Single solar cell ideality factor determination using a fixed point method

The modeling and extraction of solar cell parameters are the crucial steps for simulating and optimizing the photovoltaic systems to meet specific properties. These parameters are directly related to the current-voltage characteristic of the solar cell under illumination, the latter is generally represented, by an equivalent electrical circuit whose parameters (the shunt resistance, the saturation current, the series resistance and the ideality factor of the diode) have been the subject of several researches. This paper describes an iterative algorithm based on fixed point method to calculate the ideality factor of a photovoltaic cell. The procedure uses the electrical and mathematical equations governing the solar cell behavior. The obtained results were compared to the previous works to show its effeteness

Etude de propietes de transport d'un plasma de melange air-cuivre : modelisation de la colonne d'arc

SIGLECNRS T 59075 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Evaluation des apprentissages au sein d’un environnement de type MOOC adaptatif

La problématique de l’évaluation des apprentissages au sein d’un MOOC suscite un grand débat. Ce type d’environnements d’apprentissage offre des cours limités dans le temps, organisés en ligne et ouverts à tous. L’apprentissage au sein des MOOC consiste en l’échange du savoir entre les participants et l’interaction avec les concepteurs (forum, chat, etc.) en se libérant des contraintes de temps et d’espace. En effet, le MOOC est un outil d’apprentissage en ligne et rythmés. L’évaluation des apprentissages au sein des MOOC représente un pilier essentiel pour la favorisation d’un apprentissage rythmé. Cet apprentissage libère les apprenants dans le temps et dans l’espace. Les concepteurs des MOOC ont largement investi sur des modalités d’évaluation automatisées, tels que des modes de la correction automatique (les quiz ou les questionnaires à choix multiples). Pourtant, ces modalités restent très limitées face au développement d’une pensée critique au cours d’une séquence d’apprentissage. Dans ce papier nous allons aborder les techniques et les méthodes d’évaluation qui permettent de mesurer l’atteinte des objectifs d’apprentissage dans un MOOC. Ensuite, nous présentons l’architecture d’un modèle d’apprentissage basé sur les agents susceptibles de fournir une évaluation formative et personnalisé de cours en ligne massifs

The Rising Trends of Smart E-Commerce Logistics

Smart Logistics (SL) offers a competitive advantage for e-commerce by utilizing Information and Communication Technologies (ICT) such as IoT, AI, Blockchain, Cloud computing, 5G, etc. This technology automates, optimizes, and enables real-time tracking and monitoring of shipments, predicts, and prevents delays, and optimizes delivery routes and schedules. It also provides greater visibility and control, allowing e-commerce businesses to react quickly and efficiently to changes in demand or supply. The purpose of this study is to investigate the impact of digitalization on trade logistics in e-commerce, emphasizing the significance of smart logistics for the e-commerce industry. We reviewed 288 articles published in the last decade in the Scopus database to assess the maturity of research in this area. For researchers, this study provides a better understanding of smart e-commerce logistics and identifies research gaps in the literature. For e-commerce professionals, it can help them adopt the latest technological trends in their logistics. Through a systematic literature review and network analysis approach, the study has contributed by identifying 5 clusters related to ICT application fields in e-commerce and 5 clusters related to important ICT enablers in smart logistics. We also identified several research gaps and areas for future study, including the underutilization of computer vision technology and the need for further research on product quality inspection and accessibility for people with disabilities. Additionally, we suggest exploring the power of deep learning to solve Vehicle Routing Problems (VRP) and optimizing sensing data volume for minimizing costs associated with data storage and transfer. This study provides a comprehensive overview of the state of the art in smart logistics for e-commerce and serves as a guide for future research in this field

A New Scalable, Distributed, Fuzzy C-Means Algorithm-Based Mobile Agents Scheme for HPC: SPMD Application

The aim of this paper is to present a mobile agents model for distributed classification of Big Data. The great challenge is to optimize the communication costs between the processing elements (PEs) in the parallel and distributed computational models by the way to ensure the scalability and the efficiency of this method. Additionally, the proposed distributed method integrates a new communication mechanism to ensure HPC (High Performance Computing) of parallel programs as distributed one, by means of cooperative mobile agents team that uses its asynchronous communication ability to achieve that. This mobile agents team implements the distributed method of the Fuzzy C-Means Algorithm (DFCM) and performs the Big Data classification in the distributed system. The paper shows the proposed scheme and its assigned DFCM algorithm and presents some experimental results that illustrate the scalability and the efficiency of this distributed method

Efficient feature descriptor selection for improved Arabic handwritten words recognition

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words

Distributed C-Means Algorithm for Big Data Image Segmentation on a Massively Parallel and Distributed Virtual Machine Based on Cooperative Mobile Agents

The aim of this paper is to present a distributed algorithm for big data classification, and its ap-plication for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classifi-cation method which is the c-means method. The proposed method is introduced in order to per-form a cognitive program which is assigned to be implemented on a parallel and distributed ma-chine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condi-tion is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure