Université Moulay Ismail de Meknes

Gliomas, a highly aggressive and malignant category of brain tumors, continue to pose a significant global health challenge. Originating from the abnormal and uncontrolled growth of glial cells within the brain, these tumors often result in severe neurological impairments and high mortality rates, underscoring the urgency for improved diagnostic and therapeutic strategies. Early diagnosis can significantly improve outcomes and survival. Consequently, precise segmentation of tumor tissue in medical images is critical for accurate brain tumor diagnosis and effective treatment. However, achieving high-precision segmentation remains challenging due to the complex and variable nature of tumor structures in medical images. To address this problem, we developed R2A-UNET, a U-shaped architecture that leverages the power of residual blocks and attention mechanisms. To enhance the model’s ability to capture critical and relevant information, we incorporated two advanced attention mechanisms. These mechanisms are designed to prioritize important features while suppressing irrelevant or redundant details, thereby significantly improving the efficiency and accuracy of feature extraction across varying datasets. Normalized Channel Attention (NCA) was integrated in each encoder stage, generating a squeezed vector with relevant features at the end of the contracting path. Normalized Spatial Attention (NSA) was included in the skip connection in the middle of the encoder and decoder, generating more concentrated feature maps before concatenation on the decoder side. These mechanisms enable the model to focus on specific pixel values that more accurately localize abnormalities. In our study, we evaluated the performance of our model using two MRI image datasets: the LGG (Lower-Grade Glioma) segmentation database and the BraTS 2018 dataset. Our method achieved a DSC of 92% and an IoU of 86% on the LGG dataset. On the BraTS dataset, it achieved a DSC of 94.79% and an IoU of 90.12%, demonstrating consistent and accurate segmentation performance. To further evaluate our model’s generalizability, we conducted cross-dataset validation, and the results demonstrated good performance. When trained on the LGG dataset and tested on BraTS, the model achieved a DSC of 91.12%. Conversely, when trained on BraTS and tested on LGG, it achieved a DSC of 88.39%. These results highlight the model’s ability to adapt across different datasets with varying characteristics. We analyzed the predictions using Grad-CAM to visualize the decision-making process and applied the Wilcoxon rank-sum test for statistical comparison. These evaluations confirmed the model’s effectiveness in segmenting unseen medical images, supporting its applicability for clinical use.

In this study, we used time-dependent density functional theory DFT/TD-DFT methods in its B3LYP/6-311G(d,p) formalism to model and analyze various properties of conjugated organic compounds. More specifically, the targeted systems consist of a D donor unit (carbazole), an A acceptor unit (benzothiadiazole) and various donor motifs. Quantum simulation via DFT/TD-DFT has enabled us to assess their fundamental electronic structures, boundary energy levels and optical absorption properties.Using AMPS1D software, we carried out an in-depth analysis of the photovoltaic properties of six compounds associated with the PCBM acceptor. These compounds were classified into two distinct categories. In the initial section, we evaluated the energy conversion efficiency of compounds, with performances between 7% to 11%. The second section presented the addition of a PEDOT layer between the active layer and the anode, which significantly improved photovoltaic performance, reaching a maximum efficiency of 15%. These results underline the positive impact of PEDOT addition on photovoltaic conversion and its potential for organic solar cells. In conclusion, our results indicate that these compounds represent promising candidates for future applications.

Rationally constructed materials have enabled access to optical capabilities beyond nature’s limitations, thanks to advances made in both theory and experiment. These synthetic composites allow subwavelength confinement of electromagnetic energy and facilitate unparalleled control over different aspects of electromagnetic waves (polarization, amplitude, frequency, etc.). However, the diffraction phenomenon is severely hindering the efficacy and performance of dielectric photonic components. Diffraction causes the electromagnetic wave to spread and deviate from its intended path, thereby, making the collimated light beam scatter, leading to lower power density and inaccurate targeting. This is particularly detrimental for applications requiring precise control of high-frequency with shorter wavelengths. Herein, we report on the effect of anisotropic geometrical scaling of dielectric photonic crystals to alleviate the diffraction barrier along the Γ → X path of the irreducible Brillouin region. Thus, achieving the long-sought goal of high-frequency electromagnetic wave steering. We harness the full weight of modal and harmonic analysis based on the Finite Element Method to demonstrate that scaling the direction perpendicular to the wave’s propagation reduced by fourfold the diffraction limit from 100 THz to 400 THz.

Since the introduction of semiconductors, the world has undergone numerous profound transformations during the past few decades. With advancements in technology, semiconductor products' performance requirements keep rising. Research on novel materials and device structures is required to meet the criteria. This research presents a novel GaN HEMT construction. The components of an AlGaN/GaN heterojunction HEMT are the drain, source and gate electrode. Since M. Asif Khan and his colleagues published the first AlGaN/GaN HEMT in 1993, a lot of HEMT structures have been reported by researchers; nevertheless, none of them contain three electrodes on distinct sides of the device. In this study, we constructed a 2DEG GaN HEMT and observed its features, including its drain current characteristics curves, Ion/Ioff ratio, and transconductance characteristics curves. Which were acquired from simulations carried out with Silvaco ATLAS™.

This study conducts a comparative analysis of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for Flexible Power Point Tracking (FPPT) in photovoltaic (PV) systems. The GA-based FPPT algorithm exhibits superior performance in power output, tracking accuracy, and convergence speed compared to conventional methods. In contrast, the PSO-based FPPT algorithm is designed to mitigate oscillations around steady-state operating points under partial shading conditions (PSC) by incorporating power limitation control. This allows the FPPT-PSO algorithm to effectively track the global maximum power point (GMPP) without fluctuating around steady-state points. The findings of this comparative analysis highlight the significance of adaptive FPPT algorithms in enhancing system reliability and maximizing power extraction under dynamic environmental conditions. The GA-based approach excels in optimizing power generation metrics, while the PSO-based approach specializes in maintaining stability and precision under challenging operational scenarios such as partial shading. By exploring the strengths and limitations of each algorithm, this study provides valuable in-sights into the selection and implementation of FPPT strategies in PV systems.

In this article, the electronic and optical properties of single-walled boron nitride nanotubes (SWBNNTs) in zigzag (12, 0) and armchair (7, 7) configurations were examined using density functional theory (DFT) with the Vienna Ab initio Simulation Package (VASP). The analysis shows that SWBNNTs exhibit electronic characteristics with a density of states and an electronic band gap around 4 eV. Regarding optical properties, the calculations reveal key features such as absorption, reflection, and dielectric constant, with a notably strong absorption peak in the ultraviolet region. These results highlight the potential of SWBNNTs for applications in optoelectronic devices.

Precise voltage and current regulation is essential to ensure the required power output and maximum efficiency in charging stations, particularly those utilizing Wireless Power Transfer (WPT) systems. Effective regulation techniques are necessary to manage voltage and current in Battery Electric Vehicles (BEVs) operating under various charging modes. This study outlines the controller design for different methods for charging lithium-ion batteries in a WPT charger. Initially, the fundamental concepts of WPT systems and their equivalent circuit are introduced. Subsequently, the control strategy for current regulation is detailed for the Constant Current (CC) mode, the Multi-stage Current Method (MCM), and the Pulse Charging Method (PCM). Finally, the resilience and validity of this innovative approach to controlling various techniques for charging lithium-ion batteries are demonstrated through simulations.

This paper presents a novel approach to enhancing the performance of a solar photovoltaic (PV) system by integrating a Differential Evolution (DE) optimization algorithm into the design of a Quasi Sliding Mode Controller (QSMC). The proposed method aims to address the challenges associated with Conventional Sliding Mode Control (CSMC), such as chattering and suboptimal tracking accuracy, which can significantly impact the stability and efficiency of PV systems. Simulation results show that the DE-optimized QSMC reduces tracking error to 0.05 V, while conventional SMC results in a tracking error of 0.15 V. Chattering amplitude is also significantly reduced, from 0.12 A to 0.03 A and the response time is improved from 0.8 seconds to 0.5 seconds. By leveraging the robustness of QSMC and the flexibility of DE, the DE-QSMC is fine-tuned to minimize tracking errors, reduce chattering, and maintain optimal performance under varying environmental conditions. The stability of the proposed technique is rigorously analyzed using the Lyapunov function theorem, ensuring robust system behavior. The effectiveness of the DE-optimized QSMC is validated through simulations conducted on the Matlab platform, demonstrating superior performance compared to conventional control techniques.

Accurately forecasting the energy consumption of industrial equipment and linking these forecasts to equipment health has become essential in modern manufacturing. This capability is crucial for advancing predictive maintenance strategies to reduce energy consumption and greenhouse gas emissions. In this study, we propose a hybrid model that combines Long Short-Term Memory (LSTM) for energy consumption prediction with a statistical change-point detection algorithm to identify significant shifts in consumption patterns. These shifts are then correlated with the equipment’s health status, providing a comprehensive overview of energy usage and potential failure points. In our case study, we began by evaluating the prediction model to confirm the performance of LSTM, comparing it with several machine learning models commonly used in the literature, such as Random Forest, Support Vector Machines (SVM), and GRU. After assessing different loss functions, the LSTM model achieved the strongest prediction accuracy, with an RMSE of 0.07, an MAE of 0.0188, and an R² of 92.7%. The second part of the model, which focuses on detecting change points in consumption patterns, was evaluated by testing several cost functions combined with binary segmentation and dynamic programming. Applied to a real-world case, it successfully detected a change point two months before equipment failure, offering the potential to reduce energy consumption by 27,052 kWh. This framework not only clarifies the relationship between equipment health and CO2 emissions but also provides actionable insights into emission reduction, contributing to both economic and environmental sustainability.

This work presents a Photovoltaic (PV) monitoring system using the Internet of Things (IoT). It provides a detailed overview of the key steps involved in designing the IoT-based PV monitoring system, examining the specific architectural components, including different system layers, potential software and hardware platforms, and communication network technologies used in the IoT-integrated PV system. Additionally, it discusses the existing challenges and issues of IoT-based PV monitoring systems. Furthermore, it proposes a low-cost monitoring system designed to monitor a PV system consisting of a PV module, a DC-DC boost converter, and a load.

Pedestrian detection is a vital aspect of Advanced Driver Assistance Systems (ADAS), crucial for ensuring driving safety and minimizing collision risks. While detecting pedestrians is important, it must be paired with precise distance estimation to create a robust safety solution. Stereovision cameras are well-regarded for their effectiveness and affordability in measuring depth through disparity between two images. Despite this, research on pedestrian distance estimation using only stereovision remains sparse, with many studies relying on computationally heavy dense depth maps. This paper proposes an innovative method for computing object-level disparity specifically for pedestrian detection using stereo cameras. The approach integrates Canny edge detection with ORB (Oriented FAST and Rotated BRIEF) feature matching to efficiently identify and track keypoints within pedestrian bounding boxes. This method not only improves the accuracy of distance estimation but also reduces computational demands, making it suitable for real-time applications. The approach was thoroughly tested on a Raspberry Pi 4, a resource-constrained device, and achieved promising results, demonstrating its potential for practical use in ADAS.

This study investigates the parametric analysis of a quarter-wave resonator integrated into a brick structure for optimized acoustic isolation in buildings. Starting with a brick of 100 mm thickness and period, the quarter-wave resonator was designed and parametrized. The optimization process yielded significant results, achieving an attenuation of approximately -87 dB with a bandgap width of 82%, surpassing the -72.55 dB attenuation provided by the mass law of the brick alone. These findings highlight the potential of the optimized resonator structure to enhance acoustic isolation, making it a promising solution for noise control in urban environments.

This project aims to develop an innovative technique for detecting water stress in tomato plants using deep learning and image processing techniques, and to integrate it into a mobile application for real-time monitoring. The methodology adopted includes the acquisition and preprocessing of image data, the construction and training of a deep learning model, and the development of a user-friendly mobile application. The results show a promising performance of the model in the precise detection of water stress, confirming the usefulness and usability of the developed mobile application.

This research work aims at current literature review and extensive performance improvement in Direct Torque Control using Space Vector Modulation (DTC-SVM) by considering Doubly Fed Induction Machines. In order to determine the effectiveness of DFIM, significant focus needs to be placed on its speed control design. For highly perturbed systems, traditional Proportional-Integral (PI) speed controllers fail as their gain values are a function of system parameters that inherently change such as engine properties. Therefore, a strong speed controller is necessary to realize high-performance drives. To overcome these confrontations, the proposed study presents backstepping speed control which performs better in terms of accuracy of speed, dynamic tracking and robustness against load disturbances. All proposed control algorithms have been subjected to rigorous tests and simulations using a MATLAB/Simulink environment. A comprehensive study comparing the performance of these speed control within DTC framework is carried out with a detailed insight to important metrics such as dynamic response, reference tracking, torque ripple content involved and complexity among others. This study discusses the pros and cons of all mentioned methods, which helps in better understanding for optimal speed control selection to improve DFIM performance at different operating conditions. The results of this research should help in the further development of control strategies for electric machines to make them more efficient and reliable when used.

In the general context of 3D clay printing and sustainable mix design, reaching optimal printing quality and structural integrity requires a thorough understanding of the fresh paste and solid properties of earthen materials. This study investigates the rheology of Benjellik clay using Taguchi Design plan, focusing on the effects of particle size and water content on the related fresh paste properties. A set of nine clay formulations were analysed in terms of yield stress, plastic viscosity, consistency, and flow number according to both Bingham and Bulkley models. Results revealed that water to binder ratio (W/B) mainly impacts rheological properties; it reduces yield stress and plastic viscosity while increasing the flow number. Moreover, the pre-selected raw particle sizes had irrelevant impact due to similarities of the related statistical distributions as corroborated by laser granulometry tests. Furthermore, optimal layer heights to nozzle diameter ratio was found to be around 0.55 enhancing the material flowability and scanned tracks features. Finally, the findings emphasize the crucial role of W/B along with the layer height to nozzle size ratios in stabilizing the printability of clay materials, offering a confident printing parameters area for optimizing both 3D printing clay of Fez region of Morocco.

This paper presents the output feedback pole assignment methods that attempt to achieve a robust solution where the arbitrary choose of the poles to be assigned are as insensitive as possible to perturbations in the closed loop system. The robust and arbitrary pole placement problem is formulated as a nonlinear and non-convex programming problem with a nonlinear constraint on matrix functions as well as a linear constraint. The numerical treatment is the subject of a comparative study of the proposed methods.

In every country on the globe, there are still actions to perform in order to improve buildings' energy efficiency, particularly in regions with severe weather conditions. Since walls are heat-dissipating elements in the building, designing them effectively can help reduce the energy consumption of the HVAC (heating, ventilation, and air-conditioning) systems, which are notorious for using excessive amounts of energy. The use of self-insulating construction materials with relatively high thermal resistance and low transmittance is the most effective approach to achieve universal thermal insulation in all buildings. In order to enhance the thermal performance of red clay bricks, the present study evaluates several design approaches. Five configurations are assessed, namely the case of conventional bricks, the use of aluminum shields, low thermal emissivity coating, extruded polystyrene and air cavity partitioning. Finite element method based analysis in steady-state and transient conditions enabled the assessment of the effects of each technique and their classification according to thermal performance level.

The overall goal of this study is to support the advancement of new construction methods and materials that are more cost-effective and have lower carbon emissions. There is a rising interest in utilizing earth materials in 3DCP for sustainable construction solutions, but their progress is limited by slow production rates. This research investigated the influence of various parameters on the efficacy of 3D earth materials printing using Taguchi experimental design, such as alginate dosing, scan strategy, scan speed, layer thickness, and curing age. Moreover, the impact of incorporating 2% alginate biopolymer into the clay matrix was assessed through FTIR and XRD analysis. Additionally, the Taguchi method was assessed using Fault Tree Analysis FTA to pinpoint the primary causes of failure in extrud- ability and buildability functions.

Environmental monitoring plays a crucial role in various domains, including agriculture, healthcare, and manufacturing, where optimal environmental conditions are essential for productivity and safety. In this project, we present a smart environmental monitoring system that leverages IoT (Internet of Things) technology and data analytics to monitor temperature and humidity levels in real-time. The system consists of a network of sensor nodes deployed in the target environment, comprising ESP32 microcontrollers and DHT11 sensors for data collection. The sensor nodes transmit data using the MQTT (Message Queuing Telemetry Transport) protocol to a cloud-based MQTT Broker hosted on HiveMQ Cloud. Data processing and visualization are handled by Node-RED, which subscribes to MQTT topics, processes incoming data streams, and stores them in a time-series database, InfluxDB Cloud. The collected data is then visualized in real-time using Grafana dashboards, which are embedded within a Flask web application, providing stakeholders with seamless access to actionable insights into environmental conditions. The smart environmental monitoring system offers numerous benefits, including improved decisionmaking, proactive maintenance, and enhanced productivity. Future enhancements could include the integration of additional sensors and the application of machine learning algorithms for predictive analytics. Overall, the project demonstrates the potential of IoT and data analytics in addressing real-world challenges related to environmental monitoring.

This article aims to design and analyze energy systems for a villa to address the dual challenges of inadequate illumination and excessive electricity consumption. The study employs specialized software programs, including AutoCAD, PVsyst, DIALux evo, and T*sol, to design and dimension the villa’s energy systems. A comprehensive analysis of the photovoltaic system is conducted alongside precise architectural modelling and simulations of the interior lighting and solar water heater system. Additionally, the effectiveness of motion detectors in optimizing interior lighting to reduce energy consumption is examined, and solar lamps are integrated for the villa’s exterior lighting to promote sustainability. The findings indicate that the implementation of advanced energy system designs and the use of motion detectors can significantly enhance interior lighting while simultaneously reducing energy consumption by 30%. This paper contributes a holistic approach to energy system design in residential settings, offering innovative solutions for improving lighting quality and energy efficiency in villa architecture. The integration of renewable energy sources and smart technologies underscores the potential for sustainable living solutions in modern residential designs.