From ecb22b826c96cf11331b38a531ece49570189621 Mon Sep 17 00:00:00 2001 From: liaozhaorun <1300336796@qq.com> Date: Sat, 14 Mar 2026 02:12:20 +0800 Subject: [PATCH] =?UTF-8?q?fix(factor-engine):=20=E4=BF=AE=E5=A4=8D?= =?UTF-8?q?=E5=A4=9A=E5=9B=A0=E5=AD=90=E8=AE=A1=E7=AE=97=E6=97=B6=E6=95=B0?= =?UTF-8?q?=E6=8D=AE=E8=A7=84=E6=A0=BC=E5=AD=97=E6=AE=B5=E5=90=88=E5=B9=B6?= =?UTF-8?q?=E7=9A=84=20bug=20-=20=E4=BF=AE=E5=A4=8D=20FactorEngine.compute?= =?UTF-8?q?()=20=E4=B8=AD=E7=9B=B8=E5=90=8C=E8=A1=A8=E7=9A=84=E5=AD=97?= =?UTF-8?q?=E6=AE=B5=E6=9C=AA=E6=AD=A3=E7=A1=AE=E5=90=88=E5=B9=B6=E7=9A=84?= =?UTF-8?q?=E9=97=AE=E9=A2=98=20-=20=E5=B0=86=E7=AE=80=E5=8D=95=E5=8E=BB?= =?UTF-8?q?=E9=87=8D=E6=94=B9=E4=B8=BA=E5=AD=97=E6=AE=B5=E9=9B=86=E5=90=88?= =?UTF-8?q?=E5=90=88=E5=B9=B6=EF=BC=8C=E7=A1=AE=E4=BF=9D=E6=89=80=E6=9C=89?= =?UTF-8?q?=E5=9B=A0=E5=AD=90=E4=BE=9D=E8=B5=96=E7=9A=84=E5=AD=97=E6=AE=B5?= =?UTF-8?q?=E9=83=BD=E8=A2=AB=E8=8E=B7=E5=8F=96=20-=20=E8=A7=A3=E5=86=B3?= =?UTF-8?q?=20high=5Flow=5Fratio=20=E7=AD=89=E9=9C=80=E8=A6=81=20high/low?= =?UTF-8?q?=20=E5=AD=97=E6=AE=B5=E7=9A=84=E5=9B=A0=E5=AD=90=E8=AE=A1?= =?UTF-8?q?=E7=AE=97=E5=A4=B1=E8=B4=A5=E9=97=AE=E9=A2=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/data/api_wrappers/__init__.py | 6 - src/experiment/learn_to_rank.ipynb | 230 ++++++++++++++-------------- src/experiment/learn_to_rank.py | 6 +- src/factors/engine/factor_engine.py | 27 +++- 4 files changed, 141 insertions(+), 128 deletions(-) diff --git a/src/data/api_wrappers/__init__.py b/src/data/api_wrappers/__init__.py index 6e815c5..c14ed87 100644 --- a/src/data/api_wrappers/__init__.py +++ b/src/data/api_wrappers/__init__.py @@ -29,12 +29,6 @@ Example: >>> stk_limit = get_stk_limit(trade_date='20240101') """ -from src.data.api_wrappers.api_daily import ( - get_daily, - sync_daily, - preview_daily_sync, - DailySync, -) from src.data.api_wrappers.api_daily_basic import ( get_daily_basic, sync_daily_basic, diff --git a/src/experiment/learn_to_rank.ipynb b/src/experiment/learn_to_rank.ipynb index 40422a4..cc29291 100644 --- a/src/experiment/learn_to_rank.ipynb +++ b/src/experiment/learn_to_rank.ipynb @@ -24,8 +24,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:38:49.168405Z", - "start_time": "2026-03-13T14:38:47.799348Z" + "end_time": "2026-03-13T18:10:00.178020Z", + "start_time": "2026-03-13T18:09:58.791253Z" } }, "cell_type": "code", @@ -66,8 +66,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:38:49.197280Z", - "start_time": "2026-03-13T14:38:49.189632Z" + "end_time": "2026-03-13T18:10:00.209449Z", + "start_time": "2026-03-13T18:10:00.197996Z" } }, "cell_type": "code", @@ -287,8 +287,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:38:49.207900Z", - "start_time": "2026-03-13T14:38:49.204530Z" + "end_time": "2026-03-13T18:10:00.241454Z", + "start_time": "2026-03-13T18:10:00.237620Z" } }, "cell_type": "code", @@ -350,9 +350,9 @@ " \"return_5_rank\",\n", " \"EP_rank\",\n", " \"pe_expansion_trend\",\n", - " # \"value_price_divergence\",\n", + " \"value_price_divergence\",\n", " \"active_market_cap\",\n", - " # \"ebit_rank\",\n", + " \"ebit_rank\",\n", "]\n", "\n", "# 因子定义字典（完整因子库）\n", @@ -376,8 +376,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:38:49.216389Z", - "start_time": "2026-03-13T14:38:49.211732Z" + "end_time": "2026-03-13T18:10:00.257506Z", + "start_time": "2026-03-13T18:10:00.253934Z" } }, "cell_type": "code", @@ -462,8 +462,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:02.072976Z", - "start_time": "2026-03-13T14:38:49.220376Z" + "end_time": "2026-03-13T18:10:11.844363Z", + "start_time": "2026-03-13T18:10:00.265162Z" } }, "cell_type": "code", @@ -612,18 +612,21 @@ " - return_5_rank\n", " - EP_rank\n", " - pe_expansion_trend\n", + " - value_price_divergence\n", " - active_market_cap\n", + " - ebit_rank\n", "\n", "注册特征因子（表达式）:\n", + " - turnover_rate_volatility: ts_std(log(turnover_rate), 20)\n", "\n", "注册 Label 因子（表达式）:\n", " - future_return_5_rank: (ts_delay(close, -5) / ts_delay(open, -1)) - 1\n", "\n", - "特征因子数: 47\n", - " - 来自 metadata: 47\n", - " - 来自表达式: 0\n", + "特征因子数: 50\n", + " - 来自 metadata: 49\n", + " - 来自表达式: 1\n", "Label: future_return_5_rank\n", - "已注册因子总数: 48\n", + "已注册因子总数: 64\n", "\n", "[3] 准备数据\n", "\n", @@ -646,23 +649,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "数据形状: (7044952, 67)\n", - "数据列: ['ts_code', 'trade_date', 'low', 'high', 'open', 'turnover_rate', 'close', 'amount', 'vol', 'total_assets', 'total_mv', 'f_ann_date', 'revenue', 'n_income', 'total_cur_assets', 'total_hldr_eqy_exc_min_int', 'total_liab', 'total_cur_liab', 'n_cashflow_act', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'active_market_cap', 'future_return_5_rank']\n", + "数据形状: (7044952, 71)\n", + "数据列: ['ts_code', 'trade_date', 'open', 'high', 'turnover_rate', 'low', 'vol', 'amount', 'close', 'total_assets', 'total_mv', 'f_ann_date', 'ebit', 'revenue', 'n_income', 'total_liab', 'total_hldr_eqy_exc_min_int', 'total_cur_assets', 'total_cur_liab', 'n_cashflow_act', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'value_price_divergence', 'active_market_cap', 'ebit_rank', 'turnover_rate_volatility', 'future_return_5_rank']\n", "\n", "前5行预览:\n", - "shape: (5, 67)\n", - "┌───────────┬────────────┬─────────┬─────────┬───┬──────────┬────────────┬────────────┬────────────┐\n", - "│ ts_code ┆ trade_date ┆ low ┆ high ┆ … ┆ EP_rank ┆ pe_expansi ┆ active_mar ┆ future_ret │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ on_trend ┆ ket_cap ┆ urn_5_rank │\n", - "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ f64 ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪════════════╪═════════╪═════════╪═══╪══════════╪════════════╪════════════╪════════════╡\n", - "│ 000001.SZ ┆ 20200102 ┆ 1806.75 ┆ 1850.42 ┆ … ┆ 0.920834 ┆ null ┆ null ┆ -0.008857 │\n", - "│ 000001.SZ ┆ 20200103 ┆ 1847.15 ┆ 1889.72 ┆ … ┆ 0.918182 ┆ null ┆ null ┆ -0.01881 │\n", - "│ 000001.SZ ┆ 20200106 ┆ 1846.05 ┆ 1893.0 ┆ … ┆ 0.919829 ┆ null ┆ null ┆ -0.008171 │\n", - "│ 000001.SZ ┆ 20200107 ┆ 1850.42 ┆ 1886.45 ┆ … ┆ 0.921165 ┆ null ┆ null ┆ -0.014117 │\n", - "│ 000001.SZ ┆ 20200108 ┆ 1815.49 ┆ 1861.34 ┆ … ┆ 0.922481 ┆ null ┆ null ┆ -0.017252 │\n", - "└───────────┴────────────┴─────────┴─────────┴───┴──────────┴────────────┴────────────┴────────────┘\n", + "shape: (5, 71)\n", + "┌───────────┬────────────┬─────────┬─────────┬───┬────────────┬───────────┬────────────┬───────────┐\n", + "│ ts_code ┆ trade_date ┆ open ┆ high ┆ … ┆ active_mar ┆ ebit_rank ┆ turnover_r ┆ future_re │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ ket_cap ┆ --- ┆ ate_volati ┆ turn_5_ra │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ --- ┆ f64 ┆ lity ┆ nk │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 │\n", + "╞═══════════╪════════════╪═════════╪═════════╪═══╪════════════╪═══════════╪════════════╪═══════════╡\n", + "│ 000001.SZ ┆ 20200102 ┆ 1817.67 ┆ 1850.42 ┆ … ┆ null ┆ null ┆ null ┆ -0.008857 │\n", + "│ 000001.SZ ┆ 20200103 ┆ 1849.33 ┆ 1889.72 ┆ … ┆ null ┆ null ┆ null ┆ -0.01881 │\n", + "│ 000001.SZ ┆ 20200106 ┆ 1856.97 ┆ 1893.0 ┆ … ┆ null ┆ null ┆ null ┆ -0.008171 │\n", + "│ 000001.SZ ┆ 20200107 ┆ 1870.07 ┆ 1886.45 ┆ … ┆ null ┆ null ┆ null ┆ -0.014117 │\n", + "│ 000001.SZ ┆ 20200108 ┆ 1855.88 ┆ 1861.34 ┆ … ┆ null ┆ null ┆ null ┆ -0.017252 │\n", + "└───────────┴────────────┴─────────┴─────────┴───┴────────────┴───────────┴────────────┴───────────┘\n", "\n", "[4] 转换为排序学习格式\n", "\n", @@ -747,7 +751,7 @@ "[配置] 训练期: 20200101 - 20231231\n", "[配置] 验证期: 20240101 - 20241231\n", "[配置] 测试期: 20250101 - 20251231\n", - "[配置] 特征数: 47\n", + "[配置] 特征数: 50\n", "[配置] 目标变量: future_return_5_rank_rank（20分位数）\n" ] }, @@ -755,7 +759,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6736\\2929956284.py:125: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_26384\\2929956284.py:125: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", "(Deprecated in version 0.20.5)\n", " daily_counts = df_ranked.group_by(date_col).agg(pl.count().alias(\"count\"))\n" ] @@ -771,8 +775,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:26.231967Z", - "start_time": "2026-03-13T14:39:02.079217Z" + "end_time": "2026-03-13T18:10:34.925345Z", + "start_time": "2026-03-13T18:10:11.853318Z" } }, "cell_type": "code", @@ -810,7 +814,7 @@ "================================================================================\n", "\n", "[过滤] 应用 ST 过滤器...\n", - " ST 过滤后数据规模: (6823808, 68)\n", + " ST 过滤后数据规模: (6823808, 72)\n", "\n", "执行每日独立筛选股票池...\n" ] @@ -819,7 +823,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6736\\4061767669.py:57: DeprecationWarning: `is_in` with a collection of the same datatype is ambiguous and deprecated.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_26384\\4061767669.py:57: DeprecationWarning: `is_in` with a collection of the same datatype is ambiguous and deprecated.\n", "Please use `implode` to return to previous behavior.\n", "\n", "See https://github.com/pola-rs/polars/issues/22149 for more information.\n", @@ -830,8 +834,8 @@ "name": "stdout", "output_type": "stream", "text": [ - " 筛选前数据规模: (6823808, 68)\n", - " 筛选后数据规模: (1455000, 68)\n", + " 筛选前数据规模: (6823808, 72)\n", + " 筛选后数据规模: (1455000, 72)\n", " 筛选前股票数: 5678\n", " 筛选后股票数: 1934\n", " 删除记录数: 5368808\n" @@ -848,8 +852,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:26.304464Z", - "start_time": "2026-03-13T14:39:26.241490Z" + "end_time": "2026-03-13T18:10:34.997214Z", + "start_time": "2026-03-13T18:10:34.932346Z" } }, "cell_type": "code", @@ -888,9 +892,9 @@ "数据划分\n", "================================================================================\n", "\n", - "训练集数据规模: (970000, 68)\n", - "验证集数据规模: (242000, 68)\n", - "测试集数据规模: (243000, 68)\n", + "训练集数据规模: (970000, 72)\n", + "验证集数据规模: (242000, 72)\n", + "测试集数据规模: (243000, 72)\n", "\n", "训练集 group 数量: 970\n", "验证集 group 数量: 242\n", @@ -904,7 +908,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_6736\\2929956284.py:149: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_26384\\2929956284.py:149: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.\n", "(Deprecated in version 0.20.5)\n", " pl.count().alias(\"count\")\n" ] @@ -920,8 +924,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:29.591092Z", - "start_time": "2026-03-13T14:39:26.325582Z" + "end_time": "2026-03-13T18:10:37.417614Z", + "start_time": "2026-03-13T18:10:35.002702Z" } }, "cell_type": "code", @@ -957,19 +961,19 @@ "数据质量检查报告\n", "================================================================================\n", "\n", - "[严重] 发现 1386 个全空因子:\n", + "[严重] 发现 1657 个全空因子:\n", " (某天的某个因子所有值都是 null，可能是数据缺失或计算错误)\n", - " - 日期 20200721: net_profit_yoy (样本数: 1000)\n", - " - 日期 20200721: revenue_yoy (样本数: 1000)\n", - " - 日期 20200721: healthy_expansion_velocity (样本数: 1000)\n", - " - 日期 20200220: drawdown_from_high_60 (样本数: 1000)\n", - " - 日期 20200220: volatility_squeeze_5_60 (样本数: 1000)\n", - " - 日期 20200220: roa (样本数: 1000)\n", - " - 日期 20200220: net_profit_yoy (样本数: 1000)\n", - " - 日期 20200220: revenue_yoy (样本数: 1000)\n", - " - 日期 20200220: healthy_expansion_velocity (样本数: 1000)\n", - " - 日期 20200220: pe_expansion_trend (样本数: 1000)\n", - " ... 还有 1376 个\n", + " - 日期 20200217: drawdown_from_high_60 (样本数: 1000)\n", + " - 日期 20200217: volatility_squeeze_5_60 (样本数: 1000)\n", + " - 日期 20200217: net_profit_yoy (样本数: 1000)\n", + " - 日期 20200217: revenue_yoy (样本数: 1000)\n", + " - 日期 20200217: healthy_expansion_velocity (样本数: 1000)\n", + " - 日期 20200217: pe_expansion_trend (样本数: 1000)\n", + " - 日期 20200217: value_price_divergence (样本数: 1000)\n", + " - 日期 20200213: drawdown_from_high_60 (样本数: 1000)\n", + " - 日期 20200213: volatility_squeeze_5_60 (样本数: 1000)\n", + " - 日期 20200213: net_profit_yoy (样本数: 1000)\n", + " ... 还有 1647 个\n", "\n", "--------------------------------------------------------------------------------\n", "建议处理方式:\n", @@ -996,8 +1000,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:30.132178Z", - "start_time": "2026-03-13T14:39:29.595727Z" + "end_time": "2026-03-13T18:10:37.997346Z", + "start_time": "2026-03-13T18:10:37.438005Z" } }, "cell_type": "code", @@ -1045,9 +1049,9 @@ "\n", "测试集处理...\n", "\n", - "处理后训练集形状: (970000, 68)\n", - "处理后验证集形状: (242000, 68)\n", - "处理后测试集形状: (243000, 68)\n" + "处理后训练集形状: (970000, 72)\n", + "处理后验证集形状: (242000, 72)\n", + "处理后测试集形状: (243000, 72)\n" ] } ], @@ -1061,8 +1065,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:31.913185Z", - "start_time": "2026-03-13T14:39:30.136586Z" + "end_time": "2026-03-13T18:10:39.279997Z", + "start_time": "2026-03-13T18:10:38.028508Z" } }, "cell_type": "code", @@ -1107,7 +1111,7 @@ "\n", "训练样本数: 970000\n", "验证样本数: 242000\n", - "特征数: 47\n", + "特征数: 50\n", "目标变量: future_return_5_rank_rank\n", "\n", "目标变量统计（训练集）:\n", @@ -1131,7 +1135,7 @@ "开始训练...\n", "Training until validation scores don't improve for 50 rounds\n", "Early stopping, best iteration is:\n", - "[55]\ttrain's ndcg@10: 0.625716\tval's ndcg@10: 0.559889\n", + "[5]\ttrain's ndcg@10: 0.59368\tval's ndcg@10: 0.546167\n", "训练完成！\n" ] } @@ -1146,8 +1150,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:32.028008Z", - "start_time": "2026-03-13T14:39:31.917113Z" + "end_time": "2026-03-13T18:10:39.394848Z", + "start_time": "2026-03-13T18:10:39.285414Z" } }, "cell_type": "code", @@ -1217,11 +1221,11 @@ "\n", "[早停信息]\n", " 配置的最大轮数: 2000\n", - " 实际训练轮数: 105\n", - " 早停状态: 已触发（最佳迭代: 55）\n", + " 实际训练轮数: 55\n", + " 早停状态: 已触发（最佳迭代: 5）\n", "\n", "最终 NDCG 指标:\n", - " ndcg@10: 训练集=0.6351, 验证集=0.5518\n", + " ndcg@10: 训练集=0.6331, 验证集=0.5393\n", "\n", "[绘图] 使用 LightGBM 原生接口绘制训练曲线...\n" ] @@ -1231,7 +1235,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAPdCAYAAADxjUr8AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAz/dJREFUeJzs3QecHWW5P/Bn+242vfdGTQghpNCrShFFxIaAUlREBRsWLFeKBf9evMi9NgRFREVRLCBVeqSH0CEEQirpfVO3nv9nZrPLppKym3N29/u9dz5TzpzZ98zJIPnx7PPmZTKZTAAAAAAAkBPysz0AAAAAAADeIrQFAAAAAMghQlsAAAAAgBwitAUAAAAAyCFCWwAAAACAHCK0BQAAAADIIUJbAAAAAIAcIrQFAAAAAMghQlsAAAAAgBwitAUAIKedc845kZeXly5777131NXVNb52zTXXNL52ww03pMeGDh3aeKygoCDKy8vTY+95z3vij3/8Y9TW1m7x5yxdujQuueSSGDNmTHTq1Ck6duwYI0aMiM997nPx8ssvb3Tu+vXr4+c//3kcffTR0aNHjyguLo4BAwbEIYccEpdeemlMnz69WT57VVVVfPWrX43DDjssSktLGz/X3XffvcXzn3jiiTj++OOjc+fO6ec+/PDD484772yWsQAAsPsU7safBQAAu+T111+Pv/zlL/HRj350u85PAt61a9fGrFmz0iUJMK+//vr4xz/+kQabDZ599tk01J0/f/5G73/11VfTJQllr7766vTYvHnz4qSTTornn39+o3OT48ny5JNPRlFRUfzXf/3XZuNJwtzkOnfddVe8+eab6RhGjx4dZ511Vpx55pmRn79xTUUy9v/5n//Zrs86ceLEOO6449Kgt8Fjjz0W733ve9Ow+vTTT9+u6wAAkH0qbQEAaFWuuOKKyGQy23Vuct6aNWvi/vvvjwMPPDA99sADD8SnPvWpxnNWr14d73vf+xoD249//OPxxhtvRGVlZUydOjW+853vpFWrDdf7wAc+0BjYHnPMMWlIm1Terly5Mh5++OG48MILG89v6v/+7//Syt2f/vSnMW3atPQ9ixYtivvuuy8NbZNrJdW+TSXh7wUXXJCGrp/5zGe2+VmT15PAtkuXLjFp0qT0ZwwePDgd8+c///lYt27ddt0zAACyT6UtAACtRtLu4MUXX4zbbrstTjnllO16T4cOHeId73hH3HPPPbHnnntGRUVF/PWvf02vs//++8evf/3rtOo1kbQ3uPHGGxvfm7Rj+O53v9vYUuFf//pXGtImBg0alFbulpWVpfslJSVx1FFHpcumfvGLX8QXv/jF6NmzZ9pu4UMf+lAMHz48DVmTgDUJcm+//fa0KjYJfpPK3kQS/v7sZz9Lt1977bWtfsZnnnkmpkyZkm4nVcjjx49vDHK/9a1vpWFw0lLh1FNP3c47DQBANqm0BQCg1fjIRz6Srn/wgx/s8Ht79eqVtiBokLQoSDTtD5sEq1sLi5u+J5GErw2B7bbMmTMnLrroohg1alQ899xzaVXvGWecEX379o3f/va38YUvfCFWrVoVP/rRj9KetElF7o5KQtsG++677xa3m54DAEBuE9oCANBqJAFn0gc2qU7997//vcPvTypnG8ycOTNdJ71utxRybknTc/fZZ5/G7aSitWGSsGRJAtkG//u//5seS/ro9unTJz74wQ/GSy+9lLZl+PrXv562SEgk20lFcFL5u6MWL17cuN20V2/T7YafAwBA7hPaAgDQanTt2jWtcN3ZattkYrIGSZDadL2jtvd9SSVv0gc3ac2QtGhIgt8TTzwxli9fnk5WlqwbJJXASR/dpIVDc2ja+3dnPycAALuf0BYAgFYlaTWQ9KmdOHFiPPLIIzv03qZ9YYcOHZquk8m6GiSB6bYMGTJki9e65ppr0oC06esNZsyYEQcddFC6/frrrzdW5iYB9Je//OWNzm2o0E2qcHe09UODZEK0BknbhS2dAwBAbhPaAgDQqiTh43nnnZdu//nPf96hFgI33XRT4/673/3udJ1UvTbYWj/ZhonImp6bBLXV1dXb9bMbeuI2rXzd0v6zzz4bRUVF0aNHj9gRY8eO3WLw/Oqrr27xHAAAcpvQFgCAVudrX/taFBcXN4ap27Ju3bp48MEH08C1ofL0tNNOSycGS3zqU5+KgQMHptuPPfZYnHvuuWl1bBLIJtW03/nOd9IlcfLJJ8f48ePT7eScU089NV588cX03GTCsfXr12/285OK3smTJ2/UU/dXv/pVrFixojEkTto23HHHHXHllVfGO9/5zigpKWl8/5IlS9Jl7dq1jceS9gnJsYaq2iSQHTFiRGOQ/fTTT8e0adPSYDmRhMBNA2cAAHJbXmbT/7wPAAA55Jxzzonf/e536faUKVMaJws7//zz49prr20877e//W16bhKSNp0wbFNJKPr3v/99o0m6kgrXk046KRYsWLDF93zxi1+Mq6++Ot1OwtkTTjghHcvWJBOONVwraYGQjDOpeu3Xr1/a27ZhfEkFbjKOhr62Xbp0SYPjkSNHblcv2qOPPjoeeuihdPvhhx+O448/PqqqqjY6J3n/H//4xzj99NO3eh0AAHKLSlsAAFqliy++OAoLC7d5ThJYlpWVpb1mk1A2aY+QTAbWNLBNHHjggWnFbDIx2OjRo6O8vDztm7vPPvuk/WeTatwGgwYNikmTJqVVsQcffHB6rSR8TapZDzvssPjWt74VDzzwQOP5X/jCF9JK3Pe///1pOPvXv/41DWWTn/HDH/4wDXKTXrYf+9jH0grZpoHtjkgC3CS4Pe6446JTp07p+JPx3H777QJbAIBWRqUtAAC0sKuuuiq+8pWvpAFtEjYnbRaSlgxJm4PZs2en1bJJ+Ns0HAYAoP0S2gIAwG7wox/9KL797W9vsQ9v0p/3uuuui7POOst3AQCA0BYAAHaXV155Je2Ne99998W8efOiW7duaR/ab37zm429egEAQKUtAAAAAEAOMREZAAAAAEAOEdoCAAAAAOSQwmwPIBfV1dWlPcY6deoUeXl52R4OAAAAANAGZDKZWLVqVfTv3z/y87deTyu03YIksB00aFBLfj8AAAAAQDs1Z86cGDhw4FZfF9puQVJhm5gxY0Z079695b4doNlUV1fHv//973QG7qKiIncWWgHPLbROnl1ofTy30Pp4btuuioqKtFi0IX/cGqHtFjS0REhuXufOnVvmGwKa/X/QOnTokD6zQltoHTy30Dp5dqH18dxC6+O5bfveriWricgAAAAAAHKI0BYAAAAAIIcIbQEAAAAAcoietgAAAABAo7q6uqiqqnJHdkIyz05BQUHsKqEtAAAAAJBKwtoZM2akwS07p2vXrtG3b9+3nWxsW4S2AAAAAEBkMpmYP39+Wik6aNCgyM/XWXVH79/atWtj0aJF6X6/fv1iZwltAQAAAICoqalJQ8f+/ftHhw4d3JGdUFZWlq6T4LZ379473SpBXA4AAAAARG1tbXoXiouL3Y1d0BB4V1dX7/Q1hLYAAAAAQKNd6cVKNMv9E9oCAAAAAOQQoS0AAAAAQA4R2gIAAAAARMTQoUPj6quvzvq9KMz2AAAAAAAAdtYxxxwTY8aMaZawddKkSVFeXp71L0NoCwAAAAC0WZlMJmpra6Ow8O2j0F69ekUu0B4BAAAAANhi2Lm2qiYrSyaT2a5v5JxzzomHH344/vd//zfy8vLS5YYbbkjXd911V4wbNy5KSkrikUceiTfeeCNOOeWU6NOnT3Ts2DEmTJgQ99133zbbIyTX+fWvfx2nnnpqdOjQIfbaa6+47bbbWvxPi0pbAAAAAGAz66prY+Ql92Tlzrzy3ROiQ/HbR5dJWPvaa6/FqFGj4rvf/W567OWXX07X3/jGN+LHP/5xDB8+PLp16xZz5syJk046KX7wgx+kQe6NN94YJ598ckydOjUGDx681Z9x+eWXx3//93/HlVdeGT/96U/jzDPPjFmzZkX37t2jpai0BQAAAABapS5dukRxcXFaBdu3b990KSgoSF9LQtzjjjsu9thjjzRgPeCAA+L8889PA96kYvZ73/te+trbVc4m1bynn3567LnnnnHFFVfE6tWr46mnnmrRz6XSFgAAAADYTFlRQVrxmq2fvavGjx+/0X4Stl522WVxxx13xPz586OmpibWrVsXs2fP3uZ1Ro8e3bidTFLWuXPnWLRoUbQkoS0AAAAAsJmkn+v2tCjIVeXl5Rvtf/WrX4177703bZmQVM2WlZXFhz70oaiqqtrmdYqKija7L3V1ddGSWu9dBwAAAADaveLi4qitrX3b+/Doo4+mrQ6SScUaKm9nzpyZk/dPT1sAAAAAoNUaOnRoPPnkk2kAu2TJkq1WwSZ9bP/+97/Hc889F88//3ycccYZLV4xu7OEtgAAAABAq/XVr341nXxs5MiR0atXr632qL3qqquiW7ducdhhh8XJJ58cJ5xwQowdOzZykfYIAAAAAECrtffee8fjjz++0bGkDcKWKnIfeOCBjY5dcMEFG+1v2i4hk8lsdp0VK1ZES1NpCwAAAACQQ4S2AAAAAAA5RGgLAAAAAJBDhLYAAAAAADlEaAsAAAAAkEOEtgAAAAAAOURoCwAAAACQQ4S2AAAAAAA5RGgLAAAAAJBDhLYAAAAAQLs1dOjQuPrqqyOXCG0BAAAAAHKI0BYAAAAAIIcIbQEAAACAzWUyEVVrsrNkMtv1jVx77bXRv3//qKur2+j4KaecEp/4xCfijTfeSLf79OkTHTt2jAkTJsR9992X8992YbYHAAAAAADkoOq1EVf0z87P/ta8iOLytz3twx/+cHz+85+PBx98MN75znemx5YtWxZ333133HnnnbF69eo46aST4gc/+EGUlJTEjTfeGCeffHJMnTo1Bg8eHLlKpS0AAAAA0Cp169Yt3v3ud8dNN93UeOyWW26Jnj17xrHHHhsHHHBAnH/++TFq1KjYa6+94nvf+17ssccecdttt0UuU2kLAAAAAGyuqEN9xWu2fvZ2OvPMM+O8886LX/ziF2k17R//+Mf46Ec/Gvn5+Wml7WWXXRZ33HFHzJ8/P2pqamLdunUxe/bsyGVCWwAAAABgc3l529WiINtOPvnkyGQyaTCb9Kz9z3/+Ez/5yU/S17761a/GvffeGz/+8Y9jzz33jLKysvjQhz4UVVVVkcuEtgAAAABAq1VaWhof+MAH0grbadOmxT777BNjx45NX3v00UfjnHPOiVNPPTXdTypvZ86cGblOaAsAAAAAtGpnnnlmvPe9742XX345PvaxjzUeT/rY/v3vf0+rcfPy8uI73/lO1NXVRa4zERkAAAAA0Kq94x3viO7du8fUqVPjjDPOaDx+1VVXpZOVHXbYYWlwe8IJJzRW4eYylbYAAAAAQKuWn58f8+ZtPmna0KFD44EHHtjo2AUXXLDRfi62S1BpCwAAAACQQ4S2AAAAAAA5RGgLAAAAAJBDhLYAAAAAADlEaAsAAAAANMpkMu7GLqirq4tdVbjLVwAAAAAAWr2ioqLIy8uLxYsXR69evdJtdizsrqqqSu9ffn5+FBcXx84S2gIAAAAAUVBQEAMHDow333wzZs6c6Y7spA4dOsTgwYPT4HZnCW0BAAAAgFTHjh1jr732iurqandkJ4PvwsLCXa5SFtoCAAAAABsFj8lC9piIDAAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAckjWQ9uf//znMXTo0CgtLY2DDz44nnrqqW2ev2LFirjggguiX79+UVJSEnvvvXfceeedWzz3//2//xd5eXnxpS99qYVGDwAAAADQvAoji26++ea46KKL4pprrkkD26uvvjpOOOGEmDp1avTu3Xuz86uqquK4445LX7vllltiwIABMWvWrOjatetm506aNCl+9atfxejRo3fTpwEAAAAAaOWVtldddVWcd955ce6558bIkSPT8LZDhw5x/fXXb/H85PiyZcvin//8Zxx++OFphe7RRx8dBxxwwEbnrV69Os4888y47rrrolu3brvp0wAAAAAAtOJK26RqdvLkyfHNb36z8Vh+fn68613viscff3yL77ntttvi0EMPTdsj3HrrrdGrV68444wz4uKLL46CgoLG85LX3/Oe96TX+v73v/+2Y6msrEyXBhUVFem6uro6XYDc1/Csemah9fDcQuvk2YXWx3MLrY/ntu3a3twia6HtkiVLora2Nvr06bPR8WT/1Vdf3eJ7pk+fHg888EBaRZv0sZ02bVp87nOfSz/spZdemp7z5z//OZ555pm0PcL2+uEPfxiXX375ZscffPDBtPIXaD3uvffebA8B2EGeW2idPLvQ+nhuofXx3LY9a9euzf2etjuqrq4u7Wd77bXXppW148aNi7lz58aVV16ZhrZz5syJL37xi+kf6GRis+2VVPsmvXWbVtoOGjQojj322OjRo0cLfRqgOSX/8SZ59pO+10VFRW4utAKeW2idPLvQ+nhuofXx3LZdDb/hn7Ohbc+ePdPgdeHChRsdT/b79u27xff069cvDWOatkIYMWJELFiwoLHdwqJFi2Ls2LGNryfVvBMnToyf/exnaQuEpu9tUFJSki6bSn6W8AdaF88ttD6eW2idPLvQ+nhuofXx3LY925s1Zm0isuLi4rRS9v7779+okjbZT/rWbkky+VjSEiE5r8Frr72WhrnJ9d75znfGiy++GM8991zjMn78+LSdQrK9pcAWAAAAACCXZLU9QtKS4Oyzz06D1YMOOiiuvvrqWLNmTZx77rnp62eddVYMGDAg7Tmb+OxnP5tWzCYtED7/+c/H66+/HldccUV84QtfSF/v1KlTjBo1aqOfUV5enrY42PQ4AAAAAEAuympoe9ppp8XixYvjkksuSVscjBkzJu6+++7Gyclmz54d+flvFQMnfWbvueee+PKXvxyjR49OA90kwL344ouz+CkAAAAAAJpP1iciu/DCC9NlSx566KHNjiWtE5544ontvv6WrgEAAAAAkKuy1tMWAAAAAIDNCW0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHFGZ7AAAAAAAAW5PJZGJddW2sXFcdFetqYnVlTXo8Ly8ib8M5eXl56XZyLN1P/q/hxcZz3zrWdP+ta2x4Z17EgK5lUVpUkLUvRWgLAAAAALSoqpq6qFifhK7V9eHr+poNIWyyveHYuprGcyo2OaemLrNbv6G/ffawGDekW2SL0BYAAAAA2Gkr11bHtMWrYtqi1fH6wtUxc+maWLG2IZytD2OTStldVZifF13KiqJDSUFaJZvIJP+XSapxN67MTXYbjjWe0/j6hqMbHdv4PQX5Tcp0s0BoCwAAAABsUxJqLl5VmQaz0xbXh7MN28nx7dWptDA6lxal4WvnssL6dWmyveFY8npZw+tFG51bVlSQtkFoD4S2AAAAALBJQJlUhy5atT4WraqsX1ck6w1Lxfr01/W7dSiO7uVF0b28JHqUF0e38uJ03b3J0qG4dQWNdXWZmLtiXX0gm1TOLqqvoE2WpF3B1vTrUhp79u6YLnv06hg9OxZvEsYWRcfSwqxXsLYWQlsAAACAFlBdWxd1mUyUFGZvMiM2VluXiaVrktC1Mq0ObQhjF6+uP/ZWSFuZ9mBtDiWF+Y2BbhLibhzuljSGvg0hb9eyot3253PW0jVNwtn69RuLV8f66i1/9iRvHdy9Q+zZu1NjQLtXEtL27hgdS8SMzcndBAAAAGgGyYz2z8xaHpNmLounZiyL5+asiMqauujdqSQGdiuLgd06bLIui/5ZnqG+LVpfXRuPvbEknpuzMq2IbVopu3RNVRrcbq/kV/V7dy5Nv8N02bDdq1NJFBfkx7K1VbF8TVV63WWbLMmxJPhN/gzMW7k+XbZHEox27VAUUVMQ/z1lYsSG3q2JLRXsbvFYk/ds6ZzkPybMX1FfLbwlyWcb1rO8MZhNw9k+HWNoj3J/XncToS0AAADATkgqNZ9OAtqZy9Kg9pV5FbGlDKyhcvOZ2Su2eJ0kANw0zG3YHiDU3S7zVqyLB15dlC5JYLu1StGGALNHeUMIu2HdqbRxu1ent4LZXQnUkxYLa6tqNwty3wp5K2PZmuoN6/rXk/YDyZ+h5Hga1lZuX9C7s5LWDWko26tj7Nlnw7p3x7SatrAgv0V/NtsmtAUAAADYjgBuzrJ19QHtjPqQdvqSNZudlwStBw3tHhOGdY8JQ7unv+4+d/m6eHP52nhzo/W6mLN8bRrqJeFvsjz7NqFuEuBuGuwO6dEhitphuJZUyyaVzA+8ujAeeHVxTJlfsdHryb06Ys+eMaBbWXr/mgazSVuC3RFIJn1sy0sK02VQ9w7b3bIgCXUXrlwbD078Txx+2OFRWFgYmS38edxof7PXN73y5v81oW+XsujXuTTy9ZjNSUJbAAAAgC2EglMXrKpvdTBzWVpRu7CicrOKzX36dErD2fqQtlv061K22b1Mgtv9B3bZ7HgSvK1YW71JmPtWqJtsr3mbULe4MD/2H9Alxgzq2rgkgW5rmvhqe1Wsr46Jry1Oq2kfmro4rUxtkOSOYwd3i3eM6B3v3LdP7N2nY6u8B0kAn7Rg6FZWENM7Rowe2CWKinZPj1tyi9AWAAAAaPcqa2rjhTdXpiFtUkn79KzlsWp9zUb3paggLw1Ik4A2qaYdP6R7dEl6j+6kJFRMJqTqto1Qd+W6TUPdt7bnLKsPdSfPWp4uDXp2LEnD2wMH14e4SfDXqbR1Bn/TF69OQ9r7pyxKv5umPVg7lRbG0Xv3ineO6B1H7907DcehrRDaAgAAAG1WXV0m1lTVpJOErV5fE6sqa2JNk+1ZS9fEpBnL47k3V6STRjVVXlwQY4d0q6+kHdo9DUDLinffpGFJqNu1Q3G6jBqw5VB3xpI1aZuAZEkqcZM2AUtWV8Z9UxamS/11Iu1VWh/idks/R1KJmos9S5PvIAlnk5D2wamL0s/X1B69yuOdI/rEO/btHeOGdGuXrSFoH4S2AAAAQM4GrnNXrEt/LT4JWdPgtbImrYBd02S7IZBN95uEsg3nb6+k12lDq4OkknZEv045GWw2DXWH9+qYLh8YOzA9tr66Nl6etzINcBuC3OQevr5odbr85ek30/PKigrS6t4DGytyu0XfLqVZ+RxJyPzgq/Uh7cTXlmz0nSXVzYcM75GGtMkypEd5VsYIu5vQFgAAAMiZkHbKgop4YvqyeGL60nhqxrK0PUBzKMjPS3+dvmNJk6W0MHp1LEkrNpOgdnjP8lbZB7Wp0qKCGDeke7o0SPrh1lfjLk/Xz89ZmQajyf1NlgZ9O5fW98Ud3DUNc5NQt0NxYdrft6auLmpqM+lSvWE7mTQraVdQ07h+67XkWPWG16qT/bq69DrVTV5buroy7U37/JsrNpo4q2fH4jh2n95p24Mj9uqVflfQ3vhTDwAAAGQtpH11wap4fPrSrYa0yURbXcuK0oC1U0lhlDcJXDttWHcsKYqOJQVNtgsbA9ryDdslhfmtPpDdWb06lcRxI/ukSyIJT99YvDqem70int3QWmHqgopYULE+7n55Qbo0SG5Z00C1pYwa0DnesW9924PRA7pEfjKzGLRjQlsAAABgt4a0SUCbLE9uIaRNgtYJQ7ulvxKfLPv175zTLQpao6TqeO8+ndLlIxMGpceSlhIvzl1ZX5G7obVCEuJuLbAtzM+LwoK8KMrPT9cF+flpK4Omxwob18nxDa9vOC8ZQ1LFe9Cw7mlVbbZaM0CuEtoCAAAAWQtpk8m+ktYEDSHtKCFtViQVyQ3fQYOkfUFtJtMYwiaTfiVhaxLCtteqZdhdhLYAAADQxiQ9Q5O+sLe/MC/un7Iw1lUWxE+nPRrdy0uiW3lRdOtQHF07FEf38qJ03W2T7S5lRWk4tzMh7dSFG4e0K9YKaVurHh1Lsj0EaLeEtgAAANAGJIHpUzPrg9q7X1oQS1ZXNXk1L6YtXhORLNshKaJMgtvuabhbH/J2K08C3fpgt3uT7aRXbPKr9FsLaTsklbRDGyppu8eoAV3Sik0Atk5oCwAAAK1UJpOJZ2avSIPaO1+cHwsrKhtfS8LWd4/qGyeM7B2vPPNkjBp3cFRU1sXytdWxfE1VLF9blQasy9Yk66rG46sqa9I+pslrmwaw20NIC7DrhLYAAADQyoLaZMKo21+YH3e8MD/mrljX+Fqn0sI4Yb++8d7R/eLwPXumFa3V1dWxcmrEocN7RFFR0dtev7q2Lg1rk1C3PtzdsN1kPwl568Pe6lhdWRP79uucVtEm1bT7q6QF2GVCWwAAAGgFQe2U+avSito7Xpwfs5au3Wgir3eN7BMnj+4fR+7dM0oKC3bpZyVBb69OJekCQHYIbQEAACBHTVu0Kv71/Pw0rH2jST/a0qL8eOe+fdKK2mP37R2lRbsW1AKQW4S2AAAAkENmLlmThrRJ+4NXF6xqPF5cmB/H7N0r3ntA/3jnvr2jvMRf6QHaKv+EBwAAgCybs2xt2vYgCWtfmlvReLwwPy+O3KtnnHxA/7QFQufSt+9JC0DrJ7QFAACAZuo7u766LirWV0fFuuqoWF+z0faqdLv+2KrktfR4daxcWx3Tl7zV+qAgPy8O26NH2vogmVSsa4di3w9AOyO0BQAAoMVCzHXVtbF6fU2srqyJukwm6jJRv66rXyeaHs80bNfVrxv303Xy+lbO3/Ba+nM3/Oy3xpEcy7y1ne6/NcbGMzect/F16rcra2o3ClrT7Q0hbBrGbnitJhnMTsjLizh4WPd47+j+8e5RfaNHR5OAAbRnQlsAAAC2WDG6qrI6DVxXbQhd31rXH0+2KzasV28IMpuelyy1Oxlitmb5eRGdSouic1lh2s6gU2n9unPZVrZLC2PPPh2jd6fSbA8dgBwhtAUAAGjHZi1dE/96fl7cN2VRLFlduSGArdnpitGthZjlxYVRUJAX+XnJklSW1q/r9/PSStOG1zber9/e+PzN3x/1/5+em8hL/i9v8/0Gyfsbdje8faPjb51b/77igvw0hE3D2CRsLUvWG4LXTbbLiwvS6wDAzhLaAgAAtDMLK9bH7S/Mj9uenxfPz1mx1fOS3LFjcRJUFkbHZCmpDy2T7U7pdnJsw/6GY/XbRRvOrV/KioSYALAjhLYAAADtwIq1VXHXSwvitufmxRMzljb2ak2qVQ/fs2ecPLp/+iv6TYPXDkUFkZ+cAADsVkJbAACANmpNZU3cN2VhGtROfH1xVNe+1fJg3JBu8b4D+sdJ+/eLXp1MegUAuURoCwAA0IZU1tTGw1MXp60PksA2mVCswYh+ndOg9r2j+8Wg7h2yOk4AYOuEtgAAAK1cbV0mHn9jadz2/Ny4+6UFUbG+pvG1oT06pEHtyQf0j736dMrqOAGA7SO0BQAAaIUymUw8M3tF/Ov5eemkYktWVza+1rdzaVpN+74x/WP/AV0iL5lRDABoNYS2AAAArSiofXXBqrT1QRLWvrl8XeNrXTsUpf1pk6rag4Z2N4EYALRiQlsAAIAcVV1bF7OWronXFq6OKfMr0tYHry9a3fh6h+KCOGG/vmlQe8RePaOoID+r4wUAmofQFgAAIIfC2dcXro7XFq2K1xeuihlL1kR1bWajc4sL8uOYfXqlrQ/euW+fKCsuyNq4AYCWIbQFAADYTWpq62Lm0rVpIJsGtGk4uzqmL1m9WTjboLy4IPbs0yn27t0xJgzrnlbWdikr8p0BQBsmtAUAAGiBcHbWsrfC2dcWvn04m7Q62Kt3x9grCWj71K+T/QFdy0wkBgDtjNAWAABgF62urIl/PPNmPDVzeRrUTl+8Jqpq67Z4bllRQeyVhLK968PZvZNwtk/H6N+lzORhAEBKaAsAALCT5ixbG797bGbcPGlOrKqs2WI4u2fv+mA2rZ7t3SmtnM3Pz3PPAYCtEtoCAADsgEwmE5NmLo/rH5kR/35lQdRt6HYwvFd5fHDswNi3bxLQCmcBgJ0ntAUAANgOlTW1cccL8+P6R2fES3MrGo8fuVfP+MQRw+LovXqpoAUAmoXQFgAAYBuWrK6Mm56cHb9/YlYsXlWZHispzI8PjB0Y5x4+NK2qBQBoTkJbAACALZgyvyJ+++iM+Odz86Kqpn5SsT6dS+KsQ4fGGQcNjm7lxe4bANAihLYAAAAb1NZl4oFXF6X9ah+fvrTxvhwwsEvaAuGk/ftFUUG++wUAtCihLQAA0O6trqyJW56eE799bGbMWro2vR8F+Xlx4qi+8YnDh8XYwV0jLy+v3d8nAGD3ENoCAADbZX11bbwwe0Usr4zIZDJt4q7NWbY2fvfYzLh50pxYVVmTHutcWhinHzw4bYMwoGtZtocIALRDQlsAAGCrlq+pivtfXRT3vrIgJr62JNZV16Z/jfjJlAdjRL/OjcvIfp1jz94do7SoIOfvZhI4PzVjWVz/6Iy495WFUbchfx7eqzzOPXxYfHDsgOhQ7K9KAED2+DcRAABgI7OXro1/v7IgDTQnzVzWGGomunUoipVrq2Llupp4YvqydGmQtBPYo1f5RkFusu7VqSQngtrla6vjwaRf7aMz4uV5FY2vHblXz7Rf7dF79Yr8fC0QAIDsE9oCAEA7lwSaL85dmYa0yfLqglUbvZ4Er8eN7BPHj+wTe/cqi9vuuCv2HHtEvLZ4bUyZX7FhWRUr11XHawtXp8utz81rfH/PjiUxol+nxhA3WZKq1uaY0GtdVW0sXlUZi1evr183LKsrY1FF/TrZX7K6Mqpr30qfSwrz4wNjB8a5hw+Nvft02uVxAAA0J6EtAAC0Q1U1dfHE9KVpSHvflIUxf+X6jSpmDxraPQ1qk2VQ9w6Nr1VXV0dRfsR+/TvHmCE9Ngp+k2s0DXGT9Yyla9LA9D+vJ8uSxvOLC/Jjrz4dN6rITdZdOhRFbV0mlq6p3HYIu+F4Qx/a7TWwW1mcftDgOOOgwdGtvHiX7yMAQEsQ2gIAQDtRsb46Hpq6OA1qH3p10UaBZ4figjh6715x/H594th9ekfXDjsWaObl5UX/rmXp8s4RfRqPr62qSSt3m4a5r86viDVVtWmLgqZtChJdyopi1frqjVoyvJ2karZ355Lo1bEkbcWQLh1LNzvWo2NxlBTmfs9dAAChLQAArUpShZlUbnbtUCSA2w7zV66L+15ZGP9+ZWFaWdu0RUDStuC4kb3j+JF949A9erTIJGLJhF5jB3dLlwZ1dZmYs7y+tcIrGypyX5lXEXNXrEtbLCTy8iJ6lDcNYevXvRv2myydSgrT0BgAoK0Q2gIA0Cqsr66Nv05+M66d+EbMWbYuPda9vDj6dC6NPp1Lom+6rl/6dknCvWRdGt07FGdtcqmkZcDaqtpYsa46ViSTd62tTrera+vSfq6F+Xn164K8KMzPj6JkvcnxovwNrzfZbnhv0sZg07Ay+ZlTF66Ke1+uD2qTXrVNJROFHb9f37TtwZiBXbNyb5KfOaRHebqcOKpf4/EksE1C5uQ7S77b5F4AALRHQlsAAHL+V/r/8MSsuP6RmWmFbVPL1lSly5T5W39/EoQmAW4S7L4V6m6y37k0yku2/q/GSRCa/Dr/8jVVabC4Ig1fq9J1sp8crw9mk/3648l+EtJW1dY15+3Y4ucr3BDmJn1i6zKZWL62vlo1kWS6SZVrMolYEtQO79UxclXSGiFZAADaO6EtAAA5adGq9WlQ+8cnZjX2Xh3QtSzOO3JYfGTCoFhfXRcLVq6PhavWx8JkXVEZCyqS9VvLktVVaTuA5Nfuk2Vbkl+xT3qgJoFuaWHBW9WxG8LYmh1psrqJJExN2jkkSxJKJpWyNbWZqK6rq1/X1qXXr6mtS8dbs9nx+nMzWxhCcn51bW1E9cY9Xo/cq2ca0r5j3z5pCwEAAFoPoS0AADll9tK18auJb6StEKpq6qtU9+rdMT5z9B7xvjH908AzkcyTlfwK/cjovNVrJe9fvLqyPsRduX5DqLthv6J+f1FFZayurEmD4VWLa+KNxWu2er3iwvzoloSvZcXRJV0XRbcOxfVh7Ibj3ZpsNwS1ZUUFzdJzNennmwS5aZjbJPTdaLuuLob1LE97yQIA0Dr5NzkAAHJCMhHVNQ+/Ebe/MC8ailrHDOoanztmj3jXiD471Xs1CVmT6txk2ZYktE2qdhdtCHIra+rSQLbrhkA2XcqKo6y4+Sfq2hFJD9uC/IIWmTAMAIDcIbQFACCrnpqxLH750LR4cOrixmNH7d0rDWsPHta9WSpU307HksLYs3fHdAEAgGwT2gIAsNslE3s98Oqi+OVDb8TTs5anx5JC2pP275e2QRg1oItvBQCAdktoCwDAbpNMtHX7C/PTsHbqwlWNk3R9cNzAOP+o4TG0Z7lvAwCAdk9oCwBAi1tfXRt/eXpOXDtxery5fF16rLy4ID52yJD45BHDonfnUt8CAABsILQFANpUFedL8yriqRlL0z6py9dWpxNZTRjaPSYM7RY9OpZEe7e2qiaenb0inn9zRRTk5UWXsqLGpXOTdaeSwp2a+GtTK9dVxx+emBXXPzIjlq6pSo/1KC+Ocw8fGh8/ZGh06VDUDJ8KAADaFqEtALBRoDdlfkW8NDdZVsb0JWtiULeytL/o/gO6xH4DuqQTNuWKypraeOHNlfHk9KXx5IxlMXnW8lhbVbvROcmx3zwyI93eo1d5HDSsRxw0rFsa5A7s1iHauiQ0nTxrWXp/kiD7xTdXRk1d5m3fl+S1nUrfCnTfCnYLG8PdzV7bcH7y+tLVlfGbR2fEH5+YHasra9JrDuhaFucfPTw+PG5QlBUX7IZPDwAArVPu/K0LANitKtZXxyvz6sPZl+dVxItJSLt4dWya5yWh5z+fm5du5+VFDO9Znga4+w/sWh/k9u8c5bspyF1XVRvPzF6+IYBcmlaMVtbUbXROEhomgezBw7pHz07F6fgnzVie9k99Y/GadPnTU7PTc/t3KY0Jw7o3nr9n746Rl3zIVmzJ6sqYNOOtkHbKgorIbPKd9utSGuOGdIuigvyoWFedBrtNl+SeJn8OGvZ3RhL6NvxZ2rtPx/jsMXvEe0f3T38mAACwbUJbAGgHlq+pSoPZl+atTMPZl+eujJlL127x3D6dS2JU//qq2qQydfbStel7kmX+yvWNwWfTIHePXh3rg9w0zO0SI/s1T5C7an11PD1reTw5vT6kfWELVaI9OxbHwWn1bPd02adPp41+rf/UAwc23oPkWpNm1oeZSVg9b+X6uPW5eemS6NahKMYP7R4HJe0UhnVPA+lcDxnnrViXfp6GIDv5bjY1rGd5+pka7tHAbmXbDKeT/rNJqL9ZoLu2OirW12wW8ibnNZy7ZkOlc/I1jR3cNT53zJ7xjn17N0urBQAAaC+EtgDQxixeVZmGsy9vCFqTVgdzV9RP/LSp5NfVRw3onIa0SQuE/QZ0jt6dSrdZxZkGuG82XLs+yJ22aHW6/OPZuRsFuaMH1F83CXKTALRD8bb/1SMJVp/aEKomy8vzVm5W+ZtUiSZVsUmbg4OHd08rf7enOrZbeXEcN7JPujTt7Zr8nCTITSp4kx64976yMF0SHYoLYuzg+lYKE4Z1iwMHdcvqr/VnMpmYsWRN4/1J7lXDpF5N7du3U2NAm4S1OzrJV2lRQbps68/C1lTX1qUBbhKu9+5U0uorlwEAIBuEtgDQygPa5+asaKyeTcLahRWVWzx3aI8OafVsfUBbH9QmQeaO6NmxJI7dp3e6NB1DEt4mY0gqYZPtBRVvBbl/3xDk5jetyB3YJUb0KY9llRF3vrggJs9J+tIuS1sYbGpIjw5p8Hjw8B5pWPt2VaLbKwmQD9+zZ7o0hI3J2BtC3Ekzl6eVo49MW5IuiaKCvDSETitxh9a3UygsyIvC/PwN67woyM9Lq3OTdbK/K2Otq8uk96QhpE2qaZPgvKnk54zq33lDSNsjnXCta4cd+16bU/LZTfgGAAC7RmgLAK1QUnF5w2Mz4wd3TNmsXUBDlWsS5KXVs/27xMj+ndNery2hV6eSOHbf3unSYNGq9fVB7pv1vXJfnLsiDZNfX7Q6XRqC3PRfRZ55YaPrJUFofSVt0me2R/TtsuPVnjsbNh44uFu6nH/0Hmlgmow1qWadtCE0TcLopDo3WX41cfp2XTcJqwsL8rcY6G66nwS/BUkAnJ+Xvm/qglVpO4KmigvyY8ygro2VtGOHdMupyeEAAIBd59/wAaCVqaypjf/6x0vx18lvpvt79e4YBwzq2hjSjmimfrK7Ivm1+nfsmyz1rQiaBrkN1bhJi4Xk2L59O6dVtIcM7572k02qeXNB0oN1n76d0uXjhwxJg/KkFUFDJW4S5i6qqIyaurqorctEde0mfRw2SDL1qpq6qNrJcSQtGpJJwxpaQowe2CVtXQAAALRdQlsAaEUWVqyP838/OW2JkFRifuukEfHJI4a1ir6hmwa51dXV8a/b74yT33toFBW1TBVwc0ru8aDuHdLlg+PqJzdrKgl1k4A2CXFrajNpBXQS5tbU1jVuJy0Y0mPp8cxGgW/98Y3fO6BbWauYDA0AAGheQlsAaCWenb08DWwXrapMWx387IwD48i9ekVr1payyCTULchLPlNB6FYAAADsiqz/VennP/95DB06NEpLS+Pggw+Op556apvnr1ixIi644ILo169flJSUxN577x133nln4+s//OEPY8KECdGpU6fo3bt3vP/974+pU6fuhk8CAC3nL0/PidN+9UQa2O7dp2PcduHhrT6wBQAAIAdD25tvvjkuuuiiuPTSS+OZZ56JAw44IE444YRYtGjRFs+vqqqK4447LmbOnBm33HJLGsZed911MWDAgMZzHn744TTUfeKJJ+Lee+9Nf/Xy+OOPjzVr1uzGTwYAzSP5dfrLbns5vn7LC1FVWxfHj+wTf//c4TGkR7lbDAAA0EZltT3CVVddFeedd16ce+656f4111wTd9xxR1x//fXxjW98Y7Pzk+PLli2Lxx57rLH3XVKl29Tdd9+90f4NN9yQVtxOnjw5jjrqqC2Oo7KyMl0aVFRUpOsk8E0WIPc1PKueWdqSZWuq4os3Px9PzFie7n/h2D3igmOGR35+pk38WffcQuvk2YXWx3MLrY/ntu3a3r/L5WWSWTOyIKma7dChQ1oxm7QwaHD22WenLRBuvfXWzd5z0kknRffu3dP3Ja/36tUrzjjjjLj44oujoGDLsyhPmzYt9tprr3jxxRdj1KhRWzznsssui8svv3yz4zfddFP6swBgd5u7JuLXUwtiWWVelORn4mN71cXo7ln5n2wAAACaydq1a9M8c+XKldG5c+fcq7RdsmRJ1NbWRp8+9TNIN0j2X3311S2+Z/r06fHAAw/EmWeemfaxTQLZz33uc2lCnbRY2FRdXV186UtfisMPP3yrgW3im9/8ZtqmoWml7aBBg+LYY4+NHj167NLnBHaP5J8DSUuUpIVKa5iFHrblrpcWxE///lKsq66Lwd3L4pozDoy9+nRsczfNcwutk2cXWh/PLbQ+ntu2q+E3/HO6PcKOSkLYpNXBtddem1bWjhs3LubOnRtXXnnlFkPbpLftSy+9FI888sg2r5tMaJYsm0qCH+EPtC6eW1qzurpMXHXva/GzB6el+0fu1TN+evqB0bVDcbRlnltonTy70Pp4bqH18dy2PdubNWYttO3Zs2cavC5cuHCj48l+3759t/iefv36pR+saSuEESNGxIIFC9J2C8XFb/2l9sILL4zbb789Jk6cGAMHDmzBTwIAu27V+ur48s3PxX1T6ifjPO/IYXHxiftGYUFW5wwFAAAgC7L2N8EkYE0qZe+///6NKmmT/UMPPXSL70naHCQtEZLzGrz22mtpmNsQ2CYtepPA9h//+EfaSmHYsGG74dMAwM6bvnh1nPqLx9LAtrgwP35y2gHx7feMFNgCAAC0U1kt30n6yF533XXxu9/9LqZMmRKf/exnY82aNXHuueemr5911llpv9kGyevLli2LL37xi2lYe8cdd8QVV1yRtkFokGz/4Q9/SCcR69SpU1qFmyzr1q3LymcEgG15aOqiOOXnj8a0Raujb+fSuOUzh8apB/oNEQAAgPYsqz1tTzvttFi8eHFccsklabA6ZsyYuPvuuxsnJ5s9e3bk57+VKyeTg91zzz3x5S9/OUaPHh0DBgxIA9yLL7648Zxf/vKX6fqYY47Z6Gf99re/jXPOOWe3fTYA2JbkN0N+NXF6/OjuVyOTiRg3pFv88mNjo3enUjcOAACgncv6RGRJK4Nk2ZKHHnpos2NJ64Qnnnhim38JBoBctq6qNi7+2wtx2/Pz0v3TDxoUl71vvygpfKtnOwAAAO1X1kNbAGhP5q5YF5++8el4eV5FFObnxaXv2y8+dvDgyMvLy/bQAAAAyBFCWwDaveS3NHZHaPrk9KXxuT8+E0vXVEWP8uL4xZlj4+DhPdr9/QcAAGBjQlsA2q1Fq9bHl/78XEyauSy6lxdHz44l0atTSbp+a7s4XffasN+lrGiHA94kFP7Dk7Pj8ttejpq6TOzXv3Nce9b4GNC1rMU+GwAAAK2X0BaAdunVBRXxyRueTtsVJBZWVKbL2ykqyNtiqLulwLdzaWFU12bi0tteij89NSd9/8kH9I///uDoKCvWvxYAAIAtE9oC0O48OHVRfP6mZ2N1ZU0M71kePzltTOTn5cXi1etjyaqqWLy6MhavqkzXS1ZVxpIN+xXra9IQdv7K9enydooL86OsqCBWrquOpDj34hP3jfOPGq5/LQAAANsktAWgXbnx8Zlx2W0vR10m4pDh3eOaj42Lrh2KN7zaZZvvraypjSWrq9Igd3GTMDdZJ8ebHltVWRNVNXXp0qm0MP7v9APj2H1675bPCAAAQOsmtAWgXaity8T373glfvvozHT/Q+MGxhWn7p9Ww26vksKCtA/t9vSiXV9dm4a3yaRjw3qWp71wAQAAYHsIbQFo89ZU1sQX/vRs3P/qonT/ayfsE587Zo8WbVNQWlQQg7p3SBcAAADYEUJbANq0+SvXpROOvTK/IkoK8+Oqj4yJ94zul+1hAQAAwFYJbQFos158c2V88neTYtGqyujZsTiuPWt8jB3cLdvDAgAAgG0S2gLQJv375QXxxT8/F+uqa2PvPh3jN2dP0KoAAACAVkFoC0Cbkslk4jePzIgf3DklMpmII/fqGT8/c2x0LjURGAAAAK2D0BaANqO6ti4uve3luOnJ2en+mQcPjsvft18UFuRne2gAAACw3YS2ALQJFeur44I/PhP/eX1J5OVFfPukEfHJI4ZFXrIDAAAArYjQFoBWb86ytfGJGybF64tWR1lRQfzf6QfGcSP7ZHtYAAAAsFOEtgC0as/MXh6fvvHpWLK6Kvp0LkknHBs1oEu2hwUAAAA7TWgLQKt1+wvz4it/eT4qa+piZL/O8Ztzxke/LmXZHhYAAADsEqEtAK1OJpOJXzz0Rlx5z9R0/10jesf/fvTAKC/xP2sAAAC0fv52C0CrUlVTF9/8+4vxt2feTPc/cfiw+PZ7RkRBvgnHAAAAaBuEtgC0GivWVsX5v58cT85Yloa0l71vv/j4IUOyPSwAAABoVkJbAFqFGUvWxCdumJSuO5YUxs/OODCO2ad3tocFAAAAzU5oC0DOe3L60jj/D5NjxdrqGNC1LJ1wbN++nbM9LAAAAGgRQlsAtltdXSaWrKmM+SvWx7wV62LuinUxb8X6WLRqfWQyEZnIpOcl2xutNz2+4XoN+7GF15PJxhq2H522JKprM3HAoK5x3VnjonenUt8aAAAAbZbQFoBGa6tqNoSx62N+GsjWbyfreSvXpWFtVW1dVu7YSfv3jf/58JgoKy7wjQEAANCmCW0Bsmh9dW3c8/KC+Pszc2PluuooLcqPsqKCNJgsTdYNS9P9dLv+vKbHGvebbCeTdTWorcukFbHbCmWT9gNvJy8vok+n0ujfNVnK0nYFvTuXRlFB/c/Ka3pik/0Nu5G34chb+1t+vemqZ6eSOHqvXpHf5PMAAABAWyW0BciCaYtWx5+fmh1/e+bNWL4dQenOKi7ITwPe4sKCWL62Kg1u306nksI0jG0IZRuC2WTdr0tp9O2SBLT5LTZmAAAAaO+EtgC7SWVNbdz90oK46cnZ8eSMZY3HkyD0tAmDYmS/zrG+pi7WV9XGuuoNS1VtWo2bLPXH6hqPbfn1ZPut9gVJK4P6dgY19f/Qz89LQ9c0jO2yhVC2a2l0Li3yZwIAAACySGgL0MLeWLw6/vTkxlW1yW/5v2Pf3nH6QYPjmH16b9TGoDkmC6usqdso+E0C4x7lJdGrU0mz/iwAAACg+QltAbJQVfuR8YPSytaWkPR9TfvamrALAAAAWiWhLUAzV9UmvWpvmbxxVe2x+/SOMw4eHEfv3SsK9YMFAAAAtkFoC9BMVbV/emp2PDH9raravp3rq2qTpaWqagEAAIC2R2gLsJOmJ71qt1JVW9+rVlUtAAAAsOOEtgA7QFUtAAAA0NKEtgDbYc6ytfH7J2alVbXL1lSlx1TVAgAAAC1BaAvwNp6ZvTw+/usnY01V7Ua9aj8yYVAM0KsWAAAAaGZCW4BteGnuyjj7+qfSwPaAQV3j88fuqVctAAAA0KKEtgBb8drCVXHW9U/FqvU1MWFot/jdJw6KDsX+sQkAAAC0rPwWvj5AqzRjyZo489dPpv1rRw/sEtefM0FgCwAAAOwWQluALUw6duZ1T8TiVZWxb99OceMnDopOpUXuEwAAALBbCG0Bmliwcn1aYTtv5frYo1d5/OFTB0fXDsXuEQAAALDbCG0BNliyujLO/PUTMXvZ2hjcvUP88VOHRM+OJe4PAAAAsFsJbQEiYsXaqvjYr5+MNxaviX5dSuOPnzo4+nYpdW8AAACA3U5oC7R7q9ZXx9nXPxWvLlgVvTqVxE3nHRKDundo9/cFAAAAyI7CLP1coB15c/nauP6RmTF51rL41JHD472j+0VeXl7kgrVVNfGJGybF82+ujG4ditIK22E9y7M9LAAAAKAdE9oCLeaVeRVx7cQ34l8vzI/aukx67PN/ejZufW5efP/9o7LefmB9dW2cd+PTMWnm8uhUWhi//+TBsXefTlkdEwAAAIDQFmhWmUwmHntjaVzz8Bvxn9eXNB4/fM8eMbJf57jhsZlx35SF8eT0pfHNk0bERycMivz83V91W1VTF5/74zPx6LSlUV5cEL/7xEExakCX3T4OAAAAgE0JbYFmUVNbF3e+tCB+9fAb8fK8ivRYksW+Z3T/OP+o4Y2B6IfHD4qv3/JCPDdnRXzrHy/Gbc/Pjf/3gdExdDe2JEjG+sU/PxsPvLooSgrz4zfnTIixg7vttp8PAAAAsC1CW2CXe8L+ZdKc+PUjM+LN5evSY6VF+fHRCYPjk0cM22xCr6T9wN8+e1hacfvje6bGE9OXxQlXT4yLjts7Pb+woGXnR6yry8TXbnkh7nppQRQX5Me1Z42PQ4b3aNGfCQAAALAjhLbATlm6ujJ+9/isuPHxmbFibXV6rHt5cZx96ND4+KFD0u2tKcjPSwPa40f2iW/+/cV4ZNqS+OFdr8btL8yPH31wdIzs37nFWjd8+58vxj+enZuO4WdnHBhH792rRX4WAAAAwM4S2gI7ZOaSNfHrR6bHX59+Mypr6tJjg7t3iPOOGh4fGjswyooLtvtaSRXu7z95UNwy+c343u2vxItzV8b7fvZInH/08Pj8O/aK0qLtv9b2BLbfvf2V+NNTc9K2DVefNiaO369vs10fAAAAoLkIbYHt8vycFXHtxOlx10vzoy5Tf2z0wC7xmaP3iBP265tWru6MvLy8tM/t0fv0iktvfTltW/DzB99I10nV7YSh3ZvlG/rxv6fGbx+dmW4n1z35gP7Ncl0AAACA5ia0BbZZnfrQa4vTycWS3rMNjtmnV5x/1B5xyPDuaejaHHp3Ko1ffmxc3P3S/PjOrS/H9MVr4sPXPB5nHTokvn7ivtGxZOf/cfWzB15Pg+DE907ZLw2JAQAAAHKV0BbYTFVNXfzr+XlpZe3Uhavq/2GRnxfvG9M/Pn3U8Ni3b8v0nE2cOKpfHDq8Z1xx55S4+ek5cePjs+K+VxbGDz6wfxy7T+8dvt6v/zM9fvzv19Ltb580Ij5+6NAWGDUAAABA8xHaAo1Wra+OPz81J65/dEbMX7k+PVZeXBBnHDw4zj18WPTvWrZb7laXDkXxow+NTkPiZKKy2cvWxrm/nRSnHjggvvPekduc5KypPzwxK75/x5R0+8vv2jvtuwsAAACQ64S2QOrBqYvii396NirW16T7vTqVxLmHD40zDx4SXcqKsnKXDt+zZ9z9pSPjqn+/lgbJ/3h2bkx8bXFc+r794uTR/bbZmiGZ3Oy//vlSup303f3CO/fcjSMHAAAA2HlCWyAmzVwWn/n95KisqYvhvcrj/KOGx/sPHBAlhQVZvzsdigvjv947Mt57QP+4+JYX0nYNX/jTs3Hrs3Pj+6eOin5dNq/+vf2FefH1W55Pt885bGhcfOI+zdZ7FwAAAKCl5bf4TwBy2pT5FfGJGyalge2x+/SKe750VJw2YXBOBLZNjRnUNf71+SPiouP2jqKCvLj/1UVx3FUT449Pzoq6ukzjecnxL/35uUgOfXTCoLjkvSMFtgAAAECrIrSFdmz20rVx1vVPxar1NTF+SLf4xZnjoqggd/+xUFyYH194515xxxeOjAMHd43VlTXx7X+8FKdf90TMXLomXl2RF5//8/NRU5eJU8b0jx+cun/k56uwBQAAAFqX3E1ngBa1aNX6+Pj1T8biVZWxb99O8ZuzJ0RZcW5V127N3n06xS2fOSwuPXlklBUVxJMzlsV7f/Z4/HpqflTXZuLE/frG/3z4gCgQ2AIAAACtkNAW2qGV66rj7Osnxayla2Ngt7L43ScOii4dsjPZ2M5KAtlzDx8W//7yUXHkXj3T9g7VdXlx9N494/9OPzAKc7hiGAAAAGBbTEQG7cz66to473dPp71se3Ysjj988uDo07k0WqtB3TvEjZ84KP75zJy45/Hn4sqPHpC2UQAAAABorYS20I7U1NbFhTc9G0/NXBadSgrjhnMPiqE9y6O1y8vLi/eO7hf5bz4bpUWto8UDAAAAwNYoR4N2IpPJxDf+/mLcN2VhWol63dnjY9SALtkeFgAAAACbUGkLzejBqYvS9TF790qrP3PJD+96NW6Z/GYkc3P97PQD45DhPbI9JAAAAAC2QGgLzeSNxavjEzdMikwm4qT9+8YP3r9/dCsvzon7e83Db8S1E6en2//vg6Pj+P36ZntIAAAAAGyF9gjQTO56cX4a2CbufHFBnHD1xHhoQ+VtNt08aXb8v7teTbe/ddK+8ZHxg7I9JAAAAAC2QWgLzeTulxek63MOGxp79CqPRasq45zfTorv/POlWFtVk5X7fPdLC+Kbf38x3T7/6OHx6aP2yMo4AAAAANh+QltoBnOWrY2X5lak/WI//449444vHJmGt4nfPzEr3vN/j8Szs5fv1nv9+BtL4wt/fjbqMhEfGT8wvnHivrv15wMAAACwc4S20Azu2VBle9Cw7tGjY0mUFhXEZe/bL37/yYOib+fSmLFkTXzomsfjJ/e+FtW1dS1+z1+auzLOu/HpqKqpi+NH9okrTt0/5yZGAwAAAGDLhLbQTG0IEiduMsHXkXv1inu+dFS874D+UVuXif+9//X40C8fSyctaynTF6+Os69/KlZX1sTBw7rH/51+YBQWeNQBAAAAWgtJDuyiRRXrY/KG1gcnjNo4tE106VCUBqfJ0rm0MJ5/c2W85//+Ezc+PjMyDTOXNZMFK9fHx3/zVCxdUxX79e8cvz57fFr1CwAAAEDrIbSFXXTPKwsjyV7HDOoa/bqUbfW8pNr2ni8fFUfs2TPWV9fFJbe+HGf/dlIsrFjfLN/BirVVcdb1T8bcFetiaI8OccO5B0Wn0qJmuTYAAAAAu4/QFnbRPQ2tEbZQZbupJNS98RMHxWUnj4ySwvyY+NriOP4nE+P2F+bt0hjWVtXEJ26YFK8tXB29O5XE7z95cPTqVLJL1wQAAAAgO4S2sAuWr6mKx6cv3WI/260+dPl5cc7hw+KOLxwR+w/oEivXVceFNz0bX/rzs+n2jkomNvvcH5+JZ2avSNsvJIHtoO4ddvg6AAAAAOQGoS3sgvumLEwnGNu3b6cY2rN8h967Z+9O8ffPHRZfeMeekZ8X8c/n5sWJV0+Mx6Yt2e5r1NVl4qt/fT4emro4Sovy47fnToh9+nbaiU8CAAAAQK4Q2sIuuOfl7W+NsCVFBflx0fH7xC2fPSztQzt/5fo449dPxnf/9Uqsr67d5nuTScy+e/srcetz86IwPy9++bFxMW5I950aBwAAAAC5Q2gLO2l1ZU1MfL2+Kvbdo/rt0n0cO7hb3PGFI+OMgwen+9c/OiNO/ukj8dLclVt9z08fmBY3PDYz3f7xhw+IY/fpvUtjAAAAACA3CG1hJz346qKoqqmLYT3LY+8+HXf5PpaXFMYVp+4f158zPnp2LInXF62O9//80fj5g9OiprZuo3N//8SsuOre19LtS08eGe8/cIDvEQAAAKCNENrCTrp7Q2uEE/brG3l5ec12H9+xb5/495ePSic2q6nLxJX3TI3Trn0iZi1dk75++wvz4pJbX0q3k3645x4+rNl+NgAAAADZJ7SFnZD0m00qbRPv3sl+ttvSvbw4fvmxsfE/Hz4gOpYUxuRZy+Pd//uf+OGdU+LLNz8XmUzEmQcPji8ft3ez/2wAAAAAsktoCzvhP68vibVVtdGvS2mMHtilRe5hUr37wXED4+4vHRkHDeue/rxfTZwe1bWZeM/ofvHdU0Y1a4UvAAAAALlBaAs74a6X5rdIa4QtGditQ/zpvEPiWyftG6VF+fHOfXvHVR85IAryBbYAAAAAbVFhtgcArU11bV3c98rCFmuNsCVJQPvpo/aIcw4bFkUFeSpsAQAAANowoS3soCemL42K9TXRo7w4xg/tvlvvX3Gh4ngAAACAtk4CBDvorpcWpOvj9+ujRQEAAAAAzU5oCzugti4T/365vjXCiaP6uXcAAAAANDuhLeyAZ2YvjyWrK6NTaWEcOryHewcAAABAsxPawg6468X61gjHjeijvywAAAAALUJoC9spk8nEPS/Xh7YnjOrrvgEAAADQIoS2sJ1emlsRc1esi7Kigjhqr17uGwAAAAAtQmgL2+mul+an62P37RVlxQXuGwAAAAAtQmgL29ka4e6XNrRG2E9rBAAAAABajtAWtsO0Ratj+pI1UVyQH+/Yt7d7BgAAAECLEdrCdrhrQ5XtEXv1jE6lRe4ZAAAAAC1GaAvboaE1wolaIwAAAADQwoS28DZmL10br8yviIL8vHjXyD7uFwAAAAAtSmgLb+Pul+en64OHdY/u5cXuFwAAAAAtSmgL29saYVRf9woAAACAFie0hW1YsHJ9PDN7Rbp9/EihLQAAAAAtT2gL2/DvV+qrbMcO7hp9u5S6VwAAAAC0OKEtbIPWCAAAAADsbkJb2Ipla6riyRnL0u0T9+vnPgEAAACwWwhtYSvue2Vh1NZlYmS/zjG4Rwf3CQAAAIDdQmgLW3H3y/X9bE8cZQIyAAAAAHYfoS1swar11fHI60vS7XcLbQEAAABoT6Htz3/+8xg6dGiUlpbGwQcfHE899dQ2z1+xYkVccMEF0a9fvygpKYm999477rzzzl26JmzqgVcXRVVtXQzvVR579u7oBgEAAADQPkLbm2++OS666KK49NJL45lnnokDDjggTjjhhFi0aNEWz6+qqorjjjsuZs6cGbfccktMnTo1rrvuuhgwYMBOXxO25J6G1gj79Y28vDw3CQAAAIDdpjCy6Kqrrorzzjsvzj333HT/mmuuiTvuuCOuv/76+MY3vrHZ+cnxZcuWxWOPPRZFRUXpsaSidleumaisrEyXBhUVFem6uro6XWhf1lXVxoOv1of8x+3by5+BVqLhWfXMQuvhuYXWybMLrY/nFlofz23btb25RV4mk8lEFiRVsx06dEgrZt///vc3Hj/77LPTFgi33nrrZu856aSTonv37un7ktd79eoVZ5xxRlx88cVRUFCwU9dMXHbZZXH55Zdvdvymm25Kr0f78sKyvPjN1ILoVpyJS8fWhkJbAAAAAJrD2rVr0zxz5cqV0blz59yrtF2yZEnU1tZGnz59Njqe7L/66qtbfM/06dPjgQceiDPPPDPtYztt2rT43Oc+lybUSTuEnblm4pvf/GbaUqFppe2gQYPi2GOPjR49euzyZ6V1eeCWFyNifpwybki856R9sz0ctlPyz4F77703baHSUIkP5DbPLbROnl1ofTy30Pp4btuuht/wz+n2CDuqrq4uevfuHddee21aWTtu3LiYO3duXHnllWlou7OSCc2SZVNJ8CP8aV+qaurigamL0+33HDDA998KeW6h9fHcQuvk2YXWx3MLrY/ntu3Z3qwxa6Ftz5490+B14cKFGx1P9vv27bvF9/Tr1y/9YMn7GowYMSIWLFiQtkbYmWtCU49PXxqr1tdEz44lMXZwNzcHAAAAgN0uP7KkuLg4rZS9//77N6qkTfYPPfTQLb7n8MMPT1siJOc1eO2119IwN7nezlwTmrr7pfnp+vj9+kRBfp6bAwAAAED7CW0TSR/Z6667Ln73u9/FlClT4rOf/WysWbMmzj333PT1s846K+032yB5fdmyZfHFL34xDWvvuOOOuOKKK+KCCy7Y7mvC1tTWZeLfL9dXab97lMpsAAAAALIjqz1tTzvttFi8eHFccsklaYuDMWPGxN133904kdjs2bMjP/+tXDmZHOyee+6JL3/5yzF69OgYMGBAGuBefPHF231N2JqnZy6LpWuqoktZURwy3AR0AAAAAGRH1iciu/DCC9NlSx566KHNjiVtDp544omdviZszV0vLUjX7xrRJ4oKslqEDgAAAEA7JpmCiMhkMnHPy/Wh7YlaIwAAAACQRUJbiIgX3lwZ81eujw7FBXHkXj3dEwAAAACyRmgLTVojHLtv7ygtKnBPAAAAAMgaoS3tXtIa4e6X5qf34cT9+rb7+wEAAABAdgltafdeW7g6Zi5dG8WF+WmlLQAAAABkk9CWdu+uDVW2R+3VMzqWFLb7+wEAAABAdgltaffu3tDP9gStEQAAAADIAUJb2rWZS9bEqwtWRUF+XrxrRJ9sDwcAAAAAhLa0b3e/XF9le+jwHtGtvDjbwwEAAAAAoS3tW2NrhFF9sz0UAAAAAEhpj0C7NX/lunhuzorIy4s4YaTWCAAAAADkBqEt7dY9G6psxw3uFr07l2Z7OAAAAACQEtoS7b2f7YlaIwAAAACQQ4S2tEtLV1fGUzOWpdsn7KefLQAAAAC5Q2hLu3TvKwujLhMxakDnGNS9Q7aHAwAAAACNhLa079YIqmwBAAAAyDFCW9qdleuq49FpS9LtE0f1y/ZwAAAAAGAjQlvanQdfXRTVtZnYs3fHdAEAAACAXCK0pd25+yWtEQAAAADIXUJb2pW1VTXx0GuL0u0TR/XN9nAAAAAAYDNCW9qVia8tjvXVdTGwW1ns179ztocDAAAAAJsR2tJuzF2xLv48aU66feJ+fSMvLy/bQwIAAACAzRRufgjahnkr1sUT05fG428sjSdmLI05y9Y1vvbu/bVGAAAAACA3CW1pM+avbBLSTl8Ws5et3ej1gvy82H9Al3j3qL4xdnC3rI0TAAAAALZFaEurtWDl+jSkTYPa6Utj1tLNQ9pRA7rEocN7xCHDu8f4od2jY4k/8gAAAADkNgkWrcbCiiYh7RtLY+YmIW1+XqSVtIckIe0ePWL8kG7RqbQoa+MFAAAAgJ0htCVnLapYn1bQJq0Onpy+NKYvWbNZSDuqIaQd3j0mDO0upAUAAACg1RPaklOmzK+I3z8xK62mnb5485B2v/5JSNs9DWonDOsenVXSAgAAANDGCG3JGf98dm5c/LcXorKmLt3PS0PaznHIsB6NIW2XMu0OAAAAAGjbhLZkXW1dJv777lfjVxOnp/tH7d0rPn7IkDhoaPfo0kFICwAAAED7IrQlq1aurY4v/PnZePi1xen+547ZI75y/D5RkPRCAAAAAIB2SGhL1kxbtCrOu3FyzFiyJkqL8uPKDx0QJx/Q3zcCAAAAQLsmtCUr7ntlYXzp5udidWVNDOhaFr/6+LgYNaCLbwMAAACAdk9oy26VyWTiFw+9ET/+99TIZCIOGtY9fnHm2OjZscQ3AQAAAAAqbdmd1lbVxNf++kLc8eL8dD+ZbOySk0dGUUG+LwIAAAAANlBpy24xZ9na+PTvJ8eU+RVRVJAXl79vVJxx8GB3HwAAAAA2IbSlxT0xfWl87o/PxLI1VdGzY3H88mPjYsLQ7u48AAAAAGyB0JYW7V/7+ydmxXf/9UrU1GVi1IDOce3Hx0f/rmXuOgAAAABshdCWFlFZUxuX3vpy/HnSnHT/lDH940cfHB2lRQXuOAAAAABsg9CWZrdo1fr47B+eicmzlkdeXsQ3Ttw3Pn3U8MhLdgAAAACAbRLa0qxeeHNFfPrGybGgYn10Ki2Mn55+YByzT293GQAAAAC2k9CWZvOPZ9+Mb/ztxaisqYs9epXHdWeNj+G9OrrDAAAAALADhLbsstq6TPzo7lfj2onT0/137ts7fvLRMdG5tMjdBQAAAIAdJLRll6xcWx0X/umZ+M/rS9L9C4/dMy46bu/Iz9e/FgAAAAB2htCWnfb6wlVx3o1Px8yla6OsqCCu/PDoeO/o/u4oAAAAAOwCoS075b5XFsaXbn4uVlfWxICuZXHtWeNiv/5d3E0AAAAA2EVCW3ZIJpOJnz84Lf7n3tcik4k4eFj3+MWZY6NHxxJ3EgAAAACagdCWHXLNw9Pjx/9+Ld0++9Ah8V/vHRlFBfnuIgAAAAA0E6Et221dVW1cO/GNdPvbJ42I844a7u4BAAAAQDNTIsl2+8vTc2L52uoY3L1DnHv4UHcOAAAAAFqA0JbtUlNbF9f9Z3q6fd6Rw6JQSwQAAAAAaBFCW7bLHS/OjzeXr4se5cXx4fGD3DUAAAAAaCFCW95WJpNJJyBLnH3Y0CgtKnDXAAAAAKCFCG15W/95fUlMmV8RHYoL4qxDh7hjAAAAANCChLa8rWsefiNdf3TC4OjaodgdAwAAAIAWJLRlm154c0U89sbSKMzPi08eOczdAgAAAIAWJrRlm361oZft+w7oHwO6lrlbAAAAANDChLZs1cwla+Kul+an258+erg7BQAAAAC7gdCWrbruP9OjLhNx7D69Yt++nd0pAAAAANgNhLZs0eJVlfHXyW+m2585eg93CQAAAAB2E6EtW/S7x2ZGVU1djBnUNQ4a1t1dAgAAAIDdRGjLZlZX1sSNj89srLLNy8tzlwAAAABgNxHaspk/PzU7KtbXxPCe5XH8yD7uEAAAAADsRkJbNpK0RPjNIzPS7U8fNTzy81XZAgAAAMDuJLRlI7c9Py/mr1wfvTqVxKljB7g7AAAAALCbCW1pVFeXiWsnvpFuf+LwYVFSWODuAAAAAMBuJrSl0YNTF8VrC1dHp5LCOPOQwe4MAAAAAGSB0JZG1zxcX2V7xiGDo3NpkTsDAAAAAFkgtCU1edaymDRzeRQX5KetEQAAAACA7BDakrrm4enp+tQDB0SfzqXuCgAAAABkidCWmLZoddz7ysLIy4s476jh7ggAAAAAZJHQlrh2Yn0v2+NG9Ik9e3d0RwAAAAAgi4S27dyClevjH8/OTbc/c8we2R4OAAAAALR7Qtt27rePzojq2kwcNLR7jB3cLdvDAQAAAIB2T2jbjq1cVx1/fHJ2uv2ZY/SyBQAAAIBcILRtx256cnasrqyJvft0jGP27p3t4QAAAAAAQtv2a311bVz/6Ix0+/yj9oj8/LxsDwkAAAAAENq2X8nkY4tXVUb/LqXxvjH9sz0cAAAAAGAD7RHaodq6TFw3cXq6/YkjhkVRgT8GAAAAAJArpHXt0L2vLIjpS9ZEl7KiOP2gwdkeDgAAAADQhNC2nclkMvHLh+urbD9+yJAoLynM9pAAAAAAgCaEtu3MkzOWxfNzVkRJYX6cc/jQbA8HAAAAANiE0LaduebhN9L1h8cPjJ4dS7I9HAAAAABgE0LbdmTK/Ip4aOriyM+LOO/I4dkeDgAAAACwBULbduTaifW9bN+9f78Y0qM828MBAAAAALZAaNtOvLl8bdz2/Lx0+zNH7ZHt4QAAAAAAWyG0bSd+88iMqK3LxOF79oj9B3bJ9nAAAAAAgK0Q2rYDy9dUxZ+fmpNuf+ZoVbYAAAAAkMuEtu3A75+YFeuqa2O//p3jiD17Zns4AAAAAMA2CG3buHVVtXHDYzPT7fOP3iPy8vKyPSQAAAAAYBuEtm3cLZPnxLI1VTGoe1mcNKpvtocDAAAAALwNoW0bVlNbF9f+Z3q6fd6Rw6OwwNcNAAAAALlOiteG3fnSgpizbF10Ly+OD48blO3hAAAAAADbQWjbRmUymfjVw2+k22cfOjTKiguyPSQAAAAAYDsIbduoR6YtiZfnVURZUUGcdeiQbA8HAAAAANhOQts26lcP1/eyPW3CoOhWXpzt4QAAAAAA26kwdlBVVVX885//jMcffzwWLFiQHuvbt28cdthhccopp0RxsYAw2158c2VaaVuQnxefOnJYtocDAAAAALRUpe20adNixIgRcfbZZ8ezzz4bdXV16ZJsn3XWWbHffvul55Bd10ys72X7vgP6x8BuHXwdAAAAANBWK20/+9nPxv7775+GtJ07d97otYqKijS4veCCC+Kee+5p7nGynWYvXRt3vTg/3f70UcPdNwAAAABoy6Hto48+Gk899dRmgW0iOfa9730vDj744OYcHzvorpfmR10m4og9e8aIfpt/TwAAAABAG2qP0LVr15g5c+ZWX09eS84heybNXJ6uj967l68BAAAAANp6pe2nPvWptAXCd77znXjnO98Zffr0SY8vXLgw7r///vj+978fn//851tqrLyNTCYTz8yuD23HDe3mfgEAAABAWw9tv/vd70Z5eXlceeWV8ZWvfCXy8vIaw8K+ffvGxRdfHF//+tdbaqy8jelL1sSyNVVRUpgfo/p3cb8AAAAAoK2HtokkmE2WGTNmxIIFC9JjSWA7bNiwlhgfO2DyhtYIBwzqGsWFO9T5AgAAAADIETud7CUh7aGHHpouuxLY/vznP4+hQ4dGaWlpOolZMtHZ1txwww1pdW/TJXlfU6tXr44LL7wwBg4cGGVlZTFy5Mi45pproj2YNHNZuh4/RGsEAAAAAGitmrUcc86cOfGJT3xiu8+/+eab46KLLopLL700nnnmmTjggAPihBNOiEWLFm31PZ07d4758+c3LrNmzdro9eR6d999d/zhD3+IKVOmxJe+9KU0xL3tttuirZs8q77Sdrx+tgAAAADQajVraLts2bL43e9+t93nX3XVVXHeeefFueee21gR26FDh7j++uu3+p6kujZpx9CwNEyG1uCxxx6Ls88+O4455pi0gvfTn/50GgZvq4K3LVi6ujLtaZsYO1ilLQAAAAC0i562b1etOn369O2+VlVVVUyePDm++c1vNh7Lz8+Pd73rXfH4449v9X1J+4MhQ4ZEXV1djB07Nq644orYb7/9Gl8/7LDD0nEmFb/9+/ePhx56KF577bX4yU9+stVrVlZWpkuDioqKdF1dXZ0urcFT05ek6716l0d5UV6rGTc0l4Y/8/7sQ+vhuYXWybMLrY/nFlofz23btb25RV4mk8ls70WTUDWpdN3WW5LXa2tr3/Za8+bNiwEDBqSVsUlf3AZf//rX4+GHH44nn3xys/ckYe7rr78eo0ePjpUrV8aPf/zjmDhxYrz88stpD9tEEr4m1bU33nhjFBYWpmO+7rrr4qyzztrqWC677LK4/PLLNzt+0003pZW/rcGtM/Pjgfn5cVjvujhtj7psDwcAAAAA2MTatWvjjDPOSLPNpA1ss1Ta9uvXL37xi1/EKaecssXXn3vuuRg3bly0lIaJz5pW1Y4YMSJ+9atfxfe+97302E9/+tN44okn0mrbpCI3CXUvuOCCtOo2qeLdkqTaN+mF27TSdtCgQXHsscdGjx49ojW44dok5F4Z7z9idJx0YP9sDwey8l+q7r333jjuuOOiqKjINwCtgOcWWifPLrQ+nltofTy3bVfDb/i/nR0KbZNANmlpsLXQ9u2qcJvq2bNnFBQUxMKFCzc6nuwnvWq3RxLMHHjggTFt2rR0f926dfGtb30r/vGPf8R73vOe9FhSlZuEyUlV7tZC25KSknTZ0vVbQ/izvro2XppX/4UfskevVjFmaCmt5bkF3uK5hdbJswutj+cWWh/PbduzvZnFDk1E9rWvfS2tbt2aPffcMx588MHtulZxcXEaAt9///2Nx5I+tcl+02rabUnaMLz44otpBXDTHrRJS4SmknA4uXZb9cKbK6O6NhO9OpXEoO5l2R4OAAAAALALdqjS9sgjj9zm6+Xl5XH00Udv9/WSlgRnn312jB8/Pg466KC4+uqrY82aNXHuueemryd9aJO+tz/84Q/T/e9+97txyCGHpOHwihUr4sorr4xZs2bFpz71qfT1pA9E8vOTcLmsrCxtj5D0x03621511VXRVj09a1m6Hj+kW1rtDAAAAAC0k9C2uZ122mmxePHiuOSSS2LBggUxZsyYuPvuu6NPnz7p67Nnz96oanb58uVx3nnnped269YtrdRNJjIbOXJk4zl//vOf0x61Z555ZixbtiwNbn/wgx/EZz7zmWirJs9cnq7HDemW7aEAAAAAANkIbWfOnBmXXXZZ3HPPPWnFa1L5+vWvfz0+/vGP7/C1LrzwwnTZkoceemij/Z/85Cfpsi1JP9zf/va30V7U1WVi8uz60HbC0O7ZHg4AAAAAsIt2qKdt4vHHH09bFAwePDgeffTRtJr1l7/8Zdqq4De/+c2ujocd9Mbi1bFibXWUFRXEyP6d3T8AAAAAaE+hbRLQfuADH4jrr78+7S87fPjwtHfsEUcckbYlSI4lPvrRj8aiRYtaasw08fSs+irbAwZ1iaKCHc7gAQAAAIDW3B7hpz/9aRx77LFx0kknxahRo2Lt2rUbvf7mm2+mPWqTnrRJgPuzn/2sucfLJp7e0M92/BCtEQAAAACgLdih0szbb789zjjjjHT7K1/5SpSWlsb3v//9tM/ssGHD4hvf+Eb06NEj7VF78803t9SYaeLpWcvS9fihJiEDAAAAgHZXaTtr1qy0JUJD1W3Sy/boo49O94866qi0z+13vvOd2GuvvWLlypWxYMGCdGIwWsbiVZUxa+nayMuLGDtEaAsAAAAA7a7SNulfm/S1TSQ9a/Pz33p7Xl5e2i5hzZo1UVtbG3V1dVFYuEOZMDto8oYq2336dIrOpUXuHwAAAAC0t9D2gAMOiMmTJ6fbp556anz6059O2yD861//ig9+8INx2GGHpe0RnnnmmejZs2e6sBv62WqNAAAAAADtM7Q988wz08nFkkra//mf/0n721511VVxySWXxMiRI+Of//xnY+uEj370oy01ZjaYNMskZAAAAADQ1uxQ/4KPfOQjaR/bz372s/GrX/0q7V+bLE395je/ifvvvz+ef/755h4rTayrqo2X565Mt8fpZwsAAAAA7bPSNulb+7e//S1efvnldOKxu+66K1asWBGVlZXx9NNPxznnnBOXX3553HHHHVojtLDn31wRNXWZ6NO5JAZ2K2vpHwcAAAAA7CY7PFNY0rN24sSJ8etf/zp+8IMfxIsvvpi2S9hzzz3j/e9/f7zwwgvRtWvXlhktjSY3tEYY2j0N0wEAAACAdhraJgoKCuL8889PF7Jj0sxl6Xq81ggAAAAA0H7bI5Ab6uoy8YxJyAAAAACgTdqpStsDDzxwi7+SnxwrLS1NWyUk/W2PPfbY5hgjm3h90eqoWF8THYoLYkS/Tu4PAAAAALT3StsTTzwxpk+fHuXl5WkwmywdO3aMN954IyZMmBDz58+Pd73rXXHrrbc2/4hpbI1w4OCuUVigWBoAAAAAor1X2i5ZsiS+8pWvxHe+852Njn//+9+PWbNmxb///e+49NJL43vf+16ccsopzTVWNpmEbNyQ7u4JAAAAALQxO1Wm+Ze//CVOP/30zY5/9KMfTV9LJK9PnTp110fIZp6eZRIyAAAAAGirdiq0TfrWPvbYY5sdT44lryXq6uoat2k+CyvWx5xl6yI/r749AgAAAADQtuxUe4TPf/7z8ZnPfCYmT56c9rBNTJo0KX7961/Ht771rXT/nnvuiTFjxjTvaImnZ9a3Rti3b+foVFrkjgAAAABAG7NToe1//dd/xbBhw+JnP/tZ/P73v0+P7bPPPnHdddfFGWecke4noe5nP/vZ5h0tb7VGGNrN3QAAAACANminQtvEmWeemS5bU1ZWtrOXZrsmIRPaAgAAAEBbtFM9bZNWCE8++eRmx5NjTz/9dHOMiy1YW1UTL8+rSLcnDO3uHgEAAABAG7RToe0FF1wQc+bM2ez43Llz09doGc/NXhG1dZno36U0+ndVyQwAAAAAbdFOhbavvPJKjB07drPjBx54YPoaLePphtYIqmwBAAAAoM3aqdC2pKQkFi5cuNnx+fPnR2HhTrfJZTtD2wkmIQMAAACANmunQtvjjz8+vvnNb8bKlSsbj61YsSK+9a1vxXHHHdec42ODpC3CMyYhAwAAAIA2b6fKYn/84x/HUUcdFUOGDElbIiSee+656NOnT/z+979v7jESEVMXrIrVlTXRsaQw9u3b2T0BAAAAgDZqp0LbAQMGxAsvvBB//OMf4/nnn4+ysrI499xz4/TTT4+ioqLmHyUxeday9C4cOLhrFOTnuSMAAAAA0EbtdAPa8vLy+PSnP928o+Ft+9mOH9LdXQIAAACANmy7Q9vbbrttuy/6vve9b2fHw1Y8PXNDaGsSMgAAAABo07Y7tH3/+9+/0X5eXl5kMpmN9hvU1tY21/iIiPkr18XcFevStghjBnV1TwAAAACgDcvf3hPr6uoal3//+98xZsyYuOuuu2LFihXpcuedd8bYsWPj7rvvbtkRt+Mq2xH9OkV5yU53tAAAAAAAWoGdSgC/9KUvxTXXXBNHHHFE47ETTjghOnTokPa5nTJlSnOOsd17emb9JGT62QIAAABA27fdlbZNvfHGG9G16+a/pt+lS5eYOXNmc4yLLU1Cpp8tAAAAALR5OxXaTpgwIS666KJYuHBh47Fk+2tf+1ocdNBBzTm+dm91ZU1MmV+R3geVtgAAAADQ9u1UaHv99dfH/PnzY/DgwbHnnnumy6BBg2Lu3Lnx61//uvlH2Y49N3tF1GUiBnYri75dSrM9HAAAAAAgF3vaJiHtCy+8EPfdd19j/9oRI0bEu971rsjLy2vuMbZrkxr72XbL9lAAAAAAgFwNbRMPPPBAPPjgg7Fo0aKoq6uL5557Lv70pz81VuLSPCZv6Gc7bmh3txQAAAAA2oGdCm0vv/zy+O53vxvjx4+Pfv36qa5tITW1dfHs7A2TkKm0BQAAAIB2YadC22uuuSZuuOGG+PjHP978I6LRqwtWxZqq2uhUWhh79+nkzgAAAABAO7BTE5FVVVXFYYcd1vyjYSNPb+hnO3ZwtyjI1ysYAAAAANqDnQptP/WpT8VNN93U/KNhI09v6GerNQIAAAAAtB871R5h/fr1ce2118Z9990Xo0ePjqKioo1ev+qqq5prfO1WJpOJp2c2TELWLdvDAQAAAAByObR94YUXYsyYMen2Sy+9tNFreXl+jb85zF2xLhZUrI/C/LwYM6hrs1wTAAAAAGijoe2DDz7Y/CNhI5M3tEbYr3/n6FC8U18TAAAAANBeetrS8hpbIwzp7nYDAAAAQDsitM3xScgm6GcLAAAAAO2K0DYHVayvjlcXVKTbJiEDAAAAgPZFaJuDnp29IjKZiMHdO0TvTqXZHg4AAAAAsBsJbXPQ5JnL0vX4Id2yPRQAAAAAYDcT2uagSRsmIRs/1CRkAAAAANDeCG1zTHVtXTw3Z0W6Pd4kZAAAAADQ7ghtc8yU+RWxrro2OpcWxp69OmZ7OAAAAADAbia0zTFPb2iNMG5It8jPz8v2cAAAAACA3Uxom2OenrVhEjL9bAEAAACgXRLa5pBMJtNYaTt+SLdsDwcAAAAAyAKhbQ55c/m6WLSqMooK8uKAQV2zPRwAAAAAIAuEtjnYGmHUgC5RWlSQ7eEAAAAAAFkgtM0hk7RGAAAAAIB2T2ibQyZvCG3HDeme7aEAAAAAAFkitM0RK9dWx2uLVqXb40xCBgAAAADtltA2Rzwze3lkMhHDepZHr04l2R4OAAAAAJAlQtscm4RMlS0AAAAAtG9C2xzxtEnIAAAAAAChbW6oqqmL599ckW6PH2oSMgAAAABoz1Ta5oCX562M9dV10a1DUezRqzzbwwEAAAAAskhomwMmz1re2M82Ly8v28MBAAAAALJIaJtD/WzHDdEaAQAAAADaO6FtlmUymXh6Q6XthKHdsj0cAAAAACDLhLZZNmvp2liyujKKC/Jj1IAu2R4OAAAAAJBlQtssa6iy3X9glygtKsj2cAAAAACALBPaZtnkWcvS9fghWiMAAAAAAELbrJu0YRKy8UNNQgYAAAAACG2zasXaqpi2aHW6PU6lLQAAAAAgtM2uyRv62Q7vVR7dy4v9gQQAAAAA9LTNhUnIJgzRGgEAAAAAqGcisix6emb9JGTjhpqEDAAAAACoJ7TNksqa2nj+zZXp9nj9bAEAAACADYS2WfLS3IqoqqmLHuXFMaxnebaGAQAAAADkGKFttlsjDOkWeXl52RoGAAAAAJBjhLZZnoRsvH62AAAAAEATQtssyGQy8cyG0HbckO7ZGAIAAAAAkKOEtlkwY8maWLqmKooL82PUgM7ZGAIAAAAAkKOEtlnw9Mz6KtsxA7tGSWFBNoYAAAAAAOQooW0WPD1rwyRk+tkCAAAAAJsQ2mZzErIh3bLx4wEAAACAHFaY7QG0VWsqa2L+ynUxd8X6mLdi3Yalfnv64jXpOeOEtgAAAADAJoS2O6Gmti4WrqrcLIxNl5X12yvXVW/zGhOGdouuHYp35scDAAAAAG2Y0HYbHpq6OFbHqo3C2GRZWLE+6jJvf3M7lRbGgK5l0b9rWfTrUpquB2zYPmBQ12b8GgEAAACAtkJouw1f/uuLkV/SYYuvFRXkRb8u9QFsQzCbhrNd6/eT451Ki1rqewMAAAAA2iih7TaM7Ncphvbr1RjENgSz/buURs+OJZGfn7f7vikAAAAAoF0Q2m7DHz85IXr06LH7vg0AAAAAoN3Lb/d3AAAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAghwhtAQAAAAByiNAWAAAAACCHCG0BAAAAAHKI0BYAAAAAIIcIbQEAAAAAcojQFgAAAAAgh2Q9tP35z38eQ4cOjdLS0jj44IPjqaee2uq5N9xwQ+Tl5W20JO/b1JQpU+J973tfdOnSJcrLy2PChAkxe/bsFv4kAAAAAACtPLS9+eab46KLLopLL700nnnmmTjggAPihBNOiEWLFm31PZ07d4758+c3LrNmzdro9TfeeCOOOOKI2HfffeOhhx6KF154Ib7zne9sMdwFAAAAAMg1hdn84VdddVWcd955ce6556b711xzTdxxxx1x/fXXxze+8Y0tviepru3bt+9Wr/ntb387TjrppPjv//7vxmN77LHHNsdRWVmZLg0qKirSdXV1dboAua/hWfXMQuvhuYXWybMLrY/nFlofz23btb25RV4mk8lEFlRVVUWHDh3illtuife///2Nx88+++xYsWJF3HrrrVtsj/CpT30qBgwYEHV1dTF27Ni44oorYr/99ktfT44lLRG+/vWvxyOPPBLPPvtsDBs2LL75zW9u9DM2ddlll8Xll1++2fGbbropHSMAAAAAwK5au3ZtnHHGGbFy5cq0o0DOhbbz5s1Lw9fHHnssDj300MbjSeD68MMPx5NPPrnZex5//PF4/fXXY/To0ekH+/GPfxwTJ06Ml19+OQYOHBgLFiyIfv36pUHr97///Tj22GPj7rvvjm9961vx4IMPxtFHH73dlbaDBg1K2y/06NGjhe4A0Nz/peree++N4447LoqKitxcaAU8t9A6eXah9fHcQuvjuW27ktyxZ8+ebxvaZrU9wo5Kwt2mAe9hhx0WI0aMiF/96lfxve99L620TZxyyinx5S9/Od0eM2ZMGgwnrRe2FtqWlJSky6aS4Ef4A62L5xZaH88ttE6eXWh9PLfQ+nhu257tzRqzNhFZkigXFBTEwoULNzqe7G+rZ+2mH/LAAw+MadOmNV6zsLAwRo4cudF5SbA7e/bsZhw9AAAAAEDLyFpoW1xcHOPGjYv777+/8VhSKZvsN62m3Zba2tp48cUX05YIDdecMGFCTJ06daPzXnvttRgyZEgzfwIAAAAAgOaX1fYIF110UTrx2Pjx4+Oggw6Kq6++OtasWRPnnntu+vpZZ52V9r394Q9/mO5/97vfjUMOOST23HPPdLKyK6+8MmbNmpVOTtbga1/7Wpx22mlx1FFHNfa0/de//hUPPfRQ1j4nAAAAAECrCG2TcHXx4sVxySWXpJOIJf1nk5C1T58+6etJS4P8/LeKgZcvXx7nnXdeem63bt3SSt2kX23Tdginnnpq2r82CXq/8IUvxD777BN/+9vf4ogjjsjKZwQAAAAA2BFZn4jswgsvTJct2bQ69ic/+Um6vJ1PfOIT6QIAAAAA0NpkractAAAAAACbE9oCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLQAAAABADhHaAgAAAADkEKEtAAAAAEAOEdoCAAAAAOQQoS0AAAAAQA4R2gIAAAAA5BChLbknk8n2CAAAAAAga4S25I45kyJ++56IHw2NmPlotkcDAAAAAFkhtCX7lkyLuPnjEb95V8SsRyLWr4j46zkRFfOzPTIAAAAA2O2EtmTP6kURt18U8fODIqbcFpGXHzHmYxG994tYsyjilnMjaqt9QwAAAAC0K4XZHgDtUOXqiMd+Wr9Ur6k/ttcJEe+6LKLPyIilb0Rce0zE7Mcj7r004sQrsj1iAAAAANhthLbsPknV7DO/i3joR/WVtIn+YyOO/17E0CPeOq/HHhHv/2XEzWdGPPHziEETIvY71TcFAAAAQLsgtKXlZTL17Q/u/27E0mn1x7oPj3jnJREj3x+Rl7f5e0a8N+LwL0Y8+r8Rt15Y3zKh196+LQAAAADaPKEtLWvWYxH3XhLx5qT6/Q49I475RsTYsyMKi7f93ndcEjH3mYiZ/4n4y8cjPnV/RElH3xgAAAAAbZqJyGgZi16NuOmjEb99d31gW9Qh4uiLI774XMRB5719YJsoKIz40PURHftGLH414l9fqK/aBQAAAIA2TKUtzatiXsSDV0Q898eITF1EXkHE2LPqq2s79d3x63XsHfGR30Xc8J6Il/4WMejgiIPP960BAAAA0GYJbWke61dGPHJ1xBO/jKhZV39s3/dGvOuyiJ577dq1Bx8Scdz3Iu75ZsQ934rof2DEoIOaZdgAAAAAkGuEtuyamsqIp6+PePi/I9Ytqz82KAlZvxsx+ODmu7uHfDbizaciXv5HxF/Ojjh/YkTHXs13/f/f3n2AR1WmbRy/JyG0QOiEHkA6SJcuWChrZ21YQeyuuuu6ruXbtbviqotYUNldFbtY0VVBEQELTUAUUZAO0nuHQJLves6bSSOBBCYzZyb/33WdayYzyczJJO+U+zzv8wIAAAAAAAA+QWiLo5OeLs1/X5r4gLRthbusejNXWdv8dCkQCO0ja7d39tPS+vnSpl+ld4dKl491fW8BAAAAAACAGMJCZCi6pZOl/5wsvXeVC2wrJEtnjpBumCa1OCP0gW1QmYrSoNekhERp+dfSpH8Uz/0AAAAAAAAAEURoi6KxnrWvnCOtnSuVriid/Hfpj99LnYeGp+q1RnPpnKfd+W+GSws+Kf77BAAAAAAAAMKI0BaFt321a4dgOl0h/Wmu1OevUunE8D6Kbc6Tut7gzn9wg7R5SXjvHwAAAAAAAChGhLYovAl3Swf2uIXGrB1CYvXIPXq20Fn9rtL+7dLbg6XUPZHbFwAAAAAAACCECG1ROMu/kX56z1YEk05/tPj61hZWqdLSBaOlxBrS+p+kT/4iZWREdp8AAAAAAACAECC0xZGlHZQ+vd2dt961tdv541FLqiOd/6IUiJN+eEOaPTrSewQAAAAAAAAcM0JbHNmsF6UN86WylaVT7vbXI9aot3TqPe78uNul1XMivUcAAAAAAADAMSG0xeHt3ixNesidP+XvUvmq/nvEet4itThTSkuV3h4i7dkS6T0Cii51t7R3K48cAAAAAABQKR4DHNaXD0j7tkvJx0udr/Tng2X9dQc+K/37Z2nLUum9q6VL35Hi4iO9Z8CRWS/mGaOkCfdIafulclWkqsdJVRtL1TJPg5sfD5oAAAAAAICQI7RFwdZ8L81+2Z23xcf8HIKWrSRd+Kr0377SkonSV49JJ90Z6b0CDm/XBmnsH6TFE7Ivs2rb1bPclpe1KDkkzD0uO9CN9AKBAAAAAAAgJAhtkb/09MzFxzKk4y+QUnr4/5Gq1UY6a4T0wXXS5Eekup2lpn0jvVdA/n79XPrwD9LujVKpslL/h6R2F0tbl0tblriqcds2Z57uXCPt2yatmeO2/A5c5A1ygwFv+WoEugAAAAAARBFCW+TvxzHSbzOlhESp3wPR8yi1u0haNcMtnvb+1dK1U6QqKZHeKyDbgX2uFcLMUe7rmq2l81+QarbMPvhgW16pe6StyzKD3Byhrm07Vrs2JlYdb1teZZJcgNvyLOmEq6VylfmLAAAAAADgY4S2ONS+HS5UMr1vk5LqRNej9LtHpDVzXTXi24OlKz+TEspGeq+io7fqhl+kGs393Qojmq3/WXrvKmnDz+7rrjdIfe8r3P9n6fJScmu35XVgr6vQzRXm2vll0vbfpP07pLVz3fbtky647fYHqUKN0P+OAAAAAADgmBHa4lBfPSrt3uCmWHe/MfoeoVJlpAtfkUb1diHV+Duks56M9F753/TnpM/ukpqdJg16TYrn6SGkgfjM/0if/90tNpZYQxr4nNS0X2huP6Gcq9QNVuvmrey1QNd65E59Rtr4i/TNcGn6s1LHIVKPm6XK9UOzHwAAAAAAICTiQnMziBkbf3XhXbBi1QLQaGQh1Hn/lRSQZo+Wvn890nvkb7s3uz7A5tdx0v/+5ILGaHJwn8qmbpHv7NoovTFIGvdXF9g26SfdMDV0ge2RWBVvzRZSh8vc/V70hlS3k/d4eS0anmovjb1R2rQoPPsDAAAAwN/r22xbKS2eKM0YJX32N2nq0+7rneui73MiEMUopUM2e/K1qtT0g1Kz30nN+kf3o9PkVOnk/5Mm/UP65Fapdlup1vGR3iv/Vlfv3y4l1ZN2rpXmviYlVpf63a+osGuDSr18lgZsXKD03e9JXa93/8ORrhZe9IU09gZXuR5fRur/oNTl2sgtChYXJ7U4Q2p+urRsivT1v6RlX7m/99zXpVbnSCfaWGkXmf0DAAAAEL62iJsXSZsWZ54ukjbb+SXSwb0F/1y5qq5lmzfTr1X2+TIV+csBIUZoi2wLPpGWfCnFl5YGPBwbj8yJt0mrZkqLJ0hjLpeuncwiTHnZi/J3VpUs6ZxnXA/Uj26Svh3hpvH3uEm+ZpWsL5+twMYF3pdxy7+WbKtUXzrhKqnDYCmxWnj3yVoSTLzftSAwNVq6xcby60cbCRYaNz7Jbau+c+0SFn4q/TzWbVYNfOJfpJTukd5TAAAAAEcr7aC0bYULY71QNkdIu2t9wT8XlyBVbSRVa+oW9raFj219Dls3Y+8W93nLtpwqN3CLLCe3yg5zqzWR4hP4+wFHidAW2QsZWT9TYz0uqx0XG4+MVRae+2/p332krctc1eOg193lcCxctOpqC+qOO9ldtnuju/zzv7mK23YX+betwyvneH1aMyrU0re1hqp7zT2K//5Vafsq6Yv7pEnDpOMvkLpcI9VpX/z7tGGBW2xs/U/ua6us7feA6zvrR/VPkC5+U1o/X/rmCemn99xBDtsa9HDhrVWtR6o6GAAAoDCCU7Z5z4KSyD4XWRCbFc5mntoCxekHCv65xJpS9aYuXPVOm7rTyin5z1q03GDjQrewsn1+8E5/lnatcy0VbLN2e0FWEFa9WWaI2yo71E2qy1gFCoHQFs63T7kn2Ip1pF63xtajUr6qW5jshQGumtCqCnvfFum98oeVM6SfP5QCcS5YDOr1Z2n3Jmn6SOnDG6Xy1cLXg7Ww9myRXjlb2jBfqlBLBy8bq80zflX6yacr3tpiWPhoPVvX/pA5/f81qX5XF6K2PFsqVTr0HxRmveB6Plm/2PLVpYHPSs0GKCrYkXDrA22P3bdPSnPfkFZOlV6fKtVq68LblmdJcfGR3lMAAFCSHNzvtcJy2/rMLc95a0Vlp6bFmVKHS6WGvSnUQGxX0K74VvrlIzdj1lrcFaRUWRfK5gxmvfNNpLKVina/VohihTB5i2Hss1kwwLXPZ97pL1LqTlfMYtu8HN9fplJ2Ra61VqhUT6pYW0qq4z5HUWQFeAht4cJaCzKN9dwsUyH2HpU6HaTTH5P+90fpywelxV+4cNqCyJJ6NN5Cxs//7s63v9S9aAbZY9L/IWnPJunHMdLbg6XBH7mqTD8Ftvbib0eHh/xPqtzIVlHLXnzL3qy3v0T67Ttp5r+l+WOlVTPcViFZ6jRU6jxUqljr2PfHAu6PbnYHBcxxp0oDn5MqJivqVG0snfWk1OcOadpIadaL0rofpXeGuDd4Fui3vZBpTgAAxJqDqa7XvfWytOq4uFLu1Ntynk9wU6eD573Nvj+h8LPZ0tPc+6ecAWwweM0byu7bXrTfY97bbrO1GtpfLLW7OHZmEaJkC47RXz50Qe2ezbmvt/95C2KD1bLBkNYuL+4Q1AqlGvZyW87Pm5Y15K3KtYpgW09l5TS35WXPPRVqSUm1s4Nc+8xmBWbeZZlfhyu3sIDcnof2bZP2bpX2bst93nr52sxEe7xLaraAYkNoCxfcWWVgSi+pzXmx+4h0HOz6tVqvVntxeOMCNz3DQqjWv4/8olXhZkdlf5spJZSXTv7bodfbC/s5I92bAQu57fG68jOpRnNFlL04vjpQWjfP9dy94mOpRjPpQD7TfuxFs34Xt1kIPftlF0La9J0pj0hfP+4W3+pynfueo3mRtT7QH1zvPlTYB5a+97uF0KL96LC9ORrwD1dhO+N5t9kbrA//IE0eJvX4o9Txcv+2fQAAAIWfVm3vj777z+F7XBZGID6foDdHyGvvtSystcKAjPTC3679rB2or2Bbcp7THOftd/nhTemnd6Udv0lfPea2Bt3dwfxWA6WyScf2OwLhXitj6SQ3O9IKRHIexLAFwWyRYfs8k9JDKp3or7+NjXfrh2tb89Nyh8+bfnUhrm0bf3U9c61a2A7YWOs+G7+2HU6ZpMxQNzPczQp4g+dru+cNey5KT3dBsYWs9nnSC13zhK/By7Kut+/f6qqFC6NKIzfL0raUnlKpMsf2+KFg+3dKP7wlrZkrNejm/r+srWMMCmRkBJv/RM7IkSP12GOPad26dWrXrp2efvppdenSJd/vHT16tIYOHZrrsjJlymjfvn35fv/111+vUaNG6YknntAtt9xSqP3ZsWOHKlWqpE2bNqlatTAvYBRuSye7nqA2Pf66r6VabRTzdqx10/5nvSSl7nKXWc+enn90FaclIYSyF8pnu7oeR1ZRaVPiC5K621voS6tnud5DV33upq9Egr2IWmC75ns3bcYCW5tOY+9nDhzQp59+qtNPP10JCQmH/90X/E+a8W9p1fTsy60FQNfr3IGLwvwP2FS9iQ9I055xX9do4doL1DpeMfvCaB/opj7jKmGMhebd/uAWfCvq1CqgKOMWgK8wdmOE9eG3RVNtVpUVcBirbrOAJS1VSjuQuWWeTw+eP5h5mmqldEd///b5w97PHS6EDZ6WrVy0g+sWdC38xLV7sgPswYC4VDmp1dkuwC1h7RMYt1EkdY9bX+Lnj6Rfx2d/ZjUWQrY8MzOo7RV7hUf2/GIHjyzA3bFG2rlO2rnGfYbPOl1X+CDVnmdKV5T27zi25ytjt1OuilSukntO8s5Xlratcq0qvOfETAmJbr2Ypv3dZgHyUWDc5vO6ZYuoW2Cb83/A/s62HouNDTuQYYvi+Vwwd9y+fbuSkgo+mBjxET5mzBjdeuutev7559W1a1eNGDFCAwYM0MKFC1WzZs18f8Z+Ibs+KFDAC/gHH3yg6dOnq06dOsW2/1HN3nyNu8OdP+HqkhHYGnvCtKpLqyC0AT/9Obei5id/kSY/InW7Qep8lXsCjlUWvllgay/6VjF5OHbE9tJ3pBd/J21aKL36e1dxa1NgwsmOKr92bmZgW00a8lFWYFsk1svWglnbrN+ttU6Y965rAWD9ez+/21VlWxBZ0JO9HQ1+70pX7Wvs/8X+p0qXV8yyaT89/+Sqkq0/sPW9telOtmDdNyPcQm92PdUrAAD4l9XrLJkoTXvWnQbVbi91v9FVohal77+1OQgGuDnDXKuUyxv8Wuhr32/VUBbG2vu54uqVb62ygu/3LPixYNoCXKvus/O2VarvFtulfULJHAcH9mROec/cjP1PWIVmJMJ8K5D49TNXUWuzHG3/gqwdgB1ssHU5rKowlteYsBC6Ul23HenxyhXkrs0R9GYGu7ZlpLkK2yCbZeoFrpmh65HOB8NZK1A5XEBu+2MFcfY3XDTBzexc8LHbTO12UtPMKtw6HUvUAaNjlnbAtQOx7Gb519mX2wJ3FohbyxD7LL/iG7eNv9M93tbf3DbLDKK4bUXEK20tqD3hhBP0zDOuWi09PV3169fXzTffrDvvvDPfSlurmN22bdthb3f16tXebX/22Wc644wzvJ+h0jYPe7P22V3uDdPNs92TUUk9kvn9a9LUp6XtK7OnWnS+0lURRmNf0iNVqz7VQdq7RTrzCfd7Foa1lnihv5u6UrezC03DNQVn3w4X2Fp/WpsGZPedp6L1mI5CWo/cOa9I372Q/T9gR+uan+7CyEZ93BO9PV3Ofkka/3+u35vti7WQaHG6SuSLpy329vVwF+YHK9bPe8E/vY/he1QPANEpaseuzR7yPsSnS1WPK1kfmm3FdwsqrVhh44LMCwOuIsnCWmsfEMUfagvF3setni3NfV2a917uICfYPsFaptmB6hgUteO2wNB1b+7QNWvbVsDleTY7iFBQKw4Lb63i3N7b5jpt6ApXQjVWbOr9wvEuqLWK8LT92ddZ8YhV07Y8R6rbqWQ9X4WK1zt7o/ssaaGrhbDhaFlgrRgsRFz0uauUXj0nd5WvzTCwsLFZf+m4Uw47YzGmxm1R7VwvzR7tPn8HF9rL+ox+rdSod/ZY3LrCBbsWklsrzJztd2zNFgtvbVFtyzF8MpaiotI2NTVVs2fP1l133ZV1WVxcnPr27atp0/JpSJ1p165dSklJ8QLejh076uGHH1br1q2zrrfLL7/8cv31r3/NdXlB9u/f7205H7zgALEtJu3aoFKTH7a3ajp40t+UUapC/j1BS4JAgtRxqNTuMgV+/kDx055SwN7MfjtCGdOfU3rbi5Te/SbXoyYGxH31L8Xv3aKM6s108PiLC/93L58sXfS2Sr16pgKrZyl9zGClXfBq8S9ItX+n4t8apLjfvlNGuSo6eMl7UrUWh+x3cKwe1ZhNqCh1vVE64XoFFn2muFn/Vdzyr7KOjtpjld7xSgWWT1Hcr+O8H0lvdJLSznrGNcEvqWOn1XlSy98rsPBTxX9xjwLbVijjxQFK732H0nv8KbarABASxzRuAUSM78auVXHu2qCA1w9xnQJehdVaBazSaee6zNO1ClglVKaMspWUUa+LMup1VUb9rsqwRWtthfVYs3Od4ma/pLjvRyuQuWhRRukKSm9/qdI7XyNVaei+7+BBlQjJ7aQB7aRTH1Dg13GK++EtBZZNUiBzQaSMcXcoo8WZ3vv/DJt6bgFBjPDduM2PjVEbs1Y9aWM2s2rSje31CljQbgHcvu0KFBS6FkGG9WC2wMw2C/h2rFbAnk+2LHFbfj9TuoIXqGZUaqAMC3PtfOXs87LrD2fPZu+9c9yCj73PFgGrSg/edtXGSm9xttJbnOVatwUDqTSraE875t+3RCpbzW3KzE3D9f9fo7XbevzZC44DSyYqbvEEBZZ+qYD19P7hDW/LiCvlXoOa9FN6k/5uEbkcBwWKZdxmVZpnHuCwher8MlsyI0OBVdMVN/sFBWyMpLvxkZFYQ+ntL1d6xyGuZWPe160KdSR7TbNt9yYFFo1X3MJPFFg2RQGbYTz1KW/LqJCs9GanKaP5GcqwvsN2kCZCCvs3jWil7Zo1a1S3bl1NnTpV3bt3z7r89ttv15QpUzRjxoxDfsbC3EWLFqlt27ZeIv3444/rq6++0vz581WvnuuzOWzYME2aNMmrsrXWCQ0bNjxspe19992n+++//5DL33jjDZUvH5vTnduv+K9StnylbeUaakrz+2LqDckxy0hX8o4f1HT9x6q2e5G7SAGtrtxVi5LP0I7yKYpW5VI36dSf71B8xgFNb/xnra/Uoci3UWX3IvVY9E+VykjVqio9NCfl2mL7/4lP26fuSx5Xtd2/KjW+vKY2uVPby2d+uChmFfatVuONX6j+lm9UKj37oE56IF4/17lQS2oMYNzkUCptj9qtHK1621yf4E0Vmmt2yvXaVzrG+4IDAIpPRrrKHNypsge2Zm7bDjlf7sBW73sK62BcGe9Dob2Pycle37eVb6TNiU21JbGZtiQ2VWqCTz7EHoWkPSt03Mbxqrd1uuJserCtN1a6upbW6K+V1XrrYHxsfsY5GmVTt6je1qlqsPlrVdy/NjtbS6imVdV6aWXVXtpTJsZm3oVbRrpKH9ylcge2ZI1be9y90wOZp6lblJC+r2g3q4AOxJfXgfjEzNPyOlAqx/l8t+zr0+z5IEdAFshI8/YjMXWjytu23043eecT929U2YOHn+1r9peqqD2la2h36RraU6aGd962xP3rVWfbd6q2a4HilF0FuKNsXa2pfIK37SxbL/Yr3ku4QMZBVdv1q5c3JG+fm+s5x+wuXVPrK7XTuqR22lyhhdLjDhMq2mtZ+l6VPrhbCWm7cp2WTtuthIO73GnWdbu9cWin8Rk5DhYooB1l62lLBXv9c6+Be0pXD+v/on3ut+fhRhsnqtK+VVmXb05sqmXVT9Xayico3Ra0LKJSaXtVc8ePqr1ttpJ3zM31HGPPAeuS2mtt5U7aULGt0uLDu3Dcnj17dMkllxyx0jbqQtv80umWLVvq4osv1oMPPuhV7lo7hDlz5mT1sj1SaJtfpa21aFi7dm1MLkQWWD1HpUb3984fHDJOGfWYylzgY7VymuKmPqm4JV9kXZbe+FSl9/ijMqzRdZS9qMZ/eIPifnpH6Sk9lXbp2KPe/8DiLxT/zmXeka+0Ltcrve+DoX8sUncrfsxFirOqhzJJOnjp+67f2mGeCyZMmKB+/fqFdurIvh2Km/eW4uaMluJK6+BZT7kj38j/yOi8txX/2e0KpO5WRtnKSjvjCWVYtQAQznELwL9j16YsWqumPZsUsIUtrQJp96bMU/vazlvV7Dp3mqMK7bA3ax/mKtZShvWjrGCntbJP7fIKtpp4LTf1Pe2AAuvnKfDbTAVWzXBbcJHNnLdZrYlXiZtuVVD1u0pVGvv7vV96mgKLP1fczOcVZ4viBC+236Hr9cpodpoUF/ElTfz9PmbNHAV+eENxP3+ggLdwkZNev5vS216sDOspGqXtE4rlNdeqUm08W+Xr3s35VMna167XaGErYzPs8a1Yx43lirWVYf1cvTGc7LXzywhWxlo7O6tqDeeYtJYM23/zZpcFbG2H7e7Uvra1HgJWuVgIGcnHK72lVdSe6SorUXJtXeYqcG1b8a2r9M6UkVBeaSknasX2DKXUqKg4r9J8mwLWVsP+1/Zu8w40HC3vdbN0Yr7/tzbe3GyUzBkptv5RcVSlbl7kZoP8+GbWbJiMUuWU0eY8pXW6KrSLfB/cr8CKbxRY+Inifh2f63U/o1RZZTQ6SelWgWu9h8Owfo/ljtWrV/d3aGvtEayS9d1339XAgQOzLh8yZIjXs/bDDz8s1O1ccMEFKlWqlN58801vITNb2MzaLASlpaV5X1sQu3z58kL3lti0aVPshbbWX+W/p0pr5rim+79/PtJ7FB1swSlbeMl6eAb7o1jY3evPkvcGOAoqldfMlf7dx52/drJk0wCPxQ9jpA+udef73ucei1D2GX7jQtdo3N6QDR7rejkdRonu9+M3m5dI713tnmeMTWP53bDw9UBG1GDcAjEydq3PeWbY6vUP3LXRndrXec/btNBCBrFOQKpQ0y0O5G0WwtZ2C8tmfV3Hrc1wtO/H7OPQ1mXSyhmuF96qGTn6vip3H0JbBMjburuDuEVZtKu47N/l+rRav1r7PYyFs7aomK3PUO/w76FQQDhn/RFt8TLrNRrsR2krwtviZbbmwdEsiOvH19zgVGkLgiyA9U7z2byQKPh15vnUXUc3lpPquM07XzdzPNtltaM2FPfY42IBrvXXzHtq/VSth7QF/1Vjo+0eiuG5fNmUzMXMPs/u43okpcq518CsLXPxtCNt9tnMDnrYQVJ73Vs1U1o53S3Wnfcgi7UPss/jdgDT27ocfbBpi1Zar9/v/uMWb8vZe/aEq11/8eJebyk9za2X88v/XEvErTlyQmubktLD9cC1MVvJzeiPVE9bXyxE1qVLFz399NNZ/WgbNGigm266Kd+FyPKyQNb61tqLz/Dhw7V582avQjanAQMGeD1uhw4dqubNm5fs0HbOq9JHN0mlK7rFx2Jtka3itmWZW7DMFi4LNoqv0ULqeYt0/PnF39/1aNkwf/ksF4Ief6F03n9Cc7tTn5E+/5s7bwtydbgsNIHtm4PcKpD2f2qBbb3OR/wxwh+fOZgqTX5Y+maE+6BjVQTnv+BW8gQyMW4Bn7MPVtYP1irmbCFS73SN0rf/ps0rF6p62TQFLJC18KaorFIusaYLcRKru/OJNaQKNdz5YDBr5w+3YndxsUVK7UOsfYC1zQ5E5qiAyv4Q21lq0NWFuHZA3z4sh8u2VdLMUdLsV7IX1bKVzjtd4RZpOdLq6yic7avdIm4WjG9enH15wxNdwGAf6v36GSD4OWDdj0r76UOt/mWG6lWr4Cr2cgayef+3iySQuchTldwHVIJhrJ0GD7D4+XEC/DhuF36uJb/8oOPadFZ8heqHBq/2nJ9QNvQHrdZ87177vDB3Rv6v89Wbu/DWDmTW7yZVO+7wle924HbOy9Ksl6Qdv2VeGJCa/U7qcrXU+JTIFMNlZEjr57vw9pePpfXzcl9/wctS6+wi0xIX2o4ZM8arrB01apQX3lql7Ntvv60FCxYoOTlZgwcP9looWJ9a88ADD6hbt25q0qSJV4372GOPaezYsV5bhFatWuV7H0dqj1BiQls78vd0J1fh0P8hqcfNkd6j6LVrg6tm+O6/UnDqlK0yaguWdbzcfxWFdrTOKletT8vNs1yT/FCZcI+rQrYjUhe9LjU/7dheIN68yB1xs+lOl73vPggV5keptPWnpVOkD65zR4ptCo5VZVvVTzRUp6PYRfW4tQMTUx5xFYQ24+K4k6WEcpHeK6DwDu53z82ZQWx2KJsdztrCP7lWYD4c629vlaheCFsjM4DNez5HOOuHCtWiOLBPWjs3O8RdNT2fD7EBqWYrqUYzV/mUkLlZuGsfqr3L7Hz5zMvK5Tktn/v77DS/wHrVd9L0kdLPH0nBqbFVj5O63eAqlPz2PjRW2MdmK4CY+W9pwafZj71ViHa+Uuo0xP2f+4VVjs17R/rxHWnTwiN/v1Vnl6taiCq9ylLZHNV8Ftiy+CwQu++Vbba2HbCy172VmSHuZrf2Ty7lq2VX4lqQa60NS5VxFa0z/yP9PDb7AJE913Qc7J47q6T4r1BvwScuxLV9v/WXYnluL2xoG/GmRoMGDdLGjRt1zz33aN26dWrfvr3Gjx/vBbZm5cqVuVodbN26Vddcc433vVWqVFGnTp28nrgFBbbIYfIjLrCt3kzqch0PzbGwQdv3XqnXLdKsF6Vpz0rbV0nj73Bv5IZ+6o4m+6VKxoJV0/W60Aa2pu/90u7N0tzXpHeukC4fK6Vk96gu0oehty5xga1NPbv03UIHtvCxxn2kG6ZKH93sXvisMnvJRGng81T6I3rtXC+9fbl702rmvOLClSanuoorqxgIQy8s4PB9F1fnH8QGz9t7wsKwIMebtpy9pSUm6/tFa9S+Vz+Vsko6e19kH8Bi+YCchajB1ghZH2IX5Q5xbYXqDfPdFir2+GcFwFZNFXDTrYMa9Za63Sg17R/bj78fWAWZPd62bf/NVYvNHi1ZD9dJD0lT/umqsU64xlWfRaL/sVWIz3/fBbX2PxkUX0bpTQdowfYyata+m0rlV7EXnCoNADnZa4sdjLTNglZjn/+DVbi2rZ4j7dksLfzUbd7zTmmXiVj/5yBrsWDPka1/H/oK4VCx9iU9bnKbHZwt7lYNRxDxSls/islK2w2/SM/1dEeErXrRPlgidCxw/OENacpj7o1b8vHSFR+Hd4pcQezN5P/+5J5s/ji3ePbJguExl0m/jpPKVJKuHCclty5atc9bl0qLJ7gqEwtsG/aMvqOQKJi91Mx+SRr/f9LBva4aa+CzUrMBPGolWFSO29Wzpbcuc8/19nzX5vfS4i+l7StzVxzaYpUW4LY4XarSMJJ7jJLAXodtKuPSSa7/plWGFKZvrFV3ekFs3RyhbOZU5uB5q4rNEwZG5dgN1wEd+/Bqobi91tn7w+Cp9Qw9aKd7M08Pc51th2MfhI+/wFXWhnKRFhSdvYf9+UNXtGHjLsj6HVvf2zbnS6XLF+8ja63F7D34j29Li7/IMfYzQ+a2F3q9GW2ldMYtEF2i5vXWngvX/phZjZvZVsFmoxmb7WutJK2dTN2Okd5T34iaSluEKSwZd7sLbG2FSgLb0LOjRFba3/gk6YUBrg+KVY1e9l5kp8taM/Mv/+HO97mj+EJkm7p3/ovSq793T9Svnitd9XnhpjrYE/yYy3MEtu8UObBFFLDKDRsjFmS9d5W0/ifXssOq/vs94N8jrX5nFTXWY7tMBem4U/03vSjW2II0/7vF9TS3Pl4Xv+n6d3l9x+a5qVQLP3HnV3zjts/ukpLbuAC3+emurzOVTAgFq+pcMskFtUu/yu5pGmSzVqynaX6hbPDUDujy/xg6tlZEq7OP/XasiteeZyzEzQp5cwS69vxjvX8ReTb110JR2+zAycz/Sj+96/Wi9GYZfX63a53W+arQLj5lB2pswSJrf2AL6eRcEMwCY9ufNue5sR50IM/CQgAQyufC+ie4zdpwBhf43LTI9XtnBtpRI7QtCezory3qZNUUAzIDPBQPW/HQgtrRZ0grvpXevUq68JXILKBhbNE0W625SiP3ZrE4WRXBJW9JL50ubfhZeu1c6crPXP+6w/WEfHuItOgzN/XvkjFSw17Fu5+IrJotpKsnShPvl6Y/6xZQWf6NW6QsylZhjih7I2QLotiHwb1bsi+3Bd+a9HWbHfygx2popB2QPv+7NON597WFr78fJZXNPCpuoVfttm47+S63SrRNDbMQd8VUd5DCNps6a/3P7eetAjelZ3gWZUnd7XqR2Rtn22xK96Zf3fReWzjCWufYQUfCO3+zKXr2fi4Y1OZc6dhYX0n7OzY+2fVYrpzC3zRaWWVzXGZLBESPOh2kgSOl/g9K37/q1r6wacH2ftwW77UWFlZ9awdZj6aVhb32WzBsQe1P77me00HW/swWG7awtsaRF94GgGJl7yktG7ENx4TQNtbZdBn7oGl6/okpmuFgH9qt+sqqTa3i6uM/SWc/E/4PTjvXSVOfcuet/244Fvywih0Lra3a2AKC18+XhvxPKlMx/xDk3aFuOpcdULDA16ZwIfZZVe3vhknHnSKNvcH1/vv3Se6gkh1cIDg6crubj2+VVk51X9do6aroV810YZxtM55z48pCwWCIW70pj+3RsJ5d7wxxi8+YPne6mQuH+8BtFc82bdk2q4a2xSDt9WDxRNf/3A5W2GYhm/W/tSpc+xBvFdPHUplnLRssjN1kAe2vmeHs4hwr9ObDnoNtq9HChbdtB0XHIkbrfnKBhb1uxOpq5PY6adOtrd2BBbVr5uReFMx6ndpiHxbQ2orLddqzGBDgB1ZRZp+7bIHiRRNc6wTr529FCrZZMYVNE+5waeF6JVpVvfWonfe2e38dZH2krS+kBbX2XMD7JwCIOYS2se6bJ9wHRKvs6XlLpPem5LBq0Qtecn1ebeqy9e/sd39492HSP1yPNJuO0Gpg+O7XpmFd/oH0Yn9XDWCPwSVvuykTeQNbW5jKetxc9IarDkLJ0rSfW6TMglvrwfbJX1yoZQc5EmOkn3ioqyWtUnPaSNevztqJnHSXCwYttNq7zVXhWasRexxtsSH7kGibTdGv1MC1x7EA14KuYJUoCrYus9WNVUqVruCqa1ueWfQP7+0vdptNb7bFFr02CuPcQlA/jnGbPRfa86BV4FolbkGr1BZUNbt5iXvOL3A/qrlK7OpN3IKkdt56lVoIYG0fNi6QPv6z9MV9bpEJWyTCb+02dqzNXAl9jKtcNjVbS2c+ERsLV1oVnf1Ng31pbRZCzinPxqbFW0hrB73soMyxBP0AildcvNT8d26z52irvP3+dTdl2BZm/fIhqe0FUpdrD+1NvHuT9NP77jk6Z69cm5nW/DR3gM2eB8JRlAEAiBgWIovlhci2LJNGdnU9sWyKfqtzIr1HJc+cV6WPbnLn+z/k+ruEbeG5Hq4i58rPI/Nh1hbrGX2WdGC31Ppc6bwXXGWaBbbW09TadthCGhe9KTXtW3KatCP/CkGbdv7FvVJaqlShlnTuKIL8nBZ86nqT20E4Y/3Jf/eIVLl+weGPhXAW3logbu1a7LHNVaHXLTvEtQ+LEajQ8fW4tamnY290CwXZ1C47uBTKFh7paa462ipwf/nYfYjPEnArj1t4a+F8MJg9UtWs/V1tX/OGs1ZlfbheYvu2u+B2xqjs/bDF1Oz+u17vDkRGqoLLQmoLuX940wXewUpTe/2w8CLYx9WC5r73R1/PNKvk9nrSWlA7+dC/rx30tTDfq6Y92fWo9QFfj13Az+w5zRYMm/kfN9MoqEF3V31rr992YMoO3Nh6JMHnY3sesPYHduAwvxlshcC4BaIP4zZ2sRBZKNiLZjSztggW2FpFVcsQLIqAorOFB6ySyiqX7O9hH76s2qq4TbjHfbBteVbkqo/qdpIuek16/UJp/vuut+2AYdL712QHtoNeD0lgiyhnYX73P7gerO9d7cKpVwa6qYUn/61kV5FsWyWNu8MFe8aqZU9/1FXZHI4FbBYw2tbjJvchcfm3LsC1bcuS7IWyrL9whWQ3Pd9CXKvcibbgK5QsTP3yQTdTxdjjYj2XCzOFtagVWCnd3dbvQReyWzhpm02Dt1V3bTts1WxwywxnrTL2aFoFWJsGq9i2ai+bymsHUSxEtNkQttlCatY6wVarD0ePTfsbWDuKH8ZIv3yUu9rUDja0G+SmBNv7NHu9s96Rc15xj50dIG13sb+nCVvLDJsubX2PbaVl5Xi/aRXX9j8R7EubfPzR9b4E4E/WfqbzUKnTFdLKae65wBYSs/O25e2RaxW1Vvxgi9wBAEocKm0PV2m7cIaqNeuiqGTVVbYQVCBeuuFbFviJJPtQaYHtNOtrG++qtWyaVHGxSqRXznEVVzfOdCubR7pazRZksw+l1ntz4y9SXII06LWQPg4chYwRFi5+9n/S7NHZH1isSjvS/8fhZhXp1gbB2iHYlHcbz1ap3/uvoek3av3xvCrcia6lglXEZwm4gy7BXrh1O+buk2nPadae4eB+V71rW/C8d7rfLTKY63R/vt+XlrpXvy5Zribn3KaEWq0UcdZiwg4cWIsJYwcOTr03/H1Cd6xxgZ4FqPa6EQxnC1M1GyobFri+uz+8ld12wfonWtBwwlVSpXrFcJ+/uPuzFgjW3iOoSkMXxFrfxvwWtFgxzbV2sNcXY20DzhjuFj70W1WtvRewkCZnEG2heLCSNqVHVCw+xWsuEOLWL/a+x2Y82IHqNue5g2T2fB9CjFsg+jBuY1dhK20JbQ/z4G2Z+KSqnPJHRR37kGxT4206Zbc/uAV/EPnp3x/+wU3vtMWBLh/rKmmK437+3Uda96PU5TpXkecHM/4tjfurO2/hk7XrsIV3QogXtBjz80fSRzdL+7ZJCYnSgIdcS4CC+nzGEgugPrlV2vCz+7pBD+nM4aGdmp+TBagrp2dW4U7MPV3TlElyPalzBrA5KwNDxSp8rY9qswGRWUzJQkrrX2tVyDbt/pxnpOPPD/9++M3era7Vj03l3b7SXWZBss3ksOrcY138ZtcGad670o9vSWt/yF39a9VlFtZaq4gj3Ycd6Jj+rDT5kTwHOm6XSpdX5MPap91rYfAAibUk6Xajq26Pwuc1XnOB6MO4BaIP4zZ20R4hBAL2IVZRGNratEYLbG2BkZPujPTewNjUxrOfdlMibdXYNwdJQ8dJya1D+/jYYgUW2FrIYqub+0XXa90H1VkvSQMeDnlgixjU6mxX7fnBdW6atFXR2WYtRmzc2FazlTu1Ve8jHcqEKtj5wqZ6v5Y9Bd6mzbe/pHinelsg27iP2/o/6Ko8g71wbYq89Tu1nLYg1mvPpnRbdZB3Wiaz32gBpznOp8UlaOOi2Ure8aMC1r/PNmsBYZWc1qM0XG0abFr9+9dJqTvdwp0XvS7Vbhee+/Y7awvR849S9xvd4mn2HsPG5M9j3WaPk/W9tcqwnAtOHo4tyGaVxFZVa/9rwb6NFrQ2HeDaHzT7XeFvz1hbCKuMtrYJXkuRT12LC5vtcdpjxTvDpSC2kNDUp13gnRXWtnULCFqLEz+3cAAAAEDEUWl7mMR724ONVelvi6PrTfXO9dLTHd20u3NGSh0ui/QeIafUPdKrv5dWTXeLLV31mZv2GQr2Ifjpzm4RE5vOe+KtJeqx5yhkjLLelhZ6WL9Km9Kfb4VnwE2ZTrYQt012mGtjKxIVm0dTIT/3NWnCvdLeLe6yjkOkvvdFvrds2kFp00LXEiHfENa2Usc+bnu0VsL3L7vepFbZaWxWQpvzpS7XSHXaq9ge+68ekyY/7L5ueKJ0wWjXgxsFW/eTa51gi+kc3OcuswPFnYZKna+Ukmrn/1ivnOqCWutrvn9H9nV2gMYqaq2yNrFa6IL4T2/PXtjLKvVP+2fxtHUoTFhr4baFtRZGR9P7ygLwmgtEH8YtEH0Yt7GLStsQCOzeKG1e4lZgjhZWaWmBrfWBbHdJpPcGeVk14CVvSS+d7qY+W4B75edShRrH/lhNf859OE2q56asArHAQtdet7jN+t3aYk3rf3bjZ/1P7rwt9mdT2m2zxTyCEsq7KlwLc2tmVufa5qdAbv186eNb3YEcY/t55hORW0AwLwtkQz0jID+VU1yV78n/5yojZ4xyswYszLatXhcX3rY6p2jVl4ezf6f0wfVuoS1jLWUG/OPoFvIqaWq1cbNHTr1PmvOy9N1/XQ/arx6Vvhnuql2t+rZeZ2nTIhfUWsAbbK9grKLaetS2uyjkfRs9NqOjUR/XF9raJtjfeckk6eS73L4Vx9/ZC2ufkmb+N0dY2z4zrB0QE2EtAAAAwufoy2NKCltZO5pC26VT3KlVJ7HasH+nml72vvRif1c5+Pp50pCPpbIFN58u1AfF4Ernp94dFYuYAEVmC3BZRZ5teXtiWvhpmxfmznfhrvW1XDPHbTkl1szTYqGVVL15eFssWABtvTctTLJFvaxvb3GGSdHCnrtshkj7S6XfvnMLNs0fK/020222SJ0thGUVnZXqHv392AHZty51C1dZ1bAF5cxMKTqrirVZHdY71kJRC9tt9XNbSMw2O4gYrHQ11rrHgncLaq1Xc3G/TylTwR0MsPsLHhyxhUEtRLa/ufXKDYVdG11Ya+F1cNE2O3je507CWgAAABw1QtsjWf6N+4AYDWwRjhVT3XnrTQj/sqmjthjZC/3dwiu2+M2l70oJZY/u9qY86qaaWq+84y8M9d4C/maL+Nhmq6/nbKtgB0VyBrm2bV0u7d4gLbVtUp7bqeVaKnhbSo7zDd11oQqY8k7btgWdfvdIeKZtRwurSLRAzTbrgz37ZWnWC9LOta6dwdfDpZZnSl2ulVJ6Fq2C0Xr1vnul69VbsbY0yCp5OxfnbxP77ECDVdfatuZ7t+DWT++6/3FbtKxJX9entvnpkTmoaAdorI+814bkHlel/0K/Y29D4oW1T0rfvZA7rLXK2qb9qawFAADAMSG0LUxoa738omFK2+o5bjpeuapuii38rdpx0mXvSaPPdIu6vH+1dMHLRe/BuWmxCzOMVRRRYQ24cWRTrm1rPTB3hesGa7HwU+4w13rJ7lrntmCrgpysd2vlBvmHuja1vzCV8ttWurD213Hua7u90x93lXgomAXyff7qWmRY4G19Qm0WjPVFta1GS9c6oe0gV1lZEHstt2rIL+6TMtJdy4VBr0oVa/Hoh5KFlr9/Tur3gKuWtkDc/oaRZq+Ntrhd8zNccGsBrrV2sArh/v9w1biFfa9n1f3fPinNejFHWNsxM6ztFx3vGQEAAOB7hLaHkRGX4Kp6rFrLAja/W5bZGqHRiQR30cIW17FVyl8/3/Xi/ORW6cwRRfvAN/E+N73aqnoan1ScewvERouFep3cljPMs8WvrArXtm0rss97X6+S0vZLmxe5LT92sCxnZW7OULdCsjTjeVcRbwGPvbbYdPLefw1vS4aYqOYc6DYL2i28/XGMa3Fgz50WxlpbhROuPrStkS0C+dHNrvrTWHhngXmo+uPiUNarvcXp/mzpMNAWar3UtUyw/5+x10vfvyadOVyq0fzIYa1V1h7c6y6zdi0W1lo1MWEtAAAAQojQ9jAybPGITbNctW1UhLZfuVNbeAPRw1pZnPdf6Z0rpNm2ankN6ZS/F+5nV053YW8gzlU1ASg6C1pserRtdTseen3aQbfIUq4gN0ewu2ezq9S1LW//3LxSeh05GELhprufNcJNbf/hTRfg2kJ0M55z23GnutYJVvVofzvrX2sLm8WVcq0oLNglYCvZUnpI130lTR8pTf6nq95+rmf+B1R2rs/sWZszrO2cGdaeyv8SAAAAigWh7WFk1OvqQtsV30qdhsjXrIpo1Qx3ntA2+tjCLGcMlz6+xfVrLF9d6nb94X/GqgNtQRVjC+jUbBmWXQVKnPhSmZWzKZLyOSi2f6e0NW91bvDrFa5K18Z0/4eKNgUbR1austTtBqnLddLSL114++tn0pKJbrMWFNYSw4J1+xtc+IrUsCePLJxSpaVef5ZanyuNs9Yl46VvhruKbKvEtoP3XhsEC2v3uZ8hrAUAAECYENoeRkaDrtLckdHR19YC27RUqWKd6KgKxqE6D5X2bJK+fEgaf4er+mt7mEXFfrYV1b+TEspLJ/+NRxSIlDIVpVpt3JZXerq0a71UvpoLiFB8/UpterptW5a5kG3Oq66PsKndThr0ulS5Pn8BHMoOyFz8luuZPO4O93/zxoWulUn6Afc99U6QTrrTVXH7+f0gAAAAYgah7WFk1O7g3rAHp8VWbSTft0awqfZ8mIheJ94m7d7k+l+OvcH1yWza99DvO5gqfXG/O9/jjyykA/g5TEyqHem9KFnstdqqmk/6P2n++9LOdVL3G6WEcpHeM/iZvXdqeabrDT/lEWnasy6wtQXrvLD2FN5fAQAAIKwIbY+0YI31N7QqVqu29XVoG1yErHek9wTH+qFxwDA3lXfeO9Lbl0uDP5Lqn5D7+6yKbOsyt8CR9d8DAORmPUmtdQxQFGUquNC/4xXutbh+F8JaAAAARERcZO42ijTs5U6tr61f7dsurfnenSe0jY3KvHOeddN8baX5Ny6QNizIvn7vNmnKP915WwTFPmACAIDQqd5EsjZZzF4CAABAhBDaFja0tUpbv1oxVcpIl6oeJ1WqF+m9QShY70tbMMd66O3dKr36e2nbKnfd1/9yl9VoIXW4nMcbAAAAAAAgxhDaHkn9rlJcKWn7KrcKuB8tpTVCzLbnuORtF87uXOOC29VzpBmj3PX9HnCr2gMAAAAAACCmENoWJjir09Hf1bY5FyFDbClfVbrsfSmpnrR5kfRCPyltv9TwRKlp/0jvHQAAAAAAAIoBoW1hNOzp3762uzZKG+a78xbkIfZUqitd/oFUrqqUftBd1v9B+uwBAAAAAADEKELbIvW1/Vq+szyzyja5jZRYPdJ7g+JSo5l02btSpQZS95ukOh14rAEAAAAAAGIUDTEL29c2EC9tW+m2yg3ku9YIjWiNEPPqdpL+PC/SewEAAAAAAIBiRqVtYZSpmF3ZuNxnLRJYhAwAAAAAAACIKYS2Re1r66fFyKzqd+syVwWc0iPSewMAAAAAAAAgBAhtCyu4yNcKH4W2yzJ77NbtKJVNivTeAAAAAAAAAAgBQtui9rXdulza/pt8YdkUd9qod6T3BAAAAAAAAECIENoWllWy1m7nn762GRksQgYAAAAAAADEIELbomjYy50uz2xLEEmbF0s710rxZaT6XSK9NwAAAAAAAABChND2aELbFT6otF062Z1aYJtQLtJ7AwAAAAAAACBECG2LokE3KRAnbVkq7VijiFr2lTtt3Cey+wEAAAAAAAAgpAhti6JsJalW28j3tU1Pz27R0IjQFgAAAAAAAIglhLbR2Nd2/Txp71apdAWpTofI7QcAAAAAAACAkCO0jca+tsHWCCk9pfiEyO0HAAAAAAAAgJAjtC2qBt0lBaTNi6UdaxURS6e400a9I3P/AAAAAAAAAIoNoW1Rlass1To+ctW2aQekFVPdeUJbAAAAAAAAIOYQ2h6Nhie60+XfKOxWz5EO7JbKVZWS24T//gEAAAAAAAAUK0LbY1qMLAKh7bJga4QTpTj+fAAAAAAAAECsIfU7GinBvraLpJ3rFZFFyBr1Ce/9AgAAAAAAAAgLQtujUa6KVCuzNcGKMFbbHtgrrZrhzhPaAgAAAAAAADGJ0PZopQRbJIRxMbKV06W0VKliHanaceG7XwAAAAAAAABhQ2gbTX1tg60RGveRAoHw3S8AAAAAAACAsCG0PVopPdzppoXSro0K7yJkvcNzfwAAAAAAAADCjtD2aJWvKiWHsa/tvu3Smu/deUJbAAAAAAAAIGYR2h6LlJ7h62u7YqqUkS5VPU6qVK/47w8AAAAAAABARBDaRktf26W0RgAAAAAAAABKAkLbUFTabvxF2r1JYVmEjNYIAAAAAAAAQEwjtD0WidWkmq3c+RXF2CLBFjrbMN+dJ7QFAAAAAAAAYhqhbTS0SFieWWVrC58lVi+++wEAAAAAAAAQcYS20bAYWVZrhD7Fdx8AAAAAAAAAfIHQNlShrbUv2L1ZxYJ+tgAAAAAAAECJQWh7rCrUkGq0cOdXTlXIbVslbVkqBeKllB6hv30AAAAAAAAAvkJo6/e+tsEq27odpbJJob99AAAAAAAAAL5CaOv3vrbLprjTRr1Df9sAAAAAAAAAfIfQNpSVtut/kvZsUchkZNDPFgAAAAAAAChhCG1DoUJNqXozS1mlldMUMpsXSzvXSvFlpPpdQ3e7AAAAAAAAAHyL0NbPfW2XTnan9btICeVCd7sAAAAAAAAAfIvQ1s+hbXARskZ9QnebAAAAAAAAAHyN0DZUUjJD23XzpL1bj/320tOl5V+7840JbQEAAAAAAICSgtA2VComS9WaZva1nX7st7c+M/wtXUGq0yEUewgAAAAAAAAgChDahlLDnqFrkRBsjZDSU4pPOPbbAwAAAAAAABAVCG1DqeGJ7jTY1uBYLJ3iThv1PvbbAgAAAAAAABA1CG1Dyapis/rabjv620k7IK2Y6s4T2gIAAAAAAAAlCqFtKCXVlqoeJ2WkH1tf29VzpAO7pXJVpeQ2odxDAAAAAAAAAD5HaFtcfW1XHENf22XB1ggnSnH8iQAAAAAAAICShESw2PrafnPsi5DRGgEAAAAAAAAocQhti6uv7dofpH07iv7zB/ZKq2a4841OCu2+AQAAAAAAAPA9QttQq1RXqtLo6Pva2s+kpUoV60jVjgv57gEAAAAAAADwN0Lb4tCw19H3tc3ZGiEQCO1+AQAAAAAAAPA9QtviDG2XH0No27hPaPcJAAAAAAAAQFQgtC3OvrZr5kr7dxb+5/Ztl9bMcedZhAwAAAAAAAAokQhti0Pl+lLlFCkjTVqZuahYYayY6nrhVj1OqlSvWHYNAAAAAAAAgL8R2haXhicWva/t0inulCpbAAAAAAAAoMQitC0uDXsWva9tzkXIAAAAAAAAAJRIhLbF3dd29Rxp/64jf/+ujdKG+e48oS0AAAAAAABQYhHaFpcqKVKlBq6v7apC9LVdnlllm9xGSqxebLsFAAAAAAAAwN8IbYtTw16Fb5FAawQAAAAAAAAAhLZh6mu74tsihLZ9inefAAAAAAAAAPgalbbhqLRdPVtK3V3w921bJW1ZKgXipZQexbpLAAAAAAAAAPyN0LY4Vba+tvWl9IPSqplHrrKt00Eqm1SsuwQAAAAAAADA3whti1MgIKX0PHJf22Bo25jWCAAAAAAAAEBJR2gbrhYJBfW1zciQlk1x5xv1LvbdAQAAAAAAAOBvhLbhWozst1lS6p5Dr9+8WNq5VoovI9XvWuy7AwAAAAAAAMDfCG2LW5VGUlJdKf2A9Nt3h16/dLI7rd9FSihX7LsDAAAAAAAAwN8IbSPd1zbYz7YR/WwBAAAAAAAAENpGtq9terq0/Gt3nkXIAAAAAAAAABDahjm0tfYIB/ZmX75+nrR3q1S6glSnA/+QAAAAAAAAAGiPEBZVG0sVa0tpqW5BsrytEVJ6SPEJ/DsCAAAAAAAAILQNW1/bYLVtzr629LMFAAAAAAAAkAcLkYVL3sXI0g5IK6a68416h203AAAAAAAAAPhbqUjvQInR8MQcfW33SWt/kFJ3SeWqSsltIr13AAAAAAAAAHyCSttwqXacVCFZStsvrZ4lLZviLm90ohTHnwEAAAAAAACAQ1oYkb623+boZ0trBAAAAAAAAADZCG0j0dd28QRp1Qx3vtFJYd0FAAAAAAAAAP5GT9tI9bU1Feu4tgkAAAAAAAAAkIlK23Cq3lRKrJn9tbVGsLYJAAAAAAAAAJCJ0DbsfW0zWySYxn3CevcAAAAAAAAA/I/QNlJ9bXO2SwAAAAAAAACATPS0Dbem/aRSZaXk1lLl+mG/ewAAAAAAAAD+RmgbblUaSjfOlMomhf2uAQAAAAAAAPgfoW0kVEmJyN0CAAAAAAAA8D9f9LQdOXKkGjZsqLJly6pr166aOXNmgd87evRoBQKBXJv9XNCBAwd0xx136Pjjj1diYqLq1KmjwYMHa82aNWH6bQAAAAAAAAAgikPbMWPG6NZbb9W9996rOXPmqF27dhowYIA2bNhQ4M8kJSVp7dq1WduKFSuyrtuzZ493O3fffbd3+v7772vhwoU6++yzw/QbAQAAAAAAAEAUt0cYPny4rrnmGg0dOtT7+vnnn9cnn3yiF198UXfeeWe+P2PVtbVq1cr3ukqVKmnChAm5LnvmmWfUpUsXrVy5Ug0aNDjkZ/bv3+9tQTt27Miq2rUNgP8FxypjFogejFsgOjF2gejDuAWiD+M2dhU2t4hoaJuamqrZs2frrrvuyrosLi5Offv21bRp0wr8uV27diklJUXp6enq2LGjHn74YbVu3brA79++fbsX9FauXDnf64cNG6b777//kMsnTZqk8uXLF/n3AhA5eQ/aAPA/xi0QnRi7QPRh3ALRh3Ebe6xLgO9D202bNiktLU3Jycm5LrevFyxYkO/PNG/e3KvCbdu2rRfGPv744+rRo4fmz5+vevXqHfL9+/bt83rcXnzxxV5bhfxYaGwtGnJW2tavX18nn3yyqlWrdsy/J4DwHKmyF7N+/fopISGBhxyIAoxbIDoxdoHow7gFog/jNnYFZ/j7vj1CUXXv3t3bgiywbdmypUaNGqUHH3zwkH/wCy+8UBkZGXruuecKvM0yZcp4W14W/BD+ANGFcQtEH8YtEJ0Yu0D0YdwC0YdxG3sKmzVGNLStXr264uPjtX79+lyX29cF9azN7xft0KGDFi9enG9ga4uUffnllwVW2QIAAAAAAACAn8RF8s5Lly6tTp06aeLEiVmXWZ9a+zpnNe3hWHuFefPmqXbt2ocEtosWLdIXX3xBiwMAAAAAAAAAUSPi7RGsl+yQIUPUuXNndenSRSNGjNDu3bs1dOhQ7/rBgwerbt263mJh5oEHHlC3bt3UpEkTbdu2TY899phXTXv11VdnBbbnn3++5syZo48//tgLddetW+ddV7VqVS8oBgAAAAAAAAC/inhoO2jQIG3cuFH33HOPF662b99e48ePz1qcbOXKlYqLyy4I3rp1q6655hrve6tUqeJV6k6dOlWtWrXyrl+9erU++ugj77zdVk6TJk3SSSedFNbfDwAAAAAAAACiKrQ1N910k7flZ/Lkybm+fuKJJ7ytIA0bNvQWHgMAAAAAAACAaBTRnrYAAAAAAAAAgNwIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADARwhtAQAAAAAAAMBHCG0BAAAAAAAAwEcIbQEAAAAAAADAR3wR2o4cOVINGzZU2bJl1bVrV82cObPA7x09erQCgUCuzX4up4yMDN1zzz2qXbu2ypUrp759+2rRokVh+E0AAAAAAAAAIMpD2zFjxujWW2/Vvffeqzlz5qhdu3YaMGCANmzYUODPJCUlae3atVnbihUrcl3/6KOP6qmnntLzzz+vGTNmKDEx0bvNffv2heE3AgAAAAAAAIAoDm2HDx+ua665RkOHDlWrVq28oLV8+fJ68cUXC/wZq66tVatW1pacnJyrynbEiBH6+9//rnPOOUdt27bVK6+8ojVr1mjs2LFh+q0AAAAAAAAA4OiUUgSlpqZq9uzZuuuuu7Iui4uL89oZTJs2rcCf27Vrl1JSUpSenq6OHTvq4YcfVuvWrb3rli1bpnXr1nm3EVSpUiWv7YLd5kUXXXTI7e3fv9/bgnbs2OGdHjhwwNsA+F9wrDJmgejBuAWiE2MXiD6MWyD6MG5jV2Fzi4iGtps2bVJaWlquSlljXy9YsCDfn2nevLlXhWsVtNu3b9fjjz+uHj16aP78+apXr54X2AZvI+9tBq/La9iwYbr//vsPuXzSpEle1S+A6DFhwoRI7wKAImLcAtGJsQtEH8YtEH0Yt7Fnz549/g9tj0b37t29LcgC25YtW2rUqFF68MEHj+o2rdLX+urmrLStX7++Tj75ZFWrVi0k+w2g+I9U2YtZv379lJCQwMMNRAHGLRCdGLtA9GHcAtGHcRu7gjP8fR3aVq9eXfHx8Vq/fn2uy+1r61VbGBbOdOjQQYsXL/a+Dv6c3Ubt2rVz3Wb79u3zvY0yZcp4W363TfgDRBfGLRB9GLdAdGLsAtGHcQtEH8Zt7Cls1hjRhchKly6tTp06aeLEiVmXWZ9a+zpnNe3hWHuFefPmZQW0jRo18oLbnLdpCfaMGTMKfZsAAAAAAAAAECkRb49gbQmGDBmizp07q0uXLhoxYoR2796toUOHetcPHjxYdevW9frOmgceeEDdunVTkyZNtG3bNj322GNasWKFrr76au/6QCCgW265RQ899JCaNm3qhbh333236tSpo4EDB0b0dwUAAAAAAAAA34e2gwYN0saNG3XPPfd4C4VZC4Px48dnLSS2cuVKxcVlFwRv3bpV11xzjfe9VapU8Sp1p06dqlatWmV9z+233+4Fv9dee60X7Pbq1cu7zbJly0bkdwQAAAAAAACAqAltzU033eRt+Zk8eXKur5944glvOxyrtrWKXNsAAAAAAAAAIJr4IrT1m4yMDO90586dLEQGRNHKmnv27PF6WLOAIBAdGLdAdGLsAtGHcQtEH8Zt7LLcImf+WBBC23xs3rzZO7V+uAAAAAAAAAAQSlYsWqlSpQKvJ7TNR9WqVbP66R7uwQPgryNV9evX16pVq5SUlBTp3QFQCIxbIDoxdoHow7gFog/jNnZZha0FtnXq1Dns9xHa5iO48JkFtoQ/QHSxMcu4BaIL4xaIToxdIPowboHow7iNTYUpEnXpJAAAAAAAAADAFwhtAQAAAAAAAMBHCG3zUaZMGd17773eKYDowLgFog/jFohOjF0g+jBugejDuEUgw7rfAgAAAAAAAAB8gUpbAAAAAAAAAPARQlsAAAAAAAAA8BFCWwAAAAAAAADwEUJbAAAAAAAAAPARQtt8jBw5Ug0bNlTZsmXVtWtXzZw5M/x/GQD5+uqrr3TWWWepTp06CgQCGjt2bK7rbW3Fe+65R7Vr11a5cuXUt29fLVq0iEcTiKBhw4bphBNOUMWKFVWzZk0NHDhQCxcuzPU9+/bt04033qhq1aqpQoUKOu+887R+/fqI7TNQ0j333HNq27atkpKSvK179+4aN25c1vWMWcD/HnnkEe/98i233JJ1GWMX8Jf77rvPG6c5txYtWmRdz5gt2Qht8xgzZoxuvfVW3XvvvZozZ47atWunAQMGaMOGDZH5CwHIZffu3d64tIMr+Xn00Uf11FNP6fnnn9eMGTOUmJjojWF7sQMQGVOmTPEC2enTp2vChAk6cOCA+vfv743noD//+c/63//+p3feecf7/jVr1ujcc8/lTwZESL169bzAZ/bs2Zo1a5ZOOeUUnXPOOZo/fz5jFogC3333nUaNGuUdfMmJ11vAf1q3bq21a9dmbd98803WdYzZki2QYWVpyGKVtVYN9Mwzz3hfp6enq379+rr55pt155138kgBPmJHIT/44AOvas/Y05lV4P7lL3/Rbbfd5l22fft2JScna/To0brooosivMcAzMaNG72KWwtne/fu7Y3TGjVq6I033tD555/vfc+CBQvUsmVLTZs2Td26deOBA3ygatWqeuyxx7xxypgF/GvXrl3q2LGjnn32WT300ENq3769RowYwest4NNKW5s9Onfu3EOu4z0yqLTNITU11asmsOnUQXFxcd7X9qERgL8tW7ZM69atyzWGK1Wq5B2MYQwD/mFvQIMBkLHXXqu+zTl2bVpYgwYNGLuAD6Slpemtt97yquOtTQJjFvA3m91yxhln5HpdNYxdwJ+snZ8VHzVu3FiXXnqpVq5c6V3OmEUpHoJsmzZt8t6UWlVeTva1VfwA8DcLbE1+Yzh4HYDIshks1luvZ8+eatOmjXeZjc/SpUurcuXKub6XsQtE1rx587yQ1loMWa9pm93SqlUrrxqIMQv4kx1gsTZ/1h4hL15vAf+xAiObFdq8eXOvNcL999+vE088UT/99BNjFoS2AAAgvNU/9iY0Z68uAP5kHyAtoLXq+HfffVdDhgzx2poA8KdVq1bpT3/6k9c/3hbVBuB/p512WtZ560FtIW5KSorefvttb2FtlGy0R8ihevXqio+PP2S1avu6Vq1a4f7bACii4DhlDAP+dNNNN+njjz/WpEmTvEWOco5da1G0bdu2XN/P6y8QWVZN26RJE3Xq1EnDhg3zFgJ98sknGbOAT9lUaltA2/rZlipVytvsQIst0mvnbQYLr7eAv9nMs2bNmmnx4sW83oLQNu8bU3tTOnHixFzTOO1rmxoGwN8aNWrkvbDlHMM7duzQjBkzGMNABNkigRbY2tTqL7/80hurOdlrb0JCQq6xu3DhQq+fF6+/gH/Y++L9+/czZgGfOvXUU722JlYhH9w6d+7s9cgMnuf1FvD/QoJLlixR7dq1eb0F7RHyuvXWW72pX/aC1qVLF2+VTVt0YejQofy7AD55EbOjjkG2+Ji9CbUFjWzRIuuVaavkNm3a1AuG7r77bq+p+8CBAyO630BJb4nwxhtv6MMPP1TFihWzekzbQoE27ctOr7rqKu812MZyUlKSbr75Zi+w7datW6R3HyiR7rrrLm/Kpr227ty50xvDkydP1meffcaYBXzKXmOD/eKDEhMTVa1atazLeb0F/OW2227TWWed5bVEWLNmje69915vBvjFF1/M6y0IbfMaNGiQNm7cqHvuucf7UNm+fXuNHz/+kIWNAETGrFmzdPLJJ2d9bSGPsYMt1sD99ttv9w60XHvttd5U6169enljmL5eQOQ899xz3ulJJ52U6/KXXnpJV1xxhXf+iSeeUFxcnM477zyvkm/AgAF69tlnI7K/AORNsR48eLC3KIodWLE+exbY9uvXjzELRDFebwF/+e2337yAdvPmzapRo4b3+XX69OneecOYLdkCGTZnEQAAAAAAAADgCyxEBgAAAAAAAAA+QmgLAAAAAAAAAD5CaAsAAAAAAAAAPkJoCwAAAAAAAAA+QmgLAAAAAAAAAD5CaAsAAAAAAAAAPkJoCwAAAAAAAAA+QmgLAAAAAAAAAD5CaAsAAAAUg4YNG2rEiBE8tgAAACgyQlsAAABEvSuuuEIDBw70zp900km65ZZbwnbfo0ePVuXKlQ+5/LvvvtO1114btv0AAABA7CgV6R0AAAAA/Cg1NVWlS5c+6p+vUaNGSPcHAAAAJQeVtgAAAIipitspU6boySefVCAQ8Lbly5d71/3000867bTTVKFCBSUnJ+vyyy/Xpk2bsn7WKnRvuukmr0q3evXqGjBggHf58OHDdfzxxysxMVH169fXH/7wB+3atcu7bvLkyRo6dKi2b9+edX/33Xdfvu0RVq5cqXPOOce7/6SkJF144YVav3591vX2c+3bt9err77q/WylSpV00UUXaefOnVnf8+6773r7Uq5cOVWrVk19+/bV7t27w/DIAgAAIJwIbQEAABAzLKzt3r27rrnmGq1du9bbLGjdtm2bTjnlFHXo0EGzZs3S+PHjvcDUgtOcXn75Za+69ttvv9Xzzz/vXRYXF6ennnpK8+fP967/8ssvdfvtt3vX9ejRwwtmLYQN3t9tt912yH6lp6d7ge2WLVu8UHnChAlaunSpBg0alOv7lixZorFjx+rjjz/2NvveRx55xLvObvviiy/WlVdeqV9++cULjM8991xlZGQU4yMKAACASKA9AgAAAGKGVada6Fq+fHnVqlUr6/JnnnnGC2wffvjhrMtefPFFL9D99ddf1axZM++ypk2b6tFHH811mzn741oF7EMPPaTrr79ezz77rHdfdp9WYZvz/vKaOHGi5s2bp2XLlnn3aV555RW1bt3a6317wgknZIW71iO3YsWK3tdWDWw/+49//MMLbQ8ePOgFtSkpKd71VnULAACA2EOlLQAAAGLeDz/8oEmTJnmtCYJbixYtsqpbgzp16nTIz37xxRc69dRTVbduXS9MtSB18+bN2rNnT6Hv3ypjLawNBramVatW3gJmdl3OUDgY2JratWtrw4YN3vl27dp5+2FB7QUXXKD//Oc/2rp161E8GgAAAPA7QlsAAADEPOtBe9ZZZ2nu3Lm5tkWLFql3795Z32d9a3Oyfrhnnnmm2rZtq/fee0+zZ8/WyJEjsxYqC7WEhIRcX1sFr1Xfmvj4eK+twrhx47zA9+mnn1bz5s296l0AAADEFkJbAAAAxBRrWZCWlpbrso4dO3o9aa2StUmTJrm2vEFtThbSWmj6r3/9S926dfPaKKxZs+aI95dXy5YttWrVKm8L+vnnn71euxbAFpaFuD179tT999+v77//3rvvDz74oNA/DwAAgOhAaAsAAICYYsHsjBkzvCrZTZs2eaHrjTfe6C0CZgt5WQ9Za4nw2WefaejQoYcNXC3UPXDggFfVaguHvfrqq1kLlOW8P6vktd6zdn/5tU3o27ev19bg0ksv1Zw5czRz5kwNHjxYffr0UefOnQv1e9nvZD15bSG1lStX6v3339fGjRu9QBgAAACxhdAWAAAAMeW2227zWglYBWuNGjW8gLNOnTr69ttvvYC2f//+XoBqC4xZT9m4uILfElsf2eHDh+uf//yn2rRpo9dff13Dhg3L9T09evTwFiYbNGiQd395FzILVsh++OGHqlKliteOwULcxo0ba8yYMYX+vZKSkvTVV1/p9NNP9yp+//73v3sVwKeddloRHyEAAAD4XSAjIyMj0jsBAAAAAAAAAHCotAUAAAAAAAAAHyG0BQAAAAAAAAAfIbQFAAAAAAAAAB8htAUAAAAAAAAAHyG0BQAAAAAAAAAfIbQFAAAAAAAAAB8htAUAAAAAAAAAHyG0BQAAAAAAAAAfIbQFAAAAAAAAAB8htAUAAAAAAAAAHyG0BQAAAAAAAAD5x/8DKQ00aD5KeVoAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data", @@ -1246,7 +1250,7 @@ "\n", "[指标分析]\n", " 各NDCG指标在验证集上的最佳值:\n", - " ndcg@10: 0.5599 (迭代 55)\n", + " ndcg@10: 0.5462 (迭代 5)\n", "\n", "[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\n" ] @@ -1262,8 +1266,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:32.393179Z", - "start_time": "2026-03-13T14:39:32.041674Z" + "end_time": "2026-03-13T18:10:39.655028Z", + "start_time": "2026-03-13T18:10:39.403487Z" } }, "cell_type": "code", @@ -1322,33 +1326,33 @@ "\n", "NDCG 评估结果:\n", "----------------------------------------\n", - " ndcg@1: 0.5399\n", - " ndcg@5: 0.5424\n", - " ndcg@10: 0.5346\n", - " ndcg@20: 0.5299\n", + " ndcg@1: 0.4710\n", + " ndcg@5: 0.4927\n", + " ndcg@10: 0.5003\n", + " ndcg@20: 0.5075\n", "\n", "特征重要性（Top 20）:\n", "----------------------------------------\n", - " 1. close_vwap_deviation 8189.04\n", - " 2. revenue_yoy 4009.98\n", - " 3. profit_margin 3742.07\n", - " 4. sharpe_ratio_20 1536.27\n", - " 5. active_market_cap 1361.63\n", - " 6. max_ret_20 1216.47\n", - " 7. pe_expansion_trend 1215.69\n", - " 8. market_cap_rank 1063.42\n", - " 9. debt_to_equity 886.34\n", - " 10. drawdown_from_high_60 878.19\n", - " 11. volatility_20 677.31\n", - " 12. return_20 675.57\n", - " 13. amihud_illiq_20 515.66\n", - " 14. min_ret_20 486.54\n", - " 15. turnover_rank 484.78\n", - " 16. ma_20 428.48\n", - " 17. std_return_20 413.62\n", - " 18. BP 307.34\n", - " 19. turnover_cv_20 299.52\n", - " 20. EP_rank 297.78\n" + " 1. roe 345.77\n", + " 2. ma_20 244.73\n", + " 3. profit_margin 223.10\n", + " 4. revenue_yoy 216.85\n", + " 5. active_market_cap 214.10\n", + " 6. std_return_20 149.44\n", + " 7. close_vwap_deviation 136.42\n", + " 8. max_ret_20 110.61\n", + " 9. debt_to_equity 105.78\n", + " 10. bbi_ratio 104.63\n", + " 11. market_cap_rank 104.10\n", + " 12. up_days_ratio_20 99.44\n", + " 13. turnover_rank 80.60\n", + " 14. min_ret_20 72.72\n", + " 15. ebit_rank 64.03\n", + " 16. drawdown_from_high_60 61.28\n", + " 17. turnover_rate_mean_5 57.16\n", + " 18. healthy_expansion_velocity 54.10\n", + " 19. BP 51.36\n", + " 20. EP 51.29\n" ] } ], @@ -1357,8 +1361,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-13T14:39:32.644581Z", - "start_time": "2026-03-13T14:39:32.401777Z" + "end_time": "2026-03-13T18:10:39.872615Z", + "start_time": "2026-03-13T18:10:39.663834Z" } }, "cell_type": "code", @@ -1426,17 +1430,17 @@ "│ --- ┆ --- ┆ --- │\n", "│ str ┆ f64 ┆ str │\n", "╞════════════╪══════════╪═══════════╡\n", - "│ 2025-01-02 ┆ 0.186959 ┆ 600082.SH │\n", - "│ 2025-01-02 ┆ 0.088174 ┆ 600844.SH │\n", - "│ 2025-01-02 ┆ 0.079581 ┆ 600683.SH │\n", - "│ 2025-01-02 ┆ 0.07805 ┆ 600193.SH │\n", - "│ 2025-01-02 ┆ 0.074266 ┆ 002343.SZ │\n", + "│ 2025-01-02 ┆ 0.022198 ┆ 002480.SZ │\n", + "│ 2025-01-02 ┆ 0.018886 ┆ 000826.SZ │\n", + "│ 2025-01-02 ┆ 0.018845 ┆ 600683.SH │\n", + "│ 2025-01-02 ┆ 0.016044 ┆ 000632.SZ │\n", + "│ 2025-01-02 ┆ 0.014577 ┆ 002072.SZ │\n", "│ … ┆ … ┆ … │\n", - "│ 2025-01-06 ┆ 0.185272 ┆ 600082.SH │\n", - "│ 2025-01-06 ┆ 0.137456 ┆ 000668.SZ │\n", - "│ 2025-01-06 ┆ 0.125342 ┆ 002848.SZ │\n", - "│ 2025-01-06 ┆ 0.114527 ┆ 002343.SZ │\n", - "│ 2025-01-06 ┆ 0.101892 ┆ 600193.SH │\n", + "│ 2025-01-06 ┆ 0.03183 ┆ 600683.SH │\n", + "│ 2025-01-06 ┆ 0.023015 ┆ 000701.SZ │\n", + "│ 2025-01-06 ┆ 0.018886 ┆ 000826.SZ │\n", + "│ 2025-01-06 ┆ 0.016044 ┆ 000632.SZ │\n", + "│ 2025-01-06 ┆ 0.014577 ┆ 002072.SZ │\n", "└────────────┴──────────┴───────────┘\n", "\n", "训练流程完成！\n" diff --git a/src/experiment/learn_to_rank.py b/src/experiment/learn_to_rank.py index 15a6b2a..aa3a268 100644 --- a/src/experiment/learn_to_rank.py +++ b/src/experiment/learn_to_rank.py @@ -302,14 +302,14 @@ SELECTED_FACTORS = [ "return_5_rank", "EP_rank", "pe_expansion_trend", - # "value_price_divergence", + "value_price_divergence", "active_market_cap", - # "ebit_rank", + "ebit_rank", ] # 因子定义字典（完整因子库） FACTOR_DEFINITIONS = { - # "turnover_rate_volatility": "ts_std(log(turnover_rate), 20)" + "turnover_rate_volatility": "ts_std(log(turnover_rate), 20)" } # Label 因子定义（不参与训练，用于计算目标） diff --git a/src/factors/engine/factor_engine.py b/src/factors/engine/factor_engine.py index 25c9e35..56b2f72 100644 --- a/src/factors/engine/factor_engine.py +++ b/src/factors/engine/factor_engine.py @@ -335,13 +335,28 @@ class FactorEngine: for plan in plans: all_specs.extend(plan.data_specs) - # 去重数据规格（基于表名） - seen_tables: set = set() - unique_specs: List[DataSpec] = [] + # 合并相同表的字段（而不是简单地去重） + table_to_columns: Dict[str, Set[str]] = {} + table_to_spec: Dict[str, DataSpec] = {} for spec in all_specs: - if spec.table not in seen_tables: - seen_tables.add(spec.table) - unique_specs.append(spec) + if spec.table not in table_to_columns: + table_to_columns[spec.table] = set() + table_to_spec[spec.table] = spec + table_to_columns[spec.table].update(spec.columns) + + # 创建合并后的数据规格 + unique_specs: List[DataSpec] = [] + for table_name, columns in table_to_columns.items(): + original_spec = table_to_spec[table_name] + unique_specs.append( + DataSpec( + table=table_name, + columns=list(columns), + join_type=original_spec.join_type, + left_on=original_spec.left_on, + right_on=original_spec.right_on, + ) + ) # 4. 从路由器获取核心宽表 core_data = self.router.fetch_data(