http://hdl.handle.net/10071/15079 Fri, 01 May 2026 00:14:44 GMT 2026-05-01T00:14:44Z https://repositorio.iscte-iul.pt:443/retrieve/7093cc63-2c21-43d1-b1e8-8a3b4dbbdf79/rgb_istar_description_en_main.png http://hdl.handle.net/10071/15079 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. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/10071/37086 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. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10071/37011 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. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10071/36964 2026-01-01T00:00:00Z http://hdl.handle.net/10071/36913 Título próprio: Network algorithm to model automotive supply chain structure Autoria: Barros, J.; Turner, C. Resumo: A network algorithm that models the structure of automotive supply chains, compiled from a proprietary database, is presented. An initial structural analysis was conducted using key performance indicators, including average path length, clustering coefficient, and degree distribution, to assess network configurations. The networks were then partitioned into subnetworks, with an emphasis on reflecting the operational dynamics of supply chain activities. Regression analysis was applied to each subnetwork, using the number of vertices as the independent variable, to develop an algorithm for generating synthetic networks. These synthetic constructs serve as benchmarks for the automotive sector and have shown a strong average correlation (0.94) with the structure of actual supply networks. This methodological contribution provides tools for analysing and optimising supply chain structures that underpin automotive engineering and manufacturing, ensuring robustness and efficiency in vehicle production systems. The prevalence of tree-like structures within supply networks challenge conventional beliefs regarding the complexity of automotive supply chains and prompts further investigation into the determinants of their resilience. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10071/36913 2026-01-01T00:00:00Z