http://hdl.handle.net/10071/15080 2026-05-12T01:20:32Z http://hdl.handle.net/10071/37212 Título próprio: Psychological factors influencing public perception of space tourism Autoria: Freitas, L.; Omran, W.; Ramos, R. F. Resumo: Space tourism is a novel and under-researched leisure tourism area. This study examines the psychological factors that affect tourist behavioural intention (TBI), by the mediating role of tourist stickiness (sustained experiential engagement). This study integrated text mining and Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyse 15,568 comments from space tourism YouTube videos. A Word Frequency Matrix (WFM) was used as an input for PLS-SEM to examine the hypothesised relationship. Findings indicated that perceived feasibility and social status significantly affected tourist stickiness, whereas self-actualisation had no effect. Tourist stickiness was identified as an essential mediator between these motivations and TBI, indicating that social and technological confidence and prestige may be valued instead of self-efficacy for self-growth. This study clarifies how feasibility perceptions and status motives shape space tourism intention via tourism stickiness. Moreover, it guides stakeholders in designing communication and experience strategies that build technological confidence and social prestige signals to increase public interest and intention to participate. 2026-01-01T00:00:00Z http://hdl.handle.net/10071/37086 Título próprio: Social media insights about COVID-19 in Portugal: A text mining approach Autoria: Marreiros, C. F.; Boné, J.; Ferreira, J. C.; Ribeiro, R. Resumo: The rapid spread of COVID-19 around the world had a significant impact on daily life. As in other countries, measures were taken in Portugal to combat the exponential increase of cases, such as curfews and the use of masks. Thus, in parallel with the direct consequences on health and the healthcare sector, the pandemic also caused changes in human behavior from a sociological viewpoint. The objective of this dissertation is to attain a perception of the reality concerning COVID-19. For this purpose, real-time data was extracted from three sources, two of them being social media platforms – Twitter and Reddit – and the other one being Público, a Portuguese online newspaper. The adopted approach, based on topic modelling and sentiment analysis, was validated within the Portugal context, concerning data over a period of one year, but it can equally be employed in similar situations and other countries and provide decision-making support. After the data extracting, it was prepared for application of natural language processing (NLP) tools specific to the Portuguese language, which can represent a challenge due to the lexical richness. With the gathered information, a dashboard was built, with the purpose of gaining insights on the COVID-19 pandemic in Portugal. It was concluded that the topics discussed on social media reflect the events related to the pandemic. In a final stage, these dashboards were evaluated by public health experts, who highlighted the potential of the results obtained. The data and dashboards will be made available to the scientific community upon request. 2023-01-01T00:00:00Z http://hdl.handle.net/10071/37011 Título próprio: Reinforcement learning-based adaptive quantum-safe cryptography for DN25-compliant smart environments Autoria: Noetzold, D.; Barbosa, J. L. V.; Santana, J. F. P.; Leithardt, V. R. Q. Resumo: The emergence of quantum computing challenges traditional security mechanisms, particularly in heterogeneous and resource-constrained IoT and smart environments that must satisfy DN25 requirements. This work introduces a reinforcement learning-driven model for the adaptive selection and orchestration of cryptographic algorithms. Acting as an intelligent decision layer, the system observes contextual, network, and operational metrics to recommend or enforce configurations combining classical schemes, post-quantum cryptography, and Quantum Key Distribution when available. The selection problem is formulated as a Markov Decision Process with state and action spaces aligned with protocol control flows and is embedded into a security middleware with negotiation and fallback mechanisms to ensure interoperability and policy compliance without modifying application logic. Experimental results demonstrate that the model dynamically adjusts key lengths, algorithm families, and security policies according to risk and resource conditions, increasing post-quantum cryptography and Quantum Key Distribution usage by up to 33.4% and 23.9% in high-risk scenarios while favoring low-latency classical or hybrid options for less critical traffic. The system achieves success rates above 78% while maintaining stable latency and resource usage during extended operation. 2026-01-01T00:00:00Z http://hdl.handle.net/10071/36964 Título próprio: A systematic literature review on Web3 applications in trucking logistics: Impacts and emerging trends in logistics 5.0 Autoria: Čale, D.; Ferreira, J. C.; Madureira, A.; Coutinho, C. Resumo: Web3 technologies, representing the next generation of a decentralised and user-centric Internet, offer innovative solutions to enhance adaptability, sustainability, and resilience in logistics systems aligned with the principles of Logistics 5.0. This study conducts a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, analysing peer-reviewed journal articles published between 2018 and 2024 and retrieved from Scopus, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The review specifically focuses on trucking logistics, a sector characterised by high fossil-fuel dependency, operational fragmentation, and significant environmental impact. The findings reveal that Artificial Intelligence and Internet of Things technologies dominate current implementations, mainly supporting fleet management, route optimisation, accident prevention, and risk assessment. In contrast, blockchain applications remain limited, and metaverse-based solutions are largely exploratory and confined to training scenarios. Key research gaps include the scarcity of integrated Web3 solutions, the limited consideration of human-centric Logistics 5.0 dimensions, and the lack of large-scale empirical validation in real-world trucking operations. Based on the analysis, this paper proposes a conceptual framework that maps Web3 technologies to trucking logistics areas, investment priorities, and Logistics 5.0 objectives, offering actionable guidance for Logistics Service Providers transitioning from Logistics 4.0 to Logistics 5.0. 2026-01-01T00:00:00Z