Abstract

Abstract
The rising adoption rates and integration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) into the energy grid introduces complex challenges, including the need to balance supply and demand and smooth peak consumptions. Addressing these challenges requires innovative solutions such as Demand Response (DR), Renewable Energy Communities (RECs), and more specifically for EVs, Vehicle-to-Grid (V2G). However, existing V2G approaches often fall short in real-world applicability, adaptability, and user engagement. To bridge this gap, this paper proposes EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management algorithm leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. EnergAIze enables user-centric multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. Additionally, it architects' data protection and ownership through decentralized deployment, where each prosumer can situate an energy management node directly at their own dwelling. The local node not only manages local EVs and other energy assets but also fosters REC wide optimization. EnergAIze is evaluated through a case study using the CityLearn framework. The results show reduction in peak loads, ramping, carbon emissions, and electricity costs at the REC level while optimizing for individual prosumers objectives.

Abstract
Electromobility is a critical component of Europe’s strategy to create a more sustainable society and support the European Green Transition while enhancing quality of life. Electrification also plays an important role in securing Europe’s position in the growing market of electric and autonomous vehicles (EAV). The EU-funded OPEVA project aims to take a big step towards deployment of sustainable electric vehicles by means of optimising their support in an ecosystem. Specifically, the project focuses on analysing and designing optimisation architecture, reducing battery charging time, and developing infrastructure, as well as reporting on the driver-oriented human factors. Overall, OPEVA’s goal is to enhance EAV market penetration and adoption, making them more accessible and convenient. The aim of this paper is to inform the European automotive, transportation, energy and mobility community be presenting the OPEVA manifestation, and the overall solution strategy solidified through the progress throughout the first year of the project.

Abstract
This paper explores the use of Robotics and decentralized Multi-Agent Reinforcement Learning (MARL) for side-by-side navigation in Intelligent Wheelchairs (IW). Evolving from a previous work approach using traditional single-agent methodologies, it adopts a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to provide control input and enable a pair of IW to be deployed as decentralized computing agents in real-world environments, discarding the need to rely on communication between each other. In this study, the Flatland 2D simulator, in conjunction with the Robot Operating System (ROS), is used as a realistic environment to train and test the navigation algorithm. An overhaul of the reward function is introduced, which now provides individual rewards for each agent and revised reward incentives. Additionally, the logic for identifying side-by-side navigation was improved, to encourage dynamic alignment control. The preliminary results outline a promising research direction, with the IWs learning to navigate in various realistic hallways testing scenarios. The outcome also suggests that while the MADDPG approach holds potential over single-agent techniques for the decentralized IW robotics application, further investigation are needed for real-world deployment.

Abstract
Developing real-time systems applications requires programming paradigms that can handle the specification of concurrent activities and timing constraints, and controlling execution on a particular platform. The increasing need for high-performance, and the use of fine-grained parallel execution, makes this an even more challenging task. This paper explores the state-of-the-art and challenges in real-time parallel application development, focusing on two research directions: one from the high- performance domain (using OpenMP) and another from the real-time and critical systems field (based on Ada). The paper reviews the features of each approach and highlights remaining open issues.

Abstract
O 6.º Encontro Internacional sobre Jogos e Mobile Learning (EJML) é organizado na Faculdade de Psicologia e de Ciências da Educação, no âmbito das atividades do Laboratório de Tecnologia Educativa (LabTE) da Universidade de Coimbra e do Centro de Estudos Interdisciplinares, em coorganização com a UNISINOS, a Universidad de La Laguna e o Instituto Politécnico de Tomar. Os autores partilham as suas investigações nas áreas de jogos educativos (serious games), Mobile Learning e Formação de Professores e as múltiplas literacias. As comunicações reportam estudos com diferentes públicos etários, desde os mais jovens até aos séniores. As temáticas abarcam desenvolvimento de jogos, aprendizagem baseada em jogos, avaliação da aprendizagem com dispositivos móveis, educação inclusiva, cyberbullying, gamificação, ambientes imersivos de aprendizagem, realidade virtual, realidade aumentada e inteligência artificial no ensino. Todas as comunicações foram submetidas para avaliação, sendo analisadas por três membros da Comissão Científica, através de um processo de blind review. A Comissão Científica é constituída por investigadores de Portugal, Brasil, Espanha, Moçambique e Reino Unido. O evento integra comunicações longas e breves, que estão publicadas nestas atas, relatos de experiências numa outra publicação e onze workshops, cujos tutoriais constituem uma terceira publicação do evento.