Abstract
With the growing need for AI-driven solutions in power grid management, this work addresses the challenge of creating realistic synthetic operating scenarios essential for developing, testing, and validating AI-based decision-making systems. It uses spatial-temporal noise functions, predefined patterns, and optimal power flow to model renewable energy and conventional power plant generation, load, and losses. Quantitative and visual key performance indicators are proposed to evaluate the quality of the generated operating scenarios, and the validation highlights the framework's ability to emulate diverse and practical operating scenarios, bridging gaps in AI-driven power system research and real-world applications. © 2025 Elsevier B.V., All rights reserved.

Abstract
Chest radiography (CXR) is one of the most used medical imaging modalities. Nevertheless, the interpretation of CXR images is time-consuming and subject to variability. As such, automated systems for pathology detection have been proposed and promising results have been obtained, particularly using deep learning. However, these tools suffer from poor explainability, which represents a major hurdle for their adoption in clinical practice. One proposed explainability method in CXR is through contrastive examples, i.e. by showing an alternative version of the CXR except without the lesion being investigated. While image-level normal/healthy image synthesis has been explored in literature, normal patch synthesis via inpainting has received little attention. In this work, a method to synthesize contrastive examples in CXR based on local synthesis of normal CXR patches is proposed. Based on a contextual attention inpainting network (CAttNet), an anatomically-guided inpainting network (AnaCAttNet) is proposed that leverages anatomical information of the original CXR through segmentation to guide the inpainting for a more realistic reconstruction. A quantitative evaluation of the inpainting is performed, showing that AnaCAttNet outperforms CAttNet (FID of 0.0125 and 0.0132 respectively). Qualitative evaluation by three readers also showed that AnaCAttNet delivers superior reconstruction quality and anatomical realism. In conclusion, the proposed anatomical segmentation module for inpainting is shown to improve inpainting performance.

Abstract
Purpose of Review: This paper reviews the flexibility-centric value chain (FCVC) and analyses how coordinating digital platforms along the FCVC is essential for enabling FCVC activities and supporting key actors. Based on the FCVC, the digital infrastructure needed to support flexibility provision in power systems is reviewed, with special focus on the role of energy communities (ECs) as emerging relevant actors and potential aggregators of its members. Recent Findings: We review the Grid Data and Business Network (GDBN), a platform developed by the authors to support the FCVC, with special focus on those stages of the FCVC not properly supported by existing solutions. It also analyses platforms used in local flexibility markets (LFMs), and it presents the RECreation digital platform designed to manage ECs to support the participation in flexibility markets. Summary: Digital platforms are necessary for scaling flexibility services. The GDBN offers a comprehensive approach by enabling the FCVC and facilitating interoperability with existing platforms dedicated to specific segments, such as ECs and LFMs. By addressing current limitations in platform integration, this paper contributes to a clearer understanding of how digital tools can enable an efficient flexibility ecosystem. © The Author(s) 2025.

Abstract

Abstract
The study investigates the integration of Virtual Reality Learning Environments (VRLEs) in academic teaching through the EU-funded “REVEALING” project. Researchers from Cyprus, Germany, Greece, Poland, and Portugal developed and evaluated five different immersive VRLEs, each focusing on diverse educational topics, including ancient Greek technology, sea urchin measurements, linear algebra, and historical expeditions. The study aims to determine effective instructional design principles for VRLEs and assess students' acceptance and learning outcomes. The VRLEs were designed based on literature-derived principles that emphasise ease of tool usage, authentic experiences, and continuous feedback. Students from the participating universities explored these VR environments, providing feedback through a standardized questionnaire on aspects like immersion, ease of use, motivation, and emotions. Results show that most participants positively engaged with the VRLEs, reporting high motivation and positive emotional responses, particularly for experiences involving interactivity. However, challenges like motion sickness and technical issues were noted, especially at one institution. The findings suggest that immersive VR experiences can significantly enhance motivation and engagement, but their effectiveness depends on careful alignment with pedagogical goals, design quality, and user experience considerations. © 2025 The Authors

Abstract
The learning environment is an essential part of teaching and learning. Its personalization has several advantages (e.g., guaranteeing learning quality or effective learning). In vocational education, a personalized learning environment might provide training most suitable to each professional according to individual characteristics, skills, or career path. Artificial intelligence’s ability to process big data can be harnessed to personalize a learning environment. This work intends to investigate the personalization of a learning environment using artificial intelligence (AI) in vocational training that can provide relevant training based on the trainees’ skills required. A framework will be proposed to personalize a learning environment in this scope. Its development will follow the design science research (DSR) methodology. During the process, the survey methodology (expert interviews and focus groups) will be conducted to validate the artifact requirements and evaluate our future framework. © 2025 Elsevier B.V., All rights reserved.