Abstract
This study introduces a methodological framework for constructing virtual ironic environments through the deliberate mismatching of emotional profiles in music and imagery. We conducted a statistical analysis of "happy/joy" and "angry" samples from two independent datasets to identify significant acoustic and visual features. These feature profiles were translated into mid-level semantic prompts to guide AI-based generation of visual and musical content. Our findings reveal distinct emotional signatures: happy music exhibits higher rhythmic onset rates and greater spectral variability, whereas angry music is characterized by a higher spectral centroid and more stable dissonance. Visually, joyful images are brighter and more symmetrical, while angry images feature darker hues and concentrated color distributions. Furthermore, mid-level perceptual descriptors generate the most coherent content, and we employed them to build a spectrum of virtual environments, including Sarcastic (joyful visuals + angry music) and Kind Ironic (angry visuals + happy music) spaces. This work establishes a new, data-driven approach to affective computing and speculative virtual design, grounded in the formal principle of audiovisual dissonance. © 2025 Copyright held by the owner/author(s).

Abstract
Diabetic retinopathy is an eye disease that affects people with diabetes mellitus, causing lesions that affect the retina, leading to progressive vision loss. In Portugal, it is estimated that 1.5 million people between the ages of 20 and 79 have diabetes, a figure that is expected to rise in the coming years. This increase is also likely to raise the total number of people affected by diabetic retinopathy, who will need to be identified in the early stages of the disease to receive clinical treatment aimed at reducing the likelihood of visual impairment due to the disease. Detection and classification of the stage of severity is carried out by specialists using medical images of the retina and patients' clinical data. Fundus photographs are the standard for detecting and monitoring the progression of the disease, as they make it possible to see biomarkers that characterize the stages of the disease. The manual task of analyzing and tracing images is time-consuming and subjective, which can lead to interpretation errors. Artificial intelligence (AI) models, such as convolutional neural networks, have been proposed to aid specialists in medical image analysis tasks, some of which have already been approved for clinical use. To overcome the limitations of convolutional networks, new AI models have been proposed to develop computer vision applications, achieving promising results in image classification. The vision mamba model was recently introduced, which uses bidirectional state space to obtain an efficient visual representation. In this work, we evaluate the vision mamba model's ability to detect cases in the moderate and advanced stages of diabetic retinopathy in fundus photographs and compare its performance with models based on convolutional networks. As the best result, the model achieved a recall value of 0.95 in the APTOS dataset. © 2025 The Author(s).

Abstract
Hydrogen (H-2) infrastructure is the focus of many initiatives for the planned energetic transition, but its volatility and flammability require extensive safety measures to prevent leakages and explosions. Magnesium thin films have been investigated not only for H-2 storage but also as switchable mirrors, which drastically change their optical properties when hydrogenated. Due to their lower cost compared to other hydride-forming or plasmonic metals commonly used in optical sensing, Mg-based H-2 fiber sensors have the potential to be both affordable and effective for scalable deployment in industrial settings. To this end, multilayer thin-film structures with Mg and palladium as adsorption catalyst were deposited on single-mode fiber tips, and H-2 loading/unloading processes were tested in a controlled flow gas setup. In parallel, an optical interrogation system prototype was developed, enabling fast data acquisition of fiber-tip reflectivity across multiple sensing probes at a wavelength of 1550 nm. Preliminary testing suggests fast response times of a few seconds for significant drops in reflectivity, facilitating straightforward detection of H-2 leaks using thresholding methods. Planned future work includes performance comparison with simpler sensing structures, durability and contaminant testing, and response time optimization.

Abstract
Advancements in the care for patients with breast cancer have demanded the development of biomechanical breast models for the planning and risk mitigation of such invasive surgical procedures. However, these approaches require large amounts of high-quality magnetic resonance imaging (MRI) training data that is of difficult acquisition and availability. Although this can be solved using synthetic data, generating high resolution images comes at the price of very high computational constraints and tipically low performances. On the other hand, producing lower resolution samples yields better results and efficiency but falls short of meeting health professional standards. Therefore, this work aims to validate a joint approach between lower resolution generative models and the proposed super-resolution architecture, titled Shifted Window Image Restoration (SWinIR), which was used to achieve a 4x increase in image size of breast cancer patient MRI samples. Results prove to be promising and to further expand upon the super-resolution state-of-the-art, achieving good maximum peak signal-to-noise ratio of 41.36 and structural similarity index values of 0.962 and thus beating traditional methods and other machine learning architectures.

Abstract
With the consistent adoption of iVR and growing research on the topic, it becomes fundamental to understand how the perception of Realism plays a role in the potential of iVR. This work puts forwards a hypothesis-driven theoretical model of how the perception of each multisensory stimulus (Visual, Audio, Haptic and Scent) is related to the perception of Realism of the whole experience (Subjective Realism) and, in turn, how this Subjective Realism is related to Involvement and Presence. The model was validated using a sample of 216 subjects in a multisensory iVR experience. The results indicated a good model fit and provided evidence on how the perception of Realism of Visual, Audio and Scent individually is linked to Subjective Realism. Furthermore, the results demonstrate strong evidence that Subjective Realism is strongly associated with Involvement and Presence. These results put forwards a validated questionnaire for the perception of Realism of different aspects of the virtual experience and a robust theoretical model on the interconnections of these constructs. We provide empirical evidence that can be used to optimise iVR systems for Presence, Involvement and Subjective Realism, thereby enhancing the effectiveness of iVR experiences and opening new research avenues.

Abstract
Image inpainting is a crucial technique in computer vision, particularly for reconstructing corrupted images. In medical imaging, it addresses issues from instrumental errors, artifacts, or human factors. The development of deep learning techniques has revolutionized image inpainting, allowing for the generation of high-level semantic information to ensure structural and textural consistency in restored images. This article presents a comprehensive review of 53 studies on deep image inpainting in medical imaging, analyzing its evolution, impact, and limitations. The findings highlight the significance of deep image inpainting in artifact removal and enhancing the performance of multi-task approaches by localizing and inpainting regions of interest. Furthermore, the study identifies magnetic resonance imaging and computed tomography as the predominant modalities and highlights generative adversarial networks and U-Net as preferred architectures. Future research directions include the development of blind inpainting techniques, the exploration of techniques suitable for 3D/4D images, multiple artifacts, and multi-task applications, and the improvement of architectures.