Abstract
Deep learning in computational pathology (CPath) has rapidly advanced in recent years. Research has primarily focused on enhancing accuracy and interpretability across various histology image analysis tasks, from tile-level to slide-level foundation models and novel multiple instance learning (MIL) strategies. However, it is equally important for models to provide well-calibrated confidence estimates. Due to factors such as dataset bias, overfitting, and limited training data, existing models tend to be overly confident on test sets. Promising solutions to address this issue include temperature scaling, a post-hoc method that adjusts logits using a single scalar value. However, the role of calibration in CPath is yet to be clarified. In this study, we evaluate temperature scaling and linear temperature scaling for CPath tasks, analyzing their impact on recalibration in both in-domain and out-of-domain distributions. The results show the limitations of current probability calibration techniques and motivate future work.

Abstract
DSentiment analysis has proven its importance in business and research. With the metaverse market expansion and abundant high-quality data, understanding how businesses can leverage technologies such as sentiment analysis to improve their marketing strategies becomes significant. This paper synthesizes and organizes information relevant to sentiment analysis using Virtual Reality technology. To minimize bias and ensure accuracy, a systematic review was conducted. Papers from Springer, ScienceDirect, and IEEE Xplore, published since 2022, were analyzed. This yielded a total of 12 studies included in this review after screening of 304 papers. This research shows that sentiment analysis, together with Artificial Intelligence, is crucial for businesses aiming to expand their influence in the metaverse. These tools enable high customization and optimization of interactions, making them more engaging, while providing real-time insights into the consumers' likes, dislikes and emotions. This allows companies to identify what works and what needs improvement in their metaverse platform.

Abstract
Os rápidos avanços na computação quântica abriram novas possibilidades para o aprimoramento da aprendizagem por reforço (RL), especialmente através de circuitos quânticos parametrizados (PQCs) como aproximadores de funções em algoritmos híbridos quântico-clássicos. Esta dissertação aborda desafios e oportunidades no uso de PQCs para RL, explorando o seu design, treino e potencial para alcançar vantagem quântica. A primeira parte investiga a expressividade e capacidade de treino de políticas baseadas em PQCs. Técnicas como reintrodução de dados e escalamento de entradas/saídas demonstram que os PQCs podem ter desempenho equivalente ou superior ao de redes neurais clássicas, frequentemente com menos parâmetros. No entanto, a capacidade de treino é limitada pelo fenómeno de Barren Plateau (BP), onde gradientes nulos dificultam a otimização. Esta dissertação identifica condições para mitigar BPs, garantindo treino em circuitos de profundidade logarítmica com medições locais. Com base nisso, a segunda parte explora técnicas de otimização para RL baseado em PQCs. Uma comparação entre gradientes naturais quânticos (QNG), com matriz de Fisher quântica (QFIM), e métodos com matriz de Fisher clássica (CFIM) revela compromissos entre otimizações no espaço de estados e de políticas. Embora QNGs ofereçam maior estabilidade, seus benefícios face à CFIM dependem do contexto. Para equilibrar treino eficiente e intratabilidade clássica, a terceira parte propõe políticas de PQCs baseadas em circuitos com geradores comutativos. Estes evitam o fenómeno de BP enquanto permanecem difíceis de simular classicamente, representando um caminho promissor para alcançar vantagem quântica. A parte final integra técnicas tolerantes a falhas com métodos baseados em PQCs, propondo uma estrutura para alcançar vantagem quântica provável em ambientes parcialmente observáveis, com demonstração de aceleração quadrática na complexidade amostral para atualizações de crenças via inferência Bayesiana quântica. Esta dissertação contribui para a compreensão do RL baseado em PQCs, oferecendo perspetivas sobre o seu design, treino e otimização, destacando o potencial da computação quântica para revolucionar o RL e viabilizar agentes quântico-aprimorados escaláveis.;
The rapid advancements in quantum computing have opened new avenues for enhancing reinforcement learning (RL), particularly through the use of parameterized quantum circuits (PQCs) as function approximators in hybrid quantum-classical algorithms. This dissertation addresses critical challenges and opportunities in leveraging PQCs for RL, exploring their design, trainability, and potential for achieving quantum advantage. The first part of this work investigates the expressivity and trainability of PQC-based policies. By introducing techniques such as data reuploading, input scaling, and output scaling, we demonstrate that PQCs can achieve performance on par with or superior to classical neural networks, often with fewer trainable parameters. However, PQC trainability is hindered by the Barren Plateau (BP) phenomenon, where vanishing gradients impede optimization. This dissertation identifies conditions under which BPs can be mitigated, ensuring trainability in logarithmic-depth circuits with local measurements. Building on these findings, the second part explores optimization techniques for PQC-based RL agents. A critical comparison of quantum natural gradients (QNG), leveraging the quantum Fisher information matrix (QFIM), and classical Fisher information matrix (CFIM)-based updates reveals tradeoffs in state-space versus policy-space optimizations. While QNG provides stability and informed updates, its benefits over CFIM-based methods are context-dependent. To address the balance between trainability and classical intractability, the third part proposes PQC-based policies derived from commuting-generator circuits. These circuits are designed to be efficiently trainable, avoiding the BP phenomenon, while remaining classically hard to simulate. These present a promising route toward achieving quantum advantage in RL. Finally, a fault-tolerant quantum framework was proposed to achieve provable quantum advantage in partially observable environments, supported by a demonstrated quadratic speedup in belief updates using quantum Bayesian inference. This dissertation contributes to the foundational understanding of PQC-based RL, offering insights into their design, trainability, and optimization. The results highlight the potential of quantum computing to revolutionize RL, paving the way for scalable and advantageous quantum-enhanced agents.

Abstract
A investigação sobre o feedback e a autorregulação da aprendizagem tem granjeado interesse com a explosão da Inteligência Artificial e os desafios que coloca à educação e, em particular, à avaliação dos estudantes. Contudo, há mais de 30 anos que se estudam esses processos para compreender como os estudantes regulam a sua própria aprendizagem ao nível motivacional, cognitivo e metacognitivo. Ao assumirem um papel proativo na geração e utilização do feedback estão a avaliar o seu próprio trabalho, o que tem implicações na forma como os professores organizam a avaliação e o apoio na aprendizagem.  Este artigo elabora sobre os desafios múltiplos que se colocam à IA na avaliação digital da aprendizagem, no que respeita ao feedback e a autorregulação bem como na investigação sobre a avaliação digital. Após a discussão desses conceitos e de modelos enquadradores bem como a sua conexão com a avaliação digital conclui-se que é crucial considerar equipas multidisciplinares na investigação com IA e minimizar ou eliminar situações que podem introduzir enviesamentos em termos de género, etnias, culturas e estatutos económico ou social.

Abstract
Effective communication in engineering projects is pivotal for empowering the green transition, as it fosters multidisciplinary collaboration, ensures clarity across diverse stakeholders, and bridges technical and cultural gaps, ultimately driving sustainable innovation and project success. The main aim of this study is to give a contribution to overcome these communication limitations. This research explores the critical role of communication in engineering projects related to the green transition, as part of the ECO-GT project in Portugal. Through focus groups and interviews with different stakeholders, including engineers, product manufacturers and end-users, the research identifies communication challenges and essential skills required during project implementation. The findings show that the importance of multidisciplinary collaboration, adapted language depending on the target audience, and openness to feedback are essential to achieving project goals. Key findings include the need for tailored communication strategies at all project stages to overcome technical and cultural barriers. This research highlights the value of integrating communication training into engineering education to prepare future engineers for the complexities of green transition projects.

Abstract
In this paper we present results on the normalized temperature sensitivity of UV- and fs-induced fiber Bragg gratings in a singlemode fiber with similar to 4.7 mol% GeO2 and having an Ormocer coating. In the 1500-1600 nm wavelength range, the former shows an almost constant value of 6.165x10(-6) K-1, whilst the fs-induced present some variation not related with the strength of the grating but probably due to induced birefringence. The average value obtained was 6.191x10(-6) K-1 which is higher than the former. For the UV-induced gratings in the Corning SMF-28 fiber (3.67 mol% GeO2) the value obtained was 6.143x10(-6) K-1. The achieved values are compatible with the use of Corning 7980 silica-based cladding fiber. Preliminary results also show no measurable impact of the hydrogenation process or the strength of the grating on the normalized temperature sensitivity.