University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj

The goal of this study is to present a novel and improved backstepping control (BC) technique for a dual-star induction generator (DSIG) powered by a wind turbine. This approach relies on the ant lion optimization (ALO), which is employed to determine the optimal parameters of the BC approach and improve the performance of the wind conversion energy system. The ALO approach enhances the robustness of the DSIG, enabling faster dynamic responses, greater accuracy, and consistently improved effectiveness. The fitness function of the ALO approach integrates both integral time absolute error and integral time squared error criteria, ensuring the fulfillment of effectiveness objectives. The performance of the BC-ALO approach is validated through MATLAB. The results of the tests show that the new approach reduces total harmonic distortion, minimizes stator energy fluctuations, and improves dynamic efficiency compared to the BC approach. Additionally, the method can handle uncertainties in model parameters, making it versatile and practical. Simulation results show that the BC-ALO method reduces the total harmonic distortion value compared to the BC method by percentages estimated at 29.45%, 50.44%, and 43.10% in all tests. Also, this approach improves the overshoot value of DSIG power compared to the traditional BC strategy by an estimated 100% in all tests. The proposed approach improves the response time value of the reactive power compared to the conventional BC strategy by percentages estimated at 97.65%, 97.78%, and 95.23% in all tests. The DC link voltage ripples are low if the proposed approach is used, with ratios estimated at 63.31%, 71.38%, and 71.89% in all tests. These results make the proposed approach interesting in other applications such as photovoltaic systems.

Financial markets are considered as a main base for economic growth. Due to the rapid development of these markets, the portfolio optimization problem has become one of the most complex problems in finance. This paper addresses the multi-objective portfolio optimization problem (MOPOP) with three different objectives, which maximize the return, minimize the risk, and maximize the entropy to generate a well-diversified portfolio. We adapt the original manta ray foraging optimization (MOMRFO) to handle the MOPOP issue. Our adapted MOMFRO is called MOMFRO/ED as it uses an epsilon dominance relationship to store the non-dominated solutions in an external archive. Furthermore, the external archive population is controlled using crowding distance to limit the archive size and avoid increasing the complexity of the MOMRFO/ED algorithm. The best solutions (leaders) are selected from the external archive population to improve the solution’s quality and accelerate the convergence computational. Several experiments are conducted on real-data from the major financial markets during two different periods (before and within the COVID-19 pandemic period) to compare our algorithm with three relevant state-of-art algorithms. The statistical analysis of the obtained comparative results shows the merits and the outperformance of our MOMRFO/ED algorithm in terms of IGD, HV, SP, SR, and Jensen Index (JI)metrics. In addition, our results indicate that model with Shannon’s entropy outperform those with Minkowski and Yager’s entropy. This superiority is attributed to the enhanced ability of Shannon’s entropy to efficiently reallocate assets in response to market changes.

In this study, we present a detailed numerical simulation of a high-efficiency perovskite solar cell (PSC) using the SCAPS1D simulator. The proposed design features a multilayer structure consisting of ITO/C60/AgCdF3/KGeCI3/CBTS, with AgCdF3/KGeCI3 as an active double absorber layer, which greatly enhances the overall performance. Key parameters, including the effects of different electron transport layers (ETLs), absorber thickness, the interaction between thickness of AgCdF3 and its band gap (Eg), CBTS layer thickness, series and shunt resistances, and operating temperature, were optimized to achieve maximum efficiency. The simulation results are in close agreement with theoretical and experimental data, achieving an open circuit voltage (Voc) of 0.9263 V, a short circuit current density (Jsc) of 46.6983 mA/cm², a fill factor (FF) of 87.28%, and a power conversion efficiency (PCE) of 37.75%. To enhance practical relevance, these output parameters are further modeled in MATLAB to analyze the performance of the solar module under different environmental conditions, such as temperature and radiation, providing a comprehensive understanding of its behavior in practical applications.

Chaotic systems have long played a crucial role in cryptographic applications due to their inherent unpredictability and sensitivity to initial conditions. Building on this foundation, this paper introduces an Improved Composite Chaotic Map (ICCM), inspired by classical quadratic and cubic chaotic maps. The proposed ICCM is rigorously validated through Lyapunov exponents and bifurcation diagrams, demonstrating its enhanced dynamical complexity and suitability for secure applications. Designed to expand the chaotic range, ICCM incorporates an additional parameter that significantly enlarges the keyspace, thereby improving the robustness and adaptability of encryption methods. To demonstrate its practical utility, ICCM is integrated into a novel opto-digital encryption scheme aimed at enhancing sensitivity and ensuring superior encryption performance. The effectiveness of this scheme is evaluated using a comprehensive suite of tests, including sensitivity analysis, histogram analysis, the NIST statistical test suite (15/15 passed), and key security metrics. A high mean square error (MSE) value (> 10⁴) indicates a significant difference between the original and encrypted images, while correlation coefficients (Cr ≈ 0.0001 to 0.0028) and a low peak signal-to-noise ratio (PSNR < 10 dB) further confirm its robustness. Keyspace analysis (2⁶⁷⁴) ensures resilience against brute-force attacks. Simulation results reveal that the ICCM-based encryption scheme achieves a Lyapunov exponent of 21.42 (compared to 19 in prior works) and withstands data loss up to 50%. These findings position ICCM as a robust defense against statistical, differential, and chosenplaintext attacks. In conclusion, the proposed ICCM and its integration into the opto-digital encryption scheme advance the security of encryption systems, setting a new benchmark for chaos-based cryptography. The results underscore the model’s potential to address modern data security challenges and inspire further research in this critical field.

This study investigated two lab-scale CW systems, traditional horizontal flow (HFCW) and baffled horizontal flow (BHFCW), as a treatment process in CWs filled with porous gravel and planted with Typha latifolia. BHFCW achieved average removal efficiencies of 88.65, 86.00, and 84.17% for TSS, BOD5, and, COD, respectively. Meanwhile, in HFCW, the removal efficiencies for these pollutants were 88.48, 81.07, and 77.89%, respectively. The results demonstrated that BHFCW is a reliable alternative to enhance the treatment performance of nitrogen in CWs compared to HFCW. The BHFCW removals were the best among all units: 76.59, 86.39, and 92.22% for NH4+, NO3-, and NO2-, respectively. Statistical differences were observed when comparing removal effects between HFCW and BHFCW (p < 0.05). Nevertheless, 84.15% of orthophosphate was successfully removed in HFCW. The introduction of baffles augmented the flow path of wastewater. 14% and one-day reduction in the area and HRT of BHFCW was noted relative to the HFCW respectively. The two types of flow used are suitable for wastewater treatment. This investigation of flow type showed a role in the absorption and retention of pollutants. In addition, the BHFCW could generate interest in a treatment option.

Background and Objectives This study is the first to comprehensively investigate the phenolic profile, therapeutic potential, and acute toxicity of Putoria calabrica, a Mediterranean medicinal plant. It aims to evaluate its potential for innovative wound healing formulations by analyzing the phenolic composition of five extracts, assessing antifungal activity, and evaluating toxicity, hemoglobin oxidative status, and wound healing efficacy in animal models. Methods The phenolic content of the extracts was analyzed using HPLC-DAD. Antifungal activity was assessed on solid PDA media, while biochemical parameters were determined spectrophotometrically. Key findings Ten phenolics were identified, with vitexin (20.84 mg/g), rutin (17.66 mg/g), and chlorogenic acid (14.15 mg/g) as the predominant. Methanol extract showed the highest antifungal activity against Fusarium oxysporum and Penicillium chrysogenum with rates of 57.61% and 59.62% inhibition respectively, and a Minimum Inhibitory Concentration of 8 mg/ml, comparable to ethanol extract. The latter also inhibited hemoglobin degradation and methemoglobin formation at 2.5–5.0 mg/ml. In mice, ethanol extract ointments (5% and 10%) showed no toxicity, with a 96.43% wound contraction after 18 days of applying the 10% formulation. Conclusions The current findings suggest that P. calabrica leaf extracts may offer a promising natural remedy with wound healing, antioxidant, and antifungal properties, deserving further investigation for therapeutic applications.

To meet the growing demands for energy consumption, double perovskites (DP) have emerged as a promising green energy solution, offering substantial potential for applications in thermoelectric and optoelectronic devices. This study investigates novel hybrid perovskite solar cells (HPSCs) based on Rb2AlAgI6, using the SCAPS-1D simulator to analyze their performance. The cells incorporate C60 as the electron transport layer (ETL) and CBTS as the hole transport layer (HTL). Simulation results reveal that the ITO/C60/Rb2AlAgI6/CBTS heterostructure exhibits remarkable photoconversion efficiency. Furthermore, a detailed analysis explores the effects of key parameters, including Rb2AlAgI6 absorber thickness, ETL and HTL thicknesses, temperature, absorber defects, and resistance factors. The simulations indicate an optimal open-circuit voltage (Voc) of 1.13 V, short-circuit current density (Jsc) of 34.74 mA/cm², fill factor (FF) of 83.56%, and power conversion efficiency of 29.41%, aligning with previous research. This comprehensive study offers valuable insights into the factors influencing perovskite solar cell (PSC) performance and highlights promising pathways for future advancements in this technology.

The direct torque control (DTC) approach is one of the suitable solutions for controlling squirrel cage induction machines (SCIMs) due to its distinctive performance compared to other strategies and its simplicity. However, using this approach has several drawbacks and problems. This paper presents an experimental work using real equipment of an innovative method that combines six sectors of DTC technique and neural networks (NNs). The use of an NN algorithm allows for overcoming problems of the DTC approach, such as reducing torque ripples. Using the NN technique, the operation of the SCIM inverter is controlled, as the NN technique provides the pulses necessary to run the inverter, which allows for improving the quality of the current. Therefore, the presented approach is based on the usual method, using the same estimation equations. First, the validity of the designed approach was tested using MATLAB, comparing the results with the DTC approach. The results obtained showed a high ability of the six sectors’ NN-DTC approach to significantly enhance the quality of torque and current, which confirms the competence of using NNs. Secondly, real equipment was used to verify the simulation results and the extent of the efficiency and competence of the six sectors NN-DTC approach compared to the DTC technique in terms of improving the quality of current and torque. These experimental results obtained are of great value in the field of control, as they give a clear picture of the advantage of the six sectors of the NN-DTC approach in improving the features of the control system, which makes it more suitable for different applications in the future.

Using powdered glass in construction materials has emerged as a sustainable and innovative strategy to improve the properties of concrete, mortar and other construction composites. The present research uses waste glass powder from liquid crystal displays in green refractory mortar to manage electronic glass waste. The study examines the effectiveness of artificial neural networks and response surface methodology models in predicting the mechanical characteristics and ultrasonic pulse velocity of the developed mortar. The research includes modelling with different percentages of e-waste glass replacing dune sand at temperatures ranging from 200 to 800 °C over two hours. The results show that e-glass waste powder variable influenced the characteristics strength of dune sand mortar at higher temperature, days (P < 0.05). The model predicted values in both techniques were in close agreement with corresponding experimental values in all cases. However, the results show that the artificial neural network consistently provides values comparable or superior to the response surface methodology models, demonstrating its potential feasibility through statistical measures such as coefficient of determination (R2), root mean square error, and mean absolute deviation and the variations of the residuals prediction indicate the functionality of both modeling approaches for E-glass waste mortar strength prediction. This empirical method is useful for determining the thermo-physical and thermo-mechanical properties of e-waste glassy refractory mortar. The incorporation of powdered E-glass waste into construction materials presents a promising opportunity to enhance sustainability, structural integrity, and aesthetic appeal. Utilizing powder glass as an insulating material in construction holds significant potential to improve thermal performance while promoting sustainability and mitigating the risk of explosive spalling. Its applications across various building elements can enhance energy efficiency and benefits for both the environment and the built environment.

Essential oils are promising, safe, and eco-friendly alternatives to chemical fungicides. This study aimed to develop an effective biological control agent using Cinnamomum cassia essential oil (CCEO) as potential fungicidal agent against Saccharomyces cerevisiae and Acremonium sp, both isolated from natural orange juice. The yield, chemical composition and antifungal activity of CCEO were evaluated. The essential oil was extracted via hydro-distillation, and its composition was analyzed using gas chromatography-mass spectrometry (GC-MS). The antifungal activity was assessed using the disk diffusion agar method. Minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) were determined using microdilution methods. The extraction yield was 2.8%. (E)-cinnamaldehyde was identified as the major compound (37.72%). Inhibition zones ranged from 51 mm to 80 mm against Saccharomyces cerevisiae and from 75 mm to 90 mm against Acremonium sp. Equal MIC and MFC values were recorded for both fungal strains: MIC = MFC = 6.25% against Saccharomyces cerevisiae and MIC = MFC = 3.125% against Acremonium sp. These findings demonstrate for the first time that CCEO could be a promising antifungal agent against the two primary fungal contaminants of fruit products, Saccharomyces cerevisiae and Acremonium sp.

This work presents a very compact ultra-wideband (UWB) slot antenna with an integrated long-term evolution (LTE) band at 2.6 GHz. The slot UWB antenna has a small size of 24 mm × 8 mm × 1.524 mm. Additional elementary slots (stepped slots) are etched in the back side of the antenna to obtain an UWB range by assembly the resonance frequency of each elementary slot. An F-shaped slot is integrated into the back side of the antenna to create a resonance frequency in the LTE band. For the experimental results, the proposed antenna provides two resonance frequency bands. The first mode is an UWB bandwidth between 3.1 and 12 GHz, and the second one operates at the LTE band between 2.58 and 2.73 GHz, where the reflection coefficient is less than − 10 dB. The radiation characteristics of the antenna are omnidirectional in the horizontal plane and bidirectional in the vertical plane. A good agreement is shown between the measured and simulated results in terms of impedance matching and radiation pattern.

This study explores the impact of natural weathering on the mechanical, structural, morphological, and aesthetic properties of linear low-density polyethylene (LLDPE) composites reinforced with 15% palm petiole fibers (PPF) subjected to successive chemical treatments. Composites were exposed to Biskra, Algeria’s arid conditions for 360 days. FTIR analysis identified carbonyl peak formation, signifying oxidation, while weight loss measurements revealed enhanced biodegradability in treated composites, with weight loss proportional to exposure duration. SEM micrographs demonstrated pronounced surface deterioration in sodium hydroxide and hydrogen peroxide-treated composites compared to acetylated variants, which exhibited better resistance. Notably, UV-induced cross-linking in the polymer matrix and fiber recrystallization improved tensile strength and Young’s modulus. These findings highlight the potential of chemically treated PPF/LLDPE composites for sustainable applications in arid environments, balancing enhanced mechanical performance with biodegradability.

Football match result prediction is a challenging task that has been the subject of much research. Traditionally, predictions have been made by team managers, fans, and analysts based on their knowledge and experience. However and recently there has been an increased interest in predicting match outcomes using statistical techniques and machine learning. These algorithms can learn from historical data to identify complex relationships between different variables, and then make predictions about the outcome of future matches. Accordingly, forecasting plays a pivotal role in assisting managers and clubs in making well-informed decisions geared toward securing victories in leagues and tournaments. In this paper, we presented an approach, which is generally applicable in all areas of sports, to forecast football match results based on three stages. The first stage involves identifying and collecting the occurred events during a football match. As a multiclass classification problem with three classes, each match can have three possible outcomes. Then, we applied multiple machine learning algorithms to compare the performance of those different models, and choose the one that performs the best. As a final step, this study goes through the critical aspect of model interpretability. We used the SHapley Additive exPlanations (SHAP) method to decipher the feature importance within our best model, focusing on the factors that influence match predictions. Experiment results indicate that the Multilayer Perceptron (MLP), a neural network algorithm, was effective when compared to various other models and produced competitive results with prior works. The MLP model has achieved 0.8342 for accuracy. The particular significance of this study lies in the use of the SHAP method to explain the predictions made by the MLP model. Specifically, by exploiting its graphical representation to illustrate the influence of each feature within our dataset in predicting the outcome of a football match.

Reinforced concrete domes with meridian ribs present a suitable solution for covering large spaces, but their optimization in terms of strength and stability remains a challenge not yet mastered. This work presents a new global mathematical approach to optimizing these structures, starting with a primary configuration braced at the top and bottom with ring beams. This configuration will undergo optimization via numerical simulation to ensure its mechanical performance. The main variables studied include dome diameter, rib spacing, thickness and supported load. A Response Surface Methodology (RSM) is then used to correlate these parameters. Ultimately, a cost-oriented objective function is derived, incorporating a load-bearing capacity coefficient. This approach proves effective and can serve as a valuable tool for designers seeking to optimize their projects.

This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated silicon nitride (Si3N4) ceramic inserts and coated cubic boron nitride (CBN). Key cutting parameters such as depth of cut (ap), feed rate (f), and cutting speed (Vc) were varied to examine their effects on surface roughness (Ra), cutting force (Fr), and power consumption (Pc). The results showed that the coated Si3N4 tool achieved the best surface finish, with minimal cutting force and power consumption, while the uncoated Si3N4 and CBN tools performed slightly worse. Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). The DNN-EKF model demonstrated exceptional predictive accuracy with an R² value of 0.99. The desirability function (DF) method identified the optimal machining parameters for the coated Si3N4 tool: ap = 0.25 mm, f = 0.08 mm/rev, and Vc = 437.76 m/min. At these settings, Fr ranged between 46.424 and 47.405 N, Ra remained around 0.520 µm, and Pc varied between 386.518 W and 392.412 W. The multi-objective grey wolf optimization (MOGWO) further refined these parameters to minimize Fr, Ra, and Pc. This study demonstrates the potential of integrating machine learning and optimization techniques to significantly enhance manufacturing efficiency.

This study introduces a novel arrangement for solar cell simulation using the SCAPS-1D simulator. The simulation framework incorporates advanced electron-/hole-transport layers successively ETLs (WS2) and HTLs (CuSbS2). Heterostructures composed of ITO/WS2/CsSnCI3/CuSbS2/Ag demonstrate remarkable photoconversion efficiency. A thorough analysis investigates the influence of various parameters such as CsSnCI3 absorber thickness, ETL thickness, temperature, series resistance, and absorber thickness with ETL thickness. Simulation findings reveal an optimal open-circuit voltage (VOC) of 1.15 V, a short-circuit current density (JSC) of 26.07mA cm2, a fill factor of 86.35%, and a power conversion efficiency of 24.23%, consistent with earlier research. This extensive simulation research provides insights into factors affecting perovskite solar cells and suggests promising directions for future advancements.

This study presents a high-efficiency perovskite solar cell structure, incorporating a Cs0.05(FA0.77MA0.23)0.95Pb(I0.77Br0.23)3 as absorber, PCBM as the electron transport layer (ETL), and CuSbS2 as the hole transport layer (HTL). First-principles calculations were conducted to explore the electronic and optical properties of these materials, revealing a high absorption coefficient of approximately 10⁵ cm⁻¹, making the perovskite an excellent absorber. The SCAPS-1D simulation tool was employed to evaluate the photovoltaic performance of the ITO/PCBM/mixed perovskite/CuSbS2/Ag device. Various factors such as different HTLs and ETLs, absorber thickness, ETL and HTL thickness, defect concentration, temperature, and resistance were analyzed to optimize device performance. The results demonstrate that the optimized configuration achieves an outstanding power conversion efficiency of 28.01%, with an open-circuit voltage of 1.12 V, a short-circuit current density of 29.84 mA cm⁻², and a fill factor of 83.78%. Notably, the study found that HTL thickness variations have a more dominant impact on efficiency than perovskite thickness, emphasizing the importance of transport layer engineering. The findings offer a promising pathway for further research on material optimization, stability enhancement, and large-scale fabrication, paving the way for the next generation of perovskite solar technologies.

In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recognition systems. Traditional approaches, often relying on exponential loss functions within the AdaBoost framework, struggle to maintain robustness in noisy or imbalanced data environments. To enhance detection accuracy, we propose a novel AdaBoost variant—SLFAdaBoost—utilizing a squared loss function specifically tailored for overlapping speech detection. This model demonstrates superior stability and convergence, significantly outperforming conventional methods. Experimental evaluation on the NIST 2005 corpus revealed that SLFAdaBoost achieves an accuracy range of 92.5% to 98.6%, with F1 scores between 95.4% and 99.2%, notably surpassing standard AdaBoost and Random Forest classifiers. Additionally, our model shows resilience in noisy conditions, maintaining precision and recall metrics at higher noise levels than other classifiers. These results underscore SLFAdaBoost's capacity to handle intricate data patterns, offering a more robust solution for real-world speech processing applications where overlapping segments are prevalent. This contribution provides an efficient, high-performing model, advancing the capabilities of ensemble methods in complex audio environments.

In recent years, DC microgrids supplying constant power loads (CPLs) have attracted significant attention due to their impact on overall system stability, which is attributed to their electrical characteristics that exhibit negative incremental impedance. This paper examines a secondary control strategy aimed at ensuring accurate power sharing and voltage restoration within an islanded DC microgrid supplying a constant power load. The droop control function is typically used in the primary control layer to facilitate power sharing among distributed generators (DGs). However, differing load profiles may cause the DC bus voltage to deviate from its nominal value. To restore the DC bus voltage to its nominal value while maintaining accurate power sharing, a primary and secondary control scheme is proposed. This scheme employs an integrated control strategy combining sliding mode control for the primary control level and H-infinity control for secondary control. The approach is based on a two-time-scale stability analysis, i.e., the settling time of the primary control must be faster than that of the secondary control. Additionally, compared to most existing methods, the proposed approach requires no global information and depends exclusively on DC bus voltage feedback, eliminating the need for passive loads in parallel with the CPL. A test system of an islanded DC microgrid feeding a CPL is created using Matlab and PSIM software to assess the proposed method. An experimental prototype comprising two DGs and a tightly voltage-controlled boost converter emulating a CPL is developed to demonstrate the proposed approach and confirm the theoretical results.

In recent decades, medicinal plants have attracted significant interest due to their demonstrated therapeutic properties. This study analyzed different extracts of Marrubium vulgare L. (horehound) to assess their phytochemical composition and biological activities. Quantitative analysis revealed that methanolic leaf extracts were the richest in polyphenols, containing 23.48 to 35.36 mg EAG/g DM. These leaf extracts also had a particularly high flavonoid content, ranging from 12.55 ± 0.233 to 20.56 ± 0.54 mg EQ/g DM. In contrast, aqueous stem extracts were abundant in condensed tannins, with 72.82 ± 0.772 and 65.52 ± 1.216 mg EC/g DM in aqueous and methanolic extracts, respectively. The antioxidant potential of the extracts was evaluated through several in vitro assays. Leaf extracts demonstrated the highest antioxidant activity, with methanolic extracts exhibiting stronger antioxidant capacity than aqueous extracts. Furthermore, leaf extracts were capable of inhibiting peroxidase activity by up to 42.68%, although they showed weak inhibition of polyphenol oxidase. These findings suggest that Marrubium vulgare L is a promising source of natural antioxidants and enzyme inhibitors, with potential applications in the development of functional foods, cosmetics, and pharmaceuticals. The observed differences between extraction solvents highlight the importance of considering phytochemical profiles and bioactivities when selecting and optimizing plant-based ingredients.