This study presents the development of a enhancedrectangular membrane finite element using the strain-based approach, designed for both elasticand elastoplasticanalyses. Each of the four corner nodes of the element is characterized by three degrees of freedom: two translational degrees and one in-plane rotational degree.The displacement functions of the proposed element accurately capture the rigid body motion modes.For the elastoplastic computations, the material response is evaluated using the Von Mises failure creterion, andthe nonlinear solution procedure incorporates viscoplastic-strain approaches. For validation purposes, several selected numerical examples are solved using the developed element in both linear and nonlinearanalyses.The results obtained are compared with analytical solutions and other similar membrane finite elements, demonstrating the strong performance of the proposed element.

This work presents a novel strain-based finite element formulation specifically designed for the comprehensive analysis of two-dimensional structures under static and dynamic elastoplastic conditions. The proposed triangular element features three degrees of freedom per node, including two in-plane translational components and one rotational component about the normal axis.The displacement field is derived from predefined strain functions that satisfy the compatibility equations, while the interpolation functions are carefully constructed to provide an exact representation of rigid body motions. The developed formulation is applicable to both static and time-dependent elastoplastic analyses.In the static case, the element accurately determines nodal forces, displacements, and stress distributions. For dynamic analyses, a lumped mass matrix combined with an explicit time integration scheme is adopted to improve computational efficiency.The accuracy and robustness of the proposed element are validated through several benchmark test problems, demonstrating its excellent numerical performance and reliability in structural analysis.

This study presents a reliability-based design optimization (RBDO) framework for improving the intrinsic stability of total hip arthroplasty through jump-distance-based design selection. The problem was formulated using four design variables: femoral head diameter, cup abduction angle, cup anteversion angle, and femoral offset. Jump distance (JD) was computed from an analytical geometric model, and uncertainty was propagated by Monte Carlo simulation with 10,000 samples per scenario. A total of 1,617 configurations were evaluated in MATLAB R2020b using parallel computing. The results showed a mean reliability index of 2.3945, with values ranging from -2.9677 to 3.8906, and a mean Monte Carlo jump distance of 12.0659 mm. The best-performing configuration within the explored design space was a 36 mm head, 30° abduction, 30° anteversion, and -2 mm offset, yielding a nominal JD of 20.601 mm, a mean simulated JD of 20.568 mm, and zero failures in 10,000 trials. Parametric analysis showed that abduction angle had the strongest negative influence on reliability, followed by femoral offset, whereas head diameter had a positive effect and anteversion had a weaker but favorable contribution. These results confirm that a probabilistic design framework can discriminate robust from fragile implant configurations more effectively than a purely deterministic threshold check and provide a computationally efficient basis for preclinical implant-orientation optimization.

Foam-based composites are advanced materials that combine lightweight design with high mechanical strength and stiffness, making them attractive for diverse engineering applications. Their mechanical performance is strongly influenced by foam morphology, including strut geometry in open-cell foams and cell wall topology in closed-cell foams. This study investigates the effect of these morphological features on the mechanical behavior of foam-based composites, considering both open-cell and closed-cell foams as matrix phases. A finite element-based numerical homogenization approach evaluates mechanical performance over a wide range of phase volume fractions and stiffness contrasts. The results reveal significant differences in stress distribution, load transfer efficiency, and overall mechanical response between the two foam morphologies. Open-cell foam composites, particularly those with cylindrical struts, show superior load-bearing capacity under high stiffness contrast. In contrast, closed-cell composites develop stress localization unless optimized with thin walls and dense reinforcement. The findings indicate that the mechanical efficiency is governed by three main factors: (i) phase stiffness contrast, determining how morphology affects performance; (ii) phase connectivity or interpenetration, controlling stress transfer through the stiff phase; and (iii) strut geometry, where rounded struts aligned with the load enable direct stress transfer while minimizing matrix deformation.

This study investigates how authentic audiovisual (AAV) materials, particularly TED Talks and YouTube videos, contribute to second-year EFL students’ perceived speaking development at Batna2 University. To obtain the required data, 130 second-year students completed a questionnaire that measured three constructs, including the profiled AAV engagement, instructional use in-class vs. homework, and perceived gains across five speaking dimensions, in addition to Semi-structured interviews with seven teachers, who explored classroom practices, constraints, and conditions for effectiveness. The findings indicate positive student perceptions of AAV input, as evidenced by higher engagement with AAV content. It is also reported that the strongest perceived gains were in confidence/anxiety reduction and pronunciation/intonation awareness, while perceived gains in fluency were comparatively lower. At the same time, Teachers' views converged on one key condition: AAV materials are perceived as most beneficial when viewing is systematically linked to structured speaking tasks and supported by pedagogical scaffolding.

Aseptic loosening driven by stress shielding remains the principal long-term failure mode of total hip arthroplasty (THA), affecting 10-15% of implants within 15 years and generating annual revision costs exceeding USD 8 billion globally. This study presents an integrated multiscale computational framework for the design of functionally graded femoral stems using Ti-6Al-4V/SiC whisker-reinforced composite materials. The framework systematically couples seven sequential modules : (M1) Halpin-Tsai micromechanical characterisation of three material systems Ti-6Al-4V, Ti/SiC 20 vol%, and Ti/SiC 40 vol%; (M2) analytical beam stress analysis under ISO 7206-4 oblique cantilever loading; (M3) SIMP topology optimisation at V* = 0.70 achieving 30% mass reduction (C/C₀= 0.37), (M4) TPMS lattice homogenisation for four architectures (Gyroid, Diamond, Cubic, Octet) with a corrected stress shielding index (SSI_Gyroid = +11.1%) representing an 87% reduction from monolithic Ti-6Al-4V (SSI = 82.5%), (M5) multiaxial fatigue analysis combining Rainflow cycle counting, modified Goodman criterion, and dual Miner damage reporting (D_ISO = 0.001-0.018; D_full = 0.117-1.426. The Ti/SiC 20 vol% composite in a functionally graded Gyroid architecture emerges as the optimal clinical configuration, uniquely satisfying all biomechanical, osseointegration, and SLM fabricability requirements simultaneously.Keywords:total hip arthroplasty; stress shielding; SIMP topology optimisation; TPMS lattice; Ti-6Al-4V/SiC composite; Goodman criterion; Miner damage; functionally graded implant.

Accurate detection of built-up areas in semi-arid regions is vital for urban planning and environmental monitoring. However, built-up surfaces and bare soils often produce very similar spectral responses. As a result, this similarity causes confusion in satellite image classification. Additionally, spectral overlap among urban materials, bare soil, and sparse vegetation further complicates detection. This study evaluates several spectral indices, including DBSI, NDTI, NDVI, BRBA, and BSI, combined with Principal Component Analysis (PCA) to enhance built-up area extraction from Sentinel-2A imagery. Images captured during the driest season were selected to maximize spectral contrast. Three classification schemes based on Support Vector Machine (SVM) were tested. The first scheme used DBSI, NDTI, and NDVI. The second used BRBA, NDTI, and NDVI. The third relied on PCA-derived components. The results indicate that the PCA-based approach achieved the highest classification accuracy at 95%. In comparison, the DBSI/NDTI/NDVI combination reached 93%, while the BRBA/NDTI/NDVI scheme achieved 92%. Therefore, PCA helps reduce spectral confusion and enhances the identification of built-up areas in semi-arid environments. Overall, combining multiple spectral indices with dimensionality reduction offers a reliable method for urban analysis using Sentinel-2 imagery.

This study investigates the potential for enhancing the geotechnical performance of sabkha soil from Ain M’Lila (Algeria) through cement-lime stabilization, with optimization of stabilizer dosages conducted using Response Surface Methodology (RSM). Twelve mixtures with varying contents of soil (90–100%), lime (0–10%), and cement (0–10%) were evaluated using a central composite design. Experimental testing included Proctor compaction, unconfined compressive strength (UCS), direct shear, California Bearing Ratio (CBR), and electrical conductivity (Ec), with data interpreted via ANOVA and RSM modeling. The untreated sabkha soil displayed unfavorable properties, such as 60% fines, an electrical conductivity of 16.33 mS/cm, 3585 mg/L of chlorides, and 4704 mg/L of sulfates. Nevertheless, RSM identified an optimal formulation comprising 6% cement and 4% lime, which led to remarkable improvements: UCS increased by 1007% (1583 kPa at 90 days), the CBR index rose by 442.89% (83.83), cohesion reached 38.2 kPa, and the pH stabilized at a highly alkaline value of 12.3, promoting pozzolanic activity. ANOVA results confirmed the significance of the models (p < 0.05), with strong coefficients of determination (R² values of 87.13%, 91.39%, 90.85%, 80.47%, and 89.03%) corresponding to maximum dry density, cohesion, internal friction angle, CBR, and UCS, respectively. Overall, the findings confirm that cement-lime treatment can effectively convert problematic sabkha soil into a reliable subgrade material for road infrastructure. Moreover, the RSM-based optimization approach provides a cost-effective and technically sound solution for soil stabilization in arid and semi-arid environments.

Modern factories aim to boost production to increase profits and remain competitive amid intense market pressure. This goal is pursued through production planning, which not only improves process efficiency but also ensures product quality and long-term market success. Among the key tools that support this objective is production forecasting, which plays a central role in facilitating manufacturing processes. In this study, we focus on enhancing the efficiency of industrial systems by developing smart predictive models based on Deep Reinforcement Learning (DRL). Specifically, time-series data from the rotary cement kiln at the SCIMAT factory (Batna, Algeria) were used to predict clinker production. The proposed model, based on the DDQN network, outperformed both the traditional Q-learning network and the DQN. The DDQN achieved a Mean Squared Error (MSE) of 0.0010, a Root Mean Squared Error (RMSE) of 0.0316, and a coefficient of determination (R2) of 0.9484. In contrast, the DQN achieved an MSE of 0.0014, an RMSE of 0.0374 and an R2 of 0.9374. The traditional Q-learning network achieved an MSE of 0.0052, an RMSE of 0.0725 and an R2 of 0.8865. The LSTM algorithm achieved an MSE of 0.0069, an RMSE of 0.0830 and an R2 of 0.6570. The developed model is designed to be integrated into the plant’s control system via Programmable Logic Controllers (PLCs), enabling automated operation without direct human intervention. The DQN model demonstrated strong predictive accuracy, leading to improved resource management and operational efficiency. Its application can also be extended to other industrial domains.

ThispaperinvestigatesseveralformsofSylvester-typeoperatorequationsininfinite-dimensional Hilbertspaces, focusingonboththeclassicalequation AX−XB = CanditsgeneralizedversionAX−YB = C, which involves two unknowns. We establish new necessary and sufficient conditions for the existence of solutions by employing generalized inverses under novel structuralassumptions. Specialattentionisgiven to the behavior of these equations when restricted to subspaces such asker(A+I)andker(B+I), andtocases involving two distinct subspaces. The study highlights how operator properties-such as involution and pseudo-inverses-govern solvability and solution structure. The results offer a unified theoretical frame work that encompasses both classical and generalized operator equations, with potential applications in control theory, perturbation analysis, and related areas. Illustrative examples are provided to demonstrate the applicability and relevance of the theoretical developments.

Fixed point theory stands as a fundamental pillar in nonlinear functional anal ysis, being essential for proving the existence of solutions for nonlinear dif ferential and integral equations. Krasnoselskii’s hybrid fixed point theorem, which combines the Banach contraction principle with Schauder’s theorem, is a pivotal contribution. Recent efforts have focused on refining and relaxing the conditions of this theorem. This study aims to extend the theoretical frame work of Krasnoselskii-type fixed point theorems to address a broader and more general class of nonlinear operator equations within a Banach algebra setting. It also seeks to establish rigorous conditions for the existence (and uniqueness, where possible) of solutions. The approach involves developing local variants of the classic Krasnoselskii fixed point theorems. We performed a comparative analysis of previous studies, introduced modifications to the operator equations to relax restrictive assumptions, and theoretically generalized the theorems to accommodate a complex structure involving four operators. To validate the results, they were applied to a nonlinear functional integral equation within the Banach space C[0,1]. We successfully generalized existing results by in corporating the H¨older continuity condition, which is less restrictive than the standard Lipschitz condition. The unified theoretical framework led to the es tablishment of a comprehensive set of theorems and corollaries covering a wide class of operator equations such as : Ax(ρ2)Bxρ1 + Cx(ρ3)Dxρ1 = x. Our re sults provide less restrictive local existence conditions and wider applicability in the analysis of complex mathematical systems.

Detecting arrhythmias via electrocardiograms (ECGs) is vital for healthcare. While deep learning has advanced classification, capturing critical patterns in complex data remains challenging. We propose Res_Bi-LSTM_MHA, a novel model integrating a multi-head self-attention (MHA) mechanism to selectively focus on relevant signal segments. This enhances the capture of subtle features often missed by conventional methods. By combining Residual Networks (ResNet) for robust feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal dependencies, our approach significantly improves accuracy. We evaluated the model at subject and record levels using the China Physiological Signal Challenge (CPSC 2018), St. Petersburg Institute of Cardiological Technics (INCART), and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) databases. The model achieved an F1 score of 98.01% and 99.42% accuracy on the MIT-BIH dataset. Our results demonstrate that effectively utilizing attention mechanisms offers a substantial improvement in arrhythmia classification.

Global solutions to a Gierer-Meinhardt model of two substances defined by reaction-diffusion equations are shown in this article. By employing Lyapunov functionals and investigating the regularizing properties inherent to parabolic equations, we rigorously establish the existence and asymptotic behavior of solutions under appropriate assumptions. Numerical simulations are used to corroborate the analytical findings. This research differs from previous work because it relies on spatial domains that vary over time, rather than being static.

Information and communication technology clearly affects regional development in several aspects, most notably facilitating access to services. This has led to its widespread adoption, which faces significant geographical and social obstacles that limit it s efficiency. This study aims to assess the state of information and communication technology in the Aurès Mountains of Algeria, with a particular focus on the structure of telephone services in order to illustrate the challenges that hinder regional development. The current situation of telephone communication infrastructure in the area was analysed using geographic information systems, in addition to the construction of a weighted spatial regression model to demonstrate the relationship between the efficiency of telephone services and network coverage variables. The findings revealed that the existi ng infrastructure is constrained by the challenges of terrain and by social factors related to unequal population density and distribution. Based on these aspects, solutions were proposed to improve the effectiveness of telephone services, with an emphasis on addressing the identified gaps to enhance regional development through strategic actions by decision-makers, thereby improving access to services and contri buting to bridging the digital divide in the future.

Objectives: Traffic accidents cause severe social and economic impacts, demanding fast and reliable detection to minimize secondary collisions and improve emergency response. However, existing cloud-dependent detection systems often suffer from high latency and limited scalability, motivating the need for an edge-centric and consensus-free accident detection framework in IoV environments. 

Methods: This study presents a real-time accident detection framework tailored for Internet of Vehicles (IoV) environments. The proposed system forms an integrated IoV architecture combining on-vehicle inference, RSU-based validation, and asynchronous cloud reporting. The system integrates a lightweight ensemble of Vision Transformer (ViT) and EfficientNet models deployed on vehicle nodes to classify video frames. Accident alerts are generated only when both models agree (vehicle-level ensemble consensus), ensuring high precision. These alerts are transmitted to nearby Road Side Units (RSUs), which validate the events and broadcast safety messages without requiring inter-vehicle or inter-RSU consensus. Structured reports are also forwarded asynchronously to the cloud for long-term model retraining and risk analysis. 

Results: Evaluated on the CarCrash and CADP datasets, the framework achieves an F1-score of 0.96 with average decision latency below 60 ms, corresponding to an overall accuracy of 98.65% and demonstrating measurable improvement over single-model baselines. 

Conclusions: By combining on-vehicle inference, edge-based validation, and optional cloud integration, the proposed architecture offers both immediate responsiveness and adaptability, contrasting with traditional cloud-dependent approaches.

Low-latency content delivery is essential in the Internet of Vehicles (IoV) to support autonomous driving, cooperative perception, and infotainment services. However, rapidly changing vehicular mobility and demand patterns limit the effectiveness of existing content prediction and caching strategies, which often capture either short-term temporal trends or long-range dependencies, but not both. This paper proposes a hybrid deep learning architecture that integrates Long Short-Term Memory (LSTM) networks with Transformer encoders to jointly model fine-grained temporal dynamics and global correlations in content requests. The resulting popularity predictions are incorporated into a reinforcement learning (RL)-based caching policy, enabling proactive and adaptive cache placement at roadside units (RSUs) within an end-to-end optimization framework. Simulation results across representative IoV scenarios show that the proposed approach consistently improves cache hit ratio, retrieval latency, and prediction accuracy compared with LSTM-only, Transformer-only, Least Frequently Used (LFU), and Least Recently Used (LRU) baselines. Ablation studies further demonstrate the complementary strengths of the hybrid components, highlighting improved convergence behavior and robustness under varying demand distributions.

first_page

settings

Order Article Reprints

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.