Publications Internationales

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    On computing the double point multiplication in elliptic curve cryptography
    (Taylor & francis, 2025) Nait-Abdesselam, Fadila; Oudjida, Abdelkrim Kamel; Khouas, Abdelhakim; Liacha, Ahmed
    The double point-multiplication (DPM) operation on elliptic curves, denoted as u.P þ v.Q, where u and v are nonnegative integers and P, Q are points on the curve, is a critical operation in digital signature verification. Its computational scheme sig- nificantly impacts system performances in terms of speed, memory usage, and security. This article introduces a range of straightforward algorithms for DPM, which leverage an iterative uniform pattern based on constant-time arithmetic. This approach mitigates side-channel attacks (SCA) that exploit tim- ing or power consumption measurements to compromise secret keys u and v. The proposed algorithms employ a w-bit windowing method to simultaneously recode the binary strings u and v and evaluate DPM on-the-fly from left-to-right. This one-pass recode/evaluation process accelerates DPM, reduces memory overhead, and enhances resilience against SCA. The new algorithms are systematically evaluated using precise ana- lytic formulas for speed, memory usage, and security. They pri- oritize simplicity and flexibility, enabling easy adjustments between speed-memory and speed-security trade-offs to meet various constraints. Comparative analysis against state-of-the- art methods is conducted, comprehensively examining com- plexities using NIST-recommended GF(2l ) curves, as well as twisted Edwards and Montgomery GF(p) curves.
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    Incremental fuzzy with PSO optimization for improving the pressure stability of hydrogen and oxygen recovery 5 kW PEM fuel cell system under variable load conditions
    (Elsevier, 2025) Kabache, Sabah; Reguieg, Djelloul; Essaid Bousbiat; Kendil, Djamel
    The longevity and efficiency of the proton exchange membrane fuel cell (PEMFC) is related to the stability of the hydrogen (H2) and Oxygen (O2) pressures within. Variations in these pressures may cause detrimental me- chanical limitations. Controlling the difference between the pressures is essential to preventing reactant insuf- ficiency or fuel waste. Conventional control techniques like PID controller often struggle with dynamic system variations and load fluctuations. This paper introduces two advanced control strategies to enhance pressure stability: an improved incremental fuzzy logic controller (IFLC) utilizing a (7 × 7) membership function scaling and a PID controller optimized by particle swarm optimization (PSO). Unlike previous studies that focused on smaller PEMFC systems (3 kW and 500 W) and relied primarily on conventional PID controllers, this work evaluates a larger 5-kW PEMFC system, providing a more comprehensive assessment of H2/O2 pressures regulation. Simulation results, conducted in MATLAB/Simulink, demonstrate that the IFLC and PSO-optimized PID significantly enhance H2/O2 pressures stability under varying load demands. The IFLC, in particular, ach- ieves superior robustness, quick response time, and zero overshoot, minimizing performance indices such as integral absolute error (IAE) (0.0067, 0.0165), integral square error (ISE) (0.0016, 0.0035), mean absolute error (MAE) (0.0007, 0.002). These results confirm the effectiveness of the IFLC in ensuring long-term PEMFC reli- ability and efficiency.
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    Tourism Investment in Algeria: Bridging the Gap Between Potential and Performance in the SDAT 2030 Framework
    (Université de Bordj Bou Arréridj, 2025) Badreddine, Amina; Telkhoukh, Saida
    This study focuses on tourism investment environment in Algeria and its potential to drive economic diversification beyond hydrocarbon reliance. The study used a mixed methodology, reviewing the official tourism statistics (2018-2025) and legislative frameworks and the Tourism Development Master Plan (SDAT 2030) to evaluate the current performance in relation to the strategic goals. Even with recent legislative changes under the 2022 Investment Law and the ambitious target of reaching 12 million visitors annually rather than 2.5 million by 2030, research indicates that there are still ingrained issues: tourism has become a mere contributor to GDP at 1.47% in 2023 compared to the Mediterranean average of 10%, accommodation facilities are critically inadequate at 0.1 hotel rooms per 100 inhabitants and 66% of registered tourism projects are either not started or uncommented. The study concludes that although Algeria holds considerable comparative advantages in desert, coastal and heritage tourism, the opportunities in the country could only be actualised by overcoming the shortcomings in infrastructure, institutional coordination and international marketing.
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    A Comprehensive Review of Pesticide Elimination Methods from Fruits and Vegetables Over the Past Two Decades: Optimizing Produce Safety for Sustainable Food Systems
    (Research Institute of Food Science and Technology, 2025) Meghlaoui, Zoubeida; Remini, Hocine; Remini-Sahraoui, Yasmine; Mellal, Mohamed Khalil; Boudalia, Sofiane; Brahimi, Yasmine; Negrichi, Samira; Allam, Ayoub; Medouni-Haroune, Lamia; Messaoudene, Lynda
    The increasing use of pesticides in agriculture, valued at approximately 43.2 billion USD, has raised significant concerns regarding food safety and human health. This study reviews the effectiveness of various pesticide residue removal methods applied to fruits and vegetables (F & V). A total of 57 studies published between 2005 and 2022 were analyzed, categorizing the methods into 28 household techniques, 19 advanced methods, and 10 combined approaches. Household methods, such as washing under running water, achieved removal rates of up to 90%, while peeling ensured complete (100%) elimination of residues. The addition of salt or vinegar solutions improved removal efficiency, reaching 92%. Advanced methods, notably ozonation, demonstrated high efficacy with up to 95% removal. The most effective approaches were combined techniques, integrating washing, ultrasound, and ozonation, which achieved residue elimination rates of up to 99%. Despite their efficiency, advanced methods face limitations due to high costs and technological constraints, reducing their accessibility for widespread use. This review underscores the necessity of an integrated approach to enhance food safety. Additionally, it highlights the need for further research on the long-term impact of these removal methods on the nutritional quality of F & V. These findings provide essential insights for consumers, farmers, and the food industry, contributing to the development of more effective and practical food safety strategies
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    Environmental Impacts and Behavioral Adaptations of Honeybees in Algeria: A Review of Apis mellifera intermissa and Apis mellifera sahariensis Characteristics
    (Multidisciplinary Digital Publishing Institute, 2025) Haider, Yamina; Adjlane, Noureddine; Haddad, Nizar
    Honeybees are vital for pollination and the overall health of ecosystems. Since the 18th century, the intricate biology of honeybees has been a subject of scientific inquiry. Understanding their biological and behavioral characteristics is essential for effective beekeeping, honey production, and ecosystem sustainability. This review examines the environmental impact and management practices on the health of local honeybees in Algeria, focusing on Apis mellifera intermissa and Apis mellifera sahariensis. We summarize research findings on genetic diversity, morphometric traits, behavioral characteristics, and adaptation of local honeybees. Additionally, we discuss the threats posed by abiotic and biotic stressors and highlight the importance of conservation and sustainable management. The reviewed studies indicate that environmental factors significantly influence the behavioral characteristics and adaptation of local honeybees. Notably, the hygienic behavior of A. m. intermissa contributes to their resistance against diseases and the Varroa destructor mite. Further research in these areas is important for enhancing our understanding of honeybee health and population dynamics in Algeria, thereby informing strategies for sustainable beekeeping practices
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    Adsorbents Made from Cotton Textile Waste—Application to the Removal of Tetracycline in Water
    (Multidisciplinary Digital Publishing Institute, 2025) Akkouche, Fadila; Madi, Katia; Aissani-Benissad, Farida; Ali, Fekri Abdulraqeb Ahmed; Assadi, Amine Aymen; Assadi, Amir Achraf; Azzaz, Ahmed Amine; Yahiaoui, Idris
    The adsorptive removal of tetracycline (TC) in aqueous solution, a widely used antibiotic, was investigated using activated carbon derived from cotton textile waste. The valorization of textile waste provides a sustainable strategy that not only reduces the growing accumulation of discarded textiles but also supports a circular economy by transforming waste into efficient adsorbent materials for the removal pharmaceutical contaminants. This dual environmental and economic benefit underscores the novelty and significance of using cotton-based activated carbons in wastewater treatment. In this study, cotton textile waste was utilized as a raw material for the preparation of adsorbents via pyrolysis under nitrogen at 600 °C followed by chemical modification with H2SO4 solutions (1, 2, and 3 M). The sulfuric-acid modified-carbons (SMCs) were characterized by BET surface area analysis, FTIR spectroscopy and SEM imaging. Batch adsorption experiments were carried out to evaluate the effects of key operational parameters including contact time, initial TC concentration and solution pH. The results showed that the material treated with 2 M H2SO4 displayed the highest adsorption performance, with a specific surface area of 700 m2/g and a pore volume of 0.352 m3/g. The pH has a great influence on TC adsorption; the adsorbed amount increases with the initial TC concentration from 5 to 100 mg/L and the maximum adsorption capacity (74.02 mg/g) is obtained at pH = 3.8. The adsorption behavior was best described by Freundlich isotherm and pseudo-second-order kinetic models. This study demonstrates that low-cost and abundantly available material, such as cotton textile waste, can be effectively repurposed effective adsorbents for the removal of pharmaceutical pollutants from aqueous media
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    Retraction notice to "Feasibility study of a grid-connected PV/wind hybrid energy system for an urban dairy farm" [Heliyon 10 (2024) e40650]
    (Cell Press, 2025) Bouregba, Hicham; Hachemi, Madjid; Samatar, Abdullahi Mohamed; Mekhilef, Saad; Stojcevski, Alex; Seyedmahmoudian, Mehdi; Hamidat, Abderrahmane
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    Evaluating Storage Potential and Integrity of Depleted Reservoirs for CO₂ Injection
    (2025) Zakarya, Belimane; Youcefi, Mohamed Ryad; Benbrik, Abderrahmane; Hadjadj, Ahmed
    As global industrial activity grows, carbon dioxide emissions increase, intensifying greenhouse effect and climate change and demanding solutions beyond renewable energy. This study investigates CO₂ sequestration in subsurface formations as a promising mitigation strategy to support international climate goals and reduce carbon levels. Using CMG 2021 software, different trapping mechanisms, including structural, residual, and solubility trapping, were evaluated in detail to determine their individual and combined contributions to overall storage capacity. Results show that integrating all three mechanisms increases storage potential by 30% compared with structural trapping alone. In addition, geological uncertainty was addressed through Monte Carlo simulations. For that, multiple realizations were generated by varying key reservoir parameters such as porosity, permeability and hysteresis-related parameter. This probabilistic approach allows for a more robust assessment of storage capacity variability and enhances prediction confidence. Furthermore, caprock integrity was evaluated using a two-way geomechanical coupling approach with the Bendis model. The findings indicate that injection-induced pressure reduces effective stresses within the caprock, which may promote tensile failures and create potential leakage pathways. This integrated analysis demonstrates that coupling numerical simulation and probabilistic tools support safer, more effective CO₂ storage, which offers a viable long-term solution for global climate change mitigation efforts.
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    A hybrid APSO–ANFIS optimization based load shifting technique for demand side management in smart grids
    (IAES, 2025) Faradji, Mohamed; Madani Layadi, Toufik; Rouabah, Khaled
    Cost and performance are considered important parameters to obtain an optimized configuration for smart grids. In this paper, a new optimization approach, based on a hybrid adaptive particle swarm with an adaptive neuro- fuzzy inference system (ANFIS) algorithm, has been proposed. This approach allows optimizing demand side management (DSM) using the load shifting technique. The impact of the latter on consumer profile, electricity pricing mechanisms, and overall grid performance are illustrated. In this simulation, the focus lies on modeling DSM using a day-ahead load shifting approach as a minimization problem. Simulation experiments have been tested separately on three different demand zones, namely, residential, commercial, and industrial zones. A comparative study of solutions was performed, focusing on both reduced peak demand and operational costs. The obtained results demonstrate that the optimization presented in this article approach outperforms the other approaches by achieving greater savings in the residential and commercial sectors. The study proved a significant reduction in peak demand. In fact, values of 23.76%, 17.61% and 16.5% in peak demand reduction are achieved in the case of residential, commercial, and industrial sectors, respectively. Furthermore, operational cost reductions of 7.52%, 9.6%, and 16.5% are obtained for the three different cases.
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    Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU
    (Nature Research, 2025) Ait Mouloud, Louiza; Kheldoun, Aissa; Oussidhoum, Samira; Alharbi, Hisham; Alotaibi, Saud; Alzahrani, Thabet
    Accurately predicting solar power is essential for ensuring electric grid reliability and integrating renewable energy sources. This paper presents a novel approach to probabilistic solar power forecasting by combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) into a hybrid Quantile-CNN-GRU model. The proposed model generates intra-day probabilistic quantile forecasts and is rigorously evaluated using datasets from geographically and climatically diverse regions and hemispheres: the Netherlands (temperate maritime climate), Alice Springs (arid desert climate), and Hebei (humid subtropical climate). These datasets cover varied temporal horizons (1-hour, 6-hour, 12-hour, and 24-hour predictions) and seasonal conditions (summer, fall, spring, and winter), highlighting the model’s adaptability to different scenarios. The performance of the proposed Quantile-CNN-GRU model is benchmarked against state-of-the-art deep learning models, including standalone quantile-based architectures such as Quantile-GRU and Quantile-Long Short Term Memory (LSTM). A comprehensive evaluation framework is applied, employing probabilistic tools like the Continuous Ranked Probability Score (CRPS) for assessing forecast reliability, sharpness, and reliability diagrams with consistency bars to evaluate the calibration of the predictions. Results demonstrate that the proposed Quantile-CNN-GRU model consistently outperforms its counterparts in terms of CRPS, across varying forecast horizons and seasonal conditions. To further enhance performance, a multivariate case study incorporating exogenous inputs, specifically Numerical Weather Prediction (NWP) data, is conducted. Through sensitivity analysis, the influence of these additional inputs on forecast horizons and seasonal variability is systematically explored. The study reveals that integrating NWP data significantly improves the model’s predictive skill, particularly for longer forecast horizons and during transitional seasons like spring and fall, when solar variability is higher.