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.

In this paper, we consider a general quadratic problem (P) with linear constraints that are not necessarily linear independent. To resolve this problem, we use a new algorithm based on the Inertia-Controlling method while replacing the condition of the Lagrange multiplier vector μ by resolution of a linear system obtained thanks to the Kuruch–Kuhn–Tuker matrix (KKT-matrix) in order to determine the minimizing direction of (P) and so calculate the steep length in general cases, as follows: indefinite, concave, and convex cases. This paper has an interesting topic and meaningful results.

This study examines the effect of vertical seismic components on Reinforced Concrete (RC) structures, specifically in the framework of the new Algerian seismic code (RPA 2024). Despite developments in the knowledge of horizontal ground motions, recent earthquakes, such as those in Turkey-Syria (2023) and Algeria (Béjaïa 2021-2022, Boumerdès 2003), have highlighted the significant contribution of vertical accelerations in amplifying structural damage. The current research evaluates low-rise (2-story), mid-rise (6-story), and high-rise (9-story) buildings underseven earthquake records scaled according to RPA 2024 specifications. Using nonlinear dynamic analysis, it advances current knowledge by providing insights into the specific effects of vertical ground motion on the evaluation of variations in internal forces, lateral displacements, and story-level responses, focusing on height-dependent effects, force redistribution, and displacement amplification factors. The results indicate that low-rise buildings (2-story) have negligible effects from combined Horizontal and Vertical (H+V) loading with variations of 5-15%, and mid-rise buildings (6-story) have moderate effects (20-30%). In contrast, high-rise buildings (9 stories) have significant height-dependent variations of 10-35%.

The sabkha soils of the Aïn M’lila region present major geotechnical challenges, including high salinity, significant gypsum and soluble salt contents, and low mechanical strength. To improve their geotechnical performance, this study evaluated the effectiveness of cement–lime stabilization at various mixing ratios. Mechanical tests, including compressibility, California bearing ratio (CBR), and unconfined compressive strength (UCS), were conducted to identify the optimal dosage. The 6/4% cement–lime mixture exhibited the best performance, showing a significant increase in bearing capacity and soil cohesion. Based on these results, an X-ray diffraction (XRD) analysis was carried out exclusively on this optimal mixture to examine the associated mineralogical transformations. The XRD results revealed a pronounced structural reorganization characterized by the formation of secondary carbonates and pozzolanic products (C-S-H and C-A-H), which contributed to matrix densification and improved mechanical resistance. Despite the persistence of certain evaporitic phases such as gypsum and halite, the 6/4% cement–lime mixture proved to be the most effective, confirming the suitability of this approach for the sustainable stabilization of sabkha soils.

For modern enterprises to guarantee the quality and accuracy of their fundamental business data, master data management (MDM) is essential. However, a lack of connectivity between data governance and data quality throughout deployment is the reason why many MDM programs fail. This paper introduces a six-phase MDM framework that specifically integrates stakeholder-driven governance and data quality dimensions into each phase of the MDM lifecycle. Based on the PRAXEME methodology and designed using Business Process Model and Notation (BPMN), the framework emphasizes real-time monitoring and facilitates departmental collaboration. A healthcare case study illustrates its efficacy in enhancing data comprehensiveness, minimizing redundancy, and boosting operational productivity. The proposed framework offers a comprehensive approach that aligns business and IT objectives and reinforces process transparency.

The selection of a fourth-generation fighter aircraft for the Algerian Air Force is a complex multicriteria decision-making (MCDM) process, marked by ambiguity and subjectivity. In the face of such challenges, the hybrid framework has applied the fuzzy analytic hierarchy process (fuzzy AHP), Shannon entropy, and fuzzy technique for order of preference by similarity to ideal solution. This approach utilizes linguistic variables represented by triangular fuzzy numbers to enhance adaptability and transparency in the decision-making process. Fuzzy AHP and entropy methods calculate the weights of selection criteria, while fuzzy TOPSIS ranks alternatives. The study is applied on a set of aircrafts, including SU-34, MIG-29, Rafale, and F-16, using technical and operational criteria such as range, autonomy, and climb rate. Results indicate that SU-34 is the optimal choice when using fuzzy AHP, while the F-16 ranks highest with the entropy method, with minimal score differences underscoring the precision and reliability of the methodology. This study overcomes traditional limitations, such as subjective bias and inflexibility, by leveraging fuzzy logic, enabling systematic assessments aligned with real-world constraints. The findings refine procurement decisions and provide a framework for balanced military evaluations. Future work may extend to advanced aircraft or unmanned systems.

Nowadays, the increasing tendency towards the exploitation of solar energy has yielded many technological advancements. Hybrid approaches are attracting attention worldwide to ensure the comprehensive assessment of photovoltaic modules reliability becoming a crucial issue. The present study is dedicated to the investigation of an innovative approach integrating Bond graph theory, Gaussian mixture models and Similarity-based method for fault detection and remaining useful life prediction. In this context, Bond graphs are exploited first to create a dataset covering diverse operational modes of the system. The identification and evaluation of critical sensors for fault observability is also considered, where the dataset is optimized based on the variance analysis. The Gaussian mixture model with its semi-supervised initialization is then utilized for clustering and fault diagnosis, while remaining useful life estimation is performed using a pairwise similarity technique. Validation results on a photovoltaic panel model demonstrate that the Gaussian mixture model consistently outperforms the classical k-Nearest Neighbors model across all key metrics (accuracy of 0.9396 vs. 0.7577, precision of 0.9192 vs. 0.5570, recall of 0.7849 vs. 0.5628, and F1-score of 0.8666 vs. 0.6707), highlighting its superior performance. The remaining useful lifetime model also achieves high accuracy, with Root Mean Square Error values ranging from 0.0282 to 0.0300, indicating minimal prediction error. Additionally, the R-Squared value of ~0.92 shows that the model explains approximately 92% of the variance in remaining useful lifetime predictions, underscoring its strong predictive capability. The results demonstrate the practical effectiveness of the proposed framework for both single and multiple faults. However, some limitations are noted, such as the exclusion of the transition phase in training data and the reliance on controlled conditions. The outcomes of this work are expected to provide valuable insights into the implementation of efficient hybrid frameworks, contributing to the sustainable development of solar energy.

The purpose of this article was to optimise the cost of the energy consumption of an industrial complex that was using three energy sources: industrial electricity, natural gas, and solar energy. Before undertaking any optimisation action, we carried out an in-depth examination of the company’s energy system. This action was positioned as an essential pivot for collecting energy data according to: Source (gas, electricity); Equipment or production tools; Usage (heating, cooling, lighting, ventilation, etc.). The in-depth examination of the company’s energy system made it possible to identify the energy consumption of the equipment and the costs associated with it for a year of operation. With an objective of energy and economic performance, the simplex algorithm was implemented to resolve the energy mix model and machine hours, according to the two-phase technique applied first to the energy problem and second to resolvingthe time problem.

The purpose of this article was to optimise the cost of the energy consumption of an industrial complex that was using three energy sources: industrial electricity, natural gas, and solar energy. Before undertaking any optimisation action, we carried out an in-depth examination of the company’s energy system. This action was positioned as an essential pivot for collecting energy data according to: Source (gas, electricity); Equipment or production tools; Usage (heating, cooling, lighting, ventilation, etc.). The in-depth examination of the company’s energy system made it possible to identify the energy consumption of the equipment and the costs associated with it for a year of operation. With an objective of energy and economic performance, the simplex algorithm was implemented to resolve the energy mix model and machine hours, according to the two-phase technique applied first to the energy problem and second to resolvingthe time problem.

Seismic strengthening for existing structures is a sustainable solution that is utilized to enhance building safety, reduce damages, and prevent failure in a future earthquake event. The choice of seismic strengthening techniques has to be accurate, efficient, and adjusted to make RC structures stronger in the building sector. Buckling-restrained brace (BRB) system is one of the successful strengthening strategies, that it is possible to utilize in both RC and steel structures. Therefore, this paper explores the possibility of employing buckling restrained braces in existing RC buildings and assesses the impact of different BRB bracing distributions and positions on seismic force resistance. In this work, a five-story RC building was considered, and to upgrade their performance seismic was modeled using four types of BRB systems, consisting of two types of bracing configurations with two arrangements: diagonal in the central bay, diagonal in the corner bays, chevron in the central bay, and chevron in the corner bays. To assess the efficiency of the four proposed BRB systems, firstly, the nonlinear static pushover method was conducted to investigate the lateral strength of structures. Secondly, a parametric study was undertaken using dynamic time history analysis to study various factors such as roof displacement, shear force, and roof acceleration of the original and strengthened models. The numerical study was executed using the Seismostruct software. The results and different performance levels were examined and compared. The obtained results indicate that the BRB and concrete structures can successfully work together to resist the reliability of strengthening RC structures. It was observed that the four prediction systems of the BRB models were excessively effective at upgrading the seismic resistance of the existing structure and provided significantly less damage, especially when using the chevron BRBs with the corner arrangement compared to the other models.

Cet article explore les défis rencontrés par les étudiants dans la rédaction persuasive, et aux erreurs courantes liées à la cohérence et à la cohésion en proposant des stratégies pédagogiques pour améliorer l’appropriation de ces deux compétences. Pour illustrer ces enjeux, nous nous appuyons sur une étude de cas des étudiants de 1ére année licence de français de l’université de Bordj en Algérie. L’enseignement des connecteurs, des reprises lexicales et pronominales, la révision par les pairs et l’utilisation des outils d’évaluation renforcent la maitrise de la cohérence et de la cohésion chez ces apprenants en vue de rédiger des productions persuasives fluides avec des idées bien enchainées logiquement et suivant une progression claire.

Language and translation have always been indispensable to worldwide communication; this fact highlights the importance of equipping translation students with the required competences to ensure accurate text translation. This study addresses the notion of Translation Competence from a foreign language teaching perspective in particular competence in writing in English language being an essential prerequisite in translating texts from Arabic into English. Using an Error Analysis Approach, the study maintains that defects in the students’ linguistic, discourse and sociolinguistic sub-competences in the EFL writing will result in different types of errors which will impact the quality of the translated text. Findings suggest that those language related sub-competences be given priority in the training of future translators.

This paper contributes to the study of numerical radius inequali-ties for a bounded linear operator on a complex Hilbert space. By employingthe Maligranda inequality and the Cartesian decomposition of operators, weestablish new inequalities that yield sharper estimates than previously existingresults.

In this study, a mathematical model for predicting the response of a conductometric urea biosensor was developed and numerically simulated. The biosensor features a planar interdigitated electrode array with immobilized urease. The enzymatic hydrolysis of urea generates ionic products, such as ammonium (NH₄⁺)and bicarbonate (HCO3-) ions, altering the solution's electrical conductivity. To optimize the biosensor performance, key physicochemical processes were analyzed through numerical modeling and validated against experimental data, showing strong agreement. Simulations under varying conditions supported the experimental design, improved the analytical performance, and reduced the development costs. While previous studies have explored conductometric urea biosensors, few have addressed optimizations through numerical modeling. This study addresses this gap by examining the effects of temperature, pH, enzyme layer thickness, and CO2 concentration using the COMSOL Multiphysics software. The model accurately predicted conductivity variations across different urea concentrations, with optimal responses being observed at 37 °C, 5% CO2, pH 7.4, and an enzymatic zone length of 500 μm. These results offer valuable insights for enhancing the design and application of conductometric urea biosensors in biomedical and environmental fields.

This paper presents simulation and experimental validation of a Nonlinear Compensation-based Neuro Fuzzy (NCNF) controller designed to balance the rotary inverted pendulum (RIP). Traditional linear controllers, such as Proportional-Integral-Derivative (PID) and state-feedback with pole placement, usually achieve satisfactory results in simulations on linearized models. However, their performance decreases in hardware implementation because of disturbances and unmodeled nonlinear effects such as Coulomb friction and mechanical backlash. To overcome these challenges, a feedforward compensation function was developed to cancel these undesired effects, which is combined with an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller that updates PID gains to improve the rotary arm tracking for a square-wave reference and stabilize the pendulum at the upright position. The proposed NCNF controller is validated through hardware-in-the-loop (HIL) experiments and compared with a baseline state-feedback controller. Results show that the arm angle (θ) overshoot decreased from 40.6% to 0.8% (lower step) and from 17.2% to 2.5% (upper), total steady-state θ-error from 5.75° to 0.296°, and the fitness index dropped from 41.12 to 25.23. The nonlinear compensation reduced the gap between simulation and real-time performance, while the ANFIS further improved the defined control metrics. Overall, the NCNF controller achieves more stable and precise tracking than the state-feedback control.

This article focuses on improving fault tolerance and optimizing energy consumption in the context of a mining prospection mission conducted by a swarm of autonomous robots. Two major contributions are proposed. The first aims to reduce communication between robots in order to increase the system’s robustness in the presence of failures. The second focuses on minimizing the trajectory of a deminer robot to reduce overall energy consumption. To address these goals, two reinforcement learning based algorithms are proposed: Deep Q-Network (DQN) and Federated Reinforcement Learning (FRL), both derived from the Q-learning algorithm. Simulation results examining the impact of the exploration rate α on the number of detected mines show that, with 10 autonomous robots of the same architecture and 30 randomly placed mines over 30 experiments, the FRL algorithm provides better fault tolerance and ensures that the main prospection mission is accomplished even in the presence of some robotic failures or errors. Furthermore, a second series of 60 experiments involving the integration of the deminer robot, focused on optimizing energy consumption, demonstrates that the DQN algorithm is more effective in reducing energy usage, due to improved a better optimization of unnecessary deminer movements, while successfully resolving deadlock situations that the latter may encounter. These findings open the door to the development of a hybrid algorithm combining the strengths of DQN and FRL to ensure both system robustness and minimal energy consumption.