http://hdl.handle.net/10071/15080 Sat, 30 May 2026 09:35:34 GMT 2026-05-30T09:35:34Z http://hdl.handle.net/10071/37352 Título próprio: Analyzing economic and social inequalities in housing: A visual storytelling case study in Portugal Autoria: Crespo, A.; Barateiro, J.; Cardoso, E. Resumo: Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics framework oriented towards policy monitoring. Official data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM-inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an uneven supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10071/37352 2026-01-01T00:00:00Z 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