Abstract
“Portal for sharing teaching experience in the inclusion of diversity” corresponds to axis 4.2. of the project Skills for the Next Generation of the University of Porto by supporting the development of innovative and inclusive pedagogical resources, sharing information, experience about inclusivity and ludic tools to cope with the individual differences integrated with the university's information system. This project is intended to contribute to the achievement of the inclusive priorities defined at the European level through a web platform capable of deliverable informative content and gamified resources (serious and critical games) to give adequate support for the university academy, not only to cope with the difference but to take advantage of it, dealing with human differences and specificities as an asset to the community, unlike shortcomings.

Abstract
Case-based explanations are an intuitive method to gain insight into the decision-making process of deep learning models in clinical contexts. However, medical images cannot be shared as explanations due to privacy concerns. To address this problem, we propose a novel method for disentangling identity and medical characteristics of images and apply it to anonymize medical images. The disentanglement mechanism replaces some feature vectors in an image while ensuring that the remaining features are preserved, obtaining independent feature vectors that encode the images' identity and medical characteristics. We also propose a model to manufacture synthetic privacy-preserving identities to replace the original image's identity and achieve anonymization. The models are applied to medical and biometric datasets, demonstrating their capacity to generate realistic-looking anonymized images that preserve their original medical content. Additionally, the experiments show the network's inherent capacity to generate counterfactual images through the replacement of medical features.

Abstract
Interferometric Synthetic Aperture Radar (InSAR) revolutionizes surface study by measuring precise ground surface changes. Phase unwrapping, a key challenge in InSAR, involves removing ambiguity in measured phase. Deep learning algorithms like Generative Adversarial Networks (GANs) offer a potential solution for simplifying the unwrapping process. This work evaluates GANs for InSAR phase unwrapping, replacing SNAPHU with GANs. GANs achieve significantly faster processing times (2.38 interferograms per minute compared to SNAPHU's 0.78 interferograms per minute) with minimal quality degradation. A comparison of SBAS results shows that approximately 84% of GANs points are within 3 millimeters of SNAPHU. These results represent a significant advancement in phase unwrapping methods. While this experiment does not declare a definitive winner, it demonstrates that GANs are a viable alternative in certain scenarios and may replace SNAPHU as the preferred unwrapping method. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
Maintaining process quality is one of the biggest challenges manufacturing industries face, as production processes have become increasingly complex and difficult to monitor effectively in today's manufacturing contexts. Reliance on skilled operators can result in suboptimal solutions, impacting process quality. In doing so, the importance of quality monitoring and diagnosis methods cannot be undermined. Existing approaches have limitations, including assumptions, prior knowledge requirements, and unsuitability for certain data types. To address these challenges, we present a novel unsupervised monitoring and detection methodology to monitor and evaluate the evolution of a quality characteristic's degradation. To measure the degradation we created a condition index that effectively captures the quality characteristic's mean and scale shifts from the company's specification levels. No prior knowledge or data assumptions are required, making it highly flexible and adaptable. By transforming the unsupervised problem into a supervised one and utilising historical production data, we employ logistic regression to predict the quality characteristic's conditions and diagnose poor condition moments by taking advantage of the model's interpretability. We demonstrate the methodology's application in a glass container production process, specifically monitoring multiple defective rates. Nonetheless, our approach is versatile and can be applied to any quality characteristic. The ultimate goal is to provide decision-makers and operators with a comprehensive view of the production process, enabling better-informed decisions and overall product quality improvement.

Abstract
This paper presents the examination of a planar waveguide sensor featuring a bimetallic layer, revealing its potential applicability across both the visible and infrared spectrums. The bimetallic layer consists of adjacent gold and silver slabs positioned atop the waveguide's core. This arrangement demonstrates the activation of two distinct plasmon resonances, indicating promising prospects for multiparameter sensing applications.

Abstract
This paper discusses the assessment of integrating photovoltaic-battery hybrid power plants into the electrical grids of the Azores islands and their ability to comply with advanced network services. To ensure the hybrid power plant supports the grid operational requirements, a methodology was devised through steady-state and dynamic numerical simulations. On one hand, the steady-state analysis generated active-reactive power diagrams for different voltage levels at the plant’s interconnection point with the island’s grid, demonstrating that the internal grid of the PV-battery hybrid power plant allows a significant range of reactive power modulation in different operating conditions. On the other hand, dynamic analysis highlighted the plant’s crucial role in modulating reactive current production during grid faults. Additionally, it showed the plant’s capability to automatically reduce active power injection during over-frequency events and, as a result, lessening the frequency regulation effort for synchronous generators and fast energy storage system. © Energynautics GmbH.