Browsing by Issue Date, starting with "2023-05-13"
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- Extensive green roofs: different time approaches to runoff coefficient determinationPublication . Monteiro, Cristina M.; Santos, Cristina; Castro, Paula M. L.Stormwater runoff in green roofs (GRs) is represented by the runoff coefficient, which is fundamental to assess their hydraulic performance and to design the drainage systems downstream. Runoff coefficient values in newly installed GR systems should be estimated by models that must be feasible and reproduce the retention behavior as realistically as possible, being thus adjusted to each season and climate region. In this study, the suitability of a previously developed model for runoff coefficient determination is assessed using experimental data, and registered over a 1 year period. Results showed that the previously developed model does not quite fit the experimental data obtained in the present study, which was developed in a distinct year with different climate conditions, revealing the need to develop a new model with a better adjustment, and taking into consideration other variables besides temperature and precipitation (e.g., early-stage moisture conditions of the GR matrix and climate of the study area). Runoff coefficient values were also determined with different time periods (monthly, weekly, and per rain event) to assess the most adequate approach, considering the practical uses of this coefficient. The monthly determination approach resulted in lower runoff coefficient values (0–0.46) than the weekly or per rain event (0.017–0.764) determination. When applied to a long-term performance analysis, this study showed no significant differences when using the monthly, weekly, or per rain event runoff, resulting on a variation of only 0.9 m3 of annual runoff. This indicates that the use of monthly values for runoff coefficient, although not suitable for sizing drainage systems, might be used to estimate their long-term performance. Overall, this pilot extensive GR of 0.4 m2 presented an annual retention volume of 469.3 L, corresponding to a retention rate of 89.6%, in a year with a total precipitation of 1089 mm. The assessment of different time scales for runoff coefficient determination is a major contribution for future GR performance assessments, and a fundamental decision support tool.
- Novel non-linear approaches to understanding the dynamic brain : knowledge from rsfMRI and EEG studiesPublication . Sanchez, Lucía Penalba; Cifre, Ignacio; Silva, Patrícia Oliveira; Sumich, AlexanderAdvances in neuroimaging techniques have been critical to identifying new biomarkers for brain diseases. Resting State Functional Magnetic Resonance Imaging (rsfMRI) non-invasively quantifies the Blood Oxygen Level Dependent (BOLD) signal across brain regions with high spatial resolution; whilst temporal resolution of Electroencephalography (EEG) in measuring the brain’s electrical response is unsurpassed. Most of the statistical and machine learning methods used to analyze rsfMRI and EEG data, are static and linear, fail to capture the dynamics and complexity of the brain, and are prone to residual noise. The general goals of this thesis dissertation are i) to provide methodological insight by proposing a statistical method namely point process analysis (PPA) and a machine learning (ML) multiband non-linear EEG method. These methods are especially useful to investigate the brain configuration of older participants and individuals with neurodegenerative diseases, and to predict age and sleep quality; and ii) to share biological insights about synchronization between brain regions (i.e., functional connectivity and dynamic functional connectivity) in different stages of mild cognitive impairment and in Alzheimer’s disease. The findings, reported and discussed in this thesis, open a path for new research ideas such as applying PPA to EEG data, adjusting the non-linear ML algorithm to apply it to rsfMRI and use these methods to better understand other neurological diseases.