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Abstract
Background: Virtual Reality (VR) is making education more engaging and accessible, especially for students with Autism Spectrum Disorders (ASD), promoting inclusion and the development of STEM skills in innovative ways. The literature still reveals a significant gap in terms of appropriate educational resources adapted to the specific needs of these students, resulting in difficulties in their inclusion. With the growing need for inclusive approaches in education, it is essential to find solutions to support these students. The aim of this study is to validate the data collection methodology that will enable the development of Virtual Learning Environments with STEM content for students with ASD. Methods: The Design Science Research (DSR) methodology was used to develop a VR artefact for students with ASD. In addition, the Delphi method was applied in the expert involvement phase, which will contribute to the validation of the artefact's specific requirements. Both will allow for an inclusive and distinctive approach to the development of an artefact, with the aim of offering an innovative educational experience, meeting the varied needs and learning styles of students with ASD, optimising the effectiveness of the proposed VLE. Results: The results show a strong acceptance among experts, highlighting the potential positive impact of this approach, although there are aspects to be improved to ensure a more comprehensive and effective approach. Conclusions: This study highlights the successful validation of an innovative virtual reality programme for students with ASD, highlighting the importance of interdisciplinary collaboration and the strong contribution to the advancement of inclusive education.

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Artificial Intelligence (AI) is now ubiquitous in daily life, significantly impacting society by supporting decision-making. However, in many application areas, understanding the rationale behind AI decisions is crucial, highlighting the need for explainable AI (XAI). AI algorithms often lack transparency, making it hard to understand their inner workings. This work presents an overview of XAI solutions for decision support in mobility context. It addresses the complexity of explaining decision support models by offering explanations in various formats tailored to different user profiles. By integrating language models, XAI models may generate texts with varying technical detail levels, aiding ethical AI deployment and bridging the gap between complex models and human interpretability. This work explores the need for flexible explanation formats, supporting varied user profiles with graphical, textual, and tabular explanations. By integrating natural language processing models personalized explanations that are accurate, understandable, and accessible to a diverse audience can be generated. This study ultimately aims to support the task of making XAI robust and user-friendly, boosting its widespread use and application.

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Community-based electricity markets, which are defined as groups of members that share common interests in renewable distributed generation, allow prosumers to embrace more active roles by opening up several opportunities for trading electricity. At the same time, such markets may favor conventional consumers by allowing them to choose cheaper electricity providers. Due to trends in power sector modernization, community-based electricity markets are of great research interest, and there are already some associated models. However, there is a research gap in searching for integrated and holistic approaches that go beyond economic aspects, consider social and environmental aspects, and assume the balanced co-existence of community-based and conventional markets. This work fills this critical research gap by adapting/applying the optimized tariff model, Bass diffusion model, life cycle assessment, and multi-objective optimization to the context of community-based markets. Results indicate that favoring conventional markets in the short term and community-based markets in the medium term is beneficial. Moreover, regulated tariffs should increase slightly in the short/medium-term to accommodate DG growth. Additionally, community-based markets can decrease electricity expenses by around 13.6 % considering the market participants. Thus, such markets can be significantly beneficial in mitigating energy poverty.

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Joint Communication and Sensing (JCAS) systems are emerging as a core technology for next-generation wireless systems due to the potential to achieve higher spectral efficiency, energy savings, and new services beyond communications. This paper provides a review of the state-of-the-art in JCAS systems by focusing on obtrusive passive sensing capabilities and inherent security and privacy challenges that arise from the integration of communication and sensing. From this point of view, we discuss existing techniques for mitigating security and privacy issues, as well as important aspects for the designing of secure and privacy-aware JCAS systems. Additionally, we discuss future research directions by emphasizing on new enabling technologies and their integration on JCAS systems along with their role in privacy and security aspects. We also discuss the required modifications to existing systems and the design of new systems with privacy and security awareness, where the challenging trade-offs between security, privacy and performance of the JCAS system must be considered.

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We propose a statistical duality among the preferences and endowments of the agents. Under this duality, the logarithmic prices of random trades among agents in a decentralized economy converge in expectation to the logarithm of the Walrasian equilibrium price in a centralized economy.