Abstract
Accurate segmentation of heart sound signals (phono-cardiograms, PCGs) is a critical step for the early diagnosis of cardiovascular diseases (CVDs). Although deep learning models, particularly convolutional neural networks (CNNs) like the U-Net, have achieved strong performance in PCG segmentation, the impact of signal preprocessing remains underexplored. In this study, we evaluate how different preprocessing strategies, namely wavelet-based denoising, Butterworth filtering, and their combination, affect the segmentation performance of a 1D U-Net model. Using the PhysioNet 2016 database, we evaluated segmentation quality based on sample accuracy, positive predictive value, and sensitivity. The results show that minimal preprocessing, specifically Butterworth bandpass filtering alone, yields the best segmentation performance, outperforming more aggressive preprocessing pipelines. These findings highlight that preserving the baseline structure of PCG signals is crucial for optimal learning and that lightweight preprocessing remains an essential consideration, especially when applying modern deep learning architectures.

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.

Abstract

In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees' mental well-being.

Abstract
In many e-commerce platforms user communities share product information in the form of reviews and ratings to help other consumers to make their choices. This study develops a new theoretical framework generating a bipartite network of products sold by Amazon.com in the category musical instruments, by linking products through the reviews. We analyze product rating and perceived helpfulness of online customer reviews and the relationship between the centrality of reviews, product rating and the helpfulness of reviews using Clustering, regression trees, and random forests algorithms to, respectively, classify and find patterns in 2214 reviews. Results demonstrate: (1) that a high number of reviews do not imply a high product rating; (2) when reviews are helpful for consumer decision-making we observe an increase on the number of reviews; (3) a clear positive relationship between product rating and helpfulness of the reviews; and (4) a weak relationship between the centrality measures (betweenness and eigenvector) giving the importance of the product in the network, and the quality measures (product rating and helpfulness of reviews) regarding musical instruments. These results suggest that products may be central to the network, although with low ratings and with reviews providing little helpfulness to consumers. The findings in this study provide several important contributions for e-commerce businesses' improvement of the review service management to support customers' experiences and online customers' decision-making.

Abstract
This paper presents the enhanced version of the ASV AeroCat, an autonomous surface vehicle (ASV) of the catamaran type, now adapted for collaborative operations with aerial vehicles. The modifications introduced aim to meet the growing demand from industry and academia for solutions focused on collaboration between heterogeneous vehicles. Specifically, the improved vessel is capable of operating autonomously and collaboratively in monitoring activities, cargo transport, and as a platform for aircraft takeoff and landing. The paper details the improvements made to the original vessel, the developed collaboration topology, and the experimental validation conducted in a real-world environment. The results demonstrate that the ASV AeroCat can operate both independently and in synergy with an aerial vehicle, highlighting its potential for a wide range of applications.

Abstract
This paper presents a mixed-integer linear programming (MILP) model for the dynamic and fair allocation of shared energy in small-scale energy communities. The model computes hourly coefficients to distribute energy from a shared photovoltaic (PV) plant and a community battery energy storage system (BESS). Its primary goal is to minimize the total electricity bill while enforcing quantitative fairness among members. Fairness is achieved through daily constraints on each member’s energy share and a minimax term that limits the maximum cost any single member pays for grid electricity. The model also includes revenues from flexibility services sold to the grid. Simulation results for a four-household community show weekly bill reductions of at least 78% compared to individual operation. The model guarantees each member at least 25% of the shared PV and BESS energy, achieving fair allocation in a single optimization step with an equity penalty below 3%.