Abstract
Investigating the complex coupling between the lithosphere, atmosphere, and ionosphere (LAI) requires a fundamental understanding of the physical forces governing tectonic processes and their electromagnetic manifestations. While various pre-seismic signals have been successfully identified, a persistent gap remains between the empirical observation of these phenomena and the fundamental physical laws that describe nature across all scales, from the subatomic realm to cosmic expansion. Exploring these interrelations presents significant challenges due to divergent scientific languages, specialized expertise, and unique terminologies across fields. The recently approved COST Action CA24101 "Testing Fundamental Physics with Seismology" (FuSe) aims to bridge this gap by exploring how seismic phenomena and earthquake precursors can serve as a "multi-messenger" window into fundamental interactions.At the heart of FuSe is the belief that imprints of non-standard physics, such as scalar fields or "fifth forces”, may be embedded within seismic and geomagnetic data. Conversely, theoretical insights from fundamental physics can refine our understanding of Earth’s interior by improving models of density and thermodynamic parameters like elasticity and bulk modulus. This refined modeling is essential for accurately interpreting the electromagnetic and gravitational perturbations that occur within the complex Earth-atmosphere-space system.To ensure these breakthroughs translate into practical advancements, FuSe focuses on several strategic pillars:-   Building a common language: developing a shared methodology to equip the next generation of scientists with cross-disciplinary skills.-   Interfacing communities: creating dynamic research groups that unite scientists from particle physics, gravity, planetary science, and seismology.-   Cross-disciplinary data integration: consolidating seismic data from the Earth and Moon with particle physics and geomagnetic data into AI-ready, FAIR-compliant streams.-   SME collaboration: partnering with small and medium-sized enterprises (SME) to advance sensor networks, AI algorithms, and real-time natural catastrophe alert systems.In this presentation, we outline the roadmap of the FuSe Action. We invite researchers with a background in electromagnetic precursors and LAI coupling to join this collaborative environment, where the synergy between geosciences and fundamental physics promises to drive innovative breakthroughs and unlock new paradigms in our comprehension of the Earth and the Universe.This abstract is based upon work from COST Action CA24101, Testing Fundamental Physics with Seismology (FuSe), supported by COST (European Cooperation in Science and Technology).

Abstract
Higher Education Institutions (HEIs) face significant challenges in managing and integrating diverse Information System (ISs) that support academic, administrative, and strategic operations. As digital transformation advances, the need for seamless interoperability and data-driven governance becomes increasingly crucial. This study provides a comprehensive analysis of the ISs Ecosystem (ISE) in HEIs, emphasizing the importance of system integration, Business Intelligence (BI) solutions, and Decision Support Systems (DSS) in fostering efficient, data-driven decision-making. By examining a real-world case study of the University of Tras-os-Montes and Alto Douro (UTAD), this research validates the role of BI in transforming fragmented information landscapes into cohesive digital environments. The findings demonstrate that successful BI adoption requires well-defined governance structures, seamless data flow, and alignment with institutional objectives. Additionally, the study underscores the strategic impact of interoperability, highlighting how institutions can enhance institutional intelligence, streamline decision-making processes, and improve operational efficiency through an integrated BI ecosystem. The insights contribute to ongoing discussions on digital transformation in higher education, offering a scalable framework for HEIs seeking to transition from isolated systems to an interoperable and intelligent data ecosystem. The paper also explores emerging trends such as AI-driven analytics and predictive modelling, outlining potential pathways for HEIs to further optimize their decision-support infrastructures.

Abstract
Federated Graph Neural Networks (FedGNNs) have emerged as a promising paradigm for decentralized graph learning across distributed data silos. However, the influence of underlying communication topologies on model accuracy and efficiency remains underexplored. This study presents a topology-aware benchmarking framework for federated GNNs, systematically evaluating the impact of network structure and aggregation strategy on performance and communication overhead. The proposed framework functions as a synthetic, communication-level digital twin that emulates Federated Learning interactions and topology-dependent dynamics under controlled conditions. Four learning schemes-Centralized, Local, FedAvg, and FedAvg-Fedadam-were assessed across three representative topologies: Barab & aacute;si-Albert (BA), Watts-Strogatz (WS), and Erd & odblac;s-R & eacute;nyi (ER). Results demonstrate that centralized training achieved the highest mean ROC-AUC (0.63), while FedAvg-Fedadam attained the best F1-score (0.038), balancing local adaptation and global convergence. Among topologies, BA and WS yielded higher average AUC values (approximately 0.57 and 0.56, respectively) than ER (approximately 0.39). Communication analysis revealed FedAvg as the most efficient strategy, requiring only approximately 3.8 x 105 bytes cumulatively. These findings highlight key trade-offs between accuracy, robustness, and communication efficiency in federated graph learning and provide empirical guidance for topology-aware optimization of distributed GNNs. While the experiments rely on representative synthetic topologies, the insights offer indicative relevance and potential applicability to Internet-of-Things (IoT), vehicular, and cyber-physical networks, where communication structure and bandwidth constraints critically influence collaborative intelligence. By modeling canonical connectivity patterns and releasing our code and data, the proposed benchmarking framework offers a reproducible basis for comparing emerging federated graph architectures under constrained communication conditions.

Abstract
Driving safety under low-visibility or distracted conditions remains a critical challenge for intelligent transportation systems. This paper presents Edge-VisionGuard, a lightweight framework that integrates signal processing and edge artificial intelligence to enhance real-time driver monitoring and hazard detection. The system fuses multi-modal sensor data-including visual, inertial, and illumination cues-to jointly estimate driver attention and environmental visibility. A hybrid temporal-spatial feature extractor (TS-FE) is introduced, combining convolutional and B-spline reconstruction filters to improve robustness against illumination changes and sensor noise. To enable deployment on resource-constrained automotive hardware, a structured pruning and quantization pipeline is proposed. Experiments on synthetic VR-based driving scenes demonstrate that the full-precision model achieves 89.6% driver-state accuracy (F1 = 0.893) and 100% visibility accuracy, with an average inference latency of 16.5 ms. After 60% parameter reduction and short fine-tuning, the pruned model preserves 87.1% accuracy (F1 = 0.866) and <3 ms latency overhead. These results confirm that Edge-VisionGuard maintains near-baseline performance under strict computational constraints, advancing the integration of computer vision and Edge AI for next-generation safe and reliable driving assistance systems.

Abstract
Virtual Reality (VR) applications hold significant promise for enhancing healthcare training by providing immersive and interactive environments for skills development. This scoping review analyzed the methods, and instruments used for usability assessment in VR applications designed to support healthcare training. An electronic database search identified 19 studies meeting specific inclusion and exclusion criteria. The included studies focused on two primary objectives: (i) usability assessment, and (ii) usability and feasibility assessment, evaluating not only the usability but also the practicality and sustainability of VR applications in healthcare training settings. The findings reveal inconsistencies in the reporting of methodological details essential for robust usability assessment, particularly in terms of methods' triangulation and participants' sample sizes. This review highlights the need for more rigorous and comprehensive approaches that combine both test and inquiry methods to ensure that VR applications present good usability, which is impactful for sustainable healthcare training applications.

Abstract
essential for achieving the Sustainable Development Goals (SDGs), particularly in regions aiming to balance energy efficiency, waste management, and urban development. This study explores the application of multicriteria decision-making and statistical techniques to evaluate municipal sustainability, with a focus on renewable energy, using the Alto Minho region of Portugal as a case study. The analysis incorporates 12 SDG indicators across ten municipalities, addressing energy consumption, urban renewal, and waste management. Cluster analysis revealed distinct groups of municipalities, highlighting disparities in sustainability performance. Municipalities such as Melgaco and Moncao excelled in energy-related metrics, while others showed strengths in waste management and urban renewal. The Analytic Hierarchy Process (AHP) emphasized the importance of renewable energy indicators, revealing notable changes in rankings when energy-related criteria were prioritized. Ponte de Lima and Melgaco ranked highest under energy-focused weighting schemes, showcasing their leadership in energy efficiency and renewable adoption. The findings underscore the need for targeted policies to enhance sustainability across municipalities, particularly in regions lagging in energy performance.