This paper presents a dual-layer approach for managing and controlling an AC Microgrid (MG). The MG integrates a Photovoltaic System (PVS), Wind Turbine System (WTS), a Battery Storage System (BSS), all interconnected with the utility grid. The dual-layer is structured into a Control Layer (CL) and an Energy Management System Layer (EMS-L). The CL proposes an efficient model coupled with a system control, ensuring high power quality for the AC microgrid. This system employs a Particle Swarm Optimization (PSO) algorithm to optimize the set point tracking performance. Simultaneously, the EMS-L utilizes a Sparrow Search Algorithm (SSA) with the objective of minimizing the total operating costs of the AC microgrid. This is achieved by employing a 15-minutes step time over a 24-hours period, enhancing both precision and rapidity of the system’s operation. This enhanced temporal resolution effectively models and responds to fluctuations in energy demand, supply variability and environmental factors. This synergistic integration allows for nuanced and efficient energy flow management in order to optimise the MG performance. A significant aspect of the research is the comparative analysis of the proposed SSA-EMS with PSO and Genetic Algorithm (GA), alongside an unoptimized system. This analysis highlights the hybrid methodology’s superior efficiency and effectiveness. The robust framework, combining SSA at the EMS layer with PSO at the control layer, has been empirically tested using a real world data to confirm its effectiveness and resilience in practical scenarios. These tests provide solid evidence of the methodology’s potential in boosting the sustainable and reliable operation of microgrids. Significantly, the SSA-EMS showcases notable cost efficiency, achieving reductions of 33.34% and 41.18% compared to PSO and GA. Impressively, compared to an unoptimized system, the SSA-EMS demonstrates an even more remarkable cost reduction of 49.43% and 59.10% while considering the impact on battery health constraints.

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

Background: This study evaluates the performance of a vision transformer (ViT) model, ViT-b16, in classifying ischemic stroke cases from Moroccan MRI scans and compares it to the Visual Geometry Group 16 (VGG-16) model used in a prior study. Methods: A dataset of 342 MRI scans, categorized into ‘Normal’ and ’Stroke’ classes, underwent preprocessing using TensorFlow’s tf.data API. Results: The ViT-b16 model was trained and evaluated, yielding an impressive accuracy of 97.59%, surpassing the VGG-16 model’s 90% accuracy. Conclusions: This research highlights the ViT-b16 model’s superior classification capabilities for ischemic stroke diagnosis, contributing to the field of medical image analysis. By showcasing the efficacy of advanced deep learning architectures, particularly in the context of Moroccan MRI scans, this study underscores the potential for real-world clinical applications. Ultimately, our findings emphasize the importance of further exploration into AI-based diagnostic tools for improving healthcare outcomes.

In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.

The Internet of Things (IoT) concept is tremendously applied in our current daily lives. The IoT involves Radio Frequency Identification (RFID) as a part of the infrastructure that helps with the data gathering from different types of sensors. In general, security worries have increased significantly as these types of technologies have become more common. For this reason, manifold realizations and studies have been carried out to address this matter. In this work, we tried to provide a thorough analysis of the cryptography-based solutions for RFID cards (MIFARE cards as a case study) by performing a Systematic Literature Review (SLR) to deliver the up-to-date trends and outlooks on this topic.

This study presents a comprehensive exploration of deep learning models for precise brain ischemic stroke classification using medical data from Morocco. Following the OSEMN approach, our methodology leverages transfer learning with the VGG-16 architecture and employs data augmentation techniques to enhance model performance. Our developed model achieved a remarkable validation accuracy of 90%, surpassing alternative state-of-theart models (ResNet50: 87.0%, InceptionV3: 82.0%, VGG-19: 81.0%). Notably, all models were rigorously evaluated on the same meticulously curated dataset, ensuring fair and consistent comparisons. The investigation underscores VGG-16’s superior performance in distinguishing stroke cases, highlighting its potential as a robust tool for accurate diagnosis. Comparative analyses among popular deep learning architectures not only demonstrate our model’s efficacy but also emphasize the importance of selecting the right architecture for medical image classification tasks. These findings contribute to the growing evidence supporting advanced deep learning techniques in medical imaging. Achieving a validation accuracy of 90%, our model holds significant promise for real-world healthcare applications, showcasing the critical role of cutting-edge technologies in advancing diagnostic accuracy and the transformative potential of deep learning in the medical field.