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.

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.

In this study, a numerical investigation was conducted on the seismic behavior of low-strength reinforced concrete columns, strengthened with steel bars and wrapped with fiberglass tapes and fabrics, using finite element software. The columns were subjected to both monotonic and cyclic loading, and the analysis focused on fracture patterns, failure mechanisms, lateral hysteresis loops, ductility degradation, and stiffness degradation. The results showed that the reference column exhibited brittle shear failure and insufficient ductility. In contrast, the second column, reinforced with steel bars and partially wrapped with fiberglass tapes, demonstrated 30% higher tensile strength compared to the reference column, achieving stable hysteresis loops, improved energy dissipation, and 25% less cracking. The third column, fully wrapped with fiberglass fabric in addition to the steel bars, exhibited 50% higher tensile strength and 75% reduced probability of cracking in the plastic hinge area. These findings underscore the effectiveness of advanced reinforcement techniques in improving the seismic performance of reinforced concrete columns.

The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.

Oil well drilling is an expensive process that needs a particular focus. For this reason, Rate Of Penetration (ROP) has been widely approved as a measure of drilling efficiency and adequate configuration parameters. Our aim in this work consists in the elaboration of a smart system using Hoeffding trees for predicting the Rate of Penetration (ROP) in oilfield drilling. The choice of Hoeffding trees to build our model is motivated by their adaptive learning capability and drift detection. They offer continuous, fast, and efficient learning both online on data streams and offline on batch data. To validate our approach, we used real drilling data from the “Hassi-Terfa” oilfield located in Southeast Algeria. The obtained results show in comparison to the eXtreme Gradient Boosting (XGBoost) algorithm that Hoeffding trees maintain their learning capacity and produce more accurate predictions even in the presence of drifts. This is thanks to the combination of the Adaptive Windowing (ADWIN) algorithm to manage drifts and least mean squares (LMS) filters to reduce noise. This observation highlights the effectiveness of our approach to predict the ROP while oil-well drilling. The proposed smart system offers more efficient solution to predict the ROP, whether in real-time or offline. By leveraging its adaptability to changes in data distribution, our approach ensures more accurate and adaptive predictions, facilitating drilling operations optimization and boosting the overall efficiency of the process.

Wild chicory, or Hyoseris radiata L., is indigenous to the Mediterranean region, is a plant used in traditional medicine as a diuretic, blood depurative, and against kidney stones. The present study aimed to assess for the first time the acute and subacute toxicity, to quantify the total amount of polyphenols and flavonoids, and to assess the antioxidant activity of H. radiata collected from Setif, Algeria. The overall amount of flavonoids and polyphenols was quantified spectrophotometrically. The antioxidant activity of the extract was evaluated according to two methods, DPPH and FRAP. The acute toxicity of H. radiata was carried out according to the OECD guideline 423 to determine the median lethal dose LD50 and the subacute toxicity was evaluated according to OECD guideline 407 to assess the possible pathological effects of the extract administered for 28 days by oral route. The results show that the total amount of polyphenols and flavonoids was 132.53 ± 2 µg of GAE·1 mg-1 and 96.11 ± 3.65 µg of QE·1 mg-1 of extract, respectively. The extract shows a good antioxidant potential in both tests. The administered dose (2 g·kg-1 of BW) didn’t produce any changes in general behaviors or mortality, so the LD50 is greater than 2 g·kg-1 of BW. Moreover, the daily administration of the extract with 2 doses, 100 mg·kg-1 and 200 mg·kg-1 didn’t cause any changes in body weight, behavior test, hematological parameters, and organ relative weight. A significant decrease in triglyceride was recorded in both concentrations. Based on the present findings, the extract of H. radiata has no significant toxicity. These findings offer valuable information about the toxicity profile of the traditional medicine plant Hyoseris radiata L.

تتناول هذه الدراسة تدريب القوة العضلية في حالتي التقلص المركزي والتقلص اللامركزي، وتأثيرهما على كمية كريات الدم البيضاء والتغيرات التي تحدث فيها، بغية معرفة مدى تأثير كل نمط تدريب على هذا المكون الدموي، وإن كان الكريات البيض تعد مبينا لما يحدث على مستوى النسيج العضلي المتعرض لتدريب القوة العضلية في هاذين النمطين، وذلك لدى عينة قصدية من نخبة رياضة الجودو قوامها 10 رياضيين مقسمين إلى مجموعتين، مجموعة معنية بالتقلص المركزي والأخرى بالتقلص اللامركزي، واعتمدنا على الاختبارات البدنية (1RM) لتقنين الأحمال حسب متطلبات الطريقة المستخدمة في الحصة التجريبية (10×10)، وعلى التحليلات الدموية في 3 مراحل (قبلي، بعدي، تتبعي). وبعد جمع البيانات ومعالجتها إحصائيا تم تأكيد الفرضية القائلة بأن تأثير الحصة التدريبية للقوة العضلية المقترحة يختلف بشكل كبير حسب حالات التقلص العضلي (المركزي، اللامركزي)، وخلصنا إلى أن تدريب القوة العضلية يختلف تأثيره على كريات الدم البيضاء حسب نوع التقلص العضلي، وأن النمط اللامركزي يتميز بإحداث تلف كبير على مستوى النسيج العضلي وفترة استشفاءه كبيرة لكن نتائجه أفضل، مقارنة بالنمط المركزي. الكلمات المفتاحية: التدريب الرياضي؛ القوة العضلية؛ التقلص المركزي؛ التقلص اللامركزي؛ كريات الدم البيضاء؛ تلف الخلايا العضلية.

Thermal runaway of a chemical process is a dangerous phenomenon that threatens human life, equipment, installations, and the environment. The aim of our work is to propose a methodology for analyzing and quantitatively assessing the risk of thermal runaway in a polymerization reactor. Firstly, HAZard and OPerability analysis(HAZOP)is used to determine the most critical deviations likely to occur in the polymerization reactor under study and leading to the thermal runaway phenomenon. The various accident sequences are determined and evaluated using event tree analysis (ETA). The causes of the failure of safety barriers implemented in the reactor to prevent the runaway phenomenon are determined using fault tree analysis (FTA). Finally, an economic analysis is carried out to show the economic impact of safety failure barriers on the company. Application resultsof the proposed methodology show its relevance as a decision-making tool for good industrial risk management. The novelty of this hybrid approach lies in its systematic workflow. Qualitative identification (HAZOP) directly informs quantitative frequency estimation (ETA), which in turn relies on detailed reliability analysis (FTA) to assess safety barrier performance. This integrated methodology not only provides a quantitative risk frequency but also identifies the most critical safety barriers and offers an economic rationale for investment decisions through cost-benefit analysis (CBA), thereby bridging the gap between technical risk assessment and managerial decision-making

This paper presents ELAREES, a task scheduling algorithm for heterogeneous multiprocessor real-time systems, designed to optimize energy savings while enhancing fault tolerance. ELAREES addresses the dual challenges of fault tolerance in task execution and communication reliability between tasks, alongside efficient power management. The algorithm employs a primary/backup strategy, assigning each task a primary execution on a low-power (LP) core and a backup on a high-performance (HP) core to ensure resilience against execution faults. Furthermore, ELAREES integrates a robust communication protocol that monitors data transmission over shared media connection buses, dynamically selecting optimal transmission paths and initiating retransmissions when necessary to mitigate communication errors. By leveraging Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) techniques, ELAREES achieves significant power savings while maintaining high system reliability. Simulation results demonstrate consistent power savings of approximately 30% across various scenarios, with only a minimal impact of 0.02% on reliability. This research contributes to the field of energy-efficient computing in real-time systems, offering a comprehensive solution for managing the trade-offs between energy consumption, execution fault tolerance, and communication reliability in heterogeneous multicore environments.

Le présent article propose une étude portant sur "Les Porteurs de Feu" de Salah Stétié. Dans cet essai, Stétié s'appuie sur la voix du mythe, qui s'imprègne du texte pour en devenir le cœur battant, afin de dresser un vibrant hommage à la poésie arabe. Il y attribue également aux poètes arabes le rôle d’alchimistes qui transforment l’imaginaire en réalité en se servant du pouvoir de la création verbale Mots-clés : mythe , alchimistes, imaginaire, poésie arabe.

In this paper, we investigate the connections between certain spec-tra arising from Fredholm theory of a generalized Drazin invertible bounded linear operator and those of its generalized Drazin inverse. Furthermore, we analyze the transfer of Browder’s theorem and its generalized form from such an operator to its corresponding generalized Drazin inverse. Applications to left, right, and multiplication operators are also presented.

This article presents a linguistic analysis of language dysfluencies produced in exolingual situations. The study is based on a collection of real interactions recorded between Algerian doctors in a university hospital in the country. Following a corpus linguistics approach, the oral data were transcribed according to conventions adapted to spoken language. The description will focus on communication strategies that help manage the production difficulties encountered. The objective of the article is to identify the most frequent markers of disfluencies in the corpus, such as lexical repetitions, interrupted sentences, and terminological confusions. Additionally, it aims to explore whether there is a functional link between these disfluencies and language cooperation strategies, in order to ensure the dynamic of exolingual communication in a linguistic and professional context.