Percorrer por data de Publicação, começado por "2021-02-03"
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- A Mobile Application As a Tool for Sustainable Development in Protected Areas (a proposal for the case of Arrabida Natural Park)Publication . Khodzhaeva, Valeriia; Oliveira, Fernanda Maria Fernandes; Eurico, Sofia TeixeiraProtected natural areas are designed to preserve biological and landscape diversity and ensure sustainable environmental development. Huge territories face great challenges in the management of sustainable tourism, which are very difficult to control due to uncontrolled traffic capacity and the lack of awareness of visitors about the uniqueness of the park. The best way to convey information to the masses is information technology, which almost every modern person has. The goal of this project is to develop and promote a mobile application to help developing sustainable tourism in protected areas using the case of Arrabida Natural Park.
- Minimal Clinically Important Difference for Quadriceps Muscle Strength in People with COPD Following Pulmonary RehabilitationPublication . Oliveira, Ana; Rebelo, Patrícia; Paixão, Cátia; Jácome, Cristina; Cruz, Joana; Martins, Vitória; Simão, Paula; Brooks, Dina; Marques, AldaQuadriceps strength training is a key component of pulmonary rehabilitation (PR). Clinical interpretability of changes in muscle strength following PR is however limited due to the lack of cut-off values to define clinical improvement. This study estimated the minimal clinically important difference (MCID) for the isotonic and isometric quadriceps muscle strength assessed with the one-repetition maximum (1RM) and hand-held dynamometry (HHD) in people with chronic obstructive pulmonary disease (COPD) following PR. A secondary analysis of a real life non-randomised controlled study was conducted in people with COPD enrolled in a 12-week community-based PR programme. Anchor and distribution-based methods were used to compute the MCIDs. The anchors explored were the St. George's respiratory questionnaire (SGRQ) and the six-minute walk test (6MWT) using Pearson's correlations. Pooled MCIDs were computed using the arithmetic weighted mean (2/3 anchor, 1/3 distribution-based methods) and reported as absolute and/or percentage of change values. Eighty-nine people with COPD (84% male, 69.9 ± 7.9 years, FEV1 49.9 ± 18.9% predicted) were included. No correlations were found between changes in 1RM and the SGRQ neither between changes in HHD and the SGRQ and 6MWT (p > 0.05). Thus, anchor-based methods were used only in the MCID of the 1RM with the 6MWT as the anchor. The pooled MCIDs were 5.7Kg and 26.9% of change for the isotonic quadriceps muscle strength with 1RM and 5.2KgF for isometric quadriceps muscle strength assessed with HHD. The MCIDs found are estimates to improve interpretability of community-based PR effects on quadriceps muscle strength and may contribute to guide interventions.
- A Data-Driven Approach to Forecasting Heating and Cooling Energy Demand in an Office Building as an Alternative to Multi-Zone Dynamic SimulationPublication . Godinho, Xavier; Bernardo, Hermano; Sousa, João C. de; Oliveira, Filipe T.Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption.