Avaliação de custos em epilepsia: um estudo de custos da doença sob a perspectiva da população usuária do Sistema Único de Saúde (SUS) região metropolitana de Florianópolis, Santa Catarina, 2009

Trabalho de Conclusão de Curso - Universidade Federal de Santa Catarina. Curso de Medicina. Departamento de Pediatria

GeoAI approach to Vineyard Yield Estimation

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsKnowing in advance vineyard yield is a key issue for growers, winemakers, policy makers, and regulators being fundamental to achieve the best balance between vegetative and reproductive growth, and to allow more informed decisions like thinning, irrigation and nutrient management, schedule harvest, optimize winemaking operations, program crop insurance, fraud detection and grape picking workforce demand. In a long-term scenario of perceived climate change, it is also essential for planning and regulatory purposes at the regional level. Estimating yield is complex and requires knowing driving factors related to climate, plant, and crop management that directly influence the number of clusters per vine, berries per cluster, and berry weight. These three yield components explain 60%, 30%, and 10% of the yield. The traditional methods are destructive, labor-demanding, and time-consuming, with low accuracy primarily due to operator errors and sparse sampling (compared to the inherent spatial variability in a production vineyard). Those are supported by manual sampling, where yield is estimated by sampling clusters weight and the number of clusters per vine, historical data, and extrapolation considering the number of vines in a plot. As the extensive research in the area clearly shows, improved applied methodologies are needed at different spatial scales. The methodological approaches for yield estimation based on indirect methods are primarily applicable at small scale and can provide better estimates than the traditional manual sampling. They mainly depend on computer vision and image processing algorithms, data-driven models based on vegetation indices and pollen data, and on relating climate, soil, vegetation, and crop management variables that can support dynamic crop simulation models. Despite surpassing the limitations assigned to traditional manual sampling methods with the same or better results on accuracy, they still lack a fundamental key aspect: the real application in commercial vineyards. Another gap is the lack of solutions for estimating yield at broader scales (e.g., regional level). The perception is that decisions are more likely to take place on a smaller scale, which in some cases is inaccurate. It might be the case in regulated areas and areas where support for small viticulturists is needed and made by institutions with proper resources and a large area of influence. This is corroborated by the fact that data-driven models based on Trellis Tension and Pollen traps are being used for yield estimation at regional scales in real environments in different regions of the world. The current dissertation consists of the first study to identify through a systematic literature review the research approaches for predicting yield in vineyards for wine production that can serve as an alternative to traditional estimation methods, to characterize the different new approaches identifying and comparing their applicability under field conditions, scalability concerning the objective, accuracy, advantages, and shortcomings. In the second study following the identified research gap, a yield estimation model based on Geospatial Artificial Intelligence (GeoAI) with remote sensing and climate data and a machine-learning approach was developed. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The results show that this approach makes it possible to estimate wine grape yield accurately in advance at different scales

Functional brain perfusion evaluation with Arterial Spin Labeling at 3 Tesla

Dissertation submitted in Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa for the degree of Master of Biomedical EngineeringBackground: The new clinically available arterial spin labelling (ASL) sequences present some advantages relatively to the commonly used blood oxygenation level dependent (BOLD) method for functional brain studies using magnetic resonance imaging (MRI), namely the fact of being potentially quantitative and more reproducible. Purpose: The main aim of this work was to evaluate the functional use of a commercial ASL sequence implemented on a 3 Tesla MRI system (Siemens, Verio) in the Imaging Department of Hospital da Luz. The first aim was to obtain a functional validation of this technique by comparison with the BOLD contrast, using a number of different approaches. The second aim was to accomplish perfusion quantification, by resolving some important quantification issues. Materials and Methods: Fifteen adult volunteers participated in a single functional imaging session using three different protocols: one using BOLD and two using ASL. The subjects performed a motor finger tapping task and the data analysis was performed using Siemens Neuro3D and FSL (FMRIB’s Software Library). The location and variability of the activated areas were analysed in MNI (Montereal Neurological Institute) standard space. Results: Topographic agreement between the activated regions obtained by BOLD and ASL was found. However, the results show that inter-subject variability and distance to the hand motor cortex were smaller when measured with ASL as compared with BOLD fMRI. Quantitative studies revealed that ASL allows the calculation of cerebral blood flow (CBF), both at baseline and upon functional activation. Conclusion: The results suggest that the functional imaging protocols using ASL produce comparable results to a conventional BOLD protocol, with the additional advantages of reduced inter-subject variability, better spatial specificity and quantification possibilities

The impact of public credit guarantees on the economic outcomes of Smes: evidence from Portugal

Public credit guarantees attributed to SMEs as a way of boosting credit access have been widely implemented in developed countries. However, literature often focuses on financial additionality. This paper investigates, for Portugal’s case, the impact of these guarantees on the economic outcomes of firms – we study economic additionality. We utilize firm-level data provided by Banco de Portugal and rely on propensity score matching methods to derive causal results. We find evidence that public credit guarantees have incremental effects on credit, employment, total assets, and fixed assets. There is no evidence of effects on total factor productivity, wages, or profitability