http://hdl.handle.net/10071/15079 2026-04-13T18:22:22Z http://hdl.handle.net/10071/36813 Título próprio: Application of indirect methods to optimal control problems in epidemiology Autoria: Caio, P.; Silva, C. J. Editor: Aguiar, Antonio Pedro; Malonek, Paula Rocha; Pinto, Vítor Hugo; Fontes, Fernando A. C. C.; Chertovskih, Roman Resumo: Currently most of the numerical resolution of optimal control problems is done using direct methods where the increase accuracy of indirect methods is overshadowed by the necessary analytical derivation required beforehand. With recent developments from the control-toolbox ecosystem team the application of indirect methods as become more streamline enabling a wider range of problems to be solved, like, for example, optimal control problems applied to the transmission of infectious diseases. In this work, we aim to extend the application of indirect methods to optimal control problems applied to epidemiological models, using the control-toolbox 2025-01-01T00:00:00Z http://hdl.handle.net/10071/36783 Título próprio: Are graph neural networks better than standard classifiers? Autoria: Yamaguchi, C.; Stefenon, S.; de Paz Santana, J. F.; Leithardt, V. Editor: Iglesia, Daniel H. de la; de Paz Santana, Juan F.; López Rivero, Alfonso J. Resumo: Graph neural networks (GNNs) are becoming very popular these days due to their ability to perform classification and prediction depending on node connections. Since features of samples belonging to the same class can be related, graph-based models may perform classification better than other classifiers. The big challenge for this evaluation is to know if there is a sufficiently adequate relationship between the connections of the nodes to justify the use of these models, since connections to unrelated classes can reduce the capacity of these models. This paper proposes a thorough comparative evaluation between graph models and other well-established classifiers to assess the extent to which GNNs may be superior. This will be done by evaluating the relationships between the probability of connections between nodes and changing database features. The evaluation is performed using synthetic data, which is a task that can be evaluated in future work. The results show that when there are connections of classes different from the node under evaluation, the GNNs lose their advantages over other classifiers. 2025-01-01T00:00:00Z http://hdl.handle.net/10071/36782 Título próprio: Prisec II: A comprehensive model for IoT security Autoria: Costa, P.; Noetzold, D.; García Ovejero, R.; Martín Esteban, R.; Leithardt, V. Editor: Iglesia, Daniel H. de la; Paz Santana, Juan F. de; López Rivero, Alfonso J. Resumo: This study examines data security and efficiency in interconnected IoT devices, focusing on selecting cryptographic algorithms to improve secure data transmission in 5G networks. The proposed model introduces four security levels, each applying different encryption strategies to balance security and performance. Cloud computing is integrated to mitigate computational limitations in IoT devices, optimizing encryption and decryption processes. The study evaluates multiple cryptographic algorithms, analyzing encryption and decryption times, packet throughput, and memory usage. Experimental results demonstrate that the model enables efficient encryption while maintaining security, with AES-256 and XChaCha20 showing stable performance across different packet sizes. The cloud-based implementation improves resource distribution and reduces processing delays. This work contributes a structured cryptographic model adaptable to varying security needs and a performance evaluation of different cryptographic approaches in cloud-integrated environments. 2025-01-01T00:00:00Z http://hdl.handle.net/10071/36684 Título próprio: Patient satisfaction in the digital health era: Digital literacy and digital inclusion perspective under the Donabedian framework Autoria: Geada, N.; Alturas, B. Resumo: The digital transformation of healthcare services is redefining how information is accessed and evaluated by citizens. While organizational progress is often measured by technical maturity, this study shifts the focus to the user’s perspective. Grounded in the Donabedian framework (Structure-Process-Outcome), we investigate how Digital Maturity (Structure) and Information Literacy/Inclusion (Process) culminate in Patient Satisfaction (Outcome). Using Structural Equation Modelling (SEM) with a sample of 212 participants, the results reveal that maturity acts as a catalyst for literacy, but satisfaction is strictly dependent on effective digital inclusion. This paper contributes to ‘Healthcare for Information’ by highlighting that technological infrastructure alone is insufficient without a robust healthcare strategy for health information users. 2026-01-01T00:00:00Z