QATAR22_EDA+Data_Preparation.ipynb

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   "source": [
    "<a href=\"https://github.com/ogbeiedward/FIFA-World-Cup-Qatar-2022-Prediction/blob/main/QATAR22_EDA%2BData_Preparation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "aWw2Ofd7UjIu"
   },
   "source": [
    "# CONTENT\n",
    "- OBJECTIVE\n",
    "- PRE-ANALYSIS\n",
    "- DATA PREPARATION AND FEATURE ENGINEERING\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5LCeRDXa7fb2"
   },
   "source": [
    "# OBJECTIVE\n",
    "This notebook aims to analyze the \"international_matches.csv\" dataset and from it, create a training dataset and an inference dataset, which will be used to predict the results of the QATAR 2022 World Cup.\n"
   ]
  },
  {
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   "source": [
    "#Required libraries\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_team_continent</th>\n",
       "      <th>away_team_continent</th>\n",
       "      <th>home_team_fifa_rank</th>\n",
       "      <th>away_team_fifa_rank</th>\n",
       "      <th>home_team_total_fifa_points</th>\n",
       "      <th>away_team_total_fifa_points</th>\n",
       "      <th>home_team_score</th>\n",
       "      <th>...</th>\n",
       "      <th>shoot_out</th>\n",
       "      <th>home_team_result</th>\n",
       "      <th>home_team_goalkeeper_score</th>\n",
       "      <th>away_team_goalkeeper_score</th>\n",
       "      <th>home_team_mean_defense_score</th>\n",
       "      <th>home_team_mean_offense_score</th>\n",
       "      <th>home_team_mean_midfield_score</th>\n",
       "      <th>away_team_mean_defense_score</th>\n",
       "      <th>away_team_mean_offense_score</th>\n",
       "      <th>away_team_mean_midfield_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993-08-08</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>59</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Win</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993-08-08</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>South America</td>\n",
       "      <td>North America</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Draw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1993-08-08</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>35</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Win</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993-08-08</td>\n",
       "      <td>Guinea</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>65</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Win</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993-08-08</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>67</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Lose</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date home_team     away_team home_team_continent away_team_continent  \\\n",
       "0 1993-08-08   Bolivia       Uruguay       South America       South America   \n",
       "1 1993-08-08    Brazil        Mexico       South America       North America   \n",
       "2 1993-08-08   Ecuador     Venezuela       South America       South America   \n",
       "3 1993-08-08    Guinea  Sierra Leone              Africa              Africa   \n",
       "4 1993-08-08  Paraguay     Argentina       South America       South America   \n",
       "\n",
       "   home_team_fifa_rank  away_team_fifa_rank  home_team_total_fifa_points  \\\n",
       "0                   59                   22                            0   \n",
       "1                    8                   14                            0   \n",
       "2                   35                   94                            0   \n",
       "3                   65                   86                            0   \n",
       "4                   67                    5                            0   \n",
       "\n",
       "   away_team_total_fifa_points  home_team_score  ...  shoot_out  \\\n",
       "0                            0                3  ...         No   \n",
       "1                            0                1  ...         No   \n",
       "2                            0                5  ...         No   \n",
       "3                            0                1  ...         No   \n",
       "4                            0                1  ...         No   \n",
       "\n",
       "  home_team_result home_team_goalkeeper_score away_team_goalkeeper_score  \\\n",
       "0              Win                        NaN                        NaN   \n",
       "1             Draw                        NaN                        NaN   \n",
       "2              Win                        NaN                        NaN   \n",
       "3              Win                        NaN                        NaN   \n",
       "4             Lose                        NaN                        NaN   \n",
       "\n",
       "   home_team_mean_defense_score home_team_mean_offense_score  \\\n",
       "0                           NaN                          NaN   \n",
       "1                           NaN                          NaN   \n",
       "2                           NaN                          NaN   \n",
       "3                           NaN                          NaN   \n",
       "4                           NaN                          NaN   \n",
       "\n",
       "  home_team_mean_midfield_score  away_team_mean_defense_score  \\\n",
       "0                           NaN                           NaN   \n",
       "1                           NaN                           NaN   \n",
       "2                           NaN                           NaN   \n",
       "3                           NaN                           NaN   \n",
       "4                           NaN                           NaN   \n",
       "\n",
       "   away_team_mean_offense_score  away_team_mean_midfield_score  \n",
       "0                           NaN                            NaN  \n",
       "1                           NaN                            NaN  \n",
       "2                           NaN                            NaN  \n",
       "3                           NaN                            NaN  \n",
       "4                           NaN                            NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/international_matches.csv', parse_dates=['date'])\n",
    "#df.tail(5)\n",
    "df.head(5)"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "Index(['date', 'home_team', 'away_team', 'home_team_continent',\n",
       "       'away_team_continent', 'home_team_fifa_rank', 'away_team_fifa_rank',\n",
       "       'home_team_total_fifa_points', 'away_team_total_fifa_points',\n",
       "       'home_team_score', 'away_team_score', 'tournament', 'city', 'country',\n",
       "       'neutral_location', 'shoot_out', 'home_team_result',\n",
       "       'home_team_goalkeeper_score', 'away_team_goalkeeper_score',\n",
       "       'home_team_mean_defense_score', 'home_team_mean_offense_score',\n",
       "       'home_team_mean_midfield_score', 'away_team_mean_defense_score',\n",
       "       'away_team_mean_offense_score', 'away_team_mean_midfield_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
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   "execution_count": 11,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                 0\n",
       "home_team                            0\n",
       "away_team                            0\n",
       "home_team_continent                  0\n",
       "away_team_continent                  0\n",
       "home_team_fifa_rank                  0\n",
       "away_team_fifa_rank                  0\n",
       "home_team_total_fifa_points          0\n",
       "away_team_total_fifa_points          0\n",
       "home_team_score                      0\n",
       "away_team_score                      0\n",
       "tournament                           0\n",
       "city                                 0\n",
       "country                              0\n",
       "neutral_location                     0\n",
       "shoot_out                            0\n",
       "home_team_result                     0\n",
       "home_team_goalkeeper_score       15542\n",
       "away_team_goalkeeper_score       15826\n",
       "home_team_mean_defense_score     16134\n",
       "home_team_mean_offense_score     15411\n",
       "home_team_mean_midfield_score    15759\n",
       "away_team_mean_defense_score     16357\n",
       "away_team_mean_offense_score     15609\n",
       "away_team_mean_midfield_score    15942\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VT019Ma1ZNkd"
   },
   "source": [
    "# PRE-ANALYSIS\n",
    "The dataset has a lot of blank fields that needs to be cleaned up. Before modifying any field, let's analyze the teams' qualifications on the last FIFA date (June 2022). We do this because, from these qualifications, we will create the inference dataset that enters the machine learning algorithm that predicts the World Cup matches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dJWf4Iwdczy8"
   },
   "source": [
    "### Top 10 FIFA Ranking\n",
    "Top 10 national teams to date FIFA June 2022.\n",
    "**ref:** https://www.fifa.com/fifa-world-ranking/men?dateId=id13603"
   ]
  },
  {
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   "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>team</th>\n",
       "      <th>date</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          team       date  rank\n",
       "0       Brazil 2022-06-06     1\n",
       "1      Belgium 2022-06-14     2\n",
       "2       France 2022-06-13     3\n",
       "3    Argentina 2022-06-05     4\n",
       "4      England 2022-06-14     5\n",
       "5        Italy 2022-06-14     6\n",
       "6        Spain 2022-06-12     7\n",
       "7     Portugal 2022-06-12     8\n",
       "8       Mexico 2022-06-14     9\n",
       "9  Netherlands 2022-06-14    10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa_rank = df[['date','home_team', 'away_team', 'home_team_fifa_rank', 'away_team_fifa_rank']]\n",
    "#sort out the home teams\n",
    "home = fifa_rank[['date', 'home_team', 'home_team_fifa_rank']].rename(columns={\"home_team\":\"team\", \"home_team_fifa_rank\":\"rank\"})\n",
    "#sort out the away teams\n",
    "away = fifa_rank[['date', 'away_team', 'away_team_fifa_rank']].rename(columns={\"away_team\":\"team\", \"away_team_fifa_rank\":\"rank\"})\n",
    "#merge the tables\n",
    "fifa_rank = pd.concat([home, away])\n",
    "\n",
    "#Select each country latest match\n",
    "fifa_rank = fifa_rank.sort_values(['team', 'date'], ascending=[True, False])\n",
    "last_rank = fifa_rank\n",
    "fifa_rank_top10 = fifa_rank.groupby('team').first().sort_values('rank', ascending=True)[0:10].reset_index()\n",
    "fifa_rank_top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8rO1K5nZczy9"
   },
   "source": [
    "### Teams with the highest winning percentage at home and away (top 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:42:15.425915Z",
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     "shell.execute_reply.started": "2022-09-16T05:42:15.42589Z"
    },
    "id": "bOtzjUBAczy9"
   },
   "outputs": [],
   "source": [
    "def home_percentage(team):\n",
    "    score = len(df[(df['home_team'] == team) & (df['home_team_result'] == \"Win\")]) / len(df[df['home_team'] == team]) * 100\n",
    "    return round(score)\n",
    "def away_percentage(team):\n",
    "    score = len(df[(df['away_team'] == team) & (df['home_team_result'] == \"Lose\")]) / len(df[df['away_team'] == team]) * 100\n",
    "    return round(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:42:38.952052Z",
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     "iopub.status.idle": "2022-09-16T05:42:39.095093Z",
     "shell.execute_reply": "2022-09-16T05:42:39.094267Z",
     "shell.execute_reply.started": "2022-09-16T05:42:38.952015Z"
    },
    "id": "bq4_xpA-czy9",
    "outputId": "0cc6399e-4983-41cb-ee2a-45a0ee13bc8b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>date</th>\n",
       "      <th>rank</th>\n",
       "      <th>Home_win_Per</th>\n",
       "      <th>Away_win_Per</th>\n",
       "      <th>Average_win_Per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>60</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>61</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>51</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>8</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>10</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>49</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>46</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>41</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          team       date  rank  Home_win_Per  Away_win_Per  Average_win_Per\n",
       "0       Brazil 2022-06-06     1            78            60             69.0\n",
       "6        Spain 2022-06-12     7            77            61             69.0\n",
       "2       France 2022-06-13     3            66            57             62.0\n",
       "3    Argentina 2022-06-05     4            68            49             58.0\n",
       "4      England 2022-06-14     5            64            51             58.0\n",
       "7     Portugal 2022-06-12     8            67            49             58.0\n",
       "9  Netherlands 2022-06-14    10            62            55             58.0\n",
       "5        Italy 2022-06-14     6            60            49             54.0\n",
       "1      Belgium 2022-06-14     2            60            46             53.0\n",
       "8       Mexico 2022-06-14     9            60            41             50.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa_rank_top10['Home_win_Per'] = np.vectorize(home_percentage)(fifa_rank_top10['team'])\n",
    "fifa_rank_top10['Away_win_Per'] = np.vectorize(away_percentage)(fifa_rank_top10['team'])\n",
    "fifa_rank_top10['Average_win_Per'] = round((fifa_rank_top10['Home_win_Per'] + fifa_rank_top10['Away_win_Per']) / 2)\n",
    "fifa_rank_win = fifa_rank_top10.sort_values('Average_win_Per', ascending = False)\n",
    "fifa_rank_win"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KpWPXODaczy-"
   },
   "source": [
    "### Most attacking teams in the last FIFA date (Top 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:43:18.363949Z",
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     "iopub.status.idle": "2022-09-16T05:43:18.413666Z",
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     "shell.execute_reply.started": "2022-09-16T05:43:18.363913Z"
    },
    "id": "fuiciCVOczy-",
    "outputId": "b1117e0b-4dde-4bcb-b93f-ac784964d8a5"
   },
   "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>team</th>\n",
       "      <th>date</th>\n",
       "      <th>offense_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>88.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>England</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>86.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>85.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>85.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>84.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2022-06-11</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        team       date  offense_score\n",
       "0  Argentina 2022-06-05           89.0\n",
       "1     France 2022-06-13           88.3\n",
       "2    England 2022-06-14           88.0\n",
       "3     Brazil 2022-06-06           86.3\n",
       "4   Portugal 2022-06-12           86.0\n",
       "5    Belgium 2022-06-14           85.7\n",
       "6      Italy 2022-06-14           85.3\n",
       "7      Spain 2022-06-12           85.0\n",
       "8     Poland 2022-06-14           84.7\n",
       "9    Uruguay 2022-06-11           84.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa_offense = df[['date', 'home_team', 'away_team', 'home_team_mean_offense_score', 'away_team_mean_offense_score']]\n",
    "home = fifa_offense[['date', 'home_team', 'home_team_mean_offense_score']].rename(columns={\"home_team\":\"team\", \"home_team_mean_offense_score\":\"offense_score\"})\n",
    "away = fifa_offense[['date', 'away_team', 'away_team_mean_offense_score']].rename(columns={\"away_team\":\"team\", \"away_team_mean_offense_score\":\"offense_score\"})\n",
    "fifa_offense = pd.concat([home, away])\n",
    "fifa_offense = fifa_offense.sort_values(['date', 'team'],ascending=[False, True])\n",
    "last_offense = fifa_offense\n",
    "fifa_offense_top10 = fifa_offense.groupby('team').first().sort_values('offense_score', ascending=False)[0:10].reset_index()\n",
    "fifa_offense_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:43:30.757049Z",
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     "iopub.status.idle": "2022-09-16T05:43:30.93647Z",
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     "shell.execute_reply.started": "2022-09-16T05:43:30.757024Z"
    },
    "id": "ZI2RVhsPczy-",
    "outputId": "7cc5a380-5fcb-4146-f275-2f38e1ba11da"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=fifa_offense_top10, x='offense_score', y='team', color=\"#7F1431\")\n",
    "plt.xlabel('Offense Score', size = 20) \n",
    "plt.ylabel('Team', size = 20) \n",
    "plt.title(\"Top 10 Attacking teams\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h1s9cW2Cczy-"
   },
   "source": [
    "### Top 10 Midfield teams in the last FIFA date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:44:07.187443Z",
     "iopub.status.busy": "2022-09-16T05:44:07.187131Z",
     "iopub.status.idle": "2022-09-16T05:44:07.230419Z",
     "shell.execute_reply": "2022-09-16T05:44:07.229385Z",
     "shell.execute_reply.started": "2022-09-16T05:44:07.18742Z"
    },
    "id": "OPYkt_Igczy_",
    "outputId": "7adf0372-3631-4779-e639-52254ee1e940"
   },
   "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>team</th>\n",
       "      <th>date</th>\n",
       "      <th>midfield_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>87.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>86.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>84.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>England</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        team       date  midfield_score\n",
       "0    Germany 2022-06-14            87.8\n",
       "1     France 2022-06-13            86.8\n",
       "2      Spain 2022-06-12            86.0\n",
       "3    Belgium 2022-06-14            85.5\n",
       "4     Brazil 2022-06-06            85.5\n",
       "5   Portugal 2022-06-12            84.5\n",
       "6      Italy 2022-06-14            84.5\n",
       "7    Croatia 2022-06-13            84.2\n",
       "8    England 2022-06-14            84.0\n",
       "9  Argentina 2022-06-05            84.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa_midfield = df[['date', 'home_team', 'away_team', 'home_team_mean_midfield_score', 'away_team_mean_midfield_score']]\n",
    "home = fifa_midfield[['date', 'home_team', 'home_team_mean_midfield_score']].rename(columns={\"home_team\":\"team\", \"home_team_mean_midfield_score\":\"midfield_score\"})\n",
    "away = fifa_midfield[['date', 'away_team', 'away_team_mean_midfield_score']].rename(columns={\"away_team\":\"team\", \"away_team_mean_midfield_score\":\"midfield_score\"})\n",
    "fifa_midfield = pd.concat([home,away])\n",
    "fifa_midfield = fifa_midfield.sort_values(['date','team'],ascending=[False,True])\n",
    "last_midfield = fifa_midfield\n",
    "fifa_midfield_top10 = fifa_midfield.groupby('team').first().sort_values('midfield_score',ascending=False)[0:10].reset_index()\n",
    "fifa_midfield_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:44:26.367307Z",
     "iopub.status.busy": "2022-09-16T05:44:26.366949Z",
     "iopub.status.idle": "2022-09-16T05:44:26.547587Z",
     "shell.execute_reply": "2022-09-16T05:44:26.546427Z",
     "shell.execute_reply.started": "2022-09-16T05:44:26.367282Z"
    },
    "id": "BjuoeAw0czy_",
    "outputId": "3f7adb75-779b-42c4-8c41-92a8f2aca651"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=fifa_midfield_top10, x='midfield_score', y='team', color=\"#7F1431\")\n",
    "plt.xlabel('Midfield Score', size = 20) \n",
    "plt.ylabel('Team', size = 20) \n",
    "plt.title(\"Top 10 Midfield teams\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3IkYZ_x3czy_"
   },
   "source": [
    "### Top 10 defending teams in the last FIFA date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:44:45.218555Z",
     "iopub.status.busy": "2022-09-16T05:44:45.21825Z",
     "iopub.status.idle": "2022-09-16T05:44:45.261008Z",
     "shell.execute_reply": "2022-09-16T05:44:45.260102Z",
     "shell.execute_reply.started": "2022-09-16T05:44:45.218533Z"
    },
    "id": "UkuHfpMhczy_",
    "outputId": "01ddd2ee-0785-43da-aa8c-2d572eaf0a9d"
   },
   "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>team</th>\n",
       "      <th>date</th>\n",
       "      <th>defense_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>84.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>84.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>France</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>84.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>82.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2022-06-11</td>\n",
       "      <td>81.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          team       date  defense_score\n",
       "0        Spain 2022-06-12           86.5\n",
       "1     Portugal 2022-06-12           85.2\n",
       "2  Netherlands 2022-06-14           85.2\n",
       "3      England 2022-06-14           85.0\n",
       "4       Brazil 2022-06-06           84.8\n",
       "5        Italy 2022-06-14           84.2\n",
       "6       France 2022-06-13           84.2\n",
       "7      Germany 2022-06-14           84.0\n",
       "8    Argentina 2022-06-05           82.2\n",
       "9      Uruguay 2022-06-11           81.2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa_defense = df[['date', 'home_team', 'away_team', 'home_team_mean_defense_score', 'away_team_mean_defense_score']]\n",
    "home = fifa_defense[['date', 'home_team', 'home_team_mean_defense_score']].rename(columns={\"home_team\":\"team\", \"home_team_mean_defense_score\":\"defense_score\"})\n",
    "away = fifa_defense[['date', 'away_team', 'away_team_mean_defense_score']].rename(columns={\"away_team\":\"team\", \"away_team_mean_defense_score\":\"defense_score\"})\n",
    "fifa_defense = pd.concat([home, away])\n",
    "fifa_defense = fifa_defense.sort_values(['date', 'team'],ascending=[False, True])\n",
    "last_defense = fifa_defense \n",
    "fifa_defense_top10 = fifa_defense.groupby('team').first().sort_values('defense_score', ascending = False)[0:10].reset_index()\n",
    "fifa_defense_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:44:55.011233Z",
     "iopub.status.busy": "2022-09-16T05:44:55.010919Z",
     "iopub.status.idle": "2022-09-16T05:44:55.199813Z",
     "shell.execute_reply": "2022-09-16T05:44:55.198614Z",
     "shell.execute_reply.started": "2022-09-16T05:44:55.011209Z"
    },
    "id": "RSfavsT_czy_",
    "outputId": "e9e0ba1c-a8bd-4038-f993-76c85d509468"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 Defense Teams')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = fifa_defense_top10, x='defense_score', y='team', color=\"#7F1431\")\n",
    "plt.xlabel('Defense Score', size = 20) \n",
    "plt.ylabel('Team', size = 20) \n",
    "plt.title(\"Top 10 Defense Teams\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SYurqwbTelsB"
   },
   "source": [
    "### Do Home teams have any advantage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 484
    },
    "id": "73yjPsHxAzEe",
    "outputId": "2a5560d7-366b-470f-8a0a-f8a55ab9f25c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select all matches played at non-neutral locations\n",
    "home_team_advantage = df[df['neutral_location'] == False]['home_team_result'].value_counts(normalize = True)\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(1, 1, figsize=(8,8))\n",
    "ax =plt.pie(home_team_advantage  ,labels = ['Win', 'Lose', 'Draw'], autopct='%.0f%%')\n",
    "plt.title('Home team match result', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lw-wkz7nnDAd"
   },
   "source": [
    "This question is fundamental. The pie chart above highlights that home teams win more than 50% of the home games. This is due to different reasons, e.g., players are more comfortable in familiar surroundings, additional support from a home crowd, home comforts/lack of travel, configuration of the playing area to suit the home team. When the Colombian National Team visits the Maracana stadium to play against Brazil, they tend to lose the match or draw. However, they tend to tie or win when Colombia is local in the Metropolitano stadium. For this reason, to predict the result of the matches from a Machine Learning model, one must define the home team and the visiting team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oux1hSuDczy_"
   },
   "source": [
    "# DATA PREPARATION AND FEATURE ENGINEERING\n",
    "In this section, we will fill in the empty fields in the dataset and clean up the data for teams that did not qualify for the World Cup. Then, we use the correlation matrix to choose the characteristics that will define the training dataset of the Machine Learning model. Finally, we will use the ratings of the teams in their last match to define the \"Last Team Scores\" dataset (i.e., the dataset that I will use to predict the World Cup matches)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uu3CPfusl4Hu"
   },
   "source": [
    "### Analyze and fill na's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                 0\n",
       "home_team                            0\n",
       "away_team                            0\n",
       "home_team_continent                  0\n",
       "away_team_continent                  0\n",
       "home_team_fifa_rank                  0\n",
       "away_team_fifa_rank                  0\n",
       "home_team_total_fifa_points          0\n",
       "away_team_total_fifa_points          0\n",
       "home_team_score                      0\n",
       "away_team_score                      0\n",
       "tournament                           0\n",
       "city                                 0\n",
       "country                              0\n",
       "neutral_location                     0\n",
       "shoot_out                            0\n",
       "home_team_result                     0\n",
       "home_team_goalkeeper_score       15542\n",
       "away_team_goalkeeper_score       15826\n",
       "home_team_mean_defense_score     16134\n",
       "home_team_mean_offense_score     15411\n",
       "home_team_mean_midfield_score    15759\n",
       "away_team_mean_defense_score     16357\n",
       "away_team_mean_offense_score     15609\n",
       "away_team_mean_midfield_score    15942\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:45:31.403735Z",
     "iopub.status.busy": "2022-09-16T05:45:31.403327Z",
     "iopub.status.idle": "2022-09-16T05:45:31.422868Z",
     "shell.execute_reply": "2022-09-16T05:45:31.420628Z",
     "shell.execute_reply.started": "2022-09-16T05:45:31.403701Z"
    },
    "id": "2k2rkjh2czzA",
    "outputId": "a4bf36cf-50c6-4571-edc6-ae70687ff2c2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    135.000000\n",
       "mean      86.074074\n",
       "std        3.327026\n",
       "min       82.000000\n",
       "25%       83.000000\n",
       "50%       86.000000\n",
       "75%       89.000000\n",
       "max       93.000000\n",
       "Name: home_team_goalkeeper_score, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can fill the NA values with the mean in goal_keeper_score\n",
    "df[df['home_team'] == \"Brazil\"]['home_team_goalkeeper_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:45:41.400325Z",
     "iopub.status.busy": "2022-09-16T05:45:41.399865Z",
     "iopub.status.idle": "2022-09-16T05:45:41.511674Z",
     "shell.execute_reply": "2022-09-16T05:45:41.510542Z",
     "shell.execute_reply.started": "2022-09-16T05:45:41.400293Z"
    },
    "id": "vFeJ-l8YczzA"
   },
   "outputs": [],
   "source": [
    "df['home_team_goalkeeper_score'] = round(df.groupby(\"home_team\")[\"home_team_goalkeeper_score\"].transform(lambda x: x.fillna(x.mean())))\n",
    "df['away_team_goalkeeper_score'] = round(df.groupby(\"away_team\")[\"away_team_goalkeeper_score\"].transform(lambda x: x.fillna(x.mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:45:50.13293Z",
     "iopub.status.busy": "2022-09-16T05:45:50.132607Z",
     "iopub.status.idle": "2022-09-16T05:45:50.146759Z",
     "shell.execute_reply": "2022-09-16T05:45:50.145778Z",
     "shell.execute_reply.started": "2022-09-16T05:45:50.132907Z"
    },
    "id": "IFnXnqG8czzA",
    "outputId": "39f4772b-0d4c-4a85-9d0a-a61012064dfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    105.000000\n",
       "mean      76.580000\n",
       "std        5.589988\n",
       "min       58.500000\n",
       "25%       73.200000\n",
       "50%       76.500000\n",
       "75%       80.000000\n",
       "max       89.500000\n",
       "Name: home_team_mean_defense_score, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can fill mean for na's in defense score\n",
    "df[df['away_team'] == \"Uruguay\"]['home_team_mean_defense_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:46:00.576582Z",
     "iopub.status.busy": "2022-09-16T05:46:00.576243Z",
     "iopub.status.idle": "2022-09-16T05:46:00.685258Z",
     "shell.execute_reply": "2022-09-16T05:46:00.684354Z",
     "shell.execute_reply.started": "2022-09-16T05:46:00.576558Z"
    },
    "id": "ZmtoaYaLczzA"
   },
   "outputs": [],
   "source": [
    "df['home_team_mean_defense_score'] = round(df.groupby('home_team')['home_team_mean_defense_score'].transform(lambda x : x.fillna(x.mean())))\n",
    "df['away_team_mean_defense_score'] = round(df.groupby('away_team')['away_team_mean_defense_score'].transform(lambda x : x.fillna(x.mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:46:07.821016Z",
     "iopub.status.busy": "2022-09-16T05:46:07.820668Z",
     "iopub.status.idle": "2022-09-16T05:46:07.834065Z",
     "shell.execute_reply": "2022-09-16T05:46:07.833318Z",
     "shell.execute_reply.started": "2022-09-16T05:46:07.820993Z"
    },
    "id": "-zWUGSvPczzA",
    "outputId": "7be6e463-b2ff-42a0-8eba-ee91f510ecf7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    117.000000\n",
       "mean      78.136752\n",
       "std        5.878813\n",
       "min       66.700000\n",
       "25%       74.000000\n",
       "50%       78.000000\n",
       "75%       82.300000\n",
       "max       90.300000\n",
       "Name: home_team_mean_offense_score, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can fill mean for na's in offense score\n",
    "df[df['away_team'] == \"Uruguay\"]['home_team_mean_offense_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:46:16.19468Z",
     "iopub.status.busy": "2022-09-16T05:46:16.194285Z",
     "iopub.status.idle": "2022-09-16T05:46:16.313984Z",
     "shell.execute_reply": "2022-09-16T05:46:16.312526Z",
     "shell.execute_reply.started": "2022-09-16T05:46:16.194646Z"
    },
    "id": "fgvgEg2kczzA"
   },
   "outputs": [],
   "source": [
    "df['home_team_mean_offense_score'] = round(df.groupby('home_team')['home_team_mean_offense_score'].transform(lambda x : x.fillna(x.mean())))\n",
    "df['away_team_mean_offense_score'] = round(df.groupby('away_team')['away_team_mean_offense_score'].transform(lambda x : x.fillna(x.mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:46:27.330951Z",
     "iopub.status.busy": "2022-09-16T05:46:27.330612Z",
     "iopub.status.idle": "2022-09-16T05:46:27.344112Z",
     "shell.execute_reply": "2022-09-16T05:46:27.343258Z",
     "shell.execute_reply.started": "2022-09-16T05:46:27.330928Z"
    },
    "id": "FIrZX23YczzA",
    "outputId": "1c172603-9206-4b94-a630-a214feb2404b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110.000000\n",
       "mean      77.729091\n",
       "std        5.656617\n",
       "min       63.200000\n",
       "25%       75.050000\n",
       "50%       77.650000\n",
       "75%       81.425000\n",
       "max       89.500000\n",
       "Name: home_team_mean_midfield_score, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can fill mean for na's in midfield score\n",
    "df[df['away_team'] == \"Uruguay\"]['home_team_mean_midfield_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:46:36.006525Z",
     "iopub.status.busy": "2022-09-16T05:46:36.006158Z",
     "iopub.status.idle": "2022-09-16T05:46:36.117942Z",
     "shell.execute_reply": "2022-09-16T05:46:36.117014Z",
     "shell.execute_reply.started": "2022-09-16T05:46:36.006484Z"
    },
    "id": "OckjU4rOczzA"
   },
   "outputs": [],
   "source": [
    "df['home_team_mean_midfield_score'] = round(df.groupby('home_team')['home_team_mean_midfield_score'].transform(lambda x : x.fillna(x.mean())))\n",
    "df['away_team_mean_midfield_score'] = round(df.groupby('away_team')['away_team_mean_midfield_score'].transform(lambda x : x.fillna(x.mean())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:46:43.401286Z",
     "iopub.status.busy": "2022-09-16T05:46:43.400953Z",
     "iopub.status.idle": "2022-09-16T05:46:43.417688Z",
     "shell.execute_reply": "2022-09-16T05:46:43.416931Z",
     "shell.execute_reply.started": "2022-09-16T05:46:43.40126Z"
    },
    "id": "tyhKCVFoczzA",
    "outputId": "25f9a740-d078-4979-9f54-74d984d410bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                0\n",
       "home_team                           0\n",
       "away_team                           0\n",
       "home_team_continent                 0\n",
       "away_team_continent                 0\n",
       "home_team_fifa_rank                 0\n",
       "away_team_fifa_rank                 0\n",
       "home_team_total_fifa_points         0\n",
       "away_team_total_fifa_points         0\n",
       "home_team_score                     0\n",
       "away_team_score                     0\n",
       "tournament                          0\n",
       "city                                0\n",
       "country                             0\n",
       "neutral_location                    0\n",
       "shoot_out                           0\n",
       "home_team_result                    0\n",
       "home_team_goalkeeper_score       5683\n",
       "away_team_goalkeeper_score       6254\n",
       "home_team_mean_defense_score     8019\n",
       "home_team_mean_offense_score     6995\n",
       "home_team_mean_midfield_score    6955\n",
       "away_team_mean_defense_score     8285\n",
       "away_team_mean_offense_score     7165\n",
       "away_team_mean_midfield_score    7036\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:47:12.483155Z",
     "iopub.status.busy": "2022-09-16T05:47:12.482755Z",
     "iopub.status.idle": "2022-09-16T05:47:12.586928Z",
     "shell.execute_reply": "2022-09-16T05:47:12.585971Z",
     "shell.execute_reply.started": "2022-09-16T05:47:12.483121Z"
    },
    "id": "3jyzMHpSczzA"
   },
   "outputs": [],
   "source": [
    "# Teams are not available in FIFA game itself, so they are not less than average performing teams, so giving a average score of 50 for all.\n",
    "df.fillna(50,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o4eemYO6mKL4"
   },
   "source": [
    "### Filter the teams participating in QATAR - World cup 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:47:30.45566Z",
     "iopub.status.busy": "2022-09-16T05:47:30.455378Z",
     "iopub.status.idle": "2022-09-16T05:47:30.497311Z",
     "shell.execute_reply": "2022-09-16T05:47:30.496169Z",
     "shell.execute_reply.started": "2022-09-16T05:47:30.455636Z"
    },
    "id": "_8BI1emIczzB"
   },
   "outputs": [],
   "source": [
    "list_2022 = ['Qatar', 'Germany', 'Denmark', 'Brazil', 'France', 'Belgium', 'Croatia', 'Spain', 'Serbia', 'England', 'Switzerland', 'Netherlands', 'Argentina', 'IR Iran', 'Korea Republic', 'Japan', 'Saudi Arabia', 'Ecuador', 'Uruguay', 'Canada', 'Ghana', 'Senegal', 'Portugal', 'Poland', 'Tunisia', 'Morocco', 'Cameroon', 'USA', 'Mexico', 'Wales', 'Australia', 'Costa Rica']\n",
    "final_df = df[(df[\"home_team\"].apply(lambda x: x in list_2022)) | (df[\"away_team\"].apply(lambda x: x in list_2022))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o5J41BGZczzB"
   },
   "source": [
    "**Top 10 teams in QATAR 2022**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:48:04.872235Z",
     "iopub.status.busy": "2022-09-16T05:48:04.871784Z",
     "iopub.status.idle": "2022-09-16T05:48:04.907942Z",
     "shell.execute_reply": "2022-09-16T05:48:04.907081Z",
     "shell.execute_reply.started": "2022-09-16T05:48:04.872202Z"
    },
    "id": "jYol7gOqczzB",
    "outputId": "8ec527eb-dfcc-4f22-eff1-0bf7f75c7b17"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>date</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           team       date  rank\n",
       "0        Brazil 2022-06-06     1\n",
       "1       Belgium 2022-06-14     2\n",
       "2        France 2022-06-13     3\n",
       "3     Argentina 2022-06-05     4\n",
       "4       England 2022-06-14     5\n",
       "6         Spain 2022-06-12     7\n",
       "7      Portugal 2022-06-12     8\n",
       "8        Mexico 2022-06-14     9\n",
       "9   Netherlands 2022-06-14    10\n",
       "10      Denmark 2022-06-13    11"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank = final_df[['date','home_team','away_team','home_team_fifa_rank', 'away_team_fifa_rank']]\n",
    "home = rank[['date','home_team','home_team_fifa_rank']].rename(columns={\"home_team\":\"team\",\"home_team_fifa_rank\":\"rank\"})\n",
    "away = rank[['date','away_team','away_team_fifa_rank']].rename(columns={\"away_team\":\"team\",\"away_team_fifa_rank\":\"rank\"})\n",
    "rank = pd.concat([home,away])\n",
    "\n",
    "# Select each country latest match\n",
    "rank = rank.sort_values(['team','date'],ascending=[True,False])\n",
    "rank_top10 = rank.groupby('team').first().sort_values('rank',ascending=True).reset_index()\n",
    "rank_top10 = rank_top10[(rank_top10[\"team\"].apply(lambda x: x in list_2022))][0:10]\n",
    "rank_top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ro9fGfmtrxsh"
   },
   "source": [
    "**Top 10 teams with the highest winning percentage in QATAR 2022**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:48:11.334009Z",
     "iopub.status.busy": "2022-09-16T05:48:11.333717Z",
     "iopub.status.idle": "2022-09-16T05:48:11.481754Z",
     "shell.execute_reply": "2022-09-16T05:48:11.480973Z",
     "shell.execute_reply.started": "2022-09-16T05:48:11.333987Z"
    },
    "id": "hxARVGK5czzB",
    "outputId": "46bc1966-39db-491c-def7-656d587b06c8"
   },
   "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>team</th>\n",
       "      <th>date</th>\n",
       "      <th>rank</th>\n",
       "      <th>Home_win_Per</th>\n",
       "      <th>Away_win_Per</th>\n",
       "      <th>Average_win_Per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>60</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>61</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>51</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>8</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>10</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>46</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>41</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           team       date  rank  Home_win_Per  Away_win_Per  Average_win_Per\n",
       "0        Brazil 2022-06-06     1            78            60             69.0\n",
       "6         Spain 2022-06-12     7            77            61             69.0\n",
       "2        France 2022-06-13     3            66            57             62.0\n",
       "3     Argentina 2022-06-05     4            68            49             58.0\n",
       "4       England 2022-06-14     5            64            51             58.0\n",
       "7      Portugal 2022-06-12     8            67            49             58.0\n",
       "9   Netherlands 2022-06-14    10            62            55             58.0\n",
       "1       Belgium 2022-06-14     2            60            46             53.0\n",
       "8        Mexico 2022-06-14     9            60            41             50.0\n",
       "10      Denmark 2022-06-13    11            56            45             50.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_top10['Home_win_Per'] = np.vectorize(home_percentage)(rank_top10['team'])\n",
    "rank_top10['Away_win_Per'] = np.vectorize(away_percentage)(rank_top10['team'])\n",
    "rank_top10['Average_win_Per'] = round((rank_top10['Home_win_Per'] + rank_top10['Away_win_Per'])/2)\n",
    "rank_top10_Win = rank_top10.sort_values('Average_win_Per',ascending=False)\n",
    "rank_top10_Win"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:48:19.991849Z",
     "iopub.status.busy": "2022-09-16T05:48:19.991516Z",
     "iopub.status.idle": "2022-09-16T05:48:20.193784Z",
     "shell.execute_reply": "2022-09-16T05:48:20.192252Z",
     "shell.execute_reply.started": "2022-09-16T05:48:19.991822Z"
    },
    "id": "QEAzw2oAczzB",
    "outputId": "65d0eba2-5d28-4af9-bbef-0a50f54ab3d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 QATAR 2022 teams with the highest winning percentage')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=rank_top10_Win,x='Average_win_Per',y='team',color=\"#7F1431\")\n",
    "plt.xticks()\n",
    "plt.xlabel('Win Average', size = 20) \n",
    "plt.ylabel('Team', size = 20) \n",
    "plt.title('Top 10 QATAR 2022 teams with the highest winning percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RVxHeFDwsbNb"
   },
   "source": [
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:48:32.091183Z",
     "iopub.status.busy": "2022-09-16T05:48:32.090871Z",
     "iopub.status.idle": "2022-09-16T05:48:32.099575Z",
     "shell.execute_reply": "2022-09-16T05:48:32.098881Z",
     "shell.execute_reply.started": "2022-09-16T05:48:32.091149Z"
    },
    "id": "a1FcVAFGczzB"
   },
   "outputs": [],
   "source": [
    "# Mapping numeric values for home_team_result to find the correleations\n",
    "final_df['home_team_result'] = final_df['home_team_result'].map({'Win':1, 'Draw':2, 'Lose':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:48:41.376897Z",
     "iopub.status.busy": "2022-09-16T05:48:41.376545Z",
     "iopub.status.idle": "2022-09-16T05:48:41.394297Z",
     "shell.execute_reply": "2022-09-16T05:48:41.392928Z",
     "shell.execute_reply.started": "2022-09-16T05:48:41.37687Z"
    },
    "id": "jAI_G4p2czzB",
    "outputId": "477efee0-e214-495b-8c8c-f86e4df8813c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_team_result                 1.000000\n",
       "home_team_mean_offense_score     0.165981\n",
       "home_team_mean_defense_score     0.158841\n",
       "home_team_mean_midfield_score    0.157639\n",
       "home_team_score                  0.127916\n",
       "home_team_goalkeeper_score       0.122509\n",
       "away_team_fifa_rank              0.111366\n",
       "home_team_total_fifa_points      0.015498\n",
       "away_team_total_fifa_points     -0.047726\n",
       "neutral_location                -0.066361\n",
       "away_team_mean_offense_score    -0.117055\n",
       "away_team_mean_defense_score    -0.122354\n",
       "away_team_goalkeeper_score      -0.124837\n",
       "away_team_mean_midfield_score   -0.127505\n",
       "home_team_fifa_rank             -0.186355\n",
       "away_team_score                 -0.464635\n",
       "Name: home_team_result, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.corr()['home_team_result'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DOCd7Joh1CYc"
   },
   "source": [
    "Dropping unnecessary colums."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "izXSRnBk5n1K"
   },
   "outputs": [],
   "source": [
    "#Dropping unnecessary colums\n",
    "final_df = final_df.drop(['date', 'home_team_continent', 'away_team_continent', 'home_team_total_fifa_points', 'away_team_total_fifa_points', 'home_team_score', 'away_team_score', 'tournament', 'city', 'country', 'neutral_location', 'shoot_out'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:49:03.425873Z",
     "iopub.status.busy": "2022-09-16T05:49:03.425574Z",
     "iopub.status.idle": "2022-09-16T05:49:03.432142Z",
     "shell.execute_reply": "2022-09-16T05:49:03.431248Z",
     "shell.execute_reply.started": "2022-09-16T05:49:03.42585Z"
    },
    "id": "PHis1RgqczzB",
    "outputId": "07210637-d01e-4f48-87ea-0309498b21b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['home_team', 'away_team', 'home_team_fifa_rank', 'away_team_fifa_rank',\n",
       "       'home_team_result', 'home_team_goalkeeper_score',\n",
       "       'away_team_goalkeeper_score', 'home_team_mean_defense_score',\n",
       "       'home_team_mean_offense_score', 'home_team_mean_midfield_score',\n",
       "       'away_team_mean_defense_score', 'away_team_mean_offense_score',\n",
       "       'away_team_mean_midfield_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T05:49:10.723944Z",
     "iopub.status.busy": "2022-09-16T05:49:10.723621Z",
     "iopub.status.idle": "2022-09-16T05:49:10.73044Z",
     "shell.execute_reply": "2022-09-16T05:49:10.729146Z",
     "shell.execute_reply.started": "2022-09-16T05:49:10.72392Z"
    },
    "id": "taJRn50YczzB"
   },
   "outputs": [],
   "source": [
    "# Change column names\n",
    "final_df.rename(columns={\"home_team\":\"Team1\", \"away_team\":\"Team2\", \"home_team_fifa_rank\":\"Team1_FIFA_RANK\", \n",
    "                         \"away_team_fifa_rank\":\"Team2_FIFA_RANK\", \"home_team_result\":\"Team1_Result\", \"home_team_goalkeeper_score\":\"Team1_Goalkeeper_Score\",\n",
    "                        \"away_team_goalkeeper_score\":\"Team2_Goalkeeper_Score\", \"home_team_mean_defense_score\":\"Team1_Defense\",\n",
    "                        \"home_team_mean_offense_score\":\"Team1_Offense\", \"home_team_mean_midfield_score\":\"Team1_Midfield\",\n",
    "                        \"away_team_mean_defense_score\":\"Team2_Defense\", \"away_team_mean_offense_score\":\"Team2_Offense\",\n",
    "                        \"away_team_mean_midfield_score\":\"Team2_Midfield\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 808
    },
    "id": "Z_pSWrvusVDC",
    "outputId": "e7f0c79d-b322-4538-ef2f-5ffdeff610cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b3df3b610>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FeoPiCMN7l6KBs8hMvrcBi5RSbwKPoDdUPkV5I6A11XEuWYA34AW8CIx1YFu7KaV8gGWUN/CuAcZqmpZXE8e7HEU2qE+zJo3Y9MdftZ0VlxTQriltPp6Bu583xdl5HH/3OzI27sPNx4v61/UhaurV+MVH0f7LJ9h+7UxKcpx7Cr7Zk5OMjZtZuxM58cH35CWl4tu0ATH3jyWwQyxRU66mKP0CR/+1yOq+ClLOsfuml22mMce7YZhx/dnlm8nYcoCC5DSUuxtB3VoQM3003lHhNJw4EOXpwYF733HibOu2Bm2bcN0HD+Dp601Bdh6bP1zO8c378fDxovXoXnS+eTDhcZFM+Pwx5o5+hkInY276aFFJYTFnDp7A3cOd+q0bO7Sb6z58kLiBHQFI2Z3Itk9/Iv3IKbwCfIkb3Ilut15DWLOG3Dj3ceaOfoa889nO5deFdOjQhs+/eBc/P1+ysrJ5682PWL9+C76+PtwwfhS33X4TzVvE8vXSTxjY/zqysx1/XubUqVR69RhuM90jj07nxol6Fbnwy28rrW/YoB5ubm5kZ+ewYvlqflu3mYSEJAryC2jRMo7p995K124d6dqtI98vn0//vmPIycl1OL91SVjbJgyc/QAevt4UZeex+/3lpGzSy1izMb1oOWUwwXGRXDPvMX4Y/gzFzpYxoOvMCcbGzfQ9Sez5cAVZx84Q2KQ+7e8bRXj7prScPIj8c1n89drXZvfR/oHRxsbNwqw89s35kTPbDlGUm09wXCTt7h5BaOsYQlvFcM28x/h+6FNoJRWfvt7wyBw8/Lyt5jWkRTSDPnoQgJQN+8g9fd7p865tnTq1ZeGXs41l8PV/v8+6dZvw9fXhxhvHcNe0KbRsEccP38+jZ6/hTpXB5OTTdOw82Ga6mf98gJsnjQNg3oIlZtO8/dZLxkaV1NSzvPHmbHbs2ENRURHt2rXm8cfuo2nTGPr168XCBR8ycvQUh/PraszF8Ld1m/Dx9WHijWOYZojh99/Po1cVYtjJzhhOMsRwvoMxLDbE8DGTGH654ENGSQwlhrUstF0T+n5UXhfue285qZv24+7jRZOxvWg+ZTBBcZEMmv8YPw6rWl3Y6f8mGBtWzu1JYv+HK8hKOkNg0/q0uW8UYe2bEj95EPnpWeyyUBeWyU05x9qbrd82sFR/bZ5huy4MbhFNv4/1ujB1wz7y6mhdGNquCf1M4rfXJH5NTeI3eP5jrKrm+O37cAXZSWcIaFqftob4NZ88iIL0LHZaiV9I6xiuWfIk3mGBFOcVcmTROlJ+20Pe6fO4eboT2KwhUYM6UL9nS7Pbe4cHMWTxTPyjIwA49sNWEpf8Tm7qefwahBI7oR9NxvQkamAH+s5+gHW3vun0OV8KV2IZtBTDvNTz+LpgDC9LpZfXiD2i+kkDpxAWaJo2p7bzUJcppXoBLQwvvwR80BsF2ymlumiaVpstR88A96M3Ng5SSrlpmuUxDarxXM4BK4H7gDFKqW6apv3p7ElYyKsf8ANQ1qXtR2CcpmnOf7O8Qky//WbatW5Bu9YtiAgLJTkllaHjb6vtbLmk+Jdvx93Pm9KiYnZPfJkLfx4yrsvYsJe8xBTinpuKX3wUMfeOJukN8zdtrPGNjSTm3tEAXNhxhJ3XPUepYWjGrJ0JpP38J52+e4GgzvHE3DeG0wt/tdpbVCsqJufvEw7nAyD3SDKJ/1rI2RVbobTiR8mF7YdJXbKezstfxi8+igbj+nLqi9VkOjCMqiu4+rmpePp6U1JUzFdTXyf5ryPGdcc27ed8UiqDn5xEeFwkPe4awYa3Kzda2SPtcDKrn/uClF2JpO4/TklBEX3/Mc6hBs6WI7obGzePrt/D13e8UWGIzONbDnD0t91MnPdPQmLq0W/GOFY/a9do5i7ttf88g5+fL0VFRVw/9ja2/bHDuG79b5tJOJLES6/8H81bxPLAQ3fy2r/edfgYxcXFHNh/yGoaNzc3+vbrCcCFC1msWF55yutz5zJ49unX+fSTLyvdXN65cy9Llyzn08/fZtwNI4lv3oz7H7yDf7/2vsP5rUt6vjgVD1/9c/Xnm1/n7PbyMpaycT8XjqbS/ZlJeuPhPSPYOcu5MhYU25B200cAcHZnIj/e8BIl+UUApO1K5Pjqvxj+zdPU6xRL++kjOLz4t0o9RpWHO+3v1Z8dKyko4scbXuLcvuPG9We3HyHhm42MWPYs9bvE6z0fhnXl2MqKA5JkWxgS3FT8DX2My0eWbnDqnOuKt9580VgGh4+4mS1btxvX/bpuI0eOHOX1156hZYs4HplxDy++NMvhYxQXF7Nv30Gradzc3BjQvzegl8Fly36qlKZ+/QjuvEMf+eLcufP06DWc5OTyntIbN21j4aJv+evPNTRr1pihQwfRtUsHtv9l/3CArmiWSQxHXBTDdes2ctgkhjNm3MNLNRjD/nbE8A6TGPa0EMPtEkPjOolh7etmUheunfQ6aSZ1YerG/WQdTaXLM5MIiouk9fQR7HnTubowMLYhrQ11YfrORNaMK68Lz+1K5OTqv7jmm6cJ7xRLm3tHkLD4N6u91UqLS8g8aGnQK+ty7KgLm40vrwsTl9TdutA0fv+zEb8200ewuwrxa2MSv9Um8Us3xO9ak/gdsRA/N29P+s95CO+wQLJPpvG/ia+SdbRiurS/Ejj6zUbcPN3N5qXDI9cZG8Z2v/lthXM6v/cYyf/bSeaRZDo8Mo5G13TWe4KutDVAW+25Estg+4tiuOeiGJ4yiWH0NZ2JGdmdE3U4hkJciSqP+ySEFUqpLkqpj5RSB5VS2UqpHMPybKVUCxvbxiqlHlVKLVdKJSml8gw/x5RSXymlhtnY/jallGb4aaqU8lJKPaKU+lMplamUOqeUWqeUGnnRdoFKqX8qpXYopS4opTKUUmuUUkMsHUvY5RbD7/PojXrL0Hswmq6rFZqm5QJlY975oc/LaU11nsu/gLLGxhcd3NYqpVQAsIryxs0fgOukcdM+D0ybysA+PYkIC63trLi0wM7xxuFaTy9cW6Fxs8yJ2cvJMVxgRN81AuVh/oLQmkZ3j8TNU38O68hTnxkbN8uU5hVy5KnPAHDz9KDRPZWH7qoue6a8xtkfNldq3CxTdC6LhOe/ML6uN9ryHEquKLJjLI17tgJg11e/VWjcLLN1zirSDuujpXe7YyhuTsQcIGVXItvnruHUjgRKCoqc2keH8eXzsPz8zFyz8/8lbdzH/uX6PCqdJg3GJ9jfqWO5ii5dO9CnTw8A5s9bUqFxs8x7737C338fBuDee2/Dw6NmnoMcNKgPUVENAfh+2U/k51ce5um5Z//NO2/PsdhzprS0lEdmPEtBgb7t2Ots9xqtyyI6xdKwl17GDi3+rULjZpm9H68i45BextrcOdSpz1WANtOGGT9btz7zhfFmUpmS/EK2PqN/nrl5etD2rspfz0OaR+EdopeZE7/sqNC4WUYrKWX3uz8YX9fv2tzxzCpF7Dj9pm5Rdh7HVrnuzaTu3TrRr59eN3z2+eIKjSplZr31MfsP6HXqgw/cWWNl8Ooh/YiOjgTgm29Xkp9f+Wtkjx6dcXfX/8e++OLrCo0qZbKysnnn3f8aX/fq1bVG8ltXmMbwcwsxfOsSxXCISQy/rWIM35UYViAxrD3hnWKpb6gLExb9VqFhpcyBj1aRaagLW1ahLmxlUhdue9pMXZhXyLany+vC1ndbvVVVs5Si2fXldeGJH+tmXRjeKZYGhvgdsRC//R+Vf5epC/FrM30EQXGRlJaU8vs971Vq3DRl7npCuSmaGb6nZJ84y563vjO77Z5Z35FtmPux7QOjbZxd7bkSy+DFMdxrIYZ7Z31nnL+zLsdQiCuVNHAKuyil3JRSs9DnobwHvbebP3rjUQtgOrBPKXW3he2bAQnAG+jDhTZB7yXnAzQGbgR+VErNV0rZcxURBKwH3gS6Gl6HAgOAFUqpGYbjNgY2A68DnYBAIBi4GlijlJrs2F9CACilvICJhpdLNE0rNAyPWvao0yQ741iTTFtDLN4lr+5z0TQtGX1oY4DhSqneDuTZIqVUEPAT+v846PNvjtc0rfpmdxfCDhHDuxuXUxb/aj6RppG65DcAPEMCCOnT1vHjDNOPk3PoJBe2Hzab5sL2w+QaGtXK0teW8xv3GZd9mzSsxZxUvxbXdjMu71my3nwiTWPPN/oT5b7B/jTp3fpSZM2shu2bAXDu6GnOW3nSN/E3vYeDu5dHpbk6LzejRl1jXF4wf6nZNJqmsXihflEfEhpM//4101B/083XG5cXLXTuqW+A8+cy2LdX7yHTrJljQxjXNY2HlZexw19ZLmNlPRi9Q/yJvMq5Mtb4Wv1/PeNwMmf/Mj/n29m/EsgwzE3beGjlm+VlN6QAso5b7nmSZTIfr+k29orq2xb/SH2I8KSV2yjJd92vPGPHlt+Y++KLr8ym0TSNBQv08hkaGsKggVfVSF6mTBlvXJ4/3/wIC15eXsblxKOW5+Q1na/XdJvL0RgnYjiwhmI41SSG8+yI4VErMUwwiaGnxFBiWIsamdSFCVbqwkSTurBBH+fqwkZD9bow83Ay6RbqwvS/Esg01IWNrq29huOG/driF6XXhcdXbqMkr27WhTF2xu+oSfwaOhm/GJP4pVmIX5qN+Ck3RfOp+nPjp3/fS/oO8/uxJjC2IV6GhyRT1u9FKzU/T6RWqnF6vT67UnjHWPxj6jl8rEvhSiyDpjE8bSOGKS4Qw8uWVlp7P8IlSAOnsNd7wAz02bHWA3cAA4EewF3APvQhjz9WSo0xs707eoPTcuAh9AbGLobf9xm2B5iCPryoLXPQGzY/BK4BugHTgFOG9W8opdqhN1LFAq8Z8tsd+AeQaTiX2Uqp+nYcT1Q0CggzLC8web9suT5Qa485Ghoky7oKZGqalmEleU2cy6tA2WRgLzm4bSVKqWBgNVA2Ns0iYJJhXl0hLqngHvpTnSU5+WTvSrSYLmPz/krb2MunSX28DTe1M032Y+043lHh+DSuvY9zNy9P47Jmoaenq2rUXR+goTAnn5Q9Ry2mO7G1fFjeRt2sDupQo3xD9amyc9IyrabLOVu+PqanY/+jrqbXVfoNi+zsHHbusDzt9oYNfxiXe/au/hsJAQH+jDQ0th5LOsFGk+M5w8tbv5FbUuLa87I0MJSxopx80ndbLmOnTYa+rt/d8TIW0LiescHw9Ja/raZNNaz3jwwj4KKbOBeOnjZ+zgU2tnyDJ7BJg/JtEir3PLIlbkJf4/KRpb87vH1d0ucq/SGc7Owcq8NHrl+/xbh81VXV/+BOQIA/Y8foX2uPHj3O+t+3mE136FD5zcbYZk0s7i82tnyd6TaXI3tj+PsliOEYkxj+bkcMm1mJYZzEsBKJYe2o36O8LjxnpS48s7m8LqznZF3oZ6gLz2y2XheWrfeLCqu1Bo3Y8eV14dEldbcurGdn/FKrMX6pdsbP30z8Irq1wN/QcHxydfmMRG7engQ0qY9vw1CUm7K6f2/DNQdA/lnr1x15JustzedZ267EMuhIDPNdIIZCXKlqu4eVcAFKqWvQGyEBpmma9ulFSbYppRagD+05GHhXKbVK07RikzQpQFNN08zd3fifUuoj4DPgNuBRpdQsTdOs1S490OcdXGby3nal1DZgB3rj/Vr0np0DNE3bapLuT6XUYUN+A4HJwFtWjlXbmiml0qysP6ppmvkx3OxT39AYbMkZTdPOXPRe2bCtSYDpJBBr0RuZowxpVlQhX1XxEHrvYtB7OlpT7eeiaVqqUuoD4HFgiFKqv6ZpFh6BsykY+AW9ER9gHnC7tTlFhahJfs0bAZB39DRaieV/w7Kelfo20Q4dw79Fo/L9HEm2krLier/m0eQfv/jjSucRFkin717Av1UM7v4+FGVkk7P/GOmrt5OyaC2lVXwaumzYXoDcQ87N/1FXRcRHAXA+KdVqzNOPnDIuh8c7FvPqVJhbgG+wB96BflbTeQf5GpcjajG/l0LLlnGA3tvKWmPgYZMbo2XbVKex1w3D31+Py+LFy2ykti6iXrgxj4cOuu4NXdCHfAW4YKOMZZqUsRAHP1cBQlqUb2O6L3MyKhwrqsJ8mUVZeSQu20zcuD7EXN2Z0NYxnD9QcY5j5cmeOs8AACAASURBVO5Ghwf1IbwKMnNI/H6zQ3n18POmiaEnQdaJs5ze5NrzGrdqpT93dyQhyWoZ/Ptg+XBwZdtUpxvGjTSWwS8XfmMx3d69f7Np0zauuqo7t9xyI7Pe/piUlIo94gMC/HnowWkAJCQksXrNb9We37qkLB4JLhrDtyzE8EGTGK6RGAISw9oSZKgLs2zUhRdM6qdgJ76/BTtQF1Y4VvMoi/NleocGcPU3TxHSshEe/j4UZuRw/sBxktfsIGHxb073uvTw86bRcMNDaifOklqH68LgWojfBRvxy7QSv3pd443LGQdOENisAZ2fnEj0NV1w99JvlRdm5nBy9V/snvUd2ccqX2MW5ZRPs+AZZP26w8vkusP0HOqSK7EMOhJDTxeI4WXrMnuAXFQ/6cEp7PF/ht/fmGncBMAwB+ADhpdNgEEXrc+x0LhZtl4DHgVK0Ie+vdpGnr6+qHGzbD+7KW+kqge8fVHjZlm6VUDZWC79bByrtn0G7LHyU9XHSu+1sf/7TBMrpcKBEYaXCw2xA8DQ6LbQ8HK0Uiqkinmzm1LKVynVTin1H/QhiQHOoM+JaWmbmjyXfwPZhuWq9OLsRHnj5iKkcVPUIjdvT7wiggAoSEm3mrY4M4eSHH0+Ie/oCIeO4x1ZPm1uwalzVtMWJJfnwzvK8nS7HgG+hFzVBs+wQNy8PfFuEErYoE40f/VOem56l6Cq9DhUisYPXWd8eeYHx27k12Xu3p74hesxzzptPRb5F3IpNMQ8KCrMatqalG5o9I6Ij8I3LNBiusYmPYuDom1N1ey6vL29iIjQz+9U8mmraTMyLhjnvSybG6w63XTzOONy2XC4znr44bvw9NR7Tn/37aoq7as2uXt74mMoY7kp1stYYWYuRYYy5u9EGSvrvQmQY+NYOafKP1v9zXy2/vHCl6TtPoq7tycjvn2Gjv+4jsh+banXJY74Cf0Y/eNL1O/anKLcfH5/+GMKzmdX2oc1TUf2wNPfB4DEbzc6tG1d4+3tTb16+t8w+aT1nqwZGZnGMhjTKKra8zJ1ygTj8vwF1p8BvPOuR0hMPEZ4eCjbtv7EPx6+mwH9e9Pnqu7cfddU/vpzDbGxTTh7Np1bbn2QoqLLd2AR0xierOUYTjGJ4QIbMZxmEsM/DDHsbxLD7SYxvFViaCQxvPTcTOrCPBvf/U3rQj8n6kI/k7rQVr1rWhf6WbnO8AzwpUHv1niHBeLu7YlvgxCiBnag+yu3Mub3N4jo5lxDeYxJXZj0Td2tC03jl1uH4pdr5buMaQNVUHwUI1a/QuORPYyNmwBewf7ETujHiNWv0LBf5SlXso6mUlKo9+toYKNHX32T0WL86+B1x5VaBk1jaKtXZl2PoRBXMunBKawyzPs30PDS6rdvTdMOGHoaRgC9gTVW9usJNEDvQWk6K3U6+pCgHQHLjzPCYivrdgH97Ui3G70xNtZKGlHZJKBsLMYFZtYvAB5Dn191AvDfGspHE6WU+QHydeuA+zVNszyGZg2ei6ZpaUqpd4Engf5Kqas1TfvF3u1Nd4U+nDLo5SoKuLy6hwmX4R5Q/tRiWeOlNSW5+bj7++BuuDCvieOU5JavN3ccTdPI/PMQ6av/JHv3UQrPZuDm44V/68ZE3jyYoC7N8Y4Kp+PXz7BjzDNk701yKK8Aje4ZSVAX/aLp7IotZO+29rHjWrxM/qaFdsS8KLcAL38fPP0ci3l1OrxmB426tsDNw50Bj03gpyc/q5QmtGkD2k/ob3zt5eD/qCsJCCwfeiknJ9dKSl1ubh4BAf74B/hXaz4aNYqkb98eAGzZvL3C3H2O6tqtI/fefxug36j+9JMvqyOLtcLD5H+vyI4yVpxbgKeTZczTv/yztdjkiXVLxynPo3el9flpF1h1/Uu0nDyI9g+Mpsvj4yus10pLOfjlr+z7eBWZzgxPO950eNoNVlLWfYGB5WUpO8f2oCs5ObmGMmi9J4GjYmKijHPrbtq0jYSEJKvpDx9OpNdVI5l+zy08/th9vPGf5yqsLyws5M03Z/Pu+5+SnOx4jF2JszEMqAMx7H3VSO4xxPA/FmL4nsSwEonhpeVpWhfmOlAXOvH9zSPA/rqwxEZdCHD2z8Mkr9nBuT1J5Kdl4u7tSUirGOImDSCiSzx+UWEMXjSTNde/xPm9jn33MR2eNrEO14WmcSh2IH4eTsTP0yR+RVX4LuMVUv6Z0O3FqXj4enHg4x85+MUv5Can4RcZRvNbhtBm+gi8An3p9/FDrLzmSXJNHq4tySsgdeN+ogZ1ILRtE5pe15ukZZUfdG16XW9C25TPF+8ZUPeuO67UMnhxDJtc15tjZmLY5KIYetTBGF7OpI+HsEUaOIUtnSnv6btIKbXIzu0aXvyGoVHzbmCqYb9eVra31d3nkJV1pvMt2pPOcveOumGQpmnranD/L2ia9rwD6W81/P5L07RKY6RomrZLKbUXaIc+tGtNNXBakwl8oGma9cn7av5c3gDuRx9m9kX0oWYdtQFIBm4CmqIP6TxA0zTr3XCEqAFu3uXzTJYWFltJaUhToKdx97H2cW/jOEXWj2OaD3ffysdJeHYuxRcqN+pc+PMQKfN/odkTk2jyj3G4+/vQcta9bL92pkN5De7dhtinJwNQeDaDQzNr4yOv5niYxKLERiwAigv1ngOeDsa8Ou1Y8Atdb72GoMgwOk8ejKevF1s+Xkl6wim8/X2JG9SRQU9OwsvPm5LCYty9PGo1vzXNx7v8ZkBhoe2eHQUF+hBOvj7Ve+E+8abrcHPTv1IuXuR878169cOZt+ADPD09KS0t5d57Hicvz/aNmLrKw8f+zzuAEkMMHf1c1bdx7rPVw8KxIvu2IXZcH/zqVx7kQrm50XhoFwrOZ7PjjaWUFtk/T6pfZBgNe7cG4Mz2w1xIdO2vPD4mZak2y+Dkm28wlkFbvTfLjBp5DZMmXU+gyYMSZby8vBg/fjRn09J5483Z1ZrXusY0hkUOxNCnmmN4s0kMbfX8K2NvDN+UGFYgMby0KtRP9lxjVKUudOA6o8RGXbj9uQUUmbnOSNt+hCNf/krHmRNo9/BYPP196PnGNH4a9ozd+fSNDKP+VXpdePbPw2TV4brQ2fhZ+n5h9VhOxu/i/xUPv/Lvxx6+Xuz691L2vF0+SFz28bPseHkxhRk5dH5yIt4h/rR7YDR/PDG3wn52v/kNDfu2wc3Tg95v30NAk/okLt1AXmoGvg1CiB3fl/YzrqekoMiYd2f+b2valVwGL45hoJkYtnOBGApxJZMGTmFLfSe3q/Coo1IqDFgNdLVze18b6611QTA+2qFpmj3p3K2kESaUUq0pHy7VXI/HMvPRh4nto5Rqpmma5RnKnXcKGGryuh76cL0z0BvYv1ZKTdI07StzG1+Kc9E07bxS6i3geaC3Umq4pmk/2ru9QSn6QwE+wHVAC2CNUmqgpmnWxwi1QSnVyHYqKDzr2nObiepTWlB+Q8jNy/ZXCDdvPU1JvmPzzlQ4jqf145jmw9zcGuYaN00dfXURQV3iCe3fgcCOsQR1b8mFbQftyqdfy0a0+/xx3Dw9KMkrZN+0WRSlXbBr27ogoEEoPsHme+nlZ+aQnXqeYpNYuNuIBYCHl37RV+RgzKtTQVYe30ybxY1zH8e/XjDtxvWl3bi+ldL9+tpX9Jg2DP+IYArs6DlXF0VGNiAkNNjsuozzmaSkpJJfUP7ks5eXp9m0pry99Qv2vPzq/ZtMnKQP45yfX8C33zg3RXdAgD9Lln5Ko0b68LnPP/sf1v9Wd4eE9msYipeFMlaYmUPu6fMU59v/eQfgboiho5+r+jbOfbYWmzlWmzuH0uP5ySg3N05vPsDOd5aRtiORkoJCAps0oPlNA2h71zA6PDCaBj1asHryvyv0pLAmblwf3Nz1BoAjS363a5vaEhXVkFALZfD8+UxOnTpNvklZqs0yOHnyDQDk5+fz9ZIfbKb/z+vPMmPGPQAs+/5H3nzzI3bt3kdJSSmtW8dz/313cPttN/Haq0/To0dnbpo0nVIXnCPJ0Rh6OhDD/FqO4b8viuEsMzG8zSSGkySGRhLD6uNroy7MO32+Yv1kzzVGVepCB64z3G3UheYaVkzten0J4V3iiOzXjvAOzajXvTlntx22K5/NbiivC4/WYl1Yk/Ez9ze1xdn4Xfy/Yprn/PQL7Ptgudl97J+9klZ3DsW3QQhNRves1MCZ9lcCW2d+Rs/X78Ddy4NOMyfQaeaECmmK8wr466VF9PjXbQAUZV/a6w4pg9bLYPpFMew4cwIdzcRwx0uL6G6IYfEljqEQwjpp4BS2mDb+3QNssnO78xe9fofyxs1l6PNK7kafIzG/bO5DpdRxIIbyITlF3XKLyfIspdQsG+mVYZsXaiAvRZqm7b3ovV+VUguAP4BoYI5SarOmacfNbH+pzuUt4GEgFL0Xp6MNnGiaVqyUmgh8DwxD71G6Wik1WNO0TEf3Z+JEFbYVV6CS7Dzjsj3DzrobhlC0ZzhbZ4/jbjJMo6PHKXNq3hpC+3cAIOSqNnY1cPo0rk/Hr57BMzQArbiE/dPfInNLpY7gddqAxyfQwWSYVlO7l6xn5WNzKgxLa88wrp6Gp6HtGdqoJp3em8SnI57iqvvH0GpEDwJMepid2pnAhne+I2HtTvr+43pAb9B1Rc889yiTp9xgdt2XC77hvun/JDurfO5Df3/bQ+35+enPmOVkV9/fpEvXDrRsGQ/Aj6t+ITMzy+F9eHt7seirj+ncpT0A7779X955e0615bEmdJk5geY3mi9jh79ez4YZcyg2KWP2DPPlUYUyVpRT/tlqaZivi48DlYcPC20dQ/fn9MbN5PV7WDP532il5bMGZB45xZ8vLyLzcDJ9Z91Ngx4t6fzYDWx7cSH2iLuhj37c/EKO/rDFrm1qy0svzuTWW240u+6LeV9z57QZZGWVl6UAf9tDP5eV05xs20NK26t7t060bqUPpb58xRoyM60/jDNi+BBjo8rcL75i2l2PVFi/c+c+7rr7UZKTU3j6qRmMu34k906/lQ8+/Lza8nypvPTiTG6xEMN5VYxhdi3GcLhJDL+wEsOTEkOzJIbVp+PMCcRNNF8XJny1ni0z5lQYot2eIdiNdaET3/2Ls+2vC92t1IX2OjJ/LZH92gFQv1drhxo4QW9AOlaLdWEnG/HbfFH8PByIX7ET8SsyiZ9nFb7LmB47ddMBiyNNaCWlnN6wj2Y39ME7LJCAJvXJPnamQpqExes5v+847R4eS+SA9sbvc6VFxSSv3cWOVxbjGVj+HbzwEl93SBm0XQYTbcRwZy3H8IpXxx7cEXWPNHAKW0x7iOWaaVCyyTCP50TDyy81TZtiJXmoo/sXl4ZSyg2Y7MSmU6mZBk6zNE07pZSaDiwHgoBXDHkwupTnomnaBaXUG4Z8dFNKjUWfJ9YhmqYVKqXGASuBQUAX4Eel1LWapmVb31qI6lFaUERR+gU8w4Pwjgy3mtYj2N/YOFmQnObQcQpSyqse76gwq2m9o8vzUXDKuU7NOYfKp7X1bmj9eABeDULpuOQZvCPD0EpL+fsfH5L+059OHbuuKykoIvdcFn5hgQTa+Nv4BPkZG0EvnDp3KbJnVc6ZDNY8N481z83Dv14w3gG+5KRlUpClX1QHNgwzDk2bdujyndq4oKCQ9PRzhIeHERVdaQaBCkJCgggwzL1ZnXN5Tbr5euPyooWOD0/r7u7O3Hnv0X9AbwC++Pwrnnn6tWrLX20qKSgi/1wWPmGB+EVaL2NewX7GGy45TpSxnJTybfxtHMs/qvyzNeeiz9bmE/sbe5XsfPPbCo2bpg5/tZ72D4wmODaS+Bv72dXAGd6hGaEt9QEmTv5vJ4WZ1de4UFsKCgpISztHREQY0Ybex5aEhAQby+CJk6eqLQ9TppTPkWrPsJh33DEJgNLSUp597t8W07362ns8/NBdBAYGcNttN9W5hpXqYhrDRpdZDF+TGFYiMbz0Sk3qQl8b3/1N68JcJ+rCXJO60Fa9a1oX5jp5nZF5KNnkePbd7grr0IwQQ12Y/EvdrwtN4+dXh+LnZ+W7jOlrW/kwTesTHlipgRPg3J4k1k97B+Xuhm+DENw8Pcg9fd44MlGzcX2MaTMP1r3rDimDcH5PEr9biWFTkxhm1MEYCnElkwZOYctOQEPvvdYH60N5WtIcKBsHxuxwoQBKqVZA5YklRF0xCL13LcB72O7N2xP4BxCnlOqjadrGmsycKU3TViilNgB9gZuVUq9eNB/npT6Xd9GHzo1AbyC93npy8zRNy1NKjUEf7rm34We5UmqEpml51rc2K8Z2EkB6egoTOYdOEtK7Db7NGqLc3dBKzD9N59c82ricezjZbBprxzDuJz7aSsqK6x09jpH5e/NmeYYF0vHrZ/BtqjcUHX7yM1KXrHfuuLVs5WNzWPmY7R5waYeTadyzFaFNG1iNeXh8lHE5/YiTsaghOWczyTlbscN7w/ZNjcspuxIvcY6qx33T/8l90/9pM93ffx+hT58exMY2wd3dnZIS80+pN28RZ1w+eLB6hif38PDghvGjADhzJo1f1jhWXpRSzPnkTUaMvBqAb5au4OGHnqqWvNW0DTPmsGGG7TKWcSiZhr1aEWSjjAWblLEMJz7vMkxu8Jjuy5yQCseqeIM/2ORzN31PktX9pO9JIjg2Ep/QQHwigsi3MYx3/Pjy4aTr+vC0AHdOm8Gd02bYTHfgwCH69etFfFxTq2WwlaGnM8Dff9vXy8cWDw8PJt44FoDU1LP89POvNrdpZehlduZMGqdOWZ73raCggP37D9GzZxdatYyzmK4uczSGcbUUwxtNYvhzDcWwpcQQkBhWty0z5rDFjrrwwqFkfHq1ItBGXRhkUj9lOvF9M9OBurDCsQ4719htGKzMIc0mlNeFibVcF26eMYfNdsQvsxbiF2QjfsFW4pd5sHw/yvDQliWm60uLrfck00pKzTb6hXVoalxO23FprzukDDpWBu2JYfoljuEVT5MenMI665/i4oqnadpZoGw8jJuVUvWc2I1pQ7q18WCmO7FvcemUDelaArysadpiaz/oPRaLL9r2UnrJ8NsNuPhO6CU9F0MPy9cNLzsC5scTtH9fw4HthrcGAt8qpRye5VzTtJP2/DibV3F5yvzjb0AfOjagY6zFdCG921Taxl75x85QYHiyM9hkP+YE92oN6L03849XfprWHv4tyqejLUi9eIT1cu6BfnRY/DT+rfRnAxJeWsCpz3926piu5OS2Q4A+RG1k+2YW08X0bF2+zZ+HajxfVdVqZE/j8oHldXsYzKrasknvYRwQ4E+nzu0spuvbt4dxeevm7RbTOWLosEGEh+tPZy/5+geLN5QteefdVxg/YTQAq1b+wl13PuLUzcK6LNVQxjz9fQjvYLmMNexVXsbObHO8jGUfP2vsxdmwVyuraRv0bAnovT6zT5ytsE4ziaGtm4JuHuWzXVi6UWbcl4c7zcb2AiAvLZOTax0e8KLO2rhpG6CXwa5dOlhM179/L+PyJsM2VTVixBAiIvQyuHjxMrvKYHGx/rXXw8OOuZcN82cVFztWtl2NvTHs54Ix9JQYViAxrB1n/iivC8Os1IX1e5fXhWedrAvLepDV7229LqzfS68Lc0+dI+eiutBewS1MHsY8bfk6o4zycKepoS7MT8vklIvUhWftjF+DaoxfAzvjl2Mmfqlbyq9PA5pYv80Z2KS+cTnPjhheTLkpYkZ01/OSnEZaHb1OkjJomXJTNHaBGApxpZIGTmGPlw2/g4ClSqkQSwmVUt5KqfuVUqaDth+hvH/MrUqpSvNrKqVGAw9UV4ZF9VJK+QPjDC9/1zTNZiuCpmlpwG+GlzcqpawPrl/NNE1bDZSNGTlRKRUPtXouHwKphuWZTmxvmp9MYCiwx/DWMOArpZT0yhc1Lu3H8hs9kTcNMp9IKRpMGABAUUY2GRv3OX6cn/Tj+LdoRFDX5mbTBHVtbmycLEvvjMhbrjEuZ27abzaNm68XHb58gkBDo+6xt77hxPvfO31MV3Jodfnwu+0tzNmJUrS/QX/aPC8zh2Ob6/Z8pOHNo2g9Sm/gPPr7Hs4dtdwz4nKwYsUa4/KUqePNplFKcZNhKNmM85msX189jb4Vhqf98luHtn3l1Se59XZ9loN1v27k1qkPOtxA6gqOmwxx3dzCHEkoZezdWJCRQ8om58rY8dV/ARDSPJp6Xcz39KnXJY4QQy/84z9XbujOOl5+g6msIdRslj3cqW/4/C7IzKHgvPUR9RsN6ohvRDAAics222wQdSXff/+TcfnWWyeaTaOUMg5hef58Br+uszXAiH2mmgyLOW/BEru2SUrSB++IiAijVat4i+lCQ0No11b/HziaZG7K+8vHD07EcF01xdB0aNP5NRDDtoYYJkkMJYa16KRJXWhpvkCUItakLkzd6FxdePJnvS4Mbh5NuIW6MLxLHMGGuvDkaucf+mo+dbBx+cwW2w99Rg3uiI+hLkz6znXqwhN2xq+ZSfxOOxm/Eybxi7AQvwgb8cs5cZZzhlEoGlzVBs9AX7P78fD3oaFh/saso6nknclwOL9xkwYS0CgCgMPz11oc2r+2SRm0LG7SQPxdIIaXrdKS2vsRLkEaOIVNmqatAt4xvOwPHFBKPaeUGqKU6qSU6qOUulUp9QmQAryPSa9NTdPSgVWGl8OA1UqpcUqprkqp4YbtvgMSAeceyakBSql4pdRtpj+UD6EbcPE6pZT1ia1c2zjKz/0bB7YrSxsCjKnWHNnnFcNvd+AJw3KtnIumabnAq4aXEY5ub2Z/6cA1wEHDW9cB8wzziwpRY7J2HCFjs94I2PDmwQR1a1EpTcy9o/Evmzfmv6vQLnqaPOSqNgxMXcLA1CW0eud+s8c5OWelcbv4V+7AzadiJ2U3Hy/iX7kDgNKiYk7OWVlpH0Fdm+NV3+IzOQA0+7+bCBugP8WfvTfJbG9T5elBu88fJ7hnK2Pejr622Op+LycpuxI5vlX/u3ScOIDoLpVvsvW8ewQRhgvQPz/7mVIzPQga92rNE8cW8MSxBYx84+4azXNAA8vzqwRGhjH+v4/g7ulBcX4hq5+bV6N5qQv+2r6bjRv/AGDqLRPo3qNzpTQPPjTNOBze7NlzjT1HTPXt15PM7AQysxP48CPL84GVCQ0N5tqhAwHYu/dv9uyx/ybI/z35EA88eCcAWzZvZ9LEeygsLLR7e1eStjOR04YbLi1uGkC9rpXLWLt7RhBieAJ9/6c/V/pcBWjYuzW3Jy/g9uQF9H3LfBnb/8lPxvLZ86VbcffxrLDe3ceTni/dCuifrfs/+anSPk6s+cu43O3JiXgGmL8p2PmxG/BrqJdFe3pjxpsMyZewZIPN9K5k2587+f13/aGBO26/iV49u1ZK88iMe2jTWq9T33v/U7NlcED/3hQXJlNcmMynn7xl87ihoSGMGD4EgN179rNrl30PHJk+FDHrjRfw9PSslEYpxdtvvYi3t/7c36pV/7Nr367KNIa3W4jhDDti2L9/b4oKkylyIoZ7HIjhSpMYvmlnDFdKDCWGtSh9Z6Kx8SFu0gAizNSFraePMPbGOmihLqzfuzWTTy1g8qkF9LJQF/5tUhd2f9l8Xdj95fK68O//Vq4Lw7vE4WPjOqPjP8cT2b89AOf2HTP2crQm1mSo9sSlrlMXpu9MNPaKjLcQvzbTy7/LWIpfg96tmXJqAVNOLaB3DcYPYO/7ywG9x2K3F6eaTdP1+cl4BfkBcGi++fLl29DydUeDPm3o9uIUAC4kpLD/4x8tpq1tV3IZtBXDriYxPFCHYyjElUp6+wh7zQDOAc8ADYHnraTNQR/609S9wAagMXC14cfUcfQGmlXUHX2Bzy2sCzezbhBwuXYBKRuWVQMc6X7xLXqDt5thH/Y9rlp9vgf2AW2BqUqpF6jdc/kYeBywPqmgnTRNS1VKDQF+B5oBk4A8pdQ07XIbv68a/LVrL8dPphhfn88sn4/vePIplq1cUyH9dSOvQZh35OnP6bz8Zdz9vOnw1dMcf+dbMjbuw83Hi/rX9SHK0CMy98gpTsxe7tQx8hJTOP7hDzR56HqCOsfTeflLHH//e/KTTuPTtCGNHxhLYAe9N+WJD38gz0wPvLBBnWj84HWc+3Un537bTe6hkxRn5uDm7Yl/myZEThpEUFf9JlZJbj4HH/3IbF7afPQwYYM6AXD+9z2kLFxrHKbWnNLCYvISUyyud0W/vDCfqd88i6evNxPnz2TzBz9wbPN+PHy8aDO6N50n60/Gpiek8Md/q1aVtx/fr8LrBm0aG5djB3QguFH5MyLnk1LNDoc77F+34xcWxMGftpGyO5GCC7n4hQXSpE9bOk8egk+QH6Ulpfz4xGecS7i8YmXJ/z3+Ej//8jV+fr589/1cZr0xm/Xrt+Dr68MN40dx+x2TADh8KJH33/20Wo55w/hRxpuujvTevHv6LTzx5MMAJCen8Owzr9OkqfVpow8fSjR7I9pVbH12PiO/fxYPX2+GLpzJrvd+4PSm/bj7eBE7tjctp+hlLDMhhb0fO1/GLiSeZu/slXR4cAz1OsUyctlz7PlwOReSzhDUtD7t7xtNuGF+2j0freLC0dRK+zi1fi+nNuwjqm9bwto0YezqV9j/6c+c3ZlASX4RQc0a0HziABoN7ghAUU4+O9/6zmq+vIL9aDTE8Dl74ATpe5OcPse6asajz7J+3ff4+fny46qFvPb6e6xbtwlfXx9uvHEsd9+l3zw7eCiBWW99XC3HnHjjWGMZnD9/qd3bfTHvax56aBptWrfg2msHsnXLKj744HN2795PSUkJrdu0YPrdt9C7dzcATp8+w1tvV0+e67JHHn2W3wwxXGWI4W/rNuHj68PEG8dyl0kM36qBGM5zMIYP2ojhPRfF8G2JocSwlv357HyuNdSFgxfNZN+7el3o4eNFk7G9jT2xLiSkcOAjuNEUnwAAIABJREFU5+vCrMTTHJi9krYPjiG8UyzXfv8c+z9YTlbSGQKb1qfN/aMJM9SF+2evIstMXRg1qCNt7x/FqXW7Ob1+L5mHkim8kIublwehrRsTd1N5A1Fxbj5bH7P93cor2I/oq/W6MOPACc7bmOe6rvnz2fkMNcRvyKKZ7H33B1IN32WaXhS//VWM3/7ZK2lniN/Q759jn0n82toRP4Djy7eSPKEf0Vd3Im5if3zrh3Bo3i/kJKfjHxVO8ymDjfE4tyeJg5+vMbufUWtf48yWAyT/spOMQ8mUFhThHx1BzPCuNB3XBzd3NwrOZbH+nvcoLShy+rwvhSu1DI40iWGmIYZ+ZmL4uwvEUIgrkTRwCrsYGkteVErNR58rczAQCwQDucAJYAewGvhO07S8i7Y/oZTqgj4051igCZAPJAHLgHc0TTtvZvRaUcuUUtHo8QbYrGma3TN7GxrgNgL9gGFKqXqGeV0vCU3TNKXUv4AvAU/gBWrxXDRNy1dKvYI+XG210DQtWSk1GFgPxAB3AHnIkM+VfLP8Z77/8Rez63bs3s+O3RWHJpUGTsuy9yax/563aP3BQ3gE+RH71ORKaXKPnGLP5Fcpycl3+jhH/7UIr4hgIm8eTGCHWNrOmVEpTcqX/+Poq5Z7U7r5eBExvAcRw3tYTJN/4iz7732HrJ0JZtfXG1U+F1Nov/Z0X/em1XznHz/Dlu7me6a6qtR9x1h2//uMfvtefIL8GDiz8tBu6QkpLLn9DQqrEHOAUW/eY3Fd7/tGV3i9e8l6sw2cSimiu8Sb7W0KkHc+i5+fnsuBFVurlFdXsnv3fm6/9SHmfDKL4OBAnnvh8UppDh9K5Mbx08jOzqmWY940SR+etri4mK+/sn9I5zFjhxqXo6MjWf3L1za3ad+mP8ePJzueyTri3L5jrLv3ffq/ey9eQX50e6JyGctMSGHNLW9QXMUytv31JfhEBNFi0kDC2zdl4OwHK6U5tHAdf71u+VmuX+9+h8H/fZjIPm0JbFKfnhZ6PuSlZfLbAx9ywcaDBM3G9MLD0FP/iAv1WHHEzp37uHnyvXwx912Cg4N45eUnKqU5eCiBMWNvqbYyWDY8bXFxMQsX2f+QQVFREaNGT+Hbbz6nU8e2dGjfho8/+o/ZtImJx5gw8S7S052b18qV2BvDsdUYwykmMVzkYAxHG2LYsWNb2rdvw0dWYnijxNBIYlh7zu89xobp73PVe3pd2OnJynXhhYQUfp1a9bpw52tL8I4IIn7SQMLaN6XvR5XrwiML17HLSl3o7uNFzLBuxAzrZjFNzsk0Nt7/Aed2JdrMU5MxvXA31IWu1HuzzPm9x/h9+vv0McSvs4X4ra2m+PmYxK+fhfjttBI/gN+nv0f/Tx4mamAHogbpPxdL25HAuttmWWzYcvN0t/p/kPH3CTY8MJuM/XV/COkrtQzaE8ONLhLDy5LmGkN1i9ojDZzCSNM0m62LmqYdxcn5Aw1Dav7T8GMpTVMr6+YCc+04zvNY72Falu424LaqHq8m2HsOzm6vado6wK7WZE3TktGHeHU2LxYG73d6f00dTL8QWGjy1u1VOLbZc9E0baAD+5gNzLYjnd2t/ZqmJaH3jhbikklfvZ1tgx6l0V0jCb+6C95RYXrPxaOnObt8M8mf/URpXhWHk9Q0Ds6YzdkVW4iaejWBneLxDAuk6FwWWTuPcGreGs6t3Wlx85TFv1J4NpOgbi0IaNMEz4ggPEMD0UpKKErPImvPUdJX/8mZbzfIk5h2OPK/HXw67Am63zGMuEGdCIwMpaSohIykVA6s3Mr2L9ZQnF83hhDd9OEPpCemENO9JUFR4fiGBJB/IYeM42c4tPovdi3+lTwb8wFejn76cS19eo1g+n23MXToIKKiG1JUWERi4jGWffcjcz6eR15e1W5WlImNa2ocCvfXtRs5cyatWvZ7OTuxZgfLrn6CNtOGETOkE36RoZQWlnAhKZWkFVs58PkaSqqjjGkaGx/7hGOrttFi8iAiOsbiExZI/rks0nYlcnDBWpJ/3W11F4WZufx046s0vrYLsddfRUTHWHzrB6Pc3Sm8kEPGoWROrt3FoUXrKMyw3UgQZ5jDt7S4hITvNlb9HOuoFSvX0Lnr1Tz0wDSGjxhCo+hICgsLOZKQxDffrOCDDz+vtjIYH9+Mnj27APDLL+tJTXXsGcPjx5Pp1XsEEyeO5YZxI+ncqT316oWhlOLcuQz27DnA9z/8zPwFS8jNzbO9w8vEypVr6NL1ah68KIYJCUks/WYFH9bRGI6zEMMfJIYSwzomec0OVg15gpbThhF9tV4XlhSWkJ2UyvEVWzn4+RpKqnqNAaBpbH30E06s3Eb8lP9n777DoyraBg7/Jr13SgKhhNBb6CCgFJUOKqBUG6iIFcuLr4oN+2fv8qooIhYsdBVUEBRQqnQIgVBCLwlJICFlvj/OtpBt2SQkwee+rlw5uzvnzGwmc/bsmZlnehLdOgH/qFByT2Vy8p897P78Nw45+Szc89Xv5BzPIKZdIhHN6hAQHYZ/ZAi6oIDcU1mc2pzKwSXrSf1hldvfM8zrUxbmF5D6fdX8LExbsoGFvf9LEzv1t6+M62/1gx+xf+EaGtqpv2QX9WeWfzaX30a9TN0hnUkY3p2o5nXxiwwh70w2p7buJ3XOKvbOXuF0zcXVD31E7BUtiUlKILB6BD7BAeSczCR9+372Lfibvd/9aTeUa2X1b2yDf5nqMNqmDnNPZnJ6+372V8E6FOLfRkkUQyGEEM7kndgjHxRV2J/NPRqTIiqRVQH+FV0EUUovnlxV0UUQpfBGeJeKLoIopduOL63oIohSkjg/QlSsT2N6VnQRRCnJl/qqTz4Lq77Rh2ZKNZZQ7tZfK+z05d+8t9RXFeBV0QUQQgghhBBCCCGEEEIIIYQQQgh3SYhaIYQQQgghhBBCCCGEEEIIUXnIGpzCBengFKKMKaUigNqe7Ku13lLGxalUlFL1gWAPdj1tWgtUCCGEEEIIIYQQQgghhBD/ctLBKUTZuwaY7uG+l3ps7+nAFR7s9xlwc9kWRQghhBBCCCGEEEIIIYQQVZF0cAohhBBCCCGEEEIIIYQQQojKo1BC1ArnpINTiDKmtf4U+LSCi1Epaa17VHQZhBBCCCGEEEIIIYQQQghRtUkHpxBCCCGEEEIIIYQQQgghhKg0tC6o6CKISs6rogsghBBCCCGEEEIIIYQQQgghhBDukg5OIYQQQgghhBBCCCGEEEIIIUSVISFqhRBCCCGEEEIIIYQQQgghROWhCyu6BKKSkxmcQgghhBBCCCGEEEIIIYQQQogqQ2ZwCiGEEEIIIYQQQgghhBBCiMqjUGZwCudkBqcQQgghhBBCCCGEEEIIIYQQosqQGZxCCCGEEEIIIYQQQgghhBCi8pA1OIULMoNTCCGEEEIIIYQQQgghhBBCCFFlSAenEEIIIYQQQgghhBBCCCGEEKLKkBC1QgghhBBCCCGEEEIIIYQQovIoLKjoEohKTmZwCiGEEEIIIYQQQgghhBBCCCGqDJnBKYQQwqk/m0+u6CKIUui69aWKLoIopZCkByq6CKKUwiO7VHQRRCncf3pVRRdBlNJH1XpWdBFEKRWoii6BKA0vXdElEKV10/GlFV0EIYSo8kZXdAGqIl1Y0SUQlZzM4BRCCCGEEEIIIYQQQgghhBBCVBkyg1MIIYQQQgghhBBCCCGEEEJUHoUyg1M4JzM4hRBCCCGEEEIIIYQQQgghhBBVhnRwCiGEEEIIIYQQQgghhBBCCHGRKKXqKqVeVUrtUEplK6VOKaXWKKUeVkoFlVEe9ZRSLyml1iml0pVSeaZ8ViqlnlBKVS+LfCqKhKgVQgghhBBCCCGEEEIIIYQQlYe+dEPUKqUGATOBMJung4D2pp/xSqkBWuvdpchjLPAhEHjBS5FAF9PPfUqpEVrrJZ7mU5FkBqcQQgghhBBCCCGEEEIIIYQQ5Uwp1Qb4GqNzMwt4DLgM6A38z5SsEbBQKRXqYR5dgU8xOjcLgenANUBHYBgw35Q0CpirlErwJJ+KJjM4hRBCCCGEEEIIIYQQQgghROVReMnO4HwTo+MxH7haa73K5rXflFLJwMsYnZwPAk95kMd/sU5wvEdr/Z7Na2uA75RSrwIPmMryAHC3B/lUKJnBKYQQQgghhBBCCCGEEEIIIUQ5Ukp1BLqbHn58Qeem2avAdtP2fUopXw+yusz0++QFnZu2nrHZ7uJBHhVOOjiFEEIIIYQQQgghhBBCCCFE5VFYWHE/5ecam+3p9hJorQuBGaaHEUBPD/LxM/3e6yiB1joDOHFB+ipFOjiFEEIIIYQQQgghhBBCCCGEKF/dTL+zgXVO0v1us93Vg3x2mn7Xd5RAKRUGxFyQvkqRNTiFEEIIIYQQQgghhBBCCCGEAJRStd1Jp7U+WMJDNzX93q21zneSboedfUriA2AaEK2UmqC1/sBOmikXpK9ypINTCCGEEEIIIYQQQgghhBBCVBpaF1Rk9gfcTKfcPaBSKgDrjEmnHaNa69NKqWwgGIh3Nw8bn2DMFr0ReFcp1Q6YBxwG6gBjsYbLfU5r/YsHeVQ46eAUQgghhBBCCCGEEEIIIYQQovyE2mxnuZHe3MEZUtKMtNE7fJNSaj7wKDDe9GNrKfB8Ve3cBFmD819FKVVPKaVL+1PR7+NiMf297lFKfaeUSlZKnVVK5SilDiql5iilRiilymWQgFLqqRLWSz0n+/ewc/weJTz+zU7KutQm3eKy/lvYyS/VQRnzlFInlFKrlFIvXPg3cfPYHr0XpdQym/0KlFLNXaS3bYtPOUhjfn2Zi2P5KKW+tkm/WikV4W7ZhRBCCCGEEEIIIYQQotIpLKy4H2PWpDs/JRFgs33ejfS5pt+BJcwHAKVUU4wZnC0dJOkCjFNK1fLk+JWBzOAUwg6l1FTgMexPMa9l+hkCPKCUGqa13n8xy1dZKKXqAlfYPNVbKRWntT5UAcXxAaJNP52B+0zxxWe4s3MZvhcv4ClgeAn3KzGllC/wNXCt6ak/gP5a68zyzrss+NeOofb4/kRf2Rb/WtEU5uZzLvUIx+etIm36TxSec+dz3rWoXknEjr2KsKQG+EaHkXfyDGc2pnD48yWc+m2j032bvHkXNUf0cCuf1e0nknPguN3XQlsnENW7LeGdmhDcqDa+0WHo/AJyj5wi4++dHJn1Gxl/77C7r7A6eTqdzdt2smXbTrbsSGbL9l2kZ5wBYEi/K3nu8QcruISXJr9a1ah+60DCe7fDLy4GfT6P3NQjnFrwJ8c/XURhTinaqlIEJNYiOKkRwUkNCW6dSGDTenj5+wKwc/jjZK7a4vIwXkEBBLVMMI6R1Ijg1on416kBQO6BY2zucrvnZbzEhNSKptWtfajbK4mQuCgKzudzZt8xds//iy2fLSG/NPUJoBSRibFUT2pAjaQGVG+dQHSTeLxNdTpn+HMcWr3d5WFCa8dQ78o2xHVpSnTTOgTXjEQpRc7pLI5t2sPueatJWfg3uqCwdOWtYuLj47jjzpvo07cntWrFcj73PHv37ueH7xfxv2mfc+5cjsfHrlOnFpu3LS/RPvv2HaRV8yuKPX91nx60bdeKtm1bUa9+PDExUYSFhZKddZbU1AOsWLGaT6d/xe7kvR6XtzIKrhVN03F9iO+dRFBcFIW5+WTuO8be+X+x49MlFJS2fdmo1bMVjUb3IiYpgYCoUHJOZXJi4x52ffEbaUs3Odwv8frudHv9jhLltfub5fwxaZrd17x8vWkwrDv1BnYksmkd/COCKcwv4OyR0xxbm8yuWUs5vja5RPlVpJBa0TS71ajD4LgoCs/ncyb1GHsX/MW2Mq7D2j1b0Xh0L6q1ttbh8X/2sPOL3zjopA5t1ejQiCZje1OjfUMCq0dQkJtH1oHj7Fu8nm3TF5N72vnEgIiGccT3bkPNzk2IbFybwGrhoDXnjmdwfEMKyd/9wcHf/imLt3vRBNeKptm4PtQ212Gu8TmXOv8vtpdDO2xspx3udNEObVVrl0ijUT2p0aERQTUj8fLxJjc9m9Pb95O68G92z15BYV7Jw/O1f/QGWt41yPL4x2HPcWSV68/fqqBOnVrcc9c4+vXvTXztOHJzc0nZs49vv53Pe+9/WqrPwrp1a5OS/FeJ9klNPUBio84OX+9zdQ9uvPF6OrRPombN6nh5KY4fP8WGjZv58qs5fPvtfLT+18wlAKpeHQ7ofyU33ng9nTq2pVq1KDIzs0lJSeW77xfwwYczOHv2nMflraqkDsW/gQdra7rDtnH4uZHe3/S7xP+kSqnuwHwgHNgHPA4sAU4BNYDBwFRgBHC5UupqrfXWkuZT0aSD898lDce99QCbTb/XAreUf3EqtViMzs1s4AfgVyAZ4yTUFLgX6GD6+UUp1VZr7c60ck/cCqxxkSatFMd/H3jPRRpHJ/SxGH+nXMAb45wyBni5FOVx1yGgj83jQCDRVKZ+psefKKWStdar3DheWb6XoUqp1lrrcrsboJTyB74FBpqeWgYM1Fpnl1eeZSn66nY0ffdefMKCLM95B4FvZCJhbRKJHd2bzaNf4FzqEc8zUYrGr95B7OjeRZ72j4umWlw01fp35NDMX9j10DQoxy+USXOeJqJLs+Iv+PsS1CCOoAZxxI7syZGvl7HzwQ/Rec7WF/93u2LgyIouwr9O+JUdqP/W/fiEBVufDArAJymU4KSGVBt5Fck3TSXXw7YaPbQH9d+4r9TlTPz0McIuc3aJIwDqXtmGK9+8E3+bc69vEAREhFC9dQJNR/Zg4c2vcCb1qMd5NB7ajd4l7Dy5UMeHhtHunsEor+LBZkJiowiJjSKhT3uO3pbCz3e8Rdahk6XKr6ro268X0z56jfBwa1Sj4OAgIqMiaNuuFTfedD3XDxvPnj37LlqZdifvKfact7c3s7/72G76iMhwkiLDSWrTgjsm3Mjzz77B6699WN7FvChqX9WGy9+6Ez+b9kUQ+EeGEJOUQKNRPfjlxlfILEX7AkApLnt5HI1G9SjydHBsFMGxUdTt155dXyxl5eRPyuz6JiPlsN3ng2tFc+WMh4hsUnTgure/L+ENYglvEEvDGy5n28c/8/cTn5dJWcpT/JVt6GGnDqslhVAtKYFGI3uw+KayqcNuL4+j8cgeRZ4212G9vu3ZOWspfzipQ+XjTdfnb6bxqJ5FnvcJ8MM/PJjoFvVoMqYXv014m6N/77R7jMtfv4OGw7vbfS20TnVC61QnYUgXDi7bxNKJ73D+zNmSv9eLLN5BO6wWaarDUT1YUkbtsKuLdrjTjXbY6ZmxNBvXp9jzQTUiCKoRQa0erWg2rg9Lxr5Cdgk+66Ka16H57f3cTl+VDBxwFZ99+hbh4WGW54KDg4iKiqRD+yRuvXUUg4fcSEpK6kUr065dKXaf9/Pz4/MZ7zD0ugHFXouPjyM+Po7Bg/owccJNXHPdLWSYBm5e6qpSHYaEBPP5jHcYNPDqIs/7+/sTExNFp05tuW38WK4dejM7duy+GEWtFKQOxUWlL7kBrbaTUtwJO2u+GVOifgfTfeMvMTo3jwCdtda2N24OAu8ppX7H6AuKAz4D2pckn8pAOjj/RbTWeYDDaRBKWSYrZmutXU+XuLSdBCYD79uZDbdOKfUlMAu4HmgIPAA8U05l2VvO9XGsFMcfa/q9AKNDsb/puYvRwZlnp9xrgC+VUq9i1Ik3xkzcgRfubEdZvJdMjJE1fhj/D0NKsK/bTAtSz8HawbsEGKK1rhJDzkJa1KPZh5PwDvInP+sc+9/6gfQ/t+IV4Ef1a7oSN/ZKghLjaPnFf1l39WQKsj0b+Vf/0ZGWzs3MTXs48O5czqUeJbBeDeLvGkJoqwTixlxJ3skz7H3+S6fHyj18ik0jnnWZxh7/mlGW14/PX0X66u3kpp1AeXsR1r4R8RMG4R8XTc0beqB8fdh+55sevNt/n9ga1alftzYr/15f0UW5ZAU2r0/C+w/hHehPQdY5Dr/zHZkrN+MV4EfUkO5UG301AQ1q0fCzKWzr/yCFnrRV67UHhefzOLdjP8rXm6Cm9Up4GOtx8k+fIfufFELaN8E7xKMoLpekmOZ1ufq9u/EN9Od81jnWvzuftJXb8AnwI3FwZ5qP7kVkg1gGfPoQ3w6YQp6H517b2BcF5/M5ueMA3r7eRDet4/YhgqpHoLy8yMvOYc9Pazn451Yy9h6hIDePyMRatLz1amqYZogO/vIRvun3OPlnc10fuApr1aoZ0z97i6CgQDIzs3j91Q9Yvnw1gYEBDB02kJtvGUHDRgl88+1H9Lj8GrKySj7e6dCho3Tu6PqG+AMPTuD6G4xLnFlffG83TXr6Gf5Y8Rfr1m4kNfUAR44c49zZHGrGVqdb906MGTuciIgwnnrmP2RknOGTj51/Dld2Uc3r0uP9u/EJ9Ccv6xyb3pnPkZXb8A7wo/7gzjQe04vwBrFcOeMh5vebQr6n7QtoO3m4pVPl5OZUtry3gDP7jhFWtzotJg4kumU9Go3uSc6pTNa/+E2x/ff/tI45/zziMp+eH91HeEIshQWFpHz3Z7HXlY93kc7NU9v2sXXaT5xJOYRPcCA1Ojai+R398Q0OoNm4Ppw7ms7md+d7/L7LW3TzuvQy1eF5Ux0eNtVhwpDONBndi4gGsVz92UPM61+KcyTQfvJwS+fmic2pbH7fWoct7xxITMt6NB7Vk3MnM1n3UvE6BOgy9UZL52bGnsNs/mAhJ7fsw8vPh7iuzWlxez+Cqkdw1ScPMG/Qk5zZW3wgUlDNSAByTmeSunANh1dtJ+vAcQoLColuUZcWt/UjIjGO2j1acdX0B1k47NlyHRRYWvba4WHT55xtO7xqxkPMK2U7bHdBO9z83gIy9x0jtG51WpraYWMn7RCg5d2DLJ2b5zPPsXXajxxbs4u8szmEN4ilxe39iWwaT2STeK6a8RBz+zzmXtQCpbjspXF4+fpw7niGMSv3EpGU1JxZX7xv+Sx86eV3WLZsJYGBAVx//WBuGz+Gxo0aMG/uDDp17ufRZ2Fa2hFat+nlMt3k/9zNqJHXATBj5my7ad54faqlc/Po0eO88ur7bNiwmby8PFq0aMrDD02kXr14unfvzKyZ7zFg0JgSl7eqqWp1+NWsD+jb1zjW2nX/8OZb/2Pnjt2EhobQv19v7rrrFho2rM+CeTPp1KUfJ0+eLnF5qxqpQyFKR2udo5Q6iRGBsLaztEqpSKwdnAdKmFVfjAiUAG9f0LlpW56tSqmZGGtztivvCTvlQTo4hbBDaz3ZxesFSqmJwDUYnVnDKL8OzkpJKdUZaGR6+AVGDPH+QAvTjNaK7HWYAtyF0dnYUynlpbXjIT9l+F5OAQuBicBgpVR7rfVaT9+Eg7IGAfMA87TEH4HrtNaef0O/yBKfvQXvIH8K8/LZdMOznFm7y/Ja+h9bOLfnMA2eHEtQYhzxdw4i9RX7F6rOBCbEEn+nEZLpzIbdbLzmSUsYzcyNKZz4eS1JPzxNWJtE4icO5sispU5ni+q8fLJ3lPRawnB2dxp7np/F8QV/mWP4W5xZl8zR2ctpM/9ZghLjqHFdNw59tpgMN8I3/htNuGUULZo2okXTRsRERZJ2+Ch9ht1c0cW6ZNV5ejzegUZb3TXqKbLXW2eAZK7cTM7eQ8Q/fjMBDWpR845rOPTaVyXOIyf5APun/I/sf5I5u3UvOjePuAdGlLiD8+Sc5Ryf+TPZ/yRbZpO2XDVNOjhtdHt6LL6B/hTk5TN/9EscXW8doZy2chsZe49y2eMjiWwQS9Lt/Vnzuv2OK1dOJ6exYspnHPtnDye27acgN48Ok64rUQdnzulMVj73JVs//7VYJ8Lxzakkz13JVe/cReKgzkQkxJJ0Wz/WvjnHo/JWFS/+3xSCggLJy8vj2iE3s+bvDZbXlv++ipTdqUx97hEaNkrg7nvH8eLzb5U4j/z8fLZv2+U0jZeXF926dwLgzJlMFswvvmR5QUEB9eu0o7DQzqXXBvhx0a9M+2AGv6+YS2RUBI8+PolPp39tP30V0fGZsfiYzpeLR73E8XXW9nXkz22c2XuUDlNGGp0Wd/Rn42ueta+whJq0mNAfgBMb9/Dj0KkU5OQBcPKfPexfvJ5+3z1OTFICLSb0J/mr34vNVDt/5qzLmXjhiXGEJ8Qa5V+5jbN2BnHV6dPW0rl5bO0ufrx2KrrQ2vl1eMUWDixeT/95T+Ht50OLiQPZ8sHCShtWuvPT1jr8edRLHLM5Rx5eadRhx8dHEmGqww2e1mH9mrS8w6jD4xv3sHCYtQ5P/LOHfYvXM+Dbx6mWlECrCf3Z9XXxOoxpnUDTscZXgZPb9rHwumfJy7KOczy2Npl9P61l0Lyn8I8IptOTo1ly86vFypJ96BR/TP6Y5NkrKDxfNILIiX/2sPu7P+n7xWRqdmpMzU6NSbyuK7u/+8Oj930xdHqmaB3atsPD5dQOj1/QDk/YtMNqSQm0dNAOlY83Le80xuAW5Obx49CpnNpqXfXm+LrdpHz3J/3nPEH1tolENo2nTt927FvoKrATNBt3NdXaNCA9OY19P62j9T2DPXqfldHrrz5j+Szs138Uq/9aZ3lt6bI/2b17Ly+9OIXGjRrwwKQ7eGbqayXOIz8/n61b7c96NvPy8uKKy7sAxmfhnDk/FUtTvXoM4241os+cOnWajp37kZZmnQ3/58o1zPrye9avXUL9+nXo06cn7dq2Yt1690IbV1VVqQ6vu26ApWNsyZLfGXzNTeTl5Vle/335KhYv+Z2FC2ZSr148T0x5kPvuf7zE5a1qpA6FKBPbgO5AolLKR2vtKJRbE5vtkt4obGqz7eq+9jqMDk5znlWqg7N43CchnFBKtVVKfaCU2qmUylJKZZu231dKNXKxb4JS6kGl1HylVKpS6pzpZ59S6mulVF8X+9+slNKmn3pKKT+l1ANKqbVKqQyl1Cml1DJCjeN+AAAgAElEQVSl1IAL9gtVSv1HKbVBKXVGKZWulFqilOrtKC93aK1PAuarzwalOVYVdaPp92mMTr05WKfZ32h3j4tEa30WMMdMC8IYFeNMWb6X57HGUy/TTm+lVAiwCGvn5jzgmqrUuRnaJtESrvXIrN+KdG6aHXh/Ptk7jajItW7rj/LxLnE+tW8fgJevMYZn92OfFFsjsPDceXY/9gkAXr4+1L6jeNigsrJ5zIscn7eqWOemWd6pTFKe+szyuNogx+tG/NvdPX4sPbp2IiYqsqKLcskLTmpIaOfmAJz46pcinZtmRz+cy7ldRsd/9VsHetRWszcmc2z6QrLX70Ln5rnewYETXyzm1NwVHofKvdRVT0ogrpPx3WjHV78X6dw02zhtEad2GRHvW97aBy8P6hPg2MY9bP50CUc3pFDgYZ2ufuFrNn6w0OEMKV2o+f3RTy3HbzCgo0f5VBVt27Wia1fjPX4+Y3aRzk2zt9/6iB07jHUO77zzZnx8ymcca8+eXYmLqwnA3Dk/kZNjf+asq87KffsO8sMPiwCoVi2aRo2r7qV0TFICNTsb7Sv5q9+LdKqYbf1wEemm9tV0XB+PzpcAzcb3tVzf/DXlM0unillBznn+mmJcU3j5+tDsNqdfrxxqMKybZTvlW/sdWtXbW7/6bXpnfpHOTbOTm1M5+Ivx/+ofEUx4wziPylPebOtw51e/F+ncNNv84SJOm+qw+a2e12ELmzpc9YT9Olz1hLUOW4wvXocNh1vr5+9nZhXp3DQ7vfMgWz8ybvbWubINkU2KTxBY8eA0dn6xtFjnpm1ZVj463fK4XiU+19rW4S4H7XCLTTtsdhHbYXM77TCiYRz+EcaEjAO/bCjSuWmmCwrZ9NY8y+Pq7Rq6LFtwXDRtHx4GwMpHpjus26qoQ/skunc3vid9Mv2rIp0qZq+9/iHbthvfL++5e1y5fRZe2bs7tWoZg0C++34hOTnFr1c6dmyDt7fxP/bZZ98U6dw0y8zM4s23/md53Llzu3Ipb2VR1erwprHXW7bvue+xIh1jZr/+toKvv5kLwG3jRxMZGVEu5a0spA5FhSgsrLif8mO+wA4GnJ38r7DZLh5SxTnbiwBXDdHXwX5VgnRwCrcopbyUUq9hxGS+A2O2WzBG51EjYAKwVSl1u4P96wMpwCsY4ULrYsySCwDqYIR6/VEp9blSyp1PvzBgOfAqxokgDIjEaPgLlFKTTPnWAVYBLwFJQChG7OkrgSVKqdEl+0sUY17ot6CUx6lSlFJ+wA2mh7O11udN4VHNw2BHulmP5cm2R8vhHdayfi9a6zTAvJhUP6VUlxKU2SGlVBjwE9YPt2+BYVrr8473qnxi+nWwbB/+aqn9RFpzdPbvAPhGhBDRtXnJ8+lr5JO96yBn1iXbTXNmXTJnk9OKpK8op/+0ruEdWLdmBZZECENEn06W7ZPf/Go/kdac/M5oxz4RIYTKGpiVVv0+1mU0tn+z3H4irdlpmpkTEBFMrcua2k9XSeSmZ3HSNLM+rG71Ci5N+Ro48CrL9szPv7WbRmvNV7N+AIx1Li+/vHwGy4wYda1l+8tZns1+MsvKtIYsC/D3d5KycqvT19q+kr923L7MHYX+EcHEeti+4q9uC0B6chrH19tfa+r4+hQydh8yytbHg5vlSpFw3WUA5GWdczhjzMvX2jmUte+Yw8Nl2rzm7VvRXw/sq2tzjkx2co40z170jwgmzsM6rNPHvTpMN9VhXTt1GNMqAYD8nPMcXuV4MP/BZdaZYPX6e9Y5eXrnQc6dNNYFrMznWnfb4e4yaId13GyH6U7aoZdNW8jcf9xhXpn7rDM/vdxoP52fvwnfkECSv1nO0dU7XKavSoYMsXYUf/bZ13bTaK2ZOdP4nIyMjKBnj8vKpSxjxgyzbH/+uf1oQ35+fpbtPXsdr41tu2627T6XoqpWh+3atQIgOXkvu3fvdXisnxcvA4z6u3Cdx0uN1KEQZcY2/NAt9hIopbywTrpJBxzcRHXI9p/e/qLrVrYdqY4bSyUlHZzCXW8DkzBWVloO3Ar0ADoCtwFbMUYDfKiUshcDxRujw2k+cC9GB2Nb0++Jpv0BxmCEF3VlGkbH5nvAVRgL4I4HDplef0Up1QKjkyoBeNFU3g7A/UCG6b28r5Ty6JuaaT/zt6J/WzzJgUCUaXumzfPm7eoYsb4rhKlD0jzENUNrne4keXm8lxcAc+yvqSXctxilVDiwGOhqeupLYKRpXd0qJbyjMbK6IDuHrH/2OEyXvmpbsX3cFVC3Ov6xRpVm2BzHWT7+cdEE1Km4mzZeftbBUroKh+gTl46QDsbHW0H2ObI32b95B5C5yto5H9KhZG1VXDyxHYyZVnnZORzf7Pj7yiGb8Ng12zsNzFEpePsZN3t1QeVdE64sdL7MuHGflZXNxg2Ol03/44+/LdudupT9LJCQkGAGmDpb96Ue4E+b/EoqIMCf/gOuBIyQts5uOlV21W3a18lNjt/HEZv2Zd6nJELqVCPYdH3jquPiiOn14NgoQuKrlSif2K7NCKkVA8C+H9eSf87+LN2MFOtspBAnHV+hptd0YSFn9h51mK4i1bSpwxPO6nBV6eowtE41gk1rsx8uRR36R4YAkHs6y2nI33MnMizbNTs1LnF5zcwd05X5GrVGBbTDIy7q8KiTOjyz94jl7xlax3EbDa1bw7pPSvEZgLbqDepEnavaknM6kzXPzHKatirqepkxIDUrK9tpGNfly1dbti+7rOwHsYaEBDNksHF7YO/e/Sxfsdpuul27rNfPCfXrOjxeQoL1Ndt9LkVVrQ6jo42oQceOOR6EAHDsqPX17t07OUlZ9UkdigqhCyvup7zektZ/AytMD8c5mBzzINZ+hzcvvAeslOphE+nyUzv7/4r13vSdSim7I9KVUv0A8yjWNGCj+++kcpAOTuGSUuoqjE5IgPFa6yu01tO11r9rrddorT/C6GD8zZTmLTsz3g4D9bTWg7XWb2utf9VabzD9fh9oCXxqSvugqUPHmY7AcK31XVrrX7TW67TWHwP9gEKM/+3fgBZAT631f03lXau1fhMYZTpOKODpLM6HsU7x/sbDY7ijvlKqhZOfYNeHcKq6i+Pbu2NhHkGSinVaPRh/80MXpKkI92LMLgZjpqMzZf5etNZHgXdND3srpS4vyf4XCAd+AcxXWTOAMU7is1dqQQ2N8Fjn9h5xekPGPLPS2KeWw3T2BDeyhuA6uzvNScqirzvLxycqlKQfnqbr9k+4fP8sumyaRquvHqPWrX3xCiz9SFtz2F6As7sOlvp4QpRWgKmt5qYeASdtNSfF+v8akBhf7uUSnolMNMJCZqQedXruTU85ZNmOTCzZufdiC4wOI8L0vk67ONdXdY1N4Vv37NlHQYHjoCHJNjdGG5dDyNch1/QlONi4vPrqq5Kveerj40Pt2rEMHTaQJb/OJrFhfQBmzviWrKxsF3tXXhGmsKuZLtqXeVYlQHgJr20AIhpZ97E9luu8ShYW1jY87e7ZKxym2ztnlWUtz5YTB6K8VLE0Uc3rUrt3EgB7flhlN5RqZWD+G50pwTkywoNzZERD9+sw3eb1iAvqMN8UvtvXxTrTfqFBlu0ID/7nAKKb18UvzDhOerLzMlekCDfrMKPI37V826GzOszLPMeeOasAiL+yDZFNi19DKW8vWt0zCIDcjGz2zF3lMC+/sCA6PT0WgLXPfU3u6SwX76TqadLEGL+8OyXV6Wfhjp3W8MTmfcrS0OsGWD4Lv5j1ncN0W7bsYOVKYwb8jTdeT2xsjWJpQkKCufceY8mzlJRUFi/5vczLW5lUtTo0X5uEhYc6PV5YeJhlu1nTyj9AsDSkDoUoU/cB5zD6FhYrpf6rlOqslOqplPoQeNmUbhdGBMsSMU32edH0MBRYqZR63nT8JKVUH6XUexjLn5n7CB/Ruhx7dsuJdHAKdzxi+v2dqROxGNMagHebHtYFel7werbW2uGQQ621xhiZUIAR+vZKF2X6Rmtd7M6K1noT1k6qasAbWuu/7KRbBJhjgbiapl2MUqoTxkxQgIPA+yU9Rgl8Amx28lPa4VB3ujj+RNvESqlooL/p4SxT3QFgOgmah4sOUkpdtOD1SqlAU4fs/2GEJAY4hrEmpqN9yvO9vAyYv1mWZhZnEsYAAjBmbt5SFT9sALz8ffGLMS4ccw+fdJo2PyObAtPNG3/TLAJ3+cdal1zNPXTKadrcNGs5/OMcL9XqExJIxGXN8I0KxcvfF/8akUT1TKLhC+PotPItwkoz00kp6tx7jeXhsXmOb14IcTEof198o41xRucPn3CatiAjm4Js44a1n5M2JCqOt78vgdHGuTfrsItzYsZZy7qXIXFRTtNWtKQJAyyzinYvKHapd8nw9/cjJsZoW4fSnK8xm55+xnIjx7wmUVkaMeo6y7Y5HK4rderUIiMrhYysFE6m72Trjj/45NM3adXaCD//y5LlPPaow0u1Ss/b35cAU/vKdtG+ztu0r2AP2pd51pg7eWUfsl7fBJfg3OwT6E/dfqYZw2knOPKn40gYuaezWHHvB+SdzaFGx8YMXDSVBsO6Ua1tA2K7N6f1pGvp+93jePv7cmLTXtY884Xb5biYbM+RJalDT86RRerwiIs6tLlWDo4tWofmjjO/0ECiW9RzeIyanayRFYKqRxQJK+yu1vdYgzPtraTnWtt2eLYKtcO/n/6CE5v24u3vS//vp9D6/muI7d6cam0bkDi8O4N+nEr1dg3JO5vDivs+dNpp2f6xEQTViODo3ztJ/nJZid9XZefv70+1asbfMO2g85ms6ekZls/C+Nplv+7v2DHDLdufz3Q+lnrcbQ+wZ88+oqMjWfPXT9x/3+1ccXkXul7WgdtvG8v6tUtISKjL8eMnufGme+yuD3ipqIp1uGOH0UnXtElDYmIcny+6d7PO+IuPr5xrTZcFqUMhypbWegPGkmlngBCM+9erMCbcmJcA3AUM0FpnepjNs8AbgDbl8V/T8TdgLIN2J0YHax7wsNZ6poPjVGqVcxEMUWmY1v3rYXro9OpNa71dKXUCiAG6AEucHNcXqIExgsD2m9ZJjJCgrQHHw3DgKyev/QNc7ka6TRidsQlO0hSjlKqB8bfwwThB3KS1Put8r0vKSKyLD9s78c0EHsJYX3U48L9yKkddpZSzmHTLgLu01o7joJbje9Fan1BKvQU8ClyulLpSa/2Lu/vbHgojnDIY7SoOo1O9yvG2GWVu7rx0puBsDt7BAXgHB5RbPgVnra/by0drTcbaXZxcvJasTXs5fzwdrwA/gpvWIXZUL8LaNsQ/LprW30xhw+ApZG1JLVFZAWrfMYCwtsaoxuMLVpO1ydm/rBDlzzvY2oYK3WirhWdz8Q4OxCvY+UwSUTF8bc5teWdd12fe2Vx8gwOK7FfZVE9qQKtxfQDIOnSSrTMcrBN7CQgJDbFsZ2e7vtw8e/YcISHBBIeUNsBHUbVrx9Ktm7GG3+pV64qsGeaJEydO8tADTzF3zk8UVuKwl6742LSTfDfOl/nm9hVU8vblY3OOzc+2HzbWNh8z32D31zet07+9ZVZgynd/ukx/YMl6FvSdQvM7+tNw5BV0f3NCkdfPHUvnr//7ll1fLKUgp3IuG+/rYR36eHCOtJ1xmVeKOty/eD0NrjHWNGv3n2EsvulV0EW/FvlHhtDijn5F8w8OJDfd/Zl99fp3oP5A46bv8X/2kLrI/nqsFc22LvLKuR36etgOfey0w5wTZ1h07VQaj+5Jy7sH0fbhYUVe14WF7PxiKVs/XFQkJPSFanRsTKNRPSjMy2flI9PdfStVSmio9TMtK9v1jP/s7LOmz8Igl2lLIj4+zrLG9cqVa0hJSXWaPjl5D50vG8CEO27k4Ycm8sr/PVnk9fPnz/Pqq+/z1jsfk5bmvMOoqquKdTh/wWK6dGmPj48Pzzw9mYl3TS6WJjGxPjffdIPlcajNddulRupQVJgq/F3BFa31fKVUK4zZnAOA2hhL/O0GZgPvlKbPwTSRZ5JSaibG0n7dMPpCgjAm5ewGfgc+1FrvKs17qUgyg1O40gbr/8mXNrGd7f5gdG4C1LzwQEopX6XUXUqp1RiN6ACwjaKzBc3hUF1N2XLW6GzXW3QnnfNYBTaUUqHAQowTDhhTt39zsktZ6Km1Vk5+lpXy+E+7OP5TF6S/yfR7vda62NqjWut/APMCURUVpjYDeFdr7XwBxvJ/L6+YygLwjAf7gzEj2dxRXw/4VSlVrH1VBV7+1nUmC8+7jrBbmGuk8Q4oWQjYIvnkOc/HthzedkLNpjzxKRsGPMb+N3/g1NKNZG1J5czaXRz+/BfW93uUfW98b+wbHEDj1+4sUTkBwrs0I+FxI0r2+ePp7JpcXuMBhHCf8re2BVdtCKDwvDHa3KuEbVVcHD4lPPcWmOrTp5LWZ2BMGH0/vBdvXx90YSG/TvqQ/EracVIWAvytN8XPn3c9syM31/hbBAaUbQf1DSOuwcvL+Erw1Zfuzd4EOHToKJ079qNzx3506zKQ4UPH8eEHMwgKCuL1N6Zy/6TbXR+kEvMOsLavAjfOl+b2VdJrm5LmVWB7fVOCvBoMtYanTfn2DycpDV6+3jQY3p34Pm1RXsVvLQRWj6DBdV2J697C7TJcbN7+ntWhJ+dI7xKcjwtyra9fmNfeBX9xcqsxyCC+VxJXz3iIam0b4O3vi29IIHWubsvAOU8SXDOKglzrecPeta4j4YlxdH/VaJ/553L5/b4P3N73YvMJcP/aH8quHZbke4aj/5fYbs1IuK4rQdWLBwtSXl7U6dOWxOsvdzj71svXm8tevhXl5cXW//1E+s4qOQ7WpQCbz7SK/CwcPWqo5bPQ1exNs4EDrmLkyGvtdpr4+fkxbNggRo64xs6el5aqWIcffDiDg6aZirffNoZPp79Fy5ZN8fX1JSoqktGjh7L01+8IDg7i/HlTeQMr7wDB0pI6FKJ8aK33aa0f0Fo31loHa60jtdYdtNYvO+vc1Fovs7l3f7OLPNZpre/UWrfUWodprX201hFa6/Za6wercucmyAxO4Zq99RfdUWSIjlIqClgMtHNzf1fTQJyNXrAM7XAxysGczq1YPUqpAGAu1vfwitb6ZSe7XHKUUk2xhkt1Nm39c4wwsV2VUvW11nvLoTiHgD42j6thhOudhNHB/o1SaqTW+mt7O1+M96K1Pq2Ueh14CuiilOqntf7R3f1NCoGxGLNIrwEaAUuUUj201s7jvLqglKrtOhUsrT7MdSI3FNrcYPHyc/3x4+VvpCnpiP8i+fg6z8e2HAXniueTf8b5QKm9L3xJWNtEIi9vRWjrBMI6NObMmp1ulTOocW1aTH8YL18fCs6dZ+v418g7ccatfYUoTzrX2hZctSEALz/jZl/hJdzJVFkF14zEP9z+TL3cjGyyj5wmv4TnXm9TfVbGTkPf4AAGfPYQIaZQf6te+Jq0la7GMlVesbE1iIi0v+x8+ukMDh8+Sk6udQaQn5+v3bS2/E0DFM7luJ7FVBI3jDRuvubk5PL9dwvc3i8/P5/t26zflzdv3s7in5fx2fSvWLDoC558+mESGtTj7omPODlKxQiqGYmfg/Z1PiObs0dOU5Bj03nkxvnS3L48mc1Ykry8ba9v3MwrsEYEsd2M0MHH1+/mjJMZY2CEs71y5sPU7NyEwvwCNr87n91fLydz/zG8/f2IadOApEnXUqNTY3p9cj9rpn7JtmklvQwunSAX58izR04X7QAsQR16co4sKMH52Nvf+vqFeelCzS/j36DvrMmE169JfM/WxPdsXewY22f8Qkyr+lRLMtbkdXcN1KAaEfSZ8RB+oYHowkJWPPg/l+tNlhd32mF+jvvX/lB27bAk3zPs/b80G9eHjk+NRnl5cWTVdja+OYcTG/ZQkHue0Lo1aDjiCprf1pdWdw+iRsdGLB79cpFZoQCt7hlCRMNaZB08wcbX3B98UpnExdUk0sFn4enTGRw6dIQcm8+0ivwsHD16KAA5OTl8M3uey/T/99ITTJp0BwBz5v7Iq69+wD+btlJQUEjTponcNfFWbrl5BC++8DgdO7ZhxMgJVTKqwaVah2fOZHLd0FuYP+9zatSoxpjRQxlj2t/Wo489z/333U716jFkZlbN9W+lDqt+HV7SquYqXeIikg5O4Ypt598dwEo39zt9weM3sXYMzsFYV3ITxhqJOea1D5VS+4F4rCE5KwWllA/wDda1RT/SWj9cgUWqKLazGF9TSr3mIr0y7fN0OZQlT2u95YLnlpqm3f8N1AKmKaVWaa3329n/Yr2X1zFCDURizOIs8Z0drXW+UuoGjA72vkALjAWoe2mtM5zv7dSBUuxbYgU2N1XcCTvrbQob5U44W0/z8bYJTVXSfMwOzVhC5OWtAIi4rJlbHZwBdarT+usp+EaGoPML2DbhdTJWF5tELESFMK+pCeDlRlv1CjJmmBVmu3fjVJSdTv8ZTpPhl9t9bcfs5fz2wLQi4frcCcfna6pPd8L8XUze/r70+3gS1VsZKwts+GAhGz9YWMGlKp0pTz7I6DHFb7IAfDHzOyZO+A9ZNjdZgoNdh/gKCjLGCGZnuQ4d5q627VrRuHEiAD8u+oWMDE+XgLHaunUnz059jdfemMrYG4fz/bcL+O031zMGL6a2k4eTeL399rX7m+X8MWlakZCm7oQs9TG3LzfCRV8o3+Ycay/cpb18wHUoVLMG13XFy9uYEbHbjdmbSQ9eR83OxhqPfz70ESmzV1heK8w7x+EVWziychtXfzmZ2K7Naf/4SA7/sZXT2+xdlpePdv8ZTiMHdbjrm+WsuOAcWZI6dCec7YVsOxhdhQ52VYdZB44zt/8UWk0YQOLQboTUtgZAOr3zIJveX8jub1cwdOlLABTmF5CX6fpz2i8imL5fTCa0jjHOedWUGeyZt9rlfuWl7eThNHRQh8l22qE74dVL0w7zPGyHF4azjWwaT4cnjc7NtOWbWTL6ZXShNcxwxu5DrH32SzKS0+j22u3U6NiYNg8NZc0zsyxpwhvE0uruQQCsfnwG+efca+uVzdRnJnPTjdfbfe2zGd8wbvwkMjOtn2khwa5DsJs/L7Ozym4loQ7tk2jaxFhWZP6CJWRkOB+Y2r9fb0vn5qeffc342x4o8vrGjVu57fYHSUs7zOOPTeK6awdw54SbePe9qhdm+FKtQ4D1GzbTrsPVPDL5HoYNHUjNmtY5IGvWbGDqs6+z6MdfmfL4JMDoDKyKpA6rfh0K8W8mIWqFK7YzxM5qrbe4+ZNm3sm0jqc5oPkXWutrtdbzTVOwz5k7N00iL8abKgmllBfGLL5Bpqe+xujs/Vcx/R1Ge7Dr2LIuizNa60OAeQGgMOC5C9NczPeitT6DEaoWoL1SaogH+aK1Pg9cByw1PdUW+FEpVWUWCCjMzSPvpHEB6h8b7TStT3iwpXMyN+1EifLJPWw9bfnHOV5IHsC/lrUcuYc8mxCbvcsaCsq/pvP8APxqRNJ69hT8Y6PQhYXsuP89Tv601qO8hSgPOjePvFNGW/WLdR4x3js82LJm53kP25AoXwW5eZw7ZXRIhcS6OCeGB1luDmcdOlXuZXOX8vbi6vfvoXZXY3bZtllLWfXclxVcqosjN/c8J08adRFXy3mE+oiIMEJMa2+W5VpeI0dda9n+clbZzRBauMC6NPmQa/s5SVl5FeTmkWNqX8Eu2pefTfvK9qB9ZR+27uMqr+A46/VNtpvnZnN42oLcPPbOXeUyfeIIo9MpI+Vwkc5NW7qgkA0vG+HjvLy9SLy+u1tluZg8rUNPzpFF6tDFNWOwzbVy9mH7dZiXeY51//ctX3e+n5ktJzC7+0PMbDGB73s/wu5vV6C8FCHx1QBId2MGpm9wAH1n/ofIJvEArHt5Nts/+8XFXhXPtg6Dqkg7bHjD5ZYBBRtf/b5I56at5K+Xk7HHOJ9f2H6a394Pb39fzqQexSfQj/qDOxf7iWxiDdgT27WZ5XmfQPfX5q0McnNzOXHC+NvXqh3rNG1ERLjls/DAwbKbeTxmjDWq0Uw3wtPeeutIAAoLC3niSceBv1548W3LjLGbbx5RylJWXlWxDs2OHDnG/ZOmULtOG2rFJ9G0eXeiYprQpetAFv34K7VqxRIYaHwf2ra9Skd5dErqUFSYwsKK+xFVgszgFK5sBDTG7LWuOA/l6UhDwBy/wG64UAClVBOgMnbWfAiYrzTnA2O0/lfOj++JMbsW4G1cz+btBNwPNFBKddVa/1mehbOltV6glPoDY/HkUUqpFy5Yj/Niv5e3MELnxmDMAL3WeXL7tNbnlFKDMcI9dzH9zFdK9ddaezJ1Kt51EqAMZ3pm7zpIRJdmBNavifL2QhfYb0pBDWtZts8mp9lN4ywPy3ESazlJWfT1kuZjYf9+hF2+UaG0/mYKgfWMm9TJj37C0dnLPctXiHKUs+sAvp2b41+vJnh7gYO2GtDAeuMsZ/dFnRQugN8emMZvD0xzme50chqBnZoQXq+G03NvRIM46z67PTwnljWluPLNO6l/VVsAkuetYtkjn1RwocrGxAn/YeKE/7hMt2PHbrp27UhCQl28vb0pKCiwm65howaW7Z07U8qkjD4+PgwdNhCAY8dO8MuSsvvMMt8kA4iPj3OSsmL8MWkaf0xy3b7Sd6VRs3MTQl20r/BE63vM8OCaI32XdR/bY7nOy/WNxagW9YhsalwWHvhlA+fTnc8ADqwWTkBkKACntqQ6TXtys/X18AYXt55XPDCNFW6cI811GFaCc2S6B+fI9GT36zDC5vV0N+ow93QWuaeLhtWLbBxvWfvx+Ebn5wTvAF+u+vRBSzjbTe8vYONbc13mW95K2g5d1WF4kb9r+bZDZ3UYbvMdxLaN2HNycyrhCbEERIYSEBNGjmlJC3MI3LB6Nejx/t0uy540yfoVdHan+8k6WDlmfI4bP4lx4ye5THPGVAMAACAASURBVLd9+y66d+9MYoN6Tj8Lm5giDgDs2JFcJmX08fHhhuuNccpHjx7np5+XutgDmphmmR07doJDh444TJebm8u2bbvo1KktTRo3cJiuMrtU69Ceo0ePc/To8SLPtW3b0rK9Zs1GzwtZgaQOq34dCvFvJjM4hVNa6+OAOSbNKKVUNQ8OY9uR7iyOwQQnr1UIU9jS8aaHvwLDtdb5FVikimQO6VoAPKu1/srZD8bMyfwL9r2Yppp+ewGPXfDaRX0vWussjHU8AVoD9uPRuX+sfsA601M9gO+VUn4eHOugOz+eltWejL93AEbo2JDWCQ7TRXRpVmwfd+XsO0auaXR1uM1x7Anv3BQwZm/m7D9WonzMghtZO3hyj14YndvKOzSIVl89TrBpVHzK1Jkcmv6zR3kKUd6y1hghk72DAwlu5fhmS2iX5jb7lKytiovn8BpjJLJvcADVWtZ3mC7OdE4EOLK2coxe7vHirTQc0gWAvUvW88u974MuwciSS8DqlcYs/5CQYJLatHCYrlu3jpbtv1atc5iuJPr07Ul0tDFLafY38xzeyPJEXFwNy3Z2dtmFL7vYjtm0r+hWjttXTZv2Zd6nJLL2H7fMHqthCg3rSI1OjQFjtlnWgeNO0wIkDu9m2U5xIzxtYb71/0D5eDtJWfR1XYb/P2XpiE0dxjirwy6lq8PM/cfJPmLUYayLOqxZwjq0p/5A6zlhr5Mws8rHm97T7iPW9P62z/iFNc995VGeFeVoBbTDmqVoh7ZtQXk7vy3nVaQN/RvHWRv+XLkGMD4L27Vt5TDd5Zd3tmyvNO1TWv379yYmxvgs/OqrOW59FubnG7cPfHxczyvxMa3nmp9fOc+RZaWq1aG7hg0daNl2Z23WqkzqUAhRGUkHp3DHs6bfYcC3SqkIRwmVUv5KqbuUUraLX+zGOsfpJqVUsfU1lVKDANfDDi8ipdRTGLPuwJjhN0RrXTmGOV5kSqlgjPCoACu01i57grTWJ4DfTQ+vV0pd1Dg4WuvFgDnu5w1KqUSo0PfyHnDUtD3Zg/1ty5MB9AE2m57qC3xtWiu2Ujvxo/XiNnZET/uJlKLG8CsAyEvPIv3PrSXP5ycjn+BGtQlr19BumrB2DS2dk+b0noi98SrLdsbKbXbTeAX60eqL/xJq6tTd9/p3HHin4kfFC+FI+s9/Wbajr+9tP5FSRA812nF+ehaZKzfbTycq3N6frWGwmzpYywylaGwKUZmTnk3ayopfF/iyJ0bTbJTxP3ZgxRZ+nvDWv/Lm7oIFSyzbY8YOs5tGKcUIUyjZ9NMZLF9eNmvmFQlP+8X3ZXJMs2uu7W/Z3rrV9frVldV+mzDzDW9w3L4aDDPaV256Noc9bF8HFq8HIKJhLaq1tT/4pFrbBkSYImHs/9l1R7fy9qK+aRBBzskzHPztH5f75KZnc/7MWVN+iU47aGp2sXYCZe73rKOuvO2zOUc6Wu8RpUgcaq3DQx7W4f6fS1aH+9yoQ3sCokJperNxjZqecpi05VvsplNeip7vTCS+VxIAyd+uYOWjn3qUZ0Vytx0mlkE73F8G7dC2LZg7Qu0W2ceb6qbvMrkZ2UVm6f4xaRrTa41x+rPhVet5+8dhz1mezzpYsiVAKoO5c3+ybN900w120yilLCEsT59OZ+kyV4Ga3DPWJizmjJmz3donNdWIbBITE0WTJokO00VGRtCiufE/sDf14q1RXBGqWh26o2nThlw/fDAAv/yynOTkPWV27MpI6lBUCAlRK1yQDk7hktZ6EfCm6eHlwHal1JNKqd5KqSSlVFel1E1KqY+Aw8A72Mza1FqfBBaZHvYFFiulrlNKtVNK9TPt9wOwB6gU33qVUvcAT5oepgH/AeorpVo4+fF1fMQq7zqs4YO/K8F+5rQRwOAyLZF7zOtvegP/NW1XyHvRWp8FXjA9dL6onXvHOwlcBZjvCF4DzDCtL1ppZW7YTfoqoxOw5qhehLVvVCxN/J2DCG5sdDym/W8R+oKRrBGXNaPH0dn0ODqbJm/eZTefg9MWWvZLfO5WvAKKTnD1CvAj8blbASjMy+fgtIXFjhHWriF+1R2O5wCg/iMjiLrCGLmYtSXV7mxT5etDi+kPE96piaVse1+sWqPixb9P9sZkMlcbgwtiRlxJcNviN99q3DGEwEbGjORjnywo1lZDu7Sg/cE5tD84h3qv3Vv+hRYOHdu4h0N/GeenJiOuoEbb4jfakm7vT1Qj42bs5k9+LjJDyyyuc1MmHpjJxAMz6fXa7eVa5g6TriPpNmNdxsNrdvHjuNcpPP/vDKKxft0m/vzzbwDG3jicDh3bFEtzz73jLeHw3n//U8vMEVvdunciIyuFjKwU3vvA8XpgZpGR4VzdpwcAW7bsYPNm9zoDBgy8iho1nAd9uaxrB/7ziDG2MS8vj29nz3fr2JXRiY17OLLaaF8NR1xBtXbF21fzO/oTYWpf2z/+udj5EozZgTenzeTmtJl0e91++9r20U+Wttlp6k14BxT9+uEd4EunqTcBxvXNto9+KnaMC9Xq2YrAauEA7Jmzym7ZitGag78aIdyCY6Noda/9Jeb9woNo96h1TbmDv2xwfewKYFuHjUdcQXU758iWd/Qn0lSHWz9xXIfjDs5k3MGZdHdwjtxiU4ddnrFfh12esdbh1o/t12FQDcfXqH7hQVw1/QH8w43gSSv/O91h2m4vj6f+wE4A7F34t1shfSsj2zps5KAdtrBph9uctMNb0mZySzm3wwNL1lu22z96A74hgXbzavPQUIJqRgK4NfjgUrZm7UZWrDAG79x6ywg6d2pXLM0Dk+6gWVPj++Xb73xs97Pwisu7kH8+jfzzaXz80esu842MjKB/P2Ow36bN2/jnH/cG39oOTnrtlafx9S1+u0gpxRuvP4O/vzF+etGiX906dlVV1eoQIC7O8frntWvH8f230/H19SUnJ4f7Jj3u9nGrKqlDIURlVOln+4hKYxJwCpgC1ASecpI2GyP0p607gT+AOsCVph9b+zE6aBZROdiGEK2FUXZX6gOp5VKaimcOy6qBkgzf/x6jw9vLdIyyG2blnrnAVqA5MFYp9TQV+14+BB7G+J8qNa31UaVUb2AFxv/fSOCcUmq81pU3ft/ux6fTZv6zeAf50+rrx9n/5vek/7kVrwA/ql/TlTjTjMizuw9x4H3Pbnie23OY/e/No+691xLWJpE286ey/5255KQeIaBeTercPYTQVsZsygPvzePc3uLrokT1TKLOPddwaulGTv2+ibO7DpKfkY2Xvy/BzeoSO7InYe2MC/eCsznsfPADu2Vp9sF9RPU0RsWfXrGZw7N+s4SptafwfD7n9hz26H1f6tb/s4X9B61/m9MZGZbt/WmHmLNwSZH01wy4CuG5/U9+RJM5L+Id6E+jWU9x+O1vyVy1Ga8AP6IGd6famD4A5KSkceTDOR7nEz28V5HHgc2soeXCerTBr3Z1y+Pc1MOW8Lm2/OvVJKRD0ZDUXsEBlt8X5pGxbD35x9M9LnNV9MeTn3PtD0/gG+jPoC8ms/6deaSt3IZ3gB8NB3eh+Rjjb3Q65TAbp5Xucqzx8O5FHkc3r2PZrtOjFaHx1nE+GalHLeEhzVrefBUdHjCCLWQdPsWq578krI7zDrP0lMN2O2UvFY88PJWff/mGoKBAfpj7Ka+98j7Ll68mMDCAocMGcsutIwFI3rWHd976uEzyHDpsoOWma0lmbw4YeBXTP3uTxT8vY9mylezYnkxG+hn8/P2oX78O/fr35trr+uPtbYRdfOnFd9idvLdMylxR/n7ic/rPfQKfQH+unjWZTW/P44ipfdUf0oXGpvaVkXKYLR963r7O7DnClvcX0uqewcQkJdB/zpNsfm8+manHCK1XnZYTBxHdsh4AWz5YRObeo84PCDQYZm2vKbNXuF2Wf17/gfg+bfENCqDNQ0OJblWflNkryNx3DO8AX6q1TaTZ+L6E1Dba+6EVWzjkYBZhZbD6yc8ZOMeowz6zJvPP2/M4vGobPgF+JAzuQhNTHaaXtg73HmHzBwtpffdgqiUlMPCHJ9n03nwy9x0jtG51Wk0cRIypDjd9sIgzDuqw9T2Die3clD0L/uL4+t3knMzELzyIGh0b03TslZYO0HUvz+awgwgjHaeMotEII2rKqR0H+OfteZZZh46c3lmmq1eUqb+e+JwBc4vWobkdJpRTO6yWlMAAUzs8k3qMsAva4WYHdXho+RYO/bGVuG7NiWpWlyGLn2Pbxz9zfGMKBTl5hNWvQcMbrqB2r9YA5GXnsPH1Hzwu86Vi0oNPsHzZXIKCAvlx0SxefOltli1bSWBgANdfP4TbbxsDwM5dKbz2+odlkucN1w+xfBZ+/vm3bu/32YxvuPfe8TRr2oirr+7BX6sX8e6709m0aRsFBQU0bdaICbffSJcu7QE4cuQYr79RNmWuzKpSHQK89+6LVIuJ5vsfFrJu3SbSMzKoFhNNr17duP22sYSHh1FQUMCEiZPLbP3zyk7qUFx0WmZSCuekg1O4xdRZ8oxS6nOMtTJ7AQlAOHAWOABsABYDP2itz12w/wGlVFuM0JxDgLpADkaH4BzgTa31aTvRa0UFU0rVwqhvgFVa60Pu7mvqgPsT6A70VUpVM63relForbVS6nngC8AXeJoKfC9a6xyl1HMY4WrLhNY6TSnVC1gOxAO3AueoZCGfbWVtSWXbHa/T9N178QkLIuGx0cXSnN19iM2jX6AgO8fjfPY+/yV+MeHEjupFaKsEmk+bVCzN4S9+Ze8LjmdTegX4EdOvIzH9OjpMk3PgONvufJPMjfYvhqsNtK4/Efn/7N13eFRV/sfx90nvnUDoXYoUkWoDEcGCWLHvir3v2nbV31q26a67rquurm1V7F0UxQKIDSmi0jsEQgstJCGkl/P7406SSUifhJlJPq/nmYc7M+fe+02+nDmTe+4558RBjPjmX3XGXbBtL4tG1Dwyta374JMv+fjzuTW+t3TFGpauqHoBTx2cnslfvYXUGx+lx5O3ERQTSed7f3VYmYLNO9l4xV8o86Cu9vh37aM7U26uumTx/nfn1djBGTViQK3HCU6IOey99VPvI6eNdXDuX53G7JueYsITNxIaE8Hoew6fVipzczqzpj1KsQf5BDjlsetrfW/YzWdVeb7uve8O6+DseUblZ25USgLnzXiQ+rw25jZy/HDKvYZasWINV17xG57/32PExkbz4J9+d1iZjRtSufCCazh0KLdZznnxJc70tCUlJbz7TuOmVQ8NDeWsKZM4a8qkWsvk5eXz1z8/xtNPveRRnL7gwOo0vrnxKU568kZCYiI49t7D61f25nTm/vpRSjysX7888h5hSTH0vWQciYO6M+6ZWw8rs+HNb/jlkfrvxQuJiaDLBGdEcOa67WSs3NrgOLI3pzPvqn8z9umbCUuMoevEYXSdOKzGsrvmr+ab659s8LG9IWN1GvNufIpxrhyOqCGHWZvTmX2F55+RP7lyeNTF40ga1J3xNeRw/Vvf8PM/6s5hfL8uHFvLTXPFeQX89Pd3WfPS7Fr3737GiIrthH5dOOeLv9ZattyLnS+vt4y3VK+Hw2uph3OaoR7+3Az18OvrnmD8C78l5fiBRHdLZtSfD/+eBZC/P5tvb/kvBzfrBshly1Zz6WU38sr0J4mNjeGhv957WJn1GzYz5exfN1tbWD4tZklJCW++1fCbfYqLi5l81uV8+MHLDB0ykMGDBvDcs/+ssWxqahpTL7qWjIzMZonZl/lTDgEMhlGjhjFqVM3tW0ZGJrf85v94rw2t26gcioivUQenVLDW1tu7aK3dQhPXD3RNqfl716O2Mt3reG86ML0B5/kjdY8wLS83DZhWy3vj6tu/JTX0Z2jq/tbab4AG9SZba3fiTPHa1FhqWQSlycfr3sjybwJvur10pQfnrvFnacz/F2vtM8AzDSjX4N5+a+1WnNHRfiNj9s8sOflOOl97JokThhHaMcEZubhlN/s+WcjOl76gLL/Is5NYy/rbn2Hfp4vo+KsJRA/tTXBCNMUHcshZtoldr87hwLxlte6e/vbXFO3LJmZ4X6IGdCM4KYbg+GhsaSnFGTnkrNxCxuyf2PvhfMoKiz2LVcRHZc9dwppTbyP56snEnTKc4JREbFEJhVvTyZy1gL0vz6KswMO6KkdM2tylvDPxXgZffRrdxg8lKiWe0qJSDqbtYdOni1k1fQ4lyqfP+uLzeRw/+gxuuGkakyadTMdOHSguKiY1NY2PZnzO88+9Sn6+Zxfty/Xs1b1iKtyv5/3A3r0N7zx+4P5H+GH+Yo47fiQDBvSlXXIS7dolUlZWRmZmFuvWbuS7bxfy1psz2LPHJ1anaBY75izl4wn3MuCa0+h8ylAiUuIpKyolZ+setn66mLUvz6G0OeqXtSy463+kfbaEoy47mcQhPQlLiKbgQA4Zy1NZ//o8dn69okGH6jZ5JEHhzjT+mz/4odGhpH+/mhljf0+fi8fSafwQ4vp2IiQmEltaSv7ebPYvTyV1xoKKtUN93fa5S5lx6r0MvNrJYaQrhwe37mHLrMWsacYczr/rf2z9bAn9Lj2ZpKE9CYuPpiAzh/3LUln3xjx21JPDda/Po+hgPh1G9yO6SzvCEqIpzivg0I4Mtn+1jA1vfc2hnRmex+pnts9ZykeuetjFrR4ebIF6+IOrHva97GSS3Orh/gbWw6LsPL648G90nTiMnuceR9KQnoQnx2ICAyk6mEvWhp3smLecDW99Q1FW83QStAafzprDMcdO4De3XMPpZ5xC504pFBUVsWnzVj744FOe/u/LzdYW9u7do6JTZO7c7xrdZm3btpPRY87goovO5vzzzuSYoYNo1y4BYwwHDmSxcuVaPp75Ja+9/h55efn1H7CV8KccPvKP/7Bhw2ZOOGEknTt3JDExnqysg6SmpjHzky958aU320THdHXKoRxRWgtT6mF8eBZDERHxAd+0n6qGwo8dv/oRb4cgHlo+9A5vhyAe+tFE1V9IfNa9mQu9HYJ46InYMd4OQTxUqol+/FqA/prwe9fu+9rbIYiI+L2Sop36RtNI+TMf9dq3iPApdylffiDA2wGIiIiIiIiIiIiIiIiIiDSUpqgVERERERERERERERER32E1Ra3UTR2cIs3MGBMHdG7KvtbaVc0cjk8xxvQAIpuwa6ZrLVAREREREREREREREWnj1MEp0vzOAV5u4r6tfW7vl4GxTdjvFWBa84YiIiIiIiIiIiIiIj6pTCM4pW5ag1NERERERERERERERERE/IZGcIo0M2vtdGC6l8PwSdbacd6OQURERERERERERER8nNbglHpoBKeIiIiIiIiIiIiIiIiI+A11cIqIiIiIiIiIiIiIiIiI39AUtSIiIiIiIiIiIiIiIuI7yjRFrdRNIzhFRERERERERERERERExG9oBKeIiIiIiIiIiIiIiIj4Do3glHpoBKeIiIiIiIiIiIiIiIiI+A2N4BQRERERERERERERERHfYa23IxAfpxGcIiIiIiIiIiIiIiIiIuI31MEpIiIiIiIiIiIiIiIiIn5DU9SKiIiIiIiIiIiIiIiI7ygr83YE4uM0glNERERERERERERERERE/IZGcIqIiIiIiIiIiIiIiIjv0AhOqYc6OEVEpE4Lw0K9HYJ4IGroHd4OQTw0ZNlj3g5BPBQ47HZvhyAe+Fv8GG+HIB4aGZLp7RDEQybAejsE8YAtM94OQTz0ZPuTvR2CeEhT+Pm/Em8HICLig9S+iYiIiIiIiIiIiIiIiIjf0AhOERERERERERERERER8R1WU9RK3TSCU0RERERERERERERERET8hkZwioiIiIiIiIiIiIiIiO8o0whOqZtGcIqIiIiIiIiIiIiIiIiI39AIThEREREREREREREREfEd1no7AvFxGsEpIiIiIiIiIiIiIiIiIn5DHZwiIiIiIiIiIiIiIiIi4jc0Ra2IiIiIiIiIiIiIiIj4jrIyb0cgPk4jOEVERERERERERERERETEb2gEp4iIiIiIiIiIiIiIiPgOjeCUemgEp4iIiIiIiIiIiIiIiIj4DY3gFBEREREREREREREREd9hNYJT6qYRnCIiPsYY80djjDXGWG/HIiIiIiIiIiIiIiLiazSCU1o1Y0x3YIunx7HWGo+D8QPGmChgGDDS9RgBdHe9nWat7V7zns1y7j8CD9bwlgUOAenAEuA1a+2XLRWH+LaYTokMv3ISvU8eSnTHBEqLSshK28vaWYv5+ZU5lBQUeXYCY0jsnULHIb3oOLQXKYN70q5fF4JCgwF446KH2LZobYMP13v8UAZNPYmOx/QmIiGaotwCMrfuYd3nP7L0ta8ozi/0LF4/ENKpHclXTSb2lGMJ6ZiELSqmcOtuDnz6A/umf0aZJzkzhrDenYgc2pfIoX2IHNKb8P7dCXDla/3U+8hZuKrewwREhBExqKdzjKF9iRzSm9Cu7QEo3L6XlWOua3qMUkVGZhYr16xn1Zr1rFq3kVVrN5CVfRCAs0+fwEP33enlCFuf4E7tSL5yMjHjhxNcXgfTdpP16Q/se2UW1uM62JmIIX2IGNqHiCF9CO9XWQc3XvgHDi2qvw6GdE4mZsIIokcfTVj/7oR0SARjKMk8SN6KTWTOnE/WZz9Aqe7eBYjqlMjgqybRbfxQolxt4cG0vWz6ZDGrmqktjO+dQvLQXrQf2ovkIT1J7NeFQFdeP5r6ELsa0BZGd06i+4Rj6DimP4n9uxLZIR5jDAWZh9i7IpVNMxexedaP2DaQ1+CO7Ui4YgpRJ48gOKUdtqiYom3pHPzsew68Ngtb4MH3AWMI6dWZ8CFHET64L+GD+xB6VI+Kerj10nvIW7yycYcMDyXu/AnETDqOkJ6dCYyPpSznEMW7M8j/ZS05Xy0md/7SpsfsZ4I6JpPw6ylEjauWv8+/J/P1T5snf4Od/IUN6ktovx4EhDj5S7vsbvJ+rDt/SbdeRrvfXNao0+578g32/+eNJoftb1QHWxe1g/4vqlMig66aRNdqOdz8yWJWN3MOk4f2ol21HM5sRA67ueUwwi2H+1w5TG2jOYx21cPubjnMdtXDlc2Yw/Zu9TDJLYczpj7EzgbksNv4obQf0pPkIT2J6dqO8MQYQqLDKc4t5OC2vexctJbVb3xNVmq6Z/GKiNeog1NE3H0CjPN2ENUYINr16AtcZoz5ELjUWtv6e4dqYIz5BhgLfGutHefdaI6c3qccw1mP30hYTETlixEQHhdFypCeDLloHO9d+SiZaXuafI5B553A5Meu9zjWkMgwpjxxE31OHVbl9aDQYCISouk0rDfHXDqe9695jIxNuzw+n6+KnTCCHk/eRlBMZOWLEWEEDY0mcmgf2l1yKhuv+AuFW3c36fiJ54+jx+O/9TjO3tP/QMxxgzw+jtRv7ORLvB1CmxIzYQTdH7+dwOp1MC6ayCF9SLz4VDZP+zNFaU2rgwnnj6PbY7d5FGPKnZfS/tapmIDDJ5YJSUkiJCWJuEmjyV02hS03PELxrv0enc/fdZtwDBOeuJFQt7YwOALC4qJIHtKT/peMY9a0Rzm4telt4VHnn8Ap//asLRx51wUce+uUGvMalZJAVEoCPScNZ8+1m/ny+ic5tCvDo/P5sqjxI+n02F0ERleth+Fx0YQP7kvchZPYds0fKU5r2oW12HPH0+mfdzRPsEDE6MF0fOQ2Qjq3r/J6QGg8QUnxhB/dm4jhA0mdf2uzndOXRY0fScdHf1d7/qZOYvu1D1K8rYn5O2c8Hf9x5G/uKdqy44if01tUB1sXtYP+r9uEYxhfTw4/8zCHfc8/gfEe5nDEXRcwrJ4c9pg0nL3XbmZ2G8th9wnHcGotOWw/pCcDLhnHp9MeJduDHPY7/wQmeJhDExjAWa/cVeN7gXFBhMX1IHlwDwZPm8jif33AL//9xKPzScuwZa17cjtjTDfgN8CZQBegENgMvAs8ba3Na8ZzTQAuB04AUoASYA+wAvgKZ1DRoeY635GiDk5p7XYCdV21Lr+V8ifgypYPx+e5j1Q9gPN7OQ6IOsJxXIUzWhOcqbS7uOK4HQgHzgMeA24+wnGJl7Qf2I1znr6F4PBQCg/ls/C/n7Bt4RqCwkLof9Zojrl0PIm9Upj68l1MP+t+inILmnYitxpQWlTC3vXbCQwKJLl/10Yd5pz/3kqvcUMASF+RypIXvyBj0y5CosLpNX4ow684lYQeHbhw+u+Yftb95Gf63feHeoUP7EHPZ+4iMDyU0kP5pD/1ATkLVhIQFkLC2SfS7rKJhPXqRJ9X7mfNGXdS1pScmcqElRUVk79uGyY4kIj+3Rt5mMrjlGQeJHf5ZqKG9yMwKrzxMUmDpbRPpke3ziz48Rdvh9IqhQ/sQY+nf0eAqw7uefp9chY6dTB+yokkXTqJsF6d6DX9AdZPvpOy3PwmnKVqHSxYn4YJCiK8EXUwKDkBExBAaW4+2V8uImf+Cgq37qKssJiw3p1pd+Vk1yjtvvR+88+sP+MOyvKa+Bnv55IGdmPif522sOhQPr88/Qk7FzhtYe8poxl42Xjie6Vw5vS7eP/M+yluprYwY912AoMDSWxEWxiRHIcJCKA4t4DUL35ixw+ryd6ym9LCYuJ7d2LQVRMr7sif8tY9vHv6fZTktb771sIG9KTzk3cTEB5G6aE8Mp59j9xFKzBhIcROHkv8xacR2rMzXf/3R7acc1vT6qFbG2aLiinYkIYJCiSsX49GHyryuKF0eeEBAsJCKc0+ROabn5G7eCWlGVmY8FBCe3UhavxIghLjGh+nHwod0JNOj99Tmb/n3iVv0QpMWCgxZ55E/MWnE9qzM11e+BNbz/ttM+VvKyYoqFH5y3zjU3K+mF93ocAAur35DwKjIynNySVnzsLGx+qHVAdbF7WD/i9xYDcmuOVw6dOfsGvBGgJdORxw2XjieqVwxvS7JYL6CQAAIABJREFU+MCDHJpqOTywbjsBHuRwyxc/sdOVwxK3HJaPEJ381j2830ZymDSwG5Pccvizqx4GhoXQ160eTp5+F+82cz0MCA4kqZHXZgqzc9m5aC17lm4me9te8vZmUZJfRET7eDqP6U//C08iNDaS4+69iMKDuax+fV7T4hVpAmPMWcDrQIzbyxHAcNfjGmPMmdbaTR6eJx54GTi7hrdjgD7A+cBCYJkn5/IGdXBKq2atLQZqnRvN7aJ2rrW2/jnUWr83geeAJeUfnsaYrRz5Ds4t1fKxAphljHkf+BHns+s6Y8xfrLVNG3YifmXCg78iODyU0uIS3vnVI+z8pbJtT1uwhsytexj/f5eQ2CuFkdeewfzHP2zSefZv3MnsB18hfXkqe9Zso7SwmBNuO69RHZxHnTGionNzy3crefeqRykrLq14f9uitWz5dgUXvfp74rq048Tbz2P2A682KV5f1vVP1xAYHkpZcQkbLv0jub+sr3gvZ8FKCrbsost90wjr1YkO15/DrsfebvQ5CjZuZ9v9L5C7fCN5q7dgC4vpeMfFje7gzPjoO/a9/iW5yzdWjCYdtPB5dXC2gBuuvJSj+/fl6P59SUqIZ2f6HiZdMM3bYbVKnf94LQHhodjiEjZd/iB5bnXw0IKVFG5Jp9MfnDqYfN3Z7P530+rg9geeJ2/5RvLXOHWww+0XN6qDszTrIDsfns7+17447MJy/srNZH78Pd3/cyfxZ51AWM9OJF97NrufeKfRsbYGJ/ypsi385LJH2OPWFu5csIbsLXs47r5LiO+VwtDrzmDJv5vWFmZu3Mn397/C3uWp7He1hSNuP69RFwULMnNY8NBbrH7tq8MubO1buZWNHy/g1KdupvdZo4nrmcLQa0/npyc+alK8vqz9/dcTEB6GLS5h27T7yV+6ruK9vIUrKNq6k/b3XE1oz84kXn0u+558s9HnKNy4jfQ/PUvBig0UrEnFFhXT7jeXNrpzJTAhhk5P/J6AsFDyV29m25UPUJqRVaVM/s9ryXp3NgS3jcsIHe67oSJ/26+8j/xlbvlbtJyitF20v9vJX8JV5zVpytfCTdvY/ednyF+5gUJX/pJuvaxR+Ss9kE3pgew6y0SeNLxiBGPO5/OxhR5OHegnVAdbF7WD/u94txzOqpbDXa4cjrnvEuJ6pTDkujP4yYMcznflMMOVw+FNyOGiWnK4f+VWNn28gFPccjjk2tP5uQ3k8ES3HM687BF2V6uHWVv2cLyrHh5z3Rn82MQcHti4k+/uf4U9bvVw5O3nNaqD05aW8b/BN9QyAnALW+f8wvKXZ3PRZ38hLC6KUXeez5o3v271Iwb9TlnrnALaGHMM8A7OQJ5DwN+Ar13PLwauxZnJcJYxZri1NqeJ54kF5gDHul6aAbyPM0q0FGdQ0VicDk6/dPg4exFps6y1z1tr3/L0zpCWYq1dCpRfgQ3C96bTlRaQMqQnXUf1A2D5O99W6dwst/j5z9i/cScAw6+aREBQYJPOlb48lZ+nz2HX0s2UFhY36RiDLzipYvvL+6dX6dwst/WH1az5ZBEAQy8ZT1hs5GFl/Fnk0D5Ejx4IwP6351bp3Cy357mPyd+wHYDkqyZjmpCz3GUb2fvyLHJ/2YBtYr4A9r8xmwMff9/kqXKl4W655leMO34USQnx3g6lVYsY0oeoUU4dzHhnbpXOzXJ7n/+I/I1OHWx31VnQhDqYt3wj+6fPIm9p0+vgrr+9yt5nZ9Q+aqasjO1/eJYy1/HjzjiuSefxd8lDe9LR1Raue/vbKhcEyy17/jMObHDawkEetIV7l6Wycvoc9njQFi762zsse3ZWrXft2zLLt/83veL4vc4c2aTz+LKwwX2JHHk0AJnvza7SsVIu438zKNy4DYCEaWc3qR4WrNhA5qufkL9sPbao6W1h8l3TCEqIpSyvgB03/OWwjpUqikuafB5/ETa4LxEjnPxlvT+7SudmuQMvfkjhJlf+rpjS9Py99gkFHuavPrHnjq/YzvroqxY7jy9RHWxd1A76v4bkcHkz5nDV9Dns9SCHixuQw+/dctizjeSwkyuHa9/+tkrnZrmlbjkc7GEOV3hYD6H+6U1ztu9j0yeLAYhIiiW+d8cmn0ukkZ7A6cwsASZaax+21i601s6z1l4H/N5Vri/gyXoG/8Hp3CwEzrbWnmetfdNau9ha+5O1doa19jagK3UMEvNl6uAUqYcxZpgx5lljzHpjzCFjTK5r+xljTN969u1pjLnTGPOJMWarMSbf9UgzxrxjjDmtnv2nGWOs69HdGBNijLnDGPOTMSbbGHPAGPONMebMavtFG2N+b4xZaow5aIzJMsbMMcac0hy/Ey9b6bbdpbZCxphAY8wVxphPjTG7jDGFxpgMY8x81++wzqFZxphjjTEvGmM2uHJeYIzZboz52RjztDFminGf15LD81XHsbu7lZvWsB8bjDHTjTEW584agLFuxyl/bG3o8fxF34nDK7ZXvvddzYWsZeUHztRc4bGRdBvT/0iEVqMOg5y7tQ9s2U1mHWtOpH67AoDAkKDD1ur0d3GTRlVsZ7xby0U0a8n44GsAguKiiNYamCLNJraBdTCzvA7GRhE9xnfrYGlWDvnrtgIQ0q2Dd4Pxkh6TKtvCte/W3haud7WFYXGRdDrOe21hQxRmHSJjndPJHtMt2cvRNL+YU0dXbGe/P7fmQtaSNcOZCi0wNorI0YOPRGiHCYiJInaK8/Uy++OvKd61zytx+JLoCWMqtrM+mFNzIWvJnuF8xgbGRnstf/UJiAon+hTn/2PR9nTyl/jltatGUx1sXdQO+r/ubjlcX0cON7hyGBoXSUfl0Kf0bGA9XOdH9RCossRRYGiwFyORGtky7z1aiDFmJHCi6+mL1tqa1g74F7DWtf1bY0yj/3MaY04AfuV6ep+1dmZtZa3DL++gUgenSC2MMQHGmMdw1qG8HueOiUicubD7AjcAq40x19Wyfw+c4d6PApOBbkCY69EVuBD43BjzmjGmIXPMxADf4XzAHet6Ho/T2fWpMeZ213m74syZ/QgwFIgGYoEJwBxjzGWN+034HPf5lGq8jcv1O/gZmI6zSHMKEAIkAMfj/A5X1NZB7fpd/oizFmgfnJyHAp2BYcBNwMc4/x+khXUe4aSpKLeA9JVbai23ffHaiu3Ow+u896BFhcc7Mzrn7q97qrDcfZXvd3HdBdlaRI1w/ogpzc0nd8XmWsvlLFzttk/r+h2IeFPUiAGAUwfzVtY+KUPOosqL3JEjfPviQ0CI6++50tY5RVF9UlxtYXFuAfvqaAt3LapsCzt4sS1sqMAQ5yuwLW19U4GFD3dGUZfl5pO/amOt5fJ+rLx3L+LYAS0eV02ix48gIDwMgJy5iyteN2GhBHdLITCp7Y26j3DLX0ED8xc+zDv5q0/0aSdW5Df7o7aztpjqYOuidtD/dWhgDtOVQ5+V4nZtZm8D62GKj+cwMCyYnhOdG87LSsvIStWsTnJEnOO2/XJNBay1ZUD5elZxwMlNOM8trn+zgaeasL9faJsT94s0zH9wOrLA6VicDqQCecAQ4DZgIPCcMWZ3DXdBBOJ0xn2JM9f1GuAATidbX+Bm1/6Xu477YD3xPI/TsflfnPmyM3E6MP8MdAQeNcbMccXZE/g78AWQi9Op9yecjs5njDFzrLV7G/PL8CHuV2C3Vn/TGJMIzMcZ3VkIvAB86yobBUwEfgv0xulgHmatzXbbfzBOp3QAsAWnAViGk7to4CicRqWmhZlb2h9csb2Ms9j0T8CV1cq0ugV1klxThGRu3YOt48J2xqZdFduJvTu1eFy1KcorJDw2iNDoiDrLhcZUDiJO8mK8LSGsT2cAZ8rXOnJWsHlH5T69ax2QLSKNFNq7vA6m11kHC6vUwc4tHldTBSXGVsRXsGlHPaVbp/LpsrLraQuzNle2hfE+3raEJ8YQV97Gb9rp5WiaX2gvp10rSmt4PQzp5Z22MHxo5U1GBeu3EjaoD8l3/prI44ZgAp2p5Uoysjg463v2PfV23VNnthLluSjatqvu/KVW5i+0V8PXBTuSYs+tnMQnu41MTwuqg62N2kH/19AcZvpRDsPaWA4TWkkOA4ICiUiOI2V4H4bdOJm4nikArH3n21qnJBZpZie4/s3FGaBTm2/dto8HZjf0BMaYECqvXc+x1ha4Xg/E6UcIBHaXv+7P1MEpUgNjzKlUdm5eY619sVqRJcaY14FZwHjgSWPMZ9WGcqcD3a216TWc4itjzLPAS8A04E5jzGPuHW01GAmcZ611X7X8Z2PMEmApTofcPJyRnWOttYvdyv1kjNnoijcauAz4dx3n8knGmC44sQNkATXNNfQkTudmGnCytbb6bWXfGGPeA77H6Qj+PU7HYbkLcH6XucAYa231OUa/B/7nWqQ5z4Mfp9GstTuBncaYXNdLudbaVj3HVGBoMBGJMQDk7D5QZ9mCg3kU5RYQEhlGTMeEIxFejTI27aTzsX1J6t2R8IRo8g/UvA5415GVFzJiOiUeqfBanAkNJjgxFoCi9P11li3NzqU0N5/AyHBCOrae34GIN7nXweLdGXWWrVIHU5KORHhNknz9uZhg58+WrE/nezmaIy8wNJhwV1t4KL3utrAwO4/i3AKCI8OI8mJb2BBDbziTQFdeN326uJ7S/sWEBBNUUQ/rbgvLDh6iLDefgMhwgr1UD0P7VHbMRY4ZTMeHf1NR58oFJcaR8OuziD7teLZd+QCF62ofueHvTEgwQQkN+xx1z1+QD36OBndKrhiNmvfTaoq3tY2RKaqDrYvaQf/nnsPcenJY5JbDSD/K4WblsEJhduW1mWgfyWF05ySuWPh4re+nfbOC+X958whGJA1WzzqqLckY06C7gK21jb0Lt3zwzqZ6poV1X0C8sVMuDcGZRRJgpTEmBmeQ1BU4I0IBiowx3wEPWWu/aeTxfYamqBWp2T2ufz+ooXMTANcdDuVDvbtRbai4tTa3ls7N8vctziLBpThTnU6oJ6Z3q3Vulh9nBc6IRYB2wOPVOjfLy32G0+kHlfN8+zzXVMFdjTGX4vyc5dPC3m+tPVStbHfgItfTW2ro3ATAWrsUeNr1dFq1t8sX99pQQ+em+zGyXdMFSAsKiQyr2C5qwJ10xXmFAARHhNVTsuVsnLMUcO4KHHvX1BrLxHdvz6CpJ1U8d/85/V1gZOXI1LIG5KzMlbOAyDqXxRWRBmp6HfTNz6GIoX1JvvosAIp27Wffa597OaIjL9gtN8V5jWgLfTSnAMlDezH46kkAHNqVwepXW9eosoAot3qYl19v+bJ8J6/eagsDY6MqtlP+cjNYy95/vcqGE65gTb+z2TTpBrLed9ahDE5OoMuz91X5GVubKvnLbUT+InzvdxJ7zimYAOeyT/aMWtahbIVUB1sXtYP+r7XmcJBbDte0oRwWNSCHJX6QQ4D8jIN8ceN/+PSKf1J8qP72Qtqc7Q18NJgxJgwov6Oqzo5Ra20mzgAccAbzNIb7vPsBOLMA/pbKzk1wlnObAMwzxtzdyOP7DI3gFKnGdUfDONfT9+sqa61da4zZj/PBNAZnKtrajhsMtMcZQRno9lYGkIxzZ8UHdZzu7TreWw6c1IByK3A6Y3vWUcYXfG2Mqe29XcADtXQ8n4nzu80D6rsC+h3O6M2Oxpiu1tptrtfLO6UHGGNGWmt/bFzo/qOhdyI93NV7y7YGuS3wXlpc/1rXJUXOsqzBYSEtFlN9lr4+l2OvOJWYlASOuWw8weEhLHpuFhmbdxEaGU6vk4dw8v9dQkhEKKVFJQSGBHk13uZmQit/lrIG5KzMlbOAVvQ7EPEm9zpoG1AHbUUdDG2xmJoqKCmWHs/ejQkOwpaVkXbH49iCVjcTe73c28KyovpzWurKaZCPfq6GJ8Vw2nO/IdCV169uf46SVpbXptdD7+QswO3GsICwUHbe+SjZH31d8VrRpu3suvtxbHEJ8ZecTkiXDsRfeiYZz9f5p5LfMiH+lb+6xExx7sEtKyjk4GffezmaI0d1sHVRO+j/At3/rm8lOZzolsN5bSCHTa2HgT6Sw9zdmbw5wRnLEhAYQGSHBLqNG0z/i8cy7m9XEtstmZ+f/sTLUUqNylrd2JJot+1DtZaqlIsz2CeqvoLVuA+fvhtnNOcXwAM4/QMxwPk4S9zFAn83xqyz1n7cyPN4nTo4RQ53DJWjm98yxrzVwP06VH/B1al5HfAr13Hratnrmw9nQx3vuS/C0ZBy0XWU8XWfA2/U8t5w178RQEkdnaTVdQDKOzjfAu4FQoEfjDFf4EztOx9Y7Rp521o06i6j5hbVPp6w2Mga3yvIzuXQnkxKCosrXgsMrr/JCgpxvnQXe/GPi8KcfD645jEunP47ItvFcvR5J3D0eSccVu7rv7/DyGtOIzIplsJWtM6DLaz83Qc0IGcBrpyVtfI/CEWOFPc6WH16u5qYijpY2GIxNUVAZDi9pj9ASEfn69Guv7/KoQUrvRxV84vsEE9oLW1hYXYuuburtoUBIfXnNNCVU1+80BYcGcaZr9xFlGta8oV/e4edC9Z4Oarm1/R66J2clbnFW7A2tUrHiru9j75C7HmnEBAaQsyZJ7bazhVb5F/5q03Y0KMI7encz5gzdxFlh47o6hpepTroP9QO+n87GNkhnpBacljkymGp+9/1rSCHp7vlcPHf3mFXK8hhS9XDUh/JYVlJKQfWVw6W279mG2nzlrH6za85993/Y8w9FxHbowPz7nrBi1GKD2qJxbndhzU3pIKU/6He2Kkb3Ct1GM6grMnW2lLXa/uAZ40xq3DW+gwA/maMmelv177VwSlyuOQm7hfh/sQYk4Cz+O+xDdy/vg+quv4irbidxVrbkHKBdZTxBVcBS1zb4UBv4HpgLHA10MEYc1YNH7ge585au84YcwnwAhAPTHY9APa7Ojyft9a2nVugW8jY301lsNs0re5WvPcds+56vsq0tA2ZxjU4whmB1JBpb1rS7lVbefGMP3DczVPod8ZIopIrZ4DYtWwz85+YweZ5yzjhtnMBp0O3tSh1m8qtIVNeBrhy1pAp4ESkfk2vg75zo4UJDabn//6PiMG9Adjz7Az2PjvDy1G1jFG/n0q/WtrCde99x7w7nqfYLTcNmYK9oi30oZyCM3rj9BdvJ3mwM5HI0mdnsezZWV6OqmWUuU1x1pBpSwPCnbx6qy10j/fQ/KW1livNyqFg5UYihg8krH8PCA6CBoyO8zdV8teAKUsr8teAqVCPpLhzTqnYzv6odU+dWJ3qoP9QO+j/7eDI30/lqFpyuP697/i6leXwNLccLmslORz9+6n0ryWHa9/7jq+q5TCkATkM8tEcVpexbjuL/vk+4x6+kgEXjWXjzIVs/26Vt8MSH9GEtTUbwr1SNGSIc/lUS439klK98t3t1rlZwVo73xjzIXABzjqfg3BGePoNdXCKHM698+96YEED98us9vwJKjs3PwJewvmA2AsUlHfOGWO24dwR0uDhhm3AFmut+zeKJcaYt4EXgStxpqK9Dfh3tf3Kc7efamui1nc+9yfW2g+MMXNx1vOchLNmaTucUbaXA5cbY14BrvLzdTgbeieS10Z6lhYWk3cgh4iEaKI71L04fVhMREUn6MFddS96fyTk7s1izoOvMufBV4lsF0toVDi5+7MpzHG+k0R3SKiYmnb/hpb4zuQdtrCY4gMHCU6IISSl7oHpgbGRFesFFu3KOBLhibR6trCYkgMHCUqIIbhDYp1lq9TB9P1HIrz6BQbQ47+/J/r4wQDsf2s2ux6e7t2YvKy0sJj8AzmEJ0QTlVJ3WxgaG1Gx1tEhH2gLy5nAACY+cyudjx8IwJo3v2bhQw2dJMX/2KJiSg5kE5QQS3CHutvCgJioik60Yi/VQ/fzlqTva1BZExhIYGw0pfur/wnk/6rmr+7PUff8lfjK5yhAcBDRZzgXq0v2HSD3+1+8HNCRpTrYuqgd9H/uOYysJ4chbjnM9bEcnvrMrXRy5XDtm1+zSDmsUWhs5bWZHB/KYW22zP6ZcQ9fCUDvM0aqg9PXtL4panPcthsy7Wz5SMyGTGdb23n2WWtrv4MKvsTp4AQYgTo4Rfye+1X2vGodbQ3iWsfzItfTN6y1l9dRPL6xx2+LrLXWGHMLcArQFXjQGPOKtdb921J57qKBtTXdmdKI82UDz7seGGP6A2cDtwIdgSuApTgd2eXcW90Aalfz3B9HWEPvRPpbt7r++zbdrLueZ9Zdz9dbbv/GnXQd1Y/47u0xgQHY0pq/3CT27lixnbFpZ7PF2Rxy92WTuy+7ymsdBnWv2E5fnnqEI2pZBRu2Ezx6IKHdO0BgANSSs7BelcvAFmzy6ozJIq1KwcbtRI0aSGj3lDrrYGiVOugDN1oYQ/fHbyf21JEAZM78nu33/NfLQbWseXc8z7w76m8LMzfuJHxUP2LraQvjelW2hZm+0hYaw4QnbqTHqcMA2DhzId/c85KXg2p5hZu2EzQylpBuDa+HRZu90xYWbkzDuZ8PCKhnopcAt6+4pU3+qu3zijZvJyghlpCuHevOX8/K/BVu3lZjGW+IPnkkQfExAGTP/KY1Xhysl+qgf1A76P++vuN5vm7GHMb7aA7HP3Ej3V053DRzId+2ohx+dcfzfNWAHB7YuJNO/prDOuRnVPYDRXeub/UwEc9YawuMMRlAItC5rrLGmHgqryE39kuKe/n6/th3L9uukefxurouwIu0VcuA8qlPj2/iMfoA5Stwv1NbIWNMPxq/SHCb5Zp+98+up7HA76sVKb8bJZTK9Tib69xrrbV/B0bjLPAMcGG1Yu53x9TVcd3X03A83N+v7FjiLCsbEhlGyqAetZbrMqp/5T4/1bUUrW/od+aoiu21nyzyYiTN79CStQAERoYTObhXreWixwx022ddi8cl0lYcWuKsAxQYGU7EoN61losefXTFdq6r3npTl7/fRPzZzoij7Dk/svW3j4F/Lf/RYtJdbWFwZBjt6mgLO46ubAt3+0hbOO7vV9Hn7DEAbJnzC3N/80ybyGv+T6sBZ4rT8KP71FouYuSgiu28n72zhlfektUV2yFdO9RZNqRbCuCs21ualVNnWX+W55a/sAbmL/8X31mDLbYNT09bTnWwdVE76P92NzCHKT6Yw7FuOdw65xe+aqM5THe7NpPcwHqY7iM5rEtkh8rLd74+pW6bZK33Hi2n/AtHb2NMXQMQ+7ltN/YP9tVu2/UtVef+vt/Nfa8OTpFqrLX7gPLehkuNMU25c8H9w6mu0Xo3NOHYbd2rQPnt0Te51jot9wmVnX+3tcTJrbXbgfJvaNVv7XKf6rauDtZLPAyj/BtXaJ2lWokNs3+q2B5Uy7oQGMOg808AID87l7SF3r9QX5fEPh3pP9np4Nzy/UoObNnt5YiaV9aXiyu2Ey88peZCxpB4vjOTdEnWIXIWrDwSoYm0CdkNrIPx5XUw+xA5C71bBzvdfxVJl0wEIGf+crbc+Eito23aoi1fVraF/S+svS08ytUWFmTlsnOB99vC4x64jAGXOv/Ptn+/ii9veLLWu/1bm4NzKm9eir1gQs2FjCHu3PEAlGYfIneRd2aDyvtxFSUZWQBEjR9ZdYSYm+DO7Z11/4C8n9e26ou7OXMXVmzHnX9qzYWMIfZc5zO2NDvHa/mrLjAumqixzp8iBWs3U7huSz17tE6qg62L2kH/t9Uth0fVkcO+rhwWZuWyywdyOOaBy+jvyuGO71cxuw3nMLWB9bCfj9XD+vR2u/k8Y50PzGojbcF817+RVC5vV5Oxbts/NOYE1to0Kq+fdzfG1LU0nvvIBN8fdl2NOjhFavZX178xwPvGmLjaChpjQo0xNxtj3FfZ3kRlR9sVNX2IGGPOAm5proDbCmttMfAP19No4Ldu760H3nM9vdgYc0ddxzLG9DDGXFLttXPqyXcXKu+gqX61YBVQPmXuLcaYwzogjTEXAlPriqsB0l3/9qyngWoV0pensm2xM7pvyEVj6TTs8NFIo647g6Q+nQD46aUvKSs5fLqmrqP7c2/a69yb9jpnPnpdi8Yc1b72AbzRKQlc8MIdBAYHUVJQxOwHX23RWLwhd9lGchY5N4slXTyByGFHHVam/fVnE97XWQZ270ufYqvlLHrM0Qzf8RHDd3xE98d+0/JBi7Qiecs3cmixUwcTL5pARA11MPm6cwjv49TBfS99AtXqYNToozlm28ccs+1juv6rZetgh9svJvnaswE49NNaUq9+CFvkdzeOtqi9y1LZ5WoL+108lvY1tIVDrzuDhL5OW7iylraw4+j+3LT9dW7a/jrjH2vZtnDE7ecx9NrTAeeO/8+v/jdlbSivBSs2kPujs9JF/NSJhB/T77AyidecS2ifrgAcmP7xYfUwYtQgBmyexYDNs+j4j9tbLtiyMjL+9yEAIZ3b0+6Wiw8vExhAyp9vwgQ6N3hnvvlZy8XjAwpWbCBviZO/uAsmEj708PwlXH0eob1d+Xtl5uH5GzmI/hs/o//Gz0h5pAXzV03M5LGYEGcyoewZ847YeX2N6mDronbQ/zUkh0MamMMbtr/ODdtf5+QWzuHw289jiCuHu5ds4AvlkJ2uHPa/eCwdasjhMW45XFFLDjuN7s8t21/nlu2vc0oL5rDHpGOJSK718h4AHUcdxYjbzgGgtLiEDR8vrLO8eEFZmfceLecjt+0raypgjAkAfu16mgV83YTzfOD6NwZnybfanOe2Pb/WUj5Ka3CK1MBa+5kx5gmczrOTgLXGmGdxKnkGzh0WvXEWyjgPZzrSV9z2zzDGfAacCZwGzDbGPAOkAcnA+cA0IBWIw0fmtzbG9AZOqPZy+RS6UcaYadXe+8Ja642hZy8C9wEdgFuNMY9aa8vn57kRZ/RkT+BfxpizcUZ9rgYKceY4H4KTl/HADMB9ZfjbgDeMMbOAeThTAGTj5Hg4zhqc4a76lf8jAAAgAElEQVSyz7oHZa0tMcY8B9wLHA3MM8b8A+eOmfY4HZvTgAXAcR78/AtwGsBk4DFjzOuuGAGKXXfptCpz//Qav/rgAYLDQ7notbtZ+PRM0hauISgshAFnjeGYy5w7rzM2p/PjC579sT/oghOrPG8/oGvFds+xg4l1W5Mhc+ueGqfDPe3hK4lIiGH9F0tIX5FK4cE8IhKi6Xb8QI657BTCYiIoKy3j83tf4sDm9MP2bw22Pfg/+n30dwLDQ+n75h9J/8/75CxcSUBYCAlTTqTd5ZMAKNi8k93PfVTP0WqXOHV8lefhAyqnyokZdwwhnZMrnhduTa+YPtddaPcORI0YUOW1gMiwin+rnyP7m18o2ZfV5Jjbsl+Wr2Lbjsr/85nZlWvTbtu5i49mzalS/pwzaxk1I/Xa8ccX6PvhIwSEh9L79T+x5+n3yFmwkoCwUOKnnEDSZacBTh3c+/zHTT5PwgV11cFhVetgWvphU+EmTTuTlNude42K0vez6+HphHRpX+c5C1J3HnYRui2Y/+BrnDvDaQvPeuNufnlqJjsXrCEwLIQ+U8Yw8HInF5mb01n2vGdt4VFTq7aFiQMr28Ku4wYT3aWyLczeuqdi2rlyg6adyog7nL+TD6UfYOHDbxHTte6vu1mb02u8CObP9vzlObq/+08CwsPoOv0v7H/mXfIWrcCEhRI7+STiL3Eumham7iDjxRlNPk/s+VVHp4UO6FmxHXXSsQR3rqxTRVt3kV/DNJwHps8k5syTCD+6N+1+exkhPTuT9eFXlGZkEdw1hcSrziFimDPlXM7XS8j5olE3kful3X99lu5vP0pAeBhdXv4rGc++S+7i5QSEhhIz+STiLz4DcPJ34KUPm3ye2POq5i+sf2X+IqvnL63m/FU5nmt6WltcQvbMplwLaz1UB1sXtYP+74cHX+McVw7PfONulrpyGBQWQu8pYxjgymHW5nSWt2AOuzQgh0dPO5Xhbjlc9PBbRNeTw+w2kMPvH3yN8105nPLG3fz81Ex2uHLYZ8oYjnarh0s9zGG/ajlMqqceplfLYc9Jx3La07ewdd4ydvywmgPrd1J4MJfAkGBiuyfTfcIwek8eRUCgM/5ryRMfkZXaOq/PiG+x1v5ojPkep1/hamPMK9ba6r3rdwLl8z0/4RrwU8EYM47KTs9XrLXTajjV4zjXycNwrh+fYK09WO04lwPjXE9nuWYu9Cvq4BSp3e04o/Hux+lI+2MdZXOB6t9ibsTpEO0KTHA93G0DzgF86dbLE4CXa3kvsYb3TgaOeAena0Hmx3BGcsbjjIT9m+u9A8aY44F3cRqKk1yP2hys4bUInM7I2kZalgEPWmtr6pX5K87vZTROJ2b1Mt+44l1VR0z1eRunE7UnToes+3S8aUB3D47tk/asTuOjm5/irMdvJCwmgnF3X3RYmYzN6bx35aMUebhmwuR/XV/re2NuOqvK8xXvfVdjB6cxhk7Detc42hQgPzOHL++bztpPF9f4fmuQv3oLqTc+So8nbyMoJpLO9/7qsDIFm3ey8Yq/UOZBznr8u/aRZSk3n1/l+f5359XYwRk1YkCtxwlOiDnsvfVT7yNHHZxN8sEnX/Lx53NrfG/pijUsXVH1gp86OJsuf/UWttz8T7o/fjuBMZF0vPvXh5Up2LyTzdP+TFlufpPP0+2x39b6XvubqtbBjPe+OqyDM+70MRXbISlJ9P3wkXrPufq4aynasbeRkfq//avTmH3TU0x44kZCYyIYfc/hbWHm5nRmTXvU4/WDTnms9rZw2M1V28J173132EXBnmeMrNiOSkngvBkP1nvO18bcRs6O/Y2M1LcVrEllx28eodNjdxEYHUn73007rExh6g62XfNHj+phpzpGliXdUPXrbNYHc2vsXLFFxWy/5o90eeFBwgf1IfasscSeNfawcjlfL2Hnb+uvp61B4ZpUdt72dzo++jsCoyNJvmva4WVSd7D92gc9yl/HR2qfdCbp+gurPM/6cE6dHZwhPTsTPsQZtZ/7wy+UZrTt7yuqg62L2kH/l7E6jbk3PcV4Vw5H1ZDDrM3pfNYMOTy5jhweUy2H6xuQw3MakMM32kAO969O48ubnuJUVw7H1FIPP22GHE6oI4fHVsvh2ve+O6yDEyAwNJhep4+g1+kjaj1WcX4hi//5Pste+LzpwYo03m9xpp0NxxkY9TBOh2U4cDFQPrx5A/CvppzAWrvNGPMAzvXzQcCPxphHgBU4ozrPw+m/AOf6+JGbcqQZqYNTpBbWWgv82RjzGs5ameNxOpRigTxgO7AUmA3MsNbmV9t/uzFmGHA3cDbQDWftxK04nV5PWGsz28AMoy3lGeAeIAG43RjzhLU2D8A1qvQkY8yZOOtdjsHppA7GGda/EVgIzLTWflftuJcAk3HuXhng2i8JJ3dpwHfAs9baGhdosdbmGWPG4zQKF+OM9C0G1uOM8n0W6OLJD26tPWSMOQ6nk3Mizv+tCE+O6Q82fbWUF0+7lxFXnUavk4cSnRJPaXEpWVv3sHbWYn5+ZQ4lBUXeDhOABf+dSUZqOl1GHEVMx0TC46IoOJhL1ra9bJj9C8vf/pr8zEPeDrPFZc9dwppTbyP56snEnTKc4JREbFEJhVvTyZy1gL0vz6LMR3Im0hodnLuEtZN+S/JVZxEz3lUHi506mDXrB/ZNn4VVHfQraXOX8s7Eexl89Wl0Gz+UqJR4SotKOZi2h02fLmbVdN9pC8VxaN6PpJ5xMwnTzibq5BEEd0jCFpdQlLaLg5/P58Crn2ILCr0dJgAl+zLZcv4dxF04kdjJYwnt05XA6EhKsnIoWL6BrA/nkjO7bU3ddmjej6ROvpmEK84malx5/oopSkvn4BfzyXztE5/JH0Ds2ZWj6rPa8PS07lQHWxe1g/4vbe5S3pt4L4NcOYxMiaesqJTstD2kKod+Yevcpbw98V6GVKuH2a56uNJHcrjgobfZtWgdHUf1I+GozkQkxRCeGIO1lsKsXA5s2MGOH9aw7oP55O1t2zcE+bSy1rnetLV2qTHmIuB1nM7Gh2sotgE4023Wwqac55/GmASc/omjgJdqKLYXOMdau7Gp5/EmY9vQouQiItJ4f+t2uRoKP3ZqaevvSG3thix7zNshiIdWDfPLGyHFZaGN9nYI4qFxIZneDkE8ZAL0ddSf2TLd1OvvvimK93YI4qEAbwcgHmu7q3+2Hrdsf10NYiPlPXqN174ERtz1vxbPlzGmG85ozjOBzkARsAl4D3iqfDBPDfuNo/4pat3Lj8EZrXkikIIzkGcDMBP4j7U2u47dfZpGcIqIiIiIiIiIiIiIiIjvsGXejqBFWWvTgDtcj8bs9w3Q4A5Y1xqfrXIaCN3AIyIiIiIiIiIiIiIiIiJ+QyM4RURERERERERERERExHe00jU4pfmog1NEPGaMicOZJ7zRrLWrmjkcERERERERERERERFpxdTBKSLN4Rzg5SbuqwW2RURERERERERERESkwdTBKSIiIiIiIiIiIiIiIj7DlpV5OwTxcQHeDkBE/J+1drq11jTl4e3YRURERERERERERETEv2gEp4iIiIiIiIiIiIiIiPiOMuvtCMTHaQSniIiIiIiIiIiIiIiIiPgNjeAUERERERERERERERER32G1BqfUTSM4RURERERERERERERERMRvqINTRERERERERERERERERPyGpqgVERERERERERERERER31FmvR2B+DiN4BQRERERERERERERERERv6ERnCIiIiIiIiIiIiIiIuI7ysq8HYH4OI3gFBERERERERERERERERG/oRGcIiIiIiIiIiIiIiIi4ju0BqfUQx2cIiJSp79nLPR2COKB2Pgx3g5BPBQ47HZvhyAeOvqXf3s7BPFA/ITrvR2CeKjDizd7OwTxVHGhtyMQTwRo8jB/F3D5y94OQUREROQw+pYpIiIiIiIiIiIiIiIiIn5DIzhFRERERERERERERETEd9gyb0cgPk4jOEVERERERERERERERETEb2gEp4iIiIiIiIiIiIiIiPiOMuvtCMTHaQSniIiIiIiIiIiIiIiIiPgNdXCKiIiIiIiIiIiIiIiIiN/QFLUiIiIiIiIiIiIiIiLiM2xZmbdDEB+nEZwiIiIiIiIiIiIiIiIi4jc0glNERERERERERERERER8R5n1dgTi4zSCU0RERERERERERERERET8hkZwioiIiIiIiIiIiIiIiO/QCE6ph0ZwioiIiIiIiIiIiIiIiIjfUAeniIiIiIiIiIiIiIiIiPgNTVErIiIiIiIiIiIiIiIivsOWeTsC8XEawSmtkjFmmjHGuh7da3j/G9d73xzx4ERERERERERERERERKTJmn0Ep6szaYunx7HWGo+D8QPGmChgGDDS9RgBdHe9nWat7V7zni0SSxxwMXAKMBRIAqKAg8AuYBkwD5hhrc06UnGJuDPGdAauwfl/2g+IA0qADCAVWAp8D8yx1mZ7K05pGV26dOT6G69g0mkn06lTCkWFRWzZso0ZH37GC8+/Rn5+QZOP3bVrJ1au+a5R+6Sl7WDwwLE1Huu0M07hxBNHMXBgP1I6ticgIICMjAMs/WUVH37wKR/N+JzS0tImx+uPojolMviqSXQbP5SojgmUFpVwMG0vmz5ZzKpX5lBSUOTZCYwhvncKyUN70X5oL5KH9CSxXxcCQ4MB+GjqQ+xatLbew0R3TqL7hGPoOKY/if27EtkhHmMMBZmH2LsilU0zF7F51o/Y0rZxJ2Fwp3YkXzmZmPHDCe6YhC0qpjBtN1mf/sC+V2ZhPcmbMYT17kzEkD5EDO1DxJA+hPfrToArZxsv/AOHFq2q9zAhnZOJmTCC6NFHE9a/OyEdEsEYSjIPkrdiE5kz55P12Q/QRnLWkjIys1i5Zj2r1qxn1bqNrFq7gazsgwCcffoEHrrvTi9H2PoEpiQTc8m5RJw4ksAO7bBFxZTsSCd39rfkvDMTW1DY5GObsFDCjxtB2OhhhA7sS1CXjgSEh1OWm0dx2g4KFv5EznufUpqRWedxgjq2J/yk0YQNH0JI354EtkvEBARQmpVN0ZoN5H7xDblzv2uzdXDXvkze/PIHvl+6jt0HsggJCqJL+0QmjhrMRRPHEB4a4vE5du47wLtzF7F45SZ27M0gv7CIiLBQenRsx3FDjmLqKaNJjI2qdf+r//IcP61NbdC5lr/5iMfx+pNd+7N4c84ivl++gd0Z2YQEB9ElOZ6JI4/molNGNlP+Mnl33hIWr97Mjn2ZlflLSeK4Qb2ZOn4EiTG15++7ZetZvWUnq7fsZMfeTDJz8jiUX0B4aAid28UzvH8PLhg3nO4pSR7H6o927c/kzdmL+H7ZerccJjBx1NFcNGFU8+Xwqx+dHO49UDWHg/swdfzIOutgbeYv38DNj75a8fyGc0/mxvNO8ThefxPUMZmEX08hctwIgl1tYdH2dHI++57MNz71qC3EGEJ6diZ88FGEDe5L2OC+hB7Vg4AQ5/votsvvJu/HlXUeIunWy0i69bJGnXb/f95g/3/eaHLY/kY59G/Kn3hNmfV2BOLjNEWt930CjPNmAMaYAOB3wD04nUXVJbgeRwOXA88YY14A/mSt3X/EApU2zxhzLfA4EFHtrSCgk+txIvAb4B2cDntpJU47fTzP/+8xYmOjK16LjIwgPiGOYccO5tdXXMiFF1xDamraEYtp08bDLwT+4f7buet3NxEQcPgkCZ06pdCpUwqTzzqVm2+5il9ffjM7dqQfiVC9rtuEY5jwxI2ExlRW3+AICIuLInlIT/pfMo5Z0x7l4NY9TT7HUeefwCn/vt6jOEfedQHH3joFU0P+olISiEpJoOek4ey5djNfXv8kh3ZleHQ+XxczYQTdH7+dwJjIyhcjwgiKiyZySB8SLz6VzdP+TFHa7iYdP+H8cXR77DaPYky581La3zq1xpyFpCQRkpJE3KTR5C6bwpYbHqF4l766eGLs5Eu8HUKbEn7SaNo9dA8B0W51MDycwNgYQgceRfS5p7Pn1vso2b6r0ccO7tODlOmPExBZ/WsVBMbFEBg3gLAhA4i57Hz2/+Xf5M3+tsbjxN10BbHXXFpjHQxq346g9u2IOPl4Yladz967/kzp7n2NjtWfffPzGv7w37c5lF954a+gsJjVqTtYnbqDD7/5kad+dyVdOzS94+mT73/hry9+SEFRcZXXD+bms3zjNpZv3MabX/zAI7dewphBfZt8nrbom6Xr+MNzH1TNX1Exq7fks3rLLj789meeuuPy/2fvvsOjqNYHjn9PeodQQm+h995EqoAFEAs2VMSOvf0s13ot2K79WhD1iqCiIFhAREA6gtJBeg2Q0ElCej2/P84ku0l2N9lNQrLh/TxPnsxmz8yc7LuzZd4576FxnZoe72POqk28MmWO4/jtPczmvYf5dsEa3rj3Gvp2aFFk/eycHB541/EJ2qTUdHbEHGVHzFGmL/yLe68azO0jB3jcV2+0dMNOnpk000EMTUJ49tL1fPh/N5cuhis38sqXvxQTw9W8ce919O1YNIbOpKZnMnHKLx73q6oIG9yLem89jm94wc+jwdXDCe7YimrXXMyRu14g65Bn36sirhhC/TfO/QVaGfuPnPN9VhSJoXeT+AkhKrPySHDGAh1d3J93ycU64NZy2L+3sR+pegbzuFyAGTlZ/jtXKhiYDoy2/pQFzAYWYkbDJQCRQBPMiLmRQDXgfmAV8N256KcQSqkbgMnWzXTgS+B34AjmOKoP9MA8R7tWRB9F+enUqR1ffvUBISHBJCUl8+7bk1i+fA3BwUFcPWYk42+9npatopnxw+cMGnAFyckpbu8jLu44fXpdWmy7Rx+bwLXXmZfMb7+ZXeT+unVq4+PjQ3JyCnPnLGDZ0tXs23eQjPQMWrVuzoR7bqF7j85079GZn+dMY8CFl5OSkup2f71JrfZNGP7x/fgHB5KZnMaGj+YQ++d2/IICaHF5H9rfOITI5vUYMeX/+GHEc2SleDgS1+4dNSczm9M7D+Pr70vNto1LvImQqOooHx+yUtLZP38dR1ZtI/HAMXIysohs0YCOtw2njjVC9PLpTzHj0mfJTi3F1aqVWHD7ZjT76HF8ggPJSU7j+Ec/kLR6Kz5BAURe3p9aYy8mqHkDmk95nl0jHyM3Jc2DvdiClpuZRfquGJSfH8Ftm5Z4C35RNcxIsZQ0En9fQ9LKLWQcjCM3I4ugFg2pfetIQru0IrRLK1p8+xK7LnuU3FTPR3sLm3p1omjWpCF//r2hortSJQW0bk7tN57BJziI3JRUEv/3HelrN6ECAwm9ZBDhV4/Av2kj6vz3FeLG3odOde8Y9AkNyU9upm/8h7Tla8jYvpvchLP41KhO6JALCbvqUnzCQ6n96r84kZJK2qq1RbbjW8uM1sxNTSN18SrS/95I1qFYdEYm/s0aEzH2CgI7tCGwQxvqfvomcdffgy5FxQVvsuNgLE/+91vSM7MICQrg9ssH07Ndc9Kzsvj9z83MWvI3MUdPcf9/vmT6Kw8SGhzo9j427jrI85NmkKs1PkoxakB3BndvR+3ICI6dTuCX5etZtmEHicmpPPz2VGa98QgNXSRy2kc35MW7rynNv11l7Ig5ypMfz7TFb2R/erZtRnpmNr+v2cqsZeuJOXaa+9/5mun/nuBZ/HbH8PxnP9rid2EXBndrQ+3qVvxWbmLZpl0kpqTx8PvTmTXxPhpG1SiynfCQIHq0aUrH6IY0iIqkdrVwggL9ORmfxNqdB/h5xUaSUtP5YOYiwkOCuXZIz7J4iCq9HQfjePKj720xHDWAnm2jSc/MMjFcuo6YY6e4/61pTH/pHs9jOHm2LYb9uzK4W1tqR4Zz7HQiv6zYyLKNO0lMTuPh975h1msPOIyhIx/NWkTcqQRqRIRy5qz732+qgsC20dR/7yl8goPISU7lzOQZpKzZgk9QIBEjBlD9uksJjG5Iw8kvEnP1Qx59HlV2n0d1ZhYZuw+Cnx9BbZqVeBvx38zl7PyVrvfj60Pjb97ENzyUnKQUkhetdruv3khi6N0kfqKiaRnBKYpR5glOrXUW4LSWmFL5L1opWuvia45Vfd8CnwJrtdZ7AZRSBzlHCU5r33nJzTXAWK21sxLDXyqlIjDJzWfPReeEAFBK+QLvWDeTgAu11lscNP0FeF4p1RbXF1oIL/P6f54jJCSYrKwsrhw9nrV/b8y/b/my1ezbe5CXJz5Fy1bR3P/g7bz+6gdu7yM7O5sd23e7bOPj48OF/XsDcPZsEnPnLCjS5syZBJ5/9g2++PybIonWTZv+4YeZc/jiy/e46uoRtGjZjPseuI03X//Q7f56kwtfvBn/4EBysrKZc+MbHN+wN/++2D+3k3jgOBc8ewORzevR5a7LWPtu0cRxScTviWXFc19xYvN+Tm0/RE5GFj0fucqtBGd6fBJ/TpzOtml/FEm0ntx6kD0//8mwD++jxag+VI+uR5c7L2Xd+z951N/KruG/78QnOBCdlc3em14gdcOu/PuS/9xKxoGjNHhmPEHNGxB112iOvev+NU/pew5z+PnJpG7eQ9r2A+iMLOo+cr1bCc6chLPEvjqFU9PmF/lCnbZ1H/E/r6Dpfx8jctSFBEU3IOrO0Rx7/3u3+yqMCbeOpUPbVnRo24paNSKJPXqci8eMr+huVUk1nrgXn+AgdFY2x+95iowtthLb6Ws3kXUolhqP3IV/00ZUGzeGhEnT3NuB1qT8vpSET6eRtf9QkbvTV68nddXfRL3zb5SfLzWevI/Yy8cXaZebeJYz731G0ow5RZKsmTv2kDJ/CbVf+xehFw/Cv0lDIm4eQ+Lkr93rq5d6c6oZlefn68Okp+6gc6sm+ff1bt+CxnVr8e70ecQcPcXUX5dzz5hhbu/ji1+WkKvNiZ+nxo/mumF98+/r0LwRQ3t15K2v5zJt3grSM7OYOm8FT996hdPtBQcG0LJRXbf7URW9+fU8W/weH0fnFrbPE73bRdO4bk3e/X4BMcdOM3X+Ku65cojb+/hi7gpb/G4ewXUX9cq/r0N0A4b2bM9b0+czbf6fJn7z/+TpcSMLbMPP15dlHz2Fr4NR1DSDQd3acMOwPtzwwiTOpqTx8ezFXD2ou+P2VcybX/9qi+ET4+nc0i6G7ZubGH73OzHHTjH1t5UelX79Ys5yWwzHjeS6ob3z7+sQ3dDE8NvfmPbbKhPD31bx9C2jit3u9gOxTF+whgB/Px64ZhgvflE1P28Wp86zE/LfCw/f9izpm3bm35e6ZjOZB+OIevJ2AqMbUuO2qzwqN5mx7xDHX/6EtC27ydixH52ZRa0HbnQruZJzJpGcM65nyAkd0CN/BFzS/JXojFJOz+ElJIbeTeInhKjsqv4n2kpOaz1Zaz09L7l5LimlxgA3WzfXA0NcJDcB0Fqf1Vq/ipk39Jz3WZy3egN5Z1o+dZLczKe13qG1nlH+3RLnQrfunejXz5zsmTZ1ZoHkZp7/fvA5O3fuAeCee8bj51c+FdgHD+5H/frmqfjzT/NJdzDPxAvPv8n77012Ooo0NzeXRx95nowMs+7oK4ofNerNorpEU793GwB2fresQHIzz6bJ8zizOxaAjrddjI+fr0f7OrFpP1unLOT4xn3kZGQVv4IDa177nk2TfnU6ilTnapY9PSV/+81H9HLYztuFdG5JWO/2AJz+flGB5GaeE5N/Im3PYQBq3zYKPIhb6uY9nJryK6kbd6M9jFnca1M5MelH51cL5+Zy+JlJ5Frbr37ZBR7tRxj333Ezg/r1plaNyIruSpUW0KE1Qd07AZD80/wCyc08Z6f+QOY+U5Y9fOyVbh+DGZu3c/LJiQ6Tm3nSlq4m9Q9zNbx/4wYEtClaWjH+/c85O2WG8xGkubmcfvUDdKY5iRQ6tL9b/fRWW/ceZsNO89XqikE9CyQ384wb0Z/oBlEAfDN/FVnZ7s/NvXm3eQ5UDwspkNy0d7dd0mbLHufxFjZb9x1hg/XYXjGgW4HkZp5xl1xAdP3aAHyzYI1n8dtr3kerh4UUSG7au3v0oPzlLVb7wopLVjasHcnwXuZ9PT4phQPnQbn2rfuOsGGXFcOB3QskN/OMu7SfLYa/r/YshtYxVT0spEBy097dVwzOX3YWQ3s5ubm8+MVP5OTmcvuoATSqU7IRn1VNUKdWhPTsAEDCDwsKJFbynPnfbDL2mhhEjrvco8+j6Vt2Ez9tDumbd6EzPfs8WhLVrrBdBJH44x/ltp/KRGLo3SR+QghvUCkTnEqpbkqpSUqpXUqpZKVUirX8iVLK5aQhSqlopdRjSqk5SqmDSqk06ydGKfW9UuqSYtYfr5TS1k9TpVSAUupRpdQ6pVSiUuqMUmqpUmpEofXClVJPKKU2KqXOKqUSlFILlVKVefb3p63fGrhVa13iOgJa651a63XO7ldK+SilblJKzVNKHVNKZSqlTiqlliil7lVKBbjavlKqg1LqWaXU70qpI0qpDOu5sEcp9ZVSqk9J++oppdTNSqks67mwSilVvdD9vkqpW5RSc5VScVYfTyulVlrPmeAS7MPjbVjPQ62UWmrdbq2UmqyUOqCUSldKHVVKzSjpY6WUqquUmmg9189YfTlsbWOoi/Wa2h0z462/XWXFPk4plZ3Xx1Kw/zZaJol1pVRtpdTzVmxPWLGOV0r9pZR6UynVycW6TZVS7yqltimlkpRSqdZz81OllMuRo3aP1b+t20OUUjOtxzpLmRHchdfxKDZVxciRttEMX0/7wWEbrTXfffsjANUjqzFgQPm8RFw/9sr85enfejbKECD+TALb/jEJo2bNSj660Bs1u7hH/vKOGcsdN9KaXbPMCfSg6qE0uKDtueiaxzISkjm905ycimgSVcG9KR/VLradoDs9w8mXP62Jn7UEAL9qYYT3rbwD53MSkkjbeRCAgCYyMklUfiGD++UvJ/38u+NGWpMydyEAvnf3rNwAACAASURBVBHhBPXsUi59SV+3OX/Zr1F9j7aRm5hE5p4D1jbqlUm/Krsl67blL48e2MNhGx8fH0b27wZAUmoaa7fvc3s/eQmZBi5KXoaHBBNpjVjIynE/gXM+WrLBdlHB6P6OZ7/w8fFhZL/OgJnrcu0Ol9cKO5Qfv9rVnbYJDwkiMtyUky5N/EKDbOVXM7OyPd6Ot1iyfnv+8mjrOCvMx8eHkRea104Tw/1u78cWQ+cX/hSIYQmSqF//9ic7Y47SpG4tbjvP5ky1FzbUdtFG4qyFjhtpTeJP5rOqb7VwQno7/RpfoXxCgwm7yHxHzTx8lLR150dBO4mhd5P4iUohV1fcj/AKlSrBaSXF3sHMQ3k30AoIBUKs5QnANqXUXU7WbwbsA97CzMPXBAiyfhoD1wK/KaWmKaVKMrwnAlgOvA10t25HAgOBuUqpR6z9NgZWA28AXYBwzDyVQ4GFSqkb3Xskyp+VvMn7prZEa73VVXs3t10D87hNAy4F6gD+QC1gEPARsEkpVfQyZrP+IMxcrS8Dw4EGQADmudACGAesVkq9VlZ9dtCHh4CvMGWcfweGaa0T7O5vjBn1OgUYAdSz+lgD6Id5zmxRLhLyZbENu21dam3rTqApEIgZ8XgNsEop9XAx69+ISRw+jXmuR1p9aWhtY6FS6vMSHDdKKTUVmIWJfT3As6FYBdnXjSh15sP6fw8AL2LmvK2NiXV1oBfwOKbcraN1xwE7gYeBdphy0sGY5+ZdwEal1L9K2I+JwB/AGMxjXeTxLcPYeK0+F5iTgsnJKWza6PxD6MqVf+cv9+7bvcz7ERYWyggr2Rpz8DCr7PbniYBAc51HThU/0Vivp3kJy0pJ5+RW5yf+4tbYTiTW7VHsy16F8w0wh5zOqZofesN6tgMgJyWN1K3OrytJWmM7JkN7Vu7EtE+Av1nIya3YjghRAkFdzEir3NQ0Mnc4L5+evt5W1CJvnbKm/P1tN0px/ORv5zw5BjfuPgiYkq/tmjVw2q5Hm+j85U3WOu5oWs+MPos9ccZpm+TUdOKTUqz2tdzex/lo424zGiU4MIB2TZ0n9nu0aZq/vMmD0bFN65r5UGNPJjhtk5yWTnxSqtXes/ilZ2axZIMZeeOjFE3qOp+HtarYaI3ANcegqxjaSiBu2u1BDK1jKvZkvNM2BWJYzDEYezKej2ebZMEz40cR4F9lv+YVK6S79V6Ykkb6tj1O26X+bTudFdK9Xbn3yxPhl/bHJzgIgLM/L67g3pw7EkPvJvETQniDSpXgBP4LPAIoTILsNkxCrBcmcbMNkwD4VCl1uYP1fTGJkDnAg5gEYzfr973W+gA3Ac+VoD+TMQmFj4FhQA/gDiDOuv8tpVQHYDYQDbxu9bcnJvmRaP0vnyilKtsQj4F2y/PLaqPKzJU4F5OgA1iGScL0AC4H8iaOaAv8oZRyNNeoH5ACzMAktQdh4ngJ8BgQY7V7Sil1a1n13e5/eBF4DxO7GcDlWutUu/trAiuBzkAG8CHmf+wJDAZeA1IxCa/flFLVHOyj1NuwUx8zl2s2Jgl2gfXzDHAWc5y/q5RyONmOUupaTDI6FNgPPIp5rLsDVwPzrKa3A2+66AeY5/3NwApgLCbuQ63tl4Z9TdK7lVLuT3BjUUrdDHyN+X/TMa87l2GeYwMwc8wuAIqcfVNm5PYUTAI5GZMg7Q/0xTw3T2Feh15VSt1TTFeuwsRrK+a1rhfmuMyba7SsY+O1WrduDsD+/TEuk4F7dttGPeStU5ZGX3EJoaHmyuvvvivdHDi1atfM7+PuXe6P1vAmkS3MCaXEg8fRLk5qJ+yLy1+ObOH8RHBlEFwzgurW/xW/N7aCe1M+Als0BCDj4FGXyYiMfUfyl4OsdSojv5rV8vuXvvdIMa2FqHj+1uj+7MNxLo/BrAO2Uof+5VQRIK9UrtmfZ+VNfSKr5/fP0214mwOxJwBoXLcmfr7Or/drZpXHtF/HHddYJTETklOZsWiNwzaT7cqwXXOR6yoXB+JOcONzH3LhHS/Q85ZnGHb/RB56+yvmLF/vUflOb3Xg6EkAGtep4Tp+9eziF3fS7f1cM6QnYMVv8VqHbSb/vKxI+5LIys7h6OkEfluzlXEvf8ah46cBU3I3NDiwmLW9X148io1h/TKM4R+OL4Cc/NPSIu2dmTjlF9Izs7jsgs70bl/232m8SUDzRgBkHnL9Xpi53/bZLiC6clbHqXaFrbjb+VQaU2Lo3SR+olLIza24H+EVKs2lYEqpYZgkJMAdWusvCjVZq5T6GvgVGAJ8oJSap7W2r61yFGiqtT7qYBd/KKUmAf8DxgOPKaXe0Vq7moG4F3CV1tr+TPZ6pdRaTMLFB1iMGdk5UGv9l127dUqpPVZ/w4EbgXdd7Otcs68ZsKEMtzsBk+wBmAqM11rnDW9ZD8yxRq09DTTHJJqfLLSNTUBD+xGTdn5XSn2ISaIOA15QSk3VWpf627ZSSgEfYBJcAJ8BE7TWhV/RPgAaYRKtgx3MW7pUKTUTk+SLBp7AJBvLeht5WmKS6X211vYTNK1WSv0M/Il5jn6olPpVa51f0F4pVQuTyFeYY+PuQsfUBmC2XcweUkp9qrUuOhmb0YmicS81rfUBpdRczMjsIMzxvBb4DVgDrNVaFzuRjFKqHjDJunkCuEhrXXhI4ArgI6VUo0Lr+mN7rJKB/lrrTXZN1iilZmFGc9fDXAAx00W/OmJGb47QWttP5Ljc2l9Zx8YrBQYGUKuWucI8LvaYy7YJCWdJTk4hLCyUBg3Kvvzd9WOvyl/OK4frqYceuhN/ayTLj7PnFdPae/kG+hNcMwKA5KPOR5YAZCSmkpWSjn9oEGH1K/c8Q10mjMDXupp+79y/imntfVSgP/41zXU1WcdOu2ybk5hCTkoavqHBBFTiUUFRd1+JsmKWMHdlBfdGCNdUgD++NUy5yuzjrk+25yYlk5uahk9IML51arts6wn/VtEE9zcJtMzd+z1OTlYbf03+MZiywEm58iokIzMrf8RkVA1X1ylCRFgIwYEBpGVkcuy0q6+mjl0xqCcbdx1kzooNvPblT+w4cISB3dpROzKCo6cSmLtyQ3653DuvGEKfji1dbu90YjKnE5Pzb584c5YTZ7azdP12/jdnKW8/fBPRDeq43U9vYuJnrm+Nioxw2TYiNNgWvzMexG9ANzbuPsScVZt4bepcdhyMY2DX1tSuHs7R04nMXbU5v1zunaMG0KeYhFfsyXgu+z/npx0u6NiCx2642O1+epsCMSzuGCxtDAd2Z+PuGOas3MRrX81hx8FYBnZta8UwgbmrNrFkvRXDywfSp0PRuYzzzPtzM6u27CE8JIj/G3up232pSlSAP35W7LKL+TyaezaZ3JQ0fEKD8a+En0f9G0QR3MOMhEtdv42sw66/11YVEkPvJvETQniLSpPgBJ6yfs9ykNwEQGudrpS6H9iOKT87GFhod38KZuSfQ1prrZR6DDPCLBQzsmyWiz7NKJTczNvOFqXUSsxor9rAG4WSm3nt5imlYqy+9qdyJTjt33GcnjlRSoVj+u9Ipta6cM2s++y2eb+TJNcLmNFrbYA7lVLP2yd4iktUaa0zlVKPYxKhTTBlgde7Wqc4VnnPKZhENMCbWuvCiVeUUk2B66yb9ztITOb1caNS6iNMYnI8dsnJstiGAy8XSm7mbWOblQB7A1PqdzRgP4nhPZhyyrHAvYUSaPZeAG6xtjHORV8ScB730roVM2Ix75LXnnbLKKV2Y0Zefqm1dpa0fwBT8hrgLgfJzXxa68OF/nQlZrQswCuFkpt568RYz82vrf3cCvzHyS5yMRdzZDi5v6xj45XCwm2DvFNSUl20NFJT0wgLCyU0LLRM+9GwYT0uvLAXAGtWr2f//phi1nCue4/O3HPfeACOHDnKF59/UxZdrJT8Q4Pyl7NS04ttn5WagX9oUIH1KpuoLs3pdLs5MZgcd5ptU6ve1Z++obbpn3NTio9bbmoGvqHB+FTSuIV0aUXU7aMAyIw7xclpv1Vwj4RwTYWG5C/r1LRi2+u0dAgJxiek2Onf3ePvT60XHkX5mZFP8R9+6dFmAjq0IcK6SCj72AmSZs4psy5WVinpto93IVZJelfykiupGZnFti3M18eHV+65joHd2vL5z0uYvWQts5cUHAnYs11z7hg92GVyUylF7/YtuLBLa1o3qU+1sBBS0zPYcSCWHxb/xf7YE+yPPcEdr0zmm5fvp14t5/MNeruUdFscQoJKEj//0sXvrqsY2LU1n89Zzuxl65m9rOBX255tm3FHCZKbrkSGh/Cvm0cytGc7fH0qWyGvslcghiU6Bq0YpnsYw7vHMLBrGz7/ZRmzl65n9lIHMSwmuZmYnMp/vjEXPj547XBqVnNU7Or84WP/ebQE74W5aen4hAajyvq9sAxEjL4IZR13iT8uquDenDsSQ+8m8ROVhsyFKYpRKRKcSqkITBlSKJh8KUJrvUMpdQqToOuLXYLTwXb9MfM/hlNwHsDTQBSmPKirBOd3Lu7bjElwFtduCyYJF+2iTUUIt1t2mhQGLgKcDVWKwcz3CIBSqj62+RFnaK2THK2ktc5WSn2JSbpFYkqDrnbWAaVUICaOYdjKKiu7Jp0pRYJTKRWMKUU70vrTk1prZ+U+R2CeS6mY0YOuLMckJ+srpRprrfMueS+LbdjTmPlCnfkSUz5ZYZL69sdYXqnnuS4SbXkxW42ZK7Kvs3bAHGdxLy2t9SmlVD9MsncC5nljr5X1c7812nuCddGDvbwY78fJHJsuDM3rCmZEpTMzMfPM5s3D6yzBuUprfdDFdso6NkUopUpUTzIitOJevoICbeWzMjOzXLQ0MqwTS8FBZZtoue76K/CxPhB/N93z0Zu1o2oy9euP8Pf3Jzc3l3vufpy0tOITSN7KL9A2b1tuprMcvU2OFWO/EpxMrAjBtSK45NMH8fX3Q+fm8scjn5LtwYmwyk7ZnQjUWcXHTVtx8wmqfOXu/GpVo9mkJ1FWzGIefQ9dBWMmqhYV4NkxqEpwEt8dNf91P4HtWwOQ/MsC0pY7Ln/qik+N6kS99Vz+MXjquf+g051+rKkyMu3i5u9X/FfuAH/zVTWjBJ91HNkfe5w5Kzaw18mIhC17Yvhx6VqaNYiijpPRbO88cjMRoUVPSnZr04xrh/Xlpc9n8cvy9ZxOTObNaXN495FxHvXVGxSIn4vSpnkCrBhnlOCzjiP7404yZ+Um9h457vD+LXsP8+OyDTSrV5s6NVyPKI2KjOCHieaa45ycXE7En2XV1r38tHwDr3w1hyMnznD7qAEut1EVZGbZjiV/vxLE0D8vhp4egydKEMP1NKsf5TSGb0+fz5mzKXRs3pAxg3t41I+qxPPPo5Xve0TE5YMByE3PIGneigruzbkjMfRuEj8hhLeoFAlOoCu2xNV0pdT0Eq5Xt/AfrKTmXZhRml0BV6+sxY2bLzw60Z59+dSStAt30aYi2CehymqoUwe75eJq9tnf34FCCU6lVChmHtXrgfYUTFAXVpr6B+GYOUgHYEbUTdBaf+aifd43jRAg21S1LZG6QF5ysiy2Ye+Aq1GvWuuTSqmDQDNMWVQgf77ULtbNu5VSd7vRD2e2lHAbHrHK634GfGYl1PtjHs/eQB8gL5tyEyYpPDyvfLH12pD3HF3pwSjTvHUPaK2djnq2RhhvxFy00cFZO1w8VuUUG0cKj1I9p+rVq0P1SMcn2RLiEzl69DjpGbaToAEB/g7b2gu0PoSnpZdt0vC6G8wUtunpGcyeNdejbYSFhTLzhy9o2NCUz/338/9h+TKn13ZUeqF1Iwms5vjtIyMxhZRj8WRn2E4S+QQU/5HD14pxZUwa+ocGMeKr/yOsvimZvPq174n9c3sF96p8aLsRKHklJV1RVtxyK1nSwic0mOZTniegvvmYEPf6VJL/3FrBvRKieDrTs2NQezB6zJlqt11P+FWXAZDxz05Ov/pft7ehQoKp89+J+NWNAiD+gy9IX1ukAEaVFGAXt6zs4k8KZmaZ2TYCS/BZp7ANOw/w4FtTSEpNp36tSO67djh9O7YkIjSEM4lJLN2wg49mLmD+6s2s33mASf+6nRYNi35kdJTczOPv58sLd17Nlj2HOHj0JIvXbuP4mUSnyVJvVyB+LuZ/z5NpxTiwBJ91Ctuw6yAPvvetFb/q3HfVRfTt0JyI0GDOnE1m6cZdfDTrD+b/tZX1uw4y6fFbaNEwyun2/P18adnQVkK4TZN6DOjSmqsHdueO17/kgx8WEXP8NC/dcaXbffUmAf62Y6kkc8fmJbU9OgZ3HeTBd762xXDMUPp2aGGL4YadfDRrEfPXWDF8YjwtGhYs87x2x35+Xr4BXx8fnr11dP6Fleczzz+PVq7vEUGdWxMYba4rTv5jDbnJxVclqiokht5N4ieE8BaVJcHp/BO6ayH2N5RSNTAlKruXcP3ixs27etXLn5dRa12SdsVfNnhu2RdQdzphj1Wit0AGzkqWOSpbaz9p2oli9m9/eXGBydasEq6LMQm5kihN/QP7UYCTikluQtk8V8vk+W6nuMca4Djm8bR/rGvg2WuAs34AxHuwPY9oreOA762fvOP//zBzuvpg5uq9AVMuFsz/m/dcdjRPb3HyHruSPN55z29XEwm6eqzKIzaVznMvPMaNN13t8L5vvp7FvROeIDnJNgdUaGjx/16IVQ4lJdnVwHT3dOveidatTTmp3+YtIjHR/UHKgYEBTP/+U7p2M9cYfPDeZ7z/3uQy62NF6P3ENbS5xvEIgJ0zl7P40clk2ZU39Q8pflStf4gZAZhVgrKo55JvoD+XfvEIUZ3MaOaNk35l06RfK7hX5ScnxVaCqCRlZ32suJWknO25ogL9if78aUI6mWP3+KQfOTGpdHPnCnGuaLuS7CUp86WCzXFakvJhJRF29QgiH7wdgMz9hzh+/zNoNy8cUgH+RL33IoHtWwGQ+NUMzk6ZUSb98wahdiPaS1K2NM1qU5JSmvYys7J58kOTHKtVPZxpL91Hreq262rr1KzOdcP60r1tM8Y+819Oxp/luU9mMH3ig27tB8DP15crB/Xk3emmhOb6Hfu5rF9Xt7fjDULtRp+UpGRpmnVBl0fx++QHE79qYUx77s6C8atRjesu6kX31k0Z++9JnExI4rnPZjP9xQlu7QegVeO63H/1RUycOpefV2zkkt4duaCj83Kp3q5ADEt0DFoxdHPkUWZWNk9+NMMWwxfuLhrDob3p3qYpY5//hJPxSTw3eRbTX7q3wDZe/p8pLnTD8D60aVLPrT5UVbn2n0dL8F7oY70XlqS0+7lU7YqL8pcTf6p6U1u4IjH0bhI/UWlIiVpRjMqS4LRP/t0N/FnC9QonCN7Hltz8CVNGcgsmIZGeN2JLKXUIaEShxN15ZrPdclegrF/lS/PqMw2TjNOY8qrfATsw83pmWnOp+gB5l2KWJo7bMMdBa8xIueVa6+9dtM97rp7CzAFbUvbzbJbFNux5+ljbH3efY46fknD1DbH4y2PLidb6DPC0MkNi8+b0vQZbgrPMdlVG23H1WJVHbBxpVMJ2FTbSMyMjk9Onz1CzZg3qN3A9QLV69QjCrLk3Y2M9yWE7dsNY2xXu0791P0Hi6+vLlKn/ZcBAU0H4qy+/57lnXy+z/lVmORlZpJ1JIrhGOGH1XOX7IbBaSP7cm8lxZ85F90pE+fow/JMHaNivPQDbv13C6oklLTThnXRGFtlnzuJXIwL/ujVdtvWtFpo/Z2fmUZdTaJ87vj40+/gJwvt1AuDU9AXEvTqlYvskhBt0ZhY58Yn4RlbDr47T6xAB8AkPyz/plHPcaYGJEgu9ZDA1n34AgOy4Yxyf8CS5CWfd24ivD7XffI7gXib5lTR7HvHvFncNYdUSGOBP9bAQEpJTOXEm0WXbs8mp+QnOujXdGxG5avMuTpwx8blh+AUFEiv2WjSsy4gLuzJ7yVq2H4hlV0wcrZvUd9jWlWi7kYN5+62KCsQv3vX/eTYlzRY/N0e0rtq6J3/7Nwzr7SJ+UYy4oDOzl61n+8E4dh06RuvG7hZugUHd2jBxqqlEsmjttiqd4HTrGCxNDLfYxXB4HxcxrMOIfp2ZvXQ92w/EsSvmKK2tROYf67YRc+wUfr6+NG8QxW+rixb62R9nu8Z275ET+W06Nm9IwyjXn7G9lc7MIjs+Eb/IavgV83nUJyIsf77ArMryeRTA34+Iy8wFodknz5CyYkMFd+jckhh6N4mfEMJbVJYEp/1owlSt9T/ubsCax/M66+Y3WuubXDSPdHf7VdAyu+VLgLfKYJv2Z6TrOG1l2H8jy19PKdUGuNC6+arW+lkn65fVp/hTwFhgKdAS+FoplaO1djYXbN5zNRzYkVf+1E1lsQ17xT3W9m3sY2S/rDw57iqpz7AlOO2/tZ/BjKj2ATy5LDbv8SrJ4533/PY0S3NOYqO1PlKSdtXCmpfH7rl3whPcO+GJYtvt3LmXfv16ER3dBF9fX3KclApr2crWz1279pVJH/38/Lh6jJm69cSJUyxauNyt9ZVSTP78bS4bYaZwnfXDXB568Jky6VtFW/zoZBY/Wvwo1Pg9sQT3bkO1pnVQvj7onFyH7ao3t51ojd8bW2b9LBWlGPr+PTQbZgb77/llNUufcjUFb9WRvucwYb3bE9i0Hvj6gJO4BTa3TeWbvrdELynlSymavvcI1Yb1AiD+lxUcfurjCu6UEO7L2h+Db/dO+DWq7/IY9G9mu1Yp64CjmQxKLnhgX2q9/ATK15fsE6c5dtcT5Jxw8ySVUtR65SlCBpmLelLmL+H0y++Vql/eKrphHTbsPMChY6fJzsnBz8lcjgfibInpZg3cK/Rin/Ro26yBy7ZtmzWEJWvz9+lJglOdR9cHR9evzYbdMRw6fsZ1/I7axa++6wsSCttvF/u2xcSjbdP6sGx9/j49SXBGhtumFog7neCiZdUQ3aA2G3aVIIZxpYmh3THYtLgYNgDyYngqP8GZV6I6OyeHF7/4qdh9Llq7jUVrtwHw0p1XVdkEJ0Dm3sP49axGQGPX74UB0bbPo5n7S/deWJbCBvfCN9LMuXp2zlLIddz/qkxi6N0kfqIycH+GMe+ilGqCmaZvBGYgSgawD5gBfFRM5VBP9xkC/IOtemaM1rppWe/nXKkshf03YRsV1c/DbbTENv+e0xF4VgItzMN9VBla6y2Yxx1giFKqXRls1j4J07uYtr2crNfebtnVSMoeLu5zi1XqdAjmxcMPMw/sFU6ab7R+B5aiD2WxDXvNlFJOL6dSStUGmlo38x9rrXUmZgQreH7cVUZxdsv574LW/J15/39/5cbkp5a8dZtZj6lD1lyfefW6PEpMVuHYeGTNn+sAM4dll67OpzW98ELby8pfq9eXyb4vvmQwNWuakwYzZ/ziNLnqzPsfTGTMNaMAmPfrIu68/dEq/+GssKNrzTTV/qFB1O7ovPJ4/T5t85ePrXM1tfW5M+j122g52pykP7BwA4se/ATOk/glrzXzi/qGBhPiYoRHeB/bMZmydke596s4jV6/l8jR5irdxIV/c/Chd86bmImqJX2T+RjgExJMQNtWTtsFde9UZB1PBPXqStSbz6L8/ciJT+T4PU+SfcT9agg1n3uYsEtNgZLUpas5+czr5+0x2LVVU8CUn91+wPmFO+t27s9f7mKtU1J+dvP0ZTs56Wi73/YZxs/Xs9MA+2OP5y/Xtk4YVlVdWzUGrPgdjHPabt3Og/nLXVo2dmsfBeJXzEnXAvHzcH5G+9GoIXZllKuqrq3MrDrmGHQVQ1uhpC6t3I2hLWl6Lo7B803qeuu9MDSYoPYtnbYL6dXRbp3t5d6vkpLSmBJDbyfxE6J8KaVGYaqPPoqpLhmCGZjXA3gT2KiUKo+SGy9R8qkBK71K8alKa30SWGPdHOsqeeCC/WjUUKetwP0JK6qu16zfCpiilCp+oi0XrERh3tnNa5VSDhPJSilfYLx1Mx6wrxFQIXG0RrINwZSB9QNmKKUud9B0Drak2cMe7q4stmFPAeNc3D8eWxnfRYXu+8X63UYpdXEZ9KVcuJmMtE8a7y903xzrdzNgtJvdyHvsFHCri3ZjgLzaRoUfb3d4RWzOhblzF+Yv33TzGIdtlFJcb5WSTYhPZPnyNQ7buatAedpvZru17sTXnuaWW01hgaVLVnHLzQ+4nSCtCg78vi5/ue21jufsRClaX20G76cnpBD7Z8Unyi54/kbajTUn6Q+v+IffJ3zgdPRpVZT4+1/5yzWvvchxI6WIvNo8RtmJySSt3nouuuZUg+duo9YNwwFIWrmZA/e84fQqYyEqu9Qlq/KXw0c7+RigFKEjhwGQczaJ9LWbHLcrRmDndkS99yIqMIDcpGSO3/svsvbFuL2dyMfuJvyqywBIW7OBE4+/fF4fg4N72K7b/HnZOodtcnNzmWuVSwsPCaZnO/eqZjSwG7m1YZez2SyM9TtsH4sb1HZ/xFd2Tg4/2f0f3dtWmXMiDg3uZrvw6ucVGx22yc3NZe4qM/NLeEgQPd18TBrUthWW2rDL9TG33i6Rar+eOxautV0E0bKhe6OFvdHg7rZruH92UpYwNzeXuSvNa6eJYbRb+2gQVfoYjh7Qjc3TXnH58/nTt+W3n3Dl4Py/jx7Qza3+epvkRavzl6tdPcxxI6Xykxg5iUmk/lW0xG9F8KkeTtgAc2oifcc+Mna6fo2uqiSG3k3iJ0T5UUp1xQzuigCSgWeAC4CLMNUJAVoBvyqlHNfA93y/DwPpQFJZbbciVYoEp+UV63cE8INSqrqzhkqpQKXUfYUScnuxJY1ucZQQsbLi95dVh72d1noG8K11syew0BoW7ZQ1hNnV5Z4fWb9rAx84afMCkPdt4zOtdYbdfXvslsc76cM9uJ+cKpbW+hBmTswYzGjgmUqpEYXa7AJmWjevV0o96mqbSqlmSqkbynob+l/jkQAAIABJREFUDjynlGrtYN22mBdIgKPAz4WavI95EQX4UinVHheUUiOUUp1ctSknlyqlZlgvwk4ppWpQ8HlX+P/9EEixlj9VSjkdDqiUaljoTz9hGx36jFKqY6H7UUo1wlbuORUzh6ynvCU25W7D+i2sWvU3ADePu4aevYo+DR548A7atDFXFH7yyRSys7OLtLmwf28Sk/eRmLyPjye9Wex+IyOrMfziQQD8889Otm4tedLtqacf5P4Hbgdgzer13HDd3WRmujtFatVwYtN+4v7aCUCb6wdSp1vRi8+63HUZNVqZ0npb//c7udlFE8H1+7Tl3sNfc+/hrxnyzl3l2ueej1xFlzsvBcwI1N9uf5fczKLPqaosdfMekv8yJ0JrXjeUkG5F3mKIuusKglua8pgn/zcHCsUtrE8Huh76ma6Hfqbx2w+Wa3/rPnI9UXeajwbJ63aw//aJ6PMsZqJqyfxnF+nrzQmisCsuIbBT2yJtIsaNIaC5+eie9O2PRY7BoB6daLppIU03LaTWS4873E9A6+ZE/fcVfEKCyU1N4/gDz5K5Y4/Dtq5Un3Az1ayLkNI3bePEwy9AVpbb26lKOrZoRLc2JuH109K1bN5dNPkx9dcV7I81JS5vvKQf/n4FS2iu3b6PzmOfpPPYJ3lu0owi6/dq34KgQFPEaOaiNew55HjU7cpNO1lsJbeiakTkl8bM8/e2fZxNSXP6v2Rl5/DiZ7Py+zqwW1vq1nT6db1K6Ni8Id2sEYA/Ld/A5r1FS+5Nnf9nfpnZG4f3KRq/HQfofMvzdL7leZ77rOiFcr3aRRMUYMVv8Vr2HD5epA3Ays27WbzefA6NiowoUp528fodnExwfX5q/c6DfPrzUsCMHry0T5X82lBAx+YN6dbaiuGy9Wze4yCGv62yxfDivg5iuJ/ONz9L55uf5blPZxVZv0AM//ibPYePOezLys27WbzOjGpyFEPhWPqW3aSuNUWRqo8ZTlCXNkXa1LjtKgJbmJG38VN/KfJeGNKrI212z6PN7nnUe/2R8u+0JWLkQJT13Ej8afE5229lIzH0bhI/USnk6or7KV/vA8FANjBca/2q1nq11nqx1vouIG9Or1bAY2WxQ2vQ2WeAL/Aqnk+tVqlUljk40VrPU0q9DzwEDAB2KKUmASsxcxaGYubT6w9chRmu+5Xd+qeVUvMw9YovARYopT7BJKuigKsxCbP9QHVMAq7CWcOMLyz057yRj2FKqfGF7puvtXb8qdkzd2JGm42w+rFbKfUDsBDzWCViDrbGmKsIbsA8nmASOIVNAm4E+gK3WgnTjzEjI+sBt2HiB6Yk7MuF1t+IKevZAbhbKRUJTMMk5xoCN2FGyK2iHEp3aq1jlFKDMXOUNgJmKaWu0FrPt2t2D2aUYDTwtlJqNDAVU1I0A6gJdMY8D4cAPwLTC+2qLLaRZy/m+bxGKfUGZj5RgEGYuSjzRhM+YJU+tf9/jyulbgF+wMRnnVJqCvAbcAST6G2IKSk8xupv3vD5c8kHuAa4Rim1GfgVWIt5XmRinpMXAndhe36ux+41AkBrfcxKkE+12v2tlPoM8/8ewxx7HYDLMaUBmtutm6mUugszCjQCWKWU+g/wB5CDOT6estv//2mtPZ5d3Ytic0489fjL/L5oBiEhwfz48xTeeesTli9fQ3BwEFePGcmtt5lrAPbs3s+HH3xRJvu8esxIAgPN9RzujN68a8I4/vX0QwDExh7l+efeoEnTRi7X2bN7v8OkbFWx8oVpXPnj8/gHBzLqmyfZ8OEvxP65Hd+gAFpe3pf2Nw0BIH7fUTZNnleqfbW+pn+B2zXb20qNNR7UifBGtfJvJx48zrG1Bcvhdhw/jJ6Pmrep5KNnWP3qdCIau/7IkLDvqMOkrLc78u/PaDX7DXyCA2nx9Ysc/2gmSX9uxScokMjLL6TWjZcAkL4vlhOTC19PUnI1xgwpcDu4nW0ETMSgbgTYjTLJiDlapBRurfEjqPeIeQ3IPHqKuFenENDI9XTJ6ftji3z5FiWzYfM/HLIrXRqfmJi/fCg2jp9+XVig/RUjnFztLYp15s2PqTvlPXyCg6jzyeskfjGd9LWbUYEBhF4yiHBrjuisg4dJnOps+njn/BrWo87Hr+EbYS4ITvjoS3KTUvBv3tTpOjlnEsiNLzh3X/j1o6k+wRQTyT5+kvj3PsOvgeuT91kxh8+LY/CJcaMY/+9PSM/MYsLrn3PH6CH0bBdNemY281dvYtZicwFXk3q1GDfCSZUDFyJCg7lt1CA+/mEhKWkZjPv3x9wwvB99OrYkIjSY04lJLF2/ndlL/ibXKhX80PWX4lOoxOmcFet56O0pDOrWjh7tomlarzahwUGkpmew40AsPyz+Kz+5WSMijCfGOSp0U/U8cdNljH/lcxO//0zljpED6Nm2GelZWcxf8w+zlpoRrU3q1mTcJe5/NY0IDea2kf35ePZiUtIzGPfKZ9wwtDd9OjQnIiSY02eTWbphJ7OXrbfF75phReK3ZMMOnvh4Bv07t6J3u2iaN4giPCSIzOxsjpyIZ9nGXSz4+5/8bdw1ehBN69Uq0p+q6ImbRjD+5c9MDN+cwh2jBlrHYBbz12xh1pK8GNZi3KWFT8sUzxyDA/h41h8mhi9N5oZhfejToYV1DCazdMMOZi9dZ4vhdcOLxFA4d/yVSTT57i18goNo9L9XOP3pDFLXbMYnKJDwEQOIvN5UDsjYf4Qz/3Ov4o69alcOLXA70G40b+iA7vg3sH22zDwUR1oxZTjzRrTprGzO/rLE435VBRJD7ybxE6LsKaV6YXJcAF9orVc7aPY2poJgW+AhpdREa/q10ngI6A7sAt4Abi/l9iqFSpPgtDyCyRw/B9QF/u2ibQomqWDvHkxCtDEw1Pqxdwi4Aijd2dOydSHOR3nVdHDfYEwipkxorVOtUqz/Ah7HJMPGWj/OpFv9et7B9nKUUiMx5TX7YZJzQwq3w5SyvVRrnVxofa2UuhlYjEliX2v92NuKSXY5n0ijFLTWB+ySnA2AH5VSl2utF1r3n1FK9cNM9tsfk5B3dUbibOE/lMU27MRihpbPwFZ22F4u8ITWuuglp6Yvs60E6xSgBqb8r7MSwLnYRkCeS/HWfkMxid/OxbRfCNygtS6SMdJaT1NK+QCfYJL3D1o/hRW5zF5r/atS6lbgUyAcU7P8pULNcoDntNafFNPHYnlJbM6JLVu2c+stDzL583eoVi2cF14sOhJlz+79XDvmDpKTy+ZhuP4GU542OzubGd+XPHlzuV0pwQYN6rFgUdERF4V1bDeAQ4ecz8/l7U5ti2HBvR8y9P17CIwIoc9T1xVpE7/vKL+Of4uslPRS7euid+52el+3+0YVuL1z5vIiCc7oy2xzuYbVq8FVP75Q7D6n9X2YpCMeX89QaaVtO8CB+/5D0/cewTcilPpPFq2Gnr4vln3jXyLXxcif4jR55yGn99W59+oCt0/P/KNIgrP6pX3zlwPq1aLV7DeK3ee2C+4k88gJN3sqAGbN+Z2ff3NcgX3jlu1s3FLwZIMkOD2XuWsfJ5+cSO2JT+ETHkrkg0W/f2YdPMzxB55Fp7p/DAZ164hvTVuZxBqP31vsOgmTppIwaVqBv4UOtV1Y4lenNvWmvFfsdo5cdhPZcY5Hq1UlbZs24I0HxvLMx9+RnJbBB9/PL9KmSb1afPj4rYQGezYn4l1XXsTZlDS+mb+K1PRMvvhlCV84OJHn5+vLg9ddzMgLHZe0TE3PZN6fm5j3p/NSxy0b1eWNB8bSMMr9ErfeqG2Terxx7zU88+ksE78fir72Nalbkw8fvcnz+F0+kLPJaXyzcI2J39wVfDF3RZF2fr6+PDjmIkb2c/w1KCs7h8Xrd+SP9HQkKMCf+64e4lEy1lu1bVqfN+67jmcmzTQxnLmwSJsmdWvx4f/d7HkMRw8yMVyw2sRwznK+mLO8SDs/X18evGYYI/t18Wg/56uMHfuJe/h16r31OL7hoUQ9Nr5om/1HOHLXC6X6PFrvDeeFtWreVfCUVOLshS6TKwHRDQnuZKqfpKzaQM7pBKdtzwcSQ+8m8RMVrvxHUlaEK+yWHeaFtNa5SqmpmHP91TE5oQWe7tAahJZ3DnuCNZDH081VKpUqwam11sBLSqlpmJP4QzCjkqphRgsexozwWwD8qLVOK7T+YaVUN+BJTAnTJphk3EFMecn3tdbxVSV4ZUVrnQtMVEp9hBmhORSTQKqFSSglAscxj/1yYIbW2um7g5W8G4AZyTkW6IpJzpzFJCd/wJSmdVivUWu9SSnVBZN0vRSoj6kJvReTxPtIa51ennHUWu+zkpxLrf3/rJQaqbVebN1/DBhglbC9ATNitS5mVF0CptTuauAXrXXRbzdltA27bf2qlOqBSVIPwYz4SwBWAG87uRLEfv05SqlmmBG9lwHtMTHLxiTUt2GSzj9orQ+72lZ50FqvsubmHYoZmdodaIm5CMAX89w6iBnV+Z3Wemkx2/tKKbUAuA8zSrY5JmF5FnMVy2LMyGFn6y7DJJWHYy6o8MEk3BcD/9Val9lEdJU9NufS/N8W06/PZUy4dzwXXzyY+g3qkpWZxf79Mfz0429M/nQqaWmlS47liW7eNL8U7pLFqzhxouolr861mEUb+X74v+h0+yU0GdKFsHqR5GTmcDbmOHvn/sU/UxaSnX5+lvGtzM4uWsuOix8i6rZRRAzpgX+9muisbDIOHiXh11WcnPIrWuImRLlJW76G2GvvImLslYT0741vnVrorGyyD8eRsnA5Sd/9jE7PKH5DosIM6t6Oma8/wjfzV7Ji006On0nE39ePxnVrMqx3R64ffgHBgQEeb18pxeM3j2JEv67MXrKWjbsPcvRUPOkZWYQEBdCoTk26t41mzEW9aVrPcUWCW0cNpHWTemzec4j9sSeIP5tMYnIaAf6+1KwWTrtmDRjWuyNDenbA9zwbeTaoaxtmvnIf3yxYzYrNuzl+5iz+fr40rlODYT3bc/3Q3qWP342XMuKCzsxetp6Ne2I4eiqR9EwrflE16N6mKWMG96BpXcejLh++bjjdWzdl/a4Y9sUe53RiCmeSUvBRiojQYJo3qE2vdtGM6teF2tXLbAonrzGoWxtmvno/3/y+mhWbdptj0M+XxnVqMqxXB64fVgYxvOkyRvTrzOyl69m4O4ajpxJsMaxTg+5tmjFmcM/zZuRsWUte8jcHRt1HjVtGEzqwJ/51a6Gzssg8dJSk31YS//WcSvVeGDHado2/lMY0JIbeTeInRJnLKxuRgqlA6Mwyu+V+lCLBiamwGQpMK+68ubdRWlfJLLgQVZ5SaikwEFimtR5Usb0RVVm1sObyRuHFXovsW3wjUan1VVVi3vfzWocN71Z0F0QpxA51PjJceIe6X9xX0V0QpZVVeU6cCg+cZ4nxqujgTc4KjwkhhCipNrvnyagrNyXeOrTCzklWn/KH6/mmLFrrI+5sVyl1EjOwbLPW2mlpB2vqvrx5MmdqrQtXuSzp/q7HTH0XD7TWWp+0/n4QM0AwRmvd1JNtVwaVagSnEEIIIYQQQgghhBBCCCGEEBWopJXySpy4VkoFYZKbAC4To1Yl0rwp20qUbHWwv0ggbw6Rp/KSm1WJXEYnhBBCCCGEEEIIIYQQQgghRPmxny8guQTtU6zfYR7u7z9AHcw0eJ95uI1KTUZwCiGEEEIIIYQQQgghhBBCiMojt0JnzfJo1GQxguyWM0vQPm+ehmB3d6SUGgDcBmQDE3QVnatSEpxeSilVHWjoybpa63/KuDtCuCTPVyGEEEIIIYQQQgghhBDewN25NUso3W45oATtA63fae7sRCkVCEzGlM99X2u9xZ31vYkkOL3XFYCns7zLhMbiXJPnqxBCCCGEEEIIIYQQQoiSya3oDpS5JLvlkpSdDbV+l6Scrb1ngNaYeURfcHNdryIJTiG8lNZ6UEX3QQghhBBCCCGEEEIIIYQQrmmt05VSp4GaFFPtUCkViS3BedjNXT1p/V4EjFLK4fihvG2HKqWut5ZPaK0Xu7mvCiUJTi+ltZ4CTKngbghRIvJ8FUIIIYQQQgghhBBCCFFSumLn4Cwv24H+QAullJ/WOttJuzZ2yzvc3Ede+dtbrR9XagHTreVlgFclOH0qugNCCCGEEEIIIYQQQgghhBBCVHErrd+hQHcX7QbaLa8qv+54N0lwCiGEEEIIIYQQQgghhBBCCFG+frJbdji6UinlA4yzbiYAS9zZgdZaFfcDxFjNY+z+PsjN/6XCSYJTCCGEEEIIIYQQQgghhBBCVB65uuJ+yonW+m9ghXXzdqVUXwfNHgPaWsvva62z7O9USg1SSmnrZ0q5ddYLyBycQgghhBBCCCGEEEIIIYQQQpS/hzBlZ4OBBUqpVzGjNIOB64G7rHa7gbcrpIdeQhKcQgghhBBCCCGEEEIIIYQQovLIregOlA+t9Ual1HXA10AE8KqDZruBEVrrpHPaOS8jJWqFEEIIIYQQQgghhBBCCCGEOAe01nOATsC7mGRmKma+zXXAk0BXrfXeiuuhd5ARnEIIIYQQQgghhBBCCCGEEKLS0OU4F2ZloLWOAR61ftxZbymgSrnvpqVZv7KQEZxCCCGEEEIIIYQQQgghhBBCCK8hCU4hhBBCCCGEEEIIIYQQQgghhNeQErVCCCGEEEIIIYQQQgghhBCi8sit6A6Iyk4SnEIIIVx6r1rfiu6CKIWH41dXdBdEKb0WKcegt4scendFd0GUQoNFn1Z0F0Qp7en9QEV3QQghvFr09FsruguitDLSK7oHorT85DS+EEIUJq+MQgghhBBCCCGEEEIIIYQQotLQubqiuyAqOZmDUwghhBBCCCGEEEIIIYQQQgjhNWQEpxBCCCGEEEIIIYQQQgghhKg8ZA5OUQwZwSmEEEIIIYQQQgghhBBCCCGE8BqS4BRCCCGEEEIIIYQQQgghhBBCeA0pUSuEEEIIIYQQQgghhBBCCCEqDS0lakUxZASnEEIIIYQQQgghhBBCCCGEEMJryAhOIYQQQgghhBBCCCGEEEIIUXnICE5RDBnBKYQQQgghhBBCCCGEEEIIIYTwGpLgFEIIIYQQQgghhBBCCCGEEEJ4DSlRK4QQQgghhBBCCCGEEEIIISoNLSVqRTFkBKcQQgghhBBCCCGEEEIIIYQQwmvICE4hhBBCCCGEEEIIIYQQQghRecgITlEMGcEphBBCCCGEEEIIIYQQQgghhPAakuAUVZJSarxSSls/TR3cv9S6b+k575wQQgghhBBCCCGEEEIIIZzSuRX3I7xDmZeotZJJB0q7Ha21KnVnvID1eI0CBgGdgAaYxPMpYB3wHfCD1jr7HPSlOnA9cBHQBagFhAFngThgE7AY+FFrnVDe/RHCEaVUQ+AOzPO0DVAdyAZOA/uBjcAKYKHWOrGi+incF9qgJu1uv5iGF3UhtH4NcjOyORtzgoNz/mLHlIXkpGeW2b4aDO5E6xuHUKtLNEE1wkk/k8SpTfvZ9c1iYpdsKdE2andvQauxg6nTsxUhdSPx8fMlIyGF+B2HOPjr3+yduYLcrByH614y8xnqXdDWrT7/NmYix1bvcGudyqZRo/rcfc8tXHzJYBo0qEdmRiYHDhzix9nz+GzyNNLS0j3eduPGDdi6fblb68TEHKFT+4FF/j784kF0696Jbt060bRZI2rVqkFERDgpyakcPHiYFSvWMOXL79i7p9Qfd7xOWIOadLrtYpoM6UJY/RrkZJrjdO+cv/jnq4Vkl/Y4VYrIFvWI6tKcOl2aE9U5mpptGuEb6A/AT9dMJG5N8cdBeMNaNB3alfp921KzbWNC60ailCI9PpkTW/az95c17Pv1b3RO1f/W4lsviogbriSkfy9869ZGZ2aRfeQoKQuWkfT9L+j0DI+3rYICCb6gJ0F9uhHYvhV+jerjExxMbkoqWTFHSF+9jqSZc8k5He9yO3716xA8oA9BPToT0Coa39o1UT4+5CQkkrl9Nynzl5KyaDmcB/Eqb6fjE9i6fRf/bN/FPzv38M+O3SQkngVg9KVDmfjsYxXcw6rJv35tatxyOWGDe+JfzxyHmYeOcnbeCs5M+7VUxyFKEdC8IcGdWxPcqRXBnVoS2LoZPtbr5sGxT5H611b3NhkcSPWrhxJx8QUERDfEN7IauUnJZB07TdqGHST98RcpKzd63mcvI/HzfhJD7xd3Kp5vF6xhxaZdHDudSIC/H42iajC8dweuG9qb4MCAUu8j9mQ8M/74m7+27ePIiTOkZWQSEhRIs3q1uKBTS64Z0oua1cLc3u7Kzbu5762p+bcnXDmYe666qNT99TZxpxP4dtFaVmzdy7EzZwnw86VRVCTDe7TjusE9CLaOmdKIPZXAjKXr+Wv7AY6cjCctM4uQwAATww7RXDOwOzUjQp2un5qeyY5DR/nnQJz5ORhH3ClzWql+zWr89sYDpe6jt4o7lcC3C/9ixebdJn7+vuYY7Nme6y7qWXbH4JJ1/LVtvzkGM7MICbLi17EF1wzuQc0I58dg7Ml4lm3azbqdB9lz5Dgn4s+Sm6upHh5C+6b1ubh3B4b1bIefr2+p+yqEqDyU1rpsNygJzhJTSr0MPAMU97+uBcZorQ+VUz98gMeBpzDJouJkAJ8BL2qtT5VHn0pLKTUe+NK62UxrfbDQ/UuBgfD/7J13eFTV1offlV4IgdB77x2kCdIEpYoiKGC5qNi7n+2q2LBcvV7btSDKFUFFQUAEQUA6CIp0qYFA6J2E9Lq/P/aZzCTTMikkwf0+zzw5k7PbnHX2zDnnt9darFJK9bmUYzMUHBG5G3gfCMtH8e+VUqOLeUh/C76sdWvR/lC4oM6ADvT68H6Cyrs2bfyBEyy9/R0SDp0qXEci9Hj7LpqO7eO2yN5vVvDbM/8DD7+PXV+9jZZ3Xeuxqwt7jrD0tndIOn7OaZ+vAmd2VjazujxK8knPIoErHotf73Od4mDgoH5M/uJdIiMjXO6P3hfDTSPHExMTW6D2CyJwLvt1NSOuvyPX//z9/Tkfv89r3fT0dN547X3ee/czn/osCG9W7F7sfeSHev070P+D+wl2M08vHDjBz+Pe4WIh5mmzkVdx9Xv3ut2fH4Gzy5Mj6fTwdYif50Alp7YeYPG9H5LoYo4WNYMrFfK7q4CE9upGldefxS/C9YOcjENHOPXwC2QeOe5z24FNGlBj6vv4hXv+Sc5OSOLsxPdIXrLK5f4KD/yDyPFjvdor7a89nH7yVbJOnvF5rIWl1q/FP88vFa17DHK773IWOKO7ltwDyXL9ulDr3SfxdzMP02KOcnj8y2TEnihQ+5EjrqbWv59wu99XcSWsW1tqvvUYQbWruS2TuiuGmGF/j4e8xn5lH2PDoqHhjHGXtD9HVm7ew/OTZpGY4lqIrle9Mh89eRt1q1UqcB/z127htS9/IjU9w22ZyHKhvPXAzXRv0zjf7SanpnPjPz/k+Fm7j0CJCZxpBV9MWlhWbt3H81PmubdhtSg+emQ0datFFbiP+eu389r0haSmu/cPiQwP5a17bqB7q4Yu99/17+n8udf1/WipEDgDitxPKV+s3LKX5yfP8TAHK/HR42MLNwfXbeO1r+Z7t9/9I+neupHTvo9mL+eLBas9PcYBoFWDmvznoZuoUSk/j7+LnpDuYy57vaOoOTOgd7E/k3RHlaWrjL3KAMXxzXgMaONhv+3K7k/gDg/l/g7UQIubScBcYBkQDaQCLYBHgM7W61cR6aiUSizKAYhIKDADGG79KwOYAyxFe8PFARWBemiPuaFAJPAQsA7tYWowFDsiMgaYbL1NRQvYi4Gj6HlUE7gCfY52KIkxGgpGVKt69Pn0IQJCg8lITGH7R/M58dsuAkKCaHBdN5rd2o/IRjUYMO1Jfho0gcykgt+YdXpmVI64eW7HIXZ8soCE2NNE1KtKmweGUqlNfZrd0pfU8wls/tdMl220eWhYjriZnpDCzsmLOL1xHxnJqUQ2qkHrewZTsUUdKjavw4BpTzLv2uedvMTWPjGZgLBgj2Ot0LQWfSfpG6gTa3cWSNwsLbRt25Ivv/qQsLBQEhISee8/k1i9egOhoSHcOHIo4+4YTZOmDZn5wxf06XU9iYlJPvdx/PgpunVx/+DexhP/dx833ax/8r79Zo7LMnFxF1m75nc2/bmVQ4eOcPLkaVKSU6leoyo9r+rKrbeNokKF8rz86tPEx1/kf1Nm+DzeskblVvW45pOHCAwNJj0xhc0fz+eYNU8bX9eNVrf0o2KjGgyZ+iQ/DJlARkHnqcPtQ1Z6Juf2HME/0J9KLermu4mwqhUQPz8yklKJ+eVPjq7bSfzBk2SlZVCxcS3a3HkN1SwP0etmPMvMQS+QmVwIz41SSlCzRlR563n8QkPITkom/n/fkbpxKxIcTPjAPkTcOITA+nWo9t/XOD72QVRyik/t+4WH5YibqVv+ImX1BtJ27SM77iJ+URUI79eTciMG4RcRTpU3/snppGRS1m10ase/svbWzE5OIXn5OlL/2ELG4WOotHQCG9Sl/NjrCW7dnODWzan+2dscH30/qhDe3gY7NapVpUG92vz2x+aSHsplS0jLhtT+8Bn8QkPISkzm3KRZJG3YjoQEETm0NxVHDyS4YW3qfvEyB69/jOwk3+YhAGL/4lTpGaTui0UC/Alp3sDnpsKvbE+dz1/ELySYrPhELny7kKTfd5B1Lg4JDSa4UR3K9etCQAk9FLzUGPuVfYwNyz67Dx3nmY+/J9Xy5LprWC86t2hIanoGizfsYPbKP4k9eZaH3pnOjFfvJzzU8z2WK7bsi+XFyXPIVgo/EYZd1YG+HVtQpWIEJ8/F89OaLazasof4xBQee/8bZr/5MLWr5k+I+3j2rxw/G0dU+XDOX/T9HudyYPfhkzwzeQ6p6ZmEBQdx1+Ar6dy8vrbhxl3MXr2F2FPneejD75gx4S7CQwpgw+i75YhAAAAgAElEQVQjvPi/+XYbXtmWvu2bUqVCBCfPx/PTb9tZtS2a+KQUHvt4JrNfuZfaVSo6N+SgjkWGh9Kyfg227T9KclrRRZMqa+yOPcEzn87S9gsJ4q4hPencooG23+9/MXvVZmJPnuOh975lxkv3FGwORh/mxS9+tNuvZzv6dmiu7Xcunp/WbWXV1n3afh/OYPZrDzjNwbPxCSgFocGB9OvYgq4tG1C3eiWCAwOIOX6Gb5f+zs6Dx9l58Dj3vD2N71+5l7ACnGuGS48JFWvwRpELnEqpDOAvd/vFfvGXpJRyW+5vwjngGeBTpVRCnn2bRGQG8C1wE9AEeAJ4tYjH8Bl2cXMDMFYp5c4D90sRKY8WN18o4nEYDG4REX/gXettAtBTKeUqjuhPwIsi0gLPCy0MpYiur95GQGgw2RmZLB77Fmc27c/Zd2LdLi4ePEXnCWO0eHjvYLa+61qU8kb5htVpfd9gAM5sjWHRjRPJStUrdM9ui+Hwks0Mmv0CVdo3pM19g4n+bpWTx6gE+NPm/qEAZKVlsOjGiZzfaXeuP7NpPwdmr2Pwjy9StWNjKraoQ92BnYj9OfdD/cQj3j2QGt/YI2d7/w9rC/SZSwv/+vcEwsJCycjI4Ibh49j4hz2k1upV6zmw/xATX3+WJk0b8tAjd/GvNz70uY/MzEx27/Lseenn50fPq7oCcPFiAgvmL3Eqk5WVRYO6ncjOdnEVvQUWLVzG5EnTWLVmHhWjKvDcC48z9cvvXZe/jOj5ym0EhgaTlZHJ/Fve4tRm+zw99tsu4g+e4soXxlCxUQ3a3zOYje8VbJ5eiD7GmglfcXpbDGd3HSYrLYPOj4/wSeBMvZDAb6/PYOf0ZU5C65kdh4ie9xsDPnqQxsO6UaFhDdrfPYg/P/ixQOMtzUQ9/QB+oSGojExO3f8sadvtnq+pG7eScfgYUY/fQ2D9OkTePpK4SdN960ApkhavJO6z6WTEOAcZSV2/ieR1f1D13ZeRAH+innmQY9eNcyqXHX+R8+9/TsLM+U4ia/ruaJJ+WUGVN/9J+LV9CKxXm/K3jSR+8te+jdWQw313jKV1i6a0btGUylEVOXbiFNeOHFfSw7psqTbh3px5eHjcBFK27MnZl7x+O+mHjlHt2bsIblibSnfdwJkPv/W5j7Tow5x4ZRKp2/eRuisGlZ5BlUfG+iyu+EeVp9YHT+MXEkzKzgMcvuNFss7lzkqSsmk3cTOXQGDJeJBcaoz9yj7GhmWft7/+mdT0DAL8/Zj09DjaNbFfE3Zt1Yi61Svx3neLiT15lmmL1hbIM3LK/NVkW8LWs7cP5eb+XXP2tW5Ym/6dW/HOt4uYvmgdqekZTFu0juf+Mcxru7sOHmPGkg0EBQbw8KgBvDLl8rvezA9vz1hManqmtuETY2nXqHbOvq4tGlC3ahTv/bCM2FPnmbZ4A/cPd04h4o0pi9bZbTj2Wm7ue0XOvtYNatK/Uwve+X4p05f+Tmp6JtOW/M5ztwx0amdQ19aM7N2RVvVr5niTDnrmv39rgfPtbxbZ7ffkbbRrXCdnX9eWDalbrRLvzVxK7MlzTPvlN+6/oa/PfUxZsMZuv1sHcfPVXXL2tW5Yi/6dW/LOjMVMX7xe22/xep67bUiuNiLLhfHYTf25qW9nJ5G1Zf2aDOrWhmcnzWbJHzs5fOo80xav577hfXweq8FgKH14jgVlKFaUUs8opd52IW7a9mcBDwC2X9KRRdm/iIwEbrPebgL6eRA3bWO6qJR6A+gI7PdU1mAoQroC1a3tz9yImzkopXYrpVy73xlKFZXbN6R6t+YA7PtuVS5x08Zfny0kbt8xAFredS0SULB8CS3HD8TPehjw+4SvcsRNG1mp6fw+4SsA/AIDaHW38w1PhSY1Ca6gw1sd+XVLLnHThsrKZvuHP+W8r9qpie+DFaHhCC1wZiSmELvQ2euprNCxU1t69NA3KNOnzcolbtr474dfsGdPNAD33z+OgGIKvdO3bw9q1tRfJfN+/IVUN/mWvImVsbFHmTt3IQBVqlSiaTPnEDmXE1XbN6RmVz1P93y3Kpe4aWPr5IWct+Zpmzuvxa+A8/T01hh2TF3KqS0HyEpzHyLMExve/J6tk35260WqshWrnpua036jIV1clivLBLVuRkintgAk/vhLLnHTxsVpP5B+QIfgihh7A/hos7RtuzjzzOsuxU0bKSvXk7xML9AIrFuLoObOId0ufPAFF6fOdO9Bmp3NuTc+RKXry+Hw/lf5NE5Dbh4afxt9enSlcpQLrwVDkRLStinhXVoDcGHWklzCio1zX8wlLVrPoahxw32ehwCp2/dxYdp8UrbuRXkIreiNqk+OIyAqkuzkVI7eN9FJWMlFhvvwcZcLxn5lH2PDss+OA0fZbIULvb53p1zipo3bB/WgYc0qAHyzeD0ZmVk+97PNOgcqlAvLJW46cu/1dtFm+/4jXtvMys7mlSk/kpWdzV3DelGnEKFXyzI7Yo6xOVofr+t7ts8lbtq4/ZpuNKxRGYBvlm0smA33HwWgQrnQXOKmI/cOs19Dbj9w1GWZkb07Mqhr60KFyr2c2BFzlM379Py4/qoOucRNG7cP7E7Dmpb9lv5eQPvpc6RCudBc4qYj9zoI39v3O9vv8ZsGcMfgnm49SP39/Hj+9iEEWt/zv27c5fM4DSWDyi65l6FsUCoFThHpKCKTRGSviCSKSJK1/amINPVSt6GI/J+IzBeRQyKSYr1iReR7EXF+Yp27/jgRUdarvogEicgTIvKniMSLyHkRWSkiQ/LUixCRp0Vki4hcFJE4EVkqIoUKrK+UOgfYxJyifoL6nK0b4A6lVL7jsSil9iil/nS3X0T8RORWEVkoIidFJF1EzojIChF5QEQ8Zp8WkdYi8oKILBaRoyKSZp0L0SLylYh0y+9YC4qI3CYiGda5sE5EKuTZ7y8i/xCRBSJy3BrjORFZa50zofnoo8BtWOehsvKJIiLNRGSyiBwUkVQROSEiM/N7rESkuoi8bp3r562xHLHa6O+hXn2HOTPO+t8Iy/bHRSTTNsZC4HgnUyTCuohUEZEXLduetmx9QUR+F5G3RaSth7r1ReQ9EdkpIgkikmydm5+JiEfPUYdj9bL1vp+IzLKOdYaIHHJRp0C2KQvUHWi/+Yj+3k3+RKVyPBiDK4T7lLsyV1/XdAQgLvoYZzYfcFnmzOYDxO3XuejqXtvJab+fw2rphMPuvTATYu2en34FWGFds2crwmvom6pDP28kK7XsrhgdOnRAzvbX039wWUYpxXffzgWgQsVIevUqnq/40WNvyNme8W3BPAxtJCbYQ0yFBF/eoW0aXGufp7tnup+ne2freRpSIZxaBZynl4q0uETO7dE30uXrVS3h0RQ9YX3tHuAJ8xa7LqQUSQuWAuBfPoKQzu2LZSypf27L2Q6oU7NAbWTHJ5AefdBqo0aRjMtgKG7KD7D/lsX/8KvrQkoRN3c5AP6R5Qjv5vbys1jxK1+OyOv0g8P4eSvIOH7pc92WNoz9yj7GhmWfFZvsAsTwqzq6LOPn58fQnvoaJiE5lY27Y3zuxybI1HIVstQiIiyEihFhucp74utFv7En9gT1qlfmzqG9fB7T5cKKrXtztof3aOeyjJ+fMLS7foySkJzKxr2HfO4nI8uyYWX34ZsjwkKoWC4sV3mDZ1Zsti8MGX6V60xQfn5+DL1S2zYhOZWNezz6zbjE5zlYQPtVKBdG0zo6v/GR02U3BZDBYMhNqRI4LVHsXXR+znuBpkA4EGZt3wfsFJF73NRvABwA3kHn4asHhFivuuhQr4tEZLqI5OeJc3lgNfAfoJP1viLQG1ggIo9b/dYF1gNvAe2BCHSeyv7AUhG5xbcj4YTtyWmR/QJb4o3t12mFUir/We+9tx2FPm7TgUFANSAQqAz0AT4GtopIPTf1+6BztU4ErgFqAUHoc6ExcDuwXkTeLKoxuxjDo8BX6DDOi4EBSqk4h/110V6vU4Eh6HyqQUAU0AN9zmwXD4J8UbTh0NYgq627gfroc6Y6MApYJyKPeal/C1o4fA59rle0xlLbamOpiHyRj3kjIjINmI22fQ2gYG48uXFUdwr91Nz6vAeBV4ArgSpoW1cAugBPocPduqp7O7AHeAxoCZQDQtHn5j3AFhH5Zz7H8To69+5I9LF2Or5FaJtSSbXO+vTOSErl3Hb3F8InN9i9j6p29jolnChXt0qOYHhyg/PqbUdOWfvDa0RRrk6VXPsuHjyJsrz7IupWcaprI6JeNXudAyd8Hm+jUT1ztvf/sMbn+qWJbldqcSwxMYmtW9xHpl+79o+c7a7dncXlwlKuXDhDLLE19tAR1jn05yshIcEMHqLXFmRlZbF/v+83cWWJGg7z9MwO95/1uMM8rX6F7/P0UuMfpL82VZbyUrLsEdK+FQDZySmk73Yfujl1kz0ggq1OUSOBgfY3WQVfBpvTTiHaMBguJaFXWPMwKYWUv6Ldlkv+w34bFtapZbGPyxUR/TrjFxoCQMKvv+f8X0KCCaxXA//Kfz+PX2O/so+xYdlnyz7tvRkaHETLBu4XSV3hEA546z73kSXcUd/yHjx2xr3gkZiSyoWE5Fzl3XHszAU+mbMMgOfHDSPobxRSOC9bLO/N0OBAWtZzv0jtimb2x4NbXXjneaN+tUoAHDvr3vM5MSWNC4mWDatX8rmPvyNbrPkUGhxIy/oe7Ne8fs721mjvHs55qV/dxzlYCPulWx7w/n7ipaSh1KCk5F6GMkGpEjiB/wKPA4IWyO5EC2Jd0MLNTrQA8JmIXOeivj9aCJkPPIIWGDtafx+w6gPcCkzIx3gmowWFT4ABwBXAeOC4tf8dEWkNzAEaAv+yxtsZLX7EW5/lUxEpkHuAVc8m6DjHFys4jkHtfymqRkXnSlyAFugAVqFFmCuA6wBb0oEWwDIRKeeimQAgCZiJFrX7oO04EPg/INYq96yI3FFUY3f4DK8A76NtNxO4TimV7LC/ErAWaAekAR+hP2NnoC/wJpCMFrwWiUikiz4K3YYDNdG5WjPRItiV1ut54CJ6nr8nIte7+bw3ocXocCAGnet1IPrcvxFYaBW9C3jbwzhAn/e3AWuAsWi797faLwyOMS3vFZF+BW1IRG4DvkZ/3lT0985g9DnWC51jdgng9ARVtOf2VLSAnIgWSK8CuqPPzbPo76E3ROR+L0MZgbbXDvR3XRf0vLTlGi1q25RKKjTRN6oXD51CeXhoHb//eM52hSa1fO+nqb2OY1uuiMvVV+4b6YyEFGJ+XA9Anf4dqNjCOUSL+PvR9mGdkyUtPomYeet9GmtAWDD1LM/WhCNnOPlbUX71X3qaWeFbY2JiyfKw0jJ6n92rtlkxhHwdfv1AwsP1is/vvvM9/01AQAC1a9fgxpFDWbpsFo2b6AcpX0/7gcTEJC+1yzYVG+t5EO9lnsYdsM+dio19n6eXktBK5algfa4L+4+V8GiKnsAGOvBB5pHjHgXBjIP2BxC2OkWNLVSu7s/3h44AfhUr5IyvoG0YDJea4Eb6GiE99oTHeZjmECYvqJHzdcWlILR985zt1L2HCGnThLpTJ9J8+yyaLP+CZr9/TdM/vqH6S/fhX8m9d8zlhLFf2cfYsOxz0PJkrVstigB/9+umG9S0Lzw9WADv11H9OgMQl5jMzGWuF0FO/nGlU3l3vD71J1LTMxh8ZTu6trq8U1l44+CJcwDUrRpFgL/7R9ANHASrgyfO+tzPqD5WtKbEFGau3OSyzOQF9oXDo3q79gg25MZmC20/D3PQQfQv0By0wgrHJaYwc7nr9DyT5612Ku8r5y4m5nwmx+8Ng8FQtik1y4hEZABahAQYr5SakqfIRhH5GvgZ6Ad8KCILlVKOyQdOAPWVUq7cZZaJyCTgf8A44P9E5F2lVLyHYXUBRiilHJ+EbhKRjWjBxQ9Yjvbs7K2U+t2h3J8iEm2NNwK4BXjPQ1/ueAq7nYoyp6Bj7JXNRdjufWixB2AaME4pZXON2ATMt7zWnkOH3J0APJOnja1AbUePSQcWi8hHaBF1APCSiEyz8pUWChER4EO0wAXwOXCfUk5Rtz8E6qCF1r4u8pauFJFZaJGvIfA0Wmws6jZsNEGL6d2VUo5KyHoRmQf8hj5HPxKRn5VSOYlBRKQyWsgX9Ny4N8+c2gzMcbDZoyLymVJqL65pi7PdC41S6qCILEB7Zoeg5/NGYBGwAdiolPJ6FSwiNYBJ1tvTwNVKqbwuZWuAj0WkTp66gdiPVSJwlVJqq0ORDSIyG+3NXQO9AGKWh3G1QXtvDlFKOSYCXG31V9S2KXX4BwcSUqk8AMknznssmx6fTEZSKoHhIYTX9D0fhs17EyDJS19Jx8/Z69V0Xhn4xyvfENm4JpXbNmDwnAn89dlCTm+KJjMplchGNWl590AqtapHRnIqax79jLQLiT6Ntf6QLgSG6xXcMXPW+VS3tBEcHETlyvoYHj920mPZuLiLJCYmUa5cOLVqFX0IytFjR+Rs28LheqNu3Vrs2OUmJCvw69LVPP/cG4UeW2nGPziQUGueJnqZO2kO87RcAebppaT9fUPwt1bT71/wu5fSZQsJCsQ/Sj/8zDzl+QFDdkIi2ckp+IWF4l+t6G/yA5s2JPQqncsqfV9MgcXJyHGjEMteSUvcz0mDobQgQYEEVNLrEzNOer5Ezb6YSHZSCn7hoQR68QoqLoId8tqFd29LzTceyZlzNgIqVSDq9mFEDOzB4TteJK0AIejKCsZ+ZR9jw7JPWnpGjrdW1ShP672hfHgoocFBpKSlc/K8p0d8rrm+dye27Itl/tqtvPnVfHYfOkbvDi2oUiGCE+fiWLBuKys26Uctd1/Xm26tnXOK21j42zbWbY8mIiyEJ8cO8nkslxNpGZk5HpNVK0Z4LKttGEhKWgYnz1/0ua/re7ZnS/QR5q/fwZvf/MLu2BP0bteUKhXKceLcRRZs2MGKLfpRyd1DetCtZUPfP9DfjNxzsLzHsoW2X68ObIk+zPx123hz+kJ2HzpB7w7NLPvFs+C37Tnhcu8edhXdCrhw4KuFv5FpLXi5pnPxRK8xGAyXnlIjcALPWn9nuxA3AVBKpYrIQ8AudPjZvsBSh/1JaM8/lyillIj8H9rDLBztWTbbw5hm5hE3be1sF5G1aG+vKsBbecRNW7mFIhJrjfUqfBQ4RaQr2iMO4CjwqS/1veB45e726ZeIRKDH74p0pVTeuGcPOrT5kBuR6yW091pz4G4RedFR4PEmVCml0kXkKbQQWg8dFtj1Eq18YoX3nIoWogHeVkrlFV4RkfrAzdbbh1wIk7YxbhGRj9HC5DgcxMmiaMMFE/OIm7Y2dloC2FvoUL/DAcckePejwykfAx7II6A58hLwD6uN2z2MJQ73di8sd6A9Fm3LJTs7bCMi+9Cel18qpdyJ9g+jQ14D3ONC3MxBKZU3rsYNaG9ZgNfyiJu2OrHWufm11c8dwL/ddJGNXsyR5mZ/Udum1BFgiXigQ196IzM5jcDwEALDQryWzUtguD2dbWaSu0Nu78c+RufciqlnL7Lwhok0u6UvbR4aRsenRubar7Kz2fvNCnZ+tpD4goSnHekYnnatz/VLE+Ui7E76SUnJHkpqkpNTKFcunPBy4UU6jtq1a9CzZxcANqzfRExMrJcanjl79hxPPvEy8378hezsyztcZqDjPE32Pk8zbPM03Pd5eqmo2r4Rbe+6FoDE4+fYOW1ZCY+oaJHwsJxtlew9vbpKSYWwUPzCvKYO943AQCq/9AQSoFd7X/joywI1E9S6OeWtBQqZJ0+TMGt+kQ3RYCgu/MrZ51N2PuZhdkoqfuGh+IUX8TzMJ/6R9t/rGhMfBKU4/Z9pxM1dRubZOILq1aDy3TdSYeQAAqtGUWfSC8QMfYjsRO+frSxi7Ff2MTYs+ySl2rPUhAUHeS2vxZV0klPTvZbNi7+fH6/dO5LeHZrzxU+rmLNyE3PyeAF2btGA8V7EzfjEZP79jQ6y9MhN11Ap0lXAsr8PSan2++p82TAoiJS0DJLTCmjDu4bTu11Tvli4jjlrtjJnTe5HNp2b12P8YCNu5hff52Ah7Xf3DfRu35QvFqxhzurNzFmd+7Fe5xb1GT+04OLm9gNH+WbpBgCqRZXnpn4F8wI1XHqc3I4MhjyUihC1IlIeHYYUcosvTlgijk0A6+6prIgEikhtEWkhIq2tcLI1AZt7jusM13a+87BvWz7L2ZIb+fQLKiLV0MciAFDAPxzDpBYBjsunPMXWuxodQtPVa0meMdfEHk53plIqwVWDllBje8pVER0a1C0iEiwidUWkpYMdHQNhe7OjR0QkFJiLXdx8xpW4aTEEHYI0Ge096Ambi0FNK99mUbbhiELnC3XHl1YZ0KK+I7ZQzws8CG02m9nibHqad/Pd2b2wWMJ3D3SeS1cCZlO09+0mK8+uK4VkqPU3Bjc5Nj1gO3YK7VHpjlloj1rHOq5Yp5Q65GF/UdvGCev70evLlzZ9ISDEnpctO8OdfmsnK107H/uHeL+4zou/D31lp9v3B7jpq0bPljQc0YOwqs4hosTPj7rXdqTxTb3wC/QtBW1YjSiqd9dfo6c3RXMxxrPXY2knJNguEKenZ3goqUmzboZCQ4pWHLt59PX4+elLnu9m5M97E+D48VN06zKIbl0G0bP7UEbdeBefTZpGWFgY770/kcced5kS/LIiINhh7qTnf566mzslTWjl8gz87BH8AwNQ2dkse/wzMgvwIKw0I0H2Y6/y8d2qLJtJPh5c+EKlfz5EcKtmACT+tISU1Rt8bsMvqgJV35mAWPY6O+HfqFTPi1QMhtKA43zyZR76ldB3p5/D4jG/kGCO//MDzn7yPZknzkJGJun7j3D8mfe5MEPfugTVqU7FsUNKZKyXAmO/so+xYdknPcN+7xAY4P2eypbnMi0f9xyuiDl2mvlrt7L/6CmX+7fvP8LcVZs45cE77T8zfuH8xSTaNKrNyAKG0LycSHeYe/mzoS6Tlo97DlfEHD/L/PXb2X/stMv92w8cY+7arZy64LuH4d8Rn+0XYLNfAefg8TPMX7eN/Ufd2G//Ueau3lIg+52LT+TJj2eSmZWNCEwcfz2hRXzvYzAYSo7S4sHZAbvYOkNEZuSzXvW8/7DCSN6D9tLsAHj6xvIWfySvd6IjjuFT81POczwGByyvyZ8Bm7DwrFJqeX7r5xNHEaqoXGVaO2x7i/fmuL81doEGAEucegQYDbRCC4LuKEwcmQh0DtJeaI+6+5RSn3sob7tKDQMydVTbfFEdsMVlK4o2HDnoyetVKXVGRA4BDdBhUYGcfKntrbf3isi9PozDHds97Cs0Vnjdz4HPLUH9KvTx7Ap0A2xP4m9Fi8LX2MIXW98NtnN0bQG8TG11Dyql3Ho9Wx7GW9CLNlq7K4eHY1VMtnGF79nf80lY9YoERbr+akmPTyL55AUyU+0Xvn6B3n+O/IO0ebMKIEZk+dCXX5B9vyvho+Vd19Ll5VsQPz9Ort/N1g9+5OyWGLLS0omoV40mo3vT6u6BtH1oGNW6NGXJLW/n8gr1RKMRPfCzcpPsn7XGS+mSpUaNalSo6DpcVNyFeE6cOEVqmv1zBwUFuizrSLB1k5GS6t1T0BduHqNTEKempjFn9oJ818vMzGT3LvtP/I4du1myeCVfffkdCxZ+w0uvPEXDRvV56IFnPbRSegmvXpFgN/M0LT6JpJMXyExzmDtB+Z+npVE0DAwPYchXT1LOCj29/s3vOfbbrhIeVdGj0u3HPm94O1eIZTNVgNXW7oi8czQRIwYDkPbXHs698V+f25CwUKr993UCqus09hc+nELqRqfgCQZDqcRxPvkyD7NL6Lsz22G8qbtjiP9xhctyp9/5isgRV+MXHET5IVdxbrLHtcllFmO/so+xYdknKNB+75CR6T0jkU2MCc7HPUdeNu89xCPvfk1Ccio1K1fgwZH96d66MeXDQzl/MZGVm/fw8exf+WXDDjbtPcSkp8fRuHa1XG1s3B3DvNWb8ffz44U7hucsrvw7E+Qw9/JnQ10mOB/3HHnZvO8wj/z3exJS0qhZKZIHr+9D95YNLBsmsXLbPj7+cRW//LGLTfuOMOnxsTSuZXIwesJn+2Xa7FeQORjLI+9/a7ffjf3o3qqRfQ5u2cvHc1bwy+9/sWlvLJOeuo3Gtarmq+2klDQeeu+bnMUJj47qT1fjxVumUNn5fm5u+JtSWgTO/H0rORPm+EZEotBehZ3yWd9b/BFPHpM5DtJePCtt5fLlxiMiIcA87J/hHaXU2/mp6yPnHLbd/qpbIXpzfZNYYpmrsLWOCbdcL7mx4+iWlCtRlxXCdTlakMsPhYkj4+g9OsmLuAlFc64WyfnugLdjDXAKfTwdj3UUBfsOcDcOgAsFaK9AKKWOA99bL9v8fxKd09UPnat3DDpcLOjPazuXfY8baj92+TnetvPbUxI6T8eqOGxzSen4zCia3NTL5b7omatZ+/hkMh3C0uYnnGVAmPYGzE+YzLxkJNnDN7kKO+uqH3AOZ1uxRR06v6TFzWOrd7D0lrdR2XatPH7/cf58bQbx0cfo+e49VOvSjA5P3sjGV7/N1zgb3dhD95uazsGffPd2upRMeOn/uOXWG13u++br2Txw39MkJtjzj4aHez89w6wQmUmJngIL+EbHTm1p1kyHklq08Ffi4wvvZL5z515em/gu774/kdtuH8WcHxawfHnZCyfc9elRNB/lep7umbWa5U9MzhU+Oj/hoQNt8zQfYacvJf7BgQya8jhV2+qb2S2TfmbrpJ9LeFTFg3IIBy35CDsrodqu+Qnhlx/K3TiEio/cBUB6zGFOPfQ8ysdFCxIUSNX3XyG4VVMA4r+aycWpRZmK3mAoXhzDRuYn/LOfbR4mlUy4ScfxJq7d4rZcVlwCqTuiCbuiFSEtGkBgAOTDO66sYexX9jE2LPuEO3jT5oEJpGQAACAASURBVCfkZYq1KC/MRy/c9IxMnvl4JgnJqVSOLMf0l+6lcgW7f0K1qEhu7t+VTs3rM/bFTzlzIYEJk2cz49UHcrUx8X86QNSYa7rRvF4Nn8ZwuRIeYr+vzpcNrUV6+QmH6kh6RibPfD6XhJQ0bcPn7qCyQ3jgalHlubnvFXRqWo+xr03hTFwCE/73EzMm3OVTP383fJ+DhbDfpB/s9psw3nkOXt1Fz8GXJ2v7fT6XGS979wNIS8/g0Q9nsOuQfgT4j4FXcsfgnl5qGQyGskZpETgdxb97gd/yWS+vQPABdmHwR3QYye1oQSLV5rElIoeBOuQR7koaKw/kTHRuUYAvlFJPFVN3jiF2OwBFnYCqMDkYp6PFOIUOr/odsBud1zPdyqXqB9iWEBXGjjvR86AZ2lNutVLqew/lbefqWex2yg+OeTaLog1HCnqsHefdF+j5kx88Xdl4X9ZVTCilzgPPiXaJtblTjcIucBZZV0XUjqdjVRy2cUWdfJYrFk/PrLQMUs8nEBIVQVgNT1owBEWG5YigScfP+9xX0gl7nXAvfYVb3l26r3O59jW5uVeOh+XW/8zJJW46Ev39ato8NIzIhjVofNNV+RI4K7VtQMVm2nH/6LKtpMcXZVTykiEtLZ1z585TqVIUNWt5djCuUKE85azcm8eOFWQNgmvGjL0hZ3vGt/kPT+uNnxf8yrvvTwRg+A2DyqTAmR+y0jJIOZ9AaFQE5bzMnWCHeZpYgHlaXIi/H9d8+jC1e7QCYNe3K1j/en6DhZQ9VHoGWRfi8a8YSUA1zyvT/SLK5Tz4zTrlNjhBvgkf2JdKzz0MQObxk5y67xmy43wMI+XvR5W3JxDapQMACXMWcuE9b+vPDIbShUrPIPN8PAFRkQRW9xxsxq98uZy8fxkn3AZlKVYc+8084fm7wFZW/P3xj4wg6+wlW994yTD2K/sYG5Z9goMCqVAujLjEZE6fj/dY9mJSSo64Uj3KdYQZd6zbHs1pK+TlmGu65RJWHGlcuxpDerRjzspN7Dp4nL2xJ2hmCZnL/txJ7MmzBPj706hWVRatdw7WFHPcvk56/9HTOWXaNKpN7aqer7HLKsGBAVQoF0pcYgqnL3heZKptqEXq6lHlfepn3V8Hctof0++KXOKmI41rVWFIt9bMWbOVXbEn2HvkFM3qVHNZ1mCbg5b9PIRmhkLab8d+u/36d3E/B2tVZUj3tsxZvZldh06w9/BJmtV1/4whMyuLpz6ZxcbdhwAY0asjT4y+xqexGUoHJgenwRulReB0fIKcrJT6y9cGrDyeN1tvv1FK3eqheEVf2y9uLMFuOjDM+tf3aLG3uFjlsD0QeKcI2nR8muntKsHxVyinnog0B2zLad5QSr3gpn5RXQGeBcYCK4EmwNcikqWUchfrxXauRgC7beFPfaQo2nAkP1dktjKONnLcloLMu1LK59gFzsYO/z+P9qj2AwqypNJ2vPJzvG3nd0Gf8F8S2yiljuan3Je1PH2dumbt45NZ+/hkr+Xi9h2jerfmlK9fDfH3Q2W5vnKJbFzTXif6mM/jidtnr+PYlisq5OrreJ5x1MrZPrfjkMd2zu04RGTDGoRUjCCkcnlSz3q+KWg80r6SsLSHpwV44L6neeC+p72W27NnPz16dKFhw3r4+/uTleX6K69J00Y523v3HiiSMQYEBHDjSJ169/Tps/y6dLWXGvnn7Fn7NK1Tx/M5VVpZ/sRklj/hfZ5eiD5GaNfmRHqZpxUa2Y/Dhf2+z9NiQYT+H9xPgwE6YEP0T+tZ+aynNMqXBxkxsfh3aktAnZrg7wdubBbYwL7OJeOgqyj4+Se0d3cqT3wa8fcn8/Q5Tt7zNFmnfXxQLELl154lrI9OKZ30ywrOTXy/UOMyGEqKtP1HCOgSSVC9Gh7nYXAje7rz9APFlj3AI2nRsejMD4Cfl8BDjmEX3fymXw4Y+5V9jA3LPg1rVWHz3lgOnzpPZlYWAf6uj83B43ZRuEFN38KOOgqPLep7vqZvUb8WsEn3eeJsjsBpC62amZXFK1N+9Nrnrxt38uvGnQC8eveIy1bgBGhYozKbo49w+PR5MrOyCfB3Hbr34En7Y+EGNXzLQhXjsECghRfv2Rb1asAanfLg4ImzRuD0QsOaVdi877BlPw9z0MEGvs9B+/z1ar/6NWC1vU93Amd2djbPT57Lqq063cy1XVoxYdxQn8ZlMBjKDqUlKPxW7F5RPQrYRhPs+ffceuBZAprr5Twly2fofJMA84FblSq+NQpKqe3o4w7QT0RaFkGzjiJMVy9lu7ip18ph25MnZZFlbLdCnfYDDqBF/xkicr2b4rZ4McGFGENRtOFIAxGp5G6niFQB6ltvc461Uiod7cEKBZ93pRFHRSrHvc7K32n7/FeJD8lPLWx1G1jH1CVWrs8Oeer4xGVsGydObdQXnIHhIVRq6z4qdfVuLXK2T2/0lPbYNYmHz+R4cVbv1txj2WpdmwHa6zPxSO4V1MrhIYK4uTmz4Rdgv/h3JwjltBXgT4Ph3QBIORvP0eXbPJYvS2z47U8AypULp30H92lpe/a0/yz8vn5TkfR97cC+VKqkHxjMmvmTW3G1INSsab8ZTkoq+962njjhME+rtHE/T2s6zNOTf/o+T4uDPv+6kybDtVh2cOlmfn3kU/A5BXPZI3Wr/gnxCwslqEVTt+VCOrV1qlMQQrp0oOrbLyCBAWRdiOfU/c+QedR3T+xKEx6j3CAd3CJ55XrOPP+vv4W9DJcnKX9a8zA8lNDWTdyWC+vSJmc7eVPJ5AVO3mif/0EevCEALRYB2alpZMUVPux7acXYr+xjbFj26dBUZ0ZKSUtn18Hjbsv9ucce7Kp907o+9RHgIChnerlny3S4l3An1Bly06GJXkyXkpbBrlj314Z/7o3N2W7fuLbbcq5wtIV3G9r3Gxt6p4M1n1LSMnLCvLrizz2HcrbbN8lvoDBNLvtl+2A/D3luJ05dwC+/68dxvds35Y17R5i8uAbDZUypmN1KqTOALdnYWE/igQccvVHDPZS7rwBtFysi8i4w3nq7DBillLoUiRTetA0BmGrl/ywwllC423p7k4i4FJJFxB8YZ729AGx22F0idrQ82fqhw8AGADNF5DoXRedjF80eK2B3RdGGIwLc7mH/OOxhfH/Ns+8n629zEbm2CMZSLPgoRjqKxjF59s23/jYAhvs4DNuxE+AOD+VGAra4OHmPty+UCdsUlsO//Jmz3eRm17kAEcnxbkyLS+LEb7tdl/PW1xL9VVOhSS2qdGzkskyVjo2o0ER7aR5e7CyyJRy2C542IdTlkAP8qdpJP0hJi08i7UKi27IAtfu2I7SyPm1iflzvVRAtSyxYsDRn+9bbRrosIyKMtkLJxl2IZ/Xqosk/mis87TdziqRNG9ffMDhne+fOvUXadmnj4GL7PG3hJrcuIjS7Uc/T1LgkjhVwnhYlV754Cy3HarHsyJq/WHzfh5fV3PJE8op1OdsRw938hIgQPnQAAFkXE0jduNV1OS8Et2tJ1fdfQYKDyE5I5NQD/yTjQKz3inmo+H/3EjFCz6uUDZs5/dREt942BkNZ4OJS+29Z5Mj+rguJUOGGfgBkxSeStME5rOGlIPmPv8g8FwdAuX5dcnuIORBYu5rO+wckb9p9WS9AMPYr+xgbln36drKvw5+3ZrPLMtnZ2SxYq69hIsJC6NyioU991KpqD/C2ea/n65dNDiJOrSr2esN7dWTb9Nc8vr547s6c8vfd0Dfn/8N7dfRpvGWNvu3t98zz1rlexJudrViwfgdg2bBZfZ/6qFW5Qs725mjPEUk27bPb2LGewTV9O9oXh89b4zo/cHZ2Ngt+07aNCAuhc3P3C2JdUauywxzc58V+ew/Z61Vxbb9/z/iFOav190XXlg1458Gb3HqeGsoGSkmJvQxlg1IhcFq8Zv0tD/wgIm5/aUQkWEQezCPI7ccuGv3DlSAiIsOAh4pqwEWBiLwMPG69/Q0YrpRKuxR9K6VmArbEcJ2BpSJSz1MdEQlDex6642PrbxXgQzdlXgJsV6qf5/m80Q7b49yM4X58F6e8opQ6jM6JGYv2Bp4lIkPylNkLzLLejhaRJzy1KSINRGRMUbfhggki4qS2iEgL4Hnr7QlgXp4iHwA25eVLEWmFB0RkiIi09VSmmBgkIjNFpIOnQiISRe7zLu/n/QhIsrY/ExG37mQiknfZ4I/YvUOfF5E2efYjInWwh3tORueQLShlxTaF4uzWGE5u2ANA09G9qdKpsVOZ1vcOpkJTLTrumrIYlenshVe9ewvuOPY1dxz7mp7v3eOyr11f/EK2VbfrxH/gHxKYa79/SCBdJ/4DgOyMTHZ98YtTG0eW2m+sr3juZgLLhbrsq8OTNxJWXV+o58cbs/Eoe3jaA7Mur1yOmzdtZ926PwC47fZRdO7iPI0ffmQ8zZtrQfjTT6eSmem8xqfnVV2JTzxAfOIBPpn0ttd+K1aM5Jpr+wDw11972LEjf4LbkKEDqOYlb+GVPTrz9LP6ciIjI4MfZs33WL6sc3prDMd/1/O0+ejeVOvoPE/b3zOYKGue7vjf4py55kjNbi144MjXPHDka/q963qeFhWdHx9B+7sHAdoDddFd75GdfinWjpUO0v/aS+om/ZC23PUDCW7bwqlM+dtHEtRIX/YlfDsX8tgs5Iq21N+6lPpbl1L5Vdcp4YOaNaLqf1/DLyyU7OQUTj38Aum7o12W9USF+24j0loAkbp1J6cfewkyMnxux2AoTaRu30fSH9p7oOKoawjt4BxBotL4Gwhuor0jzk+d5zQPw7q2oeWBn2l54Gdqvv24U/0iIzubc1/ohUBBtatR5aHRzmX8/ajx6gOI9ZDwwrcLi288pQBjv7KPsWHZp02j2nRspq9Vfly1iW0uxKtpi9blhLi85druBAbkFjI27o6h3W0v0O62F5jw2Wyn+l1aNiQkSN8Xzlr2B9FHTrocy9pt+1j+p/bwrVqxvMfcfwY7bRrWoqPl0ffj2q1sO+CcJWfakg05YWZvubqzsw33HKLd+NdoN/41JvzvJ6f6XVo0sNtw5Saij552KgOwdsd+lm/WC1OrVoygWR1jQ2+0aVibjpYX549rtrBtv3MY72m/rCfmuGW/AV1dzMGDtBv3Mu3GvcyEz+c61e/S0sF+yzcSfeSUy7Gs3R7N8k36nrRqxQiXc/DTuSv4erFe3NK+cR3ef3QMQYGlJTufwWAoLkrNLFdKLRSRD4BHgV7AbhGZBKxF5ywMR+fTuwoYgc6j+ZVD/XMishAYgs4puUREPkWLVVWBG9GCWQxQAS3AlSgi8jBa7AM4BjyNDn/pqdpeK9RmUXE32ttsCDr35T4R+QFYij5W8UAoUBe4EhiDPp6gBZy8TAJuAboDd1iC6Sdoz8gawJ1o+4EOCTsxT/0t6LCerYF7RaQiOjfpCaA2cCvaQ24dxRC6UykVKyJ90TlK6wCzReR6pZSj0nE/2kuwIfAfERkOTEOHFE0DKgHt0OdhP2AuMCNPV0XRho396PN5g4i8hc4nCtAHnYvS5k34sBX61PHznhKRfwA/oO3zp4hMBRYBR9FCb210SOGR1niHAZd6aasfMAoYJSLbgJ+BjejzIh19TvYE7sF+fm7C4TsCQCl10hLIp1nl/hCRz9Gf9yQ6fHVr4DqgGdDIoW66iNyD9gItD6wTkX+jva6z0PPjWYf+n1RK+Zh8LNdYy4ptCs3vL05nyLwXCQgN5tpvn2Hbf3/i5G+78A8JouHw7jS7Va+qjj9wgr8+K/iN/MWYk/z16c+0ffg6qrRvyJAfX2LHJ/O5eOg05etXpc0Dw6jUpj4AOyYt5OJB5wvr46v/4vjandTs2YqolvUYvuR1dk1ZzJmtB8hKzaB8g2o0ubk3tfu1AyAjKZWt7zlfxDsSFBlG7avbA3Bh9xHO/XWowJ+xtPLsUxNZ/OtMwsJCmTtvKu++8ymrV28gNDSEG0cO5Y479RqO6H0xfPThlCLp88aRQwkO1utxfPHeHDJ0AF9+9QFLFq9k5crf2LM7mvi4iwQFB9GgQV0GDb6aG0YMxt96uPTWvz5if/RBL62Wfda+NJ0b5r5IYGgww755hs0f/cQxa542ua47rax5euHACbZOLtwDt2ajrsr1vlIre6ixun3aElHHnpcn/tApTuYJW91m3AA6P6EvNRJPnGf9GzMoX9fzZV/cgRMuRdmyzPm3P6H61PfxCw2h2qf/In7KDFI3bkOCgwgf2IcIKz9txqEjxE9zl3rcPQG1a1DtkzfxLx8BQNzHX5KdkERgo/pu62SdjyP7Qlyu/0WMHk6F+3QgisxTZ7jw/ucE1PL8wCkj9ojTQ2hD/ti87S8OO4QPvhAfn7N9+Nhxfvx5aa7y1w8ZcMnGdjlyauJn1J/5b/xCQ6g7dSJnP51J8obtSEgwkUN7UXGMXoiRFnOUc1M8Xy94IvLG3N5pwS3tHkzlenUisLY9rHr6oeOkuAjDeX7qT5Qf0ovQ1o2p8ugtBDWsTdycZWSdiyOwbg0q3Xk9YR31YomEFRtJ+GWdUxuXG8Z+ZR9jw7LP07cOYdzEz0lNz+C+t6cyflhvOrdsSGp6Br9s2M7sFTrSSL3qlbl9UE8vrTlTPjyUO4f14pPZy0hKTeP2VyczZkA3urVuTPnwUM7FJ7Jy827mrPyTbMtj9tGbrzHhLn3g6THXMu5fU0lNz+S+d79l/JAedG5Wj9SMTH75YyezV2vPwHrVorj92m4+t18+LIQ7B13JJ/NWkZSazu1vTmXM1VfQrWVDyoeFcO5iEiu37mPOmi12G47oh5+f87PXw6fOsyWPiJeclp7zN68Xao/WjagcWRqzoBUdT98yiHGvT9H2e2c644deRefm9bX9fv+L2St15Kt61Stx+8ArfW6/fHgodw7pySdzV2j7vT6FMf270K1VI7v9tuxhzqrNdvuN6u80B79d+juT5q0CtAD62M0DOHbmgse+61ev7CTIGkofxZfAz3C5UGoETovHgfPABKA68LKHskloUcGR+9GCaF2gv/Vy5DBwPVBalrrd6LBdCz12bzQADhXVAJRSyVYo1n8CT6HFsLHWyx2paM+0F120lyUiQ9HhNXugxbl+LtrYDQxSSiXmqa9E5DZgOVrEvsl6ObIDLXa5T8JQCJRSBx1EzlrAXBG5Tim11Np/XkR6ADPRgnsv6+WOiy76KHQbDhxDh7qdiT3ssCPZwNNKKeflinoscyyBdSoQhQ7/6y4EcDZ2D8hLyQWr33C08NvOS/mlwBhXoZ6VUtNFxA/4FC3eP2K98uIUn0Yp9bOI3IHOmRsBvGq9HMkCJiilPvUyRq+UEdsUmvM7Y1l5/0f0+vB+gsqHccU/b3YqE3/gBEtvf4fMpNRC9bXprVmEVC5P0zF9qNSmPn0+fdipzL5vV7L5rVnOlS1W3PMB/T5/lBo9WhFRrypdX73NZbmUs/GseugTLh7wnIeuwXXdCAgJAmD/D5eX96aN7dt3ccc/HmHyF+8SGRnBS684e4NF74vhppHjSUwsmtN49BgdnjYzM5OZ3+d15vZMcHAww667lmHXuY8OnZycwmuvvsvHH/2vUOMsK5zdGcuSBz6i/wf3E1w+jG7POs/TCwdO8PO4d8go5Dy9+t173e7r+OCwXO/3zFrtJHA2HGzP51quRhQj5r6EN6Z3f4yEowVek1IqSd97gDPPvE6V15/FLyKcio/c5VQm49ARTj38Aio5xef2Qzq2wb+SPaRU1FMPeK0TN2kacZOm5/pfeH+7oB1QrQo1pr7vtZ2jg28l87jr1d0Gz8yev5h5i1xH0N+yfRdbtud+6G4EzsKRuiuGo4+8Ra13n8Q/IpxqT41zKpMWc5TD418mO8n3eWijlgfPssr3jcr1Pm72ry7FFZWewZHxL1Pn85cIbdOEyGG9iRzW26lcwoqNHHv0rQKPtSxh7Ff2MTYs+7SoX5O3HryZ5yfNIjEljQ9nLXUqU696ZT568jbCQz0FG3PPPcP7cDExhW+WrCc5NZ0p81czZf5qp3IB/v48MmoAQ3u0L1A/f1da1K3OW/eM4Pkp87QN56xwKlOvWhQfPTKa8JAC2nBoTy4mpfDNsj9ITktnysLfmLLwN6dyAf5+PDKiL0O7OwXkAmDL/iO8+KXr6DxxiSlO+7548tbLXuBsUa8Gb90/iucnz9H2+2GZU5l61Svx0eNjCz4Hr+ul7bd0g56DC9YyZYHzs5EAfz8eGXk1Q690fiS47E/79+rpCwmMe937ffrCfz+aK9y0wWAom5QqgVMppYBXRWQ6+iF+P7RXUiTaW/AI2sNvCTBXKZWSp/4REekIPIMOYVoPLcYdQoeX/EApdcG3dH6XP0qpbOB1EfkY7aHZHy0gVUYLSvHAKfSxXw3MVErFuWnOJt71QntyjgU6oMWZi2hx8gd0aNp0N/W3ikh7tOg6CKgJJKA9FWcCHyulUovTjkqpA5bIudLqf56IDFVKLbf2nwR6WSFsx6A9Vqujveri0KF21wM/KaWcr4yLqA2Htn4WkSvQInU/tMdfHLAG+I9Sar2X+vNFpAHao3cw0Apts0y0Z+NOtOj8g1LKOSZFMaOUWmfl5u2P9kztBDRBe7r6o8+tQ2ivzu+UUiu9tPeViCwBHkR7yTZCC5YXgb3ozzrdQ91VaFH5GvSCCj+04L4c+K9SakfBP61Tf6XaNkXFkaVb+LH/P2k5fiB1rm5PWI2KZKdncfHQKQ4t+J3dXy4lK9XlV4ZvKMW6J78gduFGmt7Sl8rtGhISFUHq+QTOboth79fLObbCsxNsenwyv9z0JnWv6UjDG66kcruGhFaNRPz9Sb+YRNy+Yxxdvo19M1aSHuddrGtk5S3MzsziwNzLdyX2L4uW06PbYO57YBzXXtuXmrWqk5GeQUxMLD/OXcTkz6aRklI4YcxGw0b1c0Lhrli+jtOn8y9cvTjhLdat/Z0re3ShZcumVKlamSpVKpGdnc2FC3Hs2R3N6lXrmfHtXE6dOuO9wcuI2F+38P01/6TtXQOp16895WpUJCs9i4uxp9i/4Hf+mrqUzKKYp4YiI2X1Bo7ddA/lx95A2FVd8a9WGZWRSeaR4yQtXU3Cd/NQqZckM4LB8LclcfkfxAx+kKhxwynXtzOB1fU8TI89zsVFazk/bUGpmYeZZy5w8MYnqHDTNUQO7U1wk7r4R4STGZdA6rZ9xM35lYQlHm8rLjuM/co+xoZlnz4dmzPrjYf4ZvF61mzdx6nz8QQG+FO3WiUGdGnN6AFdCQ0OKnD7IsJTtw5mSI92zFm5iS37YjlxNo7U9AzCQoKoUy2KTs0bMLJvZ+rXqOy9QYMTfdo3ZdbLd/PNrxtZsz2aUxcStA2rVmRApxaM7teZ0OBA7w25QUR4avQ1DOnWhjlrtrBl/xFOnIvXNgwOok7VKDo1rcvI3h2pX71SEX6yvwd9OjRj1sT7+WbpBtZsi+bU+YvWHIxiQOeWjL66S+Hn4NiBDLmyLXNWbWZL9GH7HAy25mCz+ozs24n61c0c/Luhso2OY/CMqMs8KbnBcLkiIiuB3sAqpVSfkh2N4XLmy1q3mh+KMsxj8X+/hyCXG29W7F7SQzAUksGVjLdhWabWr5+V9BAMhSS6q3PEBoPBYDDkn4YzxpX0EAyFJa1oFpMaSpCAUuWnZCgAId3HGLXOR450vrrEnknW2bjM2KsMYILGGwwGg8FgMBgMBoPBYDAYDAaDwWAwGAyGMoNZ+mEwGAwGg8FgMBgMBoPBYDAYDAaDwWAoNZjgowZvGA9Og8FgMBgMBoPBYDAYDAaDwWAwGAwGg8FQZjAenGUUEakA1C5IXaXUX0U8HIPBI+Z8NRgMBoPBYDAYDAaDwWAwGAwGQ35R2SYNpsEzRuAsu1wPfFnAuuabwXCpMeerwWAwGAwGg8FgMBgMBoPBYDAYDICI1AMeAYYAdYA04AAwE/hYKZVciLbDgIHAAOAKoDFQDrgI7AMWA5OUUicL8xlKGiNwGgxlFKVUn5Ieg8FgMBgMBoPBYDAYDAaDwWAwGAyG/CMiw4CvgfIO/w5Di5FXAONFZIhSan8B2m4LrEMLmnmJArpZr8dF5B6l1Pe+9lFaMAJnGUUpNRWYWsLDMBjyhTlfDQaDwWAwGAwGg8FgMBgMBoPBkF8u1xC1ItIB+B4IBRKBN4EV1vvRwN1AU+BnEblCKZXgYxflsYub64AFwJ/AOaAKMMLqozzwjYhcVEotKtSHKiGMwGkwGAwGg8FgMBgMBoPBYDAYDAaDwWAwFD8foMXMTOAapdR6h33LRSQaeBstcv4f8LKP7Wejw9y+opTa5WL/EhFZBMwF/IH/ikgTpZTysZ8Sx6+kB2AwGAwGg8FgMBgMBoPBYDAYDAaDwWAw2FCq5F7FhYh0Aa6y3k7JI27a+A+w29p+VEQCfelDKfWbUupmN+Kmrcw8YI71thHQwZc+SgtG4DQYDAaDwWAwGAwGg8FgMBgMBoPBYDAYipfrHba/dFVAKZUNTLPeVgD6FtNYVjhsNyqmPooVE6LWYDAYDAaDwWAwGAwGg8FgMBgMBoPBUGq4THNw9rT+JgGbPJRb5bDdA1hSDGMJdtjOKob2ix0jcBoMBoPBYDAYDAaDwWAwGAwGg8FgMBgMgIjUzk85pdRRH5tuYf3dr5TK9FBuj4s6RU1vh+3dbkuVYozAaTAYDAaDwWAwGAwGg8FgMBgMBoPBYDBojuSzXL7dTEUkBKhsvfUojCqlLohIEhAO1MlvHz6MpR0wxHq7QyllBE6DwWAwGAwGg8FgMBgMBoPBYDAYDAaDoTAoddmFqI1w2E7MR3mbwFmuKAchIsHAF4C/9a/ni7L9S4kROA0Gg8FgMBgMBoPBYDAYDAaDwWAwGAwGTZF7TQIhDtvp+SifZv0NLeJxfARcYW1/pZSaX8TtXzKMwGkwGAwGg8FgMBgMBoPB15aAXQAAIABJREFUYDAYDAaDwWAoNajsEuzb99ya+SHVYTsoH+WDrb8pRTUAEfknMN56uxF4sKjaLgn8SnoABoPBYDAYDAaDwWAwGAwGg8FgMBgMBsNlTILDdn7CzoZbf/MTztYrInIv8Ib1dg8wWCmVVBRtlxTGg9NgMBgMHrn7zIqSHoKhEHxRpW9JD8FQSLoEXSjpIRgKSfUpZXpB5N+e6K4Pl/QQDIWkye//LekhGApJ9oWTJT0EQyEQf/Poqayz9+qJJT0EQyERUSU9BEMhuQxzEf7taHNwTEkPocyRfZmd90qpVBE5B1QCansqKyIVsQucRwrbt4iMAT6x3sYCA5RSZwvbbkljPDgNBoPBYDAYDAaDwWAwGAwGg8FgMBgMhuJll/W3sYh4WgXW3GF7d2E6FPl/9u47LIprfeD499A7CBZQsGDvvcRYY+9JjEZNYkw0vfdf7k27pvebqsk1N2qMybVFY2I0GjWa2HtvIKCIokjvsPP7Y5Zlgd0FFhAW38/z8DC7c2bOgcMws/POeY8aDyxEjwfGAUOqKAXvNScBTiGEEEIIIYQQQgghhBBCCCGq1l/G795AdxvlBpot/21vZUqpIcAS9GyuCegjNyPs3V9NIwFOIYQQQgghhBBCCCGEEEIIUWNomqq2ryq00mz5HksFlFJOwHTjyyTArvnDlFJ9gVWAO5AMjNA07ag9+6qpJMAphBBCCCGEEEIIIYQQQgghRBXSNG0XsNX4cqZS6gYLxZ4B2hqXP9E0Ldd8pVJqkFJKM37Nt1SPUqoL8Cv6SNF0YIymaXsr42eoSWSmdyGEEEIIIYQQQgghhBBCCFFjaIYqHUlZnZ5ATzvrCfyulHoLfZSmJzAFuN9Y7hTwYXl3rpRqDqwDAoxvvQQkK6U62NgsXtO0+PLWVd0kwCmEEEIIIYQQQgghhBBCCCFEFdM0bb9S6nZgEeAHvGWh2Cn0UZepdlTRH6hv9vrjMmzzL+A1O+qqVhLgFEIIIYQQQgghhBBCCCGEEDWGplV3C6qOpmmrlVKd0EdzjgFCgRzgDLAU+FzTtIxqbKJDkACnEEIIIYQQQgghhBBCCCGEENeIpmnRwNPGr/Jstxmwmr9X07T5wPwKNM1hOFV3A4QQQgghhBBCCCGEEEIIIYQQoqxkBKcQQgghhBBCCCGEEEIIIYSoMTSD1UGKQgAyglMIIYQQQgghhBBCCCGEEEII4UBkBKcQQgghhBBCCCGEEEIIIYSoMQyajOAUtskITiGEEEIIIYQQQgghhBBCCCGEw5AApxBCCCGEEEIIIYQQQgghhBDCYUiAUwjhcJRSQUqpD5RSx5VSmUopzfj1ZHW3TQghhBBCCCGEEEIIIUTFaJqqti/hGGQOTnHNKKWaAmcruh/tOvkPo5TyAboBvYxfPYGmxtXRmqY1tbxlpdT9GvCqhVU5QLLx6zSwB9igadqWqmqLhbb5A9uBlteqTlFzNG7ciMcemcmo0UMIC21IdnY2EZHRLFu2mi/nzCczM8vufTdpEkrE6Z3l2iYq6hwtWvWxun7E8EFMnz6Znj26EBxcHycnxeXLV9l/4DA//LiSZctWo2ma3W2uSbwbBdF25gjChnTBq2Eghuw8UqPjObt6Jyfmryc/K6fS6mo0uBOt7riJul3C8Qj0JetqKlcORHLq+43EbjpkdbsWk/vT7+MHylXXmSVb+Oupry2uc3J1pvlt/Wk6thd12jbGPcAbQ14+GRcTid9zmlOLN3F5z+ly1VcTuTasR+Dd4/EZ3BPXkHpoObnkxMSRsmYrV7/7FS0r2/6dK4Vb81A8O7fGs1MrPDu1xL11M5zcXQGImvZ/ZOw8XL5deroTMHEofiP64hYeinMdfwypaeReTCBz33FS/9hJ+l/77W+zg7lwOZHF6/5m6/4TXLyahJuLC2ENghjeuxO3D78BT3e3CtcRe/kqSzbsYOfhM5yPTyAzOwcvD3eaNaxH386tmTSkD0H+Pla3n/n6V+w5Hlmmug4ufrfC7XU0cgwKcwmJSRw+dpIjx05y5MRpjhw/RVJyCgATRg3lzZeeqeYW1j4X4q+w+OcNbNlzkIuXr+Lm6kpYSD2G9+vFlDE34enhXuE6zl+8zOLVG9i+/yhxlxMwGAzUDwygT9f2TBkzhBZNGtncPiExmc27DrDr0HFORp4j7nICuXl5BPj60KpZGEP7dmfs4L54VML/fEd04dIVvv/5d7bsOsClywm4uroSFlKfEQN6M2Xs0Erqw3i+X/U7O/Yf4UJ8AprBQL2gOtzQtQNTxg2lRZNQm9vvPnScg8dPc/hkJNGxF0lMSSUlLR0PNzeC6wfRrV0rbh05iPYtm1W4rY7ItVE9gmaMw3dwT9xC6mLIySUn+iLJa7aSsHBNhc+F7s1D8ezcCq/O+rnQo01T07kwcsqLpO88YvfufQZ2p9n810yvL/17MfGf/GB/ex2Ua8N6BM0Yj+/gHriG1NP7MCaOlF//IqESrmcK+tCzoA/NrmfOTn2R9DJczzRb/DbefTqWqcoj4WPtb68DkmNQCFFTSYBTiJprNTCouhtRjBtQz/jVAhgFvKyUOg68qmna0mvQhkcoDG6+h/57SjK+jrsG9YtqMnbMMBbM/xR/fz/Te97eXgQG1qFnjy7ce+80xk+YTkRE1DVr06lTERbfd3Nz47uFnzPx1jEl1oWFNSQsrCHjx43g4Qfv5uZb7yHZeGPSUYUO68qATx/Czc+r8E0vcK/jQ90u4bSaNogN0z8gNepSxSpSir7vzaTVtEFF3vYOCcQ7JJAmo3pw6vtNbHvhv1BJgePkCMv/VrwbBTF04bPUaRNW5H1nd1f8m4fg3zyElrcP4Ng369j1yneV0pbq4HNTLxp99CzOvt6Fb3p54Bngi2enVgRMHkHMrNfIjbbv36//LTfR6P2nK6exgFefTjR890ncQhsUed/JvQ4udevg2aEFXj3aE/nXY5VWZ022ee8x/vnlj6RlFt5wyMrO5WjkeY5GnmfF5l18/tw9NA6ua3cdq7fu441vVpCVk1vk/ZT0TA6ejuHg6RgWr/2bdx+byg0dW9ldz/VKjkFR3MCxU6u7CdeVzTsP8I8PvyYtI9P0XlZ2DkdPp3P0dBQrft/CF68+SeOGDWzsxbZlazfz9tzvyc3LK/J+TFw8MXHx/PT7Vp6deTtTxw21sv2fvPnlQvINhhLrriQmcyUxmW37jrBgxVo+fPERWjULs7CX2mvzjn28+P7cIn2YmZ3D0dNnOXr6LCvWbuaL2c9WqA+XrtnI23O+K9mHFy4Rc+ESK9b9ybP3TWPa+GFW9/F/780hPiGxxPtpeZmciTrPmajzLP1tE1PHDeWFB+7Eyen6ScjmO6QnYR89g7Nf4bnQycsDlwBfvDq3JPD24UTdO5scO8+FAbcMJuzDpyqruUUoT3cavfFQlezbkfje1IvQj58pcj1j6sNOrahz+3CiZ/6rQn0Y+kHlXc+IouQYFNWplowJEFVIApziWooFbD0KVfA41R7gnqpvTo1nPlL1KvrvpS9gfQhE1bgX2G3WJn/0AGcPYCzQCWgLLFFKfQPcr2layU/Xlafgk/0eTdNeqMJ6RA3SpUt7Fn8/By8vT1JT03j3vc/ZvHkbnp4eTJ48nvtm3UnrVs35edVCevcZRVpaernriI29SOeuN5Va7oXnH2Xa1FsBWLjIckz/3x+/bgpuXrp0mQ8+nMP+/YfJzc2lQ4e2PPfswzRtGkb//n1YvOhLxoy7s9ztrSkC2zdh0JxHcfF0Jzctk0Ofr+bitmM4e7jRbHwfWt95E/7NQxi68FlWj3qZvHT7R9l2e2GSKbiZcDiKI1/+Qkp0PH5N6tPh4bEEdWxKqzsGk3U1lX3vLCmxfczavaw8+H+l1jN43hP4h4dgyDcQsfzvEuuVi3OR4ObVY9Ec/XotKREXcPH2pEGvVrR/YDSu3h60mzmCzEtJHP5itd0/d3XxaBdO6Kcv4OTpQX5aBglzl5K+4xDKww3/sQOpM2Uk7uGhNJ73GmdvfhJDembpOy1OFZ7qtJxcsk5Fo1yc8WhT/tEJ3n27EPafV3DycCc/OY3ExWtI33mY/IQklKc77s3D8LmpFy5BAeVvpwM6HhXLC58tJisnFy8PN2aOH0zPds3Jys1l3baDLN+0i+i4Kzz6/rf88MbjeHuWf/TK/pNRvDJ3CQZNw0kpxg3ozuDu7ahXx4+LCUn8vGUvf+47TnJaBk9+uJDl7z5FaIMgq/trHx7Kvx6YVJEfu1aRY1CUJqRBfZo1CWXbrn3V3ZRa6XhENM+/N4es7By8PD2YOWkMvTq2ISsnh7VbdrF83Z9Ex17kkX/9mx8/fgVvL89y1/HbnzuZ/fkCAHy9PZl+80h6dW6Lm6sLxyNimL98DTFx8bzz9WICA/wY0b9XiX1cTUom32DA1cWFAb0607dre5qFNcTb04NzcfGsWPcn2/YfJfrCJe576X3+98lrBNcNrPDvxxEcPxPFc+98YerDWZPH0bNzW7Kzc/jtzx0sX7uZqNiLPPLKB/z46Wz7+nDzdmZ/9i0Avt5eTL91FL07tzP2YTTfLvuVmAuXeGfudwQG+DFyQG+L+/HycOfG7h3p3LYFjRsGUy8wAG8vTxISkzl8MoKlv20iITGZxT+vx8Pdnafuvb1CvxtH4dEunMafvYCTpzv5aRlcnrOM9O2HUB7uBIzrT+BU/VzY9L+vcGb80xU+Fxpycsk+GQ2uznjacS4sLvjpO3ELbUDulURc69ap8P4ckUe7cMI+e950PXNl7lLSth/GycMN/7EDTH3Y5JtXiZjwVKX1oXK173oGIOPQKWKf/8SubWsbOQaFEDWdBDjFNaNpWi5gNaeAKjyhpWuaZn/ugdpjMfAVsFvTtDMASqkorn2A86yV/vgJ+KdSahzwX6AuMBM9GPt8FbanID/TqSqsQ9QwH384Gy8vT3Jzcxk1eho7du41rdu0+W/OnDnLu++8TOtWzXn6qQeY/fpH5a4jLy+Po0dP2izj5OTEwAE3AJCSksrKlWtLlKlfvy4z79VHV1y9mkivPqOIjS18kvHvbbtZ/MMK9u1ZT7NmjRkxYjDdu3Vi7z7rqVVrsl6z78LF0x1Dbh6/T3uXy3vPmNZd/PsYKWcv0fPlqfg3D6HDA6M58NEKu+rxCw+mw4OjAbhyIJLfJr5OfpY+YizhYCQxv+9j1PKXqNslnA4Pjub0j3+WGDGak5JBTkqGzXr8WzTEPzxEb/+2Y2TEXS1RpvGIbqbgZvyeU/x2y+tohsLHCuO2HuHc7/sY/fNrOLu50OHhsRyZ+ytaflU++1H5Grz8AE6eHmi5ecTMeJnM/SdM6zK2HyInKpYG/zcT9/BQgmbewuVPF5e7juzTMcT9ay5Zh06RdSwSLSeXeo9PK/fNCOdAPxp98jxOHu5kHo0g5p5XyE9IKlImc+9xkpb8Dq7Xx+XvewtXk5WTi4uzE3P/bxadWzUxrevdvgWNg+vy8Q9riI67wsJft/DQbdZHlVjzzc+bMBgfqf2/GRO4fdgNpnUdmocxtFdHPlj0C9+t2UpWTi4L12zlH/fcbHV/nu5utAwLLnc7ais5BoUlD94zjQ5tW9GhbSvqBtYhNu4SI26bUd3NqpXe/XoxWdk5uDg789XsZ+jctoVpXe/O7WjcsAEff7uE6NiLLPhpHQ/fYf3/myWZWdm8+x/9uPXy9GD+u/+gZdPCNKbtWzZj5IBe3P38W5yOOs87X31P/x6d8PL0KLIfTw937r1tNNNvGUGgWaYTgLbNmzC8X08+mPcjC1euIzE5lS8X/cTsJ2eW99fhkN75alFhH775PF3aFs5y0rtLe5o0Cuajb34kKvYiC1b8xsN33lqu/WdmZfPOV4sAvQ8XfPASLZsWjpBt3yqckQP6MP3Z1zkddY535n7HgJ6dS/QhwE9fvYOLs7PFegb06sIdE4Yz9YnXOH8xnoUrfuOe20YT4OdbrvY6ooav3oeTpztabh5R018hY3/h57X07YfIjrpAyIv34h4eSt1ZN9uVdjL7TAwXXvuKjIOnTefC+k9MrXBwxaNDc4JmjMOQncOlDxYR+s71mb0g5JX7TdczUXe/UuR6Jn37IXKiLhBs6sNbiP/EnuuZc1x4bS6Zh8z7sPzXMwW0jGyyT0XbtW1tI8egqG6G62OmOlEB109OCyEcjKZpX2ua9kNBcLOm0jRtNfrI0lTjW88ppbpVYZUFQ0xybZYStUbPHl3o31+f5/K/3/5YJLhZ4KOPv+LYcT3m/dijM3FxqZqbp0OH9KdRIz34tXzFr2RllRyN2KtXV5yNNycWLFhSJLhZIDU1jU8+/Y/pdZ8+3aukvVWtbpdwgvu0AeD0j38WCW4WOPrVGpJOxQLQduYIlIvlGzelaTdrJE7Gm+I7X15gCm4WyM/KYefL+ggIJ1cX2t030q56mt/Wz7Qcsewvi2Xq9yhMs3no89VFgpsFEg5HcX6DPsece4A3/i0b2tWe6uLRqRXevToAkLj09yI3IgokzPuJ7NMxAATOmAB29G3WoVMkLlxN5oGTaDn2/1uv/+wMXAL9MWRkcf7B10sEVorIzbO+rpY4fOYc+07o057fPKhnkeBmgelj+hPeqD4A36/9m9y8/HLXc9B44yfAx6tIcNPcA7cOMS0fMv69iNLJMSiseXTWXQy6sTd1A2UUQlU6fDKSfUf1a8tbhvUvEtwscPctIwgP08/v3/+8vkR60tJs3XOIq0n6NAV3jB9aJLhZwMfLk+dmTQEgISmFVRtKXpvcdfMInpwxqURw09wTd99GvUB99PQf2/disJDOtrY5fDKCfUf0G/G3jBhYJLhZ4O5bR5n6cNGqdeXvw90HC/twwvAiwc0CPt6ePHf/NECfK3Xl+q0W92UtuFkgwM+XiaMGAZCXn8+hE5anyqhNPDu3NJ0Lry5ZXySwUuDKf1aSZTwX1r1nvF3nwsyDp0lY8EuFz4VFODkR+vZjKBdnLn+xlJyoC5WzXwfjWeR6Zr3F65kr834y9WHQDDv78NApri6s5D4UcgwKIRyCBDiFQ1FKdVNKzVVKnVRKpSml0o3Lc5RSNid2UkqFK6WeUUqtVkpFKaUyjV/RSqn/KaVs3g1XSs1QSmnGr6ZKKTel1NNKqT1KqWSl1FWl1Gal1Jhi2/kqpZ5XSu1XSqUopZKUUuuVUkOs1eVoNE07DZjnfbSZA7K8/aiUGlTwuwcK7tLebdYfmlJqs5W6blZKLVVKxSilsoy//z1KqVeVUlbvDCml5hv3G2V8HaCUmq2UOmpsb5JSaotS6g7bvx1QSt2ilFqplDqvlMpWSqUqpSKVUluVUq8rpUrmmiq6/WCl1ALjNhnGv6PDSqn3lVKOFTWxw4QJhYfmggX/s1hG0zQWLVoGQJ06AQwe1LdK2nLnnbeZlr/7znJ6Wjc3N9Ny5FnrT31GRhauM9/GkTQe2cO0fPp/WywX0jRToNA9wJuQvm3tqitsuP7cRNLpWC7vs3xD5/K+CJLP6B9cGo+wI2isFOG36n87uWmZRP+622IxJ9fCD21p0fFWd5dqts7ZwUYs+Q3rY1pOXrbBciFNI+mnjQA4+/vg3afTtWhaCU5+PviPHwhA8qpN5F64XC3tqEk27TlqWp4wsIfFMk5OToztrx9XqRmZ7D5W/hulBUHRRvWtpzr09fKkjnG+pdz88gdRr1dyDApRvTbuKEz7O2FYP4tlnJycGHeTft2Qmp7B7kMlb9zbcuxMlGm5X3frx2+Pjm1wd3MFYP3fe8pVRwFXVxe6GIO0qemZJKWm2bUfR7Jxe+FDkTcP62+xjJOTE+OG6v2bmpbB7oPHy1XH0dNnTcv9e3S2Wq5np7aFffjXrnLVYc7bbORn9nUQxDE/FybaOheu2ATo50KfG6rnXFhc3ZkT8OzQnOzI81z+all1N6fa+A0368Ol6y0XKnY941NN1zOiJDkGhRCOQAKcwiEopZyUUh+hz0P5ANAK8Aa8jMsPAkeVUvdb2b4ZEAF8gD5vZBPAw/jVGJgM/KaU+k4pVZa70H7AFuBDoLvxdR1gIPCLUuopY72Nge3Au0AXwBd9DsuhwPqyBMccyHyg4HH9MUop1+IFKtqP5aGUqqOU+gM9le5tQBj66E9/9D57DTihlOpjdSeF+2oN7AdeBtoZ2+sP9AcWKaU+t7Kds1JqCbACmICeXtcNPc1wM6Af8BLwpZXtPZRSPwAbgenGbTzR/446AM8Cp4xpgmutG/v2BCAtLd1mGtctW3aYlvsat6lMPj7eTBivB1vPno1hy9YdFsudOlUYJAhvVnLUlGldeOE6820cSf2e+vMIuelZJBw6a7XcxR2FN4sKtikPn8b18A7RAyiXdti+eXjRuN47JBCfsHrlqifkxnb4NKoLQPRve8jLzLZYLjmicFSuT5P6Vvfna1ynGQyknL1ktVxN5NmjPQCG9Ewyj5y2Wi5j12HTslf3dlXeLkt8b+qJk/GGX+qGnab3lYc7rk1CcL4O51rZfyoK0FO+tmvWyGq5Hm3CTcsHjNuUR9MQ/RiLjS+ZyrlAWkYWianpxvJ1y13H9UqOQSGq1/5j+nHn6eFOuxZNrZbr0aF14TbHrR+rliSlFAYZgwKsj750cXbG30d/UOTgiQjy7HxYJMds9LSzU+2/FVQwAtfTw512La2nOezRsY1pef+x8s2CkmwWKA6q42+1nIuzM/6++kwzB4+fsasPDQYD67YU/o9tFhZS7n04Gu+e+nktPz2TzMPWE1ul7zQ/F9r3MGVlcm1UnwZP6qN2Y1+ag5Zz/WYu8Oph1odHbPVh4axIXj2qvw+FTo5BURNomqq2L+EYHGs4gbiefQY8bFzegh5MiwQygM7Ak0B74Cul1EVN034utr0zkAOsA9YDx9DnigxED6w9Ytz+TuN+Xy2lPV+jB8m+RA+gJaIHMGcDDYEPlFLrje0MB94B1gLpwI3Av9ADZHOUUus1TbM+BMhBaJqWoZTaBoxGDwB2A3YWK2ZvP+4GOhqX16H/jlehBwcLpBcsKKXcgQ3GNuSjz2e6BjgLuAIDgKeB+sAapVRXTdOsDbXzAlYDQcAbxv2mAV3R/05CgUeUUqs1TVtXbNuHgEnG5b+AeeiB9nTj/joBI9H/FopQ+qS0y4CCEcGrgSXG35cB6AU8gx6gX6aUulHTNPse6a7h2rTR00mdiYgi38bNgBMnCy+4C7apTBNvHYO3txcA3y9ebrXckSMn2LZtN3379mT69Ml89O+viIsrGtzy8fHm8cdmARAREcXv6/+s9PZeCwHGtKupUZdszi9ZMKoSwL+l9WCL1XpaFW5jvq/S62pI2rmyjyQyT097Zqnl9GEAZ1dup9vzk3Dz86Ljw2OJ/eNAiTS1ge2bEDqkCwCRP20nNy2zzO2oCdyb6ynWcqLjwEbfZkecNy27NS+Zlu1a8OxSeGMy62QUHh1bUv+Z6Xj37YwypnvLS0gi5detXP78R9upM2uJs7H6ZUXj4CCbKe+aNSx8CKBgm/KYNLQ3s+etICktgyUbdjB5aMlnhr7+6Y/C8kNsP1N09kI8d7z8OdFxl8nOzSPA14t2zUIZ2rMDI/t2wdXOFNeOSI5BIarX2XPGjBAh9W3/HzULMp09V3JaAlvM52FMy7B+naBpGmmZ+rQIuXl5nLsQX+7gVm5eHodO6NfKQQF+pmBbbXY2xtiHDRvY7sPQwoQ4kefKl8LQ08PdtJyabn2ed03TTH2cm5dHzIVLptS4tuTnG7iSmMSJiGjmL1/DXmPK3T5d29OiScmUxrVNWc+FWWbnQvcW1XMuNNfojYdx8vIgceUm0rdbf0D3elD265lzJbapTm7NQwlf8SHu4Y1Q7m7kX00h88gZUtZuI2n1n2DH1A6OSI5BIYQjkACnqPGUUsMoDIrN0jTtm2JFdiulFgG/AjcBnyql1miaZv6IThzQVNM0S586/1BKzQX+C8wAnlFKfaRpWrKNZvUCbtU0baXZe3uVUrvRR/o5oY+68wMGappmHujbo5Q6bWyvL3AH8LGNuhzJPvQAJ+iBY9PPXZF+1DQtHThi3E9BLp4kTdOOYNkr6MHNJGCopmnFJ238Syn1Pfro2hDgLfR+sKQe+qjLGzRNO2r2/l5jWtzD6COBH0YPvpqbbPy+Exhc7G8S9GDpR0opS7n9ZqEHN3OB8ZqmrS22fodS6jtgK3pQ+N/oI0JrFXd3d+rVCwIg9rztm0ZJScmkpaXj4+NNWGjlZ+69685JpuXvFtlOcTLzvqf5dfUiwsObsHvnWj74cA779x8mLy+P9u3b8OwzDxEe3oTLlxOYfvdj5OY6XoopZ3dXPIL00QbpcdZHbwHkJGeQm56Fq7cH3g2tp7K0pmD0ZlnqSr+QULhdw6Ay1+Hi6U6TUXoqz7TYK1z8+5jVstmJaWx9fC4DvnyYBr1aM3bN6xybt5aUyDhcvD2o36MV7R8YjbO7K1cOnWX37O/L3I6aQLm54hKkP3eRe/GKzbKGlDQM6Zk4eXviWk2j89xbNjYte9/QiYZvPY4qlhLYJSiAwOnj8B15IzH3vEL2Cesjjh1ddk6uacRk/UDro0kA/Hy88HR3IzM7h4sJti57LLt5UE/2n4xi9dZ9vP3tSo6fPc/Abu2oV8ePuCtJ/PLXPlO63Ptuvok+HW0/fJKQnEZCcuFomPirKcRfPcbmvcf47+rNfPjknYQ3alDudjoaOQaFqF7ZObkkGkdXNqhr+7rFz8cbTw93MrOyuXjZ9jVKceFmQco9h09aHSl6PCKajMzCed/jLieUO8C5fO2fpp9peL/Kz3RS02Tn5JCYkgqU3of+vhXpw8LPHHsOn6C9lZGixfvwYnyCzQBnx1F3WV3XtkVT3nzmgXK10xEVORfGJdgsa0hJJz89E2dvT9yqOVuE//iB+A7qTn5yGnFvFL/lcX0p3/VMYR+6hpQvC09VcK1XB9d6hRkonEKmobgaAAAgAElEQVTq4hpSF79hfaj7wETOPfJ2kYfMaiM5BkVNoWmllxHXNwlwCkdQMJ/jcgtBMQA0TctSSj2KPjKzCTAYfaRmwfp0zEb4WdheU0o9A9yFnjJ1KGB9iBYsKRbcLNjPIaXUX+gjBOsB7xYLbhaUW6OUija2tT+1J8BpftVTPB9ZhfuxLJRSPugjcgFethDcLKgrWin1Ovoo3ElKqfuNfyeWvFwsuFmwjzNKqZXAFCwHF4ON37dZCG6a76fIJ2nj6M0XjC8/tRDcLNguUSn1HPro1BuVUi2N86HWGr7GedsA0tKtHsIm6ekZ+Ph44+3jVantCAtryIAB+sijbdt2ExERZbP86dOR9Ok7hgcfmM5zzz7MB+8XHRSek5PDhx/O4dPPvyE2tnxP+9cULt6Fow7y0rNslDSWycjG1dsDVy+PUsuWrMvTrC7LaWPN6yng6u1uo2RRjUf3wNVHrydi+d+llj+3fh+/jHyZ9g+MpuXUgfT/5MEi6zPjk9j5/jJOfb+J/KycMrejJnDyKfx9G2yMKDGVyczCydsTJ7N+upac/QtHoYS8/ghoGvEfLiTppz/Iu5KEW5MQ6t43kYDbhuFaP5CwuS8ROfZRDA42qras0rMKjwEv99Ln9y0IcGZkl//v1NnJiTceup2B3doyb9UmVmzazYpNReeu7dmuObMmDLYZ3FRK0bt9C/p1aU3rJg3x9/EiIyub42djWbZxJ5Gx8UTGxjPrja/5/vVHCanlKU/lGBSieqVnFv5tenmUfi1REBzLyCr9eshcv+4dcXF2Ji8/n4Ur1zHupr7U8fctUsZgMPDZdyuKta989Zy/GM9n3+kfbb08PZg5aUwpWzi+9IzC31FV9mH/np0L+3DFb4wf0s9iH366YGnR9pWzD0E/Xz973zRuHjYAN7cSs8HUOvacC529PXGy47NGZXH29yHk5ZkAXHxvAfl2PDxWmxTpwzJ8XtQys8DbEyfv6utDzWAg7e8DpG7eQ9bxs+QnpuDk44Vn++bUmToSj5aN8WjVhKaL3ybylqdr9bzjcgwKIRyFBDhFjaaU8gMGGV/aHDKladpxpdQVoC5wAzYCY8b5IRugj6A0z1eTgJ62tDO2A5w/2lh3ED3AWVq5Q+hBvHAbZRxNmtmy6ZNdVfWjFQMpTPla2kziW4zfXdFTDm+xUEZDT3FrzV70AGegUipA0zTzvGtxQEtgnFLqLU3TbD+2WKgd0Ny4XNafAfTfV5kDnEqpMuU1cnat/NGQZeXhUXhxnJNT+ijHbOMNek+Pyr2ovmPaRJyMcxWVNnqzwNgxw5g69RZ8LaQAc3Nz47bbxnH5SgIffDinUtt6rTh7FN5Yyc8tfU6LfGP/OXuUHnCpSF35ZvNrlKeu5hMLn1GIWPZXqeWdXJ1pPqk/YSO6oSzMY+VZP4Dmt95IWsxlzq3fV+Z21ATKLCimlaFvNWPfOtnRt5XB/EO0k4c7sc98QPLKTab3cs6c48IL/0bLzaPO1FG4hQVTZ9oYEr4u27HsaMznWHN1Kf1S381VvwzKLsP/WEsiYy+xeus+zpy7aHH9odPR/LR5N80a1aeBlRGlHz11F34WgnPd2jRj8rAbmD1vOT9v2UtCchrvfbeaj5+abldbHYUcg0JUL/NrTpcy/R/Vy5T3/2hwvSAmjRrED7/8QXxCItOff4un75lEz05tcXVx4URkDHMWr2TbviO4uriQm5dnrKfsD6RkZmXz1Jufk5qu35x+8YE7qB9Uux8SAcgxy47i6lqWPtSvNbPL+bBPcL0gJo2+iR9Wr9f78JnZPDVzCr06tcPV1ZkTETHM+X4Ff+89XKQPs0rpwxVz3gL04GhCYgq7Dh1jyZqNfDDvB86ej+PpmVPKdI53ZE7lPRdm62VUNZ0LAYL/cS+udeuQsf8EV38ontzp+lPePjQY/4eqMjygV1ViHnoLQ2rJB6szdh8lYdGvNHrrMercNhTXenUIfvk+zj30VjW08tqQY1DUFAaZC1OUonZfEYnaoCt6uleAH5RSP5Rxu+DibxiDmvejj9Lsip521JrSciqcsrHOPMBVlnK+Nso4GvOfJcVsudL6sQx6mC3H6YMhK1TXFU3TbOXjMB996UvR/l+AHuxuAZxRSq1AD9hu1TTNVj4T859heyX8DNacK71I1WnYMJg6dSzf7E5MTObChYtkmT1FXZYnld2NF+GZ5Xz6ujR33DERgKysLJYsLT7Fb0nvv/sKTz2lp45aueo3PvxwLgcPHSU/30Dbti145OF7uWfGFN55+yV69erKlKkPYjBYn9PiWvMKroObv7fFdTnJ6WRcTCQ/q/DGkXMZbhw5G/vPntGM5anL2a1wfVnr8mwQQEi/9gBc3neGlAjbo2pdPN0Zuug5gvu0wZCXz+EvVnPmf1tIjYnH2d2Nul2b0+WpW2jQuzU3/fdJdr/+A8e+/q1MbakJNLObe8XTTFqijH1rqKaRqgaz9mYdjywSWDEX/8EC/G8dgpO7G35j+tfa4IqbWZ8V3Ei1JSdXn0PI3Y7RIPtOnOXxD+aTmpFFw7p1eGTycG7o2BI/by+uJqeyed9xvlj6O2u3H2TvibPMfXEmLUJLnqosBTcLuLo48+p9Ezl0OoaouMts3H2US1eTrQZLawM5BoWoXubXnHll+j+ql7Hn/+gzM2/n/MXLbN1ziOjYizzxxmclyrRv2ZT2LZuxZI1+bHl7lu1Bvrz8fJ5950tOntUv+SePHsyEobVuRguLCgKWALlluDFfEBB1tyOw8uysqZy/GM/W3QeJir3IE7P/XaJM+5bN6NAqnP/9qs9LXVoftmxadA67vt07MmXsUGY8/yaLVq4jIjqWOa8/h7NzyYfsagtDec+F7noZrZrOhd69OxA4eRhaXj6x//xScipS/j50Mv4P1ezIKlJZLAU3TfLyiX3xU7y6tsa9eRj+I/oS1yCIvEu207c6KjkGhRCOovZeDYnaor6d2xXJT2mc43A78DnQG9vBTYDScnxl2FhnilJomlaWcs42yjga88CweeCvUvqxjCq7Llt9CGb9TbG+1DTtv+jze+ahjyq9B3006Dml1Bml1IdKKUsjeK/l76vavD77BQ7u32jx6/XZeobeVLMPGD7elgNu5ry99V9Belpp3VZ2PXt0oW0bPbXi6l/Wk5ycYrP86FFDTMHN+Qv+x22TZrF9xx4yMjLJzs7mwIGj3Hf/M7zxpp6Z+tZbxvDQg3dXWnsrQ7cXJnHzxncsfnV7QZ+L1DwtrUsZ0gi5eOnpwXIzyh98zksvTInjUkra2YJ6AHJLSWdboPmtN+JkvEF0pgyjN7s8cyvBfdoA8Pez89j71v9IjojDkJtPblomcVuPsHbSm8T9fRTl5ESPl6ZSp13jUvZac5injXTyKj3lpZPxJp0hvXrSTZq3N+2v/VbL5SelknVYH+Tu0bYZlOGDuiPyNkvFV5a0s5nGMmVJZ2suJzePFz5fTGpGFnUDfPlu9iOM7deNIH9fXF2caRAUwO3DbuC/rzyAu6sLlxNTeHnOkvL9MEYuzs7cMqhwzri9xyPt2o+jkGNQiOrl7Vl43GVklX4tkWks42VHBhE3V1c+e+UJXn1sBm3CG2P+YGNggB/33T6W+e/+o8h9Wl+f0q+JNU3j5Y/nsXXPIQBG9O/JPx68s9ztc1TeZiPLq7wP3Vz5/LWnee2JmbQJb1KyD6eMZ8EHL6GZdaJfGfqwuOB6QfzzYf0zw/b9R1ixbnO59+FI7D4X2vFZo6KUmwuN3tJnyUmYv5qs4zLPNBTrwzJ8XlSm65lr34dllm8gcUlhkjHv3h2qsTFVS45BIYSjkE+VoqYzDxg9AGwr43aJxV5/gp6CFGAl8F/0FLHxQJZm/LShlIoBwgAZ/26frmbLJ82WK6sfy8K8rm5AWXNFVckM8Zqm/VMp9TVwBzAE6IMeiGwOPA08ppR6XNO0uWabmf8M44CoMlYXX87mhZVeBKjGkZ7Z2dlcuXKVunUDaRQaYrNsQIA/PsabBefOX6i0Ntx5522m5UVlSE97771TAT2l1Cuvvme13NvvfMYTj9+Hr68PM2ZM4Ysvv614Y6+h/Oxcsq6m4hHoi3dIoM2ybv5euBo/1KZfuGqzrCXpcYXblFaXd8Ogwu0ulO1p2oL0tPnZuZxdtb3U8i2m6FnIkyPiiFi61WIZLd/A/veWEbKqPU7OTrSY3J/dr31fpvZUNy0nl7yrybgE+uMabDuhgZOfj2nev9y4smbhrlzm9ebF2Z4Hp6CscnbG2d+X/Cv2nGZqNnc3VwJ8vEhKyyD+qu15Z1LSMkwBzuCg8o2I/PvgSeKv6g98TB3el7oBlhNStAgNZky/rqzYtJtjZ2M5GX2B1k3Kn/o8PLTw2Z+CemsrOQaFqF7ubq4E+PmQlJLGpSu2r1tS0tJNwbHgeravUaxxcnJi4oiBTBwxkPSMTBKSUvBwd6NuHX/TFAkxFy6ZyjcPK/1/6JtzvuPXzTsAfa7Pt56537Sv64G7m1uZ+zA5tZL6cOQgJo4cZLUPo837sHEju+rp260jHu5uZGXnsP6v3UwafZNd+3EE+rkwBZdAP1xDgmyWdfLzxtl4LsyphnOh34i+uIeHYsjJJevMOfzH9i9Rxr1l4cOOHq2bmMpkHDhF7vlLJcrXBuW7ninsw9xSriWqW9aZGNOyawPbf5uOTI5BUVNokqJWlEICnKKmM787naFp2pHy7sA4/+Ptxpffa5pm69HV2j8hSRVRSnkBfY0v04ADZqsr3I/lYF7X5VJSwV4TmqZFo4/kfMuYKrknMBk92OsBfKmU2qlpWsGwB/OfIamqfl9l/d24uNn3Abw0M2c9xcxZT5Va7vjxU/Tv34cWzZvi7OxMfn6+xXJtWrcwLZ84UeapSG1ycXHh9skTALh06TJr11lOu1ekHcbRnvHxV7hwwfKcdKAHb48dO0Xv3t1o07q51XLV4a+nvuavp74utVzSqViC+7TBt2kDlLMTWr7lNLv+LQpvxCWfji13e5JOFW5jvq/S6yo90B3YoSl12uqx/nMb9pOTZCMtEeBZzx+POnog5+qRKJtlEw4XrvdvXn1z2doj+8w5XHr549YkBJydwErfujcvnMo3J6J6noXIPh0NGD/EOpWSFMH85q6V/yW1QXhoA/adOEvMxQTy8vNxcbb8ezl7ofAGUrNG5UseEHmh8Jmats1snyfaNguFTbtNddoT4FTX2bNncgwKUb3Cwxqy7+gpYuLibf8fPVeY1r5ZmO2H8crC28sT72IjZfLzDZw8q99QDw2uRx1/2zOcfPztElM62+4dWvPRPx6t9fM1WhLeuBH7jpwk5sIl231o9mBkeBmCx6Wx2oeR0QCEBtcvtQ+tcXZ2ws/Hm6zsHOLiq+ehlmsp+0wMLr06lHou9DA7F2afufbnwoJU8U5uroS+81ip5f1H3Yj/qBsBOPfsv0mqxcGVsl/PFD57nV1N1zNldh1lPpVjUAjhCK6fR/iEozpA4eXDjXbuoyVQMAnH/6wVUkq1AXzsrEPo6VcLhn/8omma+WQnldGPZWWeG62q6yo3TdNyNU3bpmnak8A049sKuM2sWI3+Ga61v7fpN8V9fLzp3q2T1XIDBvQxLW8zblNRo0cPoW5d/UnuH39caTW4aq5griaXMtxIcjGm58vLc8ybvPG79WmGXb09COrUzGq54D5tS2xTHmkxl02jOBsYU8Na06B3a0Af9Zl2rvSnf1tMKpyLKqIM6WkNZn2lXGzfyDdfrznYjfzMPUcBcPL2xLNDS6vlvHp1NC1n7D1W5e2yJGP3UdOyW2PbUxG7NdFvPhuysslPSq3SdlWnrq2aAnr62WNnrT9UsOdEYarXLsZtysrFLFCVZ+VmR+H6wr9/FzvnC4uMLbzxUK+On137cCRyDApRvbq204+7zKxsjp2Jslpuz5HCpDVd21o/Viti9+HjJKWkATCify+bZb/+8We+Xa7P+92hZTM+f+UJPOyYV7I26Na+FWDsw9PW0xXuOXzCtNy1XasqacuuQ8dMfThyYG+795Obm0disv6/07OMc7E6svTd+nnN2dsTz44trJbz7m1+Ljxe5e0SZZexx6wPO9jqw8JUrxl7anYfurcoDMbmxpc/O5EjkWNQ1AQGTVXbl3AMEuAUNZqmaZeBHcaX05RS9ezYjXmUwdZkFw/asW8BKKVaAm+bvfWO+fpK6sey2kDhvJmPK/NJUGqeP8yWzXO27KMwZe79Sqna/+nVhlWr1pqW7777dotllFKmVLKJiUls2lzWLMi23WWWnnbhoqVl2iYqSn9isW7dQNq0sf4hoE6dADq014NxZ6NirJaryWLW7jEtt7x9gOVCStH8Nj2ImJ2UTtw2+z7wnPt9HwABLRtRr5vlEa/1ujUnoKU+kixm3d5S96mcnWg24QYAshJSOL/xYKnbZCelk5OSYayvBcpGsCb4hsJgbGpMzU61VFzK+h2mZf/bhloupBQBt+ip0fKT00jfcehaNK2EjF1HyEtIAsDnpl5FR4iZcQ1toM/7h/GDt1Z7H78e3KO9aXnVn3ssljEYDPyyVT+ufL086dmufCPJG9UvTOO376TteW7M58xsZEf6v7z8fFaa/Rzd21p/oKK2kGNQiOp1U59upuVV6y0/AGUwGFi9Ub/m9PX2omcn2w9h2UPTNOYsXgWAi4szE0cMtFp20arf+XzRTwC0bBrKnNlPlxhJeD256YbupuWV6y1PKWAwGFi9Qe9fXx8venZua7FcRWiaxhxjv7i4ODNx5CC797Vxx15yjQ9TtmwaWkppx2d+Lqxj61x462BAPxembb/258Kk5X9wuNk4m1+RU140lb/078Wm95OW/2Fjz44v5XezPpw0zHKhYtczadV0PVMmzk5Ffo6MXVWZnKz6yTEohHAEEuAUjuAN43c/YJlSKsBaQaWUu1LqkWIBoTMUjh6821LASyk1Dni0shp8PVFKjUWfU7Mgz87bmqZZihJUtB/LRNO0JOBz48u+wMdKKav/65RSDZRSs8pbT1kope5UStkaxjfcbNl0d1jTNAN6SluAcGChUsrdRj1+Sqla+/e7e88Btm7VL6zvvWcKfXp3L1Hm6aceoF1b/Ynrzz7/xjSK0tzAATeQlxNLXk4s38z7uNR669QJYPSoIQAcOnyMgwePlrKF7pdf1puWP/rgX7i6upYoo5Ti3x/Pxt1d79Y1axzzovrKgUgu7tCfem85ZSD1upcM6LZ/YDQBrfSg4/Fv1qFZGK0afENbZsQuYkbsIvp9fL/Fuo7NW2saPdn79btx9ij6e3X2cKX363cDYMjN49i8tSX2UVyjwZ3wrKcPPI9cud1i20rQNM7/oWfg9g4JpNPjEywWc/P3ovs/pphen9+w32K5mirr0CnSjR/Y60wajmfXkjdtg2bdYprL5Or8VVDs9+fVuyPtIn6lXcSvNHyv9HTUdjMYSJi3AgC30AbUe3RKyTLOToTMfhhlTE+XuHhN1bWnBujYIoxubfRA0srNuzl4KrpEmYW/biUyVk8ze8fIG3EtNiJ597EIOk97gc7TXuDluUtKbN+rfQs83PXjcOmGHZyOiStRBuCvAyfYaBzhVz/Qj9ZNiqZw3HU0gpT0TKs/S25ePv/6z3JTWwd2a0twkNVLiFpDjkEhqlfH1uGmEYA/rd/KweNnSpRZ8NM6Is/p6U3vGD+sRBrY3YdO0GnsPXQaew8vfTzPYj1JKWnk5OZaXJefb+CtuYvYf0yfemHmpDGEBlt+TnTl+q28P+9HAJo0Cubr15/F3/f6Tk7UsXVzunXQHyb8ad2fHDhecgqLBSt+M/XhnRNGWOjD43QcdRcdR93FPz/8ymI9SSmp5ORY78M3v1zA/mN6BpNZk8cRGlwyJfz2/UeKzLNqSUR0LO/M+c70evyQfjZK1w6ZB0+bzoWBk4fh1bV1iTJ177sZD+O58Mq3P5c4F3r37kDHs6vpeHY1oe8/WfWNFkVkFrmeGWbxeqburFtMfZgw31IfdqRD5C90iPyFRu9VXR969+mIk6+NMREuzjR6+3FTW1M27Ky2+c+vFTkGRU2gVeOXcAzX30QMwuFomrZGKfUJ8AQwADiulJoL/IU+V6E30AJ98p9b0efRXGC2fYJSag0wBhgJ/K6UmgNEA/WBicAMIBIIAKpydGGZKaVaAMU/tRR8SvVRSs0otm6tpmnWJ/yzXzOlVMFVm0IPUNYDegDjAPOcoV8D/7S0k4r2Yzm9AgwEehvrG6SU+g96qtx0477bA0OBUcBhwPJdh4r5DvhAKbUCPQgcAWQBDYBhwEPGcmnA98W2nWsscwswCeimlPoK2AUko/dDG2AQMN6438+ppZ565hW2bF6Fl5cnv61ZzDvvfsbmzdvw9PRg8uQJ3H+fPrXuyVMRfPSx5ZsP5XX75AmmAOR33y0r83YLFi7h8cdn0a5tK4YPH8TOHWv44otvOXToGPn5+bRt14oH75/ODTf0AODixXg+/nfltLk67HrlO0avegUXT3eGL36BQ5/9zMVtx3D2cKPZhBtofaf+NG5yRBxHvrL/hnZK5EWOzPmVTo+Np26XcEavfJXDX64mNSoe36b16fjwOII6NgXgyNw1pJ4tfR6N5rf1Ny1HLLX8ZL8lBz/+ibAR3XD18qDrsxMJ6tSMiKVbSY2Ox9nDlXrdWtBu1kh8QvWB2Re2HuHCFsd7uvfS61/RdMn7OHl60Hj+61yZs4SMHYdQHu74jx1AnamjAMiOPE/CNz/ZXY//xKJPA7u3Czct+wzojmtoA9PrnKgLZFpIw3l1/s/4jRmAZ4cW1HviDtzCQ0la8Qf5CUm4Ng4h6N6b8eqmj8pI3bSb1LV/291eR/H89HHMeG0OWTm5PPjOPGZNuIme7cLJyslj7fYDLN+4C4AmIXWZPsbKCGwb/Lw9uXfcIL5ctp70zGymv/YlU4ffSJ+OLfHz9iQhOZXNe4+xYtMuDMaRek9MGYVTsdF9q7fu5YkP5zOoWzt6tAunaUg9vD09yMjK5vjZWJZt3GkKbgb6+fD89PEV/M04DjkGhSX7Dh4h5nzhAwWJycmm5ZjYC6z8dX2R8jePsTJiRpTqhfuncffzb5GVncMDr3zIrElj6NmpLdk5OazdspNla/8E9IDi3beMsKuOXYeO8/bcRYwc0JseHVoTUj+I7JxcTkWdY/naPzkRqWf56Ne9I/dPHmdxHxu37+Nfn81H0zR8vDx54f5pXE1O5Wqy9TTQjYLr4eVh9fnJWuP/HriT6c++rvfhP9/jvtvH0bNTO7Jzcvjtzx0s+02fq7Rpo2DuvnWUXXXsOnict+YsZNSAPvTo2Ibg+kHk5ORy6mwMy37bzAnj3Jv9enTi/imWH4zbf/QUD730Pr27tKdv9460ahZGgK8P+fkGLsRfYdu+w/yy8W+yjYHUW4YPoHeX9hb3Vdtc+Nd/aL7sPZw83Wm6cDaXv1xK2vbDOHm44T9uAEHTRgL6ufDKvJV21xMwcUiR1x7m58KBxc6F0XGm1KuidHGzvyZ86Xs4eXrQdMFsLs9ZSvr2Q3ofjh1A4LTC65kr8+y/ninRh2YZP3wGdsM1tPDhAkt9GHDrEBp//TKpf+wifcdhsiPPY0jLME4X0II6U0bg0aoJAHlXEomb/bXdbXUkcgwKIWo6CXAKR/EUcBV4GQgGXrNRNh0oPgznIfRAWmP0oFbx3AoxwM1ATXqcvB/wrZV1QRbWDQaqIsD53zKUOQa8rGnailLKVbQfy0TTtGyl1DBgPnqwtDO2g38p9tRTRg3Q//4esrI+GZiiaVqRmdg1TdOUUrcDn6CnT24OvGejnvhKaGuNdeDAUabd8RAL5n+Kv78fb77xYokyJ09FMH7CdNLS0iulzoL0tHl5eSz+obQ/7UK5ubmMHXcnK5Z/S5fO7enUsR1fzX3fYtnIyGgm3X4fCQmJldLm6nD1aDSbH/qcAZ8+hJufF91fLJlGODkijg3TPyAvPatCde17dykedf1oNXUQQR2bMmjOYyXKnFq8mX3vlp5O2M3Pi7ChXQFIPHGOhMNRZW5HckQcG+/9mIFfPIJHkB+Nh3ej8fBuFste+Osomx/4tMz7rkmyjkVy/vF3afTRszj7etPguRklymRHnidm1msYbIzAK00jGyPL6j44qcjrpOUbLAZXtJxczs16jbD/vIpnx5b4jxuI/7iSafxSN+0m9ol37W6rI2nbtBHvPjaNf375I2mZ2Xz6v5KjmpuE1OXz5+7B29O+m9z33zKElPRMvl/7NxlZOXzz8ya++XlTiXIuzs48fvsIxvazfJxkZOWwZtsB1mw7YLWulmHBvPvYNELrlz/FraOSY1BYsnz1Olb9tsHiuv2HjrH/UNH+kQCn/do2b8J7zz/EPz78mrSMTD5duLxEmSaNgvni1ScrlAo2ISmF739ez/c/ry+xTinFzUP78c+H78LV1fLtm4079pFv0OdCTsvI5OFXPyq1zm/eeqFKUurWNG1bNOX9/3uEF9+fS1pGJp/ML3mN2LRRMF/MfrZifZiYzKJV61i0al2JdUopbh7Wn5cemWG1DwHyDQa27TvMtn2HrZZxdnJi+q0jeWKG5Wk7aqOsY5HEPPYuYR89g7OfN8HP312iTHbkeaLunV2hc2HYB9ZHltV/6LYirxOX/SHBlXLIOhbJucfeI/TjZ3D29Sb4Oct9GD3zXxXqw9D3rV/P1Ct2PZO4bIPFPnT28SJgwiACJgyyuq+sE2c59/h75J4v/YHa2kCOQSFETScBTuEQNE3TgNlKqe/Qgz03oafu9Eefb/EcsB/4HfhJ07TMYtufU0p1A14AJgBN0Ee8RQErgU80TUus2dM11gi56MHAZOAksAf4XdM0yxPTFFPRfiwPTdNSgYlKqX7A3egjQxsCnsafIQJ9NOSvxvqqQgf0kcP90AOUDdBHCacCJ4B1wBxN0yxeGWualgs8bBxxfB/6aEhtHaUAACAASURBVM3G6CN509DT2u4FfgN+qaKfocb45df1dO0+lMcfncWo0UMIbRRCTk4OZyKiWL78F7748lsyMysWQCvQokUzevfWb8Rv2LCFS5fKN39iTEwsfW4Yze23T2DirWPo2qUj9eoFopTi6tUkDh8+zqqf1/HdoqVkZNj/IaCmOL9+P6uGvki7WSMJHdIFr5A6GHLySY26RNQvOzn+7Xrys3IqXpGmse3ZeUSv2U3rOwYT1Dkcj0Bfsq6mknAwkpOLNhK7qWxzfjQZ2wsXTzcAIpaXfyRR3Naj/DTweVpOGUijmzoT0KoRbn7eaPn5ZMYnc+VgJJE/bTPNHeqo0jbuInL0IwTOmIDP4J64BtdFy80jJ/oCKb/9xdWFv6BlZVd3MwHIu5zI2YlPEzB5OP5jB+LesjHOvt7kJaWSdfAUSSs2kPr79upu5jU1qHs7lr7zFN+v/YutB05w6Woyrs4uNA4OYljvjkwZ3hdPdze796+U4rm7xjHmxq6s2LSb/aeiiLuSSFZ2Ll4eboQ1CKJ723BuG9KbpiGWE2TcM24grZuEcPB0DJGx8SSmpJGclombqzNB/r60a9aIYb07clPPDjhbmduxNpNjUIjqNah3F5Z9PpvvV61ny55DXLpyFVcXFxo3rM+wG3sydewQPCswErJ7+1Y8fe9kdh08ztnzF0lISsbJyYl6gQH06tiGCcP60al1+eZIFkUN6tON5V++xaJV69i66wCXrlzFxdWFxiENGN6/F1PHDatQH3br0JpnZk5h58FjnD0fx9XEZJSTE/UDA+jZuR03D+tPpzYlp3Ewd9ctI2kWGsLuwyc4GRnDlatJXE1OwWDQ8PPxollYQ7p3aM34If0Ia9jA5r5qo9Q/dnN69GPUnTEe35t6mM6F2VFxJP/2FwkLfq0x50JhWerGXZwZ9ShB90zAd7BZH0ZfIGXN3yTUgOuZK18tI+t4JF5d2+jXMIH+OPv7oOXkkncliczDZ0hZ+zcp67aD8aGS64Ucg6I6GTS5Vy9sU5omGYWFEEJY5+LWSE4UDmxevcHV3QRRQb08HHeEr9CFL7mvupsgKiBy8n+quwmiglru/Ky6myAqyJBYFYlqxLWinOXZekd3csjr1d0EUUFKycd6R6dJoMfhdTy7WjqxnLaFTKy2f15945ZLfzkAucoUQgghhBBCCCGEEEIIIYQQNYYE9kVprr88T0IIIYQQQgghhBBCCCGEEEIIhyUjOIUQQgghhBBCCCGEEEIIIUSNcX3NeCvsIQFOIWoZpVQAEGrPtpqmHank5gghhBBCCCGEEEIIIYQQQlQqCXAKUfvcDHxr57aS2FwIIYQQQgghhBBCCCGEEDWaBDiFEEIIIYQQQgghhBBCCCFEjaHJWBxRCqfqboAQonJpmjZf0zRlz1d1t10IIYQQQgghhBBCCCGEEKI0MoJTCCGEEEIIIYQQQgghhBBC1BgGrbpbIGo6GcEphBBCCCGEEEIIIYQQQgghhHAYEuAUQgghhBBCCCGEEEIIIYQQQjgMSVErhBBCCCGEEEIIIYQQQgghagwDqrqbIGo4GcEphBBCCCGEEEIIIYQQQgghhHAYMoJTCCGEEEIIIYQQQgghhBBC1BiajOAUpZARnEIIIYQQQgghhBBCCCGEEEIIhyEjOIUQQgghhBBCCCGEEEIIIUSNYajuBogaT0ZwCiGEEEIIIYQQQgghhBBCCCEchgQ4hRBCCCGEEEIIIYQQQgghhBAOQ1LUCiGEsEmm83Zs+dKBDk85adXdBFFRudnV3QIhrmuGxIvV3QRRQU51gqu7CaICDFfOVXcTRAUpJdejjs7ZWRI9OjrNIB/uxfVHq+V3JZVSTYDHgTFAGJANRABLgC80TcuopHpGAfcDPYF6wGVgN/C1pmm/VUYd1UUCnEIIIYQQQgghhBBCCCGEEEJcA0qpccAiwM/sbS+gh/FrllJqjKZpZypQhxPwNTCz2KpGxq+blVLzgAc0TXPIJ2EkRa0QQgghhBBCCCGEEEIIIYSoMQzV+FWVlFJdgf+hBzfTgH8CfYEhwH+MxVoBvyqlfCtQ1ZsUBjf3A1OBXsbv+43vzwLeqEAd1UpGcAohhBBCCCGEEEIIIYQQQghR9T4BPIE8YLimadvN1m1USp0G3kMPcj4DvFbeCpRSrYBnjS/3AAM0Tcs0vt6tlPoZ+BN9tOhzSqn/VmS0aHWREZxCCCGEEOL/2bvv8KiK9YHj39n0XiAQIKGE3nsRAekoRVRExQIIiL0Xrvfarnq9ggXbFWw/AQEVKVJFQARRpPdeUmihhfRezu+Ps9ndJLub3ZCQbHg/z7NPzubMOTPJntlTZuYdIYQQQgghhBBCiCqjOo7gVEp1A3ob335TrHGz0AfAYePy00opjzJk9QzmAY5PWjRuAmCc3/NJ41t34Nky5FHppIFTCCGEEEIIIYQQQgghhBBCiIp1m8Xyt9YSGOfDnGN8Gwz0cyYDpZQCRhrfHtE0bYuNfLYAR41vRxq3cynSwCmEEEIIIYQQQgghhBBCCCFExepl/JkO7LSTbqPF8o1O5tEIqGtlP/byqQc0dDKfSidzcAohhBBCCCGEEEIIIYQQQogqQ6PyBhQqpSIcSadp2hknd93S+POEpml5dtIdsbKNo1rZ2I8j+cQ4mVelkgZOIYQQQgghhBBCCCGEEEIIIXSnHUzncCusUsobqGl8a7dhVNO0RKVUOuAHRDqah5Fl42xpDbCWf6ez+VQ6aeAUQgghhBBCCCGEEEIIIYQQVUaBy80IWaoAi+U0B9IXNnD6V2A+6RbLzuZT6aSBUwghhBBCCCGEEEIIIYQQQghdRYxm9LZYznEgfbbxp08F5pNtsexsPpVOGjiFEEIIIYQQQgghhBBCCCFElVFQiXNwlmFuTUdkWSx7OpDey/gzswLz8bJYdjafSmeo7AIIIYQQQgghhBBCCCGEEEIIUY2lWiw7Eg7Wz/jTkXC2Zc3Hz2LZ2XwqnTRwCiGEEEIIIYQQQgghhBBCCFFBNE3LAhKMbyPspVVKhWBufDztZFaWo0/t5kPRULzO5lPppIFTCFGlKKUeUEr9oZRKVEoVKKU0pdSeYml8lFKvK6X2KqXSjWk0pdRHlVVuIYQQQgghhBBCCCGEEOVDq8RXBTpk/NlEKWVvCskWFsuHy5hH8f2Udz6VTubgFFWCUqohEHO1+9E0rfICc19DSil/oBPQzfjqCjQ0ro7TNK2h9S0rpCyNgfuAIcYy1EQfBn8O2AD8pGnaJgf3NQ14sZQ0HsA6oGeZCy2EEEIIIYQQQgghhBBCXFt/Ar3RR2d2BrbaSHeTxfJfTuYRg/5svm6x/VjTx/jzLBDrZD6VTho4hXBNy4G+lVkApZQX8C7wOOBRbHUN46st8KRSahXwsL3JmZVSkcBzxrdbgLfRh8UXUHSC49GYGzdnAbOBy8b3lxHVWv369Xji8YncMnQAkRF1yc7OJjo6jp8WLmfGjFlkZmaVvhMbGjSI4MRxW9cU1sXGnqZpsx421w8e3JexY++ia5cOhIfXwmBQXLp0hd179vPDDz+zcOFyNK2C+4VdI/71atBqwhAiB3TAr24oBTl5pMReJGbFVg7NWkt+Vk655RXRrx3N7+tPWPsovEMDyLqSyqW90Rydt54zv+9zaB+1uzajxQMDqN2lKT61gsnPziXt9CXi1uzi0LdryE60P+1AcNO6RA7oSHiPFoQ0j8AnLAg0jcxLyVzafZLji/7kzPq95fHnVjr3urUIHXsr/n274lEnDC0nl5xT8aT8sonEuSvQsrLLvnOl8GwcgU+75vi0a4Z322Z4tWiEwVM/rcTdN4WMbfvt7qLmk/cR9tR9TmV76ZN5XP50XpmL7UrOXU5i/totbNp7jPMJyXh6uBNZK4TB3dpw94Bu+Hh5XnUeZy8lsmD9drYePMmZS4lkZufg6+1Fozo16dm2CaP7d6VGoO1pP/7Yc5SDMWc5GHOWMxcTSUzNIC0zCx8vTyLCQujSshF39u1Cwzo1r7qsrsijbhih427Fv1+xOrhqE1e+W1k+dbC9Xgd92jXFq3kjDF56HYy99x9kbLVfB0vs0seL4FEDCRzSE8+oCNxCgihITSP3fAKZuw6T+ttW0v/cXfYyu6BzFy8zf9k6/tixl/OXruDp4UFknTAG9+rGPcP64+PtddV5nDl/ifnL1/H37oPEX0qgoKCAWqHB9OjYmnuGDaBJg3p2t09ITGbDtj1s23eYo9Gnib+UQG5eHsEB/jRrFMnAnp0Z3q8n3uXwnXG9S0hMYv+hoxw4dJQDR45z4PAxkpJTABh5y0D+88rzlVzC6ufcxQTmr1jPHzv2cf5yon4uDA9j8I1duGdYX3y8yqEOXrjM/OW/8ffew8RfTKBA0/Q62KEl9wztR5P6de1un5Oby+Ho0xw8HsuB4zHsPxbLqfiLpnuFfUu/vOoyujKPumHUGH8rAf264FEnjILCc+HKP0koh3OhV+MIfNo301/tmuJtcS6MGfMy6Q6cCxvN/y9+Pdo6lOWBqOFlL6+LknsK1ybXo6KyFFR2ASrGz8DLxuUHsdLAqZQyAGONb5OA353JQNM0TSm1FHgUaKGU6qFp2hYr+fTAPIJzqeaCDymlgVNUFWfRG8NsKTyT7UCv+Nc7y5GqV9D/Lz1xbHLiq89cqQBgKdDPogyzgN+AC0AQ0AWYADQHhgJ/K6UGa5pma6h7P8DNuDxJ07SDNtINNP48b0yXfxV/inAhw4YNYvasTwgKCjT9zs/Pl9DQELp06cCECfcycuRYTp6MvWZlOnbspNXfe3p6MmfOZ4y6Y1iJdZGRdYmMrMutI4bw6CPjuP2OB0k2PtRyVZEDO9L3k0fxDPQ1/9IXwjr4E9YhimZj+rJm3Pukxl64uoyUote0iTQf07fIr/3qhOJXJ5SGN3fh6Pzf+XPK/4GNazLl7saN74yn+b39ivze3dsTryA/arRpSIv7+7P+kU+5sO2o1X30mf4wTUf3trouoH4tAurXImrkDZzZsI/fH/uMnJQM5//WKsK/fzfqvv8ibgEWc877euMTHIBPu2YEjx7C6YdeJ/dUfJn2H3Rbf+pOu/YPcnNibPa3qVY27D7Cv75YRFqm+YFDVk4uB2MyORhzjsUbd/LZc/dTv3aNMuex/K89vD1rOVk5uUV+n5Keyd4Tp9l74jTz12xh6mOjuaFNkxLb5+Xn8+R06w+GUjOyOBwXz+G4eL5fu5XH7ujHxOF9rKatrvz7d6Pehy/YroN3DeHUpDfIjStjHby9P/Xee670hA7y7dGOulOfwTOidpHfG7xCcK8Zgk+bJvh2aU30n0+WW55V3Yate/jnB1+SlmHur5eVncPB4+kcPB7L4jV/8L/Xn6F+3dp29mLfwtUb+O/MeeTm5RX5/an4i5yKv8iSNZt4YeLdjBkx0Mb2G/nP53PILyj5COlyYjKXE5PZvOsAsxev5oOXH6dZo0grexGOumn4mMouwnVlw7a9/HP6N6RlmDtCZmXncPBEHAdPxLF47Z/877UnqV+nVpnzWPjrH/z3yx9s18G1f/HChDsZM6y/zX289fk8lq7fXOYyVGcB/bsRMf35IudCg6837sEB+LZrRsjdg4mb+G9yynguDL69HxHvl9+5UJQk9xSuTa5HhShfmqZtU0ptQh/FOVEpNVvTtL+LJXseaGlc/ljTtCI33EqpvpgbPWdrmjbeSlYfAZPRn7d/qpTqo2ma6aZEKeUDfGp8m2dM73KkgVNUCcZKesDWeqVM7XnpmqbZTHcdmQ98AWzXNO0EgFIqlmvUwAl8g7lx83fgHk3TLhZLs14pNR19lOdz6BMaL1dKddQ0LdXKPi27lR+zk3dhumhp3Lx+dOjQmvnzZuDr60NqahpTp33Gxg2b8fbx5u67bmXSpPtp3qwxS5fOoUePW0hLS3c6j7Nnz9Oho+2HDoWmvPQEY8bcAcB3c3+ymuaj6W+ZGjcvXLjE+x/MYPfu/eTl5tKmTUteeOExGjaMpHfvHsyb+znDR9zvdHmrihqtG9B/xhO4+3iRk5bJvs+WE7/5EG7enkSN7EGL+/oT3LgOg2e/wLKhr5KbXvZRtl2mjDY1bl7eH8v+GStIibtIYINatH10ODXbNqT5vf3ITEhl59QFVvdxw1tjTY2bydHx7J+5koQDcRg83al7Y2vaTL4F31rBDPq/51g24nVSYs6X2IdveAgAWYmpxK7cTvzfh0k7fYmC/AJqtGlAm4duIbhJXSL6tmPQt8+z8s63bTa4VmVeraKo99E/MPh4k5+WQcIXC8jYsg/l7UXgsD6E3HMLXlERRH71b2LveJqC9MzSd1qc+fyOlpNL1rFYlLs73i0aObyLxHkrSF39p/1EbgYazJ+GW4Af+anppK4tfu9Q/RyOi2fK5z+RlZOLr7cnE4f3pmvLRmTl5PHrlv0s2riTuPMJPPHhXL5/4xH8fJwfvbL7WByvfbWEAk3DoBQjenWgX6cWhAUHcj4hiWV/7mHjnqMkp2fyzMffs+g/jxNRK7TEfgJ8venSoiFtoyKoVyuEsKAAvL08uJSYyvYjMSzdtJvUjCw++WkdAb4+3NW/a3n8i6o871ZRRHwyxVwHZ/5E+pZ9KG9PgobfRMg9N+MVFUH9r98g5rZnyqkOxqHc3Zyqg4X8enYg8qvXMHh7kZ+cRuL8VaRv3U9+QhLKxwuvxpH49++Ge41g58vpog6fjOOlaTPIys7B18ebiaOH0a1tC7Jyclj9xzYW/bqRuLPnefzfH/HD9Nfw8/VxOo9fNm7lzc9mAxDg58PY226mW/uWeHq4c/jkKWYtWsWp+Iu8++V8QoMDGdK7W4l9XElKJr+gAA93d/p0a0/Pjq1pFFkXPx9vTsdfZPGvG9m8+yBx5y7w0Cvv8ePHbxBes2RdFs6rU7sWjRpEsHnbrsouSrV0OPoUL733pfFc6MXEO2+hW9vmZOXksnrTdhat2UTcuQs8/uan/PDBv/Dz9XY6j1/+2Mabn88FjHVw5CC6tWuBp7s7h2NOM2vxr3od/OpHQoMCGdKri9X9aBazffn5eNOycX1iz57ncqJrd4S8Wt6tooj89CXTufDyzJ9I+3s/Bm9Pgob3IXSMfi5s8M3rnBz57FWfCwtycsk+GofyKNu5ECBj3zHOvvRxmbatjuSewrXJ9agQFeZp9LCzPsAapdQ76M/YfYB70BsmQX9G/kFZMtA07ZhS6j3gH+gDkf5SSk0FTgKNgSlAR2Py9zRNO17Gv6VSSQOnEC5I07RKi0+jlLoLPUwswF5guKZpVocnGRuun1dKBQKT0L88C8PaFudVbDtbCtPZSyOqmQ8/eBNfXx9yc3MZOvRetmzdaVq3YcNfHD8Rw9R3X6V5s8Y8++zDvPXWh07nkZeXx8GD1kfsFTIYDPTpcwMAKSmp/Pzz6hJpatWqyYQJes/8K1cS6d7jFs6eNfdk/GvzduZ/v5idO9bSqFF9hgzpR+dO7di5y7HQqlVNj38/gLuPFwW5efx671Qu7jphWhe/+RApMRfo9soYghvXoc3DQ9n94eIy5RPYKJy2Dw8F4NKeaFbe+Rb5WfrXwOW90cSt2cWwha8Q1iGKdo8M5diPG0uMGK3ZPoqWDwwAIOFQHCvveJvcNPMN2MUdx4lbvYMRy97AK9iP7q/fx9rxJa8j089d4c8p33D8p00U5BTtqX95bzQnFv3FzfOmEN69OeHdm9Pkjhs5saiUm+UqKPyVRzD4eKPl5nH6wVfI3HPEtC5jy15y4s5Re8pEvKIiCJ1wR5nCM2WfOMX5N2eQuf8Y2Yei0XJyqfnkfU7dzOZfSSb/SrLdNH59uph6HKf+8idadvmFTK6qps1dRVZOLu5uBma+OJb2Teqb1nVvFUX98BpM/3ENcecTmLP6Lx69vfQOHsV9s2ITBcbG+388MIy7B5gbTtpE1WNg19a8//1qvlu9maycXOas3sw/xxYNyebu5sbG//0DN4OhZAaNoG+nFowZ1IMxr88kJT2TzxevZ1TfztbTVzO1X33YVAdPjX+VzN0WdfDvfeTEnqX2P/Q6WGPi7Vz6ZL7TeWQfP0X8v2eSte8YWcY6GPbUvU4/UHILDaTexy9h8PYi8+BJTj34GvkJSUXSZO48TNKCNeBx/dyCTv1yPlnZObi7ufHFm8/TvqV5FHP39q2oX7c2079dQNzZ88xe8iuP3XebU/vPzMpm6lf65+7r482sqf+kacMI0/rWTRtxc59ujHvpHY7HnuHdL+bRu0s7fH2KNuL4eHsx4c6hjL19CKEWkTIAWjZuwOBeXXn/6x+Y8/OvJCan8vncJbz5zERn/x3C6JEH76VNy2a0admMmqEhnI2/wJA7x1d2saqlqV/9aDoXfvHvZ2jforFpXfd2LahfpxbTZy8i7twFZi9dw2NjbnVq/5nZ2Uz9+kcAfL29mPXfl2hqEQ66ddOG3NyrC+P+MY3jcWd596sf6N25TYk6CNCrUxu6tmlO66YNiYoIx2AwMOFf71/3DZx1XptsOhfGjnutyLkw/e995MSeI/zlCXhFRVBz0u1c/Lgs58LTnHtjJpn7jpvOhbWedv5cWEjLyCb7WFyZtq2O5J7Ctcn1qKhsBUqVnsgFaZq2Wyl1NzAXCATesZLsGDDMxkAhR/0LqIUeZbEj8IOVNN8Ar1xFHpWq+j8ZENcNpVQnpdRMpdRRpVSaUirduDxDKdWslG2jlFLPK6WWK6VilVKZxlecUupHpdTNpWw/XimlGV8NlVKeSqnnlFI7lFLJSqkrSqkNSqlhxbYLUEq9pJTarZRKUUolKaXWKqUGlMf/pIL8w2J5sq3GzWKeRw9dCzBBKRVWuML4/9aA1y1+pxV7NSxcxjwx8k3F0sRay1gp1U8pNVspFa2UyjD+n/crpd5TStmcCEUp9YZFniilvJVSLyqldimlUo2vbUqpJ5RSdq+MlFL9lVLfK6VijMdVhvHY2qKUel8pZfep8tUc29VB1y4d6N1bn+fy229/KNK4WWj69C84dFgf+PvkExNxd6+Yi9UBA3pTr14dABYvXklWVsnRiN26dcTNTY+2PHv2giKNm4VSU9P45JOvTO979OhcIeWtaDU7RBHeQw/Vf/SHjUUaNwvt/2IVicfOAtB6whCUu1uJNI5oM+lmDMabkL9fm21q3CyUn5XD36/pI1gMHu60mVTya7vp6F6m5W1vzi/SuFko8egZDn6tN1zXH9iRkBYRJdJsev5Ljs77vUTjpmVZNv/zW9P7hsNKjpap6rzbNcO3axsAkhauKfIgotCVbxaTfeIUAKHjboUyfLZZ+46R+N1ysvYcRcupuH4rQRaNd0k//1Zh+VQV+0+eYZfxwdptfToVadwsNPbmnkTV1U/H89ZsITfP+aAIe0+cBiDY37dI46alh0f2NS3vM6YvrrTGyoiwEAZ3aw1AYmo6Meeq/5Tb3u2a4ddNr4OJP60p8jCpUMLXS8g+bqyD40eWvQ7OWU7mVdbBWi+Mxz00iIKMLM488laJh0lF5Fr/7qxu9h+NZtdB/drk9kG9izRuFhp3+xCiIvXL0XnL1pYIb1maTTv2cSVJb/y479aBRRo3C/n7+vDipHsASEhKYem6kh1uHrhtCM+MH12icdPS0+PuJCxUH+3w2987KbASzlY45olJD9D3xu7UDA2p7KJUa/uPxbDrkD4Q4faBvYo0bhYad9sgoiL0a/t5y9eXoQ4e4Eqy/szxvhEDijRuFvL39eHFCXr/4ISkFJthaG/u3ZWRA3rSpH5dDNdBJx5H+BQ5F661ei68/PUSsoznwhrjy3Y9mrnvGFfmrLjqc6EoSe4pXJtcjwpRsTRNWw60A6ajN2ZmoM+3uQPj6MrCyI1XkUeBpmkTgWHo082dA3KMP5cCQzVNm6Rpmste3MtVk3B5SimDUupD9Mr/MNAM8AN8jcuPAAeVUpNtbN8IfWj2+8BwoAHgbXzVB+4CflFKfVdaQ5ZRIPAH+vDxzsb3IegNcyuUUs8a860P/A1MBToAAehzVw4E1iqlnJvd/BpQSrXDPHT9b03TtjmynaZpKUDh035voMInnjE2SH4PrEeflLkR+jD/AKAN8AJwTCk1woF91Ub/rKah//3+xldX9Fjli42TP1vbdjr63KT3AA3R/34f9GOrO3rjr9VYmld7bFcXt440N1TNnv2j1TSapjF37kIAQkKC6du3Z4WU5YH77zQtz/nOenhaT09P03JMjO2euyejzes8LLZxJQ2GmENsHV/wh/VEmmYavegV7Efdni2tpytF/SGdAEg6fpZLu6zPfXpp10mSTpwzlq1ko3HNdlEA5GXlEP+3remA4cwG82jahkPL1jiZePQMmQn6Q+fABmWf06myBAy8wbSctGit9USaRvIS/cbeLSgAvx7trkXRnGbw9yFggN5JIud0PJnbq3+k+993mY/vkb07Wk1jMBgYfmN7QJ/rcvvhGKfzKWwUrRdmO8RTgK83IQH6/Ly5+WWPLO/nbQ6hm3MdPJAIHNTDtJy8cJ31RJpG0pL1ALgF+VdaHTQE+hN0q97/LHnp7+Seu1Qp5ahq1m8xhxwdOaiX1TQGg4ER/fVrltT0DLbvK/ng0J5DJ2JNy7062/78u7RtgZenBwBr/9rhVB6FPDzc6WBspE1NzyQpNa1M+xHiWlm/dY9peeQA6/cGBoOBEf3079vU9Ay277cfzaW4QyfM1/O9Orexma5L2+bmOrhZwhE7KnCw+VyY+JPt61HLc6F/Fb0evV7JPYVrk+tRURVolfi6FjRNi9M07TlN05prmuanaVqIpmldNU2bZm9QkaZpGzRNU8bXeAfyWaVp2m2aptXTNM3L+PM2TdN+Kdc/qBJIA6eoDj4FngUUesPiBKAv0A14CDiIHo75C6WUtZgzbug9F5YDT6E3MHYy/nzMuD3A/cCrDpTnS/SGzc+BQegxrieh94wAeF8p1QZY0DxKDwAAIABJREFUDEShh2zti95Y9gyQbPxbZiilqtpT8T4Wy8ud3HaZxXJvi+XBQFtghsXv2hZ7nbVYLnwqs6NYmsGFGyt90taF6I2KhWV9ALgRuAE9zvkp9MbChUop6xOhmC0GWgGfoH+mnYF7gcInyCPQj7UilFLD0T9TgH3Ao+ifdUf0OUyfAH4Gsm3ke7XHdrVwY099rrW0tHS7YVw3/bHFtNyzZ/nPz+bv78ett+qNrTExp9i0aYvVdMeOmRvfGjVqYHN/jaPM6yy3cSXhXfUBxLnpWVzeZ7tx5LxFY2Ktrs4POg6oH4ZfuD7XV/wW+w9/zxvX+9UJxT8yrMg6rxB9muLsxDS0fNud0zIvm0MThXdv7nR5C7kZR5xqLjjKxbeLPlquID2TrAO2p2HI2LbftOzTqVWFl6ssAm7ujcEYCi755/WVXJprY/cxvRe1j5cnrRraDFZAlxYNTct7jD2vndEwvAYAZy/Z7h2dlplFYmqGMX1Np/MAyMrJ5fddet02KEUDY77VmY9FHcx0sA76dq6cOhjQv6upjqWu22r6vfL2wqNBHdxqXp+j1HYbR475eHvRqklDm+m6tDGfZ3Yfdm7am6QUcyNjjWDboy/d3dwI8tdD6u09cpK8MnY2sOxccD2EiRaubfchfcCDXgdtX5N3aWO+Nt192LlrcsuGfsfrYHSZ6+D1xreLfl7LT88k84DtASzpW80NTb5dytaZUlQMuadwbXI9KoRwBRJwWrg0pdQg9EZIgEmapn1TLMl2pdRcYCXQH/hEKbVK0zTLrv/xQENN00rGkYTflFIzgf8DxqPPJ/mhpmn2AvN3A+7QNO1ni9/tVEptB3ajdyxYjz6y8yZN07ZapNuhlDpuLG8AcB/6MPWqor3FsrNdT/cABeh/v2k/mqYdA1BKXbT4nbWucAeM6dKN79NtpAO9QXkY+jydt2qaVnyixC1Kqe+ATUBr4CPAetd6XVdgsKZpGyx+t0sp9StwCKiNfhx+UWy7u4w/44AbNU0r3tV9A/A/pVRo8QzL6diuFlq0aArAyZOx5Nt5GHDkqPmmt3Cb8jTqjmH4+emjkObNX2Qz3YEDR9i8eTs9e3Zl7Ni7mP7RF8THF50L0t/fjyefnATof9fatRvLvbzXQlBTveEkJfaC3QbDpJPnTMvBTUqG7ipNcFPzNsknztlJiWkEp75dXdJOm3tu5qXrIYU9/H3s7sPTONqseN7OqNG6AZ6B+n6Sjtsvc1Xk2TgSgJxT58DOZ5sdfca07NW4ZBjUqiDodnPU9+TrIJQUQEy8ftzXrx2Ku5vtMFGN6pg7AcSUoZfz6P5defPbZSSlZbBg/Xbu6l+yc8mXSzcWSe+o3Lx8Liensuf4ab5duYlTFxIAPeSun49XKVu7Pq/COhgXb78OnjTXwcJ6e635dGhhWs46Got326bUen4sfj3bo4zHX15CEikrN3Hpsx/shwurRmJO69/99evUsl8PI+tYbGPtdsQ2y3n80jJKhl0vpGkaaZn6OTA3L4/T5y4WydcRuXl57DuiX2vVCA4kKMDfqe2FuNZizuj1qX6dMPt1MCK8xDaO8rWILpCWXkodNNbR3Lw8TsdfpFGEc3XweuT4udAcAt+rks6FljwbRxC1+AO8ouqhvDzJv5JC5oETpKzeTNLyjVCGaQFcldxTuDa5HhVVget1FxfXmnS7FK6ucD7IRVYagADQNC0LfaQc6OFn+xVbn26jcbNwvYYeRjQffcTfwFLKtKBY42bhfvYBhZPehAEfFWvcLEy3Cr1BDIqOdKwKLIdenHdmQ03TMoHCSZErbOiFcfTmFOPbT6w0bhaWJxF40fj2RqWUvRaxT4s1bhbu4wrm0LttlVJBxZIU3i3vstK4WXw/xV31sV0deHl5ERamHy5nSnngkJSUTFqa3v4dGWF7xFJZ3X//aNNyYThcWyY99BzR0XHUqBHCtq2reebpyfTpcwM39uzK5IceYOeOtURFNeDSpQTGjXuS3FzXm+vFzcsDnxp6T/X0eGuHsFlOcga5xsZF/7ol2vNL5VfHvE36eft5pccnWGxX9KumsPHTM8CHGm0a2txHeHfzzZFvrWAMHs7PI9L+SfOg6pgVJb7qqzTl6YF7qP51lns+wW7agpQ0CowP9NzrlG10XkXyqFfL1HM8Y8dBck85depySdk5uaYRk7VCbI8mAQj088HHSw+Rff6Kvb5b1t3WpxMjbuwAwH/nrODf/7eUDbuPcDDmLOt2HOKZj79n9i9/AfDQiD70aF1y/jNLZy8l0n7ca7Qf9xpdJv6bm5/7kH/M+Imjxs+tZ9smPD9miNPldDXK0wP3GoV10P58o5Z10KOS6qBXU/ODSL8b2tHop/fx793J9DAJwL1GMKFjRxC14lO8WjSqjGJeU9k5uSQaR1fWrmn/vBfo74ePsZHk/CX757jioiwaKXfYCa15+GQcGZnmecPjL9n/brdm0eqNpr9pcK/yj5QhRHkqUgdr2B+1U6QOXk50Kp8iddA45641h6NPkZFlDtoT72Rdvx45dy5MJ990Lgyzm/Za8AgLwbdDc9wC/TF4eeJRpyaBg3oQ8cFzNFnxCV6NS86XXB3JPYVrk+tRIYSrkBGcwmUppQLRw3WCHo7UJk3TDiulLqM30N0A2Aj+D0opD/QReQHo4WsLJQC10Ecf2h6+BT/YWbcXc5hXe+n2oTdYRdlJUxkCLJbLMvFOGvo8o/afuF6dVkDhE1T7rVB62NdCNwC2Ym7Ms7OPncafCn2ezz0W6wpb5PoopRprmuZQzKOKOrZdUUCAn2k5LT3dTkpdenoG/v5++Pv7lprWGZGRdenTR59/YvPm7Zw8GWs3/fHj0dzQcxgPPzyWF194jPfee73I+pycHD74YAaffvYNZ88611O8qvDwM48aKRwZaU9eRjYeft64W2zncF4WIy5z021FdDbnYy5j0VFep9bsovFt+hxMnV+6kzXjPgCt6MwKXiH+tHn4lqL5+/mQneT4V17DoV1pNLw7AJf2RhO7arvD21YFBov/d4Gd0QimNJlZGPx8MPjaHxlbGYJuG4AyhlFMXmJj3phqJj0rx7Ts6136/L4+Xh5kZueQkZ1Tatri3AwG3p58Bzd1bM7Xy/9g8cadLN64s0iari0bMcmBxk17QgJ8efmB4Qzs2uq6CItZpA7aGZVnSlNYB/0qpw66BZlH8tV563HQNC5+MIekJb+RdzkJzwZ1qPnQKILvHIRHrVAiZ75C9PAnKEgr/W9zVemZ5r/NcoSXLT7eXmRmZZORVfr51FKvzm1xd3MjLz+fOT//yoj+PQkJCiiSpqCggE+/W1ysfM7lc+b8RT79Tr/98fXxZuLoYU5tL8S1ZnmMO1QHvTzLVgc7tcHdzUBefgFzlq5lRL8ehARaqYNzi/Z/drYOXo+KXo+W/v/SMrPAzwdDGe41yotWUEDaX3tI3bCDrMMx5CemYPD3xad1Y0LG3Ix30/p4N2tAw/n/Jfr256r9HIFyT+Ha5HpUCOEqqv8TAlGddcR8DH+vlNLsvTCPPgwvviOllIdS6nGl1Bb0RrjT6KFH91u8CufDLK07ku2um2AZA8GRdAF20lSGVIvlssSlKtwmpRzKYovlfJp/l3JMWLZYlDguLNib9M+y+23xz2uO8WcN4IBS6gel1INKqSal/A3ldmzbo5SKcOTlzD7Lm7e3+QY1N6f0UY7Zxgf0ltuVh3vvHYXBeENT2ujNQsOHDWLMmNsJsBLCzdPTkzvvHME999xWruW8lty8PEzL+bmlR0bON35+7g40uNjLqyDHfl752eb1xfOKWbGVhIP6APnI/h0YPOcFwjo1xs3LAw9/H+oP7sTwn1/HLzyU/Gzz8ebm43iZg5rUpfcHkwHIy8xm49MzHd62qlCe5r9Xc+Cz1YyfraEMn21FC7xVH9hekJVNyqpNlVyaa8NyjjwPOyH5Cnm66/0ds0upW7ZEn7vE8j/3cOLMBavr9504zZKNu7hwpfRTf62QQBb+53EW/udxfnzzUT599j7uGdidzOxc3p69nFkr/yx1H9WB8nKtOmjwNZ9zDd5enHv5Yy5//iN58ZchN4+cE6c5N+UjEr//BQDPyHBC7q3eDWQ5Ftcs7u6l9yn29Cish85FdAgPq8HoW/oCcDEhkbEvvcPvW3aRlpFJdk4ue4+c5LE3pvPXzv14WJQjO8fxDg2ZWdk8+5/PSDU+nH754fuoVcqIOCEqW5nrYLazdTCU0TffBMDFhCTGTpnG71v3mOvg0Wgee/NT/tp1sFgddL3oLdeawclzYYHxf2p5Dr3WTj36DrEPvELCNz+TvnkvWYdjyNh+kIRZyzgx7EkSF+oNYx5hIYS/+lCllfNakXsK1ybXo6KqKFCV9xKuQUZwCldWq/QkVhUZ2mWc/3AN0NnB7UvrjpRhZ50pdLimaY6kcz4uYsWyjEsRjj4i1SFKKR/MDYDOx8VyXLkcF5Yc/Kyg2OeladpvSqkngPfQj5u7jS+UUmeBFcAMTdOK/x/L/W+w4XTpSSpO3brhhIQUj+qrS0xM5ty582RZ9KL28PSwmtaSl/EiPMvJ3telue++Uab9LvhpWanpp019jWeffRiAn5f+wocfzGTvvoPk5xfQsmUTHn9sAuPH38O7/32Fbt06MmbMIxQUVJ2ZBXzDQ/AK8rO6Ljs5nYzziUUbAD1Kv5xwM35+eVnOjxKzzMvgaT8vNy/z+uJ5aQUa6yZ9xM3zpxDUKJzIfu2J7Ne++C44PGcdNds1IqyDPuIs18Fenb61gxky5wU8A3zQCgrY9PxXpc4ZWhVpFg++lQOfrTJ+tgVl+GwrkneH5nhF6X00UtdtoSDN3ld59eFp8Znl2pm3uFBOnv7AwquUumXNrqOxPPXRfFIzsqhbM5jH7xjADW0aE+jnw5WUNDbsPsr/Fv3G6q372Xk0lpkvjqNJhO1TnIe7G00japvet2hQhz4dmjPqps5MevdbPlm4jrgLCbw56Xany+pKtGzXqoMFFuXNOhxN8s+/W0138f3ZBN0xAIOXJ4HDepPwpWMdhlyRp8U1S15e6Q8FCzsmeDlwrVPc8xPv5sz5S2zasY+4s+d5+u1PS6Rp3bQhrZs2YsEq/bPx83GsI1hefj4vvPs5R2P0S8a7hvZj5EB709YLUTWUuQ56laEOPninXgd3HiDu3AWefufzEmlaN2lA66YNWfCLPi+1o3Xwelbg5LnQYPzMtTJEpCgvBal2Ig7l5XP25U/w7dgcr8aRBA3pSXztGuRdqMhHI5VL7ilcm1yPCiFchTRwCldm2Zj0MLDZwe2KT6zxMebGzZ+B/0MPEXsRyDLOwYlS6hQQiR6K9Hq1z2K5I/CrE9t2wDwq0eGG0TKwPC5GALEObnex/IsCmqb9Tyn1E3AvMAi4ET1Mbz3043ayUuodTdNesdisvI7tKu2tN6cwduxdVtfNmbOAiZOeJdXiJtHfz3qDmyU/P72NN60cbzq6dulAyxb6FK3LV6wlOdn+KKRbbhlgatycPftHJj30XJH1e/Yc5KHJz3PmbDyv/OtZ7rh9GI8+Mo7/ff6ttd1Vis4vjabZXX2srju24A82PfelaU5NwKGws+6+engwR8LZFmfZwFg87KytfMB6ONu005dYOvRV2j0yjCajeuEfYR6Un3j0DPtmrOTEwk2M+n0qAAV5+eSmlt7A6Rnsx83zphBQX2+8+fvVOUQv21LqdlWRZZgeR0IMGYwP6RwJXXQtBd82wLSc/PNvlViSa8vPotd0hgMPGDKNHQh8nRzxkJObx5QZC0nNyKJmkD/fvfoQNYPNgQxqhwZx94BudG7ekHvfmMmlpFRe/Wox3//7EafyAWhWP5wnRg3gP3NWsHTTbm7u3paebUsLhuC6itRBB8K0meqgA+HfKoJledP+3G0zXX5SKln7j+PbpTXeLRuBhzs4MCLAFfn5mD83y3n3bMk0pvEtQwQKTw8PPn3taZas3cSPK9dzNOY0xtsXQoMDGTWkD5PvvpVpX31v2ibAv/RrKk3TeHX612zaoV/+D+ndlX8+cr/T5ROiMlg2IDpUB40PxstcB195giXr/uLHVRs4GnvGXAeDAhg1uDeT7xrGtG8WmLYJKOfpNKqjotejpX8uynQurMLhf/MLSFywlvCXJwDg170Nycs2VnKhKo7cU7g2uR4VVUXBdf0YXjhCGjiFK7Ps6pahadoBZ3dgnOvwbuPbeZqm2btrl1hMReesvBV414ltb7Wxn/JmeVwkleW4KG+apl0EPgI+UkoZ0Bt7bweeAIKBfymltmuattS4yVUf2w6KdDBdpY30zM7O5vLlK9SsGUpERB27aYODg/A3PrA7fab8Rs3df/+dpmVHwtNOmDAG0Ofbee31aTbTvfvupzz91EMEBPgzfvw9VaqB0xH52blkXUnFOzQAvzqhdtN6Bvma5uxMO3fFblpr0uPN2/iF28/Lr04Ni+2s94jOTc1k53sL2fneQrxC/PEK9ic7Mc00z6YyKPwjwwBIcmAEpoefNzfPfYmQFnqV2jntJw7Pdt25WbScXPKuJOMeGoRHeA27aQ2B/qYHFnnxl+2mvaY83AkYqjfS5126QvqmXZVcoGvHy9ODYH9fktIyuJhov0NGSnqm6aFueKj1EfW2/LX/uGn/YwZ1L9K4aalJRC2G9WzP4o07ORR7jqOnztO8vlMR1QHo26kF/5mzAoB12w9W6wbOonXQ/swIlnUwt5LqoGW+efH25xMrTKvc3HALCiD/skv1zXKYl6cHwYH+JKWkceGy/fNeSlq6qYEzPMz+Oc4Wg8HAqCE3MWrITaRnZJKQlIK3lyc1Q4JMIfZPnTOHkW4cWbfUff5nxnes3KB31OnVuS3vPD/ZtC8hqjovTw+CA/xISk3nQoL975kidbBm2W75DQYDowb3ZtTg3qRnZJGQnIK3pyc1QwJt1EH79zXC2XOhH26mc2HVntcy68Qp07JHbfvX2a5O7ilcm1yPCiFchTRwCle2B9DQR1TeCMwtwz6aAoVxaH60lUgp1YKyzTlZrWiatlcptQe9ge4GpVQXTdN2lLadUioAGG98mwX8UHGlxLKr1o1AlZqwS9O0AmAXsEsptQTYaVx1F1DYwFkex7YjZTnjSDoPz3oVkT0TJz3LxEnPlpru8OFj9O7dg8aNG+Lm5ka+jZCLLZqbH3YfOXK8XMro7u7OXXeNBODChUv8+qv1MCdFymEc7Xnx4mXOnTtvM112djaHDh2je/dONG/euFzKW142Pfclm577stR0ScfOEt6jBYENa6PcDGj51sPsBjc2P0hNOnHW6fIkHTdvE9TE/kPZYIv1ScdLb5zMTkwjOzGtyO9Cmkea5u+8tOek3e3dvD0YNOt5UzjbfTNWsOeTpXa3cQU5J0/jHhqEZ/264GYAG59tYbgmgOyTp6ymqQwB/brhHhIIQPKyDVCFQkBfC1F1w9h1LI5TF66Ql5+Pu425OGMsbv4b1Q1zKo/oc+ZtWzawXy9bNqwLG3ea8ixLA2dIgHnE2bmEJDspq4fsE6dx7xaEZ4M69utgY3MdzDlZOf2Rso/HAb31N4ZSZlewbCBzIISyK4uKrMuug8c4FX/Rfj08HW9ablQOjR5+vj74FRtpkZ9fwNEY/Ts6IjyMkCDrHRIKTf92gSmcbec2zfnwn08UmT9QCFcQFVmXXYeOcyr+kv06eMZ8vd6olA6VjvDz9cbPt+iIQ70O6t/REeE1CQm0XweFzvFzobnfbnYlnQsdplV2Aa4tuadwbXI9KqqC6+xrU5SBdMEULkvTtEtAYfy/e5VSzj2Z01neqduL1eR8PLXqa6rF8pfGuTVL8x76nJ0A/2f87CrKLqCw4W6yUqrKTnCiadouzGFla1r8vjyO7Wrjr83bAfD396Nzp3Y20/Xu08O0vNm4zdUaOnQANWvqoyl++OFnm42rlgrn+XF34EGgh3Eui7w817yoPr/9GKCPYKzZrpHNdOE3tDQtXzRu44zUU5dIP6+PgKnTo4XdtOHdmwP6qM+002X7qmk0vJtpOcZOmFnl7saAL5+mjvHvOzxnHdv/U5H9N66djB0HAT2clHebpjbT+XZra1rO3HWowsvlqKDrNJRUoY7N6gN6yL1DsbYb+ncciTUtd2ha36k83C0eDOSV8rAnz+K7072MI8AsR6P6etsPVV0dZFrUQR8H62DGzsqpgxnbD5qWPUtpvPZsoDceFGRlk5+UWqHlqmwdW+mfW2ZWNodOxNpMt+PAUfM2LW1/1ldj+/7DJKXonXmG9O5mN+2XPyzj20W/ANCmaSM+e+1pvJ0MYS1EVdCxld75Ua+DcTbT7Thgvjbt2LJiOh1u33+EJOPUG0N6da2QPKqjjB36ec3NzwefNrYjN/h1b2OxzeEKL9fV8GpibozNveh8ZBtXI/cUrk2uR4UQrkAaOIWre9v4MxBYqJQKtpVQKeWllHq8WIPXCcydQcYppUoE9lZKjUAPJSoATdN+ABYb33YEltlqgFNKeSilpqHPIwlwEvhHBZevAHjH+DYKmKOUsvkkVCkVqJSqkM9XKXW3vQZgpVQXzKGPY4qtvtpju9pYtnS1aXncuLutplFKmULJJiYmsWGDo9OW2mcZnva7uT85tE1srN5jsWbNUFq0sH0jHhISTOvWzY3bVJ1eqs6I+9U8gLupjTk7UYomo3oBkJ2UzrnNZXvocOpXPRxQcNN6hHWy/vAprFNjgpvWM5Ztp9U0pfEODaDl+EEAJJ2M5+wf1iNEK4Oi32ePEdm/AwDHF25i8z9nlSnPqih13d+m5eBRg6wnUoqg2/Wb/vzkVNK37LOe7hpzCw7A/6YuAGQdPkn2keJfr9Vfv07mTgVLN1mfg6agoIAVf+lTYgf4etO1pe1OCtbUCzOH8dt11PaDY4CdFg2plts5Y63FQ4umEbXKtA9XkrLW3Lki6M6B1hMpRfDt/QHIT06rtDqYse0AecZRtf79uxXtFW/BI6K2PtcRkLHzMGjVuz92/x6dTMtL11oPKFJQUMDy9fo1S4CfL13b2e/EUxaapjFjvh5ZwN3djVFDbrKZdu7SNXw2dwkATRtGMOPN50qMBhXCVfTv3sG0vPQ36/cGBQUFLP9d/74N8POla9vm5V4OTdOY8cNywFgHB/cu9zyqq5Q15nNhyGjb16OW58K0KnI9apWbocjfkbGt0mfTqXByT+Ha5HpUCOEKpIFTuDRN01YBHxvf9gEOK6VeV0oNUEp1UErdqJQap5T6GogHPsNi1KamaQnAKuPbm4E1Sqk7lFKdlVK3GLdbAkQDVWYyB6VUE6XUeMsX5hC6/sXXKaWcjwVn3wTM82gOBI4opd4z/s86KaX6KqVeAPYBLxrTnQFu1TTtWnSPmon+uQGMBg4qpV5USt1kPC76KKUmK6XmA+eANyqoHFOBc0qpWUqpCUqpXkqpjkqpgUqpN4Bfjenyga8tN7zaY7s62b5jD5s26RfWDz54Dz26dy6R5tlnH6ZVy2YAfPrZN6ZRlJb69LmB3Jyz5Oac5Zuvp5eab0hIMENv0W+09u8/xN69B0vZQrdyxVrT8gfv/xsPD48SaZRSfDT9Tby89Lb3latcszfo5T3RnN9yBIDm99xErU4lG3TbPjyUkGZ6o+PB//sVzcpo1fAbWjLxzFwmnplL7w8nW83rwNerKTBue8Ob43DzLvp/dfP24IY3xwFQkJvHwW9Wl9gHgG9tm30F8AzyZdC3z+EVpA/o3/yy7XlRe02bRKPh3QGIWbnNoZC+riRr3zEytusPXYLvHIxPh5IP3UMn3oFXE33U35XZy6DYZ+vbrS0tj6+i5fFV1Jlaejjq8hI4/CaUp358JC9Zf83yrUraNo6gU7MGAPz8xy72nijZiWLO6s2mMLP3De6Bh3vRUE7bD8fQftxrtB/3Gq9+tbjE9t1aReFt/D//tH47x09fKJEG4M+9x1i/U+/YUCsksER42vU7D3OplJ7TO4/E8sXSDQC4uxm4pYft0fzVRda+Y6QbH3yGjB6MT8eSdbDGpNvxMo68vTJrack62L0trU6upNXJldSdVoF1sKCAhK/1Y8QzojZhT9xTMo2bgTpvPoYyhohMnL+qZJpqpm3zKDq11q9NlqzdxN7DJ0qkmb3kV6JP66Os77t1UIkwsNv3HaHd8AdpN/xBXpn+dYntAZJS0sjJzbW6Lj+/gHdmzmX3IT10/8TRw4gItx4Y5Oe1m3jvaz0KQYN64Xz51gsEBVz3M3QIF9a2WSM6GUdSL1n3J3uPlJx2YPbPa4k+o4eJvm9E/5J1cP9R2o2cTLuRk3nlY+vXhaXWwS++Z/dhPe+Jo24horb9ueyEWWaRc+Egq+fCmpNux9t4LkyYVfJ61K97W9pEr6BN9ArqTXumwsrq16MthgA7QcHc3aj336dMZU1Zt7XS5iq8luSewrXJ9aioCgpU5b2Ea6iWD8PFdedZ4ArwKnoY1DfspE1Hb0yy9Cj6PI310RvrindLOgXchrkhtCroBdh68l7Dyrp+gO3JAJ2kaVqyUmoIMA39/xcKvGB8WbMamKxp2jUJxq9pmqaUuhu9gfARoLGxrLZcrMDiBAPjjC9rsoFHbMxlerXHdrXx3POvsXHDUnx9fVi1aj7vTv2UjRs24+3jzd13jeShh+4H4Oixk0yf/kW55Hn3XSNNDZBzvlvo8Haz5yzgyacm0aplMwYP7svWLav43/++Zd++Q+Tn59OyVTMenjyWG27Qe4OeP3+Rjz4qnzJXhi2vf8fwn1/D3ceLIfOnsPfTZcT/fQh3b0+ibr2BFvfrvTmTTsZz4Iuyf42mxJxn/8yVtH/iVsI6RDF8yevs+3w5qXEXCWhQi3aPjaBm24YA7Ju5ipQY640t7Z+8lTo9WhK9YiuXdp0gKyEVzyBfandrTssHBpoaQHdO+4n4zdbD63R79V6a3aOPgLly5DR7P11mGjlqS+JRh6by8/hLAAAgAElEQVS8rVLOvz2Thj+8j8HHm8hv3yZh5gLSt+7F4OVF4PA+hNwzFIDs6DNc+b+SDWCOCrqj6GnXu2WUadmvT2c8Imqb3ufEnSOzlLBHhaGktNw8kpeVPm9udfXS/UMZ//bXZOXk8sh7c5g0vA9dWzYiKzeX1VsOsGiDftppEF6DsTff6PT+A/18mDC8N58vXk96VjZj3/6KMQO706NNYwJ9fUhISWPDriMs3riTAmPP6KdHD8JQrDf177sO89LnC+jdvhndW0XRuF4tAny9ycnL48zFRDbuPsqabQdM+5g8si8N61wfD4cvvPUFDRe8h8HHm/qz3uLyjAVkbNmH8vYiaHgfQsbcAuh1MOGbJaXszbagUUXroFcrcx30L14HY63XwSuzlhE4rA8+bZoQ9vR9eEZFkLT4N/ITkvCoX4caE27D1ziyOPX37aSu/qvM5XUlUybfy7iX3iErO4eHX/uASaOH0bVdS7Jzclj9x1YWrt4I6A2K424fUqY8tu07zH9nzuXmPt3p0qY5dWrVIDsnl2Oxp1m0eiNHovUODr06t2XyXSOs7mP937v496ez0DQNf18fpky+lyvJqVxJtt35oF542HURLroi7Np7gFNnzHOvJiYnm5ZPnT3HzyvXFkl/2zAbo55EqaY8dDfjpkwlKyeXh1//iEmjh9K1bXOys3NY/ed2Fv66CYAGdWszbuTgMuWxbf9R/vvl99zcuytdWjejTlgo2bm5HIs9w6JfN3HEOPdmr85tmDx6qM39XE5M5q9dB0v8rlDxUagdWzWhfp3qH9Eg/s0vifppGgYfbxrOfpNLM34i/e99GLw9CRreh9B7zefCy1+X/VwYPGpAkffeFpEt/G/qhIdF9IicuHhT+FzT9ncMoP6Xr5L62zbSt+wnO/oMBWkZxtCeTQi5Zwjexs5neZcTiX+zenWOtEfuKVybXI8KIao6aeAULk/TNA14Uyn1HXpjVn/00KRBQAZwGtgNrAGWaJqWWWz700qpTsAUYCTQAMgCYoGfgY81TUu0Er32uqZpWhbwlFLqE+B+YAjQEL2BNRV9ZORGYIGmaX/Y2k8Fli8XeEwpNQN4COiL3ojtD6Shh4TdCfwCrKigYvQDRqCPwGyG3kgZgn5cngR+A2ZomhZt42+4qmO7Otmz5yD33vcos2d9QlBQIP95++USaY4eO8nIkWNJS0svlzwLw9Pm5eXx/feO32jl5uYyYsT9LF70Le3bt6Zt21bMnPme1bTR0XHcdfdDJCQkWl3vChIOxrH+0c/o+8mjeAb60vXlkmGEk07Gs2bc++SmZ11VXjum/oR3zUCa39OXmm0b0n/GkyXSHP1+Azun2Q8nHNIiks4tIq2uy83IYse7Czj0f2tsbt9wqHnupNAWkdy2+m2baQt9E3F/qWmqmuxD0Zx95l3qvv8ibgF+1HphfMk00Wc4/dDrFKSX/eun7tTnbK6r+fBdRd4nLV5r92GEZ1QEPu2N87D+tYt8Y5ii61HLBnWY+tho/vXFItIys/lk4boSaRqE1+Cz5+7Hz6dsjRSTb72JlLRM5q3dQkZWDt+s2MQ3KzaVSOfu5sZTdw5g+I3tre4nNy+f9TsPm0Z6WuPt6cHjo/qXqTHWVWUdiubMU1Op9+ELuAX4UfvF8SXSZEef4dSkN66qDtaz05u+5iOji7xPWrTOah3UcnI5PekNIr96HZ+2TQkacRNBI0qGQk39fTtnn55a4vfVVcvGDZj20qP884MvScvI5JM5i0qkaVAvnP+9/sxVhYJNSEph3rK1zFu2tsQ6pRS3DezFvx57wDT3d3Hrt+wi3ziXblpGJo+9/mGpeX7zzpQKCal7PVi0/FeW/lLyOxlg975D7N5XtI5JA2fZtYyqz7QXJ/PP6d+QlpHFJ9+VfPjeoG5t/vfak/j5ln22j4SkFOYt/415y0tGZVFKcduAnvzrkXtt1kGAmDPnefWTWTbXF1/31lPjr4sGzqxD0Zx+choR05/HLcCP8BdL9hvOjj5D3MR/X9W5MOI92+fCsGLnwsSF60o0cAK4+fsSPLIvwSP72txX1pEYTj81jdwz1jtjVkdyT+Ha5HpUVLaCyi6AqPKkgVO4BE3TSm1d1DQtBr2Rsiz7TwBeMr5spWloZ90sYJYD+byBA+FQNU0bD4y/2vyuBU3TTqD/TW9c5X4c2oemaX2d3O9+4KkKLM8GwOrxaTwmPzG+yuxqju3qZOXKtXTqPJAnn5jELUMHEFGvDjk5OZw8GcvCRSv4/PNvycy8uga0Qk2aNKJ7d33urHXr/uDCBeciVJ86dZYeNwzl7rtHcscdw+jYoS1hYaEopbhyJYn9+w+zbNmvfDf3JzIyXL9d+vS63SwZ9DKtJ95MxIAO+NUJoSAnn5TYC8Ss3Mqhb9eSn5Vz9RlpGn++8DWxq7bT4t5+1OwQhXdIAFmJqVzeE82Rees587v9OT+OzF1PTkom4T1aEBAZhndoALkZWaSdSeD0b3s49v3vpJ1NuPqyVhNp67cRPfxxQseNxL9vVzzCa6Ll5pITF0/K6j9J/G45WlZ2ZRfTJGhkf9Ny0nUaSspS344t+Ontx5m35m827T3GhSspeLi7Ub92KIO6tuaegd3x8fIs8/6VUrx43y0M69mexRt3svt4HPGXk8nKycXX25PIWqF0btGQO/t1oWG49VGXz9w9mM7NG7LzaBwnz14gITmdK6npGJQi0M+HxvXC6NYqihE3diAsOKDMZXVVaeu3ET30cULHj8S/X2EdzCMn7hwpv/zJlTkrqkwdzLuUSMyo5wi+azBBw2/Cq2l93AL8yEtKJWvvMZIWryN1zd+l76ia6du9Aws/e5N5S9fyx459XLh8BQ93d+rXrcWgG7syZvgAfK5iJGTn1s14bsJdbNt7mJgz50lISsZgMBAWGky3ti0YOagX7Zpbn7taiOtB327tWfjx68xb/ht/7NjPhYREvQ7WCWPQjZ0ZM6wfPl5XUweb8tz4O9m2/4ixDqZgUMpYB5szckBP2jWPKn1HwqbU9ds4ccsT1HhwJAH9upjOhdlx50hZ9RcJVeBcePmLhWQdjsa3Ywv9/BcahFuQP1pOLnmXk8jcf4KU1X+R8uvfUHD9Pa6XewrXJtejQoiqTGkyma4QQgg7PDzryYnChc2s1a+yiyCuUk+fK5VdBHGVGn1nK0q5cAXR982p7CKIq9R49b8quwjiKhlCwktPJKqsgsvXZKYSUYGOD7UekUa4Dje3669htbrRZFJAl9fq5Er5EJ30bb37K+2Z5INn58rn5QIMpScRQgghhBBCCCGEEEIIIYQQQoiqQRo4hRBCCCGEEEIIIYQQQgghhBAuQ+bgFOI6opQKBiLKsq2maQfKuThCCCGEEEIIIYQQQgghRAkSmVmURho4hbi+3AZ8W8Zt5ZQihBBCCCGEEEIIIYQQQohKJw2cQgghhBBCCCGEEEIIIYQQosooqOwCiCpP5uAU4jqiadosTdNUWV6VXXYhhBBCCCGEEEIIIYQQQgiQEZxCCCGEEEIIIYQQQgghhBCiCpERnKI0MoJTCCGEEEIIIYQQQgghhBBCCOEypIFTCCGEEEIIIYQQQgghhBBCCOEyJEStEEIIIYQQQgghhBBCCCGEqDI0VdklEFWdjOAUQgghhBBCCCGEEEIIIYQQQrgMGcEphBBCCCGEEEIIIYQQQgghqoyCyi6AqPJkBKcQQgghhBBCCCGEEEIIIYQQwmVIA6cQQgghhBBCCCGEEEIIIYQQwmVIiFohhBBCCCGEEEIIIYQQQghRZUiIWlEaGcEphBBCCCGEEEIIIYQQQgghhHAZMoJTCCGEEEIIIYQQQgghhBBCVBlaZRdAVHnSwCmEEEJUYwa5GnR5WoGq7CKIq2WQoClCVCblJre9rq7g8unKLoK4CoaakZVdBHGVNE2uR11dfr5cj7o6qYdCCFGS3OkJIYQQQgghhBBCCCGEEEKIKkP6e4vSSPcdIYQQQgghhBBCCCGEEEIIIYTLkAZOIYQQQgghhBBCCCGEEEIIIYTLkBC1QgghhBBCCCGEEEIIIYQQosooqOwCiCpPRnAKIYQQQgghhBBCCCGEEEIIIVyGjOAUQgghhBBCCCGEEEIIIYQQVYaM4BSlkRGcQgghhBBCCCGEEEIIIYQQQgiXISM4hRBCCCGEEEIIIYQQQgghRJWhVXYBRJUnIziFEEIIIYQQQgghhBBCCCGEEC5DGjiFEEIIIYQQQgghhBBCCCGEEC5DQtQKIYQQQgghhBBCCCGEEEKIKqNAVXYJRFUnIziFEEIIIYQQQgghhBBCCCGEcHFKKV+l1EtKqe1KqStKqXSl/p+9+w6PolofOP49m94LCRAILfQSehOlSUcRBQXBAgr2du3enx2vHftVrFdArAiCKIhYUBCQ3jshoZeE9F7O74/ZZDdkd7PZJCYb3s/z7JPNzpmZk5ydndl5z3mP2quUek0p1awKtm9SSg1QSr2glFqplDqllMpTSqUppXYqpd5TSnWuir+lPDKCUwghhBBCCCGEEEIIIYQQQtQaRTVdATeklGoFLAVan7eorfkxXSl1ndb6h0rsJh5oYuN1L6Cj+XGbUmom8JjWWldiXw7JCE4hRK2glJqtlNJKqfhKbifevJ3ZDsrUU0rNVErtUUplm8trpdS/zMufKX6tMnUpp54rzftYWcntFNf9maqpmRBCCCGEEEIIIYQQQgh3opQKAn7EEtz8CBgC9AMeBzKAYOBrpVTXSuyqkfnnQeBl4AqgJ9AfeApIxog9PgI8X4n9lEtGcIoapZRqDhyu7Ha01hdERm6lVCDQHehtfvQCmpsXJ2itm9tes0r2/QzwtNVLv2utL3VivYuB1dav1WR7KaVCgLWU7cUihBBCCCGEEEIIIYQQohaotpEnddfDQBvz80e01q9aLVtrHmjzB+APvAkMcnE/64FngZ9tjM5crZT6AuP+eyTwsFLqY611nIv7ckgCnEK4lyW4/sFT1QYqpaK11sfKKXfDP1Ib592FJbj5Csb/NMX8+8kaqZFwWtOmjbn7rmmMGj2EJtGNyM3NJS4ugfnfLmHWrNlkZ+e4vO1mzaI5eODvCq0TH3+U1m362l0+fPggbrxxAr16dqVhw/qYTIqzZ8+xZesOvvpqEd9+u4RqzNLwjwpoXI8O00YQPaQrAY3CKcotIC3hDPFL/mbP7BUU5uRV2b4aD+5M2+suJaJrDL7hQeScSydxaxz7Pv+N479vd2obkT1a0WbyYBr0aoN/wzBMnh7kpmSSvOcI8T+u5+D8VRTlF1a4bj3/byKxd40p+X3Z1c9zau2eCm+nNvFqFEn4lCsIHNwLr6hIdF4+eUdOkrZ0Fec++xGdk+v6xpXCu2U0fl3a4te5DX6dW+PTtgUmHy8A4ic/RtbfOyq2ST8fQscPJXhEP7xjovEIC6EoPYP8U0lkb95D+q9/k7l6i+t1djMnEpP54ud1rNq6j1NJqXh7edKkfjjD+3Ri4tA++Pl4V3ofx88m882v6/l71yGOnTlHdm4e/r4+tIiKoF/n1lxzaW/qhQRWeLurt+3nrplzS36//arB3DFuSKXr627kGHR/J04n8vn3P/Pn+q2cPpuEl5cXTaLqM2JAH669fCh+vj6V3sexU2f4fPHPrNuykxNnktBFRUTWC+Oibp24dsxQWjWLdrj+hu172LbnADv2xZFw/BTJaemkZWTi6+1Nw/r16N6hDeNGDqJj6xaVrqu7OXEmiS9++I0/N27nVGKy8TnaMJLhF/fk2ssG4edTBe13OpEvlvzK2m17OHkmiSKtqR8eSt+u7bl29GBaNW3kcP28/Hz2xB1l14F4dh44zI798Rw5eabkOnP74g8rXUdhSEpOYcfufezcvY+dew+wc89+UlLTABg7aijPP/FgDdewbvJqHEm9qWMIGtwL76gIivLyyUs4RerSVSTNXVrpc6FPy2j8urTBv4txLvRt17zkXBh37b/J/Huny5sPHNiDFrOfKfn99JtfcOatL12vr5vyahRJvalXEDS4J15RkUYbHjlJ2o+rSaqC65niNvQrbkOr65nDk/5NphPXMy2+eJGAvrFO7XJnzOWu19cNyTEoRO2nlPIC7jX/ugd47fwyWus1SqlPgNsw7u330lpvqOi+tNb9yll+SCk1A3gHIwZ5JfB6RffjDAlwipp2HHB09VB8BbIRuKn6q1PrWY98PIfxf+kHVPyuYeXkAL7AdRjD0G1SSnkDE85bxyat9VRgapXV0L6h5p8btdaP2qnLM8Az/0BdRAVcdtkw5sx+m5CQ4JLXAgL8CQ8Po2fPrtx882TGjr2RQ4fi/7E67d9/yObr3t7ezJ37X8aPu6zMsiZNGtGkSSOuGDOCO26fwlXjbiLVfFPEXTUZ1o0Bb9+Bd7C/5UV/iAwLJLJrDG0mD2LFjTNJjz9duR0pxcWvTKPN5EGlXg6ICicgKpxmo3qy7/PfWfPo/8BB4LjPjBvoMG1Emdf9G4Ti3yCUxoM602HaCFbcMJPME0lOVy+8Y1M63jrK6fLuIPDS3jR+/SE8ggIsL/r74hcahF/nNoROGMGR6c+Qn+Ba/5CQqy6l8asPVE1lAf++nWn08r/wjm5Q6nWTTxieEWH4dWqFf8+OxK2+p8r2WZut3LyXx9+fT0a25YZDTl4+uw4fZ9fh4yxcuYn/PnQDTRvUc3kfS1Zv4T+ffk9OXn6p19Mys9l28CjbDh7li5/X8vKdE7kotpXT283KyeP52d+7XK+6Qo5B97dy3Wb+/er7ZGRll7yWnZvHrgOH2XXgMAt/Wsm7Mx6iaaMGDrbi2Pylv/HirM/ILygo9fqRE6c5cuI0C5f/wUO3TGbyFcPsbuOxV2ZxJim5zOsZBdkcjD/GwfhjzF/2O5PGDOXR267HZLowZrtZuX4b//fGJ2RkWTrR5eTmsetgArsOJrBwxWrefeoemkbVd3kf3y7/kxc//Kps+508w5GTZ/huxV88dPPVTLrMfvKc5977nMW/rXG5DsJ5Ay+fVNNVuOAEDelFk9cfxCPYci40+fviGRqEf5fWhE8cTvzNM8hz8VwYetVgmrx2f1VVtxTl50Pj/9xRLdt2J0GX9ib6jQdLXc+UtGHnNoRNHE7CtGcr1YbRM6vuekaUJsegEG5jMBBifj5Ha21vCtPZGAFOgKuACgc4nfS71fOW1bQPCXCKmqW1zgfsdsNRqiSel6m1dr27Tt3xBfABsEFrfRCMOSf55wOc32MELm/AQYATuBwIwwhuLgfGVn/VytXY/HN/jdZCVEjXrh354vNZ+Pv7kZ6ewcuv/Jc/Vq7B18+XiROuYPr062nbpiWLF8+lb99RZGRkVngfx4+fomu3crMu8+gjdzNp0jgAPps332aZN994riS4efr0WWa+NostW3ZQkJ9Pp07teeihO2nevAn9+/fl83nvcfmY6ytc39oivGMzBs26G08/H/Izstn+3yWcXLMbT19vWlzRl7bXX0pIyyiGzX2I70c9SUGm66Nsezx6TUlwM2lHPDve+4H0hDMENatP7J2XUy+2OW2vG0zOuXQ2v/SNzW3E3j2mJLiZl57Nrg+XcWbDfvKzcghpGUWnW0cT1r4JYe2aMGzuQywe8Ti60Ilp7ZWi38vTMHl5kn02Fb/IkPLXqeV8O8QQ/fajmPx8KczIIun9+WSu247y9Sbk8oGEXTsSn5homn78DIev/BdFmdnlb/R8lvM8Oi+fnP0JKE8PfNtVfIRQQL+uNPnoKUy+PhSmZpD8xVIy/95BYVIKys8Hn5ZNCLy0N571QiteTze0J/4Ej777NTl5+fj7ejNtzAB6tY8hJy+f5et2sGDlRhJOJXL3zM/4csYdBPhVfATSlv0JPPXhQoq0xqQUY/p3Y3D39kSGBXEqKZXvV23hjy17Sc3I5l9vfs6CF+8hun64U9t+d8EvnEhMITw4gHNpFf9MrwvkGHR/ew7G8/BL75KTm4e/ny/TJ4yhV5f25ObmseyPdSz4aSXxx09x11Mz+ertGQT4+1V4H8tWrmXGO58CEBTgz43jRtGnSwe8vTzZcyiBT7/9kSMnTvPS+58RHhrMyAF9bG7H39eHi3vE0qV9K5o2akhkeCgB/n4kJaeyY98h5i/7naTkVL74fgW+Pj7cf/PESv1v3MGeuCM88uqH5s9RH6ZdPYresW3Jycvnp1UbWPDzKhJOnOauGe/w1WuPE+Bvty+nXcv+XM+M9+YBEBTgx41jh9G7czu8PT3Zc/gosxcu58jJM7z00deEhwQz4pKeNrejrRK4Bfj50r5lU+KPnyIx2b070dV2UQ3q06JZNGvWb67pqtRZvh1iaPrOo5j8fCjMyOLsrG/JXLsd5etD6Jj+hE8yzoXN//cUB694oNLnwqK8fHL3JYCXB34unAvP1/CB6/GObkB+YjJeEWGV3p478u0QQ5N3Him5nkl8fz4Za3dg8vUm5PIBJW3Y7JOnOTT2/iprQ+Xl2vUMQNb2/Rx/5C2X1q1r5BgUNa1IktRWxCVWz/9wUG4jkIWRpvbiaqyP9U2GiqdIc5IEOIVwI1rr2pJbaC5GgLOjUqqb1tpenrHi9LRLMD44a4PiD9d8h6VErfL6azPw9/cjPz+f0aMns+7vTSXLVq78iwMHD/PyS0/Stk1L7r//Np57ruJZDwoKCti1a5/DMiaTiQEDLgIgLS2dRYt+KlOmfv0Ibr7Z6Nl97lwyffqO4vhxS0/Gv9Zs4IsvF7Jp4wpatGjKiBGD6dG9M5s2O5datbbpM+MGPP18KMovYPnklzm76WDJspN/7Sbt8Gl6PTnJCB7eNpqtry90aT/BMQ3pdPtoAM5ujWPZ+OcozDEO48RtcRz5eTOjFjxBZNcYYm8fzYGv/igzYlR5ehB7h5FKqDA3n2Xjn+PcriMly89uOsihBX8xetFT1O/eirD2TWg6sgcJP5bfma3DtOFEdmtJyoHjJPy0iS73XOHS31mbNHjyNkx+vuj8Ao5MfZLsLXtLlmWt3U5e/HEaPDYNn5ho6k27irNvf1HhfeQeOMLJZ98nZ/t+cnbHofPyibx3coVvRniEB9P4rUcw+fqQvesQR256isKklFJlsjftIeWbn8Hrwrj8fWXej+Tk5ePpYeL9R6bSpXXTkmV9OrakacN6vPHVchJOJTJ32WqXUr9+suRPisyjpR+78XImDrUETjrFRDO0V0dmfrGMz5b9RU5ePnOX/cX/TRljb3Mldh8+zpc/r8Pby5N7rhnGs58sqnDd6gI5Bt3fSx/MIyc3D08PDz54/hG6trdMAd+na0eaNW7I6598RfzxU8xZuIw7rx9Xoe1n5+Ty0gdGcMzfz5c5M5+gdfMmJcs7tolh5IC+3PjQcxyIP8pL73/GgF5d8PcrG4j77oOX8PTwsLmfAb27ct3Y4Uy67xmOnTrD3IXLuOnq0YQGB1Wovu7m5Y++Lvkc/eDZf9GlnaXjeZ/O7WgaVZ835iwg4cRp5iz+mTsnVezcn52by8sffw0YAebZLz5C62aNS5Z3bN2ckZf0ZMpjr3Ag4TgvffQV/Xt0stl+l3TvRK9ObenYujkx0Q0xmUzc/PhMCXBWg9tvmkyn9m3o1L4NEeFhHD95mhFXT63patVZjZ6+BZOfDzq/gPgbnyJri+X7Wuba7eTGnyDq3zfjExNNxPQrXUo7mXvwCCee+YCsbQdKzoX175tU6eCKb6eW1Js6hqLcPE7PnEf0SxdO9gJrUU/dWnI9Ez/lqVLXM5lrt5MXf4KGJW14FWfecuV65ignnnmf7O3WbVjx65liOiuX3P0JLq1b18gxKC5kSinHczyYOTF92z+lg9XzvfYKaa0LlFIHgc5A+2qsz0Cr59U2d9OFkVdG1GlKqe5KqfeVUvuUUhlKqUzz81lKqTblrBujlHpQKbVEKRWvlMo2PxKUUl8rpUaWs/5UpZQ2P5orpbyVUg8opTYqpVKVUueUUiuVUpedt16QUuoRpdQWpVSaUipFKbVCKeUuE0vtAoqDmjbn2FRK1QNGm3/9rLwNKqVmm/+P8eWUG6WUWqqUOquUylJK7VdKva6UauxgnUHF7QQ0M788xarttHmS5eLyz1iVd1QXX6XU3UqpX5VSp5RSeUqpM0qpX5RS05RSlb6Dp5SabH4PJZvf3zuVUs8qpS6Y4Q+9enalf39jnstPP/2qVHCz2BtvfMDuPcag3HvunoanZ/XcPB0ypD+NG0cBsHDhj+TklB2N2Lt3NzzMNwjnzPmmVHCzWHp6Bm+//VHJ73379qiW+la3iK4xNOzbDoD9X/1RKrhZbOcHS0nZfxyADtNGoDxt3zwtT4fpIzGZb4r//eSckuBmscKcPP5+cg4AJi9POt5S9uM7tHUjfEKNtDpHf9lSKrhZTBcWsf1tS1rM+j1alylzvoBG9ej+8NUArHnsU4ryCspZo/bz7dyGgN6dAEie/3OpGxHFkj7+jtwDxv8wfOpYcKFtc7bvJ3nuErK37kPnud7vpP5DU/EMD6EoK4djtz9XJrBSSr77t095dhw6xuZ9xk2ZKwf2KBXcLHbjqIuJaRQJwOfL15JfUPEOldvM7R8a6F8quGnttisHlzzffvBoudssLCri2U8WUVhUxLQxA2jSwLkRn3WNHIPub8e+Q2zeadwEvGrEwFLBzWJTxo0ipokxt+K8xcvLpCgtz6oN2ziXYgSwrhs7vFRws1hggB8P3zoZgKTkVBatWGVzW/aCm8VCg4MYP2oQAAWFhWzfaztNf12xY/9hNu8+AMBVQy8pFdwsNuXKYcREG9eFny/5reLtt3En51LTAbhuzJBSwc1igf5+PHzzNQAkpaTZTUM7sn8vxg7pR6umjS6Y9ME15e7pNzDo4j5EhMtIoOrm16V1ybnw3DcrSgVWiiV+tIgc87kw4qYrXDoXZm87QNKcHyp9LizFZCL6xXtQnh6cfXc+efEnqma7bsav1AJ3dK4AACAASURBVPXMCpvXM4kff1fShvWmutiG2/dzbm4Vt6GQY1DUCkU1+ACOOvmoLYoDsplaawdfyABLvSOVUpWfUP48Sil/4F/mX3OBxVW9j2Jy5SvcllLKpJR6HWNY9W1AGyAAY3h1G+B2YJdS6lY767cADgEzMVKpNsOYI9IXaIoxQnGZUuozJwNVwcCfGBP49jD/HobRW+EHpdT95v02BdZipHbtCgRh5MceCqxQSl1Xsf9EjSkOWk5SStm6gpkIeAOJQNlhbi4wt/dSYBQQAfgBrYH7gS1KKds5m6qBUqoLRm+Yd4BLgQaAFxAJDAE+BtYopVyaUEkp5amU+gb4HOM9FIrx/u4IPAVsMr+H67wrxloCVXPmfG2zjNaaefO+BSAsLJRBgxzOde2yG66/uuT53M9sp6f19vYueX74sP1en4fiLMu8rNZxJ01HWg65A1//abuQ1hz8djUAPqEBRPVzrXNY0+HdAUg5cJyzm23fVD27+RApB40vLk1HlA0am6xGDaUfOWt3X+kJlpGfJidGGvV9YQpegX4c+OZPTq+z20nOrQQP61vyPPXbX2wX0pqU734DwCMkkIC+nf+JqpVhCg4k5AqjY2Dq4t/JP2G/bS8Uv2/aXfJ8bP/uNsuYTCYuv6QrAOlZOWzYE1fh/RQHRRtH2r/JG+TvS1iQf6nyjsxbtoa9CSdp1jCCmy8fUOE61RVyDLq/39ZaOmRdOay/zTImk4kxQ41MUukZWWzYVrGOzbsOHC553r9nF7vlenVuj4+3FwArVq+v0D6sBViNHMyt4zeQf/t7a8nzsUNsX1eaTCbGDDaO1fTMLDbscJwJ5Hy7D1quBS/p0cluuZ6xbS3tt0ZSoYoLh/W5MNnRuXChMcWXR0gggRfVzLnwfBHTxuLXqSW5ccc4+8G3NV2dGhM83KoN56+wXei865nAGrqeEWXJMSiE2ylOr5LhRFnreWCqY+q7lzHiKwDvaq2rrZeBBDiFO3sHI7ClMAKLNwODgN7ALRijDD2BD5RStvIFeQB5GOlT78UIMHY3/7zTvD7A9cCTTtTnQ4zA5nvAMKAnMB0oPoBnKqU6AQuBGOAlc317YfRoSDX/LbOUUvWd2F9N+wIjf3ZDjL/3fMUjO78yz7VaKUqpf2G0Nxj/03uAPhjBv1cwgsTzMQLc59sAxJofxe2x2Oq1WOCmCtSlFUYu82ZAGvAixqTMPYERwLtAAUbbLlZKeTm7bSszgWvMz/cB08zbG4oxD2tzwHa0r465uF8vADIyMh2mcV3157qS5/3M61SlwMAArrjCCLYePnyEVavW2Sy3f78l+NaiRTObZQBaxliWWa/jThr0MgbJ52fmkLT9sN1yp9ZZbtjW7+VwYL1NgU0jCYgKN2/LcQCxOMAYEBVOYJPIUsvSDp9CFxn98IKaRpZZt1hQM0u/hLRDZUfgWms+pg9Nh3UnJzmdDTMqnk6ptvLr2RGAosxssncesFsua/2Okuf+PTrYLVedgi7thcl80z39l79LXle+Png1i8LjApxrZYs5pZafjzcdWjSyW66nVdqnrfvLjmguT/OoCACOn022WyYjO4fk9KxS5e05fjaZ9xb+CsDjU8fgfQGlMj2fHIPub/MuI7OEn68PHVrb75PWM7ZdyfMtuys2RXxquuXeRb0w+3M/e3p4EBJk3LfYtucgBYUVH7FdVFTE8j8t7duiSVSFt+FOtuw2slL4+frQoZX967menSzXNVv2VOx6LsW6/UKD7Zbz9PAgJNDIQLFtb5xL7SeEOwroZZzXCjOzyd5RNlNMscy/rc+F1ZlpzzlejevT4F/GyPnjT8xC14HsLq7y72nVhjsdteFOq3Vqvg2FQY5BURvoGnwATZx81BbFvRHznCiba/XcryorYR68dbf51z3AE1W5/fNduHcNhFtTSg3DCEICTNdaf3JekQ1KqXnAjxij695WSi3VWluf1U4CzbXWtu5e/6qUeh/4HzAVeFAp9brWOtVBtXoD47TW1hNFbVJKbcBI52oCfsMY2TlQa/23VbmNSqkD5voGAdcBbzjYV43TWp9WSv2MMZryBqxGaZoDgMVdveZWdl/mgO/z5l8TgL5a61NWRf5USi0HlmPjc01rnQnsNG+rONiaorXeeX5ZJ83BCKhuAYZrrRPPW/6zUuoHjPbsg/Ee+ggnKaViMQK4AJsx3i/WvW9+VUqtMdejzmvXzkjpduhQPIUObujs3We54C5epyqNH3cZAQFG/PzzLxbYLbdz517WrNlAv369uPHGCbzx5gecPFl6LsjAwADuuWc6YPxdK1Y4mvu79gptbQRO0uJPowuL7JZLPWjpqBXa2m42afv7aWNZx3pbtqSU2lcjMo5aRhLlp2cTt2gtLcddTJOh3Qhr34TkPaWziSgPE53vMeYIzE3NJG7xWrv78g72p8+zRl+Ojc9/TW6yM53k3INPS+MaPS/hJDho29xDlqkmvFvWzHW9X1dLcCBnXzy+sa2p/+CNBPTrgjKnXCxISiHtx1Wc/e9XjlNn1hGHzSPomjYId5h2skUjS6D/sAuj7q65tBcz/reYlIwsvvl1PROG9C5T5sNFK0uVd+T52d+Tk5fP6H5d6NOxbDrIC4kcg+7v8BFzRoFGDRwfh9GWTghxRyvWsdnP15JNKj3T/nT3WmsysrIByC8o4MiJ0yWpcR0pLCwiMTmFvYcSmL1gKZvMKXf7dutIq2ZOTUnktg4fM74iNo2KLKf9GpZZx1n+Vu2XkZltt9z57Xf05BlaRNftALMQ4Py5MMfqXOjTqubvMzf+z52Y/H1JXvQ7mWvtd9C9EDh/PWP5TuZTQ9cz1rxbRhOz8DV8YhqjfLwpPJdG9s6DpP20hpQlf4ALUzu4IzkGxYWuuubWLG9KNCfdpLWefd5rxfNoOZMmzjotrf0L0QpSSg0CiuM054DxWusq274tEuAU7uox888FNoKbAGitc5RSdwO7MUbaDQZWWC3PpPRw7PPX10qpBzGCdwEYI+fsRzXgm/OCm8Xb2a6UWg0MwEhf+vJ5wc3ickuVUgnmuvanlgc4zT7DCHBeqZQKtArCFY/e3Ke13lAF+5mCZWTmg+cFNwHQWv+mlPoIuKMK9meXUqo/UJynaoqN4GZxfX5SSn2Lkep4KhUIcGKkVy4eYX/recHN4u3PVUpdi/H/r7N8fHyIjKwHwLFybhqlpKSSkZFJYGAATaLLv2lXUddff03J8+J0uPZMv+UBflgyj5iYZqz/+ydee20Wm7fsoLCggI4d2/Hgg3cQE9OMs2eTmDLlHvLz3S/Nm4ePF771jNEGWSfPOSybl5pFfmYOXgG+BDSq+Hx6xaM3ATLL2VfmiSTLeo3qlVm+/tnPCWnViIjOLRi98El2frCUM5sOUJCZQ0jLRnS4ZST1OjYjPyuHVfd94DBo2fPxa/FvEMrp9fs48OXKCv9dtZXy9sKznjESKP+UzY+4EkVpGRRlZmMK8MOrnNF51cXHan7JgIs60+iFe1HnjfzzrBdK+I1jCBp5MUdueorcvfZHHLu73Lz8khGT9cPtj+gCCA7ww8/Hm+zcPE6dc9SHy7YrB/Zgy/4ElqzeyotzlrAn/jgDu7UnMjSIk0kp/PDXVn7fZIzgvuWKgfTt1Mrutpau2cZf2w8Q5O/LQ5Pr9KmtXHIMur/cvDyS04y5FRtEOD7vhQQF4OfrQ3ZOLqfOOj7Hnc86SLlxx1462hkpuudQAlnZlnnDT51JchjgjB11g91l7Vs15/kHb6tQPd1Nbl4+yWnG+b9BPccjkIMDrdov0f5odltirEbBbty13+5I0T1xR8jKsXSyP3n2nAQ4RZ1X6lx4Mslh2aK0TAozs/EI8MO7hs6FxUKuGEjQoB4UpmZw8j82b1VdMCp2PWNpQ68o+5l2/ilekWF4WU3BYIqKwCsqguBhfYm4bTxH73qxVCezukiOQSHcUrr5pzMpZwOsnldJb33z1HHfYwRPM4DRWuuKzcHhAglwCrejlArGSO0K4DDKoLXeo5RKxJiv8SKsApw2tuuFMY9iEEb62mJJQH2gC44DnF85WLYNI8BZXrntGAHOGAdlapNFGB+eQcA4LKM1rzf//MzWSi4Yav6ZjONJif9HNQc4geJ0x/u01jscljRSJ08AeimlPM8bQexI8d+7Q2u9yUG5/1HHA5xBQZbzbUam3f4IJTIzswgMDCAw0FamYtc1adKIAQOMQclr1mzg0KF4h+UPHIjjon6XcdttN/LwQ3fy6qtPl1qel5fHa6/N4p3/fsLx4xXr7V9beAZY5uHKz8xxUNJQkJWLV4AvXv6+5ZY9n1eAJVtGQWaug5LGfix1LDtPek5iGkuveo621w0m9u4xdH/46lLLdVER+z7/nV0fLCXVQXraBr3b0mbyIIryC1jz2KfO/iluwRRo+X8XZZXf0a4oOwdTgB+mgCrNauI0jxDLtXvUc3eB1px5bS4p3/1KQWIK3s2iiLhlPKFXD8OrfjhN3n+CuMvvpiijWjsR1pjMHEs2Gn+f8jtu+vl4kZ2bR1aOM1lsSvMwmfjPbVczsFs7Pv7+Dxau3MTClaVPW73at2B6OcHN1IwsXv18KQD3ThhOvZDqmALEfcgx6P4ysyznRetRevYUB8iycso/n1rr36sLnh4eFBQWMnfhMq4YcglhIUGlyhQVFfH2nNLzhmdmV2w/YKS8fuiWyVw5bADe3q7MvuA+rP8/TrWfj7dL7XdJ9054epgoKCxi7uIVjBncl7Dgsu33zrzSfWhdaT8h3I0r50KPAD9MLnzXqCoeIYFEPTkNgFOvzKEwqeKdx+qSUm3oxPdFnZ0DAX6YAmquDXVRERl/bSV95UZy9hymMDkNU6A/fh1bEjZpJL6tm+LbphnNv3iRuKseqNPzjssxKGoL+2OH3VpV5HK2dcPqGEYmwQClVKjW2lHqnOLh1me11o5vtDlBKdURI7tjEEb62yttDfCqDhLgFO6oG5bRbV8qpb50cr2G579gDmreijHisBuOh3CX1w3J0aQ51h8ozpQLclCm1tBaZ5tHKd6E8T+cq5S6BCNAq4F5VbSrWPPPLeUECbdi5Bl3Zii+q3qaf7atQEoBLyAcOFNeQaWUD1CcX7W80a/rndy/vX05lVvM06vqR0M6y9fXKoiWV/4ox9zcvDLrVYXJk8djMhkfO+WN3ix2+WXDmDTpKoKCyt6o9/b25uqrx3A2MYnXXptVpXX9p3j6Wm5uFuWXH7svNLefh2/FD0+PCuyryGp+DU87+4q6pAMx4y7Gv35omWXKZKLpiO7kJmewZea3FOWXTT9k8vKg3ys3o0wmdr6/lJR9dav3rrIKimkn2lab29bkQttWBesv0SZfH44/OJPURb+XvJZ38CgnHn0TnV9A2KRReDdpSNjky0j60Llj2d3kWY0I9/K0n1axWPE8l7lOfMbaEnf8DEtWb+XgsdM2l28/eJTv/thEi0b1aRBue4651778iXNpmcS2jObqwT1tlrmQyDHo/kodh07MJevtZZzniq9jnNUwsh7XjL6UL5es4ExSMjc+OIP7p11L784d8PLyYO+hI8z6fCF/bdqBl6cn+QXG+yknz/F+Fs56ATCCa0nJaazfvptvlv7GzI+/5PCxkzww7Vq8POvurYQ8q89DTyf+zpLP0dyKfY42jAznmpED+fLH3zmTlMKNj77CA1PH0yu2LV6enuw9fJRZXy5hzZZdpdrP1c9rIdyJqaLnwlyjjKqhcyFAw/+7Ga+IMLK27OXcl8trrB61RUXbsMj82aac6KBXXY7c8QJF6WU7Vmdt2EXSvB9p/MI9hF09FK/IMBo+eQtH73ihBmr5z5BjUIjqo7XeW02b3g2MNz9vB6yzVUgp5QkUzwlT6RGWSqmWGIPK6gEFwESt9a+V3a6z6u63ElGX1XdxvVJDupRS4cDPQA8n1y+vW7z9iW+sOpxorZ0pV/4dydrjM4wA56VKqcZY0tP+qbVOqKJ9FOf2chgg1FoXKKXOYSOYXYWq5P3nQBigzM/LC4javpvsvKPlF6k+jRo1JCzMdvrE5ORUTpw4RY5VT3gvJ0YL+JgvwnMq2IO+PNddN75ku9/M/77c8q+8/BT332+kb1u0eBmvv/Y+27bvorCwiPbtW3HXnTczdeq1vPTiE/Tu3Y1Jk26nqKj29EvzbxiGd0iAzWV5qZlknUqmIMdyc83kxM1bD3P7FbowSqywAvsyeVuWF9jYV4dpI+j9zHUok4lTa/ew9a1FJG6JozA3j6BmDWh97UA63jKSznePoUHvNvx83SulRoUCdL5nLKGtG5NxLJGtr39X4b+nttNWN9jPTzNpizK3bZELbVsViqzqm7MnrlRgxdqZmXMIGTcEk483wZf1r7PBleJACUC+E/MD5ZlvWPi4MCJr87547n19HulZOTSKCOWuq4dyUadWBAf4cS4tg5Wb9/Lugl/4ad0ONu2L5/1HptIqukGpbWzYE8fiPzfjYTLxxE1jSzqTXMjkGHR/pY5DJ24KFgdEfVy4qfvQ9EkcO3WGVRu2EX/8FPfNeLNMmY6tW9CpTQxf/2jcZwjwc9wRrHXz0vNn9esRy7WXD2XqI88zb9FyDiUcZ9ZzD+PhUTePV+sRqgUFzrSf+XPUp+Kfow/edDXHTp1l1aadJJw4zX0vvFemTMdWzejYujnfLDPmbC+v/YSoC4oqei70McroGjoXBvTpRPiEYeiCQo4//h7oqphezb1VtA1N5s9eXcHOPlXJVnCzREEhx//9Nv7d2uLTsgkhI/pxskE9Ck47Tt/qruQYFLVFkSq/jCix2ur5QOwEODEG7xTf9PurMjs0D575BYjCiGtM0Vo7yr5Y5ermNxJR11kH/27DGN3nzOPx87bzFpbg5iKM1KPNMQJRJq210lorLEEg+Ui1bSXG/8iEEeicYH69qtLTWqsNVyjF779tOP/eiwWOu7Cv2vD3VpvnZjzK1i2/2Xw8N+NRANKtvmAEBtgOuFkLCDDiyBkZjvoRVEyvnl1p384YVLvkhxWkpqY5LD9q1JCS4OacOV9zzTXTWbtuI1lZ2eTm5rJ16y5uufVB/vO8Mc3uuKsu447bp1RZfatC90ev4arfXrL56P6oMRdpgVWaIS8n0gh5+hsp3vKzKh58zs+0pMSxlXbW1n6gbDrbsPZN6PW0Edw8/ucOfprwAidX7SI/I5ui/EJSD55g43++ZM0jxlwdDXq3pdtD40ttI6RlFJ3vHgPAuifmUpBd6UwetY512kiTf/kpL03mG61FmTWTbtK6vhmrt9gtV5iSTs6OAwD4tm8BTnxRd0cBVr2ms5y4QZRtHnHkX8He1nn5BTz67jekZ+UQERLIZ0/fxuUXd6VeSCBenh40CA9h4tA+/O/x6fh4eXI2OZ0nP1xQZhvP/c/oNDJpeF/aNZM55UCOwbogwGpUq/XcifZkm8v4u5CBwtvbi/8+8wDP3DeNdjHNUMrylSU8NJhbrr2COTOfQFvd6AsOLP+a6nwNI+vx+J3G9craLTtZuHxlhbfhLqwDiE61n/mz1qX28/LinSfu5um7bqBdiyal2y8kiFuuGc3sFx8pdZ82qIqnYhCiNnL5XOjCd43KUt6eNH7hLgCSZi8hZ0/dnmfaWaXa0Invi6rkeqYWp+EuLCL5G8vMVwF9OtVgZaqXHINCuKWVQHFu5inK+sKytKlWz13uta+Uqo8R3Gxuful2rfUXrm7PVXX3W6Woy6y7R2VprXdWdAPmeTwnmn/9XGt9vYPiYQ6WXfC01lopNQ/4N0YQ2RfIAeY7XLFikjFGZTZwVMg8xD7cUZkqUPz+C3TlvecE63TGDv9eJ5aXp0n5RYAaHOmZm5tLYuI5IiLCiY52fOM7NDSEQPMNu6PHTlRZHa6/3jJHozPpaW++eRJgpHV76ulX7JZ76aV3uO/eWwgKCmTq1Gt59z33msexMDefnHPp+IYH4R/l+LDzDvEvCYJmnjhX4X1lnrSsE1DOvgIa1bOsd6J0b9rWEwdgMo822fraQnSR7T4EB77+k9i7xxASE0WrCf3ZMMNyfdbx1lF4+HiRFn8aTz9vWlzRt8z6Ye0s2Z+jLu6AX6QxUvnoii1uERDVefkUnEvFMzwEr4aOs7ObggNL5v3LP5n4T1SvDOv9Fpx0PA9OcVnl4YFHSBCFicnVWrea4OPtRWigPykZWZw553jembTM7JIb8w3DbY+ot+ev7Qc4k2x0+Jg0vC8Robaz67eKbsBlF3dh4cpN7D58gn0JJ2lrDmT+unEXCacS8fTwoGXj+ixbu73M+nEnLMkMDh47U1ImtmU00fWr+5RfM+QYdH8+3t6EBgeSkpbB6UTH573U9MySAGfDSNfe0yaTifEjBzF+5CAys7JJSknD18ebiLCQklHRCScsiT9aNm3s0n76dY/F18ebnNw8VqzewDWjL3VpO7Wdj7cXoUEBpKRncjrJ8Xs0LcOq/SJc+9poMpkYP7w/44f3JzMrh6TUNHy9vYkICy5pvyPW7ddEOoOIus84F6bhGR6MV1Q9h2VNwQF4mM+FeTVwLgwe0Q+fmGiK8vLJOXiUkMv7lynj07ppyXPfts1KymRt3U++nTT/7q5i1zOWNswv51qipuUcPFLy3KuB4/emO5NjUAj3o7XOU0q9DTyJMc/nQ8Cr1mWUUhcB08y//qG1tjk9mtW0bAla6+Y2locCy4G25pfu11p/VOk/wgUS4BTuaCvGyDYFXIxr8zy2xpgXEeBre4WUUu2AshPoifN9hhHgLO6Wt1hr7XiYW8XswAhwdlVKeTqYh7ML1Tv/JsAWoB8Qo5RqqLU+VZUb11rnKKUOYLxHe5VTvLzl5e3LqYkDvbxduwlWnmnT72fa9PvLLbdnz3769+9Ly5bN8fDwoLDQdsrFdm1blTzfu/dAldTR09OTCRPGAnD69FmWL7eddq9UPcyjPc+cSeTECftvj9zcXHbv3k+fPt1p27al3XI1YfX9H7L6/g/LLZey/zgN+7YjuHkDlIcJXWg7zW5IK8s8rikHKj6YOWW/ZR3rbdkSWmpfpQPdIa0s7+WkHfEOt5O0I56QmCh8w4LwjQgmJ9H4SCtOgRvcvAGDZt1dbt273n9VyfP5ff5FxrHaH+AEyD14FM/eIXg3iwIPE9hpW5+WlmBu3qGa6QuReyABMH+JNZWT4d06/amdz5K6IKZxJJv3JXDk9DkKCgvx9LD9fzl8wnIDqUWjyArtwzrw2L654+OyffPGwCZjnycTSwKceeY5bgsKC3n2k0Xl7vOXDbv4ZcMuAGbcMq7OBjhBjsG6IKZpYzbv3MeRE6cdH4dWnbJimlR+3vMAfz8CzhtpUVhYxL44Y+aI6Ib1CQux3SGhPB4eJoIDA8jJzePkmZoJqP9TYpo0YvPuAxw5ebac9rNc67UopzOeMwL8fUuNAAZz+x02ju/ohhGEBbvWfkK4m9yDR/Ds3ancc6Gv1bkw9+A/fy4sThVv8vYi+qV7yi0fMupiQkZdDMDRh94kpQ4HV5y/nrH0vc6toesZp9XpPFulyTEoaoOiC+mgqxqvYgzqagO8opRqBXwFZAODgf/DiAlmA/9yZQdKKR/gR6Cr+aXPgV+UUo6GtWdqratleLWkqBVuR2t9FksO6clKqYrdkTNYB/cd5Wi63YVtX3C01nsw2iTX/Jhbxbv4xfwzHBjjoNzNVbxfW4onYFTAfdW0j+K/N1Yp1c1BuX/i761xf60xOhMFBgbQo3tnu+X6D7CMpFuzxmYHpAobPXoIERHGDfSvvlpkN7hqrXiuJk/P8vsQeZnT8xU4MU9ebXR6w37ASFFbr3MLu+Ua9m1f8vyMeZ2KyDhytmQUZ8O+7RyWbdDH6DyWefIcGUdL9/7VVu2nypk3zORpuZFpL3Bbl2VvNIJIpgA//Dq1tlvOv3dsyfOsTburvV62ZJkDXgDeTR1PwextDqwV5eRSmJJerfWqSd3aNAOMtIm7D9sf0b5xr+X7Rdc2Te2Ws8XTKpBVUM4xUmB17HnW0Tn7qpocg+6ve8c2gJF+dvcB+9/lN+7YW/K8W4c21VKX9dt3k5KWAcDIgX1c3k5+fgHJqUa7+dXxeSC7dTA6zmXn5LL7YILdcht3Wq5rurWvng5rG3bsJcU8bcOISyrVv1EIt5K5wTiveQT44Rfbym65gD7W58I91V4v4bysjVZt2MlRG1ruiWdtrN1t6NPKEozNP1Px7ETuRI5BIdyP1joduAwoHvlxK/AbsBZ4AWMgVxowQWu91cXdRGEM/il2HcbgJEePaktbJyM4hbv6D0ZPgWDgW6XUWK11iq2C5l4F04FPtNbFyeAPYhkFOkUp9bXWpWegVkqNAcofniMA0FpfVI2bnwM8A/gBryul1mitS3WxUkoNxPjQrlZa65+VUuuB3sDDSqktWutv7JVXSsUCzbXWSyqwmw8wgusK+FApNUhrXWq2e6XUdcDoiv8F7uf7xT/x2KNGL7wpUyayfkPZ+b2UUiWpZJOTU1i5ck2V7Ns6Pe1n85zLuhwff5ROHdsRERFOu3at2Lv3oM1yYWGhdOzY1rzOEZtlarsjP22kyz1XAEb618Qth8oWUopWV18CQG5KJifXuPaF58jPm2k/ZSihrRsT2b0lZzeX3Vdk95aEtjZGaR5ZvqnM8vQjloBngz5tOf572XSYAMrTg/o9jIBCbmomuckZJcucGd3a9YFxdHtwHADLrn6eU2vd70te2op1RNxpZHIPuXoo2dv2lS2kFKFXGekJC1MzyFxn+/9Z3bLW76QgKQXPeqEEXtobnvsQisoG3LyiGxjz/mH+4q3rbk/QwT068MmSPwFYvGoznVuVzUheVFTED6uN7zNB/r70ah9ToX00rm9Jxbh5XwIDu9nvfLBpb7xlvUjLemMHdGfsgO4O97NhTxzTX/gfALdfNZg7xg2pUD3dlRyD7u/Si3rw8dfG5d+iFavo3K7sjcGindRfEgAAIABJREFUoiKW/LIaMOZV7NWlfZkylaW1ZtY8Y2odT08Pxo8c5PK2flu3iXxzR67WzaPLKe3eLu3TlU++XQbA4l/X0Llt2c/IoqIilvxu9LsNCvCnV2zbMmUqS2vNrK+M95Gnpwfjh5dNuydEXZW2Yh3175oAQNjVQ8neaqOjpFKEjhsMGOfCDBvp7qtbyoJfSVnwq8MyAX06EfPViwCcfvMLzrz15T9RtRqX9vM6Iu80t+E1w8jeZqcNra5nMmroesYpHibCrhlW8mvW+uqYtaj2kGNQ1AZ1+xtD9dBaHzQPmLkLuAZohZHx8CiwFHhLa22/B5+bkS7Uwi1prZcCb5l/HQDsUUo9rZQaopTqqpS6WCk1RSn1MXAS+C9WAX2tdRLGAQ0wEvhZKTVOKdVDKTXKvN53QBxQayYAUEq1UkpNtX5gSaEbeP4ypZTjbvRuwhzMfNL8a3Ngk1LqLqVUL6VUf6XUixh5v4/zz7TXZOAc4AF8rZT6Xil1nVKqt9V76P+UUmuB7cDAimxca70NeNf8a09go7k9eyilLlVKzcIYJbux6v6k2mvDxq2sWmXcPLrppmvp26dHmTL3338bHdobox7e+e8nJaMorQ0YcBH5ecfJzzvOJx+/Ue5+w8JCGT3KuJG+Y8dutm3bVc4ahh9/WFHy/LWZz+Ll5VWmjFKKN9+YgY+Pj7HOUscX47VV4tY4Tq0zRp60uXYgkT3K3rztdNtoQtsYQcfdnyxH2xit2vCi9tx0fB43HZ/HJW/Y7qew++OfKDKv2+e5KXj4lv6/evh60ee5KQAU5Rew++Ofymzj6IrNJc97/t9EvAL9ypQB6PbQePwbGkGYY79ts1mmrsvZvp9M8xf2sGuG42cjeFVv+lUlc5mcm70Yzmtb/z6xdDj0Ix0O/UijV8pPR+2yoiKSPl4IgHd0AyLvvrZsGQ8TUTPuRJlTDCZ/sbRsmToktmU03dsaozgX/bGJbQfKdqKYu+wv4swpaq8bcRFenqXTL27YE0eXG56gyw1P8OQHC8qs37tDDL7mdFDzf13PgaO2U3Kv3raf38y99+uHBdO2nBF+wiDHoPuLbduS7p2MgNd3y/9g656y6fPnLFxG3FFjlPX1Y0fgdV72hw3b9xA76gZiR93A4699YHM/KWnp5OXl21xWWFjE8+/NYctu44bk9AljiG5Yv0y5tVt2lprj0ZZDCcd5adZnJb9fMeQSh+XdXWybFnTvYHR2+u6X1WzbW7Zj1ZxFK4g7dhKA68ZcWrb9duyj89hb6Tz2Vp54y3an9ZS0DPLy7bffCx98yZY9xr6njR9FdAPH89gJUZdkbztQci4MnzAM/25lOxFE3HIlvuZzYeKn35c5Fwb06UTs4SXEHl5C9KsuZeITlZBd6npmmM3rmYjpV5W0YdJsW20YS6e4H+gU9wONX6m+NgzoG4spyEGCN08PGr94b0ld0375u8bmP/+nyDEohPvSWmdqrV/RWvfSWodprQO01u201g84E9zUWivzo7mNZfFWy519DKqOvxNkBKdwb/djBJmexJif8RkHZTOB8++q3wGsBpoCQ80Pa0eAK7EEQmuDS7A/pLuejWWDgSqdI7KmaK1fU0o1Be4FGmMEra0lYvRKcW6YXeXqcsg8KfMCoBNG2lxHqXNdmY/0AaARMA5oR9m2PYyRU93GkLm654EHn+KPlYvx9/dj6dIveOnld/hj5Rp8/XyZOGEst9xyPQD79h/ijTds3wCsqIkTxpYEIOd+9q3T682Z+w333DudDu3bMHz4IP5et5R33/2U7dt3U1hYSPsObbjt1hu56KKeAJw6dYY336yaOteEv5/6jMsWP4Wnnw8jvniUbe98z6k1u/Hw9SZm7EW0vd7ojZt66CQ7P3D94zQt7hQ7Z/1I53uuILJrDJctepod7y0hLf4Mwc3rE3vnGOrFNgdgx/tLSTtc9kbtiT93cmL1Lhpd0pHwDs0Y+/Pz7P5kOWe3HqIwJ5/gFg1oPXEg0Zd2ASA/M4etb3zncp3d3ennPqD5N69i8vOl6eznSJz1DVnrtqN8fQi5fABhk0YBkBt3jKRPXP8/hYwvffr16WAZJRM4oAde0Q1Kfs+LP0G2jTSc52Z/T/BlA/Dr1IrI+67DOyaalIW/UpiUglfTKOrdfCX+3Y2RUem/byD9p79crq+7eOT6y5j63Efk5OVz+yuzmT5mIL06xJCTl89P67az4Hejj0yzhhHcOKrigYrgAD9uHjOA9xb8SmZOLjfO+JBJw/rSt1MrggP8SErNYOXmPSxcuZEi80i9+yYOx2SS/pXOkmPQ/T122/Xc+NBz5OTmcdvjr3DLxDH06tyB3Lw8lv2xjm+XGXN7N2/ckCnjRrm0j/Xb9vDCrLmMGtCXnrHtaFi/Hnl5+ew/fIRvl61kr3nuzUt6dubWa8fa3MaWXfu544lX6dO1I/16xNKmRRNCgwIpLCzixJlE1mzewQ+//UWuOZB61fAB9Ona0aX6upNHb5nIlEdfJicvn9uefpPp14ymV2xbcnPz+Gn1Br5dvgqAZo0aMGXscJf2sX7HPl788EtG9u9Fz45tiIoMJzc/n/3xx1iwfBV7zXNvXtKjE7deYz95S2JyKn9t3lXmtWKLfy2d3aRbh1Y0jSob7Bbl27xtJ0fMgW2A5FTL//nI8RMs+nFFqfJXXjYM4boTz35Ey29fweTnQ/O5Mzj73nwy1u7A5OtNyJgB1Js8EjDOhYkflz+ftz2h40tniPC1PhcOPO9cmHCyJPWqKN/JGR8SM/8VTH6+NJ8zg7Oz5pO5drvRhpcPIHyy5Xom8WPXr2fKtGF7yxQqgQO74xVt+cyz1Yah44bQ9MMnSf91PZnrdpAbd4yijCzzdAGtCLt2BL7maSAKEpM5OcNxVp+6Qo5BUdMuvAmDREVJgFO4LXNK2RlKqc8w0nleCsQAIUAWxrDrLcDPwHda6+zz1j+qlOoOPAqMBZoBOUA8sAhjuHayUuqf+YNEubTW9ymllmMEOXsB/sAxjCD0q1rrY/9Ue2mt9yulugITgPHm+kRijOpMAvZhBNC/01pvtrsh+9vPB8Yrpa7HSL3bGfACEjBGF8+8kN6fW7fuYvJ1dzBn9tuEhATz/H/+XabMvv2HGDv2RjIyMm1soeKK09MWFBTw5ZcLnV4vPz+fMWOuZ+GCT+nSpSOxsR14//1XbZaNi0tgwsRbSEpKrpI614RzuxJYecd/GfD2HXgH+9Pz3xPLlEk9dJIVN86kIDPHxhact+nl+fhGBNNm0iDqxTZn0Kx7ypTZ/8VKNr9sv5/D77e+xaUf3UfUxR0JalafPjNusFkuOzGVP+5+j7RDJ20uvxDk7I7j2L0v0/j1h/AICqDBw1PLlMmNO8aR6c9QlJldZpmzGjsYWRZx+zWlfk9Z8IvN4IrOy+fo9Gdo8tHT+MW2JmTMQELGlB08n/77Bo7f97LLdXUn7Zs34uW7JvL4+/PJyM7l7fkrypRp1jCC/z50AwF+Pi7t49axg0jLyObzn9eSlZPHJ0v+LEmNa83Tw4N7rxnG5Rd3dWk/Fyo5Bt1f+1bNefWxu/j3q++TkZXNW7PLnp+aN27IuzMeIsDfdlYBZyQlpzJv8XLmLV5eZplSiiuH9eeJu6aWzP1tS2FREWs272DN5h12y3iYTNw4biT3TS17rq+L2sc05ZWHb+X/3viEjKwc3v6s7I33Zo0a8O5T9xDg7/qcpEkpaXy+5Fc+X1I2o4dSiiuH9OPx2yc7bL/Dx07x5Nuz7S4/f9lz906VAKeLFixZzuJlv9hctmX7brZsL/0ZKQHOysnZHceRe16myesP4hEcQMNHppQpkxt3jPibZ1TqXNhkpv2RZfXvuLrU78nf/irBlQrI2R3H0XteIfqNB/EICqDhw7bbMGHas5Vqw+hX7V/PRJ53PZP87S8229Aj0J/QsYMIHTvI7rZy9h7m6L2vkH/MceaDukKOQSFEbScBTlGraa3Ljd5orQ9jBCld2X4S8Ij5Ya9McwfLZgOzndjPMzgeYVpcbiowtbL7qw7O/g0O1p+K47/N4XKrcktxMKrWUXs5s9xc5hmca69C4Evzo0KcHZqvtZ4HzHOw/MKIcAI//riC7j2Gcs/d0xk1egjRjaPIy8vj0KF4vl3wA++99ynZ2ZULoBVr1aoFffoY88L98sufnD5dsczHR44cp+9Fo5k4cSzjxl1Gt66xREaGo5Ti3LkUduzYw/ffL+ezefPJynL9S0BtcXTFFhYN/Tcdpo+kyZCu+EeFUZRXSFr8aeJ/+Js9n66gMCev8jvSmr8e+piEpRtoc91gIrrE4BseRM65dBK3xbFv3m9259UslpeaxU8TXqTp8O7EXNWPiC4x+NUPQXl4kJeWScr+4xz7bRv7v1xJXkrVBMvdWcZv64kbfRfhU8cSOLgXXg0j0PkF5CWcIG3Zas7N/QGdk1vT1QSg4Gwyh8c/QOiE4YRcPhCf1k3xCAqgICWdnG37SVn4C+k/r63pav6jBnVvx/wX7ubz5WtZtXU/p8+l4uXpQdMG9RjWuxPXDuuDn4+3y9tXSvHw9aO57OIuLFy5iS37EziZmEJOXj7+vt40aRBOj3YtuHpwL5pHSVpFV8gx6P4G9e3OgvdeYN7i5axav5XTiefw9PKkaVQDhvfvzaQxw/Dzda2TAUD3Tm15cNq1/L1tN4ePneRccirKZKJ+eCi9unTgymH9bc7/ae2Gq0bSIjqKDTv2si/uCInnUjiXmkZRkSY40J8WTRrRo1NbrhhyCU0aNXC4rbpmUO8ufPvW03y+5Ff+3LiD00nJeHl60jQqkmEX92DSZYPx83G9/Xp0bM0DU69m/Y69HD52iqSUNExKERkeSu/Ytowd0s/m/J9CXEjSf93AgdH3EDH1CoIu7VlyLsyNP0nqstUkzfmx1pwLhW3pv63n4Ki7qXfTWIIGW7VhwgnSlv5FUi24nkn84Fty9sTh362dcQ0THoJHSCA6L5+CxBSydxwk7ae/SFu+1uZc43WZHINCiNpMaS1TtQohhLDPy7uxnCjc2IeRg2u6CqKS+vi67whfYYj5cmpNV0FUQtyk2TVdBVFJrVY8XdNVEJWk89y/Q9iFzBTRpKarICppb6/7aroKopKUkq/17u4C6t9eZ8UeXiKNWEGPNp9UYx9eL8d/Ke3lBmQSHCGEEEIIIYQQQgghhBBCCCGE25AUtUIIIYQQQgghhBBCCCGEEKLWkLHnojwS4BTiAqCUCgWiXVlXa72ziqsjhBBCCCGEEEIIIYQQQgjhMglwCnFhuBL41MV1Jd+4EEIIIYQQQgghhBBCiH9MUU1XQNR6MgenEEIIIYQQQgghhBBCCCGEEMJtSIBTiAuA1nq21lq58qjpugshhBBCCCGEEEIIIYQQQliTFLVCCCGEEEIIIYQQQgghhBCi1ihC13QVRC0nIziFEEIIIYQQQgghhBBCCCGEEG5DRnAKIYQQQgghhBBCCCGEEEKIWkPGb4ryyAhOIYQQQgghhBBCCCGEEEIIIYTbkBGcQgghhBBCCCGEEEIIIYQQotYoqukKiFpPRnAKIYQQQgghhBBCCCGEEEIIIdyGBDiFEEIIIYQQQgghhBBCCCGEEG5DUtQKIYQQQgghhBBCCCGEEEKIWkOja7oKopaTEZxCCCGEEEIIIYQQQgghhBBCCLchIziFEEIIIYQQQgghhBBCCCFErVFU0xUQtZ6M4BRCCCGEEEIIIYQQQgghhBBCuA0ZwSmEEMKh2RGDa7oKohKmnP29pqsgKuntBnIMujvT9Z/WdBVEJcR8eVNNV0FU0r4hz9V0FUQlKSXzL7kzrVVNV0FUUrsNb9V0FUQlFSUerekqiMry8qnpGgghRK0jAU4hhBBCCCGEEEIIIYQQQghRaxQhndyEY5KiVgghhBBCCCGEEEIIIYQQQgjhNmQEpxBCCCGEEEIIIYQQQgghhKg1ZPymKI+M4BRCCCGEEEIIIYQQQgghhBBCuA0ZwSmEEEIIIYQQQgghhBBCCCFqDZmDU5RHRnAKIYQQQgghhBBCCCGEEEIIIdyGBDiFEEIIIYQQQgghhBBCCCGEEG5DUtQKIYQQQgghhBBCCCGEEEKIWqOopisgaj0ZwSmEEEIIIYQQQgghhBBCCCGEcBsyglMIIYQQQgghhBBCCCGEEELUGhpd01UQtZyM4BRCCCGEEEIIIYQQQgghhBBCuA0ZwSmEEEIIIYQQQgghhBBCCCFqDZmDU5RHRnAKIYQQQgghhBBCCCGEEEIIIdyGBDiFEG5HKVVPKTVTKbVHKZWtlNLmx79qum5CCCGEEEIIIYQQQgghhKhekqJW/GOUUs2Bw5XdjtZaVboybsD8/xoDDAL+n737Do+qaBs4/JtN7yTUhNASWui9SW/SsYCIoiAgVnwF62t97b0XFD8VBMRCFQUUUUSlSAfpEEJJQktCes/5/jib3Q3Zmh547uvKxdnsnDMTJrPZPc/MM+2A+uiTEi4C24FvgCWapuWVQ93/A5618lQOkGz8Ompsx6+apm0s6zbYaVsQsBloVlF1CiGEEEIIIYQQQgghhKg4GlplN0FUcRLgFKIKUkq9ADwJWAvm1jd+jQVmK6XGaZp2qoKa5gnUNn41BYYDTyulDgLPapr2fQW04T7Mwc3XgVXAJePj+AqoX5QTv/o1aTHtWsIGd8AvLIT87DzSTp7n5KqtHJm3jvzMnDKrK2xAO5pOGkjN9hF41QwgOyGVhD3RHFv4G3G/77V7bo93ZhA5oa9T9azo9iDpZy4W+/7gJU9St1eUS21ed+NLnN980KVzqpqGDesz875pDB8xiAbhYWRnZ3M8+iRLlqzi4znzyMzMKvG1GzUK5/jRrS6dExNzmqbNe9h8/tqh/bn99pvo2qUD9erVwWBQXLiQyK7d+1j8zQqWLFmFpl1db7b969ek3dRraTSwA/5hIeTn5JFy8jzHVm3l3/nryMsq5ThViuCmodTpEEndDpHUaR9BzZYNcPPyAGDF+JeI2+J4HASE16Lx4I6E9YyiZlRD/OoFo5QiKymN83ujOfbDFo7/9A9a/pW/o4d7WB1Cbh+DX/+ueNSrjZaTS87peFJX/0nSoh/RsrJLfnGl8IwIx6ddC7zbNce7XXO8WjTB4Kn316lJj5Hxzz67l6g181ZqzbzVpWovfrCIix8sKnGzq5u4i0l8/csW/tx9mLMJyXh6uNOgTghDu7dhwuDu+Hh5lrqO2AtJfLf+H7buP86Z84lkZufg6+1Fk9Ba9GrXjPEDu1EzyN/l6/615wj3vfmV6fHd1w/gnhsGlbq91Y1H/drUnDKagAFd8QytRUFOLjknz5K8+k8Svlpd6nHoFRmOT/vm+LZvjk+7Zni3bIzB+LoZffN/Sd/6b4kv79+vM03m/c/0+Ny7X3P+vcUlb2815BFWm5pTxhAwoAseobX1/jsVT8pPf5Gw4Kcy6z+fwv5r0cTUfycm/pf0rfZfRwGafP0Kfj3aOlXlvxGjSt7eakrGoLCUkHSJfQcO8++Bw/x76Cj/HjzCpeQUAMYOH8xLTz1UyS28MsWdT+DrH39j4/a9nL2YpL+fqVebodd04eaR/fHx8ip1HWfOXeTrVevZvOcg8ecTKNA06oTUoEeHKG4eMYCmDcPsnp+Tm8vB6NPsPxrDv0dPsO9IDKfiz5s+8+1dObfUbayu4s5d5Osf1rFx2x7OXkjA08ODBqF1GNqnGzePGoSPdxn039kLfP3DL2zetV/vv4IC6tQMpkfH1tw8ahBNG4XbPT8hKZkNW3fxz56DHI4+RfyFBHLz8qgR4E/ziIYM7tWZUQOvwbsM3jsLIaoOCXCKihQL2PvUVfjJbTtwR/k3p0oLRQ9upgPLgfXoKyazgCjgAaCr8etXpVQnTdPSyqktU4FtxmMFBKEHOLsAo9BXl0YB3ymlPgdmaJpWnneMBxv/3a5p2mPlWI+oQPWHdKTXB/fgGehr+p67L3gF+1OzQwRNb+nP77e9SVrMudJVpBTd35hG01v6F/m2b1gIvmEhNBjehWOLfmfro19AFQpcFeQXkHribGU3o1RGjRzC/HnvExQUaPqen58vISHBdO3SgalTb2HM2Ns5fjymwtp05Mhxq9/39PRkwVcfcuMNI4s916BBGA0ahDFm9LXce/dkrrvhDpKNN0SudI0Gd2Twe/fgZTFOPXzBu4Y/ddpHEDWxPz9NeZOUUozTFjf2ZtA7d5Wqnd0eHkfnmWNQhuI7MfiHhuAfGkLEtV04d+dxfr7rfdLiEkpVX1XmP6AboW8+gluAn/mbvt741AjAp21zgsZfy5kZz5J7qmTzgwKvG0jYaxV/EzA7+kyF11lZNuw8xJOffE9apvnme1ZOLvtPxLL/RCzLNuzgw4dvo2HdmiWuY9Vfu3jxyx/Iyskt8v2U9Ez2HDvNnmOn+fqXzbx27wR6tm3q9HUzsnJ4ad4PJW7XlSJgUFcavP0QboHmcWjw9ca9RgC+7ZsRMmEoMVOfJ+dkycZhjesH0OCtWWXV3CKUjxf1X7ynXK5dXQQM7Eb4Ow8VeR019V+75gRPGMrJac+Vqv/C35xdVs0VVsgYFJfrN2piZTfhqrPhnz088c7npGWYJ7RmZeew/9hJ9h87ybJ1f/HRMzNpGFqnxHUs+Xkjr8z9hty8oknOTsWf51T8eZav+5uHp45j4siBNq/xwseLWPnbphK34Uq1YesunnjjU9IyMk3fy8rOYf/RE+w/eoJlP//BR8/NpmFY3RLXsWTN77wyZ2Hx/os7x6m4cyz/eSMP33kzE0cPsX7+2g289OF88guK3468mJTMxR372LRjH/OXruGtJ++neZOGJW6rqFhX/pRkUVoS4BQVRtO0XMDm1EWlTIsV0zVNK/kUxytDAvAYMEfTtNTLntuhlFoMfA3chL6acTbwfDm15YSN/lgOPKmUGg18AdQCpgGJwKPl1BbQV68CHCnHOkQFCm7TiN6f3I+7jxe5aZns/2AV5zYdwM3bk0Zje9Bs0kACI0MZsOBh1gx7mrz0kq/y6/D4eFNwM3FfDAc+/pHUmPMENK5Dq3tHEdK2MU1vHUBWQip7Xv3O7rUy4hP57ZbX7Zc5m2T1+5tnzcXd1/4Mx6Dm9enz6UwAzv21n0wb16oOOnRozdeL5uDr60Nqahqvvf4hGzZswsfHm5tuGsOd0yfRonkkP6z8iu49hpOWlu5yHbGxZ2nf0faH1UKPPXo/t0y8AYCvFlpfdP7uOy+Ygpvnzl3gzbfmsGvXPnJzc2nTJopHHr6Xxo0b0KdPD75e+DEjR09yub3VTa3WjRj68f14+HiRk5bJzo9WEbvpAO7enjQd04PWtw4kODKUkfMeZsnIp8kt6Ti1yFuQn5NHwqHTuHm4UTPK+Q+gvnVqoAwGctOziF67nTN/7yf5xFnys3MJblqftlOHUte4QnTM4sf5bvhT5GWUYuVGFeUVFUHYu49j8PEmPy2DxLnfkb5lLwZvLwJH9qXGhOF4RYQTPvc5Tt74HwrSMx1f9DLKosO0nFyyj8SAuzveLZs4fY2kRT+SsvYv+/W4GWi46HXcAvzIT00n7dfNLre1OjoYE8djH31LVk4uvt6eTBvdl65REWTl5PLzln0s3bCdk2cvcv+bC1j8/D34+bg+c37XkZM8M3cZBZqGQSlG9+nIgE5R1A4O4GxCMj/8uYs/dh0iOS2TB99dxNJXZhJeJ8Spa3+09FfiLl4iJNCPxBTXX9evBN6tImj4wWMYfLzIT8vgwpwlpG/ei/L2osboPoRMHIZXRDiNv3iGY2Nml2gcYv4MRUFOLtmHT4KHGz4ujENb6s2ehGd4XXIvJuFRK7jU16tuvFtF0OCDR02voxc/+Z60zfsweHsSNKqvqf8aff4sx8fOKrP+Ux5uLr2OWsrYe4TYR98r0blXIhmDwpHQunVo0iicTf/srOymXLEORp/i0TfmGt/PeDFt3HC6tW1BVk4ua//cxtJf/uRk3Dnue/4DvnnrSfx8vV2uY83Gf3j+44UABPj5cPvYIXRr1xJPd3cOnjjNvGU/cyr+PK9+9i0hQYFc27uL1etYpsL08/EmKrIhMbFnuZh0dUxotebg8ZM8+urHZGXn4OvjzbSbRtGtXRRZ2Tms3biFpWv/4GTsWe579m2+ee9/+Pn6uFzHmj+28PwH8wAI8PPl9uuH0a19FJ4eHhyMPsm8Jas5FXeOVz9ZpPdf3+7FrpGYlEx+QQEe7u707daBXp3a0KRBKH6+PpyOP8+ytRvYtPNfTsad484nXufbD56nXi3n3tMKIao2CXAKUQU5WpmoaVq+Uupe4Dr0tLHjKL8Ap12apq1SSvUCdgABwCNKqW80TSuvTwiFd+9y7ZYS1UaX52/D3ceLgtw8fpv4Ghd3HDM9d+7vA6SeOEenpycSGBlK1N0j2PfWshLVExBRj6i7RwCQsDuadTe8QH6W/muUuCeaM7/sZMjSp6jZIYJW94zg+Dd/2F0xWpCXT/Lhkq0iSj99wWGZJuOuMR1Hf2//5n9V985bz+Pr60Nubi7DR9zClq07TM/9vuFvjh07wWuvPk2L5pHMnnUXz7/wtst15OXlsX//YbtlDAYD/fr2BCAlJZUVK9YWK1OnTi2mTdVndScmJtGtx3BiY80z+v/etI2vFy9j5/Z1NGnSkGuvHUDnTu3YsdN+auPqrvdzt+Hh40V+bh6rbn2NczvN4zR20wGST5yj11MTCY4MpcOMEWx7p2TjNOloLH8+PZ/ze6K5eOAU+dm5dJ11g0sBzqykVDa9tJj9C9YXC7Re2BfD0ZWbGPLhfTQd3YMaEaF0uHM4299bUaL2VmV1n7obg483Wm4ep6c+RdbuQ6bnMrbsIScmjjqPTcMrIpyQqTeUKOVr9vFTnHthDpl7j5B9MBotJ5daM2916cZ8fmIy+YnJdsv49e1iWj2VuvYvtOyyS1lelb2+8Cd8JR/OAAAgAElEQVSycnJxdzPwyaNTaN/MPA66t46kYb2avPPNz5w8e5Gv1vxVotSvn6/aSIExY8Hjt49iwmDzDaM2EeEM7tqaN79ew4I1f5OVk8tXa/7micmjHV73wIlYFv+yBU8Pd2aOH8Jzn195Y8wZYc/eicHHCy03j5jbnyFjl/nvVPrmvWTHxBH636l4RYRTa/p1JUo7mX3sFHH/+5SMPUfJOqCPwzr/mVjq4Ip3m0hqThlNQXYO595cSPirM0t1veoo9JkZptfRmMnPkLnL/DqavnkvOTFx1DP13/Wcf+9rl+vIPnqauP99QuZey/67pcQBTi0jm+wjJ0t07pVIxqCw5u47bqFNVHPaRDWnVkgwsfHnuHbclMpu1hXrtc++Nb2f+fS5B2nfMtL0XPd2LWkYWod35i/lZNw55q/8hXsnjnHp+pnZ2bz2f98C4OvtxbxXHqVZo/qm51s3a8yw3l2Y/PjrHD0Zy6uffUOfzm3w9SkeSO3dqQ1d27SgdbPGRITXw2AwMPXJN6/qAOdrnywkKzsHdzc3Pn3xEdpHmbN5dO/QioZh9Xjni285GXuW+cvWcu+k6126fmZWNq99qn8O8fXxZt4bT9KssTkVbevmTRjWtzuTH36RozFnePXTRfTp2r5Y//l4ezF1/Ehuv2EYIRZZowCiIhsxtHdX3vxsMV8tX0tSciofL1jG87Omu/rfISpBQRXKriaqpuK5u4SowpRSnZRSnyilDiul0pRS6cbjOUqp5g7OjVBKPaSUWqWUilFKZRq/TiqlvlVKDXNw/hSllGb8aqyU8lRKzVZKbVdKJSulEpVSG5RSIy87L0Ap9ahSapdSKkUpdUkptU4pVaoNiDRNSwAK76hH2itb3jRNOwo8bvGtx22VBdf7USnVv/D/Hmhk/PZki/7QlFIbbNR1nVLqe6XUKaVUlvH/f7tS6lmllM1psEqpecbrxhgf11BKPa+U2m9s7yWl1EallMONw5RS1yulViilziilspVSqUqpaKXUn0qpF5RS3RycP0ApNd94Tobx92ifUuoNpZT9TSSquJodIqjToyUAxxf/USS4WejgJ6tJPhILQItp16Lc3UpUV8vpwzB46PN6tj013xTcLJSfmcO2p+YDYPBwJ2qG3ZeE8qUUTa7XA5y5aZmcXrPNwQlVV9cuHejTR9/n8osvvykS3Cz09jufcuCgvih75v3TcHcvn/lXgwf1oX79UACWLvuJrKziqwy7deuIm5v+OzZ//ndFgpuFUlPTeO/9z0yPe/ToXC7trSrqdIggrLs+Tg9980eR4Gah3XNXk2gcp22nXouhhOP0/O5o9s1bx7ldx8nPLtk8li2vfMvuT36yuYpUK9D444l5putHjrT7Elwtebdrjm/XNgBcWvJLkeBmocQvlpF9TN/CO/j2MVCCPsvae4SkBavI2nMYLaf85h0FXWdenZ28fH251VOV7Dt+hp2H9SDFdf06FwluFrp9+DVEhNUGYNHPm8nNy3e5nj1H9d+BGv6+RYKblu66boDpeO+x0w6vmV9QwHOfryC/oIBpo/vSoO7VOTvep30z/Lrp4zDxu3VFAiuFLn62gixjH9S6o2TjMHPPURLm/0jm7jIchwYD4a/MRLm7ceGj78mJiSub61YjPu2am/ov6ft1RYKbhS7+33JT/9WcUsL+23uExK/KuP8EIGNQ2Hb/9Nvof013aoXIqtjytu/ICXYeOArA9YN7FwluFpp83RAiwvXPaItW/VYsRakjf27/l8RkPfHZraMHFQluFvL39eGRqeMBSLiUYjMN7bA+XRk7qBdNG4ZhsLLdxdVm3+Hj7Nyvf06/fmjfIsHNQpNvGEZEA/221KIffilB/+0l8ZIeQL51zJAiwc1C/r4+PHLnLYC+z+bKX/8sVua264fx4B03FQtuWvrPlPHUDqkBwPpNOyiwks5WCFH9yKu1qBaUUgal1Nvo+3PeBTQH/ABf4/HdwH6l1Awb5zcBjgNvou8b2QjwNn41RE/1ukYptUAp5cyd9UBgI/AW0Nn4OBjoB/yolJplrLchsBl4DeiAvsIxCH0fyXXOBMccKFzN6PodrbI3D7hkPB6plPK4vEBp+9EVSqlgpdR69FS644AG6P9fQeh99j/gkFKqhxPXagHsAp4GWhnbGwT0ARYqpT60cZ6bUuo7YBkwFj29rifgDzQBegNPAR/bON/bmI74N+B24zk+6L9HbYCHgSPGNMHVUvgwc2qY499utF5I04heoq9g9KrhR91rokpW17WdAEg+GkvCTut7LybsPE7yMf0GQvjQygta1evTGt8w/YbwqZ+2kZ9ZfVcrjR1rDhTPn/+t1TKaprFw4RIAgoNrMKB/r3Jpy6RJ40zHCxZYT0/r6elpOo4+YXsFRHS0+TnLc65ETa41j9OD39kep4eX6uPUu4Yf9XuVbJxWlOxLaSQc0gM1gY1KvtdPVeU/uKfpOHnpOuuFNI3kFXqw0C0oAN/u7SqiaS4z+PngP0j/U51zOp7M7VfHLga/7zhgOh7bp5PVMgaDgVG9OwCQmpHFtoPRLtdTGBStX9v2Td4AX2+CA3yLlLdn4ZpNHDoZT6N6tZg6qq/LbbpSBA4xv8VMWvKr9UKaxqVlvwPgFuSPf8+qMQ5rTRuLT5tIsqPPcOHTJZXdnEoRONSi/763/Tp6aflvgLH/elSN/hM6GYNCVL7ftu42HY8dZP0znsFgYPQAfbympmewbZ/9rDyXO3DM/Lmsd+c2Nst1adsCL0/9NtW6TZKS2Bm/bTb/P40d0sdqGYPBwOhB+uTs1LQMtu096FIdB46eMB337mL7NbhLu5bm/vtru0t1FPLwcKdDq2aA/rt2KTWtRNcRQlQtEuAU1cUHwCz03bk2AlOB/kA34E5gP3rK5U+VUtbyWbgBOcAq4AH0AGMn47/3Gs8HmIQexHJkLnqQ7GNgCNAFmA4UTq18UynVBj2wFQG8amxvV+BBINn4s8xRSpXozqrxvMI7yK69gygHmqZlAIXT4HzR/38vV9J+3Aa0NX4V/h+vtPheW+COwsJKKS/gV2AgevB3ATAR6IEelHwSfZ/TOsBqpVThqlBrfNF/b2oCLxrb28XY3sL8pPcppa61cu49wHjj8V/AFGP9ndB/bx4C1mElQK30TWmXADcbv7UKuA24BugJ/Ac4hR4gXqKUsr6JRBVXp5u+YDc3PYvEvSdslju/2fwrXrur3cXaVvk3rI1vaIjxWsVn4BetS3/eNywEvwa1Xa6rLESM6206PvF98dmJ1ck1vboCkJaWbjeN68aNW0zHvYznlCV/fz/GjtGDrSdOnGLjn1usljtyxBz8jmhi+6UhIsL8nOU5V6LQruZxemGf7XEat8U8Tut1cX2cVjQ3T30+k5Z/5aW88e3cGoCC9Eyy9h+1WS7jn30W57Qq93aVRMDwPhiMKahSVv5Wya2pOLuMKSZ9vDxp1cR2soYuFikQdx855XI9jUNrARB7wfY+z2mZWSSlZhQpb0vshSQ+XqYHzp+cMhpPj6t3RxS/rvqYyk/PJHNf8ZXvhdK3Wo7Dyp8c4lG/DnUf1FdJxD41By3HtZUYVwrfLhb996+9/jNPuvDtUvn9J8xkDApR+XYd0Meej7cXrZra/mzVpY35s8Oug659trIMUtWsYXv1nrubG0H++pYHew5Fk5dfFdYJVG27jKs3fby9aNWssc1yXdq2NJ9zwPZnD2supVj0X3CQzXLubm4EGbes2HPwWIn7LyfXvNLeTVbpVgtaJX6J6uHq/cQpqg2l1BD0ICTAdE3TPr+syDal1ELgJ/SA1vtKqdWapll+EogHGmuaVjzXIKxXSn0CfIEegHpIKfW2pmn2NoTqBtygaZrlhkI7lFLb0Ff6GdBX3QUC/TRN22pRbrtS6qixvQHArcA7duqy5RHMY/i7EpxfHnYCI4zHzQHTz12aftQ0LR3413idwncjlzRNs7WM4xn0IOIlYLCmaZfnxPxLKbUIfXVtKPAyej9YUxt91WVPTdP2W3x/hzEt7j70lcD3Aj9fdu5Nxn+3AgMu+50EPQj7tlLKWu626cBI9L1Gx2iadvlmgVuUUguAP4HWwLvoK0KrlcBm+k3b1JhzaPm204OkHDOnZQpqWjzljCNBzc3nJB+zn+KpSF3Nwmzul+kV7M/gpU9So0U47n7e5FxKJ+ngKWLX7eL4N3+UeNWlu68X4cP1eHXa6Quc21Tp8xdKpWVLfYbkseMx5Nv5EHLosPnGU+E5ZenGG0bi56evQFr09VKb5f799xCbNm2jV6+u3H77Tbz97qfExxfdi9Xf348HZur7dRw/HsMv6/4o8/ZWJcFN9XGa7GCcXjpuHjvBJRinFcmnZiA1jD9X0rHYSm5N2fOMbABAzqk4sNNnOdHmfYQ9I5zf57QiBV1nzuh/taSnBTgRp//taVg3BHc32ykTm4SZJ+IUnuOK8QO78vwXK7mUlsF36//hpkHFUzbPXbGhSHl7Xpr3A1k5uYzo1Z7urSt1B4VK51U4Dk/G2x2HWcfN49CraYNyb5cj9V+8F4OvN0krfid985W9v7Q9zvZf9nFz2ubCcyqTZ2Q4EcvewiuiPsrLk/zEFDL/PUbK2k1cWvUHlCCVdXUlY1CIynfijH4LrmFobfvvZ8LrFTvHWb7eXqbjtPRMm+U0TSMtQ38+Ny+P0/HnaWJMjSusO3Ha2H9hdR30n/n/8cQp11Jq+/pY9l+GzXJ6/+lbkOTm5XE67hxNGri2Y1NuXh57jQH0msFBBAX4u3S+EKJqkqkKojoo3M9xqZWgGACapmUB9xsfNgIGXPZ8uo3gZuHzGvpqunz0FXGDHbTpu8uCm4XX2Yu+Ug/0wNi7lwU3C8utBgrzaFjP82CHUqo7+kpQ0FcRznH1GuUkweL48lxnpe5HZyil/IH7jA+fthLcLKzrJPCC8eF4pZSfncs+fVlws/Aax4DC3wNrwcXCd+mbrAQ3La+TaPnYuHrzMePD960ENwvPS0IPdANco5Qq+6hQOTJ4eeBdU59hmRmXaLdsTnKGaT+9wtStrihcvQmQEW+/rvQ486+xb1hNm+U8/H2o2zMKr5AA3Lw88Klbg7D+7ej60mTG/PkmtbqUrDsajOyGh5++Wilm6d8lukZV4eXlRe3a+v9hrIMPqpcuJZOWlg5Ag/Cy31r2tknjTccLFtpP9TXtztlER5+kZs1gtm1dy4P/mUG/vj25pldXZtx5Gzu3ryMiohEXLiRw++SZ5OZeuXtmuXl54GMcp2kOxk62xTj1L8E4rUgd7h6Jm3Fl2bEfi/2ZrtaUpwfuIfrs57yzCXbLFqSkUWC8EeThYGVeZfCoXwefLvpq1Iwd+8k9fbaSW1QxsnNyTSsm64TYnskOEOjng4+Xnib7bKK9uXnWXdevM6ONaW5fmb+K5z5fzoadh9gfHcuv2/bz4LuLmL9af2t755h+9GhTfO+lQqs37eHvvUcJ8PXm4VuGu9yWK4ny9MC9pt53ufGOxmE6+cZx6FnJ4zBoTD8C+ncmPzmN+Betvl2/KhTpv7MX7Za17D+P0MrJ/GHJo3Ywvh1a4Bboj8HLE4/QWgQO6UH4W7Np+uP7eEUW39vsSiRjUIjKl52TS5JxdV7dmvb3Ow3098PHGKg8e9F2VglrIhqYg2vbjSsOrTkYfYqMrGzT4/gL9j/bXO2yc3JIStH3NnXYfwGW/efa/2uERZByu530xAePnyQjM8v0OP6C/dd2a5au2WD6mYb2LvusUaJ8FKBV2peoHmQFp6jSlFKB6ClBQU/XaZOmaQeVUheBWugpPG1slgLG/SHroq+gtJyGVJi2tD1ge4kPfGPnuT1AXyfK7UUP4kXYKVOMUqou+v+FO/qK+cnG9LBVgWUC+4DCg/LqRxv6oe+P6bAu9DS5AB7oKYetbS6nAV/bucYO9DSyIUqpGpqmXbJ4Lh5oBoxWSr2saZr9OyRmrYDCZQ/O/gyg/3+5lg+kEhUG8QByM7LslNTlZWTj4edd5Dxnufv7mK+Tnm2nJORnmJ939/OyWubC9qPErttF4r4Ysi4m4+blQY2WDYic2I9anZriGxbCwMWPse76F0j61/Y+jtZYpqct3Hu0ugoIMM8bSEtPd1g+PT0Df38//Px9y7QdDRqE0bevvq/Lpk3bOH48xm75o0ej6dFrJHffdTuPPHwvb77xbJHnc3JyeOutObz/4efExro2w7i6cXWc5pZinFaUOh0iaTdNzyqeFpfA/q+urFWBBj/z611Bhu1Z7KYymVkY/HxQvj4Oy1a0wLGDUMbUUcnLbeyfdgVKzzJnAPD1crzHr4+XB5nZOWRkuZ45wM1g4MW7xtGvY0v+74c/WLZhB8s2FJ0b1jWqCdMdBDeT0zJ4Y9FqAB64aSg1g67uGfEGf9fHoZufDwbfynvtdAvyJ/TpaQCcfX0++QmuB8yvFEX6L93x3z4tMwv8fDBU4t8+raCAtL93k7phO1kHT5CflILB3xef1pEETxyGd7OGeDdvROOvXyH6+tnklmDFd3UiY1CIypduEYyyXGVpi4+XJ5lZ2WRkOX7dtdS7Uxvc3Qzk5Rfw1cp1jB7Qg+DAgCJlCgoK+GBh0TUKlu0TxRXpPx/Hr40+3l56/2Xav99yud5d2uHu5kZefj5fLV/L6EHXEBxkpf/mF701lu7EZ1NLZ+LP88FX+m1eXx9vpt00yqXzhahulFK+6IuIxqPfY/YCTqNnTnzfuPCnvOr+FnNWQ4AmmqbFlFd9EuAUVV1HzCuNFyulFjt5Xr3Lv2EMas5A38ewI3raUVscTd20PS1MT4vqSrkAO2WKUEoFoL8QFU69fVzTtKq0IZXlz5JicVxm/egEy70o4/XFkKWq66KmafamhllOTwugaP/PRw92NwWOKaWWoQds/9Q07Qy2Wf4Mm8vgZ7BKKeXUFO6Fobay95aOm7eH6bjAib1lCnJyjec5vtlbrC4vi7py7deVb9EWdyt17Xh2IbkpxecUXNxxjGOLfqf9Y+Np85+xePh50/3N6awd5sy2vjqf0BDq9NL3/rmw/Sip0dV7tZK3t/mDUE6O41WO2dn6zXkf77K9uXTrLTdiMAZJHK3eLDRq5BAmTryeACtpazw9PRk3bjQXLibw5ltVZQF9+XD3cm2c5hv72drYqQp8agUy7NMHcPNwRysoYP2sT8krQVCoKlMWATHNwesdgGbsM0MV7LPAMXoih4KsbFJXV+/9iF1huTeQh7vtdGCFCve5zHbiddaa6NjzrPprN8fOnLP6/N5jp1n+xw6ahNWhboj1va3eWryWxJR02kaGM25AtdwWvEwZXB2H2XoZVYnjsN4TU/GoFUzGrkMkLr5814Wri6v9V/geVTkxIaG8nLrnZQpSi08my9i2n4SFP1H/5ZkEjxuMR+1g6j19J6fvebkSWllxZAwKUfksP/+5uzu+/Wx6P5Pt2vuZerVDGD+sH4t/+p3zCZe4/bHXmT3lRrq2bYGHuzuHTpxmzuJVbNq1Hw93d3Lz9PFe0vdNV4ui/efK+1HXPlvVq12T8SMGsHjVr5xPSOL2h19k9tQJdG0fhYe7G4eiTzFn0Qo27dh3Wf85X09mVjazXvqAVGMK3P/ePYk6DlaliqpDk5WULlNKNQVWoy+6sdTC+DVdKXWrpmk/lkPdoyga3Cx3EuAUVV2dEp5XZPmPcY/DX9BX6TnD0TIGeysmTRt8OFhZWVjO8TsFQCnlDazE/DO8qWna686cW4EsA8OWgb8y6UcnlXVdjlbHWm7oUqQvNU37QikVCTyKvqr0DuMXSqnj6P35kaZp0Zdds6L+v047LlIyPvWC8QyynvU3JzmdzLNJ5GeZ3zAbPB3/OTJ46oGW/BIEI/ItPiQZPOzX5WbRFmuBD2vBTUt7Xvuemp0iCe3ThprtmlC7azMubHNuYW2TG6/B4KYH4k58X7Vv5oeF1SM42HrqxKSkZOLizpJlMfvW09PDallLXsabUZkuztp15NZbbwQgKyuL777/wWH5N157hlmz7gJgxco1vPXWJ+zZu5/8/AKioppy371TuWPKzbz6ylN069aRmyfeTUGB7b2dqiq/esF42Rin2cnppJ9NIi/btXHqZuznqhg09PDzZuT8h/E3pp7e/Mq3xG46UMmtKntatvn/Xjl4vQM9jR9AQRXrM+/2LfCK0OfhpK3fQkFaVUlWUf48Pcyvl7lO7JeXY7x57+XE6+zldh6O4YG3F5KakUVYrRrcN24wPds0JdDPh8SUNDbsPMRHS39l7ZZ97DgcwyePTqFpeN0i19h2MJqVG3fiZjDw1B1jTRNKrmYFro5DL72MVknj0K97G0JuGoKWl0/skx+DdnXfSHK1/wrfo1q+/lY0a8FNk7x8Yv/7Pr4dW+AV2YCga3sRX7cmeedcT+9XXcgYFKLyWX7+y8tzPNHA9H7Gy/X3Mw/dMY4zZy/w545/ORl3jv+8/HGxMq2bNqJ1s8Z8t+YPAPycWJV4NSvaf668H3V9oshD02/W+2/bHk7GnuU/L7xXrEzrZk1o3bwJ3/2kr/Hw83Eu+0xefj4Pv/IRh6NPAXDTyIGMHeLyTmFCVBsWi6MKg5ufoWeYzETfCu6/QCDwrVLqGk3Tdpdh3f7AR8aH5yn5/W2XSIBTVHWWAaO7gE1Onnd50v73MAcGVwBfoKeIPQ9kGffgRCl1CmgAOL1kriIopdyB7zDvSfl/mqY9YueUytLR4tgyeX5Z9aMzLOvqBDg7Lc/eisoS0zTtSaXUXOBWYBDQAz0QGQnMBmYqpR7QNO0Ti9Msf4bRQIyT1Z0vfYvLRvvHxhM5oa/V545/u5Ets+aa9uoD8HAiHZS7r57WJteJVGGXy0szp6aylXa2kJuv+XlH6WxtObbgN0L7tAGgTo8olwKcoAdxT/6wpUR1V5QXnn+Mybdbn5Q1/6vvmDZ9FqkWN9v8/extc6vz89Nj9OllGMjo2qUDUS3193WrflxHcnKK3fIjhg8yBTfnzf+W6XfOLvL87t37uXPGQ8TGxvPUk7O44fqR3HP3ZD76+Msya3NF6f7oeFqOtz5OD32/kd9muz5OPUoxTsuTm5cHwz+fRZ12elb4XZ/8xO5PfqrkVpWPwj01AQxOpJ01GG/uaE6k8KtIQdcNMh0nr7iy0gg74mexgijDiYBJpnEigq+LK49ycvN47KPvSM3IolaQPwuevYtaNczJOOqGBDFhcHc6t2zMLc/M4UJSKk/PXcri5+8tco0XvtAnjkwc2oOWjUKL1XM1Kkgr2TgscDHdWllQnu7Uf1nfvj5h3iqyDp6o8DZUNUX6z4m0s6qw/6rY374i8gtI+m4d9f47FdADask//FHJjSo/MgaFqHyWAUTLvS9tyTS+5/EtQTYfTw8PPnjqfpb/+jffrt7A4ZgzGG/zERIUwI1D+zDjppG8/vl3pnMCynhblCtNkf5zIp1vprGPfX0cpyO+nKeHBx88+yDLf9nItz+u5/CJ0+b+qxHIjdf2Y8bEMbw+17x7lDP9p2kaT7/9GX9u2wPAtX268cQ9t7ncPiGqmUeA5sbjRzVNe8Piuc1KqQ3AH+j3pt/FvKVcWXgRaAisR7/PPrkMr22TBDhFVWc5rTRD07R/Xb2Acf/HCcaHizRNm2SneJXLUaCUMgAL0ANdAN+iBwmrFGNu717Gh2mA5QyQUvejCyzruuAgFWyFMOY1fxl42ZgquSv6cv27AG/gY6XUVk3TdhlPsfwZLpXj/1cDJ8uVy0rPguxcshJT8Q4JwCcsxG5ZzyBf055+GXGubVoPkBFvPsc31H5dfsbVXXpdJZvZnnwk1qI+515WQto1oUYLfbVS7K+7yUmu/quVsrOzuXgxkVq1Qqgfbv+md40aQfj760HQ02fiyqwNkyaNMx0vdCI97dSpEwF9n49nnrW9SP6VVz/gPw/cSUCAP1Om3FwtA5zOyM/OJTMxFZ+QAPwdjB0vi3GaVoJxWl6Um4Ghc2YSfk1rAA58/TubX3I2U3r1o+XkkpeUjHtwEO71atotawj0N+3ZmRvv7DbRFcDDncARevA970Ii6X/urOQGVSwvTw9q+PtyKS2D84n292BLSc803RCsF2J9Vb0tf+89yvkkfdLHxKE9igQ3LTUNr8vIa9qzbMMODpyI4/DJeFoYA5nrt+/n5NmLuLu5EVm/Dms27y12fnScef7VsTPnTWXaRoYTXsf+60p1peXkkpeYgntIIB6hjsahH27GcZhTCeMw8NpeeEWEU5CTS9ax0wSNKr6qwatZQ9Oxd4tGpjIZu4+QayO1cXWm918y7iFBeNSzv3OJZf/lxlftfS2zjp0yHXvUtf97Wd3JGBSi8nl5elAjwI9LqemcS7A/dz0lLd0UIKtXq2S35QwGAzcO7cONQ/uQnpFFQnIK3p6e1AoONGWXOBVnHi+RDWRSlj1enp7UCPTnUkqa4/5Ltey/kr23MxgM3DisPzcO6096RiYJl1Lw9vKkVnCQ9f5rWN/hNV/6+Ct++n0zoO/1+fIjd0mmkWqo+uXKqjzGe84PGB8eBN66vIymaZuUUp+j35Pup5TqqmnatjKouwswE8gG7gWeKO01nSUBTlHV7QY09BWV1wALS3CNZkBhboVvbRVSSrUEim+2Vvk+BW42Hq8CJmmaVhVf3+9AT8MK8KOmaZY5SMqiH521y+L4Guz0eWXQNC0XfQXrJqXUH8Ay9P+XcZjbfvnP8Fc5tcWp4O+iMHtzAqzbMmsuW2bNdVgu5Ugs3j1aEtC4LsrNgJZv/Vc7sGmY6Tj5WKzVMvZYBhyDLK7lsK6jJQu0aSVIK9VkfG/TcXQVT08LMG36LKZNn+Ww3MGDR+jTpwdNIxvj5uZGfr719DYtWzQ1HR865NyKV0fc3d2ZcNNYAM6du8Dan393eE5L42rP8+cvEhdnew/U7OxsDhw4QvfunWjZIrJM2lvRfps9l99mOx6nSUdj8enekiAH454DIhkAACAASURBVLRGpHnsJJVgnJYLpRj83j00GdIJgKM/bGbD419UcqPKX86x07h3DcKzYRi4GcBGn3lGmLdizok+ZbVMZfAf0A23YH2vx5RVG6AapoAurYj6tdl5+CSnziWSl5+Pu5v1HQ1OxJkDKk3CartUh2XgMaqx/b+NUY3rAzv0OuMvmgKcObn6a3pefj7Pfb7CYZ2/btvPr9v2A/D8nTdcsQFOgOxjp3Dv1gbPRqF2x6F3ZLjFOeW2e4BNhWmqDZ4ehL8602H5oOHXEDRczzhx+uF3uXSFBleyj53GvVuQw/7zijTPF8w+XvH955KrLOupjEEhKl9EgzB2HjjKqfgL9t/PnDF/7mriYGKsM/x8vfG7LPtMfn4Bh0/oYzy8Xi2CA61P7BJmEQ3C2Ln/CKfizjnov3jTcZOG9t9TOsPP1we/y1bf5+cXmNLMhterTXCQ/f5754tvTelsO7dpwdtPzsTDib1ghajmBmC+Nz/fTvxgHubFU9cDpQpwGjNPfgYYgFc1TTuiVMUlx5RpC6JK0zTtAlCYp/EWpZRrd250ln/B7OVIvLsE1y5XSqm3genGh+uB8ZcFDqsEpVQz4BWLb71q+XwZ9aOzfsW8b+YDqiJfUV1nmXPPcnr4Tswpc2cY91+9Ip3/5wig740X0q6JzXJ1ekaZji9sO+JyPWmnLphWcdbp2dJu2To9WgD6StH00yWbiR/U3DybMOOs40zLyt2NxmN7AJB1MZm43/aUqN6q6O9N+vskf38/OndqZ7Nc3749TMebNpV68hgAI0YMopZxBuk336ywGVy1VLg/jLsTH37cjXs6ObMnSXUWv808Tmu3tT1Ow3qYx+nZ7a6P0/LQ/9WpNBvbE4AT63by6wNzrop9rTJ26AEkg58P3q2b2Szn262txTlVZz/Sqzk9baGOzRsBerq2AydsT7bZfsicyrBD84Y2y1njbjDfpMqzcePf/Lz5dc7dTT5COiN9mz6m3Px88Gnb1GY5v+6W4/BgubdLOCdju0X/tbHXf20szqna/efV1ByMzT1fdTItlBcZg0JUvo6t9LGXmZXNgWMnbZbb/q/5s0PHqPKZPLpt3yEuGbdQubZ313Kp40rTsbWe5TIzK5sDR2Nsltu+75D5nFa2P3uUxra9B7mUkgbAtX272y07d/FKvlyyGoA2zZvw4f9m4e3l+t6gomooQKu0r2qot8Wxvb0ItmO+d35NGdQ7G+gAHKVofKBCyKdTUR28aPw3EFiilKphq6BSykspdd9lAaFjmOerTrYW8FJKjQbuL6sGlwWl1P+AwuVRm4CxmqaVbEPAcqSUGoXevsLpU69ommYtOlPafnSKpmmXgA+ND3sB7xjT/Nqqq65Sarqt50tDKTXJOIvFlqEWx6Y7lMYZNi8bH0YAXymlbG5koJQKVEpVqd9fZ51Zu910bGvPTpQiYpz+Nzr7Ujrn/i7ZjYczP+spDoOa1admJ+sfmmp2iiSomR6cPPPLjhLVA9DstoGm4/NbDtkpqQsb2B7vWvokq5jlm22ukKuOVq5cazqePHmC1TJKKVMq2aSkS/y+wdlteu27zSI97VcLv3fqnJgYfVZvrVohtGxp+2ZYcHAN2rTWg+EnYqrOyrfycOJn8ziNusn2OG1xoz5Osy6lE7up8m8Q9nrmVlrdom9dffrPf/n57vevqLFlT9qvm03HQTcOsV5IKVMgMT85lYytxVOLVgZDjQD8+3YBIOvgcbIPXZ17kQ3o3Mp0vNJGit6CggJ+/EvfESDA15uuUREu1VG/jjkF3M7Dtm86Auw4FGM+r7b5vLF9O7FnwYt2v/7viamm8ndfP8D0/bF9O7nU3uomZZ15L+3gcYOtF1KKGjfor1P5yWmkWUnxW94uLV3Pviaj7X5F3/xfU/lz735t+v6lpVfuBISUXyz6b7zt19Ea1+vv+fKT00jbUjVeR61yMxT5OTL+Kc8dQ6oGGYNCVL6B3TuYjleut/4Zr6CggFW/6+M1wM+Xrm1blHk7NE1jzjerAHB3d+PGocVTQYviBvY0v1dbuc56lqmCggJWrf8b0PfF7Nouymq50tA0jTmLlgPG/hvW32bZhSt+4cMFywBo1jicOS88XGw1qBDOUkqFO/NV2e200Mri2ObNSOPiqWPGh6UatEqpJsCzxof3VkbsQgKcosrTNG018J7xYV/goFLqWaXUIKVUB6XUNUqpyUqp/wPi0YNb7hbnJwCrjQ+HAb8opW5QSnVWSg03nrcciAaqxMYpSqmZmF8cYoFHgSZKqTZ2vjxsX7FULOtta/z/vk4p9aJSag962tzC1YdzgSetXaS0/eiiZ4CtxuP/ADuNAdNrjHUNUErdr5RaAZyi/FbvLgDOKKU+NgY7eyqlOiqlhiml3gK+MpZLAxZddu4n6L+XAOOB/UqpR5RS/Yw/Q1+l1Ayl1NdAHPC/cvoZylXC7mhTADByYj9qdS4eUIq6e4RpReThz39Gs7Jark7PKG6NW8itcQvp8c4Mq3Ud+r+1FBjP7friZNy8iw4ZN28Pur6o739dkJvHoc/WFrtGzU6ReNexGZsHoP2j4wjtq88ET9x/kgv/OF7JVhjABYheUi4ZiSvNtu27+fNP/QPr1Dtupkf3zsXKzJ51F62i9NmhH3z4uWkVpaV+fXuSlxNLXk4sn//fOw7rDQ6uwYjhevBm774D7Nmz36n2/vjjOtPx228+h4dH8ZdWpRTvvvM8Xl76vIPVq6/sm0vnd0cTt1Ufpy1v7kfdTsXHaYcZIwgxjtN9X/xsGmuWwnpEce/phdx7eiED37Y+TstK11k30OHO4YC+AnXNtHcoyKlyCRDKTdbeI2Rs029e1xg3FO8OxVeuh0y9Aa+m+oq/pK9+gMv6zLdbW1oeWU3LI6sJfdVxOuqyEjiqnyldX/KK3yqs3qqmbWQ4nVroqzhX/LGDPUeLT6T4as3fRBtT1N56bU883IumDdt2MJr2tz1F+9ue4ulPlxY7v1urCLyN/9ffr/+Ho6etp+X+a88RfjOuZqsTHEiLhvVK/oNdRTL3HCXdGEQKuWkIvh2L37Ctded1eBv31rv4ZfFx6Ne9DW1PrKLtiVWEv/Fg+TdamGTuPWLqv+DxQ/DpWPx1tNb06039lzDPWv+1pU30j7SJ/pH6r5df//n1aIshwE6iInc36r/ygKmtKb9urVr7LpcTGYNCVL62zZvQybiib/mvf7Hn0PFiZeavWEe0McXpraMHFksjum3fYdqNnUG7sTN46r0vrdZzKSWNnNxcq8/l5xfw8qeL2XVQr3vajcMJr2t/f2Wha9sikk7GVZzLf9nInoPHipWZv2wt0af1bCO3jhlavP/2HqTdiMm0GzGZp97+zGo9Dvvv4wXsOqBvYzPtplGE17OeFG7FLxt547OvAWhUvx5zX3qUoICquBOZqEZOO/lVVRQGW9ONC4DsKWx3bXuLapzwCeALLNY07ddSXKfEJPm0qC5mAYnA00A97Adz0oHL76zeg76PYUNgsPHL0ingOsyB0Mp2o8VxfZzbg7EJEFMObXFms7IDwNOapi1zUK60/egUTdOylVJD0HOK3wC0x7yq05qUktTjpLrov3/32Hg+GbhZ07QifxA1TdOUUhPQg8J3A5HA63bqOW/nuSpt+zMLGLryGdx9vBi4+DH2v/8DZzcdwN3bk0Zje5pWQ6Ycj+fgJyUfoqnRZzk45ydazxxDzQ4RDF35LAc+WkVqzHkCGteh1X2jCWnbGIADc1aTeqL4fjZhA9rT+r5RxG3Yy9mN/5J8JJaclAwMnu4ERzUk8mZzkDYvI4utD3/usF2eQb7UH6zPbL108DRJ+2JK/DNWVbMeeoaNG1bi6+vDmtVf8+prH7BhwyZ8fLy56aaxzLhT3+f18JHjvP3Op2VS54SbxpoCkAsWLHH6vPlffccDD0ynVVRzhg7tz9Ytq/nooy/Zu/cA+fn5RLVqzt0zbqdnT32F2dmz53nn3bJpc1X217MLuH75M3j4eDF60WPs/PAHYjcdwM3bk2ZjetJ6kj5Ok47Hs3tu6f6UthhfdEZ1zdbmtJsN+7cjoIH5hkRyzDnOXpa2uu2UIXSdfQMAafGJbH55MYEN7WdGv3Q83mpQtjo79+InNPrmTQw+3jT44kUSPv2OjC17MHh7ETCyL8E3jwAgO/oMiV84+vNtW9D1Rd9SeVmsIvTr2xmP+nVNj3NOxZHpIBVu4apSLTePlB8c75t7JXt00kimvPAZWTm53P36PKaP7kfXVhFk5eSydstelv6ur65uVK8Wtw/v7eBqxQX6+TB1dF8+Xrqe9Kxsbn9+LhOH9KBHm6YE+vmQkJzGhp0HWbZhOwXG1M7/mTAUg0HmyDor7rnPiFzyOgYfLxp/9TwXPv6etM37MHh7EjS6LzVvGQbo4/Di/znew9SWGjcOKvLYu5V5HPr364xHuMU4PBlvSr8q7It/fi4R37+OwcebxvOf58Kc70nfvFfvv1F9CblFn0ij999yB1ezrVj/RZnTwfv364RHeB3TY2v9V+OGQTSc+zSp6/8hfcs+sqPPUJCWgcGYXjf45mvxNqa9zruYRPzzjvffvlLIGBTW7NzzL6cs9gxMSk42HZ+KjWPFT+uKlL9upI1V3MIpj905gcmPvUZWTi53Pfsu08ePoGvbFmRn57D2r20s+VlfGdgorC6Txw51cDXr/tl3mFfmLmZYn650ad2c0NohZOfmciTmDEt//pNDxr03e3duw4zxI2xe52JSMn/v3F/se4UuX4XasVVTGobW4Ur22N2TmPzwi2Rl53DXU28w/aZRdG0fRXZ2Lms3bmHJmg2AHlCcfMOwEtXxz96DvDJnAcP6dqdL2xaE1q5Jdk4uR2JOs3TNBg4Z997s3aUdMyaMsXqN3zbt4Ln3v0TTNPx9fXjsrltJTE4lMTnVZr3169XG17s0cR1REbTqmSq2shRmV0xzomy6xbE/4PLKS6XUJPTshMmYs1BWOAlwimpB0zQNeF4ptQA92DMQPXVnEHrO6NPALuAXYLmmaZmXnX9aKdUJeAwYCzQCstADgiuA9zRNS6ra2zVWCbnowcBk4DB6zu5fNE1zaslZafvRFZqmpQI3KqV6A5OBPkAY4GP8GY4D/wA/GesrD22Akeg50CPRg501gFT0VAE/A3M0TSseSdN/hlzgXqXUHOBOoD96kN4f/Y/VCWAHsAb4sZx+hnKX9O9J/rr7Q3p9cA+egb50eKJ4GtOU4/H8ftub5KVnlaqu3a9+j1etQJpO7E9I28b0/mRmsTLHvt7AntdspzN18/akwbAuNBjWxWaZ9DMX+fu+j0jcE+2wTY3G9MDNW98P4kpbvVlo9+793HLrPcyf9z5BQYG89OJ/i5U5fOQ4Y8beTlpaupUruK4wPW1eXh5fL3Y+eJObm8uo0ZNYtvRLOrRvTbu2rfj0kzeslo2OPsn4CXeSkOB4n9Xq7uL+k/xy74cMfu8evAJ96fF48XGadDyen6a8SW4px+mgt++y+Vyn+0YXeXzo+43FApwRI7qZjv1DQ7hh+bM4sqDng6SeubJWs2QfjCbuwVcJffMR3AL8qPPQlOJlos9wZsazFKSX+M8toa/NtvlczRk3FXmcvGyd3QCnZ0Q4Pu30FTbpf+8kP8HRpNMrW1TjMF67bwJPfvI9aZnZvP/9umJlGtWrxYcP34afT8luzswY25+UtEwW/bKZjKwcPl+1kc9XbSxWzt3NjQfGD2HUNR2sXEXYknUgmlMzX6PB2w/hFuhHvUcnFyuTHX2GmKnPl2ocNnjT9sqyOveMK/I4acl6Ca44KetANKdnvk74Ow/hFuBHvUes99/Jac+Vqv/C37B9P6j23eOLPE5a8qvV/nPz96XG2P7UGNvf5rWyDp3g9AOvk3vG6kePK5KMQWHN0lU/s3KN9UUmu/YeYNfeov0jAc7SiYpoyOuPzOCJdz4nLSOL9xcUnxDSKKwuHz0zEz9fl3cpMkm4lMKiVetZtKp4dh2lFNcN6sWTd9+Ch4ftW+Enzpzl6ffn2Xz+8udeeGDKFR/gjIpsxOuP38sTb3xKWkYm788vPnm4Uf16fPTc7FKlgk1ISmbRyl9YtLL47TmlFNcN6cOT991us/9+27KT/AJ9O5K0jEzufeYth3V+/urj5ZJSV1xRGjguUqUUvojmOFHWMqDp8uBVStUE3jY+fMLWve2KIAFOUWVomuYwuqhp2gn0IGVJrp+Anur1UTtlGtt5bh76ikBH9fwPJ9KFapo2BZhi47n+js4vT87+DKW4fmn6sbGL5f/CuRWwl583BRv9c1m5edj4vdA0bT+wH/srL51pyz7ggdJco6qLXbeL1YP+S4vpw6g/uAO+ocHk5+STFnOOUz9u5fCX68jPdObvswOaxtaH/o/TP22j6aQB1GwfgVdIANmJqSTsiebYgt+I+9323jvR3/xB1oVkanVuSo1WDfGuGYhXsD9afj7ZiWkk7ovhzLqdxCzfTEG29RQrl2tiTE9bkJdPzLK/S/8zVlE//rSOjp0H88D90xk+YhDh9UPJycnh2PEYli79kY8+/pLMzNIFxgo1bdqE7t31/UJ+/XUj5865ln381KlYevQcwYQJY7nxhpF07NCW2rVDUEqRmHiJffsOsvKHn1mw8HsyMkp+M6y6OfnrLr4d+l/aTRtGo4Ed8DeO05ST5zj241b+nbeOvKwyGKeizKT9/g8nRt9HyOSx+PXrike9Wmi5ueSciid1zV8kLVyFllV1tvcOHGvev/hqTk9rqX+nlnz/8v0s+nkzf+4+wrnEZDzc3WhYtyZDurXh5iHd8fHyLPH1lVI8MmkEI69pz7INO9h15CTxFy+RlZOLr7cnDeqG0LllE8YN6ErjUEnnVhKp67dxdMRMak0ZQ8DALsZxmEd2TDzJa/4iYf5PVWociqJSf/uHY8Pvp+YdYwkYYNF/J+NIWf03CV/9WOn9d/HTJWQdjMa3Y0u8mjXELSQItyB/tJxc8i5eInPfMVLW/k3Kz5uh4OrYi9qSjEEhKl//bu1Z8t6zLFq1no3b93EuIQkPd3cahtZmyDWdmThyAD5eJV9J17l1M2ZPGcc/+w5x4sxZEi6lYFCK2iE16Na2BWMH9aJdC9f2Khdm/bt3ZMlHL7Jo5S9s3LaHcxcT8fBwp2FoXYb07srE0YPxKcVKyM6tmzN72gT+2XOQE2fiSUhKxmAw6P3XLoqxQ/rQrmVkGf5EojqpzHcumqadKY/rKqXKYlnqHcZ70pYKb6o58wHRctCW5MbWW0Bt9MVDn5Tg/DKjNE2W+QohhLBtUdgk+UNRjU2+eHWneLwSvF93QGU3QZTSQJ/Eym6CKIXGC++o7CaIUjo64SvHhUSVVjb3gURlcWIus6jiWm57r7KbIEqp4GJV2iZOlIiHpFOt7rwie8gfRBfd0GhMpb0JXHbyh3Lpr/IKcCqltgDd0ffgtLsBrVLqR/SsgwDemqY5PdtLKTUQWI++tVxXTdN2WSkzDz2jIkATTdNinL2+q2QFpxBCCCGEEEIIIYQQQgghhKgyrtDFeWWRGzneyvfOoAc4/ZRSNTRNs7fnS2H63QuuBDeNCrMybgdaKKVaWCnTxOJ4tFLqAoCmad+4WJdDEuAUQgghhBBCCCGEEEIIIYQQohxpmnaonC59ALjReNwS2GKtkFLKHSjM+3ywBPUULifvDix2ovz7FscS4BRC2KeUqgGEl+RcTdP+LePmCCGEEEIIIYQQQgghhBCi/PxlcdwPGwFOoAvgZzz+u1xbVAEkwCnElec64MsSniu54IUQQgghhBBCCCGEEEJUqgKuyBS15WUDkAwEAZOVUq9r1nP8TrE4Xu5qJZqm9XdUpiL34DSU14WFEEIIIYQQQgghhBBCCCGEEOVH07QczOlgo4CHLy+jlOoJTDM+/EPTtG3WrqWU0oxfMeXR1rIkKziFuMJomjYPmFfJzRBCCCGEEEIIIYQQQgghSqSgshtQ/bwBTACaA68rpZqi73uZCQwAnkCPCWYCD1ZWI8uSBDiFEEIIIYQQQgghhBBCCCGEqKY0TUtVSo0EVgPNgBnGL0spwK2apu2u6PaVBwlwCiGEEEIIIYQQQgghhBBCiCpDkz04XaZp2jGlVEfgPmA80BTwBE6jBz7f0zTtZCU2sUxJgFMIIYQQQgghhBBCCCGEEEKIak7TtHTgdeNXSc5Xpax/CjClNNdwlqEiKhFCCCGEEEIIIYQQQgghhBBCiLIgKziFEEIIIYQQQgghhBBCCCFElVEgKWqFA7KCUwghhBBCCCGEEEIIIYQQQghRbcgKTiGEEEIIIYQQQgghhBBCCFFlaJqs4BT2yQpOIYQQQgghhBBCCCGEEEIIIUS1ISs4hRBCCCGEEEIIIYQQQgghRJVRUNkNEFWerOAUQgghhBBCCCGEEEIIIYQQQlQbEuAUQgghhBBCCCGEEEIIIYQQQlQbkqJWCCGEXbKdtxCVS2ajCVHJsrMquwWilJSSdzPVnZubJCirzvLz5d1MdVdw8XRlN0GUkqFWg8pugiilgnMnKrsJQlQ4Te5KCgfkXaYQQgghhBBCCCGEEEIIIYQQotqQFZxCCCGEEEIIIYQQQgghhBCiyiiQFZzCAVnBKYQQQgghhBBCCCGEEEIIIYSoNiTAKYQQQgghhBBCCCGEEEIIIYSoNiRFrRBCCCGEEEIIIYQQQgghhKgyNE1S1Ar7ZAWnEEIIIYQQQgghhBBCCCGEEKLakBWcQgghhBBCCCHE/7N3nuFRVVsDfvekTHoHkkAooffepIOCFEVBUAHFC4jYxYbda7n3qtdyFQv6iVIEUZpUpYogvfeWDiQESO/1fD/OZGaSackkIRmyX5552JOzdpk5Z+05Z6+91pJIJBKJRCKRSCS1hmKkB6fEOtKDUyKRSCQSiUQikUgkEolEIpFIJBKJRCKROAzSg1MikUgkEolEIpFIJBKJRCKRSCQSiURSa1CkB6fEBtKDUyKRSCQSiUQikUgkEolEIpFIJBKJRCKROAzSwCmRSCQSiUQikUgkEolEIpFIJBKJRCKRSBwGGaJWIpFIJBKJRCKRSCQSiUQikUgkEolEUmsoVmSIWol1pAenRCKRSCQSiUQikUgkEolEIpFIJBKJRCJxGKSBUyKR1CqEEA8JIXYKIVKEEMVCCEUIcayMjLsQ4m0hxHEhRJZORhFC/K+mxi2RSCQSiUQikUgkEolEIpFIJJKqQanBl8QxkCFqJbUCIURTILqy7SiKIio9GAdA933dBQwGOgENUTcs3AAOAcuAFYqiFN6EsTQHJgMjgKZAEJABxAM7gOWKouwqZ1sfAS/ZkHEBtgK32T1oiUQikUgkEolEIpFIJBKJRCKRSCQOizRwSiQOhhDiPeB1wJwxt6HuNRZ4Xghxn6IocdU0Di3wAfAk4FLmcKDu1RF4WgixEXhMUZTLVtoLA57Xvd0HvA9cAoqBHCPRCRiMmwuAhaiGXYz+lzggng0DaT19BA1v74JnaABFeYVkxl4jdt1+zi/YQlFOfpX1FTqkEy2mDCWwczhugd7kJmWQdDyKiJ+2E//niXK3I5ydaDbuNpqM6Y1f20a4BflSkJVLTmIqSUcjid9xgrj1ByzW12hdaPHAIMJG9cS/XRgu3h7kJWeQcjqWqBV/E7tmX1V83FpD48YNefrJ6YwcNYywRqHk5eURGRXLihXr+PqbBeTk5NrddpMmjYi8uL9CdWJiLtGiVR+Lx0ePup2HH55I717dqFcvgIyMLCIjY1i5aj3zvl1EdnaOxbq3Kl4NA+k4bQSNh3bBKzSAovxC0mOvEbluP6cXbqEwt5J6KgT+LUKo36U59bs0p17ncALbhOGkVX9m1k74F/H7ztpsxrtREE1u70po37YEtm2MR7A/QghyUzK5fiKKiLX7iNpwAKWouHLjdQCcQ+sT8PDdeA7uiUtwPZT8AvIvJZCxcRcpS9aj5ObZ37gQuIY3wr1Ta9w6tcKtUyu0rZuhcVXPV9yUOWQfOGm1iaCnJxP09OQKdXtj7hJuzF1i97AdjfikVJZuPciukxFcTU7H1dmJsPr+DO/RjvuH9MBdW/Y2rOJcuZHKrzsOs/9MNJevp5CTX4CH1pVmIUHc1iGcCYO6E+jjabF+dm4+Z+MSOBUdr75i4om/kQZAaKAvv3/4dKXH6Mi4hNYj8JG78R7SA5eQehTnF5Afl0D6hr9JWryh0nqobd4I986t1Fenlri1boZGd11EP/gqWfut6yFAs6X/wbNPx3J1eSp8jP3jdUBK5lGvwT1xCdHNo3EJpP++i5SfqmAeba7Oo+6dWuHWsRXaNoZ5NHZy+ebRes9UbB69/kXdmkelDjo+8deSWLp+OzsPneDqjRRcXZwJC67H8H49eGD0YNy12kr3cTnxBkvXbWPv8bMkXEuiWFGoH+BHny5teWDUEFo0DrVaP7+ggLNRlzh9MYZTF6M5eSGGuIRrKLr8cSfWfFfpMUpUklJSOXnmPKfOnOfUuYucOnuB1LR0AMaOvJ1/vfFCDY/w1iP+WhJLN+xg55FTBh1sEMTwft15YOQg3LWule7jyrUkfv1jJ/tOnOPy1Rvk5OXh4e5Gs4YN6Ne1PROGDyDQz9tmO38dOsma7fs4cSGalPRMPN21hAXX446+XZl450A83Co/X0huHsXSl1JiA2nglNQWrqAawyxR8kRwCPhH9Q+nVhOCatzMAlYD24CLQC7QFngG6Kl7bRVCdFMUJbMqByCE8AbWAEN0f0pGNTZuAxIBX6AHMA1oDYwC9gohhiuKYmllegjgpCvPUBTltAW523X/X9XJFVXio0hqCQ3v6Eq/uY/j6uOh/5uzB2j9vQjsEk6LSYPZ/tDHZMYkVq4jIejz3+m0mDS41J89QwPwDA2g8cgeXFzyJ/tf/gFsJDL3axtGvy+fwL9tWKm/O2ldcAvwxr9tGI1H97Ro4PRpHsKgH2fj26L09kEtJQAAIABJREFUg7JHsD8ewf40HNaF5vcPZOeMzynMrsSiSy1hzOg7WLjgC3x9ffR/8/T0ICDAn549ujBt2iTuHvswkZExN21MFy5Emv27l5cnixd9yV1jhpf6u1arJSgogN69u/HojIe4d/wjnDsXcTOGWitocntXhn7+OFojPXXxADc/L+p3Dqftg4PZ+MjHpFdCT1uN78/Qzx6r1Dh7vngf3Z6+G6ExzcTgFRKAV0gAzUb04NqjkWx+7Asy45Mq1V9txmtIL0I+fgknbyPDlIcb7n7euHdshe+EEVye+TYFcQl2te9zz1BCP7z5C0h5URb3S91y7Dh2gdfnryEzx/A7kJtfwOmYBE7HJLBq11G+fOYBGjcIsLuPdXtP8P7ijeTmlw78kZ6dy/HIyxyPvMzSrQf5cOa99G0fbraNp+f+wqHzsXaP4VbGe2gvGn32Qik91Hi44eznjUenVvjfP5zY6e+QH2ufHvrdO4RGHz9vW1BiF15DexFqaR7t1Aq/CSO49Kj986jvPUMJ/ejmz6P50XVnHpU66PjsOHCc1z6bT2a2YTNkbl4+pyNiOR0Ry6otf/PVW0/TOKS+3X2s2LST/3y3jILC0r+FcQnXiEu4xuotu3lx2n08OHqoxTbe+3oJa7bvsXsMkvIzaMyDNT2EOsWOgyd47fMFpjqYGcfpyDhWbd3NV68/USkdXLdjP+/NW0pufkGpv6dnZnP8fDTHz0ezZP2ffPT8NPp2aWu2jaycXF757Ef+OlR6U0lqRiGpGVmcvBjDii1/88WrswhvFGL3WCUSSe1CGjgltQJFUQqAU5aOC6F3VsxSFMWiXB0hCZgDfKMoSkaZY4eFED8DS4GJQEtUr8h3q3gM8zEYN/8EHlAU5VoZme1CiM9QvTyfBxoB64QQXc2MG1TP0xIuWOm7RC5KGjdvDfw7NGHAvKdwdtdSkJnDqbnrSNxzBic3V5qO7UPLKUPxaR7C0MUvsvHONynMst/Lr8srE/TGzeSTMZz+ej2ZMdfwalqf9k+MIaBjU1pOHkJeUgbHPvjVYjt+bcO4Y/lraAO8KczJJ+LnHST8dZKcqyloXJzwbhZM6JBO1O/d2mx9baAPw5bNwbNhEACxa/cTtXwX2YkpeDTwJ3zCAJrc3ZvQwZ3o/81T7Jj6id2fuTbQpUt7li75Bg8PdzIyMvnwoy/ZsWMP7u5uTJx4N4/OmELrVs1Zu2YRvfuMJDMzq8J9XLlylc5dLS84lDDn5aeY9OA4ABb9tNyszLKl87jzTrWtQ4eP8/kX/8f5cxF4e3sxauQwnnzyH7Rs2Yz1a3+id9+RJCWlVHi8jkZg+ybc/vVTuLhryc/M4ehX64jX6WmLu/vQbvJQ/JqHMGrBi6wc/SYFduqpMIpNUJRfSPK5S2hcnAhs27jcbXjU90NoNBRk5RL9xyGu7D5NWvRVCvMK8G/RkI7Thus9RMf8/AorRr5xS2wiKIu2bTih/3sFjbsbRZnZJH/3K1n7TqBx0+IzeiB+949EG96IRt+9Q+z4ZynOqrhHsjAKJqHkF5B3IQacnXFr06zcbaQsWU/6H39b78dJQ+MlH+Hk7UlRRhaZW/dWeKyOyNm4q8z5bhW5+YV4aF2ZPuo2erZpSm5+AZsOnmHlzqPEJibz1BfL+PnN6XjasRv96MVLvPXDOooVBY0Q3HVbJ4Z0aUU9P2+uJqexds8J/jp+kbSsHJ776ldWvvMYjer5mzZktCnI19Oddk1DOB5xmey8qou+4Ii4tQsnbO7Lej28MW85mXtPonFzxXfMQAIevBNteCOazH+byLGz7dJD44mzOL+AvPOxCBenCumhMdknLnDl5c/tqnuroW0XTkOjeTTp21/J3ncCoZtH/R9Q59Gw/3uHmHH2zaPG50/JLyD3QgzCjnk0w8Y8ipOGJksN82jGlroxj0oddHzORsXx8n+/Ize/AA83LdPvG0mvjq3JzS/gj10HWbl5F7HxiTz57lyWffI6nh5uFe7j950HePfrnwDw9nTn4bF30KtTG1ydnTkbfYkFqzYRl3CND/7vFwJ8fRjRv4fZdhQjLyNPdzfaNm9MzJWr3EhJt+/DS8pFSIP6NGvSiD0HjtT0UG5JzkZd4uVP5ht0cNwIenVsperg34dYuWU3sfHXePJfX7Psv6/g6V5xHTx6NpI3v1xEcbGCRiO4e3AfhvTqRD1/XxJupLD2z338degkaZlZPPvBPFb9700aBQeVakNRFF78+Ht2Hz0DQLvmjXlozFCaNQomKyeXnYdP8fPGHcQlXOeJ977i5/++gr+PV5V8RxKJpGaRBk6JxMFQFGWOjeNFQogngHsAV+A+qtDAKYSYiBomFuA4MEZRlGwLYykAXhBC+AAzgOYYwtqWRVumniVK5KzJSByIHu8+hLO7luKCQrY9+CE3Dhs84hJ3nyEjOpFubz6IT/MQ2s0axYlPVtnVj3d4MO1mjQIg6VgUm8e9R1GuehklHY/i8uYjDF/5BoFdwmn3+Cgilv1l1mNUo3Vh4HfPoA3wJvPyDbbd/x8yokvL3TgSSfTK3WhcnEzqA3R6/h69cfPEJ6tKfaaUU7Fc2XaMtIgrdHp+HI3u6Kp6gm44aNfnrg189sm7eHi4U1BQwMhRk9i3/7D+2J87dhMREc2HH7xJ61bNeX72Y7z73qcV7qOwsJDTp89bldFoNAwa2BeA9PQMfvvtDxOZceNG642bW7b8xd33TKWgwDDd/LVzL5u3/MWG9T/RtGkYb735As8+90aFx+to9HvnIVzctRQVFLJh8ockHjHoafyeM6RFJ9L3jQfxax5C55mjOPSZfXqacvEKf7+5kGvHo0g6E0dRXgE9Zo+rkIEzNyWDff/6mdOLt5kYWm+cjCFizR6GffkkLe7qg194CJ0fHcnhz3+za7y1mQZvzELj7oZSUMilaW+Qe+yc/lj2vuPkx8RTf850tOGNCJg2zq5QhXmRcSS+9w05Jy6QdzYKJb+AoKcnV2hRtyg5jaLkNKsyngN76D1vMv74G6WOGM0++nkTufmFODtpmPf8JDo3b6Q/1rttMxrXD+CzFduITUxm0aZ9PD52UIX7mP/7bop1xslXJo3g/iGGRdsOzUK5vXtbPv5lC4u37Cc3v5BFm/fz2uQ7TdoZ2bsD9w3qRvumoXpv0pFz5tZ5A2fIWzP1ehgz9S1yjhr0MGvvCfJj4gl+dRra8EYEzbiXa58vrXAfeRcvEf/PeeScuEjuGVUP6z87yW7jipKdR94F6Y0LEGw8j/7jDXLKzqOx8TSo7DwaEcfVd78h5+QF8s7cpHn097ozj0oddHw+/L9fyM0vwNlJw7fvPEfnNs31x3p3akPjkPp8tnAlsfGJLFyzmScevLtC7efk5fHh978A4OGmZcF/XqZlE8Pe6/Ytm3Jn/x5MfeUjLsZe4YP/W8aA7h3wMGPE6d+tAz07tKZ9y6aENwpGo9Ew7fWPpYGzGpj1j0l0aNuKDm1bERTgz5WEREbc90hND+uW5MMflht08O2n6dzaEM2jd8fWqg4uWk1s/DUWrtnKEw9UPIT2/FWbKC7W3Y9On8gDIw33tB1aNuWOvl35+MeVLFq3jdz8Ahat28Zrj95fqo0te4/qjZt9O7fhy9eewMXFYPbo2aEV/bq04/H3viT+ejLf/LLBpA1J7USGqJXYwjR2l0TioAghugkh5gkhzgshMoUQWbryN0KIVjbqhgshXhBCrBNCxAghcnSvWCHEL0II05Wc0vUfEUIouldTIYSrEOJ5IcQhIUSaECJZCLFDCDG6TD1vIcTLQoijQoh0IUSqEGKLEGJYZb4LRVGSgJJEgs2tydrBK0blmZaMm2V4ATV0LcA0IUS9kgO671sB3jb6m1Lm1bSkDJTc6QwqIxNjrmMhxBAhxEIhRJQQIlv3PZ8UQvxXCGExiYYQ4p9GfSKEcBNCvCSEOCKEyNC9DgghnhJCWN0sIoQYKoT4WQgRrbuusnXX1j4hxMdCCKtuZ5W5tms7gV3CadCnDQARP/9VyrhZwpl5G0m9cAWA1tNHIJzNGw1t0WbGnWh0N7gH31ioN26WUJSTz8E3FgKgcXGm7Uzzat9u1ih8modQXFTMrsfmmhg3jSkuMHUyFhpBs3H9AMi8dJ2Tn602W/fkp6vJvKymlW3/1F02Pl3tpWePLgwYoOa5/OHHZaWMmyV8+tm3nDmrOm4//dR0nJ2rZ//V7cMG0LChGopm5aoN5OaaehlOfWiivvz0s6+XMm6WsG37Ln75dQ0Aj86YjL+/X7WMt7ZQv0s4ob1VPT237K9Sxs0Sjn+3kWSdnnacNgKNnXp67VgUpxZs4drRSIry7NvHsv8/v3Bs3gaLXqRKscKu1xbo2w8f3cuufmozbp1a4dGzAwCpKzaXMm6WkPzDKvIi1DTd/g/fDXacs9wTF0hZvI7c4+dR8qtv35HvPYafybTV26qtn9rEyagrHLl4CYB7+ncpZdws4eHhfQgPUTfLLNl2kILCige2OB6hhqn083IvZdw05rG7BujLJyLNh7W8b1A3RvbuUKlQubca7p1a4dlL1cOU5VtKGVZKuPH9anIvqnoY+Ih9ephz4gLJi9aTc6x69bCuUXYezTE3j843zKMBUys5j1bz+fO91zCPpv5WN+ZRqYOOz8kL0Rw5cxGAe2/vX8q4WcLUe+7Qh5pcsm67SYhZW+w6dIrkNDXA1OS7hpUybpbg5eHOS9PUPd5JqekWw9DeOaAnY4fdRovGoWjMpEqQVB1PzXiIwf16ExRgJqqEpMo4eTGGI2fUZ797h91WyrhZwtS7hxHeKBiAJRv+tOt+9Nj5KAD8vD1LGTeNeWziKH35uE7emLV/7tOXX3v0gVLGzRL6dG7Dnf27A7Biy9+kZVQ8cpREIql9yF9cicMjhNAIIT5Fzc/5GNAK8AQ8dOVZwGkhxEwL9ZsBkcDHwBigCeCmezVGDfX6uxBisS1Dlg4fYCfwCdBd994f1TC3XggxW9dvY2Av8CHQBfBGzV15O7BFCDG5Yt+ECSWejlUWxlUI0Qnoqnu7V1EU88kFy6AoSjrwo+6tG1DtCRN0Bsmfge3Aw0AzwB31e+4AvAhcEELYtBwJIRqgnquPUD+/l+7VE5gLrBJCmJ1PdWF6twEPAE1RP7876rXVG9X4azYWamWvbUcg7E7DYmrkLzvNCykK0SvUsFtaP0+C+5nPt2CzrxHdAEi7eIUbR8znXrxxJJK0iHgAGg3vbnJcaAQtH1L3H1zddYqko+bbsYZ3eDCuvuoO+oSdp1CKze9GU4oVru5UI3IHdg7HM6yeWbnaztixBkPxwoW/mJVRFIWffloBgL+/H0MG31YtY5ky5T59efFi8+Fpu3fvBMDFi9FERERbbGvT5h0AuLq6muTqvNVoOsKgp+d/taynF1Ya9DT0Nvv09GaRl5pJ0jnVeOTTxP5cMbUVr9v76stpK7eYF1IU0nSL3E6+3nj07nQzhlZhNJ7ueA1TN0nkX0og51DdyFTw5zGDR/rYfp3Nymg0gjF91RT2Gdm5HDwfU+F+CorU28SGQZY3anh7uOHv5VFKXmIbn+F99OWU5Zb1MHX1dgCcfL3w6lM79bAu4m00j6Zam0dXG+ZRz1p6/jRe7ngbz6MH68Y8KnXQ8dm+/5i+PHaY+ecDjUbDXUPUc52Rlc3Bk9YjupTlTITBW7Z/9w4W5Xp0bI3W1QWALXtkKFRJ3WD7/uP68tihfc3KaDQa7hrcG4CMrBwOnqqYDgJ6o2jD+oEWZbw93fUhZc0ZUU9HqptVGofUo0mo5ee7fl3bAVBYWMSOgycsyklqD4qi1NhL4hhIA6fkVmAuMBsQqIbFacBgoBfwKHAaNRzzt0IIc/FKnIB8YB3wDKqBsZvu/yd09QGmAG+WYzzfoRo2vwbuAHqghmeN1x3/WAjRAVgFhKOGbB2Maix7DkjTfZZvhBB2rbrq6pWsLp+1pw0LDDQqr6tg3bVG5QFG5eFAR+Abo791LPO6YlQ+pJM5VEZGb2EQatLWFahGxZKxPgT0A/oCzwJxqMbCFUII8y4LBlYB7YAvUM9pd2AShu/2LtRrrRRCiDGo5xRUj9rHUc91V9Qcpk8BvwGWkr9V9tqu9dTrpTqgFmTlknzCsjEpca/hMq7Xs+JOq16N6+EREqBry3T3tjHXdMc9QwNMjIpBPVrhGaq2c3mz4cFWo3XBq0l93IP9ERqBNbT+hjwPudethxPLMTpuKZ9nbaffbT0ByMzM4vARyw8QO3cadlzepqtTlXh5eTL2btXYGh0dx85d+8zKBQaqu4CvXbtutb1riYbjAwb0rqJR1k6Cexr09PpJy3qasM+gp8E9ar9zuZOrumdJKbr1Hlw8urcHoDgrh9zTFy3KZR84aVSnXbWPyx68Rw5AowsDl75mew2P5uZxVOe96a51oV2TEItyPVo30ZePRZj3rrRG0wbqQtKVG6kWZTJz8kjJVAN2NA22vPAkKY1HD1WnirJyyDll6vleQtZ+g7HJo0ft3hxSl/DoYTSPnirfPOrerZbOo3ca5tG03+rOPCp10PE5qvMcc3fT0q5FE4tyPToY7juPnq3YBtTUjEx9OdDPx6Kcs5MTvl7qJtXj56IolBt+JHWAo+dUfXJ309KuueWUIT3atzSqY+pdaYumOoPklWtJFmUys3NISc/UyTcwOZ6m0+VAX8t6XPb44TOWfxskEonjIHNwShwaIcQdqEZIgBmKoswvI3JQCPETsAEYCnwhhNioKIpx3JIEoKmiKAlmutgmhJgH/AA8gppP8lNFUaxZJXoB4xRFMU7odVgIcRA4irqxYDuqZ+cgRVH2G8kdEkJc1I3XG5gMfGalL0u8hEG/zXoH2omxC0FFty0eA4pRP7++HUVRLgAIIa4Z/c3ctuJTOrmSGBJZFuRANSiPRs3TebeiKGUT7e0TQiwGdgHtgf8B/a2MvScwXFGUHUZ/OyKE2AScARqgXofflqlXEusyFuinKEpmmeM7gK+EECbx3Kro2q71+LZUowRnxCSiFBVblEuPiNeXfVuYhg2y2U8rQx3jtsyRZtxXy1CyLhkMWfW6t9CXU89ewrtZA7q+dj8N7+imN5bkp2VxefMRTny6mszYa5SlIMtgz3bx8bA6Flcfd7OfwZFo00Z92ImIjKHIykLAufOGh4uSOlXJ+HGj8fRUv+8lS1dalMvMzMLf3w8fX2+r7fkYPRi1a1v7jXmVwb+FqqdpNvQ0JdKgO/526OnNxC3QBz/d50qJuFLDo6l6XJuHAZAfFw9Wzll+lMEg5hpe/jynNxPfewxR++tKeFqA6AR1gadx/QCcnSzvSW1mZHCMTrhR4X4mDO7Gu4s2kpqZw687DjNxsGn0gu/W7zLID+pW4T7qKtoSPYxNsKqHeZGXTOrUJK7NGxG+6hO04Q0RWleKktPJORVB+h97SF33F9gRes4RKe88mmc0j2qtLP7WJL73Gs2jdSQ8LUgdvBWIvqwu0TQOqYezk+Xwwc104TGN65QXDzetvpyZlWNRTlEUMrPV4wWFhVxKuEazRpY3IEkktwLRl68C0DjYhg42NNbBqxXuZ8KIAbz7zVJSM7L4ddNOJo4YaCLz7fLf9eWJIwaYHHd305KRlUNGtmU9Bkodj7xUsflCUjPIHJwSW0gPTomjU5IPcqUZAxAAiqLkonrKgRp+dkiZ41kWjJslxxXUMKJFqB5/t9sY069ljJsl7ZwA/ta9rQf8r4xxs0RuI6pBDEp7OpYLIURvDF6DlyntGVlZgozKFbprURQlB8jQva227f867805urdfmDFulownBdUQDNBPCGHNojK3jHGzpI1kDKF3OwohfMuIlNzlHTFj3CzbTlkqfW3XdjRaF9wCVSNRdry5r8BAflq2Pp+eR2jF83uVeG8CZCdY7ys73rBr0DO09KVqbGT0aRHKqM3/ovHoXnrjJoCrryfhEwYwavO/CB7Q3qT9jOhEivJVO3QDG16Z9XV5DwE8Gzqe14xWq6VePZ13kI3FhtTUNDIz1f0LYY0spse1m4emTNCXF+vC4Zrj3DnV0Nq2TUuCgixfawP6G7w2w8Kqfry1BSetC+46Pc2yoTvGeupph57eTLrMGo2TLi9L5HqTn2KHRri64Byg/hwVXrW8CxqgOD2TYt1inktIkFXZmsClYX3cdV5U2YdPU3Cp4gsmjkheQaHeY7K+v43NFp7uuGvVkHlXk9Mr3Nc9/btwly7M7X+W/ME7C9ez49gFTsfEs/XwOZ77ajkLN6ke74+O7kefdqa5lySmCFcXnANVPSy4at3wXJyeRZFeD2s+HL1LPX88urTGyccLjdYVl5AgfO7oQ6NPnqfF+i/QmskHe6thPI8WVGAeda6l82iJN2r2odMUxNWNeVTqoOOTl1+g99ZqEGg9z6KPlyfuOkPl1RspFeonPMxgpDx0+oJFubNRcWTnGjaqJly3fl8skTg6pXXQcioDAB8vD7t1EODeobfpw9z++/9+4Z9fL2HHwROcjohl676jPPfBtyxcsxWAR++7kz6d25i0UZIHNPryVX1eXXMcMfLatGesEomk9iE9OCUOixDCBzVcJ6jhSC2iKMpZIcQNVANdX8BCEg4QQrigeuR5o4avLSEJqI/qfWjZ/QeWWTl2HEOYV2tyJ1ANVhVaRdLlilyBqtsKMFVRlOyKtGED41U2iwY7K2Si5hm1HjOicrQDmuvKVq8L1LCvJfQFLMWfWmKljcO6/wVqns9jRsdKLDoDhRDNFUUpV7yc6rq2axsunm76cmF2rk35wuw8XDzdcDaqV+6+vAyekMYelJb6KcHZU1vqmKufp77c492HcHZ35ey3v3N+4Vayr9zAIySAlg8Po92sUbh6uzPg22fYcMdrZF8xLI4V5eSRuPsMoUM64d++CU3v6UvMb3tNxtH0nr74tzN4Arh4Vfxz1zTe3obvKzMry4qkSlZWNl5ennh6WfdsrShhYaEMHKjm5tmz5yCRkTEWZdet30zfvj1wdnbm3Xfm8MSTc0xkWrRoxiNT79e/9/b2MpG5VTDW04Jy6GmBTk9d7NDTm0X9Ls3pOH0EAJnxSZxZdGt5s2g8DfNdsY0dzADFObloPN0RHu42ZW82PmOHITTqfsy01VtreDQ3jyyjBVQPratNeXdXV3LyCsjOy69wX04aDe9PH8ugzq34fuNuVu06xqpdx0rJ9GzThBmjpHGzImiM7juKs2zPnUpOLni6o6nBuVMpLiZz9zEydhwi92w0RSnpaLw8cG/fHP8H78StZWPcWjWh6dL/EHXv8xTEWw/l7siUPn/ln0c1tXAe9b2nbs6jUgcdn6wcw3kz9rK0hLvWlZzcPLJzbZ9vY/p364Czk4bComIWrdnCXUP64O9TenNRcXExc38qvYfdeHwSya2I/Tpofb3FHE5OGv71zFQG9ejI9ys3sWrrblZt3V1KpmeHVjw63rxxE2Bwz04cPx9NUXExXy5dx1uPTzKRiY2/xm/bDWsvUo8lklsD6cEpcWS6YriGfxZCKNZeGLwPg8s2JIRwEUI8KYTYh2qEu4QaevSk0askH6atrbmWt/2BcYKj8shZ37ZvhBDCGzVcacmWzlcURanqJCvG26DsWdEvqVNxF4PyY5xPc6+Na8LYSGtyXRhhLWmj8dbNsudrke7/QOCUEGKZEOIfQogWWKfKrm1rCCEaledVkTYrgpObi75cnG87sm5xfgEAzm62F3tN+tIa9VVgva8io7E4lenL2cNwY+/s7srxj1Zw+J0lZMYkUlxQRGbcdY6+v4xjHywHQOvnSYen7jLp48QnK/Xj6Pu/x+jw7Fg8GgYinJ3waBhIh2fH0vd/j1GUV2BxLI6Am5thkSg/v8CKpEqebnHe3a1qF5cmTxqPRre4Z817E2Det4u4rPM2nfnoFBb8+AUdO7bFxcWFgAB/Jk8ez5/bVuLp6UF+vm687rXXmFdZjHWnqBx6WlQJPb0ZuAf5MPzbZ3BycUYpLmb77G8pzK24Uag2I4wMYoqN+Q5A0Z0zTS08Zz53q4EJinPzyNi4y4b0rUO+0XlzcbYcDqwEVxdVJq8cOmqOqPgbrNt7gogrpmHVAU5EXmH138dITKnO27dbC00F9bDkHkeUw6BdXcQ9/m9iHnqDpPm/kbXnOLlno8k+eJqkBWuJGP00KStU45hLPX+C3zRJPX9LIVxvzXk0vQ7No1IHHR/jZwdnZ9u+Ga66yBx5ebafOYwJrhfAhDsHAXAtKZWH53zEn/uPkZmdQ15+AcfPR/HEu3PZfeQ0LkbjyCvHs41E4sjkFxjrYHnuR3U6aKduRF1OYN2O/UTEmU8fcuJCNKu27SExyXze+IkjBlI/QPU0XbHlb177fAEXYi5TUFBIakYm63bs5x9vfEpOXr7+80g9dgyUGvwncQykB6fEkalvW8QspVyDdPkPNwOmSYfMY2trrjWPSX3yDxuelSVytu8iACGEG7AGw2f4WFGUj8pTt4IYx/cJRvVILRdCCHcMBkDrsZ4qR5VcF8aU81xBmfOlKMo2IcRTwH9Rr5v7dS+EEFeA9cA3iqKU/R6r/DNY4JJtEftwD/bH1dfT7LH8tCxyrqZQlGu4mdS42v450riqhhZ7jBHGhkKNi/W+jMPNFpXpy3jMuUnpnP5qndk2znyzgTbTR+DewI8md/XmwKsLSh2/cSSS/XN+oPeH03BydabLnAl0mTOhlExhTh5H3vuZXv9+BICCzNq1uzA0NBh//7JRmVVSUtKIj79KrtEOaldXF7Oyxmh1i0o5Fdx5bYvJk8cDkJuby6/L11qVTU/PYNz4f7Bu7WIaNKjHlMnjmaKrb8xrr/+b556dSf36QWRk2OPQXvN42tDTrKsppY3s5dBTp0roaXXj4unGyIUv4qULPb3/P78Qv+dMDY+q6lGMvPiEjfkO1DB+AMW17Jy5dW6NNlzdY5O5bR/FmVUZkKJ242p03gqJYfDKAAAgAElEQVTKkWstv0CV0ZZDR8ty5EIcz8z9hYycPEIDfXnynsH0bdcMH093ktOz2HH8Al/99hd/HDjD4QuXmDd7Ei0a1nwIx9pOcQX1sOQeR7HDC7eqKM6wEmmhsIgrr36BR9fWaJuH4TviNhIaBFKYWJ239DWHkn+LzKNdDPNoxta6NY9KHXR8jJ8dCgttG6lLNgdptbafOcrywj/u4/LV6+w6fIrY+ESe/ffXJjLtWzShfcum/Pr7XwB43sIbHCUSAFcXYx0sz/2oTgfL8dxflsNnInjm39+QkZ1DaL0Anpp0F307t8XHy5Ok1HR2HDzBVz+v54+/D3HkzEXmvfU0LRqXThPj7enOF6/O4on3vyI5LYP1fx1g/V8HTPp6dspYFq3dRkp6Jh7utj1TJRJJ7UcaOCWOjLEx6TFgTznrlQ2y/jkGw+BvwA+oIWKvAbm6HJwIIeKAMNRQpLUGIYQz8CuG/IvfK4rykpUqleGEUbkrsKkCdbtg8Eost2HUDoyvi7uAmHLWM++2UEkURflKCLEcmATcAfRDDdPbEPW6nSmE+LeiKG8YVauqa7vG6DJnAs3vN00MDxD5y072zv5On6sPwNnD9gNiifdkYTnCTJWlINMQXszF0/pNrLGXZmGZcLbGfSfuOUtxgfkbfaWomKt/n6bZ+H5oA7zxalKfzNjSl1jksp2knI6jw7NjCRnUUR/Ss7igkCvbj3P0X8tw8TbYrPPTbId4vZm89+4cpj480eyxhYt+ZfqM2WQYLdR4eZo3pBnj6al+3qwqXIDr2aMLbduoKXbXrd9CWpptD6QjR0/SvedwXpnzNPeNH0NwsGHPwcGDR3nv/c/Y+Ps23nxjNqAadB2RXi9PoPUE83p6fvlO/ny+tJ66lENPXXT6U2CHnlYnTloX7pw/m/qd1BCbx+Zt4Ni8DTU8qurBOJxiecIlanQLdEo5wtneTHzvGaYvp/12a4URtoWnURiw8oSdzdEZY8oTztaY/IJC5vzfajJy8gjy9WLxa/8gyNcQoKNBgA/3D+lB91ZNmPT+fK6nZvDmD2v5+c3pFeqnLlJsdN9RnpCXQqeH5QmlWWMUFZPy6xaCX50GgGfvDqSt/auGB1U9lD5/5Z9HyxMW/GbiV4fnUamDjo+xAbE8IS9zdL+XHnZEgnF1cWHuG0+xeutuftm4g/Mxl9EtAxHg68344QOYOXE0H83/VV/Hu4pTakgktQ37dbBiRsP8ggLmfPoDGdk5BPn58NMHLxFktJE6OMifB0YOokf7ljz48odcS07jjbmLWPbfV0zaate8Mcs/eY3vV/7B5r1HSUo1PPt3aNGEWRNHMbBHR+b9uhFQc4dKaj8l87FEYglp4JQ4MsbbFbMVRTlV0QZ0uQ5LEqktURRlihVx65ntawAhhAZYjGrIA/gF1SBWXRjnrLwb+KACde+20E5VY3xdpNpzXVQ1iqJcA/4H/E93zroA9wJPAX7A60KIg4qirNFVqfS1XU7CyilXLZ6exXkF5CZn4BbgjUdogFVZV18PvQEwOz7Zqqw5shMMdTxCrPflofPuAsiKL70r2vi9rXEYy7oFepsYOAGST8awc8bnCCcN7g380Lg4k301hWKd11yzcf30smnnL1vtrzaSl5fHjRvJBAUF0LBRiFVZPz9fvLxUI+ily/FVNoYpU+7Tl3+yEZ7WmKtXr/Hc7Dd5bvabNGhQDx8fbxITr5OerkbqbtgwBHd3ddHzzFlrEccdm6K8AnKSM3AP8MbThu4Y62mWHXpaXQgnDXd88zQN+7UH4OzSP9n3r59reFTVh5JfQGFKGs7+vjgHB1qV1fh46RfvCxJuWJW9qbg44zNKNb4XXk8ma9eRGh7QzUXr4oyflzupmTlcS8mwKpuelUOO7jcjOKBiKc53n4rUt//g0B6ljJvGtGhYj9F9OrBq1zHOxCZw/lIircMaVKivuoaSX0BhchrOAb64BFvPbqHx8cRJr4e1O6debkScvuzSwPr84siUPn/ln0cLa9k86l2H51Gpg46P1tUFP29PUjOySEyyvo83PTOLHJ0BJjjIvmUbjUbD+OEDGD98AFnZuSSlpePm6kqQv48+1UVcfKJevnmY9WcbicTRKa2D5sPClpCemW23Du4+eoZryWr7D44aXMq4aUyLxqGMHtiLVVt3cyYyjvPRl2ndzDSjUr0AX1599H5effR+bqSkkZmTS6CvD966ef7qjRR9aFqpxxLJrYE0cEocmWOAgupR2Q/4yY42WgIl8RN+sSQkhGiDfTknq5tvgQd05XXAFEVRiq3IVwpFUY4LIY6hGuj6CiF6KIpyyFY9XX7QR3Rvc4Fl1TVG4KhRuR/wdzX2VWF05+cIcEQIsRo4rDs0ETXMMFTNtV2esZTLYvZTqDW7v3n2zv6OvbO/symXduEKbn3a4N20AcJJg1Jk/vL1aWEIP5IWYT4ng61+zLVlDl/jvi6WNrSlnTe0I5ysp7E2Pl5caF0tlaJiswbTgE5N9eUbR6OstnGzmT5jNtNnzLYpd/bsBQYM6EOL5k1xcnKiqMi812ub1obUtOfOXaySMTo7O3P/xLEAJCZe549Nf9rVTmLidRITSy94devWUV8+ePCY/YOsQf58/jv+fN62nqZcvIJ77zb42tBT/+YG3UmxQ0+rBSEY+vnjNL2jGwARa/fy1ys/1PCgqp/8iEs49/TFtXEoOGnAwjlzDTcsCuRHxZmVqQm8hvTCyV811qWv2wHF1XZrU2sJDwniyMVLxF1LprCoGGcLvznRVw2baZqF2EoTX5ooI2NM2ybWF3jaNgmBXepcF51wQxo4y0FexCWce/ni2iTEqh5qmxv2m+VFVlv2gKqhDm2gz4+8hHOA7XlUazSP5kXWnnnUe0gvnHXzaNraHXVyHpU66PiEh4Vy5MxF4hKuU1hUhLOT+Qw+0Zev6svNbGyqLA+eHm54lolcUlRUzPlo9fpoFByEv4+3uaoSyS1FeFgIR85EEHfVhg5eMdbB4Ar1EWWkv23Dre/Bb9c8jFVbDX2aM3AaE+Tva2IwPWv0zNOxRdMKjVVSMxTXtR8/SYWxvjorkdRiFEW5DuzTvZ0khLAnIZCxkd9a/MRZdrRdrQghPgVm6N5uAyYoimI7OUXl+dCo/J0ut6Yt/ouasxPgB925qy6OACWGu5m6/KS1EkVRjmAIKxtk9PequLYdgusHVM83F083Ajo1syjXoG9bQ52DFfeWy4y7rvfibNC3jVXZ+n1aA6oHWtal0pdq4r5z+rJXE+unxbuJIaxpztWKRw8WGkHYqJ7qWK7c4MYhx/QS3L3nIABeXp5079bJotzAgX305T26OpVl1KhhBAWpXofLlv1m0bhqD/eNH6Mv28rr6ehcPWjQ03odLetpSB+Dnl6tJdfroA+m0XJsXwBithxh2zPfQB0IcZN9+DSghlZ0a9/SopxHL4OhPvtw7clHWpfD05bQtaW6wJOTV8CZ2ASLcofOx+rLXVpYX+Qpi7HRtNDCwr+545aMrZLSZB9SdcrJ0x33Di0synn27mBU52y1j6syaFsYFh4LrtUeT/3qIPuQ0TzaoXzzaM4ROY/WJqQOOj5d26nnLSc3jzMRsRblDp0y3Hd2bdu8WsZy8OQ5UnXpN0b071ktfUgktY2ubVR9ysnN44yVTTyHThs2KHdtE16hPpw0hvvKIhubcYxzgTpZMLbaYvMeQ0SDEf27W5GUSCSOgnw6lTg67+v+9wFWCCH8LAkKIbRCiCfLGLwiMOyDnCqEMMmvKYS4CzWUaK1BCPFPoMR1ag8wVlEU20HxqwBFUZYBq3RvuwJrLRnghBAuQoiPMITNjQRMA+VX7fiKgX/r3oYDi4QQFpMACCF8hBDVcn6FEPdbMwALIXpgCH0cXeZwZa9th+DSHwYHYEs5OxGCZvf1ByAvNYuru+1beLi0Sb2R9W3ZkKBu5h98g7o1x7dlQwAubz5scjzr0nWST8YA0OC2drh4mz+9zp5uBA9QF0syohPJuWY9pIs5mj84GK9Gqt374uLtKMWOaZRZs+YPfXnq1PvNyggh9KFkU1JS+XNHedPOWucho/C0i35aXiVtArRt25KJE9So21u37uTixdrlXVvVxGwy6GnriZb1tNV4g57G76n5BcK+b02m7SQ1PfXlXafYPOsLi96ntxqZW/fqy77j7zAvJIR+AbwoLYPs/SfMy91kNH7eeA3sAUDu2UjyzpX9eawbDOnSWl9es9t86vLiYoX1e08C4O3hRs/WTSvUR8Mgw63FkYvWPc8OXzAsLBvXk1gmffM+fdl/gmU99Lt3KABFaZlk7qsdemgWJ02pz5F9oMazQFQrGUbzqJ+1efRewzyaVUvOn5OfN16D5DwqddDxGdq7i768Zpv554Pi4mLW/amea29PD3p2bG1WrjIoisI3y9YB4OzsxPjhA6q8D4mkNjK0d2d9ec32vWZliouLWbdjPwDenu707FAxHWzYwBCB5MiZCKuyh04bjjesX/Ew3ZGXEti0W13n6dOpDU1DZUQSieRWQBo4JQ6Noigbgc91bwcCZ4UQbwshhgkhuggh+gkhpgohvgcSgC8x8tpUFCUJ2Kh7eyewWQgxTgjRXQgxUldvNRAF1IqEHEKIp4G3dW+vAC8DzYQQHay8XCy3aBfTMOTRvB04J4T4r+476yaEGCyEeBE4Abykk7sM3K0oivVkUlXDPNTzBjABOC2EeEkIMUh3XQwUQswUQiwF4oF/VtM4PgTihRALhBDThBD9hRBdhRC364zUm3RyRcD3xhUre207CknHovRekS0eHERQd9Pd1e1mjcKvlWp0PD9/E0qhqRdeg75tmRL/E1Pif6LvZzPN9nXu+z8o1tXt+f5UnNxKq4WTmws9358KQHFBIef+7w+TNgBOfak+3Lp4utHj3YfMynT/52RcfdSE9RcWm9817x5sOTdFg37t6PGuGho4PTKBM9/+blG2tnPw0DF27VIXHab94wH69DbdJfn87Mdo17YVAHO/nE9hoakz+qCBfSnMv0Jh/hXmf/+ZzX79/f0YNVJddDxx8gzHj58u95hDQy2H1WnUKJRVK37ExcWF3Nxcnp39RrnbdVSuHYsifr+qp20eGESDbqZ62nnmKAJ0enryh016XTMmtE9bZl36iVmXfmLIp+b1tKroMXscnR8dCageqH9M/4zi/JsR5KB2kHviAtkH1YVPv/uG49bF1HM9YNo4tC0aA5CyaC2UOWcevTrS5sJG2lzYSMgHtsNRVxU+YwYhXNX5Oe237Tet39pGx/CGdNN5cf729zGOR5pGlV+0eZ8+zOzkYT1xcS69k/3guRg6z3ifzjPe580fTD3Ne7Vthpvuu16+4zAXL5vmigb4+2QE24+cB6C+vzetwyoWeqyuknPiAlk6A4T/hDtw72qqh0Ez7sWtpaqHSQtM9dCzd0c6RK2nQ9R6Gn70XLWN1bNPRzTeVoLZODvR8D/P6MeavnV/7crbWw2UnUfdzc2j0w3zaPJC8/No24sbaXtxIyEf1tA8urruzqNSBx2fjq2a0a2d6kG9euvfHD8XaSKz8LctRF1WIx1MvmsoLs6lH4kPnjxPp7Ez6TR2Jm98/qPZflLTM8kvKDB7rKiomH9/+zNHz6p9Tx8/kkYNKhYSXiJxVDq2bEo3nSf16m17OH7edGPvwrXb9GFmJ48eYno/euoCncY9QadxT/DG3EUm9Xt3ao2b1hWAXzft4kKs+VQnu46cZvsBNV1C/QA/2pgJT2stV+jVG8k8+8E8CouKcXVx5tUZEy3KSmoXiqLU2EviGDjcYrhEYobZQDLwJmoY1H9akc1CNSYZ8zhqnsbGqMa628scjwPuwWAIrWnGG5UbUr4ck82AmKoagKIoaUKIEcBHqN9fAPCi7mWOP4CZiqLclKQmiqIoQoj7UQ2Es4DmurFawvyKXtXgB0zVvcyRB8yykMu0ste2Q3DorcWMWPMWzu5ahv08h1NfrCVxzxmc3FxpOrYvLR9Sd1WnRyZwZp79apgRdZUz32ygw9N3E9glnBFr3ub0V+vIiLmGd9P6tH/yLgI6NgXgzDcbyYhONNtO3Lr9XJkwgIa3d6H5/QNxr+/HhUVbybqShGdoIC2nDKXh7epu4+STMZz/cYvZdsZs/4Br+85yZesxUi9coTivAM+GQYSN7E7Tcf3QOGnIS85g52NzKc4z/8DtKMx+4S127liDh4c7v29cygcfzmXHjj24u7sxceJYZj6qGnPPX4jk08++rZI+7584Fq1Wdd5evHhFhep+/dUH1AsKZNXqDRw+fILUtDTqBQUydGh/Zj76EL6+PhQVFTHriTmcP2+60HIrsvvtxdyz+i1c3LWMXjKHo1+u5cqeMzi7udLi7r60m6LqaWpkAse/q9zPZesJpXfFB7ZvrC+HDe6Ed5hhUSktJlEfQreEDo/cQY/nxwGQmZDMvn//jHdj6yGl0yITzBplHZnE9+fRZNnHaNzdCPvhfZK+/ZXsfcfRuGnxHj0Q/wdGAZAXdZnkH1bZaM0yvveWvm3StjWEpfIc2B2Xhoad0flx8eTYCIVb4lWqFBSSvta+vLm3Ci8/OIJHPlhAbn4hsz5dyozR/ejZugm5BYX8ceA0K3eqacebNAjg4RF9bLRmio+HG9NG3sbXa/4iKzefh/+zgAeH9aBPu3B8PNxISs9ix7ELrNp1lGLdA/6z44ai0ZgEPCEuMZmjEaVv87Lz8vX/l/VC7dehOUG+tTG9fdWS8O53hC//CI27G00Xvsv1b5aTtfcEGjdXfMcMJGCSuhEjL+oyN75fbaM1y/iNH1bqvVtbQzhxr0HdcGlkCJufH5ugD92prz9uGI2/e5OMbQfI2neSvKjLFGdmo9GF9vR/YARurZoAUHgjhYR3bedvvhW4+v48mpbMoz++T9K8X8nafxyNVovPmCqcR8eVnkfdys6jjYzm0diKzaNpdXwelTro+Mx59H6mzvmQ3PwCHnv7f8yYMIqeHVuTl5fPH38fZMWmXQA0CW3A1LHD7erjwMnz/Oe7n7lzQE96tG9FSL0A8goKuBBzmZWbdnFOl3uzf/cOzJwwymI7N1LS2H3ktMnfSijrhdq1XQsah9RHUnGOHD9F3GVDCP+UNMP3HHclnt82lH4Gv2e0BS9uiU3mTJvA1Nc+VnXwnbnMGD+Cnh1ak5efzx9/H2bFFnVJsklofaaOLbucahsfTw+m3zucr5atJysnl4df/ZgHRw2mb+c2+Hh5kJSawZ8HjrNq626KdVGtnntoLBqNqc/We98uJSUtk9v7dqV988Z4e3qQkp7B/hPnWb55F5nZuWg0grdmTapwrlCJRFJ7kQZOicOjqFsq3hVCLEY1Zg1FDU3qC2QDl4CjwGZgtaIoOWXqXxJCdAPmAGOBJkAuqkHwN+BzRVFSzESvrdMoipILPCOE+AKYAowAmgKBQAaqZ+RfwK+Kouy01E41jq8AeEII8Q3wKDAY1YjtBWSihoQ9DPwOrK+mYQwB7kL1wGyFaqT0R70uI1Fzp36jKIrZ+JaVvbYdhZRTseya9SX95j6Oq48HXV8zDWOaHpnA9oc+pjArt1J9HftgOW5BPrR4cDABHZsyYN7TJjIRS3dw7EPr4Ux3zZrLwO+fJXRwJ0KHqK+y3DgayY5HPrVonNS4OBF2Zw/C7uxh9njquUv8/dQ3pJ6xHjbQETh27DSTJj/OwgVf4Ovrw7/ef9VE5vyFSO4e+zCZmVlV0mdJeNrCwkKW/lyxRUeBoHfvbvTu3c3s8aSkFJ565jWW3+K5N41JOh3L1ie+ZOjnj6P18aD3K6Z6mhqZwMZHPqagkno65NPHLB7r+uRdpd6fX77TxMAZPqqXvuwVEsA9q9/GFkv6PkfG5VvLEyLvbBTxz31AyMcv4eTtSf0XHjGVibrM5ZlvU5xl/89HyIfPWzwWOLP0zui0VVusLsy7hjfCvZMuD/LuIxRZ2YVdF2jbOJgPZ47j9flryMzJ44tVpoaKJg0C+PKZB/B0sxiN3yozx/QnPSuHJdsOkJ2Xz/yNe5i/0TQMoLOThmfGDWFM345mWoGjEZd468d1Zo+lZuaYHPv+xSl1wsCZeyaKS09/RKPPXsDJ25Pgl0z3u+VFXSZ2+juV0sNG/7XsHVhv1oRS71NWbDUxrgA4eXngN3YwfmMHW2wr91w0l575iILL5jeB3WrknYniynMfEFoyj774iKlM1GUuPVq5eTTUyjwa9FjpeTS1PPNoZzmPliB10PFpG96Yj16ayWufzSczO5cvFpsaopuENuCrt57G08P+jC1JqeksWbeNJetMo+8IIbhn2G28PmsSLi6Wl1GjL1/lzS8WWDxe9th7zzwiDZx2snLdJtb8vtXssaMnznD0RGkdkwZO+2kbHsZHL0zntc8XqDq4xPQZuElofb56/Qk83e3TwZkTRpKWmc2SDX+SnZvH/FWbmL9qk4mcs7MTz0y6mzGDeptvSIGTF2M4eTHG7GFfL09en3k/d/Y3vwYjqZ0UIz0pJdaRBk6JQ6Aoik3roqIo0ahGSnvaT0IN9fqyFZmmVo4tABaUo59/Uo5wqIqiPAI8YuHYYFv1byaKokSgfqZ/VrKdcrVR0c+vKMpJ4JlqHM8OwOz1qbsmv9C97KYy17ajcGXLUTYMe5U2M+6k4e1d8Ajxpyi/iMyYRGLX7+f8j1soysmvfEeKwr4Xviduw0FaThlCYOdwtAHe5CVnkHQ8iouLtxP/p+3cO4XZeWyf9BFNxvYhfMIAAto3wdXfi4L0LJJPxxHz216il++ymjdz34vfEzKoI0FdwnGv74ezpxu5SRmkno0jdv0BolfuNhuO11FZv2ELXbvfzjNPzWDkqGE0ahhCfn4+EZExrFy5nq++/pGcnMoZxkpo0aKZ3ji5detOEhMrFmH8w4/mcuFCJP3796JRo1ACA/1JTU0nKiqWtes2Mf+HpSQlpVTJWB2J2K1HWT78VTpOv5MmQ7vgGeJPcX4RabGJRK3fz6kFWyjMrQI9lVQZmX8eIPquJwmYOhbPQT1xCQ5CKSggPy6BjN//JuWndSi5NyWFd7nwGTtUX67L4WmNGdylFcv/+ShLth5k14mLJKZk4OLsROP6/tzRvS0PDO2Ju9b+TARCCF56YDij+3Rk1a6jHI24REJSGrn5BXhoXQmrH0D3Vo25b1A3mgZXPNeRBDK2HyBi5FME/mMs3kN66PSwkLzYeNI37iZp0foa18Mb364g92wUHl3boG3ZGKcAX5x8vVDyCyi8kUrOyQjS/9hN+qa9UFw3chmXkLn9AFFj1HnUa7DRPBqbQPoff5OyuHbNo75G82hqHQ5Pa4zUQcdncK/OrPj8bZas28bOQydJTErBxdmZxiH1uKNfdx4cPQR3rX0bfQC6t2/J84/cx4GT54i+fJWk1HQ0QlAvwI9eHVszdthtdGodbrshieQWZXDPTqz49HWWbPiTnYdPkZiUqt6PBtfjjtu68eCowbjrwszagxCCl6fdx5hBvVi5dTdHz0aScD2Z3Lx8PNy0hIXUo0e7ltw3or/VvJnTx42gaWgDjpyN4OqNFFIzs/D2cCcsuB5DenVi3O398Pe59TfYSSR1DSHjCUskEonEGj+FTpE/FA7MIzfqdmiyW4EvGwyp6SFIKslg9+SaHoKkEjSd/2BND0FSSSKm/lLTQ5BUEienumfUuZUoKjINJShxLFpufKmmhyCpJJqgsJoegqSSFCdG1/QQJJVE236YDA9YQToH31Zja5LHr+5x2PMlhPAAngImoKZu06JGAtwAfKEoSmwV99cWmIYa4TEMcAMSgQhgO7BUUZSYquyzBOnBKZFIJBKJRCKRSCQSiUQikUgkEolEIqk1KDJEbYURQrQANgItyxxqrXvNEEJMVhSl0inbhJrT733UqJhlbY1NdK9hqKnW/lfZ/swhDZwSiUQikUgkEolEIpFIJBKJRCKRSCQSiYMihPBG9dIsMW7+H7AMyAGGAK8CPsAvQoh+iqIcq2SX3wEzdOXDwA/ASSATqA/0AsZD9VmqpYFTIqlDCCH8gEb21FUU5VQVD0cikUgkEolEIpFIJBKJRCKRSCQSE4plesWK8hLQSld+WVGU/xod2yuE2AH8BXigelQOtrcjIcQ0DMbNfwNvKKb5MDcB7wkh7E/UawNp4JRI6hb3AD/aWddh445LJBKJRCKRSCQSiUQikUgkEolEcisihHABntG9PQt8UlZGUZQ9Qoj5wGPAICFET0VRDtrRlzfwse7tBkVRXrcmryhKfkX7KC8y07tEIpFIJBKJRCKRSCQSiUQikUgkEomk1qDU4D8HZAjgqysvVBSl2ILcAqPyvXb2NRnw15X/ZWcbVYI0cEokdQhFURYoiiLsedX02CUSiUQikUgkEolEIpFIJBKJRCKRmNDfqPyXFblDQLau3M/Ovibo/r+hKMrekj8KIeoLIVoIIXzsbLfCyBC1EolEIpFIJBKJRCKRSCQSiUQikUgkEgkghGhUHjlFUS5X91jKSTuj8jlLQoqiFAohIoBOQNuKdiKE0AA9dW9PCiEE8BQwG2hmJHccmAv8aMWbtNJIA6dEIpFIJBKJRCKRSCQSiUQikUgkEomk1lCs1Gio2EvllKstkQ9LDLJZiqKk2pC9hGrgrCeE0CqKkleBfsIAb105GViJ+VC3nYHvgbuEEBOrKw+nDFErkUgkEolEIpFIJBKJRCKRSCQSiUQikTgmJUbHzHLIZhmVvSrYT4BReTSqcfMCcDfgoxvHGAxepGOBDyrYR7mRHpwSiUQikUgkEolEIpFIJBKJRCKRSCSSWoNCjXpwhtVk53bgpvu/PJ6Sxh6b7hXsx7NMn1eB/2fvvuObqvoHjn9OOtJ0L0oXq2XvVYbKBpmCgoCCCiruiQu3Pq6fA/VxgvORrcgURIZsWbJnWS1QZoHulabj/v64aZLSJG1CSxs4b159cZN77nm26m8AACAASURBVD0nvTm3yf3e8z23KIpyyeL5P4UQ24C9QCTwpBDis6pI5ysDnJIkSZIkSZIkSZIkSZIkSZIkSZJE1c2tKYSojKjt/Yqi/HLFc3rj/54V2F5rsZznYN36Kx5/fEVwEwBFUS4LIT4AvgY8gOHAlw7WVS4Z4JQkSZIkSZIkSZIkSZIkSZIkSZJqjGqeg9PVZBn/r0jKWctRmBVJaWutnhIr7ZRdYbEc52A9FSIDnJIkSZIkSZIkSZIkSZIkSZIkSZJUtZpVwj7OW3nuDNAZ8BFCBCqKkm5n+5L0u5cURcm3U86aM4ACCOPj03bKWq6r5WA9FSIDnJIkSZIkSZIkSZIkSZIkSZIkSZJUhRRFOVxFuz4EjDAuNwW2WiskhHAHYo0P4x2tRFGUHCHEKaC+8Sk3O8Ut1xU6WldFaKpip5IkSZIkSZIkSZIkSZIkSZIkSZLkDKUa/7mgfyyWe9gp1xFzitpNTta1wWI5xk65WIvls07WZZcMcEqSJEmSJEmSJEmSJEmSJEmSJEmSa1oHZBiXxwkhhI1y4y2WFzpZ13yL5TvslBtusbzRybrskgFOSZIkSZIkSZIkSZIkSZIkSZIkqcYoVpRq+3E1iqIYgC+ND5sBL1xZRgjRFXjQ+HC9oijbre1LCKEYf07aqG4pcMC4/KwQooWVfVi24RKwoCKvw1FyDk5JkiTJLlu3+0iSdG1UySQFkiRVnLv8yuTqFEV+mnF1SrE8hq5M9sHrgIe2ulsgXaXi5BPV3QTpKmlqN6juJkiSVPN9AowGGgMfCyEaAr8CeUAv4FXUmGAe8KyzlSiKUiyEeAxYjZru9h8hxMfAWmOR7sDLgK/x8VOKouQ6W5898tu6JEmSJEmSJEmSJEmSJEmSJEmSVGO46FyY1UZRlCwhxGBgGdAIeNj4YykTGKsoyp6rrOsfIcQY4H9AIPCBlWIFwNOKovx2NXXZI1PUSpIkSZIkSZIkSZIkSZIkSZIkSZILUxTlONAOmATsANKBXOAI8DnQWlGUpZVU13ygJTAZiAeyLer6BmiuKMrUyqjLFjmCU5IkSZIkSZIkSZIkSZIkSZIkSZJcnKIoOcDHxh9ntq/w/AKKoiQBLxp/rjkZ4JQkSZIkSZIkSZIkSZIkSZIkSZJqDEUpru4mSDWcTFErSZIkSZIkSZIkSZIkSZIkSZIkSZLLkCM4JUmSJEmSJEmSJEmSJEmSJEmSpBqjGKW6myDVcHIEpyRJkiRJkiRJkiRJkiRJkiRJkiRJLkOO4JQkSZIkSZIkSZIkSZIkSZIkSZJqDEWRIzgl++QITkmSJEmSJEmSJEmSJEmSJEmSJEmSXIYMcEqSJEmSJEmSJEmSJEmSJEmSJEmS5DJkilpJkiRJkiRJkiRJkiRJkiRJkiSpxihGpqiV7JMjOCVJkiRJkiRJkiRJkiRJkiRJkiRJchlyBKckSZIkSZIkSZIkSZIkSZIkSZJUYyiKHMEp2SdHcEqSVCMIIX4RQihCiJNXuZ+Txv38YqdMiBBishAiXgiRZyyvCCGeNa5/u+S5q2lLOe1cZ6xj3VXup6Ttb1dOyyRJkiRJkiRJkiRJkiRJkiSpZpMBTqlaCSHqWwRonP6p7tdxrRh/X08JIeYLIY4JIXKFEHohxBkhxCIhxF1CiCoZmW0Z9DP+rKngdjfXpOMlhAgAtgDPA00Br+psjyRJkiRJkiRJkiRJkiRJkiRJjpEpaiXJRQgh3gVeA4SV1VHGn2HAc0KIOxVFSariJvUQQkQrinKmnHL3VnE7HPUE0Mi4/DGwBEg3Pj5fLS2SAPCJCqHJg/2J7NsWn8hgivILyT51kVNLtnH0l1UU5Rkqra7IXq1peE9vQtrEoA3xIz8li5S9iRyfuYZza/fZ3bbL5w8TO7p7hepZ1OlZcs5ctrleo/Ug9q4e1B0UR2DzOnj4eZOfmkXawVOcmPcPpxZvdeh11XR160bx1BMPMnBQH+pER5Kfn09C4inmzVvCt1N+IS9P7/S+69WLJuHYNoe2OXnyNA0bd7G5fvCgvtx33yg6d2pPrVrBZGXlkJBwkvkLljL1u+nk5uY53V5X5RcVQusH+lO/d1t8I4MpMhSSceoix5dsY/+0VRTqr7KfCkFQwwhqt42ldttYwtrEENq0Dm5aDwAWjnyfs1vjy91Nvd5tqd0mhrA2MfjXrYUuxB9PPx0FOflkJl3k7NZ4Ds5aS3ri9X/ad48MI/i+ofj0jMMjvBaKoQDD6fNkLdtI2qylKPp853cuBJ4x0ehaN8GrdWO8WjdG26QBGk/1eCXdM4ncf/fb3UXoU2MJfWqsQ9Ve/moWl7+a5XSzXc25y+nMXrWNjXuPciE1E08PN+qEBXNrXAtG94lDp/W86jrOXkpj7todbDuYyJmLqeQZCvD28qRBRCg3tWrIyF4dCfH3tbv9+j1H2XH4JMfOJHMxLZPiYoVAP29a1I+kf+eW9Itrjrub21W31RV5RNUiZPxt+PWKwzMilGJDAYZTF8hYtpGU6cuuuh9qY6PRtWmMd5vG6Fo3wqtpfTTG82biXa+Qs+2A07v37dGBBr+8bXqc/N/ZXPxijvPtdUEekbUIHjcU315xeEQYz6NJ58lctpHUGX9e/Xk0NhpdmyboWqvHT9ukgen4nRzzMrnb7J9Hy+xSpyVwRF/8+9+EZ0w0bkEBFGdlU3Ahhbxd8WSt3kbOP7udb7MLkn3Q9Z1LvszsP1axYfteLlxKwdPDgzoRYdzarRN3DemDzkt71XWcuXCJ2X+sZMvug5y/mEJxcTFhIUF0adeCu4b0oWG9aLvbp6RlsG7bbv7dG8+RxCTOX0qhoLCQQD9fGsfUpe9NHRjS+2a8KuHvtis6dzGF2X+uY8OuA1y4nIanhzt1aody680duGtgj8r5PHMxhbnLN7B132HOXLhMXn4+3jovGkTV5uZ2LRh5azdCAv3K3c/6HftZvGYr+46eIC0zGx+dljrhtejXtR2jBnTHuxLebze6lLR09h86woFDRzhw+BgH4o+SnpEJwLCBfXn/9eeruYVSTVEsU9RK5ZABTqm6nQVa2Vlf8m1uB3B/1TenRotADW7mAAuB1cAxQA80A54G4ow/fwsh2iuKkl1FbdGjjnwcC3xkq5AQwhMYdcU2VimKMh4YX2kttK2v8f8diqJMstGWt4G3r0FbJKOofu246avH8PT3Nj3n7g3aIF9C2sbQcExP1t47meyTyVdXkRB0/uRBGo7pWepp78hgvCODqTOwI8dnrWXbSz9DFX+I8ouNoMf/JhLQMLJ0W8KD8A4PIqpPW2JGd2fjhC8ozL2Kiy41xJDB/Zj2y5cEBPibnvPx8SY4OIi4jm154IExDB12HwkJJ69Zm44eTbD6vK+vDzOmf81tQ24t9bxWqyU0NJjOndvz0IR7uWPEeA4fPn4tmloj1O/bjn5fPIbWop96eINXoC+128TQ/O6eLB0/mYyr6KdNR9xC388fuap2CjcNt017weo6t0B3vAIbENa6Aa3H38q2T+ez69slV1VfTebbqxMRk1/Ezc/H/KS3F7pAP3StGhMwsj9nHn6LgiTnAr3+t/cm8qNrf/EhP7G8e6uuH+t2H+G17xeQnWf+O6A3FHDwxDkOnjjHgg27+HriGOrWDnG6jiWb9vLetCXoDYWlns/M0bP3+Bn2Hj/D7JXb+OixO+naMrbM9l/PX8OPSzdY/bN5MS2Li2lHWLv7CDNWbOHTJ0cRERLodFtdkV+fOOp89jxu/uZ+qPH2wj3QD+82jQgefSsnH3gHwynn+mHgHb2o8+nEympuKUKnJeq9x6pk367Ct3cnoj57wfp5tHVjAkf1J2nC2xQ4efwC7uhN1CfPVU5jAe8urYn86Fk8o2uXel6jDcI9NAhdy4Z4d2xB4j9PVVqdNZ3sg65v3bbdvPrJd2Rb3Fyozzdw8NgJDh47wYIV6/nmP89RN7K2nb3YN++vtfzflJkUFJb+W5h0Lpmkc8ksXLGBFx66i7tv62d9++XreP/raRQVF5dZdzktg8s797N5536mzf+LT197ksYN6jrdVle0bvs+Xv3iF7JzzTe06vMNHMxO4mBCEgv+3sQ3rz1O3Ygwp+tYsm4b706djd5QUOr5zOxc9h45wd4jJ5i1dC0fP/cAXds2s7qPnDw9L3/+P9bvKH1jSXpWIelZOew/dpJ5q/7hy1ceJSY6wum2StBjyN3V3QRJkq4TMsApVStFUQoAm7czCmEarJijKIrztz1eH1KAScAURVGyrli3UwgxB5iNGlBsBDwHvFNFbfnDWM+92AlwAkOAINTg5grUEabVLcr4/9FqbYVkEtSyHrdMfRJ3nZaC7DwOfrWE5M2HcPPypN6wLjS6pzf+sRH0mvECfw14g8Ic50f5tX15pCm4mbr/JIe+XUrWyYv41Q+j+eNDCG5Vn4Zje6FPyWLvh3Pt7iv3fCprxnxsv8yFNKvPa0P86fPrJHyiQgE49cc2En/fSF5yGrraQcSM7Ea9oZ2J7NmaW6Y8ybpxnzr+YmuQtm1bMHvWFLy9dWRlZfPRx1+zbt1mdDovRo0aykMT7qFJ41j+WDydzl0Gkp2d43AdZ89eoE273uWWm/TSk4y5ezgA02f+brXMr7OnMmCAuq8dO/fyxZc/cOTwcfz8fBk0sA9PPHE/jRo1YOkfM+ncdSApKdaP8/UktEU9+n/7JB46LYbsPHZ+s4Szxn7aeGgXWoztTVBsBEN+eYG5g9+gwNl+apGjoMhQSMrh02g83Aht5thFoPyMHM5ujSd5dwIZSRfJvZhOYZ4B79pBRHdtRrNR3dEG+HDTK6PJz8zh4MwKZV13KdpmMUT+92U0Oi+KsnNJ/X4uOVv3ofHS4j+4O4GjB6KNiSb6+/9wasQzFOc4PiJZWBwwxVBA/tGT4O6OV9MGFd5H2qylZC7/x349bhrqzvoYNz8firJyyP57i8NtdUXxp84zacrv6A2FeHt58uDgW4hr1gC9oYAV2w4wf/0uTl1I4cnPZzPnrYfx0Tk+mmD3sSTe/HERxYqCRghuu6UNvdo1pVagHxdSMvhj0x7W7zlKRk4ez345h/nvPU50WHCpfVzOyEJRQKf1oHf7ZnRu3oC64SFoPdxJPHeJ2au2mQKyD388nd/+88gNM/LBq3kMdb+ahEanpSg7l0tT5pGzZR/CS0vgbd0IvnsA2pho6v/8JseHPudUP8T8fYliQwH5R06Bhxs6B/qhLeHP3YNndG0KLqfhERp01ftzNV7NY4j+cpLpPJoy9Xdytu5DeHkSMKQHQXepx6/uj29z4vZnr/r4KYYC9EdPIdzdHDqPlvC5qS11fngTjZeWooxs0mYvI2fbfopS0hE6LdrYOvj27oT7DXSTgeyDri8+4RQvffgt+nwD3jovHhw1hE6tm6HPN7B8w1bmL1/PqbMXeOKtz/j1i7fx8dY5XMdf67fyzle/AODn4819dwygU5tmeHp4EJ94il/mLSPpXDIfTp1FcIA//bt3LrOP1LQMioqL8XB3p3unttzUviUN6kTg463j9PmLLFi+js27DnDqXDIPvfoxv331DuGhwWX2cz2KTzzNS5/+hN5QgLeXlgeH96dTq8boDQUs/2cH81dt4tS5izzx/rf8+snL+Ogcn0Vod3wCb3w9neJiBY1GMLRnF3p1ak2toADOX07jj7VbWb9jPxnZOTzz4VQW/PcNosNDS+1DURRemPwjm3YfAqB5bF3uHdKbBtHh5OTp2bDzAHOWrSPp/CUef/cb5nzyMkF2sltIFRdRO4wG9aLZ/O+u6m6KVAMpyBGckn0ywClJLsLWaEOL9UVCiMeB2wFP4E6qLsA5HTXA2UII0U5RFFs5jkrS0y4BcquoLY4quaJWYLeUdM10fOde3HVaigsKWXP3R1zeaR4Rl7zpEFknkmn/xt34x0bQ7NFB7P90gVP1+MWE0+zRQQCk7Elk1fB3KdKrb4PUvYmcWbmLfvNfJ6RtDM0fG0TCr+vtjhgtLiwi44hzo4haPXe7Kbi579MFpV5T2oFTnFu9h4zjZ2n93HCi+rWjzuA4Tv+53am6aoLPP30Hb28dBQUFDBw0hq3bdprWrV23iePHT/DRh2/QpHEsz018hHfe/czhOgoLCzl48IjdMhqNhh7duwKQmZnFokXLy5QZPnywKbi5atV6ht4+joIC8+li/YYtrFy1nj+XzqR+/Tq8+cbzPPPs6w6319V0+8+9eOi0FBUU8sfYj7iwy9xPz24+RPqJZG5+/W6CYiNo9/Ag/v3cuX6aeuwsG96YRvLeRC4fSqIov4BOE4c7FOBUior5sfWjKMXWvgid4OSqXez930pGL3sXr0BfOj8/gkOz19oo77pqv/4oGp0XSkEhpx94Hf2ew6Z1uVv3Yjh5jrBJD6KNiSb4geFOpXzNT0gi+d0p5O07Sn58IoqhgNCnxjp0Yb4oNYOi1Ay7ZXy6dzSNnspa/g9KfuWlLK/JPp71F3pDIe5uGqa+cC9tGtYxrevcPIa6tUP4fO4qTl1IYfryzTx2Ry+H6/hp6UZT2qeX7xnI6D6dTOtaxkTRN645k+esYMaKLegNhUxfsYVX7x1cah8Bvt48O6ovo3rFlQmyNq8fycAurXh56nxW/nuQpORUpq/YwqPDejrcVlcU+dZDaHRalIJCTt73Jrm7zX+ncrbsI//kOSJeeQBtTDShE253Ku1k/vEkzr39Hbl7j6E/pPbDsGfuvurgilfLWELG30ZxvoHkyTOJ/vDGGfFXovYbj5jOo0nj3yBvt8V5dMs+DCfPUvtl9Twa8uAdXPpytsN15B9L4vx/pqLfd9R0/Go9PcbhAKdbsD9RX7yExktL3sEEku5/k6KU9FJl8nbGkz53JXjcOJeBZB90fR9NnYk+34C7mxvfvfcibZo1NK3r3LY5dSPD+fzn3zh19gLTFizn8XvucGj/efp8PvpO/QzkrfPil09eo1F9cyraFo0bMKB7Z8a98B7HTp7hw+9m0S2uDd5XBOF0XloeGDmY+4YPINgiYw1As9h63HpLHJN/mMP0hctJy8ji2xkLeGfiBEd/HS7po59/R28owN1Nw3dvPUWbJjGmdZ1bNaFuRBifT1/IqXMXmbb4bx6/a4jDdfy0YAXFxs/yLz84irsG9jCta9moPv26tmPy/+Yzfclq9IYCpi9ZzasPjS61j1VbdpuCm13bNOXrVx/Hw+J8GdeyMTe3bc5j737NuUupTPntzzL7kCru0fvH0LJZY1o2a0xocBBnzyfT/87x1d0sSZJckKa6GyBJV0sI0V4IMVUIcUQIkS2EyDEuTxFCNC5n2xghxPNCiCVCiJNCiDzjzykhxG9CiAHlbD9eCKEYf+oLITyFEM8JIXYIITKEEKlCiHVCiMFXbOcnhHhJCLFbCJEphEgXQqwSQvS5mt+FoigpQMkEgmVziFWeg0BJUNPqHJtCiBBgkPHhjPJ2KIT4xfh7PFlOuYFCiGVCiEtCiFwhxFEhxGdCiCg72/QsOU5APePT4yyOnSKEWGdR/m2L8vba4iWEeFIIsVoIcUEIYRBCXBRC/C2EeFAIcdVXD4QQY4zvoTTj+/uAEOI/Qojr4tbrkLYxhHVpCkDCnPWlgpsl4qcuI+PoWQCaPNgf4e7c/F1NJwxAY/yCsv31aabgZomiPAPbX58GgMbDnWYP2+3+ThMaQYPhNwOQffoSBz5faLXcgc8WmubvbPHkbVXSlmshrmNbunVT57n8+X+/lgpulvjs8+84FK8Oqn7qyQdxd6+aC299+3QjKkpNJTR/wZ/o9WVHGY67d5Rp+alnXisV3Cyxes1Gfpu7GICHJowlKOi66I42hbWNIaqz2k/jf11fKrhZYvf3y0g19tPWD/RH42Q/vbgnkX2/rCJ5dwJF+c7fh1JesDLr9CWOL1HnbPUODSDoilTRrs6rdWO841oCkD5vZangZonUnxeQf1ydrjvovqHgxDHT7ztK2owl6PceQTFU3X1DAbebR2dnLFxdZfXUJPsTz7DrqHp8bu/WrlRws8R9A7oSE6neLDNr1TYKCoscrmfv8dMABPrqSgU3LT0yzHyRcN/xsjf2TBzVj/sH3WJzBKmbRsNr9w3Gw/ge+3v7IYfb6Yp0bRrh00nth6lzV5UKrJS4/MMi9MfU4xx6v3P9MG/vMVKmLSVvTyX2Q42G6P97CuHuxqVvfsdw8lzl7NeFeLVubDp+ab+vLBXcLJHy40LyjccvePww58+j05dc9fELe2E87sEBFOfqOfPou2WCm6UUFNpedx2RfdD17T+SwK6D6neEO27tXiq4WWLc8AHE1FE/x836Y2WZFLPl2bhjH6np6rx/Y4f2KxXcLOHrrePFh8YA6jybi//eWKbMvXcM4Nn7R5UJblp6ZvxIagWr3xtWb95JsZV0tteb/cdOsuuQ+t3hjj43lQpulhg3tA8x0eEAzPpzrVOfZ/YcSQQg0M+nVHDT0iOjBpmW9xrLW/pj7VbT8qsP3VUquFmiS5umDLilAwDzVv1DRpbjmYck1ZMT7qXnzZ0JDb4xR6dLFacoSrX9SK5BBjgllyWE0AghPkOdn/MRoDHgA3gblx8FDgohHraxfQMgAZiMmkq1HuockV5AXdQRin8JIWZUMFDlD2wAPgU6GB8HAT2ApUKIicZ66wJbUFO7tgX8gADUuSFXCSHGOvabKKPk6pLjnwodUxK0vFsIYe2b4GjUkaSXgbLDpJxgPN7LgIFAKKBDTcc7EdgthOhYGfVUsC1tgMPAV0BvoDbgAdQC+gA/ApuFEE5NBCKEcBdCzAVmob6HAlHf3y2AN1HTEl993qNqFj3AfMgSfttgvZCikDhPTV+oDfSh9s3W58sot67+7QHIOHaWlF3W515M2ZVAxnH1AkL0rR2cqqc8fjHheAaoI5EubDhgMxCjFCuc36Bm5g5pE4NPnVpV0p6qNmyYOVA8bdpvVssoisLMmfMACAoKpFfPm6qkLffcc6dpecYM6+lpO3RoDcCxYyc4fvyEzX2tWLkOAE9PzzJzdV5vYvqb+2n8XNv99PB8tZ96BfoQdZNz/fRaMlik0XXTelRjSyqfb9+upuWM+ausF1IUMhapwUK3AD+8O7e+Fk1zmMZHh28f9SYJw+nz5O24MWYsWLvLHEwZ1q2d1TIajYYhN7UBICtXz/bDts9ZtpRcRIyqZfvikp+3F0F+6ty7BUXOfbwM9PWmcR31I9Hpi9d/Wm8A/35dTMtp8/62XkhRSF+wFgC3AF98u9aMfhj64DB0LWPJTzzDpe/mVXdzqoXl8cuwd/wWqinO3QJ88elSPcdP4+9LwFD1gn7G4rUUnLtULe2oaWQfdH1rtpjTVQ7r181qGY1Gw2191JtHs7Jz2b4v3qE6Dh0z/+28paPt49+xdVO0nurnxVX/7HCojhIeHu60bd4IgKycXNKzsp3ajytZs22vaXlY765Wy2g0Gm7rqab9zcrJY/sB+1l5rDF9ngmzPSe5n4/OlFLWWhD1YIJ6s0PdiFrUi7Q9F+jN7ZoDUFhYxLrt+2yWkyRJkq4NGeCUXNlXqIEtgRpYfADoCXQCHkIdZegOfCeEGGplezfAgJo+9WnUAGN74/+PG7cHuAd4owLt+R41sPkt0A/oCEwASm63nCyEaAksAGKAD43tjQOeBTKMr2WKEMKpmdWN25VcVXbsk73jZqMGUcNRX++VSkZ2/mqca/WqCCGeRT3eoP5OnwI6owb/PkYNEv+OGuC+0naglfGn5HgstniuFXC/A21pCKxHDYpnAv8H3IF6zPsD3wCFqMd2sRDCmSvnk4GRxuUjwIPG/fUFvgPqA9ajRS4krJM6yLogR0/qPtsXZi9uMb+da8XZHZhtlW/dWnhHBBv3VfYO/NJ1qeu9I4OrJKioDTLP06G/ZD8to+X6sM5NKr0t18LNN8UBkJ2dw85dtr8AbthgvmP2JuM2lcnX14dhQ9Vg64kTSWzYuNVquZAQ9SL/xYv2Lw5eTDav79at7Dw815MIY58z5Oi5uN92Pz231dxPIzo63k+vJTcvD2JuVW96KC4qJj3xQjW3qHJ5d2gBQHFOHvqDx2yWy/13v8U2zau8Xc7wG9gNjTENXObi62+uVFt2G0dv6rQeNK8fYbNcx6b1Tct7jp12uJ76xvmnzl6yHXTMztOTlpVrLG/7wmF5DMZRY24aUU7J64NPnNqninLyyNtfduR7iZxtlv2w+m8O8YgKo/az6kils69PQTHcGKP9rqTraD6P5h2o2edRv95xpvNk1t/bTM8LLy0e9SJwu0HnbpR90PXtNo7e1Hlpad6ovs1yHVs1NW9zyHZ/tSY90xxkDAkKsFnO3c2NAGO6/L3xxyl08oYfg0V2GDfN9X9Jdvdh9cZinZeW5rG2p5zo2KKRxTZlR1eWp74xIHn2YorNMtm5eaQZj3f9yLL3oWcYA84hdkbhXrl+5yHb5xZJkiTp2rhxJl+QritCiH6oQUiACYqi/HRFke1CiJnAn6ij674UQixTFMXy28F5oL6iKOetVLFaCDEV+BkYDzwvhPhMURR70YhOwHBFURZZPLdTCLEdNZ2rBliDOrKzh6Io2yzK7RBCHDO21w8YC3xupy5bXsTcr+c6sX2FKYqSLIRYiTqa8l4sRmkaA4Alt8xOv9q6jIHb940PTwFdFEWxvBq9QQixAliBlfOaoig5wAHjvkq+UaQriuLsMJBpqAHV3cCtiqJcvmL9SiHEUtTj2Rn1PfRDRXcuhGiFGsAF2IX6frG8vXO1EGKzsR0uzb+Rmk4o62QySpHtFD2Zx81pmQIa2sxGbFNAY/M2Gcftp3gqVVejSHJOWw90aYN86Tv/NQKbROPu44UhPYe0+CTOrtpNwq/rKcqzPkdcQU6+adnD31o8Hov1OquvwZU0bap+WT2ecJIiOxcCDh8xfzks2aYyjRg+GB8f9fc9a/Z8m+Wys3MICgrEP8DP7v7861pxEQAAIABJREFULb7YNm9Ws4N5VyvYmL41o5x+mpZg7jtBTvTTqqZxd8M7LJCIjo1o/9gQAmPUoFH8b+spyCmbrtiVecaq6UwNSefAzjEzJJrTjXrGVHye02sp4HZz9v4bJT0twInz6keLumHBuLvZTpnYICLUvI0To7ZG9urIO78sIT07j7lrtjOqd9kbTL5fvKFUeWekZGabXlODSNfMSOAobUk/PHXebj/UJ5j7odZKKuJrLeq9x9F4e5G2aC05W27ckSkVPX75Fsev5Nx7renamoM7+iMn8WrViLDn78PnpjYI4/mjMCWdzD83cunrX+2nr72OyD7o+k6cVi/V1I2sbf9vYbT5RqATSY6l8/W2SK+enZNrs5yiKGTnqp8XCwoLOX0umQZ1HJvioKCwkH3xasAvJCiAAD/fcrZwfSfOqJdt6obXsn8Mo8LLbOOIkf278c6U2aRn5TB3xQZG9e9epsx3v/9lWh7Vv+yIYJ2XlqycPLJy8+zWZbk+4bS1y4mSJFWmYmSqWMk+GeCUXNXLxv/nWwluAqAoil4I8SRwCHWkXS9glcX6HMBmwnxFURQhxPOowTsf1JFztq+Kw9wrgpsl+9knhPgH6I6avvSjK4KbJeWWCSFOGdvaDQcDnEKIzqgjQQHOAFMc2d5JM1ADnLcLIXwtgnAlozePKIqyvRLqGYd5ZObzVwQ3AVAUZY0Q4gfgsUqozyYhRDegJH/mOCvBzZL2LBdCzENNdTweBwKcqOmVS27nfPiK4GbJ/qcLIe5C/f27JI3WA68QNUiUdy7VbllDRi4FOXo8fLzwjgx2uK6S0ZsAueft15VzznzXp3ek7ZEqHr46anc13+Wtqx2IrnYgkT1b0+KJ29j46Fdc3lH2DuKsE8kUGQpx83Qvd1RmWGfzBSufKOdHzVQXrVZLrVpqu8+esf/lLz09g+zsHHx9fagTXfnzId57z0jT8oyZtlN9HT58nK5dO9KsaSNCQ4O5fNn6+6XbLeZRm3UcvLjhSty0HuiM/TSnnL6Tn5GLIUePp48Xfk7006rgFx3KuC3/tbn+1Lp9/PPu7GvYoqonPD1wD1ZHIBResH0XO0BxZjbFOXlofHR4WATKagqPqDDTKKrcnQcpOH19jbS1Jd9QYBoxGRZsfxSBv48OndaDvPwCLqRmOlzX7d3bsftYEks27eX/Ziwj/uR5erRrQq1AX86nZLB08z5TutyHbutGlxbOTfE+bdlmCo0BhlvjWji1D1ciPD1wD1H7YcH58vphDkU5ebj56PCs5n4YMLQHfj07UJSRzfn3rH7FuiGUOn4XrH7UN6kJ51FtI/MNKj5dWxP5wdOIK+aOcw8JJPi+2/AbcDNJ979JvhMprV2J7IOuL99gIC0zC4DaIfZHIfv7+aDz0pKnz+eCjc/utsRYfI7fsf8IzRtZnwUmPuEUuXnmG+LOX0pxOMA5/691ptd06y2Vn7Gmpsk3FJhGTNYOCbRb1t/X2+IYOp7K/o7eN7E7PoEl67bxwQ+/cSjhND3jWlErKIDzl1NZuu5f1vyrpst96M4BdGnTtMw+YqLD2XvkBCfOXCA1I4tgGze87rIYtelMWyVJkqTKJQOckssRQvijpnYFsDshhaIo8UKIy6jzNXbFIsBpZb8eqPMo+qGmry2RAoQBbbAf4PzVzrq9qAHO8srtQw1wlp153Q7jPI/zUPu0ghp4s337YeVZBGSh/s6GYx6teY/x/xnWNnJCX+P/aaipZW35mSoOcAIl6Y6PKIqy325JNXXyKCBOCOF+xQhie0pe735FUXbaKfczLhzg9PDxMi0X5JY/eqowNx8PH69S21WUu695JGShxQhKa4pyzevdfbRWy1zacYyzq3aTuv8k+ssZuGk9CGxah9i7exDaviHekcH0njOJVXe8S9qBU6X3n5dP8qZDRPZqTVCLetS7vSunFm0pU0e927sS1Nx8wcrd1/HXXd38jGmcALJzbN5PYpKTk4uvrw8+vvZHtjqqTp1IundXB5Vv3rydhISTNssuWbqSrl074u7uzjv/mcTjT0wqU6ZhwwaMHzfa9NjvOr772rK/GSrYTz2d7KfXUl5KJutfn0bCsn9tzoPrqjQ+5vNdcTl3oAMU5+nR+OgQ3rpyy15r/sP6IIzp2zIW2pg/7TqUozdnAPDWepZbXqf1JC+/gNx865kD7HHTaHjvoTvo0bYxPy7dyIINu1iwYVepMnHN6jNhiPPBzX0JZ5i1Sk0LXjvYn1G9r9mU6dVG4+t4P3Tz0aHxrr5zp1uALxFvPAjAhY+nUZRiP43+9cyZ46fx0ZU6/15LbgHmzyER7z4BisLFT6eTvnA1hZfT8awXQehDIwi8sx8eYcHUmfo6iUOepDi7/NfmqmQfdH05FsFEb135x6UkOJabZ/+73pVu6dgadzc3CouKmL5wObf1uZmgKwJbxcXFfDWt9KWnnAp8LrZ05vxFvpquXk7y1nnx4KghDm3vikodQy/r36st6bSe6jHUO3YMAdzcNLz/9Dh6dGzFj/NXsODvTSz4e1OpMnEtG/PQCOvBTYCeca3Ze+QERcXFfD17CW8+NqZMmVPnLrJojfm7u+VrlCSpaijK9fV9Xap8MsApuaJ2mEe3zRFCzKngduFXPmEMaj6MOuKwHWDvKlJ5t3MetbPOMg9QRcrZz41oQQjhh5oKNdr41MuKolyTSaoURckzjlK8H/V3OF0IcQtqgFYBZlZSVa2M/+8uJ0i4B3Ve1fKvBjqv5KpcEyFERf/KegDBwMXyCgohtEBJfs7yRr/+W8H6bdUVXX4pmBkx9mqqscnNyzw1aXEF5pYpNhQYt3P88LppLeoqsF9XkUVb3K3UtfOtmRRklr1/4PLO4xyftZY2k0bS8plhePh40XnyBJYPKDuF775P5xN+S3M0Hu50/e8j+NULI3HeP+Qlp6OrHUjMnbfQcuIdFOUXmNruzOuubl5eFsExQ/lT8eYbL87rvCr34tLYMSPQGIMk9kZvAkz9bjqPP3Y/0dERPPzQPXh76/j0sykcPnwcPz9fBg7szYcfvIaPjzcGgwFPT090Fbjo4qrctY7106Kr6KdVIedCGrP7qkkfNG4afMKDqdezNc3u6kHP/7ufgHph7PxmSTW3snIJi4CYUs75DkAxHjNNDTlmlvyH9gKgWJ9P1rKN1dyaa8dgcdw83G2ncyvhaSyTX4HzrDWJ5y6xZNNejp+x/jFl3/EzLNywmwaRtagdZH9E6ZVSMrJ54Zu5FBYVIwS8O+F2dBUI2ro6jaP9MF8tI6qxH4a/+gAeoUHk7j5M6pwV1daOmsDVzqOWQTmNl5azz08mY9Fa03OG46c5N+m/KAWFBN09EM864QSNGUzK9/Y/E7ky2Qddn+V3B/eK/C00jlrONzh2s094rRBGDurFnCV/czEljfteeI/nHhhNXJtmeLi7cTgxiSmzFrF553483N0pKCx0uJ48fT4T3/+KLGMK3FcevYewckalXg8s5xt17Bg6+XnmzHmWrNvG8aSzVtfvO3qCBas30yA63OqI0lH9uzNn2XoupqYzb9U/6A0Gxg/rS4OocHL0ejbuPMjn0xeSl2/A3d2NwsIip9sqSZIkVR4Z4JRcUZiT25UaEiSECAZWAh0quH15t+TaGzFpmvSjnJGVJeXK//QHCCG8UEc0lryGyYqifFyRbSvRDNQAZ28hRBTm9LQbFEU5ZXszh5TkOrQbIFQUpVAIkYqVYHYlqpT3nx1BgDAulxcQTXayLSVOX+X2NunCg/AM8LG6zpCRQ96FNIr05i8DGs/y/xxpPNVAS5He8REqRfkWdXnYr8vNoi2FVuqyFty0tPej3wlpH0tEt5aEtG5ArbhGXNpeOlVtyq4Etk36mc4fPYCbpzttJo2kzaSRpcoU5uWz+905xH0wXn2cXbPuDo2MDCcoKMDqurS0DM6du4Beb26zp6eH1bKWtMaLUXn6yn2tY8eOAECv1zP39z/sls3MzGL4iPtZ8scMateuxT1jR3CPcXtLr772Ac8+8zBhYaFkZZXJIu0SfMKD0Nrop/kZOeRcSKMw37F+6nYV/bQqFBcWkXrEPLfW5UNJnFqzh4Oz13LH3Ffp+vJoAhqEs+YFR7KI12yKxSi+K1MUWiOMx6y4hhyzEl5tmqCNUe/DyV69leLsa5GYombwtDhuBYW25y4uYTCW0VbgPHulXUdO8fR/Z5OVl09kSABPjOhN1xax+PvoSM3MZt3uI3yzYC3Ltx1g55FTTH3xXhpGVeyjUE5ePk9+PotkY+rcZ0b2pXNzh5KUuKxiR/uhVi2jVFM/9OnckuBR/VAKizj72rdwg98p72rnUcv3mz4+sVRw09LFydMIGN4HjdYT/8HdrusAp+yDrs/yu0NhRf4WGgPZWk/Hg9TPT7iLMxcusXH7Xk6dvcAz735RpkyLRg1o0bgBc/9U7yX30VVsxHZhUREv/N83HElMAmDU4N4M61d2/sfrkaeHs8fQ8c8zOw8d5+kPppCVm0dkrWCeHHMbXds0w9/Xh5T0TNZt38c3c5ay/J8d7Dp0jKlvPkXDuqVTDPv56PjylUd5/L1vSM3IYun6f1m6vuw95c/cM4zpf6wmLTO71ByukiRVjWL5N1EqhwxwSq7IMvj3CLC5gttdmRz/C8yBwUWo6T73oQaV9IpxDLwQIgmogznoVCMIIdyBuahziwL8qCjKi9XQlHWogbI6qIHOUcbnKys9raWa8Fet5P23F3Mq3oqwfhuhfTXh9TqlzaSRxI7ubnVdwm8b2DrxewpyzEEsjwqkg3L3Vr88WG5XUYUWKbhspZ0t4eZtXl9eOltbjs9YQ0S3lgCEdWlWJsAJkPjrBtIOJtHymWFE9GhlSulZXFDI2TV72fP+r3j4mePihozyU7xeS+++M4lx942yum7a9Lk8OGEiWVnmNvv6WA+kWfLxUV9vTiUGMuI6tqVZU3VQ9JKlq8jIKH+Oul2799Mh7lZenvQUd44YQni4+WL+9u27efe9z1n212reeH0ioAZ0XVGXl0bSbKT1fhr/+wZWP1e6n3pWcT+9llIOn2brJ/Po+cH9NB/dg2N/bOH0hgPV3axKUZxjPt9pKpB2VmMcgaxUIIXftRRwex/Tcsai1dXYkmvPx2IEUUXSzuYZy1Qkna0lQ0Ehk6bOIysvn9AAX2a8MYHQQHMSkdrBAYzu04kOTesz5u3vuZSexRs/LGTO24+Uu+98QwHPfDmHQyfV+ZfHDbiJ+wfd4lD7XJll6k9H+mGxgykPK4PwdCfqgycASPllCfr463tuxopw+vjlVM951LK92f/stlmuKD0L/f5jeHdsgVezBuDhDhUY3eiKZB90fT4WGVJyK5AGNM+Y1tSZgJOnhwdfvfUsC1du4Lelqzly4rQpJWJwoD8j+vfg4buH8vH35nnb/SowpYaiKLzx2Q9s3K7O/di/Wydefezecra6fpQ6hhVIO2v6PFOBdLaWDAUFTPrsZ7Jy8wgN9Gfmhy8SanEjbnhoEHcN7EHHFo24+6WPuJiawetfTefXT14us6/msXX5/dNX+XH+clZu2U1Kuvm7Y8uG9Xh01CC6d2zF1LnLAHXuUEmSJKl6yQCn5IpSLJZzFUVx+IqkcR7PkgnUZimKYi9QVeNyhwghNKgBxNuMT/2GGuy95hRFUYQQM4FXgNcAL0AP/F6J1aShjsqsba+QMegbbK9MJSh5//k6896rAMt0xnZfbwXWl6dOBctVyUjP4vwC9KlZeAX7oYu0f9g8A7xNAcDcc6kO15V73ryNd4T9unwiQ8zbnUuxU9K2jKPmeLZ3hO1TSNr+k2yc8AXCTYOudiAaD3dyL6RRbBw1V3/4zaay6Raj0FxFfn4+ly+nEhoaTFR0hN2ygYEB+PqqQdDTZ85VWhvuuedO0/LMctLTWrpw4SLPTnyDZye+Qe3atfD39yM5+RKZmVkAREVFoDPeuX0o3l7mcddWlF9AXmoWumA/fMrpO9oAbzyN/TTLiX56rZ1YuZOeH9wPQMNBna6bAKdiKKAwLQP3oADcw0PsltX4+5rmjCs4f/laNK9iPNzxH6QG3wsvpZKzcVc5G1xftJ4eBPrqSM/O42Kq/ZsyMnPyyDP+zQgPdix97Kb9x7mYpp7T7u7bqVRw01LDqDAGd23Ngg27OHTyPEeSLtCkru1kGYVFRbz47e9sjz8JwPDu7XnurlsdapurUwwFFKZm4h7sj0dEef3QBzdjPzRUQz/0738T2phoig0F6I+fJmBI2ZFF2kbmOcG9mtQzlcndc5SCM1ebUKTmUY9fBu7BAXiE25+lpCacRy3rLTx/qUJlhZsbbgF+FF2+8h7g64Psg65P6+lJoL8v6ZnZJKfYf59mZuWYApzhoc5dDtBoNIwY0JMRA3qSk5tHSnomXlpPQoMCTFNdJJ0z/65j60aVu8/3v53On2vV+Rpv6diaD158xLSvG4HW04NAPx/Ss3JITkm3WzYzO9fiGDp2CW7T7kNcTFX3f/egnqWCm5Ya1o1kcPdOLPh7E4cSkjhy4gxNGpSdtadWcACvPDSaVx4azeW0DLLz9IQE+ONnPE9cuJxmSk0bW8f+d1xJkiSp6skAp+SK9qCObBPAzTg3z2Mj1HkRQQ0OWiWEaAr4OrH/qvYdcJdxeQlwj6IoxXbKV7UZqAHOklv0FiuKUv4wqYrbjxrgbCuEcLczD2cbqnb+TYDdwE1AjBAiXFGUC5W5c0VR9EKIY6jv0bhyipe3vry6KhQxmxXpyEBV1daJ37N14vfllss8ehavLk3xq18b4aZBKbL+NvZvaE4fk3Hc8cGwlgHHgIaRdkpeUdcx5wJtjk6CrhQVWw3cBreub1pO2Z3oVFuqyoMTJvLghInllouPP0q3bl1oGFsfNzc3ioqspydq2qShafnw4bIjXp3h7u7O6FHDAEhOvsTyFdZTtpUnOfkSycmlLxi2b9/KtLx9+x7nG1mNVj/3PaufK7+fph47S1TnpgSU00+DYs19J82Jfnqt5aVkmZb9osubZtu1GI6fxj0uAM+6keCmARvHzDPGfFHHYEydVhP49uqEm3Gux8wl66C4Oj/iVI+YyFrsOppE0sVUCouKcHezPnvBCYuL8Q0iazlUR+I583mtWT37F+ia1Y+ADeY6bQU4i4uLee37hazfo9740b9TC94YP8Shdl0v8o8n4d6pJZ71Iuz2Q6/YaIttqmz2AJtK0qtqPD2I/vCpcssHDLyZgIHqDVinX/gv6ddpcCX/+GncOwWUe/y0FsfPkHDtjx9A/rFTgDEopilnphPL4IqNz2TXC9kHXV9MnUh2HTxK0rlk+38Lz5w3LTeoa/+7XkX4eOvwuWLkb1FRsSnNbHR4LYICrN8UVOLzn38zpbPt0LIJn732FB7uN94l2Jg6Eew6dJykC5fsH8Oz5ssqDaIdm3Eo8Yx522Yx9u/hbh5bhwV/m+u0FuC0FBoUUCZgGm/xmblVw/oOtVWSJMc5en1NuvHcOLcOSdcNRVEuAVuND8cIIRy7mqOy/GRpL2/io07su0oJIT4DJhgfrgZG2gn4XROKosSjHpN848/0Sq7C+BGUYMyjVq15oJLrtaZkAj8BPFNFdZS83lZCiHZ2yl2L11ulLv6rXgD18PEiuHUDm+XCujYzLV/a7vhoueykS6ZRnGFdm9otG9alCaCOFM05bf8ueFsCGpvv6M294Nyd8UIjqDtIjWHnnL3M5R2uOUpw0+btAPj6+tChfWub5bp372Ja3mzc5moNGtSHUONd3L/+ushmcNUZd44wX7Avb15PV3fe2Oc8fbwIa2W7n0Z2MffT8y7wfvUJN98dXtNT6joqd+dBADQ+OrxaNLJZzruTOVCfu/NQlberom7k9LQl2jVWR+vk5ReY0rxas+PwSdNy20YVTcygcnczfxUsLCeIXGgRGHC3M/rk3V+WsnybOhq6R9vGfPDI8BtqtIqlnO1qn3Lz0aFr1dBmOZ/Olv0wvsrbJVVM3g7zeVTXsmafR3O3HzQte9oZXQ2owT6gWJ9PUXqW3bKuTvZB19euRWNATT976NhJm+V27D9s3qa57f56Nbbviyc9MxuA/t072y37/ZzF/G+emsK0ZeMGfP32RLwcTCN/vWjXNBYwHsME2zfT7ThovsG1XVPH5ut2s/icUVTe5xmLuUDdbARby7NyszmzSP9bOtgpKUmSJF0LN+a3Tel68J7xf39gnhAi0FZBIYRWCPGEEMJy8rDjmOc3HCeEKDO/phDiNuDJympwZRBCvA2UDJnaDAxTFMW5SQIrmaIoXRVF8TL+LKvk3U8DSiZS+UwIUSY1qxCiB/BwJddbhqIoK4GSmeZfFEJYn4TQ3K5WxveSI77D/P78XghRJggvhBgLDHJwvzXOmeU7TMu25uxECGLuVOftyk/PIXmTcxcezqxQv4gENIoipH2s1TIh7WMJaKQGJ8+s3OlUPQCN7u1tWr649bCdkrbF3t0TH+OosmMz1qAUu+Zda4sXLzctjxs32moZIYQplWxaWjpr11V0amX77rVITzt9ZuVlzW7WrBGjRg4F4O+/N3DsWM0aXVvZEleY+2mzUbb7adMRaj/Vp+dwdnPNv0DYcLD54lTKYddLAW1P9t9bTMsBI/pZLySEKZBYlJFF7rZ916Jp5dIE+uHbvSMA+vgE8g/fmHOR9Wpvvhln8Ubrc+oVFxezdLM6r5eftxdxTW3fgGBNlEUKuF1H7Y/g3XnkpHm7WtY/dn8yZzkLNqh/azs3b8DkJ0bZHKlxI8hctdW0HHRnX+uFhCBweC8AijKyyd5y7fth+vzV7G9wm92fxLteMZVP/u9s0/Pp86/fGxAsj1+AveN3h/qZrygjm5yt1XMezf33AIXG9I++vTuVHqVpwSO6tjr3JsZA3nU+IkL2QdfXu2t70/LiVRutlikuLmbJ6k2AOi9mXOtmVstdDUVRmDJrIQDu7m6MGNDTZtmZi1by9YwFADSqH82Ud18oMxr0RtK7cxvT8uI1W6yWKS4uZsm6bQD4+eiIa9nEoTqiapszsew6dNxu2R0Hzeujwuynr7Ym4fR5VmxSrxN0ad2U+pFXO2uQJEnlKUapth/JNcgAp+SSjAG0L4wPuwPxQoi3hBB9hBBthRA3CyHGCSF+BM4DX2MxalNRlBSgJAg3AFgphBguhOgghBho3G4hkAg4N4SrkgkhngLeMj48C7wENBBCtLTz42F7j65DUZRk4A3jw/rATmPQOk4I0U0I8X/ACtTfy7U4XmOAVMAN+E0I8YcQYqwQopPFe+hVIcQWYB/Qw5GdK4qyF/jG+LAjsEMIMd64795CiCmoo2R32NyJi0jZk2gKAMbe3YPQDmXvrm726CDTiMgjP61AKSw7Ci+sazPGnpvJ2HMz6fK59Tj34R+XU2zcNu69cbh5le4ebl4exL03DoDigkIO/7C8zD5C2sfiFWbzfgoA2rx0JxHd1TvBUw+e4tK/1key6cJtzy1S++bmdHhHTQ2cmXCe+O/+sltnTbZ9xx42blQvMD1w/1106Vz2LtfnJj5C82bqHdpfff0ThYVlB6X36N6VQsNZCg1n+enHz8utNygokEED1eDNvv2H2Lv3YDlbmEVG2h79EB0dyYJ5/8PDwwO9Xs8zE1+v8H5d1cU9iZzdpvbTZnf1ILx92X7a7uFBBBv76b6fV5j6mqWoLs148vRMnjw9kz6fVd39KA36d8C7nH4a2bkJcc/eDkBRQSFHF1u/4OKq9PuOkrtdHUUXeOeteLUtO3I9+IHhaBuqowTTpv8BVxwz706taHp0GU2PLiPiw/LTUVcW/yE9TOn6MhatuWb11jStYqJpbxzFuWjjbvZaSZs4ffkWEs+pKWrH9uuMh3vpYOL2+BO0Gf82bca/zRs/LCyzfafmDfAy/q5/X7OdY6etpzn8Z98x1uxUzwFhQX5W09NOWbiWmSvUc33bhnX47zN34+lx46Xis5S39xg5/6r9MHhUP7zblb1gG/rQ7XgZ59a7/L+y/dCnc0tanVhCqxNLiP7k2apvtGSi33fUdPyCRt6Krl3Z82jIhDtMcyOm/rK47Hm0cyuaJ/xJ84Q/ify4Cs+jxcWk/KgGVDyja1PrybvKlnHTEPHO4wjjTQdpsyv7ftSaR/ZB19eqSSztjaM4F67cwN74ssGraQuWk3hanVZk7NBby6SB3b4vntaDxtF60Dhe/+wHq/WkZ2ZjKCiwuq6oqJgPvp3B7kPqCMMHRw0hOtx6ErFFKzfwyQ+zAagXFc73779EgF9NnPHo2mnVqD7tm6vfHRau3szeI2VvDJ32x2pTmtmxg3uV/Txz4Cithz9O6+GP8/pXZZOFdW7dxDRCdu6KjRw9ZX2qjI27DrLmX3VqkbDgQJpaSU9rb67QC5dTeebDqRQWFePp4c4rE+ze6y5JkiRdIzf2t07J1U1EDTK9gTo/49t2yuYAV15tfQz4B6gL9DX+WEoCbsccCK1uIyyWo1DbXp4GwMkqac01pijKp0KIusDTqK//6yuKXAZGApU3TMt2WxKEEF2B+UBL1LS59kZpOjMf6XNAJDAcaAr874r1J4DRQIIT+65Rdrw5g1sXv4m7TkvvOZM4+OUfXNh8CHcvT+oN62oaDZmZcJ74qc53x6zEC8RP+ZMWTw0lpG0Mty5+i0PfLCHr5EX86ofR/InbCG5VH4BDU5aRdaLshd7IXm1o8cQQzq3bx4UNB8g4ehZDZi4aT3eCmtUl9i5zkLYwV8+2F36y2Z7Baz7k4tZ4zv69h4yjZynOL8A7KpQ6AztQf/jNaNw05KdmsfGRryjOt/6F21VMfP5NNqxbjLe3jr+WzebDj75i3brN6HRejBo1jIcfUoO5R44m8Nnn31VKnaNHDUOr1QIwY8Y8h7b99psPqRUawoKFf7Jz5z7SMzKoFRpC79638PBD9xIQ4E9RURGPPj6JI0dcvgtWyMa3ZjBi4Zt46LSnZrkqAAAgAElEQVQMnTWJnV//wRljP200tCst71H7aVrCeXZ/f3V/NpuO7FbqcWiLuqbluj1b41fHfJd2xslkUwrdEjH9OzDgmyc5uWYPZzYdJPXIWfIzc3Dz9CCgfhj1+7an4ZDOaIzpObd/sYj0RNspQF1V8ntTqffrZDQ6L+r8/B4p380ld+teNF5a/AZ3J+guNQlAfuIZUn9e4HQ9AXeU/vikbWZOK+bTvQMeUeY72w1J58grJ4VjyahSpaCQzD+cmzf3evHS2IGMf/8n9IZCHp08gwlDuhHXtD76gkKWbzvA/HXqCIJ64SHcN+Amh/fv76PjgcG38O3CteToDdz3/k/c3bcTXVrE4u/tRUpmDut2H2bB+l0UG0d6PTOyb5mUs7NXbWPq4vWAGgB9dnQ/zl6yn569fnhomQuY16Nz//mB2Hkfo9FpqT/9HS59+zvZW/aj8fIk4LbuhIwZAKj98PKPi5yuJ3BEn1KPvZqb+6Fvjw54RFv0w1Pnyd1Rc1JS12TJ735H/bmfoNF5UfeXd7k8ZS65W/chvLQEDOlO0N0DAfX4pfxU9iaCigoYccV51PL4db/i+J20fh5N/eUP/Ad3R9eyIbWeGYtnTDTpC1ZTlJKOR90IQh64He/26si2rLXbyVq+yen2uhLZB13fpEfvYdwL76HPN/DI658wYdQQ4to0Iz+/gOUbtjLvr3WAGlAcN3yAU3X8uy+e/5sygwHdO9OxVRMiaoWQbyjg6MnTzP9rHYeNcy7e0rE1D48eanUfazbv5D9f/g9FUfD11jHpkbGkZmSRmmE7FXRUeC28vbROtdmVTHpgJONenYzeUMAj//mKCSP6E9eyCfkGA8v/2cm8VeqlrXqRYYwbZmO0tR3+Pt48eMetfPPrUnLy9Nz3ymTuHtSTrm2a4u/rTUp6Fmv/3cuCvzdRbMyK9Oy9w6ym0H/3u9mkZWTTt2s7WsTWxc/Hm7TMLLbtO8LvKzeSnatHoxG8+egYh+cKlUrbtfcASRbz56ZlZJiWk86eY9Gfq0qVv32wjaw00nVPzsEplUcGOCWXpahnuHeEEDNQ58rsDcQAAUAucBrYDawEFiqKknfF9qeFEO2BScAwoB6gRw0ILgK+UBQlzUr2WqmaKIryjBBiBWqQMw7wBs6gBqE/URTlzLU6XoqiHBVCtAVGoQaf44BaqKM6U4AjqEHohYqi7LK5I9v7LwBGCCHuQU292xrwAE6hji6efL28P9MOnOKfR7/mpq8ew9Pfm7avlk1jmplwnrX3TqbwKufJ2/Ph72hD/Wl4d0+CW9XnlqlPlSlzfPY69n5kO07u5uVJnQEdqTOgo80yOWcus+mJb0jdazt1qcbDze5+0g+fZtOTU0g/ZD9toCvYs+cgY8Y+xrRfviQgwJ/333ulTJkjRxMYOuw+srNzKqXOkvS0hYWFzJ7jWPBGIOjcuT2dO7e3uj4lJY0nn36V36/zuTctXT54ihWPf02/Lx5D6+9N15fL9tO0hPMsHT/5quez7PvZIzbXdXii9L0k8b9vKBPgBHDTehA7MI7YgXE291WQl8+2T+ax5wfXHSFtT358Iuee/ZCIyS/i5udD2PPjy5ZJPMOZh9+iOCev7A4qKOKj52yuC3m49J3tGQtW2Q1wesZEo2utjrDJ2bSLIjt30d8ImtWL4KPHRvLa9wvIzsvny3llUxHWCw/h64lj8NE5d4H04aHdyczJY9aqreTqDfy09B9+Wlr2Hjp3Nw1P39mHITe1KbNutcWF+otpWYx//+dy6132yTNE1bKdyeB6oT+USNJTH1Hns+dx8/ch/KVxZcrkJ57h5APvXFU/rDPZ9siysMfuLPU4bd5qGVypIP2hRM48/RFRn72Am58PtV8cX6ZMfuIZkia8fVXHL8rO6M7QR0eWepw+/2+r51HFUMDpCW9T54e30LVqRMBtPQi4rWwSmay12zn7zEdOt9XVyD7o+prF1uPjlx/n1U++Izs3jy+nlb1xsV5UON/857mrSgWbkpbBrMUrmbV4ZZl1Qghu79eN1564Dw8b2QnWbN1lmv8xOzePx9/8tNw6/5+9+w6TrKoWNv6uGXLOOQxJUCQpoAjKECRJuCqgogKCgiLmLHoNmK/yIYo5EM1kEAkCAiIYQIKoxAHJEiWnWd8f+5Td9HSo7q50qt/f8/Rzuqr2OSxY1Oqqvc/e+4df+mhbltTtNc9ffWW+8oH9+fjXj+KRx57giOPn/A616grLcOQhB7Hg/PMNc4WxHbDHjjz0yGMcf8b5PPbEk/zwxLP44YlnzdFurrmm8+69dmXnLUfYRzXh6utncfUIe74uutCCHHLA69hhi5H7AtScE047i1POPHfY16646lquuOq5ddIBTkkjcYBTPS0zxxy9ycybKYOUE7n+fZSlXj88SpsZo7x2FHBUE/+cTzP6DNNGu32BfUd4beZY57dTs/8Oo5y/LyP8uzXz+qB2v2aUWbWj5auZ16s2n6a5fD0L/LT6GZdm85mZxwHHjfJ6/Uc4gdvPuYJfb/Mx1n7rDqy47YYssPziPPvUszwy625uPf0y/vnjc3j28acm/w/K5LIP/IB/nfEn1nzTViy5werMu8TCPHn/w9x35U3ccOx53HH+yHvv3PSz3/HEvx9iqRevyWIvWIX5llyEeRdfiHz2WZ68/xHuv3oWt51zObNO+sOYsy4v++APWH7L9Vhyw9WZf5nFmGvB+Xjyvod54O+3cuvpf+TmE34/7HK8dXX6Geew0Yu35d0Hv5Udd9qGlVZcnqeeeoobbpzFCSeczpHf+jGPPz65gbGGNddc7b+Dk+eeeyF33z2+lau//JVvcN11N7LFFpuy0korsOSSi/Pgg//hpptu4dTTzuKHP/oJ9903+uykfjTr3Cv42XYfY4P9d2DVrTdkoep9+tAtd3PD6Zdx9VHn8MwTLXifTtIln/8Zd1z6D1Z4yTossfZKLLDUIsy/5CJkJk8++Cj3X3cbt/3+Wv5xwsU8dk9/D6A9cv4fuXmXd7LEPrux4JabMPdyS5FPP81Tt97Jw2dezAPHnUY+0RNbeQOwyG4D+xdP5eVpB5u50dr88tB3cPw5l3LRlddz9/3/Ye65prPKskvwyk1ewOu32ZT5q2XZJiIi+NBeO/Cql63Pib+7nCuuv5U7732QJ556mgXmnYeVl12CF689g923ejEzlltq7AtqDg//9k9cv9O7WGrfXVl4642r9+EzPDnrTh4682LuO/qMnnof6rkeOe+P3LTTO1li391YaKtN/pu/p265g/+ceTH3H3N6z+TvmX8/wM2vfT+L7bkdi+68JfOutQrTF16QZx58mCeuvI4HTzyXh8/uryXZm+F7sP5mvmQjfnXk5zj+lLO58E9Xcve99zP33HOxyvLL8sotNuENu2zL/JOYCfnidZ/H+/d/HX+88u/cfNud3PfAQ0ybNo2ll1iMTdd/Pru98uWsv84aLfw3mnpmbrI+vzrsEI4/43wu/Ms13H3fg+XzzHJL88qXvYg37DRz0p9nPrzf7uy85aaccO7vueLvN3Lnv+/niSefYoH55mXl5Zdm4xesxe7bbzHqvpn7v2Z7ZqywLJf//QbuuvcBHnzkURZeYH5WXm5pttp0fV6z7eYsvsjUXnZYknpNOM1XkjSa41d4k38oamyfe6f2Eo/94PBlt+p2CJqkbee/v9shaBJmHP3mboegSbp+r590OwRN0vRps7sdgibh2dlzLgWpenneuYd0OwRN1hOtWSlH3TNt2dW6HYImae6lVu+LSQqdtNACq3WtT/KRx242XzXgp0xJkiRJkiRJkiRJteEStZIkSZIkSZIkSeoZiYvKaXQOcEpTQEQsBqw0kXMz85oWhyNJkiRJkiRJkjRhDnBKU8P/AD+e4LmuNy5JkiRJkiRJknqGA5ySJEmSJEmSJEnqGbPTJWo1umndDkBS+2XmUZkZE/npduySJEmSJEmSJEmDOYNTkiRJkiRJkiRJPSOdwakxOINTkiRJkiRJkiRJUm04g1OSJEmSJEmSJEk9I3EGp0bnDE5JkiRJkiRJkiRJteEApyRJkiRJkiRJkqTacIlaSZIkSZIkSZIk9YxMl6jV6JzBKUmSJEmSJEmSJKk2nMEpSZIkSZIkSZKknuEMTo3FGZySJEmSJEmSJEmSasMZnJIkSZIkSZIkSeoZzt/UWJzBKUmSJEmSJEmSJKk2wnWMJUlTUUSsBPyrerhyZt7WzXg0fuaw/sxhvZm/+jOH9WcO688c1pv5qz9zWH/msN7Mn6TJcAanJEmSJEmSJEmSpNpwgFOSJEmSJEmSJElSbTjAKUmSJEmSJEmSJKk2HOCUJEmSJEmSJEmSVBsOcEqSJEmSJEmSJEmqDQc4JUmSJEmSJEmSJNWGA5ySJEmSJEmSJEmSasMBTkmSJEmSJEmSJEm14QCnJEmSJEmSJEmSpNqIzOx2DJIkSZIkSZIkSZLUFGdwSpIkSZIkSZIkSaoNBzglSZIkSZIkSZIk1YYDnJIkSZIkSZIkSZJqwwFOSZIkSZIkSZIkSbXhAKckSZIkSZIkSZKk2nCAU5IkSZIkSZIkSVJtOMApSZIkSZIkSZIkqTYc4JQkSZIkSZIkSZJUGw5wSpIkSZIkSZIkSaoNBzglSZIkSZIkSZIk1YYDnJKk2ouIRVp8vSNaeT2NzRxKkiRJkiRJalZkZrdjkCRpUiLiEmDbzHysBdf6AfCWzJw++cjULHPYnyLiZmA2sH1m3tDkOasAFwCZmWu0MTxJqp2IWAPYDFgOWAD4Vmbe292oJKk+rKOSJPUPBzglSbUXEbOBC4EdM/PxCV5jGnAMsBdlYMXBsQ4yh/2pymsC62XmtU2eswZwPeaw50TEWsDeDHQKzs+QweuIeCGwCvBoZv6uK4FqWOav3iLiRcDhwOZDXnpOfY2IdwKfAh4CXpCZT3cuSo2l+qyyFc8dXDkkM+8c1GYeYC7g2cx8siuBTlER8b/tuG5mfrYd19X4WEf7g3W0t1lHJXWaA5ySpNobNIhyPvCq8X6JiYi5gJ8D/wMEcGtmzmh1nBqZOexPDnD2h6oj6SvAeyhbXET10hy5jYidgNOBZ4DVMvP2DoerIcxf/UXEzsAvgXkYyB8Mn8OFgTsoHb67Z+ZJnYxVI6vyeASw6pCXhubwIOAbwCPACpn5aOeinNoGfW5pKT/PdJ91tD9YR3ufdVRSp7kHpySpH/yS8kV1K+Dk6o7NpkTEvMApDAyM3QRs2Y4gNSpzqIZFq+OklytWy3wXeB8wndLh96uRGmbmr4Gbq7a7dyQ6jcX81VhELA/8FJgXuBbYEVh4pPaZ+TBwavVwx7YHqKZExNson1VmUD6r3MdzB1kG+wFl5thCwKs7EZ+eI8b4mUgbdZF1tD9YR2vFOiqpYxzglCT1g72AkykffLcDTqhm9I0qIhYAfg3sUJ37D+AVmXlLG2PV8MyhGt5UHc1hD4iIbYD9q4dfAGZk5p5jnNa4YWHrdsamsZm/vvA+YEFKTXx5Zp7VxEyUCyg5fHGbY1MTquWhj6wenkdZ8nKZkdpn5lPACQx8JlKHZOa0kX6A1YE/UfJyJrAHZRbZfNXPqpQbQ86s2vwJWL06V91lHa0562h9WEclddqYHYeSJPW6zHw2IvYETgJeBewE/CIi9sjMZ4c7p1p66EzK3h0BXAVsl5n3dChsDWIO+0NEnDfCSz+OiLE6kualfOldhrKs0dmtjE0TdkB1/HVmfqLJc/5YHddtQzwaH/NXfztQauLXMvPBJs/5R3VcrT0haZzeR+l7uQbYqep4H8tFlJsTNmpnYGpORCwKnEOZObZ3Zh43TLN/VT8nRsQbgaOBcyJi48x8qGPBajjW0fqzjtacdVRSuzjAKUnqC5n5TES8lrKc0HbAbsDxEbFXZs4e3DYiFqcMnryIMjD2Z2CHzLy/w2FrEHPYF2ZSOpAGLyMUwCbjvM5NwBdbFJMmZzNKTn84jnNuq47LtT4cjZP5q7/GPmN/HLXVc/2nOi7U4lg0MVtT3oeHN9kpD3BDdVy5PSFpnN4HrAF8Z4RO+efIzOMjYgvgQOADwP+2OT6Nzjpaf9bR+rOOSmoLBzglSX0jM5+KiN2AMyhfgvYAngbe3GgTEctQ7hx8IWXg5RLKXaD/mfOK6jRzWHsXUjofGrasHv8FGG0GZwJPAHdS8vmzJpYOU2c0lv+aNY5znq6OftfoPvNXf408jGd5tsZexo+0OBZNzErV8cpxnNP4G7hAi2PRxLyW8lnll+M45xeUjvnXYMd8t1lH6886Wn/WUUlt4ZdWSVJfycwnI2IXytKlrwD2ioinM3O/iFgROBd4HmVg7DxgNwdSeos5rK/MnDn4cUQ0Zt7um5nXdj4itcCjwGLA0uM4p9EJ5Yzq7jN/9XcXZTm31YFLmzxn0+p4azsC0rg1bvwZTyf7ktXRJfl6w4zqOJ58NNquOmordYJ1tP6so/U3ozpaRyW1lJv0SpL6TmY+TtnH8RLKINg+EXEcZXbZ2gxsar+zA2O9yRz2jWOqnwe6HYgm7Kbq+IJxnLNjdfxbi2PR+Jm/+ruI8jdvj2YaR8Q8lNkOCVzQvrA0DrdXx9XHcc4W1fGmUVupUxoz29cbxzmNtk+P2kqdYB2tP+to/VlHJbWFA5ySpL5UDXrtRNlrJYA3AKtVL58M/E9mPtGl8NQEc1h/mblvZr4lM+/sdiyasLMp7793RsSY3x0i4gXAvpROwV+3NzQ1wfzV31HVcdeIeOVoDatO+WMoe1wl8P32hqYmXUB1s1YzjSNiUeDtlBye176wNA5XUnL4kYgYcwZZ1eYjlBxe1ebYNLajqqN1tL4uwDpad9ZRSW3hAKckqW9VezJuB/yZ8mE6gZ8Cu2emdwHWgDmUuu4IyjKnawDfiYgRt7ioOg3PBuajLG9qp2D3mb+ay8wLgJ9T/gaeFhFfjohNBzWZEREvi4gPUWbd7kH5W/mdzHQWbm/4LiUnW0bEvqM1jIglKTdxLQc8A3yn7dGpGY16uDZwQURsOFLDiNgAOB9Yp3rqe22OTWOwjvYF62j9WUcltUVk5titJEnqYREx1l2ZiwIbAbOBi6vjaDIzt2lFbGqOOZR6V0S8kTKbAeA24AwG7or/AaXDcHNKJ0RQ3p+7ZeYZnY9WQ5m/+ouIeYETKKsajPYFPqrjicDrMvPZdsem5kTEYcB7Kfn7FSWfP6seHwg8Rnkf7gUsUp32qcz8XOej1XAi4hfA7gy8B68G/gTcUz23LLAJA0sqBnBCZja1LKrayzpaf9bR+rOOSmoHBzglSbUXEbMZ/YvquC5HGRyb3qLrqQnmsN4ioh2dP5mZI842U2dFxJ6Uu+cXZfj3aqND8BFgn8w8qVOxaWzmrz9ExNuAD1Nm5A7nNuALmelslR4TEQF8E3gHzQ2uHJ6Z7297YGpaREwHvgYczMBqaCPV0wSOBN6fmc90JkI1wzpaX9bR+rOOSmoHBzglSbVXDY61koNjHWYO660N+QNz2HOqJb8OAnYBNgQGD0D/DTgV+Hpm3tOF8DQG89c/qr1SNwaWAaYD9wFXAJenX/B7WrUU9EeBLZlzy6AELgU+l5lndjo2NSciXkiZBb8tsBYDgykA1wPnAt/NTPeM62HW0fqyjtafdVRSKznAKUmSpEmJiE+147qZ+Zl2XFeTFxHTgCUonYL3uyduvZg/qbsiYmHK0vuDB1f+mpn3djUwjUtEzAMsXj18MDOf7GY80lRiHe0P1lFJk+UApyRJkqRhRcRN1a+HZeY3uxqMxs38SZIkSZL6lfsaSZIkSRrJSpS74v/a7UA0IeZvCoiIDYDdgaWAm4HjM/P27kYlSfVhHZUkqZ6cwSlJkiRpWBFxK7AisGlm/qXb8Wh8zF/9RcQmwJHAM8BOmfngkNcPrF4fvH/VI8DumXlOxwLVpETELsCeDAyu/CAzL+9uVFJ/sI5ODdZRSZqanMEpSZIkaSSXAa8B1gUcIKsf81d/uwAbA2cP0ym/GnAEMG3IOQsDP4+ItTPz350JUyOJiK2AnwNPAOsPk8dDgY8POe2tEbF/Zh7boTAFRMR5bbhsZuY2bbiummcdrTnraH1YRyV1mgOckqTai4j/bfU1M/Ozrb6mRmYO+1NE7D2Z8zPzmFbFogn7NvBa4H0R8dPMfLrbAWlczF/9zQQS+M0wr70TmBt4HHgj8Ftge+BoYFHg7cChHYlSo9mJMqPopGE65dendMo3Zo49ACxO6av5bkRclJmzOhjrVDeT8n6LMdo1o3Edl03rvplYR+vOOlofM7GOSuogBzglSf3g07T+Q6+DY531acxhPzqKiec1AQc4uywzz4uILwIfA06PiLdm5r+6HZeaY/76worV8aphXtuNUiu/m5knV8/9KiI2A94H7IAd871gC0qezh3mtXdQOm8fALbNzCsiYmPKQMzilMGVj3YqUHEhdqT3I+to/VlH68M6KqmjHOCUJPWLVtwh2OAH8u4wh/2plXlVh1Wzq58ErgZeCdwUEb+ndBI+ADw72vnOpO4u89cXlq6O9w1+MiJWBNag/L37xZBzzqZ0zK/T9ujUjOWr49+GeW1nSg6/mZlXAGTmnyPim8D/Att2JkQBZObMbsegtrCO1p91tCaso5I6zQFOSVI/WG0S5wZl+b6PAEviYEy3mMP+1ExeFwSeB+wF7A78HjgAeKyNcal5n2bghoEEpgMvr36a4QBZd30a81d381THBYc838jhY8Cfhrx2d3VcuF1BaVwagytDl1VcgzKzLIGThpxzUXVco72hSVOCdbT+rKOSpGE5wClJqr3MvGUi50XELsBngA0aTwG3A59vUWhqkjnsT+PI67XAyRGxJ/AT4BuU2WbqDUNvGvAmgnoxf/X2b2AFSgftJYOeb9TISzNz6Ezc+arjg6gXNN5ziw55vjG48lBm/nXIa42ZZgu0LSpp6rCO1p91VJI0rGndDkCSpE6LiO0j4jLgZMrAWAD3AO8F1szM73QzPo3NHPanzPwFcDSwFXBgl8MRkJnTJvPT7finOvPXF/5M+Ru3f0RMA4iIJYHXUGas/HaYcxqzVe4e5jV13l3V8flDnt++Ov5+mHMaM80eaEtEmrSImBYRS0XEKhExvdvxaFTW0fqzjvYh66ikVvBLqyRpyoiIrSPiYuDXwMaUL7r3AR8CVs/MIzLzyW7GqNGZwynhF5S87tvlOCSpFxxTHV8OXBwRX6XMQFoUeAY4fphzXlYdb2x/eGrCpZS/a++IiAUAImJ1YDfK4Mo5w5zzvOp41zCvqUsiYnpE7B8RF1GWNb0buAlYe0i7nSPiKxFxSDfi1Byso/VnHe0T1lFJreYStZKkvhcRWwCHAq9oPEW5k/OrwBGZ+Wi3YlNzzOGU0rhTfu1RW0nSFJCZJ0XEryh7FL8UeAkDS/V9JTP/Nbh9NQOiMSvp4k7GqhH9AHg9sD5wTURcTvk8Mx+lc/cnw5zT+LxzXUci1JgiYhnKyiGD34MjmQWcCmREnDHM0pnqIOtoX7CO9gHrqKR2cAanJKlvRcRLIuJs4HeULzgB/Af4NDAjM7/owFhvM4dT0irVce6uRiFJveP1wLuBi4AbgAuB/TPzkyO0XZby9/KMjkWoEWXmecDXKTmZAbwaWKp6+UOZee/g9hExHwOzki7sXKQaSTXgdRplcCwpq00cPFL7zLwGuKx6+Oq2B6hmWEdrzDpaf9ZRSe0SmdntGCRJaqmIeBHwWWDHxlPAI8ARwFcz88FuxabmmMOpKSLmBs6nLAt2VWZu2OWQNEhELAG8BdgWeCGwRPXS/cA1wLnAjzPz/u5EqNGYP6m7ImJnYA9gOeBO4Jiq035ouz2Br1A6gGdm5i0dDVRziIj9KDPIngZ2zcyzqudnU/K0XmZeO+ScjwGfB87PzG06HLLUl6yj9WUdldQuDnBKkvpGRKxHGRTbtfEUZcmaIynLD93XrdjUHHPYXyJilbFbMQ1YnLKn6sGUgReAQzLzS+2KTeMTEQdSloReoPHUkCaNLxWPAR/IzO91KjaNzfxJ0sRFxDnA1sCRmfnuQc+P1jG/A2XP+Dsyc6VOxitJvcY6Kqld3INTklR7EfF8ypKlr6V02gbwBPBd4IuZeU/3olMzzGHfunkC5wTwB+D/tTgWTVBEfJRy93RjUOwh4ArgrurxcsBGwKLAgsC3I2KxzPxKp2PVnMyfJE3a+tXx1HGc0/jsumSLY5GkOrKOSmoLBzglSf3gagYGxZ6kLH3yhcy8s6tRaTzMYX8aOktsLPdTBrU/l5lPtiEejVNEvBA4lJLLO4EPAb/MzKeHtJuLsmTY/wErAJ+LiDMy828dDlmDmL/+FBGLAAsD08dqm5m3tj8iqe8tVh3Hs5JI4/35bItjUQtYR6WOs45KagsHOCVJ/WAaZVmTpAyOvQp4VcR4x1b+KzNzjRbFpuaYw/70libazAYepsz2vCYz/QLbWw6mdC78G9hspE6+zHwG+GlEXAz8CVi6OvcdnQpUwzJ/fSIiXgkcBGzBwP6pY0n8zt9zImJhYDWaH1y5sO1BaSz3A8sAK1NmwDdjrer477ZEpHGzjvYP62gtWUcltYV/pCVJ/SSARaqfyXCD6u4xh30kM4/udgyatK0p76cvNjODITP/FRFfBr4GbNPu4DQm89cHIuII4J2Nh92MRRMXEW+jDK6sR/N5dHClN/yN0jG/Cc0vr/g6Sv7+1K6g1DzraH+wjtaadVRSW1jgJUn94EIc0Ko7cyj1phWr4yXjOOf31XGFFsei8TN/NRcRe1Fm00LZm/pk4C+UmRCzuxWXmhcR04ETgF0aT3UxHE3MyZQbRg6OiMMy84HRGkfE7pR8JyX36iLraP1ZR/uCdVRSWzjAKUmqvcyc2e0YNDnmUOpZjSWDx/O9obFUmJ2G3Wf+6u/A6vgvYOvMvLGbwWhC3g7sWv1+N/BjHFypm+8DH6QsrXh2RAWgQrcAACAASURBVOyTmdcObRQRywDvoex3nMA1wC86GaiGZR2tP+to/VlHJbWFA5ySJElqm4hYACAzHxvh9XcBewJLUfbh/HZmnta5CDWGW4HnU5YrbXYWYGNp0zGXRFXbmb/6W5/SwfcZO+Vra+/qeC3w8rFmraj3ZOaTEbEbcAHwYuDqiPjnoCbHRcRCwOqUmWUB3Ae8NjNdoaT7rKP1Zx2tOeuopHaZ1u0AJEnqNRGxZLdj0OSYw94QEbsADwN3RsTCw7z+I+Bw4GXA2sD2wMkR8bGOBqrRnEPpYPhgRKw3VuOIeCEDd1yf3ebYNDbzV39zV8cruhqFJuP5lPfUoXbK11dmXknZO+4PlLq6zqCXNwDWpPSxBfBH4CWZeUOn49SwrKP1Zx3tA9ZRSe3gAKckSUBEzB0Rr42IU4Dbux2Pxs8c9qTtKV9QT83Mhwe/EBFbAPtWDx+jdDo9UbX/bDXQou47HHgSWAi4OCI+ONwNBBGxZER8ELgIWLg65/CORqrhmL/6m1UdF+pmEGqJf47dRL0sM2/IzM2BVwBfpcxE+jtwHWWW/JHA9pn50sy8qWuBaqhZ1dE6Wn/W0ZqzjkpqNZeolSRNaRGxGWXJmz2BxbocjibAHPa0l1Lutj5/mNcOqI53AJtl5m0RsTJwMbASZb+kd3UkSo0oM2+JiAMpex0tBHwZ+FJE3AzcQ8nvssBqDCwnlcCBmekSp11m/vrCicAhlKWDL+pyLJqY64ENgSW6HYhaIzMvpnxeUT1YR+vPOtpnrKOSWsUZnJKkKSciVo2IT0bEdZQP1QcAizPQsesX3x5nDmtjmeo43N3WO1By9Y3MvA0gM/8FfIOSxy07EqHGlJnHALsCd1JyMw1YgzKAvVn1e2M5qTuAXTLz2O5Eq6HMX+19jbIf6nsjYp2xGqsn/Yzy/tq524FIU5R1tP6so5KkYYX79EqSpoKIWATYgzLTb3MGZqoAPAP8DjgBODEz7+lKkBqVOayfiHgcmAfYKDOvGvT8usDVlAHOF2TmPwe9NhM4D3g4MxftbMQaTUTMBbwa2BZ4IQN30d8PXAOcC5ycmU93J0KNxvzVV0Q8DzgNWBr4BPBT9yCrj4iYF7iUstfYdpnpTVhSh1lH6806KkkaiQOckqS+FRHTKLPE9gZ2AeZrvFQdk7LH2Bcy877OR6ixmMN6i4hHgPmBbTLzgkHPHwh8G7gnM5cbcs4GlP04n87MeTsYriT1nIho7D+1AGVWfFY/91L2Lx5NZuYabQxPTYqIZSjLZG4MHAH8BPhHZj7R1cCkKcA62h+so5Kk4bgHpySp70TEhpQBsTcwsERmY0DsGuBYyj5kAJc5MNZ7zGHfuB1Yk7JnzgWDnn8VIy8l3Ji1eW9bI5Okepgx5HFj9YJl5mw6B+9m7gER8ezgh8AHqh8iYthzBsnMtN+mQwbl6jn/3YfkcLzMYffNGPLYOloz1tH6sI5K6jSLgySpL0TEcsCbgDdTlt6DgQGxOyh3eB7XWCYzIr48x0XUVeawL10ErAUcHBHHZea9EbEJZVYuwFnDnPP86nhXJwKUpB53dLcD0KQN7X0fszdeXTNSbsxZvVlH6886Wh/WUUkd5QCnJKn2IuI3wDbANAY+OD9MWcLmOOC8dE32nmYO+9a3gH2B1YCbIuI64AWUz6D3Az8f5pytKXfLX9uhGDWKiFgPOAV4FpiZmbeP0X5Fyn64AeyYmde1P0qNxPzVX2a+pdsxaNI+0+0A1LSRcmUOa8w62hd8D9aHdVRSR7kHpySp9iJidvXr08DZwPHAyaPtx1Gdk8AbMvMX7Y9SozGH/Ssi3gf8H2XwuuFp4PWZedKQtotSlrWdHzggM3/YsUA1rGqm9IeAszJzxybP+TWwPfD5zPzfdsan0Zk/SZIkSVK/mjZ2E0mSaiEpM1Qer36e6W44mgBz2Icy8/8BGwGHAt8HPgusP3RwszIT+BNwIXB6p2LUqLakvDdPHcc5p1BmAG7Tlog0HuZPkpoUEe+ufhbvdiySVEfWUUmd5gxOSVLtRcT5wCsoHbKNP2wPAL+k7Nn4+2HOcfZfDzGHUm+KiPuBRYFXDPc+HOGczSn7r96fmUu1Mz6Nzvz1n4iYH3gxsBywAGW1g/90NyqpPwz6bLleZl476Pnzquf3y8xbuhWfWsM6KrWPdVRSp7kHpySp9jJzq4hYBdgbeBPwPGAJ4ADggIiYRVny9PjM/GfXAtWIzKHUsxaqjo+M45xG20VaHIvGz/z1iYhYGfgCsAcw96CX/sygPYsjYn/gQOAhYDv3r+49EbEGsBkDgyvfysx7uxuVxjCT0jG/YJfj0CRYR/uHdbSWZmIdldQGLlErSeoLmXlrZn4uM9ehfNn5DmUGYACrAYcA10bEHyPiPV0MVSMwh1NDRKwUERtHxCuqO+jV2x6ojsuN45xG24dbHIvGz/z1gYh4CXAFsBcwD+XvYozQ/DRgfWBrYLuOBKimRMSLIuJC4DrgaODLwKeAZYa0e2dE3BMR10fE3MNcSu3T2Pt9sa5GoZazjvYH62gtWEcldZQDnJKkvpOZl2XmQcDywGsp+4k9Q/kSuzFwGAPLoL4sIlyCr8eYw/4SEQtHxKER8S/gFuAy4HzKwPXgdq+PiF9ExPe7EaeGdX113GEc5+xYHW9scSwaP/NXcxGxGOVv4BLAXcBBwHojtc/Me4Azq4evanuAakpE7Az8HticgYGVkQZXjgHmB1YHdu5IgGqYVR39795HrKP9wTpaG7Oqo//dJXWES9RKkvpWZj4NnAScFBFLUO7Y3ZsyQAZlgOxdwEERcRFwAnBiZt7VjXg1J3NYfxGxFvBrSgfD4E6I4Zb7uhQ4rpwWR2fmxR0IUaM7C3gZZano72Xm30drHBHrAm+j5Pc3HYhPozN/9fduysyUe4HNMvNWgIiR+nQBOBfYDdi07dFpTBGxPPBTYF7gb8AHgYsZYZZ0Zj4cEacCr6fccHBSh0JV+byyDvCRiNiGMkvs6UGvfy4iHhznNTMz929VgJoQ62jNWUdrxToqqaPCpeQlSVNNRKwD7EMZLFu5ejoHHS/JzFd0IzY1xxzWQ0TMB1wFrAk8ChwJXAicTsnTepl57ZBzzqEsCfa1zPxwZyPWUNXs6Jsp+xvdA7wtM08foe2uwHeBZYHHgDUy8+5Oxao5mb/6i4g/AS8CDsnMLw16fjYj19GZwHnAfZm5dAfD1TAi4iuUzvhbgI0y88Hq+dFy+DbK+/GKzHxxh0Oesqqb6S6lfG6Bgc+WMeRx05ekdMxPb0F4miDraP1ZR+vDOiqp05zBKUmacjLzH8DHIuLjwFaUGYGvARaifIDevIvhqQnmsDbewcDg5ssz868w5h3zZwLbUPZhVZdl5r0R8XbgWMrsh1Mi4ibKXfN3Vs2WB15OWXI4KB0X73BwrPvMX19odBBeOI5zGnuvLtLiWDQxO1DeV19rdMo34R/VcbVRW6mlMvP+iNgYOJjyWWRFyoyxVSk5vJPnzkRSPVhH6886WhPWUUmd5gCnJGnKyrKMwXnAeRFxEGWvx72Bmd2MS80zhz3vNZQvsl9vDG424crquFZ7QtJ4ZebxETEN+DZlJuAalCWHB2uMWj9KGRw7roMhahTmr/bmq47j6QxcsDo+3uJYNDGrVsc/juOc/1THhVoci8aQmf8BvlD9AP+dJQaw3dBZYqoF62j9WUdrxDoqqZOmdTsASZJ6QWY+lpnHAvsBx3Q7Ho2fOexJz6+OZ4/jnPuq42ItjkWTUL231gS+BFxdPR0MzPi7Cvg8sKaDY73H/NXaPdVxPDNQNqyOd7Q4Fk1M48by8fS/LFodH2lxLNJUZB2tP+uoJGlYDnBKkgRExOoR8X3gesrejqoZc9iTGndMj6djYd7q6NJFPSYz78rMj2fmBpQ8LVf9zJeZG2bmJ13WtHeZv9q6rDru2EzjKGuAv40ycH1Ru4LSuNxVHYfOnB7NptXx1hbHoonZirI/+M3dDkQTYh2tP+to/VlHJbWFA5ySpL4UEUtGxJh7pkTEOhFxLGWPjv2AeRhYqk9dZA77QmM25oxxnLNudbxr1Fbqqsx8JjPvqX6e6XY8Gh/zVyvHU/6mvTEiNhyrMfA1YIPq96PbFpXG4yJKDvdopnFEzAMcSBlcuaB9YalZmfm76sflSuvJOlp/1tGas45KahcHOCVJfSMiXhIRp0bEw5SliB6IiPsj4rsRMWNI25Ui4mjKUn17UZa9CeBvwJs6G7kazGHfubw6vmIc5+xN6Yz4Q+vDkaR6ycxTgPMpf+N+GxHviIhlBjWZKyJWiIg9IuIi4D2UGnpiZl7ShZA1p6Oq464R8crRGlad8sdQ9spN4PvtDU3qf9bRvnBUdbSOSpKeIzKz2zFIkjRpEfFm4IfAdOacvZfA3cBWmfnPiNgD+B6wyKC2fwY+X30BVheYw/4TEfsAPwaeANbJzFur52dTcrpeZl47qP17gcOq13bOzDM7H7WaUS3f9gbKnfRrUnJ2E3AycExmzu5ieBqD+auXiFgM+C2wESVXozYHLgVemZmPtjs2NScifgq8DngK+DpwAiVPCewCPAhsDhzAwBKM387Mgzsf7dQVET9qw2UzM/dvw3U1DtbR+rOO1oN1VFKnOcApSaq9iFgVuBaYj/KF9ElKR+1TwFrAApQvPucD3wJ+ycCg2MXAoZl5TofD1iDmsD9FxDTKLM71gVnAO4HfAM9S8vlCytLCGwPvBV5fnXpRZs7scLhTXrUk9Heqhydm5q9GaLcAcAplH53h/BnYITMfaH2UGon562/VjJRPAQcBi47Q7DHgm8D/ZuZTnYpNY4uIeSmd8Tsx+uBK47PNicDrMvPZdsemAYNuwGrZJSkd89NbeE1NkHW03qyj9WAdldRpDnBKkmovIr4AfJTyQfr/AZ9s7O0QEdMpywx9iTIz8CFgMeB24O2ZeUZXgtZzmMP+FRGrUAahV6Lk9zHKgDXAvcDCwLyN5sCNwOaZeU+HQ53yIuK1lJsHEnhBZv5zhHbHUJaBTobf7zaBszNzx3bFqjmZv6khIhYEtqTcGLIM5e/ifcAVwLmZ+VAXw9MYIuJtwIcpSycO5zbgC5n5nRFeVxtFxCxG75hfAFh60OOngPur35eg7ANPdY17KZ95yMzVWhqoJsU6Wm/W0d5mHZXUaQ5wSpJqLyIuAV4CnJmZO4/Q5v+AD1QP7wQ2zsw7OxSixmAO+1tELAF8A9iT0ok0nKQMzrzDmWPdERGHA+8GLs/MjUdosxHwF0q+ZgP/R9nb6A5gE8qSYY3l37bKzAs7ELowf3UXEe+ufj3WGtj/IuIFDD+4cnnaSdOTIuLFlM8pK1Hq5o+AvzZmh1U35G0A7A+8jTLIskdm/qU7EU891tGpxTpaP9ZRSe3gAKckqfYi4t+Uu/32ysyfj9BmXeBqSqftRzLzqx0MUWMwh1NDtRTxqxi+M+K0zLyui+FNeRHxO2ALypLPnx6hzRHAwZT34Rcz8xNDXl+Cstz00sB3M/Ogtgat/zJ/9TbK3sTnVc/vl5m3dCs+jS0iDqt+/ZKrEPSXiFiB8lllEWCnzDx/jPZbUpbkfwjYyBvyOsM6Wn/W0f5lHZXULtO6HYAkSS3Q2EPlhlHa3Djo9z+1MRZNjDmcAjLzlsz8Vmbul5k7Z+aOmfmmzPza0MHNiJivW3FOYatXxz+P0ma76vg0MMdNBpl5P+WO7KDMCFTnmL/+NLP6WbC7YagJ76Usqb/U4Ccj4uaIuDEi1uxOWGqBD1Bu/Dh8rE55gMz8HXA45WauD7U5No1tJtbRurCO9i/rqKS2cIBTktQP5qqOj4/UIDOfGPTw3+0NRxNgDgWUfZEi4qPArG7HMgUtWR3vGO7FiFgSeB5lFsSlmfngCNe5qDrOaGl0Gov5q7fG37jFuhqF2mFVyvtpnjHaqXftTKmd49n3vdH2Va0PRyOwjvYv62j9WUcltYUDnJIkSeq6iFgsIj4F3AJ8nnKHrzqrcaPBSHtYDJ7Rd9ko12ksKbbIpCPSeJi/eptVHYfdh1q18Fh1XGrUVqqjlarjE6O2eq5G25VGbaVWmlUdraP1ZR3tX9ZRSW0x19hNJEmqjWY3lnYD6t5lDvtARCxDWQpsZcpSmLOAczPzsWHaLgZ8GDgIWJiyNCaUvTnVWQ9SZgGuRNkjZ6iXDvp9tGVQG3fXP9uiuNQc81dvvwbWAT4SEdsA11HqZ8PnImKkWbcjyczcv1UBakw3AOsBe0fERZnpZ5X+8SgwH+VGkdHq52CbVsc5Pvuobayj9Wcd7V/WUUltEf6tkCTVXUTMpgx43cFzv8QONaPJdpmZa7QsQI3JHPaHiJgbOAw4gDlvpHsQ+ERmfntQ+/cAn6LswdoY2Lyrusa3M/PRtget/4qIC4HNgW9l5ruGef0ySqdEAitm5l0jXGc34CTg9sxcuY0haxDzV28RsQRwKdDYX6zxRT2GPG76kpS/hdNbEJ6aUK1C8ClKru4CbqJ8VplZPfdnSgfveGRmbtPCMDUBEXEGsCNwO/CizBx1q4TqRq+/ACsAZ2XmTu2PUtbR+rOO9i/rqKR2cYBTklR7gwbHYoymQ7/kjtjOL7KdZQ77Q0T8Cng1I+cngf2BnwAnADsNansr8BXgh5n5ZJtD1TAGdSo9DmyamX8b9NpWwG8pObw8MzcZ/ioQEZ8DPg5ckJlbtzdqNZi/+ouIRYCDgW2AFYF5KfuOJXAno9/YM6zMXK2VMWpkETEf5X22WQsu1/hM5OeZHhARO1L2gkvKUvrvB07NzNlD2k0DdqHcqLVa1f5VmfmbzkY8dVlH68062r+so5LaxQFOSVLtRcQsWrxkqV9kO8sc1l9EbAf8hpLHZ4GfAVcDTwHPB94ILEi5a/dM4K3VqbcCnwWOycxnOhy2BomIFYF/AvMDDwPfpSwV9nzgwOr5BA7IzB+Ocp0rgPWBwzLzQ+2OW4X560+DbgBaLzOv7XY8Gl1EzAXsAWzLwODKlpQc/oXxzzwiM7dqZYyamIg4DHgvA59XH6AsB35P9dyywIbAEgzcvPX1zHxfh0PVENbRerGO9i/rqKR2cIBTkiRJkxYRx1IGMR8DtsjMvw55fWXgIsq+nA1HAQdn5uOdilOji4i3UQbGhvuSEMAfgc0zc9j9GSNiQ+Dy6vxdM/OMdsWqOZm//mPHfP2Zw/4REe8FPgcsUD01tNY2OuQfBz6ZmYd1KjaNzPdg/ZnD/mEdldRqDnBKkiRp0iLiH8BawBcy85MjtNkLOI7yRfZ3Ln/ZmyLiDcDXgOWGvHQ68JbMvG+Uc39Ouev+EWBplxvuPPPXXyJiy+rXP3ozSD1VHfMAL7Rjvv4iYmlgH8rssvUoM42gzES6GjgXODoz7+lOhBrKOlp/1tH+Yh2V1EoOcEqS1ISIWIFyp2Fm5v7djkfjZw7bKyIeAhYCds7MM0doszJlz5UE9srMn3cwRI1Dtf/NS4BVKMsMX5GZs5o4b0dgGvBAZl7S1iA1IvMndV5ENGY/756ZNw96vrH/3+0jzZ6WJFlHJUnj5wCnJElNiIh1KXcTZmZO73Y8Gj9z2F4R0ehs2CAzrxmhTVD250xg08z8S6fik6S6qwautwI2o8zQXQA4JDPvHNRmHmAu4Fln4HbWSEsoVs/PBtZ35pHUXdbR3mYdlSSN11zdDkCSJEl9ISgdErNHapCZWcY4gbKviqaYQTcazM5Mv4vUjPnrnojYGTgCWHXIS18F7hz0+K3AN4BHImKFzHy0QyFqYB+xacO8FsM8J6mDrKO1YB2VJI3LcH8wJEmSJKmd7KSqN/PXQRHxNuAUYAblv/19jJyDHwCNJcNf3Yn49F8PVceVuxqFpDlYR2vDOipJGhfvupUkSVIrbRIRS7WiXWZe2KKYJKmWImIt4Mjq4XnAwZn5j2q5vjlk5lMRcQKwP7AdcFxnIhVldvMWwCci4mbg+iF7xbk/UI+LiJuqXzMz1xjm+Yl4zrXUedbRWrGO1px1VFKnOcApSZKkVvrRGK83OiaaaednVUlT3fsotfAaYKfMfKqJcy6idMxv1M7ANIcfAC8HXgr8DWDQsuwBXDPocbPS5aA7akZ1HDqIMoOJc0Cm+6yj9WEdrb8Z1dE6KqkjLPCSJElqFZetlKTW2prSsXd4k53yADdUR5f466DMPDYi1qMMpkwfpol/I3vf0eN8XvVgHa0J62hfsI5K6igHOCVJktQKn+l2AJLUh1aqjleO45xHq+MCLY5FY8jMD0fEEcBWwIrAvMCnKIMr3wHu6WJ4GkNmvmU8z6s2rKM1Yh2tN+uopE5zgFOSJEmTlpkOcEpS6zWWZRtPJ/uS1fGhFseiJmTmbcCxjccR8anq1yMz89ruRCVNadbRmrGOSpKaNa3bAUiSJEmSpGHdXh1XH8c5W1THm1ociybm1uqn2aUxJbWWdbT+rKOSpGE5wClJkqRaiIh1I2J2RDzT7VgkqUMuoOw5tk8zjSNiUeDtlBlL57UvLDUrM2dk5mqZecPYrSW1wQVYR2vNOipJGokDnJIkSaqb6HYAktQh36V0sm8ZEfuO1jAilgROBpYDnqHsVSZJU511VJKkPuUenJIkSZIk9aDMvCIivg68F/hhROwInDCoycsiYkNgc2AvYBFKR/6hmXlLxwPWHKoBkzcALwc2oOzttwjwH+A+4ErgQuBnmXlft+JUERHPtviSmZn2vXWRdbT+rKP1Yh2V1EmRmWO3kiRpiouIdYGrKR+up3c7Ho2fOaw/c1h/5rDezF93REQA3wTeQel0H7FpdTw8M9/f9sA0qohYEPgSsB8w3+CXBv0+OJ9PAD8EPpaZj7Y/Qg0nIma3+JLWyx5gHa0n62g9WUcldZJ3P0iSNERELDnMnZ+PUu4K9c6gGjCHkqR+keWu5HdGxMnAR4EtmXO7mQT+AHwuM8/scIgaIiLWBk4D1uC5HfGzKTOOHgEWAhZmIJfzA+8Eto+IXTPzn52LWEMkZaDkLOChLseiFrCO1o91tPaso5I6whmckiQBETE3sCuwN7B9Zs43xinqMeaw/zl7rP7MYb2Zv94QEQsDGwHLANMpy/P9NTPv7WpgAiAiVqQMkqxI6ZS/G/gxcColT08MajsfsCHl88u+lH3/AG4FNs/M2zsXueA5M48anfOnAccCv8nMVi+7qC6xjvY262i9WUcldZIDnJKkKS0iNqMMiO0JLNZ43o7b+jCHU4eDK50XEedVvx6bmT9uwfUWBf4HIDOPnuz1NDrzJ3VeRJwO7FQ9/AHwgcx8uInzFgK+ChxA6RQ+MzN3blugGlZEvIzyuXIPYHEGVv64F/gpcExmXt6l8KQpwTpab9ZRSZ3kAKckacqJiFUpH7jfTFnyBgaWvZkNXJyZM7sQmppkDqcmBzg7LyKepiz7tW1mnt/teDQ+5k/qrIjYgoHl8A/LzA9N4BpfAT5YXWPLzLy4tVGqGdXKIDtTPm/uCMzDQCf9P4FjgOMz81/diVDqT9bR/mEdldQJDnBKkqaEiFiEcgfh3sDmlMGwxoDYM8DvgBOAEzPznq4EqVGZQznA2XkRcTtlqa+NM/OKbsej8TF//SEipgMvAp5HmQmxMGX/sfspHYR/zczZI19BnRIRXwfeBfwDWG8iS/FFxFzAVcDawJGZ+e7WRqnxioglgNcDbwJeWj2d1c/vKEsv/iozH+lOhBqLdbQ+rKP9yToqqV0c4JQk9a2ImAbsQBkQ2wVo7MnYGBRL4HDgC5l5X+cj1FjMoQZzgLPzIuLXwPbAXpn5827Ho/Exf/UWERsCHwVeBSwwStNHKPtbfTkzr+5EbBpeRFwJvBD4SGZ+dRLX+QDwf8DVmblBq+LT5EXEGpTPpW8EVq+ebuwzdwpl6cXfdCk8DWEdrR/raP+zjkpqpWndDkCSpFaLiA0j4jDgdsoX1T2A+SmDYn8DPjKo+WUOjPUecyj1jB9Q3ndv73YgmhDzV1MR8SXgL5S/fwsysGrBcD8LA28A/hoRh3YlYDWsUB3/PMnrNM5fYdRW6rjMvDEzP5WZawKvAL4PPET5nPp6ymoi6gHW0dqyjvY566ikVpqr2wFIktQKEbEcZbmTN1Pu+ISBWX53AD8BjsvMq6r2X+54kBqVOZR6T2aeGBHHAW+KiB8B78rMR7sdl5pj/uopIr4H7M/A38AHgUuAy4H7KDONFgaWAjYCXgYsWrX9eEQsmZkHdTRoNSxcHR+a5HX+M+R66kGZeXFE3AjcChxCWWkkRj9LnWAdrTXr6BRiHZU0WQ5wSpJqLyJ+A2xDWZmg8WH4YeBE4DjgvHRN9p5mDqXeFBF7A78F1gf2AXaLiNMo+xo9AIy6L1JmHtP2IDUi81c/EbE78FbKUm0PU5ZWPCozHx/lnPmB/YAvUDpyD4yIszLzlA6ErOe6F1geWHaS12mc7woVPSgiFgBeQ7kpb2ue+/nV/Y67zDpae9bRKcA6KqlVHOCUJPWD7arj08DZwPHAyZn5RPdC0jiZQ6k3HUXpIGxYnNIR0YwEHCDrrqMwf3Xzmep4L7B5Zt4w1glVp/2REXE28HvKjKRDKftYqbNmUTrmtwYms3/Y1oOupx4QEQG8klJD/4eyn2OjM/5flBvyjs3Mf3QnQg1iHa23WVhH+5J1VFI7uAenJKlfJGUmyuPVzzPdDUcTYA41ltuAt1DusFfnDN6jaujjsX7UfeavJiLi5cDzKX8P92umU36wzLyegfq4bkS8rMUhamxnUd47b4mIpSZygeq8/Sj/H5zVwtg0ARGxQUR8lfIZ5ExgL8p+jo8APwa2zsxVM/MQO+W7zzraF6yjfcY6KqmdnMEpSeoHv6NsTj8f8Nrq54GI+CVlz8bfdzM4NcUcakyZ+RBwdLfjmGJW63YAmhTzVy/bVserMvOMiVwgM0+PiKuA9SirI1zSquDUlOOBTwBLAD+NiF3GsxpFRMxL2XN8CcqqFse1JUqNbcDVwAAAIABJREFUKiJWAN5ImWW0buNpyo14ZwPH4kojvco6Wn/W0T5gHZXUKQ5wSpJqLzO3iohVgL2BNwHPo3yhOQA4ICJmUb4oHZ+Z/+xaoBqROVRDRCwDrAOQmRd2OZwpLzNv6XYMmjjzVzsbU2abnD7J65xG2Xd140lHpHHJzJsi4lvAeyjLI/4+Ig7IzL+MdW5EvAj4HrAR5f+Db2fmTW0NWHOIiHOBLZlzP7hjgZ9k5j3dik1NsY7WnHW0/qyjkjopMnPsVpIk1UhEvATYB9iTMkgGA3uQ/YUyUPb/qufekJm/6HiQGpU5nLoiYh/KUkWzM9Ob8SRNGRFxHbAG8JrMnPC+bxGxG3AScGNmrtWq+NSciJiHsm/cTAY+u/yBMmByOXAXZVm+hYDlKB3xuwKbNS5BWdli+8x8qmOBC4CImF39+jhwKmUv4r9N5pqZeetk41JzrKP9wTpab9ZRSZ3kAKckqW9FxNzAzpRZgTsBc1cvDf7j9w3gc5l5b4fDUxPM4dQzaIAzM3N6t+ORpE6JiLuBpYCXZ+aEl0Ss9oy7GLg3M5dpVXxqXkQsBPwI2L16qpmOl8YslxMpewf+px2xaXRVx3wrO8rSG7Y6xzraP6yj9WUdldRJ07odgCRJ7ZKZT2fmSZn5amB54N3AnylffBpfft4F3BERv42IgyJiuS6Fq2GYQ6l3RMRaEXFoRJwbEddExI0RseaQNi+MiJ0iYstuxanhmb9aWLQ6PjTJ6zQ6dBeZ5HU0QZn5SGbuCbyesixfNPHzV8qqFLvbKd91zeRrPD/qHOton7CO1p51VFJHOINTkjTlRMQ6lOVP9wJWrp7OQcdLMvMV3YhNzTGH/csZnL0nIqYBX6HshTR4L50E1svMawe13Ymy79UzwGqZeXuHw9UQ5q8+Bs14eE5eJnCddYGrsY72jIhYD3gFsAFldtnCwMPAvcBVwIWZeVX3IlRDRHyq1dfMzM+0+poannW0f1lH68M6KqmTHOCUJE1ZERHAVpTlT19D2cMD/CJbG+awd0TETS261EKUTgtz2CMi4vvAfpSBsdspeyDtzggdiBFxIzADeH9mfr2z0Woo81cfdsxL0uRYR6X/z969R0tSVgf//+4ZFDIwILdRAWEAMQiCQsAbotwMAQkRf4go6ghIImAiwWU0yetdf+uN0cjlNWrUAAajGI0YRBIxOFwMCrxcBRFBASEGZLgPCszMfv946jg9Z86l+5zurq46389avepMnapae81evU937XqeR5LmFuevliQ1XkS8ufrx3F6mosnylM9FwEURcQKlQbaE0jDTEJnDVlhMuaE02ymEfPpuhETE/sCxlLz8/8D7M3NldQNxMv8C/AWwH2CDrEbmr7H2jIjNZnH+tn2LRCOno/GyyjXJpElZRzUp66gktYdFXJLUBmdSbt5eBaz1pG5EbA4cD5CZH5roApn5GHA2cHZEbDGwSDWZMzGHbfEosGwW528AbNqnWDR7f1xtv52Z/6vLc66otjsPIB71xvw10z/WHYAawTXJGsrmylBYR9UN62hDWUcljbEASJLmgkXABygNtAmbY50y878HHZB6Zg5H3+3ANsD3M/OgmV6kYw1OjYaXUN53X+jhnLuq7TP6H456ZP6ax5ut0tzh+30w/H+V5g7f79IcZ4NTkiRJ/XAlZZraPWqOQ/21qNre3sM5T1Zbv2vUz/w1ywfrDkCSGs46KknSHOKXVkmSJPXDFcBrgU0iYrvM/FndAakvlgNPAzbv4Zytqu39/Q9HPTJ/DZKZ3piXpFmwjkqSNLfMqzsASZIktcIVHT/vWVsU6rexRvVOPZwzNkXxjX2ORb0zf5IkSZKkVrLBKUmSpH64GrgYuARYOIvrXAYcDRzTj6A0a9+hrG1zYkRM+90hInYC3kJZ9/Hbgw1NXTB/WkNE7BwRqyJiRd2xSFITWUclSRodNjglSZI0a5m5PDP3rV6fn8V1bsvMszLzrH7Gpxk7jTLN6fbAZyJi0iUuIuKVlIbaepTpTT83lAg1FfOnyUTdAUhSw1lHJUmqmWtwSpIkSZpQZt4TEW8DvggcCxwYEed3HPKOiAhgL2BHys2+VcBbMvPRoQesNZg/SZIkSVJb2eCUJLXJCRFx7wT7F439EBHv6+ZCmfmhvkWlXphDacRk5pci4kngs8CzgD+hTGEK8NZqOzaK4VFgSWaej0aC+ZMkSZIktVFk5vRHSZI0wiJiFatv1vZFZs7v5/U0NXMojb6I2BQ4AfhD4AWs+bDkjcC/Aadm5kQPKahm5k9Q1o4DbgDSv5PNZA6bzxw2m/lrPnPYfOZQ0hhHcEqS2qKfa6D49E89zKE0wjJzGfBh4MMRMQ/YBJgP3J+ZT9YanKZl/iRJkiRJbWKDU5LUBvvWHYBmzRy2WERsBhwF7A1sByykNFamkpm5/aBj08xk5irgvrrj0MyYP0mSJElS09nglCQ1XmZeXHcMmh1z2F4R8Xrg05SmJnQ/UtdRuJIkSZIkSZqQDU5JkiQNRETsB5zN6qbmHcD1wIPAqrri0sxExHzgj4ADgF0oU5wC3A/8CPgu8M3MXFFPhJqK+ZNa4y7g6LqDkKQGs45KUktEpg/HS5Ikqf8i4juUZsqDwFGZeUHNIWmGIuJQ4P8AW3burradXyh+Cbw9M88dVmyanvnTmIjYGbiBMg34dFOFSxqAiNgIeDVAZp5VczjqkXVUqp91VNIYG5ySJEkaiIh4ANgQOCkzT687Hs1MRLwD+Luxf1IaYrcD91T7ng4sZs2G2Tsz85ThRanJmD918sZ8M0TEImBHgMy8pOZwJHWwjjZDNXPFlgCZeWfN4UiSBsQGpySp8SLi5f2+pjeThssctlNEPARsAOyZmVfXHY96FxEvAr4PzAMeBj4KnJGZ9407bjPKVF9/BWwErARelpk/HG7E6mT+NJ435pshIpYAZwCrMtOlhRrIJnV7WUeHIyIWACcDhwPbUz6b/Bg4B/hMZv5mmvPH8mQdbSib1JK6YYGXJLXBUtacYm+2Ev9GDttSzGEb3QY8H1i/7kA0YydTmmMPAXtl5k0THVQ1zP42Ir4F/Bdl5O7JwOuGFagmZP6kZovpD9GIOoiqSY2fSaWeRMSzgAuBHcZ2VdsXVq+TImJJZl7czeUGEKK6MNsmNeUhkRuwjkqawry6A5AkqU+izy8Nnzlsn69QcnFg3YFoxvamPDDwN5M1xzpl5o+Bv6Hkve8js9Uz8ydJ9fIzqdSDatTe14DnUN4/TwJXURpdK6t9WwP/GRHvrCtOTa1qUl8NfBDYhfLA64aUBvUngJsj4hXdXm4gQUpqBZ9+kCS1wQfrDkCzZg7b6e+BN1Oesj43M6+qOyD1bONq+70ezhk79ml9jkW9M38a7y7KdMSSpJmxjg7W4cCelAe0/gU4ITPvB4iIzYG/AN5Buaf9sYjYPDPfU1ewWtu4JjXAE8D1wLrAcym5G2tSvzszP1FLoJJawQanJKnxMtPmWMOZw3bKzEcj4mDgX4FLIuKTlCmJbuliSiKNhl8C28ziXNXL/GkNmfkQcFbdcbRVRPysT5faoE/XkdRn1tGBO7LaXgm8PjN/u4xJZv4KeFdEfI3S/Nyq+vfTMvNtww9Vk7BJLWlobHBKkiRpYDLzzohYAlwEvKd6ETHtTEOZmX5Wrd93gWOBVwA/7PKcfartRYMISD0xf3NQRCyirFtFZl5SczhzzWLKDd3ZTqfXz3XJ1QOb1ILfjkDbEspn2ZrDmWv2oNTA0zqbm50y84cRsSdwPrA7cFxELATelJmrhheqJmGTWtLQxCR/KyRJkqRZi4h3AB+nrP3eyw3fzMz5g4lK3YqI3wX+L2VqqRdn5i3THP8c4AfAU4A9MvMng49SkzF/c1P1UMkZwCofFBmuiFhFuTG/HFg2i0ttAGyKfwuHriOH/WhSB+awFhGxADiZMpJse8rajT+mzCTymelmEomInSlrPlpHhywifg08lfI55Jppjt0A+DfKw1lZ/XxEZj7ZkUPfg0MWEb8AtqA0nP95iuOezuomdQJfoaNJbQ4ldcM/0pKkxouIrasf787MlbUGoxkxh+1UTU/7yeqfq4BLgeuAB6t/a8Rl5k8i4nDgn4EfRMSHgC+OTTM1JiI2pqy3+t5q1xE2x+pn/ua82TZo1LvbKdNCfz8zD5rpRTqa1KrPo/SnSa0hi4hnARcCO4ztqrYvrF4nRcSSzLy4m8sNIERNbew7wnrTHVgth3EQZSTgIcChwLci4tUDjE/T26za/niqgzLznojYh9VN6iOBBRFxRGY+OdAIJbWGIzglSY1XPWm9Ctg1M2+a4PcLKFPdOFXbiDKH7RQR36NMjXk3cHBm3lBzSOpRRIxNU7ol5UZhVq+fA/dWPz8d2JbVNwFvpeR8MpmZ+w8kYK3B/M1NHc0xRzwMWUScA7wWWJaZm8/iOuawJtUUtdsA3+lTk9ocDlE1tex/Udb/gzKDwfXAusBzWT3QYxXw7sz8xCTXceRYTSLiFsqo26Mz84tdnjMfOBt4HeWzzfeBd1dbczhkEbGc0qB+WWZe3sXx67K6SZ2UJRZeDWyH70NJ03AEpySpLaZ6unZbYCnli6x/+0aXOWyfXSlfUt9nc7Ox9mHNteCiem1fvSby7Oo1/j392+n6+huiprAP5k8apisoDc5NImK7zOzXeo4anispa6nuUXMcmpnDKc3NpDRMThibtSAiNgf+AngH5fvExyJi88x8T13BakLXUz6j7A901eDMzJUR8QbKyOtjgb0o0xGrHndTcrgDMG2DMzMfj4jDWN2kPgD4D0qTWpKm5A1CSdJc4hRDzWcOm2XsSdtra41Cs3EJNrSazPw1SDVyrB826NN11LsrOn7eE7DB2Tw2qZvtyGp7JfD67Ji2LjN/BbwrIr5GaX5uVf37aZn5tuGHqkksBV4DHBYRJ2Tm8m5OqnJ9XEQ8ApxEmb1C9bBJLWlobHBKkiRpUH4K7A5sXHcgmpnM3KfuGDRz5q9xFrN6pOxs2NSuz9XA2Lp+C2dxncuAo2cfjmbAJnWz7UGpgad1Njc7ZeYPI2JP4HzK59TjImIh8KbMdI34+p0HnAqsDxwPfLyXkzPz5Ih4GHjfAGJTd5Zik1rSkNjglCRJ0qB8Gfg9yhoqF01zrCSpeBRYNovzNwA27VMs6kF1E3ffPlznNuC22UekGbBJ3WybVdsfT3VQZt4TEfsA/0aZzv1IYEFEHJGZTw40Qk0pM++IiI8Cz2SGf8sy8wMRsYzSZNPw2aSWNDQxyQNNkiQ1RkSsojypu0tm3jTB73fGxelHmjlsp4h4CnApsBtweGaeV3NIkjSyqilqtwG+k5kHzeI6S4Az8G+mpDkmIpYD6wEvy8xp1/6LiHUp09UeQvku8l3Kg3nb4XcPacYi4kOUJvV9mfmXM7zGn1I1qTNz1g8PSWonR3BKkiRpUJ4JHAf8A/CNiDiHspbKLcBj052cmXcONjzNRERsBTwDWABcmZm/rjkk9cD8jbQrKdPU7lFzHJLUVHdT1v7bAZi2wZmZj0fEYcDZwOuAA4D/AN49yCCltsvMWY++zMzTgdP7EI6kFrPBKUmSpEG5ndVrwQVl+q8juzw38bPqyKjWpvoL4C3AFh2/2gW4qeO4IylPWj+UmccNM0ZNzvw1xhXAa4FNImK7zHTtP0nqzfWUBuf+wBe7OSEzV0bEGyjTgx8L7EV5IE+SJI04bxpJktpkz4jYbIL92479EBF7UxotU8rMS/oZmLpmDtsnJvlZDREROwDfpkzX1pnDida6+AFlFERExFmZedkQQtQUzF+jXNHx856ADU5J6s1SyoM6h0XECdW6uNPKsn7XcRHxCHASsOXgQpQkSf1ig1OS1Cb/OMXvxm7kLu3iOo4cq485bJej6w5AsxMR6wHnU0ZDLAc+BVwCfGui4zPz9oj4HrAfcChgg6xG5q9xrgYurn5eOIvrXIb1d+RUD3AdBexNeeBgITDd2n6ZmdsPOjapRc4DTgXWB44HPt7LyZl5ckQ8DMx6ek31n3VUkjSeN/4kSW3hyLDmM4ctk5ln1R2DZu144NmU5tjemXktQMSUb9cLKFPDvWTg0Wk65q9BqpFG+/bhOrcBt80+IvVLRLwe+DSrG9fdfuaZaKS1amJzZfRl5h0R8VHKOvCbzvAaH4iIZZSRoBoR1tF2sI5K6jcbnJKkNvhg3QFo1syhNJpeQ7kxdOpYc6wL11XbHQYTknpg/qSaRcR+VFM/V7vuoKwT+CCwqq641BubK82RmbMefZmZpwOn9yEc9YF1tB2so5IGwQanJKnxMtPmWMOZQ2lkPbfafqeHc5ZV26f1ORb1zvxJ9XsP5Sbug8BRmXlBzfGoRzZXpNpZRxvOOippUGxwSpIkSZrMBtX20R7OWbfaPtnnWNQ78yfVb0/K6JP3e1O+sWyuSPWyjjafdVTSQNjglCSpCxGxM3ADsCoz/fvZQOawXhGxEDgAeD6wGfA7TD0tUWbmscOITVNaBjwDWAxc3eU5O1fb/xlEQOqJ+ZPqN6/afr/WKDQbNlekellHm886KmkgvLknSVJvul0nQqPLHA5RRMwD3gu8E1i/29MoX4BtcNbvauBg4OXAv3Z5zpsp+bt8UEGpa+avJSJiM+AoYG9gO8r6VfOnOS0zc/tBx6Zp3UZ5uKfbv4EaPTZXWsA62mjW0eazjkoaCBuckiRJGqQzKTeTAlhJGVG2iNJAuQvYmNXTaCZwH/DY0KPUZL4GvAr444j4u8y8c6qDI+IkSjMtgS8PIT5Nzfy1QES8Hvg05WY8dP+gTg4mIvXoK8ALgAOBS2uORTNjc6XhrKONZx1tPuuopIGwwSlJkqSBiIgDgTdSbg6dSRnFuSVwPUBmblMd97vA8cCJwAPAqzPz5hpC1tr+CfhzYFdgaUScCPx7x+8zIgLYAzgJOJKS70udfmokmL+Gi4j9gLNZfTP+DkoNfRBYVVdc6snfU0ZGnxQR52bmVXUHpJ7ZXGkw62grWEebzzoqaSAi04eRJEmaTsf6jZmZ001lpBFkDocvIr4CHAH8KDN3rfZNmoeI+EPKNJq/AHbLzIeGHLImEBFbA5cBW1GaX48BC6pf30cZDbHu2OGUJ7T3ysx7hxyqJmD+mi0ivkNZv/hB4Cgbz81UvQ//FdgJ+CRwDnBLZv6m1sDUlYjYAPgBZT3jfWyuNIt1tB2so81mHZU0KPOmP0SSJEmakRdTGiqf6ubgzDwPOAvYBvizAcalHlTTmr6AMmXpKsrUUlG9NgfWY/WoiK8CL7Q5NjrMX+PtSamj7/emfHNV78MlwCPAe4BrgOURsXKa14paAxcAmfkoZT3jm4FLIuKjEbFrRKxXc2jqjnW0BayjzWYdlTQojuCUJKkLjv5rPnM4fBHxGGVk2AGZ+b1q347ATZQbTQsy8/Fx5/wB8G3g2szcfcghaxoRsQ1lTcc9KGupzqesq3oNcF5m3lJjeJqG+WueiHiIsk7xnpl5dd3xaGYi4h3AxykPmXe79h/4mWWkVJ8lLwI26+G0zEyXh6qRdbQdrKPtYB2V1G8WB0mSJA3a/R0/P9Lx8yLKdLSdxkaOLR5kQJqZzLyDsg6SGsj8NdJtwPMpI2/VQBFxMGU6RSijqC8FrsP1/xplFs0V1c862nDW0XawjkoaBBuckiRJGpR7gK2BTcbtewJ4CrArazc4t6m2TlckSfAVyhTDB1Ju6Kp53lVt7wYOzswb6gxGvbO50njW0eazjjacdVTSoNjglCRJ0qDcQGlw7gR8DyAzV0TENcALgaOB88edc3y1vWNYQao3EbEV8AxgAXBlZv665pDUA/PXOH8PvBk4KSLOzcyr6g5IPduVMi37+7wp31g2V5rNOtp81tHms45KGoh5dQcgSZKk1lpKmX7ogHH7z672HxYRZ0XEqyLiiIg4vzo2gW8ONVJNKSIWRsSHI+IXlObzDylN623HHXdkRHw1Ij5XR5yamPlrrsx8FDgYuBm4JCI+GhG7RoSj3JtjbO23a2uNQrNhc6XBrKOtYB1tPuuopIGIzKw7BkmSRl5E7EwZjZaZOX+64zV6zOHwRcS2lHWPHgcWZ+Y91f51gB8Au1O+6K5xGqUBs3tmPjDEcDWJiNgB+DawHWuul5PALpl5U8exi4Fbq+NekZmXDS9STcT8tUP1N+wiYLMeTsvMdNammkXElZS/d6/MzIvqjke9i4gHgYXAHpl5Td3xaGaso81lHW0+66ikQXEEpyRJkgYiM39Oaao8D3i4Y/8K4JXAl4AVlGbKWOPlfGBvm5ujoRrdcD6wPfAY8DHgkMmOz8zbqaYjBg4ddHyamvlrh4h4B2XUymasrpfdvlS/L1Ny8eq6A9GM/bTablxrFJox62jjWUebzzoqaSB8CkmSJEkDUzVMJtr/APCmiDgB2IHyufTWzLx/iOFpescDzwaWUxrP1wJETHm/7wJgf+AlA49O0zF/DRcRBwOfrP65CrgUuA54sPq3Rt/pwBHAn0TEhZl5Xt0BqWdfBn6P0lxx9FjDWEdbwTrafNZRSQNhg1OSpO7cBRxddxCaFXM4gjLzEeDquuPQpF5Dmcr01LHmWBeuq7Y7DCYk9cD8Nd+7qu3dwMGuW9VIzwSOA/4B+EZEnAOcA9xCGVk9pcy8c7DhqQs2V5rNOtp81tHms45KGgjX4JQkSZI0oYi4jzKV1L6ZeUnH/lVMsIZj9bsXUJrWT2TmesOMV2syf80XEcuApwFvzcwz6o5Hvet4v0GZYrGXmzCu/zcCImJrYCNKc2VPSmPF5kpDWEebzzrafNZRSYNigZckqRIRi4AdATpvBKs5ImI+sCX4JWjURMQ8YF/KtJfPABYAf52Zv+w45qmUz6crM/PxWgLVeBtU20d7OGfdavtkn2NR78xf882vtt2OwNVoikl+VjPczprNlSOrVzcS773VzTraDtbRZrsd66ikAbA4SJK02kHAGZS1WPwbWYOIWACcDBwObA+sBH5MebrzM5n5m2kusSNwA+ZwpETEIcBpwDbjfvVx4Jcd/34rZfqiRyNii8xcPqQQNblllIb0YrqfSnjnavs/gwhIPTF/zfdTYHfKSFw1k9Pjt4PNleayjjafdbQdrKOS+s4bf5Ikrc0P2zWIiGcBF7J63bexPLywep0UEUsy8+JuLjeAEDUDEXEc8BlW5+Q+YDMmnlrq88BHKNMXHQacPYwYNaWrgYOBlwP/2uU5b6bk9/JBBaWumb/m+zLwe8CrgYtqjkUzkJln1R2DZs3mSrNZRxvOOtoK1lFJA+EanJIkVSJiCWUEZ2bm/OmOV/9UU8v+F2U9DoAngOspUyU+l9UPZa0C3p2Zn5jkOjtTRnCawxEQETsAN1KmBvse8PbMvHma9f8+BxwLnJ2Zbx52zFpTR138DbDj2NTPk+UwIk4C/q763SGZecHwo9YY89d8EfEU4FJgN+DwzDyv5pAkqVGso5Iktde8ugOQJEmiTEm7J+Wm+leBLTLzhZn5fMqamp8AVlA+u3wsIv53bZGqF39OaU7fCBycmTd3cc6l1Xa3gUWlXvwT5WGD9YClEXFQRHSOkM4o9oyIL1HeqwlcanNsJJi/5nsmcBxlNO43IuJLEXFoROwYEVtP96o5dkkaBdZRSZJayhGckqTGi4if9elSG1BNnenov+GKiG8AfwRcAbwkJ/iAEhEvAv4F2IpyA/5zmfm2ccc4gnOERMTNlCmHj8vMf+zYP9UIzpcClwEPZ+bThhmvJlbd3LuM1e+9x4AF1a/vAxZSRltDmYr4NmCvzLx3yKFqAuav2TrqJZT89PIFPjPTZWkkzWnWUUmS2ss/0pKkNlhM+aI623UXfeqnPntQ/v9Pm6i5CZCZP4yIPYHzgd2B4yJiIfCmzFw1vFDVg62q7XU9nLO82i6Y8igNTWbeGREvAE4HjgDW7/j15p2HUkZgH5+ZDwwxRE3B/LVCTPKzGqb63HIA8HzKQ3W/w9Q5zcw8dhixSS1nHW0J66gkqZMNTklSmzwKLJvF+RsAm/YpFvVms2r746kOysx7ImIf4N+AfYAjgQURcURmPjnQCDUTY83qXpqVY+/Bh/oci2YhM+8HjoqIvwJeRXkoYRFlfdVlwDXAeZl5S31RajLmr9GOrjsAzV5EzAPeC7yTNR8ymPI0yt9Rb8yPEJsrjWQdbQHraHtYRyX1k1PUSpIar5qidhvgO5l50CyuswQ4A6c3HbqIWE5ZI+5lmXl5F8evS5mu9hDKl9bvAq8GtsMpakdGxxS1x2TmWR37p5qi9n3AB4ArM/NFQwxXE4iIDTPz4Rmeu09mLu1zSOqB+ZNGQ0R8ETiKcgN3JeXBgkWUv4V3ARtTHrSj2ncfZTppMnPbYcertc2mueJnUmn2rKPNZx2VNAjz6g5AkqQ+uJLywXePugPRjN1dbXfo5uDMfBw4DDiHkvsDgP8ANhxIdJqppZT8LOnm4IjYCHgb5abERYMLSz04PyLW6/WkiDgA+NYA4lFvzJ9Us4g4EHhj9c8zKTfkDxj7fWZuk5kbAs8FTgNWAQ8AB3lTfqScCbyP0kBZBfyK1SOO7qJMsR8d++4D7gDuHGqUUgtZR1vjTKyjkvrMBqckqQ2uqLabRMR2tUaimbq+2u7f7QmZuRJ4A/AFypegvSgNT9UgIlZFxIqI2Klj92cpzcpXRMRbpjl/U+Bc4BnACuAzg4pVPdkL+HpEdL20RUT8PmUa6d8ZWFTqlvmT6jc2PeaNmXlMtcbtWlNpZeZPMvMk4DXA9sC3qwd/VDObK1LtrKMNZx2VNCg2OCVJbXBFx8971haFZmMppUl5WER0O10NWRwHnFKdv+VgwlOX1lg7JTOvAU6t9n8hIs6JiCM6DnlpRLwhIj4F3Aq8nHKz4sOZecewgtaUVgJ/AHyxm4Mj4mBKo3o94L8HGJe6Y/6k+r2Y8rftU90cnJnnAWdRll/4swHGpe7ec0VWAAAgAElEQVTZXJHqZR1tPuuopIHo+kleSZJG2NXAxdXPC2dxnctY/cFbw3UepRG2PnA88PFeTs7MkyPiYcqUNxot7wTWpeT18Oo19mX2sx3HjTVHT8nMjwwvPE3jGMpT1q+LiIcy8/jJDoyIPwS+Ssn3XcB+Q4lQUzF/LRIRCymjHZ4PbEYZZRtTnJKZeewwYtOUFlXbWzr2rRz7ISLWrabe7/Q1yvv3MODDgw1PXei5uRIRZ1G+V/wZ5nBkWEcbyzrafNZRSQNhg1OS1HiZuRzYtw/XuQ24bfYRqVeZeUdEfBR4JrDpDK/xgYhYRnnaUyMiMxM4MSLOBd4DvIK1ZxFJ4HLgI5l5wZBD1BQy858iYhPgk8AfR8QDmflX44+LiD+iTBH9VMo6Oftl5s+GG63GM3/tEBHzgPdSHhjpdpaDoNRWb8yPjvs7fn6k4+dFwC/GHXtvtV08yIDUNZsrDWcdbQ3raHNZRyUNhA1OSZI0EjJz1qMvM/N04PQ+hKM+y8wLgQurJ+d3o3zJnQ8sA67NzPvqjE+Ty8xTqybZe4F3R8SyzPzE2O8j4v8D/hl4CnA7pTl2ex2xam3mrxXOBI6i3GxfSambiyg33u8CNgY2qI5N4D7gsaFHqcncA2wNbDJu3xOU992urH1jfptqu97Ao1MvbK4015lYR5vMOtoe1lFJfeUanJIkSRqazHwkMy/JzK9l5jmZ+V2bm6MvM99PmVIqgI9FxDEA1ZqqY82xnwH72BwbPeavuSLiQOCN1T/PpNwAPGDs95m5TWZuCDwXOA1YBTwAHJSZ2w43Wk3ihmq709iOzFwBXFP9c6LlEcamk3Y96tFwT7WdqLkCpbkyns2VEWEdbQXraPNZRyUNhA1OSZIkSdPKzD+lNMMC+GxEnA6cTWmO3Qrsm5l31hiipmD+Gmvspu2NmXlMZj7A6nWMfyszf5KZJ1Gmad8e+HZEbDTEODW5pZT33QHj9p9d7T8sIs6KiFdFxBERcX51bALfHGqkmozNlWazjjbfUqyjTWcdlTQQUZZFkiRJkmYuIlZRbiJcBSzvwyUzM/fvw3XURxExHzgXeBUl3wH8lDKt6d11xqbpmb/miYjbgWcBJ2TmZ6t9O1NuFGZmzp/gnM9TbhR+IDNds6pmEbEtZY33x4HFmXlPtX8d4AfA7qzdbAnKDd3dq2aMahQRJwMfB76ZmYd17D+RsjRCUhotX6Ws77gEOKja/7eZ+Z6hB63fso42n3W0+ayjkgbFBqckqdUiYjPKeit7A9sBCynr/k0lM3P7Qcem7pjDZuhocPblckxyw0n1i4j1gH8HXg7cTGmO/U+9Ualb5q9ZIuIxYF3ggMz8XrVvR+AmSs1dkJmPjzvnD4BvU9Y33n3IIWsCEbGY8tnlvzPz1x37N6ZMiXkEZTQ1lLx+Gzg+M+8abqSaiM2VZrOOtoN1tNmso5IGxQanJKm1IuL1wKcpDTEoH5C7YWNlRJjD5uhocHabo+mYwyGKiIt6PGUh5UbEjcBka6g6CndIzF97ddyY3z0zr6v2bQn8glJzF2fmL8adsztlNP2DmbkJGnkRsRDYAVgHuDUz7685JI1jc6W5rKNzg3V09FlHJQ3COnUHIEnSIETEfqxekwPKk3/XAw8Cq+qKS90zh431vMy8qe4g1LN96K1BPXbszhOcM/Y7n6Qcnn0wf211D7A1sMm4fU9QbgLuSrlJ32mbarvewKNTX2TmI8DVdcehyWXm7ZPsfwB4U0ScgM2VUWUdnQOso6PPOippEGxwSpLa6j2UG7QPAkdl5gU1x6PemUNpeC7BhlaTmb/2uoFyY34n4HsAmbkiIq4BXkhZI+78ceccX23vGFaQ0lxnc2WkWUelBrCOSpoJG5ySpLbak3Kz9/02xhrLHEpDkpn71B2DZs78tdpS4BDgAOBTHfvPBl4EHBYRZwFfBdYHllTHJvDNoUaqaUXEPGBf4CXAM4AFwF9n5i87jnkq5V7NyvHrAkqakaVYR1vDOipJ6uQanJKkVoqIh4ANgD0z06cAG8gcNkvHGpy7OEWtJPVHRGwL3AY8Tlkn7p5q/zrADyhrqY7/Uh+UUUe7V9O+aQRExCGUNca2GferNf5uVlP0nQ48CmyRmcuHF6WmY3Oleayj7WEdbQfrqKR+mld3AJIkDcht1Xb9WqPQbJhDSdKclpk/B7YDngc83LF/BfBK4EvACsrN+LH1VM8H9vam/OiIiOMoI8EWU/K0jMnXzP08MPaQ12HDiE/dqZortwLfAT5ImcZ0CbDxuEPfCjwC3BsRfo6tmXW0Hayj7WAdldRvNjglSW31FcoXngPrDkQzZg4lSXNeZt6embdl5q/H7X8gM98EbAbsAbwY2Dwz/zAz76ojVq0tInZg9bSYFwE7ZeaiyY7PzCeAr1M+A/3+4CNUN2yuNJt1tNmso+1gHZU0CK7BKUlqq78H3gycFBHnZuZVdQeknplDaUgiYutBXDcz7xzEdbUm8ze3ZeYjgFO5j64/p9x7+RFwcHXjfTqXAscCuw0yMHVngubK2zPz5mp6/rVk5hMR8XVKDn+fstajRph1dORZRxvOOippUGxwSpJaKTMfjYiDgX8FLomITwLnALdk5m/qjU7dMIeNs221vbvWKDRTPx/ANRO/bwyL+ZNG136U99MpXd6UhzJ9H8CzBhOSemRzRaqXdbT5rKOSBsIvrJKk1srMOyNiCeUJwfdULyImmwWl89T0b+QIMIfNkZl31B2DZmXaN5VGmvmbAyJiHrAv8BLgGcAC4K8z85cdxzyV8j1/ZWY+XkugGm+rantdD+csr7YL+hyLZsbmSktYRxvLOtp81lFJA+GNP0lSa0XEO4CPU9ac9uZvA5lDaWiOrjsAzYr5a7mIOAQ4Ddhm3K8+Dvyy499vBU4HHo2ILTJzOapbVttebrJvWm0f6nMsmhmbKy1gHW0062jzWUclDYQNTklSK1VTm36y+ucqyvQm1wEPVv/WiDOH0vBk5ll1x6CZM3/tFhHHAZ9h9YM+9wGbsfqGb6fPAx8BNgIOwzWrRsHdwA7AdpTPMt14WbX92UAiUq9srjScdbTxrKPNZx2VNBA2OCVJbfWuans3ZY2HG+oMRjNiDiVJc1pE7AB8qvrnRcDbM/PmiJjwQZ/MfCIivk5Zs+r38cb8KFgKPAdYAkz7MEJEbAS8jXIz+KKBRqZu2VxpMOtoKyzFOtp01lFJAzGv7gAkSRqQXSlfaN5nY6yxzKEkaa77c8qDyTdSHva5uYtzxm4c7jawqDShiFgVESsiYqeO3Z+lfJ55RUS8ZZrzNwXOpawNuIIy4kz1W0oZ+bekm4Ntrowc62iDWEdbaynWUUkDYINTktRW86vttbVGodkwh5KkuW4/ys29UzLziS7PubXaPmswIWkaa6wZnpnXAKdW+78QEedExBEdh7w0It4QEZ+i5O7llJx/ODPvGFbQKmyutJJ1tHmsow1mHZU0TE5RK0lqq58CuwMb1x2IZswcSiMkIgJ4AfB8yrpVv8O4G1DjZeaHhhCaumD+GmurantdD+csr7a9rHOlwXonsC5wPHB49Rpbj+yzHceNvSdPycyPDC88jbNWcyUiTgVOojRXDgK+3nHISyPiBcBewBuADbG5Mkqso+1gHW0W66ikobDBKUlqqy8Dvwe8Gqc0aSpzKI2IiFgCvB/YpsdTbZCNAPPXaGM3b3u5yb5ptX2oz7FohjIzgRMj4lzgPcArWHtGrQQuBz6SmRcMOURNz+ZKc1lHW8A62grWUUl95xS1kqS2Oh24AviTiPjDuoPRjJhDaQRExEeBfwQWU244TPVign+rRuav8e6uttv1cM7Lqu3P+hyLZikzL8zM/SmzU+wDHAG8Hvh94OmZuZc35UdTFicCB1LWkksmrqGXA6/KzJPriFMTso62iHW0uayjkgbBEZySpLZ6JnAc8A/ANyLiHOAc4BbgselOzsw7BxueumAOpZpFxIuAv6TcgLgQeBflIcmrq33rAJsAe1Cexj4UuAx4bWbeU0fMWs38tcJS4DnAEuCs6Q6OiI2At1Hy6+wHIyozHwEuqTsO9S4zLwQujIiFwG7AIsq68cuAazPzvjrj04SWYh1tHetoc1lHJfVTlBH+kiS1S0SsYvV0J9HxczcyM30IqGbmUKpfRJwJvBm4HXhOZq6IiJ2BGyjvs/njjj8e+BRlnasXZeYTw41Yncxfs1R/91YBu2bmTdW+3YCrqkOOzcwzO45NYJeOYzcFvkaZtu9JSs5dt2qIJsqLmsUcNpt1tPl8DzafOZQ0TN74kyS1WUzys5rDHEr1einlBsVpmbliuoMz89MRsR/wGuAE4JQBx6epmb/mWeNvXWZeExGnAicBX4iIg4Cvdxzy0oh4AbAX8AZgQ0rOP+xN+VqdERHL+3CdrKZilNQ962g7WEclSdNyBKckqZUiYslszs/Maacv0mCZQ6l+EfEIsAD4g2o6KSLiucCNlJt/62Xmk+POORQ4F/hhZr5kyCGrg/lrlslGPEREAP+HMo3wVF/gx27qn+K6VfUYN/vErC/HBCOtNVgdObwKsLnSMNbR5rOONp91VNIwOYJTktRKNreazxxKI+Ep1fbejn2Pdvy8OfDf4865q9o+e1BBqWvmrwWyPJV8YkScC7yHMnXivPGHAZcDH8nMC4YcotbmrBPNt0cfrtHrEgsaEOtoI1lHm886KmngbHBKkiRJmsyvgC0o07WNuQdYSbkx+FzWbpA9s9ouHHh0mo75a5FqFO6FEbEQ2A1YBMwHlgHXZuZ9dcanNTzPdccaz+ZKC1lHG8U62nzWUUkDZ4NTkiRJ0mRupDTIdgQuBcjMJyLiRmAX4HXAf447503VdnzjTMNn/looMx8BLqk7DqnlbK60mHVUGgrrqKSBGz8dgyRJkiSNuZTy9PW+4/afU+0/JiI+GBE7R8QLI+LvgSMoU0k5vVv9zJ8kSZIkqZWiTEMvSVJ7VVMQHQA8H9gM+B2mni4lM/PYYcSm7phDqR4RsTNwA2Xdxq0y8+Fq/wLgR8Bi1l4XJ4D7gRdk5l2oNuavWSJiFSUfVwHL+3DJzMz9+3Addakjh7s4aqWZzGGzWUebz/dg85lDScPkFLWSpNaKiHnAe4F3Aut3exrlw7jNsRFgDqV6ZeaNEbEv5XvDOh37H6v2nw3sNe60HwFvsjlWP/PXWHv04RpjfwslaS6yjkqSNAfY4JQktdmZwFGUL6crgWXAIsoX1buAjYENqmMTuA94bOhRaipnYg6lWmXmxZPsvwPYOyJ+F9iZ8t3ip5l5zTDj09TMXyNNNUOBJGl61lFJkuYAG5ySpFaKiAOBN1KaXmdSRgBuCVwPkJnbVMf9LnA8cCLwAPDqzLy5hpA1jjmUmiEzfwL8pO44NDPmbyQ9zyndJGlWrKOSJM0B8+oOQJKkATm62t6Ymcdk5gNMMMVQZv4kM08CXgNsD3w7IjYaYpyanDmUJElNsi2wHXBL3YFIUkNZRyVJXXMEpySprV5MaYZ9qpuDM/O8iDiL0lT7M+DDA4xN3TGH0oiJiKcAuwPPAzapdt9PWbfx6sx8sq7YND3zJw1WNfWzmm3bant3rVFIc5R1tBWso5KGxganJKmtFlXbzic/V479EBHrZubj4875GnAMcBg2x0aBOZRGREQsAN4LHEdZ+3YiD0TEPwAfyUzXwh0h5k+SumNzRZJmxzoqaZicolaS1Hb3d/z8SMfPi8YfCNxbbRcPLBrNhDmUahQRWwPXAn9BGfUXk7w2Ad4NXBMRW9UTrcYzf5IkSZKkNrLBKUlqq3uq7Sbj9j1R/bzrBOdsU23XG1RQ6ok5lGpWTWl6AfBsShPsZkoTbB9gx+q1D6V5dlN1zA7ABRHhbDE1M3+SJEmSpLaywSlJaqsbqu1OYzsycwVwTfXPoyc45/hq65Qqo8EcSvV7K/Bcynq4HwV2ycy/zcxLMvOW6nVJZn6c8tDBR6rzdqrOVb3MnyRJkiSplWxwSpLaaillJMoB4/afXe0/LCLOiohXRcQREXF+dWwC3xxqpJrMUsyhVLfXUt5T52bmezNz5WQHZuaqzHwf8A3Ke/S1Q4pRkzN/zbItsB1rrj0tSeqedVSSpDkkMrPuGCRJ6ruI2Ba4DXgcWJyZ91T71wF+AOxOuem7xmmUkX+7Z+YDQwxXEzCHUv0i4l5gU+DQzDy/y3MOBr4F3JeZE62VqyExf5IkSZKktnIEpySplTLz55Snd58HPNyxfwXwSuBLwApKQyyqX58P7G1jbDSYQ2kkbFRt/7uHc35ZbTfscyzqnfmTJEmSJLXSOnUHIEnSoGTm7ZPsfwB4U0ScAOxA+Xt4a2beP8Tw1AVzKNXufmARZcq3a6Y5dsy2HeeqXuZPkiRJktRKjuCUJM1ZmflIZl6dmVfYGGsmcygN3NWUEdIn9nDOCZTpo7ttqGlwzJ8kSZIkqZVscEqSJEmazJer7T4R8Y8Rsf5kB0bEgoj4PLBfteufBx6dpmP+JEmSJEmtFJlZdwySJA1URMwD9gVeAjwDWAD8dWb+suOYp1KmOV2ZmY/XEqgmZQ6lekREAJcCL6WM6vsV8FXgh8C91b6nAy8CjgA2p4wYvCwzX15HzFrN/EmSJEmS2soGpySp1SLiEOA0YJtxv9olM2/qOO4E4HTgUWCLzFw+vCg1FXMo1SsiNgbOB15c7ZrsC0RU28uBQ6q1clUz8ydJkiRJaiOnqJUktVZEHAd8E1hMuXG7jNU3cMf7PPAQsAFw2DDi0/TMoVS/qtH1MuBPgR9T3oMTvX4MvB3Y2+bY6DB/kiRJkqQ2cgSnJKmVImIH4EZgPvA94O2ZeXNErKKMXllj9F91zueAY4GzM/PNw45ZazKH0miKiGcCzwM2qXbdD/yoc8pojS7zJ0mSJElqg3XqDkCSpAH5c8rfuR8BB2fmE12ccymlObbbIANT18yhNIKqRpjNsIYyf5IkSZKkNnCKWklSW+1HGeV3SpeNMYBbq+2zBhOSemQOJUmSJEmSJK3FBqckqa22qrbX9XDO8mq7oM+xaGbMoSRJkiRJkqS1OEWtJKmtxhaZ7qXRtWm1fajPsWhmzKE0ZBGxCXBA9c9bM/Pqcb/fAfiPaS6zHHhFZt4/gBA1BfMnSZIkSZorbHBKktrqbmAHYDvKuozdeFm1/dlAIlKvzKE0fB8ATgR+Dew+we+fCiymPIAQk1wjgQ8Cf9r/8DSND2D+JEmSJElzgFPUSpLaainl5u2Sbg6OiI2At1Fu7F40uLDUg6WYQ2loqvfQcdU/P5qZt0xzyh0TvP6H8r49JiI2HFSsWpv5kyRJkiTNJTY4JUmNFxGrImJFROzUsfuzlEbXKyLiLdOcvylwLvAMYAXwmUHFqomZQ2kkHAasS5ni+fTpDs7Mbce/gGcD9wLrAUcMNFqNZ/4kSZIkSXOGDU5JUlusMdVeZl4DnFrt/0JEnBMRnTdrXxoRb4iITwG3Ai+nNNM+nJl3DCtorcEcSvXar9p+KzMfnckFMvMx4MuU9+3L+xWYumL+JEmSJElzhmtwSpLa7J2U0SzHA4dXr6x+99mO48Yaa6dk5keGF566YA6l4XkB5f31n7O8zn8B7wB2m3VE6oX5kyRJkiTNGY7glCS1VhYnAgdS1nNMSiOs8wVwOfCqzDy5jjg1OXMoDdUW1Xa2I6DvrLZPn+V11BvzJ0mSJEmaMxzBKUlqvcy8ELgwIhZSRqQsAuYDy4BrM/O+OuPT9MyhNBQbVtsHpjjmDspaj1P5dbXdaNYRqRfmT5IkSZI0Z9jglCTNGZn5CHBJ3XFo5syhNFCPUppaT5vsgGptx29Oc52xxtjyPsWl7pg/SZIkSdKc4RS1kiRJkgDGRkJvN8vrjJ3vyOrhMn+SJEmSpDnDEZySpDY5IyL6MeIkM3P/PlxHvTOHUn2uBZ4NHACcMYvrvLLaXjfriNQL8ydJkiRJmjMiM+uOQZKkWYmIVUC//qAFpTk2v0/XUxfMoVS/iPgT4NPAE8BzMvPOGVxja+AW4CnACZn52f5GqcmYP0mSJEnSXGKDU5LUeB3NsejTJW2ODZk5lOoXERsBP6eswXg5sH9mPt7D+U8Fvgu8DHgQWJyZDw8iVq3N/EmSJEmS5hLX4JQktcnzMnNeH142xupjDqWaZOZDwN9QHjR4CXBJROzUzbkRsSNwCaU5lsDf2hwbLvMnSZIkSZpLHMEpSWq8jtF/u2TmTXXHo96ZQ2k0REQA3wAOpbwnE/ge8O/A1cCvgOXA+sDmwG7AHwD7URprAXwrMw8devAyf5IkSZKkOcMGpySp8WyONZ85lEZHRKwH/APwxmpXN18YxqaX/mfguMz89SBi0/TMnyRJkiRpLnCKWkmSJEm/lZm/ycw3A0cA17B6ZN9Ur2uB12XmG22O1cv8SZIkSZLmAkdwSpIaz9F/zWcOpdEVEbsArwB2BTYDFgKPAMuA64CLM/OG+iLUVMyfJEmSJKmN1qk7AEmSJEmjq2p+2QBrKPMnSZIkSWojp6iVJEmSNBQRsXNErIqIFXXHot6ZP0mSJEnSqHAEpySpDbattnfXGoVmwxxKc0vUHYBmxfxJkiRJkmplg1OS1HiZeUfdMWh2zKEkSZIkSZKkbjlFrSRJkiRJkiRJkqTGsMEpSZIkSZIkSZIkqTFscEqSJEmSJEmSJElqDBuckiRJkiRJkiRJkhrDBqckSZIkSZIkSZKkxrDBKUmSJEmSJEmSJKkxbHBKkiRJkiRJkiRJagwbnJIkSZIkSZIkSZIawwanJEmSJEmSJEmSpMawwSlJkiRJkiRJkiSpMWxwSpIkSZIkSZIkSWqMdeoOQJIkSdKccRdwdN1BaMbMnyRJkiRpJERm1h2DJEmSJEmSJEmSJHXFEZySJEmS+iYiFgE7AmTmJTWHox5FxHxgS4DMvLPmcCRJkiRJmpBrcEqSJEnqp4OApcBFNccxJ0XEgoj4XxFxbUQ8EhEPRsTlEXFSRKzXxSV2BG4HfjbYSCVJkiRJmjlHcEqSJEkahKg7gLkmIp4FXAjsMLar2r6wep0UEUsy8+JuLjeAECVJkiRJ6gtHcEqSJElSw1VTy34NeA6lOfkkcBVwA7Cy2rc18J8R8c664pQkSZIkqR9scEqSJElS8x0O7Akk8FVgi8x8YWY+n7Km5ieAFZTvgB+LiP9dW6SSJEmSJM1SZGbdMUiSJEmqWUT0a83FDYDNgMzM+X26pqYREd8A/gi4AnhJTvBFLyJeBPwLsBWlEfq5zHzbuGN2poz6NH+SJEmSpJHlGpySJEmSABZTml6zXXvRJyjrsQfl//60iZqbAJn5w4jYEzgf2B04LiIWAm/KzFXDC1WSJEmSpNmxwSlJkiSp06PAslmcvwGwaZ9iUfc2q7Y/nuqgzLwnIvYB/g3YBzgSWBARR2TmkwONUJIkSZKkPrHBKUmSJAngdmAb4PuZedBMLxIRS4Az+hWUujY2AnO96Q7MzEcj4iDKdLWHAIcC34qIVw8wPkmSJEmS+mZe3QFIkiRJGglXUqan3aPuQDQjd1fbHbo5ODMfBw4DzqHk/QDgP4ANBxKdJEmSJEl9ZINTkiRJEsAV1XaTiNiu1kg0E9dX2/27PSEzVwJvAL5AaXLuRWl4SpIkSZI00mxwSpIkSYLVDU6APWuLQjO1lNKkPCwi1u/2pCyOA06pzt9yMOFJkiRJktQ/NjglSZIkAVwNXAxcAiycxXUuA44GjulHUOraeUAC6wPH93pyZp4MfIjS5JQkSZIkaaRFZtYdgyRJkiRpliLiQ8Azgfsy8y9neI0/BV4DkJn79jE8SZIkSZL6xganJEmSJEmSJEmSpMZwilpJkiRJkiRJkiRJjWGDU5IkSZIkSZIkSVJj2OCUJEmSJEmSJEmS1Bjr1B2AJEmSpNEWEZsBRwF7A9sBC4H505yWmbn9oGPT9MyfJEmSJKltbHBKkiRJmlREvB74NKUpBhBdnpqDiUi9MH+SJEmSpDaywSlJkiRpQhGxH3A2q5tidwDXAw8Cq+qKS90xf5IkSZKktrLB+f/au7tQy8o6DODPO3Mq8yMxZZCoZhS8yI8JBaGi7yxpLEgILzIoiSALioogiOoius4soaJAoUihIhHpTgYl8kJQsURMCysJw8/8AGWcfxd7Teymvc7sfTznrLPW/H5wePdee63NA/+rzXPetQAAgD5fz6wcezrJlVX1u4HzsBrzAwAAYJJ2DR0AAADYsS7O7Fal31aOjZL5AQAAMEkKTgAAoM+R3wu/HzQFG2V+AAAATJKCEwAA6PNwt540aAo2yvwAAACYJAUnAADQ58bMnuF46dBB2BDzAwAAYJJaVQ2dAQAA2IFaaycnuTPJviTvraq7hk3EKswPAACAqbKDEwAAWKiqnktyIMkDSW5vrX23tba/tXbCwNFYgvkBAAAwVXZwAgAA62qtnZfktiRnrHBZVdXaFkViBeYHAADA1NjBCQAA9GqtfSnJPZmVY23FPwZmfgAAAEyR/8gFAAAWaq0dSPK97u3hJHckuTfJ0917djDzAwAAYKoUnAAAQJ+vdeujSQ5U1X1DhmFl5gcAAMAkuUUtAADQZ3+SSvIt5dgomR8AAACTpOAEAAD67O7WewZNwUaZHwAAAJOk4AQAAPr8uVtPGzQFG2V+AAAATJKCEwAA6PPLJC3Jx4YOwoaYHwAAAJPUqmroDAAAwA7UWntVkjuSXJjk41V1y8CRWIH5AQAAMFUKTgAAYKHW2puTnJrkJ0kuTnJT9/dgkheOdX1V/W1LA7Iu8wMAAGCqFJwAAMBCrbXDSY78YGhzr5dRVbW2+alYlvkBAAAwVX6wAgAA62k9rxkH8wMAAGByFJwAAECfq4YOwCtifgAAAEySW9QCAAAAAAAAo7Fr6AAAAAAAAAAAy1JwAgAAAAAAAKOh4AQAAAAAAABGY23oAAAAwM7XWjslySVJ3prkjCSvTY4HgUAAAAYXSURBVNLWuaSq6jPbkY1jMz8AAACmpFXV0BkAAIAdqrW2K8k3k3w1yUnLXpZZQbZ7y4KxFPMDAABgiuzgBAAA1nN9kiszK71eTvJEkj1JKsk/kpyW5OTu3EryeJIXtj0lfa6P+QEAADAxnsEJAAAs1Fq7NMknu7fXZ1aMXXLk86raW1WvS/KWJNcmOZzkqSQfrqqztjctRzM/AAAApsotagEAgIVaazcmuSLJH6tqf3fsvCT3ZcEtTFtrH03ymyR/T3JhVT2zzZGZY34AAABMlR2cAABAn7dldtvS65Y5uapuSXJDkr1JvriFuViO+QEAADBJCk4AAKDPnm59cO7Yy0detNZes+CaX2X2vMfLtzAXyzE/AAAAJknBCQAAHMuTc6+fnXu95+gTk/yrW/dtWRpWZX4AAABMioITAADo81i3vv6oYy91r/cvuGZvt56wVaFYmvkBAAAwSQpOAACgz33deu6RA1V1KMnd3durFlxzdbc+soW5WI75AQAAMEkKTgAAoM/BzJ7HeMlRx3/eHb+8tXZDa+2y1toVrbVbu3Mryc3bmpRFDsb8AAAAmKBWVUNnAAAAdqDW2llJHk7yYpJ9VfVYd3wtyZ1JLsqsDPufyzLb/XdRVT21jXE5ivkBAAAwVXZwAgAAC1XVX5OcneT8JP+eO34oyQeT/CLJocxKsdZ9fGuSdynHhmd+AAAATJUdnAAAwIa11k5Jck6StSQPVdWTA0diBeYHAADAGCk4AQAAAAAAgNFwi1oAAAAAAABgNNaGDgAAAOx8rbVdSd6X5O1JzkxyYpJvVNU/5855dWa/MV6uqhcHCcpC5gcAAMCUuEUtAACwrtbaR5Jcm2TvUR9dUFX3z533+SQ/SPJckjdU1fPbl5I+5gcAAMDUuEUtAADQq7X22SQ3J9mXpCV5olsX+WmSZ5KcnOTy7cjH+swPAACAKVJwAgAAC7XWzklyXff2tiTnVtWevvOr6qUkv86sQPvQ1idkPeYHAADAVCk4AQCAPl/O7JmMf0pyoKoeWOKaO7r1wi1LxbLMDwAAgElScAIAAH3en6SSXNPt7lvGQ936pq2JxArMDwAAgElScAIAAH3e2K33rnDN89164iZnYXXmBwAAwCQpOAEAgD7VrauUXad36zObnIXVmR8AAACTpOAEAAD6PNqtZ69wzTu79S+bnIXVmR8AAACTpOAEAAD6HEzSknxqmZNba6cm+VxmOwdv27pYLOlgzA8AAIAJUnACAABprR1urR1qrZ07d/jHmZVd72mtffoY15+e5LdJzkxyKMmPtior/8/8AAAAOJ4oOAEAgCPa/JuqujvJ97vjP2ut3dRau2LulHe01j7RWrsuyUNJ3p1Zofadqnpku0LzX+YHAADAcaFV1dAZAACAgbXWDmdWbl1QVffPHW9Jfpjk6u7z3q/o1muq6itbFpSFzA8AAIDjiR2cAABAr5r5QpJLM3umY2VWhs3/JckfklymHNtZzA8AAIApsoMTAADo3QG44LxTklyYZE+S3UmeSHJPVT2+LUFZyPwAAAA4nqwNHQAAABiPqno2ye1D52BjzA8AAIApcItaAAAAAAAAYDTcohYAAJi/xeldSZ7fhK+sqvrAJnwPSzA/AAAAjicKTgAAYL4g25Svy6wg271J38cxmB8AAADHE8/gBAAA5rWhA/CKmB8AAACTp+AEAADmnV9V9w8dgg0zPwAAACZv19ABAAAAAAAAAJal4AQAAAAAAABGQ8EJAAAAAAAAjIaCEwAAAAAAABgNBScAAAAAAAAwGgpOAAAAAAAAYDTWhg4AAADsCGd166ODpmCjzA8AAIDjRquqoTMAAAAAAAAALMUtagEAAAAAAIDRUHACAAAAAAAAo6HgBAAAAAAAAEZDwQkAAAAAAACMhoITAAAAAAAAGA0FJwAAAAAAADAaCk4AAAAAAABgNBScAAAAAAAAwGgoOAEAAAAAAIDRUHACAAAAAAAAo6HgBAAAAAAAAEZDwQkAAAAAAACMhoITAAAAAAAAGA0FJwAAAAAAADAaCk4AAAAAAABgNBScAAAAAAAAwGgoOAEAAAAAAIDR+A/M/UzYetq0wAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4), dpi=200)\n",
    "sns.heatmap(final_df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-09-16T05:49:29.588706Z",
     "iopub.status.busy": "2022-09-16T05:49:29.588338Z",
     "iopub.status.idle": "2022-09-16T05:49:29.605047Z",
     "shell.execute_reply": "2022-09-16T05:49:29.604222Z",
     "shell.execute_reply.started": "2022-09-16T05:49:29.58867Z"
    },
    "id": "HKqMcyd3czzC",
    "outputId": "63160a06-c040-450f-a977-dd1a0fb10d68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9321 entries, 0 to 23920\n",
      "Data columns (total 13 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Team1                   9321 non-null   object \n",
      " 1   Team2                   9321 non-null   object \n",
      " 2   Team1_FIFA_RANK         9321 non-null   int64  \n",
      " 3   Team2_FIFA_RANK         9321 non-null   int64  \n",
      " 4   Team1_Result            9321 non-null   int64  \n",
      " 5   Team1_Goalkeeper_Score  9321 non-null   float64\n",
      " 6   Team2_Goalkeeper_Score  9321 non-null   float64\n",
      " 7   Team1_Defense           9321 non-null   float64\n",
      " 8   Team1_Offense           9321 non-null   float64\n",
      " 9   Team1_Midfield          9321 non-null   float64\n",
      " 10  Team2_Defense           9321 non-null   float64\n",
      " 11  Team2_Offense           9321 non-null   float64\n",
      " 12  Team2_Midfield          9321 non-null   float64\n",
      "dtypes: float64(8), int64(3), object(2)\n",
      "memory usage: 1019.5+ KB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "1st6r-I8dCOJ",
    "outputId": "36e53a7a-252a-47f3-ce63-a69ed86c30af"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-909a3443-9b74-4548-bf68-661bdca8395e\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Team1</th>\n",
       "      <th>Team2</th>\n",
       "      <th>Team1_FIFA_RANK</th>\n",
       "      <th>Team2_FIFA_RANK</th>\n",
       "      <th>Team1_Result</th>\n",
       "      <th>Team1_Goalkeeper_Score</th>\n",
       "      <th>Team2_Goalkeeper_Score</th>\n",
       "      <th>Team1_Defense</th>\n",
       "      <th>Team1_Offense</th>\n",
       "      <th>Team1_Midfield</th>\n",
       "      <th>Team2_Defense</th>\n",
       "      <th>Team2_Offense</th>\n",
       "      <th>Team2_Midfield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>59</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>86.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>35</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>67</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23908</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Wales</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23909</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23918</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>28</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23919</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23920</th>\n",
       "      <td>Korea Republic</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>9321 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "                Team1        Team2  Team1_FIFA_RANK  Team2_FIFA_RANK  \\\n",
       "0             Bolivia      Uruguay               59               22   \n",
       "1              Brazil       Mexico                8               14   \n",
       "2             Ecuador    Venezuela               35               94   \n",
       "4            Paraguay    Argentina               67                5   \n",
       "9              Sweden  Switzerland                4                3   \n",
       "...               ...          ...              ...              ...   \n",
       "23908     Netherlands        Wales               10               18   \n",
       "23909          Poland      Belgium               26                2   \n",
       "23918           Chile        Ghana               28               60   \n",
       "23919           Japan      Tunisia               23               35   \n",
       "23920  Korea Republic        Egypt               29               32   \n",
       "\n",
       "       Team1_Result  Team1_Goalkeeper_Score  Team2_Goalkeeper_Score  \\\n",
       "0                 1                    67.0                    79.0   \n",
       "1                 2                    86.0                    79.0   \n",
       "2                 1                    71.0                    68.0   \n",
       "4                 0                    74.0                    81.0   \n",
       "9                 0                    76.0                    81.0   \n",
       "...             ...                     ...                     ...   \n",
       "23908             1                    81.0                    74.0   \n",
       "23909             0                    87.0                    89.0   \n",
       "23918             0                    79.0                    74.0   \n",
       "23919             0                    73.0                    64.0   \n",
       "23920             1                    75.0                    70.0   \n",
       "\n",
       "       Team1_Defense  Team1_Offense  Team1_Midfield  Team2_Defense  \\\n",
       "0               67.0           69.0            68.0           80.0   \n",
       "1               86.0           86.0            85.0           77.0   \n",
       "2               71.0           75.0            75.0           72.0   \n",
       "4               75.0           78.0            74.0           83.0   \n",
       "9               77.0           81.0            78.0           78.0   \n",
       "...              ...            ...             ...            ...   \n",
       "23908           85.0           83.0            84.0           75.0   \n",
       "23909           75.0           85.0            76.0           81.0   \n",
       "23918           76.0           77.0            78.0           76.0   \n",
       "23919           75.0           75.0            78.0           71.0   \n",
       "23920           73.0           80.0            74.0           70.0   \n",
       "\n",
       "       Team2_Offense  Team2_Midfield  \n",
       "0               83.0            79.0  \n",
       "1               79.0            78.0  \n",
       "2               75.0            73.0  \n",
       "4               88.0            85.0  \n",
       "9               76.0            79.0  \n",
       "...              ...             ...  \n",
       "23908           73.0            78.0  \n",
       "23909           86.0            86.0  \n",
       "23918           76.0            78.0  \n",
       "23919           72.0            74.0  \n",
       "23920           79.0            71.0  \n",
       "\n",
       "[9321 rows x 13 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ODvtpbrbz3Ui"
   },
   "source": [
    "Exporting the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "yExreJrr5H-G"
   },
   "outputs": [],
   "source": [
    "final_df.to_csv(\"./data/training.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ztEdYY8VMHeT"
   },
   "source": [
    "### Creating \"Last Team Scores\" dataset\n",
    "This dataset contains the qualifications of each team on the previous FIFA date and will be used to predict the World Cup matches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "DVoNJ6acMF4_",
    "outputId": "d339d098-91fc-434d-ca21-e6830d5aa31d"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>team</th>\n",
       "      <th>date</th>\n",
       "      <th>rank</th>\n",
       "      <th>goalkeeper_score</th>\n",
       "      <th>defense_score</th>\n",
       "      <th>offense_score</th>\n",
       "      <th>midfield_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2022-06-05</td>\n",
       "      <td>4</td>\n",
       "      <td>84.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2022-06-13</td>\n",
       "      <td>42</td>\n",
       "      <td>77.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>2</td>\n",
       "      <td>89.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2022-06-06</td>\n",
       "      <td>1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>2022-06-09</td>\n",
       "      <td>37</td>\n",
       "      <td>67.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        team       date  rank  goalkeeper_score  defense_score  offense_score  \\\n",
       "0  Argentina 2022-06-05     4              84.0           82.0           89.0   \n",
       "1  Australia 2022-06-13    42              77.0           72.0           72.0   \n",
       "2    Belgium 2022-06-14     2              89.0           81.0           86.0   \n",
       "3     Brazil 2022-06-06     1              89.0           85.0           86.0   \n",
       "4   Cameroon 2022-06-09    37              67.0           77.0           78.0   \n",
       "\n",
       "   midfield_score  \n",
       "0            84.0  \n",
       "1            74.0  \n",
       "2            86.0  \n",
       "3            86.0  \n",
       "4            75.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_goalkeeper = df[['date', 'home_team', 'away_team', 'home_team_goalkeeper_score', 'away_team_goalkeeper_score']]\n",
    "home = last_goalkeeper[['date', 'home_team', 'home_team_goalkeeper_score']].rename(columns={\"home_team\":\"team\", \"home_team_goalkeeper_score\":\"goalkeeper_score\"})\n",
    "away = last_goalkeeper[['date', 'away_team', 'away_team_goalkeeper_score']].rename(columns={\"away_team\":\"team\", \"away_team_goalkeeper_score\":\"goalkeeper_score\"})\n",
    "last_goalkeeper = pd.concat([home,away])\n",
    "\n",
    "last_goalkeeper = last_goalkeeper.sort_values(['date', 'team'],ascending=[False, True])\n",
    "\n",
    "list_2022 = ['Qatar', 'Germany', 'Denmark', 'Brazil', 'France', 'Belgium', 'Croatia', 'Spain', 'Serbia', 'England', 'Switzerland', 'Netherlands', 'Argentina', 'IR Iran', 'Korea Republic', 'Japan', 'Saudi Arabia', 'Ecuador', 'Uruguay', 'Canada', 'Ghana', 'Senegal', 'Portugal', 'Poland', 'Tunisia', 'Morocco', 'Cameroon', 'USA', 'Mexico', 'Wales', 'Australia', 'Costa Rica']\n",
    "\n",
    "rank_qatar = last_rank[(last_rank[\"team\"].apply(lambda x: x in list_2022))]\n",
    "rank_qatar = rank_qatar.groupby('team').first().reset_index()\n",
    "goal_qatar = last_goalkeeper[(last_goalkeeper[\"team\"].apply(lambda x: x in list_2022))]\n",
    "goal_qatar = goal_qatar.groupby('team').first().reset_index()\n",
    "goal_qatar = goal_qatar.drop(['date'], axis = 1)\n",
    "off_qatar = last_offense[(last_offense[\"team\"].apply(lambda x: x in list_2022))]\n",
    "off_qatar = off_qatar.groupby('team').first().reset_index()\n",
    "off_qatar = off_qatar.drop(['date'], axis = 1)\n",
    "mid_qatar = last_midfield[(last_midfield[\"team\"].apply(lambda x: x in list_2022))]\n",
    "mid_qatar = mid_qatar.groupby('team').first().reset_index()\n",
    "mid_qatar = mid_qatar.drop(['date'], axis = 1)\n",
    "def_qatar = last_defense[(last_defense[\"team\"].apply(lambda x: x in list_2022))]\n",
    "def_qatar = def_qatar.groupby('team').first().reset_index()\n",
    "def_qatar = def_qatar.drop(['date'], axis = 1)\n",
    "\n",
    "qatar = pd.merge(rank_qatar, goal_qatar, on = 'team')\n",
    "qatar = pd.merge(qatar, def_qatar, on ='team')\n",
    "qatar = pd.merge(qatar, off_qatar, on ='team')\n",
    "qatar = pd.merge(qatar, mid_qatar, on ='team')\n",
    "\n",
    "qatar['goalkeeper_score'] = round(qatar[\"goalkeeper_score\"].transform(lambda x: x.fillna(x.mean())))\n",
    "qatar['offense_score'] = round(qatar[\"offense_score\"].transform(lambda x: x.fillna(x.mean())))\n",
    "qatar['midfield_score'] = round(qatar[\"midfield_score\"].transform(lambda x: x.fillna(x.mean())))\n",
    "qatar['defense_score'] = round(qatar[\"defense_score\"].transform(lambda x: x.fillna(x.mean())))\n",
    "qatar.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jpEwbIaM6NhY"
   },
   "source": [
    "Exporting the \"Last Team Scores\" dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "fs3i-xDS6Zop"
   },
   "outputs": [],
   "source": [
    "qatar.to_csv(\"./data/last_team_scores.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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