Abstract

Abstract
The European Union's Artificial Intelligence (AI) Act defines robustness, resilience, and security requirements for high-risk sectors but lacks detailed methodologies for assessment. This paper introduces a novel framework for quantitatively evaluating the robustness and resilience of reinforcement learning agents in congestion management. Using the AI-friendly digital environment Grid2Op, perturbation agents simulate natural and adversarial disruptions by perturbing the input of AI systems without altering the actual state of the environment, enabling the assessment of AI performance under various scenarios. Robustness is measured through stability and reward impact metrics, while resilience quantifies recovery from performance degradation. The results demonstrate the framework's effectiveness in identifying vulnerabilities and improving AI robustness and resilience for critical applications.

Abstract
The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring system stability and efficiency. This paper presents the development of an optimized energy management system integrating renewable sources, with a focus on green hydrogen production via electrolysis, storage, and use through a fuel cell. The system aims to promote energy autonomy and support the transition to a low-carbon economy by reducing dependence on the conventional electricity grid. The proposed model enables flexible hourly energy flow optimization, considering solar availability, local consumption, hydrogen storage capacity, and grid interactions. Formulated as a Mixed-Integer Linear Programming (MILP) model, it supports strategic decision-making regarding hydrogen production, storage, and utilization, as well as energy trading with the grid. Simulations using production and consumption profiles assessed the effects of hydrogen storage capacity and electricity price variations. Results confirm the effectiveness of the model in optimizing system performance under different operational scenarios.

Abstract

Abstract

Abstract
Integrating Internet of Things (IoT) technologies into physical education (PE) presents opportunities for improving the methodologies for collecting, analysing, and managing student performance data. However, it also introduces technical challenges, particularly related to the real-time handling and protection of sensitive data in dynamic training environments. This paper presents a comprehensive solution outline based on a private local network architecture that supports scalable sensor data processing, real-time database integration, and mobile application interfaces. The proposed distributed system ensures data integrity, low-latency communication, and secure access while enabling educators to monitor student performance in real-time and review historical data. The system supports more personalised, data-driven training strategies by providing actionable insights for sports education. © © 2025 by SCITEPRESS - Science and Technology Publications, Lda.