## 2 Materials and Methods
### Experimental Methods
#### 2.3.2 Analysis Methods
 \tag{9}\]  
in which \(A_{1}=\left[\frac{w_{1}}{w_{1}+w_{2}},\frac{w_{2}}{w_{1}+w_{2}}\right]\), \(A_{2}=\left[\frac{w_{3}}{w_{3}+w_{4}},\frac{w_{6}}{w_{3}+w_{4}}\right]\), \(A_{3}=\left[\frac{w_{8}}{w_{5}+w_{6}},\frac{w_{6}}{w_{5}+w_{6}}\right]\), \(A_{4}=\left[\frac{w_{7}}{w_{2}}\right]\), \(A_{5}=\left[\frac{w_{8}}{w_{8}}\right]\). Therefore, the first-level evaluation matrix (\(B_{i}\)) is calculated through \(B_{i}=A_{i}\cdot R_{i}\). The secondary evaluation matrix (\(B\)) is calculated through \(B=A\cdot R\), in which \(A=\left[a_{1},a_{2},...,a_{k}\right]\), \(R=\left[\left\{B_{1},B_{2},...,B_{k}\right\}\right]^{\mathrm{T}}\). Finally, the evaluation grade is determined according to the principle of maximum membership degree. (2) Analytic hierarchy process method  
Analytic hierarchy process is a decision-making method that analyzes the problem, extracts the influencing factors, analyzes the relationship and characteristics between the various factors, and conducts qualitative and quantitative analysis. I. The interaction between various elements of the evaluation system is analyzed to establish a hierarchical structure in this system. II. The evaluation factors at the same level are compared to the previous level. A pairwise comparison judgment matrix is constructed, and its consistency is tested by Equation (10). A nine-point scale is generally used to indicate the importance in the judgement matrix, as listed in Table 5. \begin{table}
\begin{tabular}{c l} \hline \hline
**Scaling** & **Meaning** \\ \hline
1 & Indicates that factor i has the same importance as factor j \\ \hline
3 & When factor i is compared with factor j, one factor is slightly more important than another \\ \hline
5 & When factor i is compared with factor j, one factor is obviously more important than the other \\ \hline
7 & When factor i is compared with factor j, one factor is strongly more important than the other \\ \hline
9 & When factor i is compared with factor j, one factor is extremely more important than the other \\ \hline
2, 4, 6, 8 & The median of the above two adjacent judgments \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Table of importance of factors. The consistency index (\(CI\)) is calculated as:  
\[CI=\frac{\lambda_{max}-n}{n-1} \tag{10}\]  
where \(\lambda_{max}\) is the maximum eigenvalue of the matrix. Then, the random consistency ratio (\(CR\)) can be calculated by Equation (11). \[CR=\frac{CI}{RI} \tag{11}\]  
If \(CR<0.1\), the judgment matrix can be considered to pass the consistency test. The \(RI\) can be found in Table 6.  
III. The relative weight of the influence factor relative to the influence factor of the upper layer can be calculated according to the judgment matrix, and the weight (\(w^{(0)}\)) can be obtained. The eigenvalue method is used to calculate the weight. IV. The combined weight of the elements of each layer for the system purpose (i.e., the overall goal) is calculated, and the alternatives can be ranked. Entropy weight (EW) method  
The EW method is an objective weighting method based on the principle that, if the degree of index variation is smaller, the reflected information amount is also less, and the corresponding weight should be lower. Assuming that there are n evaluation objects and m evaluation indexes, the following forward matrix can be formed:  
\[\text{X}= \begin{bmatrix}\text{x}_{11}&\text{x}_{12}&\dots&\text{x}_{\text{1m }}\\ \text{x}_{21}&\text{x}_{22}&\dots&\text{x}_{\text{2m}}\\ \vdots&\vdots&&\ddots&\vdots\\ \text{x}_{\text{n1}}&\text{x}_{\text{n2}}&\dots&\text{x}_{\text{nm}}\\ \end{bmatrix} \]  
The forward matrix (\(X\)) is normalized, and the normalized matrix is denoted as \(Z\), in which each element is denoted as \(z_{ij}\), and it needs to be satisfied as \(z_{ij}=x_{ij}/\sqrt{\sum_{i=1}^{n}x_{ij}^{2}}\). The probability matrix (\(P\)) can be calculated where each element \(p_{ij}\) is satisfied as \(p_{ij}=z_{ij}/\sum_{i=1}^{n}z_{ij}\). Moreover, \(\sum_{i=1}^{n}p_{ij}=1\) ensures that the sum of the probabilities for each indicator is 1.