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
Unmanned Aerial Vehicles (UAVs) are a promising solution for next-generation wireless networks due to their mobility, rapid deployment, and ability to provide Line-of-Sight (LoS) connectivity. However, deploying multiple UAVs in realt-ime to meet dynamic, non-uniform traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose the Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) algorithm, a scalable algorithm that jointly minimizes UAV count and optimizes 3D positioning based on real-time user distribution and traffic demand. In contrast to prior works that assume static user patterns or fixed UAV counts, EMTAD dynamically adapts UAV deployment to maximize spectral efficiency and satisfy user-specific Quality of Service (QoS) requirements. Simulation results demonstrate that EMTAD reduces the number of UAVs required and achieves superior aggregate throughput compared to baseline approaches.

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).