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. © 2025 Elsevier B.V., All rights reserved.

Abstract
The capabilities of the most recent language models have increased the interest in integrating them into real-world applications. However, the fact that these models generate plausible, yet incorrect text poses a constraint when considering their use in several domains. Healthcare is a prime example of a domain where text-generative trustworthiness is a hard requirement to safeguard patient well-being. In this paper, we present Physio, a chat-based application for physical rehabilitation. Physio is capable of making an initial diagnosis while citing reliable health sources to support the information provided. Furthermore, drawing upon external knowledge databases, Physio can recommend rehabilitation exercises and over-the-counter medication for symptom relief. By combining these features, Physio can leverage the power of generative models for language processing while also conditioning its response on dependable and verifiable sources. A live demo of Physio is available at https://physio.inesctec.pt.

Abstract
This paper presents a simulation model of a Time of Flight distance sensor applying SimTwo robotics simulator in order to contribute to a mobile robotics application, in an educational context. The objective is to observe the sensor behavior, inside the simulation environment, face a set of experiments, such as an abrupt difference of distance, several angle inclinations and measurements to the maximum sensor range. The tests were performed using SimTwo being a high performance, open source, versatile, real time simulation environment, in which is possible to configure an specific sensor adding its features, which allows to achieve a realistic simulation. The results represented the expected sensor behavior for the proposed scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The adoption of sustainable dietary patterns that consider simultaneously nutritional well-being and reduced environmental impact is of paramount importance. This paper introduces the Dietary Pattern Sustainability Index (DIPASI), as a method to assess the sustainability of dietary patterns by covering the environmental, nutritional, and economic dimensions in a single score. Environmental indicators include carbon footprint, water footprint, and land use, the nutritional quality is evaluated through the Nutritional Rich Diet 9.3 score, and the economic aspects are considered using diet cost. DIPASI measures the deviation (in %) of an individual's diet in relation to a reference diet. The case study utilized dietary data from the Portuguese National Food, Nutrition, and Physical Activity Survey (IAN-AF 2015-2016), which included 2999 adults aged 18 to 64. The Portuguese dietary patterns (covering 1492 food products consumed), were compared against the reference Mediterranean diet. Results indicated that the Portuguese dietary pattern had a higher environmental impact (CF: 4.32 kg CO2eq/day, WF: 3162.88 L/day, LU: 7.03 m(2)/day), a lower nutritional quality (NRD9.3: 334), and a higher cost (6.65 euros/day) when compared to the Mediterranean diet (CF: 3.30 kg CO2eq/day, WF: 2758.84 L/day, LU: 3.67 m(2)/day, NRD9.3: 668, cost: 5.71 euros/day). DIPASI reveals that only 4% of the sample's population does not deviate or presents a positive deviation (> - 0.5%) from the Mediterranean diet, indicating that the majority of Portuguese individuals have lower sustainability performance. For the environmental sub-score, this percentage was 21.3%, for the nutritional sub-score was 10.9%, and for the economic sub-score was 34.2%. This study provides a robust framework for assessing dietary sustainability on a global scale. The comprehensive methodology offers an essential foundation for understanding and addressing challenges in promoting sustainable and healthy dietary choices worldwide.