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Sétif City, located in the Eastern High-lands of Algeria, faces a resurgence of urban flooding, a phenomenon exacerbated by the soil sealing resulting from rapid urbanization (+351.67% between 1986 and 2021) and the under-sizing of drainage infrastructure. In the context of a lack of integrated spatial assessment tools, this study aims to evaluate and map the flood vulnerability of the urban area by coupling Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP). The methodology integrated seven vulnerability criteria (five physicals and two socio-economic), whose AHP-based weighting was judged reliable (Consistency Ratio: 6.4%). The results reveal that 45.65% of Sétif’s urban area (339.22 hectares) exhibits high to very high vulnerability. The AHP analysis identified slope (33.7% of the weight) and land use (29.1% of the weight) as the major determinants of this vulnerability. Critical areas, notably Ararsa, Yahyaoui, and Aïn Sebaâ districts, are characterized by the combination of gentle slopes and a high density of infrastructure. This work confirms the relevance of the AHP-GIS coupling in providing local authorities with an essential decision support tool for the revision of the Master Plan for Development and Urban Planning (PDAU) and for more resilient urban planning.

This work aims to improve the performance of electric vehicles (EVs) based on linear induction mo tors (LIM). The Particle Swarm Optimization (PSO) method is proposed to tune the PID regulator of the Field-Oriented Control (FOC) technique. The main objective of this study is to develop innovative solutions that maximize the efficiency and precision of electric vehicles on various paths. The LIM model is imple mented using the d-q synchronous reference frame and takes into account the end-effect phenomenon. This phenomenon occurs due to the termination of the mo tor’s physical structure, which leads to distortion in the magnetic field at the ends of the motor’s primary (sta tor). It is also highly nonlinear, which increases its complexity and makes control difficult. To overcome this issue, the Field-Oriented Control (FOC) technique is suggested to achieve better efficiency, dynamic per formance, and greater control flexibility of the motor. Furthermore, the use of the (PSO) optimization tech nique enables the determination of optimal control pa rameters to maximize the performance of the (FOC LIM) system under different operating conditions, such as speed variation and disturbance load. A compari son between the PSO-PID and conventional methods in terms of response stability, steady-state error, and rise time is conducted using MATLAB/Simulink. The results demonstrate a more efficient, precise, and high performing electric vehicle system.

This work aims to improve the performance of electric vehicles (EVs) based on linear induction mo tors (LIM). The Particle Swarm Optimization (PSO) method is proposed to tune the PID regulator of the Field-Oriented Control (FOC) technique. The main objective of this study is to develop innovative solutions that maximize the efficiency and precision of electric vehicles on various paths. The LIM model is imple mented using the d-q synchronous reference frame and takes into account the end-effect phenomenon. This phenomenon occurs due to the termination of the mo tor’s physical structure, which leads to distortion in the magnetic field at the ends of the motor’s primary (sta tor). It is also highly nonlinear, which increases its complexity and makes control difficult. To overcome this issue, the Field-Oriented Control (FOC) technique is suggested to achieve better efficiency, dynamic per formance, and greater control flexibility of the motor. Furthermore, the use of the (PSO) optimization tech nique enables the determination of optimal control pa rameters to maximize the performance of the (FOC LIM) system under different operating conditions, such as speed variation and disturbance load. A compari son between the PSO-PID and conventional methods in terms of response stability, steady-state error, and rise time is conducted using MATLAB/Simulink. The results demonstrate a more efficient, precise, and high performing electric vehicle system.

This work aims to improve the performance of electric vehicles (EVs) based on linear induction mo tors (LIM). The Particle Swarm Optimization (PSO) method is proposed to tune the PID regulator of the Field-Oriented Control (FOC) technique. The main objective of this study is to develop innovative solutions that maximize the efficiency and precision of electric vehicles on various paths. The LIM model is imple mented using the d-q synchronous reference frame and takes into account the end-effect phenomenon. This phenomenon occurs due to the termination of the mo tor’s physical structure, which leads to distortion in the magnetic field at the ends of the motor’s primary (sta tor). It is also highly nonlinear, which increases its complexity and makes control difficult. To overcome this issue, the Field-Oriented Control (FOC) technique is suggested to achieve better efficiency, dynamic per formance, and greater control flexibility of the motor. Furthermore, the use of the (PSO) optimization tech nique enables the determination of optimal control pa rameters to maximize the performance of the (FOC LIM) system under different operating conditions, such as speed variation and disturbance load. A compari son between the PSO-PID and conventional methods in terms of response stability, steady-state error, and rise time is conducted using MATLAB/Simulink. The results demonstrate a more efficient, precise, and high performing electric vehicle system.

Mobile Wireless Sensor Networks (MWSNs) enhance traditional wireless sensor networks by allowing sensor nodes to move, resulting in continuously changing network topologies. Although this mobility enables advanced applications such as disaster response, intelligent transportation systems, and mission-critical monitoring, it poses major challenges for secure and scalable key management in large-scale deployments. Most existing key management and key pre-distribution schemes are tailored to static or lightly mobile networks and therefore suffer from limited scalability, excessive memory consumption, inefficient key utilization, and increased vulnerability to node capture when applied to highly mobile environments. This paper proposes a mobility-aware, zone-based key management scheme that integrates an enhanced composite key distribution mechanism with dynamic key refinement. The network is partitioned into logical zones, each maintaining an independent key pool to confine security breaches and improve scalability. To adapt to mobility-induced topology changes, sensor nodes continuously refine their key rings by preserving only the cryptographic keys associated with persistent neighbor relationships. This selective retention strategy significantly reduces storage overhead while strengthening resilience against key compromise and unauthorized access. Comprehensive analytical modeling and performance evaluations demonstrate that the proposed scheme achieves higher secure connectivity, stronger resistance to node capture attacks, and improved scalability compared to existing approaches, particularly in dense and highly mobile MWSN scenarios.

Quantum Key Agreement (QKA) is a cornerstone of quantum cryptography, facilitating secure key distribution among multiple participants. Existing QKA protocols often suffer from scalability issues and increased computational complexity as the number of participants grows. This paper proposes an efficient Circle Multiparty Quantum Key Agreement (CMQKA) protocol based on the BB84 protocol. This protocol enhances quantum resource efficiency and ensures equal participation in a circular topology. The key feature lies in the optimized use of quantum resources, minimizing the qubit overhead while ensuring high security standards. By achieving a qubit efficiency of 1/2n, it significantly improves the multiparty quantum communications. A thorough security analysis is conducted to demonstrate the protocol’s resilience against common quantum threats.

This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.

Designing scalable and interpretable control strategies for decentralized multi-agent systems remains a challenge in reinforcement learning (RL). This challenge is particularly evident in pursuit–evasion tasks, which require coordination under partial observability, without explicit communication or centralized guidance. Although deep RL methods achieve strong performance, they typically operate as black boxes, limiting trust and deployment in safety-critical domains. We propose a Multi-Head DDPG architecture that decomposes control into three interpretable force components - pursuit, cohesion, and separation - weighted adaptively to generate context-aware actions. This design enables emergent role differentiation and interpretable self-organization in the model. In grid-based pursuit–evasion benchmarks, our method outperforms DQN, PPO, and standard DDPG in terms of success rate, convergence speed, and generalization, while also yielding transparent collective behaviors. Overall, the results show that weighted force-based behavioral decomposition provides a principled pathway toward achieving both high-performance and explainable multi-agent control.