QM-7063-Learning-Practice-4/Schrick-Noah_Learning-Practice-4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learning Practice 4 for the University of Tulsa's QM-7063 Data Mining Course\n",
    "# Cluster Analysis\n",
    "# Professor: Dr. Abdulrashid, Spring 2023\n",
    "# Noah L. Schrick - 1492657\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import pairwise\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "from pandas.plotting import parallel_coordinates\n",
    "\n",
    "\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 15.1\n",
    "The dataset on American College and University Rankings (available from www.dataminingbook.com) contains information on 1302 American\n",
    "colleges and universities offering an undergraduate program. For each university, there are 17 measurements, including continuous measurements (such as tuition and graduation rate) and categorical measurements (such as location by state and whether it is a private or public school).\n",
    "\n",
    "Note that many records are missing some measurements. Our first goal is to estimate these missing values from “similar” records. This will be done by clustering the complete records and then finding the closest cluster for each of the partial records. The missing values will be imputed from the information in that cluster. \n",
    "\n",
    "a. \n",
    "    Remove all records with missing measurements from the dataset.\n",
    "b. \n",
    "    For all the continuous measurements, run hierarchical clustering using complete linkage and Euclidean distance. Make sure to normalize the measurements. From the dendrogram: How many clusters seem reasonable for describing these data?\n",
    "c. \n",
    "    Compare the summary statistics for each cluster and describe each cluster in this context (e.g., “Universities with high tuition, low acceptance rate...”). (Hint: To obtain cluster statistics for hierarchical clustering, use the pandas method groupby(clusterlabel) together with methods such as mean or median.)\n",
    "d. \n",
    "    Use the categorical measurements that were not used in the analysis (State and Private/Public) to characterize the different clusters. Is there any relationship between the clusters and the categorical information?\n",
    "e. \n",
    "    What other external information can explain the contents of some or all of these clusters?\n",
    "f. \n",
    "    Consider Tufts University, which is missing some information. Compute the Euclidean distance of this record from each of the clusters that you found above (using only the measurements that you have). Which cluster is it closest to? Impute the missing values for Tufts by taking the average of the cluster on those measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        # appli. rec'd  # appl. accepted  # new stud. enrolled  \\\n",
      "mean     -1.885729e-17      1.131437e-17          3.630029e-17   \n",
      "median   -3.685865e-01     -3.338810e-01         -3.688211e-01   \n",
      "\n",
      "        % new stud. from top 10%  % new stud. from top 25%  # FT undergrad  \\\n",
      "mean                9.051500e-17             -3.771458e-17   -2.074302e-17   \n",
      "median             -2.712639e-01             -8.127227e-02   -3.957697e-01   \n",
      "\n",
      "        # PT undergrad  in-state tuition  out-of-state tuition          room  \\\n",
      "mean      3.771458e-17      7.354344e-17          2.130874e-16 -8.297208e-17   \n",
      "median   -3.224580e-01      8.181657e-02         -1.102035e-01 -1.838341e-01   \n",
      "\n",
      "               board     add. fees  estim. book costs  estim. personal $  \\\n",
      "mean   -2.753165e-16  3.771458e-18       1.131437e-17       9.805791e-17   \n",
      "median -7.045916e-02 -2.782627e-01      -2.989446e-01      -1.641725e-01   \n",
      "\n",
      "        % fac. w/PHD  stud./fac. ratio  Graduation rate  \n",
      "mean    2.413733e-16      1.508583e-17    -3.846887e-16  \n",
      "median  1.675262e-01     -1.443072e-01     2.410147e-02  \n",
      "  [0 0]  [0 0]\n",
      "-------  -------\n",
      "      0  AK\n",
      "      2  AK\n",
      "      9  AL\n",
      "     11  AL\n",
      "     21  AL\n",
      "     25  AL\n",
      "     31  AR\n",
      "     37  AR\n",
      "     38  AR\n",
      "     45  AR\n",
      "     48  AZ\n",
      "     49  AZ\n",
      "     62  CA\n",
      "     76  CA\n",
      "     77  CA\n",
      "     78  CA\n",
      "     80  CA\n",
      "     89  CA\n",
      "     91  CA\n",
      "     94  CA\n",
      "     95  CA\n",
      "     96  CA\n",
      "    107  CA\n",
      "    109  CA\n",
      "    111  CA\n",
      "    119  CA\n",
      "    120  CA\n",
      "    121  CO\n",
      "    122  CO\n",
      "    125  CO\n",
      "    126  CO\n",
      "    129  CO\n",
      "    133  CO\n",
      "    138  CT\n",
      "    139  CT\n",
      "    145  CT\n",
      "    147  CT\n",
      "    148  CT\n",
      "    150  CT\n",
      "    151  CT\n",
      "    152  CT\n",
      "    153  CT\n",
      "    155  CT\n",
      "    157  DC\n",
      "    159  DC\n",
      "    160  DC\n",
      "    163  DC\n",
      "    167  DE\n",
      "    168  DE\n",
      "    171  FL\n",
      "    173  FL\n",
      "    175  FL\n",
      "    180  FL\n",
      "    185  FL\n",
      "    187  FL\n",
      "    189  FL\n",
      "    193  FL\n",
      "    207  GA\n",
      "    209  GA\n",
      "    219  GA\n",
      "    227  GA\n",
      "    229  GA\n",
      "    234  GA\n",
      "    235  GA\n",
      "    238  HI\n",
      "    243  IA\n",
      "    244  IA\n",
      "    245  IA\n",
      "    246  IA\n",
      "    247  IA\n",
      "    249  IA\n",
      "    250  IA\n",
      "    251  IA\n",
      "    257  IA\n",
      "    258  IA\n",
      "    259  IA\n",
      "    261  IA\n",
      "    262  IA\n",
      "    263  IA\n",
      "    264  IA\n",
      "    267  IA\n",
      "    268  IA\n",
      "    269  IA\n",
      "    271  ID\n",
      "    274  ID\n",
      "    276  IL\n",
      "    286  IL\n",
      "    293  IL\n",
      "    296  IL\n",
      "    297  IL\n",
      "    301  IL\n",
      "    303  IL\n",
      "    311  IL\n",
      "    316  IL\n",
      "    318  IL\n",
      "    319  IL\n",
      "    320  IL\n",
      "    321  IL\n",
      "    322  IL\n",
      "    323  IL\n",
      "    325  IN\n",
      "    328  IN\n",
      "    329  IN\n",
      "    330  IN\n",
      "    331  IN\n",
      "    332  IN\n",
      "    335  IN\n",
      "    339  IN\n",
      "    348  IN\n",
      "    351  IN\n",
      "    358  IN\n",
      "    359  IN\n",
      "    361  IN\n",
      "    364  IN\n",
      "    365  IN\n",
      "    367  KS\n",
      "    368  KS\n",
      "    369  KS\n",
      "    375  KS\n",
      "    376  KS\n",
      "    381  KS\n",
      "    386  KS\n",
      "    389  KY\n",
      "    394  KY\n",
      "    397  KY\n",
      "    399  KY\n",
      "    402  KY\n",
      "    404  KY\n",
      "    411  LA\n",
      "    414  LA\n",
      "    417  LA\n",
      "    420  LA\n",
      "    427  LA\n",
      "    432  MA\n",
      "    435  MA\n",
      "    437  MA\n",
      "    440  MA\n",
      "    441  MA\n",
      "    442  MA\n",
      "    443  MA\n",
      "    450  MA\n",
      "    451  MA\n",
      "    453  MA\n",
      "    455  MA\n",
      "    456  MA\n",
      "    459  MA\n",
      "    461  MA\n",
      "    462  MA\n",
      "    463  MA\n",
      "    466  MA\n",
      "    476  MA\n",
      "    478  MA\n",
      "    480  MA\n",
      "    482  MA\n",
      "    483  MA\n",
      "    493  MD\n",
      "    499  MD\n",
      "    504  MD\n",
      "    510  ME\n",
      "    512  ME\n",
      "    513  ME\n",
      "    514  ME\n",
      "    519  ME\n",
      "    521  ME\n",
      "    524  MI\n",
      "    525  MI\n",
      "    526  MI\n",
      "    527  MI\n",
      "    536  MI\n",
      "    537  MI\n",
      "    540  MI\n",
      "    542  MI\n",
      "    543  MI\n",
      "    550  MI\n",
      "    551  MI\n",
      "    555  MI\n",
      "    557  MI\n",
      "    562  MN\n",
      "    563  MN\n",
      "    564  MN\n",
      "    566  MN\n",
      "    570  MN\n",
      "    571  MN\n",
      "    576  MN\n",
      "    577  MN\n",
      "    578  MN\n",
      "    581  MN\n",
      "    583  MN\n",
      "    586  MO\n",
      "    588  MO\n",
      "    590  MO\n",
      "    594  MO\n",
      "    597  MO\n",
      "    598  MO\n",
      "    604  MO\n",
      "    605  MO\n",
      "    606  MO\n",
      "    607  MO\n",
      "    613  MO\n",
      "    614  MO\n",
      "    615  MO\n",
      "    616  MO\n",
      "    617  MO\n",
      "    625  MS\n",
      "    626  MS\n",
      "    628  MS\n",
      "    629  MS\n",
      "    632  MS\n",
      "    637  MT\n",
      "    641  MT\n",
      "    645  NC\n",
      "    646  NC\n",
      "    650  NC\n",
      "    653  NC\n",
      "    654  NC\n",
      "    655  NC\n",
      "    657  NC\n",
      "    658  NC\n",
      "    659  NC\n",
      "    661  NC\n",
      "    665  NC\n",
      "    666  NC\n",
      "    669  NC\n",
      "    672  NC\n",
      "    673  NC\n",
      "    675  NC\n",
      "    676  NC\n",
      "    680  NC\n",
      "    681  NC\n",
      "    683  NC\n",
      "    686  NC\n",
      "    687  NC\n",
      "    688  NC\n",
      "    690  ND\n",
      "    692  ND\n",
      "    695  ND\n",
      "    696  ND\n",
      "    697  ND\n",
      "    701  NE\n",
      "    702  NE\n",
      "    703  NE\n",
      "    704  NE\n",
      "    709  NE\n",
      "    711  NE\n",
      "    712  NE\n",
      "    715  NH\n",
      "    716  NH\n",
      "    719  NH\n",
      "    722  NH\n",
      "    723  NH\n",
      "    725  NH\n",
      "    731  NJ\n",
      "    732  NJ\n",
      "    735  NJ\n",
      "    736  NJ\n",
      "    737  NJ\n",
      "    738  NJ\n",
      "    741  NJ\n",
      "    743  NJ\n",
      "    744  NJ\n",
      "    745  NJ\n",
      "    749  NJ\n",
      "    750  NJ\n",
      "    753  NJ\n",
      "    755  NM\n",
      "    756  NM\n",
      "    768  NY\n",
      "    770  NY\n",
      "    771  NY\n",
      "    776  NY\n",
      "    777  NY\n",
      "    781  NY\n",
      "    782  NY\n",
      "    788  NY\n",
      "    791  NY\n",
      "    792  NY\n",
      "    793  NY\n",
      "    794  NY\n",
      "    800  NY\n",
      "    802  NY\n",
      "    803  NY\n",
      "    813  NY\n",
      "    814  NY\n",
      "    822  NY\n",
      "    823  NY\n",
      "    824  NY\n",
      "    825  NY\n",
      "    827  NY\n",
      "    830  NY\n",
      "    832  NY\n",
      "    833  NY\n",
      "    835  NY\n",
      "    836  NY\n",
      "    837  NY\n",
      "    838  NY\n",
      "    839  NY\n",
      "    840  NY\n",
      "    842  NY\n",
      "    843  NY\n",
      "    844  NY\n",
      "    845  NY\n",
      "    847  NY\n",
      "    850  NY\n",
      "    859  NY\n",
      "    868  OH\n",
      "    869  OH\n",
      "    871  OH\n",
      "    873  OH\n",
      "    874  OH\n",
      "    877  OH\n",
      "    878  OH\n",
      "    881  OH\n",
      "    884  OH\n",
      "    888  OH\n",
      "    890  OH\n",
      "    891  OH\n",
      "    892  OH\n",
      "    893  OH\n",
      "    895  OH\n",
      "    897  OH\n",
      "    900  OH\n",
      "    902  OH\n",
      "    903  OH\n",
      "    906  OH\n",
      "    910  OH\n",
      "    911  OH\n",
      "    915  OH\n",
      "    916  OH\n",
      "    927  OK\n",
      "    928  OK\n",
      "    930  OK\n",
      "    931  OK\n",
      "    932  OK\n",
      "    938  OK\n",
      "    942  OR\n",
      "    944  OR\n",
      "    949  OR\n",
      "    951  OR\n",
      "    954  OR\n",
      "    957  PA\n",
      "    958  PA\n",
      "    962  PA\n",
      "    964  PA\n",
      "    966  PA\n",
      "    968  PA\n",
      "    970  PA\n",
      "    973  PA\n",
      "    974  PA\n",
      "    976  PA\n",
      "    977  PA\n",
      "    978  PA\n",
      "    985  PA\n",
      "    986  PA\n",
      "    987  PA\n",
      "    988  PA\n",
      "    990  PA\n",
      "    991  PA\n",
      "    993  PA\n",
      "    995  PA\n",
      "    996  PA\n",
      "   1000  PA\n",
      "   1008  PA\n",
      "   1009  PA\n",
      "   1013  PA\n",
      "   1016  PA\n",
      "   1019  PA\n",
      "   1020  PA\n",
      "   1022  PA\n",
      "   1023  PA\n",
      "   1024  PA\n",
      "   1025  PA\n",
      "   1026  PA\n",
      "   1028  PA\n",
      "   1029  PA\n",
      "   1030  PA\n",
      "   1031  PA\n",
      "   1032  PA\n",
      "   1034  PA\n",
      "   1035  PA\n",
      "   1036  PA\n",
      "   1038  PA\n",
      "   1040  RI\n",
      "   1042  RI\n",
      "   1046  RI\n",
      "   1047  RI\n",
      "   1050  SC\n",
      "   1051  SC\n",
      "   1052  SC\n",
      "   1054  SC\n",
      "   1058  SC\n",
      "   1059  SC\n",
      "   1060  SC\n",
      "   1063  SC\n",
      "   1064  SC\n",
      "   1074  SD\n",
      "   1078  SD\n",
      "   1080  SD\n",
      "   1083  SD\n",
      "   1086  TN\n",
      "   1088  TN\n",
      "   1089  TN\n",
      "   1094  TN\n",
      "   1095  TN\n",
      "   1097  TN\n",
      "   1100  TN\n",
      "   1101  TN\n",
      "   1104  TN\n",
      "   1106  TN\n",
      "   1109  TN\n",
      "   1110  TN\n",
      "   1114  TN\n",
      "   1116  TN\n",
      "   1117  TN\n",
      "   1120  TX\n",
      "   1124  TX\n",
      "   1126  TX\n",
      "   1130  TX\n",
      "   1131  TX\n",
      "   1137  TX\n",
      "   1138  TX\n",
      "   1142  TX\n",
      "   1145  TX\n",
      "   1151  TX\n",
      "   1153  TX\n",
      "   1155  TX\n",
      "   1157  TX\n",
      "   1162  TX\n",
      "   1163  TX\n",
      "   1165  TX\n",
      "   1167  TX\n",
      "   1171  TX\n",
      "   1175  TX\n",
      "   1176  TX\n",
      "   1180  UT\n",
      "   1184  UT\n",
      "   1187  VA\n",
      "   1188  VA\n",
      "   1191  VA\n",
      "   1193  VA\n",
      "   1194  VA\n",
      "   1195  VA\n",
      "   1197  VA\n",
      "   1203  VA\n",
      "   1205  VA\n",
      "   1211  VA\n",
      "   1213  VA\n",
      "   1217  VA\n",
      "   1220  VA\n",
      "   1221  VA\n",
      "   1222  VA\n",
      "   1226  VT\n",
      "   1230  VT\n",
      "   1231  VT\n",
      "   1235  VT\n",
      "   1236  VT\n",
      "   1237  VT\n",
      "   1238  VT\n",
      "   1245  WA\n",
      "   1252  WA\n",
      "   1256  WI\n",
      "   1257  WI\n",
      "   1261  WI\n",
      "   1267  WI\n",
      "   1268  WI\n",
      "   1272  WI\n",
      "   1273  WI\n",
      "   1274  WI\n",
      "   1283  WI\n",
      "   1284  WV\n",
      "   1291  WV\n",
      "   1301  WY\n",
      "Colorado Christian University\n",
      "475    3.771458e-17\n",
      "Name: # PT undergrad, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "raw_university_df = pd.read_csv('Universities.csv')\n",
    "\n",
    "# a: Remove all records with missing measurements\n",
    "university_df = raw_university_df.dropna() # 1302 rows -> 471 rows\n",
    "\n",
    "# Normalize\n",
    "university_df_num = university_df.select_dtypes(include='number') # get numeric cols only\n",
    "university_df_num = university_df_num.drop('Public (1)/ Private (2)', axis=1) # drop the discrete column\n",
    "university_df_num_norm = (university_df_num - university_df_num.mean(numeric_only=True))/university_df_num.std(numeric_only=True) # normalize\n",
    "university_df.update(university_df_num_norm) # merge\n",
    "\n",
    "# b: hierarchical clustering using complete linkage and Euclidean distance\n",
    "university_dist = pairwise.pairwise_distances(university_df_num_norm, \n",
    "                                     metric='euclidean')\n",
    "pd.DataFrame(university_dist, columns=university_df.index, index=university_df.index).head(5)\n",
    "\n",
    "uni_hclust = fcluster(linkage(university_df_num_norm, 'complete'), 6, criterion='maxclust')\n",
    "\n",
    "Z = linkage(university_df_num_norm, method='complete')\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "fig.subplots_adjust(bottom=0.23)\n",
    "plt.title('Hierarchical Clustering Dendrogram (Complete linkage)')\n",
    "plt.xlabel('University')\n",
    "dendrogram(Z, labels=university_df_num_norm.index, color_threshold=2.75)\n",
    "plt.axhline(y=10.5, color='black', linewidth=0.5, linestyle='dashed')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()\n",
    "# reasonable number of clusters for describing the data:\n",
    "# At distance of 10.5 (horizontal line in the dendrogram image) data can be reduced to 9 clusters\n",
    "\n",
    "# c:  Compare the summary statistics for each cluster\n",
    "cutree = cluster.hierarchy.cut_tree(Z, n_clusters=[5, 10])\n",
    "clust_stats = university_df_num_norm.agg(['mean', 'median'])\n",
    "print(clust_stats)\n",
    "\n",
    "# d: Use the categorical measurements to categorize\n",
    "state_table = tabulate(university_df[['State']], cutree)\n",
    "pub_priv_table = tabulate(university_df[['Public (1)/ Private (2)']], cutree)\n",
    "print(state_table)\n",
    "\n",
    "# e: Other external information\n",
    "# There are multiple external factors that can explain these clusters. Notably, that these clusters are\n",
    "# built with only partial information. Since the pre-processing step removed all entries with NaNs, the\n",
    "# total number of entries was reduced from 1302 to 471, which is a very large amount of missing data.\n",
    "# Second, school funding priorities can affect some of the school data. Depending on how funding is allocated\n",
    "# to sports, liberal arts, research, campus maintenance, events, etc, the underlying data may change.\n",
    "# The socioeconomic factors involved with private vs public universities may also change the data.\n",
    "\n",
    "# f: Compute the Euclidean distance of this record from each of the clusters that you found above (using only the measurements that you have)\n",
    "tufts_df = raw_university_df.loc[raw_university_df['College Name'] == 'Tufts University']\n",
    "tufts_df = tufts_df.drop(['# PT undergrad'], axis=1)\n",
    "tufts_df_num = tufts_df.select_dtypes(include='number') # get numeric cols only\n",
    "\n",
    "tufts_dist = pairwise.pairwise_distances(tufts_df_num, Y=university_df_num_norm, metric='euclidean')\n",
    "\n",
    "# Closest cluster:\n",
    "print(raw_university_df.iloc[np.where(tufts_dist == tufts_dist.min())[1][0]]['College Name'])\n",
    "\n",
    "# impute missing (from raw data - non-normalized)\n",
    "tufts_df['# PT undergrad'] = clust_stats['# PT undergrad']['mean']\n",
    "print(tufts_df['# PT undergrad'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 15.4\n",
    "The file EastWestAirlinesCluster.csv contains information on 3999 passengers who belong to an airline’s frequent flier program. For each passenger, the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.\n",
    "\n",
    "a. \n",
    "    Apply hierarchical clustering with Euclidean distance and Ward’s method. Make sure to normalize the data first. How many clusters appear?\n",
    "b. \n",
    "    What would happen if the data were not normalized?\n",
    "c. \n",
    "    Compare the cluster centroid to characterize the different clusters, and try to give each cluster a label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SniPZuXOnACAeeOABp3WMHj3aLS3+nmOB/g7169cX2dnZ8vvff/+9ACD++9//uu2bUnmuE+kec+LECSGEECtXrhQAxJYtW7yuIzMz0+vvojzfJV988YUAIP7++2/5PX/z+iNHjgi1Wi1uvfVWYbfbnZaVfteCggIRHx8vJk2a5PR5enq6iIuLc3vflclkclv3iRMnhF6vd8rbKiOv8GbEiBHCYDAIi8UihBBi9uzZIjU1VQghxPz580XDhg3lZadOnSoAiLNnzwohPP8GGzZsEADEp59+Kr8n/fb9+vUTNptNfr8i6ff3XuWJdE5kZmaKqVOnilatWsmfXXHFFWL8+PFCiJI84cEHH5Q/W7p0qVCr1eKff/5xWt/ChQsFALFu3Tr5vTp16jjlG67bLut8lPKHe+65x2k56TdYs2aNEEKICxcuCJ1OJ2644QanMs4zzzwjAHhMwyuvvCIAiIyMDG+HiMrAbm+1zHvvvYfffvvN7Z9U2+bL8uXLcdVVV6Fu3bq4ePGi/G/QoEGw2+34+++/nZYfNmyY3K1BotFo5PEDDocD2dnZsNls6NGjB7Zv3+62Tdd1ZGZm4u+//8aECRPQtGlTp2U9TTPrOkbgqquuQlZWltxtyJNvv/0WnTt3xq233ur2mbSNQPejLPn5+VUyONFgMECn0+HPP/9ETk5OwN8Pxjngi6ff6/jx4/LrVatWISIiApMmTZLfU6vVePDBBwPeF2+io6NRUFAAoKS7wbfffosbb7wRQginfR4yZAjy8vLcfu/x48c7jZG56qqrAEDej61bt+LChQu47777nJYbN26c06BkpbFjx8JgMMivpXU88MADTuOnbrjhBrRt2xY//fQTAODcuXPYs2cP7r77bkRHR8vL9e/fH506dfK4rWBctyNGjHDal549ewIA7rzzTqeWmJ49e8JisXjsqqeUlZWFunXrev188uTJHvO19u3b+1yvJ/5c//743//+B6Bk0gAl15bCQM6xQH+H22+/3em4uZ6LFaG8TjyRWh5//PFHv3oWuFKe7yaTCRcvXpTHpnja17Ly+u+++w4OhwPTp093Gy8n/a6//fYbcnNzcccddzj9DhqNBj179iyzW7Ner5fXbbfbkZWVhejoaLRp08Zjmisjr3DVr18/GI1GbNu2DUBJF7g+ffoAAPr27YsLFy7gyJEj8mepqalyC5XyN7BarcjKykKrVq0QHx/vcX8mTZrkNO6rIumv6L1KMnr0aBw9ehRbtmyR//fW5W358uVo164d2rZt6/T7SxN7BNKtvazzUcofHnvsMaflpFY4KQ///fff5VZ0Zf7jmo8oSdd8VUy3XVux21stc+WVV6JHjx5u70uFWV+OHDmC3bt3ey3MXrhwwel1amqqx+WWLFmC119/HQcPHnS6KXpa3vU96abg77TcrgGSlCnk5OQgNjbW43eOHTuGYcOGlbnuQPajLLGxsUEpkJRFr9djzpw5ePzxx5GYmIhevXph6NChuPvuu5GUlFTm94N1DngSGRnptt66des63fjS0tKQnJzs1t2iVatWfm+nLIWFhXIgmpmZidzcXHzwwQf44IMPPC7vus++zjmgZB8AoHXr1k7LRUREoEWLFh634XocpXW0adPGbdm2bdvK3Vyk5Twdn1atWnkswATjunU9BlJBx7Wbi/S+P4UbIYTXz1q3bl2h8UBK/l7/ZUlLS4NarXbrDuP6mwV6jlXkd3A9FytCeZ140r9/fwwbNgwzZszAm2++iQEDBuCWW27B6NGj/ZpkJjs7GzNmzMCXX37pdo3l5eW5LV9WXn/s2DGo1WqfAbEUBHib7dHbPUPicDjw1ltvYf78+Thx4oTTeMX69esHlGagfHmFK+W4n549e2L9+vWYNWsWgJL7aGxsLNatW4cmTZpg27ZtuP322+XvGo1GzJ49Gx9//DHOnj3rdA16+g285VPlSX9F71WSrl27om3btli2bBni4+ORlJTk9fc9cuQIDhw44Pf9zZeyzkcpf3DNm5OSkhAfHy8fO2/HMCEhwWuFkPQ7BVJZQ84Y/JDM4XDgX//6l9vMKZLLLrvM6bWy1kjy2WefYdy4cbjlllvwxBNPoGHDhtBoNJg9ezaOHTvmtryndQTC2+xDvgpS/gh0P8rStm1b7NixA6dPny6zH7Qn3jI518kCgJIaoxtvvBHfffcdfvnlFzz//POYPXs21qxZU+bUwME4B7ypDjNFWa1WHD58WA6upfFJd955p9dxRa6tppVxzlX0OqjotgI9370dg/Iem/r16welwO6Jp2ukKgVyjgXrd6ho/ud6nXiiUqnwzTffYOPGjfjvf/+LX375BRMmTMDrr7+OjRs3OrVEejJy5EisX78eTzzxBLp06YLo6Gg4HA5cd911HscNBmNfpfUuXbrUYwG7rPFjr7zyCp5//nlMmDABL730EurVqwe1Wo1HHnmk0tJcls6dOyMmJgZr167F9ddfj+zsbLnlR61Wo2fPnli7di1atmwJi8XiNOHOQw89hI8//hiPPPIIevfujbi4OKhUKowaNcrj/gQ7n6rIvUpp9OjRWLBgAWJiYnD77bd7nSnT4XCgU6dOeOONNzx+Hsi92d/ftjICFCmvDGR8Ijlj8EOyli1borCwsEI1rN988w1atGiBFStWOF30L7zwgl/fl2qL9u7dW+40lKVly5Zlrr+i++HqxhtvxBdffIHPPvsM06ZNC/j7Ug1Qbm6u0/tSrZGrli1b4vHHH8fjjz+OI0eOoEuXLnj99dfx2WefAfCeIQfjHKiIZs2a4Y8//kBxcbFT64+nGZLK45tvvoHRaMSQIUMAQJ6ly263B22fmzVrBqCkllFZA2m1WnHixAmPDyX0to5Dhw651WIeOnRI/lz639PxCeSYBft8D1Tbtm3x+eefuz2vJBB169Z1uz4sFgvOnz/v9J4/178/mjVrBofDgWPHjjm19hw6dMhpuUDOsVD/Dsp0KK8TX3r16oVevXrh5ZdfxrJlyzBmzBh8+eWXuOeee7zmMzk5OVi9ejVmzJiB6dOny+9LLTPl0bJlSzgcDuzfv9/rhDRSK13Dhg3Ldb1/8803uOaaa7B48WKn93Nzc8tVEA1GXqHRaNCrVy+sW7cOa9euRWxsrFOX1z59+uCrr76SWyCUwc8333yDsWPH4vXXX5ffM5lMbtdRZaa/rHuVP0aPHo3p06fj/PnzWLp0qc9t7dq1CwMHDiwzKKlo0CLlD0eOHEG7du3k9zMyMpCbm+uWhx85csSptSwzM9NrhdCJEyfQoEGDgLqckzOO+SHZyJEjsWHDBvzyyy9un+Xm5sJms5W5Dqk2RFn7sWnTJmzYsMGvNCQkJODqq6/GRx99hFOnTjl9FqzasmHDhmHXrl1Os624bqOi++Fq+PDh6NSpE15++WWP6ygoKPA4lbekWbNm0Gg0bmNu5s+f7/S6uLgYJpPJ6b2WLVsiJibGaXrNOnXqeLzBBeMcqIghQ4bAarVi0aJF8nsOhwPvvfdehde9a9cuPPLII6hbt648hkij0WDYsGH49ttvPRaIy/Ok+x49eiAhIQELFy50mq3nk08+8btQ0aNHDzRs2BALFy50+t1+/vlnHDhwQJ7JKCUlBR07dsSnn36KwsJCebm//voLe/bs8TvNwT7fA9W7d28IIeRxC+XRsmVLt+vjgw8+cGv58ef694f0XJW3337b6X3XGREDOcdC/TsAnq8TT3JyctyOlxR0SOesVIHhet572k/A/dgF4pZbboFarcbMmTPdWi2k7QwZMgSxsbF45ZVXPI5TKut612g0bmlevnx5mWPavAlGXgGUBDSZmZn4+OOP0bNnT6eWjz59+uDQoUP4/vvvUb9+faeCuKf9eeedd/xuLa1I+v29V/mjZcuWmDdvHmbPno0rr7zS63IjR47E2bNnne4vEqPRKM9gB3i/R/pLeiiz6zkttTpJefigQYMQERGBd955x+m38HUtbNu2rVY8pDaU2PJDsieeeAI//PADhg4dinHjxqF79+4oKirCnj178M033+DkyZNl1m4NHToUK1aswK233oobbrgBJ06cwMKFC9G+fXunwpkvb7/9Nvr164du3bph8uTJSE1NxcmTJ/HTTz9h586dQdnPb775BiNGjMCECRPQvXt3ZGdn44cffsDChQvRuXPnoOyHUkREBFasWIFBgwbh6quvxsiRI9G3b19ERERg3759WLZsGerWret1Wtm4uDiMGDEC77zzDlQqFVq2bIkff/zRrY/y4cOHMXDgQIwcORLt27eHVqvFypUrkZGRgVGjRsnLde/eHQsWLMCsWbPQqlUrNGzYENdee21QzoGKuOWWW3DllVfi8ccfx9GjR9G2bVv88MMP8rNi/K2N++eff2AymeRByevWrcMPP/yAuLg4rFy50qnLy6uvvoo//vgDPXv2xKRJk9C+fXtkZ2dj+/bt+P333/16To1SREQEZs2ahXvvvRfXXnstbr/9dpw4cQIff/yx3/34IyIiMGfOHIwfPx79+/fHHXfcIU913bx5czz66KPysq+88gpuvvlm9O3bF+PHj0dOTg7effdddOzY0e9zNdjne6D69euH+vXr4/fff/fYX3/79u0ea4JbtmwpFwLuuece3HfffRg2bBj+9a9/YdeuXfjll1/czld/rn9/dOnSBXfccQfmz5+PvLw89OnTB6tXr/bY4ubvOVbVv0Mg14mrJUuWYP78+bj11lvRsmVLFBQUYNGiRYiNjZULfgaDAe3bt8dXX32Fyy67DPXq1UPHjh3RsWNHXH311Zg7dy6sVisaNWqEX3/9FSdOnCj3vrRq1QrPPvssXnrpJVx11VW47bbboNfrsWXLFqSkpGD27NmIjY3FggULcNddd6Fbt24YNWoUEhIScOrUKfz000/o27ev2zPAlIYOHYqZM2di/Pjx6NOnD/bs2YPPP//c7+vaVTDyCqC0NWfDhg1uz7qSpljfuHEjbrzxRqc8dOjQoVi6dCni4uLQvn17bNiwAb///rvH8UvBTr+/9yp//d///V+Zy9x11134+uuvcd999+GPP/5A3759YbfbcfDgQXz99df45Zdf5DHT3bt3x++//4433ngDKSkpSE1NlSd28Ufnzp0xduxYfPDBB8jNzUX//v2xefNmLFmyBLfccguuueYaAJCfdzd79mwMHToU119/PXbs2IGff/7Z4732woUL2L17d1AnAQpLVTOpHFU2aSpKb9OO9u/fv8yproUomQp02rRpolWrVkKn04kGDRqIPn36iNdee02eSlOaZtXTFJUOh0O88sorolmzZkKv14uuXbuKH3/80W3qZl/rEEKIvXv3iltvvVXEx8eLyMhI0aZNG/H888/LnyunuvR0HKTpWL3tZ1ZWlpgyZYpo1KiR0Ol0onHjxmLs2LHyVLT+7ocQ/k11LcnJyRHTp08XnTp1ElFRUSIyMlJ07NhRTJs2TZw/f15eztN2MjMzxbBhw0RUVJSoW7euuPfee8XevXudpuS9ePGiePDBB0Xbtm1FnTp1RFxcnOjZs6f4+uuvndaVnp4ubrjhBhETE+M2JWlFzwFvU13XqVPHbVnX6YOl/Rw9erSIiYkRcXFxYty4cWLdunUCgPjyyy99Hl9pilnpX0REhEhISBBXX321ePnll92meJZkZGSIBx98UDRp0kRERESIpKQkMXDgQPHBBx+4rdt1emBP+ytEyTSzqampQq/Xix49eoi///5b9O/f3+NU196mHP7qq69E165dhV6vF/Xq1RNjxowRZ86ccVvuyy+/FG3bthV6vV507NhR/PDDD2LYsGGibdu2bumsjOvW236UlS8pPfzww05T1iq35+2f8rq22+3iqaeeEg0aNBBRUVFiyJAh4ujRo+W6/v2Z6loIIYxGo3j44YdF/fr1RZ06dcSNN94oTp8+7TFP8OccC0b+6U9+VJ7rxDVv3b59u7jjjjtE06ZNhV6vFw0bNhRDhw4VW7dudfre+vXrRffu3YVOp3NK25kzZ+Q8Pi4uTowYMUKcO3fOLf2B5PVCCPHRRx/J10zdunVF//79xW+//ea2/0OGDBFxcXEiMjJStGzZUowbN84t7a5MJpN4/PHHRXJysjAYDKJv375iw4YNfl/XFckrfCkqKhJarVYAEL/++qvb55dffrkAIObMmeP0fk5Ojhg/frxo0KCBiI6OFkOGDBEHDx50u2bKuo7Lk35/71WeeDsnXMFlqmshSqbnnjNnjujQoYN8jnTv3l3MmDFD5OXlycsdPHhQXH311cJgMDjlNYGcj1arVcyYMUOkpqaKiIgI0aRJEzFt2jS3qf/tdruYMWOGfF4NGDBA7N2712PetWDBAhEVFSXy8/PLPE7knUqIII68IyKqBN999x1uvfVWrF27Fn379g11cmqELl26ICEhAb/99luok+KX48ePo23btvj5558xcODAUCeHiKja6dq1KwYMGIA333wz1Emp0Tjmh4iqFaPR6PTabrfjnXfeQWxsLLp16xaiVFVfVqvVbSzWn3/+iV27dmHAgAGhSVQ5tGjRAhMnTsSrr74a6qQQEVU7q1atwpEjR8o1aRI5Y8sPEVUr99xzD4xGI3r37g2z2YwVK1Zg/fr1eOWVV5jpe3Dy5EkMGjQId955J1JSUnDw4EEsXLgQcXFx2Lt3r9/994mIiMIBgx8iqlaWLVuG119/HUePHoXJZEKrVq1w//33Y8qUKaFOWrWUl5eHyZMnY926dcjMzESdOnUwcOBAvPrqq24P4CQiIgp3DH6IiIiIiCgscMwPERERERGFBQY/REREREQUFmrkQ04dDgfOnTuHmJgYvx96SEREREREtY8QAgUFBUhJSYFa7bttp0YGP+fOnUOTJk1CnQwiIiIiIqomTp8+jcaNG/tcpkYGPzExMQBKdjA2NjbEqSEiIiIiolDJz89HkyZN5BjBlxoZ/Ehd3WJjYxn8EBERERGRX8NhOOEBERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhYWAg5+///4bN954I1JSUqBSqfDdd9/Jn1mtVjz11FPo1KkT6tSpg5SUFNx99904d+6c0zqys7MxZswYxMbGIj4+HhMnTkRhYWGFd4aIiIiIiMibgIOfoqIidO7cGe+9957bZ8XFxdi+fTuef/55bN++HStWrMChQ4dw0003OS03ZswY7Nu3D7/99ht+/PFH/P3335g8eXL594KIiIiIiKgMKiGEKPeXVSqsXLkSt9xyi9dltmzZgiuvvBJpaWlo2rQpDhw4gPbt22PLli3o0aMHAGDVqlW4/vrrcebMGaSkpJS53fz8fMTFxSEvL4/P+SEiIiIiCmOBxAaVPuYnLy8PKpUK8fHxAIANGzYgPj5eDnwAYNCgQVCr1di0aZPHdZjNZuTn5zv9IyIiIiIiCkSlBj8mkwlPPfUU7rjjDjkKS09PR8OGDZ2W02q1qFevHtLT0z2uZ/bs2YiLi5P/NWnSpDKTTUREREREtVClBT9WqxUjR46EEAILFiyo0LqmTZuGvLw8+d/p06eDlEoiIiIiIgoX2spYqRT4pKWlYc2aNU5975KSknDhwgWn5W02G7Kzs5GUlORxfXq9Hnq9vjKSSkREREREYSLoLT9S4HPkyBH8/vvvqF+/vtPnvXv3Rm5uLrZt2ya/t2bNGjgcDvTs2TPYySEiIiIiIgJQjpafwsJCHD16VH594sQJ7Ny5E/Xq1UNycjKGDx+O7du348cff4TdbpfH8dSrVw86nQ7t2rXDddddh0mTJmHhwoWwWq2YMmUKRo0a5ddMb1QxQggYrfZQJ4OIqNowRGigUqlCnQwiIqoCAU91/eeff+Kaa65xe3/s2LF48cUXkZqa6vF7f/zxBwYMGACg5CGnU6ZMwX//+1+o1WoMGzYMb7/9NqKjo/1KA6e6Lh8hBIYv3IBtaTmhTgoRUbXRo1ldLL+vNwMgIqIaKpDYIOCWnwEDBsBXvORPLFWvXj0sW7Ys0E1TBRmtdgY+REQutqblwGi1I0pXKcNgiYioGmFOH6a2PjcIUTpNqJNBRBQyxRY7esz6PdTJICKiKsTgJ0xF6TSs5SQiIiKisFKpDzklIiIiIiKqLhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWAg4+Pn7779x4403IiUlBSqVCt99953T50IITJ8+HcnJyTAYDBg0aBCOHDnitEx2djbGjBmD2NhYxMfHY+LEiSgsLKzQjhAREREREfkScPBTVFSEzp0747333vP4+dy5c/H2229j4cKF2LRpE+rUqYMhQ4bAZDLJy4wZMwb79u3Db7/9hh9//BF///03Jk+eXP69ICIiIiIiKoM20C/8+9//xr///W+PnwkhMG/ePDz33HO4+eabAQCffvopEhMT8d1332HUqFE4cOAAVq1ahS1btqBHjx4AgHfeeQfXX389XnvtNaSkpFRgd4iIiIiIiDwL6pifEydOID09HYMGDZLfi4uLQ8+ePbFhwwYAwIYNGxAfHy8HPgAwaNAgqNVqbNq0yeN6zWYz8vPznf4REREREREFIqjBT3p6OgAgMTHR6f3ExET5s/T0dDRs2NDpc61Wi3r16snLuJo9ezbi4uLkf02aNAlmsomIiIiIKAzUiNnepk2bhry8PPnf6dOnQ50kIiIiIiKqYYIa/CQlJQEAMjIynN7PyMiQP0tKSsKFCxecPrfZbMjOzpaXcaXX6xEbG+v0j4iIiIiIKBBBDX5SU1ORlJSE1atXy+/l5+dj06ZN6N27NwCgd+/eyM3NxbZt2+Rl1qxZA4fDgZ49ewYzOURERERERLKAZ3srLCzE0aNH5dcnTpzAzp07Ua9ePTRt2hSPPPIIZs2ahdatWyM1NRXPP/88UlJScMsttwAA2rVrh+uuuw6TJk3CwoULYbVaMWXKFIwaNYozvRERERERUaUJOPjZunUrrrnmGvn1Y489BgAYO3YsPvnkEzz55JMoKirC5MmTkZubi379+mHVqlWIjIyUv/P5559jypQpGDhwINRqNYYNG4a33347CLtDRERERETkWcDBz4ABAyCE8Pq5SqXCzJkzMXPmTK/L1KtXD8uWLQt000REREREROVWI2Z7IyIiIiIiqigGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQVtqBNARES1hxACRqs91MnwS7HF5vHv6swQoYFKpQp1MoiIaiwGP0REFBRCCAxfuAHb0nJCnZSA9Zi1OtRJ8EuPZnWx/L7eDICIiMqJ3d6IiCgojFZ7jQx8apKtaTk1pmWNiKg6YssPEREF3dbnBiFKpwl1MmqNYosdPWb9HupkEBHVeAx+iIgo6KJ0GkTpeIshIqLqhd3eiIiIiIgoLDD4ISIiIiKisMDgh4iIiIiIwgKDHyIiIiIiCgsMfoiIiIiIKCww+CEiIiIiorDA4IeIiIiIiMICgx8iIiIiIgoLNf4JdEIIGK32UCejRii22Dz+TWUzRGigUqlCnQyiWoX5t/+Yf5cf828iUqrRwY8QAsMXbsC2tJxQJ6XG6TFrdaiTUKP0aFYXy+/rzRsoUZAw/y4/5t+BYf5NREo1utub0WrnjZOqxNa0HNZQEwUR82+qKsy/iUipRrf8KG19bhCidJpQJ4NqmWKLHT1m/R7qZBDVasy/qTIw/yYiT2pN8BOl0yBKV2t2h4gobDD/JiKiqlKju70RERERERH5i8EPERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGEh6MGP3W7H888/j9TUVBgMBrRs2RIvvfQShBDyMkIITJ8+HcnJyTAYDBg0aBCOHDkS7KQQERERERHJgh78zJkzBwsWLMC7776LAwcOYM6cOZg7dy7eeecdeZm5c+fi7bffxsKFC7Fp0ybUqVMHQ4YMgclkCnZyiIiIiIiIAADaYK9w/fr1uPnmm3HDDTcAAJo3b44vvvgCmzdvBlDS6jNv3jw899xzuPnmmwEAn376KRITE/Hdd99h1KhRwU4SERERERFR8Ft++vTpg9WrV+Pw4cMAgF27dmHt2rX497//DQA4ceIE0tPTMWjQIPk7cXFx6NmzJzZs2OBxnWazGfn5+U7/iIiIiIiIAhH0lp+nn34a+fn5aNu2LTQaDex2O15++WWMGTMGAJCeng4ASExMdPpeYmKi/Jmr2bNnY8aMGcFOKhERERERhZGgt/x8/fXX+Pzzz7Fs2TJs374dS5YswWuvvYYlS5aUe53Tpk1DXl6e/O/06dNBTDEREREREYWDoLf8PPHEE3j66aflsTudOnVCWloaZs+ejbFjxyIpKQkAkJGRgeTkZPl7GRkZ6NKli8d16vV66PX6YCeViIiIiIjCSNBbfoqLi6FWO69Wo9HA4XAAAFJTU5GUlITVq1fLn+fn52PTpk3o3bt3sJNDREREREQEoBJafm688Ua8/PLLaNq0KTp06IAdO3bgjTfewIQJEwAAKpUKjzzyCGbNmoXWrVsjNTUVzz//PFJSUnDLLbcEOzlEREREREQAKiH4eeedd/D888/jgQcewIULF5CSkoJ7770X06dPl5d58sknUVRUhMmTJyM3Nxf9+vXDqlWrEBkZGezkEBERERERAaiE4CcmJgbz5s3DvHnzvC6jUqkwc+ZMzJw5M9ibJyIiIiIi8ijoY36IiIiIiIiqIwY/REREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhQRvqBBAREVHlEkLAaLWHOhlVqthi8/h3ODBEaKBSqUKdDKJqicEPERFRLSaEwPCFG7AtLSfUSQmZHrNWhzoJVapHs7pYfl9vBkBEHrDbGxERUS1mtNrDOvAJR1vTcsKupY/IX2z5ISIiChNbnxuEKJ0m1MmgSlJssaPHrN9DnQyiao3BDxERUZiI0mkQpeOtn4jCF7u9ERERERFRWGD1Ty0XjjP8BFM4zxYUbJx9iIiIiEKNwU8txhl+givcZgsKNs4+RERERKHGbm+1GGf4oeqEsw8RERFRqLHlJ0xwhh8KFc4+REQ1WU3qPl6Tu2qzazRVFQY/YYIz/BAREQWmJncfr2ldtdk1mqoKu70RERERecDu41WHXaOpqrApgIiIiKgM7D5eOdg1mqoagx8iIiKiMrD7OFHtwG5vREREREQUFhj8EBERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYYPBDRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWFAJIUSoExGo/Px8xMXF4V+Dh2Dt8Rw0GDoVN5j/xNkzp9GxY0dMmTIF9913HwBg0qRJsFqt+OSTTwAAH330EebMmYNDhw6hVatWeO655zBu3DgAwF133YWoqCi8//77AID58+fj/fffx65du9CkSRP85z//wahRowAAI0eORHJyMt566y0AwJtvvokvvvgCmzdvRsOGDfH+++/j1ltvBQDcfPPNaNOmDebOnQsAePXVV/HTTz/hn3/+QWxsLD777DPcdtttsNlsGDJkCK688kq89NJLAIAXX3wR69atw2+//Qa9Xo/ly5dj9OjRKCwsRP/+/TF48GA8++yzAICnn34ae/fuxY8//ggA+PKbFWjY/d9wmPLx1NibMPr2kXj88ccBAI899hhOnTqFb775BgDw9ddf49FHH8XZs2fRtWtXTJw4EVOmTAEAPPDAA8jLy8Pnn38OAFi6dCleeOEFHD9+HO3atcNjjz2GSZMmAQAmTJggH2cAWLRoEd544w0cOHAALVq0wIwZM3DXXXcBAMaMGYO4uDjMnz8fAPDuu+9i8eLF2LFjBxo1aoQ333wTI0eOBAAMHz4cTZs2xRtvvAEAeP311/HNN99gw4YNqF+/Pj7++GPcdNNNAIChQ4eiY8eOePXVVwEAL7/8Mn799Vf89ddfiI6OxrJlyzBixAiYzWb861//Qt++ffHiiy8CAJ5//nls3rwZv/zyC7RaLVasWIE777wT+fn5uOqqq3DDDTfg6aefBgA8+eSTOHToEL7//nsAwMqVK3HvvffiwoULuPLKK3HHHXfg0UcfBQD83//9H86fP4+vv/665Lf58ks88cQTOH36NDp37ox7770XDzzwAADg3nvvRXFxMZYuXQoA+OSTTzBr1iwcPXoUbdq0wVNPPSUf53HjxiEiIgKLFi0CACxcuBDvvvsu9u7di2bNmuGVV17BmDFjAAB33HEH6tevj3fffRcA8Pbbb+PTTz/F1q1bkZycjHfffRfDhg0DANx6661o2bIlXnvtNQDA3Llz8f3332PdunWoW7culixZgltuuQUOhwPXX389unbtipdffhkAMHPmTPz5559Ys2YNDAYDPl76ORI6XwthM+PFe0di8KCBmD59OgDg2WefxY4dO/C///0ParUa3333HcaOHYucnBz07dsXN998M5588kkAwNSpU3Hs2DGsXLkSAPDtt99iypQpOH/+PHr06IG7774bDz/8MABgypQpyMrKwhdffAEA+Pzzz/HMM88gLS2NeYQij/jhhx8wfvx4ZGVloXfv3hg+fHiF8ohPl36GPw5dQIOhj+NG21qcTjvpNY9Y9OFi/HHoAupf9xBG6Hfh6OFDzCMqKY9wQI27n5oNANjz23Is/mBhtcojvvrqK9x+++0wGo249tprMWDAgGqTR9gdAlsiuwJ2O66w7oZGrWIeEeRyRKvL2mC5uTOyVr2Da9o0xKR7JgJgOYLliMDyiO+//x6rVq1CXl4eYmNj4UuNDn7OZ2ah12sbAAD7Zw5BlE4b4pRVL8UWG9pP/wUAjw+FDs/D8BHIb83zourwWJcfj13l4zGmYJBiA3+CH3Z7IyIiIiKisMDgh4iIiIiIwgKDHyIiIiIiCgsMfoiIiIiIKCww+CEiIiIiorDA4IeIiIiIiMICgx8iIiIiIgoLDH6IiIiIiCgsMPghIiIiIqKwwOCHiIiIiIjCQqUEP2fPnsWdd96J+vXrw2AwoFOnTti6dav8uRAC06dPR3JyMgwGAwYNGoQjR45URlKIiIiIiIgAVELwk5OTg759+yIiIgI///wz9u/fj9dffx1169aVl5k7dy7efvttLFy4EJs2bUKdOnUwZMgQmEymYCeHiIiIiIgIAKAN9grnzJmDJk2a4OOPP5bfS01Nlf8WQmDevHl47rnncPPNNwMAPv30UyQmJuK7777DqFGjgp0kIiIiIiKi4Lf8/PDDD+jRowdGjBiBhg0bomvXrli0aJH8+YkTJ5Ceno5BgwbJ78XFxaFnz57YsGGDx3WazWbk5+c7/SMiIiIiIgpE0IOf48ePY8GCBWjdujV++eUX3H///Xj44YexZMkSAEB6ejoAIDEx0el7iYmJ8meuZs+ejbi4OPlfkyZNgp1sIiIiIiKq5YIe/DgcDnTr1g2vvPIKunbtismTJ2PSpElYuHBhudc5bdo05OXlyf9Onz4dxBQTEREREVE4CHrwk5ycjPbt2zu9165dO5w6dQoAkJSUBADIyMhwWiYjI0P+zJVer0dsbKzTPyIiIiIiokAEPfjp27cvDh065PTe4cOH0axZMwAlkx8kJSVh9erV8uf5+fnYtGkTevfuHezkEBERERERAaiE2d4effRR9OnTB6+88gpGjhyJzZs344MPPsAHH3wAAFCpVHjkkUcwa9YstG7dGqmpqXj++eeRkpKCW265JdjJISIiIiIiAlAJwc8VV1yBlStXYtq0aZg5cyZSU1Mxb948jBkzRl7mySefRFFRESZPnozc3Fz069cPq1atQmRkZLCTQ0REREREBKASgh8AGDp0KIYOHer1c5VKhZkzZ2LmzJmVsXkiIiIiIiI3lRL8EBERhQMhBIxWe6iT4VOxxebx7+rKEKGBSqUKdTKIqJZi8ENERFQOQggMX7gB29JyQp0Uv/WYtbrshUKsR7O6WH5fbwZARFQpgj7bGxERUTgwWu01KvCpKbam5VT71jQiqrnY8kNERFRBW58bhCidJtTJqNGKLXb0mPV7qJNBRLUcgx8iIqIKitJpEKXjLZWIqLpjtzciIiIiIgoLDH6IiIiIiCgsMPghIiIiIqKwwOCHiIiIiIjCAoMfIiIiIiIKCwx+iIiIiIgoLDD4ISIiIiKisMDgh4iIiIiIwgKDHyIiIiIiCgsMfoiIiIiIKCww+CEiIiIiorDA4IeIiIiIiMICgx8iIiIiIgoLDH6IiIiIiCgsMPghIiIiIqKwwOCHiIiIiIjCAoMfIiIiIiIKCwx+iIiIiIgoLDD4ISIiIiKisMDgh4iIiIiIwgKDHyIiIiIiCgsMfoiIiIiIKCxoQ50AIiIiIgo+IQSMVnuok+FTscXm8e/qyhChgUqlCnUyqAIY/BARERHVMkIIDF+4AdvSckKdFL/1mLU61EkoU49mdbH8vt4MgGowdnsjIiIiqmWMVnuNCnxqiq1pOdW+NY18Y8sPERERUS229blBiNJpQp2MGq3YYkePWb+HOhkUBAx+iIiIiGqxKJ0GUToW+YgABj9EREQUROUdZB+Mge8cjE5EZWHwQ0REREERrEH25R34zsHoRFQWTnhAREREQRHqQfYcjE5EZWHLDxEREQVdVQ6y52B0IvIXgx8iIiIKOg6yJ6LqiN3eiIiIiIgoLLBKhoiIiIiqnfLOHFgZgjEbYbBxdsPyYfBDRERERNVKsGYOrAzlnY0w2Di7Yfmw2xsRERERVSuhnjmwJuDshuXDlh8iIiIiqraqcubAmoCzG1YMgx8iIiIiqrY4cyAFE7u9ERERERFRWGDwQ0REREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWGPwQEREREVFYqPTg59VXX4VKpcIjjzwiv2cymfDggw+ifv36iI6OxrBhw5CRkVHZSSEiIiIiojBWqcHPli1b8P777+Pyyy93ev/RRx/Ff//7Xyxfvhx//fUXzp07h9tuu60yk0JERERERGGu0oKfwsJCjBkzBosWLULdunXl9/Py8rB48WK88cYbuPbaa9G9e3d8/PHHWL9+PTZu3FhZySEiIiIiojCnrawVP/jgg7jhhhswaNAgzJo1S35/27ZtsFqtGDRokPxe27Zt0bRpU2zYsAG9evVyW5fZbIbZbJZf5+fnV1ayiYiIiIjKJISA0Wqv8u0WW2we/65KhggNVCpVSLZdUZUS/Hz55ZfYvn07tmzZ4vZZeno6dDod4uPjnd5PTExEenq6x/XNnj0bM2bMqIykEhEREREFRAiB4Qs3YFtaTkjT0WPW6tBst1ldLL+vd40MgILe7e306dP4v//7P3z++eeIjIwMyjqnTZuGvLw8+d/p06eDsl4iIiIiokAZrfaQBz6htDUtJyStXsEQ9Jafbdu24cKFC+jWrZv8nt1ux99//413330Xv/zyCywWC3Jzc51afzIyMpCUlORxnXq9Hnq9PthJJSIiIiKqkK3PDUKUThPqZFSJYosdPWb9HupkVEjQg5+BAwdiz549Tu+NHz8ebdu2xVNPPYUmTZogIiICq1evxrBhwwAAhw4dwqlTp9C7d+9gJ4eIiIiIqNJE6TSI0lXaMHoKsqD/UjExMejYsaPTe3Xq1EH9+vXl9ydOnIjHHnsM9erVQ2xsLB566CH07t3b42QHREREREREwRCSMPXNN9+EWq3GsGHDYDabMWTIEMyfPz8USSEiIiIiojBRJcHPn3/+6fQ6MjIS7733Ht57772q2DwRERGRT56mLS5rSuGaPN0vUbhiB0UiIiIKa/5MW+xpSuGaPN0vUbgK+lTXRERERDVJeactrsnT/RKFK7b8EBEREV3iz7TFtWG6X6JwxeCHiIiI6BJOW0xUu7HbGxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFTmdCREQVJoRAscUmv5b+NkRo+ABIIiKqNhj8EBFRhQghMHzhBqeHRPaYtbrk/2Z1sfy+3gyAiIioWmC3NyIiqhCj1e4U+ChtTcuB0Wqv4hQRERF5xpYfIiIKmq3PDUKUToNiix09Zv0e6uQQERE5YfBDRERBE6XTIErHWwsREVVP7PZGRERERERhgcEPERERERGFBQY/REREREQUFhj8EBERERFRWGDwQ0REREREYYFT8hAREVGNI4SQnyFVbLHJ7yv/BgBDhIYP2SUiGYMfompGeUOvDXwVSmoyFqiIQkcIgeELN3h8uG6PWaudXzeri+X39eb1SkQAGPwQVSu+bui1gWuhpCZjgYoodIxWu9/55Na0HBitdj5/iogA1PDgRwjh8W+imiqQGzqFFgtURNXD1ucGIUqncXu/2GJHj1m/hyBFRFSd1ei79j1Ltsl/3/nhZqx4oA9rYanW8HZDp9BigYqoeonSaVgJQUR+q9G5xZ6zeVDrowAAO07nshaWahXe0ImCK9jj6SpzPBvHlBERVQ6WrIiIqNar7PF0wR7PxjFlVNFgPdjBOQNyqi0Y/BARUa1X08bTcUxZeAt2sB6M4JwBOdUWzFWJiKjKhXLCmuo8no5jygionsE6A3KqLXgGB1l1ekZLdX2+CpvOicKbEAJjPtwsv67qCWs4no5qklAH6wzIqbZh7h9E1fkZLdXp+SpsOicKb0arHTtP58qvOWENkXcM1omCSx3qBNQm1bGZujqSms6JiIiIiKoSqxIqSaibqasjNp0TERERUSgx+KkkbKYmIqLazNMY17LGmnLMJxGFGkvnREREFBB/xrh6GmvKMZ9EFGoc80NEREQBKe8YV475JKJQY8sPERERlZs/Y1w55pOIqgsGP0RERFRuHONKRDUJcysiIiIKOuWECN4mQuAECERU1Rj8EBERUVD5mhBBORECJ0AgoqrGCQ+IiIgoqPydEIETIBBRVWPLDxEREVUaTxMicAIEIgoVBj9ERERUaTghAhFVJ+z2RkREREREYYHBDxERERERhQUGP0REREREFBYY/BARERERUVhg8ENERERERGGBwQ8REREREYUFBj9ERERERBQWOPE+ERFRJRFCwGi1+718scXm8W9/GCI0UKlUAX2HiCjcMPghIiKqBEIIDF+4AdvScsr1/R6zVge2fLO6WH5fbwZAREQ+sNsbERFRJTBa7eUOfMpja1pOQK1MREThKOgtP7Nnz8aKFStw8OBBGAwG9OnTB3PmzEGbNm3kZUwmEx5//HF8+eWXMJvNGDJkCObPn4/ExMRgJ4eIiCjktj43CFE6TaWsu9hiR49Zv1fKuomIapugBz9//fUXHnzwQVxxxRWw2Wx45plnMHjwYOzfvx916tQBADz66KP46aefsHz5csTFxWHKlCm47bbbsG7dumAnh4iIKOSidBpE6djTnIhCK9BxiK4qMi7RVajGKQY9J161apXT608++QQNGzbEtm3bcPXVVyMvLw+LFy/GsmXLcO211wIAPv74Y7Rr1w4bN25Er169gp0kIiIiogpRFhq9FQA56URgfBXEAylk87j7p6LjEF0FOi7R7fshGqdY6dVQeXl5AIB69eoBALZt2war1YpBgwbJy7Rt2xZNmzbFhg0bPAY/ZrMZZrNZfp2fn1/JqSYiovIQQqDYYvNYcGEBhSpbZQUovgqNygIgJ53wXyAF8bIK2Tzu/qnqcYhlkcYpVnWreKVuzeFw4JFHHkHfvn3RsWNHAEB6ejp0Oh3i4+Odlk1MTER6errH9cyePRszZsyozKQSUQhVtBm+KgWzyb8qVVXgMebDzdh5OtfpPangIhVQiCpDZQYo/hYaQ1WYq4mCWRDncQ9cZY5DLEuoxylW6lny4IMPYu/evVi7dm2F1jNt2jQ89thj8uv8/Hw0adKkoskjomog2M3wVamiTf5VqapqRl0DHyXORkaVqaoCFE+FxlAX5mq68hbEedzLL5zHIVbaXk+ZMgU//vgj/v77bzRu3Fh+PykpCRaLBbm5uU6tPxkZGUhKSvK4Lr1eD71eX1lJJaIQqm7N8LVVVdeMKgszLKBQVQskQJG6aipf+xLOhcbKwmNKVSnoZ5oQAg899BBWrlyJP//8E6mpqU6fd+/eHREREVi9ejWGDRsGADh06BBOnTqF3r3ZHYIonIWyGb62ClXgwcIMhZK/55+nluc7P9yMFQ/04fgRoloq6HemBx98EMuWLcP333+PmJgYeRxPXFwcDAYD4uLiMHHiRDz22GOoV68eYmNj8dBDD6F3796c6Y0ozLHATERVyVPL847TuRw/QlSLqYO9wgULFiAvLw8DBgxAcnKy/O+rr76Sl3nzzTcxdOhQDBs2DFdffTWSkpKwYsWKYCeFiIiIyC//PHlNqJNARFWgUrq9lSUyMhLvvfce3nvvvWBvnoiIqhFP4ynYnahsgcyAWJ4ZCDntuDsDu9wS/L/2yjvzJ6+90GObLlElCnQK54pOo8xMlaoTb+MpPrvnyhCmqvqryAyI/s5AyOeiELkr77UXyMyfvPZCj8EPUSWp6BTO5ZlGmZkqVSe+xlOQd1UxA2JNfy6KspeJPz1OKjMdRqudD/WtJXjthQceeaJKEoopnJmpUnX1z5PX4Kq5f4Q6GTVOsGdArMjsf8qW7FAW9oUQGPPhZvm1NDtbVfNWweX6UF8GQDVTdbr2KLhYQiKqApU9hTMzVaruOJ6ifKrLDIi+WrKrurBvtNqdHqYbqtbEsiq4WBlVs1WXa4+Cj78qURVgJkpENZk/LdnhXNjnQ32Jao7wy6GIiIio3FxbsmtLYb8i44hYwUVUc/BKJTeBzlDmr4rOZOYPDjCl2iJY12GwrzteY1RbC/oTl2yV/5bGEfFcr36qy9gzqrlqX+5FFVLRGcr8VZ6ZzPxaLweYUi1QWddhMK47XmNUW+0+kyf/LY0jqo1BXk1WncaeUc3Fq5qchGKGsmAK5z7nVHtU5+uQ1xiVh2tLpq8WSdbc124VmR6cY88oGHhmkFeVPUNZMNWWPudErqrLdchrjMqrrJZM1xZJ1tzXXsGcHry2jj2jysfgh7yqrf26awPWooYPXodU0wXaksma+9ormNODM2+k8uJZ46Iig4yDNbCYhVXyhbWoVN0EOjMWhS9fLZmsuQ8vnB6cQoXBj0IwBxlXZGAxC6vkC2tRqToRQmDMh5tDnQyqIVhbTxKeC7VHoA0HFWksCEYDAc86heoyyJiFVfIXa1Ep1IxWO3aezq3weth6REQUHN6CEX+CjkCDi4o2HATaWBCMBgKWrr0IxSBjFlYpUKw5o5qirAdI+nsjK2/X5GB0S2aXZHdCCLdja4gI/QQdROHK32DEW9ARaHBR1Q0HwWggqFWlJinTDcbNiYVKIqLyE0I45cXKB0hO/GRbudcZjK7J5e2WXFO6JPsKEP0NApX3Ul8Bjuvv0WPWavRoVhdLJlxRoX0govKpaDBSkeCiMhsOgtlAUKtK91KmWxNuTkREtdmdH27GZ/dcKb9WPkBy99k8T18pU6i7JteELsmBBIi+gkDpXgr4DnA8bUc6TkQUWoEEI8EILmpKw0H1T2GAasLNifwTSPeW8nZpYTcWosqx43RupRaAq7Jrck3qkhysAFEZwPgT4Gx9bhAA1JjjRBQOakowUtVqzRH558lrcNXcP0KdDAqSinRvCaRLC1sKiWom3tTLVp4A0Veg5yvACfWDeF27WVL5eKp0LKtysaKViJ66VQZjvUTe1Jo7h6EaPAGdgqequrewpZDIN6kw5K1wQtVXsAPEUAc4vrh2s3TFiRnK5k+lo6fKxYpUInraprQNVk6Gt0Af5h4Ilvio2quM7i01qRsLUah4KwwpCycc2F7K35s1a7SDz1c3S28F7FCfvxWdmCLY51F5Kx0rUonoa5uBrNdbcMvrrGYqz8PcF4/u4Pf6a03ww2dE1F7s3kIUGmUVhsoa2B5O+XIgN2vWaFctb+dxKM/fYExMUZnnkT+VjsGuRJS2Geh6fQW3vM5qpvI+zN1ftaZEaeLMMkRElUZZGPK3cFLeKa2rA2WtvD8tOIHcrMOtu211CoIDmZjhzg83Y8UDfSql8ByMrt2VeR6FotKxvNssK7gNl+ustqqMh7nzjCAiojKVp2BS3imtQ81Xrbw/LTjebtbh1N1WGfBUpyA4kC7UUle6KJ3WaX+CPV4o0K7d4XQeBYqzDtY+lRGIM/ghIiJS8LdW3lvNcm3uqqsMAny16CgDwmAHwVXdkiSEwJgPN8uvgz1eqDafL4Gq6Nid6jwpB1UfvNqoVvM2oLSyp+4kosCV9Wwvf5/nFczr11OtfLjWvLsGAaFq0anKvFk6J3eeznV639sYAw68Lz+O3aGqwuCHai1/B5SWd+rOYBXUAAZbRHa7A8MXb/Z7HISv53kFs7DEWvlSrkFARVt0asLseK5TaCufKejaFS5Sq8aI9zey8F5OHLtDVaXWnEnKDLQ6Da6sDsoqpCsFUmCXVNeCe0UGlJaV2Qb6ENayHrzKmyOFu1yjLWjP9mJhqXxcWy0q48Gh0v25MmbH8/fe72/XPcB9Cm3lMwWVLV89Zq1G1ybx2OHSQgTUvvPR17O/AO9lgkCetcSxO9VboBPCVDe140oEMPjNf+S/K3OGlpom0EK6UlkFdnm5GlBw93dAqb/dWYL9ENaK3hxD8VRuospS3md7hWt3tGDwdK8o68Gh5SHdnytjdjyT1SH/7S2oCWbXPdeWL2XgE8zCeyAPewQqN28v69lfgOcygbcubV2bxHvcTlWM3anM+2ZtvidXdEKY6qDWBD9Kyhlawl2wC+me1IRarcrsulKRh7AGo7AWiqdyE1Umf65Xzy3azt2QXNWUwkUoeLpX+HpwaHl5WmewZse7au6f8t/eKkGD3XUPcO4KJwlW4T3Qhz0CQLem8Vg68UqoVKqQPAjVU5nA2/c8tZRVlD8te5V536zt9+RAJoTJKjLL50F1yn+rb2m1HDxlQFSqIoV0T1jLWiLUYwJC8VRu8i6QbqbelKf7qTfV6YYTLLW9cBFqwbiXBtL9PNA81J91V1YlqKdtGyqxlaI8+fv2U7no8MKvAKr2Qaj+lgn8bRULpHuicjlly56vILiy7psVWXd1DRa82frcIBgi1Bjz4Wa3SUGA6tsSVKtKPZWZAdUGoS6kU+WTbka+CuBGi12uIfVWsK4JmW51VJFupt742/3U6/er0Q0nWBjwV67y3kuVBVSjpfIePO6rcqGyK0EnLtlaaesui3uwYSszf6iOD0L1VgmrPH8cDgfuXLxFfi0FMWVxbdnzJwj2p2K4vJW9ruuWgjNPgQJQfYMFb6R987Y/StUp/w19CsIIZwer3mrDFKVROg0MERq/C+Debpw1IdOtjqqim2mgqtMNpzJUZsElmPzJX8o7kLyq+Kp9d61xf+CzHVWRJDeVUQnqcJTu9+4zoXtwr69go7ytMNVp4PoExfir0Ys2O3VJrIwumJLKrBh2XXexxeZXoADUzLy7pjwaoOYc0RqOs4NVb7Xp+QLBKIDXxEy3ugl2N9NAVeYNp6pq+P1RE1q0/clfyjuQvCrSLvE1OYBrjfve8/mVmawqNeA/f4Y6CWUqz3UQyoHrngLpPYpgJ9gPxq0KviovPAWQwRrrVlGBptuXmpAfA7Us+ClP/9CqUhmzgyn7hgKhrxWsyWrr8wUCLYBXxxqamqqm3AQC5VrDrxxkTp75k7+UdyB5ZVPeU6qyQOpp2u1QKVbMIudJIGkLZkGzogIZuB7M8841D6kNyqq8kAJIpepwj/A33bWtbFmr7szKvrjVebrrQAqkvvqHurYOBfMk9WfQdm16JpBSbXq+QHXIXKnmcn2IoyFC4/Fp98GgbEGqiV1O/eVP/hJIF6ZApkGuKcfU27Tb1fWe7u84oOpc0KzK7krlzUNcz/XqVMldViApBZCVpazui96ufX/TXZ5yRHXuxlurSkXKvrjVebrrQAqkoegfWp5B27XpmUCh7KpUlcrzHAIg8AwrWGPdakrBrbZwrZ2VumktmXCF/F4wB5cPnve327aqe15RHv7kL9I9ovTacQ9CfXWVk1RmBZmrQFs/fKXB17Tb/kyB7vp3ZfcK8XccUGUWNCuqJlSU3fnhZqepsf2dBKGqKQPJquhN4U/3RX+u/WCmu7p245VU7zOdnFRV/9DKHLTtK3Mvb81FTVMd9rO8UwUDgWVYwRzrpnx2hVJtOS/Ky1twWdGg0lPtrGvtZWXOsBnMypxgT2RSFV2sfRUepGsw0Ly6MgvXgTwsNJBjH2iA7doD5Nv7ewftoaaB8nVu+FPQrMi566nVNtBzvroNJXB9JpAUEFd2OgP9Hao6kAxWl1lpwiRfFS5VnabKwuCnBglFzUywBm2XFaAFq+aiugvGfrpm9OU5HhUJcAPJsIIZSCufXaFUG86L8vI3uPQVVPpz/KrqGWr/PDkABp02aJU5lTGRiafniHx7f++AKjI8FUxd+bp2PHWh8ZRXS4Gx6/T2gRRk/C1MVtZ4oEADbNceINnFlqA/1NQfrlM1u+bVZd3PK3Luemu1DeSc9/a8nGCoaICizI9cj3Owg9uaNhlSeWf9A/yrcCnP/panG6+/+Wl5A18GP+RTRQMub902JNLJXN1rCSQVHaxa0f30dEP67J4rA9kF9+35GeBWtIWxrO3488wKj+utBudFqFTVzH5V9Qw1g04T1G6nlTGRiafniAxbsMGpVtrXIGd/uhO6pffStePrGnTNqytSkPF3hreq4E+g6O/3q5LrVM2BFhorcu76arX195z39ryc8grmORUZoZb/dj3OZQW3gY5DqWmTISnzgUBbrPypcCnvc538+V6gY+Jc89JJS/w/r6rXr1ZFKtJNJNAas+oyf34oBNLnU6kiNReVKdiDVcuzn8G+IQFV16IYyHaUx8bb9cqHtTrzN4j11hoA1IzjVd6uQJU5kYlrdxx5m5dm5VQW2D2N4yxrMHR5rtGKFGSUhzLUUw4ru7H1mLUa3ZrGY+Gd3fz/foiCN9fjVt5WeqBi5+7fTwzA1Zem6q5oIFiRfQjurIGK5y75sS7l2K+KjEOp7MmQgjkTYEVbrPypcAm2QMfEuZaHAjmvanXwo3wwmaSi3UT8PXGqYv58bxdKdSnABNLKoVRdB176c2FK04/78xtU1/2sDpQDvqvievXFn5kPJeWZAREITtDhz/lUnWebkvgaq1CRG3ogM2yWN0/d8uxATPp0m9MNuces1ejSOE5+rSyMB9qdsLxdXqWCTJHZhiteLvmtQ3mvcC2E+0qL62QC20/lYsLH/s2uBoQ+eJN4aqX3dQ9Xqkhr6JQvSh82W9EZ87z1NKjqskigAa203xXteRGl01R4fJG375c3b/aWX1W0xSrU5ZPKnjSiVpe8TDYHol3eq2g3EX8LuBWdP7+sGgAAfnVpCMagx2Corq05gOfMI1Krdvrc1dbnBsEQoXabhlzKqKTB+VE6bbUIRGuqYFyvFemaUJ6ZDyWBdOGrqqCjOs82BZQ9VqGyu6BUtLZUpYLH2Tl3KgrwysK4r+6Ernl3pFZd7i6v0kDm0Ys2ye+FctzCmEWbnJqXAk1LTXyAqmsrfVndEn11g5S+70+w4W0WXE/fB3xXxOw4nev27KXKGifiS6ABraceEuUpk3gbB+Wr+6jyeLmOT1J+vzx5s6/8Snn+eGuxck1feYM516n2A+2a6kllB1+1Ovgpiz/dRDw9ZyfQWtJA58/3pwZgyYQryuzSYIjQVHjQY7AEeiJX1Yxo3jKPKJ2yT/EmrHQZ6Cn9nt6mIZcG54f6iewVCXg9ZWqe/q6qgNrb9eqpZaasLnH+prkyZz5U8vTQYkllHd9AataqatYno8X/sQqBdkHxdpOurP79/qTP23F1LST1mLUaXZvEV6jLazDGgQTLTg9TQ29Ny0GxxYY6+ogqTUuoBDqhhVJZQXpZvH0f8NwVXUnZ6iIF4JUxTqSy+dtS7nrf83QNGiI0HitQR7y/0enYuI5P8jZ9u795c1n5lXJfPe2b6zlQni6insrH/gTvoVb9zsgq5M/J7+s5OxVpPvRVQA30YVne+mZWp5tdIPzpMug67bFrIcbfWjFvx7rYUvpE711n8jBswQYsnVh6Mbt2OfnnyQEen3Tvz/GuaD9f1wJUMALeslo8QjFXv7frqDxTdpcnzRWZ+dBTJYpbmry0FCnP9WAGQoFUSCjHXShvkMEOhJyvKd9dwly7oJQV6Hu7Sfvq3y+EkLuJKccNeKP8zKAYlO2N8rhOUBxX10IS4H1cUXlU1ex9/uiYEou950pacqSacE9d1msDb/tV1vgK13PO30KvN2UFXsUWm9fryLXwrrxveepeWR2myC4Pf4MDb4Fk1ybxbtesvy1W5Wn1CLQyyNM54Cl9ZU044umeVtkPdJVU5NyqviXgaihYA8ACKaBK2xRCIL/IjN5z/wIAWBUZjtSlwbX50p+ChKda88p+QrinplZ/al+VXKc9du3qV56uK1ueHYiJS7Z6fGDdjtO5GL3Ie5cTZfcV10KTMsMospce6yK7HZEO99ohKb2u++WNMkP2NZha+dDEsgKtQFo8QhlQl7dlpjxprkgzfCAPK3alPNdD1XK720u3rYmf+D/2IlBlzTDnVz6quDl6u0l7a22QurVKPD0zxpUygHHt2uWJ8rju8TJjlXLAerB4OrbOLU9VV2CVAh+gtDBtsjl8fKPmMts8H1flGEeJ8jdwPZeUy3kK0suaBlv5udSF+45Fm+TzMZAulcr7j6fuldX1QaRl8Tc4KLbYPN6DlIGPMsCvLP70YlL+rTwHvOUx3vLYT8b3cFvWNfjyNbbJW1c7T38LIWCxWD0uU5H7IIOfAMhBhtUov1eeyNNbi0x+QTFiY6I8bvOjjz7C8VNnAXQHAMybNw9AVwAlXSSGL9zsdAH6Kpwr0x5orXn3JnH4YkJ3qFQqaPX6cj00zXWbvjLakgtKlDl+QlmYDaTrivPvJ3w+qdtXjZdrja+nDMN0RQNszSlE5KX3O63dhx5xdbC3gmMwlOlatuwLAIkA3APe8g6orKqH61aUr5YZIQSKHQ4YLXZcPbvkmBTZ7RB2FaLUas/nsRCApbj0taUYgAaIiCqzUFuedPoz1XdFAk0hBBwW5zEH0v/Km4vFYoUhQuNX3hbsweRl3QiVymzZFgL49GYADzotc3njOKfr3Fu/fU8zK7o+M8aVMoDx1LWrPJQD1iuLayGn/39C1yp08cO9iLq7Xci2X5l8ndOuv8E9iml7fZ1LkVoV7lzs/GDXsgIXR3FpGSbSZgEiIt3GBvk7YYtrHlAZM5IGgxACVqv3QrfdXnpM7HYj1OpI+bWvCghl8Lfl2YEAIAeiEl+BTzBbxnzln77GC3rKY6SKUk957B2L3CuAXFviz9x3P9BkBADg1ISJaPvZEgDuY9WVwfHFD/fK71/8cC8M93fGxx9/7FT2/fTTpbjvnvEVrgAMi+BHKvgAQLFLzbuwlx5Ar4UgxXru/vlu7MjYB+AlAMDk3ybjsxs+cutypcxcHMVGOGwaqAwGt0kIntBuxH9svQAACyaPQbPLLsNNz77stF2r1YrTp08jQgWMiyzpB24VpdvbsHUitqWNcvrOjtO5MJYRXZen1nzb6Ty8Mf4ORAgbUtq0x6gZcyr81F9PA0ElroXEQAcp+moKFkI4XWxZn+z3cy9KfPfqTAD9Lv39EoC+ALwXykytIgGtGqYhjUrfzyuSgyFlK192cRGumrOuZB+tRhgiov06zufOnoMU/ES6dLsp72B3by0eyt/JarZDVLBV0Futj6f3HI6S68uuuJ71Ggv0mpJzQ602OM0QdtP2o9iSXwTYHE7BJ7RqXBlXB993beWcdiGAj4YAp3YC+Ljkvf+0AlRmoPEVwF3fAbo65QqC/GlB8nWeCyFgNZkAAFZFMGM1mWF12DxWSgghkLlwN/LSSgsq3m4u//nPf9CiaSOMHTsKiwc/DLNNhwfWvBbwfpbHf1+fDaDkRpi1ZJ9TWqMe6OL15v7L41djyOt/O79vLUbU2XU4GbkORUKPDuaS39G1gsNbv3tPglVQ2aufADUE2l9Kky++KmQQpPS45llGa9V3VfoNMTBABZwudArSa6KZvV/G9A3Pur2ftbT0HuN6vwlk2l7lveeLWS9ip62v/NmO07koMnsPXE6NnwDTrt3Aja8AAI707Qd9l85Ak9udtzGn9J5WUVaTGVq9LijrKq9PlyzFmbNnIOVzS5Z8Kheit20fiQtZewGU5HP//HMlYuO6AhgDAIjSe29VUf5ORUv242f9Tkj3YH8oAwMlIQRsZrNbHq+8z7qOE1y69DN528qKUG9jleR98JDHfDPreQx/7iX59ZTmmXj3ZAIA50oeZXon/ToJQEnAg5078PO20hZAYTTCpNV5Lf9FQgXN6UKsRWzJB6cLYSk2u5V9M84COTk5iI6OhhDlL2/U+uDHqeADeCz8SLrHRuGrzi1QR6Px2G3CaDViZ+ZOqNRATLunAQC7swCjzYioiCh5e2mjxyBn9z6nzCXSboGhS2c0+fxzfD9vp7zOgqN78JAojbrPHdoPm9nidX+mTp0KAHjjjdlYPPhhCAAvFJcGS22vb46D/ztZkpZltwO4F4AUhQ8H4Pli81QbbTWZsWDyGKf3IkRpplqSVjMiIiOdlhFCQFgdTjcwh6WkcKykbJlQXsRjd59QrsypgBlo1yNDhNrpeBbn5SEipg4AQK3SOl1sxrNFfq8XALKOHcBDYg8AoFPyGEgnlnJfPHU33NO3Q8l31u1zej8qQg1DhMYtwB7wdX90TmyHpdctDSh9dyuOo6euDp4CLX8Ld0IIp/P4oyfWoklqHG57ohvU6rLHOnhan1Otz6I9iLy/Mz79dCmkDPzTT5fi3onjsH3H7cjL2w4AMNt0UN6w9NqS3zourju6d/sKKpUKxQ5H6fXvEnwCwOa8Ily02hClUZdWgFiLgdObEKUCTkaOdk7smS3A7EZlBkFCCBhtJUFasXKgvdUIqDQwaA1eg0Vf5/k3Lz2Hi0dKzh2rSgs0nwSgpPLEW6WEsDpgScuHASqnm4u5yOR2cwGA06dPw2otbQ2qKtknDuEhxy5oVBGIxmNOaXVY7E7ng/Lm3n/bYTlfH77zGH664jIoz0KV2oKYdk9DOCJQeKjkuvLUMmo1meXXnrp+ff556TbLyzQwGcbNkYiyGMte2Iu9+gmIVplQrDrt8XNPFXAqQ6TT58q/y1tpMfbcd1iScgsAwHRVQ0T+c8Hv796fqwcKvsWCJkPdPlMGCbWJNr243PcbJeW9J0ulBZo7Bykrvl6CxYP/A6NNjylr/uP0mWnXbkTaLfj5u5KyhACwUVwtf37fyUWIEDa39c6/tmR514qQOq1moOjoCyXr8nL/WDB5DJJaXAagf+A7C2CYbhe+tXQu13cB4H+IwtfnTjnlcxfOAYsXL8b48aORl7cdei2wePDD8nfy83ZACn7Kui9Kgbv1TD4yIk9hXOQpqNVW9O33pdfKo5X/fQaR9pL7lTAaITSlXW8dDge+nD4N5w4fcMvjm7ZqhVEz50KlUjndM9M/3IWMiyXbLtk/FaS8atfuyQCcK8jhJf+Rfv+LACxGk/x+zvlTGBd5Elahxufm7m7fM1qN2JO9DTHttkFvEYi0O1dgOIxGiOjSfVS2pllNZuj1eo/pkSQnJ+P8+fMAgLfffhsA0LBRUyjz46jWs3yuQ6lWBz9COJwLPoDHwo9kW34xWv2zt7QmGHDuNrFsBHDp91l500rc+sOtAEqCH6kgI4xGGHfsQCQgZy4S485dOD56LHLqTsATMEAIK8yXgomGqS1x4cQxACUnfuk+CEBxX4qIcO6bboYexyLaAkNKXu+0W3EsagKiHCaMcTQpXXDndvy8bWPpeo1GQFtaE+OpsGV12ORg5/4PPkOEvuTmaTWbsGDynR6PoVTDbEnLh1HxILLzszYhrlkc6kxoL7/n1DKhUcm/y/Zik1yQwae3AHd/53E7wmh0DrCKjRAR0U4Z1TcvPYfzRw/JmceHD02U96lx647oixtK1wnvGVxy4+ewaL4Df1395qXtl/52GlUEGkc2xlppvxQ/mN7DFRal9lLQ+PQWFN/9lVuADQC7M3djzM9jsGigew2RNzuKjYB0rmtUgF3RogYzDNpo3L1qrMeWzLLYLA7knCjAEzDI72WcyMeK/2zHsCe7B94d0upwrvU5U4TzC3Y4ZeYZZwGTKU8OfABAr7U43bAkeXnb4HAYodE4dyPd06c9xu45ie0FxU7vS4Go07VfFikIatILmLDKrU/+3T/fjZ2ZO0teOyKgDGZVaiu6NuyKJdctkVuDXQurQtHapzynzx89BCkXiBA2PHRigVOyzh3aD2N+HgyxcR5/h4iUaFjPFQIAspaUFjKnTp0Kq9WKt956y22bkv368SgWNmR+nQijRofbLlXwBIuycsWVxWJBxlnpfBBoWH8NcOLSNaLIP3YUm3DjjqP4sWOKz99R2RVYKmykHTki5xVHXvkV0i1yjH4bIlQOZJ5XwzX4kQoxJo0Ot/o4HrOvnYFHIz6QX09KagikeU/fb4gBAPwLBfJ7poHJgFYN4/pIRFtNEIp7hRRU69V6nBo9Brl7D7jU7l8ONCkpAH03ZyakWv3lM5/F8OdLCw2363bgK0tX7wlT8PV7eWLqk4DI9ZmXXtmgtZ3GIyc/xPDmj8nLCAj8lLUeQCu379dpPRNFR6b7vb03+z+DR/8qOQbP7nsZL3dwb40JtvnXToXZHlgrRyAtig9nqyG8HHfpPL1wzorWLQCLVg/TwGRErj7vtmyrtf/gzH33o2j/YRhjW2BqjhXm3Hfkz/snjcRCP9Lz1HIHpl86XdImTMTOyx+SP5MK0gCQeeIE0Kwk+Lldtx1fWfx/UK3rvTkx+UVknH9Rfv0D9Phct8HjOn9DDLTwPIbs7NmzsFhKK0ev6rcJQgBr1/V02uLYPSf9Tqsvm3X34krL+27vOxwOfP/OTvn1929uRfrhAwDc8/jzRw7ii+lPYtTzrzrdM61nigBFPbReU3pvNBXp4Br8iHmdADinRfl7AcDxV1cDcC5zeqUoH3typG8/oPsVcgvjL2++DCkPWjB5DFJbt3UqjwEu973z7udw2rnzkPJj08BkGI77X/Faq4MfjchFsb2B/HpTr3ZoEKEBVCoU2x1uNe+SzXlFKHY4UMdukrtNAEDxeRXQvCSgkAIfABjw9QBcnnA5ll631OnHarX2H6hUqpIf/RLTnj3ApUoW5bJS4AMAeUsPy39fXLQHDSeW9n/+5JNP/JuiV6XCnigVYto97TkKdzhgNSuaU812WIUKWp3nrn8R+khEREaW1I6aS2sD3PotX6phBuBcywzAkpaPyKLSC2vXrkmQLsiSmolLN0CtGnujJiLaYQTOAkWKcRdCCLl1zbhjB0wandMNPr5TO2zo+ri8/PmjhzwWEAGgTWEXp8wiAgITotajY7//4QHVxyWthB5uGp72W34fAkuXfQ4gCQCwcvkifDj4bbyAV3BMVXJh3rrjGFZ0aSnvq2lIIxz7ZwgMZ00Y89s98rra1W2HAzkH5Nd7L+7FPb9MhFsNDkpvelbh+eIftvMYvtnxKORA/j+tYGzaGTu16W4tmcXWYkCU3rxtWdlwxEXL3TZ97X/GiXxYzXboIiuetVjOFDj9PgCwbNkypLYo+fuqfpvcghubrRhr1/UEANjtxVCrDc4rUKncAh8l+dr39OHDu4C3PdQ+nt4IFF0E6jSQA6Bia7Ec+ACASm11CmYBYMeFHSi2FiMqIspja3HdTu3QdNkyqFQqp1Y2iVQhIYTA8lnPIf3oIfmzBZPv9NotVQp8AMB6plA+xl988QXOnj0rf7Zs2edIbQHYFSGE49LfAsDOzqWFnGC4P0cNrY/C9MVPSvNrtdqGhvXO4b3BT2Ciapnbstvyi1FsL/0dPZ2tykomqbARAeChEwugUUWgWfPHsBaAFXYsUbkXnkwDk5F0aopce+vL/Gunolhbei4Wq9TYE6VCg5ZPYenrdqdA8ktTARpHlgRyRi8VMnaoIQBMWj4EQEkwMODr/lCpLHhzWSQanSpyq4Az7T6EJ2IMcDgsyM/bjYewGwCQBeDrF58BcI3Xo3X3qaX4tOldTu/dYzQjyqHYd40fVQaKZSKE59/FBgeMERew+Jq3kYfYkrwYQP0zD0JlK7s18uFsAV3BpxjUYiyOakqXb58BPJ5kQUHhUnzQbKLPdZgGJAEQiPwzw+syL/R/FdO0b3m9R3iirBhTFuiXLFmCTpevBuC5UhEAvvnpBWzsMwfmgi/83p6r+qnPOZ2vpr17AbV7oKZRRSApMgXjIv8EAGi1Rui1Fphs7st2OmPFz2kl55ldrUNOTAEed2hgyXvXazpU6sCCZkOkEaZrSiutI3aVVlz8hhg4YIZBVwx4uRR9VWxK+RwAqFSR2LX77pIXWkWFbJHR9VZU5nbUl/bRKYjqPBvRhU8j0qVMdnzivcipO1GuTMzOLh0jdM87i6HV6bDw3tLrL/3IQViN/rUcCwCv4AX59T79eNRRmVGsKs1/7God/ug3BxZF/qtRRSAR9fEH7Fhy6TzwRG6FVpSPvXXHNe7cJQc/WUf34yGxW/7s/JFDQHPnyuilyz7zax/LI/D+KTXImH2ZTgFOz40HMHzncQghnGrf9/TtgGNXdcSmXopBlgH2pd6duRtjfroDaWPHye+dvu8+QNGUpywwCCFgKVjuth6NKgLasyVN42sRC82ZIqemzfPnz+PcuXPya4eHus2Rl7/mpZ5D+g6w8u19+OiJtfJ7Hz2xFh/831/4du42OBwOtyDHajZdqh190qnV5+uXnoXFaJSDEmVLTOKTPZA8ozeSni0dWJc+t3SgnKV4KxYPfhiLBz8MY7HzFJJGTclxEwAm/TFFfn/yr5NhLyqCcUdJV0Gp+f7n76Yi0m5B0e4DJS0SuQZMzdHKtRgJzVs6rV+jikCDSOcWQF8ZpNqlNc5S8I3H5ayw48L50xgXuQXjIjcjod7nMEGPY6o28jK7C424ZccRt+8Wq1TYk1V6vioDH8npszuw8r/PyK/vO7kIj5/6FBFy4czzPuwsMGJkwh04ETkaJyNHI0plhjhT2s2pbb228t/3/DoRaePGy6+PDfoXDnXrjrQ77pDPDU+FcckPb+2UzwdlN6LivDxYTSaPgZNbEA2Bn3XugzCV537JINWSAEejibp04yoNHv9Z2xNbt42A3V762yn/3tSrHY5d1dHzte/wcAV9Pa707xSXGsbXWgGL/wVcOj6Tfpvk9HHHBh2xYdQGbLxjIzrU7yC/P/m3SbBlZZW0Frucy8adu5B2++2wmGwez2mtTo+IyEioVCqnwEc+Vof2Iz8zA1aTyamgr6Q855WBDwCkp5+GAPCydgZMQxrBNKQRinQG2ByAQ62DMbYFHs/W4L6TizyuO2CKc2BAkvP4AwGBHzL/8ZB+727dcwaOS8tMSmroto30+x/AE7kGPJFrQPYJHwOSfWwlwh6hWK7Ug2mfYC1isRoGTIhaD53WgrkovW6L1c5VpFoBPJFrwJNZDjnwcd32s9e+IXfTzo2IKang0hYjpt3TiGn3NFRqK/RWoNEp5+5U2tatIADs6nR/Sd6V737fyT95DGsRi1+hRx2tCW8Mfq6klemSOir32l8h7FCrFF2ZNWqn7wSDwylYgM/7smlgMqbY7dChpEvz7xG7S1o+Lp27Fql7djm65UrrLwmKStiglSuv3ln3htd0e6OF49J9Ygsupp+ApXgzFg9+GK8Pfk5Os2lII9RrNf1SnmAFYAMc6U7rcb1WAIGOnX4vTcelNEZ1eAUqdWkwePq++y4t7blCy9N5Ly4FBGX9zuXpRnl/1C639x5M+wS/IRIRKufKW+FyPtpdSjymgcmYcP5TfGcqmWDI073k0ppQr35pd/LtO+5Afv6uS5+UTbrODVC53bM0lwJvh3TMhjTCjnqdIFR6p/NYANikvqb0tUv54sOHJmLFnJlu2z72qvOkCs6/V+k5IPUOktIgNOrS/ZPG0UIFFfwNSEu3YxqYjCJdScAm/wJC4LkvnH+P1F9/AQBo1DpMzYnEQ2kry2w1tsKOc+fP+VzGv1/Js5AGP++99x6aN2+OyMhI9OzZE5s3e59CtDx2FLof3B0FJV3bRuwsbWkRQmDUruPoubG0sHnrjmNw2Jy/71p8aFu3tMAIITDqjd0w7y0NVMx79yHt7rtLv6/WIT+uxaXFrYDDvVbJ35u+RlOSNjPca2K2xXXChOTSLm8qlwtte9fHceFUEXRQyTd/3aUMMONEPpbP2Yplz011CnIWTL4Ty56binOHnQvkGccO451xI/Dl80/iwvydSJ9VOsAtY+5WXPxwLxyK7UsZ/oSo9fL4DACweNgP4FILVs5O+Qa/O2sLTioK5pEd2qPF338p9s/zdKCZJ0t/b08EBP6n2y4fo5LEqqG9uhjR7Z5GjFlZ4+h+A5LWocz81Gob4uKyPN4G9xaZ3d6TC2g+qDwUYhVDHtHp8t+8fnd7XCcUqyOBpM7OBUIAB7MPyn8fObcHqh3bnAriQEmtzcmRI2Ex2XDhpPfCYsaJfFhMNnzx3FSnMWMfPjQRb48djmXPPo6i3BxYTSa5YH5x0R6nddjgQKbadRulGToArF3XSw5wrNZCbNs2HAUFzuvJz9+B9dvHyq/7bCrdz54bD2DYjmO43eXaH7bzGBwWD61D6Yob6Lnt7p+f2QJ8OBDF5kLsueicjr0X92LS75MghMA+KcAVAsPf2IGj/a5yX9clpj17nfMQxe//9UvPysGoZPKCJWioCPQ/fOgevD12OL59+Xmv2/DFDD1OqlqXpketg9mqPKMDq8H1SZQUcDxVTFhh93A+wGcRc3exBdd3mY8ilQp7IkuCDYPFhJ+/m4r/fTcVOx2l4xl8FdasUBa8XG62ipdaocLUHC0eOrFAvrFbIOXTepxSlXbjMl2qbVcJcalSrKR7ihDOLRtSa/TiwQ9Dq1VMYAOV5wouD8GB7chRONQ6FMU2A2ADhPt9R6Mqaal1QMg11lKhuXXjNxEh3CcgUMEOtWtQpFXjHosVT576DB8iyu070JSs8/8KDIjwUW7RaGwQAP6jCBgd0OH5L3xV65UQl8aJZqsLnYIQm6ZkH/s2vLnMdQRKuJyJJk1pcDuh0IZHTn4oF5JLllfeK0rzNQHgNUxzWXlpAdU1QJG6XP+BOhgXuQURKsel+85Ft+MnVDqn88O8tyQfcmjcA1vpfJDS175Dydg4uUP6pXMjvsXzTi1J3gIpfxTqCjwGVgICbdv97fTenWucyws2uJ+fapUWDSJTvNxLLi1z6VjJaSgs7QZs8tKH65ufXigpwJ/81qkA77odtdpeUnmEF533RwDPLys9j5XlQmlNruWLC8edK0s1qgjEob7zel1anaT9cv0lpIplk0ol91RwaCL8/s2iI/Px8uCXSlrFtGrc2XE2BErKagCgtwJtXBpDT9xyS8l2oPK4fwCgdclLPP2mrgy6YjmoU47f90fIur199dVXeOyxx7Bw4UL07NkT8+bNw5AhQ3Do0CE0bFh2IbCitheUNhlevt59cOXuQiOu330aP0EFzaXTx+hygzyYU1qQirQIXOah9duyv7RQ5RSXezjRPN30vV+4wnNGecm+SD3UoqSArVPcT+1uF5rLWoXAmd3vewzMMo4d9vCNEu0Ku8N6utDtfevpAlx4pbSFweqlsGR3icOlmlHXW53eImBVBJimfftxavK9petRZuQuBQlfbHDgorrArTag0NAaepUeQlXsszVNWoen38qfWsAilVouoPmis7jXx6k1OgACl3f+BXFxF7F48MPIQAM8pnLvWyygAtJ3lQSVnrYnhM8ChnnvPpy4626gwSSvyzgcDnz94hO4cPKo3I1IKf3YYacm/OTUtrgazgUSh4ej7XqjkuTn78Df/3geDCsAzCi+RS4lF7tcdzsL3bsO7Cww4rr9VqxQR6KOw+T710voAGSWttaJc9sxaUl3wMOx3XtxL+78X2mFgt4KtD3rtpgb874DwNUl16a1sLRGMOPYYSx9+v+gVpfWvi/+v0mwW9z7frh2KQCkApiHIO4Stdr79VORQo43Dof37mMql19BrlUt49raGdcBwzu/C5E3ByohMP1SDzmHWocCRT5ot3u+0QoI/KrbKb+OjszHC/3nYpqqZFxUhGJshy0iyu/jUaxSA7BDbxGX0pIqpcRt+xJlRVexSud2PwKcK7rKwwwLNBqbS4tLoI8zsEGv9hD4KJeBe6DnnhY90hQBY2H9h9Hp9DP4Mmce1nd/HG/X9fablRR8AcCqGK9g1mqhAZCiT8G9cf9DSvdNeGGN5/unV4puezpFlzqtWounTn2Ofi1vwwWtBUW20o6zRp1a0fm7hPJeoczXzNDjhOoyp2UjLm3GrtEFUDvvfvxU8H1+CJfrz3YpD5Yq8QDn4wkAWsQBUIxVvHT/9XUtB0poLKgTm+v0XlK+c9HVteUHAGzakrT4arn1uk24lK0uBXs/PjIeertASSXCBWgU3apdt6NW2y5VHjn/nlp7NC5LL1J8z/n68tZKr+Spkvx/XvJy1/UXqSORcOlvp8pULxVAnverNC/aE9sOxepIPJQYV7IaT+fYpfGsDrX3IMs54PYv+FGryz8rZMhaft544w1MmjQJ48ePR/v27bFw4UJERUXho4/KHmxdVXYabWjU/0/5RuDpZiPR2QJs5hX+9df0VAgESn50TxeW/D11TOkLaeI6ANvL6KfvrUXKF09BmzdmlOTkUsuVJA91PS7veszVnmo2D5YGocrrtKybqzfeWqGs2mif3/OWyRbA9/eA0toY3xsQeHwFoFbr8ehFe2kNs0rlFhh4K7BI2/GWZXgL4kvXC2yMGOQjiQKWgi9x4eTRsvZGlp12yu09m9t5L9Cxk/dWLW+MiHQrTPhjt9GGVlf9gpu7vOP71qkIfIBLXRd9BLHH8ktbIP0tqNo0Jeejp2vzYtoJXDhReqw9BT4AoHcd+wSpNaXAw9KXvqP3PjbKU21xxXkv2Nldzljppmf2NcL2kp1xHSFUeuitQKtLk5G5VkTZCr52+o50vGxwIEvtXKljUxQA9Vbn4yAVTqM0MZe2o2hJUSi6dB3WMbl95ERZqFNu16KOKKm5VRIC05eVL/iR0iu1VCkLua5djMrkxyQINq3vbjaejpnNkAqTruzfW1qv6zpcA+iq5Hr9Ke8VGo3v+1R602chADjUWg8tP4HVX8d4KHZILUrWwpVlft+1ssGmcQ7rpHXZCla4fVcT6HmkYPVyT/bvu94LyN4qeLyVrYw6PSwRpYGtsrXCn8I6AOhszsfBqnHeN5XK93q8VZK75lOSQpfyR462tGzo6XyQKPNAVzaXINioUmHfpfuezsfp7NBEeK2Udi0/easoD5aQtPxYLBZs27YN06aVRtZqtRqDBg3Chg0b3JY3m80wm0u7CeXllcwx7jB7vzkDgKOoMOCmME/2i/poaslElgawGz2fmDajQKGX2kOJRaWB0VIS8TtsBW5TycZF1EWB2bm/diGMMKvcu0gJYUZxkR0OlecT3m5UQ3UpPTajA4V2B+xqHTINiYDF+xSbDocx4CluDRqDW7q9yUMhzCoz7HYziopKLyojbE77UmCyI98icEyrcjrm9c7bUWj3foM3alQ+j7FEo4JTmq2ww6wyQ6Wyuh1Xu0kDm8mOfABGSxGEsMKiWK+0LhMsTr+VSmVFUZEDeVB7/Z0kWWbh9dyS6C0CDTPsyNcDZvMFed8cKgssliKn45kPlcdtZli00FsE9ms1HrcXmVdyrnhjV+tw0dDQ6zkkhBUWs+fpd72v01Lmea9SWaHVZqIowNlh86Ep89j7slHbAukWHeoI92vQk2J4zyNcqYvKzjMAID8yCkZLUbmuTYlWONyOsXTOexMRYXG7FgpMdsBckm6jpQgOez5MVmuZebE/zJYCOKxWt2sTAC4i3ymtQpj9vraAkmvYbrLLx1uZF5dcz84Rv8phRYG5yO0YCWGGsag0r7KZrPI6jXYbjJZiWKxWaEXJOZ2HIpi07nl1kckOu9EOm8mOQrtaTgtsxU77boIFdn0eioocMMIqr6PIZMNFs/O5prcIJJ2zw9PRsAu7x7xLYkRJegtRDJPJOb02o9Xjb2yyFENnscjvS/dbk7kARqsFReYiOOBcunIUFQI2DUzmIsBSDKuH39sKu1saJIV2O6x2G0yWIjjMDrd1m8zFcFjssFktbusotllhtBSj0KyByXTpd/Ry3jqKCgEIp89L3istdxiLbHBESOu2wWi1otBcXPJb2UrXbbY4YLernfZRmb8JUSzn3Wa477MRibio0QJ2G4yWfKc8QKuNcTtPpfuO67rsRhtUhe73T6MWMFryYLGUjq+wq7UoNBfBrDIjMjJHTl+By/WmLrA55WHyuqzuNWgqR+k5ZFaZ3Y692WS+dH445M9Ml46p67lQrDi+hVCjEMUwa81O56LZUnJOF3gpQ5Ucq3yn+6acFg+/AwBk6PSob7bBaCmCxWqV84mSdDhvR6Nxzz8BwG5yPmZS/i4pK5+PUGvLzMu1WqO8X8Uu+1JosiPfLFCgElCpS84Hi9oO46XrUV7HpXuG0a1c45wHAkCBySHnRTaT9zJEoYdzWKJxuUflo8jn/UlKi3RdAqX5oT8t8CoRzH4Lfjp37hwaNWqE9evXo3fv3vL7Tz75JP766y9s2rTJafkXX3wRM2bMqOpkEhERERFRDXH69Gk0btzY5zI1YqrradOm4bHHSp8D4HA4kJ2djfr161foifJERERERFSzCSFQUFCAlJSUMpcNSfDToEEDaDQaZGQ491/PyMhAUlKS2/J6vd7t6a/x8fGVmUQiIiIiIqoh4uLi/FouJBMe6HQ6dO/eHatXl85T7nA4sHr1aqducERERERERMESsm5vjz32GMaOHYsePXrgyiuvxLx581BUVITx48eX/WUiIiIiIqIAhSz4uf3225GZmYnp06cjPT0dXbp0wapVq5CYmBiqJBERERERUS0WktneiIiIiIiIqlrIHnJKRERERERUlRj8EBERERFRWGDwQ0REREREYaFGPOTU4XBApVJBCAFpiJJKpZL/CSGgUqlgt9ud3pP+aTQaACUPQFKr1bDb7VCr1fK6NRoN7HY7NBqN/B1pGbVa7bQe5fYdDgeEEHA4HIiIiJDX53A4oNVqnV5L61GpVE7bttvt8nel7UgPbvX0t9VqhVarlfdTWr9y/6T997RutVot77PrMZKOs7R/AORjKn1X2n9pH/whfUfarj/L+3p4rfS56//+rMfXuqXfUvqNlMfV9dhJ27XZbPJriXTMpOWVv5W0nPI8VJ6/0u8gLQOU/BY2mw1arVb+XbVardN5LK1f+tv193LdZ+V5ozxnXNMpvWez2aDT6eRjJF0zyutC2qbNZkNERIS8Del6UB4H5fGUtu26Tmn7ymPlesykv6XtaDQaefuux0B5LJTngXTcXfMS5XakY6A8tsp8RvpNlMdPuW3l9pXbltKsTL/yGnM9z6VzS/rc9ZpVnisWiwURERFOeaPrMZeOqfI3sFqt8u+nTKfy+Hu6/qRtA3C6JqT9Ueapvrheo1arFRqNxi3vVqZNuS3l+l1/e+m4+dqmtK+un0vbtNls8jFQbtPTOeOJtH5lupXrkfIU5TXien1Ir32ds8r7jnL/pXVL35HyEuU1J1Hmc9L57bp+Kb3K/XK9V7jmG8rrQ5l2fx9a7jpUWUqX9LfyN1X+nso8XvrnWh5Q7oPrb67clus1rNyesgxQXp7ux2UdE09lB+XnrnmgMi/z9j1PpPPB07YlyrxKOp7Sa+U9QpkHKa8b5f1MmU9Ky0vLaLVap3KR9B1lvub6WymPhzJdynNXeZ4or0XpuwDcznfl9pX5rPL4ui4nbde17KG8RlzzIymPdr2/uJY5pGtT+Xsot61ct+t1r7xPK9fler9UljWV92mpvOJ6LSnzKel7ynxamScp16EsPwDwqyzpS7UOfnJzc7FmzRp8+umnaNmyJfbt24eDBw/iuuuuw8WLF9G1a1fs378fKpUKOp0OhYWFsFgsUKlUyMzMRFRUFBISEuBwOFCvXj1ERkbiyJEjSExMRJMmTXD27FlcvHgRZrMZmZmZaNOmDTIyMqDT6dClSxf88ccfaNu2LaKjo9GiRQvs3r0bBQUFaNCgAfLz86HT6eBwOFBcXIzo6GjUr18fGzZsQFFREZo3bw4hBOrWrYsjR44gNTUVRUVFsNvtSElJwcWLF1FQUIDMzEx069YNaWlpiI6ORtOmTeWCps1mg8lkQtOmTVFYWIgLFy7gmmuuQXFxMXbt2oXs7GzExMQgPj4eO3bswMmTJ9G5c2d06NABf/31FwoLC9GmTRtERETAYrHg5MmTiIqKQlRUFOrUqQONRoNmzZph/fr1GDx4MDIyMrB161ZcdtllSE9Ph16vR25uLuLj4+UHzcbExECj0eCaa67BwYMHYTQa0b17d1y4cAEnTpyAyWSCxWLBgAED8Mcff8BsNqOoqAgxMTFISEhAfn4+6tWrh/79+0Or1WLHjh3Q6/XIzMzEyZMn8eyzz2L16tXYtGkTxowZg02bNiE/Px8mkwlCCGzduhUdO3bEiBEjAJRcAMXFxcjIyMD58+fhcDiQlpYmPzC3a9eucDgciI6ORkZGBqxWK5o2bYoDBw7AaDTiuuuug91ux969exEZGYkNGzZApVIhNTUVdrsd+fn5yMzMxJEjR9CwYUMcO3YMXbt2hdVqRUJCAho0aIC0tDRs3LgR1113HbKzs1FYWIiioiLExsZCp9Ohfv36uHjxIlq0aIEDBw4AADp37oxdu3ahffv2cDgcsFqtiIyMxMmTJ5Geng6j0YhmzZpBq9VCp9NBq9WiTp06sFgsOH/+PFJSUmAwGJCQkIDz58/jzJkz6NWrF3JycrB79260bt0ahw4dQmJiIgwGAwoKCnD8+HG0aNEChw4dQu/evWG323Hs2DEkJycjOzsbPXr0wJYtW5CQkAC73S4/WPj48ePIzMxEamoqNBoNYmJiYDQakZeXh6ysLLRu3RpmsxkmkwlGoxGJiYlyBrVnzx6YzWbEx8ejffv2AICTJ0+iXr16uHjxItq2bYvdu3ejQ4cO0Gg02LFjB+x2OxITE9GpUyfUq1cP6enpKC4uxoULF5CZmYnY2FgkJCTAarUiOjpaLhjbbDZcuHABe/bsQYcOHXDq1Cl07NgR586dQ926dSGEQE5ODgwGA2JjY6HRaGAymVBYWIi6deuiuLgYOTk5aNSoEVq3bi0X1DMzM3H27FlotVrExcXBbDajVatWOH78OOrWrYuioiKkpaWhefPmyM7OxoULF9C2bVtkZ2fDbrcjJycHLVq0QGRkJIqLixEZGYmLFy+iYcOGWLduHXr16oXz588jKysLDRo0gMViQVRUFCwWC+rVq4dGjRrh2LFjOH/+PJKTkxEZGYnWrVujuLgYRqMRkZGRMJvNMBqNSE5OxunTp5GXl4eYmBh0794dmzZtQm5uLgwGg7ycVqtF+/btkZ+fD6PRiISEBFy4cAE2mw3Jyck4fPgwBg0ahO3btyMiIkI+r1NSUmC1WuX9PnnyJFq3bo1+/frh+PHj2LFjB1JSUpCUlIRz586hffv2OHv2LM6fP4+jR49i4MCB8rl85swZ6PV6XH311QCAw4cPo169eti1axfS0tJgtVqh1+tx/PhxNGrUCI0bN8axY8cQFxeH/v37AwD279+PjIwM5OTkQAiB9u3bo2PHjti/fz/S09NRr149pKSkICsrCykpKVizZg0cDgdefPFF/Pnnn1i/fj06duyIvLw8+ZrfuHEj+vbtiwMHDiA1NRXR0dGIiorCwYMHUVxcjJMnTyIpKQmXX3456tati5MnT+LkyZMYMmQI0tLSkJubiwMHDqBp06aIi4uD3W7HjTfeiNdffx1msxnXXnstVq1ahY4dOyI3NxfR0dEwGo2w2WyoU6cO9uzZg6SkJGg0GmRnZ+PGG29Ebm4u9u7dC5vNhm7duuF///sfYmNjER0dLV97eXl5uOyyyxAfHw+tVotffvkFrVq1gsPhQJs2bRAbGyvntWlpaYiIiEB8fDw0Gg0sFguys7NRv359NGrUCA6HAw0bNsTmzZvRqFEjnD17Fl26dIFGo8HGjRsRExODs2fPonXr1tBqtdDr9RBC4Pz58zh//jzi4uLQuXNnbN26FQ0bNkRubi5yc3PRqFEjJCQkoKCgAFqtFmfOnEH79u3RoEEDNGnSBEVFRTAYDDh16hT0ej0cDgfOnz+PK664Aj///LO8TakwlJGRgZSUFKjVahgMBpw/fx6ZmZlISkqSj2lxcTH0ej0aNWqEOnXq4Pjx47DZbIiMjJSXa9CgAfR6PbZu3YrOnTtj1apVuOaaa2CxWLB582bYbDa0aNECjRs3RmZmJuLi4rB//360a9cOR44cQWxsLGJjYxEVFYXs7GwkJSUhIiICWVlZsFqt6NGjBwoKCqBSqRAVFYWlS5eiV69eiImJQW5uLrRaLQwGA7Kzs7Fjxw40a9YMBoMBUVFRyM3NxaRJk7B69WocPXoUhw8fls9zrVaLv/76C3Xr1sU111wDq9WKNWvWoEOHDjhw4ADi4uKQkJCAjIwMHDx4EA0aNEBmZiYsFguuvPJKZGVloVmzZjh16hTatGkDnU6H6OhonD9/Xi4vdenSBfHx8WjZsiVWrFiB5s2b47PPPkOPHj2QmZmJrl27YteuXejWrRuOHz+OBg0a4OjRo9ixYweaN2+OxMREFBYWwmw2o1evXsjIyIDBYIBWq0V6ejqysrJgt9sRHR0tn6NSvmaz2VC/fn1cffXV+PXXX/HHH3/AYDCge/fuOHfuHCIiImCz2ZCWloZevXohNzcXVqtVzp+le3BWVhZyc3PRpEkTWK1WWK1WAJDLf0IIGI1GXHbZZcjKypLLNBkZGbhw4QJuuOEG7N+/Hw0bNsSJEyfke8Pll1+Oixcv4siRI2jdujWioqKQmZkJm80mrzM1NRVmsxmHDx+GwWCQ83mDwYDTp08jNjYWZrMZycnJ2Lp1K7p06SLnhY0bN0aTJk2g0+nka8tkMiE7Oxs33HADbDYbCgsL5XyrUaNGqF+/Pmw2G44cOYLi4mJceeWV2LFjB8xmM4YOHQqr1Yrk5GRkZmbi3LlzyMzMlO8FarUaZrMZVqsVRUVFOHLkCJKTk2EymZCbm4tWrVqhQYMGcDgciI+PR2ZmJgoKCnDx4kXo9XrodDrExcXBaDTKxz0pKQkpKSk4ceIE4uPjkZ2djcsuuwx79uzBiRMn0KxZM1itVlgsFrRr1w4HDhxAnz595LJnQUEB1Go1CgoK5HyhQ4cO6NGjBwYNGoTo6Gi/K02Uqu1sb1lZWbjjjjvw22+/hTopRERERERUDWg0Gvzf//0fXnrpJURFRQX8/Wo75mfZsmVYu3ZtqJNBRERERETVhN1ux5tvvokJEyZgz549cm8Tf1W74Cc9PR0ff/wxoqKi5CY8IiIiIiIioGSM0O+//46ffvopoHHoQDUb85OVlYXJkyfjv//9b6iTQkRERERE1VR2djaSk5MD/l61afkxm81YuHAhtm3bFuqkEBERERFRNaTT6aDRaPDWW29h7NixAX+/WrT85OfnY8uWLWjWrJk8SxAREREREZGkQYMG+Oijj9CtWzc0atSoXOsIefBz8eJFDBo0CLt27Qp1UoiIiIiIqJoyGo1o2rQpUlJSyr2OkAY/+fn5eOaZZ3Dw4MFQJoOIiIiIiKoxrVaL119/HZ07d67YeoKUnoBlZ2dj3rx5WLVqVaiSQERERBRyGo0Gdrs91MkgqnZiY2ORnJyMF198EZdddhm6detW4XWG5CGnWVlZuPHGG7Fhw4aq3jQRERER+aFbt27Yvn17qJNBYU6j0WDnzp3o0KEDVCpVhdcXkpafAwcOwG63IzExERcuXEAI4i/yU2RkJDQaDYCS2TUuu+wybNu2DTabzef3dDodIiMjYbVaMXjwYOzbtw8NGzZEUlISrrnmGhw+fBhDhw7FTTfdBLvdjl69emHbtm0QQqBDhw64/vrrsXXrVvz9998AgISEBJw/fx42mw0ajQYWiwX169eHVquF3W6HXq9HdnY2mjdvjsLCQmRlZaFJkyYoLCxEeno6a9SIQqRbt27IyMiAyWSCyWQCABQVFVXqNtVqtfzQO6lGPSIiQs4/2rdvj+PHj6OwsLBS00ElVCoV9Ho9TCYToqKiYDKZ0KlTJ5w/fx6FhYUwmUwBPaRQpVJBrVZXi3xdus/Vq1cPFosFVqsVVqsVUVFRsNvtMJlMEELA4XB4Leu4nq/Ssnq9HjabDXa7HS1atMDZs2dht9sRHR0Nu92OgoKCgNKq1WoREREBi8WC5s2bIz09HVFRUTAYDGjcuDF0Oh22bt2Kdu3aoX79+khKSkJGRgYSExORmZkZ8IMkiYJBq9XinXfeQceOHYO2zpC0/NjtduTn52PdunX45JNPkJGRgbVr11Z1MsJaZGQk9Ho98vLyvC7TsWNH/PDDD0hISABQkikbDAZkZ2dDpVIhIiIC3bt3x+HDh52+Fx0djWuvvRaLFi2CXq9HXFwciouLodFooFaroVKpYLVaERERgZycHKjVatStWxd5eXlQqVSIjo6GVquF1WpFUVERNBoNYmJiMGvWLLz33nswm81ISEhAx44d0bt3b9x///2oU6cOZs2ahf3796NHjx646667ULduXRiNRgDA/PnzMWPGDPk1UBJQZWZmVsLRJap9tFptmZUeErVajfj4eMyYMQP3338/rFYrbDabXHj6z3/+g++//x579uwJOB1qtRqdOnXC2bNnkZWV5VSg1Ov16NChA0aMGAGbzYZ33nkH2dnZcDgcaNCgAUaPHo06derg6NGj6Ny5Mx544AHceOON2LJlC4QQMJvNAacHAO69915s374dW7ZsKdf3Q0GlUqFevXrIzc2ttCBCpVLJv49Op8P111+PBQsWQKfTYdGiRdi6dSu6du2K0aNHw2AwYOjQodi5cyeAkvPNZDLJgY5arYYQAiqVSg4AJk+eDL1ej127duHnn3+u8mCoS5cu+OWXX7Bw4UJs2bIF/fr1wz333IMFCxZg8+bNsNvtOH36NC5cuID4+Hj8+9//xu7du7FmzRqn9TRo0ADLli3D3LlzceDAAcTExODUqVOwWq1o0aIF+vbti4KCAnTu3BkXL17E6tWrodVqccMNN+Cff/7Btm3bygzkDQYDGjVqhFatWkGlUuHaa6/FsGHDkJiYCJVKBY1GAyEELBaLU6Xn3LlzsW3bNnTo0AHnz5/H2rVrsX///ko7pkRKsbGxWLx4MYYPHx7cFYsQcjgcYu3atUKv1wsA/FdF/+rWrSu0Wq14+OGHvS6j1WrFH3/84fW3s9lswmq1iqlTpzp9b+DAgcJgMIjVq1cH9Tyx2+1i48aNokmTJiIyMlK8++67olu3buLo0aPC4XAIIYTYuHGj/J7yuzabTaxfv14kJCQIAEKj0QiNRhPy34H/+K+6/9NqtfLfOp0uoO9++OGHHq9lh8Mh/vnnH9GiRQuPeb9arZb/bt68udvnKSkp4quvvhIpKSlunz322GOia9eu4vDhw2L9+vWiSZMmQqvVioiICNGqVStx7NgxOZ84fPiwcDgcYtGiRUKlUol69eqV6xg1aNBAHDlyRHz44YdCpVIJACIyMjLkv52vf1I6AYioqCiP7wfjn1qtltfZuHFjcezYMY/5td1uF3a7Xbz//vvyd1NTU+VzITk5WcTHxwsA4uabbxYARLNmzcTRo0fFP//8Izp16iTq1q3rcz+D/S8iIkK+z7neezZs2CC6du0qvvnmG9GmTRvRvHlz0b59e3HgwAHx3nvvua3rtddeE0IIsWjRIqHT6cR7770nEhMTBQCxbNkysXHjRnH55ZeL48ePi40bN4o2bdqIdu3aiSNHjoj58+cLtVrt856mVqtF48aNRYcOHcS3334runbtKo4dO1bmfVfaN2nb69atEy1bthT169cv8/gEWq5r2rRpyK8L/qt+/959990KliA9C0nLz/+3d3exUVRtHMD/u52ZnZ2Z3dJlKQVLLY2WlooahWJAExMRq0igkqCGmhDRBDAaEhP1whtjgvGCG2OMMVRRo6bEENIYEy6MFUQTkICNjaF8SJGPtix0t7s7Ozs7u/tekHPcLS0febUo/f8SQjo7H8/MnDnnPGdOu0Iul8OXX36Jt956C5WVlejr65Ovh0OhEOrq6qCqKurq6uD3+5FMJjEyMgLLsuA4Dn777TcoioKFCxfCtm1omgbHcXDkyBE4joPZs2cjkUhgwYIFUBQFfX198DwPyWQS4XAYlmXJ7xTy+/1QFAWFQgHV1dUYGhpCOBxGc3MzFEWBbdvw+/3QdV3GXygU0Nvbi4qKCui6DlVV0djYiHg8Dl3XcejQIRiGId9ueJ4nR09DoRBSqZQcFTMMA67rwvM8BINBmKaJhoYGORXswQcfhOd58DwP9957L0ZHR2XspmkikUjIkVXLsuS+dF1HTU0NNE2DqqpYvHgxLMtCY2MjLl26hN27d6O3txee52H69OlIJBKor6/Hli1bsGjRomvew87OTvT09ODkyZN45JFHsHTpUkQikeva9kb19/fj4MGDaGxsRGVlJQzDQG1tbdnnY5eVftbd3Q3btvHQQw/hhRdewNDQkJx+EwqFAFwukwDk9BwAmD59Oi5evAjgcjmJRCLI5/PIZDJQFEW+ycrlcuOOPJZOaRjLsiz4/eXfNZzNZpHNZhEKhWCaJhzHQTweB3B5ZDubzcryJqY9VFRUQNM0mKaJWCwm96WqKhRFgaIoMAwDqVQKwWBQlmcRu6IoZXEUi0W53OfzwfM8mKYptxOjsONtW6p0nVwuB5/PJ9f3+XzQdR3JZPKa+7zasa4Vq3hjGY/Hy+7r9RLXbrw4xp5fNpuFaZooFApQVRWZTAaBQADpdBqhUEiur6oqDMPA0NDQdcdwtbcuqqrKsls62l4qGo0ik8nIt6mqqsrrFQwGUSgU5OhxVVUVFi5ciOHhYZw6dQrt7e04cuQIzp8/j7a2Nly6dAmHDx/GhQsXEI1GYVkWYrEY2trasH//frz22mvYtGnThPH29/djcHAQn3/+Ofr7+1FTU4OZM2ciGo3CdV2Mjo5i+fLl+PrrrzEwMIBIJAIAWLRoETo6OmDbNg4ePIjff/8dAwMDsG0bK1euxJIlS2QdIOqLixcvYvbs2XjggQfk8tJ6Ys+ePUgmkwgGg1i1alXZM+zz+dDQ0IBcLodp06bhtttug+d5qKqqguM4mD9/Pl566SXU1tZiz5492L17NxobG/HOO+/gwoUL8Pl8MAwD6XRalnnDMFAoFGSbJu5vsVhEMBiEpmmIRCIIh8NQFAXTp0/H4OAgcrkc+vr64Pf7UV9fD8/z4Louzp8/f8X1ra2thed5uHTpElRVRSAQwDPPPIOffvoJgUAAGzduxHvvvQfXdfH444+jv78fra2t+PDDD3H27FkoigLLshCPx2EYBjRNw5133on29nbs2rULx48fRyaTQTQaRWtrK+LxOJLJJC5evIhZs2ahpaUFLS0tAC5Pc7/99tvR0dFxzfq6u7sbW7duRW1tLVauXIljx45hzpw5aG5uxoEDB9DU1IShoSF4nocVK1bI+6lpGp577jmcOHECzc3NKBaLCIfDGBgYwNGjR5HP5+WsgpaWFhw8eBCu62LZsmUYHBzEuXPnkEwm4bouqqur5bpLly6FaZo4ceIEHn30UcRiMXR3d2Px4sXYuHGjbOfGnov42bZtDA4OAgAaGhpQW1uLb7/9Fm+++SZM00QoFMLatWuxfv16WRYjkQgqKyvR3d2NpqYmPPnkk2X7F8+O2J8ovzt37sTAwIAsL7quw+fzob6+Hu3t7aiurkZDQwNs256wnZzoWR177B9//BGffvopzp07B8/z4DgOVFWVU9Hr6urQ2tqK7777DjNmzMDZs2fhOA78fj9M04TP58PcuXNx8uRJDA4OoqWlBa+++irWrVsHx3FkXSaeGVVVoWkaXNdFNpstq998Ph9mzJiB4eFh6LqOmTNn4vTp09B1Ha2trUin02V9IlVVcejQIcyZMweZTAZnzpyRdaaoF0VbrigKgsEgPM9DJpOR7a5t2wAw7lRG0zTlWzPgcpvheZ6MWxwHgKxrAoGAvF/FYhHpdFrW9+Ia3HPPPTh16hQMw0BDQwMCgUBZf3hgYACxWEy2s+LYgpj+K/7XdR3hcBjJZLJsRgxw+S1tPp9HKBSS9Z6I2bZt1NTUYM6cOejq6kImk0FtbS2OHTt2RdkpvRajo6Nl90xMgxWxBYNB2f93HAdz587FG2+8gRdffPG6yumNuqnJD3C58GQyGWiahlQqJV9p67oOTdMAXG7YxyMKoOiYlC6PxWKYNWsWbNtGZWVl2fqJRAKVlZVQVVV2FFVVlYW+srISiUQChmFcse+JYhAxlsYq9pFIJOQDIypEMRVMdIoDgYDcn2EYsnOUSCRg2zZmzZp1nVeUrodIPEdGRqDrOizLAvBXZVZaUYXDYVy4cAHBYBB+v192XkQHRmwnKkhRuYjpGuLei8Q5l8vJSkmUzVL5fB65XA66rsvEKR6PIxAIQNM0ZDIZBINBVFRUIJ1Oy4RC/IvH43J6iGEYMrEYm4CXxj5e8jL2/MZud7Vtx9tP6TURREzXs8+rHetasfr9fuRyObiui3Q6XfacimRkLDFvX1EUWReNF0fp+aXTaYTDYeTzeXluIgkKBoNl6/v9fgwPD8tjj42jtJEV9UVpbABkfVJVVYVUKiXLgOd5soyJjklVVZXsqCiKIhslMUhSLBaRyWSQz+dhmiY0TUOhUEA2m4VlWfKZMAxDJvvZbFbWkblcDpZlyTrsv0h0HkrvSTQaBTBxOzQe0aFQFAWBQADxeBymaQKAvK5+v18OjOm6jnw+L6+baI/Giw8orzfE1OXS9kW0F6XTjcV9E/sfr/1MpVLIZrOyzCeTSVmHiJiy2WxZHSXaLnG9buQ6jSebzcrj3QhRJku3E+UXgPzdUMMwMDo6CsdxEI1Gkc/nUSgU4LouXNeV98nv98vnfmyZ+H/P8e/Yx80mkm8xECjaJ1G3if5U6cBMKbFcrKOqqmyXXdeVA0jA5c6xaFfFfQL+uo7RaBTnz5+Xg6KizzVe+wr81TcrFAqIxWKyDIv4RVseDAahqiqKxSJs25btrmjD8/l8WULh8/lk+SlVKBTkNqXJj2iLAoHAFW2SbdvI5XIIhUJwHEf2ZyeqGwqFAtLptOyDAJAxiQEuEavrurJtE9dTPHcAZF0h+qJX64OL9WKxWNkzoihK2bUQv9tnmqb8fbaRkRHZ1xUvH/x+P0ZHRxGNRv/RZ+SmJz9EREREREST4epDtkRERERERLcIJj9ERERERDQlMPkhIiIiIqIpgckPERERERFNCUx+iIiIiIhoSmDyQ0RE/xo9PT3w+Xzyu60msn79eqxevVr+/PDDD2PLli3/aGxERPTfx+SHiIgm3c8//4yKigqsWLGibPmSJUvkd3bciF27duHtt9/+O0MkIqJbEJMfIiKadJ2dnXj55Zexd+9enDt3Ti7XNA01NTXyW8rHEl9KOVYkEkEoFPrH4iUiolsDkx8iIppUqVQKXV1d2LRpE1asWIEdO3bIz8ZOe9uxYwemTZuG7u5uzJ8/H4FAAKdPn75in2OnvdXX12Pr1q14/vnnEQqFUFdXh48++qhsmz///BNr167FtGnTEIlEsGrVKpw6deofOGMiIvq3YPJDRESTaufOnWhqasK8efPQ0dGBjz/+GMViccL1bdvGu+++i+3bt6Ovrw/V1dXXdZxt27Zh4cKFOHz4MDZv3oxNmzbh6NGjAIBcLofHHnsMoVAI+/btw/79+2FZFtra2uC67t9ynkRE9O/D5IeIiCZVZ2cnOjo6AABtbW1IJBL44YcfJlw/l8vhgw8+wJIlSzBv3jwYhnFdx3niiSewefNm3HHHHXj99dcRjUbx/fffAwC6urpQKBSwfft2LFiwAM3Nzfjkk09w+vRp9PT0/N/nSERE/05MfoiIaNIcPXoUBw4cwLPPPgsAUBQFTz/9NDo7OyfcRtM03H333Td8rNJtfD4fampqMDw8DAD49ddfcfz4cYRCIViWBcuyEIlE4DgOTpw4ccPHIiKi/wblZgdARERTR2dnJzzPw+zZs+WyYrGIQCCA999/f9xtgsHghH8A4WpUVS372efzyT+WkEqlcP/99+OLL764YrsZM2bc8LGIiOi/gckPERFNCs/z8Nlnn2Hbtm1Yvnx52WerV6/GV199haampkmJ5b777kNXVxeqq6sRDocn5ZhERHTzcdobERFNim+++QYjIyPYsGED7rrrrrJ/a9asuerUt7/bunXrEI1GsWrVKuzbtw9//PEHenp68Morr+DMmTOTFgcREU0uJj9ERDQpOjs7sWzZsnG/wHTNmjX45Zdf0NvbOymxGIaBvXv3oq6uDk899RSam5uxYcMGOI7DN0FERLcwX/Fqf1+UiIiIiIjoFsE3P0RERERENCUw+SEiIiIioimByQ8REREREU0JTH6IiIiIiGhKYPJDRERERERTApMfIiIiIiKaEpj8EBERERHRlMDkh4iIiIiIpgQmP0RERERENCUw+SEiIiIioimByQ8REREREU0J/wOckSYRudHEpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/noah/.local/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID#   Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  \\\n",
      "0  0.957102 -0.392262   -0.136428  -0.717780  -0.098242  -0.062767   \n",
      "1 -0.842588 -0.161087   -0.107379  -0.515236  -0.098242  -0.058526   \n",
      "2 -0.167650  1.103818    0.467739   0.071777  -0.098242  -0.062767   \n",
      "3 -0.301522  0.639719   -0.084433   1.022084  -0.098242  15.646299   \n",
      "4  0.538585 -0.001928   -0.127412   0.830568  -0.098242  -0.048809   \n",
      "5  0.089763  0.417981    7.087067  -0.043229  -0.098242  -0.062767   \n",
      "6  0.219325 -0.046890   -0.156236  -0.668227   9.038254  -0.062767   \n",
      "7 -0.929265  0.876328   -0.078554   1.535043  -0.098242  -0.062767   \n",
      "\n",
      "   Bonus_miles  Bonus_trans  Flight_miles_12mo  Flight_trans_12  \\\n",
      "0    -0.580379    -0.639577          -0.194036        -0.217088   \n",
      "1    -0.472057    -0.394837          -0.148568        -0.159181   \n",
      "2     0.658657     1.810074           4.088700         4.348639   \n",
      "3     3.179691     1.714614           0.033293         0.059695   \n",
      "4     0.394766     0.636493          -0.135599        -0.144842   \n",
      "5     0.065275     0.079326           0.352770         0.395268   \n",
      "6    -0.101665     0.617851           0.087549         0.220347   \n",
      "7     1.475799     0.890097           0.007000         0.021263   \n",
      "\n",
      "   Days_since_enroll    Award?  \n",
      "0          -0.955105 -0.461496  \n",
      "1           0.820704 -0.192639  \n",
      "2           0.208676  0.907008  \n",
      "3           0.239873  0.337527  \n",
      "4          -0.520677  0.347496  \n",
      "5          -0.115867  0.324977  \n",
      "6          -0.072464  0.051784  \n",
      "7           0.932570  0.700216  \n",
      "0 :  1, 1, 2, 9\n",
      "1 :  0\n",
      "2 :  6, 5\n",
      "3 :  1, 0, 6\n",
      "4 :  1, 1, 4, 1\n",
      "5 :  7, 0\n",
      "6 :  8\n",
      "7 :  4\n"
     ]
    }
   ],
   "source": [
    "raw_airlines_df = pd.read_csv('EastWestAirlinesCluster.csv')\n",
    "\n",
    "# Normalize\n",
    "airlines_df_norm = raw_airlines_df.apply(preprocessing.scale, axis=0)\n",
    "\n",
    "# a: hclust with euclidean and ward's\n",
    "Z = linkage(airlines_df_norm, method='ward', metric='euclidean')\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "fig.subplots_adjust(bottom=0.23)\n",
    "plt.title(\"Hierarchical Clustering Dendrogram (Euclidean Distance and Ward's Method)\")\n",
    "plt.xlabel('Airline')\n",
    "dendrogram(Z, labels=airlines_df_norm.index, color_threshold=2.75)\n",
    "plt.axhline(y=65, color='black', linewidth=0.5, linestyle='dashed')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.show()\n",
    "\n",
    "# b: Text answer\n",
    "\n",
    "# c: \n",
    "kmeans = KMeans(n_clusters=8, random_state=0).fit(airlines_df_norm)\n",
    "\n",
    "centroids = pd.DataFrame(kmeans.cluster_centers_, columns=airlines_df_norm.columns)\n",
    "#pd.set_option('precision', 3)\n",
    "print(centroids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 :  1, 1, 2, 9\n",
      "1 :  0\n",
      "2 :  6, 5\n",
      "3 :  1, 0, 6\n",
      "4 :  1, 1, 4, 1\n",
      "5 :  7, 0\n",
      "6 :  8\n",
      "7 :  4\n"
     ]
    }
   ],
   "source": [
    "# Cluster membership\n",
    "memb = pd.Series(kmeans.labels_, index=airlines_df_norm.index)\n",
    "for key, item in memb.groupby(memb):\n",
    "    print(key, ': ', ', '.join(str(item.index[0])))"
   ]
  }
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