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.

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

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

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

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

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

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

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

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.