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.

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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.

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.

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.