Abstract
Segmentation of lung nodules in CT images is an important step during the clinical evaluation of patients with lung cancer. Furthermore, early assessment of the cancer is crucial to increase the overall survival chances of patients with such disease, and the segmentation of lung nodules can help detect the cancer in its early stages. Consequently, there are many works in the literature that explore the use of neural networks for the segmentation of lung nodules. However, these frameworks tend to rely on accurate labelling of the nodule centre to then crop the input image. Although such works are able to achieve remarkable results, they do not take into account that the healthcare professional may fail to correctly label the centre of the nodule. Therefore, in this work, we propose a new framework based on the U-Net model that allows to correct such inaccuracies in an interactive fashion. It is composed of two U-Net models in cascade, where the first model is used to predict a rough estimation of the lung nodule location and the second model refines the generated segmentation mask. Our results show that the proposed framework is able to be more robust than the studied baselines. Furthermore, it is able to achieve state-of-the-art performance, reaching a Dice of 91.12% when trained and tested on the LIDC-IDRI public dataset.

Abstract
Mineral identification is a challenging task in geological sciences, which often implies multiple analyses of the physical and chemical properties of the samples for an accurate result. This task is particularly critical for the mining industry, where proper and fast mineral identification may translate into major efficiency and performance gains, such as in the case of the lithium mining industry. In this study, a mineral identification algorithm optimized for analyzing lithium-bearing samples using Laser-induced breakdown spectroscopy (LIBS) imaging, is put to the test with a set of representative samples. The algorithm incorporates advanced spectral processing techniques-baseline removal, Gaussian filtering, and data normalization-alongside unsupervised clustering to generate interpretable classification maps and auxiliary charts. These enhancements facilitate rapid and precise labelling of mineral compositions, significantly improving the interpretability and interactivity of the user interface. Extensive testing on diverse mineral samples with varying complexities confirmed the algorithm's robustness and broad applicability. Challenges related to sample granulometry and LIBS resolution were identified, suggesting future directions for optimizing system resolution to enhance classification accuracy in complex mineral matrices. The integration of this advanced algorithm with LIBS technology holds the potential to accelerate the mineral evaluation, paving the way for more efficient and sustainable mineral exploration.

Abstract

Abstract
Sound-based uroflowmetry (SU) is a non-invasive technique emerging as an alternative to traditional uroflowmetry (UF) to calculate the voiding flow rate based on the sound generated by the urine impacting the water in a toilet, enabling remote monitoring and reducing the patient burden and clinical costs. This study trains four different machine learning (ML) models (random forest, gradient boosting, support vector machine and convolutional neural network) using both regression and classification approaches to predict and categorize the voiding flow rate from sound events. The models were trained with a dataset that contains sounds from synthetic void events generated with a high precision peristaltic pump and a traditional toilet. Sound was simultaneously recorded with three devices: Ultramic384k, Mi A1 smartphone and Oppo Smartwatch. To extract the audio features, our analysis showed that segmenting the audio signals into 1000 ms segments with frequencies up to 16 kHz provided the best results. Results show that random forest achieved the best performance in both regression and classification tasks, with a mean absolute error (MAE) of 0.9, 0.7 and 0.9 ml/s and quadratic weighted kappa (QWK) of 0.99, 1.0 and 1.0 for the three devices. To evaluate the models in a real environment and assess the effectiveness of training with synthetic data, the best-performing models were retrained and validated using a real voiding sounds dataset. The results reported an MAE below 2.5 ml/s and a QWK above 0.86 for regression and classification tasks, respectively.

Abstract
Achieving consistency in immersive learning case descriptions is essential but challenging due to variations in research focus, methodology, and researchers' background. We addresses these challenges by leveraging the Immersive Learning Case Sheet (ILCS), a methodological instrument to standardize case descriptions, that we applied to an immersive learning case on ancient Greek technology in VRChat. Research team members had differing levels of familiarity with the ILCS and the case content, so we developed a custom ChatGPT assistant to facilitate consistent terminology and process alignment across the team. This paper constitutes an example of how structured case reports can be a novel contribution to immersive learning literature. Our findings demonstrate how the ILCS supports structured reflection and interpretation of the case. Further we report that the use of a ChatGPT assistant significantly supports the coherence and quality of the team members development of the final ILCS. This exposes the potential of employing AI-driven tools to enhance collaboration and standardization of research practices in qualitative educational research. However, we also discuss the limitations and challenges, including reliance on AI for interpretive tasks and managing varied levels of expertise within the team. This study thus provides insights into the practical application of AI in standardizing immersive learning research processes.

Abstract
This study demonstrates the application of a tomography-like (TL) method to monitor grape maturation dynamics over two growing seasons (2021-2022) in the Douro Wine Region. Using a Vis-NIR point-of-measurement sensor, which employs visible and near-infrared light to penetrate grape tissues non-destructively and provide spectral data to predict internal composition, this approach captures non-destructive measurements of key physicochemical properties, including soluble solids content (SSC), weight-to-volume ratio, chlorophyll and anthocyanin levels across internal grape tissues-skin, pulp, and seeds-over six post-veraison stages. The collected data were used to generate detailed metabolic maps of maturation, integrating topographical factors such as altitude and NDVI-based (normalised difference vegetation index) vigour assessments, which revealed significant (p < 0.05) variations in SSC, chlorophyll, and anthocyanin levels across vineyard zones. The metabolic maps generated from the TL method enable high-throughput data to reveal the impact of environmental variability on grape maturation across distinct vineyard areas. Predictive models using random forest (RF) and self-learning artificial intelligence (SL-AI) algorithms showed RF's robustness, achieving stable predictions with R-2 >= 0.86 and MAPE <= 33.83 %. To illustrate the TL method's practical value, three hypothetical decision models were developed for targeted winemaking objectives based on SSC, chlorophyll in the pulp, and anthocyanin in the skin and seeds. These models underscore the TL method's ability to support site-specific management (SSM) by providing actionable agricultural practices (e.g. harvest) into vineyard management, guiding winemakers to implement tailored interventions based on metabolic profiles rather than only cultivar characteristics. This precision viticulture (PV) approach enhances wine quality and production efficiency by aligning vineyard practices with specific wine quality goals.