diff --git a/mcmthesis-demo.pdf b/mcmthesis-demo.pdf
index ca8b3e8..31a5a82 100644
Binary files a/mcmthesis-demo.pdf and b/mcmthesis-demo.pdf differ
diff --git a/p3/interaction_heatmap.png b/p3/interaction_heatmap.png
index 3934590..b878899 100644
Binary files a/p3/interaction_heatmap.png and b/p3/interaction_heatmap.png differ
diff --git a/p3/tornado_analysis.png b/p3/tornado_analysis.png
index b17a43e..b7fdfc0 100644
Binary files a/p3/tornado_analysis.png and b/p3/tornado_analysis.png differ
diff --git a/p3/water_sensitivity_analysis.py b/p3/water_sensitivity_analysis.py
index 0af7c1b..b6070f6 100644
--- a/p3/water_sensitivity_analysis.py
+++ b/p3/water_sensitivity_analysis.py
@@ -806,7 +806,7 @@ def sensitivity_buffer_days(baseline: BaselineParameters, save_dir: str):
 
 def tornado_analysis(baseline: BaselineParameters, save_dir: str):
     """
-    Tornado图 - 多参数灵敏度排序分析
+    Tornado图 - 多参数灵敏度排序分析（马卡龙色系简约版）
     """
     print("生成Tornado图...")
     
@@ -825,7 +825,7 @@ def tornado_analysis(baseline: BaselineParameters, save_dir: str):
     baseline_annual = baseline_demand['annual_supply_tons']
     baseline_initial = baseline_demand['initial_transport_tons']
     
-    # 定义参数范围
+    # 定义参数范围 - 根据论述调整（删除Confidence Level）
     params = [
         ('Recycle Rate (η)', 'recycle_rate', 0.80, 0.95, baseline.recycle_rate),
         ('Comfort Factor (α)', 'comfort_factor', 25, 100, baseline.comfort_factor),
@@ -833,7 +833,6 @@ def tornado_analysis(baseline: BaselineParameters, save_dir: str):
         ('Sickness Rate', 'sickness_rate', 0.01, 0.04, baseline.sickness_rate),
         ('Medical Water/Event', 'medical_water', 3, 10, baseline.medical_water_per_event),
         ('Buffer Days', 'buffer_days', 15, 45, baseline.safety_buffer_days),
-        ('Confidence Level', 'confidence', 0.95, 0.999, baseline.confidence_level),
     ]
     
     results_annual = []
@@ -883,13 +882,6 @@ def tornado_analysis(baseline: BaselineParameters, save_dir: str):
             demand_high = calculate_water_demand(baseline.population, baseline.survival_water, baseline.hygiene_water_base,
                                                   baseline.comfort_factor, baseline.medical_water_per_event, baseline.sickness_rate,
                                                   baseline.confidence_level, baseline.recycle_rate, int(high))
-        elif param_type == 'confidence':
-            demand_low = calculate_water_demand(baseline.population, baseline.survival_water, baseline.hygiene_water_base,
-                                                 baseline.comfort_factor, baseline.medical_water_per_event, baseline.sickness_rate,
-                                                 low, baseline.recycle_rate, baseline.safety_buffer_days)
-            demand_high = calculate_water_demand(baseline.population, baseline.survival_water, baseline.hygiene_water_base,
-                                                  baseline.comfort_factor, baseline.medical_water_per_event, baseline.sickness_rate,
-                                                  high, baseline.recycle_rate, baseline.safety_buffer_days)
         
         # 计算变化百分比
         change_low_annual = (demand_low['annual_supply_tons'] - baseline_annual) / baseline_annual * 100
@@ -914,76 +906,109 @@ def tornado_analysis(baseline: BaselineParameters, save_dir: str):
             'swing': swing_initial
         })
     
-    # 按swing排序
+    # 按年度需求的swing排序（统一排序顺序）
     results_annual = sorted(results_annual, key=lambda x: x['swing'], reverse=True)
-    results_initial = sorted(results_initial, key=lambda x: x['swing'], reverse=True)
     
-    # 绘图
-    fig, axes = plt.subplots(1, 2, figsize=(16, 8))
+    # 按相同顺序重排initial结果
+    param_order = [r['param'] for r in results_annual]
+    results_initial_dict = {r['param']: r for r in results_initial}
+    results_initial = [results_initial_dict[p] for p in param_order]
     
-    # 图1: 年补充量Tornado
-    ax = axes[0]
-    y_pos = np.arange(len(results_annual))
+    # 马卡龙色系
+    color_decrease = '#A8D8B9'  # 薄荷绿 - 减少
+    color_increase = '#F7C5A8'  # 杏橙 - 增加
+    color_decrease_dark = '#7BC08F'
+    color_increase_dark = '#E8A07A'
+    
+    # 绘图 - 简约风格（缩小尺寸）
+    fig, axes = plt.subplots(1, 2, figsize=(9, 3.5))
+    fig.patch.set_facecolor('#FAFAFA')
+    
+    # 统一的参数标签
     params_sorted = [r['param'] for r in results_annual]
+    y_pos = np.arange(len(params_sorted))
+    
+    # ========== 图1: 年补充量Tornado ==========
+    ax = axes[0]
+    ax.set_facecolor('#FAFAFA')
     lows = [r['low'] for r in results_annual]
     highs = [r['high'] for r in results_annual]
     
-    # 绘制条形图
+    # 绘制条形图 - 分开绘制正负部分
+    for i, (l, h) in enumerate(zip(lows, highs)):
+        # 负值部分（减少）
+        if l < 0:
+            ax.barh(i, l, color=color_decrease, edgecolor=color_decrease_dark, 
+                    linewidth=1, height=0.65)
+        # 正值部分（增加）
+        if h > 0:
+            ax.barh(i, h, color=color_increase, edgecolor=color_increase_dark, 
+                    linewidth=1, height=0.65)
+    
+    # 添加数值标注
     for i, (l, h) in enumerate(zip(lows, highs)):
         if l < 0:
-            ax.barh(i, l, color='green', alpha=0.7, height=0.6)
+            ax.text(l - 2, i, f'{l:.0f}%', ha='right', va='center', fontsize=8, color='#555555')
         if h > 0:
-            ax.barh(i, h, color='red', alpha=0.7, height=0.6)
-        if l >= 0:
-            ax.barh(i, l, left=0, color='green', alpha=0.7, height=0.6)
-        if h <= 0:
-            ax.barh(i, h, left=0, color='red', alpha=0.7, height=0.6)
-        # 如果跨越0，绘制完整范围
-        if l < 0 < h:
-            pass  # 已经绘制
-        elif l >= 0:
-            ax.barh(i, h - l, left=l, color='orange', alpha=0.7, height=0.6)
-        elif h <= 0:
-            ax.barh(i, h - l, left=l, color='orange', alpha=0.7, height=0.6)
+            ax.text(h + 2, i, f'+{h:.0f}%', ha='left', va='center', fontsize=8, color='#555555')
     
-    ax.axvline(x=0, color='black', linewidth=1.5)
+    ax.axvline(x=0, color='#333333', linewidth=1.2)
     ax.set_yticks(y_pos)
-    ax.set_yticklabels(params_sorted)
-    ax.set_xlabel('Change in Annual Supply (%)', fontsize=12)
-    ax.set_title('Tornado Diagram: Annual Water Supply', fontsize=14)
-    ax.grid(True, alpha=0.3, axis='x')
+    ax.set_yticklabels(params_sorted, fontsize=10)
+    ax.set_xlabel('Change in Annual Supply (%)', fontsize=11, color='#444444')
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_color('#CCCCCC')
+    ax.spines['bottom'].set_color('#CCCCCC')
+    ax.grid(True, alpha=0.15, axis='x', linestyle='--')
+    ax.set_xlim(-65, 115)
     
-    # 图2: 首批运输量Tornado
+    # ========== 图2: 首批运输量Tornado ==========
     ax = axes[1]
-    params_sorted = [r['param'] for r in results_initial]
+    ax.set_facecolor('#FAFAFA')
     lows = [r['low'] for r in results_initial]
     highs = [r['high'] for r in results_initial]
     
     for i, (l, h) in enumerate(zip(lows, highs)):
-        width = h - l
-        left = l
-        color = 'steelblue' if width > 0 else 'gray'
-        ax.barh(i, width, left=left, color=color, alpha=0.7, height=0.6)
+        # 负值部分（减少）
+        if l < 0:
+            ax.barh(i, l, color=color_decrease, edgecolor=color_decrease_dark, 
+                    linewidth=1, height=0.65)
+        # 正值部分（增加）
+        if h > 0:
+            ax.barh(i, h, color=color_increase, edgecolor=color_increase_dark, 
+                    linewidth=1, height=0.65)
     
-    ax.axvline(x=0, color='black', linewidth=1.5)
+    # 添加数值标注
+    for i, (l, h) in enumerate(zip(lows, highs)):
+        if l < 0:
+            ax.text(l - 2, i, f'{l:.0f}%', ha='right', va='center', fontsize=8, color='#555555')
+        if h > 0:
+            ax.text(h + 2, i, f'+{h:.0f}%', ha='left', va='center', fontsize=8, color='#555555')
+    
+    ax.axvline(x=0, color='#333333', linewidth=1.2)
     ax.set_yticks(y_pos)
-    ax.set_yticklabels(params_sorted)
-    ax.set_xlabel('Change in Initial Transport (%)', fontsize=12)
-    ax.set_title('Tornado Diagram: Initial Transport Volume', fontsize=14)
-    ax.grid(True, alpha=0.3, axis='x')
+    ax.set_yticklabels([])  # 删除右图纵坐标标签
+    ax.set_xlabel('Change in Initial Transport (%)', fontsize=11, color='#444444')
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_visible(False)  # 隐藏左边框
+    ax.spines['bottom'].set_color('#CCCCCC')
+    ax.grid(True, alpha=0.15, axis='x', linestyle='--')
+    ax.set_xlim(-65, 105)
     
-    # 添加图例说明
+    # 简化图例 - 放在图的下方中间
     from matplotlib.patches import Patch
     legend_elements = [
-        Patch(facecolor='green', alpha=0.7, label='Decrease'),
-        Patch(facecolor='red', alpha=0.7, label='Increase'),
+        Patch(facecolor=color_decrease, edgecolor=color_decrease_dark, label='Decrease'),
+        Patch(facecolor=color_increase, edgecolor=color_increase_dark, label='Increase'),
     ]
-    axes[0].legend(handles=legend_elements, loc='lower right')
+    fig.legend(handles=legend_elements, loc='lower center', ncol=2, fontsize=9, 
+               framealpha=0.9, edgecolor='#CCCCCC', bbox_to_anchor=(0.55, -0.02))
     
-    plt.suptitle('Multi-Parameter Sensitivity Analysis (Tornado Diagram)\nBaseline: α=50, η=90%, N=100k', 
-                 fontsize=14, y=1.02)
     plt.tight_layout()
-    plt.savefig(f'{save_dir}/tornado_analysis.png', dpi=150, bbox_inches='tight')
+    plt.subplots_adjust(bottom=0.15)
+    plt.savefig(f'{save_dir}/tornado_analysis.png', dpi=150, bbox_inches='tight', facecolor='#FAFAFA')
     print(f"  保存: {save_dir}/tornado_analysis.png")
     
     return results_annual, results_initial
@@ -991,10 +1016,12 @@ def tornado_analysis(baseline: BaselineParameters, save_dir: str):
 
 def interaction_heatmap(baseline: BaselineParameters, save_dir: str):
     """
-    双参数交互热力图 - α vs η
+    双参数交互热力图 - α vs η（马卡龙色系简约版）
     """
     print("生成双参数交互热力图...")
     
+    from matplotlib.colors import LinearSegmentedColormap
+    
     alphas = np.array([1, 25, 50, 100, 150, 200, 250])
     etas = np.array([0.70, 0.75, 0.80, 0.85, 0.90, 0.95])
     
@@ -1019,52 +1046,64 @@ def interaction_heatmap(baseline: BaselineParameters, save_dir: str):
             annual_matrix[i, j] = demand['annual_supply_tons'] / 1000  # 千吨
             capacity_matrix[i, j] = demand['annual_supply_tons'] / baseline.total_elevator_capacity * 100
     
-    fig, axes = plt.subplots(1, 2, figsize=(16, 7))
+    # 马卡龙色系（中等饱和度）
+    # 图1: 淡蓝→蓝紫→玫粉（年补充量）
+    colors_annual = ['#C8E6F0', '#A8D0E8', '#98B8D8', '#B898C8', '#D098B0', '#D88898', '#C86878']
+    cmap_annual = LinearSegmentedColormap.from_list('macaron_annual', colors_annual)
+    
+    # 图2: 薄荷绿→淡黄→杏橙→珊瑚红（运力占比）
+    colors_capacity = ['#A8D8B8', '#C8E0A0', '#E0D890', '#E8C078', '#E8A070', '#D88070', '#C86868']
+    cmap_capacity = LinearSegmentedColormap.from_list('macaron_capacity', colors_capacity)
+    
+    # 缩小图片尺寸
+    fig, axes = plt.subplots(1, 2, figsize=(10, 4))
+    fig.patch.set_facecolor('#FAFAFA')
     
     # 图1: 年补充量热力图
     ax = axes[0]
-    im = ax.imshow(annual_matrix, cmap='YlOrRd', aspect='auto')
+    ax.set_facecolor('#FAFAFA')
+    im = ax.imshow(annual_matrix, cmap=cmap_annual, aspect='auto')
     ax.set_xticks(range(len(alphas)))
-    ax.set_xticklabels([f'{a}' for a in alphas])
+    ax.set_xticklabels([f'{a}' for a in alphas], fontsize=9)
     ax.set_yticks(range(len(etas)))
-    ax.set_yticklabels([f'{e*100:.0f}%' for e in etas])
-    ax.set_xlabel('Comfort Factor (α)', fontsize=12)
-    ax.set_ylabel('Recycle Rate (η)', fontsize=12)
-    ax.set_title('Annual Water Supply (kt)', fontsize=13)
+    ax.set_yticklabels([f'{e*100:.0f}%' for e in etas], fontsize=9)
+    ax.set_xlabel('Comfort Factor (α)', fontsize=10, color='#444444')
+    ax.set_ylabel('Recycle Rate (η)', fontsize=10, color='#444444')
     
     # 添加数值标注
     for i in range(len(etas)):
         for j in range(len(alphas)):
-            text_color = 'white' if annual_matrix[i, j] > annual_matrix.max() * 0.6 else 'black'
-            ax.text(j, i, f'{annual_matrix[i, j]:.0f}', ha='center', va='center', fontsize=9, color=text_color)
+            ax.text(j, i, f'{annual_matrix[i, j]:.0f}', ha='center', va='center', 
+                   fontsize=8, color='#333333', fontweight='medium')
     
-    plt.colorbar(im, ax=ax, label='Annual Supply (kt)')
+    cbar1 = plt.colorbar(im, ax=ax, shrink=0.8)
+    cbar1.ax.tick_params(labelsize=8)
+    cbar1.set_label('Annual Supply (kt)', fontsize=9, color='#444444')
     
     # 图2: 运力占比热力图
     ax = axes[1]
-    im = ax.imshow(capacity_matrix, cmap='RdYlGn_r', aspect='auto', vmin=0, vmax=150)
+    ax.set_facecolor('#FAFAFA')
+    im = ax.imshow(capacity_matrix, cmap=cmap_capacity, aspect='auto', vmin=0, vmax=150)
     ax.set_xticks(range(len(alphas)))
-    ax.set_xticklabels([f'{a}' for a in alphas])
+    ax.set_xticklabels([f'{a}' for a in alphas], fontsize=9)
     ax.set_yticks(range(len(etas)))
-    ax.set_yticklabels([f'{e*100:.0f}%' for e in etas])
-    ax.set_xlabel('Comfort Factor (α)', fontsize=12)
-    ax.set_ylabel('Recycle Rate (η)', fontsize=12)
-    ax.set_title('Elevator Capacity Usage (%)', fontsize=13)
+    ax.set_yticklabels([f'{e*100:.0f}%' for e in etas], fontsize=9)
+    ax.set_xlabel('Comfort Factor (α)', fontsize=10, color='#444444')
+    ax.set_ylabel('Recycle Rate (η)', fontsize=10, color='#444444')
     
     # 添加数值标注
     for i in range(len(etas)):
         for j in range(len(alphas)):
             val = capacity_matrix[i, j]
-            text_color = 'white' if val > 60 else 'black'
-            ax.text(j, i, f'{val:.0f}%', ha='center', va='center', fontsize=9, color=text_color)
+            ax.text(j, i, f'{val:.0f}%', ha='center', va='center', 
+                   fontsize=8, color='#333333', fontweight='medium')
     
-    # 添加100%等高线
-    cbar = plt.colorbar(im, ax=ax, label='Capacity Usage (%)')
+    cbar2 = plt.colorbar(im, ax=ax, shrink=0.8)
+    cbar2.ax.tick_params(labelsize=8)
+    cbar2.set_label('Capacity Usage (%)', fontsize=9, color='#444444')
     
-    plt.suptitle('Two-Parameter Interaction Analysis: α vs η\n(N=100,000, Buffer=30 days)', 
-                 fontsize=14, y=1.02)
     plt.tight_layout()
-    plt.savefig(f'{save_dir}/interaction_heatmap.png', dpi=150, bbox_inches='tight')
+    plt.savefig(f'{save_dir}/interaction_heatmap.png', dpi=150, bbox_inches='tight', facecolor='#FAFAFA')
     print(f"  保存: {save_dir}/interaction_heatmap.png")
     
     return annual_matrix, capacity_matrix