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 paper focuses on evaluating the settlement and bearing capacity of compressible soil reinforced with stone columns, by applying load compensation through embankment bodies of varying heights at the level of the column heads and the surrounding soil, the latter is a soil formation consisting of a clay layer containing within it a group of columns embedded in the layer in the form of a square mesh resting in turn on a layer of marl, through a load distribution and transfer mattress located above them spread along the group of columns and the same loads are applied to both models of the unit cell and the isolated column in order to perceive how the three models of distinct geometric shape act and produce an effect on the results of instantaneous settlements obtained for an applied static load. In addition, the bearing capacity for the isolated column model is estimated, based on the theoretical principle of Datye (1982), describing the failure mechanism of a top-loaded isolated stone column embedded in a compressible layer, which was analyzed and developed by Greenwood 1970. Using a calculation code (Flac 3D), numerical simulations were performed to obtain and analyze the settlements for the three models, and the bearing capacity for the isolated column model. The numerical results are estimated using other methods and analytical approaches available in the literature that support this line of research. The comparison of settlement results between the numerical models related to the study of this analysis to indicate that both column group and isolated column models show deviations and quantitative differences correlatively low estimated in percentage between − 5.8% and + 8.8% explaining a suitable and adequate convergence. On the other hand, divergences were recorded are more considerable between the column group model and the composite unit cell ranging from + 64% to + 127%, as well as between the isolated column and the composite cell from + 51% to + 126%. These results highlight that the composite cell systematically reduces settlements, while the group model can be considered as the most significant, especially under high applied loads. Regarding the bearing capacity, some analytical methods present a suitable and almost similar estimation of numerical values with reduced percentages varying between − 1.18% and + 1.34%.
Preventing major accidents remains a critical priority in the process industry, given their potentially catastrophic consequences. However, the rare, stochastic, and dynamic nature of such accidents presents significant challenges for conventional risk assessment approaches. This study develops a novel dynamic risk assessment framework that addresses these challenges. The proposed methodology combines Bow-Tie analysis (BT) with Dynamic Bayesian Networks (DBNs) to create a robust decision- support tool for managing major industrial risks. Initially, the BT approach identifies basic events, safety barriers, and potential consequences across multiple equipment operating states. These elements are then systematically integrated into a DBN model, which provides enhanced flexibility in modeling the dynamic failure mechanisms of both basic events and safety barriers through continuous precursor integration. The framework’s effectiveness is demonstrated through a practical application examining a furnace system in a gas treatment unit at Hassi R’mel, Algeria. This case study validates the model’s capability to provide real-time risk assessment updates, offering significant improvements over static risk evaluation methods for industrial safety management.
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.
Direct Normal Irradiance (DNI) is a key variable for solar resource assessment and the design of concentrating solar power plants, yet it is less frequently measured than Global Hor-izontal Irradiance (GHI) due to high costs and operational constraints. To address this gap, a novel two-stage deep learning framework is proposed that first forecasts GHI and then reconstructs DNI through an advanced separation model, eliminating the reliance on dedicated DNI measurements. The framework leverages bidirectional recurrent neural networks (BiLSTM/BiGRU) to capture temporal dependencies, combined with a wrapper-based Mutual Information (WMI) feature selection method to optimally integrate diverse radiometric and atmospheric quantities. Validated on 15-minute data representing arid, dust-prone, tropical, and temperate climates, the WMI-DL consistently and significantly outperformed four conventional empirical separation methods (Erbs, DISC, DIRINT, Engerer4) across RMSD, MAD, MBD, and R2 metrics. At Gobabeb and Desert Rock arid sites, RMSD is reduced to 10.4 W/m2 and 20.7 W/m2, respectively, with R2 of 96.6% and 90.5%, while at Tamanrasset, under arid and frequently dusty conditions, RMSD reaches 18.5 W/m2 with an R2 of 93.7%, markedly outperforming empirical models. Even in cloudy temperate climates, the model achieves RMSD values of 30.5 W/m2 in Bermuda and 36.3 W/m2 in Palaiseau, with R2 ≈ 91%, demonstrating robustness across diverse atmospheric conditions. While direct DNI forecasting with BiGRU achieves slightly higher accuracy, the WMI-DL framework provides a cost-effective, adaptable, and robust solution for high-fidelity DNI estimation, outperforming both conventional separation methods and ECMWF reanalysis benchmarks in regions lacking direct measurements.
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.
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.
Traffic congestion remains a persistent problem that compromises road safety. This phenomenon is often amplified by driver behaviors, particularly those characterized by the herd effect. This study aims to model the emergence and dynamics of the herd effect in traffic jams and to simulate a strategy for mitigating this behavior among drivers. To achieve these objectives, reinforcement learning (RL) was employed within the frameworks of memoryless Markov chains and multi-phase Markov chains. The results demonstrate the effectiveness of Markov chains in accurately modeling the collective behavior of specific drivers. Likewise, the simulations illustrate RL’s capacity to regulate the herd effect and optimize individual decision-making during congestion. The findings suggest that traffic authorities may consider implementing RL-based strategies to mitigate herd behavior, improve traffic flow, and enhance road safety.
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 proposes a novel hybrid model for wind speed forecasting (WSF) based on a three-stage framework comprising decomposition, feature selection, and forecasting. The proposed approach employs Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to decompose wind speed time series into Intrinsic Mode Functions (IMFs). A distinctive contribution of this study is the use of sample entropy as a feature selection mechanism to identify the most relevant Intrinsic Mode Functions (IMFs). The selected IMFs are then integrated through a classification-based fusion technique, significantly enhancing forecasting accuracy and distinguishing this approach from conventional methods. Two distinct forecasting approaches are evaluated using multiple performance metrics, including RMSE, MAE, MAPE, and R². The first approach applies the fusion technique directly to the original wind speed time series, while the second incorporates ICEEMDAN to decompose the time series. Experimental validation using two real-world datasets from Algeria demonstrates the superiority of the proposed hybrid model over individual forecasting models, yielding significant improvements in prediction accuracy, robustness, and stability. These findings underscore the effectiveness of the three-stage framework, offering a reliable and efficient solution for short-term wind speed forecasting, with potential applications in renewable energy management and grid optimization.
