Airline_Quality_Service (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FOXGw608gh-Q"
   },
   "source": [
    "# **Airline Quality Service Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0hvDpOoCgudn"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "0lej7IwMG-2d",
    "outputId": "8e777444-2775-4492-9756-91946da7548a"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ijbpYlgFhp93"
   },
   "outputs": [],
   "source": [
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials\n",
    "# Authenticate and create the PyDrive client.\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1Xk4PDAYkJYN"
   },
   "outputs": [],
   "source": [
    "link='https://drive.google.com/open?id=1tmzZKQKEvxt61TxjHchFfJkpqklVgdzP'\n",
    "fluff,id=link.split('=')\n",
    "downloaded = drive.CreateFile({'id':id}) \n",
    "downloaded.GetContentFile('airline.csv')  \n",
    "airline_data = pd.read_csv('airline.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "airline_data=pd.read_csv('airline.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 185
    },
    "colab_type": "code",
    "id": "TtixSsYZk4NP",
    "outputId": "3b3edc83-655b-4288-9bfa-f1dd1c06f326"
   },
   "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>airline_name</th>\n",
       "      <th>link</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>author_country</th>\n",
       "      <th>date</th>\n",
       "      <th>content</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>type_traveller</th>\n",
       "      <th>cabin_flown</th>\n",
       "      <th>route</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>seat_comfort_rating</th>\n",
       "      <th>cabin_staff_rating</th>\n",
       "      <th>food_beverages_rating</th>\n",
       "      <th>inflight_entertainment_rating</th>\n",
       "      <th>ground_service_rating</th>\n",
       "      <th>wifi_connectivity_rating</th>\n",
       "      <th>value_money_rating</th>\n",
       "      <th>recommended</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adria-airways</td>\n",
       "      <td>/airline-reviews/adria-airways</td>\n",
       "      <td>Adria Airways customer review</td>\n",
       "      <td>D Ito</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2015-04-10</td>\n",
       "      <td>Outbound flight FRA/PRN A319. 2 hours 10 min f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Economy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    airline_name                            link  \\\n",
       "0  adria-airways  /airline-reviews/adria-airways   \n",
       "\n",
       "                           title author author_country        date  \\\n",
       "0  Adria Airways customer review  D Ito        Germany  2015-04-10   \n",
       "\n",
       "                                             content aircraft type_traveller  \\\n",
       "0  Outbound flight FRA/PRN A319. 2 hours 10 min f...      NaN            NaN   \n",
       "\n",
       "  cabin_flown route  overall_rating  seat_comfort_rating  cabin_staff_rating  \\\n",
       "0     Economy   NaN             7.0                  4.0                 4.0   \n",
       "\n",
       "   food_beverages_rating  inflight_entertainment_rating  \\\n",
       "0                    4.0                            0.0   \n",
       "\n",
       "   ground_service_rating  wifi_connectivity_rating  value_money_rating  \\\n",
       "0                    NaN                       NaN                 4.0   \n",
       "\n",
       "   recommended  \n",
       "0            1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline_data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rWNXUTVaxFrJ"
   },
   "source": [
    "# **Analysing the DataSet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "7-4RDqs9n9PH",
    "outputId": "6e609c24-5da1-41ed-f8e0-d0c132f15b61"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "airline_name                     0.00\n",
       "link                             0.00\n",
       "title                            0.00\n",
       "author                           0.00\n",
       "author_country                   0.04\n",
       "date                             0.00\n",
       "content                          0.00\n",
       "aircraft                         0.97\n",
       "type_traveller                   0.94\n",
       "cabin_flown                      0.07\n",
       "route                            0.94\n",
       "overall_rating                   0.11\n",
       "seat_comfort_rating              0.19\n",
       "cabin_staff_rating               0.19\n",
       "food_beverages_rating            0.20\n",
       "inflight_entertainment_rating    0.25\n",
       "ground_service_rating            0.95\n",
       "wifi_connectivity_rating         0.99\n",
       "value_money_rating               0.04\n",
       "recommended                      0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round((airline_data.isna().sum())/len(airline_data),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d9JGI01HoLdn"
   },
   "outputs": [],
   "source": [
    "airline_data=airline_data[airline_data['overall_rating'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "ccuVw6MNl-ux",
    "outputId": "df809c07-d18d-4e48-d299-24c448eb7aeb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "airline_name                     0.00\n",
       "link                             0.00\n",
       "title                            0.00\n",
       "author                           0.00\n",
       "author_country                   0.02\n",
       "date                             0.00\n",
       "content                          0.00\n",
       "aircraft                         0.97\n",
       "type_traveller                   0.94\n",
       "cabin_flown                      0.05\n",
       "route                            0.94\n",
       "overall_rating                   0.00\n",
       "seat_comfort_rating              0.17\n",
       "cabin_staff_rating               0.17\n",
       "food_beverages_rating            0.18\n",
       "inflight_entertainment_rating    0.23\n",
       "ground_service_rating            0.94\n",
       "wifi_connectivity_rating         0.98\n",
       "value_money_rating               0.03\n",
       "recommended                      0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round((airline_data.isna().sum())/len(airline_data),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IrmWpsrXn5aQ"
   },
   "source": [
    "# **Calcluating Total Reviews to Each airline**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "tVZHjIYPmFEg",
    "outputId": "6bf8d9ba-86bd-4c71-f71c-c7645363f5bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total airlines Considered for Analysis :  357\n",
      "Total Reviews Analysed :  36861\n"
     ]
    }
   ],
   "source": [
    "\n",
    "airline_names=airline_data.airline_name.unique()\n",
    "print('Total airlines Considered for Analysis : ',len(airline_names))\n",
    "total_reviews_each_airline=[]\n",
    "for i in airline_names:\n",
    "  temp=airline_data[airline_data.airline_name==i]\n",
    "  total_reviews_each_airline.append(len(temp))\n",
    "result=list(zip(airline_names,total_reviews_each_airline))\n",
    "print('Total Reviews Analysed : ',sum(total_reviews_each_airline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "cCDqd6w3n_mc",
    "outputId": "0462f0a4-0799-49d9-91e3-d74ff81c0441"
   },
   "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>Airline_Name</th>\n",
       "      <th>Total_Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>spirit-airlines</td>\n",
       "      <td>966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>british-airways</td>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>united-airlines</td>\n",
       "      <td>839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>air-canada-rouge</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>emirates</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Airline_Name  Total_Reviews\n",
       "295   spirit-airlines            966\n",
       "97    british-airways            896\n",
       "333   united-airlines            839\n",
       "20   air-canada-rouge            715\n",
       "138          emirates            690"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(result,columns=['Airline_Name','Total_Reviews'])\n",
    "df=df.sort_values(by='Total_Reviews',ascending=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PR4Pxod7QIwk"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "colab_type": "code",
    "id": "486Z7T_QpMTi",
    "outputId": "cba41212-608a-4b0d-e267-1f12dea4bd17"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "plt.style.use('seaborn')\n",
    "plt.xlabel('Airlines')\n",
    "plt.ylabel('Reviews')\n",
    "plt.bar(df.Airline_Name[:10],df.Total_Reviews[:10],label='Top 5 Most Reviewed Airlines')\n",
    "plt.legend()\n",
    "plt.savefig('top_10_most_reviewed_airlines.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NyT2jwkUrHIN"
   },
   "source": [
    "# **Finding Polarity of Each Review**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GPn-vQVOrGU6"
   },
   "outputs": [],
   "source": [
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T6s6fwPZnxIn"
   },
   "outputs": [],
   "source": [
    "result1=[]\n",
    "num=1\n",
    "numlist=[]\n",
    "for review in airline_data.content:\n",
    "    analysis=TextBlob(review)\n",
    "    result1.append(analysis.polarity)\n",
    "    numlist.append(num)\n",
    "    num=num+1\n",
    "result1=np.array(result1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9h842fKvWqPK"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uMzY1z-crnVX"
   },
   "source": [
    "### **Plotting Polarity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "colab_type": "code",
    "id": "XgL31hv7rrFC",
    "outputId": "78133247-8d3c-42f7-abc7-97348932c3f7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.scatter(numlist,result1,label='Polarity')\n",
    "plt.xlabel('Reviews')\n",
    "plt.ylabel('Polarity')\n",
    "plt.legend()\n",
    "plt.savefig('polarity.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hF9njJ_9HKs1"
   },
   "source": [
    "# **Generating Word Cloud**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bSLxoOExGiun"
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-68f19605a8d5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mairline_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m   \u001b[0mtext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "\n",
    "text=''\n",
    "\n",
    "for i in airline_data.content:\n",
    "  text=text+i+' '"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "colab_type": "code",
    "id": "mJn2PRcUGr6Q",
    "outputId": "a13ae1c9-9785-4749-ff45-a941ce10cd9a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V3yZwF1d1EapVlEwhxY2TXNLeQz2cJIiXNq0c2jQwJkLJBGG8gWv1EJsaUtgIOlAyTaQrgCFh7cKSeWJdJ9IbuFYvrtVLVWsEgozdARhiEzGUHGc9WAIgY3WQL3Zttq7comy3vu3NZtrymM1+MNeUvV00gpDlUo2VSp2OdJJyvUml6bNYqrBea7Beq9PXkeH8wgqeYzNQzJF0Hep+SMKxMe3/AFKew/x6Bc+2yKcS2zp8pT2H2bUySkqSrs16tcFyqUYUa0p1H6UkSkps1bLKpDwHIaDmh3TlUvR3bN17n18q8d0fvsHiagWjDfPLZRaXywz1dfCzH7+LA3t6mZ5f56kfnGZ2cYMwipFC0Ned5eF7xrjvrmGiWHPq3AIXLi1Trja5OL1CvRkggP17ennwyOi7tlq+GZSUFL0Uacvl5MYCCMFgKk9fIourbk5Vr5yfw3MsPvXABLal+N7L55mcW+Xo3oHNMgurFY7s6ecT947jORax1tiWZH61yp9+62V+4ZG72NVbwADffukMr16YY99wNz2FDH3FzKaFY61c54++8RJHx/u5a6yXu8b6UFJS90MmBrv44qPX+2S8NbzvCfmDCmNCjFkjlfnfsKxdiGv24DbL6FX8xtcxxied/mdY9jhCOODchzEh9dof4TefxvU+jRASpVqKhpQdCOEgRQeWNXzHZNa6jO3sIpX5Jyh1c6XDmBClekmn/wmWvQ8A27mbMDhOGLyK1ovvSA5xm9F4HYU0j3/yri3fZbIJRsa6sO3WarSnL0ehM41SAtk2KRY6MxhjUO2VVUchzeBwsXVvcZU4hdiqAmyrF4qbG0xvb4WRQsr7SDqGlO0AAikEvZlfRaKpRzFCWHQkhgBDoGMibXCd1qurjaGQ/AJKSDxrHD8OEULgKWfTUSdht0zTka6iTUDK3kNiG3M1gJIePenPY0wMiE1Szrhb2znrHgHk5u/GaDLuwS3fuVZPu9Uk67WTJK0MPfYwG8EKofbpT4zRl9jVWsXfpK22a99rCfh2WthWilzSI+G03sFy3Wd2rcRGvcFKpcZiqUqtGTC3Wqbhh1xa3mhNwI5DynWYWyvj2hYdqQS2pTDG0JvPgID9g91MLq5htGHfYNcNhCyl4OBwL2dml9Fa09uRZWGjwlKpiqUkq5Ua3bk0rm2RTXp05VIcGOphfr3C3FqZtOdskv8V/OgnF5maWeOzjx/iwbt3cezEZb75/ZPsGizyyY/uB+Br33mNqdk1Pv3IAe6a6Gd2YYNvP/cGf/XtVxkf7SafTfLb/+CjnJ9a5n/+/af4uSeO8JnHDiHlNYrQFcXnGuXo2n65U7jyfAll83DXKCfW5vjGzCleXL7EPcVB7u8aYW+2i7S9/bZPOuHQ8EPmVsu4lkUQxXRct+c+2J1npKeDTLJVh40iCCPWynXm18o8f2KKY2/MbJYf6MwTRjGlaoMfvzHN5NwqtiXRGpY3qoSR3lQU7yQ+UITcGiQ+2jQxtMw4Agslr5iyBdr4RHEJQwQolPCQ0msPppjY1DGmta8nhI0UHlLY7d91+/eQK3ZyJZMI3BvlICTWtfbv9g17pkLYKDmGZQ1tS8YA2tQIw5MoNYgQTsusvXm9gxBua++ZELi1J+mdgJQ5LLXrlmTcks1Gyu5NMt68XnViiDGm8W6KeUtIKZByq2n4CjlfW+b6V0nKn/JyoI1K6PPc3EWUlNzXPUikNZ6yqEcBScvh+MosQ+kOhtoOYVOVdRYbVfZkO5ECUpbLcrNKynLQwOXKOo5STOS6SFg26pqJs+qfJIhXyLp3v6kVSYgbzetbf7eu+yy5Pq3BtWX6vFGW/GnW/SUKbi8Ft3V/wa3vcy0ZiWv+/1bQmU3x0QOjm5978mm+8MDBG8rdPdqygIRxzB985yU+d/8B+vIZvvf6BXJJjw/tu6rE/Nrj9wHw0f2jhHGMEnLbMaSk5LFDuwmjGKUkUgiGO/N8eN+uzTIfbr9Gw135ze8ODfeidWslfv3e9PxyiUI+SSGXxLYUhVySYkeK1Y0aAH4Q8YMfn+OLP3MPRw8MUexI0VXIUKo0+H+//jKvnJrh0YfGuRWMNvjNENezQbbmvDjSxJFGKoHVfqekvH1fEYHY0p/aGEIdE+qWeVcKQdbx+Od3fZzJygrfnTvLU7OneWnlMl8au5fH+7eX+cN3jfJn3znOf/yrF+jMp+gtZvilR49sKePaatutDSkFjqX4H37lEwx251Cy5UsAEMWaH56Y4m+eP8W//6efZ6Azz0qpytTC2tbnahtx9XUWsbeDDxQha+OzVn+K5fpf0QjPg4Gks5e9nb8LtCaR9cbTLFT+jHp4Bs8apif9S3SmPg/ENKMZLpd+l0ZwFm0ict7DdKe/SMa9G9A0oxnmyv+ZUvN5tPFRwmMo/99SSD6xRQ5DQKV5nEul/4WB7G+R8x7GEtebiy2kKnLLCcRE6HgFredZWfr49T8CGsf5UNvR6w4RsmmtZraDEAnEm4SmtMq5NynXeuXgnQ/MvysI4oic6zFXK/Od6XNYUhLoGE9ZxEbTjCJ6k62xdaG8yqsrc0Ras9aoM97RSdb2mCyvcbm6gSUkrrJQQhDqmAe7t1pPct4D5LifO6/Xvzk8lWQ4OfE2r74ynn46cisheeyuMZ4/PUUzjJgY6GJioPOm5a3bIKVb7XPfDK2J/sZn7ilmOXbiEmsbdeLYsLZRZ2W9xsRYD1obllerNP2Ins4MyYSzWVc65ZLNeMwtlW6o83pUS3VefuY0hx7aQ6YjRaPSJI40cxeXcDyHvl1dYMxWv55bPQuCpOXgx9EmcZWDBqWgid5mPhpNF/n18Ye5tzjEl8+9yIvLUzcl5JVSjd5CmseO7uYjh8eQ4vZW8Lal6Clk6O/K8Y0XT/PFRw7TlU+zWqphgIRr44cRSko6MkmEgGdemWS1VN9Sj5KyFTpZqt5WW9wKHyhCXq1/nYr/EwqJT5Av/Bswhtg0UCJNbGoYE6N1wED2N7BVJ8u1r7HWeJqM+wBSWEyu/Wt6M79CMjcOaBarf8Za/TvYqgtjAhYqf4ohYF/376NEqu0c04Pgyl6bQJsG641nWKz8CSO5f07KPYgSqZtI/CaDQiik7EDKHMn0byC2qUfKToS4vUF/I24kRkPc8op+R6QpbunZuoPbR9Jy6HATlIIm634DP4qwpaIc+KRsh3oUEumre6aR1vg6ZsD12JPtZLFR4VJlnZTt0OkmiUzLs9sYCHS8Ze+tNUnduX7TJiLSlZb/glBoEyKFQ6RrWDKFFDbahIS63LYkCbQOUDJJHDdxrY7NeoJ4HSkchJCtMCssYuMjhcVa42Wy7gSuKm4Jv3orqDZ83ri4yL37h/CDGCGg6YccPzuD1oZd/UXKtSbVus/+sR76O3J88sg4Soq2t7RFpe4TRjEJ1yYIY7Ipt2VSv43J/+2YeG92zRMf3cfU7Cp/+OfP839+5QUyaY9DE/18+mNt501x5frr6xKY2+z/KIxZnlnj+DOnaVSarC6WeOjTR1C24tKZOSZfn6ZroIPDHx7H8W7ucHUFtpTsz/fwzMJ5Dnb0EeqY782f5UfLF0lbrcVGPQo4vjpD0nIYTOaxpORSdQ0/jsjaN4/XNgbeuLzMt186yx8++WMsSzE+2MVvf+Fhcqlbx3nnUwn+1a8+wV88c4J/+Z+exA8iMkmXTz+wj0/dP8HewU4mhrv4r//9V0i4Dkd291PMpUi4V8dhbyHDPXsH+ONv/YQfn54mnXD4F196jJGejttq62vxgSLkanACWxZbntSqtWHfMl1f0T4NWe8Bks5+lEjiWYM0oymCeBGBpOIfR5sAq23i9qN5Us5BongVQ0Q9OE1/7rfxrF0ILGzVtWV/K9JlNhrP0QjPM5j7Z6ScAy1P17cR4gO0wpucgwTNH2Dbh1HWIDdmM7WArZOQwMagN83229YtsxjTxJgaxkQIYWFMhNYbRNE53uuuv7y+wQ8uTvHi9Axr9QZKSPqzGX77ofsZzGWxlOIHF6d48vRZLqy2TERZz+Wju0b4tfvvuWXdxhgqfsC/+c73GMnn+MXDB+nPvvMkMl87dYYn3zjDz0yM8+juUbJvkpDidpC0bHbnigykckRGo41BCUGkDXZ7tZx1PFxlMZot0Oml0Ua3PLuVRU8iw+d2HcASEltKNKCNxpEWtry1OfidohycI4w3iHSVZryMNhFpewTX6qLpn8KSCdL2KI1okSBew5YZwFAJJtFEjGR+ASk81puv0ohaTjwtwhWAbnuQa5rRKmnn9hPg/F8/eYUXL8/wxPhuvnCwtacax5pStYkBXr8wj21JMkkPSymaUch6uU6tETC7tIEfRIwOFPnJ6WmkEKQSDknPwbEtPMfCsiQnJ+f5pU8cfU/UUj+IKFUbfOSBPdx/eJhUwiWT9ujIJ5FC0NmRwvMsltdqNJoBCc9Ga0O97lOrNem6ZlUrZNs5bnPPuPVEypLkimkyHSniSNM/2o2QgpWZddYWNlq+CWkPcZsm64Tl8Gt7HuQPzr7A7538PpaQjGaKjKaLmz4BsdHM1ct8Z+4NqqGPADzL5khh8Kar40sL67x8ZpqHDgzzG599EKUki+sVfnTyEj96/RKfuG8vHz08xn0TQ+QziS3XCiGwlGR8qIt//Jn7qfshWhssJSlkk9i2Yqgrz2/+7ENU6j5SCnIpjyfuH6czl9rs+1TC4YH9wwz15Iljg6UEPR1vbxH1gSLkWFdxrC4skd0kQYHc4mzgqG6USCGEbGncKIwJMEJgTEBP+hex5FXNxVZFXGuQeniO2NRxVU9b4xeI64hQmwZhvIRBUw/PkHIOvW0yBpCyiOd9lsB/nnrtyzjeYyg1AEa3w4mqKDWA4z609To1gMAiDI7hN0eQbY9mKYso1Yodtez9IJ7Ebz6NEBmUNUgczxM0n8YQtSe79walZpO/PnWGb587j6MUQ/nW/misNSnH3lSABrJZHhgaoDud4tTiMq/NLzDacXtaZ6hjTi8tEeuYZhjdEbmXqlVeX1jinoH+draodw4lJUnpkLScG7yyr4UQLZNfQtmbn6E1YfVab2/V+E4hhYUfrxK2V8mWtNFEJKweQl2mEc5jyxyNaI5Ql9GECNN6XxyZ39ynjnSVyNQw17Sp2LyHvenBfbuYLZV5fXGJw/1X98m1MVTqTV6/MM9KqUql5rNvVzfZlEeiHRpXa/gkEw5KSc5PL7O8XiWfSZBOuji2ouEHzC5vYCnJRqW5GY75XsD3I1585SIXLi1jW4pcxmP/3j4+/qEJXMfmoaNjvHZ6ht6uLPv39LK4UubV07Okki53jV/1Ps5lEjiOxcziBvPLZQq5JAZIZhLc/bF92I5N4IdIKfCSLh1dWZJpj6AZMnZoCGXd3vxnC8mBfC+/OfEwq34dg6HLS6NEa/4eTOXxlM2DXcP0JjI02nHHrdVyjt7k9gr1WqXORq3BgdEeDo72YAxoranUm5v71YVsy8GrFm1QDau4KoUUikDXiU0EwjDY3cGl2nFSVoGU1YHTzsQnrZhs3qe7mMaRSQyaSthEihqRkSgsYkIs16enJybntBwZK+EKy7UFXJUmb/fgqNvLm/GBImTH6iPQKzSjKWxVAAzaNBHiqllCCLWFJEX7O0tmsVURKZJkvQfasZ1lMKBkCiWS2KqTkv8irjWMxEWbOkJYm8SsRIq0ezeWzLNWfwpH9ZL1Htpccb9VCJHEce4jlf5tguBFmvWvXpEYECjVj/Ru3LtynLuJ3A8Rhq9Qr/0RQqSQsgsv8TObhGzbR3C9JwiDl2jU/wtSZgEPIRwc98NEwStvS+Y7gfMraxybmcVRis8f3Mfh3p7NeMt8IrGpMQ/lcxSTCcq+z1NnzvPa/MJt3yNp2/zmA/fRkUhQTL73SWQ2Gg3Or66RdV32dBa3DdV5t0yfN8NStcp0qUwhkWC08OaKTjOKeG1+gbTjMFYskLB6aW3jhCjhtZRgFI7sIGPvwZUFHNXRMkObCNV+T9OMtctfVSQEgoTVh6MK7XhzgUG3CbnWIvB3cBZOwrU5MNaL59ikEy71ZkB3IY0lZctbQxsyqVZMs2tbVGpNegoZkp5DwrNxbYtme2WqpGRsoPOmYUErtTqX1tfJJxLsLhbetszXwxjFQzJ2AAAgAElEQVRDGMV857k36O3KMDHWTSrhoo1hdb3GCy9fpLuQ4Z5DQ3z2sUN84/sn+cGL5/jRy5OEYYxjKz7+4Qm6r1khZ9MJHjg8woXpFf7kqy+STSfYu6uLJz66n+7B4pZ7A2Q6UiTSLqEfUejJ3fZ4jGLNxalVRnuKHMj2EscGKVtJYCrVJpXVJulul34vT0GmcGzF4koFz7bJeQkctb21p7eQobsjw6vn55le2gAEdT+gt5DdDEkCWGleohGX8XWNpMqTsHIsNy/iyATrwRz7ch9lPZjF13U2gjnSdpGM1UUpXCDSPk1dYzR1DxpNIy6xFsxQdIbIOb2s+TOUwkVydg85u4fF5nl8XWOhcY6CM0DGKm4r+3b4QBFy3vsQG40fst74PvXwLGCQIkkx+albXidQ2KqbrtQXKfvHCOJFpHAxJiTp7CPj3oOjuulIPELVP8ESppVMwURkvftItLNqKZki5ewnZe8njFdZq38bKZKk3cNbSFmqHhLJX0DKAtzCi1QICTJPIvn3UGqEODqL1iVAbB4cYVk3ZuhR1kArTjgYJo7nwERI1Y24ZuWvVA+JxM9hWSPE0SUMMVL2YNkTYGJC+8CWuGXbPgzYWPatvS+VNYaX+BxS5G/4zbbvaZnhrQPbXHkVlzY2WKvXOdTbw+O7xxjIba/9OkrhJBIkbJuOZGLbMttBtJNH/PyhW8vx08RCpcrfnD7DPf39rUn6fZDy9eLaOt89P8n9gwO3Rcg1P+AvTpziSH8v/dks+UQGy9mqjF7xLfCsTjyrpUx6dAFbPaavLZu0B7FVnoTVg6uK26T0NLxTvwXPsRkfvhobv11IT2c+hRACY6C3mG3/zpZrBrtzLVluIcr0xgZPnjnHfQP9d5iQYXm1yrPHzvP5jx/mw/eNkUl5xLHmtTfm+IunjnPm4iL3HBpifLSbIIw4fX6B9VKdhOewd1cXhyb6t3hGO7bi0YfHKXakmV8utbKYuTfSwrXEm30b5tgo0pw+P0+t0TKhY8xm49bqPo1mQL0R4AcRaxt1ugppllYr9PfkSCUdHHv7ebSnkOHhg7s4N71MrRkgBPR2ZNg71MVQ99U5atmfwlMZjNFUo1UC02AjnGMwcZBKtIwf19BGYwuXZlyhFq0Tm4h1f5Yubxer/jTVaI1IB1SjVRabF7CES9ouUo9LBHGNZCIPCOYbZ+jyRrGl+5bf8w8UIWfcewFB2T9GPTwDRuK20/sJLDoSj2PLqytKV/VvrmiVSNGX/VVWa9+gGV1GmyaWzJAwuwGwVSeFROskm3p4AWMipPBImUMAOKqXtHMYW3aiZIbezJeYK/8BejNM6iosawgr/Vtbhb/Wk/Aa5xohJEIk8RKPAx/f/P7WENj2QWz7xtCNLXLYe7DsPVvFMDHa1JD2ARAptPEBieXci7T2t8OWtsagXotb3df1PobLx95U+pof4McxKcch67774VzvNaJYM1+p8tL07B2doN8Jwjjm8kaJV+YW2Nfd9ablY61ZbzR4buoSg/ncpmfsWyHJm5XNOLvf9Mq3hNsofv3K7trPN5tDb2c1GMYxM6Uyx2fnmOi8uWf224OhVvfx/YhkwsFzWwdaBFFMvRlgjCG16VUtuGtigLsmBt6kThgZKDIycPuruLcluTHUGgGT0ysoKVBSbmZoU6r1uTy1TLXmU2sEbJTrBFFM55t4cUshuHtPP3fvuTHr3JZytHKiS6FaaV2MxhIOnd4ulvzJluka08r3IKyWNcYYZDuFriUdDDEb4Ty1aL1F4G0fHkcmcNxB8k7vZtpX2vnf5ZuE9V2PDxQhS+GQ8x4i5z207e+jhX+15XPGu5eMd+/mZ0d10Zf91euuMm0CirFVkZ70L9Na1V5xFmsNmrRzkLRzoP1dgBSSgew/bocjGYwJuGpupn39FQeVCB0vIUSi9bYb1fo3ul3Gx+gKQmRACIxpJYO4Y6FOW542xI8uE+s6tupqy6AA3dofNz4JZz+WvPUL2orFNhji1j4+GoEk0k0QAku4yPYeYaw1K/U6K9U6QRxxcX2dehCwXKvx6vwCnt0K1cknEox05N9y5qUrqAchi9Uqq/WrYQkZ12U4nyNhb5OYxRi0MSzXaqw3mq2cxdeFYNhS0ZdN05VKbbluvdFgpVajHraUMc+yKCaTrdzK7WQWpWaT1XqD5WqNl2fmWKxWubxe4iczc5sxq3kvQW8mRfodKCblps96o0EtCAjiGG0MtlRkXIeudIqUc9ULdq3eYK1eZ6nd9guVClNr6xybmd0sU0wm6c2kSdg2tSBgpVZntV7n5OISS9Uas6UWkV9xass4Lj2ZFPnEVivGar3OWr0llzYGRynyCY9iMrmlPxarVVZrdbrSKSwhWa7XqAchxoBnK4rJFMVkAtk+aAKungy1VK1R8X20Ma0Up5l06/Si64ZQpPVmOzXCkCCOEbRiUPOeRyGZ2JRJG8NipTWOBnM5Ih2zWq/TaPsiJGy71dfJ1vssgPVGg7V6g6VqjVfmFlioVJla39jSroVEgp5Mekt/vBUIIeguZujuzHBmchHbUqRSLmsbNU6enUMIwaHxWxPTewUpW7IbwHNbTmYtBaf113MsENBVSOO5Fk0/olRp4Ln2lpzjbxed3i7W/Glq0Tp5t5+s3U2gG0gkKasDS7pk7G4aUcus3WnlKbiDaDTL/kViE5G1eygFSyRUhrzTR0JlkcLCVVujY/oSE6z4l2hEJXJ29+Y8eDsQ5laeJH8H0MrDXGqltRQemAYIG0zQ/hsDVtujOUaKDFpvYPAxpoZSI62cz8h2kgMFQqF1GcsaAWy0XqLZeBLL3tcuI1HWUIuEZQc6nieKTmNZ+xHCRps6UnZhWYN3/HljXacWvAImJIhmNhUAREt+AViyk2zi8Vs6rMUmpBmX8OMqjkwSmxBLuGwEl7Bkkg5nGKc9UGtBwNdOvcFfnnyDxUp1c1K0lSJp2wgBKcfho6O7+J2PP4J93X6RH0V87fQZ/u33nuXzB/bxO594dFuZzq2s8uevvs7fnD5DbDRr9QYPDQ/yrz/xGONdN65WIq1ZqlT5L6+8xsuzcyxUqlSDgFoQEMYa17IYK3Twj+47yhcO7OPLx47z5WPH+dyBCTKuy4uXp5la30AbTVcqxSf27uGXjhyiw2sd8/j81GWePHOWl2fnmSuVqYUBSbuV9/rKFPPo2Ch//+gRjvS//XSv3z57nu+en+Tcyior9TphHJNxXPb1dPLzhw7wkV0jqHZ4zrfPXeCpM+d4bWGRhXIFP4pI2PaWPNuf2TfBPzx6hN2dBV5fWORrp87w4vQ0lzdKlJs+CdvGsyyuzJNH+/v50tHDPDK2C2gRWqnZ5Gsn3+B7kxeZWt9oZU9KeNwz2M/P7p/g7r4+nPY9//jYK/z5a6/zhYP7yHku3zx7jsnVdWKtKaaSfGLPbn7x8CG6UsnWysoYyk2fFy7P8JUTJzm9vESsYTCb4XMH9vHawgI/mZnj7919mN96sJXAY6PR5Ftnz/P0hUlmNsqsNxtgoJBM8ODwIJ/dP8Hh3h6UlIRxzH988RhfOXGSf/qhh9hoNnj6/CTTpRIY6Mtm+GS7r1OOgxSC7124yFNnzvHK3DzzlQqNMGq309V2/eT4Xv7B0cO3ZZG4Fq0puhVXIZEcP32Zbz5zkjOTizTDgFw6waE9gzz28AQHx3sRSDQaScuF+p2GKPpRRKXpE8Qxtmopet42Cu7t4MpRj9f6w12b/vTaVKlX/t4pvwltdLu+m89rrbS9V9pMtOXRW0i1Vc+tw920iblVBrqb4QO1Qn43EIXniONphMwSR+cAF62XESKLMXWEcICYOF5ukZV9CGWNgdnAGAHKoKNZwugsEF2NGRY2tt3KDmSMj1K9KNWHMdW2ElAhDF7FEGJMGWOaaF1FqX6ESIJ86zFstwMhbFzVR9U/BkK2nOKwwEStl1ckQVgYAgQ3j+GrhUucLT9FPVonZXXhqQyts4tDOtzRLWUdpTg60E9nKkWsNd+fnOIHk1Mc6u3ms/sncJXCkpLudPqGrERvBcP5HP/V/ffwmX17mStX+Bdff+qmZU2bNH732ef57vlJfvHwAX7tvnso+02ePH2WEwtLfHjXMP/NRx7ePNTgCv761BvkPY+xQge/fPgQpWaTpy9c5PeefR7Ptvj5g/vJJxIM5LJ8anwvR/p6+fHlGZ6+MMmnJ8b5yK7hzfp6MmmGO248lvGt4BtnzjG5ts7uYoHH944hgJMLyzx78RJnllf4w1/8eXozrbOhRzvyfO7APu4d7OeZySleX1jkk+N7eHDoqvLXn8vSnWkpU12pFI+O7eJATxcnF5f48rHjPL5njI+NjpBur/QKyZZl40q71oKA//W5H/FXJ0+zv6uLz0yMk3ZtTi8u8+2z53ltfpH/8ROPcXTg6lF1tTDgT15+lY6Ex0Auyy8fPkQ1CPju+Ul+77kfYVuKLx46SGcqiTGG709e5H9//kVKzSYf2TXCgZ5u5ssV/uClnxDG+oYDGTYaDf761BtsNJvcN9TPQC5LLQh54dI0XzlxkpVand95/BE601dXO1U/4P/40YukHYfdxQIfGhlmuVbnB5NT/IcfvkDSdfjc/glSjsNIPsdn9o1zdKCP5y5e4qWZWT41vpcPjQxt1teXzdCffev55g0GXzdpxg0SKsXuPTl+fewojbhOKdzAkS6DiSEs6VAKSyRUkkZcI21lsN5m3Pa1OL2wxO8/9xKvzc5zsK+HX3ngKB/bs+tt1XVl3F9LU1v+fU2q1DvtZSFvIyLmesfBljzXZfq7jXreyqr4WvydJ2SEahFkvNYiWKpI2YNSRbSutj24Q6QsQjsuUsfLWPYoYXAcHXcQxdO09kIGAQutN9phRa2OkzKDlAWMLhNHUy1dVzWI9TxS9gBW+54FpOpHx0sYXX53HhcLxxqmQ13JM3ytPkorMBF5Q8jX9VDCIWP30eHsIjYBnsrh6yr1qIa+fk9dSnYXC4x25DHATKnMselZhvM5Pjo60lol08rh/E5eQkcputMpOlNJBnK5LWkjr0dsDMvVGt88c47Hdo/ypSOHGchlWyY1y940b+YSHu51K3ZbSb509DCf3TeOoyxCHfPY7jF+8yt/yXfOXuDje8bIJxIM5rL0ZTMsV2us1uo8N3WZvZ1FHt09ujkxSSHekRIC8C8f+9jm4QSqPVnMlssMn8jx1ddPcWx2lk+P78WRkl2FDoY78syVylxcW2dydY193V08uvuqEnVtGsiuVJKOZIKK72+OlF0deT68a5iOtolaCrF530YUcXx2nq++foon9u7h1x+4l+F8DoGgFgR8/Y0z/P4LL/HlYy9zdOCzW55DCPjCof383MH9uMoi0pon9u7h1/78K3z//EUe3z1GZyrJ5No6P56eIdSaL919mF9/4F4sKdHG8NGxEf7dD37I5Or6lroHcln+3c9+GtdSWFKhZMt56+HhIf745ePMlcu8sbzMR9KpG2T6tfuP8ujYGI5S+HHEh0aG+e+f/CZPnj7LE3t2twi5I89gPsdCpcLMRonXF5aY6Orc0q5vt68rUZkz5dP0eD3UohqvlY7T7faQtjLU4zqr/grL/hL1qEY1rtDvDdLr9ZFUN0tY9NZwsK+Hf/tzn+b/PvYqc+XKHalzB9vj7zwhW9bo5qENGN3aCxUWV8j3Klldsey3PT5FEunlEXgo1d/6XtjE8Rw6WAdMm9AzCJHFsg8hkKjNwyAslOwhCI4jhMRx7kWqToTwMGrotuOEV5tVnl+e5Fx5if/u4Mff1DzVMqHI9h72tiVu676e1cFI+sObrSNRGHTbdO1gyav1CyGwhID2ZGRJSeujxFHqpofLv1UIIVCipc/ab5KqMIhiFipVgjhmtNBB1nM35ehOp+hMp1hvNCg3mxSu2xu9d2CAw7295Dyv7ZVrMZTPMZDNMluuEESt1ZmSLZcORylUe/9TSbH5+U6hmGyl9bvWPNaTTrG3s0CoY+ZKlc10hVfSPNpKbZqxLSlv2gdStg5WdJTCli0TqHWLfqv6Ps9enKIRRjwxvoehXG5zz9SzLcYKBTqTSd5YXqHUbJK5Zu/8cF8vd/f1kt9sV8NQPstgLsd8pYrfPlv68kaJqfUN+jIZHtk9uqWO+wcHGczlmCltJQ5LSrpSqRvaabSQpz+T4dLaBsu1rSkRAR4cGmJ/dzc5r5WVyzMWQ/ks3ek0lzY2NuPRb+jrdjvdqbFtS4tur5fXNl4m0D4pK01kIowxZOwsSlhIIck7HRgM3V4vtnTesbkaWmPFVoqEY28qXjt4d/CBJuRaVOdsZZJXN06yEqzzhf5PMpIaxJG37zQhhHcT56nrB/KNW+1CdLRLXhvnaiHdTGtPtk16rdjoVLvstUQ1gitSQIxUXYDT9rq+fQefyGjW/RoLjTfPT3uN5G+h7PZQwkKqq+a3Ky/+ldCW93tqTSnEZmxjPQi3JIYP4hg/irCkJO3c2Bf92QzFVGJzYr+S8SflOmw0y3ckyfxbwVqjwbGZWc4ur7Bcq1H1A+phyFK12kqhGcc/tezifhQzubaOMYb//OIx/vzVE1ssFeuNJrPlChnXodRobpq9AfoyGTpTqa3tKhVpx2Gt3ths1/VGg41Gk8FclsHrQuaSjk1nKkniOiKMtGahUuVHl6aZWl9nvdGgHoTUgpCL660DOcL4xmQvQ/kc+YS3KZMUYtP3Yb3R4PpQrncDSZViLL0XTybYk55gODlGxs6iTUxkIpRQCASxidsx35qkSt6WafVOwBhDGMd86/R5Xpmdp9z0Gchn+fDYCEcHW7HA2hjqQcBXXjnJzHqZtXqdZhiRch0eGBnk0wf23pZjY80PeH1ukRPzi+zpKvLixWkWKxV2dxV5fHyM/b3dRFqzWKny5OtnOL+8hhBwoLebR/aOMtzRip2OYs1sqcw3T53l/PIaSgru6u/l0b2j9OcyXF4vcXJ+kblSmctrJVKOwyN7d/HN0+fIeR6fPTTBePed9qL/gBOyJSw63QK707t48fJxymH1bR4MfXuhRrcDKZMgby8RhRAOaptD4t8KYWZsj4e6xhjP9fzUSXC7+73fifgKbCXpzaTZXSxwbGaWo9N9HOjpphlG/PjyDOuNBvcM9G+bHjNpOzeczyoAJcRPlYxjrZkvV/ijnxzn/MrqZhKUgZxHrA1KSC6tl/hp6getibe1ZZFPeKQcZ8uISDkOg7ks+UQC11JbVqvJtrPY9ZBSXHMCMa09Yh1jSYm3zTm5rVXq1XobYcjppWX+9HjL5Dqcz5HzPIrJFEEUUWo2qQbBts+TcpwbklKItkw/rb62pU1O5jEYCm5nWwZxg/K79WSsn957GGvNN0+f4yeXZxnqyLOnq8jU6jrPnLuIrSRHBvrwo4inTp/n7NIqd/X1MKLzfOPUWYI4pjebvsGR82aItGZmo8zXTrzB4+NjjBTzDBfy5BLuppf8YrnKX752isVylaNDfWhjeGNhGYPhkT2j7Cp2ML1R4m9ef4PVWp37RwaItObk/BJgeGTvKJWmz+vziyxVahzs6+brr58l0DG7Owv84PwUe7qKDHVsH73xTvCBJmRXOQwl++l0C3x19ht3NIvRe4kXliepR0HrJJ/KCpXQJ2O73F0cYle6iCMtmnHIK2vTnN5YIDaa3sT2yTU2gjrnyktcqCxTDX0sKSk4KR7vmyBpuUghuFRd5XRpgYVGGSUEA8k8RwqDFN23e6jF+x+q7UT2D+85wh+99DL/34mT9F+6jECw1mhwuK+XT43v3TZD0JUDB95rhHHM0xcm+fqpMzw0MsTH94yxu1gg47o0w4gfXJzihcvT2157o2vNnYEUYlOJeWJ8D7sLHVjb5NR2lCKXSGyRQEpxWyFvrvX/s/eeQXbd55nn7+Rwc+qcGxkEQII5SVQwrWTJliXbO7bXYeyZrdo0VbOzobZqx7U1H2b3k7emXN7x7Ixrxklla8fWKliJFMUgkSAIgsih0Tne2zfHk/fDuX3RTTSABoMMh+cLie7T555z7vn/3/S8zyuhSTKO79NyHGLvcprajtMb6Qehctb3rs3w4swsXzp2lGcnJxiIxYioCqVWm4Zt8/bq2q6fJQkC4n3iZL7byN7t3z8JBEGA7Xn8xdsXeHJijJ8+vJ++WIRXZub5wfVZTi0sc2J4ENv1eOnaLPv7snzswBQxXWO5UsN2XSbSqXtK7fuBz3qtzlQuzTNT40Q1FcfzEIWQIT9fCp2Bf/L0ozwzPYEfBHxduMxbi6v0x6IMJeLcKBQ5Nb/MP37qYZ6eGsfxfDw/4O2lNYaTcbKRCEGXhf/Jg9P8eHYJx/X4qUP7eGd5nXIrzLD8g0G+B9i+Q9EqsdLewPEdZFFizBwmo6YQELjemEMVVaaiYV237XW4XLvOuDlCVI4w05hDFmVc36Pu1lEEhQGjj2EjbABvuC0WW8t0fBvLs/ADn7gSZcgYIK0maXlt5pqLtNwOEJBWUwzoOWLKnQ3dqxs3uFhZZV88h+W5tD2HhmNxrZbntw48TZ8ewwt8Knaba7UNbtQ3iSkanxs9vuM8Hc/h9cIcrxdmqTsdZFFCJNRE/kh/qAC23q7y0vo1rtfy+IR9uVerG6y1a3xp/CSqKN0XxufDgCpLHBvoR5NlREHA8XziusYTuREeGhricP+9tafcDiHhSsIPAtqO+4ElOR3f562VNZqOzSf3T/Ox6clevXaxUtnRj73bNclSOPt1L1rfYnfiEQG0HOe20aGhKBzpz/GDG3O0bIeJdIq0ae4wFUEQ4AXBnsYW7oZsxCQbMSm32lzfLNIfu7meSq0W+UZzxz1VOh0ubuSRRJFfevA448lkbxxivtGgZlnv6Tp2gySE37XnB3Rc5+5/cJ+huFGlUW0zfmBvbXhBELLRL68XODrQx+tzi6iyzGq1xmaj1eNKBEFA23HCdjlR6PIQRBxPwLvnTIOAJks8Nj7SKydsRdj1jsVGrUHTsnlmeqKXhXlodIiXZ+ZZrdZo2TbrtZCT8OTkGHJ3fT4yNsxL12fZqDXIRMxee58my+SiEcbSCXRFIaKq4Sxn7/bDfd4r/o4Z5O3Dr33WO3nOlM+z1FoDQgWVpdYaz2QfJSpHeCn/I5JqomeQa06dv1r+Nj838ikmImN8c+0FYnKEmBKlbFdxfJcRY5AvDP80qqhwoXaFi9WrBMCmVaJolRiPjPD8wHOYksG5ymWu1m/Q9sKZn6ZkcDJ1jCOJ/Xetcy+3yoxHM/zq9BPEFZ3X8jf41+e/zfNDh0mqJhFZ41PDR3kgOcRfLp7lbOnWSGi+UeT7a5cREPjyxMMciPfj+h6r7SpxVUcUBF7L3+BSdY2HM2M813+Apmvz3dVLfGXuTZ7MTTIWyYSErJ8w/CDAdj0c36PtOD1xiY7rUG6H06FkUUCT5V5vquv7WK6L5wdU2p2uPrZPzbKodjpIgogqS72ot+O4vDq/QKnd4n/4yNM8PjaKqX7wgxo0WSZphC1kV/MF5kplTEUhCAI0RSauae+J/CMKAgktzHIUmy026g1MVaFlO5xb2+Cd1fXbqk6ZqkJC17E9j6uFTeZKZTQ5NCSmohDTtR3ZAVkUSZsGmiwzs1nkRrFMp0uyUiSJmKphqgpRTeXpiXG+dvEKf33lGtmIycFcFl2Ww03ZdWk7Dqai7NobvhdMpFLsz2T4/sws3702QzZiEtVUbNfn9PIy6/XGDodBEUWimobvB6zX690eaoG6ZXFqaYW5UvkDiy0NJXyuXuBz5V3P1VAU4pqK+gERvd4N1/FoVFsYEQ1VVyjlaxgRjU7LolnroBkKsWQEURLYXKsS+AGJTBTNUKiVmlgdhxsXlinla3s2yD4BHdcjCALeXl5jdrPcY+grksR4V5JVkSROjg5zvbDJ20urmKpC3bIZSsRI34MsLoAoQFzXkYVb+3w938f1fGRJ2jGDWpGkcI/wfFzfx/N9ZFHcQa5UZQnfD/eRIAi1trfOIYqh0RcAhK3pWPd02XvC3zGDfBO273CxepUbjQW+NPJZRswhilaZ3732BwwZfRyI3n2cW9NtEZVNns48ypAxwKnSWb6/8TLP5B5jQO/jh/kf81j6QR5OHadglzhVfJuYEmV/dILV9gZfW/0OXxj+FKPGAF7g843V73GxdpVBo49+/c7R13gkw1O5KfbH+wiCgI8PHuT/uPAd1tpVDnoD6NLdDcebm/M4vsfHBw7xRO7m/fZ109tBEPBa/gZRWaNPj1FzLCBgLJKm7Tm8XVxiyEwivw9R//eKlm1ztVBkrV6nblmcX9vA9jzmyhW+e22mu7lpHB/sJ22aOL7PcqXKtc0iLcem1GrjBaFC2GvziyxVqpiKynQmxb5sqELm+T6rtTqKJFNqt5kvl8NFJwjIooDZ3VzfL1PWVGTGU0nGUgl+tLCILIWMXy8IOJDN8NjoyG31vO8ERRR5emKcU0vL/OXFSyxUKmRMk416g3yjGeoS71JjhbA2OplOkTFNfjg7h+t7ZEwTx/M4NjDAo6PD5La1AKmSxFAixvHBAd5ZXeePzpxlJBFHFARGEgkeHR1mfzaDLssc7e/jV04+yJ+/c54/eP1N9mXTvR70fKNFw7Z4fGzkPRvkkUScx8dHubiR5wezc6zV6+zLZGjYNpfzeWRJJLEtjZ02DR4ZGebs6hr/5rXXeWJsFEkUWShXaDkOSnfAxAcBU1WYSCfpj0Z5ZXYBCFnwrudzpD/HY6MjOyL6DxKtRodzP55h37ER+kfSnH7xEhOHh1i+kWf+yirjBwY5dHICx3F59RtnQYD9x0cZGMty/Z1FKsUGVtsmmtj7MBZJEEgZOknD4Ncef4inpsaJ61qYBfG3ulRAU2Q+98BB/vl//hZ/8uY7mKrCieFBnts/eUvJ4f1A7+ree77PSqXGSDIOQcBqpYYiSSQMnYimkjANXN9nrVpnOBnH932Wy1UMRSau6z8xQty7cfGyYHQAACAASURBVN8b5CAI8Dwf2/Z6Xq8ggCJLqOq7L/+mt1R1ajTcJmk1yXgkFD3IdQlga+08g3r/zc8g2FZ/2bk0D8amGTGHUESZpBonJkeoOXUG9L5wSgw+duDg+aH+syiIOL7LplVisbnCny9+bUdt53gyQtNt3/W+E6pBRusys4XwvJoo0/HcPZNJCp0GUVkjp+++AbiBz2anwYvlK3xr+cKO+l1U0ajY7TuOBISb8pO27eH7oYKNLIvIstTzlN8NRZKIqCralse5C5ardf7tG29yanG59zNRELi4nufieh4ISUP/+jPP88SYScOy+d71G/z+j0/1jtckmWKzxR++eQYI07S/evIE/+zZpwiCAEWS+OjUBN+7doP/7bsv9P5OlSQypsnDw0P80oPHeHBocNt1K6iSdEvkKRAa8GhXuWnH7wSBg7ks/+Kjz/KHp9/i1NIyTdslrqnossyJwcFQA/c24WwQhBmfIOgKKwjhdCJZlHj+wDQNy+Lrl6/y3Ws3EAQ4mM3ymcP7mUyl+d1Xf4QqywTBzoEKoiDw2Ogw/+3TT/BnZ8/x6twCLcclZejkItFbxksKgkDWNPmd5z/OH7z+JufW1jm9vIKhKDw3NcnxwZsRlaEo/OajJzmQzfCNK1d5Z3WdSnsJVZIZTsQ4OTzEs5MT294HsUee2v4Mgq6qky7LRFS1x9YWBIFnJ8ZI6hpfeec8p5dWObe2wXQmxW8/9gjFVovvXJvpRfh90ShfOHIIx/P45uVrfOWd8xiKykNDA/zKyROsVuv8cDYkIG2pYqmSRERTkSWxp328pfAkCje/awEBx3eQBAlREENt5cFB/tmzT/HHZ87y2vwCbcclqeskDX1Hbft26FgOrrs3cqosiSiKhCSJeK5HZbOO1QllRzc3qqQHEtiWw8i+fh7+2GHstsPply6R6o/TN5zm9A8ukUivsP/4KMef3MfclVVWZgs7r8dx8Xy/l7HqOA4t2w7TzrKMJkt86sh+Xrh6g6RhcGggR8dxKLfa6IrCdDaN5/vMbJZIGDr/9UefYDqbQRTCFH8Y0X4wBlBXZMbSCfbl0vzRqbP85pMP4wc+37syQzpisj+XxVAUJjNJhpNx/uytc/zmkydp2y7fvnydoWScicyHI8q0F9z3BhngtVM3+MM/fY31fNjak05G+MxPHeNXvry7pvUWgh39wzshhA2JO5iJfhDQ8a0df6GIKnJXdWXbVoEAfG7wE3x1+Ru8uvkmsiBzKDbNw6lj3fNDVDH5l0f/OYak9f5aFMTe+e6EcOD8+xswH3DnpowtmbpfmnyUL4yeoH8bMUxAQJdldPHOkbjvBywuFfkPf/oaV2c2MA2V5z9+hE9+9Ah92d1Vib58/Cg/e/Rwryd3N+zPpvm/Pv8ZPP9OM4LpsXJThs5vPHKSX37oxO0vVgijSoCaZfHD2Xn+z5de4eTwIIf6cuEGKwh0XJfrm0XeWFphqVrj97/4M8Q1jV968BhfOnYEeasndxsyEZPf/+Ln8YMAY5f5xIYsc3J4kAcGPr3DoRIQ6Ngu9Y5N3Lh9pLCQr1ButMglomiKzOx6ifG+JIOpGJ8/eojnD+yj0uqgKTIxXUUWQ+Pwf3/x84iCwGa1iWtoRPSbhi2qqnxkaoInx0dvOrsQpvt2qe/KoshUKsX//vwnQgdh2893O/6J8VEeGR3G928eK3adge3Hf+n4Ub5w9HBoXLb9PCB0jv7lxz5GOmYS2VZOUCSJB4cGOdrf3+usEAUBRQyHB/zcA0d2pCNTpsFvPHKSXz35YG9VSEJYRw9GAj53+ACCAJbXwg5s/ouHDvPlE4dwggbNYBXBSZJQw8zKWCrJH/z8F8LvWlH49vpfcyR+hInIBAARVeHJ8VEeGRl6V+pc2pPh+Tf/7kVe/tE1HPfuNconHp7iS59/mAcOd4dICOC7Pp7r47s+saTJ/uNjXHh9hpf+6i36RlJYHZdYIoKqyXz85x5h5sIyoiSimxrKtnnkW/jdH7zG2eU1litVOq7LqzcWmHgrxXP7J/kvH38IRZL4p888xlfeOsfvvfw6q9UasiTx8OgQP//gUaaz6a1L48rGJv/Vn32tu/ZlHh4b5ssPPcATk6N8UJjMpPinzzzGv3vtNL/2n/4CsVtD/vJDD3BsOAzEDvXl+PXHT/KHr5/hF/6fr6BIIo+Mj/CF44c51J/l6sbmB3Y994K/FQbZ83zaHYd2OyRJtHUHx7nzy5pU4sSVGEutNeabS4yaQxStCjONeX5m6KdIqUl0UaXhNil0ikTlCHPNRYpW+V3tA7fXUi07NcbMER5Jn2DI6EcXNQwpnOebUVMklDhvFM/wbO5xIrJJ2a4gIhFVPhgFnbshq0dZapYpWs1df6+IEjk9SscLhyok1Z2pqr3U1Wzb5U/+31OcvbBEo2mFeskvXSYeM/jc88dvOX65XOV7l2YoNVuMZ1I8NDbIdO7WQRaSKGLcI+mn3Gpzdb2A6/t87OAUAXB+eZ2VSo3jIwOMbJOoXKnW+MM332J/NsO/+OgzpMybc5iDABbKZeKaymvzi8yVyhwfHAjHQd7GgRAF4Y6Myy3RknffU9t2aLYtVEW6rUG+ulLg2kqeRMRgLBfOCm5ZFmduLPOZhw+hSBKO6+E5HpKi7LgOQwyJW6dnltk/lGWiL42phdewUWlQrLc4PNJ322zGu+9BEEAXd24bi4UytuMxNZDZcZ7bGeqt+357dolHpodRZGnXtpcgCLAcl3bHxkjFdxjYngjMbc7/7vOJQjhIQu3KIJ6bX2MwFSMbjyB0ST01p8TV+lsogoYqahhSmFny8TG5mWUK8OkEDV7dfJWoHKVkl2h7bc5VzrHYWiShJBg1RynbZQ7HD6OJGqfLp5mOTpOW7z7ty7ZdWm0Hdw8G2bLdbmoYNENlYDTDt/74NYYmczRqLarFBqV8jbnLq6i6QjRucvSRCV75xllymylG9/UzdWSYa+8scu2dRXzfJ57auT/91lOP0HHcsLbKluMjYmo337OopvILJx/gs0cP4nge4QhUmZiuYTkulzfy/N4PX+dffe6TDCcTyJJIudXimxeu8f0rMzw2PrxjLOS52TUW82VGc0mmhjLoiky12aHRsXj+8H4emxglae4u7atIEtPZDP/T8x/pkfu2UtFb76MqSxwayPG/PP9R2o6DIAiYqkJc15BFkf19GYaTcQRBIKqp/HfPPYkqSUQ1lf/mo0+gSNKOHvoPCveNQV5r1ahYbRqOjSEpHEn337UFYqOzyQ8LP6ZolynbVb699gPOlM7zcPo4+2OTHI0fxPJsvr76vVAQXBB5JH2cfdEJTEnngcQhTpfe4T/N/wVxJYYh6YxHRvbc5uAGLgutZTasAqZkoIkah+P7eDxzkrSa4ovDn+GdykWu1WfxAx9VUnk88xAnEke6EpUfLh7NjHOutMKr+RkGjDj7430hqatVZSqW67Gtv7t6iVc2ZogqGmk1QtlusdqucDI9hire+RXxfJ+ZuTzNlo3vhxmJfKHGZrGx6/E/urHIaCrB45OjzBSKvHhllkqrwwND/QiCwAuXZ3h8apSzS+tsNpqMpRNM5dI0OjbnlsMWr4lMislsilPzyxTqTQ70ZTk0mOtFhY0ua1YgXHhtx9nBiAy6vbKL5SqT6TR90VunLS1XRWqd8DwfFgnH833Oza9RqrfYN5hFkSSuLOcpNdrUWh1OTA4S1TXeuLbIernGvsEsbdslGdGJGRqlRpuAsDfzzetLtG2XI6P9xAyNhXyZMzdW8H2fvmSUWsvizevLnLmxwkRfisFUjKsrBc7NrbFerjE5kGayL73D+XRcj+trm1xeyhPVVQ4O5yhUmxQbLSrNNlP9abLxCKdnllnZrHJy3wgTfSmSEYMfnL+B63kcGetnMBVjsVBhbqOMpkgMZxIEQcDXXr9AodJg31CG/mSMmbUi8xslcokoxycGEUV45eIcMUNnsj/NzNIma6U6pWaLZMQgEzPJVxrU2hbJiIFluzxxcIzLy3k2qg1GMnFSUZPNWpNSvUXLdjgxMYiuyHzv7WtkYhGOjvcz2b0PL/BoulUkFEw5SsUuMGhM4vkOltfpPZeO1+F64zojxggZLUPBKiALMmk9jRM4VOwKK+0VkkqSS7VLDBqDtLxWb2Tl3ZCIG6QSBrV6B8u+OwN+C5qhcujkBOn+OEZEw/cCYikT1/EYne5HViRiSRNRFImnokiKSCRmoOoK6b44ju0iSgKqttOxzEbvHEBs6U8nDeOWiV8QsrALjXDy10gqwUQ6hSBArd3pEfy23jvfD1gt1njtwhyNjo0kCvQlo2x06lxdytOfijGUjrORr3H5xjqVRpvx/hSTgxk2q02uLuWJGhrHJgcYSN+elyEIISG0P757OU+RROK6FioKCuIOPkXuLs/j/eC+McheENB0bDzfx9CUPZnEiGxwJH6AjtfhaPwgmqigiAoDeg5VVOnXczyefohRcwi7W+cZM4dIq0kkQeJAbJqYHKXsVFFFhYhs8nDqOANGjohk8HPDn2LQuFlrHjYG+fTgx+nTs6y2N7hen+Op7CPE5SgCIqvtdVbbGyw0lzkQm+JE8ghJJU7ZqeH4LpqoklbTWL6LE/jUnQ5Fq8F0rK/b77j36SBnioucLi4wUytwo56naDX5nbNfx5BUPjV8hIOJASajWT4xeJDXC3P86eybKGKodRxXdH77wLMYssJTuSlqTocr1TV+78oPkQQBRZDoM2I8mL57GklAIGKqiKLAls3TNQV9lyHnAKuVGocGchwcyLJUrlLvWNQ6FudX18lGI+QbTd5aWCVfbyIJcHVjk6ZtE9U0VspVPnZomqSpYygK/bEotbbFfLFM3NAYTMR6KcGt56jK0q4OlqEoDCXinFtd568uXmYqnUaTJayupObp5RWuFDY53JdjJB7/UDo8BUEgburkKw3qbQtNkVgr14kZGgeGspy6tsTHj+8jGTHQFImpgTSpqNHjUGxBFASSEYNyo0zLsqk2O2xUGt3jZBrtcFZu3NDCza1SRxQEdFVB1xTG+1KkIrdupIuFCjNrxW4NMeD778wQNzWihsZkf5q5jXLI5ZAloqbGWC5sJzpzY4VKs03c1Hjj6iInp4dZLFSwHJejY/3EDY2mZRMEMNGfIhePYKoK6ajBZk2jUGtwY73I0bE+UlGT1WINzw9YLdVpWTYDyRi1lsXrVxeRRJHRbIJCN9pXZYlysw0CzKwVcb0CcUMjbur0JaK8cmmOzz5yCFEU6E9FGUzHe5GeJEgklAyD+hSKqGL7HWJyCg8XZZt6nhu4VOwKR+JHyGpZFEGhYBVo+yHnQhVV3MAlq2V5ufAyFafCsDGMKe2NLPX8c0c4fmQEy3axbY+O5dDu2DSbFt//4WU2S42u87sTkiQSS5pEEztV5AI/IOhjh2zoaLS/x1sQBAEjcvP+Pug2R02WmMykeXBkkD8+dbYnI+sFPrlohEfHR7ZdL8RNjWTUoC8ZZaI/TTyiExB2JJTqLTw/YG69RNTQGB9Is5gvh+97t/zjej6vnJ/jyx+9Q/nqXbA8F0kQws4M38P1PWpOm7bnMB7JhM8OgbbnYEgKshjqmju+29Nyt30PVZSwfQ9NlHECD4EwEykSlkjvJj163xhkQ5LxggDLd7tax3d/KaJyhAcSB+94zIDRx4DRt+vv4kqUeLcnuNWyWF2roMkSimSwsF5m/9B+ItsYgCk1QUpNYPsOC80VNq0inxn8OMPGID4+bxTPcK0+S9vrIAoiEdnkSOIA5ytLWJ6LIEDJcihb673Ic6lZxA08DsYHesy+Z/v30XJtBoybKVZNlPntA89yLDWMJslEZI0BI44sSEzHQraqKsmoYvg7EQFVVngyN0VWi3KjsUnTsVBEiaweRZdkBAT6jDifGDzIiJlkpVXB8T1MWWXYTKLspdatSDz3zEHqDYv1jSqGofDQ8TEOHRjc9Xipq1G8VVpLGjoJQ+fU7BJDqTiHB/q4sl5AlURysSgdx0GXwxYhU1M5MhSyzq/nizRth5RpsF6rU+9YDCb2NklHEAQG4lF++aHj/PWV67wwM8tr8iJAt7/QJyDg0ZFhnj+wb1e1rg8CoiCQiZosKTKu6+H5YbFkLJdksj/N985eR1NksnET19MZSMWIGRqNjnXLeXKJKEuFCq7n98hJlWaH4XScqYE0Z26sMpSJc3A4R77SwHI8EqZONh5hsj+9a+p3s9ak3rJIxwxMVcXv9izvG8xycDjH1eUCvh+QMHVEUWAsl6TS6LCQLxMzNfoTsZAzIYkQQDZusn8ofFdL9RbxiM70QIaIrrJcrFJtdYhoKpbjUm60UGWZgWSU2bViL32dMHUODOW4spInX2kwlI4zkknS7Ni4vs9ioULM1MjFI9iuR7HeQhBFxvpS9CcifOutK5iqStzUGckmGErHe5k4XTIZMw+R1u7c8iMLMmk1zfXGdcpOGTuwabgNam4NL/BQBAVTNonIETRJY72zzvHEcXTp9tPTtuPAvgEO7BsIyXy+j+142LZLo9Hh7IUlytUWvr97OnvLwO74mXirZMi7j/ugjfDNMYYisiQxlkrwiw8fY65YxnZDQxXVFaayaaazN9n2giAQj+hkE1GiukJ/KkpEV/E8n0TUYL1YC9vnbJepoQwHRnLMrhZZK5bJJSKkYgaGqtCy7q0H/EY9jyQImLLGWruKKauYksJSs4TtuxiSQt6qU+w0OJkZp0+Pk+/UmG9s4gU+MUUPya2+2zO6VadFRNZJqxGSqklWj/7tMciaJGP7HhvtBknNYJyfLNOt1baZmc1jmiq1RofZuQKJhEHEvHUzFhGIKzFiSpQL1SssNJfx8Flv50kocQb0nQ7Atdpa6IGJImKXiZ3VYgwaSWzfZb5RYCrah9K1f4/nJm/5TE2S+dXpx8MZvu0GOT3GaCRFEEDbdeh4Dv1mDNf3kbtsTy/wMWWNE+lRDsQHaLo2GT2CIorM10rkDDBkhYwW4+m+WJfsE0oCuoGHtAdSmSJLPPf0QSRRZCNfwzRUjh0d4cB0/67H7+vL9CLjpmUxmk6SMHTK7Ta25/Gxg1O0bJuFYoVSs8VAIkZfPEK11UFXwtc1AOaLZWYLJWK6huv5WK5Hvt5koVSh1u6wVq1jqgoLxQqr1Rr91Ri5WJSoFtZ9UobBZw8fpD8W42qhQKXdwfZCOca4pjGSTHC4L8f+7M76do+kszUcS3jvm1nLslkpVVkt1ujYLrIcDnzQVeWmoMK7aHm261GstVjIV9ioNFgvN4gZKsubFVZLdXRVoT8ZAwKurhRQJImjYwMghC0hvV7KbsQRBAGXljYYSidIR40d95KI6OiKRLnRRk3KTA2kubKyiaEqO8hJhqpQqDW5tlIgomv0J6OslWtossxQOk7M0FEVaUfqXxQFkqbOpaUNRrJJFvMVZtaLXVJZ2C9eabaZy5fJVxuslcOhHaoioyo3sx6SFE6mkrrX05eMUG9blBtt+pOhJGPTsjGUsF9963HGDY21Up2ortGXCDd9VdTJaLs7ktuhiRoTkQku1y7jBR6TkUnichzLt7B9G1VUicpRJEEipaSQBRlN1O65lUYQwvszJBFDD7NOqnIrw/92cD2/m3INjW/Dsai7Fjkt0i11eMiihBf4iITHiNxalw8CDz+wQ519ttagRxB4CIIEgYsghO+O5zfwgw5e0EJARpHSgIQsujw2PsBj44OEe4yF65e7BvvOnRyW41KoNlnKlynX22xUwnKYrirhEBkhfFdV+ea7Ojlw91r9dlTsFhWnhd41wkNGkhEzRdFq0HAtgiCg7nZoezZHvGEIoOF0WG6VaXs2EVlDIIyuE6pJ3enQci1yeoy2a9P2bPr0uwcM941BFhFouw41OxyEfac2kA8DQRBurMmEydJy1yO3XVzXQ5Z3GiZJkJiIjHIscZjF1jKr7XUEBLJahiOJAwy+KyLP6XF8P0DpvvySIDJgJOjXE1SdFkK3MWr70O7bYa1V41J5A1MO24aisoooiKy1asiixGKjTFozMWWFjudSsdpIoojtedQdiwezChFZ5XRhmX2JLHFVww9C3WOtGzXbvosiSoxE705aEEWBXCbGFz93ck/P+eGJYV6fXWJ2s8xgIsbhwRyaLHNiZBDH8zBVhQdHB2lads9AarJM0jQY7pKyBKA/HqXeCaVAFVMipmm0bQfPC9BkhUqrg0A4OEJTFGzXw3LcnkEWBYGYpvGx6Uk+Nn2rAwTdli4/wCWArkQgAbQsZ9umFZCK7b1vcztsx6PZsZGkkCwVBEEvhSoIsH8oi67IpKMmfuCjK3JIcLQdHNcjoqlUm20kUaDRsVGVUA1srVSj0bGZ6EsRNzXOzq4ykIwRNzUkSWQgHSeiKWTjEYbTCZY3q8RNnXR0Z9p6LJek1bGZzZcQRQFDUxnNJogZoRjJaDZBImKgSiL1js1Kscb+4SwPTQ9RPd9BFEMilaZI9HXZ4VvQFJkHJ4dYKdaIGhpxU6MvEcFyPHRFJhnRaVsOLcsmETGoty0yMZNU1ECRJNIxk4PDObKJCDFDYzAVQxJETk4PcWkpT9t2kCURQ1WIGRq6piBJIgdHciiyxMHhPpY2K+SrDRKmTkTfO0FHFmUyaoans0+HK7ebzvR7bG8Rx3co2kX8wOdw7DCa9OFkWW4Hy3GZK5QxVJlcLIKhKlTsFtdqeTrRLJbn4BGgifI2hr1AQtXpM3YaDstdwPEKiGIMWYiFU938Kl7QRhJjCAToyn4IFByvgOuXsL0NQESXxxAEhSBwEQUdUdAJ8HD9Epa7hICKqR5ElYZD495FLhlBV2QUWcb1fFqWjesFGJpCo20xmuu+h6LISDZBzNTwvICFjXLI4JbvrUNlNJJGbAlYvkufHiepmviE7adRWadst9gX68eQFLJaNCwTaRH2x/vpeA4BoaBIVNaIyhotz0YURBRBouq0up0Fd78mIbhbo+lPCHW7w4srN1hpVnmif5wHs0OI3fTmD169yr/9jy+zvtFte0pF+PynTvAb/+jpD+zzq9UWC0tFjhwa4o3TsyTiJrGYTi4TxdwlSt6OjuviBqH6iySKRORbWwe2sL0X9L3gmwuXybcbOL5Hw7HYn8hyIJnjaqVAUjN4cWWGhzLDyKLAZqfFZqeJISvEFI2EqvPM4CQRWeVPrp/BlFXcwAvT25Lcm/sbBPBAeoCp+K3s5w8CvZ5YgTsS97xuX/PtGMCu7/faaD4MdGyHUq0FAciyRKXRJhHRWdgoE9VVEEKj+uD+4Q/l898rzs2tMZcvcWJyCM/3+dbpK/zW849h7KJC5nedjju14/hBQBAEdxwZ6fthP/r283iej3gH3e8tdbWtElX4fbODbftesVVjvRN7fCuC/CBHYW6h6TaZbczScBs8mn4U+S7kyL3Ashz+x9/5KpeurWHbYcr6qcem+aUvPsqJozv5HqVGi6+eukDC1Hhi3xhjmSRLzTJvbi7gBwE1p0NCNXB8D0kQ6XihHOqR5ACP5SZ2nGuj/kc43gqqNIIkJfH8Oo6XJwg6yGIGWUqT0J9DFhO0nes4Xh7b28APmqEBDhwEQUVAQRINvMDC92v4gYUgqOjKPqLqQ0h7HMpzJ+zlXb0TdptWt9Ueuj1oevc7vXXM9r3IDwJWWxWarkVWi5K5jR7Edtw3EbIuK6R0g81OE8tzP7RN9nZIJEyOdxVqnn5i/z397XKzyma7SdWyyOgGJ/uGbxvpvt+o35AVREEgpZlMxtNkdJOy1SbfbtBwbTK6wWQ8xbVKgbiiMRyJ4wcBFbvTnVscGrCRaIKYolGy2jRsCwFoODaO77E/kfvQjDHsPc17t0X1XrWQ94pitcUP3r6OripMDKTYrDZ5+OAosiSidUVpLNv9iWdz7obRXJJGx+LFd65j6iqffvjgbSMGURAQpTtfu9jt2b/jMeKtQxiku/Tcbtcghg/WMO6ljeuDEqPYDRE5wrHksQ/t/Hf9fE0lHTVwPL/reAhIooTZTa0mVIOkalDoNGi6NgEBiri79oEqD6HJw0hiAtcrEQg2pnKoG/k6yGKaABcIUKRsGE0LOoqUQUDC8Qr42IiiieUuIYlJQEYWTWQpjSKmCIIPRvd7L+/qnbDrBDvh5k/vtK+/+3eiIDASubfS631jkDuuS8O2aLkOzm0IC/crzC7rbjASYzjy4TByt/Dc0DR+EITvXECP/fdY31jo2wUBpzeXOZTqYzyawpTVm29RcNOD++TwgZ7H9+4kyU/aGbpf4fk+HcvBUBUy8QgX59ZZLlRY3CiTiOphet9xuyn/++eZJSM6Tx4a54mD48D72p/+AX9L0bIdGh27K2EZru8hI87g8Hbxn23rv7uP7PaqJPWPsXtBrRcv9n4iixni+lPbPmELPiACPtXOy9jeOroyhaEcRBTef2T8dwX3jUGGgJiq029GiSp3ThGHKYOfzFXtBbqs0HJsCo5DRFbIGB/eCyZu98S6/9NrGej++MHMUChhKb4rZhFuPXZrUe447H56uH+DGMrG+cVPnEQUw+kyP//cCVRZ4uDoTSGNILj/HJjtLST/gL+/yMZMgoAeGXK3KG77+r/dug8lQ4NtfwG7GeObEHc5divylohrTxHgIQgKAvI/7DfbcN8YZJ9wY9Mkedeh4zuwC7X/bwqe7zNfK7FcrxIAJctk0LXDyPRDwm73ftNIC7cdKHAv5/sHhBKSMfNmCi/aTft+CAOh/l6hVm5y8c1ZAE48tR/HdtEMFc/x8Dz/JrdAEGg1LOIpE0kUsCwX3/fRDRVVU9hcrzJ3ZZVowmD/sVGqxSaRmI7reIhS2H8LIMkStuX0FomqKsiKCF1+gvghpq7/ptCybXRFZq0StgTeCXtb/7uZ8vd2rCjurf3r7yPuG4OsCCKeH4qDdLw71xNEUdgWoYQpmcWlIldn1lldr1KptbAsF1EUiEVDYtb0RB8H9vUTMbU9aDq3RgAAIABJREFU1Ze2sLxa5pXXr3Pl+np4nbLIyGCKX+8SygRBIK6GQhUVq8NSvRKyPfuGbjnXqTNznDozT6FYByAe1Xns4UmevceaNUC5EpLQFleKFDYbVGptLCuUFJW6gvMRUyOZMMimowz2JxgeSpFORd5TRPf9H17m7Pkl6s3OXY8dGUrx1KNTHD303slOc4ubvPjyFRZXSgAcmO7n8586QSx6czHnN+vcmM0zv7RJsdyk2bJxnJAVbxoqfdkYYyNpDh0YIBE3PpAapW27rK5XmFvYZHmtTKnSot0OP1fTFExDpT8XZ2wkzfRELnze9/C+3Qn1RofFlRILi0U2NmtUKi06lovthC11iixhGCqJuE4mHWWgL87oUJpcLnZHCdi9wLJd1tarzMzl2SjUKJWbtNo2dldJSpYldE0mFtVJpyL0ZWOMDqcZ6IujqjujINty2FgpoygyF07NkuqLUynUKKxXUVUZRZORZYnhyRyFtQqqJmPGdGqlJpXNBn0jKSYODmK1bYobVVzb5fwbN0hmoixcW8PzfBwrrO1LSjjkREDAcVxqpSbZwSRmTGdgNE0itXfVpXqjw8paheXVMqtrFar1Nu2OjWXdfAamoRCPGaRSEQb74owMpRjoS9yR3PZhwFAUDgzkSEXMXUVf7gbH8dgo1Jhb3CRfqFEoNmg0LDqWQxAEO77vXCbG8FCK/VN9xKLaB0LKA9gsNZhf3GR5tUxhs0Gt3g6FPxwv1D1XZaKmSjJhks1EGRpIMjyYJJkw3/ezrjc6LK+Wu/dfp1xp0rFcHCfkN8mKjGkoJGIGmXSEgb4EI8Mp+nLx97XW7huDbPseghD+t2TdeRqSJArIkoTremwU6rzy+nUuXV1lZbVCudqk1bJx3K6eqq4Qjxr098cZG07zxKNTHDs8jGmoe3po9UaH85dWeO2NGQBUReLIwSF+vft7URDoN6OsNSN0XBfH96nZuxut1bUKp87MsbBUBCCbjjLQn9j12NuhUGxw9vwil66usbRSYrMUvqhb9+y6IdNbkkPiUcRUiUV1UkmTXDbG8GC4cB46NoYs710ZbHa+wI9OzVAs766LvR1HDg6ybyLH0UP3dGs7UKm2OHNukQuXVwCo1ds8/dg0kYhGp+Pwo1M3OHthicWlIoVinXrDutmmJomoqkwiHjoj42MZHj4xzrHDw2TS9z76bqv96drMBm+dW+TGfJ71jWroBDTDz/U8vzeBLBE3yGaijI9kOHpoiGNHhhkaSL7nZ1GutLh4ZYXzl1dYWCpS2KxTrXdoNi1sx8Pragdv3bdphN95MmGQzcQYHEiwb7KPBx8YJRrR7mmzaLYsZuc3efv8IjfmCqznq1RrbepNC7v7vCEkZCmKhKErRCJa79kP9CeYHMvywOEhBvuToWESRaJxg8lDQ8xdXWV4Msf6ooPdcRAIDbYoitTKTVZmC0QTBrblomoyjVob3VTptML6qO/5uK7H5noFgoDCWgXbchEA3VQRRIF2w0IQBXRDoVZukshEWZ3fJJmOkMzc+X0IAljPV7lyfZ0r19dYXC5RKjcpVZq02w627faGQEhi+PwNQyEa0UgmTDKpCIMDSZ5/7ghDA0kU5f0NjNkrGh0b2wsV1raYx3f73reM8LWZDW7MF1hdr1Ao1qnW2qEx7IT3usVi3vq+YzGDdCrCyGCSY0dGePCBEbKZ2HtyRH0/oFCsc/rsAleur7OyWqJYalJrdGi17VBAx/MRxVBwRtMUIqZKPKaTSkToy4XOwcHpfo4fHblnR6hcbXHpyirnL68wv7i11to0m1Z3f711rUWjGsl46BQMDiTZN5njxNFR4jH9ng3zfWOQA6DfiBIEQW+Cz+0gSSJ+EDC7sMl3XrzIyz+6RqFY33VgtON41OodltfKvHNhmaXVMuVyi8ceniSdfP+e1NbVK920lykrjMXe++Z7O3i+z8pqmRdevsKP37zBwlKJzm3UaDzfx7N9bNul3uiwnq8BoSOTSkV49KEJjh8ZQZb37snqmoJhqCj1Dq7nfSjDue+EdtthPV8jFjP4/kuXePGVK8zOF7B3GTJi+x6249FoWqysVbh0dZW5+U3KlRZPPjrNQN/eZw8HQUC7bfP6W3O89sYMZ88vUao0d5UutGwXq/vMl1fLXL66xuVroeP0zBP7ODDdf8/Rw+p6hVdfn+HVN2ZCzfDm7unHIAh23Hd+M8zCCAJEIhoPPTDG9ESOaGTv/bClcpOz55d46UdXOXdxmXKlddtjfd/DcT1abXuH06brCgen+4lFNQb7w3VhRjT2HRthbN8AggjZwfDnQxNZJEnE7X6nuqni2C7RhEmj0iKejuD7AbqhYpgqiiozeWgIzVDwXB9JljAiOo7toqgyuqmGuuUNC4IAM6YzOJHD6442VDXljt+H43hcvrbGG2/NcebcIgtLmzRb9l2fQbNldbXcQ8c7nYrw5CN3n7/+QaLjOFRaHRzP31NGLAgCLl9b44c/usbFK6ssLBVpte9+r1vf9/ziJmfPL3J1Zp38Zo1nn9jP2Ej6nvZXx/FYXC7yvZcu8/pbs6yslndd3xC21Hmej2W71Opt1rotsbIkksvGePrxfRw/OrLnzwZY26jy2hszvPr6da7P5mm8h7VmGioPHhtjcixLPHbvqfn7xiCrotQby7aX+uv6RpVvv3CBr3/7HexumjYRNzB0BUWR8P2AdsehXu/0DJfrepx+e55KpQkCPPnIFMl7GMZ9O1ieR0RWSesGSc1gKnFvKjF3g+f7VCotvv6dc3z7hQvU6jcjcFWViUY0TF1BVmREIeyvtG2XdsfpRc7heQJaLTv0MKV78xwP7OvHdlyKpUY3dRMuSNt2WVopUyzv1Nf1g4BSq8VqrY4sigzGY2iyzGq1RtNxSBsGshQql/UGitdqTKRSu24gzZbF9RsbbBRq/NGfv06j2QkJK5pMNKpj6CqSGPayttsO1Vq7d9+O63P+8grtjoMkiXziI4d2VWB7N4IgoN7o8PrpWf7sP7/J4nKxN6dWEgUMQyUa1dFUGVEQcD2PZsumVm/juuFmce3GBpulBqVyky985kEO7ruzLOP2z261bF58+Qrf+N451jeqPSdIlkViER3TVFGUcN14fnDzO9+WSg4CaDbDyOJeooVOx+Ht84v81bfOcv7Scu+zBSAa1YmYKqoqI3VlOh3Ho2O5tFoW7Y6z4zxb17IVMRkRjYmutOpUd2zg8GRu1x79kalQZCfwgx2kxC1sRbjBLlHg1v8HQRB2JHQ/f+lGntxgglhy97UfBAGu63Pm3ALf/O553j6/uGPNbUHXFLRuel0g3F86lttrhdu6hqH+BOlU5J4c4PeLVCQs0ZiaQso09vS9zy9t8qNTM6yuV3f8PCx/qZiGirJtj9naX7cGYPh+wNWZDSq1NoIg8KnoA2T2WBJwPZ+NQo2v/fVZvv3CBSz7piHWNZlIRMPQ1VDDXaBnjNttm1bLxvX83nnabRvf9+/adrcdzZbFD169wje/e46VtcrNtSaJvfdd6c5431prne5as7avtZbdi6LfS7B33xjkgICFRplzxTWmYhmOpgduSxtotx3OXuhGKkHAYH+CoYEk05O5UMjDCOUUN4t15hY3mV8ssp6v9dJrM3MFvvmdc8SjOo+enEBV3vtj2GoIT+kGiij1xDXu9Rxtx0GT5V3rnJblcv7SCl//9ju9zU5RJHLpKGOjGcZG0uQyMSKmhiSLWB2Har1NodhgfaNKudKk3uhQb1rEozrPPXXwnuupTzwyxRNdL9/1/PBlbFmUqy3+/C9P89qpmR0bseN5XN8s8v3rNxhPJXl6YpykofPO2jpz5QoTqSRxTUOVJfZl0gQBvDK3wFgyuatBrtU7vHFmjnKlRb3RQddk+nNxRkfSjI9myKajqIqMZbsUinVmZvMsLBV3CPHPzOV57Y0ZJscyHDtyd++51bY5d3GZf//Hr/ayDIIgkEqaDPYnGBtOMzyUIh7TUWSJdsdmPV9jdr7A8mqZUrmJ7XiUyk1eef06fhDwT37tI6T2UOMKArg6s85fv3ChZ4xlWSSViDA2kmZiLEN/Lk4koiFLIrbtUW902Cw1WM9XKRYb1JsW9UYH23b56NMHiUb27rEvr5b44WvXuHB5JWSSiwIRU2NkMMXURJbB/gTxmIGqhuphzZZFudJivVBjY6NKo2VRq3dotSyOHx1lbPjufe13eibCXdKfd9oAw9nnN/89Or27tj3cLE9cn8vzH7/yY67f2OhFaYIQGuFUMkIyYdCXjZGIm5hmGEB0OjalSotSOVxvjaZFu+3wkacP3HOp4P0iF4+Su80ko90gCAL7Jvs4fGCQ9XwNrVt6iUb1kIMykKAvEwtrxJKI1XHZLDWYW9xkcbnE2kY1JOQBG/kar70xw/Bgko8+dWBP991sWpw9v8Q3vnu+dx5VlenLxpgYzTA6nCabjmJ0B9ls7XH5zXp3j2vRaIZ7XDYT48lHp+/peV2b2eA7L17sGWNZFkkmTMaGb661aERHlrtrrdmhWGqwnq+x2S2Z1Rsd2m2bjzx5gHjs3uv2cB8ZZFEQ2BfPdnvhhC0ty12PLVXCGo4si4wMpfjy5x/mEx85jK7fqpBVr3c4/c4Cf/IXbzAzl+95rhcur/Dm2/OMjaQZHX7vEa0XBCzUypSsNhWrTVaPMPkeIuRrm0VGkwkS22Z2bqHR6PDCy5d3jGIbG0nzxc+e5KNPHSB2h9SIZTmsrFe4cHmVdy4uYdsuJ0+Mvy+ikSyJRCMa0YhGPGaQTJq3nG9LuSwTMXlmYpyBWBQ/CDg5PETSMFioVNBlGVWWeG1+kaF4nKSu39YJq1RbVKphylRVZY4dGeYXf/ZRjh4aukVJLQgCmi2Lb33vAl/9/05TKO40ym++Pc/RQ8N3fAae5zO/WOSrXz/TM8YQ1v2f/9gRfuq5I4yN7j6UoV7v8N2XLvHtFy4wu1DAdf3QoXhrjqmJLF/87Ekk6c71e9/3eeHlK1SqrZ633p+L8+lPHuNzzx8ndZvoDsB2XAqbdS5eWeXt80vML27yyEMTmMbemf9nzy8zu1DoPbeIqfHkY9P89i8/QzYbvW2q1/N86o0OV6+v89a5Ra5cX+PEAyP09++9THAvcHyPpmOT1HZugH7g4/hhylURVSBAQMTHR0QkIAh3ml0Ul6r1Nv/hT15lZi7fM8aiKJCIGxw+MMjHnz3Eow9NkIjfGnluZVVm5zc5f3mZs5fmeeqx6Xt69h8EbNfrqrD5KJKEfJf3DWBqIsdDx8a4MV9geDDJE49McfzICCNDqVvkgyHcotsdm1Nn5vj3f/waq2vlXqR6fTbP+UsrPPXoNKp6dzNTLDd45cfXesYYYGo8yy/+3KM8fnKSyB1KLZ2Ow+JKifOXljl3aYWIqe7J4d6Ol169SqnU7K21vmyMn/74A3zh0ydIJW8f5TuOR6FY59KVVc6cX+L6jQ0efnCcaOS9fd/3jUHeaDXYaNdxfI+orO1J13l0KM0//pVnePbJ27OUYzGdpx+bpi8b43/9V39JpdbuRbVvvj3Pvqk+RoZS78t7nYinOSiFY7fc9yBqEgA3iiVmS2WeHB9lIBrdcT2W7XJ1Zn1HSvinPnqEZ5/cf0djDKBpClPjOabGc/zMTx/H94N7SuW8V5iKyoNDg+iKwl+cv8gTYyOU222KzTaZiIkkCKRNA0UUubiRp2ZZfOmBo3etd6mKxAOHhvif//tPk05FdzWqgiAQjej8ws8+QqPZ4RvfOdera24WG9yYK9BsWTsY2+9GodjgjbfmePvcYu9nmirzG//oKZ59cv8dPeBYTOfnf+Ykhq7w1a+/xY25AhA6FV/71v9P3XvGWHan6X2/k8/N+VaOXV2dI9nkcIbkcGYYhuSk1azWa8m7tmFIMiAblg3oi/zNgD8bEGDDsCEJCrurWWmTNLMzy0nMZDN27qru6u7K8eZ47on+cG7druqu3CS3/QAEUbe6zjn3/MP7/t/wPJd55dsniYT0HfuE10/IG+sELpwf4bUXT+5ojAFURaavJ0FfT4KXXjiO63r7Lm6ZXypuygX3dsf4r3//GbK75N8lyT9ZPP3kKE8/Obojfea6c+xuoCbcFGbmfo+3uyEEvJHQYrpS5D/cvco/O/+tTdc2nAYT1Wt4uByNnMZyLQJygLpdJygFabkGuhRERd10z0bT5NdvTXDl2vwmBziTivD6S6f4O98/38nDe7jtDdw/RPjfwiMc1jhzsp9jx1Ocf8WlK+w7rP6+47Hew7uVQ/BF4dZSjnqrRa7WYLw7zaGu1K77qa4pvPDsEZ56coRMKrLrWlzPmT73tcMossT/8X//irV2PtU0bVZzFZZXywz27x4dqTdMpqbXNn32g1fP8tQuxhj8OoXxQ12MH+rid14/7xed7XOPu3V3ZVOE7/yZIb73yukdjTH4kcre7ji93XG+881jB1prG/HYGGRdllFFiawepmcPbFfRiM7pE308/eTIrtdWFInB/iR/90dP8u/+9MNOscLScom702tUqk1i0YPnkt9ZvEfdMukKhhmKJOgK7k0GcCOm8nkOp9NbLgLH8ajWNhcYhMMagcD+G2K/qBac3VAxWlxaWubT+UVs19+4JFFktV6nZBgd1aF4IEBfLErDtIjsInMoCJBMhPnH/9232q0Nuz/Hqy+e4qPP7m0yLpWawex8gRNHH25NW8fMfJ4PP7nzwLVOcvbUwJ5Dv994eozpdsrEcVxc16NQrPP2e7f4zvPHOqHOreFRq7dwnftOWDCg7Lo5bYWDjHlzQx4a/JaexAHqLXa7d8Vq8ed3r7HSqJLUgpzP9OF4Hh8szxBTdb7TP0ZE1fizO1epWi2eyg4wFkszUVzl89ziw+Q3bdieTVAKUTQL3K7eICSHmS3epWgWiKsJalaVM/ELDARHkIX722ClavCf/+ZSp/4AIBLWee2lU/zo9XOEghoeLlW7yGfFX2K5BjElw1DwBCVrlcXmbSJykqHQSap2kU8Lv6BkrtAbHCOt9TPfmGTVmCWuZhkKniSuZvb9TveCkKbQME1GM0my0fCe2QNDQY1QUN0X26AoClw4P8zwQIpKpdlxZCpVg5W1yp4MsmO7DxUsxiI62ja66tthr7S8D6JWb+G490/nwYC6rwLIdTzq/vrYGGRNkgkqKgFJJq3vnmPrykY5faJ/T/lfQRAIBlRe/OYx/vJnn9M0fHF0x/WYXypydybHuVODB3puSRA4n+mlaproskxKP5hhTwQC2K6zjfC4QCSsbap6nJnLs7JWZaB371ypD75Tx3U7hOhfNNtUWFM52dXFcMKvoI1qOo7ncranB0n0hbqDqsJavU4mFCQVDO76DOGQzhNnhxgaSO4a8l1HJh2htzvO9FyeZtP3gI2WRaG0fftWpdrk7vQa92Zznc8kSeSFZ4/uq50jEtYYHkzT1x3v9FNbtsPnV+d49muHdzHI/piv5QVc258TyysV5hYKHB7dWtpyy6sccFwDARVNlbFtf87V6i1uTC5x4dzwvq6zY1gej4niGgktQEL1Iw5T5RxhRePlwcM4nsef3rlCWg9yOt1DNhDmw+VZJkpr9AQjPNszjOHY/GZh6qFrK6JKUA7TdBroUoC01sVaa4VwMIbj2YTlGBEltkkSsd4wmbi1zMJSadM6/NqTozz9xAiRsK/73HJa5FrzJNQuJCQ8QJOCdEsjiIJE2VxjrTVHb2CMqJLmWPQZgnKM+eYkNbtEREnSdGrcqX3GE8lX9vU+9wpdkbFdlxsLqwgCRAN7M/z+3N7fnBEEAVWRGR5MM3VvtWOQTdPesSp9I2TZT4NtPKXevrfK4UNddGX2nu446HyPhDQkUezoTC+vVpiZy++5CPNR7r0Rj41BDkgKhm0zVythOA5H4jtPoFQizNjI9sUZD0IUBVLJMIdGMlRqBkZ74POFuq8idUAeeEEQ6AlFyQRcRIE9SWw9dA1gIB6j1DRwPPehgdU0mSOHezblQt+9OIWiyHzn+aOMDKX3XKTleR6W61I1fW90vlqmOxQhpPjGQRIFTMdBESVajk1M0w9krCVBIKKphDV1UygyommdnxcrFW7n8iiSxOme3Sd+NKLzxJlBlH0U4cmSSDIRJqApHYNsWw6NHTaKXL7G/EKxo6gjigIjQyl6uqKo++gjFUWRdCpMNhPpGGTHdrkxuYhp2Tv+rSDAkbFuFpfL1Gx/rK5cnycU0vjud05y7HD3l5p66G+TyKxvqKu5Cj/5i4+xLIfzZwbR1EenPBTxRU7eX54hIMmcy/Sy1qxjuQ69wSg1y2Sl4aexnu8ZoTcYoeXYrDRqdAcj9ISiVExjSwZmVdTIaF3ElQSiIKGLAY5FTyMKIq7nIgoiASm06TvUGy0mp5Y7lfQA4ZDGyWN9DA2kOo6YJChE5CST1Y9IKj0Mho5Tt8vU7AKOZyOLCpZroIgasqgRkmOoYoCmXcXzPAJymIicRJO+PIrdaEDH8yAVCRINfDXMWIlY0K+CbsO2nQ5pym4IBTUOjWRZy9c6n/36rQlEQeSFZ8fp7018Kapc6zh8qIu5xSJW1V/z1ycW+Mu/vsRrL57i+JGeryTNB4+RQXY8l5VGlfl6maQW3LGRXRD8MFJ2n56TJMLwYJqbk0sdg1wqNzZNgv3Caz+7/5//2UFUiCRBJB0Kom3Rgx0O6nzr2SN8dnmGer2Fh39a+s07E8wvFjl6uJsTR3o4NJolsks41XBsJvJr2K7H4USKcqvFcq1Gw25Xb4sSDdskpQeRJZEnuvoIKsqBjPJ2CijrCKkqo8kksigS03ffNAIBlfFDez8driMYVDcVpTiut6NBXMtXWVwudX6WJJGRwTSatr2s5nYIBbVNuWrX84kPfBpIb9vTtiiKfPMbR7h8fZ5Gw8T1PPLFGu9dnGItV22PeS9HDnd/oexI6zh9vJ+rN+ZZXParZ5tNi6s3FjAMi08uT3PiSC9HD/eQSYcfqUshJCss1stoktxRL1tr1vnV/BSKKHIi2U13MMynawtcK6wgiyLHE1lajs07i/eQhK0ViiRBIiAFCWwwenH14WLLjTO00TCZure66fe9PXF6u2ME9PvpIVEQkUWFvLGAhIzh1LA9k3xrEcezUUSNgBRBRCIqJ7lV/YQufZiokqbhVCiaK8SUDDHlywlXgy9NGtQUUuEYqfAXxbewM2RZ3DSfXZctI35bIZkI8uzTY3x2ZabjCM8vFnnjt9eZns1xbLyH40d6GBlK76llcb94/plxrlyf99NE7dTSBx/fIV+ocaS91o4e7iYS0b9Ux+CxMciiIODiYXtup9JtOyiKz0C1cZHsFT1d0U1sOfV6i3JlZ2awneB5Hkv1KrlmHcOxyQRCHE1k9rUAPGC+XO60/DzojOi6zNmTA7z0zeP89r1JypUGnuexvFIml6sycWuJazcWGB1OM9Rug+rvTRIMqA9t+K7nUWm1WKxV6QlHKLcMco1GpxhNFPwTcq7RIKjInMl0A18OeXNM1/dkiGG9gEQhk9p/fl4WxU1tM+utLduhVG6SL9x30lzXY2GpxH/8q0/QtP29i1y+2jkdr8O27/dKittEVAQBThzp4dvPHuWNN2+wvFrubBQffXaPidvLXLu5wKGRDEP97THvSxIN61+INz84kOQbT4+RK9SZuLXkt7q1LK7eXOD2vVVuTi5xaDjD8GC606mQSYW3rMbdDq7n8enaAqdTPST1IKVWk7CiMh7PMFcrocsKp1I9xFSdq/llHM9lOJogrYfIGw2WGlUCskxPaG9zYmsto/swWtZDPbgDvYmHqqlbbpOl5l0OR58kpmTImYuk1F56A2NYnokiqOhSGFXUGA2fpWGXkQSJtNaPKIgYTo2AFCUg7Z81bj8wLYdivUlE19AO4DS5rkel2qRQqlOp+G1cRus+O9k6OYfjeDiuy5Xrc9uSaeyGcEjn3OlBvvPcMd56/xbNps/ENr9YbDOlLXHlRpbRofYe1+fPd02Vv5C6mGPjPXzr2aP8zW+vs7jspyyKpQYffz7NxO1lrt9c5NBImqGBNIN9SQb6EkQjgS/85PzYGGTLdYmqOjFVx3R3DnOoirRli9NeEI8GN20a6w3ej6Jp63oeK80aqw2/L/poYv+eb9kwKBk+s86DEEWReCzAj3/wBIoq8fmVWeYXizQNC9txWc1VWc1V+eize/T2xBkf6+LIoe62YU6QToXR24ZEFSVG4wks18XzPBJ6gKQe6ISsPc+j5TjcKuYYiETRZPmR5SRdz6PVsphdLtLfFUdXFRzXpdYwcT0XVZFpGhZdOxhbWRIJ6ir6AZyw/aLRNDdtLI7jcmNyiRuTS1/YPZqGheN62y5AQRAIBjVeffEkkixy8dN7TM/mOh58qdzgk0szfHJpht7uGGOjWY6MdTM8kKK/1+fUfZRWG11TuHBuBM/zw7a376x0CuMMw+q8j2QixMhQmiOHujg0kqWvJ05/b2JPfbcesNasMxSJE1MD3HMK6JLMkUSGI/H0JsH3b/aN+mu0/W56ghFOJLu2jMIcFLbtUKluds5TidBD79H1HBpOlW59BFXUqdtlYkqapOqTnWzMS/cFDndarQRBICRHO9XWG//dFw0Pj4phMJ1roCvynvmsXdelWmt1DOHCUonVXIV8oU650qTRNH2yF8vGtlxsx8F2XGzbxXV3P0xtB0kS6cpE+b0fPYmiSFy9scDicqlNh+uyuFxmcbnMxU/v0t+bYPxQF0fGehjoS9DfmyCVCO2pvWo7BIMqL3/7BJIk8sEnd5iezVOtGbiuR7nS5NPLM3x6eYaervZaO9TN8KC/1rqyj7bWNuKxMcilVhNFFOkKRAhIyo5tT7IkbspV7AfqA2FHx/WwbKfdDrT/pS0KAn3hKGvNGjXT3F2pahukQ/dFH7bayERRZKAvwR/+F88wOpThw0/vcm9mjXyhTr3RwvN8wo7Z+QKz8wXefHfB7DBYAAAgAElEQVSSkaG0z+F8vJ+RwRTpVARNlemPxOiLxPzcddTn0t6oiVw1W6iSxNmunkcWJQDf0y5Wmly8OoPrQqpNWbqUq2DbLqlYkPnV8o4GWWrz1n4VsCx7U8vLlwG7zQm8G7q7Yvz4++cZHkzz/sUpJu8sk8vXqNaMzua3vlm9d/EO/T1xzp4a5OypAUaHM3Rnox1nbL9IxIO88I0jDPQlefO9Sa5cn2N1rUqp3Oho7BaKdQrFOp9fmSURC3LyWB8Xzo8wPpqltydOMKhuG+ITBYGvdw9xJb/EWrPOcCTBoVi7PecBQ/vgPPwiDTG02bna7FMbEdDVTRG1lmnTqEOvepSl5m0cxyOjDSNaYWzRfSh8LwgCEtJDn32xT/8wmqZFMhSg0bL2fKdm02Rxpcy1mwtc/PQuNyaXdqRLXYco+kWhQju69yA8z8X2WtTtHGE523ZG1t+JgOvZOF4LRQ4yMpTmH/zBc/zm3Uk++uwes/N58oV6p6DVNB3uTue4O53j129NMDaa5clzw5w82svQQOqRDHN3NsrvvH6OoYEU7300xeTUMrlcjUqt2VlrSytlllbKvP/RHXq745w9NcC5U4McGk7TnY098oHhsTHIuqwgCSIRRaUnFN0xZ3lQWjKg3SC/+TPX9XCc/VGtbYTlOLien0s2XWdbZ8IDtnMhA4pMRNM6nNjbIRLWefXFkzz95AifX5nlvYtT3Lqz6gtMtFWHwA+L3r6zyu07q/zm7QmeuXCIF184xuiQz2f84PNtfJ9RTed89/YtQQeB7br0ZGLcW8yzlK/Qn421c6h+xMNx3C2pE9chiuJXRj1otwULNiKgKw+pFj0KZEXa86YcCmo8/8xhzp4c4PrkIu+8f4trE4tUqk3qjVYn5+Y4LjPzBWbmC7z53iRPnBni9ZdPMX6om3BYO1AdgKbJHD/Sw6GRDLPzeX7z9gSfXp6hUKxTa7Q6uXDX9cgX67z1/i3evTjFscM9/PD1s5w+3kcqEd5SVEEUBAYj8W253x3PxXBMLNchIGkoooTp2piujSQIaKJyoCLKreB59/mRN0JWpE35+VK5wdVbSxwZ6eJo7HlWchXcpseSWacrLZE+gHjJlwHX9ehLxEiGg8SDu5+OjZbFzdvL/PSNK7zzwe0HWt5EdM2f/4osIUliR3FPEgUURUZVZXL5GvlirbMHdZ4Fh5q9yq3KLzkR/wGm00QUpHYntkTLrdKwC2T1IwTkONFogB+9dpZnvzbGR5/d44OP7nBnJke12vSV1dpFd5btc43fvLVEb3eM554Z54VvjDM0kDpwnjkYVHn2a2OcOdnPjVtLvPP+La7eXPCjA437NJmO4zK3UGBuocBb701y/vQQr79ymqNjXYRD+oHD6I+PQZZkmo5N3qgTUlS6d+jldVw/VHIQmBt4ZtchS+K+KncfhCbLeHh4Hijb9EWCP4Fs9+GQNPh82HWz1WG62Q3JeIjvPH+M558ZZ36xyHsfTfH+R1Pcnc51DMr611zNVflPv7jE51dn+f3fucDL3z6xJ+aeLwqiANGQTjIWpFBpsJwro6sypuVgmBaeB/lybccip68SsiwhKxJ0ZPVEnjgzxLEjPY9UwLQRPdkY0j4djGhE55knR7lwdphcocr7H93h7fdvcWd6DaOdvljPjZcrTX7zzgSfXZnl7/34Kb738mmCwb0pnG0FTZU5PNrF2EiWRtNnZ3rng9tcn1ikWGpgbzBmjuNybWKBG7cW+cF3z/DD184xPJDcsfDM8/z6Eb8C2m+Lq1kGH+SmWGwU+HpmnKFQmlvVZSYriyTVMGcSgyS1cKf+QRb9Td5pX0doX0faQ2hYFAXktrHZaJSttpJX52fbV0QyTZvnnxpjbrGIrikP/d1WcN37fNsbh8Hfj4R2TYF/YPDpt/0Q/UEK9n5x5RbRgE4mEkLvkWGHkLXneVyfWOTf/8XHfPTpvc7ngiCgqRL9fUlOHe9jbCRLX3ecVDJEJOxzqauK0vku/+YnH/CffnG5Qw6y8fqe5xJXBhGRqViLlM2F9vioeLho4sOOTDoZ5rUXT/Hi88e4N5vjvYtTXPz0LtNzBRzb3SRys7hc5k//8mMuXZvj9374JC98Y3xf9QwPIhLWefr8CE+cGaJQqPPeR1O888Etpu6t0Wyam9ZapWrw5nuTfHplhr/346f53suniYQPRpX62BhkTZRwXJdiq9lpydkOtu1iWXszXA+i2bI2FfQoioTyCLkHAMO22qw77HhCNtv5kAchAL936iTAviv4ZFlkaCBJf+8FfvTaWWYXirz17iRvf3CLlbVqZ5PwPJhbKPJv//RDNE3hW88eOVCI/iAQBIFYWOfseB8evn61tN7v2N6gTo71PBbGGHzjE9BUam0yFkkSOTSS5fWXTu2LD3onSFtEavb+twLZdJTvv3KG1148xdxSkfcvTvH2+7eYmS9sOqGUyg3+1R+/h6rIvPjNY7syu+0F6+xMX79wiLV8jc+vzvL2+7e4emNhU8jXdT1++sYVREHgh6+dZXgwve01G7bJO2uT3KmtMBRKcTI2wLJR5p3Vm4QVnbVWBQ+Pi7nbLBklDke6KZp1qrbBG0tX8TyPb3Ydo1uPcTF3h9lGrmO0x6M9e/pekigSDKhUa/eFJJqG9VC0xHU9anWDQrnB/HKRcycGKJabNIyde27XClVK5SbRiE4krGNZDqIgUKkb6JrCxNQyxw/7LTaNRot60ySgK/T37J1rYB2vnTlK07IIqArx4M5jXi43eeO3N7h0Ze7+u5BEerpi/MM/fJ6zpwYI6ArChtD0fuauKIhoUgRdimE4VSrWEpKoEJJTfj7eLmB52+usK4rE2GiWkaE0P/7+E9ybzfHme5O888EtCsVGZz/3PLh9Z4U//rOLaKq8I4PjXiGJAulUmO+9fJpXv3OS+aUSH3zsr7Xp2fwmNapq1eBf/dG7qLLES986diCyqcfGIJuuQ8Fo0LBMFEnC9bxtQ2xGy6JWN7AdF3mfYea1teqmDUvTZEIB9cCbI4AmK50TMkDTtjpFUhuxTj7+IARBQDmAKMX63wqCgCj6xnlsJENvV4xXXzzJR5/d45dv3uDOvTWctne+lq/y7//iY544O0QsEvhKjOC6p7juAEjiw6Hpr0Yldm8I6j5Lz1re9/S9dnWzIApfmZ7tTljfEEVRQlEkhgdSdKUjfPu5o1y+Ps+v3rzBlRsLHWesaVj85K8+5vjRXsaC6iNVhq6PmSxLyLJEd1eMb8eOcuHsMPdmc/zmnQl++85kh+nKtl3efP8Ww0Npervj2+b3PsrfISxrfK/vHIuNIpeKM3w9c5jT8UH6gklOxQfQJYU1o8pAKMVYpBtZELlcnCGhBkmqYX628Dm/M/Aky0aRE7F+TicG0MS9F9vIskgsEthkkFfWKpsK/LoyUX7w0mnAnyfff/E0uqZgO86udS2VqkG94cv1GS2LZCJEb1eUucUCT50ZBuDdj6colOp0ZaJ0p6MHPuVloqHOaXy3NX7pms93vt4KKAjQ3xvnf/pHL3J8vGfPBbSm5fiqXA9AQCIgJegNnkZEJhhNtmsC/O/mev5cUcWtDZh/khaQRBFZljg23sNQf5IffvcM716c4pe/vcHcYrGTOplfKPIXf/05T5wd8h2JR9jcH1xrQwNJsumzvPCNI1y9scCv3rrJ5WtzncimaTn8h//8KceO9BAK6fu2T4+NQdYkmaiqkWtJmI6zY3bNdX3xgFK5sa+cjYfP0dva0IMaiwRIJbfnKxUloX2aa1/D46Ee1rpl0rStdi91jRuFVS50bSY3t2yHUqmxZ+aag2CdMUeNyUQjOtFIgP7eBD/9myt88PFd3Las3NJyiRsTizxxdujABT+P+pyPM1LJEF3ZaIepy3Fdbt9deSg39rhAkSWUSIBwyB/z3u4Yv3rzJj//9bXO6WFltcLk1DJdmcgXIjm6DlkSkUMawaBKLBogk44wMpjmX/7Rex2jXCw1uHNvjeW1CoPbCLmUrQY9gThpLcKqUaFut1BFhYCsEpI1ApKKKsnokgJ4BCUNwzGpWgYJLUxMDXIhNUpAUtEllZgaJKr439P1HFzPRhIV1jmkt0IgoDLYn2R+qdj5bHa+0BE1Af9dRzf0lcfanOaqt7vhjIR08sU65VoT07R9Ba2AyvxSkdHBDMtrFSRRIJUIk20XONbq258cd8J+DMGdab84dB3RSIDTx/s5caQXTdt73URlg+TpRvhFbBKi4L8r6YE2Sp/fe/e2NPDrDjRV7qhRRcI6g31J/uJnn3Pp2rzfJWLaLCwWuXlribMnB77QSODGtRaLttdad4yf/fJqZ62trvlrrbc7vivv/IN4bAyyIkqcTHYzGEkQV/VdJ0Gp3GRmLr9ng+x5HpbpcPvuaocUBPxK0mxm+3y1psgPkEq41OotbNvp0DfqkkxQVtBlBTxoWNZD15lf8Mn6d8szfVEQRZFkIsTZU4OsrFaZns2z0Ca7sCyHe7N5Tp/o/1sxyI+Kdb1a1/MQBNoFYf7YtFo2bluft7crdiDjn81E6eu5X2jkOB6z8wUWFktEIwG0R0xxfFlYVyQ6fqSXWs2nulx3KtZPDk3DIh774u8tCL4+9MhgGl1T+ODju9y8vYRl+dXkuXyNQqG+rUEeDXcx28jx6+VrAAyEduc/jqtBeoNJblWXsFybvkDS11QXJKT2uDuuSd2eo9S6SUwdJ6qO4Qlbb/6hgMrYaJb3P77PYe5TKBY4fqSXWDTQ+a5bff/dkIgFEMQ0vd1+QaMiSwQDKueVIcJBjfMnB/wNX5EI6Io/r3e96qMjX6htouUNh3zK171WDK/39a+3Yu4XB60495nwIjx5bpjZ+QKLy2VW1nxlNqNlMz2b5/Txfg4YfNzl3gLRiK/+VW+YXJ9Y4u6ML46xzltQb7T+/2uQRUGgKxihi701+a/mKlyfWOT86cE9LQbLcrg+scDKamVTHrc7G9tRqzUU1DZNTNf1aDRaLCwW6e9LIkkCAVlhNJYiHfBP2kH54TDZ5etzncnyVSIYUOnKRkklwx2D7HkehmHuqe3mccXNO8s4jkskrFEoNYiEdaIhnZV8FUWW8DyPnmzsQKmIVCLEyGCaeCzYOR01miZvvX+LrmyU7mz0sT7l65pCJh2he8MpH/xUz5ftEEqSSCIeZGggtSmqYNn2lqendYyGM0iCQNlqEJF1ugNxdEnhWLSPiBLo1Fb0B5PYnkNU0dEllROxPjRJJiSrpNQIYVnnZLyfjNZm8RPAdpvUrFlkMURUPcR2DZXhkMbxIz3omtJR2TJaFp9enubQcJonzg49EiOapilk1YfbotZFO8Ihn7/gq16X687tOiRJ3Bfpkut63JhcYnm1jLULJeyXgVBQo7srRjwW7Oyxnuf5mgVfskujaT5ZUXdXtGOQwZ837jYFvDvhsTHIFdOgYVttHmWR7mBkx02vUGxw9cYC92bzjA5tXywC/gkqX6zz0zeubvIEI2Gdwf4k3V3bHxkiYZ1YJICiSJ3NpWlYvP3BbX78gycI6D6tZE8osi1j0Nx8gU8uzRyIotNxXEzLRhRFVEXatyFwXX9ibvzegiAQDh+8NP9vG57nMXl3BQ9fEWY1V6WvO46TclnNV+nNxtp5v72IeD6MQEBldDjDyaO9vHvxvnDBuxenGBpI8ezXxkglQvsaC8/zW+saTZNIeOcIkOv6RCqC6Kcg9jtOnufRatlUH2BNCgXVPbWOtVoWHu088T5zYOvfs1RpbCqe1DRlR+WesKJzPN7H+v65/n4ORzdznPcGNxc4dQfidOkxXDzEtpzhsVjfhuexEQSJgNyD4+5cLKppCsODaY6Nd2/Kwd+YXOKtD24TiwU5NJzZVw5+o20VhL2dpL9qZ0/XFRRZZJ0SxTRtiuW9sRc6jkuuUOOnb1yhuEG7ez+wbRfLtpFE8UA9xK7rtQlL7u9xoiD462yX9e8fTh5xrZn2QwxloQfoeveKx8YgF1tNVppVGpZFXAvQFYzs+CpN02bq3io/e+MKP/juGXq748jyw608luWwmqvwwcd3eO+jqU39dYdHs4yPde/oDeq6QndXlHQyzNKKT6tntGx+9dZNjo73cGy8Z0uKSq8dNl1cLvGLX1/jxsTilgVdu6HVspmey7OwXKSvO04iHiIWDaBr8q7eesu0WVoucX1ikaWV+9zMsiwyPJA6MLnK3z4EMqkIkiggiiJSViQZ87m3E9EA2VS4XRl78I1toC/BN54eY+L2Mrk2jebqWoWfvnEF23E4d2qQrkyEYEDbdhE7baKJas2gXG5QKNUxDIsXnj2y46brOC7zSyVm5vNkUhFSiRDxWICAvntBlj/fq1ybWGBmPt/5XBAE+noTBPTdi5zW8jVm5vMoskQmHSERCxIOabtuMI7jUqka3Jhc5Oatpc6JWBAglQyT3EVbVkA40JD55Btb/6HtGbieSVDuxvPcHV209ZD/qy+eYma+QLHkU9TW6i3e/+gOePCdbx6lvzdBIhbcVnHMdV1apk215tPy1moGR8d7DkT1+1Ugk44QCmpUqn6+ulIzmJxaZi1fJZ0Mb/kdPc/DNG0Wlkt88PFd3vnw9o6CLTuh2TSZnsuzlq/S2x0jEfP3OHUPtJhGy2J+scj1iUVW8/fbrTRN3iQIsh0cx2VhudRJf6aSYWLRAMHA3tbaWt5fa9MbIlGCINDXnTgQe9djY5ADsoKI2O5BDu9IYqAqfr9gqdzg57+6SrNp8tK3jpNKhNE0GUkUfYNoO+TyNT65NMNf/fzzTbnjSFjnwvlhjh3eXWVodDjD+KEullcrnRPA9Fyef/uTD/jR6+cYGUxvYCTyGX8Mw2ItX+NXb93g7fdvY7QswiEN23Yw9qiAAtA0TC5fn+df/tE7HDvcw8ljvRwZ627TtWkoin+KEUTfF3Tbz2eaNitrVd567xYffHKnU0wmyyI93XGOjffsu/fa89Z7Cr12RaPbaQvZ6Bm7bfGGpmF1CATEDdWej6wSJAq88PThzjP519z4nB7Z9O6a2jshGglw9uQA33r2CD9940onN3b7zgr5Qo2JW8s8/cQIg/1JArrS2ZzXKz1t26HRNFlarXB3eo3JqWVmZvOMDKV57uvj7ORL2bbD3ek1/vn/+2v6exKcPOYT2/d2xwmHdH/MZandgrLOMuVhWTa5gs91/ea7k1Sr9wuCujIRxg917UnjdXm1wh//x4uUyk1OHO3l+HgPw4MpYrEgqiKhyFJHhN3D39Rsy6FSM7g1tcJ//pvL5DZEg+KxIKND6X3J6O0XnudRd5oEJB3btZEECcdzsFwPERnHMwhI3Qj+OXrb6wR0hWe/NsYnl6f54KO7nYrrfKHGG29eZ+reKs89c5gTR/2csiyJbce4Pe7ttZ/L17g3k2PyzjLziyX+9//1R/TvQyrVX2e+cXfb681o061ujMI6jl83YWxYa+vtSbC39qTx0SwXU2GWV8p4+CIb1ycW+OVvb/DcM4cJBf19RhD8tW1ZDk3DYnG5xLsfTvHzX1/FshzisSD1emvH1MRWqDVaXPzsHn/2nz7h2Hhve4/rIp2KEAioqO3+8E17nO07PUsrZX711k0++Xy6o+amqjJ9PXGOjHXvapBtx2VmrsA//39+TVcmyomjvRwb76G3J05ky7V2P2qZL9b55PNpfvPORMeZAcimIxw+1LVJVGaveCwMsud5SIJI3TYptpqEZJVsILKtUe7vS5BNR7k7vcZqrsrPfnmVX701wfiYz6UbCQewbYeV1TJ3ZnKsrG7O3SqKxDNPHeKJM0N7qjg9PJrl7KkBLl+f31Rxefn6PBO3lxltE/xHI3qHZ3huscDd6VwnzJ1JRTh/epD5Jd+b2+cLwjQdLl+f5/L1eQBi0QA9XTG6s1HisSCaqiBKAqZpUyo3mF8oMrdYoNG874SIokAmHeHvfO88odD2J7v7t/W9YKNltzcG3ys0DIumYdI0TCoVg5XVyiZx71rd4O50jngsSKDNPx3UFTRdQWoTH8iyhKbJj0y0sdUU+aJoFXu6Y/yd759nebXCJ5emfc5zfLrIX755g1+/dZNIWKe7K0okrKMoMq2WRcMwKRTqlMqNTX2K+xVbdxyPidvLTNxeBvwcZ1c2Sm9XjEQihK4pyLKEadpUKk0Wl0vMLRQob9gcBHz2od/53nm6MtE9h1vXi3TmF4v8zW+uo6r+abm/2+dGDwRUFFnqFDmurlWYWyg+VCehKBLPfs03YLIs+rlK70EHik3H1o1j1/ldu4Vn/ccHnToXj8+LNziXOM5Sc5WEGqNi1TCcGl2aTd64hCyGiWtHtrjLhnsLvnb6P/zD5ymVmlyfWKDR9PXTWy27wwwlSQK9XXHisSDBoIrnejQNi1K5Qa5Y3xQNi8WCe1I98pn27jOfmZZD0zAxmhYNw6JabVKtGpuuVSo3uDW1Avg0nwFdQdcVdE3pOE2KIqFr8rYRjlMn+hn7JMOde2ud1Fa+UOdf/NG7fPjpXc6cGCCbiSDLEoZhsbJW4fadFSanVqjWDATBd7q+9/Jp3nxvkrmF4pb32RGeR6NpdTijwS+4Xd/jotGAv8cJAoZpUS41mG3vcRslHkVRYKA3wWsvnd5XRMJxPCan/O8EnxMKqXRlYvR2xUiurzWlvdaqTRaXy/5ae0CYSNdkfvjaWXq6YgdqL3wsDLKLx51KjnsVXxWn0GpQs0yi6tbe/Mhgmh+8epbZuQL/57/4DUbLpmX68nDXbi7c/4ebnckOnjw7zI+/d37PQu+6pvC1J0fJF2r8yZ9/hOPcv2rLtLk5ucTErc3CAxtPjIoi8V/+7lOMjWT4q7++tH+DvAXKlSblSpPJqeUtf79VLiedCvP6S6f47rdP7nhC23iNN9+b5C//+hJLK2WaTQtrCw7mBzeb+cUSP/nLj/nTv/pk0+eC4L9LXVc4eribH756lqefGN39Qf4W0Z2N8U//x1f4Nz95n7/+1bVNYTnX8yhXm5SrB1cL2w9q9Ra1e2vcvbe2pT3ZasyDQZXXXjzF9185QzBw8JCpaTosLJZYWCxt6QRtlzt85sIo33/5NKPDGVzPo2lZGJZNUFVQJAkPqDQNNFn22x0FCCi+42Y5Dk3TJ7domCYhVcOwbXRZIqiqD9zfY6axyMnYYaYb/h5Qses0nQaHwofoCX6TmjXDXusK0skw//R/eIV/9Sfv8e6HUw+JTjiOx9xikbnFAxifbfAnf/YR712cIpev0Ww9TEgCD6+1yakVbt1ZfWhMREFA0xWCAZWnnxjh9ZdOcfzI1nS4AV3h5W+dIJev8c6H92smbNvl8rV5rlyf70QV2hQcmyJT4ZDOP/lHL3Lm1AA3bi0xv1g8sMjERhRLDYqlBjdvbS3qstU9+nsSvP7yKb793NFHune9bnK3vsa96b2vNV1XeO3Fk3z/lTNEwgej7nwsDLKIwEAoTsFostSoMFsrYboO3+4be+jfruejDo9mGR5IEY3q/NF/uMjtu6udMM92COgK3/vuGV576RT9PfE9V+AKgkBXJsr3XjlDNBrgJ3/+cUf5Zh1b3VcUBF8Q4vef4fyZIWRJIhLem+rKOnRdYWgwxehwmnszuYfus5eJHw5pnDnRz2svneLsqcF9FS5Uay3W8jVK5ea+qj/Xw9sPotEwaTYt8vn6gXNOXxX8U5hHJKzz3/z+NzhzYoBf/OY6V67PbyKP2AuCAZVjR3p4+Vsndn3/siwxOJDk8EiGiamVTXUPsM6JvvP9dE1h/FCW73/3DM9cONQ+Lext3LsyEUaHMywulyhtUdyz2zQQBYFsJsIPXz3Lc88cpjsbQ0Dg5soq796ZwXQdxjNpAorCbLHEcrVGTzTCSCrhV8eaNoIAFaOFLAkUGwYhVcWwLNbqDS4M9nFhsB91w4nPw2PZWOPjwjXu1GepWDUajkFM1rDdbhQpTL/2CrC7J7p++k4lQ/yDP3iOc6cG+NVbN7l6c2Hfc1YUBEYGU6jq7vUaxVKdXGGzmMFesNW+5+LhNFo0myaFYn3HNJkgCBwe7eL3f/wUkYjOb9+Z3NS+tE7j+SB0Tebo4R7+8Pef4dh4D7om05ONEQpq+5JhDAVVhgZTDPUnmZkvPPT7vbyLaETnwrlhXvn2CU4d699zWkyWRAb7E4yNZLhxa2nTaRv2ttY0TebwSJYffPcMzzx1qJ0WOliM7rEwyIIgkNACpAMhKpbR0QTeCvFYkEwq3A7PeFw4N0xfd4JrN/1w7vRsvlM8I0ki0YhOdzbG8SM9nD01wPBAmmQytG8uZ0kSyaQivPzCcY4d7uHS1Tkmp5aZXfCJA5qG5curBVUS8RBDbf7Xk8f66OuNEwyoePgTR1PlPasJ6brCqWN9/LP/+TXm5ovcmVljbr7Aar5KqdSgVm/RMm0c26fsVFUZXVdIJcL0ZKMcGskwfqiboYEkyURo36TrruvhbhB+eFR4tCtx3c2tFo8r1pl6IhGdJ84OMTKUZnouz9TdVe7cW2N+qUi5TZvYalkosoymycSjAVLJMH09cYYH0z6bVjZKIh7cVeTBL7pL87/845eZXyhyd2aNmbk8K2tViqUG1VrTl6WzfM7m9TFPxEJ0Z6IMD6U4MtbNyGCadCq871xWVybK3//dp3nhG0eYns0xPZtjcblModSgVK7TMm0sy8F1PKS28EAkopNNRRjoS3D0cA+HR7NkMhGi4UCnslsSRRLBAEFVIaQqzJcrKKLESNLXAU+HggQUham1PIuVis9L3XIJKDKxgI4A9MeipEOhTWQ94NMzPp08gyaqnIgeRhEkXDySahgPl4p5l5ZdJhN4kr1ulustXM9cOMT4WDdz8wVu3VlhejbH3EKxI+himrZPyqPKhEMaibhPLDM8kGRsNMtgn7/2dsN6qPqLWhbrTvE6h/ZOUBSJsZEsf/93v8aFc8N8fnWOqTurLK6UaTR8jn1ZFgmHdLJt8peTx3o5cerlUMAAACAASURBVKyPnq4YWlt4pb83QTSi79Mgazx1boSB3iRz8wXuzKwyN18kV6hRLDV8ARXL6exxmiajawrpVISebKxdnNvFQF+CZDxEYB/FVJIkMtCX5J/89y8yt1jk3kyuvdYqFEsNKtVmRwLSddtrTVOIx4LttZbmyFg3o0MHW2sPQvAek2bUUqvJZGmNqUqesKzyTPcQ2UAYz/NYzVW52ybQ1zSlo/ML9yddudIkV6hSqRp+v6XtdkrZQ0GVZCJEOhluFyccPMPoGxOPQqFGsdxo02EuUq3+Gab5GYosoWkK0bBOKhkiHvOlBtf3gLkFhdXcOWT5G6iaTH9PgqGB3UkQPM+j2bQoV5sdCs5Wy8a0HVzH7eTmRElElkR0XSEU8NmTYrEguqbsOSKw8Z7zi0Vm5wsPndIeFeF2y9l6oY/nubheA8fNoUh9lMoW92bWKFeatKwbeOQZ7H2dE0f3r0I1PZtjaaXcKeoLBlX6e5ObyD/2g/XceaniO0TrHOW24yKJApLkt28EdIVQUCMa1YmE9AO1dDQNi0p7zBvrY27avkPT3rwlyb+nrvkhymgkQDwWIBjQHokStmXazKwUuDa9TLNpcjSbodEymVhcY61c42hPhlQk5H9fxSe5CId0EvEg0YjOg0VFVaNFxWihSCKSKFJttZBFX/zBwyMW0Ck1DOZLZRRJIhUK4rgumiyjyhKW4yAgENU1guoDMqqey8X8ZUZD/eTMEhE5REwJIwo2rrtKqXUTxzMZjf5dLM9p83UJtFwL8FBEGQH/IGC6Ni4ei80cfYE0ESXYaY8pV5pUqgbVutEZd8dxfXrFtixsS7TIeRX0oMy3R060197uAzFxe4m1XG3LUHXTMfm0cI/RSJasFkPeo5ayB9hBGzkp0J9KMB7def2st0nm8jVKZZ9Z0G7L0/r7qUQgoBKL6CQTIaKRwKbvNr9YZGGpSKNhEg7r9Pcm6NmhrfShezdNypUm1bpB07BotSws272/x+E7dpIsdtZXPBogFg221dh2eBeeC2yvFGgYFpWaP767rjVVIRhUiUZ0v47gEdfaOh6LEzL49iob8EvsVVEiG/AZuNbDxdtVaK739iXiwW1ZUVqmzcpqhen5PKOD6UdSAREEAVkSyGaiZNvPZFselUoFw5h8+N4POIo92THGRr9GKLS/HIcvWK8SDKoPTfC1Sp1rs8tYjusX8YQ1jvZnWS7VuF0o0uPZjHXvbvS3uudAX5KBbdiVHsSV0gTdeoasfv9enufiumUarQ9AENDkcSQxheXMYtk3aBgJNOUIINFofYggyMhillhM5NjRNRy3jGWXEASFePhgkpDDg+kdhQ32i4CuUjVMEukQ44e6qBkmU4s5BtJxkpFgJyRt2Q41w0QU2NIYLxerVBsGqWiIZGTruRvQFQK68kgVyp7nYdoOn04v0DAt+hJRJFEkV61jOg4BVcHzPE72dXF1fpmKYTKcijOQjBGNBoinQgQdnfMnB3E9j+h8mImlNZ4eH6IvsXfar4iuEdb804sgCKRCwc7zrX/muB7DqQQhVSG0IU+8voluJ9HpeR6rrTwnYmOU61UicpC4GsV2mzTRCchd2K6fZpipryAJIo7nYrk2Hh6yKBOQVPDgdnWep1LHKJpVMlq8c79AQCUQUHfkLfA8j6JZ52YFCq2aTzLiWFwrz9G0W/QFU6iiTMmsU7WbuJ7HSDhLUgtTjFcxIyZhWWM03EVGvz/mFatBYb5MWJFBsukJpenSo+RbNeYbBYKyymAwTdVqktVjRBSdicoi3XqchtNisrxIrlVjfItnnplc5PalGZZncrSaJoGwTt9olhNPj5E6EUcQBEzD4rd/9hG5Up1TXx9nZCjz0HXm76zw4c8vkeyKcfKZcbL9/r7RrBvcvjTDrUszVPI1ECCRiXL47BDDx/oItwtrW02Tq+9OcPvSDN/9g+dYXC6zcHWWcr6GJEtk+5OMnxti5Hg/Hi62ayAKcltbWcDxWniei4CI41nIgoogSLTcGrZr0HJqqGIQXY4DHo5noQg6jmejiBooBsmkRiYVRtijw/NF47EwyK7nUbVaNG3fW901aL8BLdNmZj5PvdFC11Rc1yWbjmC0bNbyVQLtUN7yWplqvUU8GqBlOsSigU5vaDCo0ZON7aklZEsICqKYRBS78LwW0Gr//6uhySzWm9xcWGMgFUMWRS7enmMwHWdiYZXrcys8eaifQ13JfUUGbNdmrrFE3WmiiSoeHkk1hu055FpFVFGhR88iixJ3arOoosLV8iSKKOPgUjRL6KJOVouikqduvEVAO9/JRXleC9tdwXaWQZDQ5MN4XgPHreHhYFnTWPYcIOBhg/fVT9WWbVNtmaTbhiNXrxPVdVqmzaW7C1SbJqeGunHxuD67wmqpRjSo05eKkomHWSpUWC3VyMTCKLLEUqFK3TBptiz6UlFy1Qb5agPTcTEsG1kUyca/HD3dWsvk0+kFEqEA3bEwS6Uq93JFQpqCYdkYls2J3i4aps18oUyjZRJQFUKaiqa0+eXb1esBVTkwqcxutJNRXSOqb78Ot5vDHh7zzWWmqrPcqc1iOC1M1yIsq4QlD8utoohRQGDZKNCwDcJKENO1MBwTSRARBRFZkLhdW+BMYoya3cT27I5Iw16/nyxK6KJfQOficb08R9GsIQsSd2srGI6F67nIooSIyMf5O5yIDzDXyNNybAKyQkaLbjLI62g5FpqocLM8j0g/jueSb1VZatpUzSaGa6FKCiFZ41ppDjUpE1UCKOLW6+fmJ3d56y8/ZnZiCdu0EUQBx3a4/O4E0xOLvPaHz5Hs9p2SqSuzXH3/FgAjJ/ofUsm7/M4kP/837/D1189x/Cm//qeUq3Lp7Zu8/VefUsnXkGQ/GmK1bCY+vcczr57hiW+dIJIIYTZNrn1wm1/8u3eRFYnlmRz55TK2ZWPUW3jAvevzvPrffgO1v0TTKRKSU6hiCNtrYThlPM8jKCeRBKVtlDUsr4nttqhZK0iiSsIbRhF1qtZ6QayAKgYxnApxdZCgnPxCujQOgsfCIHt41CyTitWibpto+xAdbxomH352D72dw6g1Wlw4O4zruty6t4oADPen8PDbcSamlgmHA9QbJjPzOVbzVUJBjWrN4PypwQM9vyhG0PTnEaU0nlfHcxt4XgPPa+I4y1jWDWDrnIrnedu2cuwHiiSSjfnsUXdX81iOQ6HWwHZdLMeharSIBvae32i5Fp8Wr6FJKgIiTafJiehhJFFiuj6H47k0HYOApHOvPkdciWK6JnmzRM4ssmrkCUoBKlaS45EoqjKCLHUhCBquV0cQAijSELaziG3Po6tn0JQj1I03ARfTuoUkxlGVIxjmFSxn/sDv5iDwPI9Kq8XtXI5k0J8XV5ZXON3dheDAYr5CodakPxVDV2Uc12OlVKPaaLFSrPLU0UFK9SZLhQqqLBPWVa5ML3XYn5aKFVKRIIZpMbNapFw36EnsjTb2IBAE6E1EiQV0AqqCYdtossRoJsnkcg7Ttik1m6TDQfA8FkoVio0mIW3/5AZ/GxAQiCsRcmYRWZSo2w3mmyt0a1E0zaZmzRNW+gGPtBYjB2S0OIZj0nRaiIJAsy1oMR7xDV1YDiAJj0ae43oel4szPJc9xmAoxd8sXma2kedQuIsj0V5UUeJPpt/jUKQbD0ioIdJahIjycPGngMChSDdHIj38xdzHLDZL9AUT9AUTrBoVZho5wrKG2T71l60Gprt1qsnzPKqFOj//128zdXWW8XPDHL9wiHAsSG65xKW3J/jrf/02kXiIV//gWQJhnWNPjjL52T2mJxbJLxXpHrpP9WmbNpfeuYkgCoyeHCDVE6fVNJm6PMPP/vXbNCpNXvidC/SOZnFdjztXZ/nol1eplRuE4yGe/PaJzrMZDZM3//xjjj4xwte+e4Z4JkJ+qcQnv77Gez/7nFi3zvH/ysT0GjhuC1GQMNwaLaeKgIjtGSTUIWrWKpbXJCRnsD0D2zOxnRaGU8L1QhRa93Bx0KVoO1VRJ6xkD8yt/UXgsTDIeKBLCl0Bn9RjP6/DcVyK5QYvP3+M6bk85WoT07LJJCMcHs6wuFJmer5Af0+cheUSC8slfvz6eW7dWaVWb9HTFcNs2cwtFh/BIMcIBF4nEHj9/lfyXDyvhmH8kkr5f8N11zb9jeO5mI6N47kYrklEDiAJYlsw4f6UEBCwPaed49reaDdNi2uzK1iOSyoSpFhvYtoOkYBGpdFiZq3EqcHdSVDW4XouRbPMc9mnWG6uUbPrWJ5NQo4xHOpnxchzuzaNLuocj47RpadZbK4w21gkrkTJaikcz2XBWONUbJCg9jTVxs/Q1CoC+CdjZBAkPBw8t4Ht5HDcCo5bQhBkXM/AcQt4XhNhF4FG1/VwHAfPBUkWcV0PSRJwbLf9OxdZkTqkMYIo4LkeoihQqxpoAQVlA3Vey3GYL1e4vLRMd9g3lJNrOQ4lkwzEY/SmonQnIlwY72et0kCZW+VoX4ZjA1n+5RsfU2u2GMwkKNUMGi0Ty3ExLYcj/f6/+b9++j6aIlOoNMhV6pwf62e4a/+6t1vBshy/H7bN693flySgKJzq7+btyXuUGk1atuMrNUkiiiRiOy6fzyyyUq6TigR9gQ7boWa0KNYNmqZFpdlClkSK9SaVpkGl2SITcTZVO29Evd5iYalEq/Ww4ICiSIwOZw5c0/Hgd+zrS/DN9FPIotwpmrM9GxkbKBCQM8iCX1h1LDq0fZGTcF/soEvf/3jYrkPdNiiadWq2Qc02CMkaNdtgxfCJNzRR5v9j782D7LruO7/POXd/ay+v970b+0IQIECCogiKFKl980i2JcuW5SWOM56KnVQSV+KqOIlrktRMkpmUx5mUx/YslZk4iiSLEmVTEklxA8UVIIh9RwPofX37fXc7+eO+3oAG0A2AJJSZLwtF4L13l3PuPed3zm/5fg2pkdAtFAohJH3JHN+58jpNZoqeZDMp/frFswBSuo0pDRBwpTzNpJtHobA0A4HA1kzGKnOYUsMLA/wopOi75P0KltSpBDUSukUURpx8+wKHXjrJvo/v4Iu/8wT9W2PK0cAPGNzew+lDF3nmr17k0S/swU5abH6gn87BVkYvTHL68PAKgzx+eZoLx67Qv6WLzoEWDFNn7NIU7x08w9jFKb76n32az37zscVnvffjO/C9kFeefpvDL55g94GVIbyGljSf+82PMbSzByklfs2nsTXD//lH/w9HXzvHI7/5KIZmY8oUlWAGXdg0GF0IITGkQ8boJIhqzNQuktSbKQfTJI0chnCwtQyBqmFIh4TejKWlyHujddf3gof2wzHK94ZBBk7PTzLplml3UvSm1jMQBIYu0TS5OLgvDE9z6fIMsq5fq+LML2pePAmVKx5B3QWHErS3ZWm5iWpUvItVK1ZOoQrRxI0nkzgG4SBlhtVKLaqBx0h1llLgMlKdZWe2h0YzRdGvxlSAQiIRSCEpBy5diSb0m6zWM47Nx3YMknEs/vTvXqPkegghaEg4TBfLWJM6O3ra1uF6A11qaEiM+iR3pTLGmDuJAmxpopRCingS8qMAhECopV7KWQ0MGe34wRWqtbcQwkIKp25cBZEqIEkiZBY/HMX1DhGEE3jeKUx9I5Xaz/D8UyB0DK37JncLgR+Sny9TLddwEha+H5LO2JQKLrWaj1v1SKbixKpIKRJJi2KhSjJlc/TQJYY2ddDakUXW3XBzlSonJic5Oj5JseYhBAw0NpGyTKSItVn9wKfkxkkvUggyCTs2TgLCSOF6Pq7nx5KdQRgrxNR/szDs/TDEDyNc36dS80mugdryVpidK/Oj547xnaffIZdL8V/+/qexGkyOXh3HC+KYsa5paFJg6jpJyyTr2KRtiyuzBebLVQxdww8jLk7Pc2Z8GikF5yZnaEo5HBuZYGSuwIWpOXLpJC3p1TOILw5P80//7CdcHJ6+LtO3rTXD//6PvkZry80562/Wxmd/cpTvfv8QLbk0/8Xvf5KgpUx3sjU2WMCcV6AQTODIWTThEKmlhcH7xRddDT2uVmY5WxzDDX2GS1N8pGUzL02eoBb69CRyDKbasDVjMZktbdjM1IpodRaxy+Vpmq0U3fpSLkbMYmhjyPj9Sun24mI978UliTkrRW8yx7OjR7hUnkKXEi8KuFie5EJpgozhMFKdZWO6gzCMOPTSSdxyjc6h1tioDi/RPwZ+QK6jkdOHLpKfKdHYmqFzoJW+LZ2cP3aFM4cv8chnd6MbGmEQ8caP3qNSqrHj4Y201pNUJ6/Ocu69y9gJk77NnUxcnlnRV4k6p/vk1Vkq15QRPvjkTnIdDYv0wIZl0NCcJtuUojBZpTf14KLNzDEErJRxDCKXUHm0OVtJG21kza7F7xbQam9e/Hs1zKOI6smlAZr4cDxD94RB1qRkf1svBT92HWXNtdfqChHLcAkh6hOlqBvdGvmiSzJhknBMTFNn17Zu2nJp3n73Eps3tFMoupy+MEF3R8NNeUd95ROoAA0NXep4kceEO0G3043G7a3wvSgg75fxo4jZWpFDsxf5SG4zY+48l0oThEqR0ONYmi41epI3TkoSxHHk//uVdxECepqzXJ3J85X9O9na3cqLxy8wW69tXM+tSlHv1/p/utCphTXm/SJJzSGpJ9iSGeLZsZdothqoBFV2NWxjzstzoXyZ9rAFS/ZiOdvriVsChIZAkFAH4ntftsiwze0rrm+ZG5e18OY3LgSUCy4n37tCMV8llXFIpiw0XcN2YmrL4XOTXL00TTqbYGBTG+/87By9Ay14tYCBoZUkMR2ZNF/YuoUHe7oZaGys98fScG7Jpnjn3FV+8MYJBtubFqn1EPHv5koVzo5McejcCA0phzCKVhyv1ekNB9qb2NbXTr5U5bWTl3hq92ppN+vD1HSBcxcmYxYhAafPjvPpp3bQ3ZxFEI+X5aVXO7qW2v7RTf1o13z/sS0DK87/Wwf2ruk+kkmLvr4cfhBSLLmU6xnpd6OuY3JqqY1CwPEzI5z3jrChoQdbi8fNuDtNRquwtyFNqGpo4jZzRNaBtOGwr3mIfc1DKz7/1YFHY8/MKlm+vzZwgL88/wLfHPoYrXaG7195m9HKHN2JJYOcMmy+2L3U77/Q8yDAIkPeghqWUortDT2LQhsLb9yT7TtXXFNFismrMwR+wF/9D9/lX/7J36zaHqlJyoUqYRChGzob7+vlvVdPc/n0KOOXp+gabCP0Q17723exEyYbd/XRkIs9SpWSy/xUgavnJvjDX/jfbthnQzt7qBZdrGVzcK6jAfOaxanQJJqh4dcrPq51LS//ty5tOhIr23wz9CUfWvNvF6mD6y/yAi3w8ue68H38zOMF4LW/WQ33hEEGSJs2KcOKJ7R1uAsaMgm+8ZX9mKZOX1cTYRTF2Zr12llNizlYF1yVUopFxZYtQ234YYSU4oZuN4BSUOJ08SRj1XGareZ4JSsElbDMYHIQW1sf2QfELvqskeBsYYxWK0ODmaIS1hirziEQtNkZdKlR9Kvo4uY9MtDaxH/81IOLE52UcbaqXaelfGRLH9E6jXFCS/BLPZ/FEAZ9iU52q20IBBFx7atWN6ya0PhG/99Dq2cl6kKvZ0DGHghDLLBDXfuqrSWLce03XK16TE0UKBaqtLRnyDQkSWdtjh2+zJad3RTnqyAE/Rtj43Pl0jSWGRvqSrm2KrWhJiT5qsv/fOQlgkjxkb5eHurpJmvbbO1tZaijGaUUpqGzeyhC12LO219/ci+6Fr9vT+zaEC8UNYlScawf4D/61EOL4RkhxF2tyW5tybBxqJWjx6/S3pbl/p3daJpcUyKWfgfygteit7uJ//z3niKoj8UzZ8d5+ofvcnAZG9TtIm5jG8dOjNDRnuX+nT3MB8P0JjpI1MejABK6TU9qV90JeeuF3fuJG8UmTanzeOt2Xpo4QaQi+lOtDKZa13TO1WratTWNLVBRLLbx1Fc/QveGG3vPOvpb0I14fhza2cvg9m7e/ulx3jt4hraeHJfPjDF8epRHP/8AjW3LEtHqBinX0cDnf+tx5A2oJDsHW0lmEwTLSitN+3rBnnsFZ85O8MyzRzh6/CpSCh7Zv4Hf+MajK57uqTNjfP+Zw5w8M4ahazx+YAtf++X9t3z77hmDLOP6pfUfJ8XS7nbZA79ReQSwVPaka5g3+d3SuSJSWoqskWXSnUAg+HjbU5wuniJQt1efa2sGfclWWq2GuHa1nuHZbKVj6TshmXTznPCvohRUQo+ktvoKP44F3niHb+rrf8xSCBxtIY6lsWBWr+1XpRTJhQXJsvibIW7er2vzKqz9fUgmbbbd38OGrR1xrFiLwxgtbVmcRCzqEYUKIRcI4uPaSsPQ8b2QVMa+rhxuulLm9PQUv75nD1II/vb0Gba2tpCxLAxNWzRecVuWjk1YSxSV1g24uhPvY7JUU2OSz35qF488vBHD0GhrzaxpdQ5315WraZLkssqFdComxbkbaG5K8rlP7+KjH4nb2NqS5uP+fnJmA3o9o7jFiisLNHlnZA13AzdTstKFZGOmg85EYz0cZGBrxiID1wI5zarnveaLNYekpCDX2YSuawzt6OGhT+7EuQGpRTLjLBrTTFOSvi2dvPvKaY7/7Bwf+cxu3nruKH4tYO8T22lsWdIKd5I22eY0QRCy+7GttPWuXnppmDp2wqK4nOvgZmun21i7el7AwdfP0dyU5L4dPes/wTL8+IVjSCn4jV/7KB3tWRzHvO5Wf/jseySSFr/zG4+Ry6VJpew1zWb3jEG+21hPmcKtkDYyZMMKtcijyWzCVx7H8kfpdDoxbjPWIIXEkmKR4WYBlrY0metSI6nbMS+tvDek29Y6AXzQmq6aLkmmbJLXpAKYdUEHi+v7T4iVjHDXSXeGEaWaj63r2LpOsVYjWCai8UG3ca3QdY2mxuSa2KE+UNzF7rq2jUopuvRWdLE0ngyp3zXWq/cDkYoVukrlGumUgxkYmIZO4If4YchkoUSl6tHRmiVhGwRhRLlSI2GbeH6I1OLERKVULFUoBJ4fUqrUaMomKFU9LENDqXjjYi9bKGqa5P5Ht/Czv3uXU4cusn3/Btp6cyveaaUUXi1A05fCcpqu0bu5g95N7YxenOTsu8O889MT9GzsoHdzB/YyJsCW7iaGdvZw+cwYR149zZd/76n64mJZLk6dG/9Gu+e7hZGxec6em0BsWJt+wY0QKcXVkTm2bu5g2+ZOmpuTi4smWKqauXxlhkf2b2DL5g4aGhJ1QZU1eKju6O7+PYEpTRqNRjQkUmhERNRClza7HeMODOWtHpCjmeuigfsPuB636uObfd+UcNjSkuNnly8TKcWmXI6MZd2WJ+c/4P3FyvBIDE1oH6aH+pYolVzOX56mUHJJOCZCsJjnIYSgVK7FKlvNKTxfcu7SJEGoqFRrzOYrpJMWzY0pNCnw/TBmScsmuHR1htGJPKmESb7kMjVboqM1y7YN7Ysa6FJKtj+0gT2PbePskWG+/xcvsH3/RppaY9KT4lyJ6bF5srk0j31p7wpD2znQyoZdfZw+fIlXv/8OV8+O8+lvHCDTnF5hWJvbs+z66GZOH77E8//v64RhSM/GDpyERc31mBmbJwhChnb2sPPh28udcGs+h49c5sy5cUpFF0PX6Olu4qF9gzQ2JnFrPi+8dIqjx69y/vwkF4enOHVmjIaGBDu3d7O9nll++coMh49cZnwiTxhGNDYk2L6ti+3buhYXOk//4BD5QpVLw9O41Zi1ra01w46t8e8qFY/vPXOYcqXGyNgcb71zkcnpIu1tWXZu62LrGlgGf+4NchRV8Ly38P33QIVIrQnLOoCu9wOgVEAUzRME5wiDyyhVQqkKoCOEg9RyaFovut6LlKsz8MSJTHPMeDOY0sIQOhvTd558c7cQl1iVCcOrhOE4UTSDigoo5aIIEUgQJkIkkLIJTbai6YNImVmRVHUrBMFFPO8QYXAFhIGu9+I4n192HzXCcJQguEQUThCpEkrV4jlRWEjZgKa1o+tDSJlDiDvf9UfRPGEwErc9mo7rwJUHaEhhI2UjUutE1/uRsgkh1v7KZyyLne1tnJqawo8i0qaFpel3ZY5XCmbnSoyN55mcLpLPV3BdnyiK0HWNZNIi15xmsD9HY0MSw7hJjkPZ5aVXzzA7W7ruO8cxOfDIJnLNqcWM1ZthcqrA629dwHV9du/qZeNQG7VawOR0gQsXp5ieKVGr+ei6RiJhLt5j6/uodQyx8Xrp1dPMXiPqArFwx6OPbCLXnL5p3LHmBbxy8AwTUwU2b2hn08Y2pJSM1Olh5+fLeF6IpkuyaYfuzkb6+3OkktYtF3blSo2R0XmuXJ1lfr5CzQuQMua2b2vNMDTYSjaTWOT1BnBrAaOTeSpVb1FPe8Fjo2kS3w9palhiMytVPSamCnh+QM0LqdUCMikH0zGZmC8yV6hgmhrDI7M4tkGuKcXsfJl8waW5YaW3REhBY2uGT/7qR0lkbIZPjTHxrdcx7ZjmM4oUUhPs+dg21DX5FamGJL2bO0g3Jjn00gkipdj31A4S17i87YTFxt39fPrXHuXgM4d440dHOXrwDFq9yiAMQjr6cvRsWns55rU4c26CQ+8OE0URmbRDEMQa7UtqVHFIU4pYJ9qxTdJph2TSWsGeV3V93JqPVecUuDg8zXyhSlNjiq7OWIgombRQKuaat2yddMqO9cnr5xFSkEpaMb2mjOmL0ymbZNK66fhdjnvSIKtV2brEqkkRSpWp1Z6nXPrXgI+mdaLJNnS9nyiax/dP4dXewPeP4PunUCpPFJUQQkeINJrWgW5sxjTuxzT3ohubiY310rViwosZZmozdDpdHxqt2rWIohJhOEoYXCIILxH4ZwiCYaJonCiaJYqqQADIuORIppGyDV3vRjd2YBg7MYydSNmwJsMcBBeoVP4ar3YQsDCtB+u114IwGMXz3sHzD+N7a9z+UAAAIABJREFUxwjDK0RRHqVitSAhHDQth6b1YZi7MM09GMYONK2V9W5j4thaicA/hecfIfBP4PvnCMNRlCqilAtoSJlEyhZ0fRDD2IFh3odhbEPT2m55zbzrMlmKJ/+Wuh/87atX6cpkFukf7wRHjl7h2ImrnD0/ydXROWZmSpTLNcI6gX0m7dDZEa+s9z+4gYH+3A0rASoVjxdePMmps+P4/gIRfjx+cs0ptmxqp6kxuSbJzYnJAt99+hCT0wVs26C9Ncvpc+O8/uZ53js+wtj4PJWKh2lopNM2G4fa+cJndr3vBrlS9Xj+xZOcPjuBH1zfxs2b2mlqTN3UIHu1gGefO8Z7R6/wiY9vR0iYm6vw5jsXOX12nKmpIjUvQNclTY1JNgy28tC+QfbtGbhpedbo2DzvHbvC4feucPbcBJPTBVw3QJOCdDrmbN+1s4e9e/oZ6MuRWNht1nfCuq7R0ZIBQawxHdZr6MMorhDRNXRd0tvRSBCEJB0TyzSIlKKlKRUbHCkoVz10TdLanKYh42AaOqmEhewS5JpS6Ku4hXfs30BDS5pTb1/g8pkxSvMVpBSkGpO09+XY8dAGzGt0haUU9Gxo5+O/+BDnjl4hlU0wuKMHYxW978aWDI98bjftfTmOv3GO6dE5fC/AckwaWzMMbO9mYGtc0mjYBpvu7+PJX95Pa3cT2jU5HY0tGfZ/chfhMq7vqyOzTM8U2Xt/P488vBGpCapVj0wm5ti2LYPHD2zB0DWiKGLvngGefHzbdffZ2pLmsY9urh8HP37uOEeOXuHi8BTdXY2Yhs5nP7ULgDfevsD99/Xy2U/tIptZSuhNOCZf/NxuAH768in27unnyce3k06vPYfhnjLIUVTED0cJw4mYLrEOIWxs4z6EvDWtYBjOEEUFwnAKz3uDSuVb1NyXgJXkBEr5KFUliibx/SPU5PNY9hMkE7+CYe5GqSVCeCkECS2BshSNZuMds/fcDSjl4ftHcKs/pFZ7jSA4D1xPSr/892FYJAxH8f3DUP0BhvkAicRXse0nkLJtnQsNjyiaJopKRNE81cr3qFa/RRAMr3ofSnkEQZ4gOE+t9iKGcT+JxC9iO59A0zrW0e6QKJqnVnuFauU7eN5bKFVY5Zc+UeQSRTMEwSlc9zkMYydO4gvY9pNoWu9NFyHT5QqHRkcxllmxw6NjPNjTTRt3Tm/50qun+enLp/D9gHTaobkpSUd7NuYN9gMKBZejx69y9PhVJqdK/L0v7mHjUNuqBsexTfY9MEB7W5ZKpcZcvsLo2DyTU8Xbvj/X9cnnKxw9cZUf/ug9jh67Sibj0NYaG17fCymWXQrFKu4qxB93Gwtt7GhviNs4X2F0/PbbeHV0jhdfOc3ZcxOMTxRobEjQ19uMUjH738REnld/dpZzFyZRkeKxRzeTSV9fTTE9U+InLxznR88fZ3auTDbj0NGWRa8bgErV4/jJUY4cu8rwlRm+8Jn72bK5A9syMAyN5sZkzCO+qRPHNlbNml6Yh9rrnP6CJeqKhe9Sy7il23LxM4oitSK6cqMFRfdQG91D64uttvfl+MJvP7Gm39oJi+0PbWD7Q9fL6S5HImVz4Ev7OPClfdd9p5RP54Dia7+fA0Ii702kuZuerkZOnR7jwqUpDEOju7uJttbMCk/EWmAaOvl8ldm5cizk48Zei1Jp7apVdwP3lEGu+ScoVL5NtXaQMJxF05qIogK61klb0z/DkmtxE9cIwxHc6g+pVL6N7x8i3vFmEMIizoZVQFCnt4w5p6Nohmrl+4TBRbIN/xO6vhGIdyS6iJVgpmvTTLmTWNLisdbH369uWBOUquC6P6Fc/mtgeVG9jhA2QpjEj1ey1N5qfcca7yx87x1K4SgAjvNZhFiP+pFCRRV8/xiu+xyV8r+rG8bY87DU12LZtSv1a0f4/iHK5SIKn2Ty6whx69Kx2BjP4rrPUiz8Y6JohqWpyUTIBAJjWZv9Zc/Yw/ffISyOEIYjpJK/haZ3c6Odctoy2djcTNZeWt3OVas4xt1Jrtu5vYsrV2dJpWy2belgoC9Hc3MKXZfMzJY5cXKUv/vJUcbH87z46ik2bmijp6uJROL6XXI6bfPVrzy4+O/hKzM8/cxhvvv9Q7d9f1GkOH12gvMXp7hwcYp9ewbYu6efjvY4rDM7V+bs+UmyGYfuNYqP3AnSaZuv/eJSrejw5Rm+98wh/uYHh2/rfOcuTHL67DiNDQkefWQjD+8boqOjgTCIuDo6xw+fPcKxEyOMjceGuauzkT339y0er5QiDBXPv3iSZ549QrFUY9OGNvbvG2T71i7SaRvX9blydZYfPX+Mk6fH+OnLp3Gc2GU62J+jMZPgoV39a77nlQx+t8a9WjZ0W1AukXcIv/znqGgSCLGbf8DO7T1YlsFrr5/nuRdPoGuSB/cO8uTj20mlbl13vhAiOPTuMG8fvsTcfAWlYjU/w9C5rZTuO8A9ZZCr3jtIkaA5819Rqb1KU/r3KVW+hyJCk2t3ibnuj4lUkTC4gBBJNL0Hy/wIurEFKRtQyicMR/Fqb+L7R+oTewjU8Lx3KeT/hGzDP0bTuoiJxy02pDYylNpApCLcaH3i9O8HpGzANO7HM97A948CBkJYaFoXhrkTXd+AprUiRKLe3hF8711q3uuoaJ4F4YswHKNafRpN68KyHrvhKno1RNEc5dK/xHV/ROwat9D1fkzzAXRjWxyzRRKGk3j+YWq1l+vXjnfQQXAW130Ww9iOZT1802vFbuo8rvtj8vP/DUu7cA0h0hjGNkxrH7o+gBBJlHLrbvTX8P1jRPXrRtE41crfgFJksn9UX7jECFUEdUa2XDJBczKxWBOvUBjaBrK2RaSiW/Ld3qofH9m/gb17+rHtOLN2OXq7m7lvezdNjUn+8t+8wvRMieHL00xOFejvu7Vq1d2ahn/25jl6e5r5ypf28sXP3n9dWdgTj229S1e6DdxhI0ulGtmMzZc+t4fPfGInqWXxz6HBFu6/r4c//G+/zbnzk5w+O8Hw5Rl27+pdfK5RpJiaLvK9HxxieqbMQ/sG+NpXHmLXzpUlNVs3d/DwQxv4o//+u5w6M8bB188xNNBCf18z2irvSKRCFFE9QBdvHiIiUGpR1ehOsvujOhOVLuP3/mblofcShEyjO59Bsx4mqP4Av/xnQEydvGGwlY1DbeTzVV546SQvHzxDU2OSAx9dYuISYkmL/VraVM8L+Od/8VM+/YmdfP2X95NrTvPyq6d5/a0LH2QTgXvMIKNChEjUY3wautZFNv07jE7/Kknnk+ja2oL/vv8eoNC0HpzEl0kmv1FP2Fo+oShU8hvU3Bcpl/8Kz3uj/nlArXYQ130ex/k8gUiBinlx3dClFJQYrlzkQMvH7mrTbweG+QCW9SgQYdufxrKfQNeH6q5YycpZS6FUFd87RqHw39UFL+IX0/PeJPAfqxvFtbMZKVVYNMZCJEml/z6O80U0rfOa6ysS6usEwQny+T/G944BHgCBf5aa++NbGmTw8L1jFAv/K8td4qa5m2Tyt7GsRxEywUrCEYVSv41Xe5lS6c/wvMPERnka130ew9xJIvGVxV9fKl3GVyEZI4UlTXSpY0qDWlijHFZxtSrHihdos1tpsZpQKCIVYUgTL6xh6xaRUljSvKXBNk39pvrIUgoefKCfv/72m3Vt2lin9YOEaejs3zvIp57agfY+l6V8GHho3xD37+xZUSu9gGzGYc+uXqani8zMlpmbL+P74eIz87yAnzx/nELJxTA0nnhsK1s3rx56SacsHvvoJqani4xN5Bm+PMvUdJH21uuTSCfck8zWLpE22mmxNhIqj/HqcdywSE/yATJGO3eyGsl7I5wpPM9DLd8E4oXFz/NO+vzFSRKOSXNzCscxaWxM4jjGoojLAtJpm0rFY3auTM0LMA2dSEVIIfH9CM8LaG5MYlsGU9NFTp4e48rIDLt23pyy927jnjLIQuh1dhcXpXyCcARNNhBFRVA3jo9ejwgpW3ASXyKZ/E2kbLxBfNTAsh8HAVGxQuAfrX8eUq18G8vcS0l1UAwKzHgzzHmzAMx4M6uc64OHpnWQTP0OieTX6xnTyRU7vuthYJi7yGT+mNnZ30apIqBAeQTBZcJwFF0fuMnxqyEANNKZ/xrb/gSa1n6DTGaFYewgnfoHFAr/C0FwAoAomsH3TxOG82jajV3mQXCBSvU7RNHkUmuM+0ml/1NMcz9CJFZ9xkKAaT1KUnnAX+J5bwKKMBylUv4WjvM5IM6infbmmKrNIBF4yseSJoPJPo4VTrEpPYQA5v0CxaDMleooaT2JIXRmvTyakLhRDU1oPNC4k5R+8xrgtexIGpZlV3tegL+KcP37ib7eZjZuaKuX5NQ9BZHi6rlx2npzjF2cpDhXprWneZG/+OcJmze00dHRcMPa+tZcBsuMQxQ1L4gn8rpB9v2QQ0eG8byA3u4mWppTq2bSLpyro70Bu54cNTNbYmJyFj0zxeXy22jCpD+1n0gFDJfexA0LCCS2TFH0J7lcfhtHy1IOZrC1LBeKr1AN8+TsIZrMfsrBDOPV4wgETVY/rc4WzhSexwtLtNqbabB6mKtdZso9C0CofMIwYnauzIWLU3R1NNDUFNfTLkhNhmGEbRvMzpZobEzhuh5OnYIYIApHCL3XifzzEI4QhSMYqf+E0HuTyD+Obj2K7nwRobWjolmC6vcJ3GdRUQmpd6HbX0Kz9iNkIyqaJ/TeIXB/gAouggoQei+680to5gOIm3hHhy/PxJnzkwUQkE073Lezh717Vs5jG4fa2L6lk5+9cY6fvnyKvt5mPvHxHezZ1UsiYfDFz+7mxy+c4JkfvUdLLk1jQ5Kd2z5YYwz3mEG2jG2E0RxSZDH0Hibm/gCBiWkMIdeQ0LUcpvkAtvUEmtbEjVaUcZF6EtPch+N8nqJ/jIVdYxCcxA/OkjI7MbUcKT1Nt9ODImLem7vDlt4dCKGjac1AM2tZNceTQwLd2I5p7adWewXqWdBRNEMYTt+GQTaw7Cew7Y/dxBgvXNvCtD6CYWwiDC/VY8pRXLoUXkTTdq96rFI1guBcPTlvYeWrk0h+HcPYg5TJm7ZfygSW/dF6pv3J+kKkVk8wO1j3MphYmkmT2UBEhBvW0ITkSnUUL/LJGCnGqhMYwsDRYpUeW1okNIeR6jh+3YOS0Jw102AGQcjVkTkuXJpiZHSe2bky5UqNWs3H90OCIGJ0bL7eB7FAygeJllyaXPPK7OIoinjnuaPs+8R9nDl0kfFLU+z86GZyXY1rKqu6V2BZBs3NqZty2DuOgabFbQ9DtbjrUkrhhyEXLk0RBCFjE3n++V++SDp542zafKHK+EQeiJPGxgrDOF5ER2InurA4U3iebdlPkzU6yZqdtFgbSRtxNUCzNUDW7KLR7OFq5RBeVMHRMky5ZwkiDwGEymMofQBN6Fwuv0EY1UjoDYxXT9QVj3K02psIlMe5mTd4+52L5HJpJibyFAoVurqamJsrMzVVJJWysCwD09BiPnRgbr7M3j0D9PU2x6ELFaCCy0TezzAS30S5z+CX/xzNPIBmbCYKLxP6R9HQCWsvENZeRne+jBBpouAkgfs04KPZnyCuAkmiGdsR1lMgNEL3pwTV7yFkEs188Ebdyu5dvfR0N+G6Pp4XIIDm5jSGoeH7IZomyecrjFydZfOGdrbUvRheLaBaqlEsuiQSJh/Zv4HeniaKRRfHifkfkgmTllyaQqGKbetUKz7JlM3v/tbHyGQckqvkcyzgD37vKZqbUjjO+nJO7jGDvBNFDSFspHTQZBNK1bDN3Wiy5dYnWISJYWytlzDdylAJpGzGNO9H03oJw2GA2L3rH8c0d2PLzkVSEFMaNBjrSX56v7E+d5MQEikdTGMXXu0NFPGAi+t3r6/xvPX5TBKJLyPljY3xckiZqdchNxCGlfq1XcJw8obHhOE4vnd0mYSlwDC2Y5p76qGIW/eBlA3oxlZ0vRffP07szi5Rq71W32Gb9CW6Y7UXVD2eHJe8DaX6abWasWVsiA2hx5J30kKXOprUCFUYi84LHecGFKfLcenyNK8cPMOpM+NMTRcpFF1c10cpha5LdF1Dyjjj+sOC45jXGSylYHYiz8EfHKJjoIVUQ5IwiOJa1Z8fe4zjGFiWcVNXvJTL6XyXYo9KKVw3oFzxYlrbiseZsxNrvnYYRlS8Il5EXTJQI++PoUkTW0sjhY6jZ7G0FKaWwtGyJPRGTC1F0Z9AFxaO3oAhEzhallpUxNJSZM1O/KhK3hvDlmlsrQFd2JSDORSKjNmJFxYJI8V8voqU8XumaRqTkwUmJvO4ro9l6RiGxnzeo1x2kVLWQyza9e5t4aBZj6CiSYLqd5DGZkAR1l5BheNE8iph7VWkcT+6/SQIBxluxiv8j0T+e0jjPoTWhTQ2IfQuhGhmgd/WL/8LVDjBzeQQc83xohFgZGSO8+cnuFSYolwnWymVaqTSFqOj8+i6xoaNbXR2NjAxUeDQO5c4cXyEIAyZmSnT1dVAQyax+MhrtYBTp8aoVGp0dDTQ2JDASZhs3NB+S26grZtvTQKyGj50g+wHV+pEDstRQWDgmA+glI+U6fghrRFS5pBaV1368NYQwkDKVgxjx6JBBgiCc0TRHEJ2kvfzTHvT6EKn0WyiVVqrKrf8fEAg67vZhU2XUj6o9ZavyPpiZm89q3qNR8kWhFju8Qjqu9bVEYZj+MFpljIeBab1YJ3oY60laAJN60LTuusGOd55+94hqJfYNZpLMT2FWnZk/IztOrf3tfHhTqdt1c9Xb0vE+ESe7z79Dq+/dYFC0aWzvYEd27poa8nUdyd6PFFKyb/6tweZmr798qU7gaZJpLayTVIKdn50C4WZYl2IvpFkxkH8nMUhDX0V47JGKBW7rBcIM7o6G9i2pZPGOonHrdDZ0Uh/l8SU01ytHEYTJk1mL/IWmt8CQaPZx3TtHMpXpI1WLC2JH1WQ9VCVQKPZHGC6dh4lFGm9jQYzgRvmGavEITnHtOje2IYmJblcCtPUqdUCmpviOu5UyiYMI86em0BKyeBAC5mMQ3NTauV8JwyEbALZgNDaQTbF/6bO7a6qqGiG0H8HiYdf+hdLfRgOEwkTFU0htG6UqhH5x1HBCAofFU2iwrE6p8Da9Ik9LyCfr+K6Pr4X16prmqTZTxGp2MPhuj6+HxFFEaWyi+f5sd5yEKLrWrwgqUu0zs2VKRVdokgxM1Oip6epTuCypsd8W/jQDXLZfY4wimOzAkGkXJSqIetlMGFUQtfaySR/CamtrcBa09qQ8sau6tUgRQrd2LCigigMrxJFBQyhIYSk6BeY9WbJGFlKfpE2u52knkTeI0Qha4dACJuV/ROx5A5e62lMdL2vHqNfe212fO0lV45SYb00aXVE4RRhMLzsE4FhbFtTqdRySJFFiOWJNAFBcHHV/ITVjOuNDO5aDPECfD/kpy+f4sWXT1Oq1Ni9q5dHP7KJHdu6aG/LkkyYi4xNQRjxnaff+dAMMlw/goQUDGzvQq9zLjuuTzLr/Fy5q+HO2E+FAEOXLBQEt7Vk+MQT29m4xlpeXZdoZkhFpZmpXUQR0Zt8EE1apI12pJDoIp7rLJkka3bjaA0IJG3OFsL6WHG0LKZMkjIEphYvcDWh057YRqBqSKFjaxkcvYFyME3Rn8AQNj3Z++hP5lbQdC4xW8X/r1Y9XNfD9yMG+ltIJMxVBErEspwVbVmpI8QehRChaqioGLu4o6VQnzQeQBqb4xhyOEJYe47IPx0bdKGBqhAvlNc+JzU0JNi0qR2vLlJRKrlkswkMQyOTdjBMjUzGWVx0bNjQhhCQSMSsXY2NCebnK9hWTLpimTpa3VsVhRHNzen3PbnxQzfIkSrHSVsIwmiSIJxECgdd60SpED+4SBCOk0p8bs3nlDKLFOsj1hfCQpMrs7ijcDqun0VhSpNmqznWQw59JmsTaELHkhbmTZSWPigoFRGTYcyhVJkoqgBefecboAhZMLpKeQT+0VU8E+uDwETTelh31mddZ3klbjzwIpUnDMeXfaIIgmFq7ouwjp15FI4RRctdixFKFVAEi8ksqyEMIybG8qSzDomEydREAa1OPOB5Aa1tscLN1EScWGJZBoV8hYamVEwsv3DXSlHzAn7ywgnKVY9sxuETT2znwCObVuUsd13/rsoy3g2oSHHx6BU2PjDA5VMjzIzNM7gz3in/fHqL1g8hBLZtkExYlMouxXqmdTabWLOhD8OIqNJBi5cj05ggDBSEgsY65a8QsXynQZoGkUQXsQ6wqSfpNh4iCANCT2EIg5S9FM4TQmJrGTZkHiNSMW2uEIK00Ua7s22FBOVK0pCV9+c4JtvqPM836QlWjv1r3dmA0JBaO3ril9Csj12zeI2PD2rPEVR/iGYdwEh+E0SWyH+HyHvvVt24Ao2NSRqXiY0sYPnCYwGWpbN37/X5Mk1Nyzx3y3K6Pqh3+0M3yE3pf7D490Llu4ThOCnnCxh6N0r5+MFF5op/vq4pXwgH1qvCJIzrsvmUqoDy8SOfeX+eMIrYmd2FJS1AMVwZjmsEP0Qo5RFFBaJomjCcIPCPE4RXCINRlJojiop1cgyXmCgj/hMzl93hZC+0dXsi1gu1mHW/PL4dUSr+07t0/rBOlhLBDVyGgR/ys1fPsP2+HvoGcrz5+jkyWQfHMZkYz7NrTx/JpMXBl09jWTq51gwXzk2y+4H+awxyHJcaHZsnDCN6upvo7GhY1RgrBePjeXz/g82svhWiSHHstTNIXWNieIqxi1Nkc2kGtvcgtH9/DLKhawz2t3Di9Chj43lm58r4fnDTUrblCPyQ6fE8Y8MzDG7rpFb1F6knDVMnlbFREZTyFTwvQNc1vJpPIm1TLdXwaj4116e5NUtL5+o5LXKZ1+rD0YKWCNmI0NqJ/PfQzP0o4RBXdlRBmDGRT1QA5SLNB0BkAJcoOI1S13OzrxXLDehapSvXi3KhiooUyaxz3bnCIMSt1DBM4zrq0ZvhQzfIyxGGY0RRGV1bcP3o6HoPXnCKSFXWcSYdse6mybordQmxOEOALS36E/1AXFhfCSuk9BQDycF1XuPuIV4BugTBBVz3OaqVZwiCU9yYPlNc80fe5LdrQxxDX/sO9fYQ1Hfy75dhUvXY+U0WJwIMU6Na8SiXavheiG2bbNke7yBeev4km7a0E9Vl5IQU7Nk7QGd348orKUUQhIvz4kLi1rW784XfvXXoEpXKB0vdtxZYCYvX/+4wB37hQdKNKZykfR3Zwv/fYRgaex/o5/zFSUrlGsdPjjI00EpPd9MNY9MLZUUQG10nYVHMV3njhROYlkG56CI1SUd3Ext2dJGfKzM2PEMq43D5/CSWbeAkTBCQSNoIKcg23VsLtmshtF406wBB5VtIbQPC2AL4qOAcUhtEGFtBZkDYRN67SG0QFV4mdJ9DRTMsDBalvHpMepZYICiCaBaFBJEAYa0rbHY3cPTgaWrVGo98fi/6NSVvpfkyRw+eoWuojYF16C/fUwZZYBJE53G9d3GsfShcKu5P6lPlenz3MdvN+nG9oIUf+URBhbBuEEpBkfOl8xxoeew2zn834VGpfI9y+a8I/JPc2OUblxtJmarX6lqAhVIlwvAqcCdZvIL3/xWKeP+M8dqgaZINm9p59pl3SadtPC8k0+Bw7MgVTp0YQUqBZRk8cmAz3/3Wm0xPFukbaCHXml5xHikFiYRFwjEoBCGjo3MUS9eTfYRhxNWRWX7wt+8ulp3cK9B0yRd+90lK82UaWjIU58rohva+69nea7AsnU8/tZNnf3yUcsXjuZ+eqNevJshkbpzbkC9U0XWJCBUjl6aZGpsn25SkXHSplGokUha5jizpbIJivorvBeRnywghqJRc+je1c/zti2zalakf8+GzBt4MQrah218EFeKX/5QoGI0zs41NGMnfArYhjV3ozmfxy/8Kv/x/IPVB9OTfj2PO9fycyHsbv/wXhN5hUEXAx535Mog0euq30e3PxYllHyBOvnGW4lyZ/Z/ZfZ1Brrk+r/zNm+x96r6fX4OcsB8nqMwyOfeHKHziPZhBY+b30NcjQIDHtWISt0Z0XWKREA7nyhcpVt5a3MG4ocusN73Oc99tRFTK/xfl8r+ti0osGGMNTevGsh6r02f2I2UrQjh10ox4d6xUgFt9hmLpT1ckWtybMOueC8lSOyWp9B/UlaLuzBAIJPIW59E0ycZN7XT8zuNxUoeId0go2L6zOxZ/t02kJvn1334MFcWC8eZ16jcCw5Ds2zPAq6+fY3K6yPd/+C41L2D71i4MPS4/eefdS/zw2fco1ks3qu763uXlOzEFRHdz86pgbrLAsVdPUy1XiSLFtoc20rgK69Rq97X8Xu5oV63UirLs+Lwf3C5dCEFjQ4Jf/erD/Ot/9xoTkwX++ttv8t7xq+zd3UdPVxO2ZVDzfGZny1wZmePU2TFs2+Azn7iPh/YOsP2Bfjbt7EZIiYoiFPGizahn2XcPtNDW1Ri3lTh+b9oGXf05DEsnitSHxqAmtC6MxK8Rz7MamvV4XC9cD/uZeh9xopdAySb0xK+gOV8kXlzH8zoyBegI2YLu/DKa/dn69zpCZtDMfQgRcwxIcw+W/o/iuV3V1QBF7OkTIhXvkj9gLB9nq31XKbl46xReuacMsqH3kE1+naR9gCCcRQiJprVh6oNIsXZiEBVVrjOutzxGeUTXGCchEnQ6fejWdox6TNqLaouMXR8WvNrbuO7zBMEFFhYeUraSSHw1FomQjUiZrGchG8uMcQylagiZQiDveAoLwpBXj1wkX/K5b0Mn47MFUk7MfHVlYo5syqEh7dCZy5Ks65KuBzF5i1Xn5F6IKQlM834MY8ddcJmLxUF/s3swTJ2GRm3ZMfFgtOuF/wsLtkzGuSE/sBBgmQZf+dJeLg5PM3xlhnePXmH4ygzJRNxnnh9QLtdwXZ9v/uojHD8ONoJ4AAAgAElEQVQ5yqEjw6yGKIqYni7x7affYWa2RK0W4Lo+xZK7mJmdz1f4J3/6Y7IZB9uOa29tS2fP/X3sf3BohXzcWhBFEQe//zapbIKGlgz5qSLBNbXSQRCTZjz73HHm8xVqda3ZfL66qM40O1fmj//h0ySTFrZlYFk6lmXw8IOD7H9wCNuK+zVcaOP33mF2bvU2zucr/JN/9mOy6WvauLuf/fsG193GtUAIgaYJHtm/gSAIefpv3+XS8DSHjwxz7vwElmXE4YhI4Qchbi2gUqkx2N+C5wVxba8tMW1jxaS+/J3RDW0xeXA5rt2NfRgQQgexzAMkHGBZP9fnSzcIKNZqpE0bS09dNyYgLtVCpBDXqKgJbel8Qtig2deNUgVcnJ+j4k/T4sQJXePlEp3pNC2J+N9BFFGs1XAMA1tfafLCKGKqUiZtWjjG6mpby+G5PldOj1IpVpm6OkOlWOX4z87UhShiRJHiyCsnqRSqOKvQst4M95RBFsLC0LvQtTYiVQUEUjosxTzXhiiaRUWrSfLdGDE5xeiKzzStFVNvxtYzGHV+YoUiY7y/2q+3Qq12kCA4xwIftJQ5bPuTJJK/gq4vuEdu/mItJTLdGZSCuUIFy7SZK1WZmitTrvpIAXOFKkGomC+59LU33nZKiRQNaFobQbCU5BFF8wiRjGvUPyDciGLxVr9bDk0TDA608JvfeJRXXzvLiVOjTE4XmZgsYJo6TY1Jtm3p5OEHh3hgdz9CCM6cHV/1XEpBseTyxtsXmJouEgYRQRjXWC7M8UEQcXF4GikFmiYX/6TSNrt29qzbWCkFbtll854B8tNFSvkKtYpHFEaLbutIKSanirzx1gXm5soEYURY/7MA3w85c24CKQX6svtqa02zd08/1A2yipbaOD1zkzZeWtlGXZNkMg737+x+XwzyAjIZh0ce3khLS4YjR69w8vQYo2NzTE4V8LwQXZekUzatuTS9PX3s2dXL5o0rXas3e1/uRnbv+dLb+FGN3sQOEvqtPRl3E3NuleOTk2zLtdCRvvtj9dzsDKemp2iwbVoTSfwwYqJcYq5a4WP9cY5PLQyYr7lIIa4zyF4Y8ubICNtbWunOZLDq34+XirhBQG+2YYWRDv2Ak2+d49hrZzn11nncssu/+ZPvrqzDVxB4Afcd2MrA9rW7q+EeM8hBOEa19iauf3TFDleTDWSTX1+zuEQUjRNF0ygVrIk9CkCpIoF/esVnmtbDtOci1SgZPYMlbVJGClt7/wb4WuAHZ4iipV26pnVi20+i671rO4GKCIMRlLpzFihNE2wfbMexHTQpSdkmmqYhgPZcBkPTCKPoOkWj9UBqrWjaYN09D6DwvSN1yssPziBXPJ9jYxOkLZPuxixpy8L1fQquR8I0SJoGfhjhRyGlmkfSNDE0iesHeGGIJgQNCQehCTqHmvhkZgf79g4wM1ei7HogBQ0Zh429LfT15Zj3XIa2tPE1ez/ZlI1I6ZydnKanMYul64REaCmNL31hN/lKFV1oMQGCiuLghBAYUsMNfSxN53Jpnqxp02A6bBpsW6FwtICO9ga+9osPUihW6e/LrSwDIXap7jqwlb6tXZx+5yKpxiSJjAMijn27nk8UKTo7Gvj8Z3YRBCFhqJBCxLFTGef73ijxadOG9sV3RSkFApobk3z5i3uo1YLFjFml4t3Rzbg9BvtbSNYTzpSKwwyf+9QuHq7vwPtuwb+9eWM7X/+lhyiWXDYOta2aDQ/Q1Jjkgfv76OluYs/9vczOVahWPYIgpm60bYNM2ibXnKa9NbMuwXo/cikHeULl0Wytb3JfQEpvIlQ+mrg70qHrQS0IuJyfZ96tkjAMNjbnaE0kOT83y0y1QlsyRZPj4AYBrckkScPkrbERNjflGC7MM1upkjJNerNZ2lMrx/pUuczBK8NMlEpsybUQRBGWrmFIyUw1llGMlOLd8THcIMBpacUJDa4U8pybjfUIUqbJRLlENfA5MT1JX7aBJsfh3YlxrhTy7O3oojudWVxM6JbO0H19OEmHcr5COV9h36d2oWlLXgspBcmsw6Y9A3QMtK6rv+4pg+x6R6jWfkYQTuIHl7Ct3Xj+GTSZI0qsPbkligoEwWWiaBptDUZcKS+mZ/RPrPhcNzYRqgxj1REuq8sktARZI4suDAZTH1aGtarXGi8lcwiZRTeG1na0iohUBf8u1CEDSCEZ7GpGCAOloLXxWgN55/JumtaFYWylVnuehVrqWu0gTuLLdbauD2aiqfo+714do6cxSy6VJAwjRvIFZitxLPXB/m7mKlXOT8+iS8lQrplircbZyWlCpcg6Nn1NDeSSCU5PzbClLce2TZ1cmZ9nslgmjBQKxYa+Nlw/YLJYYiKssO8jg7SlU7x7dYwr83la0ymQguHSLDU9oPehFuZqFRzdxI/iBDhL0/HDEFvXma1V6EhkyZRSbMq00JtqwtFX77Ncc4pPPrnjhn0gpGDLviEs26R3cyeWbZBpSiGlpFCscn54Gs8LGOhtpq8/h2loaJrEMDRsyyCdtGmskzUopYiiuDY7CCP8IMQyNBBQdX2qrsflsTk29rXQP9RKf1cT6aRNEIZMTBfx/JCejgaCIELXJK4X4Nix0k/8vsXvXqlSI190yaRs+vqb2b9vEMOMJ9Cb1Z/39ebo671e7jKKIipVn5nZEh1t2VgxSRO0NKdoycULGM8LCYKYj7ypMXnbrGCVsMBo9RRSaLdtkNvsD68aBGKPiRsEWLrOG1ev0p5KUQtj3umrhTxnZ2cW3cmWrnPwymVaEkkOj49hazop68allX4YkrFsmp0Etm4gBdjaSrNWCwKmKmUqvo8mXIbzcwzn55FCkDSWXOuWpnFiapLBxiaKXo25avW6qxqmwdYHN7Bl3xCz4/MU58p86XefwrCWxpOQYhUSlbXhnjLIXnAeIRJkkr9I2X2B5swf4tZep+q9uc4ypoj/j7w3D5Ljuu88Py/vrLu6+m4AjcZNgCRI8CZESdRFW7Zk0bJkjY/wMTOesTe8Ebvh+cNe73onNjZmPLPrHe+G1x5rbY9nrJFleSTbkkyRNIcU75sEQIC4gW4AfXfXXZXne/tHVjfQ6OpGA4QoavcbgUBHZtXLozLf7/2u7zcM3yEIDuE4fdcoh1fE8QxB8BpSXg4NLmnsDup7aKspZrzpjpaoxNJ+kEQgCrEq87ta43Ot7yrVJgheI4yOsRTyvlno/vy995CbrvdjWreh68OdynCIohN43lNoWhFd33TdLQ/J/ZLX9T2zswpOWxaOYXChUuXQpWmkUtQ8j31D/cw3W5yaW2DPQB+2oXNyts7hyWkyls2mYo5YSgayGeYaDYbzWfqzkgvlGnONJlnbYqbRZM9AHwuNFuWWx0vnJhjtKTCQXempRjLmUrNKJWhRCzzqkY+lGcRKYmsGtq5TDTxs3aAZBVT8Nq04YCx746pMURAxPT63Ylur4dGqJ4tlzw+ZuLRItdZCCDh09AKZtEMh55JO2wigmE/Rc0Vv9uxCnWY7IIolQRiRTSe59EbTxzR0jp6aor8nw7HTU/QW02TSNp4XcmGqTLnWQtMElqmTSztcmCpjGhr1VoDZWQTousD3IxYqLXaP9XN6Yo4olqRTNrm0jetYywIS3bDYbOGYJinrSmY58P2QU2dmaLdDWm2f3lKWIAiJYknKsS4vMsK4Q6l5fe9BrEJq4TzjzUNMNI/gGlmMTr3E5tQ+LN2lGZZpyzqxDAlkCy9uomsmo6nb0YVJO65RDqZoxmXSepFeewu2nkra6pTPhdZRMkaRdlwnlB4po0DRHMI1bm5KzjVN9vX1s7evn9998Tku1Cp8bGw7e3v7eG3yEm9MTbGnt5cgjhOaylaLII7xwojb+we5a6g7L3RfOs3WQhFT09lVKtHjulS8y46bEAJdCHb0lJhrtQilJJIxUSxxDIO+VJqc7XBsbpZdPSVu6evnm8ePEUnJYDqDhuDOwSH0Lix0Qgh23DGK3w6SIryblNf/QBlklEITNppIHghNy5B2H6Hc+DLyOpvEw/AIvvckprEd3Rjt6kUloawKgf8iXvsxrqzSNK07MIztuOYAt+hFtmd2oAsd9wccrk74o4sI4XTUkkDKKlF0EsMYo7vMZOIZK9UkDA/TbP5JQmf3PlalvhcIYWMYu7GdT9BqfoUlUpNW6ytoWhbb/hi6PnxFvUF3LJGAKFVHyhqJJOSeDZ9HzrHJ2BaOaaBrGmEcU/d8dE2j4LrLK5KtPUV29pXIuw62oVNwHfozGbYUC8zWG0RxjBCCuufjRRFSKXozKcZKPcRTM9Q9nzPziyy0WjT8ACkVQRzTCgJqnk/d8+nNptiSKVBZbLEl00O64x1rQsPUNAIZ4UUhpm7g6iatKGAxaOHoxnUX1i3B90IOPfsuhmks3+ZzRy+y776d7L5rG45tsnm4SDGfotUOGOrPUyqmyedSWJZOpdpCKpWoBZF4TucvLdBo+eSzyXvl+RFzi3VqDY97bx9lqC+HVIrB3hwpx0ITGhJIpyyq9TZnJubZtbWPKI4Zn1ykWm9Ta3hYpkEu42BbieqPaSThfD+IuTCViC3s2TaI66y/uD42PcemQo6tpcs95UIITFMnCCLePnKBeqPN9rE+FsstoihmcCDJ0xbyKXw/vKFq8kiGzPvjnGu+xUz7DK6epRUldTH9ziiW7jLjn2WieZhAJnzP9XABS3MYcfegC4NGtMj55lucrL9Ev7ONB0o/lRhkJO24zpPT/5492YO04xrNqELKyLMjey/bMnejbzDVtxHoQpC1bExNAxQpy6Ydhsw0G3hxSNZOij0v1qrLYWZIwsmWvnFDF0lJIwhY9NrUfJ+qnyxIF9ttqr5H1fcYymSIpOKt6SnuGhpmb18/x+ZmyVgWtq4nxZooTF0nkpLpRoOC6yx70lfizof33axbtIwPlEHWtDRShsstT17wJroocP19qAZKtfG8xwFJKv2L6PpIh3d1yWBFKNnE97/XaR+6nD8WwiWV+vxyuNvSLSyuxyteasmQXXmSURKlYqAbheQGrs7Yjaa9uayWFEcTeO1vYZq3oWmFzuIjefghRqkQKeuE4SGajT8hCF7qVC23+WExyoaxBdf9HIH/KlH0LqCQ8TT12r8jdI/jup/BNHeRaBvrLFsMJTu0oRFS1onjcwTB6wTBEXSth2LP/31d55GyTM4tlCmlUwznc+we6GN8sUzWtim4DgvNJq5pLq+qLcMga9ukLBPL0LFNg7lGk/lGiyCK6cumMTUNU09yX6lOpWc7CnFMg56Ui6nr1Noes40WE+UqxZRLXzbN9mwv27K9S5TKK0kJl/SLOy0zV37mRtMHhqkzsn2QQn9ueQwhBLlS4r3nsi4Hbtuyoh1p6VCT0xVQULoiJy2EYGSgwKHjl9g06NDyAuYWG3hBRE8hTS7j0lvMkM049BYTvWEhwLEMirk0fhBjmwbD/Xmm5mo0WwG9xQxbR3qQUmEaSfh7sdIilpIgiGm0fMY2l7gwWUYprtk2NFWtoWuCvOskeXBNwxQai4tNGk2f/r4MY6Mlmq1EBnGwP08u47BYblLqyXDm3OwNvWG2nmJn9n5cPc/R6n9l0NnB/uIjqz63GEySNgo82PvTZIweAtnG1tIIIeh3xuh3kkX6kjFfgkLRiBZwjCx3lT6LAF6e/2tO119l0NlJ1rw5+taGpi3XUgAUHJcHR7ZwYnGet2am2JTNcXDzFmq+z9PnzzGczVFyU1i6viGD7BompqahCY1WGDBerXBsfg4/iji5MM9gJsvh2SQfnLNtHMOgFQUYmkYzDDk6N0O2cxxNCDJW8pmsZTNerfD0+bPcO7KJXaXVqQtIGLkCLySOYrqtu+yUhWVvPKUm1AeIYscLDiFlGV3roxW8xEL1dwFBLv0Fipn/BtNYHbqI4zkajf+LZuPPWWoB0o1toDzieAo68oq2/SFMcz+a3oNSEXF8kcB/uZNLvZK838JxP0U+/7+g69cmi08Ma6cvbhkJr3Qc1/D9p6nXfhcpk95lXR8lnf4Z3NQXESJ9BTn7lZOkxnrGOgheo1b9XYLgxcsbhYNp7sV1fwrT3I2m5TqGeI4wfJfAfwHffx1oAw5u6rN47ceWr92y7ieT+Wc47uqXfgme9xSNxh8R+C8kZ6mVSKf/CZnsr11XHrfV+muajT8mDN/p3JMxMtl/Tjr98+t+T8oWQfAa5cV/1jnvK++5g2FswjB2o+sDnQVHiFItYjlLHE0QxxeXr1eIDLb9EXpKX+56rPUQyaRoStcSvmFJpw/gJvLdLnkKmnh/FMWWpgEpl3Suuk0LSQhQoTrtaILF6Qq6qZMv3XhxnVRqecGQHEW8J/GHpfGuXpQs/T6Hjl/CsU1GBvJkUuu3pXz7nRO8dXEShSJj2WzrLfK52/euOt6V530zF0EXW++uaZDPNF7nXOMN+uytXY31El6c/xqtqMaB4o/SY29Cqph6tMD/c+ZX+dmt/4Z+ewxNaLxd/i4z3hn25j/K5tTN9/6uRNIfr5bfG9l53pcWst+vJ/7p82ep+j4Pbx2j3G7zlXcO8VsHP9L194mlRCq1nKq6GlIqZsbnOPryKabOzRL6EVc+yEIT3P/pO7nlno3V98AHzEO2zb10aicxjS2k7I+gVAvT2Hpdfci2/RE0LY/nPUkUHkXKedrtv8fznuDyTy07nM5XVBoLB8u6m1zut9G0ja0QG40/IgzeXhZ1uCzs4C8f40oe5ji+QL3++zQa/57E8BpoWhpIo2kphJbGth/EcR7BMLoXY5jmHTjOx4njSeL4fLJReYTB4U6luLHiOhP6yYS/Wog8qdTnyWb/e6LwDGF4hJudS/5+QQgXy7qbnp4/oVb7nwjD01wmgPGIonNE0QQIbZnEfilXzLK4xnuHftXL+/2iZriZBv5aiKXizNQC333jOBcXarT9YFlecAm9+TS/848+yeHnj7PrwBiprEtlvo7tmO/JIIur/n+v6DbOldv27hhM8osbKLQ6uG0Ld4wktKhCJPnQ6zne9/sX1IT5nqqnLzsUidf8fnpnV743gtXv1fcDu0q9vD09xVeOHCJn23xm59opK02Idd/Bd189zbe//BRHXjhJpuAyd6nMpu0DzE8uEgQRtx/cQ/zJ26/r/D5QBlkIk1jWieNZYlXtqBRJgugMtrG7Q+CwEUgc+6OY5q20ml/D958G/HXJQjStH8f9NOn0L6Drw6wlNHA1wuAQQfBSxwhfVlRaOxQsO2IPS9zcAikTjzjxijU0UcCy7lvzmEKYuKnPg7Bpt766rO+bGN61Wpl0dGMH6dSXcFM/idCKmOZuoujMTam2fj+QrGJTWNYB8oX/g3b7b/Ha3yWOL5AsrOLkn1o/EC+Ei2HswnY+/B7OY/XfNxPvt3JSyw/4N3/9DFv6Cuzd3I9rmavOIe1YKKU48fpZRveMkMq6zF9axM04DI313zB95s2+1mv1i5vGxvOS7TDk+bPjpCyTbaUe2mFEb2blPLTR/vQbgSZ0BBrNuNJ1/3s90kTrCFmzF4FGOZhEqZi8uTEZyfcCASuqQN+v570/leZDW0a5JxpBF0k4fS1c65zOHBonDCJ+7jd/gkw+xZ/8ztf5jT/+Fbymz7e+/BS5ngzODzMxiBccodl+DD86jkZqxdNWyv2Ljid5bSjZQggH2z6Irg9iBx8mDF4nDN9BykWkbCKEgRBZdGMzlnkblnU3prkf3RjlenK7SnkdY3yjnLJJnncpT57Qsfndc89XQNP6cN0fwzDGCILXCII3iaNzSLnQaYkSCJFC03oxjFFM63Ys615Mc+9ybtww96D53yNe42X/IEIIgSIJz2taHtt+iDA8ShQeI4rOEsczHfL5hFhGCAchcuh6L7o+gm5sxzR2Yhhj6Mamax7v/09oBwG3jw1xx7Zh0o61KmychOgTpaJ3XztDsS/HxPFJNu0cXEmM8P8hHJ2aRSpFyjKZrjfQBGzv7elaefv9QNYo0eds5UTtBcrBJJbm8EAnX3wtHKt+j1n/PBPNw4TSx4vr9Dvb2Jm9F00Y6CSFXy/Pf51WXMMUDlszd5LW31/ykPcTpq6T13W4CZo4tYU6lmNy24d206p5WI7J4GgvuqHziZ85yD989QVmJ+bZsX90w2N+oAyyHxwijKdI2R9F13pWrKA07XoekhBFjKblMc3b0fVRLOsu4ngq8UyVD2ggnE7bzDC6PoSmZTqV1z7dKCfb4QXmW0/ix1Pk7bsppT6Kk/opPJGn4R8liGeRKkTXUjjGMFnrNtLWnjUrn5ehIIjnaYan8aJx6uEi87WvILTHEMLAEBlsY4C0tYeMtRtN2B3qvgE0LY9h7MC2P4SMF5KweScMH8RlWtFFmnGV2J+C4GkQz6EJC13LoskahvUwrj6AY92CYe5d/zS1EqF+Kw2tQhDPIaMYo/ksVljH0PNYWi+OsYmUtR1L70NbI5RmWQcQ2f+WKJ7Bjy7SCCeot14H/wJKhejCxdL7SVu7SFu7sfSVfYjJYsnAMEapBmep+BdBQc5+hKy5mSCapOq9TCs8mxS+CYGBwNFy5PQ9pOwHMY1r9acrorhGPXiXZniCIJolVq1OgVh3CDQKzoMU3QcxtNUpFqViIlmn5r9FKzxLGC8Qd4TkTa2Ia24lbd1CytzCer6PVCHt8ByX6n8BwJbcP8c2BlAqpBWepR4cxYsuEqsWIDC0DI4+Qtbej2NsQtdWE1NYhs6P3L2HN89cYny2TNa10XVtxVlkXJtHH9jHvY/sZ35ykfmpMsPbB9i0a+g9eTgylkxOLHDhzCwzl8rUyk3arYA4jNH0hF7SdkwKPWlKA3lGtvbSN1TASa3t3dTKTV579gTH3jh/zePne9Lces8YBw7uWrWv2vaIpMSPYsqtNqau3/C1KqU4fmiCo6+fpzxfp6cvy233bWfXrWsvDFN6jrH0HTh6Zpncw+y0P/XZozhaBlfvni4oWsOYmk3J2oRCYgqblJHH0lJEKkChGHFvQRM6ofTIGr2U7M3o2rVD4PMzVd5+8TTvvnWZ1vXWu8e48+BOCqWNpxd/mCF0Db2j2KYbGqmsy/T4PMPb+tm0Y5D6YoPqQv3aA12BD5RBjmUFQ+8nl/pCp4XlxqCUTDj3SDhXdb2ErpeAOwCoBC1O1mbImS55y8WSBiUzjVKSWNVoei9jG1uwjK0rWmnCeJ6F1lPUg3eIZZ2UtYNyVKYSBTQlBDIhO9DRaccQxiGCPnrcD6OL7jyusfSoeC9RCSZpxSFerAjiC8TqOErFCAwMLYWl9+Ka2yi6H6LkPoyl9yJEIhlpGJuvoMxMgrUV7zUqwfPUghm86AJhXEaqNgqV5J20DKbWg2MMU9RvwTH2XjHGqjtKzT9Muf0CteBC59p0YhWg4nfQ/BPoWhpD78HRByk491JKfYy0tbvraIaxDSmyNLw3KEfnaYQVvOgYkayjiNCFg6mXSJlj5OwDFN0PkbFu6Wrgm+Ep5r1X0YSDbd2Dre1gMTpFLQ5pxxDLGEUTLY6x4piWDPFlQCn1MLbRPTSnVEw7Gmeh9TQV72Va4TnCeB65nItfrQqmCQdDy2EbIxTUPV1+5xbN8CQLraeo+YdohxNEsoJUAULoGFoexxghY+2j6D5Ij/sQAqvrM6NUjBdNMVX/SwD6U59BCJ2q9xqL7edoBMfw4yniTpRAF2lsfYDRwq9i6X3orDbIUinmKg0uzFcIwohW2sG4qpjFD6Pl/stcT4Z20yPfmyXXc2MTsFKKhZkabz5/kuOHJrh4bp756Sr1agu/nVSuarqG2RHqyBVSFPuyHPzUbTz4yX3rGuR20+fIK2d5/K9fu+Z5DG7uIZVxuhrknX0lzswvsthsYegam4v5G87tl+fqvPDEOzz794epLjYpljLMTVUZ3TGAZRtdf2tNGOSMQQq51eI6ObOPnNm35vGG3J3AzlXbl4q6JDG99hZ67Q0y/F2BeqXF4VfP8OR/eWN5m1Kwe//m980gv1s7wmhqGymje+RUKUUj8jlauchoppc+O8u832Der2NqBhpwsVWm184mJCGGzbRXxYtD+p0cWcOhHDRRwP7i6rkxX8oyO7HA9PgCA5tLDG3t44mvPMe9n9rP3MVFWnUvaRG8DnygDLKhDyS8t3IWIYbfAwPT+qUJJ6rTHK1OMezmSRs2GdOmaKUSpmrZphW8jlR1DL0PjdULA6UimsEZZht/x3zrCZRS2PogrrEVRUQQL+BFF2iFZ2gFZ9CETcG9D0OsXskqAmr+W0zWv5pooOglXGMLupZGoCGVTygrtKMJmuEpmsFxdJGix30IU18tTK4UeNE4U/W/ZLH9HEqFWPpAx6DZgEAqj0jWCeQ87fY5TK1A3lltRJbgRReZaXyTueYTxKqBpfeRtnajCQchNGLpE6saQbxAJZxAEzY55641xwviRRZazzDd+C80gqPoIo1jjKBrGQQ6sWrjR5OU2y8nxiWaZDD7BbL27cvFWlcjjMtUvdcTA918HFPvIWVuRxM2UvkE8Qx+PEO7NUE7HAcBQ5kvdCUG8eNp5pqPM1X/KkG8SMrcStb9CLqWRamQUC7QDM7gx9MIBLYxTI/7ESy9h5x9J9pVutqxbFEP3mGq/pfMtZ5AoOEaW3CMETRhIZfHPEkjOE4zOA5K0ZP6MGCtec2Xz3eKZniS2ea38KKLGFqelLkd0JDK6zw/4wiMNalkw0jy4rvjfGz/DvaPDZFLOatC1pZx2WgMjHZvA9koZCyplls8+Y3X+YdvvsHMpTJxtLroTsqYKIxpN32qi00unZ9n9/4t12S+0gyNTM6lUMoQBhFhEBGFCfHE9WCkkAMBlbaHa5psLt54OHfqwiKn3rnE3GSSIpq5VObwK2eoLjbp6csQRxLd0JBSEXXO128HyFhR6MuiaYLAj7BsI6lI1rXknikwHQPtfQqj3wzUwiqtuIEfBxhCJ28VMYTJrD+FVJIeq4UmdGIAACAASURBVISjp6iFFZpRA0d3yZl5pJLM+tNYwuJw9U367IE1DTJArCTT7RrNKGBrppfJdoWJ5gJ50yVlWJypzzLeXCBlWAw4OabbVS61y4ymexlxi5yqzxAp2dUgj94yknC4a4JsMc0dH93Ld/7kaeYuLFCdb9A7VGRw69oLpm74QBlkTSsQBW9QbXwFx74rUfjowLHuRNduDoPMydosedPF0U2m2zVykYMqJFWUQtjYxnboCElc3d8JoIhoRWdp1yfIWHvocR8iY+3F1ArEqk0zOMVC+ykq3ss0wmNM1r+CbSRG8eoJUcMib99NzX4bS+8lbe4hZW7DMvoQmESyRjs6x0LrKcrtl2hH48y3niBljnU1yKCYbz1FxXuFWDbJO/dQch8mY+3G0PKAIJJVvGiSZniSVniaovshXHNtWr7F9guU2y8RyTJZ+zZKqU+QtfZhaAWEMIhkDT+apBmeohWcpugeJG3u6DqWUjEV7xWmGn9Fw38X19xMwXmAgnMflt6PJizCeJGa/zaL7WcTA9t6EiEsHGMTlt69+j2SVcrei+hamrR9CyX3Y2StfehahljWqQdHWWw/Td0/Qis8w2zz25Tcj2AbQ1edn6TmvcVc87sE8TyuOcpI7hfoTX0cQ8snpArhWaYbf8Nc8zFCuUDK3MZo4de6nptSklZ4nunGN5ltPoahZSk499HjfhjH2IyupZGyTSs8Rdl7kYr3GjX/EOPVP8Q1t+KaW6+5MK16r1DzD4PQKbkfJWvfhm0MAjqRrNAOJ2iFp0lbe9BF98iTpgk29eYpZlysThhOiJVLgRulf+wG3wt5/dkTfOPPnqNRay+voR3XIpN3cdN2onSkEo7sKIhotwJMy2B4S+maXpibsrnlzlGkVLSaHu2mT7vpE/iJvvDkxALBNWQtlVJcrNToz6a5fXiQi5Ua07U6A9nMDXnJgZ94/VdCSoXX8rl0ukkcSyzHRMaSKIgJ/JDKfJ1202Pbvk30DBaYOj+HaRmJE+BYBF6I5ZgMbOllo/ZYIDCExWh6P+YVc+yVHbBXznvfj4Krae8Ss/40oQzRhU4h6CFn5jlaO4QudHrtAfqtASa9i5SDBdJGlj67H1NYnG2eJG8WiWS0yvVaWry1Wz6apuGWHHbnB3l+9hS1sE3asJf53XOmy92lMc435mnFAe04ZHu2HwUYQsPQNAyhEcnuaaptt21h085BnJSNnbK495H9zF5Y4OyRCUpDBT786L2M7bu+OpUPlEGO5Sx+eIRY1mh4j63YN1T645tmkAtWimrYYtaT1EOfrOl0ClZCpGrhWLuJ4jnWzuMpYlnHMUYZLfw6Oft2NHE5fJaz95N37uTEwv9A3X+Hsvcivf6ncIxNmHpxxUhC2BTdB7GNoc4EvTqcmFd3kbfv5Uj4T/CjKer+Yfx4dg1ZBUU9OEwsWxhaloHM5xhI/8SaeewwrqAJp+txl9AIjhPJCrqWpjf1STblfnlNyskoriVpAq27PmkYl5lt/h2N4BiOMcJQ9osMZ392xf0D6Ek9hGtu5VLtP1APjlDz36DqvUZf+kfWuOoQqSBj7mNb8TfI2ftX7C+49+EYw1yQf0o9eJsgmqHqv0X/VQZZqoBmeJpWeApDy1F0HmQo+4Xl/QKdtLWb/vRnCOJ5Zpt/Szscp+4fppR6eNV5xapF1XuNueZj6MKlx32IXaV/iSbSKya6gnsvOecAovpl5prfphG8y0LrKYayX0TT1y/gmW58E9fcxpbcL9GTehjzqvckWVZKQFvT29Y1wWh/kb956R3SjkXWtTE6IiFLKOXS/NZPf2zdc9kIpFRUFhp88z88R6vuLRvjbCHFzls3se/AKCNb+0hlbZSCdsOnXm0yOb6Ipgu27Lh2RXcm53LwkVs5+MhlXm6lFL4X8crTx/jz33ucqYmFdcdoBiEnZuZo+gECwbmFReYaLW4dGkC7DgapJZT6c/T05bAckyiMsB2LgZEimZzLU3/5IplCajlvXuzP4aRsvIZHebbGee0Shd4cF05OUZmr46QsTNsk8ENGtg0wtLVvuf9ZSoWmCaRUK6Icy33RQmCJLI+O/DaxUsQyiUzEShF26CsNXSOMYjL2yvfyZhnndtzG1VPsyoxSDSu8WXmVPmuAnJGnx+rlVOMYc94Mru7SZw/Sjpu8Wz9Cvz3I3vx+eq0+zjZPcXU0tFpucvT1c5x9dwo3a3PPF/ZxsjGNADKmw/ZMH9NelaOVS5iaTt5yOdi/k29dfJsFv0HasHB1i6xp4+gmuY7j1g3pnEu6oySmlCLfm+XnfvNz7+m+fKAMctb9PFn3M133bbzl6dr4+NAeXpk/x4XmItsyJfb3LHmHBrpepOG/hOiIYK8XLhxIf5aUuXWVMRHCwDZGGMn+HCf830YRUPFeI2vv72KQkwKltLU617METZg4xhA5ez8L8SJ+PEssm+sQ41/JVBUlzGeqez6yu5e9EoKl4jaBQiZFY0p0NfKGvv6iqey9QDs8j1IhBfcB+lI/uur+LaGUepia/yb14Ah+NE3Fe2VNgwzgGMOU3IdXGeMlJAulO6gHbxOrNu3w7KrPhLJMKMsoIgwtT8bqXujmGCM4xggAUvm0o4tdP9cMTlHz30SqFq6xjS35X0MTqa6/RcrcxkD6x5lrfgeQzLeepi/9o5jXMMgKyUj25yg4D6FUCi9OPD+1pOEO2Fr3HOUSdE1j7+Z+do/0EUuJUgpN0y736ipIdbR7l6QWhRDIzmR+PeHSwAu5eHaOc8enlrdpusYnHr2LH/+ZBxgevTksUVdDCIHjmmRy7jUZugCOTE7z5sVJXpu4RN51KKYc7tw0vCZRxLWwZccABz91K82Gx+T4Atv2DPGZn32ATM5lYHOJbE+aymydVr1NKuei6zpSKga2lMiXMlQXGixMVSgNFij0ZfFaAbIsk2sRSR1AMwiptNsUUy4Nz8fQ9A7hRhLxCOOYjGOz2GyRd5zkf9dFKkkkJXONJtW2x0ghx6mZBe4d24RtJHSr5lULtPcKW7Nx9BT1qN6p34lxtRSa0LizeB8XmuPExOhCZ8jdRMnuY96fRUNDkLBzXY3J8/N8+z+/xNHXz9M7mOczP/MgW0aS50kTGpoQbMv2c3/vDhQSveNY/OMdHyZWcWfcy1d5a2FtD3dJHCUKIqIgIo4lmULybifpF4Wma9f1bnygDHIiNm+TrOmvDhPcvPyIq5s81L8DiWKqVWXRb9Jjp1GEhOElNGERROdXKCp1OVtyzp3oaywUdOFScO5DEyaxCmhFZ4jke2kvElhaLwIdiDv0opLV/dKCrLWPqvcKQTzHZOPrKDR6Ux/fkPHthoy1m3L7BdrROWab30ETDv3pH18zfLweav4hwri8nDe29LXlyXSRxtR60EWaUNZohefXHds2Rsg5d66539SKGFqyIFIqIpJdKiBXFARqa4aLhdCXowSKGLncV74SXnSBZngagYlt9JEyx1gr8iKwMLUeDK1AJMs0wxPE6/TOL8E1RsnatzIfCJ6deYuT1RkkklhKNKGRNmx+/ZaHSRlrF0FZhs4n7txJreVz+NwUE3MVbts6yL7RAZSCIIrRNYFSitOHxhm9ZQQnZTMzPo9pG/QOX7sNZwmtps+5E1Mrto3uGGD/fdsZ3Fxc41vvP+4Z3URvOkXWsSmm3I5s5Hubhw5+6lbu+9gtqI5ClGEYaLrgwR8/0GH5AiVlchwBu+8aW2ZFEwIe/dVPdlJrgndfP0ttsUEYRHitgCYx33jrKNO1BnduHiJlWSw2E9rQWEkWm23CWLJnsI/tfT0UXZfxxQp51+PY9Cxbe4qMFHJ4YUTBdQnimOdOnydtW9w+PEgxdZmv/WYja+bYmd3NCwvfo8cqMexsZiyzgxP1o5xvnabfHqTfHqTPHuSZ2ccp2f0dmdGVv0dlscH4yZkV2/QuhjtxL1Zu167TxiilOHt4gsf/07O8/uRhhBD8b9/9TYoDBQ49e4x202fH7aPXlUf+QBnkMDpFvfVNGt4/rDCGhj5If+FfY21QYnAtKKV4fPLYsgcBcKlVod/JsiPXj0DH0HtJ6/djG9vQtfUnh6WinO7Q0LU0tjFAOxwniGaIZXONzybeeOJNJTnOIJ4hlDWkbBGrhNTEj2c6rSydr6xBbziQ+QxV71Uq3is0gmOcr/weM41vUHDuoeA+SM7ev2YFbzeUUh+n5r9F2JqnFZxhovJHzDW+Q965i4J7kLx9V6fA69rjtcJzRLKBIuRi9U+Zbnxj3c+HcblzzQqpWkjld4rTVsPU8jj6yJpjCWEt/14KhexComLoBQw9D2hEskGrixcNEMSzBFHy4mvCxtZXV8Emn5vDj6ZRhNT8Q7w2+el1rhak9IhkNflbtVHKS6rt11GlSlvb0bU0/WaOT4/cyuZ0kTmvzoGeLYQy5snJYyyR5q8V8ZFSMT5b4X//5veYWWxQb3v80qfu4batg8zWmrx47DzztSb/+BP3cPKt8/RvKmFaBmffmSCVTVHsz6NvkHAj9EPmZ6ortm3a1kexL3NTCpOmvVkaYZNm3CJrZBjLbLlmYVw3GJqGEGDoGrZhUG61afg+m4s3trCFhA+8mzLQlduUunwPNH1lmPjKUP3O/aNsvWUEw9CxXBOv7VPKpBjKZ9lUyHNiZo6q56N1GMY2FfMYmkYsJTO1Bo5hMFGuYlTrVFoetZSPLFeZazRxTIPpWoP7xzZzdHIGNXRzmeN2Z/chAEMzcXSXAWcIW7P58aHPIwBLszGEQcEsEqkIQxiYWiLxOugMY2gGUkkyxuVaglqlxczFMvXaStnEbvPSRreth9NvneeJrzzP+WOX2HlgG+88f5wlnW6l4LUnDmMY+g+vQW4HrxPLGmnnk/jhIbKpR2l5z6KJXMd7fu+wNYOc6SyvmlpRgKktvQwJkYQfniaMZzD0QfQ1MrWgoYsUa3nuQghQeqeQSidWTWQXisqlntLJ+tdoBMcI43ki1UQqvzMRa2iYCKFf1XKzdrWoqZUYLfw66dYe5ltP0Q7PEcWHklae9jM4xgh5515K7kexjaF1FhVL4/WwKffLOMZWFlpP0ghOLPe6LrafxzaGKDj3dAqVRrvmo5fymJGsdbx7RSgXCeXiusdeMYaSSBWuaZA1YWNoa1M4Xt1X3u0e6sIhZe4gZW6jHY6z2H6GrH17pw0pCft60RRzzcepeIksqG0MkrdXV5UrJLFqE3eoU6XyuobJ17lipAo7hnRtGFoegbGcE7M1g3YUIpUiUpJ5v5nkCa++/CvghRH/+Zm3GCrmePSBW/nas4fwg2TBkrJNHMvglaPnuS+V49Azx6jM1nAziaTiLfftuC5DGseSZmNl9ClbcLGvoby0UQg0IhVj6zZZ88ZbcKRSTJSrgKDoukxWa0yUqwzlcxjfx4rmjRoG27Ww3cv3LOtYfHjHVjQhcEyDwVyG4zNz5Byb4UJuOeS8lMpIWRYf3504ObGSy8ImQRyTsiyyts18o8lYbxHXWj/lcb1w9MtzhC70ZUnb3BURKSEEhmYuF5sJkURoluVvBSsWWgszVS6dn0dJ9b4Q1Zw+PIHX8Pj0L30Ey7F45/njy/tKQwUqs7Uf8j7keBFNS+Ha9xDLeTLOj2Abeyg3/hB1k/iW9xaGsDR9Of9QsFzkiok5IoynE49vXfpMtRxGWg9CJIozSsVcLQciVUArPMP58u9T898mkjVS5g5Kzt24xiimXkQTLkIYKBUz0/hbqv6ryHVC6clLo5OxbsHQcuTs/dT9d6gFh2gE79IIjtEKT9MMT1HxXqHkfpQe98Odqty1xtRImdsZyGTIWntpBMeo+W9TD47SDE8k7V3hWSreq/S4D9HjfgTXvIqdRi39FwEKQyuQsw+QsTYuf2gbg2uSjSTnqa+7fyMQQidvH6CdeoSp+ldphWeZqP4h883HMfQ8SoUJiUtwEj+eIW3tYTD9aPeeZkWHcU2iCRvX2Na18Gs92MbgNb07IVbSXI6kCrSigKOVRBTh3t6tmNr6r3oYx7xyfILf+PxHuGfXZp548+TyPtcyybo2lZbH4GgfBz5+K0Nj/diOiZOx6R0qXtcEmOTdVqakDDMJ3d4M2JpFIENi4qTO4gYzn6dm53lt/BJHp2YZzGUIophSJv2+coxfD0xdp+8KWs+0ZWEZBrahryrOWqqiThVyV4h6rETaSnSgbcPA6cLh/f3AtTzX9RYFc1NVLp6bW3P/zUZ5pgKaYM89O6hdZXht1yYKo1XP+bXwgTLIyYtjkORFJYoA09hOGI1fI5+7cfQ7WSphmxO1GSpBC0c32ZJeCk1LpGqjixyWOXYNqk7V8WLXKqxKPhPLNonOs7Uq7BjGZeaaj7HQ/h4Q0+N+lN7UJ8nat2Lrg+haZtnAxLJF1XuVmr+xsKAQOq65BccYJmPtJR/enRji4BSN4BiN4DjtcAIvuoRSEaXUx9ckylgazzGGsfUBMvZecs6BzninO+O9ixdd7DBEtelP/RiOeXVBhEAjkYbUhE3evpOBzMarEhPDc62J4b1Plo4xTF/6R5DKY6bxDer+IRr+u8ueuSZMLL2P3tSnKLoHE++5a3+vSChaSf45xjDD2X90Xedi6kW4FtPbVciaDroQnG8usCc/SJ+dRb+WapRKiD/yaQfjqoInP4yotXzSKZuB0T723L2NkR2D1MtNvJZ/XXlFKSVRGBMGK9MFS3nR9wqpFNWwRjmsIpUkZ2QoyjzmBtinrkbKstjWW8TSdfKui2saDOWzH1iDDKvvYW+me7eDWOPvK6FrGv3ZHw7WrTCMmLm4yOT5+fftmLqhg1IE3mpncfr8HLquYa9DXtMNHyiDbBjDCFlBCANN5Kg0/iOaSKNpecR16RF3x9LDeqwyyYLfRCpFJWhhCI3R9FKBUtIcoJSXFPes8+4F8SKWPtD13BIKzpAwLqOIMbS+VYQRoSwz33oKiNFFhoHMo/S4H1oz7BqpxnXrsSQV34NJWNW5Gz+apua/Tdl7kYXWf6UZHGe2+R0cY9O6BvnyeDqW3oul95KzDxDGc9T8Q53xnqYVnmO++TiOPrzCICf3XmDpvWjCTqrEUZh6z3v2am82hDAwtCymVkATLmmzv0Ocoq6gMh0hY91CytyGvgarXKIolMXQckSyQaSamHrhuvL3N4J5v8F0u0bedOmx0lxoLbI7P4Cu1uZo13WNfaMDvHryArqm0fJDGl7A+GyFczOLHD43xa2jQyilmDw7Q6Evx5nD48xfKrPzzq1ki5fbuKSUlOcaLM7VaDcDfC/Ab4f4XoDXDpmdLDM5vrLl6NyJKZ7+1tsU1+kv1g2NOx/cyeDm9QvIIhURyYhQhiz4ZVzdZdi9fsGEzcU8Um1itt7sVFm711QkUkpx6fw8b794+prja7pGaSDHfQ/fct3ndi20WwEzlxZZnK1Tr7Tw2wFRFCPjjc8fmi4o9uU4cHAntnPtd/TKWxNFMfVKi9nJCguzNbymj9/p+zZMHTdtkyum6R8uUOrPYVobM0VxLGk1PBrVNvVqm0a1Rb3WplZu8vqzJ6gsNoDkd2g3fb779VexNjD2lp0D7Nw3gnsdYhDD2wa4dGaGl77zJsX+PFEUc+HEFBPHJ/neN16l0JdjYMv1Eeh8oAyybd6BVA10LYdr30ut+VUUkrTzqTULrISwMYw9OM4nWZJStKz963Jfn63Ps68wzFi2lzcWxplqV2nFAYYQaGh44QkiuYjh9KDhsJZVboVnSJljXXOwihAvmiSUFUB2mKhWGlqpfLxoIjlno5e0uaOrMU5aAjy88EInj3xj0ITV8Zo3UXDuxY8uUfMP0QiO0Y4mWEkHsJHxDGxjiD5jiLxzgCiusNh+jmZ4hmZ4uut4KWsH9eAdgngWL7pIEM8utw99UBDLNlXvDaYb30QRM5z9WQYzP7kqNLwR2Ho/jjFCPThCEM3QDM+SMXdzMzz5tbDgNaiEbbZmSiz4Tc7W54kH5LqNCrZp8BMP7OPxN07SaAdMLdaRShFEMXOVBpom+MmDt6Gk4tSb5wFBs9ZiYbJM/+aeJG/XCTlHYcyR187yxnMnWZyr06i1aVTbNGptWg2PKFwdxjv88lkOv7x+ft1JWfyLf/uldQ2yJgR9dolyWGXRr1CLGsx68zdkkCFh6DoyOUMx5TKQzWDqWlJxvcZzoBScOHSBP/iXf3PNsU3b4PZ7t980g6yUIgpjLp6b4+y7Uxx7a5yJU9PMTlaoV9sEXkgcb1yC1DB19t21lb13btmQQdaNZMFXr7SYODPLicMXOHFogvFTM1QXmzTrHgqFbZsUShkGN/ew49ZN7L1zC1t3DdE3lL/m+9Wotnj+u+8wc2mRuakqc9MV5qaqVObrBP7lqItSUK+2+fK/+vaGrvXTP30fg5t6rssg77prjJkLCxx5/ji6oRH6EY//xXO0Gx71SpNHfu4hNu/qXuy5Fn7gBjmKZxHCQRMpLHNsebuhj+Da9yFljUS1KOlvVUoRd3qENSFAZHDcL2E4XwSSfaamIxDESnYtee+x08z7DQIZUfZb+DJkvLFA3rLoszzCeApD72P9SVNRab9E3j7Q4am+fBylFJGssdh+etmAXhZJWDVMB4JEmnFlCDwRqvdoBu/SCs90CqLWOqOkxE+qdkeAYq3wtsDS+3GNMRrBCSJZR3aRYFzyxqX00IS5Ju0igKn14phb0f23COL5Tp57tUHO23dRbr9AEM/RCI5Rbr9Ef/rTaxKJAJ1q6LhTVHVjPaDXgyCepx68Qzs6m+T0Uw+vSaxyLSSCEXuoB0cJZZnZxt/h5H8lKcRaY8ykeM3vRA6uX8wgZdg4msmcV0cgOrqu6yuYWYbOw7fvwLFM/uGtUxTSDpVGm6Pj0+zdMsAn7tzF/rEh4iim0J/n1Fvnuf/Td5IvZUnnV6Z2okhy/K1xXnjiHdrNa7dt3WzYuk1Kd2loTUzNIGveOIfBiZl50rZJT8plpqP2JNXwup6ybmi4KYs4lshYEcdyBQvW9wNKKfx2yNnjU/zdf3yeV585Trt1+Z0WQiA0sUwYsiZEEi0xTQM3bZHNpzZcH2DZFs2Gx8kjF3n8r1/jndfOrUpNAERBTLPucen8PG8+f4qRsV4+/hMH+MSjd9E7uD416exkhS//628ve9s/SAyO9vHxLz1A71CBlx97m223bebiqSmGxvp59Fc/xe0f3nPdOuE/cINcbX4F27wV17ob/QrSDCEMNLJI6tRb3yCb+hyGPoAXh9QiD6kUrm6iC41YSea9BhJFOwoo2Wl0TcPUDEpWetVEVLLTnKnPMd5YQBcalmZwojbDaLrIoF3E1IeQyltX2QcE861/oOgeRBMpDC3XmWAVsfRoBieYrP9VRywhRd65B1tfWTilYWEZA3jRBH40hRddwjaG0XARiE5/a1L4NV75g65GcwVUUjRV9V4jZW5H13JowkiK05bugVJIIiJZoxmeRqo2lt7T8czFqvEgpu4fwjL6MbUSmjCAKysuVUfFqEYrPEMk65havtPvu9rgFJwHyFhP0o4maATHmGn+LbbeR9a+s2OArhiXGKWiRFlKtXHNrRji5rC1rQepgo5GdEIzOtf8LkX3Qxha5iojmhAUCGGhC7vrgiVlbqfg3Mti+1mCeJapxtdJmdvocR9C17JXFA4mVehSJT3NrfAMaWsXplbkel/TrZkSkYp5e/ECjmbwhdED2Bsks3hgzygP7BklkhLZ6WM2dA3ZYXEydI2PfekBgnZIrpShWW2hm/qKdhxB4s3miqmuoUgZS7x2sMJTtmwDyzbXZeByXBPTuvZ1NMImUklCFZHXc2zPbN3QtXdDyjKZa7SYrtepewFZx1pXelEI6B3Ic+BDO2nUPBq1Ns16G9+LiKOE1jG8zkKfjSAKY84en+QP/ue/4WyHcEUIlpWy3LSN7ZjEkaRZ9wj8MCEWucI4a7ogk3PpGyrQO5Cnf6TInjs2Y9kbSynphuDlp47xwuPvcP7kdFIl3Wnz0jotZEoqoigmDGOUVAk96dk5/u4/vUC92uYX/rtHMK21F6G6oZMrpPH9qwyyUvheuMJQCy25no0saN20fUPUsD0DBR7+4gM8/MUHkkVXxwe50ZTUD9wgt7xnsM29CNGNulFDEzlqzb8iZR/E0Ac4XpvmRHUGTQjypkuvk2EkVeR0fZa0aWMIjRNzM4w3F9mVG+CR4b2rqM82p4u8U7nE+eYCW9Ml7iptYXd+EAiJoilscww/PL1OIZlAEylAcnrxf6WUepiCcz+2Pkis2tT8N5ltfIsgngagN/VIR9xhZa7R0HMU3QeZqk8glce58r9jKPsF8s496MLFj2epeC8x2/gO7WiClDlGOxxf30tWAScWfguBTtbeT9a6DdccxdSS3slQlmmFZ5lrfnfZ487ZBzpiBF3GI+ZM+XcJ4lky1q1k7dtJGWOYeg8CnUhWaUfnmW89QSM4gVRt8vYBsta+ruNpwmYk9/OEMhGYqHqvcDw4Rd65h7xzD5bes2zgvegS9eAYreAUWXsfO3p+B+Mm0aeuh0R1aQ+GliWI5zhT/ldQXvmCaUJHFxlcc5Si8wD9mc+RMreuGktgUHDuYyT3s5wr/z6xrHFy4X8k7xyg4NyHY2xGYCJVCz+epRmcpu4fIZRz3D7wZxhWbt3IRDecqc/yxsI4/U6Og/3byZvXr5ymC4F+hRFvegHnZ8rs29zP8VfPcOuDu7AcE8tZ7dHYrsnnfvEhPvmT9yC7eIYzFxf5mz9/njeeu1zJ/dCnb+ejP3YHA5vWCUdrgp6+a3scKcNlLL2FIWegK5vT9eBju7bz6vhFTs8tMJzPcu/otbmJ9929lVsOdLoMOnwBYRAzc2mRP/23j/HqM8fX+/oN4cLZOb7xZ88tG2OAVMbhE4/excOfvYMtOwawLJM4jqksNHj1meN8/Y+fYX6mupxXHt5S4rM/f5BPf+n+TksRsIFOkiWceucS509OU55PyWxAcwAAIABJREFUKo6zhRS7bhvhljtHKfXnsR2TWqXJySMXefvlMyzMVJcjhOX5Bs/+/SH237+NAx/ahbFGT/umsV7+1Z//01XPle+FfPdrr/Cdr74MJMa4py/L7/zhL66rCraETM4hW1g7SrdhvMdM1A/cIEvVRAgb0bUXViCEi1T1ZW9VIAhklIStFcx5dfKmy0RjkYMDO5hqVTE0jd25AbZle6/oMb6MF2bPcm/vGD+55QBHypcYbyZFL6JDnSnDkJR91zrEIAJDy7El/ytM1v6C+eaTLLSe6Xg7CqkCYtVCoFNw7mdz7pdxjJFVqyZL72Mo80Wq3qu0wnFa4WnOV/7PTk5aoFTiIWvCYXPul8k5Bzi18DsE8ew6d1SglCSQC4Tt71H2XkTD4LK3KpEq6kgxRuTtexnKfGFNisgluswgXqDivUjVe71TObw0nkIREctkvKy1n8HM58k7d3cfTQhS5g625H8VUysy1/wuoSyz2P4eFe8l6NzDxPOOOn24nV7c9zi5bhwK2xgmZx9gsf0MCSPaSiSebJnIr9MMT7PQ/h7be36zo/a0spfS0vvoT38WDYfx6h8SyTI17zAN//gVxlahlOxwcoed+6vf0Au+OzdIr5PldG2Wr517nXYccn/vGLf3bFqXretKXP2sXpyr8OXHXub3/ulnOHNkgp0HtpLOr1HBKwS5Qops3u1agiilXDVJprMOfUMFhrasbZA3wpQVK8l48yL1qEEtbFCyixStG1doqrTbXKhU+H/Ze9MgOfLzzO+Xd2Zl3VVdfR/oxtE4B8dgTs7N0YjiLZKKlShLuyvtylbs2mtZDsXG7gdbjvCuIhz2hkK217bWpqxrbVGkSJEaiUNySM4M58YMgMENdAPo+6r7yDv9IasvoBvoxjEcMfx86e7qrMysrH/m+/+/7/M+z9ViCcf3Gcikycc3T4EvXzfphhYuURJJZkzULdRit4tm3ebymal1ExxNk/nH//UnOP7UKOlcHFmJVp2iJJDrTPHsZ46wc18v/+5ffZWJsXkCP2RmosjF0xM8+vH9t00db4QP3hnHd31UVeaR5/bzwhePM7y3G0WVVqVW/ZCnP3WYyfFFvvknr/HG985ht6LMX63S5MX/+CaHHh7ZNCArqkxXf+amcWU1HeKp1YmnQMQS7+rPbKkuLLZT+tvBskpaIh19r2EQUi3Veec7p4mnY4weHyHdsb0FxE88IEtiHs+fxA9KyNKNjDQXxzsXCfG3T3VXsoAiStRciy4jhS4ppBSdzww8QFIx6DHSBGEQ6dZKCuIGTzTbd3H8qLbhBn5buSvaThRM4tpjCILWZkVv9CWFBEGDjPE4MWWIpeb3KVvvYHmThDhIQoKEup+M8Ti52HMYyuDK+a+FgEJM2cme/L9hvv5tyvbbWO4kblBGFDRUuYOEeoCs8TRp/TiioLXFSDaHKGiMZP8lZestmu4lbG8WL6isSDCueg0fI60/Qko/3iambTxoBWSGUv+MkvU6DecCljfd3p9F2O6vVcQsCf0QKf04af04MWXXTYzy9eeoYCp7GEj9p6T1xyJXLOcsljfVlrMMkdsevoYyRFw70HaD2p6V2Z2g5U4w3/gWi82/wwuqpPTjxNW97QzD6iQkCB0cf4mGc46ac6Y9mfoD9hd+H0FIret9FQQJTSpQiH8aUx2l1HqFqn2Klnet7YkciZ2oYhpN7sNU95DSj2GqIwhs/wEuCAItz2GiUaLoNOg3syw6db43c46H8zsoGNvPMtiez3ylHj3oJJFv/4eXiadiyKrMnmPD7D66yv8QBGGF4LURJEm8KT0oCJHJ+2YPYogcgmpulZzWQVbdjL0aktMy5LUslm+vaBXfKd6dmGYgk+bxHYNcLZa5tLDEvq6ObSuKCYLQTtveezLfwkyZi6cnsNbUjI8/vZf9D+4gW0iu0+0WBAFJEjBMjeG9PTz2/H6+85ctluaq+F7A9PUiV85O3VFAdu3IL/uZzxzhhS89xI49XejGRhNAjZ0xlRe+eByr6fDm988B4Fge509NUCk2ULrkDfXGo3Fy83cqyes1qKONQZKkW46pu8HpV89z+eQ1Dj0xyuEn91FaqPIH/+KPKM5VcG2Xxz/7IE9/8RF6R7ZOKPyJB2RTf46m9Qph6GHqzyFL3UQ+rg1s5ySVxlfQ1MOIbfaxKWvsiOdxA5+YrCAL0czPaM/8jRvS0xvdAA/mB7lWX+Jao4gmyexOFqI+yOhxg3wbMf9Ig8lCFuNo2jE0uYd87PmoLSn0EQW1bTjfgyoVNl3ZCYKAiEZCPYicTJPzPo7f3kckcmGgSjk0uaftpRyyM/ev8YIGSfUgN9ZoIyUbkZzxFHF1H25QbptQOKsZBkFCFHRkMYUmdbXFR1avWdlpcro8wdtL4yxadf7xzifoNY4TU0ciGcugrSKGT2S+sbo/hBxfn7zEYHyGpztvfUNLoo4u9KNIGeLqKK5fbF8/t32eCpKgI4tJFCmHImY3ZLPnY89hKrux/TonyzavXnmVX9v51KbHzcWeaQuMqDfJXfqBxWLzO8zV/4oAh5zxNAXz0+3WrChrsYLQxw8t6s5ZZut/Sdl6nbp9mpY7QVyN3ZTxEQQZRcyS0o9FJhix59pkOhsI2tdRQxLjKFIGVepAEm7mP0A0oYmre9nb8e8AMOQBFHF1zM62Kkw2y4wkOjic7SepRLKmH5Sm1q0sTlyZ4m/ePrfptVqLxWqTluMhiAJHnztArdiIHoKSSDq/PeLKVuCHPlW3zKX6OSBk2NxD02vQ8OtIjkzLayKLMp16z03vnWhOE4QBMdkgo9y5zOVSo0m52UKXZWirRDm+f19b1u4ExYUq1y+vz5o98OgImXx8UxMNQRBQNZlDDw3zxvfPsTRXBaC8WGfqLnp5dx/s4+Fn9jI82n1LZraqKew+1M/ew4N88PZ4xMAOQ1p1m4kr86SyJtKGwfyjg5nxBRYmisTiOs1aiw9eu8Dlk9f48r/8HAuTS1w7N8XkxZm/XwE5bjxPEJRp2W9iOyfb7UoyYdggCCoIaCRjX0ASV40MbpV228rNklFjvNEaQ0CgQ89jytq21XzCMHLzkESdmLijbRpwe5SW6lw4Pcnlc9P4nk++K8XPfeE4hjxEfTHDqbfHmZ5YIp2Nc+DoIMN71gYNgazx5C33LwgCspRq6zFvH4ookdcS9BhpvjX5Pp8fOEpfLIshp1lerLU8h6LTwA18huKrK5WmZ3O6/CohCmxhDAqCiCwkkdU7rwvHlBFiygi271FafJsTxUv82i233/y7srwJqvYJWt510vpDFMxPktKP3vL4kqDTdK9Qtl4nCC1cf4EwHIENJg+Rv7CCoQxgKAPb+Zg37EdCkwsU5I11sQUE3MCn4dm4gU/FafJgfoiDmV6SymrmYnKhzLuXpji2awttZ2G40gGwbMxOCEuzZaQNtJnvFg2vxvXmGLqoo0sGF2tnSCopWl5jZZXco99czxUQiMsmlm+hiiq6dOcP9QtziysOSJbnUbcdEtr97SEHsGyXxVIDSRJImjqeH6BrCp7no6kKtuuhqfKKgEuzblNeXK8U1T9c2JIUafdgHmNN+aDVsCgv1e/43I9+bBfDe3u21CZlxnW6+rPku1I0ahFfJwhD5qdL+N6d3x8fFlp1C0TIFNJUSw3e/d4H7HlwmEc+cZjiXIU/+m+/Snmhuq19/sQDsiIPkYh9jpbzJrZ7gSAoAyKCoKEqu9HVhzC0Y5vUmO8MV2oLCAh0GylCIiGFfjP7oSjwXDg9yVs/ukAQBGRy8XVM02UpzomxBa5dnieTi98QkO8/TFljb6qHrBbnj8d+vOE0peg0OF2eQBHldQH57zssbwrHXwJ8FCmLoQzf/k03yXXe/7as28HyXUpOE9t3KdlN6p7FkdwAPbH1q8UQ6Mok+PnHDt52nxenFrg+X6I0V6FRbdGqWwRBwPVzU3QPd5LrvrcuTS2/yZK9wNHMo5FxfeV9NFGj7tWoemVSSpqUevMxRUFkINaLG7iIgriqe3wHiOsq+7pW3cgKcZN0bPsEuTtBqdKM0vohzC5WiYik0YSjVG2yd2cX+YyJKIqR21NrfQeGmdC3ZDEZi2vrtvM8H+cOW4oMU2Pn/l4y+a2re8WTBulcnGuX2g5NYUi9Ym1LwOQnBaldl2/WWpQWqlx+/yq/9DufxUjoxJoOvh/geX8PpTNVZReqshM/qBEEFaIe4ySSlORObReXV7A3GwrAeH2JuKKhiBKzzSopVWe7ohh3dk4hl89N06hb/Px/8ji79vXgtNOAAB1dKZ779JFIXOD0xv66dws38JhuVZhoLOGFAQLQZaToNTLElc3rvsvvnWlVeGtxjBPFq2Q0E7VNmjuWjVadAlByGpwuTVB0GsiCRE8szY54VP+tuxZXG4uUnAZhCBnNpD+WJaUYtHyXV+cv8rHCLmJyVNO+Wl+k5TvRKl1SKDlNrtTnCENoeDaiIFDQkxS0JAICLd/lYnWWuVYFLwzoMlIMmfmVksatsJyGh4itHplCbF6+CEIX25ul7lwABETRQJM6tyDveX+xaNcp2g32JDspOQ0W7RpBeHOPe3c2wcOjA+wb2FpKrTMVp1qsc/7tK0iyhCAKXDs/jZG490FKFhQMyWDenqbmVYhJJpIoI4sKMcnEC11KztKGtWRd0tAl7a57fw/1bK7vfr/huh4hUKm1GLu+RBCEJEyNcq1Fo2nT35Mhl96cXLbV3mfP89dJ7IuiiHiHNdeO7jTZjuSW26QAFFVat5oOw4gxfa/7tm3fo2y1kERxRaOiELs7WdCO3ixTl2b54dfexG45GKbOoY+Noqgy1aUaohj1c28HH4mAHEFAEpNI96CtJQxD/GCJIGxGhDBBRxJXL36HFmfJbrBg1Wj5LnnRvOv2iNudz8xEkXq1xcxEkUbdplSsM3ZhlmQmtgnxYWMEQUBxoUal3MRzPFRNIZ01SWfjeJ7PlfMzDO3qRNMVZidLOLZLOhcnZmp4no+vhJwpT/LDuQu4bbZ6TyzNCz0HOZy5dZrI8X0u1+Z4Y/EKF6uzpFSDoh05GY0mu4nJGoIgMFZf4KXZM0w3S7iBz6CZ5zf3PIciSLxbvMYbi5dZsKJUTl5L8GBuB4937KJo1/nXJ7/KXz75n68E5NcWLjLVLPH5/mPk9QSvLlzkxwuXSMg6F6uz1DyLZzr38ULPAQSg7DT4/uxZrtYXqXkWGdXkV4cfZzhR2FAkZi00qRO5Td5qeROUWq8gGE+3e8wjZnnEhHbwgya2P0ux9TJl63UEQSGh7kdXetq92j85JBWDkUQHPbE04/VFwpC2mM567B/oYmf31jIc2XiMpx/YSb43g3xSZufhQQxTR4+pZLvuvYdxQknSH9vB+doHyILCjvguNFEnLidQBJWW32TRnrspIIdhSNNv4Yc+buChiArJu3B8+kmgZbu4XoAoCvhBgGmo5DImZkwlUzNQVZlcxlwhxumGSjwZY2Fm1dJyaa5C/0jhtpKUxbkazpqeXt1Q77j9p9CdRjO2NxkVxJvZzUEQbMjOvxvMNeqcWpzFkBUkQSAIQ54duLtxsevIEBMXp3nrb0+SyMZ57hcfI9mWfi3NV8j3ZkgX/p6xrO8PAmznLI53GVnqQZF7kNQHVv77WGGEV+cvc62+xFA8xwOZ/vt7OiF85xsnOPv+dSavLuI6HsWFKulsnONP7Obzv/zYlndVWqrz0jff44N3r1KtNEln4zz4+C6e/eQDWJbLf/dbf8bv/s+/Qv+ODr72x68xeW2Rn/38g+w73M/SQo3hfd2MJnsYTXZjyjpLdo3/7dLLnClP8UC6/5b1MVPReLZrHyklxndmPmBfqofP9q/WWJueTRCGSILAU52jHEz18U5xnP/h7It8tv8oGdXkT8d/zFOdo/zayJNoksKLU6d4cfokO+IdaLdxJFqwanxn5gO+PPQoD+WHeWtxjO/OnOHB3BDD8QLvFa8RAgNmjn+66xkqbpPfePMrXKrN02WkSdwmA2AqO0moe6k7p2k6l5io/CFN5yop/SiylEFEIQgtHH+BhnOJsv0mdecchCGGMkB/8tdXmPm25yGJApIg4gYBru+jyRLyBm14W4XtefhhgCrJt7T/G07kGTSzuIFPXyxDbyyNJt1sn2fqKuYNdcYwDHE9PxIGCUEUIhehzkycX3jyAXzP57FPHUWPaYiSGAkq3AcWqypq9MYG6TUGV3yct1K7DQi53pyi5jaoeVHb0+H0xj3x9wobrebups6cScZ4+PDQyt/7d60nrgVhZMe5fIxU1qR7IMv4hdUe5LPvXWPXgT6M2MY170hrHy6cnqBWaa28nsqadN9GK3wzJNLGhl7PHwVcLheZa9Sj9iZBoDNm4gb+hm2xW8XQvj4+/5sv8PAnjqCoMrvWfGeJTJyPffZB+vfcTDq8FX5KA7KIrh1GUQaJVt7ra2dLdp3JZonpZgVREOk3s+S0jRmt9wKCKPAP//nzAPzh//i3lBbr/KP/4nnyt2Eir8XyTf9Xf/o6C7MVvvgPn2D3gV7eePk8P375HLIi89izexna2cnsZIl0xoQQSgs1ykt16lWL+ekyfaMdXKzN8s3JE3hB1F97pT7H4ezqg+9usS/Vw4FUL6okk9PidOgJFu0aRTu6IZb7ZAGGEx1crs9xtjLFkcyqZeNG5xIS4gcBohClnIQV9YJVFPQkT3eOIgoCGdWk20jT8h3swNvU2XoZoqjTGf95vLDBXP3rWN40U7U/Yqr2lQ22jswyREHF1EYZTP0G2VjE7g7DkPfnZugw4wwkU1wpFTmzMMfRrh4GU+k2uYsVcYN19nLtn0EYScMGYbhSTDk9P8dMvcYDnV30JpIr+1l+f9jeVhElFFFCR+HRwjDvLV2/7fe6PL4cz+fs9Tmuz5doOR6JmMZwZ5adPXkIQmauLrA0XWLPsR3opk48E9+O2dO2sPz9bm9MhvQaXXi6jxu4qKKy7XG93XSp3ya8LUMQbjZuvZfPlhu5Ll19WUYPD/Dj755ZEdr4wV+/z/EnR0ll4zcpX4Xt823ULH707ZORQEcbnX1Zdh+8swWKqisbtoOtu57b/j7vDQQBBhJp3CAgJKTDMJmuVxlM3l12J9OZIrPBc3zfwzvvaH8/pQE5wLJPYHuXUKReZLkXQz288t9X5i5zPDfELww9yIml64zVFxlNdSH9BAbKduD7Ae+8eonP/IOHGd7TRSymMTLaxbXLc5x9/xpPvnCAHbs6mZsqoigiqaxJJp/A83yW5mtMTxXJ1Bf4g/Mv8buHf54hM48VuPzbD759T89TFWXUDVa7AgLL6nIbYUXdc80WTuDjBBExoktP8XO9D/BvP/gWw4kCju/xfPcB9iRXiW+yIGKsYdYKLAe5rT1kdbmXwdR/Rlb/GEutl6napyJLySBy2pIELWoZk3uIq6Ok9eMktaMoa1rlxsslvnXpIqok8mB3L7IostBs8LXzZ0GAJ/uH2JHJ8N3xK8w16hzs6CRrGDi+z3Amiy7JfP3CWX52ZBcvXr7ITL1O1jDQZJm643B+aQFDVvhY/yCHOrtWRm3JaXK2PMN0q7xyLpbv8UF5iiO5gVuuBvwgZGx2if/pr15hbGYJRZZQZAnLifyrDw/38uXHDnL+b0+zOF3C9/wVYZCO3gz5njtbVd1riIjoks6cNc9Y4zpJOcHhOxAGsT2flutiqApNx6Fm21iujySK9CYTCGI0lluuix8EjBWLxFWVIIy0DfYWOlAkiSAMUbbZs7xdpHImo4cH2HWgj0tt7klxocYf/t63+dSXH+Whp0fJrGlNcx2PK2en+fP/5ftcPD25YsrQP9LBoYeH6ey7syC1LI95MyJzHEkwuN88nc3weM/guueK2NZ4/6jhpzQgi2jKXiQpH/WziusfFjFZpeZazLeq1FyLhmczb1UxJJW0unH9xPZLOGFILv4l4vozdMYebdcbPxyEIVRLTVoNm69+5VX+9uvvrjAsG3WLgZECsiwxtLuLi2emsC2PRMpgx+6ImHL18hwz00WGvD5agUuPkSalxnh/7jyLdu02R18PuU2MKDmNTbbYOL04GM+BAJdrc+yI59FFhav1RZbsOqPJbgxJxZBUxuuL5LU4RbvBeH0B249qXF4Y0PIdHsgM8MvDj6FLCmklhinr+OGqmtZmq5FrV+awWy7dAzlkWcK2XMy4RrNho+lRW8nMRBFdl0l3HMKdSTJ14RCPP78b27LRDQWr6WK3AuwGmMk0mUw3khBb12vek0jSk0gwnM7wUE8fE9UKs406e3MddCeSfO38WQoLJqO5PA/39HFmYZ5ziwsMpdP0JpJIgsDF4hIDyRQZ3SCtR6SpuuuQ0XUeKHRiqiovXxvnYKFzZSYzb9VwA59uI7XSGtj0HCabxduuSizH5d//zRskDI3f/sLT9HekUCQJ2/W4NLXAa2ev8Wc/ep/nO3OMnbrO9Ng8sURUAtAM9SMTkAVBgACankUYRgSv7SIEZqo1fjR+laSuMZBOIYsiLdfDCwKmKhVShk5c1fjhlXFe2LMTURDQZJkgDJlYqlBuWWQMndFCAeU+C8yJosjQ7i4+9yuP83/8m29RLjYIg5Brl2b5k99/ib/5j2+S7UgQS+h4jkdpsc7SXIXifA277eWb6Ujw1CcP89jH92+Jnb1V+IFDw73GXPMHZPQHyOiHIuObLQTmeqXJ1JV5zJRBrjOF7wUomkyzbhFPx6iXmyTSMeQtEKfULWq5A1wfX+D6+ALlYgNFlTl0bBBdV3n1++eo11ocPDqIY/ukMjH6h/KUFuuMX57jkSf3bPkYm+GnMiBHziZxAm8Myz2DIvWRiH1i5f9ZzWTeqrFg1Wl4Nn4YcrI4Ra+Z3jQgR/tV0eReEupe4truD+OjrDk26KaKJIk89tw+9hzsQ1tmMwqQSEX1mx07O3n9++coL9V57Nl9JJIGpWKDqasLOLZHt5HiSGaQ3z//EjFZI6fF24Ew2td4fYGXZiJCVtlp8mdX36DbOM/TnXvZl+rBlDW69BTD8Twvz53namMRQ1L5p7uevmUNWAASisEXB47zfukav3/+JQBiksbTnaP0mdED/YuDD/HXk+/x3dkPyKtxHN+jQ4+IEUEY0vBszlSm+MqVV5EEgYRi8GznXvakbt8eVi01mb6+xNJCFU1XiScMpq4tsrRY49CDO5i6tkirYZNIxchVkwhCDl0KqM138P6bV+gfSiIr8VUSSiyJLN5MDNFlGV2WSWk6KV1nrlFHl2R6E0k64wkqdiSC8FBPHz2JBKfn56jYNl4Q4AUBfhBieR69yRQnL19Ak2SOdHXTcF1qtk1vMgoQFctat+7vNJJk1BiquFpjdgKvbbRy6weS5wecHJvmd770DA/t7icR06KUeRDSkTKxXZ+/fPUU/9UnH0c1VPr3dKPHomBnJj+cVqCtwgs9Fp0l5u1Fkkp82ynSIAw5v7DAYqOBKAgsNVvkY7Ho90YTx/e5WiyTjhkUW02KzRaztTquHxCEIU3HJaFpTJSrHOr+cJjaZlzn8GM7+dXf+lm+8cevMXFlHtfxWZytUFyooaoykiIRBgGO7a1rtxzZ18MznznCo8/tI72NlqWtQBBEREEhCB0sbyFKX2/x69AMFc/zuXzqOqfbrmG9OwpRt4AoEAYhux4Y2FJA3g7KxQZWy6V3IEcQhLz7+hU+/qnD7NjVybUr81y7skA6a+K5/oqb2VpnrbvBT2VAhrZblJhCFEyCMJJjXB4Je1Nd1Fx73apKEkTiskbNXrWLMxRl5cEmIiMKCpa/hBs0SGm7+LDTL7qusGt/L4IA3X1ZBkcK+H5Ao24RBiGSJNLZm6Fes7BaDomUQSafYOGVC8zNlOnf0UGnkeTLOx5lulVGEgS69TQHUr30xjIICCQVg32pHgbNHAcz/RiSgiYpFPQEcluGMKOZPNKxk5QawwsDVFFCa6epvzDwIFlttR2jU0/x5aFHGYp3oIoSD+eHyaomC3aNMAzJaXFG4gUMScUPAz7bd4QL1RncwCelGEiiRELWMRWd8foCU80Sv7TjUTRRAULOlKc4V50hq5kczw3Tb65fqX1p4CG6jRRxWcf3A6rlJrbtku9M0dOf5fzpJrIsMnVtkelrS+iGiqJIWC2HXEeCmKkxfnGWuekyPX05FuerdHSlIIRWc3NrwZxhMF4pIYkCjh/VvRVJQmorPu3t6ODC0iJj5SKW57Erm8UPQ75/dYxcLIYsisQUhelaDUWSGEpnIh05QUAVxUg56oY0fErRQYlkM8fri0y3KuxLdROT1NuOVEGAuKGRMnVURVpJ54migKbIJGIaCVMn25Xm4Mf2YCaNDSUMPwoQBZGsmqHhNRHb12k7QVkUBHbmc/QkE8QUBUWWMZWoFp2NRfrcTcdFV2QGMym6knFiqoIuy4RAXFNxfJ/ORPyWzlD3EqIkks7GefjZvdTKTb72lVeoLDUIwzBia3s+tu0iSSJ6TCWVMensy7Jzfy97DvWx+2A/2UJi25Kgt0Ok69/CVIYIcNhq6QjAathUFmtUFmsIYkQgtC2HhakSfSOdTI3NM7Cnm9g9brvzvQBFkejsTmNZLm+/donxi7NUy01SmRiz02UGMwUqpQZnTs7R1ZtmaGfh9jveAj5SAXmtbOJd7ytoISChSF0ErPfm7TJSdN3wHYZhSMvzuLC4SMt1cXyfQ11dZPRIdlAUVCRBbZs93J8+05NvjTE7VeLU2+PMTpVwHI9m06a7L8uhB3cgCAIf/8xhTr41xqvfPcO7P75MGAbETJ2do93kCkkMU0PVZFzHQzdUOnvS0Wdr2HT2pNEllSPZQY4wuOE55LQ4HyvcevWvijIDZo4BM3fT/x7tWE9mSKsxnuwcXfk7qRgcyw1tuF9JEDfd76JVY6K5RNVt8bm+o0iCiBf6zLYq1F0LO/DYl+plV3J9T+3jhV0rv3f2ZBAlEVkWSWX2GyFrAAAgAElEQVTi5ApJ9h0eRFFlWk2bdNZE01U0XcaM65hxHUEUqFVbJFIx+nfkSSxEQgaBH6LewgrwQEcnM/UaqiST1hXSukZa11EkiWeHhjlY6OJqpUTLdRlIpskaBlXb5nqlTFLTeHZomNPzcxwsdJIxDCq2hako7MzmSGgaIPDkwNC6Othy696sVeVsZQY/DJhtVZlqlRhNdiHd4IlcbVosVptAlLI+ONTFKx+M0bQcOjMJZEnEdn2uz5c4NzHPo6ODkXFE9sNpIwrDEC+MSDhhGAmeiAjMWpV2f3uKgHAdZwAiDoIiKqSUJHF5++cqtAMyRE+MtaSkDtO8cWMEoBBfPU42ZlBqtoipH670o2N7TIzNMzE2j+f6aLrM6OFBRvb1YJgagR8gSiKarpBIx8h3pRjc1Uk6G79v7OhlgxhNbl/PbWiLi6JAR1+WdEcCI6ahGlGG0Ihp9AwXosniWkWwG+dcd9E7VSk1uXBmasVhbHqyyOTVJbIdUXtpLKbiOh7Nhk2jbtN7h8z0G/GRCsi2e4YwtNHVo3cdlIOwRRBWAQlZ3JopQUhEyBgrtVctuRwZPaqTCYJAELq4QZW0uoc7XR3v3t9Lo26hrek9Xm5BqFVaLM5ViCd1Bo0CiiKzNFclviYleLjthHL+9CSLc5XIRENXMUx15TwfeXo0qo8mDQRJJNuVYtehPnbujYRIJEnEcTxUVcb1fGRZQpbE6AHoBfhBgCJLuK6PLIsEQdQK43o+mipjbEGS715DESXSagxFkPj+3FkkQcIJPIpOg92JLvLa7fWUu/uzdLUJK8uBaWS0e50mzI3153UOMoJAoXtrvIHhTHaFUX0jeeSFkWiS0J1IROk3lnXIQ/Z1FNqp4oCvnjvDQCpNStMZL5foiicYza+O5ed2bGyZWbKbVF2LATNL0WkwXl9alw1axvhckZdOXAJWGewvn7rC2GyRzkwCVZZo2S6TSxVqLZtPHh+9aR/3E1bgMV5bAARMWaXiWpiyynhtCTvwWLLriILIoUzvSjtZGIaUnApz1gJe6KEI8rZT1jduvV2GtKmqmB9yMPZcn+lri3zz//4xr33nAwCOPbGbT/7iIxw8PrxuHH+YEAUVRUzi+g22GyHNVIxdhyJthLWTos7+iIuS6UiufFmRYcnq6j4Ml0VPViVftwPP92k2bJLpGEeO78BzfYIgxIip5AtJEikD3w/IdSRIJI1tiaHcCh+pgNy0fkQQVtDUQ5u63DS8RWw/SkEvX2gRiZTav45YIwgaoIJgIwomtwuggiCgyzI7szmuVyILx7W9nkEY4AY1Gu4MhtTJrZS9lgOs43kosoTvB0hiZPL+2HP7VgZHEESuVI7rM7tU5diTu3no6VEEYbUlRhZF/CDE96NAKYkiQ/t6GDnQix9ErTFmu9cwaKenPvmlhyiWGjQth3rTpn93F4WhPIVCkrFrC2RSJo2WTcxQsWyPfMYkHtdpWS7lShPL9hAFKNda9PdkWCrWCcIolZPLmh9aQK7XLGQ5mtEnFYMDqT6uN4q8On8RUYiIZbuTXTyYG6Kgb60B/8YbUxBubp267fabIAyj78mxPVRNvq3LzNqWpeV9L79DFAQe6xvg1PwsC80Gg6k0OzM3Zw42QkLRScg6i3Y90naWtEih6IZzb9kuM6X1Wruj/VHqrdpc9QLPxA0ycYPp4vbIf3eLhmvz+sI4miizK1lgzqoxYGZIqQbXG0XeXbpOSjXYl+5Gbl+5kBDLt5BFGQ0V+Scs0vJhoV5tcebEVV57KQrGCPAzX3iQw4/u3JL94P2CICjIYgLfHaPujpHRDm5oEnP7/awZu8INPwFZkVA0GVGMeA9hGGK3XKrlJnpMvckO81aQFYn+oTwje7pIt+VJwzBk976eFRvJRt1ieqJIvpC8Z+lq+IgFZEFQEEKDjawKl7FgnWe6+T5B6CKtOPCEPJT/J0iCwvK3JAgqgiDie/P4wSKaOsrtgvKKmwtged5Kny6AKMgoYgJVSuKGjVvuy/MDak2besvGUBVcP2qXaFnuSk1OFAV0TSamqRSrDb739kU+dniYRCyq4dmuj+N6qLJE03IwDRXH9TF0lYvX5+lImbRsjyAMGezOoCoymiqjtoPAmYvTGLrKUH8OSRKp1izK1RbnL82wd3c3nucjSxJ+EKBrMvG4TrHc4PL4PC3LZXGpRrna4rHjI7z/wQRDfTmSSYOUt72ZdhiGBEFIrdIiDENUTW5bwIkoioTnBbhuFMBqFQsEiMVUBFHg/AeT6IbK4HAHmqaQEU0+lz4CGTBNHceJyCmCAxW3iaJEVmuNhoWAQMxUkWSJVtOOVvtSJNOnavK68wvDkFrVwvcDNE1G0xQ8P6DVtBFFASMW6f026tE2shz1dcqyiKYp+EFAq+lgxnUW52qUi3V6B3PEEzq25dJqOYiiiKbJKIpMrRpdCz2moqky4ias1qSh8vjgQBTowwAPj5LdRBbFKP2MgL5G8GPZUCKuaAzGs5wsTSIi8HhhZKX+vxaPjA7yyOj60kUYhrQcF9v1CdoTQE2R0dWbhUXuN0RBICYrxGWNDj3OnFVFkxQSbVZ9QU+Q1+LrWuxEQWQ4PsjwJiWZn1ZUy00un5laWYSKooiiyZGesuu3A8m97YfeGkKC0MYL6hhSFwLbz1hsBaIoEovrmEmDWjkqw7iux6k3r/D4zxzATOhb/uyZnInraGjaal/1jbaP87MVyqUGHZ0pevruXYfBRyogy1IPLhM43hiKNLAu5gloCILIUPxj6GKKpl+kz3wQL7C4WHkRhPVrVlEwEIUYkpRDkYfYiia2KAgYsozt+xEBZ80K2Q9t/NBGEjQcP9Lb3iwol6pN3j0/yUBXhhNnJ9nRl+XyxCITcyU0VSYdj5GM6+zszTHS34HjBvQV0py9MkdvIUV/V5pyrcm58TkWSnVqTZtCOk7SjGqatuuSMqNU+lyxxmK5TjZpcnDXKtNYU2RaLYeFxRqLxTpTs+Wodpo06CqkOHH6Gof29jF2bQGrLZ0XNzViMY35xRo9XWl2DhWYnq0gSSL5XALH9WhZ22QTtlPx3/h/3yIMQob3RH2z+UKSvsEc87MVxi/PsWdfL9/8i7fQNIVHn9yDbqi89dolXMfj4NEhBoc78P2A114+jyjCI0/sYfzyHHMzZRRVRhRFevoydPVkeO3lc4iSyPHHd9Hdk+b1H11gYa5KvpBk/wP9DI2sn9G2mi5/87V3qFUt9h3sY2S0m/nZCifeuIJuqhx7eISu7jQvffsUtVqT7t4skiiQLyTZva+HWrXFW69d4oVPH+a1H5wjntDJdyaJxTTOnprk1ImrxBM6u/f10N2b4cWvn8D1fA4eGWT0QC/J1M3M/oCQb09+gB8G2L7Lkt1EEkTyuklWNenQ48iiyLHc4MoonLdqvDJ3iXOVWfwwYtFKgsjXrr/Pb+37OJKobPowXM7q1C2bty5OcHl6kYblkIzpjPYXODzcg6mpN/kY309kNZNf3PHQyt/DidV0/UiyY0UIZSv9pOvEKTYR/giDiAF8L4PW2uNGv689j+hHEIQr/bt3euwbsy2BH/DNP/4x1VKT3Yf6SWdNFFW+oU94efHS1rCWxMhSU9yaKtpWEOLjhy2C0MGQexDu0JtgK8h3JukdzHF+OSA7Pl/7P1+hf7jA0O7O9udfo0rQnoyHYZTyluRo9ds3eHs52R07O9mxc+u2ilvFRyogi2IKu/Ut6s2/RlX2IQqrq7FM4jdR5MhuzQ1bNNwFas4MbmhRdWcjM4l1Y0hCkYe3HIyh3VbjuuiyTMWy8NeskFveAhXnMl7QRL5NCnz5S18o1dFUmeGeHI2WQyKmU643aVkupq7SU0ghCpAwNVKJiL3Z35VGkSXmFms0LYeBrmj167o+6aRBsdqk0mjRaDlUG1H7TDoRo1htrKyOAY49MEgQRCnsXSOdBEFbFQoQJYGh/hyyJLJruLDSd5hKGBw92M8D+/siNjDLz64QqZ222e6N2qhbXDw7TXdvhsefGUXTFd545SL1moXnRXWahdkKfQN5fD/kE58/RqEzMlUf2JEn35Hk6CMjTE8UOXdqkp/59GEyWZM//Q8/xGq6HHloB74fMj1R5Pr4IuVSk0J3mlQmxrf/8h1+4VceY3G+ytGHhxk90Ldh6qpRt2g2HI49MsLogV4mri1xdWye7r4Miirz4tdP8OVff5L52TKf+OwR+gbzzE6XuX51kfffuUo8oaGq0er3+GM7OfnuVVzHY+LaIo2axbGHRxg92Eu9ZvHK986Ryph0dCY58eYYiaRB8uAGrXZh1EOsCBIpNUZ326lJEgQ6tASmonG5OsfaiWG3keILg0c5XZpkplXl0Y5h3MDnr66/f1s1rSAMmS3V+N0//y5TixWy8RgxTaHStPjGG2fY21/gX3zuSXpzd681fy+xndHo+wGthk210sT3/XWvN+s2tUoTI6ai3KN64FpYLYdqqYljeWteDXFdj3KxjhHT0HTljlXP0rk4ew8P8Hd/8fbKa6fevMLZd69GqzyBm4KxQNRalEgbdA/k2HtkkKOP72J4tAdJvjcBOfL5VnCDKlKgE3Izl+FeYWBnJ6OHBzl/cgKIJkATV+b5vf/yzzj21G527e8jmTERBLBaLvVqi2qpSWmhxvGn9nDwoWHMxK3lde83PlIBObJc3Isi70S4oXl8LcmrQ9+DJKjMWqcRkNid+sQNzOeQMGwShFb7vTKScHu1npUVsufdtEI2lR50KUeIj4B4y7RLJhnjicOr1n0xXeXInj78IFipbyiyhK5GN2DK1Dm0s4cgCNBUBQF46MAgR/2+6ByWiQuiiO8HuH6ApkQqQIulBsVqk5G+/LpgGZEcVqUZQ3H5OkYDVW2zKsU1/amCICCJIjdnUIWV920XfhDSajkkUzFUNaqrCkT188CPaq5iO/g+/6kH+OF3PmD0QC979vciitGsNSKWBTiuRzyuo+kKtuUhCKCqkRiDKAtYlodju2RzceJxnY//3CGMmEY8oUcP2k2YpNl8nOc/9QCnT1xbmSj4boCeUklnTJ5+4QCiKJBMGWiGiqxIdPakmZkq8d5bY/QN5njw0Z0gRNedthqZbUdOXrG4Frm+hJF3bSZnousKDz+xi66ejUlioiDwxaEjLGs4r2R+EFYca3qM1LpxKAoCoiDhBgFFu0HRbmC3Hbq8IIjmpZsMW8vx+N9ffIPuTJJfe/44vflUJFEaBFyaXuQHp8f48x+8x29/4altj4H7hc0mh3/3F29x7r3rVMtNrJaD1XSwWg6O7RIGIVbLoVJcFbUpzlf51p+9zg+//T6iGKUmdUNFN1S0mIpuKOw51M/xp0bpH9m8Xnjm3au888MLTF1dxGrZtJrRsW3LJWhb8a09ruv4XHj/Or/9D/5XRFFAbJdAdENBax+/0JvhyOM7Of7krQl1ZkLn8GO7+IXfeIZv/vFrWC2HwA9xfO+W72s1HarlBnNTJc69d40f/PV7HHl8N5/+8qN0D2yNs3ArBKGDFzRRpQwNd6rtqHZ/0Nmb4cjjuzj73jUunloNyotzFX74rZO8/tLZlcVHEIarzyAvoGcox+iRiEDmBwH1pr1CeG3ZLum4gSJLkeSo5WCoCtptjDvuBB+pgKwp+1CkjXVUJXG9nJvlV2h6S3QbRwjb6bnVh1OI58/iuJfxwzqSGCduPM+tVsphGFK2WvzNpYsookxa11DXEGEkQUXaotm5LImYa1jUgiBgaMpKQLvxQSJJAsYNUXD5/Ru9Z+1riiSRS8U2DDbrCUMbv36r92znf5vBMFT6BnJ878VTlEt1unozGKbG1cvzXBubJwyilN3cTJkr52cpFes0Gw5BEJLNxZm8toTnBeQ7EnR1p/nOt97HiCmM7O5ieqK40nYiIJDLJ8h1JDl/ZpJGw6av/UBZJmJshuJinbGLc8zNlkEQ6BvMIQoCV6/M09mdonfdfqL3yLLUrlGL2LZLOmOyOF/lxJtjXDo/E6mkjXTieT5vvnqRqYkl8h1J9h3q581XL9CoW/S22aKbXeu1IjU3joMwDFesL2/EcCJPEAacKk2CIPDx7lE06da3uuv7vHlhgt/50tPsG+jE1NUV5repa5QbFl977fQt9/FRwaUzU7zzygWatajmvzzx22xC6XsBtXJzpfYItFO3YjSxbXcg7D7YT//GxHYA5qdLnHrrClcvzraPG+D7IYG/+arQtlxmri+t/C1Kwrrjdg/kKPSkOf7krT+zbbksTJeolhpIkthm1tweYRjie1FQslsu9apFrdKiXm3xy//8eTq6U3eVvhYEGUVKYSqDqFIWcROy7r2ArEiMHh7gi7/+JH/9J69z+q0xIHq+NOs2zfrmugGe669csGuzJa7OFEmZOj0dKS5PLiKJ0TN8vtxgoVTj2J5+dvXlb0vc3PZnuKd7u0tIYgpJXL+SDcMQz59sN/dHqLmzNL0iSaUPAYGKM0GHvidauQrR41kU0whijNCbIQyXyV+3hirJDKXTaJJCWtcxlDsfPBsN4jsZ2Lfbj6bK92Wmdq+gKBLd/RkefWoPqqaQTBnoevTTtryIiKVIpLMmu/f1sGN3J/mORCSCsreHfGcSVZFJZU2y+QTprIkoiWSyJrv2dpPOmIQhdHanUdRoZZPJmWi6Qiodw0zoHH1omExu835UM64xONJBZ0+aeEInlY7R3ZuhqzeDbigkUzHMuM4jT+wm1bamEwToG8jx1PP7UVQZWZGImRp7D/XRvyNPLp8gm0+g6jKFrhRGTCWZiqHrCrIS+aQmUgax2NYYsNthe2dVk73pbjr0BAgCnXoCRZBuPf5CcD0fU1eR10xgBEFAkUUMVcb17l+68V6i1XBo1Cysu1BP8r0An4BlY8JWw7mt2bxre7d98N8OgR8S+P6KklazbuFY7ubbB1Fa9scvfcDJN64wM1HEtlz0mEZXf5Z01mybPqz/7sMQfM+n2XAoL9ZYmqu2V9UBxfkq7712ia6+DL/4m8/d8WcB2tkwm5pzGVVMclfNwVtAImVw5NGdJJIxzjwywoWTE8xMLFEpNrAaNkEQIisSmqEQM3UyHQlynUlG9vagtnuam5ZDpdFCFARK1SbTixX6O9NMzJdxPB9VkVEV6SbbyHsBIbzXTtD3GGEYUq7/exKxzyFLEWlpqnmCeescCbmbkICiPcax3K8iCatWY0HQwnbP4XiXUKR+YvrtLQ6DMKDpunhBZHGnrUlbn7s0w9i1RQ4f6Ke36/5qWI9PLHJtskgubXJwb+8d78fxfequjSJKJNTttT7YrseZ63PkEjEKqTjnJ+epWw7dmQQ9uSRNy+X01ZloQqAoBG3Jx509OfLJm43Tl8kTy0zPlZq2sJoOj0g1wjo26PJ2YlvMf+0NvVmAufFYW0HQ5gtE57O6Cg1D7prItNF+lok8Hz7rdWPUWjb/6isvMtCZ4emDIwwW0miKTNN2uTS9yGtnr2I5Lv/Nl3/mro9VLTc5/dYYE2PzK6/tPtjPzv29JO/Qi3ct3vj+WSbHF9bJQ94tuvtzjD4wcEvjhbHzM1w8NUG5WL9nx40nDEb29bD3yMas8fELM3z/Gyd45W9PMzdZQlYkDh7fwaFHRugeyJFIGsiqvH4Mh2ta9CyXWrXF1PgC7756iStnpqJOAkVicFcn//3/9evEU8ZNCl7lpToXT00wtsbycWRvL6OH+0msISn6oUPDvc5880foUoFu8zkkcZUbND9d5sLJ60xdWwSistzo4QF2H+xbp9WwXfh+QLXY4PqVeZbmq9SrLRzLJQwjRUNFk9H1yPs5mTHpHcoTTxlIksgHYzNcnlwkk4yxoytLud6iM5ugWG1G75Ul8imTuLGxteXd4Ce+tPKDMqJgIggKQVAnCG80LAhpWC8T059dCcgxKYcp57H8clvusQfhphVASIgXqX9tQR1mmfIkCeKG4uqXxhd4+bULdBdS9z0gT0yXeP2dMUaGOjYMyI7vc71Wpmg1o5S1HiOuaIxXiwDsSGZIqBpj1SWWrCZ9ZpR1mG/WaXgOtu/TF0+S0gyuVUtUHRtNkuk2E3TGopWk6/mcvjrL/oFOsokYH1ybY6ZY5djOXvIpk2K9yStnxsmnTGKaiuf7uH5ANmFsGJCXA+8ybgxygiAgbEC4Wrtd9P7b3wA3Hmsr2Egy8E72s9Xz+TDZyluBpkh89tH9/N2Ji3z7rXPkkrFIGMTxKNaaSKLIzx69c/H8arlJeamOKArkOpM89vx+XGcPzbpNIrV1Gc7SQg1Fk2/ZxvLIs/u2tK/NSkhrUXEbjNVnSCsmhfjqfR/pqrd4p3iRutdCFiT29Q/ys6MPrdkmoOa1eHvpAi3fRhYlDqWG6Y3lCcJIiCWyExXx2p7Zru+vZOYEQUCW2vwJ38f3A3RllSlcr7Z499WL/OjFU8xPlVE1mR2j3Xz+Hz3BwePDaIay5YBRKTbQYxqVYp25yRKe6zM/XWbq6iI79/ciquvvj3QuzkPP7OWhZ/becr8CIqqYJqMdRhZNhBv6wgs9aQqb8CjuBpIkkulIkOm4vWDQjcgmY+zoyRI3NDqzCQa6oklYZzba11bGzZ3iJx6QW/Zb6OphZKmA5Z7Gdk4h3NA47vkzEK6SEww5QzocpOEuEOITk/I3kawEQUYU1Ih27y9xqzYlANo9yNcrFRw/Mmjfmc0SV+/9LOhusWQ1ObU0w0Stgi7J9MST5PQYVypLaKJMyWrxYGcvc806M40aSUUnIOSd+Umcth/o9VqZ/bkC7y1MM99qYMoKRwu9KwF5GTFNRZEiIlZcV/GDkImFMvWWQzYRI2Fo+EGIriqEtvuRu1b/P7YGRZJ4+mBUIH3t7FXOTszj+T6aIjNUyPDE/mEe3NV3R/u2Ww5jZ6e5dGaSeMrggYdHyHWmuHZpjlbTZvfBPlSgvNSgUbOQFREzaaCqMrOTRQQhki8UJZHTb4+hGypDe7pIpGJ3JXoxXp/FVHQKWnrTcbtkV/ju3Al2J/oYiq81igixAoczlatcrk+zYJf5pcHnGDQ712wR0vQsTpfHGG/OsmhX+SfDP0dvLE/dtpmvNVa0DpZ/2q5HdyqB7Xm03Ihc6ger9e+RfDa6HwWBqauLnDtxjfmpyG4zkYrx9KcO8+CTe7Z9H6ayJnsO9XHmnU7mJktA1Do1fXWJHaPdd1z5FQUZXc6jy7dvJdoMy+1tcHN71/L/2eD17aJoF1FElULWpCe/OQn4fj7jfuIB2XZOocrDIBVo2T+m3vo2qrxrzRYhQVBZR5dveSW8wKJD34MdVJlunqTD2APrbMEFRCGFJCQJgiq3DchEwgrTtSoTlQoN1yUfixFfI4EXhCGNls3ViSUcx0NRJfKZOLG2xqrr+TQaNpVaC6edLtNUmVzGxFhj3u16PvWGTaXawnE9BEBRZDo7EqsOTmsQBAHNlsPMfJWOXJwFux7pPifSxBWVuutwsbTIC4O7yOkm//adl9mb7WB3Ok/VsWl5bnRjhyEHc50MJbP83okfkFQ1dElmRzKDqSik1qS1RVEkl4ihqzKiIJBNROkbz/d5++Ik6bjOI3sGsFyPcr1FJmFQrLXQtrjS8UMPN7BwgyZuYBOEHiE+ISEiEqIgI4saqhhDEQ2kbSouhWGIHTSouXMEbWanKWWJyRlEYVVm0QttbL+OG1j4uISh3x4pAlL7HBTRQBPjazgKd47o4RLiBk2coIkX2Pgrnz3y8xUFCVnU28eNIXCb+u8NcAOfIAzRpIiBbvkuhnT71VJIyLMP7OSpQyNUGxa26xHTVRK6elemA/WaxeTVBRZnKhimhuf62C2Hs+9dJRbTGR7tIQxcTr89xuxEkXxXioGdBVRN4cX/5y1yhSTHntiNqimcPzkROf0AQ7u77jgge4HPN6ZeY3eyn090H9+0a0IVFbr1DJkbXKNEQaRDS/PPdn+Ot5bO89WJH930Xun/4+7Ng+w6zzO/39nPPXe/t/e9gcYOkAAIghRFUqRoSaYoyfJuZyzLE8fLxM7ETnlmKkslVUlNTZKJq+JsM1WxM/aMFVnjkTzUOBK1UAv3BSBI7FsDve/dd79nP1/+OLcb3egG0ABIkTPPH2Tfg7Pfe877fe/7vM8jKXQnivzh3l/gtaVz/OuJH6/921y1zomJaSQJ6o6HpWsMF/OEQuCHIQu1BteXS2vBxvYDCskEnZkUWdNEkSQmrs4zP11a22cyk+ChJ+7dhc5KmRtkeoUgtmf8EIuaYRhRb7iEURSL8AQhScsgCKM1FUTb8WL535bMryRJGLq6du+CIGqVvG60bcYlpPgYq2Wqi7VLJJQEHWYHGTWNqZhEIqIRxiYdKTWFKqs0ggZ+5CNLCgnFxFTev1apDz0gF7N/tPa3qnSSTf4dcqnfXFsmhGBm6deJpTAhEgGqbOJ4ZcbrryEBOzJPb2o4F0REoonAR1EKrA/G60dbq5AkiaSmc6S7h6VmE1PVMG7y0GzaHu+cneSvnj/B7HyFfC7JL37uKI8eHSabSVCp2rx+8hovvnyR+aUqQkBbIckvfu4hjj4wQDppEkWCuYUqL795hZffvMrCcg1Zlugopvm933iKXcMdG04uEoJaw+WNk9f506++wn/yqx/n8IMDvNoYZ7ZR48meIYYyed5bnF17+cpSLKPpRxFeGOBFYaxPLStkdRNdien7u3Nt/L+X3yWIIp7pH6E3dWNUaBkanzt+Ix312ZaOsRDwxIG4pete0q6RCAmES9WbZ965xIx9liX3Gs2ghBvWCEWIqaaxlDztxg56rEN0mfvI6J2okrEWTO94HAKmmqf44dz/RjOMX1rHCr/C0cIvYioZIkKcsMqic5Xxxgnm7UvUgyWcsIogRJNMLDVPwRiiK7GfHalHsdQ8GibrJVq3i1VRCF842EGFafs0M82zLLnXW3KwDSIiDNkioeYo6sN0J/bRnzxKSm1Dk81tXbsQgiWnTj1wGUm340oh2KIAACAASURBVEUB58ozHC70o95mQBFFgvH5Mj3FDIauUkhvv5YrREQofAQhiqS3BlU3UqvFjgw79/eQb0sxONJF3452fD/k8Md28c5LlwmDEIRMtdRgYFcnj/3UAVzHZ+zKHLqh8thnDtLelUU3NHqHinT1Fzny2MiWgwQhBF7k40b+mn63IsnostZyCIN6YLPi1Zi2lykYGVa8GjISqqySVuOAJBDUAxtLNflsz6ObDCzuF5oiY6gKth8QET/nXZk0YyslKrZL2Y4Z4qauIgS0p5LkrFg/efX9VS01adRuSJxqukLbfZTTPDfAXUcgk2RIZkwkKW5fEiKMS4Oo9/QMrEcY2UiSckcZzUbT5Z0zEwRhxI6BNuaXahzY083SSp32Yopyxeb65DK5TIJ81sJ2fDRNZsdALCLjuAHlSjNuj4wiZDkuAfh+hJWIlR2zmRsBdaI5yWjjGp1GBzuSwwQi5GTpHZzQ5njhYfJ6gVPld1lwF7AUi73pPezN3L8P8io+9IC8Hgn9+JZ9aro6jCzFN23ZHaXiT7X0rOOZVt1foM3YtWEbITyEcBDCJYpWhc1vH0AiIWj6PkrLa9aPNrJK5xerWAmd//TLn6C9mOJr3zzJ1755kkIuybEHB9E1hcHeAj//3BF2DXcSRhF/+fU3+do3T9DZnmHvSBe1usP3XrrAy29e4bmfOsRjx3agKAqXR+fo68mjqi2pNuKaabPpceK9Cf75v3qJ3/nSEzx+fARfiah6LitOk/FamYxusivfxj878yYJLU49NwOfN+cmObk4zWA6x47s5p5CNwypei5OEHCtskKnlWLnFuvdjPuZJNaDRc6Wv8Xl6o9iQRduUi8CmsEKzWCFJfcaF6vfJ6v1sDvzNAdyz5LR7l0dxw4r2GEFTU6w6I7y1tK/YrxxYstzCISH7VVZ9ia4WnuZt5e/wsPFX2Vv5qew1FsTe24NQTMscaHyPU6V/gYnKBMRbb720KUZllh2x7hS+yGWUmBf9lPsy36GgrF1S+DGowjmnSpLTp0d6TYagctL81fYnekkrZm3fALqjssf/t/f5Pc+/xiP7Rsindj+zNMJy0w13qTqTzGYegIvatKdOMzdyXZAMp0gYcUvaN1Q2bGnm0RC54Wvvcmhh4c5+PCObe3zB/Pv8t25E1xrzAISg8lOnuk8whd6PoYvQv7J+a9yoTpBzW9yqnyVvxx/EYAHssP808O/jYTEslvlH5//ClfrMwB8aehT/PLAU3d1PbfDUCHPQD5H2XY4OTHD07uHUWWZwUKcPj/YE//O16djN1+52KA6FrcwhXCPCebF2TJzUytrn2VZptiZRVZk6u472N4lDG2ApP4AmnIvz8C6YzX+moS2k6z58duu5wchqaRBIqGTTpksrtSJIsG18SWuXF+g0fBwXI/5JZViLkkmnSDbEvcIw4iXXr9CX0+O2YUq6ZRBteYwOr5IEIR0tmVIJg2effrA2vH6rD4GrH4mm5OMNq7zaPE4j8jHGWuMcaF2iS6zi6yWpaDniYgwlfdXJ/wjFZA1dXjL5YXsf4EsxbVNVTIw5AyGnCKldgACVU5w84MqSwaSpMZCHpKx4d9LzSYrtk0hYVGwEuu2kUhqGs4WwiAA7cU0jz+8k907OpFliV/+wjHeOz/FlesL7Bhso5BLsndXF1EUC3+EUcQzj+/lj//597AdHyHg/JVZpmZLHHtwiM9+8uBay1L28BD6OsKG3NK+fuPkNb7x7Xf58i8+yhPHR0iYOt8aPcMjnf18ae8RLq4scrWyzM+NHGRfPp5dm6qKJsn0pjJ8bnhvbJQhyQjAUBRkSeYfPfQUX796hi/u2M9gpsCbcxNcKi3dMSBvl1h1M4SImGqe5s3lf8mScw0vam5DtScOlRV/jvdKzzNrn+d429+hz3rwro8PcUCu+fMsu+O8sfTnVPzZbZ6DwAlrvLX0FRrBCgdzz5HXt19PDUXAvHOJt5e+wqx9rnXtd8oDxms0wxKny99k2bvOodwXGEweu+1M+Xp9iVcXRrlSnee90hSqJNNj5TAU9bbfWiRgpdakry2HeZeG7yXvOl5Uo83chx2UqQXTdJoHkdk6Te65PtNjy7zywmnGr87T1pNlZH9P61/j9R3bY+LqAqffGMV1/TW2fXt3jtHzM1SWG+w/OkjXTbZ3J0tXeHXpHMeLe/mdkc8RioiK3yClxM+5Jin8g32/xLXGHP/i2gscze/i2e7jsV+1fENnuWBk+G8PfIkL1Un+7fQr9ySKczvEpjiQS5g8MTJ4w3d9XbsZrMvmbXEfk+kE5rqUvd3wuPjuxD2lrafHlnjvjVEmrsbMd1mRyRVT7NzXQ8AEZfuHhFEVSdYxoh7cYIyK8yqKlCKXeArbv05CG0JXe6g5J/DDJUDCD+cRRGTMRzG1QRZqX0OWNNxgmoS64/YnBZiGRjadwPUDpufKzMyVaC+kmJ4t0dGRRVEksukE6ZRJWyFFFAnKFXtte0WRWCnHKefujiyppEnQ0vdeqTSwrI0zdE1S0VuZlEV3gXdK7+KEDmktRShC+hN9vLHyJoEI2J/ZR5fZxfuJj1RAvqHGJYiEjedfxfFO4ngnKWT+CF0dIq11t3yJA1TZoOxNbKlNK4SHJJkk9MNoyiDrg4ihaszVFrm2XGJ3W5GhQjzai1qeyLqiUHddopss66yERldHFk2LU77FfJJsOkG1ZtNouqRTJtfGF3njnetMTK/guD5N22OpVCcM49nQcuvvwd4C1jpa/83M7jCMOH1+mtMXptmzs5OnHtuD1aLZ7863c7W8zA+nRknrBg+0dWEoKsZNqluaotxysJw3EjzQ1s31aonRygpZw2RX7v6VebZCELlMNd/jjaW/YNkdIxA3+jRVySCtdZDX+zHkFEgSXlin5E1R9ecJhIMgxI1qzNrneW3hzzje9mv0J4+0zES2j0awzOXaj6j685S9qbXaclrtoGAMklCyyJKMFzap+LOUvGkCsZoSFLhRnYuV75PROjHkFJZ65/RgJAKmGqc4ufLXzNnn8cW6FKNkktN7SWudGEoKSUg4UY2qP8eKO05EiCDCi5pMNU8TiYhIhOxM37qFryeR45Ndezha6Kc3mUdGwlQ0dPn25hCKLDHQkaPWdNbqc9uFEBFB5OKFVSJC6v78ppTm4K4ueofaMUwNVVPo6s/z6Z8/husGZHIxOeuhJ3evCdzohkbfcDvZfJIwjEjnLBKWwf6jgwzu6kRVZVJbaIDXA5tm6JJSE/Ql2tEVFT8K1xTPAPJ6moLfQG+lqDvN3JqX9Nr9QKJgZMjrG80r3k9IkoQiSSRuU5+/3XfWO9xGe3eOsUtzQMxAf/5fvkrPYJHO3vy2mOuBH3Lu5Bjf+8YJ3nnlMr4Xk2fTmQSPfnIfRkJD0E1C2wWSRFI7QBDV8IJpsuaTSJLCUuNvUOUcmlJEEyFeOIsTTGKofVj6PiRJp2R/j4S/k4S+C0PpptT83rZkNE1To7+3QNQitvX35ElaOs9+8iBnL83guT6D/UV6OrNYph6z11tCLIoic+zw0Fp8sBI67cWI3s4sUctqVlM33vuJ5iSTzSl0RafD7KQZNllwFmiGLclYSVDzaziRy5K7TJve9h9WDXkVQoSE4SJucAnPP4cXXAcBipJD1/YhS/HDp8pGrNIVrpDWulh0LiNJMp2JgxtJN5KKEAF+OAfISJKOLMftOKstBkvNBu22xaDIraWEdEUhaLncuEHMttZateRVh5/VvxUlbksIo9jR6MzFaX78+hVkSeLx4yMYusr0XJnRVo+dEKyJK9xJzKNSs6nUbLKZBJOzpbi200rFjGSL5AwTJwhIajoFIxEPN+4yl7wv30GbmcQOfCxVI2ckqLoOTd9vDU58lpt2y5PX2GAJGUQRZdfBC0MGs7lbCvyHwqfkTfL28ldZdEaJiB94TUrQbe1nKHmcojGEIafjACvF27hhnbI3xVjjLWaaZ3GjOoFwWHCv8PbyV0lpbeT1/rsKyiVvipq/iB/ZSJJMmz7I7swnaTOGSChZVMkASSISAW5YY9kdY7T+GlPNd9f20QxLXK+/RUEfwlIP3/GY885lLlZfZNY+vxbcFUljZ+px+pNHyGrd6EoSRYolU4PWtVe8Ga7UfsysfYFAOPiRzax9DlPJkNba6TB3bXm8hKpTMJLMO1VeWxhFCIEsyXxx4PBt1boMTeVLn3yIE1emkGWZnV1FLFPb8L1KSGuSq+uR0wcJhUfNn0ZCpdc6vokAZ91EvkpYBombRFHWm80rioyVMtZIW6s926lMgmSq5VG+BYdhV6qXfquNV5fOMdlc5FBumP2ZQYrGR0uD+/3A4EgnIwd6uXhqglqliet4nH9njH/2P3yTB47vYGhPF21dWayUiaLKiFDgeQGNmkOl1GBuYpmxy3OMX5lncnSBaiVWKjMSGjv39/BTP/tQPJOXEihyGlnSUJUsgb9CJFwMtR+BhxvMIGsWosUjiIRLJBxkycBQ+4hnykvIkkVO24mu9iLL2+MoKLJMwrwRNFOtjspES0nOD0IyaTMm1rb09lchSRK5TGITC9s0tA2fV7E7vRs7tAlEiCHrmIqBH/kMWYMYsg6SxGj9GnvSu8nqWZbdZWacGTrMdt4vfOgB2fOv4gVX8IPrRFENkPGDCdzgAoX076MqPahKN4p8YzbiRnUWnUu4YZWU1knZG9802hLCJxI1gmCOKKoRCRvLeASIBfqFuCE6sbYNYPs+XhAgSRKjpRWSuk5HMv4V+H5Ire609i/w/ZCG7WG0NJpHxxaZminx3DMH+fjDO1EVmbdOjaGtm/1arZdOpWZzO2iawu4dHRzc28urb13lG98+xa/9/CMkLYOkppPU7p9kktYN0rrBxaUlZqo1SqqDKsvUfQ9DUfDCkCsrK1Rch6QWPwCqLGGqGqos0/Q9hIDBbJZbpbGbQYnzle8yZ59fm5FqksXO9GPsyXySzsQeEsrWLQad5m7y+gBJtcBo7VXssEIofObsC5wp/S0PF/8jUlrxlse+GV7UwKOBKpl0mXt5IP8z9FkPklCym2Z0QkS0mTtJqkX8yGbeucxqAnHeucSKN06PdeC2A4K6v8x4/W0mmu+sBWNdsjiQ/ywj6Sdo04fR5MQWbRwRXeZesno3p1a+wYx9Bq/Fyp5pniGn9dJmDCPfgnm+5NZZcur0JOIZf6xxfft7FEYRl6YWef3iOBcmF2jLJDG0je5AuWSCv/fcxzZtm1CLtEsqltpGJIINNqj3g1v1gd9OIanLzPOpzmOcq44xYy/z8sIZLlYneLz9IPsy/2FZMqZzFg89vpvZiWXeePE8TtOjWXc5+fIlpq8v0t6dI52zYqUuRUJENyQyGw2H8lKdpbkKTtNbC1rJtMn+o0M8+8vH6duxdaDRlHa8cIGK/WMkScfS96Ir3TS89/DCWfxwMc5IoCBJWuvdrJDQdlH3TuOGcwRRBYn7k53saNvcY7wdhcRbZR3ajDhDuD6ACyEQhljLoCy4i/iRRzO0UWSVlHprBcB7wYcekKvNr+EH40hSAk0dwlB3I0kJ/HCStPXFLbdJqe1ktV402aLd3IMsqcg3fbnxfFdBkmSE8AiCGdhG/V1upXolYKnRpOl7QByQKzWbi1fnOHpogKSlc+bCNE3bo6sjQyZt4vshQgjSaRNFlllcqfP2e2PY7o0e6r7uPKmkwYWrc4yOLdLTFQezUqVBLmOtjd4sU2ewr8hjD+1AliSe/857/Oj1yzz92B6S1vvbGz1ZLTNeKdOTSmPpOovNBjvzBSpOnFquex7LdjPOFsgKqiyT0FQ6kykqrhNnhLY4nSByWXHHuVp7eS0YA/RaB9mX/RQ9iUOo8q0HFoaSotc6hCKpeGGTK7WXYvY8AVdqLzGcehRDSaHJd5MyksjpPezP/jQ704/fsp1KkmQSSpb+5FGaYWnD7N4JK1T8WeywQuo2/ZULzmWmmu/RDGKijCaZ9FoP8kDuC2S0zlvWgiVJRlcsBpIP4UU29WCJJfcaIKgHy8zYZyh5UxSNoQ3bBVGIEwYsOjXcKGBnph2rRTpRtsGKjSLB/v5O/DCM62zhRulJXd18r+r+HE5YWfscCo8l5zwFY4T3IyjfLRRZ4WBuiMFkB+ONeU6VrnKxNokXBRsC8uqZbabz/fsDSZLYua+HT//cMTRN4fRb15ifKiEEzE6uMDu5cuedtGBaOj2DRfYfHeLhT+zdxGI31cEWK9pCUdIIzcf2ryHLCbL6kyiSSd2NkCUDS9+HjI6mdqLIKRCCXOIJUsZhas7byJJGUj+EocaiR5OjC1w9M8muB/rp2xHzYN74/lkWpsscemQHg7u68P2QyavzjF2a5WOfPoRuqoxfmmNuYplqqYHvBWi6Slt3jsE9XXT2FmJv74bLy996j87ePDv295LJ3xAt8r2A+akV3vrBeR56cg/dg23oxg3ew6rCHkh4fizzuyM5zJwzhxu6dJmdtOv33l+9FT70gBxGVSRJQ1OH0NQBZDndmq2INZr9zUhpHUhIOGEVJ6zEPaI3KXVJkoWm9IAIWwYEN2YykRBrT+T6h1GRJAqJBCOFIqukmtRNkpPVusOPXr+MrimcOjvJUH+R3Ts6yKRMerpypEfnefPUGOWKTa3hUG9s1LUd6i9yYHc3L781ygs/Ok9fTw5FkqnWbZ54ZITujo2zxUw6wfEjQ8zMV/j2D87RUUxzaF/vhvrz/aLNSiIh0ZlMrg1GelJpHD+gaCVIajoN36Ph+SiyRCgEiiTTnUoTRrd+nTXDMjP2OerB4toyQ04xkn6CDnP3bYPxKmRJod0cYUf648zY56kHMenEDstMNE+S1/vI6j132MsNGHIybmNKP7at3mZdthhKHuct5SvYYXmNjNUMSjSD0i0Dsh85zNjnWPbG15Yl1BwHc8+S0bo21SxvhcHkQ4zWXqHizeALG0FI1V9gqnl6U0B2Qp+pZpkVt4kXhVwoz2GpOrIk8VBx8LZkMENT+dWnDt/KKhjYzHMAqPvz1PyZtX17UYOSd32D9vxPEiWvhgBMWWd3ph9D0Sn5da7VZzesZyo6mqRSD5pU/QYpNbY//aDqxR8UTEvn4MPD5NvT9A61ce6dcZbnq9QrTZp1F8/1CYII0ZJqXfU81g2NRNLAShnkCik6+/LsPzrEoePDdPUVNg34LX33TZ/3IBhmtlqjO50hlCVyiR4kSaLp+UQCNEVFleMSIOJJpssOxeTTJHW9RZiNjzF5dZ5vffV1nnF9eofbYy/nv3iFy6cn+NIfPktHbwGn6fLOy5d49YXTPPz0PhRN5tUXzjA5Oo/dcAmDEM/xSaRMHnpyD5/55UexUnHP+/e+/jbFjgw/95uf2BCQm3WHt354gb/4428zsKuTzr6NBMFGw2VpKZZBVRSJ3t4CWS1DVvvgyh8f+q+vmPmHuP4ZHO8dms6LgEQkXEDgeCeQ5SyylEFV2tYUvOygzJJ7lUawEKcXJOi2HmS9MIgkyWhqL5q6WXoylqMTawIQN7aRMFSVh3s3b1PIWTx2bCeHD/Tx0htXmFus0tGW5nM/dYiB3vgHfGhvL7bj89rbo4xNLNPbleUXPvcQrh+SThlIEiQtg0eP7sAwNF47cY1L1+ZQZJmujgxBi4yQTSfo68nTVkghSfHnL/70g8zOl7k2scSOwbb3NSAf6ere8HlvW2z+3pFMbVDG2WC2Tvw4daXSt26l8ReZar63YVm7sZM2Ywemsn1JO002KeiD9FoHuVT9wdry8cYJdqYeJ6N1bztjkNW76Ukc2PbxZUnBVDJk9R5cp04o4j7N1RTyrVD151nxxtdmjzIqabWd/uTR21p33gxNTtBmDDNrn6fix2UON6yy6FzZtG4gIup+TEaUJYklt04i0HFCnzYjRUciTVIxNnUPQFyr6y5kEELQdH1cPyCMBKoiYWoapr41KcxSi2iKhdZqS/Siesui9MNRbLtWn2XJrWAqBqaiMWuv0AwcdqU3PtM5LUVPosisvcLrS+fpMPOkVJO9mQGEEMw7JSp+g+uNOaq+zbxT4nxlHEsxaDdzmIrOrL1MPbAZb8xTD2xm7GXOV8ZJqQnazSyapDLrrFD3bSYaCzQCh6nmIucr46Q1i3Yji/k+9DdrusrQ7i76htt5+gs1Lrw7ztToIgszpTULyjCIe3A1Q8UwNTI5i7auLJ29eYb3dtM9UNhU0789JJYbNt+/dI1P7BzCDgL2dLShSBKz1Rpl26EjlaIrk2Kp3mS8VObd6Rk+uXsnw4U8yrqyQyqbIN+WZmZ8CUQs41ldaWClTCordcpLNXwvoLxcp9CRIdMi+gkhOHh8Bz2DbZiWzvWLs7z8rXf54fPvcODYDvYcHiCTT3Lksd28/r0zTF1bZHhfD3rLea9aanDq5Uv0DbezY1/vJu3s5eU6J94eJYqgszNDd3dug13tB4EPPSCrShFVeYqk+RSRaOL517DdN3G8t1go/zdo6gCGup9s6tdQlbg3r+xNUPEmyRvDhMKn5s9CSwFpOy9mJwiIhMALQhperO1sbpGOW4/Hj4/w+PGRtb+3QnsxxXPPHOS5Zw5uWP7f/9HnN3wu5JM88/hennl8a4/TB/f38eD+G201siyRz1r8V3//2Tte2/uFrfoeN2Qgbvr/zRAiwg4rrHgTG5b3WAdJbIOdfDOSaoHuxP4NAbnsTtMMy0QEKNvsvcyoXbQbt/HQ2wISEkm1uEF8Jow8wujWbkIr7vhaqhrAUJIUzeG7TK/HSGsdGEqKVeshN2qy4k1sIqvkdItjbYOYisbZ8jR9Vp6hVJELlVn+3dRpHioO8GC+n6ye2HQMIQR+GLFSa3BhYoGZlWqs1GXoDLTn2NXbTi5pbmJfp/UeRKsbQZZUgshFk5P8JNLVq4YknusThhFWKlZWemPuAldLUwgV2lM5juRH+Ez3wxu2NRWdZzqP8P35d/i3068iI3MkP8LezAChiPjhwru8tXKRklfDi0LKK3XOVsYYSffwM70fZ8Bq51szb3GmfI2KX8KLHF5aqPD28kUOZIf4md6Pk9dT/O3065wpX6ceOAQi4PvzJ3ll8QyH8yM81/MoA8lb+ytv6/rXGa5IikRbd5bHOw8Ridh3PYwEihyTT0FqZS7i2mgs7HN/eu1hJFioN3hzfJJ8IoGhKtRcj3cmZ2hPJTk+2McbY1PkEybT5dqWGbVU1iLfnmZ2fJkoihg9N02umKKtJ4fdcCkt1mIf4qpN71B7i1yr8Bv/4LMb9jNyqB+B4N/9xSuMX55lz+HY3/iRZ/bz9o/Oc/XcFHsOD9C3owPfC1mcKTN6bprPf/lxzC3KgKqqYJo6TsuU4m4QiWhtwreV3Oet8KEH5PWQJQtTP4ipH0SI3yAUZWznNZruDxHr2kVU2SCldZFSO5hqnsQNK0QE2/baNFWVUAgCETFeLpM2DB7subt+slXlJUG0wfbxo4z153wjWS9vYMSupmTvd4bjC5dGsLKhvgiQ0/vQ5c3mE3eCoSTJaRtT0xEBFW8aJ6iS1LbXspVQs6TvWlxEQpPMDfckNue7tZtQ2ZvCXnftqmySUtvXZth3A1UyNqTXBWEsuyncmBl+E6q+TclrkNct/nbqNBXf5vf3Ps03J08zmCxuGZDDKOLy1AL/41//kPGFMpahoWsqTdcjCCIeGO7mP/vCx9ndezPRR6IZLBEKn4zWF7dphTXQVh23tvc7Eq0AslZNahG6okjAqlOWBCISaxKIkiRRKzc5+8YVlmZKPPulJziW30N6TGd8dobBvT3sOTK8tr4QcTeELMfbHswNcyA7tOk8FEnmVwae5lcGnl53zzfrKP/2yHNARNM9gedfwdQfxNAOrFsbfnfkOWIf9mj9Xlp7iBAiaH1ev85qOle0lnHTOvFnP4iw3SBWoJIkgjDENHQajkej6VK3PRZKNTpyKfq78siyhOcFmIZGw/Foy6W4X48TQ1V4eKCXlaZN3fWYrjjMVGrM1xukTRPbD2j6Hj+9b4S5Wm0DwXUV6dYM+dyJ6wRByNWzU7T35Ml3pFmeq1BarKJoCs2aw57Dg2vBcXVAtjoo0TSFVDqBJEs06jfixcjBPgZ3dzN2aZaxy7P0DrdTWqzGfskSPPm5wxsY/qtIpQw6u7KMXV+kvX17vtCr59YIbNzIJyEbmKrR+kbvvP1HKiBvhIwi5UkmPk3SfApJukGTLxq7yOvDRMInpbaRafUmbxcJVeWTO4cJhUCGLVN420HJvcjF8l+yK/tLtCfuTaziJ4lQeJTdy5wv/T9U/TFkVAbTn2Ew/SwpbX1K786qZneCH9k4LTW19UhpHWjy3avbyKgYShpDTuFGN+zt7KCyobf3jvuQU+jbbLm4HzTCEl50g0lf9xd5e+krnFr5+l3vKxQ+QbTxGoWIcMM6qrr5XqqyjCLJlL0mFypzNAKXimfjR8EtBUls1+dPvvkKAx15/uCLTzLUkUdVZLwg5PzEPN8+cZE/++5b/E9/97mbthTUg3mCyCGt9eCGVaabJ8gbI3f1TDoNl/deuczYhSmsdIKRBwbId2T40TfeIvBDjn/6Adq685x+9SJjF2bo29nJ0af2kUgnyLWlWZppaTpLkM4n0Vvpx6mr88xPLHHg0V1IksR3vvIKn/qVj2Gl40GJF4Y0/XiQpCvKmg5BybHJGyaBiGh4PnXfJa0b9GVu7giQUOUCQStl7wdjBOE8urYTSdKpNf6GlPUc1ebzhOEcitxGwjiGED4N98dIgKaMkEo8RaX5DYJwAcv4GIa2Hy+4TNN9HQkZXd1D0nySSvOviKImlvEIlfowpy5VmJ4vk00nMPU4qOiaQj4dWyZWajYr5SaXJxYp12Lt/B19bQx2F2jL3V8mQ5Ik3DDk6++d59ryCg8P9DJdqdLw3LXAlEuYFC2LP3/rFNeXSxzu6960n1TWorO/QLPusjRb4fKZSfY80E//SCeTVxdYWayRsHQaCKuHFgAAIABJREFUdYfe4Rucjde/e5aTP77IxNV5Kit1HNvDbngb9LhXcfSJ3Tz/L15i7MIsRz6+h+X5ChdPjbP7wQG6+grIWwwUXDegWnUIQ8HSUhUhtjdpK/s1Xpx/mwiBIevsSvezLzO0rW0/sgF5dcYpYcBNswBdttbkDgdSj8UzvLsQ4F+tFd8vIhEQRE2EeP98Vz9I2ME8E/XvAIKH2v8REjIJpQ1jnQzesn2amebLHCr8vS0JddtFKHz8aHNrly6byPfws4tnRAqanNgQkL3IJhTbM6JXZR1F/sm4d/mRvWE2LIjwhYMfbm/wcCcIxC2ve3emE1PWqAUOv1d4inro8t2Z8wyn28jpWw9GgkhwZXqJLz9zjH39HVgttmkkBId39lBtOvzlD05t2q7sjjPTOEHNn2HROYeEgqnmbtmSdSuEQcTSzAodfUWOPLWPRqXJmdcuk0iZFDqyvPz8CZ799SfZc3QYw9RZnq8wdmGG/Y/s3NAGJUkSkiyvhRkrbSLJEu/++AKdA8XYc3sdzi8ucr1cQpVlJCkmcR7q6OTq8jLLdhNNVsglTExVvUUftxSLPreOGIkqQbSAJnoBCTe4hOoNocoFVDkmDYXhckwgU0da2tA6spzFMp/A9c7jBxNEwkZCXVsHEVB3vt9atoOm+zpjsyUWS50oqkImaWLqGsuVBk3XY0dvkemFCpqqkEnpCBGrB6qKjK4pDHXnkW/RVrZddKZS/NLhgwggCEPaUkkeMw3cILaRNDWNjGHw+I5Bjvb1EIqIYtJa03VYhZHQyLdnMBMasxNLjF2c4ekvHGHnwV5eeeE0c5MrmJaGLEv0DnfgND2+87U3+M7X3mTP4QGe+dljFLuymJbOu69e4QfPn9x0rg88OsIb3zvL+JU5LrwzRnm5zvJcmZ/7raeQla013vP5JMePD1OvuYyOzm/7vtSDJkU9SzVoYIcOTuhtMLa4HT6yAfl2kKQbD5ypfHgN/xl9iAOF32rNLj/a6WoAP2rQ8GfI6iO0mQ/ErWKSvNYyFkYudX+KZefcfR8rEsGm9KxE3J52ry8BCXnT7DoQLuE2B0SypKL8hH7ygYgdrD44iFvOdtOayUimHS8K0WWVi5U5Pt6xkzYjRWqLGTXE7X5tmSSqIrde1NLa8lVf3nx688zDUtvpsY7hRXWSWgcyCvo9OGMJBJqhkSmmyLdnsGsO1ZU62bY06UKSvcd2MD+xhF2PTRcUVcZuunfcb6aQIpVN8tqrp1hZqHDosd1o6xzV6r7LQqOOoaqosowdBNRcl7FyCVPVSOo6WcPE9n1q7p2PF+tLBwgCIEBEDpraR615Ii7JGUdRlR5c/zyedxlD24Oh78P23iYIF5Elk5AVZMkiEnU8/yqGthdd20vD+QGqXERWchjyIXb27aC3rR1JBlPXUGQJ1w8IwohCJhmLYkSgqnJLdyGKhT5kaa298n6gqwptyXUDPElaM66JP8bff8Y0yJjGhmXrIcsyybRJoSPDxVPjNOsOXf1FCu0ZCu0ZlucqgCCTT5ItJGnWHV7/7llkReaxn36AXYf6SVgGdsPl4rsTW/axZfNJdh3q552XL/Hmi+fQDRUrbXL08VsbQzQaDpOTK9hNj3p9O999jKKeY0ZdZsEtkVaTFPTMtkuAH3pAdsOA95ZnWHGbdFsZhlIFnNDnveW4TWEgnaPHynB6ZZaa5zKYzpFQdJadBhXPIRARu7PttCeSnFycohn4DGfikehcs4oXhliqjh+FWJrG/nwXSVXnfGmegpGgJ3kjBRUJn4Y/y5z9Js1gjijy0ZQ0beYDFIz96EqaSATYwSJXK/+GQDhIyOzMfBFDuUFUCiOXmj/BdOMlhjOfZ6bxCnV/EoCuxCO0JY6gygZh5FL1x5lvvokdLLTaLtJ0WY+SN/ciREjZvcKye5b+1DNM1l6kGcwhSxq9yU+QN/YSCY+KN8qScxo7XEKIiITaRrf1GGltAEU2WLTfZa75OjV/gpJ7GTcs895SDZDYkfkCGX2YJfs08803WXEvUPWucXLxf2Z1kLEv/2Usreuu6soS0hatPdFa3+e9xWSxSc5URt72eUlI9+eMcRe4+Zw0OUFeHyB9H76w6xE7QG0MkM3Aox641DyHim/TDD2c0OdMaZrf3f0kCfXWKWRdVXjm8C6+d+oKi5UGvcUsmirTdH2uz61wbmKe3b1tXJhYWNumvz1LKpEipXUTiYCM3kMYedjh8j1dk6opa61VyUyC7qEORs9OEvohnYNtNCpNpq7Oxyp5mkLCMpifXObSO2PMjM4zcWmGfEeGa2cnuX5+Gt8N6OgtkMpZ6IbGwtQKz/zSx1DXqY1ZmsbOQoG+dGatdJVPJDje24+laZiqitXSt1e2KLgG4TKOdxbXvwBIqGovgoCG/SKyXABJRZYs/HAKWUqgRcOoSg9COHjBdRQ5RyTqBOECrn8JRc61ZH8NonAeP7iOohTQ2YGhHcTxThLho6n9FDI5lFx+w096PfloVYTo5i6J9zNDdD9CHOuRSBq0ded497UrFDuzpHMWmq7S0Ztn7NIMru1z4OEdaC2FQ0WVcW0fu+7SqNqsLFS5cnqSM29cRdM3Z/ZkRebAsR1cPj3Je69doau/yK5D/RS7bu17rOsaxWKKIBvS0ZndlrudJElYqslwsoeCnkaXNYr69urP8BEIyJfKi1wuL+JFIYt2gyW7QVo3WLBrDKYLaLLMe8szVD0XXVG4Vl2hGcQKUYaioMoKL81eYyCVpRn4GIrKlcoiC806kRCkNIOJoEQj8NiX7+RCaZ4eK8P16jJyprghIAsR4UV1Gv5sTKGQNereJE6wEouTJx8lZiUq6EoGx1tmwT5Bt/Uo2XXM3YiAhj/DterfoMoWQdRARiUQDhE+EhKR8Kn6Y1yt/DVh5GEoWWRJw4/qhMIFJELhUfWuc736PLKk4kd1ZEknFE5MKFqTeaxgh8tracJl5yx+WGc483nS+gCKpKMrGbQw1fIZTqArWSRk5JaqkiIZKC3ikoTaGmDEP6J7SV3LkoZ602xWEBEKDyGie9pnrJm8MeWryuZdeyX/JLBKxFpNK5tymgHrCEOp4+/b/o2byHGiNWCZd6rM2hWyegI78FhxG0S3mE2vIhKCxWqdNy9OMrZQoi1jocoyjh8wX6pTbtjs6mnjqy/dkBH90tNHGenV8aIqQeSRFl14UZN5+wxD6XZksf0ykm5oDO3rxWrJwyazFruPDCJJcdq50JUj357BSpmEoUA3VJJZCzOh0zPUTraYQk/oyIpMvj3DzkP95IppZEVGN1SK3VnCIEI3N7Zv9aWz9GeytFs37qUkSeTMjWz4W7FsJUlBVTpAP4SqdKIqXciSge9PICsZkuYzON67mPphVLlIGJaww9dAUkiaT6PIWWz3JJbxKLJkIUkGEmqcspYUkomnkKUUjneWdOJZINboV+UOJCmxaXx5u/v9kyjV3CsSlkFnX4FXXzjDw0/tRTfjkklnX1xO81x/7W/D1PnE54/wnX/9Fq98+zSn37iKoipoukquLUUYbp0x69/ZQd9wO2feHKXZcDjy8d1b9tavwrL0TeYTd0Lsw+6R1izSmsWiU6Ye2qS07fFWPvQ32YXyPALoTWapeA6zdhVfJBlKF3i8O2ZIfnfyMo93D7MzU+SFyYuMVlfYnW1jX76TlGbwJ2deZsGu8fmhAwyl8vzN2FlGq8vsz3cymM5zqRKP6vflOnlh8iIzzSopVSejb3zoJEnBUjvoSz5FUutBkUzm7bcYr32bZfcM3clHkFvr7M39OvP2m5Tdy1telyDEC6s0gzmGUs+S1HrwowaqnECRdexgkQX7JMvOWQ4WfpeieRBFMnDCJXQ5hyJpBMTB3Q0rOEGJwfRnSChtuGEZXYnXEZJJxthBQu0gqfUgIXG99reM116g03qYtN5PwdxPwdzPsnOWpj9Hp3Wc3blf3VDna08cJqX1tVpXHA4Wfvu+asiqbGzJpnbCKqHwt+1rvIpIRATCxb2p99dQkqh3QR76SUGXky1SU3y+8e+mQK/1wAd2TFPRUCWZDjONLisMporYoU8gItQ73G9JAlPTeOLA0Ibl6YRBe2ZrVrwsSzhBhWX3SmwnKTz8yKYRLHG3+ldGQmfnoRv2kpIakulS+NhnDxOFgoAqmpJiYHc3SGxQkeoa3Mj8zq8T12nWHOYnl9EMjQOPDG0KSp2p7Ukf3iqYKXIOy3h0wzIh+jC0gy2VwJBK42stC9ksQowSRssoUju6NoQQAWG4iK7tQdd2QyvV33TfIBJNNKWHSDhEUQ1FyZNKfKbFWdl+ScAJ5mn6Y3jhEoIQTc5TSHwMCQVBSMl5CzdYQBCRUHtI6/vRblEKFCKi4V/DC0uk9d1ot5C9vRMq7ntEkUdCG8RUO7DSJgeODbMwVeLok7vXNMz7Rzp5+On92HWXkYNxK6huanz82QdBkpi4Mo/TdEkkdfYeHiJTSDJ5dY6O3sKmY1ppk7aeHNlCklwxzd4jH4yU6mRznqBVRrvemKHDKNBlbq8L5EMPyGnNYDFo0Aw8epNZdFmm3Eq5TdRKJLU4cFY8h8l6mTASJBQVTVZIqjpqq56cMxKU3CZyq8fO0nRUWUaTZVQpthzsS2ZxwoDzpXmeG9hHR2KjOISEgi6nCZQszWCWSAREwicULn5Y3XZhfv0ee5OfIKMPo8gG+rofeTNYYMU5T8E8SF/qqbXl+s2CFUKgSCa9qU+Q1gZiwRPlxktEkTQMOUMUudS860SEqJKJF5YIhdNqPbmLU36foMsJLCWHIukbyEdVfw4/su+6HzcULs2gtM59KUZSLbT6Xj9aSGpFDDmJHZaBWLlrVWXsg4IiySiKzI50O4K4fceLAg7melDvoAxmGTr/8BeeuutjVrxJvLBGM1hiyb2EImkUzZEWV+BeCxMCL6xR8ydoNw8jKxIr9gUKxj50efvpPwC74dCo2OTa0vTv2h5LVgixTtzk7mrhABt10WWSxhM4/rsEYgFdHcaUHyIM5/GCa8hSmmTiGeLM243tDHUXiKi1ThbLfHLd/rc/mA2iJsv2Kyw1f4wfVZGQsbQB8onjcUAWASXnBBXnXZr+ddLGAUbyf3DrgEzIiv06Ffc0Q7nfuueAPFf///DCEj2pn8VUO0gkDR54dIQHHt2o8dA71E7v321HiAhBhBCxwEkqk+Azv/TIlvs+cGx4y+WeG2DXHZKZBCOH+ih0fDD8o7Jfx4ti/owbendVnvvQA/LxjgFemLjIstPEUnVGsm0Yisr3pi5Tdhx2ZIo81jXISzPXeW95hsFUnv35zpamcuxZ3J3M8MneXfxo+ipOFDCcLrBf6cBUNExFI6uba+L6w+kCl8qLJBQN/Sa2XyQ8St4lJmrfoeHPEgqHQLg0/BlSWh93O+qXJImU2rsl4zQUDn5UJ2ds7dqzHrKkkVJ72Kry6oYVFp1TzDZexQnjftBAODhhqSXYcP8tTPcCRdKw1AIZrYvSOnGQZXeMgeQxLO7O4NwJayzfJDKiySYZrQtTeX8F3t8PFPR+EmqOsj8NxJKSy+4YQeSiSB8M0zuIItzIx49upOyc0Ge0tshgsojCvbX33Q5ZvR9dTiEIsdS2uKzQKrncKyLh0wwXWHbOt373sOxcJKX1ocuZu9p3sStHsetuhWgEdjCJIifR5RwS956BkSQZTRtA0wY2/oM2RIKtAwqAohSxlFvbbG4XTX+cpeZLSJLGSP4/R1eKRMJDkVY1zk1G8n8fPywxWvo/cMI7sYklVCWLoXahSO+f7eCdEEQ1/KiCpmTRpO0NAlZ72z0n7tWeHV/m6rlpkpkEDz66tbjT/UKSJB4u7CcSEaGIqAXNO2an1uNDD8htZpJf2/0Q4SoDsPWiOlToJhRizbh7aE+RSMT6uDc71/zBoXj0uCOz9TqHit0IIWJNVeBYex9t5k31NyGwwyXeXfxfMZQCB4u/S0YfouHPcm7lT+/x6qRWMN7iBSJWj3tnT1BpbT837UIIFuwTXK18nbyxlweKv4+h5Flxz/HG/H93j+f8/iGhZOkwRjYE5OnmGXZnnian9XCzw9KtIISgHiwx0zyzYXlRH97SqemjgII+RFJddaISBMKj7E0z71ymJ3HgrkQztouS1+BMaZqZZmUtK+KEAWfKUzzesQtNvmEjuh7r2drSuv9uB0JEGEo6bsOKPLyowZJ7gT7r0bvaz3rYwRIL9inm7LdoBLOARFYfRpOSH/h3HXPXA8bKf0o+8TDFxBMYygfjE/6TgBNMI4jIGgfImUdus+aN1q3bQZZUelJfeN/ObzsQCKreGZbtV2mznqZgbp+HYTdcLpwco1pu8uaL55i5vsSTnzvM7gcH7rzxvZyriJ+mRmhT8RuYso6pJtYU0u6EDz0gr2IrezjlpmXbeRRvtY4bBvx4dpS5Zo1jg/3kjZtbOAR+VKfiXeeRzi/F9VQ03LCEH9U2p5LvE6psocsZqv71e96HIMQO43pdf+qZVj+xoOHPEd2DIpTUIqy1uly5X3u0jNbFUOoRLtVuyF2ueBPMNs+T03pJadtjHHtRg0XnKjP22Q3Lh1OP3NZt6cNERuugwxhhzj5PPYj9sJtBmVMr/4aOnt0fSN17wa4hIbEr07HW3tQIXVbcOjIREMItv9OI7T1hG1EP5ql6UzSCBbywhi9sqv4kvdYj9zxHTqpdDKefpTPxEFltCFhtdfzJvK4iEVBx3yVjHLzzyh9xhFETEC3y5r+/cIIF6t4obdbTd165BSEE1ZUGf/kn32FuYoWO3jyf+oWHefy5B7fFmL5XlPwqL86/jSFr1AObHaleHi1u77f0kQnId6LKr8rp3XYft1lHV1Q+3jVMEEWkNGOL2YmEIulocoKye4U28wFcUWKq/gPK7iWS2vYdhbaDpNZNR+Io50p/xtXKN+i2HkOVTRr+DLqSIaHeWeNWQkGRTCIRUPPHyBm7qfmTXK8+jxuW7vqcVDmJIefwRZN5+wTt5hGCyEZXMnct9ABxHbloDNGbeIBp+3RrqeB89Tsk1SIj6SfQlc29resRCp/J5inOV76zoRZtKQX6k0ex1LtLff+kIEky/cmjLDhXuFp/GQBfOEw1T/PG0p9ztPBLWErurnt13bCOHzmktc1etUPpIv3JfFzKac0k/SjAVDQkqU7ZuYKlDaPIyVaGRkIQAiF17wJJfR9h1ESTswgiZElDtHgUqrK12IchZ4hEgCzJdFkP4oZVottofG8HcfCVaQYLTNRfZNWCalf250hqN5SeIhFQcU5xrfx/caD9nzBd/wYl+w3CqEnWPEx38gtkzZhEF0Q1lpuvMtv4Jk4whyqnyZsP05H8NCl9BAmZujfKaPl/p+mP0/QnuVb+Pxmv/DmSpJA1DtGd+lmKiUdp+uNcKf0xeeM4PemfRW1xGEr2CRaaL2Kq7Qxm/2OEiPCjMqfmfoc9xf+amneRZftl3GAeSxuiK/Uc7dbT+GGFc0v/JR3Jz+D405Tdd/HDMqbaQ0fyaTqsT6PIt39ObsbllX9KxT2N7U8RRFUqzrtMVr+KLudptz7JUO437/p7ubLyv7DUfIUIn6xxiIHMl8kY+zatt2y/wULju9S8CwTrzEYUyWJX4R+QNQ4BIAhYcV5jtvE8DW8URU6RMw8zkPkymhxnviarf8VC83s0/et4YZnGwlXkFv+kO/Uz9KZ+/v9n7z2DNLnvO7/Pv3M//eTnmTw7O7szmwMWkUgkQIKkKJA8SqYk66hTMq/KF1T2le5clu0XDm9cV2Wfy/Zd6Xy+k3zKRx1FUoQEiiRIECBy2gRg4+zk+OTYuf2in0m7MxsAkgJ0+lYBUzvTT/fT3f/uX/5+0XdxyoUQ9A1n+a3/65fxvQBVVbDSBmbS+LF2nHuhx55EP8NmP4udVRKy/jebGOS9QBKCpLo7ZaMQAl3OcSD7n7PUfoGSfQZNTpPRJsnqBzeiRdsvM9P6FqXuWTr+Ci1vgfOV/5epxp9TNE4yYj2GfhtGQpVS9Cfuoxussdh+jrnWd+N6k7AYTz2JKd868hNCUDCO0vJmuNZ4itnWM+hShuHkR2l6c9xpxCMLjZx+iD7jFBeqv8cl/gRdznKi8A+3vQhvF0JIpNVB7sr/DKWlKZywDUQ03GXO1L5BN6gzkXqE7C7yiS2/xLXWy1ysf4+yM725X2Tuzn+RrDryvjrBf9wo6HsZT36EmrewoWfshC0u1J+h7VWYSD3CcOIYCTm3ayrWC23q3iIVZ4ZV+zJNv8SAcYh78l+8YduGa+NFAWEUogqZESuHKskcSPWjyRFd18UN1rCdt/CCKoqUQurVEv2wQUI9QNefphlUibVvDaIoQJWzpOV7d/x+imRQNA4TRh66nMIPbRQpgXif9WonqNPy5tmb/OQGs9tWRrkYEUHUoe6cY6r22xjKIEPJLxBGLoqUQpHSvWvYoNp9hdnGH1AwH6Ev8UmCsEPVfpUg6jBofY60fhRd6Wcs/Us4QYl31v4H+hKfIKffhyKlUOUsphJ3gYeRh+0t4CrVHi98jCDq4ASr2yYIoiig400zU/9dLG2CgvnRHmmKgiYVemcR0vUXmK3/e9L6cYqJjyGQqDtnWG5/G1kk6beeuKPrN2h9loLxMOXuC9ScN0mqh+hLPI4kNHTlznj71zGU/AI580GWW3+B7S/2VPm2o+G8w3LrKWTJZDzzZYKoQ6nzAyr2q0wW/ylJbQKplx1quhcJwg5Z425yxn10/UVWO8+gSjmGkz+DKmfImw+SUPew0v42VftVhpM/S1KN+24MdRRF2j1zKYRAURUG92yWHNzAp+J0CMKIweSPNuu5jrO1KzS8NiWnjhv6TCZHPjzEIB8kqFKSseRPkVbH8aMOsjBJaWP0m/f2Uj4SimRSME5gyv2EeD3PJ2a7SiiDaHIaWejk9CPc2/dbqFKSnWoz8RjMAOOpz5LTj+D16CAVYZLRJxAiHmHJGscYCj5Lw6uQ025s7EqpY+xLfZ6CcZIw8lClJFn9IAl5AE0ZwA1tdDmegUuqIxzK/T1MubjjC1MIiaQ2xkTmi7TcWQI8VJHoncMm3LCLF9pYt+F4aJLJiHmCu/Nf5K3KV3HCFiE+JWcKN2yzbL9LXhsjrQ6hSwkQAi/s0vRWqLhzrDlXabhLvUYhUITBROphJlOPosvWHZGV/KShSiZj1j10gxpOtU3TXwEiOkGFa+2XqXpzZJqDJJU+TCWDInQEUq8xz8EOGnSDRvzTr9Hyy0hCwlJuHOkAaPk2s+0KJadFQtbwev0JAug3AtxgFS+s4PorRITIwiRCxo8aBGGHMLKx/RmIQJEyBHQJwnYvItn5OsfylFkgfg5iaYQfhZPU00NHQpHjCPTmzldE0XwcQxlkvRKs9PjvbX+JUvc5THWEweSTqFKOMHIIojY1+zQN5Txp/SiKSJLRT+GHDYRQsNQJcuaDaHK+t87e+1qLCMnqd5PSDiOEEmcgrmsWCyOPgvEwWeNuhFCQhMFa57s03PN3bJBT2qFeRmWNjj+Dpe2nkPgowHt2lpLaARLROA3nHLa/uOM2Le8SXlglrR+jYD5KGLmAoGK/iqmMoEjpjfsokEhqBxmwfhpVytD1F2g6F6jarzFg/RQqGRLKHgxlkJZ7mab7Lmn9JDnjvt7n1wU3bkQYRTRdhx/Oz9D1PLKGybFiP1W7y0y9xkgqjaVpvFNa467+QXRZ5vXlBYaTaUrdDnONGjnDZCSVptzpUHW61B2HQ/ki+7N5EuruJYADyTGCKIifBSHIqekPDzHIBwmSULDUQSx1dw9SEQmKxkm4RYOhpQ7dMqqUhIqlDu+aDreDLk4IqrJ3Y67thu8jJcjok2T07V2DiWQ/V5qvAjp98jgQRxiDid07OwFUKUFOP0hOP7jrNg1vjaq7yIHUg7tusw4hJAw5zZHMp/Ejh4v179HyS4SRR82dp+Eusyifw1LyKFJMTOKHDp2gSjeob6PfNOQ0+6wHOZn7PCl14D2l0X/SSCp9TKQeJYxCLjef7UXKcV181b7Eqn0ZTUqgSxayUBEIAmLaUTdo4/W0wddhyrt3DOd1i1W7Sclu4YYBZk9rVxKCPr0PSztIBBjKnp7Dl0YIiSBsE0Y2ipQiqR1FwkCWEjj+InavBhnhxbzy16HhztMNNmUmvbBD1ZmiaBzm/RiwuHyUpOpe3ujmHTDvQd5hzEYSGmn9BElt/0bEvxVeUKVqv44u97HQ/OrG92p7V2l7V0j5h3tkNRJE8mbvhJB6mYL3v86S2kGS2gF05cZSwzosbT8p/TC6EquRmcooipTG3XJ9bxcxRzasN2sJIf2EnhdBFAVb+P2jnqLVeg/A5powlAFS2mES6t7eliGWuo+q83rvM73ziDYVsG73PLwgYLpepdTpEEQhXhjQ9XPIQmAHPhW7w1iYoWp3eLu0wpCVYrpeo9zt0nAdmo5NpdvlcrWMLsvoskJa13ljeYGcYd7UIPcbeS40rlH32vG4lgV5/fZGrD74b7T/ROGFDiVnjtn2WYSQKOpj+JFH2Z7Dj1xSagFTTmMHTRpeCVXSyaiDyEJh1Z5CEjLL9uUbuJ+jKMKLbNbsaYLIR5NM0moRJ+zS9qvoUoJ0rz655swCEVltEF1K0PDWsIM2VXeRdm++9nYgCZmU0s/J7BdQhcFM+w3KzrWNaLkTVOncpOatCoOMNsxo4hSH059kwNzdWfigQQhBVh3haObTmHKaq60XKNlTdIL1dGeEG7Zxw/Yt96WIeMwrs4vDmNctDqT7sQOPqtslr8eRpSQEmpxFUQtsGvetxnKT0FSVN6NvIVQkoSNLyd1VooIKLW954yXpBi2a3mKvq/S9Q5YMNDlNxblIVpsgjNxdRVyEUDDkIXaKlmIRDhsvqKBKWRx/ZaPPRJGSZI17SWo/mhGY+H7uPDWhy307OgvbtxlAbGn2i42qRPQeGjT/upBSD1FT3qDhniNqBoCg682Tby6fAAAgAElEQVRQNB/FUAa2ReeKlNkoK8QQCKERRe6u6+1OEEXQ9T3ypsloMkNGjx27bLuJ7fuYisredJbvzlxlLJUlb5ost1p4YcCAlSSKYKHVQEJwuJDjSKGf//P1FzfUwXZD1W2waJdY6pZwAhdTMZhIjvxtDXmD7k7cXmIziiI8x2N1vkLg+phJg2TOotu0adba6KZGKp/E6ThYmQS6qbE2H7MA1UtNPMcjXUiRzCSQFZmV2RJRFJEfyGJlTGTl9lN5QeTRCep0gjqZnn5vyZlh1b5GRETLr2DKadywS9Mro0k6Lb+KJWdZ6F7AlFPb5P829+tTd1eZ6ZwjiDxMOUk+GMULbTp+HU02afplVMlg1b6GIlQ6QR1LzlLzlnHDLm5w435vBSEEKbWPU7mfpaCPM916lZJzjW5Qww6a+JG7IcYgCQVFqGhSElPJkNNG2ZO4mzHrvh2bmT7oEEKQVIscyXyKPmOCqeZLrDgX6fhV7KDRU6zyCCO/Z8gkJCGjCA1VMtAkC0NOkVYHGTSPMJo4tfNxEPQbaR4oarhhQEHfiTBlpydh53SsoYxgKCM3br4FCaUPTUqi9Bpt3KAVCyC931JCFBFGPgJBUh2h4y9Dj/TnxoZPcdMuYoGEImUpmI8wmvq7N9TrZWFs+Z3Y/G9XUXoBQo4FJLZsE4SdXlfzjZBQbnlNJNQPdAnmdpDUDpBQxyl3n8fr1tDkIqYySr/1BLKwtt07wZZsxE2wbXXuek+2Q5EkhpNpTq8sMZ7NciBXQBKCtU6blXaLIIzo+B7DqTSlToeG4/KLR46jSjLT9Sptz2PASnJE72OuUSep6mgbNJu3/g6HUntJqxY1t0VKtW6bDeJDbZCjKCKMIkIi5J5UHMQ/hYiJErwwJKGoG/PMN0MYhDQrLb73Jy+SSBqMTA5SHMmxPF1ieXqVdCFF30iOZrXNgXv2MTBW5LW/OsvwRD/nnr9As9bh7o8fY2j/AIHr8+JTbyAkiYkTYxx/9BDp/O0TWBhykqI2ih/a5LVR8toIr5S/ysHUQ+S0Ic7Xvsd8522GzcMcSj+ME7R5pfxn5LURRhNHyelDtPzKDWIMIQEtv0pCTqNLFqqkU3bnyaqDHEo/Ss1d4mztO2S0AY6mHyOp5Hix9BUgYtA8wKh5lJI7x6o9dYd3K4YmW0ykHmUkcRcVZ5q51tsstq/gRg18OgjAUJLoIkXB2EuffpCivp+0ViAIIxodG0mInvbruvB8HPREUbwmFDlOb+lSkqK+HztoAKDLKSx55/rrzRA3pw3QZ0zi+ja+H5CShnakBt0NURgROAKlMsDdmS9hp0usOVdY6Vyh2l3EiZpEsofnO4SuwNAtMmYfJnkKib3xNVAGetKj4HRdZFlCVmNGrGa1haopaIZGSv3JETYke9H6uiEJFK9XU35/hsUJ6zTdGQrGUdywRcOdIa8fQSdzR/sWCFQpS0LdS9u9QoSHIuJ9RHg7Rt0CGUlo+GGvtt7rA4klXuP/FJHG9pfxw+YGv3zXn8MNysCtyX4+aLijiPQmm0aRj+0vktKOMpr6RRLqnt03vm1ICKFCFOKF9S3NZHEafKfI0w9DlloNsoaBpWpcrVZQJJlyt8NKu4Umyyy1moxnchzrG6Bu26Q0nbv6LFquw0KrSVLTyOoGedNEV2RAMGAlbyndO2QWaPt2vPaEQkLWdxzr3QkfaoPshSFVp4sTBOR0nbrjYKoqbc9FEoK6Y1N1uhzLD5AzNscGWp6DG/qokowhqxuECQCeG1BeqPC5//HnyA9mef5rr6GoMp/+5Y+yPL3Gd/7wh+w5OIzTjQUuyks1Dt23n5HJIfSExoNP3s3aQoUXvvE6+cEcA2MFXvv2WUYPDd2RQd4NUY9kYnPQPI4k/MiLF6aIiETU8yTFDYtVERp9xhinq39JQR/lSPpjPX3hiBCfAH9THYYQb33USKwfPep5rO+vi9aQUwwnThB1x3BaZXJpk0zCQJElVEXG8wMMTcV2PbqBhKVENLsOr1+ex1BlDo70EUYRsiwRhiGKLBNGEa7nM5BNoSoKe6y72WPdjAxhE57rEwQhYRjvU1HlXsbEJ0JwT/pLPFD4FZq1NuXVJhnNIm+m8P0A1/ERgKzKKIqM63iEYYSixPuRJAnf81m8sswzf/RDHvr8vRy4Zz+6VCDrn6DRaGKlTfpGCrRrHeYvL5HMWozuHWJtrkw6lSJyI5yWT6DZcRZnrkwibTK0fwBJgje/e46BvX3sPTaKaW0a5DAMaXYc/CDc8NItU9sQs3+/uD6ik4WKpd58ZC8IQxzXx3bjjIihKeiasqG2BHFEKUkaHX8VWei4YZP1xrE7hamO0p94gqnav2au8cfkjPuRhIYblAgjh6R2iLR+ND4fIRBCxVL3U3fOkFDHcIMxFMlCk/vi1L9IkNIPs9x6inL3BRLqXjr+NFX7dbywfsff768Tfq+Zzw0qBFGHMHJwgwpOUEZCRZFSCCEIwi5BZBOEXfywRRi5uGEFJyghUHpjSgIvbOD6ZVQ5Sxh1cfy1XoZSRhYJpPfAUieEhCbnEEKl3HkeXe5DIKHK+V4a/MaZ/pbn8szMFP/g7gfIGwm+cuEcfhjy+Ng+Hh/bTq35C4e3zwg/OXGIsMfyJV8XyP3aiXtu/X0RJBWTZHKEEbPvto0xfMgN8mq3xdPTF0lrBscKA3xr+iJPjh+i5bnMtmp0ff8GRi6A3774A769cIH7imN8af/9nMhtpuWEJMgNZDbSy57jo+px5IEQBH6AkOJoOgwCAj9Yt30bCP0Q1/HIFFKousonf+kRisPvb15WFipH0h/lbO27BJFHUR/jUOoR6v4aL5b/FEvOMJn8CBm1jzeqT5GQ0zhhB4nD1+0pwgttumGTtt9g1Zkmr42ybF/lxdJXSCkFDqQeRJcsXi7/R1RJZ0CfJK+NMN89z0z7LIpQsdQfzfxvq+twaXENaVlgGRqmplJpdjA0FVNTcf2A8YEcfRkLzw9YqTVJmTrPnp/CUGUaHQdZkkjoKmEYkU2aDOXvnKP23GvXmJ8pUVltMjpe5Ni943RaDi9//x0812fiyDB3PzTJy89eIPBDjt+3j3xfirmrazz3rbMIITh81xiH79rD9795mvJag4MnRjl2915yxRRhGOE6Hp1GB1VTEALWZsu88vRbWGmTUx8/hu/6NGtt6uUmT//O9/lH/8evcvrZt5k8Nc7afBlNV0kVksxfXMLuOMxfWuLn/+nnyRR3H98o1zv8d7/9FO9OrxAEIYos8Vu/8kk+9+ix93Pb3hfK9TZf+8E5/uz7Z5EkwS984hRPPnKUgfzmeVjqEGPJT7LQeg43qrM//TnMmzRE3QyqlGPA+mkUKcls4w+Zb/4JEKLL/fQlniCtb78WklCYzP0Tpmq/zcXyPwcExcRjjKZ+AU3OoskF9qS+hBusMVX71wRRi5R2lLzxAKYy+j6uzE8ec43fZ7H5dbr+QpyCByrdl1CkNDnjAU72/wtAsNT6JnPNP6HtXiXCAyLOrvxXSJJJSj3I3YP/BkVYKHIaQx1mqfV15pt/2isnGFjafvZl/j4F86PviXIzq9+Da5WZa/4Ry+2nUaQ049kvM2g9iSbfaJCTqsYn9u7n65feAQT3DA4znrl9ClXBe2fSa3jtjYzDpeYsSSXBscz+2/rsh9ogh1GEEwTUnC5nSkuEETRdl4V2nYbjIISgG2wW4KMowo9C3izPs9StcbGuM9OqbDPIMTZvxJEHJjjz/Lv8h//tm6TzSe594gSaofKNf/VtRg8M0Wl0kWRBcSjHxTem+M4fPM/Eqb0cf+ggz/7py/TvKTB2ZISh8VsTfVyPjDZIUi0iIyMQZLUhHir+XG9kQkUWKv3sZ3/ybgQSstCQhMzH+n4ZqTeCol7XSNINmrxTf45PDfyXWEqWc7VnyKqDHEs/RoCHQO6xSAny2hAIgSxUZBTy+sjGkP/7ZfFah6GpjBYzWIbGcrVJs+OQtUyySRPPDwjCEFlISEIgy4KUqZHQVbwgoG27WIZGphcRNnvG+b08SLVKi77+DKc+MsHslVXOv36NwdE8nhPwc1/+GIoqo+kKR+/ey8Uzc9hth7XlOisLVQ4cG+HkA/uRFZkXv/s2QhLsOzTE7JVVFEXmwY8fQTNU8gNZMn0Z9h4dRdEUIiJGDw4xtK+fgfF+6qUG02/PsTy1QrvWJooiTn7sKNfOzjB3cZED9+4n8ALmLy2RzFnkh7J4rr+rNCD0JBnDiCAICcIIQXTT7X3Xp1mN+yXCMIyjBEXGbjm0Gx3sts3gvn46jS6JlEEUxZmBMIhwbZdMMYWi7v5a8fyAqwtlvvKd0zS7DgL44++8yZF9AxSz1kZEIpCxlAEmMn+HKIqouVcIZBd5y8tXoJAzPsIDw/8BVcpxPZNXvdyiVe+QSBlki0ny+uM0a2MMp1WyBQtJUqks25RLClF/B9PSWVuo8Nw33+LkI/s4eOh/QSlGVFYbuHUBUrE3XSFhKAMczP9WL30a0ag4tFY9cimNfH+WbtthdaFCppDgwZFv4LRUygsevrdCKmuhmxpTb8/z1g8v8dBPHePUxP+DopisznXRtCrpvIUlHWVEHabTspm5uMToRD/tps0PvvEGI/v6mTy5h2TapFZqUa+0yPWn0Q2Vv/i957n/E8dIpEyy2ccoFh9l5kKJN0rvcujuvSTTCcIwZOHqKmbiSe4qfA5Zi7Nt61c2NqQ66+/CmMTkcUL8bdv1KvTIwiSMfK5W/2/coMJE9r/GUicQQsILalTsV7hQ/l+5f+gYhhhkf/Yf90bvNmUJVSnNeOa/YCz9pW3NhQCaXGAg+QWS+uPUnDZOEFKxVVKaRte3N0tWRNi+z+VamclsgZ85eBRDUQh6vQcXqms4vs/+TJ6UdnNeijCKnxshuCFSvhnerF3A6dmdxe4aB1K3T9P5oTbIuiwzns5xT98wsiThhwFpzWAimyfovXRkIbZdeFlITKaKLHSqjCcLDCc2xygkWSI3kOHJLz+OlYkXSnE0zwOfuQu77aCqCmbSAAH9Y0VMSycMI3L9GYLxkKH9/QhJIpmNP5sfzCLJEomUSSJ1Z0w78XdVtmn9yijIcnLbYwMKUW8GS8SvW0wl3fv35v/XoUo6g8Yks53zyEIhpw2RVvvQZJMIc8t+I+QtHZCi9+Dttt/3isFcioxlIEsSjucThCGqLKPIEu/OrdLsxulWx/NJJww+emw/kiQ2DIwsCRRZZnatSqPjEIQhtuORMO6MmjIMIzRDIZVNICSB3XWRFYlkxiSZ3rx3mq4iJNEjrffwvIBcIUkybRIEIY1qG8PUsJI6B4+PMrQnfrEIIZBkCd/xeO3bZ7jvU3cBoGgKmqmhqDJ226G8UMG1PaIwvst9ewq88Z0zyKpMpi9NMptgaH8/KzMlrFyCdN6itFBl+u15VmdLqLoaG3z1vTlM775yGdf2aFaalBarFEfypAsp5i8uMX5sNF57ESxcWaGyVEVIYCR0wiAikTE59fjNI2/H9anUOzQ6sWpXBNSaXWotG88LkHWJpjdPxX532+fW7DOY2SLaFiERIQSyMJClG7vOl2dKzFxexvcC9kwOoBsq77w+g+OELE7N8+CnT5DOWbjtgKvn59HNNR76qRPYXY+1pRpECqpcYG26wuwVO9ZVVkKyhfXISUbrEZXMXVlh+mIVWZYZP5ylvhJy9Z1LCCE4/cNLPPaFe3jpW2eplVqYSZ2B0QKDewu4jo/dcYj11fs4/fwloiiitFxn7MAgxaEsMxerLM+WOHDXGEIIfNenstKgbyg+tmN7tJtd5q6scP7Vqzzxxftp1bsszZRYni1z8qEDjB0cJPJbON0qURhhdxzOv3oV1/ZYnC5x3+NHGNnfj6rtbg4UydpgJNsNXW+ehnOOYuIx+qxPoIg43e2HbUJcZhu/jx81iOjfUSVKCLmnMnVjhksImYYreKfiItBQJYkwgpVOh/PlFdqeS0rT0WWFMAqxg4CimeC5hWkGE0lGkxk6vsdqp0XRtDbKW/WuTSZh0HH8XjNibHyjKNpw+lebbY6PDmB7Pook0XZcdFVGEoKu59N14nLhWDGOwO/OHur1M0HZrZNQbj8j8KE2yBnN5P6BUfrN3kLpdVOnevOS65HAesQken//pYkHeGL4MEU9yZi1mXoVQqDpKv17NlmyVE3Zpq8aNxFFJNLmtv0CJJJGPAze40k1LH2j4ehHR9V2fdVu+79v1aWpCI1h8yBptQgIEkq6p5Z08/3ezr7X8UZplrdri+xN5nls8OYjSrqqoPciqqSxdeRDcHCkyEghjampKD0jnU9tetRb7++4lCeXTKAqcQ36veDapWUWZ8tIksTASA4hiW2d8fVKmwtnZrny9iKe43Po5B7MhMq5169Rr7bpG8oweWyEt1+fpttxGdyT32YYkzmLx3/xETRDRVFl+kYLpAup2MkDcgMZ7vnkSXzP567HjqGbGoqqcPcTJyCKyA1mUVSFUx8/TqvWRtVVNFMjlbd49GfvR5Ik8kO598XTqxoq85eX8GwP3dSwOw71UhO7ZWOkDBavLNPak2dpaoXA80nlk/huLGun6AriFsdWZAlDUzaeIwBVkTE1ZVMsPuq9nMWmAbjT7mMrk0BRZFbmKuimRjKT4PLZOVK5BJ7j49oea4tVVuYrLE6vkc4lkeXYATMSGgOjBVRVJpWzAMHaQpVkOsHw+I0MevNXV0gkDQbHCuSKKZbnKtTWmpx46ADV1Qbl5TrtRpcwCEnn4k5jz/ZJZkzSOYv+kRySJDF3dYUTD07i+wF2x6FRiXs7sn0phsf7EJLA6n2/wlAG09KprNRZnF5jabZEsxp3eEdRxNB4kUa1Tbdt49jbqUxd2+fS6dnYIbG9WA0pCPFCm5o7hx00GTKPbYxF2mGDIPTxIwdDTmPswuu/noq2/SUcfw1Z1fADl7Y3Rbn7AqYyHNek32P/iSwkwihivlXnvv4RrtYrlOw2C+0GqiSR0nTCKKTm2HR8jwvVEmvdNpaqkTVMOi2PumuT1DQq7S7lWpukofPOwiqSEARhSKPrIAkJU1PoS1tkEya1dpfFaoPXpub52OF9LNWalJvxmKKpqxiKstGTIYQgp206FKas34rxeRs+1AbZUBQMZfdGqd2M4OHMIIczt08ftxPP9k77FtJ1xlGIO7oZPwlIQsZU0htR9I8aQRRypjrPdxbf5eODB29pkLfi+muaSybIJRO7bL19+5SpkzJvPud5K6RzFpmcRa6YpNCfRghQt6RfVU1meKyAmdBJ5xLk+pIk0wZCkkhYOqlMgpG9CaSesUllE1ipTe9YNzX2nxwjCqP45aolNjIxAImUiXnQIIoipC0pstED2wlmiqN5iiP5jSRFImUycdf4+zr3dQzvH0BRY4OpGSphEGK34whxYG8fqqZgZSwO3rsPWZUxLYO1+Qrdq8vQGxvUb3IfVFVmtD/Lg8f2cvrSApIseOTkPkb6s73ueNCVLHlxBGVLrTGIHDTp9psiwzAkjCLazS7l5ToH7xoj15+mttZE1RSMhMbyXJnKSp1u2yGZTsRRsKEiSRIL11ZJpI2NtH2j2qFWau54rFTOorxcZ/7KKiDQdAVVV5m/ukK72cW0dDRDi7v1cxayLIOIsy2aoWL1MjDZYorSUo3KSoOhvYW4d0VXMS2DdC52TnQjzqaUlmr0DedoVDusLdboNm1CP+4Yl3uOSKPSZnRyANf2KC3VmL+6SraYZmR/H4XBDLW1JooaXwtZkXuyrTErXN1dpORMkdYG0aQEfugQRG5cztrFIKtylv7EJ2m47zLf+CMkSSOKQsLII4xcxjN/Py4tvEfFLktVOZgrktNNRpJpJCHwwpCRZBpdVkiqGgJB23exfR9Nlhmx0mR0naFEioSiktXjrmvhwUq9heP5LNWacZClazS7Nl3Xj6Nk4vGplXoLLwiZKVVpdIdYqNRpdG0sXSehayCg2Y07v6MoIohCnNAljEJW7AqGrDOauL0o+UNtkP8WHzzU3C6z7Qor3QZuuDORwwcRCUunOJBhZLxIIrkpPpLv23RcEkmDA8dunMstDma2GdFj944T9uREd3Tm5N29tN2cveu3+XGNq6YLKVI7TAOsR7SpXBJEvN061J5h0RNx78HNIEsSw30Z/u6n7+HY/kEkSfDA0TGGi5v0gqpkoUrbaVEtdXCDA/l2IEmCQn8aju8hkTQwLZ0TH5lgcbqEqspkCkl8P0BRFQoDWZLZ2ChqusrR+/fFWYYIJEmifySHosrk+3fW4R07MEgURjjdOBLNFJPsOzJMdbXByL5+CgMZJk/swfd8cn1pJElsZEkmjm82gR06tZfSUo1oIKJ/JE86n0TVlG3OGcDkyT0b1yaVSzB2YIC+4SxhEKGqCscfmECSJYbGixQHM2i6Sq4vRRiE8X0yNE48OMnidAlZlsgWk8iKRBBKaJKFK7UpO1OsOZcII5eUOoAimXhhd5vAyw3XXGj0W59CV/rpeNP4YRt6pDQJZR858/73paGsyQpDiRRDiXjtHcj2OKqv45lYl0DcyGD2fp9QVQYT8dpu2g77+nLIkoRlaHh+QNLU8YOQ+UqdKIoYzqZIGjp7i1l0VeX+iT2kDJ2RfJoR0iQNnaShEUXQdjavS8WtM99dI4oi5rsrDBl9jCZur4dIRDfr8Phb/C3uEG+UZ/k3F3/IxfoyPzd+D79x5PH3tb9mK641Jq2dFLp6Wslth1bbQdcU8rntda7L11YZHshgJW4ePc9cWcFKGmRyFqr+N8dPXa02+a1/9RTvXFsmCGN98f/+1z7J5z/6wZUWdIMm9nV0kav2aUYSj2DegdxmEIQQgazEBm1dsF70ioVCCMJerX5rmj+KIsIgQpJj5yjwg40egN2wdd/rPQZRGN30MzshDMI403YLpywMeg6fJIjCaFupDNhwCGHnTOFO1yIIXbpBnba/Rjeo44YdkkofslBJKAW6fpWEkiN5i7G2Dwvia9DjMGCzs3quXEeSBMPZ1AbPwTp2ui3rDsB6Zmyus8JcZxVZkqi4DUbMPu7K3t5s+gf2zRNTPAbU3S5Nz6Hru3jhJmG3JslYik5OT9yUCMEJfNbsJiv2jemmtGrQb6TIaLduuGp5Dqt2g7bvMmCm6dOT+FFI2WlTd7vYgbcxt2bIKlnNpM/YntqZaVWoOG3yeoKRRBY/ClmzWzTcLm4YP/SGrJDVEmQ1E0O++axoEIa0fIeq06HtOxvXR+1dm4KewFL0m3YIluwWq3YTVZIZMNIkVR078CjZLRqejRcGcepWyFiqTkG3Nq53FEV4YcCq3aQbeNiBx4urU8y1K7hhwFK3zhvl2RuOmdNMBow01k3UtwBc1+fS1RUc1+fgxAAJU0OWJSrVNhGQSRnoukqp0uLK1Cq5bIJcNoHj+pSrbVRF4lvfe5svfOauWxrkvZMDN/373xh8wEooO6EblFjtntmmmrTWfYt+4xQmt2+Q5euM4U7Zh53q7UIIZGXz97fDsHf9vm+VCdkNt2vAt253fakMuCGqvh47XQtZ0khKfVt0yjf5CECQ/BCy5N0MW0uKW6/EnkLmuu1uYz9b/j1oFBgyi8hCpu137qj/4QNpkKMooht4XGmu8cOVq5yuzHGtWaLstPGjEENW6DNSHMsO8/jgAR7pnyCt7qxxuWY3+erMW/x/V14mjOK6UhDFJAkP9+/n1ycf4tGBW3PZXmms8rtXXuJsdYFfP/AQvzh+H1PNEn8xf56X1qaYb1dxw4CkorMvVeSJoUP8yuR28YXfufwC35g9y5Ojx/mHhz7Gsl3n67NneGVtmrLTQhYSe6w8j/ZP8PGhgxzJDGIqO6fp3NBntdvk1dIM31+6yLv1ZSpOmyAKKegWR7PDfHr4CPcU9tBvptCknW/1s8uX+PdXXmbATPMrkx/heHaYc9UFnpo7x5uVOSpOG1lI5HWLk7lR/rO9p3i4P56pC6KINafNv7v8IlPNEjOtMhW3g9dLVX9t5jRfnz1zwzE/PXyEX598mJP5m9MyVhsdzr67wOpag07XZe9oHkmSeP6Vy0hCcPTQMIcmB0gkNAwjdl5c12d2vsyzL16ikLOwnfenzfs3Ex9sq6xKFhltH/oWaT0nqCDfggv6b3H7WO8iDqM4mlYVGWVjZPD69s73RwL0nxpkSabj2z1BoAhDvv11+4E0yCERF+rL/IOX/piWZ8fpoi2LpOt7TDfLTDVLPLd8mV/Ydw//+PDjaJJ8g1FOKBoH0gM82j9Bw7OpuR3m29Vt88l3Ai8MWOk2udRY5b9942vMt6vxjFvv7xW3TdiMmEzv7k2W7BZfmz3Nn8+dYbnb3PisT8il+goX6su8UZ7hVycf4lPDR25geomiiIv1FX7n8kt8b+kCXhgib9lmzW7x/aWLPLt8ic/vOcHf2/8AR7NDN02DtX2H+XaN6WaF3774A5qeveHZeYQsdGpYikbF2RRBCKOQqtvm5bUpvDBAkSQSikbbcwiJsBSN9A7Zi5yeQJVvHXUMFNPsHc2zZzjLJx49xFq5xTPPX+DTjx2lr5Dk9//jK+QyJsnk5jEqtQ4Xrizz+COHGB7I8G//8IcbI0R3ivVqznpqKyLakUo3vqxio5v+TkgFNo+xnvra/Riix7z2frr2t75v14+9zkq0LTXHZhT1Xo8XhhFBuLPYwvWQZWljnZtK34Ye+Ca3t4a+g9JVFMXHWP/ukiSQet97+/lt4bZfP8f1ayrd2T37oOD68yPamQBz65pcv5dhFHFhcY2u67HW7HBsdIDxYnbbvsMw2hjfkYRA2pJKv9naiY/5/tZrtOWe7fRMrO93fc3slpaHuHQRbfmcfJOSQHzO4bbjxRwIOzsl699z6zqvBU2eWnyehc4qQRTwaN/dfKL/vtu6Bh9IgywhKOhJPto/ScVt83D/BMezQ4wksshCYqlb5/vLl/jm3FlKdos/m3mLn/66oR8AACAASURBVB49xmSqH/U6zdSsluCJoUN8dGAiXjxE/LPXvsrrpRtTqbcDO/A4XZ7nhytX8MOAX5l8kHvyeyjoSezAY7pdZrXb5FhuZ0lFgNfKM1xsrDBm5fjNY5/kRG4EXVJY7NT52uxpnl2+xLnqIt+YPcOoleVYdvu+ZttVfvfySzy7fImUavBw/36+MHYX+5NFJCGYbVX4+uwZfrBymafn30YWEr8++RATt3ASvjF7hrbvsC9Z5MnRYxzJDmJIKhW3wzu1JUxZZTK9WT9SJZnD6QH+6GNf3vjdV2fe4qszb9L2Xf7OnpN8+cAjNxwrpiy9vaW3VYc2DCNcL8A01A3KzOsf1DAM8fwQ01Ax9Hi79xoQhlFEtdnlzQtzvHNthasLJWrNLs2Og+cHKLKEZeoUsxZDxTQHRvs4tn+Q/cMF9JvMdG5FFMFyucHbU8u8M73MzHKV5XKDdsfF9QMMXcUyNAYLKfaPFDl1YJgTE0OkrPfWHKPI8kaa1vND5ldrvHjuGm9emGel0qLe7mJoCvm0xcGxPh48vpcTE8Nkknd+vGdev8QffOt1lkqNm2433Jfhn/3Sxzm+f7Ob3I+6lO0LzLd/wET68zhBDV3OcL3u6fRShX/5p89z7uoSAL/2uY/w6Y8copix4gad1Rqvvj3LW5fmWSw1qLW6Gx21w30Zjoz388CxvRwdH0R7j7Pbf13wgpBri2XOXFrg4uwas8tVmh2bVschAnRNIZM06c8lGRvIcWhvP4f39jPan0UgsHSNrucxOZCnmNo+zbBWbfF7T7/Od169SBRFfOahI/zcx+9ibDAeEw3CkMVSg+feusrpSwvMr9ZodhxURSZt6RSzSSZGitw1OcyRfQMUMrfP+w5Qb9mcvrTAmxfnubpQolRv07VdZFkmkzQY689y4sAIj5wcpz+XRNnFwQ+jiN/437/K9FKFMIz4xH0H+PknTjExsnPp45V3ZvjKd9/i7allAFKWwSMn9vGbX3p8x+2DIOS501f557//TK8eLfif/5vHOZk5QL+epx10KerZLVTHN8cH0iALIRgy0/zm8ScIwpCkqpOQNVQ5ZqwqGhYF3SIha/zLC8/S9lzOlBcYTeS28VJD7N3osoK+xQCokvyex5G6vselxgrDiQz/06nPsT/VR0rVUYRESMShzAB+GKDfpP7rBD5j2Tz/5OjHOZQZxFRUJAQZzaTffBRVknhq7hzv1pf53tLFGwzyN+fOcqYyjyErPDl6nF+e+AhFPYkuKwji+ew9Vo6UavD0wtu8sHqVPVaOfakC0i4jByvdJk7o82j/JL86+SADZgpTVon1cgOOZYcQCBLK9llhRcjk9C2jO4oaM2shMGR129/eC3LZBBeuLPOdH7zLvrECxw8N8fWnT2MYKkMDWYIw4vy7C5y/sEguk8AwVEaHsjz17XMMD2bxveCOuGQhftlML1X47quXePn8NKVam67r4bg+fo/vOup1g8hSi7mVKuevyjz/1hSmrvL4PRP87OMnGe3fnaqv3ury6juzvHDmGlcWStRbXbqOh+sFGwxlUbQZmcyuVDlzZZHvvHKB/SMFfvrhI3zi3oN3HH2osowsBIulOt97/TLffuUiq9UWHdvdOK4QgqVyg6sLJV48e42j+wb53KNHefD4+B1dR8cLqLVsqs2bq4NZpobvb0YYAoHtV6g679JnHMcNW9Tda6TUsbj7essaDoKQRtvZOMbscoWu7bHsN/j2qxf5/htXWC434vPzAvxeJCMJiaVyg7enlnjm9cvcf2QPP/PYCSZHf5x10hDXm8Lzp5DlPgztFGHYwvbOEgQrWMZnkKRbPy9eEPDyuWmeef0yF2dWe2vHx/V9giAi7J2jEILlcpOphRJvXpjH0BWGixk+8+BhPv/R48xV6ji+z7XVKooskxoobH7TKKJtu1Qa8WzzcrlJ142zisuVJs++cYWnX3yHld7acb1go5FspSKYXqpy7soSZy4v8IWPHeezj9yaqjWKIhzX59uvXuSvXr7AwlqdVsfB9nx8P4hFgxCsVJpML1Z4/cIcT/3wPI/dPcET9x9k7+CNwjGSEFiGTsf26Doei6UGpVp7V4M8u1RlbqW2sZ5aXZcLMysbjv/1j1qz47Cw1qDSiLfXVJkhs4iRiAOBFbuCIuTbriN/IA0ygCorDCeyO56GLquMWjmO54awFB0v9FmxGxsP248TIRGSEPzC+H2cyI0wXasxHzSoOw6lTpsgitB7c4ZP7j+ELt+YRs9qJkezgxzLDW9r3NJlhUEzwwPFcd6tL3OussA7tWWanr3RSFVx2pyuzLNmt7ivuJdH+icYSWS3GR1DVhlMZPjUyBEuN1Z5pTTNO7UlZlsVxlM7L0QvCtiXLPKZkaNMpvuQtxpuSd61lv3jxsR4H/mchQCymQRDA/FPIQTplIFpaGRSBmMjeVRVIZdNQATZtIVpqBw/PEyxcPvzqx3b5ZW3Z3j6pQu8c22JUq1NsFvKO4rwgwg/CLFdn2YnnkVMJYwNspPdcGlujT9//jxvTy3T7robqcHrEfY6fv0gpOt41Jpdyo0OtZZN1/H57MNH7swgKzJL5SYzy2/z9Evvslxu3HB+URThhgGuF9Bs29RaXert2GH4+L23r2Q0VEhz3+E9LORTdByPju3GP7subfvmtf2QAC/sEEQudlCh7S8R4nGrdMdyucnMcoWzlxf5zmuXWC438IMb3wtBFNJ14mtab9nUml1aHZdf++wDjA/fuSKY7Z6DyAMhEUZdZCmLJNJ0nReBAF07harsBaEQRg5iQ/9aQiAThGV201JeRxRF1Ns2f/b9s7xw9hrTS2Vand3XThRFhH7s4HXwoBUTWRSzFpIQjOTSqLJMpd0hc4sZ/mY7ZlKbW6ny9Evv8q2XL7C41rihJBFdt15t16eYvXV0HIYRa/UWX/nuaX54eor5tRqud+PYZMTmOa07DJVGh8VSg88+coxTB4Zv6C4fG8xyfmqJruNRbXQo13fXHp9fq21zID0/oNLoslZrUcxaNxjWRttmtRo3DMuSxGhfFlWS0SSZfdYwe8wBTPn2BTU+sAb5Vl9flWRSqkFGM1izW9iB/yMRtb6d72UpGk8MHcJUVMIoYrpeo+26rFcq3CAgpWm7RmY5LcEeK79jF7UkBPuSRUYTOd4sx01VS506qUxskK81y5TsVs+AFphM7a4mcig9wIiVhRKsdBtcbZZ2NcgSgvFknhO5ke3G+K8BERElZ426WyOn5dkzXIijRSkeQUhamy8PIQRJS2dwy4xoFEWkNwg5bp+cxfUDnj89xTeeP8/bU0t0bK+3B0gnDcYGcwzkUiRNHUWW8IKAVsehVGuzWKpTqrVJJw2OTwyRvkWKdyCXwvECuo5HRCyKMdKfYaQvQzFjYZk6miLheAHlepvLc2tML1VodV26jseFmRW+/gO468Aww8XMBqnGrc/R5/nTV6m3bVaqTQYKKY6ODzJczGCZGkEQUm12uDS3xtRimWbbodlxOHN5kSiCoWKGA6PFXWtqWzExUuCLHz9Juxun313Px/MDri1W+MO/eoOus3sfhyHnKBonaHqzuGGTgnEMrac8dDPMLlf5xnPnmVoos7BaI5M0mRwtsm84TzZloioyHdtjYa3O21NLrFRbBEFIpdHh5fPT5NIJfuPnH92oQ98uPH+WIFxDoBBGXSQpgSSy+MEqspylbX+flPlZBCpigw5XgJAQt9GsFkYRHdvlD7/1Bs+8domlLY6GpsoUMxZ7BnIUMxaGHhO72G7swK2UmyxXmjiez1Ahw9F9QyAgYxq8Mb0QM+SJnbWm19Fo2yyXm7x+YY6/evkC86t1kqbG+FCe4WKGhBlL3HZdj0q9w8Ja/DwUM9Ytsw5RBCuVJl/7wVmefvEdSrV4iiKV0Jno3bt8KoGqKvh+QK3Z5dpShXeuLdN1PJbLTZ5762pPllXhyPj2iYm9gzkShkqlAdVml3J9Z83qVtdhpdKk3XVR5LjBzfMDbNfj2mKZQjoB8o0GeaXSAmIWuonRArIsmO0s0/JtUkqCEfP2sy4fWIMMcepwqVtnvlOj4rRpeQ5O6OGFIUEUNxp1/djT/kkYY4gNZko16DdTCGA0lY6jmCgioaoI4u5jS435VndCQtHI3mTUKquZG81QbuhTctqs810tdxvYvYa0nJ4gf5OUcFLVN8anWr7D6g6jX+vQZJmslritEbAfJYIooOk1WLIXCaOQ0cQYSSWJHdisOquokooqqbT8JpqkYwc2mqSRUlIs2Yt0gg55rYAiYgH4nJYniAIWuvOMJvYw3Z7CCz3yWoG8lkeXdzeUZy8v8K2X3+Xs5UUcL1a+SSV07jk8ysmJYUYHsrGxNOLxKz8IaXcdqo0uy5UGi2t1JEli72AO7RaaqSP9GR44OkbS1MgmTcaH8oz0ZRgopMilEiQMFVWWcf2AWrPD1GKFF85e45Xz06zV2rhewOW5NV46N83nHjmKcpssZevG3NBU7jk0ymN3T3Jkbz/9+RSmrsb0gS2ba0sVXjo3zcvnZ1gs1ek6HueuLvH1H5zjH33xEVKJnefCtyKbMsnuwOF+9soif/q90zc1yJJQMJUCqmQRRh6ypCOJW0tGrlablOpt/CBkck8fD58c59SBEfb0Z0lbBqoixy/xSpNzVxb57muXeHd6BT8Iqba6vHD2Gp975Cjjw/ltjZK3hk8Y1gGJCJ8waCKJBrKcRZVHiKIQ3ke3ctd2eeb1S/xlz2CFUZypGxvMcergCIf39jNczJBLm+g9pjXH82m0bUq1NkulBvVWl7GBHPl0AojQFJn5cp2sZbKv7+ZZgWqzw3OnrzK9VKHWsrn74Aj3HdnDxEiRgd7akSWB7fnbnICBfLyeb4Zaq8Or78zyly++y1otjl7Hh3I8etd+7jowwthAlkwydqb8IKTRtplfrfHau3N87/VLrFZbVJtdXjwXO1TDxTSZ5Oa6GxvMY+pxhq/e6lJtdGLRmuvez4ulBrVmlyAMGS6mMTSVqcUyrudzeW6New6N3iCpU2/HRhxAUSQmR/swFB3NV6m4K8x3VhBAQd+ZVOZ6fCANchCGVNw2b5XnOFNd4EpjjVW7gRP4hBsddxF24NPwnDuuEb4fSCKeM14/Zt5MbNNaXsdWppjrIYv/n7v3jLEsT8/7fiefc8/NsXLu6hynJ/WEndnZ3Vkud0dMYqYoUqQMEIY+CLAsQIBtGIJgwLJM6INhm5ZFWCIB0iR3l9xdbZhdTs6hp3PuyrluTif7w7l1u6uru6p6pmd3xs+XRt9w6p5z7/m///d9n/d5BORtslBVkru98NDRyu0+1/acbiauitI9x5m6xxJlVFHC9f1Nx7kTiiihiNJP9VpCuJGyfItVa4WKU8ENXPbFDqCJGlKHoFeyi0w1bhKRTepuHU1UORg/TMUps9ReouJUiEgRFFHBDVwkQeRm4wYZNcP5ylliShxDjuBvUxKsNdr86N0rnL2+2A3GmUSEJ4+O88JTBzk42rOJZXonfD+g3rKoNy2SMWPHrFwSRZ45McGxPX0U0jF6MvF7anCn4xFG+zL0pGM4rseP3r6MHwQ4rsfrH93gSw9PYu4yILsd95o9Qzl++ZkjPHVsfMvfTcUiDPemGcgnkSSR7795kVrTomU5/OS9q3zt1H72Dud3LMt/Ethejbozz2D0WQCm6y9iyr3IgrHtRsByPMBjpDfN107t5yuP7iOf2tyyiEY0cqkoY30ZRFGg1rS6pJ9Stcl7F2cZKCTvy+FHFMxOpisgoAAekpjF9VcRhQiasgdBkHHcORx3DklM43qrBIGD7U7hesu43hKKMIRwhyKZ43rMr1b4yxdPd4OxQPgdfuXRvTxzfILBQnKH6+LSaFrhewXw/IDptRIxQ0OTpR1Z5pV6m9fP3ESVJY7vHeAbTxzg4QND9zRxCQJo2w6O622rre77ATcXivzw7UvdwJaKGXz9iYN89fH95FPRLZ8rHY8w0ptm73Aez/N58d0rrFcaLBdrvHdxhqN7+jh1eLT7+oF8ErPzOdu2S7ketifuJCpOLax3206jfRmyCZMbC+tYjsfVmdWuiMytcww2laxlSWTPYBZBDFBEmV49Q91tff7Hnop2kxcXLvGn195koVlhOJomp8fIaVHiqo4hq2iizEq7xg/mL3zsEaaPizt/t3fVtd7m/W4Q4AT3Dg6u73fdqgSETZm2IkndETA38HEDH2mbnbfre3iBjyiIyOK9WaQbIwo/bfi+hyhI9On9aKLOXHOGiehm/WsncKk6VVpeCzdwqQc+lm+R0XKIgsSatYohGYiIXK5dIK1mSatpAgJiSpy83kNcjm9yzroTF6aWOX9jiUpHGcw0VE7sHeQf//zD25KzNiCKAnFTJ34f7OeJgd2LXAiCwN7hPI/sH+Kts1NUGm08P+D63DqOe38SpQlT5/FDIzx2eGRbI46JgSxfOD7O9GKRdy7MdBagFq+fmWIgn/xUA7IbtKg5czTdVURkqvY0ae1Ob++7Q1Uknj42zjMnJrYE49thGiqPHhzm6uwaU4uhMpjluJy/ucQLT9+fX7QsDxDghzO7gkAQ+GjKBPXWDwgCFxDxgzaeXyYIHAJsPH8NEPD9CgIanr+OHPRuCci1psUHl+e4MrPafSyXivKNJw/ylUf2korvTARTZQk1Hune434QcHlxjZRpkDKNrjnCvWB3+rYnDwzxC08f4okjo9uuF4IQ9qsNbfvj1loWF6eWOX1lHgjH3x45MMzPndpPLrk1GN+OXDLK1588wLW5NUrVJn4QMLtS5o2zUzx2cKS7EcgkIiSjYXXEcT2qjTar5drWgLxYotYME7zBQpKhQgr9bRnbcbk+v4br+ai3lfUdz6dSb1Hu9JwVWWKsP4uLS9GuUtDSHE5MoN3Fr/le+MwFZC/wuVxZ4k+vvcV8Jxj/0b4v8FhulKRqdFnCQRDwwfosry5f+6kH5E+KtudQc9r3fL7mtmm44U5NESUSyq0bLqtFUTuBteZYVJ02Oenui47je9Rdi5ZnY8jKtmXynxXKTpnLtUu0vCYpNUVAgBe4tLwWltfG8kP3FUUK2wGKIOP4DtfqV6m5VWJyKB5hSiayqLBsrWD7Nk9ln0EWFQ7Gj/Bh+T3qTpXJ2D6y2uZ+zgYf5pUPr7NSvFXSH+vL8Pyj+3YVjH9aUGSJbNKkL5eg0mgTBAHFWhOvw/re7YZqYjDHnsFcKIy/02sHspzcP8h7F2e7rZm3zoVl3VRs+2z1k0ASNCRBZ7n1Pqpg4gc2Arvzuu7LJTg22U9/bufvbiCfZLCTDXu+j+v5zK6U75oN+RsayYRtqYDwMdf3UcQJDC3cSIbVsfC3lYz+V4AHhOROVR4B40ubjq0p995oBAGsVxq89P61TY8/fWych/cPkojp+IHX+VQhC9nv/NuhNuHj4/o2AQGqqCEKoQjIc4cmaDsuhiIT1XfO4iKGytPHxjm5b/CBfe8zi0XOXV/E7mwqNUXihacPEjfvLvR0JyaH8gz1pLg0tUyjbVOutbg+t0ajbRPrqPNJokhvNkE0olGqNqk1LVZK9U29bT8IQo5G08LUVbIJk95MnHwqxsxyifnVKvWWhdHpzwNU622K1Sau5yOKArGITm8mjo/PpDiE47tUnAZm4GHKu1t7P3MBueHazDRKzDSKxBWd3x1/jGd7JrewfP0goOXZVO3tRyo+iyjbTRaaZfxO5no7giBgoVlmuVVFIOwDD5i3FpY9sRwxRUcA5ptl5holspq55ccbALONEsutGo7vk1IjjEQzfNrYmBv26Sii7RAoNs6/4lTwAg9FVCg7Za7VrzDXnMUKLAp6Dwklge3ZmLLJqrWKKqq0vCa2b6OICpIoYcpRMmoG27fQJJ3l9hIXqudpuU1SavoenyMUQLgys0q1U66SJZGxvgwn9w9+Clfok0FTZaLGrXvB9Xxsx+1q8u4GQ4UUvZnduX2l42H5OhpRqTYsggCuza3RtOxucPo0YMo9DMe+TLF9EddvsSf+S+jy7n6/h8d66cnEdnU9FFkiHtGJRTTK9Ra+71NrtLfMt9ueR822sX0PTZRYbzUJBKhbFvP1KiOJFP0dPonr++iyhO355M0oIH/s6xQQUG1YXJpe6T6mKRKPHhymkDOpuxWCwEMUZXzfQxV1LL/VCbwhr6Ll1Vm1FvACl35jjKicQBAEMts4qd0NB0YKjPdn0HfIend9bkHAQme8DkJ+TsI02D/Ss+vqiygI9GfjJGMGjbZNEIREq5sL6xyZuDUu2p+LE+8E5GqjzUqHiLWBtuUyv1qh0bZDElnCJGZq9OfizCyXcD2PG/PrJKI6uhquWcVak7UOY1tXZYZ7kwgClOwq31t4g/nWCq7v8WTuKM9+XoVBPN/D7vQ6BUIC1d1OZKFV4d21aZqes2X2+LOOotXgcmWZ2UaZ4ehmMoUXBJwuznGpskRCNRiLZTdlthk9ypF0PzONIqeLs7yz1svBVB+qsPUa/GTxMpcry0RkldFYhsn4py8Kr3X63w3XpmQ3d1y002qGxzKn8AIPuXMOGyVsHx+xkxXd7n0cBD6CIHIocQSxM/MsCiKr1ipxJUFKSSEgUNB7SKsZIEASpG5P+k7MrpQp11uhGQGQTZoMdZiZnzUIgrBFp3hD8Ga34TGTiNyX0EcsotGXTVBthEHBdjyW1msM5VMYn9I1anlrFNuXKEQeQhQUblS/S7/5JBF5Z8bqYIcEtFsostgVcgl7n+4WiuhHK0u8OjtFxojw/OgerpTWSWrh2rTealFut3l/aYG1ZiNsL6Sz7ElnOgH546PRsllYLW8aExvrz5JLRZltX2SqeTF04ZKTqKJOIAQogoqIRESOYvttml6NttcKs+fAYyJ2GPVjuC4N96bvW+BjOwSE2f+GcIwiSwzeZsO5W5iGtimAh6NKm5nUffkEsc50Rq0RZsi348b8Gs3ONc4mTNJxg7ip059PAtP4fsDVuVUOjvV0y/ulapP18kZAVhjpCTeMJavGkeQEeT1F3W2R1VKfX2EQU9bI6lEUQaLl2fzN9IdMxHOMRDMookTdsThTmud7c+d4Zekqmijjb7l9Pvs4V17gfzn3Ir859nCo1CXJLLerfHfuHN+fP0/RbnI8PcgXe/du2pCIgsALg0e5UVvnrdWb/O3sGVqew88PHGLYTCMKInPNEt+bO8f35s6x2KrweH6M53r3/VQ2Lv2RJFnN5HJlidPFOf5m+kO+3LefuKJj+R5Vp4WIQFTRWKrXma6UsX2PuVqVstViTyrLeDKF5Xl4fkDZatF0HGRRRBZFxhIpxlPhD38jMAmCwEp7menmTUQkhs3RjlyggCpuFjK5E0EAi2tVbOcW4S0dN8mndx6xeVAIgoBitcnNhXWmloosrlY7xBOL5m1iIbYbjlltN0e5G8RNfVOWvR0EQSDSmV1l+tbjpWoT23Ux+HQCsiKaqFKUc8X/AIjkjePIwu6CbDYZ7ZJ4doU7q0uhDuWmxwxFZiiRJGtEWGnVma9V2J/JcX51haiqElPV7rijJIjENJXRZOoTVxCabbvLPN7AUCFFRFcJRJ2c1hdKiyKiiBo1p0QzqKFLEZZaM2hS2DfWRB1NjGDIURzfQRXvPyBnEybRHUxa7gfNlk251sayw3vPdlzOXF/gt//7/3xfwk21ptUlY0FYNarf9n+AvmySWMeTuNq8RcTawNW51S7rP5uMko6bxCI6/dmQHe0HAVdnVjetE8XbZpo1VWa0M78+YOapO63wF9ReRxLEz68wiCJKjMWyfLF3kh8uXOR0cZZ/9cHfklYjCAK0PIey1UKTZE4VxinbTd5YuXHXYxWtBmdLC3xUnKPdcSNqeQ4Xy0vYvseVygr/8eqb/GjhEroko0sKcUXnhcGjpPXIpzaPOxbLMmCmuFhZ5H86+wPiqo4kiLQ9l+VWhXWrwVg0y/P9BziS2mrAMBbL8htjJwkI+GB9hm9On+aNletEpNCPtu3ZLLdqlOwmD2WH+aXhYxxK9v1UAsy+RA+HU/1cqCwyXV/nT668zrdnPkIWpbCcF3g8lZ/gqwMHUSWFtucyX6+iSzJ9Zoy267JQr1EwoxTbDdZaTSzXRRQEeqIxesxbhgNvrtyk4dqkNIOVdpnZRoOEatJ0ZkirJiIwEc+hbivTGTIlbxfHiOjKtgGrblucWVoGoC8ep9puM5nN0nbdUBSmA8fzUCQJQ7l70LIdl4+uLfDWuSmuzKyyXmnSbNsddmrYz/T8W8pgt2sLfxKoioy8CwejDciStIVN22zbdxXceFDwAxfbq+H6TSTBwPIq+Nx7SuB2RHQFpWO5uNSscrW6ihv4RCSFotXCkBVOZAaIq7sPSqOJNIVIDEUSkQSBtB4hF4kQVcProohi2GMnrFPIorhJHfDjwnE9Gq3NIiqpuIGqSKSNYXJaWJYNWdIiru/g4yEJMk7gsNqeo+qUSCgZCvogumSiih8vqBq68kDlRdu2S9t2ulufoPPYzHLpEx3X94NuT3oDPZlYty/dtsPRrEbLxuzc59dm12h3A7JJOh7pVoY2bDqvzK5uEispVjeXrDcCsiFqKKqMIWkMR3rQPs/CIIIgMGSm+a3xR8kbcd5evclco8TV6gqyIJLSIuyNF3giP85EPMfba1P3DMgVu8UH6zP87ewZXN/DDXwc3wtHh4KAotXgg+Is58oLKKKELIjEFJ0nCxMkNePOGfAHhrhicCo3zuO5MV5eusqV6jJVu4UoiOT0KM/27uXpwh4ey43c1VpSk2QeygxhSAr7Ez28vz7DVH2dqt0GAmKqzpCZ5ucGDvJ4foxDyT6iO1gdPrhz03m+/wCqJPHa8nVu1tdYKJYJCEIpTTWCF/gICKR1g8O5AsPxJIaiIAkCnh/OV8Y1jbRuMBJPdoNQVNUw1VuBYa5ZpuHalJ0mZbuF7ckoghHKPwJTjSLDsQzb5UoB0Gw7m0g8KGJhRwAAIABJREFUqixtq0Xddl2ajoOhKKEJebVKuR2S9NyOfKDjhZKdw8kkI8nkFjONlVKd771xgXcuTHNzoUi51toU4FQ51Ow1NBVNlVEVCUWWaLRsFterWzKA3UIQQOoYMOwWkihsYWNbjreF+PRgEaCIMUbjX0dEpuEu7TrbVGUJSRIpWU2m6kXmGxUSqsFINM3NWhHH96g5bSKysu3kwe2IqipmZ2MlCAJxLbwvN4LunaYLD2rzG6rAbSatbsz8GpJJIN2axoCtegyqoJHRmmiigSnHtp002AmyJCFtM8J0v7Ac965qXA8Cd25cDU0JM3xDpda0qLcsVsp1Ro0wiF6bW6NlOyiyRC4ZJRkNNz3JuEE2YbJarrOwVqHaaFNIx/B8n3KtRbXRRhDA1FUGcrdGzxRBRtlhJPVu+MwFZAiJTEdT/eT0KMfSA5TtFi3PRkIkruoMm2lGY1kMSUGXFDRR5kCyF+MOenlcNXgoM7RJf3knKKJESo1sWbB6Iwm+PnCYw6l+ston66MEQMGI8XB2hIl4jtlGkYZjIwoCSTXCaCzDSDS7LSs6puicyAzRH0lyPDPIUrPaZWZHZJWCEWcinqOgx7fdqR9K9vE744/SdG0Ob2OIUWq3eGtuFlEQON7TR968+zUQBIGJeEg825foYbFZoeluMDxloorOZDxPRjPRZYVeU6bX3OwbvdENjavalmPfjoOpXlzfQxREbC/MouOKgSErRKRQKU3ZTZXjjjVmpzAjCSJJXUcSRWq2xUqjQbEVmhbUbQtdVpBEkagaivdvZMoQLtiNts3f/P1HfP+tS13pSlWWGCwkmRzM0ZdLkIpFiBphMFbkMBhLksj1uTW+/+aljx2Qw89wn69nq1OSKPKpujgKSAiCSNNdIasfxvYru7YB3JgXlwWRvB4jqmjEFI3+SAI74yKLUmeDen8ncNfxxjse+zSqUNt9XXeWQu/8f0xJEuPBTAqEp3br+I7tsrxQ5qP3btI/mCHXk6DVstA0hSAA1/UwoxqyLHHjSmjWMDbZQyJt3rVCo8gSI71pHj04jPwJsqG4abBvaDNfRhQE8ukYyZhBrWnRbDssrVcZLqSotSyW1ms4rkchHSOdiKAqMoIAUUNluCfFarlO23JZWKsyWEjRbNuU6+EmWldlsqnoAynnfyYDMoTiGCPRzBZmsON5nF9Z4dLKOqcGhziU6uPQPQJJRjN5umcPT/fsXn/3XigYcb7cf3dm6nsL86iixEgqRVzb+UvZ2MUmZZ2jWi/9JQNJEkll42iGgijdIjK1GxbLs+vhY6JAOp9A1cPMrFZqogfweHIEkuDaHmZ85z6b5/tcXl9jIB5nTzzPvmTPju+pWm1+PHU99GyOJ+4ZkCEMWH2RBH2RndVp7neG+3YcTIbuQF1rtdueC4KAuKrveCwBMDR5k3iB05F5vBciikJ/PI7jeViex0gySURRuv/XZRldlokoCkld30TCclyfd85P8903LrBcrBEEIWnq4FgPTxwZ5dBYL4OFJHEz/B7vvDyaIvH6mZs7nNW9EQR0SuHBrhc9z/e7fb5bn0PGsz1mry2j6grpfLzLqHcdD1VXcCwXVZMR75OkA+AFFnVngaa7TETO0/bKeIFzX+NdUUUjqmxWFNubLGzzju1RtEvUnTpD5mb2fUBAzalTd+sYkkFKfXCjcrIkot9RrWlZzr311X+KCAJo1Nu8+9oVUi+cwPM8bl4JWzkBYFsOpqljxjQq5Sa6rnLuw2mOPzpOPBlBUyTU2wKzLIUqd7/91Yd27ZZ2N4jC1ooOQCEdJRWLMLtcpmU5LK/X8HyfueVSODUQQH8uSSoW6d53EV1ltC/Ne5dmAZheKnJ4ope1SoNKPZzwMQ2NgVzigWzGPrMB+V5wPI8Xb1xnuVHn1ODQz/rjAPD9q1eJaRovaNquAvIGrLbDzJVFrpyeQVElhvf1EU9H0XQFw9Qxohr1SotLH0xhxg1qpQYj+/vJ9aWolhos3FxBkkWGJnuJxAyKyxUmDm8/qhMEAU3H4T+dPc1vHz7GRCq9K0UiQ1Y4mCuEo1jqz8Zo4l64222w4QG7m3cnosamUlyjZVNt3DsD1WSZ3titrH5PJtPxbb3lI3w3BEFA23b45ktnKVab3VGlA6M9/NqXjvPYoZEdGaa+H3TZ4B8XbdvBclzkXQoW2I5H/Y4+ZtTQsFs2N87NYsYjOJZLvdokGjfCXqbrUlmvM7Kvj2gisq1a013/pl+j4cyT1CawvCpVe4asfui+jvEgFkgv8CjZZSzfYrY5R8kuk9dzrNtFgiAgqSYwJIOyU2GmOUtOy5BQ4rS9NkW7hI9PRs3Q9lqYsokqqjTcJj4+CWXn0TNFlojeocJWrDa33TD+tKBqMrmeBNlCnGOPjmFEVC6dnWN9tYYoCLiux/pKjXgywlNfOkgqG+U//PEPmDzYTzwZQVdD4ZBwgjr8bVfrbSK6uqOgyMdBIRUj1ZFybVk2y8VQD/zG/Hr3nhrIJ0jfJvdq6iqjfbeSwunFEq22w3ql0V0joobK4APSK/jcBeT/P6HdtJi6tIjvedQrNtfPzWGYGnpEpTCYYWC8gCAKqLpCKhtndb7IzJVF2k2LG+fnqVeaxJIRRFFkaLKH+RsrjB8aAO69GLm+z2K9zt9eucTzYxOMJlO7+qx5M8rvHT3xwM79swJBgEI6hnrb2ESx2mS5WOvqBe/uODtvAPwgoNawOHN9sds7MzSVxw+P8PD+oV2Ne9jOVpLP/aJca1FrWLtiIgdBQLPtsHrbmIggQCZhoogi8XQU1/a4+MFN5q+vMLS3l6GJAqdfv0J5vU4yG8OM6XCfDH9J0FClOC13LdSwFoJOGfunqyZXcapcrF6m7taxfAsBkYXWIh+WzyAiMGwOsTc2iSHpKGIYRPzAp2SXOV0+Q82tcyRxiIpTpdfoIatmmGnO4gc+h5M7q4FFdIVM0uwKjQBML5VotOz7qhZ8WhBFAU1TKK7VSKWjqGpIGBTCnSmC4KEoErVqkyAIUDWlWzEydIV4VEfXFFpW2NqZW61g2S66Kj/wc8t3MmQIeSPLxdBY5MZCscvfGMglSd6mvR3Rw5K1KAj4QcD0UpGmFbpM1RohbyQa0RgobF5HfT8IvUO4v43h5zYgu75PzbJw/M7CJitosowoCHi+T9t1sTyPIAiQRRFDUVA7fTzLdWm7bteu8fbngyCg5bpdZR7X9/GDAK1TgoQwS284YflMEcUOSen+IYoiZkxnaE8PU5cW8FwfURKprteJRENGoOf51MpNXNslN5AmnjIpLodze2bcQFZkHNulVm5SKzXwXB9J3rqwO55Hy3Upt1u8MTeD43lUrDZrzQaGHJ6XociYyi1GYBAENByHdud6QEiYicjKFpLSxvehySGb2nK9znWV8YKAhu2EPRkl9LUWb/sbXhDQdh0sL/Q8FRFCdrIsI4u7U2f6JBgqpEhEdSQxJJWtVxrMLpdptG6p/TwIuJ7PWqW+KbvpzcbpycR2VaLzfZ9as91ldn5crJbrlGpNejKxHV/r+QGVeovF9Wr3saiukktFSSRNWrU2C1NrNGtt4ukoqWyMockeFqfXGZ7sJZWNfaySdUQuMGB+gcXmW7h+k5HoV3ctDPIgcb1+g5gSZV98krnmPKfLZ7lav05KSZLVMnxYPkO/0bdJ4EcURJJqkqPJw8w057hev8He+CTzzXlKdgk/8LsKczvBNDT6cwkMTem6j00tFlkp19njep+qfOluoKgyw+MFXvreWQ6dHCYS00nYLrIi4XsBggg9/Wl+/J2P0DSFfYcHiXac2CRRpJCKMZBLcnUu1IouVhtMLxbZN1p44OeWjpuk4gayJNK2XVbL9TBDXggzZFkU6cslNs3oq4pMIR0nbmqU621mV8o0205X8QvCDHmg4zYXBOFERKnZRpXDCQtJ3H1Q/lwGZFEQmCqV+XdvvM6rMzOIAvzhQyf5uT17iKoai/Uaf33hAj+8do2m6zCRTvP7x0/weKfE/erMNH9x7ixX18Oy06FCgd88fJgnhoaxPY+/PHeOqtVmsV7n4uoKa80mX5/cy3/96GMIwDvz8/zbN16j5TgcyhcotVs80j9w3+cRT5s89cJDYdnykTECf3PPUBAFjKjGN37vaTYKswK3EWw2aj2d1+45OnTPL/7S+hr/+exp3pqbZaFWww18/vmPvr9pI/G7R47zr578wia923//9pt88/IFSu2wX/LlsQn+6YmHOd7Tu+n4U+US/+2Pf8CXxyZYazb54Y2rHMjl+c1DR1mq1/i3b76Gqaj8i8ef4omhoS5LNQgCpssl/vz8GX588zrLjQZJ3eCJwUF++9BRDuYLKPcQ9HhQkESBycEcM0slSh22882FdT64PMsXjk88sL8T+AHtO3qxhqZsys63w3q1yfRSmWrj3rKru8HNhSILq5UtNnV3w1q5zo359e6MpiDA3uFCqFNsaozu72NkX2+XiSd00oInvnYUggDhY7JyBUQicoHx+AvdR34WcHwXTdTQRA1FVAgIcHyHqGoiCzJP5U6RUBLU3Ftzret2kYvVy7S9Nhktgx8E9OgFbjammGnOcSC+j2FzdypwAqH2+N6hPB929J4t2+XdCzOM9qY3lVN/FoiYGl/6xlEc20NRJQSELSQ0Adh3qJ+AAFGUNq1xvdk4Y/0Zrs6FOt2u5/P9ty8xUEiiJR5seJJEgUwiHGlaKdWp1Nu0LJub82u4vk9vJlT8urNSpakS4wNZPrwyH3pn11ssl+rUWhZSR8O+J31rg7XeaPEX75whH4/y+PgQ/an4rn+9n8uAvFFKfGRggN8/8RDXSkX+9csvMZ7OsCed5s/OfITt+fwPz36RbCTCmeUl/v3bbzEQT9AXi7EnneG/eeIpdFmm5Ti8eOM6f3PhAk8MDQMhSeMH16/xW4eP8gcnHkIUBAxFxlAUrqyt8X+89y6/c+QYp4YGWao3+HdvvE6pdf8SnoJwy6tXEAQCIdjy/O3/dhHc9p7bxiy224X1RKO8MLmPh/v6eWV6iu9du8IfnjjJRDrTrRyMJlNbREh+/9gJnh+f4PzaCn918fxtqlB3fKQAbM/nu1evcLKvn5N9A5xbWeaP336DpK7ze0dP8K3LF/nz82foi8c5WughAN5bWuD/fP89bpaLfHFknL5YjIVajVdnppmrvMofHH+I50bHP7UseeO4zzw0wdnri11z8quzq3zntQtMDubpze5OZnIniKK4pR9Ya7a3tSHcgB8EfHhlnjfOfnxC1wauz61x4eYyD+0bvKs94u24PL3CW+emuv8XBZEnj44SNe6YrRQ2/05vL7F+nO/u1nt+tiXZMXOE05UwKwbIqmnGo6O8U3yfrJqh3+hDE1Wu1q5zo36DnJZl2BzG9V1W2qu0/XZXpamgFag6NRBA2+UssCAIZBMmzz082Q3IAD965zLDPWnS8ch9qZI9aGx8T6p2a/zrzm8sCIJNVZLbfw+j/RmO7xvgldPXaVlOGJDfvMTD+4d45ODQlvvlk37WTNwkl4yyUqpj2S43F0uslOr4fsBIX3qLragghFnynoEcH11dwCdgaS20Wm22bRKmTk86vuk9MV0jFzXxfR/X8+7rF/y5DMgAhWiUk339ZCIRIopC2jC4WSoSBD7rzRb7czkO5/MokoTlumSMCO8uzPO1iT2U221enppitdHAC3xulIpokozv3yLLDMbjHMjlGE4muxfU8VyW6jXWmg2en5ggoetkIyYD8Tj6Dv63AP9k8gl+cfg4EUkhb2wtWe124dr8g9nde1K6wbFCL0OJJLOVCoIgcDhf4GRvf7dkrUjSFlpvzjRJGSEL/CdTd5/3voWA/liCZ4ZHgXBe99LaKr+y/xDPjYxRsSx+eOMaVSvM8OarFV6dmWa5UeP3jj3EsyOj6JJM23Ppi8X45qWLvDk3y55MluHEp2vycHCsl4NjPSyuV6nU27Rtlw+vzPHv//IVfuMrJzg01rstMWmjxXFzoYgqS+RS0S3EFFkSyaWiXd3kIICltRpTnTnkewVH1/N54+xNvvvaeaYWip/4XG3X49WPbpBLmrzw9KF7WuidvR76BV9fWAfCDVomEeHxQyOb+s/3+g3+rPubDwIZLc0jqRM4gRs6pgkSumSQVkNVPEMMe8dmMsK+2CSKqKBJGkORAfYn9qJ2nJtCe1CJfqOPrJq5r2sTM3Ue2jfInoEs1+fX8Tu2f3/1k9PUmm2ef2wfQ4XtuSAbVoGr5QZRQ6Vnl1rmu8FO57Ld87oqc2CkwFPHxvjh25cBqLcs/uTbb1Kut3j62HioErcDyrUWV2ZWaLZt9o/2UEjfvSWQSUTIpky4CY2Wxekrc13G+khP+q4tKlWR2DOUC+9/L5xZXi2HQTwRNejJbg7Ic8UKvckYpWar69q3W3xuA7ImScTUcNbUkGUMWcFyPZqOix8EGLKM3un56opCRFGoWhZ12+Yvzp1lIB7n2dFRJFHgpakprq2vb8r70oZBVFORb2MgW56L3elLp4xw8YwoSqdPsHOfbMhMM2RubwS+E/7u/3mNvuEse44MEk/tfh5aFkXkjrzfRkZsyApRVev2xu+EIAhIgoAkiuiyvK2H8wZ6YzEG4+HNXjBNbpSKHM4XyJkmg4nEpms4VSlzfnWZpG7wlbGJTaNUj/cP8fdTN7heKjJbqWwbkG3rHXy/hCQPEQRNHPs0ZvSf7PraQMim/NrjB1gtN3jr3BS2E9q0vXVummK1yb6RPBMDOQqpKFFDQ5QEHNen0bIpVhssrdeYXSmxXKzz/KN7efLo2JaALAgCEV3hxN5BXj9zg7btYjkuL39wDVWWePr4GAP5JIosdxfQqcUi71+e5e3z01ydXUMUBVIxo5vJ3y9URSIIYGG1wrdeOcfCepWT+wYZ7k0Rj+j4vs96tcWFm0u8fuYmZ64tdEeeTEPlF54+TE82jvQx+sKfRyiiQkrtBLuNkjyg35bhCoKAdofnbRAExIl3e8szzVmKdplevYestnvbTQg3cn3ZOL/784/wx3/xMsXKLavB77x2nquzq+wdyjPSmyabMNE1BT8IsB2XSr3dEbUIszpVkfj5Jw4+0ID8SSAKAkOFFM8/to/Z5TIXp8KxqRsL6/zFjz7kzNUFJofzDBaSoViHLOF5PpYbqm2tVxrMr1ZYXKuyUqoz0ptmsJDaJiCHGTKE1o/vXpjpPjfSe6+ALDMxmOvyXi5MLVOshjyORNS4i1FLgK7IDKQTJI37kyj93AbkUrvFjVKJyWyW1WaT5XqdbCRCXyyUt1tpNFhtNEjoOquNBgu1Gl+f3Evbc7mwusLJvj4eGxxkqVbbInoA3FV/VBEl4pqGJIpcWFlhMpul3G6x3mxg7CJD3kCrYVFer+P7Pv0jO4vl347Lp8Mf0NDkzrPDPwuYSkiu83wfTQpJWUl9Q9VI2mRjt9poMF+r0nZc/vf339mUnNdtm5lKhWwkQtnavmfq+6u4znWCoE4QtPHc6/h+Gdt+H4ImsryPABtRiCNKPfh+Gd9bQFGPbjrO3pE833jyIL7v8/6lOVqWQ71l8eHlOa7Pr1FIx0hGDTT1FnnQsl1qTYtKvcV6tYnteJyY7L/raJIghPO733jqIFfnVplfKeP5ATcW1vm7185zaXqZbNJEU2Qczw91jEt1biwUWa/UySRMjk32I4ki3339wsf6fsb6Mgzmk0wvl7g+t0ax2uDi1DK5pImhqQSBT61pM7dSZmGt2hXcj0U0Th0e4edO7UfbgQFrOx7vXJim1dHhth0X273178JqpStTCFCpt/n2q+d4//IsqhyqkqmyhNL5V1VkHj4wRERTfiZZ924EQe72/O3rR0QyGI4MklZT92VYvwFdVTh1eITlYo1vv3qOhdUKruezsFZlrdLg4s3lbvVFkSUCQmGOpuVQa7Qp1prUGhYD+STPnHhwvIgHAdNQOTLex6996Th/9ZPTXLi5jN+5L5aKVc5eXySTiGAaKlJHotT1ws1wrdmmVG1Ra1l4nk9EV/H8e48FpuMRckkTgdDh6Uan+qMqEv25xF2rRbIk0pOOkYga2KU68yvlLis7GdW3tLQUSaLtujQaNlkzQjZ2H4nTrl/5GYIuy9iezzvz83y0vMRKo8FYOs1YOk1/PM5DvX1cKxb55sULGIpCqdViPJ3iQD6PJAqMp9NcWV/H8i5ieR7FVuueesO3QxZF+mJxHh0Y5K8vXmAslUKVJFqu2806d4PiSpWrZ2cxTO2+A/JnHZIQyjL6nf64gHBbZi1we0/Q8lws18P2PWYq5S3H2pvJMpwMBf23hwJ4+N4q4COKBSAgCBp47jRBYCMIBr4QRQpa+EEDz53aEpA1Rebh/YPIkkgqFuH0lXnm1yr4fkCl3qZS3x2ZSlGke5KZZEnkxN4Bvvb4fn7w9iXmVio4rsfMcomZ5RICoUm75/uberDDPSm+cHycJ4+NMb9S5r+8efFjSVfuHc7z7IkJZpbDY1ybW+uaw98NQqdM/ciBIf7B04d25Q9tOQ5/9ZOPqNTbW4LxRoC2bpNMrDUtXnznMpoibwrCinwrME8O5TFU5b5MBz5LyGpZsp+gHSqKAtGIxtefPIiqyLx+5gaXpleo1Fuh+1axxlKxtqvj3E0042eNRFTn6ePjKLLIj965wrkbi5SqLZpth5l2aVf61hFdIR2PdN2Y7v0aE9PQqLesLnO9Jx0nGYsg32UdFwUBQ1MY6klRrrW6xExBgGTUIJ/a7OilqwoZM4Lj3b/t0WcuIAcB2K5LvWkhyyEJplRroSkyES0sDR/M5bsL/dX1IpIA/+joMQbiMXRZ5omBYWRB5MLaKrbnUYhG+fVDR8hGwoX9G3v38f7CAlfX1xlKJHhsYLA7uiQKAqPJFKaibBHAEASBghnlVw4c5K/Pnefa+jrDySRPDQ0znExt0lm+GxzbZXmuyAevXubSh9Ok8/Eusevo43tQNJnyWo3FmXVq5XBuLxLV6RvJke3ZqnrValgszxVpN22G9hTQIxrtpsXNSws0a20CPyCZi9EzmNlU3v50s4zdCnKEFQdNlumLxfijk4/edebXUBTykZ12mD4h080lCDwEQcf3q0hiD4Is4bozKMphgqCCbU8hinkk6e7qbqah8dihYXqzcUb70nx0dYGlYo1KrUW9ZWO7Lq7rh4xRQUSRRXRNwdRVElGdbDLKRH8W4x6LgtC5uX/xmSMYmsK7F2aYXSlTqrVCBSYvDMSaImNoCqlYhEImxmOHhnny6BiD+SSW7ZKM6hSr91+2Hu5JsXekwORQnpip8cqH15lZKrNebYSzoI6HJAmoihwSVjJxDo338uyJCQ6N9+78BwDX9fno6gKNlrXrBSk0Gri32EVoQvBpOjB/9iEIAul4hF985jDDPUneOj/Ntdk1VssNqo12aPjhejheqKcui+H3aGgK0YhGOm6wd7iway/snyYEQSAW0Xju4Un6cwlePX2TS9PLLBdrVOotGm0bx/Fw/aC7adXV8Nzipk4yZjCQS3By/1BX/ONuEEWRRNQgn45Sn78l/jMxmMXQ5Htu+ERBYHIwy6WpZazO2GJEU7smFLdDl2UiqkLS0NEV+b7mxT9zAdl2XBbXq8yslCmko4z2qlyZXcXUFUb7MsQjOk8ND3M834vjet1+wO1wLY8TmV6+MjbRLUH4vk+53iIe0fjC8AjPjIze9e+LwDOjt55rtG0kUUBTwjKdKklMJNP84ZGH6MvcXy/NtlxuXJjn9GtXmLm2TDxlsr5UQRAF9h0fRtFkFqbXePvF8yxMreH7PnpE48BDo3z9d57oHkcgFBVZmSvywWuXwyymEH6WM29e44NXL1MtNfA8n2QmysPPHuDYE3tQtTDDkEURgXB2+G7l+p8W0oZBxjAIAuiLxemJbv0ud/3pBAUEDYJW2Ee23sHz55GkfsBHlLJ4XhPXvYksq6ja0/c8lCxJjPdnGS6k+Mqj+zh3fYnr82ssrFWoNSyalo0fBCiShGmEN2VPOsZYX4YDYz0ko8aOv4t0PMKvffk4D+0b5KOr81ydW2O1w/yUJTE8bsJkcjDH0T199GUTaKocEklMnZP7hljuWMjpqnLPbZAiS+wZzKHIEp7vMzmUJx7RURWJ5x/dx7E9/Zy+Ms/5m0uslRvUWxaaIpOI6oz0pjm6p5+xgTia5uL6FvIu2MGyJHJ4vJeW5dyVkQ+hm1PdWSamhEHeC2xEQUG8x4jbvc5R1xT2DGY3/Z24ubNk6u1IxyMcGO0hnYxQtS0G0oktntM/K9i+i+07mNItX3hNkXn88CjHJgeYXipxaWo5nE0u1Wm0bFqWTcNvk9IjpKIm2WSUgXyCycEcY/0ZTEMLW0cE2L6DKipbnO0UWWKokOTYnv7utc0mzF2ZSzRdC1EQ0MT7bzFIosjBsV72j/Rwc3Gdy9MrXJ9fZ3m9RqnRoNJuEZMNdE0mFYuQS0YZ6kmxZzBLfy6BuQtWdk8mxiMHhjYF0pP7t2d0S5LA0T39zCyVqTbDSlkmbjIxmNtyjq7vc2FhheVKHc/3iekaprY7VbzPVED2fJ+5tQqnr83jeT4jvWlkMcxCrs6vkUmYxAyNluV0pAcDsgkTPwhotMJFUhJFri+s47qhXm+2IxRu2S4vnb7GEwdHiZlayL7ulNE29GLvLFe4nsf5qSUMVWGsN4OqhKIXpVqTYrVJTzqGbTk4rtclfMiSiCyJNC0HgXDBUDra1GZM5+mvHycaN3j7xxfYd3yYZ3/hoU1/M9+X5ku//DCKpuC5Hu+9dJEX//pdfv63T926Tp7PlTOzXD8/R643yZd+5RGiMYOVhRJ/+j9/l9/4Z19h/EA/QRDwrf/7ZT549RL9ozn6R3NIgkjGCD1S56pVSh2XooDQQk6TN4zaw5vQ6wy6O56PT4DQ6d84nofQKU9/3JxlNJniUK7A30/f4DtXL/FL+w50rRI938fyXHRZwVSPIngyAAAadklEQVRVpG1ubEEwkKR+BCGCLyxD4CGIMQLPwveLCIKOIChIYh5JzCEIIqK4UxkcZFmikI5RSMd47uHd66H7fkCzaeF6PrIsIctit/wsiQK2E45C+H5AbzrG0BMHUVQZ3/NpWw6CIKBrCqIk0G6HWbPrhu5RjuMSkWT+8VdOMjGWJwig2bSoN9pdQXzP8/G88A+amsq//EfP0WhY+EGALEuIArQtB9t2SZkGX31sH19/8t6qUWV7mpnGR2S1/ZhKHkUwEAQJ1293rP4UJEEhCHycoI2iB/yv//wbCIi4gUUQuAhI4WsIcAMLy6typvjnPJb/BWyvSctbx5RzqFKso1ntAQGSoCEJMrbfxA6qCJ6ILOpIQliBGMgn+Re/89yWzxwKA3VKi52BfVmUUCUJSRBoug6qKOEGPgcnezm8t4/1dpNXZm9yJNdDK3AQHAFNlhA7s7UbUqcbGc+dmc/GPeMHAZ7vo0jSPcmeQceKtOXZ4TohqXiBjxd4eIGPLEjIosRiq8hsc5WjyTE0SUEWJFqehRcEqLLMxGCG0YEUth+WXhVRRhJEXl45w/74IGk1jiLKuIGH7Tv4+B1DFoE1q0LRrjJo5FElBcd38YPQQNKMKvzqV4/yy88fColpAWiSghf4VJ0mkiCiiOEmzwt8PHw0UUEURC7VZtBEhRGzB03c6qjlB0HnHDxkQUISRAJAFUMZzYbXRhdVegpRcnmDZx4ZRxElVtplrtUXOJGa6B635dl4gY8ihjPQdaeFT0dhLwBT1rcEzP0jhV3N4N8OWZJ49qE9PPvQzuuAKku4figqNVeqkomajOd3R+b9TAXkYrXFmeuLfHh1nv5snFKtwWAuQSpmMr8WKgX5fsDl2VUuz66wZyDHSJBmvdLkL1/6iFqrTU8q1rWp++j6PKlYhKPjvXh+wLdeO8dysc6R8V5Ge9N8cGWOC9PLDOVTPH5wZIse6cJalZc783EPTQ6wf7iArsr84N3L9GcT7BvK88qZG1ybX0OVJWRJYiCXYKiQ5IfvXkEQBJ45Ns7kQHZX4g9BELC2VOblv/uQqcuLANTKDVzHx/cCxI4ZwPSVJa5fmOeJrx7hq7/+OIapYVsOqwtlZq4t83/9m7/tjOiErz/62ATVUoP+0Ry6LHM4nyeqqvyns6cpt9sMJEKjhIlUmkf6bwkWOL7Pcr1G3bG5Vlyn0g53vtdLRaKqSlRVw7Ez+ePpzg4nUzwzMsrZlWX+t/fe5kpxnSP5UC97qV7n/aUFnhsZ4x8eONQVErkbNP0Ld72Wmv4cIHcWTx/HP4MoDyPLYx/r8+4WrbbN379yifn5EiPDWYYGM1hWaPGYTplcurKILEtUKk3WS3VGR3Ic3N9PqdTg9TevoqkKpx6bIJeL8dIrl1hdq1HIxzl8cIC25XDm7CzDQ1nGx/I0mhZ/973TVKttDh3oQ1VlllaqrK3VEQQ4eWKEyT09fOs7H9Bo2IwMZ9m/t5f5hRJnz8+RSpo8cnKM4aHtBSZaXoXr9RfxfY+x2Bcx5RzTjdepO4tktD3k9AO0/TJTtZfxA4eJ+FfD19RfpeYsYSo5svpeXL/NbONNVNFEIMAPXGYaryIIEqoYpeWVWGmfp+ms4gc+/eZJUuoIFyvfpukWicgZhqKnyGjbL4zvLc3z/tI8nu9jqipeEDCaSHIwW6AvGucHN65yKFdgtlrh/aUFkrpOXyxO03H4Lzeu0HZdTvUP8WjfIBFZpdlR5hMFActzMVWVum0TURRkUcTvqAMKgkCl3WauUuFQoUA6cveNnxt43Kgv8srqGSRB4snsQRbbJVbaJdbtKv1GlhGzh5uNRd5au8Byu8S+2CCDkRw/WHqPol1jX3yIyVg/K+0y75euIgkiE7E+clqSHy29z/X6AocSIwxHCsy31ni/dJWorPOF/FHyWpIfL39IWo2S11LMNle4Uptnza6gigoT0T7mmqusWGUyWgLHc3gks4+ldpHL1TnyepLxaB+LrXVWrDJFu8bx1ARZNcEbq+dpuhZHU+Psiw8ybG4OfpZn8+OlD5hprjAW7SWvJ2l5FidSkyiixPcW3uap3BFeXz3HklVkMjbAqNnDtfoCLy2fZqVdZl88vBY/Wf6QVavCnlg/mqgw1Vii6VnEFRPX9/iHQ19A/QSWkx8HTcsmG41QbbXZU8gwmtudPDF8xgJyJh7hwEgBRRYZ6UmxdzC/5TWiKDDam6Zth5R+y3a5OrfKRH823Jl4HuV6m2TOYKwvQ8tyOHdjkW+cOshIT5pfeOoQqahBtWlRb9mM92V56shoV+P0dvTnEoz2pknHwtlLVZKwXY9H9g3x4bV5/CBgcb3K5EAu9MVdLTO1VKRSb9GbjpOKG3zz1bP80T84RWYXqjPNWptv/8dXGBwv8M/+za+iR1Tee+kif/0nL216XbXcJJOPM3d9mdOvX+HxrxzuPBOgqjL/+k//KcnsBu1fQFYkND0MmrIoMpRI8T8+8xx/duYM/+/Fczi+R8Yw+PWDR7oB2fY8Plha4F/++IdULQs3CBccgItrq8iiSC4S4Q+On+SX9++syXsvnOzt5797+hm+ffkir85M8+MbN5BEgZRhsDeTYTyd3tWM991x632ucw7XuYAkDSLLn9z9azu8+/5NbNtlfDTH6loNz/PZN9nL935wBlEU+I1ffZTX37xGX0+Sxx4Z5+KlBX744jmSyQi9PUkiEZVvfecDfuvXHmdltcqTpyYZH80hyxL1RptGw2ZtrYZje7z86iU0TWHvZILLV5fw/YDBgTSPPTKGKAr8/csXabcdMukYz39phHjc4PzFeZZWqgwMpPFcn5+8fIHf+52ntj0nTYxSMI6QUAY4V/pLZNGgxzjCsPkEK+0LrFoXGDRPMRaTWLeuMt98j9HoF2i4qwyYj5DT91Gyp2i564zHnkMUVM4W/xxRkOmNnGC5dRY3aOP4TUQk+s1HkUWdK5Xv4Zpt4sogSXWUABdJ2LksudpskIuY5CImN8slGrbFSrPJaCewLjZqOL5PVFH54vAYB7N5VlsNVpt1TvUPcyCT488ufsR4KkNUdnlrdpalWo3RVJp0xGCqVEaXwww4qess1GrYnocuyXiBf89Rwg3MN9e4VJ2loKeJyQbfnHuDQTPHgJHlsex+LlSmKdk1RsweKk6Tn+t9BFEQ+Pvl08iCxGRsgBv1RVzfQ5VkTFnnhf7HkYQwox8y83yp5wQDRg5ZlIgpESKyxv/X3r39xnHdBxz/njkzZ+47e+NlSYqkaNmWbElOLV+SxqljtGlg5CEveSsKFP0Liv4Pfe9rgT70sc8BWrRoagdFYjf1RY7tOLYkS6RNSaR4515ndi59mCVNSjIlpgayQc/HsASby11yMTO/nXN+l+XuOh/v3eL11kt8u3GBD3dvMsxT2mkfVypeaV4kzoe8sX4VW1q8OnGZ5d4a62mfX+/eRBkmC/4kvTTmjfUPmPcneDqcYymc4aerbzM32WTBn6Ji+bzcOP/QmcC9LKaTDrhQWeBK/Ulycm511nh76zcseFNYhkk/i+lmAy5HSzxXfQLfdMiKnLuVeX408xLKsHhj/QMEgieDWVZ7m9yLdznjTfCtcI6V3j26eZ9TbHp9Y6qeS1EUPDXdpFUN/3AbgxhGueSrDrIrTfnAHqcY1cWasmz2rSyTxVadf/q3d5hrRnz72QWMe3tlm7TQY7PokqQZUhplPe1ovmw1cPnji4t8srzOLz9e5tJSi6XW8bsEaZTfoyxZLl0ZBkaWY5nyMMPVEAJHmRRFmTDQixP6SUqj4lHxHP70ypMPpNLL0d3y/s7xnsTxYEivM8ANHepTFe6t7nD9o9UH3qenLs/xzAtLfPLuLd7/xTVc3+Hyd56gOV1lYqbGB29d57UfX6FS89nZbJPEOY6nDt8/W0penV/k2eYkgzQly3NMQx4mvUGZun9pcop/+NGPyYrjZQRFXq5UmNJgwvORQjAfRfz9D39EqBRV26Eo4K8uP89PLlyk6XkI4M/OLnF5corpIDzMt7YMgydqDf76W1f4yYWLJKOlcNMwcE2TquNinXIwwcHveZRpnkPKORAK8ZifmMt9Ng6TzQRfDT4/aZhEpxsjDQPXVTx5bppGPUBKgWEIkiTF82woCpRtEgQOBdDuDPA8m3rNoVb1+P73zmNZksB38D0be1TTLA0D0yxLP4qiYH9/QKXi4rmK5y6eYWOrg61MalWP4TBjEA/pdAeEgY03mq8cxyl5XuC5ijBwOPsYmf4Hd7C2DBnmPfIixRQ2tiw/+O0lXyJ5h6ToooyAbrpRzsCWPkr6mIZDUWTkZNgywhAmQpRDlQ1hAWK0V1kghcKVNYQwyIuMujrHRzv/jCMjZrwX8M1H/7yGKMvsyr7rBmUaXnG4xJrkGZ1hQmU0oc21LMzYQAqDSc+n5rijPvY5t/f2WW93KIDAVsxFEde3tuj1E3ylWGu3sS2LuuNSANu93iN/vqRIifOEuqoQWT6vTT3HF717VCyfqhVgCIOMAlNILCGxZTlydT/t4ZsOrrS5VF2kqkK24n1C08M9Uk4lhYFtWCjDZKW7zq3uGsMiw5GKTlomA1qGOXrHy39d06amAnaHXZI8xTNtXNNGGeW1o5sNsAwfV9o07Yi6XWEnaVO3K0SmT1pkgEAKA2VIlGEe6/F9ILJ8Xpm4yKf7X/DO9mc8HZ6hpgL+Z+tTduIOLzWeZtKp8erEc/x2f4X3dq5xoTKPFLJMBJUKA0E77aEME9e0eSaap9JzySlwpI0lzMNz9TQ2Bx12kz6uaeGOlugjy2Mj7rDc3uLZWgvPVAzSId00oZ8mKGky53+1utpNEj5d26AZ+MzVHj2C9qixCsj3y/Kcrb0uV6+vcuP2JqFrl92Ntve5ev0OWZ5zdrpO6NusbbexTMm9nS5xkhK4Zb3wwUXTNAzmJqr87L3rnJ+fJPJdVu/t8eXGHlIIBvHDMzwbFZ/Ve3v8slhmcbpOPEx559Mv+Pz2Jp+srDMYpggEQpTZf42Kx1Q95JPlNXrxkPmp6gOZe9Nn6rQWm7z7899y87d38AKbv/zb1wkil2euLHLtgxVWPrtLWPXIs5yofjzL2PUdJmdqVH8Y8Na/f8Tb//ERXugwf26Sv/ibH/Lrt6/zj3/3U/I8x7ItXvz+BV587cJhopEQgkDZOIZFvx9z984uji1xXbPcs0wzTEuS9TPquU08GNJohnieYjAY8sXKFlubbc49OcXazW38cyb9XsJsEJLEKSu3N8v5znUff2Bwr7vHzGyNiu2QiwIpBd20rG8VlPvUga2wLVkuD6YJrlT0syGddMB20kEaBqHlMEiHVJXHIBtiGpI0z7CliS1PPpSF4ZHmdjkogwGWIYmzFM8sX8cScjQopKCXJVCUwfd2d4+FsE5BjistPt/fpqJsJt3ga1/z6Senee/qMitfbtGarqKUJI5TZmdqhKHDW/99g0E85LPra9xa2cQyJZeenSPPC24tbzAYVJibKfecpDQOj59eL+HWyia//uhL2u0BS4sTXLo4x7vv36LXj5lp1ZBGGfilNEjTHMuSLC1O8p9vfsLefp9Wq0oYODi2ycoXW0xPRSyceXQ/5GHe5U7vXdb7H1K3l/DMSbbjz9mMr2EISWBO0s92aA/XcM2vlugMJILyuPPMJu10jVvtN7FlBUNY9LNt1nofsjn4DIHAMsrxjYaQh/c2hjDpphtkRUI33SSwprGMk9tFioN/Rh+cbCmZ8UP+5eY1FqMq7TjhUnOKvTjmzS9usbK/iy3Nw6THr4afwFK9RsMvP1RGjoOvFK8uLpLmOWpUc6pkWTFQFGU5nwCCE0axNlSFKafOjfZt+lnMnNtEIDCEcSSIFbjSxhCCN9av8lQ4x7PRIu9vX6ObDphxGzQQGKMgeNS0U+e97WvsBG2SPGUz3qOd9vBNFykMNuM93t+5zo3ObRoqZJAP8aV9LIAevIfG6O+z/jR7SZeV3jrTTh1P2ghEeVd+5CJXUyF3+1v8autTzgUzTDjHtwL3hl1W+5vcHWzhmy5pkeGbLpHy2Yj3aNpVOumA1f4Ga4NtqiogyVNcqVCGyc/W3uN8ZZ7zlTNc3blx+F5IIUdVFie3ET7JbtJnO+7SIGCY5yx3trANk6py2Rh0uLq1yiAbshV3qSmPCSegqtzDYwXKnvVKSgbDsgnSaYxdQJ6IfJQpy56iCFxbcWFhirmJKpO1gMBVtOoVXjp/BoTAMg3e+2yV118+z2QUcPPuNq1GhSdmGviuoiUrvHLpLLaS/MlzS+x3B9RCD8+2mK6HOLaJbZnHmoMfdX5+kkbkoUxJ4JZzOi8ttTjbajBVC/juxUXqo+XuiVqAMs2yBMZ3cJVFLXQfSBarTVR44dXz1CfCcpi7bWFaEmVbfPf151h8eoZ+LyaMPFzfptseHNa1/uAnLxI1AsKqh7Itvv2Di2zc2aHaCFCO4sXXLlBtBOxudcjS8rlnlyYeOnFnZXmDfj/hzp1dpCFYv7ePYZRlWNIwqEQu/X5COsypRC6gGA5Tut0BSZJiGAY722U29+3bZZ1gv5ewvd1FSkGzGaJsE2kYtGZrfLh9B0eadIYxSZ6R5mUCixDiMCEvy3M24y4tt0LD9siLgkGe4psKKQxu7G2MTn7BIBtiCMETlSYtNzrWVe1hlts7rHZ2kcLAMS1CS7E16CNEMWqJaDLpBnTShPVemyTP2Ox3We3usRsPmA8jNvs9zkVNGrYPX3Pjfma2TlEUJElGENi4jkWWFbSmI1xXsb3TZXe3x0TFo1r1qNd8ajWf4TClUQ9wHYtq1SPwba48v0h9VLJmmgYTzZBvXZ4ny3LqdZ/Adw4vQJXQKaebmZIwcPDcnNf+5AKt6Ygrf7SANCWViksldIgqLju7PXxPUauenODmyjot93myIiEvslHyVUBnuE5a9FFGgGW4DPM+VbWAabgIwJVVWt7zeGZz9Dw1JuzzeLKOaThE1hmUEVK3n8Azm7hmFSlsDAxsWZbmLAbfY7X7KxaC7+KZDfaT23SGa3jmyUkylyenyYsCz7JoOC5JntN0PZQ0iWybxajGYqXKfhKz1e8RjjrWTfsBk16AEIIfLJ5j2g+IHJfaaJXnwGzlwdKho1PSjv73w0SWz4XKPFXLx5GKqgqIVEBk+SjD5EKlHBQTWT7P154kyYeElseEHY2SzAoqlkfF8vFNl2n3+D7llfpT7CcdqirENCQVy6OfJaNkKBNX2jwVztFy6kw6VQRlVnRguphC8ufTV7ClRdOOUIbJnNekYUd00z7tYR/fdHCkYsZt0lAhAsErExdp2BVCy6WmAizDfGgTFEcqWqOl+vJuu8Iwz6ipkKoVoAyTgoIZt0FkefimS12FSGHwQu1p+llMaLo07Qgpyml7oemNVvIKIhUQmA79bBZ5ysE0jrRIsozdpE9BwRedHSLlkBflGstO0iPOhuRFgSPLJMX28KB8qiAvYHV3H2VK+sMhg+TRfeqPEsXvs+7lG9DuDXjz6ufUQhfXtri71ebyUouF6Qc30g+6RBmjC1ieF6N60pM/UR0uT59ick05hqvMyhzXnr4ff/Qld27v0u8n2E6Z/BQPyixfz1Ncfm6e3d0emxttnjrfotEI6HYG3L69Q7cbMzkV8f67t3j5O+d4752bCCHKWsE0w3EsanWfIHDodRNeeHmJf/3yN1SVy2bcpZeW81wtaaIMSS8dUlE223GPOEsJLZspJ6SqXOK8TJapKo9f3Vsuu6iJ8g42sGzOV6dYDOo48uR9u7fWlvl4ew1HWky6AU9Xm/zi7nLZwN/xSfKUhaBGZxhzs72NoLyTV4YkzjIiZdNNE55vznG+OvnIfcKTjpuf/9enzM7WmG3VcF3r2MW83P443TGT58Ujj7X7H3Pa1yovFWUtcPkcXxUbCQ4GneSIEy6CB98hjvx5kjSPudl5A8cIMQ2XXrpFzV6iYZ++29TR858jWw7FqHPc486+/iYVo2Etgq+/Bh2UKB29C86L/MTvAciKvLy7HWWEf1O/4+M8Vz7a5nrYkvXRrwsE3WzAcmeN1f4ml6tnmXbKPuFHH3P0mM0pDn+vg+d61HvxuHppwnq/PcrLh41Bh9CykcIgPdy6K5BCUrFssqIgK3IWgvrh+XTj3haf3LlHexDzwtlZzk8/WBr1dcbuDvm0XGVx5alZrq1u0u7FPLMw+UDnlAMHvZkPHM1EPslpL44HrzWmcfhQq1UlScoSCD+wy3KdUXlMGDo0J0Js2yRNM6zRjGVlmzSaIa6ryiX6ZoAhBPVRsM6ynEYjZGIyJIo8pDTYlh0EMB/UAIFjWsRZihQGvqWwhCTOU1xp0RnGKCkZ5jlyFITTPKOfDbENyYwXEVo2SZ6VvbhNm5ryHqsdiWVIGo7HpBsy6Qa0/IhzUZMkz5jxK+wnAzxLgYA5v4o96sJWtR1saTJIU/aHA2q2i/kYx8RJx83iQpMwdMquXkcOlN/1uHmcY/T+x5z2tYS4/3w5/q6XXz/5juS0RXKGMJlxn2c7vkGSd6nZS4e1y6d1//l/9P//vk7V+1tsft1j5H2P+bpAd9TRAP5N/o6P81yP+vmOfv2g1WXLqTPjNh/6mKOv/bu8F4/LMxWLQf3wtc6GjcMPIIIHV0Hu/9mg4OxErZw/nwzLrY5TnGR/8HfI2v9NkRfHRucVRQEFx1o/3v+Yr3Pts7vs7HSZmoqYma2hlPnA8+VF8ZCL+HEHj7m/L9OxOzuOnyCPY6W9Q14ULIa1x3quo1+7/+9xXfXQNO0Plw7ImqZpmjYGxqM/nKZpmqb9P6cDsqZpmqaNAR2QNU3TNG0M6ICsaZqmaWNAB2RN0zRNGwM6IGuapmnaGNABWdM0TdPGgA7ImqZpmjYGdEDWNE3TtDGgA7KmaZqmjQEdkDVN0zRtDOiArGmapmljQAdkTdM0TRsDOiBrmqZp2hjQAVnTNE3TxoAOyJqmaZo2BnRA1jRN07QxoAOypmmapo0BHZA1TdM0bQzogKxpmqZpY0AHZE3TNE0bAzoga5qmadoY0AFZ0zRN08aADsiapmmaNgb+F18IprD3Bj7RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#wordcloud = WordCloud().generate(text)\n",
    "\n",
    "# Display the generated image:\n",
    "wordcloud_fra = WordCloud(background_color=\"white\", mode=\"RGBA\").generate(text)\n",
    "plt.imshow(wordcloud_fra, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n",
    "plt.savefig('wordcloud.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "colab_type": "code",
    "id": "BWEc4mqwLDCk",
    "outputId": "58b840e4-67e5-4c01-d0d4-0004dc20877f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 2718.5, 944.5, -0.5)"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VHR3B7d/PezUGZzMUSu+8qbQ7oaW2Ut75JEwch+dlGbK2BmmG6auAz5ClW0i3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\nsXk9g8FA+uP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83/cQxzEWiyUK3RnbP5qmQRzHSJIEnudhMOjDlx6R3fNWsMxSopAYY3Tv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Lmy+9zwPBeOY+P65g6DQU/ZEPV6XczmC1WljcIAmqYjigLc3j2g3+1iOOw/ORdpQgJa9w9T\nABR4lXwQhoEP13ERhD70maa6C/P5HHGSEie2EWpfnC9X6Hc7yFlQzHMdpGkGy6ZKc17k8D2f+HCg\ninBVV4CmYTqbU6fe91XHOk4SbDZbDgJbfLgsx5KFRoaD/v7z0jRUcYztd9/B8H14l5dcXGz2ApX1\nZoP1egPTMuFWDnzfhyGOB4BplmEyncJhb+M4STEeDTFfLNUZQWrY1CnaxjH6ZQ/XN7fKDuRhMlU8\nvTZ6QKEnBCl9npyMYRoGq99q6r2RRcxet4Mw8Fkdnfbl7TZGVVcYtOyWet0u3r3/AMMw0IlC/P7b\n79DtdJAwt7gsSxRloazCdF3Dar0hIZl+D2EQYjQcwHW9vULhbpp1fr81BVNbLFeUnFsWvUcsTGZZ\nVGw8PT1h9VsP/V4PP759h29+9fXe5643G/zhD9/xnNCfBYGPb371NamV8l6VM5RN3q/OXQnZgZQB\nnO3Y+OLlCyRJirfv3qMTRarjtVO2FiowL4qSugsZCccdr1HQmec4Ds5OTyEv9DDJbQ8hpFjhTujG\ntiwFG/7j27eIwhC2bcFzPby4ulLdRV3XkeUZDi+GIJtd3N7dQ0LXnwo4ZQcpbYkuEcrKRuD7ePni\nSq3l5xIhip32udXyfui7ydtd8og1jYRQ/uFvfwNAI0E3Lkjruo5ffvUlJrMZzk9PYZgGsjTD17/8\nCgLUIfrmV7+k9VqUKvDu9boM95/i4vwc5+dnOx/4J699n+4kuO1FgT3FD2VZkn8k6wwslivULDpH\nRWVNvTvyLCYo9rlCOXxqVFWFbRyj2+3ANvcbA3ItZlnOscwRkcIkVfFDXhRYbzY4GY/gOPRMfI8S\nVN3QlUWNpunIskwV8ibTKUajIRc6qBAzY74rKcRSga2uG5RFgcFwgM16A2issJpmCIJA2d8QgoX+\nW0JFXdfBZDpThdCiKJVydVEUDNfO6Ex1HMznC5ydnipOvaHrSDPyA8/yHKJpFL3LD3ys12ts41g9\nA13X0e/39gpzcn0DOyQL7Q8CRVMwl7eBbVlIs1R9n6QltO19sixXcwsBBGHAeh77BQgZ49R1A8PQ\n4TiOakhIep2u79Cd6vPzjKkRMVMldNiVBU0nKqLs5FZVjbqq0OlE6p1uj0/FEX8J4y86GVUPqanV\nhtCGC2icdNABJBR2Wv5eN3S1mclqvxBkI1Kzqq7CrTMEpapr2CzWMOj38N/+y9+hrCqGVhnqsFut\nNwh8D3GScnWcFo/f4tIRDr1BGAZYrTcMHQb/jIskzRREod/tfhYSRNc1BCxBLruwz82fFM2JgkDd\nv7Qd0XX9EW9IDikeAgCCja+VqplFvCTf95jzSEmxY9tHAxw5/OEQ8WSCMk2RrYhTppsmTPe4D6hp\nmuh3O3sJoxAuABIXKqtSbYqmaSghqEW9QlVV6p6PDccmcSvZwdZ0ghsemwvJ25BQj7ZZs+s4WK7X\nSNIUrrNfwaO5JuiuvAfHIbGUnLu98vrSjLq6URgi4KBKCIGElTv1g9uwLBP9bndvbhohgLUg5c6q\nenTvddOg24n2bSl0XSVcsusteQh1s1NT7vDmvOvk7vOkTMPEaDTgKiUFDxlXF9Wc7k2ttr/eNarE\nXl1eKNiyyarF5OX4GHbExCP1J5vNFtPpHF++eYWqrvHjj89zfbSWcIWmSVgqVDGj/Y2WZeHy/Kwl\nCkOdsGOQdzk+5322LAtvXr/CdrvFze0d6rrG6ckYg34PP/z4FgAQRbuOzMsXLxAnCe7uHpAxvO/Y\nXGhcYf+Cob6So6XuTMPewdk0DfKiRCcK6cC1dTRilywVZcVwRDrsJfqkLEsMBn3MF0veB3VKeg2T\n4eMCYRCiqgje3OlEmE5nO+RI01CiDfLkS7MMr754sSfcI4RAtd3CORkT5FzXiTva1KirGq676x6G\nQYAoChUUazKZ4uz05InZJ7E70zRRlBWiKEQYBJjO5ggDH1meq+KTVFCU54Xv+9hsNlyQ7GK13qAT\nRarQFCcJZrM5up0O5osF8jzHaDTEZrNVVihZnqPbIcXizWaL8XiE5XLFyB8dy+WaoGatorhlWbg4\nP4e0Bru6uMBsPke/30cUhqQOze9eURS4urzAlK+DuvZ9PDxMVPdgby3alurmUseCPAKnsxlOTsbo\nRJFCEZF6ZQe40LBcLuH7PjpRhC4jEbqdaO+9kPYfr199sUMJGboqJlqWheFwiI/XN6pLeHF+hk63\ng4/MfZTihUII3N8/KIsbqYYJ0P7aNA3evf+ATidCv9fHeDTC7d2dUiu9uDjHcrniwFZXhUcNwOnJ\nGO8/XCvVz163i8GgD6k6fDiaplYKynIvfphMFc/Q4gKRZ7hYuEu8ffcO0oLs4vxMccMPh+/76tyR\ndiVplmE+XyDNMkynM1RlhV6vi7PTE9ze3WG1JrHBszPiua7Wa/z4lr7PcRyMRgyF1SRkU7DIGomN\nybNL4+cxZW6f57msOptjOByQ8FRNhbs8z9GJIvT7PQhNQ1kR9aLX7ZDa7mKB5WoFQEPDgm/j0Yg6\n5FxwlN87nc0VUg0aYJs/36NXxjYEXW2UEj4VRSmxCRwHdV2x6FhJFAEAi8USwNMcRplcts8/nSkd\nx06CNM0IdaFTokmxgqM6cd1OxOuNbGWCMEAYkrWdhKVTM8amBKYke6/Al9oW1GzxfW8viSo5QfZ9\nD2VZIgx8xEkC3/MZZktCj67roixKuB75KZcVPRPJecyyjGHd1PQxmDu+2W5gmqTOvt1u6WfLUnVc\nSb02gOe56j4bidLg4o3fbkRgX9BHNPRdBouPfry+wcXFuYoPDp/JerNRxSzZ7ImThD3nXXLFABXB\nsixHmqbcJKJOaV3XdL6b+7FN+1eTGxZ1VbPqfEP/XdXQdbLwk6q4u+dmcTxGPrqu62C93ih4tADZ\niSVJCtN6nnv7lzz+opNROZQgimgFw0ykBweROndrGt5kDytmhk5dsqqpFLRDMHlfNzjY1fa5ILf3\nE9ww3ER+nGHo+OWXryF9ybxWIrWPu9/fyAa9XbIpE2i5+cl7rPPP6Yzu7ulTQ3CFSG/xvmS1iTzq\naiWO0h5SsKD9HbZlKZiCHE95Gj11bbbvw7y8RJEkcBWH7ukhu5WHf0Zekqnq6Ox9p2jzdJ+Gxu3+\n/HFV+tjIGQ5Lz3v3uRJWWBRkz3EIJ5HwsB1Mm9RZJZTYwI63auj6nlqwhIwcS6iPzQ2JPZBVzDEl\nU8s09+C4ci41bV+VdAdt3k0RQTgzTuA0pZypawT5Gg77mM0XkBY4wSdEtBzHxmw+x2y+UAdSJ4rw\n8DBBt0Mefv3+Ltl+fP+AY1tYLFfq0Gsn8FL2HiAYTLyN6XDexuy9+ezlPZq3Xq+Dh8kM/V4XFXfK\nPnV/q9WK1P2YU35sSJsQy2yjJWjdeK6L6XSGr7/+iirmeYHFcgnLNFWi/tRchIHPoh4zClZE86yc\nOx2aJpIkVYIuhlkh8HyQ6rhU2TWh6TpBNj0XRVlQwOqSh6Om63BsRyVl7SKipmkkwOA4SNIEtk1C\nN9JmJgrDJ+c1u75GuVzCvbiAblkwHEdB7dpDJis52wJ0IrJWemrkRYkwDNXcGIaBQb+PJE1wMhpj\nG29hW5ZCwlQVWd5stluMhgOyndINjIZDrNdrVbRJeR5934PnUpC32WxUYOp5HievGQVQloWCA1XD\nNJBltVrT7SE7NnL0el2lIp7nBeIkxpdvXiuIm8vfI0dZVsom4/DdkhZHUgPAsnT0uj1V3CUPPxuL\nBQkzzeZz2LaNL9+8pmQpzXBzc4vLi4tH3Z/A9/H7+RxJkijYuu97ePPmlVKiPhmPlDWQxYgPgiAS\nhM1xbNXxjToRPEYytD1WHcfGm9evkRc5TMPAar2C67no98lmZzDoI88LgsZyRzxNU0A3oXPRZTwe\nsp4EdYHTNMPJeKy0IuRzkIM6Ozv/wkG/h8zzAAj2PaUz4YuXL1QALBPuIPBxZV+oz2vqBvGW7Cl6\nUQRN1xBvNkQnyWlN98IAmhDIkgTzssBgPEZVFCjKAqZpocpzVNBwMhxiu90SNFADQ3dL1WWWZ4g8\nRwpWhpZqnU3TKC7zZkP+jfS+7tZwGIbIGBFB5xXtW5ZJCAyf7Y96va5ST06zlGk9pHhvWxZx3Hlv\nME3jqF7C5wxNIw6o+4RwWfhMsRwAI+Ysxf1+aszmC2zjLUQj4LgOhv0BOSIUxWMOJh82tbTuAlTS\nYrN9Td2QgA4l+TrroEAVlbKM0GxGVUIqaZcVdTpNdk8wTQvbOFZqr+B7EY2AYZqMeKI9brFcImIa\nkmkYsFn0U8aHVBRzkLJQ6Ga7xTZO9mxYHNuByehCyyJuvM0QaOk1TQ2RbN/ajRWqFfScERAAVNwp\nr0Oe6WVZYTDoq31NIiZlc0jybeXfya5lv9VEandWCYIdMWqAnpHc/5qG/LNL1oeQnxkGgUIYzBcL\nSPiudOKAtisi7ZCYVDAIAiq4ygZEp9WgkNaFYRQ+Qyr5yx9/0cmo7Hi1vRUBrpSjBlqplDRifsoi\nQ0JGJSFYQVeMfT4P8RUF5osl/vs//jNGw/4efIUEj0wM+l1S7DIMVYV/bhzb2B4FGs9/xE8eMgmv\nmIwtv6+WlVDdOPqdh9Lc/GEAdhyNqqrpUH+G/H/0mgwDzmd6HMnqo/Q9lLChttDH/6ohOyI5S/a3\nB/Eqd7AmOahj+BgeStUwqE6WhPTpxmNIkFSPE81+0P1z5sY4di3YPdfnhgYoBd2mEezNpqPRBESe\nYzgYwDQMbOMYpmGqTsFw2GdonIPxcAiHLQmock7JoWVZ8DQXL19cYj5fIklTtk4woOsaxmMyR5cd\nJfm+np6e4OP1DTbbGL7vI4xIIClNU3Q75Den6zrSlNT1+r0uC5QYiMJQ8cw0jVAQ1BnqwjB0dLsd\nJUhlWiZOT044AIlV5dYyTXS7XYKLgkSCGnlw9vuo6wbr9Qau4+Cp10TyXvK8QK/X3eOGSA9Aaauj\nacTzi2MSNxgNB8QJ1KC6k+vNFpZtw3UcXF2e859t4Ps+GiEY8mSoDjYlMgT57nKnXdd2/CHJL2sn\nykmaQgif5PK5GBEnpDBusvfjcNBHzP67dACb0HVGpOgM2a6oGylVEp8b3suXgKbBDAKyq9KktdDj\nn+v3eqojUFeEdJFelO1hWTZeXl2ie5AAD/o9DPrkjdjWMXjz+pX6bw1g2JpLnM2MbHkKFtLodDqY\nzefI8hyDQR+ObTMXnOCNls22I7qONEnQ1A08z8OYTd0FgO12izAIVNB1+O4KQZB2x3WQZzkc10Fd\nN1it1qiqEkKAE7hCdaAK7pBQd3O4x0U0TYu8JFnAi4I4qKBS+j42olE8fvleygRW+iIWRaHQR5Zp\nIk4SDPp9fPHFC0U5kJxrx7LQ7/fQNNQJDHwfSZqS+qRhoKor2A4p+86XK4RMTxkOiOOYJCl836P7\nrEpEYQjX7ah503UDrkOeewSlb6DpuyBaJr913aBuaji2A9chsSkKagl1lcUJ4uUK4CSuaRpYjq2K\nBpq2g8geg9UZhqHsIXZzvu9V2gjqGBV5BggBUzeQJgmqsqIuHu+JjusysoDPgYIsIHRdg24YyLOM\nzx8dhm6hzAt0ez3lU7zdUsfG9VzF0ZOdw6qukCSJEo6SCYhUFpVWVrLj0zSkMeF7HmzbguM4KMsK\nXeZ62jb5z/YY5RTHCVyHBXAEWdS5rot+DyRCtFwpX8mfMuQaPEwGj42iKBS1oa4bUkblwsExuPLh\n+9eJQriug9lszirB9L7UdRvaTMP3vCfjJPm549EIRZ7DdaiL2O9TESgMAgS+v5dsLZYrWJYNyzJJ\nu0Gh8nae6aQVksI0TWX500Zj6YyIsixSNg6DliuBpvN7USkKQpbl6IQhc61NgK/tcJ7a+7gsph/d\nt9Zr3N7ds2dwhsuLc5yeUIdfIo4IIdRHnud49+EjsiwjUTTDQJKm+PDhI631PEcYBHj54gpVXeP9\nh48oyxJxHKPT6eDNq1ewLGPv+/O8wGazQRiFiLcEP9YNA2mSIggDNHWNsqwIMSWkcjkl+2EQIAxC\nZf8kKSZSY0OKndH+Rz64Ekosn3e7afGXID70pxh/0ckocLxTVZYV4iRWlT0hKCFwjqggSrn6R5LO\n/NmiqjCfL1FzMG8Y1OlarjcYDnr43//rPxyF4t1PplitNyjLipWB/2MXVBs2IjhH1zWy4Fhvtliv\nN0zcJ16Orml7kOL2ODZPe38i5KH7eTCauq4JaqqTb1JTk5qraRpwLFt1LmTga/NGSzyMRMFoZYf3\nqY7s/59DHsxFURytPFCi9xkFhyM/KwW4NDzRBYQG0WrlCSEUBKcqq725ObSD+NR3f/ZoVS81TSDw\npRUSi2+xKInv+6yGSIljGATIcyLwS85nUZSoKhIXcB3iJ8sDs9vtIGpqBSMrywqdKKLOzGaNMAwY\nUiMVP08ZdgVACHQ6HQwtU3HzBCiJvvLP1TsieUBt249er4skSRGwibbv+6ibGp7vs9k5K0wKaWFD\nolG+53LgJqhrWlaoqxrQNPR6XdTc0Tk2BL+oUkFXDukPuFyvMR4PW2gNoSxB5GEnJd+haRgO+son\nlDpwpPApi3plWaLf7yrYXpbncBwHKXsABr4Hkw/JdkdT8sTkkNB+jb9TOwhM2pDZTy+rfVufHfz7\nANnieQh/8QulbK6+56CGIpMkyQ/stXjlh8O2LFX53j0P9seUxZm9db97fyS3SAAtJUaNOb8aTNPG\nxfnZo++URQA5HMchlXZBQX/773tdQgis1uu9oFOOpmkwnc1433TgBwy/44Kh6ziwbZttuBwVOLXL\nnu3gVdOg3tE22qfdWZVzIJNWTdeRxAnyIleUkc12QgG9BpRFidFwqMSzvv32B0j7Nt/3YNmUDG7i\nWIn6JW6KPM8RJwmiKERd1ej3e3h4mKBiRf0kSdDtdPD2/Qf4vo/laoUsp4DwqzevVTHMb4nvtIfs\nqmvAHrSxvR5lN0WOqqR9S9N0lHmOMi8Q9bvoDPYtvj5n7Oa9vdapG9TpdiHY+qyNphLtX1vXpus6\nTrhbroEg+l4bmSLvle9NM004A3vvXgEAfJw3aYMGDSX1TonAJzEYqW7tODuxKOlwQJ9DxaFjdkrd\nFhKq1+2irCrM53MuBPaV1RCAZ2D1z8SrZ68AACAASURBVI88L3B9e4uzkxO4LnVxpSVRlhOvPctJ\ntXzNnV7PJX/UyXSG169ekkWdZcI0TCQpWTJNJlPYto3RcKBiQYffrfV6gzrPAeZYt3Uj5DiGYjoc\npmHAbD2zNvIqTTMV9xiGTtDtIEDd1JjN5zgZjx89S13X0et21Tkq/79ksRw1x9qO6qb27TBAEPjq\nZw+7iup7Du7zWEHx2H0LARIX7HXR7XTwuz/8Ad1OF6v1GtPpHL/46kvkRY537z8gCAI4joMXlxf4\n3bffqXOormvM5gt8/YuvYDs2vv3uB4yGQ8Rs1/TVmzf4/o9/ZNHNx9eQZZnyAQWAKOpgG2/huA58\nz0PO2hemaWKxWJIThMAeInLv+ZkGUcZ0TcUdtN/qvM/8Nfc8P2/8xSejxwZxUvar2e1gqa5rxugT\nEXi1WsOyBuyBuDN+lxtVt8uHLVcIdU1nEZ/H8Fs5wiBAlueq+/IfPeqmwWQyg26QMpnvEQzB4S5J\nludALg2wiQMlvU+PjecSF02jDvFzfLn2KFmxVgOUmXZRlMS5FSnyolA8ySzPccLcls025kp+AFea\njGskdrNabz75vceGDByhaT8JCiQ34SiKnlT20z9jLo5+NmS39AmOysGfN02DzWaLqq4RBgF1Eaks\nr2Cfz93Hv3ccVvYAoK4qulIhlHdfURSs6uvslOOKUnVbpO2BxZLrhiEUDM9l6yX5MzIJk+8z8Uhl\np1dTEu+y05immUo6pcBPnhcEkePq/eE9Oc4Oni47bvK5y0OVIDU6DOgtP1+h1lPd7HhXQghk+dNQ\ncUomFhiPBrDt3VrM8xwfr29hW9aekA8p+IYK1cHfTO+FEIo3T9etw/N296jxv5GfZVkGbMtW9ylF\n1MDXXZYl0jRDktL7WZUVqzZLNXIdpmnBcSy4jgvXdZQYVTuYf27I4kBZlsiyvPVd1F1sf5dl0TOT\nsFed4e6HHX0JJzVaegCf2uMUJzxOsE0S5CwCBkAl7qHvqzWoaeSZ1+5EVBWdIYfw1PYoK+LdHr7m\ngU9Je54Xas1LFVVN01T36REsX9dxcXG+E0YxyLJD3pOkVYScOJOHI+3fTUM2COv5mhTLy1IpTdN8\n7ywVHLn3tkbdNLi5vYNpmhiPhkqhVGoZeK4HpibDsmyYpoG//7vf7n2GYZA3pG3b2Gy3SvBrtV5D\nA1iBnJIXxya7CuK7Q82LZVnodTuYL5ZI01TZlBwbdV0jzTKkaaqU3+umVoG2oUsRMFslKqRuTfce\ndDsIOhG4XbKbSyF5mNRxUetO1/ZoPPK5FEWBbZywz2mplPEN3YBlmcqmQyqjfs6e3b5nmWinSYo4\nSVSSLqlNUlwm8H34rITd/o7HrgYHlBydBG+kyvax0TAfsY3o0Y2dRoVlmmzDsz/KstxDMdi29dkd\nUgGChD5MJnBcB2mS4fR0jOvrGxRlSd33usF6s0Hg+5jPFzg/O2XuogtD1zGdEZS81+0gTlJcnp+x\nh/qnmw15XsBzPVjW430lL8hGUIrjtfceyW3MsmyPeiRFyibTKTpRhNV6rbr7eZFjtVrDONhv1puN\ngpRKRwRd1xFFIVzHUc+sqmvEMWtFGIayJ2wnnFTUzBkJQ9DyIAhgcNyXZRnSLINj28xF9WBxgq7r\nGjpR5+i8aRrx05M0hbS1Mk0Dkyl1Kn3fg+e5eIcPSJlfaxiPUXye56Lb6xI82CZxRNXE2BI3/yl6\nTCMEDBaTojiWGwr8LXmWI88LRKGm+MdFyd10w0CWZ4xWqFUMoyDee/e6ry4s/+w/4/irTEaPQZba\nG36eF1jxC9HrEfSsrhusN1u4DnXiiqJEpxOSMqr7uEPY7UTYJgn+73/6F4yHA/UyEKRigLIscXF2\nehSG+R8xZBdUCAHN3nUtq6pCXpDamuc6qhIG/PteCk1vV8ie/7eObaMThtB0slcxdLKeEEIovyrT\nNFCUZMtCggKVUnBs26YIDro/NUTr/9sjSVOs12sIULVWBk2fGlJZUohGbcZPfvcnIK+HQwZCylNs\nr0tG99v+Numn5bAH1+HB9icfn3E7um6oIEImmjvxIXo/y3LXAZdQIZk8apqmbGokj6lthyNFPiQ0\nkFQJDaQpJbeWZWG73Srxlyh6LMfv+96eVP3+9et78K52wkffrzNyomWB1EJT1HWt1nFZVur9sxuh\nOt8NB6KiESzvrqukQPr0WiyW8+b1TuglTTNWM97BeuumgcadCMn1AsO9bYZjikaozpNj29CNHXcm\nOhCkAl9jmmYM7V1zl/HTD586gdRlPD89UaqeTw3Z1Y0TsrWJ453X3KeG5LSNhgMKoGznUSBNcvya\n4rAeG+2Aa7VeYzKdIU2zJ65hg5muw/NcjIdDdA9E1QA8aXzeHtttjA8frz9rf4jCEK++eKG6xYdd\niPZnFkWhoNzyuqq6xs3NneLuep6L16++gMnv1XQ2Y5XcAk9dzpzvq9frYjQa7llp6dwdomdv7u1Z\nbVVXSUEQAoonJe1IDMNAv99HHMe4ODvDar1W0OTlckWJkk6Ks40giFuapuj1ugp+OBwM4LkeXCdF\nw+9FluUIQxbf44RguVpjsVwiy3aFhucGQVypIDAaDVm0Tmcl+kztBXI/CNli7mEyIRss0D74JXNi\n5ZqfzReYzxdKfOmpIYWxBlyM0pmfVlU1IyMaVZzbPxsbLFdrzOYLpGn67Dul62RLNhz00WN6wrHY\nSgiByXSq7kvTNLx8cYlet/vkOZhlOd6+e080Ah6OY+PL16+efFeEEFgsV7i9u+fvAS7OzxU89XOG\nZVowTBN1VSOMAoRBgKqq0e10YOgGttt4ByuvK0IzcHKYZSTQRp3HkAsGpC5uWZ+G/pKS/+5e8qLA\n3d096rqGZVuoqxrzxRInY/Kdlu/Hw2SKbSwh+QLrNSWZwwHFnbLAIotKkr9OSuz7e+12GxOqII5V\nZ5ioCpZah1me4/aO/KZty0Zc15gvlzgZEWdb7ouz+RyL5ZIFPXWmBKxxfnoK0zSR5wUmkyk8z0NR\nllgsV/A8V4keNY14rIoOWj+9bhc3t7dwbBtv3rxSImSyU6nO1kPlxtbQdf0RYk/yOtfbLc7PTvf8\ndNtjOOhDCDq75Fso91CArJB83yfKyXBABUj4EE0DTdchWjSW9n0dXRefWZz9ax9/lcnopwZBy6hj\n0tQN6obV1JoaWV6AjIyJu9AWWmkfDhvetB4mM8xmC7XJUBXpb1FVFVa39wh8D4N+71H18+dcs1D/\nvRP8eO7ft5METSO1svaQsE3qBlYoS10FNbq2szL5OS9JGASPOsd1XR+97qqqsIljmNzhIJhmxclE\nRvyErGJ4J3mL2rb1KEmkA71RgglPDU3fBUHGwfV4rgvbsiAEFCdK03ZG20/Nh2PbiPUUCV+v2Qrs\ngR1f4ufMpexWb2Py9wxan50z98o0nq/MymAnO8KN+7lDBeyCDamfuT9d13dqjBqpNRoGCSvorPzZ\nrrrK+3McW1UX6XnsIKBBYKhnYhiGsm4IAl/Nt8OdkqYhmxgZUD01nuqFH/uZsqoQxzEChh7L4I+6\nMjUmkzlL5hdkAdLUGPR7mM2WODsbsRDEGkHow3MdTKcLum6D4OiSD7harmFaVOw4GQ8VRxMAe8/G\naBoByzJgmpbqHshuLiWXBIfexDF8z6OkV5BasvQOlOJAx+61rmsslis8TKYqIPjc0TRCqex+TmWn\nLCvm+m4/KwE9vM71ZoMkSXByMsZ4NNz7+9l8gSRNoIFQFqcn40c2YIDcBgUmkynuJ9NPJihN0yCO\nExJwyXOcjEfqGclOrLzzsqpgmaYSV5KVfZksfM75IGkBABXCjqEutnFMEDJdh27o+5/LkDI5v3le\nKAj5x5sbMk3/xBAg8S9Sh81wdXmuuidSzOrgGAKwv76qusa//tvvoOsG3rz+AoHv449v32G9XuPy\n4gKnJ2P0e11IQRNS76bAW+JSparmyWgEgGCE/V4PksvV1DVGwwEWjNaRybAQAtttjLuHB8Rx8pPO\nZUmFqOIagxaPW9c1htGz4mjdAO1zoFUQrLnIBIcsRW5u7rBcrT7rOiQ/frFYUjGJOzztLk1V1TgZ\nj9T1FmWJu7t7LFfrz3qv6KxNkGVkWXZ+drpXcGgPqVIsR5blEJ2nY5Qsz1QxUY4iLxgt80QyCuwl\n0FIc8HOHbVmq4BFFoaITXV1eIE4SlahonQ48z8XpyQkqpnvYNiWxYRYQ6k6jGCcIqHss96qnuu5l\nVULXd8JLQgjMZjMIAFdXl7QfNAL3kwdMplP47JNelCVW6zVOxiO1pg1Dx2YbK0eDwRGPyZwVuD1v\nXyAOoIK77/t48WKsCp7tgsV0OgME8OLyUglVTaZTPEynbG1kI0lT8hAeDlRCl2UZPt7cqIQaoPe7\n3+vBMAy8+/ABuqbj/PIMd/f3SNIEA/E4GSQEQc7om5QQDqaFQb+P6WyGj9c37AdLSJg0y7BarZEX\nBZbL1aPYd/85VEq8rmZHAfK/3Vc/bqOfYrbQkgVM27IVd1sIgeGAklENAOTz/0/C8/xTjv+UySg0\n6h5FLYVNaWJcVTUdekUJz/PUiyqTl6auoWk6xsMB/q//439DxYIbwA6CRQIfxLGSlV4BqGqJ7E58\nakjITlGSJ5asIuY5deBkEC4rdxIGVFYl2mI15KtYMQTH3LPuMAzaIOOEEik1RQyVibja9lOSKFk1\nloNgCiZtkNuY5kkIDgwcNa9F0zCckYQ+JKyhYWU99eyaGobhwjJps5a8uEYlose7W3I4TLjfbmMS\nDiBUFQmstKrZ8hnYloU4SbDebvcOPwmxkvMaBj622xjL1RoOmzU3jVBJuDRi/jnDc11krIZJVgGG\nUnQ7FGkxWBREzrdlkQiDVEj8Uw1N2ykXrzfbPY7wIfxM0/YhijLpagfRQuyLPMkkXAYnz3FO6LAl\nOA2EVE1lqXWdIHuu61AXWdvnuex+npJICcmRf0bPTxYnWtfLMvrrzRYagO4B3w8Aet0Ik+kc3W6E\nWYt7vrtn4h+maY6qrnF2uuNbUdClodslj8SHCYlhSKgtQR5z7qo2ME0HcUIFtII5XEKQ4qVhGEq6\nXhYxwiBQ3m/eEeidHHVd4/5hgglbrhwbUthIBt7HkqowDD4LUmcYOgSOd/ENXScPWoZgN6JhO5H9\nZLHia7a5c7frUJoYusTtyvJcqVI+HppKvlXwy++5YRjqDJEQ2fZcSW/R8WjI3bIUKfvVlWWJNMtg\ncefOdV10ux2EQaC8OCUCQs6h/N9T8x4Ej1V1AeKYDocDQpMYJibTmRLlOhyyE71eb/YSUdnZlFzS\npq4V77g9Ntstbu/u8eLqsgVto26dvLamIcGuoixICEzXcXd3j9lsjl9/8yvYFinxvv/wEa9eviA+\nKHNipXm9bduI44R418Desz020oR8BE/GY/YGpSGEwHK1ws3t3ZP2Z20xNPkMDu9bFo3kNdi2jf5n\nnO0AJaZVRaI6Nzd3WCyXj75fxhaiIeSEXAcS3eE6pIS9QzPRnFuWybGJrhLRjx9v9jwU6d9rXOij\ngkbDcYaytAMVkxaLJeq6xovLCwW3bw8JGZfXRzHK8Ya9EAJJkj5a03XT0HviP+ZVAqQk3Ibotn2T\nP2dIu5zD0elE6HChvixLeP5jQaEtc5ZPT8YqRpRc6aZpYHBHElqpkFHt27NMi6gMQsL2CZ4bBAHK\nokDJuhikpF+iyAsSd+PYoV0EsCwbTbN5tqCwi7+YltDiR5vcVW+r48pRliWSJMFwOFBIINJqIHsq\nqXIex2QR1el01BnseR6CIMBmu1HdaunzKc9U13VYDdhE2oo326MoKAG/vDiHZVmYzxf4eHODr968\nxldfvsHH6xvM5wv89m++IbGiJEVRFDg7OUFRlciLAr7n4+L8XFG3zs/O4Loubu/vcTIaIYpC5HmB\nt+8+4GScsu+s4AKuA8exlX9zWZEg12q15qICUXp83/sP0Sf5ax3/KZNR27IxHPTgMCwhioiTI4U+\nNE1jDiIJk9R1wQmZ7CbSQSgDSyksY1kW6qpClhfKioHgh5WqpMjDzWYjYOB5LmFV1yhKeVju5K6l\n8qIpBNxWUF3VFQqpmKrh0b8HAPB9lxVJslNXyd8dfAxdStIM2yRBj/m3hkFQRf2IQqXOwk7HYBO6\npiEKQmyTGHlBnQOaYwcuSEJfVtLAFSblR6pRptj+vZytMAyoG5GT/YOmk/2E67rYbGMAxH2QfB3Z\ntXIdB3XQIMvp/iS80bHtRzpTmrbzbc2LAmWZqGT78MCSqp8E1eLOEwCdP7t9wJqmCYcTqMNhmgYc\nYUNwkq5zgtyNQsVZy3OGJHIXsCyrXVdb15QJdZqmWC9zGKZJfA4NEPW+L29T18qwvj3k30mrkMMh\nu5ACZD1TlKUqYhxC2zVNh2nq/DgFsoz8xtrJpGkaewe4aHV/yoJEqg4TWmnDIIsvhqFDNAICLAqg\nAQYokBaCuhGyM16WFcm16xp3FilpN3QdVVmoYA5CsH+ae/AMDXSiSCEtDruumq4pXqqCDB0EYPJ3\nFXfLDrvnpCy746keBnAudz6l/L6ukX+oTAwti/jCuq7D5+JaIxpltRJFIc3DE0WSpmkwnc4wmU4f\nQXKt/4+99+yS3DrTBJ8LbwIIn5GmMsvRU5SaUve0uqdn5sy/3rMf9qyd2ZG61aRIFm0VWZU+vIU3\n8+G99wbCZFaxd9VO9Z6jo2JkBAJAABeveYyuw6sR1M0wTWgqPfhLfj1GcYwgCBCEEfI845SI1zdD\nFEVBq9nAcrlCWRTQDQOuQ4kOKVpSol0WVBxkeY4ojjCdzDZsWvI8x2A0Qq1WkzzunKt8132P7G3u\nOO4sy2QhSkmXh2ajTjBvhQR2iiJHFMUYTSaYzxcbKIj+YAjbtrgdTQN1v5D0AiEzQ+iAtUJ2zXXx\n9PFDXohSg4EUQHP0+wMJqd3+fSbTKdrc63L7PIqkN0nJaoMS6d01pyzJmzPlRYgQC2vW6zB5AknH\nTE2t8XSK2daEbb5YYDZfoNVsENcxpylEo/ABVUV/MMTV9TVc18X1zS3ef/cdpLzpSOeOnkkFb/6g\nBC6vrrlAiILxeIIHD07w+Rdf4rB3AMu2oKgKNeQ0smbLuYiLVyPudMabx9sNoMViiaurm8qzlUJV\nyE5FFHoav/dKXjgmSYwgCBEExF/2PW9jkncfkmj73qU1iCbLU+6rralk4UDiQKa8PvMiRxInWIWh\nLASEuuu+qKJQsjzH9fXtRiFKzzUHrWZD8kLp3i25MOBKepbK33e+wI02wMnx4cZ9wxipomqqKq+f\nOE74vbN7fwm+6L4IwhBt7E76AMpt0srvJWxLsiyjIpVRMSUanyb39Rbq3wmnX9VqLvGh53NkaQZV\nU6VWwSoIUfRzdLsdqWLe7XTQHwzR7bQ5TPgGTx8/RhBSEdRutxCFEZKU6BBCLbp6P2q6Bk/35GRU\nQIEXy6X0nAXoeWAYhkzVBMpqvlzCMMn7c7labqBj7gqR75RFiaxYN+tUrsVwFwKmKIudhoMoXEWT\nQsDoq7oaIq+t2qYxxilb/N/rc3JPzsu58zXXhappmHJLMoBQJHEUSz/vn16+gutSk5P8a+uYzeZY\nLlc4PjqUn+t22qSKvFqh025L1JGicCQV9zVVVIWrZ6/vK8/zoDCGTqfNKUMKaq5Yz9/cbvFt3B9/\nlsUoJSbrQ1eVNUSKgXt6FgUYn5gyRrAoQ9NlUUMPywSaqm+oddJiJJJWSjpU3p3ZTkRr7q7XopjS\nVuW4q1j114XrONiz2Y3IODwhCEPya2w0NqY+IjlI+cRBwE9Mw7gTFnPX34RxfQnAr9VokRLduBLS\nELwKYxUPaYULNhCXjmBPaZLA5F6bukbm6WL7CmMIwhDT6QyrIJS8BFFQEoeFFk2hahdFayW6xWIB\n8H9blinVPhVFge/VeCLPC8ytokH82zKpuC5kQbT7XvqdbADr3zXPMsRhCLtWk9Dwab8P3TBR4zLu\nuq6j4fsbMCW53colQu/V0Kj7uHr+AqPLS3QfPIDdauLVs68JtvabX9NvXRSY3tyi8+BkR+ygLEtc\nf/89Og8ebFyDcRiCgcHgog51z6s0ZvYf7/ZzTwhXrCc/3AuYTzaLspSJTMlb7NUCWkCykziV14Nh\n3D3Bl0kRL6rjmHOP0wxlXsCyyW6ACmL67QxDR5pweXa2C1+tcsLui+qnxDRPSPynfCqj6xqW3Gev\nOulmFVfcne2yXZsInXPh9vnlidfSLCOeKUdVOPbdyfNytUJ/OMK2Z2+j7qPb7Wwo526HX1YEiHhy\n8SbBGDVT2q0WLNNAzavJyYD4nmg2JyhXlsKxHXRaLfieh/OLS26lQCFEluo6pygw4PrmltAUmoaD\nbnfvdEVcY4qi4OjwAPV6nawtwBO2opTNvpOjQ1imif5gKAuOLMswGIzg2I7cvpgw131/xy6mLMnO\nIAyjnelNURSYTKbAnmKUztfOo0VGs1HnzytaM+4TWhGFmaZpOD46RKNeR9UzWYRh6JyPa+C2P6gU\n4SWm0xkadX+D3yx4ucPhCEeHPRx0u/j9P/wBeVHgoNvFgL/OGEOn3YLneTg66sEyTVzf3EqIbhhG\nOM4pCXz08IyUdYNwQ1hwO9FWFIblcgVD1yWcMU1T3Nz2dwpRx7HRO+hu8KW3C/yydNFu0e8rxFn2\nXftifXodLWAymSKKuVq16+Co14PLn0078EXHQbPZkEiA+8Swqs/yyWQqi13xt067hd5Bdy/qyTB0\naqR4NVxeXW9MyoX/ZKPu7zRXdUOXxWiapcjybO/1lnLtBwByDVquVgDAn9n74a5pkm7YplkmoW9W\nqxVenV/KJqOqqrBMA6cPTjaeRUTTWB+vQHpkWYYszWDZFhiAZrOJ0WjM/T2pYNE5z9S2LZ4vabi4\nvIRpmtSo4lZPYttChXsVBPQ6sFPAM4Wh3WxKH+BqiPdqmoZut4Obm1uEAXl6app2J8KhGqKgTNMU\naZpJnYT7lFtF3rrPKg6VoYfw1N1sHpcy31o/s+5vPorBhxgyMMYkD/vl+QXKsoRj29I7WVVVtFpN\nGvwUBVZBiOlsDse20O12kSQpLq+uYVsWt5Fa5y2KwnB6coKbfh+T6RSaquLRw7MdsVNhOSVCTJkV\nLmBWjT8X25V/jvizLEa3w77DwgTgxRQvVElAJZc8NHHBJklCkxPOUVMURcJyc15kyQKXP+AEBEvA\naMRrlKyWckCXF4UscteCKFtwQX5Di78J6wHxGbFvYntpRv5QwjdsO+EQxZlYWMTftotVwaMSN6g4\nDhFFUWA8nsDkEEDLshBHMSwuly4gQbWai+ViiXa7tcFR8DwPWqqBgSGMIvheDUEQQNfX3LzthEFV\nybZDWAdkGRWvruvwhz5N08Io4t5yvHNarqG9ZaXIKPIci/EESRii1mzAdF0sRmOkcQy/3QZTFQTz\nOdIohmFZcJsNrKZT5EmKEiXq3S7KssR8MESeZ/A7HWi6jnC5xGo6hWFacBt13L58ifHVDY6ePkGz\nd4AsTZFGMewaTd7CxQJpFCOJY3jtFkzbxnIyRbhYAAxo9nrQtya1wWyG2+cv0Dzsod7tQDMMdB6c\n4Or75/L3Wown0HRdnsM4CLAYT5CnKZx6HXEQYD4cIg5W8DsdlEWJn774AppO2/K7HTkp/jlRlnQt\nx3FCQgqMeG2OYyOKImQpeXN6HnUwV8sAtmORKl6aIQgiDtkWHdifz22mBgfB08S/M+4Bpqnk16dq\nKlRNQZKkKMufB1cnuJYoztfcWNsyMRiMaZrD7z3bthCEEW77Q6gcvu55tQ0UgvIzj7EK76R7upSJ\nlb5HwGk7hAJ3FbLHQOIOR4e9vd31bd6NgD3dB88VCtbCkxKMoIPHRz25DdEsFMklweq8jcadrus4\n6HY3RIAE7833aryIrsM0TD5Ffv35bDUb6LTbiKII88VSWpYkXNiHmj4NHHQ7iOMY09lcfna5WmEV\nrOB7JPoxGI5weXUN3/NweXWNvCDo6Xg8IeVSlxQ8f24oAp6+FQQhJnP5MAzhebU74b4iGGM47B2g\n1WzsPGNEE0wgfrqdNlarAIvlulgJowgJF8QisZecmmuMwbRMrFYBAjdEybeTFfd4QjNu76Lr6LTb\n0h6huvY7W0296nGUZcktbWzJqSxL8ggXRYII13VwenIilWJHozFc15VTeEPXKwJyKgbDETpcuDCO\nY2qUZpnUWgjDEIPhCGenD+69xsR+1FwXp6cndxa34pgAus7fRBALoAbDcDja+N3rdR+HvYN7lXgF\nguj4sIcf47UtT1EUGI3G8L1NVWKhKh0E1DDJspxg6HvUSuMklmuKrmvwfQ+rYIWypFwq5c3yagge\nYXUSL6gFCqdi5HnOlWjXGg3bAjKaRv+tqSqJbFkWwBiyNIXv+1itVlgul9B1HTXXxWA4RBCECKMI\nTGFoNhpIUyq0vZoH13Wkz+f2+bvvXtM0DbquI4pjQj7doymSpkQZO+h2pcPD6/QnStoQCs6nNt5A\nYAlYX1tBGMKv+xJVJhom4ve0LBvzxZJPgy35fAnDEKZp7lCH7oo4jjEYjmSRbZomvJoLx7KhazoC\nbt9UFQ/UNI28iZcrgD+rdcOAzX2Ufd9Dq9ncGY4wxlCv+xKSLV7bPi/3NXnexp8u/uzP+r03NC+u\nFECKsyTpeiqlivdscWP4hyUESdF1JJxnI/D/cZLAMAzJ5xAm78IoWuOFpBS7KArECYmSCM6p+D7B\n4cmLAgbWCbp4TVUUJLxwFIukmJ4laYpVEMIWnA9AWl/keb7XFwsAprMZFnOaJIr98zxPJjzi3Fm2\nRZ3P5Qp5Rgp1ccykh6Fp0mcFHEqcW8dxuInwkvNBhcLdelJLx0gTCkV+lqHVbMA0DLkgy/OEDGWp\nIFMpQXJsW8L35MR2KwEbvDrH+PoafreLNEmwms0wOL+A6/sYXlyi9+gM3/3+H3Dy7ju4+PZbPP7l\nJ/j+9/+AzukDrKYzpHGMPMuxoX8aogAAIABJREFUHE+gaCpmgyEOHz3Ci88/R+PgAGVRwqn7SKMY\neZrwqRwAMAzOz1GWJQ4enuHq+x8QrQI4vo/R5SVOP/gAr756hlqzgdHVFerdXe+2khues+qDi7GN\nZmVR5Hj17Gv47TY0Q8fLL7+CVath8OoVzj7+GFmSYD4cQTN0zIcjHL3zFHEQQvHUO61m3iSkcit/\nWLqujfl8yYVPqDgOViGKvICq0YRcdGujOIFpEUxpuaQkpsr/3DkP5eZUD+AdeVuVnXuDm+jt2vIQ\nPNaySHFbTDQButYICpnvNGLKskS97nFF0DoUVUWr1YDCFLTaTYIdM7aRMDUaPrI0k1xaxoBOuym7\n0NJiCmsBE9oXxqdj6/tOJGNxQqq5tm1iFYRwbFM2qQSnmc4LuN+kIbcbrAI5sRDh1lz07khkBZQ9\nCiNaB8DVjVVVojHovK2bceJzt/0BDnsHlPjFEQ46HeR5IX3ZoijGcrWSgkTVxKQaXs0lTnhFZIka\nCevGWtU79L7QNE3CX23bltCwu6LdbklhO/Bjn80Xklvmex66nTY0TcPV9Q1s28JysUSccJ6TY9/7\nPLorrC1+togSQBAG3Gw9w3g8gdpV70Xa1FwXzTt4mOPxBHme4+CgC6F0Xa/7G8VonmdIEoLga6q2\nwWc9PTnGi59e4vmPP+LR2Smt0WW5MRlijBTuVYUEnx4/eoiXL89xeXWFw94BCW1V9u9154sBsAxT\n8qzTNMV0Ott4j65rODrsSd5jnueYzucwTAOvLi7AALiuS2JlrsubCVewbYsggRz6J8TEfM/Dcrnc\nmbzeFZqmycnwarXaUJsW9AEAmE5nsGyyXKuuN2VJNIJtK7ayLDGbzTfuBU3TcNDp3FuIynPHGGzb\nRr1ex2AwlK+vggBBGG5wZQHSCRCtFEGh2O55lWXJVanp3jd0gxdzqoSkx3G8tyivHofKhahE0fzk\n0SOUKGXeJPZf7Mt0NpMaEaZpEge50ZDDhdWKmgKe56EocrSaLSQpWZBFcSy3G4YhLMtCWZQ4O32A\nwXCI0XhCfHJdQ6vZlM01yr9ylBwiTFzqtUp8q9nEbX+A29s+TeM5zDjPiw2VWaHwTOrfDAkjlNR9\njQuUJS8sNRo67EE47AtVVdFqNDAYDaGPNDiOgzRNMeTNGZt7xno1F7PZDLf9Pqn6KgrmyyWiOMbx\n0eEbr2NGxdNUqMrruo469+xuNRs7FJJOpw0GXljWfUlvEVP+x48eoizKvWviP6Vp/Tb+eeLPvhi9\nL0S3RkRZlrAqiX3VwqM6ERChaRpBXfnCRIUrk5DD6vifVLo2oWjigaOqKlf8LaEpyg7HVEwydW6y\nznhRVfXCy/mDURfTBxAEOM+JvxFGkTTMBvfvq7nOnYlLmqTQuUepgDyImz8MI8RxDJfzyRhj0m9V\n1zW5r7quSehe4ThyUlGWpZQ2Fx6QeU5wGc+rAfzYFEUhYSO2lt2udmIZnRz534ahS8hX9XfKshxB\nECDP8w1bhrIsMbm9Re/xYzQPSfzi+R/+Ed0HD9A6PsKX/+f/jTiMUGs2cPLeuwjmc8RhCN2ycPzO\nU4yurhHMF1iOSSVVNwwS8RiPYLkuTt5/T+6D124jSxJ0HpzQces6alw9j//IOHh4Bq/dwvM/fIY8\nS1GWBU1j63Xoe7qAXrsFt1FH6/gIzhYURb6n1eKfpe/JswyaYcCuebBrLlRdx9GTJ1A1DRfffQfT\ntlFrNOB32mhs+cCVZUn8tCQmpVx+LaEk3zAGBsteK1nuu2dEI6TqZbfv4cF4AQYwKloYKR9TwZhL\nfmVZlFBUBWEYw7IMAIwXkUxCtBhTpCKxLKA0lSdGCVzXlgiCavHHGAODeJ1VikPuS6iSP2oYxXAU\nxpOKArZlIuJcV4KjK9J0nuDKtL+maSBNUqRZBtu2YPGEOooTUpnWNCgKo79zgSvD0FHkVNyYBvk3\npkVW2X/iRaVpJtUaFUWBZRoIgmjDhmM23xTJUBQFnXbrXv/hMIwwnc5gWiaGwxEchyYHk+kUQnHY\ncRzyDOXd9sODrlR71Q0dYUTrR38wpImaYVBB+QbND03TYJibxeibWHXsC9uyaHKCTVRInKw1Aarh\n2OR/t1yuC/hgFXBlcA1FWcj13HVs+HyNW60CdDqttZcqbwq8LibTKXzPh2mZMvmtQt51TZMiHFEU\nYTSeSEGUfcEYCQLdrQiaSaSQAlGsWNw/t5DnJ01TjgrKNzQETNPEh++/t9EYsEwT7z59WtkHhnef\nPpX3l+s4+PCD91Ft9r7z9MkbWcQXZYk4SaVoTFmWRN/YUoNu1OtwK6I5KtcDEPvZabc5PNXDfL5A\nq9mE53nwPQ8vX12g7tOEPooixFGMJWNoNhsYT6Y7+7Qv6j4JlM3nC0xnMzBGdBOT21kcdDswTROz\nBdnPCG49Tck0zv0mTvPG8RdkS1QN13Vg29ad9+92MMbg12oYjcZyLRDK0duwe1EciutLTNO2C+Qq\n99niXq3E189lsSoaOOtjKTfg95pOsGCAq+rec79IKCioUS80K6rbJTswDUVBeQXlCJADA9/zwLjw\nTiq0EXRN6oSEFUXjKIpx2+8j50VpWZa4vLqWk1WhPitynMlkiuVqCZTUpK0ee86fRavVCreDgTwe\nBqDb6dwp4EXX+gq+5nMa2Vr46j5oqWgGgVHzYzql69F1HbTbbfls0DQNR0eHGA5HuO0PaEKpaTjq\n9STMveQKtWLvNHX9bFGVtX1bFVlHv0mxkXdX196iWA9/ACa9nEWxT881ymcEfYthP3XobfzrirfF\n6M+I+7oq+yBNaZrSYsJhn1mWyWRX3JSMMdl1E0Wn4GvqmoY8TelG0/WNjmj1Bq3exDovgHPeYcrz\nnBOyqVAVya84lrrvIcuy9cSHwwqrxeW+Y+4KwSFgh/PgOPYGbBiANP1lvKvX7a5hUwBZagh4s/jO\n6ucNQ8diuaQCgduZdNstsjgosdENFZ+tToqqv40oGsT+qCpNYheLtQiJCMOysJpO4dRJNc50HATz\nOSy+4KqaRlxL3mQATxJY5RzbXg2aYaDZ60HVNWRJgtHFFaLlCoqqwOCTjSxJkEQRDNNEUZbIswxF\nlpF3FWM0TeSiPIZto8gLhIsljp4+udMvsRplUSBPSSBECCPlaYaiyJFnGXTThO15mN7cov3gGBYX\nAWGqsjFNZaqCOAyRxjG0re5sHMdcvIELoYjzAuw8YMTn4jihCaiqSnhPmlIBBcYl/9MMSpZD1RSY\nho4gjCBEgarfL9EIfLIKMFi2CaE+HYUxNN4QydIMtmNxXnNJ1hYFXU+aLsSU1gXzWmxp87wSD+mO\nc87tA9I02+CDqgodR5ICq1WIGheiyoocaq5wuJ+CkHusVrcvLZK4B5qYzIJPBkIOqWOKgNtv/k8o\nBoNxhWAw2fgR+5fn1KCphmmaO9OQajDGJCTeNAyAMTQbdWk9Q2JTFibTKcFdOY9p3/Zi7ilbq7kI\nwxieV9uBVt61D9uQ8TXagCKKYhiGLv1r74JlSSGorZjPF/BqNdj25vcIj9NqMZqkqfT9G43GUBQF\nN/0+NE3DKgjg1Vx4ngvXcVGWBR6enb4xxG04GiNJyP5ATPTEWmcYpPI6XyzAFIYwpEZDFMdw7+Du\naqq2UZTFCdm9FEUO13VhmgYXqasIs/DnxLoYXT/fiqJEEAayUKgWKiK2r+19/51mKb569jVmszl8\n38MvP/nFnbSAOI7x+Zdf4YP33pVq0YZhyPOyWq029kFMd6vXYJIk/Jot4Dg2DEPn4l85+f9ynuN4\nMsHRYQ+r1YrD7EO4NRcN38dkNtvrT74d1aJEbEM00gzDkMURQJP1NV2ImrNJksC2LQlDr0aSphsF\nHAB499y/d4UQmqs2pgQct3ocsvmVChGj3WI0y3LElX2yLIsE+wxT7us+oa48J2h8dZ/23efjyQSz\n2QyGYeKw15PN7G6ns73JvZFmGRhIHTqOIhwdHu6s7VWPajHVE818OiYTJ8dHe1tn1eaMoiio+z68\nWg2j8RhFXqDTaW+I+g2HQ0RRjIcPz+TxFgX5uo6nE/h3KPSLayZLM9j2eh1rNZvI/LVaN8CQZamE\nCuc8b637/saEnqg06+eJwu/7Xu8AeZYhz3NZjKdZBlVRsOT2M+Bib4ILn2UkZlfLa8iyjE+L1znb\nfLFAo07PDSHIl/FnnhCKEzlqURawbYIMlyVkwy8KIygqCbiZhrnXV/xt/OuKt8XonyiE6ptQIQO3\nmRCFqrB2EV17XdP5hGeNic+5kilNeChZ306+gd0Ca8N8fKtIqS7g4j0E51hvqwrzBSAfRKKAEEmF\n4KJuT7hEUVvdvzRN5UNGYaRYmOfE5xJJs6ZqqNVc5HlOUuqMIU1SmFxMiKwq1qR5VVWQZhmSOEHO\nhZYsy4JlmcRFjCMpbuHVakh5YiigxIvFErWayyFLu78hYwzH776Dn776GovP/ojDxw/Re/wIl999\nj5fPvkbv6WM4vo9gsUAURag1mzAdB36nA6YosFwXiqbB8T1cfPsdLr77Dt3TUzR6B/DaLfz0xZdw\nG3UcPnkM26tB0TS8+vIZHnz4Pqa3fcxHI6xmM2imCculKSUY4LfbCBdLoCyRZSnOv/4Gpx9/BNv3\n+SBy/bvYvg8opBAXL1fov3yJcBXg/LvvcXB6iqsffkCSJPjxq2c4e/99hIsFFFXF6OIKRZ6j1mxC\n1XSoqgqv1QIYQ/fBA1x89z0YYzh6+mRj+uz7Pspy07eMCpEYZkX+Xz4MGSnCWtaaa00egiWYTbxf\nRVVRqznraaTKUFMdvn3sJK8q53sqCvFQhWKp4ECnKRUHQuwkz3KoOsFtlZK2r2kq0mTNZ9uXRL9J\nMAYYOqEABO+synnWdR2Os77HRMe4KArkWQ7XsZDlBRiYnOIKeJZAEtRqpAprgrxMHcfiYkzaeuLL\nO9eMkXgXNbvUjXtJ2CwJBfBtmKHLVTfFOiOKjmqDTNeIZhBFMalW83UDKOE4JMYWxzHq9bUQVxTF\niOMEqyCQPsE11+WTFgW+X8NC2DC90Tm/+7fJsgznl5dyPWi3W3cWo8ICa99vum9KyxjbsbgoioLD\n/UwwhcGxHCkWx0AiJjpX/gSAavlSbTTuC9/zKJHn0OqcawAsVyuiOeg6Ou0WFEVFEAYIghC2dTdE\nV8D6RARBgOvrGximiRNdRxzHsMytAqvSbKrud1EUWAUBNJV8/FSV3jOdztAfDvHeO0/xpqGpKh6e\nneL5i59weXWNTz7+6E4fvyzP8erVBR4/PINXq6FVEZbZp+JqGPrO76zrOt5/952N91X9xgHg7PTB\n+j94Y7bZaMiXOlv+tncem6bKddEwDBwd9u58b3X7opC4T506juMNVIDgSP7cYlQ0CavLQZqtG+3r\nY9Fg6IYsRklROd/IQYgTmsrtWlx8jnQk1vudZfnGtSiaeSIsrs1RjZT7qJKNhynPzc85XpGXTacz\ngl93Om/0+U2hHOWNLKzEvolnkKAyiO8Tdkuu425cowRl1V7rp15yRFIURTB0nXOZI5xfXhIqjuuR\nCGXfWq0Gx7axCgKp3SEaNfPFHLZtYTqbwdB1uK6L+Xwhhw6KosCIyb/TskyyF0sS+H4b48kYUZyg\n5joIghBerQbXdTCZTKXloFerScXvIAjhez7m84UcYoynE/7sqoExzpNXGDRFg6aqXDSyQJqRD7F4\nlkVRDEP/p9OJ3sY/X7wtRv9EYb5B4kRdX+7ZVlYKR0YLjrDKEEkLA6QIiVjsGCM5bSHAk3MrGOEb\nJhT9BFyhOhEECMqkVRbAoigQRzEMnhyJhFNAzUq+eJUgmwRhO0E8IX1vl64syVfMtkmKPggC8trT\ndWR5jkbNB8DIT45zTVRVpc4+X5CAu5XLmKJwiwoqeAn6o8v9XSzForlpFi9gHNshzJNd10EUxWg9\nekRdWcvGcDJF4/QU6mqF0jCQKwyts1MsVwG8Xg+L5RLH770LVdNQP+giT1PMxmMcPnmMLE0RBSFG\nN7cwvRo6pw/AmILR9Q2KIkeN8zanwyGyPEf77AyMMYQB2cnEUQSr5uL0ww9w/fwFbK+G+kEX/Vfn\n6PcHULjVguM4VMwpCvRmA6s4wWByjm6ng7NffoKLyysoloXhdApWr6PpEzS5QIksSdF7/Ajz0Qh5\nmuHo3XdooqMqsDsdzOcLqLqOB7/4GAAQhJFURhT2QLP5HLZl8zx1zV9cBQGKXEAVFTCIwpFUMMuy\nRMJ95ERCL5JIhTEYpkHddqHECwGlX4t5iIlXmqQk/sMhPCq/hi3LlPccA01dxbVM3wd+n5HMOwrq\nLmdZtua58ntG2+GX7obgpooQCZa4lBXGYBo64nidoFEjix+3osEyOUy1JI6vgD4Jz1Qhsa+Z6rpQ\nLAvkecanK3TOnYpQ2z6VyyrUjbhLm/BWRVHldFJTVQRhxOHBERr1uhQLIi4gfediseRw0QZBqIuc\nbFsWS/hcCTuOE/S6XZn0igKiyu1ce/qVKIo1R17wosmDkf67au+yHYwp8D0PpmmQfccd06v7JqZV\n1MV2oioULKt82DRJMZ3NeMMxlwgWYQ1QFAUMw8B8viBBEy54RWgZ4q3ui95Bl6b9cYwwDNHksP4o\njmFbFhaLJdIsg1dz4Xs+VqsAaZpgn+APQNDHaoKvazoMw+CiTwY0TZe2Zhvnas+2FEWRdlxi6pEk\nCV5dXGA8meD4sAfGFIL5Vpq0cRwDjMnkm+DlClrNJsaNCW77/b2/RxTHZIVVmd6J30YI4uQ5oSGq\nYRpr+5T7Cv/tCLnq63aRuv3drwtN1Taa1DHXUhDoB437RQvLjCzP0Gw0MJnO4NgWDMMgWyjDQJEX\nSNMEpmnS57i9WGWnEHIF/XXcdczr/S+KfOO8AkSHKItioymgqiTgKNYIwVUWz++yLBFGEfJ8rews\n7hfBRaTGdYY0TeRaSdD4dWEtpsjVmM8XuO33sVqtMByN4PsebJvE8PqDAdI0Q73uo9UkLuZkOsV0\nOoWqquh2urBtQsjc9vtrz8nX2I/0B0OEYYg05b6Wx0dIkgSD4VByUsU9OZ5MMJ/POey6C9M0kSQJ\nbm77KLlXsmFs5owEPzaxWC5l7lQWBTkGzOZc7Xo/ioLumQZ0XcdsvkCe5TAMA5ZtSU4nNawh72nT\nMKDrBtyaS57koOaiojDYlk1NUNAkX6DTSO9Eg+/VsFwFRMHgom22bROPeDKF4zh0rWsqfN9Dzq1U\nXNfBfLEkUbgkRa1Wk+gIyzJhWRaSmLRSXMfdUXHO8xyLxQKMCyCqBTWiHT64WHskv41/7fG2GP0T\nxM/pxGmqCmVLGU8kVwL7LjrPDICiVpLlCoxW5e8R0udiMqsqKmLuecUAyZ8xdJ13MEuUfOIqtqUo\nClTBY6vsE3Ev6MFEEEHy8VSVu+G81eMRfDRNp26drmkSwlyWnE9bkt9kECYk886nr/edU1EQK6pK\nk0GenKQJ5+Plu0VnWd7/GBYeY4wBuqFB1VREvNMskpsiz7FYruDYBEuO2fr8ysm0psHmkF5NN2A6\nQrZfgWYYKPIcluus4b5gEDYMqqoiDkMEiyVqjQYMy4TBO9sHZ6eYWhbyLMXZRx9CtSya7PFGhbgO\n8oygiC7n/8Yc8t1qkjBOmmZ80VZhWRbOPv4I0XLJFXi7KAqCi8kEhkGqzoppeZ7nvDlSoihIlCLL\nMmg6PXyyiAopRWEoICZIBE1Wt35XTVV3stuqPYKm6RInK4rRDSuUyuRewHwZo8mohDszhrwspKUQ\nNXwIXq0oZDlE95+GfCMRW9vQvBFp7WcG8WdUFMW60FmjFzT53WLarOvqzmSkLEtc3/Qxmy/geS6O\nD3u46Q/QbbfA+DnYzLnXB6Np63tRwKKq5zXLUtz2B3AdB5Zlyum2SJyFYXoYRVgFGhzHhmUSh1JR\nVfpukHp5mmWIpzMURUHc2DTFcrWUfCpxLFTE0HZXAXkKpwJqXpkcbv//XaGq5M+YF6QKetjr7bV2\nofVkf6JXckj3vppO5Zz+auqecZVPhSlyDeGMKpmU+Z6HIAx5UmrIY7HM+x/RjK+ZghNHTQfasRIl\nLi4vYVvEZaV78m4VVrr3NtEttD9UMDQa9Te+7EWTQVyrURzhiy+f4adXrzgX+3fQNA2/+fRXaNTr\nWK0C/OPnf5Qcx26ng1998vGdzYLq93z7/Q/4/ofn0DQNvufJ5Fo8B6bTGY4Oe/JarYZ4dgDAcrnE\nbX+AVquFNE2xWC7RbbcxHBH3WVWpudLtdniCbYOBrIJc10FZEk/y8KBLDcE3iCrl5vrmBmEU46Db\nwcXFFYqywPHRIUajsUQ1pGkqBZI0TcXVzS35RPM1OghDPDo7g+97G56c4lxdXd+80X69Lrah7yJs\n24JQMRKiO+vrERuemoZuSG0B8s1UuK97jiiON85hFW4sirRqTuA4Ng4OuojjGEdHhzBNWp9enV9w\nPm0Dl1fXUBVCxNzc3OLByTGiOMari3M8ffwYN7e3yPMCnXYHl1dX9yoWT2czLFdLnJ6c4OWrc5jc\n6/rV+QVarSYs08Tl9TU0lYSZhsMRjo+PEAQBzi8u8ejhGS6vrqFpGprNFl6dn++dph50uhiOhhgM\nRxwGRHlBp91Co76mR4lCX/jX0zOfmkFViomuaRuf2w5B16hOYoW9Tcgb/aKQFU0B8Z2mSbBy8f02\nf8Y3mw3SONA1WCXdzwKJM18sSROAD14SrocwX9AQYjqdodVs8kZ2Tk0QfuUJ3r7n+fy6YCiMqsCg\n8rNsEd/Gv2y8LUb/hWNf9z0XnD5l7U8qFv8S91s8MECqV5aV18qiRAEq+MQDUExhBAeA3ry2eilz\nEk0ilTNsCEGoTF0rtIlx0h0hkn3JJcpFIb3/c+tOFkEKXye1vQ1dJtU7soJROYdWvHE91Shk0bcd\nmqZJK4F1sU1QQ6FOnOc0OSu4sJJjW1BVEinRuCdZwqXoVU2FUen0V6MsS5iVrjBAIkJiMqWZFhq2\nDcvZ9J4TNi3bUYX2ZWkKhdEibeo0gXBsG6cnx3v95QDA77ThtVvI0hRJnJC/rq5Bs22peLwBxwbB\naquvGQZBNdMkQRrH0DUNxhasajFfwOa2DXKfyxJqZWKXJimf5AgfOwOv8+0TRRwV2sLDU+GTQFIA\nVjWuolvZTnWbYkqR8MJBURToXEzkvhDHsA1bzFI6FyU/Pp1z2KrfqWlCTbLgt/wa5pmmpRQf0nUV\njAl7i90GUJwkSNIUx4cHWPKJep7lErabpClBo0AP67XoEsCU9ZRoexLCADlRXO+zxpMKi59nHb7v\nwa8L8TFNWgOomgaFKUizVIqF0N/Ed5UoSn1DzCIIQozGY8wXy53E+v9LBGEokRppmt5RjN7dXHNd\nZycZXn9u9/osynKHsyTeI6cwjKHVakJhys7+vK7AZnc063zPwwfvv0f3OoDBcMhtYO54djBFLsdi\nDfU4TxGgBicDeyNbEUVR0GjU+TVGiemv/+KXKMsCcZLit3/1lxKiXJQlPvvjF8iLAv/57/4WRV7g\n93/4R3z19bf49Fef3Dn9AYDxZIqvnn2N33z6FzjodvDD8x8RhZE8b1mWUSMHQFkW8hkmojo1GY0n\n6LTbcF0Xl9fXmM/n0lKid0CWQc1mgyCFSYosSzEYjrBYLiHU2I96vdeqLm+HUND1PR+L5TXm8wXS\nLEWn3eZq4VRodNtt7n9s8MS/gMIUtLtNDEdjRGEElcN+BYLpTxd3wNT5Oi/EtOIoRulzhEueb8Ck\nbduSzTSDW4oIKG4QhLIxVZZb4kWaJuG01ddMvq6ahglD1/nUMiXRNV1HsxFiMpvCNAzUajV4ngfX\ndTEcjRCEIVarACfHR6jVXDQa9R1e7M7xgjj7iqJA18ghoShytFstQgYslpjO5ijLAj7nhtqWhdF4\nzEUjqXHgOA5HlqzPqaAquY6Dul9Hq9UCQM2uxXIJizfUqlPmyWSCZqOxMwlsNurymZSmKeVG3N6k\nLLk3MBdCqq41sqhjJOJVjSAI4boOL1oVlGUhhYsSrsHgug6SJIVj2xLZUr032q0WHMfhrg1cyLIo\nCBlQUBO92WhI31FylIg5okpBKQWQNnm4b+PfZrwtRv8Vhqqq/2RoQVUwCBziaGjGWuBHWSccjNEi\nVhYlcpC/n1KBpzFGix9NlATMl8N+y2IjKWG8qMjzQvrLMSRgILEB0yJrCU1VkWYpde3uSLIKvsjG\nSYwkYVJxb3uxFEGJRik7h6JzF0UxGEvksWuqiiIvsFytOLyPPi+4pKIja3DuFGMMxtbiJs6v/H1U\nFZPRGF999jn8Rh0f/vIT3F5d4/k332IynpAwkGGg0zvAOx+8h2a7vZEMVpPSyWiE6/NLDPt9rsKZ\nUnJqmmi2W3j0zlN0D3t3LrhFXmA0GOCn5y8wHgwRRxH/vTSYtoVGs4mj0wc4Ojnem5AWRYHJaIyX\nz59j2B8gDiMS0FBVmJaFeqOBowcnODp9IOFVG79DWSKOIrx68SMuXp5jtVxwjqSOerOJh0+f4Ojk\nhCC5lWSmKAp888cvMRmP8cEnv4Bbc/Hjdz/g8vwc4SpACfIL7Bwc4OkH76NRkb3f/v75dIoX3/2A\nwc2NTEot24Jl2xtFomEY+OVf/hpORcylLEvkWY6by0v89PwFZpMpsjSBoqpwazU8eHiGsyeP7+Rd\nrZZLfPmHz6AbBj75zacAgJfPX+D8xU9YLpcoC1JUrjcb+MWnv0KdK52K4rkoqOEhrkt6bb3b9P+C\nt733EpDbYwoDSprKiYLaNEUSu1b83f5c9Vxs/ZHzW9fTCkoOCHWRcluqKpRTCGIoAvmgKaiZ+8Vz\niqKQiUqeFxiNxxJety8EeoPxYkdwiBgj1eKqN+p21H0Pvu9RMX1n8nI3SE/h0467Gjo7sW/NKoW6\n5ZoiIQRv9nLw74kiL/bCPBhj0mtPcgzvE0eqHArxsxzAdWSjcjabwzSMN1JjVRRlQ22VMSaFcFQl\nh2muhc+CIMTtYIDf/tVDPXUsAAAgAElEQVRfwvfIV/nJo4d49vW3+MVHH96rlDocjWCaJh6cHEPX\ndTx6eIavv/tOfqemafQcwJ2PG7kfrutgNJlIZVzTMAm5IzQNNJWL/eWYTqeIohgHB13keY5mo4FV\nEMhprBCMeR1dpyxLjMYTtNstSYVxHIer8GewTBI1qtVc1GoubvsDrFYrBEGIJE3hODZMwyR+dpZD\n1VSslisYzcadx/v/R9x1d2yLHUVJIrEXQswL2IXaCgXjkK/Zwv5FVZnMKUSYhsFRRK/byU2ERQmB\n9KJ1Vsb+f94bvueh3x/g9vYWvu+hXvcRRdE6F+FbE2v29pYZ7lthgMlkipt+H81GHWEUwavVpB1M\nyKkxRVFisVyCAXhwcoyiKDEak0has9GEaRoYjccc/koiaf3+gBAmUYRWq4XZfIZ+f4AwiqSN1Wg8\nQVHkqNfrsCwLo9EY4nnRbrVJOIvrLozHEymeKFBuRVkR2WIMft2/s5G7LfKlKIpU0q+ue3etjW/j\n30+8LUb/ncV2oSKSre3Xy5IUe6nAKwCoKMscabk2NwdXAi2REbyFJ06CaF9dGMTSkGUpwEjsRPwN\noMVE1zTkRQHLMuXnBeeGMQaX27+sggA1Dl3NswxBGMniJ8syhDzZZIzUBIuy5JBaSqAUwSHNMrm4\niUmU73t82qmubQnKEqqqkHAOfv5CF0chrl6dYzqewDAMfPPFl8jSDAZPRJbzOWaTCQY3N/jN3/4W\nvaOjne+YjMb4b//b/4FgxRV2DROqRsXzbDLBZDTC1cUFfvPbv8bp40e705eiwE/Pn+OPf/8HhEEA\n3TCogFfINmi1XGJ428dkNEK3d7DzMC/LEpevzvGP/+/vsOKm34ZpgikKsjRDsBpj1B9g1B+g1e3s\nTEfKssRyscAf/vv/wPXlFSXCpglFVRCHEc5nL3FzcYl3PvwAH/3qkw0LkbIoMOwPcHV+Ds/3MZ/N\ncPXqnBSkdR1lUWI6mWIyGqN/c4O//s9/h9aWsERZlhje9vH3/+2/YzaZwnFd+FzleTGbY8yN31VN\nhWXZ8Or+RrImuH1fffY5fvjmW+RZRubduoYsyzC4ucXg5hY3l1f49Lf/ATXP2/kN0jTF9cUFVFXD\n0/ffxffPvsGP3/9ASbGuEVwxDPnEfDM5IdELIfpErzF2p07LnWEaBgxDx83tAGmWIYwiSlgrCT1N\nI+6/xvc1GraLoqIoMByNMZnOcHJ0iEbdx6uLCyiKCl3T0Om00B+MkKakaNps1KFvWTZUj3+9zRFu\nbvs701nLNOHWSMhMKHcKHq9AR5QlcH5xgelsvudbKBbLFTyvBlXZhTlvHi8l0NtUhel0Bsuy9hYa\n5R5pI3EuR6OxVHYVYhtiIkKUBMHPz6Fp6r3KxdXQDR015X61SMYYVwd+s8kBrZvWGu7OX3vTCXVZ\neY68zlahKIh/uG6CMq4nkG1w/PdFJvQR+PWjauqGYqmwLRN88H37KY6x3WrD4wripmVS80jXsVwF\nmC8W6HY6eP7jT3BdF/V6HcvVFdqtJnzPI76kZeL5ix/RabdhGAYWiwVOuVVXlVZTDcbI9kxVFPit\nJhzHxmoVwDJNzn8nRd+aW4PjOHh4dorxZIKHZ6dYLJcc8UDP5iRNoEPHbD6Hu8cfXFVV9A46Fdut\n+6KKq9r9210Nc03XYBi6vE6SOJHc0qqgkuCXVs+/bduYcA9YamhnUFVDck9FWJa5V+V6Owxdh2WZ\nGAxHJJgzneKo14OmaTi/uMR0NkMcxxIxVKu5GI1GQAnMZrMNLYLtyLIcOZ/iiTzENE3omi4bJIvF\nEicnxyiLAte3t3BsR3LqbZvE3IajERpZhtl8LieLAKGNHmjHYApD3a8jTmIJnV1PKUv5GWFBIwQZ\nx5MJHIc4s77nYTQa0zF6NSiqik6HbFpqtRqWyyUOuuQ92x+QTYtpkjXX0dEhFssFjg8POaJHNFkM\nKu4VReo0CHX2JC0lbed1aKL7Yj/y5G0R+u813hajf6ZRliWZIfORZ1kWUtCnmgSqbM0HpQmOgjCK\npUKpKAqr1io11915UJGNggJgzTMUU56Y+yMafLoqOHPgUKNqMqOqKgxdJ8/QooTFIUkAh8vwovWu\nh+W+1wVscF+sC28A2LSJEX8TsVos8NVnn+Pg8BDvfvQharzLP+z38dVnn2M+neGLf/hH+P+1Dqe2\nOSHy6z66vQNouo7DBydk5mwYyLMMt9c3ePbZHxGsVvj2y69wcHRIk75KBMsVvv7jl4jCEGdPHuPd\nDz+AU3PBwJAkMebTOW6vrlBvNWWRXI04ivD1519gtVzi+PQB3v/Fx/R5RjyOxXyOwc0tLJu8F0VS\nIX6bNEnw+e/+HlfnF2i0Wvjgk4/R7lLiE4Uhfnr+Ai++/Q7fPfsajuvg6Qfv703Ovv/6GzAGvPvR\nhzh78hg25+NenZ/j2edfYDaZ4tsvn+Gv/9PfbUJ60xRf//ELTMcTHBwd4te//Q/wfPJLWy2W+Ox3\nf4+r83M0W2381d/9DWzX3eDolGWJH77+Bt89ewbDMPHRrz7B8ekDGCZ5gd5eXuObL77E5fkFdMPA\nX/7tb3e8XeW+JAmeff4F+tfXePrB+zg+fQDLtlDkBeazGcIgkNeGUEAV0Hi6yujaZ5XCQdwX1eLp\nrod1r9tBjUOkdJ1sKX7uQ3zfxDDbgv1pGonrJEmKZqMuObxHhwcycSrLEgfdDrzafguXahA0N0B/\nMNxcg1QV3U4brWZzxyN43zbuHRuDivHr6xvouo7eQXe/8iVfe+ajuWy8kG0RTQQEd2rnY8Uu/F+s\nEYqioCgLrJYBYo7AKP0SeZ5JH1axzlYT09dFmqZYLJawurv3tfDfy7IMruP8LBhbEISYTKdotZqw\nLUtOB9/kWoqiCP3hEKZh4qC7bhwxpkj7MhGGocOyLMzmcxx0OyhBnDzHeT1Fw3Vd8veMY6nEWS2Y\nXdvh0zjGp+jKBnx1Q21WIS57mqYYDIY0HTXXqJxWsykVneu+h8nU3nieJEmKLKXzrGoqFosFVivi\nOqdpit6WL7M8ft2Q2xDft82BFucvDCP8+NMrPH3yCAAw5E22+XxBHD2+P/3BcMciSGFU2FQ9nLdD\nrDPLJYnKNJtrqCxjZBGUZqmcYItrVvxdVRRYponVikSM0jRFJj3BY3k8hm4AZYnziws06nX4vi8n\n7gJeTbY8OpI02RQvugOZoqoqmo2GvMZF7rBYLDAcDukcM4bxeIKyLHF1dY3lconHjx9jsSRrkDTN\n8OWzZxwequLq+hrNZnODe1iWJWbzmbRBiuMYLydTPH3yWPKJoyjG0WEPNddFmmU46HYxnU2haToe\nPHgAVVVxdHyEwWCA2XyObqcjBReFiJlQsi3LkkOaFd7kXa9XoiFWlvRsMA0DCp9MC49qoU4uaBkb\nVC1FwZr2waTNGVNIZ4IBXO3b3Mh1qsrO4vuTJOGChAQVf5OGwdt4GyLeFqN/psEYu/uhJBaRSsJQ\nhYwpCpOk9Zz7l4rpRFnun8LSg6ggYRTDIF8uxuDYDokZVZRJa66DOE6QZTEUVUGNd3nFA0/nqo/b\n/AbiLqw7rdUHeVVshxbftUKwEEJRtj7DGMOYeyIqTJGCIwAZL88WCxz11glGURTwfB+//pu/hlvt\ncnIvsN/9X/8PxoMhri4u8PT99zYeqIZp4i//7m8l9K/6N8/3kWcZPvvd32M+m2G5WOwUo6vlEsFq\nBcM08MEnH29MDl3U0Gy3cfr44Z2Qv2AVYDmfQ9M1vPfxR+hxZcA8z2EwBS3TxMnZKVnm8N9OURTY\n3HT68tU5Ls8vYDsO/uo//g3aXOkTAFyvBr9RR5ok+PH7H/D82+9w8vBs4xyJyNIU7//iY3zym083\nElHP9xCuAnz71TMM+30q6Pz1lG05n2PYH0BVVbz74QdotFry+/1GHe99/CH6NzdYzGcbnmji2plP\np1QIg+HjT3+Fd7aKZb9eh6Iw/OF//B6Xr87x6J2nOLwD7pwkCa7OL/Dpf/grPHr36YbQWKviz5vn\nBcIogWloyHO6RnVdRZ6vpzhJmkmxiDwvYBgaVquIW3rsFmZ5XuB2MEAcr2FtE2WGo153I3GpJpLV\ne0vCR/m0SRSFItmoTsr2RZpmuLi8IpuK3gEXTbmFaU7QO+jeqT4qvmM8nW1AbBkjvl63036jQop6\nRneD7cqyRLPRQKNeR8whhPuiKIkfXvfr8nO2bUtOsH7H1ISEvTa3Kq7jRqPO0SCupMsLNWnhwScg\njT+neSC8pPe9fnF1zdXWCxz17ob47z8WoTpN++Jwa583CU3X1wqclWNpNRt4+eoVnn39LUzTwIOT\nY1iWhQ/eexdff/Md4ihGXhR49eocv/zlx1y1OcRkMsVgNEIYRri4vEKt5qLdauGANxP+/g+fodvt\noN8fbKxxWZ7JfSDbpE2LkiRNSSSvso+qqsprI8ty1H2Cym769Zb8f+uJp2mRn6HrOgQzLQvYtoXr\nmxvYtg19DzcZDHKaBWBn7a9GWZaYTKd0n4wnODo6hMq55sLKKs8y1JoNTGfzDZg0QM+nLM8A7H/u\nl2WJ65tbRBF50g5HI1JGt20sFgu0Wi1EUQhFUWEYBi4ur+DVyCZtNpuh1ztAo17fgF5mOVmW6JqG\nqMIXFeioKIqB+hpSrnNee1EST7ZWc7mS+LrhbN6Rt+i6jiN+HwG0Ds/nCxwdHmIwHKJWq+Hly1dI\nUqK/CE2Ofr8vkQBFUaDVbHIExBwxF0x79PBs4zzO53P0Dnrkf7xaYcV9aVdBAE3Xccb1HEhhPESj\nXker2eRrfgjTIM/ek+NjrIIAtmVxxecc0ylNbB+encpr4k1oW2VJTRzK7SzibKYJdG7roqqEWImS\nGLP5HL7n8Wu3xGQyhe978DwPq+WKkFH8vmFvgKYQSChgvw7K23gbr4u3V82fadz30Ku8ae/nBHzs\nTRdJABxyymDwKSPJfGtriEdl+zqHZ+6LlKuuiakSIApRAGAEdeP7lvNESsAS6cFdgER3qUOvcrgy\nbWi9LTFhjbmHqW1biJMURZ7LorQsdlPZ47OzDR6iOKbe8REa7ZaEej5+5+kGVFZwqvYFUxR0ej1o\nOgkDJfGudYWiqqQSm+VYzOZocihP9Te+LxFVVQVMVVCkKRazOQ6ODjc69GJb+37vPM9x/uNPKPIc\nhyfHaHV3vdk0XcfJ2SlevfgRy/kC0/FkbzFqOTYev/t053uYoqB3fITvv/4GSZwgisKNYjQMQqkW\nWvM35d8FBNwwDERhiGC5Qrvb3dj+9cUlgtUKjVYTpw8f7pwrRVFweHICx/kKy8UCt9fXODg6lEJc\n27dKt3eAh08fg6xtyj3nkbiOqqogywsp6pUkGX9NiFkVMGyNuvZZBsMQ1jD7f0sxUTjors3Tt0Vn\naAJJfGJqfqwhrqLYNHTiU1YnlGu4493rhqZpOOodwLSIb6coVEx6tV20RFmWMsl3uZBFwCcqImzL\nQqvZePMiqrIu7IvVKsBkOkVeEA/tqNeTvMrtSJKExD/eMMqSbGWqv7Vo3FXX2+ppKHnRW/A1kOxc\nVCqS+br0On6cqqhw3d0inzEFdc+DopKoy89NEskiwZbT0PtUOLcjyzIEYbghLpLnOQ4Pe3h39RSj\n8RimaaLL4YGPHz2Erus4v7hEURT46KMPcHhwgCRNMZlMcXF5haIocHTYw81tH96qhka9Dse28R9/\n+9d48dNPWCyWeOfpE3TaLVkQqZwrTTZP9FtUxXBofc+gVq4BxhhajYbkXYdRhDhJsFytkOU5VquV\nnGKFYShVSxXGUK+TWEwQBCQcxhuenvd6ZMDrQogSPXn8ENPpDNPpTPL0FFUFUxTUbBuDwQitVlMW\n4eJ6FLZp20Vq9feZLxZ4/PChnNYFYYjFYIDTByfEbW01EQQh5vMFXMdGt9PGZ3/8ArZlIY5isAaj\nqRhfO8SEztD1jQaZQLwkSYLFcolarcbzAUP6Gwv+aFzx0xR+zQAvrkXDhCkba7A4ZsuiRoKwefG8\nmlRkVRUVYRii2+2QDVJKWha6TjxjxwaCUEOj7stzL9ahVquF65sbDIZ0T/V6PRiGAd/zMZlO5T7M\nFwvkeSb3dzQeYzQeo9NuoV6vI4qoudJo1GUh7/se+oNYbmMVkEewZZl7ESaCUtBuNUktX1Vh2zS1\nV9ptxHEs4flCnLGsINl6/D5jjGxbDG65J/i/3U7nrTDQ2/iTx9ti9G38rHijInbPZ+4zrK/6Cb4u\nyrJcW3Jw/zp6TUeWZrLjmeWZVF0T8DIBTxE2NwxMwoEVlYynhTeliINuR36HrnG1T0YP7rLYhO0q\nioLmHeI6umGg0WpicHOL5XyOJElh3+NjCEBaYaAkexvqfEM+SKrh+T6anTZur67x+e//AfPpFA8e\nPYLfqG+IpNwVjuuic3CAi59e4qvPPsNyscDZk8eoNxsbQi37thNHEWYTegBbti3/vR15noPx7v1y\nvtj7nprnwd3zwGWMEWdFJUGtPNv2wFSkj+421xDgvLRy/QCuRlkUGN6Sf6FpWQi4wMN2pEkixVwW\n0zn31l3zaKrROz6CqmlyekL7ILr7XA2Q9LbkZD4vCm4nQ4mPppJSIHH1MjmN0XhTZZ9XqKZRs+f6\npg+dX1+KoqB30JXwrioqgjFxvTH+b/4aAMM0NrhaAgIpOuB5nmM2XyAMQyyWK8nX1nRNfjcAzBcL\nJGlCZudbk1Gh2urYtpyiVMO233waB9Ak6z5eo2VZaLdaMAyDDNLvaaaF4euL72qI4qQaqqrd2WQS\nMZ1OsVyuOP+MGmGEDMmQ5TmOD3v3fp4pbO81L2yewihCkqTcPuPNSciMKRhPpiRA9gYKutXI85xE\na5JU8ukIClnioNul6RNKhGGEIAhx2DuAV6vh/ffexXQ6haIwTLj1j6oq+M2nf7FzvY/GJDbU7bTx\n67/4lXz95Pho4xhWy4WEKNu2jfliKf8uvKmrXphRFKPRqG9cd2Ia+t47T0npXEzQILxvc6xWAbod\nEqjr9Q7Q6x0gCMh7se77b3TeypIskqo82PWxMJycHENVFDTqddls1XVNNmyEmJiuEW9T1/UN8Z/l\ncoVWs7FxTRcFQcVL/px5dXEByyRfWYU/u29u+2jU61guVwiCAL3eAQaDOXR9goNuB1mWw+PNQYPz\nuZOE4NhJmsJI17xPRSF7JdO0YFlk/ZRlOcG1bQvL1QoApLdovCFetIaLRlFMyrRhCIfzyJM0RZ5l\nKMoSdd/H2dkpNFWV01VVXcO0cz7V1lQVNZd8ynVDBwPQaqrQNE2eQwCYTmdS5dUwDDT5OW/U63sF\n/QAq/vqDofQEFc0DyyK9Ck3V5PnYt84lSYKbm1s0Gg3c9gckcLWFiirLkttnEdpLURTM5nPomi7V\nl4MgRBwnSJIYum4gTBKsAqILVNVuAezYEm17ur6Nt/GniLfF6Nv4NxWqSh6XKi8aFaZA1YgHUbW7\n0BlB0xgjRUWC566LU2qjcniuopASoUpTrhIlL15oggt2v52OCEVVod+h/MgYg23TIp8mKbI0RdWo\nUBRRi9kck9EI8+kMURgi4xYvcRwjTXYnoiJMy8Qnv/5UKup+9fkXePHdD+ge9vDg4Znkmd51DJqu\n4xef/gWyNEX/5hbffvkVXj5/gc5BFycPz9A7PoJzh6BKzNX9AOD7Z1/j+bff7f2Osiz5cXOhqz1h\n2dbaZmg7+PR7n8NdzavBcmyslisMbm7RqsA6i6LA8HaAOIph2hZqW4lhlmUIuVH74OYW//v/8r/u\n/35A/gZZlqEsqlP5zbDFtERhUJT9x0PFApmKRxGZvFORWqKEtsG5UVUVrkNWCKap82krsK2KK6wR\negfdCvSPhEXkaWRsbyFbjbIsUXNdyf0CKDGazRfodtbQS8e2YR4dyqL56PBgw3ah1+0iimMwht1p\nd4W7JL5zG0b+c4qnsiwRRqGcrOyLyXSKKIrQbJIFgnbP9oMwRJpm0mpFcNmq+1VtzqVphtWWHYRl\nmtIjdrFcQmHc9oJD/cXxOo4jhV9EgaHrGizTeq2ViqHrUO6YdpUoMRgO34izux0q9xsUCJA8z7nK\nafHaSZ9pmjRpmy84pNyUNg5CG0Dae/Fr2K25yLMMlmlAeAOL/djX8Lnt9xHHMbqd9s7fADF1zjml\ng16ruS4GyqjSICowmc4Ibgrg+vqGpm1lCV3TOJdbkUXd8dEhgjDEcDiC67pyMsoYWThtnxfHcWRy\nv20rs29/kyTB7WCAw4MDUpJmkKrPdG+XCFOyqxGKpkxhiOIYpmHIAivP6T2uY28Wo6sloijeKDCq\nnGXLsjhlhsnGr81ty9qtJp9Ekn2X69Bxq9yXVVynuqbB0A2pnJskCWJ9jYDSdQ0G96S0uRCY4IK7\nto0h3680SSUPV0SVq21ZJo4Oe5JuUz3v4vpSFAXL1Qo3N7cyD8jzHLZtI01T2LYlJ7CmacJIdQ5l\nJkRH1ee2XvepoC5L6LoO0zSRJsmdhahowldFJE3LhGkYsjlimAYMXYfrODt2NQAQxQmCMIJh0Dpc\n3IH6EMcroMbiuVQWRF+ixn1BDYw8k9NPkRe9jbfxLx1vi9G38W8m1kn0lr0Kj10oyfrv1fdKexau\n5icSeOo0g8P86IEkOBXGFvfprv1jjDrTRU5WOQxrP0dhq1Cd0gEcrrha4dsvvsL5y5eIgpCr1JGE\nvcqFT+6b0jDG0Okd4G/+63/B+Y8/4tWLHzGbTPHqxY+4fPkKzXYb73z4Pk4fPYS6x5KCMYZGq4nf\n/pf/hIufXuHlixeYjMa4ePkKVxeXqDcbePr+e3j45Al1jyufpykxHY9pWXcK+6y/CzucVxGqqr1G\n53V/OLUazp48xjd//BLffvkViqJAt3cAxhhGgyG+e/YMAPDwyWN49c1itCjWnnyaru/ArDeC/63m\n13AXlYaKrF0/1n3nXFUp6SOri8rnt95bVcAV2xPWB9vvK8sSw9FoQzjnoNvZgCK+SdR9H6PxRBZg\nZVliNBqj5ro8QVVR9zfVcbe79o5j3498KDfPD1MYUBl6C4jb60I0c8bjyd4poQjLMjEcjQAIyFrr\nTghakiSYLxZoV9AO0+kMQRhKyPoBV5YmEZn5RuIPAJ5Xk5DF0WgMyzI5VUAlcZYk4ZMkBQpTYFs2\noT4Kun5sx34tRO4+KzBd13FydLRRkL1J0ISQfCuFUJhQOrY4N/LefVIUdDptmnrzYkz8NovFAmAM\nZw9OYFkWkiTBjz+9RBRF6HQ66LSJ7z2ZTnF7O4CmqTg5PoJt20iSFBeXl8iyNVz2vmMIglBC4hkj\n2LFj23L6BgDz+QLT2Rx1f+2Pq3OfyhIKvJpDvMn5AgB5o3qeRyI/eY4gCKQ6vCiMJMwAPw9NVPAp\n18JZYjSeEMy2KNDptOUkmAQCFYzHU16oKwg5jFPnXMSa66Je99FsNDCbL+Q9kSQphqMRjo8O5TVD\nQmQtuQ+MkX9m/j/Ze69mSY7sTPDz0CL11aokUAXZjZZskt1NjiA5L7vPM/9v92Ft9nHXbGdnlrNL\nsslho9kCaKABlFZXixShhfs+HHfPyLx5qwogSGODecwKhbo3M4SHR4Sfcz5R1TOc23kqiVL/BmYt\nbFTSpca4LEuyPZHj4boEGc+zjOZuQxTLlVzSuq5R1TWyPNPPH8ZmxYvUe1V9PssyMCmg1LwffM/D\n1tYmcZ9lgqZ8nYWggoEqihmMoSjKmWum4Pf2XFfSsqwZxFdd1yilCnApCwVFUaCqaxTSAotQW1TU\n8XxPv+uSlN75pmFID9tKeyD7vqfn5qIuJWPs0v2ouOfqb4WK0esYxlCrcV1CcJfxLyCWyegy/tWH\nZZmv7BS9TpAicY3JZKJhnp7nAUKQeXzd8HptZDJlUeA3H/4CTx8+hu04pMC6u4Og1YJlWzANE5Px\nGH/93/5SdxYXheou3H3vXVy/fQunR8d4/uQpjvb3cXp8jOHFBeIowtvfen/h4pUWvwElrTev4+zk\nFM+fPMXhixe4OD3Dr4YfIhqP8f53vwOrUcVtdrfe+fb72Lt545Vj9XIe3KsXbtPq73Rhcvfdd1AW\nBR5+fg8f/8Mv9aKQcw7HdXDnvXfw9vvvLYS/qZ9t7W7juz/6g9cqPHDxss9Mf5ckKQzThCcXBPNi\nQUmawZYL4FpCVZsq0YxNBbimf6aw32ZYloXNjfUZqDnB077c/FYLn26njbPzC/3zLM/x4uAAO1tb\nr+U3qfYPQIu9zHcwuIImmiYs05rxFk1TWoy+rDuoOqonp2cYXQH/hkxWW2GIN27dfC2PULXNMPDh\nyUVwu92Cbdsoq1IL+yh47unZ+Uxn13FsbS7PGMP21iZqzvW1sW1bL1CvCnXtv2okSYKT0zO0Wy14\nvncl13hRcE6K65a0AuENWsKrxi4viimsWyYwT589w+nZOd68fQtHxycYjyf47gffwseffArGDLRb\nIX7z0cf44NvfgmWZ+OjjT7C1tYnxZILjj07xg+9+B5998QUmkwgbG+s4Oj7B9ubmlceg4OjjSQQu\nBAxQ4j4Y9JGkib5/OOc4PDyCwRjW19aIP7/gnrMHxK8OAkpOVwZ9/RkAOL+4wPHJKXUyTRNVVcJ1\nXPR63de6T1QnzXEclGWloe5xniNNSTHYcWysrgwQxwl6PeIcTiYRWu0Q/V4Pk0kkxXWIE99qhei0\nWzNWRwp6rUTBrtICMB0DDqaoAIAKNALQPOiroiliVJTlDJYl8H2d9IFhhrPt2A5smzqYSvynlvNf\n2cHMB+ccT589A+ekOruxvj5zTxGq5GrhtPnwvOl1V+e+v7+P1dXVGTir+n2e56jk8d5/+BBrq6vY\nPziE8nb2PQ9RnJDfuuOg2+lgEkVwHKL9rK6uIIpiEhozSYXZsiwMRyP0ul2sDPqIYhIVUu+F5rE1\nhea4tCN6HSVbw7a/FA98Gcv4p4xlMrqMf9XxdUJU6pqjyAusbWxAdDqoOYdlKrVDQ/PJbMeeSebO\nTk6x//Q5AOCt93ktLbEAACAASURBVN/FW++9q/0TVRCM9PVgXgAlvN2VFbR6Pdx88w18/ttPsP/s\nOe59+hl2rl1Df2Uw872yLLVSMEALz97KCtY2NxCN7uJ3H/8Wzx49xsMv7mN7bw8bDV6W47mwZJcn\nyzLyJ/0nhv7keT4Dz2KM6a6maZnY2NjGYGUFzGBwXBftThfdfg+GScb1U66xBcu24HpU8c6zQv7s\n5dBIGuvX69oVVYU8SpB5DhgYqrqG5zjIi0KPE/FPKwghO80AoiiBaZECYppl8FwXnXbYSFKnHE8V\nhmFoj18VZ+cXME0D1hXd6KuCMYa11RXEcYKsISISRTGePH2G1ZUBOp32DEytmWRzziXEnMRfirLE\nztbmTDfLMAlirwQz/MBH2uDrplmG07NzrK+tXkpkAXUdqNsznww2YzyJkKYpOu0WWl/CNiXLMjx7\nsY+tjQ0EYQDXdS8pMcdxgv3Dw5kxAsj+QPkQMsYWLqSvCs0bF0ImTXPnJaaJfHNE5sdH8evrul5o\n13NVMEZQ4SiO0e124MMDIURsrWT+snvcsW30e13tZa3OZXdnG2/cvoV2u43ffvo7JEmKp8+eY293\nB0maIi8KnJ4SUHMSReilKeq6xtnZOaIoxvHxCT749vtYXVlBJDmoLzsHpjpAjcJOt9PGeNLBUHpa\nApQwPd8/wKDfQ7/XnbGyUHNadT/rukZRENc0jmOsra0i8H10O13tFYvG8+XLcI6LkkTy2q0WOCf7\ns0G/j4vRUFobORI6S+q8JPDVxySKYFu2Fp2ZQUWsryFpUCk45zg6PkGW51iRtj3z56qOhwuBqqyQ\n5RmiKEYUxej3u1ifE4BTyAQhKGn03KmIURNqq7qbAFEH6rieoTGYEtKtRKaiKNaJlmXZM6r7gLQ1\niiJEcYztrS0t3KM6+0o5XUF0K+kfDUBb95C6LpO2TQZceXyq2wr5fFLjUlUVRuOJhCZbSJIUruti\ndWWAXreLDamw/0zO6/lYbbx7AaDTbs9YOa0MBjBNE2VZIo4TMDCsr61qYSxL2qcoaz0huLQIAyA7\n+0vo7TJ+32KZjC5jGV9TCM4xPDvH7vVrGj4E0IIgz3OMZIep3W7PyPxPxmPq/jgOtvd2L3UNSaAg\nuiTusiiUkl9ZlsjyHHleYHdnG+999zs4OzlFlqaYjMfozQktKcVXteDnvJQLCxP91RW8990PcHZy\ningywWg4xPrW5pS74/vodLtIohjHB4e4++47r4Tqfh1RV7VObIQQODk8wmcf/xbdXg/f/6M/RCAt\ngcpSLoYYk90dWptWVQ3TIJjYyvoaDp4/x3g4RDSZoDcYvGLvkPu9nBDOBwNDGPpIkoysHMIAo3GE\nMPCkqitxozbCFUAASV1J/pajBWA4F5cWYk0eXV3XSOXiKkmSma7peDIhWX+bjMjpeJt8VANlWdKi\nkMmTkpA2y7KwubmOF/sHMx1L6pAe4uT0jLxnnak4Dqlc1ijKAkVRSu86Lk3uZ8dGccZUJb/X7WI0\nGmvYtBACxyenyPOclB5dR1swFWWFJEkwHk90AmtZJlphiNF4MpOoqCTCNF/vlefYNgyTvBHjOMHj\np8/QCkOEoa+T76qqEMcpJtFkZmwAIAwDrKwMXgtyXlU14iRGXROygv7mMonkqHmNZI6LmmYZnj5/\nQcJmpqkFzgwJ9TNMA4Hvw7YkH+01aAbzYZkmBv2eTh5saddi2a8ew/FkgjwviJcnv686f00kguJ0\nqkRrZUAL+sdPniIMAg0f3dvdgeu6ZMMiC3WWZc3wCRefAyVuzTBNE1sb6yiLEnEy5URXVYXjk1Nc\nXAzhyXmplIR1ElrS/VgWyj+TYUXaSL3Mv/N1QgiB589fkHBOVWJlMNCJU1cqugpB97167qVZRol/\nvyc5ywnW18hWK0lTeK6LwPexvbWJ5y/29TuEc46LiyHG4wk8zyOIbyOJqWtSAi6KQj8/Fee1yxeL\nMZ1fXGh/TdX1KyRsVV8PizwoGWMIJZ+22cVjoM7pSHZym4WpRQJcRVFgMolQlRWiiGDBvueRLc1o\nrJPQ3Z1tZFmO84tz3LxxA1Vd4+nz57i2t4f9/QPiMjs20jTDxsY6up0OXuzvS+9MC0ljngghcHx8\nTAVE18Gg10MhC4uaAjRXOKuqSnuyq8JGXXNNq1BFm8D3qfsJqXDPvBlkS+D7M0W5RUiHZSK6jN/H\nWCajy1jG1xj7z57h1p03EbSmYj8qUbo4Pydl0+2tmZeqhhQJgsXNw3CKPMeT+w+0/ceiUC+6Xq9H\nKrwNMRjXdTEuR2BsMR9RdUHUsRA3aLYzyCQosbmQVGFZFvZu3sDxwSHOjk/w9NFj3Hzj9iUhInVM\nvK4X8la/TBiGoblsKs5PT5FnObytQJ+PkGITi2CezXPcubaLB599jjRJcO/Tz/DtH3wP9oIFvBAC\ndVWBGabkoV19jJxzCTETiKJE++MmSYrAd5HlBRjIbsJ1HCQJeRPmeQHmk7qiUnQ0DCYX9sorVMFv\nmd5XkqSAAA6PTmYWxkqZNc8LEvWC5GaCEvowDFDXHGVVw2BMq17a0oO3024D28DB4dGMPYPiUuXF\n1cJar4o0TZHluVabbLVCDPq9mS6nEpkZjsZ6fk47MdOE0zJN6mAGAeI4mVECZoyUed0rBMbmo9vt\noNfr4tnzF8gy6sIPRyPt46fOf1F4novtrc1XQhlV5HmOp89eSA7b6xE7q6qa6ew1Q3UEd3e2NXqg\nCZt83ahrKirUda0hxd3u66nCKoEX5aM6PbjZz/meh3a7jZpz9LodEoxyHaysDHB4fAzP8+A4NgQn\nXnUYBiRGY5o4OT1Db4FKrZob5CMJ3RFuwt5d18Xu7jb29w8xiaKZ75dVhTKKLv38nzKEEBgORzg5\nPcPdN9/AaDRGlmVYGaxgEk3gez6GoyGKosT21qZ+nj17/gKtMCRxoZosyEiJeIhnz1/grbt34Ng2\nevK6HRweztzDtbSqiRsc2q8SjNFzTAmeKdu2eQ61I71E1b6HoxE67bbmoip6QLNDq6LJF1URBAE2\nLQtJmuhxqaoKZ2fnuLa3C9/3sX9wgLOzcxLQUpB3+a6FEKjqCp12Gxvr6zg+OcF4PIbruojjBLdu\n3QQDECfT8VGFAM8nRdyiLPVzLElTJEkKyzTJEigv4Dg24jjGxXAkhZpIVTdNU+kHGqKUkOVAdnYB\nzHpAyVhkObaMZXwTYpmMLmMZX1MwxjAajvCrn3+Iu+++g7DdBiBwfnKKj3/5a5RFgcHaKrb2dmde\nqt1+H47rIM8LPPjiHlyfqvKcC0STMe59+hmOJAflqnj26AlOj46wvrWJTq9HQhAGydifHZ/gi08+\nRZblaLVb6PYv288cvtjH88dPsL61iW6/D8/zYJgGeM0RRxHuf/Y54jiGF/jor6zMfJ8xhr0b13Hw\n7DmeP3mKj37xD4gmE2zv7WmD8qosEU0inB4dw3ZsvPPtb31pHmMzVMLePI6w1YZpWTh8/gI//5uf\nodVua26uYRjwfA/91RX0JAyq+d1ev4833r6LT379ER7du4+qqnD99i2E7RYMRuqUaZLg/OQMcRTh\nve9+ANuZ2qMsiqqqkGa5VElkMA0DjmtT57Cs4VKjUkNxi7KEwRnsINAFgsD3MJ4QVM00mgquSk2X\nwrIsrK2Sp9zKoI+VQV//7vjkFJ7nIghmExIhAM6pu9NMXj24l7q9vW4Xju3g+PQUk8nkpV6eV4VS\nu57uX53A9IcGIxVLIQTOL4YzfEnVnVoUrutic2NdewK6rttIRhVHl79WsscYQ0va0Ozt7uDg4Ahx\nEuvxvmobqtuztbmBUF7D1wkBoQtIX0eooo8QAp7nYTQaIcvp3v8yEYYBWVB8CZ6piizLMByNqeMp\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y2syH7dhwHZq7aZbJZ9vV/p6WZcrtTccxz3OdlFaNQoi6/lVVg563lk4ip9eBVL4N2e2E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\nf5FnRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import requests\n",
    "from io import BytesIO\n",
    "response = requests.get(\"http://www.pngmart.com/files/7/Modern-Plane-PNG-HD.png\")\n",
    "\n",
    "mask = np.array(Image.open(BytesIO(response.content)))\n",
    "wordcloud_fra = WordCloud(background_color=\"white\", mode=\"RGBA\", max_words=1000, mask=mask).generate(text)\n",
    "\n",
    "# create coloring from image\n",
    "image_colors = ImageColorGenerator(mask)\n",
    "plt.figure(figsize=[16,16])\n",
    "plt.imshow(wordcloud_fra.recolor(color_func=image_colors), interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sStx2XOemArv"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "paXcx0dPrxpQ"
   },
   "source": [
    "# **Providing Sentiment Value to Each Review According to Rating**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XiUQPa3AoVlj"
   },
   "outputs": [],
   "source": [
    "sentiment=[]\n",
    "for i in airline_data.overall_rating:\n",
    "  if(i<=5 and i>=0):\n",
    "    sentiment.append(0)\n",
    "  else:\n",
    "    sentiment.append(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Yq5wSiIfX7iQ"
   },
   "source": [
    "### **Sentiment from polarity**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-A90E47xXJLm"
   },
   "outputs": [],
   "source": [
    "sentiment=[]\n",
    "for i in result1:\n",
    "  if (i>0):\n",
    "    sentiment.append(1)\n",
    "  elif (i<0):\n",
    "    sentiment.append(-1)\n",
    "  elif (i==0):\n",
    "    sentiment.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oY8KTbVoYfAP"
   },
   "outputs": [],
   "source": [
    "new_airline_data=airline_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uPvrZYXtYtXZ"
   },
   "outputs": [],
   "source": [
    "new_airline_data['sentiment']=sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jhVnemSSZFgJ"
   },
   "outputs": [],
   "source": [
    "### Shuffline the Dataset for Training ###\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "new_airline_data=shuffle(new_airline_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "colab_type": "code",
    "id": "6mJF256DghkJ",
    "outputId": "695e601e-8aea-4b5b-b3de-d419d16a34b4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_sentiment_count=new_airline_data[new_airline_data['sentiment']==1]\n",
    "negative_sentiment_count=new_airline_data[new_airline_data['sentiment']==-1]\n",
    "neutral_sentiment_count=new_airline_data[new_airline_data['sentiment']==0]\n",
    "\n",
    "temp_array=[len(positive_sentiment_count),len(negative_sentiment_count),len(neutral_sentiment_count)]\n",
    "x_axis_labels=['Positive_sentiment','Negative_sentiment','Neutral_sentiment']\n",
    "plt.bar(x_axis_labels,temp_array,color=('green','red','blue'),width=(0.2,0.2,0.2))\n",
    "plt.savefig('sentiments.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sW_OEGPxoyQA"
   },
   "source": [
    "# **Logisitc Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Emyr7gWIo2XM"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JlrhTdHjo9Nw"
   },
   "outputs": [],
   "source": [
    "model=LogisticRegression()\n",
    "vectorizer=CountVectorizer(ngram_range=(1,2))\n",
    "x_l=vectorizer.fit_transform(new_airline_data.content.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "CdpFCgWgpAJl",
    "outputId": "41bd650d-1730-4b73-a5bb-34042d81ad10"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\kunalverma\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\kunalverma\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_l[:29488],new_airline_data.sentiment[:29488].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "DQpRwrSVpUhv",
    "outputId": "a86a6c02-a5bd-4b47-f5b0-52a72611fc19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9182151091821511"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_l[29488:],new_airline_data.sentiment[29488:].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "M9fNtgaHna3Y",
    "outputId": "86a1ab3e-19b8-4798-a9b9-9cc14a0c670a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Outbound flight FRA/PRN A319. 2 hours 10 min flight. I thought drinks/snacks for sale but sandwich soft drinks were served complimentary. Inbound flights SKP/LJU/FRA CRJ900. each 1 hour 30 min flight. Skyshop menu was in a seat pocket and drinks/snacks were for sale. All flight crews were friendly. Security check at the Ljubljana airport for transit passengers was chaos however it's possible to go to a gate within 30min.\""
      ]
     },
     "execution_count": 106,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_airline_data.content[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kC4MkAs5IaeV"
   },
   "outputs": [],
   "source": [
    "y_predict=model.predict(x_l[29488:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "19y3NJYpIi_n"
   },
   "outputs": [],
   "source": [
    "x_axis=[]\n",
    "n=1\n",
    "for i in range(0,len(new_airline_data[29488:].values)):\n",
    "  x_axis.append(n)\n",
    "  n=n+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "avVw5fWLJpKd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "colab_type": "code",
    "id": "BcGq1xQKJign",
    "outputId": "8895f94f-d030-48fd-bae8-79970d47494b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aa07ab4978>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_axis,new_airline_data.sentiment[29488:],color='red',label='predicted')\n",
    "plt.scatter(x_axis,y_predict,label='actual')\n",
    "plt.legend(loc='best')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "50jXWs7HHsCq"
   },
   "source": [
    "# **Linear Support Vector Machine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_V029Ywolo0C"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-XSb4DqKH501"
   },
   "outputs": [],
   "source": [
    "clf = LinearSVC(random_state=42, tol=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "OgeaaG4KH---",
    "outputId": "9184812b-3f0c-4aee-885c-3a84c3e2a1c9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\kunalverma\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=1e-05,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_l[:29488],new_airline_data.sentiment[:29488])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "U-ITN9ksIP4l",
    "outputId": "a14a142c-1427-4d2b-9a23-d40cbb4d9fef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9197070391970704"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_l[29488:],new_airline_data.sentiment[29488:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tSlhmc7HdcbI"
   },
   "outputs": [],
   "source": [
    "y_predict1=clf.predict(x_l[29488:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "colab_type": "code",
    "id": "bc4xhR5yK2d5",
    "outputId": "a28d3218-e02d-4650-9c5b-40c4f78e7a9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aa036e7358>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_axis,new_airline_data.sentiment[29488:],color='red',label='predicted')\n",
    "plt.scatter(x_axis,y_predict1,label='actual')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4mfLbnlgQkIg"
   },
   "source": [
    "# **Preprocessing the Reviews **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4jgaB84lQpGw"
   },
   "outputs": [],
   "source": [
    "t=new_airline_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dLsB_JVIRoQ8"
   },
   "outputs": [],
   "source": [
    "pos_sentiment=[]\n",
    "neg_sentiment=[]\n",
    "net_sentiment=[]\n",
    "avg_rating=[]\n",
    "for i in airline_names:\n",
    "  tempdf=t[t['airline_name']==i]\n",
    "  pos=len(tempdf[tempdf['sentiment']==1])\n",
    "  neg=len(tempdf[tempdf['sentiment']==-1])\n",
    "  net=len(tempdf[tempdf['sentiment']==0])\n",
    "  pos_sentiment.append(pos)\n",
    "  neg_sentiment.append(neg)\n",
    "  net_sentiment.append(net)\n",
    "  avg_rate=tempdf.overall_rating.mean()\n",
    "  avg_rating.append(avg_rate)\n",
    "clustered_data=pd.DataFrame(list(zip(airline_names,avg_rating,pos_sentiment,net_sentiment,neg_sentiment)),columns=['airline_name','average_rating','pos_sentiment','net_sentiment','neg_sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0FXCr0X2UqBp"
   },
   "outputs": [],
   "source": [
    "clustered_data=clustered_data.sort_values(by=['pos_sentiment','average_rating'],kind='mergesort',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "4x-bZA1cWBD0",
    "outputId": "14976ff9-1504-432c-ad3c-73092b0aff88"
   },
   "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>airline_name</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>pos_sentiment</th>\n",
       "      <th>net_sentiment</th>\n",
       "      <th>neg_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>british-airways</td>\n",
       "      <td>5.881696</td>\n",
       "      <td>703</td>\n",
       "      <td>2</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>emirates</td>\n",
       "      <td>6.246377</td>\n",
       "      <td>558</td>\n",
       "      <td>1</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>spirit-airlines</td>\n",
       "      <td>2.902692</td>\n",
       "      <td>556</td>\n",
       "      <td>15</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>united-airlines</td>\n",
       "      <td>3.356377</td>\n",
       "      <td>523</td>\n",
       "      <td>5</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>lufthansa</td>\n",
       "      <td>6.993333</td>\n",
       "      <td>511</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        airline_name  average_rating  pos_sentiment  net_sentiment  \\\n",
       "97   british-airways        5.881696            703              2   \n",
       "138         emirates        6.246377            558              1   \n",
       "295  spirit-airlines        2.902692            556             15   \n",
       "333  united-airlines        3.356377            523              5   \n",
       "215        lufthansa        6.993333            511              1   \n",
       "\n",
       "     neg_sentiment  \n",
       "97             191  \n",
       "138            131  \n",
       "295            395  \n",
       "333            311  \n",
       "215             88  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustered_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1630
    },
    "colab_type": "code",
    "id": "5VCmW4hZWIeU",
    "outputId": "16b95b00-a269-4b01-b43b-714e8e0e1a41"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.bar(clustered_data.airline_name.head(10).values,clustered_data.pos_sentiment.head(10).values,label='Positive Sentiment',color='green')\n",
    "plt.bar(clustered_data.airline_name.head(10).values,clustered_data.neg_sentiment.head(10).values,label='Negative Sentiment',color='red')\n",
    "plt.bar(clustered_data.airline_name.head(10).values,clustered_data.net_sentiment.head(10).values,label='Neutral Sentiment',color='black')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend()\n",
    "plt.savefig('sentiment_per_airline.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lzpCq6CJijfS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v2CkQIFPikXH"
   },
   "source": [
    "# **Preprocessing Reviews and Removing Stop Words**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m8vN2Oz3jdv8"
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import sent_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import string\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "RLmJas4Cosa2",
    "outputId": "636672c6-edb2-4aef-caf0-aa789a331e3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "VKFYMEYAo1wF",
    "outputId": "53f27671-7cf2-460a-cf24-ddee573363c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9BaY0ZrMjiIN"
   },
   "outputs": [],
   "source": [
    "stop = set(stopwords.words(\"english\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vQSDMxa1irCB"
   },
   "outputs": [],
   "source": [
    "def preprocessing(text):\n",
    "  le = WordNetLemmatizer()\n",
    "  words = word_tokenize(text)\n",
    "  words = [x for x in words if not x in stop]\n",
    "  words = [le.lemmatize(x) for x in words]\n",
    "  return \" \".join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hbZZSe0Bj-js"
   },
   "outputs": [],
   "source": [
    "reviews2 = [preprocessing(x) for x in new_airline_data.content]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KoeCiIPkkc2X"
   },
   "outputs": [],
   "source": [
    "new_airline_data['reviews_processed']=reviews2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NNQGD-IZlKfw"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "exZACuNzkoNY"
   },
   "source": [
    "### **Logisitic Regression on Processed Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0hO76ighktak"
   },
   "outputs": [],
   "source": [
    "logic_model=LogisticRegression()\n",
    "vectorizer_p=CountVectorizer(ngram_range=(1,2))\n",
    "x_l_1=vectorizer_p.fit_transform(new_airline_data.reviews_processed.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "QiG_o3SBo1Ls",
    "outputId": "bc92bafc-8008-4447-b9d5-c69cbd1b3e70"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logic_model.fit(x_l_1[:29488],new_airline_data.sentiment[:29488].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9HfqtPhgo-VW",
    "outputId": "6ca359f5-b07a-40cf-ab42-4e85d044d42e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9115692391156924"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logic_model.score(x_l_1[29488:],new_airline_data.sentiment[29488:].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-3oq7TAFpHY1"
   },
   "source": [
    "### **Implementing Linear SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qyoMfJBepLrb"
   },
   "outputs": [],
   "source": [
    "clf1 = LinearSVC(random_state=42, tol=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "ekVMoqewpV3M",
    "outputId": "b41dfa71-dac5-48ea-81fc-89408533ae1f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=1e-05,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.fit(x_l_1[:29488],new_airline_data.sentiment[:29488])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "r6sEwRNUpYTb",
    "outputId": "53890b5e-ecce-446b-ffcf-c697cd1c4bbd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9092635290926353"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.score(x_l_1[29488:],new_airline_data.sentiment[29488:]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bqg3zYRAcgG6"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iucgAG0apokI"
   },
   "source": [
    "# **Working With Neural Networks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "colab_type": "code",
    "id": "SBaGPNOfpzBK",
    "outputId": "90b67a89-507b-4f9e-cca7-5189394a826b"
   },
   "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>airline_name</th>\n",
       "      <th>link</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>author_country</th>\n",
       "      <th>date</th>\n",
       "      <th>content</th>\n",
       "      <th>aircraft</th>\n",
       "      <th>type_traveller</th>\n",
       "      <th>cabin_flown</th>\n",
       "      <th>...</th>\n",
       "      <th>seat_comfort_rating</th>\n",
       "      <th>cabin_staff_rating</th>\n",
       "      <th>food_beverages_rating</th>\n",
       "      <th>inflight_entertainment_rating</th>\n",
       "      <th>ground_service_rating</th>\n",
       "      <th>wifi_connectivity_rating</th>\n",
       "      <th>value_money_rating</th>\n",
       "      <th>recommended</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>reviews_processed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31578</th>\n",
       "      <td>singapore-airlines</td>\n",
       "      <td>/airline-reviews/singapore-airlines</td>\n",
       "      <td>Singapore Airlines customer review</td>\n",
       "      <td>A Turner</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>Flew economy from Frankfurt to Singapore. Food...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Economy</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Flew economy Frankfurt Singapore . Food decent...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             airline_name                                 link  \\\n",
       "31578  singapore-airlines  /airline-reviews/singapore-airlines   \n",
       "\n",
       "                                    title    author author_country  \\\n",
       "31578  Singapore Airlines customer review  A Turner      Australia   \n",
       "\n",
       "             date                                            content aircraft  \\\n",
       "31578  2014-01-09  Flew economy from Frankfurt to Singapore. Food...      NaN   \n",
       "\n",
       "      type_traveller cabin_flown  \\\n",
       "31578            NaN     Economy   \n",
       "\n",
       "                             ...                         seat_comfort_rating  \\\n",
       "31578                        ...                                         4.0   \n",
       "\n",
       "       cabin_staff_rating  food_beverages_rating  \\\n",
       "31578                 5.0                    4.0   \n",
       "\n",
       "       inflight_entertainment_rating  ground_service_rating  \\\n",
       "31578                            4.0                    NaN   \n",
       "\n",
       "       wifi_connectivity_rating  value_money_rating  recommended  sentiment  \\\n",
       "31578                       NaN                 5.0            1          1   \n",
       "\n",
       "                                       reviews_processed  \n",
       "31578  Flew economy Frankfurt Singapore . Food decent...  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_airline_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mZVXyfEHrlsd"
   },
   "outputs": [],
   "source": [
    "reviews_array=np.array(new_airline_data.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IXD2vA0Trt57"
   },
   "outputs": [],
   "source": [
    "sentiment_array=np.array(new_airline_data.sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cT6LO9DltGFo",
    "outputId": "5c61cd93-d1b6-43bb-ec77-aa84ab9af800"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import imdb\n",
    "from keras.layers import Dense,Conv1D,MaxPool1D,Embedding,Flatten,Dropout,GRU,LSTM\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.models import Sequential\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from keras.optimizers import Adam\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5uWiCZeur4yr",
    "outputId": "4f8a3bb0-8805-40c4-b56f-789b9f453015"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37678\n"
     ]
    }
   ],
   "source": [
    "token=Tokenizer()\n",
    "token.fit_on_texts(reviews_array) \n",
    "vocab_size=len(token.word_index) +1\n",
    "print(vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QiKcZJgttOI5"
   },
   "outputs": [],
   "source": [
    "l = 0\n",
    "for i in reviews_array:\n",
    "  l += len(i)\n",
    "  \n",
    "avg_length = l/len(reviews_array)\n",
    "review_training = [x[:int(avg_length)] for x in reviews_array]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fdiIIJGNtmTt"
   },
   "outputs": [],
   "source": [
    "# encoded = token.texts_to_sequences(reviews_array)\n",
    "# l = []\n",
    "# for i in encoded:\n",
    "#   l.append(len(i))\n",
    "# print(l/len(encoded))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K5VkzXbgtw_N"
   },
   "outputs": [],
   "source": [
    "padded_docs = pad_sequences(encoded, maxlen=38, padding='post')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "10zOA3UBvOaK"
   },
   "source": [
    "## **LSTM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "0DkyborJt9k3",
    "outputId": "dfe1130e-5f15-453c-896f-55e6af7f8118"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 38, 64)            2411392   \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 38, 64)            33024     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 38, 64)            0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 64)                33024     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 2,477,505\n",
      "Trainable params: 2,477,505\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "n_model1 = Sequential()\n",
    "n_model1.add(Embedding(37678,64,input_length=38))\n",
    "n_model1.add(LSTM(64, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=True, return_state=False, go_backwards=False, stateful=False, unroll=False))\n",
    "n_model1.add(Dropout(0.5))\n",
    "n_model1.add(LSTM(64,return_sequences=False))\n",
    "n_model1.add(Dropout(0.5))\n",
    "n_model1.add(Dense(1,activation=\"sigmoid\"))\n",
    "n_model1.summary()\n",
    "n_model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "colab_type": "code",
    "id": "UTG7KVS0utds",
    "outputId": "52952d97-9ca4-4783-e50a-82c7b40f6319"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 29488 samples, validate on 7373 samples\n",
      "Epoch 1/5\n",
      "29488/29488 [==============================] - 40s 1ms/step - loss: 0.4716 - acc: 0.7725 - val_loss: 0.4018 - val_acc: 0.8283\n",
      "Epoch 2/5\n",
      "29488/29488 [==============================] - 38s 1ms/step - loss: 0.3562 - acc: 0.8549 - val_loss: 0.4038 - val_acc: 0.8204\n",
      "Epoch 3/5\n",
      "29488/29488 [==============================] - 37s 1ms/step - loss: 0.2973 - acc: 0.8836 - val_loss: 0.4231 - val_acc: 0.8142\n",
      "Epoch 4/5\n",
      "29488/29488 [==============================] - 37s 1ms/step - loss: 0.2439 - acc: 0.9078 - val_loss: 0.4939 - val_acc: 0.8013\n",
      "Epoch 5/5\n",
      "29488/29488 [==============================] - 36s 1ms/step - loss: 0.1928 - acc: 0.9301 - val_loss: 0.5099 - val_acc: 0.7991\n"
     ]
    }
   ],
   "source": [
    "hist = n_model1.fit(padded_docs,sentiment_array,epochs=5,batch_size=100,validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HfDE2R2Ru7ak"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bu3DrUM3vwXY"
   },
   "source": [
    "## **CNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "qHleXlmpv7cG",
    "outputId": "77668ea4-4b91-4b98-97f1-1d8a68e3e08d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_5 (Embedding)      (None, 38, 100)           3767800   \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 36, 64)            19264     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 12, 64)            0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 768)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 64)                49216     \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 3,836,345\n",
      "Trainable params: 3,836,345\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "n_model2 = Sequential()\n",
    "n_model2.add(Embedding(37678,100,input_length=38))\n",
    "n_model2.add(Conv1D(filters=64,kernel_size=3))\n",
    "n_model2.add(MaxPool1D(pool_size=3))\n",
    "n_model2.add(Flatten())\n",
    "n_model2.add(Dense(64,activation=\"relu\"))\n",
    "n_model2.add(Dropout(rate = 0.2))\n",
    "n_model2.add(Dense(1,activation=\"sigmoid\"))\n",
    "n_model2.summary()\n",
    "adm = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "n_model2.compile(loss='binary_crossentropy', optimizer=adm, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3434
    },
    "colab_type": "code",
    "id": "-SPkWfY8wDzp",
    "outputId": "68cea884-d801-4df8-a474-7686d9fc55e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 33174 samples, validate on 3687 samples\n",
      "Epoch 1/100\n",
      "33174/33174 [==============================] - 23s 688us/step - loss: 0.6859 - acc: 0.5677 - val_loss: 0.6800 - val_acc: 0.5837\n",
      "Epoch 2/100\n",
      "33174/33174 [==============================] - 22s 654us/step - loss: 0.6750 - acc: 0.5908 - val_loss: 0.6750 - val_acc: 0.5837\n",
      "Epoch 3/100\n",
      "33174/33174 [==============================] - 22s 660us/step - loss: 0.6706 - acc: 0.5908 - val_loss: 0.6711 - val_acc: 0.5837\n",
      "Epoch 4/100\n",
      "33174/33174 [==============================] - 22s 666us/step - loss: 0.6650 - acc: 0.5908 - val_loss: 0.6655 - val_acc: 0.5837\n",
      "Epoch 5/100\n",
      "33174/33174 [==============================] - 22s 650us/step - loss: 0.6575 - acc: 0.5908 - val_loss: 0.6567 - val_acc: 0.5837\n",
      "Epoch 6/100\n",
      "33174/33174 [==============================] - 22s 650us/step - loss: 0.6460 - acc: 0.5915 - val_loss: 0.6425 - val_acc: 0.5891\n",
      "Epoch 7/100\n",
      "33174/33174 [==============================] - 23s 680us/step - loss: 0.6284 - acc: 0.6121 - val_loss: 0.6218 - val_acc: 0.6222\n",
      "Epoch 8/100\n",
      "33174/33174 [==============================] - 21s 643us/step - loss: 0.6046 - acc: 0.6722 - val_loss: 0.5949 - val_acc: 0.6813\n",
      "Epoch 9/100\n",
      "33174/33174 [==============================] - 22s 653us/step - loss: 0.5754 - acc: 0.7240 - val_loss: 0.5653 - val_acc: 0.7266\n",
      "Epoch 10/100\n",
      "33174/33174 [==============================] - 22s 653us/step - loss: 0.5463 - acc: 0.7511 - val_loss: 0.5368 - val_acc: 0.7513\n",
      "Epoch 11/100\n",
      "33174/33174 [==============================] - 22s 668us/step - loss: 0.5190 - acc: 0.7679 - val_loss: 0.5122 - val_acc: 0.7624\n",
      "Epoch 12/100\n",
      "33174/33174 [==============================] - 22s 654us/step - loss: 0.4961 - acc: 0.7791 - val_loss: 0.4915 - val_acc: 0.7711\n",
      "Epoch 13/100\n",
      "33174/33174 [==============================] - 22s 650us/step - loss: 0.4740 - acc: 0.7915 - val_loss: 0.4739 - val_acc: 0.7792\n",
      "Epoch 14/100\n",
      "33174/33174 [==============================] - 23s 684us/step - loss: 0.4568 - acc: 0.7988 - val_loss: 0.4589 - val_acc: 0.7920\n",
      "Epoch 15/100\n",
      "33174/33174 [==============================] - 22s 662us/step - loss: 0.4407 - acc: 0.8070 - val_loss: 0.4464 - val_acc: 0.7979\n",
      "Epoch 16/100\n",
      "33174/33174 [==============================] - 22s 652us/step - loss: 0.4279 - acc: 0.8146 - val_loss: 0.4363 - val_acc: 0.8042\n",
      "Epoch 17/100\n",
      "33174/33174 [==============================] - 22s 651us/step - loss: 0.4152 - acc: 0.8216 - val_loss: 0.4278 - val_acc: 0.8082\n",
      "Epoch 18/100\n",
      "33174/33174 [==============================] - 22s 671us/step - loss: 0.4042 - acc: 0.8270 - val_loss: 0.4205 - val_acc: 0.8142\n",
      "Epoch 19/100\n",
      "33174/33174 [==============================] - 21s 645us/step - loss: 0.3949 - acc: 0.8335 - val_loss: 0.4149 - val_acc: 0.8150\n",
      "Epoch 20/100\n",
      "33174/33174 [==============================] - 22s 667us/step - loss: 0.3867 - acc: 0.8370 - val_loss: 0.4102 - val_acc: 0.8205\n",
      "Epoch 21/100\n",
      "33174/33174 [==============================] - 23s 692us/step - loss: 0.3798 - acc: 0.8412 - val_loss: 0.4065 - val_acc: 0.8183\n",
      "Epoch 22/100\n",
      "33174/33174 [==============================] - 23s 685us/step - loss: 0.3734 - acc: 0.8447 - val_loss: 0.4034 - val_acc: 0.8205\n",
      "Epoch 23/100\n",
      "33174/33174 [==============================] - 22s 677us/step - loss: 0.3662 - acc: 0.8477 - val_loss: 0.4008 - val_acc: 0.8221\n",
      "Epoch 24/100\n",
      "33174/33174 [==============================] - 23s 681us/step - loss: 0.3606 - acc: 0.8503 - val_loss: 0.3987 - val_acc: 0.8226\n",
      "Epoch 25/100\n",
      "33174/33174 [==============================] - 23s 684us/step - loss: 0.3556 - acc: 0.8527 - val_loss: 0.3968 - val_acc: 0.8223\n",
      "Epoch 26/100\n",
      "33174/33174 [==============================] - 23s 683us/step - loss: 0.3503 - acc: 0.8571 - val_loss: 0.3956 - val_acc: 0.8237\n",
      "Epoch 27/100\n",
      "33174/33174 [==============================] - 22s 663us/step - loss: 0.3447 - acc: 0.8585 - val_loss: 0.3943 - val_acc: 0.8245\n",
      "Epoch 28/100\n",
      "33174/33174 [==============================] - 22s 675us/step - loss: 0.3403 - acc: 0.8598 - val_loss: 0.3934 - val_acc: 0.8253\n",
      "Epoch 29/100\n",
      "33174/33174 [==============================] - 22s 672us/step - loss: 0.3354 - acc: 0.8632 - val_loss: 0.3929 - val_acc: 0.8270\n",
      "Epoch 30/100\n",
      "33174/33174 [==============================] - 22s 656us/step - loss: 0.3318 - acc: 0.8653 - val_loss: 0.3922 - val_acc: 0.8248\n",
      "Epoch 31/100\n",
      "33174/33174 [==============================] - 22s 654us/step - loss: 0.3279 - acc: 0.8658 - val_loss: 0.3919 - val_acc: 0.8242\n",
      "Epoch 32/100\n",
      "33174/33174 [==============================] - 22s 668us/step - loss: 0.3234 - acc: 0.8707 - val_loss: 0.3922 - val_acc: 0.8256\n",
      "Epoch 33/100\n",
      "33174/33174 [==============================] - 22s 669us/step - loss: 0.3186 - acc: 0.8708 - val_loss: 0.3918 - val_acc: 0.8248\n",
      "Epoch 34/100\n",
      "33174/33174 [==============================] - 22s 657us/step - loss: 0.3145 - acc: 0.8739 - val_loss: 0.3919 - val_acc: 0.8242\n",
      "Epoch 35/100\n",
      "33174/33174 [==============================] - 21s 647us/step - loss: 0.3104 - acc: 0.8756 - val_loss: 0.3921 - val_acc: 0.8248\n",
      "Epoch 36/100\n",
      "33174/33174 [==============================] - 22s 670us/step - loss: 0.3074 - acc: 0.8781 - val_loss: 0.3926 - val_acc: 0.8256\n",
      "Epoch 37/100\n",
      "33174/33174 [==============================] - 22s 667us/step - loss: 0.3032 - acc: 0.8789 - val_loss: 0.3929 - val_acc: 0.8256\n",
      "Epoch 38/100\n",
      "33174/33174 [==============================] - 22s 666us/step - loss: 0.2990 - acc: 0.8813 - val_loss: 0.3934 - val_acc: 0.8229\n",
      "Epoch 39/100\n",
      "33174/33174 [==============================] - 22s 661us/step - loss: 0.2967 - acc: 0.8813 - val_loss: 0.3940 - val_acc: 0.8240\n",
      "Epoch 40/100\n",
      "33174/33174 [==============================] - 23s 684us/step - loss: 0.2921 - acc: 0.8849 - val_loss: 0.3948 - val_acc: 0.8223\n",
      "Epoch 41/100\n",
      "33174/33174 [==============================] - 22s 664us/step - loss: 0.2881 - acc: 0.8864 - val_loss: 0.3957 - val_acc: 0.8226\n",
      "Epoch 42/100\n",
      "33174/33174 [==============================] - 23s 684us/step - loss: 0.2840 - acc: 0.8885 - val_loss: 0.3967 - val_acc: 0.8215\n",
      "Epoch 43/100\n",
      "33174/33174 [==============================] - 22s 670us/step - loss: 0.2811 - acc: 0.8896 - val_loss: 0.3976 - val_acc: 0.8221\n",
      "Epoch 44/100\n",
      "33174/33174 [==============================] - 23s 679us/step - loss: 0.2775 - acc: 0.8914 - val_loss: 0.3989 - val_acc: 0.8199\n",
      "Epoch 45/100\n",
      "33174/33174 [==============================] - 22s 657us/step - loss: 0.2734 - acc: 0.8927 - val_loss: 0.4007 - val_acc: 0.8186\n",
      "Epoch 46/100\n",
      "33174/33174 [==============================] - 22s 652us/step - loss: 0.2703 - acc: 0.8943 - val_loss: 0.4015 - val_acc: 0.8169\n",
      "Epoch 47/100\n",
      "33174/33174 [==============================] - 22s 663us/step - loss: 0.2668 - acc: 0.8964 - val_loss: 0.4026 - val_acc: 0.8183\n",
      "Epoch 48/100\n",
      "33174/33174 [==============================] - 22s 659us/step - loss: 0.2632 - acc: 0.8978 - val_loss: 0.4041 - val_acc: 0.8183\n",
      "Epoch 49/100\n",
      "33174/33174 [==============================] - 21s 645us/step - loss: 0.2603 - acc: 0.8988 - val_loss: 0.4057 - val_acc: 0.8172\n",
      "Epoch 50/100\n",
      "33174/33174 [==============================] - 21s 640us/step - loss: 0.2564 - acc: 0.9022 - val_loss: 0.4075 - val_acc: 0.8161\n",
      "Epoch 51/100\n",
      "33174/33174 [==============================] - 22s 674us/step - loss: 0.2532 - acc: 0.9034 - val_loss: 0.4093 - val_acc: 0.8158\n",
      "Epoch 52/100\n",
      "33174/33174 [==============================] - 22s 673us/step - loss: 0.2501 - acc: 0.9053 - val_loss: 0.4108 - val_acc: 0.8172\n",
      "Epoch 53/100\n",
      "33174/33174 [==============================] - 22s 664us/step - loss: 0.2465 - acc: 0.9067 - val_loss: 0.4134 - val_acc: 0.8150\n",
      "Epoch 54/100\n",
      "33174/33174 [==============================] - 22s 665us/step - loss: 0.2426 - acc: 0.9076 - val_loss: 0.4148 - val_acc: 0.8164\n",
      "Epoch 55/100\n",
      "33174/33174 [==============================] - 23s 683us/step - loss: 0.2399 - acc: 0.9095 - val_loss: 0.4167 - val_acc: 0.8164\n",
      "Epoch 56/100\n",
      "33174/33174 [==============================] - 23s 690us/step - loss: 0.2368 - acc: 0.9101 - val_loss: 0.4192 - val_acc: 0.8153\n",
      "Epoch 57/100\n",
      "33174/33174 [==============================] - 22s 664us/step - loss: 0.2327 - acc: 0.9132 - val_loss: 0.4212 - val_acc: 0.8153\n",
      "Epoch 58/100\n",
      "33174/33174 [==============================] - 22s 676us/step - loss: 0.2298 - acc: 0.9140 - val_loss: 0.4236 - val_acc: 0.8156\n",
      "Epoch 59/100\n",
      "33174/33174 [==============================] - 22s 658us/step - loss: 0.2268 - acc: 0.9156 - val_loss: 0.4259 - val_acc: 0.8153\n",
      "Epoch 60/100\n",
      "33174/33174 [==============================] - 22s 664us/step - loss: 0.2242 - acc: 0.9170 - val_loss: 0.4283 - val_acc: 0.8142\n",
      "Epoch 61/100\n",
      "33174/33174 [==============================] - 22s 668us/step - loss: 0.2204 - acc: 0.9185 - val_loss: 0.4307 - val_acc: 0.8145\n",
      "Epoch 62/100\n",
      "33174/33174 [==============================] - 23s 690us/step - loss: 0.2169 - acc: 0.9201 - val_loss: 0.4336 - val_acc: 0.8148\n",
      "Epoch 63/100\n",
      "33174/33174 [==============================] - 22s 678us/step - loss: 0.2136 - acc: 0.9219 - val_loss: 0.4358 - val_acc: 0.8120\n",
      "Epoch 64/100\n",
      "33174/33174 [==============================] - 22s 671us/step - loss: 0.2109 - acc: 0.9223 - val_loss: 0.4385 - val_acc: 0.8126\n",
      "Epoch 65/100\n",
      "33174/33174 [==============================] - 23s 681us/step - loss: 0.2073 - acc: 0.9254 - val_loss: 0.4413 - val_acc: 0.8126\n",
      "Epoch 66/100\n",
      "33174/33174 [==============================] - 22s 664us/step - loss: 0.2051 - acc: 0.9250 - val_loss: 0.4445 - val_acc: 0.8134\n",
      "Epoch 67/100\n",
      "33174/33174 [==============================] - 22s 652us/step - loss: 0.2010 - acc: 0.9280 - val_loss: 0.4474 - val_acc: 0.8126\n",
      "Epoch 68/100\n",
      "33174/33174 [==============================] - 21s 646us/step - loss: 0.1981 - acc: 0.9274 - val_loss: 0.4504 - val_acc: 0.8118\n",
      "Epoch 69/100\n",
      "33174/33174 [==============================] - 22s 662us/step - loss: 0.1952 - acc: 0.9291 - val_loss: 0.4537 - val_acc: 0.8112\n",
      "Epoch 70/100\n",
      "33174/33174 [==============================] - 22s 668us/step - loss: 0.1919 - acc: 0.9320 - val_loss: 0.4570 - val_acc: 0.8104\n",
      "Epoch 71/100\n",
      "33174/33174 [==============================] - 21s 635us/step - loss: 0.1896 - acc: 0.9337 - val_loss: 0.4603 - val_acc: 0.8110\n",
      "Epoch 72/100\n",
      "33174/33174 [==============================] - 21s 638us/step - loss: 0.1858 - acc: 0.9354 - val_loss: 0.4644 - val_acc: 0.8115\n",
      "Epoch 73/100\n",
      "33174/33174 [==============================] - 22s 662us/step - loss: 0.1834 - acc: 0.9362 - val_loss: 0.4682 - val_acc: 0.8101\n",
      "Epoch 74/100\n",
      "33174/33174 [==============================] - 21s 640us/step - loss: 0.1808 - acc: 0.9371 - val_loss: 0.4719 - val_acc: 0.8080\n",
      "Epoch 75/100\n",
      "33174/33174 [==============================] - 21s 636us/step - loss: 0.1780 - acc: 0.9371 - val_loss: 0.4741 - val_acc: 0.8099\n",
      "Epoch 76/100\n",
      "33174/33174 [==============================] - 22s 667us/step - loss: 0.1748 - acc: 0.9397 - val_loss: 0.4778 - val_acc: 0.8088\n",
      "Epoch 77/100\n",
      "33174/33174 [==============================] - 22s 662us/step - loss: 0.1718 - acc: 0.9406 - val_loss: 0.4817 - val_acc: 0.8074\n",
      "Epoch 78/100\n",
      "33174/33174 [==============================] - 21s 648us/step - loss: 0.1689 - acc: 0.9425 - val_loss: 0.4855 - val_acc: 0.8069\n",
      "Epoch 79/100\n",
      "33174/33174 [==============================] - 22s 653us/step - loss: 0.1665 - acc: 0.9440 - val_loss: 0.4891 - val_acc: 0.8061\n",
      "Epoch 80/100\n",
      "33174/33174 [==============================] - 22s 677us/step - loss: 0.1640 - acc: 0.9447 - val_loss: 0.4935 - val_acc: 0.8050\n",
      "Epoch 81/100\n",
      "33174/33174 [==============================] - 22s 666us/step - loss: 0.1610 - acc: 0.9461 - val_loss: 0.4976 - val_acc: 0.8034\n",
      "Epoch 82/100\n",
      "33174/33174 [==============================] - 22s 654us/step - loss: 0.1590 - acc: 0.9472 - val_loss: 0.5014 - val_acc: 0.8031\n",
      "Epoch 83/100\n",
      "33174/33174 [==============================] - 22s 665us/step - loss: 0.1558 - acc: 0.9482 - val_loss: 0.5052 - val_acc: 0.8028\n",
      "Epoch 84/100\n",
      "33174/33174 [==============================] - 23s 688us/step - loss: 0.1535 - acc: 0.9489 - val_loss: 0.5103 - val_acc: 0.8007\n",
      "Epoch 85/100\n",
      "33174/33174 [==============================] - 21s 647us/step - loss: 0.1508 - acc: 0.9511 - val_loss: 0.5143 - val_acc: 0.8009\n",
      "Epoch 86/100\n",
      "33174/33174 [==============================] - 22s 652us/step - loss: 0.1480 - acc: 0.9518 - val_loss: 0.5182 - val_acc: 0.8007\n",
      "Epoch 87/100\n",
      "33174/33174 [==============================] - 22s 672us/step - loss: 0.1459 - acc: 0.9529 - val_loss: 0.5224 - val_acc: 0.7996\n",
      "Epoch 88/100\n",
      "33174/33174 [==============================] - 22s 669us/step - loss: 0.1430 - acc: 0.9542 - val_loss: 0.5272 - val_acc: 0.7977\n",
      "Epoch 89/100\n",
      "33174/33174 [==============================] - 22s 659us/step - loss: 0.1406 - acc: 0.9550 - val_loss: 0.5316 - val_acc: 0.7979\n",
      "Epoch 90/100\n",
      "33174/33174 [==============================] - 22s 672us/step - loss: 0.1379 - acc: 0.9561 - val_loss: 0.5375 - val_acc: 0.7979\n",
      "Epoch 91/100\n",
      "33174/33174 [==============================] - 23s 680us/step - loss: 0.1356 - acc: 0.9570 - val_loss: 0.5415 - val_acc: 0.7960\n",
      "Epoch 92/100\n",
      "33174/33174 [==============================] - 22s 663us/step - loss: 0.1328 - acc: 0.9592 - val_loss: 0.5453 - val_acc: 0.7947\n",
      "Epoch 93/100\n",
      "33174/33174 [==============================] - 22s 667us/step - loss: 0.1304 - acc: 0.9597 - val_loss: 0.5511 - val_acc: 0.7947\n",
      "Epoch 94/100\n",
      "33174/33174 [==============================] - 22s 665us/step - loss: 0.1282 - acc: 0.9607 - val_loss: 0.5572 - val_acc: 0.7917\n",
      "Epoch 95/100\n",
      "33174/33174 [==============================] - 22s 673us/step - loss: 0.1261 - acc: 0.9614 - val_loss: 0.5609 - val_acc: 0.7917\n",
      "Epoch 96/100\n",
      "33174/33174 [==============================] - 21s 647us/step - loss: 0.1240 - acc: 0.9620 - val_loss: 0.5661 - val_acc: 0.7917\n",
      "Epoch 97/100\n",
      "33174/33174 [==============================] - 22s 669us/step - loss: 0.1207 - acc: 0.9638 - val_loss: 0.5713 - val_acc: 0.7909\n",
      "Epoch 98/100\n",
      "33174/33174 [==============================] - 23s 687us/step - loss: 0.1189 - acc: 0.9644 - val_loss: 0.5763 - val_acc: 0.7890\n",
      "Epoch 99/100\n",
      "33174/33174 [==============================] - 22s 659us/step - loss: 0.1165 - acc: 0.9647 - val_loss: 0.5817 - val_acc: 0.7890\n",
      "Epoch 100/100\n",
      "33174/33174 [==============================] - 22s 661us/step - loss: 0.1144 - acc: 0.9665 - val_loss: 0.5870 - val_acc: 0.7879\n"
     ]
    }
   ],
   "source": [
    "hist = n_model2.fit(padded_docs,sentiment_array,epochs=100,batch_size=100,validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "colab_type": "code",
    "id": "LVmM60URwUqA",
    "outputId": "b99eb4b1-d35b-4401-bf85-cb124a042f31"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa03f688400>"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tLvIt+eN+7UOhFl448id2BpowMPDme6hylVPhLCcUD7O3dz/hxNk/c3lmKxXO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\nnWFzJiIisjN/AYpCpEpF14/SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history[\"acc\"],label=\"acc\")\n",
    "plt.plot(hist.history[\"val_acc\"],label=\"val\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "uWBCoVRD5By1",
    "outputId": "c1515d83-09c9-4d3e-e250-612ce907de37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_7 (Embedding)      (None, 38, 100)           3767800   \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 38, 100)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 34, 64)            32064     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1 (None, 8, 64)             0         \n",
      "_________________________________________________________________\n",
      "lstm_6 (LSTM)                (None, 100)               66000     \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 3,865,965\n",
      "Trainable params: 3,865,965\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_conv = Sequential()\n",
    "model_conv.add(Embedding(vocab_size, 100, input_length=38))\n",
    "model_conv.add(Dropout(0.2))\n",
    "model_conv.add(Conv1D(64, 5, activation='relu'))\n",
    "model_conv.add(MaxPool1D(pool_size=4))\n",
    "model_conv.add(LSTM(100))\n",
    "model_conv.add(Dense(1, activation='sigmoid'))\n",
    "adm = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "model_conv.compile(loss='binary_crossentropy', optimizer=adm,    metrics=['accuracy'])\n",
    "model_conv.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 391
    },
    "colab_type": "code",
    "id": "kCJfJ2_w5DR0",
    "outputId": "bc17cd8b-040a-46cf-c0b3-e3a8867bb9df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 29488 samples, validate on 7373 samples\n",
      "Epoch 1/10\n",
      "29488/29488 [==============================] - 73s 2ms/step - loss: 0.5655 - acc: 0.6930 - val_loss: 0.4516 - val_acc: 0.7951\n",
      "Epoch 2/10\n",
      "29488/29488 [==============================] - 71s 2ms/step - loss: 0.4169 - acc: 0.8122 - val_loss: 0.4189 - val_acc: 0.8088\n",
      "Epoch 3/10\n",
      "29488/29488 [==============================] - 72s 2ms/step - loss: 0.3694 - acc: 0.8420 - val_loss: 0.4149 - val_acc: 0.8150\n",
      "Epoch 4/10\n",
      "29488/29488 [==============================] - 70s 2ms/step - loss: 0.3337 - acc: 0.8600 - val_loss: 0.4201 - val_acc: 0.8107\n",
      "Epoch 5/10\n",
      "29488/29488 [==============================] - 71s 2ms/step - loss: 0.3034 - acc: 0.8761 - val_loss: 0.4287 - val_acc: 0.8085\n",
      "Epoch 6/10\n",
      "29488/29488 [==============================] - 71s 2ms/step - loss: 0.2734 - acc: 0.8903 - val_loss: 0.4478 - val_acc: 0.8056\n",
      "Epoch 7/10\n",
      "29488/29488 [==============================] - 70s 2ms/step - loss: 0.2396 - acc: 0.9064 - val_loss: 0.4779 - val_acc: 0.8016\n",
      "Epoch 8/10\n",
      "29488/29488 [==============================] - 69s 2ms/step - loss: 0.2024 - acc: 0.9228 - val_loss: 0.5058 - val_acc: 0.7978\n",
      "Epoch 9/10\n",
      "29488/29488 [==============================] - 69s 2ms/step - loss: 0.1579 - acc: 0.9423 - val_loss: 0.5606 - val_acc: 0.7922\n",
      "Epoch 10/10\n",
      "29488/29488 [==============================] - 71s 2ms/step - loss: 0.1177 - acc: 0.9579 - val_loss: 0.6562 - val_acc: 0.7854\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa03da84e10>"
      ]
     },
     "execution_count": 87,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_conv.fit(padded_docs,sentiment_array, validation_split=0.2, epochs = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fVgPdd3Dvtwh"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H5_nykBWmC7f"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pVALw-Z0mDxs"
   },
   "source": [
    "# **Features Passengers Concerned About **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wvN8xCykmIWV"
   },
   "outputs": [],
   "source": [
    "lower_reviews=new_airline_data.content.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W8ndG-lCmQes"
   },
   "outputs": [],
   "source": [
    "features=['','security','check-in','facilities','people','passport','arrival','waiting','access']\n",
    "x=np.array([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wxcBStF-wtEz"
   },
   "outputs": [],
   "source": [
    "new_airline_data['lower_reviews']=lower_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LPSfvTnQwYd9"
   },
   "outputs": [],
   "source": [
    "avg_pos=[]\n",
    "avg_neg=[]\n",
    "avg_net=[]\n",
    "for feature in features:\n",
    "  temp=new_airline_data[new_airline_data.lower_reviews.str.contains(feature)]\n",
    "  avg_pos.append(len(temp[temp.sentiment==1]))\n",
    "  avg_net.append(len(temp[temp.sentiment==0]))\n",
    "  avg_neg.append(len(temp[temp.sentiment==-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "colab_type": "code",
    "id": "AAKc-QJyxYhF",
    "outputId": "2dacf396-d8d9-46cf-8ec3-4f36ae3248ac"
   },
   "outputs": [],
   "source": [
    "# w=0.2\n",
    "# fig,ax=plt.subplots()\n",
    "# plt.bar(x-0.2,avg_pos,color='green',width=(0.2),label='avg_pos')\n",
    "# plt.bar(x+0.1,avg_neg,color='red',width=(0.2),label='avg_neg')\n",
    "# plt.bar(x+0.4,avg_net,color='black',width=0.2,label='avg_neut')\n",
    "# plt.xticks(rotation=45)\n",
    "# ax.set_xticklabels(features)\n",
    "# plt.legend(loc='best')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Airline Quality Service.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}