Alternative-classifiers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42b0a1b0",
   "metadata": {},
   "source": [
    "# ALTERNATIVE MODELS\n",
    "\n",
    "## With ICC-paper feature vector\n",
    "\n",
    "(You can run only the next two cells and skip to the next step if you already have the model pickled)\n",
    "\n",
    "Once the loader has loaded the data and applied the transformations, we can load the resulting parquets and prepare for model training. We then split the data into training and test sets and train a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8327cd7c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records: 469565\n",
      "Number of used features: 128\n",
      "No. of available unused features: 0\n"
     ]
    }
   ],
   "source": [
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "\n",
    "phishing = pq.read_table('floor/phishing_2307.parquet')\n",
    "benign = pq.read_table('floor/benign_2307.parquet')\n",
    "\n",
    "from transformers.drop_nontrain import drop_nontrain_table as drop_nontrain\n",
    "phishing = drop_nontrain(phishing)\n",
    "benign = drop_nontrain(benign)\n",
    "\n",
    "# realign schemas (parquet files save in nonsense orders)\n",
    "benign = benign.cast(phishing.schema)\n",
    "\n",
    "# concatentate tables\n",
    "data = pa.concat_tables([phishing, benign])\n",
    "df = data.to_pandas()\n",
    "\n",
    "print(f\"Total records: {len(df)}\")\n",
    "\n",
    "from transformers.cast_timestamp import cast_timestamp\n",
    "df = cast_timestamp(df)\n",
    "\n",
    "used_features = [\n",
    "\n",
    "    # IP  ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"ip_mean_average_rtt\",\n",
    "    \"ip_entropy\",\n",
    "    \"ip_count\",\n",
    "    #\"ip_v4_count\", \"ip_v6_count\", # Moved RDAP and RENAMED to rdap_ip\n",
    "    \n",
    "    # Newly added for ICC\n",
    "    \"ip_v4_ratio\",\n",
    "    \"ip_a_aaaa_to_all_ratio\",\n",
    "    \"ip_as_address_entropy\",\n",
    "    \"ip_asn_entropy\",\n",
    "    \"ip_distinct_as_count\",\n",
    "    \n",
    "    # DNS  ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"dns_A_count\",\n",
    "    \"dns_AAAA_count\",\n",
    "    \"dns_MX_count\",\n",
    "    \"dns_NS_count\",\n",
    "    \"dns_SOA_count\",\n",
    "    \"dns_TXT_count\",\n",
    "    \"dns_CNAME_count\",\n",
    "    \"dns_soa_primary_ns_len\",\n",
    "    \"dns_soa_primary_ns_level\", # renamed\n",
    "    \"dns_soa_primary_ns_digit_count\",\n",
    "    \"dns_soa_primary_ns_entropy\",\n",
    "    \"dns_soa_email_len\",\n",
    "    \"dns_soa_email_level\", # renamed\n",
    "    \"dns_soa_email_digit_count\",\n",
    "    \"dns_soa_email_entropy\",\n",
    "    # \"dns_soa_serial\", <-- Doesn't convey any kind of comparative information\n",
    "    \"dns_soa_refresh\",\n",
    "    \"dns_soa_retry\",\n",
    "    \"dns_soa_expire\",\n",
    "    \"dns_mx_avg_len\",\n",
    "    \"dns_mx_avg_entropy\",\n",
    "    \"dns_domain_name_in_mx\",\n",
    "    \"dns_txt_spf_exists\",\n",
    "    \"dns_txt_avg_entropy\",\n",
    "    \"dns_txt_dkim_exists\",\n",
    "    \n",
    "    \"dns_zone_level\",\n",
    "    \"dns_zone_digit_count\",\n",
    "    \"dns_zone_len\",\n",
    "    \"dns_zone_entropy\",\n",
    "    \n",
    "    # Newly added for ICC\n",
    "    \"dns_resolved_record_types\",\n",
    "    \"dns_has_dnskey\",\n",
    "    \"dns_dnssec_score\",\n",
    "    \"dns_ttl_avg\",\n",
    "    \"dns_ttl_stdev\",\n",
    "    \"dns_ttl_low\",\n",
    "    \"dns_ttl_mid\",\n",
    "    \"dns_ttl_distinct_count\",\n",
    "    \"dns_soa_min_ttl\", # why is this not in NOMS?!\n",
    "    \"dns_txt_external_verification_score\",\n",
    "    \"dns_txt_dmarc_exists\",\n",
    "    \n",
    "    \n",
    "    # TLS  ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"tls_broken_chain\",\n",
    "    \"tls_expired_chain\",\n",
    "    \"tls_total_extension_count\",\n",
    "    \"tls_critical_extensions\",\n",
    "    \"tls_with_policies_crt_count\",\n",
    "    \"tls_percentage_crt_with_policies\",\n",
    "    \"tls_x509_anypolicy_crt_count\",\n",
    "    \"tls_iso_policy_crt_count\",\n",
    "    \"tls_joint_isoitu_policy_crt_count\",\n",
    "    # \"tls_iso_policy_oid\", <-- Basically a random value, doesn't make much sense when collected like this; policy OIDs need more research\n",
    "    # \"tls_isoitu_policy_oid\", <-- same\n",
    "    #\"tls_unknown_policy_crt_count\", <-- Abandoned, no useful values\n",
    "    \"tls_root_cert_validity_len\",\n",
    "    \"tls_leaf_cert_validity_len\",\n",
    "    \"tls_chain_len\",\n",
    "    \"tls_root_cert_lifetime\",\n",
    "    \"tls_leaf_cert_lifetime\",\n",
    "    \"tls_subject_count\",\n",
    "    \"tls_unique_SLD_count\",\n",
    "    \"tls_server_auth_crt_count\",\n",
    "    \"tls_client_auth_crt_count\",\n",
    "    \"tls_CA_certs_in_chain_ratio\",\n",
    "    \"tls_common_name_count\",\n",
    "    \n",
    "    # Newly added for ICC\n",
    "    \"tls_has_tls\",\n",
    "    \"tls_negotiated_version_id\",\n",
    "    \"tls_negotiated_cipher_id\",\n",
    "    \n",
    "    # LEX ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"lex_name_len\",\n",
    "    #\"lex_digit_count\", <-- abandoned, almost the same as \"lex_sub_digit_ratio\"\n",
    "    \"lex_has_digit\",\n",
    "    \"lex_phishing_keyword_count\",\n",
    "    #\"lex_vowel_count\", <-- moved to sub\n",
    "    #\"lex_underscore_hyphen_count\", <--- same as nonalnum\n",
    "    \"lex_consecutive_chars\",\n",
    "    \"lex_tld_len\",\n",
    "    \"lex_sld_len\",\n",
    "    \"lex_sub_count\",\n",
    "    \"lex_stld_unique_char_count\",\n",
    "    \"lex_begins_with_digit\",\n",
    "    \"lex_www_flag\",\n",
    "    \"lex_sub_max_consonant_len\",\n",
    "    \"lex_sub_norm_entropy\",\n",
    "    \"lex_sub_digit_count\",\n",
    "    \"lex_sub_digit_ratio\",\n",
    "    \"lex_sub_consonant_ratio\",\n",
    "    \"lex_sub_non_alphanum_ratio\",\n",
    "    \"lex_sub_hex_ratio\",\n",
    "    \"lex_sld_norm_entropy\", # <-- newly added feature on 24-09-29\n",
    "    \n",
    "    # Newly added for ICC\n",
    "    \"lex_bigram_matches\",\n",
    "    \"lex_trigram_matches\",\n",
    "    \"lex_sub_vowel_count\",\n",
    "    \"lex_sub_vowel_ratio\",\n",
    "    \"lex_sub_consonant_count\",\n",
    "    \"lex_sub_non_alphanum_count\",\n",
    "    \"lex_sub_hex_count\",\n",
    "    \"lex_avg_part_len\",\n",
    "    \"lex_stdev_part_lens\",\n",
    "    \"lex_longest_part_len\",\n",
    "    \"lex_shortest_sub_len\",\n",
    "    \n",
    "    # RDAP ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"rdap_registration_period\",\n",
    "    \"rdap_domain_age\",\n",
    "    \"rdap_time_from_last_change\",\n",
    "    \"rdap_domain_active_time\",\n",
    "    \"rdap_has_dnssec\",\n",
    "    \n",
    "    \"rdap_ip_v4_count\", \"rdap_ip_v6_count\", # <-- used for NOMS, but now prefixed with rdap_\n",
    "    \n",
    "    # Newly added for ICC\n",
    "    \"rdap_registrar_name_len\",\n",
    "    \"rdap_registrar_name_entropy\",\n",
    "    \"rdap_registrar_name_hash\",\n",
    "    \"rdap_registrant_name_len\",\n",
    "    \"rdap_registrant_name_entropy\",\n",
    "    \"rdap_admin_name_len\",\n",
    "    \"rdap_admin_name_entropy\",\n",
    "    \"rdap_admin_email_len\",\n",
    "    \"rdap_admin_email_entropy\",\n",
    "    \n",
    "    \"rdap_ip_shortest_v4_prefix_len\",\n",
    "    \"rdap_ip_longest_v4_prefix_len\",\n",
    "    \"rdap_ip_shortest_v6_prefix_len\",\n",
    "    \"rdap_ip_longest_v6_prefix_len\",\n",
    "    \n",
    "    \"rdap_ip_avg_admin_name_len\",\n",
    "    \"rdap_ip_avg_admin_name_entropy\",\n",
    "    \"rdap_ip_avg_admin_email_len\",\n",
    "    \"rdap_ip_avg_admin_email_entropy\",\n",
    "    \n",
    "    # GEO ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"geo_countries_count\",\n",
    "    \"geo_continent_hash\",\n",
    "    \"geo_countries_hash\",\n",
    "    \n",
    "    # Newly added for ICC\n",
    "    \"geo_lat_stdev\", # <- Puvodne to pridal Adam, ale pak jsme dali pryc, ze to budem resit jinak.\n",
    "    \"geo_lon_stdev\"  #    Ale muze byt helpful pro identifikaci, jestli lokality jsou blizko sebe.\n",
    "]\n",
    "\n",
    "\n",
    "print(\"Number of used features: \" + str(len(used_features)))\n",
    "\n",
    "# NUMBER OF AVAILABLE AND UNUSED FEATURES\n",
    "missing_columns = set(df.columns) - set(used_features) - set([\"label\"])\n",
    "print(\"No. of available unused features: \" + str(len(missing_columns)))\n",
    "\n",
    "df = df[[\"label\", *used_features]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31032311",
   "metadata": {},
   "source": [
    "## Shortening feature names (for paper purposes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98013788",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>ip_mean_average_rtt</th>\n",
       "      <th>ip_entropy</th>\n",
       "      <th>ip_count</th>\n",
       "      <th>ip_v4_ratio</th>\n",
       "      <th>ip_a_aaaa_to_all_ratio</th>\n",
       "      <th>ip_as_address_entropy</th>\n",
       "      <th>ip_asn_entropy</th>\n",
       "      <th>ip_distinct_as_count</th>\n",
       "      <th>dns_A_count</th>\n",
       "      <th>...</th>\n",
       "      <th>rdap_ip_longest_v6_prefix_len</th>\n",
       "      <th>rdap_ip_avg_admin_name_len</th>\n",
       "      <th>rdap_ip_avg_admin_name_ent</th>\n",
       "      <th>rdap_ip_avg_admin_email_len</th>\n",
       "      <th>rdap_ip_avg_admin_email_ent</th>\n",
       "      <th>geo_countries_count</th>\n",
       "      <th>geo_continent_hash</th>\n",
       "      <th>geo_countries_hash</th>\n",
       "      <th>geo_lat_stdev</th>\n",
       "      <th>geo_lon_stdev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>misp_2307:phishing</td>\n",
       "      <td>93.0610</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>0.249146</td>\n",
       "      <td>23.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>misp_2307:phishing</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>misp_2307:phishing</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>misp_2307:phishing</td>\n",
       "      <td>3.4960</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.182703</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.154588</td>\n",
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       "      <td>600</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>misp_2307:phishing</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>benign_2307:unknown</td>\n",
       "      <td>165.7385</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.332193</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.143050</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1800</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>469561</th>\n",
       "      <td>benign_2307:unknown</td>\n",
       "      <td>3.8575</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.292193</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.157442</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1800</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>...</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>0.149881</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>172</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469563</th>\n",
       "      <td>benign_2307:unknown</td>\n",
       "      <td>20.5390</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>130</td>\n",
       "      <td>1.19615</td>\n",
       "      <td>2.3587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469564</th>\n",
       "      <td>benign_2307:unknown</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.258668</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.164918</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>469565 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      label  ip_mean_average_rtt  ip_entropy  ip_count   \n",
       "0        misp_2307:phishing              93.0610         1.0         2  \\\n",
       "1        misp_2307:phishing               0.0000        -0.0         1   \n",
       "2        misp_2307:phishing              -1.0000        -0.0         0   \n",
       "3        misp_2307:phishing               3.4960        -0.0         1   \n",
       "4        misp_2307:phishing               0.0000        -0.0         1   \n",
       "...                     ...                  ...         ...       ...   \n",
       "469560  benign_2307:unknown             165.7385        -0.0         2   \n",
       "469561  benign_2307:unknown               3.8575        -0.0         2   \n",
       "469562  benign_2307:unknown               0.0000         1.0         2   \n",
       "469563  benign_2307:unknown              20.5390        -0.0         2   \n",
       "469564  benign_2307:unknown               0.0000        -0.0         1   \n",
       "\n",
       "        ip_v4_ratio  ip_a_aaaa_to_all_ratio  ip_as_address_entropy   \n",
       "0               1.0                     1.0                    1.0  \\\n",
       "1               1.0                     1.0                   -0.0   \n",
       "2               0.0                     0.0                    NaN   \n",
       "3               0.0                     0.0                   -0.0   \n",
       "4               1.0                     1.0                   -0.0   \n",
       "...             ...                     ...                    ...   \n",
       "469560          1.0                     1.0                   -0.0   \n",
       "469561          0.5                     1.0                   -0.0   \n",
       "469562          1.0                     1.0                    1.0   \n",
       "469563          0.5                     1.0                   -0.0   \n",
       "469564          1.0                     1.0                   -0.0   \n",
       "\n",
       "        ip_asn_entropy  ip_distinct_as_count  dns_A_count  ...   \n",
       "0                 -0.0                   1.0            2  ...  \\\n",
       "1                 -0.0                   1.0            1  ...   \n",
       "2                  NaN                   NaN            0  ...   \n",
       "3                 -0.0                   1.0            0  ...   \n",
       "4                 -0.0                   1.0            1  ...   \n",
       "...                ...                   ...          ...  ...   \n",
       "469560            -0.0                   1.0            2  ...   \n",
       "469561             1.0                   2.0            1  ...   \n",
       "469562            -0.0                   1.0            2  ...   \n",
       "469563            -0.0                   1.0            1  ...   \n",
       "469564            -0.0                   1.0            1  ...   \n",
       "\n",
       "        rdap_ip_longest_v6_prefix_len  rdap_ip_avg_admin_name_len   \n",
       "0                                 0.0                        13.0  \\\n",
       "1                                 0.0                         0.0   \n",
       "2                                 0.0                         0.0   \n",
       "3                                 0.0                        19.0   \n",
       "4                                 0.0                         0.0   \n",
       "...                               ...                         ...   \n",
       "469560                            0.0                        10.0   \n",
       "469561                           28.0                        10.0   \n",
       "469562                            0.0                        13.0   \n",
       "469563                           64.0                         0.0   \n",
       "469564                            0.0                        11.0   \n",
       "\n",
       "        rdap_ip_avg_admin_name_ent  rdap_ip_avg_admin_email_len   \n",
       "0                         0.249146                         23.0  \\\n",
       "1                         0.000000                          0.0   \n",
       "2                         0.000000                          0.0   \n",
       "3                         0.182703                         23.0   \n",
       "4                         0.000000                          0.0   \n",
       "...                            ...                          ...   \n",
       "469560                    0.332193                         26.0   \n",
       "469561                    0.292193                         23.0   \n",
       "469562                    0.249146                         23.0   \n",
       "469563                    0.000000                          0.0   \n",
       "469564                    0.258668                         21.0   \n",
       "\n",
       "        rdap_ip_avg_admin_email_ent  geo_countries_count  geo_continent_hash   \n",
       "0                          0.149881                    1                   2  \\\n",
       "1                          0.000000                    1                   3   \n",
       "2                          0.000000                    0                   0   \n",
       "3                          0.154588                    1                   1   \n",
       "4                          0.000000                    1                   0   \n",
       "...                             ...                  ...                 ...   \n",
       "469560                     0.143050                    1                   2   \n",
       "469561                     0.157442                    1                   2   \n",
       "469562                     0.149881                    1                  10   \n",
       "469563                     0.000000                    1                   6   \n",
       "469564                     0.164918                    1                   1   \n",
       "\n",
       "        geo_countries_hash  geo_lat_stdev  geo_lon_stdev  \n",
       "0                     1800        0.00000         0.0000  \n",
       "1                      184        0.00000         0.0000  \n",
       "2                        0        0.00000         0.0000  \n",
       "3                      600        0.00000         0.0000  \n",
       "4                      144        0.00000         0.0000  \n",
       "...                    ...            ...            ...  \n",
       "469560                1800        0.00000         0.0000  \n",
       "469561                1800        0.00000         0.0000  \n",
       "469562                 172        0.00000         0.0000  \n",
       "469563                 130        1.19615         2.3587  \n",
       "469564                 600        0.00000         0.0000  \n",
       "\n",
       "[469565 rows x 129 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shortening = {\n",
    "    \"lex_stld_unique_char_count\": \"lex_stld_uniq_char_cnt\",\n",
    "    \"lex_sub_max_consonant_len\": \"lex_max_conson_len\",\n",
    "    \"lex_sub_non_alphanum_count\": \"lex_sub_nonalnum_count\",\n",
    "    \"lex_sub_non_alphanum_ratio\": \"lex_sub_nonalnum_ratio\",\n",
    "    \n",
    "    \"dns_resolved_record_types\": \"dns_resolved_rec_types\",\n",
    "    \"dns_domain_name_in_mx\": \"dns_dn_in_mx\",\n",
    "    \"dns_txt_external_verification_score\": \"dns_txt_ext_verif_score\",\n",
    "    \"dns_zone_digit_count\": \"dns_zone_digits\",\n",
    "    \"dns_soa_primary_ns_digit_count\": \"dns_soa_prim_ns_digits\",\n",
    "    \"dns_soa_primary_ns_len\" : \"dns_soa_pri_ns_len\",\n",
    "    \"dns_soa_primary_ns_level\": \"dns_soa_pri_ns_level\",\n",
    "    \"dns_soa_primary_ns_entropy\": \"dns_soa_pri_ns_entropy\",\n",
    "    \"dns_soa_email_digit_count\": \"dns_soa_email_digits\",\n",
    "    \n",
    "    \"tls_percentage_crt_with_policies\": \"tls_percentage_with_policies\",\n",
    "    \"tls_x509_anypolicy_crt_count\": \"tls_x509_anypol_crt_count\",\n",
    "    \"tls_iso_policy_crt_count\": \"tls_iso_pol_crt_count\",\n",
    "    \"tls_joint_isoitu_policy_crt_count\": \"tls_isoitu_pol_crt_count\",\n",
    "    \n",
    "    \"rdap_ip_avg_admin_name_entropy\": \"rdap_ip_avg_admin_name_ent\",\n",
    "    \"rdap_ip_avg_admin_email_entropy\": \"rdap_ip_avg_admin_email_ent\"\n",
    "}\n",
    "\n",
    "df.rename(columns=shortening, inplace=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d63ab1",
   "metadata": {},
   "source": [
    "# NaNs will be replaced by -1\n",
    "## Some classifiers cannot handle NaN values\n",
    "Replacing by -1 gave better results (for xgboost) than 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "207df674",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle NaNs\n",
    "df.fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "564247b4",
   "metadata": {},
   "source": [
    "# Train-Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3460ca64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import Config\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "class_map = {\"benign_2307:unknown\": 0, \"misp_2307:phishing\": 1}\n",
    "\n",
    "labels = df['label'].apply(lambda x: class_map[x]) # y vector\n",
    "features = df.drop('label', axis=1).copy() # X matrix\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "  features,\n",
    "  labels,\n",
    "  test_size=0.3,\n",
    "  random_state=42,\n",
    "  shuffle=True, \n",
    "  stratify=labels\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f06005",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5375765b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.98\n",
      "Precision: 0.98\n",
      "Recall: 0.71\n",
      "F1 Score: 0.82\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import f1_score, confusion_matrix, classification_report, ConfusionMatrixDisplay, det_curve\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Random Forest parameters\n",
    "params = {\n",
    "    \"n_estimators\": 270,\n",
    "    \"max_depth\": 9,\n",
    "    \"min_samples_split\": 2,\n",
    "    \"min_samples_leaf\": 1,\n",
    "    \"random_state\": 7,\n",
    "}\n",
    "\n",
    "model = RandomForestClassifier(**params)\n",
    "\n",
    "# set to True to cross validate\n",
    "do_cross_val = False\n",
    "if do_cross_val:\n",
    "    kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "    results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='f1')\n",
    "    print(\"F1: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "\n",
    "# at last, train the model\n",
    "_ = model.fit(X_train, y_train)\n",
    "\n",
    "# If you want to evaluate the model on the test set\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')\n",
    "print(f'Precision: {precision_score(y_test, y_pred):.2f}')\n",
    "print(f'Recall: {recall_score(y_test, y_pred):.2f}')\n",
    "print(f'F1 Score: {f1_score(y_test, y_pred):.2f}')\n",
    "\n",
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=model.classes_)  # Compute confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)  # Create display object\n",
    "disp.plot(cmap=plt.cm.Blues)  # Plot confusion matrix\n",
    "plt.show()  # Show the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528d61bf",
   "metadata": {},
   "source": [
    "# Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2402d7dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.97\n",
      "Precision: 0.94\n",
      "Recall: 0.71\n",
      "F1 Score: 0.81\n"
     ]
    },
    {
     "data": {
      "image/png": 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zGx0VAAC8JpQRAAAAQDoSFxeno0eP2i8QHj58WHFxccqRI4e8vLzsK5grVqxI+QAgSdhsNns5ERgYqMDAQN25c0eOjo6qVq2afddVo0aNlC1bNqPjAgCAZEIZAQAAAKRhcXFxOnbsmH2F8qFDhxQbG6vs2bPby4cmTZqocuXKlA8AXgubzaabN2/ad2QFBgbq7t27cnR0VPXq1e2laMOGDeXu7m50XAAAkEQoIwAAAIA0JD4+XsePH7df4Dt06JBiYmKULVs2NW7cWN7e3vLy8lKVKlXk5ORkdFwAkM1m0/Xr1+3PW0FBQbp3756cnJxUo0YNe2nasGFDZc2a1ei4AADgb6KMAAAAAFKxhIQEe/kQFBSkgwcPKjo6Wu7u7mrcuLF9hTHlA4DUwmaz6dq1a6+UE/fv35eTk5Nq1qxpf15r0KCBsmTJYnRcAADwJ1FGAAAAAKlIQkKCTp48ab9Ad+DAAUVHRytr1qxq1KiR/cawVatWlbOzs9FxAeAfs9lsunr16itjnR4+fChnZ2fVrFnT/rzXoEEDubm5GR0XAAD8D5QRAAAAQAqWmJiokydP2i/AHThwQFFRUcqSJYsaNWpkXyFcrVo1ygcA6YLNZtPly5ftO8ICAwP16NEjOTs7q3bt2vaxTg0aNFDmzJmNjgsAAP4fyggAAAAgBUlMTNTp06ftK4D379+vyMhIubm52cuHJk2aqEaNGpQPAKB/lxOXLl2yP28GBQXp8ePHcnFxsZcT3t7eqlevHuUEAAAGoowAAAAADJSYmKgzZ87YV/fu379fERERypw5sxo2bGgfP1KjRg25uLgYHRcAUjybzaYLFy7Yi4mgoCA9efJELi4uqlOnjv15tV69esqUKZPRcQEASDcoIwAAAIDXyGKx2MuHoKAg7du3Ty9fvlSmTJnUsGFD+86HWrVqUT4AQBKwWq32ciIwMFDBwcF6+vSpMmTIoLp169p3TtStW1eurq5GxwUAIM2ijAAAAACSkcViUWhoqH18yL59+/TixQtlypRJ9evXt6/QrVWrljJkyGB0XABI86xWq86fP29/Xg4ODtazZ8+UMWNG1a1b1/68XKdOHcoJAACSEGUEAAAAkISsVqtCQ0PtK3D37dun8PBwubq6qn79+vYVuLVq1VLGjBmNjgsA6Z7VatXZs2ftO9aCg4P1/Plzubq6ql69evYda3Xq1OF5GwCAf4AyAgAAAPgHrFarzp0798oK2+fPnytjxoz28oGLWACQelgsFns58Xulcu3atdnRBgDAX0AZAQAAAPwFv473+M/Z48+ePVOGDBlUr149xnsAQBrDuD0AAJIGZQQAAADwO2w2m718+HXnw5MnT1658WmTJk1Ut25dZcqUyei4AIBkZrFYdObMGXspvX//fr18+VKZM2dWgwYN7K8LtWrVkouLi9FxAQBIMSgjAAAAgP9gs9l08eLFV8YuPX78WC4uLqpTp459BWzdunWVOXNmo+MCAAyWmJj4m3IiIiJCbm5uatCggf11o0aNGpQTAIB0jTICAAAA6ZrNZtOlS5fsF5GCgoL0+PFjOTs7q06dOvbZ4PXq1aN8AAD8ocTERJ06dcq+o27//v2KjIyUm5ubGjZs+Eo54ezsbHRcAABeG8oIAAAApCs2m02XL1+2XyQKCgrSw4cP5ezsrNq1a9vHa9SvX19ubm5GxwUApHIJCQn2ciIwMFAHDhxQVFSUsmTJokaNGtlL72rVqlFOAADSNMoIAAAApGk2m01Xr16173oICgrSgwcP5OTkpFq1atlXqNavX19ZsmQxOi4AII1LSEjQyZMn7a9LBw4cUHR0tLJmzapGjRrZX5eqVasmJycno+MCAJBkKCMAAACQpthsNl27du2VnQ/37t2Tk5OTatasad/50KBBA2XNmtXouACAdC4+Pl4nTpywv2YdOHBAMTExcnd3V+PGje2vW1WrVqWcAACkapQRAAAASNVsNptu3Ljxys6Hu3fvytHRUTVq1LCvMG3YsCHlAwAgxYuPj9fx48ftY50OHjyo2NhYZcuWzV5OeHt7q3LlypQTAIBUhTICAAAAqYrNZtPNmzdfueH0nTt35OjoqOrVq9sv0jRs2FDu7u5GxwUA4B+Ji4vT8ePH7a95hw4dUmxsrLJnz67GjRvbS/fKlSvL0dHR6LgAAPxPlBEAAABI8X4tH379cfv2bTk4ONjLhyZNmqhRo0bKli2b0VEBAEhWcXFxOnr0qL2UP3z4sOLi4pQjRw55eXnZS/mKFStSTgAAUhTKCAAAAKQ4YWFhr+x8CAsLk4ODg6pWrWq/yNKoUSNlz57d6KgAABgqNjZWR48etb9mHj58WPHx8fLw8JCXl5d950SFChUoJwAAhqKMAAAAgOFu375t3/UQGBioW7duycHBQVWqVLHvfGjcuLFy5MhhdFQAAFK0mJgYHTlyxP66euTIEcXHxytXrlz2nRO/lhMODg5GxwUApCOUEQAAAHjt7ty588oNp2/cuCFJqly5sn0FZ+PGjeXh4WFwUgAAUrfo6Gh7OREYGKijR48qISFBuXPnfmWsU7ly5SgnAADJijICAAAAye7u3buvjF26fv26JKlSpUr2iyCNGzdWzpw5DU4KAEDaFh0drcOHD9tfk48eParExETlzp3b/prcpEkTlS1blnICAJCkKCMAAACQ5O7du/fK2KVr165JkipWrGgfD+Hl5aVcuXIZnBQAgPQtKipKhw4dsr9uHzt2TImJicqbN6/9NbtJkyYqU6YM5QQA4B+hjAAAAMA/dv/+fQUHB9tXWV65ckWSVL58+Vd2PuTJk8fgpAAA4PdERkbay4nAwEAdP35cFotF+fLlsxcT3t7eKlWqFOUEAOAvoYwAAADAX/bgwQMFBwfbL1RcvnxZklSuXLlXdj7kzZvX4KQAAOCfiIiI0MGDB+07J06cOCGLxaICBQq8snOiZMmSlBMAgN9FGQEAAIA/9OjRI/tFiKCgIF28eFGSVKZMGftsaS8vL+XLl8/gpAAAIDlFRETowIED9gUJJ0+elNVqVcGCBV/ZOVG8eHHKCQDAKygjAAAA8BuPHz9+ZezShQsXJEmlS5e2X2Tw8vJS/vz5DU4KAACM9PLlSx04cMD+nuHUqVOyWq0qVKjQKzfELlasGOUEAKRzlBEAAADQkydPXhm7dP78eUlSqVKlXhm7VLBgQYOTAgCAlOzFixfav3+/fTflqVOnZLPZVLhwYXsx8Ws5AQBIXygjAAAA0qGnT5/ay4egoCCdPXtWklSiRIlXdj4UKlTI4KQAACA1Cw8Pt5cTgYGBOnPmjGw2mzw9PV8Z6+Tp6Wl0VABAMqOMAAAASAeePXumffv22UcohIaGSpKKFy/+ys0nCxcubHBSAACQlj1//lz79++3vycJCQmRzWZT0aJFXxnrVKRIEaOjAgCSGGUEAABAGvT8+XPt27fPvvPhPz/o/+eIBD7oAwAAI/26YOI/37NILJgAgLSIMgIAACAN+HUEwq+rDH8dgVCkSBF5e3vbxy4VLVrU6KgAAAD/09OnT+3lRGBg4H8dJdmkSRPuYwUAqRBlBAAAQBIaOnSojhw6mKTHzOjqqomT/FS/fn37rx06dEgnT55UWFiYAgMDdfr06d/cHNLb25vyAQAApGpPnjx5ZdTkuXPnJEklS5Z8ZbdngQIF/uv3x8fHa8SIEbp///7fOn+ZMmU0fvz4vxsfAPAfKCMAAACSSHx8vDJmzKiy+bKoUkH3JDvuplP3NHDgp5o3b54kKSoqSlmyZJEkFSxY0L7zoUmTJipWrJgcHByS7NwAAAApyaNHj14Z63T+/HlJUunSpVW9enXdv39f3333nbJlyyZJOnbsmOrUqaNsRcoqo3uuv3SuqEe3FfXotu7du6f8+fMn+WMBgPTG2egAAAAAacWvazz6exVTpxr/Xp0Xm2CRq4vT//yeP/p9STr/IEZWq9X+czc3N82cOVMeHh567733KB8AAEC6kSdPHr377rt69913Jf27nAgODlZgYKDWr1+vZ8+eaerUqZo2bZokKSEhQZJU89Mlci9U+i+d61FosA5O7mI/BgDgn6GMAAAASGLXHkXpuzP31a5yPl1/HKWTt1+obL4sCr3zUnndM+rNyvm09vgdZXV1lkfmDMqcwUlXHkaqYHZXFcqRSc5ODsqcwUnn7kYoUwYnJVisvznHkCFDDHhkAAAAKUuePHnUqVMnderUSbNnz9bp06dVr16933zd8+tn9PL2BRWs+6aeXjmhuOcPla1YJb385ZKcXDIqU84Cypg9jxydM+jpxcOSg6NsVosBjwgA0i7KCAAAgCRWMo+b3q6aX48j4lShgLsqFPj3yKawpzF6+DJOx289V6OSOWW12RQRm6ioeIuyZXJR5gxO8syZ2X6cRqVySpJcnBwNeRwAAACpSaZMmV65x9Z/ylGiqtwLlVZs+GNFP7qtuIhnypS7kLIXqySb1aLE6EhF3r2mhJiXypDFQx6la+hRaPBrfgQAkLbxyRYAACCZ5M6aUZL049mHOnLzubK6Oit3lgw6c+elwp5Gy93VRZ4emRUVZ5GTo4NiE606ERauFYdu62lUvMHpAQAA0pa7R39U5P3rcs6UVZly5NWzKycV9eCWXDJnU+a8nkqIfiFJsibGK+LuVYPTAkDaw84IAACAZNa2Ut7f/X3vMq/eTLGmZ/ZkTAMAAJA+FazT9nd/P1/1Fq/8PObpveSMAwDpDmUEAABAMjh0/Zku3I9QqTxuio63qF5xDzk7OuhueKxiEiwKexotD7cMirdYlTmDkyoWcNfVR5HK6Oyol7GJstmk3FkzqFSeLEY/FAAAgFTv+fUzenx2n7IWLKXEuGjlKldPDk7OevnLJUlSzJM7yle9hZ5dPaX4yGdyzphZUQ9vGRsaANIYyggAAIAkYrPZJEmXH0aobL4sqlMshyTJw03affGxnkTF60VMgsrnz6osGZ0VHpOg6HiLEi1W3XgSLdlsyuOeUfncXSVJd8NjFZtg1eOX0YqNjTXscQEAAKRW8fH/Hn0Z9+KxHByd9PjCIdmsFsU8uy+nDK6KDX+sDG7ussTHKix4vRwcnWRNTJA1MUFRD29KkiwWbmQNAEnBwfbrp2YAAAD8LXfu3FFAQIBWrFih69euypoM764cHR3Vvn17mUwmtW7dWi4uLkl/EgAAgDQiMTFR/v7+Gjt2rO7dvy/93ctfDo4qW6a0Jk+erA4dOsjBwSFpgwJAOkIZAQAA8DdER0dr69atMpvN2r17t1xdXdWxY0d17NhRBQsWlKOjY5KdKzIyUqdOndLKlSt15swZ5cmTRz169JDJZFLlypWT7DwAAACpndVq1fr16zVu3DhdvXpVnTp10siRI1WgQIG/dbzr169rwoQJ+vnnn1W9enX5+fmpdevWlBIA8DdQRgAAAPxJNptNhw4dktls1vr16xUREaFGjRrJZDLp3Xfflbu7e7JnCAkJkb+/v1atWqXHjx+ratWqMplM6t69u3Lnzp3s5wcAAEiJbDabvv/+e40ZM0Znz55V27ZtNWnSJFWrVi1Jjr9v3z6NHj1aBw4cUIMGDTR58mR5eXklybEBIL2gjAAAAPgDt2/f1sqVK+Xv769r167J09NTPj4+6t27t0qUKGFIpoSEBO3YsUP+/v7atm2bbDab2rZtK5PJpDfeeEMZMmQwJBcAAMDrZLPZtGvXLvn6+ur48ePy9vaWn5+f6tevnyzn2rlzp3x9fXXy5Em1aNFCfn5+ql27dpKfCwDSIsoIAACA/yIqKkpbtmyR2WxWYGCgMmXKpE6dOsnHx0deXl5JOobpn3ry5InWrl0rf39/nTx5Urly5VL37t1lMplUtWpVxggAAIA06cCBAxo9erT27dunOnXqaPLkyWrWrFmyn9dms2nr1q0aM2aMzp8/r7ffflsTJ05kfCYA/AHKCAAAgP/HZrNp//79MpvN2rhxoyIjI9WkSRP5+PioY8eOypo1q9ER/9DZs2ftY5wePnyoypUry8fHRz169FDevHmNjgcAAPCPnTx5Ur6+vvrpp59UuXJl+fn5qV27dq99AYbFYtG6des0btw43bhxQ126dNGECRNUunTp15oDAFILyggAAJDu3bx5UytXrtTKlSt148YNFStWzD6GqVixYkbH+1sSExO1c+dOmc1mff/997JYLHrjjTfk4+Ojdu3aKWPGjEZHBAAA+EvOnz+vsWPHasuWLSpTpowmTpyod9991/AdqwkJCTKbzZo4caLu378vHx8fjR07Vp6enobmAoCUhjICAACkS5GRkdq0aZP8/f0VFBSkLFmyqHPnzvLx8VHDhg0N/1CblJ49e6Z169bJbDbr+PHj8vDwsI9xql69OmOcAABAinb9+nWNHz9eq1evlqenp8aNG6eePXvK2dnZ6GiviI2N1ddff60pU6bo+fPn6tu3r0aNGqX8+fMbHQ0AUgTKCAAAkG5YrVYFBwfLbDZr8+bNio6OVtOmTeXj46N33nlHbm5uRkdMdhcuXJC/v78CAgJ0//59VahQQSaTST169OCDMgAASFF++eUXTZo0ScuXL1eePHnk6+urDz74QBkyZDA62u+KiorSggULNGPGDMXGxurjjz/W8OHDlTNnTqOjAYChKCMAAECad/36dfn7+2vlypUKCwtTyZIl5ePjo169eqXb7fOJiYnatWuX/P39tXXrViUkJKh169YymUx688035erqanREAACQTj18+FBTp07V4sWLlSVLFo0YMUL9+/dX5syZjY72l4SHh2vOnDmaO3euHBwcNGjQIA0aNEju7u5GRwMAQ1BGAACANOnly5fauHGj/P39tX//fmXNmlVdunSRyWRS/fr1GU30H54/f67169fL399fR44cUfbs2dWtWzeZTCbVqlWLPysAAPBaPH/+XDNnztT8+fPl7OyswYMH67PPPkv1F+8fP36s6dOn66uvvlLmzJk1fPhwffzxx6muXAGAf4oyAgAApBlWq1V79+6Vv7+/Nm/erNjYWDVv3lwmk0nt27fnA9+fcOnSJfvNvO/evauyZcvKZDKpZ8+eKliwoNHxAABAGhQREaH58+dr1qxZSkhI0MCBAzV06FB5eHgYHS1J3b17V5MnT9bSpUuVK1cujR49Wh9++KEyZsxodDQAeC0oIwAAQKp39epV+ximX375RaVLl7ZfQC9cuLDR8VIli8WiPXv2yN/fX1u2bFF8fLxatGghk8mkt99+W5kyZTI6IgAASOViYmK0aNEiTZ06VS9fvlS/fv00cuRI5cuXz+hoyermzZuaMGGCAgICVKhQIY0bN069e/dOcTfkBoCkRhkBAABSpRcvXmjDhg0ym806dOiQsmXLpq5du8pkMqlOnTqMFkpCv/5Z+/v76+DBg8qWLZt95FXdunX5swYAAH9JfHy8vvnmG/n5+enhw4d67733NGbMGBUpUsToaK/VxYsXNX78eG3YsEGlSpXShAkT1KVLFzk6OhodDQCSBWUEAABINX5drW82m/Xtt98qPj5eLVu2lMlk0ltvvcVq/dfgv+1C+fVm4OxCAQAAv8disWjVqlWaMGGCbt26pW7dumn8+PEqVaqU0dEMdebMGY0ZM0Y//PCDKlWqpEmTJumtt95iwQeANIcyAgAApHiXLl2Sv7+/AgICdPfuXZUrV84+hqlAgQJGx0uXrFarAgMDZTabX7k/h4+Pjzp06MD9OQAAgJ3VatXmzZs1duxYXbp0SR06dNDEiRNVsWJFo6OlKIcPH5avr6/27t2r2rVry8/PT82bN6eUAJBmUEYAAIAU6fnz51q/fr38/f115MgR5ciRQ926dZPJZFLNmjX5UJaCvHz5Ups2bZLZbNb+/fuVNWtWdenSRT4+PmrQoAH/rwAASKdsNpu2b98uX19fnTlzRq1atZKfn59q1qxpdLQUbe/evRo9erSOHDkiLy8vTZ48WQ0aNDA6FgD8Y5QRAAAgxUhMTNSuXbvk7++vrVu3KjExUa1bt5bJZNKbb76pjBkzGh0Rf+D69etauXKl/P39FRYWppIlS9rHOHl6ehodDwAAvCaBgYEaPXq0Dh8+rEaNGmny5Mlq1KiR0bFSDZvNph9//FG+vr4KCQlRmzZtNGnSJNWoUcPoaADwt1FGAAAAw50/f17+/v5atWqV7t+/rwoVKshkMqlHjx7Knz+/0fHwN1itVu3bt09ms1mbNm1SVFSUmjZtKpPJpHfeeUdubm5GRwQAAMngyJEj8vX11Z49e1SzZk35+fmpZcuW7JT8m6xWqzZt2qSxY8fq8uXLeueddzRx4kRVqFDB6GgA8JdRRgAAAEM8e/ZMa9eulb+/v44fPy4PDw91795dJpNJ1atX5wNrGhIZGanNmzfLbDYrKChIWbJkUadOnWQymdSwYUM5OjoaHREAAPxD/3kT5ooVK2rSpEl6++23eU+XRBITE7V69WqNHz9eYWFh6tGjh8aPH68SJUoYHQ0A/jTKCAAA8NokJibqp59+kr+/v77//ntZLBa98cYbMplMatu2LWOY0oGbN28qICBAZrNZN2/eVLFixeTj46PevXurWLFiRscDAAB/0aVLlzRu3Dht2LBBJUuW1IQJE9SlSxc5OTkZHS1Nio+P1zfffCM/Pz89evRIffr00ZgxY1SoUCGjowHAH6KMAAAAye7s2bP2MUwPHz5U5cqVZTKZ1L17d+XNm9foeDCA1WrVgQMH5O/vrw0bNigyMlJeXl4ymUx69913lSVLFqMjAgCA33Hr1i1NmDBBK1euVMGCBTV27Fj5+PjIxcXF6GjpQkxMjBYuXKhp06YpIiJC//rXvzRy5EjlyZPH6GgA8D9RRgAAgGTx5MkTrV27VmazWadOnVKuXLnUo0cPmUwmVa1a1eh4SEGioqK0ZcsW+fv7a+/evcqcObM6duwok8kkLy8vxjgBAJCC3Lt3T5MnT9bSpUvl4eGhUaNG6aOPPpKrq6vR0dKliIgIzZs3T7NmzZLFYtGnn36qIUOGKEeOHEZHA4DfoIwAAABJJiEhQTt27JDZbNYPP/wgm82mdu3ayWQyqU2bNsqQIYPREZHChYWFKSAgQP7+/rp27Zo8PT3Vu3dv+fj4MBMZAAADPXnyRNOmTdNXX32lTJkyafjw4fr444/l5uZmdDTo3/djmzVrlubPny8XFxcNGTJEn376qbJmzWp0NACwo4wAAAD/WEhIiMxms1avXq3Hjx+rWrVqMplM6tatm3Lnzm10PKRCNptNhw4dktls1vr16xUREaGGDRvKZDKpU6dOcnd3NzoiAADpwosXLzR79mzNnTtXkjRo0CANGjRI2bJlMzgZ/puHDx9q6tSpWrRokdzd3TVy5Ej961//UqZMmYyOBgCUEQAA4O959OiR1qxZI7PZrJCQEOXJk0c9e/aUj4+PKleubHQ8pCHR0dHaunWrzGazdu/eLVdXV73zzjsymUzy9vbmBpkAACSDqKgoLViwQDNmzFBMTIw+/vhjDR8+XLly5TI6Gv6EX375RZMmTdLy5cuVN29ejRkzRn369GGnMgBDUUYAAIA/LT4+Xj/++KPMZrO2b98uBwcHvfXWWzKZTGrVqhU3LESyu3Pnjn2M0+XLl1W4cGH16tVLPj4+Kl26tNHxAABI9WJjY/X1119rypQpev78uT788EONHj1aBQoUMDoa/oZr165p/PjxWrNmjYoWLarx48erR48eLOYAYAjKCAAA8LtsNptOnz4ts9msNWvW6OnTp6pZs6ZMJpO6du2qnDlzGh0R6ZDNZtPRo0dlNpu1bt06vXjxQvXr15ePj4+6dOnC6AgAAP6ihIQEmc1mTZo0SXfv3pWPj4/Gjh2rokWLGh0NSeD8+fMaO3astmzZorJly2rixInq2LGjHB0djY4GIB2hjAAAAP/VgwcPtHr1avn7++vs2bPKly+ffQV6hQoVjI4H2MXExOj777+X2WzWzz//rAwZMqhDhw7y8fFR8+bNWfkHAMDvsFgsWrduncaPH69r166pS5cumjBhgsqUKWN0NCSDkydPytfXVz/99JOqVq0qPz8/vfHGG3JwcDA6GoB0gDICAADYxcXFadu2bfL399eOHTvk5OSk9u3by8fHRy1btpSzs7PREYHfde/ePa1atUpms1kXL15UwYIF7SVa2bJljY4HAECKYbPZtHXrVo0ZM0bnz5/Xm2++qUmTJqlKlSpGR8NrsH//fvn6+mrfvn2qV6+eJk+eLG9vb6NjAUjjKCMAAEjnbDabTpw4IbPZrLVr1+r58+eqU6eOfdyNh4eH0RGBv+x//b02mUzq0qWLcuTIYXREAAAMYbPZtHPnTvn6+urkyZNq1qyZ/Pz8VLduXaOj4TWz2WzatWuXRo8erRMnTvB3AUCyo4wAACCd+nUFub+/vy5cuKACBQrYV5CXK1fO6HhAkvl1x4/ZbNZPP/0kZ2dnvf322zKZTGrRogU7fgAA6ca+ffvk6+ur/fv3sxoedjabTd99953GjBmjc+fOqV27dvLz82OXDIAkRxkBAEA6Ehsbq++++07+/v7auXOnMmTIoPbt28tkMjFbH+nC/fv3tXr1apnNZp0/f1758+dXz549uRcKACBNO378uHx9ffXzzz+rWrVq8vPzU5s2bbhPAF5hsVi0YcMGjRs3TlevXlXnzp01YcIERl0CSDKUEQAApHE2m01Hjx6Vv7+/1q1bp/DwcNWrV88+hil79uxGRwReO5vNplOnTslsNmvNmjV69uyZatWqJR8fH3Xr1o3xZACANOHcuXMaM2aMtm7dqnLlymnChAnq2LGjHB0djY6GFCwxMVH+/v6aMGGC7t69q969e2vcuHEqWrSo0dEApHKUEQAApFF3795VQECAzGazLl++rEKFCql3797y8fFR6dKljY4HpBhxcXH68ccfZTabtX37djk5Oemtt96Sj4+PWrduzRgnAECqc/XqVY0fP15r165V0aJFNX78ePXo0YNdsPhL4uLitGTJEk2ePFnPnj3Thx9+qNGjR6tAgQJGRwOQSlFGAACQhsTExGjr1q0ym83atWuXXF1d9c4778hkMsnb25sPoMAfePjwodasWSOz2azQ0FDlzZtXPXr0kMlkUqVKlYyOBwDA77p9+7YmTpwos9msvHnzasyYMerTp48yZMhgdDSkYlFRUfryyy81ffp0xcTEaMCAARoxYoRy5cpldDQAqQxlBAAAqZzNZtPhw4dlNpu1fv16vXz5Ug0bNpTJZFKnTp3k7u5udEQgVTpz5ozMZrNWr16tJ0+eqHr16vLx8VH37t358A0ASFEePHigKVOm6Ouvv5a7u7tGjhypf/3rX8qUKZPR0ZCGvHjxQnPmzNGcOXMkSZ9//rkGDx6sbNmyGZwMQGpBGQEAQCp1+/ZtBQQEyN/fX1evXlWRIkXk4+Oj3r17q2TJkkbHA9KM+Ph47dixQ2azWT/88IMcHBzUrl07+fj46I033pCLi4vREQEA6dSzZ880Y8YMLViwQC4uLhoyZIg+/fRTZc2a1ehoSMOePHmiGTNm6Msvv5Srq6uGDRumTz75RG5ubkZHA5DCUUYAAJCKREdHa8uWLTKbzdq7d68yZcqkd999VyaTSV5eXtyMEEhmjx8/1tq1a2U2m3X69Gnlzp1bPXr0kI+Pj6pWrWp0PABAOvHy5UvNmzdPs2fPlsVi0aeffqohQ4YoR44cRkdDOnL//n1NnjxZS5YsUY4cOTRq1Cj17dtXrq6uRkcDkEJRRgAAkMLZbDYdOHBAZrNZGzduVEREhLy8vGQymdSxY0dWvgEGCQ0Nlb+/v1atWqVHjx6pSpUqMplM6t69u/LkyWN0PABAGhQTE6OvvvpK06ZNU2RkpP71r39p5MiRvO7AULdu3dLEiRPl7++vggULasyYMTKZTOweBfAblBEAAKRQYWFhWrlypfz9/XX9+nUVK1ZMPj4+6tWrl4oXL250PAD/T0JCgnbu3Cmz2azvv/9eNptNb7zxhkwmk9q2bctNQwEA/1h8fLyWLl2qyZMn6/Hjx+rTp4/GjBmjQoUKGR0NsLt8+bLGjRun9evXq2TJkho/fry6du0qJycno6MBSCEoIwAASEEiIyPtY5gCAwPl5uamzp07y8fHR40aNWIME5DCPX36VOvWrZPZbNaJEyeUM2dOde/eXSaTSdWqVZODg4PREQEAqUhiYqICAgI0YcIE3b59Wz179tS4ceNUokQJo6MB/1NISIjGjBmjbdu2qUKFCpo0aZLat2/P+yAAlBEAABjNarVq37598vf318aNGxUVFSVvb2+ZTCa98847ypIli9ERAfwN58+fl7+/vwICAvTgwQNVrFhRJpNJPXr0UL58+YyOBwBIwaxWqzZu3Khx48bp8uXL6tixoyZOnKjy5csbHQ34044ePSpfX1/t3r1bNWrUkJ+fn1q1akUpAaRjlBEAABjkxo0b9jFMt27dUvHixWUymdSrVy8VLVrU6HgAkkhiYqJ+/vln+fv7a+vWrbJYLGrdurVMJpPefPNNZcyY0eiIAIAUwmaz6YcfftCYMWMUEhKiNm3aaNKkSapRo4bR0YC/LSgoSKNHj9ahQ4fUqFEj+fn5qXHjxkbHAmAAyggAAF6jiIgIbdq0SWazWfv27VPWrFnVuXNnmUwmNWjQgFVCQBr37NkzrV+/Xv7+/jp69Khy5Mihbt26yWQyqWbNmjwHAEA6tmfPHo0ePVpHjx6Vl5eX/Pz81LBhQ6NjAUnCZrNpx44d8vX11enTp9WyZUv5+fmpVq1aRkcD8BpRRgAAkMysVquCgoJkNpu1efNmxcTEqFmzZjKZTOrQoYMyZ85sdEQABrh48aJ9jNO9e/dUvnx5+fj4qGfPnipQoIDR8QAAr8mhQ4fk6+urwMBA1a5dW35+fmrevDkFNdIkq9WqLVu2aOzYsbp48aLat2+vSZMmqWLFikZHA/AaUEYAAJBMrl27Jn9/f61cuVK3b99WqVKl7GOYChcubHQ8ACmExWLR7t27ZTabtXXrVsXHx6tly5YymUx6++235erqanREAEAyOH36tHx9fbV9+3ZVqlRJfn5+evPNNykhkC5YLBatWbNG48eP182bN9W1a1dNmDBBpUqVMjoagGREGQEAQBJ68eKFNm7cKLPZrIMHD8rd3V1du3aVyWRS3bp1+XAJ4HeFh4drw4YN8vf316FDh5Q9e3Z16dJFJpNJderU4TkEANKAixcvauzYsdq0aZNKlSqliRMnqnPnznJ0dDQ6GvDaJSQkaPny5Zo0aZIePHggk8mksWPHqkiRIkZHA5AMKCMAAPiHLBaL9u7dK7PZrG+//VZxcXFq0aKFfVVzpkyZjI4IIBW6cuWKfXfVnTt3VKZMGfn4+KhXr14qVKiQ0fEAAH/RjRs3NGHCBK1atUqFChXSuHHj1Lt3bzk7OxsdDTBcTEyMFi9erKlTp+rFixfq27evRo0apXz58hkdDUASoowAAOBvunz5sn3e+507d1S2bFmZTCb17NlTBQsWNDoegDTCYrEoMDBQZrNZW7ZsUWxsrFq0aCEfHx+1b9+e+84AQAp39+5dTZo0Sd98841y5cql0aNH68MPP1TGjBmNjgakOJGRkZo/f75mzpyphIQEffLJJxo2bJg8PDyMjgYgCVBGAADwF4SHh2v9+vXy9/fX4cOHlT17dnXr1k0+Pj6qXbs2I1QAJKuXL1/aR8EdOHBA7u7u6tKli3x8fFS/fn2egwAgBXn8+LGmTp2qhQsXys3NTSNGjNCAAQMokYE/4fnz55o9e7bmzZsnJycnDR48WJ999pnc3d2NjgbgH6CMAADgD1gsFu3atUv+/v769ttvlZCQoNatW8vHx0dvvfUWN5cFYIhr165p5cqV8vf31+3bt1WqVCn7GCfmLAOAccLDwzVr1izNmzdPjo6OGjx4sD7//HMuogJ/w6NHjzRt2jQtXLhQWbJk0fDhwyn1gFSMMgIAgP/h4sWL9jFM9+7dU/ny5e1jmPLnz290PACQJFmtVgUHB8tsNmvTpk2KiYlR06ZNZTKZ1KFDB7m5uRkdEQDShcjISH3xxReaOXOm4uLi7ONlcubMaXQ0INW7c+eO/Pz89M033yh37tz2cWcZMmQwOhqAv4AyAgCA//D8+XOtW7dOZrNZx44dk4eHh7p16yaTyaQaNWowAgVAihYREaHNmzfLbDYrODhYWbJkUefOnWUymdSwYUOewwAgGcTGxmrRokWaOnWqwsPD7TfeZfEKkPSuX79uvxF8kSJFNG7cOPXq1YsbwQOpBGUEACDdS0xM1M8//yyz2azvvvtOFotFbdq0kclkUrt27bi5IIBU6caNGwoICJC/v79u3ryp4sWLy8fHR71791bRokWNjgcAqV5CQoKWL1+uSZMm6cGDB/Lx8dHYsWPl6elpdDQgzbtw4YLGjh2rzZs3q0yZMpowYYI6deokR0dHo6MB+B2UEQCAdOvcuXPy9/fXqlWr9ODBA1WqVEkmk0k9evRQ3rx5jY4HAEnCarVq//798vf314YNGxQVFaUmTZrIZDKpY8eOypIli9ERASBVsVgsWrNmjcaPH68bN26oa9eumjBhgkqXLm10NCDdOXXqlMaMGaPt27ercuXKmjRpkt588012gwIpFGUEACBdefr0qdauXSuz2ayTJ08qZ86c6tGjh0wmk6pWrcqbVgBpWmRkpLZs2SJ/f3/t3btXbm5uevfdd2UymdS4cWNWEwLA77DZbNqyZYvGjh2rCxcu6O2339bEiRNVuXJlo6MB6d7Bgwfl6+uroKAg1alTR35+fmrWrBmf74AUhjICAJDmJSQk6KeffpLZbNa2bdtks9nUtm1bmUwmvfHGG9z0DEC6FBYWpoCAAJnNZl2/fl1FixZV79691bt3b5UoUcLoeACQYthsNv3000/y9fXVqVOn1KJFC/n5+al27dpGRwPwH2w2m/bs2aPRo0fr2LFjatKkiSZPnqz69esbHQ3A/0MZAQBIs0JDQ2U2m7V69Wo9evRIVatWlclkUvfu3ZU7d26j4wFAimCz2XTw4EGZzWZt2LBBERERatSokUwmkzp16qSsWbMaHREADBMUFCRfX18dPHhQDRo00OTJk+Xl5WV0LAC/w2azadu2bRozZoxCQ0P1xhtvaNKkSapevbrR0YB0jzICAJCmPH78WGvWrJHZbNaZM2eUO3du9ezZUz4+PqpSpYrR8QAgRYuOjta3334rs9msPXv2KFOmTOrYsaN8fHzk7e3NGCcA6caxY8c0evRo7d69W9WrV5efn59at27NyBcgFbFardqwYYPGjRunK1euqGPHjpo4caLKly9vdDQg3aKMAACkevHx8dq+fbvMZrN+/PFHOTg46M0335TJZFLr1q3l4uJidEQASHV++eUX+xinq1evqkiRIurdu7d8fHxUsmRJo+MBQLIIDQ3VmDFj9P3336t8+fKaOHGi3nnnHUoIIBVLTEzUypUrNWHCBN25c0c9evTQ+PHjVbx4caOjAekOZQQAIFWy2Ww6c+aMzGaz1qxZoydPnqhGjRoymUzq2rWrcuXKZXREAEgTbDabjhw5IrPZrHXr1unly5dq0KCBfYxTtmzZjI4IAP/YlStXNG7cOK1fv17FihXThAkT1K1bNzk5ORkdDUASiYuL07Jly+Tn56cnT57o/fffl6+vrwoVKmR0NCDdoIwAAKQqDx8+1OrVq+Xv76/Q0FDlzZtXvXr1ko+PjypWrGh0PABI02JiYvTdd9/JbDZr165dypAhg9555x2ZTCY1bdqUi3YAUp2wsDBNnDhR/v7+yp8/v8aMGaP33nuPnbVAGhYdHa2vvvpK06ZNU1RUlPr3768RI0YoT548RkcD0jzKCABAihcXF6cffvhB/v7+2r59u5ycnPT222/Lx8dHrVq1krOzs9ERASDduXv3rlatWiWz2axLly6pYMGC9jFOZcqUMToeAPyu+/fva8qUKfr666+VPXt2jRo1Sv369ZOrq6vR0QC8Ji9fvtTcuXM1e/ZsWa1WffbZZxo8eLBy5MhhdDQgzaKMAACkSDabTadOnbKPYXr27Jlq164tHx8fde3aVR4eHkZHBADo38/Xx44dk7+/v9auXavw8HDVrVtXJpNJXbp0Ufbs2Y2OCAB2T58+1fTp0/Xll18qY8aMGjZsmD755BNlyZLF6GgADPL06VPNmDFDCxYsUMaMGTVkyBB9+umnPC8AyYAyAgCQoty/f1+rV6+W2WzW+fPnlT9/fvsYpvLlyxsdDwDwO2JjY/X999/L399fP/30k1xcXNS+fXuZTCa1aNGCMU4ADPPy5UvNmTNHc+bMkdVq1eeff67BgwdTmAKwe/DggX3HVObMmZUjRw6FhoZSSgBJiDICAGC4R48eKTg4WGazWTt37pSzs7P94lXz5s0ZwwQAqdD/KpfffvttValSRW5ubkZHBJAOXL58Wf3799eZM2cUFRWlAQMGaMSIEcqdO7fR0QCkUGFhYWrevLmuXbumRYsWqV+/fkZHAtIMyggAgKF69+6tgIAASbKP9ejcuTNzOgEgjbDZbDp58qT8/f3tY/ecnZ0VFxcnR0dHo+MBSOOqVKmi0NBQde7cWXPmzFHBggWNjgQgFbBarbp165aKFSsmBwcHo+MAaQZlBADgf9q8ebMWfDFflsTEJDtmydJltHz5cvsbujFjxmjZsmXasGGDGjVqlGTnAQCkPHFxcerUqZPi4+O1Y8cO+2vBqlWrtHTpUlmt1iQ7V5YsWTR37lyVLVs2yY4J4PWw2WwaO3asrl279pe/18PDQ35+fvaFLZcvX9b58+fVoUMHLigC+I34+Hj16tVbv9y5a3SU/8rJyVH9+n6kHj16GB0FSBKUEQCA/+mNNm10IHC3WlfIlSTHu/44Wqd+ealbt27J09MzSY4JAEj9GjVqpEuXLqpNq5ZJcjybTVq1Zq1mz56tQYMGJckxAbw+v/zyi4oUKaJihQvIs2C+P/19MbFxOnrmvDZv3qx33nknGRMCSCtOnTqlGjVqyKNcfbnmTHk7px6H7FHNyuV16MB+o6MASYIh3ACA/8lqtapxqRya37mCJCk2wSJJcnX5/RuQxiZY/uvX7L/2TF2WnU7Sla8AgNTParWq3Rtt9M3XiyT9+0bYrq6uv/nvv2Lbj9t5vQFSqYSEBEnSwgmD1aRu9T/9fZFRMcpd6w379wPAH/n1vULZnhPk7lnxT32PJT5WThlc/+fPk4LNapGDo5POLvlc1vjbSXpswEiUEQCA33X/RZw2n76vigWyKsFiU1RcoioVdNfKI3fUuJSHQu5EqFX53Dpy87meRsWraM7McnZ0UMUCWXXzSbRyZ82gRxHxyunmoiuPoox+OACAFOrWrTCtWrtO1apU0ZOnT5U5UyZFx8Qom7u7EhISFB0To6qVK+n8hYsqVLCgEhIT9Px5uK7fuKns2bNJ+vcFhV9/AEj9zl6+rsfPw/VOSy8dOXNBDx4/VdVypXT+6k25ZsygQvlyK29uD2VwcdGR0+cUFR1rdGQAqVT0ozAlRr+UHByVpUBJPb1wUJbYKDlnyioHJ2dlyl1EGdxzKvzaSWXMlkfOrm5KiApX1iLlFfXgpiLvXJKDg5Py1Gyt6Ic3lTFbHsU8vi3XXIX04topZcpTRJbYKMU+u69MuQvLwdFZcS8eycUtmzJk9dCL6yHKnK+oEqJeymZJUMbseWWzJirm6V0pq9F/OkDSoYwAAPyu/NkyqmO1/HoRk6BMLk7K4OyoxxFxypUlgw7dCJd36Zy68zxaFQtkVURsoh5GxOlxRKIyODuqRpFs/+8Y/14lUjqPm5EPBQCQghUt6qme3boqPDxcpUqWUHx8vBwdHbV563cKfx6uVi2b68WLl8qfP5+eh4frydOnioyMVKGCBdSgfj2j4wNIBpXKlFCTutW1aPUWWa02Na1XU9ExsapctoReRkbp4ZNnOn3hikoXK6IWDWsrMirG6MgAUqnMeTztOyPiXjxW/rpv/+Zr4l48Vr5abX/za1H3rykxNkq5K3srMfqlXNxyKD7imeTgoMi7V5QpTxFlK1bld8+fpWCZ//rrmXIWlNgZgTSEMgIA8Kdky+QiSdp+7pFyurkoS0ZnuTg5KvDyE5XLn0VFPJzl4eaix5Hxyp8toxwkhd55KYvNptJ53OSWkZccAMAfy549uyTph+07lDtXLrlnzaoMLhn0085dqlSpoqpWrqRcOXMqMjJSrhkzymK16OChw/LwyKFy3KwaSHO27tqnSmVK6vmLlzp39boePnmmSqVLqHLZkipWuICOnD6nKzdvy2K1qmKp4kbHBZAGZMyW+0//+vMrx5QpV2E5Z8qql2HnFBf+SFmLlJe7ZwU5ODrr2aUjin54S9aEOLlk8VCWAiWTOz6QonFlCADwl7xRMc/v/n6T0jlfUxIAQFr2Tvvfrkj8T+yGANKH9i0a/+7vt2hY2/7f7IwA8Lr9350S/1eeas1fUxIgdaCMAAD8ocM3nuv8/UiVypNZ0fFW1SuWXc5ODrr/Ik7PoxN062mM8mTNICdHB2V0dlSVQu66/CBS8RabEixWvYhJVIncmY1+GACAVCJ4/wGFhp5V2bJlFBUVJa9GDeXs7Kxz5y/oxs1bssmmbO7u8vZqrOD9B5SYmKjs2bMrg4uL6tWtY3R8AEkk9PJ1nb96U2VLeCoqOlaNalWRs7OT7j18okdPn+mX+4/0ZrOGOnv5up6/iFB0DPeMAPB6PLt4SC9vX1CWAqVkiYuWR7l6cnB0VszTuwq/elxu+UvIvWhlvbh2Spb4GDk4OsklS3ZlL1nD6OiAoSgjAAC/696LOGXJ6Kw6RbNLkjwyS7svPdHTyASFxySofP4syunmoojYREXHW5RotenaoyjZJOXJmlH53DMqS0Zn3Q2P1aUHkYY+FgBAynX79i86fSZEkuSeNasaNqiv8xcu6vHjx7p67bqKFfVUXGycMrpmVFRUtB4+fKT1GzfLZrMpf/58cs+aVU+ePLEf48WLF0Y+HABJ4OGTZ8qTM4f2HTsji8Wqew8fy9U1ox4+eSoXZxclWixavPpb2STl9sguJydHoyMDSKVe3jor2Wx/+uudM2WVR5k6irhzWfEvnyjqwQ1lzl1ElsQ4uWTOpviXz/TwxHY5ubjKwclZlvgYRT28KUfnDH8pV8yTO5L7X300QMpFGQEA+J8yZc6s07+8VKsFx5LsmC7OzsqWLVuSHQ8AkPoVKlRIGzZsUM16DZP8uABSn3z58snDw0Nzvln3l7/XxcVFpUqVSoZUANKi/Pnzy8HBQeeWDjI6yv9UpFNnoyMAScbBZvsLtR8AIF04cOCApk6dqu3bt6tAgQLy8fFRu3btlDFjxr99zHPnzslsNisoKEiFCxfW0KFD9f777ytzZsY3AUB6Fx8fr/Pnzys0NFRjxozR48eP9dlnn6ljx45ycHD4w++PjIzUjBkztH37djVr1kwjR45UoUKFVLp06T/1/QBSnri4OIWHhysqKkqtWrVShgwZtH37drm6ur7ydQMGDNCOHTu0d+9eFS1aVFmyZJGbm5tBqQGkRvfu3dP9+/f/0vdERUXp008/1eXLl7VgwQJVrVr1v36d1WrVlClTtHXrVo0ZM0Zvv/3798T6vxwdHVWhQgVlyPDXdlQAKRVlBABAkmSz2bRz505NmTJF+/fvV4UKFTRq1Ch17txZzs5Jt5Hu/PnzmjZtmtauXSsPDw999tln6t+/v7Jnz55k5wAApC4JCQny8/PT5MmTVaNGDQUEBKh06dJ/+TgbN25Uv379lDFjRi1fvlytW7dOhrQAXqePPvpIq1at0smTJ1WuXLnf/P7Lly9VrVo15cqVSwcOHJCLi4sBKQGkJy9evFCbNm10/vx57dy5U3Xr1v3dr7darfr444+1aNEiLV68WH379n1NSYGUh4GKAJDOWSwWbdy4UTVq1FCbNm0UHx+v7777TqGhoerevXuSFhGSVKFCBQUEBOjq1at69913NXHiRHl6emrUqFF69OhRkp4LAJDyXb58WQ0aNNDkyZM1ZswYHTx48G8VEZLUqVMnnT17VpUrV1abNm00YMAARUdHJ3FiAK/Lpk2btHTpUn3xxRf/tYiQJHd3d61du1anTp3S2LFjX3NCAOlNeHi4WrZsqYsXL2r37t1/WERI/97d8NVXX+mTTz5Rv3799NVXX72GpEDKxM4IAEin4uPjtWrVKk2fPl1XrlxR8+bNNWrUKDVp0uS1jrS4f/++5s6dq0WLFikxMVEffPCBhgwZIk9Pz9eWAQDw+tlsNi1atEhDhgxRoUKFtGrVKtWuXTvJj124cGEFBAQk2bEBvB63b99WlSpV1Lx5c23YsOEP359Onz5dI0eO1K5du9SsWbPXlBJAevLs2TO1aNFCt27d0q5du1S9evW/9P02m01DhgzRnDlzNHfuXH322WfJExRIwSgjACCdiY6O1rJlyzRz5kzduXNHHTp00MiRI1WrVi1Dcz1//lxffvml5s+frxcvXqhHjx4aPnz4/1wFBwBIve7du6c+ffpo586d6t+/v2bMmJEsM94vX76snj176vTp0xo7dqxGjRqV5Dv+ACS9xMREeXt76/bt2zpz5oxy5Mjxh99jtVrVsmVLXbhwQSEhIcqdO/drSAogvXjy5ImaN2+uu3fvavfu3apSpcrfOo7NZtPIkSM1ffp0zZgxQ0OHDk3ipEDKxpgmAEgnwsPDNXnyZHl6emrQoEHy9vbWuXPntGXLFsOLCEnKkSOHxowZo7CwMM2cOVO7d+9WhQoV9O677+rkyZNGxwMAJJFNmzapUqVKCgkJ0fbt2/XVV18l281my5Qpo0OHDmn06NGaOHGiGjRooCtXriTLuQAkncmTJ+vQoUNavXr1nyoipH+PQVm5cqUSEhLUp08fse4SQFJ59OiRmjZtqvv37yswMPBvFxGS5ODgoKlTp8rX11fDhg3TlClTkjApkPJRRgBAGvfw4UONGDFCRYoU0aRJk9S5c2ddu3ZNK1euVIUKFYyO9xtubm767LPPdP36dS1ZskQhISGqWbOmWrVqpeDgYD5YAkAq9eLFC/Xu3VudOnWSt7e3zp49qzZt2iT7eV1cXDRhwgQdOHBAz549U7Vq1bR48WJeT4AUav/+/Zo4caLGjh2rhg0b/qXvLVCggFasWKEffvhBX375ZTIlBJCePHjwQN7e3nr8+LGCgoJUsWLFf3xMBwcHTZo0SRMmTLAvmADSC8Y0AUAadevWLc2aNUvffPONXFxc1L9/f3322WfKly+f0dH+EovFok2bNmnKlCkKDQ1VvXr1NGrUKLVt2/a13tsCAPD3BQUFycfHR+Hh4fryyy/Vs2dPQ57Do6KiNGTIEC1evFht2rTRN998o/z587/2HAD+u+fPn6tKlSry9PRUYGDg3x6rNnDgQC1ZskTHjh1T5cqVkzglgPTi3r17atq0qSIiIrR3716VKVMmyc8xZcoUjR49Wr6+vpo4cSKfcZHmUUYAQBpz8eJFTZs2zb6t/dNPP9WAAQP+9Bb3lMpms2nHjh2aMmWKDh48qMqVK2vEiBHq1KkT878BIIWKi4vT6NGjNWfOHDVq1EgrV66Up6en0bH0448/6v3331diYqKWLFmid955x+hIQLpns9nUqVMn7dmzRyEhISpSpMjfPlZsbKzq1KmjhIQEnThxQpkzZ07CpADSgzt37sjb21txcXHau3evSpYsmWznmjlzpoYNG6YRI0ZoypQpFBJI0xjTBABpxPHjx/XOO++oQoUK2rt3r2bPnq1bt27J19c31RcR0r+3sr7xxhs6cOCA9u3bpwIFCqh79+4qW7asli5dqri4OKMjAgD+Q2hoqGrVqqUFCxZoxowZ2rt3b4ooIiSpbdu2Onv2rBo3bqyOHTvKZDLpxYsXRscC0rVly5Zp8+bNWrZs2T8qIiTJ1dVV69at061bt/T5558nUUIA6UVYWJi8vLyUkJCg4ODgZC0iJGno0KGaM2eOpk2bpqFDhzJKEmkaZQQApGI2m02BgYFq2bKlateurXPnzmnZsmW6fv26Pv3002S7IajRGjVqpB07dujkyZOqVq2a+vbtq+LFi2vOnDmKjIw0Oh4ApGsWi0UzZ85UrVq1JP27LB8yZIicnJwMTvaq3Llza/PmzVqxYoW2bNmiKlWqaN++fUbHAtKlixcv6tNPP9WHH36ojh07Jskxy5Urp3nz5mnJkiXasmVLkhwTQNp38+ZNeXl5SZKCg4NVrFix13Lezz//XAsWLNDs2bP1+eefU0ggzWJMEwCkQlarVT/++KOmTJmiI0eOqGrVqho5cqQ6duyY4i72vA6XL1/W9OnTFRAQIHd3dw0cOFCffPKJPDw8jI4GAOnKrVu35OPjo/3792vIkCGaNGmSMmbMaHSsP3Tz5k35+PjowIEDqSo3kBbExsaqbt26io+PT/KRSkk5+glA2nft2jU1bdpUGTNmVGBgoAoVKvTaM3z99dfq16+f+vfvrwULFsjRkXXkSFv4Gw0AqUhiYqLWrFmjKlWq6K233pKLi4u2b9+uU6dOqXPnzumyiJCkMmXKaPny5bp+/bp69uyp6dOny9PTU0OHDtX9+/eNjgcAaZ7NZtPKlStVuXJl3bp1S4GBgZoxY0aquaBfrFgxBQYGatq0aZo3b55q166ts2fPGh0LSBeGDx+uixcvau3atUl+bwcHBwctXbpUWbNmVc+ePWWxWJL0+ADSjitXrsjLy0uZM2dWcHCwIUWEJPXt21fLli3TokWL1K9fP1mtVkNyAMmFMgIAUoHY2Fh9/fXXKlOmjHr06KHChQtr37592rdvn9q0acMNrv6fIkWKaP78+bp165YGDhyopUuXqmjRourXr59u3LhhdDwASJOePHmiTp06ycfHRx06dFBoaKh9vEFq4uTkpGHDhun48eOyWq2qWbOmZs+ezUUAIBn9+OOP+uKLLzRz5kxVqVIlWc6RI0cOrV69WgcPHtTkyZOT5RwAUreLFy/Ky8tL2bNnV1BQkAoUKGBonvfff18rVqzQsmXL9MEHH1CkIk1hTBMApGCRkZH6+uuvNXv2bD148ECdOnXSiBEjVK1aNaOjpQovXrzQokWLNHfuXD158kRdu3bViBEjVKlSJaOjAUCasGPHDvXp00fx8fH6+uuv9e677xodKUnExsbK19dXc+bMkZeXl/z9/RnvAiSx+/fvq3LlyqpTp462bduW7Itrxo8fr0mTJmnfvn1q0KBBsp4LQOpx7tw5NWvWTHnz5tXu3buVJ08eoyPZrVmzRr169VKPHj20YsWKdDsJAWkLZQQApEBPnz7VggUL9MUXXygyMlK9e/fWsGHDVLp0aaOjpUoxMTFavny5ZsyYodu3b+utt97SyJEjVbduXaOjAUCqFBUVpaFDh2rRokVq3bq1li9frvz58xsdK8kFBgbKx8dHL1680FdffaUePXqwGxFIAlarVa1atdL58+cVEhKi3LlzJ/s5ExMT1aRJE/3yyy8KCQlR9uzZk/2cAFK2kJAQNW/eXIUKFdKuXbuUK1cuoyP9xoYNG9S9e3d16tRJAQEBcnZ2NjoS8I8wpgkAUpC7d+9q8ODB8vT01IwZM9S7d29dv35dy5Yto4j4BzJlyqQBAwbo2rVrMpvNunLliurVq6emTZtq165dopcHgD/v6NGjqlatmsxmsxYuXKjt27enySJCkry9vRUaGqq3335bvXr1UpcuXfT06VOjYwGp3qxZs7Rnzx6tXLnytRQRkuTs7KzVq1frxYsX+uijj3j/B6Rzp06dUtOmTeXp6ak9e/akyCJCkjp37qz169dr06ZN6tatmxISEoyOBPwjlBEAkAJcu3ZNH330kYoXL65vvvlGn3/+ucLCwjRv3jwVLlzY6HhphouLi3x8fHT+/Hlt3rxZL1++VMuWLVW7dm19++23zAUHgN+RkJCg8ePHq0GDBsqePbtOnz6tf/3rX2l+p0D27Nm1cuVKbdiwQXv27FGlSpW0c+dOo2MBqdbx48c1evRoDRs2TM2bN3+t5/b09NTSpUu1ceNGLV++/LWeG0DKcfz4cTVr1kwlS5bU7t275eHhYXSk39WxY0dt2rRJ3333nbp06aL4+HijIwF/G2OaAMBAoaGhmjZtmtavX6/cuXNr0KBB6tevn9zd3Y2Oli7YbDbt2rVLU6dOVVBQkMqVK6cRI0aoW7ducnFxMToeAKQYV65cUc+ePXXq1Cn5+vpq9OjR6fJ58t69e3rvvff0888/a8CAAZoxY4YyZ85sdCwg1YiIiFC1atXk4eGhgwcPGvY88uGHH2rNmjU6efKkypYta0gGAMY4cuSIWrVqpQoVKmjHjh3Kli2b0ZH+tB9//FHvvPOOWrVqpY0bNypjxoxGRwL+MsoIADDA4cOHNWXKFP3www/y9PTUsGHD9N577ylTpkxGR0u3Dh8+rKlTp2rbtm3y9PTU0KFD1adPH/6fAEjXbDabFi9erMGDB6tQoUIKCAhQnTp1jI5lKJvNpoULF2rIkCEqUqSIVq1apVq1ahkdC0gVevfurW+//VZnzpxRiRIlDMsRFRWlmjVrytXVVUeOHOGCHpBOHDx4UG3atFHVqlX1448/KmvWrEZH+st27typ9u3by9vbW1u2bJGrq6vRkYC/hDFNAPCa2Gw2/fzzz2rSpInq16+v69eva+XKlbp69ar69+/PRW+D1atXT99//71CQkJUv359DRw4UEWLFtX06dP18uVLo+MBwGt3//59tW3bVv3795fJZNLp06fTfREhSQ4ODhowYIBOnz4td3d31atXTxMnTlRiYqLR0YAUbfXq1QoICNCiRYsMLSIkyc3NTWvXrtWFCxc0YsQIQ7MAeD2Cg4PVqlUr1ahRQzt27EiVRYQktWrVSj/88IOCgoL01ltvKTo62uhIwF/CzggASGZWq1Vbt27VlClTdPLkSdWqVUujRo3SW2+9JUdHOuGU6tq1a5o5c6bMZrMyZcqkjz/+WJ9++ulru8kiABhp8+bN6tu3r1xcXLR8+XK1adPG6EgpUkJCgiZNmqTJkyerVq1aCggIUKlSpYyOBaQ4169fV7Vq1fT2228rICDA6Dh28+fP12effaYff/xRb7zxhtFxACSTvXv3ql27dmrQoIG+++67NDFiMSgoSO3atVPt2rW1bds2ubm5GR0J+FMoIwAgmSQkJGjNmjWaNm2aLl26pKZNm2rkyJFq1qxZmr/ZZ1py7949zZkzR4sXL5bVatWHH36oIUOGcGNxAGnSixcvNHDgQK1cuVLvvPOOvv76a+XKlcvoWCnekSNH1KtXL/trxkcffcRrPfD/JCQkqGHDhnry5Il9R1FKYbPZ1K5dOx0/flwhISHKnz+/0ZEAJLGff/5Zb7/9tpo0aaItW7akqYkEBw4cUJs2bVS9enX98MMPqXa3B9IXluQCQBKLiYnRl19+qZIlS8pkMql06dI6fPiw9uzZo+bNm3NxIpUpUKCAZs2apbCwMA0bNkwBAQEqUaKE3n//fV25csXoeACQZIKDg1WlShV9++23MpvN2rRpE0XEn1S3bl2dPn1avXr1Ur9+/dSuXTs9ePDA6FhAijB27FidOnVKa9euTVFFhPTvsWsrVqyQk5OTfHx8ZLVajY4EIAlt375db731lpo1a6Zvv/02TRURktSwYUP9/PPPOnPmjFq3bs14YaQKlBEAkERevHihqVOnqmjRovr000/VqFEjnT17Vt99953q1q1rdDz8Qzlz5tT48eMVFhamqVOnaseOHSpbtqw6d+6s06dPGx0PAP62uLg4DRs2TN7e3ipSpIhCQ0Pl4+NDef4XZcmSRYsXL9YPP/ygkydPqmLFitqyZYvRsQBD7dmzR9OnT5efn59q165tdJz/Kk+ePFq5cqV27dqlOXPmGB0HQBLZtm2bOnTooNatW6fpGz3Xq1dPu3bt0oULF9SyZUuFh4cbHQn4XYxpAoB/6NGjR5o/f76+/PJLxcbGqk+fPho6dKiKFy9udDQko9jYWK1cuVLTp0/XjRs31Lp1a40aNUqNGjUyOhoA/GmhoaHq2bOnLl26pMmTJ2vQoEFycnIyOlaq9/jxY3300UfaunWrTCaT5s+fn+JWhAPJ7fHjx6pSpYrKly+vn3/+OcXfK23YsGGaO3euDh8+rJo1axodB8A/8O2336pz5856++23tXbtWrm4uBgdKdmdOnVKzZs3V4kSJbRz5055eHgYHQn4rygjAOBvun37tmbNmqVly5bJyclJ/fr10+eff64CBQoYHQ2vUWJiojZs2KCpU6fq3LlzatiwoUaOHKk2bdqwqhhAimWxWDR37lyNHj1apUuX1qpVq1SlShWjY6UpNptN/v7+GjhwoDw8PLRy5Uo1btzY6FjAa2Gz2fT222/r8OHDCgkJSRXvj+Pj49WgQQOFh4fr1KlTzF4HUqmNGzeqW7duevfddxUQEJAuiohfhYSEqFmzZipcuLB2796tnDlzGh0J+I2UvTQBAFKgS5cu6b333lOJEiW0evVqjRgxQmFhYZo5c2aq+KCFpOXs7Kzu3bsrJCRE33//vRITE9W2bVtVq1ZNGzZskMViMToiALwiLCxMzZo107BhwzRw4EAdP36cIiIZODg4yGQyKSQkRIULF1aTJk00fPhwxcXFGR0NSHZfffWVtm3bphUrVqSa98cZMmTQ2rVr9eDBA33yySdGxwHwN6xdu1bdunVT165dtWrVqnRVREhSlSpVFBgYqLt378rb21uPHz82OhLwG5QRAPAnnTp1Sp06dbJvNZ8xY4bCwsI0duxYtkBCjo6OevPNN3Xo0CEFBgYqT5486tKli8qVK6dvvvlG8fHxRkcEkM7ZbDatXLlSlStX1s2bN7V3717NnDkzzc5QTimKFSumoKAgTZ06VXPnzlWdOnV07tw5o2MBySY0NFRDhgzRJ598onbt2hkd5y8pWbKkvvrqK/n7+2vNmjVGxwHwFwQEBKhnz57q1auX/P395ezsbHQkQ1SqVElBQUF69OiRvL299fDhQ6MjAa9gTBMA/A6bzab9+/drypQp2rlzp0qUKKHhw4erd+/eypgxo9HxkMKdOHFCU6dO1ZYtW1SoUCENHjxYH374odzc3IyOBiCdefr0qfr166dNmzapV69eWrBggbJly2Z0rHTnzJkz6tmzp65evaqpU6fqs88+S/Fz9IG/Ijo6WjVr1pSLi4uOHj2aKstOm82mXr166fvvv9eZM2e4DxyQCixfvlwffPCB3n//fX399de8tkq6fPmymjZtKnd3d+3du1f58+c3OhIgiZ0RAPBf2Ww2/fjjj2rUqJG8vLx0//59rV27VpcuXdKHH35IEYE/pWbNmtq8ebPOnz+vpk2basiQIfL09NSkSZP0/Plzo+MBSCd++uknVapUSXv37tWGDRu0cuVKigiDVK1aVSdOnNDHH3+swYMHq3nz5rp9+7bRsYAkM2jQIN26dUvr1q1LlUWE9O8RawsXLlTu3LnVvXt3JSQkGB0JwO9YsmSJ3n//ffXr148i4j+UKVNGwcHBioyMlJeXl+7cuWN0JEASZQQAvMJisWjdunWqVq2a2rVrJ6vVqh9++EFnzpxR165d0+1WT/wz5cuXl7+/v65du6auXbtqypQp8vT01PDhw/XgwQOj4wFIo6KjozVgwAC1adNGlStX1tmzZ9WpUyejY6V7rq6umj17tvbs2aNr166pUqVKWrVqldiwjtRuy5Yt+vrrrzVv3jyVK1fO6Dj/iLu7u9asWaOTJ09q3LhxRscB8D989dVX6tu3rz755BN99dVXFBH/R8mSJRUcHKz4+Hh5eXmxAAIpAmOaAEBSXFycAgICNH36dF27dk2tWrXSyJEj1bhxYzk4OBgdD2nMgwcPNG/ePC1cuFDx8fHq06ePhg4dqmLFihkdDUAacezYMfXq1Uu//PKLZs6cqf79+/N6lgKFh4fr448/1urVq9WpUyctXryY+1AhVfrll19UpUoVNW3aVBs3bkwzzzfTpk3TqFGjtHv3bjVt2tToOAD+w/z58/XZZ59p0KBBmjVrVpp53kkOYWFh8vb2ls1mU2BgoIoWLWp0JKRjlBEA0rWoqCgtWbJEs2bN0v379/XOO+9o5MiRqlGjhtHRkA6Eh4frq6++0rx58/T8+XN1795dI0aMUPny5Y2OBiCVSkxM1JQpUzRx4kRVq1ZNAQEBKlu2rNGx8AfWr1+vf/3rX3J1ddWKFSvUqlUroyMBf5rFYpG3t7du3bqlkJAQ5ciRw+hIScZqtapFixa6dOmSQkJClCtXLqMjAZA0a9YsDR06VMOHD9fUqVMpIv6EX375RU2bNlVcXJwCAwNVokQJoyMhnWL/EoB06dmzZ5o4caI8PT01bNgwtWzZUhcuXNCmTZsoIvDaZM+eXaNHj1ZYWJjmzJmjwMBAVahQQR06dNCxY8eMjgcglbly5YoaNGigiRMnavTo0Tp06BBFRCrRpUsXnT17VhUrVlTr1q31ySefKDo62uhYwJ8yefJkHTx4UKtXr05TRYQkOTo6KiAgQHFxcerTpw/j1IAUYOrUqRo6dKh8fX0pIv6CwoULKygoSJkyZZKXl5euXLlidCSkU5QRANKV+/fva9iwYfL09NTUqVPVvXt3Xbt2TStWrOCCDQyTOXNmDRw4UNevX9c333yjCxcuqE6dOmrevLn27t3LB18Av8tms2nx4sWqVq2anj17pgMHDmjChAlycXExOhr+goIFC+qnn37SggULtGzZMlWvXl0nTpwwOhbwuw4ePKgJEyZozJgxatSokdFxkkWBAgW0YsUKbdu2TQsXLjQ6DpCuTZw4UaNGjdKECRM0adIkioi/qGDBggoODpa7u7uaNGmiS5cuGR0J6RBjmgCkCzdu3NDMmTO1YsUKZcyYUQMGDNBnn32mPHnyGB0N+A2LxaItW7Zo6tSpOn36tOrUqaORI0fqzTff5KZsAF7x4MED9enTRzt27FC/fv00a9Ysubm5GR0L/9ClS5fUs2dPhYSEaOzYsRo5cqScnZ2NjgW8Ijw8XFWqVLGvtk3rf0c/+eQTLV26VMePH1elSpWMjgOkKzabTePGjdOkSZM0efJkjRo1yuhIqdqjR4/UrFkzPXr0SHv37lWFChWMjoR0hDICQJp27tw5TZs2TevWrZOHh4c+//xz9e/fX9myZTM6GvCHbDabdu7cqSlTpmj//v2qUKGCRo4cqS5duqT5D/wA/tiWLVv00UcfydnZWd98843atm1rdCQkoYSEBPtFl9q1aysgIEAlS5Y0OhYg6d/vUbp06aKff/5ZISEh8vT0NDpSsouNjVXt2rVlsVh0/PhxZc6c2ehIQLpgs9k0atQoTZs2TTNmzNDQoUONjpQmPHnyRM2bN9fdu3e1Z88eVa5c2ehISCdYXgkgTTp69Kjat2+vSpUqad++fZo3b55u3bqlkSNHUkQg1XBwcFDr1q21b98+7d+/X56enurZs6dKly6txYsXKzY21uiIAAzw8uVLvffee+rYsaMaN26ss2fPUkSkQS4uLpo4caIOHDigx48fq0qVKlqyZAmj+5AiLF++XBs3btTSpUvTRREhSa6urlq3bp1u3rypwYMHGx0HSBdsNpuGDh2qadOmac6cORQRSShXrlzau3evihQpIm9vb50+fdroSEgnKCMApBk2m0179uxRs2bNVLduXV26dEkrVqzQtWvX9PHHH7N6Calaw4YN9eOPP+r06dOqXbu2+vfvr2LFimnmzJmKiIgwOh6A12Tfvn2qXLmyNm/erBUrVmjz5s3KnTu30bGQjOrVq6czZ86oZ8+e6tu3r9588009ePDA6FhIxy5duqSBAwfqgw8+UKdOnYyO81qVL19ec+fO1eLFi/Xtt98aHQdI02w2mz7//HPNnj1bCxYs0Oeff250pDTHw8NDu3fvVokSJdS0aVPuVYXXgjFNAFI9q9Wq77//XlOmTNHx48dVvXp1jRo1Su3bt5eTk5PR8YBkceXKFc2YMUMrV65UlixZ9Mknn2jgwIHKmTOn0dEAJIO4uDiNHTtWM2fOVIMGDbRy5UoVK1bM6Fh4zX744Qe9//77slqtWrp0qdq3b290JKQzcXFxqlu3rmJjY3XixIl0eY8am82md999V4GBgQoJCVHhwoWNjgSkOVarVZ988okWLlyoxYsXq2/fvkZHStNevHihNm3a6MKFC9q5c6fq1KljdCSkYeyMAJBqJSYmatWqVapUqZI6dOigzJkza+fOnTpx4oQ6duxIEYE0rXTp0lq2bJlu3LghHx8fzZo1S0WKFNGgQYN09+5do+MBSEJnz55V7dq1NXfuXE2dOlVBQUEUEelUu3btdO7cOTVo0EAdOnRQnz599PLlS6NjIR0ZMWKELly4oLVr16bLIkL69xjNpUuXys3NTb169ZLFYjE6EpCmWK1W9evXT4sWLdKyZcsoIl6DbNmyaefOnapUqZJatGihgwcPGh0JaRhlBIBUJzY2VosWLVKpUqXUq1cvFS9eXAcPHlRQUJBatmwpBwcHoyMCr02hQoU0d+5chYWFafDgwVqxYoWKFSumDz/8UNeuXTM6HoB/wGq1avbs2apZs6YsFouOHTum4cOHU7anc7lz59a3336rb775Rhs3blSVKlW0f/9+o2MhHdixY4fmzZunGTNmqGrVqkbHMZSHh4dWr16t/fv3a+rUqUbHAdIMi8WiDz74QMuWLdOKFSv0/vvvGx0p3ciaNat27NihGjVqqFWrVtq3b5/RkZBGMaYJQKrx8uVLLV68WHPmzNHjx4/VuXNnjRgxQlWqVDE6GpBi8O8ESBtu374tHx8fBQUFadCgQZo8ebJcXV2NjoUU5saNG+rdu7cOHTqk4cOHa8KECcqQIYPRsZAGPXjwQJUrV1atWrX0ww8/sPjn/xk7dqymTJmiffv2qX79+kbHAVI1i8Wi9957T6tXr1ZAQIC6d+9udKR0KTo6Wm+99ZYOHz6sbdu2qWnTpkZHQhpDGQEgxXvy5Im++OILLViwQFFRUTKZTBo2bJhKlixpdDQgxYqJiZHZbNaMGTN069YttW3bVqNGjeKDMpDC2Ww2rV69WgMGDFC2bNlkNpv5EIjfZbFYNHPmTI0dO1bly5fXqlWrVLFiRaNjIQ2xWq1q3bq1QkNDFRoaqjx58hgdKcVITEyUl5eX7t69qzNnzih79uxGRwJSpcTERPXq1UsbN27UmjVr1LlzZ6MjpWsxMTHq0KGDgoOD9f3336tFixZGR0IawpgmACnWnTt39Pnnn8vT01OzZ89Wnz59dPPmTS1ZsoQiAvgDmTJl0r/+9S9duXJFAQEBunnzpho0aCAvLy/t3LlTrEUAUp6nT5+qS5cu6tWrl9566y2FhoZSROAPOTk5acSIETp69KgSEhJUs2ZNzZ07V1ar1ehoSCPmzJmjXbt2KSAggCLi/3B2dtaaNWsUHh6uvn378v4K+BsSEhLUrVs3bdq0SevXr6eISAEyZcqkrVu3qlmzZnrzzTe1Y8cOoyMhDaGMAJDiXL16VR988IGKFy8uf39/DRkyRGFhYZo9e7YKFixodDwgVXFxcVHPnj119uxZffvtt4qJiVHr1q1Vs2ZNbd68mYtVQArx600Dd+/erfXr1ysgIIAVtvhLqlWrphMnTuhf//qXBg0apBYtWuiXX34xOhZSuRMnTmjUqFEaOnQoK2P/B09PTy1ZskQbNmzQihUrjI4DpCrx8fHq0qWLvvvuO23atEkdO3Y0OhL+H1dXV23evFmtW7dW+/bttW3bNqMjIY1gTBOAFOPMmTOaNm2aNm7cqDx58mjw4MHq27evsmbNanQ0IM2w2Wzau3evpkyZor1796pMmTIaMWKEevToIRcXF6PjAelOdHS0hg8fri+//FItW7bU8uXLKd7xj+3Zs0cmk0kRERFauHChunXrxox//GURERGqXr26smfProMHD3I/kj/wwQcfaO3atTp16pTKlCljdBwgxYuLi1OnTp20c+dObdmyRW3btjU6Ev6L+Ph4devWTdu2bdP69evVoUMHoyMhlWNnBADDHThwQG3btlW1atV07NgxLVy4UDdv3tSQIUMoIoAk5uDgoGbNmmnPnj06cuSIypYtq/fee08lSpTQggULFB0dbXREIN04ceKEqlevrmXLlmnBggX66aefKCKQJJo1a6bQ0FC1bdtWPXr0ULdu3fTs2TOjYyGV+eSTT3T//n2tWbOGIuJPmD9/vgoXLqyuXbsqLi7O6DhAihYbG6sOHTpo165d+v777ykiUrAMGTJo3bp16tChgzp37qxNmzYZHQmpHGUEAEPYbDb99NNPaty4sRo1aqSwsDCtWrVKV65cUd++feXq6mp0RCDNq1OnjrZu3aqzZ8/Ky8tLn3/+uYoWLaqpU6fqxYsXRscD0qzExERNmjRJ9erVU5YsWXT69Gl9/PHHrFxHksqRI4dWr16ttWvX2seA7dq1y+hYSCXWrFkjf39/LVy4UKVKlTI6Tqrg5uamtWvX6sKFCxo5cqTRcYAUKzo6Wm+99ZaCgoK0bds2tWrVyuhI+AMuLi5avXq1OnfurK5du2rdunVGR0IqRhkB4LWyWCzauHGjatSooTZt2ig+Pl7fffedQkND1aNHDzk7OxsdEUh3KlasqICAAF29elXvvvuuJkyYoCJFimjUqFF69OiR0fGANOXq1atq2LChxo8fr5EjR+rw4cMqW7as0bGQhnXt2lVnz55V+fLl1bJlSw0cOJBdcPhdN27cUL9+/dS9e3f16tXL6DipSrVq1TR9+nTNnTuXG74C/0VUVJTatWungwcPavv27WrevLnRkfAnOTs7a+XKlerRo4d69OihVatWGR0JqRT3jADwWsTHx2vVqlWaPn26rly5oubNm2vUqFFq0qQJK0GBFOb+/fuaO3euFi1apMTERH3wwQcaMmSIPD09jY4GpFo2m01LlizRoEGDlD9/fgUEBKhevXpGx0I6YrVa9eWXX2r48OEqWrSoVq1apRo1ahgdCylMQkKCGjVqpEePHunMmTNyd3c3OlKqY7PZ1LZtW504cUKhoaHKly+f0ZGAFCEiIkLt2rXTqVOntH37djVq1MjoSPgbLBaLPvroI61YsULffPON3nvvPaMjIZVhZwSAZBUdHa0vvvhCJUqU0Pvvv68KFSro2LFj2rVrl7y9vSkigBQof/78mjFjhm7fvq1Ro0Zp7dq1KlmypEwmky5dumR0PCDVefDggdq1a6d+/fqpZ8+eOnPmDEUEXjtHR0cNHDhQJ0+eVKZMmVS3bl35+fkpMTHR6GhIQcaPH6+TJ09q7dq1FBF/k4ODg8xmsxwdHeXj4yOr1Wp0JMBwL1++VOvWrXX69Gnt3LmTIiIVc3Jy0tKlS/XRRx+pT58+Wrp0qdGRkMpQRgBIFuHh4Zo8ebI8PT01aNAgeXt769y5c9qyZYtq1apldDwAf0KOHDk0ZswYhYWFaebMmdq9e7fKly+vd999VydPnjQ6HpAqbN26VZUqVdKJEye0bds2ff3118qSJYvRsZCOlS9fXkeOHNHw4cM1btw4NW7cWNevXzc6FlKAvXv3aurUqZo4caLq1KljdJxULU+ePFq5cqV+/vlnzZ071+g4gKHCw8PVsmVLnT9/Xrt371b9+vWNjoR/yNHRUYsWLdKAAQP00UcfaeHChUZHQirCmCYASerhw4eaO3euFi5cqPj4eL3//vsaOnSoihYtanQ0AP9QXFycAgICNH36dF27dk0tW7bUqFGj1LhxY3Y5Af/Hy5cv9dlnn2nFihVq3769lixZoty5cxsdC3jFwYMH1bt3b/v7tw8++IDn83TqyZMnqlKlisqUKaNdu3bJycnJ6EhpwtChQzV//nwdPnyYsWhIl549e6ZWrVrp+vXr2rVrF/8O0hibzaZBgwZp3rx5mj9/vgYOHGh0JKQClBEAksSvK6e/+eYbubi4qH///vrss8+YkQqkQRaLRZs2bdKUKVMUGhqqevXqadSoUWrbti0XsQBJ+/fvV+/evfXkyRN98cUXMplM/NtAihUREaFBgwZp2bJlateunZYtW6a8efMaHQuvkc1mU/v27XXw4EGFhISoYMGCRkdKM+Lj41W/fn29fPlSp06dYmcc0pWnT5+qefPm+uWXX7R7925VrVrV6EhIBjabTcOHD9fMmTM1a9YsDR482OhISOEY0wTgH7l48aJ8fHxUsmRJrVu3TqNHj1ZYWJimTZtGEQGkUU5OTurSpYvOnDmjH3/8UQ4ODnrzzTdVtWpVrV27lvnjSLfi4+M1cuRIeXl5qWDBggoNDdV7771HEYEULWvWrFq6dKm+++47HT16VJUqVdJ3331ndCy8RgsXLtT333+vFStWUEQksQwZMmjt2rW6d++ePvnkE6PjAK/N48eP5e3trbt37yowMJAiIg1zcHDQ9OnTNWrUKA0ZMkTTpk0zOhJSOMoIAH/LiRMn1LFjR1WoUEF79uzRrFmzFBYWJl9fX+XIkcPoeABeAwcHB73xxhs6cOCAgoODlT9/fnXv3l1ly5bV0qVLFRcXZ3RE4LU5d+6cateurdmzZ2vKlCkKDg5WsWLFjI4F/GlvvfWWzp07p3r16ql9+/b64IMPFBERYXQsJLOzZ89q8ODBGjBggN58802j46RJpUqV0ldffSWz2ay1a9caHQdIdg8fPpS3t7cePXqkoKAgVapUyehISGYODg7y8/PTuHHjNHLkSE2aNMnoSEjBGNME4E+z2WwKDg7WlClTtGvXLpUqVUojRoxQz549lSFDBqPjAUgBTp06palTp2rz5s3Knz+/Bg8erI8++oixBEizrFar5s+fr5EjR6pEiRJatWqVqlWrZnQs4G+z2Wz65ptv9NlnnylPnjwKCAhQgwYNjI6FZBAdHa3atWvLwcFBx44dU6ZMmYyOlGbZbDb16NFDP/74o06fPq3ixYsbHQlIFvfv31fTpk314sUL7d27V2XLljU6El4zPz8/jRkzRmPHjtX48ePZIYzfYGcEgD9ktVq1bds21a9f377CYf369bp48aL69OlDEQHArnr16tq4caMuXryoVq1aafjw4fL09NSECRP07Nkzo+MBSer27dtq3ry5Bg0apP79++vkyZMUEUj1HBwc9MEHHygkJET58uVT48aNNWrUKMXHxxsdDUls8ODBun79utatW0cRkcwcHBy0aNEi5cyZU927d1dCQoLRkYAkd+fOHXl5eSkyMlLBwcEUEemUr6+vpk2bpokTJ2r06NFiDTz+L8oIAP9TYmKi1qxZo6pVq+qtt96Ss7Oztm/frtOnT6tz585ycnIyOiKAFKpMmTJavny5rl+/rh49emj69Ony9PTU0KFDdf/+faPjAf+IzWbT6tWrVblyZV29elW7d+/WnDlz5OrqanQ0IMmUKFFC+/bt06RJkzRz5kzVqVNH58+fNzoWksi3336rxYsXa+7cuapQoYLRcdKFbNmyac2aNTpx4oTGjx9vdBwgSd2+fVteXl6Ki4tTcHCwSpUqZXQkGGj48OGaPXu2pk6dqmHDhlFI4BWUEQB+Iy4uTkuWLFGZMmXUo0cPFSpUSPv27dP+/fvVpk0bttkB+NOKFCmiL774Qrdu3dLAgQO1dOlSFS1aVP369dONGzeMjgf8Zc+ePVPXrl3Vs2dPtWvXTmfPnlWzZs2MjgUkC2dnZ40aNUpHjx5VXFycatSooXnz5slqtRodDf/AL7/8ovfff1/t27dX3759jY6TrtStW1cTJ07U1KlTFRgYaHQcIEncunVLXl5e9rHOjCGDJA0aNEjz58/XrFmz9Pnnn1NIwI57RgCwi4yM1Ndff63Zs2frwYMH6tSpk0aMGMHICQBJ5sWLF1q0aJHmzJmjp0+fqmvXrhoxYgQ3tkOqsGvXLplMJkVHR2vx4sXq0qWL0ZGA1yYmJkYjR47U/Pnz1axZM5nNZhUqVMjoWPiLHj16pDfeeEMPHz5USEiIPDw8jI6U7lgsFrVo0ULnz5/Xtm3bVLt2baMjAX/b9evX5e3trQwZMigwMFCFCxc2OhJSmEWLFql///4aMGCAvvjiCzk6si4+vaOMAKCnT59qwYIF+uKLLxQREaHevXtr2LBhKlOmjNHRAKRR0dHRWr58uWbOnKnbt2/rrbfe0siRI1W3bl2jowG/ceXKFc2bN0+LFi1SixYttGLFChUsWNDoWIAhdu/eLZPJpKioKPn5+alPnz7cbyAVady4sfbv3y+z2SwfHx+j46Rbt27dUrFixZQ5c2ZFRUUZHQf4W5YsWaK+ffuqZMmSCgoK4r0R/qelS5fqo48+krOzsyIjI5UxY0ajI8FAlBFAOvbtt99q4cKFOnz4sKxWqz766CMNHjyY1QwAXpuEhAStWbNG06ZN06VLl1S3bl2VKFFCAQEBjIRDivD8+XP7yuEvvvhCAwYMYEUX0r3nz5+rb9++2rhxo4oXL67r168bHQl/0tdff61Dhw5p+fLl3P/NYH5+fnry5InmzZtndBTgb/Hy8tK+fft04MABNWjQwOg4SOFq166t48eP68yZM6pSpYrRcWAgygggHcucObNiYmLk6+urgQMHKnfu3EZHApBOWa1Wbd26VT4+PoqMjNTKlSvVq1cvo2MBioqKkre3tzp06KCRI0caHQdIMRISEtShQwflzZtX33zzjdFxAACvmcVikdVqlYuLi9FRkErExcWxKwKUEUB6dujQITk4OKhevXpGRwEASf9ebbtjxw517txZzs7ORscBAAAAAABJhDICMNiPP/6oWTNnKDEhwbAMNWrVZnswgBTDarXqvT59dO3a6xn7kTFjRk2cMF4NGzZ8LedD0vH19VVw4N7Xft4cHjm1+OuvVaBAgdd+bsBIT58+1fvvf6BHj5+81vPmzZNby5d/oxw5crzW8xrt8OHDGjvGV7ExMa/1vLnz5NXyFSuUPXv213retOL06dMaMWKEoqOjDctQsGBB+fv7swIZv2vJkiVaGbBKVuvfuyyYI3s2LVz4lTw9PZM4GdKS27dvq3///nrxIjxJj9uiRUuNHTs2SY+J14MyAjBY586d9cPWLWpX0ZgRSefvR+rCg0hFRkbKzc3NkAwA8J9u3LihEiVKKG/ZmspWsHiyn+/K3o36eMAALViwINnPhaTl4uKiYrkyq5pnrtd2zvhEi7aeDNPq1avVvXv313ZeICX44Ycf9Oabbyp39VZyzvz/sXefAU2dbRiA7wwIe+/lHiC0uMW9V9XiFttPrXVvbV1Va92zbquiba2tglurddWF1r1Qgoh7gEDYkACZ5/tBQx1skpyEPNcfNeN972A4JznPO2x00qdCkonke2dw4sQJdOvWTSd96otp06Zh7dq16N9Qd5vCpufIcDYmGadOnUKXLl101m9lMn/+fCxcuBADv2Bnucn4uDj8E3EBt27dQqNGjVjJQAyDn/8neBYvgqN/6zI/l2FUSLxyEDt27MDXX3+thXSksvj1118xfPhwDO4XrLF9127cvou3SckQi8UaaY/oFq1/QAjLVCoVmlazw/oBfoXenydXwsxEe5vLHX2QhLFh0VCpVFrrgxBCykJ9PGo2bC48Asq3GZ5Clge+qVmpHpv5KoaOgQZKpVJhVPu6GNqqdqH3v3sOLel8WtrzrVSeX4yg9wwxRur3vd/XqyGwLb4IqJTlgWdqVvBneUkzRIiYcMYof+dUKhV8PeywYdAnGm+7qGNeQmYeGiy+YJQ/b01RqVTw8vbBT9t3FvmYvLw8mJmV//eiONFRD9C6aX36PyQlUqlUcG3aC3W+XAgAZT5eJ145SO8zUiL1e+S3n9aU+jl5eVKYmRU9s2vj9p2Ys3hVhbMRdlAxghA9IJEqcO15OrgcDmo6WyApW4bM3Pxlm3hcDpQqBnweFw29bXDkQRI8bc2QnisHwwB1XC2RK1ciK1eBHJkSAZ7WSBbL4GotwPOUHGTlKQAAWbkKeNnnf7Dgczl4npKDT71skJglZe11E0JIcbKS3gC4AnC4sPeuhQThdWQlvYa1syfsvGrCxMIaqc+FUCnkMLdzAs/UHNau3siIewoTgQXkeWLwTMyglOfBtW4jJD+5D0snD8jzJDAxs0D6myfgmwqgkOWx/VJJBSRl5uLq46T8c6ibDZIyc5GZIwMA8HgcKJX551BzUx4sBSaQKZSQSBV4kyqGp4MlFEoVEjJyUM3ZGpm5cmTnyuFiawZXG3OY8LiwEPBx90UKrMxNIJEqkC6m8yYhkoRnkLx9DA6HC0uPmhDHP4aVZ20kR56DubM3TCztoJJLweWbggEDnqk5pBlJsKn2KVIfXIDAzhUqpRw5Cc/g0SYEb/7+FRbu1WFTJQCyzGSY2rlC/CYGKqUMjJIudMUmZSNVLMs/zrlY4nGSGLVdrXDjRTqcrEwhlatgyuciKUsKEx4Hddys8So1By7WAmTlyZEmkcPB0gTutmZ4lCiGCY8DTztzpOfI4ONgARXDIDtPAYlUiYxcGdsvt9J4FPMQqSnJ4HK5qFW7LpISE5CRkQ4A4PP5UCgUMDExQaMmzXBofzg8vbyRnpYGhmFQ168ecnNykJmZgRyJBJ8ENkCyKAmubu6IvHsbEokY7h6eeBsfhxo1a4PL5SIzMwPi7GwoFAqWXzkxJDmJz5Fw5QCsqwRAnp0KrsACAMDhcpGXEgcLtxowtXUGl2+KjMc3ILB3R/YrIUws7dgNTgzO5Ws3kZAkwoDgHli1YSvc3Vzh4+UBPp+PjMxM1KpRDTGxT6FQKODi7IisbDHy8qRwcXaCQGAKNxdnODk44PK1m3j46DHbL4dUABUjCNEDlgI+gqrnr4GbnC2Dn7tVoY9LzpahT6AbDtxNQEauAq1r2iNXroS1IP9XOVeuwrUXGfCyM4OTlSmcrEyL7LOBjy0A4GEiTWsjhOgnG1fvgpkROekiVG/R4737czNTIZNkQ5qTCcfq/uCZCJAtioOphTVy0kUwMbcEA6agDTe/Ju8939rFGwBKPYOC6CdXW3M0r+0KABBl5aKeV+Fryodfe4akzFz0qO8DV1tzOFmbQZSZA3GeHNZmJlCqGLT1dS/0uR38/1siRSpXYuyvVzT/QggxIJbuNQpmRkgzk+Hg2xzSzGR4th743uPe/rMfsswUOPi1gLmzD8Txj2HhWg0KqQQAYOFWHXJxOqp0HVnwHIGdS/6ftq3y288Q6eIl6bU6rtZA/mEOydlSNK/hiORsKT4LcAOQP7CJw+Hgr6hEJIvlqONmjSqO5pAqVLA1NwGfy4VCpYKNuQm6+LmAy+UU2VdCJhXoNaWu738z30VJSagXUPgMF1FSEpRKJR7cj0SHTl3A5/ORlZkJLpcLhUIBuVyOly+eoWlQ/ueZTl27F9tvdNQDzb0IUulZuFWHe4t+kEsykfn8HuTZaXD0bwMTS1uYWNhBkZuNnISnkGWnQWDrDGsfP1j7FL6qAyHFaRWU/10sSZSM6ZPGAAD+2HcY6ZmZ6Ni6BXJz8xDgVxdZ2WKkpqUhNS0DNatXRVDjBu+107l9a8Q+e67z/ERzNLNYFyFEY5ytiy4g3HqVgRsvMmBlxoeztSkuPU1HRo4CNuZ8VHEwB5cDCPhcKFUMbr3KxL03WZBIaWQMIcTwWdi7vPfv51eOI/3NY5ha2sDCzhmvb59FVsJL2LlXg41bVXB5fEhS3oLHM0F63FOWUhNdc7ExL/T24/deo6qTNWq72yLqTRpORL7Bm1QxarnZIqiWK1QMkC6R4tqTJAjj0nWcmhDDJrB1fu9PAEi69RfSH10H39waZo4eSI+9gVzRK1i6VYO5SxWoZFLkpcSBa2oOhSSDpeSGydla8N6fQP7ApguxyfC2N4eXnTmE8Vk4/ygF6RI5PO3M4eduDT6Xi3+epOLu6wyWkhs3F1fXIu+7ce0KfKpUhbe3D6Lu38PpE8eRmZkBL28fNGjUBJZWVnjx/Bnu37urw8TE2KQ//AcWrlVh4VYdkoSnEN05idyUNzBz8oSVtx/4ZpbITYlD/IU/kPUyiu24xIC5uuR/Xjh8/BSq+niiipcnomJicfHKdbx6EwcfLw80bvApqlf1QWpaOi5fu8lyYqJpNDOCEAPS3d+l2Pvb1nbUURJCCGHXh7MkPuQVWPaN+Ejl1aO+T7H3d/nES0dJCDEOro0/K/Z+R/9WOkpiPNSzJIrSpFrhs8YI+3oG9yn2/jbtOugoCTFmLiUctx3q0XGbaFbvHl2Lvb9F00Y6SkJ0jYoRhOi5a8/TEZ0gRi0XS+TIlAiqbgc+l4OETCnSc+SIS88Dn8eBhSkPHAAtazog+m02pAoVuBwOUiQy1HaxRC0XS7ZfCiGEaMTbqCtIeRENe+/aUOTlwMO/OTg8PtJexUClkEEqyYbXpy2R8iIaktQE8PgCmNk6wN2vKdvRCQuuPk5CdFw6arnbIkeqQPNaLuDzuIh5m4E3qWJYmPJhIeCjQVUnXHuSBAcrM6gYBo2rO5fcOCHkI2kxV5H9+iGsPGpBKc2BvW8QOFw+8lLjoZTlIjf5DZw+aYfMZ/egkkvB4fHAM7OEfe0mJTdOinT1WSoeJmSjlovVv98ZHMDncvA2Mw93XqWjTW0n3H6VAV93a9RyKXxJWKJ7Vy5HQBj1ALXr1EWORIIWrdqAx+fjofABeDw+3rx+iU5dP0N01H1kZmSAw+Ggjq8ffKpUZTs6qUTS/z1uW35w3JamJ0CWmQxZVgocP+2ArGd3oZTmQiXPg8C+8KUtCSmtS1dv4H50DHxr1YAkJxetmzcFn89D1MNYqFQqeHt6ICk5BWKxGB7ubqhbqwbbkYmGUDGCED2QmCVF1NvsQu+zMuOjaTU7AICDpQnOPkpBqliOjFw5/NytYWvBR45MhVSJHAqVCgfvJYIBAxdrAdxsBPARmCM+Mw95isI3/3tEe0YQQvRUynMhTCysP7rdxMIaAis7pL58CEaphFKpgInAHOKUBJjZ2EMhzcWrO+fB5fHAMAykOVngCcyQ/KzwNZQl6cnafilEix4nZCLqTVqR91ubm8DO0hQx8elQqhgoVSqYm/KRkCGBCY+HHGkeOGIOXqeI4WprDlM+F6nivCLbzJMrtfVSCDEY2S8fQGpbeMGOb24NE0s7ZL+JAaNSQqVUgCewQF7aW5haOUAlz0PKgwvg8PhQKWRQyaUQcDhFLvth7HtGpIjzEBWfWeLj3mbkQqFUQfg2Ez4OFrjzKh05ciUSMvNgacrDhdj8c13km4xij2O0Z4RmpKel4kHkvRIfZ21tA3s7e8QIhVAqFVAoFLCwsETC2zjYOzhCJpPh3JlT4PP5kMlkkIjFMLewQEZ60UsKCqPua/KlkEouN/k1eObW4FvaQfzhcTv9LUwtHaCUS5Fy/zw4PD6Yf4/bCildRyBlc/eBEBzOf3sWWVtbwcHOFlExsVAqVZArFbA0N0d8QiJMTEwR+/QZwOEADIO3iUnIzfvv/PToMS3Da8ioGEEIy7y8vHAwUYwuG2+xlsHezg7m5oWvs00IIbrm7OwMExMTXAmdq7M+vbxomR5D5OXpge0XYrH9Qqzu+6b3DDFCnp75m7nfXfUFa30bEy8vLyRn5aHzuqs679sYf96a4uXlBYlEgnbN2VtihMPhwMPDg7X+iWHw8fZC7JnTSLl7utxt0OchUhL1e6Rpx14abbdWrVoabY/oDodhGIbtEIQYM4VCgejoaCgUhW80LZfLsX79eoSHh6N9+/b4/vvvYWVV/NRqhmFw9OhRrFy5ElWqVMHy5ctRpUqVIh9frVo1ODg4VOh1EEKIJiUlJSEuLq7Ujz958iTmzZuHrl274vXr19i1a1epnysQCODn5wcul1ueqIRFmZmZePq0+JFRO3fuxKZNmzBs2DCMHz/+vRFZ73rw4AEmTpyIatWqYePGjbC2/nhWjpq9vT2qV69eoeyEGKpXr14hJSWl1I//5Zdf8Msvv+Dvv/8GwzDo1KkTRo4ciWHDhpW6DScnp2I/y1ZWKpUKMTExyMsr22yFCRMmgM/n48aNG5g8eTIGDRpUpuc7OzvDx6f4vXZI0RiGwaNHj5CTk1Pq56hUKgwcOBAeHh5Yv349ACArKws9evRA//79MXHixDJlcHd3p2IEKVFeXh4ePnyIwi4Lnjp1CnPnzoWdnR3Onj1b6PNtbW1Rs2ZNbccklcCzZ8+QkZHx0e137tzB6NGjMWvWLPTr16/gdoVCgS+//BJ8Ph87d+4En//xWPpatWrBxsZGm7GJllAxghA99vLlSwwcOBD37t3Djz/+iAkTJhR5EaUwDx48QP/+/ZGQkIAdO3ZgwIABWkxLCCHsmT17Nnbv3o0pU6Zg3rx5yM7OpuKCkWMYBgsWLMCCBQswf/58zJ8/v8Rz6O3bt9G5c2dUr14dZ86coUI9IRoQEBCAgIAA7NmzBwAwYMAAPH78GJGRkewGq8S8vLwwZMgQnDp1Cg0bNsT27dvZjkRKcOjQIfTt2xdXr15FUFBQwe2zZ8/Gpk2b8OrVKzonEZ1at24dZs6cCblcDrlcDh6Px3YkUsnk5eXhk08+gYuLCy5duvTRd7ebN2+iWbNmWLVqFb755huWUhJtoG/phOipY8eOoUGDBkhKSsI///yDiRMnlqkQAQCffPIJbt++jc8++wwDBw7EhAkTIJVKtZSYEELYIxQK4e/vD39/f+Tk5ODFixdsRyIsYhgG3333HRYsWIClS5fihx9+KNU5tFGjRjh//jxevXqFdu3aITmZ9hMhpCKEQiGEQiFCQkIKbgsJCcH9+/cRExPDYrLKKz09HfHx8QXnRKFQyHYkUgKGYbBkyRK0b9/+vUIEAEydOhVKpRIbN25kKR0xViKRCHZ2dmAYBqmpqWzHIZXQokWL8OrVK2zfvr3QQWRNmjTB5MmTMW/ePDx//pyFhERbqBhBiJ6Ry+WYMWMGevXqhZYtW+LevXto0qRJuduztrbGnj17sGXLFmzfvh0tWrSgAzkhpNKJiooqGH2r/jcxTgzDYNq0aVi+fDnWrFmD2bNnl+n5gYGBuHjxIpKSktC2bVskJCRoKSkhlV9YWBjs7OzQpUuXgtu6desGGxsbhIWFsZis8lIXH9TnRKFQCJVKxXIqUpxTp07h7t27mDNnzkf3ubi4YNSoUVi/fj2ysrJYSEeMlUgkgqOjY8HfCdGkBw8eYOXKlZg7dy58fX2LfNyiRYvg4uKCMWPGFLqcGDFMVIwgRI/ExcWhbdu2WLNmDVavXo2jR4/C3t6+wu1yOByMGTMG165dQ3p6Oho0aIDDhw9rIDEhhLAvKysLr169gr+/P9zc3ODg4EAjQY2USqXC+PHjsW7dOmzevBlTp04tVzv16tVDREQEMjIy0LZt2zLtX0IIyccwDMLDw9G3b1+YmpoW3G5mZoY+ffogPDycLixogVAoBJ/PR506deDv7w+xWIzXr1+zHYsUgWEYLF68GEFBQWjXrl2hj/n2228hkUiwZcsWHacjxiwpKQlubm4FfydEU5RKJUaMGIE6depg5syZxT7WysoKW7duxd9//43ff/9dRwmJtlExghA9cerUKQQGBuL169e4dOkSvvnmmzIvy1SSBg0a4O7du+jQoQP69OmDqVOnQiaTabQPQgjRtejoaAD5o0A5HA78/f1pZoQRUiqVGDVqFLZu3Yrt27dj3LhxFWqvTp06uHTpEvLy8tC6dWu8fPlSM0EJMRI3b97E8+fP31uiSS0kJARPnjzB3bt3WUhWuUVFRaFOnTowNTWFv79/wW1EP0VERODq1auYM2dOkd/9vLy8MGzYMKxZs6ZMm2ITUhEikQje3t4FfydEUzZu3Ijbt29jx44d7w1WKErXrl3xxRdfYOrUqfRerCSoGEEIyxQKBebOnYtu3bqhSZMmuHfvHpo3b661/mxtbXHgwAGsX78emzdvRuvWrWm0FCHEoAmFQvB4PNStWxcACpalIMZDoVBg2LBh+PXXX/Hbb79hxIgRGmm3Ro0auHTpEjgcDtq0aYOnT59qpF1CjEFYWBjc3NzQtm3bj+5r3749XFxcaKkmLVDvoQTkX8S2tbWlc6IeW7x4MQIDA9G9e/diHzdz5kykpqZix44dOkpGjJ1IJIKHhwfMzc3pAjDRmJcvX2LOnDmYMGECmjVrVurnrV27FhwOB1OmTNFeOKIzVIwghEUJCQno2LEjli1bhqVLl+L48eNwcnLSer8cDgeTJk3CP//8g8TERNSvXx9//fWX1vslhBBtiIqKQq1atWBmZgYA8Pf3x+PHjyGVSllORnRBLpdj8ODBCAsLQ1hYGP73v/9ptP0qVarg0qVLMDMzQ5s2bfDo0SONtk9IZaRUKrFv3z4MGDAAPB7vo/v5fD769++PvXv30n4GGsQwDIRCYcH+SerZglSM0E/Xr1/HuXPnip0VoVa9enUMHjwYq1atos83RCdEIhFcXV3h4uJCxQiiEQzDYMyYMXB0dMSSJUvK9FxnZ2esW7cOYWFhOHHihJYSEl2hYgQhLDl37hwCAwPx+PFjnD9/HrNnzwaXq9tfySZNmuDu3bto0aIFevTogZkzZ0Iul+s0AyGEVNS7o0CB/JkRCoUCsbGxLKYiuiCVStG/f38cOXIEBw4cwIABA7TSj6enJyIiImBnZ4c2bdrQhT1CSnDp0iUkJCQUukSTWkhICOLi4vDPP//oMFnl9vbtW6Snp390TqRlmvTTkiVL4Ovriz59+pTq8bNnz0Z8fDx27dql5WTE2EkkEuTk5MDFxYWKEURjdu/ejdOnT2PLli2wtrYu8/O/+OILdOnSBWPGjEF2drYWEhJdoWIEITqmVCqxYMECdOrUCZ988gnu3buHNm3asJbHwcEBR48exapVq/Djjz+iffv2iI+PZy0PIYSUBcMwiIqKKhgFCuRvPgyALhhXcrm5uejduzdOnTqFI0eOIDg4WKv9ubm54eLFi3B3d0fbtm1x7949rfZHiCELCwtDtWrV0LRp0yIfExQUBG9vb1qqSYPU5713z4n+/v549OgRDTjSM5GRkTh+/HiZBqT5+vqib9++WL58ORQKhZYTEmOm3rBaXYygDaxJRSUnJ2PKlCkICQnBZ599Vq42OBwOtm7ditTUVMydO1fDCYkuUTGCEB0SiUTo2rUrFixYgPnz5+PUqVNwdXVlOxY4HA6+/fZbRERE4MWLFwgMDMSZM2fYjkUIISUSiURISUl5bxSonZ0dvL29aSRoJSaRSNCrVy9cvHgRx44dK3GtbU1xdnbG+fPnUa1aNbRv3x63bt3SSb+EGBKZTIYDBw5g0KBBxS49w+VyMWjQIOzfv58ulGtIVFQULCwsULVq1YLbAgICIJfL8fjxY/aCkY8sXboU1apVK3b2UGHmzJmD58+fIzw8XEvJCPlvw2pXV1e4urrSzAhSYdOmTQPDMFi3bl2F2qlatSoWL16MjRs34vr165oJR3SOihGE6MilS5cQGBiIBw8e4O+//8b8+fMLXUOXTS1atEBkZCQaNWqErl27Yt68eVAqlWzHIoSQIqlHgb5bjFD/m2ZGVE7Z2dno3r07rl27hpMnT6JTp0467d/BwQFnz56Fr68vOnTogCtXrui0f0L03ZkzZ5Cenl6qi6whISFITU3FuXPndJCs8hMKhahXr957I+1ptqD+iYmJwYEDBzBr1izw+fwyPTcwMBCfffYZli5dSvutEK1RFx9omSaiCadOncIff/yBNWvWwMXFpcLtTZo0CY0aNcKIESMgk8k0kJDoGhUjCNEylUqFZcuWoV27dqhduzYiIyPRoUMHtmMVycnJCX/99RcWL16MpUuXolOnTkhMTGQ7FiGEFCoqKgpmZmaoUaPGe7dTMaJyyszMRJcuXRAZGYkzZ86wtsyhra0tTp8+jfr166NLly64ePEiKzkI0UdhYWHw8/P7qEhcmMDAQNSpU4eWatKQdzevVnN0dIS7uzudE/XI8uXL4eHhgaFDh5br+XPmzEFMTAwOHz6s4WSE5BOJROBwOHB0dKRiBKkQsViMMWPGoGPHjhgyZIhG2uTxeNi+fTtiY2OxYsUKjbRJdIuKEYRoUUpKCnr06IHvvvsOs2bNwtmzZ+Hu7s52rBJxuVx89913OHfuHGJiYhAYGIjz58+zHYsQQj4iFArh5+f30UyzgIAAvHz5kjY3q0TS0tLQsWNHxMTE4OzZs2jevDmreaytrXHy5EkEBQWhe/fu+Pvvv1nNQ4g+yMnJwdGjRxESElLsEk1qHA4HISEhOHz4MHJzc3WQsPJSKpWIjo4utAhEm1jrj+fPn2P37t2YPn06BAJBudoICgpC+/btsWTJEjAMo+GEhOTvGeHo6Ag+nw8XFxdIJBJIJBK2YxEDNG/ePIhEImzbtq1UnwtK69NPP8WMGTOwePFiPHr0SGPtEt2gYgQhWnL16lXUr18fN2/exMmTJ7FkyZIyT8NlW9u2bREZGQl/f3906tQJixYtounAhBC98uHm1WrqizHR0dG6jkS0IDk5Ge3bt8eLFy9w4cIFNG7cmO1IAAALCwscO3YM7dq1Q8+ePfHXX3+xHYkQVh0/fhwSiQSDBg0q9XNCQkKQnZ2NEydOaDFZ5ff8+XPk5eUVeU6kmRH6YeXKlXBwcMDIkSMr1M7cuXNx7949nDx5UkPJCPmPSCQq2NtSvawOzY4gZXXz5k2sX78eixYtQvXq1TXe/rx581C1alWMHDmSrlMZGCpGEKJhDMPgxx9/RJs2beDj44N79+6ha9eubMcqN1dXV5w+fRrz5s3D/Pnz0a1bNyQnJ7MdixBCoFKpihwF6uvrCy6XSyNBK4HExES0bdsWiYmJuHjxIgIDA9mO9B4zMzMcOnQI3bp1Q+/evWnZDGLUwsLC0LhxY9SsWbPUz6lduzYaNGhASzVVkPp8V9TMiOfPn9PIZpbFxcXh119/xbRp02BhYVGhttq2bYugoCAsWrSIZkcQjROJRAVFCCpGkPKQy+UYMWIEGjRogMmTJ2ulDzMzM4SGhuKff/5BaGioVvog2kHFCEI0KD09HcHBwfj2228xdepUXLx4Ed7e3mzHqjAej4cffvgBp0+fxr179xAYGIjLly+zHYsQYuRevnwJiURS6ChQMzMz1KpVi0aCGri4uDi0adMGGRkZiIiIKNUa9GwQCATYt28fevfujf79+2Pv3r1sRyJE5zIyMnDixIlSbVz9oUGDBuH48ePIysrSQjLjIBQK4eTkVDCa+V3+/v5gGAYPHz5kIRlRW716NSwsLDBu3LgKt8XhcDB37lxcv34dFy5c0EA6Qv7zbjFCfUyhYgQpi1WrVuHhw4fYsWOHVlcIadOmDUaOHIkZM2YgPj5ea/0QzaJiBCEacuvWLTRo0ACXL1/Gn3/+iZUrV8LExITtWBrVqVMnREZGombNmmjXrh1WrFhB0+EIIaxRFxqKukBNa2QbtlevXqFNmzbIy8vDpUuXUKdOHbYjFcvExAS7d+9GSEgIBg8ejF27drEdiRCdOnz4MORyOQYOHFjm5w4cOBBSqRRHjhzRfDAjIRQK4e/vX+ia3H5+fuBwOHROZJFIJEJoaCgmTZoEGxsbjbTZrVs31K9fH0uWLNFIe4SoJSUlFRQjnJycAFAxgpRebGwsFi5ciG+//VYnM5pXrlwJS0tLjB8/nmaKGQgqRhBSQQzDYOPGjWjRogVcXFxw9+5d9OzZk+1YWuPh4YFz585hxowZmDVrFnr27InU1FS2YxFCjJBQKISdnR08PDwKvZ/WyDZcz549Q+vWrQEAly5dQo0aNVhOVDp8Ph87d+7EV199hWHDhmHHjh1sRyJEZ8LCwtCmTZsij8nF8fHxQcuWLREeHq6FZMYhKiqqyOK8hYUFatSoQedEFq1duxY8Hg+TJk3SWJscDgdz5szB+fPnce3aNY21S8i7MyP4fD4cHR2RlJTEcipiCFQqFUaNGgUvLy/Mnz9fJ33a2dlh8+bNOHr0KA4dOqSTPknFUDGCkArIzMzEgAEDMGnSJIwbNw6XL19G1apV2Y6ldXw+H0uXLsWJEydw/fp11K9fH9evX2c7FiHEyKg3ry5sFCiQX4xITk6mkVwGJjY2Fq1bt4aZmRkiIiJQpUoVtiOVCY/HQ2hoKMaOHYuRI0di8+bNbEciROuSkpJw7ty5Mm1c/aGQkBD8/fffSElJ0WAy45CXl4cnT54UumyhGhXo2ZOeno7Nmzdj3LhxcHR01GjbvXv3hq+vL82OIBqjVCqRkpLy3pJvLi4u9HmalMqOHTtw6dIlbN++Hebm5jrrt0+fPujduzcmTJiA9PR0nfVLyoeKEYSU071799CwYUOcOXMGBw4cwLp162Bqasp2LJ3q1q0bIiMj4eXlhVatWmHt2rU0LY4QojPqJSmKor4oQ8tSGA6hUIg2bdrAzs4OERER8PLyYjtSuXC5XGzatAlTp07FhAkTsGbNGrYjEaJV+/fvB5fLRb9+/crdRv/+/cEwDA4cOKDBZMbh0aNHUCqVJZ4T6XzIjo0bN0Iul2PatGkab5vL5eK7777DX3/9hXv37mm8fWJ8UlNTwTBMwcwIgIoRpHTevn2L6dOn4+uvv0a7du103v+mTZuQk5ODGTNm6LxvUjZUjCCkjBiGwdatWxEUFAQbGxvcvXsXffv2ZTsWa7y9vREREYHJkydj2rRp6NOnD1WiCSFaJ5PJ8OjRo2JHgdaoUQNmZmY0EtRAREZGom3btnBzc8PFixfh5ubGdqQK4XA4+PHHHzFr1ix88803WLp0KduRCNGa8PBwdO7cuUKjvp2dndGxY0eEhYVpMJlxKGkPJfV9iYmJNPNEx7Kzs7F+/XqMHDmy0M3FNWHQoEGoXr06nWeIRqiXY3q3GOHq6krFCFKiiRMnwtzcHKtWrWKlfw8PD6xcuRI7duzAhQsXWMlASoeKEYSUQXZ2Nr744guMHTsWw4cPx9WrVw1mHWttMjExwerVq3H06FFcvHgRDRs2xO3bt9mORQipxB4/fgyFQlHshRcejwc/Pz8aCWoAbt26hfbt26NatWo4f/48nJ2d2Y6kERwOB0uXLsUPP/yAOXPmYP78+TSDkFQ6r1+/xpUrVxASElLhtkJCQnD58mXExcVpIJnxEAqF8PHxKXZjZHXxngr0urV161ZkZ2dj+vTpWuuDz+dj1qxZOHjwIB4+fKi1fohxUBcdaGYEKYtDhw7h0KFD2LhxI+zt7VnLMXLkSLRq1QqjRo1Cbm4uazlI8agYQUgpRUVFoXHjxjh27BjCwsLw008/wczMjO1YeqVXr164d+8enJyc0KJFC2zevJkuuhBCtEJdYCiuGKG+ny686LerV6+iY8eOqFu3Ls6ePQsHBwe2I2kUh8PB/PnzsWzZMixcuBCzZ8+mcyOpVMLDw2FmZobPP/+8wm0FBwfD1NQUe/fu1UAy46HeQ6k4NWvWhKmpKZ0TdSg3Nxc//vgjhg4dCm9vb632NWTIEHh6emLZsmVa7YdUfkUVI2gDa1KUjIwMTJgwAb169arQco2awOVysX37drx+/RqLFi1iNQspGhUjCCmFX3/9FU2bNoWpqSlu375doc35KruqVavi8uXLGD16NCZMmICBAwciKyuL7ViEkEpGKBTC09OzxJE3AQEBEAqFUKlUOkpGyiIiIgKdO3dGYGAgTp8+DVtbW7Yjac2sWbOwZs0arFixAlOnTqWCBKk0wsLC0LNnT1hbW1e4LVtbW3Tv3p2WaiqjkvZQAvJnMvv6+tJsQR36+eefkZycjJkzZ2q9L4FAgOnTpyMsLAzPnj3Ten+k8hKJRDA3N4eVlVXBbS4uLkhJSYFSqWQxGdFXM2fOhFgsxubNm8HhcNiOgzp16uD777/HypUrcf/+fbbjkEJQMYKQYkgkEgwbNgzDhw/HF198gRs3bqBOnTpsx9J7AoEAGzZswP79+3H69Gk0bNgQkZGRbMcihFQiQqGwxFGgQP7MCIlEglevXukgFSmLv//+G926dUNQUBBOnjypkQuZ+m7q1KnYvHkz1q9fj3HjxlGRjBi8R48eITIyUiNLNKmFhITgzp07ePLkicbarMwyMzPx+vXrUp8TaWaEbshkMqxYsQIhISGoWbOmTvocMWIEHB0dsWLFCp30RyqnpKQkuLi4vHdR2cXFBSqVCmlpaSwmI/ooIiICoaGhWLFiBby8vNiOU2D69Onw8/PDiBEjoFAo2I5DPkDFCEKKEBMTg6ZNm2L//v347bffsH37dpibm7Mdy6D069cPd+7cgZWVFZo1a4bt27fTSFBCiEZERUWVOAoU+G8ZJ7r4ol9OnDiBnj17ol27djh27BgsLCzYjqQz48aNw44dO7Bt2zaMGDGCRhkSgxYWFgYbGxt069ZNY2326NEDVlZWCA8P11iblVl0dDSAkpctVD9GKBTS53Ed+P333xEXF4fZs2frrE8LCwtMmzYNO3fupH1XSLmJRKL3lmgCULD5Ou0bQd6Vl5eHkSNHokWLFhg9ejTbcd5jamqK7du3486dO9iwYQPbccgHqBhBSCH++OMPNG7cGCqVCrdu3cKQIUPYjmSwatasiWvXruGrr77CqFGj8L///Q9isZjtWIQQAyYWi/HixYtSXXjx9PSEnZ0dLUuhR44cOYLg4GB07doVhw4dMsr9l77++mvs2rULv/32G4YOHUojtohBYhgGYWFh6N27t0Z/j83NzREcHIywsDC6aF4KQqEQPB4PdevWLfGxAQEByMrKwps3b3SQzHgpFAosW7YMffr0Qb169XTa99ixY2FlZYVVq1bptF9SeRRWjFD/m/aNIO9atGgRXr16hdDQUHC5+nd5uWnTppg0aRLmzZuHFy9esB2HvEP/3i2EsCg3N7fggnmfPn1w69Yt+Pn5sR3L4JmZmWHLli3Ys2cPjhw5gsaNG9MoZUJIualHgZZmSQoOh0PLUuiRvXv3ol+/fggODsb+/fshEAjYjsSaL7/8EmFhYQgPD0dISAjkcjnbkQgpk3v37uHJkycaXaJJLSQkBDExMXjw4IHG265soqKiULt27VIdT2m2oG7s27cPz549w3fffafzvm1sbDBp0iRs376dLhyTcimuGEEzI4jagwcPsHLlSsyZM0evr5ktXrwYTk5OGDNmDA1w0CNUjCDkX48fP0ZQUBB+//137NixA7/99hssLS3ZjlWphISE4Pbt2+Dz+WjSpAl27tzJdiRCiAESCoXgcrnw9fUt1eMDAgJoZoQe+P333zF48GCEhIRgz549MDExYTsS6wYMGIADBw7g6NGj6NevH6RSKduRCCm1sLAwODs7o0OHDhpvu1OnTnBwcKCNrEuhNJtXq/n4+MDa2prOiVqkUqmwZMkSdOvWDQ0bNmQlw6RJk8Dj8bB27VpW+ieGTSQSFSzLpGZtbQ2BQEDFCAIAUCqVGDFiBOrUqYNZs2axHadYVlZW2Lp1K86cOYM//viD7TjkX1SMIAT5o1caNWqE3Nxc3LhxA19//fV7GzYRzalbty5u3LiBkJAQfPXVV/jqq6+Qk5PDdixCiAGJiopCzZo1S72Pj7+/Px49egSZTKblZKQoP//8M4YOHYqvvvoKO3fuBJ/PZzuS3ggODsaRI0dw+vRpBAcHIzc3l+1IhJRIpVIhPDwc/fv318rvs4mJCfr164fw8HAayVgMhmEQFRVVqpmCwH+zBakYoT1HjhzBw4cPMWfOHNYyODg4YNy4cdi8eTNtOEzKTL2B9bs4HA5cXFyoGEEAABs3bsTt27exfft2mJqash2nRN26dcMXX3yBKVOm0HtYT1Axghg1qVSKCRMmYODAgejevTtu376NTz75hO1YlZ6FhQV+/vln7Ny5E3v37kXTpk3x6NEjtmMRQgxEWUaBAvnFCIVCgSdPnmgxFSnKTz/9hBEjRmDMmDEIDQ0Fj8djO5Le6d69O44fP46IiAj06NEDEomE7UiEFOvKlSuIi4vTyhJNaiEhIXj16hWuX7+utT4MXVJSElJTU8t8TlQvd0g0i2EYLFmyBG3atEGLFi1YzTJt2jQoFAps3LiR1RzEsEgkEuTk5MDZ2fmj+1xcXGjpL4KXL19izpw5GD9+PIKCgtiOU2pr164Fh8PB1KlT2Y5CQMUIYsSeP3+OFi1aYPv27fjpp58QFhYGa2trtmMZlaFDh+LmzZtQKBRo1KgR9uzZw3YkQogBKMsoUOC/NbJpJKjurV27FuPHj8eUKVOwefNmvdzcTl907NgRJ0+exI0bN9CtWzdkZ2ezHYmQIoWFhcHb2xvNmzfXWh+tWrWCh4cHLdVUDPV5rSznxICAAMTExEChUGgrltE6deoU7t69i7lz57IdBa6urhg1ahTWr19P5xNSaupR4x8u06S+jUaVGzeGYTBmzBg4ODhg6dKlbMcpE2dnZ6xduxZ79uzBiRMn2I5j9OgbITFKhw8fRoMGDZCeno5r165h7NixtCwTS/z9/XHr1i307t0bX3zxBUaPHo28vDy2YxFC9JRIJIJIJCrTKFAHBwd4eHjQhp06tmzZMkybNg2zZs3CmjVr6DxbCm3atMGZM2dw//59dO7cGRkZGWxHIuQjcrkc+/fvx6BBg7RaYOTxeBg4cCD27dtHF86LIBQKYW5ujmrVqpX6Of7+/pBKpXj69KkWkxkfhmGwePFiNG3aVCv7qJTH9OnTIRaLsWXLFrajEAOhLjZ8uEyT+jYqRhi33bt34/Tp09iyZYtBDuT98ssv0blzZ4wdOxZisZjtOEaNihHEqMhkMkybNg19+vRBhw4dcPfuXTRo0IDtWEbPysoKu3btwvbt2/Hbb78hKCiIviARQgqlXlqiLMUIgDax1iWGYfDDDz/gu+++ww8//IClS5dSIaIMmjdvjnPnziE2NhYdOnRAamoq25EIec/58+eRkpKCQYMGab2vkJAQJCUl4eLFi1rvyxAJhUL4+fmVafk7mi2oHREREbh69Srmzp2rN+c8Ly8vDBs2DD/++CPtR0RKRb0MU2EzI6gYYdySk5MxZcoUDBo0CD169GA7TrlwOBxs3boVKSkpejGDzZhRMYIYjdevX6N169bYtGkT1q1bhwMHDsDW1pbtWORfHA4HI0aMwPXr1yEWi9GgQQMcOHCA7ViEED0TFRUFgUCAmjVrlul5/v7+NDNCBxiGwXfffYcFCxZg2bJlmD9/vt5clDEkjRo1wvnz5/H69Wu0b9+evvwTvRIWFobatWujfv36Wu+rUaNGqFGjBi3VVISyLlsI5C9V4erqSudEDVuyZAk+/fRTfPbZZ2xHec/MmTORkpKCHTt2sB2FGAD15w0nJ6eP7qNihHGbNm0aGIbB+vXr2Y5SIdWqVcPixYuxYcMG3Lhxg+04RouKEcQo/PXXX6hfvz4SExPxzz//YPLkyXRxRE8FBgbizp076NatG/r374+JEydCKpWyHYsQoieEQiF8fX3B5/PL9LyAgAA8f/6cpuRqEcMwmDZtGpYvX441a9Zg1qxZbEcyaIGBgbh48SKSkpLQtm1bJCQksB2JEOTl5eHw4cMICQnRyWdpDoeDkJAQHDx4kD4PfkClUiE6OrrMMwUBmi2oaTdu3MDZs2cxZ84cvfuOWaNGDQwePBgrV66ETCZjOw7RcyKRCI6OjoV+znZ1dYVYLEZOTg4LyQibTp06hT/++ANr1qwpdAkvQzNp0iQ0atQII0aMoOMiS6gYQSo1hUKBWbNmoUePHmjRogXu3r2LJk2asB2LlMDGxgbh4eHYvHkzQkND0bJlS7x48YLtWIQQPVCeUaDAf8tSPHz4UNORCPIvio0fPx7r1q3D5s2bMXXqVLYjVQr16tVDREQEMjMz0aZNG8TFxbEdiRi5EydOICsrCyEhITrrc9CgQcjMzMTp06d11qchePHiBXJycsp9TqSZEZqzZMkS1KlTB3369GE7SqFmz56NuLg47Nq1i+0oRM+JRKIiLzarb6fZEcZFLBZjzJgx6NixI4YMGcJ2HI3g8XjYvn07YmJisHLlSrbjGCUqRpBKKz4+Hu3atcPq1auxatUqHD16FA4ODmzHIqXE4XAwbtw4XL16FampqWjQoAGOHj3KdixCCIsYhoFQKCzXKFBfX19wOBy6+KIFSqUSI0eOxNatW7Fjxw6MGzeO7UiVSp06dXDp0iVIpVK0bt0aL1++ZDsSMWJhYWGoX78+6tSpo7M+69Wrh4CAAFqq6QPq81l5zon+/v54+vQp7SOgAZGRkTh27Bi+++67Mu3doUt+fn7o27cvli1bRpvBk2KJRKJC94sAqBhhrObNmweRSIStW7fq3cyvivj0008xY8YMLFq0CI8ePWI7jtGhYgSplM6cOYPAwEC8ePECERER+PbbbyvVgdOYNGzYEHfv3kXbtm0RHByMb775BnK5nO1YhBAWvHr1CmKxuFwXXiwsLFCzZk1alkLDFAoFhg0bhp07d2LXrl34+uuv2Y5UKdWoUQOXLl0Ch8NB69at8fTpU7YjESOUlZWF48eP62Tj6g+FhITgzz//hEQi0Xnf+ioqKgr29vZwd3cv83MDAgLAMAzNFtSApUuXomrVqjqdLVQec+bMwfPnzxEeHs52FKLHkpKSaGYEKXDz5k1s2LABCxcuRI0aNdiOo3Hz5s1DlSpVMGrUKKhUKrbjGBUqRpBKRalUYt68eejatSsaNmyIe/fuoUWLFmzHIhVkZ2eHQ4cOYe3atdiwYQPatGmDN2/esB2LEKJj6lGg5VmSAqBlKTRNLpdj8ODBCAsLQ1hYGL788ku2I1VqVapUwaVLl2Bubo7WrVvTKC6ic3/++Sfy8vJYKUYMGjQIOTk5+PPPP3Xet74SCoUICAgo14ArPz+/gjZI+T169AgHDhzArFmzYGJiwnacYtWvXx/du3fH0qVL6aIbKVJxyzQ5OzsXPIZUfnK5HCNGjEBgYCCmTJnCdhytMDc3x/bt23H58mVs376d7ThGhYoRpNJITExEp06dsHTpUixevBgnTpwoOGESw8fhcDBlyhRcvnwZ8fHxCAwMxIkTJ9iORQjRIaFQCFtbW3h5eZXr+bRhp+ZIpVL0798fR44cwYEDBzBgwAC2IxkFT09PREREwN7eHm3atKELiUSnwsLC0KJFC/j4+Oi872rVqqFZs2a0VNM7yrtsIQBYWVmhevXqdE6soGXLlsHd3R3Dhg1jO0qpzJ07FzExMTh8+DDbUYieKq4YYWJiAgcHBypGGIlVq1bh4cOH2LFjR6EbmlcWbdq0wciRIzFjxgzEx8ezHcdoUDGCVAoXLlxAYGAgYmJicO7cOXz33XfgcuntXRk1a9YMd+/eRVBQED777DPMnj2b1j4lxEhERUXB39+/3Mvu+fv7IykpCcnJyRpOZlxyc3PRu3dvnDp1CkeOHEFwcDDbkYyKm5sbLl68CHd3d7Rt2xb37t1jOxIxAqmpqThz5gyrS9GEhITg1KlTSEtLYy2DvpDJZIiNjS33TEGAZgtW1IsXL7B7925Mnz4dAoGA7TilEhQUhHbt2mHJkiVgGIbtOETPKJVKpKSkFFmMAPKXaqJiROUXGxuLhQsX4ptvvkH9+vXZjqN1K1euhIWFBcaPH0/HRh2hq7XEoKlUKixatAgdO3ZEvXr1EBkZibZt27Idi2iZo6Mj/vzzT6xYsQKrVq1Chw4d8PbtW7ZjEUK0rCKjQIH/lneKjo7WVCSjI5FI0KtXL1y8eBHHjx9H9+7d2Y5klJydnXH+/HlUq1YN7du3x82bN9mORCq5AwcOgGEY9O/fn7UMAwYMgFKpxKFDh1jLoC9iY2OhUCgqfE6kYkT5rVixAg4ODhg5ciTbUcpk7ty5uHfvHk6ePMl2FKJn0tLSoFKpitzAGsgvRiQlJekwFdE1lUqFUaNGwcvLC/Pnz2c7jk7Y2dlh06ZNOHr0KH3G0BEqRhCDlZycjG7dumH+/PmYN28ezpw5U+yJk1QuXC4XM2bMwMWLF/H06VMEBgbi77//ZjsWIURL5HI5YmJiKnThpWbNmjA1NaVlKcopOzsb3bt3x/Xr13Hq1Cl07NiR7UhGzcHBAWfPnoWvry86duyIK1eusB2JVGJhYWHo0KFDsSNmtc3NzQ3t2rWjDXiBgvNYvXr1yt2Gv78/4uPjkZ6erqlYRiM+Ph6//vorpk6dCktLS7bjlEm7du0QFBSExYsX0whg8h51kYFmRhi3HTt24NKlSwgNDYWFhQXbcXSmT58+CA4OxoQJE+i8qANUjCAG6fLlywgMDMS9e/dw+vRp/PDDD+DxeGzHIixo2bIlIiMjUb9+fXTp0gXz58+HUqlkOxYhRMOePHkCuVxeoSUp+Hw+fH19aSRoOWRkZKBz586IjIzEmTNn0Lp1a7YjEQC2trY4ffo0GjRogC5duuDixYtsRyKVUHx8PC5dusTKxtUfGjRoEC5cuIDExES2o7BKKBTCy8sL9vb25W5DXdync2LZrV69umBJD0PD4XAwZ84cXLt2jc4Z5D3qIkNxxQhXV1cqRlRib9++xYwZMzB8+HC0b9+e7Tg6xeFwsGnTJuTk5GDmzJlsx6n0qBhBDIpKpcKKFSvQrl071KxZE5GRkejUqRPbsQjLnJ2dcfLkSSxcuBCLFy9Gly5daPooIZWMehRoRWZGALSJdXmkpaWhY8eOiI2Nxblz5xAUFMR2JPIOa2trnDhxAkFBQejWrRvOnDnDdiRSyezbtw8mJibo3bs321HQt29f8Hg87Nu3j+0orFLvoVQRtWvXhomJCZ0Tyyg5ORnbtm3DxIkTYWNjw3accunevTsCAwOxZMkStqMQPVKaYgTNjKjcJk6cCDMzM6xatYrtKKzw9PTEypUrsX37dirWahkVI4jBSE1NRa9evTBr1izMmDED586dg4eHB9uxiJ7gcrmYO3cuzp49C6FQiMDAQDqBEFKJCIVCuLu7w9HRsULtqDfspKUJSic5ORnt27fHq1evcP78eTRq1IjtSKQQFhYWOHbsGNq3b4+ePXvi+PHjbEcilUhYWBi6d+8OOzs7tqPA3t4eXbt2RVhYGNtRWCUUCis0UxAATE1NUadOHZoZUUZr164Fl8vF5MmT2Y5SburZEefOncP169fZjkP0hEgkgkAggLW1dZGPcXFxQXJyMlQqlQ6TEV04dOgQDh06hA0bNsDBwYHtOKwZOXIkWrVqhVGjRiE3N5ftOJUWFSOIQbh+/Trq16+Pa9eu4a+//sLSpUvB5/PZjkX0ULt27RAZGQlfX1906NABS5YsoQ9LhFQCmhgFCuTPjMjOzsbr1681kKpyS0xMRNu2bZGYmIiLFy8iMDCQ7UikGGZmZjh06BC6d++OPn360AZ8RCOePn2KW7duISQkhO0oBUJCQnD9+nW8ePGC7SisyM7OxsuXLzV2TqSZEaWXnp6OTZs2YezYsRUeHMG2Pn36wNfXl2ZHkAIikQiurq7gcDhFPsbFxQVKpRJpaWk6TEa0LSMjAxMmTEDPnj3Rv39/tuOwisvlIjQ0FK9evcKiRYvYjlNpUTGC6DWGYbB27Vq0atUKXl5eiIyMRPfu3dmORfScm5sb/v77b8yZMwfz5s1D9+7dkZKSwnYsQkgFCIVCjVx4oTWySycuLg5t2rRBZmYmLl26VKFNUonuCAQC7Nu3D71798aAAQNoo19SYeHh4bCyskKPHj3YjlKgV69esLCwMNr3d3R0NICKL1uoboNmC5bepk2bIJPJ8M0337AdpcK4XC6+++47HD9+HJGRkWzHIXogKSmp2CWagPw9IwDQUk2VzMyZMyEWi/HTTz8VW4wyFnXr1sW8efOwcuVK3L9/n+04lRIVI4jeysjIQN++fTFt2jRMnjwZERER8Pb2ZjsWMRA8Hg8LFy7EqVOncOfOHQQGBuLKlStsxyKElINEIsHz588rvCQFAHh7e8PGxoaKEcV49eoV2rRpg7y8PERERKB27dpsRyJlYGJigt27d2Pw4MH44osv8Ntvv7EdiRgohmEQFhaGzz//HBYWFmzHKWBpaYlevXoZbTFCKBSCy+XC19e3wm0FBAQgIyMDb9++1UCyyk0sFmPdunUYOXIk3Nzc2I6jEYMGDUL16tWxdOlStqMQPSASiUosRqjvp2JE5REREYHQ0FAsX74cXl5ebMfRGzNmzICvry9GjBgBpVLJdpxKh4oRRC/dvn0bDRo0wIULF3DkyBGsXr0aJiYmbMciBqhz586IjIxEtWrV0KZNG6xatYqWbSLEwDx8+BAMw2hkFCiHw4G/vz8tS1GEZ8+eoXXr1gCAS5cuoUaNGiwnIuXB5/Px66+/4quvvsJXX32F7du3sx2JGKCoqCg8fPgQgwYNYjvKR0JCQvDgwQM8fPiQ7Sg6FxUVhZo1a8Lc3LzCbanPq3ROLNnWrVuRlZWF6dOnsx1FY/h8PmbNmoUDBw4gJiaG7TiEZVSMMD55eXkYNWoUmjdvjjFjxrAdR6+Ymppix44duHPnDjZs2MB2nEqHihFErzAMg82bN6NFixZwcnLC3bt38fnnn7Mdixg4T09PXLhwAd9++y1mzJiB4OBgWueSEAMiFArB4XDg5+enkfbUy1KQ98XGxqJ169YwMzPDpUuXUKVKFbYjkQrg8XgIDQ3F2LFjMWrUKGzatIntSMTAhIeHw97eHp07d2Y7yke6dOkCOzs7o9zIWhObV6tVqVIFlpaWdE4sQW5uLlavXo2hQ4fCx8eH7TgaNWTIEHh4eGDZsmVsRyEsK00xwsbGBqamplSMqCQWL16MFy9eYPv27eBy6fLwh5o2bYqJEydi7ty5RrtPlbbQu43ojaysLAwaNAgTJkzA6NGjcfnyZVSrVo3tWKSS4PP5WL58OY4fP45//vkHDRo0wI0bN9iORQgphaioKFSvXh2WlpYaaS8gIAAxMTGQy+Uaaa8yEAqFaNOmDezt7REREQFPT0+2IxEN4HK52LRpE6ZOnYqJEyfixx9/ZDsSMRAMwyA8PBz9+vWDqakp23E+IhAI0KdPH4SFhRndfgdRUVEamSkI5B8jaLZgyX755RckJydj1qxZbEfROIFAgBkzZmDPnj14/vw523EIi5KSkgr2hCgKh8OBi4sLkpKSdJSKaMuDBw+wYsUKzJkzR2MDviqjxYsXw8nJCWPGjDG6zxvaRMUIohfu37+PRo0a4eTJk9i3bx82bNgAgUDAdixSCX322WeIjIyEu7s7WrVqhfXr19NJhRA9p8lRoED+zAiZTIanT59qrE1DFhkZibZt28LNzQ0XLlyoNGthk3wcDgc//vgjZs+ejW+//RZLlixhOxIxADdu3MCLFy8QEhLCdpQihYSE4NmzZ7h9+zbbUXRGJBIhOTlZ4+dEmhlRNJlMhhUrVmDQoEGoWbMm23G0YsSIEXBwcMCKFSvYjkJYIpFIIJFISpwZAeRvYk0zIwybUqnEiBEjULt27UpZZNUka2trbN26FWfOnMEff/zBdpxKg4oRhFUMw2D79u1o2rQpLC0tcffuXfTv35/tWKSS8/HxQUREBCZMmIApU6agX79+yMjIYDsWIaQImhwFCvy3RjZdfAFu3bqF9u3bo1q1ajh//jycnZ3ZjkS0gMPhYMmSJViwYAHmzp2L77//ngrxpFhhYWFwd3cv2ENGH7Vr1w6urq5GtVSTegaDJs+JAQEBePjwIW3QWYQ//vgDb968wezZs9mOojUWFhb45ptvsHPnTsTFxbEdh7AgOTkZAEpVjHBxcaFihIHbuHEjbt++jR07dtAg4FLo1q0bBg8ejClTptB7X0OoGEFYIxaLMWTIEIwaNQrDhg3DtWvXKu1oE6J/TE1NsWbNGhw+fBjnzp1Dw4YNcffuXbZjEUI+kJKSgsTERI1eeHFycoKbm5vRL0tx9epVdOzYEXXr1sXZs2fh4ODAdiSiRRwOB99//z2WL1+ORYsWYdasWVSQIIVSKpXYt28fBg4cCB6Px3acIvF4PAwYMAB79+6FSqViO45OCIVCCAQC1KhRQ2Nt+vv7Iy8vD8+ePdNYm5WFQqHAsmXL0Lt3b41+DtFHY8eOhaWlJVavXs12FMIC9QVWKkZUfi9fvsScOXMwfvx4BAUFsR3HYKxbtw4cDgdTp05lO0qlQMUIworo6Gg0btwYhw8fxu7du7F161aYmZmxHYsYoeDgYNy9exf29vYICgrCli1b6OIMIXpEPXtBk0tSALQsRUREBDp37oz69evj9OnTsLW1ZTsS0ZGZM2di7dq1WLlyJaZMmULnPPKRixcvIjExEYMGDWI7SolCQkLw9u1bXL58me0oOhEVFQU/Pz/w+XyNtUmzBYu2f/9+PH36FHPmzGE7itbZ2Nhg0qRJCA0NpQvNRkj9f17SnhEAFSMMGcMwGDNmDBwcHLB06VK24xgUZ2dnrFmzBnv27MHJkyfZjmPwqBhBdO63335D48aNwefzcfv2bQwePJjtSMTIVa9eHVeuXMHIkSMxbtw4hISEIDs7m+1YhBDkXxwxMTFBrVq1NNpuQECA0c6MOHv2LLp164bmzZvjxIkTsLa2ZjsS0bEpU6bgp59+woYNGzB27FijGVVOSic8PBzVq1dHkyZN2I5SombNmqFKlSpGs1STUCjU+Ah9V1dXODs7G+05sSgqlQpLlixB165d0bBhQ7bj6MSkSZPA4/Gwdu1atqMQHVNvSO3k5FTiY11dXWkDawO1Z88enD59Glu2bKHP/+Xwv//9D506dcKYMWMgFovZjmPQqBhBdCYnJwfDhw/HsGHDMGjQINy4cQN169ZlOxYhAACBQIBNmzZh7969OHHiBBo1aoQHDx6wHYsQoycUCuHr6wsTExONtuvv749nz54hJydHo+3quxMnTqBHjx5o164d/vzzT1hYWLAdibBk7Nix+PnnnxEaGoqvv/6a1osnAPI36z148CAGDRoEDofDdpwScTgcDBo0CAcOHIBcLmc7jlapVCpER0drfKYgQLMFC/Pnn38iOjoac+fOZTuKzjg4OGDcuHHYvHkz0tPT2Y5DdEgkEsHBwaFUn7ddXFyQnZ2N3NxcHSQjmpKcnIzJkydj4MCB6NGjB9txDBKHw8G2bduQkpJiVOcGbaBiBNGJ2NhYNG3aFOHh4di5cyd++eUXugBC9NKAAQNw584dmJubo2nTpvj5559pCQtCWKTpzavVAgICwDAMHj58qPG29dWRI0cQHByMbt264dChQ7Q8IsHw4cOxa9cu7Nq1C0OGDIFCoWA7EmHZ6dOnkZ6ejpCQELajlFpISAhSU1Px999/sx1Fq169egWxWKy1cyLNjPgPwzBYvHgx2rRpgxYtWrAdR6emTZsGuVyOjRs3sh2F6JBIJCrVfhHAf/tKqDe9JoZh2rRpUKlUWL9+PdtRDFq1atWwaNEibNiwATdu3GA7jsGiYgTRuj179qBhw4ZQKBS4efMmhg4dynYkQopVq1YtXLt2DUOGDMGIESMwdOhQSCQStmMRYnQYhtHKkhQA4OfnBwCIjIzUeNv6aO/evejXrx969+6Nffv2QSAQsB2J6Ikvv/wS4eHh2LdvHwYNGgSZTMZ2JMKisLAw+Pv7G9RmvZ988gl8fX0r/VJN6pkL2vi/8ff3x+PHj5GXl6fxtg3RmTNncOfOHaMc+erq6opRo0Zh/fr1tGytESlPMSIxMVGbkYgGnT59Gn/88QfWrFlTqn1BSPEmTZqEhg0bYsSIEfS5uZyoGEG0Ji8vD2PGjMEXX3yB4OBg3Lp1y6C+2BDjZm5ujm3btuH333/HwYMH0aRJE6MaQU2IPtizZw+ysrK08mVYPStg8uTJGm9b3/z+++8YPHgwBg8ejN27d2t8ySti+Pr3748DBw7gzz//RL9+/SCVStmORFhw48YNhIWFISgoiO0oZcLhcFCvXj388ccfuH37NttxtEZ9vrK3t9d425mZmWAYBitXrtR424YmKysLPXr0QJ06ddChQwe247Bi+vTpyMjIQKtWrdiOQnREJBKV+iL1y5cvAYD2FjEQ9+/fx+DBg9G+fXsaGKwhfD4fO3bswMOHD9G7d29aSaMcqBhBtGL37t1o3Lgxdu7cidDQUPz++++wsrJiOxYhZfbll1/i9u3b4HA4aNy4MaZPn07rYxKiI97e3hAIBOjUqZPG2+bxeGjdujWCg4M13rY+adiwIYYMGYLhw4fj119/BZ/PZzsS0VOff/45jh49itOnT8Pa2hpPnjxhOxLRsadPnwKAQWxc/aHu3buDw+FU6n2AGjZsCC8vL60sddunTx84OzvTxWcAT548gUKhQO3atQ1i3xRt8PLyAgAaiGUkVCoVbt++XerBP+3bt0fVqlUxZMgQLScjmjB69GikpaXh22+/NdpjmjZ8+umncHFxwYkTJxAdHc12HIPDYaiEQzQsOjoa/v7+4PP5uHXrFgIDA9mOREiFSSQS9O/fHydPnkSfPn1w8OBBtiMRQkix0tPT4eDgAHNzc4jFYnC5NAaFlGz27NlYvnw5pkyZQqMejVBaWhocHBzYjlEuDMPQhRaiEZGRkfj000+N+v0kFovx9u1b1K5dm+0oRMtycnJgaWmJKlWqFMx6IJXH2bNnce/ePSpGaEF8fDxWrFiBH3/8kWaelxEVI4jGSSQSjBw5EuPHjze6Db9I5Zabm4uJEyciODgYPXr0YDsOIYSU6O7du6hXrx7tEUHK5OXLl/D29gaPx2M7CiGEEEK0bO/evWjevDm8vb3ZjkIIMQJUjCCEEEIIIYQQQgghhBBCiFbRwsFGbsOGDdi/N1wrbVtaWeHHNWtRr149rbRPCBtWrVqFP48c1mib9Rs2woYNGzTaJiH6buPGjbh69Wq5n8/j8fD111+jXbt2H933ww8/4Mzf5yoSr1h1atfEL7/8orWpzlFRUfjxxx81uomwi4sLli1bppW1xonhYBgG06ZNw/Pnz9mOAiB/XfL169fTXiZa8vvvv+PEiRMaacva2hrz5s3T6ahZlUqFr4YPx5Mnz3TWp5mZAAsX/ICWLVtqva87d+5gw4YNkMlkGmvT1NQUkydPRoMGDd67/cyZM1i2bDlkcrnG+vokwB9btmzRWHva8tdff2H16lVQyBXlbqNR48YGt2xddHQ0pk77BmKxhO0oRbK0tMDqVSvx6aefsh2F/Ov06dNYtnwFZDLNHSvMzARYtHABrZpRTrm5uRg2dCjexsdrrE03d3f8unMn7etaSvv27cOmTZuhUqm01kdIyCCMHz9ea+3rG5oZYeRqVKuCvPQkNK9mq/G2D0SKsGzZMsyaNUvjbRPCFndXF5gqJGhaXTPrKT98m4Xot1nIzMyEjY2NRtokRN8xDAMTExM4WvBQ26V8H4JvvExHl67dcez48Y/uM7ewBGzcYF2jQSHPrJjcxGcQP7+Hly9fokqVKhpvHwCmTZuGtWvXomUNzRxnMnLlEL7Nxvnz5wst3hDj8fz5c9SoUQP+9eqhahUfVrPExccj8v6DgrXZieb5eHlCnJ6Meh7WFWqHYYArz9KwYcMGTJw4UUPpSqZ+v1rXaABz1+o66VN07SAmjB+PjRs3ar2vMWPGYNu2bWhZw15jbf7zLB1jxoz5qEgwZMgQ7AnfhzptgjXST8rLGIiePkBKSgocHR010qa29O/fHyf+Oo7enduU6/kPHj1FVOwzSCQSgyror1ixArNmzYJ7y/5sRylSwj/7sWDBAnz//fdsRzEar1+/RmpqapH3z5kzB6fPnodLk14a6zPx6gFMmjgR69ev11ibxuTGjRto1qwZWvl6wsOh4sUDUWYOLgjfICIiAq1bt9ZAwsqvY8dOuH7rDhq07aqV9qNvXIK9tQWePnmslfb1EQ1DMnIqpQp9P3XGrE5VAQB5chXMTArf4LK4+wpz8Xm2ViuHhLBBpVLhi2Y+mNyxVpmelydXwszk47W3j91/i5G/3aHfFWJ0lEolZnSojpCGbuV6/siwh5AXMcKTUang3u5/8Ow8quA2lTwPXBOzQv/94X3FyYj5B9GrBmr1d1Yul6Oepy32faWZmYVxGXlosvpWkT8vYjwUivyRwZs3rEOrluyMUFQqleDxeLgXGYmGTZsXZCKaJ5fLMKK5J6a2r1rhtmovvKrzY4j6OFu133ewrRNU+GNKefwu7eOkbx/p7DOZXC5Hwyp22DvUT2Nt9twRXej/k0qlgqdvI3Sdvvmj+xSyPPBNzT76e3GeXTuJoz/8zyA+v6pUKrRs9Cl2rPiu2MflSaUwK2R/pf0nzmPItAUG8VrfpVKpYG7rhIAxRc++VsrywDM1K/hTVxiVEhwuD1kx/xjcz9WQqS9ql8SuTjPUG1144eDD90pp3jt58Q/p/7kC1D+7ZV+2Ql3Pogcq5ckUMDMt+RLv86RMNJm5m/5PykDFqPBpi44YuyS01M+RSfNgKijdcTVs7fcQXvyzvPEMEhUjCOIzpDgalYwe9Zzw4G02XqdLYWHChZOVCcz4PEhkSvC5HFgJeJApVciRqZCYJUWL6nZIFsvgam2KmKQcmPG5sDHjITNPAT639EULQgzN67QcHLkXj56feuD2yzQAgLudOR4nZkOqUMHGjA83WzO8SJFAwM//vfGyM0d6jgw+jhawMOUjTSKDXKnCg7hMll8NIey5H58NUx4X/u6WeJycAysBDz72ZnidnoeaThaIjM+GjRkfvq6WBeebqAQxPGwEEEuVMC+mbVl6IrIe3wTf0g7S9ATIM5PgENgZuYnPoVLKwTURgG9hC4U4HQyjAk9gAWnaW4BRwcy5CkxsncHhmSInLgZKqQQmVg5glHJkP7+ns5/PMWEyZAoG/u6WSJXIIVMykCpU8LQTIDtPgbQcBRws+HCzESArT4EcmRLedmZIyJbB3cYUCiWDpGwZeHRKJh94+fIVVCoVuFwu6tapjfMXIyAWi2FjYwMTPh/VqlWFs5MTrt+8CbFYglo1a4LP58HN1Q0ODva4cvUaMjIz4ejgADMzM8TFxyMnJwd+vnVhbm6ON2/i3lt6yczMDHZ2tsjMzIJ6Uvar16/ZevlGJTZJgoP3EuHvYY3X6bmo6mAOHpeDWJEEdmYmiM/MQ0MfGzxKksCMz4WHrRmcrU1hyuPg3ptsSGRKmPA4YHMyfV7Km/y/cLiwcKsJWWYSFDmZ/97EB6NSgMPjw7p6QyTfPAqBgwcUkgyAYWDhWQcquRSMSgEuzxRySTrAqGDp44+8pBcwsXWGLEMEE2tHSFPfQCnL1elrS5fIcPC+CP7uVvnHeYUKMqUKHrYCZOcpkZYjh4OFCdxsTP89zquQladALRcLJGXJUM3RDNYCPqISxBBLlcX2JU4T4eHZfXCq5gswDHIyUqBSKmDt5AHOv5vVq5QKmJhZQJGXizxJJuR5OXCt+Qly0pNh6eiKpCcPYO3siezkt7r48WhMWmYW9p84jz5d2uB6ZDQAwMvVGUmp6XgVnwjfGlUgkyvg4eoEUWo63Jwd8fRVHOysrfA2KYXl9BUjjo+FLCsVHA4Xlh41Ic0QQS7JAABweHwwSgX4ZlZgVErkJL2Ee/PeSLx+FAI7VzCMCiaWdgCHC7k4DebOPv9+XkqAUi5FXsobmNq5AACkaYngW9hAmScBT2ABnrklOFweTK3sIc1MhlycAQ6PD6iUMLVzBaNSQCnX3BJlpGRxcXEAgLAhvrA35+Ps43SIpUo08raGKY8DFQNs+icej3KzkHT9KJwbf4aES3th5ugBWWYywOXC3NkHAnt35KXGgcPjg2siAKNUQqWQQWDnCml6Aixcq4EnsIA47hEUOVksv+rKI/KFCDFxqQhuUhMn772Ej5M1pHIlEjMkYBgGtdzt4elojd2XHqKGmx0CfJyRnJUDVzsL3HsugpejNWRKJd6kZLP9UgxSZloyrp06iKadgvH4/g0AgKOrJ96+eAwOlwsHV08kx7+Ee9VakEulUCoV4JuYQpyRCkbFoIrvJ0h89Qx2Tq7ISEmEtb0TUuJfg2diguyMomcrVVZUjCDwtBPg8wBnJItlaFLFFk3+XXUiT67CgcgkSBUM2tayR55MCVszPvhcJXLlfNyLy4aDhQmcrEzRysqU3RdBiA75OFgguL4nkrOlaFo9f2p6qliKNIkMWblyVKvpCBXDoI6bDcR5ckhkSgjfZsLd1hxe9vnTux0s839nPvESs/Y6CGHbp57W6BvogsxcBao5muNPYTIexIvRw98JGblyfOppjew8BYQJYmRLlciVq9CuVv6IICsBD8VddjFz8oalT738i1eetQEAssxkWNdoANHVA5Bnp8CmTvN/vxAroczNBofDBdfMErKsFFh41gEA2NRq/F67urxQ1dPfGQAKfj6mfC7CbifiaXIOuvo5wtveDCpV/swHiVQJEz4XaTlyNPb5b8m3qo7miMvI01lmYhiqVq1SMDMiKSkJA/v3++gxSUlJ6BMc/N5tv+/eg/T0DHTt3AlmZmZQKpXIys6ClaXle218EhBQYoZ7kZEVeg2kdOq4WqJvfTdk5soLjiO7bsQjV65C7bqW8LA1RVaeEj725kjKkiLqbTbcbQRoUcMerWr+t3yQtvbIKQ0zJ++CmRGyzGRYen88k0B09QAyYq7CuXEPcEzMILB3h0KSAfGrqPwLovZusPLxf+85VlU/AQAI7N3/7ccLPNPiytyaZ29pir6f/nsedDCDKZ+LPLkKcqUKpx+lITNPgbqulshRfw/jqZArV+FtphQtq9sVtNOsav6Su+svJxTZl5WDC/w6DoAsVwwOhwuXmvmv/+HZfcgTZ6Bao/bgC8whFWeBy+VCKZNCIc1FZuJrePjlnwurNe4AAMhMeKGln4h2ONjaoH/39khKSUOLhp9gz9HTuHL7Pj7v1BpNA+tBpVIhSyxBzNOXiE9MRla2BC0b5y8fF/3EsF7rh6w86wCe+X+XZibD2qfomTi2NepDmpkMt2afAwDe/rMf2a8fwumTdjB39gGjUkGalgCFVAJGqYCFW3XYVC35eG/pUfiMcp4JXUNgQ1K2DAI+F35ulshTqBCfKYOvmwXquVnC7X4ynqts4Nrsc0gzk+HZ7ouC5yVcOYDslw8ADhfmTt5gGBWkGSLIMpOhkuWCy+PDvu5/s9jsajcBALw6tELnr7EyCqzmgrqeDhBl5qB7g2oAgEyJFLFv0yDOk6OulyOeJqSja/1qyM6VIfpNCvg8Lvx9nNA5sGpBOzbmH88AIyWzdXBGUNe+yEwRoW6D5gCArPQUBDTvAC6Xi8vHwpCdngYbeyfYOrlCpVQiV5INhVyOtKR4qBgV6jXJXxbLwdUDAODskb9k6p0Lf7HzolhExQhSwPmdgsKJ6BQ4WprA2coUeQoVLjxJg6+rJTztzeBkZYIcmQrpOXLwuBxce5EJDgeo62oJO3N6SxHj4WwtwF8PEuBoZYqMHDlM+VwwANIkcvh72oDP5SIpKw8KpQq1Xa0hVahw91U6PvW2A4/L3pd6QvSNrTkfJ6JT4G2XP8JTmCBBslgGX1dL1HO3RBUHM9yLy8bbTCmi3ooR4FG69VJ5Zpbv/dvU1hmpd05A4OQNnrk18kQvkP3kJiy8fGHlUw8C5yrIiYuBPDMBWU9vg29pBwv3mtp4yWVi+++59UR0Cqo7mcM+1yT/Z5Qtg69b/s+Iz+XgXlw2cmQq3HyVCQcLE9R0Npy1rQl7XF1di7390JEjcHZyRlpaGkxNTcEwDOLfvkXgp5+Az+fjbUICrKysEHHpMmxsrFE/MFCH6Ulp2ZqbAABOCJNRx8US6bkKCN+K/zuOeFjlH2vfZCMpW4Y7rzPR0Efze8pVlKmt80e3pbxzXBe/FuYXLP49rpu5Vof4xT3Is1IgfiUE19RML47rH7J95zuUmQkX55+kw9tOAOtcHoQJYiSL5fB1tUA9dytUsRfg+sss3IvLRn2vsu8HYmr+/jnUxMwCNq7eSH39GEqZFJJ0EZyq+cGtTgNweTzERV2r8OvTF65ODjhy5hJ8PN1gbWWJB4+eISklDQG1q+MT31qo5pV/gSgxJRXX7wnRrL5/CS0aFkEhvz/FPYYnsIC5kzckb59AJZdCmiGCtY8frKvUA4fLR9rDf6DIyYaDX3NtxiYa1qmOQ5HXbfg8Djj/ToR7970gunUCZk7eUJhZQZoaj6znkbDy9oV1lXqw8qqLrOf3kJcaD0alhImVfZEFKFJxLrb/fb6/HBOHai62SJdIEfUqGaLMHPh5OyHAxxFVnG3w+G06zke9RvsAdvcJq0xsnVwK/m5j7wQAuHn2KJw9fGBuaQ1R/Cs8vn8TPrX9UaVOAFy8qkIuy4OCZoK9h64ck0J1r+dU7P2Nq9BGu4QAwGefuBd7f5NqmtmAlpDKrqTzTisNbfDp2LB7sfd/OBNCn+jqZ0TIuz6cHfGhFs0LX8uf6Kfu/sVfjHx3NoShcCrhuG7n10pHSTSnu1/xm0J3rKO5z5e1WvYo9v7qTTtrrC99ENy5+A1b2wU11FES/efa+LNi73dpqJ3NXIl23XqdhdfpUtRyNkeOTIVmVW3A53KQmCVDikQOppAJKy6Niz/OOtQzvONsZdCjUY1i729Yo/ABJ0SzmnT8vNj767emY+WHqBhB3nPtRSYeJkr+OzFVswWfC8Qk5kAiUyJZLIObTf60LplShSY+NniSnAsVw0C9lCwVKogxuPo0BQ/fZqGWqzVyZAoE1XAEn8vFw4Qs8LkcPE+WwMacD3MTHhIy8/B5fQ88SsiGUsVArlQhITMP9Tzod4UQALj2IuPfc48FcmTKf889HDxKyoFcqUJajgJta9rj3OM0+LlZlnvEf2bsNUheP4SFRy0opTmwrdMM4PKRExcDnsCiYGaELD0BfCt7KKU5sPT2g5mTt4Zfcdlde5GB6Hd+RkHv/IyUKgZpOXJYCXho4GWDay8zwQFQ28UC3va625CSVD4Rly7j/oMo+NatA4lEgjatW4HP5yMuLh7pGRnIyMhAq5YtcPL0GVTx8Ubs4ydo3bIlqlatwnZ0UoRrzzMQnSjOP5bIlQiqZgc+l4OETClSxHKIZQoEVbPD9ReZqOJghlouliU3yiL1cd3coxZU7x7X4x8hL/k1bOs0Q27SC6hkufn7BOVmwcKjtl4c14ty7UUmHiZJUMsp//+oWdX872MJWTKkSuTwsTdDfIYU9hb8Cs+Ae/PgCpKfR8PRuzbk0hx4BTQHl8eDOCURfIEZpJIscPkmcPSpraFXx67LNyPx4NFT1K1RBZLcPLRqHAg+j4eox8/A5/GQIEpB+6BG+Of2fZgJTOHpWvJMgsoqLeYqsl8/hNW/n5nsfYPA4fIhjnsEnpklFDlZyEtLgLWPH6w8K8f7w1jEZUihUKogTJDA206AO6+zkCNXITFLBrFU+V4xIv3RNYhfR8Pi32OsXd3894E0PQGyrBQoJJmwr9cKWc/uQinLhUqWB4GDO+z0eHBPZXHlUTyi36Sitrs9cqRyNK/rAR6Xi0fxaVAoVajibIM3qdmQypVwt7NELQ/DG2xgCGJu/YNXj6PgWa0O8nIl8G3UEjw+H6kJccjLlcDe2Q2vYqPA5fLg7FkFntXrsB1ZL1AxguBlWh6Eb/PXrbcW8ND032JCsjgH++4mQqkCbMx4cLcRwNyEi4RMKRQqBhwAx4QpcLEyKShQpErkBW1lSqSsvB5CtO1Zshjt67oU7BfhYGmKsw9FSBFLkZEjQz0PW9hbmCBHpkS6RA65UoUDt+PAMICrjRncbM1Qw9kK8em5ePiWNvUixuv6y0w4WuYvHSJXMbj6IgNKBkjIksLMhAeRWAoTLhcKFYPnKTlgALzNzMPz1Fw8Tc5FNa+i2855+wTiV8L3buOZWYNvaQdJ3CMwKiUYpQLcfzdiNLGyhyQuBlwTM3D4ppCL06HMkyAv5Q0Ukv82mpe8jtbGj+Ijb9NzcebR+5uZKZT5PyOVCkhU/4yypTDhcQvOy/fisuFoaQI7cz5uvc5CTJIE8Rl0Pibvu3jpEmSy0k0Xr+fni9jYx0hJTcW9yPvw8fGGVCqFQCBATk4OHsU+BofDQVxcPBzs7XHv/n08e/68xHYj7z+o6MsgJeLg3pssnIn5bwPe5yk5SM9RIClLCg9bAU5GJyNProIoWwZzUx44AGISJXC0NEGaRIYXqfn75Ijz2FteQPw6Gjyzwpcjeve4DpUSKqWiYINdEyt7ZDy6mn9c5+VfPFPmScA1NX/vuP4uaaZuNyt+k5aLM4/S3rvteWouMnIUSMzKgIeNACcfpiJPrkKyOP+4f+t1NhiGgSmPg+ep7+8J9CY9F/WL6EuclgjR0/9+71JePUJuRgpUSiWkkkyYmFng9b0IcLhcZKckwMrBFVweH5L0ZCTF3svf+PpfyS8eauxnoAtvk5IR+fAxrK0sYG9rDeHj51AqVVAoFLAwN0N8YjJMTU0gVyhw4OR5MGDg5uSAuEQRoh+XfDzTV1JxBrJeRpXruXmpb8EoFch6JYS5sw8yntyBUpoDeU4WeCam4JqYgWsiQNqja8h8Hglrb9+SGy0kH9EdL6/8D85zT7ws9nEWbiJk//u+4ZtZwa52U4jjYyHPSkVO4guYOXtDJZeCayIAh8NF8u0T4JqagcPlAwyDrOf3wTP5bzCMro+rldWDl8mQKf7bMS8+TQyFUoWo1ynwcbbGradJyJHJkZAugaXABE8TMwAArrYWiEvLRq5cAQB4nlT4+Y8ULyXxDV7G3P/o9tTEOKgUCryMfQAXzyp4+uAmpLk5kGRnwsRUgNSEN+Dx+JDKcxB17Tzk0o/38kt6Y7jnmfKiYoSR8/L2wbGrV3FMqJ0ThPqER0hl4eXtjX23IrHvVpzG2rS1sYGlpX6PPCRE0/x86+JA5CMciBSVu42eRWyS6+HpiRcXfkPihd/K3XZxTExN4ehY/BIaFVGvXj2k58gw7A/NXewRCExRo0bxU7lJ5efm5gZLS0vMX7CI7SgAAIFAAG9v/R2hbujq+Qfg7PnzOBubVvKDS8DlclG3bl0NpCo9Z2dnmJiY4EXYfJ32q6vvL/Xq1cMvv0jx1Z4Yjbf7IS8vL6TFPcMf49trrB8ra2vY2Oj/LF8vLy8cOnQIQX1GlrsNBwcHmJkZ1kxDLy8vqJQKXJ+r38ts0fUC3WnatClevXqF1NTUIh+zbt067Nq1Cze/76LRvun/ufw8PPL3shm3/ZxG2/X09NRoe5WZt5cXLpzfhe8GtNRaH61bt9Fa2/qIwzDqxXWIMcrJyUFMTNEfgCUSCXr16oVOnTph1qxZAACxWIyePXuiW7dumDFjRpHPtbS0RJ06dcDh0Ea9pPIQi8WIjY0FACgUCixfvhxHjhzBuHHj8NVXXxX7fj958iQWLFiARo0aYeXKlbCwyJ9aX7VqVa1e2CREHykUiiK/DF24cAEhISHgcrmIi4sDl8v96DE8Hg9OToXvoZCVlYUnT56UKseaNWtw+PBh1K9fHxs2bCjVc7y8vIrc9FdT0tPTix25fuvWLfTs2RNDhgzBsWPH8PDhw2KPP9bW1gXHHGLcxGIxkpOTy/T4Pn36QKlU4vDhwx9dfMzJyUG/fv0gkUhw9OhR2NnZlbptJycnWFuXfQNeUjoqlarE/+smTZrg9evXWLlyJYYMGVLk48zNzVm58JyUlIS4uNIPABk5ciQEAgE2bdqEq1evYtKkSfjjjz9KXUgRCATw8/Mr9LyjDWlpaZDL5cU+Zvz48Th9+jS6deuGjRs3FvtYExMTODh8vJ+EUqlEdHR0iX117NgRGRkZWLx4Mbp2LX6N6ypVqhR5HtYnCoUC0dHRUCgUBbfdvHkT48aNw6pVq9CuXTsAQG5uLj7//HMEBQVhwYIF77VRrVq1Qn+u+oxhGMTGxkIikWikvR9++AEXLlxAq1atsHjxYo20aWFhgbp169L1Aj1S3LFi69at2LlzJ/bs2YPq1asX3P7gwQMMHz4c3377LQYNGvTR83R9XK2M4uPjkZiYWOT9f/zxB7Zs2YJz586hc+fOGDZsGIYPH17k411dXalAVAZyuRxCoRAqlarYx02ZMgUpKSn4448/AADffPMN3r59iz179pR4nPPz84O5ubnGMus9hpBiLFu2jDExMWFev3793u2LFi1iTE1Nmbi4OJaSEcIusVjMfPbZZwyfz2d27txZ6uedPXuWsba2Zho0aMAkJCRoMSEhhmvXrl0MAAYAk5KSotW+vvjiC8bNzY1p2LChVvvRtK1btzI8Ho8JDw9nANDxhGiFUqlkevfuzVhbWzMxMTFFPu7Zs2eMg4MD07lzZ0ahUOgwIakoKysrxtramlm4cCHbUSrszZs3DIfDYX799VeGYRhGJpMxjo6OzPTp09kNVkGdO3dmPDw8mK5du2q1H4VCwXA4HMbU1JRZu3atVvtik0qlYho3bsw0adKEUalU7923efNmhsPhMFFRUSyl01/du3dnPDw8mI4dO7IdhbDg4cOHjImJCTN37txC7x87dixjZWX10XUjohs9evQo+N3s3r0706lTJ5YTGZ/Xr18zXC6X2bZtW8FtJ06cYAAw169fZzGZfqLSJCmSWCzG6tWrMWLEiI+m0E+cOBGWlpZYsWIFS+kIYY9IJEK7du0QERGBv/76C0OHDi31czt06IDLly8jMTERQUFBBbMsCCH/EYlEBSNDRKLyL+NU2r7s7Oy03o+mCYVC1KpVCw0aNCj4NyGatnTpUhw+fBi7d+8udmR59erVsXfvXpw9exZz5szRYUJSETk5ORCLxQZ5DCzM3r17YWpqit69ewPInyXQv39/hIeHlziaUZ/p6jyVmpoKhmEqzfuhKIcPH8atW7ewfPnyj0aqjhgxAtWqVaPjWCEM9fMSqTiVSoXRo0ejatWqRf5uLFu2DNbW1pg4caKO0xGlUolLly6hbdu2AIC2bdviypUrJc6EI5q1Y8cOWFhYICQkpOC2zp07o0qVKti2bRuLyfQTFSNIkTZv3oysrCzMnj37o/tsbW0xbdo0hIaGIj4+noV0hLDj6dOnaN68Od68eYNLly6hc+eyr8P66aef4tq1a7CwsEDz5s1x9epVLSQlxHCJRKKCpR90UYxwcnKCSCQCY0ArV0ZFRSEgIADVq1eHubk5oqLKt0ElIUU5fvw4vv/+eyxYsAA9e/Ys8fEdO3bEypUrsWLFCuzdu1cHCUlFqZdwcnR0rBQXGMPCwtC9e3fY2toW3BYSEoI3b97g2rVrLCarmHfPU9ruB4BO+mKLQqHAnDlz0KVLl4Llmd5lamqKRYsW4c8//6TP5x/Q1fuQ6J9ffvkFly9fxrZt24rcO8XW1hYbNmzA0aNHcfjwYR0nNG6RkZHIyspCmzb5ew60bdsWOTk5uH37NsvJjIdCocDPP/+ML7744r3lR3k8HkaOHInw8HBkZtLG4e+iYgQplFgsxqpVqwqdFaE2ceJEWFhY0OwIYjRu3LiBoKAg8Pl8XLt2DfXr1y93Wz4+Pvjnn39Qr149dOjQAUeOHNFcUEIMXFJSEtzd3QHophjh5uYGqVSKrKwsrfalKQzDQCgUwt/fHzweD35+fjQzgmhUbGwsvvjiC3z++eeYO3duqZ83bdo0DB48GMOHD8f9+/e1mJBoQlJSEoD8jc0N/QLjkydPcOfOnfdGJAJAy5Yt4eXlhbCwMJaSVQzDMBCJRHB1ddV60Vz9HnB1dS14b1Q2u3btwqNHj7B06dIiHzNo0CB88sknmDVrlkENUtAmhmGQlJQENzc3JCcnG/RMI1I2iYmJmD59OoYNG1ZoAe9dffv2RY8ePTBx4kSD+UxdGVy8eBHm5uZo3LgxAKB+/fqwtrbGxYsX2Q1mRE6cOIH4+HiMGjXqo/u++uoryGSygn0kSD4qRpBC/fTTT8jKyirYtLowtra2mDp1KkJDQ/H27VsdpiNE944fP4527dqhdu3auHLlCqpWrVrhNu3t7XHmzBn07NkTffv2xU8//VTxoIRUAiKRCO7u7jA1NdXqBRGVSgWRSFSwgZuhXIxLTExEamoq/P39AQD+/v40M4JoTFZWFoKDg+Hp6Yldu3aVacNJDoeD7du3o3bt2ggODi5yk3qiH9THPG9vb4O/+BweHg4rKyv06NHjvdu5XC4GDhyI/fv3v7eBsaFIT0+HQqGAt7c3ZDKZVkdWqt8D3t7eBnM+LIvc3FzMnz8fAwcOLFjisDBcLhfLli3D5cuXcerUKR0m1F/Z2dmQSqXw8vKCUqlEWloa25GIjkydOhV8Ph+rV68u8bEcDgebN29GRkYGLXWmQxcvXkTz5s0hEAgAAHw+Hy1btqRihA5t27YNjRo1KvTc4uHhgV69emHbtm1U4H4HFSPIR9SzIr7++mv4+PgU+9hJkybB3NycZkeQSi00NBSff/45unbtirNnz8LR0VFjbZuZmSE8PByTJk3C+PHjMWvWLBptRIyeehSoi4uLVi+IpKenQ6lUokqVKgX9GgL1LIiAgICCP6Ojo+nYQSpMpVLhf//7HxISEnDkyJH3ppqXloWFBQ4fPozs7GwMGjTIIC8AGwv1Mc/Hx8dgjn+FYRgGYWFhCA4OLthv6F0hISEQiUQ4f/48C+kqRv3/oh4Eo83/J/V+TR4eHgb9fijKTz/9hISEBCxatKjEx3br1g2tW7fG7Nmz6dwK3b4Pif44deoUwsPDsWbNmlJ///Xx8cGiRYuwefNm3LhxQ8sJyYf7Rai1bdsW//zzD+0boQOvXr3CyZMnMXr06CIfM3r0aERFReH69es6TKbfqBhBPvLTTz8hMzOz0L0iPqTeO2Lbtm00O4JUOgzDYN68eRg9ejTGjx+P/fv3F/olt6K4XC7Wrl2LNWvWYMWKFRgyZAhkMpnG+yHEUIhEIri4uGi9GKFuu0aNGu/9W98JhUKYm5ujevXqAPJnRuTk5ODly5fsBiMGb+HChTh27Bj27NmD2rVrl7udqlWrYt++fbhw4UKxs2wJu0QiERwcHODu7o7U1FSDLRw9ePAAMTExHy3RpNagQQPUrFnTIJdq0uV5Sn3uVS8JVZlkZmZi6dKlGDFiBGrVqlXi4zkcDpYtW4b79+/THjgw3M9LpPwkEgnGjh2LDh064MsvvyzTcydOnIj69etj1KhRdDFcy+7fv4+srKxCixG0b4Ru/Pzzz7CyssKgQYOKfEynTp1QtWpVhIaG6jCZfqNiBHmPelbE8OHDS5wVoUazI0hlJJfL8dVXX2Hx4sVYuXIl1q9fDx6Pp9U+p06divDwcOzfvx/du3entTaJUVKvj63LYkStWrXA4/EM5st1VFQU/Pz8Co5J6hkStFQTqYijR49iwYIFWLRoEbp3717h9tq3b4/Vq1fjxx9/xJ49ezSQkGjau8daAEhJSWE5UfmEhYXBwcEBnTp1KvR+DoeDkJAQHDp0CHl5eTpOVzHq81KdOnXe+7e2+lK/H3JyciCRSLTWl66tXr0aOTk5+P7770v9nObNm6NXr16YO3eu0Q8S0uX7kOiHBQsWIDExEVu3bgWHwynTc/l8PrZv3w6hUIi1a9dqKSEBPt4vQq1BgwawsrKipZq07N2Nq62srIp8HJfLxciRI7F3715kZGToLqAeo2IEec+WLVtKPStC7d29IxISErSYjhDdyM7ORs+ePbFnzx7s3r0b06dPL/OHsPIaOHAgTp8+jdu3b6NVq1Y044gYnaysLEilUp0s06Ru283NDc7OzgazZrp682o1d3d32Nvb0ybWpNxiYmLw5Zdfom/fvvjuu+801u7kyZPx5Zdf4uuvv8a9e/c01i7RjKSkpIJjLWCYFxgZhkF4eDj69esHExOTIh8XEhKCrKwsg9sDQCQSwcTEBFWrVtV60fzD4pShnBNLkpSUhDVr1mDy5Mnw8PAo03OXLFmCFy9e4Oeff9ZSOsOQlJQELpeLqlWrwtTU1CCPFaT0IiMjsWbNGsybNw81a9YsVxsNGjTA5MmT8cMPP+D58+caTkjUPtwvQo3P56NVq1aIiIhgKZlxOH78ON6+fVvsEk1qw4cPh1wux++//66DZPqPihGkgEQiwcqVKzF8+PCC9bNLa9KkSTAzM6PZEcTgJSYmom3btrh27RpOnTqFwYMH6zxD27ZtceXKFaSlpaFZs2Z4+PChzjMQwhb1F1z1UhHavBiSlJQEExMT2Nraar3woSkqlQrR0dEFsyGA/FG/AQEBNDOClEtGRgY+//xzVKlSBTt37tRo8Z3D4SA0NBR+fn4IDg5GcnKyxtomFffusjyAYV58vnbtGl69elXkEk1qvr6++PTTTw1uqaakpCS4uLiAx+PBxcVF6+dEV1fXgveDIZwTS2Px4sUwNTXFzJkzy/xcf39//O9//8PChQsr1UyRshKJRHB2dgaPx9P6ZzPCLqVSiVGjRqFu3br49ttvK9TWwoUL4ezsjLFjx9LGvVqg3i+iTZs2hd5P+0ZoX2hoKJo0aYLAwMASH+vm5kYbWb+DihGkQFn2iviQnZ0dpk6dim3bttHsCGKwYmNjERQUhKSkJFy+fBnt27dnLUu9evVw/fp12NnZoUWLFrh06RJrWQjRpXeLEbqYGeHi4gIOh2MwxYgXL14gJyfnvZkRQP4FE5oZQcpKpVLhyy+/RHJyMo4cOVLsFPPyMjc3x+HDh5Gbm4uBAwca7L4ElZH6GOjs7Fzwb0MTHh4ODw8PtGrVqsTHhoSE4NixYxCLxTpIphnq/yMAOjsnGvJMmQ89f/4c27Ztw8yZM2Fvb1+uNhYsWIDU1FRs2LBBw+kMhy7fh4RdP/30E27duoXQ0FCYmppWqC0rKyts3rwZZ86cMbhCsCG4f/8+MjMzP9ovQq1t27aQSCS4c+eOboMZiZcvX+LUqVOlmhWhNnr0aERHR+PatWtaTGYYqBhBAOTPili1ahW++uqrMs+KUJs0aRIEAgHNjiAG6cqVK2jevDksLS1x7do1fPLJJ2xHgqenJy5fvoz69eujU6dO2L9/P9uRCNG6D4sRWVlZWlvjWyQSFYwANZQv1+qCQ2HFiNjYWKNf15qUzfz583HixAmEhYWVeymG0vDx8cH+/ftx6dIlTJ8+XWv9kLJRX2C0tLSEpaWlQRwD36VQKLBv3z4MHDiwVPt6DRw4ELm5uTh69KgO0mkGG8UIR0dHcDgcg3s/FGb+/PlwcnLCpEmTyt1G1apVMXbsWKxYsQJpaWkaTGc4qBhhHOLi4vDdd99hzJgxaN68uUba7NGjB/r164cpU6YY7e+Ptly8eBFmZmZo0qRJoffTvhHatWPHDlhbW2PgwIGlfk7Hjh1RvXp1bNu2TYvJDAMVIwiA/L0i0tPTK7ROMM2OIIbq8OHD6NixIwICAnD58mV4e3uzHamAra0tTp48ib59+2LgwIFYt24d25EI0SqRSAQejwcHB4eCL77aWtrl3S/Xrq6uBvHlOioqCvb29h+tex0QEACFQoHY2FiWkhFDc+jQISxevBjLli1D165dtd5fmzZtsHbtWqxbt47Wy9UDKpUKycnJBleQfdfFixeRlJRU4hJNalWrVkVQUJBBjdDV1UVgiUQCiUQCFxcX8Pl8ODo6GvxSPA8ePMDu3bvx/fffw8LCokJtzZkzBwqFAsuXL9dQOsNiiIM3SNlNnDgRVlZWWLZsmUbbXb9+PaRSKWbMmKHRdo1dUftFqPH5fLRs2ZKKEVogl8vx888/48svv4SlpWWpn6feyHrfvn1IT0/XYkL9R8UIUqG9Ij40efJkCAQCrFy5UkPpCNGuTZs2oW/fvujVqxdOnz5d7inc2iQQCPDHH39g+vTpmDp1Kr755huoVCq2YxGiFUlJSXB2dgaXy9X6utUfXuQxhAsv6s2rP1zXv169egX3E1KS6OhoDBkyBAMGDNDpxYEJEyZg2LBhGDVqFC0bwLK0tDQolUqDK8i+KywsDDVq1ECjRo1K/ZyQkBCcPn3aYEbovnsRWJv/R+qivy760pU5c+agevXq+PrrryvclouLC7755hts3LgRcXFxGkhnWNR7lwCV471BPnbkyBEcOXIEGzZsgJ2dnUbb9vDwwIoVK/Dzzz/T0sMaolQqcfny5SKXaFKjfSO04/jx40hMTMSoUaPK/Nxhw4ZBoVBg165dWkhmOKgYQTQyK0JNPTti69atNDuC6DWVSoVZs2Zh4sSJmDp1KsLCwoocVaAPuFwuVqxYgY0bN2Lt2rUYPHgwpFIp27EI0bgPCwSA9jZVfffLtYuLC9LS0vT+w3pUVNR7m1er2dvbw8vLizaxJiVKT09HcHAwqlevjl9++UWjG1aXhMPhYMuWLQgICEDv3r3pghaL3l0ST/2nIRRk1aRSKQ4ePIiQkJAyvYcHDBgAlUqFgwcPajGd5nx4ntLm+VDdh/pPQ/79/Oeff3D8+HEsXrwYJiYmGmnzm2++gaWlJRYuXKiR9gyJIQ7eIKWXlZWFCRMm4LPPPkO/fv200seoUaPQvHlzjBo1ir7DasCDBw+QkZFRqmIE7Ruhedu2bUPTpk3x6aeflvm5bm5uCA4ORmhoqFFvZE3FCCOnib0iPqSeHbFq1SqNtEeIpslkMgwZMgQrV67E2rVr8eOPP4LLNYzD4YQJE3DgwAEcOXIEXbp0QUZGBtuRCNGod7/wantT1cIKHykpKVrpSxOkUikeP3780X4RarSJNSmJUqnE4MGDkZqaiiNHjpRparmmmJmZ4dChQ5BKpejfv7/eFwArq8KKEYZ08fnUqVPIzMzEoEGDyvQ8V1dXtG/f3iCWapJKpcjMzHzv/yg9PV0rewMZ+vvhXQzDYNasWQgMDMSAAQM01q6NjQ3mzJmDX375xaiWRFQoFEhNTX3vvZGdnY3c3FyWkxFNmTt3LtLT07F582atDVDgcrnYtm0bnj17ZrTLnWlSSftFqNG+EZr34sULnDlzpkwbV39o9OjRePjwIa5cuaLBZIbFMK6+Ea3ZunUr0tLSNDIrQs3Ozg5TpkzBli1bkJiYqLF2CdGEzMxMdOvWDfv378fevXsxZcoUtiOVWZ8+fXDu3Dk8ePAALVu2xJs3b9iORIjGvFsgMDU1hZ2dnVYuiOTl5SErK+u9NZDV/eur2NhYKBSKQmdGAPn7RtDMCFKcefPm4cyZM9i7dy+qV6/OWg4vLy8cOHAAV69exTfffMNaDmNm6Befw8LCEBAQULBEXVmEhITg4sWLePv2rRaSaY566SRdFM3V//dOTk4FfRnq6PcTJ07gypUrWLZsmcYHG40dOxYeHh6YN2+eRtvVZ+r324fvQ23t50V06+bNm9i0aRMWLVqkscGpRfH398eMGTOwdOlSPHr0SKt9VXYXL15EUFBQiSs7mJiYoGXLloiIiNBRsspv+/btsLGxKdPG1R9q37690W9kTcUII6beK+Krr75C1apVNdr2lClTaO8Ionfi4+PRunVr3L17F3///Tf69+/PdqRya9GiBa5evQqxWIygoCC6AEkqjaSkpIICAaC9C2QfXuRR96nPF1/Usx6Kuvjm7++Ply9fIjs7W5exiIHYv38/li1bhhUrVqBTp05sx0GrVq2wYcMGbNy4ETt37mQ7jtFJSkqCQCCAjY0NgP+OtYawZIBEIsGxY8dKvXH1h/r06QM+n499+/ZpOJlmFVYwevd2Tffl6OgIPp9f0JchFafUVCoVZs+ejbZt26JLly4ab9/MzAwLFizA/v37cfv2bY23r4/Un4sMafAGKR25XI5Ro0ahfv36mDRpkk76nDt3Lnx8fDBmzBiDON/oI6VSiUuXLpW4RJMa7RuhOXK5HL/88gv+97//wcLCotztcLlcjBo1Cvv370dqaqoGExoOKkYYMW3MilCzs7PD5MmTaXYE0RvR0dEICgpCeno6/vnnH7Ru3ZrtSBVWt25dXLt2DS4uLmjZsiXOnz/PdiRCKuzdmRGA9jZK/PAij7aXhNIEoVAIT09P2NvbF3q/evmmhw8f6jIWMQAPHjzAsGHDEBISolczEcaMGYOvv/4aY8aMwc2bN9mOY1TUx1r1khyurq7Izc2FRCJhOVnJ/vzzT+Tk5JR5iSY1Ozs7dOvWTe+XalKfj97dVPrd2zXd17sDAVxdXZGSkgKlUqnxvrQpLCwMUVFRWLZsmdaWm/nf//4HX19frXyH1kcffl7S5vuQ6Na6desQFRWF0NDQgkKktpmbm2Pr1q2IiIjAr7/+qpM+K5vS7heh1rZtW4jFYty9e1e7wYzAn3/+iaSkpHJtXP2hr776CiqVCr///rsGkhkeKkYYqZycHKxcuRLDhg3T+KwItSlTpsDU1JT2jiCsi4iIQMuWLWFvb49r166Va0q/vnJ3d0dERASaNWuGrl276v0Xa0KKI5fLkZaW9l4xQltLRXy4WaeFhQWsrKz0+st1UZtXq/n6+oLL5dJMKfKetLQ0BAcHo3bt2tixY4dON6wuCYfDwebNm1G/fn306dOHBrDo0IeFX/Xf9Xl2mFpYWBiaNWuGatWqlbuNkJAQ3Lx5E8+fP9dgMs1S/1+oi+XqP7V1Tvzw/cAwjEGN2JTJZJg3bx6Cg4PRrFkzrfXD5/OxZMkS/P333zh37pzW+tEX6s9F6vefeikvQzhWkKK9ePEC8+fPx6RJk9CwYUOd9t2hQwf873//w7fffqvXn7v1VWn3i1Br0KABLC0tad8IDdi2bRuCgoKK/T5WWi4uLggODsa2bduMcpYQFSOMlDZnRajZ29vT3hGEdfv27UPnzp3RsGFDXLp0CZ6enmxH0jhra2scP34cISEhGDx4MFatWmWUJzRi+D5cl1j9d13MjNBmX5oiFAqL3LwayB9tVrNmTdrEmhRQKBQYNGgQsrKycPjw4QpNKdcWgUCAgwcPQqlUon///lrZnJd8rKhihD4fA4H84tqpU6fKvUSTWs+ePWFhYYHw8HANJdM8kUgEW1vbgjXBzc3NYW1trbVzoqEWp9RCQ0Px6tUrLF68WOt9BQcHo0mTJpg9e3al/8wtEolgaWkJS0tLAPn7ednb2+v9sYIUjWEYjB07Fk5OTli0aBErGX788UdwuVxMmzaNlf4NWUREBIKCgmBmZlaqx6v3jaBiRMU8e/YMf//9d4U2rv7Q6NGj8ejRI1y+fFljbRoKKkYYoZycHKxYsQLDhg2r0Iii0pgyZQpMTExodgRhxdq1azFw4ED0798fJ06cgK2tLduRtMbExAQ7d+7EnDlzMGPGDEyePNngptYT8uGSFIB2ixF2dnYwNTUtuE1bS0JpQnZ2Nl6+fFniSBzaxJq867vvvsP58+exd+9erc2E1QQPDw8cPHgQN27cwJQpU9iOYxQ+XJbHUIoRhw4dglKpxIABAyrUjqWlJT7//HO9nlH6YYEA0O450RCLU2pisRiLFi3CkCFDdDIDmsPhYPny5bh16xYOHz6s9f7Y9OGxAtD/wRukeOHh4Th9+jQ2b94MKysrVjI4Oztj9erV2L17N/7++29WMhgilUpVpv0i1GjfiIrbsWMH7OzsKvz5413t2rVDzZo1jXIjaypGGCFdzIpQs7e3L9g7wpBG1hDDplKpMG3aNEybNg0zZ87Erl273rvgWFlxOBwsXrwYW7duxebNmzFgwADk5uayHYuQUvtw6ST137WxqWpRF3n09VwVHR0NAMXOjFDfTzMjCJB/sWHVqlVYtWoVOnTowHacEjVv3hybNm3Cli1b8PPPP7Mdp9L7cFkeR0dHcLlcvb/AGBYWhrZt28LNza3CbYWEhEAoFOrtMZOKEaW3bt06ZGRk4IcfftBZn+3atUPnzp0xZ84cKBQKnfWrax8eKwAqRhiytLQ0TJkyBX379kXPnj1ZzTJ06FC0a9cOY8aMQU5ODqtZDMWDBw+Qnp5ermIE7RtRfjKZrGDjanNzc421q97I+sCBAwa1LKImUDHCyKj3ihg6dKjWZ0Wo0ewIokt5eXkICQnBunXrsGnTJixfvhxcrnEd6kaPHo3Dhw/j5MmT6NSpE9LS0tiOREipfLguMZA/W0GhUCAjI0PjfRnSl+uoqChwuVz4+voW+7iAgACIRCK9fR1EN+7fv4/hw4fjyy+/NKiZBqNGjcLo0aMxbtw4XL9+ne04ldqHx0AejwcnJye9PnYkJCTgwoULFV6iSa1Lly6ws7PT29kRhY1I18YMPpVKheTk5Pf6srKygrm5uV6/H9RSUlKwatUqjBs3DlWqVNFp30uXLsWjR4+wa9cunfarS4V9XtLnmaSkeDNnzkReXh42bNjAdhRwOBxs3boV8fHxrC0XZWguXrwIgUBQ6v0i1Bo2bAhLS0tERERoKVnldvToUYhEIo0u0aQ2dOhQMAyD3377TeNt6zPjukJHsG3bNqSmpupkVoSag4MDJk+ejJ9++klvR5ySyiE9PR1dunTBn3/+iYMHD2L8+PFsR2JNr169cP78ecTGxqJFixZ4+fIl25EIKZFIJIKVldV769pra93qpKQkg1p2QCgUombNmiWOxlHPnNDXkb5E+1JTUxEcHAxfX1+Ehobq1YbVpbFhwwY0atQIffr0QUJCAttxKqXc3FxkZ2cb1OwwANi/fz/4fD769u2rkfZMTU3Rt29fhIeH6+W6/0WNSNf0/1FqaipUKtV7fXE4HL1/P6gtX74cKpVKp99v1Ro2bIgBAwZg/vz5yMvL03n/umBoM0lJ0S5fvowdO3Zg+fLl8PDwYDsOAKB27dqYM2cOVq9eTcuMlsLFixfLtF+EGu0bUTGhoaFo0aKFVpYBdHFxQZ8+fRAaGqqXn0W0hYoRRkS9V8SQIUNQvXp1nfY9ZcoU8Pl8mh1BtOb169do2bIlhEIhzp07h969e7MdiXXNPLPBZQABAABJREFUmjXD1atXIZPJEBQUhHv37rEdiZBiFfWFV32fLvrSxpJQmlDS5tVqNWrUgEAgoGKEkVIoFBg4cCAkEgkOHz6s0ankumJqaooDBw6Aw+Ggb9++kEqlbEeqdJKTkwHAoGaHAflLNHXt2hX29vYaazMkJATPnz/HrVu3NNampuhqBp+6PUN7PwDAmzdvsGnTJnz77bfvzarUpUWLFiEhIQE//fQTK/1rm6HNJCWFk0qlGDVqFIKCgrQyursiZs6ciVq1amHUqFFQqVRsx9Fb5d0vQq1t27a4fPlypV5WThuePn2Ks2fPavX3ZvTo0YiNjTWqmStUjDAioaGhSElJwZw5c3Te97uzI+iDC9G0Bw8eICgoCDk5Obh69SqaN2/OdiS9UatWLVy9ehVeXl5o3bo1bRBG9FpRsxUA3RQjXF1dkZeXh+zsbI32pQlRUVElbl4NAHw+H35+fjS6zEjNnDkTFy9exL59++Dj48N2nHJzd3fHoUOHcOfOHUyaNIntOJWOekSzIc0Oe/HiBa5fv66xJZrU1PtP6NtSTQzD6KxobsjFiAULFsDa2hrTpk1jLUPt2rXx9ddfY+nSpcjMzGQthzYwDFPkZ7Pk5GS6cGxAVqxYgadPnyI0NFTvljA2NTVFaGgorl+/jq1bt7IdR2+Vd78ItTZt2tC+EeWwfft22Nvbo1+/flrro23btqhVqxZCQ0O11oe+0a+jENGa3NxcrFixAkOHDtX5rAi1qVOn0uwIonHnz59Hq1at4OrqimvXrqFOnTpsR9I7rq6uuHDhAlq3bo3u3btX6nVtiWEr7MKLnZ0dTExMNHpBpLiLPOoc+kQkEiE5OblUMyMA2sTaWO3evRtr1qzB2rVry/1FVZ80bdoUW7ZsQWhoKLZt28Z2nErFEC8+h4eHw8LCAr169dJouzweDwMGDMDevXuhVCo12nZFZGZmQi6XF/p/JJVKNVo0N8T3AwA8evQIv/76K+bOnQtra2tWs3z//feQSCT48ccfWc2haWKxGHl5eYW+N7SxnxfRjtjYWCxZsgTTp08v9WdJXWvZsiVGjhyJ2bNn4+3bt2zH0UsREREQCARo2rRpuZ7fqFEjWFhY0FJNZSCTyfDrr79iyJAhWp1tzOFwMGrUKBw8eBApKSla60efUDHCSGzbtg3JycmszIpQU8+O2Lx5s15/sCWGY8+ePejatSuCgoIQEREBNzc3tiPpLSsrKxw9ehRDhw7F0KFDsXTpUr1cioYYt8IKBNpYtzo9PR0KhUJnszAqSj3LoTQzI9SPEwqFNGLRiNy9excjRozA0KFDMWHCBLbjaMzw4cMxbtw4TJw4EVeuXGE7TqWhPsY5OTm9d7urq6vergMfFhaGXr16wdLSUuNth4SEICEhAZcuXdJ42+VV1OwV9b81+f+UlJQEMzOzjy7o6/smxXPnzoWXlxfGjBnDdhR4enpi0qRJWLNmjd7+DpVHUYUqbbwPiXYwDIMxY8bA29sb8+bNYztOsVasWAFzc3OaEVmE8u4XoUb7RpTdkSNHkJycjFGjRmm9r2HDhgEAdu7cqfW+9AEVI4yAelYEG3tFfIhmRxBNYBgGK1euxBdffIEvvvgCx44dY31ElCHg8/nYvn07fvjhB8yZMwfjxo2jNSOJXimsGAFofnRmcaNA371fXwiFQggEAtSoUaNUj/f394dYLMarV6+0nIzog+TkZPTu3Rv+/v7YunWrwW1YXZK1a9eiWbNm6NevH+Lj49mOUymIRCLY29vD1NT0vdtdXFyQmpqqd58NoqOjERUVhUGDBmml/aZNm6Jq1ap6tVSTLs9T6nPvh8cOfd6k+NatWzh48CAWLlwIgUDAdhwA+cvk8fl8LF68mO0oGmNon5fIx3bu3ImLFy9i69ater+PlL29PdatW4eDBw/i2LFjbMfRKyqVChERERWe+Ur7RpTNtm3b0LJlS/j5+Wm9LycnJ/Tt29doNrKmYoQRCA0NZX1WhJqDgwMmTZpEe0eQclMqlf9n77zDmyrbP/7NTvdI0zJayiijpQWUWbCACr6un4CCUBUVZK+yoSDiomyhgCBFUFwgCqIiThQISwEZTUspo3Q36UzbtNn5/VFPKR1J2pycc5Kez3W91yvJyfN8e3LyjPt+7vvGvHnzsGzZMrz55pvYt28fBAIB3bKcBg6Hg9WrV2Pv3r3Ys2cPnn/+eVRVVdEti4WlydRJAPkGEWL+qV/sUiKRgMvlMs74IpfLER4eDj6fb9P1RAh+SkqKI2WxMAC9Xo8XX3wRGo0GR44cafFpOSYjFArxzTffgM/nswWtScLSWGs2mxmXIuDgwYPw8fHBk08+6ZD2ORwOJkyYgMOHD0On0zmkj+bS1DxFfG9kz4mNFX8ODAxEVVUVKisrSeuLLOLj4xEREYFXXnmFbim1+Pv7Y9myZdi9ezcyMjLolkMK1pwRTFsvsTxIYWEhFi9ejFdeeQUjRoygW45NjB8/Hk8++SRmz57NyLGHLpKTk1FaWophw4bZ1c7w4cNRWVmJK1eukKTMdbl16xb+/PNPSgu+T58+Hbdu3WoV0SusM8LFqa6uxrp16/Dqq6/afKLS0SxYsAA8Hg+bNm2iWwqLk1FdXY1x48Zh586d+Oijj/Dee++53AlQqpg8eTJ++OEHnDhxAo899hgKCwvplsTSyqmoqIBGo2mQkgK4XyiRLIjNdf2+uFwupFIp45zlthavJggODoaPjw9bxLoVsGTJEpw5cwbffvstQkJC6JbjMIKCgnDkyBFcvXoVs2bNahUnxhxJYwVpgfsGRiatCcxmMw4cOIAXXnjBoSfgY2NjUVJSgt9//91hfTQHpVIJPp8PPz+/B1739/cHl8slfU50lucBAP744w+cOHECCQkJ4PF4dMt5gHnz5kEikWD16tV0SyEFhUIBLpcLiUTywOs+Pj4QCASMezZYHoQo7P7BBx/QrMR2OBwOdu7ciaKiIsanlaKSkydPQiQSYdCgQXa1w9aNsJ2kpCT4+/s7tHB1fYYOHYru3bu3ilpprDPCxWFSVASBRCLB3Llz2doRLM2iuLgYI0aMwC+//IKjR49S6qF2VZ5++mmcOnUKGRkZGDJkCO7cuUO3JJZWTFOn74jXyE5JIRAI4Ovr6/C+7MVkMiElJaVZBQc5HA5bxLoV8NlnnyExMRGJiYmIiYmhW47D6d+/P3bv3o19+/Zh165ddMtxaixFRhDvM4VLly7hzp07iI2NdWg/UVFRiIiIYEyqJiJagct9cLvuCKe5Mz0PZrMZy5cvx6BBg0gvZk4GHh4eWL16Nb744guXOBCgVCoREBDQwOlD1PNi0rPB8iC///47vvjiC2zatKnRyCcm06lTJ7zzzjvYtm0bLl++TLccRnDy5EkMGjTI7ghYtm6EbWi1Wnz66ad47bXXKI06JgpZHzlyxOXHV9YZ4cIQURETJ05kTFQEwcKFC8HlctnoCBabIIzl6enpOHnyJP7v//6PbkkuQ79+/XD+/HkAwODBg3Hx4kWaFbG0Viw5I8guqqpQKBrNj030z6TFX2ZmJiorK5sVGQHUGNZcwRDC0jiXLl3CtGnTMHnyZMycOZNuOZTx2muvYd68eYiLi4NMJqNbjtPSlPGZiUVpDxw4gKCgIDz66KMO7YfD4SA2NhZHjx5lRPrKpqJXAMfMiY31xcTn4fDhw7h8+TLWrVvH2OjoN954A507d2bUYcCW0tRYATC74H1rp7q6GjNnzsTw4cNri+I6G/Pnz0dUVBSmTp3a6usbmEwmnD592u56EQTDhg1j60ZY4bvvvkNRURGmTp1Ked+vvfYauFyuyxeyZp0RLsyePXsYFxVBIJFIMG/ePHz44YdseCeLRf79919ER0fDYDDg/PnzGDBgAN2SXI7OnTvj3Llz6Ny5M4YPH47jx4/TLYmlFWItMkKlUpGWK97S5pppzggiuqE5kRHE9WlpadDr9Y6QxUIjCoUCY8aMQZ8+ffDhhx8y1iDnKDZt2oQhQ4Zg7NixyM7OpluOU9LUGOjh4QF3d3fGjIFGoxFff/01XnzxRUrS8UyYMAFqtRrHjh1zeF/WoHKeaqqvgICA2veZgMFgwMqVK/HUU0/ZnTfdkQgEArz//vv48ccfcfbsWbrl2IUzrZdY7vPee+8hOzsbH330kdOuEQQCAZKSknD16lVs27aNbjm0IpfLUVJSQpozYvjw4aioqGDrRlhg9+7dGDp0KMLDwynvWyKRYOzYsdizZw9MJhPl/VMF64xwUepGRYSFhdEtp1HY6AgWa/z6668YNmwYOnTogHPnzjH2WXYFAgICcOLECYwYMQLPPfcc9u7dS7ckllaGUqkEl8uFv79/g/fIzlvtTCf9kpOT4ePjg+Dg4GZ9LioqCnq9Hunp6Q5SxkIHRMFqg8GAw4cPu2TBamsIBAJ88803EIvFeOGFF6DRaOiW5FSYTCYUFhY6hYFRJpMhLy8PEyZMoKS/sLAw9O/fnxGpmqgyAhMFqhvri8/nQyKRMOZ5+PTTT5Geno6EhAS6pVjlxRdfRJ8+fbB8+XKnrnFDRJI2BpPGCpb7JCcnY+PGjVi5ciW6d+9Otxy7GDBgAObMmYNVq1bh3r17dMuhDbLqRRCwdSMsc/PmTZw8eZLWtODTpk3D7du38ddff9GmwdGwzggXZc+ePVAqlYyMiiAgakfs2LGDjY5gacCnn36KZ599FsOGDcNff/3V5EKYhTzc3d1x+PBhTJ06FVOmTMHbb7/t1BsoFudCoVBAKpU2evqV7LzVznTSTy6XIzIystkn23r27Fn7eRbXYeHChTh//jy+/fZbtG/fnm45tCGVSvHdd98hOTkZM2bMYOeqZlBaWgqDwdBkCiAmjYEHDx5EaGgooqOjKetzwoQJOH78OMrKyijrszGomqeIPRjT58Tq6mq8/fbbiI2NRZ8+feiWYxUul4u1a9fizJkzTh1x3FRxc4A5zwbLfUwmE6ZPn46wsDAsW7aMbjmk8P7778PPzw+zZ89utXM9WfUiCIRCIYYMGcI6I5pgz549kEgkeP7552nTEBMTg/DwcJcuZM06I1wQjUaDdevW4ZVXXmH8SXIiOmLz5s10S2FhCGazGe+//z4mTZqESZMm4ejRo/Dw8KBbVquBz+dj586dSEhIwDvvvIOpU6eyaV5YKMFatAJxDVl9WdpcFxcXMyaPKuGMaC4SiQRt27ZlnREuxCeffIIdO3Zg+/btGDJkCN1yaOfhhx/Gnj17sH//fuzYsYNuOU6DpZR4QM14ywQDo16vx7fffosJEyZQmmZk/Pjx0Ov1+O677yjrszEszVNkfkdEO1T0ZQ8ffvghFAoF3n33Xbql2Mz//vc/DBs2DPHx8U6basPa2owJzwbLfXbv3o3z588jKSkJIpGIbjmk4O3tjR07duD48eP45ptv6JZDOSaTCadOnSI9Nd3w4cNx5swZxux3mIJGo6GlcHV9iELW3333HaMi9smEdUa4IERUxJtvvkm3FKsEBASw0REstRgMBsyYMQOrVq3Cu+++i927d4PP59Mtq9XB4XAQHx+Pzz77DPv378eoUaNQWVlJtywWF8fShlcqlQIgr4imtbQDAFBUVERKX/ag1+uRlpbW7OLVBGwRa9fh77//xowZMzBt2jRaw8aZxiuvvIIFCxZgwYIF7Ak/G7HmjAgMDGTExvf3339HcXExYmNjKe23ffv2GDp0KA4ePEhpv3XR6XQoLS21+B0VFxeTcliE+K6Z/DyUlZUhISEBU6dOZfxBu7pwOBysXbsWycnJjEj91VwMBgOKi4stPhsqlYpNlccQ8vLysHz5ckyZMgUxMTF0yyGV0aNHY/To0YiLi6M9ao1qyK4XQTB8+HCUl5fj6tWrpLbr7Bw5cgTFxcWYNm0a3VLw6quvgsfjuWwha9YZ4WJoNBqsXbsWL7/8stMs1hYuXAgAbHREK0etVuP555/H3r17sW/fPqxatcppC265ChMnTsTx48chk8kwfPhw2jejLK6NJWeESCSCj48PKSfwtFotVCqVVWcEE077paenQ6/XtygyAqgpYs1GRjg/BQUFeP7559G3b99WX8SxMTZs2IBhw4Zh3LhxyMrKolsO47HFGcGE8e/AgQMIDw9Hr169KO87NjYWJ06coO0+2JI6CSDHaU78jUSx6sb6ovt52LRpEzQaDVatWkWrjpYQHR2NUaNGYdWqVdDpdHTLaRbFxcUwm81Wn0P2QCEziIuLg1gsxoYNG+iW4hC2b98OtVqN5cuX0y2FUk6ePAmhUEhavQiCfv36wc3NjT3IUY+kpCQMHz6cEfVW/P39MW7cOCQlJTltdJ0lWGeEi7Fnzx4oFAqniIogqBsdwYSTqCzUU1hYiMceewx//vknjh07hkmTJtEtieU/Ro4cCZlMhtzcXAwePJgthsviMCw5IwDyDCLWjDxEqgomON+IqIaWOiOioqJw9+5dqNVqMmWxUIhOp8PYsWNhNptx+PBhl0m7QCZ8Ph9ff/01PDw8MGbMGFRXV9MtidEolUoIhUL4+Pg0+j4TjM9VVVU4evQoYmNjaTmYMnbsWHA4HNpSgtjiMKp7nb19+fv7QyAQNNkXnc9Dfn4+tmzZgvnz56Nt27a06bCHNWvW4N69e9izZw/dUpqFLVEzADMOb7R2jh07hm+//RZbt26Fn58f3XIcQnBwMNasWYPdu3fj7NmzdMuhDKJehJubG6ntsnUjGpKWloZTp04xIiqCYPr06bh79y5OnDhBtxTSYZ0RLkTdWhFdu3alW06zWLRoEQA2OqI1cufOHQwePBiZmZk4deoUnnzySbolsdSjT58+uHDhAoRCIQYPHowLFy7QLYnFBbGUHxsgzyBizchDpIRiwuZaLpejbdu2kEgkLfp8ZGQkzGYzUlNTSVbGQhVxcXG4ePEijhw54rSGOCoICAjA0aNHcePGDUybNq3VFrm0BcLx25SRPzAwEFVVVbQ6MX/66SdUVlZiwoQJtPQvkUjwxBNP0JZah2pnhLWDAEVFRTAajXb31RLef/99iEQiLF26lJb+yaBnz5549dVX8d577zlV2lNr9URYZwQzqKysxKxZs/C///2PtjGTKmbNmoUBAwZg2rRpThdp1BJMJhNOnz5NeoomguHDh0Mmk7F1I/4jKSkJAQEBtBaurs+QIUMQERGBpKQkuqWQDuuMcCE+/vhjFBQUOFVUBAERHbF9+3Y2OqIVcfHiRURHR4PL5eL8+fPo27cv3ZJYmiA0NBRnz55FeHg4HnvsMfzwww90S2JxIazlJQbIK5RobXPt4eEBDw8PRmyu5XJ5i+tFAEBERAQ4HA6bqslJ2bNnDz766CN8+OGHpIfnuyJ9+vTB3r178cUXXyAxMZFuOYzFUs0c4P7YSOcYeODAAfTt25fWw1WxsbE4e/YsLam/qHZGWDoIEBQUBJPJhJKSErv7ai537txBUlISli9fDl9fX8r7J5O3334bpaWlTjU2UfkcsrScVatWoaioCLt27XL5FMc8Hg9JSUm4efMmNm7cSLcch5OSkoLi4mKHOiPYuhE1aDQa7N+/H6+//jqjopA5HA6mT5+Oo0ePoqCggG45pMI6I1yEurUinC0qgoCNjmhd/PTTTxg+fDjCwsJw9uxZdOrUiW5JLFbw9/fH77//jqeffhpjxozBrl276JbE4iIUFRVZzEsMkFdEk2iDiIBoqi8mbK6Tk5NbnKIJANzd3dGlSxe2iLUTcv78ecyePRszZ87ElClT6JbjNMTGxmLx4sVYvHgx/vzzT7rlMBJbTsID9KWqU6lUOH78OOWFq+szatQoiMViWgpZKxQKeHt7QywWN/q+u7s7PD09SZsTmfo8vPXWWwgMDMScOXMo75tsOnbsiJkzZ2LDhg0oLi6mW45NKJVKuLu7w8PDo9H3hUIhfH19GZHWsrVy+fJlbNu2DW+//Xar2Uv37t0bCxcuxHvvvYdbt27RLcehOKpeBEH//v3h5uaGU6dOOaR9Z+Lw4cMoKSnB1KlT6ZbSgIkTJ4LP5+OTTz6hWwqpsM4IF8GZoyIIAgICMGfOHDY6ohXw8ccfY9SoUXjiiSfwxx9/NFk0j4V5iMVifP3115gzZw5mzZqFlStXsukwWOzG2uk74j2yToH6+PhYPPUSFBRE++ZarVbj7t27djkjALaItTOSl5eHF154AQMHDsTWrVvpluN0rF27Fo899hhefPFF3Lt3j245jMNWZwRdDtmjR49Cp9Nh/PjxtPRP4OXlhWeffZaWVE3WviOA3DmRic/D1atX8dVXX2H16tVwd3entG9HsWLFCphMJqxbt45uKTZhzVEFMOfwRmvEYDBg6tSpiIyMxIIFC+iWQymrV69Gu3btMGPGDJfeh548eRIDBw4kvV4EAVs34j67d+/Go48+im7dutEtpQF+fn548cUXsWfPHpcqZM06I1wAolbEyy+/zMgfT3MgoiM++OADmpWwOAKz2YzVq1dj6tSpmD59Or799luX2WC0Jng8HrZu3YqNGzciISEBr7/+eqvI28niOJrjjLB300GlkcceUlJSAMCuNE3E59nICOdBq9XihRdeAJfLxTfffAOhUEi3JKeDz+fj4MGD8Pb2xpgxY1BVVUW3JEZhbQyUSCTgcDi0jYEHDhxATEwMgoODaem/LrGxsbh69SrS0tIo7Zd1RgArV65E165dMWnSJEr7dSSBgYFYtGgRtm/fjpycHLrlWMVZ1kutlW3btuHq1avYs2dPkwXoXRUPDw/s2rULf/75Jz7//HO65TgEk8mEU6dOOSxFE8Hw4cNx+vRp2uoCMYHU1FTIZDJMnz6dbilNMn36dGRkZOCPP/6gWwppsM4IF2Dv3r3Iz8936qgIAqlUitmzZ7PRES6IXq/HG2+8gXfffRdr167Fjh07wOPx6JbF0kI4HA4WL16Mr776CgcOHMCzzz6L8vJyumWxOCm2OiP0ej1UKpXdfTnD5loul4PD4SAiIsKudiIjI1FQUMDOqU6A2WzGnDlzcOXKFRw5cgRt2rShW5LT4u/vj6NHjyI9PR1Tpkxx6ZOTzcVajQA+nw+JRELLGFhYWIg//viD9hRNBE8//TS8vb0pT9VE1TxlMplQWFhosS8vLy+IRCJKn4fTp0/j+PHjeP/9913OyLpw4UJ4eXnhnXfeoVuKVayNFQAz1kutkczMTKxatQqzZ8/GgAED6JZDC//73/8QGxuLhQsXuuQa19H1IgiGDRvW6utGJCUlQSqVYsyYMXRLaZLo6GhERkZi9+7ddEshDdYZ4eRotVqsXbsWL730ktNHRRAsXrwYJpOJjY5wISorK/Hcc8/h888/x2effYbly5e7fIGt1kJsbCx+/fVX/PPPPxg2bBjy8vLolsTihCgUitrC0U1BVlFVZ9lcJycno0uXLnZHjxGRFWyqJuaze/dufPzxx9i1a1erNS6QSa9evfDJJ5/gwIEDbD2y/9BoNFCpVFYN3UFBQbSMgd988w0A4IUXXqC878YQi8UYM2YMDhw4QKlDy5Z5iozvqLS0FEaj0WJfHA6H0ufBbDYjPj4eDz/8MMaOHUtJn1Ti7e2NlStXYt++fZRH3DQXW5xidI0VrRmz2YzZs2fDz88Pa9asoVsOrWzZsgVGoxGLFy+mWwrpOLpeBAFRN6K1pmqqrq7GZ599hkmTJjE6GpnD4WDatGn4/vvvkZ+fT7ccUmCdEU6OK0VFEEil0traEc5S4IulaRQKBYYPH46zZ8/i559/xsSJE+mWxEIyjz76KGQyGQoLCxEdHY0bN27QLYnFybD1FChgfxFNW3IgE5trOk9Ty+Vyu+tFAEBYWBiEQiHrjGA4Z86cwbx58zBnzhyXSktCNy+++CKWL1+OZcuW4ffff6dbDu0UFhYCsByFRrxPR92cAwcOYOTIkZBKpZT33RSxsbFIT0/HlStXKOvT1lz9ZMyHRFuO7stWjh07hnPnzmHt2rXgcl3TVDFz5kwEBwdj1apVdEuxiK1rM7prbLU2vv32W/z000/Yvn07vL296ZZDK0FBQdi4cSP279+PP//8k245pHLq1CkMHDjQ4SmtRSIRBg8e3GqdEd9++y1KS0sZWbi6PhMnToRQKHSZQtauOcO3ErRaLRISEvDSSy+he/fudMshFTY6wjW4efMmoqOjkZeXh9OnT2PEiBF0S2JxEFFRUTh//jy8vLwwZMgQnDlzhm5JLE5Ec5wRZERG2NJXdXU1Kisr7erLHuRyud31IgBAIBAgPDycdUYwmJycHIwdOxbR0dHsuscBvP/++3jiiScwfvx43L17l245tGJLSjzifapPO2dnZ+PMmTOMSdFE8Pjjj0MqlVJWyNpsNjcrTZM9TnOmPQ9GoxErVqzAo48+ipEjRzq8P7oQiUR499138e233+LixYt0y2kUs9ncrALWbCo8aigrK8O8efMwatQoRqeUoZLJkycjJiYGM2bMgEajoVsOKVBVL4KgNdeN2L17Nx5//HGEhYXRLcUqvr6+GD9+vMsUsmadEU6MK0ZFEBC1I7Zt28ZGRzgp58+fx5AhQyAWi3H+/Hn06dOHbkksDiYkJARnzpxB7969MWLECBw+fJhuSSxOgi2GF19fX/D5fLsMIs0x8hC66KCoqAgFBQWkREYANXUj2CLWzESj0eCFF16AUCjEN99843L50ZkAj8fDV199BX9/f4wePRpqtZpuSbTBNONzXb7++muIxWKMHj2a0n6twefzMW7cOBw8eJCSzX95eTl0Op1N35FGo7HLac605+Grr76CXC7H2rVrXT6d6yuvvIKIiAjEx8fTLaVR1Go1qqurbXo2DAYDysrKqBHWyomPj0dlZSW2b99OtxTGwOVysXv3bty7d89l0lalpqaiqKiIUmdEeXk5rl27Rkl/TCElJQVnz55ldOHq+kyfPh337t3Db7/9RrcUu2GdEU4KUSsiNjbW5aIiCNjoCOfl+++/x2OPPYaIiAicOXMGoaGhdEtioQhfX1/88ssvGDNmDMaNG4dt27bRLYnFCbAlPzaXy4VUKrXLIKJSqaDX6xnvjCCiGMh0RsjlcvbkIsMg8j5fv34d3333ndXnkqXl+Pn54ejRo7h79y4mT57can8LTDM+1+XAgQN45plnGJl2ZMKECcjJycHZs2cd3ldzvqO617e0L6FQaPWeU/E8aLVavPXWWxgzZgwGDhzo0L6YAI/HQ0JCAk6cOIE//viDbjkNIL5vW2ps1b2exXGcO3cOH330EdasWYOQkBC65TCK8PBwxMfHY/369UhNTaVbjt2cPHkSAoHA4fUiCFpr3YikpCQEBgZi1KhRdEuxmYEDByIqKgpJSUl0S7Eb1hnhpOzbtw95eXkuGRVBEBgYiNmzZ7O1I5yMXbt24fnnn8ezzz6L3377Df7+/nRLYqEYkUiEL7/8EosWLUJcXByWLl3qEqGELI7DllQAQM2m2J7cxMRnmb65Tk5OhlAoRNeuXUlpLyoqCuXl5cjOzialPRZy2LlzJ/bt24ekpCT07duXbjkuT2RkJPbv349Dhw5hw4YNdMuhBYVCAV9fX6tFGoOCglBUVERZyob09HT8+++/jEvRRDBkyBAEBwdTkqrJ1nmKeN/eOTEoKMhqFIK9c68tJCUlISsrC++//75D+2ESzz33HAYNGoTly5czzkFqq1OMjOeQxTo6nQ7Tpk1D//79MXv2bLrlMJL4+Hh06tQJ06ZNc/p958mTJympF0EgEokQHR3dqpwRzlK4uj4cDgfTp0/HDz/8gLy8PLrl2AXrjHBCiFoRsbGx6NGjB91yHMrixYthNBqxZcsWuqWwWMFsNiM+Ph6zZs3C3Llza8PtWVonXC4XGzduxNatW7Fp0ya88sor0Gq1dMtiYSC2pk4C7D+daevmWiKRgMvl0ra5lsvl6NGjB2kpe4gIC7ZuBHM4ffo05s+fj/nz52PixIl0y2k1vPDCC1i5ciXi4+Pxyy+/0C2Hcpoz1ppMJsoOAx04cABeXl54+umnKemvuXC5XEyYMAHffPMNDAaDQ/uiOjLC1udBrVY7LMVZRUUF3nvvPbz22muIiIhwSB9MhMPhYN26dbh8+TLjUps2p7g5wEZGOJpNmzYhLS0NSUlJ4PF4dMthJGKxGLt378bZs2fx8ccf0y2nxVBdL4KgtdWNOHToEMrKypyicHV9XnnlFQiFQuzbt49uKXbBOiOckH379iE3N9eloyIIAgMDMWvWLGzbtg0lJSV0y2FpAp1Oh9deew3r1q3D5s2bsXXrVnC57PDCAsTFxeHQoUM4cuQInnrqKTanLEsDbM1LDFDnjODxeAgICKA1TRMZxasJOnToAC8vL9YZwRCys7MxduxYxMTEYOPGjXTLaXW88847eOqppxAbG4vbt2/TLYdSmmN8Jq53NGazGQcOHMDo0aPh5ubm8P5aSmxsLIqKinDixAmH9qNUKsHj8eDn52fxOn9/f3C5XMqcEQBQWFjY4r4ssXXrVqhUKrz99tsOaZ/JDBs2DE8++SRWrlzpcEdXc1AqleBwOJBIJBavI6OeF4tlbt++jXfffRcLFixgazBaYfjw4Zg0aRKWLl2K/Px8uuW0CKrrRRAMHz4cKpWq1dSNSEpKwsiRI9GlSxe6pTQbHx8fTJgwAXv27HFq5xFrLXQy6taKcPWoCIIlS5bAYDCw0REMpby8HM888wy+/vprHDhwAAsXLqRbEgvDGDt2LH7//XdcvXoVMTExyMnJoVsSC4Ow1UFAXGOv4YXP58PX19fhfbUUs9kMuVxOWr0IoOb0JVvEmhlUV1djzJgxcHd3x9dffw0+n0+3pFYHj8fDl19+CalUitGjR9tVANjZYKIz4tq1a7h58yZjUzQRPPTQQ+jWrZvDUzUplUpIpVKrh3rIcJoz4XkoKirCxo0bMXv2bHTo0IH09p2BhIQEpKen49NPP6VbSi1KpRISicTqHMXhcGhbL7UGzGYzZsyYgTZt2rRKZ11L2LhxIwQCAebPn0+3lBZB1IuIjo6mtN8BAwZALBa3ilRNcrkc586dw7Rp0+iW0mKmT5+OrKwspy5kzTojnIxPPvkEOTk5WLVqFd1SKIOoHZGYmMhGRzCMvLw8DB06FBcvXsSvv/6KCRMm0C2JhaHExMTg7NmzKC8vR3R0NHtCm6UWW4skEteQYXixJXKLrs11dnY2ysvLSXVGAPeLWLPQB2FUSE1NxXfffQepVEq3pFaLr68vjh49iszMTLz++uuMy9fuKJRKpc1jLXG9ozlw4AAkEglGjBjh8L7sgcPhIDY2Ft999x00Go3D+rH1OwLImRPpfh7Wrl0LoCbfe2vloYcewoQJE/D222+jurqabjkAqH0OWZrmiy++wIkTJ7Br1y54eHjQLccpkEgk2LJlCw4dOoTjx4/TLafZnDp1itJ6EQQikQiDBw/GqVOnKO2XDnbv3o2goCCnKlxdnwEDBqB3797YvXs33VJaDOuMcCKIWhETJkxoNVERBIsXL2ajIxhGamoqoqOjUVRUhDNnzlAeSsjifISHh+P8+fOQSCR45JFHWsXJCxbr2JqXmLimtLQUOp2uxX3Z0g9A3+aaiF4gM00T0d6NGzcYlQaitbF9+3Z89tln+Pjjj/HQQw/RLafVExERgc8//xyHDx+uNYi6OraOgR4eHnBzc3N43RyTyYSDBw9i3LhxpNXIcSQTJkxAeXm5Qw1czZmnAgMD7S5gbUtfAQEBtdeTSVZWFj788EMsXry41Ttn33vvPSgUCnz44Yd0SwFge9QMYP9zyNI4RUVFWLhwIcaPH4+nnnqKbjlOxcsvv4yRI0di1qxZDqt14wjMZjNOnjxJm12lNdSNqKqqwueff47Jkyc7xbqjKYhC1seOHUNubi7dcloE64xwIlpjVARBUFAQZs2axUZHMASZTIYhQ4bA29sbFy5cIP0EL4vr0q5dO5w+fRr9+/fH//73Pxw8eJBuSSw0Y2teYsD+vNXOsLmWy+Xw8vIiPV1FZGQktFptq8uRzxT++usvLFy4EIsWLcJLL71EtxyW/xg9ejTeeustvPnmm/jpp5/oluNQzGazzWMgValXzp8/j6ysLManaCLo0aMH+vTp49BUTc2dp1r6HVVXV6OiosKmvgQCAfz9/Ul/Ht555x14e3tjwYIFpLbrjISFhWHKlClISEhgRH215jrF2MgI8iFSVW/dupVuKU4Hh8PBrl27oFAosHr1arrl2AxRL2LYsGG09D98+HCUlZXh+vXrtPRPBYcOHYJKpXLKwtX1eemllyASiZy2kDXrjHASdDpdbVREeHg43XJogZ2QmcHhw4cxcuRI9OnTBzKZDMHBwXRLYnEyvL298dNPP2H8+PGIjY3F5s2bW02KDJaGKJVKBAQEgMfjWb3W3rzVVBl57CE5ORmRkZHgcDiktks4jdm6EdSTmZmJF198EcOHD8e6devolsNSj9WrV+PZZ5/Fyy+/jPT0dLrlOIyysjIYDAZGjYEHDhxA+/bt8cgjjzi0HzKJjY3FsWPHUF5e7pD2qZqnCKc+Xc/DjRs38Omnn+LNN9+El5cXae06M6tWrYJGo8GmTZvoluIU6yVX5q+//sKnn36K9evXo02bNnTLcUq6dOmCt956C1u3bsWVK1folmMTdNWLIGgNdSN2796NJ554Ap06daJbit34+PggNjbWaQtZs84IJ6E1R0UQsNER9JOYmIhx48ZhzJgx+OWXX2wqAsvC0hhCoRD79+9HfHw8Fi9ejAULFjjlJMpiPzk5ObUpIKxBpTNCKpWiqKiI8rRG169fR8+ePUlvVyqVIjAwEFevXiW9bZamqaqqwpgxY+Dp6ckWrGYoXC4XX3zxBdq0aYPRo0c7zMhMN81JiUdc50gDo8FgwDfffIPx48fbVMeHKUyYMAEajQbff/+9Q9qnyghMfI6u5+HNN99ESEgIpk+fTlqbzk67du0QFxeHLVu2oKCggFYtBQUFzVqbsc4I8tBoNJg+fTqGDBmCKVOm0C3HqVm8eDHCw8Mxbdo0p9hnnjx5EgMGDKCtPohIJEJ0dDT++usvWvp3NNevX8eFCxdcat6ZPn06srOz8csvv9Atpdk4z8qvFXPz5k3MmjULTz/9dKuNiiBYsmQJqqqqMGjQILqltCpMJhOWLFmC+fPnY/Hixfjyyy8hEonolsXi5HA4HCQkJGDnzp3Yvn177QafpXXx0Ucf4caNGzZdS4YzwtaCjEQBt6NHj7aor5Ygl8uRnJyM1NRUh7SvVCqRkJDgkLZZGqLT6TBlyhSkpaXh6NGjNqUiY6EHb29vfP/998jNzUVsbKxLOiR27twJAEhLS7PpekfXzZk2bRqUSiUeffRRh/XhCDp06ABfX1+8+uqrMJlMpLat1+tRUlLSrMLBxcXFLXKaE98tHUWK3377bRw5cgTvvPMOu5+ox9KlS8HhcDBw4EDSny9bSU5ORnFxsc3GraCgIJSVlbW4nhfLfcxmM5555hlkZGQgKSnJqRy1TEQgECApKQmXLl3Ciy++SLcci+Tk5ODbb7+l/dBKVVUVfvzxR9y5c4dWHY5gyJAhEAqF+L//+z+6pZBGv379EBAQgGeffdYpHG51YUc3J+DHH3+EyWRCTEwM3VJoJygoCAKBALdu3aJbSqvh7t27CAoKwqZNm5CYmIgNGzawCyMWUpk5cyaOHDmCH3/8Ee3bt8eff/5JtyQWCpkwYQLGjx9v07V8Ph8CgQC//vprs/vJzc1FaWkptFqtTdfPmTMHISEhGDJkSLP7aikhISEIDAzE/PnzHdL+Sy+9xK4lKKRfv344cOAAPvroI/Tu3ZtuOSxW6N69O3bu3Injx487VdogWxk/fjw6dOhg8ya8srISycnJDjUw8vl8pzxgNGjQILi5uZGeTo+IXLPVCG00GmE2m3Ht2rVm93XixAkAsPkEbkVFBS5dukSKgZzIb/3ss8/a3Zar4efnB29vb2RlZaGqqooWDWFhYejUqROWLFli0/XEM/Hvv/86UlarQC6X488//0RgYCAiIiLoluMSREdHw83NDUeOHIFKpaJbTpPweDxwuVzaHfSTJk2Cl5eXS2bAMJlMaNeunVMXrq4Ph8NBjx49SF+PUAEbK+4ELFy4EDExMRg4cCDdUhhBUVGRS+f0ZRpvv/02ioqKsGjRIsybN49uOSwuyqhRo/Dxxx9j4sSJWLZsGS5evEi3JBaKaE4hUL1eD71ej9OnTze7HyL6wtYCzoMGDUJWVlaz+7EHHx8fhxbN/vLLLx3WNktDAgMDER4ezhasdiImTJiATz/9FKGhoXRLIZ0hQ4YgMzPT5utv3LgBtVqNiooKh0T17Nu3D3v37nXKDfTPP/8Ms9lMunbCmJuXl2fT9cR1ly9fRt++fZvVF5GGQ6PRwMfHx+r1N27cQFlZGTQaDdzd3ZvVV31++eUXmM1mNlqsCe7cuYOTJ0/C09OTlv7d3Nxw9+5dm68nUkpdunTJKZ2LTCI8PByLFy/GwoUL6ZbiUsjlcuzZswfe3t50S2mStm3bQq/X037oc/r06S6VxqguxcXFLhmNJ5PJUF1dbVP9RSbBMbNVQ1lYWCygUqlw7do1xMTEOOWGkcW5+PfffxEYGMgWRmdpkh9++AHdu3dH9+7dm/3ZXbt2YfLkyS65EGVhYWEhE6VSiV9//RUTJ06kW0qrwWQyYdeuXZgxY4ZNRgWj0YiPPvoIM2fObLYBKycnB2fPnrU5MjE7Oxvnzp2z+XqW1gPx3LbkOWRhYWFhaZ2wzggWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFofCpmmiiLt372LunDkoV5WR2m5IaCj27//MpfKeAcCyZcsgO3vOYe17e3pi69Yt6NGjh8P6cATl5eWY9PrrUCoK6JbSKDw+H3HzF2DMmDF0S2FxIvR6PV577TVkZ2c7pP0BAwZg8+bNDmmbpSFmsxlvvPEGbt20rUiqrXh4emLzB1vQs2fPFrexceNG/HD0OxJVNcTL2xtbE7ehW7duDd47ePAgdu38EKZmFhjj8fmYOy8OL7zwQoP3du/ejS+/+AKOPlsS1CYIn3zyKby8vBzaD9PIysrCe++9h8rKyha38cwzz+CVV14hURULWeTl5WHG9OkoLSl2SPtDhz+KNWvWOKRtwDHrQk8vL2zZmmh1jfz1119j54c7mj2eNRcuj4c5c+dh3LhxDmn/zp07mDt3HspU9BUtD5QGYO/ej50qbVFKSgoWLloEtVpNWptDY2KQkJBAWntM54cffsCWDzbDoNe3uA2z2Yw7dzNQpdEAuB/B7ufvj3Zt25Gg0n54PC5mzZyB2NhYuqXYhUwmw+q334HGxtpjVCMUCLByRTxGjhxJtxTSMZlMeH3SJNy+bXv6MNrgAEHSAIjFYoc0LxaLsXTpUoSHhzfrc0ePHsXWrVtgNDh2zu7cpQs++eQTh0VJzZo1C1evJwMO2Pb4+fpg584PGZeq02g0YtKkScjIyHBI+71798aOHTsc0rYl2MgIiti9ezdmzJiBF3pLSUt1k12qwd+Z5bh27Rp69epFSptMwGAwQCAQwCu4O3w6OebvypF9g02bNmHRokUOad9R/Pnnn3j88cfxaHcpAjyFdMtpwC8pCgwb8T/8+OMxuqWwOBHJycno1asXoofEILRjR1Lbvn7tKlLlyaiqqoKbmxupbbM0TnZ2Njp06ICHgj3RJcC+vNJVOiO0hprCiCfSSzF37lxMmjSpyeuJvNdNFah7YsTjcIcGAzv62qWrKcxm4Nsr+UhMTGy0xs5jjw7Hlb/PYkR3/2a1+1taMQYPG4Gff/mlwXuRPSNQUliAR/tHtVS2VZQlKvxx4Rr+/PNP2gvrUc22bdsQFxeHzg/FAC1Yv+Xfug6Jrzeys2zP1c9CHQcPHkRsbCz+r2cAxAJyN85Xcytwp1gLowON9SdOnMCIESPwWDd/SDxafjDJYDSjQmuA2QycSC/BggULLKZn4nA4WLhgAa78fRYjewa2uF9b+C1FiYGPDMevv/3mkPZ37dqFWbNmQRr9Qot+4y3BbDbDWF0Os8kEo0aNivQL2Lp1K4YOHdro9UFBQQgKCkJqaioMBoPN/XTs2BGenp5ITU21qfA0UQhTr9dbra/06aefYtu2bXh2HDmO1tRrl5FxK42UAtnOwpgxY/D7z8fwdFTbFreh1Rvxw7U8eHbsBfd2DQ9BOAKTXguj1rbi2hweH6qbFzCk/0M4dfIvBytzLHPmzMHOXbsQ8kjDgyFMIOf8D3gldjz2799PtxTSuX37Nrp27QrvsL5wC+pMtxyLlN++DH3RPXiL+YhoS/4BnjO3ixEfH99sx+1zzz2Hk3/+gf97bDDpmgjuZOXh72s3cPv2bXTp0oX09ktKSiCRSOAZGgnP4OY5Y6xhNpuhOPctPv74Y7zxxhuktm0vt27dQrdu3dB/YDS6hIWR2naKPBnJ166itLSU8qLlbGQERZhMJgh4XGwbaznHtUZvqt0M1f3vxriWW4GnP7rmsou2Ls/OQodh9uUlNeo04AkbeqULr/zmlPeN0Lz+hZ4I8bfNyKfRGyEW8Jr8NxkYTWbwuBxM/vQyTEbnu68s9EI812+98z4GDXmk9nWNRvPAqZK6/67/XlN8+/UBTH39FYefGme5D/F9Lh3REUO7+Fq8trF5jnjt3+wK/F/StQfe2759O7Zv395ibXwuB68/1gmLRnSBRl9jIKw/HtoyRlq65ufUoibnF5PRiMe6+iHxhW7/tWN5nieYfjANOlPjBk2TyYSxIwZjbVzjhkONVgexSNjgv5vDvTwlIp+f55Tzpr3o9Xq4efrg1S3ft+jzJ/dvwI2fXc8w4CoQz/SW57vBXVjzm7b0u7T1NwsAX14qwJLvb5EjtAkI/etGdUWwb9NzorX9xcgPryAl735kwJYtW7BlyxaLfQdIJHi0hxTbYvu0SLut69EZn/8LdRPjHxmYTCbwBEJ0nbKt6Wv0GnAF5J1yzf7+A2T/8GDE5vz58y1+ZuLEifj888+b1Y+/JABPjHwcBw9+bfNnHn/8MdzLysGdW+lWr/Xy9sH72/davEar0UBkw3rtu68+wTsLZ9is0xUwmUwY3EWC7S/XFB9v7h5NozeiQqPHD9fy0GHUQvj3se1EfP3nuTnPt1FbjX/mRcKo09is07NLXxhdYH9oMpng37En+s1+cB3alL2hPrZe11I0xbkuu04j/q5O41bCt/v9IukmnQZcG+6prdfZg9lkBIfLQ86ve5D59duYNbQDZg3rSHo/MVv+gb4F0VQmkwnDBvTGnvebdxiX2DvYsoc4czkZ/5u8zGHPIdFux1ELIO37FIDm/64sXa84f5iRvyFC04q33sawx0bUvk6GreTHo0fwWuw4Wv5u1hlBMRfuqaCo0OHZngG4nF2z6G/rLcLV3EqUVesRFuCOh4K9kFWqQZXOiOT8SoyOkiKjWAOplwDKCh0kHgLkq3S4U2TbiQRnpTLvNnLPH0W7gf+HkvRLAAA3SVtU5t4GuFzoKkrgJmkHo6YK1cV5EHh4o+2AZ1GcdgEAIPT0g0FTCb6bF8S+geDyhShJvwiz2eT0hsm7hWpkl1aDy+EgLNADZ28XQ601wkvMB5/HQQd/d0g8hLicVYZALxE8hDyUVesR0dYbGUVqpBVUgMfh4MnIIGQUVyHQS4Sskip0lLjjbmEVBLyak2G5ZdXwFPHB5XJQXq1HiJ87BHwOtHoTMkuq4O8hhEZvRJCXGAaTCcpyLYJovjcszsud27dgRk1oeV5uDhQF+Xjqmedw+1Y6dDodxGIxfP38UFJcDJPJBHcPD+TmZMNkMqFjp84ICmoDgVCIP379GYFBQeDzBUi+fs1qvyyO4UaBGqVVeoyKkuL75EIEegoh4nORUVyNyLYeuFNUDT93AfLLtRgdJcWuM7kI8haiS4Ab/N0FSCmoSYsTvugA+B5+KL3+B4zVlfAK6wcOXwiYTDBqq2CoKIZX1/4AgOR3nwQARL1VE0FQ/zP39syCskKHv++Vwk3AQ1pBJTxFfIS38cRNRSVEAi6kniKUa/QoKNeik8Qd94qrMKpXG1y4VwoA8HMXoFJrhLeYD6mnEEI+F6n5lSit0oHP5VidX4oqdfg+uRDP9gzAlZwKcDiAiM+FzmBCkLcQ+SodOknEEPK4uFtcjbJq66dgS1QVkP2bCi6Xg26h7aEoKkVpRU3qDD6PB4PRCAGfB3exCB5uYmj1eqirNMjML0RImwDwuFxo9Xq0DfBDrrIEYSFt4OXhjht3syEWCXH1pmPCgp2Je9fOobK4ABHDR+Pvwx8BANy8/dF14AiUFWShMDMdXpI2EHt6w9M/EO4+EmReO4fibMuni1mYwT+ZKqg0RjzbMwDX8iqQXaqBu4AHiacAbnwu1DojeFwOvER86IwmVOmMyC/X4ZHOviis1CHIS4i7xdXQGkwI9BRCYzBRuk7/N7scl7PK8WykFJeyavYX7XxEuJpTUbO/kLrjoWBvZJVW1+wv8ioxulcgMoqrIfUUIqtMi4DoF9B25FQADcfO+uNt9ncboMn4B+XVXJy7U1y7Hv37bgmyS6rR3s8NXaQe8BLzIc8th8FkhsRTCDcBDyF+brhdqIabgAe1zgAxnwuNwYS+ob64nq1CW183+LoJ8HdGCQDAYKJmzV6Vlw59RTE4HC7c2oZBV6aAoaomyo7D5cNsMoDD48Orc18U/fM9RP7tYFCXwWw2w719N5h01TBUlcOkrYJHaBT05YUQ+AShMuMKOFweeGIvmE0GcAUiVGZeRzsfEfZN6IY/0ktRqTWiX4gXhDwOTGagSm9EsdqA/h28cKdYg9nf3kJGRgbcg8PRZfIWm76jkkvHkHt8B7IyMzEo1AtvP9nRal9bTuUgMysLuTl5CBo+EYFDX26yn6ILR2DMvoLL52UoUhTgiVFjcfLXH9EupCN0Wg0KC/JhNpvRMaw72gaH4LuvPkFo567oEdUHxYVKSAPbIE1+FdI27aDX6ZCb2TrnmVK1Dkev5OD/erfHtewyeLvVRDgpyzUwmsxIyVNh9qNdcTW7FCJ+jaMiyEeM9IIKeLvxodPXGJC0xblQpZ0HuFy4twlD2Y0zMGqrwHfzBIcngCggBAIvCSru/AuhjxQ8sQcMahU8QsJRrciAOucmOFwuJA8/CY3yHgQ+UmgKsyEOCIamKBv4z1Clzr0Jo06D5Y93wPAwH6vP1HN7U2CoKAbaetBzgx1Aec5NaMtrxgqv9mFQK7NQnp0GDpeHDjFjUZ59E5oyJapL8tC235OoyEmHtrwYHkGhMBn00JYXQa3IhDRiMIw6DcS+UqgLs+ER2AEcLh9VyiwYNGroq8vBE7nDqK0Cly+CwMMbAncfGKrKoddUgsvlw2w2gS+qOaCorSil+c44Hk1RNsrMZoDDhXvbMGiKsqDOqbn3QYNfgDr3JnQqJbQl+Qh4+H9Q596CvqIYbtIOMBkN0JcXobrwHnx7DIZJp4HQJxCaomyIpSHgidyhLcmHtiQfRq0abkGdYNTUrKU1xTngu/uAw+PDbNCB7+4DgZcE+soSGNRlgNkMoW8QzCYD1Lk3AQCFFTqcv1sKLgcIk3rg73tlyCqpRntfMbpI3eEl4kOeXwGD0YwATyHEAi583QRIU1RCzOeic4A7+Dwu3IU83FLW6DADMNphNC4uK8e3v5zCmJGP4O9rNel02wcFQFlcitTbmRjYJxxFpSrkKYvhJhJCwOcjuK0UlepqGE0meHt6wM/bExk5BejYPghGownl6irkKYpgMpmRmUdNKnF17k2YDDoE9n8WFfeuge9eExmvK1PAbDKhMjsFHZ6ahfKMq+AJRAAAoW8bqPPSwXfzAt/NCxweHzyRO9R5t2oivUymmhB3BnP3zh2IRGL4NmIr0et0ENWzlXjUsZWE1rGV3LyRCqAmGuTa1Su0/T2sM4JiBnWs+aEUVuowILTmv0vUemj0RugMZgR6CZFeqIavmwB6owntfUT461Yp2vmI0Na75n8AEOwrhpBPTSgxXXi2C0P76NHQlBVC0mMgAEBbXgxpr+HIOXsYerUKvp17gxsggke7LjBUV6Lgyh8Q+wTAbDbDu0PD0K3A3jXpJchKlUUXnaUetZERhRVajOrTMCdoYYUWz0S1afDa7UI11FojHu0RgHKNAX5uApSodeAAuJ6jQoCnCA918AUA9INfkxr6dWz4XuB/zycLS0sI7xmJbj3CweVy0SM8AgCgVCjQb8BAHPzqcxQVFuKRocMR1KYtTCYjysvLIRaLoa6shKqsDOERNbUExox9sbZNNjUKfYS38cDQLjWGulFR0trX7xZX4/ebJRjR3R9eIh7a+4pw7p4K4W3cweVwIOByEOovhn9+zYbcM7QXVGnn4NN9MAzqMpj0GuiKc+EeEg7vrv3B4fJRmXEFPhH30+hoi7Ih8JLAIzjiges5XD7a+YoR2dYLHA4Hvdp7AwBU1Xo81j0AQj4X3/ybB1W1AY92k0Ak4ELiIcS5uyUQ8nnIV2kg4nPRP9T3gb91QJ20T9bmlwBPIUZFSVFYqUOuSovCCh2Ghfki0FMEoxnwFvOQWqCG3mhGgKcAI7r745srSott+vt4Iebhmt+MorgMkV0fzHV64OfTKC1X47H+UajSaOHl4Qaj0QSJrxeyC4rQPtAfQ/rUzJmdg+/PG/0juwIAvDzYFGcde9eEtVeWKOHuI0F1RRmCI/pBpciBu48EbbpEorJEgcJ7N2HU6+ATGIywAY8j58ZlKOVnaVbPYo0BoT5wF/JQWKnDwFAfDPxvja7Rm/DNVQW0BhMe7eqHap0JPmI++FwOqvUmnL+nQoCHAD3bejZInynPJy+XvjUeDvFGsK+4Rv9/e41PL+RCbzTjse7+EPG5uKGohJDHRYlaj7beIqQrq9C3Q80YyOdy4NamCzxDo1B8+bjV8VYsDUVlxj/wdhNgcJeaOgeFFVo80+vBdDPFlTpUaAwo1+gR0c4LIj4POaXV8BLzodYakFNSjc5Sj9o2+ne6n8Lu8fCa9E/fXMoBFRna66a40akK4RES0eh1OlUhzCYj1Fly+EYOB4fLh6G6HBwuFyZdNcDlQVemhGdoJADAv3fD0+oCbykCvMTILtNicCcflFUboDGYkKvSIbyNO/qHeoPP5eBCZjkqNPcd0iL/tjZ9R+qcG3Cvk8Kiva/Ial9XcisR6idGZlHNZ9zadoW2KLvJfjT5t6HKuoK+0TEAgOJCBR598jkAwDf790BTXYWhI5+CUCTG3fQb6Dd4GMpKipD87z8IahsMf2kgBj/6RK3Gmymt8/CIn4cQox8KRmGFBgM71/wOiiu16B7khcOXs+Em4OFyZgna+rrBaDKjUmPAv/dKUVatQxe+JzpIavaDIkl7+PSIBlDzjEoHjmrQl05ViIB+Tzd4zavzQ6guuAODWgV9eSH4XhKYDXrwxB5Q56TBbNBD6BsIr84PQRzUEXe/WImebd2tPlOXsivg7S5Clci+lJ1Mwzv4fqYLTVkh/MMehn/Yw7X/9gmNgE9oRO2/A8JrTvLr1Crk/XMcBo0aQb0fBU8oAt/NAzp1OcxGIypybkHo7Q9JjwEt0iXyanrf7iqIA0JqIyN0qkJ4d34I3p0fqv23Z0gE8N/YrVMVwrd7jQ3JoFaBK3IDly+E4uy3KEs7B/+oRwFOzW/HoFZBnZsOs0EHkaQ9vEJbnvq04u41FAGQegkR3bnmOyms0OLpyPspDb/9Nx95Kg2e7hkIsZAHk8mMCo0BV3PK4SbgIsBTiFDJ/d9N3X0Gz45aDBJfb4x9chgUxaUY/HDNvrmoVIW+bbuhf68e+OrHEygrr8TIIX0hFglhNJqQla+EwWCAl4c7qjVaRHXrhOA20gfa7dW9JnXWmcvJLdbWHDzad4e071M133G3mu9YV1EMj3bdoDh/BDyBGOV3LkPk37YmHWJ1BUpTTkPk1xYmvRZu/83PAODbrc7vjeE2wh7hEYjs3adRW8nXX32Owv9sJW3atIXRZERFeTlEjdhK+g0YWNtmQX4eLX8LwDojaEPqKcTxlCJIPAQoqzZAyOfCDAMKyrXo2dYTfC6gKNdBbzRD6imE2Qz8m12B3u09weMy+0dCNmLf+4OdyFuCvH9+grs0BDo3L6juJUNbVgjvDuHwDo2ER2AoStIvwmTUo/T2v/D7b2Hgyki9GncA1H/9p+QCSDyE8BLxIeRx8GdaISLaeqNnO2/4ewiRml8OHzcBNHpT7Sm3XsE+tWkLWFiowNPT84F//33+LCQBUnh7eUMoEOLC2TPoGRWFqF594C8JQElxza7ZbDYj+fo1RPXqTYdsFgtI6xjojqcUIcRXBC8RDzeVVSis1CE8yAOR/817V3Iqcae4GirNg9EAkr5P12/2AXwiYmy7/r9FpofoweWPz38nEX+SKxDi5wYvkR7yvAooK3SIaOuJyHbe4NdGjGnQt4P1v9sSxPzvJeJBwPvPGRPkgZ5tPeHvzkdJlQF6oxEmE3C7sHmnq4Mkvg1ecxeL0aGtFLdzCqDR6qAoKUNkWCj6dO8EPo+Hs1dv4I8L1zBiEPv7sUTq6R/h4RsAkbsXeHwBbv/9B4K69IRfu45w9/GHrroSvm06wGQ0ojAzHdJQavJ3s5CHtJ5D4c/0EnSTuqO02gB5vrp2zOrZ1gOh/mKcy1BBbzTjem4FerWnv7g7of94SiHC23igtMoAeV5lje42HujU1g2h/mJcyalAaZUeFzLKMKiT7wNt2DLellz5tWHfjaxHL9wtRgeJO8qqdJDnlENZoUVEOy/0DPABn1sTacvEfY3QR9rkexW3L0IcEAKD2gvqLPl/jotweIT0hDggFBUZV6AryUWFUQcOTwBPC0atpyMsF6we0c0Pd4qqG7xu7Tvy7tofRX/nNquvmM4++DP9wZPVlvrhCoQP2G0k0prY6BM/HUWX7uFQlZUgLfkaigoL0C08Ch06hSE4tBPSU5ORk5kBdUU5+j8y3KKm1oTU6346DYmnCD9dz0OIvzu8qvTILavG1awyhLfzRmR7H4RK3JGuqEB2SXWtM6IuTT2/jb0u9JGi6NJxiAJCwBN7oTLz/jPt2aEnxNJQqG6eg9nYMErT2jM1PMzXyl/t/NS1U1j6d+7fP0HkLYHQ0w98kRsUV/+ET4dw+HSMhNBLAqNGDROPB9W9FPBF7vBq35Wyv8FZqf88W/o338MHhZeOQ+AlAc/NExy+ACXX/6p9zgWe/jWOZg4XxupKVOXdgns78r6DuvPjcbkSIX5ieIp4SMmvQGGFDuFtPdGzrRdCJW64lKmCskJHWt+NESS577gK8POp/W8PNzFC2wfhZkZ2zX6huBSRXTvhoZ5dwefxcOHqDZy/koLoh3o6VJ+t1P2OhV4SFF46DnFACPRuntCU5KE84xo864xl5XevwKSpoFGx/dS3lQQGBeHHo0fQoUNHeHl54+7tWw/YS0I7dcbZ0ychELS8rpijYJ0RNPJ0zwCL7/cP9aZIiXPRbsAzFt8noh9YHqR+lER9BnRqXkFVFhYq+L/Rz1t8f9ijj1OkhIUMrM17MXVqTPyUUlT736qb51GVnQq3tl1h0lbBu/sgcLh8aEvzwBN5QFdWAJ6bl9XrzVbyjj8TaTnR3ACSCl835z6QwahHLZ+ye3KI6zvuySBi6P9ZfL9D1CCL77M4H9Z+q0/0sGyMo4unezZtTAeAmC6Nn6Bt7lhrjfqREg10dLN8f5mINUeAbz3nuCXO31MhtaAKXaVuqNKZMKhjzYny/HItitUGdPAT4e/M8gc+Y/U7UinAEzVMidNUXwXlOogFXBSr9Sgo1wHwsKkvQ1V5gz4A4PFnRlv8mx8a4Ljiqa7EM70aRr3X5eFQfzwcChRW2F67oSnqR0vUR9LniQav3VBUIaNY0+B5ylVpYTYDWoMJWgPzcq/TRfuBlu0XRDSENIL9fTgKqZXn3D9qOCU66kZINMbwbvStK0aNGGLx/ZFD+lKkpGVY/Y572j4/OxPW7CVPPmN5/0IXrDOCJs5nqJBaoL4/gXfyAZ8L3CioglpnRGGlDm28RdAbTTADGNDBG7cKq+Ep4qGkSg9/d+Z5tqiiKPUcyrNS4dmuK4zaKkjCo8Hl8VFVlIPS9EtoFz0KpbcugcPjgy/2gMDdG57tyK0670ycu1OM1LwKdA3yQJXOiOjOEvC5HKTml4PP5SK3rBrtfd1gIApDBXjgcmYZAjyF0BlNkHgI0b0N/af9WFoXZ2WnIL9+Hd169ECVWo0hMcPA4/ORknwdRqMBioICdAjtiB4RPXH54t/g8/ng8fjwl0jQrXsPuuWzNEJT816aogp6oxklVXoMD/PD2YwydJbcTwtU+Pd3MOl1gMkIQ5UKXJE7ylJlAIcLXVkBhD6B4PD40GWl1Ob61BbnwmQ0QJ0lhyggBOV3LsOkrbLqjDh3twSp+ZXoGvjfeNnJD3weB6n5lfAQ8hAqcUNqfiX83AXoGtiy/MeFlTrsPZ/X6H0wmszgcjio0BowMNQHJ9JL0NZbhCqdscULNtm/qUi+lYkeHdtDrdHikYfCwefxkK0oQlm5GpFdOyDldjbyCksQGdYB3Tu2b2FPrs+9q2dRcEcOaWg36Kqr0LHPEHB5PKgUOdBVq2EyGhDUJRK3/v4dApEbAA6qK8rols3SQs5nlCGlQI2uUvf/xoOa0/yZJRpUao3IKtXg6YgApBRUQmswoa23CGFSZqQjOZ9RhpT8SnQNdEeVzlSrPb9ch6JKHSq1NX9PurIKXuKa0aX89kVwBGKbxtrKzOs26ahZg5aja6BnzT3sUrMGTSuogM5oQqi/O+4VqyH1EqFbEPPXmnWN80ZtFXz+M86rc27AqFXDq0tfqDOvw6itBofLhb6iGN7dBkEcENKgraJKLXLLtDAYTZDnqxHiK8LlrHJU6U0oKNfBXcjF3aJq5JbfT1JVVXAXlfeuW/+OVIVQ371c+7mbiipcz62E0Qyoqg1wF3Ahu1OGKp0RJVUG+Ij5MMMMRaUO+C84qPzmeZiMhib70pbkNVoj6dK500hPvY5OXXugukqNftFDwePzkZOZgQpVGSJ6PwzZH7/A198fgW3aoVNXds3WGOduFyE1T4WuQV6o0hkQ3SUAfC4HybkqcDkchPi742JGCdr42J8iV5V2HursVLi1q3E6+XQfBHD5qMq5Aff23aHOSYOuNB8eIRHgudWcys0t0yLUT9Tks8sBByqNASq1FgIfKwJcnMLUc1BlpsKrfc24ERAeDQ6Pj/LMVHB4fFSXFiAwaijKM1Ohr66AyaCHyKvl6ZpYGlKWdh6V2Slw/8+x6tMj+r+xOxUcLh+a4hxIej0O1a2LMBsNMJsMEHj5w6cr+d/B+bulSMmvaLDXyC2rqRPTwd8NV7LL0cZb1OK9RkuQXUpG8s276N45BFXVGjzSLwp8Hg/y9AwYjCaEtgtErqIIGp0e7QIl6N6p4bxGN6Vp56HOSoH7f3ZC3/++58rsVHgG94A67xb0laVwCwyFB4mRL3TSlM0kJzsLqtJSRET1wsW/z8NoMKBj5y6MspOwzggK0RtNkOfVFOT0EvEw8L/Ih8LKKhz6twBGU02u6LbeIrgJuMhXaWEwmcEB8KO8CIGeAgh5IngKechXaZGjoiKDKn1U5KRBldEw71x1cR7MRgPK78nhHtgBpbcuw6itgl5dDq5AhMLrJ8Hh8WHS61BdnAc3v7Ywah8McdZWqqj6MxzC9dxylFXrbbrWS8zHwNp8hRU4eDEbRpMZ3mIB2vnWhATfUlbUFgi8paxEoJcIIgEXIgEX+SoNdEbbTrbkqzRox7x5icVJuH7tCvj8mmnJy8sb0UMewY3UFBQVFuLO7VsI7dgJWo0WIrEIfIEAd26lIzcnG3w+HzqdDlqtFtVVVdBU3/+9p6bI6fpzWj2p+ZXwd7u/zPAS8eDrxkeaQg2jCTCYTHAX8JBfroWAx4XBZMYPyYUwAyivU7T53pdvNrvvO3vjGn2dz+XgbqEaybmNn+jMK9PUGIbyytHBzw2Xs8r+K1SrhYeQh6s5NXOHkMeBRt+4Y0OttTw264xmDAz1xk1lFYrVOmQUVyPETwSt3gSRgIsqnemBe1Gk1qFIrYel2LaMXAWuNVFk2tvDDf7eHki5kwWjyQSDwQh3NxHylMUQCvhIz8wFBxyYYcbl1NvQaBuGhmfkWq5Z4epUV6pw89wvKM65g+ryElQUFcAnsD1uyI7BoNVAq64ATyAAXyiGIuMGjHo9jAYd/NqGovDeTbrls9hAcn4l3AUPpqX0FPHrjFlmGEzmemOWCd8nF8IMM6QeAuSptKjW318vpSupqxmRnFeJ0qr7Yw9h1EjJq0SIn7jGUFhnLONwgO+vKxHoJfyvfhgfd5P/gir5L5v7DJBIkFNajeScptfUeaXVMBjNkOeWo4O/Gy7fK6nRodLAQ8RHRqH6v+s00OobX2vmllbD18EHRY16HSozra8XeGIv8Nx9a/Lom4wwGw3gityhK8kH39MPJVd+BVcgBofHh1Grg8lggKYoGwb1g/eIw+Uhr0yDuO/u2KyxU6dOOHPmDLIOvWvzZ/wlAegQGoqD5y9AXpBl02cef7w/9ODizr8/o+Tfny1ey+XykJZ89YHXFHk5MBqMuCm/hvYdOuH65b9RXVWFivIyCIUilBYXQSAUoqykGHfSUqHV1Jzsv3Mz1ea/y5XIV2mQnFPW4PW8sqqa9UhuGTr4e9z/7ZRVw13Ex21lBcxmoLCiZs2rzk6F0M9yJFJjVOXehK68+D+nUzl4QneUpp4Bh8OFtjQf2jIFOFw+AA5KU07Dq2Nv8AQi7L+osLkPnt7+6A2mUK0qQlkj9glLCNy8IPTwRXnWDZhNRpiMBvBF7qguzoPQyw9GbTUU1/4Cl8uHyaCDUa8FTyhudj/VpQoAnZv1GWejMlMOnrj5Bnqemyf4HvfHbpPJAJ7QHdrSmrHbpNeiOPlPcP77DswGHbhCMSoym18DoSr/NgDgcpYKv6UWNnj/blEVSqv0UJRr0c5HjJ/lSmgMJigrtHAT1MzPZjMg4HGQUdQwVWt+WfPSt9YlV1GEqzcan3e8PNzh5+OFlFv3YDSaoDeYavYLiiIIBQLczswFh8OB2WxGnrIY1ZqG+4XraXdbrK05VGbfgDgg+IHX1P+NZWaTEYbqcnCF7ij9z4GuLc2HTqWo/X5L5Kdgqj8umc2ML2B9/dpVeNRL05SbnQ2DwYDka1cR2rETLv59AVVVapSrVBAKhVAoCmptJX/98fsDdhIASJFTU+ejMVhnBEUEB9f8WP636yqp7XK5XLRpYzn9jrPB4/EQGNQGd47twp1juxzWD/GdOBPt29ecWJ2y/1+alTTNgKdYbwRL82jTpg24XC4WzJnpkPYlEglEIra4OlVIJBKIxSK89+s90tt+99138eyzzzb5vo9PzfE7lapx49jLsRNw6N90HPo3n3RtdWlqfgnu0AFfys60aC3Q78nGC1UEB4fg2B9/4NjpS81us7kQc1BrokePHuBwODiw4qUWt/H4iIYFbFmYAfFbHfOxbSf9m91+e8upVuyF+E2+8WUKqe0mJCTgySefbPJ9DoeDjRs34quvvsITW86Q2ndjvP6onYV6LEA8A9ff/Z/D+rCFL774AhERjRfNDgoKQlBQEJYsWQKDoWHu/qbo2LEjPD09sXx5PEwm6weLOBwOevToAb1ej9u3b1u89tixY3jrrbcwYeRAi9c1h9Y2xwQHB+OHH1QYufmkfQ1xOMg8sgGZRzaQootsNAV3EfIoec8JXQQHB6OqOB9/LmfunB4cPI5uCQ5BKpVCIBDgzldv0S3FJjzc3fBzSiF+TmnojCCDpuYKSwQHB+Onn37CkAlzHaDoPgKBAFKp5VSRLcXLywueXl64990m3Ptuk0P6YKKNMDAwEHw+H/GL5zukfW9v7wa1KKiAY24svpKFdMxmM27fvo3y8sZPY164cAFz5szBjh07MGhQTc7hpKQk7N+/Hz/++CP8/RvP59+uXTu0bdv8UxBMp7S0FHfv2uZZ/eabb7B+/XqEhITgu+++s+kznp6e6NatGzgc5hXNs0Z2djaUypafUi0qKsKoUaMwceJEzJgxo/a15557DpMmTcLUqVNb3DaPx0NkZGTt6XYWFlvJz89HXl5ek+9v2LABx48fx9GjR+Hr6wsAuHv3LsaPH49FixZhwoQJTX62U6dOTY6hLI5BqVQiOzvb6nVDhgyBVqvFpk2bMHz4cIvXenh4oHv37naN25WVlbh50/aT6s8++ywKCgqwcuVKjBkzxqbPeHl5oWvXro3q1Ol0SElJaWAUyszMxAsvvACgxsBT/5ABj8dDz549Gy0+Vl1djRs3bjSaLqMxDh48iM2bN6NDhw44fPiwTZ8BahbCISGt09lcXl6O6noniV555RXk5eXhxIkT4HA4OHToEObNmweZTIauXR8M/ZZKpeByuVRKZmkGd+/eRWlpqdXrpk2bBh8fH5w8eRIrVqywaUzo0qVL7ZzlKLKyslBYaNngsXXrVhw+fBi9e/fGjh07LF5r6xpZr9dDLpfbZOQGgPXr1+PSpUswGo0YPHgwFi9ebNPnLI1/ZGBtj2aJ3NxcjBo1CuPHj8ehQ4dw5syZFh1+CAgIQGhoaLM/Rydmsxk3b96EWt10BNCbb76J7OxszJ49G7NmzcLBgwcRFtZ06lwqfi9MwmAwICUlxaqDSavVYsiQmlzue/bswUMPPfTA+6WlpSgoKHjgtY4dO8LNzQ3N5dSpU1i0aBGEQiHOnj1Lyl6Zy+WiZ8+eEAqFdrdFJyaTCampqdBqW56dgljvLV++HGPHjgUAHD9+HG+99Ra+/PJLdO/evcVtCwQC9OzZEzwez/rFTohCoUBOTg5p7V29ehVTpkwBAJw9e5bUg2thYWHQaGyLCBo2bBhu376N1atXY9q0aVavF4lELRonDQYD5HI5jEbLKWsB4ObNm3j55ZeRlJSEDz74AF26dME777xjUz/BwcEICrJcg88eioqKkJmZafU6s9mM6OhoGAwGbN26FY888ojVz/j4+Fico+ikoKAAubm5Fq8xGo147LHH8Oqrr6K0tBQymQzff/+91bY7duwIiYSGWiVmFkYwfPhwc79+/cwmk6n2teLiYrOHh4d5xYoVNCpjPqtXrzZ7e3ubfXx86JbiFCxcuNDs7e1tLikpeeD1uLg4s6+vr7msrIwmZSwsjXPr1i0zn883r1u3rsF7b7zxhjkgIMCsUqloUMZiD5WVlWYAZg6HY05KSqJbTqO4u7ubPT09ze+//75D+5HJZGYAZgDmixcvOrSvN9980+zt7W328/NzaD+uTHp6uhmAed++fbWvaTQac1BQkHnWrFk0KmNxFCaTyezv729+7733zN27dzfPnTuXbknN4tVXXzW3adPG3KdPH9o0DB061Dx+/HjzCy+8YH700Udp00Em33//vRlA7f//+++/dEtiFCEhIeZFixaZKysrzXw+37xz5066JTklWVlZtWuEb7/91qF97dmzp7aviooKh/bVGnnjjTfMbdq0MVdXV9e+ptfrzR07djSPHz+eRmWtj8OHD9c+65mZmbTpkEqlZl9fX3N8fDxtGuqzbds2s1AoNFdXV5vj4uLMnTp1oltSsyktLa39fj/55BO65VDClStXzADMp06dMn/77bdmAObc3Fy6ZTUJe0yLAZw7d672lFXd0wf+/v6YOXMmduzY0WTKCZaaE7i+vr5QqVR2nVRoDSgUCuzatQvz58+Hn5/fA+8tW7YMGo0G27Zto0kdC0vjrFy5EkFBQZg3b16D995++21UVlZiwwZmhqazNA0R4eXj42NXtJejUKvVqKqqgq+vr8P11W2fir58fX1RWloKna5hvlcW62zfvh1SqRSxsbG1r4lEIsyYMQP79+9HWVkZfeJYHEJBQQFKSkoQGRmJyMhIyOXOVY+I+N3TNdaazWbI5XKnvX9NIZfL4evri2HDhtX+m6WGzMxMZGdnIyYmBh4eHnj44Ychk8noluWUEL9bHo9HyRrB29v7gX5ZyCE7OxufffYZFi1aBLFYXPs6n8/HsmXLcOjQIaSnp9OosHWhVCpro1bpetaNRiOKioponZ8bQyaToX///hCLxYiJiUFGRgapUSlUQNxPT09PRt1bRyKTySAUCjFgwIDaSBAmz7usM4IBrF27FuHh4Rg1alSD9xYuXAiNRoOdO3fSoMw5UCqVtWFF1sLUWzsbN26EQCDA/PnzG7zXtm1bTJ8+HR988AHr/GJhDBcvXsShQ4fw7rvvNhpuHhwcjPnz5+ODDz6wmOaJhXkQC0OJRMLIRSKhKSAggJLNP1UbIqVSiYCAAAA1oc4szUOlUuGTTz7B9OnTHzAmAMCMGTOg0+mwd+9emtSxOIrk5JoCf1FRUYiKikJycrLNqdGYAPG7VyqVtOjOz89HSUlJ7f0rLCyEQmF7AVymkpycjKioKPj4+CA0NLT2OWEBTp8+DQC1BpGhQ4dCJpM51e+GKVC5Xqq7r2bi2syZ2bx5Mzw9PTF9+vQG773++usICgpiD1dRCBOe9eLiYpjNZkr2GrZiNpshk8kQExMDAE5h1G4MKvdxTOH06dO1TqSgoCB069atdi5mIqwzgmauXbuGY8eOIT4+vtF8wm3btsXkyZOxZcsWVFVV0aCQ+SgUitq6Ga1loGkJCoUCO3fuRFxcXIOoCAI2OoKFSZjNZixduhQ9e/bEa6+91uR1y5Ytg5ubm825LFmYAWGIatOmDSPHbkJTUFCQw/UpFAoEBgZSFoXBzpkt55NPPoFGo8HMmTMbvNemTRtMmDABO3bssCknL4vzIJfL4ebmhk6dOiEyMhJFRUVO9ftRKBRo06YNDAYDLZE7RMQAERlR9zVnhoj2AOBSER9kIJPJEBERUWvsi4mJQU5Ojk25vlkehMr1Ut01gis4DJlCYWEhkpKSMG/ePHh5eTV4XywWY9GiRfjss89sqrnGYj8KhaK2tgFd8zkT90J37txBQUFBrTOCMGo7qzOiTZs2rWIsq+9EAmrmXSZ/b6wzgmbWrl2Ljh07Wiy+unTpUpSUlODjjz+mUJnzoFQqa6vet4aBpqVYioogaNu2LaZNm4YtW7aw0REstPPzzz/j5MmTWLduncVibL6+vnjzzTexd+9epKWlUaiQxR6USiU4HA6Cg4MZOXYTmkJCQhyuT6lUIigoCEFBQQ7vS6FQoH379rX9stiO0WjEtm3b8OKLL6Jdu3aNXhMXF4d79+7hhx9+oFgdiyNJTk5Gz549weVya43PznIK3mw2075WTk5Ohru7Ozp16oSwsDCIRCKnuX9NodPpkJaW9oAzwtn/JjKpbxQhCjAz2TDCVIg0a1QY1RQKBdq1awcOh8OuEUhk69at4HK5mDt3bpPXTJ8+HZ6enti0aROFylovhOPNz8+Ptn0I8RujYq9hKzKZDBwOB4MHD659jelG7cZQKBTg8/lo165dqxjLbt++DYVC0cAZIZfLUVpaSqOypmGdETSSnp6OQ4cOYdmyZRAIBE1e16lTJ7z00kvYuHEjm9+5EZRKJUJDQ2v/m6UhSqWyNirC39/f4rXLli1DVVUVtm/fTpE6FpaGGI1GLF++HEOHDsUzzzxj9fpZs2YhJCQE8fHxFKhjIQMiPJqKyIOWQDhLOnToQMlJxMDAQAQGBlLSFzFnMmXj4ywcO3YMGRkZiIuLa/Kavn37YsiQIdi6dSt1wlgcjlwuR1RUFADUGtOd5RS8SqWCXq9Hx44dAdCzVpbL5bXOHB6Ph4iICKe5f02Rnp4Og8FQ+1xERUUhJyeHrRmDmlPgaWlpDxhFJBIJevbsyeiUEUyF6jVCmzZtWlVqE0ejUqmwY8cOzJw5szZSqDG8vLwQFxeHPXv2sPeeAqj8XVnSAAChoaGM+c5Pnz6NXr16wdfXt/Y1wqhdUlJCn7BmolQqIZVKGbvPJJumnEhmsxlnz56lUVnTsM4IGtmwYQOCgoLw+uuvW712+fLlyMnJwRdffOF4YU6ETqdDWVkZ2rVrx9giqExg48aN4PP5FqMiCNq1a8fWjmChnS+++ALJycnYsGEDOByO1etFIhHef/99HD16lLETLsuDMGETYAnCWdKmTRsUFRU5NO0OVfeiuroaFRUVCA4OblUF3cgiMTER0dHRGDBggMXr4uLicPr0aVy9epUaYSwOxWQyISUlpfYEvLMZ04nfeZcuXR74N5XUTWcE1BjuneX+NUXd1FN1/z8lJYU2TUzhzJkzAPCAM4L4t7OdrmUCVDsjmLw2c0Z27twJjUaDhQsXWr127ty54HK5SExMpEBZ64YJz7pSqYSbmxuCg4NRVVUFtVpNi4661I9qA+6P5c60x2bC90slMpmsgROpU6dOaNeuHWPnXdYZQRPZ2dn47LPPsGjRogYFEBsjIiICY8aMwbp169g8xHUgCla3poGmuSiVSnz44Yc2RUUQsNERLHSi0WiwatUqvPDCCxg4cKDNn4uNjUWfPn2wdOlStkCiE1B3kehoY39LqKvPbDajuLiYkr4cOY8Rc2ZQUBA7ZzaT69ev46+//rIYFUEwZswYhISEsMYEF+Hu3buorq6uPQEPoLaItTNA/M47d+4MPp9P+e/eaDQiJSXlgftH1FcwmUyUaiGT5ORktG/fvrYOW48ePcDj8ZzmuXAkMpkMHTp0QIcOHR54PSYmBjdv3mTnnmZC1RrBaDSiqKiIXSOQSFVVFbZs2YLJkyfX1uKwhL+/P2bOnIkdO3awhwIdDBOM1YQGumtXEOTn5+POnTsNnBFMN2o3Rv3v19VtA405kTgcDqMPAbDOCJrYvHkzPD09MX36dJs/Ex8fj1u3buHw4cMOVOZcECkm2EVT0xBREQsWLLD5M+3atcO0adPwwQcfoLy83IHqWFgasn37duTl5SEhIaFZn+NyuVi/fj3OnTuH77//3kHqWMiCKBxHhbG/JdRdxBL/dhREAWtHz2NE28TGh50zbWfbtm1o3749nn/+eavX8vl8zJ49G1999RV7j12A+ifgif9OSUlxCmN63QKZdKyVMzIyUF1d3SAyQq1WO3Ux4/rRHiKRCN26dXP6iA8ykMlkGDp0aIPXCUMJETnBYht110ulpaUOS9tcUlICk8lUu0ZgUznaz969e1FSUoIlS5bY/JmFCxdCo9Fg586dDlTWutHr9SgpKaHdGVH3tw3Q74wgjNaNGbWHDh3KWKN2Y9R19BgMBsbWTSADwonU1Lx76dIlVFdX06DMMqwzggaUSiWSkpIwb948eHl52fy5/v37Y+TIkUhISHB5z56t1DessIumByFqRcybN8/mqAgCNjqChQ5KSkqQkJCAadOmoVu3bs3+/BNPPIERI0YgPj4eBoPBAQpZyKL+aSCmjd/EBsHR+nQ6HUpLS2v7UiqVDjNwEn8D3ZsvZ6OoqAhffvklZs+ebbHGV12mTp0KHo+H3bt3O1gdi6NJTk6uTdlGQBjT7927R58wG1EqleDz+fD19aVlrUxECtSPjKj7njOSnJz8wN8EOFfEjKOorKzElStXGhizgJoiraGhoU5l0GIC9ddLRJQj2bBrBHLR6XTYsGEDYmNj0blzZ5s/17ZtW0yePBlbtmxBVVWVAxW2XupGCtNpQ2LaXkgmk6FLly6NRvEQRm1neSaZ5uhxJE05kYjX9Ho9/v77b6plWYV1RtBAYmIiuFwu5s6d2+zPrlixAteuXcPx48cdoMz5IAYVqVTKLpoaYdOmTeDxeM2KiiBo3749pk2bhs2bN7PRESyUsXbtWuj1eqxevbrFbaxfvx5paWn45JNPSFTGQjZURh60BKr0FRUVAbi/+TcYDA4rgFp/zqR70+MsJCUlAQCmTZtm82f8/f0xceJE7Ny502GnWFmogTgBX7d+EWFMd4ZT8EQRRy6XS8taWS6XQyKR1BpbgJo1po+Pj1Pcv8aorKxERkbGA5ERwP30U6350Nj58+dhNBobNYoAbN2I5mI2mylbj9Q95Mfuq+3niy++QE5ODpYvX97szy5duhQlJSX4+OOPHaCMpf6zXlhYSEukI/Hblkgk4HA4tP/mGkv1QxATEwODwcBIo3ZjMH2fSSYymQxhYWEPHJohiIyMhK+vLyPnXdYZQTEqlQo7duzAzJkzIZFImv35YcOGYfDgwVizZk2rXugSKJVKeHt7QywWs4umehC1IubNm9eiZw2oiY5Qq9XYsWMHyepYWBqSlZWF7du3Y/HixQ8YLZrLww8/jJdeegmrV69mRCEwloYQeYmZvEgkFrEeHh5wc3OjbPNf9zVH9OXn5wehUMjOmTai1+uxc+dOvPLKK82eS+fNm4eCggJ88803DlLHQgX10/EAzmVMJ8YyALQ5I+o7czgcjlMXsU5NTQWARp0RJSUlKCgooEMWI5DJZAgICECPHj0afT8mJgZXrlxBRUUFxcqck7KyMhgMBkqdEcRpYibW83IWjEYj1q1bhzFjxqBnz57N/nynTp3w0ksvYePGjeyBBgdQf+3tyINA1nQEBgaCz+dDIpHQui4vKyvD9evXG031AwA9e/aEn58fI43a9SGizpm8zyQTS04kLpeLIUOGMPJ7Y50RFLNz505oNBosXLiwRZ/ncDhYsWIFzp8/j9OnT5OszvlQKpW1RsvWUpzGVuyJiiBgoyNYqOStt96Cj48PFi1aZHdb77//PoqKirB161b7hbGQDpGXOCgoyOHG/pZQ11nC4XAcasCj2hlB9EGkhGLnTMscPnwYubm5mDdvXrM/27NnT4wcORKJiYnsfXZStFotbt682SAdD2FMd4aUPI2tlamksXRGQI3h3hnuX2MkJyeDw+EgIiLigdeJv9NZ/y4ykMlkeOSRRx5wPtUlJiYGJpMJ58+fp1iZc1LfQVD3NUf0JRaL4enpiaCgIEbW83IWDh8+jFu3biE+Pr7FbSxfvhw5OTn44osvSFTGAjSMFK77GtU66Jyf63Lu3DmYzWanNGrXp27Uua+vLwQCAaP2mWRCOJGa+t6Amnn33LlzjEthzTojKKSqqgpbtmzB5MmTG83DZitPP/00evXq1ezirq4IUfQTqFmk6fV6qFQqmlXRT2FhIT788EPMnTu3xVERBMuXL0dlZSUbHcHiUK5fv47PPvsMb731VrNq6TRFp06dMGvWLKxfv95huXVZWk7dvMQcDodxxZTrOksAOFQfcS+kUmltf1Q4IwIDA6HVatnTqVZITEzEY4891qgx1Rbi4uJw8eJFXLhwgWRlLFRw8+ZNGI3GBifggfspeZhO/bUylWOtVqtFenp6o/cvKioKaWlpTnnqVy6XIywsDG5ubg+83qlTJ7i5uTnFc+EItFotLly4YNEo0qNHDwQEBDiFQYsJ1F0vicVieHt7O3yNQBzCqNs/i+2YzWYkJCRg5MiR6N+/f4vbiYiIwJgxY7Bu3To2QoVkFAoFPD094e7u7vC1d1Oo1Wqo1Wra5uf6yGQytGnTBl26dGnyGsKordfrKVTWfOo6cYnxzFXHsrNnz1p0IgE135tarcaVK1coVGYd1hlBIXv37kVJSQmWLFliVztEdMRvv/2GS5cukaTOOalvWAHYRRNQExXB5XJbHIFTl/bt22Pq1KnYvHkza7RicRjLly9Hly5dmpWT3RpvvvkmOBwO1qxZQ1qbLORQNxqA+H8mjd11N//E/ztKn1KprN0Q+fj4QCAQOKyvukbJ1hC2bC///PMPLly4gLi4uBa38dRTT6Fr165ITEwkURkLVRAn3J3ZmF5/rVxWVgatVktJ32lpaTAajU1GRhgMBqSnp1OihUyaivbg8Xjo2bNnq42MuHz5MjQajUWjCIfDYetGNAMq10vsGoEcfv75Z1y7dg0rVqywu634+HjcunULhw8fJkEZCwETbEhM2wsRqX6aimoDaozaVVVVjDNq16exfZyrjmW2OJH69esHsVjMuHmXdUZQhE6nw4YNGxAbG4vOnTvb3d7YsWMRFhbW6qMjGptIXHWgsZXCwkLs2LHDrloR9WGjI1gcyV9//YWff/4ZCQkJEAgEpLUbEBCAZcuWYefOnbh79y5p7bLYT2MLcCaN3VTqqxuiTUVKKLo3X85EYmIiOnfujGeeeabFbXC5XMydOxfffvstcnJySFTHQgVyuRwhISHw8fFp8J6zGNMb+91TFTFIRAg0ljPdmYqA16exOiIEzhIx4whkMhk8PDzw0EMPWbwuJiYGf//9N2VOMWdGqVSCz+fD19cXgOPXI+y+2j7MZjPWrFmD6OhoDBs2zO72+vfvj5EjRyIhIYFN90giddfexEEgqp91Ju2FNBoNLl68aNGRDAB9+/aFm5sb44za9ambhgtg3j6TTGxxIgmFQgwaNIhx3xvrjKCIL774Ajk5OXblDawLj8fD8uXL8d1339UWUWuNsIumhpAZFUEQHByMqVOnYtOmTWx0BAupmEwmLF26FAMGDMDYsWNJb3/+/PmQSqV48803SW+bpeXUzUsMMG+RSLUzguiHir7q5qYlXmNpSF5eHg4dOoS5c+eCx+PZ1dbrr78ODw8P7Ny5kyR1LFTR1Al44L4xncmn4OsWcQSo/90nJyejQ4cOjTpz/P390a5dO0bfv8YoLCyEQqFo8rmIiopCSkpKq0yrIpPJEB0dDT6fb/G6mJgYaDQaXL58mSJlzkvd1EkAdWsEJtbzcgZOnz6Nc+fOYeXKlRaNg81h5cqVuHbtGn7++WdS2mN5cO3t6INAljQAzHBG/PPPP9DpdFadEUKhEAMHDmScUbs+SqUSXl5etakUmbbPJIvq6mqbnEhAzbx75swZRjk1WWcEBRiNRqxbtw5jxoxpUOjMHiZOnIjg4GCsW7eOtDadCbPZ/MCiydfXF3w+3yUHGlshs1ZEfdjoCBZH8M033+DSpUvYsGEDaYv2uri7u+Odd97BgQMH2E0vg1AoFLV5PAHmLRKpdJbUTYvgyL5MJhMKCwtr+5JIJOByuYy670xi165dEIvFmDRpkt1teXl5YfLkyUhKSkJ1dTUJ6liowtIJeMKYzuRT8EQEBF1OSEv3D6gx3DP5/jUGoddSZER1dTUyMjKolEU7JpMJZ8+etcko0qdPH3h6euL06dMUKHNuiPUSAVWHI4h6Xmz0ZPNISEhAr1698PTTT5PW5tChQzF48GCsWbOGUYZEZ4aqtbc1DRwOBwEBAbUaioqKaHFknz59Gt7e3jbVRyOM2iaTiQJlLaP+QS9XHcv++ecf6PV6m50RRUVFSEtLo0CZbbDOCAo4fPgwbt26RVpUBIFQKMSSJUvw1VdftboFLwCoVCrodLragYbL5TLOoEU1mzdvBofDITUqgiA4OBhTpkxha0ewkIZOp8PKlSvxzDPPkBLK3BSvv/46wsPDsWzZMnYRzxAaWyQyaewmHN2EsyQoKKi20Jwj+qLiXpSVlcFgMDwwZ0qlUkbdd6ag0Wiwe/duTJo0qdET3S1h7ty5KCkpwZdffklKeyyOp7y8HJmZmRaN6UxPydPYycu6rzsaa84Ipt+/xpDL5RCJRAgLC2v0fWdOP2UPcrkcZWVlNhlF+Hw+oqOjGX+6lglQuV6iMlLTFbl06RJ+++03rFixgtQDVkS90HPnzrEOPJJgwj5EqVRCIpHURpIFBQXBZDKhpKSEUh1ATVTbkCFDbIoEjomJQXFxMaOM2vWpe2AZcN2xTCaT2exEio6OBo/HY9S8yzojHIzZbEZCQgJGjhyJ/v37k97+lClT4Ofnh40bN5LeNtOpv8Ei/tsVvZ62QNSKmDt3bq2HnWyWL1+OiooKfPjhhw5pn6V1kZSUhLt372Lt2rUO7YfP52Pt2rU4ceIEfvvtN4f2xWIbjW14HWXsbwmNnZgCHGPAa+xeOGIeq1/MjfhvV1yc28uBAwdQVFSEuXPnktZm586d8X//939ITExknaJOQkpKCgBY3ORFRUUxOs1Q/bWySCSCj48PJWtlwplj6f5FRkbi7t27qKysdLgeskhOTkZ4eHiTqYjatm0Lf39/Rj8XjkAmk0EgEGDgwIE2XR8TE4OzZ8+2ynRWzaGpNQLZ80hVVRUqKyvZNYIdrF27FmFhYQ5JO/v000+jV69erb5eKBkQ2TXotiE1pgGgvpabwWDAuXPnbHIkA8w0atensX2cSqVyuTpFzXEieXp64qGHHmLU98Y6IxzMzz//jGvXrmHFihUOad/d3R0LFizAvn37kJ+f75A+mEpTzojWumjavHkzADgkKoIgJCQEU6ZMYWtHsNhNeXk53n33Xbz22ms2efPt5bnnnsOQIUOwbNkyRoeVthaaWoAzZfymSl/9dINEX45yegBoFSeF7MFsNmPr1q14+umn0bVrV1LbjouLg1wux19//UVquyyOQS6Xg8fjoUePHk1eExkZiYyMDMYa0+lcKxPOHGtpmgA4Vf07a9EeHA7HKSM+7EUmk6Fv375wd3e36fqYmBioVKpWd5+aS2PrEZ1Oh/LyctL7Adg1QktJTU3FkSNHsHz5crvrTDUGER3x22+/4dKlS6S335qoqKiAVqul/Vlnyl7o2rVrqKystNkZwUSjdn2aurdE6kpXoLlOJKBm3mXS98Y6IxyI2WzGmjVrEB0d7dAUJLNmzYJIJMIHH3zgsD6YCLtouk9RUZHDoyIIli9fjvLycjY6gsUuCIfWu+++S0l/HA4HGzZswLVr1/DVV19R0idL0zRmgCdeZwJUbRCIDREVp3caM0q6ag5Vezh16hSuX7+OuLg40tt+9NFHERkZicTERNLbZiGf5ORkdO3aFWKxuMlrCGM6YXhnGkqlEt7e3g/8DVStlZOTk606c8LDw8HhcJwmisBsNkMul1s9RMH0iBmyMZvNkMlkGDp0qM2fGThwIAQCAaMMI0yEqvVSU2sEpqzLmM66desQHByMiRMnOqyPsWPHomvXrg6PKHd1mHKglSl7IZlMBpFI1KwsLkOHDmX02N1YGi7idVeBcCI1Z96NiYlBZmYmsrOzHajMdlhnhAM5ffo0zp07h5UrVzqkMCuBr68v5syZg127dtGSY44uFAoF+Hw+fH19a19rrc4IIipi0aJFDu+rbnQEU08CsjCb/Px8bN68GfPmzUNISAhl/Q4ePBhjxozBm2++CY1GQ1m/LA2hMg1SS6i/iA0ICACHwyFdX1OpkwDyT+8olUoIBIIHaiC01jnTEomJiYiIiMCIESNIb5vD4SAuLg4//vgj7ty5Q3r7LORi7QQ8cN+YztTT3fXHWoC6371cLrfqzHF3d0dYWBhj7199srKyUFFRYfW5iIyMRHp6usulhGiKjIwM5OXlNeuEppubG/r168dogxbdaLValJWVUbJeYtMft5yMjAx89dVXWLJkCYRCocP64fF4WL58OY4cOeJU0WRMo6m1N9VpfOrPz56enhCLxbQ4IwYMGACRSGTzZ2JiYpCVlYWsrCwHKmsZTaXhAqhPgeVICCdSv379bP7MI488UvtZJsA6IxxIQkICevXqhaefftrhfcXFxcFkMmH79u0O74spKJVKSKVScLn3H+PWeIKjqKgI27dvx5w5cxweFUEQHx/PRkewtJh33nkHIpEIy5cvp7zvtWvXIicnBzt37qS8b5YaiNoQVBj7W0r900p8Ph8SiYSyk4h13yOzr8DAwAcOR7DOiAfJyMjA999/j7i4OIcdInn55Zfh7++PHTt2OKR9FnIwm81ITk62egKeMKYz9RR8/Q05QN1a2Zb7B9QY7pl6/+pD6LQlMsJoNDK6wCeZyGQycDgcDBkypFmfI1JGsHV0Goc4lEDVGgHAA3vJwMBAVFVVMaaeF1PZuHEj/Pz8MGXKFIf39corryA4OBjr1q1zeF+uiqW1N5VpfOrPzxwOh3JbVkui2gDmGbXr0lgaLqlUCoA5h97IQCaTYeDAgc1yIkmlUvTo0YMx3xvrjHAQly5dwm+//YYVK1Y4NCqCIDAwEFOnTkViYmKryeXf2AYrMDAQpaWl0Ol0NKmiHiqjIgiI6IiNGzey0REszeLmzZv4+OOPsXLlSvj5+VHef/fu3TFlyhSsWbMGZWVllPfP0vjmmjD2M+HESmNFHAHHnBBs6iQiQP7pHYVC8cDCnOiruLgYBoOB1L6clR07dsDPzw+vvPKKw/pwc3PDtGnTsHfvXtJzfrOQh1KpRFFRkdUT8AAYXR+gqbWyo8dawpljy/2Liopi7P2rj1wuh7e3N4KDgy1e17Nnz9rrWwOnT59GZGRks9d1MTExyM/Px927dx2kzLlpbI3g5+cHHo/nkDWCRCKBQCCofY1pUatMJD8/H/v27cOCBQtsrpdiD0KhEEuWLMFXX32FjIwMh/fniiiVSnC5XPj7+9e+RvXJeaPRiKKiIlrm57qkp6ejsLCwWVFtQI3TMjw8HKdPn3aQspbTWOSLSCSCj4+Py4xlhBOpud8bUDPvMuV7Y50RDmLt2rUICwvD2LFjKetz8eLFqKioQFJSEmV90klTGyzAtYrTWIKoFTFnzpxajy9VELUj2BPmLM1hxYoVaN++PWbPnk2bhtWrV0Oj0bCnimiisc018W8mLBKp1EdsiCQSSe1rjjq9Y2nOLCoqIrUvZ6SyshJ79+7F1KlTHW5QmDVrFqqqqvDpp586tB+WlkMYkV3VGaFUKh16Gl2pVKK4uNjm+6dQKJxi7U6k7rJ20MzX1xfBwcGMfS7IpqVGkSFDhoDD4TDmlCbTaGw9wuVyIZVKKV0jMGFtxlS2bNkCkUiEWbNmUdbnlClT4Ofnh40bN1LWpytBZNeoW2ic6me9pKQEJpOJ9r2QTCYDl8tFdHR0sz/LtGLIBEzfZ5LBzZs3W+REAmq+t9TUVBQXFztAWfNgnREOIDU1FUeOHMHy5csfGOQcTUhICF599VVs3ry5VeRDb+qUJ9B6Fk0ffPABTCYTpVERBB06dMAbb7zBRkew2Mz58+dx5MgRvPfeexZzSDuatm3bYuHChUhMTEROTg5tOlorxImVxsZvJozdVC5iFQpFg3SDjjq901S6FkJHa2f//v2orKykxFEaHByMsWPHYvv27TCZTA7vj6X5JCcnQywWo0uXLlavjYqKYqwxvam1sl6vh0qlcli/tqYzqnuNMxjubU09BbSeItYFBQW4detWi4wifn5+iIqKYqRBiwk0dsKX+De7RqCfkpIS7Nq1C7Nnz36ghqWjcXd3x4IFC7Bv3z7k5+dT1q+r0FgtJarT+DBlLySTydCnTx94e3s3+7MxMTG4ceMG4w40NbWPCwoKcpmxzF4nEgCcOXOGbFnNhnVGOIB169YhODgYEydOpLzvZcuWQaFQtIrTdq39BEdxcXFtrQiqoyII4uPjoVKp2OgIFquYzWYsXboUvXr1wssvv0y3HCxZsgSenp5YvXo13VJaHY3lJQZapzOisXmMyr5a05xpCZPJhG3btuH5559HSEgIJX3Onz8ft2/fxvHjxynpj6V5yOVyRERE2HSoiDj9zzRjemNFHAFqfvdyuRxubm7o3Lmz1WvDwsIgEokYd//qo9frkZaWZlO0B8DsiBkyIQwaLXFGEJ9jnRGNo1Qq4ePj0yAnOFVrBIlEwqh6Xkxj+/btMBqNmD9/PuV9z549GyKRCFu2bKG8b2ensWed6jQ+TDm939KoNoBZRu26KJVK8Hi8B9JwAczZZ5KBPU6k0NBQBAcHM2LeZZ0RJJORkYGvvvoKS5YsgVAopLz/bt26Ydy4cVi/fr3L54C2tMFyFa+nJYioiMWLF9OmoUOHDpg8eTIbHcFilR9//BFnzpzB+vXrKY0Yawpvb2+89dZb+PTTT5GSkkK3nFaFUqmERCIBn89/4HWmnFghNNR38jpCX1POCEf01dgJaVcs6NYSfv31V6SnpyMuLo6yPgcNGoQBAwYgMTGRsj5ZbKc5J+C7du0KoVDIuFPw5eXl0Ol0tJx2Tk5OttmZw+fzER4ezrj7V59bt25Bp9M1KzIiMzPT5WvDyGQydOrUCe3bt2/R52NiYnDr1i0UFBSQrMz5oXuNQNTzau1rhMaoqKhAYmIipk6d2uh35Gh8fHwwZ84c7Nq1CyUlJZT378xQ+buypAGg9/R+Tk4OMjIyWuyMCA0NRUhICCOM2nVpLOoccD1nREu/Nw6Hw5hDAKwzgmQ2btwIPz8/TJkyhTYN8fHxuHfvHg4ePEibBkej0+lQWlraYAAXi8Xw9vZ2mYGmKYqLi7Ft2zZaoyII2OgIFmsYDAbEx8fjsccew//+9z+65dQyffp0dOrUCcuXL6dbSquCymiAltCUsyQwMBBFRUUwGo2k9kXFvdBoNCgvL2/Ql4eHBzw8PBhx3+kkMTERffv2xeDBgyntNy4uDn/88QfrEGUYJpMJKSkpNp+AZ6ox3dLJy7rvOwKitoKtREZGMu7+1YeIciCKU1uDqREzZGOPUQS4f7qWCYYRpkF39KSj+nIFkpKSUFFRQeuhwLi4OBiNRmzfvp02Dc4IE551pVIJsVgMT0/PBhrUajXUarXDNRBj7iOPPNLiNphi1K4LE75fR5KTk4N79+7ZPe/++++/lDxnlmCdESSSn5+Pffv2Yf78+Q4vfmiJ3r1745lnnsHatWtdNhcxkZuuqYGGCadrHQmdtSLqExoaWhsdQfeAxsJMPv30U6SmpmL9+vVWiz5SiVAoxJo1a3Ds2DGcPn2abjmtBkuLRLKN/S2BKGxXn8DAQJhMJlILflm6F2TOY0Qu+6b+LlefMy2RlpaGX3/9FXFxcZSPT2PHjkXbtm2xbds2SvtlsUxmZibUanWzjelMcyo15Yzw9fUFn8932O++uc4c4P79c2RRbXuRy+Vo06ZNgxSDTREeHg4ul8u454JMysvLce3aNbuMIu3atUPnzp0ZZ9BiAlStEUwmEwoLC9k1go1oNBps3rwZEydOpCy1Y2MEBgZi6tSpSExMREVFBW06nA2qfle2aKi/7qQyfapMJkO3bt0aREQ1B8KozaQMGZb2cUqlktHrDFsgy4lkMBhw4cIFsmS1CNYZQSJbtmyBSCSipPihNVasWIHU1FT88MMPdEtxCE0V/QFqBhomFhEkCyIqYvbs2bSEhTYGGx3B0hRVVVVYvXo1xo8fj379+tEtpwHjxo1D3759sXTpUqdfnDgLjaUCAO4b++kON1cqlU3qA0Dq/GLpXpDZD7GpaayvoKAglzgp1FK2bduGoKAgvPjii5T3LRQKMWvWLHz++ee0P/cs92lO8WWCqKgoyOVyRs0jTa2VuVwupFKpw9bK9+7dg1qtbvb9q6ioQFZWlkM0kUFzUncBNdHaXbt2ZXzEhz2cO3cOJpPJLmcEwMzTtUzA0hqhtLQUer2elH5KSkpgMpnYNYKN7N+/HwUFBYyIrF68eDEqKiqQlJREtxSnQK/Xo7i4mHYbkqXfNkDuXqMp7I1qA2rGbqPRSLtRuy5N7eOCgoKg1+tRVlZGvSgSIcOJFBERAT8/P9rnXdYZQRIlJSXYtWsXZs+eDV9fX7rlYPDgwRg2bBgSEhIYtTEii6ZOexGvufKiacuWLbTXiqhPaGgoJk2axEZHsDQgMTERhYWFWLNmDd1SGoXL5WLDhg34+++/ceTIEbrltAosnUgi3qcTqvTp9XqUlJRYnMfImr9b85xpidLSUuzfvx8zZ85sUCCUKqZPnw6TyYQ9e/bQ0j9LQ+RyOXx9fdGuXTubPxMZGck4YzpRxNHPz6/Be4783RNpiZobGVH3s0ykuamnANcvYi2TyRAYGIhu3brZ1U5MTAyuXbsGlUpFkjLXwNp6hMgUQEY/ddut31drXSM0hsFgwPr16zFu3Di7n3syCAkJwauvvorNmzdDo9HQLYfxWMuuQWWaJjr3QiUlJZDL5XY7I8LDw+Hv70+7UbsudN9bRyOTyTB06FC72uByuYw4BMA6I0hi+/btMBqNmD9/Pt1SalmxYgUuXryIP/74g24ppEMMIo2FYDGlCKojYGJUBMGKFStQWlqKXbt20S2FhSEUFRVh3bp1mDFjBrp06UK3nCZ57LHH8OSTTyI+Pp60U2YsTWOpcBzg2KKqttDUaSWy9VnaEJF9eofQTPfmi2ns3bsXBoMBM2bMoE2DVCrFSy+9hA8//BAGg4E2HSz3IU7ANydtF3Finkmn4IlUBfWLOAKOXSsnJyfDz8+vWc6ckJAQeHt7M+r+1UWtVuPOnTvNiowAap6L5ORklzwYBtw/WWtviruYmBiYzWacO3eOJGXOj9lspmy9ZC3jQGtdIzTGwYMHkZGRgfj4eLql1LJ8+XIoFArs37+fbimMx5LjjYgComK8buq3Tdi2HL0XOnv2LADY7Yzgcrl45JFHaDdq18Va1Ikzj2dkOZGAmu/+woULtNo+WGcECVRUVCAxMRFTp05llIF45MiR6NevHxISEuiWQjpKpRJeXl5wc3Nr8J4rL5q2bNkCo9HIqKgIAqJ2xIYNG9joCBYAwJo1a2A2m7Fq1Sq6pVhl/fr1uH37Nj7++GO6pbg0RF5iJp9YaWqD4OnpCbFYTJo+aycR615DRl++vr4QCoWN9kX3PacDg8GAHTt2YMKECXaFOpNBXFwcsrOz8d1339Gqg6WGlpyAJ4zpTDoF39RYBjg+MiIyMrJZBmoOh8PoKIIbN27AbDa3KDKiqKjIJcdYrVaLf/75hxSjSNeuXREYGMgogxbdqFQq6PV6ytYIddut3xcT6nkxAZPJhLVr1+KZZ55Bnz596JZTS9euXTFu3DisX7+ePdRgBWvPOlVpfJqan/l8PiQSicPnDJlMhvbt26NTp052tzV06FBcuHABOp2OBGX2YS3qHKB/n2kPZDmRiDaqqqrw77//2t1WS2GdESSQlJSEiooKxhmIORwOVqxYgZMnT7rcSROFQmF1g+Vqp5BKSkqwbds2zJo1i1FOr7rEx8ez0REsAICMjAx8+OGHWLp0aaMRTEyjV69emDhxIt555x1GFeFyNYqLi2EymSgx9rcES84SDodDqgHPUh0HRxgampo3iBPSrjZnWuP7779HZmYm4uLi6JaC3r17Y9iwYUhMTKRbSqtHp9MhLS2t2SfgCWM6k072N3U6EHCsM6K5tRUIiCgCJpKcnAwOh4OIiIhmfY6JETNkcfHiRWi1WlKMIhwOhxEpI5iEJaMpsa4mc40gEong5eXV4L2goCCYTCYUFxeT0pcz88MPPyA1NRUrVqygW0oD4uPjkZGRgYMHD9IthdFQeRDIErbYshwJWVFtQI1Ru7q6mlajNoGlqHM/Pz/w+XzaI/DtgXAidezY0e62Hn74Ybi7u9M677LOCDvRaDTYvHkzXn31VYSEhNAtpwGjRo1CeHg41q5dS7cUUmmqMA1QM/jodDqUl5dTrMqxbNmyBQaDAUuWLKFbSpN07NiRrR3BAgBYtWoVJBIJFixYQLcUm3nvvfdQVlaGzZs30y3FZbFkgCfb2N8SiCKOVGwQiMVwY846Kp0RgYGB0Gg0rc4Jl5iYiJiYGDz88MN0SwFQEx1x9uxZXL58mW4prZr09HQYDIZmn4AHmFcfgI7ICJ1Oh5s3b7b4/qWlpTEyXaJcLkfnzp3h4eHRrM916dIFIpGIUc8FWZw+fRpeXl7o3bs3Ke3FxMTgn3/+YfPe/4el1Enu7u7w9PQkfY3QmGHSFU4Tk4HZbEZCQgKGDRuGwYMH0y2nAb1798YzzzyDtWvXwmQy0S2HsSgUCnh4eDQ6llP1rKvVaqjValoOCwBAVVUVLl26RIojGQAeeugh2o3aBJacTUzYZ9rL6dOnSXMiCQQCDBo0CKdPnyZBWctgnRF2sn//fhQUFGDZsmV0S2kULpeL+Ph4HDt2DNeuXaNbDmlYO+VJXOMqlJSUIDExkZG1IuqzYsUKlJSU4KOPPqJbCgtNXLlyBV9++SXefvvtZm/c6aRDhw6YM2cONm3a5NSnJpiMpUUicD9fK11Y2vwTr5O5+W9qQ0T26R1rDnzimtbClStXIJPJGBEVQfDcc8+hY8eObHQEzbSk+DJBVFQUo4zp1tbKpaWlpKdVsNeZo9PpcPv2bVI1kUFLUncBAI/HQ0REhEs6I2QyGQYPHgwej0dKe0OHDoVOp8PFixdJac/ZoXK9xK4RrHPixAlcvHiRkVERBCtWrEBqaip++OEHuqUwFibYkAoLCwHQtxf6+++/YTAYSHNGCAQCREdHM8oZQZejx5Go1WpcvnyZtO8NqDkEcObMGdocmKwzwg4MBgPWr1+PcePGoVu3bnTLaZIJEyagY8eOWLduHd1SSMPaaS+A/iKoZEJERTAtFVhjdOzYEa+//jpbO6IVs2zZMnTv3h1vvPEG3VKazYoVK8Dn8/Hee+/RLcUlsba5DgwMpHXsplKfpXmM7NM71sLBCT2thcTERHTo0AGjRo2iW0otPB4Pc+bMwcGDB1FQUEC3nFZLcnIy2rdvDz8/v2Z/ljCm37p1ywHKmo8ta2XCKEIWRDqiljoj6rbBJFqaegpgdvqplmI0GnHu3DlSjSK9evWCt7c3IwxaTECpVILP58PX17fR98lcj7BrBOskJCSgb9++GDlyJN1SmmTw4MEYNmwYEhISWl3qTVuxNC/6+vpSksaH7r2QTCaDn58fevbsSVqbdBu1CSxFnQPO7Ywg24kE1HxvpaWlSE1NJa3N5sA6I+zg4MGDyMjIQHx8PN1SLCIQCLB06VIcOnSIMRske7Flg+WsA0196taKoLvIpq2w0RGtl99//x2///471q5dCz6fT7ecZuPv74/4+Hjs3r3bZcZLJmEpLzFA/yLRlg0CFamTqOzL1eZMaygUChw4cABz5sxh3Bj1xhtvQCgUsnMnjbT0BDxw35jOhFPwloo4Ao773cvl8hY7cwICAtCmTRtG3L+6FBcXIz8/367nIiUlhXYjDZlcv34d5eXlpBpFeDweBg8ezDoj/kOpVEIqlYLLbdxcQ9UagQn1vOjm/Pnz+Ouvv7BixQpS0qM4kpUrV+LixYs4ceIE3VIYCZUHgSxpAKjZazSGTCbDkCFDmhxbWgJh1E5JSSGtzZagVCrh6ekJd3f3Rt+ne59pD45wIg0aNAh8Pp+2eZd1RrQQk8mEtWvX4plnnkGfPn3olmOVSZMmITAwEOvXr6dbit2YzWaLJzj8/PzA4/GcdqCpz9atW6HX6xldK6I+nTp1qo2OqKqqolsOC0WYTCYsW7YMgwcPxujRo+mW02Lmzp2LNm3aYOXKlXRLcTmIsbupzRzdi0QqnSWW0iKQ2ZfZbLa4+ZJIJOByuS4VTWiJ3bt3g8/nY8qUKXRLaYCvry9ee+017Nq1C1qtlm45rRJ7TsATxnQmnIInIh6oTr1iz/0DmBlFQDhH7ImMUKvVuHfvHomq6EUmk0EoFGLAgAGkthsTE4OzZ8/CaDSS2q4zwpQDCxwOB0FBQa1mjdAYCQkJCA8Pd4q9zYgRI9CvXz+sWbOGbimMhKq1tyWI31JAQECTGgoLCx3iwNbr9aRHtQH0G7UJrI2bzjyWOcKJ5OHhgYcffph1RjgbP/zwA1JTUxmdN7AuYrEYixYtwmeffYbs7Gy65dhFeXk5dDpdkxMJl8uFVCp1CWdEaWkpEhMTMXPmTKeJiiBgoyNaHwcPHsSVK1ewYcMGxp8csoSbmxvee+89fPPNN/jnn3/oluNSMGETYAlbnCWVlZWkOFltMTSQsWAuKyuDwWBosi8ej4eAgACXmDOtodPpsGvXLrz66qstOrlNBfPmzYNSqcTBgwfpltLqqKysREZGRotPwAPMKWJty8nLuteRhT2RJQBz7l9d5HI5BAIBunbt2qLPMylihixkMhn69+8PsVhMarsxMTGoqKhwqTqHLYXK9RKVjg9n49q1azh27Bji4+NJNQI6Cg6HgxUrVuDkyZM4d+4c3XIYBxOedaVSCYlEAoFA0KQGk8mEkpIS0vu+cuUKqqqqSHdGuLu7o2/fvox3RjjrWKbX63H+/HnSvzegZt6VyWS0pHZj/ojKQMxmMxISEjBs2DAMHjyYbjk2M336dHh6emLz5s10S7ELaxssgP4iqGThjFERBJ06dcJrr72G9evXs9ERrQCtVouVK1di1KhRGDJkCN1y7GbixImIjIzE0qVL2byrJGLLiRWyjP0twdrmn3iPjDzrliL8iL7ImMesFXMDnHdx3lwOHTqEgoICzJs3j24pTdK9e3c8+eSTSExMZMceiiFy5tp7sp8JRmdra2WxWAxvb29Sf/eEM8fe+3fnzh1GrRvlcjnCw8ObNBxZo3379vD19WXEc0EGZrMZMpnMIUaR/v37QygU0m7QYgK2rhHsnSeqq6tRUVHBrhGaYN26dejYsSMmTJhAtxSbGTVqFMLDw7F27Vq6pTAKa9k1AGpsSLbshYjryEYmk8HNzQ19+/YlvW06jdoEtjhxy8rKoNPpKFRlP45yIgE131tOTg4yMzNJb9sarDOiBZw4cQIXL150mqgIAi8vL8ybNw9JSUmkF6yjElucEXQXQSWD0tJSbN26FTNnzkSbNm3oltMi2OiI1sOuXbuQlZWFhIQEuqWQAo/Hw7p163Dq1Cn8/PPPdMtxGWw5sUJcRwe26rN3frGWOonoi4z7QGh1xZNCzcFsNiMxMRFPPPEEwsPD6ZZjkfnz5+PKlSs4c+YM3VJaFcnJyeBwOHY9H5GRkbhz5w7UajWJypoPHWtlIle0vZERZrOZtmKKjZGcnGzX38ThcBAZGcm49FMt5fbt21AoFBg6dCjpbYvFYgwYMIB1RsC29YhGo0FFRYXd/RDtWerL1dcIjXHr1i0cOnQIS5cubbEzkg64XC7i4+Nx7NgxNsqoDpWVldBoNLTbkKjaazSGTCbDwIEDIRQKSW87JiYGubm5tKYktOZsIt5zNluoI51IjzzySG0fVMM6I1pAQkIC+vbti5EjR9ItpdnMnTsXXC4XiYmJdEtpMa3FsOLMUREEnTt3xmuvvcbWjnBxVCoV3n//fUyePBkRERF0yyGNp59+GsOGDcOyZcvY/MUkYesikenOCHv1ERsiKk7v2BpN6OwOfGucP38ely5dQlxcHN1SrPLEE0+gR48eTr1Wc0bkcjnCwsLg5ubW4jaioqJgNptx48YNEpU1H4VCAS8vL4t/C9lrZblcDi6Xa5czJyIiAhwOhzFRBGaz2e7UUwAz00+1FJlMBg6H47DsAEw4XcsEqFqPsGuEptmwYQOkUikmTZpEt5RmM2HCBHTs2BHr1q2jWwpjYEqkMF17IZPJhDNnzjjkdD2A2swIdDqTbY06cbbxTCaTYdCgQQ5xIkkkEvTs2ZN1RjgD58+fx19//YUVK1Y4ZU50iUSCGTNmYMeOHVCpVHTLaRFKpRI8Hg/+/v5NXuPszggiKmLGjBlOGxVBsGLFChQVFWH37t10S2FxEEQqrnfeeYduKaTC4XCwYcMGyOVyfP7553TLcQmYHhlhbYNAFJujYvNPpqGBz+fD19fXYl/OPGfaQmJiIrp164Ynn3ySbilW4XA4mDdvHr777jtawqZbK/YWXwbuG9PpPgVvbawFyP/dJycn2+3M8fDwQOfOnWm/fwQ5OTlQqVR2PxdRUVFIS0tzutQQjSGTydC7d2/4+Pg4pP2YmBgolUrcunXLIe07AzqdDqWlpYxxRrSGNUJ9srOzsX//fixatIj02ihUIBAIsGzZMhw6dKhV/5bqYuuz7ug0PtbmZy8vL4hEItJ/c2lpaSguLnaYM4JOozZge9Q5QN8+syU42okE3D8EQDWsM6KZJCQkIDw8HKNHj6ZbSotZtGgRqqursXPnTrqltAilUgmpVGqxiJSzL5oSExOh0+mwdOlSuqXYDREdwdaOcE1yc3OxdetWLFiwAO3ataNbDukMGDAA48aNw6pVq1BdXU23HKemqqoKlZWVFk8kkWXsbynWFrECgQD+/v5O54ywVJSb6MuZ50xrZGdn4/Dhw7XRoc7Aq6++Cm9vb3z44Yd0S2k1kHECnjCm030Kng5nBBn3D2BWFAGhg4zICIPBgPT0dDJk0Yqj6kUQDB48GBwOp1WnaioqKgJg/QQ3QJ4zQiqVWuxLrVbTnn6OSjZv3gxPT0/MmDGDbikt5vXXX0dgYCDWr19PtxRG0Jy1tyPT+Fira8DhcByyLpfJZODxeIiOjia13brQZdQGbEvDRYxzzrTncbQTCaj53tLS0ii/L86xI2MI165dw7FjxxAfH+80m9nGaNu2LSZPnowtW7Y4pXHYlg1WUFAQSkpKoNfrKVJFHmVlZU5fK6I+K1euRFFREZKSkuiWwkIyb7/9Ntzd3V3CcdYUCQkJKCgowPbt2+mW4tQQC3trxn6JRELLItEWZwlATnE7W9INklXAztqmh9BRXFwMg8FgV19MZefOnfDw8MBrr71GtxSb8fDwwJQpU7Bnz55WZQCii8LCQigUCrtPwANgRH0AW9fKZEdGkHH/oqKiaL9/BMnJyfD09ERoaKhd7RDODKb8XS0lLy8Pd+7ccahRxMfHB717927Vzghb1ggSiQRcLpeUNYK/v7/FmgjOmme9pRQWFiIpKQnz5s2Dl5cX3XJajFgsxqJFi/DZZ58hOzubbjm0o1AowOVyIZFImrzGkcWjgZpT7oWFhZTPz0CNM+Lhhx+Gp6cnqe3WJSYmBjdv3qRlH2dLGi6xWAxvb2+nckacPn0aPB4PgwYNclgfxJxOda0657Wo08C6devQsWNHTJgwgW4pdrNkyRKUlJRg7969dEtpNrae9gKcc9G0detWaLValzLudu7cGa+++irWr1/Pni53IVJTU7Fv3z6sWrXKYeH6TCAsLAzTp0/H2rVrUVJSQrccp8WWE0nE+3Tk8rTFWUK8b68+pVJpdUNE1ukda6mngJqFu9lsRnFxsV19MZGqqiokJSXhjTfecDqjwpw5c1BeXs6miaMAsk7AAzXGdLpP9tvqhFQoFKTk5lcqlVAqlaRFRuTn5zNiPCKiPexNzevv74927drR/lzYC+EgcKQzAgCGDh3aqp0RtqyXuFwupFKp3esRW9cIdXW5OomJieByuZg7dy7dUuxm+vTp8PT0xObNm+mWQjtKpRIBAQHg8XhNXuPI4tEAUFJSApPJRMteyNFRbQB9Rm3ANicu4BhHjyOhwokUEhKC0NBQyudd1hlhI7du3cKhQ4ewdOlSiycHnIXOnTsjNjYWGzdudLr8pbYsmpwxHxxwPyrCFWpF1GflypUoLCxka0e4EPHx8ejQoYNThzDbyqpVq2AwGLB27Vq6pTgtti4S6UoZ1BxnCRknEa1tiMg6vdMcB76zFXSzhS+++AKlpaWYM2cO3VKaTWhoKEaPHo1t27a1+mKujkYul0MkEiEsLMzutphgTLd1razT6VBeXm53fykpKQBAWmRE3TbpRC6Xk/I3AcxwUtmLTCZDWFiYw/coMTExuHv3LvLy8hzaD1OxJXUSQN56pDWvEeqjUqmwY8cOzJgxw+KBEWfBy8sL8+bNQ1JSklMe0iQTW551R6fxoWsvlJmZiaysLIc7I0JCQtCxY0danMlMP/TWUqhwIgH0pNhinRE2smHDBkilUkyaNIluKaSxfPlyZGdn48svv6RbSrNozqLJ2ZwRiYmJLhcVQdClSxc2OsKFOHPmDH744QesWbMGIpGIbjkOJygoCIsXL8b27duRlZVFtxynhMrNdUugcoNgyzxG9EVGFIarzpnWMJvN2LZtG0aNGoXOnTvTLadFxMXF4caNG/j999/pluLSJCcnIzw8HHw+3+62COM1XYZnW4o4AuT+7pOTkyESidClSxe72+ratSsEAgHtKY0MBgNSU1NJifYAmJG+y16oNIoQ/bVGlEolvLy8rBaDp2o9Qnc9LyrZtWsXqqursXDhQrqlkAZRLysxMZFuKbRiy7Pu6DQ+VB58qgsxlj7yyCOktdkUdNWNIKLO/f39LV7nTHXyMjMzkZ2dTdm8e+XKFVRUVDi8LwLWGWED2dnZ2L9/PxYtWgSxWEy3HNLo2bMnRo8ejbVr18JoNNItx2ZsDT0nrnUWysrKsGXLFsyYMQNt27alW45DIKIj2NoRzo3ZbMbSpUvx8MMPu0TaOltZtGgRfH19sWrVKrqlOCW25CUG6I+MYMpJRCr7csY50xZOnDiBlJQUxMXF0S2lxcTExKBPnz6t3ojgaMgqvgzcN6bT5YyoqKiAVqul9Hcvl8tJc+YIBAL06NGD9iiCO3fuQKvVkuqMyMjIwP+z997hUZ1n/vf3TK/qvSIhAUIS1SCBwbhgMDYSboAmTrGTbMrGG+9usimO09dOdTZONpvsvmsnm/1lJYwbAhsb44KxYUQ10qigjvrMaCSNptfz/jE+QmVGU3TKjKTPdfkymjlznnvOnPPcz3NXs9lMy/nYZmJiAk1NTawYRdLT01FcXIwPPviA8bGikVD2ugB7awSBQMBZPy82sVqt+M1vfoPHHnsMWVlZXItDG8nJyfjKV76Cf//3f4fRaORaHM5gc+09nwzA/H0NmJDh7NmzKCkpmXIsMgkXRm0gtKxzILacEWw7kbxeL86fP8/4WBTLzogQePbZZ6FQKBZlKZInn3wSHR0dePnll7kWJSRcLhfGxsaCKhKpVAqlUhlTKViLOSuCYuXKlfjMZz6Dn//858vZETHMa6+9hvPnz+MXv/gFeLylo0YUCgV++MMf4n//939x7do1rsWJOULdBKSnp3Myd4fqLElPT4der4fX6414rFBKqFBjLWTB7HA4MDExEXTTI5fLIZPJYmZxHirPPfcc1q1bh127dnEtSsQQBIEnnngCb7zxBtrb27kWZ1FCkiSt5XgoYzpXUfChRl5S8wId8y1dzaspoqGJNTU+nWWagOgoPxUJH330EUiSZMUZAXAXXRsNhLNGoKNnBFuOj2jn+eefx9jY2KLci3/jG9+AzWbDf/zHf3AtCmew+VwFQqfTQSwWB+1hlp6eDpPJRJu9hK2sNoAbozawOOcyNp1Ia9asQUpKCqt6d+lYkSKEiuL++te/HnOND0Nhy5YtuPvuu/HMM8/ERD3iUBuMUsfEykRD9Yr48pe/vGizIiiWsyNiG7fbje9+97vYs2cPdu/ezbU4rPPFL34RxcXF+M53vsO1KDFHqJuAtLS0BRv7IyGciCmPx7OgZuZsRT2GozO5cgIxRWdnJ15//XU88cQTC24+yzU1NTVITU3F73//e65FWZT09fXBZDLRFgEPcNsfgHqOg80xiYmJ4PP5C14rU84cOq9fWVkZNBoNp3sTjUaDtLS0oNlyoVJSUgKCIDjP+IiUs2fPIjMzk5ZSXKGwc+dOaDQajI+PszJeNMFWBLfX64Ver1+Sa4TZOJ1O/OpXv4JKpYrZso7zkZmZic9//vP4t3/7N1itVq7F4QQ2M44CQe2Fgq1L6cxcHB0dRWtrK2vOiNWrVyM1NZV1Z3I4QW86nS4mbJ9nz57FbbfdxspYBEGwHgSw7IwIwnPPPQcej4d/+Id/4FoUxnjyySdx7do1nDx5kmtRghJqtBd1TKw4I373u9/Bbrfj29/+NteiME5RURE+/elPL2dHxCjPP/88rl+/jl/84hdci8IJQqEQzzzzDN588028++67XIsTU4SzufZ6vQsy9kdCOM4SYGEbBLZ6RixWnRkKv//975GcnIxPfepTXIuyYCQSCb7yla/gL3/5y5IuscAUlHGYzsh+Lo3poT73PB4PqampC37u+/r6YDabac+MMBqNGBwcpO2c4UJntgwAyGQyFBUVxbQzYufOnaw5d3fu3AmSJPHRRx+xMl40Ec4awWAwwO12RzTO+Pg4PB7PklwjzOZvf/sb+vv7F3Ww0b/8y79gbGwMzz//PNeisI7b7YbBYOD8Xg/n2aaOXygffvghALDmjCAIAjt27IhaZ0RaWhqcTmfUr6fZdiIBvnukoaEBDoeDlfGWnRHzYDQa8e///u/4yle+guTkZK7FYYxdu3Zh27ZtePrpp6PeQ7gYDStGoxH/9m//tiSyIiieeuop6PV6/H//3//HtSjLhIHFYsGPfvQjPPLII9iwYQPX4nDGgw8+iIqKCnzrW99iPXo/luFiAR4ObMkXyYYoUt28GHVmKExOTuLPf/4zvvzlLy+aXl9f/epX4XA48MILL3AtyqKjqakJcXFxyMnJoe2clDF9YGCAtnOGSqhNHAF6nnuqnBHdmRHTz80FTU1NtH4nIHabWNtsNly8eJFVo0hhYSEyMzOXZKmmcNcjo6OjEY8z/TzBxlosa4TZeDwe/PznP8f999+P0tJSrsVhjMLCQqhUKvzqV7+C0+nkWhxWMRgMIEmS83udi73Q2bNnkZubi/z8/AWfK1R27twJtVrNmlEbiP59Zriw7USixrLb7bh8+TIr4y07I+bhj3/8I2w2G/75n/+Za1EYhSAIPPnkkzh37lzUL/jCWTQttNY2WyylrAiK5eyI2OTf/u3fMDY2hn/913/lWhROIQgCv/zlL3H58mUcPXqUa3FihlDTo6ljuHBGsCFfOBui9PR0uFyuiKN3lqqh4c9//jNsNhv+/u//nmtRaCMzMxOHDh3C73//e3g8Hq7FWVRQJYbojPimjNhcRMHrdDqkpqaG1NOJjrWyRqNBfHw8rc6cvLw8KBQKzrIIbDYbOjs7GXFGxGJmxIULF+ByuVg1inBRMiIaIEmStfVIqM10gcW1RpjNK6+8gvb2dnz3u9/lWhTG+c53voP+/n787W9/41oUVonEhsREkG6ozzZVHpAuZwSbczfgM2o7HA5cunSJtTHDKcNFHR/NnD17Fnl5ecjLy2NtzA0bNkChULCmd5edEQGwWq34zW9+g8ceewxZWVlci8M49913H9atW4enn36aa1HmRafTQaFQQCaTBT12oeUt2MBoNOI3v/kNvvSlLy2ZrAiK733ve9BqtcvZETGCXq/HL3/5S3zta1/DihUruBaHc2677Tbs378fTz755JKLLoqEcOoSU8ewPX+HGlGjVCohFosjli/Ueu7AwhfMWq0W8fHxEIvFQY+NFQd+MDweD37/+9/j4MGDi2799sQTT6CnpwfHjx/nWpRFBd3NlwEgPz8fCoWCkyj4UDfkAD1rZSqDgE5nDo/H4zSLoLW1FV6vl/b7ory8HFqtdqqfT6xw9uxZxMfH0+6cCcbOnTtx6dKlJRW4NDk5CYfDwcp6ifpcqAZaLvp5MQ1Jknj66aexe/dubN26lWtxGKe0tBT3338/fvazny2pwIZw195MlfEJda8hFAqRlJS0YP1sNptx5coV1p0RbBu1w8k65yroLVy4cCIJBAJs27Zt2RnBNc8//zzGxsbwrW99i2tRWIHKjjh16hSrHsxwCbWmN7Dw8hZs8Lvf/Q42m21JZUVQFBcXT2VH2O12rsVZJgg//elPwePx8L3vfY9rUaKGn//85+jt7cV//ud/ci1K1DM2NhZyXWLK2M/mIjEcZwlBEAuKEAw3WwGI3NAQ6qaHGkur1Ua1zgyFN954A11dXXjiiSe4FoV2tmzZgu3bt+O5557jWpRFg8vlQltbG+1GVoIgOIuCj2StvBDobl5NwWUWATXu2rVraT0vlxkzC+GDDz7ArbfeCj6fz+q4O3fuhMvlQkNDA6vjckk4a4SFRk/rdDqIRCLExcUFPZbq52UwGCIaK1o5efIkrl27tqT2N08++SQ6Ojrw8ssvcy0Ka0Sy9mZiH8K2flar1fB4PKw1QaZg26g9OjoactZ5QkICBAJBVActc+VEAnx698MPP2TFWbnsjPCD0+nEr371K6hUKhQWFnItDms8/PDDKCoqws9+9jOuRQlIuIYVh8MBk8nEsFSRMb1XxGKL3gyVp556ajk7Igbo6urCn/70J3znO99Z1P1zwqW0tBSPPvoofvKTn2BycpJrcaKacDYBCzX2R0I4zhJgYRsENjdE4epMm80Gi8US0VjRwnPPPYeKigpUVFRwLQojPPHEE3j//ffR2NjItSiLgo6ODjidTtoj4AFfFDxXmRFsGTtcLhdaW1sZu34tLS0RN+ddCE1NTSgoKIBSqaT1vMXFxRCJRDHVN8LtduP8+fOcGEXKysoQHx+/pEo1hbNGkMvlkMvlC14jhJLVFCulTcKByorYtm0bdu3axbU4rLFlyxbcfffdeOaZZ2I+ACVUdDodZDIZ5HJ50GOZutetVivMZjOrzoizZ88iOTkZJSUlCzpPJOzcuRMfffQRK0btcOZNHo+H1NTUqJ7Lzp8/D4/Hw5kzwmg0shI0seyM8MPf/vY39Pf34zvf+Q7XorAKn8/Hd77zHbzyyitoaWnhWhy/DA0NISUlJaRjo33R9Pvf/x5Wq3VJZkVQUNkRP/vZz5azI6KY733ve0hLS1uUkcYL5cc//jHMZjN+9atfcS1KVDM8PAwgtEUidRybc3c4i1jquEjlGxwchFQqDWlDlJiYCD6fz5ozAmC/PBadaDQavPPOO4t6rnrggQeQk5OznB1BE9euXQNAb/NlirKyMk6M6cPDwyEHDqSlpWFsbAwulyuisdrb2+FyuRi7fg6HA52dnbSfOxiNjY2MNLIVCARYs2bN1H0XC1y9ehVms5kTowifz8eOHTuWlDOCzfVSJGsESr7FwNmzZ3Hu3Dk8+eSTtJaZiwWefPJJXLt2DSdPnuRaFFYYGBiYyiQKBlM2JDb3GhRnz57Fjh07OLm/KaM2G873cK9tSkoKBgcHmRRpQXzwwQecOZEqKiogFApx5swZxsdadkbMYmBgAJ///Oexa9cuRhah0c5nPvMZJCQkoLKyMupqQpIkiVOnTuGNN94I6fhorgenVqvxk5/8ZMn0JJmPp556CsPDw7jjjju4FmUZP/z0pz/FkSNH8KMf/QhSqZRrcaKOnJwcfO1rX8MzzzyD1157jWtxopZnn30WQOibWLb7F4TTxJE6LlL5nn76adhstpBqYPN4vAUbGkL9Ts3NzQAQ02XHKisrIZPJ8PDDD3MtCmMIhUJ85jOfwQsvvIATJ05wLU7M8w//8A8AEFIvsnAZHR2F0+nEH/7wB9rPHYiRkRG0t7eHfG9Q88Po6GhE433zm98E4Lsv6UYgEMwYgy3GxsZw6tQpxhwGra2teOGFF2ImIvkzn/kMAHBWLYAgCLz99ttobW3lZHy2eeaZZwAAExMTIR2/kPVIOGsEav22WIJv7HY7du/ejby8PNx3331ci8M6u3btwpo1a1BVVbUksrufffZZ3LhxI6RjExMTIRAIaN+H/Pd//zcAhJzZutC9UGNjI9577z3OeoOsXLkSAPDpT3+a8bHCdUY0NTVFbWUOkiTxr//6rzCZTJw4kaRSKVwuFyuBXcvOiFmcO3cOALB582aOJeEGkUiE1NRUmEymiKOkmIIgCOzYsSPkPh7UxvL9999nUKrIePbZZ+FyubB7926uReGcoqIiAMCVK1c4lmQZf/zXf/0XAOD+++/nVpAo5sEHH4TX68Vvf/tbrkWJWh577DGsXLky5OaAJpNpSh+zAaUnQjVImkwmNDY2RtS8/Ktf/Sp27NgBiUQS0vEejwdvvfVW2OPY7Xa0traGXKrwwQcfRG5ubkwb8m02G9LT0xkxjEYTVDmJ119/nWNJYp8HH3wQW7duDfl5DIeamhqkpKSgsrKS9nMHIi0tDWvWrAk5u5vH820F1Wp1ROPt378feXl52LBhQ0Sfn4/NmzcjNzcX+/fvp/3c8xEfH4/c3Fx84xvfYOT8jz/+OO0Nv5lk+/btWLlyZchGHrrZvXs3eDzeop/XKb785S9j7dq1WLFiRUjHWywWvPfeexGNde7cuZDXCLfccguKiorw+c9/PqKxog2dTgeXy4UVK1bEzLNIJwRBYM2aNfB6vejq6uJaHMb5/Oc/j+rq6pCPF4lEePPNN2mV4cEHH0ROTg4OHjwY0vFmsxltbW2wWq0RjadQKCAUCsP63nSSmZmJwsJCbN++nfGxTp06BYFAAJFIFNLxjzzySNTudwiCQGFh4VQgABccOHAA69evZ3wcgoyVsAwWYaoRW6zgdrvR0dHBSVoQnXR0dGDVqlW46667cPr0aa7FmUF/fz/OnTuHw4cPcy1KVGA0GtHf37+kn7topampCV6vlxWFFMucOXMGhYWFyM3N5VqURUFaWhr0ej1cLtdUdCyT3HbbbTh79ix6enpCMgCUlpaipaUFY2NjSExMZFQ2giAgFArDdnxQEY+bNm3C5cuXGZIuujAYDEhMTJwysC5mmpubp+rPL7NMpBw5cgQ1NTX4xje+gV//+tdci7PMMn4hSXJJGoxDQSqVwm63h51p4/F4IBAIkJycHHFmVKyj0WhQWlq6ZO8tkiTR3Ny8vP+eBfVspKSkQK/XcyZHZWUlGhoaMDg4uOQraQSjoKAAvb29MJlMUCgUXIuzTIgsOyOWWdT87//+L/bt2xdyn4lllllmmWW4p7u7Gx9//DEefPBBVsbT6XR4++238cgjj4R0vFarxenTp0M+fiF8+OGH4PP52LZtW9if/Z//+R/s379/ufH8Msss4xeSJPGf//mfeOyxxyAWi7kWZ5lllgkTjUaD/v5+7Nu3L+zPvvLKK1i/fv1UOZVlllnGx8mTJ5Gbm8upo2Z8fBzHjh3Do48+ypkMsUJnZyc0Gs1yJYcYY9kZscwyyyyzzDLLLLPMMssss8wyyyyzzDLLLLPMMsswCvO1D1jg0qVLePK734EtwnpqkUIQPHz20UfxxS9+kdVx6eLixYv47nefhM1up+2cBEHg8489GlI9ya997Wu4+nFoDXwipaioEH/5858XXLKhq6sLzzzzTMQ1+xZKSUkJfvCDH3AyNl38x3/8B2r/9v8YO3/x6jV4/vnnl1SqbX9/P7761b/HeIhN7tiGz+fjia//Ax566CGuRaGNzs5OPPH1r2PSOEHbOQkeD5//whdjJvLl3XffxdP/+lM4HQ7GxhCJxfj+D36I22+/nbEx6KSjowNPfP0fYKKxCeCWikr85je/oeVcv/71r/HKa8cAGsNPhEIBvvHP/8RYLdof/OAHOP1uZHWwpyMWifCD7z+FO+64gwap6KexsRFPP/007DSuxSKFIAg88sgjIdczjja8Xi8ee+xRdHd20n7udRs24g9/+APcbjc++7nP4caNPtrHmI5UIsGPf/wj/P53v8PgQD+jY227dQd++ctfYmhoCF/+8lcwNj5O+xgikQjff+p7uPPOOwH41i9//9WvYIKBsdaWleNPf/oTCIJAS0sL/vGf/hlms4X2cQBAJBLiqe89OdUDzmAw4Itf+AJGR+kt7VFVfSDknnmBGBkZwZe/9CWMjRlokmomRcWr8MILL4S9Dm9ra8MvfvELWudAgiBw+PBhHDhwgLZzLoTf/e53OHr0KK3n5PP5ePzxx+ete97d3Y3HH/8HGKO4QXGcUonnnvstVq1aFfJnvF4vHn3sMXR2dTMoGSDg8/HP//SPURltbTQa8dhjn8eIlt4my0zA4xF49HOfpc1m5vV68djnP4+ODmZ6XUgkYvzkxz/Cjh07gh77+uuv49e/+iXcNPVa1Y+OQj86ivT0DBQXF9NyznCwWq1oatLA4/GAx+MhJycb0hD76XGBRCzGD3/4Q/zf//0fGps0jI/H4/Hw1a98GZ/61KeCHnv27Fn8+Mc/hoPB9T2Pz8OXv/yVkOSZjt1ux2c/+zkMDA3RIgcB4NDBh8Nuer0oMiOefPJJ/OxnP8PDG1JZHfeDzglkFa7G1WtNrI5LF9/5znfwi1/8Amt309e3oPfye1i1IgdXr8xfn3p8fBxJSUmQ55VBlrOGlrG9bic89pubDefYEKwDrXjttdeQl5c341iCIFBSUhJySvrPfvYzPPnkk9i1lf26+b0DI7gxpIVWq+WseRwdlKwuhnGkHzuKkmg/d6fegqv9k7hx48ac33ox89e//hWf+9znsOaOh8Hj87kWZw5d50/i9h3b8eabJ7kWhTb+8Ic/4PHHH8ehbUUATY6v9zQDWFFcgguXYqOu/xe/+EX89S8v4EA5PeXvXB4vzA7PjNfe75jAgQcexFNPPTXneD6fj7Vr10bUS2JgYCCs+q9KpRKFhYXo7OyExRLYmFVXV4df/vKXeGh96oIconaXBzaXF92jNvSM2fHRRx9BKpUGPF4qlWL16tXo6emB0WgMeNzuu/fCSkiQuJq+Rrq6y2+g6p678eqrr9J2zumIxGKIU/KQVLRxQecZOPcaHvvcZ/Hf//3fNElGL9/97nfx85//HHeXcV8PuKFLj9Vry2NmLppNZ2cniouLsTlXiYLkwM9NuLRpLdAMWzA2NoahoSGUlZUht7wSCZkraBtjNk2n6vDZz34Wf/3rX7G9IB7Z8QsvoUSSJEwODzzem1u/zlEbesfsuHDhAt555x1897vfRcnt90Mgorehd8u7L+Nzn/0Mnn/+eQC+EnKPPvooHliXCgGPviCS6zorGofMU2vmX/3qV/jWt76F1O3MNKocvVCPz336U/jzn/8MADhx4gSqqqpwz+aViJfRU/ZKfX0QNlIIrW5hDo4XX3zRZ6DfuQEyCb29Zjr6dbjU1ove3l7k5+eH9dmnnnoKTz/9NPI37KRNnuHrV1C2tgSXL12k7ZwLYeXKlbA7Xbh15+0LOg9Jkpg0TsDrJXH+ozNYV16OP/zhDwGPf/vtt/Htb38b2+6roW3NSiskifOv1+G5557D17/+9ZA/1tXVhaKiIqStvgXxWYWMiderfgP37L4D9fX1jI0RKW+//Tb27NmDxHV3Qqigf29NJ+PNH2BVXiY0jR/Tcr7u7m6sXLkSypWbIctY+O9Pkl64rZPAJ2bRsWuncfjwYXz729/2e/yaNWum1uaHDh3Ciddexv7She2JPF4Sk3Y3LvWbQILA1gJuftPBcRtahidx4OCnorL/msVshst1s4fee6fewP3334/XXnsNGcXrkVrAbM/bTvUpbFpXig/PfhD02Mcffxx//OMfobo//BJ6ofLmex+itHwdzpwJLs90Ll68iK1btyK5dAckSZkLlsPQcg4pciH6b/SE9blFkRnh9XqxIlWB5x4K7FG3u7yQCHlz/h3s2Pl46kQ3Lpm94QscJXi9XqTkFuHeb/1H0GPdTntIG5PTv/8XePquhDQ2AGTt+xpSt96MqvS67OAJ5x/H3zEehxUff6cSjsm50T6Bohnu2bsHJ998K6isAOByuZCZloI3/vPpkI6nk7c+vIQH/+FHcNHkcecKr8eLBzZm4Pv3+p5Tu8tnfJQI+VN/U/8Olw86DDj831em7qulAvV9933rPwI6I6hnN9RnmBa5PB7w+Hwc/+lj8Hq5j/alE6/XC4lIgGc/txMS4fwq1O5yzznG32vf/OtZaCZj5971er1YnxOP5x4MPYqNYrZ+JUkSm569At3k3PvklVdewSuvvOL3PF/4whfCNi5rNBqUl5eHJzCAv//7v8d//EdwPSkWEPjdw6vnPWa+9YXO5MTmX12Ed1qMyK233hp03K9+9av44x//OO8xBF+Agvv/GYUH/hEA4HHawQ8wH8z33nRcvxlndM71er0ouveLKNzz6By5QpURAGy63qjWDW63G0WZifh/X6HPEBcuHq8XfB4P3z5yCdcmY3etQf3O39uzAhUr4kP6TChr/pMto/hibRu8Xu/UGLu+8BTy120P+Bl/OjccPaxtuwyPx7dO+tG9hSjLDL8h4+zv9tz7ffjF6Rt+j926devUv/f907OQKhMDnnf69wj1OxmHumc8h9S/f/fwGvAjcEYE+t3ebjPgc/+veer8Xq8XYmUSCj/7ixl7h9l7iel/h7IXoXAZ+v1+r+e+tBcpcaFFk9qdbkhEgdcT//ZaA/50ujmkc80HJdtvnjiMlPib95Pd6YJEJAwg28z3Ah37/pXrOPDtf49ornW5XEjJWYmHfkGfY/udf/82nL2XaDvfQvF6vTio+jS+/YOfBjzGbrdDIpHM+fd0nvnhk3ip7maW+fnz57Fp06Z5x+YLBPjCT/8rZFldDjuEYknAv+mC2itcfe942PcNdfyWzz2FzLLg66TphLMvcj7zOXiidP1AXYOiz/4c4qTsm6+HOH+FM89FCun1gODx0fW378M7coG281LffcUD30RS2a657zt9ewpeiPr2+gvfxNjHb8947ciRIzhy5Ijf47dXVuCj8+opWSoLEvDcw4GDa0OxQ1b9f424fGMCQj6BR7evwE8f4KZXxf+ev4F/OdqIX//pLzNed9jtEPuZk2YT6nGRcPa9t/H5h++b8/prr70GANj52W9h1a30GP4DzQ/Hf/E1eE2hZcZ6vV6sX7saz//6xwGPsTsckCygX9cXvvlD3NBOhP056hkq+cxPocwNLzDc3x6s/aVfwnbx5bDlWBTOCIp2nRUGiws8AihKlUFrcsJocwMABDwCbi8JAZ+AVMiDXMSH003C4vSgf8KO7Hgx3F4SI5NOrEiSYNLugcnhRppChDSlCAI+AZmQh6sDZuQliqE3u9A/bgf8r91iiusfHIPH6UBa0TpYjaPwulywjGuRmL0SHrcLZsMwErIK4DAb4bSaIE9KhzwpHTy+EEKJDMNtlyGWKyGUyGHU9iOc7ZJjtB+THRdAej3gCcUg3S5IUvNh6roMl3kMypWbPzmShDg5BzZtD0iPC6KETLhMo3CM9oMvUUCgSIBj0oCvbM/E/eUpON0+DrPDg1tylRDxCXhJwOrywGBxY0ueEr9+bwD9feGn2Ld29WF03Agej8CqFbk4c/EaLFY7lAoZhAI+8rPSkZoYjwtN12G22lCUlwWBgI/05EQkJ8Th/QvXkJIYDwGfD7PVBovNpywtNjvEQiGy01MglYgxMWlG79AI1q0uRGs3s6UA2KRTb8FLV4ZRnqWEy0vC7HBjXXYcXtdokZsoxbrsOFwbmARBAEqxAAUpMnw8YIRMyIfTQ2LC6kJ+khSpShFEAh5uGGyIlwpwpS9wRPBSwHDjOuzmcRAED0m5xbCMaWE3TwAAeHwBvB43RFIFvB43JoZ7UXLHQ2h7/1UokjNAkl5IFAkgCB5spjHEZ+TD63bB43ZhtKcFYnkcpHFJEEikGGq5BL5ACJ5AiPj0XFjGdZAoE0HweCA9HphGhyAQS6FISofX64FtchyQ0BedGk0094/B5fFCxOdBJhZAKRXhSrceCokQKzPiYTDZ4fJ4kZkgw6jJjr5RExQSIdLiZZi0ObEyLQ58Pg89ukkMGMwAwxsCujHZ3TBYXDjXY0SKXIgJmxsSIQ8bshVo11khE/FhdngwbnPD4vSgIEmCOIkATo8XaQoR9BYX0hVCtIxYoJu0I/PuLyJl28MYbzwNj80MZdEtIAQiwOuFx2GF22SAsngLAKDnf7+L/v6BsGUeHBwEAKx+/AWIkrJCGqvpJ/egp6cHQj4Px79YCgB+9cvrLQYcbx6DutcIrcmJ6rIUnGobQ06CGA43Ca3JAZL0rU2y48WovTKCwmQpyjIV0JudSFeK8F77OLwkicLP/Qry/PKQ5Gv79SH09PiiUE5+uRxEAPkef60XNn0/RtTHkL51P0w912Ab7QdfJIMoLhk8kRQehwUEjw+BVAmvywmP0wrH2DAS1+6Ac1IPcUI6jF1Xkbi6AvaxYXgcVgDMpm5PdDei74OXkFBQBodpDHyRFB6nDUJZnG+ectqQsKIMk/3XIU3OhNfthtM8Bkl8KmxjI+AJxfA4Y8Mhen3YCIPZAR5BoChdiQ/bdbA43FBKhBDwCeQny5GskOByrwEWhwsFqUoI+ATS4qRIkotwpk2LtDgJxi1OmB0upColU2vf9pFJZCfe/K2MNifykuRIlIuhN928Pn0GC0hB7M/ZzcMWjJicOFCeij98MID0OBGy48UQ8AkYbW4UpkjRobPC7SWRIhfC5PDA4fYiVSGEiM9DqlKEJJkAmiELPCSJpiHznDHGB7rAI3jgCYTwuJwQSqSIS8vB6I3rAACxPA5Oiwk20zgUyRnwOB3gCQRIzluF8aEeuOxWJKTnwTQ6BGVqNrxuF3qunoFQLEPmqvVwT4v8axw0ITNONHO+FfCwIUeJdp0VUhEfFocb49ZP5ttkKeIkvnVTmkI0Ncdc6TdBnJCGVV//q+87zJpjJls/wvCp/4SuuwU8Hn/e7yYUS+GyW+GwmqDr0iB/o8+ZZpscR1J2IaRxSdD3tkIokUEkVcBmmvD7W6l7jRi1OLG/NBWX+nylZLLixRiYsMPh9qJdZ0VWvBjrs5UwWFxwuL1IkgvhcHuhFPMh4BGQifjo0FvhcHtxbdDkdxxLfwtIjxsCeQKcY8NwGrVI2rAHNm03SLcLPKEYfFk83JZxwOsFTyyDc2zIt0ZKzYcwPhU8vgiWgVZ4HRYIFEkgPS64zP7LTF1sH4Ld5UZ1xSpcbPeVQchKVuL6gAF2lxtxMjEyExW4PmhAVpISZrsT6fFypCXIIRTw0XB9ADKxECQJ9OvpXeO29AxBKOBDJOBDJhHDYDRDP2HGA7s24uX3L6MgMxV56YlouzEChUwCggD042bweARGDEbs2boWXYN6ZKUmIEEhg1rThcbOwQXLNdB0DhaDFqtvfwCat/4GkSwOIpkCaSvLYei7DoLgwaQfhDQ+GaTXi4TMfPRefg/5G2+H02aCPDEdkvgkjFy/gkltH+QLv1S0Mtjfh2Mvv4j99z+ESw3nAQBZ2Tn4+MolTIyPoWjVamy8pQJ9vd2wWixounYV9z9cg56uDqSmZ0A3MoyujnYUJEvxx4eLgu51W7RW/PNrXSBJYKi7DabxURAED5kFq9B64QwcNgukciX4AiFSsvOhTExBV+NFxKekQyyVw2qaQE5xKbR9XRjqbAXB52Hj7fuh6+9GfEo6RgdvICk9G2O6IUhkclhNRtjMkxAIRb5Ic5cLcmUCRFIZ+tquISE1A1JlAhxWM1xOBxLTMuGwWeH1eIJcucAYepphHdehYHs1dNd9WTDy5CzYJvQYv9GKtJKtsBtHYTWMgC+WgMcXQJGaA5fNDNLrgVAWB7EiASZtL5Tp+SA9HjhtJpj1gyAIAvbJMSA5mZbfnylsI92w6/sAggdZRhHso32wDF4HweMhbdtDsA5eh9Ooh2N8GMkb98A61AGXyQBJah5IjxuuyVHYdTcQv2YbvE47hPFpcBj6IU7JA18shWNsGKTLAeekHkJFEnhiGTy2SRA8ATwOC9zWSZBuJ3giKUQJ6QBI2LS9EEiVEMWngfR6fPIx8d11vTC2S8CXxsF8owkEwYMwLgU8oRggeHCOD4Pg8aEs3Ain8ZNyVgQPXqcNooQ0WPpaEL+qArahdmzMVuBn+wuCPld//GgIV/tnlk6csLpwrEk3R499PGjChNWFolQZNubEoW/cBqvTi6YhE+5fl4Yegw2pShF0Jif6JpxI3lIFx42PMW5x4lznKHg8AkVpCnzYMQqrwwOFRAAhn0BekgzJCjEu3xiHxeFGYaocfB4PaUoxkuQiqLsNiJf6jJT94zZkxvv2mAIeAbvLA73Zicx4CSasTnhJIEkuAo8AnG4vuvW+9U5HWwvGDKPg8XgoLF6NgRu9aG9rBp/Hw4FDj6CjrRl6nRYjQ4PYva8KnddbMTaqR+6KArhdbhhGdejr6UbFjttgt9mRmp6Ogb4byM1fAalMjpGhAYwbDNDrRpC3ohBOpxPa4UHkFxRhVK+FSCSCSCyByTiB5NQ0uN1uvP36MQBA+VOvAzz+1BpGmr0a3S/8E/S9rXA7bVh7x4NQv/jvKNh8B0Y6riGjeD1M+iHYLUYk5xZB39OKxOwCDLVeQeqKNVCmZsFl95Vit5smYNT2I3fdNiiTMzA22A15YhrMY1rI41NgmRiFPMzY2daObujHxsEjeFi9cgVGdKMY/6R0noDPh9vjgVAgQMXGcrx4/BRyMtMxbjSCJIHSVSvR3N4FiViEnMx0pKcmQyQUoqmtA4bxCQwOawFe5M4MY/c1mAeuI6NiP8Y7fA58SVIWzIPt8LrsEEjjIEnKgGWkB3yhGF63C9LkbDgt45Cl5oH0euBx2mEZ6UYkeTSLyhmxKu3mRktvdmJthv9lyItXtNCanLi3NAXpShFSFELoTE5MWF1QSvjwkMBtRQl+P3vnKl+kUFEq8HqzAdq5+5OYY/VtvnqadrMRidmF4AtFaD59BAMaNVZsvgPxGXnwejzgC0QgSRJ2sxFejwe5n0SEFW7dPXWu+PRcePpCTyOWZhRCnlsKEDzwxb4NsNOoR/ItPq+n7txL8FiNSCi7HTbdDQgVSXBbJmDqvgICJMRJ2VAW3QKv27dpW5MuR/+EA9sL4jFhc8Pu9mLQ6ERJhgxb8uMg4BG41G+CKMKKNiUrb5YA0hrG8fDe2+YcozWMw2Kzw2iyICkhDhKREG6PBx03BiERi2B3OEEQBHbeMn+EbuUGX5pZ/zC9dWe5pChVjoc3ZcJoc0Eq5EMk4EFvcuDgpiwcvTIEdfc4qtalQyzgwUsCV/omIBLwYXV5IBcJsGftzFJspVlKAMCmvNAiIBcryfmrpzIjLOM6pBaWBjw2c81mWMZ1WHP7AwCAltNHoO/SYMWWuxCfQS3ELbCO68AXiiCNS0L6qg2+cfLmj/qejTQuEUBsGALDZXNhGsx2F3gEAZlYAIPJDqvDheEJC+RiAbKSFHC6Peg3mOD2kBAJeFBIhCjPm7mpSY2TIidZgYnoLefrF6VEAC9JoqpsZlryqNmJihXxeOljHYx2N24vSoREwIPJ4YbZ4caEzYORSScUYj5KM+TYudK3WJZmr4FjtB/xq7fDbZmA12WH0zAIWW4J4oq3gOAJYBloRVzxFoiSMgFEvnmV55fD3PNxSGMJpT73erxMhP4JB5LlQqzNkM/QLaUZcnSM2kC0jKHyk2hsvdmJvSW+3/p/LwzD6vJi9+okSAQEOvRWbFsRD63JiasDJqQpRUhRiFCaKZ+SL9i1MF4/h6QNeyCOu5nGPTCPfEI+AWlqLjIqD8Bh1CNhdQUSVlcA8EW4DH90FF6XA8nld8DjtEEgjwchEIDg8SFOSIM4wVciMHXj3QAAhSwOfDHzNWQTCtch77aH4bQYocgshNftQv9Hr8HjsCF9wx2QJKTCaR4HXyyDebgHpMcNUVwSZKm5kKXmAkDIGRRcszrzph7TTdpx/+a5JQd1k3bctyEHAHD0Qi8mrE7cWZIJA+lASVY8zHYXhAIeRB6fkXbrSp/O3F4cuMRjcUbc1L+PXenD+GQUlvIIk9JMOSpWxENvduJrt+VMvf7SxzqMW1xIlglQlimHh/Q5Vi1ODwQ8AgQIbMhRTh2/Jd93bcatc7NFUgtKkLpijc/4IfE9C5ZxPfLX+6J0rxz/M1x2G4oq90AgkoD0euCwmtBz+X3EpWZDIBQhPiMX8Rm5U+dMzC6Y+rdAeLOMzrpsJbwkUFU2cw00Nd9e1WLC7sYdxYkQC3gw2z0w2T0w2t0YmHAgSSZAikKEzHgRxLZEKPLLYbj8xpw5hif0bWiz1mzyRbTO+m6T2gHYzRPILt0KgseDRBEPgsdDUm6RbyNPklPfHwAyP1k7AIBUmeD3t6pcEQ8+j4D+k+8CAAaLL+Br0uHBXauTIBbwoDc7IeLzMGpxY9TiREaceEa2yNZ832ftLv9RzMrCTb5SrgQPsixfVp/TqIeycBP0516CyzSKuNXbIYpP922u7SaAxwPcHngcVsg+KYMS94kjmEKo8J9BsmVVFlLiZNBNWFC5xncPPn/qKlxuL3ZvLAABX0ZEQXoCdBMWGEw2yMRCrFH6dOruDTdLj1xoHwIQvgM+EJtW+8oo8QhiRrkm3fgkXC4PLrR0w+3OR35mMkwWOyx2JwR8HtweLz61x6c3UhJuPid3by2FMIKyibPJKfftKy3jOpTtfQQA0HL6RTSd/CtW7awGXySBMjULTosJk/oBDLVeRnrxBiTlzayrnrvuVrR/UA9Pb3TV0s/OzcOBhw5Br9OiYruvFr1hdBQ2mxUOhwNp6Zlob2tBQkIiXC4XVhSuxMnjryE7NxfrNm5GZlY2ZHI50uPEQfe653qNcLhvPgtZhTcjX40GHbbundvLzWjQYfNd1XNe0/Z2wG4zo2z7btjMk5DHJ8I8YYDb6cBgdyvkcYnIXTX/nnbF2sDlFhdSZja5oBSZZbfCOq5DxlpfGUr7pAGK1Bykrd6MjvdehMM8gdxNd4EvEoP0eGDWD8DrdsHrcYHHF0JRUApFavas8/qi0q+//beIZWMLaUbhVGaEb07bCGXhxqm/5blrIf9EzTiNesSv8j3DbqsRPJEUoxd8Jagohyy8HvClcbAOXvcZ7RMzEDcVHBoacUUz50lJah4wMrKQr+kXedYqKPLKAIKAIndmeR7tRy/BZZ1A4trb4LYaIYxLgcdmgmNCC7d5Ah6HFalbqwAAfKkcmSJR0Ofq6qAZBckSXB2fWeU+QSbEgfK0OXrM5vTA4SGRphShXWdBglQAl4dEdoIEH/VMICtOjMxP/uMTBERpBXAOtSJR7sH2Ip8u0JscuH/jzPuTev2+dTfL6xy91I/LvWO4Y00aitMV8HgBs92FzHgJekYtKEpVYFN+4IxHip5RX0na4jVrp14b1WmxfvMWrN+8ZervNaXrsKb05vtbtvnmtEnjBCRSGUQiEV478v/Q8OEHuO2uPSAIAlk5uTBOjKO3q/MTJ8RKHDgYeq+D3u5O1P75vyDPX4exq29OrWGckz5bWeqKEqy6dR/MYzpUHnocAJC+snTq/26nHY1v1cHjckAWn4I1t1VPrc0mdQOQJ6ZBLFdi4/7PTY1JrWPiUn2lVOUJKUCImREUJcWFoO5Ord6A8pKbOutvr76OCaMJe27bho+br6NiUzlMZgucLhf6B4fR0duH6j23zznn9lt8cr321nsRZUZQxBeuhzJ3DRxGPZI+2RfeePvP8LrdSNtwJ0gAXqcD0uRseOwWX7Cay47kkpmZwfKMQtj6r4Y9/qJyRkwnVTG3FuYbzQYkywWIlwogFvLwXsc4StJlKM1UIEkmQCsJjFldEPIIXNdaYbS7sTpNhnjpor1MM5AofJNn+4fHEZ+eB7FMibH+Dp9xs2At0laWIyEzH/oeX8TUUOtFZJVsCXLW4PAlM51GonjfZstw+Q1IUnLhtihh6dN8olBLIM8thTS9EJNdl+AYG4S3zQHlNKV379r5IxhuL0rAux3juLHAQKP0ZP8TuvrjVuRnpSFOLsO1ti5oDeMoKy7AutWFKMjJwNlLTb5anx+3IDkhDqtW5Pg9z2KG8tYDwIUbE0iWi6YyId69bsDaTAVKM5XYkBuPlmEzHC4v5CKgecgEo92FihWJEaX1LwXkicH7isgT09Dx4XFI41MgkinBE4jQe/EdpHzynEvjk2D7pOSZ1+OGtuMa0ovZ75cS7SgkN+/j8+3DyEtVYsLiwOC4BVd7R1Gam4Sy3GQIeASaB8bg8ZK40q2DXCLE6qzgC8Jox5+eTVGI8EaLAbkJYihtfGiGzdCbXVO6VsADrg6YYXd5oRk2o3Ra0EDy5nvnHW+2EWghRDLWfLpFLJgZD0JdmzeaR7EqTYYJm/uTa+FESbocpZkK5CdJcHXAjK5RG9q0FsRLbq41gsmXtGFPWPJNny3F8TMNmoZr70KetQou8zhMN3y6VpFbAmV+KQieABPtF+C2mZCy/q55ZWISkdy3Phm+/DaUWSvhNE/A2NsE+4Qe8XkliF9RBnl6PkZbz8M+rsVY5xUkFc1fuiKaSYvz70ChXn/94wHkJsmhkAjRNDAO3aQda7PjUZaTiPwUBa7cGMPQhA2XekZxSwE9vV1ikelz1BvNo755ScxH34QDlwdMM57Fcz1GuL0kNENmlGWFluMrks48Tp5489mSxadAlp+C0b52eJx2mMd0SCssRcHm28Hj8zHUFrykaaDvQpGiEPm+V6IECpsbmmEL9CYnSjLkKM2U+4wnAyaMWd1onJUx4G+O0atvlsnx992EEhniM/JgGh3GxFDP1HfKWrM5ou8U6Ps19BqRnyTFuM3lmzdNLpRkyFGQKf1k3jTBZHeHPcbs/Yap8yKEymTwpUoQAiFMHRcg+2SfIVQkwW0ag8ftAsHjwzbSCWlGUdhjpiXIZ/w7JU6G9sExOFxu6CYsWJuXik0rM8Hn89DSp8e713pw5/qCec5IDwrp3EhKtaYbeRlJUJok6NeP4fL1GygtzEL5yhwI+Dx8eK0Dl9t6sXnNCkZlm76OFUpkyC7bBkNfOzxOByzjOqQUrEV2aSV4fD56Lr6DwWYvsksrGJWJTlLT0qf+nZySAoVcifz8AnRcb4PDYYdeO4KSsnKUr9+EjZu3okXTiNNvvo7d99wsTxJsr7tndRJatf77XMUn+98n+Hs9PjkNIqkcyVn5GO5ph8thx6RBi5ziMuSVrAefL0DntYZQvjajyKbdM5I437XpOXcCyrRciKRKjHY3wjauQ9KKUiQXlvnWNv3X4fW4oWu7hLQ1t3AlOq2IZq3v5vtbIIvH6OU3IE7JBV+qhJmyteSUQJFXCklqPsw9V0EQPJhvNEGRH36ZUzaYPa/rL73xybyuACEQYrz5A8hzSyBOzoZQkQSnUQ++VAGhMhmm3kYoV6yb+myw52pnYTzUvYENSNP1WLJcCIVYgPwk6VTmHqXLNuUqfQGyfZOob9Kjujxw39tUpf+o9+mvv944jNwkGZQSF5oGjdCbHCjJjENZdhwEn/R98CygTXDKtDkr2N9x8QkAgLeOv4rsvBVQKMfQ0vQx9Dot1qwtR0n5euTmF6LxykWYTZEb46avYVwmA3r+erO/hyLJ/xzXqX4bKfmrYTONQ9vZOLWGSS/y2TiH2q6goHxu2S86SU+deY/JpVLkZ2ehrasHdocT2ksGlK0pxsayNdiyvhQfXriKM+pL2FXJ7BxF7Q1HLr4OZW4JXOYJTPZq4DDqoMxdi7gVvj2hqa8FLssEbeMuDSv7J9xbOv8EQ0VALXVW7aia9322FnzBjDGJZbdP/ZvKjOgcteF59TCKU6WwOr2oXOHzZA8aHbA6PViVKsOFvklM2MLfxITKgbsC1xAGgHt3xc6CmQ3uK0uf9/2tKxLYEWSJURzkOc/fyKwyXmzs3zy/8aCiOIMlSbgn6GJ+ZcLUv8lpi2Pj9fOw9rdAmlkMr8OKuNWVIHgCOMaHwBfL4TRqwRfTU3ghkrHO9xrRMmKdo19atRYMTzr9jnNvkIZ206+FZlopmGDyOcYGQfBmRhMGkm/Q6IDbG3gTkrZlfl2bsGrrvO+zSXbF3Fqx08ncdDdLknALlR0RiNtWz69XlyLBnsU9a+gtxbHmtvl1bMEmenRs8DnmpuP7/y77IlMDzS/O8eF5zxWN32khBNtnxK+lt49L1db5+yxVrJ4b/com1Ts3zPv+vm3sGyOLd+yf9/3Vu+5nRxAGuffAA/O+v3XbzJ4Ikza3373ujXE7Ju1u5CVK0K6zzsiMWAizsyVmU7Z997zvc0XB9vnvnbTV4UX7L0ZSgsyBCTTPgWyQekuQYJ7y2wO+F2gd3aazQi7iwekmMWh0AJgbHOCPYLrs9uK5Taq9TlvI56eYniHhDy6aYe+tmn9e277rzojPPXn9PKyDbVNrGGlWaD0M2VrDhMP998x/HfbduYMlSXxkbJl/n5W4mt494ZJwRpzvMaJlxILiVBmsTg8qC+Ih4AGtI1ZYnB7ozS6sSJJg3OYGnwdszYtDh942pcRT5EIUpzFfjiAa6G/8CPouDZLyVsFltyJ33a3g8fkY7W2D1+NGfEYeJnUDcNos8HrcSMwqQFJucfATh8n0jZLHYUX8Jxsly0ArZNmrYRvpgnNsCKKkTPClcb70PwBDE3YkSfnQDFuQmyDG5b5JWF1ejEw6IRPx0DpiBQkSfWMOhNXcIgTOXmpCU3sPVhfmwmqzY8fmcgj4PPSP6DExaQZB+GJTy1cV4GLTdThdbsgkYsQr5Vi3ujDI2Rcf57rH0DJsRnGqHFaXB9sKEiHgE2gZNkPAI9A9aoWQT0AhFsDl8eKW/AR06i3gEwRcXi+SZSKszqD5R1wizPecA4DX7UJa8Trou5vB4/ExNtCJxOyVyFpLX2T6YuKj68No7jdgVWYCrA43tq/OnMqGEPB5sLs8sDndqCzOwIXOEYiFAoj4PGwpWpwGw/M9RrRoLShOkcHq8qByhU/ntmmtcHlIjFlduL0oEed7jchP9EX2GFvOwm2dBLweX+q4WIaJlrO+mq8TIxDFp4HgC+A06mEb6gSSI48cHb1QDxBESGO5bD4HgdHqxOCEA26P169+GZywY3bQ0c21xyebmoKb18HjJcEjCJgcblTkx+O6zoqrA75aXYbLb0AgTwjtWozrAPjKLwzMI58ngDNivO08TH3NkGf5dG3imm0geAKY+1ugyFkDy3AnbPp+KPPWQp5Fv64PFX3LORh7W6DMKYbHbkXK2m0g+AJM3mgBwRcABBCXsxojV077+kl43YjLWQ152txSR7HOuQ4dmgcnsCo9DhanG9uL0yDgERiesEFvsmPU5MCdazNwsXsUcrEASQoxitKXg20CPY83xuwwOzzQmpy4a1USzvcasSJJgqLUyNf9N659BF2XBsmf6Ni89T4dq+tuRuqKtTD0d4D0epBTurBN3fmeCTRP299sK4j33QuTToyanTA7fK+peyeRn+TLqnFOGuAwDMLrccPSp4E4JReTXZfhdVhhG+mO6u/VpvXVdF6dJkOr1oJEmRDFC/idAjllrINtECVlwjmhg8uoAyEQAl4vJOkrIsqOAICPWvqh6dNjVVYSrA4Xbi3JncqG4PN5vv4RA6PISYnDqmz2a9R/eK0Dmu5BrMrLgNXuwK3riiHg89DaOwy3x4P8jGR09OsgEQmRFCfDqjzmAyz6Gz+CvrsZybmr4HJYkVO+HTw+H/qeFrhsFiTnr4G+qwk8gRBxablzyjXFCuc/PIPmpkYUr14Dq8WKbTtug0AggObaVeQVFKK7sx1CoWiqtJPN5UW8hI/WEQs8JODyeCET8jA86YSQT6B71AYCBJoDZEaEy/XLH6K/vQmZBavhtFmxavOt4PMF6O9oRvbKEgz3XIdUETejFBTXDGs+gqGnGQk5q+B2WJFZth0ET4Cx3maQXjfMugGs2HYfhjXn4HZYIU1IgyI1B8r0xbdmmA/j9fOw9LVAmuWbA+NXVwI8Aez6GxAqEnxz4OQoeCIJRImZkGVGNv+xxUTbeZj7myH7ZE6P/2RNaxlo8QX0GAaRWH4HJjsvgfS4QXrdECp9hvpOvQ2Ng2Z4SMBoc0Mm5OFs1wSsTg/GrG7ESwQgQUI76UQozoJAemxwwgEPSSIvUYKWkZl6zNx9FV5X6M4OinOdo2gZmkRxuhJWpxvbViZDwOOhZXgSAh6BjHgJBsZtvrHSlcFPSAMNH32ANk0jVq5aA5vVgq233gY+X4CRoQG43S7YbXZ4PG4kJCZh5arw5g7H2Mw1jKn3WkifY2sNEyofNFxGU2sHVq9cAavNjp0VmyDg89E/NAKz1YbVhStwuakFHo8HedmZWL1yBeMyGVrPwXSjBYps374wqWTbVEaEIncNLEOdcNstkCRlQEHDvnDROCPGzI4ZUYXTUYr5qPgk60FvduLFK1p4vCTiJHxkxokhFfLQY7DB7SVBADiuGUWaQoSMON9EMGR0+I0uuLEIGlhbjQZoOxun/hbJlBArE6HvaQXp9cDrcUMokcE0OgypMhFGbR8EIl8DKLfLgZ7L78HlsE193jjSF5aN39rfCnFy7pzXHYYheD0emPuaIUnJxWTXFXgdVritk7Drb4AnlIDgC2AfHYDLqIeyeAtE8ni80mTAK02GkMa+Z294Cw6dYRxvnJk/FXVFdjocDid6BkZwWdMOj9eLOLkMaSmJsNsdsNqd+PCyBgQBkCSQmhgPoYCPgZHAfSHev9gY8L1Yo9dgQ9Ogz+CmFAtQ8UnWg17rQN2lQd9zKRUgK14CsYCA1emB2e4GCOCtZh3S4sTIiBNDAh6GJ+1wenzPZfOQ/4aFSwVd5zWfQS5EqOd8tKcVXj/PucthhWVCDx5fAM8nTTQdNvOMuSIUTPpBIDe6G79Fgt3pRuON0am/lRIhEmRitHxSisnt8UIqFmB4zAKhgAe3hwRBAK82dCE9QYoUpRAGk33qHDdGTUCM1LWn0Jkc0Az717lDkw54vCSaR8zITZTgSr/PKD486YBcxAcB4JhGjzSFCINGB5LkYhguHIPhwrGQx8+9N/xorexsX9Rp39GfhvW5goICuDxePPFqV9Bjp69DlGI+EqQCtGkt8HgBt9cLmZCP4UkHhHze1JrjWJMeaQohViT5GoUOvf67sOUDgH+cRz6CL4B1pBuTvU0zXrcZBn3N5280Q5Kai4mOy/A4fbrWqrsBnkgCQiDCWOt5GLs+hiL35obBPjYM5K0MS9ZwmRxox0RPE4RSJUSKBEz2tYL0+OYsgVgG29gQRIpEeJw2WPWD4AlEcFkn4babYRXL4LIYP3HYRDfaCQs+uK4N+Xg+j8DFnlF4SBLjFickIj50RpuvrruXxP871w0SQIpCjJFJO4YmbEHPCQDtw5OAPPaDbzTDFshnNQcbMjrg8XrRPGxBbqIYV/omYXV5MDzpvDkvffIsDk44ZvQeaB2xzhljpKMJfMFcg4G+tw2WCT1IjwcOyySEEhl6r5zxNd8dHYbZoAWPL4B5XIexwW6kFaydcw4AsBhvrmWbhsxznJ0AoBALkCARoG3E12jb7SV9c4zR8Ynu8aK+aRQkSWLc6gIBAvYJHbqef2Le6zfScQ3CWT1hFvq9TGM6AHO/a9OQeU7ZzcEJBzweEs3DPh1yuc/k+62MPh3SrrOAJAGRgDenR0TLyFwDrNMyAcsNzdzXDUMgPR5YPtlrmKbvNXS9U3sNj80Mr8sBQiCG1zGzD5ZjQgdgbj+txh4tUuJuXkOlVIREuRit/Xp4PL51gkwsxLDBBKGQj5YbehAE0NKnR692AhmJN3dTncNjc86/EK519EMhm1n6QymXIEEpQ0vPEDxer08+iQiDo77ebR39WlCbF5FQAJtzZh+Vpq6F9bSYNIygS/3mjNfGB7tgnxyHxTACRWoWOj46AbfTDqfVBL5AhPGhbvB4fHjcLgy2NCBj1c2+BPpuDZIib0XACN1dnWi6NrOm9vXWZhj0eng8HkwajZDJ5Dj7/jvg8XgYHhqCXq+DQCCAXqdDX28PBAIBegzWkNYjFF6PG31toRnrZjPY1QrTmB5erwc28yTEEhlaL5wBQRCY0A3BODoCPp+P/vYm9LZcQU5R4J51gbBbI3eaGLo1EErnGleFUiXEigSM3WjxrRncbggkUlhGhyGJSwRAoP/yu+Dx+SBJEsbhbpBeLxzmiRnnsYwOAcncZiwFw3xDA5dpPKLPOgxD8Hp9c6D4E3uLx2GFxzoJQii6OQeax2EZbIfXGVkfQLv+RkSfC4a5rxn8aSUF+VIFBPIEWAbafHYsrxt8kQyO8WEIFInwuuwY07wHgieA1+38pPG2BIRQina9DT85FVovgO2VM3XZyKQDTbPskMH02McDphl6LFHKw0jrWQj5BNpGgLPtofcM7dSZYbS50NBtQE6iFO+26mB3eaCdtEMqEoAAQAIQ8QmMGOf/DTs+aYjbfO3qzBqvYdDR1gLDqB5ejxumSSOkMhnOnXkXBI+AdngIaemZ4AsEcDkdaPr4Muz20Naojk+O63rhn/y+r+3WIC7N//NK19rMqO0Pa5msGzXg4+Y2v+8NDI3A7XajsbUdK3Ky0HClEVabHYNaHeRSKZqvd4IkSWSkpaB/aBg2+9zfrm9wGBBIQxdoFpO9jfB6bupzm2EIXq8bxhsayFLzMN5xGR6HFS7LJCy6G+CLfHOCoeUcJrquQvlJrxbrSE9Et8uicEbk5ORg0ubC3j9GpmgXwr37YrfkTk5ODqyT4/jfv7+D1vPed9/8aZEAoFQqoYyLQ3/9s+ivf5bW8acjEAhw+vRpxMXNjAokCAIlJSUBPjWXkpISeL1eHPzH8IxYdJGWloaUlNiu+5yTm4c33n8fb2iYMQyJxSIkJy8+w/d85OT4SnX87R+itzTJvu2f51oEWqGu+V0/eTXIkeFxoDp20sVzcnLQN2ZjXOd++ctfxpe//OU5r/P5fKxd63+ROB9lZWXo7++HXh/6Al+pVKKwsBBPPPEELJbAG+Z33nkH//Iv/4K9f/w4bLkCER+nxOl33gV/nuaOUqkUq1evxje/+U0YjYFrr6oeeQTXP3oJIx+9RJt8AJCzn7mU5uzsHHS/+QK633xhweeinttopLi4GCabEwd//z7XogAADh28nWsRIiY1NRVCoRA/eGP+CP9IiFMqoVQqkZGRAT6fjzd/+w3ax5hNUVERCILAN17tYHScpMQEnHr7NK5evYq/+7u/w1+/Pn+Zi0iZ/hxS/773T+E3PAyGXCZDfLyvz0x2djZIrxeNP9lL+zgU2dk10/7tM4Qc/PnLtI6xYf264AcFYeqaf+O5BZ/LH2KxOKK9ytq1a+G0WXDsh5+mVZ7bP/UIredbCDk5OTj+ylEcf+UoreetqqrCj3/844DvNzY24tFHH8VPPhXd5XbC1dHUXK/+76cYkugmuXdFp62Hmmtafx8b+6yt9+yj7VzU79/1fz+g7Zyzefzxx/H5z/u/tmvW3AzMyc7OxssjFuz9j8h7Jk3H7SFxtmMUZztGgx/MIPffGZ33vT+++tWv4i//8z/44M8/wwd//hnj41UcPhzScTk5ORgc0aGyil7dNpuamprgB80iK8vXkLvxT/MHp4RDReW2sD9DkOQCuplECSRJorW1FTZbaF41ADh9+jS+853v4JVXXkFubi4eeOABbNq0CT/4QeiTGkEQWLt2LSSS2IpopSBJEi0tLbD78bJNP+b222/Hvn378NJLL+Gvf/3rvEYggiBQWloKsdh/s53pjI6O4saN0Dzlo6OjuOeeewAAdXV1KCoKLU0wJycH6en0lEIxGo3zXit/fPGLX0Rvby9Onz6NxsZG7NmzB7W1tbjjjvAcQImJiRCJwkvZizZsNhtaWlqCHtfT04ODBw/iueeewze/+U18/etfh0qlCvq53NxcpKUFb9682Ojq6sLExMSCzjE2NoY9e/bghz/8Ie644w7cdddd+OY3v4mDBw8u6Lx8Ph+lpaUQCmM8hWwaJEmis7MTk5OTAY/57Gc/i7S0NPz617/Ge++9h3/5l3/BSy+9hBUrVvg9Ppx5Mxrwer1obm6G0+m/T8JsnnrqKZw9exZ33HEHfvSjH4X0GZFIhNLSUvB4vOAHRwEkSaKjowMmU+AsLbVajccffxz3338/1Go1Tpw4Me85CwoKkJRET51Xi8WCtjb/kTnT+cpXvgKNRoOamho8/vjj8x4rEAhQWloKgYCZuBaj0YjOzk6/79XV1eG3v/0t3nnnHbz99tt4+umn8fbbbyMhIWHOsbFwLw0PD4e9vqBob2/HPffcg7/85S/4u7/7Ozz55JP43Oc+F9G5CIJAXl5eVF+rYIyMjGBwcDDocd/61regVqtxzz334Mknnwx6fH5+/pShdXh4GENDQyHLtGPHDtjtdvzqV78Kef0nkUiwdu1aDA8PY3h4/l4OFNeuXcMXvvAFAMCHH34Y8v6ksLAQiYm+Pgzd3d0YHw8eZfuf//mf+Otf/4o1a9bg+eefD3q8v+cw1PXLo48+io6ODjz22GP44he/GPT4vLw8pKb6mjGSJIn29naYzf4z+aZjNptx++23AwD+8pe/oKysLOhnhEIhSktLZziNb9y4gdHR+Q1J1D5ULpfjzJkzQcdZvXo1FIqFlybt6enB2FjwTIsrV67gS1/6EgDg3LlzIe1BFrIOn5iYgMPhCPh+e3s7brvtNvzf//0fHn30UXz/+9/H3/3d3wU8niAIpKamTpXI5Rqr1YrW1tagxz3xxBO4cuUK7r//fnzjG/M7PENZY4eyZg2HH/3oR3jvvfewc+dO/Ou//ist51QqlSguLg77t9JqtRgYCJ6R09bWhk9/+tP4/e9/j23btuHxxx+H0+nEf/3XfwX9LJ/PR1lZGWPrnIXS19cXVoBNMB555BEUFhbi5MmTeOaZZ7Bnzx5azsuEzSzU3//kyZP4/ve/j3vuuQd9fX3461//GvQzYrEYa9euDWkt5Ha70dzcDLd7/n6kf/zjH/G3v/0Na9aswX//938HPM7r9WJkZAQFBQWQyyPrk/fAAw+gtbUVjz/+eNC1vD96e3thMpkidjDP5h//8R/hdruxatUqnDx5EidPnlzwOSmo32psbCxkuyLgWyP94z/+IwQCAc6fPx/y/MPj8VBaWhqSTvR6vWhpaZlXt1FoNBo8+uij+OMf/4ivfvWr+P73v48DBw7QKs9sBgcHMTIyMu8xjzzyCLq6uvCVr3wFjz766LzHlpSUQCYLM7uaXKI88MAD5ObNm6f+/t73vkfGx8eTdrudQ6mijxs3bpAAyKNHj5IEQZAvvPACJ3J8/PHHJHwZZuQ777zDiQzhYjQaSYlEQv7iF78gSZIkvV4vWVxcTH7uc5/jVrAo54UXXiAJgiAnJibIbdu2kSqVimuRFj3UNddqtSRJkuRdd91F7t27l2OpYpOOjg4SAFlXV0eSJEnabDYyLi6O/MEPfsCxZNxx9913k1lZWeS+ffu4FoVTnn32WVIqlZL/8z//QwIgjUYj1yLNobS0lExPTye/8IUvcC3KvOzevXtqjhoeHiYBkP/zP//DsVTc8Pzzz5MEQZCTk5Pk1q1byUceeYRrkWKCnTt3kpmZmeQDDzzA6DgWi4UEQBIEQf7pT39idKxXX311aq3c09PD6Fhf/epXydTUVLK4uJjRcUiSJAsLC8nU1FTy8ccfZ3QcSn8DIOvr6xkd6w9/+APJ4/FIAFG59zx69OjUtejv7+daHPLPf/4zSRAEaTQaF/Xe4JZbbiEzMzPJT33qU1yL4pd7772XzMrKInfv3s21KCHzzW9+k0xJSSGdTidJkjf3PAMDAxxLFl243W5SLBaTv/3tb8msrCzye9/7Htci0cK3v/1tMjc3l3z22WdJmUxGejweTuT40pe+RKalpZGrV69mfKwVK1aQKSkp5Ne//nXGxwqG1+slU1JSyB/84AfkK6+8EjU65YUXXpjScRMTE1yLQz733HOkSCQi7XY7WVpaSn7pS1/iWiSSJEkyOzubTEpKIv/5n/+ZkfPHbujTAjAajXjjjTdmRFurVCoYjUa8+eab83xy6aHR+Gqs3nLLLSgsLJz6m210Op3ff0czx44dg91un0qdIggCKpUKr776asQRkEuBhoYGlJSUID4+HhUVFVCr1VyLtOipr6/Htm3bpiLaqqur8d57780b5b2Mf+rq6iCXy1FVVQXAF936wAMPoLa2FmTsJyJGhE6nQ0JCQszM3Uyh0Wiwdu1arFvnK7fR3NzMsURziYXfymg04v3330d1dTUAICMjA1u3bkV9fT3HknGDWq1GWVkZlEolKisrl3VmiLB1r1NRq2yMxeZamc25gq2xuLh+AGiNbKYLnU43le0RDfpArVZj7dq1iIuLW9R7g2jXwdEu32y8Xi/q6upw8ODBqQySBx54AEKhEEeOHOFYuuiiq6sLDocDZWVlKCsr48zmQzcajWbqO1mtVvT09HAix2LUmaHQ3d2N0dFRVFZWoqLCV/qpoWH+HqxsoNPpprINo+E6qdVqbNq0CWKxOGrW8iRJMn4vLUlnxKuvvgqn04nD0+p9lZaWory8HHV1dRxKFn00NTVBoVAgPz8f5eXlaGpqCv4hBqAeALFYHBUTRijU1tbi1ltvRV7ezUbZKpUKk5OTtKanLTbUajUqKysBAJWVlejp6YmZ3zwWsdvtOHXq1JRhD/DVoHU6nTh16hSHksUeJEmitrYWBw4cmJGmqFKp0NHRgatX6a+NHQvodDokJycv+ee4qakJ5eXlWLNmDfh8Pmf6NBAejwejo6NISUmJ6t/qzTffhNvtxv79N/tTVVdX46233gopFXqx0dDQMLXBq6ioQFdXV9ASMcv45iU27nXq/GyNRZV2Y2OslJQUGI1GRp87q9UKs9nM6m+VmJjI2vWbPm40EW3yTZ/nFuvegDL8RLMOjnb5ZnPu3DkMDAzMCEBNSEjAvffei9raWg4liz6oNWl5eTmnNh+6odbe5eXlU39zAbUXGh8fD7nMbSRYLBZYrdaoeU4px8PWrVuRlZWF3NzcqHFGRKuOq6iogEajCam8JJMYjUa4XC5G76Ul6Yyoq6vDzp075zRLUqlUqK+vn7dJ5VKD8iYTBMGpl1yn00EulyM9PT0qJoxgjI6O4u23357T62DNmjXYsGHD8gIoABaLBU1NTTMmYyA6POiLlXfffRdWq3WGM6KgoADl5eVLNtI4UjQaDVpaWuY893fddRdSU1OX5HPv9Xqh1+uRkZEBrVa7ZLNDqD4bZWVlkEgkKC4ujrqoM4PBAJIkkZGREdV6tr6+Hhs2bJjh6K+urobZbMb777/PnWAcYDabodFoZjjwgWWdGQyn04nx8XFkZmayZuBmY/2q0+mQmZkJwFdPm0m0Wi0yMjIAMBvZT52brevH4/FYmQN1Ot3U9WP6t4qE6b8v1/rAYrGgsbFxan5brHsDk8kEu90etTqYcpakp6dDr9fD6/VyLVJQamtrkZOTg1tvvXXG6yqVCpcuXQrYl2opotFokJqairS0NJSVlaG7uzvmbWKTk5Po6+tDWVkZMjIykJSUxKkti9LPTAaMUHNHtMwjarUaxcXFSE5OBoCoyWyLJh2n1+vR3d09Yy3v9Xpx6dIlTuWirguTa+Ul54zQ6/U4ffq034a4NTU1sFqtywa4aVDeZAAoKyvD8PAwDAYD63JotVqkpaUhLS0tKhfts3n55ZdBkqTfBsAqlQrHjx9fLoHjh0uXLsHr9U5Nxvn5+UhPT190G45oor6+HitXrsSaNWtmvF5VVYXXX389aEOuZW5SW1uLxMTEOQ3fBAIBDh48iLq6upjYvNHJxMQE3G43cnNz4XQ6aWuiGGt0d3fDZrNNNUQtKyuLuqgzSrfm5ORErZ51uVx44403ZjhPAd/1XLFixZJbv83WmQUFBUhNTV3WmUGgDNzZ2dkwGo2Mls5k87miDIVsZKLpdDrk5uYCYNaYTp07NzeXleuXkpLCyl5Dq9VOBcVxbQjxB+UsiY+P51wfXL58eUnsDaj7ICcnJyqN/ZSzJDc3Fx6PJ6RG6Fzidrtx9OhR1NTUzGlEvH//figUiuWKGNOYbfMBorOcaDhQjofpgbVcrb2nz/ls6MxoWcs3NDRMzd2Az9B+6dIlzu0LOp0O2dnZ4PP5nF8nSpdR12nt2rVQKBSc6zjqumRnZzN2jZacM+Lo0aMgCAIPP/zwnPcKCgpQWVm5JKNX/eF2u9Ha2jqlkCgFxYVHWafTTTkjonHRPpva2lrcddddUzX4p3P48GHY7XYcO3aMA8mim4aGBsjlcpSWlgLw9dmIFg/6YoQkSRw/fhzV1dUgCGLGe9XV1TAYDDh//jxH0sUWJEmirq4ODz/8MEQi0Zz3VSoVBgYG8NFHH3EgHXdQi5f8/HwA0Wl0YQNKb1J6lEqBj6ZMEeq3WbFiBaxWa1RGxH300UeYmJiY6slCQRAEqqqqcPz48ai6pkyjVquhVCqnnMnLOjM0qHudmpeYjOynSiexEaU4PXCHybGcTicmJiZYmden/1YGg4FRAwZb1w+4GSUbHx8flXoxmvZdarUaCoUCa9euBbB457np93o0GvunrxGm/x2tvPPOO9Dr9X4DUGUyGQ4cOLCk+7nNhqqGAfiMoQRBRF0Gb7hoNBrweDyUlJQA8K29ufhONpsNJpOJVZ25YsUKjI6OwuPxMDZWMOx2O65evTqVzQb4MiNsNhvnAVlU8EZqairnc5larUZaWtrU/cHn87FlyxbOddz0e4kpB/mSc0bU1tbi7rvvnqoRNhuVSoU333wz6hYAXNDZ2Qmn0zllPCkuLoZQKFx2RgRhcHAQH3zwgd/FD+BbZG7fvn3Z6eUHtVqNLVu2TDXNA3xe4gsXLnCqTBcrV65cwdDQ0JwoYwDYsmUL0tPTcfz4cQ4kiz0aGhrQ09MT8Lnfvn07cnNzl9xzT83XK1eunPH3UkOj0SAxMXEqRbusrAwGgyGqrkcs/Fb19fXIysrCpk2b5rxXXV2N/v5+XLt2jQPJuEGtVmPr1q1+dWa0RdVGE9S9XVRUNONvpsZi08DNxliU84aNuYI6d2FhIQDmy1sstt8qUqJJvoaGhiWxN4h2HRzt8s2mtrYWxcXF2Lhxo9/3VSoVWlpaODeKRgM2mw0dHR1TzgiZTIaVK1fG/LXRaDQoLi6GRCIB4Ft7t7e3s95fjG2dSRAECgoKQJIkJxVNKD7++GO4XK4ZmRGbNm2CQCDgPOo/2nRcZWXljMBQqok1l85SnU4HPp+P/Px8uN1uTExM0D7GknJG9PX14cMPPwxoLAKAQ4cOwePx4JVXXmFRsuiEUkCUYhIKhSgpKeFEMVHey2iYMIJx5MgRCIVC3H///QGPUalUOHXqFKcKItogSXJG82qKiooKmEwmtLW1cSTZ4qW+vh6JiYlzaqkCAI/HQ1VV1ZIrexIptbW1yMzMxG233eb3fR6Ph5qaGhw9ehQul4tl6biDmq9Xr1494++lBpX+Ti00uW6k5w+dTgepVDrViyHafiuSJFFfX4+qqqo5JRcA4LbbbkNcXNySmbNIkpzR8I6ioqICRqMR169f50iy6IfNeWn6hpfpyH62NtfUubOzsxmP7KcyS7KysmaMzdRYaWlpjPenoDJLosUQ4o9oMtSo1Wq/89xi2xtQPUvYcJJGQiyt5+x2O1599VWoVKo5md8Ud999N5KSkpZckJA/2tra4PV6p9amAHdZBHQyvfQU4PtObreb9fUR9azk5eVBqVQyrseSk5Ongp+4fE7VajUkEgnWrVs39ZpMJsO6des4jfqn+hlGg47zer24cOGCXx03MjKC/v5+jiS7uQ5g8l5aUs6II0eOQCKR4MCBAwGPycjIwO23375cQxA+bzKVvkTBVRNrtjYIdFBbW4t7770XCQkJAY85ePAgvF4vXn75ZfYEi3IGBgYwPDw8ZzLesmULCILg3IO+GKmvr8e+ffsgFAr9vl9VVYXr168vG7WC4PF48OKLL+LQoUMzIvdmU1NTg9HRUbz77rssSsctOp0OQqEQBQUFUVGXkyump78DvihfqVQaVRu96XqW+juaaGtrQ1dXl99MLgAQiUTYt2/fknFG9PX1YWRkZI4Df1lnBker1SIuLm6q5wFbzgiAuch+t9sNg8EwFbjDRk1qthwf068f02NR14/Jmv1UlGy09sJzOByYmJiIiiCwgYEBDA0NLYl5TqvVIjU1NSqMiP6gnCX5+fkQiURRJ990Tp48icnJyXkDUEUiER566CHU1dUt+VJNVGAMVSYZiM7eZuFAkiSamppmrL2p78f22pt6VhabzgxGQ0MDNm/ePMfOUFlZyencPT4+DrfbHRXOiLa2NkxOTvoNxgXA6XVi415aUs6I2tpa3HfffYiLi5v3OJVKhffeew8jIyMsSRadzJ7AgZtecjaVNkmSM+q4ms1mWK1W1sYPh46ODly6dGnexQ8ApKen46677lqOxpgGNdnOdkYolUqUlZVxXjdvsdHX14ePP/44oGEPAHbv3g2JRLJcqikI77//PkZGRoI+9xs3bsSqVauW1HNPLWT4fH5U1OXkAofDgevXr8+IzuLz+Vi7dm1UbfS0Wi3S09ORkpICgiCizkBWX18PmUyGO++8M+AxVVVVuHz5MgYHB1mUjBsC6cz4+HiUlJQs68x5oOYlsVjMeINe6rminHxMjWUwGECSJCuBO9MNK+np6axcP2ozzORY0zfeXq+Xsexl6jtQ90W06cXpzhKmf99gUPPYUtgbUPdfXFwcxGJx1OlgylkiEAg4vy+CUVtbiw0bNkz1UwqESqVCb2/vorqPIkGj0WDFihVQKpVTr5WXl0Or1TLaU4lJtFotDAbDjLV3QkICcnNzWV97U89KamoqK8ECbKw5QsFfVhvgc0a0tbVhfHycA6lurmGo68T1NSIIArfccsuM1zMyMpCfn8/p3DTd/kr9TTdLxhlx/fp1XL16NaixCAAeeugh8Pl8vPjiiyxIFr3MjuQEfF5yo9GIgYEB1uQwm82w2+1R4+WdjyNHjkChUGD//v1Bj1WpVDhz5sySMJqEglqtRl5e3lRE0HQqKioWVfRTNHDixAkIBALcc889AY+RyWS4++67l50RQairq0NhYSG2bt0673EEQUClUuHVV1+F3W5nSTpuoRYyADiPPuGK69evw+Px+NWn0ZgZIRAIkJycHHW/VX19Pfbs2TNV+9cf+/btA5/Px4kTJ1iUjBvUajUKCgqmnq/pcB11Fu1Q9zrA/LzEVpTi7GwFJiP7dTod4uLiIJFIWLt+crkccrmcsbGozBI2fis2o2QjwZ98XEWONzQ0ID8/HxkZGXPeW2x7A+peJwgiau+LWFjPmUwmHD9+PCSbz2233YbMzMwlXxEjkM0HAJqbm7kQacFQ6+toWHvrdDokJCRAJBKxqjOlUilnz6lWq0Vvb++ciH/gpnP54sWLbIsFILp0cENDA0pLS/0Gy3O9lqfupYSEBAiFwuXMiIVQV1cHpVKJe++9N+ixiYmJuOeee5ZU9OpsbDYbOjs7Z3iTgZsTOpuT+HTvZTQ7I0iSRG1tLQ4cOACZTBb0+AceeABCoXDJO70o/PWLoKisrIRGo4HZbGZZqsVLfX09du3ahfj4+HmPq6qqwocffrjc3yQATqcTL7/8MmpqagLWpZ2OSqXC5OQk3njjDRak455Y2bwyCaUvp6e/Az592tzcHDWNhqP5t9Lr9Th//vy8mVwAkJSUhJ07dy6JUk3++kVQVFRUoLGxERaLhWWpYoPp9zqTkenT6xJTJU/ZMnB7vV6MjY0xNhbbzhymxxodHZ2RWUKNzQTUeakoWS6N/f6YfS+5XC4YjUZOZAkUWQssvr1BNOtgIPrlozh27BjsdjsOHz4c9Fg+n4/Dhw/jxRdfXFTN0MPFXzWMoqIiiESiqMrgDQeNRgOJRDLVNJqCK2cEpVfYyFyMBqdmoOxdACguLkZiYiJnUf+zddzY2Bhn/Rzn03EVFRW4fPkyZ7JR9y2T99KScEZQRuIHHngAUqk0pM+oVCqo1Wr09PQwLF100tLSApIk5yim/Px8KBQKVhXT7HTw6a9FE01NTWhpaQkpEgPwpQru27dvSTu9KFwuFy5fvjzvZOz1enHp0iWWJVucmEwmvPfee0ENewCwf/9+eL3eJWM8D5e33noL4+PjIT/3q1evxsaNG5fMcz99AR6NtbHZoKmpCTk5OUhMTJzxenl5OSwWC3p7e7kRbBbRbGh4/fXXAQD33Xdf0GOrq6vxzjvvLGpDvNPpxOXLl+d14Hu9Xly+fJllyWKD2fMSU/f6+Pg4PB4PK5H9/gJ3mJpv2cx4Y2teYjPwaXZmCZfGfn/MNtRMf41NqL1BoHluse0N2JqXIiWa1wjTqa2txfbt25Gfnx/S8SqVCiMjI3j//feZFSxKmZiYwMDAwJwAVKFQiJKSkqjK4A2HpqYmrF27dk4vv/LycvT29sJkMrEmy/N4D+UAAQAASURBVGJ04AejoaEBmZmZU725pkMQBKeZbVqtFiKRCPHx8Yz385oPs9kMjUYz71rebrejsbGRZcl8sHEvLQlnxMcff4zr16+HbCwCfJtZmUyGI0eOMChZ9BIokpMgCNY9ytMXxSkpKTNeiyZqa2uRmJiIu+++O+TPqFQqXLx4EV1dXQxKFv00NjbCbrcHnIxLSkqgVCqXfE1Pujh16hScTieqqqqCHpuZmYmtW7cul2oKQG1tLcrKyuY4budDpVLhxIkTrC6EuYKtCORoxl/6O8BNpuF8sBm5FS719fWorKz0W5JoNlVVVXA4HHj77bdZkIwbrl27BofDEdCBX1paCrlcvqwzA8CWMX26gZv6P5NjyWQyyOVyViL7Z38nJiL7qcwStq4f4LsflEolxGIxK9ePjV4Y4aLVahEfHw+xWMxpEFhTUxNsNlvAeW6x7Q2mz0vRpoOB6F4jUBgMBpw6dSosm8+WLVuwcuXKJRMkNJtA5Yyo12I5M2K+tTeb5af8GXWZ0Jkejwejo6NR8ZxSEf+BqgZUVlZCrVZzkhU4PXuESx136dIleL3egDpu48aNEAqFnOg4p9OJ8fHxZWcEHdTW1iIlJQV33XVXyJ+Ry+Worq5esoqpqakJBQUFUCgUc94rLy9nVTFptVoQBIHk5GQIhUIkJSVF1aId8GXf1NXV4eGHH4ZIJAr5c1VVVZDL5Uu+VmVDQwMEAgE2btzo930+n48tW7Ysmg0H19TX16O8vBwFBQUhHV9dXY0333wTDoeDYcliC4vFgmPHjoW16QGAw4cPw26349ixYwxJFj1ES4QOlzQ1Nc2JOAOArKwsJCYmRsVGz2KxwGKxzPitokXP2u12vPXWWyFlcgG+0gIlJSWLulRTQ0MDRCLRss6MAJIk58xLTGYQUGMwPdbs70S9xsZYLpcLExMTtI8zNjY2lVlCjcXk9aPGoEoSsJVZMn38aMDfvcSFPlCr1RAKhUtinpveswSILh1MMfu+jTb5AODll1+G1+vFwYMHQ/4MQRCoqanByy+/vCT3ORqNBgKBwG+z7/Lycmg0mqgqIxcKXq8Xzc3NftfeJSUl4PF4rNuypj87DocDk5OTtI9jMBjg9Xo5f049Hg8uXLgQMMgU8GW2jY2NobOzk0XJfESTjlMoFFi7dq3f9yUSCTZs2MCJjqMa1zN9Ly16Z4TX650yEguFwrA+W1NTg8bGRrS0tDAkXfSi0Wj8TuCAz6Pc2toKt9vNiiw6nQ4pKSlTaXbRaNBSq9Xo7e0N2ygpk8lw4MCBJev0olCr1diwYcO8ZdSoJj6xtiCKNtxuN15//fWQsiIoqqqqYDKZcObMGQYliz2OHz8Oq9UaUl3a6eTl5eHWW29d9M+93W6H0WicsZDhsi4nF0xOTuLGjRt+o7O4yDQMxHRDHPX/aNGz7733HqxWa8jOCMDnQD1x4sSirQFN6UyxWBzwmIqKCs6izqKZyclJOJ1OVqIU2Xyuphs72IjsZ8OYzvb1ozJLmB5rdsYg9Vq0MF2+hIQECAQCTuRraGjA+vXrl8TegCoREo06GPDvLDGZTLDZbBxLNpPa2lrcddddU89VqNTU1GBiYgJvvfUWQ5JFL01NTVi1apXfYMqysjKYTCb09fVxIFnk9Pb2wmKx+F17SyQSFBcXs17lY7HpzPlobW2F2WwOGPEPAFu3bgUATko1Tf89mO7nNR8NDQ3YsmXLnFJi0+GqnBVb99Kid0acO3cO/f39YRuJAeCee+5BQkLCojcY+SNQahvg85I7HA7WPJnT00KB6FugAb4G6ZmZmbjtttvC/qxKpUJzc3NURMdyxXyNOCkqKiowMjIScwuiaOP8+fMwGAxhGfbKy8uRn5+/qCONI6G2thZbt26d0xwtFFQqFU6dOrWoG4P7i6qY/vpSgEoDD+TcZzvTMBD+Fp2jo6NRYcyvr69HYWEhSkpKQv5MdXU19Ho9Z/VomaahoWHeiDPAZ6QbHh7GwMAAS1LFBrPv9fT0dLjdbkYi+3U6HUQiEeLi4qbGZMPAzWRkv7/MEmp8ugm0GWbKcTS9DBzTJaGiwdgfiOny8Xg8pKamciKfWq0OOs8tlr2Bv3s9moz9/pwlQHSt5wYHB3HmzJmIbD5UudVlm89MqLVrNATNhAO1ro6Gtbe/coPA4nZGqNVq8Hg83HLLLQGPSUpKwqpVqziJ+p8evCGTyaBQKFi/TiRJhqTjKisr0dHRwbq9gK0yo4veGVFXV4ecnBzs2LEj7M+KxWI8+OCDqK2tjfmIi3AYHx/H4OBgQMXEdp1rNjcIkeDxePDiiy/i8OHD83o2A7Fnzx4kJiYuyQUQ4EvDb29vD2nDAXDjQV9MHD9+HOnp6diyZUvInyEIAtXV1Th+/PiSmgvnY3x8HCdPnoxo0wMABw8eBEmSeOmll2iWLHqYvZCJxnIUTKPRaMDj8fymvwM+fXr9+nU4nU6WJZuJvw2M1+vF2NgYl2KBJEkcP34c1dXVAevO+qOiogIpKSmLstfN6OgoOjs7Q3LgA8s6czaUgZ6NeWl6XWJqLDb6EDA5ltFonJNZQo1PN/7mJZvNxkhzejYDn/wZ+6Op5I1Wq+U8CIzaGyyVeS7QvBQtxn5/z+L016OBF198EUKhEA888EBEn1epVKivr2dkfolWSJKctxpGbm4ulEplVATNhINGo0FCQgKysrL8vs9mVvLExATcbjdnOpMqw8omDQ0NKCsr81vufTpUZhvbzLYtcqHj+vv7MTIyErKOu3DhAhtiTUFdDypzJC0tDUajkfZSdovaGeF2u3H06FEcPnwYPF5kX1WlUqGrqwuXLl2iWbrohZqcAymm1NRUpKWlceaMiLbMiPfffx8jIyMRGyVFIhEeeugh1NXVLUlDLzW5BnNGpKeno6CgYFHUhuWS+vp67N+/P+w5saqqCn19fWhsbGRIstji1VdfhdvtxqFDhyL6fFpaGu66665F7YT0F4E8/fWlgEajQVFRUcAyE2VlZXC73Whvb2dZspnodDoQBIGUlBQA0fNbXb16FYODg2FlcgG+WuL79+9flNlcoerMzMxM5OXlLevMWbBpVJtt4Gay2TNbgTuzncyJiYmMRfbrdDqIxeKpzBKmI0rZ2GtQmSVcG/vnIxqCwJba3mC24SdadDCFvyjZ6a9HA7W1tdi3bx8SEhIi+nxNTQ2sVuuiDGIIxPDwMMbGxgIGoEZTOdFwoLI9AgWxlJWVQa/Xs3L/zl5zJCUlgcfjMabHpFLplBOAek7ZdmqGEvEP+Ob3jz/+mPUMsEBrMzahdFYwZ8TKlSuRnJzMuo7T6XRQKpVT+1emHOSL2hnx7rvvQqfTRWwkBoA77rgD6enpS6rBcFNTEwQCAVatWhXwGDbT26anUgHR1zSrtrYWhYWFYUWaz0alUqGnp4d1r2c0oFarkZycHFKpG67q5i0W2tvbcf369bANewCwa9cuKJXKRWnci4Ta2lrcfvvtAaNuQkGlUuGDDz7A4OAgjZJFD7M311zW5eSKQM2rKagNINdRZ1qtFsnJyRAIBAC4beg2nfr6eiQkJESU3VpdXY2WlhZ0dXUxIBl3qNVqpKSkoKCgIOixyzpzLjqdDnw+H4mJiQCYvdf9rV9tNhvMZjPtY7FlTJ9tWGGyJBR1/aZnllCv042/68fEOLMzS6ixokUvzi7DBXCz72poaFhSewOdTge5XD6jZwnAvQ6moOSg1nFU4EK0yNfV1YWLFy8uyOZTWFiIioqKRR0kNJtgAajUe1yvUcMl2Nqbeo+N70U9I2xkw7GpMwNhMpnQ3Nwc1MgO+OZut9uNq1evsiCZD7vdjsnJyajQcfn5+cjIyJj3OIIgONFx/tav1Ot0sqidEbW1tSguLsamTZsiPgefz8ehQ4dw5MgReL1eGqWLXjQaDdasWeO3kREFm15yfxFEer0+Kn4Ph8OBl19+GTU1NWGVkJjNrl27kJmZuaQWQBRUv4hQrl9lZSUuX77MeUmTWOX48eOQSCTYvXt32J8ViUTYt2/fkooYCsTIyAjefffdBW16AOCBBx6ASCTCiy++SJNk0YVOp0N8fPxUk12qLme0bF7ZYL5avIAvqjg7O5vzqDN/xifqdS6pr6/Hvn37IBQKw/7s3XffDbFYvOjmLKpfRKg689KlS0uqaXwwdDodUlNTp7IDmY7sZ+O5slgssFqtrGyuZxtWqH/H8vXzN1Z6ejrMZjOsVivt4wCYMxbXcy2F0WiEy+Xi3FmiVquX1N5g9v1HGfuj5b6Y7SwRiURITEyMGvnq6uogl8tRVVW1oPOoVCqcPHkS4+PjNEkW3TQ1NUEmk80b3FBWVoa2traYWUc4nU5cv3593rX3ypUrIZFIWFl7z84qov4d6zozEBcvXgRJkiFlRpSXl0MqlbIa9e9PB3Ol40K5RsDNclZs2j79ZY9Qr9PJonVG2O12vPLKK1CpVAsyEgM+xTQ4OIizZ8/SJF10E8x4Avgmj87OTsbTqtxuNwwGw5xFezTUsgaAt956CxMTEws2Sk53ekVDw1C2IEkypObVFBUVFXA4HMulgiKkvr4ed999N2QyWUSfr66uxsWLFzE0NESzZLHF0aNHwePx8NBDDy3oPPHx8bj33nsXrRNydt1pILoiQJlGq9VCr9fPG50FREfU2ewNjEKhgEQi4fS36u/vx9WrVyPK5AJ83+HOO+9cVNlcXq83bJ1pt9s5v7+iidkbLCqyP5YNA7PrzVNjMfWdpmeWMD3W9OuXnJwMgiBoH4skSb99EgD6SxJEiyEkEP4MZ2zLtxT3BrPnpWgz9s9+FoHoum9ra2tx4MCBiPc3FIcOHYLb7cYrr7xCk2TRjUajQWlp6byle8vLy+F0OtHZ2cmiZJFz/fp1uN3uedfefD4fa9euZWVtpNPpIBQKER8fP/UaWzqTC6dmQ0MD4uLiAvbKm45QKMTmzZtZjfqPBh3sdDpx5cqVsHTcxMQEOjo6GJbsJrPvJaaqGyxaZ8TJkycxOTmJmpqaBZ+rsrIS+fn5i9ZgNB2SJIOmtgE+L7nX60Vrayuj8hgMBpAkybmXNxB1dXUoKysL6rwJBZVKhZGREZw5c4YGyWKDjo4OjI+Ph+wZ3rhxI0QiUczXhuUCg8GADz/8cEFRQ/v27QOfz8eJEydolCz2qK2txd69e5GUlLTgc9XU1ODixYsxs8gPh2jfvDINFXEVTD9EQz3e2b8VkwbaUDlx4gQEAgHuueeeiM9RXV2NDz74YNFEOV6/fh1GozFknblp0yYIBIJlnTmN2annQOxH9gfaXDMV2T89s4Qai43rx+fzkZKSwkhmic1m4/S3ipaMwUCZL+Pj46xlHizFvQGb81IkRPN6rqmpCc3NzQsODAR8vZZuv/32JWHzAXzXLpQ1KnVsLBBta2/q2ZkeHM2WzhQIBEhOTmb1OVWr1di6dWvIvSkrKyujJjOCrd6tjY2NsNvtIeu4rVu3AgDr12n6NRKLxYiPj192RoRKbW0t1q9fj5KSkgWfiyAI1NTU4KWXXoqZFLVIGRoawsTERNAJvLS0FADziinQhAFwX6fSYrHg2LFjtCx+AN9EU1BQsGQWQMDNSZWaZIMhFouxYcOGmK8NywVvvPEGvF4v9u/fH/E5kpKSsGPHjkUVaRwuvb29OH/+PG3P/f79+6FQKBZlXyJ/m9doKkfBNE1NTRCLxSgqKpr3uPLycvT09MBkMrEk2VxmR2UC3P9W9fX12LVrV8TNKAHf8+XxeHDy5En6BOOQhoYGEAQRco8qqVS6rDNnwZZRjapLPP25Sk5OZqRxZaDSPwAzkf1szRVsjTXfXoOJsQQCwZzMkomJiagoMzTfvTQ6OsqKDNR8tZT2BtG+XorGNQJFbW0tEhMTsWfPHlrOp1Kp8N5772FkZISW80UrHo8HLS0tQW0+KSkpyMjI4DxoJlSampqQnZ09Y471R3l5OTQaDeOlb9h05HH9nFJZbaEa2QFf1H9fXx+Gh4cZlOwmgXSc3W5npJ+XPxoaGiAUCrFx48aQjk9ISEBJSQnrGSRs3LeL0hlhMplw4sQJ2oxFgE8xGQwGnD59mrZzRiOUcyFYZoRCoUBBQQHjiilQ6jnAfWbE8ePHYbVaacm+AW46vV5++eWo2JCwgVqtxpo1a8IyNrHtQV8sHD9+HFu3bkVmZuaCzlNdXY133nkHFouFJsliiyNHjkAqleLAgQO0nE8mk+HAgQNLxhkRTRGgTKPRaLB27Vrw+fx5j6M2gi0tLWyI5ZdAUZlc/VYmkwnvvvvugus/5+TkYPPmzYumb4RarUZJScmMdP9gVFRULOvMaQQy+tF9r/vb8FKR/UyMRRAEkpOTp15jKnCHzbmCrbH8/VZUSQImrp+/zBKAfsdRJFDOkunrcraDwJbi3iDa10vRtkagIEkSdXV1eOihh+btdRkODz30EPh8Po4ePUrL+aKVnp4e2Gy2oDYfwLdOjaXMiFAqVpSVlcFiseDGjRuMyuOvZC0Taw5qLC6f0xs3bkCr1YZcfgjAlOOCLUO7VqtFQkLCjPmCCx23YcMGSCSSkD/D5lreX+lKgJn7dlE6I+rr62Gz2WgzEgPAunXrUFJSsuij1jUaDeRyOfLz84Mey0Z6m78NQlxcHEQiEefOiNraWlRUVKCwsJC2c6pUKoyPj+PUqVO0nTOaCacmLEVFRQU6OzthMBgYkmrx4XA48Oabb0Zce306VVVVsNvti94xG4ja2lpUVVVBoVDQdk6VSoXm5uaYWeiHSjSn9bNBqBuikpIS8Hg8zqLOPB4PRkdHo+q3OnXqFJxO54KdEYBvzjp58uSicPKHG3EG+DZ67e3tUdFnKxpga17yt35lcqzk5GQIBIIZ40yXg86x/H0nuiP7bTYbTCYTZ7+VUChEUlISK9ePqcaQkRCoDBf1HhtEMs/F8t6AJMmoXy9Fq3wXLlxAT08PrQGoSUlJ2Lt376K3+VB7jlAN97GSGRGOM4I6nkkCPTtjY2O0VlyxWCywWCycPqeUQyEc205OTg6ysrJYc0YE+j2o99gg0rV8Y2Mj7aU3/TE5OQmn07mcGREptbW12L59e0gG9VAhCAIqlQqvvvoq402buSSURkYUbDTd1Ol0kMlkkMvlU68RBMF5auj4+DhOnjxJq8ML8F3T0tLSRb8AAgCr1Ypr165FNBkD7HnQFwNnzpyByWSixbBXXFyMNWvWLMlSTa2trbh27Rqtmx4AuPvuu5GUlLSonntqcx2ogTVbdTm5wuv1QqPRhBRxJpVKUVRUxJkzamxsDF6vN6oMDcePH0dZWRktzv7q6moYjUacPXuWBsm4w2KxoLGxMSIHPuAz2ix1XC4XxsbGAs5LdMKmM8JfBBtTjSvn28jTGdlPnYut60cQxNQ1Y3Ks+a4f11HmlAyz7yWmGlf6w2az4dq1a2HPc7G8NzCbzbDZbKzMS5Ewn7NEr9czXuZmPmpra5GZmYldu3bRel6VSoXz58+jt7eX1vNGExqNBsnJycjIyAh6bHl5Obq6uqI+K95kMqGnpyektXd2djYSEhJYsWUFmvPpLH3Hps4MhFqtRmFh4ZTOCBU2M9u4dkYYDAZ0dHREtJb3eDy4fPkyQ5LdhM3166JzRhgMBrz11lu0G4sAn2Iym814/fXXaT93tBBK82qKsrIyDA4OMtoY0t+EAXC/QHvllVfgdrtx6NAh2s+tUqlw7NgxVjyfXHLlyhV4PJ6wJ+OCggKkpqbG5IaDK+rr65Gfnx/ysx2M6upqnDhxgtMNCBfU1tYiPj4e+/bto/W8IpEIDz/8MOrq6haNkX5iYgIul8vvQsbhcHDaH4ENbty4AYvFElJ0FsBt1Bmbi85Q8Hg8OHHiBC2ZXACwYcMG5OTkxLwD9fLly/B6vWE78IuKipCUlLSsMzH/Zp3uyH7q2Zm9KWfLwC0SiZCYmMiqMZ3OsdjOLElJSZlTUo+t34pNY38w/MknlUqhVCpZke/KlStwu91hz3OxvDeY717n2tgP+G/wDvjkc7vdmJiY4EQuj8eDI0eO4NChQ0HLYYZLdXU1pFLpoiyhSkE1r57eWDkQZWVlIEkSra2tLEgWOVS501DW3gRBsFblY7HpzEBEEvEP+JwRFy9ehMfjYUCqmfj7PZKSkhjp5+UPKjAo3OtUVlYGmUzGio5bdkYsgJdffhlerxcHDx6k/dxFRUW45ZZbFlX06nRCbWREQRk2mZzE/UXoANzXqaytrcXtt9+OrKws2s9dU1MDi8WyaGpcB0KtVkMqlYZtICcIYrkGdhiQJInjx4+juro6pAVnKFRXV0On0y2pSFuSJFFbW4sHH3wQYrGY9vOrVCr09PTE5EbaH4EWMtFUjoJJQu2/RMFGpmEg/PVmov42m82sO8bPnz8Pg8FASyYX4NMZ1dXVOH78eEw7+9RqNeRyOUpLS8P63LLOvAmb85JWq0ViYuKcOuZM9afwF7hDdxax0+nE+Pi437kCoDeyf755aXR0FG63m7ax5rt+TPSMmP2d2DT2B4PNa+EPtVoNmUwW8l6UIpbnufnmJbfbzWjQXyjM9yxOf59tzpw5g5GREUYCUBUKBaqrqxetzQcIvZwRgKl1R7SXk21qagJBEFi7dm1IxzO99rbb7TAajZzrTL1ez7ih3+l04sqVK2EHmQK+qH+LxYLm5mYGJJuJv6x9Pp+P1NRU1nRcSkpK2JnfAoEAt9xyCys6br57ie7qBovOGVFbW4s777zTrwGbDmpqavD666/DaDQycn4u6erqgt1uD9l4smrVKggEAkYn8WjMjBgZGcF7773HyOIHAFauXImtW7cu6gUQ4POeb9myZUaN41CprKzEhQsXOI8WigUaGxvR19dHm2EP8F3/lJQUHDt2jLZzRjuXL19GZ2cn7aXZKHbu3InMzMxF89zPF1UBREc5CibRaDSIj49HdnZ2SMeXlZVBp9NxoteC/VZsy1RfX4+0tDRs3bqVtnNWVVWhp6cnZmoe+4PSmZFEgFZWVqKhoSGmnTF0wOa9zub6db6x6JxrA2WWMBHZT53LX+kkkiRp7Q0QLb9VNDsj2JKvoaEBt9xyy5LaG0SbDp5NtMpXW1uLgoICWtcK06mpqUFjY+NUtP1iwuFwoL29PWSbj1wuR2FhYdQ7IzQaDYqKiiCVSkM6vqysDG1tbYz1FIsWnen1ehnvG3bt2jU4HI6IMiM2b94MPp/PmqGdax1XUVERUYAoW+WsdDod+Hw+EhMTZ7yelpYGl8tFazbconJGDA0N4cyZM4wZiQHg8OHDcDqdi9IIR23SQ/WSi0QirF69mlEvJtVIbTZc9ow4evQo+Hw+HnroIcbGqKmpwcmTJzlLfWUDtVodkfcc8HnQJyYm0N7eTrNUi4/6+noolUpa66ny+Xzcd999iz57Zzp1dXVITU3FnXfeycj5+Xw+Dh8+jBdffJGVNFWmWXZGaEJOfwdu6l02ooJmo9PpIBaL5zRl5+q3On78OPbv3x9S76pQueOOO6BQKGJ6zlqozhwfH0dHRwfNUsUWbM5LgdavaWlpMBgMrET20+2MCFR6SiKRIC4ujvaxEhIS5mSWMPVbsXH9XC4XxsfHA94X0aAX2boWgViKewOdTgcej4fk5OQZr0fLemm+knMAN/I5nU68/PLLqKmpoS3rezb79u1DfHz8oizV1NbWBo/HE1YGUllZGSdr1HAIJ9sD8H0nt9vN2Noo0LMjk8mgUCho12NJSUlzHLlsPadqtRoikQjr168P+7NyuRzl5eWMVwcgSRJ6vZ4zHez1eqecEZFQUVGBwcFBDA4O0izZTKjSlbP3YUzcS4vKGfHiiy9CKBTiwQcfZGyMnJwc7Ny5c9FEr06nqakJqampYWWVMJ3e5i+VCuA2gqi2thZ79+5FUlISY2McPnwYLpcLr776KmNjcMng4CAGBgYi8p4DwJYtW0AQxKIpacMkx48fx759++Zs6BdKdXU1mpub0d3dTet5oxGv1ztVlzaSaL1QUalUGBkZwZkzZxgbgy20Wi0EAsGcqAqqLiedjU6jkXD6LwG+MpBisZiTqDNKz87e0DPRlDYY7e3taGtro61fBIVYLMbevXtj1hkxMDCAoaGhiHUmFTm61HWmVquFUqmcEzVJbUzpvNfnW7/SGdnv8XgwOjoacCy6vxMwN3WfqbECjQPQ+1vNVxJ2dHSUtkh7SmZ/Y3EZZEURqAwXQP/v64+hoSH09/cvub2BVqsN2LMEYFcH+0On0/lt8B4fHw+hUMiJfKdOncL4+DijAahisRgPPvggamtrF11WYbgBqAC35URDJdy1N/X9mfpei1Vn+kOtVmPTpk0RlzKmMniZZHx8HG63mzMd19HRgYmJiYh1HPU5pq8Tm/fSonJG1NbWYt++fUhISGB0HJVKhbfffpvzxQHdhOtNBm423WRKSc8XoWMymWCz2RgZNxC9vb04f/48Y6VaKLKysrBr165F6fQCbk6ikXqG4+PjUVJSEpO1YdlkaGgIFy9epN2wBwB79uyBSCSKWeNeOHz44YcYGBhgdNMD+DbSK1euXBTPPTV3zzZw8/l8pKSkcG50YRKn04m2traw9KlAIEBJSQknZYQC6VnK8MDmb3X8+HFIJBLs3r2b9nNXV1ejoaEBIyMjtJ+baShdF6nOTExMxOrVq5e8zgx0r1OR/WyVaaLepwODwQCv18tK2YFAmSVMjcXG9Qs2lsfjoa1mP5vXLxIClRShXmNavqW6Nwh0/8XHx0MkEnF+XwRq8E4QBGf3bW1tLUpLS8PuOxguKpUKnZ2duHz5MqPjsE1TUxNyc3MRHx8f8mfKysowPDxMa4k8OqFKnYaz9k5KSkJWVhZja+9AmRFAbOtMfywk4h/wzfstLS2MlsLnWgdTumnLli0RfT4rKws5OTmM6zg276VF44zo6urChQsXGDcWAcDDDz8MgiDw0ksvMT4Wm0TijCgvL8f4+DiGhoZol4dqnDmfZ47tBVBdXR2kUikOHDjA+FgqlQrvvPMO5+m5TNDQ0ICcnJyQ66n7g626ebHMiRMnwOfzsW/fPtrPrVAocOedd6K+vp72c0cbtbW1yMvLw7Zt2xgdhyAI1NTU4OWXX2asfilbBFrIAOw1wuSK9vZ2uN3usDfJXEWdBYoKFgqFSE5OZvW3qq+vx+7duyGXy2k/97333guCIPD666/Tfm6mUavVyMvLQ2ZmZsTnWNaZ7M5LgZ4ruhtXzre5phpX0hXZHyizhBqLjeunUCggk8loG4vKLAl0/ShZ6CBQU0ggOpwRwe4lrVbLaIS4Wq1eknuDQPMSZezner0U6FkEuFnPWa1WHDt2jBWbzx133IG0tLRFESQ0HY1GE9EalfpsNELJFU1rb61Wi4SEBL/ZAmzpTKVSCYlEwuhzqtfr0dXVFXHEP+Cbu0mSxMWLF2mUbCbz6WA25jK1Wo2SkpIFBc6zoeMC3UuJiYkQCATLZZr8UVdXB7lcjv379zM+VkpKCu6+++5FpZjsdjs6OjrCnsAp5wUTiinYonj6MWxRW1uLqqqqObW1meChhx4Cj8fD0aNHGR+LbRZSE5aioqICTU1NsFgsNEm1+Kivr8eOHTsYKylWXV2NDz74YFH3NnG5XDh69ChqamporWEfCJVKhfHxcbz11luMj8Uk8xn9osHowiSRpL9TxzOZaRiIaPmtDAYDPvroI0YyuQDf2m379u0x6UBdaMQZ4NOZjY2NsFqtNEkVe7B1r3u9Xuj1elYiy4JF+nk8HtoaV7I5V7A1lsFgAEmSrP5W80XJclkOJti95HQ6MTk5ydj4dM1zsbY3iBYdHIhok+/48eOwWCyMVykAfFmrhw4dwpEjR2KuMfp8NDU1hb1GXbVqFYRCYVQ7I8RiMYqKisL6HLX2ZoJo0JlsZDBduHABQORZbYDv/oqPj2e0BFEwHUd3P6/Z0KXjLl26xKicbN5Li8YZUVtbi+rqakai6fxRU1ODs2fPor+/n5XxmCaSRkYAsGLFCsjlckY8ysEmjOnHsEFLSwsaGxtZicQAgOTkZOzdu3dROb0AwO1249KlSwvyngM+z7DH41l0qbN0YbFY8M477zBm2AOAqqoquN1uvPnmm4yNwTWnT5+GwWBgZdMDAKWlpSgrK4v5536+SLpo2FwzSVNTE7KyssJ2ApaXl8NsNuPGjRsMSeafaDE0nDx5Eh6Ph9Ggkurqarz99tusl3hcCC6Xizad6Xa7ceXKFZokiz3YmpcmJibgdrv9PldyuRwymYy2sYJF2wP0GtO5NqzQPRbb12++zBKmjf3BoK4FF/sut9uNixcvLsm9gVarjQodHIhoWSNQ1NbWYuvWrVi5ciUr49XU1GBwcBBnz55lZTymmZycRF9fX9g2H6FQiDVr1kRt34impiaUlJSE3duvvLwc3d3dMJvNtMu0GHWmP9RqNdLS0rBixYqIz8Hj8VBRUcFo1L9Op4NAIPCbmUBdu9HRUUbGtlqtaGxspEXH2Ww2xnv2snUvLQpnhEajQXNzM2tGYgC4//77IZFIcOTIEdbGZBLqhi4tLQ3rczweD6WlpaxnRnBRy7qurg7x8fGMlLwJRE1NDc6dO8e6cYpJNBoNrFbrgj3DpaWlkMvlMdeoji1Onz4Nu92OqqoqxsbIycnBpk2bYjLSOFTq6uqwevVqbNiwgbUxVSoVjh07FlORfbOJts0rm0RS8hBgNtNwPqLlt6qvr8eWLVsWVIooGNXV1bDZbHjnnXcYG4NumpqaYLfbF6wzy8vLIZVKl7TOZOten2/9ysRYUqnUbzAWF84IOiL758ssmT4WHcz3W8XFxdFasz/Y9QPoKwkVCTqdDnFxcZBIJHPeY9oZ0dzcvGT3BoGahQLRsV6KljUC4HP0njx5krUAIQDYtm0b8vLyUFdXx9qYTNLc3Awg/HJGALNZBAtloWvvlpYWukValDrTH1TE/+w+geFSUVGBhoYGxnvR+pOTaR13+fJleDyeBeu4TZs2gc/nM6bjXC4XxsbGlp0R4VBbW4vExETs3buXtTHj4uJw3333xXz0KoVGo0F+fj7i4uLC/ixTikmn04EgiCnHw3REIhESExNZWwCRJIna2lo8+OCDfuv+McWBAwcgkUgWzQII8CksPp+PzZs3L+g8fD4fW7ZsibnasGxRX1+PNWvWoLi4mNFxqqqq8MYbb8DlcjE6DhfYbDa8+uqrUKlUC15ghUNNTQ2sVmtMNwcPVpud6801k0S6IcrJyUF8fDyrGz2r1Qqz2TxvPWg2fiun04k333yT0UwuAFi9ejWKi4tjyoGqVqshEAiwadOmBZ1HIBDglltuWbI6kyRJ1uYl6jxsPFfzba7pLmk6n9GUzsh+KrOEresH+HdGEARB+1jzGZ2ny8MFwZ4P6hgmUKvVS3Jv4Ha7YTAYonq9FKxnBJvyvfLKK3C5XDh8+DBrY/J4PNTU1ODo0aOLYq/T1NQEPp+PNWvWhP1ZrsqJBoMkyYj6YABASUkJCIJgzJY137Njt9tpycgYGxuD1+vl5Dn1er1oaGhYcMQ/4Iv61+v16OnpoUGyuQT7PahjmECtVkMmk0W0P5yOTCbD+vXrGdNxVGYIW/dSzDsjSJJEXV0dHnroIYhEIlbHVqlUuHLlCtrb21kdlwkiqR1IUV5ejubmZng8Hlpl0mq1SElJAZ/P9/s+m029Ll++jM7OTlazbwBf06GqqqpF5YxQq9VYt24dZDLZgs9FedCXmYnX68WJEycYN+wBvkhjo9G4aNKXp/PGG2/AZDKx/twXFhaioqIiZp97p9OJiYmJeaMqDAbDotjUzcZsNqO7uzuiDRFBECgrK2M1BT6UCG429OyZM2dgMplYm7OOHz8eM/WfGxoasH79er+lXcJlKetMk8kEh8PBSpTifOVuqNfpbGAdaBwqsp/OBsxsRPazff0CZZbQPVYo1y9anRGJiYng8/mM6YOGhoYluTeYr2cJwO5e1x/BnCVpaWkwGo2w2+2syFNXV4ddu3YhKyuLlfEoVCoVDAYDTp8+zeq4TKDRaFBcXOw3AyoY5eXlMBqNGBgYYECyyLlx4wbMZnNEtiyZTIaioiJG1t6LUWfO5vr165icnFxwxD8AbN26FQAYm7/n+z2oXk5M6rhbbrkl7DJi/mBSx7F9L8W8M+LChQvo7u5m3VgEAPfeey+USmXMGoymE6k3GfB5ye12O7q7u2mVab5FMcBuNEZtbS3S0tJwxx13sDLedFQqFT7++GO0tbWxPjYT0OU9B3we9MHBwahbFHHNhQsXoNPpWDHsbdy4EdnZ2TEdxR+I2tpabNq0CatWrWJ9bJVKhZMnT8Zkc3C9Xg9g/oUMwFxdTi6h0rwjde6znQIfijNCr9czbrivr69HXl5exOuQcKiursbIyEjM1BRXq9W06sz+/n4MDQ3Rcr5YIpR73el0wmg00jKWUChEfHx8wLHYKP1DZ7PBYJkldBrT2S5zFSizhKmx/JGUlAQ+nx+1zggej4fU1FRGo0aX4t4glHt9cnKSNWP/bEJxlgA3131MotVq8c4773Bi81m/fj3WrFmzKCpiLCQAlatyosGg5FmILYvu77RYdeZs1Go1CILAli1bFnyulJQUFBUVMRb1P9/vIZfLIZfLY0bHtbW1YXx8nJbzTYfteynmnRG1tbXIyMjArl27WB9bKpXigQceQG1tbdSlq4WD0WhEf3//gjIjANDuUQ7mjGCrTqXX68WRI0dw8OBBWryZ4bJv3z7ExcUtigXQxMQEWltbaZuMKS98rERAsUV9fT1SUlJou87zQRAEqqurcezYsZieB2czOTmJEydOcLLpAYBDhw7B5XLhlVde4WT8hTBfQ1AgOiJAmaKpqQkEQWDt2rURfb68vBytra2sZY2Esuj0er0YGxtjTAaSJFFfX4/q6mpWyqFt374dSUlJMVGqaWxsDO3t7bRuYIClqTPZnJfYNHDPV0KFzrEmJyfhdDqjxrBitVpp6avE1vUDuDX2h8J8UaMAc/uupbw3CCUKFWDH2O+PUJ7F6ccxydGjR8Hj8fDQQw8xPtZsCIKASqXCq6++CpvNxvr4dEGSJJqamiI22ufn50OhUERdE+umpibExcUhJycnos+Xl5fT/p2ocoPRojPNZjOsVuuCx5qNWq1GaWlpRKXe/VFZWcnY3M2VbXFgYACDg4O0r+UvXrxIy/mmQ31/KlNkNmlpaZiYmIDT6aRlvJh2Rng8Hhw5cgSHDh0KWMqHaWpqatDW1oZr165xMj4dLNSbnJaWhpSUFNo9ytHijDh79iwGBwc5M0pKJBI8+OCDMe/0Am5OmnSk8gFAZmYm8vLyYqY2LFvU19fjvvvuY21erKqqQk9PDyPNv7jitddeg8PhwKFDhzgZPzMzE7fffntMOiGDLYqZrsvJJRqNBitXroy41ERZWRlcLhc6Ojpolsw/1G/grzcTwI6hobGxEX19faxkcgG+3gn33ntvTDgjLly4AIA+nZmdnY3s7OwlqTPZNKqxuX5la6xg148q40PXWCKRaN7MkukyLXQsNq5fsChZOseKlPnqaQPMybeU9wbB+stwHbwRqrOEDflqa2uxd+9eJCcnMz6WP2pqamA2m/H6669zMj4daLVaGAyGiANQeTweSktLozIzoqysLOKAlrKyMmi1WlqdfsF0ZlJSEng8Hm16TCwWQ6lU+n2fSacm1byaLioqKnD16lU4HA7azknBlQ6mnCt0Xafi4mIkJiYyouN0Oh0UCkXAfSzd91JMOyPOnDmDkZERzozEALB7924kJyfHpMGIQqPRgM/nY/Xq1RF9niAIlJeXM+KM4GJRPJu6ujrk5eVh27ZtjI8VCJVKhY6ODly5coUzGehArVYjMTGR1qbKTHrQY5Hu7m40NzezZtgDgDvuuANyuTwmjHuhUltbix07diAvL48zGVQqFd59912MjIxwJkMkBIuqoF5frM6IhZQaYjsFXqfTITk5GUKh0O/7bDiOjh8/DqVSyWqGa3V1NRobG3Hjxg3WxoyEhoYGJCUloaioiLZzLlWdqdPpwOPxkJSU5Pd9Ou/1YOvX9PR02iL72SppGsxoyuPxaHV8zJdZQvdvxcb1o3qWBNvXcNUfIBRnCVPlcZfy3kCn00EmkwXsWcJ18Ea0OEtu3LiBc+fOcWrzWbVqFTZt2hTzNh8g8lKiABix+SyUha69qc82NzfTJVLQZ4fP59OWDUetOdjQmdMxm81oamqitRJDZWUlnE4nPv74Y9rOCQAOhwNGozHo2owpZ0Rubi5tvW4IgmCsb0Qo61fqODqIaWdEXV0dCgoKaPXGhYtQKMTBgwdRV1cXM80QZ9PU1IRVq1ZBLBZHfA4mmm4GSxemJgwmr7vL5cLRo0dRU1MDHo+7x+XOO+9EampqTC+AAN+GY+vWrbRey4qKCly6dGlRNsONhOPHj0MkEmHPnj2sjSmRSLB3795F0zdidHQUb7/9NqebHgB46KGHwOfzcfToUU7lCBedToe4uLiAzfGoupxcNmVkioXU4gWA5ORkZGZmspYCH0pZDuo4pqivr8c999wDkUjE2Biz2bt3L4RCYdTPWWq1GhUVFbSWr6qoqMDFixfhdrtpO2csoNPpkJqaGnD9kZCQAIFAQFszSTaeK4vFAovFEnQsNhpk0j0WW/NSKJGSdNTsD/X6cWV0DlaGC2DOWdLQ0LBk9wbB7j+mm6oGI5izRCQSISEhgXH56urqIJVKceDAAUbHCYZKpcLrr7+OyclJTuWIFI1GA4lEgpUrV0Z8jrKyMrS0tETNGsLlcqG1tXVBa++ioiKIRCJa196LVWdO5/Lly/B6vbTaYtetWwexWEx71H+wTBXqPSbmMmotTyeUM4Luqils30sx64xwOp146aWXUFNTw0qN4flQqVTo6+vD+fPnOZUjUhbqTQZ8iqmjo4O2Blsejwejo6NBHwaPx8NI8xaK06dPw2AwcG6UFAgEOHjwII4cORKzTi+SJGltXk1RWVkJm80WdVEaXFFfX4+77roLCoWC1XGrq6uhVqsXhYH5pZdeAgAcPHiQUzmSkpKwd+/emHNCBqvBDXBfjoIJ9Ho9tFrtgjZEALtNrIMZQpRKJcRiMWO/1dDQEC5evMhqJhcAxMXF4Y477ojqbC4mdabVaqU1AjAWCDYv0VmzPxQDN3XcQscBAkdeUmPR9Z3myyyhe6z5rl9ycjIIgqBlrFB6RlAyLYRQDCFMRWWGAlfOEpIkaW3sSREre4Nghh/K2M9lZsR88gHsrOdqa2uxf/9+1vc2szl8+DAcDgdee+01TuWIlKamJqxdu3ZBZXzLysrgcDjQ1dVFo2SR09HRAZfLtSBblkAgQElJCa3zhU6ng0AgQEJCQsBj2NKZVClWup9TtVoNhUIRcZ88f4hEImzevJn2qP9QnRF0XyO3241Lly4xouMMBgPtz2GoDvIlnxlx6tQpjI+Pc24kBoAdO3YgJycn5gxGwM1GRgs1npSXl8Pj8aCtrY0WuQwGA0iSZGUzNx+1tbVYvXo11q9fz9gYoaJSqTAwMIAPP/yQa1EioqurCwaDgXbP8MaNGyEQCGKiNizTjI+P48yZM6iqqmJ97HvvvRcAYrqWKkVtbS12794dsMwQm6hUKpw/fx69vb1cixIyoWxeuTS6MMVC+y9RMNFILxDBfiuCIBg1NJw4cQJ8Pn9q/mCTqqoqvP/++zAajayPHQodHR0YHx+nXWdu3rwZfD5/yelMNo1qbDsjgo1lMpkW3HQ1WGYJNRYb108gECA5OXnBY1ksFlit1qj6rbguxxNMvrGxMVqzDbq7u5f03iBajP2BCOYsAZiXr7W1FdeuXYsKm09ubi527twZkzYfgJ4AVOrz0dLEmpKDDlsWnd8pWLlBgD2dKRQKkZSURPtz2tDQgC1bttDeo7KiooKzzAidTkdrtkFTUxNsNhvtOm7r1q0AwMh1mu8aSSQSxMXFLTsjamtrUVpauuAJlQ54PB4OHz6Mo0ePRk3KWqiMjIxgbGxswdextLQUAH11rkOdMKYfSzc2mw2vvvoqVCoV59k3ALB9+3bk5uairq6Oa1EigvJwU5MnXUilUmzYsCEmasMyzZtvvgmPx8OJMyI1NRXbt2+P6kjjUBgYGMDZs2ejYtMD+DJOpFIpjhw5wrUoIfP/s/feAW5dddrwo95HZXrvfcaJS2LHSUgvkMQJkJCYzhLa0neB3X1pL1k6H2UJu7AEduElYCchJDjV6c3xOI6rNL33kTSaUe+69/tDvmPNjMqVdJtsP//YI13dc+495/x6EbpyzRYsFgvkcnne9f17enowMTHBSD35TOB7rZ544glcccUVaaOt2cJtt92GSCSCgwcPcj42HbDFM9VqNbZs2XLe8Uyu9jpVl5iLyP5sZOV8mw0KyZnD1Fhc6ho2mw0SiSRjZgnTxn66oJtlA8RLWTIFypByvuoGmepzA/w7qfiW5/bv34+ioiK8853vZG2MbLB37148//zzrDQDZhMEQaC/vz9vo31ZWRlKS0sFk3VksVhQWVmZd2NzKiuZKUP0ucgzE8FWVhsQj/qfnJxkdL6Z+hkC8XcUCAQY1b+OHDkCqVSKbdu2MXZPIF49oa2tjZUMEi73UkE6I/x+P/7+97/jnnvu4Xsqa7jnnntgs9nw8ssv8z2VrMCUN7moqAj19fWMeZSzEYrZKgvz1FNPwev1CsYoKRaLcc899+CRRx4RfA3UZOjr60Nra2vewkIysOFBL0QcOHAA27ZtQ01NDS/j79mzB88991zeEZh84qGHHoJcLscdd9zB91QAAFqtFrfddltBRWEJQXnlA2azGZ2dnSmbQdNFb28vSJLEwMAAQzNLDTqGELayWHw+H1544QVenKcAUF9fj4suukiwDtS+vj60t7fDaDQyfu/zkWdylbFFGajSnSupVIqSkhJGDNwikWitBEMyMNVskEtawdVYdJwRTJUkoJtZAuTvOMoFlLMkHb1hownqkSNHzmvdQOiZpHzKCEDc4Llv3z685z3vSdmHjGvceeedAM6WdC0UTE1NwefzMRLIy2UGbyYwUeEDiD+Tx+PBzMwMA7M6N3lmIubm5rC4uMhK717qnkwa2m02G/R6fVo6wgaP6+vrw5YtW6BWqxm7JwWmeRxJkpzvpYJ0RjzxxBPw+XyCckZs374dLS0tBWUwAuLeZJVKhcbGxrzvxWSdazq1S/V6PeRyOWsC0P79+7Ft2za0tbWxcv9csHfvXiwvL+PFF1/keypZg43a1xR27dqF4eFhVvuHCB2RSATPPPMM57XXE7Fnzx4EAgG89NJLvM0hX+zbtw/vete7oNfr+Z7KGvbu3YtTp05hcHCQ76nQAt1Iv3Ohv0giLBYLIwpRV1cXRCIR61FnBEHAbrfTchyxsVYvvPACgsEg7zTr6aefFmRWK9s8c3BwEE6nk5X7CxFc0SU68itTY9lsNhQXF0MqlaYdJ3FeuYJuuZZ8I/uDwWDGzBJqLCbeH5DecSSXy2E0Gjl5f2wYQugiG2cJk/yArchaoDB0A7rBG3w2sOZzfsePH8fo6KhgAgOBuIPy+uuvL0ibD5B/ACp1DyFlRjDhYKHeC5O2LDpnx+Fw5CWDBgIBeDwezs8pZQRnwxlRV1eHiooKRp0RdNeDupYpsC3Lnzx5krEgUI/Hg2AwyOleKkhnxP79+3HJJZfkXQqBSYhEIuzduxd/+9vfEAqF+J4ObVgsFnR3dzNS6623t5fRMk0qlQoajSblNWzWsna73XjyyScFJfwAwMUXX4z29vaCE4CCwSBOnjzJCsMCzjLCt956i5X7FwJef/11uFwu3qKMAaC9vR0tLS2CjTTOhNHRURw7dkxw5/6d73wn9Hp9QZx7KqqCbqQfk3U5+QRJkowpRGq1Gs3NzawreisrK4jFYrxlsRw4cAAdHR1obW1l/N50cdttt2F1dRWHDh3ibQ7J4Pf7cerUKdZ55tGjR1m5v9AQiUTgcDgEU/qHqbHoKNdMRvbTVeTzieynfsvV+xOJRBmj8pnaF3TfHx/OCC73EoXzXTfwer0Ze5YAhVOmiQ15bt++fSgrK8O1117L+L3zwd69e/H6669jbm6O76nQhtlshsFgQFVVVd736u3txdjYGO+Z8D6fDxMTE4w4WGpra1FUVMSoLYvO2SFJMq/Sd1zKHIno6+tDfX09KioqGLsnBZFIxHjUPx88eHV1FUNDQ6zyuGg0ihMnTjByPz72UsE5I5xOJ55++mnBGYuAOGNyuVx45pln+J4KbTCV2gbEPcozMzOMNIOk0/QHYE9Ae/zxxxEKhXD33Xczfu98QDm9HnvsMd4FgGxw4sQJRCIR1jzDLS0tMJlMgk/HZhMHDhxAdXU1tm7dytscRCIR9uzZgyeeeAIEQfA2j1yxf/9+aLVa3HrrrXxPZR0UCgXe8573YN++fYI33rtcLoTDYVqCTDAYhNfr5Whm7GJmZgYej4dRfsp2CjxfCgwQz8p48sknec2KAOJZrZWVlYJzoB4/fhzRaJQ1ntnW1gaDwXDe8ExK0aez11dXVxEOh3Mei05dYmosLgzccrkcBoOhYIzpXNIlOpklTI7FtbE/G9CZn1qthlarZWx+57tukO1e51r+y8ZZEo1GGc+0IwgCDz30EO66666MZ5RrvPvd74ZCoSiofm5UwAwTfTB7enpAEATvGdsDAwMgSZKRQCCRSMSo7H0u8sxEsBnxD8Sj/t966y3EYjFG7kdnPZjq50WBcoSz9Z62bNkCpVLJGI+74IyggcceewyRSERwRmIA6OzsxEUXXVQQ0avA2UZGTDUBp4ww/f39ed+LDsEA2HNG7Nu3D1deeSVqa2sZv3e+uOeee+DxePD000/zPRXa6Ovrg1KpxJYtW1i5P+VBF3qjOrZAkiQOHDiAPXv28N5sfc+ePVhcXMSxY8d4nUe2oOrS3nHHHVCpVHxPZxP27t2LsbExwb/XbASZxOsLHUymv1P3YTszIpu18ng8jDrA33rrLdhsNl4zuYB4L6bbbrsNBw4cEJSj78iRI1CpVIzJZxshFotx6aWXnjc8M1u6lG+Uol6vh0KhyDgWFwZuJsaKRCJYWVkRnGFleXk5L0MFl7oGH8b+bECnjBnArN51vusGdMqEAewZ+zOBb3nujTfewNzcnKDKclMoKirCLbfcUjA2H4DZANTu7m4AzJU0yhUWiwUikQidnZ2M3I8p2TscDsPpdAqOZ9rtdkaCBSORCI4dO8ZaxD8Qj/r3eDwYGhpi5H50eLBEImGknxeFI0eOwGg0spYBLpPJsH37dsZ4HB8O8oJzRuzbtw9XXXUVIylmbGDv3r144oknCiLac2JiAoFAgDHG1NHRAYlEwohH2Wq10hKKy8vLGa9Tabfb8fzzzwtS+AHipXC2bt1aUALQkSNHsH379rwbu6bDrl27cOTIEUEZlLjCwMAAJicneY8yBoDLL78cRqMRTzzxBN9TyQqnT5/G4OCgILPuAOCaa65BWVmZ4M99tsrrudI3wmw2Q6fToa6ujpH79fb2YnFxEQ6Hg5H7JQP17uk0KgOYNTQcOHAAxcXFuOyyyxi7Z67Ys2cPxsbGMDw8zPdU1tDX14cdO3awGg26a9cu9PX1nRc8ky5dovZ6PnSJS/mVrjE937HoNOUGmKHrdHtulJeXgyCIvGgkV+8PoL8v+OoPwOW7oHC+6wZc0qVcQNdZwtb89u3bh9raWuzevZvR+zKFvXv34tixYxgdHeV7KhkRDocxPDzMWICDTqdDQ0MD702szWYzmpqa0pb1zga9vb0YHBzMq+8RkJ2jEWCGZ2bKxiwvL0csFsPKykrOY1E4ffo0gsEgq5kRO3bsgFgsZizqnw/bYl9fH3bu3MlqoCglyzMBq9UKsVicsXRleXk5wuEwI9VwCsoZYbVa8eKLLwrWSAwAd999NwKBgOBS/pOB8vwyxZgUCgXa2toY8SjzmRnx17/+FQBw1113MXpfJrF371489dRTcLvdfE+FFthsUEdh586dWFlZwdjYGKvjCBEHDhyAVqvFNddcw/dUIJVKccsttxQEDUzEvn37YDKZcP311/M9laSQSqW466678NBDDwm6BBafBm4+QTWvZkrgZLqRXjLYbDYoFArodLq017ER9XjgwAHceuutjPSryhfXXnst1Gq1oGjWkSNHWI04A+I80+FwYGJigtVxhIBsmkoD+Ucp0pVf843sz8bAzUXkpUqlgk6ny3ssg8EAuVye9jom1oqr90c3swQ420+Ja9DpGQEwnxnBBZ0Tqm6QTc8SgHt5ic/MiEgkgr/+9a+455570jZV5xO33HILtFqt4IOEAGBkZATRaJSxAFRAGE2smerVRqGnpwfhcDhvekH37Gg0Gmg0mrx5pslkyujUZfKc9vX1QSaTsVoaWqfTobu7m5Gof7r9DAHmeBxJkpzJ8jMzM1hcXMz7XjabDaWlpRlpLpN7SZjUPQUeeeQRiMVi3HnnnXxPJSUaGhpw2WWXFQRjMpvNMJlMjDaeYaqJNZ/OiH379uH666/P6GHmE3fffTeCwSD+/ve/8z2VjFhaWsL09DTrxPjSSy8FAMHWhmUTBw4cwI033pixLARXuO2223Dq1ClMT0/zPRVaIEkS+/fvx5133pnRAMIn9u7di/n5ebzxxht8TyUlbDYbpFIpDAZD2utMJhPEYvE55YxgUiFqbW2FXC5n3RlBtzcTdT0TmJiYQH9/vyAyuYC4AfWGG24QjDNiYWEBs7OznDjwgfODZ9psNmi1WqjV6rTXMVGzPxv5lSTJnCP7Y7EYlpeXOZGV6RpWmBqL7jiJc2N7rHzKW9DtWUJdwzVfDIfDWF1d5XR+lG7ANp0Tsm6QTc8S6nouQddZYjAYIJVKGZ3fiy++iOXlZcFmKwNx2eGOO+4oiH5uVAYDk84Ipmw++YAKBGIKTAUCnas8k8KRI0dw8cUXQ6lU5n2vdGAq6t/pdCIajXLK48bGxrCyssI6j6Puz4TTho+9VFDOiP379+PGG2/MyBT5xt69e3Hw4EFG0qDYBNORnMDZppv5MuVsDoPb7UYwGMxrPApzc3N4/fXXBS38AEBdXR2uuOKKgnB6UcSRbWJsNBrR3t4u2NqwbMFqteLIkSOCMewBwE033QSZTFYwpZr6+vowPT0t+HN/2WWXoa6uTtDnnm5UBdN1OflEJBLB4OAgowqRTCZDR0cHqynwdPks001Vn3jiCcjlctx4442M3I8J7NmzB2+++eZaORo+QfEwth34xcXFaGlpOS94Jt29zlRkPxfK3MrKCgiCuOCM4GisfGr2c/n+cgEfzhKu6JyQdQO6+89gMEAmk/GSGUHHWSISiRjft/v27UN7ezsuvvhixu7JBvbu3YuhoSGcPn2a76mkhcViQXV1NYxGI2P37OnpwdzcHFZXVxm7ZzZwOBxYXFxkNBCopKQEFRUVecve1FmgE9haSDyTAhdZbUCcP/T398Pj8eR1Hz54MMVzKIc4W6ipqUFlZeUFZwTbmJmZwaFDhwRvLAKA973vfYjFYnj00Uf5nkpamM1mxpsj9vT0wOFw5FVrzefzwefz0U6dBsCYAeGhhx6CQqHAu9/9bkbuxyb27t2L559/Pq9Gi1zgyJEjqKysRE1NDetjMVk3r1Dw1FNPQSQS4V3vehffU1mDXq/H1VdfXTDOiH379qGqqgpXXnkl31NJC7FYjHvuuQePPPJI3vVM2QJdQQbgx+jCBsbGxhAOhxl1RgDsp8DTbVgqk8lgMpkYW6sDBw7g2muvhVarZeR+TOCWW24BADz99NM8zySu5FVXV1/gmQwiG7qUb5kcuucq31J12SjX5eXleUX2080socbi4v1ptVqoVKqcx8oms4SpteK6QTRdZDM/an3zDTrr6+tDZWUlamtr87oPHQiVztHd62wY++mA7vwAZsuLBQIBPPbYY9i7dy+rtdaZwA033IDi4mJBBwkB7Nl8AKC/v5/R+9IFJR8LUfa22WzQ6/W0Mge44plFRUVQKBR5n1OHw4HR0VHWg0yBOO0mCAJvv/12XvfJhcfli76+PrS1tcFkMuV9r3QQiUSM8Ti6e8loNDKWDVcwzoj9+/dDqVTi9ttv53sqGVFeXo5rr71W0IwpFAphZGSEcQJOMbp8PMrZKlgAc02z9u3bh1tuuQVFRUWM3I9N3HnnnSBJcq3HhVBB9YvgQqDctWsXTp06hUAgwPpYQsGBAwewe/duwZUV27NnD15++WXB9zWJRqN4+OGHcffddwuifn0m7N27Fw6HAy+88ALfU0mKbJXXc6GBNRvp78DZFHi20v/p1ggHmGuq6nQ68dprrwkqkwuI78Vdu3YJolTTkSNHOFHygDjPPHnyJGPZpUJFNnQpn72ebV1iIHf5NVsDdzQazTmClUtaQXcsykCb61hUZkk2gU+5jkW3wShwli9yWfYl26jRYDCYd6QqRefOZ90g2+ANPhpY8zG/p59+Gh6PR9A9QinIZDLceeed2L9/v6BLNTFdzggAOjo6IJVKeWtibTabIZfL0drayuh9e3t7836mc5FnUnjrrbcAsF/xAgA6OztRVFSUd9Q/3b5h1DXLy8uIRqN5jcm1LH/06NG8epAB9PeSWCxGaWkpIzS/YJwR+/btw2233Zax0aJQsHfvXrzyyitYWFjgeypJMTw8jFgsxriXvLGxESqVKi+PcrZCceJv8sHo6CiOHTtWEMIPEH/26667TtBOr1gshqNHj3KSygfE0/mi0SiOHz/OyXh8IxAI4LnnnsNtt93G91Q24bbbbkMkEsHBgwf5nkpavPLKK7BarQWRdQcAF110ETo6OgR77rMVwM+FzAiLxYLy8nLGHYI9PT1wuVyYm5tj9L4U+MhiefbZZxGNRgVLsw4ePMirYT4ajXLOMyORCE6cOMHJeHyBK7rkdDoRiURojaXRaPKK7M9WuQbyi+znilZwNRbX749uZklZWRlCoVDexv5skI2zhAm964JuEIfQ5SW+5rdv3z5s3boV7e3tjNyPbezduxfT09M4fPgw31NJCo/Hg8nJScadEXK5HG1tbbz1jbBYLOjo6MjYuDlb9PT0YHx8HH6/P+d7nIs8k0JfXx9KSkrQ1NSU133oQCwW45JLLsk76p9uP0Mg/35eQNw+c/LkSU55nM/nyztLiQ+9sCCcEUNDQzh58mTBGIkB4N3vfjekUikefvhhvqeSFJTHt7u7m9H7SiQSdHd3c5YZwWQt63379kGr1eLWW2/N+15cYe/evXj99ddZM1bli4GBAXi9Xs48w729vVCpVIKsDcsGXnzxRQQCAcFFGQNAfX09tmzZIohI43TYt28fmpubsWPHDr6nQgsikQj33HMPHnvsMcFF+QHnZ5kmNtLfAWYyDdOBj7U6cOAAtm7dykkJomyxZ88e+Hw+vPzyy7zNob+/H36/nzOeuWXLFiiVynOeZ3K117ORX/MtvWKz2aBUKmmVO+PDGZFLlHA2mSWJY+WCbNZKr9fnVbM/22dKnB8XyNZZQv0mV1zQDeIQurzEx/zcbjeefPLJggkQAoArrrgCVVVVgg0SGhgYAADW5FQ+MyPYeiaSJNfeWy7gimcSBAG73c7pOT1y5Ah27tzJWQm1Xbt24ciRI3llHtHtZwgww+NOnDiBaDTKGY/bsWMHxGJxXjwuGo3C4XBccEYkw759+1BUVCSomuiZYDQa8c53vlOwjMlisaCmpoaWhzBbdHd359XIyWazQSQSoaSkJOO1crkcBoMh78NAkiT27duHO+64AyqVKq97cYl3v/vdkMvleOihh/ieSlIcOXIEYrGYM0OvVCrFjh078Oabb3IyHt944okn0NraKtjooT179uDpp5/OO9WRLYRCITz66KO45557BF+XNhF79+6F1+vFU089xfdUNmFpaYkW7QbOLWcE0459AKirq4NWq8XJkycZv3cgEIDH4+FU6IxEInj66acF6TwFgK6uLjQ1NfHa6+bQoUMQi8XYvn07J+PJ5XJs27btnOaZJEnCarVyQpeyMXDnO9bc3BxKS0tp8S6unRG5RvY7nU5Eo1FO3h+Vuc6V4yhbZ8TS0lJOY+UCai/RAROGmkOHDkEkEnFG54SoG8RiMdjt9qze+/ngjHj88ccRCoVw9913530vriCRSHD33Xfj4YcfFqS+Y7FYIBaL0dnZyfi9e3p6YDabOS9RRZIkzGYz49keQFwWBJCX7J3t2QkEAvD5fFmPs7q6ilgsxtk5JQiCs+bVFHbu3ImlpSVMT0/nfA+uAwIOHToEhULBirMsGTQaDXp7e/PicVQP2gvOiA1YWVnBfffdh/r6elpNYISESy+9FG+99RZ+85vf8D2VTfjhD3+IxcVFVu7d39+PY8eOYXR0NKff22w2FBcXQyqV0rqeiUYz3/3udzE0NITdu3fndR+uodfrUVVVha985St5pROyhU9+8pMgCAIajYazMWdmZvDoo49iZWWFszH5wPz8PH77299Cr9cL1pDe09ODlZUVfPGLX+R7KknxwQ9+EC6XCzfccAPfU8kKbW1t0Gg0uOuuu/ieyjq8/fbbcLlctEtzlZeXw+FwCFJ5o4tDhw5hfHwcIyMjjN9bJBLB6/Xi61//OuP3ttvtAOjVm6euy5fPfuELX4DL5RJsFpJIJILBYMCvf/1rTg2BifjsZz8LgiBoRSgzhdnZWTzyyCNwOp2cjcklXnrpJYRCIbz44ou0rs+nQW82fRwSx8oFP/nJTzA7O0vrWoPBkHdkfzbPRP0ml3ES70FnrFyf6fvf/z4A0O6jkc9Y2bw/KtP5//v//r+cxsoFv/jFLzA5OUnr2uLiYojF4rz4wWc+8xmQJEkrq4cpCE03oLKGX331VVrXM9kgmg6oBu/ZnEWn04lwOJzzmARB4CMf+QiMRiPq6upyvg8fuPbaa2Gz2fDJT36S76lswpe+9CUQBMGKHW1ubg5OpxP79+9n/N7p8Kc//Qkejwfz8/OM35t6T/norucizwSA+++/Hy6XK+/eBNmActh++tOfzvkeXK0Hha997WsIhUKMlxBLh7m5OfzhD3/IuWoC13uJguCdESRJQiwW47rrruN7KlmDyuQQYmPA5uZm3Hjjjazc+8tf/jLKy8tRVVWV0+8PHjyYlRIYCoXw/PPP5zQWhUgkApFIVHBGSQC46qqrIJVKBWmQ3rJlCy699FJOx/zCF76AsrKygmhCng/kcjnkcjne85738D2VlLj66quhVCoF11ybglQqhVwux9atW/meStbYsWOH4HoodXZ2oqGhAV/5yldoXR+LxUCSJE6dOsXyzNhDS0sLSktL8dnPfpaV+99xxx2s8GqqtjHdpu3BYBCLi4trkTO5QCQSrdV/FSre/e53Q6FQQC6X8zL+1VdfjZtvvpnTMT//+c+jvLycU8Mgl9i+fTvq6urwpS99idb10WgUoVAoJwfj888/D5FIRDvD1uPx4PDhwzk5Pu69917ccssttK4lSRJyuTynHk5OpxNzc3O0FVwqkCiXCL1Dhw4BAG0F3u/3Y3p6Gm63O+uxPv3pT6O3txfV1dW0x3rppZeyHockSfT19dHOFLn88svR1NSEj370o1mPlSs++tGP4vbbb6d9vUKhwLPPPpvzeNdeey1uuummnH+fC4SmG+zevRu1tbW0ZYdwOLx2FrnA6dOnQZIkbaMjQRAAkFdfDop2chl1zRR27dolWF1827Zt6O7uZmVuH/vYx1BSUoKLL76Y8Xunw7Zt21BSUoIPf/jDjN9bIpHgHe94B+64446cfr+6uor5+XnaPJOSw7nimVNTUznxTCBu1zEajZwGw23ZsgXV1dW46qqrcr7HoUOHaPNguVwOiUSC5557Lufxdu7cybl95rOf/Syqq6tzdjq+/vrrAEBb//H5fBgfH4fX681pvDWQBQCCIPieQs4o5LnzBaVSSWazNQFkdX0qFPJaFfLcL+AC+EQhn51CnjtJkuS3v/1tEgD5wAMP8D2V8w6f+9znSADk448/Tuv6O++8kwRAHj58OOcxCYIgY7FYzr+/gAvgAl/60pdIAOTDDz+c9W/b29tJAOTq6iqt68vLy0kAZDgcznqsbBCLxUgApMlkyvq3p06dIgGQt9xyC63rDx48SAIgP/axj2U91oc//GESAPnSSy/Ruv7mm28mAZAWiyXrsbKFVCrNSdcIhUIkALKyspKFWXGPSCRCAiBLS0v5nsp5hY997GMkAPLgwYOcjPf73/+eBEB+4xvfoHX997//fRIAef/99+c1biHLtYU89wtgDseOHSMBkHv27KF1/TPPPEMCIO+9996sx/rABz5AAiBfffVVWtdff/31JAByaGgo67EKFdnKPx6PhwRANjY2sjwzYYHS8/r6+mhdf9VVV5EAyPHx8bzGFZEkx0XeLuACMuDo0aPw+/20PaBHjx6F1+vFNddcw/LMLuACLuACLoApxGIx/OY3v8FnPvMZWk3FLoA5BINB/PGPf8QnP/lJWtFy1PWf+tSnOJjdBVwAf4hGo/jtb3+Lz3zmM1lHkk5OTuL48eN473vfy8r1+eDgwYOoqqrKqYbxAw88gA984AO0y4c98MADeP/73591iU6v14v9+/fj3nvvpX39vn378IlPfCKrcXLBqVOnYLVac8pU++tf/4pLLrkE9fX1LMyMezzzzDOoq6tjpVfSBSRHOBzG73//e3zmM5/hZDyCIPDrX/8an/rUp2iVTaauvyDPXcAFAL/97W/xwQ9+kDbPzPZ6Ch6PBw899FBWPPOhhx7Cxz/+8azGKXQ8/vjj6O7uRmtrK+3re3p60NLSwvLMhAOXy4VHH30U//AP/0DrerfbjUceeSTvvXTBGXEBF3ABF3ABF3ABF3ABF3ABF3ABF3ABF3ABF3ABF3ABF3ABrIJeh2CG8bnPfQ6nTuReU5DC7e9+D+3a1LngoYcewn/9569AcNikJRtotFr89Gc/XxeZMjU1he9973t51e+67LLL8IUvfCHtNf/zP/+DP/zP/4AkiZzHAQCRSIyPf+IT+MhHPpLXfZKBJEl8+tOfxoDFnNPvyysq8Yc//lEwdZSPHz+Of/mXf4U/x8Y0iVAqFLjvvu/g8ssvX/d5MBjEF7/4Rd6ad25ER0cHfvjDH66LTvzv//5v/PlPf8p77yVCpVbjRz/+CS+9A7773e/i6Wezr+NMF2qVCj/64Q+wfft2xu/d19eHb37j6wgysCc3QiqT4atf+5e13jvp8OCDD+KB3/4347RaIpXiy//0z1nVUmYCBEHgH/7hHzA+Ps7aGDfccAO+9a1vbfrcbDbjq1/5Z/jyrQGZJdo6OvG73/2O13q7b731Fr7+f/6Nlf0MALqiIvzy/l+hqakJH//4xzEyOsbKOACg1Wjw85//DF1dXTn9PhaL4Utf+hJmZmYYnlnu2L59e9I9mwt+/etf48E//wVMRONUlpfjj3/8wzpZ4Qc/+AGeeOppMDJAEojFInzi3o/Tkp2WlpbwqU9+EiuO3Pt9bMQV77gKP/jBDxi7Xy6477778PzB3OvWbwSfcgAQrxf9rW9+AyEW+szt2n05fvKTnzB+XyHh9ddfx7f/73cQDIYYv7fRoMevf/1fOTfVXV5exsc/fi+WHQ6GZ3YWZaWl+N///R8YDAaQJInPfOYz6M9R/0kFtVqDH/7oR+vOyK9+9Ss8tH8fwHBso1giwec+/wXatckffPBB/PdvH0CMYE43KNJq8Ytf/BwdHR0Zr52bm8OnP/OPWKHZED0biAC8f+89rPWmYgMvvfQS/v2738urofVGSCQSfOHzn8Odd97J2D3pYHx8HJ///BfgzLHOPl3odTrcf/8v16KxnU4nvvGNb8DBIt1IBEEQeOutI+t6HSgUStTV1uZ1X7vdjkVr6ka3EokEDQ0NUCrp9VrKFw7HMqxLSyAhOjO+GA0N9VCpsstKSIQIwF133ZlX02s2MTAwgC9/+Z/g9fl4m0NFRTn++If1svLy8jLu/fjH4Vi2Mz+gSIQ9t9+Br371q3ndJhAI4CMf/jAWF5htlv6Oq6/B9773vYzX/fznP8ejf/sbo2NvhFarxc9/9jN0dnayOk4ycJ4Z4XK5YDAY0F2hQWdF7of+yJQbMaUe84tWBme3Htddew2O9x3CDV3CbL76yLEF/OAHP8C//uu/rn32s5/9DP/8z/8MY9cVOd3TtzCKmGcZ4XA4bZrljm1bMTM6gKtbTTmNQ5IkvKEY+qacqG5sxZ8e/HPG31RUVKC8vBxDQ0MIhTIrG6urq7juuuuwpaYI7eXZNXq1e0N4ZXgZr7zySl4Nc5jEN7/5TXz3u9/FRTfdk/e9Tj//MD732c/il7/85brPDx8+jN27d0PbeDFkRSV5j5MPgtZJBJbGsbi4iIqKirXPe7s7sTw3iStbctt7yfDI8UV885vfxH333cfYPelCV6QHNMUobtvByv2nX3sEX//61/Hd736X8Xt/5StfwU9/+lPcta2S8Xs/abHhtjvei4ceeijjtVfsvgyDp4/hunZm9+wz/TZce9Mt+Pvf/87ofTNhYmICzc3N2HHpLjTTTCnNBm+/dQTWxYWkzby+//3v4+tf/zruvJhZvkeQJDzBGIgkIsfsaggj9gCeffZZlJWVrX3e2NgIg8HA6DzS4V/+5V/w4x//GHdeXJb54ixBkiQePWXH/fffj9tuuw0NDQ2o7dqB0jp2UoCPP7sfP/7xj/GRj3wE8/P0BWiFQoHOzk689tpruPrqq9FVbUSNKXdZjSkML7owveyFz+fLOo09GVrbOzFrd6GkJzdZiULIacOy+VW8+uqreMc73rH2ucFUjLCsCIY2dpp1L596CRd1tuDokb6M1z7yyCN43/veh1t7y6GS02tYng6n5lwYtfkRi8V4dR7qtBqUKglsr81OvksEQZJwB2MgSRIvjjhx77334h//8R/T/qapqQmrq6tYzcLoaDAY0NDQgPHx8ZRNFH/yk59g3759jNBefziGUDRulB23BzG1GsSRI0dSNrnUarWorKzE6Ogo7TFEIhE6OzsRCoVoO87lcjk6OzsRi8UwMDCw1vQ2E2pra6HT6TA4OJiy2fcPf/hDPPzIX1F6Wf5NI0mSRCzgAUnEAJKE0/wSvv3tb6cMTKivr4fJZILVasXCwsKm71999VV8+ctfRtvl74JCw3wD5YB7FWN9B/Hss8/ipptuwvLyMkpLS3FRYwU6avPbT/5gGKFoPNDjueNj+MQnPrGuVNAde25D1LeKKzqYlQMPnprFpbvfgeeef57W9ZdfcSWOmwdRs/06hmZAYuzlR/DTn/4U//RP/5Tx6gcffBAf+tCHULP7Doil9JrJpkMsHEQsHDcKr46fQkmRCk/8/fGk1+r1ejQ1NeU0DkmSGB0dhY+mkbKmpgYmkwkDAwOIRqMpr7vvvvvwxJNPofta5krQjbz5DK68bCeee469AK5k+PWvf41//Md/RN2VdwJs8TySxMzrf8X999+Pz33ucwCAv//977jjjjtQ1LwNEgX7clgs5Id7/Dgu23ExmupqGLvv86/3YcUfheniGxi7Zz5wDbwBccSHLVfdktd9SJJE0OcBEYtiZvAk1HIxnn7qqZTXU3LA0NAQglkEHVRVVa2dObo8s66uDhqNBkNDQyBJEn/4wx/wy1/+Ettuzt+GlAs8KzaMvvXSJrvagQMHcPvtt+OdF9dBp8qPbhIkCbc/vBYDZJ5xICJS4PkXX0r5m8bGRqjVagwODqZ8t6dPn8ZHP/pRXNliQkWRIq85Ujg558b4cgAxGkGUdfX1iMRIbLvsHRmvpYNgIIBQcH3Q3aEXn8GXvvSlpA3h1Wo12traMDc3h+VlekFNxcXFtIM3OM+MoBb6S1fX4l1dxeu+C0YIKGX06gz+4pVZ/MniZ3x+iSAIAte0l+CXd29Z+ywYiUEpy6zM0b0uH7wyurrp4EQiESh0RnR+JbMBLxmsr+/H+P/+c8briFgM7+ouxQ/uiEeLRGPr5yGVrF/Hje/jz2/N46uPDQEAnAOD2LZtW8YxRSIRvva1r+FHP/pRxmsT8ZUbW3FDV3nKuSTDtMOPXT94hTbR5wIEQaC4qg7v/rf/yup3kVAQMoVy3WfLE/1Jn40SLNvu/Q+oK/mpk0cSMYjEEqyaX0b/zz+4SdglYgT2bCnHd25tT3ufbM5g37SHt7UmCAJN130AHbd/DrFwXDiRyNevVywc3PTZRqS6xjl6lLVnIwgC7ZV6/Pg9neve9cZ3n/g33XWxeU/QnjdBxHBTZyl+dld3xjGy2Rcf/eNJXvYFNebXvvFtXHv92frUwWAQSqUy6d8bv0uH3/7X/fjut/5PyrFLi1T4j/e0ZbwPxbPp8O5vPDWBv51KL8TcfPPN6/42FZfAZl2CRMIuL6VAEASay3T4j/eeffZgJL4W1PNlI6dsxDODcZ5Nre91//Av6Nh1/abrIqE4HdhIt7PBSN9z8Pl8qKmpQSQSyeq3X/va1/DjH/8YAPDTD+7E9kb+AjJiBAGJWIzHjk7iU79/g7HzSBAEqnbfgY7308+0SEZjfdYpvPZPl22aF0EQqLn2w2i4hX59bzp0nsLA77+CmGeY1rXU3H7xvh5oFHGxPxeaTGHf0Tn80yP9tK9nCwRB4APby/GZK5IbTTae1WRn9yuPj+KF4bN06Xe/+x1+97vfpR3XVFySU5bJt7/9bXznO99Je01ziWoT/cmG3gQjBGZWg7jm/hObvtu5c2fa39Y3NGJ6apL2WADwznfdggHLaUzPzNL+zbe+9S1MTIzjQRpBSBTUahUu230FXnwhvWFaVd2Opg//CGJZ6nNERIJr3yf+PxGLL/4Ppv7yzXWffec730m5fga9Hv0DA6irq0trXLjpCz9GUWnVps+jZ2Q/Kc3zvxGe5UX88n3da2d9TYa460rcvCMe0BAMR6GUp1b7k33fP23DO766/jw88MADeOCBB9b+lknE+MwN3fjGe1IH1AQjUShl2ZkcPvnbV+Ah6Ge7xggCtZfciHd88T9oXR8NBzO+77mjB7OQQ+PX7fjsLyGWymn9BkhO94NOG5759MXrMsDnXUirKz/xxBO49dZbaY9L4S9/+Qs++MEP0r5eoZDj4x+/F//1X5l10aqObbidps5KZz0e/c4/gCC4zdoF4msrlSmw43O/yvq31PrS4e8Lbz21br9RslvHlx6EVK3Peuxs4V8YwclvXoMf/NsXsWv7RUmvCQZDUCozG2QTr7v2rnsxJm1ExyfOnk0iHISYBr2je102sPz0A6gv1eGj//c3aa9LZj9J/Ozwk3/B//vOWRnPjfRnFAA+/elP4ze/ST/uRkilUuzZswd/yyI6XqvR4NJdl+GlF19Y+0ypKcL7vvHrlL9J9rxMwTE/iZ/cvS2prAwAP9y7C5XGsz2mNvKMxL9T8ZOvPPgm/to3seHTQNo1KSuvwJVX7Majj2Z+t9/d04G2cnrVUjLJ1Q8emcNX/zZA614EQeCWuz6MT34lub4SCgahoKn/WxfmcPvO1qRBHb/4xS/wi1/8IunvvvnNb+Lf//3faY1B4ejRo9ixI3OgLS9lmhLRN+WC1RPGrd0lODHngUgEKKRihKMEyovkWHSF0VishFYhgWXRB28ohuvbmYuIzgR3MII3x1cgFgMtpVrMrvgxZPVCIhLhvduqMGz1wuYJYdEVxE1dZRixeeHwhlFfrEY0RmLZG8KUw4/dzcUIRmIo1SkwuxJAnUkFqViE2dUAhpa8UMjEqCxSIhSNwReKodKghEYuwak5N8p0chAkIBGL4PCF0VSihiuQOhoBAFzDfYi4rCi59HbY+x6DwlSFqM8JkiShrm6Hf34YYpkCClMVZPpSiCVyuIbfRMC68RCnxpQjgEdPLKKnSgexSLQu4rXWqMKI1YdQjIBJLUMoSkCnlEIqFkEtl+DQxCokMgW6/i0ecbx6+gXEAl7oWnZAJJUDBIFYyI+oxwFd6yXwzw1i/H++jLGxMVTolfjDPa14YWQV3lAMO2p1kEtEIEjAH4nB4Yvikjod/nLMiv/3djwt8PD4CmyeIG7dUonjM06IIIJSJkYoSqCiSIEFVxBNJRpIxCLMrPgxvMS9sEMXtqkh+J0OiMRilNS1wj41jNKGdtinhkGSJLSmMqzMT0AqVyAWiUBfXoOAexWGynqIJRKEfB4EfelTTQO2KUTcdkAkhrqiBc6hNxAL+iFVaSGSyKAoqYVMVwzPxHHEgj6oyhshkkgh15dCqjXBPXIEInGcCBOREGIhH+SGCojEEpCxCCJeJ6QqLWLhAORFpSBjUYjEEhDRsxkv/sXUJUwmlv3464lF9Fbp4PCGEY6RsHlCaCpRIxIjseQOorFYDVcwCk8wijKdAuU6OaQSMdRyCY7PulBjUMIZiGDJxXxKf7YIrlixPHQEErkKrplByNQ66Os64Z6Nn1OloRQRvxuBlSVoKxrgXZpG7e7bsTwUj4qVa42IBLyQqXVQGsoglsqxPHQEABgtZ5UKA4teRGIEDGoZFl0h2Dwh3NhZiollP8IxAkqpGHqVDKv+CAiShFouwYIzCIIE6k0qlOrkkEvF6JtYhTMQRUOxCg5fGNmYQG2eEP5+agm39pbj1JwbM6sBqOUSlGjkUMok8IWjkIrF0CkkCMUI+MMxLLlCuKLFBLsnjPIiBSaW/VhyBbGz0YjZ1QBiBIn849tyx9TEOPrefANGowkL83OwLi3i5lv2YHx0BOFIGAqFEgajEasOBwiSgFqtwfzcLAiCQENjE8rKKyCTyzFgOQ3H8jIqKqsQi0UxPJhZABqx+eHwRSAWAS2laozY/GgrU+OFkRXUGZTQq6QIRQnIpWKQJAmVTAKbJ4wtVVq8MraKMp0c0RiJcUcA92wtx+EpF9S1XWj+2M8AZKb5jrefxMLTv0IoFGIkEj4bPGFZRjhKoKdSgwhBwheKYUuVFk8NOFBrUGBLlRanFrwQAdAqJGgsVuHUvBcqmRiRGInVQAT1RmV8X0tEmF4JwqCSbiofsTI/ialTh6HUFmF+5DREIhG0xlJI5UqIxCK47YsQiSWo77kE3tVlhPxe2GdGYaysA0kQMFU1YHHMgur2iyGRSiFXabAwchoypQoEQcDr9SISiaBh733QtV6a8Z0DwMgvP7wuynl62YtQhIjvgwo93hhegi8UgU4ph1QiQl2JFiVaJd6eXIYvFEFTWRGkYhHKilQwaRU4MeWAOxCGRByPJozGSJTplQhFCMikYkzbPWvRUIFwDL5QBDsaS7HiC2F62YvOKgN8oSgmbcmjyfNB2LMCx+CbEInE0FS1wDs/Am11G7zzIwAJKAyl8C1NQiJTgIhGoCquRti3CnVpHUQSKaIBD1aG30p5f79tCmH3MlaH+yAvKgVAIrRqhUgkhrqyGSBJBOwzMLReAt/SGCRyFaJBHyQyJWKRIAwt2+GePA2FqRKxkA8ShQau8eNQlzcg4nNl/bxPW6wQiUTorSqCwxeGWi6BPxxDkUqKaIyEPxxDT5UOw1YvKvVKyCRizK4GMO3wQyYRo6NCC18oinE7f2n+G2H1hPHWtBsxgozzfk8Y7mAU22p0UMnFCIQJzDqDaCuLywYVOjnsvgjKtTJMOoIYtAWgbdyKxg/FS05lOiPLhx/F4vNxQ2z3vz4OsVyZ8TdkJATLD27H2Fhcpnn2U71J5dc/HF3Cgju8Ti86veBFkTIuS9k8EcRIEgOLPnzmimqcnPdCIY07KiqK5Bix+aFTSjC4FA/Uavvs76AorqF17s333Qz7sgMll70XlTd8gta7mH3sx5iencXc/AI+dmkF7t5amlEu/8TDY5ibm8PczAwurdPhvnc2ZPzNy2NO/OjFWUzPzMKw5TrU3vHVpPOzvvQHhJ1L8M0OxGVKiQxkNAyxXAVFcTX8CyMAAKlKB7FcBSIUQDTogX92APqOeMnSiG8VqrJGhOwz0ClleOQjHRnn9+zQCn7x6jympqYQi8Xw/Vsasa1Gu+535gUv/uP1BdinBuFamoFCU4Sl0VNxWmAsidN8kRie5QWIxRJUd18C3+oywgEvHLOjMJTXgSQJGCobYBu3oKItTvNlSg3mB98GEUuuEw7O2hEIR7BnVwfGFhx4e3QenbWlODmxhAqjFrdf1ok/v3QKOrUCxUUqqOQyDM8to6akCLWlepgn4yVb2z//v5AbK5PuiZk//jPmV3x4/OgE9mxvRN+oFXqNHC5fGE5/COV6NVRyCUgALl8YXTVGTNrdCIRikEpFCEViqC3WQSYRQa2QYWTRCbFIhGA4vZ6bDIFVGyZefxwNu2+DbfgoAEBTXAXn7Ahi4SDkmiKoiyvhnBmGpqQKkYAXKmMZVGfkZuvgEUgVagAkIv7ceM7y4BGEPQ5U77oNjpG3EVy1wtDYCyISQsizgpBrGaaWrXBOWSCSSKEyVSHksiPotMLYdBGioQDCnhWQJIEf39aELVWajHvw5v82Y25uLqf5zs3NQSwS4elP9mQc59CkG//+3DQmJyehrm5H88fjxuVk+2Lh4H8jFFhB/0t/Q/e178Ghff8BXXEF9GU1EEulCHpcMNU2Y3lqGEQsCo2xFCGfB9FwEBpTGaQyOTSmMqj1xZg4+jLEEt5NVlgeOIyg07q2tgCgMlUi5FqGe3YIprYdCLsdCKwuQSJXQiSRQl1Sg2jAC5KIQaYugkyjh882DU1ZHUgihojfCyISgt8+AyKF8y2wOAYiGoZIJIaqsgWuwTcQC/khUcZ1cmVpLWTaMzp5yAdVWSOQoJP75wbjgX4QgSRiCLusEMvi84u4l6EsrYdILIH/jA1oanYe4UgUYrEI7c2NWLLZseqKnwepVIJoNAaZTIqdW3vx8BMHUVNZjlWnGyRJoru9GcFQGNFoDHK5DI5VJ1weH6IKN5xDh9eeIbg8A//cECCWoHz3e+GbH0bYZUN4ZRHF226Cf34UEY8DytI6kLEowu5lBO1T0HfsBhEOQq4vQ3B5FsrSWpCxGIhIEBHvKiJuB6SaIsSCPohlSsh0pjP2hTACi+OQFZUg4l2FVxrG2889ih03vhdvP/coiorLIVMoYZsZR237FlinR6HRm+C0LWDHTXfi+T/9BwyllSivb4VGb4JEKoNtZgxSiQhP3tsDAGnPz0XVWuz5nQWTk5NQFBWj7UtxZ3wmPusdP4bJP38dM1OT2FWvw/+9OTPPfGnUiR+/NIupmRkYL7oBNbf/M2xvPATX0cdgnRyCz+mASCxCaV0bPI4lBDxxeVIskYCIxSCRSlHbfQlOv/Ao9OXV8LudAEmivLED4aAfQa8b4aAP1W0XwbNiQ1FJBRZGTkMskYKIRREJh6DRmyBXaRD0urG6NAudKb1GP7SwiullL4waBRZWfbC6/Lj5ojqMW92IxGJQyCTQqxVY9YbidgSFFAsrPhAkifpSHcqKVJh1eKGp70XTR35C69063vo7Fp79NeZmpnF5YxG+eWN90ndrXvTip6/M49ScC4NLXtzaW463p50AgCq9EiM2L4JRAkVKKSqK4n9X6ZXwhqIo0yrWbBxHJlehkkuglIox6cguoH7VYcfxw69BJBajoaUdy9YleFzx7FyJVIpYNAqpTIaebTvx/N8fRllVDdzOVYAk0dTejYnhfsgVSnjccVtw88d+BnVtFy35cPDH713Lmv3f97ejUidPuwebSlT4wJ8GMTc3VxjOiF0NcU/v/x5ZAEECO+uKoJKJIdPI4AnFEIgQmHQEoVNK1q7lEkVKGXY3x50fdk8IW+sM2FpnWPu7q1KHrkrd2t+7GuPXugIRqGQS/P1U3KitkIpRpJSCIEnoVVIsuILwhaK4pr0UlzQYU47fXZVbOq++fRcAIOyygyRi8E1bYOi9GiKxFNGAG8qSWoSdVkT9bsSCXuiat8N08Y2IeFZoj9FQrMJ7t1auPatcKsZfjy/CGYjiouooKg1KEAQJTygKmyeEJXcIWoUEuxqNaCxWQ6ElEVqehUxXDE1NF4hIEGHHPNS1ndDUdkMklsIz/jaiHgfE8rN1BEu0Csw6Q9jdqIczEEUwSmDeFUZnhRqX1McNIYNWH1pKz/7msjNr+D9vTIEggV1NRihlEhRLxPCEopCKRTg6tQqTRo7LW4pRomUmDYsNlDWcrV3qXbHBZZvDjPkwuq6+AwAQDYegL6uBd8WKcMALIhZFw8UJvSFKKqHMkCauKmtYy4wIu+wovXRzanrYZUfJ9rP1/G1v/hWesWMw9l4NVWUrQMQQDXgQdlohVeuhb0sflbcRRDh1CmNTiRp3ntl7jcVqyM8o5A++NQd3MIqbOkuhkIlh0sjhCUbhC8dwcs6NGqMS2+sMm8r5fO+57KIBmYa6tAaGhh5AJIKxKZ6JFfa5ULH1Wsy++Xd4FsZRcfE1a8KrWKrA/JGnIJbKINeZoK/bXOOvcms8Vd3yh9yiuLPBtjo9fKEoRCIR2s9EDdg9IYwv++AKRLG7yYhwlECJNr4e3lAMaoUEGrkU7RVnowyu7zwrrBRr6EeWAUCZToHbL6qA3RPCzkYjdjbG6WowEsMjxxcRihK4pq0YgUgMBqUMMrEYUrEYhydWUaKVo7tKhxLt2THLixRrBlS+0NbRiZ4tF0MsFqO9M17732a1YvulO/HwX/4Eu92OK95xNcorKxGLxeBxu6FUKuH1euFyOdHRFc8U2XnZ+t4wJ469nXnssrMOALs3jMsa9bB7w7h7a/m66/560oZlXwS7G4pQZ1Ri1O5HQ7EKVncYKpkYjSYlVgMRNJiUsMoqoa3vhePY09C370bU51xH94taL4FILIVvbhDq6sw1mtnCbT1x+uAKRKGSiSGXimH3hnHnxWX460kb+qZcuLWnFEqpCDESOD7nQThKwB2MwqSW4caO9Vmf3ZXxPS7ZUPqwrKEdla29EInFqGjuXvfd8Wf3I+BxoWXH1fC7V6HSGSASi1Fc3QifywF1kQkaQzG2XHvHut+17IinPyeWWdTU9SC0PJvxnYedi1BsKM9XX6Jdy4ywuQO4Y0fDpvdlcwdw69az6bgP903g7Qk7ru2uRl2JFjGCgCcYwYo3BIKMIhwlsLstvo8uaUqtoFzWenavWV3MZ8LKdSYUd+4GAIRcdhR37sbUc/8DEARMXbtBxgjoajoQDXgQC/oQWJ6DXF8CVcmZSHxjBUSS1O5KdVkDSJJE+SW3Ihr0QSQSQdJ+9lwtvPEIoj4X/EWlUBbXAAQBkViKWMiHsMsO97RlU5knamyZRg94suvt9K6ecmgU0jjfLFEjEiPw+MklBCIxXNNeghKtHM5AJO4sdgXhC8Vg0sjw3m3rI7mHrMIJ1qgxKNFdoYFYhLUSVH89acOJeQ+2VmtRqpGjRBvXJ5a9Edg8YWgVEnRXaFCilaNIKYVcT48ueSdPQFV9NiNT29CL1dMvpf2NZ/xtFHXsXvuNWi5NKb9W6xVY8kTWdB27N4xL6+Oy2oovAvOCF+5gDDd3FcOy5I0/VzAGqyeMEZsPdSYVeiq1WPHFjbja+i3wTp3K+Ez6rishPyMTqiqaab0Lz/jbUJbWA/Z4hkhbmYqWXF6mOytb1xgyy/In5r1I5MLK0oaU8xNJpIBIDF3TNsSCPkAkhkQR1wMi7mXo2y+D/c2/wutcguGi6yHVGiFRF0GqLkJodQHG3mvXxhFJZDBo6M2vs1yDRPRWaTb9zuqNRzhXtl0MqVwBkUiMsqb1/XzMzz2EoNeJhu1XI+B2QqUzQCyRQF9eC4W2CEQ0CrW+GJ1nZH0KLTtvgGd5Men56Kwtxc07WmFzetHTUI6ehjhNnbI5sbTqxZHhWVy1pQExgoTHH4LN6UNrdTGkEjEayo0YnXes7SXPxPGke0IsV6DapMEdlzTB5g5gd3vFujk8fHgMLn8Y1/ZUQ1+ixYTNDblUAg8RgUIkgwgEGkrPllrb2RKfo1IuRXY5fYDKWIamK+9AYNWGiq64Hhx0OxB0ORD2u6GragJJEChu6kU44AECwOr0EGKRCEqae1HLQImnks6dEEvlCDrtKOnYCSIWRTTgwdLxFxD2uVB+0TUIe50oqu1ENOhDLOhD0GmFurgaRbVxucc5Ge/1kWwvbdyDb0658pZVDRo5rfPbU3l2rysMFWlphcJYAYUnhu5r3wPvig2X711fS//Us3+BbaIfHe+4DVK5EgQRg1ztgW/FCs/yIrqveffate1XxHXN08/tz+s580VJ12UAsLa2ABByO6AuqYGpNR59PfPaI2vrLJErEHI5IJErEfY6oa+Pn3l1SfWme5tat+H4b76cdFxVZctaZkTYZUdJCp28ePu7YHvzr/BOnYKh52qABCIuO8QyBcJOK8hoBFKtCSWX7Ek6jkQd5wMNtdVrmRFWuwO9nckzpX/9x/0gCBLbe7tQV12JcDiMBasdkWgU4XAEN18TL4Op12lglxbB0HHZ2lyLmraiqGnr2t/a2i6gtmvtb317/P1GfS6IFSr4jxwAAIhlCkjVRSAJAlJ1EfzzIyCiYShMVTCcsX2lAjXewnMPQGvUYceN74XbYcOOG8+WErPNjMH8xrPoveJmKDVamCpqMHLsddR3boXX6QARi6G0phEAIFeqUKSK84lijQxdFZp1Z6e7QgOpWIS3Zz3rAnYVRcW0dSFNXc/a76pp8MxBqw8x4uxYyrJ6aOt7sfjcbwGIUN54VrfyrNhQ2XL2/onwrNhAEDEsjJjRtvM6iCVSBH1uiEQiELEoQn4vbNMjaN52JQCgbWd62umYT29nuai+BHKpBGKRCO1VhvhauAMYt7rg8odxeXsFIpEYSoqU8AQicHiCMGmVCISj6KiK6/sSsRhyYxVtGUadIM/Vpnm3W2vi/OmiGj3ayrVrdgYAcPjCcHjDcIeiaCw2gSBI9Fbp4AnGoJCK4fCHEY4R6K0uwnUdZ/WdgcXs5GhjcelamSaH3YrWrt513z/91z/D43KCBHDRpbtBxGLQ6IqwYrfieN9rqK5rxI7Lr0b/iTNO+vpeBG1TGfcfGQ1BoTubBLClUovjc560e/DIVHbBUrw7I54ecKBYLUVVkQLBKIG+aTc6y9XortTCpJbCH4ph0ROGRiHB2LIf4SiJxmJ2UogyoVSnoP33G2MOFGvl0CmlkEvEeGnYjq4KHbqrimBSyzGw5IFGIcXhiRUUKaXQKqSoL2Y++tMzdhTKklpEVTr4ZiwIu+zQ1HRCU9cNZWk9fHODiLiX4Rk/Bl1zbg1u9WeiGp+22FBrVEKrjGLBHcKpeTc6K7TortKh3qTCiVk3xBtqLRYnGLOTwdBzNQDEvecJ2FjiayMuqSvC6YWzB/0p8xKKNXJUGVQIRWM4PLGCrsqi+Hpo5Dg6vQqtUgqVTALzvAtFSj7joeljxnIEhoo6KNQ6LI2ehnfFhvLmblS09MJYVY/5weNw2eYRi4QhlspQ1ZY87TId5PrkxqLEz5ePPQ1FSS0kKh28CftMe2afecbfhvXQw1CYqmHovDzp/XIBtfeeslhRrJGjRCOHTiHFS8MOdFVq0V2pg0kjWyP6oSiBUZsPdm8IXRU6GNTCWWepcr1CK9foMX/kKahLayFT6eCcMiPotENf1wlDQw805fVYGTuBaNAH+8CbUOhLUVTNfH8BuqDKf1B4a9qJWqMKOkUEozYfbJ7w2ppIJSIcnXLC7vEjEiOwo97A2DwS6TC1L0q1coSixNq+qDGoUKKTY2AxbuiQScQYs/vQUqpJfWOekNjoCwDKysvx5ON/Q219A7S6IkyMj6LvzTfQ1dOL3i0Xo76xCSfefguT42OQSCTYcWl64ZwOSs84aUq16x1ET5/JFNApJJhcCeKtGU+cfxerUG9U4MScFyv+CBQr6x2Lmeh+UeslsDtyi/JjEnrV2T19dNqNYo0MOoUEDcUqvDy6gs5yDbortbi4WosTc15EiRhkEhEsC170VNFL5VWok18nV2lgrKzD8tw4ouEgPA4bKlu6UdV2ESRSCayTw1AXpQ5k2Ag67xwArE/fn/KasqLkzQU3fq5WSFFXrMHIoguhaAw2VwBdNUZsqTVBKhHj+NQynjfP4YZe5moS5wuFvhRLR59CUV0XIl4nfPOjCLls0NV2oaghHhxhP/USQqtWuKbM0Df0Zr7pmfsCm+m79ehTUJXWIqLSIbgyD/fkCWhru6Crj48FAEHHPNCSm1yWDhTffH7QjuYyDZz+CCzzbti8YXRVaNFddcbQOuuCLxSDed6N3mrm69wzBY3ibCr8Gk0KSDDrDOH4nHdNp6g3KvDmpBsSkQjHZj2bek1kOiP6risRfH1fVr8x9FwNIrq+gWsq+fXZwZV1hvdEets35UadUQlnIArLohd2b+Tsc5nitHbZG8bR6fVZr3SeKRnoPNfqqRfWfUZHLgfW96/J9Jsrm/QIRzdndyabX2BpHOGVeL8GyYbz5h59CzJdMSQqHRRSGdwDb6wFPsm0JkQ9K7Adehjqmk6IZWdlCDrze7J/c3PZjb9TSMTYfyLeoFOuSk7zZUo19BV1WJkbRywchHfFhrKmblR1bIdYIsHC0HGosqD5iSgznB3ziSNDqCvVQ6dSYMHhwcnxRXTVlaG3oRz15QYc6p+BUbtZz065J0RnHd8becGTx6dQV6LFqi8E84wDNncAXdUmNJUVoaFUh0PDi7jpotyagqeDyni279OS5TB0FfUIeVbhmh2Bf9UGU0MXipt6oCtvgHN2GP6VJaCZHk2nC6UhTvvFEinkWiMkCjUMpbXwzI+CiATX5Hl9Qw+K6ruwPHg46X0y7cEbGaoWQef8vjGx2ciUal/Y33wUFEHTms6ux+BrT0BjKIFSa4BUrsTYkRdQ3tyN8pZeqPUm+FaskMoUmD71Juov2p303nyDWlsgblgGgPkjT0FRVBzPvJLKYD350tr6iiRShIaPYvrVh6GrboGpJXNp6lRIp5M7jj0dt/n4Emw+Z+icsqQenskTiPpW4RruWwtazYTy0tT7orK8FCUmI4bGJhEMhWC1O9DT0YqtPZ2QSiR464QZl27dfK42PkO6v52Dh87Qbi1EUhlWT78cf6a6OO0Ou+xxA3kkCP/CKNRV2enBRcXre8TJlWo0bdkJ+9wEIqEg3A4bqlu7UdO+BRKJFFP9x3Ds+b9h+w1nexNlOjtXtxgQidHjY+vm1noJPGPrg8fonFNnYHOGjbq6Hb6BV9Z9pjOVbbou8TtKD7FNjyTVQxbH+jF+/A00b8uv9xoF7QbbW1mRKq5PlGgxvexBKBJb4yFb6uJO80PDyR3xdGQY+5uPrPss1bs9OrM+Sy7RztA3uYq6YjWcfsrWEQ9Up2wdA4seLLlD6N3sf8wZxaXrgwJffvpxVNbWQ6Mrgm1hDoMnj6Glswdt3Rehuq4RIPvgXNlcWpSuXrgRmfYg5byhC96dERkPVb1wFaB0uKW3Iu33l6bJhmASuW60XPCunvRNP5M1HHYNH4Z/dgCqylYQIT+K2ndBJJYitLoAiUJzJmVvYZMyd3jKhYElP1pLVfCHCexqOOsRbi9TY9IRxHSCASzTelzTvp4RTmeZPsUXut5xW9rvm7Zz03y7JMM+M55xKrGFW3rK035/aYNh3d+tZcIzOidD9c70zbXKe5MbEoSATGtyDcPNpnOZw8Z9USi49Y70DTrfcQ1TDRzTI6Ohptlw9o9DcSNUZppvhUQhvPP5ru70+3XdszKAnqvS0/a6bvoN730z/fDNWNK+89DyLDS13ZlvRgOJWRLJ8A6GG50yhYpL0tPbyl3JowlzQXmGsQxtlzI2Virc0puePl7Zmv58CxEZDXcdyQ13GemScwkS1WYFi44MS0TWl4JMJb9aPWcbL256rm76tPa1MWdez0XnmbChnMiEI4Df9y2mlclDG5wKdm8k6W+GbHH5u71UhbdnPZjfUEoz1fyifidS9LbO2SmTaq2GbH5o5GKEoyTG7OsbQZoXfTgx5133G6r3UDp0ZJDnG7cxI8/ftjN91iHVY2IjUr13MpY6f+HWbQ1px2LDEbERDbvT91Eoa2fe4ZsMmeT5ym2bm/ueXvDi6Ixn0/6bXo2Xflz2Rjadq1xBR68esq7XjdPRCiLkR7ID2Zlhnzdfyo38yjQy6msXX8P6HDIaYFPQuVxxx83p1yqZIyJblOxI/0ym3qvzHiMRW69NL+d1XLp5vFRnZ94VQowgUWdU4vCGaPFcdKFUPHPQ6oNELEKFTo5hmx8LSXime+xo0j4B6ZBJD2nYkn+wWybkwkNykWGANPzemro0aWY7A/v23mvedUfa73ddvZm3eKdOwT18OO3+I4I+yI3r9bVjsx4secJp+YTdG940Xjrw5owYtvmx6A6htUQNfySGXQ16SMXA4JIfaoUE/Ys+mNRSxAgSUokIVzQZMGzzwx+Ob56NwqlQ8Ob4CgYW3Wgt08IfjuGyJhOkYhEGljyQikWYWPbDqJaBJElc1mTCqTk3pBIRJCIRtjMYHUzBNXwY/pkBqKpaEQv5oT+z2eJlMNoRWBxDNBCPpFIU5xeleHhiFf2LXrSWqeEPE7isyRAnxs4gnIEoqvUKTDoCa53oQ14XQo55ELEofDMWKEpq4R4/BiLkR9TvhlimgFimgEgiRdA+vTbOsjeEeWcI0RgBy6IPtQYFjs244Y8QWHKHMWLzQwQRxpbjCsLQogfTjgBayzVn1qT4zLwC8IVi8IWjiBIkLm0w4viMExq5FIuuQNJnFAqmTh7C0rgFpfVtCAf8aLj4coglEiyOmuN1Zx2LaN15A+YHj0EqV8C5NAtjVQNqu5lzPlFwDR+G78weI87sMYjjaybTGhB22hCwTkKuL4XcWMloY+w3J1YwsOhFa9mZtW00nvFEe+PnzeFHU7EaK/4wRAC21uoxavdBK5fCd4aWCNUgbR94E86pARTVtCIa9KO06zKIJFIEVhYRctkRcCyi6pKb4Zzqj5cnAOCcPI3mGz/K78SRsC6lGvgjZ9dl0RXC6Xk3ZGIRrusoQf+iF4uuILqrdIxnJaSaA7U3ZlcD0MgluKzJiGGrD6v+MNrKtKg1JY/+FgoOvf4q+s2n0dbeAb/Ph91XXgWJVIqlhXmsrMQjNLt7L8Lxo/GeIZFIBK3tHairb2B0HocnXRiw+jbx8CGrH5EYiRV/BFe3GHF8zoPKoniEb2BpAt6p0/ESbn4XxAo1nAOvAyIxws4lyPVlEEmkCLvs8E4cY3S++eDwpAsDS76zwlfj2WeNESSW3GFABFzfZsKwzQ+7N4yuCg1qjblncU6cOITFMQvKGuI0vvHiyyGRSuCyL0KmUGJlYRpaY+m6lOtUCDnmIDOUp+ezUjmcQ28isLIIYHPZN7p4c8SK/rkVtFbo4Q9Hsbu1HFKJGHMrPrw1bsP1PdVYcgagU8nQWsF96c1UcAy+Cc/0ALTVcVnJ1HkZRGIp/LZpRAMeqIqr4Rw/AZnWiIhnBbr6bmircudlK4NvwjszAM0Z2cx4Zjzv7AA0NR3wL8T7C0jVOmiyjPSjg7is6jnLO8/IqovuIOyeMOyeEK7vLEX/ogehCIFKvRItBeDET0WXpldD8AajsHojuK7ViGGbf60XQ2BxjB5dmumHZ6RvbSz74b8hGvBk/F3oTLQ+APjD0ZTyq90bWVfKYd0zLfnQWqqGPxxboz9L7jDs3gi84RguayjC2zMe1BjORuwtv/V3QCyh9VxhnxtSbRHcY0chkinp0ejp0zCdSehedEVQqZOnlcldwSgWnAFQ+bkOXySlHK+WizFi9YMEiYGls4YA7/RpyIurk84vtLIIYLNhNpVhYk0PWhpHxGWDSCoDCALK8ob47/zpdQ21XAwR4kp4IuZWQyjRSNf9xkyjJMP0qUOwjVtQXNeGSNCPuovicr1toh+lDV1wzI6CJGKo6c7dUXloYBqWKRvaakrgD4ZxeVcdJBIxhmbtiMYI1JUZMGd3weUPoaHcsPY7+1uPAxAlfe8bnW2JeHN4Kc4PKvXwh6LY3VYBqUSMwflVVBnVWHIGoFFK18pssIFFy5tYmeyHoaYVkZAflT27IRJLsXpGdlabKrAy1Q+pXAl1cSUMNexkGNsH3oRregC6MzympDMu07unB1BU14HVidMQS2Vr5X8AYM4ZglElTbv/XMHourIsucDpC9PSqxP3sd82mZZuRjwOKDf0b50+eQjWcQtK6tsQDvpRv7bHB2CfGkL3Ne/GwtCJ+P1dDpS39KKkjr+Mb7pIubazQyCiYWjK6uGZi/esIYgotBWN0OUhP6RCIq1LtPmEnEsQy5SIuO2IeJYhVRsg1Rrz0sdf6zsG8+AI2lsa4fcHcOWu7ZBKJOgfHoNWo0YgGEIsFkvZCJsOnEOH4Zvth5p6no64nBReXYRr9ChKL70N7rFjIM/0zCGJKLS13VCW1uY03sixNzA3akFlYxtCAT9at10OiUSKVdsCYtEIIqEgvE4HSmsaUdEQL13lDqQ/O2q5GCfnvIgknNGQ20HL/hULehGynbV/rWTgmUNLcZ7Zn8gzp05DZqwCGUvejyQVUukgSxODUKg0CAcD8LscMFU1oKyhPfMNc0S2PCSwRFOec9nhGT3b823A6sfpeS9iZLxEr1omxuvjTvjDMQxaUwcox20NHrSWajfYGjzwhWLYUW/A4JIHRrWctUDY44dfw+iAGQ2t7Qj6/di660pIpFKMDZoRi0ZRXd+EmYk4/XGtxsvxh5bnIFEXZbS/+ofeRNBlXxtr1hWEiERaPjGXpY2eN2cEQZDYeSbrwW4L4+ETVsQIEkVKCSqLFFDLxVjxRxAlSIgAPHrKhjKtHBVnjBrOQAQA+7XQ551BmOfTN/tNhE4pxc5GqseEB/vfnos/l0qGqiIlFFIxlr1hRGMEHj+1CJBAWZECMok4q3EAwOlLvtghzypWTj4HAAhYJxD1OhFyLkFhqoLj+DMgwkHEgl54xo/FN5tYAiIWgWf0KEJZlMaYWQ3CvHA2dWneFUSMING/6EWtUYljMy74wzEsukPQyCUYt/tAAlhyB0GQJGKREMZ//8XUA2yASCRCS0sLHn00iC8+Np75B2cQI4GdTUYML3mw7A1jwu5DnUmNUDQGhVQC/xlnxOMn4w6IMp0Cq75sK5VyA59rFYujp6HQ6FC/5TLYJofgc9qxMj8OQ0U9ouEQpHIFxBIpht54ClK5EmKJFGKpFPNDxyGVxxVVz+rmdK1EuAbfQHg1eerbRvgXxxH1u+AeOQJFcTVWzC+DCAcQDXgglsrX9ljQPg33+HEUNdNLT3UlKP0bMe0IwDzvhk4hxc4zzgS7J4T9b88jRpIoUkpRpVdCIRFh1OZF9IwQ8Ey/DWU6BdQyCVQyMRy+MMzzbqx4+XVuehbGsHqmRiwFqUoHudYA18wgiFgMRCwKqUIN/8oCFFojCJLA4omXIJZIEYuGQURC0JQ3bLpPwE2/D0wucHhDm2iXTiGFQSXF4JIHMYJENEas1SGXS8QIhkk8fmoJIIFSnRwLziAC4fWCktUdzKqB9ezqelpN7Y1hqzfh3KsQihJQSMUIRmLwBqNr8zCqZZhZDZzhLXEsuIKoz+mtMAPL6VMoKlpvsNXpimAwGDFgsSAWiyISjUKt1mBxYQ5GUzH8fh+WFhchlUoRDofh83qhUCrhXF1du8fI0GDacV3+ECwZjCc6pQQGlRRDtnh90ihBQi0TY9EdgkwiRpQgccBiBwnA6glBLhEhaJvEzMP30X5+U3EJFApu+/es+EKwLKx/dp1Cgp31RRi2+eHwhTHpCKDWqEAoQkAhEyMUJeLPa44/r1EtxexqEK7A+gac/g0NORdHzVDpDJvmYJ0chHd1GSQRQ8DrhlypxtixVyESieC2L0FXUg6xRAr7zBjmhk6iorlr0z18bie0Wi1kMhkWn/vvrN5BS8tZBfXQsBW+EP1GomNL8fquR8ZtqDFp8GL/AoKRKKyuAFRyKQ4cmwYJQC4VY9FJPwPxrXF75ouyhN86BddUnF4GHAsgiChc0xaoS+uwOnoMsZAfEZ8bYpkcIZcdIqkMYe8KYpEQAisLiIUD8FunUt7ftzgG95R50+feuWGE3ctnmle6IVGo4bC8DpFIjKBzEcFVK0QSKchoGCGXHbEk/ZMCy7NAlkfj9Lwb2jPl9HRKKQwqGQaXvAnnV4IFd5xGx2XUJYAkz9DoAAKROI0eXhJOz4hRu38drcqGLsklIqilIgQWRrKmSyuOZUz88atZzZU6V+nkV5NampT+GFRSDFlTP9MT5mWQAJa9YahkcQvgzF+/l9X8SkuKMW1+GS7zy7R/U/+uW+BzreLJgVk8ObC5XFEy1NTUIBwO4ZXXXsd9z83Q+o1GrUZDfS3Gnn8e3rGjKa+T6cvgm7as+0yq1EGqNsA/NwSSiIGMRSFWqBFeWUTEZQcR9kMsUwJn9BKn5TUAgDsQoa1rGPR6NDQ0QCKR4L8OLaS8bmnkFNSGzVl29qm4LE/GYgj53JAp1Zg6/uqZptaL8DqsEEuk8K7asDI/gbLG9TQ/Vc8Iy7QVVcVnM190KgWMWiUGZ2yIxYi4bKaQYXHFA5lUgtEFR7zJLUisevyQy+L0Yubhf0/5TDKJGJM2D8wzm9d/ftWLKEHAMruC+hIt3p6wIRCOYnHVj+EFadyY7g9hdtmLCsP6UsVzK17osyyO4LXNYnl8Pc312uNGP8eEBdryeliH3kY0GEDY54JEpoDXvgCxRIJAcAUr08OIhs7S26A3O70cAJwTZohl60taumeHEUqg+VKFGjbL6xCJRAisLCHgtEIsliLossFnnYbSWA6RSIT7X0+9lzaipia3gMKamhoQJEl7rysUcjQ2NiL0zDMZ6aaovBaLo6fX/pZrdFAWGWGdGABJxPUamUINt30BKr0JQ4eegUwR11tJAONvv4xI6GxwoNs+j0qeghiikdBaL4+NCCwvgIxF4ZqyQF1Wh5XRY4iekR8kMjlCbgfEYimIaBjRcBASqQKx0Oagx2iSzwBg5eRzaz0j0mGdzce43uYjksgS9PEZRD0r0DaudxSEVuIZzKcGhqHMIHvPLSwhGo3idP8wGmqrceTYafgDQcwvWaFRqyESAU63BytON7z+AIKROXink7+/VJCqtJBqDPBRtJuIQiJXI7S6CKnWCMfxZyGWKyE6825jIR+CyzOI+p1p7xsNeLBqdWN2+NS6z1etcyBiUcyOmFFSVY9J81GEg34EPC5I5XLIFCqIJVKMHnsdU/3HQMRiiMaIrGxSjY2NOHjwYFb2L6lUirqGRvzt+AlYlujxTK1Gg4a6Oky8+MKa0V0kEmN+5HSGX8bhtM0hFotiYdQMY2U9ZvqPrjWvlsjka+d04uSbmB08kVQPSYRjbiLt96dnHChOUh4wGx6ilIkRXBrPSp4rK69ATV09Hj16DKcXUmdAAIB5wbMpE23BGUQ0RsKy4EadSYVjM3EHxqIrCI1cigOuuI1BLvUjGDlr5xixZSdHz06OYdhyMul31oX4Wo30n0ZVbQPMx48g6PfD63ZBppBj1bEMiVSKSDiE6bERiEQizD/5i6zGb22NO4X//SC9/QfQ50mcOyM0Gg2KdDr87JVZ/OyV2bzutX3rxcxMKgVqamvx4Kuv4cb/eJPVcfLBxoXu7OyESCTC0C8/lvM92zvi90g7bl0dnnjSjJdH6Ckf6XD1VVfhZz//ecbrKioqUF5ejg9/+MMIhTIbj0OhEK6/7lr86NkR/OjZkZzmVlVVlfkijlBTU4Ogz4P//sTVjN1vI+rr6yGTyTD+4NcZGSNf6A0GFBevL09QU1eHg8+P4OAgs8apXAV5JsYdeeFPmHzhT6yOwdZ9lz1B3PjLI6zc/2aa866prccjR46yMo/Lb+N+X5SWlkIul+MbX/sn1sZoa0vekK6mpgbhKIGbfn0q6fdsQS6X4eWXX4FKdTYzpbGxERKJJM2vmEVNTQ1WfWHc9OuTrI5RXFwMuVyOp371DdbGAeLGz7m5OczPz2e++AwUCgU6OzvxoQ99CDu2b8d3Hz/B4gyzQ1Vlxbr9kQ/qamvwysvPwPr2M4zcr7p6fUHY2poaDLz6Fyy8+hdG7p8M77jj3Zkvwtm5vec3qY242aKqMn3pSy5QU12NB98ex4NvWxm75//5P/8Hd955Z9prmpqasLq6itUEB28mGAwGNDY24oMf/CA8Hk/Sa/7whz/gl7/8JSu012DQ44UXXlzX2D4RWq0WVVVVGBmhLyuLRCJ0dnYiFAphfJyeMUYul6OzsxOxWAxf+cpXQRD0SszU1tZCp9NhcHAwZamJ3//+9/jP//xPnL7vJtrPkA1+9atfYffu5DXs6+vrYTKZMD8/j4WFzQbk/v5+fOhDH8K+f0m/t/IFddaLioqg1Wrwg4deww8eeo3RMTaekXs//nE8cfwEnjg+xeg4APCRK+mXcaqrrcGRhx/G37/MbJkfuvIzdd0r30xfridXbNu2Hb/73QNJv9Pr9Whqasrpvu9///tx6aWXwuulZxyrqamByWTCpz/9aUSjqQMVfv3rX+OBBx7A7z55dU7zSoXrL/koo/ejA2ptX/rXzeVO2BoLiBsAZXI5xn7/JdbHTcQXv/lDVu57/Nvs0OZc4Abw/Q++g9F79vZuwR//+IeU3xsMBjQ0NOALX/gCgsHNQSapUFVVBZPJhIGBAdo8s66uDlqtdo1nPvXUU/jmN7+J+/+Bm/LdqbBRVqb+ft8vnmNlvI72NvxlX+rG942NjVCr1RgcHEz5bhcWFnDrrbfic/uzc6ZlQk01PRtjTU0Nnvv7w3ju7w8zOv5GfO9738M73/nOTZ+r1Wq0tbXhE5/4BJaX0wczUyguLkZdHT3+LSKzLSDGABwOB6amppJ+NzU1hTvvvBN33303Hn30UbzxxhuQSpP7TDo6OqDRsJc2HolEYLFYaB/8dPjiF+Me0KNHj+Jzn/sc3v/+9+d9T41Gg/b29k2OA7fbjUAguXf90ksvxczMDH784x/jwx/+cNJriouLU75zCqFQCP39/SmVgtnZWbz73XFF+cCBAymN+iKRCN3d3axFv9rtdszMbPbiPfDAA3jooYfQ0NCAsrIyfP/73990TVlZGWprc0v1YwMkSWJwcDDl2vp8Plx11VW45557sH//fjzzzDMoLU0eW65QKNDV1ZVUMXW5XFhZyT2a3mw24/bbbwcAnDhxAnp97lEs5eXlUKvXR0sFAoG0CikAnDx5Evfeey/e+9734qWXXsLzzz+fdhy1Wo2Ojo6MTjg24PF4aBsAPvKRj6CmpgYHDx7Efffdh3e9K30tTQBQqVRrTkqmQRAEBgcHaQlVP/rRj/DUU0/hkksuwU9/+tOM10ulUvT09NAyRofDYfT392ek1ffddx9GRkbgdDpx00034fOf/3za6yUSCXp6ejLSQzZgtVoxN5c5U21iYgLve9/78L73vQ+PPfYYXn/9dVrzbW1tRVHR5rBDkiQxPDwMny99hMhGvPTSS/ja174GlUqF119/PavfAnFjU1lZ+r5DbIMkSQwMDNDaz0NDQ/jgBz8IAHjxxRdp0TmdTofW1laIRCLa6wvEjZS/+c1v0NLSggcffJDWb7RaLdra2vI69x6Ph7bgmQzT09O45ppr8OEPfxh//vOfYbFY8uL1lZWVUCpzL32ViEAggIGBgbTXXHXVVfD5fPjhD3+I66+/PuV1yWSFbOh6f38/PvKRjwAAXnnllU1N65MhW9lpcnIyLV8/duwYPvWpT+G9730vXnnlFRw8eDDt/Zqbm2EwGGiNzRbcbjdGR0fTXvOhD30I9fX1ePbZZ/Hd734XN998c8pr+ZQDgDg/HRgYoBVsc8MNN2B1dZW2HNDU1ASjkZtedXwhm/f3mc98BmazGXfffXdGOQCIG5Gam5vzmt/U1BQcDnoBXF//+tfxxhtv4Nprr8W3v/1tWr8pLS1dp/wvLy9jeno6zS/iuOaaa+DxePC9730PN92U3liY7IzQoaUAMDg4iA996EMA4vJCMvkjERKJBN3d3ZDJZGmvo0BHDsxWN8iWj05MTNByUj7++OP47ne/i4qKCjz55JO07t3V1cWYM54LEASB/v5+hMOp64cHg0FceeWVeN/73oeHHnoorb0AyH5PMAWSJDE2Nga3m16mzE9+8hP09fVBJBLh0ksvxde+9jVav0uUESn4fD7ajqKNeN/73oe3334bH/3oR/Gtb32L1m9sNhsWF89mWul0Osjl8jS/AGKxGHbujJcX+8///M+1/1OIRCIYHx9Pqber1WrU1dXROmeHDx/G5z//eYjFYvT19aV0sKdDNBrF+Pj4Gq1QqVSor6/fNP7PfvYzPPbYY7j44otx//33Z7xvZ2fnJpuFUECSJEZGRnLaSyMjI3j/+9+Pu+66C0888QRee+21nN57KrsaXd74/PPP49/+7d+g1Wrxyiuv0BqTKVvx/Pw8lpaW0l7z9ttv49Of/jQAoK+vL6Mu3tLSQkt39Pv9GBxMX9GAwqFDh/DFL34Rt99+O44dO4bHH3+c1u+Y0BlzBS9lmoqLizdFO1OYmIin8ezduxcPPfQQdDodurrSp/6wBZlMhq1btzJyr5mZGXzgAx9AIBCA0+nE9u3sNcwqKipKKeStrKxAp9MhGAyivDx905V0UCgU2LYtdbmdRGWgvLyc1edNh9LS0qQG+R/+8IfYunUrOjs78eqrr/I2v2wgEonSnoW+vnhZo71792L//v0QiUQ5PZder8/LgTA0NLT2f5VKhcbGxpzvlQwqlSrt3gOAt956C1KpFHfddRceffRRQRg6U0Gn09FaJ4IgMDU1hY997GMYGhqC1+vlfd+KxWJ0d9NveqvX6xEOhxmft1wup0WrFxcXsXPnTiwvL8Nut/P+/tKhvLycFo0eG4vXlr/nnnvw8MMPQ6/Xo6Mjcx+BVBCJRDn9/ujRo/GSA4FAwSnNFCgDLx0kGukrKyvR2ZldnwW66wsAf/7zn6HX6zk/8zqdDjrd5qa9dHHqVDzC+z3veQ/+3//7fwiHw3ntTSahUqnSvstwOAyfzweJRAKtVpv1e6dL1wGsU3CqqqrW0qGZRGNjY1pe3NfXB5lMhve+97149NFHUV9fj5KS9E3b+UZRUVHadxyLxTA1NYVPfOITsFgsguCZ6SAWi9HT05PxOoIg4HK5oFAooNFoBP1MXILu+wPiRlC9Xg+xWMzZ+2toaEBDQwOta6PRKPR6PQiCyHl+JSUlGc9wJBKBx+OBVCrNic4BmWkpBZvNtvb/qqoqtLczW2ecjhzItm5ANzvh4MGDUKvVcLlc2LZtG28OUDYhFovR25u+gfHx48dBkiTe//7346GHHgIAQdIzkUiUFV+2Wq245JJLIBaLYbVa83omjUaTszHV5XKhqKgIPp+PtrxZXl6ecd02IvFsGwyGpM+7axczzY4pxydBEGhqakppT8yEjQ6TZBCJRNDr9QiFQoLcl9lAJBLlTHMpQ/jevXvxyCOPwGQy5e2cTwRd3nj48GFIJBJ4vV709vZmdJIxierq6k1ZHRuRmCFaW1vLWHUVtVpNe/+9+OKL0Gq1uP3223HgwAHWA/eZAPtNF7KExWJBeXk5rrjiirW/Cx0ulwuzs7Po6elBT08Pb8/k9/vh9XphMBjWMQ42kHh/tsfKBRaLZW09hoeH00ZuFAosFgvEYjGuu+46qNVq3vaZENbeYrGgra0NF1988drfhY6pqSn4fD7e6UiusNlsnNCeVKCitAr1/aWCxWJBZWUlLr/88rW/+QC1vkA8I+1cB5d0zmazQa/Xw2azpc0IExosFgtMJhPe8Y53rP1dKKD28PkiL1ksFrS3t59TPHNychKBQOCco/krKysgCIJXflrosNlsMBqNgn1/XMlLlFP9fKJzQtANqPUNBAJZZ6GeS6De/9VXXw29Xn9O0GiSJGE2m9fxHb7kNq7oCHV/pVLJyVhUsBMXY13gs/FzWltbu+bA4ZtuAsgra5st2Gy2tWwIPnlcT08Pent717L9hQ7BOSPMZjN6e3tRXFyMyspKmM3M1ufiA9Sh7e3tRW9vL61yImyAUq6Li4s5IeBUpIfQiHgwGMTo6OjaekSjUQwPD/M9rbxhNpvR2toKjUaD7u5u3s6OzWZbi6bla+0pOtLS0gKFQnFO0BHqGah9W2jPZLPZUFJSwtuemJychN/vX3t/MzMzcLlcvMyFSVB7vaysDGVlZbyeeyoKU2g0nw3YbLa1cjpc8NOSkhKEw2HaZQKEAGpvGo1G1NbWFhTNotaUC5pFOZsSx+Ua1Fq1trZCLpcX1FqlQqHzzFTgcm+eiyAIAna7nRNdKFdwJS9xTecu6AZxnG/yUiqYzWY0NDSgqKjonKHRVqsVDodjje84HI6M5V3YAEXnzkU6wtXZSaTDhRQIxDQoullZWQmTyXSBbqaAEOZHrVVXVxdEIlFB0FTBOSMojw6AcyaSyWKxQCKRoL29HT09PfD5fCl7ZrAJ6mBUVFRwQsBLS0thMplgtTLXXJAJDA0NIRaLoaenZ60kx7myz4Rwdmw2G8rLyyGVSnkhxiRJrr0LiUSCrq6uc2Z9jUYjKisr0dPTg9nZ2YIyplutVlRUVMDj8aTse8ImqD1ARSsB8VrthQ4hnfuKinhTW6HRfDZgs9lQVlYGuVzOCT+trKxc+3+hQCh7Mxckykts72er1Yry8nJIJBLeeaZUKkVnZ2dBrVUqWCwWFBcXo7y8HD09PZieni4oZ14qcLk3z0VQmSVc6EK5gDIilpeXs24E41ovvKAbxHG+yUupUMgyQiok0zX4eC6n04loNMqZDANgjWaxPRZX8jB1TgstEIhpUOdUJBLxek4pOwIgTF2I7/lFo1EMDg6ip6cHarUazc3NBUFTBeWM8Pv9GBsbW8eYCsGjkwlmsxltbW1QKBRrz8bHc1HMora2lnXGRBlquGBM2YJ6993d3TAajaiurj5n9hlV57Gnpwf9/f2IxWKcz4MixmVlZbwI2YuLi1hdXV33Ls6V9U0UBoDCcaJFIhGsrKygpqYGAD9MmnLmVFVVoaOjAxKJpOD3hc/nw8TExNp+4DOyzGq1rtXTFBrNZwNc0rnEd1sohotQKITh4eGCleeoPVxTU8OZwltaWsrL+s7NzcHlcp0XPPNccEBTe4SLvXkuYqMuJLSIV4fDAYIgUFtbi0AgkHPzWjpI3Etc8DE+dYOFhQXB6AZWq5VXeVgooGg0EF+PoaEhRCIRnmeVH8xmM5RKJZqbm9HY2AiVSsWrzYfiE2w7NVUqFaqqqjixL1VWVkIul7M6FkEQsNlsa+e0UGRvpuFyuTAzMyMIWV7o60HtTZ1Ox8v8xsbGEAqFBLFW2UBQzojBwUGQJLkmKPT29mJiYqLg6ylaLJa1Z6qurobBYODFiEgJPHV1dZx4rqmyIUITtCwWC+rr69eafPf29haMUTcVbDYb7Hb7OqNkMBhcawjP9Vz4XHuK8Ca+C75KozGJRDpCGdMLZd9StR2pBlV87QvKMKVQKNDW1lYw7y8VBgYG1vHMnp4ejI2N8ZJ5QglhQq7DzSS4onNUlCyfZycXDA8PIxaLrZPnCiky3Wq1QqfTobKykhNnBJ88MzGSEzgrEwnNSJstEnlmZ2cnxGJxwdN8IL5fFAoFqqurC4YeCAnUO6uvr0cwGGTV2J8LqPlxQfNtNhs0Gg2qqqrOSzrHl25gs9lQV1cHkUh03p7h1dVVzM/Pr5MRIpEIRkZGeJ5ZfrBYLOjq6oJEIoFEIkF3dzevNp/6+nrWI/u5PNtUhhXbY62uriIWi6G+vn5t3PMRVABH4jkdGRlBKBTifC42mw1VVVXQarWCXA+h8LjEtSoEmVdQzgjqhXV1dQGICwwkSa51cS9EJKaFAuA1xclms8FkMqGyshIOhwPRaJTVsYTsjKDWAzg3UkM3GuD5jJzne+0tFgvUajUaGxsBxN+F1+vFzMwM53NhCuFweF2UcaEZ06l90NzcvO5vLpFomALOjXNvsVggEonWeCbVsIoPnsn3uecaXD0vpRA1NDTwVsYnFyRmIAJneVIhNFMDuFeu+eaZWq12Tenu6emBx+PB7Ows53NhCqFQCCMjI2v7TqVSoaWlpeBpPrA587jQnUZcQwjySDpwOb/zjc4JQTeIxWJYXl5GRUXFed33hTJyCkFvZRJC0TWofdXS0rLub7bGOtfoiND5BFegSs13dHQAiO/naDTKi9OQbx6SCXzPz2KxoKysDKWlpQDia7W0tCTIZt+JEJQzwmw2o6mpaa0pZCE130iFpaWltUZGFPgqpZF4SAB2O9HzfSDTIbGcERBfj8nJSXg8Hh5nlR8sFgsUCsWa0FFRUYHi4mLe9xlfEfDd3d0Qi+PkjVrrQqYjw8PDiEajgqAjuYDaB62trev+5gobnTnA2fdXyIYcs9mM5uZmqNVqAGcd+VzvC6oMl1BpPhvgWiGqrKxEaWlpwbxbi8WC2tpaGAwGAGcj0wuJZlHr63K5WI0Cu8AzmQfVG6xQeWY6JO4XIUb2Cx1UZolQs82o+bS3t6/7m62xKMeW1+uF3+9nfazznc5RPUu4iO4WMsxmM6RS6ZqR02QyoaqqqqBpNEEQ6O/v36Rr8JGBY7PZIJPJ1pxv54IzgiRJzmXv1tbWggoEYhpmsxmtra1QKpUAzjoNuT6nwWAQbrdbsCXgAWHwuI0yLyB8B6+gnBEbI9Y1Gg2ampoE/xLTYWNaKPX/4eFhhMNhTuey0RnBNhGnBC0h1XVzuVyYnZ3dtB5A4URsJkNiWijAXwYOl4JCKmykI9XV1dDr9ecEHaGijIGz0TaFYExPrF3KRxkfypmz8dw7HA5B0adssXGv63Q6NDY2cr7XKcc2JSQW8julg0Q6x7ZQTN270Bw9ibWgAUCpVKK1tbVg6HCiDEP9zQaousR888xEBaa2thZFRUUFs1bJkI5nFjoSaQ8gzNrJQsbG9yc0mko5S+rr6yEWiznT1QDAbrezMo5QdINEOseXbrCRp5+v59disaCtrQ1yuXzts0Kn0VNTU/D5fJsyI/x+PyYnJzmdC3XeuGiUnlia2+/3s1Ze3e12IxQKceLIo+5N9fMSGp/gChv1TIPBgJqaGt7pptDWI9FZIhT7V0tLC+RyueBpqqCcERs9OkDhRzKZzWaoVCo0NTWtfdbb24toNIrh4WFO52K1WlFeXs66AhONRuFwONaEfbvdLph6/RvrqQGFF7GZDEI5O06nE5FIZG2fcS1kx2IxDAwMrHsXIpHonKAjlCGfQm9vLxwOB5aWlnicGT3YbDZotVqo1Wpe9kUyp/C5EP2b7Nzz0bCKWs/zJdJvo0LEtpIHFJ7hYqPhBygseY4rgyVVhosvnhmNRjEwMLCONlLBDIWyVslgNptRV1cHvV6/9llvby/sdnvBnKFUoGR5LgKLzkVQ76+4uBhisVhw+4Gan1QqRUlJCWdGROpvNiAE3WBjxDpfukEiTxdqhC8XEIreyiQ2lkwG+NM1KDpiNBpZj+ynnJps25cSDdJs0xGr1QqZTAa9Xl9QsjeTIElSMOeUWnshBjoD6+fHB48LBAIYGxtbt1YymQydnZ2Cp6mCcUasrKxgYWFhHQEHCt9LbrFY1qWFAmcjtfjwKibWEmOLMS0vL4MkyTUBlyAIrKyssDJWtqBq31Hpz0Dh1xJOlhYKxM/O6OgogsEgZ3PZ6Ll2Op2cZgBNTEwgEAick3Qk2TNR3wkdFO0BwIux2mw2o7q6ep0zp7GxESqVqiDeXzIsLy9jaWlJEHtd6BErTGPj87rdbtborM1mg1wuX1OICuHdut1uTE9PC2Jv5oqNRjq23vvGvbSysoJIJMLKWMkwPj6OUChU0GuVDIXOM9OByyzncxHU+5NIJIKs2c+lvMTVXhKCbhAMBgVB5843eSkZNvbTpNDT04OJiQnWIuvZhsVigcFgQFVV1dpnFRUVMJlMvNl8xGIx65H9fNERLp5JJBKdt+fUarXC4XBcoJs0kGx+XFauGBwcBEEQglirbCEYZ8TGRkYUenp6sLi4CIfDwce08kYyZkvVReSLMWk0Gmg0Gs6YReJnfINKC1UoFOs+L4TDmgrT09Pwer1Jz04sFuM0AyfZ2rOV9p0MyTJfgPi7GBoa4tTIwySS0ZFCMqbz7YxIFqUtkUjQ1dVVEO8vGVLxzN7eXszPz2N1dZWzuVDrWVpairKyMkFlw7EBLulcokJUKFGU6eQ5u90u+GdILCnCdvBGsr3EZbO5dDxzcHAQ0WiUs7kwiWQ8s7m5GQqFomBpPgVqb5pMJtbL+JyL4FseyQSu5sc3nTtfdQObzQalUgmdTifI/ccFFhcXsbq6mtJhXKhlkyldQyQSrX3GV9lkruiIz+eDz+fjxRnBdiAQlelRKLI300hWVYD6e2pqitNeq4l6JrUeQipTvXFvhkIhTt8PtVZU70gKhVDSWzDOCLPZDJlMhra2tnWfF0rzjWSgItY3Cj8A9ylOBEHAbrdzwpg2plIBwqlpmyzdDCjs1NB0QjbAbWoo344os9mM4uLiNQGCQm9vLyKRCEZGRjibC1PweDyYnJxMakzv7u4uiH2bKNTxlRmxUZgCCvvcm81myOXytabgFPiI/t1YhisajcLpdHI2Ptfgks4J3XCWDBaLBWKxGJ2dnes+L5TSaG63G+FwGOXl5VAoFNDr9azJMBvLcAHc80zKiZiI3t5ehMNhjI6OcjYXpkBl5mzkmVKpFF1dXYLff+kQCATg8XjWIvvP51rWuULoNJWr+Xm9XgSDQZSVlUEmk8FkMp2zQWpC0g02RlwvLy8XrNM3V1A0eCON7urqgkgkKlgaLSRdI5GOsGlMTzzbxcXFEIlErI4lkUhgMpk4CwQChMknuIDZbIZSqURzc/O6z6lzSwUecQGr1QqDwQC5XM6LsT8TKFk+UZ7mmsc1NjZCp9Ot+7y3txdutxuzs7OczSVbCMYZYbFY0N7evq6RERDvYi+TyQrSGTE5OQm/35+UMXHtJU+sSwxwx5iE1CAuVVooEF8Pm80miHlmC7PZDL1ej+rq6nWf6/V61NbWcm6UlEqlMBqNvKx9sqgUoLDLM1ARQkKgI7mCKnkCcB9h4vF4MDU1ldQJ2dPTg/7+/oKM4rdYLOjo6IBMJlv3eXt7O6RSKefnPtHZBAjHAc0GEhUituncRoWI6zI+ucBsNqO1tRVKpXLd583NzVAqlYKnWYkyDMC+vESV4eKTZ25EIfPMVJk51GeF+EwUKMNLIr09l2ktGxB6xCtX80sMHAPYD1K7oBvEsdFITJJkwVZ/yBUWiwVqtRqNjY3rPler1Whubi5IGh0OhzE8PJySn46MjCAUCnE2n41yOdt9HBL73LB1tq1WK0pLSyEWizmXvYXGJ7iAxWJBV1cXJBLJus87OjogFot51zOFtCY2mw1GoxFyuZw3HpdK5qW+FyoE44xIFbFeKM03kiGV55/6bHJykjOvXmL0HfUvm4xJrVZDo9FAp9NBoVAIgmAsLS3B4XCkXA9A2Ic1FVIJ2QD30RiU0VkkEq2lfXOpKKcixlRptEKlI8mijIH4+haCMT2ZUMfVnNM5c3p7e+H3+zE5OcnJXJhEKp4pl8vR3t7Oy7kHhCkkMo1EhYhtOpfs3XJZ3iIXpDJwU6XRhE6HNzoj2DbS8ckzU0VylpSUoKKiQvBrlQxmsxkSiQQdHR2bvuvt7YXFYhE8z0yFZLL8uUxrmUZiZgkgTGfORprPRVYWF2MJkc7xoRucb/JSMpjN5k39NCkUasby8PAwotFoSl0jGo1yVjY5GAzC5XJxWg2DK/vSxrPDlexdCIFATCOVnqlSqdDa2sp55Q2u1j4X8D2/VGtVV1cHnU4naJoqCGdEuoh1oHAjmSwWC0wmEyoqKjZ9x3VdRC6V60QCLqTGP6lq3wGFXUtYSGcnkRgrlUoUFRVxtvahUAgjIyOCeRdMwWKxoKWlBSqVatN3PT09gjemJ9YlBuK0JxaLcdbTIJ0zpxAiBpIhE8+kDG5cIZkQJgSazxYSn1cul8NgMHAWnUV9JlSQJJnS8AMUBh3m0uCbuL5qtRparZaz9Q0EAhgbG0uqwACFsVbJYLFYkmbmAPFn8vl8mJ6e5mFm+YNLWf5chNDfn9/vh9frXTc/h8PBShkfPpyuAPe6QTAYxOjoqGDoXKHxdDYgJL2VKaSzMXR3d6+7hm1QAStcOiNKSko4GYt6Ji763BRaIBCToErNC+WcCp1uJs6P635eq6urmJ+fT7pWfPWsyQaCcEbMz8/D6XSmFBQow4qQm28kA+WlShaxznVdRL6ETmosIXgvzWYzVCoVmpqaNn1XqLWEI5EIhoaG0holZ2Zm4HK5OJlPsrXnihgPDQ0hFoulpSOFtr5Aam83UBg12BPrEgPcCxHpnDmVlZUwmUyCfn/JMDs7C7fbnVa5NpvNnPHMxHOv1+shl8sFJSQyDS7p3MaSHdRnQoXNZsPy8nJGeU7IkemJZbgAbiKGKXDNMwmCSCs/FBptBAqfZ6YDl4afcxHJdCG2jP25IJkRka0yPjabDSKRCMXFxQDYLwnFt24gFDp3vpW13IhYLJaynyYQX4+lpSUsLy9zPLP8YDabUV1dDaPRuOk7g8GA2tpaXm0+bEX2W61WFBcXQyqVro3FBR1hMxAoGAzC7XYXlOzNNKhS80KxqSTKysXFxZwa++kgcX5U1jyXdg4geSUe6nMhy7yCcEak8yZTn7tcLszNzXE5rbyRzvOvUqnQ0tLCmaeKqktcVFQE4CyzYMNYlShoJY7FNywWS8q0UKAwozFGRkYQiUTSGiUB7poM8alwUISWikDZiJ6eHkxMTMDn83EyH6aQjo5UVFTAZDIJet9SShZftR7TRWkXQsRAMmTimb29vXA6nVhYWOBkPonnnsqGO5eVa67oHKUQcRUJxgQoOpxOnhN6ZLrNZlsrwwVwH7whJJ45Pj4Ov9/PyXyYQKbMnOrqauj1+oKj+RQS6xIDcb56LtNappHMSCekmv3J5pf4OdNjlZSUrNUDP1fpHB0bA5e6QeK70Gg0UKlUgubpTGNiYgLBYDDtegCFl7GcqjwlBS51jWR9rwB2Ivu5PNtWq5UT+1IypzAgbNmbadChm3a7nbN3krjPuDb20wHfPE4qlaKtrS3p9z09PRgcHBRM0MVGCMYZodFoUF9fn/T7QmRMmUrGANwzpvLy8rUsjfLycgQCAVaEr40HUigN4tIZdYGz61FIGTiZDD8dHR2QSCSc7zMKXK69xWJBXV0d9Hp90u+5Lo3GBGy2eFP1QjampxKKudwXQlEQmILFYoFOp0NdXV3S77nkmVQZLiE6oNlCMjrHhkFwo0LEdRmfXGCxWKBUKtHc3Jz0+0KQ51LJMFwEb3DNMxsaGqDT6ZJ+39PTA5IkMTg4yMl8mIDNZoPD4ShonpkOyWitkCL7hQ7qbFGOXaFFvFLz4CIiN5lhz263s5K1xjedq6urWwvG2wgudYONZbhEIpFgdGSukMnI2dLSArlcXnA0mq6NgQtw7dTk6mxzZV/a+P4KIRCIaVgsFhgMBlRVVSX9nktZniAI2O12QeuZfPO49vb2tSCVjejp6UE4HMbY2Bgn88kWgnBGUFFMqSLW6+vrodVqBZ1ishFUI6N0RjAu02aSlQKgPmcafHoHU4GqfZdpPbxer6AjNjfCYrGgqqpqrZzERiiVSk6bDCXbZ1xF7WUSBLkujcYEMqXeUd8J+Zk2CnVFRUWQy+Wc7ItMzhwg/v6Gh4cRDodZnw9ToHhmshKAANDQ0ACNRsPJvvB4POvKcAHCoPlsgqvSOht7F1D/F3IktNlsRldX11q07UZUV1fDYDAInmZtfOfRaBROp5OTsbha33QZBMDZjAkhr9VGUHMtZJ6ZDqlk+UIracIXrFYrTCYTZDIZAOGVyaHmkViGK/FzJpGM9sRiMaysrDA+Fp+6QSY6x6VusFEepv5/LstLG2E2m1FcXJy0nyYAyGQydHZ2FhSN9ng8mJyczKhrTE1NwePxsD4fq9UKg8GwZpxk2xmxcT8vLy8jFosxOk4kEsHKygondGSj7E0FAgmFT3CBdKXmgbjTUKFQcHJOnU4notGoYHUhylnCJ4/LFHRJXSdECMIZkcmIWIiRTNRcU6W/A/HNQRnL2EYyZkF9zsVYfBMMqvZdpqgFQNgRmxuRScgGuIvGCIfDcDqdvJacSEeM1Wo1mpubC2p9LRYLFApFyihjIL6+IyMjCIVCHM6MPmw2G8Ri8ZrDjMum9nScOT09PYhGoxgeHmZ9PkwhE88Ui8Xo7u7mZK+fb8p1KoWIzegsvqJtcsG5IM+lkpeYlmM2luGixhJK1phGo0FTU5Og12ojLBZLyt5gFHp6ejA0NFRQDmgKXMry5yKE/v5sNts6Z4lGo4FareZEV2MrC4Nv3SATneNSN0jG04WgI3MJSkZIZeQECi9jmcqqoWMQ5KJsMpd0LpmjkSAIxkvfUQ53LmVvIVb54AqZZHmJRIKuri5OzmmywCwhrcfq6mpSZwkX8yNJMuNalZaWory8XLA0lXdnRCwWw8DAQFoCDhReJJPZbEZtbS0MBkPKa6hn5koA4oIxeb1e+P3+TYIW9TlfoBMtV1NTA71eX1D7LBMBAs6eHbbLT20sKUL9n63yFolwu92YmZmh/S4KBVSUMdUYLBl6e3sFbUyn6q8nRkpz6Yyg48wBhBsxsBHRaBSDg4MZeSbVxJptnG9CezqFiGk6t7GkSOJYQgSdDERA+HQ4WfkSgHl5KZ0jj22e6XQ6MTc3d87yzFSZOcBZnjkyMsLhzJgBVwbkcxUb3x+bxv5csHF+ALsGNy70Qj51A5fLJSjd4HwL3kiGTIFjQHw9CqlsstlshlgsRmdnZ8prOjs7IRaLOdtniXtMpVJBp9MVNB1J5chj65kSM0vYHEuICIfDGB4eFowsL3S6yef8FhYWsLq6Kpi1ygW8OyPGx8fTNjKiIPTmGxtBx0hMpTjx4YxgqxN9qgMJsNM4iS4sFgtMJlPKtFCgMCI2E+Hz+TAxMUHLKOlwOFiPvEm19uFwGG63m9WxM9UgpVBI6wvQoyNU9pVQn2tj1AzAHZM2m83o7OxM68wxGAyoqakR7PvbiLGxMYRCIVrK9cDAAOOp0huR6tyfq5F+qZ6XjTI+haYQTU1Nwefz0aLDQo5M51q53jhWMBiE1+tldKyNoJM1BpybPLMQs2ApbNyblKPyXKW3TINLY38uOBedEXzqBlQUulDoHPUuqDJcgLD2H9sIBoMYHR2lRaPdbjdmZ2c5mll+sFgsaGlpgUqlSnkNVTaZD5sPwI5cHovFsLy8zAkdSVW2lK1AICHzCbZBlZqnc077+/tZ6TOUiEJ1RnDRz+tcsH/x7oygE7FOfR8KhQTbfGMj6Hj+pVIpZ3URNzZWkUgkKCkp4UToFELkVqbadxSE7DncCErIpmOUBNiP/E5VUiTxO7ZgsVggkUjQ0dGR9rre3l4sLS0VRH1lgiAyppcDcWN6bW2tYPdtMqGOq8h5Ou8PKKxzn6lpPYWenh4Eg0GMj4+zOh+qDFdxcfHaZ2VlZXC73QgGg6yOzQfS0TmmFb1CU4joGriFHJmerAyX0WiEVCrlJNKPS54plUrR3t6e9rre3l4sLCywUkeeadDNzDGZTKiqqioYmk8hWRNHjUYDjUYjWJogNGzUhQBhZfJxNb9oNAqHw7FuLKqf17lE58xms6B0g41luABh7T+2MTQ0hFgsRktGAAonY5lOyWSA20jyjXSEDdlxZWUFBEFwcraTZQqXl5ezFghUSLI306CrZ3LVa9Vms0Emk62rNsOVsZ8O0vE4tnmK2WyGRqNBQ0ND2ut6e3sxNjbGa5WaVODdGWGxWFBaWrrp0G9EIUUyud1uTE9P02JMXHiqktUlBtjxkqfLjOAzcotOtBxwNmIzEolwMKv8YLFYIBKJ0NXVlfa6pqYmKJVK1vcZtb4bS4okfscWLBYLWltboVQq015XSHRkZmYGXq9XMHQkV3AVobMRdOooUhDy+9sIi8WC8vLydecsGbgqA2i1WlFaWgqx+Kw4IYRsOLaQjs6xEQmWzDBltVoFWb7AbDbDYDCgqqoq7XVCzuZKVoaL6nPDNM3ik2eazWa0t7evy7pJhkLimXQzc4DCovkUktUlBs4vI0m+SJWpKZTMEq7mt7y8DJIkN9E5ir8wCb51g7a2NigUirTXcUXnkvH0srIy+P1+1rPhhAA6/TQBoLa2FjqdrmBoNN3AJ674DlcZ6cmyFTQaDVQqFSv2Ja1WC7VavfYZW3Qknex9PsBisaC6uhpGozHtdVzSzbKysnUBxdT6CEHPtFqtkMlk0Ov1a59xyeO6u7vX6eDJ0NPTA5IkMTg4yOp8coEgnBF0FAfKYVEIjIlqZJSNQsSmYSGZg4D6mw3PtUgkWhclS6Wj8qUshUIhjIyM0F6PSCSC0dFRDmaWH8xmM5qbm9cx5mTgqsmQzWZDUVHROocAV80B6WQiAfHSaHK5vCDoCN3UO+oaoT4TXxEm09PT8Hq9tBWEqakpeDweVufEBOjyzLKyMpSUlHBy7pOtL/XduYZ0ChEb/DTZuw2FQoLcq3QaUwJnI9OFSLO4lpeSleFKnAdboEtH2traIJPJBLlWG3Gu8MxU4HJvnougMkuE/P64kpe4pnN86QZ06RxXusH5Ji9thMViQW1t7TqjXTIUUtlkm80Gm81Gm+/Y7XZW15okSV7pCBW8wZU8nDgPtsfios+NEECXblZXV0Ov15/3dJOaX6LuIzQeRwUuC5Gm8u6MoGtEBAqnlAadRkYUuEhx4lLotFqtKCkpWVejXS6Xw2g08kYwqNp3dI2SQGGkhtIlQAA3ZycZszAajZBIJKwLXnRTZGUyGWel0fKF2WyGXq9HTU1Nxmt7e3sFa0xPFQnGdhmfbAxTFG2gSp8JGXR5pkgk4u3cs1W2SAhI9rwGgwEymayglS8mcC7Ic9Se5aLEQbL1NZlMrPTzSkQ2WWMymQwdHR2CXKuNMJvNMBqNGTNzgPj+m5iYKKho5FSy/PkUsZkPVldXEYvFBOuM4NJZwrUzgk/dgA5P4ko3KDSezjTOBRlhI+iWp0y8hs3ncjqdSTPo2OATXNuXksllifNgCoUWCMQ0CkHPFBLdTDY/KhOQzfnFYjFapUkBQKvVoqmpSZA0lVdnBN1GRhQKxUtOt2QMwE2KE99CJ1tj0QXdtFAg3ti7srKyIPZZNkIdF02Gkq29WCxGaWkpq2tvtVrhcDiyeheFsL50o4yBs3REaMZ0qi5xKiGCzfTKbJw5HR0dEIvFgt8XgUAAY2NjguKZfAlhfCHZ854LkWD5IhwOY3h4WFB7Mxckq0sMcCcvsdXPKxGLi4tYWVm5wDNxNpO5EHAhMyI/CP39pXOW+P1++Hw+xsZKVuOaGosLOselbiAknlRIPJ0NZBNE19PTg8HBQUHUhE8Hi8UChUKB5ubmjNc2NzezXjY5E51jMrLfZrNBoVBAp9MlHYtJcBUIlC6zhJrHuQyPx4OpqakLdDMLJJsfF/28JiYmEAwGBbVWuYBXZ8Tg4CAIgsjKSz42NoZAIMDyzPJDNkbi2tpaFBUVseqpSlavE2DPS57MGcFngy6z2Yza2tp1jW/SoRCiMex2O6xWa1aZEX6/H5OTk6zNKVnUAsB+1F42EfBA/F2wXRqNCWRDRzo7OyEWiwW3b5PVXwe4iZzPxjClUqnQ2toquPe3EQMDAyBJMiueOTo6ymoGSrJzL5fLYTAYBCEkMo1UdI7put4EQSRtQijUrJNsMhAB4UamJyvDBbAjwyRbX2osofFMs9l8TvHMrq4uiEQiwdP8RFitVsjl8k0lToRiTBc6UmU9lZeXC6Jmf7r5JX7P1FhqtRoajWbTWFzUX2drrERQZzsbnsS2bpDsXZSUlEAkEp3zZ9jlcmFmZiar9QiHw4Ivm2w2m9HV1bWuIkQqUGWTubD5JHM0Mh3ZT8kwG/UsruxLbPTzonozFYrszTSooMZszinbvVaTycpqtRparVYQ68GXLJ8LjxOizMurM4JSiDI14KXQ09MDgiAEH8mUjeefqovI5uaw2WwwGo2bGhWWlTHfiT5dZgRfBCOb9QDA+nowgWyZBRflp/jKirFYLFCpVGhqaqJ1fU9PD9xuN2ZmZlibU76IRCIYGhqivW+VSqUgjenpInQSv2cD5+K5z4VnxmIxVhtWCTEbjk1w9bypUu25KOOTC7LJQASEm83FpQyTrMkkNRab62s2m6FWq9HY2Ejr+p6eHrhcLszNzbE2p3yRbWaOWq1Gc3Oz4Gl+IpLVJQbOr1rW+YBPeYQOuJwfl3ybb90gGzrHpm6QqgyXVCpFcXGxIIxqbILi9XRpNCVLCJ1GC03X4JKOcCnDcEVHhM4n2IbFYoFIJKJVah4422t1eHiYtTnxJSvTBV/zs1gsKCkpSTp2MvT09GBhYQErKyuszSkX8O6MqK+vR1FREa3rKcYkNOU1ETabDXa7PWvGxOYz2Wy2TVkRQPyQkCS5Fr3M1FjngjNicnKS0ZRopmE2myGXy9HS0kLr+qqqKhiNRt72Gdue4c7OTkgkElrXC9UIlojR0VFEIhFB0ZFckEqoo/YJW/siEolgcHCw4N/fRlgsFjQ2NkKr1dK6nm2eSZXh4uPc8wWu6FyqckFUeQuhvVuLxYKqqiqYTCZa11OR6UJLGU4nw7hcLoRCIUbH4uPsWCwWdHd3QyympwIUAs8cGRlBNBo952h+ItLtl0AgwHtkv9Bhs9kgl8s36ZzUeeebpqYrEQcwO790e8nj8TBagYBPOtfV1SUY3WBlZQUEQZxX8lIiLBYLJBIJOjo6aF1fWlqK8vJyQdPobPovUejp6cHAwABrZZNtNhukUummahBc0xEmx0lVOomNsVLRYZPJBIlEcl6c05aWFqhUKlrXs11uPhgMwu12C5pu8snj6FaAAIQry/PqjMgmpRqIN99obGwUtJc825QZ6trBwUHWUpxSpQ+xUbc9XQkLNuvDpwJV+y7b9SBJUtAZOBaLBR0dHZDJZLSuZzsDhxIU+Fh7i8WS1frW1dVBp9MVBB3JRsAVYvodxYQ3CpBUGR+29gXlzMn23NtsNkFEWaRCtjyzqKgI9fX1rO0LypGdKj1VyO8yF3BJ51LV9GZjLCaQ7d5UqVRoaWkRnDMinQwDMCcvUVGyfKxvtmtVX18PrVYrOP6SiHOFZ6ZDulIA1PcXkBrpMksAdntY0QHlLNlYhqu4uBgikYhx/sIFneNTN8iWzrGtGxQaT2caZrOZdj9NCkKn0dPT0/B6vVnrGl6vF9PT06zMKRWdY4NPpDvbTPa58Xg8CAaDvMreYrEYJSUl58U5zWY/m0wmVFVVsSbLU+9bqHSTcpYUAo9ra2uDTCYTHE3lPTMiG8UBEG7zDQoWiwVKpZJWIyMKVIoTW3UR03mTqe+ZQCwWw/Lycsqx7HY7qw2UkyHbtFBAuBGbiciWAAHsnh23241wOMx5mhpBEOjv78/qXVCOGSGvr8ViQWVlJYqLi2n/pqenB3a7XVAGCZvNttbEaSPY3Be5GKbYju5gAkLjmakyX6jPhLQXmYDX60UwGOSkL1KhvVuh7c1cwZW8lKoMFzUWW+sbi8UwMDCQ1VqJxWJ0d3cLbq0SYbFYUF1dDaPRSPs3PT09sFqtvCuzdMHV3jxXker9UcZ+vt9fKiMiVcaHKyMi9T0T4Fs3yIbOsa0bFBpPZxrnioyQiGz7LyVey+Y+S7bH2CjxmYknMcVbuTw7NpsNMpksaZ/RC+c0Oc5nukntca7nFwwGMTo6mtVayeVytLe3C46m8uaMcDqdmJ2dzdqgKnQvOdXIiG5aKMB+Pf90zcOo75mAw+EASZIpDTWxWIzzOmVmsxlisZh27TtA+LWEc0kLBeJnZ3h4GOFwmPE5pWqYRX22srLCSubP1NQUfD5fTu9CqOsL5OZsoq4X0nOlElQBdhs75eLMaWlpgUKhENT7S8Tq6irm5+cFxTPTnXshCIlMI9PzOp1Oxsr4WK3WlAoR203RsoXH48Hk5KSg9mauSEWzmJaXMvFMpvt5UZicnEQgEDgn1ioR+fBMoSlmqZApa+dco7dMI9X7k0qlKCkp4Z2mppofwDzNT1Xjmms6x5ZuMDk5Cb/fLyg6l+ld8L3/2ARJkjnT6LGxMfj9fpZmlh/MZjP0ej1qampo/6a6uhoGg4Fzmw9V4pPpnhFc2JfSGaTZoo3JSt+c6+eUqg5wgW7SB535sdHPa2hoCLFYTFBrlSt4c0bkErFOXT8/P4/V1VU2ppU3cjESl5SUoKKignMvuUajgVqtZowxZfJeJl7DFSwWS9ZpoYCwozFmZmbg8XhyyoyIRqOsNBmis/ZsRB/mUhYNiL+LwcFBVow9TCAXOtLc3AylUimofZvOGcF2ZkS2e0IikaCrq0tQ7y8RuURgUdfPzs7C5XIxPqdUtVWBc7OpKpd0LlWULDWWkAyPVEnDXPamkCLT09UlpvY4V/IS0/28KOSSNUZdPzAwgFgsxvicmEAuPLOlpQVyuVywNH8jhB7ZL3TwJY/QBVfzS0fnSkpK1ubCBPjSDfKRl9jSDWw2GxQKBXQ63abvhLD/2ITVaoXD4chpPUiSxODgIEszyw/Z1mwHuMnA4YKO+Hw++Hw+Tmw+megIk/28hM4n2EQ+dHNiYoKVXqtC1zMz7c1gMMhKPy9qrajekHRB0R4h6ea8OSPMZjOkUintRkYUhBzJRBBE1vXrKbDlqaLqEnNBWDNFjQLcOyNyMUoCwvQcUsiHWQDsRM7z5YiyWCwwGo2orKzM6ne9vb0Ih8OslUbLBz6fDxMTEzkb04W0b1NF3wHsCnW5GKYAYZ97s9kMmUyGtra2rH7HJs/MVIYrGo3C6XQyPi5f4JLOFZJCZDabIRKJ0NXVldXvhCbPpatLrFQqUVRUVPDBGxaLBcXFxSkjsFOht7cXoVAIY2NjjM8pX+SamSOTydDZ2SlYmp8Iqi5xsv0ikUhQUlIiKJogRAidpnJpRAwEAknHksvlMBqNBU/nzGYzTCaToHSDTAEGy8vLgnX25otcA8coQ5tQabQQbQxcZaSnK0/DhlNTLBbDZDJt+o6tQKBkEAKfYBNmsxkKhQItLS1Z/Y46A2w0RrbZbNDr9VAoFJu+Y9PYTxeZnCWJ1zAJs9mM+vp6FBUVZfW73t5eOJ1OzM/PMz6nXMGbM8JisaCtrQ1yuTyr37W1tUEqlQpGeU3E9PR0TiVjAPYi8dPVJQaYJaxCzYzIdT2WlpZYiUzMF2azGTqdDnV1dVn9zmg0orq6mjWjpEQiSVqvmW1inG1UCnBWwBUiHRkYGABJkoKiI7kiVV1igD2hjnLm5Pr++vv7Oe9tQwcWiwXt7e1Z88z29nZIJBLWzn06pQdgLk1bCKCjEHHljGCrjE8usFgsaGlpgUqlyup3QotMTyfDUJ8zub7p6hInzodJUMaTbHmmkHvq5JqZQ/1GiM+0EekMP4AwyhUIHUI3MnE1P67pHB+6QS4R6wC7ukGm9SVJEg6Hg/FxhQCqn2ZTU1NWv9NoNGhqahIkjY5EIhgaGsqZ7wwNDbFSNlkIdITpPjdWqxWlpaVJS6Cfz4FATMNisaCzsxNSqTSr33V2drLWazXTelDX8IVMzhLqGqaRj32T+r1QwKszIpeXKNTmG0DuEetA3FPFRooTdQDS1SFlkoCrVKqkUbJ6vR5yuZxTgmGz2WC32/M6rGx4efNFrkI2EN9nbDILsXgzSWGbGOcSlVJaWory8nLB0pFcoowB4RnTMxmr2UivpJw5ueyLnp4eeL1ezMzMMDonJpArz1QoFGhra2Pt3KdzNlHXnCuw2WwZFSIma+Sm49sABOMsz3VvSqVSQUWmZzLSMS0vpYuSTZwPk8h1rcrKylBaWipYnpltbzAKlEwkpJT1ZMgky5/rRpJ8QWWWcKEL5YpMNJ/p+utc6YV86Qa50Dk2dQM6PJ3vPcgWLBZL1v00KQjVYTw6OopIJJKzjSEajTKegRMOh+F0OjnhE3zQkVTjJM6HibHSPZOQAoGYRq50k81eq0Knm3zNL9e1qq+vh0ajERRN5cUZkWsjIwpCLaVhNpthMBhQXV2d9W+puohUhBdToITXdF5FJgXcVMq1SCRidCw6yDUtFABaW1shl8sFuc9yJUBAfJ+x8UzpyvGoVCrodDrG1z4cDmN4eDjndyFkOtLc3Ay1Wp31b3t7e+H1ejE9Pc3CzLJDurrEwNkyPkz3/8nHmSPEJuCAcHlmpjJcwLmlXKd7XoVCAb1ez2gkWKZ3K5RI6Hz3plCEYjoRw0w2dk01DlX6jOn1DYVCGBkZERwdyRdmszmnzBwgLhN5PB5BOqATQUeWP5doLdPg8mzngnRluABmy/hwqRfyqRsIjc4VEk9nGkKUX/NFrv2XEn/D9HNxmfVE7VWqJFOysZi2LyUDVR6HC5rFZp8bvpFPqXmAPVle6HQz3fxMJhPEYjHj83O5XJiZmclprcRiMWu2wFzBizNiaWkJKysreRlUhRjJlE/EeldXFyspTlyn46Yah+mx6IBKC21ubs76tzKZDB0dHYIxklCIRCIYHBzMi1lMTU3B4/EwOi8+1n54eBjRaDTndyHUaJt8nU3UPfhGurrEAHvG6nycOdXV1dDr9YJ4f4lYWFiA0+kUHM9Md+4NBgOkUuk5ZSDjks4JPTWZgs1mg81mE9zezAXpynABhS8vDQ0NIRaL5b1WQkM+PFNofUtSIV1dYuCCMyIT6OhCfNbspzM/psr42Gw2iEQiFBcXpxyrkOkcpRsIjc4VCk9nGgRBoL+/P6/1WFhYwMrKCsMzyw8WiwWVlZUpz1E6mEwmVFVV8WLzYSqy32azwWQyQSaTpRyLCzrCZCBQKBSCy+U6L8/pzMwMvF6v4IJd0609ZeznOzMi1fzY6udFVW0RGo/LFbw4I/KJWKd+t7q6ioWFBSanlTfy8fxTdRHZ8JLLZDLo9fqk3zPtJU/XFJFrZclsNuecFgoIMxpjbGwM4XA4b2M10+Wn+FA4KEJK1XjNFr29vRgbG4Pf72dyWnkjHzpSXV0Ng8EgiH1LJ/oOYF6oy8cwJRKJBHnumeCZ/z977x0g11XeDf+m9z67s71JWu1KK7n3Dqab4BDTAoEUSEJ5IZg08gYCJhQHvuSFJE4AY0wwoTkQAzbY2OAuW7YlS7va1e5qi7bP7PTe7r3fH6O7mt2dcmfm3DN3xvP7R9qZO+c595SnnPMUn8+HjY0Nkt0que/5aLhmUtpp8blyBhF/ICmFseX5cC1rUyqe6aXyEgONf0hXSypRIDdXs7OzSCQSJLtVM2qRmb29vTCZTJLj+Tvh8XhgtVqL1gxqNl5LGjQP+6uBkP7lP1crLYfDUTQveKPzuVo81gHxbINSY2E0GqHVaptyDy8sLCAej9ekIwDSuzCuRe4A4pwxCEk1CZDx7JeKcw5JWuVqMzXzZQQJO9PtdhOPGqnHYX8lqJeMUygUGBkZqer3hw4dwuTkZN2cL3aiLpcRExMT0Ov1GBwcrOr3UvL+5ZFOp6suZMRDjJuqUqmTgNwmicfjRGpVlNuQtHOy1nIoCUjLY5NHrUr26Ogo5HK5aOusGMRixj09PQUL4wkBnxptamqKaL9qgdfrxcbGRk2H6VK58RaS4zr/OVKo1UCQyvjlY2JiAgaDAf39/VX9XgyZWS4NFyCNPNwkQet9yxlEfBofKYztxMQENBoN9u7dW9XvpaTPCZVjJHSCesnMvr4+mM3mqn4/NjYGlmVx+vRpov2qBbVG5khJZpaCEN5TT89+qUNIZEn+c7Qh9DKCRLqHUjmueVqbm5tEao/V69K1p6cHVqu1qt+LYRskEglEIpGSzhvNpi/xqPUSfHh4GCqVSnI8mtQZA0nQ5HO0LyNoOLvSvBSWGiYmJmCxWNDT01PV78VwdhViZ9bbEaNeMm54eLhg0WwhGBsbQzKZxNzcHNF+VYu6XUYcPHiwYEErIRgYGJBc8Y3Z2dmaUsYA4uRbK8fASRZXkRLD4MNCa52PcDiMlZUVgj2rDRMTE3C5XEUVjXLQ6XTYu3dvXdaZlDzggfMRFVLiI7wQlxofqQbllDqr1QqVSkV0XdR6mQPkxu/06dPIZDLE+lUr+LVercwcGhqCTqcjui74NFzlDIRmyoEsxCAidVgEFL/I47+TgkE0MTGB0dHRol625dDb2wuz2SwJz3QhB76ZTAahUIgIrZbMrB3NJDNLQQjv4ThOMkXtpQaPxwObzVY0sqTehTDLHSLStNVcLhey2SyCwSARWvXgc7XwAzH4HO9gIKXsAbQwMTEBm82Grq6uqn4vxbTJsVgM8/PzNcud+fl5Is6gPDweDywWC7RabcHvSV9GlNvbJC41s9ksfD4fFUegcrq3Xq+H0Whs2n1abap5QJxaq8FgENlsVrK2EH9Z0mgyTmrRZnVL01SLQSSXy3Hw4EFJGK88+L5UmzIGyN1Ura+vEw0TLlVYBSDvbVOOFq2DqbNnz9aU+w4Qr8BULajV6xsgn9cvk8nA7/dTn/tax0Ks1Gi1YHx8HGq1umovYyA3v1I4TC+Xl1iMova1powBcuOXyWQwMzNDqls1Q4oys1waLv67ZlHahRhEpN5X6NhK4aKn1rUpJc90IToMULu+lEwmS6bh4mlJTWaaTCYMDAxITmZqNJqqaoPxGBsbw9TUVN1lZikI1eWbhd+SBk1bqBq43e6SlyUGgwE6nY6a4xjfp1pQT9ugFpkkhm3QSDKdNPj5qPaQExAvH321mJycBMdxRM4YSHqS05QTQmixLFvzmZbX6wXHcVT2Dt9GKYfPZt+n1UKpVGJ0dJSoLi91vhkIBJDNZqn2j+O4mueqvb0dbW1tkuGp1C8jai1kxEMqxiuPiYkJdHV1FS18KARi3FQJVTprFUyxWAyxWKys10ckEqGSb7jWsFAA6Ovrg8lkktw6q3XvkPYCLJdShP+OVHoLAIhEIlhcXGxKPjIyMlK0IJgQSOUw3e12w+l0lqzZQvqwemJigshlDt+WFMAwDCYnJyW31stFvvDfNcvhmFCDiASfK+clm0+rnuA4johMkspBg5C6V0Dt+lI9ZGY4HMbS0pLk+EitqDUyB8jpROl0GmfOnCHYM7Kgpcs3K8qNH8nD/mpQrn8kazDROrBs2Qbn8UrTl/JBSkeQUtrkiYkJyGQyHDhwoOo2RkdHIZPJqJ758J79tBxQ+edqpZPfXjFapC5qLRZLyfQ3zbhPM5lMzanmAfK6vNT5Zj3653a74fP5JCfjagH1y4j5+XkkEomavbsPHTqEU6dOSSY/KgmP9X379kGlUhHfyKU2icPhgEwmoyIsSBZOKofx8XFYrVZ0d3dX3QbvsSmFQxIAiMfjmJubI8KA+DzLJCCUGadSKUQiESI0JycnAdR22QRIr0g5qcgXvq16ohzvAcS5jKj1Msdut6Orq6vu48djbm4OyWSSmMwkkQcakL6SSBpC35dEGp9GMYj4CEQSa1Mq0VxSMq4TiQSxFA4kosb430uFNwLNJTNLoXUZURtoHvZXA6H6UjMeIpK0DUikbeN/L8ahmtPpLPqMVFIvkkQ6ncb09DSR+QgGg1hdXSXUs9owPj6OPXv2QK/XV92GXq/H3r17qZ75AGR0R5Zlsbm5KSk+QsoRiLbdKgXMzMwgk8kQ2ackLw2lbmcK7Z/P50M2myVCs9ZC4zykpMtTv4wg4bHO/z6ZTGJ+fp5Et2oGiZt/lUpFPMSpHGNVKpVwOBzUhEX+s2Ki1tx3PKR0c8iHhZJgQAA5z+96zP34+DjkcjlGR0dramdsbAxra2vw+/1E+lULSHkZ84fp9V635fIoAuSVCBIHU4C09j1JmRmPx7GwsECiW/B4PJDL5UXTcAE54zoYDCKdThOhWU9UcuFe64FRoxhEJNdmOp3G7OwsiW5VBSFpuGw2GxQKRUPqSxMTE1AoFBgZGampnbGxMaysrBDJJ18rSMlMp9OJjo4OyfD8nRBSxNFoNEKv19edJ0gVUueptPrHMAy8Xm9J3cxisUCtVjcsn5PL5UT4HEnboFzNEkAaMp00pqenkc1miegIgHQilknIHUCcCBwafMTv94NlWUldRpByBJKynBALJHX5SCSCpaUlEt2Cx+OBUqmE1Wot+gzpw/5KIHRtchxHLAX/xMQEdDodBgcHa2pnbGwMs7OzSCaTRPpVC6hfRoyPj8PhcKCjo6OmdvgDJync6kSj0ZoLGfEg6YmfSqUQCoXKHgiS8MaQ2mUEqUPJQ4cOYWpqqi5Mbid4YVFLWCgA7NmzBxqNhtg6q+SQjqTBsXfvXuh0uprakVIRn+XlZYTDYWLrtt68UYhSR9ITjNTBFCCN8eMxPj6Otra2sny8HEjLTI/HIygNF/9so4OmjBNykcfvnXqmLRgfH4fZbEZvb29N7UjhoIEv/FtqfuVyORFDtB4yc3x8HMPDwyWjbYRASjKTVGQOIJ1UYYUgpIgj0Ly5rEmgEp5aD9Dqn8/nK5tukFSUSL343L59+yRnGwh1zuFTHjcLeJ5aq17e398Po9EoGR5N8oyBdGQEDScw/velaJlMJmi12pppud1uGAwGGAyGos+Q4iNSlxNiYXx8HJ2dnSWdy4RADL7Z3t4Oubz4cbXL5SJ62F8J+MsSm81W9BkxZNzBgwdL2t5CcOjQIbAsi6mpKSL9qgV1iYwg4bHe3t4Op9MpCYOIVMoYgGyIkxBFkP+ehCenTCYrGYLK578W21hKp9NEct8BuTlNpVKSyCU8Pj6OoaEhGI3GmtpRKpU4cOAAsb3jdrthMplKKv/8GiQ196QUweHhYahUKknwEVKeCXwb9X4nmmkHALKXOWNjY5ifn5eEUUjqgqWjowN2u53ovhcyv0BzXEYIMYhIva/QsU0kEohGozXRqgWk9DneM72eBw1CjGuADM9yu92C0nDxz5IAKT6yf/9+KJXKussXgKzMJF1LiySEFHHkv28GXksaLMtS10cqhVCeT+OCgP+eBJ+jbRuQ4nOkbYNXmr7EY2JiAt3d3SUP64RALpfj4MGDkuDRXq8XGxsbxGw1t9tNJH21kAg6gMxhuhCZxF9q0ooUzu9XtRC6T91ut2Tql5AAKb7Z29sLs9lMTJevhG/WQ3bz/StlA0lVxh08eHCrvXqjLpcRJA6LZDKZZIwHEoWMeIyNjSEcDmNlZaXmtipROkkIJofDUbKIoFqths1mE13Rmp2dRTabJXYoCUhjs5JiQADZw2ohioLdbodcLicaGUFiLFQqFUZGRiQzvyaTCX19fTW3dejQobofpgtVIkKhEFKpVM30SB9MAecvmusJqcrMSgyEZjCuhbyv1WqFUqkkcmDUCGNLam0C9T8MpnngK2R+SdXzAnKHFOPj40R4o1qtxv79+yUjMy0WC3p6empua2xsDHNzc4jH4wR6RhY0dflmBB9ZItXxE5J/HSBnq/FtiU2rXrYBCZlE2jYQekjMP9ssaCYdgQepuiT5bfBt1oJQKIR0Ok3NAZVvqxytRtKHhdJKJpN1dQQiDZJ2Ju3zpXraQrT7x7IsTp06RWSuTCYTBgYGJMFTqV5GpFIpTE9PEz1QlULI3vj4OJGUMQDZVBpSExakaJUDP3b8rV8t4NOjSGWdkVbqSBSzFTL3crkcbW1tRObe48kV3yY1FlLiIyS8jIHzB/IkFNxqICT/OnCeN5HwChofHyd2mTM6OgqZTFb3dZFMJjE7OytJmSlk3/PRcM1gXNPkc1JXwAEgk8lgampKkhfk1YAfR37NFgMtfUmhUMDpdBKZX7fbDZ/P15KZJXDo0CFwHCeJC+idaF1G1Aapj18llyW1pvGRml0ohm3QqPpSvWW6GCB1CQ7k5mNychIMwxBpr1qMj49DrVZj7969Nbe1d+9eqNXqupz51OLZ7/F4oFarYTabBdGqBbQcgYRGljTbPo3FYpifn5ekLi/1+RDSP4PBQKye18LCAuLxuCRlXC2gehkxPT0NhmGIDqIUim+Q9Fjv6+uD0WgkspGlZlyTolUOExMT6Orqgt1uJ9JevQ9JgFye1/X1daJ7JxqNEikyRHvuSXrA8+2QSo1WC0jyEf4wvV7rls9LLCTlCUBGiSCVMgYA9Ho99uzZU/d9f/r0aeIyc2ZmhkgkipB9r9VqYbFYmkJpF8rnXC5XTV5njWIQzc7OIpPJEF2bc3NzdYvm8ng8ZdNwAY2pL7VkZnnwkc315vmF4PF4oFKpShZxBJozlzUJVHJIV4+c/ZX0L//5amnpdLqy6V5bfI4snxMyFnya42bZw5FIBIuLi0TnI5lMYm5ujkh71WJiYgIjIyNQqVQ1t6VUKjE6Okr0zIeGZz+/nsvZW6T4SDlbksSlZigUQiaTkbzuTRqTk5PgOI7oPp2amkImk6m5LdqH/ZWiWWRcvUH1MoJUISMehw4dAsMwOH36NJH2qgVJj3U+xInETZXb7YbVai1bsNDlcsHr9dbkbVDJhhQ7rxvJ+QCkUcyW93AnyYAAMhE4brdbUHHdWg/peExMTECj0WDPnj01twXk5jcYDGJ1dZVIe9Ugm81iamqK2LrV6/XYu3dv3datUKWYXzek1gWp/QFIY9+TjPICcu+UzWYxPT1dc1tC930983CTRCXvW4vSGQwGkclkytJyOByQy+V1G1sx9DmO4+pWTI3WZRNAX2aOj49Dp9NhaGio5raA3Fz5/X6sr68Taa8a8JE5pGSmwWDA0NBQ3Xl+IQjJSww0D68lDX5MhBQmBegfMtHsn9BDxEblcxqNhojHOkDONuBrlpQbC6VSCYfD0TR7mGQ6o/x26s2jpXrGIJSPkDhMp7m3haT8JUGrUj7cLPt0fHycWKp5ILee0+k0kVqrtGVIpaiHjLPb7ejs7Ky5LSA3V8vLywgGg0TaqxZULyMmJibQ29sLi8VCpD0pFN/Y3NyE2+0mfghG6pZc6AUBy7I1VaKvZEPSiIwgOR9jY2M4c+YMEokEsTYrxfj4OFQqFYaHh4m019PTA4vFQn2dkZj78fFxHDhwoGR9kkoghbogZ86cQSqVIr5u6/VOQvMSk0rjQ/oyB5CGx8DExAT6+/vLhkILBSmZKTQNF9A8qUNo8TmhF3kk0/hUg4mJCXR0dGx5c9aKAwcO1DU1WiWXTcFgEOl0umpa9fCmOnDgABQKRc1tAdKQmaQjcwDp5CTfiUrWSzwer2utKCmCjywpZ3vWy+OVdmSE0LUUDodryjzQ4nM5BAIBMAwj+EC1GfQlIDducrkco6OjRNprb29HW1tbXXk0x3GinDGQiMDxeDxQKpWCIuiA2g7Tae1toZHCJGgJ5cOk69zUGxMTExgaGiobFSwUpJxdU6kUQqGQpO3Mesg4UhkggPqn9OZB/TKC5GGR2WxGf39/XQUT6Zt/4HyIUzabramdSjYJ/zwNWmIyDD73HWmvhXp6bAK5vbN//36o1Woi7ZEqMkRTUeBBWhHs7+8nlhqtWpAOvQPqe7AiVKnTaDRE0viIcZlz6NAhbGxswOv1EmuzUpCWmVarFb29vTWvCz4NlxR4Pg1I0SAiQasWkF6bvGd6PXlWJfpStXVu6iEzSXtyDg4OQq/XN53MlMIFdCHQ1OWbEZWkFOGfpwmhlyX8xS+tQ0SgsfgcaZlEyjZoFJlOGhMTE8TqafKo94XxysoKwuEw8TOGSCSC5eXlmtrxeDxoa2uDXF76aI/2mU80GkU8Hq+KTiwWQyKRkJTuXW9HINIgzTedTic6Ojpq3qe87JEq36zHZQnpuRoZGYFSqay73ks9TRNJwwGof/ENkoWMeBw6dAipVKrmECdaBgzDMPB6vYJpbW5uEimcXAik0xkB53MJ13udkWRAAJnQ0EgkglQqRY0ZsyxLnBnL5XIcPHiw7vPrcrnK1nepBGNjY3U7TOfzEgvxtCCxLkinjMlvq55CWqoy85VmXFdiENXq2dgoYyvVtVktaOlLQovV8rRIyMxTp04RnSupyMzOzk44HA5ibR46dAhra2vw+/3E2iSB1mVEbRA6fk6nEzKZrC6XEUIuS/g0PjTSq9S6luplG0iRzzWKTCeNZtMRAHFtDRLrTMgaI+HZT+tSk+beERpZQoKWlCDVfSp1vkn7siSdTmN6eproXKnVagwPD9c99R21y4hwOIyzZ8+KsuDr7Z01OjpKLGUMQO4QTEiOSqD2PKR+vx8sywrekNlsVrT8ZBMTE5DJZMTCQgHAaDRicHCwbutMjLBQILfOTp8+XVORIX7NCF1nPp+vpoifpaUlRKPRpuQjYrwT3zZt8LxHSCghibD0iYkJ4pc5e/fuhVqtrtu6CIVCWF5eluRar3TfN7rSXsn7tre3IxAIVJ3GhzeIbDZb2WfrNbZ8BKIU12a1qKRmBP98tXTy2ylHq9Z6XgsLC4jH48SdGVoykx5o6fLNCqHjR+KwvxoI7R9A5rJbinyumW2DSvWHZtm/YvHo2dnZmtKH1YKJiQmYTCb09fURa7O3txdms5namQ8Jz35aMommrcHzxnKRJSRoSQVerxcbGxsNzzfrMR+0+zc9PY1sNivJuaoV1C4jxEhnxLe3tLSEUChEtF2hEMNjva2tDS6Xq+abKqFFfwwGA3Q6XdWhv5VuSL5vYmB8fBx79+6FXq8n2m49i9murKwgFAqJsncymQxmZmaqbkNobYD8Z6r1kADOe46IMRaTk5M1HfrUAjH4yL59+6BWq+uyboUavACZoptijJ9KpcLo6Gjd9j2vHIix1hcXFxGJRKpuo9J97/F4as6HW0/Q5HNCi9XytOpRtG1ychIcx4myNtfX12uqX1UN+JQiQnQY/sKz2nGvdC1xHFdTdJsY6YyA3FydOnWqqWTm8PAwVCpV3b3EdkKoLu9wOCCTyZqmsCYpCB0/oD48tdL+0biMoM3ngOa1DdxuN9RqtaDaX/UqxEoaHo8HHo9HlPlgWbZuaZN5L3JSOduB82mTaZ35ALUdjiYSCUQikYr2Ni0+UosjkNTlhBgQ086cm5urqX4VP75CnAz5+aBpZ1a6NuPxOKLRaNX0xIjKAs6fb9bTRqd2GTExMQGFQoGRkRGi7daz+IZYHutA7TdVleTrlMlkNSm41SidYt1gSnU+aoFYhwkkvAArDaPL/001mJiYgMViQXd3d9VtFMLY2BiSySTm5uaItisEiUQCZ86cIT6/SqUSo6OjdVm3NI1roHn3vUKhwP79+4m2S0Jmejwe6PV6wWm40ul03RwGSIAmn6v0Iq8e3ll8BCKfwpAU6uWZHo1GBafh0ul0MJlMNXv60ZSZNpsNnZ2dVbdRCGNjY0gkElhYWCDarhCIFZmjUqkwMjJSdy+xfFSSl7henv1Sh9R5Ki3njVgshlgsJoiWWq2G1WptKD4nVdtAaBouIDcWtUbDSQFi2a0HDx7c1j5tSNnWoMVHKtnbfJ2bWviITCYTlI6RREooKcsJMTAxMQGVSoV9+/YRbXdsbKzmWqsejwdmsxlarbbss+3t7UgkEjVdflQKfv6FXpbk/6YaTExMoKenR1AasUowNjYGv9+PjY0Nou1WAmqXEePj49i3b5+gRVUJRkZGoFAo6uLJtLS0hEgkQvxGEajdE7+SvMRAbYyVttJZCmJ4ywG5+VhdXUUgECDedjlMTEzAYDCgv7+faLt2ux1dXV01rTOPxwO5XA673V72WVLMmLRXCnDeK6AefEQsL2OgfhE9NJU6sS5zgPPF8urhMTA+Po79+/dDo9EQbXd0dBRyubzmfV/J/PK/aVRUYxDRuoyoNb1FNRgfH8fQ0JCgy6hKUC/P9Ep0GP65Wua3krzE+f2rBrxO1JKZwlDPKNhCqCQvMf9cI/NaMSD1QyZa/aO5lmjbBlLmc5XOL8uykqtbUynGx8eh0WiI1tMEAJPJhIGBgbrw6Gw2i6mpKdHkztTUVM1pk6V25qNSqWC322ui5XQ6oVAoyj77SnAEIo3x8XGMjo5CpVIRbZe/NGxmO7PSyxL+N9VCzPNNvv16gdplxMmTJ7cWJ0loNBrs27cPJ06cIN52OfA0xboln52dRSKRqOr3NI3r1dVVaDQaGI3Gss9aLBao1WpRGIbH44Hb7RZtPoDcOqaNkydPYmxsTFAew0px8ODBmvaOx+NBW1uboL6RYMYnT54UhRm3t7fD6XTi5ZdfJt52OfA0SXsZA+eLSNE+TN/Y2NjyiCmHWtP4iHkwNTY2hnA4jKWlJeJtl4NYMlOr1WLv3r01rfVqlMR6el3UimoMomrft9Kx5TiOelojsdamSqXC/v37qetz/FzRMuRpykyxPDldLhfsdntLZoqMSnV5p9OJ1dVVMbvUUKgksgRo7ssI3hNaaH2KRuJzYskkEraB1A/VxMCJEycwMjJCtJ4mj1rt1moxOzuLVCol2hlDOp3G7OxsVb9Pp9MIBAKC15nD4cD6+npVtGieL62srFRkS+b3r1I0giMQaYjFNw0GAwYHB5uab9Lu3/j4uChzNTg4CJ1OVxddngeVy4iNjQ088cQToh3kTk9P4z/+4z+oGw8f/ehHAUDQIXylCIfDAIAvfvGLVf2+kjoO/HPVbpLPf/7zSKVSiMfjZZ+tNSVUKXzmM58BgKrzBZYC7wHKzzktZDIZ3HfffTXVdSiFEydO4Be/+EXV+eOF5tkGAL1eD6PRWPXcnzx5EhMTE5ifn6/q9+Xg9Xrxj//4j6K0XQof+9jHAIB41BgArK2tIRqN4t577yXedjEsLi5iaWkJDz74oKDnXS4XMplM1Wl8br/9dgA5jymx8Ld/+7eitV0Ia2treOqpp0STmTMzM7jrrruq/n0l+54/GPvyl79cNb16o5L35cOEv/CFL4hOqx4Fa71eLx577DHRDgOmpqZw9913U9Xn/vmf/xkABF861qIvVTK/RqMRer2+alonTpzAqVOnRDmclslk8Pv9uOOOO4i3XQ5/8Rd/AQDEo8aAHL8Kh8O47777iLddDSrV5Z988kncf//9YnapocBHA9AqEF0p+MuSSvpXbRqff/u3fwMgXF7Q4nO12gY8n5ucnKzq9+VQq20gdZlOGhzH4dvf/rZo83Hy5Ek88sgj1DMV/NVf/RUACHJKqRS8Z/onPvGJqn7P15USus5+8YtfYHp6uiqnmUrS0/B9qnY9/+d//qfgVD8kLiMq2af1cAQiibNnz+K5554TzSN+YWEBX/va16r+vdT5ZiX9czqdkMlkVffvt7/9Lc6ePYszZ85U9ftSkMvlSCQS+Ju/+RvibQvuAw0idrsd3d3dW4dGpPGhD30IF110EfHwzHL43d/9XRw+fJh4/i4AuPXWW9He3o4bbrihqt8//vjjAHL5jYUgGo1ifHy8qoP8D37wg7jmmmsEF43OZrN4+OGHK6ZTDq9+9avR0dGB17zmNcTb7unpwf79+3HrrbcSb7sUlEolurq68Pu///uitH/77bdjYGCg6gu1xx57rKLoHYVCgV/96ldV0XK5XDAajXj3u99d1e/L4Z3vfGfV+60W3Hrrrbj22mtF8SB673vfC6fTiUsvvZR428XQ3d2Nffv2VXyAf/To0aroXXPNNWhvb8fAwEBVvy+Fa6+9Fl1dXXj9619PvO1ScDqd6OrqqtowKYc///M/x8UXX1zVbzmOw5EjRwRfYF599dXYs2cP/uiP/qgqelLAo48+imQyKehZi8WCnp4evPGNb6yYTjQaxfz8vOC8p2q1GgDw9NNPV0yrWlitVvT09Ii2Nj/ykY/g8OHDVPW5973vfRgcHMR1110n6PlIJILnn3++qguT3/zmN4JlJsdxNclMq9UKrVaLt7zlLVX9vhze9ra34aabbhKl7VK49dZbcf3114tyKPS+970PTqcTF110EfG2q8Fjjz0GAIJTon3oQx/Ca1/7WjG71FB47rnnAEBwZHEqlYLb7aZ2qHHs2DEAAMuygp5nGAYsy1Z1gHTTTTfBYrHgggsuEPR8JBLB0aNHBfctHzRtg/7+frS3t4vmLFaLbZDJZHDixAnB+hJvR//2t7+tip4UwBdk/shHPiJK+5/4xCfQ29srqCA4SbzpTW9Cb28vLrnkEuJtX3TRRejr68Mtt9xS1e8r5XOf/OQnMTo6KtizOx8PP/ww1Gq1YFrRaBRPPvlkxXSA3N57xzveIehZpVIJlUpV1fkSn4Y7lUoJep7XT59//vmKaUkFLpcLnZ2dop3Nvv/978dll11W1W85jsPRo0cF801eP3r00UerolcNnnjiiYpqVGg0mqpl3PDwMJxOJz70oQ9V9ftyuOWWW6qyWYmBa6EpceONN3IAuPn5eUHPHzp0iAPAeb1ekXvGcQA4pVIpOp0WxAcATi6XV/S8yWQSsUctNBq+/vWvcwC4z372s/XuSgsCkE6nOQCcy+Wqd1eoAQAnk8lEpzM3N8cB4G644QZBzx85coQDwN12223idqyFbeju7uYAcMlksuLfAuAUCkVFz1sslorptNAcuOSSSzgA3Pr6er270pD42Mc+xgHg7r//fkHPv+Md7+AAcE899ZTIPcvha1/7GgeA++IXvyjo+b/7u7/jAHD33HOPyD3juN7eXg4AF4/HK/5tyzbIIRAIcAC4kZERQc8vLi5yALhrrrlG5J610Ey4/fbbOQDcD37wA9Fp8fpPIpEQ9LzRaOQAcCzLitqveDzOAeB6e3sr/u2vf/1rDgD33ve+V9Dz9913HweA+6u/+quKabVQHslkkgPAdXd3C3p+c3OTA8CNjY2J3LPzAMDp9XpBz6ZSqVec3VwJyLvitiAJ/PCHP8QjjzyCwcFBQc8/+uijePjhhwUV6KwVNL04WxAXv/71rytaM48++qiggnYtvHLwgQ98ANlsFn/6p39a7660IAAqlQr333+/KN5hUsWjjz4Km80mOp2hoSHcfffdeNvb3ibo+SuvvBJ33303brvtNpF71kI+nnnmGRw9erSqNEEPP/xwRd6IjzzyiOB0CC00Hx544AE89dRT6OjoqHdXGhJ33nknRkdH8da3vlXQ8/feey9uuukmXHvttSL3LIcPf/jDkMvl+OAHPyjo+c9+9rPo6urC+973PpF7luNzR44cERxhn4+WbZCD1WrFfffdh1e/+tWCnu/v76/o+RZaAHJpQffu3Yu3v/3totN67LHHMDMzIzi18IsvvoiZmRnRo111Oh0eeOAB7N+/v+Lf3nzzzbjrrrvwx3/8x4Ke//3f/32EQiG8//3vr5hWC+Wh0Wjwox/9CJdffrmg551OJ374wx/iiiuuELln5/GLX/xCcEYGtVpd0fOvNMg4TiJV2lpooYUWWmihhRZaaKGFFlpooYUWWmihhRZaaKGFpoSgyIhYLIY/fN/7sLG+JnZ/tsFmd+D//v3f43N33IFQkGyhote+/g341Kc+te2zRCKB973vvVhfI/eeHZ2duPfe7xTM9/rss8/i05/6e6QE5qIuhSuvvqamAqEPPvggvvLlf0I2k6m5L0LhbGvHt+65p6g3zNmzZ/HhD32I6Nyvb7ghl8vhclWeJ7ESrKysIFwk150MMgwMDqK7u1s0+h6PB3NnZsFxHJRKJfr7+6EiWJPAZDbj/331axgeHi753LPPPot//ud/RobiuiqGhYUFuDc20NXdBaOh9qLzrg4X7r33OyXrbXi9XvzJ+98Pr7fyIldDgwO49957i+bF/vrXv47v3vc9sCz5++Th4b2451vf2soJeubMGXzso/8H4SqLTO9ENBrFyuoaNFotLr74Ymr54U+fPg2vz7/tM5lMhra2NthE8MpbX1tFIBBET08PzGYL8fa3IANe8+pXYe/evbj7m98Ey1Ze3FIIHA4nrrjySjz04C8AGn4MMhne+a7fx4c//GHxaVWIf//3f68pyq+jowN33nnnVt2HeuL222/H80dfINpmX18v/us739kqzAgAP/jBD/Afd/072CqKr5aCze7A17/xDXR2duK73/0uvvHNu8Ewlec5Fwq5XIY/fN976+YVl81m8bd/+7c1F6bes2cPPve5z0Emk+Hxxx/HHZ/7HNKpyuuG1YIbb7wB//iP/4i7774b/3XvveC42udNqVLhb/72k7jwwgvxp3/2Z/Dt4PliQ61W4e//7/8l5t38yCOP4Etf/AIyVdR0EwKjyYR/+X9fxcjIiCjttyA+vvCFLxApRJrPE6SC73//+7jrP/4TTIW1K5QKBf7iYx8tGRXzxBNP4I477qiqXmIpXHfddfjCF75AtM164fHHH8dn7/gc8TECgJvO8f+lpSV8+EMfQpDwWU8+AoEAPF4f9uzZC5nAege1Qgbg2muuxsSpSQQCQeLtHzwwgm984xtb+zWVSuG9730flldq0w12wma14K67/h39/f1Vt/Gb3/wGn/vHzyMllo5xzg767Gc/K077EsfXv/51fO++71ZVy6wc3vp7t+HjH//41t8f//jH8fwLLxKno9Nq8YXP/yPVCAex8cUvfhG/ePAh6nSj0SiWl86C41i42l2iRFjLZDL8wXvfiw984AO1tSMkMuLpp5/Gddddh+v3WNBuomM4p7Isfj7hwwc+8AF885vfxO9d1AE5IeXopaUQNpMyhCPRbZ8fOXIEV199NW68aBgd9toLI7n9Yfz2+AyeeuqpgiG/f/EXf4GvfvWreNtFwqqxFwLLcXhpKYJFfwLPP//8NsO/EPbv3w+O4zAzM7Pt87/5m7/B048/hlsOOqvuSyEkMwwSmd0KZCTN4PnFML761a9uKxqp1WoxMjICmUyGe+65B3/yJ3+C37uoE3JCevHPxz2wW0y44WLxjC6O4/DDR49C7+yGtX9UNDql4J8fBxfx4M2Hql9b+UhnWcTSuQMkDsBjp734y7/8y4KFtfk5XFtbw4c+9CH84uc/w6v2i5/+qxyOno3AaDLhxsuFFe8rBbcvgMeOHMcTTzyB66+/vuhzP//5z/E7v/M7uOSmN0FnFM5TPCsLmDn+HKampooeEIwcGMPCug+WA2TTCSTd84jMHcP8/PxWmre77roLH/7wh3HbhS7UwoY5jkM4yWDKHcVyIIWbR+iui9/MBGDsGIJjuLrizZVi5eivoNCZYB+9WlQ6obljyAZWcclFF2L61Em85uLSl4RCwLIcwvHklmIbS2Xw9MQC2hx2qNkErtkn7oUuADw944HR2Ynp2TnRabEsi6mpKcEG91VXXgGzWob9LmGFZfPhjaZx2h3Dd77zHRw6dKjs806nEz09PZiZmUE8HhdMp6OjA52dnSWfSSQS0Ov1aB86ANfew4LbLoXg+lksjx/ByZMnt73fq266ES8//wxu3k/u4i+ZZfHzCS9+8IMf4B3veAeuvPoaHJs4DecFZA6COY5DNhEB8i74gmdeQn+HEz/+0Q93Pe9wONDT01PRWgKA3t5eWK1WTE1NIZvNlnx2ZmYG73znO7Gv3QCXWViqhJ1YCSSw6ItjYWEBAwMD+LM/+zN86557cP2bxE/zwGN24hjWF2eRzWZx0QWHsDY/jev3Wmtu9xenfHjr296Jt7zlLXjHO96BS26+FWpt5almqsULD/8P3vPu38e3v/1twb9ZWlqCz1fYaeFTn/oUHn34l3jzmDjy8v6XN/GVr3xFtKL0YoNlWZw+fVpw8VEeg4ODSCQS2NjYEPS81WoVnPp2J3i7Syj/5vnB5OQkmDKXt+FwGDfeeCN6HEYMtlfn9JBlWCxuhrEeiOEXv/gFurq6Sj4vl8tx4MABRKNRLC4uCqKRbxtUUiD8T//sz3Fyahadl7xG8G8AYO2FX+LmG6/DQw8+WPSZD37wg/jmN7+Jd7/73RW1XQjxeByJRAKTk5NYWFjYKk5eCDabDf39/ZibmxNcpBXI6QK9vb0197US/Nmf/Rnu/tY9GH3V7xFtd2P6OIKrc8hms/jOd76DP/zDP8Tbb74CCoU4FwWPHT0FXyyNvqt/R5T2C8E7/QIyQTeSiTjar7wVMkXpM5py4DgOTCICjmWQ3DyL+NosHnnkETidubObyclJvOc974F5/1XQOGpzeORYBtl4GOA4BMd/g09/+tO49dZbCz4rhB989rOfxc8f/CXar3hLTf3K9Y1FNhHe5hQVWz2NbMiNo0WKSstkMnR3d2NlZUUwHf69YrEYFhYWBP0m/xyLJg4dPADv+jJuPFydjCqGZyaXAI0RS8u5cYvH4zAYDDD3HYBl4GDN7TPpJJh0AgDgPvYo3vOe9xQtqm02m7Fnzx4sLCwgGAwKpmG326u6SGMYBpOTk2V18nz09/dDp9Ph9OnTAIAbbrwJCqMdPQeFpZwiBfeZcWwuTOJdb7iu/MNV4rcvjKO9swcnTtbmCCHIVZo9543w+VuGMOQorNAnMyy0qvICROhziQyDn0/4tmj/6zvGBLTNQKsq7EWcj7ufXsKdv1ne9TlP658++LsY7i1+iJtMZ6BVlxcoZ1Y8uPQDX9pqtxC90S4Lvnrb+cPGLLP9bkip2M7Mdo7fX//vDBb9uU0s5CbxissuRTLL4sTx3UrS9Xus2/pSDELncDOaxkV3Pg+2xH3Xxz72sV2fffWrX8VHP/rRrXH7yltHy85robkv9Nmj00/i7Tdfjjv+tLBSJXRuS4HjOPzg18/j0Ns/gT2v3n1YTwNPfeUD6PU8i6+9o/hBV7H9svNzhuVw6HOPIxDfHt3wla98BV/5ylcKtv3JT34SX/ziFwEAl/SZ8V/vq/0CoFowLAeFXIbr/uUobnn9DfjC7cJyQvJIptLQarZfws4vr+PQmz9QdG/z4L9//2f+DSbb7ou+dCoJtWb3YdLMy8/hjve+pmT7LMvCcckbMPTufyz+TCYJuUq76/+lEDr9LCb+6W3baLMsC61Kga+9rTR/yOcNhfjEnb9ewP2PLwEA9rXp8V/vJXP4KRR9n3oCw296P4Zf/0cFv2fSSSjU5cdI6HP/++eXoP2KW7H/nf+36G8LtSW0fR5Lv/42znz/M2BZFq+9eBh3fey8J+BOnpb/dyl+9+F//Sl+9eL0rs+DwQA++Kph/N83lz9ELwShchoAPvezcfxqvrIDpmrx+c9/Hp/+9KcFPy8D8InX78EfXFb6sL8QfjGxiT/74XRFeb8/9alP4XOf+1xFdGQyGZaWltDT01P0Gf6y6ap3/QXGXi2s9kQ2nYSyxPpcmz6Ob3/w1bv4F8sweNWwvaiesZNnCNE14mkGP5/wbtFiGRZtF70OBz/wz4LeZSd27r2lh+/G9H2f2vXc3JkALr648KXm+9//ftx9990V0dXp9fiD97wH3/jGNwT/5q53HsDBTlNFdHgcmQ/g9755fCtqkWVZ7D1wIT525zfL/paXW8Xkl1A88qNv4z/+4f8AABiGxS0Hnfjcm4YKPitU7wSAjcgkWJbdWhN/9Jm7oNXXHg2Zj0wqCVWRd/etLpbVDfLx4osv4rLLLiv5zCW9Rnz1recvmUvtlUrGCgAenQ1X1F+p4Z/+6Z/wyU9+suLfOZxt8Pu8FXmSPvLII3jNayo7FAeAb3/72/iTP/kTwc/r9Tq85z1/UBE/+MI7r8IbLqrOc/mST/4Y64EYAOCWW24R9Jt3vPOdePSRh+HzC/dmz7cNhEImV6Dv2rfi8o/8KwDh+tGzX/njslF4LMvioosuwne+852tz5LJZNF8+MW+W1hYwNDQdt5VTD7w+MxnPoPPfOYzJZ8phBMnTuDwYXq6M8uy6Bw+jDf+9V1bn5XTAXai0PMnHvoJyL9dAAC4A0lEQVQv/Pr/3b5FAwC+8Xd/shWRLRTJVAZajaro3zze+tf/D+NxC67+6L8VbIe0/g8AUw/chVM//BIAYOT9X4VcqSrbRqnvlh76d8z/6PPbPnvta1+767mBd90BQ+8BAKVtv1LfTX35NgRPH9n6+4477sAdd9xR8FkAeNe73olfPfxrBPzFMwGYhy7CgT/9atHvd6LYWEzc9UH4Xv51wd+U2nc6gxGJWLTo94Xwtre9HY//9jFsVpDh4Mtf/jL+8i//siI6tYJlWdx69QF84Q+Fy6dkOgutuvRR8J0/fhL3PTW79TcvL/fd+lH0XH0rgMptVv75ZHATj3zwgm0Rsffddx/uu+++or+tlm8+/fTTuOaaayr6zV//9V/jn/+5MlvCYrHigkMH8eTTzwDIya8b3nk7rnj7Rwo+XykvFYonvvU5aJM+fONTf170mULnW5Xgb/7fd/HkqaWqf8+jorwtC74E3OE05DIZ9rbp4I6kEUrkbouUchmyLAelQoZLekx4YMKLLrMGwUQWHDjsb9cjlWWRZQG1QoZAPAuW4zDWacCCP4k2owqeSAZOowrLgSTUOw7hf37SjTTDYqzLBF80jTTDYTOSwqBTjyzDYT2cwqBDh1Ayi2gyizaTBi6TGkqFHHq1AseXQ+i2avHi2RBOb0RLKp/HZpYxMb+G373+Qjz03Cn0uWxIpbNY94XAARjuaUdPuw3fffh57O1uw+E93fAEI+iwm/H0yTlceXAQ4VgSE/PCwuR+Pr6Ze7dOI+QyID/rSq9NixlPDKksB7tBhVSGhUmrgFIug16twMR6FBd0GXDnm4fw6EwA0RSDS3tNUCtkYDkgnmHgi2VxWZ8J//nsGl5YWUYiw8F51e+h8zW5sJrAyUfhPfITxDNeHFkIbs3vjCeO4XY9VoIpzHsTYFgOOrUcToMah7qMOLkagUopR5bhwHIcVoMpDDl1sOpUkMmAyfUoWI7D0HvvhGHgAgROPgomEYVp76WQKdUAy4JJxZGN+GDalzPEpv/5nbturSfXo8iwLNTn5tKoUeL4cghGjRJ7nHr4YmlkWA4dZg180TSWA0kYNAq0mzQIJzMYcuph06txYiUMhuXgC0Xx9IkZyGUyDPd1YMMXQjCa81ZSKuTIMixUCgUuOzCI+3/7InrabAhEYuA4YGSgE4lUGqFoArFEChcO98ETCKPDbsHcqgfJdGZrbcX9G3CfehYymRzm7r3YGH8a2WQMKr0JcoUSxvZ+aCwOeGdeQjYRg6lrEHK5ElpbOzRGKzZPvwCWyUKlN4PNJBH3bcDQ3gu13gwmk0vtlQrn0g+kYyEY2nogUyjAZjNIhf0IJdLwRtN44WwQh7vNWAsmwXAcfLE0Xn+gHS+vhDHiMuLpMz4c7DIjzbCY9cRgN6hg1iqRZTi0mTTQquQIxDP4P9d1400H7GXX2e/fN40zZ84AAEz7LkcydhpH5gPn1pUeT88HEEsxMGmVUMpl6LNr4TCocWwphFiawaBDD6VchjaTGnaDCi8vh5FlOWiUcgQTWYQSGexx5vjJRjgFs1aJaJpBp1mDJX8CRq0SQw49/PE0goksuiwaxFIMUlkWq24v7n/4Sfzuzdfg+ZO5W+vudiem5peQSmdgNurR1e7A3NIaNGo1MpksejrbEAhFMNDtQibLIJPN4sjxSUF7Ox9TLz6N7qH92FxbQjadgsnmQCaVgs5ogkKpgkanx9r8NGRyOSJBYUpPJuJHeOYoZEoV2GwaCrUOGns34uu5yCeFzgSFWgcmlQCTjCC2NAnLaE4YZ2MBaNsHoTLaEF+dhlyjB8dkEFl4uSi95xaCcEfSuGWsDS8uhQEAXRYNXl6JIJjIYG+bHhf1mLEUSCCeZjC+FsWth9ux4EugzajGqfUodM5e6Acvgmz5ccy4Y/DF0tvXRpqBSaOEUiFDn00Hh0GFY0vh3Npw6nJrw7h9bQBAhuGgVyugkMtg0Srhi6URSTFQK2QIJxmkGRYcByQC7m37MhFwIx3LpZ+SK5RgmSzkChWcw5fg7DM/hd7RjXQ0AI7jYO0bBZNJgmMYyJUqpCIBcBwL++AhRNbnobW2IxFwQ2drR9y/ATabQTq0icD081AZrUj615EKeNB+yWsRW58Hl01DrtJCqTchHdqETKVB0rsCtdkBtcmBbCoOLpOCxtaBpH8N+o4hKHUmRFfOXRJwLDQ2FxK+87ImEE3gyORZ2Ew6rPnCcPsjeMPlIziz5kU6y0CrUsFq1MIfiYNlOei1aqx6Q2A5DgMuO1xWI1QqBcYX1nBxjxFfeFPOy4bf9z856YUvmsKzs5uQy4G97WY8P+/Fki+Gbpsee9qNMGlVmFgNIstwcBo10KoV6LXrMeeJQKdSIpbKQqOSI5VhccmAHSeXA+i06hFLZWHQKLHki8EbTcETTiJ37C8+VlZWoHN2Y++HvpUbxzLyauJzb8DJtSj+52UPxjoN8MUzyDAcPJE0Bh06KOQypLMsXGY11kNpDDq0UCvkmPcl8PRcbr197w9G4dArS/LUHpsGf/Tf05idnYVSa8DoX/+PoP7Flycx9+3bsba2VvIygod3aQaTv/0JRq5/C1YncymbTG1d8C5OI5tOQmM0w+TshHdxGqa2LqQTURht7TDYXVCoVFgefw46kxXxcADxoLconc1oGg+Me/CmA2344bENdFk02IzmeECfXYtOswYrwSSUChk0SjkYlkM6y8FlUm/peUaNAuNrUZi1Siz6ErtopIIebDz3AFyX34LgbC6UXGvvQmx9FjKZHOmIH1pHF5hUHEnfGlR6C1xXvBnR1WkwqQQiiyfRcdXvIr4xj9jaLMw6FX703pGyss8by+A9953GwsICdG192PvBbwiaq9CpJ7D0P1/E4uIiDnbo8f+9ZU9JWhadAh/9yRxOrkZwZjOOtxx24Scvb6DbokUgkcnpKi4DTrtj0Crl6LJq0GbUQK2U4fhyGEq5DC8tFU65t3xmCiG/FzK5HD1Dw1g+cxq9e0fw4uO/gqtnAEaLFelUCiq1GhzHQaPVI7C5gT0HL8Lxpx+Fva0D2WwGqwuzuPn33ouH/vvr6BrYi6HRCxD0eWBr68D62TkoFEpsLM1v08VnN+O4/2UPRlx6ZFkOMgAWnRJroTQ0Sjky53h4vt0w701ArZBjxKXHqY0YVIrcc/lYX5hBOhmHTCZH5+Aw1uZPo2toBDPHnoXZ3oZMOgmlSoPg5jqUKjW69ozAu7IIR1c/IgEvvGuL6B+5EEAualGpUsHi7EAiGoarbw/kCiU8y/PIJHNrMRqqLNUIr/OOfvy/oTTZd62XtV/9BxKhkzh6NgyrTon1cAruSAavHbFj3ptAhmGhUclh0Sq3bCu9WoG1UAosB/TbtWgzqqBWyPDsQhjBRBYZhsX+dj2CCeEef1LFysoKtLYO7Ps/9wIov98AwPv8/2L94f8EAPzL7+7BaLu+5J67uNeEN31jvCKv2l19VCnwv398oCwfefxMCF96bAmLi4sw9I1h6A+/UvK9UptLWP7pl3D0jBvRZBqH+pxY9kWRYRhYdGq0W/TIMCymVv3IMlwugqLNjIXNMPa4zFDI5VgLROG68b1ov/73BY3f4vf/AWeXluHzB6qyDfb96V3QdgwJojXx+VsQ21zG2Sfvh3VwDOmwHwpNzjFSJlcgvrkCY9cQtNZ2yJVqeE8/D5XWCCaTBiDM8eHJJ5/E+vo6brvtNrz44otYWFiAwWBAe3s7dDodotEolEolzGYzUqkUYrEYVldX8apXvQputxtLS7kDmX/9vb3Y59SVHItDXUbc+q0JzM7mDvgOffpXgtZtOrCO6X/9I6ysrFC9jOCxfPJZxPxuDF/3O9iYeRkaQy4KJ+bfAMey8MxN4PK3/x+sTx/bOlwzOjrgOzsNtcEMjcEEuUIFlVaPtakXEFxb3HUW88yJGXgCYdx6w6U4eioXEdvVZoMnEMbUwiquGNsDbzCKNW8AOrUaSqUCvS474sk0lja8GBnoQjqTRZcz95sOhwVzK+5cP+NJZJOqovq/TK4AxzKQK5RwDl+Kxad/CoOzG6loAOA4WPtGwKRTYNksFEr1Nv0/vD4PHa//W9sRcZ8FwCETPx/1Epx5HtmID22X3YLI4gkkN5eh0OihMjtydloyBplCAYXWBDabBpuKIxVYh3X0WmTCm1BbXQjNvgiHUY3vvXt/wTU26Y7hn36zgujiSSTWZ+G49BZEF08itbkEuUYPldkJuUoLJhWDTK6EQmcEl02DScWRDmzAMnpNjpbFhbR7Dq8fseEvbugpu7f/4VeLWD57FgG/D91v/Ajsl96yaz2vP/INZMObcD//ANovuwWhMzndTGPrQnz9DCCTQWvvQmJzCfrOPWAzKXBMFnKlGplobqxN/WOIbywg4TkL8/6r0P+OfwAgjOev/OxfEDz5KFw3/gHar3+3oN+c/eFncXZpGZteHz5ybRduOegoOxbv+d5M1XKiVqz5wvjJM5N4y5UjeGEmZxd2OUxY3gwhlWVwenkT3Q4zLtrTieXNEIw6NbIMi/52K+Y2Aui2mxBNpmHUqnFsbh1tFj3cgcKXN5HVWaw++7/ouvLNCM6fgMqQy/6QDHjAsQzCZ09h75s/jMDcy1CoNQAArc2FyMoMVHozlDpTzg7mWHzpliFc2G0oO7av//r4eb759w8CckXZeWQSEUx++W1VzcnKygr0XcPY8/6vASi/zgLHH8bKz/8Fy0tLeOMBOz56XTfe8u3TiAU8WDrxDGRyORy9+xD1u5GMBAHknTsoVegevRRTv/0JTO3dSIYD4MDB2T+CbCqBZDSETDIO177DiAc2YbC7sDHzMiCTQWeyIhbYhN7qBJNJQy5XwLc0Cw4cTi+swBuMQCaTYbi/C25fEMFIzvFAoZCDYVgolQpcfnAv7n/0CLrbHQiEo+A44MCeHkzOrUCrUaGn3YF2hwVqpRLPj88imU7j7PomkbRgFV1GDDp0W5ERm9E0DnQUTk/w7efXwXLA4S4jeqyac4eGaSQyLEwaBaIchysHzoeSHu7KeSx1mnOLtduiQSKz3ZvhzYdzkQqhRAaDDj3USjnuP7aOo4tB3LDPgR67DizLQaXIGa/+WC48/vIBKwDgVftzXsn7XUYk0gxkk8WNhYuHezHc64InEMGbrspFZNzz4LNIpNJ43eUHoFGrML3kxrWH98AfimFiYQ0OswFtVhN+9/oLc+/isEAhMLfQmw+1nXu3LHQqOX42volgIoObhu04sxmHXa9CJMVg2Z8AywHRlAJXDubey65XQaMAloMpXD1oQTCRRTLLYjWUxmiHHpf1m6GUyzDljmHIocML3pwXnK5jD1LeZahMDhh6DiCofwwGRQBXnWt3M5rGVYNWbEbTuKDbhAu6z3vf3X/cjSOLQbx5rA0sm0sVlWU5xPQMPNHcBdU1Q1bEz6X1MfQfQsq7DMv+q5GNBcFmkkj7VqHvHYV532WQyZWIrUzBvO8yqE270zdc3GdBLJWFTJa7gPHF0oinGWyEUzCo5ei0apHOsFgJJKGQy8ByHCw6Jca6tnsMXj5ghUIug8NixLUX5DzMPP4wxvYUPqjx+MN4+6svx/cfeQ7BaBw3XTKCRCoDkz63B7IMgxenFuCwGNFmM6PNlmPE/MbU2zvgOphLz5IIejBw7a27aCSCHvRd+aatv+cf/zE2Z15E10Wvgrl7LziWQSYeRSqThMpggkyugG2wfFjcrOVeWJK5w4LXjDgRSTG47NxeAIDNSArLgQSem/fjzYc7kD1nvA869Iils4inGShkMnRbtYinc8bqqEtfdp09uxiCw6jZosNm0tCpFbhqyHaObhpvObw76mgzksYbx7anfbn/2DqCiSxuGnZAo5KDZTkYNLmLuOVgEkNOPX73XLs8Ls97R2A7f9Io5eh2OXHb666H2xfA1RflxvEbP/wFMlkGr732UsiQ86rp6WiDLxiGQa9FMBzFdZdu9wKvpv7G6KXX4pHvfx0cy+LwNTdDpdFCqdIgEYsgEvAh5HPD1TOIvRdcjpmXnxPUptraDkPfQUAm3zLO0mEvLPuvgufZ+5EObsB2wc1QGW1Q6s1Q6sxI+9fAMlmoLW3Q2DoAAMaB84ZNNlqcN16Zxx+uOMfDfbEMEpncZU+7SY0ZTwxWnRIZhkO3RYNn5oOwG1TotGhy/Ippg8qUSzcx7DKAn6fSayPHI+8/vnFuTdjhi2XQbdUikmLgDqdg1SvBccAlfbxs2S2fbv/JNHQ217Z9aSsSZjr90LcAjoV9z4UwtPWAyaYR92+AYzJIx8LQOzrQfuDKrefte3LRP3pH57l/uyBXqqC1d8LUPwaZTAZj934AQCq0CevenPcOk05i7akfg82m4Dx8EzR7LgLHskiFPGBTcWTiYciUqm2pnvjf8tDlhWM7LQYcGuyAXC7DSG9uT3mCUVw63IsfPP4yvKEYrh0bgMtmOpeKKbV1CeuPxDHal/uNw2yAKZvFcjAFh0GFAx0GJLMsfnzCC4dRg6v35ebEE07iTRdsDwf3RVOIJDIIJzM42G2BRqnAij8Ok1YFXzQFrUqBdJbdauOyoe1RQz12PQDgxHIA8NKJjAAAtbkNxv5D8L30UEl5FZl7EYAMh7uM+L0L2xFKZHF8JYJYmsWN+2xgWQ4cx0GtkGHem0AkxYDlOFwzZIXdoML4Wk65P9xpwHNnwyV56m9mzu9HjckuqH/hmedg6K8scsXZN4wDN70VUb8HvYdy6zoe8iEe8iEVC8HeMwSOYdCx7zBS8QiS0SDSyRjSq3PoPnAZ9l5x3htrbfp4UTptRjXecqgdm9E03p0XVXL/cTdOrkUhl+WcMFiWgyeSwWY0DZVSBo7jtvQTAFv/HywQrauxtqPjyrcgFdqEbX8uYjQd8cExdiNkcjnWnv4xIosTcBy+CYauYYBl4Tv1NBQqNdIRP4w9I1DqTDAPXoCN534Gq0EjSMdaD6e39SFfx8qfJ0PvwS29h2Oy0Pee50Gd5vK0fj6Ru+w53G3CwU4TNiNpvPXCji2Zef1eG+IZBgc7jYikslgNpnByNYK9bQZct7d0iqzevaPgk4AEvW6MXX4dgl43Xv3WP9h65rcP/DdioSDGrrgeDJNBW1cvZk++CIPZglDAC7PNgde9448R9Lrx5veer/dia8vJG6sjx2MWpye2pTLY16bHbRe2I5ZiIJcBOrUC3z26jniGxc377dAq5WA4DkvBFLzRDGIpBhqlHJf25XQv3q5Q7Ujz0Tk4vBUZEfJ5sP+SaxHyeXDJq3PpOpLxKGQyOXxrZxH2e9C1ZwRtPYNIp5LQGU2wtXXBt7EMhVKFQ9e8BkrVbk8ys/18Pl6jxbbreyEwDBxCePq5XXtbLlfAqFXhYIcBchkw3J7jj5vRNC7uzem533/JDX88g9ePOrbGqdOigTuchj+WQSSZxaV9Zrx2pND8z1fVXylBY3GW3W/RheOIr0xB3z0CpcG69dvDnUbM+xIl99yp9VjNfRTKRw526rd+o7Z1luX52VgYyz/9Ei7f68IbLupHKJ7CkMuM/31hHoueMK4Z6YLDqMWlQy5Ekmn4o0lMrPhg1KrQYc3pSTLIoOsaLmurhWeeg+2Cm8+lgMkd8gqxDZ47G95mGxgGDiG+clqQXSiTK2Bo60X/9bchHQvBP3sc6Ygf7RfcAI3BCrXBgkwiiuDiBORyJdR6Mxz7L4Piwa8DEFafiE+36na7ce21126lVr7nnnvg9Xpx6623QqvVYnNzEwqFAtFodCtNbUdHx1aqpcOdRsxsxkuOxYm184d7Kq1ekEwPTPwG5r1003zsRO/hnA4aC3jQM3ZeR3DPnkAqFsK+a98E99xJ6K1tSMcjiPk9WD75LDr2XwTX3u36yJ4rXouob2NXKptrLhiGXC6Hxx/CVYf3AQB8wQh6XQO47MAQvv/IEQQjMdx8+Ri0ahUYlsXyhg9ZhoFRr0UskcLlB/cAAFyOnDzgbXODXgsl9AL1/7vBcRwce8/p/5k04n43OCYDJp2CUmeE6+BVW887dur/zlyaM/+Zl7eesQ5fAblShXRoE9bhK4DhnG6y9sT3kY364bzk9ZCrNEiHfZDJFGDSCXBMFknvMix7LwEAaB3d0K8V5yO8PDAOHIah9wDSoU2Y910O7MutHfdTOVr2i14PuUqLTMQHyBRg0wkoNHqkvMsw7cnRkqu1GLAryu7t46tRdFs04BOv6XsOFOQjMoUSKr0Zrivecn4MkNPN7GM3QCaXY/2Z+5GJ+KEyOaCxtp1LxxQBk4whm4ye08sOQ65SQ+3oFqRjZSM+yNU6KHQ5HUDXuU/QmRSTCEPj6AGY3CH2SBV8jja6HGa89ZoD8ASjuHI0p8n5wnFcNdqHHz81AYVcjoP97fBH4uhxWhBJpOANx8F5co7IPW3nz2b5/z85sViQlql7H7qvvhXJ4CYcI7m5TIV9MHXvx8rT/wO5Wgf/7EvQOTpzKb8SUQRmj4FJJ+A8kNuD2WSOFx7qMggaW7Xy/OWyof8w/Md/VXYejf21XdyqbS5BPDq2MgV9z/nU7IN2LZaDKShkgMHWjr4Lco6gsYAH7UOF+U4s4MGBV9+GiV//EMloEAMX34hsKgm1Prevs+kUNqaPQ2Mwo33PGPZcUTwKZu7or7E6/yJGBs+fcXr8IYzt7dv6+/u/fCrHT6+8ACdnzuLysX2IxBLIZLMIR+JY9fjx5hsu3dX2zVfmxvSpY1M466ccGZGPNuN2ZfyhSR8cehWCiSzsehU2o2lshNM42GmARaeEL5aBVZfzdk1lWBxbjmDAroXdUFlKHIsu9/xDEx702rQwahQ4sxnDZiSN0U4jDnaa0O/Q4TfTPoQSGTw3H4DDqMa+9srzOrfbcpP/s2dOYqS/A4FIHCfnVuEORHBwsBN7urox0OHAqYV1MCyLZyfmYdCqccHe8l6IhfDMfAAOgwpGjQIqhQy/nfFjtMOAvnPjtB5KgeU4cADObMaxt+28wvrGA6VzyV7WZ8aTc9s94RyXvHHr/5tH7gfCZ7f+5ud31zyf8ubGXZuLytiMZDDaYcDBTgP67VocX4mAYTmolduNwXxahWDeVzpE3aA5v1SfWwigz65DMJ7BWiiFl1ciOHBu7pUKGWLpLFJZFi+cDcKqU5Wc+/YStUHa7Wb87Mlj6O9wwBzR4tT8Ktz+MMaGenBobw8GOtvw/Kk5ZBkWz5+awxXnFKBC0FkL51fnP1967kFozQ6o9EbIlSqsHXsM1v4DsA2OQWN2IBXJecpzbBb+uZOw7xHGXNtMOaFo02+fj6Nng+i16WDSKDGxFoEnksKBTtPWGB5fDsEfy0ClDFe0zl67344vPHa+ALza5gIC5xlVW5GaM/znD0144DCoEUhkoDq3hjbCKRzsMkIpl2EjnEKaYeEyqaFRyPGjl9Zxy6F26NXCvJ94uBy5w4IHHnsWB/cNIBCK4sTpObi9AYwND+Lw/iEM9nTguRNT0KrVeGF8GiqVEheOFJ/jcnjh0QfQNzyGWDiAs6dPIuh1o294DP0jh9DeM4DJo0/Au7ECRYGDjlJQaLev78jsUShNDih0JmiUKoQmn4a+ZxTGvoNQGu1gUnEgFctF26yfgb5zb8Xvks8Xnl8Mot+uRSCexcRaFJvR9DmeYNziCf5YBuNr2/Pi7jRCyq+NTfRatTBqMphYi+R4focRB7tydCY3oggnsjgyH4BBo8Th7vLpS4rty+XnHoRt4CDS0QACC+NIBDywnduPMoUSvtljiG2uwjm8W0gXgnLHHGks5w+uvCceg7F7HzKxIMJnJ5AOemDsOwBzf06ZjixPgs2kEJg5CtuwMGPUqNuuDLdbjfj5kUn0tVth1mkwv+7Hc5NLODjgwqHBTgx02HBsdgWeYBRHTy/h8pHzysrOPf+lR7enOGwvkLPeYdTAqFWh32nAzEYEqSwDTziJA10WHO61QSmX4YUFH347tYGbRjsEvRNNlJNX1rEbt/39zHwQgw4dgolsTi5G0xh15e+BKGJpBidXo9Crt/Picjz18v7dMqpc/2wX3IzYcuXRWwBgtJ/fE3qLA2qdAdbOfgTWz4JJJxH15xTprpFLIFcoMHf0UXQfKC2/C2GnbqFXK9Br08Iby2A1lNrSLa4askApl+HFpTCOr4RxUY/w2jv5+0x97gLU/cKD0LX1IqMzIXJ2AumQB8beAzAPjEEmV8J78jdgmSyCM0dhzdtvQnSsIYcOf/Wz8we7QvWewPhvt31ejtZOR6A2k/qcTqyDUZvBlDu2gz/qcHw5DHckhaOLwR2X9sVhdbq2/ctDqzOgvbsf7pVFZFJJBLxuDAyPYWj0AigUSrz01CMFfycUBk1Onj806cPwOe/93L7KYNSlz+0rmwbPLoRh0ihwfCWCi3qEpauynLsI4f8FsHVRodYZ4Ojqx/rCDDKpJMI+N3r2jaHv3HudOfF8wYsIkii0ZqKLJ4DE+XHhwe+hhyZ9GHJoYdcrC47T8ZUovLEMnpkP4pohq6j9ryfK7TfLgfO5k+Orp7d9V35/V5cObSeE8JHfzO52CCn1bhy7/cDdos/Jf71aiYsG27Hmj2HeHYInnMDBHjsO9zmhVMjw7MwGHjmxhNdecF7eC5EthVDuvW4etuEfH92eLaAau9Az8TSMnYNIm6yIrp6BN+iBpf8ArANjMLj6sXnqGchk1dcccLnO86yf/OQn2LdvHxwOB44fP46NjQ0cPnwYF154Ifbu3YtnnnkG0WgUr3vd67a1UW4sLu3dvZbKjYXzst9BOiCstonYMNjO886Vk8/C2tmPRCQAz5lxxPwetA0dQPueQ7B25lKGcSyLtckX0FWBntBuP38o6rCeO4t58qWcPa7X4uTsUs4e39ODQ3t7oVQocGpuGSse/9ZlRDkU0/+XnvsFbANjSEUD8M+PIxn0bNnj8nP6fyYehvvUkW0XEkKhztNLNl98CPqOIWRjNkTPTiAd2oShN2ev6VwDiK1MgsumEZo9Csu+8vrIy6vbvdjzafleegg61xCyRjtiSxPIhDa3Du517QOILhxHNupHZP44TEMXlaXF47ohC350fHsNmELrObZ8CmnPwq5+qU0OeF58CGqTA0pd7gwkPPcSjOfGQWW0IxPJZYEIzhyFKu8iuRIe4j/+q4p/53vh59s+F8Ln8s9A6oV26/m0lA5z7gxHr1Whr92CmVUvkmkGnuAKDva345K9XVAo5Dh2prp+a63n51JjdmDt6IPQt/VCqTch6VtFcO5lmPtHYekfg769H+GlSXhO/BbtF9y0rR0hY7sTQuaRSVSWlqsYhNDyvbi9PtEbDzhw+88Wt32Wzz93wmBrx/RTP4elow+asAmb85OIBXI81bXnECyd/Vg/fQwyeWVnXjzy+SoA6HVa9HW2YXpxFal0Bm5fCGN7e3HB8CCUCjkm5pbw4uQcLj1Q/dmXEFR9GbETZRWsAgZ1TfTGik8mAPxOAQ/bavE715Q++L3yIJliMW88WLrSealir0cWQ5jciGNfmw7xNIsrB8572exv12PBl8RqMAXgXE5Z7xLWH/0WdJ37wKbi4LKZom1v72PpAtfX7SnsERaaPoL48uQWPfP+KyGTK5EKrEGhMSAdckOhEXZh9Kax0nN70zC5Ity/c33pnJ83X1Z78R4A26IjCqHjcPEiydWg3Bhet/f8WuMjI2Y24/jWc+u71tjZQBJWnRLeaAap7Pb0CNmIH+C2f1YK5fb1zkOU4SqKx+bjLa8uXVj4NVdfUlP7+bjs5tJFuy656XyeXqGREYVQ68VfpSjHtwrxhFQwF9ZdEZ2x0nQu77dW1F4p9FLcj67LStMSegFRDm++6kDJ7284vFvZ8MYyu/Z8qRpA+dgZLbETUryEAITIKs+2YsaAELlo3fb3U3NBAMALSxGshFK7eOpGOA2tSg5fLIO10PYiyOX6l9xchMpceq9UgpHr31zy+wM3/i4ROuXG8MZ9ZIpel9tvHVcULm5ZSseKpVkMt+nwwtL2C1cheg8ApPxrgmntb9djZnN3EdxysrNcREQluOq1pWXZtW8gU+i0rPNDQS//6sFHShTD2NWFD2JJotCa4ZjS+nnZw6Id/KdZIYR3Z0IeyJQqxJZPbf3uxGoUzyyEStpPO6P1q4UQW23as31/l3uv+NpsQVq3XFLaNn3t4b5dn1UjX4rZBqc9cXSa1QVtAyG0CtmFPVeU5t9dl76u5PeV4K1vfWvJ7wvlH395NYon5oIl5zeS2r2Wyo67ZwFMQniha1oYvq60fjB42auI0fqd60vbY1ce2keETt+VpWumkNT/2y4tc4G6r7D+X4yPTG4Uj+Cq5LJWCK3TnjgMajnSWQ7eWAbIK8tUUI5lsyhmPrSXGQf7wd19S4c2t51jFeIfTDwMhd6MbMSPbCxYsn87f5uN+HMFvM/5ds1uJgryufVwChmGQ4bhCvI5qeDNV5Su+XjDITJnmV2Xl+bRjv271/T4WhQvLUdEkSEkUv1WI6tWQyl867l1MGxlaYz2l+GpAxffUHH/i+EtN5Y+D7rq8H5itEqByGXEkYUQJt1x7HPqEM8wuHIg58k2tRGDXqM4V2cCCKcYaJVy3LDXimlPHAq5DOFEFja9EiMVHCoemQ/g1HoE+9oNiKcZXDVog1Ihw9RGFIpz3rYrwSRevd+BY8thxFJZ2A1qwd5gPJ4+eQYTC2vY3+tCLJnGtYf2QKGQ49TCOrIMg1AsCaVCjmsO7cEz4zkPeatRh2sOVX6DdGQhiFPrUexr1yOeZnHVYG4M10MpRFMMOADBeAZXDFhxbCWMWIrZiio5sxnHydUoGC6X6kmvkuOpuSDiaQb+eBazngQ45OpqAOdSuQTd0HUOI7Y0AY2zF5lYEED5DXNkIYhTGzHsa9OfG/tcP1eDKQQTWXRbNFgLp2DTn4948b30EJRGO8AyyMZDkGv0CE4+BcjkSAc3oLa0Q6ZQIh3aRDLoKUEdeHbej8n16K65Xw+lEIhnsB5KwqhV4soBK06tR7eYwFVDlYfNP31iBuNzK9jf14F4Mo1rLtgHpUKBdW8QnkAYyXQGV47twcTcCsKxJDLZLMb29KDPVdow3An3qWcRWDwFS/cwsqkY2g9eDblCidjmCtKxEFIhHzouuB6+2WOQq7RIRXzQmBxoH63ukDI3hhHsazMinjk/hqc3osiyuVosN+134vhyCIk0g05LzvN5NZSGWaPAxHoMvVYNXloKI57JpWDTq+WQQYZQMnsuz3sOTCrO33+VRW5f583tkHVrD7y4FIJBrUCPVYtwMotL+iw4thyGRatE5lxKlEr291MvjmN8ZgH7B3sRTyRx7aWHcrfAM4vIMgx8wTBcDhsO7R/E+PQCsueK4LU7rNg/2Fum9d2Yefk5eNeW0TW0H6lEDKOXXgu5QomAZw3RoB+xSAgjl1yDufGXoNZoMPPy8xXT4BGaPoLY0iT0XfvApOKw7L8SkCsRX5kCk4rBtOcSxFdOg0nFwGXTgFwBfdcwtM7K36so3wqnMb4agVIhw6uHHTiyEET/uXQqCe8KVALz+G7RmQ+c5zsZBlcNWs8p3tFc2q5AEga1AlcNWnFsOYyNcBpXDlrQa9udwkUI3KeeRXDxFMw9w8gmY3AdvBoyhRLBpSmotAZkU3GkIgEYXf2w9AyXb7AI/FPPIrI0CcO5ubKPXrUVDcExWZj7DyGyMoVMNAi12QmV3gxDV+WRLADwzMQCJhbdGO5xIp7K4JqDA1Ao5Fj3hbEZjGJ5M4RbrhzF6eUcD06kMshkWVi0CkxtxMBwQIZhkWHKy4lnZzdxai2IYZcZsXQWV+9tO7eXEwjE0ui06nDWG0O7WYvhDrKOCrUgHd5EyrcKlsluycbw3EtgU3Fk42HIVRrIVRowySh4eXlkIYTJjdh5JXrQAqUcOO2Og2E5bITTgAy4ediOZxaCMGuVCMRzh4sroRSyDFuSpy4Gz/PTVMQvqH/JzSUiXpRnTzwDz9wEHH3DyCTj6LvgGsgVCkS861CpdQhvrkKuUFQVGZGPcnpFr02LlWASNr0K+/Ii9SqFf+pZRPP2m+3cfouunAabTcPUPwbf+ONQGW3IRPww9eecDUKxNFaDpedqLZhCMHk+/35icxnRxZNl9R42m0b0zAtbv5v3ldfnlvLWBFBadnqjaayFUnjdASeeWwii366rKlI4HxNHn8Li6XH07NmPZCKOscuuhUKhxOb6MjiOQ9jvhdXZjt69o+UbK4GcbRHDPqc+z7YAzgZSiCazUCvlGG7T48hiCAN27bYozkox/dLTWJ4ZR+fgfqQTcQxfcg0UCiWWZ0+he88o1hemoTOa0TVU2qCvBd7n/hcsk9m1ZtJBD4qd4hQbo41wGpvRDKJpBlcNmHF8JYpOs7qmMZIyUiGvIN4oUyjBJCJIh87XtVkOJWFSF9ZtZzxxyCBDIFl7bY1gLFWSj/C0Tq2fv4xIbMyV5SNMenfdnGem13Fq2YfhTivi6SyuHu7M2cmrAWQZDv1OE5Z9EUSSGfQ7c57n4dPPgs2my/Ks+Poc4qvTQK8VQGnbYLqIbeA7+nPI1FpBdmF+5MfmqWcRPHsK5nP2UtuBnG4WWpqCSmdEKuyDTK6AxlK7U9oTTzyBEydOYHR0FLFYDDfccAOUSiUmJibQ09ODM2fOwOVy4cCB844ey8Ek9Cp5yfn1xs9fLmaScXievT+XLqfcuK9O1/xOJLF84hl45s/rB72Hc/qB+8w4AMDS0Y/Aypmt501tXbD3Vn9h8PTL0xifW8b+/k7EE2lcc+HwVkRElmFxeF8fnjkxg6HuNuzv76qKxnl7fB+yqfgOezwIvaMb0Y2Fref1ji6Yu6vTyQEgePoIosunoO/M6STWkZxOEls9DYXGADadAMdmkY2FoW3PRZr4S+gjM57djgpA8cPU2MoU9N37kdiYQzbihUJvhcpog+5cxPyJ1Sjajaqi+ohFqwQHDt5oGvZzPo7Bid9C1zO6az2nghuQofBhfeD0EUSXTm3pZvw4pALrCM2+gPbL34zw/MtbPDybiEIhU0ChtyC2MgWwDFgmA7lGj7R/HUqjDWw6kdPX0wmwmTTia7m1GJ4+ApbJCtLNkptngZ7c+dFaKAWLtrIzEKngmVNnMXHWg+FuR87+O9AHhUKOFW8YHMchnsrAoFVjtLd2Rybv5LMInZ2EqTs3l47RqyBTKBFemoS5dwSR1VlwLLPtUmI5mIJDrywrQ9LZPFlw5H5kooGy85jcrC2NUMJ9VpAunw5tIrZ4Yut3nmgGWYZFhXcRWNrBV/sOXwOZQgHv4mmYnF2I+tYR83tg69kDZ3/1lwVPH5/C+OxZ7B/oRiyRwrUXjW5FQygVCgz3d+HZE6cx2O3CyEBp58JaUdFlxPhabKsOQD5MWgWu6M8pM5ueNH503A2G5WDWKtFpViOeYRFPM7m6Aingf0540G5Uo8OshkmrgDuS3ipCyiOR3s6wTq6Gt+62jBoFrhiwYtodhS+awYI3jl6bFqksC41SvkXrgRMb4AC0mzSIp7MYX80VXD2zWTr354kzK+dqA2hx1cEhTJ3dgDcYxdzqJvo77Eils9ColYgl08hmGfzkiePgOA4uuxmpdAYnzuTyy51Z2Sw7pt5ICuNrkXPvZMG0OwZfrMA7ZRhkGQ4PjLvBcUC7SY14One5M+tN4o5HhG22Ky4bRjLL4sTx3yK0IzXARrt+K5d1MawGU2AYDqfWo+i1afHSUgTxDIP1UAoGtQJz3jg4DlAr5XCZ1JDJgLWH/k1Q33jsLLaZP/cmjXJr7r3RNOa9MfTZdFvjlMwwiKay+N8TuYOYdpMGKoVsa+4ZlsP86iZOzJYfL5NeC5tJj1MLq2AYFlmGyRV73QxArVQiwzD4n9/kCsx02C1ot5mxtOFDIJxbX96Zl2BoE5aySy5XwDv9AliWQSoSgFKtRTzggcZoQTaVwNlnHoBcrgCTzeQKW2XS2Dj5ZNH2EsFNuMOJrffe9l4aJaw6FaY2cim1sgx7rthhEmpFriD5Ayc2AC6X/sEXzRWJ/vHL5dczj717c4pMfPkUVi0aPHXGL+h3+89dSm6EU7jnmWUwXI6/uEwaRFNZHD9XsHjanRtjp1GFdpMGgXimLI1khsXCyjpePj0Hk1GPqy/OHTR5/AF894Ffg2FYWEwGdLkcYDkOc8trOL2wDJksdw7Q4bQhlckgkUpjfmld8FgAgFqjg8Fsw/LsKbAMAyabhUanh9+9BqPFjnQyjpPPPAqFQolo0IfgprCDxKTnLKJnJ7Z9lvKtgWMZRJdOQevsRXjuWJ5BrkZw4oktRYvNpKHrGELSu4zsuSJusaVThUghmWF2pVoyahSw6pQ4vRHbtpbWQymolDIkMud5ViCegUIuQyroRirogUenFLwuAEAhA15aCoFhOQRiGehUcrgjKajOrVnIgLnNOJxGNRyGXPqxncVtOQ4ILc/APz9eklbcuwqWYRBYmIChvQ+b0y8im0ogHQtBodJAodZCrlDCfeoIfGdehrWv8KEbm80i7l5AeLEwvaRvDRyTReTsBHRtfQjOvgTmXJ0IuUqdyx3LH1iunYHW3lHw4CGa5xm55Ang5Pzu9WnSa2E16jB51gOGZXNzpVFhzReGSqlAlmHx06cncjLTaoBBq8ZvTkdxbGW7TFApZFjcjGF8uXhtkdVAHAzDYWIlgD6HAS8u+JBIZ7EeTECvUWJ2IwwOwFogjlQJj9PFzSiAylI5Vouenh4kvKuY+9bHBD0vA3B0MQyXSY0My+LZhRAYLnfpr1XJ4YmmoJLLzxXileFbz63CaVAhw7BwR3LRDp/+5aLg/u3btw/ZZExw/4BcKrSuLmFG+eb8JDb6Tp7/e/E0YsFNcAyDVCwMlVaPxWNPQCaTI+Jdh9HhglyhRDTggW9lDu2D5w9k3LMnC5EAkHMU2alnGDVKWLXn+Mi5GlR61Xk+cmYzDpksZwwv+hLoMJ9PQ1ZIJ016lwvuOaXOBKXBisjyFDiWActkodDokQysQW2wY/PYw5CrtMgmouA4Dr7xJwBwCCXS+NhP5wSNIwAMDg7isccew9KP7hD8G51ej4GBATzyyCOC9bnHTvvORbwCWZbDs/MBsByHjXAKWqUcnkiu3kaW4fDtIyvgOGDWE8OCL4EXz+4uYB3weTA/+XJJmkuzUwj5N8EyDOKREDQ6PU4e+S1kcjl8G2uwt3dArlRidWEWZyaOoX+4ePTo8tzUtr/PBpKYWD+/NtbCKTAsh1MbOX3z2HLOQF0P5/RNGYApdwztRjVWQykkz3nOucMp5GteS1MnoDUYUQyrc1OI+DfBsgwS0TA0Wj2mjj4BmUyGoGcNIe8GFAoFlmfGsTh5DD17S0fEhnxuAMIP3Xidd/H7nyr6jMeu3TY2PEzaczLYc04Gsxz0KjnWw+dkI8vh5xNecADckfNjtK2/cXp1ecRAT08PkoGNingjADicbfD7vPiXx1fLP5xHqxr09PQgmWEE8xG9XrfFD4TykSemVsGXKpxzhxCIpeAOxtFlM+DBYwu51BzhBHRqBY7ObuSKY5p08IYTsBo02Dz2EPzHHhL8Tv3XvBOzp21V2QbL//tPgn8jkysQ21zB4pM/RirkA8cySMfDUGp0cI8/BZlMhoR/A1qba0tfCixOIO5dBazlIzDdbjeOH99d52h5eRnZbBYvv/wyBgcHceTIEcTjcaysrODUqZye/NJLL2FhYWGrVuBXfiu8SOq+fTkeUem6rXYN1oJYYBPuM9vlengzpyd75iZg6ejD6tQLyCbjSMbCUKrUiAc3IVcowWTSyKRT4MAhkzqvv/rO7r5ceXlmCQpFYQ+2FY8fWYbFyTPLGOhw4oVT84gnU1jdDMCg02Bu1QOO4xCMxJBM7748DEZiSISTJfV/lc4EtcGK4NIUWIYByzBQanSI+9ahMdkQdS+f0/8VYNJpBJdnkE3t1sl5hFbPX8ZEz45Dptyuzyb9q+CYc/ZaWy/CZ14Ckz5nrynVkKu1kMnPrelTTwAyGXzRVFk+Ejs7DuRFn6d9a+AYBrFzdmEkzy5MbZ6FXKXNHagG1hFbPAk2nYRMZ8WRxSUcOSssIuddr+qH7fQMNp/9cdFntM5eRM7uHv+kfxUcyyCydAq6tl6EzrwENn3OFlKq4Z94HDK5EpmoH2wmDbXZicCpJxGePiKobzx0BiP8x34J/7FfCv5N/9Vvx8LcDH58wlv+4XOoxx4FgLPuAE4u7D4/WPWFkWUYjC+60d9uxQszq4inMlj3R6DXqHL6dSyFJU8QnfbtaeTm1wvbeuGlKQQ7tztfh5enkQ57z9U7DUOh0WNz4ilAJkPSv4FkwA2ZXIlkyIPYxiK0tg7I5HL8+9PCU0Rt8c17Pi74N0B1c9LT04OUd6kiXd5isaK3rw9PPv0Mnl0IQyZXwL88W9Iu4uFdPI1YMDd+qegOu8u3jsjm2hZPXXzpcTDp4rpbzO9BIBzGienFgt+vbPhy/HTmLPq72nB0YhbxZAprm34YtFpMzi3nzm7CMaRShaNzF9ZKO5ALhYwTUAZ7bm5uS4GgjTvvvBN/8zd/Q7zd/fv24fTMzLbPFhYWMDQ0RJzWmTNnsGfP7miJf/qnfyL2blarBY8++hjk8tJu6Pv3527Rpqe3KwFf/vKX8f3vf59IXyrB9773PYyOnj/I0+l02L9/P2QyGX71q1/hDW94A1F6crkMbKXXlFVAJpdXnIam0XDnnXfiNa/ZXTxHq9ViZGQEa2tr+PznP4//+I//qEPvdkOMuZ+ZmdkSjIXw4osv4rLLqvMaVigUcLvdcDgKR9rc/JrX4LFHH62q7XJQqdXY9HhgseTyC/70pz8tG7LeCKC+L+XybQaBmBgYHMKlF1+E+//nf0Sls394H6ZnCqeFEAM33nA9fvv4E6LTYVkWU1NTSKfT5R8G8I63vw2zZ4QfUO+ERqPBAw88gPb20ul1AMDpdKKnpwczMzOIxwt7vhVCR0cHOjs7Sz7DMAzaXS74fT7B7QqBXC7H2trathzc73nPu/G97/03UTo8nnzySVx33XW47ba34X/+535RaOTjqquvxr//225HC4fDgZ6enorWEgD09vbCZrNhcnIS2WxpL+xAIIBbbnkTEonaPPBsViuWlpdhNBrx2c9+Fp/5zGdqaq8a9PT2YnlpCW98w+vxy189TKzdT3ziE7j11ltx3XWFU0+Ijb/7u7/D5z//ecHPLy0twVdkD37ta1/DvffeS6hnhfHf//3feNe73iUqDbHAsixOnz6NVKqyS5XBwUEkEglsbAhz/rBarRgcrC6VBcdxFfHv3t5eWK1WTE5OgmFKp4jKZDK45ZY3YXNT+GFZMZiMRvzq4Yeh05WOKpXL5Thw4ACi0SgWFxcFtZ1vG3g8wg8xPvWpT+PBB38h+Pl8vP/978c3v/nNot9/7nOfw6c//emq2i6FtrY2PPxwcX5ms9nQ39+Pubm5reLXQuB0OtHbW3kUcy0QUzb09PRieXkJjzzyyK7aG82C3r4+LC/VXuS1EDRaLX7z2GNb+9XtduONb3wjBBztVYx//dd/LZiaDBDGD+666y7cfffdxPuVj87OrqK8QiaTobu7Gysrwi8M+feKxWJYWFgo/wOc53M76yKKjde99rV45Ne/FqXtKy6/DM89fxRAzmZoa3ch4CdrM/D4+Mc/jj/4gz8o+J3ZbMaePXuwsLCAYDAouE273Y7+/v6K+8IwjCCdPB/9/f3Q6XQ4fTpXl+q2t70d83NnyvyqcfH6170Ov/zVr8o/WAKCLiMAYGVlBW63u6LGg8Egbr45l2P1e9/73tZBuFDYbDYMDQ3hzJkzCIV2e3Hl45e//CU+9alPwWq14lEBB3TDw8MwmXYXkRLynv/wD/+Axx9/HNdffz0+97nPlXzW5XIVvY1jWRaTk5MlFejXvOY1CAQCuOOOO/DGNxbPqTc0NASbrfJURDyy2SxOnTpV0YYDgHe+851YXFzERz7yEbznPe+p6LdOp7MscxAy9z/+8Y9x5513ore3Fz/96U9LPsuPtUajKflcMdx+++148cUXccstt+Cv//qviz6XTqexuVnYW0gul2N4eBgqVXGPX47jMDIygkwmg2984xtb+0goGIbBzMwMstksjEbj1qFyIRw7dgx/+qd/CgB49tlnoVaXL9BoMpmwb9++ssKW4zisrq4ikxFWk6QQ5ubmti49nnnmmbKHa8WQTCZx9uxZtLUVD0P8/ve/j3/5l39Bf38/fvzj4t4dANDe3i7IKFhcXCx4yPCxj30Mx44dw6233opPfOITu77v7u5GR0dxr65EIoGpqamySifLsrjyyivBsiy+9rWv4eqrS9fJAHLeAPmHiBzH4cyZMwiHd0fa5OPo0aP40Ic+BAB47rnnoFQWD77b3NyEzWYTdBhbCF/60pfw3e9+FxdffDG++93vCvpNOBzG2bNnt30ml8vhcrlKXuS+613vwsLCAj784Q8XVZIKIZlMYmVlBUNDQ2UvigHA7/fjta99LYDcWix10bUT+/btg1arxalTp8AKuAB58MEH8Q//8A+w2+145JFHBNFwOBxob2/H1NRU+Yfz8Pd///d46qmncNNNN1Vs1B44cKDswUg9wDAMvN7Chz9f+tKX8PWvfx2HDx/GAw88UPAZs9ksmffy+/1lDa1IJIKbbsoVnPuv//qvbakpCqGrq2sXr06n04LW569//Wt88pOfhNFoxOOPP162/3a7feuQUCgNHnfccQcee+wxXHPNNfjCF74g6DcymQwHDhyAVru7eDstJJPJorrR9PQ0brghl1f25ZdfLipHrFbrli7EsixOnTpV0QUKkNPtv/a1r2FgYAA//OEPK/otkPOUtlgsSCaTmJycLCnPPvKRj+DEiRN461vfio9/vLhXnEqlwsGDB6FQKLCwsAC/X3gEHpAbiyuuuAIcx+Hf/u3fcOWVV1b0+3z6JMAwDE6dOiVIjzpx4gT+5E/+BADw9NNPC1qjRqMRw8PD1A9PWiCHTCZTcp3ffvvt+NnPfoZXvepV+MY3vlH0uXyeIBUU4+mLi4u47bbbAOT0mXydFcg59Bw8eLCkrSXEDgcqsw0AYM+ePbBarWWfawRUIhuuv/56xONx3HnnnXj1q19d9nme/wM5W0/oAeP4+Dj+6I/+CEAunZbBUD71IMMwCIVCsNsrqzX0zW9+E/feey+Gh4fx7W9/u6LfArnzJo/HI+jd/viP/xjT09N43/vet2WXl0JfX98um3ZtbQ3r66Wj9xOJxNZF/T333IPDh0vXRrVYLDU7JfMHu0J1jGuuuQapVApf+cpXcOONNwr6Tf56eqVBiA4FAB6PZ+s88cc//rGgC/aRkZFte0yIzQAAP/vZz3DHHXegvb0dDz1UPupOq9XiwIEDTaWLRCIRzOxwfheKU6dO4X3vex8A4PHHH4fRWDzCdyc4jsPCwgLS6TSGhobKnu3dfffduOeeezA8PCzY+YWULSQ4TVNPT0/FIS6Tk5Nb/3c6nbjkkuqKwQphgE8++STUajXC4TAuvPDCqo0AIe/JMAwsFgsYhqn6nYDcIdjY2FjR71mWRSgUgkajgcFgqIlWOSiVSlxwwQUV/y4SicBkMkGj0YjSPyFz//Of/xxmsxnhcFjUMQJySjEv6MSkFQ6HtwxPuVxelTeWUMUhX6D09vYSDS+UyWQ1t7e8vLz1f61WW7VnGoBtUTiF8JOf/AQmkwmRSITY/A4MDGBgYGDX56lUClarFTKZrCpaOp0OF19cusA6kFMaeCPOYrFURUsmkwk6GM8XuP39/SUvU2pFOp2G2WxGJBKpaE1Uw+ei0SjMZjPUarWo+35i4nzKrWpl5kUXXSTouccffxwajQahUAgXXXSRoMsSHpX2i5TMlBIUCsWuww8e8XgcJpMJwWCw6DNSgt1uL2ug5+9th8NR1Tyq1WpB6/PIkSNQKBSIRqM4dOiQoAvySmnwYFkWFosF2Wy2odamVqstagDkXxZyHCdoDcrlchw6dKjiftx///1EZKZWqy0rz1KpFCwWS0Uyc3BwsGKdwev1bhn01cpMklAoFGUPi3jk60vd3d0FdY8Wmg8qlarkPud1mHA43BAyKR/FeHosdj7lckdHhyB9eCfK2eE8xLANGgVCZUMymUQ8HodCoYDJZKp4nAplkCiG1dXzqdW6u7sr+m2l+Pa3vw2z2Yx4PF713Bdyfi2EeDwOs9kMpVJZNa2urq6y6Tnz7X673U5lTSsUCsE6RiwWQyqVgkwmg9lsfsXtuWogRIcCsC0lXVtbW1VjK8RmAIBHH30UOp0OwWAQF198cVNdMghFNbyQR37UZldXV0UOigBw6aWXCn72O9/5DsxmM2KxGPX9Jvz0oQrkh2FWEpJZLS2r1QqWZSv2gqqWltjvxB8i0qBVDViWxebmZt37x89HKBSqOEy7Wlo01jOQY2I0aPGXd1JcZ/XgIz6fr+IooWpp0VpLBoOBCi3+4JAGLZvNJjodmnyuHmudYRgEAsXrP5CkJUX+Igaa8X3rsTYBFI0+IU2rNVfV07LZbPB6vWXTyZCgRZMP63S6hlsXHo9ny+BvtL63IB5afK52WrRsg0YFnw3AYrE0pa5MY+/QlnEymUySPIHvE4219EpDPfZOOp0um1mhhd1oVj63E1QuI4xGI5VBdDqd2+iKScvhcFATFk6nU5LMmL8sqXf/+PkAUDQ1EklaNN6X5tzT3DvVgOZlCT8WHMcVzd9MAhzHtdYSAVoOhwObm5uCU7JUg2AwiGw2S3WuaByCNaPMlAr49w0EAhWnvZEqaBqHtNdmvXUY0vB4PFupSWjtbbEdgZpVZpKGx+PZ8lhstL63IB6alc81m23QyKBta/CpqWnNfSQSQSJRvFh1rUgmkwiHw1RlnN1ulyRP4Pv0SrIbaIEfT5VK1VS6fDPC4zlfu5PWXMVisW1RhzQg+mWEWq1Ge3s7lUHkU4JUWtuiEvAGUUdHBzwejyhFgnjwY8bTkhqk0j+3270192L2Ix6PIxqNUnlfvv3Ozk4qtPiwbamuM5fLBbPZ3DR8JBKJIJlMwuVyUVtLtGjR2Iv5tMT27M8fPzHXBJBbc1qttqnmio8s6ejogNvtFlVmSgUej2erXoLYnv20wB/80NoHNPmIy+Wi4tlPC263Gy6Xi5ojEI25CoVCyGQyVPWveuu21YCWDtNCY4Hnc83k2d+MtkEjox62Bg3P/nwbWUyHR75tXlcWE263GzKZTLIyjn9/qfavkeHxeGAymdDe3t5UunwzgtflFQoFVRkntmP3Toh6GcEPIi3jlc9NL7ZBlE6n0dvbi2w2K+ohGD9mPT09klR+pNI/j8ezVUhYzH7w66qnp0d0hd7tdkMul6Orq4vK3uns7ITFYpHsOms2PsK33dvbK7pnv9vthkqlQnd3N7W1pNfrRb8UdrvdVPY933Zvby81o4eWkkhj/AKBALLZLHp7e18xobr5fESKPLUauN1utLe3UztooDF+0WgU8Xgcvb29YFm2aTxeeT5CS2bS5MM9PT3weDyiy0yNRkNF/yIN3vhveZS2wIN3pOrt7QXHcU1zQd6MtkEjI19XpjEfLpcLTqez6WQcLVvD4XBQ2TvVwOPxbNWclGL/Ghn5fLNZdPlmBX9B0NbW1jR8rhBEj4xob2+nFhnR3d0NrVZL5RCxv79/299i0dJoNOju7pak8sP3aWBgQPQokXL9oDUfQO59xVbo+XApWsKC1j6tBrT5CM211N/fD4ZhRE1vQXv8aNCKRCJIpVLU54pGNBxtmalWq5tGZkoBiUQCkUik6d6X9trs6uoS3bM/X6bn/93ooDVXfKQwbT6czWYRDAZFpSVlnagUGrnvLYiDZpXBzWgbNDI8nlzOcVrRazTmPpPJwO/3U5dxYqf4lLqcyL8skWL/Ghm0+WZvby/kcnlrHqsArbnKZrPw+Xx1k3FNcRnBG0S8RykNYbFnz55tf4tFK98TUWrpLfjLkt7eXiSTSUSjUep9SKfTCAaD6OrqEj2XdT3mvpkOkKsFrf7xefJ6e3uh1+tba0nCtGiPn0qlQn9/v+ie/S2Z2fjgw1ub7X1bfKRxQFNmJhIJ9Pb2UnMEajaZSRqN3PcWxAG/Dvbu3bvt70ZHM9oGjYxm1BF4p8OhoaEtumKBb5unJbbDo5TlhNT718igNbb5KXrb2tpa81gFaM2Vz+cDx3FU+FwhNMVlBG8Q0TReR0ZGtv0tFi3+nep12F8K+Zcl/N+0wR/80Jz7ffv2bftbLFr8QaHYaXykLvRp9Y9fS7QOaOVyORXjcOf4ie3ZTyP8k297cHCQimc/LT638wJaLPCRJc0mM6UA/v36+vpgMpma5n1p8WG+iCONfcC3vX///m1/NzpoX+bQkplKpbLpZCZpSF2fa4E+WnyuNtC0DRoZ+fNBw7Ofpq3R3d0Nm80mOi2r1Yru7u5ttMWiJWU5kd+/ZqrnJQXQmvtgMIhsNkstvWszgrYu39nZWZcUn6JfRuQLb7EUen7Q+AkTO6efQqHAwMAAlEolNWHB/y0lSKF//FzT2qx2u52qouByucCyrGhpfPjIkkZRSmgwY1p8xOl0bhW5pcVHxPTs573HmvEQLJ/Pib0u6rHWxaalUqkwMDAAhULR9HlDaY4tTezkw2JdkNNemzKZDH19faJ79tNEs/KRtrY2KsUQpe4IVAx8qD0NHaaFxkH+BbnY9bxoohltg0bGTl1ZLM9+hmHg9XqbUsbVw9aQ4mF/fv84jmuael5SQP46o1EDpdlsIVpgWbZuMq6pLiPyCx4mk0lEIhHR6ACgdkve3t4OhUJBZSPz78T/LSVIoX/5h5JiF2Li39dgMMBgMFBTFPi/xQDN8asGqVQKwWBw294W61IzX3DS4CMulwtmsxkajaYp+Ajtyxy5XE6lANvO8aMVEcUXnRQDO2UmDV6mUCheEaG6/Fi2tbU11cFF/j4QM2f/zotGGpfCKpVKkvKvGvA1S/L3ttgykyYfsVqtUCqVTSEzScPr9YLjOGrFKVtoDLjdbjgcji0+1wzrolltg0YGLb7p8/nAsiz1A1VatgYNB8/8yBIpFrXP7x/QODJY6uA4bts+DYfDSCaTotCiqcs3IwKBABiGoaZfA/U7CxTtMoI3iGh4ztfj5pqn14qMaIfdbq9bcRqeJn/w05r7yukA2/eOlFIS7EzDlU6nEQqFRKHFj4XT6aQ2vzKZrLWWqqTV1tYGuVxObfxsNhsUCoVotHZGlvC0xUCzykwpgA+1V6vVTfO+6XQagUCgtTYbADtlZiqVEs0RiI8scTgcLZkpAezcO2Kn+GyhMfBK4HPNYhs0Mupla/h8PmSzWdFoGY1G6PV6anJHp9OJmuJzZ2QJT1tKaFQZLHWEw2Gk0+ltY8vzUtKot7d9o2Pn+Pn9fmQyGdFo6fV6GAyG5oqM2KkoAOIKJtoGEQBqURj1POwvhfwoEafTWbfLCLPZDK1W2zQHF3yoPQ0PiZ3MLpPJiKbQV4Od/cv/TAxavPdYs6ylVCqFUChE1UDgb9YTiQRisZhotGhfDMrlclE9+/NlpthRGB5PrmaJ3W6nMn78+7wSFNL8920WL0rea44mH2lraxPd47UZD+loy0yn07kVKUyLj4i5r2g6UpHGzrkXM8VnC42DFp+rnRYt26BRwXHc1jpra2sDQM/WEDONTz1sDbFp+f3+bZElPG2poBEuSxoVtPmmSqWCxWJpGluIJgrNlVgRTPXWEUS7jKinQRSNRhGPx0WhxYeKA+JOWL5BVM/D/lKo9+IFCs9Hox9c5B/88Gl8aBz8SFHo0+Yj+fMrdnoLGmsp/4BbbM/+nd5j+Z+JQateBoJY4Ys782vytMWAx7M9skTsUF0a4ycVSEEukkYhPizmPsiPLBHbs7/Z5oo2H2m2g5p8mSlVR6BioLlPW2gcNKMMblbboFGRX6xWbM/+es59y9YQH/lpuAwGA3Q6naT618igrcu3t5+PZhXTs78Z0ay6fCGIfhnhcrngcDhEVeh3ekzl0xeTFg2DSKoelvl5iYH69W/n3Ivp2U/LMy9fWIidksDj8cBisUCr1VLJiV8p8seCxt7Oj3oS27Of5lpyuVyipzTivceUSiUVwUlj/PjIEtpz5XA4IJPJqMpMMUN18/eVlPiLGCik1DX6wUX+2rRarVCpVNR0rHz6YtJqlrW5M7Ik/zMxaOWPn5iOQLSMpfy13mh1bjwez1ZNMynqcy3UB83M55rNNmhU5PNN/l8x50Or1cJoNFLXEcRKfcdHltC2NfgUVFLiCfn9k8lkTcOzpID8saW5d8T27G9GeDweqNXqrcgS/jOxaNHgc8Ug2mUEf6PjdDq30luIffsG0DFead9ci02rGuQrgvy/9SpgvXPuxegHy7LY3NxsurkvtHektM7cbjcsFgs0Go3ohStp8RE+DVdrLZGjJZZnfyE+RyOyRKlUwuFwNOVcNbtRsfN9U6kUwuFwnXtVG/KLctO4IK/X2pSS7KsWHo8HNpsNarV6y7O/0WVmfs0SnlYzyEzSoGkLtdA4oBlBTgvNaBs0Mupha/C6SD59sWgBuXfKZrMIBALE6QQCga3IEp4WDYdH/l8pyTip96+R4Xa7oVAotnREq9XaFLp8M4IfP5lMtpX6jhafY1lWtNR3hSBqZAS/2AHxGWu9DKJQKIRUKkWcFs1DsGoglf7Rmnu/3w+GYago9LQPQHk6vEIvtXXG90/snP201lJ+Gi7+X7GVTl6Q0VpLNDz7acwV7b3IR5bQoEVj/JLJJMLh8DZazR6qS2tsacLj8cBkMkGn0wFojrWZn5eYp8UXkW9kNKPMzE+dxP9L41JYbFqkkT8fJpNJ1BSfLTQGCjlSNYNnfzPyuUZGvexWsdP4NKutwUeWiE2rGkjlfKkZ4fGcT9ELNIcu36zIHz+9Xg+j0di0cyXqZQT/QgC9Bc8bEGLcHu00iMRMbyF1g0gqwoKWx0qh9xVLoc8Ptedp0dg7Yiv01YA2H9kZUigGHynkQRSJRJBIJIjT8ng8W95jPC0a46dUKkWrc1MosoSnTxo0PXTyeRkgfpg2T0vMYoM70w3yNMVKCVVv8Ac/tNJG0kIhPkxjH4iZ4tPn84HjuF18pNHX5k4+0gzGJt9m/r4S0xGIliMVaeTPB+813PJEfGUjv1gt0Dweqs1oGzQyPB4PlEolrFYrAHrzIWYan/yi3EB9bA0xHB7zI0t4WlKScW63W9KXJY2MeunyrcuIytGMunwxNN1lhEqlgt1upyYs8j8nTUvKBlEhj2ufz4dsNkutDzsVBTE9+2nPfT0OJQFprjMa/dvpPSamZ3+xtSTWpWY9+LCYtApFlvD0SaMQnwsEAkin06LQqsdciRmqS5NvSgH5RRyB5nnfeq1NMS/Im3Vt0pqrnZElvONMS2bWD43c9xbEQYvP1QaatkEjg58PGgfctOY+Go0imUzW7TIinU6LkuJT6nKC5lp6paFeurzYnv3NCFpzFYvFEI/Hm/cygr+5BsQ7UOUNIhq0dnpniS2Ydr4T7cP+UvB4PLDb7VCpVABy/eM4jmqOsVAohEwmszVOYhboLeSZl/85aVr18PgBpFfcjlb/dnqPienZv1PpFHst0ZpfWut2514U07Pf4/HAarVuRZbwNMUowrVzrsQaPz6yhKbMpLHWpYCd78vn7G/096XFR3ZGlgD0+EizrE1ac8VHlvC0VCoVHA4HtUvh/M9J05KyTlQKjdz3FsRBi8/VBpq2QSOj2HyIlcq4HraGxWKBWq0WjZZKpdqKLGkWu7AaSL1/jQxaY8un6KWhyzcraM3VTj5nMpmg1Wqb4zKiUHiJGKFAXq93W6g9T4uGQSRmQZFC48dxnGQq0RfqH/85Lew8+OH/L9Z8aDQamEymbTRpzb0YaXx2RpbwtKQUfkyLj+xMncT/Xyw+sjMNV34fSKLQ+ImRs59hmG3eYzwtGnOlVqths9mojR9A7xKSRrpBMWnlFz7O/7dZFdKda5P37JcST60GtMKFd0aW8LRo8BExU3zSBC0+UkxmikVrZ82S/D6QprXznaTkCFQMHMdRDetvoTGwc582i2d/M9oGjYxC85FKpUTx7K/X3IuZ+q5Q6iSgsW2NalGof81Qz0sKoKUj0LQzmxX10uXrkeKTapomMRR62gfS+QaRVquFxWKhJiz4z6UAKfSP9gFyvqIgdhqfnbfJ/OckEQqFkE6nJatkF7ssoXEzzNOicdghdnqLQvuUdHoL3nuM5i0+f7ANiL/v8+kAdA7BXC4XvF4vGIYhSqeYzKQRWcKH6jarQlqIjzSDV1ehfRAKhZBMJonSKSTTxeQjOp1u61JYqVSK5tlPCyzLUpeZtGjR0In4NqXsCFQMsVgMiUSiZfy3sA0ej2ebI5VCoYDT6WzoddGstkEjg9aZAH8oXY/IXv7/zWZrtLe3Ix6PS+awn6YT2CsNxfgmy7JE6dDU5ZsRfGRJs+nyxSDKZcTOHIvA+ZckrdDXU1jQpCU1ZiyF/tVz7nmFngYtscI1pcCASoFPw7Wzf2Lk7K/nWuI9+xuZj9Aev/zIErFp5b+TWJ79O/OvA+cPwUinvmtWmSkF8KH2Fotl67NGf99iBz8A+UvNeqxN3sFATFq0EAgEwDBMXR2BaPARjUbzinUEKgap63Mt1AfNyOea1TZoZNDim8U8rsWSBXK5HHa7nQqt/HcSM8Wn1GWc1PvXqMhkMvD7/bvGNpvNIhgMEqXV4pu1odT4kU595/F4IJPJ4HA4dtGiBVEuI4qF2gONfwjWuozIYWf/DAYD9Ho99csIhUIBm8229Vmjz32pgx9ae0esAr2Volj/AHEuNfO9x3harbUkDMXmSizP/nrxYbGKcO3MSwy0ZGYjohkPfiKRCFKpVGttNgBoy8z8yBKeViPPVSlHKqmvi2JzH41GEY/H69WtFuqMFp+rnRYt26CRUW9bQwzPfo/HA6fTCYVCsY0WDRnHp/gkTYuPLJGyjJP6+VejgueNNPcpjQwGzYhifC6ZTCIajRKn5XA4oFQqt9Fq+MsIPjxnZxgdIM6C5w+I8mltbm4SDzvyeLaHigPiGkT5tHhPYKls5EJjQTsEixdYcvn5ZSxmuGah9yUdQlko1F6sXNb8OBXap1JISVCqf2LwkZ2HiLTXEmlaOwu8A/QNBLE8+2nxHlq0Cq11MefKaDRCr9dvfSZWscFCByHNHKrbzAc/tPhwfhFHQFzP/nrrMKRBe65cLhc1mUljX/GOVLRSQpFEqbknHcHUQuOgxedqp0XLNmhUpNNpBIPBbfNht9uhUCio2Rr535Gk1Wy2Bi8LaOydalAoDZfT6WyKOjf1Bm2+mZ+iFxDPs78ZUQ9dPh+0ZZwolxGFhIVYxZ535pbj6bIsS/wQrBAtMQ6kA4HArsgSQDp5KgvlJQbo96/Y3Ivh2V+MFunNWugiT6w0Pm63G0qlctfBT34/6oliNUHyvyNJq9BFo9frJX4IVmwtifFOfNs8dDodTCaTKLS0Wu027zF+PGnxfNJ0CkWWiEWrWH7N/O9I0ir0TtlsFoFAgDitQvtKCvxFDBR6XzF0BJootDbF1ud2HvwA5A9UafERmqAtMwuNnxiOQLT2VaHx4x2BpL4u3G53wVB7/rsWXplo8bnaadGyDRoVhc58eM9+sXh0vsd1o9saNGkV2jt8DUwp8IRCa4mv5yWF/jUy6q0fulwuJJNJRCIRorSaEYX4HG1dvuELWBdiJmJ59hc7LMrvh9i0aNz8i0WrGhTKSwzQ71+puRcjl3Wzzb3H40FbW9u2yBIpeQF6PJ5dlyVi5ewvNr+kPftLHXA3+lraeYhImw+TPgQLBoO78hLztGjMlcFggE6na8nMBkKx9/X7/chkMnXqVW0otDbFytnfWpu1gY8sya9ZQltmMgwDv99PjE6zykzS8HgKpxThv2vhlYlm5XPNZhs0MmjbGoVSiuT3gyStQu8UDoeRTCaJ0UmlUgiFQnWTcfxhvxR4QiPLYKmDH7/8A26r1QqlUtnQunwzgo8sUavVW5/R5nM0U3yKdhmx0yACxD0E20mH/44UyhlEJMOOpM6MpdI/WnOfSCQQiUTqagyLlRpmJx2xFPpqUOiyhPfsb1Q+Eo1GkUwmm+5gRQqHiKSLcNEev515iWUyWUPPFU2ZKRWUGlsppL6rBh7P7iKOQPPyETE8+2mh0KVwozsChcNhpNPpppOZpFFoPvgUn1Lvewviodg+FSP1HS00o23QyKi3rcF79tOce5IOj4WKcvN/iyXjeNkgJq1q0MgyWOrweHan6G10O7NZUWj8xCpqT4vPlYJolxE7DSKgsY3XUgZRJpNBKBQiRkvqzFgq/aM196UUBdIFevmDn/xQe54Wjb0jlkJfDQr1D2hsPlJq75A+BCvkPcbTojF+Ynj2lzrg5vtBCvUwsJpJZvL51wvRSqVSTRmq24wKeKGDH6Cx12ahIo48LZZliXr208QrTWaK4QikVqupOFKRRqH5ECvFZwuNgVKOVI3s2d+MfK6RwY9Dvsc1QG8+xPLsl4qMIwmPxwO73Q6VSiU6rWog9cuSRkaLbzYOCo2fQqGA0+lsyrkSrYD1zhyLgHg5XnfSMpvN0Gg0ogiLQnlr+X6QgtvtLmgQSSX3dKG6Bvzfbrebmserx1O4oHh+H0mg1PtyHEfU49Xtdu8KtQfEyd9Gc59Wg3rzETGKBZXiI6Rz9vN5AHceItIaP5lMRpxWNBpFIpGgxofz286nJcYhWD3XutVqhUqlojZ+QPMppMlkEqFQiMrapIlSfFgMpXgnLb1eD6PRSHT8aOpzNFFvPkKbD2cyGaLRcIVqlvC0pL4maO7TFhoD5fhco66LZrQNGhlutxsWiwVarXbb57TmQwxa2WwWPp+v7jKOdIpPmjpCNfB4cmm4dl6WSKV/jYx66/IOhwNyubw1jwJAa58yDAOv11t3W0jUyIidoHX7xocdiSEsaN2Sl/KSrXd6C957zGw2b/u8vb0diUQCsVhM9D6k02kEAoFd86HVamE2m6l5LeR/T4pWPfeOWLSqAa3+xeNxRKPRop79jc5HdqKR1xLtvVgssiSVSiEcDhOjVaiAFE+LRmSJGKG65eaq2RTSYhF0Ukp9Vw1K7W0ahdR4Wo3KR2iC1vjFYjHE4/FdtEwmk2iOQM0mM0mD5j5toTHQrDK4GW2DRkYz2hperxccx1HR50pFlgBkU6XQ0hGqhdT718igpSOwLFuQFl/UvjWP5UFrH/h8PrAsu4sW7RSf1C8jSC543iDaycDFoFVOWJCmVeydEokEotEoMVrVgO9focsSgI6CywtnmnNfKGww/3tStAq9k8vlEiWND43xqxa0+ldsLYlxqenxeCCTyQqm4QLo8RExPPvryYctFgtxz36afI7W+PE1S5pNZkoBxd5XDM9+mqC1NpPJJMLhcGtt1oB682GxZKZCoYDNZtv2eT34cL0dgUpB6vpcC/TR4nO1gaZt0MgoNR9iePbXU8ap1WpYrVbitPiMHvloZFujWpTqH+m02K800Jp7PkWvlNeZ1FFvPqdSqWC325s3MmJzc5OYQl8sBDWfFil4PIVz+fOV6EnTKvZOAL2CIsUghf7Rnnubzbatqj1PJ78vpGgVeyeGYYil8eEjS2iMX7UoNRak5xegt5aKpeECyO6dUuNHMmc/7z1Wz7nijcNG5cPNutaVSuWuQ0S+CJcUeAxJ0Bxbmii3Nknpc/z40NwHOxVw3rO/WeeKJB2A3lwVq1kC0OPDyWSSStRvNSgWag80Nu9poTYU43O8Z3+jrotm5HONjHK6MqlUxizLYnNzs+nmXiq2hs/nq/thf6n+NXI9LymgxTcbA83K50pBtMuIYi9GMo1PsRBU/jMxPNZ3GkRihB2Vuszhv68npNA/2nNfiI7RaIRer2/IueeVQ6mGQxZLwwWQ9+yXwlqyWCxQq9UNuZaKpafhPyP9ToUuhcWiRYvPlaLFF9slRYdvtxAtMd5pZ2SJWEW46o1iBz+ANHhqtSi1NrPZLLGc/bTXpsPhgFKp3Pa5GOnKaIGPLHmlyEzeEahReT5J+Hy+gilFgMbmPS3UhmKOVI3M55rVNmhk0OKbfr+/YEoRnhZpWcC3S4OWFGwNKRz2N6oMljqKpegFcmMbCoWQSqWI0GrxzdrAR5Y0G58rBeKXEXwRx0IvRrogRrGiP/xnpHO9F6JDk5ZUiisW65/T6YRMJqPSv2K59wG6c086ZKoYLdLhmkL2Tj1TEpTyknW5XERz9vNjUSw9Fo35FavODQ0+QpsPF4osEYtWoXciXYSrVGQJ6UKJzSozpQC32w2r1bor1B5o3PfNZDIFizgCjc9Hmm1tlvJwcrlcRFN8ut1uyGSyXakreVo05koulzeszCSNcnuHdIrPFhoDzcjnmtU2aGRIxdYgmcbH7XbDYDDAYDAUpEVD7pBO8ckwTFGPa6nIuEaVwVJHJBJBMplsOjuzGVFu/Hw+H7LZLDFaOp0ORqOxIK2GTdNUzksWILfgPZ7C+dd5WjRuk2nScjgckMlkdb9VLNY/mh6vHo8HJpMJOp1u13eNOvfZbBY+n6/kRR7JvQMU36fpdJpogd5KUa5/+c+QoGW326FSqQrSasS1lE6nEQwGqY1ffrs7acXjcaKe/fXmw6Sj4WivdblcDrvdXpBWI651qaAZ37dcBB1Ano/QqHPTjHNFm48UiizhaTUiHykXWcL3RYooN/cMw9Td47UF+mjxudpp0bINGhWlPK5JF3suN/csy8Ln8xGjVW9bgzStcpElfF/qBT7doFT718igzTeVSiWsVmtBWq05LI1yc8VxHLHUd8UyGPC0GjYyQkoGUTQaRTweJ0ar3gaRVNJbSEHBFdKHRju4KBVqbzabiabxKXfwk/9MPUCbjzSb0lnqUpjP2U96LRXykm3NlTA6fJuF6OQ/Q4JWscgSMUJ16y0naKIZ35f22rRYLAUjS9rbczn7SXn2t+aqdlrNxodLyUypOAIVA825b6Fx0OJztdNqtvEjjVAohEwmU3CceM/+RtWVm03GSV1OlLosMRqN0Gq1rT1XJeqxd4odcJP07G9GNCufKwWqlxF8egtagwiQK/wjBYOINK1qUOqyBJDOZQRJz34pKAqkc7x6PKUjS/L7Uw9IiRk3omd/qfETw7O/lPdYfn9I0KIxfqUiS0jTKjVXfOq7lsyUPmhekNNCqbVJOme/kLXZaHyEJmg6GJQbv0gkgkQiQYUWDT4sFUegYvB4PNDpdAVTikhBn2uhPmhmPtdstkGjotR88J+TnA+NRgOTyVSQTn5/SNCioc+ViizJp0UCpeZKCof9NM9AXmkoNbZiRDCV0+VJefY3IzweD1QqVdHIEv4ZUrRKzRWtFJ9ULyNIK/QeT+FC2YA4aW1K0SItLGjQqgal8nXyn9O6jKAx96Wq2vO0aM09aaWE1t6pBh6PB2azGVqtdtd3drsdCoWi4caCT8MlhbVEmpZU+DApz36afK7UIaJSqYTD4WjKuWo2o6Lc+5L07KeFcpeajSqTmnFtejyeojVLxHAEKjdXJC41M5kM/H5/08lM0uDno5AnohT0uRbqg2blc81mGzQy6mFr0OBz5eY+m80iGAzWTIePLKm3jJPJZHXnCY0sg6UOjyeXordQWnutVguLxdLimxIBf0FQLLKEf4YUrVJzxTAMAoEAEVqlQPwygi/iqFarC37f3k6u4Jzb7S57+0aCFm8QlaIVCASQTqdrplWqMDP/eT2Lv0ilf7TmPhAIFK1qz9MiXZyLFq1idGw2GxQKRd3XWbH+8Z79jcZHvF5v0TRcPC3Sa6nQAbcYtIq9E5/eguZckRDStPdiscgSMWjRWOupVKpsZInP50Mmk6mZllRAa2xpwu12w2g0Qq/XF/y+EdcmX8SRlmc/LZQaP94RqNHmSkikcCPKTNIoNR98ik+p9r0FccCyrCDP/ka7IG9G26CRIRW71WAwQKfTNdzc0x6/YpElpGlVA6mcLzUj3G432traIJcXPvZtRF2+WVGOzxkMhqabK1EiI4q9GEDeu7sYLT5/OQlaQgyi/OdqQSkvWZ6WFG6u6x36Syulg5D3JRWq6/F4oNfrC4baA+Q9JMop9PVeZ1LgI7TXUjgcRjKZJEKrmPcYT4vG+JH07C9V4B2gP1c0xo8mLZKhukJlZrOE6goJtQcazxtIKmuTZJ2bUnmJgdZc1UqLNh8OBoNEHIH4yJJSjlRSXROl5qOV3uKViUAgAIZhWnxOJFqNOn6k4fEUL1YL0JsPknyOpj4n1NYgkRKqlMd1Pq16weMpnoYLqH//GhlS4ZukU0I1I6QyVzRlXNNeRqjVathsNmrCIv+5WmlJ2SASclni9XrBMIxofSinKPCe/Y0491LYO6RpVQNa/ePTcNG41BS6lkhdajbbWipV4J2nw/enVgjhc36/n4hnv1TmSqPREAvVpck3pYBgMFg2gg5ovPeVytokmeKzWdcmrbliGAZer7clMyWERu57C+KgxedqA03boJHh8XjKelw3mq0Ri8WQSCQkcxmRSqUQiUSI0JKynJD6ZUkjQyq6PO/Z35rH4qDJ52KxmCR0hIa9jChnEJGkxbdRKpd//nO10ir3TmIf9peCkMsSjuPg8/lE60M4HEY6nS46TiQ9+1uXEfUBrf6V8x4j6dnfWku10+HbKwSSHhceT/nIEoCMZz+t8SsXWUKSVrm5ara8oeXel6RnP03QWpvlHAxI0mod0tWGcpEljewIJGWdqBQaue8tiIMWn6sNNG2DRobQ+SDp2V+OFgk6fHuFYLVaoVKpiNEqF1mS36daaUlZTki9f40MWmObSqUQCoWKnpeSpNWsoDVX5TIY8Ck+G/YyotQiJJVqhjeIaNAS4iWb/1yttMq9k9iH/aUgpH/8c2L2IZ9WsX6Qmg+1Wg2LxVKUTn6faqVVjoGTSOPDH/zQGL9qQat/tNeSXq+H0WgsSie/T7XSKvdOPp8P2Wy2JjrlCrzztGjMlUajgdVqpTZ++X0SmxYJOnxkCc25onFxJAWUW5sKhaLuqe+qAa21yUeWSIGPNOrabFaZaTKZoNPpitLJ71OttMq9Uz0dgUpB6vpcC/RRbp86nU7IZLKGWxfNyOcaGULmI5lMEqlNIpW5J5kSij94LBZZ0oi2RrUQ0r9oNIp4PE6xV80BWnNf7oCbJK1mhZT4HK25EqWAtRDP/loPwcoVuuG/I1VgqFQRR61WC7PZTIxWuXfin6sHpNA/2nNfKmyQZIHecmNLSikJh8NIpVJUxq8acBwnaJ3RKB5GmlYpOvwhGE0+Umt6C7/fX9J7jKfVbHNFks8JobW5uVnzIRjt8bNYLNBoNAW/J12Eq96gObY0IWRtBoNBpFKpmunw7ZWiRWptarXaopfCKpUKdru9oeaqXLFaoDn5MG2ZyXGc5OrclAu1BxqT97RQG9xuN9RqNcxmc8Hvec/+RloXzWobNDJo6cqJRAKRSKTp5l5qtkY9D/uFjkXrILtyCBlbEhFMLb5ZG5LJJEKhUNPxuXIgehkhNNSehGd/Oc9L/jsaoW00adWbGUuhf1Kae9JpfGiMLc3xqwbl0nAB5HL2S2ktqdVqop79zbaW+MiSYgXeSdMScghWK61yeYmB3DuxLAu/318TLSmtdZK0pIByofZA472vUH0OqP1Ssx5rs5iDAUlatFCuZgmQeycS0XBS4iO8I1AjyUzSkLo+10J90Ix8rlltg0ZGs9oaMpkMDoeDCq1S70QyxafUZZzU+9eoEJqiN5PJIBgM1kSrxTdrg5DIElIXR/wc8PWPStESG0QvI4QaREDjCaZSITOkaUmZGZfrn8FggE6nE/0yQi6Xw263F32mEQ/VpKbUkSrQWymE9g+oPWe/x+Mp6T3G02qktVTJISKttUQivYWU9iKpIlzl8q8DjSszy81VM4XqNuPBTzQaRTKZfEWuzUabK6HjR8oRqFRkCU+rkeaKpswkDaFzH4lEkEgkaHWrhTrjlczngMayDRoZUrM1+EixWmk5nU4oFIqStGjIOIVCAafTWTOteDyOaDQqaRnXqDJY6uB5oZT2aWsOC0Po+CUSCSJ8zuFwQKlUlqTVcJcRlSgKJBa8EINoc3MTLMvWTEsqBhGNw/5SKNc/krkUS/Whra2taI5FoPGM4Vgshng8TsUbm6ZCXw1o8xFah4i01pJQ7zG+T7VA6FyR8uyXymUEKVq017rQyBISHhfNdhBSCs34vrTXppDIElKe/a25qo1Ws8lMmo5UpFHJ3NcawdRC46DF5xqHzzUq0uk0AoFAyfkg5dlPk881o60h1OOa7xNtCLks4T24X8l7rhrQ5pulUvTytEjYmc2Iesi4UmjIywg+rxSNwp8eTy5aoZSi4HK5wDAMAoFAzbRoeHkKKeJIs6DITggpfAyI7/EqtA+kQnWF0Ko1pxo/XqVo8Wl8SNBSKBQlI0toFCIvBiFjQZqPlAKpwpW09o6Q8eMLaZOgpdFoSnqP0Z4rUpfCUpkr2uOXyWQQCoVEp9VMhrwU5CJpCFmbpJXiUg4GLpeLmGf/K3GuaPMRUo5AUuHDfN04qa0LjyeXUqRUqH099bkW6oMWn2sc26BRwTurlRonhUKBtrY2Yjyad8orhEazNWjSErJ3SDk8VgP+sqRU/1QqFbG02K8k0OabQs5LSXj2NyOEXEY0Ip8rB+qREQaDAXq9vuYD1XLFWPL7QYsWCTp8W2LTqgaBQKCs9xggfv8qmftaPSSE0qp1swqdexKMwe12C4osye8XTbjdbigUCthstqLPkCpcKXR+WZat6RBMSOE9nlYj8ZFyBd7z+0Frrmr1uAiFQmUjS3haNObKaDRCq9W2ZKaEIfR9SXj204KQtcnn7G+tzfrC7XYLiizhn62VlpDxy2azNTkCNavMJA232y0o1J5/toVXBpqVzzWbbdDIoG1r2O12qFSqknTy+1ULLSHvFAwGkUqlqqYjJLKEp0VDxtWzqH0jy2Cpgx+vUhd5VqsVSqWyoXT5ZoTb7RYUWcI/WystIXMVDoeRTCZrolUOxC8jyhlEALn0FkIXfC20hHrJkjgEE3KZk0+LNqTSP1pzn0wmEQ6HqbwvzbEVMn719JDweMqn4SLp2U9jLcViMSQSiVfkWqIdUlhrES7a41cuLzGp1He0ZabQi9VmCNUVOrYcx9Ul9V018HjK12YCmpePkPDspwX+nUpdCvOOQI0yV5FIBKlUqulkJmlIXZ9roT4Quk8bybO/GW2DRobUbA1SaXwqmftaHB6FpE7ivycl40pF0JGiVQ0aWQZLHR6PZ6vOYTHI5XJiEUxCzkv5Z1vYDiG8x+FwQCaTNZWMI34ZUc4gAhrLeK3EIEqn0wiHw1XTkjozlkr/aM19JYpCrQo9H2rvcDjK0qKxd0gp9NVASP+AxuIjle6dWi81y3mP5dOqBULGj/fsb9a5qgXNKDNDoRAymYwgWslkEtFotGpaUoGUlDpS8HjKF3EEGmttCslLzNMikeKTFloys3aZScuRijSEzAef4lNqfW9BHFTiSEUi9R0tNCOfa2Tw713K4xqgNx+8Z3+jzD1tW8Nms0GtVotOqxpI/bKkkdHim40DIeNHqqi9lOaK+GVEuRsxgN6Ct1gsUKvV1IRF/vPV0lKpVJI1iF5plxFC39flctVcoNfjKV/Vnu8Ljb1DilY1aEbBWcneyWazNXv2l/Me42nRGD8Snv1CCrwDjXkIJqW1zofqNorMlALS6TSCwWDTva/U1iYJz/5KHAz4fjUCpDZXtPkwCUcgWpfCpCF1fa4F+mjxucbhc40Mj8cDk8kEnU5X8rlGslsZhoHX65WcjKs1xafU5YTUL0saGVLjmw6Hg0hR+2YErbliWRabm5uSkXHEC1gLGUQSBX/dbnfZiw/+EKwWWpXk8s9/vlpaQgwiEuNXDdxut6DLEpfLhVgsJkpxmkwmA7/fX3budTodTCYTkbkXGnJWKy0hF3m09g4pWtWAVv9477FytEwmU805+4WuJVJ8pNnWEu3xUyqVZSNLXC4XkskkIpFITbRojB+ff70cLblcTkxm0uCbUgCvpDXb+7b4SGuudoKPLClHi3cEapS5krpOVAqN3PcWxEGLzzWObdDIaEYdwev1guM4anZ/flvFwPellpRQUpcTUu9fI4OmnVmJZ39rHneD1lz5fD6wLFuWFqkaTOUgSpqmciDhJRuLxajQqvSgoVZaQt8pHo9Tr0RfifcY/zxpCPX44Z8hMfdCQlDzn6+WltB3qjWXdSN4SNCcXxqe/R6P8DRc+X2rllYl49cInv1C54qUZ7/QyJL8vlVLi5bMFFKzhAStSiLK8p9vVAh9Xz5na6O8L621mUqlEAqFJLU2G83jVWp8mJTMFFqzJL9v1dKSsk5UCo3c9xbEQYvPNQ6fa2RUMh+N4tkvdO41Gg0sFkvNtMxmM7RabcnnXgkyjuYZyCsNtOY+GAwik8lQy5DTjJAan1Or1bDZbI0VGUFrEGkfSAsxiGw2GxQKBTVhwT9PE1Lon9ANxD9T63xYrVZBYYP5fauWltB3qiWNDx9Z0ixKCQ1mTIqW0DRc+X2rlpbQd6olZ38ikUAkEpHUXJE6BKPF55p1rQvJv26325siVJfm2NIErUvNejgYlMtLbDaba07xSRPNykekeClcD0egYhCaUgRoLN7TQm0Q6khFqp4XLTQjn2tkVKore73equgITSnC02qUuZeqrVGPw/5K+ldrWuxXGlp8szEgNLIEaL65qttlRC0KvRQNIhKV6KVw2F8KUuifFBUFEgo9rbHllUGpMKCdqOaypNpDMCmuJavVCpVK1RBrifYhopDIElK0pGggRKNRxOPxqunw7QihRUNmkirCVW8IPfgBGksBr2RtZjIZhEKhqunw7QihVevatNvtUKlUJZ9rJI9XKUaWkKIlhM4rwRGoGPx+P1iWlaw+10J9INSRqpH4XLPaBo0MWnwzEAiAYZhXrIyjbWvU47C/UWWw1FHpAXcwGEQ6na6KVotv1oZgMIhsNtt0fE4IiF1G8EUchea6AqpnJvzvhNKiwcBJ0aIxftVCaP+cTidkMlndLyNozYdMJqNGq1ZBXM3eqSWNT6XgL0uE9q8Wz/5KDhFpriUSQqYZ+bDT6SwbWUKKlpB3qrUIFx9ZUslcVZszlvZcCaEDNIdC6vF4YLFYyobaA7WPLS1ks1n4fL6m5CNC12ajzBXPE4SOHwlHICnJTL7OTSPITNKodO+00lu8MtCMfK5ZbYNGhlRtjVr4nMfjgV6vh9FoFESLhtypNcUnH1kiZRnXqDJY6uBT9NK0M2mczzUjKuVzXq8XDMNUTUur1f7/7d13eBzXfTf67+7MFuwCWPTeQVRWSSwiqeZIbrLjlpv7Rkn82olLIl/7faPXuoll55H9xjfP9Zs4udd5JcuSpdjXeRPLiWXLTiSRFFVISSTF3tCIjl1gsbvYvjO70+8fg0URSezMggCLf5/nwfOQ4Dk8M3POnDJn5hwUFRUZSuummYwwO8sC5L8hRjZerk/ts2mtx8Z21yotI9evvLwcFotl3Td/MXp8LMuivLx8TY4vEAigsLAQLpcrZ9j1yo/VpmXmU/vVbjhndMOsbJjVbtBrltnjWxonn7RKS0tzvj2WTetmKEtm3x7LHls+zObVat7sX6/rZyat1W7Ctd5tppkvS9arrN8Km9GtZ9lcL9lNHNezHjH6ZUkgEMh7gvxWzKv1bjONLDeYTetmaDMFQUAsFluX63etmc17WZYRjUbX+rDIdUb13M0zNrhZaZpmuJytdhNUs3mvqirC4XDead1oY43VphWJREx9WZI9tvVidhku4MZpg290611vsiyL0tJSQ2lRHi5nNq80Tct76bts3ZNrD+BsWjfNBtb5PFhZzSy5mQFRMplEOp3OOy0zjUW+52TmU/vsw/7rMXO9HtfiWh/Daj7VXY/zze5qbyQtj8ezqmV8zC4psjTOeljvemS9yvN6pWVm6aTVrtmfjWd0UnhpnHzSutXyar3LekVFBRiGMZTWaj/Vvd7txHq6Fc93vcumkU0cs2ml0+lVvdlPeUX18FJm2szsi0A3SrlYz7wnNw+q526eeu5mlUgkIIqioeu02jf7b9W8vxXHGvkwM1lSVFQEh8PxG3nP5eN63DtGH3CvdlP7W82tWs8ZcV0mI1bboTd7EYHVfXZ0ow2IVptWPsxMlgA3zmSEIAh5v9l/I3YUVruMTzAYNPVlydLjWw/rOVliNn85jrvhH4KZKUurXbM/GAyaents6fHlk9Z6XD9RFBGNRm+4vMpO+FCbeeNZzwny9WKmbK52zf58yuaNXo+spxu5zUwkEshkMuuS1nrUw9frRaCryX5qb2RJEZqM+M1B9dzNMza4WZmpN7PhVpMfdrsdxcXFhtJZenz5pLUe/Tkza/kvTSsfZvLqejzsX89nIL9pzFzbbN26XveOpml5f8F0KwoGg2BZFiUlJTnDrnc9t9ZLfF7zyQgjHYXVduhvxcHrejbs+bhRJkvWK+9v1I7CtUhrve6dfJiZLLkWb/avxwNaM8twZdOisrS6tPJ9s9/MBu/ZcKvNKyNtps1mQ1lZ2S2ZVzf7oMLs+a7mzf71YqYesVqtqKyspLJ5nQSDxr8suVleBMruWXKrtZnXmtk3EbNxyK3tVq3nbrWxwc3seow11qOeM5v3kiQhHo+bTsfMlyXZtG6GFx7zcTO3wTe6YDAIq9VqaInegoICFBUV3RR9+VtRMBhEZWUlrNbcj+bXu56TZRmxWCyvtIy4ppMRRjdxBFa3IUYwaG5zrmwcs8xs4phNKxKJQJIk02llj+9G3XTsRjm+9cr77K7263G+63ltzVy/1W7Qmw8zx8cwzKofgq1HWQqHw9A0bV3LktFGZr3K0rV448JsXuUzOFzve9HolyXXIq31KOvZL0vMpHWzf6q7Xtd2PQWDwYWlFYy4GcqmmU0cs2mt5s3+9WLm+rEsu+qv4dYjr8xsVpsNdzO0mdeamfwoKSlZ1RKf5OaQfZHKzL1zM7zZfyuODW5mN+q4taioCE6n84bP+/W+fna7HR6PZ83TyseN8nzpVhQMGl+iF7g5+vK3KjPXLzsxf6vk1TWbjNi3b5+hWeusTCaDgwcPmk5HVVWcPHnS8NI72c+XX3vtNdNpnT17FgAM71ae/YQlG8+M7PEZHfwnk0mcPHky753UzXrrrbcAAA6Hw1B4juNw6dKlvDesvRJBENDX12c477Nv0Lz55pum0zp+/Lip8JIkIZVKYXx83HRaBw4cAMMwsNlshsInk0m88847ptMB9GthZtDhcDiwb9++vNLKx4EDB0y90a6qKvbv3286nXQ6jcHBQaRSKUPhs5Oshw4dMp3Wu+++CwCG60dRFBGLxeDz+UyndeDAAdhsNkP76QBAKpVauLfNOnTokOHrZ7FY4HA48sqrYDCIYDAIQRAMhc++VXDs2DHTaeVTz42MjOQ1kDd7LTKZDF599VXT6SiKglOnThmuN7Nt0Hq0mYqiQNM0nDt3znRaN4JoNIrp6WnDe1JlBwRHjhxZy8Natf3794NhGMPLD6TT6bzKi6ZpOH78uOmymU/fsa+vD4qiGJ74yvbnTp06ZTqt9fTqq6+aajM1TcurHuZ5HkNDQ4br/GwdevjwYdNpmW0zBUFAJBLBzMyM6bT2798Pm81meLCeSqXyOqe18MYbbxhuezRNg91uzyvvyc2jv78fsiwbrueydfyNXs/dimODm9nBgwdhsVgMfakC6OPWY8eO5bWk0euvv27qWQLLsnmNW+PxOKampgynlR2z59Ofy8YxOlZLp9Pwer15vZ2cbeOM4nker7/+uul08vXqq6/CarUafpk5mUze8H3oG8X+/ftNLa8jimJe40xZlnH27FnTffn1LGc3utdff93wWFLTNDAMk1c9l0gkMDExYbqee/vtt02nZdQ1m4w4duyYqUpydHQUfX19ptPJZDKIxWIYGBgwHB7QO+1mnTlzBgAwPT1tKHx2IHTy5EnTaWWPz+gDt4GBAcTj8XV7ay9bYRj96mNgYACCIFzT9eASiQTS6bThvM8eaz6VXXYwbPT4s5MQFy5cMJ3WoUOHoCiK4Y724OAgAoFAXhNR4+PjuHTpkqGwqqoinU6vaQX0XmfOnIHX6zUcPhQKmZ44ArBw75gtS/nUI0ePHgUAw/Xj6OgoAH1QadahQ4cgSZLhsjQ0NISZmRnTAwRN0+D1ejE0NGQovCzLEAQhr4mPbHkdHh42FD47iMwnLbP18ODgIERRzKuee/fddxGNRg2HHx8fx8WLF02nk8lkEI/HDZf1bNnJp97MPtDw+/2GwmfbzGxbe7MZHx+HpmmG74NsR/NGf3Bx5MgRJBIJww+DR0ZGDF+DpURRRCQSMVw2s9cvn3o4O1FmdJI3+xbQiRMnTKe1ns6dO4fJyUnD4efm5hb6N2bEYjEIgmA4r7IPQ/OZpMq+bGF0+YvVtJmHDx+GJEmG+7ZDQ0OYnp6+IfZ9GRkZMdwuAvrkeb4vspCbQ3Zif2pqylD47BekN3o9dyuODW5mb7zxBjRNM/zwbHBwEHNzc3mtHDE8PGyqnkulUnk9rPb5fFAUxXR/Lp+8z/YBjT4QHBoagqqqhu/rpY4cOQKO4wy3WcPDwxgZGTGdTr4OHToEVVUNP88aHBxEMBi8qb+oXi/Hjx9f+NLUiKmpqbxeqOZ5Hslk0nAfbDXjzFvV0NCQ4fvOYrEgmUwuPF8yY2ZmxlQ9l70v83mx2zDtGjlx4oR28OBBw+FPnTplKvxSzz//vDY1NWUqvNfrNZ2OoijaE088ocmybCi8LMvaE088oSmKYjotn8+n/fSnPzUV/vnnnzedTr4ikYj2ox/9yHD4aDSq/eM//uM1P46f/OQnWigUWrPwWZIkaU8++aSmqqqh8KIomgq/1MjIiPbCCy+YCv+LX/zCdDqapmkvv/yydvHixTULv1pvvvmmduzYMcPhDx8+bCr8Uj/+8Y+1cDi8ZuGzzJYNQRC073//+6bT0TRNu3Tpkvbiiy+aCv+rX/0qr7R+/etfa/39/WsWfqmnnnpKS6fThsKqqqr94Ac/0DKZjOl0zNZbsVhMe+6550yno2madvz4ce311183HP7EiRPaa6+9lldazz//vObz+QyH/+lPf2oqfFa2zTTaBq6mzbxRPP300xrHcWsW/no4ffq0duDAAcPhz5w5Yyr8Uv/6r/+qTUxMGA7/s5/9zFT4LFVVtSeffFKTJMlQeLNl+Xp58803tePHj5sKn2+b+aMf/UiLRCKGw//4xz82FT4r2wYabTMzmUzebebg4KCpNtBs+LX0yiuvaOfOnTMcft++fdr58+fX8IjI9XYr13O32tjgZjY+Pq7927/9m+HwExMT2s9//vO80tq/f7+pem7//v3a2bNn80rrmWee0ZLJpOHwP/zhD02Fz0qlUtozzzxjODzHcabCL3X+/Hlt3759hsOfO3dOe+WVV/JKKx9jY2Omysbo6KipZya/yY4dO6a9+eabhsMfP37cVPil/vmf/1mbmZlZs/C3uv3792tnzpwxHP7AgQOmwi9ltt569tlntUQikVdaRlg07QZ4vYcQQgghhBBCCCGEEEIIIbesa7ZMEyGEEEIIIYQQQgghhBBCyJUY2jlnbm7O1FqN7xWNRhEIBNDS0mJog5p0Og2Hw7GwEalRgiAgHo+jqqrKcBy/34+KigpTm/tomobx8XFUVVUtbJCdi6qqGBsbQ319PQoKCgynlT3GyspKwxsdAUBNTQ1qa2tNpQPoG4pmN0AzoqOjA263G6Ojo4Y3rikuLkZ7e/sV/y0UCuW1ca82v3al0c20AKCyshLJZNL0vht1dXWYnZ01vClQdjM5o5szZaXTafh8PrS1tRneXBHQN8TSNM3UnhLNzc3gOM7U2oJFRUVob283tXH91NSU6fX1fT7fwj4Azc3Nhu+fSCSCYDAIq9WKtrY2Q/ePJEkYGxtDeXk5KioqDB9jKpVCMBhEa2urqevhdrvhcDgQiUQMhS8oKEBzczOGhoZMrVlttVrR1dWF0dFRw/tJVFVVoaamxlR9AGChnh8cHDQcp729HeFw2NS+Q/nc81nNzc2Ynp42Vc8xDGPqnACgu7sbiqIYXu+WZVn09vYiEomYqgcdDgfa2toW1pU1qqOjA4FAAIlEwnCc8vJyNDQ0YGBgwHBZqq6uRlVVFQYGBkyVpdbWVpSUlBgOv1Y4jjO8106Ww+EwvPfIUi0tLSgtLTUdLx/ZTYmNYFkWPT09iMVipvqDdrsdPT098Pl8hut+p9OJ7u5uTExMmKoTSktLIUmS4c1HsxoaGiDLMmZnZw2Ft9ls6O3tNd1HzZemaQv7cBlVX18PRVEMnxMAuFwuNDU15VXPqapq+B7Jt57LlqVgMGh4XxqPx4O2tjaMjY0Z3oMC0Ou5xsZGDA8PG94kuqGhAZWVlYbTMEJVVfT395taa72pqQkejwf9/f2G+4FdXV15taXk+ojH4xgbGzMcnmEY9Pb2IhQK3bD1HGDuvLL1QSAQMFXP5duPzreeMzN+u9GIooj+/n5D18lisSy09Wb6lIA+rtY0zXC9nr22s7OzC/s7GVFYWAibzWZqvzZAHw8VFxcbXtM9WzZDodDC3mhGZPs+MzMzhs+rtLQUzc3NGBkZMdX3qaysREFBgal9KPKtE2ZmZhAIBEzFyXeMV19fb+o54K0glUoZHmfabDb09PQgHA4b3hMX0Mc0PT098Pv9hvOypKQEra2tGB0dNVUnVFRUoLGx0XD4m0k0GsXExIShsPnUB4D+bGnDhg0YHR01XCd4PB60trZiaGjI8F5AAFBbW4uamhrD4a8o1zpOqVRKKyhwagDy/7FYTIW3WPJLx2K1ru44b6Efi8WS15rfjzzyiKl0Nm3apL3wwgumj+9K69rHYjHNbrdf92uX68dms61LOsw6lme3y5VXPDPrPJ44ceK6513uH3N1VfZnPeue7p6evOJt2rTJdJzPfOYzpuOUlni0u/buNRWnuqpqXfPZ5SowFb63p1u75977TKez5667tU295vLrS1/6kmZ3OEyntTmP/K2vrc3r+n3hC18wHefTn/606Thl5RWG92xaS9u2blm3slleVrpu53zHbdtM53tBHm3F5z//edNxHn744XW75jabTbOY7Kd+4xvfWJc80jRN+853vmP6nFiWNX1OALTOrm7TcXbv2att3Wyu/nn44Yc1p9N8Pffoo49qVpPt7be+9a28ysXjjz9uKrzT6chr/fCVPPbYY6aPu9Dt1j73uc+ZirNr165retxk7aiqqtXk0XY/9NBDpuuEr33ta+t6bvV15s7r0Ucfzaue6+o234/eeedu7bZtW03F+fKXv7yu1+9a+9QnP2nqfO+4/fa86lqr1Wq6Xv/sZz+bV1r5/jQ0NJgK/8gjj2gMw5hO5+tf/7rpON/85jfzOidXHv25xx57zFQZGhkZWdd8stlsv3F7uHR3dpi6Rl/5ylfyepb153/+56bj5Nv/ync/hBuZJElaaWnJmtcHgP4swWycfMZdVqtV8/v9q7ouOV8VjkajSKcz+Pr7m3BPe8nC7w9eiiIlKNjeWAQ7Y4GqAbyoIMxLcNkYqJqGzXWFmE2I+Oy/DOLBL/0VJDEDgUuiefNOsDY7VFWFmOaQScVhYRiwNgc8VXX44X/5OL56fwve312upzUURiqjYHtzMeyMFaqm6WlxEuIZGZVuO7Y2FOGP/mUQ1Vvvw8e++Bc4/9Z+pLkkNmzdNZ+WAiHNg0/GkeGSKCqtQCzkxy+f/DZe/PJeuOz6mwsH+wNIZmTsaC2FnbFCmU8rIyqwWi1grBa8MRjEy5c4fPbvXwQADB09AIFPomnjTjA2OzRVhZjhwMcjSCei8FQ3oP/Qr5EePIRnfq9n/pwiSAkytjddfk6yqqGkwIaeGjde6Z/D9w55cfhHfw2LxYJ975xBik9j5+ZOOGwsFFUFnxYQS3FIZ0R4itywAPjSXz+NmZkZ1NfX58riZXw+HzZu3Iif/OQneOmll5BIJLBnzx7Y7Xp+cRyHQCCAu+++G08++SR+8YtfLLzZtu9PNl+9bEgKwpyMHU1F+NDTF674Ntzc3BxEUcQ3H+zA7ja9rB0cnNP/ryYP7OyScsaJiPEy6koc2FhbhCcOTeKliyG8/dN/wL7Dx5HkeOza2gu7ndWPO51BPMHBVeCAqmoochfg97/61/C4HPj5l+/Gq31+pDISdrSWw84yUFQNvCgjnpbAWCywMRb01pfglfPT+H8ODKFj2y58+mvfxdnD+5DhUtiwdSdYuwPafDnjEjHIkghHgQv//uzfQWBd2Pan/y8AIHD6IOR0EqWdO2C12aGpChSBhyJkYGEYQFVR3NyLU3/zB/jovTvx3/70jwEALx98E8kUh93bb4PdboOqqkjxacTjCYiShOKiImzu6cR9n/gD1BVa8OTvds2XMwXbm4qWlDMVYU5CWlJQUWiHL5bB37+hv+n6wtN/i3P9l5DkeNx52+aFdDg+jVgihXgyica6GnS1NWP3Jz5r6q3GbNif/dkHUV7oAAC8et6rX/f2KthYK9TsdedFJNIiRmbjeP7ICPZ96faF/+fgUHj+nK5871S47XDarPjCT/vx7U9sxK62Mj3eZfc2wIsyMqKCeFqGp4DF9w9PIVG5FVv+4C8BADOnX4WcTqG8cwesrB3QFMgZHiIfhypJYB0FKG3bgle/9gHs/NQXse2DD+HSsQMQ+SQaNi7Wc1KaA5+IQORTKCyvQXVbLwDg2YcfgKoq+JfPbMRpb/KqeSXIKsrdNnxz3wSCc2GUbn0/Gj7+VUTPH4SSTqFow3ZYWDugqlAEHnIyDE2VwTgL4W7ejMmf/XcEg2OoryrH89/5M+w7chYpLo2dmzvm6xENfDqDWIpHJJ6CxWLBXz3zbxgfH0dXlRvf+52OFcsSLykoKWAxExfxvUNeeL1T+MRdW9DbUoMkL2BnTzPsrN4ucBkR8VQadhsLq9WCs8PTeO7lo3oe/c0XwVgtOHDqkh6vu1GPp2rgBAmRJA9RUlBZ4sa+E0N48Z0+7PvTrfP1zvzxNRbP1zvzx8dLsDEWbKsvAgC8eCGEH7wzg866Mjz1xfvx6rlJJNMidmyoWThGXpAQSWXAWq04OxHEr894wQkyyu54EPUf+S8AsOK1L+rYgemX/ie83n4ko3P4vdsq8dmdNTnrxv/6y1GMj49DFAS0/97jUEQeSjqF4g3bYWXt0DQ9DSkZhqYqsBdXwFW7Af3f+yPMzQWxp9WDxz/UumJeWSxASQGLoxMJ/Ns5/UuofYeP4+D+l5FKJrF9152w2x1QFQU8zyEejyHDp1FZXYOy8nL8/icfxPj4OBrLXHj2D7fi4EBIb8uaS2Cfv4c5UUFGUpCWVCQyEv7H/hFMTEzA3diLtj/6e0PXL3zyPzDz8hMQBOG6v7Hr83rxn/Z04Av3b8Sr56fm66zqxTpLkBFPi8iIMsoKnfjeK2cx7I8bKpuyqqGswIauahf+oy+MJ97yIZPJwO12r/15+bz41JYKfHF3bc6y+dVfjWJiYgJpnkfT7zwGz8Z7c+YhAIw89cWFN13/1x92o8Jty5nWQ/80hPHxcQDA5r98CbAyOdNS0kn0/+3vAhYLtn/rFQBA+NxByOkUPNk4S+4fq80BWBkofBIjP/0WAOAHj/0xvLNhJPkMdm1qh51loWh6HyueSkOUZRS7C/A3//RSXl9w5p9PPlQVO/GTP+zN2f5VFtoR4SU8/tIoAOCj/+f/RCI0A2G+TVraR03HI7C7CgENqGrrxT//t99GaC6Msts/jPqP/lcAue/TmVeexJT3PDKpOP73bZX4412567lHXtTruUxGwF9+oBkZWTU0nvjcz4YxOjoKVVXx3HPPYdu2bSv2U++66y5s37594Y3BzY/vM3ROMhfFwN89hOHhYbCMBf/x+U05z+ntsTj+r1f1Lz+NfjFtNO9by1146nc3rDju4kUVJQUs5jgJf/u6FxMTE9i+fTuefvrpnH357373uzh8+PA1O2aytlRVxazfjw98+ivY9aHfxfm39iPDJdH+nvFuOhmHLIsocBfjjX99BuMTE9A0Dd/7g+2YjvJIZeT5MY91+ZjHakGBjcHf7R9c13oOAKZn/PjC7lr8zpaKnPfcH8/XB5qm4bY/+XtkIrOQ00mUvWdsJQsZZKKzKCivg6epB+98+3cQnJtD6bYPouFjjwAwUM/tewpe72nIGQ7/afcGtFQW6v2AtvmxiwbwgoQ4L6LAzkKQFPzo0NC6X79rzeedwj09ddjdUZXzfF85N4XB+S8bfv+J1wAA48dfhcgnUduzY6HtkTI80okIoKlgHQVQZQmvP/HnAIC/+d82IpQU9Gc+LSULbVy2T5nMyChx2fD9NycW+gidDz8DR2WTof7Ihb/6EADgt/77L2BzFWHm1KuQ0ilUdO2Y72PrYzyJi8NiZVDWvhWpwCTe+e7nMTs7iz956OOoKi9FkuOwa9tG2G22xWcMyRTiSQ7377kDf/jfvo2xsTEoioIN//n/RlHbbYjM90eu1J+HpsLTuQt9331o4euLp363A61lzhXvg94aN37nR32m2zgh7MOlJz8Pnudx7x99HZqmGeojvPjtz5ku09m377v+5Em4atr165BZch3mj01KhGCxMmBdHrjqOuDb/wxCx36BB/7HqwAA/2k9r8o7d4BZmle8nleapkGVRZz9x28gGAyirKwsv0J/E/JNT+MP7qjCp7dX56w3/48XRjE2NgZJktDy0F+hqGOnoXvn0j/8Z4yO6v3K731qA7oqC1ZMa1tDIX77hxdNl00pFsDgP3wGPp8P27Ztuy7Xc63wPI9oNIavfmov7DYGybSAnZ0Ny56PRJJpFBbYAQ34y396faE+2PVnTyMxMwo5ndSfSc23cfo9kIDExWEvLEFp+za88fUPLdSPRp7Nbm8qwoefvoDx8XE43MV437dewPR83Vh5hbpRSnNwesqhKjKOP/VV+P3+VX0dYXjdn401bmyuK8TL/WGUu2zorXYjI6uYjgvoqXZjY60brNWCgVkOjNUClrGAExVIqgYAqO/cggyXQGFJBfhkFALPIRUJoKZ9I7p2PQArw2Dy4nEo85+h99a64YtmUO62obemEIKsYiYmoKfGjY11hWCtFrw+FEF9iRMeJwuLBbCzVlQ1tGJuZgpd2+8GF49CEjMIz3rR2LEJHbfthpVhMX7xFASew9yM/mnalgYP3hgMorzQgd66YmQkFdPRDHpqi7Gpvhis1Yp+fxyyooFlLGCtVhR6SlHXtRV9h/4drbftRToRgyxmEA/6UN2+Ec1b74SVYTBx7ghcnnIor0moLHLo17BvDntaPYil5flrmEZPtRs7movBWi04NpHQG15ZhY3Rl3y5rbsNv3rzOO6+vRfRRAqCKME3O4dNG5qwe0sXWIbBqf5RuF1OjHqNfeZ4NfX19ZiYmMB9992HSCSCTCaDqakpbNmyBXfddRdYlsXx48fR3d29EKfAzsIbE+bzK1s2RPTUuLCxRi8bJ71JjEcycNhW/mR1U13hYt7XFul5H8+gp6YQG+uK9P9rMo6MrKKq0I4CG4PaYgcKXU7c1rMBk9MBVJZ5EIknwXFpBMJRbOpoxfv33AGWYTAwOoXAXBQAUFbogDfMYe+GSkR5EYKkwhdNobfOg13tFWCtFvTPxDEV5iDKKnrrSgAA5TWNCE1PomfH3UjFo5AEAeFZHxo7NqFzvpx5L11EhudgZVg4y+pQ0roFM8dfQkXvHohcDKqYAT83jeLmHpR37YKVYREdOQNVykBOpwBY0NHWgtu2bMQvXz6Ae/fuQiQaR0YQMDXjx+aeTuzZeQdYlsGJMxfAp9NI8TwcDjuqiyzwRjNXKWcesFbg5FQSiYwMt30xP27b1A1JllFRVopoLIEUzyMQimBT1wa8/547wTIM3j5xFnORGNwuc8uNZW1tLseRS7OoKHSit7EMgqTAF+HQ21CGTY1lYBkr+nwRRFIZRDl9GZgt9UV4uS+0vD6Iz9cHtXp9cGw8DlnVUFVkRzCpx9vc4IE3kkZ5oR29dR5kJOUK93YC3giPpjL9gae7qgmlbVvge/clVG3cCzEVhSIK4Od8KGnuRUX3LlgYFvHJfqiKDEXUl/mqaulGbHYKLdvuQjoR1euDgF4fNG3W64PJc0ehKjI0TUNd1zY4XIVIp+IIJMUV8+pSMI1oWkJZoQMJAM6qZghzXni69kDmYlClDMTwNFyNPSju2AGLlQXnG4AYnYGa4cC4PEAUqCorwaR/Dnff1oNokoMgSPAFwtjUnq1HrDg1MApfYHHZqBqPPWdZOuNLIcpLiKcXl4Ror6tAd1MNKjxuRJM8uIyIQDSJjS21eOD2LjCMFX0TflyaWvzcc1t7LV56dxB3bWpFNJlGRpLhC8WxsaUGd/Y2z8cJIJLgYQHgtlsX2qU9LUuPT0JPtWvh+AZmeczEBZS5bCi0681eTakb3rkE9nbXIcoJECQZ05EkehsrcGdnDRirFUeHZsAJpcAZfbLOWdUKYc4LW1E53A29y667u3EjLFYWqfEzkJNhFFS3AnP9AIANFQXwxoT3XEO9fszW+0cm4qgpdixcCznDobT3LshcFKokIB2ZgbuxB57OnXr+evv1wXaGh8NTCaRn0VjqyJlXA7M8AkkRTlZvW1iWhXdqEnvuvhexaBSZTAbTXi96Nm3Gjt16fT/Qdx7+mRkw7OJyhpXFTnijaezZUIYYL0GQFEzHM+itKcTO1lKwVgtOTMYQ5ReXF7GX1qKweTPCp15eseymxs/AVb/YvtwINtR44J1LYm9XHWKcoJfNSAobG8pwZ0cNWMaCI5dmwVqtKCt0wsEmDZXNM74U/AkRF/0cSguML8V4rbQbKJtnplOoXVI2XY0bc9Y/8aEjKNv2AThKFj+XD6YkuOzMin2E2aRez2W5m7cgcmZfzrqusHkLAMDuKkZRyxaETr6Mku69kObvHzEyg8Il90/k/OtQFRkqu7j0USzB4e7buhBNcMiIEryBCDa1N2D35g69jzUwDsZqQU15ydpnzHtUFDrgi2Wwt60EUV6ebwPT6KlxY2eLR88nXxIRToKdWVwqsKZzGxzuYrhKKpBORiGlU0hFgqhq24j2HffDyjCYGTyN2s6tcHvKoIlpOKtaDN2niUvH4KxqBWbPAzBWz530JpfVc5yo4O62kiXhhWX94SPjcRQ59D5K5ZJycfvtt2NsbCxnPzW7bKLFajVc92TLEgCUuBw5z2kgwGFj7dpNHlYV2/VjWFaPLL9OZ3xJxDMyEpnFNrixsdFQX76zs5MmI25C9e29i+PdRBSSkEFk1ouG+fEuw7AYPPk2tt37YZw99DKQ0V8+iPEi9nZUIspJEGQFvii/bMxzdHQOrNWCak9+ffzV6qrMXY8MBDhUFi3WByVtW8C5PXAUl0NMxSBnUsjEQvA09aCqZRMsDIvQxbchpZNwFpcBMg9nVbPhOsFZ3Qp1+jQAvS/QXl2MiiInopwATpARTKTRW1+G921sAMtY8c6QH7Wlt8ayZ80VheiqK815vs0VRRgMx2FzOFHdsRXDb/8HGrbshZCMQhYFJEPTqGztRf0mfTw0038CipgBF1tcHjiRlrGnvVzvU8oqpmM8emuLsLO1TB+P+5PgRBnVnsVlj90tW8BNXsjZVpVufQCswwVZ4FHWvhWz5w4tjvEkAXxIH+NVzY/xwsOnEZscgJVZ7Jd1tzejuqJs8RkDn0ZgLoJNnW14/94dYFkGb588j5rK8oU4Mp+EKqThbuxd6Iu4G3vgbto433/ph6YoUNJJOD2L8bbUFaJ/llu5b+ZbXB7b7i42ND7hfQOwsov3TlX7JqiyZKiPUFRenXc5KmzZAt43BE/PXsip+XFNWL8Wxdlxja8fYnQWisDDaneCsTsXxuOV83mligJScz54lozHIyP6RGE6bHy5tltNR6XLYP/LvhDH3bTJ0LOE4o4dcBQvLl29pdaNkbn0imldmFlc3pKxOw33Kd0tW3Cr622uAstYUVHsQjSVRiojIRhLYWNzFe7f1g6GseL0yAwqPIsvtpS0bYWFYa/YxtVse9/CcylNleEsLAEAeFz2nM9mBwIcIvzi8skFnnKUtW0BF/TOpxWFnEkhHQ2itLkXddm0vEOwssa3OFiJ6ZHvg73lK/77jubiZX/3RhfX499838dWjNu1635w8cWHYA9uWnnt1Y9tufKacHfcv3I6vbvuAwAImcUb5SNb6laMs7N18bxfOu+H/nUKsPHe314xXtfuDwKAXoHPP3N7cOPKa9E/0LU4mzsc5Bf+/PH7dq4Y774dmwAABY7VF45PfepTK/77/fffj9OnTy/7Xa6ycd+GEgDAn72Yez3QBzetvN7ffZ1XT+sTD+xdMe7OLd2YnF58APqRbQ0rh2+rwM42Pc/+/cziGwE7Hvj4ivE6b9sNALA7C5B93FG38yMrxqncfPfCny1L1hn95IMfWDHeb929+7Lf5Spn93Xo65K/0r98n4hPfPB9K8b78PtWvr5GfPT2lhX/fdcGvcMTTi7WHw9uXLk+eKB7sUyMhBbvm49sWXnvlJ2tZdjZqt9zThuDbK3QsGvlvKrovvx+7Lln5fqgc/eV8/GBrjKUua5+397eqL/V//i+xbU9y+94cMW0iuffZgCA4Ds/W/jzx+/bcaXgC+7bvgnj08vXJsxVlu6e/2ru3y8uL0sf27t5xXh39raib3z55Olv7+5dOU5PEwDgSP/E4vGZaJfGI4tl6qPbr7x3TdYHtrVg2H922e9yXXdPr34PJ4aOLvt9rmP8QFcZfnx88bqXdO1CSdeuq6fTeeX2IFdeZa+FP7H4EPbBj31y5Th36vd85D1r/n9k88oDk/d1VWAkePl660auYejYL1cMcz189I7WFf/9A1v0svn3L51Z+F2ufL97yRenL54P5X9wq5DzGNs8ePbo8vs0Vx6Wbbu8rnugsxTl7qvXc9k+wrcPLl/H1khdp6SXr4tauX3lOFW79H5idODIwu/et70XPa1X/5r0vjv0L1vxo1+v+H+vlVxt4N3tepv+7kRs2e+7c7RLrbffe8Xf57rupVsfQHL01PJjNNAPfPbY4gODnc3F2NVSfNXwH+i++tuNRvqp72Wk7pG52LLf5WxfmopxeDS2YpjVMlqPHBhcvv+UkWv01ltvrerYyPWTa7y77d4PX/a7e7qq0VvnuWqc92/U+8zfO2hun6Rrycg99171OfrsdTs+dMXfG6kT4peOLftdrjHMB7c24f87bGw/ppuBkfN9o295u91x10dXjNOy/bcAAP7BxTZk74ZybGu8etnc2aq3cT84PLns90baqvdqvHPl8lKz5R4AQCqwPK1PvP/uKwVf8OF778R3n31+4e8lPXtQ3H77VcN7Oq7+bCfXfXDH/Ngwy+j4RAgvz6t8+whmVeToly29FpHzry38Odd4vHqznldzA++u4uhufkb6X99/x1xffumzBDNp7Wgquux3Ru5TMW58b4Sb2W/vWvmFu3s3Xz7ezNXGXem5VD5tKZC7fqzovAORsfMrhjHK8GTEQIDDaDiDjooC8JKCO+ffxBqY5eByMAgkRFgtQLGThaiouK2hCENBHqGU/lakf+QiAhODqGrugpjh0LbtLlgZBvHQDFRZgphJg09E4CrWG5qhAIfpmICOShd4ScHu1pJlX15EOAnFBSw21rhx2psEJyoQZRWRwDQO/stTqG3rgpDm0XXHXlgZFlOD+gVr6t4C33Af+GQM0yMDAIB3x8IYDXLoqC4ELyrY3V6+8DUEO79JjzfK44GeavTNxDETS0PTnBg/+w5mRy6isrkTYoZH67a9sDIMZkf7IKY52OxO1HZuhW/gFBLhWTAZCc8dndbPSVRwZ6t+Df1xEUlBRoSXwVot2NFUjP7ZFKJpGWNh/aHq26f7cWFkCl0tdeDSAu66rQcsw+DiyBQKXU7Ekxy2dbfh7TP9GBxf/Wehhw4dwrlz59DT0wOO43DvvfeCZVmcP38emzZtwuDg4GUbhh2diKN/lkdHZQF4UcWdLfrs6HRcwImpJH6rowRDQR5qjo2w+vxJDMxy6KhygRdV7G4rmb9OAi5MJ8EyFhQ7Wexo9qDPn0I8LS88WHvr5HlcGBpHV1sjuHQGd2/fDJZh0D8yiQKnA60NNTh2dgCzocUB25HhEPqmY+isKQYnyNjTUbnwRYTbzoITZWysL8GpiTAGZvQNEBOREA7881Ooa+uCkObQvf0uWBkWvhH9TeiG9h7MjF9CMjoHSRQgqlGMvfIsCus7oAg8ynv0ryfSET+EeAhCLIjq29+P6PBpqLIIZv7T1UnvNJ549ifo7mgHx6dxz279zYsL/ZfAsgxC4Qju2b0DF/r1QYOiKlBVFfG0hueOzizmRav+Fu50TICiATNxAbtbPDg6EV/2UPLdMxcwEwihq70FfDqDu3feBpZhMBMIIRyLo76mCqOTPjgddlMb5S51fCQAbziFjtoS8IKMPZ01YBkr+n0RqJoGf5RHeZETt7dWYiyob3h0dDyGPn9qsUws3DsCUoKCqWgGjaVOyKqG3ho33hrVv3y5OB3HRV8cHdVF4EV5yb2dgNvOIJ6WoKgabm8uRd9MAjFehBwN4NLLP0RxfScUgUNl756FGWcLwyATC6Fq092IT/ZBSidhK9Ab3LB3BO++8DQqmjshZXg0b9Xrg8BoH6wMCz4eRvPWPZgeOA3W7kAmlYCm6m8xnphKwBcVLsuvi34O/riAXS0eTMcEiLIKsIAQ8cN/8DkU1HZAFXgUd90Ji5WFGJuF1eaEmAhC4RPQFH22W+YTyD4CfPvMgF6XNNeBywi4a1sPWMaKi6NesIwVtRWluDCy2PmO8dIVy9JsQkQoJSEpyNjT6sEZXwqzS8qSNxjFD379Nroaq8BlRNy1qW3hawhZUbGlvR5HL44jEF18s+edvgn0TwbR2VAJXhCxd2PLwtcQLGPFVDCGD9zRgXcHvZiNJOfLRhz9AQ4dFa4lbZP+BQBjtcAXF1DsYObrVQ5j4cWNmd4ZnEafN4zO2lLwgoQ93XVgrFYMTkfgdtiQFmUE44sTWwAQHzoK3tt/2bUXojNgHG6IsVlIiTlIqSiy7x5NRjJ47pj/srpxMprRl9ZI6W+BLZWa6gfnG4SrTq8zSrp3w2JlkQlNgnWXQIwFIMaDsHuqFr7OCSbFK+ZV9lrMceJCXoVSi5tPH337EPovnEdHVzd4nsede+8By7KYnBjDwMUL+OBHPoaRS4OYHF8+kXxkNIJ+fxIdVW697WzT317zJzKI8hJqPU6cnIyZvn5MweWd2OttMpTEMwcvorO2BLwoY09nLVjGgvNT+gTNlqYK9HnDiHEC0oJ+312tbA4GePTNcqgpssNlZ3B7QxGOTsQxOmd807Br6Wpt92xChNNmRZiTEEvLWLqtdq58FCLTsFiXfwV5cioJX1y4Yh8hnpbRUOLAUJCHKC/fdDdnmYkHACx+DRAdPApuqu+ye0cITyM+chJVuz6GyPk3wBaWIjl+biHe6cEJvHl6AF3NteDTIvZu6wTLMOgb9UJWVLTWVeLdvlGkM8Y2br/WrtYO9s9yUFQN/riAD/ZU4MTk8o2aJ8+9g+DoRZQ36W1T03zbFBzrQ2VLL8Le4YW2KCvXNc+EJmArXj45Mhm9cj03HRegaYAgq5fVc/2zHIZD6cvGFpPRDFIZBb64gA92l+GML4mMtHiMJ0+exOHDh1fso15puSQj9Q/e00e92v0xEODQVeXCeDiDwQB/WVrXSiIt69f1PddoNiEglJKQyCi4q82D/lm9v7CUkb68mY0RyY1l6NTb8F66gNrWLohpHp137AXDsIgG/bA7nIhHgnA4l7+hf3YqgneGg+isLgYnytizQR/zDPgTkGQVW5tK8c5wELwoXyXVtTU6l75iPbL0fntvPRIdO4fwwDEU1XdAFnhU9OzW++3jF+CubkbKPwaLlYHDs/xljdxtSxCM/fIvRI4MzaLPF0FHrWfZOGZgOoq6UhdmY/rbrtduwbbry8j5RrnFusd3/h2ExvpQ1tgJSeDRsHmP/szHP4ng2EU0bbsHUZ++/EjMP7EQ77wvjpMT0cXnMW2LX0TIqorN9cUY8KfACzKWfotmtr0KDbyLpH8MxQ2dUDIcqjbq4zw+5IPIxeGuakRiWj++dHTx5cWRSR++/79+ga62pvlnDFvBsgy8/iA0TQPHp1HkXn6/pSYvIDl2Gq5avT/iyfZHItOApkEReGiqDJlLQJEW+xZnfEm8dim64n0Qzyy/R42UZykehBhd/kA6nz6CWcmxM4heeGOhX+bpWrwOieETKNvyW8iEfbBYWUjJOWSC+jg01HcEsck+FNd3Ql4yJk9MDUCVJZS0bUF09CwUSUByenRVx3gzm7jKOHOl/hdgrH/NOJZ/+Xl2OoW3xuIrlk1evLy8GBo/J8OXxbvVDPnm8PTLJ9BZXw5ekLC3twkMY4VvLgFtfqlot9O+LE5k+DRmTx9EUUMH5AyPyl69jUtH/GDsTgixEIRkGO7qlmXxVuq/MlYLaorsGAou778G+o4gNtGH4oZOyBkO1fP1Y2yyH57GbsSmBmBlGH358mvA8GSEqgE752e5gkERPzsdgKJqKHayqC22g5dU8KKC6bgAC/TORFXh4kFG/F6U1jZgevgcymqbMXnxXYgZHplUAozNDpvDqS+hdF5/+0BRgZ3NHgwFOIQ5CWMhPxpLnRBkFQ7WCl5SMJsQMBzkoGlAVZEdqqZBlkS4ikvgu9QHVVWgyBIcThcigWkUlpThzOv/DpuzAAzDIsPpD7MK7Cx2tpVhaDaJuZSA0VAKTWUuCLICB8uAFxXIiopfnvEBGiApGrhEFPFZH1RZgn/4PEprmjF14bi+FmIqAdZuh83uxOiJN6HIIqRMGpKk6ucUnD+ncFo/J0mFw2YFL6qQVRUzFwRo0FBVaIek6IMit8uJkiI3Lo54oagKZEWBy+nAdDAMu80GSZYx4puFpmnvHUeZNjIygpMnT0JRFMRiMbhcLhw8eBAcxyEUCmFgYACapuHSpcW3ZtKijOmYAFlRcdHPobHEgVNTCfCSitmECJfdijeHY9CgQbpCZbiUplmws9WDoUBKv05zHBpLC5blRyIjYTqW0a9TkQMZSUGKz8A7G4KkyDg3OIrm+hocOzcAPi1gJjgHd4ETp/ouQdM0KIpeSUZSAgqdLEpcNvTPxKCoGmRVhcvOYibGw85YISkaLs0moGlAlNcf+iVjc1AUCZOD51BZ34Lhc8chpnnwyRhYuwPRwAysDANZEsEnY0Ahi7LunUh6hyAk5pDyj8JV1QRFFMDYHbAwLGaOvwTG7tQb4nQSmiJDlCTs3XkH+odGEAqHMTw2gdbGemQEEU6HHRyfxr+++DIsFn38XF5aAlGUkHFY4SlgMRDgoKiApKpw2Rj4EwJsjBWyquFXF0LQAPT7F99cnprWy9C5/ktoaajDsdMXwKczmJkNwuUqwOCIvuZsbWU50hnhCrmXm8PGYteGagzORBFOZjAWiKOpohCCqMBhZyBIMiZDCUyFkgjE9ArSbWeW1wdz/LJ7Jy0p6PenYLEAlwIcrAuFCVe5t+frkfl7ezLCA5oGxmqBKAmo7N6F+HxeJWfG4K5qgiIJYGwOwGLB9PGXwNicsDAsJD4JaICiyHAWlSA41g9V0eseu9OFRMiPguJSSBkeI8dfW5iYENMchIx+ftMxAbI6f++UZu8dZf7eYfDmcBQagBgnwOkphCaLYFwecL4BQFWgKhKsDhfEiB9sYSlUMQ2rTS9XqiQiE5yADUAwEoM3MAdJlnFueAIttZU4fvES+IyA6VAU7gIH+ka98IeiC/mVkbScZenXF+agARhYUpYC0SQkWcG50Wk0V5fh3YEJ8IIE/1wcrgI7RqfnoEHD0NTigxC304EStxP9k7NQFA2SrMDlsMEfTsDGMpAUFb946wI0AEleACeqKHQw8Dizx6dBUjS4bNZlx5fKyHjxQghVhfaFB1oTgTjOjoegqBrivIACuw2H+nzgBBmRZBoelwMagJHZxWuRGj8DtqgcUBXIfBxWhwux/rcAixVibBZ2T5V+zWUR/MzQwmTEbFJEa7nzqnWjBRbEMzK80TS6std9zgtneT1SkxfhrGxEYvgkFJGHzCdgZR2w2h2wWFmkQ1MQ4iEUFtogyMbzaiSkP/iWZRnTXi9kWcLF82fR2NyCU8ePguc5JOJxOOwOvPnafrAsi2Ri8SFnKJHBdCwDWdFwcTqJxrICnJyMgRcV+OMZuO0MhgMcfLHFTk56dgypifM5r5841YfU2PI3rq83SVHhcTvQ74vo5UxWUeBg4Y9ysLMMRmbjsED/ZjLK6Q9LViqbTtaKOU6ChZMwFc2gqtCOtJTfBO9qnJhKoIC1QNGAeFqGy2bFW6N6PkZ4GR4nCw0awpy4MBkRPfcqHJXNufMxHkI65AUaNwIAvLEMFFW76n0wEkpDg4YYtzggDx39OaRUNGdamZD+1ZjIJyCEfVAVGcnJiyiobER8+CRUUV9X1co6ELnwhl53J8MQYosPGiZn51DkcuL88BSaaytw/OIoeEHATCgKt9OJEV8A0IBAJLZe2bNgLiXAF52/32ZSaCx14uTkfDsRF+CyM7BYgF+eCywrR4OHfgVHoQeqoiCTisPmdGH81JuwWKxIhv1Izs3CyrDgokEkIyEUFhYiNX4WbHFlzmvO+0eRHDuN7NRhICmi3UA9NxVNo2M+jqYBHidzhXtEhI2xzNdZel9lbsnDrsnJSZSUlODMmTNobW3FkSNHwPM8fD4f+vv7YbFYEI1Goc13iDVVRfDIz/WBbo7zSgcWJ11jvLBi3/ZSkIcFFlz0X/4F2LWSkdSc1yjbn+ubXTyOwcFBQ3357JrE5ObS/+4baOrequ/vlIzD4XSh/903YbFYEAv64amsAcOwiIcDmB0fQnOt/oakN8Kj2GnDBV8UTeVunBwPgxdl+GNpuBwsxkJJaABC8eszOe5PiqgstK14v8Uz+vG2zcfhQ17YXR7Exi/AXdWEyKWTUAQeIpcAH/IujK0SviHw0RCKitxITZyHraTGUDuWHD218PD73ZEA7IwFiqohwYsosDM4PDADXpAQTmbgcTugaRoCMR75r6J947jojWBDdXHO8/WGkwBskIQMEkEfFFlCcPQ8imuaMdN/HJKQhpCKg7E5MDNwAlYrA0UWEfUtPkD2RdMoc9lwcTqOxjIXTk5EwUuLfcrxEAcNQDCRQfa94fDxXwMMa6i9kgW9P8qFvFAVGdHxCyisakJoaLG8WG12pKOBhfETN7f4FcFMYA4NNZU4NzCC5oYaHDvbBz6TwUxAf8ZgsVgQS6Tg9QdQ1aiXzsycF7aiCiRz9OU1WUQmvvgg1hsT4GAsK94HwSUvFYlcAkJ4Gqoig5u6CEdFIxKjp6AK82nZHIvjQnkx3sixAyhraDfUR4j6J4GtG/IqR9n9IDjvADRVgSZLYBwuCBE/bIWliA28s3B8mixC5hP6Ml5z0wt55a5qQnjoJGRhvj9nsyMTn1s4Jy58c+/RshqzCQFNJXYD48wMSubjRM7sh62k2lhfPuIHoH8d7I0JKLRbVyybkfTiEr2KmDHe/5q5fl/krZfpUBxFBXZcGJ9FU1UJjg/59OcjkSRcTpueV1wGgWhiIQ4f8sJqsyM2fgGuqiaEL83fB5z+HN1qd8JqZRHqewfppP7cIs6LOZ/NDs7yULH44DgdD4N/zz0Xmr/nRC6B5OwEGLsTUjoJfm76snPLS64drlOplFZQ4MxrJ++FH4vFVHiLJb90LFbr6o7zFvqxWCyaz+czvaP5I488YiqdTZs2aS+88ILp43vxxRcvSzsej2t2u/26X7tcPzabbV3SYdaxPLtdrrzi/fznPzdctk6cOHHd8y73j7m6KvuznnVPd09PXvE2bdpkOs5nPvMZ03FKS0q0u/buNRWnuqpqXfPZ5SowFb63p1u75977TKez5667tU295vLrS1/6kmZ3OEyntTmP/K2vrc3r+n3hC18wHefTn/606Thl5RWaLMum27FrbdvWLetWNsvLStftnO+4bZvpfC/Io634/Oc/bzrOww8/vG7X3GazaRaT/dRvfOMb65JHmqZp3/nOd0yfE8uyps8JgNbZ1W06zu49e7Wtm83VPw8//LDmdJqv5x599FHNarK9/da3vpVXuXj88cdNhXc6HVoymbymef/YY4+ZPu5Ct1v73Oc+ZyrOrl27rulxk7WjqqpWk0fb/dBDD5muE772ta+t67nV15k7r0cffTSveq6r23w/euedu7Xbtm01FefLX/7yul6/a+1Tn/ykqfO94/bb86prrVar6Xr9s5/9bF5p5fvT0NBgKvwjjzyiMQxjOp2vf/3rpuN885vfzOucXHn05x577DFTZWhkZGRd88lms2nhcHiN7ogbU3dnh6lr9JWvfCWvZ1l/8Rd/YTpOvv2vM2fOXO/Les1JkqSVlpaseX0A6M8SzMbJZ9xltVo1v9+/quti0bTc79HPzc3B6/XmCnZV0WgUgUAALS0tcDqdOcOn02k4HA5YrdacYZcSBAHxeBxVVSvvN7CU3+9HRUUFbDbj+yxomobx8XFUVVVd8TPwK1FVFWNjY6ivr0dBgblNwfx+PyorK8Gyxrf4qKmpQW3tymvlX4miKOjv74csG/s8t7OzE263GyMjI0gmk7kjACguLkZ7+5XXag+FQvD5zM9sa5qGdDoNl8v4hmGVlZVIpVJIp829/VNXV4dAILDwdUUuiqJAkiRDZX+pTCYDr9eLtrY2MAyTO8I8lmWXff1hRHNzMziOw9zcXO7A84qKirBhg7k3JKamphAOm/sEz+fzIZVKLRyn0fsnEokgGAzCarWira3N0P0jSRLGxsZQXl6OioqV195fKpVKIRgMorW1dWHDTCPcbjccDgcikUjuwAAKCgrQ0tKCwcFBGKi6F1itVnR3d2N0dBSCYOyLlqqqKtTU1JiqDwAs1PODg4OG47S3tyMcDiMWixmOk889n9Xc3IyZmRlIkpQ7MICOjg4wDGPqnACgu7sbqqou+4psJSzLore3F5FIxFQ96HA40NbWhqGhIVPLp3V0dCAQCCCRSOQOPK+8vBwNDQ0YGBiAKBpbrqa6uhrV1dWmy1JraytKSkoMh18rHMcZzkMAsFgssNvthu+1pVpaWlBaWpo74DXA8zyGhoyta50tm9Fo1FR/0G63o6enBz6fz3DdX1BQgK6uLkxMTJiqE0pLSyFJ0kJ7YVRDQwNkWb5s6cmrsdls6O3tNd1HzZemaRgYGDBVnurr66EoiuFzAgCXy4WmpqYbtp7LlqVgMAi/3587AgCPx4O2tjaMjY0hHo/njjCvvLwcjY2NGB4eBscZ++qhoaEBlZUr7+thlqqq6O/vN9xWAUBTUxM8Hg/6+/sN9wO7urryakvJ9RGPxzE2lnv/vSyGYdDb24tQKHTD1nOAufNyOBzo7u5GIBAwVc8VFBSgubkZQ0NDpvrR+dZzZsZvNxpRFNHf32/oOlksloW23kyfEtDH1ZqmGa7Xs9d2dnbW1DJzhYWFsNlsiEajpo6vqqoKxcXFhr8iy7ZVoVAIMzMzhtNxOp3o7u7GzMyM4fMqLS1Fc3MzRkZGTPV9KisrUVBQgKmpqdyB5+VbJ8zMzCAQCOQOuES+Y7z6+npTzwFvBalUCsPDw4bC2mw29PT0IBwOY3ra+JvtDocDPT098Pv9hvOypKQEra2tGB0dNVUnVFRUoLGx0XD4m0k0GsXExIShsPnUB4Bez7W3t2N0dNRwneDxeNDa2oqhoSFTz0Vra2tRU7O6bwANTUYQQgghhBBCCCGEEEIIIYTka/1edyCEEEIIIYQQQgghhBBCyG8kmowghBBCCCGEEEIIIYQQQsiaoskIQgghhBBCCCGEEEIIIYSsKZqMIIQQQgghhBBCCCGEEELImqLJCEIIIYQQQgghhBBCCCGErCmajCCEEEIIIYQQQgghhBBCyJqiyQhCCCGEEEIIIYQQQgghhKwpmowghBBCCCGEEEIIIYQQQsiaoskIQgghhBBCCCGEEEIIIYSsKZqMIIQQQgghhBBCCCGEEELImqLJCEIIIYQQQgghhBBCCCGErCmajCCEEEIIIYQQQgghhBBCyJqiyQhCCCGEEEIIIYQQQgghhKwpmowghBBCCCGEEEIIIYQQQsiaoskIQgghhBBCCCGEEEIIIYSsKZqMIIQQQgghhBBCCCGEEELImqLJCEIIIYQQQgghhBBCCCGErCmajCCEEEIIIYQQQgghhBBCyJqiyQhCCCGEEEIIIYQQQgghhKwpmowghBBCCCGEEEIIIYQQQsiaoskIQgghhBBCCCGEEEIIIYSsKZqMIIQQQgghhBBCCCGEEELImqLJCEIIIYQQQgghhBBCCCGErCmajCCEEEIIIYQQQgghhBBCyJqiyQhCCCGEEEIIIYQQQgghhKwpmowghBBCCCGEEEIIIYQQQsiaoskIQgghhBBCCCGEEEIIIYSsKZqMIIQQQgghhBBCCCGEEELImqLJCEIIIYQQQgghhBBCCCGErCmajCCEEEIIIYQQQgghhBBCyJqiyQhCCCGEEEIIIYQQQgghhKwpmowghBBCCCGEEEIIIYQQQsiaoskIQgghhBBCCCGEEEIIIYSsKZqMIIQQQgghhBBCCCGEEELImqLJCEIIIYQQQgghhBBCCCGErCmajCCEEEIIIYQQQgghhBBCyJqiyQhCCCGEEEIIIYQQQgghhKwpmowghBBCCCGEEEIIIYQQQsiaoskIQgghhBBCCCGEEEIIIYSsKZqMIIQQQgghhBBCCCGEEELImqLJCEIIIYQQQgghhBBCCCGErCmajCCEEEIIIYQQQgghhBBCyJqiyQhCCCGEEEIIIYQQQgghhKyp/x/ILA2Dbt4ZcwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Decision Tree parameters\n",
    "params = {\n",
    "    \"max_depth\": 9,\n",
    "    \"min_samples_split\": 2,\n",
    "    \"min_samples_leaf\": 1,\n",
    "    \"random_state\": 7,\n",
    "}\n",
    "\n",
    "model = DecisionTreeClassifier(**params)\n",
    "\n",
    "# set to True to cross validate\n",
    "do_cross_val = False\n",
    "if do_cross_val:\n",
    "    kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "    results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='f1')\n",
    "    print(\"F1: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "\n",
    "# at last, train the model\n",
    "_ = model.fit(X_train, y_train)\n",
    "\n",
    "# If you want to evaluate the model on the test set\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')\n",
    "print(f'Precision: {precision_score(y_test, y_pred):.2f}')\n",
    "print(f'Recall: {recall_score(y_test, y_pred):.2f}')\n",
    "print(f'F1 Score: {f1_score(y_test, y_pred):.2f}')\n",
    "\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# To visualize the trained Decision Tree\n",
    "#plt.figure(figsize=(20, 10))  # Set the figure size, you may need to adjust this to fit the tree\n",
    "plt.figure(figsize=(20, 10))  # Set the figure size, you may need to adjust this to fit the tree\n",
    "plot_tree(model, filled=True, feature_names=X_train.columns, class_names=['benign', 'phishing'], rounded=True)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('decision_tree.pdf', format='pdf', bbox_inches='tight')  # Save the plot to a PDF file\n",
    "\n",
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=model.classes_)  # Compute confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)  # Create display object\n",
    "disp.plot(cmap=plt.cm.Blues)  # Plot confusion matrix\n",
    "plt.show()  # Show the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e08f9e",
   "metadata": {},
   "source": [
    "# ADABoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3324519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.99\n",
      "Precision: 0.96\n",
      "Recall: 0.87\n",
      "F1 Score: 0.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# AdaBoost parameters\n",
    "params = {\n",
    "    \"n_estimators\": 450,  # Number of weak learners to train iteratively\n",
    "    \"random_state\": 7,  # It will control the randomness of the algorithm\n",
    "}\n",
    "\n",
    "model = AdaBoostClassifier(**params)\n",
    "\n",
    "# set to True to cross validate\n",
    "do_cross_val = False\n",
    "if do_cross_val:\n",
    "    kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "    results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='f1')\n",
    "    print(\"F1: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "\n",
    "# at last, train the model\n",
    "_ = model.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')\n",
    "print(f'Precision: {precision_score(y_test, y_pred, zero_division=1):.2f}')\n",
    "print(f'Recall: {recall_score(y_test, y_pred):.2f}')\n",
    "print(f'F1 Score: {f1_score(y_test, y_pred):.2f}')\n",
    "\n",
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=model.classes_)  # Compute confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)  # Create display object\n",
    "disp.plot(cmap=plt.cm.Blues)  # Plot confusion matrix\n",
    "plt.show()  # Show the plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72f0315e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter tuning\n",
    "run_grid_search = False  # <--------------- TADY JE POTREBA TO ZAPNOUT (pokud chces tunit)\n",
    "if run_grid_search:\n",
    "    import pandas as pd\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    from sklearn.ensemble import AdaBoostClassifier  # Updated import\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from sklearn.model_selection import StratifiedKFold\n",
    "    import matplotlib.pyplot as plt\n",
    "    from numpy import array\n",
    "    from numpy.ma import masked_array\n",
    "\n",
    "    class_map = {\"benign_2307:unknown\": 0, \"misp_2307:phishing\": 1}\n",
    "    labels = df['label'].apply(lambda x: class_map[x])\n",
    "    features = df.drop('label', axis=1).copy()\n",
    "    X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3, random_state=42, shuffle=True, stratify=labels)\n",
    "\n",
    "    params = {\n",
    "        'random_state': 7,\n",
    "    }\n",
    "\n",
    "    clf = AdaBoostClassifier(**params)  # Updated classifier instantiation\n",
    "\n",
    "    # this is your grid of parameters to search through, every combination will be tried\n",
    "    grid = {\n",
    "        \"n_estimators\": [150, 200, 250, 300, 350, 400, 450],\n",
    "    }\n",
    "\n",
    "    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "    grid_search = GridSearchCV(\n",
    "        estimator=clf,\n",
    "        param_grid=grid,\n",
    "        #n_jobs=-1,\n",
    "        cv=cv,\n",
    "        scoring='neg_log_loss',  # 'f1',\n",
    "        verbose=3,\n",
    "        return_train_score=True\n",
    "    )\n",
    "\n",
    "    grid_search.fit(X_train, y_train)\n",
    "\n",
    "    clf = grid_search.best_estimator_\n",
    "\n",
    "    scores = pd.DataFrame(grid_search.cv_results_)\n",
    "    col_names = ['mean_train_score', 'mean_test_score']\n",
    "\n",
    "    scores = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "    col_names = ['mean_train_score', 'mean_test_score']\n",
    "    means_df = scores[col_names]\n",
    "    ax = means_df.plot(kind='line', grid=True)\n",
    "\n",
    "    plt.rcParams[\"figure.figsize\"] = [12, 12]\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "    plt.rcParams[\"figure.dpi\"] = 300\n",
    "\n",
    "    max_ids = means_df.idxmax(axis=0)\n",
    "\n",
    "    for i in range(len(max_ids)):\n",
    "        for col_name in col_names:\n",
    "            value = means_df[col_name][max_ids[i]]\n",
    "            id = max_ids[i]\n",
    "\n",
    "            color = 'r' if max_ids[i] == max_ids['mean_test_score'] else 'grey'\n",
    "\n",
    "            ax.scatter([id], [value],\n",
    "                      marker='o',\n",
    "                      color=color,\n",
    "                      label='point',)\n",
    "\n",
    "            ax.annotate(str(round(value, 3)) + \"_ID=\" + str(id),\n",
    "                        (id, value),\n",
    "                        xytext=(id + 3, value))\n",
    "\n",
    "    print(scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a78d55f",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56383738",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 25895, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.020045 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 12450\n",
      "[LightGBM] [Info] Number of data points in the train set: 328695, number of used features: 128\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.078781 -> initscore=-2.459023\n",
      "[LightGBM] [Info] Start training from score -2.459023\n",
      "Accuracy: 1.00\n",
      "Precision: 0.99\n",
      "Recall: 0.97\n",
      "F1 Score: 0.98\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9973    0.9988    0.9980    129772\n",
      "           1     0.9858    0.9679    0.9768     11098\n",
      "\n",
      "    accuracy                         0.9964    140870\n",
      "   macro avg     0.9915    0.9834    0.9874    140870\n",
      "weighted avg     0.9964    0.9964    0.9964    140870\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# LightGBM parameters\n",
    "params = {\n",
    "    'objective': 'binary',  # binary classification\n",
    "    'boosting_type': 'gbdt',  # Gradient Boosting Decision Tree\n",
    "    'n_estimators': 700,  # Number of boosted trees to fit, equivalent to number of boosting iterations\n",
    "    'num_leaves': 38,\n",
    "    'random_state': 7,\n",
    "}\n",
    "\n",
    "model = lgb.LGBMClassifier(**params)\n",
    "\n",
    "# set to True to cross validate\n",
    "do_cross_val = False\n",
    "if do_cross_val:\n",
    "    kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "    results = cross_val_score(model, X_train, y_train, cv=kfold, scoring='f1')\n",
    "    print(\"F1: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))\n",
    "\n",
    "# at last, train the model\n",
    "_ = model.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')\n",
    "print(f'Precision: {precision_score(y_test, y_pred, zero_division=1):.2f}')\n",
    "print(f'Recall: {recall_score(y_test, y_pred):.2f}')\n",
    "print(f'F1 Score: {f1_score(y_test, y_pred):.2f}')\n",
    "\n",
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay, det_curve\n",
    "print(classification_report(y_test, y_pred, digits=4))\n",
    "\n",
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred, labels=model.classes_)  # Compute confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)  # Create display object\n",
    "disp.plot(cmap=plt.cm.Blues)  # Plot confusion matrix\n",
    "plt.show()  # Show the plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ec33b7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter tuning\n",
    "run_grid_search = False  # <--------------- TADY JE POTREBA TO ZAPNOUT (pokud chces tunit)\n",
    "if run_grid_search:\n",
    "    import pandas as pd\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    class_map = {\"benign_2307:unknown\": 0, \"misp_2307:phishing\": 1}\n",
    "    labels = df['label'].apply(lambda x: class_map[x])\n",
    "    features = df.drop('label', axis=1).copy()\n",
    "    X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.3, random_state=42, shuffle=True, stratify=labels)\n",
    "\n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from sklearn.model_selection import StratifiedKFold\n",
    "    from lightgbm import LGBMClassifier  # Updated import\n",
    "\n",
    "    params = {\n",
    "        'objective': 'binary',  # binary classification\n",
    "        'boosting_type': 'gbdt',  # Gradient Boosting Decision Tree\n",
    "        #'n_estimators': 50,  # Number of boosted trees to fit, equivalent to number of boosting iterations\n",
    "        'random_state': 7,\n",
    "    }\n",
    "\n",
    "    clf = LGBMClassifier(**params)  # Updated classifier instantiation\n",
    "\n",
    "    # this is your grid of parameters to search through, every combination will be tried\n",
    "    grid = {\n",
    "        \"n_estimators\": [700],\n",
    "        \"num_leaves\": [38],\n",
    "        #\"max_depth\": [19,20,21]\n",
    "        #\"learning_rate\": [0.05, 0.1, 0.15]\n",
    "    }\n",
    "\n",
    "    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "    grid_search = GridSearchCV(\n",
    "        estimator=clf,\n",
    "        param_grid=grid,\n",
    "        #n_jobs=-1,\n",
    "        cv=cv,\n",
    "        #scoring='neg_log_loss',#'f1',\n",
    "        scoring='f1',#'f1',\n",
    "        verbose=3,\n",
    "        return_train_score=True\n",
    "    )\n",
    "\n",
    "    grid_search.fit(X_train, y_train)\n",
    "\n",
    "    clf = grid_search.best_estimator_\n",
    "\n",
    "    scores = pd.DataFrame(grid_search.cv_results_)\n",
    "    col_names = ['mean_train_score', 'mean_test_score']\n",
    "\n",
    "    scores = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "    from numpy import array\n",
    "    from numpy.ma import masked_array\n",
    "\n",
    "    col_names = ['mean_train_score', 'mean_test_score']\n",
    "    means_df = scores[col_names]\n",
    "    ax = means_df.plot(kind='line', grid=True)\n",
    "\n",
    "    from matplotlib import pyplot as plt\n",
    "    plt.rcParams[\"figure.figsize\"] = [12, 12]\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\n",
    "\n",
    "    plt.rcParams[\"figure.dpi\"] = 300\n",
    "\n",
    "    max_ids = means_df.idxmax(axis=0)\n",
    "\n",
    "    for i in range(len(max_ids)):\n",
    "        for col_name in col_names:\n",
    "            value = means_df[col_name][max_ids[i]]\n",
    "            id = max_ids[i]\n",
    "\n",
    "            color = 'r' if max_ids[i] == max_ids['mean_test_score'] else 'grey'\n",
    "\n",
    "            ax.scatter([id], [value],\n",
    "                      marker='o',\n",
    "                      color=color,\n",
    "                      label='point',)\n",
    "\n",
    "            ax.annotate(str(round(value, 3))+\"_ID=\"+str(id),\n",
    "                        (id, value),\n",
    "                        xytext=(id+3, value))\n",
    "\n",
    "    print(scores)\n"
   ]
  }
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