This paper proposes a smart system for managing the operations of grid-connected charging stations for electric vehicles (EV) that use photovoltaic (PV) sources. This system consists of a mobile application for EV drivers to make charging reservations, an algorithm to optimize the charging schedule, and a remote execution module of charging operations based on the open charge point protocol (OCPP). The optimal charging schedule was obtained by solving a binary integer programming problem. The merits of our solution are illustrated by simulating different charging demand scenarios.

Background/Objectives: Cardiovascular diseases (CVDs) remain the primary cause of mortality worldwide, underscoring the critical importance of developing accurate early prediction models. In this study, we propose an advanced stacking ensemble learning framework to improve the predictive performance for CVD diagnosis. Methods: The methodology encompasses comprehensive data preprocessing, feature selection, cross-validation, and the construction of a stacking architecture integrating Random Forest (RF), Support Vector Machine (SVM), and CatBoost as base learners. Two meta-learning configurations were examined: Logistic Regression (LR) and a Multilayer Perceptron (MLP). Results: Experimental results indicate that the MLP-based stacking model achieves superior performance, with an accuracy of 97.06%, outperforming existing approaches reported in the literature. Furthermore, the model demonstrates high recall (96.08%) and precision (98%), confirming its robustness and generalization capacity. Conclusions: Compared to individual classifiers and traditional ensemble methods, the proposed approach yields significantly enhanced predictive outcomes, highlighting the potential of deep learning-based stacking strategies in cardiovascular risk assessment.

Due to the rapid evolution of information technology, distance education has undergone a sustained expansion. Within the scope of open distant learning, MOOCs (Massive Open Online Courses) tend to open up access to education to all by bridging geographical and economic barriers. However, the evolution of this learning mode is facing some challenges such as the low completion rate of the courses as well as the heterogeneity of the learners’ profifiles. In this paper, we aim to personalize the MOOC contents for each learner in order to improve their academic performance and to enhance the online platforms effificiency. The idea is to build a system that takes into account the heterogeneity of learners profifiles and offers each learner a path adapted to their needs through the exploitation of their interactions with the learning environment. To this end, we suggest using the PSO method ”Particle Swarm Optimization” in order to construct the optimal choices of learning paths in the system. Furthermore, we conduct an online learning case study to show the effectiveness of the proposed approach.

The emphasis of recent works is focused on the use of rules based systems that describe the system behavior by simple rules which increase the flexibility and easiness of programming. In the context of the distributed test where a set of parallel testers exchange 110 messages to perform the test, some potential problems of coordination can arise amongst remote testers. These problems are usually known as controllability and Observability issues. We present in this paper our approach based on the use of a rule based multi-agent system to overcome such problems.

Currently, massive open online courses (MOOCs) are experiencing major developments and arebecoming increasingly popular in distance learning programs. The goal is to break down inequalitiesand disseminate knowledge to everyone by creating a space for exchange and interaction.Despite the improvements to this educational model, MOOCs still have low retention rates, whichcan be attributed to a variety of factors, including learners’ heterogeneity. The paper aims toaddress the issue of low retention rates in MOOCs by introducing an innovative prediction modelthat provides the best (optimal) learning path for at-risk learners. For this purpose, learners at riskof dropping out are identified, and their courses are adapted to meet their needs and skills. A casestudy is presented to validate the effectiveness of our approach using classification algorithms forprediction and the ant colony optimization (ACO) algorithm to optimize learners’ paths.