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
Time series forecasting is an important tool for planning and decision-making. Considering this, several forecasting algorithms can be used, with results depending on the characteristics of the time series. The recommendation of the most suitable algorithm is a frequent concern. Metalearning has been successfully used to recommend the best algorithm for a time series analysis task. Additionally, it has been shown that decomposition methods can lead to better results. Based on previously published studies, in the experiments carried out, time series components were used. This work proposes and empirically evaluates METAFORE, a new time series forecasting approach that uses seasonal trend decomposition with Loess and metalearning to recommend suitable algorithms for time series forecasting combinations. Experimental results show that METAFORE can obtain a better predictive performance than single models with statistical significance. In the experiments, METAFORE also outperformed models widely used in the state-of-the-art, such as the long short-term memory neural network architectures, in more than 70%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$70\%$$\end{document} of the time series tested. Finally, the results show that the joint use of metalearning and time series decomposition provides a competitive approach to time series forecasting.

Abstract
In the study, a new method for analyzing Electrocardiogram (ECG) signals is suggested, which is vital for detecting and treating heart diseases. The technique focuses on improving ECG signal classification, particularly in identifying different heart conditions like arrhythmias and myocardial infarctions. An enhanced version of the differential evolution (DE) algorithm integrated with neural networks is leveraged to classify these signals effectively. The process starts with preprocessing and extracting key features from ECG signals. These features are then processed by a multi-layer perceptron (MLP), a common neural network for ECG analysis. However, traditional MLP training methods have limitations, such as getting trapped in suboptimal solutions. To overcome this, an advanced DE algorithm is used, incorporating a partition-based strategy, opposition-based learning, and local search mechanisms. This improved DE algorithm optimizes the MLP by fine-tuning its weights and biases, using them as starting points for further refinement by the Gradient Descent with Momentum (GDM) local search algorithm. Extensive experiments demonstrate that this novel training approach yields better results than the traditional method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper compares the performance of Simulated Annealing and Tabu Search meta-heuristics in addressing a parallel machine scheduling problem aimed at minimizing weighted earliness, tardiness, total flowtime, and machine deterioration costs-a multi-objective optimization problem. The problem is transformed into a single-objective problem using weighting and weighting relative distance methods. Four scenarios, varying in the number of jobs and machines, are created to evaluate these metaheuristics. Computational experiments indicate that Simulated Annealing consistently yields superior solutions compared to Tabu Search in scenarios with lower dimensions despite longer run times. Conversely, Tabu Search performs better in higher-dimensional scenarios. Furthermore, it is observed that solutions generated by different weighting methods exhibit similar performance. © 2025 The Author(s).

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
Abstract Companies adopt agile methodologies for various reasons, primarily due to their adaptability to change and evolving business demands. In this context, addressing non-functional requirements (NFRs) may not always be a priority and can present challenges for agile teams. The focus on User Stories present in agile methods and tools often does not offer explicit alternatives for documenting NFRs. In this research, we perform a survey to explore five different strategies for documenting NFRs, to identify which fits better for different types of quality attributes and to understand the strengths and drawbacks of each one. As a result, the participants considered certain strategies as being more or less suitable for specifying different types of quality attributes. For instance, while Story Labeling was rarely recommended for security requirements, using Story Sub-sections or Verification Rules were highly recommended for this kind of quality attribute. Our results also evaluated the strategies considering several factors, such as the level of detail and requirement duplication. As a practical implication, the results of this work can provide guidance to agile development teams in choosing the most suitable alternative for each NFR documentation.