University Mohamed Boudiaf of M'sila

This paper focuses on the study of the effect of chemical treatments of fibers by alkalization on the flexural properties of polyester matrix composite reinforced with natural fibers. The used reinforcement consists of Alfa fiber, extracted from the plant Stippa tenacissima from Hodna Region (Algeria). Alfa fibers are subjected to alkali treatments with NaOH at 1, 5 and 10% for a period of 0, 24, and 48 h to 28 °C. The composites reinforced with layers of Alfa random costituente a rate of 40% by weight. Influence of alkaline treatments on the flexural properties is studied to determine the optimum conditions of alkaline treatment. The experimental results show that the bending behavior of composites made from alkali treated fibers are better compared to the untreated fiber composite, For a fiber processing Alfa 10% NaOH in 24 h, the flexural strength and flexural modulus improved by 23 MPa to 57 MPa and from 1.16 to 3.04 GPa. However, the flexural properties of composites decreased after alkali treatment with 5% NaOH for 48 h. This is mainly due to the reduction of lignin that binds the cellulose fibrils together.

Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array’s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.

This review aims to briefly outline the Hubbard-U scheme and to investigate the effect of its inclusion in standard Density Functional Theory on electronic, structural, and optical properties of ZnO wurtzite structure. To remedy the miscalculation of optimized lattice parameters determined by Local Density Approximation and Generalized Gradient Approximation approximations to experimental values, the effect of the implementation of Hubbard-U correction for only d state or for both d and p states was investigated for theoretical data collected from previous works. Further effort was devoted to studying the incorporation of Ud,Zn and Up,O to reproduce correct band gap, to exploring various regions composed of valence and conduction bands and their origin, and to revealing the nature of chemical bonding. This review also provides graphical and tabulated values extracted from earlier works on optical properties of ZnO wurtzite structure. It summarizes the effect of Ud,Zn and Up,O in several exchange–correlation functionals on the intensity and location of major peaks composed real and imaginary part of dielectric function as well as on the shift of main absorption peak and optical absorption edge. A comparison was made, wherever possible, between theoretical and experimental results.

In this work, we studied the aptitude to sintering green bodies using γ-Al2O3 transition alumina as raw powder. We focused on the influence of the heating rate on densification and microstructural evolution. Phase transformations from transition alumina γ → δ → θ → α-Al2O3 were studied by in situ X-rays diffraction from the ambient to 1200 °C. XRD patterns revealed coexistence of various phase transformations during the heating cycle. DTA and dilatometry results showed that low heating rate leads to a significant reduction of the temperature of the α-Al2O3 alumina formation. Around 1190, 1217 and 1240 °C were found when using 5, 10 and 20 °C/min of heating rate, respectively. The activation energy for θ-Al2O3 → α-Al2O3 transformation calculated by Kissinger and JMA equations using dilatometry method were 464.29 and 488.79 kJ/mol, respectively and by DTA method were 450.72 and 475.49 kJ/mol, respectively. In addition, the sintering of the green bodies with low heating rate promotes the rearrangement of the grains during θ-Al2O3 → α-Al2O3 transformation, enhancing the relative density to 95% and preventing the development of a vermicular structure. En este trabajo, se ha estudiado la capacidad de sinterización de muestras en verde a partir de γ-Al2O3 de transición en forma de polvo. El trabajo se ha focalizado en la influencia de la velocidad de calentamiento sobre la densificación y la evolución microestructural. Las transformaciones de fase de alúminas de transición γ→δ→θ→α-Al2O3 se han estudiado in situ mediante Difracción de Rayos X (DRX) desde temperatura ambiente hasta 1.200 °C. Los diagramas de XRD han revelado la coexistencia de diversas transformaciones de fase durante el ciclo de calentamiento. Los Análisis Térmicos Diferenciales (ATD) realizados y los datos de dilatometría han mostrado que velocidades de calentamiento bajas conducen a una reducción significativa de la temperatura de formación de α-Al2O3. Detectándose alrededor de 1.190, 1.217 y 1.240 °C cuando se utilizan 5, 10 y 20 °C/min como velocidad de calentamiento, respectivamente. La Energía de Activación para la transformación θ-Al2O3→α-Al2O3 calculada mediante las ecuaciones de Kissinger y JMA usando métodos dilatometricos han sido 464,29 y 488,79 kJ/mol, respectivamente, y mediante ATD 450,72 y 475,49 kJ/mol, respectivamente. Además, la sinterización con velocidad de calentamiento baja promueve la reorganización de los granos durante la transformación θ-Al2O3 → α-Al2O3, el aumento de la densidad relativa al 95% y la prevención del desarrollo de una estructura vermicular.

A wide literature review of recent advance on monitoring, diagnosis, and power forecasting for photovoltaic systems is presented in this paper. Research contributions are classified into the following five macroareas: (i) electrical methods, covering monitoring/diagnosis techniques based on the direct measurement of electrical parameters, carried out, respectively, at array level, single string level, and single panel level with special consideration to data transmission methods; (ii) data analysis based on artificial intelligence; (iii) power forecasting, intended as the ability to evaluate the producible power of solar systems, with emphasis on temporal horizons of specific applications; (iv) thermal analysis, mostly with reference to thermal images captured by means of unmanned aerial vehicles; (v) power converter reliability especially focused on residual lifetime estimation. The literature survey has been limited, with some exceptions, to papers published during the last five years to focus mainly on recent developments.

To determine the total phenolic and flavonoid contents, antioxidant and antimicrobial activities of methanolic leaf extracts of ten Algerian fig (Ficus carica L.) varieties (uniferous, biferous and caprifig tree). Phenolics were extracted by Soxhlet method and analyzed by the Folin–Ciocalteu colorimetric method. Flavonoids were determined by aluminum trichloride assay and the antioxidant capacity was determined by the 2,2-diphenyl-1-picrylhydrazyl radical scavenging assay. The antimicrobial activity was studied with the disc diffusion method and a macrodilution broth method was used to determine the minimal inhibitory concentrations and minimal lethal concentrations. The mean extract yield was 14.10% ± 0.66% (n = 10). Leaf extract of biferous followed by uniferous varieties had the highest total phenolic contents [(52.296 ± 5.232) and (48.973 ± 2.015) mg gallic acid equivalent/g of dry plant extract respectively], flavonoids [(14.388 ± 0.333) and (14.136 ± 1.082) mg quercetin equivalent/g of dry plant extract] and antioxidant capacity [IC50 (798.754 ± 108.590) and (825.004 ± 110.835) μg/mL]. Antioxidant capacity of fig leaves was significantly correlated with phenolic contents (r = 0.748). These extracts showed bactericidal activity and moderate antifungal activity, and the minimal inhibitory concentrations and minimal lethal concentrations were determined on Bacillus cereus and Staphylococcus aureus. All tested extracts contain phenolic compounds and exhibited an antioxidant activity and an antimicrobial effect against Gram-positive and Gram-negative bacteria. Further researches on identification and purification of phenolic compounds are required.

The first-principles calculations are used for a comprehensive study of the novel halide double perovskites by using the CASTEP code which is based on DFT. The correlational function of the GGA-PBE with the ultra-soft pseudo potential USP plane wave was utilized to carry out the present study. For all novel halide double perovskites, the lattice parameters were determined first, and the compounds were subjected to the mechanical stability criteria. From 0.56 to 4.99 eV, the Cs 2 ABF 6 materials are found to be direct band-gaps. The dielectric functions of the materials of interest are large near and in the middle ultraviolet regions. They become smaller in the far and extreme ultraviolet regions. Furthermore, the Cs 2 ABF 6 are found to be elastically stable, ductile, anisotropic and relatively low hard materials. A comprehensive analysis of the optoelectronics and thermoelectric nature with a given band gaps are carried out. This reflects the use of Cs 2 ABF 6 in solar cells, energy storage devices, photovoltaic devices, radiation detectors, photonic crystals, light emitting diodes and thermoelectric generators. Thermoelectric coefficients such as the figure of merit thermal and electrical conductivities, Seebeck coefficient (S), and are also examined to check the possibility of these materials in the field of thermoelectric. The computed value of ZT reflects (ZT ∼ 1) Cs 2 ABF 6 materials and reveals that such type of materials holds a virtuous route toward thermoelectric applicability.

The structural and electronic properties of calcium chalcogenides CaX (X = S,Se,Te) under high pressure have been investigated using the full potential linearized augmented plane wave method within density functional theory. We used both the local density approximation and the generalized gradient approximation (GGA) that is based on exchange–correlation energy optimization for calculating the total energy. Moreover, the Engel–Vosko GGA formalism is applied so as to optimize the corresponding potential for band structure calculations. The equilibrium lattice constant for CaX compounds agrees well with the experimental results. The pressures at which these compounds undergo a structural phase transition from NaCl-type to CsCl-type were calculated. A numerical first-principles calculation of the elastic constants was used to calculate C 11 , C 12 and C 44 . The energy band gaps at ambient conditions in the NaCl-type structure and the volume dependence of band gaps in the CsCl-type structure up to the band overlap metallization were investigated. Besides this, the nature of the chemical bond in these compounds was analysed in terms of electronic charge density.

In this paper, an Artificial Neural Network (ANN) MPPT controller has been proposed. The data required to generate the ANN model are obtained from the principle of Perturbation and Observation (P&O) method. The neural network MPPT controller is developed in two modes: the offline mode required for testing different set of neural network parameters to find the optimal neural network controller (structure, activation function, and training algorithm) and the online mode which the optimal ANN MPPT controller is used in PV system. The inputs variables for ANN are the output power derivate (dP) and voltage derivate (dV) corresponding to a given insolation and operating cell temperature conditions, which they have significant influence on the ANN response; the output variable of ANN is the corresponding normalized increasing or decreasing duty cycle (+1 or -1). The proposed neural network MPPT is tested and validated using Matlab/Simulink model for different atmospheric conditions. Results and analysis are presented, many contribution have been demonstrated (response time, MPPT tracking, Overshoot).

In recent years, the network technology known as Internet of Vehicles (IoV) has been developed to improve road safety and vehicle security, with the goal of servicing the digital demands of car drivers and passengers. However, the highly dynamical network topology that characterizes these networks, and which often leads to discontinuous transmissions, is one of the most significant challenges of IoV. To address this issue, IoV infrastructure-based components known as roadside units (RSU) are designed to play a critical role by providing continuous transmission coverage and permanent connectivity. However, the main challenges that arise when deploying RSUs are balancing IoVs' performances and total cost so that optimal vehicle service coverage is provided with respect to some target Quality of Service (QoS) such as: service coverage, throughput, low latency, or energy consumption. This paper provides an in-depth survey of RSU deployment in IoV networks, discussing recent research trends in this field, and summarizing of a number of previous papers on the subject. Furthermore, we highlight that two classes of RSU deployment can be found in the literature-static and dynamic-the latter being based on vehicle mobility. A comparison between the existing RSU deployment schemes proposed in existing literature, as well as the various networking metrics, are presented and discussed. Our comparative study confirms that the performance of the different RSU placement solutions heavily depends on several factors such as road shape, particularity of road segments (like accident-prone ones), wireless access methods, mobility model, and vehicles' distribution over time and space. Besides that, we review the most important RSU placement approaches, highlighting their strengths and limitations. Finally, this survey concludes by presenting some future research directions in this domain.

One of the most leading death causes in the world is brain tumor. Solving brain tumor segmentation and classification by relying mainly on classical medical image processing is a complex and challenging task. In fact, medical evidence shows that manual classification with human-assisted support can lead to improper prediction and diagnosis. This is mainly due to the variety and the similarity of tumors and normal tissues. Recently, deep learning techniques showed promising results towards improving accuracy of detection and classification of brain tumor from magnetic resonance imaging (MRI). In this paper, we propose a deep learning model for the classification of brain tumors from MRI images using convolutional neural network (CNN) based on transfer learning. The implemented system explores a number of CNN architectures, namely ResNet, Xception and MobilNet-V2. This latter achieved the best results with 98.24% and 98.42% in term of accuracy and F1-score, respectively.

The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhances their reliability and facilitates a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, incorporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.

The chemical composition of propolis from Bulgaria, Turkey, Greece and Algeria was investigated by GC-MS. All of them contained mainly flavonoids and esters of caffeic and ferulic acids, which indicated that their main source are buds of poplars of the taxonomic section Aegieros. Some Turkish samples contained a low percent of diterpenic acids, while in Algerian samples significant amounts of a hydroxyditerpenic acid (M=322, its structure not determined by its MS) were found. All samples showed significant antibacterial and weak to moderate antifungal activity.

One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO-PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO-PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.

This paper presents a new improved Direct Torque Control of doubly fed induction motor using PI controller tuned using genetic algorithm. The classical DTC control of DFIM is well known and is widely used due to its simplicity and easy implementation. However, the performances of conventional DTC are often characterized by high torque and flux ripples as well as variable switching frequency. To overcome the previous drawbacks of the classical DTC control, a new combined DTC-GA method using PI controller optimized by genetic algorithm is presented. The GA selects PI parameters that optimize the performances of conventional DTC. The PI parameters are optimized in offline mode where several values have been tested; then the best PI gains are used in online mode. The proposed DTC-GA method applied to DFIM has been validated using Matlab/Simulink environment. Many improvements related to torque and flux ripples overshoot and response time have been demonstrated. Which torque and flux ripple have been reduced more than 64.44% and 50 % respectively. The speed overshoot and torque overshoot have been reduced about 90% and 86% respectively. In addition, more details are presented and discussed in this paper.

This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.

Abstract The complex density‐functional theory (DFT) calculations of structural, electronic and optical properties for the three phases: wurtzite (B4), zincblende (B3) and rocksalt (B1) of ZnO compound have been reported using the full‐potential linearized‐augmented plane‐wave (FP‐LAPW) method as implemented in the WIEN2k code. We employed both the local‐density approximation (LDA) and the generalized‐gradient approximation (GGA), which is based on exchange–correlation energy optimization to calculate the total energy. Also, we have used the Engel–Vosko GGA formalism, which optimizes the corresponding potential for band‐structure calculations. The 3d orbitals of the Zn atom were treated as the valence band. The calculated structural properties (equilibrium lattice constant, bulk modulus, etc.) of the wurtzite and rocksalt phases are in good agreement with experiment. The B4 structure of ZnO is found to transform to the B1 structure with a large volume collapse of about 17%. The phase transition pressure obtained by using LDA is about 9.93 in good agreement with the experimental data. B1‐ZnO is shown to be an indirect bandgap semiconductor with a bandgap of 1.47 eV, which is significantly smaller than the experimental value (2.45 ± 0.15 eV). While B3 and B1 phases have direct bandgap semiconductors with bandgaps 1.46 and 1.57 eV, respectively. Also, we have presented the results of the effective masses. We present calculations of the frequency‐dependent complex dielectric function ε (ω) and it zero‐frequency limit ε1(0). The optical properties of B4 phase show considerable anisotropic between the two components. The reflectivity spectra has been calculated and compared with the available experimental data. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Context: M’sila city occupies a privileged position in the central part of northern Algeria. The climate of this area is continental, subject in part to the Saharan influences of which vegetation is steppic. Aims: Highlight traditional usage of plants despite environmental characteristics. Methods: An ethnobotanical survey in the city of M’sila was conducted during the period 2011-2012 in collaboration with traditional practitioners, herbalists and healers. A total of 85 adults were able to determine the species and answer questions about the traditional use of plants in artisanal processing, nutritional and medicinal domains. Results: Medicinal plants recorded in the city of M’sila were 36 divided into 16 families and 31 genera. Lamiaceae family predominates (27.8%), followed by Asteraceae (13.9%). Leaves are the most frequently used (27.4%), the aerial parts (18.5%) and thus the seeds (16.3%). It appears that the population is highly dependent on these plants that allow them to treat different pathologies (digestive, stomach, diarrhea, constipation, vomiting) with a percentage (18.6%), carminative (5.7%) and antidiabetic (12.2%). In general, the remedies are administered orally. Indeed, therapeutic use forms are: the tisane or decoction (44.7%), infusion (27.1%) and powder (12.2%). Conclusions: The ethnobotanical survey conducted among traditional healers, herbalists and healers in the M’sila city has created an inventory of 36 species and a database that collected all the information on local and traditional therapeutic applications as well as all the diseases treated.

A first-principles approach is used to study the structural, electronic and magnetic properties of ZrCoTiZ(Z = Si, Ge, Ga and Al) quaternary Heusler compounds, using full-potential linearized augmented plane wave (FP-LAPW) scheme within the GGA. Our calculations predict that ZrCoTiSi, ZrCoTiGe, ZrCoTiGa and ZrCoTiAl are half-metallic ferromagnets (HMFs) with a HM flip gaps of 1.03, 0.90, 0.68 and 0.59 eV, respectively. The half metallicity is found to be robust with respect to the lattice compression and is maintained up to the lattice constant contraction of 6%, 7%, 9% and 8% for ZrCoTiSi, ZrCoTiGe, ZrCoTiGa and ZrCoTiAl respectively. These compounds show a Slater-Pauling behavior and the total spin magnetic moment per unit cell (mtot) scales with the total number of valence electrons (Nv) following the rule mtot = Nv − 18. The formation enthalpies for both structures are negative indicating stability of these systems against decomposition into stable solid compounds.

Actinobacteria are found spread widely in nature and particular attention is given to their role in the production of various bioactive secondary metabolites. Tests on soil samples show that there can be a diversity of actinomycetes depending on the climate, the area it is growing in, how dry the soil is, and the quality of the soil. However, it was agreed after tests in Yunnan, China, that the genus Streptomyces sp. is most important in ecological function, representing up to 90% of all soil actinomycetes, and therefore helping to show the important characteristics needed of the soil actinomycete population. Streptomycete compounds are used for other biological activities, not just for antibiotics. It has been found that metabolites can be broadly divided into four classes: (1) regulatory activities in compounds, these include consideration of growth factors, morphogenic agents and siderophores, and plants promoting rhizobia; (2) antagonistic agents, these include antiprotozoans, antibacterials, antifungals, as well as antivirals; (3) agrobiologicals, these include insecticides, pesticides, and herbicides; and (4) pharmacological agents, these include neurological agents, immunomodulators, antitumorals, and enzyme inhibitors. It is found that Streptomyces hygroscopicus is one of the very best examples because it secretes in excess of 180 secondary metabolites to locate simultaneous bioactivities for a given compound. Increasingly, both its agricultural and pharmacological screenings are being used in conjunction with antimicrobial tests and have revealed several unusual aerobiological and therapeutic agents, which were hitherto unknown for biological use as antibiotics. Since streptomycetes are now being used increasingly to screen for antimicrobial activity, reports show the existence of secondary metabolites with other activities that may have been missed. Currently, nearly 17% of biologically active secondary metabolites (nearly 7600 out of 43,000) are known from streptomycetes. It has been found that soil streptomycetes are the main source used by bioactive secondary metabolites. However, recently there have been many and varied types of structurally unique and biologically active secondary metabolites found and obtained from marine actinomycetes, including those from the genus Streptomyces. Also, compounds that are synthesized by streptomycetes exhibit extreme chemical diversity. Diverse form made from from simple amino acid derivatives to high molecular weight proteides, and macrolactones from simple eight membered lactones to different condensed macrolactones. Berdy (1974) introduced the first classification scheme for antibiotics referring to the chemical structure. On the basis of Berdy’s scheme, (1996) recognized that both low and high molecular weight compounds from 63 different chemical classes are produced by streptomycetes.