Abstract
Engineering is considered important in solving unsustainability. However, it is a complex problem that must be viewed, analysed and studied from various perspectives and taking with the contribution of various areas of knowledge. This work studied the use of interdisciplinarity as a contribution to interconnect ethics and sustainability with technical-scientific contents of electrical engineering. The research intended to use interdisciplinarity to help engineering students recognise that engineering is not ethically neutral, and that, therefore, the problems (and solutions) must also be analysed from an ethical and sustainability perspective. A framework was developed, and a pedagogical activity using interdisciplinarity was applied. Results show that, after the activity, students recognise that ethical values influence calculations in the area of electrical installations; and move from a single view to identify different alternatives, perspectives, motivations and multiple objectives. This leads to studying more alternatives and hopefully better overall technical solutions.

Abstract
This paper presents the development and implementation of learning-based detection and tracking methods using multibeam data to detect marine litter in the water column. The presented work encompasses (i) the creation of acoustic videos and the application of multiple post-processing techniques; (ii) the training of multiple You Only Look Once (YOLO) detection models, specifically YOLOv8, across different variants, acoustic frequencies, and input types (both raw and post-processed); (iii) and the development of a marine litter tracking system based on DeepSORT. The results include a multibeam multi-frequency data study demonstrating the potential of acoustic image sensing for detecting and tracking marine litter materials in the water column.

Abstract
Sales forecasting is very important in retail management. It helps with decisions about inventory, staffing, and planning promotions. In this study, we looked at how to balance the accuracy of predictions with how easy it is to understand the machine learning models used in sales forecasting. We used public data from Rossmann stores to study various factors like promotions, holidays, and store features that affect daily sales. We compared a complex, highly accurate model (XGBoost) with simpler, easier-to-understand linear regression models. To find a middle ground, we created a hybrid model called LR XGBoost. This model changes a linear regression model to match the predictions of XGBoost. The hybrid model keeps the strong predictive power of complex models but makes the results easier to understand, which is important for making decisions in retail. Our study shows that our hybrid model offers a good balance, providing reliable sales forecasts with more transparency than standard linear regression. This makes it a valuable tool for retail managers who need accurate forecasts and a clear understanding of what influences sales. The model’s consistent performance across datasets also suggests it can be used in various retail settings to improve efficiency and help with strategic decisions. © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)

Abstract
Purpose: Our aim was to test the capability of the NeuroKinect 3D-method, as a movement visualization technique and quantitative analysis to differentiate ictal movements such as hyperkinetic and focal seizures with manual automatisms. The dataset is extracted from the NeuroKinect dataset, which is a RGB-D-IR dataset of epileptic seizures. The dataset is recorded with Kinect v2 and consists of RGB, Infrared (IR) and depth streams. Quantitative 3D-movement analysis of 20 motor seizures was performed. Velocity, acceleration, jerk, covered distance, displacement and movement extent of Regions of Interests (= ROI: head, right hand, left hand and trunk) were captured. Results: Among the analyzed seizures were 10 hyperkinetic (n = 7: 4 male, 3 female; mean age 39.6 years (SD f 9.7)) and 10 focal seizures with manual automatisms (n = 10: 2 male, 8 female; mean age 39.2 years (SD f 17.6)). Hyperkinetic seizures exhibited higher mean velocity in all ROIs (e.g. head = 0.62 f 0.28 (m/s) vs. 0.12 f 0.07 (m/s)) as well as higher mean acceleration and mean jerk in most ROIs; these differences were statistically significant. Mean movement extent, covered distance, and displacement for all ROIs were larger for hyperkinetic seizures, however not significantly. The duration of ictal movements (80 s f 38 s versus 26 s f 14 s; p = 0.001) was significantly longer in focal seizures with manual automatisms. Conclusions: This new visualization technique allows to reconstruct tracked movement via 3D viewer and supports a 3D movement quantification which is capable to differentiate seizures characterized by movements, which may help to localize the epileptogenic zone.

Abstract
Detecting malware in computer networks and data streams from Android devices remains a critical challenge for cybersecurity researchers. While machine learning and deep learning techniques have shown promising results, these approaches often require large volumes of labelled data, offer limited interpretability, and struggle to adapt to sophisticated threats such as zero-day attacks. Moreover, their high computational requirements restrict their applicability in resource-constrained environments. This research proposes an innovative approach that advances the state of the art by offering practical solutions for dynamic and data-limited security scenarios. By integrating natural statistical laws, particularly Benford's law, with dissimilarity functions, a lightweight, fast, and scalable model is developed that eliminates the need for extensive training and large labelled datasets while improving resilience to data imbalance and scalability for large-scale cybersecurity applications. Although Benford's law has demonstrated potential in anomaly detection, its effectiveness is limited by the difficulty of selecting relevant features. To overcome this, the study combines Benford's law with several distance functions, including Median Absolute Deviation, Kullback-Leibler divergence, Euclidean distance, and Pearson correlation, enabling statistically grounded feature selection. Additional metrics, such as the Kolmogorov test, Jensen-Shannon divergence, and Z statistics, were used for model validation. This approach quantifies discrepancies between expected and observed distributions, addressing classic feature selection challenges like redundancy and imbalance. Validated on both balanced and unbalanced datasets, the model achieved strong results: 88.30% accuracy and 85.08% F1-score in the balanced set, 92.75% accuracy and 95.29% F1-score in the unbalanced set. The integration of Benford's law with distance functions significantly reduced false positives and negatives. Compared to traditional Machine Learning methods, which typically require extensive training and large datasets to achieve F1 scores between 92% and 99%, the proposed approach delivers competitive performance while enhancing computational efficiency, robustness, and interpretability. This balance makes it a practical and scalable alternative for real-time or resource-constrained cybersecurity environments.

Abstract
During the COVID-19 pandemic, significant challenges arose in the on-site monitoring of patients’ clinical signs due to healthcare system overload, lack of resources, and the need for social distancing. These obstacles hindered readiness in identifying and responding promptly to cases, highlighting the importance of investments in healthcare infrastructure and technologies for effective monitoring in emergency situations. This study explores the use of a wearable, wireless, and scalable system for remote monitoring of physiotherapy sessions with an emphasis on applying human activity recognition. It employs a variety of sensors and equipment for the classification of physiotherapeutic exercises from a distance. The sensors and equipment provide data to a web platform that allows, for example, determining posture and classifying the activity performed by the patient through measuring the angulation between body limbs. This platform includes the design of wearable accessories, 3D-printed, portable, and wireless hardware construction. The web part consists of a remote server, a microservices environment including the provision of a web portal (https://bionet.ufpe.br) for user interaction, as well as data storage and processing, providing the information. Currently, a testing protocol is under development to be executed by volunteer physiotherapy specialists and their respective patients, with approval from the ethics committee (CAAE 71106023.0.0000.5208). © 2025 Elsevier B.V., All rights reserved.