Abstract
Deep learning techniques have demonstrated significant potential in computer-assisted diagnosis based on medical imaging. However, their integration into clinical workflows remains limited, largely due to concerns about interpretability. To address this challenge, we propose Efficient-Proto-Caps, a lightweight and inherently interpretable model that combines capsule networks with prototype learning for lung nodule characterization. Additionally, an innovative Davies-Bouldin Index with multiple centroids per cluster is employed as a loss function to promote clustering of lung nodule visual attribute representations. When evaluated on the LIDC-IDRI dataset, the most widely recognized benchmark for lung cancer prediction, our model achieved an overall accuracy of 89.7 % in predicting lung nodule malignancy and associated visual attributes. This performance is statistically comparable to that of the baseline model, while utilizing a backbone with only approximately 2 % of the parameters of the baseline model’s backbone. State-of-the-art models achieved better performance in lung nodule malignancy prediction; however, our approach relies on multiclass malignancy predictions and provides a decision rationale aligned with globally accepted clinical guidelines. These results underscore the potential of our approach, as the integration of lightweight and less complex designs into accurate and inherently interpretable models represents a significant advancement toward more transparent and clinically viable computer-assisted diagnostic systems. Furthermore, these findings highlight the model’s potential for broader applicability, extending beyond medicine to other domains where final classifications are grounded in concept-based or example-based attributes. © 2013 IEEE.

Abstract
In online three-dimensional packing problems where items are received one by one and require immediate packing decisions without prior knowledge of upcoming items, considering the static stability constraint is crucial for safely packing each arriving item in real time. Unstable loading patterns can result in risks of potential damage to items, containers, and operators during loading/unloading operations. Nevertheless, static stability constraints have often been neglected or oversimplified in existing online heuristic methods in the literature, undermining the practical implementation of these methods in real-world scenarios. In this study, we analyze how different static stability constraints affect solutions' efficiency and cargo stability, aiming to provide valuable insights and develop heuristic algorithms for real-world online problems, thus increasing the applicability of this research field. To this end, we embedded four distinct static stability constraints in online heuristics, including full-base support, partial-base support, center-of-gravity polygon support, and novel partial-base polygon support. Evaluating the impact of these constraints on the efficiency of a wide range of heuristic methods on real instances showed that regarding the number of used bins, heuristics with polygon- based stabilities have superior performance against those under full-base and partial-base support stabilities. The static mechanical equilibriumapproach offers a necessary and sufficient condition for the cargo static stability, and we employed it as a benchmark in our study to assess the quality of the four studied stability constraints. Knowing the number of stable items under each of these constraints provides valuable managerial insight for decision-making in real-world online packing scenarios.

Abstract
With the huge amount of data that has been collected over time, many methods are being developed to allow better understanding and forecasting in several domains. Time series analysis is a powerful tool to achieve this goal. Despite being a well-established area, there are some gaps, and new methods are emerging to overcome these limitations, such as visibility graphs. Visibility graphs allow the analyses of times series as complex networks and make possible the use of more advanced techniques from another well-established area, network science. In this paper, we present two new efficient approaches for computing natural visibility graphs from times series, one for online scenarios in O(nlogn) and the other for offline scenarios in O(nm), the latter taking advantage of the number of different values in the time series (m). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract

Abstract
Technological evolution is very present in today’s world. The Internet of Everything (IoE) is one of the next steps in this evolution. WordPress is a website-building tool that is used today to its minimum capability. The main problem is how to make WordPress customizable and reliable to support device networking and communication. This can be done by implementing a new architecture that supports WordPress as a powerful tool, allowing for scalability and maintenance. The adoption of a recent trend known as DevOps is an important step in creating and developing a strong website and ensuring its integration with multiple devices. It emphasizes continuous testing, delivery, and integration. This article is based on a literature review to justify using micro-services architecture and the DevOps approach to build a reliable and robust working tool in WordPress that can be part of the IoE. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

Abstract
The current era where living demands an accelerated digital transition mainly focused on encouraging a smarter, healthier, and more sustainable mobility, in all its dimensions – a must concern for the young generations. The convergence through several digital services and APP can be an attitudes and perception changer within the group of academic mobility users’, promoting a more sustainable and better mobility choices that impact on the academic user’s mobility routines. Thus, encouraging a global shift to shared and active mobility services and systems bringing significant contributions to environmental sustainability and, also, to users’ health. The Academic Mobility as a Service (AMaaS) provide a digital service with mobility alternatives to support the academic population geographically located in different faculty campuses and Higher Education Institutions (HEI). The AMaaS applied to a restrict group is helpful to test innovative transport solutions and its high cybersecurity vulnerabilities. Despite the shortage of AMaaS case studies and the lack of security reference, it is imperative that a cybersecurity by design is planned and included in AMaaS design. In this paper AMaaS critical cybersecurity challenges, and potential risks are discussed and AMaaS Security by Design framework is described. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.