Abstract
This research examined the domain of intelligent transportation systems (ITS) by analyzing the impact of scholarly work and thematic prevalence, as well as focusing attention on vehicles, their technologies, cybersecurity, and related scholarly technologies. This was performed by examining the scientific literature indexed in the Scopus database. This study analysed 2919 documents published between 2018 and 2025. The findings indicated that the highest and most significant journal was derived from IEEE Transactions on Vehicular Technology, with significant standing to the growth of communication and computing on vehicles with edge computing and AI optimization of vehicular systems. In addition, important PST research conferences highlighted the growing interest in academic research in cybersecurity for vehicle networks. Sensor networks, pose forensics, and privacy-preserving communication frameworks were some of the significant contributing fields marking the significance of the interdisciplinary nature of this research. Employing bibliometric analysis, the literature illustrated the multiple channels integrating knowledge creation and innovation in ITS through citation analysis. The outcome suggested an increasingly sophisticated research area, weighing technical progress and increasing concern about security and privacy measures. Further studies must investigate edge computing integrated with AI, advanced privacy-preserving linguistic protocols, and new vehicular network intrusion detection systems.

Abstract
The rapid evolution of the automotive industry has driven the emergence of Connected and Autonomous Vehicles, raising significant concerns about the cybersecurity vulnerabilities inherent in their complex networks. This systematic review investigates cybersecurity in Connected and Autonomous Vehicles, focusing on internal and external networks and addressing four key research questions: (RQ1) What security controls exist in CAV networks? (RQ2) What methodologies are employed in cybersecurity studies? (RQ3) How effective are these methods, and what limitations do they present? (RQ4) What are the key themes, common approaches, and future research directions? Peer-reviewed studies published between 2019 and 2024 were included, using IEEE Xplore, Elsevier, MDPI, ACM Digital Library, and Springer as data sources. Following PRISMA 2020 guidelines, 111 relevant articles were analysed and grouped into seven themes: Authentication, Blockchain, Intrusion Detection Systems, Vehicle-to-Everything communication, Network Operation Centers, Security Operations Centers, and Systematic Reviews. The thematic synthesis highlighted study objectives, methodologies, and implemented security controls. This review identifies significant gaps in the literature, particularly in integrating Security Information and Event Management systems and the real-world validation of proposed security measures. It underscores the need for adaptive cybersecurity frameworks to address evolving threats and highlights the importance of collaboration between academia and industry. Furthermore, future research should prioritize the development of advanced security protocols, address scalability challenges, and explore the impact of emerging technologies such as Artificial Intelligence and 5G. Providing awareness and training is also essential to mitigate human error. These findings are a foundation for designing more resilient and secure Connected and Autonomous Vehicles systems.

Abstract
The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge-cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by >= 7%, (H2) CO2 intensity (g/km) by >= 6%, and (H3) station peak load by >= 20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO2 intensity by approximately 8%, and peak load by approximately 25% under demand multipliers >= 1.2 and EV shares >= 20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge-cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities).

Abstract
Understanding people's face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as a substitute for in-person meetings. We assume that when two individuals engage in a phone call connected to the same cell tower, they are likely to meet shortly thereafter. Testing this assumption, we evaluated two hypotheses. The first hypothesis-that such co-located interactions occur in a workplace setting-achieved 83% agreement, which is considered a strong indication of reliability. The second hypothesis-that calls made during these co-location events are shorter than usual-achieved 86% agreement, suggesting an almost perfect reliability level. These results demonstrate that CDR-based co-location events can serve as a reliable substitute for in-person interactions and thus hold significant potential for enhancing contact tracing and supporting public health efforts.

Abstract
Os casos de estudo sobre organizações e empresas são um veículo de comunicação de excelência na área da gestão. Este livro reúne uma série de casos de estudo que abordam a inovação e a diferenciação, a internacionalização, o marketing, a evolução estratégica, os modelos de negócio (e como são afetados pela tecnologia), as aquisições de empresas, e tem ainda um caso de estudo sobre a responsabilidade social (área de crescente importância para todo o tipo de organizações). A estratégia e o marketing são áreas de saber muito próximas, sendo dadas em conjunto em várias escolas de negócio no mundo inteiro. (...)

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.