{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd4001d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f5bc96c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('UpdatedResumeDataSet.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5cff7568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Resume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills * Programming Languages: Python (pandas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\nMay 2013 to May 2017 B.E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Areas of Interest Deep Learning, Control Syste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills • R • Python • SAP HANA • Table...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\n MCA   YMCAUST,  Faridab...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                                             Resume\n",
       "0  Data Science  Skills * Programming Languages: Python (pandas...\n",
       "1  Data Science  Education Details \\r\\nMay 2013 to May 2017 B.E...\n",
       "2  Data Science  Areas of Interest Deep Learning, Control Syste...\n",
       "3  Data Science  Skills • R • Python • SAP HANA • Table...\n",
       "4  Data Science  Education Details \\r\\n MCA   YMCAUST,  Faridab..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f95a6d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(962, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6bd2eec",
   "metadata": {},
   "source": [
    "# Exploring Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7ededc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Java Developer               84\n",
       "Testing                      70\n",
       "DevOps Engineer              55\n",
       "Python Developer             48\n",
       "Web Designing                45\n",
       "HR                           44\n",
       "Hadoop                       42\n",
       "Blockchain                   40\n",
       "ETL Developer                40\n",
       "Operations Manager           40\n",
       "Data Science                 40\n",
       "Sales                        40\n",
       "Mechanical Engineer          40\n",
       "Arts                         36\n",
       "Database                     33\n",
       "Electrical Engineering       30\n",
       "Health and fitness           30\n",
       "PMO                          30\n",
       "Business Analyst             28\n",
       "DotNet Developer             28\n",
       "Automation Testing           26\n",
       "Network Security Engineer    25\n",
       "SAP Developer                24\n",
       "Civil Engineer               24\n",
       "Advocate                     20\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c185808b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(df['Category'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1ef8a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Data Science', 'HR', 'Advocate', 'Arts', 'Web Designing',\n",
       "       'Mechanical Engineer', 'Sales', 'Health and fitness',\n",
       "       'Civil Engineer', 'Java Developer', 'Business Analyst',\n",
       "       'SAP Developer', 'Automation Testing', 'Electrical Engineering',\n",
       "       'Operations Manager', 'Python Developer', 'DevOps Engineer',\n",
       "       'Network Security Engineer', 'PMO', 'Database', 'Hadoop',\n",
       "       'ETL Developer', 'DotNet Developer', 'Blockchain', 'Testing'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d340e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fKOC7x9y9HMCpRPQ7/9c6+FpqLQXwMGPM4z9Hc5fHvOb/txTAmf7P4wE8TUQjATAAcpfjP2aMWQGAiHbA16ara4eCxQDWHl5n2OVa2QBeIqIM+Ir/HuzHt3vcdLB/0O9bxtgh/9eb4Wsd1gHgAGPs8HlfAPDzHs77OmPMC2BHlxHJwxsaNvm/NsGXFE4AMBXARv+19QAOr4dSALzaj+8jIpQWlyTDl+yfC9+OXlHdiIYuQ+eJ+dZqi5fVmEJ4udEA/gzgz1ZXwbcAngfwUrzmQF0IY+A4Loh4Eqi+NwHcC1/dteQutxOAsxhju7se7J8u7Wk0wOn/V8GP/7e3A/iUMXaGP+H8rJvjj31M1xi6u9YDAP7OGHuTiBYCuLWHePqjuxgGMlLV9fHU5d87GWOPdD3QP3X+NGOspJvzOCJ9HWBpcYkZvuT/PPhqyEXV33eGTonpXWxEinvG7Jb+7AwOhhn+j79bXQUfw5cQro3XHIjxsVmOi2x8TaD6ngBwG2Ns2zG3fwDfGr/Da+Qm+2//EMAv/buJccx0cHfi8eOGk0sGGNvHAM4houRjrtX1nD8d4Dn7YxeAAn/SCvhGswbiAwCXEZEJAIgoi4jS4Pt+Vvk/BxElEVFegGJWTWlxyaTS4pJH4Ps/eRK+Hb5RlQACQJqWReQ0dqAkJbc2y7LqU/kifKPsTwOotroKHrK6CiaqHBPHcYMUdS8UkcY/FfqPbu66HcD9ALb6E8EyACsBPA5f/a+tROQG8BiAB3u5xN3wTQf/Br46YQOJ7QciugPA50SkwDe9egl8I3+vEFEVfG2r+lM8+Ng1gVf1cl27f13j+0TUCODbAcb9IREVAvjGn0N3ALiQMbaDiP4E4EMiEgC4AVwNoHwg5w8H/lp+5wC4EsAJKocTEqlaiunnq9FF9eFW2y8evt+/K62ugo3wreN9gY8Oclzk4HUCubBERCbGWIc/Af4XgL2MsfvUjktt235xZYGrPOEq+JLx5D4Ojyq1DlfT+RtSY+p77uq3//dp5XU37M1RO44+tMI3SvhQvObAHpVj4TiuD3w6mAtXV/g3ivwA34jDI70fHt2sroLFVlfBW1l//fRV+HZvxlwyFC+LBrVjUNPi5bVmtWPohwQAvwawy+oq+NDqKlihcjwcx/WCjwRyXJiyugq0AC4AcB18O5sBAPuuW1CJA5ZwHxEKiuXrTB43E2NuWlgUXY6y5qfCqovLAGyFb/Pbi/GaAzG/w5vjwgkfCeS4MGN1FaRbXQV/BlAB38ahCUcdcMqmmG2flqaNzYLRaZaWFrVjGIIJ8NXi3G91FfzG6iqIUzsgjuN8eBLIcWHCn/zdB98moJsBpHV3XP78tmTF2GkPZWzhIl3njsnve9zEOmffR4W9HAB/A1BhdRXcaXUVWNQOiONiHU8COU5lVldBstVVcBeAA/BN/fY67SfJkOjkzTFZsDdWC0bPXVAj931UxEgAcCOAMqur4FGrqyDiyzRxXKTiSSDHqcTqKkiwugpuh6/jyg0A+r3xIfPkqiQGb8wt6M3QKRFd0HuwlhTXJagdQxBoAVwBYI+/3mCm2gFxXKzhSSDHhZjVVRBndRXcBF/y9ycAA14jZUpkZvfM3dUBDy7MpWm9ahdLDjmNxt6Rl+8wqh1HEGngqze43+oquM/qKuh2GQTHcYHHk0COCxGrq8BgdRX8Ab7k7zb4psUGLXnVrpj7+03TxV6B+6ycJqvaMYSIDr7lEAf8awbVapHHcTEj5l5EOC7UrK4CndVVcD18yd9qBKjGX/poV4Yrq745EOeKFMkaIeZ2Rk+aWhdr6yCN8K0ZPGh1FdxqdRVEQn1EjotIPAnkuCCxugo0VlfBVQD2Afg7etjtOxSGVZvbA33OcJagib2C0fMW1WjVjkElZgC3ANhndRX8wuoq4K9XHBdg/I+K44LA6io4A8Au+FreZQXrOllzWzIUnSMayof0i0EUDQK8XrXjCB2GRcsaYn1aNBXAwwA2WV0Fi9QOhuOiCU8COS6ArK6CMVZXwYcAXgMwLNjXk7XQYMXm2mBfJ1wIRJSsccVMwWi9obMlJdUdqyOBx5oA4BOrq+C/VlfBcLWD4bhowJNAjgsA/47fexljWwEsC+W1M0+pjGcsdgbHLDFUMDq/oLFD7RjC0OkAdlhdBXfz9YIcNzQ8CeS4IbC6CsjqKriIMbYbwG+JKORFfeNSvAmeqftiplyMRed2qR1DqEybWRM72f3AaAD8Hr4agz+zugpirnQQxwUCTwI5bpCsroIiAJ8DeIaIMtSMJWHVTjUvH1IWnSdmCkYvWFIXcxthBigdwGMAvrK6CsarHQzHRRqeBHLcAPlLvtzBGNsMYJ7a8QCAZawj053e2Kp2HKFg0cVKoxSvd96imN8U0l+zAHzvnyLmiTPH9RNPAjluAKyuguWMse0A/qjG1G9PiADtWZtjoqhwmpbFxPOWOaGt2WBgotpxRBAJwO+VNs3bpcUlxWoHw3GRICaeTDluqKyugnSrq+AFAB8QUVjuTMxe2GRRZGfUFxZO0VJMFIweOaohZnZBBwpjUGr+OtcC4L3S4pJnSotL+Egqx/WCJ4Ec1werq+ACxthOAOepHUtvNDpo2fKtUb9BJEEW9WrHEAozZtfyzQ4DZN+a9qVjR2qh/8uLAOwsLS45R82YOC6c8SSQ43pgdRWYW53DngfwPBElqh1Pf2ScVhandgzBFieLxlgoibNoWY1J7RgiCfPQoZo75k075uY0AC+VFpe8XlpcourmLY4LRzwJ5LhuWF0Fc71eto2ILlA7loGIt3iTXOP316gdRzCJJIiJssemdhzBRILHPW1ma0S88QgXdf+YUcsckrGHu08DsLW0uOSUUMbEceGOJ4Ec14XVVSA124f9hTH2uSBQrtrxDEbC2TuifpgsXeeK6iQwOaW1WZbBp4P76eCXxoaOz/OPHQU8VgqAN0uLS/5VWlwSE0sKOK4vPAnkOD+rq2CE282+FUX6PyKK2L8NywRbpjuppU3tOILJonNHdb/kwrH1MdMVZaicNjid/17Q0whgd64CsLG0uITXFeRiXsS+0HFcILU4hl2uKGyLLNNktWMZKkEAac7c1Kx2HMFk0Xk8ascQTLPm1fLSMP108MmRdVJ73EBrA46FLxH8dTBi4rhIwZNALqZZXQVJjZ3DXhcEelwUKWqKzGYvbUj3Su6oTZQsOm9UV4xesrw2Xu0YIkH9Xk2t9MGUwS7b0AK4v7S45N3S4pK0QMbFcZGCJ4FczLK6CpZ4PGyHLNNpascSaFoD9N7F26K2XEy6LnoLRouSy140vsOsdhzhzuOG0nT3PF0ATrUCvk0jKwJwLo6LKFH7RMpxPbG6CjRNtmF/Z4x9JEmUrnY8wZJ+xoGoGdk8VoomfLq1BJolo7lF7RgiwcHXMg/JdSkJATpdOoB3SotL7i8tLtEG6JwcF/Z4EsjFFKurIN/tZt9JEl1PRFG9+zIxS0lxjS6vUzuOYEjUCFG7u3PcxDqX2jGEu9ZaoZm9MDsnwKclAL8G8EVpcUlWgM8dMETUcczXlxDRg/7PbyWiKiLaTEQ7iOh8daLkIgVPArmY0WQbtsTjYVtkmWJmV6D5nG1R2UbOLEtRO8o5d2FNTLTFGyyvl7Hqe2d6BK8YrNev6QC+Ky0umR2k8wfbfYyxSfDVRnwknHqcc+GHJ4FcTDjUlF8iCPhQkiim1lplTOnM9MS3RV0PWo0gaAxCdJaJWXpiXYLaMYSzik+SKjR7coO9kcMC4NPS4pKfB/k6QcMY2wvABoAXHed6xJNALqpZXQW68rq8N+PihL8KQuTW/hssUYQgnbapQe04giFd54q+5FZrb8/OdUbtKOdQdVqp0/nofEuILqcB8Ehpccm/S4tLwmk0Te+f7t1MRJsB3NbdQUQ0BcBexlh9SKPjIkrMvShyseNATV5+W5v3h4REMaZbRWUV16Z5RbeidhyBZtG5HWrHEGg5uY1WtWMIZxX/GtciOnSh3rjxSwCflBaXhMsmMjtjbNLhDwA3H3P/9US0G8AGALeGOjgusvAkkItK6cm/XVJy3cx3zGahQO1Y1KY3waDM2xF15WIyorBg9KSpdVH3PQVK1SbDIXl9UbZKl58L3zrBvlrThYP7GGOjAZwL4BkiCkQZHS5K8SQwBhDR/xHRD0S01T+FMLOXY58iolWhjC/QLCnX/8phT/rgvTenFN1318hKteMJB6ln7ou6shcWnRJ1PZLnL67hL9jdcDngar9vQYLKYWTDt3P4IpXj6BfG2GsAvgPwU7Vj4cIXTwKjHBHNArASwBTG2AQASwFEZWJk1q+m9JTfPm7rTP8nY6IIAPfdOT/j4w9SonJN3ECk5HvSXAWHournEH0FoxlbtKwhSe0owtGBZwpqpFazSe04AOgAPFNaXPL30uKSSPj9uw3AbyK5FzoXXPwXI/plAGhkjDkBgDHWyBirJqKbiWgjEW0noke7q5lHRFOJ6HMiKiWiD4gow3/7tf4aVFuJ6MUQfz/dysu5zKTTN39p70y9vOvtjInSFReepD+wX9/R02NjhensrXa1YwikVC1JascQSAZjR2tikoeXhzlG40G5Xnxrap7acRzjegCvqFFYmjFmOubrpxhj1/g/v5Uxdm+X+0oZY6MZY1E3as4FBk8Co9+HAHKIaA8RPUREC/y3P8gYm84YGwdAD99o4RH+2lIPAFjFGJsK4AkAd/jvvhHAZP/I4i9D8l30wqi9ZlhLk2WPw57UbV0vt0tnOm3JSntHhxDT662yZrZneUwdNrXjCJREDUXVFPew4Y3tascQbhQPvPV3z5XCdCDrTAAflBaX8D7PXMQKy78sLnAYYx0ApgL4OYAGAC8R0SUAFhHRBiLaBmAxgLHHPHQ0gHEAPvKXIfgTfGtiAGArgOeJ6EIAqiZWBs0V0wRK+R7MnNHbca0tiamnL1tWE6q4wpEoQRRO2Rw15SLiZcmodgyBNO2EGqZ2DOHm4JuWCk1VWjhPkS8A8HlpcUmvzz8cF654EhgDGGMKY+wzxtgtAK4B8BMAD8E3yjcewGPwrXXpigD80KUUwXjG2HL/fScD+Bd8yWUpkTrTcgbNT08UhezPiHQJ/Tl+9468nGuvmFIe5LDCWvZJ1Sleio4NFXpR1MmkRM3o7qKltbw+YBdtDUKr99mAt4YLhokAviotLhmpdiAcN1A8CYxyRDSaiLo+OU0CsNv/eSMRmQB0txt4N4BU/8YSEJFMRGP9C4xzGGOfArgBQAKAkC/Y1msuuFgURrxOpB3QaNB/X56a+9RjuVXBiivcGeKZSZm9K2rKxaRpndGx1pO8yuz5TeE84hVSzMtw6G/THIIii2rH0k/D4EsEI6GEDMcdwZPA6GcC8PThjRwAiuArIPoYgG0AXgew8dgHMcZc8CWHdxHRFgCbAcwGIAJ4zj+NvAm+mlStQf8u/GSxUNDL594oCUWPE8mDKKdBdMsNS1M3fB3fHPjoIkPKWXvCqfvBkFh00dE6Lj7B2qzXs0hJeIKu4suECs2OYaHqDBIoqfC1mlumdiAc11/EGF+GwkUGWSyUJGHinZI4/joicUhT0DqdzfrFppc1lkyXPlDxRZIdVy9o1lRaIn7k6W+7PZVv1yZHwpRhr6adsKf8vx98Fm47YFVhb4et/IpTBMlmiNSaiW4AP536/p0vqB0Ix/WFjwRyEUEWCzWSOPlBSZzwm6EmgADgcBjiT150stXlQlSsjxsow9mbo2InqkXniYp2eCfMqeHPxX5ljxQ1R3ACCAAygOdLi0uuVTsQjusLf+Lhwp4sFupkcdp/JGHcz4mEgP3O1temWs5ZuTAqC2f3JXu2NdNjsEV87910HTuuvmUkWrS8Nqp2Og9WzXZdtbxuvFqt4QKJAPyjtLjkJrUD4bje8CSQC2uyWGiUxRPWiELRhURCwF/wSzeMyrv5hrExt2NY0kAWTtpcp3YcQ5WmRcQXjBYEj2vqtNZEteNQm9sJd+u988OhK0gg3VZaXHKL2kFwXE94EsiFLVksNMvinNckcfQZ3TQ0CZgnH5mV89pLGbVBu0CYylx5KJHBG9GLgpM1QsR32EhObW0RpSD+gkeIAy/kVcvNiWa14wiCW3kiyIUrngRyYUkWCxM10oJ3JHHE8r6PHipB+M2VJ8Zv32qyBv9a4cOUxMzu6XsiulxMvEaM+Np6hePqoqqd32A0V0oN9N/puWrHEUS3lhaX3Kp2EBx3LJ4EcmFHFgtTNdLC90Qhf26orqkoGv2qFacoLc2SK1TXDAfJZ++K6OcAgygaBHgjenPP7Hm1ET+lPRSKAm/t3bNJYGK0j4bewkcEuXAT0S8AXPSRxcIMWZzzuijkzQz1tTs74pJWLipuUDyxUzcpbbQzw51R36J2HIMlEFGKxtWpdhxDseTE2micAu23svdSKzXlGSlqxxEit5YWl/xB7SA47jCeBHJhQxYLc2Vx+ouSOGK2WjFUlGVmXXr+7JjZKEIE6FZtblM7jqFI17kidjpVkpy2MUWdMZsEtjdRm+fJuZlqxxFiq0uLS36tdhAcB/AkkAsTslg4XBImPiUKhfPVjuXTD8fn//3OkTFTOiZ7fkuGonVE7DR4hs4TsbFbspojdhQ2ECrvn9IpujVR08FmAO4vLS75hdpBcBxPAjnVyWLhcFEoelgSJy4M5i7ggbj/rvkZH72fWq92HKEga6FhxVtq1I5jsCw6JWILRo+fVOdWOwa1VH4TV6nZMiJD7ThU9O/S4pJL1A6Ci208CeRUJYuFuaIw8n5ZnLqIwiUDBMCYKP3iohWGA/v1HWrHEgqZp1aYGYvM/RXpOiVi13DOW1gb8SVuBsPRCUfnPxckqx2HygjA46XFJSvVDoSLXTwJ5FQji4UZAuXcI4szTyQSRLXjOZbbpTOdtmSlvaND8KgdS7CZU72J7kn7I7JcTCQXjF5yYl2C2jGooezxUfVSpzHiy/sEgAjgxdLikmlqB8LFJp4EcqqQxcJUgdL+qpHmnUIkhu2aoNaWxNTTli6L2KnSgUg8Z0dEjqilaCOzYLRWZ2vLzHLGXCJUt0tbI308OZprAg6UEcDbpcUlw9QOhIs9PAnkQk4WCxMI5ps10qIziWS92vH0Zc/OvJxf/WxqmdpxBFv6WEemO7Up4gpmJ8iCTu0YBiMnrzGid2UPhscFT/O982Iu8e2HdADvlRaXJKkdCBdbeBLIhZQsFsYBuhKNvOw8Il3ElMZ4/ZUpeU8+kleldhzBJAgg7VmbW9WOY6DiZNEYiesZJ0+ri9gNLYN14JXsKrk+OV7tOMLUaABvlBaXaNUOhIsdPAnkQkYWC3WA9GuttOxCgUwRVhyW6NYbl6Su/yqhSe1IgilrUWO6IrsiaseqSIKYpPHY1I5joBYsqYmpF/uWarEJL5+Qo3YcYW4ugGdKi0vCZpMcF914EsiFhCwWSgB+ppEW/VQQkiKyOKzXK2kuPHOlXFutidjixH3R6qFjS7dG3AaRdK0rwpJAxhYsboiZqT+vl7Gae07wCkzkrzl9OwfAPWoHwcUG/gfJBZ0sFhKACyRx2sWikDlC7XiGwukwmE9eeLLV6aDIm3/sJ8sZB+PUjmGg0nVup9oxDITR1NGSkKhE5IaWwSj/KLlCsz87Ve04IshvS4tLfqV2EFz040kgF1T+BPAUUcj/iSQUTVU7nkCor0u1nHvKwqjtKJJg8Sa5xh6sUzuOgcjQeSKqjE/BiIaYqD8JAJ2t1OF6bF4sF4UerPtLi0tOVzsILrrxJJALtvlECT+VxdlziShqft9Kvx2Zd9Pvx0Vtj+H4s7dH1LpAiy6yBmannVAbkeV4BqP8gYlW0aWLmVHPABIArCktLpmidiBc9IqaF2Uu/Mhi4ThA/plGWjqHSI66shBPPXpCztoXMqOyhmDGJFuWJ7G1Xe04+itNxyJqIf2iZTVGtWMIhUPfGQ9pNo7OUjuOCKYHsJaXjuGChSeBXFDIYqEFwDUaaclUgYzpascTHILwu6uXJ27faoq42np9EQSQfOamZrXj6K9UDUXMSBORosye2xz1L+pOO5wd989PVDuOKDAMwPOlxSX89ZoLOP5LxQWcLBYaAFwrizPHiUJ6odrxBJOiaHSrVpzibWmWXGrHEmhZS+tTvaI7ImrZJWoip2B0QmJbk1bHov659+CTI+qkNnNMjHiGQDGAW9QOgos+Uf9ExIWWLBYKAC4ThYJJojB6ttrxhEJnR1ziyQuLGxQPi6p1XjojDN6F2yOiQLZZlvqdbHQ47kKn8350Ov+JTueDx93PGIPD/SY6nPeg0/kPKF7fj8DLOmBzPoxO5/1wKz8cOd7uegZe1v/mHyPH1EdtiaHDGvbJdeJ7vDVcgN1UWlxystpBcNGFJ4FcoJ1ElLhIFmctJKKIWqc1FJXlmVmXnDcn6jaKpJ25PyLWcsqCIBvF/peJ0WuugFF7LYzaa467T/Huhpc1waj5HXTyGXC4XwcAeJQtkMQpMGiuhMvzhf+2nRCETAjU/+Y3J8ypiernXY8bSuM9czVRtA8sXBCAZ0uLSwrUDoSLHvyvlAsYWSycAMjnaaUlc4iksO8JHGiffTQu/29/HVmhdhyBlJSjpLhHVdSrHUd/pGtdnYE4j8e7E7I4GUQEUcgFg8M/0icCcIPBAwKBMQUu5StoxPkDOv/i5bWmQMQZrg6+nlEp16TxtYDBkQjg1dLikph7fuWCgyeBXEDIYmEGgKs10tKpFLUbQfr2j7vnZ370fmpEJE39ZTp7W0QUYrbo+zkSSAS76wl0Oh+Ay/PtcXd7mRVECUe+FigejLVBFifB490Lu+tJaKQlcCvr/cli//ekCILHOXmaNWoTJGud0MLWzOKt4YJrEoCH1Q6Ciw48CeSGTBYLjfBtBBkrCmlj1I5HTYyJ0i8uXGHcv1cfNcWAM6d1ZHnMbQMaZVOYFxd8/QSu+/6V4+575uAGXPD1E7jg6ydwzlePY8YHd8HqsqPFZcPlG57DOV89js/q9hw5/jffr0WDo+9qNRatu1+1DQ2aX8Ko/RX0mkvhVr6Bx3uwH48iEOlg0FwCo/YaiEIWPN5dkIRxcLhfg931PBRv36sBUtObWwQhOldJMC9D1b0zXIIii2rHEgMuLi0uuVLtILjIx5NAbkhksVAEcKlA2UWiMHqW2vGEA7dbZzxt6Up7R7sQUV0seiKKEMTTNjcM5DEvlH+HYcaUbu+7eNhMrJl9GdbMvgzXjFyAKUk5iNfo8UHNDqzMHIcnZ16EZ8o2AADW1e/FGLMFqbq+O9ll6Lz9qhh9eP2eQCZIwlh4vZXH3B8PxlqPfO0bGTz6+k7Px9BIi+BWtkCgLOjks+D0fNjntYvG1UfEqOpglH+eWKHZnRezswAquL+0uGSm2kFwkY0ngdxQnQRoZmmkOfOiqSPIUFlbE1NPW7YsagpJZxfXpHoFpV9JVp2jDV817Mfp2RP6PPaDmp040VIEAJBIgNPrgdurQADB4/XihfLvcPGw/r3Opfej7ApjLjDmPPK5x7sXAh2dt0hCIdzKJjDGoHgrQNAdtfHD620EY22QhAIAbvjW6xMY63sgcs6CmqgcJbO1odP57/k8AQwtDYCXS4tL+r8rieOOwV+0uUGTxcKJAFZppMXjiHRRu85psPbszMu55vKpUbFjWB8HozJ3R7/Kxfxt18e4dtQi9LU53KG48U3jASxOHw0AKM4owjeNB/Gr0pfx8xFzsbbye5ycOQ46Ue5XjClakvo6hrEO2FwPo9P5D9hc/4IkjoEkjobLswEuj2/0URRGQ6AkdLruhcP9GnTyaUedw+n5EFppOQBAEifCrZTC5noIGqnvDSKLl9fF9+ubiTBlD41rER16rdpxxKBcAA+oHQQXufp80uS47shiYSaAqyVhfIIopPc95BOj3lg7JW/K9Maqy35ZHvGts1LP2qttXTe+12O+qN+HJI0BhfEWfNfce/67rn4fJiZmIV7j2+hoknX4x9SzAQBtbgeePrge90w6E3/Z/h7aPA5cmD8DExJ6/jEm9aNgtCAkwaj99XG3a6QfRxuJ6LjEryu95oIfz0cmGLX9W5olyc7OkaNtfc9rR5jqLfoqzddjs9WOI4ZdXFpc8sbU9+98Te1AuMjDRwK5AZPFQi2Aq4gSNJI4Ybna8YQ3wp9LlqSu/yqhSe1IhiplmDvNPayqsbdjtrQewrr6fTjl84fwf1vexMamcty09a1uj/2wdseRqeBjPbb/S1xWMBsf1OzAmHgLbh53Ev615/Ne44uXxbCuaZiZ1dQ60MeMH1WJ2VMOYe70KiycdfxA7D//1oq506swd3oVZk0+hCT9QbQ0K2hsUFC8qBqzJh/C22/8uKfn/LPqUFMduKWqLgfcbX+fz6cj1fdIaXGJRe0guMjDk0BuMM4gIEsjLZxPJEVMuy61eL2S5sIzV8rVVVqb2rEMlfHsrb1+D9eMWoh3F16NtxZchTsmnorpyXm4fcIpxx3X4Xbg++ZKLEgbedx9FZ3NaHR2YGpSLhxeNwQQCIDL23vyohNFnUxK2G7GGT+5rl+7l4/11ocZ+HJjFj775vhR0Gt/m4AvN2bhy41ZuPn2JMyZr0Nikoi1L3Xg/AtN+HBdJh64z9fa+r23bZg4WYOMzMBNAB18bli11JIQdaObESgFwONqB8FFHp4EcgMii4VjAawoKZqc/dhUhy5F44yaUijB5HQYzCsXndzmdFC/NleEq8yZbZkeY+eAk9m1lZuwtnLTka8/rd+DmSnDoJeOr7H30N51uHKEb33diZYivF29DZeufxYX5s/o8zrp2vD9fZy/qKb/BQUH4dWXO7DqHF/3PFkm2O0MLieDIAAeD8O/H7Di2t8EbkliY5lUL7w5jbeGCx8nlxaX/FztILjIQiy62p1yQSSLhfEAbp+amBL3jymzL5QEQXIoiuP+Pazhg/pkXiC2H6ZM31v+xv8+zVM7jqHYsya7THhxTr7acXTn91tQ+11rfFhOi23c9ZTdkuEaUKeHCaMqkZAogAi49GdxuORn3c+82mxeFBVUYtOObCQmibBavbji4nrU1yv48x1J2LnDBXO8gAsuCsygneKBd/evF7RqKi1JATkhFyidACZOff/O/WoHwkUGPhLI9YssFgoALhGJDDeNnbJcEgQJ8E3B3Vgo5dw9vrbSKLodKocZ9r7fODLvT78bF9E7hrNOqkr2khKW7x4tOrdL7Ri6o9N3WgeaAALAB59lYN2GLKx904LHHm7HV1/Yuz3u/XdsmDlLi8QkXwWa+HgBL79hwWffZGHiZA0+eNeGU88w4torG3DxeXX4dv3Q/lTL3kmr5AlgWDICeKa0uCQqSxFxgceTQK5fdAJbDrCpt4ydWmDRGzKPvX96kj5nzUyrZ0aiNWpq4wXL04+dkLP2hcyI/TkZE1iccsKuarXj6I5F5w3L5DQnv7FtMI87vH4vNU3EytMM+H5j9znuqy93YtW53bckvuuOVvz2xgS8+lIHJk3W4sFHU3HbzS2DCQcA0NYoWJWn50b8bvcoNhvAH9QOgosMPAnk+jQ/baRlVJzy+yVpCZOXWDIX9HScWZZNq8ezjJLRteXhvEBffYLwu6uXJ27bHGdVO5LBSj5rT1iONFh04bnkcsq0ugEH1tnpRXu798jnn/7PjsKxx9dMtFq9+OoLB0465fjN0fv3ulFbo2DufD1sNt/6QCLA6Rh8rnzo71Ptgkfm5cXC262lxSWT1Q6CC388CeR6NT9tpATgcrMk7L9u9PQ8kYRef2eICMst+rznZzS3jzJ19FpOJJYpikZ39skrvU2NUkS2EUsb5bK4s2vDruxNqjY8a58uXFIz4F30DXUKihfVYM60KiyZU43lKwxYeqIBTzzahice/XFg8e03OrF4qR5G4/F/mrff0oI/3eqr477qXBPWPNuBZfOqcc31g9sgUvGluUKzvSAs11xyR5HhmxbuX6V1LmbxjSFcr+anjTwRwPkX5E3Pmp6ct3Qgj1UY875c6ap87EBKLiOh9/YRMSo7r6bqy+/fzBSlPtprhKGyTxPKPfedGFabXKpszoYLN6alqh3H0RjbUfmEJ86sRPQLsr0D9oorVkLsNA54bSOnmj9Off/OO9UOggtffCSQ69H8tJHZAM7N1ie0T07M7nEauCcikXB+rjbvyemNDZk6e8ROfQbTofKMrEvOnROwjSKKwjBvRhXOPb222/u/+NyOudOrcMKkQzhpqW9Z4mALC2fNac1U9Paw2gwUrwm/gtEmc3tzpCeAAFD26JhGngBGnJtKi0sK1A6CC188CeS6NT9tpAzgCgD2c/KmLpGEfjZw7UaeUZv25HSHflVWY0XgIowen/1vXP69d4yqDMS5/v1AG0aP6f6/qrVVwe+ubcILr6Zj/eZsPL0mDQAGXVhY1kDGis11gYg7UAyiaBCghNXCwOEjGjr7Piq81f6grZY/m8jLQEUePYCH1A6CC18BTQKJqOOYry8hogcHea6FRPR2l89nd7nvKSJaNbRo+xVDt4VniehaItpJRM8T0alEdKP/9tOJqPs+WJGnGEDevNQR5hxDYuFQT6YRBM3VI+TcBybVViVIrojvnBFo/7xnXuaH76Y2DOUcVYc8+PA9Gy66tPtacGtf7MQppxuQk/vjjlNgaIWFM0+pTGAInx25AhGlaNxhVTB6+qzasPn5DIbbBU/r3xZ0v/WYiwQnlhaXnKd2EFx4ipSRwIXwbXsPF1cBOIkx9hPG2JuMsdX+208HEPFJ4Py0kZkAztAIYs1yS+GKQJ57XLw+6/mZ7ViY0nJ8I9QYxpgo/uLikwz79+oHncCU/K4Jt92ZhJ627uzb60ZrixcnL6vBghOq8MJz7QCAVeeZ8MlHdpx1Si1u/FMiHn+4DeddaILB0PfTQ1wyi3dP2xtW5W7Sda6wmqJeuLTGqHYMQ3HwxdwqqTGR9weOYC4vbjTrVweuXQwXNUKWBBJRKhG9SkQb/R9z/LfPIKKviWiT/9/RxzwuH8AvAVxPRJuJaJ7/rvn+4w/0NCpIRK8TUSkR/UBEP+9yewcR3UFEW4hoPRGl+28fRkTf+OO7vYdzPgygAMCbRHT94dFO/0jlqQDu8cc5nIg+I6K7iOhbItpzOHYiEonoHv91thLRL/y3ZxDROv/jtxPRPP+xT/m/3kZE1w/+f6Fv89NGEoCfAHCekzt1hknWJgf6GgZJMtwyVsi6vai2Qid4wrK4rxo8bq3xtKWnODrahQGX13n/HRtSU0VMmqLt8RjFw7B5kwsvv56O19624J6/tmLfHveQCwsnrdoZVptaMsKoYDSR4pk1tzliiyo3HxIb8eoM3houQjEG94Ym6bPTvjKPAHCb2vFw4SfQSaDen8BsJqLNOPqX7h8A7mOMTQdwFn5sdr0LwHzG2GQANwP4a9cTMsbKADzsf+wkxtgX/rsyAMwFsBLAanTvMsbYVADTAFxLRIcTGiOA9YyxiQDWwbf27XCM//bH2O3KesbYLwFUA1jEGLuvy+1fA3gTwO/9cR5u2yMxxmYAuA7ALf7bLgdg9V9nOoAriGgYgAsAfMAYmwRgIoDNACYByGKMjWOMjQfwZA/fa6BMATAu15DomJiQPa/Po4dgbqo+d83MVvuE+LawWlemJmtrQsqpy5YNeGRtwzcOvPeODeNHVeLyixqw7jMHfn5J/VHHZGZLWLLcV0okOUXE7Hk6bN92dL40mMLC6YXODLelcfDVhwPMolMUtWM4LDHJ2qzRRMyMy1G8CmN198yCwMSwSvK5/mlz05ZffG8qv3G7caHDS0YAV5v1q8erHRcXXgL95GT3J0CT/InMzV3uWwrgQX9y+CYAMxHFAYgH8AoRbQdwH4Cx/bzW64wxL2NsB4D0Ho65loi2AFgPIAfASP/tLgBv+z8vBZDv/3wOgBf8nz/bzzj68lo311kO4GL/z2IDgGR/bBsBXEpEtwIYzxhrB3AAQAERPUBExQAG1XmgP+anjTQAuBhAw9m5U4olQQj6jsZEjRx/30Rv2vUj6srCbUG/WvbuzMu56rJpZQN5zC1/ScKOA7nYticH/3k2FfMX6vDoU2lHHXPSSgO++dIBj4fBZvOi9FsnRnXZRDLYwsJEgPasTUH7vRyocCoYPaqwvvs+bxGg7IPUCvlgVoracXADY/e4nY8eEDad9rV54t4OcUSXu0QA/1IrLi48hfIdqgBgVpckMcuf5NwO4FPG2DgApwDob1HVrkV2j3unSkQL4Us8Z/lH/DZ1Obeb/VggUQGOKjAb6EXch+Pseh0C8KsuP4thjLEPGWPrAMwHUAXgWSK6mDHWAt+o4GcArsaPI6jBcBKAuAVpIy3ZhsQxQbzOUQQiOjVLl//cjKbmYYbO5lBdN5y99erk/Mf/nT/kdZNdCwuPLtRg6XI95kz1FR++6NI4FI3VHDl2KIWFsxc0WxSNIyymYdN0CJtuJrPm1kTkKGBHM7W7/zP3uPaQXPhijLFvGu3l524w44XKuJ66hcwz61f/JKSBcWEtlNX1PwRwDYB7AICIJjHGNsM3Enj4xe6SHh7bDmCgC5PjAbQwxmxENAbACf14zFcAzgPwHHzr4gaqHUD3WzOP9gGAK4noE8aYm4hGwfczSAFQxRh7jIiMAKYQ0bsAXIyxV4loP4CnBhFXn+anjcwCcLJWkGqXWsb8vM8HBEGGXpvy6FSX59lyW/nT5cl5RBH5+hkghNv/uDh17LjXmmfNax3QmrJ5C/SYt8BXzu2ynx/9Z3PtbxNw7W8Tun3cU2t+HDlMTRPx4ef9zwE0Omhx4tZyvDVD9eLRyRoKm5p8i0+s68/zQdip+Mfkdo1bG5Gxx6IGh7Pxjl167xarpT9/f/eY9avfbLPf2B70wLiwF8pX2WsBTPNvhNgB32YPALgbwJ1E9BXQ4zv4twCccczGkL68D0Aioq3wjTau78djfg3gaiLaCF8SOVAvAvi9f5PL8F6OexzADgDf+6fBH4EvIV8IYDMRbYJv3eQ/AGQB+Mw/dfwUgJJBxNWr+WkjBQAXArCfkzt1pknSqraQXRIE6dJhmrzHpzbUpmocMf0k5fVKmotWrZSqq7QRUVLHclp5WOwgTZTDo2C0ILqdEyZZE9SOY6AqN5gqNZtG8lHACOD2Ks7nyp3l521ISd5ijUvr+xEAfOvpb+nzKC4m8LZxHOanjZwO4Jo8Y1LLr0YtvFokISz6rzoUxfngPlb/Tm1yTBepTUlrrFu/7b+pWh0L+6HRHX+aWqPZOiJDzRgUxrxLPjcLao8kWzLrazbufF3Vn8VAOWxwlF1xsldqN4VFIs31bFurvfK2nYlJjS7tYEoQuQGMbrPfeDDQcXGRJexfVLjgmp820gjgIgD1Z2RPWhIuCSAA6ERR+7vRUs69E2oPmUR3WNV+C6XG+pT0s1cuOqR2HP2RcPYO1XdliERCksaj+ujp2PF1YbFGciDK/jOqnieA4c3qcrXctI3VXLvFkjPIBBAAZAC3BjAsLkLxJJA7CYBpfHxmXK4hcZzawXRnaqI+e83MNs8JSa3Vaseilk0bR+T+8bfjA9ZjOFgs4+2ZnpQW1XcKp2nV70ozZ0FN2GxQ6Y/6PZpa6aPJvCZgmPJ4vZ7Xqxxlq9Ynmb9sTgjECPOFZv3qIXeD4iIbTwJj2Py0kdnwJYHVKzLHLiEK33JgcbJk+us4ZP7fmNpymZQBF1OOBs8+PjPnlTWZYdWd41iCAJLP3KT6Du8MndvZ91HBtfjEugS1Y+gvjxtK0z3z+luZgQuxfR32qks36mz/2Jee72FioN5cCOAFpGMeTwJjVNfNIDOT87My9PEj+nqM2ogIS9P1eWtmNrePiesYUp/dyCQIv79meeLWTXGtakfSm+zFDRav7FI1Uc/QeVS9vqxxdAwfYY+YfrsH1mZVynUpCWrHwR2t0+Nuv3uXcuiKUkvWIYc+GBuvzjLrV/dUToaLATwJjF2TAIwBUL/MUrhE5VgGJEWrSXxwsif5qoK6cmLemNrZpCga3dknr2RNjZLqI1090Rqg8y7epurUfbpOUfX3IjO7yarm9QeitUZsxkuz+DRwGPEy5v24zl5+9jcJuvfqkrKDeCkCcEcQz8+FOZ4ExqD5aSMlAOcCaF6cPnp0stYYcbtvRSLh7Bxd3lMzGhuydLZWteMJJVtnXOLKRSuaFE/4bu1PP+PgYBesB+b6KheMnjC5zq3m9fvL62Ws+t4ZHsEr8teCMFFlc9T98nu59S+7LHl2rxSKmpcrzPrVc0JwHS4M8T/82DQDQDoBbfNTRyxSO5ihyDVo056c7jSek91QoXYsoXSoIiPz4nPmhu1GkcRMJdlVWKZaT+gUlQtGL1hco1Xz+v1V/klShWZvbn/ry3FB5FA8tn/vd1dcuDE9fW+HMdS1WvloYIziSWCMmZ82UgffKGDDMkthUbxG31Pf5YghC4J85XBN7r8m1VYnyq5OteMJlXUfj82/+y+jwjb5NZ+9XbXRsESNoOImB4aFSxsS1bt+/3RaqcP1yPyIqmMYjRhjbH2TveLc9Wbh5UMpak3LLzDrVy9X6dqcingSGHvmAjAT0DkntWCB2sEEUlG8PvO5Ge3C4tTmiKipFwgP3jsv6/130urVjqM7mZM7M92J1g41rm2WJdWmo/V6W2u6xRX2O23LHxhvFZ06Td9HcsHS4HQ2/maL0Fiy3ZLb5pHV/p35i8rX51TAk8AYMj9tpAm+dnR1yzOKxpplfdRNAxkkSX9TkZh9x9jaSr3gCdvNE4HCmChe+dMVxn17DGHXYk8QIcinbWpU49qyIMhqFRjPHdYYdv8Xx6r63nBI821hltpxxKoj7d7WpyRvtppT1Y7Hb7pZv/p0tYPgQosngbFlEQCtAHLOTomuUcBjzU7R56yZ2eqcnGCtVTuWYPO4tcbTl610drQLYVc/MXt5XZpXdCtqXDtdp07B6KkzalTvmtIbpx2u9vsXJAT6vGWdTbjg6yeOfCz439+xpmzjUcd8Vr8H5331H1zw9RO46JunsLmlEgDQ4rLh8g3P4ZyvHsdndXuOHP+b79eiIcpaiG9rtVdesMHo+U9ZWp4XQrgVZ73drF/N84IYwv+zY8T8tJFxAFYCqF2eUVhklnXh8u4zaBI0svneCSz9dyNry0UoqiQioWJtTUg5ZcnysEt4dSYYlAU7VCkXk65TZyRwwZI6taf1enXwmYIaqdUc8BqG+cZkrJl9GdbMvgzPzroEOlHGovRRRx0zIykfL/iPuXncSbj9h/cAAB/U7MDKzHF4cuZFeKZsAwBgXf1ejDFbkKqLC3SoqrC6XK03/zDkdm/BNg7A+WoHwYUOTwJjxyIAEgGuWVE+CtiVQEQnZ+rznpvZ1Drc2Kl6J4tg2rc7N/uqS6eF3Y7htDP3qZIUZWjdKoyMer3zFzUkh/66/dN4QK4T356aF+zrbGwqR5YhARn6+KNuN0gaHO5MZFfcIPg+l0iA0+uB26tAAMHj9eKF8u9w8bCZwQ416Dxer+eNKkf5qvVJcV80BqTdW7DdZNavDrcRSi5IeBIYA+anjTQDOBlA3fy0kSNiYRTwWBadNvnhKa74S/PqyxCiAtOMedHp/Cdsrqd6PEbxVqLd8Ue4lW0AAC/rgM35MDqd98Ot/HDkOLvrGXhZ3y1533ptct5j/8oPq40xybmeVNfwypBvXrHoQ19IPM7c3mKK80qhvm5/KB546++ZKxMF/2n/g9odONFS1O19n9btxllfPorrSl/BzeNOAgAUZxThm8aD+FXpy/j5iLlYW/k9Ts4cB52oaqWfIdvfYa++dKPOdv++9LwAtnsLttEAVqgdBBcaPAmMDYsBSABcJyQPO0HtYNQiCYJ4cb42/z/TGurStfa+M6ohcitfQaCe994w5oXT8z5EYeSR2zzKFkjiFBg0V8Ll+cJ/204IQiYE6k/XKMJf/rQ47ZsvEsJq1NN0zraQb9JJ17KQj2YMH9UQtiWKDr5uqdBUpQW9/pzbq2Bd/T4stYzp9v5F6aPx6tyf497JZ+LhvesAACZZh39MPRvPzroEY8wWfNGwD4vTR+Mv29/DDZv/i62tVcEOO6AOt3v7WaklM0jt3oLtOrUD4EKDJ4FRzj8KeBKAutFx6SkWvXm42jGprcCkszw93a49JaOxMljX8DIrPN7dkMXpPR7jVr6GJIwDoevyLBGAGwweEAiMKXApX0Ejzu//tb2S5qJVK6WqQxpVNkZ0J2t6e6Ynrj2k8aRoKeQjcjNm1YZlFxdrvdDqfX52SDoDfdW4H2PM6UjuY9nblKRcHLK3ovWY/TuP7f8SlxXMxgc1OzAm3oKbx52Ef+35PJghB0wI270F2zKzfnX3Q7lcVOFJYPRbDF9m4VpiGR2zo4DH0oqi9jej5Jz7JtQeihPd9kCf3+l+G1ppBYDuB6O8zAqPsgOyePSaJ1mcBI93L+yuJ6GRlsCtrIcsTgbRwMq5OR0G88pFK9udDgqLnaqiBFE8dXNIp4STVSgYvXhZTcA3XAwV8zJU/W2aQ1DkkExHflCzEydmdJ8/VHa2gPm7He5qq4XbqyBe1h+5v6KzGY3ODkxNyoXD64bgXzXo8obdxvfjqNDuLdiuUzsALvh4EhjF5qeN1AMoBlCXpDHqhxlTJqgdU7iZlKjPXnNCm3dOUkvAdrB6lJ0gMkIUei7D5nS/Da1cjGPXZxHpYNBcAqP2GohCFjzeXZCEcXC4X4Pd9TwUb//3fTTWp6SvOnlR0EY7ByprRU2Kl5SQJaVmWTSE6loAQKS4Z8xuCXW7rz5VfJFQodk5zBKKazkUN75tOojFaT/uCl5buQlrKzcBAD6u241z/SVi7trxIe6ceNqRjSIA8NDedbhyhG/U+0RLEd6u3oZL1z+LC/NnhCL8QXEoHtvD+92VF25MVaPdWzBdaNavDttNTlxgEAvfHvTcEM1PGzkfwKUAyn+SN33utOS8JWrHFM4+rbeXr96VkuliQ3sX73S/D7eyCb73WB4wOCEJY6HXnHvkmA7n3YD/b4/BBoIMrXwGZHHskWMc7rchiUXwehsBMMjiRNjdz8KguWJA8Vx0+Tflf/37tqDvCO2PnXcXVcpfjg/JtCQAnLjO6B7q/2d/Jac01W3e/2pYtWG0tcNW+bNTBdGuD+uyNZGIMYZvmx0Vf92VnNrm6TKcGV3+2Ga/8U61g+CCh48ERqn5aSNF+OoCNkkkCGPjM3penMYBABal6fPWzGzpLIprH9K0pVYuhklXApPuD9DJ50MUCo5KAAHApL0BJt0fYNL9AZIwDlr5tKMSQK+3EYy1QRIKALjhm1YmMDbwdrzP/ueE3Jeez6oZyvcUKCmr9oa0TVm6LnS9pEcX1atSl7A35f8e28wTwMDzt3truNHX7i1aE0AAuNqsXx2Wu925wOBJYPQqBJAKoGOpZUyhXtJE4g61kEvWahL+OVlJuWZ4XZkAb0CnLl2eDXB5NvTrWKfnQ2glXz93SZwIt1IKm+shaKT+bxD5EdEffrU8ceumuNZBPDigUgvc6a68mpC1kkvXhq5g9Kx5tWH1fFqzTVclfzkukjcnhB23V3E+H37t3oIpC8DZagfBBU9YPWlxAVUMoBMApifl8Q0hAyASCWdl6/Kfnt7YlKO3tQzlXJJYAIPmEgCARpoJjXR88Vu95mzI4vhjbrsAgpACABDIBKP2Shi110MWxw0qDkWRdWefvJI1NUqq91M2nr0lZLuEM3TugQ+dDtLi5bVh80bL5YS79W/zo6PVRpjY1mqv/MkGo+fx8Gz3Fky/VjsALnh4EhiF5qeNzAIwFkDT5MScrCStkY8GDEK2QZv6n2lO0/nZDWHXhWMwbJ1xiScvPKlJ8ai7EDhrljVTMXYGfEd2dyx6b0jaBYqiyzFhUnt830eGxsE1edVyc2LYJKWRrGu7t4bwbfcWTDPN+tV8ICFK8SQwOi0G4AHAFqSN5H+8QyALgvzz4Zq8hybXVSfJzrAtBNxfVZWWzIvOnluhZgySDIlO3lwXimtZtN6QjNikWVqGNGIcSE0VUoPw3xm5ascR6RTm9bxZ5Sg7O3LavQXTdWoHwAUHTwKjjL849HwAdVn6hLgcQyIv+BkAhWZd5nMzO8Vlac1h1ZJtML74ZGze3X8ZpWoimHlyVRJD8Nu6pWoRkkXt4ybWqT7NDgCKAm/93bMFiq3pyoDb32GvvvRbXed9+9Lz3ZHT7i2YzjLrV/MZpSjEk8DocwJ8xaE9yzMKpwuhaBQaI/SiqPtjoZi9elxtpUFwh8WL/mA9eO+8rPfeSgt5P9/DTInM7J65O2C1GXuSpBW0wb4GAMxdUBMWxYHL3kmtkCsyeG23Qer0uNvv2aVU/azUklnp0IfN9H4YkABcrXYQXODxBCGKzE8bKQM4GUCDAKKRcWmTVA4pKs1M1uesOcHqmppgrVU7lsFiTBSvunSFcd8eQ7taMSSv2hX055+EEBWMXlJclxCK6/SmvYmsytNz+WjNIHRt9/ZuXVLPVd5j2yVm/Wo+KhpleBIYXcYDMAOwz04tGKYXZb47MEjiZTnungnMcsOo2nIJSkg2HwSax601nrZ0pbO9TQzZDtqu0ke7MlxZ9c3BvIZBFA0CgtulRKOxd+TlO1TfMFB53xSb4Nbwmm4DVGVz1F35vdwSRe3egsUCgDcciDI8CYwS89NGEoBTALQBwLSk3EmqBhQDiAgrMvR5z89sto40djapHc9gtFkTUk5duiwkmzS6Y1i1OagjkUSEVK2rI5jXyMppsgbz/P1R8XVcpWbriFjfvDAgTsVjf3i/q+LCjanpezqMfAq9f36idgBcYPEkMHoMA5APoMUs67TZhsRCleOJGWk6TdK/p7oSfpZfXwYW/M0OgbZvd272Ly+ZXqbGtbPmtmQoOkdQ11em69xBLUczaWqdKiOphzk64bA9sIAnMf3EGMOGJnvFOevNeOlQai5/GRyQM8z61SHtyc0FF//tjx5z4esvhsXpo8eKJPBpoRASSRB/kqfNf2JaQ32G1t6mdjwD9c5/J+U/+uCwkO98lrXQYMXmoK6tzNC6XcE8/7xFNSHZfNKTg4+NaZA6jfyFuR8anc6mGGn3FixxAE5VOwgucHgSGAXmp43UwZcENgDA2PiMiepGFLuGmXTpT023687IbFS1BMvAEf5y06L0r9clhnxaO/OUynjGgrdsz6IP5ppAhkXLGpKCd/7e1e3S1sifTMxR6/qR4nC7t3PXpyTFSLu3YOJTwlGEJ4HRYSwAGYB7zDSWkH7DV8w1qkK18h+xTiOKmmtHyrn/mFhbFS+5QtYibaiYV5IvXrVSrjqkCWnMcSneBM/UfUErF2PRBi8H1Bs6W1JS3aqMBHpc8DTfM4+PAPYhhtu9BcuJZv3qFLWD4AKDJ4HRYQmADgA4+ads/LAlrXlF936Tlv7ky1b89PMyd3pjq7rhxaYJCfqs52e2Y35yS5XasfSX06k3r1y4st3poKDuqD1WwqqdQTt3qg5BK2uRX9AY1E0nvTnwcvYhuSGZ17LrAW/3FjQygHPUDoILDJ4ERrj5aSOTAYwB0AwAE+awcYfvi0tm8SPOqs0vfOzjhMQHXq1XTv223GPsjJiRqWhglCTDn8cJWX8uqq3QkkfVDQT91diQkn7WSYtCuj7QMtaRGaw3KykaQROM8wLAtJk1IU2WD2upEpvwygm8NVw3eLu3kLhQ7QC4wOBJYOSb6v+XTV/K0pLSkNbdQcl5nrTRPzuYN+q5t3XGO96ocs/bVumV3J4QxhnT5qfqc184odU23twWEdP0W0pH5N543YTyUF2PCNCetTkopVYSNELQNgAsWFIX8ulYr5exmntO8ApM5M/fx+Dt3kJmllm/ukDtILih408iEWzFSiNphrX8TMpoHy2YXMOWnecd39djBBFCxnhHVuHvd+Tkr3lNkX//XoVr/P7aUPRxjXWJGjn+/kne1GuH15UHu4BxIDz/5Mzcl57LqgnV9bIXNlkU2Rnw0VKTJBmDU7rH6523KPSbQso+TKnQHMjmmxu66PS4O+7Z5TnE272F1AWhuAgRKUS0mYh+IKItRPQb6qMdKhHlE9GA4yOi//NfZ6v/mjN7OXYaEf1zoNcINzwJjGy5UrLdKad11mtyrEUjp3h7/IXtjkYHbd68ttyiO76zZD27tpOu+KTMnV0bkUWPI4VARGdk6/KemdHUnG/obFE7nt4R/eHa5Ylbvje3huJqGh20bPnWgG8QEYmERI0n4MsgzAltzQYDC+loU0cLtbsfm5cZymuGMy9j3k/q7OXnrE/QvFuXzFvmhVaodgnbGWOTGGNjASwDcBKAW/p4TD4GmKQS0SwAKwFMYYxNALAUQGVPxzPGvmOMXTuQa4QjngRGtqkkwClovVsWnCxuSUkTBt3yyBDPTMNPacgvfOjz5JRH1jYpZ39d5kloU23Re7TL0mtTHp3qivtJTkN5MMujDJWiyLpzVq5kjQ1yUAs6H5ZxWllQWh2m61wBLxg9clRDZ6DP2ZeKf05sE91a3toMQJXd1+7t9l2WPJsiBW3dJ9ejMWb96ql9HxY4jLF6AD8HcA355BPRF0T0vf9jtv/Q1QDm+Ufzru/luK4yADQyxpz+azUyxqoBgIimE9HX/pHIb4kojogWEtHb/vuNRPQEEW0kok1EdJr/9kuI6DUiep+I9hLR3YcvRkTF/li2ENHHvZ0nmHgSGKFWrDSKABYAaASAWXN0YwJ17oQMJXn0RZX5o556zxh3z+u1niWbKhSNI6gFd2ORLAjSzwo0eY9ObahJ0TjDNuG2dZoSVy5a0aR4WNCXDMRbvEmu8fsDXjw6Q+sOeBI7Y3ZtSMuNHPrOeEhTOjorlNcMR0favX3L272FgZDXDGSMHYAvd0kDUA9gGWNsCoBzARyenr0RwBf+EcT7ejmuqw8B5BDRHiJ6iIgWAAARaQC8BODXjLGJ8I0QHvum8v8AfMIYmw5gEYB7iOjwjvRJ/muOB3AuEeUQUSqAxwCc5T/n2f04T1DwJDByFcBXvd0BAAUjpJGBvoAggNJHOy1jfr0nd/iaN0j3p3cqXVN3V3kp/NezRZJRcbqMZ2d0SsXpTT1OPaitqtKSedGqeSHZKBK/akfANyxZdJ6An3PRshpToM/ZE6cNzo77FqhWlDoc8HZvYelMla57+A2YDOAxItoG4BUART0c3+dxjLEO+DZa/hy+xgsvEdElAEYDqGGMbfQf18YYO/b5ZDmAG4loM4DPAOgAHN69/zFjzMoYcwDYASAPwAkA1jHGDvrP2dyP8wQFby0WuaYD8ABAwXDJnJgodrsrOFAkDeTsGR05mLEZjo7Ntpqvkxrs7xUaNPujZ4G6wry46JunkKaLw/1Tzj7qvveqf8DTB9cDAAySBjcWLscoczpaXDb8btNraPc4cNWI+ViYPgoA8Jvv16Kk6ESk6vo3u6kTRd0fxiBnSVpt5a07klM7FVkX2O9u6L74tCj/rtsaKv5w8+6gPillTLRl7U5qaZObE82BOqdFF9iNISR43NNmtiYG8py9OfjkiDqpPS5mS8I0Op1Nd+7UKd9bLTH7MwhTeWb96nFt9hu3h+qCRFQAQIFvdO8WAHUAJsL3rsDRw8Ou789xjDEFvuTrM3/C+FMA3wPo6/mD4BvV231MrDMBdJ2FUODLu6iHc3Z7nmDib6Ui0IqVRhm+NnGNADB/oT7go4C90ZlgGLa8Oa/ovq9S0/7zSgv7yRdl7pTmiOuXe6wXyr/DMGP3hfAz9fF4dMZP8OKcy3F5wWzcseN9AMAHNTuwMnMcnpx5EZ4p2wAAWFe/F2PMln4ngF1NS9LnrJlp9cxItIZsV+5A/Ovvc7PeeystqGVuBAGkOXNTc99H9l+6LrAbOJJTWptlGSGZDq7fp6kV358ck8mP26u41vjbvX1vNQf1jS43aCeH6kL+adSHATzIGGMA4uEbpfMCuAg4Uhi+Hb6ZssN6Oq7ruUcTUdfX0kkAygHsApBJRNP9x8UR0bEDaB8A+BURkf+YyX18K98AWEBEw/zHHx7lH+h5howngZGpAL5hYhcAFI6VQ5oEdmVO9SaOPLc6f8x/PjIn3P9avefkjRUeg62nd2Nhq87Rhq8a9uP07And3j8xMRtm/+Dc+IQs1DvaAQASCXB6PXB7FQggeLxevFD+HS4eNqCN2kcxy7Jp9XiWUTK6tlwmJaxqOTImilddusK0d7ehPZjXyV7akB7IOpbJWgroZorCsfUB32jSHY8bSuPdc3V9VMSIStut9kMXbjC6HuPt3sJdsJNA/eESMQD+B9/avT/773sIwE+JaD2AUQAOb9baCsDj33RxfS/HdWUC8DQR7SCirfBNGd/KGHPBt6bvASLaAuAj+F5/u7odvinnrUS03f91jxhjDfBNO7/mP+dLgzlPIBAL/lpvLsBWrDSeBWAFgENaLYmPP5N6gyxT2OyOUzxQ6rYbaq3vFzBxw6gMQZHDvmjrDZv/i0uHzUKn4sRzZd8eNx3c1bMHN6Csswk3jTsJHW4H/m/rW2h2deJXoxbiQEcjTJIWK7P6LNnYLw0OV8tNP+iV3R2msOrVaY63Nq7fvjY+zqwEbafqrgdGVUgfBWYErMnpal21PjUhEOcCgBtu/qTyV7/dlxOo8/Vk70uZZfT8vPxgXyecWN2u1r/vke3reLePSKEASG2z3xjmJa+47sTe28sIt2KlkQDMhL9N3ILFurxwSgABQJQgZk6yZRXeuD07b81rbuk371e4isrqwrUUyhf1+5CkMaAw3tLnsd81leONqq341ahFAACTrMM/pp6NZ2ddgjFmC75o2IfF6aPxl+3v4YbN/8XW1qG1DU7VaRL/NcWT9IthdWUUlILHg9NmjU85ZcnyOm8QQ0o/c3/AunGYZSmgO+yWLK8NekHi1lqhmb0wO2amgY+0e/smKY4ngBFFBFCsdhDc4PAkMPKkAkgBYAOAyVO1qk0F94dWD13+Qmtu0eoN6RnPrG3DZZ+VuTPqw+od45bWQ1hXvw+nfP4Q/m/Lm9jYVI6btr513HF72+tx+w/v4W+Tz0KC5vhOZI/t/xKXFczGBzU7MCbegpvHnYR/7fl8yPGJRMJ5ubr8J6c3NmTq7EFprTYY+/fkZF916Yyg7RhOzFJSXKPL6wJxLlkQZJPoDsgyBVFy2YvGdwRs00p3mJeh+m8z3YI3NlrD8XZvES9k6wK5wIqJJ5goMxI/bo9HwXD11gMOlCmRmUecXpdf+MiniUn/erVROWN9uSeuPeCdHAbqmlEL8e7Cq/HWgqtwx8RTMT05D7dPOOWoY2rtVvx+02u4bfxK5BmPr9RR0dmMRmcHpiblwuF1QwCBALi8gVvSl2fUpj053aFfldVYEbCTDtE7r0/Ke+SBYYeCdX7zOdsC1kYuXecKyO+aJaM56G9iyj9NLNfszk0P9nXU1ulxd9y7m7d7iwInmvWr+brNCMSTwMgzE0AHAIweIyfGxwsRWSw1KceTMvrS8rxRz76rN61+o9q9cEulIrsC3jd2KNZWbsLayk0AgMf2fwWr2467dn6IC75+Ahd989RRxz60dx2uHDEfAHCipQhvV2/DpeufxYX5MwIak0YQNFePkHMfmFRblSAFJqkZGsIdNy9K//LzxKC0G8yY0pnpiW8LSGcOiy4wI4HjJtYFtXC6rY06nY8s6HttQgTr2u7tnVre7i0KpAAI+k5WLvD4xpAIsmKlUQfgQQA1AJTLfx43fVmx4SSVwwoYtxOumtK42o73R8rSlgKLwET+zrIXNo/Hds9uoeWzxkTVu0hotfa2z79/ScrKdgVsHd9h+9ZayvDMgvyhnudf+9wVa6tShrzG7vZ736++5IqKoPXv3XnnuCr5m7Gq/58GS7XdUXfbDpO8u6ObIXUukv2hzX7j3X0fxoWTgI8EEpGFiF4kov3+rdbvEtEoIsokorV9PPZUIrrR//mtRPS7bo65lYiq/FvGD38kDDLWXxLRxYN5rEoK4JsKVgBgTJEmYqaC+0PWQpM7uz236LbvM3KeW2sTfvlxuSu/ulHtuMKVQZIMt4wVsm4vqq3QCR5V2/o5nXrzyQtPabfbSQn0ubOKa9O8onvI503XBqbTzdIT6xICcZ7uVG/WR20C6FQ89kf2uyp+8m1qOk8Ao9IytQPgBi6gSaC/wOF/AXzGGBvOGCsC8EcA6YyxasbYqt4ezxh7kzG2uh+Xus/fE/DwR+tg4mWMPcwYe2Ywj+0Pf4PrQP6Mx8OfABoMJGVkisMCeO6woo+DseCkxryif36RkvroK83ec78q9yS2BrU2XaSam6rPXTOz1T4hvi0gmygGq6khOX3VSYuHth26G3oTDMq8HdVDPU+6jg35b1Gjtbdn5zoDPtoJAC4HXNb7FkTduriu7d5e5O3eotlcs3512HU64noX6L/GRQDcjLGHD9/AGNvMGPuCiPL9xQ9BRBuIaOzhY4joMyKaSkSXENGDg7mw/7GvEdH7RLSXiO7uct/l/qbQnxHRY4ev0XW00X/fXUT0rf/Yef7bRSK6h4g2EtFWIvpFl/P+vsvtf/bflk9EO4noIfjazQSklpi/NMwMAC0AMH+RLk+SjqtaHpXiLd6kUT85lDf6qQ/izH/7b63nxO8rFJ3d2fcjY0eiRo6/b6I37foRdWUC1OvtvPX74bl/+PWEgO8YTj1zn3bI5whAweic3Mag7c4++OywGrklPmT9iEOh0els+t0Wof7G7ZbcNo98/JZ6LproAMxTOwhuYAKdBI4DUNqP414EcA4AEFEGgEzGWH8ed9j1XaaCP+1y+yT4KnuPB3AuEeUQUSaAm+Br2LwMwJhezisxxmYAuA6+noQAcDkAK2NsOnz9eq8gomFEtBy+nboz/NedSkTz/Y8ZDeAZxthkxligXhDTASQBsANA0VhNXoDOGzGIgLSRLsuYq/fmDn/+TVF/y1uHXDN2VnkF9ZKecCIQ0alZuvznZjS1DDN0BrTt2kCseWpm7gvPZA955K6rlHxPmqvgUMNQzpGkEYacSE6aWheUDi6NZVK98Na0qKkJyNu9xSw+JRxh1BqXfxnA4ZYM5wB4ZYCP7zodvKjL7R8zxqyMMQeAHQDy4EvSPmeMNTPG3H1c6zX/v6UA8v2fLwdwMRFtBrABQDJ8yd9y/8cm+Eb8xvhvB4Byxtj6AX5PfRmFLg2nc/OkqHnBGAxJhpQ11ZZd9KetWXlr1jrFaz8sd4+qCGpP20iRodcmPzrVZb44t75cnQLdRDdetyx5y/fm1kCe1XT21iG1ajPL4pALRs9fXBPw6S7FA2/D3XNEipLWaD/wdm+xbFHfh3DhJNBJ4A8ApvZ1EGOsCkATEU2Ab+TuxQBdv+sUoQJAAgbU5P3w4w8/Fv7H/6pL0jmMMfah//Y7u9w+gjH2H/9jAlLS4hhHSsPIMoSUVDFouxMjjdYA/bClLXmF936Tlv7ky1Z28edl7vTGVrXjUpMkCNKlw7R5j09tqE3VOEK+ltKryNqzT17JGhvkgE3bZ81sz/KYOgZdFkcniloteYZQhoixRcsaAr6h4eBb6ZXyIUtElnrqyup2td7yA6u5ZrMlu96ljappba7fJpr1q4c84s6FTqCTwE8AaInoisM3ENF0IlrQzbEvArgBQDxjbFuA4+jqWwALiCiRiCQAZw3w8R8AuJLIt57Iv9PZ6L/9MiIy+W/PIqKgTHusWGnUwzfF3AoA02ZoLZI09PVN0SgumcWPXFWbX/jYxwmJD7za4Dnl23KPsTMM6umpY0SczvLsDJtmpaWpMtTXtttMiScvWtHkdiMgdahECaJwyuYhjfam6Vwdg32swdjRmpjkCWiLxrYGodX7zJyIrpOnMK/nzWpHOW/3xgGQ4VsexUWIgCaBzFd08AwAy/wlYn4AcCuA7tYHrQVwHnxTwwPVdU3gZiLK7yWmKgB/hW8q93/wTRMPZHH34/7HfO/f2PIIfGsHPwSwBsA3RLTN//3EDeJ76Y9c+EYevQAwfoI2pqeC+ys5z5M65oqDeSOfe1tnvOONKvfcbZVeyR2UNV3hTCuK2t+OlnLunVB7KE50D2lKdaCqKy2ZF6+aG7AOJ9knVad4afBrQC0696BHJocNbwz4iOqhv091CIocsW3SDnTYqy/bqO28b296Hm/3xvlNVzsArv9iolg0EZkYYx3+kcD/AniCMfZftePqrxUrjSfCt3ayEgD+ek/S2QXD5SJ1o4pMLgecNRvNdZ3vjdbI2/Itga3gE/7a3Z6Ov+4S29Y3J4R0OcFV16+rKLl1V0DevOy8a9wh+auxgxo9+/sed+VbNSmD2rH/0yu+Lv/LvdsDtiGrfJ25wn3vioh8Q9fpcXf8ez+18m4fXDeeabPf+FO1g+D6J1ZeAW/1b+zYDuAggNdVjWbgJsC/HhAALBliQMrOxCKNDtq8eW25RX/daMl8bm07XfFJmTu7Nigtz8JRnCyZ/joOmX8aU1sukxKyUdGH7p+b9e6b6QGpY5hy1p5BL4XI0HkHPYq4cGldwOoD2jtgtz+0IOJ2zXoZ835aZy/j7d64XgS2VyYXVDExEhjJVqw0SgD+BaAegDKsQDLfeW/y9SqHFXVaqsWmhv9ldnj/Ny5ZajXHxKL2Rqer5aYf9J5d7abUUFxPkp22D796RRk52jbkZRM7rl7QrKm0DHiTxid19vLbd1kGPppHXmVPzX+g17OATHnu/Fthpfz5hIh6M8fbvXH9xAAktNlvbFM7EK5vsTISGMky4NuprADAxMlavis4CBIzleRRF1fmjXrqPWPc3a/XeJZsqlA0DlVbsQVbilaT+OBkT/JVBXXlxLxBfzfocWsNpy09xdXeJg5hh66P4ezNg1qfl6rFoAqsxydYmwOVANb+oKuOpATQqXjsjx7g7d64fiMA09QOgusfngSGvxx0+X8aPkKKyr6i4UIQQOljnBljfr0nd/iaN0j7f+9Uuqburh7KZoRwJhIJZ+fo8p6e0diQrbO1Bvt67W3xyacsXl7nHWLOmT3bmukx2BwDfVyydnAFo0eObgjIDnO3E+6We+dHxEgzYwzfNtnLz1lvxguVvN0bNyB8c0iE4H/V4W8c/F1CACAzS+IjgSEiaSDnzOzIKbplc2bu82sd4tUflbuGD61rRbjKMWjTnpjuNJ6T3RCwnbw92b83J/uXl8wsG8o5JA1k4aTNA15jGC+Lg1rXd8KcmoA8Vx58Mbdabko0B+JcwXS43dsftlvyeLs3bhD4usAIwZPA8DcGQBvga5uWmsaLRKtBZ4Jh2InNeUX3fZWa+vgrLewnX5R7Ulqias2LLAjylcM1uf+aVFudKLuCUfD8iPfemJj/yAPDDg3lHJkrDyUyDGxI0SCKhsH0Vl60vHbI3UaaK8VGvDYjrHcDu72K64UKZxlv98YNER8JjBA8CQxjK1YazQASADgAoGisnKzRUMDbVnEDE5/mTRx5bnXe6P98aI6//7U6z0nflQ9majJcFcXrM5+b0S4sSW0eUpLWO8IdNy9K//LzxEHvzDYlMbN7+p4B9SgmIqRpB5bgCoLHNXVaa+LAojuaosBbe88sCEwM2zZqh9u9PXowLZ+3e+OGKMesX52udhBc3wa1SJoLmUx06RdcNF7Dq/GHESIgtcCdnvrL/VB+tl+p3Waoanu/AOK3oyyRXAAYAAySpP9TEbIXN9ZW/GVnSrrdKwW8FRTzSvJPz16p+fy7l23Zuc5BTdMmn71LaN84ZkCPSdO5bbVOfb93KCentraIEg3pBa3s/ZRKTVlWwGoMBpLV7Wq9b49s+7zREvElX1yeL+FWNgIgCJQOnbwK1KW5ksuzDm5ls/8rL7ysHibtn8DghcP1HBgc0EjLIItjAQB21zPQyqdDoLCfwQ9HMwC8pXYQXO/4SGB4y0GX3sdZWVKKirFwvRAliFmTbVmFJduz8ta85pau/6DCVVhWx1hk7yeZnaLPXTOz1Tk5wVobjPO7nPq4lYtWttvtpAzm8WmjnRnujPqWgTwmQ+se0K7vwnF1Q+qy0t5MbZ4n5oXdMg6FeT1vVTvKzv4mKe7zxtAWDw8GL7PCpXwNg+YaGLXXAWDwKFuPOkYjzYdRey2M2muhlU6EKAwDkQEeZQskcQoMmivh8nwBAPAoOyEImTwBHDw+JRwBeBIY3goBHJm6Sk0VeRIYAbR66PIXteYW3bUhPeOZtW249LMyl6WhVe24BitBI5vvncDSfzeytkyEMqhkrTdNjcnpq1YsrhrMY4kA3arNA1qbmaEf2JrA2fNqhzRjUnn/5A7RrQmrXt+H2739fW96flS1e2NeAG4wpoDBBaKeB3zdyhZIwkT/V6LvcfCAQGBMgUv5ChpxfiiijlZ8c0gE4ElgmFqx0kgARgI4Ug8tIUlIVi8ibjBMicw84oy6/KJHP0lI+terDcoZ68s9cR0BKTcSSgIRnZypz39uZlPrcGNnc6DPv3XT8Nwbrp1YNpjHZs9vyVC0/a/pmK4d2GaSJSfWDnooqHK9qVKzeWTYjLJ1etwdf9vtOXR5qSWzwm6IVzueQBIoHhppHjqcd6HTeScIOkjiqG6PZcwFj3cPZHEcAEAWJ8Hj3Qu760lopCVwK+shi5NBpAnltxBteGvTCMCTwPCVCMAAwAX4RjzMZp4ERrKkHE/q6EvL80Y9847OdOcb1e4FWysV2TXkwsmhZNFpkx+e4oq/LK++DAEuMP3C0zPyXngme0AbPQBA1kLDirfU9Pf4NF3/10JLktM2pqhzUEmgoxOOzn8uCIu/WS9j3k/r7eXnrE/QvB2l7d4Ys8Pj3QGj9vcwaksAuOFWNnV7rMe7C6KQByLfUlQiHQyaS2DUXgNRyILHuwuSMA4O92uwu56H4i0P4XcSNbLN+tV8I2OY40lg+EpDl00h+cMksyxTWE0pcYMjiBAsYx2Zhb/dmVOw5r9Mc+O7la6J+2oGWu5ELZIgiBfla/P/M62hLl1rD2CZHKIbr1uWvLnUPKA1fgCQeWqFub/rL5M1Qr+HdyxZzQOO5bCy/4xqkDpMAes3PFg1dkf91Zukltt2WvJsihS1Q1se7z4IlASBTCASIYlje0zePMoWyOLEbu9zej6GRloEt7IFAmVBJ58Fp+fDYIYerQjAMLWD4HrHk8DwlYwu/z8jRslhMaLABZashSZ3dntO0e2lGVnPre2kX3xc5s6rGXTZlFAqMOksT0+3a0/NaAxYgWmvImvPWbkSDfXygErumFO9ie5J+/s1ipioEfpd/Hj8pLpBjdTW7dHUSv+brGpruMPt3i74NjVtV7sp6p8/BIqH4q0AYy4wxuBR9kGg40sdMuaAx3sQknD8bKXX2wjG2iAJBQDc8OUxBMYiasA+nAxXOwCudzwJDF/ZADxHvsiRov5JPBgUhWHejCqce/rxm1tffqEDs6cewuyph7B8QTW2bXUCABobFBQvqsasyYfw9hs/lpQ7/6w61FR7jjtPoBjMzDT85Mb8wgfWJac8+kqz99yvyjyJrYPqkRsqWlHUXj9Kzr1vQu2hONE9pF20h9ltpsSVi1Y0u90Y0Mho4jk7+nW8UZKM/Z3KnrewdsAjZx43lOZ75qk2DeZr92arOHd9XEy1exOFXEjCONhcD8Lm+gcABlmcAZdnA1yeDUeO8yg/QBJGdrvez+n5EFppOQBAEifCrZTC5noIGolvEBkkngSGOV4nMHzlAjiygSDdwncGD8a/H2jD6DEy2tuOnyrMy5fw7v8ykJAo4qP3bbjuqiZ8/GUm1r7UgfMvNOHMc0xYdUotVp5mxHtv2zBxsgYZmaH5k0mweJMSfnIoyXv+Ida4X1Pb9EGeS1hXlC46dAGv1xcIkxL12WtOaOtcvVOo/qo5ccgbIaoPWTIvPGte2UtvfpHf38ekj3Vk7k5tssoNyb1ueBCJhCSNu7PZre2zC8iSE+sS+nv9ww68kl0p16XkD/RxgdDkdDbduUunlLZmhHVnkmDRysugxbKjbtNIM4/6WpamQsbUbh+v11xw5HOBTDBqrwx8kLFlhNoBcL2LjbeIkSkbXZLAlBSRjwQOUNUhDz58z4aLLu2+TMTMWTokJPqqY0yfqUV1lW+UT5YJdjuDy8kgCIDHw/DvB6y49jeh30wpCKC0kS5L4TV7c4c//4aou/ntQ+4ZO6u8wsBbnwWbSZKMfxkvZN5cWFuuIc+Q58++/rww/85bx/R7qlkQQJozN/drDV+6zt3nDm2tztaWmTWwItatNWIzXjoh5AnY4XZv56xPSSpt5e3euLDBRwLDHE8Cw9CKlUY9ADP8O4MBICFB4COBA1TyuybcdmcShH78lj/7ZAeWnuhbKrbqPBM++ciOs06pxY1/SsTjD7fhvAtNMBjU/XORZEjZ0zqzC/+0NSv3+bVO6doPy10jK+pVDaobi9L0eS/MbOksimsfcmwP3T8365030ur6e3z24kZLf3ZcW7RuZ1/H5OQ1DmjTi9fLWPXdMz0CE0P6i8LbvXFhjCeBYY4ngeEpGcCRkR6dnkSjkaKqplewvf+ODampIiZN6Xv2dN1ndjz7VDv+fEcSACA+XsDLb1jw2TdZmDhZgw/eteHUM4y49soGXHxeHb5dr36bYJ0R+vylLXlFf/smLf3JV6y4eF25O72xVe24DkvSahL+OVlJuWZ4XZkA7+BHLZkgXnPZirg9Ow39Ssi0eujY0q19bhDJ0Hv6LHo9eVrdgApjl/8vuUKzPydko3Btbpf11h+81ddstmTXu7SmUF2X4wYg36xfHT3FyKMQTwLD01FTv2MK5SQSiL/DH4AN3zjw3js2jB9VicsvasC6zxz4+SXHD0xt3+bCtb9sxJq16UhKPv656q47WvHbGxPw6ksdmDRZiwcfTcVtNw+6akhQxCV740esqskrfOzjhMR/vlqvnPJtuWLoDMgmjaEQiYSzsnX5T09vbMrV21oHex6PR2s4ffkpbmur2K8pZsvpZX0mRBZd33npgiU1/V5/2dlKHa5H54Wkt/fhdm+rvkkyfd449PWXHBdEGvjan3JhiieB4SkNXXoGDyuQ+VTwAN3ylyTsOJCLbXty8J9nUzF/oQ6PPnX0IE1lhQcXnVOHR55MxYhRx5dg3L/XjdoaBXPn62Gz+dYHEgFOR/iW80vO96SNvuJg3ojn39Ya/vJmlXvu9kNeyR28Lc39kG3Qpj4+zWm6IKdh0BV329vik09demKdtx+behMylGRX0cFeex2naVkfz32MLVjckNTf+MofmGAVXbqg1+A72GGvvnyjtiPq2r1x0YxPCYcxngSGp1wAR+YcMzLEfr8Ycb174tE2PPGob2bx7r+2oLnZi99e24S506uwcNbR7Wtvv6UFf7o1EQCw6lwT1jzbgWXzqnHN9eE/My+KEDIn2LMKb/ghO3/Na4r8u/cq3OMO1Pa3oHKgyYIgXVGgyfv35LrqJNnZ2fcjjndgb3b2L386s1+JZPw523tNfFO0vRdeN5o6WhISlX4ldYdKDYc0G8dk9efYwbL5271dVmrJLLcbEoJ5LY4LML5DOIwRY+E7qhGrVqw03grABKADAEpuSiieOFk7s9cHcVw/dFqpvfbz1GbX+0Vx8qF0Vd5c2BXFcf8e1vhh/WDalzGU/PnjQ1ddd6DXx3q9YHsuPbFDaknodmt4s8tlPeub1B6z+fGTDlS8+/n/+tzl67TDefCKFR6pzdxnuZnB8DLm/bzBUXnvnpSMaO72wUW1e9rsN96gdhBc9/hIYJhZsdJIADIAHFnTFRcndF/jhOMGyBjP4oafWp9X+NBnScn/XtvkXfVNuSe+bVAjc4OlF0VdSaGUvXpcbaVB6HuX7tEIq29dlP7Fp0mNvR0lCCD5zE3NPd0fJ0m9Jm3TTqjt17vjg08Prw1WAhgr7d64qMeng8MYTwLDTzx8RbyP7Ew0mIjv/OMCLjFLSR51cUXeqKffM8Td/XqNe/HmSkXjcPX9yMCYmazPeeEEq2tagrXX9XvHYkyULzn3ZO2hCm2vtf6yltanekV3tzt8ZUGQeutwsmhZTZ+JXcMBuU58Z0pe3xEPjFPx2B+LoXZvXNQrUDsArmc8CQw/ycDR7bIMBoEngVzQCAIofYwzo/C63TnD17xB2v97p9I9ZU+1l5SgrxUxy3Lc3ROY5Q+jasslKP0uyeJy6uNOXrSyw26nHh+jM8LgXbi9qqf703WubpNAIkWZPbe516lyxQNv491zNUSBewpljGFjs73i3PVxWBND7d64qMeLl4cx/iwTfuLRZWcwAOj1xKeDuZCQNJBzZnbkFN66KTPn+bV24eqPyl0Fh3qdeh0qIkJxhj7v+ZnN1lGmjn5fq7kxOe2s4iU9JnkAkHbm/h47fli0rm4LPiYktjVpdb3vHj74ekalXJ2W2L9I+9bkdDbdsFWou2GbJdfq0egDdV6OCwN8Y2MY40lg+DGhy/9LfLygkeXedzJyXDDoTTAUnNicV3T/Vympj7/Swi74ssyT0jKgLhoDkabTJD00xZ34s/z6crB+1IIBsG1zQe7vfzWxxx3DSTlKintU911VLHql2x3EI8fU91pj0VovtHqfnzWITS3H87d7Kz9nfUrSd63m9ECck+PCjM6sX61TOwiue5LaAXDHSQZw5MXJ7Y433HFzYUVmlk3MzLZrsnNs2qwcuz4hUeGJIRcy8WnexPjzqhLZuVVoPCjXNX2Q66DPxqaLdn1An9xFEsSf5Gnz5qQ01P1xm1lf49Sb+3rMi8/MyJ04panuwksPdZtEmc7e5nTecfxGX4uu+/7LJ8yp6fHNMfMyVN073alR5IS+4urLD1b7odt2JCbUu7QBX1fIcWEmCUCfnXy40ONJYPhJQZeewWX7s0wfvTXvuFcwIsWt0bjsOoPDGRfncCck2j0pqTZvarqdMjI7hcxsu5SVY5Ozc+zazGy7wWBgvLAsN2REQGqBOz31yv1Qrtiv1G41HGp7fzjEjSMzBEUO2O9YvlGX/tR0u+vhA50V/61O6aNUC9H//WZZwsjRax0zZ7cfl5RmTuvI2mmyunUd8Ue9cUrvoWD04uW1Pa7BLf88sUKzK7/P0jG9aXO7rH/fI3V+3mgJyGgix0UAngSGKZ4Ehp8kdEkC3S5Tt+uDGBNlp1MvO516WFuAQxW9n1QQPE6N1ukwGB1Os9nhTkiyK6mpNpaeYUNGpk3MzLaJWTk2bXaOXWfJdOplGbxNHdcrUYKYNcWWnTVlG5z2bY6a9QlVtvdGa+WduemB2DChEUXNtSPF3IWptVU3/5CUaPVoelzf5/XK2ovPWtn+eela2ZLpPioZFUUInhM3teHVhQldb0/R0nHPf4LgcU6eZk1EN7/+tjZ0Ov89P32wma7CvMr7Na5D/9yfkuXyiuFfcZzjAidg62e5wOJJYPg5Ogl0G3t84RsIr1fSOuyS1mE3orkRwIHejmZMlNx2nc7pMBidrvh4uzsxxaakptpZeoaNMrNsYlZOp3+U0aFLt7j4eo8Yp9VDl7+oNReLNqCj+du22s/TWlzvjY3X1KYmDPXcExL0Wc/PbLfdtYuqv2hK6rFXrs0WF3f60qW2Lza/p5c1R2dxw0+pN1S95mESk47cnqQRjvu9TU1vbhEEsnR3/vKHx7bIDv2gRu8Odtir/7zDbCi3J/KpXy4W8c0hYYongWFEFgtp7ATDKAAGUWJ2SfQ6HHZZhQbxRIpHo+/s0Og7O+LQUAdgT2/He72y7LLr9E6HKc7hik+we5KS7d60dBuzZNqEzGybmJVt02Rl2/l6xhhgSmLmEWfUmXFGHZorpYaGj7Jt7OOxqVK7adBvaIySZLhtHAyf1ddU3bU7OcPh1XQ71FhVlWP4yamz2l5+/5uj1hLGJTGNddp2a/LGSUdG4OJl8bhagEXj6rstXl29VV8lfzluwAmgzePueHg/tb5Vy6d+uZjGRwLDFE8Cw4u8Z2ciEyWmCILXJIgsiXkGN/IQWoLgduuMbrfO2N4Wj5pei3b0vJ4xzWIjS4btyHrGnBybLjPHodfr+XrGSJWU40lNuqwM3p+Weet36apb3h+miF+PyRDcmkE99yxMM2SNj2/t+PMuQd7WmqLt7phvvhlvvv0PbVU33fXDUf180884IHs2TjrytVYUtTrB7XV45SMJ5ZwFNcf9rrkccLf9bb55IAF7GfOua3BU3uNr9xYBf8McF1R8JDBM8SQwvBjcbrHF7Ubl4Rs0UrJeFKDCaGDwDHU9Y2KyXUlJ4esZI4kgQrCMdWRaxu6E+5qdrprSuIqOd0fJ8tYCC0EY0P9XslZrum8CY/+tLet4eG+OQWHicaOCjz18Qubkac11K8+uObJjOH+c27Alr7rRWJ6Zcvi2VJ3NU2mLP9KSbfHyuuPW6h1ck18ttST0exq3xu6ov22nUdzVbuFTvxznw5PAMMWTwPCixzHdQghyzBeOPXY9Y9kg1jOmpdmQnmFHRuaP6xmzchy6tHS+njHUZC00ubPbczG7FLa27ztqv0hucr5XZJIrMvrdIk0kolUZiaYZibX223eaHfva4o6abmIQ6Zqfnxg3snBt2+hxHUemhvWrttjwtx/fU1n0Dm+lzZf3SbKzc+Ro21GF2ZsqpAbh9en92g3sVDz2Z8q9DbzbB8cdh08HhymeBIaXbtZMCfz/aED4esZIYjAzU8HJjSacvA6tNWJT/ScZ7d4PxyZLLQn96pKTqzPpH5rk1q6try5/dJclr2vypXg1htOXntq0fufL7vhEjwwAw2a3Ze59uNOm6fRtuLLo3W4AOgDIzGpqBXBknaCiwFt312xB08dIJWMM37U4Ku7YmZRq9WiGVD6G46IUHwkMUzzBCC96HFebIoDNSbluDGo9o0NvcDhMx6xnzMi0CRlZfD3jYCVkKMkJPzmU7D3/EGvcp6lt+iDPjXWFmbJT3+vPUCZROD/dmDcnqbbmhk2JcXV2/ZE6fx12U/LKeSsOfb71rWxBAGQZkuekzYc0r8zJB4BM449dQ8ZPrnN3PW/ZO2kVmsqM/N6u3eR0Nq3epfN812rhyR/H9YyPBIYpngSGl+OTQCKeRISRLusZ41r7tZ7R7dRoXT2uZ8zKsUlZOTY5K9uu5+sZfQQBlDbKZUkbtReeX+71VHxrbGh8d5jJvH20rmuJl2PlysaMZ6bbHA9XtlX+92B6zuHbyyozsq84Z07Zf9Z+lQ8A+SurkmvXKkxgImXqf+zOM39RzZG1ge2NZFWempvd0zswt1dxvXrIU/P4weQcBcevSeQ47ih8JDBM8SQwvBhw3EigwF9gIpjXK2sddnnA6xmNRofTHO/w/Lie0UaZWTYhM9sWU+sZJRlSwZzO1II529Fh3e7Z+78EB74odMUdyO32RUVDku7aXClnSeqhyj9uSrW0ubUyAHz4UVH+fbfVtl1/836zOZHF7Z+xuypuQ1FWulY80jpu8bL6I6MVlfdNtWk8crcFnXdY7Yf+zNu9cdxAJKgdANc9ngSGl+M2hgB8JDB2HL2esb4/6xk1LptO53QeWc+YYlfS0mw4sp4xx6bJzrZrM7Mjfz2jKR7S5LNaTTjrGzRUb7DVfpFejw/GJuobk49L1sbq43JemNXWsXqfW/miOlMHEO772wJT4SRrR/GpjabkM3fLrg1FSJZFEQB0+k6rJdMVDwAVX5krNduG5xx7Tt7ujeMGLaKfe6IZTwLDi4zjk0A+Esj1QBDcLp3J7dKZBrye0exwJSTYlUhdz5ia6TWknluTj3NrUHNArq/7IMeu+bwoTWMzHtlNbyCN6c8jZHyWVN68eke22eWVpCsvKZY/2fiqbVihPa0085DdVJVuAIDcvHoHgHh7B+z2B+andP3Gfe3enJX/3J+azdu9cdyghO1zSazjSWB4kQB4u95AfCSQC5Dj1jOW9358t+sZU23edF/SeHg9oyY7x65Lz1BvPWNGgTst48oDUH5+wFuxVV9V82ZefNKWQoPk0QhEhEXJCUmTZja13LRdK/zQHh9/0oJT6r/bvVavO3OLQ/PgSYkSKcqEydVGACh7bEyjbDMeGQU82Gmv+fMPZn25PTFfje+N46IEfx0LUzwJDC8S+EggFyYGvZ7R5HCa4+2exGR7SNcziiKEYZPtWcMm70Jnxy5vVamxwfvJGK/4fUF6okab+I/JXu/LFZUdjx3MSj1pwaL2j77+X9zBJ+2ODKOIefMbhNqd2hr504k5AG/3xnEBxpPAMMWTwPDSXRLI/3i4CHDMesbavo4/fj1jcoqvPmN6xpH6jNqMLJsxO9uhTUxWBvRmyGiCMGpBZyoWlKK99Tvbwf8lKeL/JirnU3rC7JSG+j/+kJp443WTan5+SqmQWToteeHSZqq7cZFRZIyta3CU37MnJZO3e+O4gOGvY2GKJ4HhpZskkO8O5qLRQNczejxarUMxGGwwGh2KOc7lSE5y29ItdiUjq1PIzLGJ6Zk2Q2a2zZg3wi4Zjd4jU9NxCWSYsKoFWPUZDu0WXYlfZLkf+HgUe2NjrvK/nGZzYbq1rfGDDFtjpcFw+07JvbPdkh/kb57jYg1PAsMUTwLDy3FrAvlIIMcBjEmSw2GSHA4TmpsB+Mop9Vh7TCCXV5TcnVqdA/EJHUJams2ZnOgQEuKdUkq23ZC1eIdr2mKvrn67yZtd1Fj72N1T4l+sSE3j7d44Lij461iY4klgeOFrAjkuALxMI3jdmji324iO9mRUVfrawVm0ne4CU6egpNidw7JbTW5LR73bBo+p6IB02diDbVYtOaubzLZDFWahqTFOZ7fpTYzx/t0cx0UnngSGF74mMAAY88LmehBEZhg0lxx1n1vZBJdnnf8rDXTy6RCFDHhZBxyu58DggEZaBlkcCwCwu56BVj4dAplD+01wQ6IX3cqYuM6OkaZOYZhBoVyjIOebtLJB0MjWJCu053/LPv4uzS19laOV0xoLdG6N0p7sbjJtzGO/mt6siT9viydlVh2zDHPrWlsk19bN8R3btyTYd/2Q4DmwLwE11XGStdVkcLt0cby/N8f1SVE7AK57/MkrvHQ3HcxHAgfIrXwFgdLA4DjuPoGSYND8HER6eJTdcLhfg1F7NTzKFkjiFMjiRNhcT0IWx8Kj7IQgZPIEMIwRvCzfbGsfm2DrKIp3u4YbIVtkyRxH2jgiigeOtBFGU2aFRzzve2XSXKd89aUTPY1fFMi//+M38W88PLlNXljpmF6WLias/kq6+heLhPlfpLEL85jcmuh2uKbta8yZe4hOuHJ/qk4Pbdfre73AwX2Gzq2b4zt+2Jbo3LMrgZUfjBca6k2aznaDUVE0RiKK+VaAXMzjSWCY4klgeBFx3Egg83Z7JNctL7PC490NjbgILuWL4+4Xhbwun+eCsbbDXwFwg8EDAoExBS7lK+jli0MTONcns+x0jjR1OEbFObwjTIqQb5A0WXqdViuIZkBrxtH52REtRTudCWfvoJlTPZqODsLSmQuU2oP52qemt3riT2i1tfxNR1edUq6/4sIi+QHNNPnDL/+LP94wteOcd8ebF6e2NlzSONpkWjcxsZwUb8fIigZpzkFX5symuNRMr1kQgOGjbMbho2zGM86pOe7aHR2Csn2LuXPb5gTHju0JngP7zKz6kFlqbTFpHHaDEZA03YTMcdGGJ4FhiieB4aWb6WCvp9sjuW453W9DK60AY84+j3UrGyGJowAAsjgJdveLcCuboJWK4VbWQxYng4i/RoeaBEUpMNlbRsc57GPiFO9wIzRZBslskiQjIGm7ju71hDEvmqb+0Jp+9h7d9LEeHQAc2Ktlpy1dRq2tmeLNYw94XePqxS1bzaYOxW1o2pTAxi3czR7ckMOu//1yza13fKg/87wD7mt/tiTp443JmukJDbaL8100bs+wVOwZBuuTQEVig6tz0j538qw6YeQUp06jOb5YtsnkFU+Y02o+YU5rt8PJFWVa5fvvzO7tW+K9e3eZvRXlZmqojxM62uMkj9sgEQl8FHEIvKwVDvcrYKwdAEEWZ0AjzTnqGMVbD4d7LbysGlppOTTSfP9j+RKRAOJJYJjiSWB46S4JdKsSSQTyKDtBZIQoZMGj9FrZGB5lP9zKdzBofgEAINIdWT/ImB0u9+fQyxfC4X4NjNmhkeYeNYrIBUa61t422uRoH2N2e0aavEKeQTQlaeV4kSjF10VxYC1HPYKbWWdt6cw796B2ZL434fDtn39s9lx23kmCy2Wm4oxytiglWbCt+NLz0Qc5XkgMpoOZwur7vxOnjJqMBYeqndP+tFwz8S8fsC0HXpEvOG12y7elhYkbNxMmxlsbfz7M7iqK12fGt6Rq4j9N1eBT4IDG4XJMOlBvmFvhzZzSlmwyM2N/4s3Nd4q5+Q3i6asajrvP7Sa2e5epbdvmeNuOrQmuvbsTWGVFvNDUYNLaOg0mxmTDgH44MUmAVjoJopAFxpzodD0AURgBUUg/cgSRATr5FHiUHUc9ki8RCSg+oxWm+pUEElEHY6zvt9+DQERlANr9X4oAXgNwO+vPUM7ArhO07yGAulkTyEcC+0vxlsOj7ESHshuABwxO2F0vQa8595jjauDwvAaDfAmIjn+tdno+hkZaBLeyBQJlQZYmwu5+FgbNFSH6TqKPXvC4RppsLWPMTueYOIUNM5IuQycnaEXRDAg9TuX2l1t2eB3LSq3Dz6nSj0k6+u/88YeybLf9cZmOMY2Qpmv3/mq4geyGNlY43y5tW53sFJ0SmStyZJNxvXfBwu2Oe78Yq30uocXTcVMxtHe/1/HWJ18mPvvE/vabb1is22KNT7l6czxGmFqclwxrd89KNJsEIkguncb0bVE2vi1CDfPCXlDdSLMPdKad0GBIy/WkDmZVoCwzGje+3TxufLsZOHTc/a0tknvbZnP7ti2J9p0/xHsO7ktATZVZam016l1OXRwgDiyDjkICmQF/wkakhUhp/iUg6V2OMQFkgkfZfcyj+RKRAOIjgWEqXEYCFzHGGonIBOBR/8dPVY5pQIhIZIwF/Bedwevh80H9o5WLoZWLAQAe5QBcyrrjEkAva4Xd/Rz08jkQhNTjzuH1NoKxNkhCAVzeGvhGogiM8QHZ/iDmZXlGe+tok6NjdJzHOzKOSdl6KS5elsxElA4EdnrdbeqwS6u+qytYUWfR6pF47P3XXzWuee3zJyT56v95cVNRs8cgJmhq5613yhrS1tbGI9ErQGAiKrbpXDfdsUNeOmuisHqX6P5Loc7U+PsVErvrvc6LLquJW3HKy87zT1tUueuH/Jx9HYnaP21L1Oab2jovG9HimmU2J0j+qVsiAYaD2Sk4mJ3S/jxQZ2pzOqbtsSbOrWG5E21JWu0Ahzd7kJDokectak6at6i52/sP7tfbfLuaE517diUoFWXxQn2dSdPRbjQqHo0p1jaseL0tULzV0Mk5fR8MvkQkwAI6qMMFTr+TQH+C9gaARPheGf/EGHuDiO4CUM4Ye8h/3K3wjew90t3xvV2DMdZBRL8EUElESYyxZiL6PYBz4Bsq+C9j7JaerskY+1t3xx/zfRCAuwGsgG/q9S+MsZeIaCGA2wA0ARgNYB2AqxhjXiJaDuDP/nPuB3CpP9YyAE8AWA7gQQAv9vfn2QMnjiuqyaeDh8rl2QAA0Egz4fJ8DMZscLgP/yoKMGqvOXKs0/MhtNJyAIAkToTd9SzcylfQSMtCHXbYS5BcttFmu3VMnNM5yuQV842CPk0rJ0iCkAhIicF8j+lJaWnTXrCxJX9BS5YkI//Y+10ueE9fPt+6bdOYIwWlLx9dbhsXl2QAgBGn1ogA0FiXJMabnF4AaNyW6Jn9s1rdqJEHO7/ZO8L0v6SquuUUl978+5O1yh3vODJHuHUfff1hzj/uHtZ03+oFRkXR6Mo6zMabN5uNFoO9/arRTc2z4gyZEh09AmfoMGsNn01Lw2fAAcHNWkbtc8gzK9x5s62atAw2tCHQXgwbbjcMG243nHbW8T38bDZB2bk9rn3b5gT7ju0J7n17ErxVh8xSc6NJ57DrTYAU8N7OamLMCbv7OWjllSDq37fGl4gEVFvfh3BqIMZY3wcRdQBIAGBgjLURUQqA9QBGApgE4H7G2AL/sTsAFAOo7u54dswF/YnUNMZYY5fbNgP4BYB4AKv8nxOAN+FL4Np7uOaY7o5njK07PB1MRGcB+KX/+BQAGwHMhC/xex9AEYBy/+ePAPgMvinqFYyxTiL6AwAtY+w2f+wPMcbu7vOH2A+yWHgtgBEAjry118qnXShQwvBAnJ/jBkMmxTPcaG8dE+ewjY7zeIebSJepl8xGSQr5mjRXTm1z3EWlnVnTO7IEsfv2HvV1suPEuScqjfWZR+b6R8dZ7Q9OVnQSidQyfJ9z+n2l2tYWgY3Pv5wKjO3O/0xj2vZxe9on/3VT3OcfJ9ouPHOVQSKFPT2twZVpMGgVnd2ZdNe77anDPCkAsGeHznPx2YucVYdyjlpPkKhx2n4xprF+UYIuQ0NSnwleZ2aNzT19rztxRoOcV+g2SFJ4DM7V1moc2zfHd2zbkuDY9UOip+yAmWprzJo2q1HvcWvjACFi6pcypsDufhqSMBIaaV6Pxznd/wOR5sjGkK4c7rchiUXwehsBMMgiXyIyQJ+02W9conYQ3PEG8ladAPyViObDt24tC0A6Y2wTEaURUSaAVAAtjLEKIpK7Ox5An63l/dcCfCNsywFs8n9tgi+R/E8P17y2u+PhG9U7bC6AF/xTt3VE9DmA6fC9U/mWMXYAAIjoBf+xDvgSw6/8sycaAN90Od9L/fh++suJY/tWMcV1/J5Djgs8xrzI1DnbRsc52seYXe6RJiblGkRTkkaOFwa5USNQXIVldYkXbVFGjHNkopd2cZu/j2tbteIkndMRf2S4RyLFe+v4dkGiOAIA3fLdCgB8uz7BAZDe5hEFwAP97jyTonzvXbCkxZCaXt3ZUJdlvOkHk/PhqW6N7NBrm244ScRd79WnFrjTRhU5pK+3viuVXDuu9YXnZ8Yz5sveWlxaw+qtWfn/lNyuy0c1lJ2UoknTUc8bOIzVGQa8kQH2BrDfaHN4Z+1p1M+qENOLbMl6o3rzjhaLS2cpbtAtLT5+w4rHA7Z3t6l96+b4zh3bEl17dyWwynKz0Nhg0nZ2GozMq+nXpphQYIzB4X4VAqX2mgD2hi8RCQg+EhimBpIE/gS+hGvq/7N31uFRXOsf/56Zdclu3AULwa1o0QpQpBRKS6lQ763feqn86i3tbW/dnRoVqNzSUqhR3BOCJxB33azvjpzfH7uBjdtaYD/Pw0Myc+bM2c3uzDuvfSmlnNsL1nihXQ2XBy4Op0Ki7Y1vE0KIFkAagBy4jMEVlNL3Whna2jnbGw+PMW3R3C1K3eN/p5QubeMYSzvzdZUWRiAFH8qlCOF1VAznSNfaDIO0Dnu6VkAfNaOMV0j0spOFGoGPBlIqghuXUxpzxWFJVF8utqPx362Kq7331ll6KsqbeKkezKiojJNq4wHALrfw6TPMSgDYv1dvB6C0iRIK8JBwclJ+gnEmpVP5g4/tFe67LRF5Vm3YF4VVhdf2YVMlDqWk5v45UY6nfm1IGsLpGJbghbcO6S+9stRw9SUzaYNJfzIn0cpLZW8cTkh7n+H5K/pXFS6Mk4RriLzdklLWolKwf4xM4v8YiRIiUmFIYZVs6glb5Jg6rS6atmn4+huJBGTQELN20BCzFihtsd9kZPnsrDDTgaxw29FDeu7EcR3KS8Ik9fVqhdOh1AKs34xbgRaCFzPBkDhYHK8DAOSSmRBpAwBXiohITbA63gSFAwCBk98Ktfzuk2HjUIqIV/CJEUgIiQTwp/vXOLgKUBqfXMZRSp0dHD8dgJNSus39+80ArJTSz3yx3mCkK+HgRwD0p5TeQQiZAeAvAH0opQWEkCEAPoArvDqNUlpOCPl3W+ObzV0AdzjYnXf4DgCRUnq1OxfvaQDnunPwEgFwlNKqNs7Z3vjGcPAiuMLFc+DyKOyBKxycAWAdToWD18FVoLIJwF4A51BKjxNCVACSKKU5rYWye4KUHbQUwDR4eEtlkvNmsUziBG/MH+LMg4Eopqpshgyt3ZKh5YX+GkiTVGxYmFSqDfTa2kIkgijMOFCacFmuWhcndsr4eezBjOpP3j07CmCbPORNDDdUPzMM0Yy7BqJ25o6a8bcXRgHAtUvGGP74bYxeznDCb1OsLADw1/9embGgLhYAhqYurm8wRIQDIt4bXVWZrlXGAgAndYiyh3919hvjPGkpOx1EvPnyScW//zE4BWhZcMFCoIv6VlZensjK9YyiRQFLR/DR9SZm6tE63cRyaWRfZ4xEQoKlqK/LFBUorNlZOsvB/eH2nCN6oSBfx1RVamVmo0rF83LtmVawcobwptG2/A5fnsBdG2CmlL7ky2NONzq8kBBCJHB5qL4E8DMhZA+ALABHG8dQSg+5PXillNLGtvltjm+Fv91ffAbAD3AZcqCUbiCEDAKw3X1dMAO4EkBVa+dsb7zHuX4AMBHAfrg8fQ9QSisIIRlwhXmfBzAMLuPvB3dhyDUAVhFCGnN8HoXLU+ltWikM4UKewBCdIlLqsKRrbQ2DtE7nAK3IpKkYdbRCqmcJEwFIIoKnGUDrCFInh7mZZUmLCiPUetqpEk6Bp/TSC8+u37V1aItSbw3r5B4ezCsYciqEnXphyclf8o+HSwHAKbInv3PO/YkEC1wpuTfdsZd78enzATB49JA24tOxdqeKlciknJzhnp0rOf7Ir+b+YxwaAJDJKfPxmq2p6386Xn37DedL7U6Vvsk6wZLv8hLivssTMSel3HhVMmicRKPr7HsjqQ7XYs1ErWkN0CBz8MLY42XqKQV81HBThEpDgr31VRNS0uyqlDS7at5FlS32ORxEPHJQazywX287lK13Hs/Ri6XFYZLaGo3cZlNqQKXKACw5RM/xWziYEDIGwMtwpYPVALjG7SS6E656AB7AYQDL3b8LhJArAdwB4Fy4jUJCyEYAOwHMgKsm4npK6Wa3I+hTuBxHR+CKXN5GKd3jr9foTTr0BBJCRgD4gFI6zj9LCgxut/B9lNJ5gVqDlB10AVwh7uJT2yZNlLADZgZqTSGCDxnhuX4am2GQ1mHL0PK0j4bIExQSnUoi6ZU3SEFtsTGL9lYmzymPVajR6ddgqGedMyfPdJSXJLfq1Xx1RHnxCL3qpDFZm5LvGP/mrpPFGoMSF3Nmc4QUAH6fUi9KGIaxK03CkK9/ZQkBnE5gSPIVFrtdrQaAWQml1uUDNCfz+wSW4+VP/lKbNtzRJFRtMki4Ky6cXpa5v2+7paNTE8rsV6dyYl+ZvkcFNly/0lrptFxT+NhqVUS8EE2Y09eTVl0ldRxwFazYjh4OFwpOhKG8LExqNKhVHKfoVQUrZxgPGG3LX/TlCdxePQuAhQAWUEqrCSFLAMyilF5HCCmDKxrpIIToKaWG5p5Az9/dRuBeSum9hJA5AO6hlJ5HCLkPrtqEfxFChsLl5JrQW43Adl0D7vj4nQDu8stqQljRvDAETntAVhIi4FAqIklpbxiotZsGaTm+v7tQQ+8q1IgOZKGGN+DDDSbZ0j11qefUJkhlLdu8tMfRwyrThefNkdksEa0agBfG15R6GoAAwM46wsPdldrpIKLFrDt5/XNQgUrAQGHTstXFjC0mRVTKZMBFi/eRr79wFRSsL0tUTY8tbJgQptcBACtIJY7H5kYVPvVrWepwe0LjXFo9L/3fpj9SV76TVvrEw9P1fBuFEpvKEhSbyoAxkbWWa/sZmcGKcGV3IqHSE4mROJEYWf8xUBNmtuLsY7Was0tIdIYtWiYjPmtBEwiiYzj5OTNr5OfMbJmBI4rA8Ry1OTtTZzl0INyRe1RPiwrDSE2VVm4xq9SCINWEIs0Bo9ZP55EDGArgd/ffmgXQGJ3MBvAlIeRHAD92cr7v3f/vBU5eoyYDeA0AKKUHCSHZPV10IGnXCKSUvgvgXT+tJaBQSjfC1Q4mkNjRrDiF0lA4+ExAy3K2dK3VmKF12gdqBdJHzShjFVK9lGF0AKtrkSXQi+ESqurVV+0195lgTGRZdDk38ZefoutuvXa2ThSUrb4pcXKb7bZ+TKTnNl5u54ecazrpZdy/R2+kYPUn10TFk9+7skwtjUlxFQ488nSObPXXY0WeVzAAsOJgvPzz8fW2MFahBABWlLL2x+fEFjy+rjRtpC3R85xX31KQeO6cr+1L555fUlAcn9TW69lbG6neWxuJDK3RcGN/Ax2p1YYzpNXuNx3CGjUqrBujsq0bgwKGF4URBRWKqSccUaMMOm0E9B1O0IthGCA9w6JJz7BoFi8ta7HfZGL5wwfCzNmZeuuRQ3ruRK4OZSVhkvo6jcJh92/ByhmIV/LmOwEBcIhSOrGVfXMBTAVwIYD/c9cVdETj/VfAKXvptHqSCO4koTMPB5obgbB6s/o4RIBhIIh91XZDhtZuGajlhH4aIktSSsK0UokGgNLbihrBhHNAUZX+qiwufoQtkZCW6h6d4T/P9K9+48WpUUBbDfVEvDCsoUHGKuM8t1qnZRmVqlOtZXZvj7ABp4wiSkQO7ushdzTOhAUNKgDQRwiSaVNzy//8a1g8ABg5meKFXGnFMwOporGAgRWkrOOJufH5//draZ8x1iaGYFKqXbH54M9Jzy4fVPn+uxP1Im27d+BRU5j+3swwpCitDbf2N5jPClfGs4TpnjUIgBElDJPZP07I7I9KAKXx1Q3stByDbkKFLDKVi2FZcvo8WXQCrVaQjJ9Urx8/qV7f2v7SYrktO0tvPrBfbz92WC8U5uuYygqtzGRUK3leriXopmUeAvCfJ9ABIJoQMpFSut3dqi4drty9ZErp34SQLQAuhytn0ASgq0LQW+ASpPibEDIYrhqCXkvICAwuWhqBtCHUX6mXEi2zmwdqbMaMMKdzgJayaSpWHSmX6llCekWhhrfgRueURV15iI3u7+ywzUtbiCLFNUvG1f69YVRLrT8PbupTU5iiVrbIxYueVdyk583+vVFNNboJbIArH1F5NFUFnNKRffqVLN1fIwYLFK4Ckm2VEXEb40sKZ+i0J8/DiizDPTUn/sSj60r6jbW08Po98vyR2PmXFDuXLZpZX2uIatcALrKpdMsPqHQxMof5ln71tZOj5AkShulx3F9SHq3D19E649eAQWlziuOOV6inFArRQ81RChXxe+PvYCMx2aFMTK5UXjC/ZcEKxxF65KDGeCBLb2lUWCkuDGNra7QKm1WpprTtXpAhAPjPEyjClVf/OiFEB9dF9lW4Cjm/cG8jAF5x5wT+DGA1IWQBXIUhneFtACvdYeBMuMLMDd59Gf6jUy1iQvgHKTuoD1yteEo8typllz8aqDWF6Bg54bkBWpshQ+uwDdTytK+aKBKUUp2CZQPfbC9AiESgwtRDJXFLc1ThCUJkx0e0jdnE8LOnnmstzOvT7hN7usbc8PZoTsOSpsUBXN/S2kGvbmmyhnNHz67IOZFy0lv47eTy6mhWddLAjPnsW3OYnp6sup179szC7INpJ40+CRGEbyZXGyIYZZN5BSKI7EO/lfafYG61upnngDuvHmX++ZcxKlcAs2N0Eqftxr51lefHyOJkPvhMUYjgM0qqZdNyLRFjajX6WDEqlDvXNepqJU6XTrPedvSQns87rkd5mVbSYNCoOKdCCzBnxhNf20Qbbcv9ZQj6FEIIC0BKKbUTQvrB1acwvaOehMFKyAgMIqTsoAS49ItLPLcrpJffT9pRHQjhJ6hIk1V2Q4bGbskI44T+GipJVkk0eqlEF7ppuhAlHC/OzipNXJyv10bQTrdAaYv8EwrrnOlziNkY1W7VsIQI4tfja+sj5fIWBqfkvnWFaVONTbyDg+OXGExWnb7x9y/OLqtMlKhPeSof/qXY05Dbu11vuGj2JXrPdKCBemPtWyMEPdusIlUkAiUPrC8ZcLapzTY3f/wSYb/9uvOIxa7vdOGGiuWd16TWls1LkEQrWYnPVDn4iAYLmXysNuzsUja6vyNGIiW9t/ooCBBFIP+EypKdqTMfOhDuyDmqp4X5Oqa6SiOzmFRqQZCpT/PeiCIAqdG2XOxwZC/A3ZrubzRKxwAPUkrXBXZV3SdkBAYRUnZQBIAX4dEiBgAU0sX/IkQd1/pRIXxBmMRpHai1NWRoHc50jUj6qBlljKtQI3RDbAVBYXMwC/dWJM8vjVZo4JUHlr//iDBct+QCDc+rO/SiPJpRUXhubMswsChz8Klf/CDIFaeqZAUB6BNx/cnwLgB8MrG0LE2mOVnha5mzu3DEzXlN5jt78IWlRaVxTXL+bh5UVrgkRt3yvBBB7/utZOBUU5sFIWYzI1x90UTLrt2Dw7qSay4lgnB5Sl3JxYkkXCuVdjWfqUuIEk4QRp+oVE7N56JHGsPVYV3OnwrRAVYrIxzODjNlZ+lthw/qnSdy9SgtDmPr69QKu02pBTrWoA5yqo225TGBXkSI1jnTXdTBhgktWsQAlDoaQkagb5BAEPpqbPUDtXZbhlYQ+6khS1RJwjQSiRqAyt1RJEQb8DqjRbpkT3Wf86rjZQq02xevK7z9alr1iidmRIFKO7SOJoYbqs+JUaS0to+ee7BcriBNPHIHMrVO2qwS1EmbSUZmJ2mBvCablj+xR3rrjU3biL57JC51YnhFeYpUHe+5nQED8aXZSQcd6yuHnm9sNRdSoxHZNX9sDVu1sqD2kbunazlB3amqII6y7MrC6NQvCkXxooS64itSRXW4TO4TWTmGl7LMrowEflcGygHwqRX1kmk5Rv24KkVEEh/NdLeM2YeUFPO4+fpqVFUIYBjg6uu1uOWOpk7pzf/YcMXiSqSkuZ7p5l+kwoOPhKOmWsCVl1aiwSDikSfCMW+By+G69OJKvPxGJOITvH/LVKlE9qwJBv1ZEwz61vaXl8nt2Zk688H9evuxI3q+IE/HVJRrpaYGtYrn5drOphUEkMJALyBE24Q8gUGGlB30BlzG4Mn8Apnk/DkskzA2cKs6PYiV24wDNXZTRhjHD9CIbKqKVUfIpTqWBN+NLNjhYmsMqiv3GJPObkhkJd7tX3PLtSNr1n4/Nqoz3jEN6+RWTTDaNZLWZfD0b62ujEoWmhhh772aYnrm8dlNxr82rqhiuFJ38kFLhIiUb75zKJRNnwKGJV1aZTDpm3g14lUW+6dn2SEjkhb5eiIVYfnXBsOoeQ369l5HdZXUsfiCc+ryjqfGtzeudUTMjjWUXpPGyWIVinYLZ7yJoLHY6cScas3kYkQPskbJFSQompVXlPOoqBAwcpQcJpOI6RNK8eXqWGQMOmVjb/7HhjdfacA3PzZ9tn73zQYolQSLLtVg8fwKrN+YgHVrrdif5cDyR7tV0O5TOI7QY0c1pgNZOutht8JKUaGOra3WyK0WpYZSWTCkEX1vtC2/ONCLCNE6IU9g8FEDV8n6SSOQUluoQrgLKBneMUBrNWRoHfYMrYA+akYZr5Do5CwbBjBhIe9e93H2LanWLct09B1pTWQY7/ads9mIMG/GdFPOkQFRnT3mmaG1FRqJqvUijOTK+uYGIABk7Y0UWpxbFJt44RgwqMhV1KZ5NIEGgFv/vY977plzmhxbblUr3i4y1N6VqmlhBDKEgea9mfrd3Abz2IUNbcq7Rcdw8n/2ro9/9T8DSl5+bnIUpdIuFIAw+K0yIvG3SmByhKH8xr4OkqJW+DxywJrVCvw+Ktn++ygUMgIVhhRWyqeesEWOqQvTRcEnnsnOEBcvQVy869am1TJIz5ChvFRAxqCOj5VKCWw2CqeDgmEAnqd4540GfP1DtwvbfYpUSsnQYaawocNMYc1SyQEAhnoJdyArzHQwO9x25KDOVbBSGiYxGNRKp0OhBVh/pLeEPIFBTMgTGGRI2UG3AhgMj5J6CTtqmJQdvihwqwpOCBVpqtpmGKixmzO0vNBfS6XJSkmYTta6VyhE9+GGn6iIuOoAYgc6fGJclJXIbbOmzKaGuthOey4ujK8pvTtdmtjWfvbf6wv6nGtIa779vHHnGo4d66f33Pb4qLyG6WGRTWKG9Rdtrhp7XVkTrx/PAYMSrmqwO5Util5eHltYO0qlb7MSuu7q36zjLm7o8PWdOK6yLJ49q6GmOjqho7FtMVJnrL6ln9U5QKNIDETNAR9TZ2SmHqvXTSyXRvVxxrASEhCHQ2EBh7nnlWPbviSEhZ1y+G/+x4Zll1UhIVGC+HgWT78QgUGDZWhoEHHjsipUVQl48tkIHDnsRJiOweVXnZ6XlPw8pTU7U2c+lB3uyDmqFwvzdaSqUiMzm9RqgZdpvFSwcpfRtvw1L8wTwgeEjMAgQ8oOWgTgAgCljdtYZkCqTDLpmoAtKggIlzqt6a5CDUe6RmTT1IwyRi7VS5gzvvWCz6AQKT/5cGns0qOKiGSh0965rrJjm67h8gVzVJxT22mvRJzcZls51kbaapkiSjgh9cs1TrmyZYhySNIis9EU1cQr9+DwE5bZ4VFNKm6r+56wTXx1T4vjH7pteOUXX0xo4RrSSu3ilxManFpG0aYXr+Ga9aYxiwydsigeuHNE0aqVZ8X3xFuTrrHU3tbPZB2qUyQxAapAFeUOTjzreJVqaoEQNcwUodKQNj2i3sRsFjH3vHLcu1yPCy9qWkxtNIpgGECjYbBhnRXL763FvsNNHcqGegHXXlGFz7+NxcP318JQL+L2u3QYN+HM6PxktzPi4QNaU3am3nr4kN55IkdPS4rDJPW1GoXNptSAtkx/aIOFRtvyH3251hDdJ2QEBhlSdtAUANfBw4XOkJhwufSCOwO3Kv8hJQLfT20zZGjt1oFaXuynIYoEpSRMLZEEQ27LGYHIcoJ4fnZp4qV5YdooUe/Lc332UWLNo/eeH0GprAt5mSI+Pau6PFWtaDN/jj83qzjj38daDROn6K/hKZU1eXi4a0ieZUFUZBNLgWectN93P1CptGmxVkM9S0f0vcouiLIWBuLE2Ar7MwMVCqYde8t2/YaaYQvqO2VU79mpM1yxcLbTatH1qLoyWWFruLV/Q8PYCHli8z6K/obvX1IrmZprjhhbowyPF6JJe29WN+E4iiUXVeKc85W4/a6OOxUNSy/Gxm0JiIw69dY8dF8t5l6owolcDoIALL5Mg8sXV2Lthm6kbZ6GVFTI7AezdI0KK3z+CR2pKNfKjQ1qJc8plQBpTLEYbbQtzwzoYkO0SciLEnwY4eqrdBKR1hsppTidWklRKiJB4TAO1NpNg8KcXH8NlaSoWE2ETKpjCIlytWAKdWPxJ4Lc7iQL9pYnLyiJUmrRarWtN7n/jqHVX382IbqVgvh2ualPTWGqWtFuJXLE/BOtXtvyjiutrSXLOziWb75NIspI0VGJrd8wvsl4XbhAZkw/7Pzjr5EtjMDtlXGKDTF5ltkRkW328VN+NDPqGPt76cB5dW2Gshs5a3yD/mDht/T6yycW/rVhSHJ3pcuK7UrdQweVumiZw3Jz3/qaKdGyeCkTGK1cyfGkSBxPiqz7GKjWmaw4+1iN9uwSJnqgPVoqIz1O2KWU4vZ/1SA9Q9qmAVhZwSMmlgUhBHt3O0BFiojIU2/tiVwOFeUCJk9V4sB+J5RKAkIAhz3kNGkkLs6piJtdrThvdnWLfaIIsbRYU37sSETVzm1x+QFYXohOEvIEBhlSdlAagEfRsmH0fYRIfdYg1peoWc4+QGNtGKR12gdqBdJHzSjiFBK9jA0JtgcDvNZslV66uzp5ZlWcTOn7qhmnE+Ki2VMa9u8d1OVyy7ZUQTzh42qNA9/7Pay1h6ZVK5MrHrjzghZ5jdf1La29KlnTIp+v5rLfLRMur2vxvSvMU/BTRl1BPHsNNiIhgvD5xHIhTqpt//N905+F/efVdLqtzrqfY6pvu3amhONUPS5TDZNwthv71FWeHyuNkweJso3I8qIwIr9KOS3PETnSoNOGd6/waPtWOy44pxyDh0rBMK7PwGNPhaOk2GXnX3dTGN5/24iP3zeClRAolQTP/icC4yeeehuuubwK//dkOPoNkKK6SsAVl1TC2CDiocfDsWBhr7wMB4panSzPZ6kkIXpOyAgMMtppGH0TIeqgjkMwEMU0lc0wUGu3ZGh5ob8G0iQVGxYmDRVqBCNcdG2D8oo9hqQphkSJ1D9RgZpqqWPm2bO46sqELueFtacK4glzyx8FfS+oTWtt3723jSz89otxLQyvK1Iqy27oo2hRiGE866Bh9GOH9K3NNXfSVGv2oYxW0xQGasz1b4526iWEbd99f8Pfhf0vrOq0IWgysvyl86aXHsjqm+qNyICK5Z3LUurK5ycwkSqJ1C+5ep2FT6wysFNzDLoJlYqoFC6GYUOtnHohO3SyvImBXkSItgmFg4MPI1ptGG2rDyYjMFLqsKRrbQ2DtE5nulZkU1WMKloh1bOEiQAkEaGPVvDCpZTXapfts/Y9y5zEMOixtFtnyc7UGhfNnqNw2HXdMjaWD6wujpS3VAXxRGQEmji1ps0eg4cPtG4/2vmW4WAAUB/uqxCEg2BbseWeeCkLiy4YiNbOdcysCV9dUlFyWbKyTcUQAMCHM1KPCxsL+i+sTGt3nBttmCBZt+nP1I/fO1H25PLpYaIo65HhZhUksnfzY1I/KhCEy5LrCi9Jgl4rlfntM9EektIYPVbF6I2rAIPK5hDH5VarpxSK0UMtUQolCeUI9w5yA72AEO0T8gQGIVJ20OsAzGjSMHradJZJm+bvtcgIz/XX2AwZLkUN2ldD5AlKqU7JskHRGDZE53EOya+MuHK/EDfE0e3WI91l9ddxtffeMlMviopuFSWMDzdUrxiGqI5aVnCTD5YOeuBQm7l2Q9MWVzTUR7QIB8+Nqy25b6CkVYNN89a31rhk2qrRMWnwbHNxaUobhpiIT8dWVqWqVB0WddCrNhUMuKQ8raNxnpSVym2LZs2sLS2Ob9/Q7AIMRLogoa7kylRRHeEjFZKeQiFCGFRcJZt63BZxVq1aH0tD4cbg5TGdLO/pQC8iRNuE3DXBSQ0AHTyMQFE0VLE+DIZQKiJJaW/I0NrNGVrOVaihZrV6qTSMISQaCKXv9UYoFcFPOFYafcVhaVQaH5COt08+PLD6w7cmRwEdhEbbQMM6uUcH8wpCOpaQC1+Q0+5+Y4Om1dQEu8C0Obc1T1OHZFOrRuBDz2Q6br22LSOQwYPZ4cqV4ywOOdO+/iv5fGparrClcMBlpZ0ODSckOpQ7Dv6c9Mz/DS5+7/UJMd7QmBXBkB/KopJ/KBMxM6ah9No+TmmcQhFUuq8EDCRHUmPEI6moeQ+oiDSYyeRjdbqzy9iofo4YiZSEKsqCh5AnMMgJeQKDECk76GYAQ+HRMJohidFy6Xm3emN+LcvZ0rVWY8apQg1lrEKqlzLMaX3xFKiIq7Z/ihiFFq+OvqTJvnVlh7AyfwcAQCWRYfmgmUgPi0W904r7Mr+Hibfj1v5TMT02HQBwz77VeGjwLEQrgjPdUWQEUTw3uyR+yXGtLkYMiN6VwFN62UWT6ndsHtYjj9KrI8qLR+hbVwXxhI80mNM/+k3NtNFypKJCZh878JpWiyAmRjRUPDcMrTbCFs7PLB54R06b5x+SfLHBaIzUt7X/ovja4n+nSzpcPwCIl24tTL+ypMsazEcOaUyXzJ1tac3L2VPOjmiouKGvjaaplUGTjtIWooQTxDEnqpRT851RI4zh6jCEBXpNZzhn6WR5ewO9iBBtE/IEBicVAM7y3CDSilpKRZGQzouFMxDEvmq7IUNrtwzUckI/DZElKSVhWqlEA0B5pnn3VhXuQR91FCyCo8W+BKUO74+7AmFSBbZWn8Czh3/DyglXY335YcxLGIqZ8YNwx95vMT02HZuqcpERFheUBqAgdXCYn1mWvLAoUqWjPm/z0haGetY5a/L5jrKSlB4ZgBfG15R2xgAEAMlFmTUM03Yj4p1bIowAWjUCbQJhgTYeiDPT9EDbHsZb78myPP/Eufq29v9YHpk8I6aieLhe2eHrYL49OzVH3F444KrCLhV+DBpi1mbnrdbeccNZhf9bMzIJ8F4vwK11uritdTqM0Jmqb+lrcaZrA6NC0hkYXsoyOzPiuZ0ZKAfApZXXSaflGPXjqpURiXxMWw8IIXxGyBMY5ISMwOCkDC2KQwQRcNQCylYF4qNldtNAjc2UEcZxA7Qik6Zi1ZFyqZ4lJFSoAaDSbsTW6hO4ru9EfFm4u8X+EeGn0qqG6RNRZTcBACSEgUPkwYkCGBDwoohVhXvwyujFflt7Z+DVFqtk8Z7qtAsqYuUqdNmT5E1yjqjM88+dI7FaInpkJcfJbbbb+jHtVgI3QolIE86patfg3LMzyt7WPrvIMEALSWEAAFsTrrUYYVKHodXXc8u/TyT+99mJDRynarOg4tFDkdGrxjeY1JKOK+WZ1RNTczimKP26/JSuGFsMA7z18Z7Upcvy6q5dMova7dpOvXedZX+DNvrmTC0GqC11t/Y3WYbrFIkMCe6KXWlBfAQK4iMMK4FajcVGJ+XUaM4uRvQga7RcQYKiNc5pTJVOlhfSvQ9yzmzLIHipQrOG0QBAqbVayUp1rkINhz1Dy9M+aqJIUEp1CpbVAowWvm/z1iv579E/cWf6jFa9gM35qWQ/JkX1BQDMjh+MR7J/xi9lB3FH+nSsLt6HuQlDofCL7nrHcBH1RsXle+rTptclSGSBNf4AYN3P0XW3XD07TBCUPby2iHh+mNEgY9tWBfGEH5tTrtai3YKXA/uj2sx9sQsM25YRCAD1x5UG9WhbqwYcwwBLrspu+OLjCW0agSZeqnj2iNTw7FDaKT1W9qfxKTkCU5R+w4nkripqTJ5eF7E//xvh6ksmF2zfPDDVS/qvJ8m1qCPu3q+OSFTYjLf2a6gfHylPZEnwyzeyZrUSG0Yl2zeMQiEjUHFoYaV82nFbxOj6MF0kgrIIppeTFegFhOiYoP/inqFUA2gRzvl3//KyhUnsYEJITEhRo/NsrjqOCJkKg3Rx2FNX2O7YPbWF+Kk0Gx+OuxIAoJEq8NoYV/6gkbNjZf4OvDhyEZ45uA5G3o4r08ZhuL5D4QevwyVV1qmv3GvuO96UxLDBkff08oq+1a88Pz0KkPTY6LgxrbooVa3odDhbt/Boi4em5hTmhbf5hbEJ7YdPrdkxIka3/dl59JmDiV9+MrpVNZJGttfp4v6uqig8J7b9NjeNsGvHphwTSdHAm4532RBUqUT2u182pa35+njlvbeepxQEhdc/I6V2Zdgjh5RhUS4VktKpAVQh6SqMyBImu2+skN0X1QDKY2uNzNRj9bqJFdKoPs5YliUBldY7TdgX6AWE6JhQYUiQ0lqbmKvSBgy8bcCQywK3qt7Jmzkb8WvZIbCEwCkKMPMOnBM7EE8Pn99kXK6pCvdlfo/Xx1yKVHVLx8B/j/6B6THpKLLUQQDF7PjBuHffGrw37nJ/vRQ4BxZVhi/L5OOG2hODKbvpmiVn1f7522ivhB/TNaaGt0fz6s56l3id0Zr+6a+KjpoJp0UsM7ZlDOkknO3Hs61ttj3i0srrBr2+qV1v0bVLJhT88dvwtPbGSIggfD2+1tBRw2tPhFn7igfckpPU3Xy22hqp85I551TkHkv1aY5omISzX9+nrnJWrCRGzkp6bQspQe7gxLG5VeophUL0cFOEUt12nmmIdrlEJ8tbHehFhGifkCcweCkEkAIPI3B3XXV54JbTe7k9fTpuT58OANhTV4gvCna1MAArbA24P/N7PDVsXqsGYJGlDjUOM8ZEpCDHVAk5IwUB4BRb7THsVSgVwY/NLYu68hAb3ZcLSJuXtjCbGX7O1HOt+Sf6eMUAlBBBfG6ojWeJvNPXJsmCrCqGJWntjTEZWZ7n5dq2zCib2H5lvKQgNsJhp/b28siefTkz9o/fhnBA27kCPGXZhw+q8fZop9Ce9J0n7PrRyTkCU5x++9FEhul6Dl5kFCf7a9f6lDdf7lf6n6emhlMq9UmjZSMvVbySG5v67gmeuyq1rnBBEKqQdAbWIZeyW4YmOrcMRSkArn9JjWxarjl8bI06PF6IDtaimCAkVBXcCwgZgcFLPkvoKCWLMBlDIxUMDXMKtVoL7xTUElkoVOEFVhdnAgAWJ4/CBye2ooGz4YUjGwAALGHw+cRrTo59O3cTbh0wFQAwK24w7sv6Hl8X7sG/+k/22fpEIojC9IOl8ZflqvTxgt8bPHdEQb7COmfaBcTUEO21UGNnVEGaE3teub6jMbu2RRgIIW02FXaKpN1rIQGD+gJ5bVyGs83Yf0KiQzlsVEHhgcx+7a4/x6yO/K7YUnBZijyto3U3IvljZHKOQErS7zyS0F35tNvvOZE4b2GZ9eJZs0qrKmN8lsNgEyXS9/NjUj8uEIQlSXVFlyZDFxYkKiTdQXo8KYoeT4qq+wio0hmtZHJOrfbsEhKdbo+RykivCH8HgHqdLC8/0IsI0TGhcHCQkqYZeH6YRHyFIbBICEwMQQNLUPt/Q2aeH6sMSwv0+kL4DlHq5OmczNLERYXhmnAaFPl+zdn0V3j91ZfM0fK82msPkp1VBfGEG3W8YtCTezvsjffc44OK33l1SrstWv6YWs+3F4LujKpHdqbWOGfaZdqOX4OIlWdVVaSolV3q68dPPVCSfvehbhuCjTx8z/Cizz8aGwf4PoePgUgvjK8vuTJVUHUlDN4RFTYjHj+wFrVOCxgQLEwegaWpY5uM2VNXiHszv0ei0mWDzohJx439J3ul/6fI8qI4Mq9SMTXPGTmqQafVQ++t13Ya8KdOlndeoBcRomNCnsAgJUUlHgOwE0Cx5/Zap6U8ZASenvAqq51duLciZV5ZjEId+ErftnjvjdSaZ/7vnAhQqdfag3RFFcSTsIsOc50ZdyArsu3SXzeCSHmWbfuayO9LUeGS9jMyho8yhSWnlheXFCV00BOQwQMH9OrPxlkdMobtdEm/ZNOwpByBKR1w34G4nhQvPPdydsollxc0XH7RbIPZpPepIogIhvxYHpn8YzlwXkxd2XVpnCRe2XMVEgnD4O6Mc5ARFgcL78BV2z/F+Mg+6Ktp6vAdFZ7Uojm8N/p/MoKEYfamx/N701EJoDSpysBOzWnQj6+UR6ZwMT011Hs5oVBwL+FM/pAGO5VwKdM3uSmW2RoqArOcEL6CC28wMzf/Wdhv5c/MgCVlaQo1fJKz5Q1uu35EzTOPnh/lTQMQAJ4ZUleh6UQPPU8EjcUeN8zaqRYyJ3IjOnzg5Wn7CZ7s0aQonqcdJoE+8fyuThVFVDoU2neO06rOjPVEsnVIYu5/RlQIAu3QsG2PUWcZddn530WfNzu7ABA7rK72Bn9URSRcvis25uEDqMy32HqU4xwl1yAjzOVIVUvkSFNHnuzv2RFt9f9c1md8t9cjKYnRk68mpzb8++K441cs4I6+mlFSvEdVZLdRa7cn7b2EjMBeQsgIDFI2VeU64FIOaWIQHDNWlgRmRSG8jTOu2iC9f11R+ke/KfvOqUmVyoNXwsVuJ+LMs6cb/rd6fJS3LxsXxteUjgjvWE2jOWRuViUraT+Xr5Ga6jB1R2N42r5RxQgSxlAqqe1onllzq6LC9HWdelj7sTwyOdtgK+54ZFMk2wcl5qwYWSl0wihtD6mUkk++2ZH20aqf6qQya31P5uoK2+t0sdftiYu/M5OpOWK0lfQ0LanMZsAxUxWG6lumzh4wlGLp1o9w595vccJcDcDV/3N7TT7u2Pstbuo/2ev9PxmrUi75a0SS46n5KcWXXao8snx8dd6vkQWGKtLh5+c0oWVHfi9ACBEIIVmEkP2EkH2EkEnu7WmEkIPdnLOgvXzhZmM/JYR0SSmAELKtO+vyF6GcwCBmasyAawGMg8sreJIVIxbcq2B7X9VdCBfOfsVVumVZXPwIawLDIOhLDcvLZLZZky+g9bWxXvdQxslttpVjbUTGsl1Wb4j++Js6XVTHTX4dDiL2j7kerrbObbNmQlVtRAc5a8zNfxT0nVOb1tE533+rT+nTD5/fqeILrYSzrxrfwHVGTaQ53FnHygc8nBkt6aQx3B4mE8svXTCtNGtPvy5J1nmD/mpL/a39TKYRekVSV1VIrLwTN+3+Etf1nYRzYgc22WfmHWBAoJLIsKX6BP579A/8MOVfTcYYOTse2v8jXhy5CC8f/dPn/T+FqHoTpuTU6SaWSaL6O2IkEnK6NXyt0MnyfKIzTQgxU0o17p9nAXiYUjqNEJIGYC2ldGg35iwAcBaltKYTYz91n+e0aX0T8gQGN8fRigRIpd1U4P+lhOgpzpG5ZWH//aFi8CvbYhJHWRN7gwG4e4euYfKIiyW+MABPqYJ03QDkBhdUdcYABIB9u/UNHRmAAMBR2mFI1LEvqVPe2htuyU+UyS11nRlr4qWK545ILd15IJfuGRif+8yYGp6jncqNbA+tVpCs/euv1BWvrS9nGIe5p/N1heMWdfg92XEpy3bJzVtrbIVCB6H5RnhRwANZP2B2/JAWBiAAaCRyqCSuP9nk6H7gRQEGZ9Po7AcntuC6vpOwvvwwMnRxeGzoHLyV848XXlXrsDXhWvaH8anmBxYm5i25mBx5bnhZ4RZtocWE00VibbufzhMGoIX3mhCiIIR8Qgg5QAjJJITMcG9nCSEvubdnE0LuaHackhDyGyHkRvfvy9zj9hNCPvcYOpUQso0QktfoFSSEaAghf7q9kwcIIQs85jW7/59OCNlICFlNCDlKCPnS22o+3SFkBAY35WhF1b7I0oHsRYigQSQC5aYcKI58Z3XN4Kf2JcQMcHapGjSQfPlJYs3FsxdqnU6tTzwVblWQbnkMNIsOtqkD3Jyd26IsnRnHie3oxrlhD6RGiWLH1hrDAFdcs7/ThtS2Ol3c31X2bn2vpfsGxOU+NbaW46iz49Edc9W1RfE7j6ySJKeVdjlM3VNK7cqwRw/FpS7Zrnb+XmEr5MS2dR4ppXjq0K/oo47ElWnjWh1T4zCj0bg+aCiDCEAnPZWy6dn/0y5yYED81v8TABhOKpHuGJTA/WdOavkVS8KO/ntK7fE1cYW1JUxVZz5nQYovw59Kdzj4KIAPATzdypjbAIBSOgzAUgArCSEKADcB6ANgFKV0OIAvPY7RAPgZwFeU0g8IIUMAPALgHErpCAD/9hgbD2AygHkAnndvswNYSCkdDWAGgP+2YeCNAnAXgMEA+gI4u4uv3+uEqoODmwqgpbfoUEN5wZSY/gFYTojOIrKcIM7OKk1cnK/TRtIu57sFmgfvGlL91ScTo331nDhAY25YkiLrVu9DUWlzxo8yx7fy1WiV/XsjO3VHd4odewIZm1JmrGZq9LG0wxyi5U8cTlz5wVlmUZR1KnVjxbHopBH62trutFGR7u8Xd/wJUtn/8V3h3uhdFxfnVGzb/0vy809mFL/18qRoQNJlb21PqOVkqueOxaW+cYKzX59WVzg7rqUKyX5DCX4tO4T+mmhcvu1jAMCtA6ahwu5yqC1OHoU/K45hTXEmWEIgZ6V4bviF8Lw3B6L/Z3tI8hMikZ8QWb8SqNFYbDj7WI1mcjGiM2zRMnnbjcqDDF8agTZK6UgAIIRMBPAZIaR5CHgygDcAgFJ6lBBSCCAdwHkA3qXUlUdLKfX01P8E4D+U0kbD8BwAqxtDxM3G/khdUYPDhJDG5v0EwHOEkKkARACJAGLhuod7sotSWuJefxaANABbuvomeJOQERjEbKrKNU+NGVAOQA3gpDfjmKmyxiHwFjkr6TDZPYR/ERQ2B1mwryL5wpIopRY+lenyBRwHevEFkw2ZuwdH++ocEiKIK4Zau6QK0oQL9pdLpKTTLXRyjkV2qpWKQ0SnKmQNx8Is+tiGDo1AlUpkZ849UvPbzyM6ZQR2R03EE+mBvrHHH2Oq+j21QyeTkU63nWmP5Y8fTV50abF58ZzZ5fV1kT7J82oPEy9VvHo8NvXdPJ67KqWucEEiE6l2q5CMDE/GnlnL2z1+SeoYLEkd0+b+50dedPLnCLkaH4+/yjsL9wKsWa3E+tHJtvWjUcAIVBhWUKmYesIeObo+LCwS4YFeXxs44KfKYErpdndBR/NrVVtPhwStRNbcbAVwASHkK+pyHbc31tM73XiuK9zrGEMp5dx5hq0Z7Z7HCggCGywUDg5+9gMtm5BWOUyhkHAQwYcZLeTGvwr6fPY/pF9RkqrUotcZ6DXVUseEoXMtmbsH+/QG41IF6X7T4OjZRV16b6sqwjrVssXZyfCbfV98pw20Z/+bGQ8InQ7TutREuG6HYaWH02JOPDrJ6HTQNsOoXSV9kEWTdWJN/KLL9hQCfoqTNsMuSqQfFMSkXrQtXPlBnrOogXMaArGOQMGILJHu7xcrvDEzteraJeFHbzrPmPNFcmHlcUlZT1sFeZl9Olme1z577UEIyQDAAmhecb0JLqMMhJB0uORXjwHYAOBmQlxFVIQQz5zix9zzvO3+/U8AlxJCIlsZ2xo6AFVuA3AGELx9XpsTMgKDn6NwfdCbEMoLDA642BqD5O71RQM+XafoN786TaZoWcjTGziwX2OaOGwhqapI9GnV+fhwQ/U5MYpue0i5/iU14XEdh2IbEUXAblN1SrLMIbT55N+UrLROG8kxsU75qLF5ZZ0dDwDv5UelFVls3e4HKj2aEn3i4bNNDjvtdN5kRzAM8Np7+1K/WbvGpFCaOqyi9BU8ZdmviqNTFm6L1L2SwxfXOBxnSsuVJkgqIsOYbyelmu65OOH4ZQvFoy8NLi3aoS6yWWin8l99iK/boTTmBGYB+AbA1bRla6e3AbCEkAPuMddQSh1w5RAWAcgmhOwHcHmz4+4CoCCE/IdSegjAswD+cY99uYN1fQngLELIHrgM0KPdfoV+JtQiJsiZGjNAB+AVuD68JxkUFhdzU//JtwRmVSG4PqU12qsy7QmjLb2iyrc9fvwurvbfN83Ui6LCp5rUGtbJrZpgtHe1KbQn0gd/LUw929Tpp+zsTK1x7vSlnZLee2pwReGU6M7pFsd9/k2DRodOGZeHDmqMs85eqiHofOuTWLnd9Nk4q6wraiLN4fuX1PR5botariCd8oR2FrudEa+5dFLxlo2DUoKhuvGc6Pqy6/s42QSlIrbj0ac3FCKE9JIa6dTj5vCxterwOCHaz3+ihTpZ3o/+PGGInhHyBAY5m6pyGwBUAU3Di0eNFVVOgbcFZlVnLs5hJ8q1L/xYPui1LVFJZ1mSersB+PSj6dV33DA3wtcGIAA83Q1VEE9EuYNLGGvsUnX1jm1RnZOQAGDvrCcQQH2uqtPtPIYMNYel9Skt7ex4oPtqIp5IjidF5S2fYvW2YoVCITJf/29L6lsf/1zNSmwN3py7O/xVHZ5wxa7Y2OXZqMwz90yFpLdDwECSkxJFPzwnre5fl0TnXD3Heuz9vkVlh+QlnNM71ePtwAH4y8fnCOFlQkZg7yATaOp1oAjlBfoLCpFykw6VhL+5pnrws3viYwc5/J4g721EkeKyCyfUvv/G9GiA9bkhe2F8TenIbqiCeELPO1Au7WLBQ+buqE7nsDnEzr8N1qzYLoVQnnyhc1JynnRXTcQTaV5iZP6D0+x2q/elyxYsrojJOr5KNXBwQVHHo33Pznpd7PV74+LvyGRrDjfYSmgozAXWEKZi145NsT50UVL+ZRezR54aWV6wUVdoboAvjPftOlne6dLr8IwhZAT2Do6hlSqiYmt9gf+XcuYgspzAz9pXFPvRGsOg5QeTIlN4n1XM+pMGA8tNGjHbtPWf4d0uzugKcXKb7bZ+TI/PFTU3v8stMo4djui0ZWfvQno9zUztVIi5kXNn1USFR9R22Uv16KHIaAvPddqb2RrSgviIgvtnOHyRL6YP56V/bN+Q8sjTf5QS4gwKjdyDRk3UbVlxSTfslTXsrbMViZ1oAn4mwPBSVrpnYDz/8uzUiquW6I7cNt2Q+01CYXUhWyF2oj1SJ/jNC3OE8DMhI7B3UNDaxiMNFSFPoA8Q5HanuHhbYdLKH2wZt+WmhEWLwdqOocvkHlOaxw1ZKJQWpXY7LNs1uq8K4gmXWl4XmSTGdPW4slJ9p89rFzufPCUpjtV3Ncx6z0N7uuyZ6omaiCeS4tjwwvtnOG0W6hMlkJvvzEvcmv01YuMruhT29iV5FpX+vgNxKVftVJi3VNsKeTEwlc3BirQ4Vk++nJLacMfiuNzLL+SOvjawpGSvqthho91NMwoZgb2QkBHYC9hUlWsAUAOgiXTXoYbySqfIe60C8EyH15ituHZjQdpnP4npy4pTVWH0tNJnXv9rVN35Ey9WWM0Rfms62xNVEE+Ui/Z3yxtmMas7VbwBADahaxn09fnyTsnCNXLNTYUJcoW5y9Ws2+p0cRur7AVdPa45kpLY8MJ7z+WtZt8YgskpdtWeo/9LvOambUVdaYvja8ocirD/OxyXumSH2rmhwlbobEeF5EyFtarkkj9HJtmfnJ9cdNmliiMPja/K+y2ywFBFOvt5rQCQ5cMlhvARISOw99CiX6AISitsxhOBWc7pAx9Vb5TcuaGw/8pfZP0XVqbJla02+ezVvPJC3+obll4YLggqvzUn7YkqiCeihBPiJxq67AU8cVxlASSdziG0d9EINGdHd7k/27IbsroVMl1xLDq51gvtUCRl0fqie88VLCb0KMTcHk+/eDDll43f2jVhhkpfnaM71HEy1YpjcakXb9Pih1J7oV3ggyJ8HWwQyhDpobQY8e3z0mpuuDTy6PUzTTmfphZVHJOW8jxty5u6QSfLO+NzMHsjISOw93AEreQF5piqjgVgLacFzuSKOvkjvxSnf7BBk3ZefapEGvju7b7gusvG1Lz83LnRgMRvlcynVEGYHr+n4oyDZd1pc7JzS2SXDB2bwHTp/eH3pag6HtWUB/7vaCLDOLrsiePcaiICFXvcGFhSHq0rvuc8ajHCZ0n8w0eZwg7kfxsze/7+AqDraxapAVbnB7A4XobF8Qqc/NZWx/FCHiyO12FxvAKr4333sWZYHe/C4ngVnHDo5Fib8zOI1AizIJW/fjw2dcE2veyLQkehuYc5l6c7kupwLfP9hBTz/YsS8y5biKPPDyvL26Qps5mbFJeEQsG9lJAR2HsoQCtyONtq8nJDic9dwzm4oFKz4qeywW/9E5E83pzMsKfn98BsZvhpY89r+H3dmKjO6ux6i56qgngSOS9P2p3j9uyK6lLYzyZ2vo8fALA5CZE8R7muHKNQiMycBYe75dHrqZqIJ5LKyLDie86Hj6pEXeeQgHzwxc60lat/NMjkli6FzgEGcskcqOX3QCW7FU5hOwSxqWORUhsc/E9QypZBLb8bCpmr9y8v7IeEHQ2V7BY4+c3ubUfAMAlgyKl6HqfISj5yqZCo3zvhLGpwOut79opPfxinXCLZNjhBfGluQunlS3TF956Xa/w79W+41DhC9EJOy5vfaUo9XLI2TTwi9U6rvdZhDooWDcEMpSK4cUdK9a99XzX4+Z2xcUPsPQ5TBjOFBXLr+MELuLycvp3OifMWPVUF8YSPr26ISuO61BuwkSMHO1J6aoqdZ7p0PWRECVNXIu2yesYzL2XFA3y38tJ6qibiiaQqIqz07pmMyQCDN+Zri3POr4nMzl+lGz0up7CzBS4MCQPLJAIACJGDJTGgtKnjkhOyIGGGgCF69zGNKbwsAA4UPAgIKBXgFLZCxk5t9VwCZZmvS6JTFm6P1L98jC+pdjgCpojS23DkRg6oemWCRCfLOyOVW04HQkZgL2FTVS6Fq19gi0rVE+aaUEi4DURGEPlzM4tiPlhTN+jR7MSoPlyXc8t6G1v/0Rumn3Wx1NgQ7VWliM6gYZ3co4N5hbeUJGSLsuq7O1VxUXiXegraRKbLDbNNh8K7XAARGcXJxk440c2mxgweOKBXe6u4ga0J15bePUtiqodPvWBqtcj+9PvG1Bff+K2CYR1dCr+KYj0EsQws07TNpEhrQGGD1fE+LI43wAn7AABSdiR4MRc25yeQSc4FJ+yAlB0FQmTtnoeCIT9XRCZduiMm6snDYnmp1R5UOY1BzE+BXkCI7hMyAnsXWWglL3BXbUHICGyGIHVwwsIdhYmfrrFk/DsnRRcnds0t1Ev58O2U6ssWLAzjOU23Qqg9paeqIJ6IDC/GT6ntdm9GY4OmS+uwCV3zBAKAc29S+5ZFGzz/6p4ol7Jx16l0KLTvHhd7pCbiiaRWrym76wKZsda3hiAALL26OG73kVWytL4lnQprU+qAjfsCcuk8ENK8XkuEIJZCKbsGStl1cPB/QRSrQYgCKtk1UMtvB8skghePQsIMhZ37HjbnlxDEjjtrbawOj79yd2zsA9moPGG2dUn7+QwkZAT2YkJGYO/iOAARrnjHSfIttfVGzl4dmCUFF4LKYsOyTYVpn//ID7y2MFWtpz02SASBYsq4Uiy5qGUU7ttVZkwaU4JJY0owc1oZDmS7HDQ11QJmzyjDxFElWPvTqR69Sy+uRHmZb9qV3XHj8JonH5oZDSoNyPd6flzPVUE8EaccKVOqiLrjkS2pqJDZqSjr0rF2gXS5iIU5mBrVnUa76YMsmr79S0u6elwjP5RH9VhNxBO2PkxddtcF8oYadDF3r+vExDrlmzN/Tb7rwY0lIG23uKJUgI37ElJ2JKTs0Bb7CdFBwqSDEBkYooaE6QOBNv2OOvg/IZPMACfsB0MSoZBeDAff+fS13fW62Bv2xiXcto+tORRSIWmNQ2N+W3E80IsI0X1CRmAvYlNVrgOuVjEtvFr55poj/l9R8MCHG0zsbX8U9v1sraT/4vJUuQpeC4W+84YRAzNad6ylpknw6x/x2LY3Cfc/pMddt7pSY1Z/Y8bSKzXYsCkBb7ziyr1ft9aKEaNkiE/wbhGy3U7EWVOmGX78dkJUoL7SMTKb7fb+PVcF8ST8wtxuv5gdWyK7XPlqF9guh4NZh1zaUMV0Kx/qmZd2dsvAbcQbaiKeSBrCVBV3zVU0VHe6N1yPuPfhnKS/dn7NR0TVtPC0UUph59aAIdGQSaa0eryEGQyBFoBSAZQ6IYjFYMgpx7Eo1oBSIyRMX7hkbQkAAtq1Wh4AwGGTJur2rLik6/bIDHtCKiSehLyAvZxOX2QJIQIhJIsQsp8Qso8QMqk7JySE3EwIWdadY3uKe+2rejhHGiHkYDePu7wn53azE2hp4OyqLTzUytjTHmdiVZ3swV+LBny8Xt1nVm2qVAavhkFLS3hsWGfFVde27lAcP1EBfbjLdhg7Xo6yUpeXTyolsNkonA4KhgF4nuKdNxpw5z3erdOoKJfZxw+Zbz+cPVDv1Ym7hIgXhjf0WBXEEz6mzhjTz9ntJtO7d0R1uYm6XexeOxvDEV235NimzKiLjIyq7nao0VtqIp6wRo2q4q45KkMl8UtxxICBVs3+E98nXHLF7kJAOGmdCbQQvJgJQWxsAfM6eOEonPxOOPmdrrUyMZAw6bA6X4fV+Tak7FlgmVM1RA5+A+SSmQAACTsCnLAXVufbkElaLxDpDAVWVfj9B+JSrtipsGxyqZB03aI8vQgZgb2crlz0bJTSkQBACJkFYAWAaV09IaX03a4e4w0IIYPgMnqnEkLUlHpfR7MD0gBcDuCrHs7TmP9HAJy8+h82lleZOHutVqrwix5soHEOKKrSL8vi+g23JRLS0jPqLR66rxZPrYiAydTxg//nn5hx3iyXfb74Mg1uXFaFr78048lnI/Dhu0ZcdqUGKpX3PHV7doY1LJk/R+l0hAW0ufWNadVFaWqlV6qBG5FelFlHPPt5dJFD2ZFd9tTYBaZbDxCOfQlSzDB051Dc/+gesvyuC7p1LNCoJlJRMCNWmdbtSZrBmjTKyrvnEvryLzXhcTTKW/O2x8tvZ6ZedlW+4cpFszmbNSxawqRBq1jR4XEyydQ2jTql7NQzN0M0UMtv8dp6KxwK7eOH47ThUqf1pj51ZefEyuJkDNulQqTTgDIAuwO9iBA9o7t3pDC4WpaAEDKdELK2cQch5E1CyDXun58nhBwmhGQTQl5yb3uCEHKf++eNhJAXCCG7CCE5hJAp7u0sIeRFQshu97H/cm+PJ4RscnskDxJCprjHfur+/QAh5O421nw5gM/h6md0ocd621pDGiFks9vr2arn071/pMfvWwkhwwkh09xrzCKEZBJCtACeBzDFva2tNXbIpqrcBgD5cP0NmpBvqT3c3Xl7C84xx8p0L39fOfi/22MSRtgSvVOD2jq//WJFdDSLkaM7vrZv2mjD55+a8OSzLntUp2Pw7U9x2Lg9ESNGybD+VysuXKjGnbdUY9llldi1o2dqf6s+S6hdNGuR1ukI61ZhgrfwliqIJ5SINH5adY8eZgrywrv8vjgpy3Yr5ysrtdva0ldcWxyvUJp65HVzq4l4NZePNasVVXfP09SVMX7LNR430aA/UPBN5NRzDhVQ9I5waz0nU72QE5e6aFsY1pSccSok/xvz24pQjmQvpytGoNJtwBwF8CGAp9sbTAiJALAQwBBK6XAAz7QxVEIpHQfgLgCPu7ddD6CBUjoWwFgANxJC+sBlyK13eyRHwFUtOxJAIqV0KKV0GIBP2jjPEgDfAFgFYGkn1lAF4HxK6Wj3sa+3MueHAK5xv950AHJKaTaA+wDc5l7nFAA2AMsBbKaUjqSUvtLGGjvLVgAt4op76gpPSyNQJALlpmUXR723unbw41kJ0f25WH+cd+d2O9b9YsWw9GJcf1U1Nm2046ZrWhZlHjzgxJ031+Cr1bGIiGyZVvbCswbcu1yPNd+YMXKUHG++H42nHut+IeZD9wyufuCOOZGUygKa0+tNVRBPhAnHylRa0qOCHkOdtlu6zwJtUxarTSQNYSpTffd77V13S6atu8cCLjWRR7ykJuIJa1Epau6ZF1ZbynitErkj5HLKfPnD1rR3P/1frURqM/jrvD3FIkjkb55wqZB8VuAoNPOcz9RYgoieRrVCBAFduYnY3AZMBoDZAD7roBeYEYAdwIeEkEUA2npC+t79/164QqYAMBPAMkJIFlw5cJEABsDler6WEPIEgGGUUhOAPAB9CSFvEEJmu8/bBELIWADVlNJCAH8CGE0I8Xx6b20NUgAfEEIOAPgOwOBW1v4dgHmEECmA6wB86t6+FcDLhJA7AehpN24sHXAQrUhAHDCUVVh4x2nT9V6UOnlh/q7ChE/WmAbdeyRZHy/4NdT9+DMROJyXggM5yfjo82hMna7A+582bTNYXMTjqksr8d4n0eif3jKaeCKXQ0W5gMlTlbBaXfmBhAAOe9cfoDkO9KKZZ9d98dHk6GCo6XpwYHWRt1RBPNEtONqj4xsMEo7n5d0KJQtU7NZ3tT5H3e2b/j3LjyWyrL1HRsMxszrCW2oinjBWpbz2nnn6mmLGrz3z5i2sjM46vko9eFhex/1cgginyEo+KYxJvWhrhOad41yR4fRVIckDsCXQiwjRc7p1J6GUbgcQBSAaAN9sHoV7DA9gHIA1AC5C29qCjU1PBZzKUSQA7nAbnSMppX0opRsopZsATAVQCuBzQsgySmk9XF7BjQBug8s715ylADIIIQUATsAVSr24gzXcDaDSPfdZAFqElyilVgC/A1gA4FK4n4wopc8DuAGuAo4dhJCMNl57d6kGUIJWQsJ55ppeXyAiKG12evmWgpSVPzgH3pifqomg3c4N8wUfv2/Ex++77tn/ea4edXUi7r2zFpPHlmL6xNImY59+vB6PPuF63li8RIOvPjfj/ClluP3urhWI1NZIHBOHzbXs3TkkKPodjg83VJ8bo0j19rxCuNESm+HodkEIAOzaHt7Q3QbTPKXd8qbZsuK6nZggl1Nm/sUHe2wseFNNxBPGppTV3nNhRE0h6/W520On56Xrt/yR+viK38sI4/R3DnePEMAw35ZGpSzaHql/6RhfUmU/7VRIvgiFgk8PumUEuo0aFi4Zs0IAgwkhckKIDsC57jEaADpK6a9whVlHduEU6wHc4vawgRCSTghRE0JSAVRRSj8A8BFcHr0oAAyldA2A/wMwutlaGQCXABhOKU2jlKbBZbQ1Dwk3RwegnLpaAVyFZr35PPgQrlDxbkppnfuc/SilByilLwDYAyADgAmAV5routVD/kErIeF/qnIzvXGOQMDrjWbyrz8L+372P2bAZaVpCg1UgV5TI1OmKfHNj67Kw+tuCsN1N7ns0jfejUZhZSq27E7Elt2J2Lg9sclxn34Vg34DXB7C6BgWG/5JwI6sJCxY2PnuIAezNaYJQxehsjyxWyFOb+NtVRBP2AX7qhmmaxq+zdm1Parb4VW+m157ui+1Rw8qTz6fHQ+03TOvc3hXTcQT1iGX1t03P7I6X+JXQxAAbrg1P2F79iomPrG8230VAwUFQ36piExasjMm6vFDtLz49FEh+SzQCwjhHbqTE5gFV27d1ZRSgVJaDOBbANkAvoRL2gxwGTxrCSHZcBksXSmG+BDAYQD73O1Y3oPLQzcdQBYhJBMuT95rABIBbHSv61MADzWbayqAUkqpp4tmE1yGa3seh7cBXE0I2QEgHUCrT6KU0r1whaA9cxHvcheq7IcrH3AdXO8P725T0+3CEA8OoJW/3wlzTV213dyrQihcXE299L7figZ8vE7Zb25NqlTe0ut6pvK/72Nr505bpLLb9EFTeehNVRBPKETEnVvZ7SKLRrKzorqdG8eL3fMESsqjdTZL9zsORETysgmTc3tsYHlbTcQTxiGX1t8/L6oqT9JNybvuk5jsUO46/HPSjbdvKe6u7nKg2VSjj1+2Ozb2/v2k8ripV6uQbB3z24oTgV5ECO9AQg3QewYhJAGuUHQG9WMD0akxAwhcxTYKuLyMJ5mbMHT4eXEZC/21lu7i7FtSrVuW6YgfaU1kmJY5jmc6zz0+oPqdV6dGAWzQvDfz42pK7xkoTex4ZNfhR+eUZzyR2aNQMACMHXRhaUVZXLfWuGpcZVWcUtEtfWnVsz+WJAxzJHXnWAA4cVxlmT7mcgXQdQ3j5rw2oqJ4uN576i2eiFKO173wc1Vsf86rVeGd5WC21rhk3myrsSE8ruPRwcsgrbn2lr5m6xCdIonxgVfdh/xrzG8r3g/0IkJ4h8Bnl/di3E2vdwJ4xJ8GINAkJNzCc/Jn5bHDnCj0MLTkO5wjjpeHvfRDxeBXt0YnjrYmhQzApogixeUXja9959UZ0cFkAPpCFcQT7cIjXimgqq0O63YagZOi299jc3Z0j6pz+/W3qgdkFJd2PLJjvK0m4gnDSSUND8yPrciRemWtXWXocFPY/rzvYucuzCwEvFsR7U+OmDSRd+6PS75uj6xhV62tyNvV3T7CDlfkL8RpQsgI7AGU0s8opcmU0u8CtIRMNGoheWAXOD7PXHMgMEtqHQqRcpMPlkS8vbpm8NN742PSnb36Kd5XmIwsN3nkLNPmv0cEWdNv76uCeCJozba4obYee5bsdkZ0cspuy7Jw3dABbkTYl9ojGTgAeOalXVpvRGd8oSbiCcNLWePy+XHlR2UBMQQlEpB3P92d+sX3PzTIFWa/yNz5ikKrSv/gwbiUy3eobP9UBb0Kyf/G/LbCEOhFhPAeISOwF7OpKrcartzJFhWjW6pP7PX/iloispzAX7C3KO7jNQ2DHjiUFJEk+EWBoDdyIkdpGTvoIqG4MM3r+XY9xa0K0uNQbVsw87OqWJb0OAy6d5eugaD7hSVOsfueQPZEfCTnpM7uHg8Ak6bUh8fEVXvFsHKpidgLvDFXazC8lDU9ND++7JC8w4KNkmIe82aWY9zwEkwYWYJ33mhoMeb1/xoweWwpJo8txcRRJYhQ5qO+TkBNtYDZM8owcVQJ1v50Ku1y6cWVSB9UGZGd/7V+7MRjhd1q9B1EVDnlmieOxKUu3q7mfy23FzqFoIzmfBToBYTwLiEjsPfzB4AWVaMHG8oq651WvydwNyIo7A7x0q0FyZ99b8+45XiKNkrUB2otvYHf10XWnTvxYrnFHBlQCbjW8IUqSHNiZ5Z6pQ3Qrm1RPWol4hC67wlkRJbUF0t77JVa/vjuHhvDjfhCTcQTRpAw5kfmJZQdaN8QlEiAZ16IwK7sJPy+OQEfvmvE0SNN7eU779WfrLJ/7OkInD1VgfAIFqu/MWPplRps2JSAN15xGY/r1loxYpQM8QkSqFQi+/1v/6S+/M6vlT3ttxgMNPAy5Ys5sakLt4cxq4vthTaBD5b2OPlwtUQLcRoRMgJ7P4fgqkBuUVF70FC2z9+L4bUmK7nh74I+n/2E9CtL0pRa9DhEdrrz2kt9aq67bEG4wKu8qrzhDXylCuIJNyyvMiyiZW5rd8jaF9WjvEKH2LMUTOPBiB5Xrl56RWmcSm30ilybr9REPGFECWP+v/mJpVmKNptVx8VLMHKUq8Bdq2WQniFDeWnbS1rzrRmLL3VdOqRSApuNwulwNVvneYp33mjAnfc0jfpfekVp3O6jq+R9BxR7vWl2ILAKEtlbebGpF23Ty1cWOAvNXMBVSD4M9QY8/QgZgb2cTVW5TgB/AWhR0fhH5dED/sov4WJqGyR3ry/q/+mvsn4XVqXJFAialibBzI1Xjq556enzogBJ0BSAeOIrVRBPNAsPea3lx/GciB550RwienST4/clK3tyfCM33b7Pa+/JMbM6YnUx59Mee4zIEssT85JK9ik7NMAKCzgc2O/AmHGtXyKsVhF/bLDhQncvzcWXafDX7zZcPL8Cyx8Nx4fvGnHZlRqoVC1vX9ExnPyfPeuS733k7xJCuB7J8QULTpGVfFoYnXrRtgjNW8e54vrAqJDwAD721eSEEMHdgu6Qu43aPe4ev+0dM5IQMsfj92sIISIhZLjHtoOEkLQO5rmLENJqMRkhZCMh5BghJJsQcpQQ8iYhRN+1V9cxhJACd89jvxMyAk8PtqOVZtZGzu4ottb5VE/YmVpeo/i/tSUD3/8jLG2GIUUiRdB5s4IRq5UI08ee2/Dbz2dFtaIAGBSM85EqiCeCyuqIH2nxWq5hVXn3K4MBwC70zAhkDiVHCkL3eg16cuf9uYmsxN4yca6bvJsfleoLNRFPGJEltifnJRXvURW1NcZsFrHssio891IkwsJav/389osV4yfKEe6253U6Bt/+FIeN2xMxYpQM63+14sKFatx5SzWWXVaJXTtaps7d9UBu0t97vhGjoqt7cz++JghgmNWlUcmLtkeG/+eoUFJpt3vFW9xJfh7z2wpffn4aZWmHADgfwBwAj3dwzEj3OE9KADzSxXPfBbQrTHAFpXQ4gOFwqYv91MX5Aw4hpM37csgIPD0ogytfQ998x46afJ+EhLmheRWa538sG/zGpqiksZZQm5cuUFwkt44btMB5Iqdft6tYfY2GdXL/N5iX+0IVxBMyZ3+FREJaii53A54HtdvVPXpP7WLPXi/jlEsaKtge5wVKpZQsWnLA0NN5TuE7NRFPCGWI/em5yUU71S0MQY6jWLakCpdcpsGFF7WdJbLmWwsWL2ldHOeFZw24d7kea74xY+QoOd58PxpPPda6Y6xff6s68/gPCUuv2VkICMFccdtFGKyrjEi6bGds9GOHaHmRxe4PFRe/9QWklFYBuAnA7cSFghDyCSHkACEkkxAygxAiA/AUgCVuD+IS9+FrAQwhhAxsPi8hZCYhZDshZB8h5DtCiIYQcieABAB/E0L+7mBdTgAPAEghhIxwz3klIWSXew3vEUJYQsgthJD/eJz3GkLIG22Nb2Wd97g9mAcJIXe5t6W5PZEr3V7J1Y3eS0LIGELIP4SQvYSQ9Y0iGG4v5nOEkH8A/Lut1xUyAk8D3D0D16MVGblddYVFJs7uFd1KSkVwEw6XhL+xpmrQc7vj4gY7AtIstjezdbPeMHX0xdIGQ4xXwoa+wq0K4nPN5pjZxV6Twjt8QGsCepa72FNPIAA0HNFZezoHADz+3IEEeDGk6Us1EU8IZYjj2Tkphds0J5WLKKW4/V81SM+Q4va72rbTGxpEbN1sx5z5LR0zJ3I5VJQLmDxVCavVlR9ICOCwt/8n+89r+1N//H21Ra1p8Plr9zeba/TxV++Jjbs3i1TlmmylPiqQLgSwwRcTtwWlNA8u+yQGwG3ubcPgkntd6d73GIBv3B7Eb9yHigD+A+Bhz/ncodZHAZxHKR0Nl5zrPZTS1+FyosyglM7oxLoEAPsBZBBCBgFYAuBsSulIAAKAKwCsBrDI47AlAL5pZ7znOscAuBbAeAATANxICBnl3j0QwPtur6QRwK1uad03ACymlI6BK2T/rMeUekrpNErpf9t6TSEj8PQhGwAHtAzHHm6o6JE3UGQEkT8/syjmozX1gx4+kBSZyndLUeFM5+P3kmuWzl8YxnMar3i+fMX8uJrSkeG+UZvwhBtYVK2PoV7LN9yxNdrc0znsQs89n459iV75++r0vHTy9Fyvas3+UB6VfMBg83nhBAED5/MXpBZu1hYCwI5tDnzzpRmbNtpOtoHZsM6Kj9834uP3T9U7rP3JgnPOU0Ktbnlrevrxejz6hKt+aPESDb763Izzp5Th9rs7dv6OGdegP1DwbfQ5sw4UUPi3sb8/2NcQFnPTvrjEW/ZJ67INtmLRu9bgh2N+WxGI96zxuzgZwOcAQCk9CpdRmt7OcV8BmEAI6eOxbQKAwQC2uiVmrwbQ3VSXxnWdC2AMgN3uOc8F0JdSWg0gjxAygRASCZfxtrWt8c3mngzgB0qphVJqBvA9gCnufcWU0q3un79wjx0IYCiA391zPgrAU7XoG3RAKH/rNGFTVa5tasyAzXB9YJrkwawrP7RvdETydCnDdkmTV5DZnWR+ZnnSRcWRKh1N8eZ6zzTuunl49ZpV46KD/bnL16ognqgXHfBq4v6+3ZE96tEH9DwcDABkf2oEpQfgjUj6ilf2xkwZmcH31MPpySOHIqNXjW8wqX2g/+wJAQPuxTmpBcJvhROnI9Xg6NPhMVcs0+KKZa0v69OvTj17Rsew2PBP1wIRUiklK7/dnrbu5xPVt107U8JxKq9UpAcTx8zqiH/vV0ekKK0Nt/YzNpwVIU9kSY9kCK0A3vXW+joLIaQvXJ6yKnQxaZpSyhNC/gvgQc8pAfxOKV3aw3WxAIYBOAKXl3IlpfShVoZ+A+BSAEfhMuqoO7WmrfGe62yL5oY9dY8/RCmd2MYxHbYXCu47UoiusgVACy9EA2dzHDdVddobyKstVlzzT0Ha5z+J6VcXpap01GshuzMNu52IF0ydZlizakLQG4C+VgXxRJA7uPgxJq+qxhw7EtHjN9jO99xyY00apamOeKWCM62PTZUxpNCrqhy+VhNpDv/y7NT8P/UFfjlZJ7hgflX0/rxV2mEjTxT28v7SbVJkU+mWu1VI/u6ZCsknY35b4ZV0os5CCImGy/B8090AfBPcYVNCSDqAFADHAJgAtPUg8ymA8wBEu3/fAeBsQkh/9zwq91zoYB7PdUkBrIDLI5cN4E8AiwkhMe79EYSQRu/i9wAugit83eiNa298I5sAXORenxrAQgCb3ftSCCGNxt5SuO73xwBEN24nhEgJIUM6ei2eBPtdKUTXKABQjlY+0L+VH94hdqBvzEXWG9k7fi/sv3KttP+iijS5EkHXuLg3UVkhs08YOs92cP9AfaDX0hluSKsu9KUqSBNm7i+XykiXPNMdUV6q7/Hn1SYyXrkmGnI0XtPtfe7l3WHeVsPwtZpIc4TXZqXlbYjw2/k6QhsmSH7958/UZ17aUMYwzh6nEQQrVU655qkjcakXb9fwv5TZChxdUyERALSZS+ZllI0tYuASQNgA4En3vrcBsISQA3AZVNdQSh0A/gYwuFlhCICTRRyvw906zR2ivQbAKkJINlxGYYZ7+PsA1rVTGPKl+5iDANQAFrjnPAxX+HWDe//vAOLd++rhUvNKpZTu6mi8x7r3wWXA7gKwE8CHlNJM9+4jAK52HxsB4B3361wM4AVCyH4AWQAmtfUmtwY5XZ+EzlSmxgyYBle+Q4vqvLsGzliUqo4c1nw7l1RRq7lqnzVxnCmRYUMPBt5g3+4w46Vz5ygcjjCvGjq+YoDG3PDOaE7ty6bQnkS8811VRKLo1dzSZN11DkDSo/6U06IMZU8MIT0ueOJn7y3KuPW411Iozsq4sLSyPC7RW/MBgJQIwqrxtQ2RcnkL2UlfwdzyR0HfC2rT/HW+zlBWKrctmn1+bWlRQlLHo3s3KpZ3Xp1aVz4/no1SSiQdNfL/dsxvK5Z0MCaEH3D3OlxLKR3q7blDN/zTj71wNfZsERb+s/LYNs/fnQMLK9XP/lSW8dY/kckTTckhA9A7fP1FfM1FMxdqeosB6A9VEE/4PmW13jYAT+SoLD01AAHAJvRcvxgAkJnm1XyzR5/e7fW/jT/URJojvnNe2om1UQX+Ol9nSEh0KHccWJt08783FwO8T1voBBqrIJG9kxeTumCbXvFJvrPI1L4KyX/a2RfiNCF00z/N2FSVa4bLnR7bfN8BQ1lFpb0hjzvrWJn+1e+rBr+4IzZ+mD3Bt53gziwevX9Q9f23zY2iorzD7xalIiyO12F1ftpiHydkwuJ4zf3vHQiiSwZapGZYHe/C4ngVnHDo5Hib8zOItHuqUv5QBfFEsSjb6+G37VujvCKpZRe8Ew6WVEZqrWbqtdd50SXlsRqtwestTvyhJtIc+v65acd/iinw5zk7wyNPHUnesO1bpy68zh999wIKR1n2s6LolIu2RWjfyOWK65wt9KX/GvPbir0BWVyIFlBKC3zhBQRCRuDpyt9w/W1b/H2zhv++ZdBjWQlRfblQmxcvIvCULpo9qW7l+1M6XQDCCVvBkNb/DAyJgEp2E9Tyf0MuOQd27nsAAC/sh4QdDZXsFjj5ze5tR8AwCWBI19v6+UMVxBNRyvEJEwwtHlB6yt5dPa8MBgCb2KNKyibUn1B6Vd7rlrv2eeU1NscfaiIt+GhG2vE1cQV+PWcnGDTErM3OWx23YPHeQkDskQ51b0AEQ74vi0q+eHtUxIojXE2dky9w73oxkOsK4T9CRuBpyKaq3Gq4El9bWBjr1wjVxXlioIXITyvqayXOCcPmWnZvH9rp3CqRNoAXj0HKjm11P8ukghCl++cU0JNePhYABwoeBASUCnAKWyFjp3Z53WqW84sqiCf0nAPlMjnxesHRkYPecWTaee+FxC37Y7zaW+3Wu44nSqQ2gzfndOEfNZEWrJyWlvttQoFfz9kJGAZ486O9qat++t6oUJh6rP7SO2CwoSoq6uLtkWlPHVauGfPbit8CvaIQ/iFkBJ6+bABOVfcSOR8uiTVPlfUxzPn5B0t+ANd1WnHooNo0fuhCWlGW1KU2Og5uLeSSC9CZFlicsBsS1tXNQMqOBC/mwub8BDLJueCEHZCyo0C6UWj79JCacn+ogngSOS/PJ42yS4r0Pc4HBLzrCRT2pXq1D59EAnLJFdle0xP2xF9qIs0hX0xJy12VWODv83aGydPrIg4UfhM+YfKRAm9XZwczf1fLVgV6DSH8R8gIPH0pBHCUKLiB0gTjebI0w3nSGIud1Tp+3rzN+mN9vXDaSSj5m59/iKmbM3WRymYN75IBwgtHQIgaLNNxsScvnAAn7IFcMhsAQIgCKtk1UMtvB8skghePQsIMhZ37HjbnlxDEwg5mdDE/rqZ0VLjKrw3A+aTK+qhUwau9ARsxNmi8YszaBe95AiX5ceFOB/Wqd+3/njmYSAjnFVm65rSnJlJhM+Jfu77C4i0f4NItH2JV4e4WY4ycHfdlrsFlWz/Csu0rcdxUDQCod1px/c4vcOnWD7GxMufk+Hv2rUa13QSyanJazhfJnfvg+hmFQmS++2Vz2usfrq1iWfuZEEE5CFePuxBnCCEj8DRlU1UuZSNsf8rTDAPZKGsNq3X+yCiFXYSFjVLg7z9smzueJURbrHhyQPWt18wPFwVllz1HglgIXjgCs/0F2LlVEMQ82Jwt1X0EsRx2/nsopVfB1Te0KQ7+T8gkM8AJ+8GQRCikF8PBdyzx6U9VEE/ki/b7xItVUS63UyprKTbbDeyi94xAQhlSXyjzajhRqxUk08875rMHuEcORUZbeK5Fj0MJw+DujHOwevKN+GTCVfiuaB/yzE17CH+Stw3p2lh8ffb1eGrYPPz36B8AgPXlhzEvYSg+GX8VPivYCQDYVJWLjLA4RCtczlLm20mpxz5NDdrmzYsuLY/dl/uVYkBGYYvWW6cZzxhty4PzjxDCJ4SMwNMYRf/6vWwY9yurEAsIgyYd49d8ZzlkNIrNK8JCdIAoUly5aFzt2y/PiAbYbuXSyaWzoVE8BI3iQSikS8EyfaGUNW3HJVIDbNwXUEovBcNEt5hDFGtAqRESpi9cktEEAAGlHQkD+E8VpMlZWV6MP7vOJ8VI2zdHeM245CnLejP0ZzoY4fVijhWv7I0FhO4qQLRLW2oiUXINMsJcTly1RI40dSSq7E1txTxzLcZFumqM0jSRKLM1oNZhgYQwcIg8OFEAAwJeFLGqcA+W9Rnf5Hj2+wmpOR/3CVpDMCKSl/21c33K8if+LPWVNzbAHAHwXaAXEcK/hIzA05h1ay0igDUA9M33CTzoP3+FvIFdwWRkuSmjZxn/+XOkT7xoTn4nnPxO989/glIr7NxPsDheh8XxZpOxDn4D5JKZAAAJOwKcsBdW59uQSdovEPGrKogH4tRDZQol8Yq3rjm7d0R1RQWhQwRKvVYVyu1NUXprrkYSkx3KoSMKyjoe2T06UhMpsxlwzFSFofqmPbXTtTH4yx3uPWgoQ4W9AVV2E2bHD8b2mnzcsfdb3NR/MlYX78PchKFQsC3TQ9mfxqfmfNCviIpBagkCuO3uE4mbMr9GTGyVV+X8goBnjLblXi1mChH8hBRDTnMumKdmATwHQAKXRuJJGAbkrQ+ibg4PZ0PtYjog77jSMnf6HNZsiuy1Unr+VgXxJOyVNeUx/XifGJ8Lzp9euG9Xutfa3KybXGtVsBKvGKyihBP6fbcGLOulJtRusvaGNcybsSTMV5XdbamJWHknbtr9Ja7rOwnnxA5scoyZd+C/R/7AMVMl+mmiUWipxaNDLkB62KmOQEbOjof2/4gXRy7Cy0f/hJG348q0cRiub5ofK1ywtzj9X7lJhAnuLqaP3Dus6LMPx8UBbK9oDN8OxwAMDhmBZx4hT+Bpzrq1FgEub2AL75Uogq79yfqH/1fVu/hrQ2T9OeMvlvdmA1BCBPE5P6qCeMLH1hqj+3I+8z4W5od7teKYp9RrChoML2Xry9iajkd2jZFjjLrE5AqfeaJaUxPhRQEPZP2A2fFDWhiAAKCRyPH4sLn4atJ1eGrYPNQ7rUhQ6ZuM+eDEFlzXdxLWlx9Ghi4Ojw2dg7dy/mkxF7tuTPKxt9NLxCD2CALAs/89kPLzX9/ZfNHI2888FzIAz0xCRuCZQSaAOrjEr5vwy/+suWWlfKhlTBu8+XJa9dWXLNALvMrvxpM3eXBgdVGUH1VBPJEtzKzzZStCQ53Wq61YBNF74WAAMB7W27w5XyOPPbfLp94nTzURSimeOvQr+qgjcWXauFbHmzg7OLfN+GPJfoyKSIbGQ8mvyFKHGocZYyJSYBc5MCAgAJxt9GSWbBidnPNGRoko0qA2TkaOMeqy87+LPn9OdiEgBvVa2+AYgC8DvYgQgSFkBJ4BrFtr4QD8AKBlhQGAr780/x5KC2jJTctG1bzw5PnRgCSoQ1Id4W9VEE9ERqBxU2uifDW/oV7C8bzcq0YgR71rdDj3JfmkN+LcBZUxWl19pS/mbuTd/KjUIqu9Yr+hBL+WHcLuukJcvu1jXL7tY2ypPoHVxZlYXZwJAMi31OLSrR/i4i3vY1tNHu7LOL/JXG/nbsIt/V05q7PiBmNt2QFcu+PzNo1KAJD8OTI559XBZaIQ3IagVErJx6t2pH7yzU91UpnFq0oxfuABo2253/SjQwQXoZzAM4QL5qllAFa4f23RAuKpFeEL0wfKhvt3VcGJzUaEuTNmmHOP9NcFei09Rc1y3NcTGmz+bgrdCHf2odJBDx7suCFiN9nwa3TN9UsXetXI/GpcZVW8UuG1PFlBbbWnf/k/uS/y2955rV/pc4+d67P3FwDi5HbTynFWmYxhvdKQuzvwUw6WDrjnYJy3cyt9gcnE8pcvmFaauadfqh/FeLrLX0bb8nMDvYgQgSPkCTxDWLfW4gTwFYBWb5grPzL9xfPeDYP1RkqKZdaxgxY4TgcDEAiMKogn+gU5HQ/qAbu2R3s91OoU4VWvCGtRKYy1jE/aMf3rjhOJUpnVp56nigCpiXgi2Tw0MeelYRWC4L18TV+h1QqSn//6K3XFa+vLGcZhDvR62kEEcG+gFxEisISMwDOLTAB5aKVI5MRxvmF/pnOn/5cUPGzfojNMHX2xtKE+xietTPzNvLiaMn+rgnjCRxrMsemOhI5Hdp8DWZFeNwo4H+SgGY5pfGIMMAyw9Or9LTz73qY9NRF/Id06JDHnhRGVvcEQBICrri2K33lklSQlrTSg71s7rDTalmcFehEhAkvICDyDcPcN/BpAGFoRrf3ofeNmh536JIk92Pn0/aSaJfMWhnFOrU/yt/yNWxUkouORvkOyILOG8XGLj7zj4V4v2HGK8HqOjD0z3mfX2oefPJRIGKfFV/M30paaiD+R7hiUkLNiZGVviVrExTkVW/f/knzHff8UA7xX+1n2BEqpBcCjgV5HiMATMgLPPHIA7AMQ23xHXa3o2LrZvsn/Swos99w6rPr/7p8dRansNPk+uFRB5H5WBfGEQkT8OVU+N0Jrq8M03p7TIcL7RQiZaXqvz+lGrRbZ82YdrfbV/I2YeKlixRGpOdB55NJdGQm5z46u5vkO5XGChgf+71jynzu+4cMjassDvRYAIIS8aLQt91nD8RC9h9Pkpheis6xba6FwSQPJAbRIsl75sWm3yST2tuq2buF0QpwzfWr9d19OjD6dvgqBUgXxRBibW64Og09zEe12RnRySq+fwy543xPI1oRrLcaWBVne4rlXMuMA4aREnUgNsDo/gMXxMiyOV+Dkt7Y4hlIbbM7PYXG8BovjLQhihftYM6yOd2FxvApOOHRyvM35GTbXkviN1fZCX72OziLdmx6f+/RZtTzXewzB9EEWTdaJNfEXL91TCLTRF8c/lAJ4MYDnDxFEnD53vhCdZt1aSxmAvwC0yNdyOKiw/lfrn/5flX+pqpTaxw+ZZzuQmREe6LV4kwEac8NlKTKfVot2hrCLjvg8b2vvLr2BgHj9GubwQTgYAOqPKw2+mBcA4uIdihFj8j28TAzkkjlQy++BSnYrnMJ2CGLTbjIO/m8wTDzU8n9DIb0EDn4tAIAX9kPCjoZKdguc/Gb3tiNgmAQwJAwrjkYn1TocAdcdl2b2j8t9cmwdx1Gv6zP7CoYBXn13X+o3a9eYlCqT15uId5IHjbblp6P2cYhuEDICz1zWwlUd1qLh7OpvLIeqq4TTTRfzJFn7tMZJwxcxNVUJLZpn92ZaUwVxCDyWbV+JpVs/wqVbPsR7x1vKRX+Wv/Nk77dLt36IcetfQIPThnqnFdfv/AKXbv0QGytPVfnes281qu1tO7UEnckaO9juc0/kzm1RPrmROXzUkc6aHePTXnfPv7o7nLpjtQwJA8u4ngUIkYMlMaDU2GS8SKvAMv0BACwTA5HWQ6QmuAIEHCh4EBBQKsApbIWMdfX4a01NJFBIs/vFHn98fD3npI5Ar6UrTJpSH56d/03E5OmHC6h/4+ubjLblocbQIU4SMgLPUNattdQD+AlAXGv7v/rc9Gswi7h3l+9WxdVeeO4itcOu6+1any14cGB1cXNVEBnD4t2xS7Hq7Ovx1aRrsa0mDwcMTe37ZX3G46tJ1+GrSdfh9gHTMDoiGTqZEuvLD2NewlB8Mv4qfFbgKhzfVJWLjLA4RCva7s/MzM+q8kc/t6y9kT4JqdkF39SyCHtTvdrUujlDh5vCUtLKSppvF8V6CGIZWCa5yXaWxIMXDrrWJhaDUgMoNULKjgQv5sLm/AQyybnghB2QsqNAyKmvjKeaSKCRHuwTe/z/JhidvcwQVChEZtVPW9Le/vTnaonE1uDr81FKeQC3+/o8IXoXISPwzOZPABYALVqibN/qKDuQ7dzt/yX5jscezKi+5+a5EVSUB33D2a7iVgVp0Q6GEAKVxHXz5qkIXhTdYl2ts778CGbFDQYASAgDh8iDEwUwIOBFEasK92BZn/HtriXu/DK/9Fg8nhPhk7+jXfBNRbOkMDbC19X3Tzy/S+n5O6UO2LgvIJfOAyFN64RkkmmgsMPieB1OfhsYEg+AASEKqGTXQC2/HSyTCF48CgkzFHbue9icX0IQXSmBjWoivnw9nUV6JC36xCOTjE4HDZoK3M5y4aKKmMzjq1QZQ/KLfHkeQsibRtvyA748R4jeR8gIPINZt9ZiA/ANgFbVEd550/iX1SoGtCWENxB4ShfPmVT3ybtTowE26Fv4dxU1y3H/N5iXkzbkCQQq4vJtH+P8v1/H+Mg0DNW33rrPLnDYXpOHc2IHAgBmxw/G9pp83LH3W9zUfzJWF+/D3IShULBtd9HhRpyo0IbDL3mWVRU6n/RztIm++YgQMKgrkPs0l27mnOooXXhdBQBQKsDGfQkpOxJSdmjL9RAFlNLFUMvvhEJ6KSi1gCFN/3QO/k/IJDPACfvBkEQopBfDwW9w72XwYLZO7RSFoPDASY+lRJ94+Gyzw977DEF9OC/9fdvvKY8+80cZIU6vpzlQSisAPO7teUP0fkJGYIgdAMoB6JvvqK8THb/8ZP3N7yvyIoZ61jlpxBzzzq1DA9ozz5d0pArCEgZfTboOv067DYcaynHc1Ho3kU1VxzEiPBE6mcuZpJEq8NqYS/D5xGuQERaHzdXHcU7sQDxzcB0eyPoB2YaWaaPahYf8Uq3J86AOu8onHke70I6rtIeYD0T6/P2564G9IqUUdm4NGBINmWRKq+MotYG62+1xwm6wTJ8m3kJRrAGlRkiYvgA4uFqLElCPgtxgUBPxRJqbHJX30BSLw9Y7+53+6468hK3ZXyMuocKroXZCyANG23JjxyNDnGmEjMAznHVrLTyALwGEo5UG0mu+sxwuKuRy/b4wL3D0sMo0bshCWlaS7NNcrEDSFVUQrVSBMREp2F6T1+r+DRWHT4aCm/PBiS24ru8krC8/jAxdHB4bOgdv5fzTZIygsdjjh1v90prm4P4wI8B4vVE0ANgExmdGIL8vxedqNDfcmp/ASI4YeTETgpgHi+N1WByvgxeOwsnvhJN35XeKtAoW56uwOF6GIOZAIZ3fZB4HvwFyyUwAgIQdAU7YC6vzbcgkU5uMCwY1EU+kJxIj85ZPtdlttFdWwCan2FW7j/wv6dqbtxV5tv3pAZuNtuWfe2GeViGECISQLI9/ywkhP7h/Pk4IafDYN4kQspEQclY786URQmyEkExCyBFCyC5CyNU+WPd0Qshab8/b2wgZgSEA4BCAXQBavYG//7bxl97UmBUAfvkpum7W5ItVNktEwETvfU20zN6hKki90woT54qO2QUOu2oLkKZuoRoIM2fHvrpiTIsZ0GJfkaUONQ4zxkSkwC5yYNxZhc5mrc7InKxKVkJ8Ypg1Z8fWKJ8pZNgF77edaYQ9mhTlD7WL6/5Vb9AqVkAt/zfU8juhlt8JCZsBmWQ8ZBJXTifLpEIjvw9q+T1Qyq4EIU3SCaGUXQ6GcUmNM0QDtfwWqOV3txpaDgY1EU+k+QkR+Q9Ms9utvdMQBICnXjiY8us/39q1YfWVHY9uHT8Vg9gopSM9/j1PKV1IKR0J4AYAmz32bevknCcopaMopYMAXAbgbkLItb56Ab6AuAh6GyvoFxjC97gbSH/t/rWFysTxXL5h00b7X/5dVff5zzP9q29eNj9cFJSnXQHIKUT8Z7ihQ1WQGocZ/9r9FS7b+hGW7ViJ8ZFpmBLTH6uLM7G6OPPkuL+rcjA+qg+UkpZF02/nbsIt/V3en1lxg7G27ACu3fE5rkwb12Rc7KxSr6t3tEXmniif9Yazi4zProuMIGEMpZJaX83fyPLHjyQxjMMnesWtESxqIp5IC+MjCu6f4bBZqM8l9XzFsJGmsOz872IuuDCrAOh6Sx5CyAqjbXm2D5bmNyileQDuAXAnABBC1ISQjwkhu93ewgXu7TsJIUMaj3N7HMe0Nd4TQkgEIeRHQkg2IWQHIWS4e/sThJDPCSF/EUJyCSE3ehxzv3vObELIk+5taW7v5dtwKXMlNz9XsBEyAkMAANattdQCWIU2vIEfvmvcWVUlBEVLiLYQRYpll4ytfePFc6IByWlXAOJJZ1VBBmhj8NWk6/D12dfj27NvwI39JwMAFiePwuLkUSfHzU8cjhUjWlwbAQDPj7wIKWqXwzFCrsbH46/Ct5NvwLlxGSfH8IMKq3TRtKWL0UccOxLhs2uXlfft07vxkP5kvprdLuKcs0tx9lmlmDCyBM891VKsJ+eoE+dPLUOMNh9vvHyqk0hNtYDZM8owcVQJ1v50ys5ZenEl6uuczKx5R/zajHhrnS4o1EQ8kRTHhhfeP8NpNVO/GcTeRiIBef/zXWkrV/9okMktnS4sopRmAXjadys7ibJZOHiJD86xD0DjBecRAH9RSscCmAHgRUKIGi5HxqUAQAiJB5BAKd3bznhPngSQSSkdDuBhAJ957BsOYC6AiQAeI4QkEEJmAhgAYByAkQDGEEIa8yQGAvjM7ckMqu9Da4SMwBCebAaQDyCq+Q5RBP3oPeP/BIEGvEFsa5hNDD91zEzj3xtG+c0QCRTBogriiWrhAb9WZFaU6Xymi2wXWZ96kB37kk66W+Vygv+tj8fWeFTdTQAAWDpJREFUPYnYvDsRf26wYffOpm9leASLF16OxB13N62DWf2NGUuv1GDDpgS88YrLOFy31ooRo2SIT5DgmZey4r2UU9ZpVhyNTqpzOHzu6ewKkpLY8KJ7zxWsJho04erucM75NZHZ+at0o8flFHbkcaWUcoSQq4225f5I42keDv7GB+fwfKifCWA5ISQLwEa4olcpAL4FcIl7zKVwyaO2N96TyQA+BwBK6V8AIgkhjV+4nyilNkppDYC/4TL8Zrr/ZeKUgdqYS1NIKd3Ro1frR0JGYIiTuItEPgGgQSu6wvszndU7ttr/aXFggMk/obCOHXwRV5jXx6datcFAa6oggUZQ2J3xY8ytNh33FRaz2me9CO2CbxtdswdSo0R3I3ZCCDQa12WY4yg4jqJ5o5/oGBajz5JDIm26QyolsNkonA4KhgF4nuKdNxpw5z2utyYm1ikfM/5EmS9fS3M4yrIPH1STYFAT8URSHq0ruvd8ajGhV1fIqtUi+9PvG1NffGtdBcM62jRqCSFP9fYwcDNGATji/pkAuNjD6EyhlB6hlJYCqHWHcpfgVIpTq+Obzd9a5Ig2+99zOwGwwmPO/pTSj9z7e1X6QcgIDNGEdWsthQDWAWjV0/TuW8atdbVCUDSIBYC//4ioP2fcxTKzMUrZ8ejezwPpLVVBAs6s/RVSKZF1JrTZyL49DkQo8/HT967rZXuhzfKypnUUucdUZkDis4Ifm8D41AhkbEqZsZo56S0TBIrJY0sxIKkIM85V4qxxnXNyLr5Mg79+t+Hi+RVY/mg4PnzXiMuu1EClOnVZf/7V3ZGA6FO5uuYEk5qIJ5KKyLDiu8+Dxdi7DUEAWHpVSdzuI6tkffoVt6jKplTcB+D5ACzLJxBC0gC8BOAN96b1AO5o7ItKCBnlMfxrAA8A0FFKD3RifCObAFzh3j8dQA09pbO4gBCiIIREApgOYLd7zusIIRr3MYmEkFb77QY7ISMwRGv8D0AdgBbeFo6D+MmHpp9Ekfr1xtIab7+aVr1s8QI9z6uDxivmS8aFG6rPi22pChJooucUKoHOhTYBl9Hz+CN1OPf8U3Z7R6FNT3Zsi/JpWM8usD7/PBmOhp20dFmWYMvuRBzKS8bePQ4cPtS5CK5Ox+Dbn+KwcXsiRoySYf2vVly4UI07b6nGsssqsWuHHRmDLdq0vmV+1wEPJjURTyRVkWHFd88k5gb4XKbN18TEOuWb9q1Lvnv5xhJCOBsAUEqdhDBXGW3LfV6B7kHznMDOGKC/EEJK3P++a2V/v8YWMXCFed+glH7i3vc0ACmAbELIQTTNe1wNVzXxtx7b2hvfyBMAziKEZMNlQHu2pNkF4Be4euo+TSkto5RuAPAVgO2EkAPu8/bKVmRnxM0zRNdYt9Ziu2Ce+kMADwEwAWhi8O3e6ajYtsW+cfJU5TkBWSCAW64dWbP2+7HRrXvxTz9OqYJIg+oFc/1KayPiaTTQGNp0La+t0CYAvPeWERdepMa+vaeEJtoKbX79Q2yL4/fu9F1lMADYRN+H2u2Z8SymNbVD9HoWk6cq8Od6GwYP6Zq09QvPGnDvcj3WfGPGyFFyLL5Mg8sXV2Lthng8/eJO1VUXJ3lz+Z3ApSaycpzVIWPYoGrTJKkO15bcNcuc+Mp6g1bfskl+b+Oeh3KS5i0ssV06b05+bXXEu0bb8sP+PD+ltE3POaV0I1x5eJ7bpncwXwGANiM7lFIbgH+1sa8SzeyatsZ7ro1SWgeg9co4IIdSelMrx78G4LVWxrfsoRTE9GpPICHkEULIIXeJdhYhZLzHPgkhpIYQsqLZMRsJIccIIfsJIVsJIQNbmfdTQki+e0wOIeQzQojXE/E7apoZSNattRwF8BfaCAu//bpxS2kJ33rXYR9isxHh3AkzDGu/Hxd1phiAQMeqIIFCtWh/k6rLjkKbZaU81v7PiutuavrQ3JnQZiNHDvpW/MUhuIxAh8Bj2faVWLr1I1y65UO8d3xzi7EF5lpcu+MzTNzwIj7P33lye73Tiut3foFLt36IjZU5J7ffs281qu0mICstHHCFwQ0GV/qczSbin79sGDCwbVm+1jiRy6GiXMDkqUpYrS4jmhDAYXelMk0/rzYyIrKmvKvvQ0+pcCi0754IHjURTyS1ek3pXbOlxjq0nbPQi0jPsCr3Hl1dBlfYNESITtNrjUBCyEQA8wCMdpd1nwfAMz9iJoBjAC5tzAXw4ApK6QgAKwG82MYp7nePGQhXBdDfhJCuPZ4HGEJ63Lh3NVyewBZublEEfeVFww92m+i3JNiyErlt3OALHTlHBuj9dc5goCuqIP5ElDn4+HHGJgUhHYU2H7qvFk8+Gw62mYRzZ0KbjRQXqeUWx1uwOF6DxfEKHNzvLdYmiFWwON6Gyf4onPymU2umZlgd78LieBWccOjkdpvzM4juFCABDCNSKsoYFu+OXYpVZ1+PryZdi201eTjQTCovTKrAfYPOx5V9mvZMXF9+GPMShuKT8VfhswKXcbipKhcZYXGIVmghqdOpzQ1oqKgQMH9mBSaNKcE5k8ow/VwlZs9V4eP3jfj4fdd6Kit4DO5bhLdfa8CLzxswuG8RjMZTzvmnH6/Ho0+4NH8XL9Hgq8/NOH9KGW73qCa+9+G9Ld4jf/BDWXCpiXgiqdOpy++6QG6s7f2GoCDAzEpwpdG2POBpOqcTlNInKKWntWHda41AuPrZ1VBKHQBAKa2hlHpWwi2Fy1VbBGBCG3NsAtC/vZNQF68AqABwAQAQQmYSQrYTQvYRQr4jhGgIIRcQQk7mIbglaX5ua3zz8xBClhJCDhBCDhJCXvDYbiaE/Nd97J+EkMbQWz9CyG+EkL2EkM2EkAz39k8JIS8TQv4G8ELz83SFdWstFgAfAYhGK263kmLB/PVX5h/80SB2xzZdw+RRiySGulify24FE51RBQkU4nkHymRy0mqozzO06UnmXieuu6oaw9KL8b/vLbj3zpomhSBAy9Dmm+9H46nHTt2nTQ3hYSrZDVDL/w2V7E7wYg4EsajJHISooJDOh4xtqpvLC/shYUdDJbsFTn6ze9sRMEwCGHLK0SpQkSeEQOVuns1TEbwourVSThEhV2OILh6SZq0FJYSBQ+TBiQIYEPCiiFWFe7Csz8lgBepzVcahw2TYvCsR2/YmYXtmEh58xGXMXXdTGK67ybWe2DgJDueloLgmDUVVqTicl4KwsFPn+/SrGPQb4PIeRsew2PBPAnZkJWHBwlOt0JbdUBivUJgC0rol2NREPGENYaryu+bIG6pJULW16Sosi1t1sryCQK8jRO+jNxuBGwAku8O1bxNCpjXuIC79o3MBrIWrAfLSNuaYD+BAG/uasw9ABiEkCsCjAM6jlI4GsAeubua/A5jg0YRyCYBv2hl/EkJIAlwG2zlwNZ4cSwi5yL1bDWCf+9h/ADzu3v4+gDsopWMA3AfgbY8p093nu7eTr609DsJlLLeaVPTbL7YT+/Y4OysF1C0++yix5tI5C7WcU9u1OFmvp3OqIIEiam5+EwOwM6HN7JxkHHD/u3CRGv99PQrzFpwyVjoKbZaVym2ATEVO2p4CmqWsAnDJnLFMMlp2OmIBcKDgQUBAqQCnsBUytqkeruCS24JARVy+7WOc//frGB+ZhqH6hE69N7PjB2N7TT7u2Pstbuo/GauL92FuwlAo2FPvhzUr1q/yGtf8KysgEmrBqCbiCdugVVXcPUdpqOqdhiDP4zudLM9n2sAhTm96rRFIKTUDGAPgJgDVcBlc17h3zwPwN6XUCmANgIWENOn99aW7ceTZcBlQnaHRBTABwGAAW91zXA0g1a3R+BuA+e4w7FwAP7U1vtncYwFspJRWu+f5EkDjXUkE0Nh88wsAk92exEkAvnPP+R6aKn18R6l3mjq7JeW+AlALoFWP1Gv/NfxZXSX4pALx/juGVj9yzwVRlMp67We1u9yQVl3UGVWQQMCnVNRHJYtNqjY6E9rsiI5Cmzu2RhgBgFIRFsfrMDuehYTpD5bpXLRcyo4EL+bC5vwEMsm54IQdkLKj0DzTgxddRiBLGHw16Tr8Ou02HGoox3FTdafOo5Eq8NqYS/D5xGuQERaHzdXHcU7sQDxzcB0eyPoB2YZS0MxUv+Z43vfI0cT2esv5kmBUE/GENWpUlXfNVRkqiV9VVnqK04kiiQQ3BHodIXovvbo62G3obASw0V2mfTWAT+Hy/J1NCClwD42ESy7mD/fvV1BK93TxdKMA/AmXMfg7pbQ17+I3AG6Dq73KbkqpyZ2P2Nb4RrpS4UDhMt4NboHu1vBqnt66tRbrBfPUbwN4DIAZQJNEL6cT4huvNKx+9Mnwm2Wy1sODXcXphLho9pSG/XsHRXtjvt6GWxWkc26nACBftN8IINxzW2NoszmNYc3mvPNhyz/tp1+darXVGNr0ZM+OaAcAEMJALb8TlNpg476AIFaAZTruV02IAirZNQAASm1wcv9AKb0Sdu57UGqDTDIZLJMKvtlDlFaqwJiIFGyvyUN/bdc+kh+c2ILr+k7C+vLDyNDFYXb8YNy7bw3e01+ut9uoVaEkfklxkMspM3fBodqfvx8dkFYWK45GJ43Q1dZGBFufSzesWa2svHsuoS//Uh0eR4P+uiOK1CmTkQU6WV6v73sYInD0Wu8KIWQgIWSAx6aRAAoJIWFwScCkUErTKKVpcBlm7Rlh7Z2HEELuhMvT9htcvYLOJoT0d+9XEULS3cM3AhgN4Eac8t61N76RnQCmEUKi3B7LpXCFfgHX32ix++fLAWxxN7HMJ4Rc4rHGEd15fZ1l3VpLAVweylarhXOOcYb//WD52RvnqqmWOiYMnWfdv3dQeMejTz+CURXEE1HCCQkT6wPSGPVgdmST2C8hSrBMHwhiTluHtImD/xMyyQxwwn4wJBEK6cVw8BsAAJxIhXqnFSbOVZBiFzjsqi1Amrpr9kuRpQ41DjPGRKTALnJg3FmFTtHVxq0+X+HXooSnX9yfAPCOjkd6H46y7COHVAg2NRFPWLNaUXXXvLC6MiYoq5o9EXj8WyfLywr0OkL0bnqtEQiXtNlKQshhd4PHwXA1fFwEl1i054XuJwAXEtIlL9WLhJD9AHLgCtfOoJQ6KaXVAK4BsMp93h1wC1u7PZNr4SogWeve1ub4Riil5XD15PsbwH64cgB/cu+2ABhCCNkLV87gU+7tVwC43r3GQ2i7x5E3+Ruu3MhWDcHV31gOHT7o3NeTE2Rnao0Thi4i1ZUJLYpnzhSCUhXEA3H6oTK5kgREoaUoXy8TqRmu1l8ApRwE4QQY0jXHjSjWgFIjJExfABxczngCSl1SqxylQo3DjH/t/gqXbf0Iy3asxPjINEyJ6Y/VxZlYXZwJAKhxmDFn41v4qmA3Psrbhjkb34LZw8Z6O3cTbunvyuyYFTcYa8sO4Nodn+PKNFc1sTk7yp9NfREZxcnGTTru93YxjRw1aSKDUU3EE9aqktfcM09XWxK8hqDVKq6O0uS/G+h1hOj9kGBN1g3hghBippQGjUF0wTx1GFyGqAi0lF9Sq4nkv69H3qQPZ7scTln9dVztvbfM1IuiwqeyXcHMuHBD9fPDENVKW6OgIezVNeUxffmA5CqmRlxt4rg6rZ37Dq7MCAoJOwxyyblw8q5WLDLJeIjUBKvjTVA4ABAQyKCW3w1CXDU2NudXkEtmgmGiIFIzbM7PAdghk5wPKTsUn5xVWZ6mVvj8NXIZRdWD/rPdr6HH3GMq8znjLlcCvpXHaxsRK8dWV6SoFH7Vm+4qgsLujPzv2vqoZKFlx/IAYreJBQolM1Qny+tVGrUhgpOQERjkBJsRCAAXzFOnA3gYQAmAFp6MkaNl0fct198kkXS+T+GTDw+s/vCtyVEAG7TGj69Rsxz39YQGWzA2hW6Ej69uGPjun7pA2KiGegk3NPVaqT/O/d7oitJ0rdLrDeKbIzK82Pe71YJESvxa+T79rNlFJ3JTAtZ7Mk5uN60cZ5UFm5pIcwS5g4t46efa6FQhKAxWnqcOAKMiVflHAr2WEKcHvTkcfEYQbAYgAKxba8mBq+q61bYxWfuc1b/9Yv2lM3MJPKWXzJtY9+Fb06LPZAMQAJ4eUlsRzAYgAMgW7q8PlJNy57aIBn+d2ym20nfGBzCihKkrkfq9IvWZl3ZpAukAqHAotO8FqZqIJ6xDLq2/b35kVb4kYCF0TxwOenPIAAzhTUJGYIjusg7AUQCtPiF/sdKctT/TsbO1fY00GFhu0ogLzDs2DwvKZsj+xKUKokwO9DraQ2R4MX5KTcCqJndtj7J1PMo7OAT4zUIyHQr3qRZya0yeXhcRHVNT1vFI3/F9EKuJeMI45FLD/fOiq04E1hA0mcRPE8ILPg3kGkKcfoSMwBDdYt1aCw/gA7hyA1v1Vv5nhWF9W/rCOUdU5nGDFwplJSkBaVcRTASzKogn4tlHy5Vqou54pG/Izoz0W1WpU/SfEejcmxQQOcoHHtsT8Ot/MKuJeMI45RLDAxdGV+ZKA2I4m03iXq2WuTEQ5w5xehPwi0CI3su6tZZaAO/CJSvXIslc4EFXPF3/ndEo1jU57ufouplnX6ywWiKCUgnDvwS3Kogn+gVdb8PiTfJPhPstb87uRwVW5mBqlChSv2u+Ll1WHKdUmQLaHDnY1UQ8YTippOHB+bEVx6Q+aYzfFjarWC2RYJZOlufXSvIQZwYhIzBEj1i31pINV2i41VBmTbVof+OVhlVOp6tlz8sr+lbfdOX8cEFQBmUPPH9zfRCrgngiRNWbYvo7A9q8uq4mzG+60XY/hoNZh1xqqGQCIll2w637/BZib4tgVxPxhOGlrPGh+XHlh2V+MQQ5jjotFjorVlfQKyXtQgQ/ISMwhDf4HsAxAK0aCQf2O2tWfWFeff3S0TWvPH9uNCA5owtAGumvNjcsDWJVEE8kF2XWMkzg2tZYrYzg5JQ6f53PIfr3pRqP6gKi63v38pwklrUHXHFixdHopDqHo1cYOgwvZU2PzI8vOyT3ab9DUaS0vEy4oV9iYaYvzxPizCZkBIboMevWWpwA3gZgQiv6wqII5t3X2cQ/NlB71xTyTl8kRBBXDAteVRBPKBFp3IzqgDav3rdL30BA/Ha9sgn+NXjt+xIC0rNPKqXkwsUH/apa0hq9QU3EE0aQMOZH5iWUZit8ZgiWFPMvDxtQ9Lmv5g8RAggZgSG8xLq1lgYArwFQuf8BADgnozx+TD+7vEwts1gPfSaIZbsCtsggIthVQTwRxh0rV2sR0AKeXduj/Oopswv+fVghWWkBKwx66oXsBBDeHqjzN3LUpIlcUxLcaiKeMKKEsTw2L7E0S+H1CueyUn79sAHF93l73hAhmhMyAkN4jXVrLUVweQTjAEisFklkzlH97JoqRZ3ZJP0dIJyT//M3UawPbIVBgBkXbqg+L1YRsEa9XSXsomN+L1poTta+KL96iOx+9gSyDVqVqR4B8cjpw3nppKm5FYE4d3PeyYtKLbLag2ItnYERWWJ5Yl5SyV5lkbfmrK4SjlRVChd6a74QIdojZASG8Crr1lr2AfjOZJQOyzmqP6euRnHMapXtOBUGFqmDX7+aUkuvudB7EzXLcf83mJcHsyycJ4LeaI3LsAc8b/H4sXC/Xqtsgv9Cz40YctQBa5Xy/Ct7YoBgCMUyeDBbp3aKgqPjscEBI7LE9tS85OLd6h4bgrW1QvmRQ86p0yaU+r13ZIgzk5ARGMIX/FKYrz1gqJPnOBySwy13OzgH/+dXlDoDnpDub54eUlse7KognjALMqsY1v8GUXOqKnV+qwwGAJvofyPdmhUXsAeDPv1sqvRBxX5tfdIWvUVNxBNCGWJ/ek5K0Q5Nt6ucGxrEhr27HDPmzyoPaNueEGcWAb+4hzj9WLfWIlaUaZ7heXYvgFbbn1Bab3Lym7+iNPC5SP7CrQrSa8LAFCLiz60ID/Q6eB7UYVf5rTIYAOw84/drI92XGtCHg2f/u0sbLP36eouaiCcEDBzPXZBauFXbZUPQahFt2zbb51+yoOKYL9YWIkRbhIzAED6BE47YAbwJV8VwVGtjRFpSyQnbvqBUOO1DH71FFcQTfnRehUYPvxpfrXEgK8wI+LeK2i763wiUlEfrbBZq8fd5G5lwdn14bHxVUHgDgd6jJuIJAQPuhTmpBZvCOm0IOh2U2/i37brLFlVs9uXaQoRojZARGMJncMIRA4BXAEiB1qtLBTG/lBN2fEmpwPlzbf6l96iCeKJdcDgo/iY7t0WZ/X1Om8AEpGVLfZ4ioO1aHnpid9C0LOpNaiLN4V+6ILXgb12HhqDAU/HvP22PLF1U+bU/1hUiRHNCRmAIn8IJR0oBvApX/0B5a2ME8XgRJ+z5mvaSHmFdpbeogngiaCy2+OHWgBeEAMC+PVF+l8uyiYHp32jOjg7od2Dx0rJYlbohaPLxepOaSHP4V2an5v8R3ubaKaXYtNH+6icfml7yx3oIIeZmv19DCHmzi3MUEEJajeyE6J2EjMAQPocTjhwB8BGARLi8gi0QxKN5vLDvW0rFgLcj8Sa9SRXEE2ZeVhXLkoB4w5qTczjC7wUTgcgJBABhb6o6EOf15OY79wVVekZvUhNpjvD6zNS89REFre3btsXx+XtvG+9ft9bS+1ydIU4bQkZgCH+xFcDXcGkMt+pl4cVDObyY/T3tjfGfVmCJQJ8bZuV6gypIc2JmlQS0ObQn5WU6v4fR7QHyBLJ58ZGcW2c7UNxxX26iRGJrCOQaPOltaiLNEd86Py3v18gCz21bNtl+eOOVhuvWrbUExUMvIWQ+IWQnISSTEPIHISTWvT2SELLBvf09eEg+EULuIYQcdP+7q73thJA0QshRQshKQkg2IWQ1IcSvFf8hWidkBIbwC5xwhAJYB5fOcAqAVr1MvLD/EC8e+ul0sAMfTK8uipbLe13ohBuSX6WLbCn/FyisFrXfi1MCZQQyIkvqiqQB9XpJJCAXLz1gCOQamtPb1ESaI757XtqJn6MLAGDLJtuGN181Xr5urcXfaQ5KQkhW4z8AT3ns2wJgAqV0FFwP6w+4tz8OYIt7+//gunaDEDIGwLUAxgOYAOBGQsiotra75xoI4H1K6XAARgC3+u6lhugsISMwhN9wG4I/AVgLIBVtfP54Ye9+QTz2iz/X5m16myqIJ5pFh4KmbU/OEbUZkLSaS+pL7ALTatqCPzAdjAh4Qc7jzx1IJISzBXodnvQ2NZHm0A/OSfvpBe0fb75qvGTdWksgvmM2SunIxn8AHvPYlwRgPfn/9s48Pqr63P/vZ7KTELYQNhVQUXHFvVZEbdWWqq1LrbWb3a/39tbb9me3a1u1ttXeLta11qp1aa3W3aKIimAQkR3CEiAsYYcA2deZOfP8/viekSHOJAGSzAl53q/XvDL5zjnn+8zJ5MznPN9nEVkG/BA4wR+fBPwdQFVfhQ+62kwEXlTVRlVtwN3cn9fOOMBmVZ3tP/+7v62RZkwEGj2KLwSfA97ACcGk8V4Rb+6CqFf+Rk/a1lX0tq4gicT6NbeOOLUhMEks7783JC0lQmKEJF3Lj9HFh6c9i7x/oZd53sfW7Ey3HfvS+7qJJLJEVr3/z7lrr5s6pTGIRfLvBe5T1ZOA/wASP4PJlmVSXdvau+a1PU7vX+45BDARaPQ4Ea8sBvwTeAcYQ0oh+N6cqLdhRg+a1iX0tq4giegnl+7IzJS0ecHasnBeUdqSFDzVHs9KBgitOHyI52na49/u+OPCYoil5Rykojd2EwFYIqvmPZfxxlVTpzQGtRvIACBeI/L6hPES4IsAIjIZGJQwfoWI9BORfOBKYFY74wBHiMg5/vPrcEvQRpoxEWikhYhX5gFPAHNwHsEU25WUeLGNvaaI6qXD9/SqriBtKZ68Ke3ZqYmULR+StrnTJgLDOZm1OzLSng17xJiWfuNP3BiY4tFxels3kcVSNv+5jDeunjqlcXu6bWmHW4FnRWQWkChUbwMmicgi4BJgE4CqLgIeA+YBc4GHVXVxqnH/WGXA9SJSiisZ9udufk9GJ5BDIQDf6L1kZYzPAm4ATgNS1tTKypg4MTPjqI/3mGEHwNDsluYnz2qS3lYUOk5k3Obd4//wXqASWU444poddbWDhqdj7lc+uruuf1Z6PLoZN75RMfai6jHpmDuRhfMG1Hzmos8NCFpoQ//MSMs/z66N5mdmFaTblvZ4L1a64rXsmZdOndLYK2sddhUiMgaYoqonptsWY1/ME2iklYhXFgEeApbjZ54l3+7dd6Ne2avBLR/TO7uCJNLvqmVpa1mWivq6grQtq0c1fUuhrYtGBWJJ/vSzageOPGxn4LyBfjeR+qBeDlRV39y58t0XNi/5fF8XgEawMRFopJ2IV9YK3A+swdURTLHdvAXR2LIXVDUQtbUS6Y1dQRLxclojI8+sS4vHLRVbN+c0q2alrZZYJEba4vJkyZjBQRE4P/vVvOx025CMoHYT8TTm/XvrsumvbV35lRllW5en254goKoV5gUMJiYCjUAQ8cpagHuAClxnkaREvcXLo97CpzWNXpq29NauIPtwcen2rGzp8VIs7fH+7CFpzaKMxNJ3s5HRkJ9XXyVp7SMc59NX7Sgu6F8TsExhR9C6iURjXuTZTYten1G55tslleUb0m2PYXSEiUAjMES8sibgLlxsYEqPYDS2ojzivf931WjaS0X05q4giRRdWhG4ZewFc4vS+vcNp9njXL26IDClRL7zg4WBuelKJEjdRMJetPXJinlT5u6p+K4JQKO3YCLQCBQRr6wB+AOwknbKx3ix8o3h6DuPqobTUkcuzo+O2bWxN3YFSSQyZnvVkFGx4nTb0ZblpUPSuh4a9kirCGxZMjww1+f/+t66UVlZTYHwTLbFdRMJpzVbuDkabnpk/XvPl9Zs/R8TgEZvIjAXGcOIE/HKmnHFSxfQjhCM6ZbK1ugbD6s27+pB8z7grEG1uy4elpuyvE1vIe/KpWkV0qnYVDEorckR4Vh6RaAuHjMwnfMnEgrB575UGsjPCcCD64emrZtIfaSl7i9r331qTX3lD0oqy3tN6RrDABOBRkCJeGVh4EFcQemxpPisqu6pa428+mhM63v04uu6gkR6ZVeQRGKZEW/EOTWB8wIC1FT375/O+Vtj6c3MyNw5pH9Tgzak04ZEbr59xUiRSFO67UiGEpJ0dBOpbKnfee+amY9sbKr6cUlleSDjJg2jPUwEGoEl4pVFcYVHp+I8gknj7pTGltbIv5+Ixfas6inbenNXkERiFy7flpMreem2oy1VezLDXjQ7zSJQ0p6eW70uLzBLsP37e5kXXrIqsN06erqbyLqGXRvuWv32g7taG24pqSyv6ql5DaMrMRFoBBq/xdzTwL9wdQRTZLBGoq3RV//lxTa/19029fauIIkMuXxdIOrRtWXue4Nr0+1kbU17qgE0Li0OVDmkO+5aNAy8SLrtSEVPdRNZsGdj6f1r3rmvxYv8tqSyPLDL5IbRESYCjcAT8co04pVNAf4KjABStDZTDUfffjPiLX1etXu+qIZmtzR/92gZ3B3H7mmiI3fVFI2OBqo2YJz57xe1pNuGllj6V/q9RaPT6g1ty8hRrXknTajYlm472uPmFUOGNkYj3bKM7mnMm7ptxex/bJz/oMJ9JZXlzd0xj2H0FCYCjV5DxCubhcscHoRreJ6UqLdkeTj6ziOqrTVda0GM3/byriCJZF+1pCbd3rZULFtSlHY/XLNH2peDMzcMHxRu1bSXQkrkzj/NGxTczj3d102k1Ys2PbFh7ltv7Cj7C/BQSWV5uEsnMIw0YCLQ6FVEvLJlwG+AbCBlaZaYbt7ZGnn1oZjWdlm5hm+M2bVpbC/uCpJILCMaGzlxTyATQgA2rEtvZjBAq5d+hSwakuqN2YEphgxw8qn1hYeN3h64VnKJzK4aMOKdLuwmUhNu3nXPmhlTS2u23gX8vaSyPO03KYbRFZgINHodEa9sPXA70AKk7NSh1De3Rl550ottmXOwcx4SXUESiJ23cltuP0lbS7aO2LO7f4ol/54jCMvBAPXLhgTO43TLb+YFqrtMMn7TRd1EKhr2lP+u7M2p25prf1NSWT6tpLI8sF5Qw9hfTAQavZKIV7YD+CWu3/AYICP5ljENR6e/EfFKXzjQVnOHSleQRAZ9em1g//ebmkJeJJyXcrm/p2j2JBDnKLLo8MCJ9cmXVw4tHFCdlrp8neVgu4nEVGPvVJbPvnvNjNebvPBtJZXli7raRsNIN4G4yAUBEblSRFREjuvk9t8T6V5PioiMEZEvJPx+hojc0wXHnSsiS0Rkk4js8p8vEZExndx/goh8KuH3T4vITw7Wrv0l4pXVAX8CXgNGAylLnUS9xcv8OMHa/Z3nUOgKkki0eE9d8ZHhwC5rL5w7sCYI9RdbArAcDJBRdvgQz9PALT/e+MOFgcpcTsaBdhNp9iL1j294/7WXtiydDtxWUlm+vhvMM4y0YyJwL9cB7wKf7+T23wO6+w59DPCBCFTVBap648EeVFXPVtUJwC+AZ1R1gv+o6OQhJgAfiEBVfUVV7zxYuw4Ev5bgs8ADuBjBlJm7Md20ozXy2kMxravo7PEPla4giWRduaRKQsEQOMmYN6coEBmXzV4oEOcoFM3KqN6WsTvddrTlW99ZPzIruzEwdQxT8eD6oaM370c3kZ0tdRW/K3vztdKarVOBO0sqywMVk2kYXYmJQEBECoBzgW+QIAJF5AIRmZLw+30i8lURuREXizZDRGb4r10nIstEZLmI/DZhnwYR+a2ILBSRt0TkLBGZKSLrReTT/jZjRGSWiCzyHx/1d78TOM/30n0/0R4RGSwiL4lIqYi8LyIn++O3isijCXN0SjSKyFEi8rpv56y4R1RErvHf01IRKRGRbNwy7LW+Xdf65+Q+f/vHROQeEXnPn/+z/nhIRB4QkRUiMkVEXou/drD4JWTe9+2K0m6cYF1Ta+TlJ73Y1rkdHTc/IxL5+fje3xUkkVjI0xHn7wq0V3PJwqIDWrbvapq9UIoQg56nbuXAQAjjREIh+OJXg9lyMBElJD/qRDcRVWVx9eY5v1355qzqcNMzwF+sBIxxqGMi0HEF8LqqrgGqROS09jZW1XuAbcCFqnqhiIwEfgt8DOclO1NErvA3zwdmqurpQD3wK+Bi4EqcaAGoBC5W1dOAa4H4ku9PgFm+l+6uNmbcBixW1ZOB/wWeSHjtOOATwFnALSLSmUzLh4Dv+nbehPOsgfMWfkJVTwE+raph9vUgPpPkWCOAicBlOCELcBXOs3kS8E3gnE7YtF9EvLKNuPOylvbjBGPh6FuvR6KL/6UaTVmP7vYT9mwvyOr9XUES8T6yenu/AilItx3tsXbN4ECIr5aAxAQChBcdlvZs6WT89LaVoyQUbky3HR3RUTeRcCza8vzmxa8+sWHuMkXvKqksf9EygI2+QGAucmnmOlxXCvyf1+3n/mfihN4uVY0C/wAm+a+Fgdf958uAd1Q14j8f449nAX8VkWW4pc3jOzHnROBJAFV9GxgiIvFg+ldVtVVVd+ME5rD2DuR7Qj8KPCsiS4C/4IQcwGzgMRH5FilF1Yd4SVVjqroyYe6JwLP++A5gRiePtV9EvLJa4C7cOR8NpKzpF42VlrVGp/45pnUfKiVxKHUFSWTgZ1YHPrNxV2VhIBIhmmPB8QRK6eghmuZexsno1y+WcfHksl3ptqMzpOomsqe1YfPdq2f8e/bu9YuBW0oqyxenwTzDSAt9XgSKyBCcB+9hEakAfohb6hTc0mLiOUolKNpbLowkFFaNAa0Aqhpjby/c7wM7gVOAM3A18Do0PclYfJ7EZQ+PFD13EwgBNQmxgRNUdbxv5w3Az4DDgSX++eqIxPmlzc9uJ+KVRYBngD8DQ3HFpZOiWlXXGnnp8ai3brpqLAaHVleQRLxBdY3Djm0NbEIIQCQi2trSL+2ZwRCs5eCMxn65dXtCgexP++s/Lh4OXuDK2CQjsZtITFUXVW169zcrpr2zrbl2GvDrksryQGc8G0ZX0+dFIPBZ4AlVHa2qY1T1cGADznO1ETheRHJ8L9vHE/arB+ItneYC54tIkYhk4DyJ7+yHDQOA7b4w/DJ7PW6Jc7SlBPgiuNhFYLeq1u3HnB/g77dBRK7xjycicor//ChVnauqvwB248Rge3al4l3gaj82cBhwwYHY2ln8OME5uHqCMdqJEwTViPfuu/1D00ozpb7pUOoKkkjGZxbtCoWCs8SZjNLFhbUQjFI8QRKBADWrC7qlFdrBMnx4OHfCGeu3p9uOzhDvJtIYba1+csPcZ5+smFceQ/8KPFpSWZ72VoWG0dME+guhh7gOeLHN2PPAF1R1M/AvoBS3xJu4TPAQMFVEZqjqduCnuCXOpcAiVX15P2x4ALheRN4HjgHiMTalQNRPyvh+m31uBc4QkVJc3N31+zFfMr4IfENElgIrgM/447+LJ7zghOdS3Ps8Pp4Y0snjPw9sAZbjlpvnAvtdrmV/iXhlFbhztRYYi1t6/xADsmLHFudsk1MKXr7PizX0ii+0/UGJMfzjO1N6RIPCvDlFgYkva/GCIUbjNC8eEdjr9Z13zx+kaOBLxgAsqW3e+fPSN15eUrNlM/DLksrykp4sAC0inn/tXOFf238g0v7NmbQpF9bOdvskMxpGR0iAW0AahxgiUqCqDf6S8jzgXD8+sNvJyhifgUuWuQaoAz4obZEd0sIx/bxP9M+iJDvEToALiseN+8SI4z+dm5EV6CSKzhI5fc328bcsDvRSMMC3v3xWxdRXJoxJtx0AuaFoZOp5jYFJyIgOqWk47m/TAvt5PHfCpZs3bRh1eLrtSEWISOPwnMVTi7OXNQGLcN6/Hs9uFpEGVS3wnxcDTwGzVfWWdva5ALhJVS/r4Nid2s4w4gT2ztI4JJniJ57MAm7vKQEIEPHKvIhX9hpueTiMW9YOgTI8N3ZufiYb4gIQYGZlefnvyt56YFNj1YqesrE7KbyyLBBlVzpiddmQwCzBtsRCgRGAAJl7BhY01nFAIR89wW13zktZrD3d5IX2rDo2/8Wni7OX1eMS6u5NhwBsi6pWAt8G/tsPw+lsubBU2wEUisiLIrJSRB6MexlF5M8issD3QN4W31hE7vS3LRWR3/tjQ0XkeRGZ7z/O7aFTYvQw5gk0+hxZGeP74cIAzh+cHRs6Kjc2JD+TqSIk/WeYPOL4Ey8oPuZT2RmZgf2Saw+vsKHp6Men5GRkSGAEViqOHfn5XU2NhUPTbUectyZVexkSnNjA3Ftf2XzYac2B9badNObq7TXVQwLkcW5tHZo5b8vI3DWzRNiGq/23KZ0WJXoCE8aqcaW96nE5Ky0iMg74p6qe0dbDJ65bVartXsdVmNjoP/+Lqj4nIoNVtcqPW58O3IgL0ZkDHKeqKiIDVbVGRJ4CHlDVd0XkCGBaPFnQOLQwT6DR54h4ZU3Ao8C9RdmanZdBeSoBCDB1+8rlv1v11n3rG3Yv6Y03TaHLF1f2BgEI0NTYL1B1Gb2YRtJtQyJNpcWBjrv7/k8XBuIfRFXxYjsWNYdffsTT8p0tMeYCt6dbALZDvHpCZ8uFtbfdPFVdr6oe8E9ckiPA50RkES62/QR/nzqgBVcd4yqgyd/2IuA+f+XmFZx3cX+TAY1egIlAo0/iZw/PH5Kj12aEWIJLGslJtf3u1oame9fMfPmZTQv/Vhdp6RV10eIMu3hbIEqudMTqsvwGyEz5N0gHUY0FahndWzg60F/EX/+PipE5uQ1pbbOm2rwn4r33WDg6bTE0R7Y2Z3x7/p7yB4Oa/SsiR+JKeVXS+XJh7W3XVoiriIzFNQH4uN9g4FUg169rexYuce8K9ta0DQHnJJQMG6WqaV8+N7oeE4FGn6aksnw38AfgMVxNweL2tp+7p2LTr5ZPfXD+no1vRWOxQHmJkhE5af3OwsGp6yQGifffLQrcl0zUeVMCQ+bGYYNbWzTQrcy+8s0lTR1v1fWoxjwvVvFOS+SFx73Y2gxgPvDziFcW2LheERkKPAjc59eT7Wy5sFTbAZwlImP9WMBrceW5CnFVJ2r9El2T/fkLgAGq+hrwPVzHK4A3gP9OsDM+bhximAg0+jwlleWxksryGcDPge24Ti4pC3ZH1Is9tXH+7HvXzLx/W3PNmh4y84AouGpFu/1Sg8TC+UMCV3A4aCJQCFFVkRPIotFxfvTzVaNCodYerWkY0/rN4ej0h8LRd9ZCNA/XevOhiFcWuBsLIC9eIgZ4Cye44okanS0Xlmo7cDF+d+LKcW0AXlTVpbhl4BW4UJjZ/rb9cQl7pbjatvFSZDfilyATkZXADV16BozAYIkhhpHApOJxmbjezlfjOsZ0mMF80bDjjvvYsGMm52VmByueLb+p5agnX8nMzJRA1btLxSXnXrypbPnYQLXqe/rsyl3DcnMCk6gCoF8uqRh3zfYx6bajPW64/syKV186dUx3z6MaafJia9+OePPWAoNxlQee9dtHGobRAeYJNIwESirLoyWV5VOBm4HVuFjBdmuzvbVz1apfr3z9vhW129+LaXAK5sqnluzsLQIQYMvmgYGKBwSIxILlCQSILjwiEL2V2+P23y0ZAdFu80KrasyLbX2/NfLSAxFv3i5cTN1vgUdMABpG5zERaBhJKKks3wn8yX9kAUewb9zNPjRGw5GH181+85F17/2lsqV+Q48Y2QHFn9gS2MLCyWioKwhc0kM4FhxRHydj9WFF0agGKmGlLUOLIzlnfGR9t9QBjWnNunD0rT+Ho28tUpqKcEkOP4t4ZSsjXpktbRnGftBrvASG0dP4raQWTyoetxq4HPgkroRCyuzglXXbK1eu3P7EBcXjxl047NiLC7Ny07KUGDlu066BxRqoZcz22Lwpt0k1K3AernCMwInAkJcZqt6aWTl0tDc83ba0xx13zR988TlHxyDUJc4G1eZdEW/5G15s5WZcL/D1wN8iXllQy74YRuAxEWgYHVBSWd4EPDOpeNwcXI/mo3CxgilLTsysLC+ftWvt2stHnXzq2UPGXJCbkdWjXq78K5cFOoO0LXPfHVwPBFEEBtKzVL9iUMvQ0bvTbUa7HHd8Y/+xR2/dtGHt4QcV5+ni/tbNiHjzF0FsODAEeBwoiXi9oxOOYQQVWw42jE7iF5r9DfAIrkTDKNr5H/JU9aUtSxfdvnzqvYuqNs+IxLweydT1clojI06vD7SXqC0L5hUFsoZba0BFYOuiUSmz14PE7f8374BDElS9Vlfy5cV7It7c9RA7AlgI/DTilb1tAtAwDh7zBBrGflBSWe4BsyYVj1sGfBY4D6gFUpbtaPLCkScr5pYMyc6ff+XhEyYeVzjsrAwJdd//3ieWbs/KlkBl2XbEitKidJuQlFYvmCIwY9noolhsiYZCIh1vnT7O//iewYOLdm+r2l00srP7qHqRmG6dG4nOf09pEGAEsAm4N+KVlXebsYbRBzFPoGEcACWV5TUlleUP4zyDVXQii3hPuLH54XWz3/zjqun3lNdXLuiuTOKhn9qY2x3H7U42VQwMpGerJaCewFBzXnbdrlBaO3N0lpt+tqBTQlU1FvViW+a0Rv59dzg6o0RpGIoLEXgYuM0EoGF0PVYn0DAOkknF40LAacAXgEG4dk4dLm8eWVA06NKRJ04akz/k5JCr7n/QRMZuqxp/96zBXXGsnmT04OvrY15O4LKDbxq3o+LSkXlj0m1HMjL/3+sbx5xfOzrddnSGccOv293S3D+pu1c15sV0x6KIN79EtaYJGI5zUEwB3ox4ZY3J9jMM4+Cx5WDDOEhKKstjwAJ/ifg8XKHpobjkkZSt5dY37K6+d83Mlw/LGzhj8sgTPnJM/+LTM0MZB+URy7t6aT2uaG6vYc/urHAQBSBAixfc1daWxSMyOL93lMT7+g1Lmh+467x9xpz4q1wa8RaUqO6pw7VsLALeA16MeGW9qke3YfRGzBNoGF3MpOJx/YFLcCVlBNeKrsOiw4Oy++VeOvLEM08cMPLsnIzM/P2dN5YViY556vlodo70quXgqa8M2/XtL38mkOVsvjmmcuMXR+cE0tvmDaprPPbxqfv9OUkHra0SO2bEVxpjXk5/VS8c0+0LI97iOapVDbgbpn7AEpz425hWYw2jD2GeQMPoYkoqy+uB5ycVj5uBa9T+cfa2oEsZB1gdbmr5e8W8WbkZWXM+NeKECacNPvyj+Zk5gzo7b+zjpduyc3pXQgjAvPeLAlvOpjnAnsCM6sL8hlpqCwYwIN22dEROjoau+vziHU8/UTw/6i2arzS24MTfEFw/2+eBDVbs2TB6FhOBhtFNlFSWVwH/mFQ87i3gMmAibnm4XTHY4kWiL2xZsuClLUsXXjz8uPFnF42dOCi734iO5iu6bEPg2q51hmVLhgSuIHOcZq9rYjW7i+o1/WoLzmwKtAjcuYNdzz+TMefF51aVRrzVHk74jQDKcOJvrYk/w0gPJgINo5vxW9A9Mql43DScZ/AcnAjcgfMQJiWG6rQdZSun7ShbeW7RkWPPKz564rDcwiOTbRs5fGdN0RHesO6wv7upWDcoK902pKLFC3YJlqalw+DMQHQp/BBrVsm6xx4OzXno/sx1/tBgoBBYB9wPrDbxZxjpxUSgYfQQJZXlW4C/Tioe9xLwMeAiXD/inUC7haRn716/Yfbu9RuO6V9cdEHxMacf1b9oQnYo84PYv9wrl9YCA7vN+G5kz57CwMa1tcS6puVZd6GLRxdCcERga4s2L18WLr39Z5lD5ryX87oXzajCJXzk4dq8/QWwHr+GERBMBBpGD1NSWb4L14ZuKi6b+FLcl+RuoN1yGGvqK3evqa+clpsr8886auhV5+ecogMz+4047NyqoS4HpXfR2BjyIuHcAUH1tzV7oYx029AemZuHDWxp1qbcPElry73t26IVc2a3LHzlpaaylmb1qmr6nZWdnXVaczRjDVCKK/diy76GETBMBBpGmiipLK8DXp1UPG46cBZwBTAGqMZ1IUlJJL/x6PmyY9XCzCXvjh9UPPTK1eEzjh2ffVJOjuR1t91dycK5g2pFJLAlbZq8kLQTvhkIqjfkVo84vrXHRWBLS6xp5fLIkldfaVy0YnkkXrg6Gxg5tLi5sq4mZ96WTVkPR7yyLT1tm2EYncNEoGGkmZLK8hagZFLxuPeAU4ArcWKwAdgDbbtWqGQMbDlcsmIlAGXVlbvKfsnU3Dx54/LP9Dvu7HNyTx01KuNICXhLMYC57xU1EuC6hi1eKDPoIrChtCjK8Vt7ZC5VZfs2b8Oc2S0LX3mxaVVrq8ZLHxXgEj5agH9nZWnJhg1bUrZS7ElExAOW4b7vyoDrVbVJRBT4u6p+2d8uE1fOaa6qXuaPXQH8EiduI8DPVfWlHn8ThtFNmAg0jIBQUlkeBRZOKh63GDgOt0x8PK7G4C78uMFQQfjIULbXIlm6TzHdlmb1nn26ccWzTzeuOOrozAGTL+t38vEnZB8/eEjG8B5+K51m6eIhHdZPTCctsWBnBwNEFx3Rj893rwisqvJ2Ll8aXjZtatPydWujcS91Bq67Rw4ulOFvwPypUxqDVvKnWVUnAIjIP4AbgD/iQi9OFJE8VW0GLgY+OJEicgrwe+BiVd0gImOBN0VkvaqW9vSbMIzuwESgYQQMvwPJSmDlpOJxw4FzcbUGc4H6jAGtYyXb29TeMdatjdbe96e6WcCs40/IGvyxi/NOCKIgXLdmcKCvQc3RjMx2mr4Egow1I4dEIxrJzJIuzbKur4tVl60ML5/+ZvOypYvDiTccBTjvbQyYD8wEyqdOaQy2y9QxCzg54fepuJut54DrgH/i4nQBbgJ+o6obAHwheAfwQ+DLPWaxYXQjgb4A90USli7iPK2qd4rITOAmVV2wn8ebAIxU1ddSvH4G8BVVvfEAbE1qkz8+Aoh7BNaq6mf39/j+sR4G/qiqKw9k/07OMRK450Bt7E5KKst34ApPTwFOAp0c6hfJk6xYI84D025WMcDKFZGqlSsigRSElTsLAx3D2BILBf4aGYplhqq2ZFUWj40e9N+zvi5WvXpVeEXJzJaV895v3Z7wUgYuyzcb55X+O7Bg6pTGuoOds6fwl3snA68nDD8N/EJEpuDE4aPsFYEn4DyBiSwAvtPNphpGjxH4C1wf5IOliy5iAnAG8CERKCKZvoDbL2HZSb64v4I1Gar6za4wJhX+OdgGBE4AJlJSWd6K60+8MJQf+ZuEOBu4ELcc14SLHezQE5MoCMcfnzX445ekTxBGIqLh1n4De3re/aE3iECA+uWDWorH7n+rXVWlak9s+9rySPnsWS2r2gg/gP7AINxnay7O67eul3j94uSJyBL/+SzgkfgLqloqImNwXsC210jhQ/G4SccMo9fSKy5wxr6IyCXAbThP0Drga6raICJnAncD+TgP0cW4oOY8EZkI3AGMB0biEg92i8hDOG/eZSJSANyLE40K3Kaqz4vIn4EzcWVMnlPVWw7Q7seAOv/4w4EfqepzIhIC7gPOxxU9CwGP+q/N9O1bICIN/vu7DOdl/Iyq7hSRocCDQLxl2vdUdbaI5Pvv5yTcZ/1WVX1ZRL6KWwLKBfJF5OvAFFU90X/t07hepkcBL6rqj3z7vwH8GNgGlAOtqvrfB3IuDpSSynIFtgBbJl+W/wru73khLqEE3PmtpRNfVGUrI1VlK50gPPa4rEETJ+Uefez4rKNHjsocm5nZtUuLyVi6qLAWQgO7e56DIRzLyFRVJOA5NpFFh+VweedEYDisLVs2RdevWB4un/l2c/nWLV7bskR5QBFO8OwEngAW9SavXxs6urF+BefxuwCX3BJnBe5alRj/dxouVMMwDglMBAaPxLtWgDtU9Zn4LyJSBPwMuEhVG0Xkx8APRORO4BngWlWdLyKFOA/RL4Az4mJFRG4FTgcmqmqziFyQMNfPgVpVPcnfNt639mZVrRKRDGC6iJzcicDof4hIfDn4TVX9of98BK592nG4i+9zwFU4UXoSbsmpDLcs05Z84H1VvVlE/g/4FvArnDC8S1XfFZEjgGk4cXQz8Laqfl1EBgLzROQt/1jnACf772tMm3kmAKfihPRqEbkXl5zxc9yXQD3wNrC0g3PQrUyd0hjBfUGVTr4sfxBwIjAJOBonAjstCFevilSvXhWZD8zPzZOM8y/IHT3htJyjxx6ZefTAQRlDu8N+PzN4YHccuyvxVKOZbikxsMjy0UWx2KJYKJQ8kaW6yqtctzZSvnB+a/m7JS2bI5EPeY1zcMIvA/e5eRlYBGydOqXxUPd8PYq77i1rcz38PfCsiLytqhX+deJ/CfiqgWHsD4G+sPVROrpr/QguY3S2753IBuYAxwLbVXU+gKrWAak8GK/42XBtuQj4fPwXVa32n35ORL6N+7yM8OfvSASmWg5+SVVjwEoRibc5mwg864/vEJEZKY4ZxhWdBViI83TG7T4+4b0Wikh/4BLg0yJykz+ey15v4ZuqmqqExXRVrQUQkZXAaNwX5DvxfUTkWeCYFPv3OFOnNFbjlrpmTb4sfzAunumABGFLs3rTpjavnza1eT3wxmGHZxRMPD/vyOPGZ409/IjMI/PzQ4VdYfOShUXBzrjw8VSjmQG/Vma05mTV7MzYNXhEbChAY0OsduuW6Mby8kjFu++0rNuwPprMi5co/JqA6bhEjw29bLn3oFDVLbgbybbjS/yb7H+LSBYuQ+hHqrqkh000jG4j0Bc2IymCEzDX7TMocjKdj1VJ1ZXiQ/EuflmEm4AzVbXaX9LNTbJvZ0lMZJA2Pzsioqpx+zz2fn5DwDltha04VXi1qq5uM3427XfmSLQxPk+w1wMTmDqlsYouEoQAWzZ7DU//vaEUX/ifcGLWkAmn5Rx+5FFZh40YmTFq4KDQsNAB1CRcXTY48OVXAKIai+YQ3MYhitJI8+6tr+Wvqh28Y/a891sq1pZ/UMalLQU476vgQiqm426o1k+d0hjocj0HiqoWdHZcVWfi4h7jv78AvNBdthlGujER2Pt4H7hfRI5W1bUi0g84DFgFjBSRM/3l4P64i3w9Lri7M7wB/DfwPfhgObgQJ5hqfc/dZBIukl3Eu8D1IvI4MBQXm/PUfuwft/t34DKi/bv1acB3ReS7qqoicqqqLj5AG+cBd/nnpB64mn2zuANJO4LwKH+TMFDl/+wUK5ZH9vgdIpYA9C+UrDPOzBk5/vjsww4fnTlq2PCMw/r1C3X4mduxbUCgM4PjRFWj6bYhkXAsGt6pe8L1mQ1LNsn2zStl3eYqqW3m1aSbZ+ASO/rhhN92XBjGSpzH75AUfoZhdA4TgcGjbUzg66r6k/gvqrrLT174p4jk+MM/U9U1InItcK+I5OEE4EXADOAn/jHv6GDuX+EE5nKcB+w2VX1BRBbjgqTXA7M7+T4SYwJ3q+pF7Wz7PK4O3nJgDS4Lsd22aW240be7FPeZLsEVhL0d+BNQ6nsFK3BJJfuNqm4Vkd/4tm3DfYnuj41pp40gLMR5BifgYkRzcSKhzn90ejmwvk4jM6a3bJwxvWVjfOzIozILTz0t57Cjjsk6bMSIjBEDB4WG5eWF9hF9zU35Aw76TfUA0ZimTSg1RsM1NeGmHbtaG3Zuba7ZsbZ+146NTbvrso+u+lzGgNbZEqIlyW75OOEnuP/jZbhl3vKpUxr3JNneMIw+iuxdXTOM9CEiBX6G8xCc1+1cVd2RbrsSSbAxE3gRl8H8YrrtOlgmX5afgfMmH4vLhox7CRWowbWvOyhqa7LH1FQNOumj52UuO+mUWHFhYfbg737ziuFCXpFIVv7BHr87+efZOyuH5+YWd+ccYS/a3BBt3VMVbqrc2VK3c3NT9Y5VdTt31kaak9aBzDqi9pOZg5vLQ9mxdTgBPxAX46e4On5zcTdVFVOnNHbay2sYRt/CRKARCPxSMANxiS7/p6qPpdOeZIjI73He1VzcEvT/6CH4DzT5svw8XLb2cThROAInLgQnCOuA/VoirdyZN2Ht6gH9mxqzZ7V9TcjPDYWKh4gMLgpJwWDIKxTJLRSyB0B2oUhGt5eraY8nzty54/B+uQdVRzGmqi1epLYx2lpTH22tqQ031+wON1Rtb67dU9FYVVUdbkrm0UuGAP0yBjWdmX1EPaEcbz1OqC/DF33A7j6Q0WsYRhdgItAwjHaZfFl+f2AULkv6BFxWdFyYeThR2Eg7iSYbNxSct7mif31LS9aS/Z1fyM8VGVQYksIBIgWFIv0KIbdQyM5HMvOEzDzcI9evOdmlPHz6jm1HFeSNbDseU9VwLNrUGos2tnrRxmYv0tjkhRubouHGhmhrY32kpbE20ty4q7WhYWtTTV1UYweScZuNS+YoYK8Q3y5Z3prssTWbMwe2LgWqTPQZhnEgmAg0DGO/mHxZfghXz3EULq7wBNxyclyktOBKjjThxxauWjFo8s4deSsj4cyNSQ/aZeRmi+TlCLnZIjk5kJMjZGVDKAQiSCgEEgIJCSL+uHuNUKgws35cvwzZLZK5PSReVPC884oaWodke16LF4k0eeFwsxcJN0RbwzXhppYuvHoKLnmjAOdpjvljjbhY3DKcl29zSWV5e5nthmEYncZEoGEYB83ky/JzcaLwMFxM4VjcMrIALF1UdM7uytx5qqHduKSlQGaljsrzLhyWoztyMijrpikEJ/LycKIvxF4P6k5grf/Y4T/q/S4xhmEYXY5lBxuGcdBMndLYgmthuA54B2DyZfmZuDZcQ7OzvbdVJRc4HNe2MIO9nsMQrkRNC65GYytpEokxJaYHf13Mwgm9+EPY69kTYDfOu7fJf+wAKksqyy2BwzCMHsU8gYZh9ChZGeNDwACcQBzgP4bjlpiH4rpYZLG3TE1cKEb9RyThp+c/75IOF8NzvYkjcrUhN8PVQEyYO9O3KTvhZ8ifV9ts24zz6u3AlRPaBVQDe4CaksryQNUdNAyj72KeQMMwepSIVxbDiaLqZK9nZYyPL5kW+o8BuILnAxPGCv2xfNzSajb7CrIDuruNxPDEibwj2NtBpxUn7GpxmbjVuALbNbiYvSb/ZyPQ2Jc8eiKiwB9V9f/5v98EFKjqre3scwEQVtX3utiWr5LQJz3FNsOAR3Ae6SygQlU/1ZV2JJnzPVX9qN97+KOq2ulC+P65ehnYkDB8k6q+lXSH9o91BvAVVb1xf/c1Dl1MBBqGESgiXpniRFfco9YhWRnjM3FCMAd3XQux1zPX9nni7x57vYlebSTkZYgXxnkZw0DUYvLapRW4SkTuUNXdndznAlypoS4TgX7tzs7wS1zbzbv9/U7uKhuS2JShqp6qftQfGgN8gf3rhgQwS1UPqMh9In4v92T93LuM+HvuzjmMrsVEoGEYvZ6IVxZfKm5Kty19jCjwEPB94ObEF0RkKPAgzqsKrh3lVlw3H09EvgT8D/A3XDLRAJyH9QJVLRGRWcDX/LFHgSNxf99vq2qpiNyKiy8dg4uzfCNh7kuBnwGXtxGnIxK3U9XShH1+CHwOdyPxoqre4o9/Bdc/XYFSVf2y30N9iqo+52/ToKoFvufuFlx7vgnA8fHXgDuB8X73pseBq4Dv+i0uEZHZwH8m2pQK36s4Fddy86P+ef2MqjaLyJk4b2ej//pkVT3Rt+0mVb3MP3dH+Of0COBPqnqPf+wv4bowZeOKjv+Xqnoicglwm39+1gFf84vnV/h/n0uA+4CnO7LfCA69ooG7YRiGEVjuB74oIm3bAN4N3KWqZ+J6bT+sqhU4YXiXqk5Q1XdwrSKPByYCC4Hz/JaYh6nqWpzwWKyqJwP/CzyRMMfpOPHzhfiAiFwJ/AT4VBLv5P3AIyIyQ0RuFpGR/j6XAOOAs/BbKYrIJBE5ASduP6aqp+BEa0ecBdysqse3Gf8Jzqs3QVXvAh4GvurPfwyQk0IAniciSxIe8Y4+44D7VfUEXGjC1f7434AbVPUc2k+wOg74hG/vLSKSJSLjgWtxHZsm+Pt/UUSKcKL6IlU9DedR/EHCsVpUdaKqmgDsZZgn0DAMwzhgVLVORJ7AeY+aE166COcJi/9eKCL9kxxiFjAJV1boDuBbuAzz+f7rE/EFjqq+LSJDEgTnK6qaOOeFuC43l6hqXRJbp4nIkcAngcnAYhE5EefFugRY7G9agBNZpwDPxcWkqlZ14pTMU9UNHW/Gs8DPfQ/k14HHUmz3oeVg3xO4Ie5FxInnMSIyEOifEG/5FKn7pb+qqq1Aq4hUAsNwPdxPB+b7f7c8oBL4CE6oz/bHs4E5Ccd6puO3awQR8wQaPY6IHCYiL4tIuYisE5G7RSS7B+b9avzO3//9YRFpe7feVXONEREVkdsTxopEJCIi93XHnIaRRv4EfAOXqBMnBJzje74mqOooVa1Psu8s4DycR+o1XALQBUCJ/7ok2Scep9m2cPZ6XMLQMakMVdUqVX1KVb+ME5qT/DnuSLD1aFV9hL3JQW2J+u8Pcaoo8frVqWLeqtoEvAl8BrcMvb+xgol9pT2cUyfZudrf/R9POA/H+kk+gouljI8fr6rfSNjfCpj3UkwEGj2Kf8F8AXhJVcfhLtYFwK+76PgZ7bz8VVwMEQCq+k1VXdkV86ZgPfvehV8DrOjG+Q6I/QiqN4yk+B6yf+GEYJw3gA8ydUVkgv+0HifU4szFxbXFVLUFWAL8B04cghODX/SPcQGwO5mXz2cjLtbuCX8pdx9E5GMi0s9/3h8Xi7gJmAZ8XUQK/NdGiUgxMB34nIgM8ccH+4eqwHnMwIm4zvS3bvu+wS0J3wPM76SXsV1UtRqoF5GP+EOf389DTAc+6793RGSwiIwG3gfOFZGj/fF+/hK20csxEWj0NB/DxY/8DcDPJPs+7gLcz/fWvSwir4vIahG5Jb6jiHxJROb5cTF/iQs+EWkQkV+KyFzgHBH5hYjMF5HlIvKQOD6LWyb6h79/nojM9MsmICLXicgyf5/fJszZICK/FpGlIvK+uBITiMg1/rZLRSTusWhLM1AWnwMXa/OvhGNfLiJzRWSxiLyVcOxbReRR3771InJjwj4vichCEVkhIt9OGP+GiKzx9/lr3NsoIkNF5Hn/fMwXkXMT5nhIRN5g3xgrwzhQ/oCr8RjnRuAMESkVkZW4hBCAfwNX+v+H5/lLkptxQgOc+OsPLPN/vzV+HFxyxfXtGaGqq3Gi8VnZGz8X53RggX+sObg4xfmq+gbOEzdHRJYBz+GWVVfgblDfEZGlwB/94/wVOF9E5gFn0zlPWCkQ9a8Z3/dtXYjrvf23dvZrGxP42Q7m+QbwkIjMwXnwajthG749K3Gxf2/45+hNYISq7sLdRP/TH38fF1No9HZU1R726LEH7ovhriTji4GTcRea7bhCwnnAcpx4G4/78sjyt38AV/MK3HLN5xKONTjh+ZO4DEGAmbg6YiT+jvMObsIVKs4E3gauSDh2fP//A37mP18GjPKfD0zyfsb4tn8a+D2undp0//3d528ziL0F278J/MF/fiuufEYO7kt1T8L7Huz/jJ+bIb79FcBgnEdiVsIcTwET/edHAGUJcywE8tL9mbCHPfrqw//fXQOEuvCYBQnPfwLcne73aY/gPmwZyOhpUsXYJI6/qap7AETkBVxgeJTkAcvg4lmeTzjWhSLyI1xv1sG4Jdh/t2PTmcBMdXe7iMg/cHFCL+FqxU3xt1sIXOw/nw08JiL/wi1vp+J14HZcvbu2wdOHAc+IyAhcTFFiMHmyoO0twI3ish/BFbwdh+u28Y76y0ki8ix7Y6LaC85vG1RvGEYPIa70zK+BH6hql3S88blURH6Ku6HdiJ+BbBjJMBFo9DQr2FvKAAARKcQJmnU4oddWJMZ7zD6uqj9NcswW9QuUikguzkt4hqpuFlcPK7cDm9oLpo6oatyeePA0qnqDiJwNXAosEZEJceG6j+GqYRFZCPw/4ATg8oSX78V1W3hFXKzTrQmvfSho29/mIlywfZOIzGRvb9pUxIPz9xF7vii0YG7DSBOq+gTdEIqhqs9g2bpGJ7GYQKOnmQ708++C44kcfwAeU5ctB3CxH5CcB1yB87qlClhuS1zw7RYX5J0YP5MsMBtcYPr54rJ3M4DrcCUqUiIiR6nqXFX9Ba5Q7eHtbP4H4MdJROIAXJFX6CDOKWH7al8AHocr2wAwz7d/kLgkj0SRnSo43wgQIuL58V4r/JixH4jIAV+fRWSiuPjZVf7j2x3v1anjVvixs/H4tHsO4liviStpYhhGmjBPoNGjqKr6y5kPiMjPcTcir+GKwMZ5FxfLdzTwlLp2R4hIPGA5hGvr9R3cckfi8WtE5K+4mL0K9tYaA1eH60ERaQbOSdhnu798MgPnVXtNVV/u4K38TkTG+dtPB5a2855XkDwr+FZc8PpWXKD12A7mfB24wQ/MXu3vg6puFZHf4MTsNmAle4PBbwTu9/fJxGVa3tD2wEbaaVZXnBf/RucpnOi/pb2dkiEiw/39r1DVReIK/U4Tka2q+moX2Hqhdr5FXEq0+3v2ZqpqtDvnMIzejuxd6TKM9COdaALfWxERDydOM4Ey4PoE72fbbceQ0Gy+o/MiIgXqWjhlAi8Cj6rqi52w6avA73DxhgW4sja36d5is12CvyzfoKq/78rjHirI3tZi8d+PxN3AFOFulO7E1c7LwXWJ+IuIPIMLkXjN3+cxXOzrBNz91i8Sjvdx4FZVPc/frgUXnjAMF5M2RVxJlb/h4lNDwNWqWt7Gzgrc53B3m/GZuJuQC3F1/r6hqrPElWN5DJdJWoZLmPqOqi6IHwv3uUvVAu0oXJePobiWcd9S1VWSpCWdqs6WNq3kNKGTiGEYH8aWgw2j52hWV2j1RFzCSXseuTG4ZvOd5VZxPUmX4xJMXtqPfZ9R1VPV1W28E3hBXPuoXoMcYrUOVXU97vpcjCv5Uauu/dqZwLdEZCyuR+u1AOKKrX8c51U/AZfElMgCfzzOGOB8XEzrg34s7Q24TNIJOHG2JYV5MxKWg7+fMJ6pqmfhegTHPZj/hQthOBmXIHU6yUnVAu0hXH/d03H9ex/wxz/Uki7hWB9qJWcYRnIOqQun0ftR1cdI3T7pUGIWcLK4jiK7VfVuABH5NS6T+Avs22y+GhgpIq/jCty+qKo/8ve5DtcDVICXVfXH/ngD7svyMlzNws+o6s72jFLVGSLyEPBt4PvJPDG4Ej5LgSNVNeZ7e1aztxn9hzw3iXP4cYkP4rK31wFfV9Vq35u0BNc5otAfnyci+bgkmpNw16xbVfVl34t5KS4ONB9Xg/JQIp7wcwnusxKPbx2AE01TgXvE9dn9JFDie89SZeAnjv3Lz0gtF5H1OE/dHOBmETkMeKGtFzCBVMvB8Sz5hTiRCS6z/24AVV3uhyUkY4N+uAVaAc4z+GxCdnuO/9Oy3g2jCzBPoGH0ML7XajJuafgR/KQQP9bx88A/+HCzeXDLfNfixNC1InK4uDZ4v8UJoAnAmSJyhb99PvC+usb3JTgB1xkWsbcQ7Ic8MapaixOB5/vbXA5MU9VIsu2THP8JXKLMyf45SIx7y1fVj+I8SI/6YzcDb/tenwtx8Zjx9mTn4JbVDykB6C8He7gySII7pxP8x1hVfUNdd42ZuBuAa3GeQXDxp2e0OeTpuFjROB/KwPdDDz6Nu2GYJiL7e07jGe0fZNHT+TZmyVqYhYCahPc9QVXjHur2WtJZ1rthdBITgYbRc+T5nr0FuOLUj6hqBbBHRE7Fb2CfrNSMz3RVrfW//FcCo0mocegHwcdrHMKHaxyO6aSdAi7OkL2emCXAX4AR/jbP4C9F4oTrMx1sj3/MAbji2vHs68cT7AX4J4CqluC8OwNx5+Un/jFn4jx/8ViwN7UL2m0FiYR4t/vUBW1PA/5TRLL8149JEMFPA1/D9d6d5o/dD3w1ngkuruXZb3HFzuNcIyIh39N7JLDaF57rVfUe4BVc8faD5V1cX1zE9ek+qbM7qmsNt0FErvH3FxE5xX/Zst4Nowuw5WDD6Dk+yABtw8O4gq7D2ev9Ssb+NoxPWuOwE5yKC+L/wBOTZJtXgDvE9VI9HddlJb+d7TtLqhqRV6trB/YB4uo0Hipen/gNQhauMPqT7G1R9jBOwC/yl3p34UongRNDT+CWQMPwQbb7l4C/+kukAvxJVRMLpq/GlUEaBtygqi0ici3wJRGJADuAX6awdYaf5ARQqqpfaed9PQA87i8DL8a1Tut0GzNc+7c/+5UBsnCidymW9W4YXYKJQMNIPy/ivnCz2JsMkqqmYVvmAnf7ZUCqcTUO7z1QQ0TkfFw84IWqWiciG0TkGlV91hcgJ6vqUj8TeR4u3muKumLdKbePH19Va0WkWlzP2FnAl9m3JuO1OJExEZcMUSsi04Dvish3VVVF5FRVXXyg7zGIqGpGO6/FcCWU/jfJaxFc68C24yU4L3EqZqtqYlIHqnoHcEcHdo5JMX5BwvPd7PU6twBf8kXmUbhyShvbHGs3cGLC/r9PeL4BF+/Ydr7d7PVEJ47f2p79hmHsi4lAw0gz6rqKzMB50T7wsOA3m8clylSn2PdAahy25VpfdPXDZRZfrapl/mupPDHgloSfxZUuoRPbx7kel5HaD1eS5msJr1WLyHv4iSH+2O3An4BSX1hW4JJdjODTDyfqs3Cfz/+MeywNw0g/VifQMNKMnxCyCLimnYzMQx4/O/gm9YuDG4ZhGN2LJYYYRhrxg+XX4pI++qwANAzDMHoe8wQahmEYhmH0QcwTaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0QcxEWgYhmEYhtEHMRFoGIZhGIbRBzERaBiGYRiG0Qf5/0zuCzG/Onm2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = df['Category'].value_counts()\n",
    "labels = df['Category'].unique()\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.pie(counts,labels=labels,autopct='%1.1f%%',shadow=True, colors=plt.cm.plasma(np.linspace(0,1,3)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5ef849",
   "metadata": {},
   "source": [
    "# Exploring Resume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8a97c061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Data Science'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "66bc53d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Skills * Programming Languages: Python (pandas, numpy, scipy, scikit-learn, matplotlib), Sql, Java, JavaScript/JQuery. * Machine learning: Regression, SVM, Naïve Bayes, KNN, Random Forest, Decision Trees, Boosting techniques, Cluster Analysis, Word Embedding, Sentiment Analysis, Natural Language processing, Dimensionality reduction, Topic Modelling (LDA, NMF), PCA & Neural Nets. * Database Visualizations: Mysql, SqlServer, Cassandra, Hbase, ElasticSearch D3.js, DC.js, Plotly, kibana, matplotlib, ggplot, Tableau. * Others: Regular Expression, HTML, CSS, Angular 6, Logstash, Kafka, Python Flask, Git, Docker, computer vision - Open CV and understanding of Deep learning.Education Details \\r\\n\\r\\nData Science Assurance Associate \\r\\n\\r\\nData Science Assurance Associate - Ernst & Young LLP\\r\\nSkill Details \\r\\nJAVASCRIPT- Exprience - 24 months\\r\\njQuery- Exprience - 24 months\\r\\nPython- Exprience - 24 monthsCompany Details \\r\\ncompany - Ernst & Young LLP\\r\\ndescription - Fraud Investigations and Dispute Services   Assurance\\r\\nTECHNOLOGY ASSISTED REVIEW\\r\\nTAR (Technology Assisted Review) assists in accelerating the review process and run analytics and generate reports.\\r\\n* Core member of a team helped in developing automated review platform tool from scratch for assisting E discovery domain, this tool implements predictive coding and topic modelling by automating reviews, resulting in reduced labor costs and time spent during the lawyers review.\\r\\n* Understand the end to end flow of the solution, doing research and development for classification models, predictive analysis and mining of the information present in text data. Worked on analyzing the outputs and precision monitoring for the entire tool.\\r\\n* TAR assists in predictive coding, topic modelling from the evidence by following EY standards. Developed the classifier models in order to identify \"red flags\" and fraud-related issues.\\r\\n\\r\\nTools & Technologies: Python, scikit-learn, tfidf, word2vec, doc2vec, cosine similarity, Naïve Bayes, LDA, NMF for topic modelling, Vader and text blob for sentiment analysis. Matplot lib, Tableau dashboard for reporting.\\r\\n\\r\\nMULTIPLE DATA SCIENCE AND ANALYTIC PROJECTS (USA CLIENTS)\\r\\nTEXT ANALYTICS - MOTOR VEHICLE CUSTOMER REVIEW DATA * Received customer feedback survey data for past one year. Performed sentiment (Positive, Negative & Neutral) and time series analysis on customer comments across all 4 categories.\\r\\n* Created heat map of terms by survey category based on frequency of words * Extracted Positive and Negative words across all the Survey categories and plotted Word cloud.\\r\\n* Created customized tableau dashboards for effective reporting and visualizations.\\r\\nCHATBOT * Developed a user friendly chatbot for one of our Products which handle simple questions about hours of operation, reservation options and so on.\\r\\n* This chat bot serves entire product related questions. Giving overview of tool via QA platform and also give recommendation responses so that user question to build chain of relevant answer.\\r\\n* This too has intelligence to build the pipeline of questions as per user requirement and asks the relevant /recommended questions.\\r\\n\\r\\nTools & Technologies: Python, Natural language processing, NLTK, spacy, topic modelling, Sentiment analysis, Word Embedding, scikit-learn, JavaScript/JQuery, SqlServer\\r\\n\\r\\nINFORMATION GOVERNANCE\\r\\nOrganizations to make informed decisions about all of the information they store. The integrated Information Governance portfolio synthesizes intelligence across unstructured data sources and facilitates action to ensure organizations are best positioned to counter information risk.\\r\\n* Scan data from multiple sources of formats and parse different file formats, extract Meta data information, push results for indexing elastic search and created customized, interactive dashboards using kibana.\\r\\n* Preforming ROT Analysis on the data which give information of data which helps identify content that is either Redundant, Outdated, or Trivial.\\r\\n* Preforming full-text search analysis on elastic search with predefined methods which can tag as (PII) personally identifiable information (social security numbers, addresses, names, etc.) which frequently targeted during cyber-attacks.\\r\\nTools & Technologies: Python, Flask, Elastic Search, Kibana\\r\\n\\r\\nFRAUD ANALYTIC PLATFORM\\r\\nFraud Analytics and investigative platform to review all red flag cases.\\r\\nâ\\x80¢ FAP is a Fraud Analytics and investigative platform with inbuilt case manager and suite of Analytics for various ERP systems.\\r\\n* It can be used by clients to interrogate their Accounting systems for identifying the anomalies which can be indicators of fraud by running advanced analytics\\r\\nTools & Technologies: HTML, JavaScript, SqlServer, JQuery, CSS, Bootstrap, Node.js, D3.js, DC.js'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Resume'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639b4549",
   "metadata": {},
   "source": [
    "# Cleaning Data:                                      \n",
    "1 URLs,                                                \n",
    "2 hashtags,                                             \n",
    "3 mentions,                                                     \n",
    "4 special letters,                                             \n",
    "5 punctuations:                                             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "16656511",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def cleanResume(txt):\n",
    "    cleanText = re.sub('http\\S+\\s', ' ', txt)\n",
    "    cleanText = re.sub('RT|cc', ' ', cleanText)\n",
    "    cleanText = re.sub('#\\S+\\s', ' ', cleanText)\n",
    "    cleanText = re.sub('@\\S+', '  ', cleanText)  \n",
    "    cleanText = re.sub('[%s]' % re.escape(\"\"\"!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\"\"\"), ' ', cleanText)\n",
    "    cleanText = re.sub(r'[^\\x00-\\x7f]', ' ', cleanText) \n",
    "    cleanText = re.sub('\\s+', ' ', cleanText)\n",
    "    return cleanText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8b2c86ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'my webiste like is this and a ess it '"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanResume(\"my #### $ #  #noorsaeed webiste like is this http://heloword and access it @gmain.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e752795a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Resume'] = df['Resume'].apply(lambda x: cleanResume(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ba537207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Skills Programming Languages Python pandas numpy scipy scikit learn matplotlib Sql Java JavaScript JQuery Machine learning Regression SVM Na ve Bayes KNN Random Forest Decision Trees Boosting techniques Cluster Analysis Word Embedding Sentiment Analysis Natural Language processing Dimensionality reduction Topic Modelling LDA NMF PCA Neural Nets Database Visualizations Mysql SqlServer Cassandra Hbase ElasticSearch D3 js DC js Plotly kibana matplotlib ggplot Tableau Others Regular Expression HTML CSS Angular 6 Logstash Kafka Python Flask Git Docker computer vision Open CV and understanding of Deep learning Education Details Data Science Assurance Associate Data Science Assurance Associate Ernst Young LLP Skill Details JAVASCRIPT Exprience 24 months jQuery Exprience 24 months Python Exprience 24 monthsCompany Details company Ernst Young LLP description Fraud Investigations and Dispute Services Assurance TECHNOLOGY ASSISTED REVIEW TAR Technology Assisted Review assists in a elerating the review process and run analytics and generate reports Core member of a team helped in developing automated review platform tool from scratch for assisting E discovery domain this tool implements predictive coding and topic modelling by automating reviews resulting in reduced labor costs and time spent during the lawyers review Understand the end to end flow of the solution doing research and development for classification models predictive analysis and mining of the information present in text data Worked on analyzing the outputs and precision monitoring for the entire tool TAR assists in predictive coding topic modelling from the evidence by following EY standards Developed the classifier models in order to identify red flags and fraud related issues Tools Technologies Python scikit learn tfidf word2vec doc2vec cosine similarity Na ve Bayes LDA NMF for topic modelling Vader and text blob for sentiment analysis Matplot lib Tableau dashboard for reporting MULTIPLE DATA SCIENCE AND ANALYTIC PROJECTS USA CLIENTS TEXT ANALYTICS MOTOR VEHICLE CUSTOMER REVIEW DATA Received customer feedback survey data for past one year Performed sentiment Positive Negative Neutral and time series analysis on customer comments across all 4 categories Created heat map of terms by survey category based on frequency of words Extracted Positive and Negative words across all the Survey categories and plotted Word cloud Created customized tableau dashboards for effective reporting and visualizations CHATBOT Developed a user friendly chatbot for one of our Products which handle simple questions about hours of operation reservation options and so on This chat bot serves entire product related questions Giving overview of tool via QA platform and also give recommendation responses so that user question to build chain of relevant answer This too has intelligence to build the pipeline of questions as per user requirement and asks the relevant recommended questions Tools Technologies Python Natural language processing NLTK spacy topic modelling Sentiment analysis Word Embedding scikit learn JavaScript JQuery SqlServer INFORMATION GOVERNANCE Organizations to make informed decisions about all of the information they store The integrated Information Governance portfolio synthesizes intelligence across unstructured data sources and facilitates action to ensure organizations are best positioned to counter information risk Scan data from multiple sources of formats and parse different file formats extract Meta data information push results for indexing elastic search and created customized interactive dashboards using kibana Preforming ROT Analysis on the data which give information of data which helps identify content that is either Redundant Outdated or Trivial Preforming full text search analysis on elastic search with predefined methods which can tag as PII personally identifiable information social security numbers addresses names etc which frequently targeted during cyber attacks Tools Technologies Python Flask Elastic Search Kibana FRAUD ANALYTIC PLATFORM Fraud Analytics and investigative platform to review all red flag cases FAP is a Fraud Analytics and investigative platform with inbuilt case manager and suite of Analytics for various ERP systems It can be used by clients to interrogate their A ounting systems for identifying the anomalies which can be indicators of fraud by running advanced analytics Tools Technologies HTML JavaScript SqlServer JQuery CSS Bootstrap Node js D3 js DC js'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Resume'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9034b60c",
   "metadata": {},
   "source": [
    "# words into categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "72994ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "af36ab05",
   "metadata": {},
   "outputs": [],
   "source": [
    "le.fit(df['Category'])\n",
    "df['Category'] = le.transform(df['Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0c4bd0dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 12,  0,  1, 24, 16, 22, 14,  5, 15,  4, 21,  2, 11, 18, 20,  8,\n",
       "       17, 19,  7, 13, 10,  9,  3, 23])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Category.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c915950",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ['Data Science', 'HR', 'Advocate', 'Arts', 'Web Designing',\n",
    "#        'Mechanical Engineer', 'Sales', 'Health and fitness',\n",
    "#        'Civil Engineer', 'Java Developer', 'Business Analyst',\n",
    "#        'SAP Developer', 'Automation Testing', 'Electrical Engineering',\n",
    "#        'Operations Manager', 'Python Developer', 'DevOps Engineer',\n",
    "#        'Network Security Engineer', 'PMO', 'Database', 'Hadoop',\n",
    "#        'ETL Developer', 'DotNet Developer', 'Blockchain', 'Testing'],\n",
    "#       dtype=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a65059b",
   "metadata": {},
   "source": [
    "# Vactorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d0e3603a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "\n",
    "tfidf.fit(df['Resume'])\n",
    "requredTaxt  = tfidf.transform(df['Resume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7d911c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<962x7351 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 164261 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "63d3c383",
   "metadata": {},
   "source": [
    "# Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0f67afa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab60004d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(requredTaxt, df['Category'], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8d542f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(769, 7566)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bc6048f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 7566)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06b1be4",
   "metadata": {},
   "source": [
    "# Now let’s train the model and print the classification report:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d24938b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9792746113989638\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "clf = OneVsRestClassifier(KNeighborsClassifier())\n",
    "clf.fit(X_train,y_train)\n",
    "ypred = clf.predict(X_test)\n",
    "print(accuracy_score(y_test,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "aa41007d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 14, 15, 17, 15, 14, 10, 14, 15, 11,  6, 23,  4, 11, 13,  4, 19,\n",
       "        8,  8,  9, 12, 11, 17, 22, 19, 16,  5,  8,  3, 24, 20, 18, 22,  7,\n",
       "       23, 23, 22, 18,  7, 20, 10, 20, 14,  8, 15, 15,  8, 11,  4, 22,  1,\n",
       "       24, 14, 15, 22, 23,  8, 15,  3, 17, 18,  3,  0, 15, 15, 15, 16, 21,\n",
       "       13, 18, 12, 23, 22, 12, 13, 22,  8,  7, 19,  4, 24, 14,  7,  1, 24,\n",
       "       13, 12, 10,  9,  8, 22,  9, 23, 11,  9, 23, 11, 15, 23, 13,  4, 17,\n",
       "        2,  5,  6, 10,  0, 19, 20, 10, 22, 10, 15, 10, 15, 15, 22, 15, 14,\n",
       "        6,  0,  4,  5,  7,  9, 13, 23,  6,  9,  9, 21, 11,  5,  3,  9, 24,\n",
       "       19, 13,  8,  3, 13, 13, 11, 20, 16, 23, 21, 24,  7, 21, 20, 15, 22,\n",
       "       19, 15, 23,  9, 15, 15,  6,  2, 20,  7, 11, 23, 24,  8,  3, 20,  2,\n",
       "       10, 22, 15,  2, 11, 23,  1, 23,  6,  3,  3, 24, 24, 12,  5, 23, 18,\n",
       "       22, 20, 20,  3,  6, 15])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7594d627",
   "metadata": {},
   "source": [
    "# Prediction System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "716d3471",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(tfidfd,open('tfidf.pkl','wb'))\n",
    "pickle.dump(clf, open('clf.pkl', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06b1805c",
   "metadata": {},
   "outputs": [],
   "source": [
    "myresume = \"\"\"I am a data scientist specializing in machine\n",
    "learning, deep learning, and computer vision. With\n",
    "a strong background in mathematics, statistics,\n",
    "and programming, I am passionate about\n",
    "uncovering hidden patterns and insights in data.\n",
    "I have extensive experience in developing\n",
    "predictive models, implementing deep learning\n",
    "algorithms, and designing computer vision\n",
    "systems. My technical skills include proficiency in\n",
    "Python, Sklearn, TensorFlow, and PyTorch.\n",
    "What sets me apart is my ability to effectively\n",
    "communicate complex concepts to diverse\n",
    "audiences. I excel in translating technical insights\n",
    "into actionable recommendations that drive\n",
    "informed decision-making.\n",
    "If you're looking for a dedicated and versatile data\n",
    "scientist to collaborate on impactful projects, I am\n",
    "eager to contribute my expertise. Let's harness the\n",
    "power of data together to unlock new possibilities\n",
    "and shape a better future.\n",
    "Contact & Sources\n",
    "Email: 611noorsaeed@gmail.com\n",
    "Phone: 03442826192\n",
    "Github: https://github.com/611noorsaeed\n",
    "Linkdin: https://www.linkedin.com/in/noor-saeed654a23263/\n",
    "Blogs: https://medium.com/@611noorsaeed\n",
    "Youtube: Artificial Intelligence\n",
    "ABOUT ME\n",
    "WORK EXPERIENCE\n",
    "SKILLES\n",
    "NOOR SAEED\n",
    "LANGUAGES\n",
    "English\n",
    "Urdu\n",
    "Hindi\n",
    "I am a versatile data scientist with expertise in a wide\n",
    "range of projects, including machine learning,\n",
    "recommendation systems, deep learning, and computer\n",
    "vision. Throughout my career, I have successfully\n",
    "developed and deployed various machine learning models\n",
    "to solve complex problems and drive data-driven\n",
    "decision-making\n",
    "Machine Learnine\n",
    "Deep Learning\n",
    "Computer Vision\n",
    "Recommendation Systems\n",
    "Data Visualization\n",
    "Programming Languages (Python, SQL)\n",
    "Data Preprocessing and Feature Engineering\n",
    "Model Evaluation and Deployment\n",
    "Statistical Analysis\n",
    "Communication and Collaboration\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6ccfb164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Category: Data Science\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# Load the trained classifier\n",
    "clf = pickle.load(open('clf.pkl', 'rb'))\n",
    "\n",
    "# Clean the input resume\n",
    "cleaned_resume = cleanResume(myresume)\n",
    "\n",
    "# Transform the cleaned resume using the trained TfidfVectorizer\n",
    "input_features = tfidfd.transform([cleaned_resume])\n",
    "\n",
    "# Make the prediction using the loaded classifier\n",
    "prediction_id = clf.predict(input_features)[0]\n",
    "\n",
    "# Map category ID to category name\n",
    "category_mapping = {\n",
    "    15: \"Java Developer\",\n",
    "    23: \"Testing\",\n",
    "    8: \"DevOps Engineer\",\n",
    "    20: \"Python Developer\",\n",
    "    24: \"Web Designing\",\n",
    "    12: \"HR\",\n",
    "    13: \"Hadoop\",\n",
    "    3: \"Blockchain\",\n",
    "    10: \"ETL Developer\",\n",
    "    18: \"Operations Manager\",\n",
    "    6: \"Data Science\",\n",
    "    22: \"Sales\",\n",
    "    16: \"Mechanical Engineer\",\n",
    "    1: \"Arts\",\n",
    "    7: \"Database\",\n",
    "    11: \"Electrical Engineering\",\n",
    "    14: \"Health and fitness\",\n",
    "    19: \"PMO\",\n",
    "    4: \"Business Analyst\",\n",
    "    9: \"DotNet Developer\",\n",
    "    2: \"Automation Testing\",\n",
    "    17: \"Network Security Engineer\",\n",
    "    21: \"SAP Developer\",\n",
    "    5: \"Civil Engineer\",\n",
    "    0: \"Advocate\",\n",
    "}\n",
    "\n",
    "category_name = category_mapping.get(prediction_id, \"Unknown\")\n",
    "\n",
    "print(\"Predicted Category:\", category_name)\n",
    "print(prediction_id)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f2f88f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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