National School of Applied Sciences Oujda

Wind energy is becoming an increasingly promising technology and a more significant player in energy production. However, due to the nonlinear nature of the system and the impact of external conditions, an effective control strategy is crucial. In this paper, we employ Sliding Mode Control as a Maximum Power Point Tracker (MPPT) on a Buck-Boost converter to connect the DC load to the wind turbine generator. All system components are modeled and simulated using MATLAB/Simulink, and the results demonstrate the promising potential of the SMC method.

The development of ion-sensitive field-effect transistor (ISFET) sensors based on silicon nanowires (SiNW) has recently seen significant progress, due to their many advantages such as compact size, low cost, robustness and real-time portability. However, little work has been done to predict the performance of SiNW-ISFET sensors. The present study focuses on predicting the performance of the silicon nanowire (SiNW)-based ISFET sensor using four machine learning techniques, namely multilayer perceptron (MLP), nonlinear regression (NLR), support vector regression (SVR) and extra tree regression (ETR). The proposed ML algorithms are trained and validated using experimental measurements of the SiNW-ISFET sensor. The results obtained show a better predictive ability of extra tree regression (ETR) compared to other techniques, with a low RMSE of 1 × 10−3 mA and an R2 value of 0.9999725. This prediction study corrects the problems associated with SiNW -ISFET sensors.

The effects of cassava peeling waste (CPW) on the properties of kaolinite-based ceramics were ascertained to develop a lightweight, porous ceramic that can be used as a building material with enhanced properties. Different amounts of cassava waste (i.e., 0, 10, 15, and 20%) were used to formulate ceramic specimens from clay samples collected in various locations of Songololo and Kinshasa in D.R. Congo. Both clays and the cassava waste were subjected to chemical analysis by X-ray fluorescence (XRF) and (CHNO-S), mineralogical analysis (XRD), thermal analysis (DTA and TG), granulometric analysis, and Fourier transform infrared spectroscopy (FTIR). Ceramic specimens were produced after sintering to 1000, 1100, and 1200 °C. Upon sintering to 1200 °C, red clay-based ceramics achieved acceptable properties with high porosity (29.1–43.9%), water absorption (15.1 to 28.9%), and apparent density (1.6–1.9 g/cm3). Gray clay-based ceramics showed lower porosity (17.4–33.5%) and water absorption (7.9–19.6%), but almost similar bulk density (2.1–1.7 g/cm3) at the highest sintering temperature of 1200 °C. As the proportion of cassava waste in the ceramics increased, the heat exchange flux decreased to 0.10 W/mK and 0.06 W/mK for 20% waste added. Despite this decrease, clay-based ceramics from DRC maintained a flexural strength of 2.6 and 2.1 MPa for red and gray clay samples, respectively. These results suggest that CPW has the potential to be used as a pore-forming agent for ceramics with thermal insulating properties.

Urban flood mitigation strategies are increasingly recognizing the potential of Nature-Based Solutions (NbS) to enhance city resilience while promoting water resource sustainability. This study presents a quantitative assessment of NbS implementation in Zaio, North-East Morocco, quantifying their dual benefits in flood reduction and water conservation. We evaluate the efficacy of detention ponds and riparian reforestation for fluvial flood mitigation, alongside vegetative swales, permeable pavements, and green roofs for pluvial flood risk reduction in a vulnerable urban neighborhood. To account for climate change impacts, we employed a modified Intensity-Duration-Frequency (IDF) curve adjusted for the Representative Concentration Pathway 8.5 (RCP8.5) scenario, forming the basis for our 100-year return period flood simulations. A 2D hydrodynamic model was utilized to simulate the effects of detention ponds on fluvial flood depths, while the Storm Water Management Model (SWMM) quantified the impact of green infrastructure on pluvial flood discharge. Our results demonstrate significant flood mitigation potential: the proposed fluvial flood NbS reduced riverine discharge by 28.95%, while the combination of green roofs, permeable pavements, and vegetative swales decreased pluvial water discharge by 44.7%. Furthermore, we quantified the water resource savings resulting from NbS implementation, highlighting their contribution to water conservation in this semi-arid region. This research provides robust evidence for the effectiveness of NbS in urban flood management and water resource preservation. The findings offer valuable insights for urban planners and policymakers, presenting NbS as a multifunctional tool to enhance urban resilience against flood risks while addressing water scarcity concerns. This study contributes to the growing body of knowledge on sustainable urban water management strategies in the face of global environmental change.

Our study investigates the effect of pressure within the range of 0 to 20 GPa on elastic properties of titanium disulfide (TiS2) using density functional theory, plane waves, and pseudopotentials techniques within the framework of the Generalized Gradient Approximation. Our primary objective is to unveil the anisotropic nature of TiS2’s elastic properties by scrutinizing the spatial variations in key parameters, including shear modulus (G), bulk modulus (B), Young’s modulus (E), hardness (H), and Debye temperature (ƟD), under variable pressure conditions. The elastic properties of TiS2 exhibit a significant degree of anisotropy due to its hexagonal crystal structure, where the arrangement of atoms results in variable mechanical responses along the crystallographic axes. However, this anisotropy tends to decrease progressively with increasing pressure. These results not only deepen our understanding of TiS2’s mechanical behavior but also open avenues for potential applications in diverse technological domains such as flexible electronics, optoelectronics and the energy industry.

This study aims to assess groundwater quality and the human health risks associated with nitrate contamination using a human health risk assessment model. 45 groundwater samples were collected in 2016 and 2023 in the Angads aquifer. According to the findings, the groundwater was brackish (TDS > 1000 mg/L), extremely hard (TH > 450 mg/L CaCO3), and contained ions in the sequence Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ > NH4⁺ for cations, and Cl⁻ > HCO3⁻ > SO4²⁻ > NO3⁻ for anions. The TH-TDS graph and the groundwater quality index (GWQI) show that the majority of samples are unfit for consumption. Based on NO3⁻ concentrations most often observed in the south of the research area, where many activities are likely to be sources of nitrates, the human health risk assessment reveals high non-carcinogenic risks for people living in the region, particularly for infants (71.1% on 2016 and 93.3% on 2023). Consumption of groundwater contaminated with nitrates over a long period could present a potential risk to human health.

In order to gain a deeper understanding of the Germanium Telluride (GeTe) system, we performed a comprehensive set of first principles calculations to study the variation of the structural, electronic, optical, and mechanical properties in the 0-1.25 GPa pressure range. To do this, we relied on the full-potential linearized augmented plane wave (FP-LAPW) method, built in the WIEN2k code. The Generalized Gradient Approximation (GGA) with the modified Becke-Johnson (mBJ) method describes exchange-correlation potential. The Geometric optimization of the unit cell involving the equilibrium volume, lattice parameters, atomic positions, bulk modulus, and its derivative, made it possible to obtain values consistent with those experimental. The analysis of the densities of states for the studied crystal indicates that the minimum of the conduction band and the maximum of the valence band are mainly influenced by the Ge-4p and Te-5p states. In addition to the Ge-5s state for the maximum of the valence band. The computed electronic characteristics reveal that GeTe has a narrow direct band gap of 0.371 eV. However, the band gap diminishes as pressure rises, and from 1.5 GPa, GeTe exhibits metallic properties. The optical properties are reported for an energy range of 0–14 eV. This compound shows high absorption peaks in the ultraviolet region making this compound highly promising for UV-based optoelectronic devices. By calculating the different mechanical parameters, including bulk modulus, anisotropy factor, Voigt’s shear modulus, Reuss shear modulus, shear modulus, elastic modulus, Young modulus, Frantsevich Ratio and Pugh ratio, it can be confirmed that the material is flexible and soft, mechanically stable. Moreover, the elastic anisotropic factor reflects its anisotropic behavior. The results obtained from Pugh’s ratio and Frantsevich Ratio confirm the brittle behavior under different pressures. As a result of its narrow fundamental gap, the GeTe semiconductor is suitable for use in infrared detectors and light-emitting devices.

Argania spinosa is a well-known evergreen tree in Morocco and worldwide due to its significant ecological value, economic potential, and therapeutic uses. In this study, native and introduced Argan trees from two locations in Morocco (Oujda and Chouihiya) were compared for their chemical composition and in vitro antimicrobial activity. The sensitivity of different bacterial and fungal strains to Argan extracts was tested using the agar diffusion, agar well, and microdilution methods. The findings of the present study indicate that each extract from the different studied parts of the Argan tree contained a distinct amount of several compounds of considerable interest, including rutin, catechin, quercetin, and cinnamic acid, the major compound was Quercetin with a value of 18.12 mg/100g DW while the minor was trans Chalcon with a concentration of 0.01 mg/100G DW. The lowest MIC values and MBC value against bacterial strains were recorded by leaf extracts from Oujda against K. pneumoniae with a value of 2.5 mg/mL. The lowest MIC observed for fungal strains was registered against Geotrichum sp with a value of 5 mg/ml. The outcomes from this work further support that Argan and its derivatives have a promising future in combating microbial problems.

Biofilm formation on food packaging surfaces is a major issue in the industry, as it leads to contamination, reduces shelf life, and poses risks to human health. To mitigate these effects, developing smart coatings that can actively sense and combat microbial growth has become a critical research focus. This study is motivated by the need for intelligent packaging solutions that integrate antimicrobial agents and sensors for real‐time contamination detection. It is hypothesized that combining conducting polymers (CPs) with nanomaterials can enhance antimicrobial efficacy while maintaining the mechanical integrity and environmental stability required for food packaging applications. Through the application of numerous technologies like surface modification, CP–nanoparticle integration, and multilayered coating, the antimicrobial performance and sensor capabilities of these materials were analyzed. Case studies showed a 90% inhibition of bacterial growth and a tenfold decrease in viable bacterial counts with AgNPs incorporation, extending strawberries’ shelf life by 40% and maintaining fish freshness for an additional 5 days. Moreover, multilayered CP coatings in complex systems have been shown to reduce oxidative spoilage in nuts and dried fruits by up to 85%, while maintaining the quality of leafy greens for up to 3 weeks under suboptimal conditions. Environmental assessments indicated a 30% reduction in carbon footprint when CP coatings were combined with biodegradable polymers, contributing to a more transparent and reliable food supply chain. CP‐based films integrated with intelligent sensors exhibit high sensitivity, detecting ammonia concentrations below 500 ppb, and offer significant selectivity for sensing hazardous gases. These findings indicate that CP‐based smart coatings markedly enhance food safety and sustainability in packaging applications.

KM combines three essential elements: individuals, procedures, and technology. The use of technology promotes the integration and development of knowledge, while procedures improve organizational structure and operations. Additionally, individuals are the primary source of knowledge and interaction within the ecosystem. This paper explores the relationship between Knowledge management (KM), decision-making (DM) and Information and Communication Technology (ICT) in the form of a systematic mapping study (SMS). KM, DM, and ICT are interconnected concepts that play a vital role in organizational performance and success. KM involves managing knowledge and information, DM involves making decisions based on that knowledge, and ICT provides the technology for managing and sharing that knowledge. These three concepts work together to improve organizational efficiency, effectiveness, innovation, and competitive advantage. The seamless fusion of knowledge management, data management, and information and communication technology can pave the way for enhanced decision-making, collaborative efforts, and well-informed choices.

The increasing amount of online data has led to a greater demand for web crawlers that can effectively extract information from web pages. One common challenge is dealing with images that contain tables, as traditional text-based crawlers struggle to process them. To address this issue, we have created a specialized hybrid crawler specifically designed to target images with tables. This advanced crawler utilizes sophisticated image processing techniques for accurate data extraction. By combining content-based image retrieval and machine learning algorithms, our approach enables the crawler to recognize and categorize images based on their visual features. Important testing on financial web pages has demonstrated the remarkable accuracy of our crawler in retrieving relevant images containing tables.

With the continuous increase in online information, the automatic classification of diverse and complex webpage content has become essential. In our study, we propose a novel ensemble learning method that incorporates six different sentence embedding techniques, which are trained using both traditional and deep learning models. Our method utilizes a stacking technique to calculate prediction probabilities. To test the efficacy of our approach, we compare it with two well-known ensemble methods: XGBoost and Random Forest. The comparison includes testing the models on each sentence embedding separately, and jointly through the combination of all embeddings in one dataset. We also compare our method with ensemble techniques like probability averaging and majority voting on both XGBoost and Random Forest. Our method obtained an improvement in classification metrics when compared to both XGBoost and Random Forest, on a scraped dataset containing roughly 15,647 labeled web pages.

Loquat (Eriobotrya japonica L.) is a popular fruit known for its sweet and slightly tangy flavor, which is widely consumed both fresh and in various processed forms. This study aimed to analyze the biochemical composition of loquat juice and investigate its metabolic benefits in mice fed a high-fat/high-sucrose diet (HFSD). Mice were fed either a standard diet or an HFSD and received or not the loquat juice at 4 or 8 mL/kg body weight for 8 weeks. Body weight, food efficiency ratio, plasma lipoprotein profile, plasma glucose, and lipid indices were monitored throughout the experiment. At the end of the experiment, additional assessments were performed, including lipid content measurements in liver, adipose tissue, bile, and feces; hepatic antioxidant enzyme activities (superoxide dismutase and catalase); hepatic malondialdehyde content; plasma biomarkers of liver injury; liver histology; and organ relative weight. Feeding mice with the HFSD resulted in a significant perturbation in lipid and glucose metabolism, obesity, liver steatosis, and oxidative stress-related enzymes. However, the concomitant administration of loquat juice significantly corrected this imbalance. Fresh loquat juice is low in fat and protein, moderately sugary, and energetically light; however, it is rich in minerals, vitamin C, and various phytochemicals compounds, such as phenolic acids, flavonoids, and carotenoids. The loquat juice could be considered a functional food and could be valorized through the extraction of active substances and their use as food supplements to prevent lipid metabolism disorders and the resulting health complications.

Three novel 4‐aryl‐2‐(2‐(4‐fluorophenyl)‐2‐oxoethyl)‐6‐phenylpyridazin‐3(2H)‐one derivatives were synthesized, and their chemical structures were established based on ¹H‐NMR, ¹³C‐NMR, ESI‐HRMS, and FT‐IR spectral data. Their in vitro antibacterial activities were evaluated against Staphylococcus aureus [MRSA] (Gram‐positive isolate), Escherichia coli and Salmonella typhimurium (Gram‐negative isolates), and Pseudomonas aeruginosa and Acinetobacter baumannii (Gram‐negative nonfermenter isolates), and the results were compared with the standard drug “Amikacin”. The results showed that one of the compounds has good antibacterial activity against P. aeruginosa (MIC = 11.57 µM) and E. coli (MIC = 23.14 µM). In order to gain molecular insights into the compounds, their reactivity properties were studied using density functional theory (DFT), and molecular docking was carried out for the compounds with E. coli MurB protein receptors (2Q85).

In this article, we introduce an extension of rectangular metric space, called a homothetic rectangular metric space, namely, the rectangular inequality in this space will have the following form: for all $x,y \in X$ and for all distinct points $u,v \in X$ each of them are different from x and y, where $\nu$ is a control function defined from $X\times X \rightarrow (-\infty ,+\infty )$. We next show the main properties of sequences in a homothetic rectangular metric space with a negative control function $\nu$ and also where $\nu$ takes values in (0, 1). Moreover, we give sufficient conditions for this new extension to becomes a metric space. Analogues of the Banach contraction principle and Kannan’s fixed point theorem are proved in this space under conditions on $\nu$.

The threat of emerging viral outbreaks has increased the need for fast and effective development of therapeutics against emerging pathogens. One approach is to modify the structure of existing therapeutic agents to achieve the desired antiviral properties. Here, we attempted to synthesize a new antiviral compound by modifying the structure of chloroquine using the N-alkylation of the primary amine (N1,N1-diethylpentane-1,4-diamine) that is used in chloroquine synthesis. Chloroquine is commonly used to treat malaria. Like chloroquine, chloroquine is used for treating conditions such as rheumatoid arthritis, lupus, and malaria. For instance, in malaria treatment, it targets and inhibits the growth of the malaria parasite, aiding in its elimination from the body. The synthesized compounds MP1, C1, and TT1 were further tested in vitro against the B.1 lineage of SARS-CoV-2. One of the compounds, MP1, demonstrated minor effectiveness, with an IC50 of XX at only a high concentration (at a concentration of 60 μM) and decreased both the number of SARS-CoV-2 copies and the amount of infectious virus. Although the synthesized compounds failed to markedly inhibit SARS-CoV-2, this could be a pontial mechanism for manipulating the drug structure against other pathogens. MP1, TT1, C1, and chloroquine diphosphate were used as ligands for molecular docking to determine the principal interactions between these compounds and the active site of the protein downloaded from the Protein Data Bank (PDB ID: 6lzg). Finally, ADMET assays were performed on the synthesized compounds to determine their pharmacokinetics and bioavailability.

This study investigated groundwater in the central Rif region of northern Morocco by analysing 55 water sampling points to assess its physicochemical and hydrogeochemical properties. Through hydrochemical analysis, GIS spatial exploration, and multivariate statistical analysis, a direct correlation was found between EC, TDS, and major ions, influencing overall water mineralization. The key findings included pH levels ranging from 6.10 to 8.52, EC from 828 to 4581 μS/cm, and varying concentrations of Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO2–, Cl–, N–NO2–, and SO42–. Notably, TDS and TH ranged from 647.19–3609.36 mg/L and 64.23–1051.24 mg/L, respectively, with a significant portion of samples exceeding WHO guidelines, particularly chloride (61.81%), sulfate (92.72%), and nitrate (12.72%) samples. The Piper diagram highlights sodium chlorides (Na–Cl) as the predominant chemical facies (70.9%), while the Gibbs diagram emphasizes the impact of evaporation on water chemistry dynamics. This study revealed the complex influence of geological and anthropogenic factors on groundwater quality, potentially leading to seawater intrusion in coastal aquifers. The observed high mineralization and hardness levels, in addition to mild alkalinity, pose public health risks, underscoring the need for continuous monitoring and sustainable management practices in coastal groundwater management to protect human health and the environment.

This study investigates the synergistic effects of alginate@montmorillonite (Alg@Mt) hybrid microcapsules for enhancing water purification, focusing on improving the encapsulation of hydrophobic contaminants. Alg@Mt microcapsules were prepared through ionotropic gelation. Characterisation was performed using SEM-EDX, FTIR, XRD, and TGA. Encapsulation efficiency (EE), loading capacity (LC), and release behaviour were also examined. Alg@Mt microcapsules effectively removed phenol and its chlorinated derivatives from water. Incorporating Na-Mt improved structural and thermal properties, EE, and LC. Increasing the clay content to 60% (w/w) raised the EE of phenol and its more hydrophobic derivative, 2,4,6-trichlorophenol, from 39.74 ± 3.1% (w/w) and 63.91 ± 2% (w/w) to 60.56 ± 1.6% (w/w) and 82.28 ± 2.3% (w/w), respectively, with more controlled release rates, following Fickian diffusion mechanism. EE increased with phenolic substances hydrophobicity, while LC and release rates were inversely related. This approach is promising for removing hydrophobic contaminants from water.

This study explores the use of ground-penetrating radar (GPR) and artificial intelligence (AI) techniques for characterizing plant roots in agriculture. Specifically, we compare the effectiveness of transfer learning (TL) and neural architecture search (NAS) for predicting root diameter and moisture content using GPR images. We conducted simulations using CST Studio Suite to evaluate these approaches on a diverse set of root sizes and moisture levels. Our results show that the NAS approach achieved significantly higher accuracy for characterizing root diameter and moisture content than TL, indicating that NAS is better suited for small datasets and can provide more precise and accurate results. Our findings demonstrate the potential of integrating AI techniques with GPR data for underground imaging in agriculture. This work contributes to developing more effective and accurate AI models for small datasets and can inform future research. Overall, our results suggest that using GPR and AI can improve our understanding of plant root characteristics and support sustainable agriculture practices.