Abstract
Lung cancer remains one of the most common and lethal forms of cancer, with approximately 1.8 million deaths annually, often diagnosed at advanced stages. Early detection is crucial, but it depends on physicians' accurate interpretation of computed tomography (CT) scans, a process susceptible to human limitations and variability. ByMe has developed a medical image annotation and anonymization tool designed to address these challenges through a human-centered approach. The tool enables physicians to seamlessly add structured attribute-based annotations (e.g., size, location, morphology) directly within their established workflows, ensuring intuitive interaction.Integrated with Picture Archiving and Communication Systems (PACS), the tool streamlines the annotation process and enhances usability by offering a dedicated worklist for retrospective and prospective case analysis. Robust anonymization features ensure compliance with privacy regulations such as the General Data Protection Regulation (GDPR), enabling secure dataset sharing for research and developing artificial intelligence (AI) models. Designed to empower AI integration, the tool not only facilitates the creation of high-quality datasets but also lays the foundation for incorporating AI-driven insights directly into clinical workflows. Focusing on usability, workflow integration, and privacy, this innovation bridges the gap between precision medicine and advanced technology. By providing the means to develop and train AI models for lung cancer detection, it holds the potential to significantly accelerate diagnosis as well as enhance its accuracy and consistency.

Abstract
Modern distribution networks increasingly incorporate intelligent automation schemes to enhance resilience and reduce service interruptions following faults. To support these strategies, this paper investigates the use of machine learning models for fault location, aiming to quickly identify the faulted area and support safe service restoration of non-faulted areas. A comparative study is conducted using three supervised learning methods: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), applied to fault location in a distribution test system adapted to include Distributed Energy Resources (DER). Using steady-state current measurements generated from probabilistic fault scenarios based on historical data, each model is evaluated in terms of classification accuracy and computational feasibility. Results indicate that the models demonstrated high classification accuracy and efficient execution time, confirming the viability of machine learning (ML)-based approaches as effective decision-support tools for intelligent fault isolation and service restoration.

Abstract
The COVID-19 pandemic has brought new challenges to Social Solidarity Cooperatives (SSCs), affecting how they conduct their activities. The aim of this article is to analyse the extent to which SSCs’ behaviours have changed in terms of environmental practices and digital empowerment following the pandemic. Behaviour changes were assessed using a quantitative, exploratory methodology based on a questionnaire survey of 80 SSCs in Portugal. The results were analysed using a range of techniques, including descriptive analysis, exploratory factor analysis and cluster analysis. The data analysis process made it possible to group the SSCs into three distinct groups, characterised by different changes in behaviour: (i) a group of organisations with some changes in the organisation’s practices, which are more environmentally sustainable; (ii) a group of organisations that show some changes in terms of the digital transition; and (iii) a third group where there are simultaneously, and more significantly, changes in practices in terms of environmental sustainability and the digital transition. This last group is the one with the largest number of organisations in the sample. The formation of clusters is influenced by the age of the organisation and its location. © 2025, Faculty of Legal Sciences and Labor, University of Vigo. All rights reserved.

Abstract
Formal verification can be a complementary approach to UCD, offering a systematic and repeatable process to address the demands of designing safety and mission-critical interactive systems. However, the practical application of formal verification often encounters barriers to accessibility for non-technical stakeholders. In the case of model checking, although the verification step is fully automated, developing the required specifications and interpreting the verification results requires considerable technical expertise in formal methods. Recent developments in generative Artificial Intelligence (AI) have driven proposals for Large Language Models (LLMs) to be applied throughout various phases of software engineering. This begs the question of whether LLMs might be used to help bridge the gap between formal techniques and tools and stakeholders lacking technical expertise. This paper explores how these questions might be addressed in the context of an ongoing effort aimed at translating model-checking counter-examples into natural language explanations, making them accessible to designers and domain experts.

Abstract
Within the context applied to Virtual Reality research, the present work focuses on a literature review within the emerging field of Transformative Experience Design. The review focuses on studies that have adopted a strongly empirical, phenomenological and qualitative approach to the creation and evaluation of transformative experiences in VR, with the purpose of finding out not only how these are being created, but also which are the main factors that enable a transformative dimension in this type of experiences. The results present a number of possible stimuli regarding the most prominent dimensions of awe and the sublime found in the literature: perceptual vastness and need for accommodation. These results are then systematized and discussed, and further possibilities are then suggested within this context. © 2025. Published by AHFE Open Access. All rights reserved.

Abstract
The sustainable utilization of forest biomass for bioenergy production is increasingly challenged by the variability and unpredictability of raw material availability. These challenges are particularly critical in regions like Central Portugal, where seasonality, dispersed resources, and wildfire prevention policies disrupt procurement planning. This study investigates two flexibility strategies-dynamic network reconfiguration and operations postponement-as policy relevant tools to enhance resilience in forest-to-bioenergy supply chains. A novel mathematical model, the mobile Facility Location Problem with dynamic Operations Assignment (mFLP-dOA), is proposed and solved using a scalable matheuristic approach. Applying the model to a real case study, we demonstrate that incorporating temporary intermediate nodes and adaptable processing schedules can reduce costs by up to 17% while improving operational responsiveness and reducing non-productive machine time. The findings offer strategic insights for policymakers, biomass operators, and regional planners aiming to design more adaptive and cost-effective biomass supply systems, particularly under environmental risk scenarios such as summer operation bans. This work supports evidence-based planning and investment in flexible logistics infrastructure for cleaner and more resilient bioenergy supply chains.