diff --git a/latex/mcmthesis-demo.bbl b/latex/mcmthesis-demo.bbl
old mode 100755
new mode 100644
diff --git a/latex/mcmthesis-demo.pdf b/latex/mcmthesis-demo.pdf
old mode 100755
new mode 100644
index 3711e29..35f9559
Binary files a/latex/mcmthesis-demo.pdf and b/latex/mcmthesis-demo.pdf differ
diff --git a/latex/mcmthesis-demo.tex b/latex/mcmthesis-demo.tex
index 8cb39a9..c6ccdf0 100755
--- a/latex/mcmthesis-demo.tex
+++ b/latex/mcmthesis-demo.tex
@@ -505,8 +505,6 @@ By prioritizing resource allocation to high-demand sites, the recommended scheme
 
 Building upon the foundation of Model I, this chapter introduces a tripartite architectural framework centered on the "Symbiotic Sites" strategy to transcend the traditional one-truck-one-site constraint and unlock dormant logistical capacity. 
 
-The model's logic initiates with a rigorous screening phase, where candidate site pairs are evaluated through a multi-dimensional filter encompassing spatial proximity, Demand Accumulation Moderation, and Demand Stability. Once viable pairs are identified, an internal resource allocation mechanism employs a multi-objective utility function to determine the optimal cargo distribution between the paired sites, effectively reconciling the intrinsic trade-off between service effectiveness and cross-site fairness. Ultimately, these symbiotic pairs are integrated as composite decision units into an enhanced global scheduling algorithm, which synchronizes their requirements with independent sites to generate a unified and optimized distribution timetable for the entire year.
-
 \begin{figure}[H] 
 \centering
 \includegraphics[width=10cm]{flowchartmodel2.png}
@@ -514,6 +512,8 @@ The model's logic initiates with a rigorous screening phase, where candidate sit
 \label{flow chart of Model II}
 \end{figure}
 
+The model's logic initiates with a rigorous screening phase, where candidate site pairs are evaluated through a multi-dimensional filter encompassing spatial proximity, Demand Accumulation Moderation, and Demand Stability. Once viable pairs are identified, an internal resource allocation mechanism employs a multi-objective utility function to determine the optimal cargo distribution between the paired sites, effectively reconciling the intrinsic trade-off between service effectiveness and cross-site fairness. Ultimately, these symbiotic pairs are integrated as composite decision units into an enhanced global scheduling algorithm, which synchronizes their requirements with independent sites to generate a unified and optimized distribution timetable for the entire year.
+
 \subsection{Model Building}
 
 \subsubsection{Selection Criteria for Symbiotic Sites}
@@ -737,24 +737,23 @@ Fig. \ref{Fairness diagnostics} presents the fairness diagnostics for 70 service
 Sensitivity analysis is used to verify that the key conclusions---improved effectiveness under feasibility constraints and controlled risk---remain stable under reasonable perturbations of a small set of threshold parameters. Throughout this section, we interpret parameter choices using the following decision rule: we prefer settings that (i) maintain feasibility and the equity floor enforced by the scheduling constraints (e.g., minimum service frequency), (ii) keep shortfall risk below an acceptable level (e.g., $R1 \le 5\%$), and (iii) maximize distribution effectiveness, prioritizing quality-weighted service (E2) when trade-offs exist.
 
 \begin{table}[H]
-    \footnotesize
-    \centering
-    \caption{Key parameters and tested ranges in sensitivity analysis}
-    \label{tab:sensitivity_params}
-    \begin{tabular}{@{}llllp{4.2cm}@{}}
-      \toprule[1pt]
-      Parameter & Meaning & Baseline & Tested range & Rationale for range \\
-      \midrule[0.6pt]
-      $C_0$ & Capacity threshold for correction (Task 1) & 350 & 350--450 & Around observed ``capacity ceiling'' \\
-      $p^{trunc}_0$ & Truncation probability threshold (Task 1) & 0.1 & 0.01--0.10 & From conservative to permissive correction \\
-      $\hat c$ & Quality threshold used in E2 (Task 1) & 250 & 200--300 & Covers typical quality cutoffs \\
-      Merge ratio & Portion merged into dual-stop routes (Task 3) & 0.5 & 0.1--0.9 & From light to aggressive merging \\
-      $l_{max}$ & Maximum pairing distance (Task 3) & 50 & 10--100 & Feasible local pairing to relaxed pairing \\
-      $\mu_{\text{sum}}$ & Cap on paired expected demand (Task 3) & 450 & 350--550 & Controls combined-stop overload risk \\
-      CV threshold & Maximum volatility admitted for pairing (Task 3) & 0.5 & 0.1--1.0 & Filters unstable sites vs. broader admission \\
-      \bottomrule[1pt]
-    \end{tabular}
-  \end{table}
+  \centering
+  \caption{Key parameters and tested ranges in sensitivity analysis}
+  \label{tab:sensitivity_params}
+  \begin{tabular}{lccc}
+    \toprule[1pt]
+    Parameter & Meaning & Baseline & Tested range \\
+    \midrule[0.6pt]
+    $C_0$ & Capacity threshold for correction (Task 1) & 350 & 350--450 \\
+    $p^{trunc}_0$ & Truncation probability threshold (Task 1) & 0.1 & 0.01--0.10 \\
+    $\hat c$ & Quality threshold used in E2 (Task 1) & 250 & 200--300 \\
+    Merge ratio & Portion merged into dual-stop routes (Task 3) & 0.5 & 0.1--0.9 \\
+    $l_{max}$ & Maximum pairing distance (Task 3) & 50 & 10--100 \\
+    $\mu_{\text{sum}}$ & Cap on paired expected demand (Task 3) & 450 & 350--550 \\
+    CV threshold & Maximum volatility admitted for pairing (Task 3) & 0.5 & 0.1--1.0 \\
+    \bottomrule[1pt]
+  \end{tabular}
+\end{table}
 
 \subsection{Sensitivity Analysis for Task 1}