The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.

Smart meters are a vital part of the smart grid, enabling energy management, real-time control, and data collection. Despite advances in technology, there is still a lack of content and limited understanding of the specific cybersecurity threats facing these devices, as well as the effectiveness of existing mitigation strategies. This study analyzed 41 articles sourced from three academic databases (Scopus, Web of Science, and IEEE Xplore). A cutting-edge study was conducted, including a comprehensive review of relevant literature on smart meters, cybersecurity vulnerabilities, and mitigation strategies. Elements were selected based on pre-assessment and classification processes, and the data were extracted and combined to provide detailed insights into the new devices. The study identified several significant cybersecurity risks for smart meters, including data breaches, unauthorized access, data manipulation, denial-of-service (DoS) attacks, and malware introduction. The study also highlighted the vulnerabilities exploited by these threats, such as undocumented communications, weak authentication, and outdated software. Recommended mitigation strategies include strengthening access and authentication mechanisms, securing communication systems, regular software updates, code management, anomaly detection, and access control. The findings indicate that although there are good strategies and methods to mitigate these cyber threats, significant research gaps remain. These gaps include design requirements, software and firmware updates, physical security, the use of big data to detect vulnerabilities, user data privacy, and inconsistencies in machine learning algorithms. Future research should focus on these aspects to improve the stability and reliability of smart meters.

Power transmission lines are of great importance for the operation of all sectors of society, such as commerce, industry and public agencies. To ensure reliability and availability of power supply, regular and occasional inspections are conducted, mostly using patrol with binoculars, helicopters or truck cranes. Research is being developed using unmanned aerial vehicle (UAV) to make this activity autonomous, faster, safer, and less costly. The present work aims to analyze research related to the autonomous control of the UAV along the transmission lines through a systematic review of the literature (SRL), apply a viable solution and to verify the possible lacuna in this state of the art. Improvements in safety, computational process and energy efficiency with low-cost were identified. The results presented can help the research community to perform the working this state of art, from the suggestions of autonomous tracking of transmission lines.

Gamificação é a prática de aplicar elementos e princípios de jogos emambientes não relacionados a jogos. Esse recurso tem sido amplamente utilizado no ensino superior em diferentes áreas do conhecimento, incorporando elementos de elearning com gamificação, com o objetivo de melhorar a qualidade do ensino. Esta pesquisa, por meio de uma revisão sistemática da literatura, discutirá a eficácia da gamificação, seus benefícios e como ela vem sendo aplicada no âmbito da Ciência da Computação. A partir da revisão realizada foi verificado que a maioria das publicações apresentou resultados positivos com a gamificação, principalmente no engajamento e na motivação dos estudantes. Contudo, em alguns casos, foi identificada uma diminuição dos benefícios em longo prazo, dependente da forma como a prática é aplicada.

Smart meters are a vital part of the smart grid, enabling energy management, real-time control, and data collection. Despite advances in technology, there is still a lack of content and limited understanding of the specific cybersecurity threats facing these devices, as well as the effectiveness of existing mitigation strategies. This study analyzed 41 articles sourced from three academic databases (Scopus, Web of Science and IEEE Xplore). A cutting-edge study was conducted, including a comprehensive review of relevant literature on smart meters, cybersecurity vulnerabilities, and mitigation strategies. Elements were selected based on pre-assessment and classification processes, and the data were extracted and combined to provide detailed insights into the new devices. The study identified several significant cybersecurity risks for smart meters, including data breaches, unauthorized access, data manipulation, denial of service (DoS) attacks, and malware introduction. The study also highlighted the vulnerabilities exploited by these threats, such as undocumented communications, weak authentication, and outdated software. Recommended mitigation strategies include strengthening access and authentication mechanisms, securing communication systems, regular software updates, code management, anomaly detection, and access control. The findings indicate that, although there are good strategies and methods to mitigate these cyber threats, significant research gaps remain. These gaps include design requirements, software and firmware updates, physical security, the use of big data to detect vulnerabilities, user data privacy, and inconsistencies in machine learning algorithms. Future research should focus on these aspects to improve the stability and reliability of smart meters.

Background: Waste collection is a critical logistical challenge in urban management, and while Internet of Things (IoT) technologies are increasingly used to optimize collection routes, a systematic, quantitative synthesis of their impact is lacking. This study aims to bridge this gap by quantifying the effect of IoT-enabled routing optimization on waste collection distances. Methods: We conducted a systematic review and meta-analysis following the PRISMA protocol, searching the Scopus, IEEE Xplore, and ACM Digital Library databases. This process yielded 11 eligible studies, providing 21 distinct samples for quantitative synthesis. Results: The analysis reveals that IoT-enabled routing optimization reduces collection distance by a combined average of 21.51%. A significant disparity was found between study types, with simulation-based approaches reporting higher reductions (−39.79%) compared to real-world deployments (−12.37%). No statistically significant performance differences were observed across different routing algorithm categories or Vehicle Routing Problem (VRP) variants. Conclusions: These findings provide robust quantitative evidence of the significant efficiency gains from implementing IoT-based smart waste management systems. The gap between simulated and real-world results underscores the need for practitioners to set realistic expectations, while our analysis supports the adoption of these technologies for more sustainable urban logistics.