Abstract
In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber-Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform's real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Abstract
Many companies have been trying to take advantage of the entertainment boom on the internet and are searching for the bestways to make money from content creators. Among these are Virtual YouTubers, who have been growing in English-speaking spaces. This paper compares their popularity and influence in Portugal and the USA, while reflecting on their impact on viewers. To accomplish this, we created a survey, which obtained 102 answers, 46 being from Portuguese people and 44 being from U.S. citizens. We noted that only 37% of Portuguese people had heard of VTubers before and only 13% watch them on a weekly basis. On the other hand, every respondent from the USA was familiar with VTubers, with 86.4% watching them regularly. When asked about how VTubers affected the respondents' personal lives, some stated that it was merely a form of entertainment, however, many U.S. citizens reported that they watch VTubers to fulfill their need for social interaction or cope with loneliness. Thus, it is evident that these creators are much more popular in the USA. Lastly, we conclude that VTubers can have a positive impact on people's lives, but they also serve as an unhealthy escape from reality at times.

Abstract
Nowadays, Higher Education Institutions (HEIs) are faced with the crucial challenge of establishing and supervising strategies and policies that are essential for decisions in various areas and at various levels. Within this context, the importance of Business Intelligence (BI) has increased significantly, emerging as an essential tool for analysing and managing data. This BI capability enables HEIs to make more informed choices in line with their global strategies. This research focuses on developing a roadmap for the effective implementation of BI systems in HEIs. Using a Design Science Research (DSR) methodology, this work proposes a structured and adaptable roadmap that covers the key factors from the design to the implementation of BI systems in HEIs. This roadmap includes not only a reference architecture for BI systems but also a set of dashboards. The roadmap was validated through a case study at the University of Tras-os-Montes e Alto Douro (UTAD), involving exploratory analysis and feedback from experts. This study stands out for its practical and theoretical approach, offering a strategic and practical guide for the adoption of BI systems in HEIs, thus responding to a need identified in the academic literature.

Abstract
Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people's daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices smart and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security.

Abstract
This scoping review systematizes the current research related to the use of both blockchain and machine learning techniques in medical imaging applications. A systematic electronic search was performed, and twenty-five studies were included in the review. These studies aimed to use blockchain and machine learning techniques to provide (i) efficient security mechanisms to support the communication of medical imaging data, (ii) aggregation of distributed medical imaging data to train machine learning algorithms, and (iii) machine learning algorithms based on federated learning strategies. Among the ten machine learning techniques identified in the included studies, Convolutional Neural Network was the most representative (i.e., 44% of the studies). Moreover, Artificial Neural Network, Capsule Network, Deep Neural Network, Gated Recurrent Units, and Neural Network were machine learning techniques used by more than one study. Although the included studies developed algorithms with potential impact in clinical practice, it must be noted that they did not discuss the generalizability of their algorithms in real-world clinical conditions.

Abstract
The scoping review reported by this paper aimed to analyze and synthesize state-of-the-art studies focused on the application of machine learning methods to enhance the cyber resilience of cyber-physical systems. An electronic search was conducted, and 24 studies were included in this review after the selection process. The most representative application domains were computer networks and power systems, while in terms of cyber resilience functions, risk identification, risk mitigation or protection, and detection of anomalous situations were the most implemented functions. Moreover, the results of this scoping review show that the interest in the topic of cyber resilience and machine learning is quite recent, which justifies the heterogeneity of the included studies in terms of machine learning methods and datasets being used for the experimental validations, as well as in terms of outcomes being measured. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.