Abstract
The authors present the Effective Solution Based Learning, which derives from Project-Based Learning but is applied to real problems in order to build effective solutions. Emphasis is placed on the effectiveness on the assumption that encourages greater involvement and commitment on the part of students, ensuring a context that is intended to be more attractive and close to what will be their professional reality. Effectiveness is measured by the functionalities considered essential for solving the problem, but also the viability of the solution to be effectively used after the end of development, without the need for continued student involvement. A brief summary of the methodology is presented in the paper, emphasizing, in particular, the criteria and requirements for choosing project themes. The results of the first year of evaluation are also presented in the paper, pointing to a clear reduction in dropouts and an increase in approved students. Considering that this is achieved with more demand and work, it is arguable that it also resulted in students with more knowledge and skills. The authors also include in this paper the results of a survey done to the students, after the project conclusion, to assess the students’ perspective on this methodology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Recently, the uptake of renewable energy has surged in distribution networks, particularly due to the costeffectiveness and modular nature of photovoltaic systems. This has paved the way to a new era of user engagement, embodied by individual and collective self-consumption, and promoted by the EU Directive 2018/ 2001, which advocates for the establishment of Renewable Energy Communities. However, the transposition of this directive varies across Member States, resulting in specific rules for each country. In this work, the impact that different energy sharing models have on the same community is quantitatively assessed. The policy analysis focuses on the regulation of two countries, Italy and Portugal, chosen for the specular ways in which their models operate, respectively virtually and physically. The analysis is supported by a suite of tools which includes two optimization problems for community's operations, one for each analysed regulation, and a set of consumer protection mechanisms, to ensure no member is losing money while in community. Results demonstrate that the sharing model impacts community's optimal operations, optimal battery size and configuration, and members' benefit. As these models are sensitive to different variables, personalized interventions at national level are required.

Abstract

Background Subcutaneous (SAF) and visceral (VAF) abdominal fat have specific properties which the global body fat and total abdominal fat (TAF) size metrics do not capture. Beyond size, radiomics allows deep tissue phenotyping and may capture fat dysfunction. We aimed to characterize the computed tomography (CT) radiomics of SAF and VAF and assess their incremental value above fat size to detect coronary calcification. Methods SAF, VAF and TAF area, signal distribution and texture were extracted from non-contrast CT of 1001 subjects (57% male, 57?±?10 years) with no established cardiovascular disease who underwent CT for coronary calcium score (CCS) with additional abdominal slice (L4/5-S1). XGBoost machine learning models (ML) were used to identify the best features that discriminate SAF from VAF and to train/test ML to detect any coronary calcification (CCS?>?0). Results SAF and VAF appearance in non-contrast CT differs: SAF displays brighter and finer texture than VAF. Compared with CCS?=?0, SAF of CCS?>?0 has higher signal and homogeneous texture, while VAF of CCS?>?0 has lower signal and heterogeneous texture. SAF signal/texture improved SAF area performance to detect CCS?>?0. A ML including SAF and VAF area performed better than TAF area to discriminate CCS?>?0 from CCS?=?0, however, a combined ML of the best SAF and VAF features detected CCS?>?0 as the best TAF features. Conclusion In non-contrast CT, SAF and VAF appearance differs and SAF radiomics improves the detection of CCS?>?0 when added to fat area; TAF radiomics (but not TAF area) spares the need for separate SAF and VAF segmentations.

Abstract

Abstract
During the COVID-19 pandemic, lockdowns and social distancing measures drove the shift from in-person to online physical exercise classes, leading individuals to explore these digital alternatives. Guided by the Expectation-Confirmation Model, this article examines older adults' intentions to continue using online physical exercise classes. Semi-structured interviews were conducted with 17 adults aged 65 and older who had participated in online physical exercise classes during the pandemic. Transcripts were subject to thematic analysis using the NVivo software program. The results indicate that older adults recognize the usefulness of online physical exercise classes because of their ability to enhance their health and well-being. Their initial expectations were surpassed, and they were generally satisfied with the experience. However, in-person classes remained preferred due to their enhanced benefits. They also felt that the adoption of online classes was involuntary; instead of an autonomous decision guided by their needs and preferences, this was a viable solution imposed by the lockdown. Therefore, their continuance intentions are limited to specific conditions, namely a new lockdown or other physical impediments. Still, considering the flexibility that online physical exercise classes offer, accommodating time and physical constraints, participants highlighted the advantages of a hybrid approach for those who may face challenges attending in-person classes. Based on the findings, this article proposes that ECM provides a relevant, yet insufficient, framework for explaining older adults' continuance intentions for online physical exercise classes, suggesting the inclusion of additional explaining factors: perceived usefulness of non-technological alternatives, necessary conditions, and self-determination.

Abstract
The current surge in the development of web applications highlights the necessity of incorporating user-specific preferences into the design process. An innovative approach to improving these applications involves the analysis of interaction data recorded by browsers, such as the number of mouse clicks and keystrokes. The data thus obtained provides valuable insight into user behavior, enabling effective personalization of web applications. The WebTraceSense project proposes the development of a web platform designed to facilitate the customization of the visualization of interaction data from websites. The platform will include a dynamic visualization component, which will support the identification of user behaviors, and a DevOps cycle, which will help streamline software cycle processes. This article presents a case study for the examination of user interaction logs from a visual data analysis tool, utilizing the functionalities of the WebTraceSense platform to facilitate the identification of behavioral trace patterns.