call_types_notebook.ipynb

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    "# Forest elephant vocalisation call-type classification\n",
    "\n",
    "This Jupyter notebook provides a step-by-step guide to using unsupervised deep learning techniques to classify forest elephant vocalisation call-types and evaluate the performance of these techniques. In this notebook, we take a datset containing three different forest elephant vocalisations and use the unsupervised techniques to automatically extract the acoustic features, project these high-dimensional features into lower dimensional space and evaluate how well these methods have been able to distinguish the call-types. \n",
    "\n",
    "## Dataset Description\n",
    "\n",
    "The call-type dataset contains audio files of African forest elephants recorded by the Elephant Listening Project in the Dzanga-Bai clearing in the the southwestern Central African Republic between September 2018 and April 2019. It has 1254 rows each representing an elephant vocalisation with the start time, end time, low frequency and high frequency annotated alongside the call-type (roar, rumble or trumpet).\n",
    "\n",
    "## Steps\n",
    "1. **Set-up** Import the libraries and functions needed to conduct the analysis.\n",
    "2. **Audio pre-processing**: Pre-process the data to isolate the vocalisations and clean them up for feature extraction. \n",
    "3. **Feature extraction**: extract the acoustic features using a pre-trained Convolutional Neural Network.\n",
    "4. **Dimensionality reduction**: Project the acoustic features into 2D space to visualise the call-type classifications.\n",
    "5. **Silhouette analysis**: Calculate the silhouette scores for the UMAP acoustic feature embeddings.\n",
    "6. **Call-Type Classification**: Train a random forest classifier and assess its classification performance."
   ]
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   "source": [
    "### 1. Set-up"
   ]
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  {
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   "source": [
    "Here we will import a series of pre-defined scripts used to automate the import of the libraries needed for each of the aforementioned steps. These are all located in the \"elephant_scripts\" folder in the main project repository."
   ]
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     "text": [
      "2024-09-16 15:21:10.226908: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-09-16 15:21:10.228892: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-09-16 15:21:10.254318: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-09-16 15:21:10.800905: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
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    {
     "name": "stdout",
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     "text": [
      "Main libraries successfully imported\n",
      "Info file loaded: /home/alastair/forestdata/alastair/temp/elephant_analysis_repository/data/1.elephant_call_type_df.csv\n",
      "Audio file paths added to DataFrame\n",
      "Vocalisation Dataset successfully loaded\n",
      "Functions for Audio Pre-processing and Feature Extraction successfully loaded\n",
      "Functions for Dimensionality Reduction successfully loaded\n",
      "Functions for Statistical Analysis successfully imported\n"
     ]
    }
   ],
   "source": [
    "%run elephant_scripts/import_libraries\n",
    "\n",
    "# Step 1: run the script for the vocalisation dataset \n",
    "%run elephant_scripts/load_data\n",
    "\n",
    "# Step 2 & 3: load the functions needed for audio pre-processing and feature extraction\n",
    "%run elephant_scripts/feature_extraction\n",
    "\n",
    "# Step 4: load the functions needed for dimensionality reduction\n",
    "%run elephant_scripts/umap_projections\n",
    "\n",
    "# Step 5: load the functions to calculate and visualise silhouette scores for the statistical analysis\n",
    "%run elephant_scripts/silhouette_score\n",
    "\n",
    "# enable plots to be auto-displayed in the notebook\n",
    "%matplotlib inline \n"
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "id": "08a668a2",
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       "...               ...           ...         ...       ...       ...       ...   \n",
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       "\n",
       "        high_freq Call-Type Behavioural_context  Combination  \\\n",
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       "\n",
       "                                        audio_filepaths  \n",
       "0     /home/alastair/forestdata/alastair/temp/elepha...  \n",
       "1     /home/alastair/forestdata/alastair/temp/elepha...  \n",
       "2     /home/alastair/forestdata/alastair/temp/elepha...  \n",
       "3     /home/alastair/forestdata/alastair/temp/elepha...  \n",
       "4     /home/alastair/forestdata/alastair/temp/elepha...  \n",
       "...                                                 ...  \n",
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       "\n",
       "[1254 rows x 11 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualise the vocalisation dataset\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451bdc53",
   "metadata": {},
   "source": [
    "### 2. Audio pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7c9d36",
   "metadata": {},
   "source": [
    "Now that the dataframe is loaded and associated with the audio files, we need to pre-process the audio files to extract the exact frequency ranges and time periods associated with the vocalisations. This helps to minimise unwanted environmental sound that may cause erroneous results. These are the steps take to pre-process the files and extract their audio features:\n",
    "\n",
    "<ol type=\"A\">\n",
    "\n",
    "<li>Read the entire audio file.<li>\n",
    "Apply a bandpass filter to exclude frequencies outside the vocalisation range using the low_frequency and high_frequency information.<li>\n",
    "Extract the audio clip corresponding to the vocalisation using the start_time and end_time information.<li>\n",
    "Zero-pad the vocalisation to a length that is a multiple of 96 (the input size of the VGGish model) and centre the recording within this padding.<li>\n",
    "Normalise the audio clip to have a peak amplitude of 1 to control for elephant distance from the microphone.<li>\n",
    "Reapply the bandpass filter to remove any acoustic artifacts that have been introduced by the pre-processing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7410330",
   "metadata": {},
   "source": [
    "To visualise how this audio pre-processing works, we can take a sample file and show the effect of each step in the pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d345d3a8",
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filename                                                  opp_731_ro.wav\n",
      "recording_id                                                         775\n",
      "start_time                                                      1.142483\n",
      "end_time                                                        5.953776\n",
      "duration                                                        4.811293\n",
      "low_freq                                                      133.862305\n",
      "high_freq                                                    1954.736328\n",
      "Call-Type                                                           Roar\n",
      "Behavioural_context                                          Competition\n",
      "Combination                                                  Combination\n",
      "audio_filepaths        /home/alastair/forestdata/alastair/temp/elepha...\n",
      "Name: 111, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Select a random file from the dataframe\n",
    "sample = df.sample(n=1).iloc[0]\n",
    "\n",
    "# Print information about the audio file\n",
    "print(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e4e92c2",
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mO1uRHY0KlPj+lQ7yyK4Do0+zCaLAs7D220qbZX3ymjg0wyZSvblFS0tLGhzkc9wcOdzjH08OPh7VUDpJZ6B2aXT9XGoO2VXF7CSpE7OsVhrtXQy4vn0q7q3sY04s+G2j1vA24V5K91HaXj7BHl7MwAxU6CLnoX+c5JeBDvSR9zRYdhj1R7Ixz9hcGwwjuyWYtdNNkIkTQdvpFMvOoP5xWUyFQpLWxSxDazTL/Ix2CLP2xAcaPS/m4Bhvh3Dctrk6wPbUHciuHrF1PJdivwukEmS1F8XW8cmIjYlGgiypvcE2ZNeNWZs4U0ls3EpSG2ZU5eBvNnkx/3gp7n2WC8mKjeKu9i5/w/6xMYcw/RSFN8YYY4wxxhhjjDHGGGOMMfIHG2OMMcYYY4wxxhhjjDHGmL7jGjZHIMflH6/0KsV/T80xiaUwQd787hZLw+0ErFD23jwrBFqDqceSVEqxFHIqczEWcXmG0ZgVxRvNMxmADnw35QwvgE1TzWsdlg5P5cvSVO9BUhmuavAWBbPmNZIfZoaSNrQehezmO0xCYijL0sIlaShk0iIjedaRZSjZsat+LLKTpJWQPfeA2FzYMMDs6l2+ni21mTwDXSMHsqy9SpbHWSy3oSxFl9kV02x9LEOpOknKB2zda8RMyo/K30nSOiiTshiyvSubYbILqQSSms2AyTOkoVwl9foiscLPScjkuSwFodZl0jlRHIqLmpgjmc16NJJPOaXC1gS6/k81oKQhlKmRpL1aRHY1sbW1FPHCzWm40IwI+r1ZtocP5vj+SH27IvRTC3BtXeEqxCrBrSINH5s+C/WPpSQ+Mhvj61pHIbuFFt+jBnOsIwdD9hKTjLMBuDXvqTP/qtphz03l9CRpQ5n1TwMqAS936DjjPjf1fcsZeE6F/v4edhy5+9pQhq4Mf2sox0yathYwqVtJWhtPIrs1+QKyW+nyWs3ZkP2+Q2mKSRjS/SgRPS4okYbybkmoBrOr2hDZNGNMf3GGjTHGGGOMMcYYY4wxxhhjTJ9xhs0RSKRIQYKCifuDRlZJvFBiGLAQnoF4mNnBKGpJykYsiq8VNJHdZIIirRvKNIKQtVeDQRMrHf4SaZHGsQL7FkyD1GhUlyS1YdAdTVSgyT2LrQTZGR32cjoxe5hmyCP2aZHPySLrIBqx2Qx59FChy54nG7Brj+RhJhdP5FIVRhDSKMfJInvmJFEWzZDd48YuywQcgBHWRZj5JEm5gD13DRYhbYsvFimx/imtkpF6D3lYGDsdc9crC/tyCRZqLogVSqVFviWeBZQLWDZnN2b762CGZQJHcUcLyNKY3kIDyGmyepggrHcARmdTuyxcqyWpDaN2x2DGOM0AGEwQpFzrsvW/Cn1kmvAxBv0RSSqk2bVrcNujmTMJEnmVjtnzLLXZs1ThTSZReGiGrE3a5GSRv8MifIfNkHV6HjqqmQTKBMPQZaNN0rlVyPJ7HIfnStpiC86F9XmuQlFqswUon2LrWQ6eHxpd/ltDB2ZPw+OVCmm+L2RhBlsGnnTyMevvpWAZ2UlSIWa+L73HRsDOLvmYZTRJUhf+Tjai1X3kUG1tx1c2xvQDZ9gYY4wxxhhjjDHGGGOMMcb0GX+wMcYYY4wxxhhjjDHGGGOM6TOWRDsCycSZVQuwTcDU4yTCaikoxUILrTehfFk2wTCn8jz1iKXMFmGhS0kq05rRME+5kGFp+OUEKen02uuLbGRkU+wep5v823IedjmVY5tpsmdO8vW7C8duDUqD0YL1ktSC0hB0VNDCnVMNlhYuSQsxK6C5LjOI7HbCepxpqgEg3uenjLA2jyqwFPdmxEfargazHWqyYsA/WmZSCiMZLom2oczW51qX3eO2GpPTkqQs1PcYyjGpgk6DFZJdDFaQnXS3hCkhG7B7LEZM/ihIsKJ1xeY2KeYuSVUxOSVapDWB0qE5wkn/7/+txiiUtYrx6GNjeaGVwBeK2B5FpZOyKX7tJpS+KUHJnxIsJJ4JuEwW9RVL8AhBXem1BaidJu4jz7SgxFKK7Sc5eF1JmmvTcQEL0cfwWaDsliSV4ThrQtnnIIE0YQNK085A7edFeEaeLLD9VpJ21Vn/UNngMpyvJw3xftxQZH4GlZbb3WQTey7Hf0P48SJ7nkH428D6EpTdSiCLvaPKxhn1j4fpYVpSJ2J+6nKXjfEQyrtl4f4qSXloSyWV2zHr73wCKekqlCFOxau/mxjYGGP6i2epMcYYY4wxxhhjjDHGGGNMn3GGzRFIN+gqDvb/ra4JA8A6tKKqpNkOi46pxg1kFwbsJheDJWQn8aLaNLJihIbwiUcBwwAwXAx+Is/zpKglvccULFZaSVBItgAjA1MwEnNjid3jiRU2biWphSPAWMQNLS4vSbsarDNp0VmaYUOLbErSCMwCOGmI9U+vC/dKfI2ktGHmTCfi73oiz577mBKb2ZMFVnyV7wpSBXohNMvuxzB6UJIyKTZ+YHCu0qvsq/eQhYVSk9AWW3/aAY1cZFkuktSBRVUbMd+LGb3vR3NkEypUAKJ2W9D3hQmtWoAV62shn5dduBK3u6zNcsAj9lMwW3UEZirRjAbojkjixeDHckm0BFYnyT3ShKFyht0j9Y/TSTKVCsz2hAmW6tyBvtDuBi/STf21KehnpBOEu9bhC6eZboNZ5rdsHuQ3WYDuOfWR29BPTSXwFqmPHMJz5ViODYqjoX8sSRN55pPQFstpZjnT4ueCOyLW5gg8syUQJsAqHVnoSytmB4iWmN8rSR2xQR7BsdsJ2P5aD6AUhHjmfS5B1o4x5tDFGTbGGGOMMcYYY4wxxhhjjDF9xh9sjDHGGGOMMcYYY4wxxhhj+owl0Y5AOmqtmsrZWwGAu6FSZzk4LCNYmDKE6a1JKAQszZQWIZWSFPijRTEZa2Gxc4nLMc212buh6d7DWZ42T6UcaNp8HcqXJaGQZqn4eShfQWUcJImqGFLpjDJUpZhp8O0mFTDbkwfZ2IX1T3VHlcu20flagVIltBhwNUFxUSpLUYDPQpmEUmySVIBrwEqXPfdohsukDMPCr9BMQxlYOLzL9fSWUrPIrhPXkV0ACqpLUiaB/FE5HkZ2VLZtJd6L7LIBk+hzbJKhtNVVBOdIL6FSZ9kggcxuzNbWED5vnEC+qJBiezgtYk4lWpP4ipR1BfZuOtCnnG1xX2gA+g/U76V2MXwWqfc+cg76BNRnkqRp2OdUrq4M/WNJKsOtdDZB8XbCSRUun0jHxZYaPPvCgwaV8pMS+Mhd9q7peZZKJyZhAsqxUf+4FnJ52OEc65/BHOuffILzQzXL9pqFLpMwWwxWkF0raCI7ScrA82cayrGV4yKyayeQSl5KzSO7QKufhwL7x8Yc8niWGmOMMcYYY4wxxhhjjDHG9Bln2ByBDEYjq0bPdmF0DI3Wl6SRgBUSr8csKqgBC7QNxYPITuLR2bTY3DoaACweiU+j+ChJiovWY/aNd6nD7nF9kT3zRJ4VtZakLIzCWmqzSLEyTM+4q8Yj+++qs37cC4OCRmAklCSNwuyHFfgO03ARoGuKxAu6ZuHYpUXjSwl2xCF47bEcywIayLCIu2WYaSJJtywzu7kWLaDNnuXJa/k9TuTZc1cyrM3BLH+JtNBuEa4BRTjQUl0+X7sxW/uGtJZdG8bpRDA6P0mbnYA9S0os0jDGucAHI2fYPBQZCkrKguyyDhxSdNsbhoXE6yHPzmtHcG1Ns2uP5HgEMM3GWceCj3E2Ns2cTgL1kZuwKHq1y2MljyqySPOJAnMWM9A/XmwlyLCE++OOOjsQba+zPXyWHws0DIcuVTCoJdjDaRZZB64VPCuN+9x0RJZgtkkFumGj0D+WuI+80mGO3a1V9tTzLe7PRjHrnydMsJfYa/9YkgZhumIRDrRCggwk6kunoEZIGLDxMxqNIDuJZ5xQ/5O21wkSZMRBH9kY89DAGTbGGGOMMcYYY4wxxhhjjDF9xh9sjDHGGGOMMcYYY4wxxhhj+owl0Y5A8sopIyaBsBohTP+VuETCQmoB2UUwHRWqAkmSQlgM+qj8ALKjhVIlaQwWIRzPs7TZKpROaiYoGLrcYbb0uSsZls68boDJ30lSmhYirTKzmSaTOqNyBpK0wFQuNJhlaeEPG+SyAhUoK7C9wVKu81B2oUR1ySRth+9mR4O1OZGH0ns5Lp1ELUeg5EMxzeyGE0h+pWHxTCqR0Ib6PmsKXLZnXZFJubSh3My6EpcK6ML9qwOfOx2w+VqCRUglqRgwSc+2GsiupTqy43Jj0mi8DtkVxZ4lJ9g/cM0N459/EXlzeJJLpZUNejdeQjhG6WxbDNk8v7tNOkGYGVRnlMTXYSprOp5n++N4HjpXklY6bH+kUmdLUIopiQxxGfrIk4PMRw7gteME0nJzLeYjd+EgX2yzaw9keD+eWGHjgspu7W5wyTgqTVaCWsA7oKTyjjr3haj01ji0o1KQIzkuA1WEc2EIzmvuHyMzSVILjvE1BTYe1xTZek/947vbZL//dLF/hS/NpfdS7N0UQuYrtgK+L7SgdC/9DWosGkV25QTngnzM1h9yj0EC+WNjTH9who0xxhhjjDHGGGOMMcYYY0yfcYaNuV9G8izCqRPxKCxaQH0iGsNtElriETxZOCVoQbxygmJ8lSwsLphlz0Ptal0ePrQIIwjXwgj7E8dZNlVlEA4eSVEIo9xLLOJmdAVGvSwMMztJcwUW4XRUkUWUPXyI55FFcOx2Y1YglpJL8ajlpTabh5sHYEH2LOvHJAWL59ts3gQwyrkEowfXw4wUSXriJIt83QuLqt6xwmI8Th2dQ3aSND7CIoPLs6x/cimW/ShJ2+tsHu6GQe0043R9ns+tgc7RyG6rdiO7UlxBdoUE0X608CuNSMzFvckAvocwdoFW01uGc9RHZu3NNNjaMZrmawdM+FM7YjdJMwjvvjb0w9Ls2jQbu5zh/j61pT4yzbCh/rEkHT/BfOSBYRgVDv3jYon34/Ay8zOi+WFkt6bA+nE99I8l6eSRJWRHM4uiBL4ihfrIyx02Hql/LElDOXYeCmPmA8632B5O/WNJKsIz7VqYlfKEcXbtmRb3H7ZU2Ts8ZWwe2Q2Psozo0gyfr7kUy3S+q86yOKaafC7QXI41MEOz2B5GdjtaK/DKPAO+ADO0QpjVGCVQrMmrd1nAfBU1xvQLZ9gYY4wxxhhjjDHGGGOMMcb0GX+wMcYYY4wxxhhjjDHGGGOM6TOWRDsCyQcZZVZJ5cylWIpronTmNBtuQcjkAjowubYkXhySfsGEtSG1psCTTYdgujdNSU8HrH+6CWTtOlAGIAObpPIMcYJ7pKod6Qzrn2KevZdcqvcF61vwuetdvpRT6S1aDH6owGQXsgn6p5hmsoi0wDAt8jvT4msFfd95KPkyVmHSB8dP8KLThaPZWtGdYWvul27YhOyOOorJj0hSbpS9m2aDPff6BHMhhvKAR0F1sN1NJuOwnanASZLCGpM+OCE8Ctml4d5O7SSp1mVrSj1ia2k94GOckMRPMUc2WaWUDVZfN3OwkDidRcUMW6uDBBWeQ1j5uwClmBIsCUpDW1p8m0r8UslXSUrBdYH6yFQiOp3h75Cq5FApLzC0JUnpLPfXCoXe+sj0HSaR5G7BQvS5DPOF1paYvyZJg9BHzsAzSWF6HNmN5rkkWhquZ3PQx6H+fj7N5QFHB1mfb5pcRHanb2LP0k0gN/Y/32B+2OSmKrLLjrAx3m5yn2ktnAt0ldpQ5POQ+si7GmxPCiO2oB0TMBk4iUt/wi1bNaiL2gz52aUKfWki3x/0UF7NGHNwcIaNMcYYY4wxxhhjjDHGGGNMn3GGzRFISoHSq3x1z+JPeTyyopzp7Vf8JozqzcLCcJI0lGXRH+MF1kGlNIselHhEUhFmSNBIsSSZD9tgEcLRHIv+aMNo+EadF32kkYbZLOufUpm9w/VlHnG3pcb6cXudzZlGyAutHwOfZwCOnxytqgwjZCVpAmbO0KjbZsj6kT6zJI3lWQZSBmbYUGhGiiRljhlidqewosH/d2IXsqvdwfeFxjSzvXNuBLdJGcqytXShzdapJhw+tQRVPlfgPjeSY2skjdhe7vCbpBGJtEhrA2aRrqRYJlcovg+bI5t0KkDZZdxHZpRpuC7Oz5WWQ2abgynjgwkeeiwP1wS4P2L/GK7pkpSDvjTdw3fU2T5K/WNJ6sBo+FaN3SNd/2kGuiQVK+x51lVZaum2GktpvaueICK9y3yhY8vMr6OZ6pKUhT5yPmZtjsPMmXKCcdbuMh+5BH3k0SLL+Ehy/sRnu2GY6bZpmNk9jGfeP2liGtk17mTttfYyu617uX9MVSgq2D/m53OqGlHrsoWqCufWSJ7//kRH5HKbzQXsH1PpFklN2I/VePW1ogtsjDH9xRk2xhhjjDHGGGOMMcYYY4wxfcYfbIwxxhhjjDHGGGOMMcYYY/qMJdGOQKpxU5lVysmtdFhqP02vl6QSrETfhkXkSDE1SQoPQsHh5TZrcwnKGUi8IGcOSyxBSasEkk1laEuLvFNSsCCmJIUhLBgIJdHoGM8nkEgYhteeakJpoC6XoNpRzyG7EwaYNEQ7ZPe4+TheiP6Eo5aRXdRi43HuNrae7VmoIDuJy6QMlVg/Dk8wCQmomiFJCneyfkxvZmMis4H1T35mEdlJ0uxdJWRH19LRHJe/iqD0VgilOGih7VICddBSmhl3YYFxKs/Qibk8YBcukvkAFpKFe1cAY46onTG1sKtstPo4XYHrEZyWKsJC9O0EhdYpcRJHHrLcYW3SfsyloD+bQNaUkoOF0UtQRizVY/9YkgLoI8fUP84lkKqCpvTdDOeYkzPdYn6LJNWgn7qzweSvjh/g/UPlxo7evMDs1jP55ajJ73HpdjYPp+eYD0ilzgbKXI6psgbaQn8t2sXOJKkTJth1JaXXw/6ZmUd2i9vYGF/pcFmyUSipF8LfWKhUnSTBn4GUB7KkklSEUqIduhFLSBJV4j53Fy6Q+RT3U7sH4XctY8yhi0+xxhhjjDHGGGOMMcYYY4wxfcYZNkcgR2Uryqb2H0W0jtV8VDdBPe1tNRqtQSOF2ffGNIwSkXgxvmbY++iGDswsogwUWKR5CxbUliQYeIKfJZ2g4CQlBdtMpXv7DkuwvyXej5Usu8digmfJwUjMowZYgVgafZqCzyJJglFTmQqLPhtcYFkuHRgJKUmdDrNttFjk27BYhk2qwNezlVvYXCjXWaHUzNEsejAzxteyHd8bRHbDWVZA95hRnslF177d0+PIrg2XM7rPSNJEkd3jTINFJa90mV05zSM2s3RBg4QdNs6WI3aPLqpqKGsLWeVSq+8rawtsslNXcXsdRoUn2EbpvAzggpRkmsN6zBia5ZiEYpHtKRlY2DqAUc9JfP00zOQNYNYOfdfweJXo2qUS85HppcswK02SStBHzsJnWTtQxdfOwYz6FN1yoX+cnuAZSOVF5n+Otll2D/Wlk5w/KxHbx4MsG+P1W9n8LzSmkJ0kZTYxfzYzBn3P77D2BnPsWSTpqDGWeU/POFN7JvG1WzBDlO41k0VmuLfB14oa3LxKGdY/2YMQGh+JZQIud1bvHz5yjDH9whk2xhhjjDHGGGOMMcYYY4wxfcYfbIwxxhhjjDHGGGOMMcYYY/qMJdGOQLLpQLlV8k1p8mg3gTxDCLUc8mn2HTELi0jSIsuS1Ib6FTmonDTV5FOsDp+nQCWoVlgx78U2S62VeDozvcd2yDqyXYUafQeB8iCTccjAgrNJCGGTzQSyf4ttZrvYZOOiAAvJZneVkZ0k5WeYbNPUPJNOooxVmNyDJM3W2ZhswTE+tMQkKcIO6xtJ2jM9hOyCvWzdO2ZpEdk1F3gsyFSzgOzSUKqk3uQyIFSahktBMrskNb6pbQ4WLJ3tMHnAkFaSlpSG/RjBh+nAa0fqrZ0xOeAfS1IEvWQq5UX3+kKa7/WNLrOlPnIrgcxZHkpQ7WkyHagmXGNyUEJMkoIVdo9L0Bdqw3uk/rEktaEsUWuR+ftUviwJxWHmI6d77CMn2Ufb8Oyy3GFntiXot0hSAcqQZneycZGZZnYz8/xsJyixNFJhfuoCPLMlkQccGGCSaFHI+nvvbnZ+SO3hA23d0gKy6yxSKa/e+seS1IRrbhf+JkGlICXuI1Oo5Gguwb650IJyp/B3ILpvJumaTmSf1pgjCWfYGGOMMcYYY4wxxhhjjDHG9Bln2ByBdMI4WWjSfuh9rJbUhZk4rZiFLYxmeJQRjV4MYEbD2gKPhi/ByMChHItmqxRYNNJSm0ekU2i0Dy1YTzNxJKlcYP2Th+8m7LDv2tUaH2e5FHvXxw2we8wEPNpmb4sXEyc0YXHRO+eGcZt0/Ny4wLJ2HjfKCsQOdXsfw0AjrJdWWERisc1LRM7B6LybFlk/rp0fRnZJilNP5Nk61eO69omgEYQx3BcGEyy5eRidtwKHxUJqEdkNRjx7bX12ANm1YFRgCkZDK2Lju5sgA9Ec2bR76B9LPGqW2nWgf5zEtpxn+14+QZQy3UnXQX+tBDN5B+F+IkmVIrOttnrrI6dT/B3SgvW0eHuxwDaKXJGfXSLoI9drrB+z0D8+pszGjiRloE/Za/9Ykppd9m62zQwjOyoa8a0EGehnjrJC9IMllqFLSZJhs7LM9vsC9JHnYZb89xd5P66bG0V2KTgeJwq97e+DQRLfnK58lSz8DQEOnxo/NmkhZH0+GLMz/5oiW1PaCfb2FNxh43j1tScNf0szxvQPZ9gYY4wxxhhjjDHGGGOMMcb0GX+wMcYYY4wxxhhjjDHGGGOM6TOWRDsCCYLVU6qLsGAoKc56D+Uss6WFUitplmaapEgrlkSDOel31fkUy8LPp1Qaog7T8Hc3uJRXM2TPva3GUs1pwfp8iqfsNlZY8dUaLE6ezzBpiOUW78duzF72djh+5lp8jK8psLlNZTuoHNsty0x2S5Kmm7CAOlQ/+X6aXbsG54wkLUAJkiEoadKAxW7TUC5EkipZpgPwiKE6svvmPOvHJ00weQ1JOnHjLLKrrbD5tXuJy1e0YVHVRVj4mRZUrXV7X6SVSomORSPIrpDic4FKnYVQaqqUZv0dwfYCcUlNc2STCpjEywD0kbtwHy1n2BhtJlAvKUOnkkqdFQ7CNLqrzvz4XIr6+wl8xQ5rc3eD+bNNuJ9srzEfVZIKaeZ/5qAvPQ/943qDy8AV8szPWIFnjS6Ukd1R5/c432ZtrimwvSydQIaY+tK3Qt9lusUmYpJzwQ/htWtdNmeWoD87lOXSey3qI0N58XKOjdtTh5mkssRl6M4eX0R2xx0zh+zqy3wuTC8wCdsulKtbhGchifuzdfg7UBf6gEmkREfSTHqvmGH9Q6XOQr6k4GsT0glkCY0x/cGz1BhjjDHGGGOMMcYYY4wxps84w+YIpBvFqxZRpjVXabSEJC22WKMzbRY234xZwcnB7CCyk6ShHCwQ3oZFqGGkmCRlYRHCZRztza6dJFBjHFbApkXjU7AEYQDbk4RjqTswGpIuk80uDz+lmTNbYWDXbJNHqVWy7D4XYaHdEEdD8v65q8rGGY2aGsiw/q5kel9wls6FwQJb94pFXj2TFhiegNNrrs3GxGCeFwPOV9i7DrvsWZpzfJw1QpjJAdtLEMSHaYes0SYMz6OZM7kU3xhoRkwHOhbzHTYXZoN5ZBeKj0dzZBPGd///atAkOZotsAx9yvkEez1dEwaycF1PsD0uwSkXifUPzWhYgVH4Ei94Tvfw0Rx7N9kEWbJUxACv1vDaYYKo62aLDYwmzGCmmTPbavx8NQ/PnxWYlbaYIKM+hmN8Z4Pto9tr7LpdmPkqSRV4EOy1j5wkU2mgxIrB5wtsHhagLz0Oz9KSNNdk42IA+sjZQTZu8x1+LmjO9v5MS6E+MnWl2zChkma5SFIhzeZCFi7ONBuGZslL0mKLPfhst7H6dWMoVWGM6RvOsDHGGGOMMcYYY4wxxhhjjOkz/mBjjDHGGGOMMcYYY4wxxhjTZyyJdgTSjWMFq0iTdKCMA1RrkSSVs6zN0ZClw8922MXbCdLCuaAWYzDLO2g0x1JcR2GhxAFYcDyCBVUlaR4WFyxnaBFqnmpOocVXS7AfKctQQkxKku4N5bSgjIMk1WAxR0oeFviksiKS1OiyNkfybL4eBKUqTcJCuxU4D9tQLiQXckmcDJyHhQHW5nFLTKOPSp9IUrvKxm4dyqQkkZvJwGLAEpO5aGH5MnhZSTU4F9IBm9dw29Ryl8uIhTGTSalHbJwtaBnZFSK2d3VjxyYZRjdikWzUd6HSaUW4ZA7luI8aQaUTLsXCfQe4HGkI7lFUbmwkgRxnGe7Ngj7yfJutg9xTlEIqxwSHRS7NNp8C9G8k/q6r0EemOzjcGiVJg/D8WePuFYb6JGnYjw24qIzk+XxNckomTMB5WM7yDm/D82c2C/sbrj35IT4Xjl1ivksbyvF2q+wdthpcqi6fYmtABvZjJP4bQjNkz0MlzGpwLgQJ9i56YlyGemxQmRRLmErSYhdKaAerz5mOEhxIjDF9wadYY4wxxhhjjDHGGGOMMcaYPuMMmyOQlAKlVok2oJE+CeoSqwRH2yxsrxOzqIDdrTpsUWqHLFKEFpsbz/PY/mEY7UcLWK50WDRbDUb6SFIBZlOsgQXUc7C9+CBk4mRhZFe1XkB2S7AguyTVYVAZjaSjEY5JoFl29Q5bBJYTJDS1YFZcDV47jFl7kwU+F06ssMqvQ3Au0Ii7+ZUyspN40dD6HrY4b6mx9XEoyyO2Gnexa9Ni0rSQdBIaMCqQRtPnEtwizZyphSyilcbwFVK9dw/pPAxTbIFsBmxvD2MedW8MgWeMsvlbyjA7Wjxd4tnle+rMrhXyKG6a9DsGMzmGqH+cIJ22CjNi6N5TgJkUa4qrF4K+hwz0kem6Tn3pXJ7v4bUqy0ClPjKNwq8nCAzvtVeQRB2g2WF+2HIHZh/ASPwk2fQdOIAmYVb7CRW25w4Vm+zCkjrQR15cZn5qC2aCN3fzc8G2GvPPyzDTrXEnu8dqh6/Nbegjw585VIfzVeJqBzl4cfpbVb3b+9S5Yqa3q0qYYPOKaBZQvPr86sYwFdcY0zecYWOMMcYYY4wxxhhjjDHGGNNn/MHGGGOMMcYYY4wxxhhjjDGmz1gS7QhkKWxptVp7i7C4XxJqUBKJpntTmgFPuV6CKdcTaSYBkAQqsZGDBQMpNDVbklLwHkdgmvvBkDobKjO5CVrAsgHlMFag/JQktWEh2QxMC0+SSr3YZrYNOBcyAZuvFVj0VZIGs6zPabHkYprFJqwr8LR5KkPXguMnnWLPgosQS1qEEiS3VZnsH1TtwfKOkjQO5WHKGShfkUDicbbV23WcygAtJJA1omOc7ppUSiFKoi0EyQfs3eRjKLETzCC7SAn0GM0RzXInVDZYfR9YTCBBQ6hDPcVW2Pt52YLywlUoQSpJYwnkRQnYP4YSYkkoZ5hfQCWEKlAm9WAwOMh883aLvz/qI9egBFUbvsIELqXotFmE6pn1BH5GBo7dSpbZDUJN1Q70HSSplGOduabA9tIAPnMTjgmJRxjTFWCxxfzjLdUSbJE/91FFNtDG4Hm21OE+Dj2TzLXYuSBJ5DeVMKPn1CRjnEKbpGtKDH1uKrUvSdmIrT9LWl7VJpQlg4051HGGjTHGGGOMMcYYY4wxxhhjTJ9xhs0RSDVoKBPsP6JuT4MVzgsTRDfsrLPIrgAWaa0HLPIkH/Ni8CNZZluCxeaGszy755GbppFdvsyiIad3VZBdkiLdhQy7No2GzMLIRVpsUkpWLJUQNWjxwwQFA6EpzUpZ7vBrN2AkL82cKcGIVhplKEkVmKowAIOcB6EdfeYkRHA9o5lzUcznK802oQV06btOsqbQ6Fzaj4N5Hi3WDNm1h7PsubMBjXxNEEmXYm3m0ywacqnN1tzZsIbsJCkfswkW0LQ0CM2ccYaNoTSirjogE2y6ycY8TRjfBSuoJ4mya8RsrtPMt8Ec98OKMB1zCGZjPvzYvcguW+J7+Owues6BezjMVqfZtJKUgT53CPfcTLa3/rEkxbXe+sjUPx5IkGJDfeQGTLLOJvBnC3RcwMehfm+SrPYK/DUoyXMTkqgspKkaBJwL1D+GRyZJ0kCaZoawe6T+cZJ+pBl+tH+GszzblI7xEJ5zaNZ/Ic1/7lzusD1krsX2rhz04VPwjJOELsgWDsWVJYwx/cEZNsYYY4wxxhhjjDHGGGOMMX3GH2yMMcYYY4wxxhhjjDHGGGP6jCXRjkAmgkFlg/0X9W10WUpoO0EB1EIKFo2OWHpmBEsL0iLLUu8lW1YSFFSs1WgBbJbOTFOuUwlS3JtdliKdTkGZrAyzayQo8NtsQKmSkPVPGspkjea47M76IrvHaSixVOvycTteYLZtOH6onFYz5PdY6zLpg/Uldo8VLInG7CSpm0D2i1CG8zCJuFO1y+6R1penkmgHA7r2NBKsuVXYZgnKXISwI0sJBloVvnB6bbq/ppVAhhJKKnXgnr0cLCK7bsz2wghKQxkzms0pm1pdGrcOhxT1kXOw4HCLaqxJiuGaQH3kBDWRsawVLURfX4FyxQkKKHfh/kihEptjmTpuk/rIrTq7drvJ7JL0DZU6G4ZypWsL7F3Ptvg91uB8HYPHMOofS9xHbkPFL+oTrC8yO0kqw3GWgXJ+vfaPJakI5ROpPGAdSn4lEYHrl4/cTOD3Upm1WpcdnAoJnrkD50IBaqetYP+Y2UlSExqn4e9FeSiJ1op4P1ZjJrffCVb3kcMEe6Yxpj84w8YYY4wxxhhjjDHGGGOMMabPOMPmCKQdh4q0/1Ce5TaLHEiSkEJt6zH72h+mWMhUNeDRAysdFpKUAxGYklSmRRLFI9qWltg90iKEG4dWkF0Scjn2blIwWito8vCYFMzuodRgdg8t0ChJLVjEdqFNC1jy/hnNsWuvhYUpC7Ao7lKH989Sm82FJ01UkR2N7Fxus3ktSYM51uZAnvXj+DpW5L1V4/1Ix24pw8JKacHZJBGJO2qs8DMt/JpPUNC5BjNsdjaY3V4W9IYjaSWeOUOz0uj+mk3gHtLMmWaP9/Zu5Awb01vaYYwyU6qwMDGFZq80E2TYdOG87MD5QfcTScql2JpZzrBr04zo6hLNVOesH1nuaXu5PN8AArjn0kx5eg5Lw6xSiUfst+E77MKzy3yCwHAoGqFh6AJOFuFmL6mQZmN8BfojS3AePmF8EdlJ0hD096vQRx6A/nGpwF/iyFENZNepQmUC6h+n+ZpCs81iWGB+d3UA2XXhmVKSsvCMXIfjcXeT7wt7m+zd0O2V+8d832xAX5pm2NDzeTtBhk0nYKlFLMMmiW6DMaYfOMPGGGOMMcYYY4wxxhhjjDGmz/iDjTHGGGOMMcYYY4wxxhhjTJ+xJNoRSD1urfriqarUjJbwdZsBK7TZSbPUbJrGORxNIjuJS7vQIt3HD/P+GV/DJJEaKyz9ONNmzzK6ll1X4nIKIZS0gvWvVWrzparZYv3TgOnwNHW9kED+Lgs/lReg+lWpxL+9j+XZuKBSZ5kAFitNIJ+Yg8/98I0zyK68ng20pbu4JNpKtYDsBipsPcvDZSpT5fJOx3UWkN2aOnvuH82PILskUpDHDvZWkrEJZRwkaRuUm5iAcyaO2Ty8cyWB3AyUcqCFTddkmQRdPeTvsBOze6TFzZejPey6EZNIieIEGnTmiKYZhwqj1cdLOmQb2lybSSc1YOHfMODrP5VNGYmHWHsJpFepuMsmKDc2spadH5rL3FekkrzDR7F3iP1j5hJIkmI4zrod9q7bbbY/Uj9akkIoYZaDxckzUFaqlOF+bznD2hzPsb2CypxJUhqOMyqLSM8PJx47ywwlFdexe1zZxsZPvQZlw6F/LEm5cfbg6SIbZ5vazD+egP6xJN0+y3zkIpSCXDPAz+eUFpQw3LZcQXZ0zkj8t5OtVTYZGlArOUcnl6TJIlv76tA3b4fsHiPaOZKWUnPIrhuvPr+i2JJoxhzqOMPGGGOMMcYYY4wxxhhjjDGmzzjD5ggkpUCpVbIGAphVUI6Lvbil+1ATi3pJBywKohizSHhJGs+x4oIDOVjUsM2j1IZgYesWzDYpl1nkUorfoiIYiDE7zaK4w4j1YzHPI0AGB1k05FDAorPLy2z8TFfZM0vSUAZGgJVZ/1Rge5JUgOlzNFOhHbEBtK3OMx9oRNLcPOvzbIFF8Xa7PIYhTfuxwfonPwWLL3f4PdII4pFRFr28ps7W+wjuH5KUo0Wn4VoRxr2PDKY93oLTsAnHtyQtddnaV0qxfSGXZk+Tgxk7ktSC2TgRjLsvBCziv5wdZ9eNO1rQd5GtObIh/nESStTBolntAc9IzMRsTSjAtWOMpvxKKsPTJc3k6NRhwfoOv8dShWU1BbBJmsi3MFVihpLCEPrIRfYsNKNhYIhnPhSX2bVnllhG62CWTYYNyOpuBqCPnKN+Xch/PulCn+2uOmuzDTPdFuf4+TydY5kcIfSRA5gl1YL+sSTl9rIxGXWgX5dm9zg0xs6KkjRRgz4ydAGzWeYfR9A/lqSwzfqHeqkJklfUiphxE66ly21mWEyQjZdPs3vMwgen59kY97hUiln2UyVePeMrjNu6C1/ZGNMPnGFjjDHGGGOMMcYYY4wxxhjTZ/zBxhhjjDHGGGOMMcYYY4wxps9YEu0IZE9616pyYhvDY1BbbSUpgMrS5nMBkwvoxEz6qhDwgoE0bbbEVRcwuSJL7Q0ClhaOpZhavGBgKstSdifWVpFdtw3lHsb5PaYrMD8bSpDkp5lUwGjIZKUk6eRoBtmVj2PtxS2eSn3bjawo5p4anIcwFX8DHN+SlAlYm7th0fjiXO+LKtICw/k8WyPp3Oo0+eKzdS9715SpBpMHHM+ztV6ScjnWP40mW8fpeJSkCpRjq0MZkGG41Zw4lKBYMpSho7ILs002FzpU30dSpocSUpJUFhu3tZjJp7qoqqFMxQtKa/WJfFQ4itqj86gLHZJ8Aolf7HND+UMqFSNJRWhLxVyzJWYZpPhcb0EJquwA68cU3JpH1nJfkco75cdhYfsKvEmq2SQpO8XOYoMTzO54zSK74rH8J4y4ycbPXTcwn3K6xiWQW1DW7qgC80eyAXvuPVCCTpLy0A+jUEm0HHxmict3d5tszuzYM4yvTdnbYP7aaJ6d47NQHrDV4j5lB47HCpRjq4f8TDIEn+eEQfjbQIbNhQ5XDdc8XCs6MRvjaaoZx11ulSK2/lRTq0uohuLnNWNMf3CGjTHGGGOMMcYYY4wxxhhjTJ9xhs0RyEg0ofQqWSdpGOGej3n2Cg2la6dYgb9mzAqJJ4n+LWaY7UqHRVbsqfPiorndLLyiAKPCKekij6SjxDDCKVuCWUUJPi3H8N3Q8Zhjda2VOyVBNkMRzpsWixYNty3iS08Ms+yn5Ra7R1q4fbbNt5vxPHuHGwfZGpDNsnHWSFAANYSZHO02iz7LwqLKjTq/x70wI+ZrswnWccCjR/iEzaUGe3rtcpZHWIewAOpwjj3PEszEieCckSQa1B7CaL9yho3HJJlKESyW2umy8dgU8wFoNm4oZ9gYxrAqyii/ql0aplhmacV6SDOBP9sUyw7OwgybQoJHqXbZmrAXZvIWdsLsA7jXJyG9+nC4G7pksuB6SVKmDP1ZeO24DR3fBBHpuUl28czJY6zBHvvHkhRtZ9mYIyMs+2m5RQeFlIMR9vNt5tuN5dgYP2p49ej6e8jAedNqMj++C7MuOi0+0LJ1Ztussnuk2TDfmGN2STh9mPVPbj7BRISUc/BcGbN5PZTlG8NSh9nSBL8sXPcaCX42ob8DZeD5Iab+ccTPyI0usyUZufQ9G2P6h2epMcYYY4wxxhhjjDHGGGNMn/EHG2OMMcYYY4wxxhhjjDHGmD5jSbQjkLvi7yu1yqvfGJyK2krD4oeSFKq3Ul7DwXpkNxfztPDOMks/rqRZyv7eFpcvysIC6pNFJhczNgjlMCZ5OjMt3NlmSlXKQjWkKIGERAxtaYFYSvsH89g2VWTfyuvbWXvlE7hMytij2Tvs3rCI7JpwjNdhCrckzcA2C3mW2h/B1PW9CQrJUjmtfJNJTYxCqQCoxCNJ6kDpLSoXkIYhHgnqFWPprXYEpRQSSAbxazO7Wpddm8oIStIg3ELaIWtzuc3GYyfmUhylNJT9g9qWWSi1mhOTKgnjHi/25iHLXaktSgWrT7pN3eNQe1ReOKTzLcH6PxwxPdf5DnOa2kvcnx2AMjkzUHo1s1JBdhMlJmklSaMjzEfOrGHPTeXGOstcti1TYS88arJNN273VmJNktSC175lll26wC7e3M77sXgCe4fDj4aypt9axNemMmJNKCM2A+XY8nl+5o6ptPEy85GpzFKuwd/hcIedfYOASlDBcQZ9K0mi9eWpd4V91DCBhC1cAui1WwmuXYd9GcJ7HIDHSuj2SuKS91SGuAAPTlSaVOJSq8V49bUCqpcaY/qIM2yMMcYYY4wxxhhjjDHGGGP6jD/YGGOMMcYYY4wxxhhjjDHG9BlLoh2BTKSOUzrYvwxBOu790CjFJWTXDtrIbjgaZtcNWPq4JI3mmDzDmhJLR53MN/G1i2mWvr4IJSQ2lBfYhbnyjWKaKgzlnejFE8m2NVib6VEmkRAMsvETV9m4laTpr7G08KlFJgPy8JEZfO10hX2nHz2ajd38iezdbNyzE9lJ0rX/vQHZ1Rrs3ZSLTPJlPIGcShXOw+U2lHxpsmfJpvmEbUH5gQJc7utQYSOJ5BeVOkvSJiWTYuvZUJY9eDbF1pRaAg2CBSg3s6fB5msVakamEmgvZQO2t9ORS6+dgS5skGSTM0c0E+FRygCfkUqdUUpw7ehEXOZoKMUkA6mk4VCO+2FroOzrRJ7JHJUzTP50Ce6jkrSuDLV74R5FJYOjTpKxw9Z/LNtWZ9pAqTHej0GZ+ThqsHc4/3XmS++dG2HXlXT8CDsPpSvsXQ8ewzWWsscVkN2a6b3I7n/+cw2yazS5hGGxwN7NWIX5yNQ3X4Z+tCRlYZuZNJV9hbLGCRRVm1DLi/qzVOosOhj+ccDWs+EcP/tmG+x9U3nhRSjxOJ1Aeq/WZXss9VPTUMc6TKAlTa8dgLh8YmOM6S+epcYYY4wxxhhjjDHGGGOMMX3GGTZHIIFSq35Rz8OivxGM/pKkrliEQzOoIrvZFIsU3hBvRHaSlIEVAwcyzG6iwDNscjBynkbcFMZYezGMZJGk2m4WakSLbFaKMNoPFiGVpG6NtRnAtIJgFBaiT1DVsN5g1958HCvSmnv4IL52UGZRaqkZNg+DY1hh4yyNZpW09n9YFF8WRtKNn8TmYQDXFEma+RHMiKmxeZhJMbtcho+zkRyLFFtXZONxF0xA2lDi/Xj8GIt8pQV0A5g1I0mzC2xuL8Miv6NZZrcnQXR+Ic3W5zUFFsU7DoqQStLeFn+HTRj1n4ZRgUMaQHZzMZwLtKC7OeJJ/a+HvBp5WPQ3goWJO3CMNgLuU3YjNj8KqSFkl6YVtSUNZNhzjxXZ8+TgXt+GhdslKTfK7jGGGZHNPey67Qa/x1KRjYsApgFEVbiPJsimCsbYeh1PLSG7Wo3tUUcfD1UEJOVOGmaGA9A/nqvhawdHTyC7TGUF2a39GnPE0tCnlKThk1iGTZBi42fhR2y+Zqrcn81koCID9JFp5vS6As9U2tNkvt36IsssPHaSjfFsnvdjELD1bHGeZU4vJ8hqHMmx+6T9WIDL1CQ1lBTGzHa2ycZPG2bOJNlfhwI2Juc7ZM44dt+YQx3PUmOMMcYYY4wxxhhjjDHGmD7jDzbGGGOMMcYYY4wxxhhjjDF9xpJoRyA1zSul/adT5gMmrzIQQ7koSfmADbdMyAqOL6Tm8bUpwzn2DbMOJRJmmqwfJamc4cVkCVR1J4ncGE2lTkFZInrtuM1T+7tMVUDaDmUFVlhBxe4Mf3+zdVY0dF2WFsXlMlBxnT0PlYzTDLvHaC+TWJOkwQKUoJpk7zDI9r4gZ6fD+qfZZXYTFSaxMTDIJXEeP8H654QpJmnypd2TyI7KnEnSmkcyaYjUAOvHxp1M2kOS0ots3hTSvV2b1/BtQawMsQRr3aoVsrmQ7XFRdUmqR+zddAT7u/fT2hzh1FI1pYPVx2k+YpIk5TSzG0ix+ZbuDiM7SVoSW1uptPFIjk84qLyl2UYR2VWyzG9JUnw7gNLG3EeG8mXQj0507Q6TGgpX4LV3cMmvdJW9m3Cerf+LtVFkN5nn9xhDHzmA/rGyCSrRz7NDSTTN7MpFNm6H17D5LyWTwCN0OmzctqB/LEnlEvNny8PsHT52YheyO3YP/53jqzvZ2Y5KnY0+EsqGF/n7a25l/ZNagv5xAplmytoCW0tnWmwudJOo4kJbKqFPaSS4SSqhaox5aOAMG2OMMcYYY4wxxhhjjDHGmD7jDJsjkLXRMcoE+49gH0mxYnMLMaxCLWlPaieyq0QjyC6rHLLbG7DC7ZKkFVZA/RFDrH8mYUFVSSpnYfRxBCMxh+D32Az/bpstsXssjLCC1SnYjzEs7idJcZdF+zSm2XOXYdHXFIx6k6QizKZamYGF7bfx7JXsY9cxwxGWdaE2e5bUJlZ4VZKOLrPIt3AaNgjHeLTCI8XqbV6IlNDtsnusnMoj6dKbhpHdwAwbP8/60m5kVyzBKFVJUY1F8XUX2dpTm+cFUEsFdp+0eG8ZRhpOwYKqkkSTC2mGTQyj6QspPs6mu2z8LKYWkV0KxhLVA5bdF8Y868oc2ayJx5TV6mvIcJ6t/wtttsZMR6wg+0DMfCZJysIj3nyX+am3L3Ef5+Rh1j+TMGq+lGNzuBvytTVVgWtcmrWZHWBra26U+7PBUAXZxXW4xsEsx9YMzwIqlti+F+TZ+KER+7Vp/hNGZohlr2Qesx7ZBSPsvUjiPvL6MWS3vsT842gmQdYM9ZGX2bM0W731jyUphNnBpZPZtVNHs98aStA/lqSnX8PeTb7E+jGqMQcwXOJnl8Y8mzfFAlsrUgkyBstpdp97IqhgAMcEzSK925aRg1mxsy3mA6xE/PciSiNY/beYUPy8ZozpD86wMcYYY4wxxhhjjDHGGGOM6TP+YGOMMcYYY4wxxhhjjDHGGNNnLIl2BBIGoYJg/+m4UczSRzugOOs+25ile7YCZteAciibo83ITpI2lFg16CIsVjrT4NWlV6DEEpULiBpQyitB4bywDdPmO7DA5xYuqUdp15gMQAglqNqzbIxHsPihJDVgoc2peSi7cBuTe5CksewUsktvSiD5AAhOgFJskoJHbEJ2mYl5ZBftWER2nb1cViANZQAGcrDAZ5q119rO19xCkUk5pMaYzM7aR7NCqeESL4jZnGPz8OatrJgrlRuUpCyUOqMylNvqbA2fS6BAUINrKSWEryZJPVUqvUSJoChFSmytj7HIhTnSiRQrBPIp0EVWF0qxtIB0iSRlYy411IC+9HEZJgW8oczneSnDnnumXkR2hTaTQC6k+fof1WFBb7gYhuwVStROUrCF+3aEThU+S4dvAN051o+0fxodNs5m5qFsr6TgduYLDeX2ILv0hhq+Nua4tcgseDjzj9N7mb8mSfHuRWTXne6tTFYZytJKUhr6yJ3drM1ciY2JYKyM7CRp/DHsTEJ95DZrTrfdziWni1m2Rmagf9yF/rEk3VVn6/hCm60/jW5v/WOJywtTNfl00HvZNkpKq187AjbGmP7iDBtjjDHGGGOMMcYYY4wxxpg+4wybI5AgDhSs8q0uA4upDcU8wqkqZkujYVd7hnvIBrzwYiHNIg0qMHpwAEaySDyKuwajz7owMC/d5lkFnWZvl4xMmfVPdZpF5UjS3gU2zoZKLPo0s8z6p9Xgka+1LrNthqy/MwkyGnZ/lc2Hk0+ZQXbZSdZe5hT+DuP1LJsiGB1Gdqn2bcguvZeHvg4PsuywTof1T62xeqFrSVraw7P25ney9WzNI1kYX+ZolnWVOYavudG3WBRoM2RtTsAi1pIUx6x/lmF0N41Ta/ElV9UOm9sZGAVOo/hoVKAkjWbYmGx1h5Dd3jQr3NuJ2buOYp6VZgyBTo+hNFs7ql2WaZIkCjcFfWRaPDmXoIY595HZ3OT+MffDwhV2j3GH3WO32fsYyEyXrf/1KfZy5udYtsBAmftCmWWW0dCC2e/cP+YDMj3P3vX0V9g7PO5Unr2SWcOeJ/0w5gPGa1k2RTA2jOwkKQjvQHap6QayGxxi56tuh8+ZRoOtpSu72ZwJt7Pz59hpc8hOkjIbmI+c3gSzg7/Lrk3PipI0XmF+UxyxTW6lxs92FOoj12CGTRJ/Nobps7TFsTyb/50Eiix7tYjsiGJNqAQp/8aYvuAMG2OMMcYYY4wxxhhjjDHGmD7jDzbGGGOMMcYYY4wxxhhjjDF9xpJoRyBRECkM9p9vSrNHU1BSRpLGIpbGTQsYh2I5s5NlLpEwkmfPA2uVarbFU4Xp19MKlFmr7mXXzhUSFGkN2V12u8yu0GbXjmBqtiTl0uzlpNMsbZ5KnS2sMEkTSdoDU/srWfYs2RSTUpB4IckYDotwgd1jcO0trEFJqYltzHCUyd9FM6xAbKrIYxionNaeRSaRQMdtoctlQPYssf4p3clS4ocGmBxGVOVrypZbR5FdHT43LVgscfmcebiOQ1WyRNC9uBv1tmApF1mUaiGTDKKF1QfiYWSXjlmx9DBuazuyNEc6kWIkO8YlVti8HAmYVFUWypdJUgilXUYLrM2RBMo39ZD1z3yL+y6ECpRYk6T6XrZXZAtsb47gM4fQP5akfJutxPTaWehTpjN8B6BSZyvLTPJnusnGRCXD/Yws9Ht77R9LUrjAxmRwHfORU2MlduExLlkeTzMN7VQJSnlBd2TvAvOPJe4j5+C5cu8i65/CHUwyWJIqZegj19g97rx5ENklkQdstpjfS/3jxTZfww+Gj0zoJPCPe32PNShr2Y65VvKAmL8wHK8+v7pxS/x0bozpB86wMcYYY4wxxhhjjDHGGGOM6TPOsDkCmQt2KxXsP3JinYZRW2lcdo1nzgykWBhfA4Y4LSao8FzKsOcZgwElSb6IlmG02GCORcMniVyihDDTZWKoiuwCuAKlUjw6Jg0j5DowYr9YYNFxo7AIvSSNL7N3M91kcyEPn1ni4ywFE9NSRTYmGlt5SGJ4Kxs/t2xlkYaPfiwrgJo9hmdJVdawSLqBFTZfl2Gk8S6YsSNJO+rsebb+mPXjw6eXkd1ym4dif3OeRTmeNMD6cc0IGzsSzwRszIwhuxxcp7IJQviOHmCLZAwj+X+0yPoxnyCSv5xmi8Vyl43xesDWiindjuwiJQiHNkc0c8GC0sHq69eaeD1qLw3nej6G/kiGz8t2yNaE5TazKye49lietUmXwmIa+sd5XkB5bp5FKVNCmHU7PsIyfiXhQ0QqTfub2SXJAqJZ+rQQ/egS87lnEigY5KCPXIL+MVgi9kEzt1t3sbEb3sr65/YtXF3i1LOYP5vZwHzFgXVsjA8ss6xbSVqGmVc16CPvqrFnuetWvk6cOLWE7Opt9m6unxtCdidX2NySpPEx5iMPd6B/PM38Y4n7yHmYMHT0AMxqTJCAftsSWyuoH19Ms37MBTxLKoIpbLtSO1e1CWOelWqM6Q8H9MGm3W7rq1/9qr7yla/o29/+trZs2aKpqSnVajVls1kNDw9r06ZNOuWUU/S4xz1Oz3jGM3T00Uf36t6NMcYYY4w55LCPbIwxxhhjjDHmwfCgPthcd911+vCHP6yrrrpK1epPvtTH9/ri2+121Wg0tGfPHt1www366Ec/Kkk67bTTdMEFF+iiiy7SyMjIAd6+McYYY4wxhwb2kY0xxhhjjDHGHAiJPth86lOf0lve8hb94Ac/kHTfw+exxx6ryclJjY6OamRkRI1GQ/Pz81pYWNCWLVv2HVq/973v6aabbtKf/Mmf6KKLLtIf/dEfacOGDT18JLMajXhRqVVe/XzE5J2ozJkk1QOWcl2IWZthzNJWp7tcIifbYKnU5QwsVppAqqoACyoO5lkKeQTlGWahbJLECxuOhLDwYovdY5ICqGmacp1j0gfFYSZT0G1wCYlOxGwDKF9B25P4O0xX4LsZZnOhcxd/h1N7WaFNChyOyra4dFIKZq+X8izdPITvsBvyd03rbH5vkb3DuTb7AbkFpRMlabHNbKsJJFooGbiulDNQFjHLZC7CMt83abHUnVCRsROxZ+ZF1aVyhk2GsYhJi2TgXJjpUwHbB8I+8uFPPagqvYpksCQtdtiakA3YWK5HbO8pxFyLKYSyKbMt5uNkoVyxJJWhvHAa+jhUqqpS5BJLIdxL56GP3IQyu0NlLl8UwXGWzrJ+TKfZ+p/JcinpwgizDRtsTHSg/xBAGVBJasM9JYX9Y+6PpCrML+hCSbS9ew6C3HUVjp82HBewe/J57nOX4Tuk5yF6Irlpka97820mD9aGY3y5Q/1jLqcFtwV87qb+sSSNwnM3lTCja8XuBOfzLvS5qY9MpfaHc1zCsArl6gIwEYMEpQ2MMf0B/Wpw3XXX6dJLL9VNN9207wD6iEc8Quecc47OOussnXnmmRoeHn7Afx/HsW655RbdcMMN+tKXvqSrr75atVpN73//+/WRj3xEl156qV73utdpYIBp2RtjjDHGGNNv7CMbY4wxxhhjjOkl6IPNU57yFEnS6OioLrnkEr34xS/Wqaeeii8SBIFOOeUUnXLKKXrpS1+qRqOhq6++Wh/60Id0zTXX6O1vf7tKpZJe//rXP6iHMMkYC45ZvagqjG6gWTOStJCaRXatmEXI5cUKELYCHnG3G0Z7j7RZIUAaPZiEXSvsR5tuzCIw7qwW8LXXF1kEWK3JIpKGYepDCkYFSlIR3mOxwqKCcqPsusECv8cyjBaldGA2lSR1YSTWwm0s2qcwwJ5lxx4u73PzIptfJw2uILtOnc2F7CJ/L+0V1ubORZYtlEvBKNUE77oGI4iXYYDcbcvs2mMFfo80C4g+d6vNs1cyMKtxT4OtkbuaB1QW8H4J4bIyAKP4xvJsbaZRhpKUT7NrF2Dx1QV47UowiexCHdyiqvaRHzpMhGuUCYBvCQN2aebMklgmeKvN/bV8ALNfY7oO4ktrJM/8c5qFHsEo4N1LPPugCyPx76qxDJu1BXbWaDR5JPVgl2XjpNIws7wAM7mGuC+UGab7PbtH6h/Dmt+SeCR+vcvmzPKPecR+vsLGxZ5dw8julgXmH28eYv6xJMFhpswSVByAl96zwOdrDp4DQ/iua/AsRP1jSboNngvG2PLYc/9YktotNsZpBvp0g6t07GywtY/OLpqJU4EZiJI0kmfjgma/52DyE/WPJWm5w9bIoWj1HzC68Dc3Y0z/QKv2+Pi4Xv3qV+t3fud3ehLhVywW9aIXvUgvetGLdP311+vNb37zAbdpjDHGGGPMzxP7yMYYY4wxxhhjegn6YHPXXXepWORf0JNw1lln6T/+4z/UaCQI3TLGGGOMMabP2Ec2xhhjjDHGGNNL0Aebg3UQ/Xlfw9xNMS4prf3LomTZ0NBisIivOxCxNO5WwHKzOwFLzU7DZ5GkvakpZPeYNHuWwRzPpaZFVeeaTBKDSiyN5HhxUZqwSwu/UhmHZpVLSNBCiaXjYdE+mh69wMajlOBdt5l80doCL2I7nGf3OTDOn4cQJ0jZX4EF5m9bZtHsx3QWkF0aS3tIhZDN7fV7mTbEEpTdqsMxIUkR7PO1RWa3pw4LFieQKilDaYgClMPIZvh6RuVhhrLMLgeLclMZB0mqQXmPHTX23NUOld7jN9mEum20za7YPaZF94XeS5PeG/vIDx0KQU6Z1SSDJWUDtkctRexDW0ns/bYSyPt1YrZuZaBnNxvzj4a5FJMrHMhCCVu4Bs/DfVTiPvIgvDalXOK+FRxmaq0wPzWdZWt14TjucyvPzljhEpwL0D+eT+ALrSuya1egf1yaTDAmoKwpFVVegVJedy5zubGNnUVkl6qwcZGbZP24FvrHkrQEpQmbXXaPVG5sbYKtdwoukVnoIxdz7CYLUFpSkrLwzJ8rMruhHF/P8g02Z+nZpRYyu+01ZCZJWun01p9twntM4nOHMbvHFNjbiY0xpr94lhpjjDHGGGOMMcYYY4wxxvSZA6qQe+WVV0qSNm3atK/o6mosLS3p6quvliS95CUvOZDLmwfJra1rFAT7j9A5Nf8M1NaxWo+vW41ZFEYdRs02AhbKMhLxYucj6XXIjtaGywQ86iUIWHTFfJv1Dy1C2OW3qAKMzJlbKSE7+sylAR7B022z6LP6nSxSNT/JnplGQkrSCevnkN0jx6eRXbeWoOBkg/VPbj2zi9tsAA1t51lAawvs3aTh+KnXWFRXZYFHEHeXmB3NIls3tozshuo8qnQXjDbuwOLLQZAgdQbSggkxbXiP1TpM2ZEUhqxNWtyYjsc2zJqRpEKKtVnMsGeByTBK8qqzsIN2Ndga0EyxedgJWCZnmCAroZfYRz78uDn+plLgaHRadDZqb2OeRbnXumwhzEawgrGkJvS5h1IshHwoxyP24XKkNFzfUtQ/bvH1n7q+1JcupNk7XFjhIfvURy5W2BrXbbMX07iDr5k5mm0Cx8QxG+eR3cljs6xBSSF07Tp1dpPZdTwDifrIle1sP1uTZ+8mmyDrolllPweVlti1wxU2bpP4GWvGWTZOs8GeZWedzcMOPDNJ3FekdKGv2ErgU9aqbI0Mu733m2hmEUzmVw6uKaUEv3aGMTwXwPbS8Jn31HnWXgNmz4bB6nYhzGg3xvSPA/pgc9FFF+37Aed1r3sdKoy6c+dOXXTRRUqlUj6MGmOMMcaYhxz2kY0xxhhjjDHGPBh6IokWx7H+9E//VOeeey4ujBon0Go0xhhjjDHmcMM+sjHGGGOMMcaYJBxQhs09POpRj9L3vvc9ffazn9XZZ5+tz33uc9qwYUMvmjYHgVpzm6T952iO5Fkx7/ECTwtvNaBcQIqluedils5cDPgwX1Niz0NTXIcKLMVdkooFWKSxxSSRqNRErcP7h0r+UIklWvS7OJokZZfZduvsJcJ69YkojbPnjqAS3PI8L7QbQ3mPwSb7YTGAFeYzGS7P0IHp/UN51o9UViSg+fqSMhXW5uAQl1kjdDpcnoEWS95UYvO1ABe+EwZ4aj+VchiGz5JOIAOSgra0OPUQHON7m3zNpYVxq212j8ud3hbQlqTRPNs3hzLMbqELZf96r9B3ULCPfPiwUL9TAdA3HR5+KmpvvMDW63aNTfQ9WkR2kpQXm0cFqPG7Fu4TEvcVK0XmIxehTOraFt9v01CyuNaB5wLoc3dDvofnoI+cH2fXzkVsn6ASYpIULrFr02/QhTH2XqIEik3VWTYXqH9cbvKLUx85m4XvBt4j9ZkkLk0W5JhhusLsKqP8jEzpdtg6NQj7++gSP38W0mxuH1dmhzvqH4/k+LtOp2HBerie5WB7Ej+TTMPfOeiaspJgrViGvjRlLM/GxGCO7wtLcA+pa/UOioGNMaa/9CTD5u/+7u/0qle9SnEc66abbtIZZ5yh66+/vhdNG2OMMcYYc1hiH9kYY4wxxhhjTBJ6kmGTTqf1jne8Q6eeeqpe/vKXa3p6Wk996lP1t3/7t9bgPgRZO3S2UqtknQQwdLWRoGJ9GDPbo8KNyC6CUQGtBAXVdtZYtM/xFVa0j0ZMSTzCiUZXtbssAmOly5eBnnzhvRdRCLNcqjyUOuzACLAsGz9pWB+2U+W98+2b1yK7H6+UkF2S+JiTKix0cnia2WUG2Lzudnn/NGEx+Pk2i3xdabD5Or7Cw0ppBOqWnWPIbrHN7pFmXEh8rZiARWwbIYt6o9eVpBocF4sd1j9rijzbbABGqU01WZs1OG5p9LkkwXrFKsPssDBmzzzfgel9kvY2uR9AiGA58HZcZ+3FvS+emwT7yIcPxw48WemAZ46vRqPL5jr1Z9elhg/gbu6fDtxUdla5P/vENTDLGkaGBzDaO0xQfLsdM993BWbYpBKs65QI7indFZqVwvonSPNnCdjWrG6V2d38g0lk9+NlpgSRhBMqNWRX2TuN28xUmF23y8ZZEyoYLMAsBUmarDHboRX2EqMmu+5dd40yQ0lLLTjQIF24VozluP9Qhf4s3BZUh/P/jir3e8ehj1zuMh9wb4NfewX+LkFVTBrUP07wa2cI59cSzMSZbfY2Y0fikrmt1OoTMRT39Y0x/aGnv79eeOGF+tKXvqTJyUm1Wi299KUv1WWXXdbLSxhjjDHGGHNYYR/ZGGOMMcYYYwyh1wHzOvvss3XDDTfoEY94hOI41rve9S79yq/8iqpVGFpjjDHGGGPMQwz7yMYYY4wxxhhjVqMnkmg/zaZNm/Q///M/Ou+88/S5z31O//Ef/6HHP/7x+td//deDcTmTkHXarPQqxUg3DbAU13bI0+bTsBDgWJalZq90WJryUsxljhZgQddWyAoGJ5FIoEXZ15SZDAyVmti+MIjsJGm6yVLSjxtbRHblUZaK2232/NsyLi6aHWd2ufX8Hk9amUN2VPKrG/Nr0wLqVBIjgjVDFxpQW06CgkjJ5MEQCYZZCqrmjJXZ+tMM2Xa8BMeEJNWg/MBMi9ntxktpguKZUG0iBSUSMikuz1WGBa9Ha+wmZ1rsHcZQcvQeawLdimnv5AP+DscLbEzWoIRqrc3WilbA5AYPNckH+8iHLuuj9coAjadNA2x+NGEt6AzUxB3L82NbtcPm2yKUP1wIeWHrZsR0oALoI6ehfzwxwPxjSSoU2Lq+Y3YI2c1A6cyjxxeRnSSVxlmfd6EE1cEgM8HGZBb6yMeuzCO7+QQSWR3o79MC6kECX5HKgy3V2L5H/d4ogTQttQ2gIxZk2E2OJJivLejPLrXZbwhUbmymxX3uqSbrn9Vk6e9hCf5uQv1jScpCH7lUYvvCcJX7V7OwL+nRjj52gp+q8LUzsNPH8mzcNhLcZA3KJw5Gq/++043hId4Y0zd6/yvo/1Iul3XVVVfpta99reI41i233KIzzjhD11577cG6pDHGGGOMMYc09pGNMcYYY4wxxjwQByXD5t5cccUVOvXUU3XxxRdrfn5ef/AHf3CwL2lWIVakeJU4W5o5kyTDZlYLyG4XDBUriBVkz4tHYU3GMGIXPnarfdCn2ANCs3uSFMCuZFh2RmWYvcM8jLjL1njUfGsWFt+GxVfT68rILsjyiPTS4BKyK8Jov0qWRzitGWBFVYsnwKgyGBm8fmaZtSdexHKhx/MrleNhajGMIKwMsrmwHk7DQp1nKknMtgOLr0432RinUdOSVIDTZn2BrT05uEZJUr7AopcHc2x+FVJs3A5nef/MwHfThWG3IbSD01qStNBi/ViNWER7F+YBNQK2pkTiRYN/3thHPrSI/tdDXg2aOdOBU322w/aJXTCzXJKKq2TS30MOZtNNZhNkycLnbsM9nGZEHwzSKbjXZ9m7KQ9zfy27jvVPusr2vc48zJyG/rEkpSahj1xgz1IcZhnohTTf68fhu5moMP84fzwvtE7THyanV5DdDPQBFzs8M4QSZGEGCfTrBhLMhbVi/ZOvst8GUjDrP4mCAc2yphSg0sE66MtKUi7DbHPUP87zd0jPL4MZ1udz0D8O+U8IeO9KwfyexTb0Z2EGuiR1Y+ojr+5XHGoZ6MaYn+WgZdjcm/POO0/XXnut1qxZozjutYaNMcYYY4wxhx/2kY0xxhhjjDHG3JsDCgW4/PLLJUmTk5Or2p511ln65je/qfPOO087duw4kMsaY4wxxhhzyGIf2RhjjDHGGGPMg6EnH2woGzdu1HXXXXcgl7wPe/fu1Q033KAbbrhBN954o2688UbNzd2dSn3hhRfqYx/7WKL2Pv/5z+sDH/iAbrzxRs3MzGhiYkJnnHGGLrnkEj3zmc/c77+dnp7Wv/7rv+qaa67R9773PW3fvl3tdltjY2M67bTT9LznPU8XXHCBisUkcjY/4bWvfa3e/va37/vf1157rZ7ylKc8qLYIs02WPt6KeUp6Gg63gZgVK50OtiO7wWAC2UnSCJRZm4VFJG9dGMbXLsH0/skSq/ydarNI3SQFsGnhzrt2jiC7iRUmP7BS4/IDVI5pYoKl13fuYHYxz0jXrl3DyI6+mT2NBMVXI5ZYuX4He26apzk1P84MJc23mZTDbJvpLmxfZmvK2B28ACoNhG80mCxNJ+xtQVWJF0u9o8rl/AhJkgROGGB7DZVunE0gGdfqsD1pBkr0UZLIjTWh/ma1w/aPdSX2zPS6krTQYtcOoYxDBi4qxXj1gqp3X7c/kg/99JHtHz/lQbVFmYNjnsoGp2EV8wHxtWgqmEF2w9EQs0txiaW5Flvkbp9lviKVv5os8z280+ntvteOWHu7d7D+lqTRKvORqytQlgzuPaOT7LqS1N3CpCljOBf2bh9AdlT2WZKmofwV9Y8nd0/ja1Nm5oeRHfWP5xNIBu+EPvLInWx+xbAfW01+jx3or1Vh/8y0mC+9pcrvEarfYY4vs4LwSaTNZ2vsd45mF/rHiWSaGXRut+E4qyWQG5ssspfYDpndfIs9Swf6x5KUgf5CMVrdX+j2UW7UGMPoX4GNHrBmzZqetBNFkS655BJ9+MMfvs9/37Vrl3bt2qWrrrpKF198sf72b/9WqdTPLpIf/OAH9du//dsKw589UExNTWlqakr/+Z//qXe+85361Kc+pUc+8pGJ7u973/ue3v3udyd7KGOMMcYYc8Rh/9gYY4wxxhhjDl8O6w8292bTpk06+eST9cUvfjHxv33d61637zB6+umn67LLLtPxxx+vO++8U29/+9v13e9+Vx/60Ic0MTGht771rT/z76enpxWGoXK5nJ797GfrGc94hh72sIepUqnozjvv1Ac/+EF98Ytf1O23366nP/3p+s53vqMNGzage7vnsNztdjU5Oam9e/cmfr6fZmNqXNnU/iPyx/JsaDRgBIYkNVssyjUXsOiYgehkZFcNeMQdKTYrSQOw8OIoLFYtSdkUi64YgAX+0rBgfXeFR1cUYZsFWPh7aA1LVRrOwJQmSdVpFjWVybN3naqwqMloJUG2GYweysBit7RovCSVM7BoMcyciVgAmBbbPAtopcsu3oEBSTRaqzTC52sKJroEMBAzDYuVTnT5XJhusptMwaLTNOliMMsjxY6CGYPtkI2JwQRr7lCRDd4WjJxehBGgS3B8S1Ihzeb2hgGaOcOu2+j2vpYKzSKowMlVimD2WtzSLcjyoYn9Y85RhZJyq/jHkjSSZ2OZzrd2DWafJQjhPi5eh+zqAUsPTpI5Wc4w42Hoz1L/uFRMsIdj/4q9a5oFRIt+S9LAWuavVdYzu/petpeloX8sSalBdmaLquy5aRZQkmwGqg5QziZIlYdE0GVbbjEfeaXL3mGSyHnqIxdH2RgP4E8DwV4+zuiZdhT2z3STZSsmyYiOoOtbyTDDdSX2+0UbZuhL0iBccwdKbOC2YX9L0nKHZ2kScnANX1/iPncrYm3W4d5O16ks9I8lqZCBe1K0erZiJ+rtOzHG9B78a/v27Ux+KgmbNm06oH//hje8QWeccYbOOOMMrVmzRtu2bdOxxx6bqI3bbrtN73znOyVJj33sY3Xdddftk2U444wz9Ku/+qt68pOfrG9961t6xzveod/4jd/QCSeccJ82yuWyXvva1+pVr3qVJibuK791+umn69xzz9WrXvUqvfvd79bMzIze8IY36CMf+Qi6v7/8y7/UjTfeqJNPPlnPfe5zdcUVVyR6PmOMMcYYc/A41Hxk+8fGGGOMMcYYc/iCP9gcc8wxCpKEGaxCEATqdg8skuVNb3rTAd/Hn//5n++7j/e+970/o6FdKpX03ve+V49//OPV7Xb1nve8R3/91399H5tXvvKVq17niiuu0D/90z9pz549+sxnPqMPfehD9ysfcW+2b9+u17/+9ZKk97///br22muTPJoxxhhjjDnIHGo+sv1jY4wxxhhjjDl8SSSJFifJhz8MiONYV199tSTp5JNP1llnnXW/dmeddZZOOukk/fjHP9bVV1+tv/qrv0p8MM/lcjr77LP1qU99SktLS5qbm/uZaMOf5hWveIWq1aouvPBCPfnJT+7ZgXQ+rCsT7T+Xs5xhBQiTdMPaHCskudxh6boNsXzUfMyLdA/lWGpoDcrFzMEUd0mqwFT8QpO1CbN6E0ElH9JQviLqsAEU1vlA60K5oRBeO26wZ0lQL1BZ2o9QpqAG5aIkKaQyCVRuDGZTrynzIrZFKBkyg+cCe2Yq4yAlW/sI9B7DBPJ3NBWfSsuNQpmUoSyXBxzMM1myLpR8CBIUX6UuTSZgHZSD614SJmCfj8GtZi+UZ2l2Ewxw+LrHs+wma13W4IKqyK4rLpN0IDyUfOQj1j9ud5A0STnDxjLdmSeLbCNdbvO1tQWdkjyUxBzIcj+jBtePBegjUynXfJ373HGPiy0X0sxvSaX5OhFBBVtsBwtlr3JEvA8x1QaC22MGSipnEuy3dXougP5VEn8/xZS3NA7lr+g4m4OSXxL3P/GCxpcKTAjPOfSMQ2+R+seSNJyjPjJ7h4MF5h93EkiiUfk7uj6moSyZxH1kujJP5OFvDTm+1u9tMdsmjKdpwu4ZyfN3WOuy517qrr4xdGO4eRhj+gb+eerCCy/c798vLi7q6quvVhAEeslLXnLAN/bzYOvWrdq9e7ck6clPfvJ+bZ/85Cfrxz/+sXbt2vWgpCUkqdX6ycabTu9/Yf7EJz6hf/u3f9Po6Og+SQpjjDHGGHNo8VDzke0fG2OMMcYYY0z/wB9sPvrRj+7372+++eZ90Xir2R4q3HLLT8rQnnzy/gvY3/vvf/SjHyU+kHY6HX3jG9+QJK1Zs0ajo6MPaLu4uKg/+IM/kCS97W1v0/j4eKJrrcZMaq/Swf6j+TbGLMMmEo+sqEF5DxrMkoHxMQGMHpSkFqwYOFFgbU4WeYHwEswqGK2wTIUIFkrNwkKOkrTSZtlKWRhh32mye2zUeFG8DCzmiOv7weGTXcPvcXA3GxfN+RFkRwtYStJaOH6yR8HngZE+8a08wmmqwSMDCfRVNxd4ik06y567XmdzpgUL1ucTFMWtwEjVtUV2bZpAUIXRrJK0AN81jQqcKLMoVUkaGmkgu2aX9c/t0+xdV2BBbkkqQNvdDdbnSaJFKdQPCGlGE0wNK3TZ2ElSfPnB8lDzkY9U/3guXlFGq0c1b9D+M4DugWZd1eE+moQUjFOmvlA7Qdr2RIHZjhfYGlyE+97wIGtPkiKYTZGBGdHVFlv/8zm+h3fr7OW0amyPSlP/OMmSmYGR+CNwfxxhWQWNWe6vDcDnnhhiWZvZ9QkyuXq86U7VS8iO+kxJaC+x8UizyJKc7TrQR87B+UpVLdYW+DijPV7tsoPlQqO4upGkNMwCl6TRAbZGDo6yc2qnw39juaP2wHv7vaFn2jzM7plq8neIVSjg246xf8wX3Sz1kVOrv5tOzN+fMaY/JJJEe6ixc+fOfX/esGHDfm03bty47887duxIfK0PfOADmp2dlSQ9//nP36/tZZddpqmpKZ199tn6zd/8zcTXuodWq3WfqMXl5eUH3ZYxxhhjjHnoY//YGGOMMcYYY/rHQVAZPXxYWVnZ9+eBgf3XVymXy/v+XK2yCJx72LJli173utftu84f/dEfPaDtV7/6VX3oQx9SJpPR+9///gMqYnvFFVdoaGho3//f+1BtjDHGGGPMT2P/2BhjjDHGGGP6xxGdYdNs/iTdM5fbf6p2Pv+T9OdGg6fc1+t1Pe95z9PS0pIk6b3vfa/Wr19/v7btdluXXHKJ4jjWK1/5Sp166qn4OvfHH/3RH+kP//AP9/3v5eVlbdy4UUPRqDLB/tO5B3JQTivBebkZshTXNEwL7UA5gzxICb2H0ira6fdQgTMnm6AoJi2WXSyzAnG5MksLT+3l90ilgSqDLJW6chK7bmmBS8tV9/R2WcusYSnpQZnJPUhSvrKI7LIw3Xsiz4tq03eTGi+vbiRhnazxYSbFJklblpkkYxvK/jVhsVIqcyZJ2QFmOxiy/YJKQ6QTFCxudFibNViwdCuUXZlp8TV3CBaip0Vak8iApLNwzYXXHs2xMZFk32zBfY7KfoVwviaof6rRFBsXNSgNs7fD5kxH7L10oZ35CUeqfzyssrJafU2qQCWfDNQba0K9QCpzJklpuBbm0qzNYobH+JXhPkUleem6XhjgBZSzA6zNNPSRW/NsHSwNcn+tdBJrs7DA2mzs6X2cZnoC+sgVttdnB5iPmuR8NVFgbQ4Ms35MjQ3hawtKCY6MrqxuJCmzxK5Nz8iS1IQ+YAr6yNn9f+PfRyVi8neS1KzCtQKuKS14nqXyZZK0rcbOgXMxPEtDyeChHF9T6G8NKegf5xJIPI5CuXS61dAx3kmgDkgVDLPwHofg72mNLr/J2Sb0fePVH6YDbIwx/eWI/mBTKPxkI2y397/Z3Vs6oVhkzmm329Xzn/983XTTTZKk3/7t39ZFF130gPZvfetbdeutt2rTpk26/PLL0TX2Rz6fv89B2hhjjDHGmP1h/9gYY4wxxhhj+scR/cGmUvlJFPdqMg612k+iw1eTh5DuLjR60UUX6T/+4z8kSS94wQv0V3/1Vw9of+utt+qKK66QdHeU4b0lJnrNxuywsqn9H1TLcGQst3lEwErIIt+SRBASWhGL6JCkLIzWoEW1V9q8oOJYkd1nDO+xNs8ifZZq7AeWJNBsgcJeFvVWT1BcdM8Miz4bGWDFyVPfYxHDccQji7fvYIUXWzAzZCVBBNjsHAt9K920iOzSAzDTrczn9foS68tdNVZ8dQVGD961cwTZSdJQkUUGzsH5NVxkc2EQZkhJPAI1gEUxSzBqeqnD3/UPl9g69ZRJFkHYge9aklYW2A+2K01mF8Esl3qCgDZYWxS/wx7XP5YkrcBGaQF2ynJqCdmFMY8+NXdzpPrH64t55VKrRzWXYaF16iPT7LMk0LWjCV1kmvErSY2QXXylzdb/sSL0wxIUbq7Psr1ieZlFuVOaVX4uyE2ztas5x3zF6alBZDc8yPxjSQpuojKIzG7XVppBwrOF5jtsnK3Zy3zK/A8W8LXTZXafuTJbA9aVWbb6nhpfJ1fgmXb3XcPIbqDE/NSFKutvSRqCbQ5AHzkD/eMkOWnlDFsjlzus1VtX2Nrzfyb4uSCE84b+hlBr8MCHkCqo8J9tEEl+VaJZ6FSRtQ7Te2AiXqJrr0Srj4tuzLPcjDH94Yj+YHPvQqr3LrB6f9y7kCrRun7FK16hf/iHf5AkPfOZz9THP/5xpVIPvEm+5z3vUbvd1nHHHad6va5//ud//hmbH/7wh/v+fM0112hqakqS9Cu/8isH9QBrjDHGGGOODOwfG2OMMcYYY0z/OKI/2Dz84Q/f9+dbb711v7b3/vuHPexh+7V97Wtfq/e9732SpCc96Un69Kc/rWx2/xFV90hKbNmyRb/+67++X1tJ+n//7//t+/PWrVt9IDXGGGOMMQeM/WNjjDHGGGOM6R9H9AebY489VuvXr9fu3bv1la98Zb+21113nSTpqKOO0jHHHPOAdm95y1v09re/XZJ0xhln6N/+7d+wpvfPi1vDnUpH+091LTQ39fy6VOqsnGbDkqqrrIRcDiWTYlIFozlYDB5KO0nS5rOZvEt6DUsh726HRd5v58XgqdxECOUwwgaV0+L50ceNziG77CjMKYZKJStbuRRTF0qd0UK7ozleaHf9RjbO8iezH7niOrz2Hl6Ysg37pwKLweeg9MFii6f2rxlh8h4bir2VY8rm+VzIZ1j/lGCRVlpkk0rxSNLRJfY8Rw2ygrzDY1zKJYSyFK1VangkpZLh8kfUcm2BPUsJSjnVoESKJAVQn6ENC6sHAdvjwjYbO5Z8SM6R6h/fXl9WJlh9vOTTwz29Ll0zi7Qas7jESq3L5lE64NcehsXJx0tsvT76icyXTk3AaueSwh1Qyut2KsfGmksi2xbCpSs7wPp708MWkV1mjPuzghJL9W2sOSqbRGVAJWkYFmWfPIaNidyJldWN/pcYFgiP97Bx1oYSyOUMPxdkAjZ+lltQwnCU9eO64jKyS0KmyJ4ln2PvpZDmPnc6YL9fUEmrjfD8sHaY+ceSNAjl0yLoH3c63D+m+xw9k9A9bk2CcnWlNJSMg79zpODLbiWQgUsF7LeqsLG6f9WJk4j+GWP6Af5g8+Y3v3m/f793715sew9veMMb6OUPCkEQ6Nd+7df0vve9T7feequuv/56nXXWWT9jd/311++LIPy1X/u1B/xx4i/+4i/0+te/XpL0iEc8Ql/4whfuowO+Pz72sY/pYx/72H5t3vjGN+pNb3qTJOnaa6/VU57yFNS2McYYY4w5ODzUfGT7x8YYY4wxxhjTP/AHmze+8Y2rRlHe8/f3HJpWo98fbCTp0ksv1Qc+8AGFYajf+73f03XXXXefiL9Go6Hf+73fkyRlMhldeuml99vORz/6Ub3yla+UJJ144on6r//6L42OssLixhhjjDHm8OSh6CPbPzbGGGOMMcaY/pBIEi2m+d4AKqGxP772ta/pjjvu2Pe/Z2dn9/35jjvu+JmIvIsuuuhn2jjxxBP1mte8Rn/2Z3+mb33rWzr77LP12te+Vscff7zuvPNOve1tb9N3v/tdSdJrXvMabd68+WfauOqqq/Syl71McRxrcHBQf/EXf6GZmRnNzMw84L0fe+yx1tU2xhhjjHkIcCj5yPaPjTHGGGOMMebwBX+wufbaaw/mfTwoPvShD+nv/u7v7vfvvv71r+vrX//6ff7b/R1IJelP//RPtXfvXn3kIx/Rd7/7Xb3oRS/6GZvf/M3f1Fve8pb7/fdXXXWVwvBu8cnl5WU985nPXPXe+ynZMBQNKRMkEPTcD1STXpIiqDfcw988JEmjWf6sJagPTh87DWuQSFJ6rIDsggp7nvQo05VNJ6in0Kgz/eK1j2IauakBtgS1d3Et5i4sYRGusHeTXcPucWATr9GSm2Viteugvnshw8VvMyU4JqHYcNxh42f37kF2XUkdqIk+CHXJ01Cfe7bJ6ynkCux9F0fZu2ktQd3kZTYHJWm2zp4nD/Wiy9CumuL697S+ECVO0FwI67Tg2l1wajUj/mP8CrxHWM5FUJ5buRTfuwaZnLb2wiVypcPW+07A7Lria/OD5VDzke0fPzgGU0Vle+QfS9xH7kLHl2r2J2E4x3ycIqx/JfHnScN1JjXG9rJgiO/hKVh/L72NFZJp1thCOH46X4+CIhuL3Sl2j11YsjJaSeBTTjKfpLiB9Xduml17sszr1RVhvcM0HT5JCvXBzXl2F6u/hP3jPK8tQs+qcw12Ts0V2TPnx/mC1l5kdo1FNh4Xqr31jyVpAJ6naY3AbAI/DAP9z7AD7eB4lKQO9n1Zm9Q/hsdUSby+UA4+NvyZQ7DUlSRpBT5QBxyIukkOTcaYvoA/2Dz5yU8+mPfRV1KplD784Q/rnHPO0Qc+8AHdeOONmp2d1fj4uM444wy9/OUvR4dMY4wxxhhzZPFQ9ZHtHxtjjDHGGGPMz59EkmiHGqQQaRKe9axn6VnPelbf7+OBeOMb36g3vvGNB/06xhhjjDHm8MT+sTHGGGOMMcYcvhzWH2zMg2N76sdKBft/9enGI1BbsXgqZSXFUqmrEUubL67yDPdQ7/I80yq0PXWYPUuSRNPOdpbeny0y2YXUUUPIbnBhdnWje9jCUuxTIywlPSixZ0kv83eYyrEU8oDKe1BpsATpzLU2e+52xKSlyjmocyFp7zZWG2BNporsqMTGdI3XJLijyubXM4/dheyKZSidtIun9s/PsecZT7N+7LTYu55ZYrIZkvSjZXaPbShTUAuZ3VKby4WsK0DpgyaThim1uAxIr+U3qUpKKc0vXEozaZE9TTZ+ZplyjvbU+e4VQtPlDlskO3DnzMZsHQ0s+WAg27Vbaa3uv6RrG1B7dI0pZ9j8rXW5VFUpzdpchpJNtS5f1x82COWF4d7T3cH20WyeH2tT65hMa/m4B663dB+2MD8jNcJl2wSfJ1Vja2s2y9ZC7B9LfOODQ7feZueHdsj9tYEC8wvmtzDfcyyzjK8dUh+5OoLstlSZX/f043eyC0sqDEAZ0h2sz5fm2BgfznBZu26DXXtuqYTsfrRUQXbtBJJfdegjL0O5MSohVq2zcSvx81AMr51KIP0eQNsi9Hup3d4W1O2VNNtiz72nwdqjUsl0H5akEGqjZgMydvn4Nsb0B89SY4wxxhhjjDHGGGOMMcaYPoM+2Pz+7/++9uzZc9Bu4lOf+pT++Z//+aC1b4wxxhhjTK+xj2yMMcYYY4wxppegXOu/+qu/0gc/+EH95m/+pv7wD/9Qxx133AFfuNPp6FOf+pTe+ta36pZbbtHll19+wG0aRlpppVZ59dWA5XDXgiV83XY0juxyUKlvReweuzQPX9L6YBTZ1WGTQ+UmvjaV1IpmWQp5qpJAowtSnmBtpsaY1IS6TJ4havCU6/oUkwEZ2MSunRpiUkzZBPmKa4eYvMdumLKfhG4XSpXAdO9uk7XXTCBfkYYKG2seCyVI1jIZsYf/AEqfSJrdymQXWnW2ni0us/YicamSQSh/Mt9mc6YDlaUqWT5f01Ai4fZlNhcWW0xORZJGCkwfrNZhcgpQpUANKJshSQMZ1un5FLt4FLNrDyRY0Jbb7B43DrB3s9xmc+aGzl3ILozZOvFgsY/80CFQSgGIZauFbExVA+avjXTYHpUN2FotSTMhW99CKBm4Ns+lvJpQTmdwEPrI0OeO5rjEUtDhZwNCYQLKjY1xeVhBCby4wa7dnGKXLW5kdpIUDDE5pjSUTpsYZv7x9AKXh6X7nqCfGvFhJjgN1QzZvkd9prEzuB+WmhxGdpt/NI/sFu5g56Z2lUsYriyzcRbDd13Jsrm1mEDitxOz8TMAfWSqTHjnEjxzS1qGPvJIka3NVOI7CfS8SP3jHPSPJSnE44e1twxVmjcN8L2dtvmd6sKqNt2Yy0gbY/oDWhHPP/98tdttve9979PmzZv1hCc8QX/zN3+jqSno+f0vnU5H11xzjS6++GKtWbNG559/vm6++WYde+yx+oVf+IUH9QDGGGOMMcb0A/vIxhhjjDHGGGN6CQptuPLKK/W7v/u7+pM/+RP993//t66//np985vf1O/93u9p48aNOuOMM3T66adrcnJSIyMjGhkZUaPR0Pz8vBYWFnTbbbfpxhtv1Pe//32123d/yY3jWBMTE3r961+v3/qt31Imw6MsjDHGGGOM6Tf2kY0xxhhjjDHG9BJ8AjzzzDP1xS9+UTfeeKP+/M//XJ/5zGfUarW0fft27dixQ5/5zGf2++/j+CfpiI95zGN0ySWX6MUvfrHK5QSp4cYYY4wxxhxC2Ec2xhhjjDHGGNMrEofsnXHGGfqHf/gHLS8v6+qrr9a1116rr371q7rzzjsf8N+USiWdddZZeuITn6hf+7Vf06Me9agDuWdzgKyJjlUm2L++7GDA6imMxFw3NRswTdI61JvPx0yHdXSVZ703A1mmIboAJT9LFa4Nmh6CNQNooYSDQPAQCvJt7GE6tcU0q5UUQH1uSUql2DgrpFnNoGKBj7NmCwrvwuGYzjMN4UqW11Ra6rCBFjXZtal2errE3yGlDetxdKIERZAgHVhHgGp0L8JhdtwAX6NGc2y9X+myfpxNUMOmDMdkDB+nDfu7lqCGDa13MwVrSS3BejNlKp4uaTDHrt0OWUdWYW2JjeEmZNeNW7oDWR449pEPb9bHk8pqdZ+xnGH76KDYepRN0VpwvO5KFm7io1l2j+UsXxMW4J5SGIR16CrQ+UziH/fYlz4o/nGf/P3mHliwTlIhw2rOwCMg948zfC4UC2yctVrwJSZw19LwCFrJMger1/6xJKWgj5wqsgcPYJ2dbod3ZDdBHUwC9Y/nO7y2CPaRy+zdDOdYgzXoH0vSfIsNyDL0zWnNIInXpqnBZa8O29vbSlCTEZY8pD4yrVfUTlBSbQUWFF2fHl7VphPBIlvGmL7xoN3LwcFBXXDBBbrgggskSTMzM9q5c6dmZmY0Pz+vQqGgiYkJTUxM6LjjjrOcgzHGGGOMechjH9kYY4wxxhhjzIOlZyfEew6exhhjjDHGmLuxj2yMMcYYY4wxhuKQviOQVqqpbrD/dMqN6SHWVsRTrpsRy/dsiaUAV1MryC4XTyK7JKwpMDsqFyVJqQrMm4cSCdFCE9l1FrnkQnYIpj4XoSxRyPuHUhxl4yy7hqW5p08YZxdO85TrdeuYhMT6U9eyBhPIsYXX/gjZtadYewG8dC7F871LaTYulu9g77C0uIjsVqa4nFY+z+S0stAuhKn9u5cHkJ0kLUG5iW6PVVeK8P1JUi7FbEMou5BEWq6QYe+mDCUNRnJsjO9qcImN7VX23CHUbWtCWbJ6gkGRYPlBRGLXrontcV1Z8sEwmnFXXa0+P9flmBNIZQDb0JduQz9akqoxG/eFiO8plLUFKFcK3d5Uhe3NcZfvPdRHDpfgs1Tg3pOHsrRSIr+SkBtl4zG7ht9j6tgxZphh+974WuYfT5zCJDGTEH2V+cedPUkaZWY56DcNQCm42h18Dy8szbM2d7OfjbJ5eA6D64QkdbtsLiwvVZDdSpeNRyqJm4QCfNd0TCx1uBNGpeDy0D8uZqGGmKTRHGtzV4OtP9vr7FmSKEs24RbbgD4yPSMfDBrh6v3diblcuTGmP/ReNN8YY4wxxhhjjDHGGGOMMcYkwh9sjDHGGGOMMcYYY4wxxhhj+ow/2BhjjDHGGGOMMcYYY4wxxvQZ17A5AqkGS0oH+9cHzaXWo7ZyKf7Nr9ZiOqfxKvV17mEwYnV2iik+zCtZ9jyjOag3XuXXLiyx/glKTHc3qrN77FT5O8xvgLabYO0Vet0211gNdy4ju9QILER0FCsUHT3yFNaeJGXYuIiLRdbe0hK+dHr3LLLL5+aQXdRk42zT/CKyk6T0HNMGHjyOiQ0HBTZnwl18LpSHWK2twgTrn8II021P38U1v7fVSshua431TzHNxJhPrNSQnSQdO7mA7MYWWZ2F6Tp7ZknKpdn4ScM6O9mAjduVBHrjQ7CsUguKdC+zYat6yGtlZKBIdxPWLKtG8CaN6TGNoKk08EFzaVYnIQfXzOk6m29Jqv5VAubjFGCdlIEsX7eGs8xno/5nbonV4wmK3OeO29RHZu1l18Nrb+h9Xc1sB46fXcxXDIahfyxJ61mdx+jUh7P2crBeUR4WQJKkFVbzNDXF/N5smvnRkhR32DhbP8veTXqWtVc+DplJkoICqxkS7WTtFSts/ufG+ZoyNsxqTmWgj3xXlfrHfE0pppkfdkKFLSob1y4iu7EF7vfuheeCXJatKSnoH0tSNsX6pwqP/MOw1FYT1u2RpOUOu8cGrJeW7rF/LEm1LvfPjTGHP86wMcYYY4wxxhhjjDHGGGOM6TP+YGOMMcYYY4wxxhhjjDHGGNNnLIl2BDIYjSgd7D/lfKrV6Pl1q0Ed2Q3ELF03HbDvjY2Yy2nNNVmbtS6TEFpZ5rICuT0sRbp0KpMBSENpiKETuKxADGUXtMTkBzQ5xuw2MMkFSUqnYOpzET53zNKUU3v2sPYkxVmWxx3sZbIL8fo1/NqPZrIUwdT1yC6aY/M6neEp6YUMm7OpQTbGqUzK4ARf96pzbPxkSkzKJTvM+mdkLetvSRrby2QWOxGUjIOyW2UohyNJ2TxbU8o5JpPFZ4LU7LJxEUI5hTCmdqwf74a12YJLcwaujwm2TXXFnmckD11ONmU0I7Y+hrEl1gyjHBeV0epr+94GW1sp9YhNuHKaH9uoVCGVP5xr8j28HjJfurrI5K+yU0wOqfAw7nOnC6wvB45he33chovwCpcM1fgos4M+cgr7x1CLU5KgX5CamkJ2MZREo/6xxH3k+JEnsWtPcRniaA/0kaG8U576xxXYoKSgxGzLE+ycWp9lcytd5I5GZoStKZW1bL8f3UP9Y35G7kJfsZRj184W2JpSyPP9aFJsPLba7B12Ih77zX1k3CSC+seSRJU/a/Ae6a45kmfnsCTMt1Z/190YOtzGmL7hDBtjjDHGGGOMMcYYY4wxxpg+4w82xhhjjDHGGGOMMcYYY4wxfeaAJNHe+c536iUveYkmJyd7dT/m50A+Lqwq+TCZZ7ICyx2ezrxbi8iuETBZosFoENnVoRSbJK2ELC18fXEY2ZVKXIolBbNhAyppkIMNFnjavKgMyOwys0vBb8ZLCSQkhsrMbv0Es0vDflzgEgnxV3/E7GB7qWdX+LUrA8gueMLDkF0WSnaM7eSSceF7bmOGXShXN8ZkFkun8RiGxnVsbqfg9EoNsHGWTbDmlqB0xsMG2UhbaLO1p1LgKfaFEaZVEHZZm9t3sn1BktYNsHWlPMCuPVFn4yyX4q5XF8qnZeHQPQiqC0ph0QcGlW0Tm/59wz7y4Uc+yCgbrD4/J6Cc1kqHDdI9XeanUhlHSSoHTMqnCfUPayHfe9YV2BwulplPGdB1K5MgDjENbamP3IL9M8t9RcwKPOcMsT1Ka7gMsTJwTC6yc0H8Negf031CUuqZzEeOy6x/gsedjK+dnmDSz0PQRw7/8hZ24ZD3TzDCnrvwCCjR+lXmW6USKO+lymycZdpsTaH+8ckVvu4tdtiaUi4ynzI3CmWIO9zn3rXIfOQ10D8eGuJS0hP1IrLLw4NTBx6SqX+cxDbB8tNzcvS3E2PMQ4IDmvGXXXaZNm7cqOc+97n613/9V0XRIX56NsYYY4wx5iBjH9kYY4wxxhhjzIPhgD/Rdjodfe5zn9NznvMcHXXUUXrta1+rW2+9tRf3ZowxxhhjzGGJfWRjjDHGGGOMMUk5oA82P/jBD3TppZdqfHxccRxrenpa73znO3XKKafoCU94gj784Q+rWq326l6NMcYYY4w55LGPbIwxxhhjjDHmwXBANWxOOeUUvfvd79bb3/52/du//Zs++tGP6vOf/7y63a6++c1v6pvf/KYuvfRSnXvuuXrpS1+qJz3pSb26b3MAhEGoINi/JmsFiniWM1yAtlU/CtntCnYhu7yYxmkUM81USRpJQf1iWF2k1eJTbOUupjc+vMT0YodPhwKrVMdbUrTC6naky3BclOG7afFaQIqgqG0Tav5CTWvlE4gxQ4IiLYDC32FcgjV+hpkOdDw4hOyCEV5LKldgmtGdGSYxlD6NPXNQZrW7JKk8wdapdJnNw9QQm/9JOHHtHLIrpFkdmf+ZZe+6WOTzNQ2HYyXPxuOjB3fjay9Os7kdRWx+4ZpTCbSvB+EWUuv2VlB7IMtrmzWgxHsAb7EZsp5cH65Bdt24Jaj631PsIx9+RIoVgplczrDBXM6w4ivtkK2tu7q8/sko9GdpLaihBGtCILZet5tsgatvY/5VJUG9w4HTWJsBrIsTLzOfEvt1klSBG2QbLsIxfNkJauXhGjZF7l8RglKCfoSbTzwA60F2mc+UpE3qI+dK7NrdGeqRSLlT2FmM+sjFiRVklyrzs0swyHxkWu7quPXzyK6Q4e/6GzMjrM0im1+pItxnNvJ3fergNLJbmWb9HXYT/IYQs+ehviL3j3n/CNZkHMiykUb92STngjYckuuyq9es7UQJ1lFjTF/oSdWqTCaj5zznObr66qu1c+dOvf3tb9fDHvYwxXGsWq2mK6+8Uk996lN14okn6oorrtDu3fwHFWOMMcYYYw5H7CMbY4wxxhhjjElCTz7Y3JvJyUm9+tWv1g9/+ENdf/31uuSSSzQ4OKg4jnXHHXfoT/7kT3T00UfrWc96lj796U+r02FRWMYYY4wxxhyu2Ec2xhhjjDHGGLMaBySJthpnnnmmzjzzTP35n/+5PvWpT+myyy7T1NSUwjDUf/7nf+o///M/NTY2pt/4jd/QpZdeqrVr1x7M2zH/S1cdxaukfNIUTqo+JUn5gKWPHqdNyC4MWGp/N+bDvBWzPNPFDksLX6xxObYwgvmwi8xspMJeTjC2esrsPvYyuYnmdxeQXb7L3mGQJFeY2g5D6QP6g9nN25idpMYdrM3iZpg+vpuluEtSkIaCAVBmLbVnD2uv0WR2kioPZ9degTpH+Z1M+kAwxV2S4HKmoMCehcqkpMegRIqkydOZJM7Elhlkl/okW1NGTuQ/MmeOphIkbK1I7UwgvbfAZCn2zA0iuxaUTltbgLI0kuBWrAIcj1NMUVO5BGvuYJbdZAPKOHRhPxbSTLKjzbv754p95EOPThwq1uoDtQXHVAznbxbOt2Nyw6xBSSG8eARj9zoJHP7FDvO7l6vMlw6hlE4SKhUoYzsC99wZ5h+3f8CkSiUpB/c9Qdk2zGCCcwH1kX+0DZm1tjJpufzmBHJae/YyOyrvlkCGODUN/fM625xLJzNfsX4r98OyexaRXZBjjkYAuzGgjou4jxyMsvk6CqWSR7aysSNJwSfZWWPwJCjnvIH5nnijkRTsYHXzckvMP57aC+9RUhv6dmvyrH+oa5dP4M9Ow6NqHi4BFag41uTKe2pn2fPk0qtPxHaUQP7SGNMXep5h89Pcddddetvb3qY3vOENmp6eVvC/wpRxHCuOY83Ozuod73iHjj/+eL3nPe852LdjjDHGGGNM37GPbIwxxhhjjDHmpzkoH2yazaY+/vGP6xd+4Rd0/PHH681vfrO2bdumOI61efNmve1tb9Pu3bv1xS9+US984QuVTqfVaDT06le/Wh//+McPxi0ZY4wxxhjTV+wjG2OMMcYYY4zZHz2VRPvGN76hj370o/rEJz6hlZUVSXdHCRaLRZ177rm6+OKL9cQnPnGf/dq1a/X0pz9dd955p84991zddNNNes973qPzzz+/l7dljDHGGGNM37CPbIwxxhhjjDGGcMAfbPbs2aMrr7xSH/vYx3TbbbdJuvsAKkmnn366Lr74Yp133nkaHHxgjcvjjz9eb3vb2/RLv/RL+9owB49u0FUc7D+5arnNxDRbERfdbMZMJ7OUYoKfYzmmX98MoYCoePmTWpcZJqrxk2X9E0INWPHHxgRZdu3MGNQ5Xjd8AHfzADShbnMNFnMoQJ3zMViLQ1K7tozsUne1kV1xjGss6+EnIbN4fJy1N8/02OMiq6ciSemAza/B/A9Yg73Wd5eUGYG1aXLMLg5hPadHsfcnSdFRR7E2H87e4em5b7L2ynDsSFLEnrtzK9MGD6t80e102Do1XGKi1q2ItTeQ4ftmtcvavL3K1inaO90EmuhtKIFdg3a0bg+ttZek9kavsY98eBEpVgRmyTIsjNSFY68esjWhRGvQSRotsCNes8vuMck2WoVtxrA2TS7N+qcL10uJ1+PA9Upge+mRBM752hFmF8NqDnSxbrA6MpKkIqtDpBFWF6dbY3t9ahv04SXlR6CP/PATkVk8OoavjX1k6K+l4Hgs5aB/rAR1QuF6lh5haw89U0rCdQz1SPYOow0bkF3wMF5z6pQs85E1MMHsYH93b+P3GDWgz91iPuXwAJ+HnZC973KGrVMrHbaW3lFjvxcloQPdyg5091d4ySlcu6cFfOR2H/1jYwzjgD7YPOtZz9J//dd/KYqifQfQoaEhvfjFL9bFF1+s008/Hbd13HHHSZLqdV4w2BhjjDHGmEMN+8jGGGOMMcYYYx4MB/TB5gtf+MK+Pz/xiU/UxRdfrOc///kqFGC0zb0olUp60pOetK/gqjHGGGOMMYcj9pGNMcYYY4wxxjwYDuiDzeTkpC688EJdfPHF2rx58wHdyPr16/XlL3/5gNowjBXNKrWKXlYnXoPayqwirXZvlgRloKIhZFeHqbVZKmcgiVp2oIzDYoun4abaLC1149AKsgtyUMorAVGD5famKlDyYR1MC0/CnTuZHZWQgNJp8dQia0/S1N4Hlr+5N/l5lhY+2VjC1y5H1yG71AnrWINUomXTemYnKR5i/ZP6Pw9jDe6eYdedq7L2JMVQ8kV1NmfiFBtnwRR7FklKLcBxQdfIcSj7R+U1JMW7F5HdzA97L2mw0mBttqHMThvuSQUo7yNJSx3mpmVhl4/Dbkwn+C4AtwVBFSlBRVYNQ7nBdtSfjxz2kQ8/FlOLSger+07rYybxSeU9V0Imf5oO+DrYgNK9WbheJ5lF3R77yJmALR5rh5l/fHejRW4LiBvMX0uVk0iiJZAXJWzdxey6fI/C8sIz7Aw4M8Wk06h/LEkjLSazVoq+iuyC47k/qwz0kTcwnxv7x084mV1XkqagpNYcm1/YP474O4xSTJo2Nc185NQiG4/4/UnSBHs31EeO9ywiu4UfJLhHMdsqlBHrhvzabWibhz7yMpREy6e47NcY3GLp70X1kL3rFrSTuNTqUG71Nlt98o+NMZwD+mCzc+dOZTIHXAbHGGOMMcaYhwz2kY0xxhhjjDHGPBgOqBKzD6LGGGOMMcbcF/vIxhhjjDHGGGMeDAd0mux2u/qf//kfSdJpp52moaH9S1ktLi7q+9//vqS79bytxd0fKhpXWvuXfIhilm7ZjHk681EpltpfzLKU2U7EJBJm2jBdX9JQmuXCDkP9mcly7wsEj62tMcPJSWaXIN07gBI0gum6oinpK7wf471QEmMRtgmfJa4ySRNJKhfY0rtYY7UOivMJpPe+w2SySjRlP2TzMGgwOQNJ0vgIsxuCEl019q6T7EjppQTPA4hqUE5likl7SFK0i73rxo9byK70GNbfQZnLMbZuZTJ0tQaTylyzjkvi5AuszxcXmfxRDcozJGEiz95NILZO3VnrvVQmlXJoh+weO1A6rQHXng7dj3qMfeTDj6FoUBkgOwZdZNUjJu2yPs/WmEKGjwk67ueabB0cgL65JA1n2dycLEN/ll53fYJ9eXIjs4M+cpA9oBjI+4f6yFS6dxZKKqd7+14kKa4xH7lYYGNnBfrHkpSfg/7VdxeQXSGB7Ku6PfaRR4eZ3SD0jyUuawdJrzC/JQlRrcMMp9k7jKfY3Gr+mJ8/i48eZoYl5od1bmc+fL3OZAQlafwo5nPnimzOLC1waUnqI6egPzuG/WPOHVAKjgKnv1oJ/FTuI6/eZrtP/rExhnNA3uXVV1+tpzzlKTrnnHOUza6+COdyOT3vec/TU5/6VP37v//7gVzaGGOMMcaYQxL7yMYYY4wxxhhjHgwH9MHms5/9rCTp+c9/vkql1aPDSqWSXvjCFyqOY336058+kEsbY4wxxhhzSGIf2RhjjDHGGGPMg+GAPtjceOONCoJAT3va0/C/ucf2+uuvP5BLG2OMMcYYc0hiH9kYY4wxxhhjzIPhgGrY7NixQ5J07LHH4n9zzDHH3Offmp8/HbUUraIPmoLa6dkE3/ym4kVkV2wz/dAsHL71gGvzjgZME3k0x7RdRwcT1F6JWZ+HHVhbZMcsu3ACnfy4zvTYVYCaqF3YXgtqF0sS1JYOjl3D2qP65XuZbrIkDQ7NILuFKtMGbrf5Ut6YZ7aFWaZhHsExEczxeRhkdyK7cIFpomdPGGYXXj/K7CQFsA5RtAJrG3VhraQ5ri3f3cv0nbfeOYbsKrtYeysJyghsWTka2W0osbV00wgUd5aUg3XQVpahHdTnrod831zswH0O1pFZajO7KhyPkrS3wfbDaoetFUM59sxZuHfFfaoFYx/58KOtriKtvufTIZWBhtNttmgW4XogSRnoC9UjNn+HAn7t4Sxrc3CIPTetGRQlcBW1nflh1KeM4TqIazxKCqiP3IQPTgfuMbAGpiQBuUeJ+8iV0T3IbilBDZt2i/nxrQXW37m5BGc7OC6CBeYjBznqH/M6MpnjhpnheuYrKsf6O0ntT1oMJJ5nPnJ3mvX3jtuGkZ0kDexk61mtwdaAu5Y3ILt1CerlroMlQrPQP64twzVK0kqbrRXNkI0f7h9zn7sKf2NZ6TK72Sbsxw7fFwZgPWHiA0SJKvwYY/pBTyokxtSTvpdtt8uL1RtjjDHGGHO4YR/ZGGOMMcYYY0wSDuiDzcTEhCTp1ltvxf/mHtvx8fEDubQxxhhjjDGHJPaRjTHGGGOMMcY8GA5IEu2MM87Qjh07dOWVV+r8889H/+ZjH/uYgiDQox/96AO5tDkAxqMJZYL9y45FAYsIXY4TaN/ArMuJTBnZtWG6bjkYYheWNJxnabjjeShfUeH6DFTqrANT+2s3LCO7FGsuEak8ezep7Uy2LRhgMnmSFEwOMkModaYaS5vvfH8va0/Sd7YcheyockY6xSO4ywNMJiE1OcDsMuy7f+vbc8hOkloLrM3v3r4e2f2fwhSySxdYur4kxVSeocnkAsIaay+gEmuSmtOsH29frrBrL7Mx0YByBpKUhnvNeIVJPuROZPeYhNEldu2JBpMw/OEi62+JrwGlNDPMJlgrKCFssgjX3BBmpNDrdhNkuPQS+8iHH+OpirKr+McSH3vVHmdKjeb5HtWFi0cZzsvBHJdOGS8wH7kwxPongv5xt8HjEOvfWkR2Kd7lrL0clxDK7YZ+UzGHzIIJuPdAmTNJ2Efu3jyN7L5/21p+bQj1M0oDzL9KTbBzqiR81mh/l50hOlB9+Ye3QtlnSY991jyySxfhWQyuPXGLz4WI+sh5dsZp72X3uBX6x5IUQFsq0ZWBS+74EJdKzm7mv4kQhpaWsO0EXCtuWWD3yP1jLpWcSbF3Q3dD6isU6MsWz9om16Z+gjGmfxxQhs25554rSfrSl76kd73rXavav+td79I111wjSXr+859/IJc2xhhjjDHmkMQ+sjHGGGOMMcaYB8MBfbB54QtfqNNOO01xHOuyyy7Tueeeq6997Wv30d7udrv66le/qnPOOUeXXXaZgiDQqaeeiqMNjTHGGGOMOZywj2yMMcYYY4wx5sFwQJJoQRDos5/9rM4++2zt2bNHn/3sZ/XZz35W2WxWo6OjkqT5+Xl1OnfLQsVxrPXr1+vqq69WEPDUP9NbmkFb6VW6fzRVQm0dnYHyU5IW20wejEqd1SPWXksJZMmgpE06gJJf2d6nmnbh44Qt9j223eHfbTNQ6ixmCkLKLUFJvZCnMwd5uKyVmISEUmytyhzD58JJE0zm4s7ZEWTXhun1kjQzzySjRmdYin1QYP1dm+Lbze69LB1+T4O9w9odbB4O5BeRnSSFS3Qi4iZZc3N8PasuFpDdUodJdtTD3u/ba/JMEmf7AptfR81xacKgzGRfunAtXWmz9la6fL5WoRRQGja5ABX1qh2+d1F5hg6UXijAh2nDfaET8/2jl9hHPvxoxl11tfp6OJRi+9nGMpMQWmyzMUr9Y0lqQNlOOj+6Ed/DqQQVlxuD61GLzxt4hFCnCuWLCrAfoX8sSZkFJiGUgu9aOShXSqWvpAQ+MvPrNq9l/vHWvcw/lqQ2lGmdm2X+8SD0jyUpKLFBTiVsp/cw2a3pJvP/JKm5hU2GYpHpscVQujduHwSJ1gX2LI0l9l6WOnzda8KzGF3F1+TZs+yY5TJnE/PsHQZF1j9hm/uzNewjQ+n3LvSPE7hTi21mXO2ysUsVx6h/LEkF+ECkzU5/3GNjTAIOKMNGko455hh997vf1XOe8xxJdx842+22pqamNDU1pXa7ve8w/7znPU/f+c53dMwxxxzoZY0xxhhjjDlksY9sjDHGGGOMMSYpB5Rhcw+Tk5P6zGc+o9tuu03//u//ru9+97uanb27mPj4+Lge/ehH65d/+Ze1efPmXlzOGGOMMcaYQx77yMYYY4wxxhhjktCTDzb3cOKJJ+rEE0/sZZPGGGOMMcYc1thHNsYYY4wxxhhD6OkHG3N4sJSaVzrYv47oUWLawN0Empu1uIXsaP2cwQyrXzFPtZ0lZQOmEljrMh3WOEHNB3hpZbKsKEZ2AOppz3NlRFoKID/CDIMhqLHcSVAIBOppq8j1nRGwjoMkdWDNkEyKtbnSgfV4JK0bWUF2qYkyaxD2d5Bi81+SdtXYGtCJ2bUX51htqsJerkveWWJ27Rp71/kKG+MRrGkiSct1NsY7EexHqO1MdaUlaSgD68N04JqbQBM9gDW5AjgPU7BuQzPBvgC7Rwm2OUQ+gej3OKxjFcI1ktbzWAmZTn43hoV7zBHPiurKaPW6WmtF11Z23UaXrf9DOX5so7YLsL5kLsGaUO+ya0fUtaP+cZ6v/xnmZiiErgv193Oj/B5T0EeOoY8c0KJBSfxjejCAm1S7Df1jWEtUkmrQf1gzRv1jXrNSKTZ4g6CK7PZUmW9O/TpJWp5h7zs3zeqOdpfZGO/W+D1mB2HNEFibanm5t/6xJC3CmrBVVrpRQ9ne+seSFEP/SnAtDaDfK/XeR4bdc1DqtOTgtccK7FkieJ6VpCV4zllur74vdOIeF1o1xvScA65hY4wxxhhjjDHGGGOMMcYYYw6MnmXYRFGkW265RVu2bNHKyorCcPUvti95yUt6dXljjDHGGGMOOewjG2OMMcYYY0z/CIKfZLTFCRRy+sUBf7BpNBp6y1veog9+8IOam5vD/y4IAh9G+8TacL0yQX6/NjmY6xkmkERrA5kJSapHzC6Eafgp8TTTSOx56l2Wsi8opSNJKdhkdYbJO1VSLHU9CnmiXRSy5+kssz7PN2Dueg52jqQYthnMLLIGG0xOp3PbMmtP0nR1DbJbaLM09ySpkiF833ENygjBNWB+DkqsSZpuMok3moo/u8K0T0Zm68hOkjpNNiaXl5jsQiXs/XydrbO1YiuUpViGQ2I8gZrKHJQ/Gc6xfaE9m0ACs8PWikZt//vlPVAZoBqUw0hiSyXRmlD9YC9dm8WlUZeiBrIrBWz+5wI2dgLx/aPX2Ec+vJhMDSq7in8scclA6iJTWZJGl6//VIKQ+shhAlmZGlwLKVTJq7qXSwOVUmxDi6A8TyoNZaCY6pYkKUvX4Sxc45qwvdlFZidxH/kOpiM7uzyG7BbbbF+WpDT0FUM4v7B/LOFFYAlK9+5tsecO4HlWkuahjzw0DyXRmlDKa4G/wwGoTUjn62KDOarb6tx/WIbTaxw+9myLraPDWe6vdWbYXpNps/5uVvmaW4XSbXX4Dql/HCaQG2vB+TrTYHbtiG2c1QTS74U0m185YBdEFlsyvaVarerv//7v9YlPfEK33367ZmZmNDExoRNPPFEveMELdP7552tgYKDft7kqT3nKU/SVr3zlAf8+CAKVy2WNj4/rtNNO0y//8i/rRS96kSqVSs/v5YA86kajoac97Wm64YYbDouvU8YYY4wxxhxs7CMbY4wxxhhjHup84xvf0HnnnaetW7fe57/v2rVLu3bt0rXXXqt3vOMd+sd//Ec97nGP69Nd9oY4jlWtVlWtVrVt2zZdffXVev3rX68PfOAD+tVf/dWeXuuAPti85z3v0Te/+U1J0qmnnqrf/d3f1WMe8xiNjo4qBYvsGWOMMcYY81DCPrIxxhhjjDHmocxNN92kZzzjGapWq5KkbDarpz3tadqwYYN27Niha665Rt1uV1u2bNEznvEMff3rX9epp57a57tmnHHGGTrzzDPv89+iKNLi4qJuuukm3XLLLZKk6elpPe95z9NVV12lZz/72T27/gF9sPmXf/kXSdITnvAEXXPNNcrlmIyFMcYYY4wxD1XsIxtjjDHGGGMeqnQ6HZ1zzjn7Ptacdtppuuqqq3TMMcfss9m2bZue85zn6KabbtLy8rLOOecc3XzzzcpkeiuhezB41rOepTe+8Y0P+Pdf//rX9aIXvUg7d+5UGIb6rd/6LW3dulXZLJeM3B8H1EN33nmngiDQZZdd5oPoYUQ+yCgT9GZyLHaZxqkkjaWYXmEZTtyZNtPDrwfMTpJSUDd1OMfkTTJMFjgR2SzTOaU6vo0mX0zSsN5Es8XajL9dRXbF9VxOJrOR1UqJ52usQahnm6pwneOBHNMbXmqzdTWJPu+2+SFkN/qDPcguDUvTtMMRZihppcuepw7ruYzmmDb4JNQQl6Q6rLPThPWuOiGzq8O6RpI01WQa3UU4dGmVJljqSpJUh7LN03Cdai4k2NtgvbRqnfXjCn3XEZ+vtA5FF/Z5gxa76SP1mNUHqKTYe0lSx66X2Ec+/MgqpWzQu+ynxTZbY4azbHyUs/ze5pq9rRsZcIl9DefYHM7Q0nbwsdMZvr7F0M9o1tnek26xa7fn+B41EjEfOb+edVD6KHYOCxagfywphsWNUoPsuQfybOxQ/1iSYrgH7Jhl/vHQD/fia6cH2LXbHfZuljvsXTfgGVCSRmG9w4lZ1uf1OrNrJ6h11YX+VaPD2tzTYM9M/WNJWoGlZKiP3IS+4kyLz4X2Et1D2Lym71ritc2om0rndaIzCayLEyeoEUWgNZQlqQnX3HJm9cHrXO8jl4997GN66UtfKkn66Ec/qosuuuhBt/XBD35Qd955pyRpZGREX/jCF7R27dr72BxzzDH6/Oc/r1NOOUULCwu67bbb9JGPfESXXHLJg77uocLZZ5+tT37yk3r84x8v6W4JuC9/+cv6xV/8xZ60f0Dz9J4D6KZNm3pyM8YYY4wxxhzu2Ec2xhhjjDHGPFT567/+631/fvWrX/0zH2vuYd26dXrVq151v//ucOess87Sscceu+9/3yOT1gsO6IPNySefLEmamprqyc0YY4wxxhhzuGMf2RhjjDHGGPNQ5I477rjPx4nVMnXu/fff//73tWXLlp7dy9LSkq644gqdccYZGhkZ0cDAgE466SS97GUv07e//e2eXeeBWLdu3b4/12o8U3k1DkgX66KLLtL111+vT37yk/qlX/qlXt2TOcjU49aqL/7EApQQKlA9A2kBygXQtNB8APOUYy5zlIZSGKUMk4LLTvBc6iDHrl1YYDnXVBJtpZFHdhKXKhisNJFdKs3edVDg35bjNtPtCEpQWirVHzkdScqm2JwJoZyWJOXSrE0qdZYeYtceHuDShJQalDShclpJ5sLoYB3ZDcB5OLPE5DDSKZ42X8mw3P71RTa/dtSgzCKUFJCkAEoaVNPsHrtQLkQSDlnJZ9kDVTJs7Zko8HdIJTboMhXHvRdAoLJPabG1Igv39nrEOqcbw07sMfaRDz+acagwWn0ejxfY8WmiwORiFtpsTYihRKIk5eCaSY+CmYD7QgW4FmYmoB8GnyU/z2WaqSRavcHeYanI/ONyhd8jVa8OCtAHpFpDeOxIQQLbXpJPc42+Zsg6MgfbTFf4XKBScIOD8NzE1IpVTyCJNg0lfo+uMR95aIj5+3ECOefZBXYooaOR+sfr4FovSTvrUDYcvpsU/D1kJcEc7LaYbQ5KouVy3OEvwz4fz7N9odplz5Jg60ogn8auvQR/+0onuEkq20qk0zrxoS+TbA5trrnmmn1/PvHEE7V+/fr92h911FHavHmzbr/99n3//rjjjjvg+/ja176mF77whdq9e/d9/vttt922T37t8ssv1xve8IYDvtYDce8AvQfKMnowHJCX9bKXvUxPe9rTdOWVV+qf/umfenVPxhhjjDHGHLbYRzbGGGOMMcY8FPnRj36078+PfvSj0b+5t929//2D5dvf/rae+cxn3udjzWMf+1i99KUv1Xnnnafjjz9eURTp8ssv13vf+94Dvt798a1vfes+2UJPfOITe9b2AWXY7NixQ+9973v1spe9TOeff74++9nP6sUvfrFOPvlklUqrV1u3rrcxxhhjjHmoYR/ZGGOMMcYY81Dkxz/+8b4/H3300ejf3Pt8c+uttx7Q9dvtti644AJVq1VJ0saNG/Uv//IvevzjH38fuyuvvFKXXHKJXv3qVx/Q9e6PG2+8US94wQv2/e/nPve52rx5c8/aP6APNsccc4yC/03hi+NYn/70p/XpT38a/dsgCNTtJtBNMT1jKbWkdLD/9Od2NITaChKkcNZDlmoeQsmHGS2x9lJ8nBUiJp/W+v/Zu/P4Ksp7f+CfOfuanCTsm4gILrjv1gVc6lXbKtK6F7B6bbUu/WmvVXtVbOu12mpdbqu2WnGrG26laqt1obgiSqutqKCsgZA9Z19nfn/kkoJA8pmck5wc8nm/Xnm9AjzMzJnlme9k8nyeAjd83EzaGGpKto13+Kh2jVEuYqkpxS0PAEaYpY0H8/i5Y2Ol+f1YSHGxAlaeHLJPnj6ZNn7AYmOSO8/WJLljk7FxXHxk5EO8noyQaOWWV99aRbUDgI4c93mCLjJGhlxvW5q/FlxkXJ2TbJfIcxEAyTwff5cxyZhFMmaNHbLfTsb72GkbdnPrbo32/IPwjdg4jlSOuxbS5P5O2Ch92D3JRmx4yBjKGgffn1W5ufthLMed4x1kxFpbgWvHxqyWmmrkyhM1U3AZPffZ2QIXDWQY3LmXIqOqTBunckuWq4XyZPSN3+QfGXNkX2gm2Vgrrl2qneuLAKC5nYtYaiLrtREmF5Nqh9vHHRsrTT5fJblttPL8iWaRGUK5Nm55zclaqt2aJH+vz5E1ssfB7cfkWhvxRQEukrOhuY5qFyVjXwPkvR4ALDKatp18XmSje11kdCIAJLNsjcz1U1m2PiZjpAHAQca+snVvO7neEFkfA0B7O9efWRb3jJwiI6cBfp8nCuT9g1xvjj/N4CHzhWvIW02Vmzsn4jbinKM57pO3ZXquIdmfuVWydDqNbJaLLB2ILMva4uetXq8XXi8f496XWlpaur4fPnw49X82jQtrbW0tav0PPPBA1ygdn8+Hl156qWsO0U3NnDkTTqcTZ599tu11vPDCC2hubt7s70zTREdHBz788EP885//7Pr76dOn4+GHH7a9ju4U9cIG2DxP2U62soiIiIjI9ko1soiIiIhI/0qn09hxx9FoaCjupUA5hUKhrtEjG1133XWYM2dOeTboSzbdNr+feyG8absvfza77r333q7vL7744q2+rNnorLPOwm9+8xu89dZbttbx3nvv4b333uu2zciRI/Gb3/wGJ598sq1lM4p6YXP//feXajtERERERLYLqpFFRERERPpfNptFQ0MrVq56AlVV/KjMgSIaTWL8DqdizZo1qKr6d1LJQBldA3S+FNvI4+GGnm26/akUN5pva2KxGBYvXtz155kzZ/b4f2bNmmX7hQ1j/fr1mDFjBs4880zccccdqKmpKdmyi3phM2vWrFJth4iIiIjIdkE1soiIiIhI+VRVBVBVxcWgDkRVVVWbvbDpyZw5c3D99dfT7c855xycc8453ba5//77MXv27C3+3uf7d1QmGz2XyWS6vmdH5WzNhx9+CNPsjAgMh8PYfffde/w/X57bhrGtEU2JRAIrV67Eiy++iJtvvhlNTU14+OGHsWTJEixcuLBkL22KjkSTypNHFlYPme5NaS47PeDk51OIFbgc36zFtQuB63iT4N/c+sG9sW4ls3Tjq/hM0kKea1vfznXYcXK+gA0ZPn+21ssdbzZruM5NRsTYSZJho3zJQ2ORIbkFct4VAEgXuP3YkC5tji8ADCHPi2yGnLcjxS1vVYL/zRYyEh1DyPPHJOcqYY8LAGwgPw+77hiZu+0m50QAgIib60vZdjuEuD53RYzfxiw5KcOqOLcfO9L8bx35XNx9rpmcw2AteS0k86WdCwwAojluP7rJfq9go1NJkRdsT3XHRhmTCxxPG+wcHZWbXS39K2fkYRk9XyStGe5c9rnI/p8M2c+a/IUZdJL9kcn1gx4Hf39sy3D9cGoVtx/Z+mp9S5hqBwAJshZqynC/MVrry/TcCPbmfKh2c30c2HkM2UcS/tGFnV4IJnkM0wWuFtqQ5s/HAlmHRcj52HIZft3ZFPd51sS5+oqdXqjGY+fJgMMem6Y4WR/TD2z8My0zBxkARDxcXVDl4epjANghRD4PkQlA5NRUWJ3g92OMrJG9Hu6+0ErWxwCwNkXeF0o8fR87LyrA18h5MuI2SX4WO1drlqy5U8QkvHl2ol6RbQiF/j1nNjtaZtN2m/5/u5qamrq+Hzt2LDW3+rhx43q9vi8LBoPYfffdsfvuu+OMM87AQQcdhPr6evzrX//CZZddVrKkBb2wEREREREREREREZHtg2l2flWaXm7zgQceiO9///vdtvnkk0/wyiuvAACOPvrobud+AYBdd911q39fV1fX9f2GDRuo7WtoaOj6vra2lvo/W7Pp/DeBAPeLAsFg34y0Gj16NK677jqcf/75AICHH34YN954I0aMGFH0skv2wsY0Tbz22mt4++230dDQgGQyiRtuuAEjR47sapPNZpHP5+F0OgdU9p6IiIiISF9QjSwiIiIiIn3phBNOwAknnNBtm7lz53a9sDn77LO3GnfGmDx5Ml588UUAwKpVq6j/s3r16q7ve3pR1J1NR+ckk0nq/yQSiV6vryfHHXdc1/f5fB4LFizAaaedVvRyS/LC5k9/+hMuueSSLQ7SD3/4w80eRu+9915cfPHFCIVCWLduXZ+94ZLuBawQnOh+2HfYzQ33djv4YaaFDPeWOOqIUe18Zu8zD7fFa3CfO5Hn2uWz/LB5cnQtPdybNS7AxTgAwLCq0nZynlruQzuq+QgJgxzPbAS4ZVpkPGAhRUZXAKgjozO+UR2l2qXIqAAAWNLKRer9fd0wepmMLxL8NnaQCUbruHszxga5fmqfGj46ye/mzgs2ditOxq5kTL5PaSeXGSXj2HJk15NmM+0AFMiOz0XGFPicZIYEgGCQjMRIc+0cZExnwMXvnyBZpTmIYeAA0EJGOdk4hEjmuRMjTeashd1k35zjhtHnwN/j+oJq5MoRsHxwEdG4QTd3vbHxKmz9lzT5eB43Wc+yvA4+J4uNgclnuWVaZOSX02EnP5czNshFfNSVuD4GAPcQ7nM7wuSNwsOdE0aQqx0AACnunDTJH6bUeLla+vixXH0MABmyxvmoNcK1W8PXx+xj8ookt8/bs2Q8YIq//keTicV7kDWyl6x72dgtAEiS8d1Z8vk8muX2d4eN5yu2Rk6SZapF3hicRIznRl7y2SUQIuvjFP/c5CQjnX3kqRtycTvcsLF/Wshykb3TZMhi2s5zU5AsLEyilmHPWZFt2XTkzZIlS6j/88EHH2z1/9s1dOjQru/Xrl0Ly7J6jEVbs2ZNr9fXk02f6QD+BVZP7KTUbtXvfvc7nHTSSVi5ciUsy0JdXd02bzDnnXceqqurEY/H8cwzzxS7ahERERGRAUk1soiIiIiIbG+mTZvW9f2nn36K9evXd9t+3bp1WLZsWdefjzrqqF6ve88994Tj/36ZKBqN4uOPP+7x/7z99tu9Xl9PvjzKx2HjF526U9RSli1b1pWPd9RRR+Hjjz9GY2PjNtt7PB7MmDEDlmXhpZdeKmbVIiIiIiIDkmpkERERERHZHu28887Ybbfduv78wAMPdNt+03/fY489MGHChF6vOxwOY//99+/680MPPdTj/3nwwQd7vb6ebDpyCOic16YUinph86tf/Qr5fB677747XnjhBSqD7vDDDwfAD5kSEREREakkqpFFRERERMrINCv3qwJceOGFXd//8pe/xIYNG7barqGhAb/85S+7/rzxl9qKcd5553V9f8cdd+Czzz7bZtvHHnsMb7zxRtHr3JZf/epXXd8bhlHU6KFNFTWHzauvvgrDMPCDH/wAHg+XBTpx4kQAfZsfJ92b6BoGt6P7XEuTjNJM2Ai/9Bnc6TYaQ3tuBCBjcCGwcYvLnwYAkFnDreTcNBtaw/Sq02Tu7soEl2tf5+FyZTMFG7nkZIYwO29Hy3If1S5iY34YzwhuPxZauP2Ta+cuhlQHn3PsdnDXDTs3DTtXCQBE89zxHu/ktjHo4vLLI25+G9enuAvRRYaDZ8n8+4LFz8mVLZA52eSxYc8JO3NYtWS582d5nPssn3Zwfa6duc06stwyA+QkNk1prk8BgFCM6wPYrHP2PGPntbDDQ87dwOZps/PSAECWXGaWfPBg5/PwOMj5GKzSzuXBUo1ceXYMBOBx9NyHsOdoIsc19Di5TmG4k5+7MUNeb6kCP48EqzXL1fvNzdw8VOwcJKvi/LxPtWSNnCVr5FSau9/ambejYxn3ucOjuM/iHsltY6GNn/er0EHOYdbG7Ud2HiL2nACA9nTPczkAQEeOu1eMCfD3x5CbOzbVLm4b15O/a+vkyzB6HosCWePkTW4b43Fy8hzwNTJ7/jSTtfkXCf7ZZVmUnZOR248dWW55Xic//0lriruHBKLcsx37jAPwNTJbz7K8NmruLHktpPJszc0tL8f+4M0GNzG3pUXOfynSnfPPPx+/+tWv8Pnnn6OlpQXHH388nnnmGeywww5dbVatWoXp06ejtbUVADBp0iSce+65Ra975syZuOWWW/Dpp58ilUrh2GOPxRNPPIGDDjpos3aPPPIIzjvvPHg8HmSz/NxbjPb2dvzoRz/C/Pnzu/7uzDPPxPDhw0uy/KJe2KxduxYAsNdee9H/Z+Mkql/OeBMRERER2R6oRhYRERERke2V2+3GU089hcMOOwzxeBxLlizBzjvvjKOPPhqjR4/G2rVr8eqrryKX63wRXFVVhaeeegouV1GvIgAAXq8XDz30EKZNm4ZEIoHVq1fj4IMPxoEHHogpU6Ygm83inXfewfLlywF0jsK55JJLbK3jhRdeQHNz8xZ/n0wmsXLlSrzzzjtIpf49QGDSpEm49dZbi/tgmyhqLxn/91bWzoNlS0sLAKC6urqYVYuIiIiIDEiqkUVEREREZHu211574aWXXsJZZ52FFStWIJfL4c9//vMW7SZMmIBHHnkEU6ZMKdm6DzjgALzwwgs47bTT0NDQAABYtGgRFi1a1NXG4XDgmmuuwcUXX2z7hc17772H9957j2r7jW98A/fccw+GDRtmax3dKeqFzejRo7Fs2TJ88cUXXbnbPdmYG1fMBENSnMZcqsdYnQlBLlbAaWMoZTrLxQBU9xDXZpdl8RE5NWRsCStpY8h+lIwvMsANmw26uf3Nxj0AgNPg1u33c0Opw0O52AXfXlVUOwAwyGgRa3WUaueu5dabW8/H7jSluHOyiYyga8/xx7DKxY33njy0hWrnIpdnJ6qqIV3aCKosOdScvWYAoCbE/RA2luL6Mzbuwe/jh/HGyUi99Wmuvx8T5M5xj41INPYe4ifzPZpsxDOMJPtn9tiwMQ7pnI3oPbLdWvKdQJqMcYh4+P4sRuaptKa5aEunQcapkHGneYuP9ykl1ciVpyVTgNvo+T6wQ5jrW9n+rTnNXUMhN39dGuR1xD4K1njtrJuNfeXWzcZS2kmbDLq5OpWNP2WjmHxkfQwAweFcTeLdI8ItkIwWtdZy9TEAOGvImKx13LrbyPiypgz/rMhGnYVdXH7RxOFcfQwATrJGbk5zUVUbMtxnYaOd7LT1kzVypIorSJJJvl5zkLFfgQBXNSXI+riBjDoEgFEB7lpgI7rYWihg46d5jeTz1VAy1pKtjwEbNXKhtHHX9TaS8dNkhFmVh1t3guzuO5I2YojJuiJR6HnluTLVx/2uguaD2UyFbfMhhxyCDz/8EA8++CCeeOIJfPbZZ2hpaUFdXR0mTZqEU089FTNnzkQoxMXh2nHEEUdg6dKl+PWvf42nn34an3/+OXK5HEaNGoUjjjgC3/3ud3HggQeWdJ1erxfV1dWYOHEiDj74YJx55pnYb7/9SroOoMgXNlOnTsVnn32GBx54ALNmzeqxfUdHB+6+++6STsIjIiIiIjKQqEYWEREREZGBZPbs2Zg9e3bJlxsKhXDhhRfiwgsvLPmyexKJRPDjH/8YP/7xj7ttZxETUb7++usl2qriFTX17Xe/+10YhoEFCxZg7ty53bZtaWnBySefjIaGBrhcLnzve98rZtUiIiIiIgOSamQRERERERHpjaJG2Oyzzz649NJLcdttt+Hcc8/Fiy++iBkzZnT9+1tvvYW///3vePPNN/GHP/wB0WgUhmHgmmuuwQ477FD0xkvvJI10j5FoiRw3NLtAvKHcKG5wQ6QLBXI8Ksko7r3kVhXIj20nbqxgcUNcfU5ueGSCjJrwkssDgGHDY1Q7d4BbpsH2QHl+Gy2yrYOMNEGBW164lh9WHG7mxkjH8lz0QYReMxAkIx9YGXLYfI48vwHAAte2JcNdiG5y1ew1AwAR8vN4nNz+LphcX5GzsY0JMvIrnieH15NxWpaLvy+kyeuLjQJKkVEKAJC30T8z2F7KRtoY3GQMpYO8Ztj9nSOvLQAg0+pQ6+JiEdm6Il1gA+Ns7PASUo1ceVJmHjmj5/OFvVewtWLc5GqCQpa/Llk2ko1pOTKqJk/e90y6Pubrm1SJIzHrRsWpdq4A1QwAXyNbZOwrstz+cYT5qCq25g7UcZGYwUYuditGRloBAMimQRvnDyubIWtk8pphe4AWG0lHbIwtG2FYTV6vbje/v+kaOcvd79k+PGGjpkywqcrkdZ0hbyBBMuoQANJk3cv+TMIOdpkeOjKO2z8GWR8DfGww+2MJtj6u9vDPdmyNnCTOXTv7RkTKo6gXNgBwyy23IJPJ4K677sK8efMwb968rolWv/vd73a12zj06Ac/+AH++7//u9jVioiIiIgMWKqRRURERERExK6if73UMAz8+te/xl/+8hdMnToVhmHAsqzNvoDOSYief/553HrrrUVvtIiIiIjIQKYaWURERESkTCyrcr9k0Ct6hM1Gxx57LI499ljEYjEsWbIEjY2NKBQKqKurw957740hQ4aUalUiIiIiIhVBNbKIiIiIiIiwSvbCZqNwOIwjjjii1IuVEsojR88R0RPDRvi11+IykeNGgmpn9jAPz0YOy062K5ex30RmEq9LccsDADaCelIVl5PNZrsm8nwOdDLGHUN3hssldnu5dq7V3DkBAM4a7tiYcW7d6XpuP26oD1PtAKAp46XbMpI25uLwOrnPw+ZFs3PYsNcMAOTIbOACedGwsdIb0vz1WpPmAsJz5H5sS3PnRLrAz8fxVgv3edYmSjyvkY3M73iOW7fPye3Hxgx/LbRkuP3DZsunyc/NzmsBAAly3TnyWmD3IzvXDQA4yZDunMUtM21y54RJpvmz7fqDauSBLWcVYKF0/SE5NQS8xLw5ABA32Xmb+POenf+qpsDXii1Zrp9Zl+QmdGF7o52quHkWAcBJzk2TIudKSUe5dq4Uf365/Nw2Oldzn9tRw9XwVoKbUwkAMuu4z9O8Jki1ayFrITuSee589JDnRMFGzZ0j56JszHDnD1sf2/nF7BRZlDSRNXIkzc1XlCX3DQB0kOdF1uSW+W4r1/fUc9Pv2pIlj2GMPNhedqIUAI3ks1gb+fML9hkHALLs3GbkuZsgr0N2fwOAz8VtY5a8ZthDY2dO6JSNeX1FpPKVfjZ2ERERERERERERERERsaXkI2xERERERERERERERMrCtACzAkcmsfE7sl0r6oXNd77znV7/X8MwcN999xWzeumlkBWCC90PLXaSUWc5G7EpdHwJGXVmkiEJIStEtQMAt4MbdMbGXNgRcnGfpzaQotqxaXVmsg8+DMkd4j4zmaQDAMi3cFEOzmqu+3NHuECtUDsXkQUA7jbuWlhHRiS05/hjGCDHZ1vk0HW3k4vDqPXwMSANae46jHi5bYxmuf1d7WbD0wAv2dZJDtl3GlxUSWuWj6VJkbkCaXJo/8Qq7pqxkc6AKBmdM9zPtXM7+OLWR567HnIsso2kGxq7K33kTveQ7RI2YvLY84xNfGBrhZzB9fUFOhSxtFQjV56Q0wM30Rc7ySIwTV4bJnlx2In3s8gaOeDgom9cNjIZSl0jh1xcfxQJcvUxADjIe4UV5z6MQcYQ2+HiUptgkT/UKbRwdaqzmq8z3NXceRZsJdfdUtr6GAA6yEg0n5N8JrFxqF10jczdpxozXORXlYe/CGM57gOF3dw9183Wx+T+BoB4lqyRU1w7NsI2Y+PnHBPC3DLZGpmtj4fxac5wkf2elzxv3WSMIAAkbcQlM9hbko9P3qPb1ifIOrUPIgzpGpmoAfJ04KiIlEtRL2zmzp1raw6TjSzL0sOoiIiIiGyXVCOLiIiIiIhIbxT1wmbcuHE9PowmEgm0tLR0PYAOGTIEgQD5K0MiIiIiIhVGNbKIiIiIiIj0RlEvbFauXEm1a2trw6OPPoprr70WkUgEf/zjHzF58uRiVi0iIiIiMiCpRhYRERERKSPTrNA5bCpwm6Xkinphw6qpqcGFF16Io48+GgcffDCOP/54vP/++6ipqemP1cuXZJBFoYdkfDvzJLDaHM0lXd4ocxTVLuzk8mztqPGQeb9k7jYA5CwuOiWb5y7boJ/MiybnDOoLhRQ578IwPlbGESS7NTIw1hkk586wMUeLk8w6Z+fjqLJxubLLjNQlueUFuPNnXCJItQOAVckqqh2bA+13csdwiJ/Pv68bkaDa5cj5eNj8+3YyxxsA/C7uxKhy25icgEBOiQMAyJHZ+yXeRAD8XI5sRnfEzS2ww8acU14yT5vd5zEyUJudxw4AcuQkY2zuttfgPvQwVFPtcuDnFysH1cgDR9YswDJ6vpe3ZUo7YVWbxd1PHHRqPzDcxc3fGHTZCPgnRdzkvCYubq6LAlkf5/P8Z/H7s1Q7tl6zyG20wyS7Lvdw7nMbQbJYtDHRsYOtkX3cOcHeb+3MVxcm5wj1sjX30DS9bhc5v8jYKNcHrElyNaCduQT95GVT6+c+d2QU1459BgT4GjlK1sgB8mcDVe7SX9dsvcbXx33Q95D9mYec6wYAqsjrMErOOeUhr1f2/gEACW6aJnqfZ8mDXbAxiY2XnG/Z6+h+vmqAn2NHRMqnD34Esm2TJ0/GJZdcgpUrV+KWW27pz1WLiIiIiAxIqpFFREREREQE6OcXNgBwzDHHAACefvrp/l61iIiIiMiApBpZRERERERE+iUSbVOhUOcQ/dWrV/f3quX/bHCsgMPoflh8jbUntaywm89iGpXlIszWOdZR7dqNGNWuzUb+43CzlmqXIGMXMqaNKC+yaSrHXbZt6Z6HwgKAx8nvHxc5nDmX5faPN8DFYVhZfqiw5SPjK7JkjE+K/cz8tRAlY+1i5LDwJLcbAQB7VnNRBS4yvqIvsFEO7NXFDvleHeMiZAAg3MhllbjIuACvhzuIYTc5Xh9AlZvrA7JkP9VOXodR8toC+Dgtenk2FpcucP0U2zeb5BmZyPP3hTSZNpEl73NtWe78CTj5aCE3+bs/WZNbd56MTotbXIRh3hrYkWibUo1cXg1GM5xGz1E5EYymlhcksxxHFLgY0MZCnGoHANE8F/nVnueut+EeP71utkbOmdz+cZBxSGx9DADtSS6rykNGGzvJ+jif4/tWD7hlsvWs4SWXZ+NGaibJGjnNfe6OHFdLs/UxAKQL3D13zwgXBczGnAEo+a/GsvWxndWyh3stWSOH1nP3XDtR0m431zbo4uqMEBlha+c5vp3rchEny/i8jWhCFhsPljXZ+tjG8znZjq57yevaznMBG3fqd3FXmIt8gMjZyJLOkfV+qtDzc2XOIk9aESmbfn9hs2TJEgCA28YP+kVEREREtmeqkUVERERESsQ0O78qTSVus5Rcv0airVixAnPmzIFhGNh77737c9UiIiIiIgOSamQREREREREBihxh8+CDD/bYxjRNtLW1YfHixXjuueeQTCZhGAa+973vFbNq6WMdZNSIl4yUAYCRPi5OYYS1E9WuJcsNuU5ZfF6Uy8G9wyRTLuB12BjiSg5TTpJxWkEyOilPRlLY4fFy+9xBDkkHuW8AwIyTMWuZ0g4174jxcSGNGe66cZNDzV0Gv3/YYe6FLHdeGOQ2Zgv8ecaODG8jR3I3prgh7p/6uAgxANhjOLeRPj93Pja38HFsrKCL28YEGS3SniGH4ef53whykucuG0vG9s0A0JzhRjGEyPtclIy5YCNN7MiQF03c5C6aRrIGAICQFaDaOcjIOIM8J/zgrlcbiZElpRq58pgwYRAxVB057mJ3OXqOVwOAkQGuLxqOGqodwEe7pAtcOxtlBjxk7eslI3lzZCxRiozTAvgamY1tY7m9fAyUwT6lk7dcM0HWx+nS/1ZvPMbliDWR92X2HAP4SKR0gdvheS5ZGAB/3bDPYmx93JrlL9jmNLfQZV7uOWe3kdzyPH7+Wmht5OoMVsBJ1sc2Cjb2mSRNHsRS18cA/1zZnOHuXSEX/3MgNsaw1D+VyPKnGRJ5rnFzjvsZVMDB9SlsfQzw54XH0fOxMSz++IlIeRT1wmb27Nn0gzUAWFbnTeKSSy7BaaedVsyqRUREREQGJNXIIiIiIiIi0htFz2Gz8QGzJ5FIBEcccQQuvPBCfPWrXy12tSIiIiIiA5ZqZBERERGRMtEcNlLBinphs2LFih7bOBwOhMNhRCKRYlYlIiIiIlIRVCOLiIiIiIhIbxT1wmaHHXYo1XZIPxptToTL6D77fbiLm0/BBJ8h3Jjh8j7ZZeYsG6GkpA6Ty+03wGW7FmzMvZKl84u5ZbJZzOk8n19qkjnieXKeHb+DzNM2+fPMIrOl2Wxww8195prqJLdAAOEm7jcm2LlFPDYiaP8V5TKoPcuHk+vmPsunMT5/up7clex8JUE7E5uQkmmuD3C7uX4qFOBO3ISNjH4fmfE+wscdw4JV2ox1AIiT8774ybxxth0ApMk+1032P2zf3Gpj/iwXeerGc6W9Hzpt5Fq7Da5t0Ml1ulnyN8qakaDa5UEGy5eYauTKMxYj4CbmRhrmZ2tAcr6ANFcL2SiFkLNK+5uZdvoYh1HaejZD9tXk7gbA175Zcg4zs8B9lgJZ1wH8HD9sjWwluOUZNubtMDxc26oI93wV3sCdZwkbzy4ecpd/EuXqVM9nw/h1O8k5FKNhqt26FPdh2NoBAIJ8WUlJp7kFusj6GACCQe4+zs5j5XNw18JwfmpLukY2yX4vTq7XTt3rI9umyXlHneScOAD/bNBClmzs/K0xG/cuNsXWRR5DN3kvDLn5/ixT4M5d5nPn++BnaSJSWqX/KZaIiIiIiIiIiIiIiIjYUvQcNiIiIiIiIiIiIiIiA4JVoXPYlHiktFQmvbAZhFocTXAa3Q8ZHmlwkWjsUE8ASJCjLgMOMsqLjOeKuLnoCgDwO7nPE3FzHWjYzcVcAEA+y8bFcMNm02SMg53hzAbZtkBHSJDrdfDxDBYZA5WP0UukWhnkegHATe7HLHmOs9FXABAhIwgCLu7czZFRJTnyswCAn4zjyJDj64Pknc5OlNf6RJBqx8apFMj9U5/go+VYYRcbiUZGn5ARKQAwLsTFV8TI6LRojl930MUd8BiZLcLGFdlIm0GWrNXZKNEqB5fvkbIRiVZr4x5LrbvA9T1uizt3DD3wCKnFisGFnuN7h2EItTwPWbtwYVGAj6xRAQBkfRXxcte610bHVUXWDyE317HnTK6PyZD3W4CPDTbYGpDcPfkcfwzZtBq6RiZ3TyFmoxii9w8ZQUrWszkbm+gjo+WqyfrYb+PZjq3t2BqZj7Qqfc2dJzMHG2JcfVxrI9aOrT/r49y6WWEbsW0FMq6uiozaHkt+FDZaGOBr5AB5rwnZyN5ja2Q26ow9H+1EZQZd3DnpJiPjasj7qx0p8pR0ouf9aBJtRKS8inph43SWvhMyDAP5PF8IiYiIiIgMJKqRRUREREREpDeKemFj2XllLSIiIiIyCKhGFhERERERkd4o6oXNddddBwB4/vnnsXjxYgDA7rvvjgMPPBDDhw8HAGzYsAHvvfce/vnPf8IwDOy///444YQTitzs0jHIYZdHHnkkXn/99S3+3jRNfPLJJ1i0aBEWLVqE9957Dx9++CGy2SwA4LXXXsPUqVN7XP7KlSsxf/58vP766/jwww9RX18P0zQxZMgQ7L///jj99NPxzW9+Ey5X8Sl27eZaOIzul9NqjqGWZScSLW1luWVaPqodM9QTAFJ5fjgzm/iQJWOg2CHcAOAnh+xXubn9yA5zjWa5WBkA6Ij5qXbs567Os0EgpWeSw8ILZLtclv9t6gAZQVVD5mFEbURspMlzd22Si95ih7izEYZ2sBEkbCwNew0CgJ+MfGE1pbl+b32av17XJNl+ilteQ5LbP6OD/Pk4xMstk/3Zt50+l42D9JMRJKvz3OeO5uycZ9wyHWR/nwMZDePkY86qPNw2pskTzUnuRx+4bcyTkT2lphq58mrkVkdjj5HBANCejVDLY6Nd0gXuunTZiId1k23TefK6tHELz5H9MBsXxUZahT18NhBbu8RzXD8Ti3L3cDvvccP5nuP5bCHXzcYVA4BJ7vJ8jquRfQ7uWLPxvgAQI6O30mTM0Xob0bRsTZIpcY1sI6kKLrIWCpC1UKnrYwBoTnLPn00Z7npdkySju6lWnRrJR9rRAW4/1nm4tbN9GcCfj2x9HLDx3LSWjKyMknmHAfJ2zz4rAvyUIVUe7rOwEdFpG32ug+xzfcQobycZsy8i5VP0C5uf/OQnWLx4Mfbaay/89re/xQEHHLDVtu+99x6++93vYvHixTjxxBNx7bXXFrPqAeOhhx7C7Nmzi1rGNddcgxtuuGGrv41ZX1+P+vp6PPfcc7j11lsxb948jBs3rqj1iYiIiEjfUY2sGllEREREysewzIqcz7ISt1lKr6gXNq+88grmzJmDSZMm4Y033kAwuO3Z0Q444AAsXLgQ++67L66//noceuihOOaYY4pZfUldcMEFuPDCC7f579v6bJs+QLrdbuyxxx7I5XL46KOP6HWvX78elmUhGAxi+vTpOProo7HzzjvD5/Nh6dKluOOOO/Dee+/hvffewzHHHIMPPvgAoVCI/3AiIiIi0m9UI6tGFhERERER6Y2iXtjccccdMAwDV155ZbcPohsFg0FceeWVOPfcc3HnnXcOqIfRYcOGYcqUKbb/32677YY77rgDBxxwAPbee2/4fD7MmTPH1sNoXV0dbrrpJlxwwQUIh8Ob/dt+++2HM844A2eeeSaeeOIJLFu2DLfeeut289uXIiIiItsb1ciqkUVERERERHqjqBc2GzO599xzT/r/7LXXXgA64x+2BwceeCAOPPDAopZx0003dfvvTqcTv/nNb/Dss88im81i3rx5RT2M7mUcArfh7bbNUB+XAdue5XNqcwXudDPJgGd2G1N5fjhhhMwkHRvgcqXHV3fQ63Y6yEzbUXGqXTrKzXXx0ZphVDsASJLz3UQCaaqd4eI+c7aFagYAcJBTLwQmc+djIcqd4/GPbGTLlzgb2EZUNZJkRje7zICLC95N2oi0Xk/OlTKGnCslRJ5nLhs5x14H97m9ZJZ3xMPNTVWIczn5drDTIgz1cw1H+/k+d7eqJNUukeeu1wSZkW2Hh8zUH0bek+LkvFgAECA/DjvXjc/i2tnJ/E6QeeMbstx9IW5xQfCWwR2XPLhrq9RUI1dejbyvYzLcju7rYwCo9XEXZkeGO0dz5CQEduY/qfGy83ZwC60ms/gBYIyfq5HH1XI1skHWTJFRXB8DALk41xd+vIKrkVM57h5VHeS3sYfpRrvk27jzzCDrY9/OfJ1hxrgJFZIfcuePm7zfBmxMqsTWOOwcNjamkoLPydWAqQK3zzeUeJ4UAAiSNTI7r4mHfC7wevgHg0ieu49/EefnFyq1Oh93Yoz0c597clWCapcg+x4ASJa4RvaS1ysAtJLzWEWz3H70khdiwMbDnUnO8cP2AQnyFG9M8fOvpQrcQpkjk7dKP9+UiJSWnZ/zbaG1tRUA0NHB/1A6Go0CANra2opZ9aBTV1fX9dD/+eefl3lrRERERGRbVCP3H9XIIiIiIrIF06zcLxn0inphM2rUKADAU089Rf+fefPmAQBGjhxZzKoHpUym8zfWnM7S/waxiIiIiJSGauT+pRpZRERERES2F0VFov3Hf/wH7rrrLtxzzz044ogjcOqpp3bbft68ebjnnntgGAZOOOGEYlZdck8++SSeeOIJrFy5Ek6nEyNGjMChhx6K2bNnY9q0aeXePDQ2NmLp0qUAgF133bWoZfkcTrgd3T/QVnvI2BRwEVkAEDS5h2gHuHGmWTLGwbCR7UKmXKDGww1dDQZKH8XiIkd7e3LckPSgmx8O25TmhuwXyCHF/jZu/wRrS78frSz3WwtWho0qsRPPwC0zZ5LDwp189EGBzFSJ5bmLIUNuY97G/rGogdxAtYeMZyB/NSFn4xdZSh354CbjBnM29mOePNZZ7qMgRkZfRSLkAgGMCHKRaHFy/6xK9DxXyEYxMp7B7+U+z3AvdwJtSPG/K+MnoxyqyDjPjgz3WexEL7nIaBo/me9T46qm2rHnd87k4plKTTVy/ylVjex2GvAQWSdVbjI2hfy9uCC5PDv1LBuzxka7MPtlo2oy4tPn52NgGC6++4dFxiWHyXq/Oemn2hVMvv/3tnD1g7+Oa0cfQfL5AQCsNFlLW1z/z8Zu2amF2BjiAhlNHSVrBwDwOrjjTZZXZHUMhNz8TdxHfm72mYSNtXOTdTQAuJxcW3Y/su0yBf48i+fJeEmyRh4WJCPRsmTWIYC1ca6TjJExxAHyuADAMLKWbkxz9X6AfPbN2IjzbCef+cnbK3xkd+9z8veFiIf9WUzPG0n+KEREyqioETZXX301qqqqYJomzjjjDJx88sl49tlnUV9fj1wuh3w+j/r6ejz77LOYPn06TjvtNBQKBYTDYVx11VWl+gwl8fHHH2Pp0qVIpVKIx+NYvnw5HnzwQRx11FGYPn26rUiLvvCLX/wC+XxnQd7TQ/9GmUwG0Wh0sy8RERER6VuqkfuP3RpZ9bGIiIiIiAxkRY2wGT16NObPn4+vf/3riEajmD9/PubPn7/N9pZlIRwO47nnnsPo0aOLWXXJBAIBfOMb38DRRx+NXXbZBaFQCE1NTViwYAHuvvtutLS04Nlnn8VJJ52El19+GW43P6KkVN59913cdtttAIAxY8bgggsuoP7fjTfeiOuvv74Pt0xEREREvkw1cv/oTY2s+lhERERERAayokbYAMDhhx+Ojz76CDNmzIDD4YBlWVv9cjgcOOWUU/Dhhx/iyCOPLMW2l0R9fT0effRRnHfeeTjssMOw995749hjj8XPfvYz/Otf/8I+++wDAFiwYAHuuuuuft++DRs24Jvf/Cby+TwMw8ADDzyAQIDLxLrqqqvQ0dHR9bVmzZo+3loRERERAVQj97Xe1siqj0VEREQGAdOq3C8Z9IoaYbPR2LFj8eSTT2LDhg147bXX8NFHH6G1tRUAUFNTgz322APTpk3DiBEjSrG6kopEItv8t+HDh2PevHnYZZddkMvlcOedd+KSSy7pt22LxWI48cQTsXbtWgDAz3/+cxx11FH0//d6vfB6vVv8fcrMI291n7+bJLNLUwU+/DKW43Kg3WTer5PM8o4V+IxsX4bLBc0UuG00ybxfgJ8zIBdls865BY6oinMrBmB1hKh2fnJenNYo9/KxQO5vAHC5yFxyg8tYd4bI+RlszFeUIzPMXeTHbsvyedrtOe7z7F/DzS3iI/OLG9L87Sbq5T44OzdN0MmdEy1Z/jz7pJ2bZ2N0IEW1a0pv2VdvzXpucQCABNn1JcjcbbZutPikfGQL3LmbLnDnjwG+uGXnX2og87Tbc+Tcb/zuoY9NgpyAiZ3Xwo4keY9NWdx9wWNxeezpAptpz2es9wXVyH2jmBp5W/VxOm+iQMy/kCJrkhQ512KMvH7tzCPD1shxcr4SLzlXFQBkyH6dnfvPYufKi1HNAAAGebuvq+LmkSi0c9voc/HzRra3c/PimGaaauck6+MA+OcmZ5Dbkb4gt8y8xT4D8vey9ixXP3TkuXXvR9bHAOAh688NGa7O6CDnq/OS88gAQICukbl1Lyfr45FZfj+2kHOoNqS5Y5ggL8MkWYMB/HM8W4blyH40Q845agc7X1ED+ewCAB1kjcyK5bltTLATFsHeXKaMFDlXWtrGz9M85M/J0kT90RfPBCJSWiV5YbPR8OHDcfrpp+P0008v5WLLasKECTj22GPxwgsvYPny5Vi3bh1GjRrV5+tNp9M46aST8P777wMAfvjDH+KKK67o8/WKiIiISGmpRi4d1cgiIiIiIrI9K+2r7u3Ubrvt1vV9fX19n68vn8/j1FNPxWuvvQYAOO+88/CLX/yiz9crIiIiIsJSjSwiIiIiIlJaJR1hk0ql8P7776OhoQHJZBInn3wyqqqqSrmKsjDs5JcUyTRNfPvb3+6amPa0007DPffcU9J1rHSshtPofui1Lz2RWlaBHf8LIAduuGeWjDnxGNwQ4A7wkV/hHDckvTXLtYsluCHcADBsWJRq5w5x+zwX587bXI4fSl3ty1Dtwn6uHRtfZidajm2bbCa7v2auWUsrFxcH8FFMUXL4eKrA75+gizt/vGREAhvv5nfyfcUQH/d5xvq5GLqQi+tTHAY/tJ+N3sqS0TlsZEcdv4kIkqd4jIzJI9N9sCHD9ym1CS4WMUlGQ9iJRHOQkSrs9crux7YMH33A3mMbMlwkTsDBnRRhF18euslYIy8d48DtnwJ5rE0b50RfUo1cvL6ukdcU2uEye47k86brqOWx12+BjCVJmHzfwcYLdxS4+2jQRpxNe5aLNUzEuRta3QgulswZpJoBAArcIpEj44bCbH0c4toBgIOsw0odLZdpsfH7nC1cs/YW7l7PRk7HbDy7JMllBsg61W0jboytkX3kMof6uOWx9TEABMiYPofBPdOydwQ2EhcA2snn8zoPdwyDLrauo5oBAArkddhExt+tj3MdWsrGfmSxtXSaPL8BIEE+q7ZnuXWTaWNoSPEHMeDk9mWIzORmk848Tr7mZmtki6g/mDbbBdPs/Ko0lbjNUnIleWGzZs0aXH311XjyySeR22Sekv3333+z37y77777cM8996C6uhovvfRSvz7kFePjjz/u+r6vox6++93v4rHHHgMAfP3rX8fDDz8MB/nQJSIiIiIDh2rk0lGNLCIiIiIig0HRTznvvvsu9tlnH/zhD39ANpuFZVnbfFv79a9/HR9++CFeffVVvPTSS8Wuul+sWLECL7/8MgBgp512wujRo/tsXZdddhnuvfdeAMDRRx+NJ598Ei4bv+UqIiIiIgODauTSUY0sIiIiIiKDRVFPOu3t7TjppJPQ2tqKkSNH4pprrsHhhx+OPfbYY6vthw0bhuOPPx5//OMf8fzzz+O4444rZvVFmz9/Po4//vhtPvBt2LABM2bMQDbbOaz4wgsv7LNtmTNnDn71q18BAA499FA899xz8HptZN/YELBCcKKHuALyFzudNn4DtNbFfZ4cOfwvVeCGcFdZfEaCg/zgbJROkIwGAwBvhPvczhC3jU4yoasqzkXpAEAqzQ3jrh7CL5PR2shFKQB85EOa/CzxDBft0ZzyU+0AIEduY62HO8fHBbjILwCI5bluvzHNfW6LvGbyZFQAAFS5uetrj7o2ql11dYpqN66N7yvaM1x/5jK463rHELeNYTd3XACgg4wMYX9zI02etxvS/O+CLI1xERvDvdw57nfauRZK+5v5TvK+UOPlr4UOMtWk1s2dj1kySiFrYxh+noxUYO+vbISZk15eeUaqqEYunf6qkX2WFy6UbrlsjRzxcn1RjoxOA4A0mWEZcnC1kNEH15GfjG1yV5F9QpDv09n4tHCMq2fTSW4/BofyUVVkkjRiTWz/Tx5DrhzpbErWis1JrkZmI0jZ+hgAxgS4Y5gg6+PmNB93zV6xObJGriajpHcZ2kquGagia+QxrdyzWDTNnY9OB9+f7RDkMgzD5M8aOnLcsXbwj5/IkNdXY4Zb9ycxbuXDvHYiv7jzJ0ZGQdrB3kEiHjY2nDt/ar38jzvZe2yWvL+ysah27q9sihlTf5gVMpJbZDAr6oXNHXfcgcbGRgwZMgRvv/02xo0b1+P/OeaYY/Dcc89h0aJFxay6JC6++GLkcjnMmDEDhxxyCMaPHw+/34/m5ma8/vrruOeee9Dc3DmBxWGHHYbvf//7W13O3LlzN/vz3//+967v//znP2PlypVdf544cSIOO+ywzdrfeeeduP766wEAo0ePxs0334wVK1Z0u+2TJ0+G2809HIiIiIhI/1GN3Ek1soiIiIiUheawkQpW1Aub+fPnwzAMXHbZZdSDKADsvvvuAIDPP/+8mFWXzLp163DnnXfizjvv3GabGTNm4N57793mb/Odc8452/y/N91002Z/njVr1hYPo0899VTX9/X19Vv8+9asWLEC48eP77GdiIiIiPQv1cidVCOLiIiIiIjYU9QLm+XLlwMAjjjiCPr/1NTUAACi0Wgxqy6JBx54AAsWLMDbb7+NL774As3NzYhGowiFQhg7diwOPfRQzJo1C4cccki5N1VEREREKoRqZBEREREREemNol7YpNOdmbB2YgcSic4MUr+fn++hrxx55JE48sgji17OtiaQZb3++utFb4MdeeRh9TBrQcbk5gFwGXxedLrALZPdnXkyGTgHPuc4VmBzxLlzPlzDz2HjGcMt0xEm57BwcLmk4XicWx6AYIHL3vbvwU2gY6W5Y5N6i8/nNcn8a5ebOx/dbm4bh9Xw+3F4B5dLnMyScwb5+PMsZ3LneCrHrXtomMuVztuYL2RVRzXVbvjoGNXOvyO37nBbO9UOAHyfcQH4sRSXp+0nz7OkjVzpLHms2WkRgi7ummH7RwDYIcD1Key8YewcTXaw+ydArtppIzI6TWaiN6TIOd3c3EZGPPx5lsxzOyhP7sj2PNefsXPd5C2+Bigl1cidKqlGtmDCIiYOYc9lNh6eLIVsYeeDzFlcu3iOnx+MnY8jUEPOYTOGu48aIX6ON7ZGDiS4l6f+PLd/vFOqqHYA6BMj8w7ZZ5L3E5eHj2HxkPPL1dZxteJQcp4UtkYFgGpyDhu2TrWz7tqqJNWuQB6bNa1cfTxkLLe/AcA3npx/qZ1bpncpd97Gk/x8YX439xyYLnA1Toasjy0b828GPWSNTC5zDDkPLtmVAQDi5DMEe9e2Ma0afE6usYv8PFmym2pM8f1Z2M2dF9XkPDupPNeOnI4HANDBfnBqvYrcEhnoippxd9iwYQDQY5b0pjZmV48aNaqYVYuIiIiIDEiqkUVERERERKQ3inphc9BBBwEAXnzxRaq9ZVn43e9+B8MwcPjhhxezahERERGRAUk1soiIiIhIGVkWYJkV+FXcCHXZPhSVHXLWWWdh3rx5eOSRR3DppZdi77337rb95Zdfjn/84x8wDAOzZs0qZtVSBCbywSIHw7psjMPNkvEMGYsbUhx2ckO48yZ/mjdaHVS7v7cPpdpNWDGEXve4eDvVzuUmow/IaLB4jIsvA4Cqai5WwL2aGzZfiHPnWSrNxU8BQDLDnRdsBFXtMO6zBHbi33/XBbn9WGjkzkfDzV+Hho+MyYpz16GZ4Y5hIUU1AwAEGrmYFHYkd6GD+yx5LmENAJDLcbECIS/3WfJ0HCN/nuXIPoDFxhRUu/kh9h3kfqTj3eg1AwEynoFFx5dxp4QtO4a4aJHmNHctRG1ELvjJEyNV4Pa3z8Hds9n7dQF9sMMJqpErTx4mmF6kQD5E+51sv84tL0teQwAQdnN9K3tPacny19FHHVyk34Qv6qh2o+Lcte7y8jFQJhlDl4hxn4WNQHat4eNzTTLKJ5vm+v9EkouM83n5GOLICK6e9U3gavNIkPvMhaZ2qh0AGOQ9yvBy14yZ4GLOAMAia2QzTd4f/eSJa6MYKrRzy8xHyShyMnYrSEZ+AUC+UNr+rNT1MQD4yOjeMBkvzEb8svUxYCMC2Vn6qKw0uc8b0+zn4T7MDiH+50DNae5zR8kuMkCmC6dsZKL5yLqiOdfz9ZW3+L5eRMqjqBE2J510EqZNm4Z8Po+jjz4ad911FxobG7v+PZ/PY926dXjyySdx+OGH4/bbb4dhGDjllFNw6KGHFr3xIiIiIiIDjWpkERERERER6Y2iZ+d96qmncPTRR2PJkiW46KKLcNFFF8H4v1k299lnn83aWpaFgw8+GHPnzi12tSIiIiIiA5ZqZBEREREREbGrqBE2ABCJRPD222/jqquuQlVVFSzL2uqX3+/HFVdcgddffx3BIB9vJCIiIiJSaVQji4iIiIiUiWlW7pcMekWPsAEAj8eDG264AVdffTUWLFiAxYsXo7GxEYVCAXV1ddhnn31wzDHHoLq6uhSrkyKFzCq4jO7zjpsMMi+azJQFgDC4H0IUyIkpmvJcVrUT/Db64KPaeci5e7Imv+58nnt/Gh7B5YibZNy4x89l6QKAt4Y7Ns5aLifb8JLzyKT5XPI6MpbYQzZ0DiE/i4c/1rnVXP41O0eL98gd6XWjpopq5lyxjmpnNXF57FaazN0GkItzbX0jueW5xnLzNLlifJ72KE+UameQ52M2xl3/HWRWPQAUrADVLk9GJ8fIXPLRfi7THgB2GtLGrTvFfe51cX5OrsY0l6nvIn+1JUXOYdOcsVN6cQeHnJoGTvKEZOecA4AQOedMxMPtSB85P50vV0u1y5kZvE+17BuqkStHyPD1WB8DQCuRDQ8ARp673tg5Ge3MP9uS4bLp2T7B5+BrHPJSp+ebKJD1cXAUX2eY5O3eHeAm4HPXcMtz1HFz4gCAkeCOYXg492GqneQ8OzX873Oyn8fwcfeJPDvHD//oAtehZI1M1seOlevpdVut3PMLWyN74tz56BnBH0PnaK5ucsa5B8vhbu4zGzZKoWw7146tFXMmVx8XLH6umzhZI48KcDXy+KFcfRxP8M8FDWSN3Jzhnn2d5Lw9AJAm5+psIedoAdh6lt9GJ/nznTy5TCc5x2y1hz/PvE7uPPM6e+6bs30wl5OIlFZRL2wefPBBAMDkyZNx0EEHIRgM4oQTTsAJJ5xQko0TEREREak0qpFFRERERESkN4qKRJs9ezbOOeccrFq1qlTbIyIiIiJS0VQji4iIiIiISG8UNcKmuroa0WgUO++8c6m2R/rBKGc13I7uh89WkfFO6QIfm8JGObTmuCHXYQc3XDdp8hEJFrmR7PDa5TFuyDUAJMmh1LuYrVQ7t5vLC3C6+GOYz3DveB1N3D53urn96IlQzQAAZFIecm3cus00FyGRi/LDihvXhal2Pi8XhzEsuJJet2sSm9tBvs8nYy4cYX7IfmBcO9XOTHHHMF9PRlLk+GHz+QR3vB3kOZ5JcPtx7FAurhIAJtc0Ue0sMsqrpYGLtayu4SL/AL4PCJIRRAHymgGA+IYhVLsmMsIsRsb2kIkLAIA68rLJkd34+BC3jUnyswCAl2yaJ6NFmjNcu0a2b7b4c6KUVCNXnuFeLzyOnqNxq8j4kkyBrDPIW09Hls+BCpExa6k813mYZDwjAGTJ/ujzGBfPk8hxffAkq4VbMfjal21XIGOIHc18ZChbP7jINEWTvDXnO/jnAkeGi+jKx7jltdVzEWseD38t1JI1snNiHbdAGzdxw8092xlB7pnWN5Y70awUfwwL68kambywC9wpYSsSLZck48aGczXyhBruWZqNpgaAjvVcrHq4lqtd2Os/UM3XOP5Wrm18/VCqXWuWP4hsrDIbJV3n5fYPWx8DwJgAG0PMbaSbrI/txJ02kTVyU6rnn8XkBsscKabFF1oDSSVus5RcUSNsdtyxMxO2rY3L2BQRERER2d6pRhYREREREZHeKOqFzfTp02FZFubPn1+q7RERERERqWiqkUVERERERKQ3iopEu/TSS/H73/8ed911F772ta/h6KOPLtV2SR+KeB3wOLofljo6yL3LayWHZQLAFzFuCHALuOHMVQUuSiFu8PE8IxwRqt34IDdEce/adnrdrGXNXKRVnY+LXfC5+FiBYcOiVDs25iiwE3eeOcdUUe0AwNUQp9rlm7hh4Q4vd45b7VQzAIDfx627UOD2T3o9P6Q5EOZyKYwIN7TfinLXtTGBi58CAPcRtVzDdu5YFz5rptqlG/lrIZ/ljo2V5s6fXI5bXmQEH6fi5BLM4Kzm1j16NNeXWjaGcGcbubbr67nMFzZGEADGh7jzx21wOzJJXq9hFxcLAQAd5HnB/vaNz8Ht74KNyBe292nlugq0Z7htdBvsfuT3dympRq48NV4DXuLcH0mm3baSMbIrY1yMbCsZDQkAYZOLWIqbXJ851MPHmo4LcPfSPWtLO/psRSMZ+QqgxsftS6+bOzZ1I7hYKTbmCAC847lYO7ZGNjeQNVMzme8GwCBrZJAxa14ft79NMpIIADLruPPRR9bHdiJ+TfLZ1zGBi6ByHUbGtrVxxxoAzM+5+NxsE7cfC+TPBtj6GADyWe4+Hh7BnbvOEBlrXM3/qGzYKK4vtUzuvpAn93fjarLYB+D1ctfX+CruWvAm+HVXk5FoIVeJY4ipVp0CTq5/ZiPMTJQ2ChgAolkyKpOoZSxy+0SkfIoaYVNVVYWXX34Zu+yyC/7jP/4D559/Pl5//XW0trbSc4GIiIiIiGxPVCOLiIiIiIhIbxQ1wsbp/PebcsuycN999+G+++6j/q9hGMjn+cngRUREREQqgWpkEREREZEyMs3Or0pTidssJVfUC5sv/4agfmNQRERERAY71cgiIiIiIiLSG0W9sLnuuutKtR3SjxalV8FpdJ9tPSE1hlqW30YWf84q7VtiB5m7mTH4OR8yFpcX6zS4S8eySp8N2pHj1p0zuZD1oX5+/7S3ksHtpHScyxr2fd5OLzMZ5XLbTZOboyWV5jLEswX+WohnyG0kz/HVbdz8HgDg+oy7DgMu7re7/dzuwcjx9VxDAN5x5K2JnCslsZxrl4rxueTROHf+5Mmsalb8C269ABAlz7NqMst/+Egu0zqb4kuLj9YMo9q1ZLnPMj7IzSMAADny2HTkuGs7Sy6vlZz/CADas6W9h3TkuOVFc/zLBR/Z9SXJKaJSBa6PaiAnDitY/HwMpaQaufIs7miHy+j5PrBjipxTy8Vdb4U+eJlnkF1H1uLmXcgWuD4YAFzkykv9u6Md5H0C4Pv/OrJGdrfw9QPLH+OOjXdlC9Uu1c4+u/D38Eyaa5vJcu2S5DG0MVUe8s3csXZ9wp2RfnJeIwDwerh1D5uwmmrnGUOe4zb6lNRy7jxLR7ljmEhw10KOrK0AwDC4z5P8gts/sTS3jVUB/hm5biRXf+aS3Dnx6erhVLvmDN/37BAit5Guj/m+gq2R28i5G0tdHwN2amRueQGyBsjY6NBSBa5tU7bnZ7u8xc+LJyLloRc2IiIiIiIlpBpZREREREREeoN+YfPggw8CAE4++WRUVVX12QaJiIiIiFQK1cgiIiIiIgOMVaFz2JQ4nUgqE/3CZvbs2TAMA/vvvz922223Lf69qakJd911FwDg2muvLd0WSsmZKMBA99kkebKDSOT5IZwmuLZVVohq5zG4odQuO0P7LW6Yeyzvp9q1Z/lhykEXN752iJeLdwmSQ/YLJj+keEM8SLXLkPFgqVaundvBn2chN7cfXQZ3jnucXLt0nh/avyHNxVqxwUnJAh+xVCBj+iZXcfFXiSyXifbXD3ag2gFA+zvcNdtMRksdWBun2u1Y1061A4BIVYpq5/JwOVDrNnAROyujYaodALSTUQUbmrl1p1dx8WV2okoSee58DLq4hboMrm8GgJCLzOgisWW100aKQ5yMJiMTEmh2HhEc5Odho9M85AKHgHsxkkPfRz6oRh5ccmTcUIG8fvPk8sJOPvLL7eDuj05wF6adWONYnqsLOshYIraeHeLj44vYWKsCGePT1ME9u7BRQwCQaOL2o9vBHZuQm3t+cNqouT1O7j6ayXP1SFOKj31lpclnErY+3qk6Sq/bynDH8G/vjqXaRd/k9mNrln8m2beG+zzjhrZT7cJV3HXodPPnWVMDd3190cbVBWyUV2NTDdUOADIruPOHTauLk8l7IRt5OW4yWi5kI/av1Ngekt0/dp5J2KZ2lsnwsoU0ADJlEUPcPd9fc3ofIDLglSxcv7GxEXPmzMH1119fqkWKiIiIiFQ01cgiIiIiIiLCKu1syCIiIiIiIiIiIiIiImKbjUGUsr2wYMHqIfAk4ORODbeNIZw+kxue3Z7n4ktCLnIbzSFUOwDIkfmWE4JcrMB+u9XT63aRKQDpNjLmghxq3riej1iqCnPD3E0yZq2+hRu6zkasAfxQc7+HG0vNxj3Y4Sdj1pJkzJqdSDQWG9vBxoo4yWH4ABAvcOdPghwOX7C4z+L18REAThd3DNnrMOzn+r0wGSEDAB057vxpzXD7O0rG+7htnY7k/iFvNSP9fCROtY/b56bF9ZFNZPRJe5a/bybypc0raM+SMUBsRwog4ebuxXkyQyJD3ofZyCeQ179IwSjAMHq+RvxO8p5Cli4Z8h4ezfH3qDDZEXscAapd1kYGzPgA17fuuecGqp2DvO3l2vm+1UHem1vXcfsnGCbvJ2R9AwANzVyNnCVrZIuM/PKT0WkA4HGXtkb2kTV3goxYA0pfI7MxeQDgcnHXrIuMoUvmuXXHybhZgI+C85A1Mlv3stcgAAT83DkZTnGx2DHy/LFTr8XIfW4nFpdh2Hi+Gu7n4pzDZH2cN/mfIbRkuUjPaI7bQck+SG3ryHL1Z4EszVNkflnOxv2VvRe7iJ/RmSjxySgiJacXNiIiIiIiIiIiIiKyXTBMEwb5y2ADSSVus5Sefu1QRERERERERERERESkzPTCRkREREREREREREREpMwUiTYIDTNHwGV0Hwgd8XN5yAEXn33JRuI709wy2flm7Mj1MLfPRjtXx6l2wb2D/Mo93D53ropR7QxyfqHqJJdnCwCBGi4bmH0VHI5zmcReG3PYsHJkprVJZjs3pchJiAB8Hufapsm5gNI2IsTZ7OTaNLeNQywu59jObwcM93LBxO1Zbs6QODkXkJ1seZM8J2NRbhsboiGqnYec/wgA3GQmep7snNkpy3I2uuYAWYUUyPtHlZfP3q8Jc31fG3ltx8jzLOSykzfOzm3BLdNpcDvcztw5NV5uG9lj2EpOQ8TOs+OwMd+ADG7DHFVw91AfA0DEy3WGQbJGZs9lV5p/bGMz8dna3E7NvVOkg2rn24Ob/8Bwk/XxWq4+BgCD7BZCca5D8tWQ+8dGdxSKcfezfInnaMnbmjeSO8dbyfvoF3FuziC2PgaANFnbsfPv2an3a71cjczO81jn5Z7D2nP8fIeJPFenssc6T877km7n1gsATR3c8zQ776ib3N9kaQUA9GwgbC3kc3IN2eUBQIiskaurufq4g3xWBPh5p4JkjTzMx86/STUDALjIejaR57axhqwV7Dw3dZCPOcwybZw6IlImtl/Y/OY3v8GwYcO2+PvGxsau73/yk59Qy7r22mvtrl5EREREZMBRjSwiIiIiMkBYFv/bKQNJJW6zlJztFzZ33XXXNv/NMDrfIl9//fXUsvQwKiIiIiLbA9XIIiIiIiIiUixbL2ysEr7l2/jgKv2v3dEOp+Hptk1TmhtK7XXww/DZeIbGAhc3VjC42CSfxQ/XDTm63y8bVQe5ocKGn1+3VeDGw6bWkkPN49zlncvx0QcBcOOKMx3cMtmIBHa4PgDkyZiEIT4u5iKV587x9hy/jWEXN2TfScZcxHL8dZiwESPB6Mhy18yGDL9/kmR8RSuXNIHWbOmvBYOMU4gmyXM8x22j10YkWpo8f6rIQ+MhM9HsxDOwUUAe8hQfEknQ664ax+UK7GS0Uu0aVo2i2o328xmGbORDQ7q0fUUdHe4BBMlKko1jHElGsq5JcOdOtp8i0VQjV76OQgouo+c+tjnFnfRxMhItQ3aaLRk+8rFAhp34HGQEspO/P1aFufrK8HNRoCCfHzJr+b41E+OOYZ6sC3zgjk0uyl/b7Wz9QNbIbH1c5ydzKQGkyNqlnawVw26yPiZrc4CPYkrwlxeNrpHTXLskWde1krFkANDG1sgZ7lpwkFFe8SQf25Yij6HbwdXIabIuYOtjAEiTMcRsjcy285DrBYDaOq5GDu3A7ccdjDZ63Y0ruP5slJ/7+c5QMm5sQ4b/cWeMjE9jo87YeDe2PgaAUX6u8Zpkz20yJf6ZgIiUHt2Dvfbaa325HSIiIiIiFUc1soiIiIiIiJQK/cLmyCOP7MvtEBERERGpOKqRRUREREQGGNPs/Ko0lbjNUnL9kxMhIiIiIiIiIiIiIiIi22RrDhvZPowzhsNtdJ8bO9THhbam83xuat7icolDBpdxGre4jOVGRwPVDgBqHeOpduvaqqh2df9qpNdtkdnJ0dYA1S4ylJtnJ5u08faefMWbTXNdi8/JnRMeG/N2tJDzL7VmuHbpAjmfQpIPOo5z8bxIkXO5uG28emfzdIeTc/z4yPl41pH53ACwPsV9IJPM6Gd3T9147poBAGeYOzb+FVwgsqspTLVL2ZgrKWdx28jun7EB7li3ZPgTsomch2i4l+sDgkP5EHrPLlw/XlvNZX6PbuLOHzaLHQCaybmf0m7uWNd6uGvGxq0dabKfIqfzANvbsxniyugW1mhPGB5Hz7VBrY/r47LkyczO8Rhy2cjiz3P3nuYC179N9FTT617fwt3PIku5+cHMLDknRgs/J0Z4OHfzySfJ+xlbH6f4uYC8bq5Y9JB1WFuae75qI+togK+RV5PzlbB1b5Kfroiukdn6eKiNOX68Lu4YNpD7nJ2vjuxSAADk9ISI7MjVV85q7pzwfh7lVgzAvYE74GyNnCPrAgf5nAEAY8n5CVuy3P5pImvpIR7yoRKAfwi3je6JEapddRU/b+TIDcSkKuBrZP45nn8mibi5422Rz1dp8jxzkvOiAoBJrjvi6bld2kY/KiLloRE2IiIiIiIiIiIiIiIiZaYXNiIiIiIiIiIiIiIiImWmSLRBKOR0wePo/tCPCnDv8hI2clMKFrfMgskNZ14e49Zbg3FcQwABJzdMOUkO101t4N+J5nPcun1+LubCU0tG32T4uLFUK/e5DXJob84kzwly+C8AhN3c/mEFyTgDj4Pfj5/GuViKjhy3H9nYFcBOLBHXMEsONWdjkwCgJcN97gCZLLIqyTUkUxsBAM5qrp8K7cCdj4bBRUOkEny0XEPST7VzgPssQTJ2pSPH93tsVInbQfZnbHwNAKM2RLVzVXP7ccK/uHiflQ01VDsACLu5CJKwi+tTWFEbsW2NGe76CjlLG8fW7iCva7IvEwl5DHgcPfcho7lkWiTy3LlXsLhz2STbAQCbNlTj4O4pfhfft6YLXP+RIVOD82Q0kJesjwHAHeGOjUnWI+kWbhsNG91RnowbK5CxO0FXaetjAAiSsW1sBPJnMe7iaiefmQA+ctBJRovaiRvLkseQje5sIWNk/fzuwSoy0tkik7eMMNmn7EB+GAB14KK3Mkmu7xme5M4zh8HX3EHyHI/muYPD1r12nj/zXCoZUMfVx85q8mYIYIePuYj6NfVcjRzycP1ZsMT1MQDEyBq5iYw1DrlsHEOyaXuu522005dVNNPs/Ko0lbjNUnIaYSMiIiIiIiIiIiIiIlJmemEjIiIiIiIiIiIiIiJSZopEG4RcDsDl6H7oNTsMl42zAYC1MW6osGVx6x4X5Ia4FmwM92SHzf+9PUi1a1i6A73u1iw3RHqfGi4Lbmymg2qXJofrAoDHzR3DTJbrWloyXqpdmIxcAACnwQ0fNcmYNTc51NxObFuajN4qkCNhbaTawdvDtb9RnoyrC3i4YxMhzx0AsMjYFzZCYn2KW6/JpU91YuNhyP7HIBfndPIHO0BGmIHsA7LkOeEjo68AoMbDHcMNZOxWKsb3Z1XsMQxwkRihEVw8w5AYm0lhLz6HwcZQJsmoQwDwEhFSAOCzce4yguTxc9opAmRQcxqdXz1ha18PWUuvinPt7JzJY4JcHcZelTkbl+8/2sJUu8aPuDq+lawp96rh6l4AGJXhMuMyGW7dHrIWslNzt6bJGpmMBnKQZxAbiQvYqZHJ+FyyrmNjgQA+NthLXtdsfQwAPjKGrpqukblaKGsj66gxzbWjI9HI5wy68wFfIzvIPtdPxl0jw0eisc8k7H2hhlx1o40+JRPj+rOQi8zU83N9FAD4R3IHvKaDq5HZ+HU72HjJNBl16CP7R7YdAPrX7YOunvePow/2oYiUll7YiIiIiIiIiIiIiMj2wbQqc8KeStxmKTlFoomIiIiIiIiIiIiIiJSZXtiIiIiIiIiIiIiIiIiUmSLRBqFV6RhcRveTNQRc1dSyfGTEqR15cg4bJxnun7QRdFwg171HNZevuvOQNnrdLbEA1S6W47JqTTJjuWAji7k1zgXqpvJk5jeZ2WpnRCg9LYWTyy+u9nPhznZyYD+Nc5m/TvKz8AnCwJ4RLk+71s9N/GKRc/dYNjLRa7xcWxfZB7B9SqKVz6p2N3DnhUlmg/uHc9uYX8P3Z01k9jabu92aI/sUW/OGce32rOZ2ZGuU60cBYMjSDVQ7R5jbj7kotx/XRrn5HQAgQfalrVk+w5yxJsnf3JmcbABIFrjPws43wM4FxrYTWZ3Iwk3cV4Iu7q7rIedysMi5RezM28HWD2muJKDrYwCYUh2n2u00vJVqx/brcRvzTZjkfa9AzufFbmMmxz960/PDkJ+FnQOPnt8DQNifodqx8024Yn6una1fOeX2z+5knREhnwvsYJ9zIuQp7rYx/12OXHeymTt3ndXk80OG71O8w8nrdS13jjeR80PlyGsLANryXB3G1sh5ct27R/h5EdvbuX6q5pMGqp0R4vvcPDnF2Pp2rkZOkPu7jZwDzY76FLduPzmnZ8LGvJHsOZku9NwuQ7QRkfLSCxsRERERERERERER2T6YZudXpanEbZaSUySaiIiIiIiIiIiIiIhImWmEzSA03B2C29H9UGA2kshOREKNl4tYSeTYqDNu3UE3/14yT45JZ2MKvF4+ViCQ4XIp1ia54cwNHSGqHRuxBgA+J5ctw8aD7VAdpdp5Pfx+zOW486w1ycUusO3yFn+eVbm482ctGVeX6YNfwKgOcpEPLvKz1Ma48xEAQi7uGMbJPoCVzvDXgrOGjPcg+x8rw11bwTR/LYxYyx3DsQHuGK5KcH1Pa5aP0/KRl02ejN5rJPtHAAi+2X006EaBANeuobmWarciwfUpABDLczsoS0YkrE1w6yXTBgHwxyZFRi/EyHyWhiR3LeRMZaIJZ7jPDY+j54gXNpaIrZHZmjtBxpcBAHurCJK3PTvRQGyN7PZy12bAy33w9fEg1Q4AmtrJGpmMmwzYiBFjja3hamQPWSOz9XG7jXsU25aNbatycefEOhs1d5Z8tmMjfquq+Eg0J1sjk1GpYRfX+cRKfzoik+Z+bBSpIWtAO7l2Ge4D+chn6ZGrudi2sUH+AWt1nOtTWsmILo+DO2/txIY3k8+0gTe4utcfJItKAE1NXNw++6wRy3PnmZ17V32qtL/LXuthI4P5ZcbIn5NtSPV87rJ9o4iUj0bYiIiIiIiIiIiIiIiIlJle2IiIiIiIiIiIiIiIiJSZItFEREREREREREREZPtgWoDZB/nxfU2RdQK9sBmUfE4DHkf3g6sS5NwQORt9X4HsdMLknA9s9K2Pn06BngeAnYPA0VBHr7uNzMluIufZSBe4HbQqyc/bMcrHZQgHyMzmoJtbnsdd+jkI/OS6s2RGLsBfDA4yTjfk5hqa5JwPALAiwXX7u8a4DGE2q35Dmgz9B79/fE6uYUOK2z//auSv1+Df11HtfFUZql0uyV2vbAY0ACTz3LFm56Zi+5SmDJ8Xzfa5Oav7edc2YjO/AcBpcOduXZrLq29Nc9vYZmOOn9Ys2QeQH9tF7h+njUlssmTXlyM3MlMobW67nneE5Xca8BA3oGSeuz7y5Bw2BfIaCvHlGtzkjZTtM+3MA8DOQeCo59bdkeXqh6YMX2ckC1w/XJ/iljmMnGcn4ORrxYCHWyY7hw3L7+YnS8qS+5EN9WCf7YIuvmM3yblp2Oehye38XHkeck4e9txlb812nn0bySl5PtnA1ciBf6yn2nmq+GuhkOI+eEsDN49MnJy/1UcePwDIkH1kc5Y7yfPk7ilY/JxTbroG5NrVprm5gACgg6yR2Tl+2sm5XGxMt0x/bvLxk55j1s7P00pZI6s+Fhn4FIkmIiIiIiIiIiIiIiJSZnphIyIiIiIiIiIiIiIiUmaKRBuEvE7A08OrOnaoZ5uNMZxZcghniIxEIxeHvI0YB/ZzszrImDMAaEhzbZNkLFG6wH0YNn4KAIawkQ/kEPLlUW7oelWKH+7td3LREOwwd3Yo9dokH5FQn+KOIXt59XQ9b4qNSVgRDVPt2B6AjQAAgCiZxpEmOwH2ul5PXoMA4K8l4/xGcDvcXMHtyeYkfy2w8VfrUlxMQYyMAbLTj6bI+E2nj1teyMmP7+8gIzE8ZIRNK9nf+2xs41Av15Y9Ns0Jbr124mbYSLTmNNeQjToMublrK2vqd5OE43IATAnK1k2xLNeOrY+DZEwqAOTJrBP21sxGxdgRy3ExUA1klE6SrHsBPuKTNcTLHeyAi48vW9FRRbULk3UBW/f6bGwjGze2nozJW5vifjRhJ0LIS95z/WS7NTGuPgb4yKEWMgaKr4/5a8EAt5FstLG3hjs47pF8zZ1ZyX3wVvJaYOOcG1Jk8QmgI8fVJC7y0ETJY1hHxvsCQIiMLI+x9XGGj4xrzXD9uI+suevIfMCEjb6+Jcm185N1Klsft2T4+6uX/Dhhol5gY6krnmXZy8YbKCpxm6Xk9BQrIiIiIiIiIiIiIiJSZnphIyIiIiIiIiIiIiIiUmaKRBuEDMOA0cMwUnYEHhtdAgBuMkOCjZpoJ8eZ+p38e0k2biJHDiH1ksN6AcBHxhc1ZbjlBckx1x5yvQDgJtuyEQAWSru/AaAjxw1fj+e582KnUIpqF3Dyw8LZy4ZtF7TRkyfIpAt2aD8bNREnI5sAIEnGZNVxo+tRTaYu1Hr4GBCLbFro4M6LdJyLmnDauF5NchvZPjdNXochG+djnoxTaSH7vZYM/TW5GgABAABJREFUmfkHIEBGQ7D3Q7Y/s3MtRNxsjBi3kekCt3/sxDOwcU7sfYG/f3ANFSggLMPo/OoJe46yEaR0fcwtDgAQzbExUNy6/Tb6dbZm8zjIiC6ylm62EUMcIJfJ1shsxBIbIQbwfVfW5E60aIrbP/E8fx/dMcTlbLJRyWzdy8ZKAfx1yEYndeT4i8FDnhdsXcBGndV4+DtfNVln1JA1skU+DpkdfM2diXEH0UnGg1nkj8Ds9LlsvBQbvVdFdmetNiKn2bY+8ryt7YP+LEVehxE3d6KxfT0AZE3uvGBr5Cx5LfRF8hXzqW0kS4pImWiEjYiIiIiIiIiIiIiISJlphI2IiIiIiIiIiIiIbB9Ms/Or0lTiNkvJaYSNiIiIiIiIiIiIiIhImWmEzSCULVg9hmV6yDztnI0Xv+ycKj7yrGRzfAs2gkFTZJxue45717kiQU6yAaA+xeXzpsn5AmI5bv9MDtuYZ8fJ7aBEngveTZLzyGTIYw0AMTJ7uzHDLTOaC1Lt2ExiAKgjs6UnBLn9nbExx09blts/OTKXOEnmITek+P3D/ibBUC+3TDbnOEXO7wEA61ZVU+28ZOZ3QzREtfugNUy1A4B2sg9g54fpC20Zrv9hc+09NuZVW5vibjZ+J9ePs9cWOf0aAD5T30vOszDUy62cvb8CfEZ3qeexK5D7kW0nkisABnEpsXNisNdGkJyrwM68kWTXCrKkRJqfpg/t5Bx4ViJAtVuXZutjqhkAIEbW8ZPC3D3cR85jmMzzj95JsibJmNxniZHHpYm8lwFAR66KasfOIzHEw+3HYIA/2OycSq1Z7tjYmVeTPd4b0uwSuQt2iIe/8UXIOpU9HxtXcPWsz5ej2gHAhjZumf9o42pzto9qIZ8VAb52YbWS86TYWa+HfMBa5+ZuSkEX/3MOdu4n9pk2Rv4MgZ0jGABqyPmc2J9fsDWAy8av0DPz7AHcvZ29/4tI+WiEjYiIiIiIiIiIiIiISJlphI2IiIiIiIiIiIiIbB8sCzArcDiRjZQg2X7phc0gtCzVAZfR/djrfatqqGVVefhxuO3k0N4YOUKajTmp9vIDydiWUTJqKEvGFABAe5bbPyN83PJyZCeftjG0P5bjhkjHyAiAT2Kl74KCLu5zB8lVm+D2j0XGFACAk8lcAZAm45BsHUMy9o+9GtjPwkbIAHxNVZ/itrExw7WrIaPqAKAtzV2IVRaXN5Ylj7Wd4etsrFUizy00lec6XYO8ZgAgQ3bk1V4yGsbGDmohz4uhHq6zYK/DgI1rwUH2K61ZNuKRWy95qAEAES8ZX0GuO0FmrfrImFWHItGE9Hk8DTdxT9ur1k8tj62Ro2T9l8jx/RvbFUY89CJpsTwZq2xxnWEH2b8N9ZV+/6TI+yhbHydsRKIti3HLZLs49t4TsBHxy7a0yLrAIJdo5/mKPYYJOgqUv4mzNbKXzLViz9t1aX7/NGW5TiBCxkV1pLiYrIKNZ5c8ebwLZJwzG5+YslFTJsgah022ZCPQ2Rqsc5lcO7ambCf7PQBIks85bHwiux9byfg7gK+R2fOsmnyuZNcL8PH9TPydXgeIdJo9ezYeeOABAMD999+P2bNnl3eDNqFINBERERERERERERGRQaJQKODDDz/EfffdhwsuuAD7778/PB4PDMOAYRiYOnVqUct/5ZVXMHPmTEyaNAnBYBC1tbXYc8898V//9V/45JNPSvMh+tjcuXO79se2vnw+H4YPH47DDz8cV111VUk+m0bYiIiIiIiIiIiIiIgMAs8++yzOOussJJPJki87Go3i/PPPx+OPP77Z3yeTSbS1teGjjz7C7bffjuuvvx5XXXVVydff3zKZDBobG9HY2Ig33ngDN998My688ELceuutcLv5EYmb0gubQWi8txoeR/fDldnh9Wkb8Qxkog0d+VAgI7/sRAgFyevI7eAWygxH3YiNjPKTVy05ohhJOgIA8JDZMmEXN163YHE73ENGBQBAHTn8eMdg97GAG6UL3IH5oJ2LAACAxlRpY8SS5BB3AMiSF4SfjBuqcpc2Og3gI9HYKC82PpE9dwAgQJ7jNeEU1Y7tKjKtIbIlEHazn4eN0+K2kky0AsDHpNAxYmxGAvgYSra/r3VzF6LXRtyMj+xz16e5vjTo4o4hG7kA8PUC24+nyTil1gy3kTnTRgcpg9r4oA8eR89xl+w9ir3fetj62MZ1WSA3ko12Cdl4YiTLAnjJvpV9fmCjdAAgR8YxpcgYHw+5bofBH0SyxAFZrqHGw/WFOwa5ugXga+R/dHAxgo1caU5HiAFAhrwW0uShYetjAKhyk9Gd5CLZ6iFB3kcBvj+LkM/IPhd3nlVX8+eZk+wrsq3VVDs2PputjwG+xsmVuG+2cy34yb6Uro9tPNvVergLjF1mgDzP1qf43M+Ak+vv2Xg3dgoS9p4JAOvJkrYj2/M9KVuJ87r0hml2flWaftrm9vb2PnlZk8vlMH36dLz66qtdfzdlyhTsu+++SKfTWLhwIdavX49cLoerr74auVwO1157bcm3oy+Ew2HMnDlzi79PJpNYsWIF3n77bWQyGZimif/93/9FU1MTHnvssV6tSy9sREREREREREREREQGkeHDh+OAAw7o+vrLX/6C22+/vdfL++lPf9r1ssbn8+H+++/H6aef3vXv2WwW//3f/41f/OIXAIA5c+bgyCOPxJFHHlncB+kHtbW1+N///d9t/ntLSwu+973vYd68eQCAxx9/HDNnzsQJJ5xge116YSMiIiIiIiIiIiIiMgj8x3/8B1atWoVx48Zt9vfvvvtur5fZ2NiIW2+9tevPt91222YvawDA4/Hg5ptvxurVq/H444/DsixcddVVeOutt3q93oGirq4Ojz32GPbaay/861//AgA88sgjvXphY2MAnoiIiIiIiIiIiIiIVKoRI0Zs8bKmWA888AASiQQAYNKkSTj//PO32fbmm2+Gw9H5WuLtt9/GkiVLSrot5eJ0OnHqqad2/fnjjz/u1XI0wmYQchqA0+DzTrtjZ34YHzm3gOEpzbb9e7182+Z0aXNlczZyjtm5e1rIrFo20rbGxrwdrVkuwLg9x3Ut7FwXdraxmpxHgs3dZueGWN7Bz5NQ4y3tu/IIH88LNo+5Icntc/a8nVRd+nmI6lPcftyQ4k60+hS/jQ1JLo89S+YcN6d7njcBANI2puNgux+2r2DnAuqL+0JTmlt5wEafGyKz5dmIZ3J6GDRn+NKLnXMgS87HwM6B0Z7l2tlhken77PkYcHJ9eNawUQTIoGYYnV89Yc9RtsZh508M2npq4zaSnXehJcN37HyNzC2P7bdas3xtxR7DiJs7iO0ZrhBj62OA79ervdw21pS4PgaADWnuc38e45Znr57lVJHXF9uukZ96hZ4Pbqcwd30N9XLHcG2KP4bsvEHryJq7kayPcyZ/vbak2BqZu2bYeTVtTA9D14psjcz+/KLVRt/MLpOtpdl5tgB+PsjWLNdH0vcZsh0AJPLcOdmRoxdJYefz3NiawcxZ6SbvMSJ2Pfvss13fz549G0Y3xfW4ceNw1FFH4a9//SsA4JlnnsE+++xT0m25//77sXjxYjQ3N2Po0KHYddddMXPmTJxxxhlwufrulcjIkSO7vt/4AssujbARERERERERERERke2DaVbuVwVKp9N45513uv48derUHv/PtGnTur7fOO9NseLxOE488URMnz4df/zjH7Fu3Tpks1nU19fjr3/9K2bOnIlp06ahoaGhJOvbmk2XPWLEiF4tQyNsRERERERERERERETEtk8//RTm/71sMgyDGi2z7777dn2/dOnSorchl8vhxBNPxN/+9reuvxsxYgSOOOIIhMNhLF++HG+88QbeeOMNTJ8+HRMmTCh6nV9mmibmzZvX9efDDz+8V8vRC5tBaFm6DS7D222bHc0ItawAmwEDoD3LvSX2khE5ZDN6ODoAuMmP4ydXHrER5cVGPqTIsdRsFAe7XgCoLvGQwSA55Nqy+P24gYwbipLDmdnh9VU2jnVbhjs4Q/zcCemyEXHIX7Lc54nnuHiGWI7MmrCBvRa6G4a7KTtRXkvauciHoIuLcWjPctvIRlcAQJ7sA9Jk1lmCXKDbxvmYI6/tLLmNXiffR7HxcmtS3DJrPeT+IWMhAKA1y+VXsFEl7JqDLn4b2dieNjK2I0fmihTIc8e0cf+Qwe2LeBouIipnXIDr1/1kjcNGEHps5CKw6ZApMtPGRtokfE5umdVuMnqVjKlJ2YgMZWvkWI7rg6vdpX+kZvthk4zTYevjWJ7PJUuSzxBhsgRk7xO1Xv6EZJ8X2XZ2xMjY4FiejA0nIz5T5LNLp9LGgf+jPUi1C7oCVDsAaM9xfUBTurRRXhkbfUqc7FTYnzWwzyQZGw8vLge38ixZh9Wn+Gc7Nl6SjatrI/vmehvxgGysXYDs7tn7TFuWP4Zkl4I88WHY4yxix6efftr1/bBhw+Dz9VwzbzqHTmtrK5qamjB06NBeb8PPf/7zrpc1hmHgZz/7GX70ox/BuUmc9meffYZTTz0V77zzDj744INer2tr2tvbcdFFF+HDDz8EAITDYVx44YW9WpZe2IiIiIiIiIiIiIiIDADRaHSzP3u9Xni93f/yfTm1tLR0fT98+HDq/3w5Lqy1tbXXL2w6Ojrw85//vOvP1113Ha6++uot2k2aNAkvv/wy9tprL6xfv97WOlpbW3HRRRdt8ffpdBorV67Em2++iXS687dsR40ahfnz52P06NE2P0knvbARERERERERERERke2DafHDpwaS/9vmsWPHbvbX1113HebMmVOGDeLE4/Gu7/1+LpHky+02XYZdf/jDH5BMJgEAY8aMwVVXXbXNtkOHDsX111+P888/39Y6YrEYfv3rX3fbxuFw4JJLLsENN9yAQIAfUbrFcnr9P0VEREREREREREREpGTWrFmDjo6Orq/uXkAMBBtHlgCAx8NFrH55xFAqler1+l977bWu70877bQet+H000+nt9MO0zRx2223Yf/998cbb7zR6+VohM0glHIk4TS6Dx0uWJGSrzdABmAnyHBOdpoEt4PP8Y14uLZs5mc0x6+bndfAT0axsvMK2Mls9jm5MNY4mTfOHpqkjSxmg8y+HRfggomHeLiA7vVpPsd3cSu3f3zksQ7YmG+CjToeEeD2ebrA3UbsbCN7TnrIEyhA5unbSfxmjw37WxF5Mhucn4UE8NCxzdy6h5Afms2gB4AkGRAedHHrtpPlzc6Xxs51EyP7PTt9LpvlPTnM9VNknDY9ZwQApMmFDiVH72dMbt1t5LwfGdMBtPTcTiRupeEi+ticyc1h4yO7I/Z+wtbHAOAk749sf8TWxwDfZ3aQcwmy/SC7HwF+Th62TnUZXEeYJvs3AGCn3+Ln6uTWPcrHT25Z6+EOYlOG+2HI+63cQfTaONbsecH+AvYQ7vIHAITICUvYeZ8cZA1ob86p0k7eY2euLRY7F4hF7x/uM5s25hwcF+I+eDs5X0lzivvQ7FxpAEBOJ0xfX3aez51GaWtk9r4wMWRjsl5SkqyR0+TPYups/KyYXSYzNyo7T6+UV1VVFaqqqsq9GbRN56zJZrmHtUwms9mf2ZE5W7NkyZKu7w855JAe24fDYUyZMsXWPDY77LADVq5cucXf53I5NDY24t1338Vtt92GhQsXYunSpZg2bRqeffZZnHjiifQ6NtILGxERERERERERERGRCvLuu+/ioYce6rbNt7/9bRx00EF9uh2hUKjre3akzJfbbboMu5qamrq+HzduHPV/xo0bZ+uFzba43W6MHj0ap5xyCqZPn45zzz0X999/P/L5PGbNmoXPPvsMtbW1tpapFzYiIiIiIiIiIiIisn2wzM6vSmNzm5cuXdrjvCr7779/n7+wqaur6/p+w4YN1P9paGjY7M92X2psatP5b9i5Y4LBYK/Xty2GYeDWW2/F448/jmQyiZaWFvz+97/HD3/4Q1vL0QubQWikNRRudJ9NEiHHM7ORMgA/DNdf4rMy7Oa30c7nYbjI4boAH1UVJSMkHOS6gzaGUjdluDHSJhmxxMaS2cFGmE2uaafaBf2ZnhsBGNERptoBwOpkDdXOQw7FtzOPHtt2mI3IB0aDjShSNsaQjSZ0kfEMtTaGpAfJeEAWe6zzNlabzHPLZPtI9jQL2uhzsybXp7RnSx9pwJZAQfIEYqMFojZijYb5uGUWLHY/cuuN29jdbFQJ247Fpq6wtYfIUGcIbqPn7D62z2TrWfYcLXV0EQAEyTRXMtkJAGCQdwv24xTIyFC2Pgb4CFQ2zrUpw+1IG6mdGO0vbY1cS9bHk8j6GAACfu6m0hLlfhiyJsn9kMZpI6qK3ecF8jqs89qZvJpr25jmLrAkeeLmyNgkgL8WqtzcZwmUuD4G+P6HrTMSdH3MLc/OutmIvhD5s5g2MlrYjgIZ3eh32ojPJTeT7ceHetn6mL8W2skoezbyvkDmWpa6PmZlVB9LH5g8eXLX942NjUin05vFpG3N6tWru76vra3F0KFDe73+UCiEjo4OAEAymaT+TyKR6PX6uhOJRHDQQQd1zavzyiuv2H5h0wcpoyIiIiIiIiIiIiIi0ldmz54Ny7K6/Zo9e3afb8fkyZPhcHS+ZrAsC3//+997/D+bxpHtuuuuRa1/05c9m74I6s6aNWuKWmd3Ro4c2fX9qlWrbP9/vbARERERERERERERERHbfD4fDj744K4/v/766z3+nwULFnR9f9RRRxW1/n322afr+3feeafH9vF4HP/85z+LWmd3Nh3ls/FFlh2KRBuEwi433I7ux/imyfHjdkbhssNCw+S4Z3YUZ1OaH+/pNLghrjtVce2qyeHjABDPc8ss9chncsQ1AIDcPfCRcWwTQ9wwxbCbz7nIkcO4m5JcpuUXHVVUu44c352yUV4mOYy72sOfZ2wEyfgAmZ1EiuX4vDE2HqY+wV0MI/xk/oANKTL+yufkjg0b92AnMpIdYk/3pSn2vsD3uex9wefkjqGdxCAyeQ9xsvvxk9E5bBwGALSVOMIsRJ4/1TZiQLJk9EqKrCvY2MZs6VNAZJDzORxwEw9TbJwOGwHD9tV24ibZ0q6R7NfZmF0AGBPk1u4mY63Y/cP2MQC/f9g4HTYuysYhxJgAF8kbdnM3irzFfer1cT7LPdFeTbWL5bmbT4x8FrJzrw+Tx8ZD3puHe/nMULbOaMtyN90MWXuuIetjAKjzcucFGw/WkuV2JFsfA3ysHbu/WWkb8bBtZO2btJNtTHCzD+fgn+PZPrclwx/DAFl/ssewiese0ZLl9w8bE8+eu+z1mqNDpwHysQlMmVKuKDbZ/p188sl46623AABz587FlVdeuc22a9aswSuvvLLZ/y3GtGnT8OSTTwIAHn/8cfzP//wP3O5t38Aef/xxZDJkh9ILS5Ys6fp+9OjRtv+/RtiIiIiIiIiIiIiIyPbBtCr3q0LNmjULwWDnL398+umnuPfee7fZ9kc/+hEKhc5fMjjkkEOw7777FrXuM888E4FA5y9mr1mzBjfddNM227a0tODaa68tan3defrppzeLQTv66KNtL0MvbEREREREREREREREpFeGDRuGyy67rOvPl1xyCZ544onN2uRyOVx55ZV49NFHu/7uxhtvLHrd1dXVuOKKK7r+fO211+Kmm27qeim00bJly3Dsscdi3bp18Hj4JBiGZVl44okncM4553T9XSQSwXe+8x3by1IkmoiIiIiIiIiIiIjIIHHCCSdg3bp1m/1dQ0ND1/eLFy/G3nvvvcX/e+GFFzBq1KitLvOaa67Bm2++iVdffRWpVAqnnXYafvazn2HfffdFOp3G3/72N6xfv76r/fXXX48jjzyyJJ/nqquuwssvv4w333wTlmXhyiuvxO23344jjzwSoVAIy5cvx8KFC1EoFHDQQQdhp512wh/+8Ad6+a2trbjooou2+PtcLoempiYsWrQI9fX1XX/vdDpx7733YsiQIbY/i17YyFYlyFBLp42w2KCLG9DFzp9Tn0pT7SJu/o3pEB+3jSN83P6JuPkM4WSBCweOkfMpsFmxI/2lDzBtz3HnRYLMtPY5+f3YmvFy6y5wGcvpAndOtOX4AYsuMkQ4TubkAyUObQYQdnHHJkDm+AZt3G285BDgMUHuGLLdVM7GyOOxAe5CDLu4AOw1SR/Vrp3MBgcAj4/74FHygwfIbGc7g3fZOWzYzGaXjXuSj86q5paXILPO7cxhM4I7LTDSz/WRUbKfYvtwAPCQ81Cwcw7YmZuAwc4jIpIxTZjErF4J8qRi50UMurhOIZrlb1INGa5G9hvczbnWy09sFSHnDKkla+Rojts/TWl+//jJjmZkgFsmO5dXCz8lI4Z6uWPjdnB1fGuGex6K5/l7eJqcN5K997BzPCZs7Ed2XkSWx8EXtGyNzM5tZJL32yp2YkTw8xi2k3OBTKnmiqGwi785r02x/Q+3jQGyDovZeTAgsfUsWx/nTP4Ed5M1coC8J9nRRs53w5aA7M9sRgf4/ZMiT8kYWSOTP/qCz8ZzPFsjM11phrxvyfbv448/3iy668sSiQT+8Y9/bPH32ey259Bzu914+umncf7553eNrvnoo4/w0UcfbdFuzpw5uPrqq3u59VvyeDx44YUXcNppp+HPf/4zAGD9+vV47LHHNmt36KGHYt68ebjqqqtsLT8Wi+HXv/411Xbs2LH43e9+h+OOO87WOjbSCxsRERERERERERER2T6YZudXpanEbf6S6upqPP744/jP//xPPPDAA3j77bexfv16uN1ujB07FscddxzOPfdc7LrrriVfd1VVFV588UU8/fTTmDt3Lt577z20trZiyJAh2HXXXXHWWWfh7LPPhtvN/0JSTxwOB6qqqjBy5Ejsu+++OPHEE3HKKafA6+V+oXxr9MJGRERERERERERERGSQWLlyZZ8u/5hjjsExxxzTp+vYllNOOQWnnHJKt23mzp2LuXPndttm9uzZmD17duk2jKQXNoPQmlw7XEb3b/km+WuoZRk2hnC2ZLjx6wEnNwzXbXDjTPlB4YCXbNxBDu1PF/j9EyWH11rkEOmmVOnjtEb6uXbscO+/NHB5P6MDZC4QgLCLHULOLY8dLswev85lkhEJ5PnIRkgAANuyNcutvIlcYEOKXDGAMJkNwUZLscfaZE9cAF4ygsRpsHFRXDs2IqGzLdcuSzZk1+y1kaSQJGPEnOS1wMe2Ae1ZLvvAIi/EUQFu3XVeO+cZ19ZPxq60kZEm7dse4b4FNp6BjcNIkVGQQbKfsNM/yuD2ofVPOMyef9NuosX9JqDL4s7RhkyUaucH/xt6bY52ql2NGaHaVZn8I+O6FPe5mzLcMhtTbOQXeUMBkChwbduz3D4f5ufuEzZu4Xh6Nbd/xoS4qDM/eW8ukOctwMdYR8kIszTZ/+dt9OtR8n7G1hk5MgYOACxyX65PcvWIn8xYspGIhgIZAxUn48HaAtzK6dRnALE82aeQz75sLd2W5bP3HOTzNBuVmTa5PspJ/jwEADxkBGZjOsMtkEveBACMCnB9aR0Z58w+a9jpzzrIGnltgrsnsZFodu5dKfLeVUVMCZAzlRksMtDZ+Vm2iIiIiIiIiIiIiIiI9AG9sBERERERERERERERESkzRaKJiIiIiIiIiIiIyPbBtDq/Kk0lbrOUnF7YDEIjnFVwO7rPEfWRofRsDisAuBw954IDgJdcdxWZIZyxEc+ZIAN1q91cu4ibX3ksz+2fFjJWttbL7Ud2XhoAGOrhPs+GjI1JLAhNNjJyM+S8Bmy+c5o8hCl2whAACTISuYqLJYfHwV+H7DwSS8kcaB85Z8gwMpMY4OdeaSE/S8TDrTtC7m8AqPNyF2LIy4WnsxnLGzIBqh0AxMnM7xh5PqbyXGaz30Z4OjkdGNrIfGc7tS07l0AN2Zfyc07x1wK7jeycbhE3dwzz5HwMAD8vzhCyD2Bz8lvIm3tODzxSYkmL6//ZOQ2yBre8HGzMp2Bx1zA7100yy09EF3QPodpVk/fcKDkR3YZ8nFsggEAPc3luxM6VQtctaXJSPQBrMwlumVnucT5CzGkAAC4bNSW7fxJ5rr9uM5NUuxoHXwt5HNy10JrhHjb+lebPM69FztvhCFLtMiZ3/sQKfF/hJgNXhvq484ft9+zEvLBz5bFz07DnY8zkH0CjjhjVrrpQTbVLG9y6m431VDsA8OerqHZ58p60E8bT62bnp2ohdzk75xQ7hxXAzzGbKXDX4fo0+wzI3xcS5MRBywufE+vl+wkRKQ9FoomIiIiIiIiIiIiIiJSZXtiIiIiIiIiIiIiIiIiUmSLRBqFRARc8PcSTsUP72aHHAB/bFCTPynSBW55pYxuHkzEwBmzkrJF8ZIrYCD8bc8R9bjtvbdNklA+ZnAQPuXIb6QxIkDFQGfIkr+XSDOjh+gDQRkb5tJPDuN12dhCpmjw4KXKIe7ONWDt2XwZKfAez0VUgXSAv2AwXIRHLcx+mmYxEBIAqMrqR7QMMMubCRjogHVfFpm+yMRcA4CVjUlrS3DY6yVgyt4PfQWTCI428LdDxbgDgIT8Pe72yNUDO4q7BLBmfKpKzUnAQsWMrjA+p5TnI3tVFxnPVYBTVDgAy4CLM2q11VLuhxo70utkaB+Cu4WiOK4aijg5yvUCjwcVauXI7Ue0KCS7WuC3P5/O0OdqodhvIY+3Lc7FbeXARpADgt7hosiS5vxuxnGrnsNGvF8h4MKfBHcOQYxi97qzFRby1ooZql2cjjGzc9qrMWqpdNsVFea1Jhqh2ATJSGQAayETGpZlGql3Q4vLA652ruBUDiBe4dbc5uFiyeKGJapfOtFPtAMBBxtO7HOSzi5P7zABQlRvBtctGqHY+y0e1G+Lis9/Zx+lYnrsOO8D1e17wmdwFg7u/JsyWHtuYFt/XVzTTAsg4yQFFkc4CjbAREREREREREREREREpO72wERERERERERERERERKTNFog1C6YLVY0xYnY8bE2rYeOfXmuGGIqbISKssOUywxssPuR4f5IaZsiMUN2T4SyxLJkg4jdIOj2zK8PsnlueONxt15iOzr+Jsjg8Ag9w/bMRS3uIastFOAOB1knFjBe6aCdvIY/OSbdnIuJYMN5w6a2Mo8hAvOzSc+yzDQ9xnGRsgoyYAbEhzETYF8vxZmeD6igiXZgCAj79aneBy1sIubhuDbjIuDnycn4M81gky+gQAAk7uGIbJXDI7fQCrys1dN26y34vn+GPDYiMk2JQkNiWFrT70m0nCas+shkFEG3pdYWp5eZOLv8rmY1S7BnxEtQOAXJ6LB/N5hlLtgv46et0fmO1UO2eKu6FlDG7/FGxEeXVk1lLt2shIJF+eizmyI1Pg4nQKFncPL/X5CABVvrFUu1ye+yyxdD3VrkCe3wDgdnPnrtPBRSy1WSvodbPXoWlymV9OJxdrF/aNo9oBQMwVodoZxhSq3Vst3HVogq+Z1jg+o9pFC1zEo0HGMbZ0/J1qBwAOBxe9xfa57DVTKPDXq0lG6jkcXH2cs3GeZd3cOZ51ctFpQ8yRVLtmG6lfbAxlzuD63Bi4WDum7tjIBe7YdKRX99jGsiowJkxkkNELGxERERERERERERHZPphWZc4HU4nbLCWnXzwUEREREREREREREREpM72wERERERERERERERERKTNFog1C9akMeorkD7q5HFa3gx+qx47qY+cWYeemqeOiPgEAo3xcvjMrlufnC3CQl2OOnBODnUfGzpw4bEty6pU+GelZRc7xkTO5/ZgnP0sP00JtJkPuoOF+7vyxs27287Bz3YwOcDvcxjREGOFn59nhlrdndZJqt2N1lFsggGiG61gaU1wm+mjyMycL/O9ZRMn5wOq83DGsJjuVKnLOFwDwkV1kxMP1jy4HX9Z4yMlXyOmc4HNyDdn+EQDWp7gdFPFw62YPjdfBb2SCnNssSZ6PLvIUT5M7kp3vTiSZXgVmbrQEuTyDnHvLsjGXQ6ml0lzdm/btRC+TzdhPWtx8AW2pL6h22Ty3PACwyPlcHOScIXbm9GTlC+1UO3YeAsMo/WN/c66Famea3JwPfSGbay7bukutUOB6n/bEUnqZYyJTqXZ+i6tnWxzcObGh8CnVDgBS6VaqnWFwNVO+wM2nYgc7D1GSmFuk3NjrNZZcRi+TnWmnmZw/ZwXZn3lcNeSaAaeDm0OVPc+SGW5OJfbcsYO7L6g+FhnoNMJGRERERERERERERESkzDTCRkRERERERERERES2EyZAjkQdWCpxm6XU9MJmEBoX9MHj6H5YcypPRruQkTIAMDLADegKkLEyGbIPC5LLA4DRIW6oeYGM01rbEqHXzcbLecjhqykypibs5vcPG5/GnhahAtewLcufZ2xLt4v7LFnyWLMxPgAwKsgNpQ6T+UUeG9GE7GayJUKaPIZ24hPZz5Mnj02cjCZsI+PLAMBJbuMwf5pbd5a7Hdu5FmJkBBUr4uGWZ+O2gEKea8dHPPIrt9P3MarJ5UXcZJYfgCHeHNWuxsvFV+TMPojtIZeZLnDXIXu9jvFz10yqADy8gWoqUlLljDpjmRbXx7Rn+BgfdpmJ1Ep6meVi5tvLvQklY1nkDbfMy5T+tT72HtUuH+LqjEKBu/5zZLwbACRSK6h2ldDnSvf4+ETyfHRwMf8AkC9wEdosNq6SbScig48i0URERERERERERERERMpML2xERERERERERERERETKTJFoA9SqVatwxx134Pnnn8eaNWvg9Xqx00474dRTT8X3v/99BAKBXi9712oL/h5iwlJkzFGGT3bBEC833DNHjgr1WKWP52khI5GqPVmq3d41UXrda5PcMW3OcJctmRaDkIsfhmuQQ819Tm6ZXgcXfWNZ/LvlHHlesLFblsW1C7lKGz8FAAVy3WwUE8BHmKXJa5u9voI27jbssan1cOcZu41uB38tZEzu3K1PckPx2fN2tyqu7wEAMtkSB9Ry7Rzgok86cty+AYANLq5tiIww9Ng4huy1UCD3Y5jsS4d6+WNY5+Mi9Xwu7oJ1Obl2eTK+DABSOe7iriLvm2PIbczSEWtsvIZUir6skWXrYsll5d4EEekDBTKarKHj7T7eEpHSy+aay70JUm6m1flVaSpxm6Xk9MJmAJo/fz7OPvtsRKP//mF/MpnE4sWLsXjxYtx77714/vnnMXHixDJupYiIiIhI/1GNLCIiIiIi2ztFog0wS5YswWmnnYZoNIpQKIQbbrgBb731Fl555RX853/+JwDgs88+w4knnohYLFbmrRURERER6XuqkUVEREREZDDQCJsB5tJLL0UqlYLL5cJLL72EQw45pOvfjjrqKOy888644oor8Nlnn+GWW27BnDlzyrexIiIiIiL9QDWyiIiIiIgMBobFTtAgfW7RokU46KCDAADf/e53cffdd2/RxjRNTJkyBUuXLkUkEkFjYyPcbje1/Gg0iurqaty9x4/gd3q7bTvEw81VECEz6QHAYXCnmpNs53dz21gw+blFLHIeiYAnR7XzkPMKAECGnAeAnaGhYJZ+AF0mz89rwDDIQ1MbSNHLzBe4z23ScyBx52M6z7//dpLzbLDXArs8OzLk50lkuf6H3Y8A4CHnQGKjXat83BwWoTA/10Ui3n0fulFDLEi168h6qHZect/YkS7xNZOx0eemyHlI3OS8Ri6D3z/sutm5u2rJ+6adYxh2c/dYti/1kPPDsPdCAIiRfYCXXDd7b8+R5208n8FhC+9GR0cHqqqqqP8jA09f1sgb62PACaD089GJiIiIDCwWgMJ2Wx9vrO3aH7scVQHuuX0giSYziJx+y3Z7fISjSLQB5Nlnn+36/pxzztlqG4fDgZkzZwIA2tvb8dprr/XHpomIiIiIlIVqZBERERERGSz0wmYAeeONNwAAwWAQ++233zbbHXnkkV3fv/nmm32+XSIiIiIi5aIaWUREREREBgvNYTOALF26FAAwceJEuFzbPjS77LLLFv/Hjl2rYwi5uo9ZcZKxMm0ZH73eAhmxMjyQpNqx8Vx5i38vGfFykUhuMtqFjecCgEgV97mdLu7YZNLc5Z1Kc1FMAB+nU+qIrr4IbmS30ePmPnMqx8UCAcCGBBeT5XGw+5vfQVnynCyQ101zhv/crBoyWmqYn4vKY+PqHEl+PxrkPh9TE6XaTfBwx7qtI0C1A4A4GVXlNLj9U0VGYA6pSlDtACBPxpKx12sux8c2ZsnzwiDj/NrS3P3QbaN/rCLvSbkSR2DaiURj7+3svThL3tvbs1y8QSKviKvtQX/VyCIiIiIiIuWmFzYDRDqdRnNzMwBgzJgx3batqalBMBhEIpHAmjVrttkuk8kgk/n3D3uiUe4HhyIiIiIiA0Gpa2TVxyIiIiIiMpApEm2AiMViXd+HQqEe2weDnb+hH4/Ht9nmxhtvRHV1ddfX2LFji99QEREREZF+UuoaWfWxiIiIyCBgmpX7JYOeRtgMEOl0uut7j6fniCqvtzMKJJXadiTQVVddhcsuu6zrzx0dHRg3bhwS+Z5jbdhINDtRI2xsSjzPRcCwkWhstBMAuJxc5I/b4iKb7KwbOW7dTouMRMtx7VLcRwHAx+Q4ShyJ5gAfVcXuczbmyAMuqiqe5481e93kKiASLVkofTHhyXMnJdtXuMhrpuDIUe3soKP3DPY842/b8TzbB3Drdji4PspL9mVAeSPRcuS5y/YV7HXtstE/GuQ9KV/GSDS6PyOvQ7bfS5D3rmShs5+w+iJbU/pFqWvkbdXHsFFriIiIiFSuzppH9bHIwKUXNgOEz/fv7Ptstucf0GyMcvD7/dts4/V6ux5agX9HPpzwzr293UwRERGRihOLxVBdXV3uzZBeKHWNvK36GNBvM4qIiMjgofpYZODSC5sBIhwOd33fXczZRolE56TOTDTERqNGjcKaNWsQDodhGJ2/ERuNRjF27FisWbMGVVVVNrdatP+Ko/1XPO3D4mj/FUf7rzjaf8Vh9p9lWYjFYhg1alQ/b52USl/XyFurjwFdn8XS/iuO9l9xtP+Ko/1XHO2/4mkfFqen/af6WGTg0wubAcLn86Gurg4tLS1Yu3Ztt23b2tq6Hkbt5G47HI5tTtZaVVWlG2ERtP+Ko/1XPO3D4mj/FUf7rzjaf8Xpaf/pNwcrW1/XyN3Vx4Cuz2Jp/xVH+6842n/F0f4rjvZf8bQPi9Pd/hsU9bFpdX5VmkrcZim50oaeS1F22203AMDy5cuR72YOh08++aTr+1133bXPt0tEREREpFxUI4uIiIiIyGChFzYDyGGHHQagM8rh/fff32a7BQsWdH3/la98pc+3S0RERESkXFQji4iIiIjIYKEXNgPIySef3PX9/fffv9U2pmniwQcfBABEIhFMmzatqHV6vV5cd911m02+Kjztv+Jo/xVP+7A42n/F0f4rjvZfcbT/Bg/VyJVH+6842n/F0f4rjvZfcbT/iqd9WBztP5HKZ1iWpXC8AeSII47AwoUL4XK58Le//Q2HHHLIZv/+i1/8AldccQUA4LrrrsOcOXPKsJUiIiIiIv1HNbKIiIiI9CQajaK6uhrtD/8AVYHKe2kVTWYQOfs2dHR0aA6nQUwvbAaYJUuW4Ctf+QpSqRRCoRCuvvpqTJs2DalUCo899hh++9vfAgAmTZqExYsXIxwOl3mLRURERET6lmpkEREREelJ1wubBy+t3Bc2M2/XC5tBTi9sBqD58+fj7LPPRjQa3eq/T5o0Cc8//zwmTpzYz1smIiIiIlIeqpFFREREpDt6YSPbA81hMwB9/etfx4cffoj/9//+HyZNmoRAIIBIJIL9998fN910E5YsWaIHUREREREZVFQji4iIiIjI9k4jbERERERERERERESkommEjWwPNMJmkFq1ahUuv/xy7LLLLggGg6itrcUBBxyAX/ziF0gmk+XevAGpsbERf/rTn3Dttdfi+OOPx5AhQ2AYBgzDwOzZs8u9eRVh8eLF+MlPfoKvfvWrGDNmDLxeL0KhECZNmoRzzjkHb7zxRrk3ccCKRqN47LHHcPnll+PII4/ExIkTUV1dDY/Hg2HDhmHq1Km4+eab0dLSUu5NrTg/+tGPuq5lwzDw+uuvl3uTBqRN91F3X1OnTi33plaE1atX47rrrsP++++PoUOHwufzYezYsTj88MNx7bXX4p///Ge5N3FAmTp1Kn0O6lqWYqhGtk81cnFUH/ee6uO+pRqZoxq5tFQj81Qf98A0K/dLBj1XuTdA+t/W8r+TySQWL16MxYsX495771X+91YMHz683JtQ0Y444ggsXLhwi7/PZrNYtmwZli1bhrlz52LmzJn43e9+B4/HU4atHLgWLVqEM844Y6v/1tTUhAULFmDBggX4xS9+gYcffhjHHXdcP29hZfr73/+OW2+9tdybIYPMnXfeiauuugqJRGKzv1+7di3Wrl2LN954A9FoFLfddlt5NnA74HA4sPPOO5d7M6TCqEbuHdXIvaf6uDiqj/uOamQpB9XIfUv1sUjl0AubQWbJkiU47bTTkEqlEAqFcNVVV2HatGlIpVJ47LHH8Lvf/Q6fffYZTjzxRCxevBjhcLjcmzwgjRs3Drvssgteeumlcm9KxVi3bh0AYNSoUfjWt76Fww8/HOPGjUOhUMDbb7+NW265BfX19XjwwQeRy+Xwhz/8ocxbPPCMHTsW06ZNw3777YexY8di5MiRME0Ta9euxbx58/D000+jubkZ3/jGN7Bo0SLstdde5d7kAc00TZx//vnI5/MYNmwYGhsby71JFeGCCy7AhRdeuM1/DwaD/bg1lednP/sZrrnmGgCdE6T/53/+Jw444ABUV1ejpaUFS5YswTPPPAOHQ4OgN3X//fdv8fD+ZR9//DFOO+00AMDRRx+N0aNH98emyXZCNXJpqEa2R/Vx8VQfl55q5N5RjVwc1cj2qT4W2X7phc0gc+mllyKVSsHlcuGll17CIYcc0vVvRx11FHbeeWdcccUV+Oyzz3DLLbdgzpw55dvYAebaa6/FAQccgAMOOADDhw/HypUrseOOO5Z7syrGLrvsgv/5n//BjBkz4HQ6N/u3gw8+GN/+9rfxla98BZ999hkeffRRfO9738MRRxxRpq0deKZNm4bVq1dv899PPfVUPPvss5g+fTqy2Syuv/56PP300/24hZXnjjvuwHvvvYdddtkF06dPx4033ljuTaoIw4YNw5QpU8q9GRXplVde6XoQnTlzJu6991643e7N2hx99NH44Q9/iGw2W45NHLCY++1DDz3U9f3MmTP7cnNkO6QaufdUI/ee6uPiqD7uG6qRe0c1cu+pRu4d1cci2y+9mh5EFi1a1DXk/txzz93sQXSjyy+/HLvuuisA4Pbbb0cul+vXbRzIrr/+enzta19T7EMv/elPf8Kpp566xcPoRkOGDMEtt9zS9ed58+b116ZVhG3tt02dfPLJmDx5MgBsNV5D/m316tVdDwV33323Ikakz5mmiQsuuAAAsNdee+G+++7b4kF0Uzon7TFNE4888ggAIBQK4ZRTTinzFkklUY1cHNXIvaf6uDiqj0tPNbL0N9XIfWfQ18eWVblfMujphc0g8uyzz3Z9f84552y1jcPh6Hrr3t7ejtdee60/Nk0EQOdvyW30+eefl3FLKtfGiJZ0Ol3mLRnYvv/97yMej2PWrFk48sgjy705Mgi89NJLWLZsGYDOSXxdLg1yLqVXXnkF9fX1AIBvfvObCAQCZd4iqSSqkWUgU31cPNXHPNXI0t9UI/cd1ccilUsvbAaRN954A0Bndup+++23zXabFmZvvvlmn2+XyEaZTKbre+Y35mRzn376Kf7+978D6IzYkK174okn8Kc//Qm1tbX45S9/We7NkUHiySefBAAYhoGvfe1rXX/f2tqKZcuWobW1tVybtl148MEHu75X3IPYpRpZBjLVx8VRfcxTjSzloBq576g+FqlcemEziCxduhQAMHHixG5/a2HTQnbj/xHpDwsWLOj6fmPsiHQvmUxi2bJluPXWW3HkkUcin88DAH7wgx+Ud8MGqPb2dlx66aUAgJtuuglDhgwp8xZVnieffBK77bYbAoEAwuEwdt55Z8yaNUu/bd6Dd955BwAwfvx4hMNh/OEPf8Aee+yBuro6TJo0CXV1dZg8eTJ++ctfbvbDOelZPB7HM888AwDYYYcdMHXq1PJukFQc1cgykKk+tk/1sX2qkYunGrl3VCP3DdXHIpVNYw0HiXQ6jebmZgDAmDFjum1bU1ODYDCIRCKBNWvW9MfmicA0Tfz85z/v+vOpp55axq0Z2ObOnbvNyBYAuPLKK3HmmWf24xZVjiuuuAINDQ34yle+gnPPPbfcm1ORPv74483+vHz5cixfvhwPPvggTj75ZMydOxfV1dVl2rqByTRNfPLJJwA65yO49NJLcccdd2zR7rPPPsN//dd/4ZlnnsHzzz+PSCTSz1tamZ566ikkEgkAwNlnnw3DMMq8RVJJVCPLQKb6mKf6uDiqkYunGtk+1ch9R/WxSGXTCJtBIhaLdX0fCoV6bB8MBgF0vpUX6Q+/+tWvsGjRIgDAKaec0m0kiWzd3nvvjUWLFuHGG29UQbYVCxcuxL333guXy4W7775b+8imQCCA008/Hb/73e+wcOFCLFmyBC+99BJ+/OMfo66uDkDnPBAnnXSSJuP+ko6ODpimCQD46KOPcMcdd2DkyJF4+OGH0draimQyiQULFuDggw8GALz11lv4zne+U85NriiKe5BiqEaWgUz1cfFUH/dMNXJxVCP3nmrkvqP6GIBpVe6XDHoaYTNIbDrBosfj6bG91+sFAKRSqT7bJpGNFixYgCuvvBIAMGzYMNx1111l3qKB7eSTT8b+++8PoPMa/fzzz/HEE0/gmWeewRlnnIHbbrtts/xfAbLZLM4//3xYloX/9//+H6ZMmVLuTao49fX1W/1ttmOPPRYXX3wxjj/+eCxZsgQLFizAXXfdhUsuuaT/N3KA2vjbbUDn/TgQCOC1117D5MmTu/7+iCOOwKuvvopDDjkE//jHP/DMM8/g3XffxUEHHVSOTa4Ya9euxeuvvw4AOPjggzFp0qTybpBUHNXIMlCpPrZH9XHvqEYunmrk3lON3DdUH4tUPo2wGSR8Pl/X99lstsf2G7NB/X5/n22TCAD861//wvTp05HP5+Hz+fDkk09i2LBh5d6sAS0SiWDKlCmYMmUKDjjgAJx++ul4+umn8eCDD+KLL77ASSedhLlz55Z7MweU//mf/8Enn3yCcePG4brrriv35lSk7qIHhg8fjnnz5sHtdgMA7rzzzn7aqsqw6T0YAM4777zNHkQ38vv9uOGGG7r+/Pjjj/f5tlW6hx9+uOs3M2fNmlXmrZFKpBpZBiLVx/apPu4d1cjFU43ce6qR+4bqY5HKpxc2g0Q4HO76nolw2PibDkw0hEhvrVixAl/96lfR1tYGp9OJxx57DEcccUS5N6tiffvb38a3vvUtmKaJiy66CK2treXepAHhk08+wY033gig8yFpY5yNlNaECRNw7LHHAujM7F63bl2Zt2jg2PQeDABf/epXt9n26KOP7pr0/L333uvT7doePPTQQwA6Rz2cdtppZd4aqUSqkWWgUX1cWqqPt001cv9QjbxtqpH7hupjkcqnSLRBwufzoa6uDi0tLVi7dm23bdva2roeRseOHdsfmyeD0Lp163DMMcdg3bp1MAwDv//973HSSSeVe7Mq3kknnYQnnngCiUQCf/7znzW5Kjrz37PZLCZMmIBkMonHHntsizb//Oc/u75/9dVX0dDQAAD4+te/rodXG3bbbTe88MILADrjIUaNGlXmLRoYvF4vhg4diqamJgDd31t9Ph+GDBmChoaGrvaydYsXL+6a4PdrX/saampqyrxFUolUI8tAovq4b6g+3jrVyP1HNfLWqUYuPdXHm6jU+WAqcZul5PTCZhDZbbfdsHDhQixfvhz5fL7rtxO+7JNPPun6ftddd+2vzZNBpLm5Gcceeyy++OILAJ2/0TVoJ8IrsaFDh3Z9v2rVqjJuycCxMb7miy++wBlnnNFj+5/+9Kdd369YsUIPozZoktpt23333buypAuFQrdtN/77tu7T0mnTyVQV9yDFUI0sA4Hq476j+njrVCP3H9XI26YaubRUH4tsHxSJNogcdthhADqjHN5///1ttluwYEHX91/5ylf6fLtkcOno6MBxxx3X9VsfP//5z/H973+/zFu1/aivr+/6XnEt0t82XtcA9JuDX7JpnM3GH8ZtTTQaRXNzMwBg9OjRfb5dlSqXy3X9JvDQoUNx/PHHl3mLpJKpRpZyU33ct1QfS7mpRt421cilo/pYZPuhFzaDyMknn9z1/f3337/VNqZpdr2Rj0QimDZtWn9smgwSyWQSJ554Ij744AMAwI9//GP86Ec/KvNWbV+efPLJru/32GOPMm7JwDF37lxYltXt16aTrL722mtdfz9+/PjybXiFWbFiBV5++WUAwE477aQHqS+ZMWNG1/fPPPPMNts988wzsKzOYfCHH354n29XpXrxxRe74jDOPPNM/aalFEU1spST6uO+p/p461Qj9w/VyN1TjVw6qo9Fth96YTOIHHjggV03tvvuuw9vv/32Fm1uueUWLF26FABw6aWXwu129+s2yvYrm81i+vTpePPNNwF0nl8/+9nPyrxVlWPu3LlIp9PdtvnVr37VlY284447qpCVkpk/fz7y+fw2/33Dhg2YMWMGstksAODCCy/sr02rGHvuuWfXb7k9+uijeOWVV7Zo09DQgP/+7/8GAHg8Hpxzzjn9uo2VZNO4B0UGSbFUI0u5qD4ujupjKTfVyMVTjVw6qo+/xDIBswK/LLPce04GAL1uHWRuv/12fOUrX0EqlcJXv/pVXH311Zg2bRpSqRQee+wx/Pa3vwUATJo0CZdffnmZt3ZgeeONN7B8+fKuP28cjgsAy5cvx9y5czdrP3v27H7asspwxhln4KWXXgIAHHXUUTj33HM3m8TyyzweDyZNmtRfmzfgzZkzB5dffjlmzJiBww47DDvttBNCoRBisRg++ugjPPLII10P+x6PB7/97W/hdDrLvNWyvbj44ouRy+UwY8YMHHLIIRg/fjz8fj+am5vx+uuv45577unqEw877DDFuGzDbbfdhrfffhvt7e342te+hh/84Ac44YQT4Pf7sWjRItx4441dk57/9Kc/1W9gbkNbWxv+9Kc/AQCmTJmCfffdt8xbJNsD1ci9pxq591QfF0f1sZSbauTSUI1cPNXHItsXw9o4plAGjfnz5+Pss89GNBrd6r9PmjQJzz//PCZOnNjPWzawzZ49Gw888ADdXpfW5uxOtLjDDjtg5cqVfbMxFWj8+PHUJKljxozB73//exx77LH9sFXbjzlz5uD6668H0Bn3MHXq1PJu0ADDnn8zZszAvffei0gk0vcbVaHeeOMNfPOb38SGDRu2+u+GYeDHP/7xZhP7yubuvvtuXHDBBQCAm2++Gf/1X/9V5i2S7YVq5N5Rjdx7qo+Lo/q476lG7p5q5NJRjVwc1cf/Fo1GUV1djfbffg9Vfm+5N8e2aCqDyPl3o6OjA1VVVeXeHCkTjbAZhL7+9a/jww8/xO23347nn38ea9euhcfjwcSJE/Gtb30LF110EQKBQLk3U0Q28Ze//AXPP/883nzzTSxfvhwbNmxAS0sL/H4/hg0bhr333htf+9rXcOqpp+r6lZJ74IEHsGDBArz99tv44osv0NzcjGg0ilAohLFjx+LQQw/FrFmzcMghh5R7Uwe8ww47DP/6179w55134tlnn8WKFSuQzWYxcuRITJ06FRdffDH22Wefcm/mgPbQQw8BAJxOJ84666wyb41sT1Qji1QW1cdSbqqRS0c1cnFUH4tsXzTCRkREREREREREREQqmkbYyPZAI2xEREREREREREREZPtgWp1flaYSt1lKzlHuDRARERERERERERERERns9MJGRERERERERERERESkzPTCRkREREREREREREREpMw0h42IiIiIiIiIiIiIbB9MVOZ8MGa5N0AGAo2wERERERERERERERERKTO9sBERERERERERERERESkzvbAREREREREREREREREpM72wERERERERERERERERKTNXuTdARERERERERERERKQkTKvzq9JU4jZLyWmEjYiIiIiIiIiIiIiISJnphY2IyHZk7ty5MAwDhmFg5cqV5d6cfvfpp5/C4/HA5/Ohvr6+ZMv9/ve/D8MwMGvWrJItU0RERET6nupj1cciIiKVRC9sREQGgJUrV3Y9SBbzNdhddtllyOVyOPfcczF69OiSLfdHP/oRPB4PHnroIbz//vslW66IiIiIbJ3q49JQfSwiIlJZ9MJGRES2C2+99RZeeOEFeDweXHnllSVd9rhx4zBr1ixYloVrrrmmpMsWEREREekLqo9FZLCyTKtiv0QMy7J0JoiIlFkul8Onn366zX/fY489AAD7778/7r///m22mzJlSsm3rVKccMIJePHFF3HWWWfh4YcfLvnyP/30U+yyyy4AgMWLF2O//fYr+TpEREREpJPq4+KpPhaRwSYajaK6uhptd/wnqvyecm+ObdFUFjWX/A4dHR2oqqoq9+ZImbjKvQEiIgK43W7qYTIYDA7qh85t+fTTT/HnP/8ZAHD22Wf3yTomT56MfffdFx988AHuvPNOzJ07t0/WIyIiIiKqj4ul+lhERKQyKRJNREQq3v333w/LsjBs2DAcc8wxfbaes846CwDw5JNPIhaL9dl6RERERESKofpYRESkMumFjYjIdmTu3LldE6yuXLlyi3+fOnUqDMPA1KlTAQDLly/H9773PUyYMAF+vx/jx4/Hueeei1WrVm32//75z3/inHPOwYQJE+Dz+TB27FhccMEFaGxspLbr2Wefxbe+9S2MGzcOPp8PkUgE+++/P66//nq0tbUV+7HxxBNPAABOOukkuFzdDx595plncPLJJ2PMmDHwer0Ih8OYMGECDj/8cFxzzTVYtGjRNv/vjBkzAADJZBLPPfdc0dstIiIiIn1L9bHqYxEZhCyrcr9k0FMkmojIIPXXv/4Vp5xyyma/Cbdq1Sr8/ve/x5/+9CcsWLAAu+yyCx599FHMnj0b2Wy2q93atWtx991348UXX8Rbb72FUaNGbXUdbW1t+OY3v4lXX311s7/PZDJ4//338f777+M3v/kNnnvuORx88MG9+hyrVq3CihUrAKDbZRQKBZxxxhl48sknN/v7bDaLeDyOFStW4I033sCLL76IxYsXb3UZO+ywA0aMGIGGhga8+OKLfRYvISIiIiL9T/VxJ9XHIiIi5aMRNiIig9C6detw6qmnIhKJ4M4778S7776LhQsX4gc/+AEMw0BjYyPOO+88vPfee5g5cyZ22mkn3HvvvVi0aBFee+01fPvb3wbQ+TB42WWXbXUdmUwGxxxzDF599VU4nU58+9vfxqOPPop33nkHCxcuxA033IC6ujo0NjbihBNO2OK3FlkLFy7s+v6AAw7YZru77rqr62H0sMMOw9y5c7Fw4UJ88MEHePnll3HLLbfg2GOPhdPp7HZ9Bx54IABgwYIFvdpeERERERl4VB+rPhYRERkINMJGRGQQWrZsGXbeeWe8+eabGDp0aNffH3bYYXC5XPjlL3+JN998EyeeeCIOPPBAvPzyywgEAl3tpk6dinQ6jSeffBJPPfUUmpqaNlsOAPzkJz/BBx98gEgkgr/+9a/Yb7/9Nvv3ww47DGeddRYOOeQQrF+/HldffTUeeeQR25/lrbfeAgB4PB7stttu22y3MRbioIMOwmuvvbZFNMQxxxyDyy67DK2trd2ub7/99sMf//hH1NfXY8OGDRg+fLjtbRYRERGRgUX1sepjERGRgUAjbEREBqk77rhji4dIALjwwgu7vm9ubsa999672cPoRhdccAEAIJ/P4+23397s3+LxOH79618DAH76059u8TC60Q477IBrrrkGQOdEpYlEwvbnWLt2LQCgrq6u29/+a2hoAAAceuih3eZ419bWdru+YcOGdX3/xRdf2NlUERERERnAVB9vnepjERGR/qMXNiIig1AkEsFxxx231X/bcccdEQ6HAQB77rkndt11162222uvvbq+//KD2YIFC9DR0QEA+OY3v9ntthxxxBEAgFwuh/fff5/7AJtoamoCANTU1HTbbuTIkQCA+fPno7m52fZ6Ntr0gXXjQ66IiIiIVDbVx6qPRWQ7YlqV+yWDnl7YiIgMQjvvvDMMw9jmv0ciEQDApEmTemwDYLOJWQFsNinpyJEjYRjGNr+mTJnS1bY3D3gbIxp6eiCdNWsWAGD58uWYOHEivvOd7+DRRx/t+g1E1qbr6c1vPIqIiIjIwKP6WPWxiIjIQKAXNiIig9DWIhw25XA4emy3sQ0AFAqFzf6tsbGxV9uVTCZt/x+fzwcASKVS3bb7zne+g6uvvhoulwsdHR24//77ceaZZ2Ls2LGYOHEiLr/8cirCYdP1uN1u29srIiIiIgOP6mPVxyIiIgPBtkNKRUREemnTB9QPPviAfnAbM2aM7XVtzBnvaTJUALjhhhtw/v9n777j7KjK/4F/5vay927fTQ8ECCBEigSifBEQlJ9YKQpSgyAqomIDsUBQFEFEqgUpAUGKSLEgohCkGYpSpQQSQspme7+9zO+PmCVlZ59nz7mbzSafN6+8Xptwzszcmbkzz+yc8zynn45bb70VDz30EBYvXox0Oo2lS5fisssuw1VXXYUrr7wSX/jCFzyXsf561h9FSURERETkhfExERERafCFDRERVVx9ff3Qz42NjUYPmlrrHkh7enpU7WfOnInvfOc7+M53voNCoYBnnnkGd955J379618jm83ijDPOwH777Ye99tpr2P7rr2fGjBn2H4CIiIiItnqMj4mINqOJWg9mIm4zVRxTohERUcWt/zD3xBNPjOm65syZAwDo6+sbdaqJYDCI973vfbj88svxu9/9DgDgui7uuusuzz5LliwBAITDYey4446GW01ERERE2xLGx0RERKTBFzZERFRxhx566FB+7yuvvBKuO3ajRA444IChn5955hnj5RxyyCFDP3d2dnq2W7eOvfbaizm6iYiIiEiF8TERERFp8IUNERFVXE1NDc4880wAwJNPPomvfe1rKJfLnu3b2tpw3XXXGa1r3333RTgcBgA8/fTTnu1uueUWFItFz///4IMPDv28/fbbD9sml8vhxRdfBAB86EMfMtlcIiIiItoGMT4mIiIiDb6wISKiMfGDH/wA++23HwDgiiuuwN57741rrrkGTzzxBJ5//nksWrQIV199NT75yU9ixowZ+NWvfmW0nnA4jMMOOwwA8NBDD3m2O/HEEzFt2jScccYZuOWWW/Cvf/0Lzz33HB544AF84xvfwEknnQQAqKqqwvHHHz/sMh599FEUCgUAwBFHHGG0vURERES0bWJ8TES0mayrYTMR/9A2LzDeG0BERFuncDiMv//975g/fz7uvvtuvPDCC0OjCoeTTCaN1/W5z30Of/zjH/Hkk0/i7bffxsyZM4dt19bWhl/+8pf45S9/Oez/r66uxu23347p06cP+//X5fHebbfdsOeeexpvLxERERFtexgfExERkYQvbIiIaMwkEgn84Q9/wOOPP46bbroJjz32GFpaWpDJZJBMJrHDDjtg3333xUc+8hGrFAof/vCHMW3aNKxatQq33XYbvv3tb2/S5uWXX8Zf/vIXPP7441i6dCna2trQ29uLRCKBXXbZBYcddhi++MUvorm5edh1ZLNZ3H333QCAM844w3hbiYiIiGjbxfiYiIiIRuK4Y1npjoiIaDO55JJLcM4552D27Nl49dVX4fNVNuvnLbfcghNPPBH19fVYvnw5qqqqKrp8IiIiIqJKYnxMRNua/v5+VFdXo+enpyAZDY335oxafyaP2m/diL6+PqtZljSxsYYNERFtFb785S9j6tSpWLJkCe68886KLrtcLuPHP/4xAOBb3/oWH0aJiIiIaIvH+JiIiGji4QsbIiLaKkSjUVxwwQUAgAsvvBCVnED6+9//Hq+++ipmzJiBr3zlKxVbLhERERHRWGF8TETbrLI7cf/QNo81bIiIaKsxf/58tLW1IZ/PY82aNZgyZUpFllsqlXD++efjAx/4AKLRaEWWSUREREQ01hgfExERTSysYUNEREREREREREREE9pQDZuL50/cGjbnLGQNm20cU6IRERERERERERERERGNM6ZEIyIiIiIiIiIiIqKtguu6cCdgPRgmwiKAM2yIiIiIiIiIiIiIiIjGHV/YEBERERERERERERERjTO+sCEiIiIiIiIiIiIiIhpnfGFDREREREREREREREQ0zgLjvQFERERERERERERERBVRdtf+mWgm4jZTxXGGDRERERERERERERER0TjjCxsiIiIiIiIiIiIiIqJxxhc2RERERERERERERERE44w1bIiIiIiIiIiIiIho68AaNjSBcYYNERERERERERERERFtExYsWADHceA4DhYsWDDem7MBvrAhIiIiIiIiIiIiItpGLF++HL/5zW9wwgknYI899kBtbS2CwSDq6urw7ne/G5///Ofxz3/+03j5zzzzDM444wy8613vQjKZRDKZxLve9S6cccYZeOaZZyr4ScbOI488MvRSx+tPKBRCY2Mj9ttvP3z1q1/Fs88+a71epkQjIiIiIiIiIiIiItrKPffcc/jCF76Ap59+etj/39PTg56eHrz00ku49tprcdBBB+Gmm27CjBkzVMvP5/P41re+hauuugquu2GKt1dffRWvvvoqfvWrX+GrX/0qLrnkEgSDQevPNJ4KhQI6OzvR2dmJp59+GldeeSWOOeYY/OY3v0EikTBaJl/YEBEREREREREREdHWgTVsPL3++uubvKyZPXs2dt99dzQ0NKC3txdPPvkkVq1aBWDtLJP3vve9eOyxxzBr1ixx+Z/73Odw8803D/191qxZmDdvHgBg8eLFWLZsGVzXxeWXX47+/n5cf/31Ffx0Y+tLX/rSJv+WyWSwatUqPPnkkxgcHAQA3HHHHVi1ahUeeeQRBAKjf/3CFzZERERERERERERERNuIHXfcEaeddhpOOOEETJ06dYP/Vy6XsXDhQnz5y19GOp1GS0sLjj/+eDz55JNwHMdzmTfccMPQyxqfz4ef/exn+MpXvgKfzze03CuvvBLf+MY3UC6XccMNN+DAAw/ESSedNHYftIKuvvpqz/+XSqXw7W9/e6jNE088gWuvvRZnnHHGqNfDGjZERERERERERERERFu5yZMn48Ybb8Rrr72Gc845Z5OXNcDaly2f/exnccsttwz92+LFi/Hggw96LjeXy2HBggVDfz/77LNx1llnDb2sWbfcs846C9/61reG/u28885DPp+3/FTjLx6P46qrrsJhhx029G+33nqr0bL4woaIiIiIiIiIiIiIaCt34IEHYv78+fD7/WLbI444Avvuu+/Q3//yl794tv3jH/+IlStXAgCqq6vx/e9/37Pteeedh2QyCQB4++23R1zuRHPccccN/fzKK68YLYMvbIiIiIiIiIiIiIiIaAP777//0M/Lly/3bHfvvfcO/XzMMccgFot5to3FYvj0pz899Pd77rnHahs3tmjRIhx33HGYOXMmIpEIJk+ejAMOOAC/+MUvkE6nK7qujU2ePHno51QqZbQM1rAhIiIiIiIiIiIioq2D6679M9Fsgdu8fs2aUqnk2W7RokVDPx900EHicg8++GBcd911AICHH37YfAPXUywW8fnPfx433HDDBv/e2tqK1tZWPP7447jmmmtw9913V2R9w2ltbR36edKkSUbL4AsbIiIiIiIiIiIiIiLawEsvvTT08/Tp04dt09fXhzVr1gz9fe+99xaXu36b1atXo7+/fyhNmqmTTjoJt91229Dfa2pqcPDBB6O+vh4rVqzAI488gldeeQWHH344Pv7xj1uty8sdd9wx9PMBBxxgtAy+sCEiIiIiIiIiIiIioiErVqzYYPbLoYceOmy7119/fYO/z5gxQ1z2xm1ef/11zJ0712Ar1/rtb3+7wcuaM888E5dccgmi0ejQv61ZswYnnHACHn74YfziF78wXtdwMpkMzjvvvKF6PIFAAN/4xjeMlsUXNkREREREREREREREW4D+/v4N/h4OhxEOhzf7dnz9618fSoM2Y8YMfOxjHxu2XVdX19DPyWRyg5ckXmKxGBKJBAYGBgAA3d3dxttZLpfx3e9+d+jv8+fPx1VXXbVJu8mTJ+PPf/4z5s2bhxdffHHU6znzzDM3+bdcLodVq1bhySefHDpuyWQSd955p2qm0XD4woaIiIiIiIiIiIiItgpuee2fiWbdNm+ceuz888/HggULNuu23HTTTfjDH/4w9PeLLrrI86XR4ODg0M+alzXrt133wmb9ZYzW3/72N6xcuXJomZdeeumI67z00kvxoQ99aNTrueaaa8Q2xx9/PK644grU19ePevnr+Ix7EhERERERERERERFRxaxcuRJ9fX1Df84999zNuv5nn30WX/jCF4b+/pnPfAbHHXecZ/tsNjv0cygUUq9n/RdAmUxmlFv5jkWLFg39fPjhh4svSw499FBMnTrVeH0jufXWW7HXXnvhnnvuMV4GX9gQEREREREREREREW0BksnkBn82Zzq0t956Cx/72MeGXsK8+93vxq9+9asR+0QikaGf8/m8el25XG7o59HMzNnYc889N/Tze9/7XrG94zjYb7/9Rr0e13U3+VMsFtHW1ob7778fH//4xwGsfeF25JFH4tprrx31OgCmRCMiIiIiIiIiIiIimlCeeuop/Pa3vx2xzYknnqh+ObFmzRp88IMfRGtrKwBg1qxZeOCBB5BMJkfsV1VVNfTzaGbKrN92/WWMVkdHx9DPM2bMUPXRtpP4/X40NTXhwx/+MD784Q9jwYIFuOCCCwAAX/nKV3DQQQdh9uzZo1omX9gQERERERERERER0dah7K79M9GMcptfffVVsa7KPvvso3ph09XVhQ9+8INYunQpAGDy5Mn4xz/+gcmTJ4t9109B1t/fj2w2u8Gsm+Gk0+mh+jUAUFdXJ67Hy/r1b2KxmKpPPB43Xt9Ivve97+HGG2/EihUrkMvlcOWVV+Lqq68e1TKYEo2IiIiIiIiIiIiIaBvU39+Pww47DP/9738BAA0NDfjHP/6B7bffXtV/55133uDvb7/9tthnxYoVIy5jNNafnZNOp1V9UqmU8fpGEggEcMghhwz9/aGHHhr1MvjChoiIiIiIiIiIiIhoApk/f/6wdVXW/zN//vwRl5FKpXD44Yfj3//+NwCguroaDzzwAN71rnept6O6unqDmTjr15Tx8p///Gfo56lTp4pp10bS2Ng49PPGL4K8rFy50nh9kvX3hebl1cb4woaIiIiIiIiIiIiIaBuSzWbx8Y9/HE888QSAtenE/vKXv+A973nPqJd18MEHD/38yCOPiO3/+c9/Dv38gQ98YNTrW99ee+019PPixYvF9q7r4qmnnrJa50jWn+Xj843+9Qtf2BARERERERERERERbSMKhQKOOuooPPzwwwCAcDiM++67D/vvv7/R8j75yU8O/XzHHXcgk8l4ts1kMrjzzjuH7Wti/ZdF999/P7q7u0ds//DDD2PVqlVW6xzJxrOHRosvbIiIiIiIiIiIiIho61B2J+6fzaBUKuG4447D/fffD2Bt3ZU777wThx56qPEyP/7xj2PatGkAgN7eXvzoRz/ybPvDH/4Qvb29AICZM2fiox/9qPF6AeBDH/oQpk+fDmDt7Jazzz7bs202m8U3vvENq/WN5Nlnn8Vjjz029Pf169lo8YUNEREREREREREREdFWznVdnHrqqbjrrrsArE3Z9dvf/hYf//jHrZYbDodxwQUXDP39oosuwpVXXolyuTz0b+VyGVdeeSUuvvjioX/7wQ9+gFAoZLVuv9+PH/7wh0N/v/7663HWWWchm81u0K61tRUf+9jH8MILL1ivczj/+Mc/8IlPfAKuu/bFWyAQwJe//OVRLydQ6Q0jIiIiIiIiIiIiIqItyy9/+UvcdNNNQ3/fYYcd8Pjjj+Pxxx9X9b/66qs9/99nP/tZPPLII/jtb3+LcrmMr371q7jyyisxb948AGvryyxdunSo/SmnnIKTTjrJ8JNs6OSTT8b9998/lGrtiiuuwM0334yDDz4Y9fX1WLlyJRYtWoRcLoftt98en/jEJ3D55ZePah1nnnnmJv9WKpXQ3d2Nf//73xt8NgC49NJLseuuu476s/CFDRERERERERERERHRVq69vX2Dv7/xxht444031P1HemEDANdddx2qq6txzTXXwHVdLF26dJMXGY7j4Mtf/jIuvfRS/YYr3HLLLYhGo0MvpHp6enD33Xdv0GaXXXbBPffcg9tvv33Uy7/mmmtU7erq6nDFFVfghBNOGPU6AL6wISIiIiIiIiIiIqKthFte+2eimYjbvLFQKISrrroKJ554Im644QY88sgjWL16NQBg6tSpOOigg3Dqqadi7ty5FV93MBjEwoULcdJJJ+Haa6/FE088gfb2dtTW1mLHHXfEpz/9aXz2s59FVVVVxdbpOA4SiQSampqw55574rDDDsOxxx5rtQ7HXZdUjYiIiIiIiIiIiIhoAurv70d1dTU6z/0MkpHK1ygZa/3ZPBouug19fX1IJpPjvTk0TnzjvQFERERERERERERERETbOr6wISIiIiIiIiIiIiIiGmd8YUNERERERERERERERDTOAuO9AUREREREREREREREFeG6QHkClm1nqXkCZ9gQERERERERERERERGNO76wISIiIiIiIiIiIiIiGmd8YUNERERERERERERERDTOWMOGiIiIiIiIiIiIiLYO5f/9mWgm4jZTxXGGDRERERERERERERER0TjjCxsiIiIiIiIiIiIiIqJxxhc2RERERERERERERERE44w1bIiIiIiIiIiIiIhoq+CWXbhld7w3Y9Qm4jZT5XGGDRERERERERERERER0TjjCxsiIiIiIiIiIiIiIqJxxhc2RERERERERERERERE44wvbIiIiIiIiIiIiIiIiMZZYLw3gIiIiIiIiIiIiIioIsr/+zPRTMRtporjDBsiIiIiIiIiIiIiIqJxxhc2RERERERERERERERE44wvbIiIiIiIiIiIiIiIiMYZa9gQERERERERERER0dbB/d+fiWYibjNVHGfYEBERERERERERERERjTO+sCEiIiIiIiIiIiIiIhpnfGFDREREREREREREREQ0zljDhoiIiIiIiIiIiIi2Cm7ZhVueeAVhJuI2U+Vxhg0REREREREREREREdE44wubCvr9738Px3Ewe/ZsFAqF8d4cIiIiIqLNauHChXAcB47jYPny5UP/nk6n0dTUBMdx8Mgjj4zb9hEREREREW3J+MKmgo466ii8613vwhtvvIGrrrpqvDeHiIiIiGiLEIvF8PWvfx0AcNZZZ8F1me6BiIiIiIhoY3xhU0E+nw/f/e53AQA/+clPkEqlxnmLiIiIiIi2DF/60pdQV1eHF154Ab///e/He3OIiIiIiIi2OHxhU2HHHHMMpk6dio6ODvz6178e780hIiIiItoiJBIJnH766QCACy+8cJy3hoiIiIi2WuUJ/Ie2eXxhU2F+vx/HHHMMAODqq69GucxvGhERERERABx33HEAgJdeeom1bIiIiIiIiDbCFzZj4PjjjwcAvPXWW1i0aNE4bw0RERER0ZZhzpw5mDNnDgDg+uuvH+etISIiIiIi2rLwhY3giSeewGmnnYadd94ZyWQSoVAI06ZNw0c/+lFcc8016O3t3aTP3nvvje233x4AcNttt23mLSYiIiIiGhs9PT349re/jV122QXRaBRNTU049NBDR1WT5qijjgIA3Hvvvchms2O1qURERERERBNOYLw3YEuVyWRw6qmnDvvCZfXq1Vi9ejX+8pe/oKOjAwsWLNikzX777Ye33noLDzzwwGbYWiIiIiKisfXqq6/i0EMPRUtLy9C/ZbNZPPTQQ3jooYdwyimn4P3vf7+4nHnz5gEABgcH8dhjj+GDH/zgmG0zEREREW173PLaPxPNRNxmqjy+sBlGuVzGJz7xCfz9738HAOy0004444wzsM8++yAWi2HNmjV48sknceedd3ouY99998Xtt9+O1atX480338SOO+64uTafiIiIiKii+vv7cdhhhw29rDnmmGNw8skno6mpCUuWLMFll12GG2+8ES+//LK4rH333Xfo53/+8598YUNERERERPQ/fGEzjKuvvnroZc0RRxyB2267DeFweIM2H/nIR/DDH/4Qa9asGXYZ73nPe4Z+/ve//80XNkREREQ0Yf3whz/EypUrAQA//vGPce655w79v/e85z04+uij8dGPfhQPPviguKza2lpsv/32eOutt/Dvf/97zLaZiIiIiIhoomENm42Uy2X89Kc/BQBMmzYNN9988yYva9bx+XyYOnXqsP+vqalp6Odly5ZVfkOJiIiIiDaDfD6P66+/HgDw7ne/G9/+9rc3aRMMBnH99dcjGAyqlrkuVmacTERERERE9A6+sNnI888/j1WrVgEAPve5z6GqqspoOXV1dUM/t7a2VmTbiIiIiIg2t3//+9/o6ekBAJx88slwHGfYdtOmTcOHPvQh1TLXxcqMk4mIiIiIiN7BFzYbee6554Z+PuCAA4yXU1tbO/RzKpWy2iYiIiIiovHy0ksvDf08d+7cEduuX59mJOtiZcbJRERERFRx5Qn8h7Z5fGGzkc7OzqGfJ0+ebLycTCYz9LM2NQQRERER0Zamu7t76Of10/4Op7m5WbXMdbEy42QiIiIiIqJ38IXNGFn/wbampmb8NoSIiIiIqEK80qGN1rpYmXEyERERERHRO/jCZiMNDQ1DP69Zs8Z4OevyfAPAjBkzrLaJiIiIiGi8rJ/qt62tbcS20v9fZ12szDiZiIiIiIjoHXxhs5G999576OdHH33UeDlLliwZ+nm33Xaz2iYiIiIiovEyZ86coZ+feeaZEdtK/x8AyuUyli1bBoBxMhERERFVnlueuH+I+MJmI3vssQemT58OALjuuuswODhotJx1D6vBYHCDl0BERERERBPJe97znqFZNr/97W/huu6w7VavXo0HH3xQXN4rr7wyFGPvt99+ldtQIiIiIiKiCY4vbDbi8/nwrW99CwCwatUqnHTSScjn88O2LZfLaGlpGfb/Pf300wCA9773vaiqqhqbjSUiIiIiGmPhcBinnHIKAOD555/HT3/6003aFItFfO5zn/OMm9e3Lk4GgA996EOV21AiIiIiIqIJLjDeG7Al+tKXvoQ//elP+Pvf/4577rkHc+bMwRlnnIF99tkHsVgMra2tWLx4MW677TYcd9xxWLBgwQb9BwYGhmbYHHHEEePwCYa37gVTIpGoWMFYIiIioi2V67oYGBjAlClT4PNxnJKN8847D3feeSdWrVqFc845B88//zxOOukkNDU1YcmSJbjsssvwzDPPYJ999sGzzz474rIeeughAGtntm+//fabY/M9MT4mIiKibQnjY6ItH1/YDMPn8+Hee+/FySefjLvuugtLlizBWWedpe5/9913I5vNIhAI4Nhjjx27DR2llpaWoXRvRERERNuKlStXYtq0aeO9GRNadXU1HnjgARx66KFobW3Fbbfdhttuu22DNvPnz8eBBx44NBtnOOl0Gvfddx8A4IQTThjTbdZgfExERETboq0+PnYBTMR6MMNnHqZtDF/YeIjFYvj973+PRYsW4cYbb8Tjjz+O1tZWlEolNDc3Y88998RHP/pRfOYzn9mk7+9+9zsAa2fXTJo0aXNvuqdEIgEAmFJ9EHzO6A99Z3qJ8bqz+dXGfYmIiGjbVh3b2aif65bQn3l9KAYiO7vtthv++9//4uKLL8Y999yDFStWIJFIYM6cOfjc5z6Hz3zmM1i4cOGIy7jvvvuQSqUQiURGfLGzubxzbvgAcIbNRNJcPU9s4yoq97b3Py22ISLa2vh8cVW7uqpdxTY9qTfENqVSn2p9tDmsfZPB+Jhoy8UXNoKDDz4YBx98sLr922+/PZTm4etf//pYbZaRdWkefE4APido0N9mqiQfgImIiMiM4/gt+zMOqZS6ujpcfPHFuPjii4f9//Pnz8f8+fM9+19//fUAgJNPPhn19fVjsYmj8s654YDx6sSiGYDmqobW8rgT0bZHGxtprrW63xXxWrulYXxMtOXiC5sK+/GPf4xSqYT/9//+H+bNk0d9jQff//4brcaYPLLCSzRuvi8Gyu3Gfftzq4z7AkA4kDTuG/LrRqwMJ+BEjPsm0Gjct75s3jfnyEWGvQw65qNtustvG/cFgJJbsOibM+5r8tJ0Hcfg+7uO7hcXlV9voZQy71vOGPcFdKNrvdh8j6eH9jHuW4L5eZl3zPdXf6nVuG+m0G3ctz48y7gvABQtvou9uRXGfW1eIgR8IeO+Ngolu++TqVDA/LsEAPFAk1G/sltEX/oVq3VT5SxevBgPPfQQQqEQvvOd74z35tAE50C+Bgd9MbHNjnWfENuU3ZLYpreoi0kLpazYJh5sENsEnLDYJuRUiW2aXDkdTtCV49Z+X7/cBh1imwFlLKKJSzWxesgn7yNNLOr3ycejWE7L61KcH6Wyedyzsebo7mKbGjSLbQYcOQ7UPHe1Z+V7djI8VWwTdWrFNoDue5svys8xAcXx9/nkX78Vy/Izteb4a55hgn753AeAKp/8O4LBwBqxTbHYq1ofERHB4rdvtImVK1di4cKF8Pv9uOSSS8Z7c4iIiIiIthgXXHABAOCrX/0qZsyYMc5bQ0REREREtOXhDJsKWrlyJc4991zMmjULc+bMGe/N8RRwYvAbjO6PueazTaaWzUbGAkDMv5Nx32xEHg03kpDP/J1m0KJvPGDe12cxqzXsN+/sWhRGK7nmM3taMzPNVwxgddl8VoCNKU6dcd9BxcgrLzmLmRs21oSWG/e1mRUEAFGYX7umlKcY960PmM+UK5TNP3PJ4ssYC+1m3DccNb9u5S0+LwC05+TRqF6WR6uN+6bKncZ9g4486tuLySzZdcIh81zVk0rm3webWZgA0A6zGbPlcbrm0abS6TTmzZuHefPm4Wtf+9p4bw5tBTTXwirIaffqS3Ic2hyQR6LnA7p7aCBUmRQ0yaD8KK95Lggp4n/NI4Lr1ohtBgrybJ4V2QF5ZQC6fF1im6nuZLFNUJHKqdORZw9FXDnu6wzIM4zSATnzgDY2nlTeTm7jyHFyyC/vo1RR/h7F/PKsuHByL7GN5rzuz+t+D7AsIF8jegItYhvNbO+QExXbaGYONrrTxTZRV57x0+HTZTLpd+VZb/mi/B0h2txc1+73VONlIm4zVR5f2FTQ+973Przvfe8b780gIiIiItqixGIxnH/++eO9GURERERERFs0pkQjIiIiIiIiIiIiIiIaZ5xhsw2aUZqpKlK5sfqQeXqfmpD5u8GyxXTAkN/uFI9bdO+3yMISM69jDcciy0I8YN65ZJUSzbxvdciucPfkwiTjvp2ZonHfZMj8IFeXzU/MeND8u5gcfSbFIW0Z87RTAwW71IY26QlnVpkfp6zFZvfnxye9mE06xkTQ/PqRLllc9AC05swvInFFChcvUUUaEe/1ysVgvWQcufixl/qSeTrGKWHzNG6pot332DQzYtHNY7ndmoloC9WMHcQ2Mxw5TVNtTI6rIoqcYHnlQ0tdWF6WJsRUZKlSjc6MKuJ/TUq0guI6nVQ8EzZGdDHjmrScps6veDCKKD5/TVFOmzUlLn+2bElOS9qalg9+tqS7Kc5Mys9JEcVjRa8izvI78oI06cY18aTmcSZT1O2jaFFOU1Zy5PTuEcgxks+VNzzjpMU22wXkWC6gyBsXzCmfKRX7u92xeyYnIqIN8YUNEREREREREREREW0V3PLaPxPNRNxmqjy+sNkGNQQjCPpGP8NmmsVI82qLkfmZkvmo7f68XbUumxHy9YrRc14ifvPtThfN16sZGefFYuIGHFgc46LdMdYWpByOTXF37ci44dgUoWuKmO/rRNB8xcWy+QkS0lQWHSM1FoPFUhbfxcGC+b5OWHwZGyLmfTWjb70UbKZSwm7mRyRvHgqFfeb3xZDFbKZsyfy6lXXM+9rM4IxohoKP1L9kNsu36I7f9YOIxlbClWcrTInLDyGNitjI78j3qa6c7nqTV4SAmlt5neJxLqt4jrKZ6b6+sOKWqJg8gn5lDJRTzCgOKG5cZUW8Vqv4cI2K25Tm8+cVM+m1s0eSIfmzRRXPnZrny4Ii1tfMLtNsc1rx/Kc5ZgCQK8szupohtwkr4hxHcT72FuTrmkZQ8fwU8eli4KhiVrjfxxk2RESVxBo2RERERERERERERERE44wvbIiIiIiIiIiIiIiIiMYZU6Jtg3yOoyrAuDGbgvQBRRoBLyGfed8muYbgiCxqaKMpYp52JuIzT5fVYZHeR5PuwUvE4jj1FMzfHftscvQAKLnmKY1MvkfraIqueslYpIGzyS6mSePgpT1jfk535XPmKwbQEB59Csh1VqfNd5jN98nmertj0rgrZsbM93WhbL7NqzMWeTMBdCvTXgwn22N+zawKmq93mqI4sZd82Xy9bw+a35tsrnk1Fun2AKDkmt3QNSlaiGhiKkG+nsUUl3ibZ431JZUPDvGAvL6w4tI1KSIHZgNFeUEpRZuqgBzH5RVxQIcqDbJuPxYU98KITb7W9WiOhybbcU9ebtOSkhtpb8dxRfrVfsWyNCn6JsfkRnvUFMQ2Mb+8I9tz8he7K6+9/8sxaK8ij+G0uHw+auL7NWl5u3ty8rUvqThoIWUas4GUnBKNaItU/t+fiWYibjNVHJ9iiYiIiIiIiIiIiIiIxhln2GyDGqMOwr7Rv6tzYFFw3GLgWsR8IDHqQnavpm0GZdkU8CxZFEnWjEryYjNLxmY2QdBive+rTxv3BQBXOYpvOC0ZsyLYAJCxOEFas+ZfCp/FccqaD8xHwGJqTzJoV8RydrX52IS4xV1yQB5E6MlmtorPYkhO3mI2gs0214YsTi4A02Pmn7kpYn6QNcWcvVQHzT9zt3rU6Kbe6DPfVwnNEFsPilq8RESjMj0sF+fWjGgvlOXYSPM8Uh/WxVg1iuu/ZtaL5nlBc5eqCcr3hZBi9r+myPmsuBwc1YV1s307snIc3leQD1x/UW7TqdgkxWQW1WjZeEDengblA/IOivr1aUU4onlsKSjCC805m1XEoppnR825BgDTo/I2LUvJsWKD4vtfduU2XTl5ezSPVZq4yyKpxybyxf7KLYyIiDjDhoiIiIiIiIiIiIiIaLzxhQ0REREREREREREREdE4Y0q0bZDfZ5aaxCITCpJBiyLp5qvFpIhFTiJLrVnzItoxRWFPLwmb1GQWfW1mVKcUqQjGSshnnpZIkx7CS5vF+WGTbs8mxWBCUSTXS0/YPHVUumR3fuyckIvyerE5xstS5qnc8hYHWVPM2IvN500XLVKLWaRTA4Co3/wzK7JTeGoMm18/NAWcvaRL5nfGZMj8+1Rr8T0OW6RFBMy32ybNHxFt2UKKFM+alL2a9KcxxX0mobyuN4blovJrsnIMoflsdYqUo2GL1MTr06Sy0qzJr9yeWED+bB05Od4eLFYmBVW1IrVcKSrvo15F2tPZSd0+mhWXzzXNPupQbJMmFov65WOmSZuWUTwbKLIhAgAUGRFRrfhdRmNYft7IKuI3vyN/tjpFOt8qxQ7Q/n4n4ZPTDwb8MbFNqcS0abR5ueW1fyaaibjNVHl8iiUiIiIiIiIiIiIiIhpnnGGzDZoUMRtlrylI6cWmGLXNmC+bGSOAbqSYl76Ced8pFjODGiNZ474hxagnL/1589kEMYv1tioKjo7k7bT5TBdNUUgvNrMgaixmrA0oRhF60Yx486IpROq5Xs3QtxFoRqB6sbmGaEbjeolbTCpqDJtfPxIB89lIaUWBZC+vDdiFI//uMt9hNufmvEbzz9xscZyqFQWJvSQtpsvazJKJWVzz1vY3W7ffcvYWEW25mhTFwidF5Iu85ipRVMRANrNUN5ZS3FMDimezBsVsnmRIbpNXzGhIFeWYOqeYYfBsV7XYBgC68vIx6ZU/GmbE5ftTwJHXNaA4ZlnFI49mlrVfsT1r28nLCipmNMUVMW1NUP5wtSE5ztScI8sVz28v9epipUxR/mwNEXl/z4zJywkF5c+fCMoxsWabNXGXr6Q7j2qC8v6eGt9HbLMs/2fV+oiIiDNsiIiIiIiIiIiIiIiIxh1n2BARERERERERERHRVsF17eqVjpeJuM1UeXxhsw0KOK5Rmp86xRRm776K+egeshYFx236AkBfwby/YfYWAEDcIi1RUzJl3DdgkZrMN2DcFR0Z87RmNun2AKC/YN4/YpHyaqcq83RImnQCXtZkzFPX2dQMj/vNJ3QuT9lNBl2VMU9716woIOql3uI42aSfqw2Zn1vxgHlfTQFYLyGf+TECgIRFd5uUaE2KVDNemqMZ4742MfzkmPn1Nm+T2tDywcP0+mNz3SKi8VOf2FNso0kLFffLF64mRSrikE+OkbuV6YEHi/L9UpPCdlJE3qb6qJwquS6ZFtuk0vJnKwzK8ZrmsxeU9wtNurOdEvLxnxnLiW1yijTZbVk5GGkOK9JUKYrOdyvDj5hfPm4JRSozRxF5VCuWo0u/J+/rZCAsttGmcQ2H5HZJRZzZpPiu+RT7cVJEPma9iudXTdxlmXV6AwWYx7VERLQppkQjIiIiIiIiIiIiIiIaZ5xhsw2K+M1mBlQFzGdfJILmo7Y1I3G8pAp2o7YHijHjvo1h8/21Q0OPcd9knTy6x0tZWXhwONG4+TGuGZBHSXnqrDXvC6A7bz4rQFNc08tudb3Gfe1UGfcsWsxmilrMsOlSjlj1MrvK/DtRY3H9KVvMktGO0q20sKIYqpcmmO/n/erspkF05s3DmSUD5n3n1Jtfq+sbBo37Jrrixn2DPl0x5+EsT5lfq1uzdmOETGdCFStXA5yINqOyK8eVmgH0mtkDmtmlccX9MaacId+ZlWc6RhTh6ayEfB+Z3NwntgkoZn2Eo/I+SiblOKClIym20c7Y7S/I9+89auQUALWKmRE9FtkA1qfJcjFQlO+XmpljADAzLs96CCmWNah4ptbM1vA78rqSitntsxPy52oI634P8FZKbrdrQp6FteOULrFNWfEsVYbcZmUqKrdRZBhIFXW/E1JMZkQdpoltVqrWRkREAGfYEBERERERERERERERjTvOsCEiIiIiIiIiIiKirUPZWftnopmI20wVxxc226CmcBEx/+jTQCWDFqmBFFN7vfgtyiu7FusF7AraxwLmeVhci1RKNn0dizl3miKKXsIWhd0jfvO0ZLZsCsNnLNL1RS1SDDZZFDqPWaQHC4fMj3GsrcG4LwDUheU0Bl6CPvPvcVfOPH1GQJEyw4umMLKXWkWKCS/T63uN+757ll36ylKP+Xfi2ScnG/dtnt5v3DfcaH79KBbM0881W1x7bO6JkyN29+MpUbPtzpRcYLXVqoloHKTzcnqhSRH5Hl2niF009/pKFufOKwrYa+JqTcyvaqP4cD7Fbw38rrwf4yH5fu1XxkCax5Z0Ud7wqKJNVVg+j0KKZ5LG6pTYZuqAnJJ79aAuNWqNYrsH8vL9NVOS97YmfXp1VI7JE4rUejvtJKf6cyK6B9veF+U2fX1yCrLEdvLnLw3K53Z9nxyL5yt0PLaL6fbRa0E5VfNghxxPv6BaGxERAUyJRkRERERERERERERENO44w2YbFPWXEFOMuNiYzUhzm0LWEWUBz+EElQUZvYRS5iPk6xQjyLwUFcUmveRS5l9r12L0oN9iRlE4bn6MJysKro5keUoexeZlVWb0M9XWyXXLRVe9bBc3nzGiKazrxbGY9eGz6KsZHTgSzWhOLwXFCDYvNjNdplSZz3TRjkyttGCteV//NPPvAwD4d9ONNB3OvKY2477pV8xnjeQ7zY/TirYa474FxchuL0mL2X39FjN7ACBbMtvX2fGbhElEFprjc8Q2yaAce4YVsx40RddtZhlvTDPro6cgx5ghxfNcNi1fe4NFxcwAxbNJNCHfI2pq0mKbuoGE2AYAAj75s73cL8f5zTl59sC0mByXaQqz53LysdfEzA0R3Uxbzayfkit//plV8swgzbr8iu9aMCIvx1cvP6P7tqsX2wBA/Sz5ux3+e4fYpjggH7f+NWGxzRrF7ClNhglNvDeguBYBQLoon9wDpcpdI4mIiC9siIiIiIiIiIiIiGgr4ZbX/ploJuI2U+UxJRoREREREREREREREdE44wybbdBA0Y+SO/pUThG/eToTzZT9saCZmj2SKos0XwFnfF6LOz7zNDuuYcoZwC4lmo2A5XptUtfZpETLWOzrFWl5Or2XHavMvxP5kvnn3W6aeeq66TPM04MBgJs3/04MLDU/Ti1t1cZ9bVJQxqLmKQkSjebp9hyf+b4qreo37gsA/rh5+krfZPN0bKF2OUWGl/7l5vfU7pz5NaDGIq2PJt2Ll6Ki8PVI/IbdTfsR0fgquvL9qL+gSPlTkNM9JR35ulhSpBfSPu/EFOmeY4rUUWXFddVn8VywPk2cr0md6yq2uTasS/cV9smF4ItleX2tWflXIlUB+TyqVaTwDYXkYz91bzn9mC+mi8kLbYq006/KTTTHNqh47k7Wycc2VCevq9QmL8dXK6ffAwBUy2nzYu+Sj3/mdfma1dZTpdokib9C32utWEBeX8Iv7yMiItLjDBsiIiIiIiIiIiIiIqJxxhk22yCfs/bPaJUtBnJoRoB5iUbNZ0AUFQUyR2JTZD1vUdjZ5zdfr802OxbDp30WVxObzxuxOD8AuwLtccVoIy9V43SMJyuKhnoJK0YFevHZDLqymLkBAL5685XHBsxn99SllSP7hlEqmV8/8nnzL6NNvlwnaH6cssvsvsehwRbjvsEda4z7BurNZ8l0/Md8lGUiaL6/pjf0GfctWcwMbG9pNu4LAHnDc9O0HxGNr97sckWr91dkXSXFLAxNDBSN6K7NfRl5lqQLeZs0s1U26wwbRVwcisqzMKJB83hzY5psCZo9pJk9M7VGni0cjsifTTNj2Ynr4o/gZMWMphY5Xi0oZrMVCnL8mVdkJ4iGKnPO9v2jV9Uuub/8vXWq5ZncrmJWYK9ihvS05IDYJplUzDBql2f6r0zrZqhnFfHf6nKPallEm5PrOqp75ZZmIm4zVR5n2BAREREREREREREREY0zvrAhIiIiIiIiIiIiIiIaZ0yJtg1akwkg6h99GpesRYoemwLa/gG5oKSXwZxd8buMRZH1Woti9jap3Pr6zPfXeInWmBfB1qRoGCuuxYz9rCL9hpdBm/Mja16sPGaT5m+V+XGKdNmlxujsMv/MJde8mH19tXn6ue4BuQCql8G8eZquqn5dod/hmR+nrjVxi/UC2eXmn3lmxjyFQ1HOWuFp9aD5Z7ZJM5rJmO8rm/XapHIEgILhuotMKUA0IRVL8v2oLSvHJTVBOQZoDMvLiWbke1wqq3vuWJOW7/EhxbOTX9EmlZK3qaSI7yJh+bmmyi+nhPKHK/O5AKCsSBtXUtx6Mop0T12K+LlGkeoupWjjWyoHF5kXdffUjj45LVZj9aDYpqw4R5b3yuua6cppWSOd8rlWzMjb8+Jbk8Q2ALB3WE6rG0zKy+lcLae6TRXlX79pfn8RzsrXo4LimBWVoZkmhXjMnXi/gyAi2pJxhg0REREREREREREREdE44wybbVBzpIiYf/SzP6otCkAmFYUavSSi8mgtL5mC+UhiW37HfEZBwGLWiM3sHG2x1OGEoubnh1u0GC2esjvGNvXst4ubn9dBi9HmrcoRnJWWUxQT9fJ2V41xX5sZegDwQm/CuO+7kuazZKrjGeO+NoUGbWZBDA6YzygqFc2/Dz2DdqPy/t0tj+r0srRPMWzSg8/ie1wfNr+32azXhs16bUcIFQwvA6b9iGh85QvtYpvGiHxNmqJ4jtDEGdUxecZPvqCbmV+peX8hf0ls4/PL+8hRPLNEovIzgj8orys/KMeS6bwuzp2ieG6pDcnPJz15eZvCfnkfaWY09CmKzqda5GebjowuXns7La/vQ4pz2/EpziPIbVKKYxvtlrc5pzhmHcpMGw88t73YJhmQz6OYos2kmPxsEFSca5rnhErGilnFLLSww18t0pbHLa/9M9FMxG2myuMMGyIiIiIiIiIiIiIionHGFzZERERERERERERERETjjPMWt0FvDgQR8Y8+pVJNyDz1lE1Ko3xRl15gOKtT5oW7AV0RSi9vDZqvO6qYUu0lFJDTI3jJDJgf43TGPE2XppCplwFF8c6RFMrmx7jF4jN35c3XW6NIOeFFUzTSi8259caAeVqylozdrarbYl8D5oXh82Xza9eARfq5uMX1I2Ox3oDF+RG2SLkJAHvW9hv3fdYindqetXJhYC87z+ow7ptLmx+nlg7zFHAl13ycT6/FubV23Zu3HxGNL59Pju/elmulI+jIKTcbw/I9KDEgx/Wpoi6O1lwPBxUpjtcMyjFKkyIFkyaVlyY+iKXM4/n1pZT3i7xiu1/sk49/ShGCvLdebqR53tWkDXuyQ45LUsp00gOKdq911YptQorPlioqzhFFjFpQpBYsK57Rd6nRxYYrBqrENisVz3wfn90mtolUyd+R1avl49+XllPi9eYV6fdKuriu6MrnbbvTpVoWERHp8IUNEREREREREREREW0VXHdi1oNRvCOlbQBf2GyD/L61f0YrYDEyXzPixotNAW2b2QQAUKcoVOnFJt+gz2JCgFWeQ4uZUCXFKDcvmsKRXmxmYAHAaotZMm+lzA9UV9b83Ny52ny9mtFWXpy8+TbbzJJZaj6JAQCQL5uf10Gf+XldEzSfsWZzDbC57lVF8sZ9ozHzkbXhiN0MG8fiM/cpixsPJxEy31/BpM39yXx/5Uvm18ysRd+y5YOHaX/b9RLR+CiXc3IbxXL8ivtpSfGsoblXaGZ8AEBCMSNVM6MhpChOrqGZGaJZk2pmhGJfa2cqvTEox5a9ithVc5/oV3y2nqwcY7dm5ZkRyxXPF5pZQWspZkbk5P1dFZDPgIjiPIoqZlRHY3JsVVbMDKmulWeXrd0mOX7tbWsU22hmz2hiv+AaeT/25+RzLaPYRz7F+QEAfkc+J5vcetWyiIhIhzVsiIiIiIiIiIiIiIiIxhlf2BAREREREREREREREY0zpkTbBtUEXUQNptA3KQpyeq7TIm1MMmzeF2m50ORIuvPmKY1M9vE6pbJNPiTzriG/edHwSNg8HZJilrWntEU6I0CTKMBb3nx3IRky/9D6NAiVFbY4P2zSdGUs9jMAJIMWJ5gFF+brrbO4ZiZC5t/FvCLlh5ewokCyF59lWpdI0vxLMbNaV5R2OIM58+tPod/8/MgOmt+b/D7z72LCZ1NQ2u5+nFUUGB5OzuZ+SkRbNM39vUERn1Yr0jTVReQUbdr4fWUqLrbRpEbVrE+T7k0T/0eCcpuwIpV0Nivfv4rK/VhQhA6ayESTKrzoyo00sa4m1V2mgnF+reJ5o6DY35p0Z/WK74hGySKeXF8wpnuAqHNSYpvG3mqxTS6t+dWafHAzBfk7ElekcUsV5O1JK2MrzTNvaSIWCqGtnus6cC1KLIyXibjNVHmcYUNERERERERERERERDTOOMNmG1QfLiLmH/0oaptZMjYGLGZQpIp2p3jQYiRyUzRr3DekKEY6Fmze5IfC5tucTpsf437LGTamo7bX9rVatbGExYwRzUhLL9oisMPpK5ivN1eyG7GV1gxT9VDQ1SsdVmPYfLbKrCrz621t1HyjS8qCycPp7TefQZG3KGYPALlW8/5vK0Y6e9GMlPVSfMt8X6ct7m1Zi31tM1MurSh+O5K8pip0BfsR0ZavPix/v+sVzy8BRbyfV1zDBgq6mFRz59DMtNVkIXAU123NvT8WkNflU+zHgaxcLF17vygoru/pzRira+6v/YpZD5rPpb2z9SjCydasvL+nReU11kbkZ9+gYjZX34AcT2qeWVu7E2IbAOjJRcQ2nTn5GWhJS4PYJuKXZ9h0ZuXtCStmpvcpzjXts2xOcdHKYHx+V0REtLXiDBsiIiIiIiIiIiIiIqJxxhc2RERERERERERERERE44wp0UjNJhVKyLKgtKmYZWoxm0wq1Ypp4V58FvvaJqVRdbV5KqVC3jzNTlYxzdxLumiXSklbbHE4AYtX3iWLc6s/b5GWyGJ/RRRpDLxUWdxtkiHLVEoWOzsRNj8/JkXMC7TbXG+zFumybNZrk26vP2eX2nDZYMy4b8Fiu2cnzAvs1sfNr7dVijQXXrIW1wBN2pCxEguYHSefxTWeiLZsNves9YV8cnwTUKTAjAZ01eI1Rd6TipRo1TH5WWMwK99fE3F5OYGA/Pk1KY41qeV6lfc4vyPvR805UlQ88PUX5O2uC8n3V0267WRI/lzaZwG/4hZYF5KXVa04H4uKZ1BNKjNNGj9NLLNamfL2jUE5Td9OVXK8V6dISRwNytcIzbVGs681adxiynAyrHgUC/JXi7QlKjtwFffcLc5E3GaqOM6wISIiIiIiIiIiIiIiGmd8Db4NSgaKiAdGP8J2lxkdxusMV5uPzO9tMR/RW+pJGvcFgJDFjAK/YgSVl6Bi9I2XgsXI60DQ/PNqRkN5KaeMu8Kinry1uOGIbwAYLNgU7zbuajUa1WaGTcji+2CznwGgKWLeP2E++QvBCo38HS3N6EUvPsWovrGgKdQ7EptZMhGLz1y0GP2UsbhW2xzjKkWRai95i+NUHbSbRWV67cqUxuecJqKxl1DM+phR3S+2adpuUGxTTMvjHFesrBXbAEBYMaMnpMgS4FdkMIgoZv1oZs+EovJycjn5vlZW3L+08ZNm9ohmBkGXIimCZos0cXLUL59Hfkfe6HplbFsvTx5BXHH8FZOZVMdWcy8PKvZjOi8H6Fll1gnNM4pmuzMFeZs0W6SZhaOJAyOK68OksPbZTo7/Ar0cC05EVEm8qhIREREREREREREREY0zzrAhIiIiIiIiIiIioq2C6679M9FMxG2myuMLm21QquSHq5jWurFMyjw3kM9vfsWxSfFlyyb9iyZNgRdNagIv2Zx5qpxcxvwYly1SA9mk6UoGzdP7AMCkiE2aHvPzI2eR1qzaIkWYTeqolEUx+6zF+TFYMO4KAGiyqJOuSbfixea8Llkcp4KioK+XaNBiZytTTwwnbZkSTVEv2FPM4hjbsElrZnOMs0XzL0SqaH6PCFmm2wsb5r8s84mHaKuVUVwLB3NyTqia/rTYpliQ71Pae5HmHl9S3FM1zwslRVpITSozjZLieGjSPTWGdbFIR14+Jv0FeV/3KuI1Tfq1dFHengHFeZRVPCNo0wUnFedIpVL4ap6bg4pYQPN7g1xZXldB+eyhSYmm2W5NuKGJhAazijx2Cpo4UxsDBxXftYTfLvUtERFtiCnRiIiIiIiIiIiIiIiIxhln2GyDor4SYgbFw8sWo6cH+8xHitgMjp1SKxcaHYmmwKKXYMh8CoXPokC736KvzXpt+toU384rRliNRDv6ajg9FpN7chaDzaeEzPd1c0RRWdVDXDEq0ovNDIpWi5lfALB/g/l1oCGWMe7brxjV66UqZH5yRZUjU4dTN00eZeyllLGYvaUoHjuSmN9iGtU4WZOKG/e1mVFkM/PL6nuctTvGqzNmMUiubH7dIqItm+ZaGNIUMB+UR4YXFde/ZCQnbxCAWEy+xxeL8jUvGJavb5rlaGbG+DWzeRQxtSbm184GH1CEO/15+SSJKKbPNITklU2OyzFUPCCfa0tTCbHNTlW6WG+vhm6xjWZmjE8RO4QD8vlY35gS24SSilk4K+Tt0f4OoVMRr0cU15FuxXL68vLx18zA00w6HlDM+Ho7rYvrViseD1pLdr93ISKiDXGGDRERERERERERERER0TjjDBsiIiIiIiIiIiIi2iq4rmNVN3S8TMRtpsrjC5tt0PTqASQCuin766udZJ4qJzdgfqr5subpW6onm6d/AgCfRQaXovnushItmKdSyisKCnrJ5c2PcdEi3V7IZ556DgCCFqncNCkUvEQVBTW9NIbNP7NmSr8Xv0XRcJs0TIa1xofMnt5p3LdqpvlnTq0YNO472G+e4isWN78GhBrMv4tlizx/07J9xn0BoCEjp9jw8kZHvXHfsMX3aWZifFJH5BUpMrysHJDTtHhpsLhu2cgoCm4T0cQ0LSqnhWqeJF9r/UE5Rkn3yQ8FQeV1LjZJbldQ3CI0zynlkpymKpuRY/h0Sk7lNJiT22iKt4eV8WZQEbJUK+qgRxS3xYQiLbDms2lSVwcVce+OCV2MOfPdcnzlKjKHdiyNiW0cRawfaVTso4R8YGsycsrisCJlIABU9VSJbTozUbHNNMUxqYpUJh1id1renkFFvDdVXgwAoOTKy4pbPLsQEdGmmBKNiIiIiIiIiIiIiIhonPGFDRERERERERERERER0ThjSrRtULYQQMAdfa6vYtb8/V6xYN43ljBP72OT0gwAYJFJpa9dOcd4GCWLFGHhkG7693DiidGnylvHJM3eOtE+OaWFl67+uHFfAEgEzNP0TI2a5+pKBMxPrpBFarJc2TwdUjprfst4O63ISeEhb5lJqadbTuPgJZwYMO5bskg95bM5xhbHKd9unkaybHGcHIvUhACQrDe//kxOm6dTyxTM93VQkV7Fi01eY5ttLpbN1+vA7hjnDdddsNhmItqyFRTXwkJOvheXFakTfYpUtqG47kboKML8YkZuNNgmpyDSpKmKxuQ4PBiRP5smJWtPjxyTpYq6B7j6kPzZqhTxtk3a3vUN5uXtbs/Ix2ylIq32GkWKLgDYsUM+jzTntiYu1cQmuS55ewIZeV2FrPy9jiZ1z5fNPjnW72+Rn2MiATmu8/vlz6ZJiVZQ/K5Ac16XlPGkJpYq2vzihGiMuGUH7gR8FpiI20yVxxk2RERERERERERERES0TZg/fz4cx4HjOFi4cOF4b84GOMNmG+S6jtHo3EDEfNSEz28+gyIzYD5NJhgzn50DAL6wed/ayXIxRC/lgvkb9WCN+Sgxf7X5jAAbQYtR/clG874AsF2p27hvdAfz/eXmzb9PLf82n721qjdp3FczmstLs7Lw53Bc1+5W1TooFxP1Eu+wu4aYciwG1YQs9rXNrMRSznyjW9vNz0vAblZih2K0q5eYYjSll2DQ/BqQz5tfe/IWM7+qLGYFZS2OEQBEFSOAh1eZkdNEtHnVxHcV22hGffsUMzjDCXn2SLpHMeo9rbvOBeLy9T9UJbepq5KnYriKW014krzdTkhuU07J+zFU1S+2aS7rZjfPbZLb+GvlGDK7TH5Off6/k+XllOR1aWY9vFvxLNevnC3b3S7PaIorZkb5FffgYlH+Pmp+n6CZgZZXzCZf3qKLLV1FmNCdl2fYTFXso2BI/o4MpOS4NKiY8RTza2b86QL/xrC8vplRRdaLPtXqiGgz6ezsxBNPPIGnn34aL730EpYuXYqWlhYMDg4iGAyitrYWu+++Ow466CCcdNJJmDp16qjX8dBDD+Gmm27C4sWLsXr1aoTDYUybNg2HHXYYTj31VOyyyy5j8Mkqa+HChTjllFNGbBMOh1FdXY3Zs2fj//7v/3DyySdbfza+sCEiIiIiIiIiIiIi2gbMnz8ff/nLX4b9f8ViEZlMBi0tLXjwwQdxwQUX4Nxzz8X3v/99+Hzym/X+/n6cfvrpuOOOOzb493Q6jZ6eHrz00ku44oorhpY70eVyObS3t6O9vR2PP/44LrnkEpxxxhm47LLLEAyajYrlCxsiIiIiIiIiIiIi2iq4rm4W3ZZmPLa5oaEBu+66K2bOnImqqiqk02m8+eabePrpp1EsFpHL5bBgwQIsW7YMN91004jLKhQKOOKII/Dwww8P/dvuu++OvffeG9lsFo899hjWrFmDQqGA73znOygUCjjvvPPG+iNWRCKRwEknnbTJv6fTabz11lv417/+hVwuh3K5jKuvvhodHR24/fbbjdbFFzbboI5MFOnA6HN9Va0yTysUDFpWDTdkk9IMAHzmddJRMs+IBn/U/ArtWHyrbdJ02QjUWKSA26XObuVx83RIKJinByqv7DXuW11rfnJ1DcqpEbxELAKHnrz5iZm0SB0FANNq5PQbXgIW166cIl2Dl7JF+qhiwTzlVTFt8XnT5p+3I22e5g8A/tmhSMPgwWeRfu5dyoK2w4l3m18/bMRD5ttskk51HZuUigDQbXgNKU7AhzQiAlK5NrFNpyI9ZFtnQmyTSOXENpqi69qYwReRr6WlnHzx0qSO8ilCArcsr8vNVuZiGn+3HIc602orsi4AQE9KbBLo6BXbNMbk9HPpvDyCVnMvDCrS+O2UGBTbAEBVQj63yyVFQfmivN1FRcpVTdpAzXk9mJIf8h9rr5EXBGBAEY5FFN+j5ogci0bS8nNnWJFuN6aI5YquvCNrlKluuxQp4frz4xPXEpG5gw46CB/72MdwyCGHYMcddxy2TVtbG772ta/htttuAwDcfPPN+NjHPoajjz7ac7k//OEPh17WRCIR3HjjjTj22GOH/n8+n8f3vvc9/PSnPwUALFiwAAceeCAOPPDASn20MVNXV4err77a8/93dXXhC1/4Au666y4AwB133IGTTjoJhx9++KjXZff0TEREREREREREREREE8I3v/lNfP7zn/d8WQMAzc3NuPXWW/GBD3xg6N9+/etfe7Zvb2/HZZddNvT3yy+/fIOXNQAQCoVwySWX4JhjjgEAuK67VaRFA4D6+nrcfvvt2G233Yb+7dZbbzVaFmfYbIO68kFkSqPPoRcZkEeoeWmIms8IqEnKI5q8BCdbVNAGgKJFUehu874B812Nct58BJxr0ddmZo+NwivdVv19MfP31rkW89kI4Rnm52ZiD/O+Mws9xn2zGfODnCtZFEm3HJkfCZvPKLDRPWA+66OkKKTsxa8YmemlWDS/3moKO3spWczcAICsYnSoF7/FqhWDkj3ZHOOsxffJZpttZsnkSnbfY9NjnB2fCb5EZCngl2diDBbk68KalHwvzitmBtTEsmKb2GS5DQD4G+TZAaVBeWaEo8gE4AvJ+6icqszsGc32FFbIcYa/T/7sAFBOy89a5Yz82UKz5HNtpk+ult61RJ49EUvLMWlfQY63tbNlNbO++hSz7wey8jmriRGKilggUSV/j8qKuFE7aVwTX/gd+TzSxLJ5xecvlOUvkg/y9hQVxyOjjCdzin05WB6f5y0iGnuO4+CUU04ZmjXz3HPPeba96aabkEqtnd06e/ZsnH766Z5tL7nkEvz+979HuVzGv/71Lzz33HPYa6+9Krvx48Dv9+PTn/40zj//fADAK6+8YrQczrAhIiIiIiIiIiIiIqINNDY2Dv08MDDg2e7ee+8d+nn+/PlwHO+X2TNmzNhg5s4999xjt5HDbMsnPvEJTJ06FeFwGNOmTcMHP/hB/Pa3v0WxOLZpHCdPnjz087oXWKPFFzZEREREREREREREtFVwXWfC/tnSrD9LZLvtthu2TTabxeLFi4f+ftBBB4nLPfjgg4d+XjeDx9bg4CA+8pGP4IgjjsAf//hHtLS0IJ/PY/Xq1fjHP/6Bk046CQcffDBaW1srsr7hrL/sSZMmGS2DKdG2QQ2hPOKB0V8A4gHzaa49imnUXiZNMS8YDstUSjYpwjQFHD3XW7ZIp9Zoni7Ltcgd46tW5EHw4CTMzw/k7N6M9z9lnq7v7VX1xn13iXcZ9/XXml+6q6aZ76/aHc0Lwzd3dRr3xV8bzPsCyGTMz81YLG/cty5hNpICAFIZ8+/EQM788w5YrDeiKJLqxTbtXSxgfq1OWVxCXJhf53Nl87RmJUUh2bFgk26vOmT+XQKAkOH3uFSZTD9EtJlVR6aLbaZG5bi1Niyn19KkmXQU1z9H+XzlFhSpvDSPXYqwPVAr3y98CcV2K/Jp+poVqWBHGGm7TmmV98jd9b3wRKPYZsfpcvwZaJTvT45P3u7m/5P30aSYfNAan1gptnl7dZ3YBgDyWfmZIRqVP7+ruJemNIXpc3KcGfDL34+MIm1cTBlmDSi+a5qoS5MSTpPKLRKoTC7XiF8OcOuUqfVe6Zf3d8k1//0FEW3ZWlpacOmllw79/eijjx623euvv47y/36X6TiOKr3Z3nvvPfTzq6++armlQKFQwEc+8hE8+uijQ/82adIkvP/970cikcCbb76Jxx9/HI8//jiOOOIIzJo1y3qdGyuXy7jrrruG/n7AAQcYLYcvbIiIiIiIiIiIiIiItnHpdBrLly/HX//6V1xyySVob28HAOy666749re/PWyf119/fejnpqYmRCJybbcZM2YM/dzd3Y2Ojo4N0q+N1k9+8pOhlzWO4+DCCy/EOeecA7//nbf4S5Yswac//WksXrwY//nPf4zXNZze3l6ceeaZePHFFwEAiUQCZ5xxhtGy+MJmG1QXyaLKYDSytrjhsCxGfIfrLUZrWBR1BoDsGvN1ZwctRvVvZz7k2wmbj9p2tdUZh1tvyHy9Tn2VcV+3a9C4LwBkU+YzkmZt323cN7hztXFfp9p8pouv03x/OTPMZxT5a81nMk1J2h1jv2Kknpeancyve46i0K+XvtfMrwHBfovvscUlMxIx31eJoN1MuckR82O8KmN+7ZoU0RWYHs7Mhl7jvqGI+f7y+c2nnPR2yYWJvWgKFo+kLmT2mdOlsc1PTERjI1vsFds0huX7TrXiOt2XlX+hkEjKy3GiutmP5T75utTXJm9TzVTFNsXlONdVVBR3i4oZRpr4VBFouEt12RWiitgh+W75Hu/bTp6t4s8qYpyI4pkiKc9Cqup4W2wTatPFevE6efZMZDv5V0KJFjkW71kln7ODabmNZsZPsSR/16ZGdXFptiQft5qQfP5PS8ozwzTXkaAips2l5GPW1SfHbEnF7EIAqAkprkcBuziPiDbV37/h/TAcDiMcHpvv2uOPPy7OBDn88MNx6623IpFIDPv/u7reySLT3NysWu/G6cK6u7uNX9j09fXhJz/5ydDfzz//fHznO9/ZpN3s2bPx97//HXvssQfWrFkzqnV0d3fjzDPP3OTfs9ksli9fjieeeALZ7Npr/ZQpU/CnP/0JU6dOHeUnWYsvbIiIiIiIiIiIiIhoq1AuOyhbDuIeD+u2efr0DdPDnn/++ViwYMFm357a2lr84he/wLHHHjtiu8HBd17uR6O6QcYbt1t/GaP1u9/9Dul0GgAwbdo0nHvuuZ5tGxsbccEFF+D0008f1ToGBgZwzTXXjNjG5/PhK1/5Cn70ox8hFjMf8MgXNkREREREREREREREW4CVK1cimUwO/X2sZtcAa2eDfOlLXwIAuK6LgYEBvP766/jPf/6Dnp4efOYzn8G1116LX/3qV5g9e/awy1g3swQAQiFdxqGNP1MmY56ZZdGiRUM/H3PMMeI2HHvssTjzzDORz9vVWt1YuVzG5Zdfjr/97W+49tpr8X//939Gy+ELm21QVzaKrMGU1aJFUWhNET4vjkVtZZv0YADgOOOTWsiJWGx30Tw1UGnAvK9bSBv39afNUymV+ixS9QHoGTBPTZZolAvZjomMxQ0laHFu9aSMu7pd5n0jEbvim/E68+PkhCyuXRZ9y4pUD16yRfNbe3XcPMVXvMb8vNyjfnRTkTe2XbucqsHL4ysnG/ed1dBj3Ld+jvm1y5cwT+WYX2HxfegxT6cWDtilJispCvVWsh8Rja+AXx6Z2ZqVfxnQkJHvD9mSoqB2Ub4v+5S3orIivZimyLtiF6loUiJrnhGcN7vkNgH5mpxZo7tu5zXpnDRxrybdmeb5SrOcbK/YpDwgH4/6al1cHVQ85miOiU8RdqQy8u8XHEc+sTVpXyc3yGnzGpp0o7Rnvt0gtsmX5fOoeZa8vvD28j5yM/I+yr0gNkFMkTKyT5mutqy4HuXLFmnsiWhYyWRygxc2Y2nWrFm4+uqrN/n3lpYWfPe738XChQuxaNEizJs3D4888gje/e53b9J2/Zo12pcgudyGz6bamTnDee6554Z+fu973yu2TyQS2H333UdVx2bmzJlYvnz5Jv9eKBTQ3t6Op556Cpdffjkee+wxvPrqqzj44INx77334iMf+Yh6HevwhQ0RERERERERERER0QTy1FNP4be//e2IbU488UTst99+o172lClTcOONNyKZTOLKK69ET08Pjj32WLz00kvw+zd8mV1V9U5tau1MmY3brb+M0ero6Bj6ecaMGao+M2bMGNULGy/BYBBTp07FkUceiSOOOAKnnnoqbrzxRhSLRZx88slYsmQJ6urkmnnr4wubbVBjNIOqwOhHQCRj5iOv0znz0cD+aosZAQGL6TmwG7mWUBRM9OKrNr9I2cywAcy3OdduvtZI1HwGhS9id4w1BUu9DHbqpnkOJ7hcV1B12L7vmWLc16m3GKGRNT8/nGny6DUvzXG72RelFvNjbDVLZtD8vM7lzM8t12JGgU2O3+hO5iGFf9bogpeNxfrMp04f8tBK475+g3vpOuWMRd+U+SyZdLv5cYpGzGcF+X12Iy+rDa/VAcduZg8RjY+kX451GhUjyBsS8gxwzXNKrFZx/QsrHxyy8vUw2Sg/dwWa5FjBqVLEEwPyZ8v1yPGQo5hJGWiU93WwSne/0GRwyC+X92M4It8XnV2nyRtUVMR9IXldofrhizmvr756lbwuAE5I3t+uYsZXSRFmDeblc602Ki8oGJOPf+x9irhRsa8BYI+XO8Q2a16QaxA4il9ZlLrkZ6l8p3w8HJ987kcU18dkWPdsF1Q8DgVt0qIQjRHX1c1Y3dKMdptfffVVsa7KPvvsY/TCZp2LLroICxcuRH9/P1599VX89a9/xUc/+tEN2tTX1w/93NbWplpua2vrBn8f7UuN9a1f/0ZbOyYejxuvz4vjOLjssstwxx13IJ1Oo6urCzfccAO++c1vjmo5vKoSEREREREREREREdEGYrEY3ve+9w39/Yknntikzc477zz0c3t7+wY1bbysWLFi6Oe6ujo0NjYab+P6s3PSaV3JhlTKPHX/SGpqajZ4QfbQQw+Nehl8YUNERERERERERERENIHMnz8fruuO+Gf+/PnW66mtrR36uatr07p1O++8M3z/mwHoui6ef/55cZnrpyPbddddrbZv/Zc9678IGsnKlebZNiSTJ79TK/ftt98edX+mRNsGdWcjyAV0BebWly2apybzKQoMeimnzdOo+EN2xcrLFjXlXYvsL4XluiKJw3F85imNCv0WfbPm73+DPebHqWyeoQcAkM6bp+sbtEhb5X/TPCVadcA8RVhgO91Ig4qbNcm8786KNBQj8NeaF4Z3W3qN+5Y7xma0hiQWMv9SBCxSfBXWmK/XFx8w7gsATqN5Gsna95in+yx2mqfbKvYZd8Vrr5mPPLJJAxmwSGtWskjVBwAr0qOPWwAgYxcGENE4iaNWbJNSFJ3vHJBTYmRL8iPx5LQct0Vyuuurm5evpW5RvmbmV8npjPw98r25lJKf0wpZeR8FBhSfSxG4Z7p0v6LQPJv2rIiIbRJp+bkr7lsttnHCiu1WpDtDk5wOxj9P+QzSsukv1DZWWiEHJKWcfD7Gg/KxDYXk70hR8UxZXC5vc2BWjdgGAII7yu0m5RTPEopH4TUvydej/rR8zgb8cnATUrTpysjrAoBWRUq8XJkBF9HWbs2ad34PNVzqskgkgnnz5uHJJ58EADzyyCOYN2/eiMv85z//OfTzBz7wAavt22uvvfDmm28CABYvXoyjjjpqxPaDg4N4+eWXrdY5kvVn+fgUqSw3xhk2RERERERERERERES0ga6uLvzrX/8a+rvXbJhPfvKTQz8vXLhwxGWuXLlyg1Rh6/c1cfDBBw/9fMcdd6BQGHkgwR133IFczmKUvuC5554b+nnq1Kmj7s8ZNtugWKCAeGD07+rKMB8dmy6Yn2qlAYsqYa5dsWHNCB8vZYtVBxLmo5gzHebHqbPdfJR6LGpekD7YbzGq3+IYAcCAokCml5JFgfZIr64I2nDanzCf7bZ9j/lsk2Cj+Xr9O43+BrWOO8O8LwA4DeaF6xwsNe7ra1MMR/NQlTSf9VHImV9v02mLWWOt5t/jvlV2o/Lq53Qa9w3MSBr3DU2tNu7rvGy+zemi+THWFOAeCz2DymLcHkwjgQlYZ5SIAPSiRWyTDMizdzUzAwcVs0fyGblNqVc307TYL1+ZNOsLxOWHDc1zVOfbckxaLMrxtj8g318CBXl7+vt0o/41Mzd7FfeevpS8vsYBeRZO9Wz5XAvE5DjLrasR22CSbqatE5c/v9Mmf7ZgUo7Tagfl45/Py88S2ZSc/SD/knwexVvl2UUAENlZ3keh9zSJbdwe+fNnn1N8rxUzYxJR+ReM6ax8rmUVsxQBIKTI4FFixEVbINd14FrO8h8Pm2Obu7u7h50lM5xyuYwzzzxz6OVGOBzGRz/60WHbnnzyybjggguQSqXw+uuv47rrrsNpp502bNtzzjkHpdLaa9573/te7L333gaf5B3HHXccvvnNbyKdTmPlypW4+OKL8b3vfW/Ytl1dXTjvvPOs1jeSu+++e4M0aIcccsiol8EZNkREREREREREREREW7mbb74Zc+fOxc0334z+fu+0ry+++CIOP/xw3H777UP/9q1vfQv19fXDtm9qasLXv/71ob9/5StfwZ133rlBm0KhgG9/+9u47bbbhv7toosuMv0oQ6qrq3H22WcP/f28887DxRdfPPRSaJ033ngDH/zgB9HS0oJQyHzA6nBc18Wdd96JU045Zejfampq8NnPfnbUy+IMGyIiIiIiIiIiIiKibcCzzz6Lk08+GYFAALvssgt23nln1NbWwnEcdHV14cUXXxyqCbPOUUcdhfPPP3/E5X7/+9/HE088gYcffhiZTAbHHHMMLrzwQuy9997IZrN49NFHN6iHc8EFF+DAAw+syGc699xz8fe//x1PPPEEXNfFt7/9bVxxxRU48MADUVVVhTfffBOPPfYYSqUS9ttvP+ywww743e9+p15+d3c3zjzzzE3+vVAooKOjA08//TRWr36n7p3f78d1112HhoaGUX8WvrDZBnXnwsiVRl+8N+gzn+YaUUzt9ZLqMD9Nw2m7NDs2ac1s0hJF8ubpxcol8+mTAb95SqNgyHxf59PmqbYGB80KUa/Tqiy2OJyIRQHucNZuu025Fl+Jcsr88zpPvG7e99WVxn0BAI2KAq8e3FbzyvBOzPy8dnzmB6qt2/zzatIweIkUdalghtPZFzfuCwDB1+Vi0F5qEuap69ycnEbEi6bwrBeblGjZnJxqxEvGIr1pd87ummd6Z5t4SRCICABKrnxP6crL17Ow3zzOW18qJY/AjKzR3gflK1O5JCfCKCseFxzFZbusSL0SjcqfTbPNmX65TXtKFxP0FuTjH8vJx81R3Cga3JTYxlWkeyu82C62CXTIsYUzSZmStaSI61x5u/1xeSd19srHrVSWj39tlZxaTJMu6K3/1optAGDvhg65UVneR9k35HTGb/VPF9tMispxqSZNTmdGTvWWVqZEi/jlz18TrOwodSIaW+HwO89mxWIRL7/8Ml5++WXP9olEAgsWLMBXv/pV+P0jXzuCwSDuvvtunH766UOza1566SW89NJLm7RbsGABvvOd71h8kg2FQiHcf//9OOaYY/DAAw8AANasWbPBDCEAeN/73oe77roL55577qiWPzAwgGuuuUbVdvr06fjNb36Dww47bFTrWIcvbIiIiIiIiIiIiIhoq8AaNt6++MUv4pBDDsE//vEPPPXUU/jvf/+LFStWoLe3FwCQTCYxefJk7Lnnnjj00ENx1FFHoapKX/O6uroad9xxBz73uc/hpptuwr/+9S+sWbMGwWAQ06dPx2GHHYZTTz0Vu+66a8U/WzKZxF//+lfcfffdWLhwIZ555hl0d3ejoaEBu+66K44//niccMIJCAbNBzJuzOfzDe2zvffeGx/5yEdw5JFHbvBibLT4wmYb5MBsxGnUbz7dpCYiF8bz0t1jMfLavL46ALsLZbXFqG3NaDgvPsUIGC9+ixk2JUUxUs/1RszPrWS1+X4GgMZe83NztWLkkpeIYlSgl7DFLAifxT3JiZof49wK89kX5TfNZ7kAwNI3zb9QO+9lPoMitKM+oNlYdLL5ehNd5uf0gMXMr5bupHHfdouZbgDwWq/5unfr6jXum7OYcfJ0V41x3ylR81mY9XXm55bNPTG7RlfU0kvU8N7msggu0YQ0DZV5gPc7cmzbrCjgXVCMRG9t0d2LfI5iJoZiJkK8Ub4XOIrQLRiQ48qSYga/T/EsEI7LMf+kpO4+tSIlz1TtyytmRlnE1evTxMnllLyu7KvybJ6BRbq4Op+X45QpB8vntr9e/mz11fJ29/bLx6yzX372zyq+jy/26macBx6VrxFVIXl/L+1rEtu8MiDH2TvXyb/AqKmTZyE5HfJ1ZlAZx0YUE3HiAZbHJppoZs+ejdmzZ+OMM84Ys3UceuihOPTQQ8ds+SM58sgjceSRR47YZuHChVi4cOGIbebPn4/58+dXbsOUrF7Y5PN5PPbYY/jnP/+Jf//731i2bBlaW1uRSqUQDAZRU1ODGTNmYLfddsN+++2HD33oQ5g5c2altp2IiIiIaIvDGJmIiIiIiIhMGL2wefTRR3H99dfj3nvvxeDgOyNg3PXynxaLRWQyGaxZswZPP/00brzxRgDAHnvsgRNPPBHz589Hba0urygRERER0ZaOMTIRERERERHZGNULm7vuugsXXnjhUKGg9R8+t99+ezQ1NaGurg61tbXIZDLo7u5GT08Pli1bNvTQ+vzzz+OFF17A9773PcyfPx/nnnsupk2bVsGPRJKAz0XAN/oUIRHFdHkv8Yh5+hYbvSm7NDuDBfPieXFFegUvJfOu8AfHJyWaP2DeN1Jrfm7Z7CsAKFmk+HEsUu0UXfNp40WL9HM+RdFQz74J83xq5VXmx7hjlXlqMQAoKtKKeClZZNxzs+ap/mxEIubp50pl8/OjqCxcOizLlGj/7Te/VvcV6o372lw/evLmfWtD5vvaJq2ZzyB2WCcasPs+JINm/QO+zfM9ZIxMVFkZR075A8jX75Aito0p0h1p4vpURpdWdM2gnPKpVpFOupSRr+f+qHzdDoXl62Q+J//aIJqU92MgJm9PoFcXMxYVjx6au1aqKH+2QFDeJl9CEYsoitd3vi4vp6NPFxvXJ+TvkZuRj78TkM+1UETeR5GcfI74ffKBLefk71pKkcYPAJ7orBHbVCuec7sL8vOGZos06Rc1qQ7DIfm4Viljq0RAcR3xT7w6IUREWzLVC5tHH30UZ511Fl544YWhB9A5c+bgqKOOwrx587DvvvuipqbGs7/runjllVfw9NNP46GHHsJ9992HVCqFX/3qV7jhhhtw1lln4bvf/e6oChgREREREY0nxshERERERFuesuugbDFYbbxMxG2mylO9sDnooIMAAHV1dTj99NNx3HHHYffdd1evxHEc7Lbbbthtt91wyimnIJPJ4L777sN1112Hhx9+GJdccglisRi+//3vG30IGp2AU0ZAUYBzYz6L2QSt/ea/aHAt6gUvU4xgG0mzxcygdNZ8xHd1wWJYv8UI6HDEfCRypNq8b6DGYpbLgF1BaZtCo7WKkUteihYzGWxG9fe/aT4yP9KeNe7btlJX+HM4r3TZFSufHpeLoHrRjF71Uu4zv34UB4y7oqVbV/R4OEHFqMaxMFi0mJ0DIFU0vw68OWg+A6vW/DIPmz2tGKDrKZ8z39ehsPn1sidnN4tqecpsZ2dKY3tOM0YmGhtt5SWKVtPFFmGffN3qycrXp4GcfA1qy0TFNgBQUMRxmplB+Yx8PY9GFbMnFKFOVY084ydUJ9+cnLBiVlCL7iY3RTHrKa+YZZ1TtGlvl2Or4Ku98vak5GP2Roc8c6xPWSy+qWZQbFMeUNzbFaFDX4/83N2blr9rIUVWj5Ti86eVj2k9inC9JiSfIxHFPgoqvmsZxWcrKWbzlBUzjF7u0/2uJK1YVskmOCUiok2o7vQNDQ345je/iTPOOKMiI/yi0SiOPfZYHHvssVi8eDF+8IMfWC+TiIiIiGhzYoxMRERERERElaR6YfP2228jGtWNGBqtefPm4f7770cmYzGjgIiIiIhoM2OMTERERERERJWkemEzVg+im3sdtFZVqICqwOhTwFSFzdP7ZNPmx9cmRU+NYZHidWyKymuKk3rxmdd2R67PPN2NT5F+wUt4O/ON9sUtitmnNEVpvUUtzpEWZeqL4UyLmW+3zTbHG837OrrsCxXXV7BLl1W0SI04I99j3NeXMN9hIYtC6VPa+o379gyYn9PpvPn3uGCRIhAAJluEEKstLiFB82xqiFukr4xYXKuDQYvrfMz8vEyGzGMIAAj5zA5yaYwzdDBGJhobIUc+7xOK1Emq5xdFurOyolx4PKC7RmYURcU1zz/huPz5y3KNd5SK8s2sqkH+bMEZMXl7BuTj4VcUeAeA3oIcdzSG5eexGsX9qalJjq2C1WITFLPyTUmTJjalTCW7okveqMZwh9jGVy1/R+qnyOnX8ivk7c4rvh9ZRZuGsC4AyJQ0wZy8rLhfbhNTtAkqYrxQTP7uR/Pylz+qjCfLrryP4pp8b0SbmVt24Fo+Z46HibjNVHkWv2ogIiIiIiIiIiIiIiKiSrAaL33zzTcDAGbMmDFUdFXS19eH++67DwBw0kkn2ayeDPmdMvzO6EfYOo75MNUBxQgoL5pCkF5sirMDQI1FQfq+lPmI2EC7+QjoaEIxlM5DqWC+vzJLzdcbbrabCWVj2pRe4747zuk27lsyn4BlNYsqONVi+pZFMcmqleYfuDliNzLfb3HtSg+aV5VP9Jl/5lLKplC6+a29qV4eHekllzVfr7ZQs5cV6fGZ/mUz6qVoMXLKZkZSKmV+TrsWs1Vs+gJAzHBWkc1MWRuMkYns+Bw5XtE8m/gtZuqvr0WRLUB7ba5WzJTWXDN7O+RtqkpmxTaRKjmGT7XIsWcc8pRVJyzvo4iyHNihe78ttvErJlmnWuQYIj5dca41hMU24bwcGzYoZgUFlbFtSTEzotSn+Y7IsXghLZ8jNUk5xadmhpWreMZ/fSAitgGAkuLL5ld8tTUzenOKa0RKMWM93Se3yWblNnUh3TN4V14+t9sz5r83ISKiTVn9hmP+/PlwnLU3ne9+97uqwqirVq3C/Pnz4fP5+DBKRERERFsdxshERERERERkoiIp0VzXxY9+9CMcffTR6sKoru1QSyIiIiKiLRhjZCIiIiKizc91J+4foorkENlzzz3x/PPP45577sH++++PP/7xj5g2bVolFk1jIBHOI2GQFakqYZ7ep0lRzNOLzyKrWapgd4rbpFIqqAoYDi8YNp9SHG6wubqb9y2mzNda6hu/O1Kk0TxVhmuRqSvdZZ6aLJ8zT4mWSJtvtBMxP6cDFoXOCxZpEQEgoUgt4cUmFSQC5hcvf9z8M8frLPLtWaTaKlv01RZq9jIjZv6dCPnN7xPbxc2/TzYpOzXpdLz4FAVvx6JvwDItUXXQ7L4Y9I1vig7GyERmUkW5EHpCce/QPL/YXNvWV4buup5XPCMUFamsNDFKuEa+9gYS8naX0vK1tKR4J+1k5W0OKFOiOYrbd6FX/mz9fXLqrOiAnDLWXyvva5/5I/GGy1HGp/GAImW1IuR0QnKjaIP8fSz0VyZFYUiRtnxSRBcrhXzyiRRRpGVtUKQXS5XkeDUSkD+b5prlV2xzlWJdABBTrC9TrMyxJSKitSoyw+amm27CN77xDbiuixdeeAFz587F4sWLK7FoIiIiIqIJiTEyERERERERjUZFZtj4/X789Kc/xe67747Pf/7zaGtrw8EHH4xf//rXzMG9BQqFiggFR/+uzuczH32mKXjoJWsxUyVVtCiwDqsa60gEFSOavNZbMh95XRww7opywXy9vqD5znLkOoaeNKP5RvLGyw3GfV/pSxr3tTm3ZsbNP3R16xrjvkHFyEEvxYL5kMKCxUwEAOjOmZ9gtWldwdLhNKTkQr9ebGZvtbxdY9y3MyMXMPZiU9w9bzmLqiFsfr1NWdxjbL7HNve2zpzFeRkx7+tazKLqzJqfWwDQUzCbRZVRjGYdS4yRicxki71im0RIvvZrCphrZBXXEu3zzoAiA4BmRH9BcV0sK+KJfFdlZhj5FeGWowgHe5fr4rZnVzWLbfoV+1qTUeGAsDx7oj4ux302GQnW11vQPeMGFc/vhX55OT5F9od0u7yvX1vVKLbxO/K5ny7K66pVzHgBdNk80kX5u615XmnLyt/ZRCAmt4nIMwcHFRlOWjK671pRcYkoWzwHEBHRpioyw2adk08+GQ899BCampqQy+Vwyimn4Oyzz67kKoiIiIiIJhTGyERERERERKRR0Rc2ALD//vvj6aefxpw5c+C6Ln72s5/hYx/7GAYH5byvRERERERbI8bIRERERESbRxkOyu4E/KOsiUdbt4qkRNvYjBkz8OSTT+L444/HH//4R9x///1473vfiz/96U9jsToaJcdZ+2e0fIqp+V7qYuYpnCIR81Q3rd0J474A0JKWpyR7mV7XZ9w33mheULpkUW/cRtmirnO43jxlTTBqdxnbLt1t3LfLIi1RxiIdUsjiu6gp0OrFJk1X34B5OiSb4uwAEFQWZh3OeJXP1KQM8VKdML/eZhSpQ7z05s1Tz6Ut01Z15sy3uyVjM3bFfL0pRXoNL5rUFF6CFtePWMz8IlCbtrs5tWXNvhT+Lex5hzEykU4yPFVs4yquhZri3EFF4e3tGnvFNtmsLk3VG901YhtNsfiGqfLLXk2R+2JKvlA6iluWLyLv68AUOXaui+ruF9O602Kblam42Cbkk++LwZDiQUdxey0MyjtSk8pXs82AfQy9jhOQlxOKyfuoMSYfsw7F83dBkUq3VxnTrszI7UKK8z+teLbrU6QfLyqOmeb3I44iHV7Ur3uA78jJcfob7tuqZRERkU7FZ9isE4/Hce+99+Kcc86B67p45ZVXMHfuXCxatGisVklEREREtEVjjExERERERERexmSGzfouuugi7L777jjttNPQ3d2Nr371q2O9ShLk8wHk3dEfer/FqFwbjsXoeJu+AJAIms/uqaoxH4kcnGz+1fSnzae6FLrN91cxbf7+19csj37z4sTNR/UDQHhFh3HfgKIoppcpMfPR5tUR82L2kR3M95cTMp8F0dQ1YNy3MWU+0w0AuvPm01VsRjU4QfNRjY6mAqqHWNL82tNUNk/NFBo0v/Y4abuC9DYjSNdkx2zsyohCipGPXqYqih97rlc5mnI4QYv1VoUspuhBVzh52H6aIfjjhDEykbe+7EqxTaG8j9gmrxg9n8/LbWxm/G8sppjREw/J6ws3y/c+X0yO3Xwd8rU92yXfK30JRZvJSbGNE9DFIn7FfaFRETMHFbNVkjPk4xHcUc7s4IumxDbTV8ttVg6aPzttzKcI752QfK4FYvJ+rK+TP1tQEad0Wj4brK81K3///YrfKfgUbSYpZqElAvL3MRCU95E/IB+PqPL3O8mg3G7P4CyxzSuqtRERETCGM2zWd/zxx2PRokVobm6GuwU/OBMRERERbS6MkYmIiIiIKs91nQn7h8hqhs35558PAGhqahLbzps3D0899RSOP/54rFwpj5giIiIiIpqIGCMTERERERGRiYq8sNGaPn06Hn30UZtVbqC9vR1PP/00nn76aTzzzDN45pln0NXVBQA4+eSTsXDhwlEt769//SuuvfZaPPPMM+jo6EBjYyPmzp2L008/HR/+8IdH7NvW1oY//elPePjhh/H8889jxYoVyOfzqK+vxx577IEjjzwSJ554IqJRs7Qv55xzDi655JKhvy9atAgHHXSQ0bLe6q1GPDD6tEjRfvNUKA0x8yLYfkWaAi+2b6Y1BQ29rF5dbdy3MWOeliitKNTopVwy31/1zfIUdy/Ft8zTZcG16Aug8y3z/aWZ+u6lPSMXXfXSb5Hia2rLauO+PkU6BC9dHVXGfXstPi9gV5B+xYD5djcsNf8eawqFesmmzfdXoWie9m7A4jh1WR7jt1K6Qs+VVra4x2wfN0+LqEnN4aUrY55+rtRufk/stFgvAJQNP7JpP1vjGSMzPj7IaFm0ZfH75GeVZYq0UJrrdESRoqxJ0aaoKDqu1a6I5+telQu4a9JD9/bXiG3q6+SYptileFZ8rVNskl2tS9OUL8nHP12UY0BNDLJ9S7fYJqFId5ZZqTgeOXl7OpXPx5pn4c7V8n6sycrP8pkB+TtbLsvbkynIMV1/Qd5HqzK62LIjJ29To+JXJ82KtLG1IblN0ZWvI+1dcvq9kuL3GEVlHJtV/I4gWzJPuUtERJsa8xo2Y6m5ubkiyymXyzj99NNx/fXXb/Dvq1evxurVq3HvvffitNNOw69//Wv4fJve+H7zm9/gi1/8IkrD3KRaW1vR2tqKv/3tb7j00ktx11134d3vfveotu/555/HZZddNroPRURERETbHMbHREREREREE9eEfmGzvhkzZmCXXXbBgw8+OOq+3/3ud4ceRvfaay+cffbZ2GGHHbB06VJccskleO6553DdddehsbERP/7xjzfp39bWhlKphFAohI9+9KP40Ic+hF133RWJRAJLly7Fb37zGzz44IN44403cOihh+I///kPpk2bptq2dQ/LxWIRTU1NaG9vH/Xn21h1KIcqgyMfVows8xKPmo8kDigK5nmxKUQNABGL4sw2hZ2rmswLmyYURUq9ZNrMRwb6gubDmH1J89HxbtpuNI9m5KEXTZFSLzazt+rD5t8nG+Ws+b4ayI5+Vt86fQXzWR+A/XXAVLTefFaiYmCxJ6fN/DgFLGYj1RfMZ1J2WZwf4ymmLNY6nMkxeWS0l6LF9SMaND8vq2Jy8WYvNtc8AOhWjDgenvkx2howPqaJKp1rE9vUKK5nNRE5Zgoo4rlwRF5XTjnrwe/I6/Mr7i/RhBzzRxrl5VQN5MU2uQE5FvMn5HjLF5OX4yj2DwCELO7B68spZg/4Fc85blFuk8/In79bMeNHG9tqZkZEI4rzqFlxzobk71q2X/6OJMtyrJEpyMspDeqyGUT88nErKELryYrfdyRD8nctq5jtnlCsSzO7sFoZW3X45Wf1mN/ueY1oLLiuY5URYbywhg0Bo3hhs2LFioqvfMaMGVb9zzvvPMydOxdz585Fc3Mzli9fju23335Uy1iyZAkuvfRSAMA+++yDRx99dCgtw9y5c/Hxj38cBx54IJ599ln89Kc/xWc/+1nsuOOOGywjHo/jnHPOwTe+8Q00NjZu8P/22msvHH300fjGN76Byy67DB0dHTjvvPNwww03qLbvyiuvxDPPPINddtkFRxxxBC666KJRfT4iIiIiGjtbWozM+JiIiIiIiGjiUr+w2W677eA4lXvL5zgOikXzUZ4AcMEFF1hvx+WXXz60HVddddUmObRjsRiuuuoqvPe970WxWMTPf/5zXHPNNRu0+drXviau56KLLsJtt92GNWvW4O6778Z11103bPqI9a1YsQLf//73AQC/+tWvsGjRotF8NCIiIiIaY1tajMz4mIiIiIiIaOIaVe4T1x2nyq1jxHVd3HfffQCAXXbZBfPmzRu23bx587Dzzjvj9ddfx3333Yerr7561A/moVAI+++/P+666y709fWhq6trk9GGG/vSl76EwcFBnHzyyTjwwAMr9kDakwsjXxp9+plE2TzVVjBtXmDdJmWVpmjfSDRFSL34LdJlWexquBbZskpF8/0VKJkfJ5u0Zq5lRWmblHs2hb+zFumBrFJ8WWSPcCzqwtdWmafLmmOxnwGgN2eebitbMp/eb/P7W6dydYtHRVMM1oumuKkXn+Uxzluc17Uh83VXB82vXUlFmh4vNvt6vNIC2NwTAfMUpSV38xTB3Zpi5G01PqYti1uW44ZuRQqy6qz8DFIVlNMU+fvl5WjTv2pS6mrStLmKy6oiu5Qq3ZlGOSNfB52QvNHaGEjzjOhCvib1KZ5/ypp36IrtDsfle9KsxKDYZlpJt5OWDVaJbRyf4rj55P2oWo7iFlFUpATTyCpj2pBiV9YrzltNurOEIvbz581ThY+WNr13WHVsmcKJiKiS1C9sTj755BH/f29vL+677z44joOTTjrJesM2h7feegstLS0AgAMPPHDEtgceeCBef/11rF692ii1BADkcu/coP1Cjs8777wTf/7zn1FXVzeUkoKIiIiItixbW4zM+JiIiIiIiGj8qF/Y3HjjjSP+///+979Do/GktluKV155ZejnXXbZZcS26///V199ddQPpIVCAf/6178AAM3Nzairq/Ns29vbi69+9asAgIsvvhgNDQ2jWpekIZJFVWD0I0FjFsXsa5Lmo+ttBq0GDUfkrjNoMTI/FDJPZ1LKmY9QyQ6aj8rxW8w2gWLkjXdf88/rrzefvQUAVe3m52a61bxAe9Ti3GyuShn3DU6xmCZjwWZ0fWs6ZrXugMW5aTPzI9dnMWMtZ76/MinzY5xXFkweTihgfs1LKApHj6QpbL6/bOZFpJUjXIfTk4nKjTxoClZ7qYkrhlt7SNSazwoqW8wKAoDXW8yu9ZnNMMFma4uRt9X4mLYs4VCz2KY5Io9or4vKcZ6mgHckKj8HhZWxf78i84BNhoH1ZXvka69mhn0oKl9MXcXH9yXk55TwNHk5AFBaUpkR/TMUxzY2WTErqlmOV8uptNim3SI+2FhSEZulBuXn3ViPvI+yfXIMmUlXZvZIVBE3Nod138fegjyjRxPBdCtn2Ek03/14Qr5maZbzWof3PXd9qzLyPnI1U/6INjPXdeCOU3YBGxNxm6nyzH8zsxVYtWrV0M/Tpo0cGU6fPn3o55UrV456Xddeey06OzsBAJ/61KdGbHv22WejtbUV+++/P0499dRRr2udXC63wajF/v5+42URERER0daP8TEREREREdH4Gacs+VuGgYGBoZ+rqkbO7xqPx4d+HhyU88qub9myZfjud787tJ5zzz3Xs+1jjz2G6667DoFAAL/61a+scoFedNFFqK6uHvqz/kM1EREREdHGGB8TERERERGNn216hk02+05akFBo5BQy4fA7U1wzGX0KpXQ6jSOPPBJ9fX0AgKuuugpTpkwZtm0+n8fpp58O13Xxta99Dbvvvrt6PcM599xz8fWvf33o7/39/Zg+fToC/jIC/tFPWbVJDRSOm6e7CVWbT68NtNtNzc12mX9FYlVymgbPvjubrzfSb566Lr3Kppi9RVqzRvOp/061XUq0YLV5SjSbVFsNYfPUQslq85RGvnq5+KingPk7/uqGHvPVdtilBSlanJs2Ka98fvPtDsTlNl4SJfNzKztonj/KJn1LY8muwOxAwfyauTRlnsKikDff7o6M+bUrETS/ztc55tc8X9D8GNukCQWAhEEqVwAIMKPAqG2r8TFtWXaIjVw/CQAiivSymqLaPkU8F2uQr2GRvO46V1wt3ztyivtabIa83b6YYl0r5HtKflCOhwLVchtfoxzguCFdnOtXHLeoXz4mNSE5bgo0yqm8nEY5xg5m5H2t2easMm7KljWprOTl+BWPahGfvN0+xe8gNCn6IgV5P07L6uKs7ICcym6gKAcT7Rap1NdXF5HPR80zhl8Rs2njyZqg/Nkaotv0WHAioorbpl/YRCLv3MTz+ZF/ub5+6oRoVPfL5WKxiE996lN44YUXAABf/OIXMX/+fM/2P/7xj/Haa69hxowZOP/881XrGEk4HN7gQZqIiIiIaCSMj4mIiIhooiv/789EMxG3mSpvm35hk0gkhn6W0jikUu8U+ZbSQwCA67qYP38+7r//fgDApz/9aVx99dWe7V977TVcdNFFANaOMlw/xUSlpfJBoDz6gn8hiyLpNrJd5qM1+vvsZl/YFPvKZcyLKkbbzEfI53vNt7mtNWnctzphMWr7pZTcyLOzRV8A7cvNZ5zYzNzoLpifm3Xd5teH+Eu9xn39CYvZJmHzkfnTqkaXZmdjqwfN91epaD6DYtWqGuO+Nb3m36feQfMZa3FFAWcvSYvZan7FCOixUmUxE6pPMQrUyysD5teA/2swv0eULe5rqe6RZ1uMZNBiJhMABA1nNBY0Q4dpA9tqfExblrArXyP7CnKsncwr2iTkGR2ljHzt7O/SXefyithCM2u10KmYYaMovN65WjHrRRHzOsvTYptwT5fYJtOti71yRXlmRL/iHAmW5F+JpF5VzLDq7xDbQPE4vUNzt9hmeUetvCDoZuK81Vsttgkukze8qDivB7Lyd2RKU5/Yxh+Q40YHuvt/PCB/tmxZPkdWpOU2fsX3empCfu5JD8jxmGbmYF4xA2ttO7lNqsB4i4iokrbpFzbrF1Jdv8DqcNYvpKpJm/ClL30Jt956KwDgwx/+MG655Rb4fN6/2Pn5z3+OfD6PWbNmIZ1O4/bbb9+kzcsvvzz088MPP4zW1lYAwMc+9jE+wBIRERGRNcbHRERERERE42ebfmHzrne9a+jn1157bcS26///XXfddcS255xzDn75y18CAN7//vfjD3/4A4LBkUf3rEspsWzZMnzmM58ZsS0A/PCHPxz6+a233uIDKRERERFZY3xMREREREQ0frbpFzbbb789pkyZgpaWFvzzn/8cse2jjz4KAJg6dSq22247z3YXXnghLrnkEgDA3Llz8ec//1md03tzqY3mUGVQvHfWAeZpiXyTEnIjD+VW8/WW/2tenB0AkjDvrymY6NnXPNsN/FHz6cgzdu017huotykabt43+7ZdKqWCZbFzUzUh89RTk2fIqQK8hHYx/y4ib5EWsVNOk+Ela5GWDABiAfNi52FFcVQvvYq0D14a68yve5Oa+o37alKeeAnGzPdVeMCuIH3UImWnaaotWzNjukKvw5lUbX5+1DSbfxddRdFdL6WSXTHasuFhYka00dtW42OaeOoUsdQOO8opuGLvUcRGRUXR+Rd0aXqjKfn6X8jJsU8pp0hTpnjma5wpb3cgIa/LVy3/aqHUJccK+azuVxQ5RQwfV8SADTE5pWt8V3mbnDo5RVtp1YDYpqh4hvQp033VKwrY9yvSBmrSndXUy/FFLCN/Z0NV8jEr5eR9pI3/Ixax/voaQvJyJkXk3y00TpJjPF9QXlcxI++jeEAXi6YU6QfbFMeWiIj01C9sfvCDH4z4/9vb29Vt1znvvPO0qx8TjuPgE5/4BH75y1/itddew+LFizFv3rxN2i1evHhoBOEnPvEJOM7wAesVV1yB73//+wCAOXPm4IEHHtggD/hIFi5ciIULF47YZsGCBbjgggsAAIsWLcJBBx2kWjYRERERjY2tLUZmfExEREREE53rOlZ1qcfLRNxmqjz1C5sFCxZ4Poits+7/r3tokoz3CxsAOOuss3DttdeiVCrhy1/+Mh599NENRvxlMhl8+ctfBgAEAgGcddZZwy7nxhtvxNe+9jUAwOzZs/H3v/8ddXV1Y779RERERDR+tsYYmfExERERERHR+BhVSjS3gnklpAdbjccffxxvvvnm0N87OzuHfn7zzTc3GZE3f/78TZYxe/ZsfOtb38JPfvITPPvss9h///1xzjnnYIcddsDSpUtx8cUX47nnngMAfOtb38JOO+20yTLuvfdefO5zn4Prukgmk7jiiivQ0dGBjo4Oz23ffvvtmVebiIiIaCuwJcXIjI+JiIiIiIgmLvULm0WLFo3ldhi57rrrcNNNNw37/5544gk88cQTG/zbcA+kAPCjH/0I7e3tuOGGG/Dcc8/h2GOP3aTNqaeeigsvvHDY/vfeey9KpbX58/v7+/HhD39Y3PaJmLLBVxsx7uskzfv6cua1DYIR81z9ADDYa1GDYi/z+gS+hPn+KrWbF8ApDpj/wqnUZ1FDYlLIuG9kut0vycLt5udXU9z8/IoGzdcbkNMIe/OZ/yLQLZrneO5ZbV6rIG9ZZygZNs+pHPCZf+a2lPmBCobNv0/RZvNtLpiXR0KmT86B7qVn0OakBoIWxykeMN/XfQXzuiwhi222YlGnqFSwuH5Yvs/IGW63ab/R2NJiZMbHtDUY9Mk3pEK5WmwTrJXX5VQrYpS8HLeFmnRxYf9/5ftl9WS51kVkZ3m7XU29nDb5s2meEUJx+b4WaJJj/qo+3bNMpFu+f9cp6tNEo4o40W9Xh22dUp+8jzr6qsQ2fmX9vdqwfB4FHHmbHMX6wrXyciL18r7OdMj7erBfflZOF3W/6qpSxIF+R/78mlo42uMmKSviT0093TJ0MVJOEbIWXItap0REtAn1C5sDDzxwLLdjXPl8Plx//fU46qijcO211+KZZ55BZ2cnGhoaMHfuXHz+859XPWQSERER0bZla42RGR8TERER0URVdoHyBKwHU67cxH2awEaVEm1LoylEOhqHH344Dj/88HHfDi8LFizAggULxnw9RERERDQxMT4mIiIiIiKauCb0CxvavIqrzNM/BaPmqXKcpoRx39hO5unBAKD0inkqJV+1eTo1X8K8ryb1gZeAYiq3F8ci1ZZdmi674QfZnPm52Z8zP06xsPlx6n7bPGVeQ6jfuG/Z/OuA9v5G475vDMipIUbyge1XG/eN15h/6NJK8/O6t8s8RZg/PGjct5QzT/nR02u+za/3mV/nASBVNE+bZ5Myqztv3rc5Yr6vBzLm15541vy+6FrsK9dydJvpbcLm1kRE42dSaYrYpqS4ruQ75TaBNXJs5JtWI7YJzpJTtAFA1Wo53VuwRt5uX7UcD7qKi2AgK8f/mnjbCSnua4r8mGVl+s3+vJxeLRmW73n5vPwrkczr8nNwqEGOGQda5OeOZYNy3LtdVUpsAwBNkwfENtVpOW3c6nb53E50yunXgjE5bVYmJR/X1b1y3Phqv64eWlCRpqxLER8HFaftYEE+/rmUfD4Gw+aptdfng+45Oqj4ao9Tol8ioq1WZZKxEhERERERERERERERkTHVC5uvfOUrWLNmzZhtxF133YXbb799zJZPRERERFRpjJGJiIiIiLY8rutM2D9Eqhc2V199NWbNmoUzzzwTy5Ytq8iKC4UCbrvtNsyZMwfHHHMMlixZUpHlEhERERFtDoyRiYiIiIiIqJJUNWxOOOEE3HrrrfjlL3+JX/7yl9hvv/1wwgkn4Mgjj8SkSZPUKysUCnjsscfwu9/9DnfffTf6+vrgui5mzZqFQw45xPhD0Ogk4lkkgqPPMuqWzWuFlLvM6984JYuMqBbbDACRWvN1+xrt6m6YKivyUHvJmpf6QGSq+b52FDm4vQRC5rUrAKCh3rzeR6Z1fMqAFUvmn7ksp5f2VLIoCZW1qDGSKdmNMGl4j5wv24t/Wq1x3x2W9Bj37X5Vzt/tpZA239cD/ebfRZuRQFUB82MEAPmyRT2Yovl2J8xLYMHvmF8zl/eb1/xJ5c03ujpifhHIW1y3tmSMkYnGT8Qv3zv8UUXNlD659ogTU9QMKepicH9Y3qbAVEX9jbiintmAooZLp6aGjbyq2GTF/UVR1zQyWVdvMb5MrgfpKO61ZUX8UsrLcUY5I+/HfE6+Fw4W5XVtP7VLbAMAiQPk2jNVOfngOv/sFdto6txleuX4NpOVzxFHEbrVhnSxZXdePiaa9UUUtWDTiueh19fUi21qFfFYNCh/P3oVdaAAIBmUv0eNIfP6ikREtCnVbxtvvvlmnHnmmfje976Hf/zjH1i8eDGeeuopfPnLX8b06dMxd+5c7LXXXmhqakJtbS1qa2uRyWTQ3d2Nnp4eLFmyBM888wxefPFF5PNrA2LXddHY2Ijvf//7+MIXvoBAYHx+8UlEREREZIIxMhEREREREVWS+glw3333xYMPPohnnnkGl19+Oe6++27kcjmsWLECK1euxN133z1if9d95638e97zHpx++uk47rjjEI8rRg8REREREW2BGCMTERERERFRpYx6yN7cuXNx6623or+/H/fddx8WLVqExx57DEuXLvXsE4vFMG/ePBxwwAH4xCc+gT333NNmm8lSpKqAaGj0KWD81dveCE/HPCvRhOSYZxVCrs0iJVrIPC2ZE7BLl+X4zLc7HFTkh/AQjcjT1L3kchbfRYtj7LeY6R5XTMv30hA2388A4FqkCbThi5mngNKk8PBSUKTb8FJUpOEYC0XLwoo9BfPt7lKkOfEyI2aeyq0uJKfg8ZIuml8DunLmX+SoxTXPtnimaYZTy8yoo8IYmahyVvjeFNvEg9uLbQI18rVHdb9WpjvbWmlSghVWyCmwA41yuiu3oLtwRxTpVCNhOf4MBORjW1bc/jTPjeGIvKBmxTZnUrqH1GpNavGAfGz9in1ULMjfo7wi/Vilim1rUssBQFtWbhdWXCJqFDFSLCC30aQpC/rk46Fpo5VVpKcuudv2NZK2TGV38z4LVMpE3GaqPOMn/mQyiRNPPBEnnngiAKCjowOrVq1CR0cHuru7EYlE0NjYiMbGRsyaNYvpHIiIiIhoq8cYmYiIiIiIiExV7Alx3YMnERERERGtxRiZiIiIiIiItDikbxvkD5XhD41+yqoTt8iHZMHtzRr3LXaZp6sBAF/UYkp2WJ7y773iykwFH61gjfncy0C9+eXEv2O9cV+r/QygpmnAuG/trtPMV2wxotp9eolx3/wy465W6dRCirQVXmJ+u+/xwFLz71Ms1W3cN9NmvsMCFqd1qGp80la19VQZ9+0v2IUjJYvt9lukn4tYpJsI+c37piyyBKZL5inzIopUHl6CQbvvsWlqxHTJLqUiEY2PGjSLbTThsq9Kvr84UUWarrx8DSt15eQNAuCPKhqFFNfqqohiOamKbE8goUjBtFO12MZpltuEpmXkDQKw+/8pnk2ny2nzsKZDbJL+y9tiG0dxQjqKc7ZKkVprIKU49gBqX+wV27iKMCg9KK8v2Sg/s2tSqxUUqdX68/J3NlvSxeFBRbOCImSLKJ5XNDFntixvkOY8ikfl61GjMv6OKI5/yD8+v78gItpa8YUNEREREREREREREW0VXNepWF2szWkibjNV3vhUFyYiIiIiIiIiIiIiIqIhfGFDREREREREREREREQ0zpgSbRuUT/uRV+SG3VhkIG+8TidqUa8jZ573viinbR5RpN7ineYMOfe2J795jYFgwTxff3llr3Ffpypk3BeT6oy7lveYY75eAAiZb7cbtqjrlNHl5x6Or7vXuG+w2GrcFwXz7+Lkrn7jvkWLmhsAkNjBvEaJr8q8mEy61aK+Scx8X4ebzadQh2p0+feH419h/nlXp+LGfQHgzUGLuiwWp9eOCfObzKwp5vWR+vt0ueuH0zKQMO5rU4cmaFHHCjCvF1Qqm5+XRDR+6su1ilbyPavUUxDbOBWqvVBO6eKNkiIEdKYoPv/0yfJygnIco4mE3UH5OdCplovhuO/eWW7TPEmxRQB8iuc0Rc1IX/wNsU10V7nOjVuWj39VWT74zT1ybNE8VVeDMzRDPrrFTsWxVXxFQjVym3CjfE8OJwbFNqm8/Lle6NMUi9LVp5kSlRvt1CTHdcGQHAu5LQ1im5iipqAmZgsr6iUBQKoonwDtWfO6w0REtCm+sCEiIiIiIiIiIiKirUIZDsqYePVgJuI2U+UxJRoREREREREREREREdE44wybbVB6IAx/cPRpoMKr0sbrjM4xT9/ixMxTEsUV08BHVLRIpdJjngIKkxrN+0417+tTTOX3FDY/TnDM3x37Wi1SfAFwLVKiOZ1d5uttkqe7e/Z9107GfX1tfcZ9i8vN+/r85udWyG+XSsmXML/VOUnztHexJl26iuEMtJh/nwJ9cvoXz74WaSCrJpmvt7HVLo1CoWx+j9GkwvASVaaSGE4wYpH2Lmu+3uayeRq3gkE61XWyOYt7BIB82ezcLLgcm0Q0EXX45BgrW6wW26RXy9eAREK+Hvua5dSdoRrdvciJKq6HfYrnrpwiXXWDnFrNSclpupy84r6TiMnL6TGPJTdZVkYRO+Tl2MRtkp+dnH12FNuUH/qvvC7FbgyH5EaBKl1crYljA668rHBE3qZcpzwiPDJVbIJQk/ydbe6RY2z/mnp5ZdCl+wo6ldlH/qAcdDbHLXO6/89gSj72gzndc3BYEUr1u0yJRkRUSXyKJSIiIiIiIiIiIiIiGmd8YUNERERERERERERERDTOrFKiXXrppTjppJPQ1NRUqe2hzSAWz8Mky5hjc7b4zItmOWGLFUfsUrC4/eZTe51u83RICJinnUHKYjpyImred7JFGreAxTEetJw2vvjfxl1LA4pUFB78H93HuK9bU2PcF+/ewbir/xBdaoHhJNo7jfu6Vz1n3BcA3IJFyr1q8+9EaDfz9Qa7zK8fjkUmSF/c/LsYKJun6YoE7NLevStpno6t2yLNVyKaM+4bqjNPExh3za89a3oSxn3rYnLaHC/V1eZ9AaApZ5aeMFU0P0Y2GCMT2dnBN1lsUxeT06YFooq8lwH5ecHxK+7pIeX9RBHnu32KNGUdPfK6Qor7ek5xD53eLLeJyynRNM+EzitvyMsBkL1/udgm8jFF3Fsnp41DXY3YxHfMAWKbkOKZZ9o/5WeTzLO6VOVOUD7XnO3qxDbJwQ6xTXaNHNf4IortCcnftXBMjjl3jOtipYTimCSCcpwaqZa3yacIZZwOeT92p+Xnk+0mydeHQECXFzjSkxTbxBzzNNJEY8V11/6ZaCbiNlPlWc2wOfvsszF9+nQcccQR+NOf/oRy2SIRPBERERHRVoAxMhEREREREZmwTolWKBTwxz/+EZ/85CcxdepUnHPOOXjttdcqsW1ERERERBMSY2QiIiIiIiIaLasXNi+99BLOOussNDQ0wHVdtLW14dJLL8Vuu+2G973vfbj++usxODhYqW0lIiIiItriMUYmIiIiIiIiE1Y1bHbbbTdcdtlluOSSS/DnP/8ZN954I/7617+iWCziqaeewlNPPYWzzjoLRx99NE455RS8//3vr9R2k4VcNoBQafSHPrvK/HSJ9+vy7A4nMcd8vT6L2jkA4A6Y5753AhbvQxNx875li4SXOfO6CCiY16+Az2JfhS0KdgCAxXFyohaXUIvP7MYtzo9a81ofbpV57Qtkzb9LgZhdKqFCm3l//5yIcV+nWpHL3UN0cp9xX1/Y4pxOmuef9ll8l6ZNVuThH0HAb14D58k289pMsZj5NdMXNb8/RaeYX+fflZDz0HvpX2N+vS2XLO/Hhh95vHJAM0YmshNWxEmlstwm0ytftwaekpdTP7tXbBPcqVpss5YcM5d75JqU/p0UcaimrkynIubQpHXMKWK9RJXcJqqLvdKd8uePaOLtkFzDyA3VyMtRPIO5QUW9JEX8WBjUxYkRxTY5DfJ5658hn4/BlLxNTlzx+avk72y4oV9ss/eUNrENALzS1iC2iQfk76xfUfbSn5DPx2m7y3Us69vkGq75jPz90MZmml8v+GAX5xGNhbLroOxOvHNzIm4zVZ51SjQACAQC+OQnP4n77rsPq1atwiWXXIJdd90VrusilUrh5ptvxsEHH4zZs2fjoosuQktLSyVWS0RERES0xWKMTERERERERKNRkRc262tqasI3v/lNvPzyy1i8eDFOP/10JJNJuK6LN998E9/73vcwc+ZMHH744fjDH/6AQsF8tDcRERER0UTAGJmIiIiIiIgkVinRJPvuuy/23XdfXH755bjrrrtw9tlno7W1FaVSCX/729/wt7/9DfX19fjsZz+Ls846C5MmTRrLzaH/GUxHgIBlGqlRKpb8xn2TVRbTAestUjgBQJ88/dtL7iXzFD/mSYlgleLLSlKR4sBL0OJS9Mrb5n0B5F+Tp517CU6Wp/V7ajVPS+SELc6QgPl30dfebr7ejPl3KbqreWoxABj8T8a4b7i117ivEzU/PxyLdI5OxCbNn8U215mn6quZY3etrllh/n0K3mnet3on81SQge2Sxn1tOC3mdVNCPeaft73d7vOmimbnZqpol1JxrDBGJhrZQFG+3vRl5XjI75OvAZrnlLq8HMc49cpYOCe/jC28oEgv9dJKsY2vWXF/zSuu7d1yCipMbZLbKOLf8tNL5eUACMUU6VD75NRRTkur2Matr5WXk5bjTVV0F5XP60BEd28rrZKfcwJV8vo0acJ9YfnTOTFFSrS4vD2hQ2aJbaa8XxezNDy6QmwzqEgNH5olP684CfmzlRW/fwhl5HMt1S8/D7SndLF7c0Tel5Mj5mmkiYhoU2P+m923334bF198Mc477zy0tbXBcdbeyF3Xheu66OzsxE9/+lPssMMO+PnPfz7Wm0NERERENO4YIxMREREREdHGxuSFTTabxS233IJDDjkEO+ywA37wgx9g+fLlcF0XO+20Ey6++GK0tLTgwQcfxDHHHAO/349MJoNvfvObuOWWW8Zik4iIiIiIxhVjZCIiIiKisefCQXkC/nF18zFpK1fRlGj/+te/cOONN+LOO+/EwMDa6beu6yIajeLoo4/GaaedhgMOOGCo/aRJk3DooYdi6dKlOProo/HCCy/g5z//OU444YRKbhYRERER0bhhjExEREREREQa1i9s1qxZg5tvvhkLFy7EkiVLAKx9AAWAvfbaC6eddhqOP/54JJPeuct32GEHXHzxxfh//+//DS2Dxk75f39GKxIwz11fKJjXzXDCFhPBLOpAAIBjUQ8mUGv+mdFcbd7Xsdhfubx5X4saJYhHzfvWWNTOAVDKtBn3dVeaF4SONFjUg9llJ+OubpMiv7iXXvO6TG7UvA6NL2RRKwhAFZ4z7uv4Lb5PZde4q6/a/Pbs+C2uezb1PnbbwbhreeZM8/UCcN4l5/r3skPkafMV+yzydVucW6XXzevulFOKnP8eCjnz+nfVCfNaUgCQLZrdUyNFOe/+WGGMTGSuryxfM7Il+V7p98n34lxRvm/6oop7q7ZOYEF+pvJXK7apUREDN9XIbQbSchvNM5Xic2lifieuu9dkFSXZYss6xTa+KQ1iG7dZriPmFhTPBYraTE59ndgm1i/X5gEAt0dxbBXxqhOSz21fUhG7amLjOrnuUnnunvJyQrrzKNQk7+/aZ98Q2zjT5DpHbkuv2KbcJcctJUVoEwjIMX1TXHceZYrysZ0c27w1komItnZWL2wOP/xw/P3vf0e5XB56AK2ursZxxx2H0047DXvttZd6WbNmrS0cl04rggoiIiIioi0UY2QiIiIiIiIyYfXC5oEHHhj6+YADDsBpp52GT33qU4hERj/iNBaL4f3vf/9QwVUiIiIioomIMTIRERER0fhx3bV/JpqJuM1UeVYvbJqamnDyySfjtNNOw047mafoAYApU6bgkUcesVoG6fRnQygFwqPul/GbpyWqjVqkQrFMh2TDTZunvHKiFl+vZnlqvieb/bV0pXlfi/RPSFmcH5395n0BdKw0T6nmKNJseKnP9Rr3jZUfN+7rmz3FuC+CFufWlGbjrm7cPJ0aADjv29W8c6ucSsNT14B5X4vvk2uR1qzcZT6C39feZd53QJeSwZM2Dc1wqu3OL2Md5teunpcsUvXBvG8qZZ7uolCyOEYAUgWz60/aJs2fBcbIRHaqfXLqrFRRvlf2puWXpFnF9ckXU1w7tamYFdelckYRBzR4p1Mcoom/2hWxTo8ipskp8jRl5fTL+WW6mKCzp0Zs439e3u5k7r/ycnoV9+waxfFIxMUmbp2cogu77yi3AeB09cqNVpunWV2fm1ec113yM5+/SU6z7XtzmbxB2thQk6ZuSo1uWdKqVsjndt8y+XcIeUWK2mxOjpsGlaluBwryNvXk+RtmIqJKsnphs2rVKgQC1mVwiIiIiIi2GoyRiYiIiIiIyITNEE0+iBIRERERbYQxMhEREREREZmweposFot48sknAQB77LEHqqurR2zf29uLF198EcDafN7MxT0+6uMZJAKlUffzW6R/qm20KJTbZJ5KCRHz9C0AgJBFChebNCz9g+Z9FakGvLjt5il6nKDFvip3G3d1c/I09pFUJeRp917auxLGfWN95sfJ+U+f+XptzmmLNF2OTdq7hlrzvgBQJaef8JQ0Pz9s+AYUaUW8WByn8oD5eYmOXuOubrddSrTMC+bp56J7jxy7jMRJjr4eyTqFN3uN+w4OmJ/TjdPM7y/hmPn1tq9LTm80kmB29KlcASDgG5+UaIyRiewEHXlcYWNEvnc0JOU2vSn5+uSfpkh3VaOMC/OKFEyap/ScInWzJt3ZKrmNm5bjA6dHcS9XxCj+uG5MqSadU21WkcrpJfk+UZdoE9v4mi2ed9fjbK+ImauV55qmnSJG19yRfJpnX81zW59iP5bWyKv6l3zMACDQKJ8j/h3rxTZun7wfO16VY5lIVN5HNZPk55NUl3w8SmWr8dsb6MuN/vdLRGOt7DoouxMvpp6I20yVZ3WFvu+++3DQQQfhqKOOQlBR2yAUCuHII4/EwQcfjL/85S82qyYiIiIi2iIxRiYiIiIiIiITVi9s7rnnHgDApz71KcRictHeWCyGY445Bq7r4g9/+IPNqomIiIiItkiMkYmIiIiIiMiE1QubZ555Bo7j4AMf+IC6z7q2ixcvtlk1EREREdEWiTEyERERERERmbCqYbNy5UoAwPbbb6/us912223Qlza/6mQGieDoc4zapFN3LVLIuysUuZc9ODb1OgC4GYv6KBZ1JFC0yAGbs6hBYWNGo3lfRboYL063eT0XAIg1rDLuW+40/1IU8ubnZq7P/AsV6TCvFeIWzNfrWKzXibUY9wWAco95HRpVznovFt8JJ2r+nSj3WNQLsrhu2dShKbXZ5X1f9WaNcd+qFvPzI5M1319v90817lsbNq9xNKXaokaaRS27YL9dbvN00SxkzdjcTy0wRiayE/TJMVZdTL7fJWrl66XjyNe2Uqt8LfGHdDUz3J7K1DrR1MLRPFOo6tPUV8nralLUHFQ8p/iV9SmDihplhYIcbxeL8hjWcpdFbcFRLsevOD/cQd3znquoK+LfZ6a8oISiDt3KXrmNQllRx9HJyPWbBlp0sXT6TbkWkv8puc5rW5/8HWnL1Iht9t9VfjYNNSnO2YK8j3r65BnAANCWk2Ow3kJlviNERLSW1QubdVxX/wC/rm2xaFconIiIiIhoS8YYmYiIiIho83PhwIXFyPNxMhG3mSrPKiVaY+Pa0cOvvfaaus+6tg0NDTarJiIiIiLaIjFGJiIiIiIiIhNWM2zmzp2LlStX4uabb8YJJ5yg6rNw4UI4joO9997bZtVkIZwoIhIa/bu6UQwS3UQxY/5uMPu8ecorxy4jmhXH4nWof5V5GjjEw8ZdnSaL9E8Bi51tkcat9Kou/YSX1/5rnrZqIG+etirkN0/TEwyZ9/U1xo37ImL+eQsvtpv3fcsuzd/rr5of4zkHdxv3DWjSR3hwS+bp52xS15VTFmnvoubHqdBul7ZqeX/CvLNF33TJ/EIfsBg4tUOtedq74I7mn9cJml/nEwO9xn0BYPKgWcq9weL4pAlljExkpzEqP6ZWJeQUPMEq+b4WUaTgyi6T2/hXd4htAMCRMzChpLjkBVb1iG18Jfn+qkp3Fo/IbTRa5W0eeEqXMm5lql5sE/Arjn9APrZOTG7jmyanhHMC8nNtfomcurRvpeIkAlAsyrH7lO0Uz9qa5zxFGsNSj5ymy6c4Hd2ifFzbuqrlBQFYNWjxbLSejCImjCnOx9h28roCO8rnWjQmp3GrU8ZWNd01YpsiLH5ZREREm7CaYXP00UcDAB566CH87Gc/E9v/7Gc/w8MPPwwA+NSnPmWzaiIiIiKiLRJjZCIiIiIiIjJh9cLmmGOOwR577AHXdXH22Wfj6KOPxuOPP75B7u1isYjHHnsMRx11FM4++2w4joPdd99dPdqQiIiIiGgiYYxMRERERDR+yu7E/UNklRLNcRzcc8892H///bFmzRrcc889uOeeexAMBlFXVwcA6O7uRqGwduqr67qYMmUK7rvvPjgOiygRERER0daHMTIRERERERGZsHphAwDbbbcdnnvuOXzhC1/AfffdB9d1kc/n0draukE7x3Fw5JFH4he/+AWamppsV0sWfKG1f0bLNS9tAJin20fZIv18MWs1iQzBmEUtiIL5L1yCPeY7zGfzOj5gsb+i5rVz4DNfr39Gjfl6AWw/tcu475KV5rVR8iXzWhCdPea5lus6zOpAAICTND/GuTbz87JtpUVtJQAtafNaMjssM9/uqmo5T7sXNyXn+Pbsm7W5WJsr9ZhfrLM9dgXH+ovm4Uy6aH79sdnTjWHzY7zaou7OtC5djYXh+KrNrwFl848LABjMGwQuAAaL4zdEjTEykTm/Ioz2+eTvt09x2XIUy9HID+juJ76g4u5RVtQD6ciKbZy42bVzk+Uo6pOo6pxUx8Qm8dm6+KnpLfnzu6683ZmCXOelsEauB+JrkGPs8oAcK3UslfdRS58uDigpzqPmFXLtVCcu76Nyn3yj15xH5UG5XlA5JX9n+3O6c78zpzj+ivOoKiB/r32OXFMq1yIvx98s1++Cos5POq2rTVVQnEftjkUNXiIi2oT1CxsAaGpqwt13340lS5bgL3/5C5577jl0dq69YDc0NGDvvffGRz7yEey0006VWB0RERER0RaPMTIRERERERGNRkVe2Kwze/ZszJ49u5KLJCIiIiKa0BgjExERERFtPmXXQVkxQ25LMxG3mSqvoi9saGJwi4BrkAHGJ88W9uQPmqcaCJhnf0LJIp0aAJTlWcuegknzz+yrNU/h5CqmP3tRpTvwErFIueBYpGIr26V/KuTML4NBn/m6U4r0C16m1shpGbz4Gi2+UBHzbfYFzbd5zaDFNgPIlszPr/5uXaqA4UTbzFMblhSpHrwU+i3SMVpct1yL62VqwCKlInSpGrwMFM379lqkvkxYpKAcKJhft9y8xTXTIuWmbVkWH8zWbdqPiMZXRpHOsGSR0nJ9mucUvyI0d5RxYSknXxA1obG/UY5RnJCcpszNySmoEFLcd6KKmCknP5xp71OxgLzd2aL8+QtleWcHJ8sxsFMrx6u+jJw2rGtATonWldXFTRG/HJzlW+T9GEjKy0mvks/rUFI+to4is16+Tz5mvcpUqpp0Z6sz8vomK07/kE/+cKqU7ornfat09hvRhKwFKNK0ERGRWmWiXCIiIiIiIiIiIiIiIjJWsRk25XIZr7zyCpYtW4aBgQGUSvIojJNOOqlSqyciIiIi2uIwRiYiIiIiIiIt6xc2mUwGF154IX7zm9+gq6tL3c9xHD6MTjCOxdmSGTBPpeQEzPOalSzS1QBrz1NThUHzNCyhlPlndqLm+9rNKlIieK23yzzlFXJyegAvxTd7zNcLoLO32rhvT848jVPAMZ+nXlKkbvDiWpxb0KTM8JDqME+Z15o1T0sGAI5FSiRNWgovtZ1p475li2uXTRq3qpJ5OgOb1Avdg+ZpIAHg7bQif4aHHouvRINFJrfuvPlNtSpgnn+u0GmTIyNr3DM3YBdypotm/TNFi1x9lhgjE5lbk5EvzkVFSjSbrLvry/XIC9KmgNY8n/gUlzx3QHEDSyhuVJrnnf/P3p2HV1Hd/wN/z92zBwJhR0VFVKwrIhZFBLVqXVAr7uBSq9Zqq1aFVoWqX7XWlfbnhhp3EdyKS+sKBQQRxYqKCgICYcm+3X2Z3x+YWyK5OZ+cc5ObS94vnzxPMGdmzp2ZO/OZO3fep15Q00TUdaK9UV23B3+QrcfKoLrecVrqeUUEdbUdEpxL/OpzZLxa3WaDv7eyjSTGCwACgljgYLV6Z/MlBPFzfnWtHw2raxBvrnpZ/nr1sirDsmvibxrU6yhP8H4MxtXbpDGqrlf9DerXlifYj+L16nUdEK4jSfRwUaKnaF5ERCRjdPUcDAZx9NFHY+nSpbBtZoQTEREREbFGJiIiIiLKHBsWbBgOpJkB2dhnSj+jGzb33XcfPv74YwDA8OHDceWVV+Lggw9Gz5494XBweBwiIiIi6n5YIxMREREREZEOoxs2s2bNAgAcfvjh+OCDD+Dx6EfeEBERERHtDFgjExERERERkQ6jGzbff/89LMvC9ddfzwvRLOLMBZwaufsOj/5jeU6XQWa+IDM1lUjILDM/FtGPMQmG9MeSwed+7Ul9/fS/uesclK89LWr1+2wn9NezQxIq3IaCHP0xOyoDZuNu6Npcr7+den+1VXtaV6H+ezEc1l9XDVGzb6NHDI4h+S79fvet1h/gJGwwvok0j7o1JuMjhQyWuyWgP1YQAPj0h7AxeuA8bpB0FTLYL6sM1nW4Tn8bW079c3kwYDDgD4DGqN5r9sdMxuzRxxqZyMzAPPUxw5erHgPRkaM+5jnc6uOEHVMfs6Mh2fE1GlaftCTj88S+Uo/1kVddp2zj3lVd61gOwTkrIBhTRzAfd2H6YiRdDvW8YoLF1Xyprst6RNTj80Tr1AuTjDsjaQMAlWF1uwPr1Ns/P6q+XqqsU1+fFPjU82lqVL/3a4PqPoeFdVauS91OUu9FBW3qBe/rer/6tfWsVY9hE2tSv64G4XisjYJ+58FszFEiImrJ6JNOj8eDYDCIwYMHp6s/RERERERZjTUyEREREVHmJOxtP9kmG/tM6Wf0teVhw4YBALZs2ZKWzhARERERZTvWyERERERERKTD6AmbyZMnY8mSJZg9ezZ+8YtfpKtP1MFcvdxw+dofLWLl6O8uvuqg9rR2XD82xh8wiyHxedVRC6kUFKofVU7FYRLv4zO4D2sSHeM0iNlx6U9r+uWDhK2/f7kd+usrahA95TGIJXLmaU8KZ7H+MSDf4P0gSCpoU53BMWRrSP8Y0uTXj4AqLg5oT5uXpx/zV12jH7dnWfrvxny3OtalLf19+tOXBwzi5wQROak4DI5ejZb+8SMW1T/BWAbHPLc7rj0toL+PWJbZvqWLNTKRGbfg8OrroT6uOPuoCx9vQn3OjVQIorWaZMdXSX1QVKS+dnJ51cdkh8+wiPqRJL7Y8ghef1TQH+Gpxiuoh52WZGbqfru9gn1NVCerz0m9BNef3zXK4qckUV41gojnHJ+6TwP61inb+AVxZwFB7S25Busn6DMABOPq5a0XJH9L4nmjgpg2f0T9OY0tKKksp3rj57pkNVKJR91uYI4g3rhetDgiIoLhEza//vWvcfTRR+Ppp5/GCy+8kK4+ERERERFlLdbIREREREREpMPoCZsNGzZgxowZ+PWvf43zzjsPr776Ks455xwMGzYMubnqO+zM9SYiIiKinQ1rZCIiIiIiItJhdMNm1113hWVte6zTtm28/PLLePnll0XTWpaFWCwzMRXdnR2Mw060P5rEjuhHoQTr2x/B1qymVj/DaYtf8GhuG0rz9GOJLIMEApdHf13bIf1p45v1X68kKiH1gvUnjdabhaJVNRZpT7spKIsjaE3EIBJtkEH0VKDcIIqtUT8isLJSfz3Xx4weBoXXob++TGK+ag32D5dB7J3bIF4sGNUvC/wx/eN8IGaQAwnAZxITaHCsbojq7x8N+m8n+Aw6XVunf160LEEmSArBoFlEqW6MpEn8pAnWyERmygPqg2RckABq+yPKNrE69bG8bqs6NuqHmmJ1hwDkCGKIXC71eS3XVr82X0y9HhP16vmgWr2y7Zh6PdqC82bDZtn5YmtIHa8luTyJC+KRG+rU29+7rknZJtKgPieVC86XIUG0FgD0FsTmNUTVy8v3q2taW7CuwxF1nVkr2K6BmHo+HmGMq0dwnRAT7CMVgvRnSWyaZHs0bVW3kWwPqcqwen2vCtalb4FEaWLDgo30RIN2pmzsM6Wf0Q0bYNtFaGu/ExERERF1V6yRiYiIiIiIqL2Mbtg8+eST6eoHEREREdFOgTUyERERERER6TC6YTNp0qR09YOIiIiIaKfAGpmIiIiIiIh0GEeiUfYJbABcWlHy+nEeW6oKtaf1G4ypUBk2y8wv9grCsVMIhvTHcyh0C0JwUzLIuzSI+rcMlpuI6o8/kTAYBwIAQnGT/Ut/2qggCzmVHKf+fh016HOsWv8YsKGxQHtag90DANDLo99vl8HbKRTXH5elssls/C1djVH945ZbmBXemkKP2Ru5GIL8/RSG5Ou/5u8a9HeQsMF+vV5/KBk0GpwX80L658S6gP6YTgCwPqDOtG9N0GCMNCLKnJhg8JH6CvW4IpYVVLap3aI+51Y3qtvUR2TnE8nYWvkRwfg0XvW505IUMpKBXiTzEYxPYwvOfaGwbD2G44JxRQR1r2Tc0Vhcvc0k49NUVqrr4ZBgWZJxVwAgTzDGX1CwvIBg35a0kVx3xQTj80hqzp7C63jJmlwvqGFCgnqjTlCuVgg+Q5B8zpAQvD82NMnGCt4SEoy95FglmhdRZ0rYslNcV5ONfab0y8xIrERERERERERERERERJ1s8uTJsCwLlmWhrKws091pIW03bBKJBN5//33cdtttuPLKK3HRRRdh8+bNLdpEIhEEAgGEw/rf0CQiIiIiyhaskYmIiIiIKJtcc801yZsZlmVh1113bfc83n//fVxwwQUYOnQo8vLy0LNnT/zsZz/DH//4R3zzzTfp73QHKCsra7EeWvvx+Xzo06cPjjjiCEyZMiUtry0tkWhvvPEGrrrqKvzwww8t/v91112Hfv36Jf89c+ZM/O53v0N+fj42bdqEvDzZI5iUXvGIAzG7/ffqHIJHqlNxGkTlmOifYxItBvQuMsidMeAp0Y/ZcRTpx/vApX8P18rTj9lxhmPa09oxs21U6NGPUjp2YJP2tDGDuKwvanpoT/tleW/taZ2W/rO53zfpxyE1RA1yyQBsDurv1731UpgAAPsW6+9beQYRYW63fgZUUUS/LAgaxFc2Rc3iK6UxNK0JC6I3UpHEX6QSt/XfT3kGWX0eg3N5Tp7+fllgeCNC912c6UfJWSMT6alPqKPMJNFZiajZtUAzS1ADSaNBBxU2KNvk56mPmb58df3s7GlwXbAdq9gs1rKZXaverq7vZSfX/UvqlG1yBfXUhnp1TNmKanXtXepXH7drBLGklWH1Ob5BeDpeL/i4Z3wf9cyKfOr9UXK973Cq30cNgvgxSaxgZUi2z9YL6ldJvRcRtElXTZLjE8QhCmLz3LWyY1aBWz2vXRN7K9usw79FyyOizFu6dCkeeOAB7ekbGhpw6aWXYtasWS3+fyAQQG1tLVasWIEHHngA06dPx5QpU0y7m3HhcBgVFRWoqKjAwoUL8de//hVXXHEF7r33XrjderWY8Q2bxx57DJdddhnsHz946NWrF6qqqmC1EgZ7ySWX4M9//jPq6+vx6quv4rzzzjNdPBERERFRl8MamYiIiIgoMxK2hYTB2MGZkuk+R6NRXHLJJUgk9L7oF41GMWHCBHzwwQfJ/zd8+HAcdNBBCIVCWLBgATZv3oxoNIqpU6ciGo3i5ptvTlf3O1RBQQEuuOCCHf5/IBDA2rVrsXjxYoTDYSQSCfz9739HZWUlXnzxRa1lGd3kX7VqFX77298CAI4++mh8/fXXqKioSNne4/Hg9NNPh23beOedd0wWTURERETUJbFGJiIiIiKibHPXXXdhxYoVAIBzzjmn3dPfeuutyZs1Pp8PL7zwAlasWIGnnnoKs2bNwrp16/DHP/4x2X7atGmYP39+ejrfwXr27Im///3vO/w88cQT+PDDD1FeXo4zzjgj2X7WrFl46623tJZldMPmvvvuQywWw7777ou33noLw4YNU05zxBFHAACWL19usmgiIiIioi6JNTIREREREWWTb775BrfddhsA4Nxzz8UxxxzTrukrKipw7733Jv99//3346yzzmrRxuPx4K9//SsmTpwIALBte6eIRQOAkpISvPjii9h3332T/++5557TmpdRJNoHH3wAy7Lw+9//Hh6PLH9+jz32AABs2LDBZNFkoLomH2FX+8cLiBs8lvd9faH2tCVe/dx7Sb5tW4Ih/dxnr0d/XJa61frLLQjory/PAP3lxuv1x+uI1+sPBBGuNtvGJuMrhWP6h9DGiP6YHfVR/dfc16f/ek3G++kR1h8MptxgDBoAcBo8URzTH2YEMYPjj8mxq6nJYDwpQd51R0xbazAGDQB836S/f32jHkogJZN9KxDVX18eh/6CK4P64xAUNuhP2xgyGBAKQCCu954IxjMTKcAamchMhWOLsk11sJ+yjXOruu5ZW1ekno9gDBufU1bPSo6HkuuIWJ36uGh9rb4uyNlFfZx02OqxZ2LV6nE14oKhJ2Nx2TFTEhnTJBgzpkZQk0cEdVm+W79O3p7XKXj9wjFs3IJ6ISx4bXFBG79hLdfM41K/jwKCa7CNQVnd8U2D+rVJxqeRjE0YEFxYVArGk6xryFG2cQuOR00x2ZiqCUHJ2sOZnnGuiChzbNvGJZdcgnA4jB49euDee+9t99MhTz31FPz+bSf7oUOH4tJLL03Z9q9//Stmz56NRCKBxYsXY/ny5TjwwAONXkNX4HQ6ceaZZ+KWW24BAHz99dda8zH6FGzjxo0AgP333188TfMgqoFAwGTRRERERERdEmtkIiIiIiLKFg899BAWLVoEALj77rtRWlra7nm89tpryd8nT57c6tidzQYPHoyjjz46+e9XX3213ctT9eWUU07BgAED4PV6MXDgQBxzzDF45plnEIvpf8Feol+//32pqPkGVnsZ3bBpXvHtubCsrq4GABQVqb/JRERERESUbVgjExERERFljp3FP51tw4YNuPHGGwFsi2m+6KKL2j2PUCiEJUuWJP991FFHKacZO3Zs8vfmcW9MNTU14cQTT8SECRPwz3/+E5s2bUIkEkF5eTnee+89XHDBBRg7diy2bFE/za1r+3n37dtXax5GkWgDBgzAqlWrsGbNmmTutsrChQsBAEOGDDFZNBkIxRxwQfb46/b8Uf3HnC1BjEAquS79O58mMW4A4DDoty9H+Kx6K3J76b9m7376H/RYPv1Dgr1RP1fI1UN7UgS3mp3OakP6j29XCWIWUqkRPO6eittgt96zT43+cj360XUNBhFwm4JGpyoE9LuNkEGcUq5H/xjQs1D/G/bBoP66NpGXrx/HGNOMu2q2xSC+sm9O+8+HzXKc+sefrQ791+wyWF2VBsetgQbHLZPIPADId+nFOZqcx02wRiYyMyAxUNkmEle/v5sEkax+QbxSb19I2SbH4Jrlp9xudfHi8amXl7e3+rU5BqsLcbtWXZc4JTmyCXWfa5vUcU8AUB1W1/CbBTWRJHJzVO9aZZuB/dRt6mtzlW2KG9RtErasEAgKamCX4DxZkKfe/wsE/WkKqN+PxYXq+L2CiLrmLA/I9qM8QVFVIkhXCwr2o4qgel37BYeRJsF1VaEgVj5fED8HAF7B7pbIyEfMRJQuV1xxBRobG+HxePDII4+0+WRMKt9++y0SiW3XbJZlieLNDjrooOTvK1eubPcyfyoajeLEE0/Ef/7zn+T/69u3L4488kgUFBRg9erVWLhwIRYuXIgJEyZ0yHVXIpHAnDlzkv+WXgv+lNEnJEcddRRs28ZTTz0lal9fX4+HH34YlmW1eOyJiIiIiGhnwRqZiIiIiIi6uhdffBFvvPEGAOCGG27A3nvvrTWfb7/9Nvl7aWkpfD71FysGDx6c/L2mpgaVlZVay2525513Jm/WWJaF22+/HRs3bsSsWbMwc+ZMzJs3D19//TX2339/LFmypMWNlXSoq6vDBRdcgC+++AIAUFBQgCuuuEJrXkZfW/7Nb36Dxx57DPPnz0dZWRkmT56csm11dTXOOOMMbNmyBW63G5dddpnJoomIiIiIuiTWyEREREREpKuhoWWKjdfrhdcreOSvHaqrq3HVVVcBAIYOHYo//elPRvNq1qdPH9E0P40Lq6mpQe/evbWWX19fjzvvvDP571tuuQVTp07dod3QoUPx7rvvYv/998fmzZvbtYyamhpceeWVO/z/UCiEdevWYdGiRQiFtj2V2r9/f8ydOxcDBgxo5yvZxuiGzYEHHoirr74a999/Py6++GK8/fbbOP3005N//+ijj/D5559j0aJFeP7559HQ0ADLsnDTTTdhl112MVk0GYjEnYhY7Y+AMYkXy3HqRZkAQCiuH1ej8RRfC7366Q0OBQDuQv3Hgm391QXYBo8jR/Szoxx5+pFEdsIgeq6H+rH5tuRvimhP2xjVP4QWCaIuUvE4THYQfbGo/kOZkYT++9j01dbqb2I4DeKUAhH990QPg+Ot260fyRI12KfjMf39wx/TX1cA0Giw7IAkwiUFk1NMyCCqr1D/7YSQQfxcLGEWXWdCdzMJEpM6BGtkIjNBqE/ekYT6m5sRwTHPLairEoLzsjQ+epeSOmWbokHq1x+XlMCW4LweSU+Um6NI8CGSoObvXdQkWl59pToWqkBQb3sF8aYep3o+CcG+FhTUWZLo44aorAKJCNZ3rSDuNBpTFx4+rzoK2COI4Eok1K8tFFa/1xqjsmJJEkEmERIUHOm6gpMc1yTHLIewkJXUrOUJ/dhtoo5iwxK9F7oa+8erzEGDBrX4/7fccgumTZuW1mX94Q9/SD7V8vDDDxvdEGpq+t/5OydHFkv503bbz6O9nn/++eT4oQMHDsSUKVNStu3duzemT5+OSy+9tF3LaGxsxD/+8Y822zgcDlx11VW4/fbbkZurjjlNxWxgAAD33HMPwuEwHnroIcyZMwdz5sxJZt395je/Sbazf/wQ+fe//z3+/Oc/my6WiIiIiKjLYo1MREREREQ6NmzYgMLCwuS/0/10zTvvvINnnnkGADBp0iSMHTvWaH7NT5YAgMcjGzf1p68pGNT/QvaHH36Y/H3ixInKPpx11lm48sorEYkYfMO3FYlEAvfffz/+/e9/49FHH8Xo0aO15mP8VUnLsvCPf/wD//73v3HUUUfBsizYtt3iBwBGjRqFN998E/fee6/pIomIiIiIujTWyEREREREpKOwsLDFTzpv2Pj9/uQXyEpKSvC3v/3NeJ7bj1kjvQkSDodb/Fv6ZE5rli9fnvx91KhRyvYFBQUYPnx4u5axyy677HA9Z9s2IpEINm7ciJdffhlHHHEEAGDlypUYO3Ys3nzzzfa9kB8ZP2HT7JhjjsExxxyDxsZGLF++HBUVFYjH4ygpKcEBBxyAXr16pWtRRERERERZgTUyERERERF1hI8//jj5pEwq559/PkaOHJn895/+9CesW7cOwLZUgHRcj+Tn5yd/lz4p89N228+jvZqj3QBg8ODBomkGDx6Mzz77THuZzdxuNwYMGIDTTjsNEyZMwMUXX4wnn3wSsVgMkyZNwnfffYeePXu2a55pu2HTrKCgAEceeWS6Z0tptCWUg1xn++/MGgwzgj0K9XMIJZnBqTQZjCEBAOjJs0YAAQAASURBVMEG/eljYf1AXJdXf2U7f2jUn7ZE/469HdbfTpGN6szjVGo26N+BB4DqkDr/PBWTPNRw3CRLVf/hSEkudOpp9ZdbJciaTsVgaCUAZseuqEHY9Nag/r7Z0+BRYJP9si6o/34IN+gXV0tr9KcFgO/1D3tGYgb7R0NEf2Knpf9erIzoT1trdLzUnhQAENQceydodKxNL9bIRHKbHGuUbTYHD1C2CQjG3ij2qL8FWpoXULZpCsviPyTjb4Sq1X2SjHnpFIzV6IbgOk0w2EVc0OfGtertUdsky3tvjKXno4zaiLpPkjO2ZCw/yfiG9YL5SM9sIcHl6Jawej02BtXXiA7BuI/1gvlU+9X186aAeh/5vE5WN0jGZ5HUe37BYHuS64raiHrrVgrqMck4nA3CcX6ighquBwrVjYg6WQLpGzuqM7W3zytXrlSOq3LIIYckb9h89tlnmDFjBgBg7NixmDRpkk43d1BSUpL8fevWraJptmzZ0uLf7b2psb3tx7+Rjh2Tl5envbxULMvCvffei1mzZiEQCKC6uhpPPPEErrvuunbNJ+03bIiIiIiIiIiIiIiIqOv44osvkEhsuy20fv16HHbYYSnbbv/UyubNm1u0vemmm3DiiScm/73XXnslf6+oqEAoFGoRk9aa9evXJ3/v2bMnevfuLX8hP5Gfn4/6+noAQCCg/qINsC0ariMUFxdj5MiRyXF13n//fd6wISIiIiIiIiIiIiLamU2ePBmTJ0/Wmvb777/H999/L2obiUTw8ccfJ/+9/c0cYNsNG4fDgUQiAdu28fnnn7d5MwhAiziyvffeux0931Hv3r2TN2zWr1+PESNGKKfZsGGD0TLb0q9fv+TvP/zwQ7unN7phc9FFF2lPa1kWHn/8cZPFkyYH9AKVfE79hwmL8/Tjfdxu/TykeINZFIrl0M9wMUisgSvXJDtG/zXHK8PqRik4e+hHXrmK9fucWysbzCwVd7X+fr1J8Fh/KnVR/R2kp0e/zyaRaA6D90OxWz8i0Okw+25BoUEyYpN+t1Hg1o/683j0F2wbRKI5Q/rbuC4ii4Jpjd9gPQNAJK7f713y9deXT5Yk0aqGqP5ySw2SIH0G72OPQ/987DRYLqAfT2gSa2iCNTKRGadlFmvczBLEAvXwqevfwoJQWpYFAB6Da5vtOd3q5TnyBFFmNep6xVGkrsUcheo2vh7qZeXUyeqnqKCmrRTEzzXG1POJxQ1O+NvJEdTDbsF+lC8sjeOCmjBXcI2fI6hpnYL55HnU8ylvVMfk1kTUK0AaxSqp5QYLEnMqw+pru6AgNq2foMaTxJ35XOrjTI7w8506weV2yDa7Jiei7Ofz+XDYYYfho48+AgDMmzdPecNm/vz5yd+PPvpoo+UfeOCBWL16NQBgyZIlOP3009ts39TUhC+//NJomW3Z/ikfh6P9n/8ZfQpWVlYGy2r/Bw62bfNilIiIiIh2SqyRiYiIiIioq2nPEzllZWW48MILAQC77LIL1q1b12b7U089NXnDpqysDDfeeGPKths2bMD777/fYloTY8eOxezZswEAs2bNwv/93//B7U79xY1Zs2YhHNb/0rrK8uXLk78PGDCg3dMb3bAZPHiw8mLU7/ejuro6eQHaq1cv8eA/RERERETZhjUyEREREVHm2LZllD6RKdnY52aTJk3C9OnT4ff78e2332LmzJm45JJLWm17ww03IB7f9jTgqFGjcNBBBxkt+5xzzsF1112HQCCADRs24K677sKf//znVttWV1fj5ptvNlpeW1555ZUWMWjjxo1r9zyMbtio7qw1q62txQsvvICbb74ZxcXF+Oc//9liMCIiIiIiop0Fa2QiIiIiIupOSktLcc011+DWW28FAFx11VUoLCzEmWeemWwTjUZx00034YUXXkj+vzvuuMN42UVFRbj++usxbdo0AMDNN98Mt9uN6667Dk7n//IvV61ahYkTJ2LTpk3weDyIRNIX6WjbNmbPno1f//rXyf9XXFysFZdtNjCAUI8ePXDFFVdg3LhxOOyww3D88cfj008/RY8ePTpj8fQTbisBj6NzQ91jMf3xOrxe/cENXIav0zYY78M2WHTc4Kk8d65+xrKVZ5AXLg0KboXDoM/uXLPBLyQZwCmXbTAmQ57LZBwJ/Z2rsKc6gz0Vd6F+n3f1N2pPu8avP1YQADg1YomamYxRUpKjv66L++tPmwjrv15p/n5r/DH944fPabaNCz2Z+RZSzOA4bzKt2+DlGrwdkDAYI81nMP4NABS79VaY13DsnI7GGpmodUWJXso2kvE1vYKaKRxTn+xtwaFEOjZNQvDNWcl1hOiYLBjjzVmqPgdbLvW1nB0SjM9SpO6zQ1iLSMbfkMyrt1c9n3yvYJyjgeoPfDw56nWUV6U+/sdt2bW15JyfJ1iPPUv8yjbeYvV8PFXq1x9PqF/bhoBP2aZQWCzFBG9uwdAziBpcC29PMoaRZGwip+DYl+OUHbMKBOsyaumP3UlEO5ebbroJixYtwgcffIBgMIiJEyfitttuw0EHHYRQKIT//Oc/2Lx5c7L99OnTMWbMmLQse8qUKXj33XexaNEi2LaNG2+8EQ888ADGjBmD/Px8rF69GgsWLEA8HsfIkSOx++674/nnnxfPv6amBldeeeUO/z8ajaKyshJLly5FeXl58v87nU7MnDkTvXqp69qf6pQbNs322msvXHXVVbj11ltxzz334LbbbuvMxRMRERERdTmskYmIiIiIKNu53W688soruPTSS/HSSy8BAFasWIEVK1bs0G7atGmYOnVq2pbt8Xjw1ltvYeLEifjXv/4FANi8eTNefPHFFu0OP/xwzJkzB1OmTGnX/BsbG/GPf/xD1HbQoEF47LHHcNxxx7VrGc30H3vQNH78eADb8tyIiIiIiIg1MhERERFRuiSy+CfbFRUVYdasWXj33Xdx3nnnYffdd0dubi6KioowfPhwXHvttfjvf/+b1ps1zQoLC/H222/j5ZdfxkknnYS+ffvC4/Ggf//+GDduHJ544gnMmzcP/fr1S9syHQ4HiouLsffee+Pcc8/F888/j1WrVmnfrAE6+QkbAMjPzwcArF+/vrMXTT+yf/xpL5PAmVBMf1drqNaPynEYpuS4PPoRLrGoQcyXwSHajuhPawkiAVKK60+bCBis56DZfeeGqH6MU6NB1J/fYNqhBQHtaV15+o/rW51+i38bp+H72GT6gMFbYkNjvva0xZuD2tO6DY5bbs3YKQDIc+lHIRQYRAQCQMggEq3JIFWx0SD9IWxwzAT0zy+SGI1UQoLYoFTchhGlulNn0wUPa2Si/wlY6ijVuF2qbCOJH5O0qWvIVbapCuYo2wDA7qU1yjaSAYdtweneTqiPgnbYLLKyWSKonk8ipD7f+yMe0fJqIuoaPhRXr8ewIAK7oFAdVWvJuq0kKbelta0kyq9GcM1aWVmgbNMjqn99sj2fV11c5bnU+1qxR1azVAuihLcK9tuKoPq91suXnoupQFw9n4igZpPWhJJDRK2jSjQvIsoukydPxuTJk7WnHz9+fPJLaZ3ttNNOw2mnndZmm7KyMpSVlbXZxnQd6Or0j9+WL18OYNujT0RERERExBqZiIiIiIiIOvmGzdq1azFt2jRYloUDDjigMxdNRERERNQlsUYmIiIiIiIiwDAS7emnn1a2SSQSqK2txbJly/D6668jEAjAsixcdtllJosmA26HDbej/fEzCYPEmqBB7FSeWz9zJhLXj28BAFvwiHwqbq9+vIDD5JF6g/wXO6ifDZQIZSZ4JtBklj9QEdbfN02itlwa78FmYcFj8KnEg/qdtg0OAibvRbOwLKAuoj/tVv1kMnzj8mlPO7yv/nK9ufrv49oqdeRLKgmD4Mwcp9nxI8epv3/VhvWXG4zp751OyzDrT5Pb0u9ztTCmpjUx2zS+Um/6oCAOpyOwRiYy40Oeso3kesYrOL+EBDWKJOS0h08dmwUAUUEEVU6B+vrHMjietxBTryOT+OIW8xFc1pU3qbc9IIsy8znV60gSmxYJqz82SQTU9Vcsoj6XSSK9A8JzW6WgxokIkszqS9QR5X1y1DGG0ZD69QcE12a5gvd1rmDbA0CdoB6rN7iW2J4kok5yfSmJ5a4Jq7dZfVT2cWBDVN3xBNJzjCAiom2MbthMnjwZVjs+cLB/PENdddVVmDhxosmiiYiIiIi6JNbIRERERESZk7DNvnieKdnYZ0o/oxs2wP8uMFWKi4tx5JFH4oorrsCxxx5rulgiIiIioi6LNTIRERERERG1l9ENm7Vr1yrbOBwOFBQUoLi42GRRRERERERZgTUyERERERER6TC6YbPLLrukqx/UiXQfC4wmDMbNMBgLJhTTH5sgFDd7iCxh0G9bkFGcitfSH8/BZJwR22/w7KXBMAGWT39d5RebhQr32KSft1sT0d+/vAZj2KxuytGeNm91qfa0uS79sVG+rS/QnnZDwGzsC4/Bus7TH+LISMhgbCWvwVhUubn67yejPhtsIwDobTBmWNw2GetM/8BXYzB2To7BqU2a6d6asMF4MGGH2fs4bustO6E5nSnWyERm8hPqUWMswSh3smOH+vgUE4xzI71myfWoB3JJCE7llmCwE0vQJck4lLZgDAtHnnphTkEJO6igSd0IwMZgT2Ubt2Q8GMG5/IvNvZVtBjb6lW0awuqx4FY2qE/y0vFofYIBUSRPg0YS6m0b9qv77RCMPVOYqx4LytWkPj4Uu9M5por6GCE51uS41G0k4/NIhlCUjM1lOr7g9jb6P0nbvIiIKA2RaEREREREREREREREXYENC7bJN4szJBv7TOmXvlvqREREREREREREREREpIVP2HRDua44cl3tf0S4Kap/l1fyGHUqJlFsJtMCgGXpR8ck4vrLTqiTElJyCR61TsUkTi1uEqfmN3lk3TQuSz9+TpAO0cZy9SfO13j/Nstz6+9ctkG0UNQgXjDHIMIJACIGy84zOkvq93trU572tHGD457JNt7sN+izYWxVgcF7YrNBJJok2iKVfXO1J4VB6h2CMf39w+fUnzZgcE4E9I+3JsdpIsocn6WOjsoRRAf5nOrzQ31EHelZIDiGbQ35lG0AoG+ROvJLcj6OhSXHVfUJQxKtZgtqmlilel0L0rfgFUbwSmoHl+BaThKdVexRR8a6BdcUkmvTfJe6z9Jzm1uwi1iCWqZW8B6pqFXHlPkE27ZJEBu3NaRukyesDSOCbRIRXCPvkq9ej5Iouy0hdX8kV6821NtMWn/nCq6Hcjw9lG2C4Y2i5RERkeENG6fTJPe9dZZlIRYz+CSCiIiIiCiDWCMTERERERGRDrPvDku+IkNERERE1I2wRiYiIiIiypyEve0n22Rjnyn9jG7Y3HLLLQCAN998E8uWLQMA7Lvvvjj00EPRp08fAMDWrVvxySef4Msvv4RlWTjkkENwwgknGHY7fSxL9hjomDFjMG/evB3+fyKRwDfffIOlS5di6dKl+OSTT/DFF18gEtn22PSHH36Io446Sjn/devWYe7cuZg3bx6++OILlJeXI5FIoFevXjjkkENw1lln4YwzzoDLlbkUO0mkQCr5bvVj5KkIntJPqSbk1Z8YQFOjLNqgNSaf1eQU668v0TPSHcA2SDWLh/WnlcVBpCaJL0ilp0f/RTdG9b99HY7rvynKDaK2TJjEIRmkTgEAYgbTm0TXmUS55QrjQNKtOqh/zNscVMdTpLIxaPY0Qtwg1mxLUH+5pT79bVziyczBOm5wbjJ5K+a7zF6vJCKkNWGTxE0DrJG7V41M6Zeum55OS33s6elVF6K5HnWkbI+4bB9sEsQ5JQRRRT6vIOZWshoFh1dbUJZI6nlbUMNKo117C15/SFB/hgTLqxTE3QVi6u1fF1XHVHkEJU1IGFUuq2PV85LEMXsEnxWEBOtogyBit1EQ77ohIKsNmwQXCpJIwJ4edRufYHvEBJtMEvdcIKi7moQxudUhdRuvs1A0LyIikjG+YfOXv/wFy5Ytw/77749HH30UI0aMaLXtJ598gt/85jdYtmwZTjzxRNx8880mi+4ynnnmGUyePNloHjfddBNuv/32Vi9MysvLUV5ejtdffx333nsv5syZg8GDBxstj4iIiIg6Dmtk1shEREREREQ6jG7YvP/++5g2bRqGDh2KhQsXIi8v9bchRowYgQULFuCggw7C9OnTcfjhh2P8+PEmi0+ryy+/HFdccUXKv6d6bdtfQLrdbuy3336IRqNYsWKFeNmbN2+GbdvIy8vDhAkTMG7cOOy5557w+XxYuXIlHnzwQXzyySf45JNPMH78eHz22WfIz1cP6kdEREREnY81MmtkIiIiIiIiHUY3bB588EFYloUbb7yxzQvRZnl5ebjxxhtx8cUXY8aMGV3qYrS0tBTDhw9v93T77LMPHnzwQYwYMQIHHHAAfD4fpk2b1q6L0ZKSEtx11124/PLLUVBQ0OJvBx98MM4++2ycc845eOmll7Bq1Srce++9O823L4mIiIh2NqyRWSMTERERERHpMLph05zJ/bOf/Uw8zf777w9gW/zDzuDQQw/FoYceajSPu+66q82/O51O/L//9//w2muvIRKJYM6cOUYXo/2LGlHgbv+gIS6DMWyK+wuCT1OIB/VT8wNrSrWnBYBARJ0znEphrv5rdugvFrH6zIyL4N3dYLyfoP54HaH/mg1w4rQMxhkxeE9YolDx1gXj+uN1OExeryC/OpVYQH88qU0GY4wAQN8c/ddc6Naf1mTf8hisa69H//1UnNAfUGqdP1d72kzq6dU/hvTx6W+nfYqatKcNxfSPAX5BfnwqJmeXHKfZualec9yvoMmgPQZYI2dnjUxdh8+hPlYNyPMr2/QsULfJK1SPHekpVB/DctbKzr/V/hxlG8kYi0U91AVSIqQ+Bsbr1W28A9Xbwy1oE1mjvj6yBeP3ALLzilswZoik5JKMc1MiuC7wCWr48oC6P9J6vleeul1Q8Pol+6OkdnUL1lGBW/0+igXVF8vSqsMnGHPSJRhUd8989YbLE7y22rB6jKuoYH+UXLf5nLLaqsSn7pM3xKdbqeuxYcE2GoUzM7Kxz5R+RqN119TUAADq6+vF0zQ0NAAAamtrTRbd7ZSUlCQv+r///vsM94aIiIiIUmGN3HlYIxMRERER0c7E6IZN//79AQAvv/yyeJo5c+YAAPr162ey6G4pHN72zWen8JsQRERERNT5WCN3LtbIRERERES0szCKRPvFL36Bhx56CI888giOPPJInHnmmW22nzNnDh555BFYloUTTjjBZNFpN3v2bLz00ktYt24dnE4n+vbti8MPPxyTJ0/G2LFjM909VFRUYOXKlQCAvffe22heubkR5BpEbulwqePb26AfZ1LgUUcctKU6qB/zFRc8qpyKryaqPW1uL/04JJMoNkT1o4HskElUjtmHMy5BVEIq0aj+NpZENKQSt/XXV6NBHFJcGFHRmlBcf1rbMNGowGUSe6c/rclrdgmiJ1IxiUQLR/T3j5jBdooaJjlGEvrr2m/Q8SJBRE4qpYIon1RCBu/j9Q0F6kYpNEb1l2sSIQkAvbx6+3UgbrZcXayRO086a2TqOgbmqaNUC3w1yjZen/rY4c5VH8udgmsZn09Wv4cb1NFBtYJrEHet+vjm8qnPNU5Baq0tOFdagmuBhGAVSc+skjO/pHb1CWquRkEUaUNUfSElmY8gfQs5Tlndky+IjYsJaihJlLNbEPclibtzWuo+S2psaW0YFpQJknjVEp867q8oRx09HBN8hrA1pH7TRgWvX1qbDVCnOCJWqx+rTEREOzJ6wmbq1KkoLCxEIpHA2WefjVNPPRWvvfYaysvLEY1GEYvFUF5ejtdeew0TJkzAxIkTEY/HUVBQgClTpqTrNaTF119/jZUrVyIYDKKpqQmrV6/G008/jaOPPhoTJkxoV6RFR7j77rsRi20rglQX/c3C4TAaGhpa/BARERFRx2KN3HnaWyOzPiYiIiLa+SXs7P0hMnrCZsCAAZg7dy5OOukkNDQ0YO7cuZg7d27K9rZto6CgAK+//joGDBhgsui0yc3Nxcknn4xx48Zh2LBhyM/PR2VlJebPn4+HH34Y1dXVeO2113DKKafg3XffhdvdyY+mAPj4449x//33AwAGDhyIyy+/XDTdHXfcgenTp3dgz4iIiIjop1gjdw6dGpn1MRERERERdWVGT9gAwBFHHIEVK1bg9NNPh8PhgG3brf44HA6cdtpp+OKLLzBmzJh09D0tysvL8cILL+CSSy7B6NGjccABB+CYY47Bbbfdhq+++goHHnggAGD+/Pl46KGHOr1/W7duxRlnnIFYLAbLsvDUU08hNzdXNO2UKVNQX1+f/NmwYUMH95aIiIiIANbIHU23RmZ9TEREREREXZnREzbNBg0ahNmzZ2Pr1q348MMPsWLFCtTUbMsU7tGjB/bbbz+MHTsWffv2Tcfi0qq4uDjl3/r06YM5c+Zg2LBhiEajmDFjBq666qpO61tjYyNOPPFEbNy4EQBw55134uijjxZP7/V64fXumG9qJyzYBnn/OqIGaRmWwV5aUqQ/RgAARAQ5w6nkuPXHoamvFwTFppCI6+fHujz6Wf+50H+9ziKD8X7y9cfrAIBwXH/ZlqX/rGpjVH/fqjGY9oBi/fdEnkt/G9dE9L95XRU2O1V5DMYLyhVkf6cSiOtvp+/qirSnHWQwzkhdWBBkn0J5UP/1+mNm5yRJtngqcYPxc0weV48a7B9hg21swuT1bg15jJbdoHk+DsbNzhGmWCN3DJMaOVV9TF1LRHBcNx3jrlk0oK4FLZf6ZJFTIKuTShrVY11ITk0JwTge9RXqsXB8OerjpCekbuPrp94gLkF5Y/yN0u1IxrCpjKjPLz0F10j9cgPKNr6wenvku9TneJ9wjEWPYHyeYkGJvrJBPYiTW7AsSZu1fvV1cG1EvV2jwqLFLygT8gRlV9xW77mSzxaigjFsnIJrUMm1l3QYx0bBOtrN3k/ZZiPmyRZIRETpuWHTrE+fPjjrrLNw1llnpXO2GTVkyBAcc8wxeOutt7B69Wps2rQJ/fv37/DlhkIhnHLKKfj0008BANdddx2uv/76Dl8uEREREaUXa+T0YY1MRERERCrZOh5MNvaZ0i+dX2DZae2zzz7J38vLyzt8ebFYDGeeeSY+/PBDAMAll1yCu+++u8OXS0REREQkxRqZiIiIiIgovdL6hE0wGMSnn36KLVu2IBAI4NRTT0VhYWE6F5ERltV58WGJRALnn39+cmDaiRMn4pFHHknrMvx+Dxzu9keTlPTRj1JyFWhPinhQf9qoQXQUABT49OPF8vP0p3UKYhdSsQWP/qec1iAqL1xrcP+3Vn/Shhr9+DhA9th5Kn6DyDy/QRSb1yDiy+PQj72TxEqk4jboc2+f2VdMBuZEtKct8hhE/Vn6kTsm36aIGezTDVH96Lpit0n0nPakAGRRDamY7NdVghiVVDY3qeNFUgkZxKmZcBiUQyGDYx4ANGnG5oUEkUGdgTWyuc6okanrkESiNQliPPPi6hrA20N9ErEEh7CE8FwkidQtzFFfR3h96Yl8lETLxYLqFRAqV88o7Fd//OAX1iJ+wbmwKabud1Rw/VPgSs+6liyrf44gWkx46B2cp47fCwiuZyrD6s8OJPVUJKbethHBOurrU6+jgPD8L4k7k2y3LQH1NWmeIKZMUi9JItEkcWfS2iwgqMGClnpfIyIiubTcsNmwYQOmTp2K2bNnIxr934dbhxxySItv3j3++ON45JFHUFRUhHfeeadTL/JMfP3118nfOzrq4Te/+Q1efPFFAMBJJ52EZ599Fg4HH4QiIiIiyjaskdOHNTIREREREXUHxlc5H3/8MQ488EA8//zziEQisG0bdoqv6Zx00kn44osv8MEHH+Cdd94xXXSnWLt2Ld59910AwO67744BAwZ02LKuueYazJw5EwAwbtw4zJ49Gy7BoINERERE1LWwRk4f1shERERERNRdGF3p1NXV4ZRTTkFNTQ369euHm266CUcccQT222+/VtuXlpbi+OOPxz//+U+8+eabOO6440wWb2zu3Lk4/vjjU17wbd26FaeffjoikW2P0l9xxRUd1pdp06bhvvvuAwAcfvjheP311+H16sfptCUnJ4oc6XPU23EX6sfdOPP07w06DeLUCgL6sWQAYNXpv+b8XvoxTCa3Upsq2h931ywS1o/ZMXkKOhjSj2GqETx+3paQQXxUsVs/HqF/jv60gZj+obtGEB/SEcIGsUQFBhGBALB3SY32tMU9A9rTDqzX3zfr/D7taZ0G8XOD8vSjL3Nd+n2uj2bug1dJzEUqW8P6/f6mMVd72lKvflRfrlP//SSJlknFJHouG7FGTp/OrJGp64gKcrpcTnXMa06h+njpzFMfnyyf+viX65LFznrr1TVgfrH6OsJToD6eRxrV/W6sVb+fXIJarKlBfR1RH1DXCtUh2fvb41D3aXCuevtbUO9rjYLauyqkrvuaBPFjkrPlkHxZfTpsYKWyTUJQB1XW5CvbxAXXVJI4wH0EnzlUC65nommsO+oFMeurmtR9KvGo96PeghpPUrs2Cmo2VxpLsxIUpW9mRGliw4ItOqp2LdnYZ0o/o09IHnzwQVRUVKBXr15YvHgxBg8erJxm/PjxeP3117F06VKTRafF7373O0SjUZx++ukYNWoUdt11V+Tk5KCqqgrz5s3DI488gqqqKgDA6NGj8dvf/rbV+ZSVlbX49+eff578/V//+hfWrVuX/Pcee+yB0aNHt2g/Y8YMTJ8+HQAwYMAA/PWvf8XatWvb7Ptee+0Ft1v/Q24iIiIi6hiskbdhjUxERERERNQ+Rjds5s6dC8uycM0114guRAFg3333BQB8//33JotOm02bNmHGjBmYMWNGyjann346Zs6cmfLbfBdeeGHKae+6664W/540adIOF6Mvv/xy8vfy8vId/t6atWvXYtddd1W2IyIiIqLOxRp5G9bIRERERERE7WN0w2b16tUAgCOPPFI8TY8ePQAADQ0NJotOi6eeegrz58/H4sWLsWbNGlRVVaGhoQH5+fkYNGgQDj/8cEyaNAmjRo3KdFeJiIiIKEuwRiYiIiIiIiIdRjdsQqFtg1a0J3bA79+Wj5+TYzbuRDqMGTMGY8aMMZ5PqgFkpebNm2fch/bIKYggV2OYE89A/bFRrEKDrHGnfmZ+TtjsQw93vv7ALL79C/UXHNMfYyD2sf6YG9GQ/rp2efT77PbIMr9b08NgjBEA6F2nfyxqFGZst6YwV3/fsg3G3GgK67+PexXpj28iycdOpbzG4L0EoNeAJu1pc/fUj9XJa9DfN3O+1R+jpK5Bf5/O8+iPvWUytlI4bjBwF8zGRynw6I8nFTbYrwfk6K9rl6V/vPUbbCeTasesUgLcmrtI3HTBmlgjb5NtNTJ1HZG4+jjnFIyH4RugPk47+6jfc1aO+r2cqJfVdj2q1PVB3j7q5Tl65ynbuNbUK9uEmtR1uMenPldK2uTmqc99pT1kdVtFrXpcFZ9L3SefYMyQxqC65s8VzCfHp24jqeMcgn0fAAr3Ur+PLJ96fJbcdbXKNuvXFCvb5AjG/7QFNV0wrq5lJOMFAUCuYCwsybGmWHC67+VV7/+RhLrfMcH4NJKyKSE8RRd71A17ezi2HHU9ti3fz7sSw/KZdhJGn5CUlpYCgDJLenvN2dX9+/c3WTQRERERUZfEGpmIiIiIiIh0GN2wGTlyJADg7bffFrW3bRuPPfYYLMvCEUccYbJoIiIiIqIuiTUyERERERER6TCKRDv33HMxZ84cPPfcc7j66qtxwAEHtNn+2muvxX//+19YloVJkyaZLJoMrFlfgnxX+x9Z3SWkfhQ6FZdXP0rJ1k9+gb/B7NHc3Hz9yBr3Bv0YpkRY/0WHmvQjrwIB/Wk9BrFCRaUG0XND9COrAKC4QP9503h1ndGydTny9A/diUb9mC47or+uEkH9aX25+vFgAGDpp1YhXqe/X8f9+q85EpHFOLQmLyesPW0iof89DpNYMpNoMQCwoT99rsGii9z6cY7SqI7WRBP6xwCT7ZTrNDgGGD7b79J8IzuMw9j0sEYmMlMfV5/L/lvdU9kmf4V6Prnrgso2Dqe6fgo1yWrSaFR9feLZoq6NnX51jRIqVx8DQ2F1vyVtcnPV101Fe6r77Oojux4piam3SbxCvf2dvdTL6xtWX9fZIfX1m+QaL69evR5D9bI6IOEXRKIJ6vtIk7o+zPep++10qvvjiqpfW0BQQ0nrHUk7n0Pdb39c3aeaiHpfCwjizlyCcl1Ss7kdshpJ8toiCYMPbYiIaAdGT9iccsopGDt2LGKxGMaNG4eHHnoIFRUVyb/HYjFs2rQJs2fPxhFHHIEHHngAlmXhtNNOw+GHH27ceSIiIiKiroY1MhEREREREekwesIGAF5++WWMGzcOy5cvx5VXXokrr7wS1o/fgjzwwANbtLVtG4cddhjKyspMF0tERERE1GWxRiYiIiIiyozEjz/ZJhv7TOln9IQNABQXF2Px4sWYMmUKCgsLYdt2qz85OTm4/vrrMW/ePOTl5aWj70REREREXRJrZCIiIiIiImov4ydsAMDj8eD222/H1KlTMX/+fCxbtgwVFRWIx+MoKSnBgQceiPHjx6OoqCgdiyNDsYQDUY1xCmJR/ft7eX31x4Gw9SeFy6c/NgoAuA12WUdJjva0VlD/RRf20x93p8A2GLOnp/7+YbSucs3GsImbjDUU1P/ug2fcEO1p0buH9qSOjVu1p7U31WlPi5j+uooH9cfdAQBPqf6+6RyUrz9tQH/snV4udZ5+KpbBVzFijfrTBkL6Y2BFEvrjuQBAzGAMHEkOeiq9vPrHzD36VGtPGwzqr+stjfo3BCpDPu1pLctsLJmo5jYOxA2KiDRgjUykZ71zjbJNLLGPsk04rL7cLeytvl6QXBM43LJzgkNwCHf1Vte3Vo76tXkj6tfW06Eea9SjHi4IziJ1fxy9C5Rt7CbZeoysU782737qDWftt5tgYeqazv56vbKN06E+lzlE1yayc5t7cK66kU+93fJd6j7lCup1yXW9e5N6fMCaoPrasUkwFg4A0Uh3jYJa8ZA+Vco2OTnqfXtLjfo9UhVSv36Xpe6zVziGTZNoXB2z8SiJiKgloxs2Tz/9NABgr732wsiRI5GXl4cTTjgBJ5xwQlo6R0RERESUbVgjExERERERkQ6jGzaTJ0+GZVl44YUXMHLkyHT1iYiIiIgoa7FGJiIiIiLKHNu2YNvZ9/RXNvaZ0s/ohk1RUREaGhqw5557pqs/1Ak2BHKR6/S2e7qQQWzMXrZ+9IvLrX4sOhWny2y4rrh+2g0cNfpxbA63fnSMyyBVJWGQPBWr11/XjrB+/FPcrz8tANSV60f8OISPkbempOAH7WldQ/Vj3ODUz8uyvAanjCL96DrvYIM3IoBEo/4xJL5ZHROSUkx//4gbLNZhkBIYadLfP3r31s9TGzS0TntaUw2b238+bJZbqL9vOr36+0duVD9uTxLHkUpgU2/tabcaROYBsjiS1gTj+u9/E6yRicwMiO+ibCM5LnxVrc7yGiaIii4uVhfJlvDzFadbXTPHV0kir9Rt3MXqc42nWLAkySk+oe5PvLZB2SZUKatFtm5WX/QMStQr23jj3yvbWKWF6jaSOtmjbuMcpJ6NK6xejwAQr1Rfj1oe9Y4bb1TvswlBaSKJRLMFEaj7771Z2cYpTNyONqiXF2xUF9f5per6SrKOds2vVbZxblS/r2vr1PtsTURWW1WG1euoIZrZCFoiop2NQdI9sNtu2/Jea2vVJxUiIiIiou6ANTIRERERERHpMLphM2HCBNi2jblz56arP0REREREWY01MhEREREREekwikS7+uqr8cQTT+Chhx7CL3/5S4wbNy5d/aIOtHdxPfJd7Y8mcTn0I6/WVRZrT9vDF9ae1uMyezS3pI9+LpHDIO7G218/fs65a7H2tIlq/Uy0+Bb9CDgrR//eccwg7goAPF79fSQW1d9OkS36/XbkqeMdUk5bkqs9baJR/73oKMnXntZ52BDtaQHA2ai/XydWV2pPG1mvv77iguiB1NNqT4poWL8sKOhrEA+Wb5bT6yjS73ev/vrvRTumf+yK1+ovt3JDnva0Pp9+nNrgQv3YO4dVoD0tAPSI661rf8wsUlEXa2QiM5bge4V7F6rr9P4F6hjZ+pA6Hrdpq/raqUB4zZKfp25nck7dnmdvdWyY5VKva+u7OnUbQbSWHVZfHwmS1QAARQXqWORwvfq1OX9Q7yNut7rmt8PqjlsO9TqyduujbOPZRRhRuqlG2ST6bZ2yTUxwSWzH1a8tGlJvD2+eIOqvh7o/DuH1pWeAetvmhAQ1myBasW6lelnhkLqm7ZGvvrYZFFXHuAVj0vpZHR/8tdvoo0WiDpH48SfbZGOfKf2MnrApLCzEu+++i2HDhuEXv/gFLr30UsybNw81NTWwbf0Pq4mIiIiIshVrZCIiIiIiItJhdBvc6fzfNwRs28bjjz+Oxx9/XDStZVmIxTgwGRERERHtXFgjExERERERkQ6jGzY//YYgvzFIRERERN0da2QiIiIiIiLSYXTD5pZbbklXPygL2Lb+GAP+mDpDNZWwX3+ckJIc/XFVAMBdYzDOiMNgDJta/W/W+jZVa08bqtU/JCQS+tNGwgZjwURytKcFAH+k/eM5NTN5T5RXF2pP6/pGP9U0x62/b3k9+imavXfZqL/cXfS3kanQWv0M+WCN/nsiGNB/zRGDsZVMBNbo97kpbLaN8zz626nXAHV2fSrxsP574vv1wvz5VmwK6I9FNThf//WaaIyaZZuHE3rH20A8M+8H1shEZnIt9bWD09Ifk2t7ccHxpSKsHucmLqwLHZb6GsHerJ5XXDBmSFFEPe6hZOyRUJP6GJ6QzCes3q6SNgAQFYwZEq5SnwNcG9V1teNjdX8Kc9X7Y15eg7JN0T7qbeYokK2jRK26Pmpar16PUcG1WpNfPc6JhFNwDR3fqt7XJGNTAcBuA9TXzu5c9T5St1V9Tfrp5lJlm95e9RhXxYLxskIx9TarjchqM0kNxi+mEBGlF2/YEBERERGlEWtkIiIiIqLMSdjbfrJNNvaZ0k98w+bpp58GAJx66qkoLNT/ZjgRERER0c6CNTIRERERERGli/iGzeTJk2FZFg455BDss88+O/y9srISDz30EADg5ptvTl8PKe0aw24k4u2Pn8kziFIq9qgf203FJMIpYRBZBQDVjfqxMzHBY/qpNEX1I+RcG/Vvx+e79WOFJI+vp+Jx6kfPmaxnAKgI6EeqOQx2r1Bcv99hg9e8Z5E6iiEl4WPzrVn82UDtaWuXmEUp1Rj0e3iRfnzU7n1qtKfNK9A/ZhZ59CPzqrfmaU+7rq5Ie1rTuKzNIf1jZmi9/hvZJP0hoH/Yg88g5cvt0D+vFbrTEz+kI6oZiaY7XXuwRiZKv5Ctrv/rBLG2eWH1ccvtUJ83++YElW28wmuW+qA6qqkhpI6XaoqqX3+sUn0MzHWp+53nSc/xX1Lz1wpeOwCE4uraQRJT1ySIjpLEVOUK4lnXb+2hbLNhjfo87Rf0GZB9S3t0/wplm/wCdcx4777qmjkSVPf7+80lyjYNEXXdVymM211cpa5fJftRVFB+hwS132556nW0CwSRiYI+S2McJeVulI8EEBGlldknndupqKjAtGnTMH369HTNkoiIiIgoq7FGJiIiIiIiIimzr7QSEREREREREREREXURNmRPiHU12dhnSj/esOmG9hlWgUJP+yPRXAX6y4zU6EeSOLz6h6u6TerIgbb4cvTj2CyDiLAtFfoZ+BHhI/KtsQ0i5Hwu/agEr0d/PYcN4q4AWQxEKv6YfgxTIK6/naSPr7cmahDFZhJd57L0Y7oCBn0GAH/cYH0ZxM+5vQbryyDWzGlwzMzN1Y9FLAjoTyuJI2lLQ0x/G9dF9Kd1GqRtmUQq+pz627g0Rx1rkkoPQSRQKrYgvqMtMbv9dQsAJKD/XiKizOnjUUfW7t9vk7JNr73Ux7xYvaBDgnIg2iSrGfwN6uNZfrH6nNpQo77OqWlSr8e4re63ZanPO7ne9MSm+YT1ZlhQSwcFbSQxxZGEej6S6yhJ/F5EUHs2xWT7WkgQC+pyqde3J0fdxuFW7yMeqOdTKIifiwi2WVgYiVoRSk/ojKSuswQfwxYItkfffHX8XDimvkYOxWUf8DQK6vStMf0YaSIi2lHaItGIiIiIiIiIiIiIiIhID2/YEBERERERERERERERZRgj0YiIiIiIiIiIiIhop5CwgYRBnHymJDiIDYE3bLqlnOH5yMlpfxa8laeXHw8AjnWScOgUy3XpH2ALguoM3La48vSPlA794U1Q2KQ/xkAobLBgA3GDsT5MxqGpDZqNU7SmKU972qDB2CiSTOlUTB6NLA6qs8xTKTVYrolSw0z0hqj+GvML8p9TsQ22cSyi3+dAg36fK+vytad1CXLZU3ELsvHbEtYfLshoZJW4QbfdBuOcmRTxeW7991NBof65qTDs1Z4WABqjevt1wuYYNkTZqF+uesyE3vuo63z38N7KNq6tjeoORdUnGle17PhqJ9Tj0/j6qI9dlqUeVywouC5wC8aMiQjqoWhUvc0kYwNuDOQq2wBAjeD6ISgY6yQmOKcmBOOoFbjV5znJB4fFgvN0THhqawip11FMMD6JZHyeYL16Wf4m9ToKC873kjFIIwZ1+E9J6i7JGDZeQXkvGS+qIF997PNF1fuRW3gdnO9S73D93PrXEEREtKN2X/3+v//3/1BauuPHdhUVFcnf//KXv4jmdfPNN7d38UREREREXQ5rZCIiIiIiIjLV7hs2Dz30UMq/Wda2rxVMnz5dNC9ejBIRERHRzoA1MhEREREREZlq1w0b205fkF7zhSt1PivHDSunc2OzIlv082rC9erHtFMuN6Qf4wYARYLHjVOJNukvt7ZJFgXQmsaI/raVxBSkUpJjEOMmeBQ/lRrDmJ0Cl/6+6bQMorbC+rFVTeoUgJQkj+unXG5U//1UYbCdAjGTEDigJqL/ogvd+tspGtHfr11u/f2yya+/rv2asVMA4HHqR0+FDI49AFDk1q9P3AaLNimLogbTeg3i1Hr28GtPWzhEf790eWq1pwWA8u/0z4udgTUyUXrVhNXvKUeh4JzlEhzkBXFnTV+r2wQbZeffvGJ1JFpCUFY31amXVxtSRwdL6vB8QUxXrkfdpkYQzSuJ3wKAIkGtJInFahTUmbZbPZ9wXL0e6yLqWrpeEC1XKYyTrhPUwAHBtWNhVPD6BfFr9UH1PhsXbP9QXL0slzBuN88lONYIdsmA4PrMKTgc9RC8jwoHq48hEr3rZdfvjYJIxP55gs8gqkSLIyIitOOGzYcfftiR/SAiIiIiyjqskYmIiIiIuhb7x59sk419pvQT37AZM2ZMR/aDiIiIiCjrsEYmIiIiIiKidDHLICEiIiIiIiIiIiIiIiJj+mH1lLUi39Yi4jUb26W9Grfqj6lQ0Ed/HBmnwbgGAGAZvENiQf37oS6n/jgBPXL0p5XkSqdSG9LfxiZj52wMmu3LTQbjo4T0V7UowzgVn/7QKOjlC2pPmyfIVE6lwmA8qS2CTOy2RAX55R0xbfFg/WOXq0R/I3t/aNBf7ib9cWiCBuPfJAyf+05AfzsNytEfFKpOkDOfypaQwZhhHv0+55bov4/dexRrT1vQI6A9LQCUbtAbJ60ppv8+JKLMkQw9E/hWfSzMCVUq2/jXqJcluabIL5EdbyTXF7agxmwKqGvvkhx13dcgGGdQMsZdQDDORVVYXQ+WB2XjcYYFNVpEUNZ4BPtaiaAG7pOvHiPOG1Rv2NX+YmWbOuEQJnHB+GolRep+F+6tXla+X32edq9Sv/7GJvW4S4mgetvXRmV1Vh+veidxCsbDWScYw0gwFBJyXOrjmnuw+prdUaB+Xw+pqVZ3CEDNt+p59fB07hjJREQ7O96wISIiIiIiIiIiIqKdQsI2/2JgJmRjnyn9GIlGRERERERERERERESUYXzCphsKVTjgkTz7nUZuj352lLtYP+rGjunH+wBAsFJ/Pdm2fr/jBhFhsbj+tPlu4fP1rTB5vSZxRsOcZtt4dWOu9rQ/CB+1b5VBt00i0Uy2k0l0XcRg2mr93RIA4DXYTJsNYqts/dQqWAX6EXI5u+ivsBI0aU8bbtQvKWqD6viLtpjsX0Vu/Q3lNzjeuh36X50ymTbSoN/n3B552tM6DKYFgKF7lmtN1xCJAAuNFk1EXVRDjfrc4XCrI8H8Der5lOyujnuSRik3rVc3dPvU1045XnVMV7WgznVAfU4p9KhrC0mNmSuJe3LIIppXNaljmkLx9MSmSUiu38KC2KzaiLrPobisDnBZ6nkFAuqas8SljvtzlqojsYpC6veRt1qwj9Sq3x+lDfnKNgDQSxAzGxJsW7el3rYeQf0miR+0PIKLwX7Fyib5h8hicovXqd//Tkv/mpqIiHbEJ2yIiIiIiIiIiIiIiIgyjDdsiIiIiIiIiIiIiIiIMoyRaN3QR98ORK5T/Qj5TzXF9O/vDS3Uj9kZFKvXnjYSMciOAuBy6T8jHzNYX7Wh9m+fZpKogVRM1pZJrJnbob+e/YYDsoUSBtF1BvFiEYOR5Bz6izWKNcu19PtsEjtleqoyWdcxg/0rrk6PSMky2MiJNEV7tJfDqb+yfC792EzA7D0RNnhPuA2W28Ojv76qI/rviYZ6WdRMa4pdBmeJHP3zGgB4++ptJ2+Y300iykY1YXXdsGBDX2Wb/M3qk2JYUAuOTFQo23i9slpHcn0SF1xH1PnVx/NATH2+yHOrY5HitiASSlDPJwTrWhpzGhGUDlFBTRS31efjgEEE6vYk12k5gnrKLyyrJWVKTUC9Hw12CIpaQTGWECRwSepJl1O98QuEtWVUcD0nueYr8ap3Nsl+tFbwvv55okHZBjnqqEerhyzGrHeBX9nG5ywWzYuoMyVglESfMdnYZ0o/XsUSERERERERERERERFlGG/YEBERERERERERERERZRhv2BAREREREREREREREWUYx7Dphg7oV4ECt6fd0zUF9PPna4LqDNVUJFnHqdgGY4wAQG2jftZ/JKaf9e80GCskYfCaXQZjyeQYjJ1TkKM/2Ickw7gtniZZdm9rTMbN8BhMvG+h/vrqmRvUntaRof2y0K09KQCzcUZMxrAJ1Lb/ONvMs1V/O9lR/U57exusrK36x49qg3G7ACBqcJ6oj+qXQibLNRkDa1iB/v4RCuu/oRIrN2tPa/XQP58CQKxeb/+KhZkCTZSNdhec/EeUblW2yc2JKNusquipbCMZm9IjPO37g+pznl9wmG+MqNeRV1AnS8an8Qjmk+9Vr2unU31MrgvLVmQooW6X61LXRJI6cbd89RgeOV71AC0BwTaTXCL0EO5rDqhff01YvT9Gt6pfmyNHfS3oKlC/OIdX3efQFvV6DAnHHZKMYdMoeP9LllYgGB93/x7q8XuDXwn2R88mZRu7LqRsAwDxuHqHM7kuJuootr3tJ9tkY58p/fiEDRERERERERERERERUYbxhg0REREREREREREREVGGMRKtG/L6YvC623+vziSa7IeGAu1pK+vytacNxMx2cbdBRJjT0p92QHGj9rQ5PvUj66lEo/r3cOv9+nE3dQbTmsbeFbn1I9UStn7sXSSh/5yrSZRSQb5+nJrbqx9717MpT3vaApdZXFaTfrcFQRKpBQL6kWglRfrLtXz6xz07pL+yvLb+sadkjf5+CQClOfoRYeV+/X2zKqK/rk3exwaHD9QJok9S2bxIfzvlFzVpTwsA1ZV6dURjTB3RQ0Rdj+QI6RNEUHl96vOapN6vDapr1QZhvKc/qo5zKhTEixX71MfkXsXq6CTJNV4wqO5zvSDqzS2IRPMK2gCAz6E+GTbF1K/NKYhN87rV+1FhT3W8lCWIFy6oLla2kbwuAPALyrr1gthzS3CJ6OqnjpmW1JmOsPrarLBQva73CTco2wBAUPB5wVpBhHYkoV5JPsG+HYqr+7P6mxJlm8ENtco2sajsWnZTg/rzmHVmZR4REf0Eb9gQERERERERERER0U7BhoWE6CsgXYudhX2m9GMkGhERERERERERERERUYbxhg0REREREREREREREVGGMRKtG1qzuSfyNMaECMX0x+uoCqtzj1OJCvJgU9kU0l8uAPT06I9vUuDSHwvC49JfriTPuyN4DV5vJK6/b5mOYWMZjFJS4NaftjFqMCZUQH+/rq1XZzCnYrJvVYZ82tNahk8E+/R3L1SoI7JT+qa6h/a0Rf8t157W10N/zI6Yweutr9Afi6oxYnaszjU4/oQNxmerjhiMY6V/mEckof9+8gnGGEjFNhg7p3ckoD8xgLqg3muW5vwTUdfydZ16fJaVFepxHHp61fMpF4xPk+NUH7RDcdk1S6Pgmqq/YJybfMG4KkVR9RhvLpd6XA2HU30C8Blcv2xPOg5onmDsmajgvOUXnCcq/Or6uaBWva9VN6rnExP02SU8tTkF7db61fvjlm/U4/318qvHS5KMhROqVfdn/VZ1jR2zZe/HsOA6tE4w1kujYD/yOtR9CsXV48UUCMZgzalUHx+k19ENguPRJsmASUREJMYnbIiIiIiIiIiIiIiIiDKMT9gQERERERERERER0U7Bts0SAjIlG/tM6ccbNt3QV/X5yHG2PxLFbZAqInkcPfW0+guOG8ZllXj0o4UKPPrxUesbCrSnrdWMjQHMogx8BpFECYPtVBHQj2ECgA1Bj/a0YYPkB7fDIIpNEAGRyvpG9WP2qTiatCfF1rD+6abRMOUvGNffvxyWwboO6EdPHVksiwNpjXuAQbxYuf7KlsR8pOIVRM20ZYsgziaVuqj+vmnymHLAYL/sYRDlmGdwnG8URGKkkhPWP9YCQF1E7/3EhA6i7PSZvUTZZnndGGWb/j71sccviDLLEWRLRYQRm8WCOKPeOeqMUr/g/LWqUh0blyOo4SXRoy7BuTwgiEBd06SO3wJkEVSSSDBJtFqd4Pz3fZU6pqtG8PpDgvqgLpK+uM+gIINNUg97+6tjw2zBsmo3qteR5NpR8v4AgErD+qQ9AoISrK9PfQ2QEJSBjWl8XZtD6nk1xtWRgEREJMdINCIiIiIiIiIiIiIiogzjDRsiIiIiIiIiIiIiIqIMYyRaN9TPF0Wus/336hpj6secU6kL6e9qlks/GshlEGcEAG6H/rIzlTsZSehvJ7/BdqqL6kdt7Zrn155WEtHQFrfBPuKw9OMIcvU3E0LCyI3WSCIdUskziK3yx/S/H2ASaQYAhW79bdzTo38M6OnRX1+2/mKRaNB/T4Tr9beT0yDmzyQWEQCcBu9jSfRIKnkG56eEwXdmag2iUBpi+seAIbKUmlb1NtzGkviPdE5HRJk11D5I2WaXXPX5rkAQA1njV0cuWmn8nmO+QTRle0UFNaMluMZb71fHnvb2qqOkCwVx05JtBgBxW93vHEG97RXUL5WCaF+v4LoxJIjfaxJEvUkNylX3SRJd7nQKYroC6jZxv3phtqBeSEDdxiFcjZL3iGReRYLrjaqwekabBNfjccE2G5Sn3tccwohdyfLynPxokbqexI8/2SYb+0zpxydsiIiIiIiIiIiIiIiIMow3bIiIiIiIiIiIiIiIiDKMN2yIiIiIiIiIiIiIiIgyjEGT3dDmkBs5zvZn2JuMQdFoMMxIQJD1m8ouuWYZ0Sbjo4TiBmPJGEwbSuivr4DBNq4QZDunUh8t0J62wGAMCQAocutP3z9Hf/+KG4znUBPR3z9MxnOoj+ovd0tIf7lxwwGhSgzGoSn1GhwDDN6LFev0x4TKyYlqT1tVrz9IyRc1xdrT1kTMvj9SYzCmiy3IQe8IdZHMDK7iMxg/y+fQP84Xu9VjRLSlPqq37EC888aKIKL0CUM91snWsLp+bBTUtg2C02ZQMG6hdGjDvQXXF05LXbv4Y+rjYlB0HaU+MUhqz80hwTg3Ho+yjfS8PKxAveEk49NIxmmVjKcYEFy/Sa6X/DF1nwuFl/K9vep1FBHUq1vq1O81x7fqfkcF1xLfVfVUtlnd5FO2aRSOBVQvqCG9TvVrCwiW1yQYMCgnTZ/QVYbUdVeOYGwiAKgOq/eR6lijaF5EnSlhZ+d4ltnYZ0o/PmFDRERERERERERERESUYbxhQ0RERERERERERERElGGMROuGqsIWfM7236uTPOabSm+f/jN9JmlIksfX29IYbX90XDOTSLQ1fv3lRg0SwnJNonIEj4p3BJNoMQCwoN/vaEJ/2ZL4gVRM4glN1pdHECuRis+pv9ywYaLRlpD++qqLqqM7UjF5T9QH1VEPHSFmEEFp8n4IG0wLAH6D2E1J9EgqBrs1ggbLLfbqLzhk8H4yiZ6rMzifAvrxqLI4ICLqauodNco2laFeyjaSax7Jka3QLYnWkh0jA4IoM4dgVlVh9XF1XUBd3OcI6n9JTZMriFeS1L8eh+xiRlLThgT1RZOgrvYLrindgjpZEpvnEjSSXu+t8avr2IhgXr286nitQr+6TUKwzST1pKSE8osj0dKz3SKCDCNPmkqSoKDurRYcHwrcsqJQkOSGNY6vRPMiIiIZXsUSERERERERERERERFlGJ+wISIiIiIiIiIiIqKdgv3jT7bJxj5T+vEJGyIiIiIiIiIiIiIiogzjEzbdUNze9tNeklzeVCSZxqlIsm5TMR3Dxi3MUG6N08rMfXGTu7DFbv3XOyQ/oD1t1GA8lxX1udrTAkB5UH/aHIMBLII6b8IfmYzp4jXoc5Hb7P2kS5Lj3haT44DJ2CgDcvW3scelv5ELCkPa07pcBsfq2iLtaU3HwOrtMxibyWBclpjBIGtm40npT5tnUPlJct5TMT0n9hDmrP+U12E4CBYRZUTUCivbGJRSLeQKjou5gvOUdAwbSbdzXOoCJE9QK4Tj6hcnqf93z1Nvj0J3VNlmQyBH2ebzOtmJyikZWERAMpadYHgSNHjV/XYJzoWSUl2679ca1Djbc1nqfSQ3J6Js4/ao99liwTiOdpN6vJwSj6ymtQSjWEUE4+pI9iPJFW+BYMi/mOClScadyXPKaqS+PvVAV4MTeyvbbMD7ouURERGfsCEiIiIiIiIiIiIiIso4PmFDRERERERERERERDuFhC17QrKrycY+U/rxhk03VBMBvBrPVnkMnseqDutP7DJYbr7L7EhXH/FoT9sYUz86nErAIIapWL/LKPboLzgU13+9NRHBs98prGrUnhSAWTyQiXyD5Rbpry5sNYiACxnsl7vk678X+/nMIo02h/T3za366WLYHNQ/eFUKIkNSMYmRrA+rIyZSaYzpv16H4dCKJoEfJkuO6ifIQZAukVJt2CBuz2C5eS79NW0SAQfoR5RmKp6UiMzURNcq21SG9lW2iXvTE3fkEARTSGOqgnH1vCoEsVCbQuqCUBJp3VMQHSU5gtcJrpvW+dUFsF+drAYA6CEoWSQfevVVr2rUCfpUIzg3ewXn4H0L1XWvIH0LALDWr15ggyDudEtIvbKL6vKVbSzBOXmjoAZOGFV+LUki6EKCN7fks5Kg4Fpqi+BaTRKbFhUc16Q8guNIg6M+bcsjIiJGohEREREREREREREREWUcb9gQERERERERERERERFlGCPRuiGPwyzeTEcgrv9IbsIghqk2YvYocLFb/y3iMFh0rsE70zZIf6kM6y/YH9PPYgsYJF6ZRprVC2IAUnEKYjZS8RnEmrkN3r9Og2nrDGKYmgzisqoiBhlOAPwGxxATkoiFVFbU52pPm+fXj1MziTWrCOm/4IhBtBhgFk3WZDCxyTY2ySaWRHOk0sOhv43jBieYDQGDgx6AEo/eiSIYN4tUJKLMqG78XNnGWfhLZRu3oCCXHFMbBbVEgzDKqyKsPh6WetUz8wpiinyCE5XkOq3er47E8gvmExLENEnPNQ1RdbsegssTSfx2jqAUrQiq+2ML4kHrouoOCV66WI7gWkoSZdcUK1K2kcSjVobVbSTvNXFEoeC9LTlGSN5rjYKaUzKfhCCidpMgmjnXKbt+lxwj8hMFonkREZEMb9gQERERERERERER0U7Bts2+0Jwp2dhnSj9GohEREREREREREREREWUYb9gQERERERERERERERFlGCPRSCxXkJWaSo5T/5k+k8cBw4bjIngc+jOIJPTvhxqNbWDwmi1Lv8+lXv2BQnoJcrpTqYmYjYvwaa3+YdBkLBmf2bAs2krUEeQp5UlCvlMwOQY4YPZMsNPSP3Z5DQajMnkfm+xbJt/EMBkLxmR8E5fBNgKAhKW/7IF5+mus0SBEfmtAf2XnGJyPTcYL8hqNu6M/LQDUa46vFDQYQ4+IujbJGBWS86lkbE/JGCYh4fHGJ7i+kBzmo4LxYCSn5nrBmCkFLvWM9ikIK9s4BOfrrxp8yjYAsDWkbiOptyXr2ivYRwbnqxtJakOXYP+ISfc1Qf0tqf08aVqPkvdsRLBfy8YQlBUtPQRjklpQt9kSVK9IyZhaTkEbybg7PsE1X2NMth9Jxp7yQn88WyIi2hFv2BARERERERERERHRTiHx40+2ycY+U/oxEo2IiIiIiIiIiIiIiLqFyZMnw7IsWJaFsrKyTHenBT5h0w35nJZWzE/M4DZvg+Ax2lRkjzy3zuMwi1KqDOvHbZksua9PPzvGICkHBS795e7Vo0572sJ8QaZBCjUNudrTAsCmUC/taaMGmVcmcVkm78Vit/6CnR79aSvC+t8PMIlwAmSRIR2h2GB95Tj1X7TJMcBkWknMRSom0WIAkGdQzZi8nzwGkXmFkgyeFGrD+sdqk7dTzCDq0+sw+46Q7i4ijSgioq6lR/6+yjb5bkmUkfrgERQcUuN2umKaZBGPUVt9YpMsb9c8dXaS5LXtWdCkbNO/R4OyTUJ0HikRtAEaojnKNl7BtaAkslMSCVbsUZ9hJRFUVYKaWbJfA7IauEBwbZAniFbzCWrXmK1+bW7BNqsSXDpKk5wlsYmStV3kEWxbQXZ5jWCH9Dol61HdRlrHNgjSywNWUDQvIuo6ysrKcOGFF7ZrmosvvhgzZ84Ut3///ffx1FNPYcmSJSgvL4fX68XAgQNx3HHH4eKLL8awYcPa2+1OJ1lPXq8XRUVFGDp0KEaPHo1JkyYZvzY+YUNEREREREREREREREYaGhpw1llnYfz48XjmmWewatUqBAIB1NbWYsWKFfjb3/6Gn/3sZ7jjjjsy3dW0CIfDqKiowMKFC3HnnXdi3333xe9+9ztEo/rjdfMJGyIiIiIiIiIiIiLaKSRss2STTMlEn4cNG4Zx48Yp2x1++OHKNtFoFBMmTMAHH3yQ/H/Dhw/HQQcdhFAohAULFmDz5s2IRqOYOnUqotEobr75ZqP+d5aCggJccMEFO/z/QCCAtWvXYvHixQiHw0gkEvj73/+OyspKvPjii1rL4g2bbsgCoJPiEjE4akgeNU/F5DEwyePrbSnST0SDQdoNBuaEtact8kS0p5U8pp5KZUA/mmxtfaH2tP642UZuiOrH5ZhEKRUZxGWZ7Nd9DOL2PA79F1wX9WhPaxpptL5Jv9+FgniDVPINzrC1Ef2N7BPEVqRiEpeVyeCpiME5psEgc89vchAwYBmsbJ9BjJvJy62NmF15eDVPT2GO2kmUlXq4d1O2kdTatuDsFBJkekpicG3hYS5XcIqXHKt7eNQnv13z/JIuKSUE6/HbCnWUWUhQt28IympGyXZrUifCieq1HEFt1T9HfQ22NaS+uPTH1OuoXni5Vy2IUB1SoH4jSaLc4rbhhXdyPupluQSxadKapUFQn9SZ5jP/KCY4juS61OtRMp+AoDZOCD9ykHwM5LYNPjghoowbOXIk/v73v6dlXrfeemvyZo3P58OTTz6Js846K/n3SCSCP//5z7j77rsBANOmTcOYMWMwZsyYtCy/I/Xs2bPN9VRdXY3LLrsMc+bMAQDMmjULF1xwAU444YR2L4uRaEREREREREREREREpKWiogL33ntv8t/3339/i5s1AODxePDXv/4VEydOBADYto0pU6Z0aj87SklJCV588UXsu+//xmJ87rnntObFGzZERERERERERERERKTlqaeegt+/7eneoUOH4tJLL03Z9q9//Sscjm23JRYvXozly5d3Sh87mtPpxJlnnpn899dff601H96wISIiIiIiIiIiIiIiLa+99lry98mTJ8NqI8t78ODBOProo5P/fvXVV9Pel1NOOQUDBgyA1+vFwIEDccwxx+CZZ55BLCbITDXQr1+/5O/NN7Dai2PYdEP5LltzjAP93PvqkH7uq8epv9w+OdqTApBl6KayJaS/3J4e/QxYr1N/XddH9JdbH9XPLY4k9O8d18fM7jtL8q9TadAfLgixDA1+57b0t1OBS39dG7yN4RbkVLfFZBwag+E+UBfVn3bfHP1BWYrc+tNuDeuXBZsNvgOSZ1iNNEWzbzRJSf54ymmlAyW0ulz9nTrH4BhgOspRneYYOCbj7xFR1yY5d+S51MeASkHN7hfUi31yZMfIHEGfaiPqY2Ykoa7penvVtb0FdX8qwl5lm6jg/NIkqNtrBK8dAPyCz1ok45j4BeOz5Ar2Na9Dva5DgnXkEdS9XmFhXehWr+8GQb0qGde1f456RpJ9pDKsXphXcDkjvcST1AlOweCBgZjBgIrbqY+od+w8wTg3tqBWlGwPANjoV1/0rrF2jm/G087F/vEn22RjnwEgFAphyZIlyX8fddRRymnGjh2L9957DwDwwQcf4C9/+YtxP5qamjBx4kS89dZbLf5/eXk5ysvL8d577+HRRx/F7NmzjZeVypYtW5K/9+3bV2sevGFDRERERERERERERNTN1NXVYfbs2fjqq69QX1+PwsJC9O/fH6NGjcJ+++3X5pMyzb799lskEtu+KWFZFg488EDlNAcddFDy95UrV+q/gB9Fo1GceOKJ+M9//pP8f3379sWRRx6JgoICrF69GgsXLsTChQsxYcIEDBkyxHiZP5VIJDBnzpzkv4844git+fCGDRERERERERERERFRN/P666/j9ddfb/Vve+65J2644QZcdNFFbd64+fbbb5O/l5aWwufzKZc7ePDg5O81NTWorKxE796929Hzlu68887kzRrLsnDbbbfhhhtugNP5vycTv/vuO5x55plYsmQJPvvsM+1ltaaurg5XXnklvvjiCwBAQUEBrrjiCq158YZNN7QhAHg1Uk1MYkVqw/qPCDfG9XOF6gWP77elVBht0BrBDeiUXt2gv9yBefna05rEEtkGcTcBg3ywJoPYKdNlm0QamcSpmexbkbjJPq2/4M0B/WOAz2kWeydIg0jJJE2pLqw/7QCDY49+KCLQaBAxWBnUX1m24YPfteGOzaBNRRKRkUoood9np6W/ndwu/QN9dVj/gFthEI0KAHlOvcjOaMJsuUSUGVtDXyrb/NB0gLJNsSAWVXKd4hfEHVUI45AH5HqUbfIFh7zvG9VtvmvIVbbp4VWvI0k8bUAQLSa5nqwOpS8MplZwDpCcy/MExaRfUEPVhiXrUd1nnzASzSnI9q0W1OiFbnUEV11EXV9ILrvqBddIDYIo3IaI7NojEFe3cwvqroSglg0l1MsqENQ7obh6H9kaUa9IybIAQJLEa0E/dpuIWtfQ0NDi316vF16v2WecOlatWoVLLrkEr732Gl588UXk5eW12q66ujr5e58+fUTz/mlcWE1NjfYNm/r6etx5553Jf99yyy2YOnXqDu2GDh2Kd999F/vvvz82b97crmXU1NTgyiuv3OH/h0IhrFu3DosWLUIotK0g7N+/P+bOnYsBAwa085Vswxs2RERERERERERERLRTSNhmX8DMlOY+Dxo0qMX/v+WWWzBt2rS0Lmvw4MH41a9+hXHjxmG//fZD7969EY/HsXHjRrz//vt48MEH8c033wAA3njjDZxzzjl49dVX4XDseCO7qakp+XtOjmxA8Z+2234e7fX8888jEAgAAAYOHIgpU6akbNu7d29Mnz4dl156abuW0djYiH/84x9ttnE4HLjqqqtw++23IzdX/cWZlPPRnpKIiIiIiIiIiIiIiNJmw4YNqK+vT/60dQNCx6mnnoq1a9fib3/7G44//ngMHDgQXq8Xubm5GDp0KC6//HL897//xYUXXpic5p///Ceef/75VufX/GQJAHg86qeJAezwxFAwGNR4Jdt8+OGHyd8nTpyo7MNZZ50l7md7JBIJ3H///TjkkEOwcOFC7fnwhg0RERERERERERERURdQWFjY4ifdcWjFxcWtPimzPY/Hg5kzZ+KII45I/r+77rqr1bbbj1kTEcQyAkA43DJHXvpkTmuWL1+e/H3UqFHK9gUFBRg+fHi7lrHLLrvAtu0dfiKRCDZu3IiXX345ua5WrlyJsWPH4s0332zfC/kRI9G6odWNIbis9j8XKMluTaU+oX+X1Gfp3/GMSgJX22CSfF9nkL+8Jay/vmoj+vmxhW79de0RZCSnYjIWjCTDty1Vcb/2tD0c+o83mozLUhPVHxxlZVgQeJ5Cjq0eNC6VIof+iTcSN8tENhkHy2kwNlOxR2/MDQCwDI63LoMxjky+xWEyDo0/avY+bkjovyfqHfXa0xbFi7SnDUN/IKtKR7n2tDmxQu1pI5b+uWl3ezftaQFZDn9rEgbvYSLKHH9wnbLNF96tyjYDwj2VbRrj6uOxZHyKXEv/vP9TQcEwZ1uD6vpGcg1W4lLXs3kudS0mueyKCxptjgTUMwLgttR9KnKqr23itroG+TZcpWzjtNX9yYH6w7ZebnW9XSscn0Xy2iTj4tmCj40swecLDsE5WTI8YEgwGE5jTFb/10Idv2Ml1OtIsm0rHBXKNvGEut9OqI81Bba6Ru0h/JwlJjj+xWyDwTuJyMjHH3+MZ555ps02559/PkaOHKk1f4fDgVtuuQXjx48HAHz55ZfYuHEjBg4c2KJdfv7/xtOWPinz03bbz6O9Kisrk78PHjxYNM3gwYPx2WefaS+zmdvtxoABA3DaaadhwoQJuPjii/Hkk08iFoth0qRJ+O6779Czp7om3R5v2BARERERERERERHRTsGGBTsLv7zV3j6vXLlSOa7KIYccon3DBgCOPPJIuN1uRKPR5DJ/esOmpKQk+fvWreov1QDAli1bWvy7vTc1trf9+DfSsWPy8vK0l5eKZVm49957MWvWLAQCAVRXV+OJJ57Adddd1675MBKNiIiIiIiIiIiIiIhacLvd6NWrV/LfVVU7Pnm61157JX+vqKhoMaZNKuvXr0/+3rNnT/Tu3Vu7j9s/nRMIyJ7W9fv1E3faUlxc3OIG2fvvv9/uefAJm25oq1UFp0bMWG5C/85jwKH/Jqiw9Kd1RHfVnhYAENCPCKuL6sfd1FsN2tM2GcT75MYKtKeNQZDdkEKOrR8tFrRkB+JUKrFae1rL1r/nHY/qry8ThY6+2tNW2eoTbsppoR8dlRDEObQ5vUMWG9GawoT+Nzz8Yf1j5saA/vqqE8SWpFJlkGawMqSOC0kl19aPzAOAzc6N2tPWxjdoT1vnKFE3SqEpro7FSCUU0j/OG8XtOfTPiY1O/dcLAK6YXmZzwtaPRCSirq3CWqds446ro4MilvrkFxRcj+Tasjo6P9RL2SYqiAuuTahr4M3O9co2DYJaJxFR12IuW/3RglPw8cMWxxplGwCI2urXb8fU/ZZEOeU71R8guRzqKLO44JxUEe+hbBOArA7Is9TzklyHbfSrz/9OQURdOK7+1vgPjep1tDWmjjGLQVb/r7aXKttItr9kP/JHK5VtonF1jJDbqa6bXYI46s1xWR0bSqg/m6io/1g0LyJKv8mTJ2Py5Mkdvpztb2609mTKXnvtBYfDgUQiAdu28fnnn+Owww5rc57bx5HtvffeRv3r3bs36uu3nR/Xr1+PESNGKKfZsEH/swCVfv36JX//4Ycf2j09n7AhIiIiIiIiIiIiIqIW1qxZg4aG/9287d+//w5tfD5fixs08+bNU853/vz5yd+PPvpooz4eeOCByd+XLFmibN/U1IQvv/zSaJlt2f4pH4ej/bdfeMOGiIiIiIiIiIiIiIhaeOKJJ5K/FxUV4YADDmi13amnnpr8vaysrM15btiwoUVU2PbT6hg7dmzy91mzZiXH20ll1qxZCIcNIkYUli9fnvx9wIAB7Z6ekWjdUAQhOIWPCG/P76zTX6bgkfVUGsL6UTdVDv24KwDItfTjbmyNddwsYqsfhU4lFtVf14mEfkxXKKYf0VPoG6Q9bSxuFonWGCzXX3asVntat1t/33I59SPkGmz9Rz6jBts4HjeINhQ80t+WwpxdtaetdxVqTzvIGq497eJq7UmNlDvb/6hus5q4LMKkNQ5LHVnTluraz7WntQyW3eTpoz2tybErFm/UnjaR0C9KTbZTnsH7ENCPY7Nt/XMxEXVtTqiPSXUOdVxnCOp4pWBCXfNtiMqiQTehn7KNQ/K9SsGYxIGouqCohfrcH4zWKNt4XepIOK9TXVfVB2W1ajgiGNDYUn/c4RXU5H6oYz3jCXUcdlQQiWVDHYeX6xusbAPItkmpRx1Bs9Kvfq+t9qvXdZMgWnBt/BNlG6dDHZPaFN6sbAMAgZA6NrDJu+M3yX/KErxnI4Jrx0RC/TmALYiL9gn6HHPLPnOICfZtoq7IBiBIGO1yOqPLTU1NLcZ8actHH32Ee+65J/nvs846Cy5X68f8SZMmYfr06fD7/fj2228xc+ZMXHLJJa22veGGGxCPb7tWGzVqFA466KB2voqWzjnnHFx33XUIBALYsGED7rrrLvz5z39utW11dTVuvvlmo+W15ZVXXmkRgzZu3Lh2z4NP2BARERERERERERER7eTmzJmDQw89FE8//XRy3JefCoVCePDBBzF+/HiEQtvGMy4uLsYtt9yScr6lpaW45pprkv++6qqr8NJLL7VoE41GceONN+KFF15I/r877rjD5OUA2Pbkz/XXX5/8980334y77roreVOo2apVq3DMMcdg06ZN8Hj0x2htjW3beOmll3DhhRcm/19xcTEuuuiids+LT9gQEREREREREREREXUDn3zyCSZNmgSXy4Vhw4Zh2LBh6NGjB+LxOMrLy7F48eIW49bk5OTg9ddfR79+bT8pfNNNN2HRokX44IMPEAwGMXHiRNx222046KCDEAqF8J///AebN//vKcjp06djzJgxaXlNU6ZMwbvvvotFixbBtm3ceOONeOCBBzBmzBjk5+dj9erVWLBgAeLxOEaOHIndd98dzz//vHj+NTU1uPLKK3f4/9FoFJWVlVi6dCnKy/+X4uN0OjFz5kz06tWr3a+FN2yIiIiIiIiIiIiIiLqRWCyGL7/8El9++WXKNoceeijKysqw997qGE23241XXnkFl156afLpmhUrVmDFihU7tJs2bRqmTp1q9gK24/F48NZbb2HixIn417/+BQDYvHkzXnzxxRbtDj/8cMyZMwdTpkxp1/wbGxvxj3/8Q9R20KBBeOyxx3Dccce1axnNeMOmG4pZYSQsde7pT9XF9Me+qPXrjyVjC3JcU7EMx75osNSZth0hbjI+gd32wFptsQQ5zx2hSpCvnYrJeAyZFBHmnLc+bRo7kgUkWc5tqfOv1J62tGik9rS5tv5YQzUO/fGRtia+0542EFRnq6diWU7taRO2/jEPkGV5p55W/xgiyT3fmZicXxoDq9LYk/bIwuBqIhKJQ31MkoxPU960TNkmFpeM4ydLGw9H1ed4yXnNhrpNLFYn6ZKSZAyzkGDMEMn4LLatP6bmjjNT7yNBg/FSM0VafwQFAx0NKT7MtDsAgDWOr5RtGqOblG0ignEv3YJr/GBYvSypUBrn1Vkkfc7G10XUHgk7O8ew6Yw+n3322Rg6dCg++ugjLFmyBN9//z2qqqpQXV2NRCKBoqIi7LbbbjjssMNwxhlnYPTo0e2af1FREWbNmoVf//rXeOqpp7B48WJs3rwZbrcbgwYNwnHHHYeLL75YdAOovQoLC/H222/jlVdeQVlZGT755BPU1NSgV69e2HvvvXHuuefivPPOg9ttNo7u9hwOBwoLC9GvXz8cdNBBOPHEE3HaaafB61WPuZYKb9gQEREREREREREREe3kvF4vDj/8cBx++OEdupzx48dj/PjxHbqMVE477TScdtppbbYpKytDWVlZm20mT56MyZMnp69jQrKvAREREREREREREREREVGH4RM23VB9bBMcGtFXdYE12suMxxvUjTpC3Cw7Kp6mbmSLtEYQZMFyiVSqGj7TntZRqP+diERCP+IrGlfHv6TiN4j44vuYiIg6U1Nsi7JNIqE+N0WiFenojljEMOo1E0wiMSlzJBF0a2JLlG18rmJlm2hcvV8HBXGAjOkiIiLiEzZEREREREREREREREQZxydsiIiIiIiIiIiIiGinYP/4k22ysc+UfnzCpov64YcfcO2112LYsGHIy8tDz549MWLECNx9990IBAKZ7h4RERERUadjjUxERERERDszPmHTBc2dOxfnnXceGhr+N+5LIBDAsmXLsGzZMsycORNvvvkm9thjD635R+JNsCxnu6eLx+q1lkdElK1MMtu31C9OY0+IiKija2TKLuFYo7KNZAwbou6stumrTHeBiIiIfoJP2HQxy5cvx8SJE9HQ0ID8/Hzcfvvt+Oijj/D+++/j17/+NQDgu+++w4knnojGRvVFChERERFRtmONTERERERE3QGfsOlirr76agSDQbhcLrzzzjsYNWpU8m9HH3009txzT1x//fX47rvvcM8992DatGmZ6ywRERERUSdgjUxEREREUgl720+2ycY+U/pZtm1zV+gili5dipEjRwIAfvOb3+Dhhx/eoU0ikcDw4cOxcuVKFBcXo6KiAm63WzT/hoYGFBUVwesZCMtq/8NVofCmdk9DRERElDk2gDjq6+tRWFiY6c6Qpo6skZvrY8AJwEpzz6kjedy9lG3iiYi6TbxB2YaIiGjnsXPXx8213TW7ToXX4ct0d9otnAjh3nX/t9NuH5JhJFoX8tprryV/v/DCC1tt43A4cMEFFwAA6urq8OGHH3ZG14iIiIiIMoI1MhERERERdRe8YdOFLFy4EACQl5eHgw8+OGW7MWPGJH9ftGhRh/eLiIiIiChTWCMTEREREVF3wTFsupCVK1cCAPbYYw+4XKk3zbBhw3aYpj3i8ZBWJBoRERERUWfrrBqZsks8EVS2sQWRaERERLTzsX/8L9tkY58p/XjDposIhUKoqqoCAAwcOLDNtj169EBeXh78fj82bNiQsl04HEY4HE7+u6GB+cxERERElD3SXSOzPiYiIiIioq6Mj1l0EY2Njcnf8/Pzle3z8vIAAE1NTSnb3HHHHSgqKkr+DBo0yLyjRERERESdJN01MutjIiIiIiLqyviETRcRCoWSv3s8HmV7r9cLAAgGU0cBTJkyBddcc03y3/X19Rg8eDBsO6HZSz6WR0RERNlkW+1i26xhslW6a+RU9THr3OwjeV/L3vvc9kRE1J2wPibq6njDpovw+XzJ3yMRddZyc5RDTk5OyjZerzd50Qr8L/IhnqjR7SYRERFR1mlsbERRUVGmu0Ea0l0jp6qPAd0vNFGmJBKMsyMiItJVXV3N+pioi+INmy6ioKAg+XtbMWfN/H4/AFk0RLP+/ftjw4YNKCgogGVZLf7W0NCAQYMGYcOGDSgsLBTPM1t1p9fL17pz4mvdOfG17pz4WjPHtm00Njaif//+me4KaeroGrm1+rir7cddCddN27h+UuO6SY3rJjWum7Zx/aTGdZNa89PFPXv2zHRXOlTC3vaTbbKxz5R+vGHTRfh8PpSUlKC6uhobN25ss21tbW3yYrQ9udsOh0M5WGthYWG3Opl1p9fL17pz4mvdOfG17pz4WjOD3xzMbh1dI7dVH3el/bir4bppG9dPalw3qXHdpMZ10zaun9S4blJzODisOVFXxXdnF7LPPvsAAFavXo1YLJay3TfffJP8fe+99+7wfhERERERZQprZCIiIiIi6i54w6YLGT16NIBtUQ6ffvppynbz589P/v7zn/+8w/tFRERERJQprJGJiIiIiKi74A2bLuTUU09N/v7kk0+22iaRSODpp58GABQXF2Ps2LFpWbbX68Utt9zSYhDWnVl3er18rTsnvtadE1/rzomvlchMZ9fI3I9T47ppG9dPalw3qXHdpMZ10zaun9S4blLrLuvGzuIfIsu2be4LXciRRx6JBQsWwOVy4T//+Q9GjRrV4u933303rr/+egDALbfcgmnTpmWgl0REREREnYc1MhERERGpNDQ0oKioCFftMgVehy/T3Wm3cCKEB3+4A/X19Rx/qRvjDZsuZvny5fj5z3+OYDCI/Px8TJ06FWPHjkUwGMSLL76IRx99FAAwdOhQLFu2DAUFBRnuMRERERFRx2KNTEREREQqvGFDOwPesOmC5s6di/POOw8NDQ2t/n3o0KF48803sccee3Ryz4iIiIiIMoM1MhERERG1hTdsaGfAMWy6oJNOOglffPEF/vCHP2Do0KHIzc1FcXExDjnkENx1111Yvnw5L0SJiIiIqFthjUxEREREEgk7e3+I+IQNEREREREREREREWW15idsrhycvU/Y/H09n7Dp7viETTf3ww8/4Nprr8WwYcOQl5eHnj17YsSIEbj77rsRCAQy3b2UOrLfZWVlsCxL9FNWVpaeF5QmFRUVeOONN3DzzTfj+OOPR69evZJ9nTx5cqa7l1Jn9XvatGnibTtv3ry0LdfUsmXL8Je//AXHHnssBg4cCK/Xi/z8fAwdOhQXXnghFi5cmOkutqqz+p2N27WhoQEvvvgirr32WowZMwZ77LEHioqK4PF4UFpaiqOOOgp//etfUV1dnemuttBZ/c7m43Bbbrjhhi65P6qks9/Z+H6l7iNb6+KOkq11ZWfJ1vqso2VrjdMVZGudkE7SGuGoo47KdFczbv369bjllltwyCGHoHfv3vD5fBg0aBCOOOII3Hzzzfjyyy8z3cVOcdRRR4n3m+783opEIpg5cyaOO+449OvXL3nO2muvvXDhhRfio48+ynQXiWg7rkx3gDKntRzwQCCAZcuWYdmyZZg5c2aXzAHP1n53hj59+mS6C1qytd+d4cgjj8SCBQt2+P+RSASrVq3CqlWrUFZWhgsuuACPPfYYPB5PBnq5o2ztd2dZunQpzj777Fb/VllZifnz52P+/Pm4++678eyzz+K4447r5B62Llv73RV8/vnnuPfeezPdjXbL1n4TtRfryx2xPkuNdU5qrBX08HxL7TFjxgxMmTIFfr+/xf/fuHEjNm7ciIULF6KhoQH3339/ZjrYhTkcDuy5556Z7kan+uGHH3DiiSfiq6++avH/I5EIvvvuO3z33XcoKyvD7373OzzwwAOwLCtDPSWiZrxh000tX74cEydORDAYRH5+PqZMmYKxY8ciGAzixRdfxGOPPYbvvvsOJ554IpYtW4aCgoJMdxlA5/f73//+N/r375/y7wMHDjSaf0caPHgwhg0bhnfeeSfTXWmXzur3ihUr2vz7brvt1qHLl9q0aRMAoH///vjVr36FI444AoMHD0Y8HsfixYtxzz33oLy8HE8//TSi0Sief/75DPd4m0z1O1u2KwAMGjQIY8eOxcEHH4xBgwahX79+SCQS2LhxI+bMmYNXXnkFVVVVOPnkk7F06VLsv//+me4ygM7vdzYfh5slEglceumliMViKC0tRUVFRaa7JNLR/c6m9yvt3LK1Lu5M2VpXdpRsrc86S7bWOJmSrXVCR7r88stxxRVXpPx7Xl5eJ/ama7nttttw0003AQCGDh2KX//61xgxYgSKiopQXV2N5cuX49VXX4XD0T0CdZ588skdblz91Ndff42JEycCAMaNG4cBAwZ0Rte6hGg02uJmzc9+9jNcc8012GuvvdDY2IiFCxfinnvugd/vx4wZM9C/f3/ceOONGe41EfGGTTd19dVXIxgMwuVy4Z133sGoUaOSfzv66KOx55574vrrr8d3332He+65B9OmTctcZ7fT2f0eOnQodt11V7NOd6Kbb74ZI0aMwIgRI9CnTx+sW7cuKz7wykS/hw8f3qHzT5dhw4bh//7v/3D66afD6XS2+Nthhx2G888/Hz//+c/x3Xff4YUXXsBll12GI488MkO9/Z9M9TtbtuvYsWOxfv36lH8/88wz8dprr2HChAmIRCKYPn06XnnllU7sYesy0e9sOw635sEHH8Qnn3yCYcOGYcKECbjjjjsy3SWRju53trxfaeeXrXVxR8vWurIzZGt91hmytcbJpGytEzpSaWkp64RWvP/++8mbNRdccAFmzpwJt9vdos24ceNw3XXXIRKJZKKLnU5yXnrmmWeSv19wwQUd2Z0u5/XXX0/erBk1ahQWLFjQ4rx1zDHH4OSTT8aoUaMQjUZx11134brrroPLlf0fF9v2tp9sk419pvTrHrfcqYWlS5cmH+G/+OKLW1yUNrv22mux9957AwAeeOABRKPRTu1ja7K1351p+vTp+OUvf5l1ERbZ2u/O8MYbb+DMM8/c4cOAZr169cI999yT/PecOXM6q2ttytZ+d5ZU62V7p556Kvbaay8AaDV2JROytd+ZtH79+uSF9cMPP5w1sTjZ2m+i9mJ9mRrrs9RY56TGWqF9eL4lqUQigcsvvxwAsP/+++Pxxx/f4WbN9rgvbZNIJPDcc88BAPLz83HaaadluEeda/uxaaZMmdLqMfrggw/GL3/5SwBAXV0dVq5c2Wn9I6LW8YZNN/Taa68lf7/wwgtbbeNwOJLfPKirq8OHH37YGV1rU7b2m6ijjR07Nvn7999/n8GetE+29rszNcfuhEKhDPekfbK13x3ht7/9LZqamjBp0iSMGTMm090Ry9Z+E7UX60vqKKxz2sZaYRueb0nqnXfewapVqwAAN9xww07xBERneP/991FeXg4AOOOMM5Cbm5vhHnWu7Z+0GjJkSMp2u+++e6vTEFFm8IZNN7Rw4UIA23JfDz744JTtti8YFy1a1OH9UsnWfhN1tHA4nPxd8q3GriJb+91Zvv32W3z++ecAtkWvZIts7XdHeOmll/DGG2+gZ8+e+Nvf/pbp7ohla7+JdLC+pI7COic11grb8HxL7TF79mwAgGVZyachAKCmpgarVq1CTU1NprrWpT399NPJ37tbHBqA5NOMALBmzZqU7Zq/WGBZFvbcc88O7xcRtY03bLqh5scb99hjjza/lbF98dwVHonMRL8vvPBC9O/fHx6PB7169cJhhx2GP//5z8lvaFD2OvbYY1FaWgqPx4PS0lIcddRRuPPOO1FbW5vprrXb/Pnzk783R7Zkg47od7Zv10AggFWrVuHee+/FmDFjEIvFAAC///3vM9sxhY7sd7Yeh+vq6nD11VcDAO666y706tUrwz2S6cx+Z/v7lXYO2VoXU9eXrfVZR8nWGqejZGud0Flmz56NffbZB7m5uSgoKMCee+6JSZMmdesnHJcsWQIA2HXXXVFQUIDnn38e++23H0pKSjB06FCUlJRgr732wt/+9rcWN4y7s6amJrz66qsAgF122QVHHXVUZjuUAWeffTYKCwsBbDvWxOPxHdosX74cb775JgDgnHPOSbbPdoks/iHiDZtuJhQKoaqqCgAwcODANtv26NEDeXl5AIANGzZ0eN/akql+z5s3D5s3b0Y0GkV1dTU+/vhj3H777dhjjz3wyCOPGM2bMuvdd99FZWUlotEoKisrMX/+fEyZMgVDhgzB66+/nunuiSUSCdx5553Jf5955pkZ7I1cR/U7G7drWVkZLMuCZVnIy8vD0KFDce2112Lr1q0AgBtvvBHnnHNOhnu5o87qd7Yeh6+//nps2bIFP//5z3HxxRdnujtindnvbHy/0s4lW+ti6vqytT5Lt2ytcTpDttYJneXrr7/GypUrEQwG0dTUhNWrV+Ppp5/G0UcfjQkTJqC+vj7TXexUiUQC33zzDYBtY2RdffXVOPfcc/Hll1+2aPfdd9/hj3/8I44++mjU1dVloKddy8svvwy/3w8AOO+882BZVoZ71Pl69eqFZ555Brm5uVi0aBFGjBiBp59+GkuWLMF7772H6dOnY8yYMYhEIjjooINajL9GRJnD0MtuprGxMfl7fn6+sn1eXh78fj+ampo6sltKnd3vIUOG4LTTTsOoUaMwaNAgANseH3355ZcxZ84chEIhXHbZZbAsC5deeqnWMigz9ttvP5x66qk49NBD0b9/f0SjUXz77bd47rnn8M4776Curg6nn3465s6di+OPPz7T3VW67777sHTpUgDAaaed1macS1eS7n7vbNsVAA444AA8+uijGDFiRKa70i7p6nc2H4cXLFiAmTNnwuVy4eGHH86ai8PO6vfO+H6l7JStdTF1fdlan3WWbK1x0iVb64TOkJubi5NPPhnjxo3DsGHDkJ+fn/xSx8MPP4zq6mq89tprOOWUU/Duu+/C7XZnusudor6+HonEtu/dr1ixAp988gn69euHu+++GyeccAJ8Ph8++eQT3HDDDViyZAk++ugjXHTRRXjllVcy3PPM6u5xaM1OPvlkfPrpp7jnnnvw+OOPY9KkSS3+3qdPH9x666349a9/3e3G+CHqqnjDppvZflBHj8ejbO/1egEAwWCww/ok0Zn9njBhAiZNmrRD4TxixAhMnDgRb7zxBk477TREo1H84Q9/wMknn4y+ffu2eznU+X7/+99j2rRpO/z/kSNH4oILLsAjjzyCyy67DPF4HJdccgm+//57+Hy+zu+o0Pz583HjjTcCAEpLS/HQQw9luEcy6e53tm/XU089FYcccgiAbces77//Hi+99BJeffVVnH322bj//vtb5FR3FR3Z72w+DkciEVx66aWwbRt/+MMfMHz48Ex3SaSz+p3t71fauWRrXUxdW7bWZx0hW2ucjpStdUJnKS8vR3Fx8Q7//5hjjsHvfvc7HH/88Vi+fDnmz5+Phx56CFdddVXndzIDmp8SAbadu3Jzc/Hhhx+2GJ/kyCOPxAcffIBRo0bhv//9L1599VV8/PHHGDlyZCa6nHEbN27EvHnzAACHHXYYhg4dmtkOZVAkEsHTTz+N119/HbZt7/D3rVu34tlnn8Vuu+2Gk08+OQM9JKKfYiRaN7P9hx6RSETZvjn7NCcnp8P6JNGZ/S4qKmrzW06//OUvcfPNNwPYlsX8+OOPt3sZlBmtFf/b+81vfpOMJNi0aRNefvnlTuiVnq+++goTJkxALBaDz+fD7NmzUVpamuluKXVEv7N9uxYXF2P48OEYPnw4RowYgbPOOguvvPIKnn76aaxZswannHIKysrKMt3NHXRkv7P5OPx///d/+OabbzB48GDccsstme6OWGf1O9vfr7Rzyda6mLqubK3POkq21jgdKVvrhM7SVp3Qp08fzJkzJ/lUzYwZMzqpV5n30y+vXHLJJS1u1jTLycnB7bffnvz3rFmzOrxvXdWzzz6bfCrpp0+UdCd+vx/jx4/HHXfcgZqaGlx//fVYuXIlwuEw6uvr8c4772D06NFYtmwZTj31VNx7772Z7jIRgTdsup2CgoLk75I4h+ZvckhiIjpSV+v3pZdemvwwcfsBRSn7/eY3v0n+3lW37dq1a3HssceitrYWTqcTL774Io488shMd0spk/3Ohu36U+effz5+9atfIZFI4Morr0RNTU2muyTSWf3uisfhb775BnfccQeAbR8iNI930dV1tX5n4/uVslNXqy8pu2VrfZYJ2VrjmOpq59tsNGTIEBxzzDEAgNWrV2PTpk0Z7lHn2P58BQDHHntsyrbjxo2Dy7UtTOeTTz7p0H51Zc888wyAbU/HTpw4McO9yZxp06ZhwYIFAIDHH38cd911F4YNGwaPx4PCwkIcc8wx+PDDDzF27FjYto0//vGP+O9//5vhXqdHws7eHyJGonUzPp8PJSUlqK6uxsaNG9tsW1tbm7wwbR4/IFO6Wr9LS0tRUlKCqqo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      "text/plain": [
       "<Figure size 1600x1600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set audio parameters used in the functions below\n",
    "samplerate = DEFAULT_SAMPLERATE\n",
    "window_size = DEFAULT_WINDOW_SIZE\n",
    "position = DEFAULT_CLIP_POSITION\n",
    "\n",
    "# Work through each of the pre-processing steps in turn\n",
    "\n",
    "# 1) Read audio\n",
    "wav, sr = read_audio(sample.audio_filepaths)\n",
    "\n",
    "# 2) Apply bandpass filter\n",
    "filtered_wav = apply_bandpass_filter(wav, sample.low_freq, sample.high_freq, samplerate)\n",
    "\n",
    "# 3) Extract audio segment based on annotation times\n",
    "extracted_audio = extract_audio(filtered_wav, sample, samplerate=samplerate)\n",
    "\n",
    "# 4) Zero-pad the wav array\n",
    "padded_wav = zero_pad(sample, window_size, samplerate, extracted_audio)\n",
    "\n",
    "# 5) Normalise the sound file\n",
    "normalised_clip = normalise_sound_file(padded_wav)\n",
    "\n",
    "# 6) Re-apply bandpass filter to the normalised waveform\n",
    "refiltered_wav = apply_bandpass_filter(normalised_clip, sample.low_freq, sample.high_freq, samplerate)\n",
    "\n",
    "# Plot spectrograms for each pre-processing step\n",
    "steps = ['1) Read Audio', '2) Apply Bandpass Filter', '3) Extract Audio', '4) Zero-Pad', '5) Normalise Amplitude', '6) Reapply Bandpass filter']\n",
    "audios = [wav, filtered_wav, extracted_audio, padded_wav, normalised_clip, refiltered_wav]\n",
    "plot_spectrograms(steps, audios, sr=samplerate, window_size=window_size, position=position)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466636ac",
   "metadata": {},
   "source": [
    "These individual steps are combined in one single function called the wav_cookiecutter which we'll use for the pre-processing the rest of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baab397a",
   "metadata": {},
   "source": [
    "### 3. Feature extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e99a65",
   "metadata": {},
   "source": [
    "The pre-processed audio files are then passed through the VGGish pre-trained CNN in 0.96s multiples and their acoustic features are automatically extracted to produce embeddings. These embeddings are then averaged to obtain a single embedding per vocalisation. This feature extraction phase involves the following steps:\n",
    "\n",
    "1. Extract the acoustic feature embeddings for each 96-sample chunk of the audio clip using the pretrained CNN VGGish.\n",
    "2. Average the embeddings across the chunks to obtain a single embedding for each vocalisation.\n",
    "3. Add in information about the duration of each vocalisation\n",
    "\n",
    "This resulting embedding encodes the acoustic feature representation of the vocalisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28053bf7",
   "metadata": {
    "metadata": {},
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing: 100%|██████████| 1254/1254 [01:21<00:00, 15.41it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<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",
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       "\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>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
       "      <th>feature_6</th>\n",
       "      <th>feature_7</th>\n",
       "      <th>feature_8</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_119</th>\n",
       "      <th>feature_120</th>\n",
       "      <th>feature_121</th>\n",
       "      <th>feature_122</th>\n",
       "      <th>feature_123</th>\n",
       "      <th>feature_124</th>\n",
       "      <th>feature_125</th>\n",
       "      <th>feature_126</th>\n",
       "      <th>feature_127</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recording_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.515972</td>\n",
       "      <td>-0.945503</td>\n",
       "      <td>0.477771</td>\n",
       "      <td>-0.668821</td>\n",
       "      <td>-0.57095</td>\n",
       "      <td>-0.932322</td>\n",
       "      <td>-0.636771</td>\n",
       "      <td>-0.477236</td>\n",
       "      <td>-0.554</td>\n",
       "      <td>-0.025346</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.184155</td>\n",
       "      <td>0.176045</td>\n",
       "      <td>0.129956</td>\n",
       "      <td>-0.311545</td>\n",
       "      <td>-0.098671</td>\n",
       "      <td>-0.591734</td>\n",
       "      <td>-0.887841</td>\n",
       "      <td>-0.081387</td>\n",
       "      <td>-0.589816</td>\n",
       "      <td>2.146929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.307767</td>\n",
       "      <td>-0.092635</td>\n",
       "      <td>0.276705</td>\n",
       "      <td>-0.187101</td>\n",
       "      <td>0.268155</td>\n",
       "      <td>-0.458426</td>\n",
       "      <td>-0.131895</td>\n",
       "      <td>-0.070607</td>\n",
       "      <td>-0.025204</td>\n",
       "      <td>-0.393817</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.12714</td>\n",
       "      <td>-0.123594</td>\n",
       "      <td>-0.055394</td>\n",
       "      <td>-0.178481</td>\n",
       "      <td>0.020661</td>\n",
       "      <td>-0.082918</td>\n",
       "      <td>-0.417923</td>\n",
       "      <td>0.137096</td>\n",
       "      <td>-0.313308</td>\n",
       "      <td>0.517146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.167978</td>\n",
       "      <td>-0.245697</td>\n",
       "      <td>0.404217</td>\n",
       "      <td>-0.207483</td>\n",
       "      <td>-0.175113</td>\n",
       "      <td>-0.335954</td>\n",
       "      <td>-0.198125</td>\n",
       "      <td>-0.103949</td>\n",
       "      <td>-0.382519</td>\n",
       "      <td>-0.161365</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.124672</td>\n",
       "      <td>0.177732</td>\n",
       "      <td>-0.220059</td>\n",
       "      <td>0.020245</td>\n",
       "      <td>0.133535</td>\n",
       "      <td>0.006519</td>\n",
       "      <td>-0.115893</td>\n",
       "      <td>0.09173</td>\n",
       "      <td>-0.229476</td>\n",
       "      <td>1.042779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.384921</td>\n",
       "      <td>-0.216846</td>\n",
       "      <td>0.39587</td>\n",
       "      <td>0.060304</td>\n",
       "      <td>0.079169</td>\n",
       "      <td>-0.448791</td>\n",
       "      <td>-0.223726</td>\n",
       "      <td>-0.132696</td>\n",
       "      <td>-0.156767</td>\n",
       "      <td>-0.39375</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.25844</td>\n",
       "      <td>-0.010367</td>\n",
       "      <td>-0.148642</td>\n",
       "      <td>-0.149439</td>\n",
       "      <td>-0.001076</td>\n",
       "      <td>-0.07927</td>\n",
       "      <td>-0.499473</td>\n",
       "      <td>0.177549</td>\n",
       "      <td>-0.326151</td>\n",
       "      <td>0.477346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.244848</td>\n",
       "      <td>0.106207</td>\n",
       "      <td>0.084387</td>\n",
       "      <td>0.010487</td>\n",
       "      <td>-0.006746</td>\n",
       "      <td>-0.389433</td>\n",
       "      <td>-0.174722</td>\n",
       "      <td>0.098153</td>\n",
       "      <td>-0.535595</td>\n",
       "      <td>-0.011508</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189568</td>\n",
       "      <td>-0.086625</td>\n",
       "      <td>-0.023329</td>\n",
       "      <td>-0.222726</td>\n",
       "      <td>-0.059364</td>\n",
       "      <td>-0.074564</td>\n",
       "      <td>-0.431752</td>\n",
       "      <td>-0.016367</td>\n",
       "      <td>0.107249</td>\n",
       "      <td>4.722631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>-0.73037</td>\n",
       "      <td>-0.318896</td>\n",
       "      <td>0.231963</td>\n",
       "      <td>-0.0481</td>\n",
       "      <td>-0.000253</td>\n",
       "      <td>-0.296795</td>\n",
       "      <td>-0.138244</td>\n",
       "      <td>-0.104653</td>\n",
       "      <td>-0.503284</td>\n",
       "      <td>-0.485252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>-0.012054</td>\n",
       "      <td>-0.083158</td>\n",
       "      <td>0.033577</td>\n",
       "      <td>-0.013438</td>\n",
       "      <td>-0.290914</td>\n",
       "      <td>-0.599987</td>\n",
       "      <td>0.283681</td>\n",
       "      <td>-0.190117</td>\n",
       "      <td>0.924552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>-0.221255</td>\n",
       "      <td>-0.191392</td>\n",
       "      <td>0.365925</td>\n",
       "      <td>-0.066978</td>\n",
       "      <td>0.110305</td>\n",
       "      <td>-0.479004</td>\n",
       "      <td>-0.294852</td>\n",
       "      <td>-0.021371</td>\n",
       "      <td>-0.220296</td>\n",
       "      <td>-0.172582</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.104506</td>\n",
       "      <td>-0.035295</td>\n",
       "      <td>-0.26214</td>\n",
       "      <td>-0.151623</td>\n",
       "      <td>0.140103</td>\n",
       "      <td>0.080468</td>\n",
       "      <td>-0.398714</td>\n",
       "      <td>-0.138962</td>\n",
       "      <td>-0.290984</td>\n",
       "      <td>0.341624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>-0.613267</td>\n",
       "      <td>-0.209866</td>\n",
       "      <td>0.203842</td>\n",
       "      <td>0.091372</td>\n",
       "      <td>0.112618</td>\n",
       "      <td>-0.296297</td>\n",
       "      <td>-0.063837</td>\n",
       "      <td>0.019286</td>\n",
       "      <td>-0.242869</td>\n",
       "      <td>-0.283673</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135368</td>\n",
       "      <td>-0.185478</td>\n",
       "      <td>-0.018262</td>\n",
       "      <td>-0.158814</td>\n",
       "      <td>0.065845</td>\n",
       "      <td>-0.181922</td>\n",
       "      <td>-0.54809</td>\n",
       "      <td>0.430584</td>\n",
       "      <td>-0.163478</td>\n",
       "      <td>0.504992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>-0.493874</td>\n",
       "      <td>-0.45921</td>\n",
       "      <td>0.425397</td>\n",
       "      <td>-0.532386</td>\n",
       "      <td>0.015759</td>\n",
       "      <td>-0.602648</td>\n",
       "      <td>-0.373284</td>\n",
       "      <td>0.036896</td>\n",
       "      <td>-0.148876</td>\n",
       "      <td>-0.357752</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.061739</td>\n",
       "      <td>0.195552</td>\n",
       "      <td>-0.120495</td>\n",
       "      <td>-0.08933</td>\n",
       "      <td>0.020437</td>\n",
       "      <td>-0.3695</td>\n",
       "      <td>-0.570631</td>\n",
       "      <td>-0.054342</td>\n",
       "      <td>-0.452039</td>\n",
       "      <td>0.755275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>0.021842</td>\n",
       "      <td>0.003972</td>\n",
       "      <td>0.350082</td>\n",
       "      <td>-0.185247</td>\n",
       "      <td>0.120597</td>\n",
       "      <td>-0.133161</td>\n",
       "      <td>-0.133748</td>\n",
       "      <td>0.17955</td>\n",
       "      <td>-0.118232</td>\n",
       "      <td>-0.12456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117081</td>\n",
       "      <td>0.038842</td>\n",
       "      <td>-0.051806</td>\n",
       "      <td>-0.15305</td>\n",
       "      <td>0.1078</td>\n",
       "      <td>0.069047</td>\n",
       "      <td>-0.060805</td>\n",
       "      <td>0.029824</td>\n",
       "      <td>-0.138636</td>\n",
       "      <td>1.226343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1254 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature_0 feature_1 feature_2 feature_3 feature_4 feature_5  \\\n",
       "recording_id                                                               \n",
       "1            -0.515972 -0.945503  0.477771 -0.668821  -0.57095 -0.932322   \n",
       "2            -0.307767 -0.092635  0.276705 -0.187101  0.268155 -0.458426   \n",
       "3            -0.167978 -0.245697  0.404217 -0.207483 -0.175113 -0.335954   \n",
       "4            -0.384921 -0.216846   0.39587  0.060304  0.079169 -0.448791   \n",
       "5            -0.244848  0.106207  0.084387  0.010487 -0.006746 -0.389433   \n",
       "...                ...       ...       ...       ...       ...       ...   \n",
       "1252          -0.73037 -0.318896  0.231963   -0.0481 -0.000253 -0.296795   \n",
       "1253         -0.221255 -0.191392  0.365925 -0.066978  0.110305 -0.479004   \n",
       "1254         -0.613267 -0.209866  0.203842  0.091372  0.112618 -0.296297   \n",
       "1255         -0.493874  -0.45921  0.425397 -0.532386  0.015759 -0.602648   \n",
       "1256          0.021842  0.003972  0.350082 -0.185247  0.120597 -0.133161   \n",
       "\n",
       "             feature_6 feature_7 feature_8 feature_9  ... feature_119  \\\n",
       "recording_id                                          ...               \n",
       "1            -0.636771 -0.477236    -0.554 -0.025346  ...   -0.184155   \n",
       "2            -0.131895 -0.070607 -0.025204 -0.393817  ...    -0.12714   \n",
       "3            -0.198125 -0.103949 -0.382519 -0.161365  ...   -0.124672   \n",
       "4            -0.223726 -0.132696 -0.156767  -0.39375  ...    -0.25844   \n",
       "5            -0.174722  0.098153 -0.535595 -0.011508  ...    0.189568   \n",
       "...                ...       ...       ...       ...  ...         ...   \n",
       "1252         -0.138244 -0.104653 -0.503284 -0.485252  ...    0.014706   \n",
       "1253         -0.294852 -0.021371 -0.220296 -0.172582  ...   -0.104506   \n",
       "1254         -0.063837  0.019286 -0.242869 -0.283673  ...    0.135368   \n",
       "1255         -0.373284  0.036896 -0.148876 -0.357752  ...   -0.061739   \n",
       "1256         -0.133748   0.17955 -0.118232  -0.12456  ...    0.117081   \n",
       "\n",
       "             feature_120 feature_121 feature_122 feature_123 feature_124  \\\n",
       "recording_id                                                               \n",
       "1               0.176045    0.129956   -0.311545   -0.098671   -0.591734   \n",
       "2              -0.123594   -0.055394   -0.178481    0.020661   -0.082918   \n",
       "3               0.177732   -0.220059    0.020245    0.133535    0.006519   \n",
       "4              -0.010367   -0.148642   -0.149439   -0.001076    -0.07927   \n",
       "5              -0.086625   -0.023329   -0.222726   -0.059364   -0.074564   \n",
       "...                  ...         ...         ...         ...         ...   \n",
       "1252           -0.012054   -0.083158    0.033577   -0.013438   -0.290914   \n",
       "1253           -0.035295    -0.26214   -0.151623    0.140103    0.080468   \n",
       "1254           -0.185478   -0.018262   -0.158814    0.065845   -0.181922   \n",
       "1255            0.195552   -0.120495    -0.08933    0.020437     -0.3695   \n",
       "1256            0.038842   -0.051806    -0.15305      0.1078    0.069047   \n",
       "\n",
       "             feature_125 feature_126 feature_127  duration  \n",
       "recording_id                                                \n",
       "1              -0.887841   -0.081387   -0.589816  2.146929  \n",
       "2              -0.417923    0.137096   -0.313308  0.517146  \n",
       "3              -0.115893     0.09173   -0.229476  1.042779  \n",
       "4              -0.499473    0.177549   -0.326151  0.477346  \n",
       "5              -0.431752   -0.016367    0.107249  4.722631  \n",
       "...                  ...         ...         ...       ...  \n",
       "1252           -0.599987    0.283681   -0.190117  0.924552  \n",
       "1253           -0.398714   -0.138962   -0.290984  0.341624  \n",
       "1254            -0.54809    0.430584   -0.163478  0.504992  \n",
       "1255           -0.570631   -0.054342   -0.452039  0.755275  \n",
       "1256           -0.060805    0.029824   -0.138636  1.226343  \n",
       "\n",
       "[1254 rows x 129 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = hub.load('https://tfhub.dev/google/vggish/1')\n",
    "\n",
    "# Extract VGGish features\n",
    "results = []\n",
    "for _, annotation in tqdm(df.iterrows(), total=len(df), desc=\"Processing\"):\n",
    "    # Apply the wav_cookiecutter function combining all of the audio\n",
    "    # pre-processing steps\n",
    "    wav = wav_cookiecutter(\n",
    "        annotation,\n",
    "        window_size=window_size,\n",
    "        position=\"middle\",\n",
    "        samplerate=samplerate,\n",
    "    )\n",
    "\n",
    "    embeddings = model(wav)\n",
    "    assert embeddings.shape[1] == 128  # Check the number of features per frame\n",
    "\n",
    "    # Store info of the embeddings of each frame\n",
    "    for embedding in embeddings:\n",
    "        results.append(\n",
    "            {\n",
    "                \"recording_id\": annotation.recording_id,\n",
    "                **{f\"feature_{n}\": feat for n, feat in enumerate(embedding)},\n",
    "            }\n",
    "        )\n",
    "results = pd.DataFrame(results)\n",
    "\n",
    "# average vggish annotation feature vectors back into original # of annotations\n",
    "results = results.groupby(\"recording_id\").mean()\n",
    "\n",
    "# Add in the missing duration information as the 129th feature\n",
    "duration = df[[\"recording_id\", \"duration\"]].copy()\n",
    "results = results.join(duration.set_index(\"recording_id\"))\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b03fc61",
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "# Save the extracted acoustic features to a csv file\n",
    "results.to_csv(os.path.join(os.path.sep, DATA, 'acoustic_features.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a08e09a",
   "metadata": {},
   "source": [
    "### 4. Dimensionality reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "534b634a",
   "metadata": {},
   "source": [
    "Now that we have the 129D embeddings for each of the 1254 recordings we need to reduce this high-dimensional data into lower dimensional space to make it interpretable to the human brain and usable in the statistical tests. This involves 4 steps:\n",
    "1. Specify the parameters for the dimensionality reduction, including the number of components (2) and distance metric we want to use (cosine)\n",
    "2. Normalise the 129D embeddings so that their mean = 0 and variance = 1. This ensures equal weighting of the features\n",
    "3. Carry out the dimensionality reduction\n",
    "4. Visualise the results to enable observation of clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af7495ca-ce88-4838-acd8-a8e7217607d2",
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "# Load the saved acoustic features\n",
    "results = pd.read_csv(os.path.join(os.path.sep, DATA, 'acoustic_features.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "015d5a84",
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "# Specify the UMAP parameters\n",
    "results_df = results # name of the dataframe where the embeddings are contained\n",
    "metadata_df = df # name of the dataframe where the call-type labels are contained\n",
    "N_COMP = 2 # select 1, 2 or 3 dimensions\n",
    "metric = 'cosine' # distance metric used\n",
    "n_neighbors = 15\n",
    "min_dist = 0\n",
    "random_state = 2204"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2d61936",
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_5</th>\n",
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       "      <th>feature_8</th>\n",
       "      <th>feature_9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_119</th>\n",
       "      <th>feature_120</th>\n",
       "      <th>feature_121</th>\n",
       "      <th>feature_122</th>\n",
       "      <th>feature_123</th>\n",
       "      <th>feature_124</th>\n",
       "      <th>feature_125</th>\n",
       "      <th>feature_126</th>\n",
       "      <th>feature_127</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recording_id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.731289</th>\n",
       "      <td>-0.912539</td>\n",
       "      <td>-3.469207</td>\n",
       "      <td>0.586230</td>\n",
       "      <td>-1.577812</td>\n",
       "      <td>-3.375247</td>\n",
       "      <td>-2.313763</td>\n",
       "      <td>-2.634366</td>\n",
       "      <td>-2.407735</td>\n",
       "      <td>-0.544476</td>\n",
       "      <td>0.886060</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.651988</td>\n",
       "      <td>0.923479</td>\n",
       "      <td>1.628321</td>\n",
       "      <td>-1.684613</td>\n",
       "      <td>-1.234323</td>\n",
       "      <td>-3.043567</td>\n",
       "      <td>-2.631176</td>\n",
       "      <td>-0.816349</td>\n",
       "      <td>-2.210517</td>\n",
       "      <td>0.192205</td>\n",
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       "    <tr>\n",
       "      <th>-1.728530</th>\n",
       "      <td>0.177136</td>\n",
       "      <td>0.591914</td>\n",
       "      <td>-0.269403</td>\n",
       "      <td>0.211791</td>\n",
       "      <td>2.116715</td>\n",
       "      <td>-0.079225</td>\n",
       "      <td>0.062523</td>\n",
       "      <td>-0.037715</td>\n",
       "      <td>1.325232</td>\n",
       "      <td>-1.215871</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.258729</td>\n",
       "      <td>-0.901633</td>\n",
       "      <td>0.302119</td>\n",
       "      <td>-0.687203</td>\n",
       "      <td>-0.427416</td>\n",
       "      <td>0.476845</td>\n",
       "      <td>-0.116217</td>\n",
       "      <td>0.490689</td>\n",
       "      <td>-0.720135</td>\n",
       "      <td>-1.079671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.725771</th>\n",
       "      <td>0.908747</td>\n",
       "      <td>-0.136926</td>\n",
       "      <td>0.273224</td>\n",
       "      <td>0.136072</td>\n",
       "      <td>-0.784480</td>\n",
       "      <td>0.498257</td>\n",
       "      <td>-0.291257</td>\n",
       "      <td>-0.232051</td>\n",
       "      <td>0.061845</td>\n",
       "      <td>0.110143</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.241706</td>\n",
       "      <td>0.933757</td>\n",
       "      <td>-0.876083</td>\n",
       "      <td>0.802399</td>\n",
       "      <td>0.335822</td>\n",
       "      <td>1.095651</td>\n",
       "      <td>1.500222</td>\n",
       "      <td>0.219292</td>\n",
       "      <td>-0.268277</td>\n",
       "      <td>-0.669469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.723013</th>\n",
       "      <td>-0.226661</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.237703</td>\n",
       "      <td>1.130906</td>\n",
       "      <td>0.879801</td>\n",
       "      <td>-0.033794</td>\n",
       "      <td>-0.428006</td>\n",
       "      <td>-0.399600</td>\n",
       "      <td>0.860053</td>\n",
       "      <td>-1.215489</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.164363</td>\n",
       "      <td>-0.211961</td>\n",
       "      <td>-0.365086</td>\n",
       "      <td>-0.469507</td>\n",
       "      <td>-0.574399</td>\n",
       "      <td>0.502089</td>\n",
       "      <td>-0.552666</td>\n",
       "      <td>0.732692</td>\n",
       "      <td>-0.789356</td>\n",
       "      <td>-1.110730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.720254</th>\n",
       "      <td>0.506432</td>\n",
       "      <td>1.538740</td>\n",
       "      <td>-1.087814</td>\n",
       "      <td>0.945835</td>\n",
       "      <td>0.317481</td>\n",
       "      <td>0.246094</td>\n",
       "      <td>-0.166243</td>\n",
       "      <td>0.945893</td>\n",
       "      <td>-0.479401</td>\n",
       "      <td>0.964994</td>\n",
       "      <td>...</td>\n",
       "      <td>0.925734</td>\n",
       "      <td>-0.676454</td>\n",
       "      <td>0.531548</td>\n",
       "      <td>-1.018849</td>\n",
       "      <td>-0.968533</td>\n",
       "      <td>0.534649</td>\n",
       "      <td>-0.190232</td>\n",
       "      <td>-0.427381</td>\n",
       "      <td>1.546677</td>\n",
       "      <td>2.202272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.719906</th>\n",
       "      <td>-2.034629</td>\n",
       "      <td>-0.485480</td>\n",
       "      <td>-0.459804</td>\n",
       "      <td>0.728184</td>\n",
       "      <td>0.359977</td>\n",
       "      <td>0.682903</td>\n",
       "      <td>0.028608</td>\n",
       "      <td>-0.236152</td>\n",
       "      <td>-0.365156</td>\n",
       "      <td>-1.737457</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280361</td>\n",
       "      <td>-0.222240</td>\n",
       "      <td>0.103464</td>\n",
       "      <td>0.902333</td>\n",
       "      <td>-0.657987</td>\n",
       "      <td>-0.962242</td>\n",
       "      <td>-1.090610</td>\n",
       "      <td>1.367607</td>\n",
       "      <td>-0.056129</td>\n",
       "      <td>-0.761733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.722665</th>\n",
       "      <td>0.629912</td>\n",
       "      <td>0.121657</td>\n",
       "      <td>0.110273</td>\n",
       "      <td>0.658050</td>\n",
       "      <td>1.083581</td>\n",
       "      <td>-0.176257</td>\n",
       "      <td>-0.807943</td>\n",
       "      <td>0.249256</td>\n",
       "      <td>0.635430</td>\n",
       "      <td>0.046159</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.102612</td>\n",
       "      <td>-0.363802</td>\n",
       "      <td>-1.177178</td>\n",
       "      <td>-0.485879</td>\n",
       "      <td>0.380235</td>\n",
       "      <td>1.607286</td>\n",
       "      <td>-0.013413</td>\n",
       "      <td>-1.160787</td>\n",
       "      <td>-0.599807</td>\n",
       "      <td>-1.216647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.725424</th>\n",
       "      <td>-1.421747</td>\n",
       "      <td>0.033689</td>\n",
       "      <td>-0.579471</td>\n",
       "      <td>1.246326</td>\n",
       "      <td>1.098723</td>\n",
       "      <td>0.685252</td>\n",
       "      <td>0.426067</td>\n",
       "      <td>0.486223</td>\n",
       "      <td>0.555616</td>\n",
       "      <td>-0.587561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551895</td>\n",
       "      <td>-1.278571</td>\n",
       "      <td>0.567802</td>\n",
       "      <td>-0.539782</td>\n",
       "      <td>-0.121890</td>\n",
       "      <td>-0.208142</td>\n",
       "      <td>-0.812860</td>\n",
       "      <td>2.246430</td>\n",
       "      <td>0.087456</td>\n",
       "      <td>-1.089155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.728182</th>\n",
       "      <td>-0.796883</td>\n",
       "      <td>-1.153618</td>\n",
       "      <td>0.363354</td>\n",
       "      <td>-1.070950</td>\n",
       "      <td>0.464780</td>\n",
       "      <td>-0.759271</td>\n",
       "      <td>-1.226902</td>\n",
       "      <td>0.588860</td>\n",
       "      <td>0.887954</td>\n",
       "      <td>-1.010139</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.807633</td>\n",
       "      <td>1.042300</td>\n",
       "      <td>-0.163686</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>-0.428931</td>\n",
       "      <td>-1.505969</td>\n",
       "      <td>-0.933496</td>\n",
       "      <td>-0.654559</td>\n",
       "      <td>-1.467893</td>\n",
       "      <td>-0.893836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.730941</th>\n",
       "      <td>1.902203</td>\n",
       "      <td>1.051928</td>\n",
       "      <td>0.042850</td>\n",
       "      <td>0.218679</td>\n",
       "      <td>1.150948</td>\n",
       "      <td>1.454476</td>\n",
       "      <td>0.052629</td>\n",
       "      <td>1.420314</td>\n",
       "      <td>0.996306</td>\n",
       "      <td>0.320096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.425760</td>\n",
       "      <td>0.087774</td>\n",
       "      <td>0.327787</td>\n",
       "      <td>-0.496577</td>\n",
       "      <td>0.161808</td>\n",
       "      <td>1.528268</td>\n",
       "      <td>1.795047</td>\n",
       "      <td>-0.151052</td>\n",
       "      <td>0.221351</td>\n",
       "      <td>-0.526216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1254 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              feature_0  feature_1  feature_2  feature_3  feature_4  \\\n",
       "recording_id                                                          \n",
       "-1.731289     -0.912539  -3.469207   0.586230  -1.577812  -3.375247   \n",
       "-1.728530      0.177136   0.591914  -0.269403   0.211791   2.116715   \n",
       "-1.725771      0.908747  -0.136926   0.273224   0.136072  -0.784480   \n",
       "-1.723013     -0.226661   0.000453   0.237703   1.130906   0.879801   \n",
       "-1.720254      0.506432   1.538740  -1.087814   0.945835   0.317481   \n",
       "...                 ...        ...        ...        ...        ...   \n",
       " 1.719906     -2.034629  -0.485480  -0.459804   0.728184   0.359977   \n",
       " 1.722665      0.629912   0.121657   0.110273   0.658050   1.083581   \n",
       " 1.725424     -1.421747   0.033689  -0.579471   1.246326   1.098723   \n",
       " 1.728182     -0.796883  -1.153618   0.363354  -1.070950   0.464780   \n",
       " 1.730941      1.902203   1.051928   0.042850   0.218679   1.150948   \n",
       "\n",
       "              feature_5  feature_6  feature_7  feature_8  feature_9  ...  \\\n",
       "recording_id                                                         ...   \n",
       "-1.731289     -2.313763  -2.634366  -2.407735  -0.544476   0.886060  ...   \n",
       "-1.728530     -0.079225   0.062523  -0.037715   1.325232  -1.215871  ...   \n",
       "-1.725771      0.498257  -0.291257  -0.232051   0.061845   0.110143  ...   \n",
       "-1.723013     -0.033794  -0.428006  -0.399600   0.860053  -1.215489  ...   \n",
       "-1.720254      0.246094  -0.166243   0.945893  -0.479401   0.964994  ...   \n",
       "...                 ...        ...        ...        ...        ...  ...   \n",
       " 1.719906      0.682903   0.028608  -0.236152  -0.365156  -1.737457  ...   \n",
       " 1.722665     -0.176257  -0.807943   0.249256   0.635430   0.046159  ...   \n",
       " 1.725424      0.685252   0.426067   0.486223   0.555616  -0.587561  ...   \n",
       " 1.728182     -0.759271  -1.226902   0.588860   0.887954  -1.010139  ...   \n",
       " 1.730941      1.454476   0.052629   1.420314   0.996306   0.320096  ...   \n",
       "\n",
       "              feature_119  feature_120  feature_121  feature_122  feature_123  \\\n",
       "recording_id                                                                    \n",
       "-1.731289       -1.651988     0.923479     1.628321    -1.684613    -1.234323   \n",
       "-1.728530       -1.258729    -0.901633     0.302119    -0.687203    -0.427416   \n",
       "-1.725771       -1.241706     0.933757    -0.876083     0.802399     0.335822   \n",
       "-1.723013       -2.164363    -0.211961    -0.365086    -0.469507    -0.574399   \n",
       "-1.720254        0.925734    -0.676454     0.531548    -1.018849    -0.968533   \n",
       "...                   ...          ...          ...          ...          ...   \n",
       " 1.719906       -0.280361    -0.222240     0.103464     0.902333    -0.657987   \n",
       " 1.722665       -1.102612    -0.363802    -1.177178    -0.485879     0.380235   \n",
       " 1.725424        0.551895    -1.278571     0.567802    -0.539782    -0.121890   \n",
       " 1.728182       -0.807633     1.042300    -0.163686    -0.018948    -0.428931   \n",
       " 1.730941        0.425760     0.087774     0.327787    -0.496577     0.161808   \n",
       "\n",
       "              feature_124  feature_125  feature_126  feature_127  duration  \n",
       "recording_id                                                                \n",
       "-1.731289       -3.043567    -2.631176    -0.816349    -2.210517  0.192205  \n",
       "-1.728530        0.476845    -0.116217     0.490689    -0.720135 -1.079671  \n",
       "-1.725771        1.095651     1.500222     0.219292    -0.268277 -0.669469  \n",
       "-1.723013        0.502089    -0.552666     0.732692    -0.789356 -1.110730  \n",
       "-1.720254        0.534649    -0.190232    -0.427381     1.546677  2.202272  \n",
       "...                   ...          ...          ...          ...       ...  \n",
       " 1.719906       -0.962242    -1.090610     1.367607    -0.056129 -0.761733  \n",
       " 1.722665        1.607286    -0.013413    -1.160787    -0.599807 -1.216647  \n",
       " 1.725424       -0.208142    -0.812860     2.246430     0.087456 -1.089155  \n",
       " 1.728182       -1.505969    -0.933496    -0.654559    -1.467893 -0.893836  \n",
       " 1.730941        1.528268     1.795047    -0.151052     0.221351 -0.526216  \n",
       "\n",
       "[1254 rows x 129 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalise the extracted acoustic features\n",
    "scaler = StandardScaler()\n",
    "normalised = scaler.fit(results_df)\n",
    "normalised_df = pd.DataFrame(scaler.transform(results_df), columns = results_df.columns, index = results_df.index)\n",
    "normalised_df = normalised_df.set_index(\"recording_id\")\n",
    "normalised_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48c425b1",
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "# Function to fit UMAP and return the embedding\n",
    "import umap.umap_ as umap\n",
    "\n",
    "def fit_umap(normalised_df, N_COMP, metric, min_dist, n_neighbors, random_state):\n",
    "    reducer = umap.UMAP(n_components=N_COMP, metric=metric, min_dist=min_dist, n_neighbors = n_neighbors, random_state=random_state)\n",
    "    return reducer.fit_transform(normalised_df)\n",
    "\n",
    "# Fit UMAP and obtain embeddings\n",
    "embedding = fit_umap(normalised_df, N_COMP, metric, min_dist, n_neighbors, random_state)\n",
    "\n",
    "results = pd.DataFrame(\n",
    "    {\n",
    "        \"recording_id\": results_df[\"recording_id\"],\n",
    "        **{f\"UMAP{i + 1}\": embedding[:, i] for i in range(N_COMP)},\n",
    "    }\n",
    ")\n",
    "\n",
    "# Merge UMAP coordinates with metadata\n",
    "umap_df = merge_metadata(results, metadata_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4707db7c",
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the style of the visualisation\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "plt.figure(figsize=(16, 16))\n",
    "\n",
    "# Define a colour-blind friendly palette with three colors\n",
    "colorblind_palette = [\"#E69F00\", \"#56B4E9\", \"#009E73\"]\n",
    "\n",
    "# Calculate silhouette scores\n",
    "labels = umap_df['Call-Type']\n",
    "silhouette_avg = silhouette_score(umap_df[['UMAP1', 'UMAP2']], labels)\n",
    "silhouette_values = silhouette_samples(umap_df[['UMAP1', 'UMAP2']], labels)\n",
    "\n",
    "# Prepare a dictionary to store average silhouette scores for each label\n",
    "silhouette_dict = {}\n",
    "unique_labels = labels.unique()\n",
    "for label in unique_labels:\n",
    "    label_indices = (labels == label)\n",
    "    avg_silhouette_score = silhouette_values[label_indices].mean()\n",
    "    silhouette_dict[label] = avg_silhouette_score\n",
    "\n",
    "# Create a scatter plot with the custom colour-blind friendly palette\n",
    "scatter_plot = sns.scatterplot(\n",
    "    x=umap_df['UMAP1'],\n",
    "    y=umap_df['UMAP2'],\n",
    "    hue=umap_df['Call-Type'],\n",
    "    palette=colorblind_palette,\n",
    "    edgecolor='none' \n",
    ")\n",
    "\n",
    "# Set the title, labels, parameters\n",
    "scatter_plot.set_xlabel('UMAP1', fontsize=24)\n",
    "scatter_plot.set_ylabel('UMAP2', fontsize=24)\n",
    "scatter_plot.tick_params(axis='x', labelsize=20)\n",
    "scatter_plot.tick_params(axis='y', labelsize=20)\n",
    "\n",
    "# Update legend labels to include silhouette scores\n",
    "handles, labels = scatter_plot.get_legend_handles_labels()\n",
    "new_labels = []\n",
    "for label in labels:\n",
    "    if label in silhouette_dict:\n",
    "        new_label = f'{label} (Silhouette: {silhouette_dict[label]:.2f})'\n",
    "        new_labels.append(new_label)\n",
    "    else:\n",
    "        new_labels.append(label)\n",
    "\n",
    "scatter_plot.legend(handles=handles, labels=new_labels, title='Call-Type', title_fontsize='20', fontsize='18', loc='best')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d8ce5a",
   "metadata": {},
   "source": [
    "### 5. Silhouette analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0aa0b8",
   "metadata": {},
   "source": [
    "We can visually assess the level of separation achieved by the unsupervised methods but this needs to be accompanied by a statistical analysis of its performance. We will do this by calculating the overall silhouette score for the data and the silhouette score per call-type.\n",
    "\n",
    "The silhouete score is a mathematical representation of the tightness and separation of each cluster (Rousseeuw, 1987). It is calculated as the mean Euclidean distance between data points within a cluster (a) and the mean Euclidean distance to points in the nearest cluster (Rousseeuw, 1987).\n",
    "\n",
    "Typically, silhouette scores >=0.5 show evidence for clustering, while those >=0.7 show strong evidence for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "255cf6d5",
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Silhouette Score: 0.5591406\n",
      "Label: Roar Silhouette Score: 0.16335147619247437\n",
      "Label: Rumble Silhouette Score: 0.8099871277809143\n",
      "Label: Trumpet Silhouette Score: 0.033587049692869186\n"
     ]
    }
   ],
   "source": [
    "# Define data and embeddings \n",
    "embeddings = umap_df[['UMAP1', 'UMAP2']].values\n",
    "labels = umap_df['Call-Type'].values\n",
    "\n",
    "# Calculate the overall silhouette score for the whole vocalisation dataset\n",
    "silhouette_scores_obj = SilhouetteScores(embedding=embeddings, labels=labels)\n",
    "avrg_score = silhouette_scores_obj.get_avrg_score()\n",
    "print(\"Average Silhouette Score:\", avrg_score)\n",
    "\n",
    "# Calculate the silhouette score per call-type\n",
    "scores_per_class = silhouette_scores_obj.get_score_per_class()\n",
    "for label, score in zip(silhouette_scores_obj.labeltypes, scores_per_class):\n",
    "    print(\"Label:\", label, \"Silhouette Score:\", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0489eb3",
   "metadata": {},
   "source": [
    "### 6. Call-Type Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b6ad3b",
   "metadata": {},
   "source": [
    "Lastly, we use the UMAP acoustic feature embeddings to train a Random Forest Classifier to predict the 3 Call-Types on unseen test data. We perform hyperparameter optimisation on the Random Forest model and cross validation on the resultant model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1563a0f6",
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 162 candidates, totalling 486 fits\n",
      "Best Parameters for Call-Type: {'class_weight': 'balanced_subsample', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "Best Accuracy for Call-Type: 0.9893162393162394\n",
      "Random Forest Call-Type Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "        Roar       0.93      0.94      0.94        86\n",
      "      Rumble       1.00      1.00      1.00       155\n",
      "     Trumpet       0.50      0.45      0.48        11\n",
      "\n",
      "    accuracy                           0.96       252\n",
      "   macro avg       0.81      0.80      0.80       252\n",
      "weighted avg       0.95      0.96      0.96       252\n",
      "\n",
      "Fitting 3 folds for each of 162 candidates, totalling 486 fits\n",
      "Best Parameters for Call-Type: {'class_weight': 'balanced_subsample', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "Best Accuracy for Call-Type: 0.9919743178170144\n",
      "Random Forest Call-Type Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "        Roar       0.93      0.94      0.94        85\n",
      "      Rumble       1.00      0.99      1.00       156\n",
      "     Trumpet       0.40      0.40      0.40        10\n",
      "\n",
      "    accuracy                           0.95       251\n",
      "   macro avg       0.78      0.78      0.78       251\n",
      "weighted avg       0.95      0.95      0.95       251\n",
      "\n",
      "Fitting 3 folds for each of 162 candidates, totalling 486 fits\n",
      "Best Parameters for Call-Type: {'class_weight': 'balanced', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "Best Accuracy for Call-Type: 0.9903691813804173\n",
      "Random Forest Call-Type Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "        Roar       0.95      0.91      0.93        85\n",
      "      Rumble       1.00      1.00      1.00       156\n",
      "     Trumpet       0.43      0.60      0.50        10\n",
      "\n",
      "    accuracy                           0.95       251\n",
      "   macro avg       0.79      0.84      0.81       251\n",
      "weighted avg       0.96      0.95      0.96       251\n",
      "\n",
      "Fitting 3 folds for each of 162 candidates, totalling 486 fits\n",
      "Best Parameters for Call-Type: {'class_weight': 'balanced', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Best Accuracy for Call-Type: 0.9919743178170144\n",
      "Random Forest Call-Type Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "        Roar       0.93      0.92      0.92        85\n",
      "      Rumble       1.00      1.00      1.00       156\n",
      "     Trumpet       0.36      0.40      0.38        10\n",
      "\n",
      "    accuracy                           0.95       251\n",
      "   macro avg       0.76      0.77      0.77       251\n",
      "weighted avg       0.95      0.95      0.95       251\n",
      "\n",
      "Fitting 3 folds for each of 162 candidates, totalling 486 fits\n",
      "Best Parameters for Call-Type: {'class_weight': 'balanced', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Best Accuracy for Call-Type: 0.9903691813804173\n",
      "Random Forest Call-Type Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "        Roar       0.94      0.95      0.95        85\n",
      "      Rumble       0.99      1.00      1.00       156\n",
      "     Trumpet       0.50      0.40      0.44        10\n",
      "\n",
      "    accuracy                           0.96       251\n",
      "   macro avg       0.81      0.78      0.80       251\n",
      "weighted avg       0.96      0.96      0.96       251\n",
      "\n",
      "Mean accuracy for Call-Type: 0.9908006475422205\n"
     ]
    }
   ],
   "source": [
    "# Define a function to handle the model training and evaluation for each category\n",
    "def train_evaluate_category(X, y, category_name):\n",
    "    # Define outer cross-validation strategy\n",
    "    outer_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "    # Initialize lists to store results\n",
    "    best_params_list = []\n",
    "    best_accuracy_list = []\n",
    "\n",
    "    # Loop through outer cross-validation folds\n",
    "    for train_index, test_index in outer_cv.split(X, y):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "        # Oversample the minority class using RandomOverSampler with automatic sampling strategy\n",
    "        ros = RandomOverSampler(sampling_strategy='auto', random_state=42)\n",
    "        X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "        # Hyperparameter optimization using GridSearchCV (Random Forest parameters)\n",
    "        param_grid = {\n",
    "            'n_estimators': [50, 100, 200],\n",
    "            'max_depth': [None, 10, 20],\n",
    "            'min_samples_split': [2, 5, 10],\n",
    "            'min_samples_leaf': [1, 2, 4],\n",
    "            'class_weight': ['balanced', 'balanced_subsample']\n",
    "        }\n",
    "        rf = RandomForestClassifier(random_state=42)\n",
    "        grid_search = GridSearchCV(rf, param_grid, cv=3, scoring='accuracy', verbose=1)\n",
    "        grid_search.fit(X_resampled, y_resampled)\n",
    "\n",
    "        # Store best hyperparameters and accuracy\n",
    "        best_params_list.append(grid_search.best_params_)\n",
    "        best_accuracy_list.append(grid_search.best_score_)\n",
    "\n",
    "        # Print best hyperparameters\n",
    "        print(f\"Best Parameters for {category_name}:\", grid_search.best_params_)\n",
    "        print(f\"Best Accuracy for {category_name}:\", grid_search.best_score_)\n",
    "\n",
    "        # Print classification report for the best model\n",
    "        rf_best = grid_search.best_estimator_\n",
    "        y_pred_best = rf_best.predict(X_test)\n",
    "        print(f\"Random Forest {category_name} Classification Report:\\n\", classification_report(y_test, y_pred_best))\n",
    "\n",
    "    # Return average of best accuracy across folds\n",
    "    return np.mean(best_accuracy_list)\n",
    "\n",
    "# Split the filtered datasets into training and testing sets for each category\n",
    "\n",
    "# Run\n",
    "X_call, y_call = umap_df[['UMAP1', 'UMAP2']], umap_df['Call-Type']\n",
    "mean_accuracy_call = train_evaluate_category(X_call, y_call, \"Call-Type\")\n",
    "print(\"Mean accuracy for Call-Type:\", mean_accuracy_call)\n"
   ]
  }
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