Abstract
Cardiovascular diseases are the leading cause of death in the world, with coronary artery disease being the most prevalent. Coronary artery calcifications are critical biomarkers for cardiovascular disease, and their quantification via non-contrast computed tomography is a widely accepted and heavily employed technique for risk assessment. Manual segmentation of these calcifications is a time-consuming task, subject to variability. State-of-the-art methods often employ convolutional neural networks for an automated approach. However, there is a lack of studies that perform these segmentations with 3D architectures that can gather important and necessary anatomical context to distinguish the different coronary arteries. This paper proposes a novel and automated approach that uses a lightweight three-dimensional convolutional neural network to perform efficient and accurate segmentations and calcium scoring. Results show that this method achieves Dice score coefficients of 0.93 ± 0.02, 0.93 ± 0.03, 0.84 ± 0.02, 0.63 ± 0.06 and 0.89 ± 0.03 for the foreground, left anterior descending artery (LAD), left circumflex artery (LCX), left main artery (LM) and right coronary artery (RCA) calcifications, respectively, outperforming other state-of-the-art architectures. An external cohort validation also showed the generalization of this method's performance and how it can be applied in different clinical scenarios. In conclusion, the proposed lightweight 3D convolutional neural network demonstrates high efficiency and accuracy, outperforming state-of-the-art methods and showcasing robust generalization potential.

Abstract
Online reviews are a crucial asset for e-commerce platforms as they provide consumers with valuable insights into products. It is important to note that these reviews are subjective and may contain biases. Therefore, it is essential to approach them with a critical eye. Despite this, online reviews remain a valuable tool for consumers when making purchasing decisions. This study focuses on developing web-based mini-games that target cognitive biases. The games are specifically designed to enhance the perception of e-commerce online reviews. A pilot study involving 85 participants was conducted to explore the potential of integrating these cognitive bias games into web platforms. The findings indicate promising avenues for leveraging these games to enhance cognitive personalization and improve the quality of e-commerce online reviews.

Abstract
Background Insufficient physical activity is one of the leading risk factors for death worldwide. Regular exercise can improve physical performance and quality of life, reduce the risks of falls and depressive symptoms, and reduce the likelihood of cognitive decline in older adults. Virtual reality (VR) and serious games (SG) are promising tools to improve physical and cognitive functioning. As part of the VR2Care project activities, four pilot sites explored the capabilities of the VR environment in a remote psychomotor training with SG and a hybrid approach with local groups of older adults performing physical activity.Objective The present study aimed to explore and measure the impact on older adults' quality of life and physical activity of using VR2Care solution and the level of usability, satisfaction and acceptance.Methods The study is a mixed method study, using qualitative and quantitative surveys to evaluate quality of life and physical activity of older users, and usability, satisfaction and acceptance of the solution. The data collection is a mix of investigator site data entry and users' self-reported data through the solutions or through online and paper-based means. Data were collected at baseline and after a follow-up of 6 weeks. Data are expressed as mean +/- standard deviation (SD) unless otherwise stated. Within the group, baseline to end of observation differences were assessed by paired sample t-test. A p = 0.05 was considered significant.Results No significant improvements in quality of life and physical activity were found. Little improvement, although not significant, in physical activity was found, comparing the Total MET average value of users who participated in phase I and II, therefore using SmartAL and Rehability. Little improvement, although not significant, in physical activity applies in >= 76 population. Users' feedback on usability, satisfaction and acceptance of VR2Care is generally positive. VR2Care was appreciated mostly for its usefulness in managing physical activity and the capacity to influence the consistency of attending physical activity sessions as prescribed by doctors.Conclusion Our results suggest that randomized controlled trial will be needed to assess correlations between specific features of the solution and health outcomes.

Abstract
This study presents the development and assessment of a mobile application - the WalkingPAD app - aimed at promoting adherence to physical exercise among patients with Peripheral Arterial Disease (PAD). The assessment of adherence, acceptability, and usability was performed using mixed methods. Thirty-eight patients participated in the study with a mean age of 63.4 years (SD = 6.8). Thirty patients used the application for three months, responded to a semi-structured interview, and completed a task test and the System Usability Scale (SUS, ranging from 0 to 100). The application's adherence rate was 73%. When patients were asked about their reasons for using the app, the main themes that emerged were motivation, self-monitoring, and support in fulfilling a commitment. The average SUS score was 82.82 (SD = 18.4), indicating high usability. An upcoming version of the WalkingPAD app is expected to redesign both tasks - opening the app and looking up the walking history - which were rated as the most difficult tasks to accomplish. The new version of the WalkingPAD app will incorporate participants' comments and suggestions to enhance usability for this population.

Abstract
The use of 3D modelling in medical education is a revolutionary tool during the learning process. In fact, this type of technology enables a more interactive teaching approach, making information retention more effective and enhancing students' understanding. 3D modelling allows for the creation of precise representations of the human body, as well as interaction with three-dimensional models, giving students a better spatial understanding of the different organs and systems and enabling simulations of surgical and technical procedures. This way, medical education is enriched with a more realistic and safe educational experience. The goal is to understand whether, when students and schools are challenged, they play an important role in addressing health issues in their community. School-led projects are directed towards educational scenarios that emphasize STEM education, tackling relevant public health problems through open-school initiatives. By implementing an educational scenario focused on 3D modelling and leveraging technology, we aim to raise community awareness on public health issues.

Abstract
The ongoing energy transition and the rapid electrification of transport increase the importance of integrating renewable energy sources into smart mobility systems. Among these, solar energy plays a central role, but the variability of solar radiation poses significant challenges for planning electric vehicle (EV) charging and ensuring the reliable operation of transport networks. This work addresses these challenges by combining Big Data approaches and High-Performance Computing (HPC) to improve solar radiation forecasting and assess its implications for sustainable transport as a novelty from previous works. A Long Short-Term Memory (LSTM) neural network was the focus, and it was trained to predict key meteorological variables - global solar radiation, temperature, and wind speed - using both the original dataset of 13 years and expanded datasets of up to 130 years, generated to simulate Big Data scenarios. Forecasting performance remained stable across datasets, with R2 values above 0.85 for all variables. The best predictive results were obtained for the original dataset, achieving R2 = 0.9884 for solar radiation, while the HPC reduced execution time compared to conventional desktop environments. These results demonstrate that larger datasets improve model scalability and robustness, but significantly increase computational demands. The Deucalion supercomputer achieved the best performance, processing the largest dataset (130 years) in 44.24 minutes, while the same task on a Ryzen 7 required 51.00 minutes. The proposed approach highlights the potential of integrating Big Data and HPC to support EV charging optimisation, smart grid operation, and sustainable mobility strategies, contributing to faster, more reliable, and data-driven decision-making in the energy-transport ecosystem. © 2025 IEEE.