Abstract
Capsule endoscopy is a medical technique for gastrointestinal examinations that is much more advantageous than traditional endoscopy. Medical specialists use RapidReaderTM to annotate endoscopic capsule video images (VCE). This process is time-consuming, error-prone, and expensive. The videos do not retain temporal markers, making it challenging to locate the annotated frames directly. Moreover, the annotated images often undergo enhancement and artifacts creation, which changes their resolution and visual properties compared to the original frames. This study proposes an approach to aid annotation using Deep Learning and content-based image Retrieval (CBIR) techniques to address this issue. A Siamese network with ResNet-18 architecture was trained to compare two medical images through their features and, with a classifier, assess whether they are a match or a mismatch. This methodology was evaluated on a dataset totalling 5792 image pairs and was subjected to several performance metrics: loss, accuracy, AUC (Area Under the Curve), precision, and recall. Various learning rates and optimizers were tested: Adam, SGD, and Adadelta highlighted the Adam optimizer with the best results. This approach produced an accuracy of 97.6% and an AUC of 0.9764 using the Adam optimizer, highlighting the model's potential to reduce manual annotation time significantly. © 2025 The Author(s).

Abstract
Data governance plays a crucial role for organizations aiming to improve data quality, security, and compliance, yet research reveals ongoing challenges in implementation, maturity, and the practical effectiveness of current frameworks. Despite the availability of numerous concepts, models, and assessments, their actual impact and relevance remain fragmented and insufficiently explored. This Systematic Literature Review (SLR) investigates how data governance frameworks influence maturity and identifies the factors that drive their effectiveness. Through the synthesis of existing research, the review aims to clarify the relationship between governance frameworks and maturity levels, highlight operational benefits, and examine implementation challenges, ultimately contributing to both academic understanding and practical advancements in data governance. Analyzing the most relevant studies, the review seeks to uncover the main governance mechanisms, frameworks, and trends shaping this field, with a central question in focus: How can a structured master data management framework improve data governance maturity?.

Abstract
Due to the increase in energy consumption based on fossil fuels, sustainable alternatives have emerged, and green hydrogen (H-2) is one of them. This fuel is a promising eco-friendly energy source but is highly flammable. Therefore, continuous monitoring is essential, where optical sensors can contribute with a fast and remote sensing capability. In this field, plasmonic sensors have demonstrated high sensitivity, but with the plasmonic band in the visible range and low definition in the infrared. It presents a sensing structure for H-2 sensing composed of inexpensive materials (SiO2 and TiO2) and Pd as a sensitive medium, which supports Tamm Plasmon Resonance. The structure is numerically optimized to obtain a plasmonic band around 1550nm, which was experimentally validated with a sensitivity of 9.5nm in the presence of 4 vol% H2 and a response time of 30 seconds. This work aims to emphasize the advantages of this plasmonic technique for gas sensing at the infrared spectral range, allowing remote sensing.

Abstract
Face recognition technology, despite its advancements and increasing accuracy, still presents significant challenges in explainability and ethical concerns, especially when applied in sensitive domains such as surveillance, law enforcement, and access control. The opaque nature of deep learning models jeopardises transparency, bias, and user trust. Concurrently, the proliferation of web applications presents a unique opportunity to develop accessible and interactive tools for demonstrating and analysing these complex systems. These tools can facilitate model decision exploration with various images, aiding in bias mitigation or enhancing users' trust by allowing them to see the model in action and understand its reasoning. We propose an explainable face recognition web application designed to support enrolment, identification, authentication, and verification while providing visual explanations through pixel-wise importance maps to clarify the model's decision-making process. The system is built in compliance with the European Union General Data Protection Regulation, ensuring data privacy and user control over personal information. The application is also designed for scalability, capable of efficiently managing large datasets. Load tests conducted on databases containing up to 1,000,000 images confirm its efficiency. This scalability ensures robust performance and a seamless user experience even with database growth.

Abstract
Self and co-regulation of learning (SCRL) are strategies that students can adopt to become more active and committed to their learning. Encouraging students to adopt these strategies is a challenge for teachers that can be met by using narratives as a teaching resource. To support teachers in this process, we present a method for evaluating, classifying, and reflecting on excerpts from immersive narratives for SCRL, so they objectively base their decision-making. The method was developed as an artifact of Design Science Research (DSR). In the Design stage of DSR, a 4-stage scheme was developed, and 38 criteria were described to identify and classify narratives that guide or encourage students to adopt SCRL strategies. In the DSR demonstration stage, we tested the method in an asynchronous e-learning curricular unit in Portuguese higher education, which uses a narrative-oriented immersive learning approach for SCRL, called e-SimProgramming. The results show that the graphic visualization of the classification made it possible to perceive the occurrence of the SCRL categories in the narratives, enabling the teacher to be inspired and reflect on the categories to be enhanced for necessary changes in the narrative in line with their pedagogical objectives.

Abstract
Detecting polyps in endoscopic images is essential in healthcare, requiring Explainable Artificial Intelligence (XAI) techniques to ensure transparency and confidence in AI models. Example-based XAI approaches, such as Anchors and Integrated Gradients, are promising but still need to be explored to improve the interpretability of models. In this study, a model was developed that achieved 91% accuracy on the test set. Anchors provided clear and intuitive explanations by highlighting critical regions, such as the polyp area, making it easier for clinical experts to understand the model's decisions. Integrated Gradients offered a detailed pixel-by-pixel analysis, covering the polyp area and other parts of the image, providing a comprehensive view of the model's behaviour. The comparative analysis revealed that Anchors are particularly useful for clarity, while Integrated Gradients offer greater depth and granularity. The combined use of these techniques improves the interpretability of AI models, increasing confidence and acceptance in critical healthcare applications and supporting informed clinical decisions. © 2025 The Author(s).