## 4 Results
### Application Phase
#### 4.2.2 Dynamic Analysis of the NCMs  
Using artificial intelligent techniques, the dynamics of a fuzzy cognitive map can be traced analytically through a specific inference and simulation process. Dynamic NCM analysis can be used to explore the behavior of the simulated system over time. As mentioned earlier, the system may stabilize to a fixed state, enter a limit cycle, or form a chaotic attractor. Each of these possible outcomes can provide valuable data to assist in decision-making. Figure 16 represents the process taken in order to perform dynamic analysis of an NCM. \begin{table}
\begin{tabular}{c c} \hline  
Simulations can analyze various aspects of NCMs, including concept activation levels at the end of the simulation, changes/trends in activation levels over time, and cycle discovery (intervals and activation levels within a cycle). This style of study investigates \"what-if\" situations by running simulations of a particular model with various beginning state vectors. After an NCM is exposed to an initial stimulus, the subsequent stable state or cycle of states may be studied to acquire insight into the system's behavior. The work of [77] suggests that simulations can provide insight into a system's dynamic behavior, aiding decision-making and predicting future outcomes. In Section 2, we previously outlined the general algorithm of dynamic analysis in NCMs as a generic representation of the analysis process. In the specific context of smart port performance, Figure 17 provides a contextualization of the general steps (Figure 5) with domain-specific elements such as the role of the smart port performance node and the thresholding operation for practical application. Next, the following figures (Figures 18 and 19) depict the experts' opinions in the form of adjacency matrices, expressing the relationships between the concepts identified in Figure 11. The only difference this time, compared to the static analysis of NCM, is that the main concept/node (smart port performance) is included in the construction of the respective adjacency matrices. Figure 17: Algorithm for dynamic analysis of NCM within suggested framework. Figure 18: Adjacency matrix of first expert’s opinion. Now, based on the above matrices, we will evaluate howthe concept of smart port performance is influencedbyother nodes of NCM. This is achieved by performing the steps of the algorithm explained in Figure 17 in the following way:  
(1) For the first expert:  
We set the effect of node C\({}_{0}\) (smart port performance) to be in the ON state and all other nodes in OFF state, i.e., we define the vector A\({}_{1}\) = (1,0,0,0,0,0) (_step 3_). We multiply the vector A\({}_{1}\) with the adjacency matrix as defined from first expert, i.e., A\({}_{1}\) N(E1) = (0,1,1,1, \(I\), 1,1) \(\hookrightarrow\) (1,1,1,1,1,1) = A\({}_{2}\) (say) (_step 4_)  
(\"\(\hookrightarrow\)\"symbol denotes the updating and thresholding of the state vector). The operation where the factor is in the ON state in the initial vector A\({}_{1}\) should remain in the ON state till the end. This method of replacing in the ON state if it is zero or OFF is called the updating operation. The threshold operation in these models is carried out using Equation (8), (here, with T = 1). The resultant state vector \(A_{2}\)'s effect on \(N(E1)\) gives the following:  
A\({}_{2}\) N(E1) = (5+I, 3+I, 4+I, 2, I\(-\)1, 1, 2) \(\hookrightarrow\) (1,1,1,1,0,1,1) = A\({}_{3}\) (_step 5_)  
(3 + \(I\), 4+I is replaced by 1 as the real part is greater than the indeterminate part while I-1 is replaced by 0 as the real part is less than the indeterminate part). A\({}_{3}\)N(E1) = (5,3,4,2,I\(-\)1,1+I,2+I) \(\hookrightarrow\) (1,1,1,1,0,1,1) = A\({}_{4}\) = A\({}_{3}\) (_step 5_--hidden pattern_ or _limit cycle_)  
Thus, according to the expert's opinion, if C\({}_{0}\) (smart port performance) is in the ON state then the nodes C1, C2, C3, C4, and C6, are in the ON state, whereas the node C5 is in the OFF state.