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.

Abstract
All over the world, there are several unoccupied spaces without adequate constant control mechanisms to reduce and prevent mold and provide good internal conditions and indoor air quality. A widespread way to reduce building humidity is through heating and dehumidification, which are costly to maintain and have high energy consumption. In addition, there are few studies on adjustable hygro ventilation systems, which do not consider the influence of temperature fluctuations. This work describes the operation of a prototype, which fills existing research gaps by considering not only the control of relative humidity (RH) but also the temperature peaks in indoor air conditions, allowing the maintenance of good air quality. The prototype Smart Hygrothermal Ventilation system uses two pairs of sensors related to RH and temperature, one pair placed inside an unoccupied compartment of the building and the other pair in the external environment, in order to activate a fan and the respective speed. The proposed prototype was applied in a compartment located on the ground floor in an unoccupied old rural building in a village near Porto during the winter period. The results show that the system performed adequately for different configurations of its functionalities. Therefore, the system offers an efficient alternative to minimize mold and the fluctuation of internal RH and temperature. Furthermore, it could be a vital mechanism for the conservation of historic buildings.

Abstract
Industrial companies operate in a context of dynamic technological innovation, in which new technologies are adopted with a high impact internally and externally, leveraging their competitive advantages. Usually, managers decide to adopt technologies, often without realising the impacts on the company, but mainly supported by a strategic vision and the pursuit of differentiation. This study aims to describe the impacts of adopting Industry 4.0 technologies in industrial companies, focusing on sustainability's economic, social, and environmental dimensions and explaining which Industry 4.0 technologies contribute to each impact. This study used qualitative methodology, collecting data through interviews, internal documents, and observation. The results of this study identified new impacts in the three dimensions of sustainability, as well as the relationships between impacts and respective technologies. This study contributes to the literature by enriching and validating the impacts of adopting Industry 4.0 technologies on sustainability dimensions and linking these impacts with the technologies. In practice, it provides important insights to managers and decision-makers of manufacturing companies in making more informed decisions on adopting i4.0 technologies.

Abstract

Abstract
This study presents and applies time delay analysis of maximum cross-correlation between quadriceps and gastrocnemius sEMG neuromuscular control with lower limb joint angular coordination of the hip, the knee and the ankle joint angles, angular velocities and accelerations to assess long countermovement (CM) and stretch-shortening cycle (SSC) at countermovement jump (CMJ), short CM and SSC on drop jump (DJ), and no CM on squat jump (SJ), with different and shared features at each CM complementing functional anatomy analysis. © 2025 Elsevier B.V., All rights reserved.