Industrial world today is experiencing its fourth industrial revolution. Predecessor of the automation industry, Industry-4.0 adds a layer of autonomy, intelligence and advanced connectivity to complex industrial systems. This layer creates an interactive and dynamic bridge between virtual systems and physical systems with their upward constraints and requirements in a constantly changing physical environment. Digital twins fall into the category of advanced concepts and technologies that enhance this connectivity. Several projects have been launched in Morocco by the government, industrialists and research communities, initiating the digital transformation of Moroccan industrial manufactories and companies. This paper comes within this framework, firstly to highlight the great potential that digital twins can offer for Moroccan industrial context enhancement, secondly to identify the challenges that can hinder the integration and development of digital twins in Moroccan industrial environment.

The objective of the present work is to evaluate the performance of a low-cost tractor equipped with a parallel hybrid engine, which was simulated using AMESim software. The tractor was evaluated with three different farming implements attached to the tractor, and each implement requires a different type of power. The first simulation was executed without any implements attached. The tractor was able to run for 170 s with the electric motor only, which resulted in fuel savings during this period. The first implement, a moldboard plow, was attached for the second round of evaluation, and the electric motor ran by itself for 150 s, which also led to fuel savings during operation. During the third simulation, the tractor was attached to a Bette Harvest, which has a very high-power demand. The obtained results show that both engines were engaged to provide the required energy. During the final round of evaluation, simulations were run for a straw tub grinder. In this simulation, the electric motor ran alone until the battery was fully discharged. Thereafter, the combustion engine was activated in order to facilitate operations and to charge the battery. The results show that the parallel hybrid architecture employed for the low-cost tractor significantly decreased the CO₂ emissions and minimized the consumption of fuel.

As the development of technology, the mobile phone world also has developed very quickly. Mobile communication and services, over emerging wireless technologies, provide anyone, anytime and anywhere access. Moreover, the intelligence is now often embedded into modern applications, such as surveillance and supervising systems. This work presents an intelligent and a pervasive supervising platform for information system (IS) security based on multi-agent systems (MAS) which detects and classifies malware events that occurs on software and hardware components and sends real time notification to smart phone users. The proposed platform is formed by two components: The first one consists of a Central Software Component (CSC); the supervisor application that manages devices and evaluates logs and alarms of the supervised components. The second one consists of an Embedded Software Component (ESC) on the Mobile Device (MD) with an ANDROID OS. In our platform, we have implemented agents on ESC using JADE-LEAP android, note that the ESC is targeted to smart phones and tablet that run the operating system Android, JADE API on CSC and the communication between these two components is performed using the Secure Socket Layer (SSL) protocol implemented using the JADE-S plug-in. Copyright © 2015 Praise Worthy Prize - All rights reserved.

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing.

In this paper, we propose a comparative study of linear and nonlinear algorithms designed for grid-side control of the power flow in a wind energy conversion system. We performed several simulations and experiments with step and variable power scenarios for different values of the DC-link capacity with the DC storage element being the key element of the grid-side converter. The linear control was designed on the basis of the internal model control theory where an active damping was added to avoid steady state errors. Nonlinear controls were built using first and second order sliding mode controls with theoretical considerations to ensure accuracy and stability. We observed that the first order sliding mode control was the most efficient algorithm for controlling the DC-link voltage but that the chattering degraded the quality of the energy injected into the grid as well as the efficiency of the grid-side converter. The linear control caused overshoots on the DC-link voltage; however, this algorithm had better performance on the grid side due to its smoother control. Finally, the second order sliding mode control did not prove to be more robust than the other two algorithms. This can be explained by the fact that this control is theoretically more sensitive to converter losses.