Université de M'Sila

This paper introduces a decentralized active fault tolerant control technique for actuator faults in a DC microgrid with load variations and source perturbations without requiring communication between distributed generators or load current measurement. The proposed control technique consists of two sections, each designed separately. One section aims to maintain the system’s stability and desired performance; the other section compensates for the actuator fault through fault reconstruction. To achieve this, a decentralized unknown input proportional-integral observer (UI-PIO) estimates the fault accuracy regardless of the load variations. The proposed linear quadratic regulator (LQR)-based UI-PIO strategy optimizes the trade-off between control performance and energy consumption, ensuring efficient operation under normal conditions and minimizing disruptions during fault states while robust to load variation. The stability and robustness of the closed-loop system under load variations are ensured through Lyapunov’s conditions, solved by the linear matrix inequality technique. The proposed decentralized LQR-UI-PIO circumvents the limitations of load variations, source perturbations, and actuator fault by allowing local controllers within the MG to independently compute control actions based on locally available information and a shared global objective. Finally, the hardware-in-the-loop setup is implemented to evaluate and validate the efficiency of the proposed decentralized fault-tolerant technique. The obtained results highlight the effectiveness and efficiency of the proposed approach in detecting and reconstructing faults, maintaining stability, and improving performance, which makes this approach of interest in other industrial applications.

This paper comprehensively investigates the existence, uniqueness, and stability of traveling wave solutions for an initial value problem of generalized nonlinear Korteweg–de Vries equations of fractional order. We apply the Banach contraction principle and Schauder's fixed‐point theorem to establish the existence and uniqueness of solutions. Furthermore, we examine the stability of the solutions using Ulam–Hyers theorems. Two well‐detailed examples and one explicit solution are provided to illustrate the practical applicability and validity of our theoretical results. The solution's parameter conditions detail the amplitude, energy, wavelength, frequency, and propagation characteristics of waves. These parameters capture the intricate balance between nonlinear and dispersive effects that shape the wave phenomena modeled by the Korteweg–de Vries equation. The solution indicates that as the amplitude parameter increases, wave height also rises due to a higher wave number (shorter wavelength) and nonlinear amplification, resulting in more waves within a given interval. The amplitude further increases with the wave number due to higher energy levels associated with shorter wavelengths.

In this study, we investigate a nonlinear viscoelastic wave equation subject to acoustic boundary conditions and a nonlinear distributed delay feedback acting on the boundary. The asymptotic behaviour of solutions is analysed by considering a general kernel formulation and imposing suitable assumptions.

The worldwide rise in soil salinization is among the most critical consequences of climate change, posing a significant threat to food security. Wheat (Triticum aestivum L.), a staple crop of paramount importance worldwide, encounters significant production limitations due to abiotic stressors, particularly salinity. Consequently, the development and cultivation of salt-tolerant wheat genotypes have emerged as an essential strategy to sustain agricultural productivity and safeguard global food security. The aim of the present study was to investigate the effect of salinity (150 mM) on the performance and combining ability of 10 hybrid combinations (F2) and their parents that were obtained through a line × tester mating design at the seedling stage. Morphological, physiological, and biochemical traits were assessed under both control and salt-stress conditions. Among the assessed traits, SFW emerged as the strongest predictor of salt tolerance, demonstrating the highest correlation with MFVS and the greatest contribution in the regression model. The results highlighted distinct responses among the studied genotypes. Hybrid H5 demonstrated particular promise, surpassing the performance of the superior parent for Na⁺, K⁺, K⁺/Na⁺ and proline (Pro). Furthermore, tester T1 emerged as a good combiner for proline (Pro), total soluble sugars content (Sug), chlorophyll content (Chl) and root length (RL) under saline conditions. In contrast, under control conditions, line L1 and testers T2, T3, and T5 exhibited superior performance, demonstrating significant general combining ability (GCA) effects for four traits simultaneously. Hybrid H4 emerged as outstanding under salt stress, exhibiting favorable specific combining ability (SCA) effects for Na⁺, K⁺/Na⁺ ratio, root length (RL), relative water content (RWC), and total soluble sugars content (Sug). Under normal conditions, hybrids H7 and H10 exhibited significantly superior performance across three traits simultaneously. Non-additive genetic effects predominantly influenced the studied traits under both conditions. The parental and hybrid combinations show promise for incorporation into breeding programs designed to improve salt tolerance under the specific conditions studied.

this study represents the first investigation of the volatile composition of Calystegia silvatica (Convolvulaceae) collected in algeria, using headspace solid-phase microextraction and gas chromatog-raphy/mass spectrometry (HS-SpmE-GC/mS). this technique allowed the identification of 44 compounds, constituting 96.9% of the total components. these compounds belong to various chemical classes, including sesquiterpene hydrocarbons (22) and non-terpene derivatives (13), as major groups. Remarkably, the volatile profile of C. silvatica was dominated by sesquiterpenoids (87.1%), particularly sesquiterpene hydrocarbons (83%). analysis of the aroma profile revealed that the main metabolites were β-caryophyllene, α-gurjunene and germacrene d. oxygenated sesquiterpenes were present in a smaller proportion (4.1%), mostly represented by pal-ustrol. other chemical classes, such as monoterpene hydrocarbons, oxygenated monoterpenes, non-terpene derivatives and apocaro-tenes, have been found in limited quantities. it is worth noting that the data presented in this study have not previously been reported for Calystegia species.

The nonlinear characteristics and low efficiency of photovoltaic (PV) systems remain critical challenges that necessitate advanced solutions. This study proposes two innovative Maximum Power Point Tracking (MPPT) algorithms based on the Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO). The primary advantage of these methods lies in their adaptive step-size optimization, leveraging multiple criteria to determine the optimal step size. A novel fitness function was developed to improve tracking accuracy, minimize ripple, and reduce overshoot. Simulation results demonstrated remarkable improvements, including up to 98% reduction in ripple, 67% reduction in overshoot, and significant improvements in tracking accuracy compared to fixed-step methods. Field validation was conducted using real-world data from the Ain El Melh PV station in Algeria on June 21, 2023. Experimental results confirmed the effectiveness of the proposed methods, with the WOA-based MPPT achieving up to 99% ripple reduction and 40% overshoot reduction under dynamic environmental conditions. A comparative analysis of MPPT algorithms revealed superior performance metrics for the bio-inspired methods. The PO-WOA algorithm achieved the highest efficiency of 98.87% in simulation and 98.94% in real data, surpassing both PO and PO-GWO. It also minimized power loss to 0.56 W in simulation and 0.39 W in real data, demonstrating its optimization capabilities under fluctuating conditions. Although its response time was slightly longer than other methods, at 0.65 s in simulation and 0.48 s in real data, it prioritized stability and precision. These findings underscore the potential of WOA and GWO algorithms to enhance PV system performance, offering robust and efficient solutions for optimizing energy output in both simulation and real-world scenarios.

During the last decades, many researchers have been interested in modeling radar clutter according to the environment in which the radar operates (land, sea or atmosphere). Several distributions were proposed in the literature. For classical radars, the most used distributions are Rayleigh, Weibull, Ki-2 and K. In the case of modern high-resolution radars, the application of these distributions becomes inefficient because it causes a large number of false alarms and degrades the performance of detection in a considerable way. For this purpose, other distributions called generalized models have been proposed. One of them is the generalized Pareto, which has been considered as the most suitable model for the sea clutter in several situations. The authors propose a method for estimating the shape parameter of Pareto type II clutter using artificial neural network (ANN). The trained multilayer perceptron (MLP) ANN framework is considered, which offers a good estimation performance especially for spiky clutter case (i.e., low values of the shape parameter) when using negative-fractional moments as input of the network. It is shown that the proposed ANN outperforms the existing MLE method, which takes a long time to compute the numerical integrals of the likelihood function. Finally, the validation of the proposed ANN estimator is checked in terms of IPIX sea clutter conditions.

Accurate regulation of the liquid level in a quadruple tank system (QTS) is not easy and imposes higher requirements on control strategies, so the design of controllers in these systems is challenging due to the difficulty of dynamic analysis of its nonlinear characteristics and parametric uncertainties. To overcome these problems in liquid level regulation and increase the robustness to the pump coefficients, this article proposes and investigates the use of an optimal hybrid fractional-order type-2 fuzzy-PID (OH-FO-T2F-PID) regulator using a combination of two bio-inspired evolutionary optimizers, namely augmented grey wolf optimizer and cuckoo search optimizer, which gives rise to the new hybrid A-GWOCS algorithm. This control mechanism was chosen to facilitate the convergence of the water liquids in the two tanks as quickly as possible to the corresponding required values. In addition, a collaborative optimization technique with several objectives is used to adjust the regulator parameters. The capability and efficiency of the suggested regulator is first investigated through computer simulation results and then confirmed by real-time control experimental results on the QTS based on dSPACE 1104 computation engine. The findings showed that the suggested OH-FO-T2F-PID regulator significantly outperformed both the optimized ADRC and the OH-FO-T1F-PID regulators. Specifically, it reduced the rising time by 17.02% and 95.21%, respectively, and the settling time by 25.13% and 74.28%. Additionally, the designed OH-FO-T2F-PID regulator successfully eliminated the steady-state error and overshoot, enabling precise regulation of the QTS, and maintenance the liquid level at the desired set point under a wide range of working situations. The robustness of the recommended regulator is also studied by considering − 50% disturbance in the QTS parameters, and the findings showed that the OH-FO-T2F-PID regulator is less susceptible to variations in parameters.

To address the challenges of global warming and the greenhouse effect, extensive research has been dedicated to microgrids (MGs) powered by renewable energy sources (RESs). This paper presents an innovative control mechanism, the synergetic simplified super-twisting algorithm (SSSTA), designed specifically for a DC-MG incorporating a battery energy storage system (BESS), a solar photovoltaic (PV) unit, and DC loads. The PV system connects to a shared DC bus via a unidirectional DC–DC boost converter, optimized for maximum power point tracking from the PV generator. At the same time, the BESS is linked using a bidirectional DC–DC buck-boost converter to the same bus, aimed at maintaining supply–demand balance within the DC-MG through charging and discharging. The SSSTA is designed to regulate each power control unit in the MG. It ensures the desired voltage level at the common DC bus while tailoring energy allocation to meet load requirements. The study shows that SSSTA improves the performance and stability of DC-MG systems incorporating solar PV and batteries. By implementing SSSTA, the stability of the MG system is sustained even under varying load conditions, thereby minimizing the impact of disturbances such as fluctuations in load demand and solar irradiation. As a result, implementing this control strategy enhances the reliability and efficiency of MGs integrated with RESs, promoting broader adoption. Furthermore, to offer a clearer understanding of the proposed control approach, the results of the proportional-integral control are also presented. Simulation experiments in MATLAB confirm the effectiveness of the designed control mechanism.

Composite insulators demonstrate superior electrical performance in contrast to standard insulators. Nevertheless, the deterioration of composite insulator and the challenges in identifying defects are the primary drawbacks of these insulators. This study investigates the effect of water droplets on the electrical behavior of composite insulators, which are widely used in high-voltage applications. Using COMSOL software, a Finite Element Model (FEM) was developed to simulate the electric field distribution on the surface of a composite insulator in the presence of water droplets. The results indicate that the existence of water droplets increases the electric field intensity by approximately 33.33% when the number of droplets increases from two to six. The simulations also reveal that water droplets significantly increase the electric field’s intensity, which affects the electric field and potential distribution on the insulator’s surface. Furthermore, the conductivity of water droplets was found to have a negligible impact on the electric field distribution along the insulator. To systematically evaluate the influence of various factors, Response Surface Methodology (RSM) was employed in combination with Analysis of Variance (ANOVA) to analyze the interactions between water droplet number, pollution, and applied voltage. The statistical analysis demonstrated that the maximum electric field intensity increased by nearly 38.3% as water droplet conductivity rose from low to high levels. RSM was used to generate a second-order polynomial model that describes the relationship between these factors and the electrical performance of the insulator, allowing for the identification of significant trends and interactions. The findings provide valuable insights for the design and development of composite insulators that are more resilient to environmental factors, enhancing their overall electrical performance.

Mn-doped ZnO thin films with varying Mn concentrations were synthesized on glass substrates using the pneumatic spray technique. Energy-dispersive X-ray (EDX) analysis confirmed the substitution of Zn by Mn in the ZnO matrix. X-ray photoelectron spectroscopy (XPS) revealed characteristic spin-orbit energy states of Zn:2p and Mn:3d, indicating strong Mn-ZnO interactions. Microstructural analysis showed non-uniform extended lines and spherical grains, with decreasing grain size as Mn concentration increased. X-ray diffraction (XRD) confirmed a polycrystalline hexagonal structure, with experimentally determined lattice parameters a = 3.1453 Å, c = 5.1353 Å, in agreement with CASTEP calculations. Optical measurements indicated ~ 80% absorbance in the visible range, with a shift from blue to red as Mn content increased, suggesting bandgap modulation. These findings highlight the potential of Mn-doped ZnO films for tunable optoelectronic applications.

In this work, the structural, electronic, mechanical, and hydrogen storage properties of B12H20N2 were investigated using first‐principles calculations. First, we evaluate the structural stability of B12H20N2 hydrides using enthalpy of formation calculations. Then, the mechanical stability is specified by the elastic stiffness constants, which reveal that the B12H20N2 hydrides are mechanically stable because they meet the Born stability requirements. The computed lattice constant of B12H20N2 agrees very well with the available experimental parameter. The study of the electronic structure and density of states of this material reveals that B12H20N2 is an insulator. In this regard, B12H20N2 demonstrated its applicability surpassing that of the U.S. Department of Energy's for 2025. Our investigation predicts the applicability of B12H20N2 hydride as a promising solid‐state compound.

This study focuses on improving the mechanical properties of Date Palm Fiber (DPF) by modeling and optimizing these properties through Response Surface Methodology (RSM) and Analysis of Variance (ANOVA). A Taguchi experimental design (L16) was employed to optimize the key parameters, including NaOH concentration (ranging from 0.5% to 3%), treatment duration (spanning 12–96 hours in 24-hour intervals), and fiber diameter (between 350 and 650 µm). Tensile tests were conducted to pinpoint the parameters that significantly enhance performance. The results reveal that alkalization has a substantial effect on DPF’s strength, deformation, and Young’s modulus. The empirical data demonstrates high model accuracy, with an R² value of 94.64%. The ε (%) model shows an R² of 93.49%, while the σ (MPa) model achieves an R² of 90.63%. The concentration of NaOH plays a crucial role, contributing 19% to strain ε (%), 16.11% to stress σ, and 17.42% to Young’s modulus E. These findings suggest that DPF, after alkalization, becomes highly suitable for various industrial applications. Treatment time has a relatively minor influence (4.66% to 4.07%), and fiber diameter has a negligible effect.

Green hydrogen represents a sustainable energy solution capable of supporting the global shift away from fossil fuels. In Algeria, with its abundant solar resources, this potential is significant. However, challenges related to water resource management and the energy cost of production limit large-scale implementation. Addressing these issues is crucial for effectively harnessing Algeria’s renewable energy potential. This study conducts an in-depth analysis leveraging advanced simulation tools like HOMER Pro to compare photovoltaic (PV) productivity and hydrogen yields in Algerian regions. The study identifies both desert regions and non-desert areas for their potential, employing innovative methods such as seawater electrolysis and wastewater utilization for sustainable water sourcing. The potential integration of hydrogen fuel cells into microgrids is also explored for enhanced energy stability and storage. The findings reveal that desert regions, such as Tamanrasset and Adrar, exhibit the highest photovoltaic electricity productivity, generating 33.5 GWh/year and 32.9 GWh/year, respectively. This translates into green hydrogen production capacities of 679 tons/year and 668 tons/year. Meanwhile, northern regions like Tlemcen and Skikda demonstrate substantial potential, producing 29 GWh/year and 26.6 GWh/year of solar electricity, which results in green hydrogen production outputs of 589 tons/year and 539 tons/year, respectively. This underscores Algeria’s ability to leverage solar energy across diverse regions. The study highlights that while desert regions exhibit high solar and hydrogen production, northern areas provide a strategic advantage due to their proximity to European markets. Algeria’s existing infrastructure supports efficient export to European markets, offering a strategic advantage in green hydrogen trade.

Toxoplasmosis is one of the most important foodborne diseases in humans, potentially acquired by ingesting unpasteurized goat milk. This study examined the role of goat milk as a source of infection of Toxoplasma gondii for humans in Algeria. Sera, blood, and milk samples collected from 106 female goats were tested for the presence of antibodies against T. gondii and its DNA, using indirect ELISA and PCR, respectively. Multiplex PCR was performed using 15 microsatellite markers to determine the clonal type of the T. gondii DNA detected. Seropositive results were found in 51 she-goats (48.11%). T. gondii DNA was detected in 16 (15.09%) and 15 (14.15%) blood and milk samples, respectively. In total, 15 (29.41%) out of 51-seropositive goats were PCR-positive for blood, while only 6 of them (6/15, 40%) showed the presence of T. gondii DNA in their milk. A fair correlation was found between indirect ELISA and PCR assays for T. gondii detection in milk (K = 0.2243) and blood (K = 0.28300), with a substantial difference in the screening ability of the tests (G2 = 38.96, p < 0.0001). The genotyping of samples could not be completed, but showed the absence of type I and type III lineages in goats from the Mila region, northeastern Algeria. The Algerian goat population is highly exposed to T. gondii, with a potentially increased risk of parasite transmission to humans via milk consumption.

This paper presents the Progressive Moving Average Transform (PMAT), a novel signal transformation method for converting time-domain signals into 2D representations by progressively computing Moving Averages (MAs) with varying window sizes. The approach aims to enhance signal analysis and classification, particularly in the context of heartbeat classification. Our approach integrates PMAT with a 2D-Convolutional Neural Network (CNN) model for the classification of ECG heartbeat signals. The 2D-CNN model is employed to extract meaningful features from the transformed 2D representations and classify them efficiently. To assess the effectiveness of our approach, we conducted extensive simulations utilizing three widely-used databases: the MIT-BIH database and the INCART database, chosen to cover a wide range of heartbeats. Our experiments involved classifying more than 6 heartbeat types grouped into three main classes. Results indicate high accuracy and F1-scores, with 99.09% accuracy and 92.13% F1-score for MIT-BIH, and 98.37% accuracy and 79.37% F1-score for INCART. Notably, the method demonstrates robustness when trained on one database and tested on another, achieving accuracy rates exceeding 95% in both cases. Specifically, the method achieves 96% accuracy when trained on MIT-BIH and tested on the ST-T European database. These findings underscore the effectiveness and stability of the proposed approach in accurately classifying heartbeats across different datasets, suggesting its potential for practical implementation in medical diagnostics and healthcare systems.

Background Monkeypox (Mpox) is a re-emerging zoonotic disease with limited therapeutic options, necessitating the exploration of novel antiviral agents. Curcuma longa (turmeric) is a widely used medicinal plant known for its antioxidant and anti-inflammatory properties, primarily attributed to its bioactive curcuminoids. Aim This study aimed to evaluate the therapeutic potential of C. longa aqueous extract (CAE) against monkeypox through phytochemical characterization, biological assays, and computational analyses. Methodology Phytochemical analysis, including HPLC, identified key Curcumin, Bisdemethoxycurcumin, Demethoxycurcumin, Tetrahydrocurcumin, Curcuminol, and Ar-curcumene. The DPPH assay and total antioxidant capacity (TAC) were employed to assess antioxidant activity. Anti-inflammatory effects were determined by measuring the inhibition of heat-induced protein denaturation. Molecular docking and molecular dynamics (MD) simulations were performed to evaluate the interactions between curcuminoids and monkeypox virus proteins. Results The aqueous extract of C. longa was prepared via decoction, yielding 7.80% ± 0.81% extract with curcumin as the predominant compound (36.33%). The CAE exhibited strong antioxidant activity with a TAC of 36.55 ± 0.01 µg GAE/g d.w., an IC50 of 0.77 ± 0.04 mg/mL in the DPPH assay, andan EC50 of FRAP of 3.46 ± 0.11 mg/mL. Anti-inflammatory analysis showed 78.88 ± 0.53%inhibition for egg albumin and 90.51 ± 0.29%for BSA. Molecular docking identified demethoxycurcumin (DMC) as the most potent compound, with binding affinities of −8.42 kcal/mol (4QVO), −7.61 kcal/mol (8CEQ), and −7.88 kcal/mol (8QRV). MD simulations confirmed the stability of DMC complexes, with the 4QVO-DMC interaction being the most stable, showing RMSD fluctuations within a range of 0.2–0.6 nm, with an average fluctuation of 0.4 nm, and consistent compactness with Rg values remaining between 1.8 and 2.0 nm, with a fluctuation of only 0.2 nm over 100 ns. Discussion The results demonstrate the multifunctional therapeutic potential of C. longa, driven by its potent antioxidant and anti-inflammatory properties. The computational findings suggest that curcuminoids, particularly demethoxycurcumin, could serve as promising antiviral agents against monkeypox. These findings pave the way for further preclinical studies to validate the antiviral efficacy of C. longa bioactives and their potential applications in combating viral infections.