## 4 MAMIC System: A New Model Generation
Furthermore, within this layer, business validations are located in \"Value Objects\" and \"Entities\", as well as the ports of the MAMIC system (adapter interfaces and domain services). * Application layer: The application layer contains the use cases needed for each \"Bounded context\" or unit of the MAMIC system. The role of application classes is to orchestrate the necessary classes to execute an action requested by the external systems. Additionally, it handles the translation of primitive data received into objects managed by the domain. The application layer should never be aware of infrastructure details. * Infrastructure layer: The infrastructure layer is responsible for communication between the MAMIC system and external factors, and vice versa. Its classes are tightly coupled to specific technologies such as frameworks, database engines, specific queuing systems, external libraries, and other infrastructure components. Within this layer, some modules translate information received/sent by other components and entry points to the MAMIC system, among other functionalities. The MAMIC model, Figure 1, facilitates the integration of these approaches by providing a modular and flexible structure. Each model can be encapsulated within its respective layer, allowing for independent development, testing, and evolution. Additionally, the architecture promotes the separation of concerns, making it easier to maintain and extend the system over time:  
* Anthropomorphic approach: Implemented primarily in the application layer, where user interfaces and interaction components reside. User-centric design principles guide the development of interfaces and interactions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Architecture** & & **Special Consideration** \\ \hline Anthropomorphic Approach & 1. & Design user interfaces and interactions that are intuitive and adaptive to human behavior. \\  & 2. & Gather user feedback through surveys, interviews, or usability testing to understand user preferences and needs. \\  & 3. & Implement features such as personalized recommendations or natural language processing to enhance user experience. \\ \hline Predictive Modeling: ARCH & 1. & Collect and analyze historical data on user behavior, system performance, and external factors. \\  & 2. & Develop predictive models to anticipate future user actions or system events. \\  & 3. & Integrate predictive analytics into the system to dynamically adjust behavior or resources based on anticipated events. \\ \hline Cognitive Architecture: RCS & 1. & Model cognitive processes such as perception, learning, and decision-making within the system. \\  & 2. & Implement algorithms inspired by cognitive science to simulate human-like behavior or reasoning. \\  & 3. & Design interfaces and interactions that align with human cognitive abilities and limitations. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Component’s functionalities of anthropomorphic approach, predictive modeling, and cognitive architecture used to combine them into the hexagonal architecture. * Predictive modeling: Integrated into both the domain and application layers. Predictive models are part of the business logic in the domain layer, while data processing and analysis components reside in the application layer. * Cognitive architecture: Implemented within the domain layer, where cognitive processes are modeled and simulated. Algorithms inspired by cognitive science are embedded into the business logic to enable human-like behavior. In summary, while the MAMIC model proposal offers valuable insights and capabilities, it presents specific challenges related to complexity, resource utilization, technological constraints, and ethical implications. * Initial complexity: Deploying this methodology could necessitate increased design and configuration efforts owing to its intricate structural nature. * Resource overhead: The modular nature of this architecture may result in elevated layers and abstractions, which could pose maintenance challenges and overhead.