Sup de Co Marrakech (also École supérieure de commerce de Marrakech) is a private five year college based in Marrakech, Morocco. It was founded in 1987.Since 2006 the college started a master's degree in Business in partnership with Sup de Co Grenoble, France.The school is affiliated to École supérieure de commerce de Toulouse of France..
The aim of this work is to study the existence and uniqueness of integral solutions for a class of non-local parabolic equations. There are two main results. First, we use a subdifferential technique to verify the existence and uniqueness of weak solutions when the initial data belong to \(L^2\). Secondly, the existence and uniqueness of an integral solution is demonstrated by extending the study to initial data in \(L^1\) space. To overcome the difficulties caused by non-local terms, the proposed strategy combines new approaches with sophisticated strategies derived from the theory of accretive operators. Non-local evolution equations and their applications are better understood thanks to these results.
Cette étude vise à analyser la littérature scientifique existante sur la responsabilité sociale des entreprises et la performance globale publiée entre 2001 et 2024 à l’aide du logiciel VOSviewer. 288 articles indexés provenant de la base de données Scopus ont été analysés. Ce travail adopte une approche bibliométrique. Les auteurs examinent les résultats sous les angles suivants : nombre de publications par an, articles les plus cités par d'autres auteurs, auteurs les plus éminents, revues les plus influentes, pays ayant la productivité la plus élevée, coopération des universités au niveau international, co-occurrence des mots clés et le couplage bibliographique. Les résultats de l'analyse de cette étude peuvent être utilisés pour améliorer notre compréhension de la recherche sur la relation entre la performance globale et la responsabilité sociale des entreprises et soutenir d'autres recherches dans ce domaine.
The advent of the Internet of Things (IoT) has notably enhanced the quality of life, with significant advancements being made in the e-health domain through the Internet of Medical Things (IoMT).The IoMT, a network comprising medical devices, sensors, and applications, generates voluminous data that frequently necessitate real-time transmission to healthcare providers for effective emergency responses.Presented herein is a framework designed to prioritize this emergent data in IoMT applications.This framework employs a Quality of Service (QoS) gateway for data processing, aiming to ensure high throughput and minimal packet loss.Simulations were conducted, the results of which demonstrate a superior performance of the proposed framework, inclusive of the high-priority gateway, when compared to traditional networks.
Within the field of computer vision and artificial intelligence, the analysis of twodimensional image data stands as a pivotal domain, specifically in the context of semantic segmentation.This intricate process involves the precise categorization of pixels within a two-dimensional space, thereby enabling nuanced classification at a granular level.In this research endeavor, we present a novel network architecture, denoted as "a-Net," strategically crafted to achieve a delicate balance between computational expeditiousness, operational efficiency, adaptability, and precision for the overarching objective of semantic segmentation in two-dimensional imagery.The a-Net architecture, grounded in the principles of auto-encoding, tactically addresses data loss concerns inherent in segmentation processes.Engineered to adeptly outline objects within two-dimensional spaces, this architecture yields meticulous masks for individual objects, ensuring the generation of highfidelity segmentation outcomes.The design philosophy of a-Net underscores not only its computational efficacy but also its straightforward implementability and training, thus imparting versatility across a diverse array of applications.Its efficacy spans the resolution of varied challenges within the domain of two-dimensional semantic segmentation, with particular relevance in medical imaging scenarios encompassing objects of both microscopic and macroscopic scales.Our investigative methodology establishes the superior performance of the a-Net architecture relative to alternative two-dimensional semantic segmentation frameworks.This superiority is underscored by commendable outcomes observed across diverse challenges, affirming the a-Net's status as a robust and versatile solution within the evolving landscape of two-dimensional semantic segmentation.This research significantly contributes to advancing the state of the art in the realm of image segmentation, offering a sophisticated and efficient solution that attains optimal precision while preserving computational efficiency.
