Université Sultan Moulay Slimane

Antimicrobial resistance in Gram-negative bacteria (GNB) is a growing global health concern, particularly in hospital environments, where cockroaches act as vectors for resistant strains. This study aimed to analyze antimicrobial resistance and biofilm formation in GNB isolated from cockroaches collected in the hospital environment. Cockroaches were collected, and bacterial isolation was performed from their gut contents and external surfaces. GNB strains were tested for antibiotic susceptibility using the disk diffusion method and examined for Extended-spectrum β-lactamases (ESBLs) and carbapenemases production. Molecular characterization of ESBLs and carbapenemases in GNB involved PCR amplification of antibiotic resistance genes, while biofilm formation was studied using a microplate assay. Seventy-five cockroaches were collected from which 165 GNB were isolated. The prevalence of ESBL-producing and carbapenemase-producing GNB was 6.7 and 1.8%, respectively. The predominant ESBL gene was blaCTX-M-28, while blaNDM-1 was the only carbapenemase gene detected. The qnrS1 gene was found in one NDM-1-producing Klebsiella pneumoniae and three ESBL-producing Escherichia coli. The qacΔE1 gene was detected in an NDM-1-producing Citrobacter freundii and a CTX-M-28-producing E. coli, whereas one NDM-1-producing Enterobacter cloacae carried both qacΔE1 and acrA genes. Strains harboring qacΔE1 and/or acrA genes exhibited biofilm-forming capabilities, with biofilm formation observed in 81.81% of ESBL-producing isolates and 100% of carbapenemase-producing isolates. The study underscores the role of cockroaches in carrying and disseminating ESBL- and carbapenemase-producing GNB in hospital settings. The coexistence of disinfectant resistance genes and antibiotic resistance suggests co-selection mechanisms, while biofilm formation enhances bacterial survival. These findings underline the urgent need for infection control strategies.

This work considers the 3-D stochastic fractional Navier-Stokes equation driven by multiplicative noise in critical Fourier-Besov-Morrey spaces $\mathcal {F\dot{N}}_{p,h,r}^{1-2\beta +\frac{3}{p^{\prime }}+\frac{h}{p}}(\mathbb {R}^{3})$. We establish the local existence and uniqueness of the solutions to the concerned equation and we prove the global existence in the probabilistic sense when the initial data are small.

The progress of two-dimensional natural convection of non-Newtonian binary fluid confined in a horizontal rectangular layer is studied both analytically and numerically. The horizontal flux density of temperature is applied on the vertical walls of the cavity, whereas the long horizontal ones are considered impermeable and insulated. Solutal gradients are assumed to be induced either by the imposition of constant gradients of concentration on the vertical walls (double-diffusive convection, M = 0) or by the Soret effect (M = 1). The study focuses on the impact of different governing parameters, namely, the cavity aspect ratio A, the Lewis number Le, the buoyancy ratio N, the power-law behavior index n, the parameter M, the generalized Prandtl, Pr, and thermal Rayleigh RaT, numbers. The mathematical model, describing the double-diffusive convection phenomenon, is presented by non-linear differential equations, which are solved numerically based on finite volume method and analytically using the parallel flow approximation in the case of a shallow layer (A ≫ 1). Representative results for the central stream function, Nusselt and Sherwood numbers as well as streamlines, isotherms, and isoconcentrations are depicted as functions of the main parameters mentioned above. The onset and the development of convective motion are investigated. The buoyancy ratio, N, is found to strongly alter the flow pattern, heat and mass transfer. It is demonstrated that the Soret effect imposes a reversal of concentration gradient.

This study aims to evaluate the potential of metal–organic frameworks (MOFs) and MOF-hydrogel composites as advanced platforms for drug delivery systems (DDSs). The unique properties of MOFs, including their high porosity, tunable pore size, and functionalizability, are leveraged to address the limitations of conventional DDSs, such as their low stability, drug loading efficiency, and uncontrolled release. This review systematically examines the synthesis methodologies for MOFs and MOF-based hydrogels, including traditional, advanced, and alternative strategies. This paper further explores the mechanisms of drug encapsulation, drug-MOF interactions, and the role of hydrogel matrices in enhancing drug stability and release control. These findings highlight the ability of MOF-hydrogel systems to achieve pH-responsive, multidrug, and targeted delivery, with reduced burst release and improved therapeutic efficacy. Key applications in cancer therapy, antimicrobial treatments, and personalized medicine are discussed. In conclusion, MOF-hydrogel systems represent a promising strategy for precision medicine, although challenges such as biocompatibility and scalability remain areas for future research.

Severe droughts have affected the irrigated regions of Tadla and Lower Tassaout, Morocco, since 2019, peaking in September 2021. This study integrates Sentinel-2 satellite imagery with machine learning algorithms (MLAs) to quantify drought impacts on fruit tree systems. Three predictor scenarios were tested: M1 (Sentinel-2 bands and indices), M2 (added historical vegetation indices), and M3 (incorporated phenological metrics). Tree-based MLAs performed best, with Random Forest (RF) and Gradient Tree Boost achieving 95.94% and 94.09% accuracy under M3. RF-based analysis identified significant crop losses: 2,121 ha of citrus orchards and 12,127 ha of olive groves, with 16,276 ha moderately affected. However, groundwater and spring irrigation preserved 5,298 ha of olive trees and 7,216 ha of citrus orchards but led to declining aquifer levels. These findings highlight remote sensing and MLAs’ role in assessing drought impacts and balancing agricultural resilience with water sustainability.

Modeling and identifying nonlinear systems are crucial challenges in many scientific and technical fields. This study examines the employment of machine learning for identifying non-linear systems, focusing specifically on two approaches: kernel methods and artificial neural networks (ANN). Kernel methods leverage nonlinear data transformations to model complex relationships by kernel trick, while ANN offers flexibility in modeling interactions between variables. We examine how these two approaches can effectively identify nonlinear systems output, highlighting their respective advantages and disadvantages. Case Experiments illustrate how well these methods perform across different system identification scenarios.

In recent decades, discrete orthogonal moments have frequently been used to represent images in a variety of computer vision and pattern recognition applications. These moments commonly employ discrete orthogonal polynomials as their basis, some of which are parametric and characterized by localization parameters. Optimizing these parameters is crucial for enhancing the efficiency of orthogonal moments in image analysis. This paper takes a new approach for optimizing the discrete Hahn polynomial parameters (α,β) using the Firefly optimization algorithm, focusing on minimizing the mean square reconstruction error. Where our method identifies the optimal α and β values for constructing Hahn polynomials to achieve superior image moments. The results demonstrate that our Firefly algorithm-based method significantly improves image reconstruction quality, yielding lower reconstruction errors, particularly in low orders of Hahn moments. Furthermore, the proposed method outperforms both the artificial bee colony optimization method and the standard parameter selection method. The experiments clearly indicate the advantages of our proposed method, especially in the lower orders of moments.

Plant-derived secondary metabolites have displayed notable biological effects and are valued for their applications in both food and medicine. This study aimed to explore the chemical composition, antioxidant activity, and antibacterial properties of Moringa oleifera aqueous extract (AEMO). Various analytical techniques, such as HPLC–UV/PDA, Fourier transform infra-red spectroscopy, X-ray diffraction, scanning electron microscopy and energy-dispersive X-ray spectroscopy, were utilised. The antioxidant potential of AEMO was assessed using DPPH, ABTS and TAC assays, while its antibacterial activity was tested against Gram-negative microorganisms as well as Gram-positive bacteria using the broth microdilution method. The obtained data illustrated robust antioxidant properties within AEMO and a wide-ranging antibacterial impact against various bacterial strains. By HPLC analysis, significant bioactive components were identified, notably including quercetin, kaempferol, and rutin. In conclusion, AEMO can be considered as a rich natural source of bioactive compounds with diverse therapeutic utilities in pharmaceutical and functional food applications.

Salinity stress significantly threatens agriculture by impairing crop growth and yield. Exogenous proline application has emerged as promising strategy to enhance plant resilience to such abiotic stresses. This study investigated the effects of exogenous proline on three alfalfa (Medicago sativa L.) varieties, two Moroccan, Ouad Lmaleh (OL) and Demnate 201 (Dm), and one European NS Mediana ZMS V (NS Med) under salinity stress. The three M. sativa varieties were exposed to salt stress with or without proline application. Growth parameters, photosynthetic performance, oxidative stress levels, ion contents and osmotic adjustment were investigated. Salinity stress adversely affected all the three varieties with NS Med being the most susceptible, as shown by the PCA analysis. In NS Med, shoot and root dry weights, plant height, and leaf number were reduced by 76%, 86%, 53%, and 65%, respectively, compared to controls. Salinity also diminished photosynthetic pigments and potassium levels while increasing malondialdehyde (MDA) and sodium contents, especially in NS Med. However, exogenous proline application alleviated these negative effects by improving plant biomass, height, leaf number, and photosynthetic pigment levels while reducing Na+ accumulation in a variety-dependent manner. Proline treatment decreased MDA levels by 26% in NS Med, 18% in Dm, and 5% in OL, indicating enhanced oxidative stress tolerance via improved antioxidant enzyme activity. Additionally, proline supplementation increased endogenous proline levels, positively correlating with growth and photosynthetic pigments. These findings highlight exogenous proline as a viable strategy to counteract salinity stress in M. sativa, promoting growth and stress resilience.

Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-invasive diagnosis of common eye diseases. Early and precise detection of these conditions is essential for preserving vision, making vessel segmentation crucial for identifying vascular diseases that pose a threat to vision. However, accurately segmenting blood vessels in fundus images is challenging due to factors such as significant variability in vessel scale and appearance, occlusions, complex backgrounds, variations in image quality, and the intricate branching patterns of retinal vessels. To overcome these challenges, the Unified Gated Swin Transformer with Multi-Feature Full Fusion (UGS-M3F) model has been developed as a powerful deep learning framework tailored for retinal vessel segmentation. UGS-M3F leverages its Unified Multi-Context Feature Fusion (UM2F) and Gated Boundary-Aware Swin Transformer (GBS-T) modules to capture contextual information across different levels. The UM2F module enhances the extraction of detailed vessel features, while the GBS-T module emphasizes small vessel detection and ensures extensive coverage of large vessels. Extensive experimental results on publicly available datasets, including FIVES, DRIVE, STARE, and CHAS_DB1, show that UGS-M3F significantly outperforms existing state-of-the-art methods. Specifically, UGS-M3F achieves a Dice Coefficient (DC) improvement of 2.12% on FIVES, 1.94% on DRIVE, 2.52% on STARE, and 2.14% on CHAS_DB1 compared to the best-performing baseline. This improvement in segmentation accuracy has the potential to revolutionize diagnostic techniques, allowing for more precise disease identification and management across a range of ocular conditions.

This article investigates thermal entanglement and quantum teleportation in a bipartite system composed of two spin-$\frac{1}{2}$ qubits, exposed to an external magnetic field along the Z-axis, within the framework of the squeezed spin model. We employ concurrence to quantify both the thermal entanglement in our system and the entanglement of the replicated output state in a quantum teleportation protocol through this system. Thus, we adopt fidelity to evaluate the quality of teleportation. It is shown that at the system’s ground state, a pure state favors maximal entanglement, while a mixed state leads to an absence of entanglement regardless of the magnetic field. At very low temperatures, increasing the magnetic field induces transitions from the entangled state to a separable state, but this transition is modulated by the intensity of interactions in the XY-plane. The intensities of interactions along the X- and Y-axes are studied to understand their effect on the system’s entanglement. Two spin squeezing mechanisms, one-axis twisting and two-axis counter twisting, are compared, revealing that two-axis counter twisting offers better entanglement. Finally, we explore quantum teleportation through squeezed spin states, demonstrating its feasibility with high fidelity at high temperatures and without a magnetic field, provided that the intensities of interactions in the XY-plane are negligible. By increasing the intensities $\mu$ and $\chi$, fidelity improves. Intriguingly, our analysis suggests that quantum teleportation, with increased fidelity, is achievable only with the one-axis twisting spin squeezing mechanism, remaining out of reach for two-axis counter twisting.

The [Zn–Al–Cl] layered double hydroxide (LDH) was synthesized by co-precipitation at constant pH and used to remove Fluorescein dye from water. Several parameters were optimized, including the solution pH, the contact time, and the temperature. The maximum adsorption capacity was obtained at a pH close to 7. The adsorption phenomenon fits well with the pseudo-second-order kinetics model, with the Freundlich model being the most appropriate to describe it. The thermodynamic study indicates that the interaction between LDH and Fluorescein is spontaneous and endothermic. It is accompanied by an increase in disorder (ΔadsG0 < 0, ΔadsH0 > 0 and ΔadsS0 > 0). The standard enthalpy of adsorption (ΔadsH0 = + 30.01 kJ.mol−1) suggests the formation of hydrogen bonds between Fluorescein and [Zn–Al–Cl]. These results are confirmed by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive spectroscopy, thermal analyses and UV–Visible spectrofluorimetry.

The twenty-first century marks a significant shift in soil fertility evaluation, driven by advancements in pedometrics and Digital Soil Mapping (DSM). Pedometrics introduces quantitative methods to assess soil variability using statistical and geostatistical techniques, enhancing understanding of soil properties. DSM builds on this by creating high-resolution predic tive maps, offering valuable data for researchers and practitioners. An in-depth bibliometric analysis on the Scopus platform (2000–2023) revealed 133 articles on pedometrics and an impressive 1,172 on DSM, underscoring growing interest in these technologies.The integration of Geographic Information Systems (GIS) and Remote Sensing (RS) has further advanced these fields, enabling extensive geospatial data collection and real-time monitoring. Machine Learning (ML) has also been transformative, facilitating complex pattern recognition and predictive analysis to improve soil fertility mapping and manage ment. A review of 364 studies from 2000 to 2023 highlights the development and impact of these technologies, detailing their advantages and limitations. The surge in related publications and citations since 2000 reflects a rising interest in sustainable agriculture and environmental management. Significant milestones occurred in 2019 and 2022 with the introduction of new soil management technologies, while RS and GIS technologies surged in popularity in 2016 and 2020, driven by satellite advancements like Sentinel and Landsat. The capabilities of ML techniques were notably effective in 2019 and 2022. Coun tries like India, China, and Iran have been key adopters, transforming soil fertility mapping into a non-invasive, large-scale process that enhances agricultural decision-making.This transition emphasizes the value of specialized publications that advocate for GIS, RS, pedometrics, and DSM, which are crucial for addressing environmental challenges. In conclusion, integrating traditional and advanced methodologies provides a holistic, adaptable approach to sustainable land management, supporting data-driven decisions to enhance agricultural and environmental sustainability. Keywords Soil fertility mapping · Remote sensing · Geographic Information Systems · Machine Learning · Pedometrics · Digital Soil Mapping · Sustainable land management · Traditional methodologie

The disposal of macroalgal waste presents environmental and economic challenges, but its valorization offers interesting opportunities. Hydrothermal carbonization (HTC) is a promising technology for transforming this waste into hydrochar, a carbon-rich material with energy and industrial applications. This study aims to optimize HTC process parameters, including temperature (180–260 °C), residence time (0–240 min), and solid/water ratio (S/W), to improve hydrochar yield, higher heating value (HHV), and energy efficiency, using response surface methodology (Box-Behnken design). The results revealed that temperature and residence time were the most influential factors on hydrochar production. The maximum higher heating value (HHV) of 18.71 MJ/kg was observed at 260 °C, 240 min, and a 0.1875 S/W ratio, indicating efficient energy conversion. On the other hand, the highest hydrochar yield and energy yield were obtained at 180 °C, 0 min, and a 0.1875 S/W ratio, suggesting that more moderate conditions favor better material retention. Analysis of the samples showed an increase in porosity and an improvement in the physico-chemical characteristics of hydrochar compared with raw waste, reinforcing its potential as a biofuel or adsorbent material. These results confirm that the valorization of macroalgal waste by HTC represents a promising solution for the production of renewable energy, thus contributing to the sustainable management of resources in Morocco. Graphical Abstract

This research examines the application of ML to predict optimal times for player substitutions in football, aiming to improve game outcomes through strategic decisions. We analyzed a dataset from Kaggle, containing 51,738 substitution instances across 9074 games from five top European leagues over six seasons. The study assessed various Machine Learning models, including Logistic Regression (LR), Decision Trees (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Multinomial Naïve Bayes (MNB), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), using an 80-20 data split for model testing. The findings reveal that Support Vector Machines and Multinomial Naïve Bayes were the most effective, each achieving over 79% accuracy and the highest F1-score at 89%. eXtreme Gradient Boosting displayed the highest precision at 81%, while Decision Trees had the lowest accuracy at 67%. In terms of efficiency, Multinomial Naïve Bayes was the fastest model to train, whereas Support Vector Machines required the most time. These results underscore the potential of Machine Learning to provide a tactical edge by facilitating more informed substitution decisions during football games.

The growing global demand for renewable energy has increased the need for efficient and reliable control systems in photovoltaic (PV) applications, ensuring optimal energy extraction and stable grid integration under varying environmental conditions. This paper conducts a detailed analysis of both simulated and practical implementations of a system that integrates a photovoltaic (PV) panel, a DC-to-DC boost converter, and a DC-to-AC inverter. The control of the boost converter is handled by an Intelligent Artificial Neural Network (IANN), and the inverter operation is managed by a Fuzzy Logic Controller (FLC). Notably, the FLC achieves a Total Harmonic Distortion (THD) of just 1.63%, substantially better than the 2.56% THD observed with the MPC algorithm, and it maintains stable output voltage even under variable shading conditions, outperforming both PSO and P&O methods. Extensive simulations carried out in MATLAB-Simulink provide a comprehensive analysis and discussion of both the simulation and experimental results. Furthermore, the development, implementation, and evaluation of electronic circuits (PCB boards) demonstrate their effectiveness in facilitating the seamless integration of PV systems with the electrical grid. The process-in-the-loop (PIL) structure employed in the programming phase significantly aids in debugging, thus enhancing the operational efficiency of the system and simplifying the resolution of software issues. The proposed hybrid technique shows superior results in various performance metrics, achieving a maximum power efficiency of 99.99%, a relative error of 0.000001, and a minimum tracking acceleration of 0.013 s. This study showcases the latest developments in control strategies, enhancing grid compatibility and overall system performance in photovoltaic applications.

Herein, nine square planar trans-arylbis(triphenylphosphine)palladium halides (PdX(PPh3)2Ar) were synthesized and fully characterized. The molecular structure of two complexes (1 and 2) have been determined by both X-Ray diffraction and described thanks to Hirshfeld surface analysis. Investigation of the antioxidant activities showed that most of the complexes exhibit a strong dose-dependent radical scavenging activity towards DPPH radical as well as in the ABTS radical scavenging test. Complexes 1 [PdI(PPh3)2(4-MeOC6H4)] and 3 [PdCl(PPh3)2(4-MeOC6H4)] showed the highest activity in the DPPH assay with EC50 values of 1.14 ± 0.90 and 1.9 ± 0.87 µM, respectively. In contrast, for the ABTS assay, quercetin (5.56 ± 0.97 µM) was slightly more efficient than the three complexes 1 (5.78 ± 0.98 µM), 2 (7.01 ± 0.98 µM), and 3 (11.12 ± 0.94 µM). The use of kinetic studies as a powerful parameter shows that complexes 1, 2, and 3 displayed the best antioxidant efficiency. The antioxidant effect of the nine palladium complexes has been also evaluated on the enzyme-catalyzed oxidation of the L012 probe (using HRP/H2O2) by using a chemiluminescence technique. As with the last model, complexes 1, 2, and 3 showed the best activity, with EC50 values of 3.56 ± 1.87, 148 0.71, and 5.8 ± 2.60 µM, respectively. Interestingly, those complexes (1, 2, and 3) even exhibited a higher dose-dependent activity than the quercetin (7.06 ± 2.56 µM) used as a standard. Taken together, the combined results reveal that the antiradical and enzyme (HRP) inhibitory activity of complexes decrease following the ligand order of p-OMePh > p-OAcPh >> Ph.

This study explores the development of novel polymer composite coatings incorporating glass fillers to enhance corrosion protection of 3D-printed 316 L stainless steel in acidic environment. The formulated coatings, GIP/MDA, GIP/MDA/0.04, and GIP/MDA/0.08, were characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), electrochemical techniques (potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), and electrochemical frequency modulation (EFM)), and contact angle measurements. Results showed that increasing the glass oxide content improved corrosion protection efficiency, with GIP/MDA/0.08 exhibiting the highest efficiency, achieving 98.1%, 98.0%, and 96.17% in PDP, EIS, and EFM tests, respectively. Computational methods, including density functional theory (DFT) and molecular dynamics (MD) simulations, corroborated the superior performance of GIP/MDA/0.08, revealing insights into its adsorption behavior and structural properties. These findings suggest that the glass-reinforced epoxy composites offer significant potential for protecting stainless steel components manufactured via additive processes in harsh environments.

Morocco’s Vision 2030, known as Maroc Digital 2030, aims to position the kingdom as a regional leader in digital technology by boosting digital infrastructure, fostering innovation, and advancing digital skills. Complementing this initiative, the Pacte ESRI 2030 strategy, launched in 2023, seeks to transform the higher education, research, and innovation sectors by integrating state-of-the-art digital technologies. In alignment with these national strategies, this paper introduces BlockMEDC, a blockchain-based system for securing and managing Moroccan educational digital certificates. Leveraging Ethereum Layer 2 (zk-Rollups) smart contracts and the InterPlanetary File System, BlockMEDC automates the issuance, management, and verification of academic credentials across Moroccan universities. The proposed system addresses key issues such as document authenticity, manual verification, and lack of interoperability, delivering a secure, transparent, and significantly low-cost solution that aligns with Morocco's digital transformation goals for the education sector.

Water resource management in semiarid regions poses significant challenges due to extreme weather and the vast, complex terrain of river basins, which imposes serious logistical constraints on monitoring and detecting hydrological anomalies. Therefore, remote sensing provides a crucial solution for addressing hydrological anomalies in data-sparse areas. This research aims to estimate hydrological response anomalies via the water balance formula (P-ET) and create a new composite hydrological response anomaly index (CHRAI) by integrating multiple satellite-based datasets, including CHIRPS, Terra Climate, and MODIS, with weights determined using a Random Forest model. Spatiotemporal variability maps of estimated (P-ET) and modeled (CHRAI) hydrological response anomalies were generated for the Oum Er-Rbia River basin (Morocco) for the 2001-2021 period. The composite CHRAI model performance was evaluated based on three hydrological drought indices, which involved mapping comparisons, Pearson's correlation statistical analyses, linear regression, and Taylor diagram analysis. As a result, strong interannual variability in hydrological response anomalies was observed over the entire study area. The comparative mapping of the estimated (P-ET) and modeled (CHRAI) hydrological responses revealed strong spatiotemporal concordance. Additionally, a high correlation (r = 0.87) was observed between the CHRAI, PET , and the indices (Flow, SHI, IDF, and DLHI) at the Tamchachat station. Based on the Taylor diagram analysis, the modeled index (CHRAI) demonstrates approximately 90% correlation with the observed index, a high standard deviation (P-ET), and a CRMSE close to 0.4, indicating strong agreement between the developed and estimated model (P-ET). This study introduces a novel approach to modeling hydrological response anomalies and underscores the need for further exploration of this methodology.