Abstract
This article investigates sampling strategies in latent space navigation to enhance co-creative music systems, focusing on timbre latent spaces. Adopting Villa-Rojo's 'Lamento' for tenor saxophone and tape as a case study, we conducted two experiments. The first assessed traditional corpus-based concatenative synthesis sampling within the RAVE model's latent space, finding that sampling strategies gradually deviate from a given target sonority while still relating to the original morphology. The second experiment aims at defining sampling strategies for creating variations of an input signal, namely parallel, contrary, and oblique motions. The findings expose the need to explore individual model layers and the geometric transformation nature of the contrary and oblique motions that tend to dilate the original shape. The findings highlight the potential of motion-aware sampling for more contextually aware and expressive control of music structures via CBCS.

Abstract
To get products delivered, clients and retailers agree on a delivery time window. We collaborated with an online retailer to develop a real-world application aimed at dynamically determining the delivery fee for each time window while ensuring the explainability of the pricing policy. This sequential decision-making problem arises as new customers continuously arrive. The objective is to maximize the final profit, given by the sum of baskets and delivery fees, discounted by the transportation and fleet costs. As multiple customers share the same delivery route, the costs are distributed among them, complicating the calculation of the marginal cost of each customer. Our study employs Genetic Programming (GP) to create explainable and easy-to-compute pricing policies to determine the delivery fees. These policies, expressed as mathematical formulas, rank price panels combinations of time slots and corresponding fees to identify optimal prices for each customer. The inputs to the GP algorithm capture the current state of the system, including factors such as capacity, customer location, and basket value. The resulting expressions offer operational managers a transparent pricing policy that allows them to maximize total profit.

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.