Twin studies have been an important area of epidemiologic research. Traditional analyses of risk ... more Twin studies have been an important area of epidemiologic research. Traditional analyses of risk use regular linear or logistic models. Regular linear regression and logistic regression assume that all observations are independent of each other. However, there is correlation between the observations in a study of twins that needs to be taken into account. Two ways to handle the correlated binary outcomes include Generalized Estimating Equations (GEE) and mixed models. In this thesis, we used univariate and multivariable GEE models to investigate an association between maternal pre-pregnancy BMI and a binary outcome variable, small for gestational age (SGA) in twins. In addition, we used splines to explore the relationship between SGA and pre-pregnancy BMI. SGA birth outcomes are considered one of the major concerns in public health issues because they could affect infant mortality as well as infant morbidity. Our data is a random sample of birth certificate records of twin births in...
In this work, we present an efficient solution to the beer bottle cap classification problem. Thi... more In this work, we present an efficient solution to the beer bottle cap classification problem. This problem arises in the Wecheer smart opener project. Although classification problem is common in Computer Vision, there is no dedicated work for beer bottle cap dataset. We combine state-of-the-art deep learning techniques to solve the problem. Our solution outperforms the well-known commercial system that is currently used by the Wecheer project. It is also more efficient than the famous architectures such as VGG, ResNet, and DenseNet for our purposes.
Accurate evaluation of recovery mechanisms in fractured reservoirs is challenging due to the larg... more Accurate evaluation of recovery mechanisms in fractured reservoirs is challenging due to the large permeability contrast at the matrix-fracture interface. Dual Porosity-Dual Permeability (DPDK) models are typically used in field-scale simulations but can be biased by their use of idealized fracture networks and matrix-fracture interactions. Unstructured Discrete Fracture Models (USDFMs) are able to capture the complex physics accurately but can be computationally demanding. Embedded Discrete Fracture Models (EDFMs) integrate discrete fracture networks with a structured matrix grid and are the focus of this study. Our study considers dense and sparse fracture networks extracted from a field-scale fracture carbonate reservoir model. EDFMs are constructed for different matrix grid resolutions, and simulations are performed to evaluate gravity drainage, spontaneous imbibition, viscous displacement. In each case, EDFM results are compared with highly refined USDFM reference solutions and...
Twin studies have been an important area of epidemiologic research. Traditional analyses of risk ... more Twin studies have been an important area of epidemiologic research. Traditional analyses of risk use regular linear or logistic models. Regular linear regression and logistic regression assume that all observations are independent of each other. However, there is correlation between the observations in a study of twins that needs to be taken into account. Two ways to handle the correlated binary outcomes include Generalized Estimating Equations (GEE) and mixed models. In this thesis, we used univariate and multivariable GEE models to investigate an association between maternal pre-pregnancy BMI and a binary outcome variable, small for gestational age (SGA) in twins. In addition, we used splines to explore the relationship between SGA and pre-pregnancy BMI. SGA birth outcomes are considered one of the major concerns in public health issues because they could affect infant mortality as well as infant morbidity. Our data is a random sample of birth certificate records of twin births in...
In this work, we present an efficient solution to the beer bottle cap classification problem. Thi... more In this work, we present an efficient solution to the beer bottle cap classification problem. This problem arises in the Wecheer smart opener project. Although classification problem is common in Computer Vision, there is no dedicated work for beer bottle cap dataset. We combine state-of-the-art deep learning techniques to solve the problem. Our solution outperforms the well-known commercial system that is currently used by the Wecheer project. It is also more efficient than the famous architectures such as VGG, ResNet, and DenseNet for our purposes.
Accurate evaluation of recovery mechanisms in fractured reservoirs is challenging due to the larg... more Accurate evaluation of recovery mechanisms in fractured reservoirs is challenging due to the large permeability contrast at the matrix-fracture interface. Dual Porosity-Dual Permeability (DPDK) models are typically used in field-scale simulations but can be biased by their use of idealized fracture networks and matrix-fracture interactions. Unstructured Discrete Fracture Models (USDFMs) are able to capture the complex physics accurately but can be computationally demanding. Embedded Discrete Fracture Models (EDFMs) integrate discrete fracture networks with a structured matrix grid and are the focus of this study. Our study considers dense and sparse fracture networks extracted from a field-scale fracture carbonate reservoir model. EDFMs are constructed for different matrix grid resolutions, and simulations are performed to evaluate gravity drainage, spontaneous imbibition, viscous displacement. In each case, EDFM results are compared with highly refined USDFM reference solutions and...
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Papers by Hai Vo