsXzD65bh18/bXlQSkqSnWpnHOpFkugj5LxmsjB96cAi4CuwABgoojsF+N7bafqZFUdrKqDO3XqFEOxXKRg9smCAjjhBJsOf8QRqS6Vcy7VYgn0RUD4YLzuWM093DhghppCYCVweIzvdXFQXAyPPmrjqR991Gr1zZunulTOuXQQS6CfBxwqIr1EpCkwFpgZcc5XwEgAETkQOAxYEeN7XRyET45R9ckyzrmQWgO9qpYB1wKvAsuA51R1iYiMF5HxgdMKgGNF5BNgDnCLqm6s7r2JuJHGrLjYsk0GJ8149knnXDjPdZMFfvQjmD698jT4pk3hpz+FSZNSVy7nXPJ4rpssN29e1VwnPgXeORfkuW6ywMqVUFYGTfxv0zkXhdfoM1h5uSW0Ag/yzrnqeaDPYM8+C8ceC2+8keqSOOfSmdcDM9i551pb/IgRqS6Jcy6deaDPYC1awKWXproUzrl05003GWjDBqvFf/RRqkvinMsEHugz0KpVsHYttG6d6pI45zKBN91koCFDYPlyyPGvaedcDDxUZJhZs2xYpQd551ysPFxkkLlzYdQoePjhVJfEOZdJvOkmgxx9NPzzn3DaaakuiXMuk3igzyAicN55qS6Fcy7TeNNNBtizB447Dl58MdUlcc5lIg/0GWDDBstO2apVqkvinMtE3nSTAQ46CN5915punHOurrxGn+b+8x/Yvt2DvHOu/jzQp7GSEjj7bLj99lSXxDmXybzpJo116gRvvmlNN845V18e6NOUqjXXDB2a6pI45zKdN92kIVUYPRruuy/VJXHOZQMP9Glo1y7o0MGHUzrn4sObbtJQy5bwzDOpLoVzLlt4jT7NzJ4Na9akuhTOuWwSU6AXkdEislxECkXk1ijHbxKRRYGfT0WkXETaB45dH9i3RER+Fu8byCZr1sAZZ8DFF6e6JM65bFJroBeRXGAScCrQB7hARPqEn6Oqf1LVAao6APgF8KaqbhaRfsDlwBCgP3CGiBwa75vIFnffbYt9d+mS6pI457JJLDX6IUChqq5Q1VJgKnBWDedfADwbeN0b+EBVd6pqGfAmcE5DCpyNPv0UrroKHn/cRty88AKsW5fqUjnnskUsgb4bEN5qXBTYV4WItARGA9MDuz4FhotIh8Cx04Co039E5AoRmS8i80tKSmItfyXFxbZodiYESVVLVAawcCE8+qitHAX2u6AgdWVzzmWXWAJ9tCwrWs25ZwLvqupmAFVdBvwBmAX8B1gMlEV7o6pOVtXBqjq4U6dOMRSrqoICeOed9A+SJSVwzDHw1FO2PWIE5ObC3r22XVpqtftM+MJyzqW/WAJ9EZVr4d2BtdWcO5ZQsw0Aqvqoqh6lqsOBzcAX9SlobYqLLThWVMBjj6VfkCwthSVL7HXHjtC1a2ic/O9/H6rdB3mt3jkXL7EE+nnAoSLSS0SaYsF8ZuRJItIWGAG8GLH/gMDvHsAPifgiiJeCAigLPCuUldn2l1/CvfdaDTrVLrkETj7ZFhERgeefD60W9f779kUQrrQU3nsv+eV0zmWfWgN9oBP1WuBVYBnwnKouEZHxIjI+7NRzgNdU9duIS0wXkaXAS8A1qrolTmXfJ1ibDw/0jz8OL78MP/85fBso0X//CzfdBN98U/11GtLGH/7+TZvgzjth2zY7dsMN9qTRtGnV9y1caG32kT8LF9avHM45V4mqpt3PoEGDtC6uukq1adPKYbJpU9Wrr1Zdt061osLOu+8+1fbtVUtLbfuBB1Qvvli1vDx0nZwce199hL//o49URVSfe65+13LOuboA5ms1MTUrZsbW1PRx4IGhRTtuuAHWr4e8PNvevBnWroWcHKuNT55sbeXBjtBx4ypPXvrxj+Gyy0Lb559vwyLB3v/QQ6H3d+kCK1fCmDGJu2/nnItFVgT6ujR9NAnL7jNhAsyaZa/DOz6DHaE9e9pPUK9ekJ9febtHj9D7g18owfeHn+ucc6kiVuNPL4MHD9b58+cn7fOKi+Hgg2H37tC+Fi1gxQro3Dnx73fOuYYSkQWqOjjasayo0TdUQUHDhjc29P3OOZdIHuhp+PBGHx7pnEtnno+ehg9j9GGQzrl05jV655zLch7onXMuy3mgd865LOeB3jnnspwHeuecy3JpOWFKREqA1akuR5x1BDamuhAJ4PeVWfy+Mkdd7ylfVaMu5pGWgT4bicj86matZTK/r8zi95U54nlP3nTjnHNZzgO9c85lOQ/0yTM51QVIEL+vzOL3lTnidk/eRu+cc1nOa/TOOZflPNA751yW80AfJyLymIhsEJFPw/a1F5FZIvJF4He7sGO/EJFCEVkuIqekptQ1E5GDROR1EVkmIktE5PrA/ky/r+YiMldEFgfu6zeB/Rl9X0EikisiC0Xk5cB2xt+XiKwSkU9EZJGIzA/sy4b72l9EponIZ4H/Z8MScl/VLSbrP3X7AYYDRwGfhu37I3Br4PWtwB8Cr/sAi4FmQC/gSyA31fcQ5Z66AEcFXrcBPg+UPdPvS4DWgdd5wIfAMZl+X2H3dyPwDPByNvw7DJR1FdAxYl823NeTwE8Dr5sC+yfivrxGHyeq+hawOWL3WdhfJIHfZ4ftn6qqe1R1JVAIDElKQetAVYtV9aPA6+3AMqAbmX9fqqo7Apt5gR8lw+8LQES6A6cDj4Ttzvj7qkZG35eI7IdVEB8FUNVSVd1KAu7LA31iHaiqxWBBEzggsL8bsCbsvKLAvrQlIj2BgVjtN+PvK9C8sQjYAMxS1ay4L+AvwM1A+OKW2XBfCrwmIgtE5IrAvky/r4OBEuDxQFPbIyLSigTclwf61JAo+9J2nKuItAamAz9T1W9qOjXKvrS8L1UtV9UBQHdgiIj0q+H0jLgvETkD2KCqC2J9S5R9aXdfAd9T1aOAU4FrRGR4Dedmyn01wZp7/66qA4Fvsaaa6tT7vjzQJ9Z6EekCEPi9IbC/CDgo7LzuwNokly0mIpKHBfkpqjojsDvj7yso8Kj8BjCazL+v7wE/EJFVwFTgRBF5msy/L1R1beD3BuB5rMki0++rCCgKPE0CTMMCf9zvywN9Ys0ELgm8vgR4MWz/WBFpJiK9gEOBuSkoX41ERLD2w2Wqel/YoUy/r04isn/gdQvgJOAzMvy+VPUXqtpdVXsCY4H/quqPyfD7EpFWItIm+BoYBXxKht+Xqq4D1ojIYYFdI4GlJOK+Ut3rnC0/wLNAMbAX++a9DOgAzAG+CPxuH3b+BKzXfDlwaqrLX809HYc9Gn4MLAr8nJYF93UksDBwX58Cvwrsz+j7irjH4wmNusno+8LashcHfpYAE7LhvgLlHADMD/xbfAFol4j78hQIzjmX5bzpxjnnspwHeuecy3Ie6J1zLst5oHfOuSzngd4557KcB3rnnMtyHuidcy7L/X8q0dVdQVMDiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 随机森林模型未出现过拟合\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "        model2, X, Y, cv=3, n_jobs=-1, train_sizes=np.linspace(.05, 1., 20),scoring='accuracy')\n",
    "t_score=np.mean(train_scores,axis=1)\n",
    "v_score=np.mean(test_scores,axis=1)\n",
    "plt.plot(train_sizes,t_score,'ro:',label='train acc')\n",
    "plt.plot(train_sizes,v_score,'b^:',label='val acc')\n",
    "plt.legend()\n",
    "plt.title('learning curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>单变量调优</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# n_estimators:700\n",
    "# xs=range(100,1000,100)\n",
    "# learning_rate:0.05\n",
    "# lr=[0.03,0.04,0.05,0.06,0.07]\n",
    "# gamma:no effect\n",
    "# gamma=[1,0.1,0.01]\n",
    "# depth:4\n",
    "# depth=range(3,8)\n",
    "# booster:gbtree or dart\n",
    "# booster=['gbtree','gblinear','dart']\n",
    "# min_child_weight:3\n",
    "min_child_weight=range(1,10)\n",
    "#可以对单个变量进行调节，单个调节不如综合调节，但是可以对各个变量确定一下调节范围\n",
    "train_score,val_score=validation_curve(XGBClassifier(),X,Y,param_name='min_child_weight',param_range=min_child_weight,scoring=\"accuracy\",cv=10,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trs=np.mean(train_score,axis=1)\n",
    "vas=np.mean(val_score,axis=1)\n",
    "plt.plot(min_child_weight,trs,'ro:',label='train')\n",
    "plt.plot(min_child_weight,vas,'b^:',label='val')\n",
    "plt.title('validation curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 随机森林分类器，min_samples_split：6\n",
    "train_score,val_score=validation_curve(RandomForestClassifier(),X,Y,param_name='min_samples_split',param_range=range(2,16,2),scoring=\"accuracy\",cv=10,n_jobs=-1)\n",
    "trs=np.mean(train_score,axis=1)\n",
    "vas=np.mean(val_score,axis=1)\n",
    "plt.plot(range(2,16,2),trs,'ro:',label='train')\n",
    "plt.plot(range(2,16,2),vas,'b^:',label='val')\n",
    "plt.title('validation curve')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}