diff --git a/PATSTAT/patstat_filter.ipynb b/PATSTAT/patstat_data_filter_process.ipynb
similarity index 100%
rename from PATSTAT/patstat_filter.ipynb
rename to PATSTAT/patstat_data_filter_process.ipynb
diff --git a/TODO.ipynb b/TODO.ipynb
index a70eb3c..6e29954 100644
--- a/TODO.ipynb
+++ b/TODO.ipynb
@@ -31,6 +31,50 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# WOS current query:\n",
+    "\n",
+    "\"\"\"(TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\")) AND (CU=PEOPLES R CHINA AND (CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=REPUBLIC OF CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN))\"\"\""
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "other keywords: pattern recognition, computer vision, image classification, reinforcement learning, support vector machines, recommender system, random forest, ensemble models, image processing, generative network, ai ethic, natural language processing, clustering algorithm, feature extraction, time series forecast, anomaly detection, identity fraud detection, dimensionality reduction, feature elicitation, chatbot, clustering, unsupervised learning, supervised learning, convolutional network, adversarial network\n",
+    "\n",
+    "# AI ETHICS keyword!!!"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# Only CPC classification? Or some basic PTC? (ASEAN analysis had some)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -73,7 +117,12 @@
    "cell_type": "code",
    "execution_count": null,
    "outputs": [],
-   "source": [],
+   "source": [
+    "# Baseline of co-publications\n",
+    "#\n",
+    "# Use address instead of CU?\n",
+    "# plus countries UK Norway Switzerland | Turkey Serbia"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
diff --git a/WOS/ai_scope_keywords.txt b/WOS/ai_scope_keywords.txt
new file mode 100644
index 0000000..9023e70
--- /dev/null
+++ b/WOS/ai_scope_keywords.txt
@@ -0,0 +1 @@
+artificial intelligence,machine learning,neural network,big data,deep learning,pattern recognition,computer vision, image classification, reinforcement learning, support vector machines, recommender system, random forest, ensemble model, image processing, generative network, ai ethic, natural language processing, clustering algorithm, feature extraction, time series forecast, anomaly detection, identity fraud detection, dimensionality reduction, feature elicitation, chatbot, clustering, unsupervised learning, supervised learning, convolutional network, adversarial network
\ No newline at end of file
diff --git a/WOS/eu_scope_countries.txt b/WOS/eu_scope_countries.txt
new file mode 100644
index 0000000..116fd3c
--- /dev/null
+++ b/WOS/eu_scope_countries.txt
@@ -0,0 +1 @@
+Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Norway, Switzerland, United Kingdom, England, Wales, Scotland, N Ireland
\ No newline at end of file
diff --git a/WOS/wos_extract/wos_aggr_concat.ipynb b/WOS/wos_extract/wos_aggr_concat.ipynb
new file mode 100644
index 0000000..6f9b387
--- /dev/null
+++ b/WOS/wos_extract/wos_aggr_concat.ipynb
@@ -0,0 +1,339 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "agg_df = pd.DataFrame()\n",
+    "\n",
+    "workdir_path = 'aggregated_yearly_from_wos'\n",
+    "for root, dirs, files in os.walk(workdir_path):\n",
+    "    for filename in files:\n",
+    "        path=os.path.join(root, filename)\n",
+    "        chunk = pd.read_csv(path, sep='\\t')[[\"Publication Years\",\"Record Count\"]]\n",
+    "        chunk[\"name\"] = filename.replace(\".txt\",\"\")\n",
+    "        agg_df = pd.concat([chunk,agg_df],ignore_index=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array(['worldwide_kwds', 'eu_kwds', 'eu_all', 'eu+assoc_kwds',\n       'eu+assoc_all', 'ch_kwds', 'ch_all'], dtype=object)"
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agg_df[\"name\"].unique()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    Publication Years  Record Count            name     region field\n0                2022        211014  worldwide_kwds  worldwide  kwds\n1                2021        197916  worldwide_kwds  worldwide  kwds\n2                2020        160472  worldwide_kwds  worldwide  kwds\n3                2019        142010  worldwide_kwds  worldwide  kwds\n4                2018        109746  worldwide_kwds  worldwide  kwds\n..                ...           ...             ...        ...   ...\n79               2015        393759          ch_all         ch   all\n80               2014        362784          ch_all         ch   all\n81               2013        329772          ch_all         ch   all\n82               2012        299804          ch_all         ch   all\n83               2011        263715          ch_all         ch   all\n\n[84 rows x 5 columns]",
+      "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>Publication Years</th>\n      <th>Record Count</th>\n      <th>name</th>\n      <th>region</th>\n      <th>field</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2022</td>\n      <td>211014</td>\n      <td>worldwide_kwds</td>\n      <td>worldwide</td>\n      <td>kwds</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2021</td>\n      <td>197916</td>\n      <td>worldwide_kwds</td>\n      <td>worldwide</td>\n      <td>kwds</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020</td>\n      <td>160472</td>\n      <td>worldwide_kwds</td>\n      <td>worldwide</td>\n      <td>kwds</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2019</td>\n      <td>142010</td>\n      <td>worldwide_kwds</td>\n      <td>worldwide</td>\n      <td>kwds</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018</td>\n      <td>109746</td>\n      <td>worldwide_kwds</td>\n      <td>worldwide</td>\n      <td>kwds</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>2015</td>\n      <td>393759</td>\n      <td>ch_all</td>\n      <td>ch</td>\n      <td>all</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>2014</td>\n      <td>362784</td>\n      <td>ch_all</td>\n      <td>ch</td>\n      <td>all</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>2013</td>\n      <td>329772</td>\n      <td>ch_all</td>\n      <td>ch</td>\n      <td>all</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>2012</td>\n      <td>299804</td>\n      <td>ch_all</td>\n      <td>ch</td>\n      <td>all</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>2011</td>\n      <td>263715</td>\n      <td>ch_all</td>\n      <td>ch</td>\n      <td>all</td>\n    </tr>\n  </tbody>\n</table>\n<p>84 rows × 5 columns</p>\n</div>"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agg_df[\"region\"] = agg_df[\"name\"].apply(lambda x: x.split(\"_\")[0])\n",
+    "agg_df[\"field\"] = agg_df[\"name\"].apply(lambda x: x.split(\"_\")[-1])\n",
+    "agg_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Text(0.5, 1.0, 'Number of WOS indexed piublications')"
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lineplot(agg_df[agg_df[\"field\"]==\"all\"],x=\"Publication Years\" ,y=\"Record Count\", hue=\"region\")\n",
+    "g.ticklabel_format(style='plain', axis='y',useOffset=False)\n",
+    "g.set_title(\"Number of WOS indexed piublications\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Text(0.5, 1.0, 'AI-related publications')"
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lineplot(agg_df[agg_df[\"field\"]==\"kwds\"],x=\"Publication Years\" ,y=\"Record Count\", hue=\"region\")\n",
+    "g.set_title(\"AI-related publications\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Text(0.5, 1.0, 'AI-related publications\\n(without worldwide trend)')"
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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44wrWIm/YuXMn48ePz9JtVfICf39/QkNDGTt2rKVLEc8ACU1C5AE//PADaWlpdOjQ4an76tSpE0lJSSY3T82roWnChAkSmh4iKSmJ0aNH5/q4O3fuZMKECfkmNAH06dOHP//8k7Nnz1q6FFHASWgSIg+YP38+LVu2xN7e/qn7sra2xt7e/pk6ZZNTEhMTLTa2vb09NjZ5+/ager0+ww13LSE4OJhChQrx448/WroUUcBJaBLCwqKiojh8+DDBwcEm22vXrk3r1q1NtlWrVg2NRsPhw4eN23799Vc0Gg0nTpwAMs5p8vPz49ixY2zZsgWNRoNGo8kwPyc5OZkhQ4bg4eGBk5MTr7/+OtevX89Q66xZs6hSpQp2dnYUK1aMfv36ZTgi4efnR9euXTO89v65KJs3b6Zu3boAdOvWzVjXw46GHT58GI1Gw/Lly43bIiIi0Gg01K5d26Ttyy+/TGBgYLbrfu6556hatSoRERE0adIER0dHPvjgg0zrAfj3339p1aoVTk5OeHp6MnjwYJKTk03azJgxA2tra5Oxpk2bhkajYciQIcZtOp0OFxcXRowYYdyW2Zym7du3U7duXezt7SlbtizffvvtQ+tbsGABAQEBODg4ULhwYdq3b8/Fixcf2h4M86OGDRsGQOnSpY2/l/S/JY1GQ//+/Vm4cKHx81yzZg0Aly5donv37nh5eWFnZ0eVKlX44YcfTPpPn/O1ZMkSJk6cSIkSJbC3t6dZs2acOXMmQz1z586lbNmyODg4UK9ePbZt25Zp3ba2tjz33HMsW7bske9PiKeVt/8zRohnwM6dOwEyfPk3btyYX375xfj81q1bHDt2DCsrK7Zt20b16tUB2LZtGx4eHlSuXDnT/qdPn86AAQNwdnbmww8/BMDLy8ukzYABAyhUqBDjxo3j/PnzTJ8+nf79+/Prr78a24wfP54JEyYQHBxM3759iYyMZPbs2ezdu5cdO3Zga2ub5fdcuXJlPvroI8aOHUvv3r1p3LgxAA0aNMi0fdWqVXF3d2fr1q20bNnS+L6trKw4dOgQ8fHxuLq6otfr2blzJ717936ium/evMnLL79M+/bt6dixY4bPKV1SUhLNmjUjOjqa9957j2LFivHzzz+zceNGk3aNGzdGr9ezfft2WrRoYVL3/QHgwIEDJCQk0KRJk4d+ZkeOHKF58+Z4eHgwfvx40tLSGDduXKY1Tpw4kTFjxtC2bVt69uzJ9evX+frrr2nSpAkHDhzA3d090zFat27NqVOn+OWXX/jyyy8pWrQoAB4eHsY2GzduZMmSJfTv35+iRYvi5+fH1atXqV+/vjFUeXh4sHr1anr06EF8fDyDBg0yGWfy5MlYWVnx/vvvExcXx9SpU3n77bfZvXu3sc3333/PO++8Q4MGDRg0aBDnzp2jZcuWFC5cmJIlS2aoPSAggGXLlhn/FoTIEZa9X7AQYvTo0QpQd+7cMdm+dOlSBajjx48rpZRavny5srOzUy1btlTt2rUztqtevbp6/fXXjc/nz5+vABUVFWXcVqVKlQx3q7+/bXBwsNLr9cbtgwcPVtbW1io2NlYppdS1a9eUVqtVzZs3Vzqdzthu5syZClA//PCDcVupUqVUly5dMozVtGlTkxr27t2rADV//vxHfj7pQkNDVb169YzPW7durVq3bq2sra3V6tWrlVJK7d+/XwFq2bJl2a67adOmClBz5sx5bO3Tp09XgFqyZIlx2927d1W5cuUUoDZt2qSUUkqn0ylXV1c1fPhwpZRSer1eFSlSRL355pvK2tra+Dv/4osvlJWVlbp9+7axP0CNGzfO+LxVq1bK3t5eXbhwwbjt+PHjytraWt3/T/n58+eVtbW1mjhxosl7OHLkiLKxscmw/UGfffZZhr+f+2uysrJSx44dM9neo0cP5ePjo27cuGGyvX379srNzU0lJiYqpZTatGmTAlTlypVVcnKysd1XX32lAHXkyBGllFIpKSnK09NT1axZ06Td3LlzFZDp3/KiRYsUoHbv3v3I9yfE05DTc0JY2M2bN7GxscHZ2dlke/rRl61btwKGIxR169blxRdfNB6liI2N5ejRo8a2T6p3794mc6AaN26MTqfjwoULAKxfv56UlBQGDRpkcnVfr169cHV1ZeXKlU81flY0btyY/fv3c/fuXcBwquqVV16hZs2axs9j27ZtaDQaGjVq9ER129nZ0a1bt8fWsmrVKnx8fHjjjTeM2xwdHU2OcAFYWVnRoEED4+/wxIkT3Lx5k5EjR6KUIjw83Fh3+tG0zOh0OtauXUurVq3w9fU1bq9cuTIhISEmbf/44w/0ej1t27blxo0bxoe3tzfly5dn06ZNj31/j9K0aVP8/f2Nz5VS/P7777z66qsopUzGDAkJIS4ujv3795v00a1bN7RarfF5+t/vuXPnANi3bx/Xrl2jT58+Ju26du2Km5tbpnUVKlQIgBs3bjzV+xPiUSQ0CZFHeXl5Ub58eZNA0LhxY5o0acLly5c5d+4cO3bsQK/XP3Vouv+LGP77Arp9+zaAMTxVrFjRpJ1Wq6VMmTLG/TmpcePGpKWlER4eTmRkJNeuXTN+Hvd/Rv7+/hQuXPiJ6i5evLjJl/TDXLhwgXLlymWYbP/gOOl1R0REkJSUxLZt2/Dx8aF27drUqFHDWPf27dsf+Tu8fv06SUlJlC9fPsO+B8c8ffo0SinKly+Ph4eHyePEiRNcu3btse/vUUqXLp2httjYWObOnZthvPQA+uCYWf17e/D92traUqZMmUzrUv+/I5hcACFyksxpEsLCihQpQlpaGnfu3MHFxcVkX6NGjdiwYQNJSUlEREQwduxY4xGJbdu2ceLECZydnalVq9ZT1WBtbZ3pdvUEt6Z82JeWTqd76DhZUadOHezt7dm6dSu+vr54enpSoUIFGjduzKxZs0hOTmbbtm28/vrrTzyGg4PDE7/2YRo1akRqairh4eHG4AuGMLVt2zZOnjzJ9evXnzr4ptPr9Wg0GlavXp3p5/3gEc3sevAzSl+MtWPHjnTp0iXT16TPv0tnzr+3dOmBK30elhA5QUKTEBZWqVIlwHAV3YNfLo0bN2b+/PksXrwYnU5HgwYNsLKyolGjRsbQ1KBBg8eGkaf9r+/0NZ8iIyNN/ks/JSWFqKgokyv/ChUqlOkaPxcuXDB5bXZr0mq1xiuofH19TcJHcnIyCxcu5OrVqyaTqbNTd3aUKlWKo0ePopQyeR+RkZEZ2tarVw+tVsu2bdvYtm2b8eq0Jk2aMG/ePDZs2GB8/jAeHh44ODhw+vTpDPseHLNs2bIopShdujQVKlTI9nvL7u/Fw8MDFxcXdDrdE3+eD0r/vZ0+fZoXXnjBuD01NZWoqChq1KiR4TVRUVFYWVk90XsWIqvk9JwQFhYUFASQ6a1M0oPBlClTqF69unE+R+PGjdmwYQP79u3L0hEKJyenp1qsMDg4GK1Wy4wZM0yOBnz//ffExcURGhpq3Fa2bFl27dpFSkqKcdvff/+d4XJ3JycngGzV1bhxY3bv3s2mTZuM77to0aJUrlyZKVOmGNs8Sd3Z8corr3D58mV+++0347bExETmzp2boa29vT1169bll19+ITo62iTsJSUlMWPGDMqWLYuPj89Dx7O2tiYkJIS//vqL6Oho4/YTJ06wdu1ak7atW7fG2tqaCRMmZDhyo5Ti5s2bj3xv2f29WFtb06ZNG37//fdMb2WS2dIVj1OnTh08PDyYM2eOyd9RWFjYQ+uKiIigSpUqD53zJIQ5SGgSwsLKlClD1apVWb9+fYZ95cqVw9vbm8jISJMw0KRJE86fP09KSkqWQlNAQACHDx/mk08+YfHixRkujX8cDw8PRo0axZo1a3jppZf45ptveO+99xgwYAB169alY8eOxrY9e/bk6tWrvPTSS8yZM4dhw4bRq1cvypYta9Jn2bJlcXd3Z86cOXz//fcsXryYqKioR9aRHjQuXryY4fM4deoUfn5+lChR4onqzo5evXpRrlw5OnfuzMiRI/nqq6+Mazs9rO7IyEjc3NyoVq0aAJ6enlSsWJFTp05l6Xc4YcIEY19Tpkxh4sSJPP/881SpUsWkXdmyZfnkk09YtGgRjRo14rPPPmPOnDmMGDGCihUrMn/+/EeOExAQAMCHH37Izz//zOLFi42T7x9m8uTJ+Pj4EBgYyKBBg5g7dy6TJ0+mbdu2mc7zehxbW1s++eQTDh48yAsvvMDXX3/NkCFDGD58eKZzmlJTU9myZQuvvfZatscSIlsscs2eEMLEF198oZydnY2XZt/vzTffVID69ddfjdtSUlKUo6Oj0mq1KikpyaR9ZksOxMTEqNDQUOXi4mJyyXZ6271795r0kX5pePql8+lmzpypKlWqpGxtbZWXl5fq27evyWXy6aZNm6aKFy+u7OzsVMOGDdW+ffsyXLavlFLLli1T/v7+ysbGJkvLD8THxytra2vl4uKi0tLSjNsXLFigANWpU6dMX5eVups2baqqVKmS6eszq/3ChQuqZcuWytHRURUtWlQNHDhQrVmzJtPPbeXKlQpQL7/8ssn2nj17KkB9//33GcbkgSUHlFJqy5YtKiAgQGm1WlWmTBk1Z84cNW7cOJXZP+W///67atSokXJyclJOTk6qUqVKql+/fioyMjLT93i/jz/+WBUvXlxZWVmZ/C0Bql+/fpm+5urVq6pfv36qZMmSytbWVnl7e6tmzZqpuXPnGtuk/10tXbrU5LVRUVGZ/v5nzZqlSpcurezs7FSdOnXU1q1bM/1drF69WgHq9OnTj31vQjwNjVJPMfNOCGEWcXFxlClThqlTp9KjRw9LlyNEvtKqVSs0Gg1//vmnpUsRBZyEJiHyiClTpjB//nyOHz9usqaQEOLhTpw4QbVq1Th48CBVq1a1dDmigJPQJIQQQgiRBfKfs0IIIYQQWSChSQghhBAiCyQ0CSGEEEJkgYQmIYQQQogskNuomIler+fy5cu4uLjIDSOFEEKIfEIpxZ07dyhWrNhjr1yW0GQmly9fpmTJkpYuQwghhBBP4OLFiyZ3FMiMhCYzSb87/cWLF3F1dbVwNUIIIYTIivj4eEqWLGn8Hn8UCU1mkn5KztXVVUKTEEIIkc9kZWqNTAQXQgghhMgCCU1CCCGEEFkgoUkIIYQQIgtkTlMu0+l0pKamWroM8ZRsbW2xtra2dBlCCCFykYSmXKKUIiYmhtjYWEuXIszE3d0db29vWZdLCCGeERYNTTqdjvHjx7NgwQJiYmIoVqwYXbt2ZfTo0cYvIqUU48aNY968ecTGxtKwYUNmz55N+fLljf3cunWLAQMGsGLFCqysrGjTpg1fffUVzs7OxjaHDx+mX79+7N27Fw8PDwYMGMDw4cNN6lm6dCljxozh/PnzlC9fnilTpvDKK6+Y5b2mByZPT08cHR3lizYfU0qRmJjItWvXAPDx8bFwRUIIIXKFsqCJEyeqIkWKqL///ltFRUWppUuXKmdnZ/XVV18Z20yePFm5ubmpv/76Sx06dEi1bNlSlS5dWiUlJRnbvPTSS6pGjRpq165datu2bapcuXKqQ4cOxv1xcXHKy8tLvf322+ro0aPql19+UQ4ODurbb781ttmxY4eytrZWU6dOVcePH1ejR49Wtra26siRI1l6L3FxcQpQcXFxGfalpaWp48ePqxs3bjzJxyTyqBs3bqjjx4+rtLQ0S5cihBDiCT3q+/tBFg1NoaGhqnv37ibbWrdurd5++22llFJ6vV55e3urzz77zLg/NjZW2dnZqV9++UUppdTx48cVoPbu3Wtss3r1aqXRaNSlS5eUUkrNmjVLFSpUSCUnJxvbjBgxQlWsWNH4vG3btio0NNSklsDAQPXOO+9k6b086kNPSkpSx48fV4mJiVnqS+QPiYmJ6vjx4yYBXgghRP6SndBk0avnGjRowIYNGzh16hQAhw4dYvv27bz88ssAREVFERMTQ3BwsPE1bm5uBAYGEh4eDkB4eDju7u7UqVPH2CY4OBgrKyt2795tbNOkSRO0Wq2xTUhICJGRkdy+fdvY5v5x0tukj2MOckquYJHfpxBCPFssOqdp5MiRxMfHU6lSJaytrdHpdEycOJG3334bMMwDAvDy8jJ5nZeXl3FfTEwMnp6eJvttbGwoXLiwSZvSpUtn6CN9X6FChYiJiXnkOA9KTk4mOTnZ+Dw+Pj5b710IIYQQ+YtFjzQtWbKEhQsXsmjRIvbv38+PP/7I559/zo8//mjJsrJk0qRJuLm5GR9ys15Tfn5+TJ8+3dJlCCGEEGZj0SNNw4YNY+TIkbRv3x6AatWqceHCBSZNmkSXLl3w9vYG4OrVqyZXKF29epWaNWsC4O3tbbyKKV1aWhq3bt0yvt7b25urV6+atEl//rg26fsfNGrUKIYMGWJ8nn7DP2Gwd+9enJycLF2GEEIIYTYWPdKUmJiIlZVpCdbW1uj1egBKly6Nt7c3GzZsMO6Pj49n9+7dBAUFARAUFERsbCwRERHGNhs3bkSv1xMYGGhss3XrVpNFJdetW0fFihUpVKiQsc3946S3SR/nQXZ2dsab8xakm/SmpKSYpR8PDw8cHR3N0pcQQohnm1KKbf9uQyll8UIspkuXLqp48eLGJQf++OMPVbRoUTV8+HBjm8mTJyt3d3e1bNkydfjwYfXaa69luuRArVq11O7du9X27dtV+fLlTZYciI2NVV5eXqpTp07q6NGjavHixcrR0THDkgM2Njbq888/VydOnFDjxo0z25ID6VfP5cWrrJo2bar69eunBg4cqIoUKaKee+45deTIEfXSSy8pJycn5enpqTp27KiuX79ufE18fLx66623lKOjo/L29lZffPGFatq0qRo4cKCxTalSpdSXX35pfH7hwgXVsmVL5eTkpFxcXNSbb76pYmJijPvHjRunatSooX766SdVqlQp5erqqtq1a6fi4+Nz42N4Inn59yqEEAXJ32f/VlXDqqp3/nlH6fV6s/adb5YciI+PVwMHDlS+vr7K3t5elSlTRn344YcmSwPo9Xo1ZswY5eXlpezs7FSzZs1UZGSkST83b95UHTp0UM7OzsrV1VV169ZN3blzx6TNoUOHVKNGjZSdnZ0qXry4mjx5coZ6lixZoipUqKC0Wq2qUqWKWrlyZZbfS34OTc7OzmrYsGHq5MmTateuXcrDw0ONGjVKnThxQu3fv1+9+OKL6vnnnze+pmfPnqpUqVJq/fr16siRI+r1119XLi4uDw1NOp1O1axZUzVq1Ejt27dP7dq1SwUEBKimTZsa248bN045Ozur1q1bqyNHjqitW7cqb29v9cEHH+TSJ5F9efn3KoQQBcXdlLvqhSUvqKphVdXcQ3PN3n++CU0FSX4OTbVq1TI+//jjj1Xz5s1N2ly8eFEBKjIyUsXHxytbW1u1dOlS4/7Y2Fjl6Oj40ND0zz//KGtraxUdHW3cf+zYMQWoPXv2KKUMocnR0dHkyNKwYcNUYGCgOd+uWeXl36sQQhQUM/bPUFXDqqqQ30LUvbR7Zu8/O6FJ7j0nCAgIMP586NAhNm3aZHILmnRnz54lKSmJ1NRU6tWrZ9zu5uZGxYoVH9r/iRMnKFmypMlEeX9/f9zd3Tlx4gR169YFDFfcubi4GNv4+PhkmOQvhBDi2XEp4RJhR8MAGFZnGHbWdhatR0KTMLnKLSEhgVdffZUpU6ZkaOfj48OZM2dyrA5bW1uT5xqNxnhRgBBCiGfPtH3TSNGnEOgTyAu+L1i6HMtePSfyntq1a3Ps2DH8/PwoV66cycPJyYkyZcpga2vL3r17ja+Ji4szruqemcqVK3Px4kUuXrxo3Hb8+HFiY2Px9/fP0fcjhBAif9pzZQ/rLqzDWmPNiLoj8sRdGCQ0CRP9+vXj1q1bdOjQgb1793L27FnWrl1Lt27d0Ol0uLi40KVLF4YNG8amTZs4duwYPXr0wMrK6qF/0MHBwVSrVo23336b/fv3s2fPHjp37kzTpk1Nbn8jhBBCAKTp05i8dzIAbSu2pXyh8hauyEBCkzBRrFgxduzYgU6no3nz5lSrVo1Bgwbh7u5uXFPriy++ICgoiBYtWhAcHEzDhg2pXLky9vb2mfap0WhYtmwZhQoVokmTJgQHB1OmTBl+/fXX3HxrQggh8onfT/3O6duncbNzo1/NfpYux0ijlKVXiioY4uPjcXNzIy4uLsNCl/fu3SMqKorSpUs/NFjkZ3fv3qV48eJMmzaNHj16WLqcXFPQf69CCGEJcclxhP4ZSlxyHB8EfkCHSh1ydLxHfX8/SCaCi2w7cOAAJ0+epF69esTFxfHRRx8B8Nprr1m4MiGEEPndrIOziEuOo5x7Od6s8KalyzEhoUk8kc8//5zIyEi0Wi0BAQFs27aNokWLWrosIYQQ+djp26f5NdIwdWNkvZHYWOWtmJK3qhH5Qq1atUzu9SeEEEI8LaUUU/ZOQad0BPsGE+gTaOmSMpCJ4EIIIYSwuE0XN7H7ym60VlqG1hlq6XIyJaFJCCGEEBaVrEvms72fAdClShdKuJSwcEWZk9AkhBBCCIv6+fjP/JvwL54OnvSs1tPS5TyUhCYhhBBCWMy1xGvMPTwXgEEBg3C0dbRwRQ8noUkIIYQQFvPV/q9ISkuihkcNWpRpYelyHklCkxBCCCEs4vD1wyw/uxwwLDGQF+4v9ygSmoQQQgiR6/RKz+Q9hvvLtSrXiqpFq1q4oseT0CSEEEKIXPf3ub85cuMITrZODKw90NLlZImEJiGEEELkqrupd/ky4ksA3qn+DkUd8scdJSQ0iUfS6/VMmjSJ0qVL4+DgQI0aNfjtt98ACAsLw93d3aT9X3/9lefPSQshhLCseYfncSPpBr4uvrxd+W1Ll5NlchsVC1FKkZSqy/VxHWytsxVqJk2axIIFC5gzZw7ly5dn69atdOzYEQ8PjxysUgghREEVHR/NT8d/AmBY3WForbUWrijrJDRZSFKqDv+xa3N93OMfheCozdqvPTk5mU8//ZT169cTFBQEQJkyZdi+fTvffvstzZs3z8lShRBCFECf7/ucVH0qDYs1pGmJppYuJ1skNImHOnPmDImJibz44osm21NSUqhVq5aFqhJCCJFf7by8k00XN2GjsWF43eH5bjqHhCYLcbC15vhHIRYZN6sSEhIAWLlyJcWLFzfZZ2dnx6ZNm1BKmWxPTU19+iKFEEIUOKn6VKbumQpA+0rtKeNexsIVZZ+EJgvRaDRZPk1mKf7+/tjZ2REdHU3TphkPoXp4eHDnzh3u3r2Lk5MTAAcPHszlKoUQQuQHSyKXcDbuLIXsCtG3Zl9Ll/NE8va3trAoFxcX3n//fQYPHoxer6dRo0bExcWxY8cOXF1defXVV3F0dOSDDz7gvffeY/fu3YSFhVm6bCGEEHnM7Xu3+ebgNwAMqD0AV62rhSt6MrLkgHikjz/+mDFjxjBp0iQqV67MSy+9xMqVKyldujSFCxdmwYIFrFq1imrVqvHLL78wfvx4S5cshBAij/nm4DfcSblDpcKVaF2utaXLeWIa9eCkFPFE4uPjcXNzIy4uDldX0wR97949oqKiKF26NPb29haqUJib/F6FEOLxIm9F0vbvtuiVnvkh86njXcfSJZl41Pf3g+RIkxBCCCFyhFKKKXunoFd6QvxC8lxgyi4JTUIIIYTIEesurGNvzF7srO0YEjDE0uU8NQlNQgghhDC7e2n3mLZvGgDdq3anmHMxC1f09CQ0CSGEEMLswo6FcfnuZbydvOlWtZulyzELCU1CCCGEMKuYuzF8f+R7AIYGDMXBxsHCFZmHhCYhhBBCmNWXEV9yT3eP2p61CfHL/btf5BQJTUIIIYQwmwPXDrAqahUaNIysNzLf3V/uUSQ0CSGEEMIs9ErPpN2TAGhdvjWVi1S2cEXmJaFJCCGEEGbx15m/OHHrBC62LgyoNcDS5ZidRUOTn58fGo0mw6Nfv36AYcXlfv36UaRIEZydnWnTpg1Xr1416SM6OprQ0FAcHR3x9PRk2LBhpKWlmbTZvHkztWvXxs7OjnLlymV6f7RvvvkGPz8/7O3tCQwMZM+ePTn2voUQQoiC5k7KHb7a/xUAfWr0oYhDEQtXZH4WDU179+7lypUrxse6desAePPNNwEYPHgwK1asYOnSpWzZsoXLly/TuvV/96zR6XSEhoaSkpLCzp07+fHHHwkLC2Ps2LHGNlFRUYSGhvL8889z8OBBBg0aRM+ePVm7dq2xza+//sqQIUMYN24c+/fvp0aNGoSEhHDt2rVc+iSEEEKI/O3bQ99y694t/Fz96FCpg6XLyRkqDxk4cKAqW7as0uv1KjY2Vtna2qqlS5ca9584cUIBKjw8XCml1KpVq5SVlZWKiYkxtpk9e7ZydXVVycnJSimlhg8frqpUqWIyTrt27VRISIjxeb169VS/fv2Mz3U6nSpWrJiaNGlSlmuPi4tTgIqLi8uwLykpSR0/flwlJSVlub+Caty4capLly6WLsMs5PcqhBAG52LPqZo/1lRVw6qqbf9us3Q52fKo7+8H5Zk5TSkpKSxYsIDu3buj0WiIiIggNTWV4OBgY5tKlSrh6+tLeHg4AOHh4VSrVg0vLy9jm5CQEOLj4zl27Jixzf19pLdJ7yMlJYWIiAiTNlZWVgQHBxvbZCY5OZn4+HiTR0Gk1+uZNGkSpUuXxsHBgRo1avDbb78BEBYWhru7u0n7v/7666mulPj555+pU6cOLi4ueHt789Zbb5kc8bt9+zZvv/02Hh4eODg4UL58eebPnw8Yfpf9+/fHx8cHe3t7SpUqxaRJk4yvjY6O5rXXXsPZ2RlXV1fatm2b4XTvihUrqFu3Lvb29hQtWpTXX3/9id+LEEI8Kz7b+xlpKo0mJZrQqHgjS5eTY2wsXUC6v/76i9jYWLp27QpATEwMWq02w5eyl5cXMTExxjb3B6b0/en7HtUmPj6epKQkbt++jU6ny7TNyZMnH1rvpEmTmDBhQrbfp5FSkJr45K9/UraOkI1QM2nSJBYsWMCcOXMoX748W7dupWPHjnh4eORIeampqXz88cdUrFiRa9euMWTIELp27cqqVasAGDNmDMePH2f16tUULVqUM2fOkJSUBMCMGTNYvnw5S5YswdfXl4sXL3Lx4kXAEP7SA9OWLVtIS0ujX79+tGvXjs2bNwOwcuVKXn/9dT788EN++uknUlJSjOMKIYTI3NZ/t7Lt0jZsrGwYVmeYpcvJUXkmNH3//fe8/PLLFCuWP+5NM2rUKIYM+e/mg/Hx8ZQsWTLrHaQmwqcWeK8fXAatU5aaJicn8+mnn7J+/XqCgoIAKFOmDNu3b+fbb7+lefPmZi+ve/fuxp/LlCnDjBkzqFu3LgkJCTg7OxMdHU2tWrWoU8dwp2w/Pz9j++joaMqXL0+jRo3QaDSUKlXKuG/Dhg0cOXKEqKgo4+/pp59+okqVKuzdu5e6desyceJE2rdvbxKGa9SoYfb3KIQQBUWqLpXP9n4GQMfKHfFz87NsQTksT5yeu3DhAuvXr6dnz57Gbd7e3qSkpBAbG2vS9urVq3h7exvbPHh6Jf3549q4urri4OBA0aJFsba2zrRNeh+ZsbOzw9XV1eRR0Jw5c4bExERefPFFnJ2djY+ffvqJs2fPZqmPbdu2mbz2008/ZeHChSbbFi5caGwfERHBq6++iq+vLy4uLjRt2hQwBCKAvn37snjxYmrWrMnw4cPZuXOn8bVdu3bl4MGDVKxYkffee49//vnHuO/EiROULFnSJNj6+/vj7u7OiRMnADh48CDNmjV78g9MCCGeMYtOLuJ8/HkK2xfmnervWLqcHJcnjjTNnz8fT09PQkNDjdsCAgKwtbVlw4YNtGnTBoDIyEiio6ONRz2CgoKYOHEi165dw9PTE4B169bh6uqKv7+/sc2Dp1jWrVtn7EOr1RIQEMCGDRto1aoVYDiVs2HDBvr3759zb9rW0XDUJ7fZOma5aUJCAmA4bVW8eHGTfXZ2dmzatAmllMn21NRUk+d16tTh4MGDxuczZszg0qVLTJkyxbgt/dTo3bt3CQkJISQkhIULF+Lh4UF0dDQhISGkpKQA8PLLL3PhwgVWrVrFunXraNasGf369ePzzz+ndu3aREVFsXr1atavX0/btm0JDg42zsF6HAeHgnFvJCGEyA03k24y59AcAAbVHoSz1tnCFeWCnJ+X/mg6nU75+vqqESNGZNjXp08f5evrqzZu3Kj27dungoKCVFBQkHF/Wlqaqlq1qmrevLk6ePCgWrNmjfLw8FCjRo0ytjl37pxydHRUw4YNUydOnFDffPONsra2VmvWrDG2Wbx4sbKzs1NhYWHq+PHjqnfv3srd3d3kqrzHKYhXz8XHxys7Ozv1008/Zbp/1apVSqPRqISEBOO2Dz74QD3qz+pRV8/t27dPASo6Otq47eeff1aAOnDgQKavmTNnjnJxccl035o1axSgbt68qf755x9lbW1t0vexY8cUoPbu3auUUuq5555Tb7/99kNrf1B+/b0KIYQ5jNsxTlUNq6rarmirdHqdpct5Ytm5es7ioWnt2rUKUJGRkRn2JSUlqXfffVcVKlRIOTo6qtdff11duXLFpM358+fVyy+/rBwcHFTRokXV0KFDVWpqqkmbTZs2qZo1ayqtVqvKlCmj5s+fn2Gsr7/+Wvn6+iqtVqvq1aundu3ala33URBDk1JKffjhh6pIkSIqLCxMnTlzRkVERKgZM2aosLAwdfPmTeXk5KTee+89debMGbVw4UJVrFixJw5N165dU1qtVg0bNkydPXtWLVu2TFWoUMEkNI0ZM0b99ddf6vTp0+ro0aOqRYsWql69ekoppaZNm6YWLVqkTpw4oSIjI1WPHj2Ut7e30ul0Sq/Xq5o1a6rGjRuriIgItXv3bhUQEKCaNm1qHH/Tpk3KyspKjR07Vh0/flwdPnxYTZ48+aHvJT//XoUQ4mkcu3FMVQurpqqGVVUHrh6wdDlPJV+FpoKioIYmvV6vpk+fripWrKhsbW2Vh4eHCgkJUVu2bFFKKfXnn3+qcuXKKQcHB9WiRQs1d+7cJw5NSim1aNEi5efnp+zs7FRQUJBavny5SWj6+OOPVeXKlZWDg4MqXLiweu2119S5c+eUUkrNnTtX1axZUzk5OSlXV1fVrFkztX//fmPfFy5cUC1btlROTk7KxcVFvfnmmxmOJv7+++/GgF20aFHVunXrh9aan3+vQgjxpPR6veq0qpOqGlZVDd8y3NLlPLXshCaNUg9MShFPJD4+Hjc3N+Li4jJMCr937x5RUVGULl0ae3t7C1UozE1+r0KIZ9HqqNUM3zocBxsHlrdajrfTwy+ayg8e9f39oDxx9ZwQQggh8r6ktCSm7ZsGQI+qPfJ9YMouCU1CCCGEyJIfjv7A1cSrFHcuTpcqXSxdTq6T0CSEEEKIx7qccJn5Rw23rRpaZyj2Ns/etAQJTUIIIYR4rGn7ppGsS6aud12CfYMf/4ICSEKTEEIIIR5pb8xe/rnwD1YaK0bUHfFUN2bPzyQ0CSGEEOKhdHodU/YY7uLwZoU3qVi4ooUrshwJTUIIIYR4qN9P/07k7Uhcta70r5mDtxfLByQ0CSGEECJTcclxfH3gawDerfku7vbuli3IwiQ0iSdy/vx5NBqNyc14hRBCFCxzDs0hNjmWcu7laFexnaXLsTgJTUIIIYTI4GzsWX45+QsAw+sOx8bKxsIVWZ6EJiGEEEKYUEoxde9UdErHCyVfIKhYkKVLyhMkNIlH0uv1TJ06lXLlymFnZ4evry8TJ0407j937hzPP/88jo6O1KhRg/DwcAtWK4QQwhy2/LuFnZd3Ymtly/t13rd0OXmGhCbxSKNGjWLy5MmMGTOG48ePs2jRIry8vIz7P/zwQ95//30OHjxIhQoV6NChA2lpaRasWAghxNNI0aUwde9UADr7d6aka0kLV5R3yAlKC1FKkZSWlOvjOtg4ZHlRsjt37vDVV18xc+ZMunQx3GOobNmyNGrUiPPnzwPw/vvvExoaCsCECROoUqUKZ86coVKlSjlSvxBCiJy14MQCLt65iIeDB72q97J0OXmKhCYLSUpLInBRYK6Pu/ut3TjaOmap7YkTJ0hOTqZZs2YPbVO9enXjzz4+PgBcu3ZNQpMQQuRD1xOv8+2hbwEYFDAIJ1snC1eUt8jpOfFQDg4Oj21ja2tr/Dn9CJZer8+xmoQQQuScr/Z/RWJaItWLVqdFmRaWLifPkSNNFuJg48Dut3ZbZNysKl++PA4ODmzYsIGePXvmYFVCCCEs7eiNoyw7uwyAEfVGYKWR4yoPktBkIRqNJsunySzF3t6eESNGMHz4cLRaLQ0bNuT69escO3bskafshBBC5C96pWfSnkkAtCzbkuoe1R/zimeThCbxSGPGjMHGxoaxY8dy+fJlfHx86NOnj6XLEkIIYUYrz63k8PXDONo4Mqj2IEuXk2dJaBKPZGVlxYcffsiHH36YYZ9SyuS5u7t7hm1CCCHytsTURKZHTAegV/VeeDh6WLagPExOWAohhBDPsDmH53At6RolXUrS2b+zpcvJ0yQ0CSGEEM+oU7dP8fOxnwEYUXcEWmuthSvK2yQ0CSGEEM8gvdLzcfjHpKk0gn2DaVqyqaVLyvMkNAkhhBDPoD9O/8HB6wdxtHFkRL0Rli4nX5DQJIQQQjxjbibd5MuILwHoX6s/3k7eFq4of5DQlIvkyrKCRX6fQoj8atq+acSnxFO5cGU6VOpg6XLyDQlNuSD9ViOJiYkWrkSYU/rv8/5byQghRF6358oeVpxbgQYNY+qPwcZKVh/KKvmkcoG1tTXu7u5cu3YNAEdHR+N92kT+o5QiMTGRa9eu4e7ujrW1taVLEkKILEnRpfDxro8BaFuxLdU8qlm4ovxFQlMu8fY2nC9OD04i/3N3dzf+XoUQIj/44egPnI8/T1GHogysPdDS5eQ7EppyiUajwcfHB09PT1JTUy1djnhKtra2coRJCJGvRMdHM+/wPACG1x2Oi9bFwhXlPxKacpm1tbV82QohhMhVSik+2fUJKfoUGhRrwEt+L1m6pHxJJoILIYQQBdya82sIvxKO1krLh4EfyrzaJyShSQghhCjA4lPimbJnCgC9q/fG19XXwhXlXxKahBBCiAJsxv4Z3Lx3Ez9XP7pV7WbpcvI1CU1CCCFEAXXk+hGWRC4BYGzQWLkh71OyeGi6dOkSHTt2pEiRIjg4OFCtWjX27dtn3K+UYuzYsfj4+ODg4EBwcDCnT5826ePWrVu8/fbbuLq64u7uTo8ePUhISDBpc/jwYRo3boy9vT0lS5Zk6tSpGWpZunQplSpVwt7enmrVqrFq1aqcedNCCCFEDkvTp/HRro9QKFqWbUld77qWLinfs2houn37Ng0bNsTW1pbVq1dz/Phxpk2bRqFChYxtpk6dyowZM5gzZw67d+/GycmJkJAQ7t27Z2zz9ttvc+zYMdatW8fff//N1q1b6d27t3F/fHw8zZs3p1SpUkRERPDZZ58xfvx45s6da2yzc+dOOnToQI8ePThw4ACtWrWiVatWHD16NHc+DCGEEMKMFp1YxMlbJ3HVujK0zlBLl1MwKAsaMWKEatSo0UP36/V65e3trT777DPjttjYWGVnZ6d++eUXpZRSx48fV4Dau3evsc3q1auVRqNRly5dUkopNWvWLFWoUCGVnJxsMnbFihWNz9u2batCQ0NNxg8MDFTvvPNOlt5LXFycAlRcXFyW2gshhBA55UrCFVV3QV1VNayq+i3yN0uXk6dl5/vbokeali9fTp06dXjzzTfx9PSkVq1azJs3z7g/KiqKmJgYgoODjdvc3NwIDAwkPDwcgPDwcNzd3alTp46xTXBwMFZWVuzevdvYpkmTJmi1/53LDQkJITIyktu3bxvb3D9Oepv0cR6UnJxMfHy8yUMIIYTICybvmUxSWhK1PGvxevnXLV1OgWHR0HTu3Dlmz55N+fLlWbt2LX379uW9997jxx9/BCAmJgYALy8vk9d5eXkZ98XExODp6Wmy38bGhsKFC5u0yayP+8d4WJv0/Q+aNGkSbm5uxkfJkiWz/f6FEEIIc9t8cTMbojdgo7FhdP3RWGksPn25wLDoJ6nX66lduzaffvoptWrVonfv3vTq1Ys5c+ZYsqwsGTVqFHFxccbHxYsXLV2SEEKIZ1xiaiKTdk8CoFOVTlQoVMHCFRUsFg1NPj4++Pv7m2yrXLky0dHRwH83ub169apJm6tXrxr3eXt7Z7gJblpaGrdu3TJpk1kf94/xsDYPuyGrnZ0drq6uJg8hhBDCkuYcnsPlu5cp5lSMPtX7WLqcAseioalhw4ZERkaabDt16hSlSpUCoHTp0nh7e7Nhwwbj/vj4eHbv3k1QUBAAQUFBxMbGEhERYWyzceNG9Ho9gYGBxjZbt241uVHuunXrqFixovFKvaCgIJNx0tukjyOEEELkZadun+LnYz8D8EHgBzjaOlq4ogIoFyamP9SePXuUjY2Nmjhxojp9+rRauHChcnR0VAsWLDC2mTx5snJ3d1fLli1Thw8fVq+99poqXbq0SkpKMrZ56aWXVK1atdTu3bvV9u3bVfny5VWHDh2M+2NjY5WXl5fq1KmTOnr0qFq8eLFydHRU3377rbHNjh07lI2Njfr888/ViRMn1Lhx45Stra06cuRIlt6LXD0nhBDCUnR6neq4sqOqGlZVDdo4yNLl5CvZ+f62aGhSSqkVK1aoqlWrKjs7O1WpUiU1d+5ck/16vV6NGTNGeXl5KTs7O9WsWTMVGRlp0ubmzZuqQ4cOytnZWbm6uqpu3bqpO3fumLQ5dOiQatSokbKzs1PFixdXkydPzlDLkiVLVIUKFZRWq1VVqlRRK1euzPL7kNAkhBDCUn6L/E1VDauq6i2op64kXLF0OflKdr6/NUopZdljXQVDfHw8bm5uxMXFyfwmIYQQueZm0k1a/tWS+JR4htcdTif/TpYuKV/Jzve3XIcohBBC5GNfRHxBfEo8lQpXokOlDpYup0CT0CSEEELkU3uu7GH52eVo0DC2/lhsrGwsXVKBJqFJCCGEyIdSdCl8vOtjANpWbEs1j2oWrqjgk9AkhBBC5EM/HP2B8/HnKepQlIG1B1q6nGeChCYhhBAin4mOj2beYcO9WofXHY6L1sXCFT0bJDQJIYQQ+YhSiom7J5KiTyHIJ4iX/F6ydEnPDAlNQgghRD6y5vwadl7eidZKy+j6o9FoNJYu6ZkhoUkIIYTIJ+JT4pm6dyoAvar3wtfV18IVPVskNAkhhBD5xIz9M7iRdAM/Vz+6V+1u6XKeORKahBBCiHzgyPUjLIlcAsCY+mPQWmstXNGzR0KTEEIIkcel6dP4aNdHKBQty7aknk89S5f0TJLQJIQQQuRxv5z8hZO3TuKqdWVIwBBLl/PMktAkhBBC5GExd2OYeWAmAEMChlDEoYiFK3p2SWgSQggh8rApe6aQmJZILc9avF7+dUuX80yTO/sJIYQQedSWi1tYH70eG40No+uPxkqTA8c6UpPg4ELQWIOzJzh5grOH4X+1juYfLx+T0CSEEELkQYmpiXy6+1MAOlXpRIVCFcw/iFKwrD8c/S3z/VrnjEHK2ROcPDJut3M2f315jIQmIYQQIg/69vC3XL57mWJOxehTvU/ODLJnriEwWdlA2WaQeAMSrhkeumRISYBbCXDr3OP7snXMPExlFrLsXCEfrmQuoUkIIYTIY07fPs1Px34C4IPAD3C0zYHTZBf3wNoPDD+/+DEEvfvfPqUgOR4SrsPd/4eou9f//7/XMm5PTTQ8Yi8YHo9jbfdAmHpE2HIolGcCloQmIYQQIg/RKz0f7/qYNJVGM99mNC3Z1PyDJFyHJV1AnwZVXof6fU33azRg72Z4FC33+P6SEzIPUxlC1nVIuWM4ihV30fB4HCvb/0JV+RfhhdFP9p7NQEKTEEIIkYf8efpPDlw7gKONIyPrjTT/ALo0+L073LkMRStAy6+f/kiOnbPhUbjM49umJBoClUmoupZ5wEqOA32qodY7l8Gj0tPV+ZQkNAkhhBB5xK17t/gi4gsA+tXsh7eTt/kH2TQRoraCrRO0WwB2LuYf41G0jqAtBYVKPb5t6r3/B6z/hygnj5yv7xEkNAkhhBB5xLR904hPiadS4Uq8Vfkt8w9wciVsN4QyXvsaPCqafwxzsrUH95KGRx4gi1sKIYQQecCeK3tYfnY5GjSMrT8WGyszH9e4eRb+/P/cpcC+ULWNeft/BkhoEkIIISwsRZfCx7s+BqBtxbZU86hm5gESYUlnwxyhkoHw4kfm7f8ZIaFJCCGEsLD5R+dzPv48RR2K8l7t98zbuVKwcihcPWqYE/RmGNhozTvGM0JCkxBCCGFB0fHRzD08F4DhdYfjqnU17wARYXBoEWis4I0fwLWYeft/hmQ7NG3dupW0tLQM29PS0ti6datZihJCCCGeBUopJu6eSIo+hSCfIF7ye8m8A1yKgNXDDT83Gwelm5i3/2dMtkPT888/z61btzJsj4uL4/nnnzdLUUIIIcSzYO35tey8vBOtlZbR9UejMefK14m3DAtY6lKgUgtoONB8fT+jsh2alFKZ/lJv3ryJk5OTWYoSQgghCrr4lHim7J0CQK/qvfB19TVf53od/N7TsOJ24TLQalaeuRVJfpbl6xlbt24NgEajoWvXrtjZ2Rn36XQ6Dh8+TIMGDcxfoRBCCFEAfb3/a24k3cDP1Y/uVbubt/MtU+HsBrBxgLY/G26HIp5alkOTm5vhA1dK4eLigoODg3GfVqulfv369OrVy/wVCiGEEAXMketH+DXyVwDG1B+D1tqMV7OdXgdbDEeweHU6eFc1X9/PuCyHpvnz5wPg5+fH+++/L6fihBBCiCeQpk/j410fo1C8WuZV6vnUM1/nty8YTsuhoE4PqNHefH2L7N9GZdy4cTlRhxBCCPFM+OXkL5y4dQJXrStD6ww1X8ep9wwLWN6LheIB8NIk8/UtgCeYCH716lU6depEsWLFsLGxwdra2uQhhBBCiMzF3I1h5oGZAAwOGEwRhyLm63z1cLhyEBwKw5s/go3dY18isifbR5q6du1KdHQ0Y8aMwcfHx7yXRwohhBAF2JQ9U0hMS6SmR01al29tvo4PLID9PwIaaPNdnrnBbUGT7dC0fft2tm3bRs2aNXOgHCGEEKJg2nJxC+uj12OjsWFM0BisNGa6KceVw4bbpAA8/yGUa2aefkUG2f6NlSxZEqWUWQYfP348Go3G5FGpUiXj/nv37tGvXz+KFCmCs7Mzbdq04erVqyZ9REdHExoaiqOjI56engwbNizDiuWbN2+mdu3a2NnZUa5cOcLCwjLU8s033+Dn54e9vT2BgYHs2bPHLO9RCCGESExN5NPdnwLQqUonKhSqYJ6Ok27Dkk6Qdg/KN4fGZpwjJTLIdmiaPn06I0eO5Pz582YpoEqVKly5csX42L59u3Hf4MGDWbFiBUuXLmXLli1cvnzZuF4UGNaHCg0NJSUlhZ07d/Ljjz8SFhbG2LFjjW2ioqIIDQ3l+eef5+DBgwwaNIiePXuydu1aY5tff/2VIUOGMG7cOPbv30+NGjUICQnh2rVrZnmPQgghnm3fHv6Wy3cvU8ypGH2q9zFPp3o9/NkXbp8Hd194/VuwklvK5iiVTe7u7kqr1SorKyvl7OysChUqZPLIjnHjxqkaNWpkui82NlbZ2tqqpUuXGredOHFCASo8PFwppdSqVauUlZWViomJMbaZPXu2cnV1VcnJyUoppYYPH66qVKli0ne7du1USEiI8Xm9evVUv379jM91Op0qVqyYmjRpUpbfS1xcnAJUXFxcll8jhBCi4Dt165Sq+WNNVTWsqtocvdl8HW/5TKlxrkp95KHUpQPm6/cZk53v72zPaZo+fbpZQ9vp06cpVqwY9vb2BAUFMWnSJHx9fYmIiCA1NZXg4GBj20qVKuHr60t4eDj169cnPDycatWq4eXlZWwTEhJC3759OXbsGLVq1SI8PNykj/Q2gwYNAiAlJYWIiAhGjRpl3G9lZUVwcDDh4eEPrTs5OZnk5GTj8/j4+Kf9KIQQQhQweqXn410fk6bSaObbjKYlm5qn47ObYNNEw8+hn0OxmubpVzxStkNTly5dzDZ4YGAgYWFhVKxYkStXrjBhwgQaN27M0aNHiYmJQavV4u7ubvIaLy8vYmJiAIiJiTEJTOn70/c9qk18fDxJSUncvn0bnU6XaZuTJ08+tPZJkyYxYcKEJ3rfQgghCj690vPZ3s84cO0AjjaOjKw30jwdx/0Lv/cApYdanaB2Z/P0Kx4r26EpOjr6kft9fbN+w8GXX37Z+HP16tUJDAykVKlSLFmyxOQ2LXnRqFGjGDJkiPF5fHw8JUvKJZ5CCCEgWZfMh9s/ZO15w/zZ4XWH4+3k/fQdp6XAki6QeBO8q8Mrnz19nyLLsh2a/Pz8Hrk2k06ne+Ji3N3dqVChAmfOnOHFF18kJSWF2NhYk6NNV69exdvb8Ifn7e2d4Sq39Kvr7m/z4BV3V69exdXVFQcHB+OinJm1Se8jM3Z2diY3LRZCCCEA4pLjGLhpIBFXI7CxsuGThp8QWibUPJ3/8yFc2me4AW+7n8E2bx9gKGiyPc3+wIED7N+/3/jYvXs3c+bMoUKFCixduvSpiklISODs2bP4+PgQEBCAra0tGzZsMO6PjIwkOjqaoKAgAIKCgjhy5IjJVW7r1q3D1dUVf39/Y5v7+0hvk96HVqslICDApI1er2fDhg3GNkIIIURWXEm4QpfVXYi4GoGzrTNzgueYLzAdXgJ75hp+bj0PCvmZp1+Rdeaaff7333+rpk2bZus1Q4cOVZs3b1ZRUVFqx44dKjg4WBUtWlRdu3ZNKaVUnz59lK+vr9q4caPat2+fCgoKUkFBQcbXp6WlqapVq6rmzZurgwcPqjVr1igPDw81atQoY5tz584pR0dHNWzYMHXixAn1zTffKGtra7VmzRpjm8WLFys7OzsVFhamjh8/rnr37q3c3d1Nrsp7HLl6Tgghnm0nb55UL/z6gqoaVlW9sOQFdfLmSfN1HnNMqU+8DVfLbfjYfP2KbH1/my00nT59Wjk6OmbrNe3atVM+Pj5Kq9Wq4sWLq3bt2qkzZ84Y9yclJal3331XFSpUSDk6OqrXX39dXblyxaSP8+fPq5dfflk5ODiookWLqqFDh6rU1FSTNps2bVI1a9ZUWq1WlSlTRs2fPz9DLV9//bXy9fVVWq1W1atXT+3atStb70VCkxBCPLvCL4erwIWBqmpYVdXqr1bqSsKVx78oq5LilJpR2xCYfnxNKV2a+foW2fr+1iiVveW9H7y0XinFlStXGD9+PCdPnuTgwYPmOgiWr8THx+Pm5kZcXByurq6WLkcIIUQu+fvc34zZMYY0fRp1vOrw1Qtf4ao10/eAUoYVv0+sANcS8M5WcDLjTX5Ftr6/sz0R3N3dPcNEcKUUJUuWZPHixdntTgghhMiXlFL8cPQHpu+fDkCIXwifNvoUrbXWfIPs/NoQmKxsoe1PEpgsLNuhadOmTSbPrays8PDwoFy5ctjYZLs7IYQQIt/R6XVM3jOZxZGGgwWd/TsztM5Q892EF+D8dlg/3vDzy5OhRID5+hZPJNspp2lTM61mKoQQQuRD99LuMXLbSDZEb0CDhmF1h9HJv5N5B7kTA0u7gdJB9fZQp4d5+xdP5IkODZ09e5bp06dz4sQJAPz9/Rk4cCBly5Y1a3FCCCFEXhJ7L5b+G/tz6PohtFZaPm38KSF+IeYdRJcKS7vC3WvgWQVafAmPWB9R5J5sH0dcu3Yt/v7+7Nmzh+rVq1O9enV2795NlSpVWLduXU7UKIQQQljcv3f+pdPqThy6fggXrQtzm881f2ACwym56HCwczUsYKl1NP8Y4olk++q5WrVqERISwuTJk022jxw5kn/++Yf9+/ebtcD8Qq6eE0KIguvYzWP0W9+Pm/du4uPkw+zg2ZR1z4GzK8f+NBxlAmi3ECq3MP8YwkR2vr+zfaTpxIkT9OiR8dxq9+7dOX78eHa7E0IIIfK07Ze2021NN27eu0nFQhVZ8MqCnAlM10/Bsv6GnxsOlMCUB2U7NHl4eGS6FtPBgwfx9PQ0R01CCCFEnvDn6T/pv6E/SWlJ1PepT9hLYXg65sB3XXKCYT2mlATwawwvjDX/GOKpZXsieK9evejduzfnzp2jQYMGAOzYsYMpU6YwZMgQsxcohBBC5DalFHMOz2HWwVkAvFrmVSY0mICttW1ODAYr3oPrJ8HFB974AaxlCZ+8KNu/lTFjxuDi4sK0adMYNWoUAMWKFWP8+PG89957Zi9QCCGEyE1p+jQ+2fUJv5/+HYCe1XryXq33MizsbDZ75sLR38HKBt4MA2c5a5NXZXsi+P3u3LkDgIuLi9kKyq9kIrgQQuR/iamJDNs6jK3/bsVKY8UH9T6gXaV2OTdg9G4IewX0afDSZKjfN+fGEpnKkYngSUlJLF++3BiUwBCWXFxciI+PZ/ny5SQnJz951UIIIYQF3Uy6Sfe13dn671bsre358rkvczYwJVyHpV0MgalKawjsk3NjCbPIcmiaO3cuX331VaZHlVxdXZkxYwbfffedWYsTQgghcsOF+At0XNWRYzeP4W7nznch3/GC7ws5N6AuDX7rBneuQNGK0PJrWcAyH8hyaFq4cCGDBg166P5Bgwbx448/mqMmIYQQItccvn6YTqs68W/CvxR3Ls7PL/9MDY8aOTvopk/g/DbQOhsWsLRzztnxhFlkOTSdPn2aGjUe/kdUvXp1Tp8+bZaihBBCiNywKXoTPdb24HbybaoUqcKCVxbg5+aXs4OeXAnbvzT83PJr8KiYs+MJs8lyaEpLS+P69esP3X/9+nXS0tLMUpQQQgiR05ZELmHQ5kHc092jcfHG/BDyA0UdiubsoDfPwp//n7tU/12o2jpnxxNmleXQVKVKFdavX//Q/f/88w9VqlQxS1FCCCFETlFKMWP/DD7e9TF6pad1+dbMeGEGjrY5fI+3lERY0hmS46FkfXjxo5wdT5hdlkNT9+7d+fjjj/n7778z7FuxYgUTJ06ke/fuZi1OCCGEMKdUfSqjd4xm3pF5ALxb413GB43HxiqHF5NUClYOgatHwcnDsB5TTiyUKXJUlv9KevfuzdatW2nZsiWVKlWiYkXDOdiTJ09y6tQp2rZtS+/evXOsUCGEEOJp3E29y+BNgwm/Eo61xpqxQWNpXT6XTo9FzIdDv4DGGt6YD64+uTOuMKts3XtuwYIFLF68mAoVKnDq1CkiIyOpWLEiv/zyC7/88ktO1SiEEEI8leuJ1+m6pivhV8JxsHFgxgszci8wXYqA1SMMPwePg9KNc2dcYXZPtSK4+I+sCC6EEHnTudhz9F3fl8t3L1PYvjCzms2iStFcmoN7Kwp+fBXiLkKlFtBuQZ5bjylNp2fr6eskpehxdbDBzcEWV3tb3BxscbG3wcY6W8dX8p3sfH/LHQGFEEIUWPuv7mfAxgHEp8RTyrUUs4NnU9KlZM4PnHwHtn0B4d+ALhkKl4VWs/JcYErV6Rmw6ABrjsU8tI2znY0xQLk52OLqYGsSrNwcbP7b9sA+e1urnLtnnwVIaBJCCFEgrbuwjpFbR5KiT6G6R3VmvjCTQvaFcnZQvR4OL4b1EyDh/0GkdFN49Suwd8vZsbNJp1cMWXKINcdi0FpbUbOkO/H3UolLSiU+KZW7KToAEpLTSEh+siWFtNZWuP4/VKUHKdf0oGUMXRnDlquDDS72tlhb5a3AJaFJCCFEgbPwxEKm7JmCQvF8yeeZ0mQKDjYOOTto9G5YMxIu7zc8L1QaQiZCxVfy3BEmnV4xbOkhVhy6jK21htkda9OsspdJm1Sdnjv30ohL+i9IxSWl3hes0ozP4zO0SUOnV6To9NxISOFGQsoT1elid/9RLBsal/eg3/PlzPERPBEJTUIIIQoMvdLzZcSXhB0LA6BdxXaMqjcKayvrnBs07l9YPx6OLDU817pA02GGG/Da2OXcuE9Ir1d88McR/jhwCWsrDV93yBiYAGytrSjspKWwkzbbYyiluJuiyxCm0gNV+rb4TEJYXFIqSamGo1x3ktO4k5zGpdgkAHzccjj4PoaEJiGEEAVCii6F0dtHs/r8agAG1h5Ij6o9cm5OTUoi7JwB26dDWhKggVododlYcPbMmTGfklKKscuP8uu+i1hp4Kv2NXmpqrfZx9FoNDjb2eBsZ0Mx9+wHnZQ0vekRrP8HLR83e7PXmh1ZCk2tW2f9ssw//vjjiYsRQgghnkR8SjyDNg1ib8xebDQ2fNTwI14t+2rODKYUHP0d1o2D+H8N23yD4KXJUKxmzoxpBkopPvr7OAt2RaPRwBdta9KiejFLl5UprY0VRZ3tKOqct47UZSk0ubn9N3lNKcWff/6Jm5sbderUASAiIoLY2NhshSshhBDCHGLuxtB3fV/OxJ7BydaJL5/7kqBiQTkz2OUDsHokXNxleO5W0nA7lCqv57l5S/dTSjF59Unm7zgPwJQ21WlVq7hli8qHshSa5s+fb/x5xIgRtG3bljlz5mBtbThHrNPpePfdd2V9IiGEELnq1O1T9F3fl2uJ1/Bw8GBW8CwqFa5k/oHuXIUNH8HBhYACW0doNBgaDABby86zyYov1p3i263nAJj4elXa1smFZRcKoGwvbunh4cH27duNt1FJFxkZSYMGDbh586ZZC8wvZHFLIYTIXSdunqD72u4kpCZQxq0Mc4Ln4ONs5tuTpCXDrlmw9XNISTBsq94Omo0Dt/xxpGbGhtN8se4UAONf9adrw9IWrihvydHFLdPS0jh58mSG0HTy5En0en12uxNCCCGy7U7KHYZsHkJCagK1PWsz44UZuNmZcR0kpeDk3/DPaLh93rCteAC8NAVK1jXfODls9uazxsA0OrSyBKanlO3Q1K1bN3r06MHZs2epV68eALt372by5Ml069bN7AUKIYQQ91NKMW7nOP5N+JfizsX5utnXuGrNeIQ/5qhhvaXz2wzPXXwgeDxUawtW+eeWIt9tO8eUNScBGBZSkZ6Ny1i4ovwv26Hp888/x9vbm2nTpnHlyhUAfHx8GDZsGEOHDjV7gUIIIcT9fjn5C+surMPGyobPm35uvsB09yZs+gQiwkDpwdrOMGep0WCwczbPGLnkp/DzfLLyBACDgstbdEHIgiRboSktLY1FixbRpUsXhg8fTnx8PIDM4RFCCJErjt04xuf7Pgfg/TrvU7Vo1afvVJcKe+bBlslwL86wzb+V4aq4QqWevv9ctmh3NGOXHQPg3efKMrBZeQtXVHBkKzTZ2NjQp08fTpwwpFcJS0IIIXJLfEo8Q7cMJVWfSjPfZrxV6a2n7/T0OlgzCm6eNjz3rmaYt+TX8On7toCl+y7y4V9HAOjVuDTDQioWqBvmWlq2T8/Vq1ePAwcOUKpU/kvfQggh8ielFON3judSwiWKOxfno4YfPV0YuH4K1n4AZ9YZnjsWNazkXasj5OQtV3LQsoOXGP77YZSCrg38+OCVyhKYzCzbM9reffddhg4dysyZMwkPD+fw4cMmjyc1efJkNBoNgwYNMm67d+8e/fr1o0iRIjg7O9OmTRuuXr1q8rro6GhCQ0NxdHTE09OTYcOGkZZmejfmzZs3U7t2bezs7ChXrhxhYWEZxv/mm2/w8/PD3t6ewMBA9uzZ88TvRQghhHmZbR5T0m3D4pSzgwyBycrWMG/pvf0Q0CXfBqaVh68wZMkhlIK3An0Z96q/BKYckO0jTe3btwfgvffeM27TaDQopdBoNOh0umwXsXfvXr799luqV69usn3w4MGsXLmSpUuX4ubmRv/+/WndujU7duwADItqhoaG4u3tzc6dO7ly5QqdO3fG1taWTz/9FICoqChCQ0Pp06cPCxcuZMOGDfTs2RMfHx9CQkIA+PXXXxkyZAhz5swhMDCQ6dOnExISQmRkJJ6eefP+QUII8awwyzwmXRrsD4ONEyHplmFbhZchZCIUKWu+Yi1g7bEYBi4+gE6veDOgBJ+8VlUCUw7J9uKWFy5ceOT+7J62S0hIoHbt2syaNYtPPvmEmjVrMn36dOLi4vDw8GDRokW88cYbgGEtqMqVKxMeHk79+vVZvXo1LVq04PLly3h5Ge7QPGfOHEaMGMH169fRarWMGDGClStXcvToUeOY7du3JzY2ljVr1gAQGBhI3bp1mTlzJgB6vZ6SJUsyYMAARo4cmaX3IYtbCiGE+cWnxNN2RVsuJVyimW8zvnzuy+wHgnNbDPOWrhkmR+NRCV6aBGVfMH/BuWzjyau883MEqTrF67WK8/mbNbC2ksCUHdn5/s726blSpUo98pFd/fr1IzQ0lODgYJPtERERpKammmyvVKkSvr6+hIeHAxAeHk61atWMgQkgJCSE+Ph4jh07ZmzzYN8hISHGPlJSUoiIiDBpY2VlRXBwsLFNZpKTk4mPjzd5CCGEMJ+nnsd06xwsfht+amkITA6F4JXPoc+OAhGYtp66Tp8F+0nVKUKr+/DZG9UlMOWwbJ+eAzh79izTp083XkXn7+/PwIEDKVs2e4c4Fy9ezP79+9m7d2+GfTExMWi1Wtzd3U22e3l5ERMTY2xzf2BK35++71Ft4uPjSUpK4vbt2+h0ukzbnDx58qG1T5o0iQkTJmTtjQohhMi2J57HlHzHcNuTXbNAlwIaa6jbE54bCY6Fc7boXLLzzA16/bSPlDQ9IVW8mN6uJjbW+Wfhzfwq25/w2rVr8ff3Z8+ePVSvXp3q1auze/duqlSpwrp167Lcz8WLFxk4cCALFy7E3t4+u2VY3KhRo4iLizM+Ll68aOmShBCiwDh24xif7fsMyMY8Jr0e9v8MM2rDjumGwFT2Bei7E16ZWmAC056oW/T4cR/JaXpeqOTJ1x1qYyuBKVdk+0jTyJEjGTx4MJMnT86wfcSIEbz44otZ6iciIoJr165Ru3Zt4zadTsfWrVuZOXMma9euJSUlhdjYWJOjTVevXsXb2xsAb2/vDFe5pV9dd3+bB6+4u3r1Kq6urjg4OGBtbY21tXWmbdL7yIydnR12dnZZeq9CCCGyLn09pjR9GsG+wVlbj+lCuOHWJ1cOGp4XLgshn0KFEChAk6IjLtym2/w9JKXqaFLBg1lv10ZrI4Ept2T7kz5x4gQ9evTIsL179+4cP348y/00a9aMI0eOcPDgQeOjTp06vP3228afbW1t2bBhg/E1kZGRREdHExQUBEBQUBBHjhzh2rVrxjbr1q3D1dUVf39/Y5v7+0hvk96HVqslICDApI1er2fDhg3GNkIIIXKHUopxO8YZ5zFNaDjh0fOYYi/Cb91h/kuGwGTnCs0nwru7oOJLBSowHf43lq4/7OFuio4GZYswt1MA9rb5c4mE/CrbR5o8PDw4ePAg5cubLst+8ODBbF2e7+LiQtWqpodbnZycKFKkiHF7jx49GDJkCIULF8bV1ZUBAwYQFBRE/fr1AWjevDn+/v506tSJqVOnEhMTw+jRo+nXr5/xKFCfPn2YOXMmw4cPp3v37mzcuJElS5awcuVK47hDhgyhS5cu1KlTh3r16jF9+nTu3r0rNyAWQohctujkItZHr8fGyoZpTac9eh7TyZXwWw9ISwI0hnWWnh8Nzh65Vm9uOXY5jk7f7+FOchr1/ArzXZc6EpgsINuhqVevXvTu3Ztz587RoEEDAHbs2MGUKVMYMmSIWYv78ssvsbKyok2bNiQnJxMSEsKsWbOM+62trfn777/p27cvQUFBODk50aVLFz766CNjm9KlS7Ny5UoGDx7MV199RYkSJfjuu++MazQBtGvXjuvXrzN27FhiYmKoWbMma9asyTA5XAghRM55cD2mKkWrPLzxqX9gSRfQp4JvA3h5CvhUf3j7fCwy5g4dv9tNXFIqtX3d+aFbXRy1T3Qdl3hK2V6nSSnF9OnTmTZtGpcvXwagWLFiDBs2jPfee++ZXVBL1mkSQognd/96TMG+wXzx3BcP/z45uwkWtQNdMlR5HVp/B9YFM0ScuZZA+7nh3EhIoXoJNxb0DMTV3tbSZRUo2fn+znZout+dO3cAw6m2Z52EJiGEeDJKKYZsHsL66PUUdy7OkleXPPy03PkdsKCN4ZRcxVBo+yNYF8wQEXXjLu2+DefanWT8fVz5pVd93BwL5nu1pOx8f2c7mkdFRZGWlkb58uVNwtLp06extbXFz88v2wULIYR4dmV5HtPFPbCorSEwlXsR3pxfYANT9M1E3pq3i2t3kqnk7cKCnoESmPKAbF8917VrV3bu3Jlh++7du+natas5ahJCCPGMyPI8pkv7DUeYUhKgdFNo9zPYFMxlX/69nUiHebu4EnePcp7OLOgZSGEnraXLEjxBaDpw4AANGzbMsL1+/focPHjQHDUJIYR4BmR5PaaYI/Dz65Acb5j03eEXsHXI3WJzSUzcPd6at5tLsUmULurEop6BFHUumOEwP8p2aNJoNMa5TPeLi4tDp9OZpSghhBAFm1KKsTvGPn49pmsn4afX4F4slKgLby8BrVOu15sbrsXf4615u4i+lYhvYUcW9QrE0zX/3TGjIMt2aGrSpAmTJk0yCUg6nY5JkybRqFEjsxYnhBCiYFp0chEbojdga2X78HlMN84YbrabeBN8asLbv4Fdwbzw6EZCMm99t5tzN+5S3N2BRb0C8XErmEfT8rNsTwSfMmUKTZo0oWLFijRu3BiAbdu2ER8fz8aNG81eoBBCiILl6I2jj5/HdCsKfnwVEq6CV1Xo9Cc4uOduobnk9t0UOn63mzPXEvB2teeXXvUpUcjR0mWJTGT7SJO/vz+HDx+mbdu2XLt2jTt37tC5c2dOnjyZYYVvIYQQ4n7xKfG8v+V90vRpvFjqRTpU6pCxUexFwxGmO5ehaEXo9FeBudnug+ISU+n4/W5OxtzBw8WORb0C8S0igSmveqp1msR/ZJ0mIYR4NKUUgzcPZkP0Bko4l2DJq0tw0T5wui3+CoS9ArfOQeEy0G01uDz85un5Wfy9VDp9t5tD/8ZRxEnL4t71Ke9VME8/5mXZ+f5+olsjb9u2jY4dO9KgQQMuXboEwM8//8z27dufpDshhBDPgPvnMX3+3OcZA1PCdcMRplvnwN0XuqwosIEpITmNbvP3cujfOAo52rKwV6AEpnwg26Hp999/JyQkBAcHB/bv309ycjJguHru008/NXuBQggh8r8M85iKPDCPKfGW4Sq5G6fAtYQhMLmVsEClOS8xJY3uYXuJuHAbV3sbfu4RSCVvOUORH2Q7NH3yySfMmTOHefPmYWv73+qkDRs2ZP/+/WYtTgghRP732HlMSbGGwHTtGDh7Q5flUMjPEqXmuHupOnr9tI89UbdwsTMEpqrF3SxdlsiibIemyMhImjRpkmG7m5sbsbGx5qhJCCFEAXH/ekwlnEswocED6zHdizes9B1zGByLGgJTkbKWKzgH3UvV0fvnCHacuYmT1pqw7nWpUdLd0mWJbMh2aPL29ubMmTMZtm/fvp0yZcqYpSghhBAFwyPnMaXcNdxL7tI+cCgEnZeBR0XLFZuDUtL09Fu4n62nruNga80PXesSUKpgXhFYkGU7NPXq1YuBAweye/duNBoNly9fZuHChbz//vv07ds3J2oUQgiRDx25fuTh85hSk+CX9hAdDnZuhnWYvAvmsjWpOj0DftnPhpPXsLOx4rsudQgsU8TSZYknkO3FLUeOHIler6dZs2YkJibSpEkT7OzseP/99xkwYEBO1CiEECKfiUuOY9jWYZnPY0pLhl87QtRW0DpDx9+hWC3LFZuD7qXqeH/pIdYeu4rW2oq5nevQsFxRS5clntATr9OUkpLCmTNnSEhIwN/fH2dnZ5KSknBweDaXfZd1moQQwkApxaBNg9h4cWPG9ZjSUmBJZzi1GmwdDYGpVAPLFpwDbt1N4efwC/y86zw3ElKwtdYwp2MAzSp7Wbo08YDsfH9n+0hTOq1Wi7+/PwDJycl88cUXTJ06lZiYmCftUgghRAGw8MRCNl7cmHEeky4N/uhpCEw29tBhcYELTFE37vL99nP8FvEv91L1ABR3d+Cj16pIYCoAshyakpOTGT9+POvWrUOr1TJ8+HBatWrF/Pnz+fDDD7G2tmbw4ME5WasQQog87sj1I0yLmAbAsLrD/pvHpNfBX33g+DKw1kK7hVCmqQUrNR+lFBEXbjN36znWnbhK+vmbqsVd6dW4DK9U88HW+onWkhZ5TJZD09ixY/n2228JDg5m586dvPnmm3Tr1o1du3bxxRdf8Oabb2JtbZ2TtQohhMjDHpzH1L5ie8MOvR5WvAdHloKVDbwZBuWDLVqrOej0irXHYpi79RwHL8Yatzer5EnPxmWoX6aw6fIKIt/LcmhaunQpP/30Ey1btuTo0aNUr16dtLQ0Dh06JH8UQgjxjHvoekxKwar34cAC0FhBm++gUqily30qd5PTWLrvIt/viOLirSQAtNZWtK5dnJ6NS1POU26HUlBlOTT9+++/BAQEAFC1alXs7OwYPHiwBCYhhBCZz2NSCtaMgn3fAxp4/Vuo8rqlS31i1+LvEbbzPAt3RxOXlAqAu6MtneuXolOQHx4udhauUOS0LIcmnU6HVqv974U2Njg7O+dIUUIIIfKPTOcxKQXrx8Pu2YZGLb+G6m0tV+RTiIy5w3fbzvHXwUuk6gwTlvyKONKjcRneqF0CB61MTXlWZDk0KaXo2rUrdnaGJH3v3j369OmDk5OTSbs//vjDvBUKIYTIsx46j2nLFNgx3fBz6DSo3cliNT4JpRQ7ztxk3rZzbDl13bi9TqlC9Gxchhf9vbC2kjMtz5osh6YuXbqYPO/YsaPZixFCCJF/KKUYs2NMxnlM276AzZMMjUI+hbo9LVtoNqTq9Px9+DJzt0Zx4ko8AFYaeKmqNz0bl6G2byELVygsKcuhaf78+TlZhxBCiHxmwYkFbLq4yXQeU/gs2DDB0KDZOAjqZ9kisyj+Xiq/7I5m/o7zxMTfA8DB1pp2dUvSvWFpfIs4WrhCkRc88eKWQgghnl1Hrh/hi4gvgPvmMe39DtaOMjRoOhIaD7FghVnz7+1E5u84z+I90dxN0QHg4WJH1wZ+vB3oi7uj9jE9iGeJhCYhhBDZEpccx/tb3idNn0bzUs0N85j2/wwrhxoaNBwEz420aI2Pc+TfOOZuO8eqI1fQ6Q2Tuyt4OdOzcRleq1kMOxuZ3C0yktAkhBAiy9LnMV2+e5mSLiUZ32A8miNLYfn/b9he/10IHg95cDkavV6xKfIa87adY9e5W8btDcsVoVfjMjSt4CHL6IhHktAkhBAiy0zmMTX9HJfT6+HPdwAFdXoYJn7nseBxL1XHXwcuMW/bOc5evwuAjZWGV2sUo2fj0lQp5mbhCkV+IaFJCCFEltw/j2l43eH4Xz8Pv/cEpYeaHeGVz/NUYLp1N4UFuy7wU/h5biSkAOBiZ8Nbgb50beiHj5uDhSsU+Y2EJiGEEI/14DymdlZF4Ne3QJ8G1d6EljPAKm/clDbqxl2+336O3yL+5V6qHoDi7g50a+hHu7olcbG3tXCFIr+S0CSEEOKRMsxjKhaMZklH0KWA/2vQag5YWXbitFKKiAu3mbv1HOtOXEUZ5nZTtbgrvRqX4ZVqPtha541QJ/IvCU1CCCEeyWQeU4XOuCztDmn3oOIr0OZ7sLbcV4lOr1h7LIZ5285xIDrWuL1ZJU96Ni5D/TKFZXK3MBsJTUIIIR7q8PXDfLHv//OYyrXDf/lQSE2EcsHwZhhYW+5U156oWwz77RAXbiYCoLWxonWt4vRsXJpyni4Wq0sUXBY9Vjl79myqV6+Oq6srrq6uBAUFsXr1auP+e/fu0a9fP4oUKYKzszNt2rTh6tWrJn1ER0cTGhqKo6Mjnp6eDBs2jLS0NJM2mzdvpnbt2tjZ2VGuXDnCwsIy1PLNN9/g5+eHvb09gYGB7NmzJ0fesxBC5BdxyXEM2zKMNJVGc696tNv8DaQkgF9jaLcAbOwsVtui3dG8NW8XF24mUsjRlvdeKMeOES8wuU11CUwix1g0NJUoUYLJkycTERHBvn37eOGFF3jttdc4duwYAIMHD2bFihUsXbqULVu2cPnyZVq3bm18vU6nIzQ0lJSUFHbu3MmPP/5IWFgYY8eONbaJiooiNDSU559/noMHDzJo0CB69uzJ2rVrjW1+/fVXhgwZwrhx49i/fz81atQgJCSEa9eu5d6HIYQQeYhSitE7RhvmMTl6Mf7wBjTJceAbBG/9CraWufIsVadnzF9H+eDPI6TpFaHVfdg+4gWGNK+Ih4vlQpx4NmiUSp8ulzcULlyYzz77jDfeeAMPDw8WLVrEG2+8AcDJkyepXLky4eHh1K9fn9WrV9OiRQsuX76Ml5cXAHPmzGHEiBFcv34drVbLiBEjWLlyJUePHjWO0b59e2JjY1mzZg0AgYGB1K1bl5kzZwKg1+spWbIkAwYMYOTIrK1qGx8fj5ubG3Fxcbi6uprzIxFCiFyllOLLiC+Zf2w+tlY2LLh+B//461A8ADr9BfaW+Tfu1t0U3l0YYVyYclhIRd59rqzMWRJPJTvf33nmUgKdTsfixYu5e/cuQUFBREREkJqaSnBwsLFNpUqV8PX1JTw8HIDw8HCqVatmDEwAISEhxMfHG49WhYeHm/SR3ia9j5SUFCIiIkzaWFlZERwcbGwjhBDPCqUU0/dPZ/4xw03aR8fdMwQm7+rQ8XeLBaaTMfG0nLmdXedu4aS1Zl7nOvR7vpwEJpGrLD4R/MiRIwQFBXHv3j2cnZ35888/8ff35+DBg2i1Wtzd3U3ae3l5ERMTA0BMTIxJYErfn77vUW3i4+NJSkri9u3b6HS6TNucPHnyoXUnJyeTnJxsfB4fH5+9Ny6EEHmMUoqv9n/FD0d/AOCDBD2tb1wGzyrQeRk4FLJIXWuOxjBkyUESU3T4Fnbkuy51qOAl85ZE7rN4aKpYsSIHDx4kLi6O3377jS5durBlyxZLl/VYkyZNYsKECZYuQwghzEIpxYwDM/j+6PcAjIpNpMPtG1C0AnT+CxwL53pNer3i641n+HL9KQAalC3CN2/VppCTNtdrEQLywOk5rVZLuXLlCAgIYNKkSdSoUYOvvvoKb29vUlJSiI2NNWl/9epVvL29AfD29s5wNV3688e1cXV1xcHBgaJFi2JtbZ1pm/Q+MjNq1Cji4uKMj4sXLz7R+xdCCEtTSvH1ga/57sh3AIy8eZu3bt8wTPruuhKcPXO9psSUNPot2m8MTF0b+PFT93oSmIRFWTw0PUiv15OcnExAQAC2trZs2LDBuC8yMpLo6GiCgoIACAoK4siRIyZXua1btw5XV1f8/f2Nbe7vI71Neh9arZaAgACTNnq9ng0bNhjbZMbOzs64VEL6Qwgh8hulFDMPzmTekXkAjLx5i7fj70DdntB5uUUC07+3E2kzO5zVR2OwtdYwpU01xresgo2s6C0szKKn50aNGsXLL7+Mr68vd+7cYdGiRWzevJm1a9fi5uZGjx49GDJkCIULF8bV1ZUBAwYQFBRE/fr1AWjevDn+/v506tSJqVOnEhMTw+jRo+nXrx92doZLT/v06cPMmTMZPnw43bt3Z+PGjSxZsoSVK1ca6xgyZAhdunShTp061KtXj+nTp3P37l26detmkc9FCCFyg1KKbw5+w9zDcwEYcfM2byckw6szIKCLRWrafe4mfRfu59bdFIo6a5nTMYA6frl/alCIzFg0NF27do3OnTtz5coV3NzcqF69OmvXruXFF18E4Msvv8TKyoo2bdqQnJxMSEgIs2bNMr7e2tqav//+m759+xIUFISTkxNdunTho48+MrYpXbo0K1euZPDgwXz11VeUKFGC7777jpCQEGObdu3acf36dcaOHUtMTAw1a9ZkzZo1GSaHCyFEQTLr0Cy+PfwtAMNu3qaj3gm6/QEl61mknoW7LzBu2THS9IoqxVyZ27kOxd0tsx6UEJnJc+s05VeyTpMQIj+ZfXAWsw7NBuD9m7fp4loJ2v4Mrj65XkuqTs+EFcdYsCsagBbVffjsjRo4aC17E2DxbMjO97fFr54TQgiRu2bvn8Gs/89hev/mbbqUbQWh0yxyW5T7F6zUaOD95rJgpci7JDQJIcQzZM6uycyKXAjA0NtxdGk8Hur0AAuElBNX4un10z7+vZ2Es50N09vVJNhfpkWIvEtCkxBCPCO+3TySby4YLoIZkpBK19cXQ6kGFqllzdErDFlyiMQUHaWKOPJd5zqUlwUrRR4noUkIIQo6pZi7sgczb+4FYFCaI906/g5uJXK9FL1eMWPjaaavPw1Ao3JFmflWLdwdZf0lkfdJaBJCiIIsNYnvfnuDr1MMk6wH2pWix1tLwTb3r0q7m5zG+0sPsfqo4TZX3Rr68eErlWX9JZFvSGgSQoiCKvYi3/3Whq9skwAYWLQ+PV+Za5H5SxdvJdLrp32cjLmDrbWGia2q0bZuyVyvQ4inIaFJCCEKovPb+f7vHnzlYjjtNcDvVXo2/dQipew6d5N3jQtW2vFtp9oElJIFK0X+I6FJCCEKEqVgzzx+CJ/I9EKGNWf6V+pI78ARFinn510XmLDcsGBlteJufNspgGKyYKXIpyQ0CSFEQZF6D1YNZf65ZXxZuBAA/ar15p3aA3K9lJQ0w4KVC3cb5lK9WqMYU9tUlwUrRb4moUkIIQqC+Mvwayd+vBPJF0UMgendGu/Sp2bfXC/lZkIyfRfuZ0+UYcHKYSEV6dtUFqwU+Z+EJiGEyO+id8GSzvxolcjn/w9MfWv0pa8FAtPxy4YFKy/FGhas/Kp9TZpVlgUrRcEgoUkIIfKzffNh1TB+crY3BqY+Nfrwbs13c72U1UcMC1YmperwK+LIPFmwUhQwEpqEECI/SkuB1cMhYj4/u7rw2f8D0zvV3+HdGrkbmPR6xVcbTvPVBlmwUhRsEpqEECK/uXMVlnSGi7tY4OrC1P8Hpt7Ve9OvZr9cnTt0NzmNIUsOsvbYVQC6NyzNB69UkgUrRYEkoUkIIfKTfyPg17fhzhUWFvZgipvh8v1e1XrRv2b/XA1M9y9YqbW24pPXq9K2jixYKQouCU1CCJFfHFgAfw8GXQoLfcoy2T4VgJ7VejKg1oBcDUzhZ2/y7sIIbiem/n/BygACShXKtfGFsAQJTUIIkdfpUmHth7DnWwB+KVefybrLAPSo2oP3ar2Xq4HpwQUr53YOwMdNFqwUBZ+EJiGEyMsSrsPSrnBhOwCLA9rw6a29AHSv2p2BtQfmWmBKSdMzfsUxFv1/wcrXahZjSpvq2NvKgpXi2SChSQgh8qrLB2BxR4j/F7Qu/NqwGxPP/wVAtyrdGFR7UK4FppsJyfRdsJ895w0LVo54qRLvNCkjC1aKZ4qEJiGEyIsOL4HlAyDtHhQuy5IGXfnk2DwAulbpyuCAwbkWWO5fsNLFzoavOtTkhUqyYKV49khoEkKIvESXBuvHQfhMw/PyzVlSowUfR3wOQBf/LgwJGJJrgWnVkSsMvW/Byu+61KGcpyxYKZ5NEpqEECKvSLxlmL8UtcXwvPH7LC1egY93fwJAZ//ODK0zNFcCk+7/C1bO+P+ClY3LF2Vmh9q4Odrm+NhC5FUSmoQQIi+IOQKL34LYaLB1glaz+M02jY/CJwDQsXJH3q/zfq4EpmOX4/jgz6McuhgLQM9GpRn5sixYKYSEJiGEsLSjf8CyfpCaCIX8oP0ifo87yYT7AtPwusNzPDDdTU7ji3WnmL8jCr0CFzsbxresQpuAEjk6rhD5hYQmIYSwFL0ONnwEO6Ybnpd9Adp8z5+XtjA+fDyQe4Fp7bEYxi8/xpW4ewC0qO7DmBb+eLna5+i4QuQnEpqEEMISkm7Dbz3g7AbD84YDodk4/jy7nHE7xwHwVqW3cjww/Xs7kfHLj7P+hOHecb6FHfnotSo8V9Ezx8YUIr+S0CSEELkp6TYcXwbbp8PtKLBxgNdmQrU3+OvMX4zbOQ6FokOlDoysNzLHAlOqTs/8HVF8ue40Sak6bK019G5ShgEvlJfFKoV4CAlNQgiR09KS4fQ6OLwYTq0FXYphu5svtF8IPtVZdmYZY3eMRaFoX7E9o+qNyrHAtD/6Nh/8cYSTMXcAqOdXmImvV6W8lywlIMSjSGgSQoicoBRc3A2HFsOxP+Fe7H/7PP2hejuo3RkcC7P87HLG7BiDQtGuYjs+CPwgRwJTXGIqU9eeZNGeaJSCQo62jHqlMm8GlJCVvYXIAglNQghhTjdOw+FfDSt6x174b7uLD1R7wxCWvKsZN684u4LR20ejULSt0DZHApNSiuWHLvPx38e5kWA4yvVGQAk+eKUyhZ20Zh1LiIJMQpMQQjythOtw9HdDWLq8/7/tWmeo3BJqtAO/xmBlOldoxdkVfLj9QxSKNyu8yYf1P8RKY961kM7fuMvov46y/cwNAMp6ODHx9WrUL1PErOMI8SyQ0CSEEE8iJREiVxmC0pkNoHSG7RprKNfMcESp4iugdcz05X+f+5vROwxHmN6o8Aaj6482a2BKTtPx7ZZzzNx0hpQ0PXY2Vgx4oRy9mpTBzkYmegvxJCQ0CSFEVul1ELXVcOrtxHJISfhvX7HaUKM9VGkNzh4P7eJC/AW+O/Idy88uR6/0tCnfhjH1x5g1MO08e4PRfx3l3PW7gOEWKJ+0qkqpIk5mG0OIZ5GEJiGEeBSl4OpRwxGlI7/BnSv/7XMvZTiiVL0tFC3/yG5O3z7NvCPzWHt+LXqlB6BthbZmPSV3MyGZiatO8Mf+SwB4uNgxtoU/Lar7yERvIcxAQpMQQmQm7hIcWWI4qnTt+H/b7d2hamtDWCoZCI8JI8duHGPu4blsvLjRuK1JiSb0qtaLmp41zVKqXq9Ysu8ik1afJC4pFY0GOgaW4v2Qirg5yA12hTAXCU1CCJHuXhwcX244qnR+O6AM2621UOElQ1Aq/yLY2D22q/1X9zP3yFx2XNoBgAYNwaWC6VWtF5WLVDZbyaeu3uHDP4+w9/xtAPx9XJn4elVq+RYy2xhCCAOL3rJ60qRJ1K1bFxcXFzw9PWnVqhWRkZEmbe7du0e/fv0oUqQIzs7OtGnThqtXr5q0iY6OJjQ0FEdHRzw9PRk2bBhpaWkmbTZv3kzt2rWxs7OjXLlyhIWFZajnm2++wc/PD3t7ewIDA9mzZ4/Z37MQIo/RpULkaljaFT6vAMv7w/ltgIJSDeHVr+D9U9DuZ6jc4pGBSSlF+OVwuq3pRpc1XdhxaQfWGmteLfMqf732F18894XZAlNSio4pa07yylfb2Hv+No5aa0aHVmZ5/4YSmITIIRY90rRlyxb69etH3bp1SUtL44MPPqB58+YcP34cJyfDhMXBgwezcuVKli5dipubG/3796d169bs2GH4rzedTkdoaCje3t7s3LmTK1eu0LlzZ2xtbfn0008BiIqKIjQ0lD59+rBw4UI2bNhAz5498fHxISQkBIBff/2VIUOGMGfOHAIDA5k+fTohISFERkbi6Sn3YBKiQFEK/t1nOKJ09HdIuvXfvqIVDEeUqr0JhUplsTvFln+3MO/wPA7fOAyAjZUNr5V9jR5Ve1DStaRZy9908hpjlh3l39tJADT392J8yyoUc3cw6zhCCFMapZSydBHprl+/jqenJ1u2bKFJkybExcXh4eHBokWLeOONNwA4efIklStXJjw8nPr167N69WpatGjB5cuX8fLyAmDOnDmMGDGC69evo9VqGTFiBCtXruTo0aPGsdq3b09sbCxr1qwBIDAwkLp16zJz5kwA9Ho9JUuWZMCAAYwcOfKxtcfHx+Pm5kZcXByurq7m/miEEOZw8ywcWWoIS7fO/bfdyfO/hSd9ajx2nlI6nV7H+uj1zDs8j8jbhqPkdtZ2tCnfhm5Vu+Ht5G3W8mPi7vHR38dYdSQGgGJu9kx4rSov+nuZdRwhniXZ+f7OU3Oa4uLiAChcuDAAERERpKamEhwcbGxTqVIlfH19jaEpPDycatWqGQMTQEhICH379uXYsWPUqlWL8PBwkz7S2wwaNAiAlJQUIiIiGDVqlHG/lZUVwcHBhIeH59TbFULkhrs34dgfhgnd/953yt3WESq1MCw8Wfo5sM76P4dp+jRWR61m3pF5RMVFAeBo40i7Su3o7N+Zog5FzfoWdHrFT+HnmfbPKRKS07C20tCjUWkGNiuPk12e+mdciAItz/y/Ta/XM2jQIBo2bEjVqlUBiImJQavV4u7ubtLWy8uLmJgYY5v7A1P6/vR9j2oTHx9PUlISt2/fRqfTZdrm5MmTmdabnJxMcnKy8Xl8fHw237EQIsekJsGpNYagdPof0P9/jqPGCso8B9XbQ6VQsHPOVrcpuhSWnV3G90e+51KC4bJ+F60LHSt35O3Kb+Nm52bmNwJH/o3jgz+PcOSS4T8qa/m6M7FVNfyLyRFtIXJbnglN/fr14+jRo2zfvt3SpWTJpEmTmDBhgqXLEEKAISRdioDocLgQbrhR7v0LT3pXNyw8WbUNuGT/lFlSWhJ/nP6DH47+wLXEawAUti9MJ/9OtK/YHmdt9sJXVty5l8q0f07xU/h59Apc7W0Y8XIlOtT1xcpK1lwSwhLyRGjq378/f//9N1u3bqVEiRLG7d7e3qSkpBAbG2tytOnq1at4e3sb2zx4lVv61XX3t3nwirurV6/i6uqKg4MD1tbWWFtbZ9omvY8HjRo1iiFDhhifx8fHU7KkeSd7CiEeIvEWXNwD0TsNIenyAdCnmrZxK2mYzF29LXg+2RVrCSkJ/Br5Kz8d/4lb9wyTxT0dPOlWtRttKrTBwcb8E6+VUqw+GsOEFce4Gm84mv1azWKMDvXHw+XxSx0IIXKORUOTUooBAwbw559/snnzZkqXLm2yPyAgAFtbWzZs2ECbNm0AiIyMJDo6mqCgIACCgoKYOHEi165dM17ltm7dOlxdXfH39ze2WbVqlUnf69atM/ah1WoJCAhgw4YNtGrVCjCcLtywYQP9+/fPtHY7Ozvs7OQfMCFyRexFiN5lCEnRu0wXm0zn7A2lgsC3AfjWB6+qYPVkq6rEJcex6MQiFpxYQHyK4dR7cefidK/anVblWqG11j7Nu3moi7cSGbvsKJsirwPgV8SRj1tVpXH5h9+WRQiReywamvr168eiRYtYtmwZLi4uxjlIbm5uODg44ObmRo8ePRgyZAiFCxfG1dWVAQMGEBQURP369QFo3rw5/v7+dOrUialTpxITE8Po0aPp16+fMdT06dOHmTNnMnz4cLp3787GjRtZsmQJK1euNNYyZMgQunTpQp06dahXrx7Tp0/n7t27dOvWLfc/GCGeZXo93IiECzsNp9uid0HcxYztipT/f0j6/6OQX5avenuYm0k3+en4Tyw+uZjEtEQA/Fz96FW9Fy+Xfhlbq5xZXTtVp+e7bVF8teEU91L1aK2t6PNcWd59riz2tnJzXSHyCosuOfCweyHNnz+frl27AobFLYcOHcovv/xCcnIyISEhzJo1y+S02YULF+jbty+bN2/GycmJLl26MHnyZGxs/suEmzdvZvDgwRw/fpwSJUowZswY4xjpZs6cyWeffUZMTAw1a9ZkxowZBAYGZum9yJIDQjyhtBS4cvD/IWkXXNwFSbdN22isDUsB+Ab9F5SczHeFWszdGMKOhfH7qd+5p7sHQIVCFehVvRcv+r6ItVXOBZd952/xwZ9HOHXVMAerfpnCfNKqGuU8zT9PSgiRUXa+v/PUOk35mYQmIbLoXrzh0v/oXYb5SJf2Qdo90za2jlCi7n8hqXidbF/plhUX71zkh6M/8NeZv0j7/xV21YpWo3f13jQt0TRHb3Ibm5jC5NUnWbzXcBStsJOWD1+pTOvaxeXmukLkony7TpMQogC6c/W/uUgXdsLVo6D0pm0ci/x3mq1UkOFqN+ucu9HsudhzfHfkO1ZFrUKndADU8apDr+q9CPIJyrHQcik2ifCzN9l59gYbT14jNtEwef1/7d17fFNVggfwX9KkoY/0/UhLS19Q3lDAAqXSWgesA8pDZ1Uc1wIqroDCjo7KqsPg6LA4qIsz7LjqLCjjsx9HeYyjdimlSAtDC+VR2gJ9Utr03abvpsnZP9KkhBQIkLQBft/PJ5+Qe0/uPecI7c9zzz33kdhQvHTvGHi72WeuFBHZBkMTEdmOEIZVtyuy+27/zwKaSi3LeYf3h6QRcYDfqBuej2SNosYifHDiA6SVp0H0PYw3PjgeT016CtMCp9n8fHWt3cguaUB2cT2yihtQ3tBhtj860B1vLp6I2HAfm5+biGyPoYmIrp+uF1CfML+zrb3ukkISw51sYXGGu9pGxAEewYNazRN1J/DhiQ+RUZlh2nZ36N1YMWkFxvuNt9l5mjt6cKikEYdKDKNJxnlKRk5SCSaFeGJWlC/iIv0wI9IHcqchfW46EV0DhiYisl5vt2F9JOOdbZVHzBeRBAAnBTB8Wv+E7dDpwDDbr5R9NUII5NTk4IMTH+BQ9SEAgFQiRXJYMp6c9CSivaNv+Bxt3b04UtZouuSWX6XBpbNExwV5YFaUL2aN9EVsuA+Uw+x32ZGI7IuhiYguT68HavOB4n1AyT7DxO3eTvMywzyB0JmGUaSwWUDwFEA2dGuY6YUeWVVZ+PDEhzhaexQAIJPIcF/UfXhiwhMI9wy/7mN3aXU4Wt6ErOIGZJc04Pj5ZvTqzVPSyAB3Q0iK8sWMCF/OUyK6hTA0EZG5lkqgJMMQlEr3W15ucw8Ewu/sn48UMO66F5G0pQpNBXYW78Se4j2oaq8CAMilcjww6gEsm7AMw92HX/MxtTo9TlQ2I+tcA7KKG5Bb0YSeXvNJ7KE+LpgV6YdZI30RF+mLAI9hNmkPETkehiai212XBig70B+UGs6a75e7AeHxQGQSEJUE+I8ZlEnb1tD0aPBD2Q/YdW4X8uryTNvd5e5YPGoxlo5figDXAKuPp9MLnK7SIKu4HtklDfhnaSM6enRmZQKUCsSP9ENcpC/ionwR6uNqq+YQkYNjaCK63ei0QGWO4XJbSYbhz+KiYCCRGuYkRd5lCEohsYDMcS4x9ep7kV2VjV3Fu5BekY4efQ8Aw3yluOA4LIxaiKTQJAyTXX3ERwiBs7VtyDpnuLvtUEkDNF29ZmW8XeWIi/JFXJQfZkX5ItLPjesoEd2mGJqIbnVCAPVn+uYlZQBlPwE9reZlfKIMo0iRdwHhswEXryGo6JWdbTqLXcW7sKdkD+o7603bR3qNxIKoBZgfOf+qo0pCCJQ3dCCrb+L2oZIG1Lf1mJVRKmSYEemDmZG+mBXlhzEqJaRShiQiYmgiujW11fZfbivJAFqrzPe7+BgCkjEoeY0Y/DpaobGrEf8o/Qd2ntuJgsYC03YvhRfmRczDgpELMM5n3BVHfqpMC0oa1kuqajFffXyYXIrYcB/ERRlC0oRgD8i4DAARDYChiehW0NNuuLOtZJ8hKNXmm+93UhiWADDOSwqc6BCTtwei1WmRWZmJncU7caDyAHqF4XKZTCpDwvAELBi5AAnDEyAfYMXwXp0eZQ0dyK9qMa2XVFrfblZG7iTBlBHefWsl+SJmhBcUMj4Ul4iujqGJ6Gak1xkecmscSTp/GNCZX2aCalLfSFKSYTkAuctQ1NQqQgicbjiNncU78Y/Sf6C5u9m0b5zvOCyIWoB5EfPgPczbVL62tQuF1a0oUreiUN2KQrUGZ2vbLO5uk0qAiSFepmUA7gjzgYszQxIRXTuGJqKbRWPJRUsBZAJdzeb7PUP7L7lFJAJufkNQyWtT016DPSV7sLt4N4pbik3b/V38cV/kfVgQtQDD3SJwpqYVP57UoFBdZQhKNa1obO8Z8JgucidEq5SY1jeaND3SBx5cUJKIbIChichRdTQa1kkyBqXmcvP9Ck8gYnZfULob8Il0mKUArqSztxPpFenYVbwLh6oPQd/38F6FkwLTAxMQ7XIXdB2jcKagA0/uO4/yxiKLVbYBwwhSuK8bRquUGKPy6HtXYoSPKyduE5FdMDQROQptp+ERJSUZhrlJVXkALkoLUrnhkSSRfZO3g6cATjfHP2EhBI7WHsWu4l34sexHtGn7H73iLY2GU0csqqui8d0pBb4DAJSYfd/P3dkiHI0KUPIyGxENqpvjJy7RrainA6j8p2EJgLKDwIUcy3lJ/mP75yWFzQIU7kNT1+tU3FSBHae+Rvr579CkVZu263u8oW2ZCm3LVLRqfU3bFTIpogOVpmBkDEn+yqF7LAsRkRFDE9Fg6Wk3TNguO2gIShdyAb3WvIwyyDAfyTgvySNoaOp6jfR6gcqmThSqNThRVYtsdTpKu/dDKz9nKiN0zuhtnQhtyzToOsIR5uuO0aOUGBPkgTEqQ1AK93WDEy+tEZGDYmgispfutr6Q9JPhVXUU0JuvNg2P4YbnuIXfCYTF3xTzklo6tChQa/ruWtOgUN2KM+oWdMnPQO6ZC5kyHxKpFpADQkgg6RqJ4bLZuMM/ARMm+GO0SonoQCXcFPzxQ0Q3F/7UIrKV7lag4rDhOW7lB4GqY5YhyTO0PyCF3wl4hzt0SBJCoKS+HbllTcgpb0ROeRNK6vrXPZI610LmeRTyEcfgKm8xbfeSD8fsoJ9jydiFmBAYxseOENEtgaGJ6Hp1aYCKQxeFpDzzZ7gBhpW2w+7sH03yDhuSqlqrS6vDqQstyClvQk5ZE45WNFne2i/tgL/qNJw8c9GOUtNmD2cP/Dzi51gQtQAT/SYyKBHRLYehichaXS2GVbfL+y63VR8HhPlCivAOvygkxTvs40mMGtq6kVvehNzyJuSUN+FkZQt6dOZtcpb3YmRoHTy8K9AuKcL5jiJ09YVDJ4kT7hx+JxZELcBdoXfB2clxHuxLRGRrDE1El9PZDFRk981JOgCoTw4QkiLM5yR5hQ5JVa0hhEBxXRtyygwBKbe8yeIRIwDgqxQYGdoAV48yNItClLYW4rzoBTr6y4z2Hm1YpTtyHvxcHH8RTSIiW2BoIjLqaLwoJP1kCEm4ZFVFnyjzkOQ5fEiqao0urQ4nKluQU96I3LIm5FY0oblDa1EuKkCOiJA6yN1L0dB7GmebC3Ba9AL9U5QQ5BaEWFUsYlWxuCPwDoQoQwaxJUREjoGhiW5fHY2GuUjGJQBqTsEiJPmOMlxmC59tCEkOvARAXavxUpthwvapCy3Q6szbM0wuxcQQF4SoaiF1LUZ1dz4KmvJR29kLdPaXU7mpMF01HXcE3oFYVSyGuw/nHCUiuu0xNNHto73ePCTV5luW8Ys2v7tNqRr8elpBrxc4Z7rU1oij5U0oa+iwKOevVGDKCFeoAtTQKYpR3nEC+Q2nUNjcCzT3l2NIIiK6OoYmuvXotEDDOaAm33CJrSbf8GqtsizrP6Y/JIXFA8rAwa+vFTp7dDhe2WyYsF3WiKMVzWjpNL/UJpEA0QFKTA5zRYBfNbplZ1HUkocj9SfRqzZf+iDQNRDTVdMNl9tUdyDEPYQhiYjoKhia6ObWVme4rFaT3/d+CqgrsnwciVHAuP5RpLB4wN1/cOtrpdrWrr61kQyv/Ast6NVbXmqLCfVCzAg3ePlUoQ1FONlwFGn1J9DbypBERGRrDE10c+jtBurP9IcjdV9Qaq8duLyzOxA4vu81wfAKGAsM8xjceluhqb0HBdUanK7WIL9Kg9zyJlQ0Wl5qC/RQ4I4wH0wOdYXS6wJqtfk4WpuLL+tOoLfGPCQFuAaYQlJsYCxClAxJREQ3iqGJHIsQQKv6opGjvvf6M5arawMAJIZHjxjDkWqC4c+eIwCpdNCrfyXG57Odrm7B6SpDSDpdpUFVS5dFWYkEGB2oxB3h3pgc6oZhykqUt5/AEfURvF9+EtpS80tzDElERPbH0ERDR9sF1BX0zzkyhqSOhoHLD/PsGzUyjiBNBALGAM5ug1tvK3RpdThb02YWkAqqW9HWPVDwA0b4uGJckAfGBnlg3HAXyFwrkN94FEfUR/Dd6ZPQ6i1DkjEgTVdNZ0giIhoEDE1kf0IAmgt9l9RO9YekhrOWi0UCgERquNXf7PLaeMAzxCGf09bY3oPTVRrTJbbTVRqcq2uD7pI5SADg7CTFaJUS44I8MC7YEJJGq9xR21WOzMpM/HThJ3ycc9wyJLkEIDbIEJJiVbEIVYYyJBERDTKGJrKtnnagtrB/UrZxBKmrZeDyLt59l9Um9ock/zGA3GVw620FvV6gorHDFIyM72qN5eU1APB2lWNcsIcpII0L8kSkvxvkTlJ09Xbhn+p/4sfKTLyWewBV7eZ39gW4BOAOleH2/+mq6QxJREQOgKGJBiYE0NtleChtt6bvveWSzxe9dzYZ7lprLIHFApEAIJUZ1kC6eGJ24HjDOkgOGAa6tDoUqVvNAlJhtQbtPboBy4f7uloEpEAPhVnQUber8c25fyCzMhOHqw+jS9cftpylzpgeNB0JIQmYFTwLI5QjGJKIiBwMQ9OtSAhA23lJuGkZOOxcab/e8pEbVnEL6B81Mo4g+UUDMoVt22kj9W3dhktrF40eFde1YYCra1DIpBijUhrmHvWFpDFBHnBXWP5T0ul1OFl3EpmVmdhfuR9nms6Y7Q90DURCSAISQxIxPWg6XGSON7pGRET9GJocXWsNUHPyCmHnMqFnwDvNrocEUHgYbtW/2rtPpGEEyT3ARue2Lb1eoKyh3eLyWm1r94DlfdycMd5s9MgDEX5ukDld/q68lu4WZFVlmeYnNXc3m/ZJIMFk/8lICElAQkgCor2jOZpERHQTYWhydCX7gG+evr7vSqSAQgkoPK0IPZ4Db3d2d7hb96+ms0eHkvo2FNe1o7i2DSX1hvfS+nZ0ai0vr0kkQISvG8YaA1JfSApQKq4aaoQQKGkpMY0m5dXmQSf6z6F0ViI+OB4JIQm4c/id8B7mbfP2EhHR4BjS0JSZmYk//OEPyM3NRXV1Nb755hssWrTItF8IgfXr1+PDDz9Ec3Mz4uPj8ec//xmjRo0ylWlsbMSzzz6L3bt3QyqV4sEHH8SWLVvg7u5uKnPixAmsWrUKR44cgb+/P5599lm8+OKLZnVJTU3Fa6+9hrKyMowaNQqbNm3CvHnz7N4HV6VUGW6tv57Q4+zukPOFbEEIgRpNN4rr2lBc14aSunbT+4Xmzst+TyGTYkyQ+ejRGJUSbgNcXrucbl03jqiPILMyE5mVmbjQdsFsf5RnFBJCEjA7ZDZiAmIgl8qvu51EROQ4hjQ0tbe3Y/LkyVi+fDkeeOABi/1vvfUW3nvvPXz88ceIiIjAa6+9huTkZJw+fRrDhg0DAPzyl79EdXU10tLSoNVqsWzZMqxYsQKfffYZAECj0eCee+7BnDlz8P777+PkyZNYvnw5vLy8sGLFCgBAVlYWlixZgo0bN+K+++7DZ599hkWLFuHo0aOYMGHC4HXIQCLvAp75aWjrMIS6tDqU1rebQpExGJXUtV12UjZguHMtyt8dUf7uiPR3M72P8HG94uW1y6lpr0HmhUzTJO7O3v5g5ix1RmxQLBKGGy67hShDrqutRETk2CRCiAGmuw4+iURiNtIkhEBwcDCef/55vPDCCwCAlpYWBAYGYvv27XjkkUdQUFCAcePG4ciRI7jjjjsAAN9//z3mzZuHyspKBAcH489//jNeeeUVqNVqODs7AwBefvllfPvttygsLAQAPPzww2hvb8eePXtM9Zk5cyZiYmLw/vvvW1V/jUYDT09PtLS0wMPD8R7V4ciEEKhr7TZcTrto1Ki4rg0Xmjtxub+hTlIJwnxcEenvjqiLglGkvzt83JxvqE46vQ6nGk5h//n9OHDhAAobC832B7gEYHbIbCSGJGJG0Ay4yl1v6HxERDQ0ruX3t8POaSotLYVarcacOXNM2zw9PTFjxgxkZ2fjkUceQXZ2Nry8vEyBCQDmzJkDqVSKw4cPY/HixcjOzkZCQoIpMAFAcnIyNm3ahKamJnh7eyM7Oxu/+tWvzM6fnJyMb7/91u7tvJ109+pQ3tBhNs+ouL4dJbVtaL3MStkA4DFMhqgAd4uRoxE+rnCW2W6+laZHg6yqLByoPIADlQfQ1N1k2ieBBBP9JyJheAISQxMx2ns0J3ETEd1mHDY0qdVqAEBgYKDZ9sDAQNM+tVqNgADzO7VkMhl8fHzMykRERFgcw7jP29sbarX6iucZSHd3N7q7+++60mg019K8W5YQAg3tPWbBqKTeMHJ0vrFjwNv4AUAqAUJ9XPuCkVvf6JEhIPm6OdsloAghUKopReb5TGReyMSxmmPoFf3hzV3ujvjh/ZO4fYb52LwORER083DY0OToNm7ciA0bNgx1NWxCCAGtTqBTq0O3VodO46tHhy6tHl0Xf+41bjds69Lq0anVob27FxWNHSipa0dL5+XXd1IqZIgM6L+cZgxIYb6uUMic7N7WHl0PctQ52F+5H5mVmahsqzTbH+EZYRpN4iRuIiK6mMOGJpVKBQCoqalBUFCQaXtNTQ1iYmJMZWpra82+19vbi8bGRtP3VSoVampqzMoYP1+tjHH/QNatW2d2SU+j0SA0NPRammgVrU6P9u5esxBjCCtXDjHGbabAo9Wj66LyxmMYyw/0nLTrJZEAId4uhpEiP3dEBbiZ3v3dr34b/40QQqC5uxm1HbWo66xDXUcd6jrrUNtRi6q2KuTU5JhN4pZL5YhVxRrWThqegFAP2/83JCKiW4PDhqaIiAioVCrs3bvXFJI0Gg0OHz6MZ555BgAQFxeH5uZm5ObmYtq0aQCA9PR06PV6zJgxw1TmlVdegVarhVxuGDVIS0vD6NGj4e3tbSqzd+9erF271nT+tLQ0xMXFXbZ+CoUCCoX9V7jelVeF51OP2/08RlIJ4CJ3gouzE4bJneAiv+jd2Qkucqn5dmcnDJM5wcVZiuFerogKcEO4rxuGyW07amQMQ8YgVNtRi/rO+v5wdFFA6r3Kwp7+Lv6mJQHiguI4iZuIiKwypKGpra0N586dM30uLS1FXl4efHx8MGLECKxduxZvvPEGRo0aZVpyIDg42HSH3dixY3Hvvffiqaeewvvvvw+tVovVq1fjkUceQXBwMADg0UcfxYYNG/DEE0/gpZdewqlTp7Blyxa8++67pvOuWbMGiYmJePvttzF//nx88cUXyMnJwQcffDCo/TEQF2dD+JA7Sa4aYozbjCHGWNa039nwrpBLzT5fXEbuJBnUCc5CCLR0t6C2sxb1HfWo7aw1hZ+6jjrT9rrOOmiv4bEu3gpv+Ln6IcAlAP6u/vB38Ye/qz8m+0/GGJ8xkEpurgU7iYho6A3pkgMZGRlISkqy2J6SkoLt27ebFrf84IMP0NzcjDvvvBP//d//jejoaFPZxsZGrF692mxxy/fee++yi1v6+fnh2WefxUsvvWR2ztTUVLz66qumxS3feuuta1rc0l5LDvTq9BAA5NexttBQEkJA06MxjAQZQ1DnJSNEfduvJQx5Kbzg7+qPAJcA+Ln4IcDVPBT5u/jDz8UPzk43tuQAERHdHq7l97fDrNN0s7vd1mnq7O3EhdYLqGyrRGVrJSrbKs0DUkcdevQ9Vh/PS+HVH4IuCkABrv3hiGGIiIhs7ZZYp4mGll7oUd9ZbwpEla2G1/nW86hsq0R9Z71Vx/FUeFqEn4tDkb+rYWRI4WT/+WFEREQ3gqHpNnbpaJExEFW2VuJC2wV067qv+H2lXIkQZYjh5R6CQLdAs1DEMERERLcShqZbmHG06HzreYsRI2tGi5wkTlC5qUyhyBiQQt1DEaIMgafCc5BaQkRENPQYmm5yHdoOXGi7YB6Krne06KJwFKoMhcpNxcUdiYiI+jA0OTi90KOuo84iEBkvpzV0NVzx+5cdLVKGIsSdo0VERETWYmhycKlFqXjj8BtXLHPpaJExEIUoQzhaREREZCMMTQ4uRBliNlp0cSAyjh5xtIiIiMj+GJoc3PSg6Tjy2BGOFhEREQ0xhiYHx7BERETkGG6uZ3MQERERDRGGJiIiIiIrMDQRERERWYGhiYiIiMgKDE1EREREVmBoIiIiIrICQxMRERGRFRiaiIiIiKzA0ERERERkBYYmIiIiIiswNBERERFZgaGJiIiIyAoMTURERERWkA11BW4VQggAgEajGeKaEBERkbWMv7eNv8evhKHJRlpbWwEAoaGhQ1wTIiIiulatra3w9PS8YhmJsCZa0VXp9XpUVVVBqVRCIpEMdXUGnUajQWhoKM6fPw8PD4+hrs5Ni/1oG+xH22A/2gb70Tbs1Y9CCLS2tiI4OBhS6ZVnLXGkyUakUilCQkKGuhpDzsPDgz8UbID9aBvsR9tgP9oG+9E27NGPVxthMuJEcCIiIiIrMDQRERERWYGhiWxCoVBg/fr1UCgUQ12Vmxr70TbYj7bBfrQN9qNtOEI/ciI4ERERkRU40kRERERkBYYmIiIiIiswNBERERFZgaGJAAAbN25EbGwslEolAgICsGjRIhQVFZmV6erqwqpVq+Dr6wt3d3c8+OCDqKmpMSvz3HPPYdq0aVAoFIiJibE4T0ZGBhYuXIigoCC4ubkhJiYGn376qT2bNqgGqx8vdu7cOSiVSnh5edm4NUNnMPtRCIHNmzcjOjoaCoUCw4cPx5tvvmmvpg2qwezHH374ATNnzoRSqYS/vz8efPBBlJWV2allg8sW/Xj8+HEsWbIEoaGhcHFxwdixY7FlyxaLc2VkZGDq1KlQKBQYOXIktm/fbu/mDZrB6se//e1vmDt3Lvz9/eHh4YG4uDj88MMPNmkDQxMBAPbv349Vq1bh0KFDSEtLg1arxT333IP29nZTmX//93/H7t27kZqaiv3796OqqgoPPPCAxbGWL1+Ohx9+eMDzZGVlYdKkSfj6669x4sQJLFu2DI8//jj27Nljt7YNpsHqRyOtVoslS5Zg9uzZNm/LUBrMflyzZg0++ugjbN68GYWFhdi1axemT59ul3YNtsHqx9LSUixcuBB333038vLy8MMPP6C+vn7A49yMbNGPubm5CAgIwF//+lfk5+fjlVdewbp16/CnP/3JVKa0tBTz589HUlIS8vLysHbtWjz55JM2+4U/1AarHzMzMzF37lx89913yM3NRVJSEu6//34cO3bsxhshiAZQW1srAIj9+/cLIYRobm4WcrlcpKammsoUFBQIACI7O9vi++vXrxeTJ0+26lzz5s0Ty5Yts0m9HY29+/HFF18Ujz32mNi2bZvw9PS0dfUdhr368fTp00Imk4nCwkK71d2R2KsfU1NThUwmEzqdzrRt165dQiKRiJ6eHts3ZIjdaD8arVy5UiQlJZk+v/jii2L8+PFmZR5++GGRnJxs4xY4Bnv140DGjRsnNmzYcMN15kgTDailpQUA4OPjA8CQ7rVaLebMmWMqM2bMGIwYMQLZ2dk3fC7jeW419uzH9PR0pKamYuvWrbarsIOyVz/u3r0bkZGR2LNnDyIiIhAeHo4nn3wSjY2Ntm2Ag7BXP06bNg1SqRTbtm2DTqdDS0sLduzYgTlz5kAul9u2EQ7AVv146c++7Oxss2MAQHJy8g3/jHVU9urHS+n1erS2ttrk9wxDE1nQ6/VYu3Yt4uPjMWHCBACAWq2Gs7OzxbyZwMBAqNXq6z7XV199hSNHjmDZsmU3UmWHZM9+bGhowNKlS7F9+/Zb/llW9uzHkpISlJeXIzU1FZ988gm2b9+O3Nxc/OIXv7BlExyCPfsxIiICP/74I/7jP/4DCoUCXl5eqKysxFdffWXLJjgEW/VjVlYWvvzyS6xYscK0Ta1WIzAw0OIYGo0GnZ2dtm3IELNnP15q8+bNaGtrw0MPPXTD9eYDe8nCqlWrcOrUKfz00092Pc++ffuwbNkyfPjhhxg/frxdzzUU7NmPTz31FB599FEkJCTY/NiOxp79qNfr0d3djU8++QTR0dEAgL/85S+YNm0aioqKMHr0aJufc6jYsx/VajWeeuoppKSkYMmSJWhtbcVvfvMb/OIXv0BaWhokEonNzzlUbNGPp06dwsKFC7F+/Xrcc889NqzdzWOw+vGzzz7Dhg0bsHPnTgQEBFz3uYw40kRmVq9ejT179mDfvn0ICQkxbVepVOjp6UFzc7NZ+ZqaGqhUqms+z/79+3H//ffj3XffxeOPP36j1XY49u7H9PR0bN68GTKZDDKZDE888QRaWlogk8nwv//7v7ZqxpCzdz8GBQVBJpOZAhMAjB07FgBQUVFxY5V3IPbux61bt8LT0xNvvfUWpkyZgoSEBPz1r3/F3r17cfjwYVs1Y8jZoh9Pnz6Nn/3sZ1ixYgVeffVVs30qlcrizsWamhp4eHjAxcXFto0ZQvbuR6MvvvgCTz75JL766iuLy57Xi6GJABhuu169ejW++eYbpKenIyIiwmz/tGnTIJfLsXfvXtO2oqIiVFRUIC4u7prOlZGRgfnz52PTpk1XHFK9GQ1WP2ZnZyMvL8/0ev3116FUKpGXl4fFixfbrD1DZbD6MT4+Hr29vSguLjZtO3PmDAAgLCzsBlsx9AarHzs6OiCVmv86cXJyAmAYzbvZ2aof8/PzkZSUhJSUlAGXtYiLizM7BgCkpaVd889YRzVY/QgAn3/+OZYtW4bPP/8c8+fPt2kjiMQzzzwjPD09RUZGhqiurja9Ojo6TGX+7d/+TYwYMUKkp6eLnJwcERcXJ+Li4syOc/bsWXHs2DHx9NNPi+joaHHs2DFx7Ngx0d3dLYQQIj09Xbi6uop169aZnaehoWFQ22svg9WPl7rV7p4brH7U6XRi6tSpIiEhQRw9elTk5OSIGTNmiLlz5w5qe+1lsPpx7969QiKRiA0bNogzZ86I3NxckZycLMLCwszOdbOyRT+ePHlS+Pv7i8cee8zsGLW1taYyJSUlwtXVVfz6178WBQUFYuvWrcLJyUl8//33g9peexmsfvz000+FTCYTW7duNSvT3Nx8w21gaCIhhBAABnxt27bNVKazs1OsXLlSeHt7C1dXV7F48WJRXV1tdpzExMQBj1NaWiqEECIlJWXA/YmJiYPXWDsarH681K0WmgazHy9cuCAeeOAB4e7uLgIDA8XSpUtvmRA/mP34+eefiylTpgg3Nzfh7+8vFixYIAoKCgappfZli35cv379gMcICwszO9e+fftETEyMcHZ2FpGRkWbnuNkNVj9e7u9rSkrKDbdB0tcQIiIiIroCzmkiIiIisgJDExEREZEVGJqIiIiIrMDQRERERGQFhiYiIiIiKzA0EREREVmBoYmIiIjICgxNRERERFZgaCIihxAeHo7/+q//umIZiUSCb7/9FgBQVlYGiUSCvLw8u9brt7/9LWJiYux6DiK6OTA0EdENW7p0KSQSCSQSCZydnTFy5Ei8/vrr6O3ttds5Q0NDUV1djQkTJtjsmBeHMqMXXnjB4iGqtlRfXw+VSoXf//73FvseeughzJw5Ezqdzm7nJyLryYa6AkR0a7j33nuxbds2dHd347vvvsOqVasgl8uxbt06u5zPyckJKpXKLse+mLu7O9zd3e12fD8/P3zwwQf4l3/5F9x///2YOHEiACA1NRV79uzBsWPH4OTkZNNz6nQ6SCQSSKX8/2aia8F/MURkEwqFAiqVCmFhYXjmmWcwZ84c7Nq1CwBw1113Ye3atWblFy1ahKVLl5pta21txZIlS+Dm5obhw4dj69atlz3fQJfn8vPzcd9998HDwwNKpRKzZ89GcXExAODIkSOYO3cu/Pz84OnpicTERBw9etT03fDwcADA4sWLIZFITJ8vvTyn1+vx+uuvIyQkBAqFAjExMfj+++8t6vW3v/0NSUlJcHV1xeTJk5GdnX3ZtixYsACPPvooUlJSoNVqUVdXh1WrVuE///M/MXr0aOzcuRNTp07FsGHDEBkZiQ0bNpiN4r3zzjuYOHEi3NzcEBoaipUrV6Ktrc20f/v27fDy8sKuXbswbtw4KBQKVFRUICMjA9OnT4ebmxu8vLwQHx+P8vLyy9aT6HbH0EREduHi4oKenp5r+s4f/vAHTJ48GceOHcPLL7+MNWvWIC0tzarvXrhwAQkJCVAoFEhPT0dubi6WL19uChetra1ISUnBTz/9hEOHDmHUqFGYN28eWltbARhCFQBs27YN1dXVps+X2rJlC95++21s3rwZJ06cQHJyMhYsWICzZ8+alXvllVfwwgsvIC8vD9HR0ViyZMkVL1du2bIFDQ0N+N3vfoeVK1diwoQJePbZZ3HgwAE8/vjjWLNmDU6fPo3/+Z//wfbt2/Hmm2+aviuVSvHee+8hPz8fH3/8MdLT0/Hiiy+aHb+jowObNm3CRx99hPz8fPj4+GDRokVITEzEiRMnkJ2djRUrVkAikVjV30S3JUFEdINSUlLEwoULhRBC6PV6kZaWJhQKhXjhhReEEEIkJiaKNWvWmH1n4cKFIiUlxfQ5LCxM3HvvvWZlHn74YfHzn//c9BmA+Oabb4QQQpSWlgoA4tixY0IIIdatWyciIiJET0+PVXXW6XRCqVSK3bt3D3h8o/Xr14vJkyebPgcHB4s333zTrExsbKxYuXKlWb0++ugj0/78/HwBQBQUFFyxTnv37hVOTk7Cw8NDlJWVCSGE+NnPfiZ+//vfm5XbsWOHCAoKuuxxUlNTha+vr+nztm3bBACRl5dn2tbQ0CAAiIyMjCvWiYj6cU4TEdnEnj174O7uDq1WC71ej0cffRS//e1vr+kYcXFxFp+vdkedUV5eHmbPng25XD7g/pqaGrz66qvIyMhAbW0tdDodOjo6UFFRYXX9NBoNqqqqEB8fb7Y9Pj4ex48fN9s2adIk05+DgoIAALW1tRgzZsxlj3/33Xdj5syZiImJQVhYGADg+PHjOHjwoNnIkk6nQ1dXFzo6OuDq6or/+7//w8aNG1FYWAiNRoPe3l6z/QDg7OxsVicfHx8sXboUycnJmDt3LubMmYOHHnrIVFcissTLc0RkE0lJScjLy8PZs2fR2dmJjz/+GG5ubgAMl4+EEGbltVqtTc/v4uJyxf0pKSnIy8vDli1bkJWVhby8PPj6+l7zJURrXRzejJe89Hr9Vb8nk8kgk/X//2xbWxs2bNiAvLw80+vkyZM4e/Yshg0bhrKyMtx3332YNGkSvv76a+Tm5prmgl3cNhcXF4tLb9u2bUN2djZmzZqFL7/8EtHR0Th06NANtZvoVsbQREQ24ebmhpEjR2LEiBFmv/QBwN/fH9XV1abPOp0Op06dsjjGpb+wDx06hLFjx1p1/kmTJuHAgQOXDWMHDx7Ec889h3nz5mH8+PFQKBSor683KyOXy694e7+HhweCg4Nx8OBBi2OPGzfOqnpeq6lTp6KoqAgjR460eEmlUuTm5kKv1+Ptt9/GzJkzER0djaqqKquPP2XKFKxbtw5ZWVmYMGECPvvsM7u0g+hWwNBERHZ399134+9//zv+/ve/o7CwEM888wyam5styh08eBBvvfUWzpw5g61btyI1NRVr1qyx6hyrV6+GRqPBI488gpycHJw9exY7duxAUVERAGDUqFHYsWMHCgoKcPjwYfzyl7+0GJ0KDw/H3r17oVar0dTUNOB5fv3rX2PTpk348ssvUVRUhJdffhl5eXlW1/Na/eY3v8Enn3yCDRs2ID8/HwUFBfjiiy/w6quvAgBGjhwJrVaLP/7xjygpKcGOHTvw/vvvX/W4paWlWLduHbKzs1FeXo4ff/wRZ8+etTqkEt2OGJqIyO6WL1+OlJQUPP7440hMTERkZCSSkpIsyj3//PPIycnBlClT8MYbb+Cdd95BcnKyVefw9fVFeno62trakJiYiGnTpuHDDz80XSb7y1/+gqamJkydOhX/+q//iueeew4BAQFmx3j77beRlpaG0NBQTJkyZcDzPPfcc/jVr36F559/HhMnTsT333+PXbt2YdSoUdfYK9ZJTk7Gnj178OOPPyI2NhYzZ87Eu+++a5rzNHnyZLzzzjvYtGkTJkyYgE8//RQbN2686nFdXV1RWFiIBx98ENHR0VixYgVWrVqFp59+2i7tILoVSMSlEw2IiIiIyAJHmoiIiIiswNBEREREZAWGJiIiIiIrMDQRERERWYGhiYiIiMgKDE1EREREVmBoIiIiIrICQxMRERGRFRiaiIiIiKzA0ERERERkBYYmIiIiIiswNBERERFZ4f8BRDHsTDvtKfkAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lineplot(agg_df[((agg_df[\"field\"]==\"kwds\")&(agg_df[\"region\"]!=\"worldwide\"))],x=\"Publication Years\" ,y=\"Record Count\", hue=\"region\")\n",
+    "g.set_title(\"AI-related publications\\n(without worldwide trend)\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "outputs": [],
+   "source": [
+    "agg_df_p = agg_df[agg_df[\"field\"]==\"kwds\"].merge(\n",
+    "    agg_df[agg_df[\"field\"]==\"all\"].rename(columns={\"Record Count\":\"All\"}),\n",
+    "    on=[\"Publication Years\",\"region\"]).merge(\n",
+    "    agg_df[agg_df[\"region\"]==\"worldwide\"][[\"Publication Years\",\"Record Count\"]].rename(columns={\"Record Count\":\"Worldwide\"}),\n",
+    "    on=\"Publication Years\")\n",
+    "agg_df_p[\"percent\"] = agg_df_p[\"Record Count\"]/agg_df_p[\"All\"]\n",
+    "agg_df_p[\"share\"] = agg_df_p[\"Record Count\"]/agg_df_p[\"Worldwide\"]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Text(0.5, 1.0, 'Focus on AI-related publications\\n(percent of publications of the yearly output)')"
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lineplot(agg_df_p,x=\"Publication Years\" ,y=\"percent\", hue=\"region\")\n",
+    "g.set_title(\"Focus on AI-related publications\\n(percent of publications of the yearly output)\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Text(0.5, 1.0, 'Share of publications of the worldwide AI-related output')"
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.lineplot(agg_df_p,x=\"Publication Years\" ,y=\"share\", hue=\"region\")\n",
+    "g.set_title(\"Share of publications of the worldwide AI-related output\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    Publication Years  Record Count         name_x    region field_x      All   \n0                2021         43927        eu_kwds        eu    kwds   914776  \\\n1                2021         55254  eu+assoc_kwds  eu+assoc    kwds  1136750   \n2                2021         63992        ch_kwds        ch    kwds   748798   \n3                2022         43361        eu_kwds        eu    kwds   800220   \n4                2022         54467  eu+assoc_kwds  eu+assoc    kwds   986893   \n5                2022         79078        ch_kwds        ch    kwds   801793   \n6                2020         34956        eu_kwds        eu    kwds   886298   \n7                2020         43969  eu+assoc_kwds  eu+assoc    kwds  1104653   \n8                2020         49861        ch_kwds        ch    kwds   668057   \n9                2019         31006        eu_kwds        eu    kwds   890256   \n10               2019         38935  eu+assoc_kwds  eu+assoc    kwds  1116403   \n11               2019         42111        ch_kwds        ch    kwds   630002   \n12               2018         23854        eu_kwds        eu    kwds   833011   \n13               2018         29913  eu+assoc_kwds  eu+assoc    kwds  1043953   \n14               2018         30454        ch_kwds        ch    kwds   534539   \n15               2017         18429        eu_kwds        eu    kwds   752558   \n16               2017         22878  eu+assoc_kwds  eu+assoc    kwds   945964   \n17               2017         21664        ch_kwds        ch    kwds   474295   \n18               2016         16178        eu_kwds        eu    kwds   741454   \n19               2016         19724  eu+assoc_kwds  eu+assoc    kwds   928272   \n20               2016         17436        ch_kwds        ch    kwds   439134   \n21               2015         14628        eu_kwds        eu    kwds   717129   \n22               2015         17655  eu+assoc_kwds  eu+assoc    kwds   901491   \n23               2015         14191        ch_kwds        ch    kwds   393759   \n24               2014         12615        eu_kwds        eu    kwds   695713   \n25               2014         15308  eu+assoc_kwds  eu+assoc    kwds   871908   \n26               2014         12000        ch_kwds        ch    kwds   362784   \n27               2013         10979        eu_kwds        eu    kwds   659641   \n28               2013         13136  eu+assoc_kwds  eu+assoc    kwds   834592   \n29               2013          9424        ch_kwds        ch    kwds   329772   \n30               2012          9626        eu_kwds        eu    kwds   631054   \n31               2012         11557  eu+assoc_kwds  eu+assoc    kwds   798881   \n32               2012          8547        ch_kwds        ch    kwds   299804   \n33               2011          9293        eu_kwds        eu    kwds   610960   \n34               2011         11044  eu+assoc_kwds  eu+assoc    kwds   773469   \n35               2011          7382        ch_kwds        ch    kwds   263715   \n\n          name_y field_y  Worldwide   percent     share  \n0         eu_all     all     197916  0.048019  0.221948  \n1   eu+assoc_all     all     197916  0.048607  0.279179  \n2         ch_all     all     197916  0.085460  0.323329  \n3         eu_all     all     211014  0.054186  0.205489  \n4   eu+assoc_all     all     211014  0.055190  0.258120  \n5         ch_all     all     211014  0.098626  0.374752  \n6         eu_all     all     160472  0.039440  0.217832  \n7   eu+assoc_all     all     160472  0.039803  0.273998  \n8         ch_all     all     160472  0.074636  0.310715  \n9         eu_all     all     142010  0.034828  0.218337  \n10  eu+assoc_all     all     142010  0.034875  0.274171  \n11        ch_all     all     142010  0.066843  0.296535  \n12        eu_all     all     109746  0.028636  0.217356  \n13  eu+assoc_all     all     109746  0.028654  0.272566  \n14        ch_all     all     109746  0.056972  0.277495  \n15        eu_all     all      81974  0.024488  0.224815  \n16  eu+assoc_all     all      81974  0.024185  0.279088  \n17        ch_all     all      81974  0.045676  0.264279  \n18        eu_all     all      68538  0.021819  0.236044  \n19  eu+assoc_all     all      68538  0.021248  0.287782  \n20        ch_all     all      68538  0.039705  0.254399  \n21        eu_all     all      59591  0.020398  0.245473  \n22  eu+assoc_all     all      59591  0.019584  0.296270  \n23        ch_all     all      59591  0.036040  0.238140  \n24        eu_all     all      51174  0.018132  0.246512  \n25  eu+assoc_all     all      51174  0.017557  0.299136  \n26        ch_all     all      51174  0.033078  0.234494  \n27        eu_all     all      42229  0.016644  0.259987  \n28  eu+assoc_all     all      42229  0.015739  0.311066  \n29        ch_all     all      42229  0.028577  0.223164  \n30        eu_all     all      37054  0.015254  0.259783  \n31  eu+assoc_all     all      37054  0.014466  0.311896  \n32        ch_all     all      37054  0.028509  0.230663  \n33        eu_all     all      33189  0.015210  0.280002  \n34  eu+assoc_all     all      33189  0.014279  0.332761  \n35        ch_all     all      33189  0.027992  0.222423  ",
+      "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>Publication Years</th>\n      <th>Record Count</th>\n      <th>name_x</th>\n      <th>region</th>\n      <th>field_x</th>\n      <th>All</th>\n      <th>name_y</th>\n      <th>field_y</th>\n      <th>Worldwide</th>\n      <th>percent</th>\n      <th>share</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2021</td>\n      <td>43927</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>914776</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>197916</td>\n      <td>0.048019</td>\n      <td>0.221948</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2021</td>\n      <td>55254</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>1136750</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>197916</td>\n      <td>0.048607</td>\n      <td>0.279179</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2021</td>\n      <td>63992</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>748798</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>197916</td>\n      <td>0.085460</td>\n      <td>0.323329</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2022</td>\n      <td>43361</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>800220</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>211014</td>\n      <td>0.054186</td>\n      <td>0.205489</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2022</td>\n      <td>54467</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>986893</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>211014</td>\n      <td>0.055190</td>\n      <td>0.258120</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2022</td>\n      <td>79078</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>801793</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>211014</td>\n      <td>0.098626</td>\n      <td>0.374752</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2020</td>\n      <td>34956</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>886298</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>160472</td>\n      <td>0.039440</td>\n      <td>0.217832</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2020</td>\n      <td>43969</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>1104653</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>160472</td>\n      <td>0.039803</td>\n      <td>0.273998</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2020</td>\n      <td>49861</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>668057</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>160472</td>\n      <td>0.074636</td>\n      <td>0.310715</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2019</td>\n      <td>31006</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>890256</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>142010</td>\n      <td>0.034828</td>\n      <td>0.218337</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2019</td>\n      <td>38935</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>1116403</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>142010</td>\n      <td>0.034875</td>\n      <td>0.274171</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2019</td>\n      <td>42111</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>630002</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>142010</td>\n      <td>0.066843</td>\n      <td>0.296535</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2018</td>\n      <td>23854</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>833011</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>109746</td>\n      <td>0.028636</td>\n      <td>0.217356</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2018</td>\n      <td>29913</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>1043953</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>109746</td>\n      <td>0.028654</td>\n      <td>0.272566</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2018</td>\n      <td>30454</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>534539</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>109746</td>\n      <td>0.056972</td>\n      <td>0.277495</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2017</td>\n      <td>18429</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>752558</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>81974</td>\n      <td>0.024488</td>\n      <td>0.224815</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2017</td>\n      <td>22878</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>945964</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>81974</td>\n      <td>0.024185</td>\n      <td>0.279088</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>2017</td>\n      <td>21664</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>474295</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>81974</td>\n      <td>0.045676</td>\n      <td>0.264279</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>2016</td>\n      <td>16178</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>741454</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>68538</td>\n      <td>0.021819</td>\n      <td>0.236044</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>2016</td>\n      <td>19724</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>928272</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>68538</td>\n      <td>0.021248</td>\n      <td>0.287782</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>2016</td>\n      <td>17436</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>439134</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>68538</td>\n      <td>0.039705</td>\n      <td>0.254399</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2015</td>\n      <td>14628</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>717129</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>59591</td>\n      <td>0.020398</td>\n      <td>0.245473</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>2015</td>\n      <td>17655</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>901491</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>59591</td>\n      <td>0.019584</td>\n      <td>0.296270</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>2015</td>\n      <td>14191</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>393759</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>59591</td>\n      <td>0.036040</td>\n      <td>0.238140</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2014</td>\n      <td>12615</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>695713</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>51174</td>\n      <td>0.018132</td>\n      <td>0.246512</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>2014</td>\n      <td>15308</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>871908</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>51174</td>\n      <td>0.017557</td>\n      <td>0.299136</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>2014</td>\n      <td>12000</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>362784</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>51174</td>\n      <td>0.033078</td>\n      <td>0.234494</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2013</td>\n      <td>10979</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>659641</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>42229</td>\n      <td>0.016644</td>\n      <td>0.259987</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>2013</td>\n      <td>13136</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>834592</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>42229</td>\n      <td>0.015739</td>\n      <td>0.311066</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>2013</td>\n      <td>9424</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>329772</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>42229</td>\n      <td>0.028577</td>\n      <td>0.223164</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>2012</td>\n      <td>9626</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>631054</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>37054</td>\n      <td>0.015254</td>\n      <td>0.259783</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>2012</td>\n      <td>11557</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>798881</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>37054</td>\n      <td>0.014466</td>\n      <td>0.311896</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>2012</td>\n      <td>8547</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>299804</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>37054</td>\n      <td>0.028509</td>\n      <td>0.230663</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>2011</td>\n      <td>9293</td>\n      <td>eu_kwds</td>\n      <td>eu</td>\n      <td>kwds</td>\n      <td>610960</td>\n      <td>eu_all</td>\n      <td>all</td>\n      <td>33189</td>\n      <td>0.015210</td>\n      <td>0.280002</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>2011</td>\n      <td>11044</td>\n      <td>eu+assoc_kwds</td>\n      <td>eu+assoc</td>\n      <td>kwds</td>\n      <td>773469</td>\n      <td>eu+assoc_all</td>\n      <td>all</td>\n      <td>33189</td>\n      <td>0.014279</td>\n      <td>0.332761</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>2011</td>\n      <td>7382</td>\n      <td>ch_kwds</td>\n      <td>ch</td>\n      <td>kwds</td>\n      <td>263715</td>\n      <td>ch_all</td>\n      <td>all</td>\n      <td>33189</td>\n      <td>0.027992</td>\n      <td>0.222423</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agg_df_p"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "    Record Count  Worldwide     share\n0          43927     197916  0.221948\n1          55254     197916  0.279179\n2          63992     197916  0.323329\n3          43361     211014  0.205489\n4          54467     211014  0.258120\n5          79078     211014  0.374752\n6          34956     160472  0.217832\n7          43969     160472  0.273998\n8          49861     160472  0.310715\n9          31006     142010  0.218337\n10         38935     142010  0.274171\n11         42111     142010  0.296535\n12         23854     109746  0.217356\n13         29913     109746  0.272566\n14         30454     109746  0.277495\n15         18429      81974  0.224815\n16         22878      81974  0.279088\n17         21664      81974  0.264279\n18         16178      68538  0.236044\n19         19724      68538  0.287782\n20         17436      68538  0.254399\n21         14628      59591  0.245473\n22         17655      59591  0.296270\n23         14191      59591  0.238140\n24         12615      51174  0.246512\n25         15308      51174  0.299136\n26         12000      51174  0.234494\n27         10979      42229  0.259987\n28         13136      42229  0.311066\n29          9424      42229  0.223164\n30          9626      37054  0.259783\n31         11557      37054  0.311896\n32          8547      37054  0.230663\n33          9293      33189  0.280002\n34         11044      33189  0.332761\n35          7382      33189  0.222423",
+      "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>Record Count</th>\n      <th>Worldwide</th>\n      <th>share</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>43927</td>\n      <td>197916</td>\n      <td>0.221948</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55254</td>\n      <td>197916</td>\n      <td>0.279179</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>63992</td>\n      <td>197916</td>\n      <td>0.323329</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>43361</td>\n      <td>211014</td>\n      <td>0.205489</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>54467</td>\n      <td>211014</td>\n      <td>0.258120</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>79078</td>\n      <td>211014</td>\n      <td>0.374752</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>34956</td>\n      <td>160472</td>\n      <td>0.217832</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>43969</td>\n      <td>160472</td>\n      <td>0.273998</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>49861</td>\n      <td>160472</td>\n      <td>0.310715</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>31006</td>\n      <td>142010</td>\n      <td>0.218337</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>38935</td>\n      <td>142010</td>\n      <td>0.274171</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>42111</td>\n      <td>142010</td>\n      <td>0.296535</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>23854</td>\n      <td>109746</td>\n      <td>0.217356</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>29913</td>\n      <td>109746</td>\n      <td>0.272566</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30454</td>\n      <td>109746</td>\n      <td>0.277495</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>18429</td>\n      <td>81974</td>\n      <td>0.224815</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>22878</td>\n      <td>81974</td>\n      <td>0.279088</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>21664</td>\n      <td>81974</td>\n      <td>0.264279</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>16178</td>\n      <td>68538</td>\n      <td>0.236044</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>19724</td>\n      <td>68538</td>\n      <td>0.287782</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>17436</td>\n      <td>68538</td>\n      <td>0.254399</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>14628</td>\n      <td>59591</td>\n      <td>0.245473</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>17655</td>\n      <td>59591</td>\n      <td>0.296270</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>14191</td>\n      <td>59591</td>\n      <td>0.238140</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>12615</td>\n      <td>51174</td>\n      <td>0.246512</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>15308</td>\n      <td>51174</td>\n      <td>0.299136</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>12000</td>\n      <td>51174</td>\n      <td>0.234494</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>10979</td>\n      <td>42229</td>\n      <td>0.259987</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>13136</td>\n      <td>42229</td>\n      <td>0.311066</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>9424</td>\n      <td>42229</td>\n      <td>0.223164</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>9626</td>\n      <td>37054</td>\n      <td>0.259783</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>11557</td>\n      <td>37054</td>\n      <td>0.311896</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>8547</td>\n      <td>37054</td>\n      <td>0.230663</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>9293</td>\n      <td>33189</td>\n      <td>0.280002</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>11044</td>\n      <td>33189</td>\n      <td>0.332761</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>7382</td>\n      <td>33189</td>\n      <td>0.222423</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agg_df_p[[\"Record Count\",\"Worldwide\",\"share\"]]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/WOS/wos_extract/wos_query_generator.ipynb b/WOS/wos_extract/wos_query_generator.ipynb
new file mode 100644
index 0000000..db1cf5f
--- /dev/null
+++ b/WOS/wos_extract/wos_query_generator.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "focal_countries_list = [\"Peoples R china\", \"Hong Kong\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "outputs": [],
+   "source": [
+    "country_mode = \"CU\" #CU-country-region AU-address"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "outputs": [],
+   "source": [
+    "# (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"computer vision\") OR TS=(\"pattern recognition\")) AND"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")'"
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "keywords_source = r'..\\ai_scope_keywords.txt'\n",
+    "with open(keywords_source,'r') as f:\n",
+    "    keywords = f.readlines()\n",
+    "\n",
+    "keywords = [c.strip() for c in keywords[0].split(\",\")]\n",
+    "\n",
+    "keywords_str = ' OR '.join('TS=(\"'+k+'\")' for k in keywords)\n",
+    "keywords_str"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND'"
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scope_country_source = r'..\\eu_scope_countries.txt'\n",
+    "\n",
+    "with open(scope_country_source,'r') as f:\n",
+    "    coop_countries = f.readlines()\n",
+    "coop_countries = [c.strip().upper() for c in coop_countries[0].split(\",\")]\n",
+    "focal_countries = [c.strip().upper() for c in focal_countries_list]\n",
+    "\n",
+    "foc_str = ' OR '.join([country_mode+'='+c for c in focal_countries])\n",
+    "coop_str = ' OR '.join([country_mode+'='+c for c in coop_countries])\n",
+    "\n",
+    "coop_str"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'CU=PEOPLES R CHINA OR CU=HONG KONG'"
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foc_str"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\"))'"
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scope_query = f'({foc_str}) AND ({coop_str}) AND ({keywords_str})'\n",
+    "scope_query"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'(CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\"))'"
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ch_scope_query = f'({coop_str}) AND ({keywords_str})'\n",
+    "ch_scope_query"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/WOS/wos_extract/wosexport1.xls b/WOS/wos_extract/wosexport1.xls
deleted file mode 100644
index 6870759..0000000
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diff --git a/WOS/wos_extract/wosexport10.xls b/WOS/wos_extract/wosexport10.xls
deleted file mode 100644
index 35ce730..0000000
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diff --git a/WOS/wos_extract/wosexport11.xls b/WOS/wos_extract/wosexport11.xls
deleted file mode 100644
index da269a2..0000000
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diff --git a/WOS/wos_extract/wosexport2.xls b/WOS/wos_extract/wosexport2.xls
deleted file mode 100644
index 04676c6..0000000
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diff --git a/WOS/wos_extract/wosexport3.xls b/WOS/wos_extract/wosexport3.xls
deleted file mode 100644
index 50db789..0000000
Binary files a/WOS/wos_extract/wosexport3.xls and /dev/null differ
diff --git a/WOS/wos_extract/wosexport4.xls b/WOS/wos_extract/wosexport4.xls
deleted file mode 100644
index d94f251..0000000
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diff --git a/WOS/wos_extract/wosexport5.xls b/WOS/wos_extract/wosexport5.xls
deleted file mode 100644
index 895080a..0000000
Binary files a/WOS/wos_extract/wosexport5.xls and /dev/null differ
diff --git a/WOS/wos_extract/wosexport6.xls b/WOS/wos_extract/wosexport6.xls
deleted file mode 100644
index 4d62a1a..0000000
Binary files a/WOS/wos_extract/wosexport6.xls and /dev/null differ
diff --git a/WOS/wos_extract/wosexport7.xls b/WOS/wos_extract/wosexport7.xls
deleted file mode 100644
index 70a8b44..0000000
Binary files a/WOS/wos_extract/wosexport7.xls and /dev/null differ
diff --git a/WOS/wos_extract/wosexport8.xls b/WOS/wos_extract/wosexport8.xls
deleted file mode 100644
index 8f65899..0000000
Binary files a/WOS/wos_extract/wosexport8.xls and /dev/null differ
diff --git a/WOS/wos_extract/wosexport9.xls b/WOS/wos_extract/wosexport9.xls
deleted file mode 100644
index 85e66cf..0000000
Binary files a/WOS/wos_extract/wosexport9.xls and /dev/null differ
diff --git a/WOS/wos_concat.ipynb b/WOS/wos_processing.ipynb
similarity index 97%
rename from WOS/wos_concat.ipynb
rename to WOS/wos_processing.ipynb
index 8768d35..0fe5e0b 100644
--- a/WOS/wos_concat.ipynb
+++ b/WOS/wos_processing.ipynb
@@ -8,9 +8,9 @@
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from tqdm import tqdm\n",
     "import os\n",
-    "import shutil"
+    "import shutil\n",
+    "from flashgeotext.geotext import GeoText"
    ]
   },
   {
@@ -119,14 +119,265 @@
     "wos[(~wos[\"DOI\"].isna())&(wos[\"DOI\"].duplicated(False))]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "        UT (Unique WOS ID)                                      Keywords Plus   \n0      WOS:000852293800024  CONVOLUTIONAL NEURAL-NETWORK; DEEP LEARNING FR...  \\\n9714   WOS:000540750000002  STATE-SPACE RECONSTRUCTION; SURFACE AIR-TEMPER...   \n9697   WOS:000600708400002  COMPRESSIVE STRENGTH; MODELS; ADABOOST.RT; DUC...   \n9699   WOS:000511965100005              STRUCTURAL RELIABILITY; FAILURE MODES   \n9701   WOS:000663142500003  REFLECTED GPS SIGNALS; SOIL-MOISTURE; OCEAN; S...   \n...                    ...                                                ...   \n3066   WOS:000528727500074           LOCAL SEARCH; ALGORITHM; VARIANCE; MODEL   \n5097   WOS:000596139400001  INDUSTRY 4.0; MANAGEMENT; RISK; ANALYTICS; CHA...   \n11369  WOS:000436774300069                                                NaN   \n11368  WOS:000846290700001  PARTIAL LEAST-SQUARES; INFRARED-SPECTROSCOPY; ...   \n11362  WOS:000480527800025  MICROWAVE DIELECTRIC BEHAVIOR; GPS SIGNALS; RE...   \n\n                                         Author Keywords   \n0      Imaging; Three-dimensional displays; Electroma...  \\\n9714                                                 NaN   \n9697   Plastic hinge length; RC columns; Machine lear...   \n9699   system reliability; jacket platform; beta-unzi...   \n9701   Cyclone GNSS (CYGNSS); Sea surface wind speed;...   \n...                                                  ...   \n3066   sea surface temperature; sea surface temperatu...   \n5097   Big data finance; Big data in financial servic...   \n11369  planetary gear; fault diagnosis; VMD; center f...   \n11368  soil fertility class; reflectance spectroscopy...   \n11362  global navigation satellite system (GNSS)-refl...   \n\n                                           Article Title   \n0      Artificial Intelligence: New Frontiers in Real...  \\\n9714   Detecting causality from time series in a mach...   \n9697   Data-Driven Approach to Predict the Plastic Hi...   \n9699   System Reliability Analysis of an Offshore Jac...   \n9701   Analysis of coastal wind speed retrieval from ...   \n...                                                  ...   \n3066   Improved Particle Swarm Optimization for Sea S...   \n5097   Current landscape and influence of big data on...   \n11369  Planetary Gear Fault Diagnosis via Feature Ima...   \n11368  How Well Can Reflectance Spectroscopy Allocate...   \n11362  GNSS-R Soil Moisture Retrieval Based on a XGbo...   \n\n                                                Abstract  \n0      In recent years, artificial intelligence (AI) ...  \n9714   Detecting causality from observational data is...  \n9697   Inelastic response of reinforced concrete colu...  \n9699   This study investigates strategies for solving...  \n9701   This paper demonstrates the capability and per...  \n...                                                  ...  \n3066   The Sea Surface Temperature (SST) is one of th...  \n5097   Big data is one of the most recent business an...  \n11369  Poor working environment leads to frequent fai...  \n11368  Fertilization decisions depend on the measurem...  \n11362  Global navigation satellite system (GNSS)-refl...  \n\n[9889 rows x 5 columns]",
+      "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>UT (Unique WOS ID)</th>\n      <th>Keywords Plus</th>\n      <th>Author Keywords</th>\n      <th>Article Title</th>\n      <th>Abstract</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000852293800024</td>\n      <td>CONVOLUTIONAL NEURAL-NETWORK; DEEP LEARNING FR...</td>\n      <td>Imaging; Three-dimensional displays; Electroma...</td>\n      <td>Artificial Intelligence: New Frontiers in Real...</td>\n      <td>In recent years, artificial intelligence (AI) ...</td>\n    </tr>\n    <tr>\n      <th>9714</th>\n      <td>WOS:000540750000002</td>\n      <td>STATE-SPACE RECONSTRUCTION; SURFACE AIR-TEMPER...</td>\n      <td>NaN</td>\n      <td>Detecting causality from time series in a mach...</td>\n      <td>Detecting causality from observational data is...</td>\n    </tr>\n    <tr>\n      <th>9697</th>\n      <td>WOS:000600708400002</td>\n      <td>COMPRESSIVE STRENGTH; MODELS; ADABOOST.RT; DUC...</td>\n      <td>Plastic hinge length; RC columns; Machine lear...</td>\n      <td>Data-Driven Approach to Predict the Plastic Hi...</td>\n      <td>Inelastic response of reinforced concrete colu...</td>\n    </tr>\n    <tr>\n      <th>9699</th>\n      <td>WOS:000511965100005</td>\n      <td>STRUCTURAL RELIABILITY; FAILURE MODES</td>\n      <td>system reliability; jacket platform; beta-unzi...</td>\n      <td>System Reliability Analysis of an Offshore Jac...</td>\n      <td>This study investigates strategies for solving...</td>\n    </tr>\n    <tr>\n      <th>9701</th>\n      <td>WOS:000663142500003</td>\n      <td>REFLECTED GPS SIGNALS; SOIL-MOISTURE; OCEAN; S...</td>\n      <td>Cyclone GNSS (CYGNSS); Sea surface wind speed;...</td>\n      <td>Analysis of coastal wind speed retrieval from ...</td>\n      <td>This paper demonstrates the capability and per...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3066</th>\n      <td>WOS:000528727500074</td>\n      <td>LOCAL SEARCH; ALGORITHM; VARIANCE; MODEL</td>\n      <td>sea surface temperature; sea surface temperatu...</td>\n      <td>Improved Particle Swarm Optimization for Sea S...</td>\n      <td>The Sea Surface Temperature (SST) is one of th...</td>\n    </tr>\n    <tr>\n      <th>5097</th>\n      <td>WOS:000596139400001</td>\n      <td>INDUSTRY 4.0; MANAGEMENT; RISK; ANALYTICS; CHA...</td>\n      <td>Big data finance; Big data in financial servic...</td>\n      <td>Current landscape and influence of big data on...</td>\n      <td>Big data is one of the most recent business an...</td>\n    </tr>\n    <tr>\n      <th>11369</th>\n      <td>WOS:000436774300069</td>\n      <td>NaN</td>\n      <td>planetary gear; fault diagnosis; VMD; center f...</td>\n      <td>Planetary Gear Fault Diagnosis via Feature Ima...</td>\n      <td>Poor working environment leads to frequent fai...</td>\n    </tr>\n    <tr>\n      <th>11368</th>\n      <td>WOS:000846290700001</td>\n      <td>PARTIAL LEAST-SQUARES; INFRARED-SPECTROSCOPY; ...</td>\n      <td>soil fertility class; reflectance spectroscopy...</td>\n      <td>How Well Can Reflectance Spectroscopy Allocate...</td>\n      <td>Fertilization decisions depend on the measurem...</td>\n    </tr>\n    <tr>\n      <th>11362</th>\n      <td>WOS:000480527800025</td>\n      <td>MICROWAVE DIELECTRIC BEHAVIOR; GPS SIGNALS; RE...</td>\n      <td>global navigation satellite system (GNSS)-refl...</td>\n      <td>GNSS-R Soil Moisture Retrieval Based on a XGbo...</td>\n      <td>Global navigation satellite system (GNSS)-refl...</td>\n    </tr>\n  </tbody>\n</table>\n<p>9889 rows × 5 columns</p>\n</div>"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wos[[record_col,\"Keywords Plus\",\"Author Keywords\",\"Article Title\",\"Abstract\"]]\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "        UT (Unique WOS ID)  level_1                           keyword\n0      WOS:000209536100003    11117                               NaN\n1      WOS:000297893800037    10831  ADAPTIVE DYNAMIC SURFACE CONTROL\n2      WOS:000297893800037    10831                NEURAL COMPENSATOR\n3      WOS:000297893800037    10831                    BUCK CONVERTER\n4      WOS:000297893800037    10831            FINITE-TIME IDENTIFIER\n...                    ...      ...                               ...\n94060  WOS:000947693400001      240                        EXPRESSION\n94061  WOS:000947693400001      240                         RNALOCATE\n94062  WOS:000947693400001      240                          PROTEINS\n94063  WOS:000947693400001      240                          RESOURCE\n94064  WOS:000947693400001      240                             CELLS\n\n[94065 rows x 3 columns]",
+      "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>UT (Unique WOS ID)</th>\n      <th>level_1</th>\n      <th>keyword</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000209536100003</td>\n      <td>11117</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000297893800037</td>\n      <td>10831</td>\n      <td>ADAPTIVE DYNAMIC SURFACE CONTROL</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000297893800037</td>\n      <td>10831</td>\n      <td>NEURAL COMPENSATOR</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000297893800037</td>\n      <td>10831</td>\n      <td>BUCK CONVERTER</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000297893800037</td>\n      <td>10831</td>\n      <td>FINITE-TIME IDENTIFIER</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>94060</th>\n      <td>WOS:000947693400001</td>\n      <td>240</td>\n      <td>EXPRESSION</td>\n    </tr>\n    <tr>\n      <th>94061</th>\n      <td>WOS:000947693400001</td>\n      <td>240</td>\n      <td>RNALOCATE</td>\n    </tr>\n    <tr>\n      <th>94062</th>\n      <td>WOS:000947693400001</td>\n      <td>240</td>\n      <td>PROTEINS</td>\n    </tr>\n    <tr>\n      <th>94063</th>\n      <td>WOS:000947693400001</td>\n      <td>240</td>\n      <td>RESOURCE</td>\n    </tr>\n    <tr>\n      <th>94064</th>\n      <td>WOS:000947693400001</td>\n      <td>240</td>\n      <td>CELLS</td>\n    </tr>\n  </tbody>\n</table>\n<p>94065 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kw_df = pd.DataFrame()\n",
+    "for c in [\"Keywords Plus\",\"Author Keywords\"]:\n",
+    "    kwp = wos.groupby(record_col)[c].apply(lambda x: x.str.split(';')).explode().str.strip().str.upper()\n",
+    "    kwp.name = 'keyword'\n",
+    "    kw_df = pd.concat([kwp.reset_index(),kw_df],ignore_index=True)\n",
+    "kw_df"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Downloading pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "43fb040512964c61bdcca3e35d4e9778"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "ChunkedEncodingError",
+     "evalue": "(\"Connection broken: ConnectionResetError(10054, 'A létező kapcsolatot a távoli állomás kényszerítetten bezárta', None, 10054, None)\", ConnectionResetError(10054, 'A létező kapcsolatot a távoli állomás kényszerítetten bezárta', None, 10054, None))",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mConnectionResetError\u001B[0m                      Traceback (most recent call last)",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\urllib3\\response.py:444\u001B[0m, in \u001B[0;36mHTTPResponse._error_catcher\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    443\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 444\u001B[0m     \u001B[38;5;28;01myield\u001B[39;00m\n\u001B[0;32m    446\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m SocketTimeout:\n\u001B[0;32m    447\u001B[0m     \u001B[38;5;66;03m# FIXME: Ideally we'd like to include the url in the ReadTimeoutError but\u001B[39;00m\n\u001B[0;32m    448\u001B[0m     \u001B[38;5;66;03m# there is yet no clean way to get at it from this context.\u001B[39;00m\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\urllib3\\response.py:567\u001B[0m, in \u001B[0;36mHTTPResponse.read\u001B[1;34m(self, amt, decode_content, cache_content)\u001B[0m\n\u001B[0;32m    566\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_error_catcher():\n\u001B[1;32m--> 567\u001B[0m     data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fp_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mamt\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m fp_closed \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    568\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m amt \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\urllib3\\response.py:533\u001B[0m, in \u001B[0;36mHTTPResponse._fp_read\u001B[1;34m(self, amt)\u001B[0m\n\u001B[0;32m    531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    532\u001B[0m     \u001B[38;5;66;03m# StringIO doesn't like amt=None\u001B[39;00m\n\u001B[1;32m--> 533\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43mamt\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mif\u001B[39;00m amt \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fp\u001B[38;5;241m.\u001B[39mread()\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\http\\client.py:463\u001B[0m, in \u001B[0;36mHTTPResponse.read\u001B[1;34m(self, amt)\u001B[0m\n\u001B[0;32m    462\u001B[0m b \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mbytearray\u001B[39m(amt)\n\u001B[1;32m--> 463\u001B[0m n \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreadinto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mb\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    464\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mmemoryview\u001B[39m(b)[:n]\u001B[38;5;241m.\u001B[39mtobytes()\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\http\\client.py:507\u001B[0m, in \u001B[0;36mHTTPResponse.readinto\u001B[1;34m(self, b)\u001B[0m\n\u001B[0;32m    504\u001B[0m \u001B[38;5;66;03m# we do not use _safe_read() here because this may be a .will_close\u001B[39;00m\n\u001B[0;32m    505\u001B[0m \u001B[38;5;66;03m# connection, and the user is reading more bytes than will be provided\u001B[39;00m\n\u001B[0;32m    506\u001B[0m \u001B[38;5;66;03m# (for example, reading in 1k chunks)\u001B[39;00m\n\u001B[1;32m--> 507\u001B[0m n \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreadinto\u001B[49m\u001B[43m(\u001B[49m\u001B[43mb\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    508\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m n \u001B[38;5;129;01mand\u001B[39;00m b:\n\u001B[0;32m    509\u001B[0m     \u001B[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001B[39;00m\n\u001B[0;32m    510\u001B[0m     \u001B[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001B[39;00m\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\socket.py:704\u001B[0m, in \u001B[0;36mSocketIO.readinto\u001B[1;34m(self, b)\u001B[0m\n\u001B[0;32m    703\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 704\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_sock\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrecv_into\u001B[49m\u001B[43m(\u001B[49m\u001B[43mb\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    705\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m timeout:\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\ssl.py:1242\u001B[0m, in \u001B[0;36mSSLSocket.recv_into\u001B[1;34m(self, buffer, nbytes, flags)\u001B[0m\n\u001B[0;32m   1239\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m   1240\u001B[0m           \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m\n\u001B[0;32m   1241\u001B[0m           \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m)\n\u001B[1;32m-> 1242\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnbytes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbuffer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1243\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\ssl.py:1100\u001B[0m, in \u001B[0;36mSSLSocket.read\u001B[1;34m(self, len, buffer)\u001B[0m\n\u001B[0;32m   1099\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m buffer \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1100\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_sslobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbuffer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1101\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n",
+      "\u001B[1;31mConnectionResetError\u001B[0m: [WinError 10054] A létező kapcsolatot a távoli állomás kényszerítetten bezárta",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001B[1;31mProtocolError\u001B[0m                             Traceback (most recent call last)",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\requests\\models.py:816\u001B[0m, in \u001B[0;36mResponse.iter_content.<locals>.generate\u001B[1;34m()\u001B[0m\n\u001B[0;32m    815\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 816\u001B[0m     \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraw\u001B[38;5;241m.\u001B[39mstream(chunk_size, decode_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m    817\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ProtocolError \u001B[38;5;28;01mas\u001B[39;00m e:\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\urllib3\\response.py:628\u001B[0m, in \u001B[0;36mHTTPResponse.stream\u001B[1;34m(self, amt, decode_content)\u001B[0m\n\u001B[0;32m    627\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m is_fp_closed(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fp):\n\u001B[1;32m--> 628\u001B[0m     data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43mamt\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mamt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdecode_content\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    630\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m data:\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\urllib3\\response.py:593\u001B[0m, in \u001B[0;36mHTTPResponse.read\u001B[1;34m(self, amt, decode_content, cache_content)\u001B[0m\n\u001B[0;32m    584\u001B[0m             \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39menforce_content_length \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlength_remaining \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m (\n\u001B[0;32m    585\u001B[0m                 \u001B[38;5;241m0\u001B[39m,\n\u001B[0;32m    586\u001B[0m                 \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    591\u001B[0m                 \u001B[38;5;66;03m# raised during streaming, so all calls with incorrect\u001B[39;00m\n\u001B[0;32m    592\u001B[0m                 \u001B[38;5;66;03m# Content-Length are caught.\u001B[39;00m\n\u001B[1;32m--> 593\u001B[0m                 \u001B[38;5;28;01mraise\u001B[39;00m IncompleteRead(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fp_bytes_read, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlength_remaining)\n\u001B[0;32m    595\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m data:\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\contextlib.py:137\u001B[0m, in \u001B[0;36m_GeneratorContextManager.__exit__\u001B[1;34m(self, typ, value, traceback)\u001B[0m\n\u001B[0;32m    136\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 137\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mthrow\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtyp\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtraceback\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    138\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exc:\n\u001B[0;32m    139\u001B[0m     \u001B[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001B[39;00m\n\u001B[0;32m    140\u001B[0m     \u001B[38;5;66;03m# was passed to throw().  This prevents a StopIteration\u001B[39;00m\n\u001B[0;32m    141\u001B[0m     \u001B[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001B[39;00m\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\urllib3\\response.py:461\u001B[0m, in \u001B[0;36mHTTPResponse._error_catcher\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    459\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (HTTPException, SocketError) \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    460\u001B[0m     \u001B[38;5;66;03m# This includes IncompleteRead.\u001B[39;00m\n\u001B[1;32m--> 461\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m ProtocolError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConnection broken: \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m e, e)\n\u001B[0;32m    463\u001B[0m \u001B[38;5;66;03m# If no exception is thrown, we should avoid cleaning up\u001B[39;00m\n\u001B[0;32m    464\u001B[0m \u001B[38;5;66;03m# unnecessarily.\u001B[39;00m\n",
+      "\u001B[1;31mProtocolError\u001B[0m: (\"Connection broken: ConnectionResetError(10054, 'A létező kapcsolatot a távoli állomás kényszerítetten bezárta', None, 10054, None)\", ConnectionResetError(10054, 'A létező kapcsolatot a távoli állomás kényszerítetten bezárta', None, 10054, None))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001B[1;31mChunkedEncodingError\u001B[0m                      Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[39], line 7\u001B[0m\n\u001B[0;32m      4\u001B[0m \u001B[38;5;66;03m# Uses stopwords for english from NLTK, and all puntuation characters by\u001B[39;00m\n\u001B[0;32m      5\u001B[0m \u001B[38;5;66;03m# default\u001B[39;00m\n\u001B[0;32m      6\u001B[0m r \u001B[38;5;241m=\u001B[39m Rake()\n\u001B[1;32m----> 7\u001B[0m kw_model \u001B[38;5;241m=\u001B[39m \u001B[43mKeyBERT\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mall-mpnet-base-v2\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\keybert\\_model.py:55\u001B[0m, in \u001B[0;36mKeyBERT.__init__\u001B[1;34m(self, model)\u001B[0m\n\u001B[0;32m     39\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\u001B[38;5;28mself\u001B[39m, model\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mall-MiniLM-L6-v2\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m     40\u001B[0m     \u001B[38;5;124;03m\"\"\"KeyBERT initialization\u001B[39;00m\n\u001B[0;32m     41\u001B[0m \n\u001B[0;32m     42\u001B[0m \u001B[38;5;124;03m    Arguments:\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     53\u001B[0m \u001B[38;5;124;03m                  * https://www.sbert.net/docs/pretrained_models.html\u001B[39;00m\n\u001B[0;32m     54\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m---> 55\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodel \u001B[38;5;241m=\u001B[39m \u001B[43mselect_backend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\keybert\\backend\\_utils.py:49\u001B[0m, in \u001B[0;36mselect_backend\u001B[1;34m(embedding_model)\u001B[0m\n\u001B[0;32m     47\u001B[0m \u001B[38;5;66;03m# Create a Sentence Transformer model based on a string\u001B[39;00m\n\u001B[0;32m     48\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(embedding_model, \u001B[38;5;28mstr\u001B[39m):\n\u001B[1;32m---> 49\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mSentenceTransformerBackend\u001B[49m\u001B[43m(\u001B[49m\u001B[43membedding_model\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     51\u001B[0m \u001B[38;5;66;03m# Hugging Face embeddings\u001B[39;00m\n\u001B[0;32m     52\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(embedding_model, Pipeline):\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\keybert\\backend\\_sentencetransformers.py:42\u001B[0m, in \u001B[0;36mSentenceTransformerBackend.__init__\u001B[1;34m(self, embedding_model)\u001B[0m\n\u001B[0;32m     40\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39membedding_model \u001B[38;5;241m=\u001B[39m embedding_model\n\u001B[0;32m     41\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(embedding_model, \u001B[38;5;28mstr\u001B[39m):\n\u001B[1;32m---> 42\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39membedding_model \u001B[38;5;241m=\u001B[39m \u001B[43mSentenceTransformer\u001B[49m\u001B[43m(\u001B[49m\u001B[43membedding_model\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     43\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m     44\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m     45\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPlease select a correct SentenceTransformers model: \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m     46\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m`from sentence_transformers import SentenceTransformer` \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m     47\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m`model = SentenceTransformer(\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mall-MiniLM-L6-v2\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m)`\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m     48\u001B[0m     )\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\sentence_transformers\\SentenceTransformer.py:87\u001B[0m, in \u001B[0;36mSentenceTransformer.__init__\u001B[1;34m(self, model_name_or_path, modules, device, cache_folder, use_auth_token)\u001B[0m\n\u001B[0;32m     83\u001B[0m     model_path \u001B[38;5;241m=\u001B[39m os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(cache_folder, model_name_or_path\u001B[38;5;241m.\u001B[39mreplace(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n\u001B[0;32m     85\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mexists(os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(model_path, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmodules.json\u001B[39m\u001B[38;5;124m'\u001B[39m)):\n\u001B[0;32m     86\u001B[0m         \u001B[38;5;66;03m# Download from hub with caching\u001B[39;00m\n\u001B[1;32m---> 87\u001B[0m         \u001B[43msnapshot_download\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel_name_or_path\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     88\u001B[0m \u001B[43m                            \u001B[49m\u001B[43mcache_dir\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcache_folder\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     89\u001B[0m \u001B[43m                            \u001B[49m\u001B[43mlibrary_name\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msentence-transformers\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m     90\u001B[0m \u001B[43m                            \u001B[49m\u001B[43mlibrary_version\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m__version__\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     91\u001B[0m \u001B[43m                            \u001B[49m\u001B[43mignore_files\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mflax_model.msgpack\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mrust_model.ot\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mtf_model.h5\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     92\u001B[0m \u001B[43m                            \u001B[49m\u001B[43muse_auth_token\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_auth_token\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     94\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mexists(os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(model_path, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmodules.json\u001B[39m\u001B[38;5;124m'\u001B[39m)):    \u001B[38;5;66;03m#Load as SentenceTransformer model\u001B[39;00m\n\u001B[0;32m     95\u001B[0m     modules \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_load_sbert_model(model_path)\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\sentence_transformers\\util.py:491\u001B[0m, in \u001B[0;36msnapshot_download\u001B[1;34m(repo_id, revision, cache_dir, library_name, library_version, user_agent, ignore_files, use_auth_token)\u001B[0m\n\u001B[0;32m    486\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m version\u001B[38;5;241m.\u001B[39mparse(huggingface_hub\u001B[38;5;241m.\u001B[39m__version__) \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m version\u001B[38;5;241m.\u001B[39mparse(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m0.8.1\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m    487\u001B[0m     \u001B[38;5;66;03m# huggingface_hub v0.8.1 introduces a new cache layout. We sill use a manual layout\u001B[39;00m\n\u001B[0;32m    488\u001B[0m     \u001B[38;5;66;03m# And need to pass legacy_cache_layout=True to avoid that a warning will be printed\u001B[39;00m\n\u001B[0;32m    489\u001B[0m     cached_download_args[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlegacy_cache_layout\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m--> 491\u001B[0m path \u001B[38;5;241m=\u001B[39m cached_download(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mcached_download_args)\n\u001B[0;32m    493\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mexists(path \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.lock\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[0;32m    494\u001B[0m     os\u001B[38;5;241m.\u001B[39mremove(path \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.lock\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\huggingface_hub\\utils\\_validators.py:120\u001B[0m, in \u001B[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    117\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m check_use_auth_token:\n\u001B[0;32m    118\u001B[0m     kwargs \u001B[38;5;241m=\u001B[39m smoothly_deprecate_use_auth_token(fn_name\u001B[38;5;241m=\u001B[39mfn\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m, has_token\u001B[38;5;241m=\u001B[39mhas_token, kwargs\u001B[38;5;241m=\u001B[39mkwargs)\n\u001B[1;32m--> 120\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\huggingface_hub\\file_download.py:780\u001B[0m, in \u001B[0;36mcached_download\u001B[1;34m(url, library_name, library_version, cache_dir, user_agent, force_download, force_filename, proxies, etag_timeout, resume_download, token, local_files_only, legacy_cache_layout)\u001B[0m\n\u001B[0;32m    777\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m temp_file_manager() \u001B[38;5;28;01mas\u001B[39;00m temp_file:\n\u001B[0;32m    778\u001B[0m     logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdownloading \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m to \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, url, temp_file\u001B[38;5;241m.\u001B[39mname)\n\u001B[1;32m--> 780\u001B[0m     \u001B[43mhttp_get\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    781\u001B[0m \u001B[43m        \u001B[49m\u001B[43murl_to_download\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    782\u001B[0m \u001B[43m        \u001B[49m\u001B[43mtemp_file\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    783\u001B[0m \u001B[43m        \u001B[49m\u001B[43mproxies\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mproxies\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    784\u001B[0m \u001B[43m        \u001B[49m\u001B[43mresume_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mresume_size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    785\u001B[0m \u001B[43m        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    786\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    788\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstoring \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m in cache at \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, url, cache_path)\n\u001B[0;32m    789\u001B[0m _chmod_and_replace(temp_file\u001B[38;5;241m.\u001B[39mname, cache_path)\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\huggingface_hub\\file_download.py:538\u001B[0m, in \u001B[0;36mhttp_get\u001B[1;34m(url, temp_file, proxies, resume_size, headers, timeout, max_retries)\u001B[0m\n\u001B[0;32m    528\u001B[0m     displayed_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m(…)\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mdisplayed_name[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m20\u001B[39m:]\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    530\u001B[0m progress \u001B[38;5;241m=\u001B[39m tqdm(\n\u001B[0;32m    531\u001B[0m     unit\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mB\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    532\u001B[0m     unit_scale\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    536\u001B[0m     disable\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mbool\u001B[39m(logger\u001B[38;5;241m.\u001B[39mgetEffectiveLevel() \u001B[38;5;241m==\u001B[39m logging\u001B[38;5;241m.\u001B[39mNOTSET),\n\u001B[0;32m    537\u001B[0m )\n\u001B[1;32m--> 538\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m chunk \u001B[38;5;129;01min\u001B[39;00m r\u001B[38;5;241m.\u001B[39miter_content(chunk_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m10\u001B[39m \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m1024\u001B[39m \u001B[38;5;241m*\u001B[39m \u001B[38;5;241m1024\u001B[39m):\n\u001B[0;32m    539\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m chunk:  \u001B[38;5;66;03m# filter out keep-alive new chunks\u001B[39;00m\n\u001B[0;32m    540\u001B[0m         progress\u001B[38;5;241m.\u001B[39mupdate(\u001B[38;5;28mlen\u001B[39m(chunk))\n",
+      "File \u001B[1;32m~\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\requests\\models.py:818\u001B[0m, in \u001B[0;36mResponse.iter_content.<locals>.generate\u001B[1;34m()\u001B[0m\n\u001B[0;32m    816\u001B[0m     \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mraw\u001B[38;5;241m.\u001B[39mstream(chunk_size, decode_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m    817\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ProtocolError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m--> 818\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m ChunkedEncodingError(e)\n\u001B[0;32m    819\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m DecodeError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    820\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m ContentDecodingError(e)\n",
+      "\u001B[1;31mChunkedEncodingError\u001B[0m: (\"Connection broken: ConnectionResetError(10054, 'A létező kapcsolatot a távoli állomás kényszerítetten bezárta', None, 10054, None)\", ConnectionResetError(10054, 'A létező kapcsolatot a távoli állomás kényszerítetten bezárta', None, 10054, None))"
+     ]
+    }
+   ],
+   "source": [
+    "from rake_nltk import Rake\n",
+    "from keybert import KeyBERT\n",
+    "\n",
+    "# Uses stopwords for english from NLTK, and all puntuation characters by\n",
+    "# default\n",
+    "r = Rake()\n",
+    "kw_model = KeyBERT(model='all-mpnet-base-v2')\n",
+    "\n",
+    "# Extraction given the text.\n",
+    "# r.extract_keywords_from_text(<text to process>)\n",
+    "\n",
+    "# keywords = kw_model.extract_keywords(full_text,\n",
+    "#\n",
+    "#                                      keyphrase_ngram_range=(1, 3),\n",
+    "#\n",
+    "#                                      stop_words='english',\n",
+    "#\n",
+    "#                                      highlight=False,\n",
+    "#\n",
+    "#                                      top_n=10)\n",
+    "#\n",
+    "# keywords_list= list(dict(keywords).keys())"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'get_ranked_phrases'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[32], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mRake\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mextract_keywords_from_text\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmy time to shine\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_ranked_phrases\u001B[49m()\n",
+      "\u001B[1;31mAttributeError\u001B[0m: 'NoneType' object has no attribute 'get_ranked_phrases'"
+     ]
+    }
+   ],
+   "source": [
+    "Rake().extract_keywords_from_text(\"my time to shine\").get_ranked_phrases()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "outputs": [],
+   "source": [
+    "def kwd_rake(text):\n",
+    "    r = Rake()\n",
+    "    r.extract_keywords_from_sentences(text)\n",
+    "    return r.get_ranked_phrases()\n",
+    "\n",
+    "kwds_rake = wos[\"Abstract\"].fillna(\"\").map(kwd_rake)\n",
+    "# kwds_bert = wos[\"A\"]\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0        [brief summary could help us better understand...\n9714     [known phase space reconstruction based causal...\n9697     [column behavior requires accurate plastic hin...\n9699     [approach needs excessive computational effort...\n9701     [proposed ann model achieves good wind speed r...\n                               ...                        \n3066     [key factors affecting ocean climate change, r...\n5097     [big data influences different financial secto...\n11369    [planetary gear fault diagnosis via feature im...\n11368    [simultaneously predict various soil fertility...\n11362    [recently developed ensemble machine learning ...\nName: Abstract, Length: 9889, dtype: object"
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kwds_rake"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0        None\n9714     None\n9697     None\n9699     None\n9701     None\n         ... \n3066     None\n5097     None\n11369    None\n11368    None\n11362    None\nName: Abstract, Length: 9889, dtype: object"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kwds_rake"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'Keywords Plus'"
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from flashgeotext.geotext import GeoText\n",
-    "\n",
     "geotext = GeoText()\n",
     "\n",
     "def extract_location(input_text, key='countries'):\n",
@@ -2751,4 +3002,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}
\ No newline at end of file