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     <p>When considering the quantitative measurement of adhesion modules, it is necessary to consider the construction of fitness functions to measure the optimal state of each cell. First, the initial values are set according to the different types of cells. Consider N pairs of Ag, Nb antigens, and antibodies. Each type of cell has one or more antigens of a specific kind. Set the adhesion function, and for a certain location, count the eight cells that are nearby. If there is a corresponding antigen-antibody directly adjacent to the cell, this cell is scored. Otherwise, no points will be scored. If there are many pairs of conjugated antigen antibodies, then the score is accumulated. If it is on the diagonal, then mark 0.5 points. This integral system can be regarded as a fitness function, according to the distribution of surface antigens and antibodies, determine the degree of adhesion, and use a number to represent, this value as the degree of evolutionary advantage.
 <br><br>
       Next, evolution takes place, that is, making the cell "move" in the direction of greater adhesion, and thus move in the direction actually expected. This is where iteration comes in. The iterative process can be broken down into steps like this
-      <br>Step1 For each iteration, each cell randomly generates a different state i than the original.
-      <br>Step2 Calculate the fitness function s(i) for the new state
-      <br>Step3 Calculate the change in score and write it as ΔS
-      <br>Step4 Generate a random number between 0 and 1 r
-      <br>Step5 if r＜e<sup>ΔS/kT</sup> ,and accept the new state; Otherwise retain the current state. In other words, the result optimization must be greedy to accept, and the result deterioration probability to accept.
+      <br>·Step1 For each iteration, each cell randomly generates a different state i than the original.
+      <br>·Step2 Calculate the fitness function s(i) for the new state
+      <br>·Step3 Calculate the change in score and write it as ΔS
+      <br>·Step4 Generate a random number between 0 and 1 r
+      <br>·Step5 if r＜e<sup>ΔS/kT</sup> ,and accept the new state; Otherwise retain the current state. In other words, the result optimization must be greedy to accept, and the result deterioration probability to accept.
       <br><br>However, as the number of iterations increases, T slowly decreases, and eventually, a definitive image will be obtained
       <br><br> The Monte Carlo algorithm can be used for specific solutions. The Monte Carlo algorithm first gives the initial position of a few cells, and then begins to evolve according to the scoring rules.
       </p></section>
@@ -185,9 +185,9 @@
     <h4><br><br>Model solving</h4>
     <p>Monte Carlo algorithm solving can be divided into the following steps: First initialize the grid situation, each grid is randomly distributed with different kinds of cells
       <br><br>The Monte Carlo solution can be divided into the following steps:
-      <br>First, the grid is initialized, and each grid is randomly distributed with different kinds of cells
-      <br>The second step is to define the evolution rules: here, the adherence function is used to measure the results. Each cell receives a corresponding evolution score based on its surroundings,Represents the KTH evolution result of the ij position 
-      <br>Third, cell changes change the results: in order, different cells iterate according to their surroundings. Always move in the direction of increasing the score. Based on this, we set a stop criterion, where we believe that the final result is when the total score stops increasing </p>
+      <br>·First, the grid is initialized, and each grid is randomly distributed with different kinds of cells
+      <br>·Second,to define the evolution rules: here, the adherence function is used to measure the results. Each cell receives a corresponding evolution score based on its surroundings,Represents the KTH evolution result of the ij position 
+      <br>·Third, cell changes change the results: in order, different cells iterate according to their surroundings. Always move in the direction of increasing the score. Based on this, we set a stop criterion, where we believe that the final result is when the total score stops increasing </p>
   </section>
   <section id="Model result3">
     <h4><br><br>Model result</h4>