123 research outputs found
Monitorização das rejeições de águas residuais para as águas costeiras e estuários dos rios Mondego e Vouga : influência no processo de licenciamento das utilizações dos recursos hídricos do litoral
O presente relatório descreve o Estágio Curricular realizado na instituição pública Agência Portuguesa do Ambiente (APA) I.P./ Administração da Região Hidrográfica do Centro (ARHC), na Divisão de Recursos Hídricos do Litoral (DRHL) em Coimbra, no âmbito do estágio do Mestrado em Engenharia Civil - Especialização em Construção Urbana, do Instituto Superior de Engenharia de Coimbra.
Teve como principais objetivos identificar e caracterizar a evolução das rejeições de águas residuais para as águas costeiras e estuários do rio Mondego e Vouga, tendo sido feito em simultâneo o acompanhamento do licenciamento das rejeições para os recursos hídricos nos estuários do rio Vouga e Mondego e nas águas costeiras, bem como o apoio na aferição das medições para a cobrança da Taxa de Recursos Hídricos. Numa parte final do estágio, foi feito, ainda, um estudo de mercado para averiguar os custos existentes para alimentação artificial de uma praia.
Em anexo a este relatório encontram-se alguns exemplos de diferentes licenciamentos das rejeições de águas residuais efetuados durante o estágio
Interpretabilidade de modelos Machine Learning para a previsão macroeconómica
Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e EmpresarialA interpretabilidade de um modelo pode ser definida como o grau de compreensão sobre o
funcionamento interno de um modelo, permitindo perceber as causas sobre o qual recai o resultado. Na
modelação estatística, pode verificar-se um trade-off entre a interpretabilidade e a precisão da previsão:
modelos de interpretabilidade natural revelam uma menor precisão relativamente a modelos mais
complexos. A previsão do Produto Interno Bruto é um aspeto fundamental para a aplicação de políticas
económicas, com particular atenção para políticas monetárias e políticas orçamentais. Nesse sentido e
tendo em conta que os modelos de machine learning preveem melhor a volatilidade económica, tem se
vindo a assistir a uma revolução do método de previsão macroeconómico utilizado pelas principais
organizações políticas nacionais e internacionais.
O objetivo deste trabalho prende-se em investigar se um black-box, Extreme Gradient Boosting
(XGBoost) pode superar os métodos de interpretabilidade natural selecionados, Regressão linear e
Árvore de decisão na previsão do Produto Interno Bruto (PIB) per capita para dados de painel e
identificar medidas de importância das variáveis para melhorar a transparência dos modelos de machine
learning. Se as organizações políticas e financeiras forem capazes de prever e interpretar corretamente
os fatores, podem implementar políticas mais eficazes.
Para a análise de interpretabilidade de XGBoost, utilizam-se os seguintes métodos, Shapley Additive
ExPlanations (SHAP), Permutation Feature Importance e Partial Dependence Plot. Através destes
métodos pretendemos mostrar que os resultados obtidos por XGBoost podem ser interpretados sem um
grande esforço computacional e garantir maior vantagem competitiva. Contudo, como será aprofundado
durante este trabalho, verifica-se que o modelo XGBoostserá o modelo com melhor precisão de previsão
para os dados de painel.The interpretability of a model can be defined as the degree of understanding of the inner workings
of a model, allowing one to understand the causes upon which the result is based. In statistical modelling,
there can be a trade-off between interpretability and forecast accuracy: models with natural
interpretability show lower accuracy than more complex models. Gross Domestic Product forecasting
is a fundamental aspect for the implementation of economic policies, with particular attention to
monetary policies and fiscal policies. In this sense and considering that machine learning models better
predict economic volatility, there has been a revolution in the macroeconomic forecasting method used
by the main national and international political organizations.
The objective of this work is to investigate whether a black-box, Extreme Gradient Boosting
(XGBoost) can outperform selected natural interpretability, Linear regression and Decision tree methods
in forecasting Gross Domestic Product (GDP) per capita for panel data and to identify measures of
variable importance to improve the transparency of machine learning models. If political and financial
organizations can correctly predict and interpret factors, they can implement more effective policies.
For the XGBoost interpretability analysis, the following methods are used, Shapley Additive
ExPlanations (SHAP), Permutation Feature Importance and Partial Dependence Plot. Through these
methods we intend to show that the results obtained by XGBoost can be interpreted without a large
computational effort and ensure greater competitive advantage. However, as will be deepened during
this work, it is verified that the fixed effects model will be the model with the best prediction accuracy
for panel data.info:eu-repo/semantics/publishedVersio
Using and Creating Microclimates for Cork Oak Adaptation to Climate Change
In Mediterranean climate regions, climate change is increasing aridity and contributing to the mortality rate of Quercus suber, reducing the success of reforestation efforts. Using and creating microclimates is a recommended climate adaptation strategy that needs research. Our hypothesis is that planting Q. suber in north-facing slopes and water lines results in a higher survival rate than those that are planted in ridges and south-facing slopes. Secondly, our hypothesis is that existing shrubs (in this case, Cistus ladanifer) can be used to create microclimatic sheltering and increase the survival of Q. suber plantations. In experiment 1, we tested the survival of Q. suber plantations in four different topographic conditions. For that, 80 Q. suber plants were planted over four different topographic conditions, where soil probes were installed to monitor soil moisture and temperature. Two years after, the results show an increased survival rate in the north-facing slope and water line when compared to the ridge area (p = 0.032). In experiment 2, we tested if planting in the shade of rows of C. ladanifer increases the survival rate of Q. suber plantations. For that, 1200 Q. suber plants were planted; 600 in a Montado open area with no shade and 600 under the shade of rows of C. ladanifer shrubs. A total of 17 months after plantation, there was a significantly higher survival rate of the shaded plants (p = 0.027). We conclude that microclimates created by topography and shrubs can have a significant impact on the survival of Q. suber plantations and discuss the situations in which these can apply.info:eu-repo/semantics/publishedVersio
The influence of change on sense of coherence on dental services use among adolescents: a two‑year prospective follow‑up study
publishedVersio
Socio-economic status, psychosocial factors, health behaviours and incidence of dental caries in 12-year-old children living in deprived communities in Manaus, Brazil
Objectives
This study examines the relationships between socio-economic status, psychosocial factors, health-related behaviours and the incidence of dental caries amongst 12-year-old schoolchildren living in deprived communities in Manaus, Brazil.
Methods
A longitudinal study involving 312 children aged 12 years was conducted in the city of Manaus, Brazil. Baseline data including socio-economic status (number of goods, household overcrowding, parents’ schooling, family income), psychosocial factors (sense of coherence [SOC-13], social support [Social Support Appraisals questionnaire]) and health-related behaviours (frequency of toothbrushing, sugar consumption, sedentary behaviour) were collected through structured questionnaires. The number of decayed teeth was clinically assessed at baseline and one-year follow-up. A hypothesised model evaluating the direct and indirect pathways between the variables was tested using confirmatory factor analysis and structural equation modelling.
Results
The incidence of dental caries at the one-year follow-up was 25.6%. Sugar consumption (β = 0.103) and sedentary behaviour (β = 0.102) directly predicted the incidence of dental caries. A higher socio-economic status was directly linked with lower sugar consumption (β = -0.243) and higher sedentary behaviour (β = 0.227). Higher social support directly predicted lower sugar consumption (β = -0.114). Lower socio-economic status (β = -0.046) and lower social support (β = -0.026) indirectly predicted the incidence of dental caries via sugar consumption and sedentary behaviour.
Conclusions
In the population studied, sugar consumption and sedentary behaviour are meaningful predictors of the incidence of dental caries amongst schoolchildren living in deprived communities. Indirect pathways of lower socio-economic status and low social support with dental caries incidence via sugar consumption and sedentary behaviour were detected. These findings should be considered in oral interventions and oral health care policies to prevent dental caries amongst children living in deprivation.
Clinical significance
Social conditions, social support, sedentary behaviour and sugar consumption directly influence dental caries in children.acceptedVersionPaid open accessUNIT agreemen
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