diff --git a/src/experiment/regression.ipynb b/src/experiment/regression.ipynb
index 5f5cfde..d6f2e10 100644
--- a/src/experiment/regression.ipynb
+++ b/src/experiment/regression.ipynb
@@ -11,8 +11,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:40.563481Z",
-     "start_time": "2026-03-08T03:32:39.994884Z"
+     "end_time": "2026-03-08T06:10:19.495013Z",
+     "start_time": "2026-03-08T06:10:18.913682Z"
     }
    },
    "source": [
@@ -49,8 +49,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:40.574743Z",
-     "start_time": "2026-03-08T03:32:40.571165Z"
+     "end_time": "2026-03-08T06:10:19.508163Z",
+     "start_time": "2026-03-08T06:10:19.502562Z"
     }
    },
    "cell_type": "code",
@@ -94,7 +94,7 @@
     "\n",
     "    # 计算因子（全市场数据）\n",
     "    print(f\"\\n计算因子: {start_date} - {end_date}\")\n",
-    "    factor_names = feature_cols + [\"return_5\"]  # 包含 label\n",
+    "    factor_names = feature_cols + [LABEL_NAME]  # 包含 label\n",
     "\n",
     "    data = engine.compute(\n",
     "        factor_names=factor_names,\n",
@@ -124,13 +124,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:40.585729Z",
-     "start_time": "2026-03-08T03:32:40.579837Z"
+     "end_time": "2026-03-08T06:10:19.519954Z",
+     "start_time": "2026-03-08T06:10:19.515047Z"
     }
    },
    "cell_type": "code",
    "source": [
     "# 特征因子定义字典：新增因子只需在此处添加一行\n",
+    "LABEL_NAME = 'future_return_5'\n",
+    "\n",
     "FACTOR_DEFINITIONS = {\n",
     "    # 1. 价格动量因子\n",
     "    \"ma5\": \"ts_mean(close, 5)\",\n",
@@ -154,18 +156,36 @@
     "    \"market_cap_rank\": \"cs_rank(total_mv)\",\n",
     "    # 7. 价格位置因子\n",
     "    \"high_low_ratio\": \"(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\",\n",
+    "    # 8. 技术指标因子（3.1 完全可实现）\n",
+    "    \"turnover_rate_mean_5\": \"ts_mean(turnover_rate, 5)\",  # 5日均换手率\n",
+    "    \"bbi_ratio_factor\": \"(ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / 4 / close\",  # BBI比率\n",
+    "    # 9. ARBR 因子（3.1 完全可实现）\n",
+    "    # \"AR\": \"ts_sum(high - open, 26) / ts_sum(open - low, 26) * 100\",  # AR人气指标\n",
+    "    # \"BR\": \"ts_sum(max_(0, high - ts_delay(close, 1)), 26) / ts_sum(max_(0, ts_delay(close, 1) - low), 26) * 100\",  # BR意愿指标\n",
+    "    # \"AR_BR\": \"AR - BR\",  # ARBR差值\n",
+    "    # 10. 成交量因子（3.1 完全可实现）\n",
+    "    \"volume_change_rate\": \"ts_mean(vol, 2) / ts_mean(vol, 10) - 1\",  # 成交量变化率\n",
+    "    \"turnover_deviation\": \"(turnover_rate - ts_mean(turnover_rate, 3)) / ts_std(turnover_rate, 3)\",  # 换手率偏离度\n",
+    "    \"vol_std_5\": \"ts_std(ts_delta(vol, 1), 5)\",  # 成交量变化标准差\n",
+    "    # 11. 收益率因子（3.1 完全可实现）\n",
+    "    # \"return_5\": \"close / ts_delay(close, 5) - 1\",  # 5日收益率\n",
+    "    \"std_return_5\": \"ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 5)\",  # 5日收益率标准差\n",
+    "    \"std_return_90\": \"ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 90)\",  # 90日收益率标准差\n",
+    "    # 12. 截面排序因子（3.1 完全可实现）\n",
+    "    \"cs_rank_volume_ratio\": \"cs_rank(volume_ratio)\",  # 量比截面排名\n",
+    "    \"cs_rank_turnover_rate\": \"cs_rank(turnover_rate)\",  # 换手率截面排名\n",
     "    \"n_income_rank\": \"cs_rank(n_income)\",  # 净利润截面排名\n",
-    "    # 8. 财务数据因子（来自利润表 financial_income）\n",
+    "    # 13. 财务数据因子（来自利润表 financial_income）\n",
     "    \"operate_profit_rank\": \"cs_rank(operate_profit)\",  # 营业利润截面排名\n",
     "    \"total_profit_rank\": \"cs_rank(total_profit)\",  # 利润总额截面排名\n",
     "    \"ebit_rank\": \"cs_rank(ebit)\",  # 息税前利润截面排名\n",
     "    \"ebitda_rank\": \"cs_rank(ebitda)\",  # 息税折旧摊销前利润截面排名\n",
-    "    # 9. 财务数据因子（来自资产负债表 financial_balance）\n",
+    "    # 14. 财务数据因子（来自资产负债表 financial_balance）\n",
     "    \"total_liab_rank\": \"cs_rank(total_liab)\",  # 总负债截面排名\n",
     "    \"money_cap_rank\": \"cs_rank(money_cap)\",  # 货币资金截面排名\n",
-    "    # 10. 财务数据因子（来自现金流量表 financial_cashflow）\n",
+    "    # 15. 财务数据因子（来自现金流量表 financial_cashflow）\n",
     "    \"n_cashflow_act_rank\": \"cs_rank(n_cashflow_act)\",  # 经营活动现金流净额截面排名\n",
-    "    # 11. 财务估值因子\n",
+    "    # 16. 财务估值因子\n",
     "    \"profit_to_market_cap\": \"n_income / (total_mv + 1e-8)\",  # 净利润率（净利润/市值）\n",
     "    \"cashflow_to_market_cap\": \"n_cashflow_act / (total_mv + 1e-8)\",  # 经营现金流/市值\n",
     "    \"operate_profit_to_market_cap\": \"operate_profit / (total_mv + 1e-8)\",  # 营业利润/市值\n",
@@ -173,7 +193,7 @@
     "\n",
     "# Label 因子定义（不参与训练，用于计算目标）\n",
     "LABEL_FACTOR = {\n",
-    "    \"return_5\": \"(ts_delay(close, -5) / close) - 1\",  # 未来5日收益率\n",
+    "    LABEL_NAME: \"(ts_delay(close, -5) / close) - 1\",  # 未来5日收益率\n",
     "}"
    ],
    "outputs": [],
@@ -190,8 +210,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:40.593766Z",
-     "start_time": "2026-03-08T03:32:40.590001Z"
+     "end_time": "2026-03-08T06:10:19.527636Z",
+     "start_time": "2026-03-08T06:10:19.523669Z"
     }
    },
    "source": [
@@ -204,7 +224,7 @@
     "VAL_START = \"20240101\"\n",
     "VAL_END = \"20241231\"\n",
     "TEST_START = \"20250101\"\n",
-    "TEST_END = \"20251231\"\n",
+    "TEST_END = \"20261231\"\n",
     "\n",
     "# 模型参数配置\n",
     "MODEL_PARAMS = {\n",
@@ -268,8 +288,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:46.956583Z",
-     "start_time": "2026-03-08T03:32:40.597894Z"
+     "end_time": "2026-03-08T06:10:27.660787Z",
+     "start_time": "2026-03-08T06:10:19.531220Z"
     }
    },
    "source": [
@@ -284,7 +304,7 @@
     "# 2. 使用字符串表达式定义因子\n",
     "print(\"\\n[2] 定义因子（字符串表达式）\")\n",
     "feature_cols = create_factors_with_strings(engine, FACTOR_DEFINITIONS, LABEL_FACTOR)\n",
-    "target_col = \"return_5\"\n",
+    "target_col = LABEL_NAME\n",
     "\n",
     "# 3. 准备数据（使用模块级别的日期配置）\n",
     "print(\"\\n[3] 准备数据\")\n",
@@ -380,6 +400,15 @@
       "  - vol_ma20: ts_mean(vol, 20)\n",
       "  - market_cap_rank: cs_rank(total_mv)\n",
       "  - high_low_ratio: (close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\n",
+      "  - turnover_rate_mean_5: ts_mean(turnover_rate, 5)\n",
+      "  - bbi_ratio_factor: (ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / 4 / close\n",
+      "  - volume_change_rate: ts_mean(vol, 2) / ts_mean(vol, 10) - 1\n",
+      "  - turnover_deviation: (turnover_rate - ts_mean(turnover_rate, 3)) / ts_std(turnover_rate, 3)\n",
+      "  - vol_std_5: ts_std(ts_delta(vol, 1), 5)\n",
+      "  - std_return_5: ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 5)\n",
+      "  - std_return_90: ts_std((close - ts_delay(close, 1)) / ts_delay(close, 1), 90)\n",
+      "  - cs_rank_volume_ratio: cs_rank(volume_ratio)\n",
+      "  - cs_rank_turnover_rate: cs_rank(turnover_rate)\n",
       "  - n_income_rank: cs_rank(n_income)\n",
       "  - operate_profit_rank: cs_rank(operate_profit)\n",
       "  - total_profit_rank: cs_rank(total_profit)\n",
@@ -393,11 +422,11 @@
       "  - operate_profit_to_market_cap: operate_profit / (total_mv + 1e-8)\n",
       "\n",
       "注册 Label 因子:\n",
-      "  - return_5: (ts_delay(close, -5) / close) - 1\n",
+      "  - future_return_5: (ts_delay(close, -5) / close) - 1\n",
       "\n",
-      "特征因子数: 25\n",
-      "Label: return_5\n",
-      "已注册因子总数: 26\n",
+      "特征因子数: 34\n",
+      "Label: future_return_5\n",
+      "已注册因子总数: 35\n",
       "\n",
       "[3] 准备数据\n",
       "\n",
@@ -405,7 +434,7 @@
       "准备数据\n",
       "================================================================================\n",
       "\n",
-      "计算因子: 20200101 - 20251231\n"
+      "计算因子: 20200101 - 20261231\n"
      ]
     },
     {
@@ -420,36 +449,36 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "数据形状: (7044952, 42)\n",
-      "数据列: ['ts_code', 'trade_date', 'vol', 'close', 'low', 'high', 'total_mv', 'f_ann_date', 'n_income', 'ebitda', 'total_profit', 'ebit', 'operate_profit', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'return_5']\n",
+      "数据形状: (7255513, 53)\n",
+      "数据列: ['ts_code', 'trade_date', 'vol', 'low', 'high', 'volume_ratio', 'turnover_rate', 'close', 'total_mv', 'f_ann_date', 'total_profit', 'ebit', 'operate_profit', 'ebitda', 'n_income', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'turnover_rate_mean_5', 'bbi_ratio_factor', 'volume_change_rate', 'turnover_deviation', 'vol_std_5', 'std_return_5', 'std_return_90', 'cs_rank_volume_ratio', 'cs_rank_turnover_rate', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'future_return_5']\n",
       "\n",
       "前5行预览:\n",
-      "shape: (5, 42)\n",
+      "shape: (5, 53)\n",
       "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
-      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
       "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
       "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
       "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1841.69 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
+      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1806.75 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 1         ┆ 33        ┆ 4         ┆           │\n",
-      "│ 000001.SZ ┆ 20200103   ┆ 1.1162e6  ┆ 1875.53 ┆ … ┆ 708.50174 ┆ 2533.9412 ┆ 921.22323 ┆ -0.02852  │\n",
+      "│ 000001.SZ ┆ 20200103   ┆ 1.1162e6  ┆ 1847.15 ┆ … ┆ 708.50174 ┆ 2533.9412 ┆ 921.22323 ┆ -0.02852  │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 4         ┆ 96        ┆ 7         ┆           │\n",
-      "│ 000001.SZ ┆ 20200106   ┆ 862083.5  ┆ 1863.52 ┆ … ┆ 713.06736 ┆ 2550.2701 ┆ 927.15964 ┆ -0.004685 │\n",
+      "│ 000001.SZ ┆ 20200106   ┆ 862083.5  ┆ 1846.05 ┆ … ┆ 713.06736 ┆ 2550.2701 ┆ 927.15964 ┆ -0.004685 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 8         ┆ 5         ┆ 9         ┆           │\n",
-      "│ 000001.SZ ┆ 20200107   ┆ 728607.56 ┆ 1872.26 ┆ … ┆ 709.74110 ┆ 2538.3738 ┆ 922.83470 ┆ -0.022743 │\n",
+      "│ 000001.SZ ┆ 20200107   ┆ 728607.56 ┆ 1850.42 ┆ … ┆ 709.74110 ┆ 2538.3738 ┆ 922.83470 ┆ -0.022743 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 6         ┆ 46        ┆ 6         ┆           │\n",
-      "│ 000001.SZ ┆ 20200108   ┆ 847824.12 ┆ 1818.76 ┆ … ┆ 730.61584 ┆ 2613.0319 ┆ 949.97690 ┆ -0.008401 │\n",
+      "│ 000001.SZ ┆ 20200108   ┆ 847824.12 ┆ 1815.49 ┆ … ┆ 730.61584 ┆ 2613.0319 ┆ 949.97690 ┆ -0.008401 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 4         ┆ 01        ┆ 3         ┆           │\n",
       "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
       "\n",
       "[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: 25\n",
-      "[配置] 目标变量: return_5\n"
+      "[配置] 测试期: 20250101 - 20261231\n",
+      "[配置] 特征数: 34\n",
+      "[配置] 目标变量: future_return_5\n"
      ]
     }
    ],
@@ -466,8 +495,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:54.939339Z",
-     "start_time": "2026-03-08T03:32:46.966714Z"
+     "end_time": "2026-03-08T06:10:37.221864Z",
+     "start_time": "2026-03-08T06:10:27.670279Z"
     }
    },
    "source": [
@@ -503,11 +532,11 @@
       "[步骤 1/6] 股票池筛选\n",
       "------------------------------------------------------------\n",
       "  执行每日独立筛选股票池...\n",
-      "  筛选前数据规模: (7044952, 42)\n",
-      "  筛选后数据规模: (4532198, 42)\n",
-      "  筛选前股票数: 5678\n",
-      "  筛选后股票数: 3359\n",
-      "  删除记录数: 2512754\n"
+      "  筛选前数据规模: (7255513, 53)\n",
+      "  筛选后数据规模: (4654331, 53)\n",
+      "  筛选前股票数: 5694\n",
+      "  筛选后股票数: 3364\n",
+      "  删除记录数: 2601182\n"
      ]
     }
    ],
@@ -517,8 +546,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:56.279010Z",
-     "start_time": "2026-03-08T03:32:54.952714Z"
+     "end_time": "2026-03-08T06:10:39.192697Z",
+     "start_time": "2026-03-08T06:10:37.226314Z"
     }
    },
    "source": [
@@ -563,45 +592,45 @@
       "\n",
       "[步骤 2/6] 划分训练集、验证集和测试集\n",
       "------------------------------------------------------------\n",
-      "  训练集数据规模: (2991506, 42)\n",
-      "  验证集数据规模: (769485, 42)\n",
-      "  测试集数据规模: (771207, 42)\n",
+      "  训练集数据规模: (2991506, 53)\n",
+      "  验证集数据规模: (769485, 53)\n",
+      "  测试集数据规模: (893340, 53)\n",
       "  训练集股票数: 3297\n",
       "  验证集股票数: 3220\n",
-      "  测试集股票数: 3215\n",
+      "  测试集股票数: 3220\n",
       "  训练集日期范围: 20200102 - 20231229\n",
       "  验证集日期范围: 20240102 - 20241231\n",
-      "  测试集日期范围: 20250102 - 20251231\n",
+      "  测试集日期范围: 20250102 - 20260306\n",
       "\n",
       "  训练集前5行预览:\n",
-      "shape: (5, 42)\n",
+      "shape: (5, 53)\n",
       "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
-      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
       "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
       "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
       "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1841.69 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
+      "│ 000001.SZ ┆ 20200102   ┆ 1.5302e6  ┆ 1806.75 ┆ … ┆ 721.52104 ┆ 2580.5045 ┆ 938.15146 ┆ -0.004746 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 1         ┆ 33        ┆ 4         ┆           │\n",
-      "│ 000002.SZ ┆ 20200102   ┆ 1012130.4 ┆ 4832.29 ┆ … ┆ 776.91820 ┆ 47.131053 ┆ 1140.2493 ┆ -0.011057 │\n",
+      "│ 000002.SZ ┆ 20200102   ┆ 1012130.4 ┆ 4824.87 ┆ … ┆ 776.91820 ┆ 47.131053 ┆ 1140.2493 ┆ -0.011057 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 1         ┆           ┆ 95        ┆           │\n",
-      "│ 000004.SZ ┆ 20200102   ┆ 17853.2   ┆ 90.75   ┆ … ┆ -69.58089 ┆ -52.61755 ┆ -24.82135 ┆ -0.000441 │\n",
+      "│ 000004.SZ ┆ 20200102   ┆ 17853.2   ┆ 90.1    ┆ … ┆ -69.58089 ┆ -52.61755 ┆ -24.82135 ┆ -0.000441 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 5         ┆ 4         ┆ 9         ┆           │\n",
-      "│ 000005.SZ ┆ 20200102   ┆ 104134.12 ┆ 29.1    ┆ … ┆ 142.55925 ┆ 385.57490 ┆ 208.12520 ┆ 0.022337  │\n",
+      "│ 000005.SZ ┆ 20200102   ┆ 104134.12 ┆ 28.82   ┆ … ┆ 142.55925 ┆ 385.57490 ┆ 208.12520 ┆ 0.022337  │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 6         ┆ 4         ┆ 2         ┆           │\n",
-      "│ 000006.SZ ┆ 20200102   ┆ 124751.76 ┆ 191.3   ┆ … ┆ 633.27582 ┆ 650.95370 ┆ 819.10495 ┆ 0.012964  │\n",
+      "│ 000006.SZ ┆ 20200102   ┆ 124751.76 ┆ 190.24  ┆ … ┆ 633.27582 ┆ 650.95370 ┆ 819.10495 ┆ 0.012964  │\n",
       "│           ┆            ┆           ┆         ┆   ┆           ┆ 3         ┆ 5         ┆           │\n",
       "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
       "\n",
       "  验证集前5行预览:\n",
-      "shape: (5, 42)\n",
+      "shape: (5, 53)\n",
       "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
-      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
       "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
       "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
       "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
@@ -609,33 +638,33 @@
       "│           ┆            ┆           ┆         ┆   ┆ 09        ┆ 45        ┆ 84        ┆           │\n",
       "│ 000002.SZ ┆ 20240102   ┆ 811106.29 ┆ 1844.3  ┆ … ┆ 1736.4093 ┆ 19.432701 ┆ 2329.7434 ┆ -0.026601 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 99        ┆           ┆ 1         ┆           │\n",
-      "│ 000004.SZ ┆ 20240102   ┆ 28867.0   ┆ 65.59   ┆ … ┆ -168.7552 ┆ -184.4013 ┆ -192.7135 ┆ -0.014789 │\n",
+      "│ 000004.SZ ┆ 20240102   ┆ 28867.0   ┆ 65.23   ┆ … ┆ -168.7552 ┆ -184.4013 ┆ -192.7135 ┆ -0.014789 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 72        ┆ 85        ┆ 84        ┆           │\n",
-      "│ 000005.SZ ┆ 20240102   ┆ 63028.0   ┆ 10.38   ┆ … ┆ -96.94997 ┆ -295.0388 ┆ -46.06373 ┆ -0.05395  │\n",
+      "│ 000005.SZ ┆ 20240102   ┆ 63028.0   ┆ 10.01   ┆ … ┆ -96.94997 ┆ -295.0388 ┆ -46.06373 ┆ -0.05395  │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 7         ┆ 72        ┆ 6         ┆           │\n",
-      "│ 000006.SZ ┆ 20240102   ┆ 261947.19 ┆ 177.64  ┆ … ┆ -6.971845 ┆ -51.5536  ┆ -5.32671  ┆ -0.013454 │\n",
+      "│ 000006.SZ ┆ 20240102   ┆ 261947.19 ┆ 176.84  ┆ … ┆ -6.971845 ┆ -51.5536  ┆ -5.32671  ┆ -0.013454 │\n",
       "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
       "\n",
       "  测试集前5行预览:\n",
-      "shape: (5, 42)\n",
+      "shape: (5, 53)\n",
       "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ vol       ┆ close   ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5  │\n",
-      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---       │\n",
-      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64       │\n",
-      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆           │\n",
+      "│ ts_code   ┆ trade_date ┆ vol       ┆ low     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
+      "│ ---       ┆ ---        ┆ ---       ┆ ---     ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
+      "│ str       ┆ str        ┆ f64       ┆ f64     ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
+      "│           ┆            ┆           ┆         ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
       "│           ┆            ┆           ┆         ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
       "│           ┆            ┆           ┆         ┆   ┆           ┆           ┆ f64       ┆           │\n",
       "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
-      "│ 000001.SZ ┆ 20250102   ┆ 1.8196e6  ┆ 1460.57 ┆ … ┆ 1791.1304 ┆ 6183.5904 ┆ 2158.1117 ┆ -0.002622 │\n",
+      "│ 000001.SZ ┆ 20250102   ┆ 1.8196e6  ┆ 1455.46 ┆ … ┆ 1791.1304 ┆ 6183.5904 ┆ 2158.1117 ┆ -0.002622 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 08        ┆ 38        ┆ 45        ┆           │\n",
-      "│ 000002.SZ ┆ 20250102   ┆ 1.1827e6  ┆ 1291.92 ┆ … ┆ -1933.116 ┆ -1110.658 ┆ -1729.069 ┆ -0.022509 │\n",
+      "│ 000002.SZ ┆ 20250102   ┆ 1.1827e6  ┆ 1284.65 ┆ … ┆ -1933.116 ┆ -1110.658 ┆ -1729.069 ┆ -0.022509 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 105       ┆ 303       ┆ 737       ┆           │\n",
-      "│ 000004.SZ ┆ 20250102   ┆ 119760.37 ┆ 57.63   ┆ … ┆ -199.1144 ┆ -126.8907 ┆ -197.3308 ┆ -0.064897 │\n",
+      "│ 000004.SZ ┆ 20250102   ┆ 119760.37 ┆ 54.17   ┆ … ┆ -199.1144 ┆ -126.8907 ┆ -197.3308 ┆ -0.064897 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 31        ┆ 63        ┆ 47        ┆           │\n",
-      "│ 000006.SZ ┆ 20250102   ┆ 307195.1  ┆ 288.12  ┆ … ┆ -646.1294 ┆ 74.343232 ┆ -637.5489 ┆ -0.048278 │\n",
+      "│ 000006.SZ ┆ 20250102   ┆ 307195.1  ┆ 285.33  ┆ … ┆ -646.1294 ┆ 74.343232 ┆ -637.5489 ┆ -0.048278 │\n",
       "│           ┆            ┆           ┆         ┆   ┆ 33        ┆           ┆ 17        ┆           │\n",
-      "│ 000007.SZ ┆ 20250102   ┆ 68219.01  ┆ 58.15   ┆ … ┆ 6.740918  ┆ 108.91759 ┆ 22.556002 ┆ 0.015649  │\n",
-      "│           ┆            ┆           ┆         ┆   ┆           ┆ 8         ┆           ┆           │\n",
+      "│ 000007.SZ ┆ 20250102   ┆ 68219.01  ┆ 57.49   ┆ … ┆ 6.740918  ┆ 783.72753 ┆ 22.556002 ┆ 0.015649  │\n",
+      "│           ┆            ┆           ┆         ┆   ┆           ┆ 9         ┆           ┆           │\n",
       "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n"
      ]
     }
@@ -646,8 +675,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:32:57.302228Z",
-     "start_time": "2026-03-08T03:32:56.290367Z"
+     "end_time": "2026-03-08T06:10:40.545732Z",
+     "start_time": "2026-03-08T06:10:39.198908Z"
     }
    },
    "source": [
@@ -701,27 +730,24 @@
       "      处理后记录数: 2991506\n",
       "\n",
       "  训练集处理后前5行预览:\n",
-      "shape: (5, 42)\n",
-      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-      "│ ts_code   ┆ trade_dat ┆ vol       ┆ close     ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ return_5 │\n",
-      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ ---      │\n",
-      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ f64      │\n",
-      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ ---       ┆ p         ┆          │\n",
-      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ f64       ┆ ---       ┆          │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆ f64       ┆          │\n",
-      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-      "│ 000001.SZ ┆ 20200102  ┆ 4.749919  ┆ 7.139221  ┆ … ┆ 1.228197  ┆ 2.569741  ┆ 1.426297  ┆ -0.00474 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 6        │\n",
-      "│ 000002.SZ ┆ 20200102  ┆ 2.92576   ┆ 7.139221  ┆ … ┆ 1.353457  ┆ -0.145372 ┆ 1.83338   ┆ -0.01105 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 7        │\n",
-      "│ 000004.SZ ┆ 20200102  ┆ -0.574944 ┆ 0.095339  ┆ … ┆ -0.560579 ┆ -0.252276 ┆ -0.513404 ┆ -0.00044 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 1        │\n",
-      "│ 000005.SZ ┆ 20200102  ┆ -0.271162 ┆ -0.301277 ┆ … ┆ -0.080905 ┆ 0.217351  ┆ -0.044183 ┆ 0.022337 │\n",
-      "│ 000006.SZ ┆ 20200102  ┆ -0.19857  ┆ 0.742213  ┆ … ┆ 1.028664  ┆ 0.501768  ┆ 1.186504  ┆ 0.012964 │\n",
-      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
+      "shape: (5, 53)\n",
+      "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+      "│ ts_code   ┆ trade_dat ┆ vol       ┆ low      ┆ … ┆ profit_to ┆ cashflow_ ┆ operate_p ┆ future_re │\n",
+      "│ ---       ┆ e         ┆ ---       ┆ ---      ┆   ┆ _market_c ┆ to_market ┆ rofit_to_ ┆ turn_5    │\n",
+      "│ str       ┆ ---       ┆ f64       ┆ f64      ┆   ┆ ap        ┆ _cap      ┆ market_ca ┆ ---       │\n",
+      "│           ┆ str       ┆           ┆          ┆   ┆ ---       ┆ ---       ┆ p         ┆ f64       │\n",
+      "│           ┆           ┆           ┆          ┆   ┆ f64       ┆ f64       ┆ ---       ┆           │\n",
+      "│           ┆           ┆           ┆          ┆   ┆           ┆           ┆ f64       ┆           │\n",
+      "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+      "│ 000001.SZ ┆ 20200102  ┆ 4.749919  ┆ 7.140527 ┆ … ┆ 1.223327  ┆ 2.58711   ┆ 1.420902  ┆ -0.004746 │\n",
+      "│ 000002.SZ ┆ 20200102  ┆ 2.92576   ┆ 7.140527 ┆ … ┆ 1.348024  ┆ -0.147052 ┆ 1.826328  ┆ -0.011057 │\n",
+      "│ 000004.SZ ┆ 20200102  ┆ -0.574944 ┆ 0.102278 ┆ … ┆ -0.557417 ┆ -0.254707 ┆ -0.510901 ┆ -0.000441 │\n",
+      "│ 000005.SZ ┆ 20200102  ┆ -0.271162 ┆ -0.29962 ┆ … ┆ -0.079896 ┆ 0.218216  ┆ -0.043591 ┆ 0.022337  │\n",
+      "│ 000006.SZ ┆ 20200102  ┆ -0.19857  ┆ 0.759033 ┆ … ┆ 1.02469   ┆ 0.504628  ┆ 1.182085  ┆ 0.012964  │\n",
+      "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n",
       "\n",
       "  训练集特征统计:\n",
-      "    特征数: 25\n",
+      "    特征数: 34\n",
       "    样本数: 2991506\n",
       "    缺失值统计:\n",
       "      ma5: 11541 (0.39%)\n",
@@ -738,8 +764,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:33:14.426213Z",
-     "start_time": "2026-03-08T03:32:57.306610Z"
+     "end_time": "2026-03-08T06:10:58.361151Z",
+     "start_time": "2026-03-08T06:10:40.549989Z"
     }
    },
    "source": [
@@ -775,8 +801,8 @@
       "------------------------------------------------------------\n",
       "  模型类型: LightGBM\n",
       "  训练样本数: 2991506\n",
-      "  特征数: 25\n",
-      "  目标变量: return_5\n",
+      "  特征数: 34\n",
+      "  目标变量: future_return_5\n",
       "\n",
       "  目标变量统计:\n",
       "    均值: 0.001610\n",
@@ -796,8 +822,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:33:14.547157Z",
-     "start_time": "2026-03-08T03:33:14.431622Z"
+     "end_time": "2026-03-08T06:10:58.523203Z",
+     "start_time": "2026-03-08T06:10:58.364995Z"
     }
    },
    "source": [
@@ -826,14 +852,14 @@
       "[步骤 5/6] 测试集数据处理\n",
       "------------------------------------------------------------\n",
       "  [1/3] 应用处理器: NullFiller\n",
-      "      处理前记录数: 771207\n",
-      "      处理后记录数: 771207\n",
+      "      处理前记录数: 893340\n",
+      "      处理后记录数: 893340\n",
       "  [2/3] 应用处理器: Winsorizer\n",
-      "      处理前记录数: 771207\n",
-      "      处理后记录数: 771207\n",
+      "      处理前记录数: 893340\n",
+      "      处理后记录数: 893340\n",
       "  [3/3] 应用处理器: StandardScaler\n",
-      "      处理前记录数: 771207\n",
-      "      处理后记录数: 771207\n"
+      "      处理前记录数: 893340\n",
+      "      处理后记录数: 893340\n"
      ]
     }
    ],
@@ -843,8 +869,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:33:15.644580Z",
-     "start_time": "2026-03-08T03:33:14.556580Z"
+     "end_time": "2026-03-08T06:10:59.948592Z",
+     "start_time": "2026-03-08T06:10:58.533879Z"
     }
    },
    "source": [
@@ -874,15 +900,15 @@
       "\n",
       "[步骤 6/6] 生成预测\n",
       "------------------------------------------------------------\n",
-      "  测试样本数: 771207\n",
+      "  测试样本数: 893340\n",
       "  预测中...\n",
       "  预测完成！\n",
       "\n",
       "  预测结果统计:\n",
-      "    均值: -0.002611\n",
-      "    标准差: 0.006952\n",
-      "    最小值: -0.104909\n",
-      "    最大值: 0.093437\n"
+      "    均值: -0.000990\n",
+      "    标准差: 0.008152\n",
+      "    最小值: -0.145569\n",
+      "    最大值: 0.102702\n"
      ]
     }
    ],
@@ -899,8 +925,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:33:20.917840Z",
-     "start_time": "2026-03-08T03:33:15.648621Z"
+     "end_time": "2026-03-08T06:11:05.252510Z",
+     "start_time": "2026-03-08T06:10:59.952123Z"
     }
    },
    "source": [
@@ -979,19 +1005,19 @@
       "重新训练模型以收集训练指标...\n",
       "Training until validation scores don't improve for 100 rounds\n",
       "Early stopping, best iteration is:\n",
-      "[175]\ttrain's l1: 0.0422897\tval's l1: 0.0535436\n",
+      "[147]\ttrain's l1: 0.0424037\tval's l1: 0.0535696\n",
       "训练完成，指标已收集\n",
       "\n",
       "评估指标: l1\n",
       "\n",
       "[早停信息]\n",
       "  配置的最大轮数: 1000\n",
-      "  实际训练轮数: 275\n",
+      "  实际训练轮数: 247\n",
       "  早停状态: 已触发（连续100轮验证指标未改善）\n",
       "\n",
       "最终指标:\n",
-      "  训练 l1: 0.042114\n",
-      "  验证 l1: 0.053595\n"
+      "  训练 l1: 0.042166\n",
+      "  验证 l1: 0.053583\n"
      ]
     }
    ],
@@ -1001,8 +1027,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:33:21.167778Z",
-     "start_time": "2026-03-08T03:33:20.923061Z"
+     "end_time": "2026-03-08T06:11:05.495565Z",
+     "start_time": "2026-03-08T06:11:05.256254Z"
     }
    },
    "source": [
@@ -1050,7 +1076,7 @@
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ],
-      "image/png": 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      },
      "metadata": {},
      "output_type": "display_data",
@@ -1064,9 +1090,9 @@
      "text": [
       "\n",
       "[指标分析]\n",
-      "  最佳验证 l1: 0.053544\n",
-      "  最佳迭代轮数: 175\n",
-      "  早停建议: 如果验证指标连续10轮不下降，建议在第 175 轮停止训练\n",
+      "  最佳验证 l1: 0.053570\n",
+      "  最佳迭代轮数: 147\n",
+      "  早停建议: 如果验证指标连续10轮不下降，建议在第 147 轮停止训练\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"
      ]
@@ -1085,8 +1111,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:33:21.213471Z",
-     "start_time": "2026-03-08T03:33:21.183426Z"
+     "end_time": "2026-03-08T06:11:05.540372Z",
+     "start_time": "2026-03-08T06:11:05.504804Z"
     }
    },
    "source": [
@@ -1125,76 +1151,78 @@
       "训练结果\n",
       "================================================================================\n",
       "\n",
-      "结果数据形状: (771207, 43)\n",
-      "结果列: ['ts_code', 'trade_date', 'vol', 'close', 'low', 'high', 'total_mv', 'f_ann_date', 'n_income', 'ebitda', 'total_profit', 'ebit', 'operate_profit', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'return_5', 'prediction']\n",
+      "结果数据形状: (893340, 54)\n",
+      "结果列: ['ts_code', 'trade_date', 'vol', 'low', 'high', 'volume_ratio', 'turnover_rate', 'close', 'total_mv', 'f_ann_date', 'total_profit', 'ebit', 'operate_profit', 'ebitda', 'n_income', 'money_cap', 'total_liab', 'n_cashflow_act', 'ma5', 'ma10', 'ma20', 'ma_ratio', 'volatility_5', 'volatility_20', 'vol_ratio', 'return_10', 'return_20', 'return_diff', 'vol_ma5', 'vol_ma20', 'market_cap_rank', 'high_low_ratio', 'turnover_rate_mean_5', 'bbi_ratio_factor', 'volume_change_rate', 'turnover_deviation', 'vol_std_5', 'std_return_5', 'std_return_90', 'cs_rank_volume_ratio', 'cs_rank_turnover_rate', 'n_income_rank', 'operate_profit_rank', 'total_profit_rank', 'ebit_rank', 'ebitda_rank', 'total_liab_rank', 'money_cap_rank', 'n_cashflow_act_rank', 'profit_to_market_cap', 'cashflow_to_market_cap', 'operate_profit_to_market_cap', 'future_return_5', 'prediction']\n",
       "\n",
       "结果前10行预览:\n",
-      "shape: (10, 43)\n",
+      "shape: (10, 54)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
-      "│ ts_code   ┆ trade_dat ┆ vol       ┆ close     ┆ … ┆ cashflow_ ┆ operate_p ┆ return_5  ┆ predicti │\n",
-      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ ---       ┆ on       │\n",
-      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ f64       ┆ ---      │\n",
-      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆           ┆ f64      │\n",
+      "│ ts_code   ┆ trade_dat ┆ vol       ┆ low       ┆ … ┆ cashflow_ ┆ operate_p ┆ future_re ┆ predicti │\n",
+      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ turn_5    ┆ on       │\n",
+      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ ---       ┆ ---      │\n",
+      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆ f64       ┆ f64      │\n",
       "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ ---       ┆           ┆          │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆ f64       ┆           ┆          │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
-      "│ 000001.SZ ┆ 20250102  ┆ 5.587779  ┆ 7.139221  ┆ … ┆ 4.533499  ┆ 3.606645  ┆ -0.002622 ┆ -0.00794 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 7        │\n",
-      "│ 000002.SZ ┆ 20250102  ┆ 3.526191  ┆ 7.139221  ┆ … ┆ -1.386218 ┆ -3.946245 ┆ -0.022509 ┆ -0.00197 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 2        │\n",
-      "│ 000004.SZ ┆ 20250102  ┆ -0.216144 ┆ -0.117733 ┆ … ┆ -0.331877 ┆ -0.860887 ┆ -0.064897 ┆ 0.006379 │\n",
-      "│ 000006.SZ ┆ 20250102  ┆ 0.443786  ┆ 1.365091  ┆ … ┆ -0.116207 ┆ -1.747611 ┆ -0.048278 ┆ 0.002845 │\n",
-      "│ 000007.SZ ┆ 20250102  ┆ -0.397614 ┆ -0.114388 ┆ … ┆ -0.079153 ┆ -0.417972 ┆ 0.015649  ┆ -0.00445 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
-      "│ 000008.SZ ┆ 20250102  ┆ 3.219998  ┆ -0.079069 ┆ … ┆ -0.385751 ┆ -1.051162 ┆ -0.066939 ┆ -0.00372 │\n",
+      "│ 000001.SZ ┆ 20250102  ┆ 5.587779  ┆ 7.140527  ┆ … ┆ 4.53796   ┆ 3.597925  ┆ -0.002622 ┆ -0.00503 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 1        │\n",
+      "│ 000002.SZ ┆ 20250102  ┆ 3.526191  ┆ 7.140527  ┆ … ┆ -1.396605 ┆ -3.929764 ┆ -0.022509 ┆ 0.002998 │\n",
+      "│ 000004.SZ ┆ 20250102  ┆ -0.216144 ┆ -0.133365 ┆ … ┆ -0.334867 ┆ -0.856969 ┆ -0.064897 ┆ 0.000258 │\n",
+      "│ 000006.SZ ┆ 20250102  ┆ 0.443786  ┆ 1.382669  ┆ … ┆ -0.117683 ┆ -1.740083 ┆ -0.048278 ┆ 0.008562 │\n",
+      "│ 000007.SZ ┆ 20250102  ┆ -0.397614 ┆ -0.111591 ┆ … ┆ 0.647925  ┆ -0.415858 ┆ 0.015649  ┆ -0.00104 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 9        │\n",
-      "│ 000009.SZ ┆ 20250102  ┆ 0.204049  ┆ 0.025538  ┆ … ┆ 0.245489  ┆ 0.579808  ┆ -0.036045 ┆ 0.003052 │\n",
-      "│ 000010.SZ ┆ 20250102  ┆ 0.584268  ┆ -0.300634 ┆ … ┆ -0.781057 ┆ -1.022311 ┆ 0.092123  ┆ -0.01003 │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 7        │\n",
-      "│ 000011.SZ ┆ 20250102  ┆ -0.488782 ┆ -0.243891 ┆ … ┆ -1.733032 ┆ -0.464004 ┆ -0.022094 ┆ 0.000949 │\n",
-      "│ 000012.SZ ┆ 20250102  ┆ -0.060654 ┆ 0.501156  ┆ … ┆ 0.722727  ┆ 0.567525  ┆ -0.029188 ┆ 0.001858 │\n",
+      "│ 000008.SZ ┆ 20250102  ┆ 3.219998  ┆ -0.075651 ┆ … ┆ -0.389118 ┆ -1.046469 ┆ -0.066939 ┆ -0.00301 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
+      "│ 000009.SZ ┆ 20250102  ┆ 0.204049  ┆ 0.03066   ┆ … ┆ 0.246551  ┆ 0.57786   ┆ -0.036045 ┆ 0.014509 │\n",
+      "│ 000010.SZ ┆ 20250102  ┆ 0.584268  ┆ -0.299947 ┆ … ┆ -0.787198 ┆ -1.017736 ┆ 0.092123  ┆ -0.00703 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 3        │\n",
+      "│ 000011.SZ ┆ 20250102  ┆ -0.488782 ┆ -0.241316 ┆ … ┆ -1.745853 ┆ -0.461702 ┆ -0.022094 ┆ 0.002963 │\n",
+      "│ 000012.SZ ┆ 20250102  ┆ -0.060654 ┆ 0.51434   ┆ … ┆ 0.727138  ┆ 0.565626  ┆ -0.029188 ┆ 0.012271 │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
       "\n",
       "结果后5行预览:\n",
-      "shape: (5, 43)\n",
-      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬──────────┬───────────┐\n",
-      "│ ts_code   ┆ trade_dat ┆ vol       ┆ close     ┆ … ┆ cashflow_ ┆ operate_p ┆ return_5 ┆ predictio │\n",
-      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ ---      ┆ n         │\n",
-      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ f64      ┆ ---       │\n",
-      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆          ┆ f64       │\n",
-      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ ---       ┆          ┆           │\n",
-      "│           ┆           ┆           ┆           ┆   ┆           ┆ f64       ┆          ┆           │\n",
-      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪══════════╪═══════════╡\n",
-      "│ 605588.SH ┆ 20251231  ┆ -0.601605 ┆ -0.161094 ┆ … ┆ 0.196523  ┆ -0.667804 ┆ null     ┆ 0.000246  │\n",
-      "│ 605589.SH ┆ 20251231  ┆ -0.152478 ┆ -0.293364 ┆ … ┆ -0.343887 ┆ 0.27582   ┆ null     ┆ -0.001414 │\n",
-      "│ 605598.SH ┆ 20251231  ┆ -0.248806 ┆ 0.149122  ┆ … ┆ -0.191229 ┆ -0.349198 ┆ null     ┆ -0.011185 │\n",
-      "│ 605599.SH ┆ 20251231  ┆ -0.482522 ┆ -0.364967 ┆ … ┆ 1.283712  ┆ 0.931037  ┆ null     ┆ 0.00215   │\n",
-      "│ 689009.SH ┆ 20251231  ┆ -0.481066 ┆ -0.119084 ┆ … ┆ 1.09954   ┆ 0.730255  ┆ null     ┆ -0.001826 │\n",
-      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴──────────┴───────────┘\n",
+      "shape: (5, 54)\n",
+      "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
+      "│ ts_code   ┆ trade_dat ┆ vol       ┆ low       ┆ … ┆ cashflow_ ┆ operate_p ┆ future_re ┆ predicti │\n",
+      "│ ---       ┆ e         ┆ ---       ┆ ---       ┆   ┆ to_market ┆ rofit_to_ ┆ turn_5    ┆ on       │\n",
+      "│ str       ┆ ---       ┆ f64       ┆ f64       ┆   ┆ _cap      ┆ market_ca ┆ ---       ┆ ---      │\n",
+      "│           ┆ str       ┆           ┆           ┆   ┆ ---       ┆ p         ┆ f64       ┆ f64      │\n",
+      "│           ┆           ┆           ┆           ┆   ┆ f64       ┆ ---       ┆           ┆          │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆ f64       ┆           ┆          │\n",
+      "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
+      "│ 605588.SH ┆ 20260306  ┆ -0.594236 ┆ -0.130348 ┆ … ┆ 0.166521  ┆ -0.648847 ┆ null      ┆ -0.00224 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 6        │\n",
+      "│ 605589.SH ┆ 20260306  ┆ 0.108199  ┆ -0.253383 ┆ … ┆ -0.32234  ┆ 0.153492  ┆ null      ┆ -0.00560 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
+      "│ 605598.SH ┆ 20260306  ┆ -0.54733  ┆ -0.01433  ┆ … ┆ -0.191519 ┆ -0.305813 ┆ null      ┆ 0.003137 │\n",
+      "│ 605599.SH ┆ 20260306  ┆ -0.409848 ┆ -0.309195 ┆ … ┆ 0.812476  ┆ 0.480653  ┆ null      ┆ -0.00217 │\n",
+      "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 3        │\n",
+      "│ 689009.SH ┆ 20260306  ┆ -0.419186 ┆ -0.168059 ┆ … ┆ 1.301768  ┆ 0.905556  ┆ null      ┆ 0.001416 │\n",
+      "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘\n",
       "\n",
       "每日预测样本数统计:\n",
-      "  最小: 3147\n",
-      "  最大: 3186\n",
-      "  平均: 3173.69\n",
+      "  最小: 1040\n",
+      "  最大: 3192\n",
+      "  平均: 3167.87\n",
       "\n",
       "示例日期 20250102 的前10条预测:\n",
       "shape: (10, 4)\n",
-      "┌───────────┬────────────┬───────────┬────────────┐\n",
-      "│ ts_code   ┆ trade_date ┆ return_5  ┆ prediction │\n",
-      "│ ---       ┆ ---        ┆ ---       ┆ ---        │\n",
-      "│ str       ┆ str        ┆ f64       ┆ f64        │\n",
-      "╞═══════════╪════════════╪═══════════╪════════════╡\n",
-      "│ 000001.SZ ┆ 20250102   ┆ -0.002622 ┆ -0.007947  │\n",
-      "│ 000002.SZ ┆ 20250102   ┆ -0.022509 ┆ -0.001972  │\n",
-      "│ 000004.SZ ┆ 20250102   ┆ -0.064897 ┆ 0.006379   │\n",
-      "│ 000006.SZ ┆ 20250102   ┆ -0.048278 ┆ 0.002845   │\n",
-      "│ 000007.SZ ┆ 20250102   ┆ 0.015649  ┆ -0.004454  │\n",
-      "│ 000008.SZ ┆ 20250102   ┆ -0.066939 ┆ -0.003729  │\n",
-      "│ 000009.SZ ┆ 20250102   ┆ -0.036045 ┆ 0.003052   │\n",
-      "│ 000010.SZ ┆ 20250102   ┆ 0.092123  ┆ -0.010037  │\n",
-      "│ 000011.SZ ┆ 20250102   ┆ -0.022094 ┆ 0.000949   │\n",
-      "│ 000012.SZ ┆ 20250102   ┆ -0.029188 ┆ 0.001858   │\n",
-      "└───────────┴────────────┴───────────┴────────────┘\n"
+      "┌───────────┬────────────┬─────────────────┬────────────┐\n",
+      "│ ts_code   ┆ trade_date ┆ future_return_5 ┆ prediction │\n",
+      "│ ---       ┆ ---        ┆ ---             ┆ ---        │\n",
+      "│ str       ┆ str        ┆ f64             ┆ f64        │\n",
+      "╞═══════════╪════════════╪═════════════════╪════════════╡\n",
+      "│ 000001.SZ ┆ 20250102   ┆ -0.002622       ┆ -0.005031  │\n",
+      "│ 000002.SZ ┆ 20250102   ┆ -0.022509       ┆ 0.002998   │\n",
+      "│ 000004.SZ ┆ 20250102   ┆ -0.064897       ┆ 0.000258   │\n",
+      "│ 000006.SZ ┆ 20250102   ┆ -0.048278       ┆ 0.008562   │\n",
+      "│ 000007.SZ ┆ 20250102   ┆ 0.015649        ┆ -0.001049  │\n",
+      "│ 000008.SZ ┆ 20250102   ┆ -0.066939       ┆ -0.003014  │\n",
+      "│ 000009.SZ ┆ 20250102   ┆ -0.036045       ┆ 0.014509   │\n",
+      "│ 000010.SZ ┆ 20250102   ┆ 0.092123        ┆ -0.007033  │\n",
+      "│ 000011.SZ ┆ 20250102   ┆ -0.022094       ┆ 0.002963   │\n",
+      "│ 000012.SZ ┆ 20250102   ┆ -0.029188       ┆ 0.012271   │\n",
+      "└───────────┴────────────┴─────────────────┴────────────┘\n"
      ]
     }
    ],
@@ -1210,8 +1238,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:35:07.142015Z",
-     "start_time": "2026-03-08T03:35:06.791043Z"
+     "end_time": "2026-03-08T06:11:05.964933Z",
+     "start_time": "2026-03-08T06:11:05.551387Z"
     }
    },
    "cell_type": "code",
@@ -1274,7 +1302,7 @@
       "\n",
       "[1/1] 保存每日 Top 2 股票...\n",
       "  保存路径: output\\regression_output.csv\n",
-      "  保存行数: 486（243个交易日 × 每日top2）\n",
+      "  保存行数: 564（282个交易日 × 每日top2）\n",
       "\n",
       "  预览（前15行）:\n",
       "shape: (15, 3)\n",
@@ -1283,22 +1311,22 @@
       "│ ---        ┆ ---      ┆ ---       │\n",
       "│ str        ┆ f64      ┆ str       │\n",
       "╞════════════╪══════════╪═══════════╡\n",
-      "│ 2025-01-02 ┆ 0.078766 ┆ 603007.SH │\n",
-      "│ 2025-01-02 ┆ 0.065013 ┆ 603559.SH │\n",
-      "│ 2025-01-03 ┆ 0.087979 ┆ 603007.SH │\n",
-      "│ 2025-01-03 ┆ 0.067351 ┆ 603559.SH │\n",
-      "│ 2025-01-06 ┆ 0.087962 ┆ 603007.SH │\n",
+      "│ 2025-01-02 ┆ 0.081732 ┆ 603007.SH │\n",
+      "│ 2025-01-02 ┆ 0.070111 ┆ 603559.SH │\n",
+      "│ 2025-01-03 ┆ 0.088554 ┆ 603007.SH │\n",
+      "│ 2025-01-03 ┆ 0.08042  ┆ 603559.SH │\n",
+      "│ 2025-01-06 ┆ 0.087152 ┆ 603007.SH │\n",
       "│ …          ┆ …        ┆ …         │\n",
-      "│ 2025-01-09 ┆ 0.036131 ┆ 603848.SH │\n",
-      "│ 2025-01-09 ┆ 0.027876 ┆ 603977.SH │\n",
-      "│ 2025-01-10 ┆ 0.027436 ┆ 002952.SZ │\n",
-      "│ 2025-01-10 ┆ 0.024641 ┆ 603848.SH │\n",
-      "│ 2025-01-13 ┆ 0.024852 ┆ 603848.SH │\n",
+      "│ 2025-01-09 ┆ 0.043328 ┆ 605118.SH │\n",
+      "│ 2025-01-09 ┆ 0.039754 ┆ 603848.SH │\n",
+      "│ 2025-01-10 ┆ 0.037324 ┆ 002309.SZ │\n",
+      "│ 2025-01-10 ┆ 0.027656 ┆ 603848.SH │\n",
+      "│ 2025-01-13 ┆ 0.050825 ┆ 002309.SZ │\n",
       "└────────────┴──────────┴───────────┘\n"
      ]
     }
    ],
-   "execution_count": 23
+   "execution_count": 15
   },
   {
    "cell_type": "markdown",
@@ -1311,8 +1339,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:35:07.151989Z",
-     "start_time": "2026-03-08T03:35:07.148773Z"
+     "end_time": "2026-03-08T06:11:05.972569Z",
+     "start_time": "2026-03-08T06:11:05.968651Z"
     }
    },
    "source": [
@@ -1332,31 +1360,40 @@
      "text": [
       "\n",
       "特征重要性:\n",
-      "ebitda_rank                     14616.823972\n",
-      "return_10                        2170.157647\n",
-      "return_20                        1597.996920\n",
-      "ma_ratio                         1346.640091\n",
-      "vol_ratio                        1307.361400\n",
-      "high_low_ratio                    918.782296\n",
-      "vol_ma20                          828.398926\n",
-      "ma20                              635.353193\n",
-      "volatility_5                      631.979581\n",
-      "return_diff                       617.399685\n",
-      "vol_ma5                           491.980673\n",
-      "market_cap_rank                   415.972949\n",
-      "volatility_20                     276.516502\n",
-      "profit_to_market_cap              256.683738\n",
-      "ma5                               236.319446\n",
-      "ma10                              173.155782\n",
-      "ebit_rank                         142.423690\n",
-      "operate_profit_to_market_cap      127.127625\n",
-      "total_liab_rank                    97.979562\n",
-      "n_income_rank                      77.705741\n",
-      "operate_profit_rank                75.762452\n",
-      "cashflow_to_market_cap             68.135267\n",
-      "money_cap_rank                     58.562898\n",
-      "n_cashflow_act_rank                47.001317\n",
-      "total_profit_rank                  36.941945\n",
+      "ebitda_rank                     12044.385847\n",
+      "bbi_ratio_factor                 1996.570270\n",
+      "std_return_90                    1900.096352\n",
+      "return_10                        1707.237531\n",
+      "cs_rank_turnover_rate            1700.674361\n",
+      "ma_ratio                         1449.246176\n",
+      "std_return_5                     1112.916802\n",
+      "high_low_ratio                   1002.711145\n",
+      "vol_ma20                          800.361497\n",
+      "ma20                              743.156761\n",
+      "cs_rank_volume_ratio              620.018650\n",
+      "vol_ratio                         614.280431\n",
+      "turnover_rate_mean_5              574.150844\n",
+      "return_20                         530.409461\n",
+      "volume_change_rate                516.283890\n",
+      "return_diff                       427.252813\n",
+      "market_cap_rank                   367.685596\n",
+      "volatility_20                     297.221084\n",
+      "vol_std_5                         245.201921\n",
+      "volatility_5                      210.447650\n",
+      "ma10                              175.131429\n",
+      "ma5                               137.251949\n",
+      "vol_ma5                           105.623339\n",
+      "ebit_rank                         102.653185\n",
+      "profit_to_market_cap               65.704505\n",
+      "operate_profit_rank                64.448026\n",
+      "operate_profit_to_market_cap       59.952241\n",
+      "cashflow_to_market_cap             40.900113\n",
+      "n_income_rank                      34.135645\n",
+      "n_cashflow_act_rank                27.246159\n",
+      "money_cap_rank                     22.246325\n",
+      "total_liab_rank                    21.635597\n",
+      "total_profit_rank                  17.579179\n",
+      "turnover_deviation                  0.000000\n",
       "dtype: float64\n",
       "\n",
       "================================================================================\n",
@@ -1365,7 +1402,7 @@
      ]
     }
    ],
-   "execution_count": 24
+   "execution_count": 16
   },
   {
    "cell_type": "markdown",
@@ -1382,8 +1419,8 @@
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:35:07.163848Z",
-     "start_time": "2026-03-08T03:35:07.158216Z"
+     "end_time": "2026-03-08T06:11:05.983915Z",
+     "start_time": "2026-03-08T06:11:05.981218Z"
     }
    },
    "source": [
@@ -1403,11 +1440,11 @@
      "output_type": "stream",
      "text": [
       "模型类型: <class 'lightgbm.basic.Booster'>\n",
-      "特征数量: 25\n"
+      "特征数量: 34\n"
      ]
     }
    ],
-   "execution_count": 25
+   "execution_count": 17
   },
   {
    "cell_type": "markdown",
@@ -1424,8 +1461,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2026-03-08T03:35:07.289782Z",
-     "start_time": "2026-03-08T03:35:07.170460Z"
+     "end_time": "2026-03-08T06:11:06.124862Z",
+     "start_time": "2026-03-08T06:11:05.993617Z"
     }
    },
    "cell_type": "code",
@@ -1475,7 +1512,7 @@
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ],
-      "image/png": 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"
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SoKtfHz582PLnz28pU0auVxOykxgF7IIFCyb0bgAAAABAkrR161a76qqrIt5PyE5iVMGWTZs2WY4cORJ6d5BATp06ZV9//bXVq1fP0qRJw/uQDHEMgOMA/D0AxwD4bOCvQ4cOuYKml7kiIWQnMV6LuN74LFmyJPTuIAEDVsaMGd0xQMhOnjgGwHEA/h6AYwB8Nrg4zjYsl4nPAAAAAADwCSEbAAAAAACfELIBAAAAAPAJIRsAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAADwCSEbAAAAAACfELIBAAAAAPAJIRsAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAADwCSEbAAAAAACfELIBAAAAAPAJIRsAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAADwCSEbAAAAAACfELIBAAAAAPAJIRsAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAADwCSEbAAAAAJKhH374wRo3bmz58+e3FClS2JQpUwL3nTp1ynr27GnlypWzTJkyuXXatWtn27dvj7GN/fv3W+vWrS1LliyWLVs2e/DBB+3IkSNhn2/jxo12xRVXuPUi+fjjj92+NG3aNM59//HHH6169eqWM2dOy5Ahg5UqVcpGjhwZY53Tp09bnz59rEiRIm6dYsWK2QsvvGDR0dEx1lu3bp3deeedljVrVvez3nDDDbZlyxZL1iG7X79+VrFixYTeDQAAAAC4bBw9etQqVKhgY8eOjXXfsWPHbPny5S6k6t8vvvjCNmzY4MJoMAXs3377zebOnWvTp093wf2RRx6JtT2F9latWtnNN98ccX82b95sPXr0iHMdj8Jw586d3fMpJPfu3dvd3njjjcA6Q4cOtXHjxtmYMWPcOvr+pZdestGjRwfW+fPPP61GjRoupH///ff266+/up85ffr0dr5SWyKwc+dOGzRokM2YMcO2bdtmuXPndqG5W7duVqdOHUsq3nzzTZs4caKtWbPGfX/99dfbiy++aFWqVAmso7M24ehgePrppy/ZvgIAAABI2m6//XZ3C0dVXQXnYAqryi6q8l599dUuuM6ePduWLl1qlStXduuMHj3aGjZsaMOHD3fVb48CsIKs8t3ChQtjPZ+qzgrs/fv3twULFtiBAwfi3PdKlSq5m6dw4cLuRIAe64V8PU+TJk2sUaNGgXU++ugjW7JkSeBxzz33nNtf5S2PKt4XIsFDts5WqMyvloFhw4a5dgSd5ZgzZ4516tTJ1q9fb0mFzozo7M1NN93kzozoTEq9evXcmZ8CBQq4dXbs2BHjMbNmzXItF82aNTun56o6eJ5Fpc7k6/7j8pEuVbS9VMXs2n5z7MTp8CdukLRxDIDjAPw9AMcA4vpssHnI/4LnuTh48KArCnrt3j///LP72gvYctttt1nKlClt8eLFdtddd7ll3377rX322We2cuVKF4TDGTBggCu2KvsoKJ+rFStWuFA9cODAwDLlLlW2f//9dytZsqStWrXKtZmPGDHC3X/mzBlX6H3mmWesfv36bhtqLe/Vq9dZ29UTdbv4448/7t4onU1QkNQPX7ZsWevevbstWrTIraMzJToDkTlzZtfr36JFC9u1a1fEbdaqVctVwYPpRerQoUPge53F0BugcQXabqFChWzatGm2Z8+ewHOVL1/eli1bFnjMhAkT3EGkEwClS5d26zRo0CBWMI7kgw8+cD+vqvQ6i/PWW2+5N3bevHmBdfLmzRvjNnXqVLv11lutaNGi5/S6AgAAAIBfjh8/7sZoq2ioTOZ1JCsYB0udOrXlyJHD3Sf79u1zOUxZyntcKAXft99+23X+nqurrrrK0qVL54K+irQPPfRQ4L5nn33W7r33Xpe90qRJ4yrfyomqmMvu3bvd+PEhQ4a4XPf111+7EwN33323zZ8//5z3JfAaWALSIHm1F6hVXD31oRRoFUK90KsfNCoqyr14LVu2dJXhC6GB8WrXVs+9vm7btq072/HAAw+4qroOIoVwVZq9Nm6NTVDrw6RJk9wZmjZt2rhxAwrQ50rbUtVeB2E4OpGgMyvvvfdexG2cOHHC3TyHDh1y/6ZLGW2pUsUc0I/kQ+9/8L9IfjgGwHEA/h6AYwBxfTZQDgmlrBVuuZap0Kls9uqrrwbWUYu3JhEL95jTp0+75apMK7tVq1bNfa/lwc9/+PBhl8M0dlot6lqu59Et3HZDqUquoKyirVq/VUxVsJZPPvnE5TQN2S1TpoyrZCu76cSAcp6XozT5m8Z3iwq+Cv2vvfaay4ahr0OiD9maXU5vis4sRKIq7+rVq23Tpk1WsGBBt0wvkn549f5r5rfzpd77jh07uq+ff/5598Zqe82bN3fLFLJ1MCjsqqrsvbDjx48P9OnrzVBrw/nQ9jVOQS0V4Shca/Y9nUmJZPDgwW7cQqjelc5Yxoz/O4CRfL1Q+UxC7wISGMcAOA7A3wNwDCDcZ4OZM2fGemF++eUXV/ENDd4qQCoTKfcogHpUCdZs48HbOn36tKtea64tLde47q+++irQoi0K0Bo+qy5fdexqCHFwe7Y3+7fW0aRs+fLlO+ubqHVUjVb12quYq2qtbmllqq1bt7riptbp27evXXnllS7bpUqVyt2Cf4a0adO6CdBCXyMVSRN9yA6dOj0cDaZXuPYCtugshKrcuu9CQrbawT158uRx/2pMeOgyHTxeyM6YMWOMgfB6M3X/uVJLgqanVzU+0sx177zzjmtliGtmO40XUGt9cCVbr9XAFSktKk2qc94vJA06Q6k/oH2WpbQTZxiTnRxxDIDjAPw9AMcA4vpssKZf/VgvkCZmViEydEZwVZt/+ukny5UrV4z1NX5Zk6EpK1133XVu2dy5c13Oe/TRR11BUeO2veq1KHCrM1hdypqXSpfW0nDfYArBqk6//PLLbjixQm98aBZ07af3M2g/lO+CfyYVcFX19pZ5eTJ4HeUwzboevCy4azhRh+wSJUq4Nmy/JzdTG3dogA9X2g8+S+O1g4dbpjMt4R7jrROfkwXBdFApZH/zzTcxgn4wDfbXFPlqcYiLxh/oFkq/OFFMeJXs6Thg4rPkjWMAHAfg7wE4BhDus4FyjYKsuos9qvZqqKwqviomKmAruOrSXMpYqlCL7lfwVZZRZfixxx5z3b7KXN26dXPt2przSkLzjlq2ta3gmcGDv/a2H7qOiouqjqurWVTh1gznXle0LuWlIcBdu3YNZDa1gSt36WSAOqE1sdkrr7zihgd762jSM7WzK+hrLiwNZ9aQXRVDQ7Nf6PeJMmTrxdMsbnqB9GKEjsvWtO2aYExvtm5eNXvt2rXuPlW0w9EZluDJyHTmRJfN0ouW0DQ1vMaga/K04Fn4Qmngv84k6QzK+Vjcq467MDuSJ/2BU3uLzlDG948BkhaOAXAcgL8H4BjA2T4baJLn4Izkdci2b9/e+vXr5yaGFk3cHOy7774LVJ815llDaHVpLgXjZs2auXHbflO+04TYHhVCFbw1rFiTranbWFdv8oYDe5cT0/xbaktX97Eq67pfQ4U9muhMJwg0DFeZ9JprrrHJkye7a2dftpfwUsDWJbx0vTX1+OtMh/r+1WagMdIK1Crxq2161KhR7j69SDVr1owYUmvXru0OEJ2B0Iut/v+zXWftUtCbrjf0ww8/dAPyvRn3NKmbbsFtCJriXu0RAAAAAHAxKCjH1ZUbn45dFU6Vb+KrQ4cOMa76FI5mIj/bsi5durhbXDQWWxlSt7iosq2bXxL8El4a6K4WBJ1Beeqpp+zaa6+1unXrugnPFLLVjq3LWGXPnt1uueUWN0mYHhNXG7VeIJ190YxxCuNaPzFUsfXznDx50u655x7XfuHd1D4eTGO1dUCrPQMAAAAAcPlIEX2uA4qRqKkKrqnv9+7dS7t4Mua1A2myBtrFkyeOAXAcgL8H4BgAnw0uTtY6ePBgxGt+J4pKNgAAAAAASQUh20fe2OpwN80WDgAAAABI2hJ84rOkZOXKlRHv0zXgAAAAAABJGyHbR8WLF/dzcwAAAACAywzt4gAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNlAErZgwQJr3Lix5c+f31KkSGFTpkyJcf+uXbusQ4cO7v6MGTNagwYN7I8//oixzp9//ml33XWX5cqVy7JkyWItWrRwjws1Y8YMq1q1qmXIkMGyZ89uTZs2jXPf9Lzap+Cbnt/z/fffx7rfuy1dujSw3q+//mo333yzpU+f3goWLGgvvfTSBbxiAAAAQDIP2f369bOKFSsm9G4AidLRo0etQoUKNnbs2Fj3RUdHuyD8119/2dSpU23FihVWqFAhu+2229zjvMfXq1fPBdtvv/3WfvrpJzt58qQL7mfOnAlsa/Lkyda2bVu7//77bdWqVW69++6776z7p1C9Y8eOwO2jjz4K3HfTTTfFuE+3hx56yIoUKWKVK1d26xw6dMjtn/b7l19+sWHDhrm/CW+88YZPryAAAABwmYXsnTt3WpcuXaxo0aKWLl06V4nSB/h58+ZZUvLbb79Zs2bNrHDhwi6wjBo1KtY6CgehFbtSpUolyP4iaVCIHThwoKtEh1LFetGiRTZu3Di74YYb7JprrnFf//e//w2EXYXlzZs324QJE6xcuXLu9t5779myZctc6JaoqCh74oknXMB99NFHrWTJklamTBlX8T4b/c7nzZs3cFMF3JM2bdoY9+XMmdOdDFCQ1++GfPDBBy70v/POO1a2bFm79957rWvXrjZixAgfX0UAAAAg/lJbAtKH9+rVq1u2bNncB3R9gD916pTNmTPHOnXqZOvXr7ek4tixY+5EQvPmze3JJ5+MuJ6CwjfffBP4PnXq83uLqg6eZ1GpM53XY3H5S5cq2l6qEvc6J06ccP+qzdqTMmVKF3x//PFHVzXWOgq0WubR+lpP66jqvXz5ctu2bZtbVqlSJXfiTN0l+p2+9tpr49wHtYTnzp3bhevatWu7EwIK0+FMmzbN9u3b50K25+eff7ZbbrnFBXJP/fr1bejQofbvv//GCO0AAABAkq9kP/744+4D/JIlS1yVVxUwhczu3bu7Cpts2bLFmjRpYpkzZ45zPKinVq1a1q1btxjL1BKr8Z8eVZP1Yb5du3Zuu2o11Qf4PXv2BJ6rfPnyrlrnUSVPJwN0AqB06dJuHa/VNT5UKVToUKUtOLCEUqgOrt5deeWV8do+cK7UJXH11Vdbr169XCBVRVjh9J9//gkc1zfeeKNlypTJevbs6U4UqX28R48edvr06cA6ajf3OjF69+5t06dPd+FWv4v79++P+Pz6/Zk4caLrWtHzzp8/326//Xa37XDefvttF6CvuuqqwDIF+jx58sRYz/te9wEAAADJppKtD9+zZ8+2QYMGuQ/xoRRoNebTC736AK62VFW4W7Zs6SpgF2LkyJH24osvWp8+fdzXGk+qMaAPPPCAC8MKFQrhavP2WlMVMoYPH26TJk1yVbs2bdq4wKGWVb+ohVeTUKlaWK1aNRs8eLALQpGo0uhVJL0xqpIuZbSlShXt237h8qL3X9QZEky/Q8HLPv30U3vkkUcsR44clipVKqtTp44LvxqvrfX0e6jWcQ3pePXVV91xr98/Vay97Sucy7PPPmt33nmn+1pjojV2+uOPP7aHH3447D7qxFpw4NfJK/2rTg5VtYMp+OsE14cffhhj/7Wf+jsRvMz7Wv+G/vzJSfDrgOSL4wAcB+AYAP9P8E98P1clWMjeuHGj+4Ac15hjVbhWr15tmzZtcmO1RZUvVbs1u7Cqw+erYcOG1rFjR/f1888/HxiXqnZuUchWyFXVXBVl70UdP368FStWzH3fuXNnGzBggPlFMzOrYq6xsaoS9u/f382avGbNGrviiivCPkYhXOuF6l3pjGXMGL4iiORj7ty5Mb7X5GBp0qSJsUzHsCrUCuBZs2a1p59+2ooXL24zZ84MrKMxzjqBo5Ctk17qDFG3h9ZRt4kcOHAgxmNUzf7uu++sQIEC8d5fdato3PXx48djLP/kk0/c74A6PYKfQ/us2cWDl+lvhvev/nYkd6HHAJInjgNwHIBjAPw/4cKp6JqoQ7YC9tmsW7fOhWsvYIsmVFJ1TfddSMhWQAhtL9WY8NBlu3fvDoRsXeLIC9iSL18+d79f1CobvH8K3WplV7XxwQcfDPsYtfqqvd6jIKTXa+CKlBaVJpVv+4bLr5L9QuUzVrdu3Rih+vrrr3cnmOLqpNAluzQxnx4bjoLzwYMHXReHTgjVqFEjMJba27ZOSGkdVaTjer7QavXhw4fdOO/gx+hvheYxUJeJVyn3bN261Z0kC/45Fy5c6IaexGfitaRM74GCVegxgOSF4wAcB+AYAP9P8I/XNZxoQ3aJEiVcG7bfk5up0hYa4MOV9YM/dHrt4OGWBV+mKPSDqtaJz8mC86WTCQoLqvpHovHd4cZ4nziTwqJO/+9nQPKloQS///57jFCqIRBqD9cwhM8++8xd/1pfq/KrWcI1h0FwyH333XddK7fW00RjWkeh15vUTOFas4qrIq75DnRiSEMuRHMQeL836lpR54VmOj9y5IjrwFDLuE5iKdg/88wzroLeqFGjGL9r6mhRRVpt7aG/gxrmoYCv51f3ibo+xowZ44aAECz/R68DrwU4DsDfA3AMwMP/E85ffD9TJdjEZ/qQr0mMdP1e75q8wdR6qg/2CgW6edauXevuU0U7HAWB4MnINImSPnhfjhREFD5UMQfOh9rDNX7aG0Otrgd9reqv6HdFQVUBWJe+0tfB16qWDRs2uOCt30cF6eeee87NTRDMm9RPj1eHyd9//+0u8RU8u7e2o+q2aPy32rxVmdaJJHVqqMq+YMGCWCeNNOGZ5ksIN7RE7e1ff/21C+F6/FNPPeV+NgVyAAAAINldwksBW5fwqlKlivvwrhZpjbFUi6PGSCtQq4W7devWrn1V92lG8po1a1rlypXDblPtqQoSM2bMcK3dGkuqUJ7QNDmUfh7va13yaOXKlW58q6p3ovZbXSNclcDt27db3759XRhp1arVOT/f4l51Il4KCUmfujc0Tlm/K3F1WyhY6xaXIUOGuNvZzuopeIeG72DB+5EhQwY3kVl8aLKzuOjvhsI5AAAAYMn9El66brSusXvrrbe6CpTaTzV+UO2hCtlqx9YkSKqG6Vq4Gqupx2gSpEg0brN9+/ZuZnAFDK2v7Sc0hWavoqjqocKIvta1iIPHpCpQa5yrxpMqJOtSZqrOAwAAAAASvxTRF3NQMRJkML5aaPfu3UslOxnzKtkaW8143OSJYwAcB+DvATgGwGeDi5O1NARSV8VJlJVsAAAAAACSEkK2TzS2OtKN8aIAAAAAkDwk6MRnSYkmMYukQIECl3RfAAAAAAAJg5DtE2+GcAAAAABA8kW7OAAAAAAAPiFkAwAAAADgE0I2AAAAAAA+IWQDAAAAAOATQjYAAAAAAD4hZAMAAAAA4BNCNgAAAAAAPiFkAwAAAADgE0I2AAAAAAA+IWQDAAAAAOATQjYAAAAAAD4hZAMAAAAA4BNCNgAAAAAAPiFkAwAAAADgE0I2AAAAAAA+IWQDAAAAAOATQjYAAAAAAD4hZANJwA8//GCNGze2/PnzW4oUKWzq1Kkx7t+1a5d16NDB3Z8xY0Zr0KCB/fHHHzHWOX78uHXq1Mly5sxpmTNntmbNmrnHBduyZYs1atTIbSN37tz29NNPW1RUVJz7tn//fmvdurVlyZLFsmXLZg8++KAdOXIkxjqffvqpVaxY0W23UKFCNmzYsFjbOXHihD333HPu/nTp0lnhwoXtnXfeOY9XCwAAALh4kkTI7tevn/uADiRXR48etQoVKtjYsWNj3RcdHW1Nmza1v/76y4XvFStWuKB62223ucd5nnzySfvqq6/ss88+s/nz59v27dvt7rvvDtx/+vRpF7BPnjxpCxcutPfee88mTJhgzz//fJz7poD922+/2dy5c2369OnuhMAjjzwSuH/WrFlunUcffdTWrFljr732mo0cOdLGjBkTYzstWrSwefPm2dtvv20bNmywjz76yK655poLfOUAAAAAn0UnAjt27Iju3LlzdJEiRaLTpk0bfdVVV0Xfcccd0d988028Ht+3b9/oChUqRCd2b7zxRnSNGjWis2XL5m516tSJXrx4cYx1zpw5E92nT5/ovHnzRqdPn96t8/vvv8f7OQ4ePBitt3Xv3r0X4SfA5UDv/2effRY9ZcqU6JMnT0Zv2LDBLVuzZk1gndOnT0fnypUr+s0333TfHzhwIDpNmjTucZ5169a5x/3888/u+5kzZ0anTJkyeufOnYF1xo0bF50lS5boEydOhN2XtWvXum0sXbo0sGzWrFnRKVKkiN62bZv7vlWrVtH33HNPjMe9+uqr7u+Afh+8x2TNmjV63759Pr1KSZ/ee+8YQPLFcQCOA3AMgP8n+MfLWvo3LqktgW3evNmqV6/u2kjVIlquXDk7deqUzZkzx7Wurl+/3pKK77//3lq1amU33XSTpU+f3oYOHWr16tVzVb4CBQq4dV566SV79dVXXZWwSJEi1qdPH6tfv76tXbvWPSa+qg6eZ1GpM13EnwaJweYhjc66jtqsJfj4SZkypWu5/vHHH+2hhx6yX375xf3eqbrtKVWqlF199dX2888/24033uj+1e9nnjx5Auvo2HzsscfcMVypUqVYz63H6He7cuXKgWV6Dj3/4sWL7a677nL7pzbxYBkyZLB//vnH/v77b9cWPm3aNLcN/X5MmjTJMmXKZHfeeae98MILbl0AAAAgsUjwdvHHH3/cjSFdsmSJGwNasmRJK1u2rHXv3t0WLVoUGAfapEkTN05U4zrVNho6VjRYrVq1rFu3bjGWqV1WY1I9+uA+cOBAa9eunduu2mf1QX7Pnj2B5ypfvrwtW7Ys8Bi1xiow6ARA6dKl3Toa27pjx454/awffPCB+3nV2q4A89Zbb9mZM2dcC6yoCDlq1Cjr3bu32wc9/8SJE13b7pQpU875tQWCw3KvXr3s33//de3eOsGjEOsduzt37rS0adO64zuYArXu89YJDtje/d594Wi5xm4HS506teXIkSPwGAX1L774wv0e6Pfh999/t5dfftnd5+2fWt11QkDt5F9++aX7Pfn888/d7xMAAACQmCRoJVsTIs2ePdsGDRrkKlOh9IFfH7q90KtxoppkSRXuli1busrwhdC4zxdffNFVi/V127ZtXZX5gQcecFX1nj17uhCuKp1OBMixY8ds+PDhrpqmalybNm2sR48eLkCfK21L1UMFDtm0aZMLHsHVxKxZs1rVqlVdRfDee++NtQ1VAb1KpRw6dMj9my5ltKVKpW4GJGU6fsLR+Ok0adK4+/WvJhbTOGgda6lSpbI6deq4E0Q6saN1vMnLQren+7UtLdfvord+6PPr8eH2RY8NfUzwfVquk18K1nfccYf7XifSOnfu7KrUek4t07r6HdSJLv1OiKra+p145ZVXqGbHcWxEOkaQPHAcgOMAHAPg/wn+/381UYfsjRs3ug/gqrRFourW6tWrXQAtWLCgW6bqrqrdS5cutRtuuOG8n79hw4bWsWNH97Umbxo3bpzbXvPmzd0yhexq1aq5qnnevHkDL+z48eOtWLFi7nuFgQEDBpzX82v7mu3ZC9VeZS9ctTBSpXDw4MHWv3//WMt7VzpjGTOePq/9wuVj5syZYZevXLnStXhrsjGPjlNNdKZArKCqmcGLFy/utqG2bFW4FcZ1Qsuj5ap+a53Dhw+7GcmDn9PrKNHvcrh92b17t+vECL5PgXnfvn22bdu2wPKbb77ZneA6cOCAC9m//vqrW/7nn3/a3r173WN00u2nn36KsW39/dAJLv0eIbzgYwDJF8cBOA7AMQD+n3DhVCRN9CH7f3M0xW3dunUuXHsBW8qUKeM+cOu+CwnZasf2eMFWY05Dl+nDvBeyNXbUC9iSL18+d/+5GjJkiH388ceuGn8uY61DqQVYrfXBlWy9VgNXpLSoNKnOe7u4PKzpVz/scm+2/bp167pKdiiFZQVYtV1rHc2LoMqxWrl18kk0g7eGT9x///2um0KdG2rR1thorwVcQx4Uih9++GE3xjuU5hXQLOH6/bnuuusCH/b1u6/ZxCOFYw2P0EkCzWEgCupPPfWU3XLLLYGTABreoX3SzOSMy45NJwT1Wkc6BpA8cByA4wAcA+D/Cf7xuoYTdcguUaKEawH1e3IzffAODfDhSvvBHzy9dvBwy9SyGu4x3jrxOVkQTO3mCtnffPNNjKDvBXlVBxXePfo+0iXKFGzChZsTZ1JY1On/7T+SLu941HWnVU32bN261bWFa0yzTgrpsly5cuVyY7PVGfLEE0+4eQq8QH3llVe661c/88wzLkArOHfp0sV1ctSoUcOto3V1gkvDKdSqre6Kvn37uuEbXvDV3AoaYqEOFE3mp+NbbemaHE0dIPo91HwJavPWPAiiSrXCu+ZS0LW63333XZs8ebIbHuL9fBrKoaEdanlX54YeoxNM2hftK+I+RgjZ4DgAfw/AMQA+G1y4+H6mStCQrfGhmvRI1/bt2rVrrHHZah3VBGMKDLp51WzNtK379IE/HIWJ4MnI1GqqCZNuvfVWS2gKJxqDrsnTgmdc9qp+CtoKKF6o1tkSzcKskHIuFveqYzlz5vR135F4aYK+4ONbreCia2JreIV+H9Tx4J3AURDWXATBNC+BTlBpAkKN89fvpq5Z7VFo13WudSwqfOv3tX379jGGS6iFRhXw4JNaaufWsAqNA/e2rxn0g2k2fc1toBNW2rY6PKpUqRK4XyFeVVkFf/3e6NjWBIiavBAAAABITBL8El4K2GpV1QdqfVhX5UtjRvWBWmOkFajVwq2WULW26j7NKFyzZs1YIdVTu3ZtFyhmzJjhqngjRoxwoTyhaUZnjf3+8MMP3ezm3jhrBQjdVBVXlU/BQVV+7xJeaqlV1RGIRFXg4I4KhVyNd/Yq1TqJpVtcNGxBv4+6RaLqc6Rx4OH2wzuZpmM+ElXRNbHf2WjuBsaVAgAAILFL8Et4FS1a1JYvX+6qcBpzee2117oxhKrmKmQreE6dOtWyZ8/uxmNqkjA95pNPPom4TbWQqsKmap3CuNZPDFVs/TyaXOqee+5x1UTvpvZxj9p1Va1TW6zGm6sNWDOwX8i4bQAAAADApZEi+lwHFCNRU3u5Zo7WmFXaxZOv4Eo243GTJ44BcByAvwfgGACfDS5O1jp48GCc8wIleCUbAAAAAICkgpDtI29sdbjbggUL/HwqAAAAAEAilOATnyUlK1eujHifLmcEAAAAAEjaCNk+Kl68uJ+bAwAAAABcZmgXBwAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCfJImT369fPKlasmNC7AcTLDz/8YI0bN7b8+fNbihQpbMqUKTHu17Jwt2HDhgXW2b9/v40YMcJy5sxp2bJlswcffNCOHDkS43ci3DYyZcoUcb9WrVplrVq1soIFC1qGDBmsdOnS9sorr8RY58cff7Tq1au759U6pUqVspEjR8ZY5/Tp09anTx8rUqSIW6dYsWL2wgsvWHR0NEcIAAAALnuXRcjeuXOndenSxYoWLWrp0qVzH/IVQubNm2dJSa1atcIGn0aNGiX0ruESOnr0qFWoUMHGjh0b9v4dO3bEuL3zzjvuOGnWrFlgnfbt29uWLVts1qxZNn36dBfcH3nkkcD9PXr0iLWdMmXKWPPmzSPu1y+//GK5c+e2999/33777Td77rnnrFevXjZmzJjAOgrpnTt3ds+3bt066927t7u98cYbgXWGDh1q48aNc4/TOvr+pZdestGjR/vw6gEAAAAJK7Ulcps3b3aVMVXjVKkrV66cnTp1yubMmWOdOnWy9evXW1LxxRdf2MmTJwPf79u3z4WtuIJPJFUHz7Oo1JGrkkicNg9pZLfffru7RZI3b94Y30+dOtVuvfVWdxJKFFz1+zF8+HCrUqWKpUmTxgXYhg0bumWqkGfOnNndgqvUa9eutfHjx0d83gceeCDG93q+n3/+2R23CtZSqVIld/MULlzY3b9gwYJAyF+4cKE1adIkcPJI63z00Ue2ZMmSc3y1AAAAgMQn0VeyH3/8cVel0wdwVepKlixpZcuWte7du9uiRYvcOqrY6UO7QkOWLFmsRYsWtmvXrjgrxt26dYuxrGnTptahQ4fA9/rgP3DgQGvXrp3bbqFChWzatGm2Z8+ewHOVL1/eli1bFnjMhAkT3MkABRy10mqdBg0auCphfOTIkcMFKO82d+5cy5gx43mFbCQPOs5nzJjh2sE9Cr46DosXLx5Ydtttt1nKlClt8eLFYbfz1ltvud+tm2+++Zye/+DBg+64jWTFihUuVNesWTOw7KabbnJdKL///nsg4KvNPK4TCwAAAMDlIlGHbI0rnT17tqtYhxsrqiBx5swZF3q17vz5810w/euvv6xly5YX/PwaS6oquoKCqm5t27Z1obtNmza2fPlyN5ZU3wePJT127JirFk6aNMm1zOoEgFpzz8fbb79t9957b5zjZJG8vffee3bFFVfY3XffHWN4Ra5cuWKslzp1aheGdV+o48eP2wcffBAjqMeHwvMnn3wSow3dc9VVV7mhHZUrV3a/vw899FDgvmeffdYd1xqvrSq7Kt866dW6detzen4AAAAgMUrU7eIbN250AVYfxiNRRWz16tW2adMmN1ZbJk6c6KrdS5cutRtuuOG8n1/ttR07dnRfP//8824cqbbnVZZ79uxp1apVc9VEr4VXrexquVUAF7XRDhgw4JyfW5X7NWvWuKAdlxMnTrib59ChQ+7fdCmjLVUqJpK63Oj4CRUVFRV2uej40GRkqVKlCqyjicUibU/3hS777LPP7PDhw3bfffdFfJ5QOjZ1ckvjrdWqHvq4b7/91k20puNYY7fVGaJgLQrmCvX6PdU4cFWydSJK47110gr+8N6T+L6nSJo4DsBxAI4B8P8E/8T3c1WiDtnxmW1Y408Vrr2ALfrgriq37ruQkK12cE+ePHncvxoTHrps9+7dgZCt9m4vYEu+fPnc/edK4UnPpTG1cRk8eLD1798/1vLelc5Yxoz/F7ZweZg5c2bYCcdU8Q2lycfUcv3YY4/FeJyOt+3bt7uv1dnhhWuN8d+2bVus59BcB9dff717nvjYunWrC9d169Z1s/aH2+fg419DJlS91lAOUdVaQz9Ugde2VGHXOn379rUrr7wyXvuA+POOASRvHAfgOADHAPh/woVT1/JlH7JLlCjhxmP7PbmZxqaGBvhwZyWCg432I9IytayHe4y3zrlemkizS3/88cfxqoBrdmeNTw+uZOuEw8AVKS0qTapzel4kvDX96sdapgCsropQkydPtuuuu861YwfTpbE0c7c6QRTAdUzqA7aOw0cffdRNfOZRB4iq0pqcLNxzhAv2ag9Xa/mQIUPi9TNpaMVPP/0U2L72QyeQgp9P3SiqesdnHxA/+pum910nQ8KdpEHywHEAjgNwDID/J/jH6xq+rEO2Klz169d3lzLq2rVrrLHJBw4ccBOMqRqmm1fN1izJuk8V7XA0XjV4MjJV+RQ01PaaGKh9Vy3gGvt9Nhr3qluoE2dSWNTp/50EwOVDYUht1grIHh3bCrf6fbj66qsDv+AK2S+//HKsAKUODP3evPbaa244g0Ktqsdq19YEfsE0d4CqzboknlrOg3355ZfuJI53kku/I/Xq1XPbfvrpp11lXPQ4bwy4fle1j94QD81LoLkN9Pvr7aeeSwFdJwM0rENzHuh625q9nDB4cY4pXldwHIC/B+AYAJ8NLlx8P1Ml6pDtfWjX5GNqm1ZlVwFCY1RVodEYaQVqVcU0adKoUaPcfZqRXLMZa9KlcGrXru2qv5qVWa3dI0aMcKE8sVCruGY7z5kz53lvY3GvOhf0eCQczVgffMLH61TQta81g72o00HhWeOxI02IprkDFIjVuaH27FdffTXGOurA0PY0q35owPZmDt+wYUPg+88//9zNrq/rZOvmUXDXpfa8bSqYq0Kuydb0+6XrYHtzG4guJ9anTx/3e6rWdlXWdb/mPQAAAAAud4k+ZOtavGo3HTRokD311FOuAq2qmVpoFbLVjq3rBHfp0sVuueUWFyg0vlMf5CNRxUyTLWmSJQWBJ598MtFUsRVqdDmjr7/+OqF3BQlEl5g72xADtWyHm9Xbo6q3fl/Ufh3pjJt+V1Qlj0ThO/iydv369XO3uOj3ULe4aCy2TojpBgAAACQ1KaLPdcAwEjW1EWfNmtX27t1LJTuZj8PUhGRxhWwkbRwD4DgAfw/AMQA+G1ycrKWOT29S38vuOtkAAAAAAFxOCNmXUObMmSPeFixYcCl3BQAAAACQHMdkJyUrV66MeF+BAgUu6b4AAAAAAPxHyL6EihcvfimfDgAAAABwidEuDgAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2UAi88MPP1jjxo0tf/78liJFCpsyZUqM+7Us3G3YsGGBdfbv328jRoywnDlzWrZs2ezBBx+0I0eOxNjOr7/+ajfffLOlT5/eChYsaC+99FK89m/ChAlWvnx597jcuXNbp06dAvf169cv7L5lypQp7LY+/vhjd3/Tpk3P8VUCAAAAEqckEbL1wb5ixYoJvRuAL44ePWoVKlSwsWPHhr1/x44dMW7vvPOOC6rNmjULrNO+fXvbsmWLzZo1y6ZPn+6C+yOPPBK4/9ChQ1avXj0rVKiQ/fLLLy6g6/fojTfeiHPfFNyfe+45e/bZZ+23336zb775xurXrx+4v0ePHrH2r0yZMta8efNY29q8ebNbX0EfAAAASCoSRcjeuXOndenSxYoWLWrp0qVzVTVV8ubNm2dJyZtvvukCRfbs2d3ttttusyVLlgTuP3XqlPXs2dPKlSvnKn+qZLZr1862b9+eoPuNS+v222+3gQMH2l133RX2/rx588a4TZ061W699Vb3+yPr1q2zOXPmWOfOna1KlSpWo0YNGz16tKsae8fSBx98YCdPnnQBvWzZsnbvvfda165dXYiO5N9//7XevXvbxIkT7b777rNixYq5ivadd94ZWCdz5swx9m3Xrl22du1aV0kPdvr0aWvdurX1798/sN8AAABAUpA6oXdA1azq1au7llZV0xQwFTYVEtSGun79eksqvv/+e2vVqpXddNNNrtV26NChrpqoimCBAgXs2LFjtnz5cuvTp4+rZCrUPPHEEy7ELFu27Jyeq+rgeRaVOnyLLhKnzUManfNjFGJnzJhh7733XmDZzz//7H6fihcvHlimEzopU6a0xYsXu/CudW655RZLmzZtYB1VpHVM6rjTSaBQc+fOtTNnzti2bdusdOnSdvjwYXcsv/zyy+7EWDhvvfWWlSxZMla1esCAAa7VXOF7wYIF5/xzAwAAAIlVgleyH3/8cdfqqoqu2l31gVyVte7du9uiRYvcOmp7bdKkiauSZcmSxVq0aOHCRSS1atWybt26xVimMZ8dOnQIfF+4cGFXLVSlWNtV2+y0adNsz549gedSlS443GosqsKLTgAoZGidBg0auJbY+FD1UD+vWttLlSrlAohCi1exz5o1qwsy+vmuueYau/HGG23MmDGunVevARBK4fqKK66wu+++O0ZnSK5cuWKslzp1asuRI4e7z1snT548MdbxvvfWCfXXX3+54/XFF1+0UaNG2eeff+7GftetW9dVxUMdP37cHfOhVewff/zR3n77bdfZAQAAACQ1CVrJ1gf02bNn26BBg8JOjKRAqw/1XuidP3++RUVFuQp3y5YtXWX4QowcOdIFBlWO9XXbtm1dZe6BBx5wVXW1biuEq9KsEwGiavPw4cNt0qRJrjLYpk0bN65UYeJcaVuq2iv8RHLw4EH33Hotwjlx4oS7BY+1lXQpoy1Vquhz3ickHB0L4eiYj3Sfwqq6I1KlShVYR63Ykbap+7QsOjra/W4F3+99rX/DPZ+3XC3ltWvXdsvUOq4qtk4OqSsj2Geffeaq3Wot97an7/V7Nm7cOHdSScu1H6H7ggsX/H4i+eI4AMcBOAbA/xP8E9/PVQkasjdu3Og+7KuqG4mqvKtXr7ZNmzYFWlL1wV7V7qVLl9oNN9xw3s/fsGFD69ixo/v6+eefdx/8tT1vkiaF7GrVqrmqucaXei/s+PHj3XhU0bhXtb6eD21f467VyhuOKoFaRyFKFfxwBg8e7Ma1hupd6YxlzPh/YQuJ38yZM8MuVydDmjRpYi3XyZ/ff//dHnvssRiP3b17d2DstcKvF6737dvnWr21roK7ZhcPfpx+z7x/9fsWSl0eos6N4Mepku5tM5hOVF1//fVu/4Or4RoiEjybuP4GiIZQaLK3fPnyxePVQnx5xwCSN44DcByAYwD8P+HCqUia6EO29+E6LprESeE6eMynZitWZVf3XUjIVjt4aKusxoSHLlNo8UJ2xowZAwFbFAh0/7kaMmSIm4hK1XiFi1AK82ob12uk8B9Jr169XGt9cCVbr9XAFSktKk2qc94vJJw1/f5vlu5gCqo6IRRq8uTJdt1118W4hJYUKVLEDTPQSSwFcAV0fcDWsfToo4+6Eztbt251J5bU6u0F+IULF7rhGjruwtEYb02gdtVVVwUq2epGUXW6UaNGblsehfQ1a9bYF198EWPfdeJIwzmC9e3b111eTGO79fzB48Rx/vQ3RO978HuM5IfjABwH4BgA/0/wj9c1nKhDdokSJVwrtN+Tm6mNOzTAhyvtB3/w9NrBwy1TK2u4x3jrxOdkQTC1mytk6/JHwUE/eF8VdP7++2/79ttvI1axRbOx6xbqxJkUFnX6f/uPy4N3bClwKiB7FIhVtdawgquvvjrwC66QrWAaekzqmNIkZq+99prrxNDxqTkKNIO45h4QtWxrTgKFbnVLKBArmGvYhLe9L7/80p3E8X4/1T2ioRtPPfWUu9SXjkvdr06U0CCn4RQ6AaWrBKiVPfhnrFSpUoz91c+l39nQ5fCHXnNCNjgOwN8DcAyAzwYXLr6fqRI0ZOvDtcKAWkR1+aDQcdkHDhxwE4wpZOjmVbN1SSDdp4p2OJr0KXgyMrXKKkToMkcJ7aWXXnJj0DV5WuXKlSMG7D/++MO+++47y5kz53k9z+Jedc77sUhYmmwv+Fj1OhV07WtNvifqglB41lCCSBOiadiDfr8UYDWp4Kuvvhq4X+Ohv/76a1cFV6X8yiuvdJXt4Gtpaz6ADRs2xNiuhmo8+eSTrnKt7dasWdPNqxD8B0cnpbSfmmgwOGADAAAAyUGCX8JLAVuX8NL1fDW2WVU4je1Um6PapBWo1cKta+pqRmPdpxm69eE+XEgVtbIqmOjSRmrt1kRNCuUJTZdHUpD58MMP3ezm3izOmtRNNwXse+65x13Ga/r06e7kgLeOTkjQRps8qJ36bN0RCsPBgTiUjhdVnNWqHemMm37X4rp8lkJy8Iz8ouq1JlvTLRKFb50Uiy/vxAEAAACQFCT4JbyKFi3qQqUqdwoF1157rWs91YRnCtlqx546daq7bq+u66tJwvSYTz75JOI2NTu4qn6aGVxhXOsnhiq2fh5d6khBWq203k3t46JJqXQZsX/++cdd5it4HY2XBQAAAAAkbimiz3VAMRI1jdVVK/DevXtpF0/G1BWhGb/jqmQjaeMYAMcB+HsAjgHw2eDiZC0Nq4xr3qwEr2QDAAAAAJBUELJ95I2tDneLa+wrAAAAACBpSPCJz5KSlStXRryvQIECl3RfAAAAAACXHiHbR8WLF/dzcwAAAACAywzt4gAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPgkWYTsfv36WcWKFRN6N5BM/PDDD9a4cWPLnz+/pUiRwqZMmRJx3UcffdStM2rUqLD3nzhxwh27WmflypUx7vv000/dfRkzZrRChQrZsGHD4tyvzZs324MPPmhFihSxDBkyWLFixaxv37528uTJsOtv3LjRrrjiCsuWLVus+w4cOGCdOnWyfPnyWbp06axkyZI2c+bMOJ8fAAAASA4ui5C9c+dO69KlixUtWtR9oC9YsKALMfPmzbOk5IsvvrDKlSu7UJMpUyYXoCZNmpTQu4VzdPToUatQoYKNHTs2zvW+/PJLW7RokQvjkTzzzDNh7581a5a1bt3ahfQ1a9bYa6+9ZiNHjrQxY8ZE3Nb69evtzJkz9vrrr9tvv/3m1h8/frz95z//ibXuqVOnrFWrVnbzzTfHuk+hvG7dui60f/7557ZhwwZ78803rUCBAnH+vAAAAEBykNoSOX2Qr169ugueqtSVK1fOBYA5c+a4SpqCQ1KRI0cOe+6556xUqVKWNm1amz59ut1///2WO3duq1+//jltq+rgeRaVOtNF21fEtnlII/fv7bff7m5x2bZtmztxpOO4UaP/PS5ckP76669t8uTJ7utgOvnStGlTF7JFJ6B69eplQ4cOdb8X4TRo0MDdPHqMAvK4ceNs+PDhMdbt3bu3Ow7r1KljCxcujHHfO++8Y/v373fL06RJ45YVLlyYQwIAAAC4HCrZjz/+uGuVXbJkiTVr1sy1pZYtW9a6d+/uqoCyZcsWa9KkiWXOnNmyZMliLVq0sF27dkXcZq1ataxbt24xlimwdOjQIfC9QsPAgQOtXbt2brtqx502bZrt2bMn8Fzly5e3ZcuWBR4zYcIEdzJAwal06dJuHYWaHTt2xOtn1X7ddddd7rFq5X3iiSfcc/z444/n8cohsVI1uW3btvb000+7YzkcHb8PP/ywC9NqBw/XRp4+ffoYy9QC/s8//9jff/8d7305ePCgO7kT7Ntvv7XPPvssYiVevwfVqlVzYT5Pnjx27bXX2osvvminT5+O9/MCAAAASVWiDtmqls2ePdt9mFf7dCgFWgUWhV6tO3/+fJs7d6799ddf1rJlywt+frXTqoq+YsUKV21UMFLobtOmjS1fvtwFYX0fHR0deMyxY8dcVVDhSGNzdQKgR48e5/zc2qba4VVpvOWWWy74Z0HioWpz6tSprWvXrhHfe53wUZVawwfCUWeDhhfoGNHvwO+//24vv/yyuy++J3U05nr06NHWsWPHwLJ9+/a559YJI52wCke/X2oTV6jWOOw+ffq459ZJKQAAACC5S9Tt4goBChxqW41EIWP16tW2adMmN1ZbJk6c6CqES5cutRtuuOG8n79hw4aBAPL888+7tlptr3nz5m5Zz549XUVPVce8efO6ZWpl1zhXBXDp3LmzDRgw4JwqixrbqkplqlSp3FhbjX+NROvp5jl06JD7N13KaEuV6v/CPy4+vffhREVFBe7TyZlXXnnFFi9e7JZ7FFi9dTSuWu+jTs5ombc8+GsFYQXrO+64wy1TINax9sILL7jQHfyYSO3q6rJQd4i25a2nidF0gkrHtZZ51eng7WiZhjCo0q1jVN0WOpk0YsSIsOO7kTDOdgwgeeA4AMcBOAbA/xP8E9/PVYk6ZAdXiCNZt26dC9dewJYyZcq4Krfuu5CQrfDgUVusaEx46LLdu3cHQrZae72ALZp9WffHl2Zz1izSR44ccScQ1BavsbNqJQ9n8ODB1r9//1jLe1c6Yxkz0r57KUWaXfuXX34JjF1Wq7WOB72nHoViTXCmCrcmEPv444/dMITQ7o0bb7zRatas6YYRiCYlu+mmm9xM3wrZv/76q1v+559/2t69e93X6uwIpa4PjbnW0AtNIBi831r/q6++coE5eP/Umq6hG7fddpubfFDHuYZFeA4fPuwmKJw6dWrgZ0XiEO4YQPLDcQCOA3AMgP8nXDh1LV/2IbtEiRJuPLbfk5ulTJkyVoAPd1YiOCxoPyItUwgJ9xhvnficLAjet+LFi7uvNbu4ThQoSEcK2ZrsSkHcowqoTjgMXJHSotKkivfz4sKt6Rd+crrrr7/edUVI1apVXcU5mKrR9913n7Vv396uueYaN8bZ60jw2r81XOHDDz+0KlWq2FVXXRX2eXSpMAVxzQqu41kfqtUFEXxMqoKtZTVq1LD33nvPVaKD/fzzzzHGVitwa/iDhmKowyJ79uxuwrNPPvnEVcJ1vHrBXieUNHQDiUOkYwDJC8cBOA7AMQD+n+Cf4M/ol23I1oRMGnuqtlSNXw2t7KmCp0nCtm7d6m5eNXvt2rXuPlW0w8mVK1eMcasKFboM0q233mqJjQJ8cDt4KFUVdQt14kwKizr9v5MAuDS8IKMuBA118OjY1CWzdDxfffXVga6H4McpwCpcS3AnhCjYigK4rnEtqlRrXLROvhw/ftzeffddNwu5wrC3H2onV4VcE5lp+17A1iR+qlTrd8Tj7VNw94asWrXKBelKlSoFlukkgYZOqJ1dM6T/8ccfrgqv31HCXOKj94T3BRwH4O8BOAbAZ4MLF9/PVIk6ZIsCtiYfUwVPY5sVAjSWVRUafdBXoFYLt64ZPGrUKHef2lrVVhtp0qjatWu76u+MGTNcoAkNHAlFFWvts/ZJwVptvJpATT/nuVrcq47lzJnzouwn4qZW7+ATNl6ngSrVmlDML6pEK+iqU0JjqL///nv3e+LRMaSg7XVp6HdG4V+30Gr4uXRb6GSWWsWffPJJ9/uoAK8Wds1RAAAAACR3iT5ka+yqJosaNGiQPfXUU64CrUq0WnAVPtWOrXGgqqhpFm5V3dTGqlmTI3nggQdchU4zg2uWZ4WFxFDFPnr0qDtBoMsw6XJMmvDt/fff92WmdFw6qi6fS2jVteDjosvJhW7vyiuvdK3dcdHJp5MnTwbOuGmCs+DL1MVHpMco1HuX0AMAAADwf1JEn0sawGUxTiBr1qyunZhKdvKl6rU6ITQWnFbh5IljABwH4O8BOAbAZ4OLk7V0RahIl7tN9NfJBgAAAADgckLIvoQyZ84c8bZgwYJLuSsAAAAAgOQ4Jjsp0fWvI9HkUQAAAACAyxsh+xLyrn8NAAAAAEiaaBcHAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsJFs//PCDNW7c2PLnz28pUqSwKVOmxLj/iy++sHr16lnOnDnd/StXroy1jTfeeMNq1aplWbJkcescOHAgxv2bN2+2Bx980IoUKWIZMmSwYsWKWd++fe3kyZNx7tuff/5pd911l+XKlcttu0WLFrZr164Y6wwaNMhuuukmy5gxo2XLli3WNubNm2dp06Z1+xV627179zm+WgAAAACSRcju16+fVaxYMaF3A5eho0ePWoUKFWzs2LER769Ro4YNHTo04jaOHTtmDRo0sP/85z9h71+/fr2dOXPGXn/9dfvtt99s5MiRNn78+Ijre8+rcK8w/O2339pPP/3kQrlOCGhbHi1r3ry5PfbYY2G3o33fsmWL7dixI3CrX7++1axZ03Lnzh3HKwMAAADgfKW2BLZz505XkZsxY4Zt27bNffhXaO7WrZvVqVPHkgoFrOeff95++eUX+/vvv13Y0s8Y7PDhw9anTx/78ssvXaWxUqVK9sorr9gNN9yQYPudlN1+++3uFknbtm0D1ehIvPfw+++/D3u/ArhunqJFi9qGDRts3LhxNnz48LCPUajWc65YscJVseW9996z7Nmzu9B92223uWX9+/d3/06YMCHsdtKlS2d58+a1NGnSuO/37NnjHv/2229H/HkAAAAAXMYhW0GievXqrtV12LBhVq5cOTt16pTNmTPHOnXq5KqASYUqngpYqjw++eSTYdd56KGHbM2aNTZp0iTXwvz++++7QLV27VorUKDAOT1f1cHzLCp1Jp/2PmnZPKRRgj7/wYMHLUeOHBHvP3HihKtiKyR70qdPbylTprQff/wxELLP1cSJE11r+T333HNejwcAAACQyNvFH3/8cRcmlixZYs2aNbOSJUta2bJlrXv37rZo0SK3jtpdmzRpYpkzZ444NjWYxseGVoibNm1qHTp0CHxfuHBhGzhwoLVr185tt1ChQjZt2jRX6fOeq3z58rZs2bLAY1Qt1MkAnQAoXbq0W0cVSrXgxoeq0TqRcO+998YIT57//ve/NnnyZHvppZfslltuseLFi7tWeP2rqieSho0bN9ro0aOtY8eOEde58cYbLVOmTNazZ093ckbt4z169LDTp0/H+3gLRxXs++67z40NBwAAAJDEKtn79++32bNnu1ZxBYpQCrQaf+qF3vnz51tUVJSrcLds2TJie258qV37xRdfdO3Z+lqtwZpE6oEHHnBhWAFHIVxt3joRIAo8avFVpVlVxTZt2rjw88EHH9iF0s+mEKWKZTAFIlUv46p66uY5dOiQ+zddymhLlSr6gvcrKVK3RKT3INx93jL9G9djz7aOhkPoxIxOKOmkT6T1dOx/9NFH1qVLF3v11VfdsaZjXsMHwu2/jpvQ5cH7LDpptW7dOnv33XcjPi+SltBjAMkTxwE4DsAxAP6f4J/4fq5KnZAVvejoaCtVqlTEdTQ78urVq23Tpk1WsGDBQMurqt1Lly69oLHKDRs2DFQTNVZa1WJtT+3copBdrVo1VzXXuFbvRdWkVZohWjp37mwDBgwwP1xxxRXu+V544QVXKc+TJ48LWj///LOrZkcyePDgwNjcYL0rnbGMGf8XvhDTzJkzw74kGi/vjV8O5nVO6GTH9u3bwz5Wx6l8/fXX7qRQuJNKvXv3dt0amsAs0j4EGzFihDtpopCtbSqYq8Mi9LGrVq1yx2a4bc6dO9f9q+q5ZjjXHAjxeW4kHd4xgOSN4wAcB+AYAP9PuHAquibqkK2AfTaqvClcewFbypQp4yp9uu9CQrbCikeBVjQmPHSZJiDzQrbGs3oBW/Lly+frpZBUIVclXeOvU6VKZdddd521atXKhb9IevXq5drrPQpler0GrkhpUWlS+bZvScmafvXDLr/++uvdyZdQ3sRnmq070kz2XjeGZgUPvZyWKth169Z1j9cEZnpvz9V3333nxnKrc+Kaa66Jcd/evXvdyYHgfVfo1odqPa86HdR1oSES4X4+JE3Bx0C4k0dIHjgOwHEAjgHw/wT/eF3DiTZklyhRwrVh+z25map+oQE+XFk/+EOn1w4eblnwJZNCP6hqnficLIgvBXi1xWsMrt5AhXi1CWvCtEg0vjvcGO8TZ1JY1On//QyIyXsfjxw54joqPFu3bnXDAzQp2dVXX+2qz5oTwKte//XXX+6xOuninXhRZVg3L4jreFZXgh6v7XgBW+P+VZkOvo62tw2to5n01aVRpUoVt0xt3epo0HWy1c3wxBNPuAnzrr322sDjtW/aRz1eLePad1Hng3dMaH/VEaF29vbt2xO2kiEdA4RscByAvwfgGACfDS5cfD9TJVjIVgDRNXt1jeKuXbvGGpetMKKQoeCjm1fN1kzbuk8V7XAUSoInh1L40Izdt956q10u9Fro9u+//7qJ1jQZ2rla3KuO5cyZ86LsX1Khie2CjwuvI0BhVBPdaTK8+++/P3C/Jq2Tvn37uknpRMMHgtv1NWmdF5LV3q1KooK8bldddVWM5/dO0OgkkC7rFdx+ou/VpaAQrYn6nnvuuViz0muYgyrjHm/MtqremrU/eMKzu+++O1aFHQAAAEASu4SXArbCgKp3GtusFm5V3BRMNEZagVot3K1bt7ZRo0a5+zQjec2aNa1y5cpht1m7dm0XlnTdbVWGQ6uHCeXkyZPu5/G+VvVx5cqVbqytN+ZagVrBS+3ACmVPP/20G7MeHPTgH81EH1cngkJy8Kz04Shse4H7fLehEB26H0OGDHG3uOhEQKRrZAd3byxcuDDO7QAAAABIIpfwUhv08uXLXTXxqaeecq2waq3VhGcK2WrHnjp1qmXPnt1VCHV9YD3mk08+ibhNjWlWJVIzgyuMa/3EUMVWy7Eqjbqp0q5ZyvW1ro3t0ZhbzZ6uYK391xheBW9aPQEAAADg8pAi2s9BxUhwGsudNWtWNxkW7eLJlzfbuCY64yRN8sQxAI4D8PcAHAPgs8HFyVoqjmbJkiVxVrIBAAAAAEhKCNk+0djqSLcFCxb49TQAAAAAgEQsQSc+S0o0iVkkuu41AAAAACDpI2T7xJshHAAAAACQfNEuDgAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAAAAIRsAAAAAgMSFSjYAAAAAAD4hZAMAAAAA4BNCNgAAAAAAPrnsQ3a/fv2sYsWKCb0bSKR++OEHa9y4seXPn99SpEhhU6ZMiXF/dHS0Pf/885YvXz7LkCGD3XbbbfbHH3/EWGf//v3WunVry5Ili2XLls0efPBBO3LkSOD+77//3po0aeK2kSlTJnc8fvDBB3Hu14QJE9z+hLvt3r3brbNjxw677777rGTJkpYyZUrr1q1brO389ttv1qxZMytcuLB77KhRoy7wFQMAAABwWYfsnTt3WpcuXaxo0aKWLl06K1iwoAtF8+bNs6QkPmFo3LhxVr58eRfmdKtWrZrNmjUrQfY3qTh69KhVqFDBxo4dG/b+l156yV599VUbP368LV682IXk+vXr2/HjxwPrKGDr/Zs7d65Nnz7dBfdHHnkkcP/ChQvd+zZ58mT79ddf7f7777d27dq5dSNp2bKlC9HBNz1vzZo1LXfu3G6dEydOWK5cuax3797uZwjn2LFj7ndnyJAhljdv3gt4pQAAAAD4IbUloM2bN1v16tVddXDYsGFWrlw5O3XqlM2ZM8c6depk69evt6TCC0PNmze3J598Muw6V111lQtLJUqUcBXW9957z1VIV6xYYWXLlj2n56s6eJ5Fpc5kydXmIY3cv7fffru7haPXWCc7FGL1OsvEiRMtT548ruJ977332rp162z27Nm2dOlSq1y5sltn9OjR1rBhQxs+fLirkP/nP/+Jsd0nnnjCvv76a/viiy/sjjvuCPvcqprr5tmzZ499++239vbbbweW6YTMK6+84r5+5513wm7nhhtucDd59tlnz+k1AgAAAJDEKtmPP/64q+ouWbLEVXnVFqsw2b17d1u0aJFbZ8uWLS4AZc6c2VV3W7RoYbt27Yq4zVq1asVqq23atKl16NAhRngZOHCgqzZqu4UKFbJp06a5oOM9lyqTy5Yti9Heq5MBOgFQunRpt06DBg1cBTI+FIR0IkHBTRX7cFTBV3hTyNZrMWjQIPc83msBf23atMl1UqhF3JM1a1arWrWq/fzzz+57/av33QvYovXVvq3KdyQHDx60HDlyxHtfFO4zZsxo99xzz3n/PAAAAACSccjWOFdVCFWxVotuKAWbM2fOuNCrdefPn+/adf/66y/XanuhRo4c6aroqhI3atTI2rZt60J3mzZtbPny5VasWDH3vaqdwdVoVS8nTZrkWoZ1AqBHjx52MZw+fdo+/vhj1+6stnH4TwFbVLkOpu+9+/Sv177tSZ06tQvQ3jqhPv30U1f5Vtt4fKmCrfHXwdVtAAAAAJefBGsX37hxowuwpUqViriOxmWvXr3aVRw1Vtur+KnarRDjtcmeD1WMO3bs6L7WxFcaD63tqZ1bevbs6cKtqubeWFe1smvsrgK4dO7c2QYMGGB+0s+r59WYYFWxv/zySytTpkzE9TVuVzfPoUOH3L/pUkZbqlT/d4IgudF7FU5UVFTgPn3trRu8vk7uqMNCy3SyQ8dpuO3pvtDlmgRN4VrHk7oRIu1HMHUqqC393Xffjbi+9kH7dbbtefvkrRef50fSxDEAjgPw9wAcA+Czgb/i+9k6wUJ2cIU4EgUPhWsvYIsCp6rcuu9CQrbawT1eJVNjwkOXaaZnL2SrndcL2KLZpL2ZoP1yzTXX2MqVK1278eeff27t27d3VfxIQXvw4MHWv3//WMt7VzpjGTOetuRq5syZYZf/8ssvliZNGve1V4nWhGUaL+/RXABFihRx29D7u3379hjbU5Ddt2+fbdu2LcbyNWvWuGEICtk5c+aMuA+hNMZbz6f9ifQYPZ9ONsW1TXVarF27NsY66v5A8sYxAI4D8PcAHAPgs4E/9Hk7UYdsjTtWtdDvyc00VjY0wIc74+AFLdF+RFqm6mG4x3jrxOdkwblImzatFS9e3H19/fXXu4q9Jr96/fXXw67fq1cvN4Y9uJKtkxIDV6S0qDSpLLla069+2OV6TdXFIHrvdAk4HR/eMr1+6rLQJGJapvA7ZswYd6LluuuuC4QWPfbRRx91E5+JToTohMfQoUPtsccei/d+6lJgGqKgcO7tQzgjRoxw+xLXOjoJpJMxWkc/k/azbt26sY5bJA8cA+A4AH8PwDEAPhv4y+saTrQhW2NadckiXVqpa9euscZlHzhwwE0wtnXrVnfzqtmq1Om+SJVdXfIoeDIyVR1VYbz11lvtcqSQH9wOHkqTqIWbSO3EmRQWdfp/JwqSIy9YKsQqNHt0LOlyXDr+rr76ajdJnsKxhi0oxPbp08cFZ01Apm2o40ET3Ck4a6iAgoseownsNGGefPfdd27uAM0qron5VHX2Tph4k5+p7V8nREJPKmkGcrWtq2MhXBhWV4NobL62q33Xdr3j/+TJk+53wvta1XCt4x0T2iYhO3njGADHAfh7AI4B8NnAH/H9XJ2gl/BSwNbkY1WqVHFjmxVoFDhUgdOYVoUHtXDrOsW61JLu04zkupZw8GzPwWrXru0quzNmzHCt3aoAKpQntNAwpFZjBSiNu/Yq1wphutyUwt/hw4ftww8/dGN8NaP5uVrcq45rWU7uNEN88AkWr+qvUKsZ45955hkXYHXdax0nNWrUcBPypU+fPvCYDz74wI2/r1OnjuuU0Ez4ura2R5daU+uIwrpuHh2nev9E7f8bNmwIO+HZ3Xff7YZAhFOpUqUYre46JhTudfk7USt78DqamE+3W265JUaHAwAAAIBLI0FDtsbBaiZvXarqqaeechVoVaLV0quQrXbsqVOnWpcuXVxoUMBRVVFjWCN54IEHbNWqVW5mcM0CrWtSJ4YqdqQwFBzENP5X+63XQZeS0kkHBWy1/OL86JJucbX06xjTCZ64JrBTNVrhNhKFdd3iokvIBV9GzrNw4cI4H3e24Qi6HF24dVRxj++YcAAAAAD+SRHt96BiJPg4AQX0vXv3UslOxryQrfHZtIsnTxwD4DgAfw/AMQA+G1ycrKUu1SxZsiS+62QDAAAAAJDUELJ9orHVkW4LFizw62kAAAAAAIlYgo7JTkq8WaDDKVCgwCXdFwAAAABAwiBk+8SbIRwAAAAAkHzRLg4AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNm4LJw+fdr69OljRYoUsQwZMlixYsXshRdesOjo6MA6+vr555+3fPnyuXVuu+02++OPP2JsZ//+/da6dWvLkiWLZcuWzR588EE7cuRInM/dsWNH93zaZq5cuaxJkya2fv36WOtNmDDBypcvb+nTp7fcuXNbp06dAvcdP37cOnToYOXKlbPUqVNb06ZNfXldAAAAACQul33I7tevn1WsWDGhdwMX2dChQ23cuHE2ZswYW7dunfv+pZdestGjRwfW0fevvvqqjR8/3hYvXmyZMmWy+vXru4DrUcD+7bffbO7cuTZ9+nT74Ycf7JFHHonzua+//np799133fPOmTPHhfl69eq54O8ZMWKEPffcc/bss8+67X/zzTfuuT1aVyG9a9euLvwDAAAASJoSPGTv3LnTunTpYkWLFrV06dJZwYIFrXHjxjZv3jxLShS8mjVrZoULF7YUKVLYqFGjYq2jwKefPX/+/G6dKVOmJMi+JkYLFy50FeRGjRq51/Cee+5xQXfJkiXufgVfvaa9e/d266miPHHiRNu+fXvgdVRInj17tr311ltWtWpVq1GjhgvpH3/8sVsvEoXwW265xT3vddddZwMHDrStW7fa5s2b3f3//vuve14933333eeq3nr+O++8M7ANBX6dJHj44Yctb968F/31AgAAAJAwUlsCUkipXr26a9sdNmyYa6U9deqUqxaq1TZcS+7l6tixY+5EQvPmze3JJ58Mu87Ro0etQoUK9sADD9jdd999Qc9XdfA8i0qdyZKCzUMa2U033WRvvPGG/f7771ayZElbtWqV/fjjj66CLJs2bXInbIKrxFmzZnVh+ueff7Z7773X/atjrXLlyoF1tH7KlCld5fuuu+46677oPVJVW23rOiEkqoqfOXPGtm3bZqVLl7bDhw+7/X355ZcD6wAAAABIHhK0kv3444+7iq2qkaryKjyVLVvWunfvbosWLXLrbNmyxVUmM2fO7MbRtmjRwnbt2hVxm7Vq1bJu3brFWKbxrxoP61FFUtXIdu3aue0WKlTIpk2bZnv27Ak8lyqRy5YtizHeVgFNJwAUpLROgwYNbMeOHfH6WW+44QZ3IkFhTxX7cG6//Xa3X/EJe8mN2rD12pUqVcrSpEljlSpVcu+z2r9FAVvy5MkT43H63rtP/2qsdDCNj86RI0dgnUhee+01957rNmvWLBes06ZN6+7766+/XMh+8cUXXTX9888/d2O/69ataydPnvT1dQAAAACQuCVYJVshRK27gwYNcq20oRRoFVy80Dt//nyLiopyFe6WLVva999/f0HPP3LkSBeKNJmWvm7btq2rPqqKrDDcs2dPF8LV5q0TAV41evjw4TZp0iRX/WzTpo316NHDPvjgA0soJ06ccDfPoUOH3L/pUkZbqlT/NynY5UzdDZ988ol7ndWSXaZMGVfJ1muv0Kz3SceGt65uHh1Dev+0TOOi1VYefL9H94Vb7tHJHZ3AURhX9VwdCTomNcmZ95xaXrt2bbe+9lNVbIVxtbUH0z7pFtfzXShv2xfzOZC4cQyA4wD8PQDHAPhs4K/4frZOsJC9ceNGF3hUmYxE47JXr17tWoG9tluFF1W7ly5d6qrD56thw4Zu1mjRjNQaL6vtKTyJQna1atVc1dwbQ6sXVZNqacytdO7c2QYMGGAJafDgwda/f/9Yy3tXOmMZM/7fxFyXs5kzZ7qqtbodrrjiCjceWtVndRL07dvXrrzyykAlevLkya4t36MhB2rt1jZ2797txl7r6+BwvW/fPtfqHbw8LuqK0AkWTbqnsdrqgBB1NQRvQ/uq770TAJ5//vnHtZ3H9/kuhEI+kjeOAXAcgL8H4BgAnw38oaJrog7ZwZdeikQTVSlcB49rVRVTVW7ddyEhW+3gHq/FWGPCQ5cpmHkhO2PGjIGALbpUlO5PSL169XLt9cGVbL1eA1ektKg0qSwpWNOvvjte9P7o5IhHJ2A01EDLdL9Cr06EeOvotdDJHLWaa5nCtmYn1/upCcy8AKLHPvroo27CufhQ54A6GXQsarvFixd3E6hdddVVgUq2OjU0NlsTtaltPJhOBBw4cCDGz+I3vQ762fTcaq9H8sMxAI4D8PcAHAPgs4G/vK7hRBuyS5Qo4dp4/Z7cTOEnNMCHK+sHBw+vHTzcMrX1hnuMt058ThZcTBrfHW6M94kzKSzq9P9+hsudXnfNuj5kyBAXlNXJsGLFCnvllVdce7/3vqjarcq+uiO0noYCKDhrJnKtoxMrqn4/9thjriNBx4Ueo7HeGpcvqmjXqVPHdUxUqVLFjbdWq7pavnWNbFWhtR+6HJf2SdvV/mhYw1NPPeUmZ9PcATr5of0IDrlr1651Y7QVsBXANRRBLuYl6PTchOzkjWMAHAfg7wE4BsBnA3/E93N1goVstfvqOsJjx4511w4OHZetIKIJxtQarJtXzVZQ0X2qIoajIBQ8GZnagdesWWO33nrrRf6JcDGpUqzQrMny1D2g8Kx2f7X6e5555hnXhq1LbukY0SW6NO5f46Y9GtetNn8FaZ2QUQu6rq3tUfDesGFDoBVEj12wYIGb0EyX6lKHg1rEdUmx4EnUFMo1a7wq19puzZo13XMH/yKqcv33338HvtfkbZLQJ2oAAAAAJJFLeClg6xJeqhhqbLMqjRq/qjZXjZFWoFaLsGaQVsjRfQpZCjDBl2EKpnZdtU/PmDHDtXZrMioFroSmCqZ+Hu9rVUxXrlzpJnVTu7EcOXLEtTd7NBZd6+iExNVXX31Oz7e4Vx3LmTOnJRUa36xjINz1xYM7C3QcxTVOXq/lhx9+GPF+zTwfHHoV5uMzdlrV67ffftvdIvGuqw0AAAAg6UrQS3hpgqrly5e7KrNaba+99lrXXqsJzxSyFZqmTp1q2bNnd9VDXdNYj1H7biRqH27fvr2bcVphXOsnhiq2JtxS5VI3Vdo1S7m+fuihhwLr6JJh3jqikwX6OrhaCwAAAABIvFJE06ua5AbjZ82a1fbu3ZukKtk4N2p7VwVeLeqMyU6eOAbAcQD+HoBjAHw2uDhZ6+DBg66TNVFWsgEAAAAASEoI2T7R2OpIN02cBQAAAABI+hJ04rOkRBOURVKgQIFLui8AAAAAgIRByPaJN0M4AAAAACD5ol0cAAAAAACfELIBAAAAAPAJIRsAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAADwCSEbAAAAAACfELIBAAAAAPAJIRsAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAABIbCH7wIEDfm0KAAAAAIDkE7KHDh1qn3zySeD7Fi1aWM6cOa1AgQK2atUqP/cPAAAAAICkHbLHjx9vBQsWdF/PnTvX3WbNmmW33367Pf30037vIwAAAAAAl4XU5/OgnTt3BkL29OnTXSW7Xr16VrhwYatatarf+wgAAAAAQNKtZGfPnt22bt3qvp49e7bddttt7uvo6Gg7ffq0v3sIAAAAAEBSDtl333233XfffVa3bl3bt2+faxOXFStWWPHixS2x6devn1WsWDGhdwPnQF0RKVKkiHXr1KlTjPV0YkfHn+6bMmVKYLmOywYNGlj+/PktXbp0rvOic+fOdujQoTifd9CgQXbTTTdZxowZLVu2bGHXCbdfH3/8ceD+Dh06hF2nbNmyHAMAAABAEndeIXvkyJEusJQpU8aNx86cObNbvmPHDnv88cf93kfXnt6lSxcrWrRoIDA1btzY5s2bZ0nJm2++aTfffLPrFNBNHQJLliyx5Gjp0qXuePJuOs6kefPmMdYbNWqUC7ChUqZMaU2aNLFp06bZ77//bhMmTLBvvvnGHn300Tif9+TJk+45HnvssTjXe/fdd2PsX9OmTQP3vfLKKzHuU9dHjhw5Yu07AAAAgKTnvMZkp0mTxnr06BFr+ZNPPml+27x5s1WvXt1VFYcNG2blypWzU6dO2Zw5c1xVc/369ZZUfP/999aqVStXSU2fPr2bxV1j3X/77Tc3c/u5qDp4nkWlzmSXo81DGlmuXLliLBsyZIgVK1bMatasGVi2cuVKe/nll23ZsmWWL1++GOvrJEVwUC5UqJA7AaRjKC79+/d3/yqUx0XHY968ecPelzVrVnfzqML+77//2v333x/nNgEAAAAk4+tkT5o0yWrUqOHacf/+++9AVXHq1Kl+7p8LRqpUqqLbrFkzK1mypGu77d69uy1atMits2XLFle1VEU9S5YsbiK2Xbt2RdxmrVq1rFu3bjGWqRKpNt/gduWBAwdau3bt3HYV0lQV3bNnT+C5ypcv7wKeR8FM4UsnAEqXLu3WUcuyqpnx8cEHH7ifV63tpUqVsrfeesvOnDmT5Cr250rV5ffff98eeOCBQNX62LFjbsjC2LFjI4bdYNu3b7cvvvgiRki/EDrBc+WVV1qVKlXsnXfecW3rkbz99tuuK0HHEAAAAICk7bxC9rhx41zI1VjYAwcOBCY7U8BU0PbL/v373cRqCjSZMsWuyur5FEIVerXu/PnzXVvxX3/9ZS1btrzg51dbvKroGmveqFEja9u2rQvdbdq0seXLl7vKqr4PDlgKf8OHD3cnIX744Qd3AiBc1T8+tC1V7dVqnJypEqzjLPgkiLomVPHXex8XdQZofLU6AXQCRicuLtSAAQPs008/dceaTvzoxMjo0aMjhntd3u6hhx664OcFAAAAkETbxRUoNH5Y1V+18XoqV6583oEynI0bN7oAq6puJKryrl692jZt2hS4rNjEiRNdtVvjem+44Ybzfv6GDRtax44d3dfPP/+8O7mg7Xlja3v27GnVqlVzVXOvmqpQrOuIK4CLxq4rlJ0PbV+dAt7s7eGcOHHC3TzexF7pUkZbqlSRq6uJmV7DYArG9evXdy3kuu+rr76yb7/91nU3BK8bFRUV67EvvfSS/ec//7E//vjDevfu7ToYIgXiYN6Jo9DtybPPPhv4+tprr3WvudrQw43jVpVbJ4N0kibcti4W77ku5XMiceEYAMcB+HsAjgHw2cBf8f1sfV4hW4G2UqVKsZZrUrKjR4+aX+JqwfWsW7fOhWsvYIsmZFOw0X0XErLVDu7JkyeP+1djwkOX7d69OxCyVTX1ArZorLDuP1c6eaEZqzVOW+OzIxk8eHBgHHGw3pXOWMaMl+fl1GbOnBn4Wq+dTqTohIO3XJOO/fnnn65dO5i6F9SmrxnCQ6VKlcp1Iihw61ruZ+sOWLVqlfslCt6XSDTJ2j///OOGSmi+guDj97XXXnMVd026lhC8CeOQfHEMgOMA/D0AxwD4bOAPdRpftJBdpEgRN+lU6BhTtXYr5PilRIkSbgyu35ObKRSFBvhwZyWCA5M3FjjcMrWsh3uMt058ThYEU7u5QraCWXDQD6dXr16udd+jqqpOOAxckdKi0qSyy9GafvUDX6sLIHfu3NanTx9Lnfp/h+t1111ne/fujfEYLdPrpoqxjs9wrrjiCvev5hLQmPu4aPt6L9XNcDYK5JpoLbR1XcMXNB5fJ0FU8b6UdDwrXOkye6HHJJIHjgFwHIC/B+AYAJ8N/HW2ywFfUMhWqNM46ePHj7sAqbbdjz76yFVV/Rjz6lG1UW3Cmtyqa9euscZla5yuQr0ukaSbV81eu3atu08V7XDUdhw8GZlag9esWWO33nqrJTS1N6sSq8nT1H5/Nuoe0C3UiTMpLOp07EtbXQ68UKiTF2r9b9++vWXIkCFwf2jngkfhWhPjiSrQauNXJ4MmoNMM7U8//bQbY6+TN6LjVmPqVSn3Zm/XGHqN79+2bZs7LvQ40fXftR21qmu7N954o+swUJDVLPAaJhEaZt977z1XNQ/X9XGpaJ8I2ckbxwA4DsDfA3AMgM8G/ojv5+rzCtmaxEmhR2NcvVmeNXZY1we+9957zU8K2ApGmsVZVU1VdjX2VuFGY6QVqNXC3bp1azfpmu7TRFSaRTpSSK1du7Y7UTBjxgzX2j1ixAgXyhOawprGfn/44Yeu0qrrg4vCnXct8vha3KuO5cyZ0y5nquQr9GpW8XOl41PzBmiCNI1ZVyi/++67Y4yn1rG7YcOGGF0Mev0Vjj1eQP7uu+/crPT6xdIxqe3qBJPCt46fhx9+OMbzHzx40CZPnux+JwAAAAAkH+ccshViFQJVYVawVVA5cuSIa+m9GIoWLepm8lZ196mnnnIVaFWir7/+ehey1Y6tsbBdunSxW265xbWC67JZcU1updCmFl9VMdWCrMCUGKrY+nl0uap77rknxvK+fftav379LLnRNcLj22ofup7ez4ULF8b5GIXm0MfpMmxxXSNbx5ZuZ6PrZMd3zAYAAACApCNF9LkOGP7/k3tpUjGu+5s4xwko4GlM8eVeycb58yZt05hy2sWTJ44BcByAvwfgGACfDS5O1lLXqi4P7Ot1stW6rWtHAwAAAACACxyTrTHPat3WZYvUth06IdnZZsROruIaVz1r1iy7+eabL+n+AAAAAAASQcj2JjfTjN+hl6rSv5qVGbHpsmeReLNbAwAAAACSWcjetGmT/3uSDGgmagAAAABA0nVeIZsJzwAAAAAA8ClkT5w4Mc77dWksAAAAAACSm/MK2U888USsS8XomsBp06Z1l/ciZAMAAAAAkqPzuoTXv//+G+N25MgR27Bhg9WoUcM++ugj//cSAAAAAICkGrLDKVGihA0ZMiRWlRsAAAAAgOTCt5AtqVOntu3bt/u5SQAAAAAAkvaY7GnTpsX4XtfH3rFjh40ZM8aqV6/u174BAAAAAJD0Q3bTpk1jfJ8iRQrLlSuX1a5d215++WW/9g0AAAAAgKQfss+cOeP/ngAAAAAAkBzHZA8YMMBdsivUf//7X3cfAAAAAADJ0XmF7P79+7vLdoVS8NZ9AAAAAAAkR+cVsjXRmcZhh1q1apXlyJHDj/0CAAAAACBpj8nOnj27C9e6lSxZMkbQPn36tKtuP/rooxdjPwEAAAAASFohe9SoUa6K/cADD7i28KxZswbuS5s2rRUuXNiqVat2MfYTAAAAAICkFbLbt2/v/i1SpIjddNNNliZNmou1XwAAAAAAJI9LeNWsWTPw9fHjx+3kyZMx7s+SJcuF7xkAAAAAAMlh4jPNIt65c2fLnTu3ZcqUyY3VDr4BAAAAAJAcnVfIfvrpp+3bb7+1cePGWbp06eytt95yY7Tz589vEydO9H8vkSRt27bN2rRpYzlz5rQMGTJYuXLlbNmyZe6+U6dOWc+ePd0yncjRsdWuXTvbvn17jG3s37/fWrdu7bonsmXLZg8++GDYy8sFU/dFp06d3PNmzpzZmjVrZrt27YqxTteuXe366693x3fFihXDbufTTz9192XMmNEKFSpkw4YNu+DXBAAAAEAyDNlfffWVvfbaay6cpE6d2m6++Wbr3bu3vfjii/bBBx/YpdSvX7+IIQiJ17///mvVq1d34/pnzZpla9eutZdffjnQCaFuieXLl1ufPn3cv1988YVt2LDB7rzzzhjbUcD+7bffbO7cuTZ9+nT74Ycf7JFHHonzuZ988kl3DH/22Wc2f/58F9zvvvvuWOtpgr+WLVuG3Yb2Wc+t2fTXrFnjfh9GjhxpY8aMuaDXBQAAAEAyDNmqHhYtWtR9rQqivpcaNWq4kHMudu7caV26dHHbU9WwYMGC1rhxY5s3b54lJW+++aY7GeG11N922222ZMmSGOsoSNarV89VWHV5tJUrV1pSNXToUPdev/vuu1alShU3mZ5+9mLFirn7NXO9gnOLFi3smmuusRtvvNEF2F9++cW2bNni1lm3bp3Nnj3bdVJUrVrVHX+jR4+2jz/+OFbF23Pw4EF7++23bcSIEVa7dm1XrdY+LFy40BYtWhRY79VXX3XVbu84DzVp0iRr2rSpC9lap1GjRtarVy/3c2kGfgAAAADJ03lNfKZQsWnTJrv66qutVKlSrm1WQUnVQbXsxtfmzZtdNVOPUautWoPVJjxnzhwXcNavX29Jxffff2+tWrVys7KnT5/ehTGFSlVhCxQo4NY5evSoC4oKlg8//PAFPV/VwfMsKnUmS4w2D2lk06ZNs/r161vz5s1dNVmvweOPPx7nz62ArJMP3jH2888/u68rV64cWEcnL1KmTGmLFy+2u+66K9Y2FNJ1jGk9j45hHcvansJ8fJw4ccK1iQdTy/s///xjf//9t7ucHQAAAIDk57wq2ffff7+tWrXKff3ss8/a2LFjXXBUG67Ga8eXQpVCkyq6aj0vWbKklS1b1rp37x6oKqpq2aRJEzd2VlVzBdDQ8bPBatWqZd26dYuxTBXHDh06BL5XABo4cKAb46vtajytQt+ePXsCz1W+fPnA+GCZMGGCC3Q6AVC6dGm3ToMGDWzHjh3x+lnVRq+fV63tCnWqvp45cyZGxb5t27b2/PPPxwiASdVff/3lxvSXKFHCvaaPPfaYGwf93nvvRRxHrTHaOlHhzV6vLghNvhdMwxdy5Mjh7gtHy3VN99CTQXny5In4mHB0gkCdB3r/9D7+/vvvrt1d4ntMAAAAAEh6zquSrTDtUSBUxVkVwuLFi7twGh9qMVer76BBg9zEVqEUghRevNCramdUVJSrcGucrCrDF0LjZzWGXGN+9bUCrqrMGoerqroCnUK4Ks06EeCNEx4+fLhrFVa1VJN29ejR47zGoWtbqqgqEF4IVVR18xw6dMj9my5ltKVKlTjblvVz671Vq7YmzJNrr73Wfv31Vxe877vvvljr6+SKHqM2bn0vp0+fdq3Z3vfBdF+45TqGvG0G03bCPSbSc+ikjYL1HXfc4e5T8NeM+y+88ILbz3DPfSl5z5/Q+4GEwzEAjgPw9wAcA+Czgb/i+9n6vEJ2aIVRlWDdzsXGjRtdeFFVNxJVCVevXu1a0zV+VzR7uardS5cutRtuuOG897thw4bWsWNH97Wqxwp32p7al0Uhu1q1aq5qnjdv3sCLOn78+MC4YYWqAQMGnNfza/uaMftCq9aDBw8OBNVgvSudsYwZT1tiNHPmTHcSRSdP9HVwAP7jjz9iLdNJD70Peq1//PHHwH27d+92Y6+D11co3rdvn5u5PHi5R63cuq67hjjo+YOXazK20Mdof3TiIty2NMZeJ2YOHDjgQrZOEsiff/5pe/futcRA49qRvHEMgOMA/D0AxwD4bOAPFUovWshWkFEVWIFT4UcVPY3TVlVYrdi6jNLZxGdyKE1spXDtBWwpU6aMC2i670JCdnDFXa3CojHhocsU5LyQrTG4XsCWfPnyufvP1ZAhQ9zkXKrGq83+QmiyLbXXexQI9XoNXJHSotKkssRoTb/6btIxjV/WyQ6PLgunIQPeMp3UUHv44cOH7aeffrJcuXLF2I4mS9NkaHp/rrvuukCg0LGlCcl0EiOU5gBQtVlt5d7zaNZyDRXQMAhNoBZMQwZ0rAXvZyRTpkxxY7q1zwlNr51ei7p167oZ3JH8cAyA4wD8PQDHAPhs4C+va/iihGy1eGvs7EsvvRRjoiq1/I4aNSpeIVtjcdWG7ffkZmrjDg3w4cr6wcHDawcPt0ytv+Ee461zrjNJq91cIfubb76Jd2t9XDQju26hTpxJYVGn//czJDZ6HZ966ilXBVaVWq3gGpevcepvvPGGu98L2Lp8ly7NpfdVFWpRi73GVev107h4jefWCR89RuPx77333kBnhSraderUcR0QmpzvyiuvdMfnM88848ZzqwKt2e3VtaBJ54I7LXS9bYVvdWto2IB3kkfPrUr1559/7uYA0P2aoXzy5MluWENiCrXal8S0P7j0OAbAcQD+HoBjAHw28Ed8P1ef18RnCiwKQ7pOcKpU/1ctrVChQrxDs4KSJo/SpGmaVTuUWnA1wdjWrVvdzaPrKes+hZ1wVO0MnnhKVXddxzgx0EkJVVE1Fj14RuzkSF0IX375pX300Ufu5IxeF52g0THlhWNNRqdqtyaLU9eAd9PltjwaD68hBwrSqjYrKOvY9Ch4q1Id3NqhMfgaS63J9m655RZXCdckZsEeeughq1Spkr3++uuuU0Nf6xZ8aTCdaNL7qOq4Qrg6ExTkAQAAACRf51XJVgDSJGehznXCJwVsBRQFE423VWVSY3DV5qox0grUauFW8FIA032aobtmzZoRQ6rakNU+PWPGDNfareshK5QnNF2yS2O/P/zwQ9dS781krXHB3thgTQan2dS9IKdwKAqBXst6fC3uVcddbzsxU9DVLRy9RvHpEtDJGr2mkYTbjlr0dezpFsnZJtZTRVyX/AIAAACAC65kq4q8YMGCWMvVPqtqX3xpHLfagW+99VbXPqyKpsaQasIzhWy1Y0+dOtWyZ8/uKo6aJEyP+eSTTyJuU7ODt2/f3s0MrjCu9bX9hKafRxNu3XPPPTGqsmof96hyq9evUaNG7nu1Pet7tUIDAAAAABK/FNHnOqjYzAVfBVlNuqUKtGa3VtVVbeQaP6ugjIQbjJ81a1Y3ZjixV7Jx8aijRDOiq4WeMdnJE8cAOA7A3wNwDIDPBhcnax08eNDN7eRLJfuvv/5yrbe6dvVXX33lJu/SNa7VBq0ZmLWMgA0AAAAASK7OaUy2ZgTXpGKalVnXCNZ4WF3H2rvcVXIWfM3lULNmzXKvFwAAAAAgaTunkB3aWa7wGG5m8ORo5cqVEe8rUKDAJd0XAAAAAMBlNLu45zyGcydZ4WZbBwAAAAAkL+c0JluzfesWugwAAAAAAJxHu3iHDh0sXbp07vvjx4/bo48+6iY/C/bFF1/w2gIAAAAAkp1zCtm6bFewNm3a+L0/AAAAAAAkj5D97rvvXrw9AQAAAAAgOY3JBgAAAAAAkRGyAQAAAADwCSEbAAAAAACfELIBAAAAAPAJIRsAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAAAgZAMAAAAAkLhQyQYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8kixCdr9+/axixYoJvRsIsW3bNmvTpo3lzJnTMmTIYOXKlbNly5YF7v/iiy+sXr167v4UKVLYypUrI76G0dHRdvvtt7v1pkyZEnG9U6dOWc+ePd1zZcqUyfLnz2/t2rWz7du3x1jv999/tyZNmtiVV15pWbJksRo1ath3330XY50tW7ZYo0aNLGPGjJY7d257+umnLSoqivcZAAAASMYui5C9c+dO69KlixUtWtTSpUtnBQsWtMaNG9u8efMsqTlw4IB16tTJ8uXL537WkiVL2syZMy2p+ffff6169eqWJk0amzVrlq1du9Zefvlly549e2Cdo0ePunA7dOjQs25v1KhRLmCfzbFjx2z58uXWp08f96+C/IYNG+zOO++Msd4dd9zhAvO3335rv/zyi1WoUMEt07Eop0+fdgH75MmTtnDhQnvvvfdswoQJ9vzzz5/X6wEAAAAgaUhtidzmzZtdGMuWLZsNGzbMVSBVjZwzZ44Lo+vXr7ekQoGtbt26rir6+eefW4ECBezvv/92P/u5qjp4nkWlzmSJ0eYhjVxw1smSd999N7C8SJEiMdZr27bt/9bfvDnO7anCrYCuKrhOTsQla9asNnfu3BjLxowZY1WqVHGV6auvvtr27t1rf/zxh7399ttWvnx5t86QIUPstddeszVr1ljevHnt66+/dicGvvnmG8uTJ4/rlHjhhRdclVydE2nTpj3n1wUAAADA5S/RV7Iff/xxV6FcsmSJNWvWzFV2y5Yta927d7dFixa5dRSO1NqbOXNm19rbokUL27VrV8Rt1qpVy7p16xZjWdOmTa1Dhw6B7wsXLmwDBw50rcTabqFChWzatGm2Z8+ewHMpgAW3N6uSqUCsEwClS5d26zRo0MB27NgRr5/1nXfesf3797t2Z51Y0D7UrFnTVVGTGr2WlStXtubNm7uTCpUqVbI333zznLejyvR9991nY8eOdeH3fBw8eNAdY97JDLWnX3PNNTZx4kRXTVdF+/XXX3f7ef3117t1fv75Z3fCRwHbU79+fTt06JD99ttv57UfAAAAAC5/iTpkK3DOnj3bVaw1fjaUQtGZM2dc6NW68+fPd1XKv/76y1q2bHnBzz9y5EgXdlesWOFag1VZVejWOGK1GhcrVsx9r/HAwaFv+PDhNmnSJPvhhx/cCYAePXrEO3hWq1bN/bwKb9dee629+OKLrjU5qdF7NG7cOCtRooQ7KfHYY49Z165dXdv1uXjyySftpptucsfA+Th+/LirPrdq1cqdoBEFblWo9b5fccUVlj59ehsxYoQ7Fr12drWNBwds8b73WsoBAAAAJD+Jul1848aNLsCWKlUq4joal7169WrbtGmTaz8WVSBV7V66dKndcMMN5/38DRs2tI4dO7qvNdZWoVDbU/VVFM4UilU196qoamUfP368C+DSuXNnGzBgQLyDp8YAt27d2o3D1s+vSr622bdv37CPOXHihLt5VEmVdCmjLVWq/wv/iYl+Hp0cUVW4f//+bplOKPz666/uNVZlOnR971/va/nqq6/c66Uuh+DlqjwHfx/XfqjrQfvy6quvBh6jY06hP1euXG6yM03Kpi4DzQOg8ddqSddjtF7w83hfx/f5L6bg1wzJE8cAOA7A3wNwDIDPBv6K72frRB2ygyvEkaxbt86Fay9gS5kyZVyVW/ddSMj2xuMGVynVIhy6bPfu3YGQrZmmvYAtCmS6Pz4U3NSS/MYbb1iqVKlcCNUM3BqLHilkDx48OBBUg/WudMYyZkycFXCdQND7o3b64EndFE41Fjp0ojev9f/HH3+MMQu4xnP/+eefbgbwYOpiULv+oEGDIu6Dnkuvq7atkyDatmfVqlVuH95//303EZ1umrlcnQa9e/d2wxYOHz4ca1+9/dTJkcQyWV3o+HMkPxwD4DgAfw/AMQA+G/hDXcuXfchWK7Fad/2e3CxlypSxAny4sxKa+drjzVwdbpnCcbjHeOvE52SBF8j1eAVsj8Ki2o81KVq4ybR69erlxqcHV7J1wmHgipQWleb/tpOYrOlX32rXrm3//POP6xbwqCqtMffBy4InPtNM48GXYrvuuuvcJGXBtEzt+mrvD51ILfi9Vnu4gvJPP/3kKtbBvPdT4+l1IsCjr3VMav90DGlyOo0r14kReeutt1zL+cMPP+xmhk9I+hkVrjSRXugxieSBYwAcB+DvATgGwGcDf3ldw5d1yM6RI4ebTEqTWmm8bui4bFUYFUK3bt3qbl41W7M+6z5VtMNRqAqejExjnjVr9K233moJSeO/P/zwQxfyFOK86zUrfEearVphLlygO3EmhUWdPvslrRKCQt9TTz3lxlKrmqyWbbV8K6Sqiu+FQo2z15h2r3qtdnrdp64B3UI7GDwK1wrrHg03UMX/rrvuCgRsjamfPn26e5337dsXON70Ot98881u7PVDDz3khgmoXVyTsins61Jf2gcFbR1fDzzwgL300kvuRIi6DTSePjiYJzTtKyE7eeMYAMcB+HsAjgHw2cAf8f1cnahDtihgK3zqEktq61ULt1p9VaXT+F0FarVwaxyzrpWs+zSOWbNyq8oYjqqoqv7OmDHDtXZrUiuF8oSmccC6nNQTTzzhrguudmRNfKYTDOdqca86bpbsxEpt/F9++aWrxOt9VTDW+6f30aP27Pvvvz/w/b333uv+VZjVZbLiS9fB1gziovZ7bVeCq+Ki8deaeV7t55rk7LnnnnPHioK5xvhPnTo1MNO7ug0U0vWeaVy+TgC1b98+3uPvAQAAACRNiT5kFy1a1FUdNb5W1U9VoFWJ1nhlhWy1Yyv8KJTecsstrjKpNt/Ro0dH3Kaqjxp3q5nBU6dO7WaoTugqtqgqq5m2tT86maDrZCtwa4K1pOiOO+5wt0h0SbXgy6rFR7jW/OBluixafNr3dYJG70VcdFm3xDL2GgAAAEDikCI6vgOGcdmME8iaNasbq5yYK9m4uFR91wkAtbXTLp48cQyA4wD8PQDHAPhscHGylrpkvcv/XnbXyQYAAAAA4HJCyL6ENCFWpNuCBQsu5a4AAAAAAJLjmOykZOXKlRHv0/hrAAAAAMDljZB9CRUvXvxSPh0AAAAA4BKjXRwAAAAAAJ8QsgEAAAAA8AkhGwAAAAAAnxCyAQAAAAAgZAMAAAAAkLhQyQYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyMYl069fP0uRIkWMW6lSpdx9mzdvjnWfd/vss89ibWvfvn121VVXufsPHDgQ5/MOGjTIbrrpJsuYMaNly5Yt7Drhnvfjjz8O3L9jxw677777rGTJkpYyZUrr1q3bBb8eAAAAAJKelEkhuFWsWDGhdwPxVLZsWRdYvduPP/7olhcsWDDGct369+9vmTNntttvvz3Wdh588EErX758vJ7z5MmT1rx5c3vsscfiXO/dd9+N8fxNmzYN3HfixAnLlSuX9e7d2ypUqMD7DQAAACBxhuydO3daly5drGjRopYuXToXtho3bmzz5s2zpOS3336zZs2aWeHChV2VdNSoUXGuP2TIELdeUquYpk6d2vLmzRu4XXnllW55qlSpYizX7csvv7QWLVq4oB1s3Lhxrnrdo0ePeD2nwvqTTz5p5cqVi3M9VbmDnz99+vSB+/S+vfLKK9auXTvLmjXref3sAAAAAJK+1An55GoRrl69ugs3w4YNcyHo1KlTNmfOHOvUqZOtX7/ekopjx465EwmqqCrwxWXp0qX2+uuvx7tSG07VwfMsKnUmSyw2D2nk/v3jjz8sf/78LsBWq1bNBg8ebFdffXWs9X/55RdbuXKljR07NsbytWvX2oABA2zx4sX2119/+bqPOuYeeugh9z49+uijdv/997sTHQAAAABwWVSyH3/8cRdilixZ4qq8Gu+qduLu3bvbokWL3DpbtmyxJk2auGpmlixZXGVz165dEbdZq1atWNVftf126NAhRlVy4MCBriqp7RYqVMimTZtme/bsCTyXAu6yZcsCj5kwYYI7GaATAKVLl3brNGjQwLUVx8cNN9zgTiTce++9rmIfyZEjR6x169b25ptvWvbs2S0pqVq1qnsdZ8+e7arRmzZtsptvvtkOHz4ca923337bvc4aSx3cst2qVSv3OoYL5hdCwf3TTz+1uXPnumNRx+bo0aN9fQ4AAAAASV+CVbL379/vwpYmpcqUKXbFVYH2zJkzgdA7f/58i4qKctXGli1b2vfff39Bzz9y5Eh78cUXrU+fPu7rtm3bukD3wAMPuBDXs2dPF8LV5u1VM1WNHj58uE2aNMlNftWmTRvXsvzBBx+YX/TzNWrUyG677TZ3IuBsFDx18xw6dMj9my5ltKVKFW2JhToU9DN5FKCvu+46K168uH300Ueuauz573//ax9++KH95z//cY/z6D255ppr3Puv5ToevG0HrxfJ6dOnA+uHevbZZwNfX3vtte511HEQbhx3dHS0Ozbj85wJxdu3xLyPuLg4BsBxAP4egGMAfDbwV3w/WydYyN64caMLK97s0uFoXPbq1atdxVNjtWXixImu2q2WalWHz1fDhg2tY8eO7uvnn3/eVVa1PbVze4FO7cyqmmt8rveijh8/3ooVK+a+79y5s6uA+kWzWS9fvtz9bPGldmuNOQ7Vu9IZy5jxf6EyMZg5c2bY5blz57avv/7a8uTJE1j23Xff2dGjR93rHvy4qVOnus6GyZMnx9iG1tP7pip3XFatWuXew0j7EkwnUf755x/3nGnSpIk1s7mOyfhsJ6GpMo/kjWMAHAfg7wE4BsBnA3+o6JqoQ7YC9tmsW7fOhWsvYEuZMmVclVv3XUjIDh7v7AW84ImxvGW7d+8OhGxdAsoL2JIvXz53vx+2bt1qTzzxhPtAHDzh1tn06tXLtdd7VIHV6zVwRUqLSpPKEos1/eqHbY1XYNW4fJ308IwYMcJNfhcamlXFVpU7eNz2ww8/7LoaNI5agT0ue/fudYE5+LniCuRq11cnRSjtX5EiReK1nYSikwk6lurWrRvrJAGSB44BcByAvwfgGACfDfzldQ0n2pBdokQJ14bt9+RmqkCGBvhwZf3g4OG1g4dbprbgcI/x1onPyYL4UGBUYFcLdXB78w8//GBjxoxxLeGagTuUxneHG+N94kwKizqdeCbt0mun1nqFZ42B3759u/Xt29f9TGq7915bdTgsWLDAVYlDX+/QroeDBw8GTo5417/W+H61+asLokCBAm6Zqt8anrBt2zb3mmoIgKhVXUMRvvrqK9excOONN7oTHAqnQ4cOdfsbvA+aiE1UZdfJAW0nbdq07sRPYqX9J2QnbxwD4DgAfw/AMQA+G/gjvp+rEyxk58iRw+rXr+9mj+7atWuscdm6RJPG7arCq5tXzdbs0rovUrDRtYyDJyNTqFqzZo3deuutlpjVqVPHtcYH0zhlBUu1rocL2JcbtV+rOq2AqvepRo0aboI7fe1555137KqrrrJ69eqddwvHhg0bYpxY0XCA9957L/B9pUqVAm3pmihPvyw6DjXru06aKHyrWq0qeTDvcd5JEY0b1wkDzZIPAAAAAAl+CS8FG7UKV6lSxY1tVgu3JrNSJVFjpBWoVaXUbNu6rrTu06zPNWvWtMqVK4fdZu3atV379IwZM1xrt8KSQnlCO3nypPt5vK9VVVVlVJVUhborrrjCTbgVTCcecubMGWt5fCzuVcc9NjHRmPOz0WR0usWHAnJoJ0G4ZZrRXLdINEu8bmfjV9cCAAAAgKQrQS/hpXG0muhLVeannnrKhUmNIVWrr0K22rE18ZTGxt5yyy1udmo95pNPPom4Tc0O3r59e9cyrDCu9RNDFVvt0aqE6qZKu2Yp19e6LjMAAAAAIGlIEU15LskNxs+aNaub5CuxVbJx6XizqGtyNsZkJ08cA+A4AH8PwDEAPhtcnKyluaGyZMmSOCvZAAAAAAAkJYRsn2hsdaSbZssGAAAAACR9CTrxWVLiXd4pHO9SUgAAAACApI2Q7RPNEA4AAAAASN5oFwcAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGwAAAAAAHxCyAYAAAAAwCeEbAAAAAAAfELIBgAAAADAJ4RsAAAAAAB8QsgGAAAAAMAnhGxcdP369bMUKVLEuJUqVSpwf8eOHa1YsWKWIUMGy5UrlzVp0sTWr18fYxtbtmyxRo0aWcaMGS137tz29NNPW1RUVLye/8SJE1axYkX3vCtXroxx36+//mo333yzpU+f3goWLGgvvfRSjPtPnTplAwYMcPundSpUqGCzZ8++oNcDAAAAQNKVMrmEPIUsJJyyZcvajh07Arcff/wxcN/1119v7777rq1bt87mzJlj0dHRVq9ePTt9+rS7X/8qYJ88edIWLlxo7733nk2YMMGef/75eD33M888Y/nz54+1/NChQ+55ChUqZL/88osNGzbMHStvvPFGYJ3evXvb66+/bqNHj7a1a9fao48+anfddZetWLHCl9cFAAAAQNJyWYTsnTt3WpcuXaxo0aKWLl06V3Fs3LixzZs3z5KaUaNG2TXXXOOquvo5n3zySTt+/Lhd7lKnTm158+YN3K688srAfY888ojdcsstVrhwYbvuuuts4MCBtnXrVtu8ebO7/+uvv3YB9/3333cnS26//XZ74YUXbOzYsS54x2XWrFnu8cOHD4913wcffOAe/84777iTAPfee6917drVRowYEVhn0qRJ9p///McaNmzojr/HHnvMff3yyy/7+voAAAAASBpSWyKnoFW9enXLli2bqzSWK1fOtfCq4tmpU6dYbcWXsw8//NCeffZZF/puuukm+/33361Dhw6uzTk4+MVH1cHzLCp1Jktom4c0cv/+8ccfrpqslutq1arZ4MGD7eqrr461/tGjR11Vu0iRIu4kg/z888/ufc+TJ09gvfr167vA+9tvv1mlSpXCPveuXbvs4YcftilTprg281DarsJ92rRpY2x36NCh9u+//1r27Nldq7n2OZhOgARX4gEAAADgsqlkP/744y5kLlmyxJo1a2YlS5Z0Vcfu3bvbokWLAuN1NY43c+bMliVLFmvRooULWJHUqlXLunXrFmNZ06ZNXaD1qKqqimq7du3cdtVSPG3aNNuzZ0/gucqXL2/Lli0LPEYtzDoZoBMApUuXdus0aNDAtUfHh1qhdULhvvvuc8+vVuZWrVq5n/1yVrVqVffaaCzzuHHjbNOmTW4c9OHDhwPrvPbaa+710k3V57lz5wbCrzoZggO2eN/rvnDUcq73U+3dlStXDrtOfLar0K0THDpJcObMGbdfX3zxRbzfUwAAAADJS6KuZO/fv98Fs0GDBlmmTLGrsgq0Cj5e6J0/f76bDEsV7pYtW9r3339/Qc8/cuRIe/HFF61Pnz7u67Zt27oK8wMPPOCq6j179nQhXNVUnQiQY8eOudZktRmnTJnS2rRpYz169HCtyWejbaslWqG6SpUq9tdff9nMmTPd80aiSqtuweOMJV3KaEuVKtoSmroObrvttsD3OvmglvDixYvbRx99ZPfff79brhMjOvmhcKtQ27x5c/d+qoqs91ihWdsK3q7o/Q5e7hkzZox7LfTa635vneCvtU1tO9x2vfX0Xiqoa6I2vcdqGW/fvr07aRDueROL4J8DyRPHADgOwN8DcAyAzwb+iu9n60Qdsjdu3OiCUPBM1KE0Lnv16tWuOuq1F0+cONFVu5cuXWo33HDDeT+/xt5q5mvRJFuqwmp7CoCikK3WZ1XNNc7Ye+HHjx/vZqOWzp07u9mp40MV7L1791qNGjXcz60AqYCnMcGRqO26f//+sZb3rnTGMmb838RhCUknCcLRDOEaKx1aSRZVoHVyQpOQqZ1bFW9VkoO35XUq6BgJ9xwff/yx6zIIPTlz4403Ws2aNe2JJ55wr69mFw9+vI4l718dU/Lggw+6Ex3ajxw5crjjS7OgR/rZEhNV3pG8cQyA4wD8PQDHAPhs4A8VVC/7kK2geTaakVrh2gvYUqZMGVfl1n0XErLVDu7xwqDGBocu2717dyBka+yvF7AlX7587v74UOVdlXO1TqvFWgFSYVCTfKmaHk6vXr1c67xH1Vu9FgNXpLSoNKksoa3pVz/WsiNHjti+fftca7xOZIRSZV5dAHofdb++/vzzz13bt8K5vPXWW25ogMZcazK8UNdee22gqi9q79YM5Rr3ri6Bq666yk2uppMndevWtTRp0gRa9jUkQZX1cHQSRdVxtfGH2/fEQvupcBX8syF54RgAxwH4ewCOAfDZwF/B+eKyDdklSpRwLbp+T26m0BYa4MOV/oPDidcOHm6ZWo7DPcZbJz4nC0RBWhXThx56KBDoNRGYZt9+7rnn3H6HUsAMFzJPnElhUaf/t38JSa+HQqlmg9e49u3bt1vfvn0tVapUrlqtoPvJJ5+48eeqDv/zzz82ZMgQN7mYHqPHK8wqcKtNX9exVku5tqFhARomIGqxV+u+OhsKFCgQ40SHaBIz0cztmlRN9Fpr3L26BdSVsGbNGtdmrqEB3vu4ePFi27Ztm5vVXP+quq73Wyc3Lofwqn28HPYTFw/HADgOwN8DcAyAzwb+iO/n6kQdstWaq4mndKkmXVoptPX3wIEDboyvgppuXjVbl3vSfQpm4SjMBU9cpeswK2DdeuutltDtB6FBWmFU4hvUPYt71bGcOXNaYqDgrMqvqtd67dUOr0nr9LVObixYsMBdukwzeqs7QC3iqih7VWu9BtOnT3ezias9X8eBxkUHt+HrtduwYcM5jUHOmjWra1lXWNe1unVZMVW2dVLDo8un6VrZGh+vQK/Ar/H26pQAAAAAgMsqZIsCttqK1eKrUKUWbo2lVSusxkgrUKvi27p1axfUdJ9mJNe420izSteuXdu1WM+YMcNVPDXRlkJ5QlPlVvuiS1J57eKqbmu5F7YvRxofHYku6xWfsc2qgse1niZNi+tEhGZrD3e/jieF/Eh0HOkYAwAAAIAkEbI1m/Py5cvdDONPPfWUq0CrAqrKo0K22rGnTp1qXbp0cRVQVYJ12azRo0dH3KbajletWuXai1OnTm1PPvlkglexRRVT/Tz6V63J+jkVsPWzAwAAAAASvxTR59qHjEQ/GF9t0JqlPLG0i+PSU9u8Kv9qb2dMdvLEMQCOA/D3ABwD4LPBxclaBw8edJMwRxJ7Ji0AAAAAAHBeCNmXkCbOinSLa1wwAAAAAODykOjHZCclK1eujHifLjsFAAAAALi8EbIvoeLFi1/KpwMAAAAAXGK0iwMAAAAA4BNCNgAAAAAAPiFkAwAAAADgE0I2AAAAAAA+IWQDAAAAAOATQjYAAAAAAD4hZAMAAAAA4BNCNgAAAAAAPiFkAwAAAADgE0I2AAAAAAA+IWQDAAAAAOATQjYAAAAAAD4hZAMAAAAA4BNCNgAAAAAAPiFkAwAAAADgE0I2AAAAAAA+IWTjourXr5+lSJEixq1UqVKB+9944w2rVauWZcmSxd134MCBsNuZMWOGVa1a1TJkyGDZs2e3pk2bRnzOU6dOWc+ePa1cuXKWKVMmy58/v7Vr1862b98eY73ff//dmjRpYldeeaV7/ho1ath3330Xdpv79u2zq666Ks59BAAAAICUSSHEVaxYMaF3A3EoW7as7dixI3D78ccfA/cdO3bMGjRoYP/5z38iPn7y5MnWtm1bu//++23VqlX2008/2X333RdxfW1z+fLl1qdPH/fvF198YRs2bLA777wzxnp33HGHRUVF2bfffmu//PKLVahQwS3buXNnrG0++OCDVr58ed5nAAAAAIk7ZCvQdOnSxYoWLWrp0qWzggULWuPGjW3evHmWlPz222/WrFkzK1y4sKuGjho1Kux627ZtszZt2ljOnDld1VbV2GXLltnlLHXq1JY3b97ATZVjT7du3ezZZ5+1G2+8MexjFYKfeOIJGzZsmD366KNWsmRJK1OmjLVo0SLi82XNmtXmzp3r1rnmmmvctseMGeOC9JYtW9w6e/futT/++MM9t8JziRIlbMiQIS6gr1mzJsb2xo0b56rXPXr08O01AQAAAJA0pU7IJ9+8ebNVr17dsmXL5kKUAqVafefMmWOdOnWy9evXJ+Tu+UrhTScSmjdvbk8++WTYdf7991/3etx66602a9Ysy5UrlwuCao8+V1UHz7Oo1JksIW0e0sj9q59BLdvp06e3atWq2eDBg+3qq6+O1zZUidaJh5QpU1qlSpXcSRl1Luh4ufbaa+O9LwcPHnQnN3SsiU5iKIBPnDjRrrvuOneC5/XXX7fcuXPb9ddfH3jc2rVrbcCAAbZ48WL766+/zvk1AAAAAJC8JGgl+/HHH3fBZ8mSJa7KqyqlWou7d+9uixYtcuuo8qhxs5kzZ3bjZlWd3LVrV8RtanyvqqPBNH63Q4cOge9VTR44cKAbp6vtFipUyKZNm2Z79uwJPJeqm8EV5AkTJriAphMApUuXduuozVntz/Fxww03uGB47733ukAXztChQ10l/91337UqVapYkSJFrF69elasWDG7XGkctV672bNnu4rwpk2b7Oabb7bDhw/H6/FesNWwgN69e9v06dPdSQe9z/v374/XNo4fP+7GaLdq1codQ6Lj7ptvvrEVK1bYFVdc4U4AjBgxwu2nd1LjxIkT7jF63+J7UgAAAABA8pZglWwFJAWaQYMGucmpQinQnjlzJhB658+f71qHVeFu2bKlff/99xf0/CNHjrQXX3zRjdvV1xrze9NNN9kDDzzgQpVCmUK42rwVyLxq9PDhw23SpEmusqq2brUQf/DBB+YHBf369eu7ard+3gIFCrgTEQ8//HDExygI6uY5dOiQ+zddymhLlSraEpK6Em677bbA9zo5oapx8eLF7aOPPnJjrD16b73H6OY5efKk+1dt3d6Yak2WphMQH3/8cZyvjbc9nZjRsfTqq68Gth0dHW2PPfaY6xbQZGdqzX/nnXfcUIWFCxdavnz53DGgareONz0u0j4mRt7+Jfb9xMXDMQCOA/D3ABwD4LOBv+L72TrBQvbGjRtd0AmeaTqUxmWvXr3aVT9V4RW196ravXTpUlcdPl8NGza0jh07uq+ff/55V2XV9hRwRQFLrc2qmmscsfeijh8/PlBZ7ty5s2sl9ouqttoPVfI1EZh+xq5du1ratGmtffv2YR+j1uv+/fvHWt670hnLmPG0JaSZM2eGXa6W7K+//try5MkTWKb3WbRcJ1U83hhqjYkO3p6qzQrHOhERiUKxTpjoPdT7FDzhmiZQ0/bef/99t23dbr/9dneiQxVzdVZMnTrVPb8mXgum40HHiarciZ3GpiN54xgAxwH4ewCOAfDZwB8quibqkK2AfTbr1q1z4doL2KJJr1Tl1n0XErKDZ4r2wp7GhIcu2717dyBkZ8yYMUbrtqqdut8vqrZWrlzZVdhFY5A1CZeCfaSQ3atXLxfKgyvZer0GrkhpUWlSWUJa069+rGVHjhxxl8PS2HOd6PB43Qxqj/fGTYsuq6XWfo2h9tbXyQ6Nsa5du3aMbQTTOgrBakvXbOSqWIe+1qKW/+BQr681CZq2qyr2f//738B9mjhNlXN1UWh8vU4WJFb6+RWu6tata2nSpEno3UEC4BgAxwH4ewCOAfDZwF9e13CiDdkKMmrD9ntyM7Vxhwb4cGX94ODhtYOHW+aFsdD7vXXic7IgvhTadRIhmFqsQyupwTS+O9wY7xNnUljU6f/9DAlFr5fa6dWCrXHvuk513759LVWqVK7VXvdrIjPdNAme6HjQGGmNgc6RI4cL15pVXJVojaXXdlSdFo1v994TdUSoqn/XXXcFArYmTdMYbh0TCvaibaozQOPCVQ1/6KGHXCeD2sXffPNNtx9qS9d2Q7ssFOy9kzHBJwISM/0chOzkjWMAHAfg7wE4BsBnA3/E93N1gk18prCj8cdjx461o0ePxrpf7bsKmFu3bnW34NmedV9oGPWoYhk8Gdnp06djXZIpsVJ1V9dzDvb777+7YHm5+ueff1zgVVVYY6MVmjWpnVdZVpVeFXtvbPUtt9zivlfbtsebME7j5tW98Pfff7trWwfPuq7XzQvBmo1cj9dzayZynbzwbhpvLbqMmOYEUGVdFXF1EKidXC3iul42AAAAAFx2l/BSwFaw1EzaqlSqhVvjaNXmqrHJCtSqGrZu3dpdV1r3aSKwmjVrulAUjgKT2qdnzJjhWrs1Y7RCeULTBF76ebyvFQRXrlzp2pM1EZjo0l6afE3t4gqkmnVdk3zpdq4W96rjAm1C0+RkcdGs4bqd7YyRJpzTLZLgjgJVvOPTYaBjSLPFx5dmNPezcwEAAABA0pOgl/DSuFa19Oq60E899ZS77rHGkGrCM4VstWOrsqiKpSqcmqlaj/nkk08iblOzg2v8smYGVxjX+tp+QlOrtCq0uqnSrsCor9Wu7FGV9ssvv3Qzb+u1eOGFF9zJBZ1kAAAAAAAkfimiKc0lucH4WbNmtb179yaKSjYShsala/Z0TeDGmOzkiWMAHAfg7wE4BsBng4uTtTRMNUuWLImzkg0AAAAAQFJCyPaJxlZHui1YsMCvpwEAAAAAJGIJOvFZUqJJzCIpUKDAJd0XAAAAAEDCIGT7xJshHAAAAACQfNEuDgAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2QAAAAAA+ISQDQAAAACATwjZAAAAAAD4hJANAAAAAIBPCNkAAAAAAPiEkA0AAAAAgE8I2bgohgwZYilSpLBu3bq57/fv329dunSxa665xjJkyGBXX321de3a1Q4ePBh4zIQJE9xjwt12794d9nk2b95sDz74oBUpUsRtt1ixYta3b187efJkjHXCbXPRokWBdb744gurXLmyZcuWzTJlymQVK1a0SZMmcXQAAAAAOCepLRno16+fTZkyxVauXJnQu5IsLF261F5//XUrX758YNn27dvdbfjw4VamTBn7+++/7dFHH3XLPv/8c7dOy5YtrUGDBjG21aFDBzt+/Ljlzp077HOtX7/ezpw5456vePHitmbNGnv44Yft6NGj7rmCffPNN1a2bNnA9zlz5gx8nSNHDnvuueesVKlSljZtWps+fbrdf//97nnr16/v22sDAAAAIGm7LCrZO3fudFXQokWLWrp06axgwYLWuHFjmzdvniUlp06dsgEDBrhqbPr06a1ChQo2e/Zsu5wcOXLEWrdubW+++aZlz549sPzaa6+1yZMnu/dNP1/t2rVt0KBB9tVXX1lUVJRbR5XovHnzBm6pUqWyb7/91lWqI1Eof/fdd61evXru+LjzzjutR48erjIdSqE6ePtp0qQJ3FerVi276667rHTp0m7/nnjiCXeS4Mcff/T9NQIAAACQdCX6SrZafatXr+7aeIcNG2blypVzYXTOnDnWqVMnV8lMKnr37m3vv/++C6iqqOpnVPBbuHChVapU6Zy2VXXwPItKnckuhc1DGgW+1nvSqFEju+2222zgwIFxPk6t4lmyZLHUqcMfhhMnTrSMGTPaPffcc077o+2qMh1KAVxV8ZIlS9ozzzzjvg8nOjrahfsNGzbY0KFDz+m5AQAAACRvib6S/fjjj7vxs0uWLLFmzZq5gKSW3+7duwfG1G7ZssWaNGlimTNndqGtRYsWtmvXrojbVNXSGyvsadq0qWtN9hQuXNiFxHbt2rntFipUyKZNm2Z79uwJPNf/a+9O4GUs//+Pf+z7vib7UrYUWSJRScS3qL6oJFKRQiJfKUSEX4uUpD0tpH0lvrJFWbMvKSFLlhAS4Rzzf7yv7+Oe/8wxc86R21nmvJ6Px3TOmfuee5urMZ/787muS5nOZcuWhfUp1s0ABcfKiGodZVp37dqVrHNVH+BHHnnEWrVq5bKyPXr0cL8/88wzlh5MmTLFli9fbqNGjUpy3X379tnw4cOtW7duUdd5/fXX7bbbbnMZ7uTatGmTjRs3zrp37x58Tu+DruGHH35oU6dOtcaNG7v3W+9nwuBc66pcXDcKtJ3mzZsne98AAAAAkKYz2RosS+XSKivWYFQJKaBVf1wv6J03b54rPVY2Vf17586de1b7f/bZZ23kyJE2ePBg93unTp2sUaNG1rVrV5dVHzBggAvC161b524EyNGjR11fYAXMmTNntttvv92VL0+aNCnJ/R0/ftyViYdSgJlYybJeo4fn8OHD7meOzAHLkiVgKUGVBdu3b3cl1tOmTXNl3npOGWG9P/o9lI5RNw90I0L9oBMuF91A2bBhgysFj7Q8kp07d7qbGroZoxsm3usKFCjguht4NKjZjh077Mknn7Trrrsu+LyuvfqTq+R9zpw57kaOBmhr2rSppTfeuSf32iH20AZAOwCfB6ANgO8G/krud+s0HWQrK6lATaXT0ahf9po1a2zLli2ur7ZXZqxstwKmevXq/eP9KxD0MqJDhgyxCRMmuO21a9fOPacgu2HDhi5rrj6+3oV/6aWXXL9e6dmzp+tnnRwaYGvMmDHWpEkT93qdm/oWx8fHR32NssbDhg077flBtU9Z7tzRX+cnBdYKijUCeP369YPPK8CeP3++jR8/3mWRFXwfO3bMDUSnvvXqaz1z5syI21QWWSOGqz++tp+cGzIqt1elg/p9J/Ua3bRZv3591PU0Crre6/79+7vjTa+iXV9kHLQB0A7A5wFoA+C7gT+UUE33QbYC7KQo26ng2guwRaNXK8utZWcTZIeOjl2iRAn3U33CEz6n4NILstWH2Auw5bzzzos6/VRCzz33nBsZWzcVlBnXdjTC9RtvvBH1NQMHDnQZ19Assa7FiBWZLS5bFksJa4e2sCuuuMKV6YfSuShYVSZfA5/p2FSGreumUm1dq0iUSVYFgMr1daMjORlslXWrDPytt95ywXxStH91AUhs+59++qmbCiw5x5DW6GaPgitdl9AB3pBx0AZAOwCfB6ANgO8G/vKqhtN1kF2lShUXbPo9uJnKuBMG8JFS/6HBiVcOHuk5ZWwjvcZbJzk3C6RYsWJuqjENzrV//34rVaqUPfzww65/djTKCOuR0PFTmSwu/n/Hd67pnDXQWMLBxlTCr3PSoG1egK27PyqdV0ZbD9E6oYGxsvcq++/cufNp11N981Wiryz/+eefHwywFTCrCuDgwYPBdb0bHwq81c/aGzxO21f/+ddeey24fVUEaJ5s3dhQ+b0y3DpOVS+k5yBVx56ejx9njzYA2gH4PABtAHw38Edyv1en6SBbQZtKqFVu3Lt379P6ZSugUr9e9QfWw8tmqwxYy5TRjkRBXehgZCrH1vzKV111laUF6husAFKBv6a9SpghTo7FA5uFzQOd2jQg2uLFi93vms86lEr9NdBc6IBnN910k6tGSEhBukb99m6KKFurbgV6lC5dOmzd0JsbGmRNc3NrJHNVCrz//vtho5ZrXm0Nsqe+2uoHr3U00rv69gMAAABAcqXpIFsUYGsKL/X1Vd9mlXAry6ngSllGBdQq4dbczGPHjnXLFCxpsCplJiPRHM0qsdZI08pcJsyAphYFocrMamAu/VRfYGXJNd1UehQ68JxGdE9uRl9TlkWTcDsa4Cx0VPhIlBHXIzEqTU9qyjEAAAAASPdTeKlUWllQZZn79evn+vaqPFjlwgqyVY79+eefW6FChdyAYZqfWa9RpjIajQ6uoEtlxwrGtX5ayGKrTFyDdykDr/mxlc3WyOKRMroAAAAAgLQnUyC56UWkC+r7rCmrNA91WioXR8pSOb36lWvQNvpkZ0y0AdAOwOcBaAPgu8G5ibUOHTpk+fPnT7+ZbAAAAAAA0guC7BSk0bajPTSfNAAAAAAgfUvzA5/FkpUrV0Zdpv7XAAAAAID0jSA7BSWcugoAAAAAEFsoFwcAAAAAwCcE2QAAAAAAEGQDAAAAAJC2kMkGAAAAAMAnBNkAAAAAAPiEIBsAAAAAAJ8QZAMAAAAA4BOCbAAAAAAAfEKQDQAAAACATwiyAQAAAADwCUE2AAAAAAA+IcgGAAAAAMAnBNkAAAAAAPiEIBsAAAAAAJ8QZAMAAAAA4BOCbAAAAAAAfEKQDQAAAACAT9J9kD106FC75JJLUvswMpwJEyZYrVq1LH/+/O7RsGFD+/rrr8PWWbhwoV199dWWJ08et06TJk3s2LFjbtncuXMtU6ZMER9Lly5NdN+JbVd++ukna9OmjRUtWtQtb9y4sc2ZM+e07UycONGdQ86cOa148eJ2//33+3Z9AAAAAGRMqR5k796923r16mUVK1a0HDlyWJkyZez666+3WbNmWSxZt26d3XzzzVa+fHkXSI4dO/a0dUaNGmX16tWzfPnyuaCvbdu2tnHjRkuLSpcubaNHj7YffvjBli1b5oJeBbY6Ty8QbtmypV177bW2ZMkSFzj37NnTMmf+X5Nr1KiR7dq1K+xx9913W4UKFaxu3bpR95vUduVf//qXxcXF2ezZs93xXXzxxe45tTXPmDFj7NFHH7WHH37YHfM333xjLVq0OKfXDAAAAEDsy5qaO9+6datdfvnlVrBgQXvqqafsoosuspMnT9qMGTNcVvHHH3+0WHH06FF3I6Fdu3b24IMPRlxn3rx57rwVaCtIfOSRR1wwuX79epe1PRMNRs2yuKxn9prk2jq6tbsREuqJJ55w2e1FixZZjRo13Dn27t3bBbGeCy+8MPh79uzZrWTJksG/9b5//vnn7oaLbkJEk9R29+3bZz///LO9/vrrLkstuhnw4osv2tq1a90+//jjDxs0aJB9+eWX1qxZs+BrvfUBAAAAIF1msu+77z4XUCkjqSzvBRdc4AK0vn37umBNtm3b5jKkefPmdaW/7du3tz179kTd5pVXXml9+vQJe04Z4S5dugT/VjZ5xIgRdscdd7jtlitXzr744gv7/fffg/tSwKUMbWhpsW4G6AZAtWrV3DrKqCoDmxwKnHUj4ZZbbnEZ+0imT5/ujlPXQNlX7VPnr2xsWhYfH29Tpkyxv/76y5WN79271xYvXuyy8cpYlyhRwpo2bWoLFiyIug1d//3799udd94ZdZ3kbLdIkSIu6H777bfd8ehmxcsvv+xec+mll7p1Zs6caadOnbKdO3e691JZebWr7du3+3xlAAAAAGQ0qRZkHzhwwAWVytxGytIqoFUgpKBX6yrLq+Bo8+bN1qFDh7Pe/7PPPuuy6CtWrLDWrVtbp06dXNB9++232/Lly61SpUru70AgEJaNfvrpp+2dd96xb7/91gXADz30kJ0rhw4dcj8LFy5sadGaNWvczQbdNLj33nvt008/terVq7v3yOsvf88997j3uU6dOi5rrCxzJMo8q1xbAW80ydmubtqo9Fvvq8ru1d9apeFat1ChQsHtqG2NHDnSle1/9NFHro01b97cTpw4cQ6uFAAAAICMItXKxTdt2uQC2KpVq0ZdR/2yFcht2bLF9dUWZSiV6VVfXGWH/6lWrVpZ9+7d3e9Dhgxxpc7ansq5ZcCAAS4rq6y5V9askuaXXnrJBeCivsCPP/64nQsKApWR142AmjVrRl3v+PHj7uE5fPiw+5kjc8CyZPn/Nwj8pOsgKn/X+6B9fvzxx9a5c2cX4HqBqvpY66aFPPnkk27Zq6++6krLQ+3YscNVCEyePDm47UiSs121qR49elixYsXcYGe5cuWyN954w5W3f//993beeee5feih4Ft9yb12pTamGzkq0U/vvOuY2PVEbKMNgHYAPg9AGwDfDfyV3O/WqRZkh2aIo9mwYYMLfLwAW5QpVZZby84myA7tf6uyY1Gf8ITPqUTZC7Jz584dDLBFAZuWnwvK8KsPcWIl1t5gacOGDTvt+UG1T1nu3PHn5NimTZt22nO6GaBA+T//+Y8r/feC4tB1CxQo4Mq9E77+/fffd1nnrFmzRty2x+smkNh2V61a5X6+++67dvDgQfe47rrrXDm6+mHr2NQtQFTqH7odHYP+Vol5rNBNA2RstAHQDsDnAWgD4LuBP1TZnKaD7CpVqrjSXr8HN9Mo0wkD+Eh3HLJlyxb83RtoK9JzyihHeo23TnJuFpwpZci/+uorV5KeWPm0DBw40PVh9yirrJsSI1ZktrhsWexcWDs08ijcKr3WzQn1K1fgryyyKgY8jz32mCsJD31O10+DmXXt2tVuuOGGRPerdZParvd+qb+8Stk9+l1tTutUrlzZxo0b566tl8lWufiff/7pug6obDy9U5tXcKVzSdhukTHQBkA7AJ8HoA2A7wb+8qqG02yQrX7GCozGjx/vRotO2C9bGUgNSqXBqPTwstkaaVvLlNGORGXCoYORaVAuZYSvuuoqS+sURGp0bfVt1jzSms4qKeoPHWkgteOnMllcfPRRus+GgjYF98oQly1b1gWnKvVWv3llszVyeP/+/V3wqz7Tmsf8rbfectORqaw8NOhTlwB1B+jWrdtpwaAGJlN/a5Vy169f3z2X1HavuOIK1/daJeXqBqCAXKXkGsleQbzWUXcD9fXv16+fvfLKK25APZ2Pui7EWlCqc4ml88GZow2AdgA+D0AbAN8N/JHc79WpOoWXAmyVGSuAUt9mlXCrVFcZOPWRVkCtEu6OHTu6LKmWaURyjSgdbS5lZSaV2Z06daor7Va/WwXlqU0lzjof73cFkCtXrnQZVmVWvRJxBauaykqly968ziqHVrB4JhYPbOZG2j5XVCavgeF0Q0PHp/dOAbaXBVZ/8r///ttlqZUl1mjpel9Dy+29Ac80UnikvvnKxCmADi3LSGq7RYsWdYOcaQ5stQVtQ0G1rqnW9Shw1zaUuVb1g9qUXkdACgAAAOBspGqQrYGzNJK3BqxSVlEBmzLRmmpJQbbKsb25k5s0aeKCIZUBq9Q3GpUdq1+uAkD18VUglRay2L/99pvVrl07+LdGKddDwZ2y1qJz9qYhC/Xmm2+GTUGWFig4Tormsg6dzzoS3VSIRlOtRSrHT2q7ugGjgD8xyl7rHJJzHgAAAACQXJkC56JTMVK1n4Ayy/v27TunmWykbcrgaxA39UEnO58x0QZAOwCfB6ANgO8G5ybW0lTLStqluXmyAQAAAACINQTZPlHf6miP+fPn+7UbAAAAAEAalqp9smOJBjGL5vzzz0/RYwEAAAAApA6CbJ94I4QDAAAAADIuysUBAAAAAPAJQTYAAAAAAD4hyAYAAAAAwCcE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBPCLIBAAAAAPAJQTYAAAAAAD4hyAYAAAAAwCcE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBPCLIBAAAAAPAJQTaSNGrUKKtXr57ly5fPihcvbm3btrWNGzeGrfPLL7/YjTfeaMWKFbP8+fNb+/btbc+ePcHlc+fOtUyZMkV8LF26NOJ+Dxw4YL169bILL7zQcuXKZWXLlrXevXvboUOHguusWrXKbr31VitTpoxbp1q1avbcc8+FbWfXrl1222232QUXXGCZM2e2Pn368K4DAAAAOCcyRJA9dOhQu+SSS1L7MNKtefPm2f3332+LFi2ymTNn2smTJ+3aa6+1v/76yy3XT/2tgHn27Nn23Xff2YkTJ+z666+3U6dOuXUaNWrkgt3Qx913320VKlSwunXrRtzvb7/95h5PP/20rV271iZOnGjTp0+3u+66K7jODz/84AL/d99919atW2ePPvqoDRw40F544YXgOsePH3fB/6BBg+ziiy8+59cLAAAAQMaV1dKB3bt32xNPPGFTp061nTt3uqBKQbMyks2aNbNYoSDyzjvvDHsuR44c9vfff1tqUmCb8Dj1HijAbdKkiQuqt27daitWrHBZbHnrrbesUKFCLui+5pprLHv27FayZMngNhSof/755y5TreA8kpo1a9rHH38c/LtSpUquHdx+++0WFxdnWbNmta5du4a9pmLFirZw4UL75JNPrGfPnu658uXLB7Pbb7zxho9XBgAAAADSWZCt4O3yyy+3ggUL2lNPPWUXXXSRC9BmzJjhsqs//vijxRIFqaGl2NEC0KQ0GDXL4rLmOevj2Tq69WnPeeXahQsXDmaKdZy6IeDJmTOnK81esGCBC7IT+uKLL2z//v2n3VRIivata6QAO7F1vGMDAAAAgJSU5svF77vvPhfALVmyxG6++WbXr7ZGjRrWt29fV74s27ZtszZt2ljevHkj9gdO6MorrzytX676GXfp0iX4t7KfI0aMsDvuuMNtt1y5ci4w/P3334P7qlWrli1btiwsw6ubAboBoL7BWqdly5auNDq5dK7K+HqPEiVKWFqi8m9dO934UKZZLrvsMsuTJ48NGDDAjh496srHH3roIYuPj4967q+//rq1aNHCSpcunex979u3z4YPH27dunWLus73339v77//fqLrAAAAAECGzGRr4CuVKqtEWEFcQgpoFfR5Qa/6DquMWBnuDh06uMG2zsazzz5rI0eOtMGDB7vfO3Xq5PoWq0RZWXUFlQrC1RfYyzgryFQf4nfeecdlclXarIBz0qRJydrnkSNHXECv86pTp47bv24qRKMssh6ew4cPu585MgcsS5aAnS1VDYRSCbb6R8+ZMye4TO/De++950q/n3/+eXfeuv61a9eOuI0dO3a4GxGTJ08+bVk0Oq9WrVq5mxfqdx3pdToutQX1vb7qqqsirhMIBNy1Te5+0yvv/GL9PBEdbQC0A/B5ANoA+G7gr+R+t07TQfamTZtcUFS1atWo68yaNcvWrFljW7ZscSNMy9tvv+0CU41arVGx/ykFdd27d3e/DxkyxCZMmOC2165dO/ecguyGDRu6rLnX31gX/qWXXnL9h72g9PHHH0/W/jSKtvoMK0OukmcF6wrqFcRHy/hq5O9hw4ad9vyg2qcsd+54O1vTpk0L/v7KK6/Y4sWLXeC/evVq9wg1ZswYFwwryNZND1UG6FxCtyHKNGukcpV8J1wWybFjx9zgdSpH16BnGnwtoe3bt7vgunnz5q6/frTtqkRdbSU5+40Fka4VMhbaAGgH4PMAtAHw3cAfSqim+yBbAXZSNmzY4IJrL8CW6tWru+yqlp1NkK0A0eOVbatPeMLn9u7dGwyyc+fOHQyw5bzzznPLk0MBux4eBdjK3L788suuTDoSjaSt0nmPglxdixErMltctix2ttYObeHeB5WIr1y50r799lurUqVKkq9Tpls3CpTF180Dj7b14IMPumqAG264Icnt6Hxat27trrXK9XV9E9JNCJWHKwAfPXp0otvTjQCNaK4bKLFMN3sUXOmmQ7Zs2VL7cJAKaAOgHYDPA9AGwHcDf3lVw+k6yFYwpzJsvwc3U6Y1YQAfKfUfGpx45eCRnvOmqUq43FsnOTcLItG2VHKtjH40yu6GDjjmOX4qk8XF/7NB0xIeg/rFq7Rbo4FrQDFlg6VAgQJubmp588033Q0BTZWl0b0feOABF0x7/bZDKw+USVZQnPBaaeR4jRavSoT69esHA2zdMVK5vTLaeoj2kyVLFlcirunD1L+7f//+wWPTMq3j0Q0CUX9xraPAXCOe64ZMLNM1JsjO2GgDoB2AzwPQBsB3A38k93t1mg6yFdApeBo/frz17t37tH7ZBw8edIGdSoX18LLZ69evd8uiBVAKvkIH5NIAXQrW1I83LdFxqRT+n2RdFw9sZkWKFPHlOFQm7w0YF0qBtTdYnEZEV1Zd/eg1aJz6TSvIjjTgmTL0kboA6EaHtuOVYSxfvtyVp0vlypXD1lWgrv189NFHbjA6zZOth0f92jUyvcfrHy6aekw3DRKuAwAAAABnK00H2aIAWyNZK7Opvs0q4dbgZiqFVfCngFol3B07drSxY8e6Zcq8Nm3a1OrWrRtxm1dffbUrsda82yrtVgmxgvLUpvPTSN0KKHU8Glzt119/tbvvvjtVjys5mXiVaSdVqi0KbqNR0By6LwX1Se1bfbX1SMo/rSYAAAAAgJgKsitWrOgymhphvF+/fi4DrUz0pZde6oJslWOrjFkjWzdp0sSVgmvarHHjxkXdpvoDr1q1yo0MrsG3lHFNC1nsP/74w+655x7bvXu3FSpUyJ2jpqSK9ZJmAAAAAIgVmQKk+GKK+jGrr7TmlParXBzpj0rvNYK6uhrQJztjog2AdgA+D0AbAN8Nzk2spQGe8+fPH3W9zD7vFwAAAACADIsgOwVp7uhoj/nz56fkoQAAAAAAMmKf7FjiTSMVyfnnn5+ixwIAAAAA8B9BdgpKOA0VAAAAACC2UC4OAAAAAIBPCLIBAAAAAPAJQTYAAAAAAD4hyAYAAAAAwCcE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBPCLIBAAAAAPAJQTYAAAAAAD4hyAYAAAAAwCcE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBP0n2QPXToULvkkktS+zBizqhRo6xevXqWL18+K168uLVt29Y2btwYcd1AIGDXXXedZcqUyT777LOwZXou4WPKlClR97t161a76667rEKFCpYrVy6rVKmSPfbYY3bixImw9VavXm1XXHGF5cyZ08qUKWNPPvlk2PJXX33VLS9UqJB7XHPNNbZkyZKzuiYAAAAAkOaD7N27d1uvXr2sYsWKliNHDhcwXX/99TZr1iyLJevWrbObb77Zypcv7wLNsWPHnrZOfHy8DR48OCzAHD58uAtiU9q8efPs/vvvt0WLFtnMmTPt5MmTdu2119pff/112ro6F51TNG+++abt2rUr+FDAHs2PP/5op06dspdfftlds2effdZeeukle+SRR4LrHD582B1LuXLl7IcffrCnnnrK3Wx55ZVXguvMnTvXbr31VpszZ44tXLjQtSu9ZufOnWd1XQAAAAAgMVktFSlrefnll1vBggVdoHTRRRe5YG7GjBkuwFPAFSuOHj3qbiS0a9fOHnzwwYjr/N///Z9NmDDB3nrrLatRo4YtW7bM7rzzTitQoID17t37jPbXYNQsi8ua5x8d69bRrW369Olhz02cONFltBXUNmnSJPj8ypUr7ZlnnnHHet5550Xcnt7fkiVLJmvfLVu2dA+Prpky6LouTz/9tHtu0qRJLrP9xhtvWPbs2d210nGMGTPGunXrFlwn1GuvvWYff/yxu3lzxx13nMHVAAAAAIB0ksm+7777XAZUZbzK8l5wwQUuYOrbt6/LoMq2bdusTZs2ljdvXsufP7+1b9/e9uzZE3WbV155pfXp0yfsOWVOu3TpEvxb2eQRI0a4YEvbVUb0iy++sN9//z24r1q1arnAMTTIVLCoGwDVqlVz6ygYVGY2OVR6rRsJt9xyi8vYR/L999+7/bdu3dod47///W+XfU0LZc6HDh1yPwsXLhx24+C2226z8ePHJxpE64ZJ0aJFrX79+i4wPtPMvPYdul9lphXoK8D2tGjRwgXjf/zxR8Rt6Fh1Ayd0OwAAAAAQM0H2gQMHXLZUAViePKdnXBXQqmxYQafWVfmyypY3b95sHTp0OOv9qwxZWfQVK1a4oLZTp04u6L799ttt+fLlrlRbf4cGhArUlE1955137Ntvv3U3AB566CHzS6NGjVym9aeffnJ/r1q1yhYsWOD6O6cmvQ+6caHrVbNmzeDzysjrmPUeRfP444/bBx984N473UjRjZVx48Yle9+bNm1y63fv3j2si0GJEiXC1vP+1rJIBgwYYKVKlXJ9swEAAAAg5srFFTwpgK1atWrUdRRwrlmzxrZs2eL61Mrbb7/tst1Lly512eF/qlWrVsHAbciQIa4cWdtTObcXlDVs2NBlzb0srTKh6h+sAFx69uzpgki/PPzww66/sa5JlixZXB/tJ554wjp27Bj1NcePH3cPj14vOTIHLEuWf9aXW+cZSue5du1a17/ZW/bll1/a7NmzXZY9dP24uLiwv3VOHgXoOj5l9Hv06JHkcaj/tKoFFJyrEsHbrtqNAv/Q/Xi/62fC49egaBpsTYG+rmvC5bEo9HogY6INgHYAPg9AGwDfDfyV3O/WqRZkJ6dkeMOGDS649gJsqV69ustya9nZBNkqB0+YBVWf8ITP7d27Nxhk586dOxhgi/oga7lflPFVX+LJkycH+xkrg6wMbOfOnaOOAj5s2LDTnh9U+5Tlzh3/j45j2rRpwd81mNjixYtt5MiRbkRvPbzBzH755RdXBh5KVQYqp9fNgUgyZ85sO3bssM8//9yyZcsW9RhUvTBo0CDXhUAD4YUekwJ5HUfoc7oZ4/3UTRmPRjvXddXNEO1Xj4xENxaQsdEGQDsAnwegDYDvBv5QZXOaDrKrVKni+mP7PbiZgriEAXykOw6hAZ43Mnak55QxjfQabx0/R/7u37+/y/yq37YX9P/6668ukI4WZA8cOND1YfcoU6ybEiNWZLa4bFn+0XGsHdrCnZcCfAX6Ko3X+xWqTp06tm/fvtOeUzm9yu81QnokKoHXlFqJlZgrg928eXNr3LixGwRO2edQ27dvd9UHWsd7T9SfXQG5+ux7dCyffPKJ60ffoEEDy0jU5hVchV4jZCy0AdAOwOcBaAPgu4G/vKrhNBtkawAqDValQbM0cnbCftkHDx50GVEFVHp42ez169e7ZcpoR1KsWLGwwchUcq1S56uuusrSw50R3SQIpQAzNNBPSIOoRRpI7fipTBYXH31arcQoKFPfaWXUlXHWe7V//363TCOda3qxhBUGHgXXCna9knKV21922WVuPmsFfRpBXf3YvcBP5ebq+66uAeeff34wwNZgdBotXO+1x6soUP95DVx37733urJ+vb8vvPCC62fvbVf70bReOofKlSsHj18D1umRUeh6EGRnbLQB0A7A5wFoA+C7gT+S+706VafwUoCtwbQ06rTKeVXCrVJgBWPqI62AWtlc9UnWXMxapuCvadOmVrdu3YjbvPrqq11md+rUqa60O2Ggllo05ZTOx/tdwaSyxAr4FASKyqJVZl22bFlXLq5B2XT8Xbt2PeP9LR7YzIoUKfKPj1fX3xutPZTKxENHak+qEeo91gBpyozrPHU+99xzT9iNBY0K7lUb6L1Xf309SpcuHbY9r2pAgf5///tfN2jepZde6krWldn2pu/yjl/XWSO0h3rsscdc8A0AAAAA50KqBtmaA1kjeSuw7Nevn8tAKxOtwElBksqxlUnt1auXm7JJWV4NhJXY6NQKSFWSrOxo1qxZXYCXFrLYv/32m9WuXTuslFkP3TCYO3eue07nNXjwYHcjQX291Rdbg7MpgExp/6QMPuFrEs55HYmC+NDXKYBPThCvGzLz589PdA52AAAAAEhpmQJ+dipGmugnoEyv+kufTSYb6ZsqAzQwnEbRp1w8Y6INgHYAPg9AGwDfDc5NrHXo0CHLnz9/2psnGwAAAACAWEOQ7RNvQK1Ij8TKmgEAAAAAsSNV+2THEg1iFo1GzQYAAAAAxD6CbJ94I4QDAAAAADIuysUBAAAAAPAJQTYAAAAAAD4hyAYAAAAAwCcE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBPCLIBAAAAAPAJQTYAAAAAAD4hyAYAAAAAwCcE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBPCLIBAAAAAPAJQTbCjBo1yurVq2f58uWz4sWLW9u2bW3jxo1h67zyyit25ZVXWv78+S1Tpkx28ODB067igQMHrGPHjm6dggUL2l133WVHjhxJ1tUOBAJ23XXXuW1/9tlnEdfZv3+/lS5dOuL+J02aZBdffLHlzp3bzjvvPOvatatbHwAAAADOtQwRZA8dOtQuueSS1D6MdGHevHl2//3326JFi2zmzJl28uRJu/baa+2vv/4KrnP06FFr2bKlPfLII1G3owB73bp1bhtfffWVffvtt9atW7dkHcPYsWNd8JwYBe21atU67fnvvvvO7rjjDrdc+//www9tyZIlds899yRr3wAAAAAQ80H27t27rVevXlaxYkXLkSOHlSlTxq6//nqbNWuWxaopU6a4QFOZ5JQ0ffp069Kli9WoUcNlgydOnGjbtm2zH374IbhOnz597OGHH7bLLrss4jY2bNjgtvPaa69ZgwYNrHHjxjZu3Dh3Tr/99lui+1+5cqU988wz9sYbb0RdZ8KECS57/dBDD522bOHChVa+fHnr3bu3VahQwe27e/fuLtAGAAAAgHMtq6VxW7dutcsvv9yVHD/11FN20UUXuezqjBkzXMb1xx9/tFijc1YAecUVV/zjbTQYNcvisuY5s/2Obn3ac4cOHXI/CxcunOztKNDV+1W3bt3gc9dcc41lzpzZFi9ebDfeeGPE1ylDftttt9n48eOtZMmSEddZv369Pf744247mzdvPm15w4YNXYZ92rRpruR879699tFHH1mrVq2SffwAAAAAELOZ7Pvuu89ldJWJvPnmm+2CCy5wWda+ffu6kmZRprVNmzaWN29e1we4ffv2tmfPnqjbVH9iZWNDKWOsDK5H2dARI0a40mNtt1y5cvbFF1/Y77//HtyXypWXLVsWfI2yvgoudQOgWrVqbh2VVe/atSvZ5xsfH+9KrYcNG+Yy96np1KlT7jrpJkfNmjXPqPJA/blDZc2a1QXqWhbNgw8+aI0aNXLXN5Ljx4/brbfe6m62lC1bNuI6Olb1ye7QoYNlz57dBesFChRwgTsAAAAAZOhMtgbPUtnxE088YXnynJ6VVUCrQNALetWfOC4uzmW4FWTNnTv3rPb/7LPP2siRI23w4MHu906dOrkgUANpKdAbMGCAC8LV99frQ6xs7NNPP23vvPOOy9zefvvtLiutwC85lKVVgKo+xfPnz09yfQWeengOHz7sfubIHLAsWQJndL6qEAjVs2dPW7t2rc2ZM+e0ZaJr7b0udLluFGjwskiv0bJIz3/55Zc2e/ZsdzMldLn24f2t633hhRe691bPRdq/Mt0PPPCAPfroo9a8eXMX1Ku0Xf3BNWBbRuFdj0jXGhkDbQC0A/B5ANoA+G7gr+R+t07TQfamTZtcsFa1atWo66hf9po1a2zLli2ur7a8/fbbLtu9dOlSN1L2P6USY/XnlSFDhri+wNpeu3btgkGfypOVNffKm3XhX3rpJatUqVIwUFXgnBwLFiyw119/3fVLPpPRwJX1TmhQ7VOWO3e8nQmVWHsUkKokWzcZVq9e7R4J6brLf//7X3eTw6MSbfW9Dt2egmuN8L1z586w5z1vvvmm/fLLL1a0aNGw5xVQqypAN1o+//xzV7Xw8ccfh62ja6/3RFlu3QxRX2y9ZseOHW65StBVQt6kSZMzKnuPBRp4DhkbbQC0A/B5ANoA+G7gDyVU032QrQA7KRpkS8G1F2BL9erVXZZby84myA4dvbpEiRLup/qEJ3xOQaUXZGvaKC/AFk0hpeVJ+fPPP12m/NVXXz0t0EzMwIEDXel8aCZb12LEiswWly2LnYm1Q1u4a64ScQX6GhG8SpUqUdf3qgs0+riut0dB7gsvvOCuSZ06dYJf9LXte++910qVKnXatrTevn37TntOVQGtW7d221QW+9ixY8HlGoxNo4arYkGl9aoAUMm+StND+2B7gfXVV18dcd+xSDd7dM2Vzc+WLVtqHw5SAW0AtAPweQDaAPhu4C+vajhdB9kK8FSG7ffgZirjThjAR0r9hwYnXjl4pOdUsh7pNd46yblZoCyuBjzTqOkeb7sKGjVXdWjw7tFo63okdPxUJouLT3warIR07OoDP3nyZJc1VnDqzS+tfs25cuVyv6sEWw8dr+j90bza6iet1+jmhPqi9+jRw2X1dW0VuN9yyy2ub7soo92sWTNXdVC/fv3TbpR4FFyrH74krGjwBmXTjQ8vyFfXAQXeGtm8RYsWrj+8bkJoH96+MxK9pwTZGRttALQD8HkA2gD4buCP5H6vTtMDnylgU6CkQatC52n2aBonlQVv377dPTzql6tlymhHUqxYsbDByFTKrL7HqUkBpMqvlUH2HjfccINdddVV7vdIAei5oJJ4Ba8aHE5ZeO/x/vvvB9dR4Fy7du3g3NMqw9bfGhjOoz7oOicF0soqayqt0D7RCrx14yC5JRfJpcHrxowZ4zLpGqxNZeTKgH/yySe+7gcAAAAA0l0mWxRga8RoZSLVt1lZUg14pVJYBYQKqJXJ1IjcY8eOdcuUjW3atGnYFFKhVDas7ObUqVNddlhBmYLy1JQzZ87TRvD2srNnMrK3Z/HAZlakSJEzfl1ysu5Dhw51j6RukCgjHo1Gb09qX0kt142ASOtoTnU9AAAAACClpelMtqiv7fLly11Gt1+/fi7gVD9TDXimIFvl2CptLlSokMuoaj5mvSY085qQRgfv3LmzGxlcwbjW1/YBAAAAADgbmQLJSV0iXXXGV/9pDSL2TzLZiA0qx9co7irVp092xkQbAO0AfB6ANgC+G5ybWEvda/Pnz59+M9kAAAAAAKQXBNkpSHNJR3vMnz8/JQ8FAAAAAJARBz6LJRolPJrzzz8/RY8FAAAAAOA/guwUVLly5ZTcHQAAAAAghVEuDgAAAACATwiyAQAAAADwCUE2AAAAAAA+IcgGAAAAAMAnBNkAAAAAAPiEIBsAAAAAAJ8QZAMAAAAA4BOCbAAAAAAAfEKQDQAAAACATwiyAQAAAADwCUE2AAAAAAA+IcgGAAAAAMAnBNkAAAAAAPiEIBsAAAAAAJ8QZAMAAAAA4BOCbAAAAAAAfEKQDQAAAACATwiyAQAAAADwCUE2AAAAAAA+IcgGAAAAAMAnBNkAAAAAAPiEIBsAAAAAAJ9k9WtDSBsCgYD7+eeff1q2bNlS+3CQSk6ePGlHjx61w4cP0w4yKNoAaAfg8wC0AfDdwF/6bh0ac0VDkB1j9u/f735WqFAhtQ8FAAAAAGKOEpoFChSIupwgO8YULlzY/dy2bVuibzxi/y5bmTJlbPv27ZY/f/7UPhykAtoAaAfg8wC0AfDdwF/KYCvALlWqVKLrEWTHmMyZ/9fNXgE2wRXUBmgHGRttALQD8HkA2gD4buCf5CQyGfgMAAAAAACfEGQDAAAAAOATguwYkyNHDnvsscfcT2RctAPQBsBnAfg3AbQB8P0wdWQKJDX+OAAAAAAASBYy2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4LsGDN+/HgrX7685cyZ0xo0aGBLlixJ7UPCPzBq1CirV6+e5cuXz4oXL25t27a1jRs3hq3z999/2/33329FihSxvHnz2s0332x79uwJW2fbtm3WunVry507t9tO//79LS4uLmyduXPnWp06ddxAWZUrV7aJEyfynqVRo0ePtkyZMlmfPn2Cz9EOYt/OnTvt9ttvd/+v58qVyy666CJbtmxZcLmGVhkyZIidd955bvk111xjP//8c9g2Dhw4YB07dnTzphcsWNDuuusuO3LkSNg6q1evtiuuuML9+1GmTBl78sknU+wckbj4+HgbPHiwVahQwb3HlSpVsuHDh7v33kM7iD3ffvutXX/99VaqVCn32f/ZZ5+FLU/J9/zDDz+0qlWrunX0GTRt2rRzdNZIbhs4efKkDRgwwL0fefLkcevccccd9ttvv4VtgzaQSjTwGWLDlClTAtmzZw+88cYbgXXr1gXuueeeQMGCBQN79uxJ7UPDGWrRokXgzTffDKxduzawcuXKQKtWrQJly5YNHDlyJLjOvffeGyhTpkxg1qxZgWXLlgUuu+yyQKNGjYLL4+LiAjVr1gxcc801gRUrVgSmTZsWKFq0aGDgwIHBdTZv3hzInTt3oG/fvoH169cHxo0bF8iSJUtg+vTpvGdpzJIlSwLly5cP1KpVK/DAAw8En6cdxLYDBw4EypUrF+jSpUtg8eLF7v/ZGTNmBDZt2hRcZ/To0YECBQoEPvvss8CqVasCN9xwQ6BChQqBY8eOBddp2bJl4OKLLw4sWrQoMH/+/EDlypUDt956a3D5oUOHAiVKlAh07NjRfe689957gVy5cgVefvnlFD9nnO6JJ54IFClSJPDVV18FtmzZEvjwww8DefPmDTz33HPBdWgHsUf/bj/66KOBTz75RHdTAp9++mnY8pR6z7/77jv33eDJJ5903xUGDRoUyJYtW2DNmjUpdCUyrsTawMGDB913vPfffz/w448/BhYuXBioX79+4NJLLw3bBm0gdRBkxxD9j3X//fcH/46Pjw+UKlUqMGrUqFQ9Lpy9vXv3ug/XefPmBT9Y9Q+cvmh5NmzY4NbRh6z3wZw5c+bA7t27g+tMmDAhkD9//sDx48fd3//5z38CNWrUCNtXhw4dXJCPtOPPP/8MVKlSJTBz5sxA06ZNg0E27SD2DRgwINC4ceOoy0+dOhUoWbJk4Kmnngo+p3aRI0cO92VZ9KVYnw1Lly4NrvP1118HMmXKFNi5c6f7+8UXXwwUKlQo+Nng7fvCCy88R2eGM9G6detA165dw5676aabXGAktIPYlzDASsn3vH379q4NhmrQoEGge/fu5+hsEUmkGy2RbshrvV9//dX9TRtIPZSLx4gTJ07YDz/84EqFPJkzZ3Z/L1y4MFWPDWfv0KFD7mfhwoXdT73XKhMKfb9VxlW2bNng+62fKiEqUaJEcJ0WLVrY4cOHbd26dcF1QrfhrUObSVvULUBl/wnfK9pB7Pviiy+sbt261q5dO9flo3bt2vbqq68Gl2/ZssV2794d1jYKFCjguguFfhaoTFTb8Wh9/RuxePHi4DpNmjSx7Nmzh30WqJvKH3/8kUJni2gaNWpks2bNsp9++sn9vWrVKluwYIFdd9117m/aQcaTku853xXS1/dFlZXrfRfaQOohyI4R+/btc322QgMq0d/6EEb6derUKdcH9/LLL7eaNWu65/Se6h9E70M00vutn5Hag7cssXUUiB87duycnheSZ8qUKbZ8+XLXTz8h2kHs27x5s02YMMGqVKliM2bMsB49eljv3r3trbfeCvt/ObHPfv1UgB4qa9as7qbdmXxeIPU8/PDDdsstt7ibqdmyZXM3W/Tvgvrahr5HtIOMIyXf82jr8NmQtmiMFvXRvvXWW10ffKENpJ6sqbhvAMnMYq5du9ZlLZCxbN++3R544AGbOXOmG2wGGfMmm7JQI0eOdH8ruNLnwUsvvWSdO3dO7cNDCvnggw9s0qRJNnnyZKtRo4atXLnSBdka6Ih2AEDVje3bt3eD4enGLFIfmewYUbRoUcuSJctpo0vr75IlS6baceHs9OzZ07766iubM2eOlS5dOvi83lN1ETh48GDU91s/I7UHb1li6+gOqEYqRepSOfjevXvd6O/KPugxb948e/75593vyiTQDmKbRg2uXr162HPVqlVzMweE/r+c2Ge/fqodhdIsAxpx9kw+L5B6NDOEl81WN6BOnTrZgw8+GKxwoR1kPCn5nkdbh8+GtBVg//rrr+6mvJfFFtpA6iHIjhEqHb700ktdn63QDIj+btiwYaoeG86c7kQqwP70009t9uzZbtqWUHqvVTIY+n6r/5S+eHvvt36uWbMm7B9Y78PX+9KudUK34a1Dm0kbmjVr5t5DZa28h7KaKhH1fqcdxDZ1E0k4fZ/65ZYrV879rs8GfYkK/f9Y3T3U3zL0s0A35HTTxqPPFf0bof6b3jqaKkZf1kI/Cy688EIrVKjQOT9PJO7o0aOuH20o3VjXeyi0g4wnJd9zviuk/QBbU7d98803bqrHULSBVJSKg67hHEzhpVElJ06c6EYT7Natm5vCK3R0aaQPPXr0cNNyzJ07N7Br167g4+jRo2FTN2lar9mzZ7spvBo2bOgeCafwuvbaa900YJqWq1ixYhGn8Orfv78bnXz8+PFM4ZXGhY4uLrSD2KaRYrNmzeqmcPr5558DkyZNcv/Pvvvuu2HT+Oiz/vPPPw+sXr060KZNm4jT+NSuXdtNA7ZgwQI3Wn3oND4alVjT+HTq1MlN46N/T7QfpvBKGzp37hw4//zzg1N4aTofTcmoGSI8tIPYnFlCU3Dqoa/sY8aMcb97I0en1HuuKbz0OfT000+77wqPPfYYU3ilgTZw4sQJN21b6dKl3fe80O+LoaPF0wZSB0F2jNE8xwq8NF+2pvTSvIhIf/RBGumhubM9+kf0vvvuc1Nv6B/EG2+80X2whtq6dWvguuuuc3Ne6gtZv379AidPngxbZ86cOYFLLrnEtZmKFSuG7QNpP8imHcS+L7/80t0w003UqlWrBl555ZWw5ZrKZ/Dgwe6LstZp1qxZYOPGjWHr7N+/332x1tzKmsbvzjvvdF/eQmmeXU0Xpm0ooNMXeKQNhw8fdv/f69/3nDlzus9qzZ0b+kWadhB79O9zpO8CuumS0u/5Bx98ELjgggvcdwVN/Tl16tRzfPZIqg3ohlu074t6HW0gdWXSf1Izkw4AAAAAQKygTzYAAAAAAD4hyAYAAAAAwCcE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBPCLIBAAAAAPAJQTYAAIhJ+/fvt+LFi9vWrVvPyfbLly9vY8eOTfb669evt9KlS9tff/11To4HAJA2EGQDAJAGdOnSxdq2bWtplQLVTJky2cqVKy29eOKJJ6xNmzYuGA718ccf29VXX22FChWyXLly2YUXXmhdu3a1FStWnNH2ly5dat26dUv2+tWrV7fLLrvMxowZc0b7AQCkLwTZAAAgUSdOnEh3V+jo0aP2+uuv21133RX2/IABA6xDhw52ySWX2BdffGEbN260yZMnW8WKFW3gwIFntI9ixYpZ7ty5z+g1d955p02YMMHi4uLO6HUAgPSDIBsAgDToyiuvtF69elmfPn1cxrVEiRL26quvulJjBWr58uWzypUr29dffx18zdy5c122eerUqVarVi3LmTOny5yuXbv2tExujRo1LEeOHC7L+8wzz4Qt13PDhw+3O+64w/Lnz++ytRUqVHDLateu7fah4/Oyuc2bN7eiRYtagQIFrGnTprZ8+fKw7Wn91157zW688UYXlFapUsUFuKHWrVtn//rXv9z+dG5XXHGF/fLLL8Hlen21atXcOVWtWtVefPHFRK/ftGnT3Pnp/D2LFi2yJ5980mWS9dA+ypYta5deeqkNGjQo7Fpq38qC67rnzZvX6tWrZ998802i5eLJOU9dqwMHDti8efMSPX4AQPpFkA0AQBr11ltvueB1yZIlLuDu0aOHtWvXzho1auQC2WuvvdY6derksrah+vfv7wJnBcDKtl5//fV28uRJt+yHH36w9u3b2y233GJr1qyxoUOH2uDBg23ixIlh23j66aft4osvdiXUWq5jEAWau3btsk8++cT9/eeff1rnzp1twYIFLohVYNmqVSv3fKhhw4a5/a5evdot79ixows2ZefOndakSRMXFM+ePdsdo8q3vWzvpEmTbMiQIa78e8OGDTZy5Eh3TLo+0cyfP98Fz6Hee+89FzDfd999EV+jINlz5MgRd5yzZs1y16Bly5buOm7bti3R9yyx85Ts2bO7LLqODwAQowIAACDVde7cOdCmTZvg302bNg00btw4+HdcXFwgT548gU6dOgWf27VrV0D/lC9cuND9PWfOHPf3lClTguvs378/kCtXrsD777/v/r7tttsCzZs3D9t3//79A9WrVw/+Xa5cuUDbtm3D1tmyZYvb9ooVKxI9j/j4+EC+fPkCX375ZfA5vW7QoEHBv48cOeKe+/rrr93fAwcODFSoUCFw4sSJiNusVKlSYPLkyWHPDR8+PNCwYcOox6Fr2bVr17DnWrZsGahVq1bYc88884y7rt7j4MGDUbdZo0aNwLhx48Ku07PPPpvs8/TceOONgS5dukTdDwAgfSOTDQBAGqWSb0+WLFmsSJEidtFFFwWfUymz7N27N+x1DRs2DP5euHBhN7CXMsCin5dffnnY+vr7559/tvj4+OBzdevWTdYx7tmzx+655x6XwVa5uMq9lQVOmPENPZc8efK49bzj1mBqKt3Oli3badtXebxKt9W3Wllo7zFixIiwcvKEjh075krLk6KMufb/8ssvu339L1b+Xyb7oYceciXqBQsWdPvUtUsqk53YeXo02FrC6gMAQOzImtoHAAAAIksYdKqcOfQ5r7z51KlTvl9CBYjJoVJxTZX13HPPWbly5VzJt4L8hIOlRToX77gVdEajYFfUH71BgwZhy3TjIRqV2f/xxx9hz+lGgMraVTrvHY8CaD127NgRtq4C7JkzZ7qyefV91zH++9//TnIQuMTO06Py8UqVKiW6HQBA+kUmGwCAGKO+0R4Fmj/99JPLyIp+fvfdd2Hr6+8LLrgg0aBVfYklNNvtvbZ3796u/7E3mNq+ffvO6HiV/VUfZa/feChl60uVKmWbN292wW7owxuMLRIN0KZ5qUPdeuutLmhPatA077w0rZoGMVP1QMmSJX2bb1sD0en4AACxiUw2AAAx5vHHH3el5QpQH330UZfV9ebg7tevnxspW6OHayqrhQsX2gsvvJBk4Fm8eHGXzZ0+fbqVLl3alWKrPFzZ4XfeeceVlx8+fNgNupZYZjqSnj172rhx49xgbJpGS9vVjYL69eu7UncNJqZAXs9rALLjx4/bsmXL3A2Evn37RtxmixYt3La0jkZnF2XYdf56/Prrr3bTTTdZmTJl3EBumu5LWefMmf+Xf9B5aXA3DXam5zXQmh8VAwrUNdDbNddcc9bbAgCkTWSyAQCIMaNHj7YHHnjAja69e/du+/LLL4OZ6Dp16tgHH3xgU6ZMsZo1a7pRuxWUK2ubmKxZs9rzzz/v+i4rs6zprUTBqQJZbVcjnSsYVkB+JnRDQKOKK8usKcB03CoP90qv7777bjc11ptvvumyylpHo6EnlsnWet65hlL5t+bF1ojhmjJMwbRGbFcArRsO6kMtmuJLwblGclegraBd2ztbGuFco8KrtB4AEJsyafSz1D4IAABw9jRP9lVXXeWCXvUzzug0X7gy6yrP9jLUqUn9uRXUK8hPOPgcACB2UC4OAABiUuvWrd2o6SrPVll4atPI5I888ggBNgDEODLZAADECDLZAACkPoJsAAAAAAB8kvodlAAAAAAAiBEE2QAAAAAA+IQgGwAAAAAAnxBkAwAAAADgE4JsAAAAAAB8QpANAAAAAIBPCLIBAAAAAPAJQTYAAAAAAD4hyAYAAAAAwPzx/wBUrbUNB11gugAAAABJRU5ErkJggg=="
      },
      "metadata": {},
      "output_type": "display_data",
@@ -1489,38 +1526,48 @@
      "text": [
       "\n",
       "[特征重要性排名 - Gain]\n",
-      "ebitda_rank                     14616.823972\n",
-      "return_10                        2170.157647\n",
-      "return_20                        1597.996920\n",
-      "ma_ratio                         1346.640091\n",
-      "vol_ratio                        1307.361400\n",
-      "high_low_ratio                    918.782296\n",
-      "vol_ma20                          828.398926\n",
-      "ma20                              635.353193\n",
-      "volatility_5                      631.979581\n",
-      "return_diff                       617.399685\n",
-      "vol_ma5                           491.980673\n",
-      "market_cap_rank                   415.972949\n",
-      "volatility_20                     276.516502\n",
-      "profit_to_market_cap              256.683738\n",
-      "ma5                               236.319446\n",
-      "ma10                              173.155782\n",
-      "ebit_rank                         142.423690\n",
-      "operate_profit_to_market_cap      127.127625\n",
-      "total_liab_rank                    97.979562\n",
-      "n_income_rank                      77.705741\n",
-      "operate_profit_rank                75.762452\n",
-      "cashflow_to_market_cap             68.135267\n",
-      "money_cap_rank                     58.562898\n",
-      "n_cashflow_act_rank                47.001317\n",
-      "total_profit_rank                  36.941945\n",
+      "ebitda_rank                     12044.385847\n",
+      "bbi_ratio_factor                 1996.570270\n",
+      "std_return_90                    1900.096352\n",
+      "return_10                        1707.237531\n",
+      "cs_rank_turnover_rate            1700.674361\n",
+      "ma_ratio                         1449.246176\n",
+      "std_return_5                     1112.916802\n",
+      "high_low_ratio                   1002.711145\n",
+      "vol_ma20                          800.361497\n",
+      "ma20                              743.156761\n",
+      "cs_rank_volume_ratio              620.018650\n",
+      "vol_ratio                         614.280431\n",
+      "turnover_rate_mean_5              574.150844\n",
+      "return_20                         530.409461\n",
+      "volume_change_rate                516.283890\n",
+      "return_diff                       427.252813\n",
+      "market_cap_rank                   367.685596\n",
+      "volatility_20                     297.221084\n",
+      "vol_std_5                         245.201921\n",
+      "volatility_5                      210.447650\n",
+      "ma10                              175.131429\n",
+      "ma5                               137.251949\n",
+      "vol_ma5                           105.623339\n",
+      "ebit_rank                         102.653185\n",
+      "profit_to_market_cap               65.704505\n",
+      "operate_profit_rank                64.448026\n",
+      "operate_profit_to_market_cap       59.952241\n",
+      "cashflow_to_market_cap             40.900113\n",
+      "n_income_rank                      34.135645\n",
+      "n_cashflow_act_rank                27.246159\n",
+      "money_cap_rank                     22.246325\n",
+      "total_liab_rank                    21.635597\n",
+      "total_profit_rank                  17.579179\n",
+      "turnover_deviation                  0.000000\n",
       "dtype: float64\n",
       "\n",
-      "所有特征都有一定重要性\n"
+      "[低重要性特征] 以下1个特征重要性为0，可考虑删除:\n",
+      "  - turnover_deviation\n"
      ]
     }
    ],
-   "execution_count": 26
+   "execution_count": 18
   }
  ],
  "metadata": {