Abstract
The hypercube is a novel viewport management and information visualization system that introduces three conceptual applications (called WorkScenes), focusing on interaction, reading, and visualization concepts. Thus, we first published the conceptual description, interaction metaphors, and the prototype in HyperCube4x: a viewport management system proposal. This article introduces HyperBook, a virtual reality-based application that aims to make e-books, infographics, storytelling, and other reading-related initiatives more attractive, by proposing the following concepts: (1) depth and surface, (2) cutting a document into pages, and (3) virtual screen enlargement. Then, we demonstrate how these features (1) integrate with virtual reality environments' attributes such as flow, presence, and immersion, (2) fit Shneiderman's visual-information-seeking mantra, and (3) solve the Egyptian scroll effect of desktop metaphor-based reading. Finally, we present the acceptability and usability study results with 31 participants who scored HyperBook at 76.29 on the System Usability Scale.

Abstract
Despite the proliferation of guidelines, standards, and best practices for digital accessibility, many platforms and websites remain inaccessible to people with disabilities. Although global awareness is slowly increasing, little has been done to overcome this issue. This study explores the potential of Artificial Intelligence (AI) and Machine Learning to address this problem, focusing on the personalization of user interfaces (UIs) in electric motorcycles. Unlike static guidelines, AI-driven solutions can dynamically adapt to the specific needs of users, creating more inclusive digital experiences. We propose a CAIA (Comprehensive AI Accessibility) framework model as a way to integrate AI into electric motorcycle interfaces, allowing users to configure their accessibility preferences and for AI to automatically adjust the display and controls of the motorcycle, promoting a human-centered computing approach and an adaptive system. The model has shown to effectively improve user models and personalization, ensuring a personalized and inclusive experience. The study concludes that AI-driven systems, when ethically implemented, can enhance digital inclusion while providing a more tailored and adaptive user experience. It also discusses the ethical implications, privacy concerns, and the role of human involvement in the development of assistive technologies and interaction design, offering a comprehensive solution to improve digital inclusion for all types of users.

Abstract
Background: Students with Autism Spectrum Disorder (ASD) often face significant challenges in traditional educational environments, including difficulties in social interaction, engagement, and adapting to standard learning methods. These barriers can hinder their academic and personal development, highlighting the need for more inclusive and adaptive educational solutions. Objective: This study investigated whether immersive VR-based STEM learning environments can support the cognitive, social and behavioural development of pupils with ASD. We evaluated usability and accessibility needs, validated the artefact through expert consensus, and measured pre-post changes using established standardised instruments. Methodology: The research followed the Design Science Research (DSR) approach within STEM (Science, Technology, Engineering, and Mathematics) to develop VR-based learning experiences adapted to the needs of students with ASD. The Delphi method involved experts in defining best practices and educational strategies, helping to ensure that the proposed solutions were appropriate and aligned with student characteristics. The study included a control and an experimental group, both composed of students with ASD and typically developing students, assessing the impact of VR on learning and socialisation. Results: The findings suggest that VR-based learning environments may support improvements in cognitive, behavioural and social skills, although causal inference is limited by the small sample size and absence of randomisation. Conclusions: This study provides preliminary evidence that VR-based learning environments may help address educational barriers for students with ASD by offering structured, engaging and adaptable environments that could support inclusion and development.

Abstract
Electronic Health Records store extensive patient health data, playing a crucial role in healthcare management. Extracting information from these text-heavy records is difficult due to their domain-specific vocabulary, which challenges applying general-domain techniques. Recent advancements in Large Language Models (LLMs) and an increasing interest in the field have sparked considerable progress in solving Clinical Information Extraction (IE) tasks. We review these applications in Clinical IE, highlighting the most common tasks, most successful methods, and most used datasets and evaluation criteria. Examining 85 studies, we synthesize and organize the current research trends, highlighting common points between papers. The presence of LLMs can be felt in the most common tasks, with novel approaches being attempted and showing promising results. However, breakthroughs are still necessary in designing reliable end-to-end systems that can perform all the Clinical IE tasks within a single system.

Abstract
PurposeThis study aims to comparatively evaluate two semantic models, ArchOnto (CIDOC CRM based) and Records in Contexts Ontology (RiC-O), for archival representation within the Linked Open Data framework. The research seeks to critically analyse their ability to represent archival documents, events, activities, and provenance through the application on a case study of historical baptism records.Design/methodology/approachThe study adopted a comparative approach, utilising the two models to represent a dataset of baptism records from a Portuguese parish spanning several centuries. This involved information extraction and conversion processes, transforming XML EAD finding aids into RDF to facilitate more explicit semantic representation and analysis.FindingsThe analysis revealed distinctive strengths and limitations of each semantic model, providing nuanced insights into their respective capacities for archival description. The findings guide cultural heritage institutions in selecting and implementing the most suitable semantic model for their needs and pave the way for semantic alignment between the two models.Research limitations/implicationsAlthough the case study explored the representation of a wide range of features, potential limitations include the specific contextual constraints of parish records and the need for broader comparative studies across diverse archival contexts.Originality/valueThis paper offers original insights into semantic modelling for archival representations by providing a detailed comparative analysis of two ontological approaches. It offers valuable perspectives for archivists, digital humanities researchers, and cultural heritage professionals seeking to enhance the semantic richness of archival descriptions.

Abstract
Text simplification converts complex text into simpler language, improving readability and comprehension. This study evaluates the effectiveness of open-source large language models for text simplification across various categories. We created a dataset of 66,620 lead section pairs from English and Simple English Wikipedia, spanning nine categories, and tested Llama 3 for text simplification. We assessed its output for readability, simplicity, and meaning preservation. Results show improved readability, with simplification varying by category. Texts on Time were the most shortened, while Leisurerelated texts had the greatest reduction of words/characters and syllables per sentence. Meaning preservation was most effective for the Objects and Education categories.