http://hdl.handle.net/10071/15080 Wed, 20 May 2026 01:12:43 GMT 2026-05-20T01:12:43Z http://hdl.handle.net/10071/37261 Título próprio: Artificial intelligence and FLIP panometry: Automated classification of esophageal motility patterns Autoria: Mascarenhas, M.; Mendes, F.; Cordeiro, J. R.; Mota, J.; Martins, M.; Almeida, M. J.; Araujo, C.; Frias, J.; Cardoso, P.; El Hajra, I.; Pinto da Costa, A.; Matallana, V.; Ciriza de Los Rios, C.; Ferreira, J.; Saraiva, M. M.; Macedo, G.; Niland, B.; Santander, C. Resumo: Functional lumen imaging probe (FLIP) panometry allows real-time assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite the development of the Dallas Consensus, FLIP panometry analysis remains complex. Artificial intelligence (AI) models have proven their benefit in high-resolution esophageal manometry; however, data on their role in FLIP panometry are scarce. This study aims to develop an AI model for automatic classification of motility patterns during a FLIP panometry exam. Methods: A total of 105 exams from five centers from both the European and American continents were included. Several machine learning models were trained and evaluated for detection of FLIP panometry patterns. Each exam was classified with an expert consensus-based decision according to the Dallas Consensus, with division into a training and testing dataset in a patient-split design. Models’ performance was evaluated through their accuracy and area under the receiver-operating characteristic curve (AUC-ROC). Results: Pathological planimetry patterns were identified by an AdaBoost Classifier with 84.9% accuracy and a mean AUC-ROC of 0.92. Random Forest identified disorders of the esophagogastric junction opening with 86.7% accuracy and an AUC-ROC of 0.973. The Gradient Boosting Classifier identified disorders of the contractile response with 86.0% accuracy and an AUC-ROC of 0.933. Conclusions: In this study, integrating exams with different probe sizes and demographic contexts, a machine learning model accurately classified FLIP panometry exams according to the Dallas Consensus. AI-driven FLIP panometry could revolutionize the approach to this exam during an endoscopic procedure, optimizing exam accuracy, standardization, and accessibility, and transforming patient management. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10071/37261 2026-01-01T00:00:00Z http://hdl.handle.net/10071/37223 Título próprio: Designing sustainable solutions: A gamified framework for empowering autonomous household recycling Autoria: Cheng, K. M.; Wijaya, L.; Teo, S. C.; Koo, A. C.; Hajarian, M. Resumo: This study explores strategies to facilitate autonomous recycling practices within families, which are essential for supporting sustainable development in local communities. A framework grounded in Self-Determination Theory (SDT) and the Theory of Planned Behavior (TPB), is developed to identify the elements influencing the adoption of autonomous recycling behavior in families. This approach combines autonomous support, integrated regulation, intrinsic aspiration, gamified recycling, societal norms, attitude, perceived behavioral control, and recycling intention to provide a comprehensive understanding of household sustainability dynamics. A webinar was conducted with 424 participants, using Quizziz to engage and evaluate knowledge of recycling. Among these participants, 197 completed the questionnaire, offering significant insights. The effectiveness of the proposed framework was evaluated using SmartPLS analysis. The results add to the expanding knowledge on sustainable development by providing practical insights on promoting household recycling. This study outlined the importance of using gamified engagement and autonomous decision-making processes to promote a more environmentally conscious society. Furthermore, sustainable design solutions were proposed by incorporating a gamified framework into recycling or pro-environmental behaviors. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10071/37223 2025-01-01T00:00:00Z http://hdl.handle.net/10071/37212 Título próprio: Psychological factors influencing public perception of space tourism Autoria: Freitas, L.; Omran, W.; Ramos, R. F. Resumo: Space tourism is a novel and under-researched leisure tourism area. This study examines the psychological factors that affect tourist behavioural intention (TBI), by the mediating role of tourist stickiness (sustained experiential engagement). This study integrated text mining and Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyse 15,568 comments from space tourism YouTube videos. A Word Frequency Matrix (WFM) was used as an input for PLS-SEM to examine the hypothesised relationship. Findings indicated that perceived feasibility and social status significantly affected tourist stickiness, whereas self-actualisation had no effect. Tourist stickiness was identified as an essential mediator between these motivations and TBI, indicating that social and technological confidence and prestige may be valued instead of self-efficacy for self-growth. This study clarifies how feasibility perceptions and status motives shape space tourism intention via tourism stickiness. Moreover, it guides stakeholders in designing communication and experience strategies that build technological confidence and social prestige signals to increase public interest and intention to participate. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10071/37212 2026-01-01T00:00:00Z http://hdl.handle.net/10071/37086 Título próprio: Social media insights about COVID-19 in Portugal: A text mining approach Autoria: Marreiros, C. F.; Boné, J.; Ferreira, J. C.; Ribeiro, R. Resumo: The rapid spread of COVID-19 around the world had a significant impact on daily life. As in other countries, measures were taken in Portugal to combat the exponential increase of cases, such as curfews and the use of masks. Thus, in parallel with the direct consequences on health and the healthcare sector, the pandemic also caused changes in human behavior from a sociological viewpoint. The objective of this dissertation is to attain a perception of the reality concerning COVID-19. For this purpose, real-time data was extracted from three sources, two of them being social media platforms – Twitter and Reddit – and the other one being Público, a Portuguese online newspaper. The adopted approach, based on topic modelling and sentiment analysis, was validated within the Portugal context, concerning data over a period of one year, but it can equally be employed in similar situations and other countries and provide decision-making support. After the data extracting, it was prepared for application of natural language processing (NLP) tools specific to the Portuguese language, which can represent a challenge due to the lexical richness. With the gathered information, a dashboard was built, with the purpose of gaining insights on the COVID-19 pandemic in Portugal. It was concluded that the topics discussed on social media reflect the events related to the pandemic. In a final stage, these dashboards were evaluated by public health experts, who highlighted the potential of the results obtained. The data and dashboards will be made available to the scientific community upon request. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/10071/37086 2023-01-01T00:00:00Z