Université Moulay Ismail de Meknes

Selective laser melting has become among the most popular laser powder bed fusion processes in additive manufacturing technologies in the past decades, thanks to the variety of printed materials available in the market, and to the mechanical and physical properties outcomes. Nevertheless, a special consideration should be given to the surface quality which not only influences the mechanical, dynamic behavior but also the electrochemical resistance of the as-built non-finished selective laser melted parts. Therefore, this work is fully dedicated to the understanding of the as-built surface texture according to four process parameters, namely building-orientation (Oth), laser power (P), scan speed (V), and hatch spacing (h); 3D topography and 2D profiles were captured resulting in substantial surface sampling for the statistical analysis; further optical microscopy followed trying to draw the interplay between the balls formation and the standardized roughness indices. The arithmetic mean roughness (Ra) ranged between 7.55 and 37.8 µm, while the best surface quality was reached for top face of the horizontal samples, power at 200 W, scan speed at 1500 mm/s, and the lowest hatch spacing of 100 µm. Moreover, it was found that the Rp statistics seems to be more adapted to the analysis of the roughness of the as-built surfaces compared to the other roughness indices. The ANOVA findings revealed that the building and faces’ orientations can be assumed as the most significant for all responses, while laser power, scan speeds, and hatch space are found to highly influence the balls dimensions. The balls’ dimensions also contribute to the smoothness or roughness of the as-built samples drawing thus an indirect relationship between P, V, and h on the roughness level.

This paper presents a novel approach for detecting moving objects in image sequences through a distribution based model. The proposed methodology leverages higher-order statistics (HOS) within a clustering framework to enhance the accuracy and effectiveness of moving object detection. The proposed method effectively combines statistical knowledge about the class of moving objects with motion information. By utilizing HOS-derived data from sample images, the unknown distribution of object image patterns is approximated. Our proposed algorithm uses an HOS-based decision measure which is derived from a series expansion of the multivariate probability density function in terms of the multivariate Gaussian and the Hermite polynomial. The clustering process, guided by HOS, enhances the decision-making process by enabling a higher-order closeness measure to accurately classify test clusters as foreground or background.

Titanium (Ti) and its alloys are widely utilized in orthopedic and dental applications due to their favorable mechanical properties and biocompatibility. Notably, titanium exhibits excellent corrosion resistance and can form a stable oxide layer, ensuring the longevity and functionality of implants in challenging physiological environments. This study investigates the corrosion behavior of α-Ti alloy in physiological saline solutions, emphasizing the role of key biomolecules found in the human body, including albumin, glycine, and glucose, as well as additional substances such as hydrogen peroxide (H2O2) and hydroxyapatite (Hap). A comprehensive suite of techniques—namely, open-circuit potential measurements, potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), and atomic force microscopy (AFM)—was employed to assess the effects of these biomolecules on corrosion behavior. The findings indicate that, unlike H2O2 and Hap, the biomolecules studied significantly enhance the corrosion resistance of the α-Ti alloy in simulated physiological environments. H2O2, due to its strong oxidative properties, accelerates corrosion, while Hap induces ion release that adversely affects the alloy's stability. The observed improvement in corrosion resistance is attributed to the formation of a stable passive layer on the alloy's surface. Notably, this study presents the first long-term electrochemical and immersion tests conducted at 310 K, elucidating the effects of bovine serum albumin (BSA), H2O2, glycine, glucose, and Hap on the corrosion performance of the α-Ti alloy.

Some alloys may be susceptible to the hot tearing defect during the solidification stage, depending on various parameters and working conditions, such as part geometry, alloy composition, casting temperature, cooling conditions, and type of molding. The focus of this work is on characterizing hot tearing in the AlCu5MgTi alloy in both permanent mold and green sand mold settings. In this study, we assess the hot tearing susceptibility of the AlCu5MgTi alloy during the solidification stage by using thermal analysis and load sensors. The rigidity point and the dendrite coherency point (DCP) serve as significant parameters to quantify the hot tearing susceptibility of the studied alloy. Our findings indicate that crack formation is more pronounced in parts manufactured by permanent mold casting compared to those produced by sand casting for the AlCu5MgTi alloy. A significant difference between the force causing initiation and the force leading to the opening of the hot tear is noticed in the metallic mold.

Introduction Determining the bittering profile of hops is a prerequisite for their use in beer making industry. To fully grasp the brewing potential of Corsican hops, it is therefore essential to perform a precise quantification of the molecules responsible for their bittering power. Objective The aim of this study is highlighting of the bittering profile of Corsican hops. Methodology A method for the characterization and quantification of α‐acids, β‐acids, and phenolic compounds in Corsican hops using high performance liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has been developed. In addition to the six α‐ and β‐acids commonly quantified in hops, seven others hop acids were identified using a new methodology based on the analysis of their fragmentation pattern in full‐scan detection mode. The compounds were then quantified as humulone or lupulone equivalents. Subsequently, a metabolomic analysis of hop cones was conducted using the method of molecular networking. Results A total of 28 compounds were quantified. The influence of both annual climate variations and transplantation on the chemical composition of hops extractives was highlighted. The molecular network elucidation led to the identification of 34 compounds. Among them, eight were previously undescribed in hops, including one previously unknown to the literature. Conclusion The methodologies developed in this study have shed light on the “bittering” potential of Corsican hops which represents a significant economic opportunity for the local brewing industry potentially establishing a new, sustainable, and profitable hops market. This work focuses extensively on the phenolic compounds and the bittering acids of Corsican hops, aiming to highlight their unique organoleptic characteristics and the influence of the Corsican terroir on their chemical composition and abundance.

The future of groundwater is one of the key challenges for sustainable water management, hence the need to monitor its overall quality. The objective of this work is to assess the overall quality and determine the spatiotemporal evolution of the Angads aquifer in northeastern Morocco in 2014 and 2020, based on the parameters NH4⁺, NO3⁻, EC, Cl⁻, and FC, as well as the Geographic Information System (GIS). The results of the comparison of these five parameters between 2014 and 2020 show a general increase in NH4⁺ and a decrease in NO3⁻ and FC at most sampling points. These changes could be attributed to a shift in pollution sources or biological processes affecting water quality. On the other hand, the stability of EC and Cl⁻ levels suggests a consistency in the inputs of salts or minerals. The quality percentages show a decrease in good, poor, and very poor quality, following an increase in average quality, from 10.52% (in 2014) to 5.26% (in 2020), 31.57% (in 2014) to 21.05% (in 2020), 31.57% (in 2014) to 26.31% (in 2020), and 26.31% (in 2014) to 47.36% (in 2020), respectively. Spatial and temporal mapping of the quality over these 2 years shows that the deterioration continues toward the east, southeast, and southwest. This is justified by very high measurements of the parameters NO3⁻, EC, and Cl⁻ at sampling points 2, 3, 4, 5, 7, 8, and 15 for 2014 and 2020, reaching 156 mg/L, 10,570 µS/cm, and 3790 mg/L in 2014 and 134 mg/L, 10,355 µS/cm, and 3597 mg/L in 2020, respectively, due to effluents from pollution points such as the Oujda public landfill, the wastewater treatment plant, and the former Sidi Yahya landfill to the west. On the other hand, in the north, northeast, and northwest, there has been an improvement in quality due to the remoteness of these pollution points. In order to protect this vital resource, recommendations need to be put in place, in particular by treating leachates so as to ensure that the quality of the water is not discharged directly into the aquifer or used for other purposes, and to avoid discharging effluent from the wastewater treatment plant into the natural environment.

The Ouislane sub-watershed is currently experiencing severe water shortages and is highly dependent on its water supply. The sub-watershed spans two communes: Meknes to the north and El Hajeb to the south. It serves as the primary water source for irrigation and drinking purposes for the local population. Consequently, it is crucial to assess the spatio-temporal variations of water quality to identify and address potential gaps; these focused on effective monitoring systems to detect contaminants, pollutants and health risks. This research project aims on the application of self-organizing map (SOM) techniques combined with cluster analysis to classify water quality in springs for drinking and irrigation purposes. The present study evaluates the water quality variations using physicochemical parameters of twelve water springs, collected during the wet and dry seasons of 2022. For this purpose, the water quality index (WQI), self-organizing map (SOM), hierarchical cluster analysis (HCA), and principal component analysis (PCA) are used as evaluation and classification methods. As a result, the SOM algorithm with a size of 5 × 5 units identified as the most suitable, based on the minimum quantization error (QE) and topographic error (TE), yielding a QE of 0.379 and a TE of 0.000. It grouped the water quality data into five distinct clusters, Cluster I represented 37.5% of the total samples, while cluster II represented 25%. Cluster III and IV each accounted for 8.33% of the samples, while 20.83% of the sampling water are classified in cluster V. Clusters I, II, and IV indicate good water suitable for drinking. However, cluster V had the highest WQI, suggesting very high contamination due to increased levels of the 10 studied physicochemical parameters. The water quality in this region (cluster V) is influenced by natural processes, such as precipitation intensity, weathering and vegetation cover, as well as anthropogenic factors like agriculture and urban concentration. PCA confirmed the clustering results obtained by SOM. However, SOM provides a more detailed classification and additional insights into the dominant variables influencing the classification processes. The results of this study suggest that SOM was an effective tool for gaining a better understanding of the patterns and processes driving water quality in the Ouislane sub-watershed and provides valuable avenues for further research to establish and monitor water quality for effective management of water resources.

In this paper we generalize the notions of n-isometry and n-equivalence introduced by Chen et al. to classify constacyclic codes of length $n\in \mathbb {N}$ over a finite field $\mathbb {F}_q$, to the case of constacyclic codes over a finite chain ring R. We show that these notions define equivalence relations on $R^\times$, give equivalent characterizations of these relations and investigate the link between them. We then compute the numbers of n-isometry and n-equivalence classes for each type of finite chain rings, as well as describe methods to find these classes. In doing so, the study of constacyclic codes on a finite chain ring R is reduced significantly, as instead of considering $\lambda$-constacyclic codes for each unit $\lambda$ in $R^\times$, it suffices to only consider units in a set of representatives of the equivalence classes.

Heart failure is among the most widespread diseases globally. With the rapid rise in the number of affected individuals and the significant disparity between organ demand and supply, the relevance of implantable devices has grown each year. However, these devices face various regulatory restrictions, and obtaining approval requires outstanding performance. This paper focuses on optimizing the design parameters of a rotor for an axial flow ventricular assist device (VAD) currently under development. The parameters investigated include splitters, inlet blade angle, outlet blade angle, blade count, rotational speed, clearance gap, blade thickness, and rotor length. The study aims to maximize pressure rise and hydraulic efficiency while minimizing the torque required to drive the rotor. The D-optimal method was employed to create an experimental design for the simulations. By comparing R², adjusted R², and RMS error across different regression models, the quadratic regression model emerged as the most effective for deriving a suitable mathematical model from the numerical results. The validity of these models was confirmed through the consistency between predicted and observed outcomes.

The phase diagrams and magnetic behavior of the $\hbox {Ni}_2$MnGa full-Heusler alloy, modeled as a mixed spin-1 and spin-5/2 Ising ferromagnetic system, are investigated using the mean-field approximation, with Bogoliubov’s inequality for the Gibbs free energy as the theoretical framework. Although the magnetic properties of $\hbox {Ni}_2$MnGa have been studied, there remains a gap in the understanding of its phase transitions under different crystal fields and reduced temperatures. This work aims to address this gap by providing detailed ground-state phase diagrams and phase diagrams in different parameter planes, offering new insights into the interplay between temperature, magnetic ordering, and crystal field effects. Our model highlights first- and second-order phase transitions, as well as the emergence of a tricritical point and reentrant phenomena, that have not been sufficiently explored in previous studies. The results found not only extend the understanding of phase transitions in $\hbox {Ni}_2$MnGa but also provide a reference for future theoretical and experimental work.

The ability to use a single camera for indoor navigation of a real mobile robot can help develop a complete navigation system, eliminating the need to use a traditional navigation system. The latter involves many subsystems such as perception for capturing the scene and extracting the necessary information, path planning to determine the optimal trajectory from the origin to the destination, and obstacle avoidance. The complexity of this system makes it highly sensitive to variations in the environment, making it inappropriate for real-time use, particularly for mobile robots with limited computational capacity. In this paper, we present an end-to-end navigation system for indoor navigation based on convolutional neural networks (CNNs). We developed four distinct deep hybrid model (DHMs) approaches using four pretrained convolutional networks (Visual Geometry Group 19 (VGG19), Residual Network 50 (ResNet50), Alex Krizhevsky’s network (AlexNet), and Efficient Network B0 (EfficientNetB0)) as feature extractors, combined with a random forest (RF) machine learning classifier. Validation metrics (accuracy, precision, and F1 score) were used to evaluate the performance of the models. During the test phase, we introduced three new and never-before-seen environments. This study revealed that the combination of VGG19 and random forest models produced excellent results, achieving an accuracy of 97.63%. This model was also able to navigate efficiently in most environments, generating a smoother path than other models.

We study the regularizing effect of two lower order terms to nonlinear Dirichlet problems with singularity. The simplest example model is where $\varOmega$ is a bounded open set of $\mathbb {R}^{N}(N \ge 2),$ $1<p<N,$ $\theta >0,$ $q>0,$ $s\ge 1$ and f is a nonnegative function belonging to a suitable Lebesgue space. In particular, we will prove how the presence of lower order terms may lead to an improvement of the summability of the solutions.

In this manuscript, the magnetic and magnetocaloric properties of the Ga2MnNi compound are investigated using Monte Carlo simulations and first-principles calculations. Initially, the electronic, structural, and magnetic characteristics of the alloy are explored. The findings indicate that the ferromagnetic state in the F43m (No. 216) structure, with an optimal lattice parameter of 5.88 Å, was the most stable state compared to nonmagnetic (NM) and antiferromagnetic (AFM) states. Phonon dispersion studies confirm the alloy's dynamic stability, while the density of states reveal metallic behavior at the Fermi level. The total magnetic moment is calculated to be 3.48 uB. Additionally, exchange interactions are computed for Monte Carlo simulations, predicting a Curie temperature (Tc) of 331 K, consistent with experimental measurements (Tc= 330 K). Furthermore, the alloy exhibited a relative cooling power (RCP) of 1133.16 J kg−1 and a magnetocaloric effect of 19.65 J kg−1 K−1 at an applied magnetic field of 5 T. These results indicate that the Ga2MnNi full-Heusler alloy is a potential option for use in magnetic refrigeration applications.

This work deals with an iterative approach for solving a Cauchy inverse problem in linear elasticity. The proposed method is based on a fixed point approach, it allows us to use domain decomposition methods like algorithms to solve the Cauchy inverse problem. The suggested method is described in detail and a relaxation procedure is developed in order to increase its rate of convergence. The boundary element method is used for implementation of the proposed algorithm then the technique to obtain its corresponding algebraic system is discussed. Finally, a numerical study is presented which shows the efficiency and stability of the proposed algorithm when the data is perturbed by noise.

This paper aims to show that there exists a weak solution to the following quasilinear system driven by the M-Laplacian where $\Omega$ is a bounded open subset in ${\mathbb {R}}^N$ and $(-\Delta _{m})$ is the M-Laplacian operator. Here we consider the non-reflexive case taking into account the Orlicz and Orlicz-Sobolev Space. The non-reflexive case occurs when the N-function ${\overline{M}}$ does not verify the $\Delta _2$-condition. We consider an approximated quasilinear elliptic problem driven by the $M_\epsilon$-Laplacian and using the Mountain Pass Theorem to obtain the existence of a nontrivial and nonnegative solution for the above system in reflexive case. By tending $\epsilon \rightarrow 0$ we get the solution in the non-reflexive case.

This article presents the design of a metamaterials-based hybrid wideband bandpass filter (WB-BPF) supporting several advanced communication applications. The suggested filter uses multiple-mode resonators (MMRs) and metamaterial complementary split-ring resonators (CSRRs). Coupling frequencies are optimized by stretching the coupled lines parallel on both sides. At the same time, the metamaterial complementary split-ring resonator (CSSR) cells are carefully tuned to improve impedance matching, reduce insertion loss, and broaden the bandwidth. The designed compact-sized bandpass filter offers a Quality factor ( QL ) of above 1 while maintaining a wide bandwidth of 6150 MHz. The realized structure requires a small physical area of 20.2 × 10 mm². A parametric study, field distribution analysis, and equivalent circuit model of the proposed hybrid BPF are performed. The resulting filter has an exceptional response with five transmission poles, demonstrating superior performance in the targeted frequency range. This wideband bandpass filter, operating from 3.3 GHz to 9.45 GHz, finds potential applications in the emerging fields of the Internet of Things (IoT), wireless fidelity 6 enhanced (Wi-Fi 6E), and high-speed wireless communications, particularly 5G technology, and 5 GHz wireless local area network (WLAN). This research demonstrates significant advances in the development of bandpass filters, offering compact, high-performance solutions for modern communications systems.

This article presents an investigation of the SIQR epidemic model of a reaction-diffusion system involving the p-Laplacian operator and by considering different diffusion coefficients. The main objective is to assess the impact of quarantine measures on the spread of the disease within a defined time and space, as well as to study the stability analysis of equilibria. The study established the existence and uniqueness of a positive solution to the proposed model. Furthermore, an investigation into the global stability properties of the disease-free equilibrium and the endemic equilibrium is conducted through an examination of their respective characteristic equations. To obtain numerical solutions for the state system, we developed a discrete iterative scheme based on the finite difference method. Extensive numerical simulations thoroughly demonstrate the effectiveness of the proposed control strategy (quarantine) and illustrate the obtained theoretical stability results.

Background/Objectives: Eucalyptus globulus is a medicinal plant extensively used by the Moroccan population for treating a range of illnesses, especially respiratory conditions. Methods: This study aimed to assess the antioxidant and antibacterial properties of E. globulus essential oil and its individual fractions (F1, F2, and F3). Antioxidant activity was evaluated through iron-reducing power, 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays. Antibacterial activity was tested using disk diffusion and dilution methods, supported by molecular docking studies. Furthermore, GC–MS analysis was conducted on the essential oil and its individual fractions. Results: GC–MS analysis identified the major compounds in the essential oil and its fractions as eucalyptol (62.32–42.60%), globulol (5.9–26.24%), o-cymene (6.89–24.35%), cryptone (7.10–15.95%), terpinen-4-ol (2.43–15.24%), and α-pinene (2.46–7.89%). Fraction F3 displayed the highest antioxidant activity in DPPH (IC50 = 3.329 ± 0.054 mg/mL) and ABTS assays (IC50 = 3.721 ± 0.027 mg/mL), while fraction F2 was most effective in the FRAP assay (IC50 = 1.054 ± 0.008 mg/mL). The essential oil and its fractions also showed antibacterial activity against Staphylococcus aureus, Staphylococcus epidermidis, Klebsiella pneumoniae, Enterobacter cloacae, Escherichia coli, and Acinetobacter baumannii. Molecular docking further corroborated these findings, supporting both antioxidant and antibacterial activities. Conclusions: The present findings demonstrate the antioxidant and antimicrobial properties of Eucalyptus globulus essential oil and its fractions, underscoring the need for further research to confirm their medicinal potential and explore pharmaceutical applications.

Background/Objectives: The use of dietary supplements (DSs) has become common among fitness enthusiasts, aiming to enhance performance, recovery, and overall well-being. Methods: A cross-sectional study was conducted in the city of Beni Mellal from April to July 2024, assessed dietary practices, motivations for supplement use, and associated adverse effects among 420 survey participants. Results: The majority of dietary supplement users were aged 25–64 and had higher education levels. Colopathy (67.38%) was the most common eating disorder, followed by digestive (59.46%), inflammatory, and rheumatic diseases (53.50%). Dietary supplementation prevalence was 88.1%, with proteins (60.81%), medicinal plants (45.13%), and vitamins (42.70%) being the most consumed. Key motivations included supporting muscle, bone, and joint strength (musculoskeletal) (83.78%) and enhancing heart and lung function for cardiorespiratory health (82.43%). However, 28% of protein users experienced adverse effects, such as myalgia, gastralgia, palpitations, and insomnia. Multivariate linear regression indicated a significant negative association of creatine with effectiveness (β = −0.485, p = 0.001). Conclusions: Overall, while the benefits of dietary and sports practices are evident, the adverse effects associated with protein supplements highlight the necessity for enhanced nutrivigilance and nutritional education to ensure safe supplements.