Overuse of the Interstate and National Highway Systems has led many urban freeways to suffer from recurrent congestion and high crash rates. One method of ameliorating these problems is through the use of Active Traffic Management (ATM). Within ATM, the practice of speed harmonization is well suited to improving safety and reducing delay. In this study, speed harmonization is applied to a segment of Interstate Highway 35, just south of downtown Austin, Texas. First, the need for congestion and safety improvements will be established. Then, the framework of a speed harmonization system will be developed through a synthesis of speed harmonization best practice. Next, the speed harmonization framework will be evaluated for its impact on efficiency through the development of before and after micro-simulation models. Finally, the trajectory files generated from simulation will be analyzed using surrogate safety measures to assess the safety impact of the proposed speed harmonization system. / text

Elastohydrodynamic lubrication (EHL) allows transfer of power and forces in gears and rolling bearings without surface-to-surface contact and is the basis for a continuously variable transmission studied in this report. Previous research constructed models and derived solution methods, but often lacked full explanations of the approach and was usually applied to limited and specific cases. This report precisely develops the numerical solution of EHL contact and includes the more general cases of elliptical contacts and side-slip. The model and numerical method are validated on known benchmark cases and test results. Side-slip is investigated and the results shown in this report. Finally, the model is used to determine the film thickness and pressure of a contact patch under identical conditions to that in a physical drive developed by Fallbrook Technologies in Austin, TX. A minimum film thickness of 0.8978 [mu]m is found, setting a benchmark for the maximum allowable surface roughness values to prevent surface-to-surface contact. Additionally, under normal drive conditions the film thickness to surface roughness ratio is in the range of ideal values for maximum life. / text

Coastal regions are the most densely populated regions of the world. The populations of these regions continue to grow which has created a high demand for water that stresses existing water resources. Coastal aquifers provide a source of water for coastal populations and are generally part of a larger system where freshwater aquifers are hydraulically connected with a saline surface-water body. They are characterized by salinity variations in space and time, sharp freshwater/saltwater interfaces which can lead to dramatic density differences, and complex groundwater chemistry. Mismanagement of coastal aquifers can lead to saltwater intrusion, the displacement of fresh water by saline water in the freshwater regions of the aquifers, making them unusable as a freshwater source. Saltwater intrusion is of significant interest to water resource managers and efficient simulators are needed to assist them. Numerical simulation of saltwater intrusion requires solving a system of flow and transport equations coupled through a density equation of state. The scale of the problem domain, irregular geometry and heterogeneity can require significant computational resources. Also, modeling sharp transition zones and accurate flow velocities pose numerical challenges. Discontinuous Galerkin (DG) finite element methods (FEM) have been shown to be well suited for modeling flow and transport in porous media but a fully coupled DG formulation has not been applied to the variable density flow and transport model. DG methods have many desirable characteristics in the areas of numerical stability, mesh and polynomial approximation adaptivity and the use of non-conforming meshes. These properties are especially desirable when working with complex geometries over large scales and when coupling multi-physics models (e.g. surface water and groundwater flow models). In this dissertation, we investigate a new combined local discontinuous Galerkin (LDG) and non-symmetric, interior penalty Galerkin (NIPG) formulation for the non-linear coupled flow and solute transport equations that model saltwater intrusion. Our main goal is the formulation and numerical implementation of a robust, efficient, tightly-coupled combined LDG/NIPG formulation within the Department of Defense (DoD) Proteus Computational Mechanics Toolkit modeling framework. We conduct an extensive and systematic code and model verification (using established benchmark problems and proven convergence rates) and model validation (using experimental data) to verify accomplishment of this goal. Lastly, we analyze the accuracy and conservation properties of the numerical model. More specifically, we derive an a priori error estimate for the coupled system and conduct a flow/transport model compatibility analysis to prove conservation properties. / text

The integration of signi cant levels of variable generation into the electricity grid has increased the complexity of power system operations. The strong unpredictability of variable generation poses an important operating complexity and demands an adequate dimensioning and deployment of system reserves. This work establishes su cient conditions for the dimensioning and deployment of adequate reserves. These conditions involve the determi- nation of reserve requirements and the design of a frequency control system consistent with such requirements. The analysis is divided into the adequacy of primary and secondary reserves, and simulations of ERCOT validated by empirical data are considered. Adequacy criteria from current practices are used to evaluate the performance of the formulation. / text

This work was motivated by questions raised from multifrequency microwave interferometer measurements taken in a cylindrical plasma on the Variable Specific Impulse Magnetoplasma Rocket (VASIMR) project. Standard data analysis based on a thin beam model neglecting refraction yields inconsistent electron densities and density profiles for different frequencies. This work focuses on the development of a model for the wave propagation through cylindrical plasmas when the plasma radius is on the order of the beam waist. For the calculations presented a Gaussian beam profile and plasma spatial profile were assumed. Both refraction by density gradients and finite beam sizes are found to play important roles and explain polychromatic differences in the electron densities and profiles. Calculations for the new model are compared to a thin beam model not accounting for refraction and experimental data from VASIMR.

The second-order least squares (SLS) method in regression model proposed by Wang (2003, 2004) is based on the first two conditional moments of the response variable given the observed predictor variables. Wang and Leblanc (2008) show that the SLS estimator (SLSE) is asymptotically more efficient than the ordinary least squares estimator (OLSE) if the third moment of the random error is nonzero. We apply the SLS method to variable selection problems and propose the adaptively weighted L1 regularized SLSE (L1-SLSE). The L1-SLSE is robust against the shape of error distributions in variable selection problems. Finite sample simulation studies show that the L1-SLSE is more efficient than L1-OLSE in the case of asymmetric error distributions. A real data application with L1-SLSE is presented to demonstrate the usage of this method. / October 2015

In this dissertation, we propose methodology to analyze high dimensional genomics data, in which the observations have large number of outcome variables, in addition to exposure variables. In the Chapter 1, we investigate methods for genetic pathway analysis, where we have a small number of exposure variables. We propose two Canonical Correlation Analysis based methods, that select outcomes either sequentially or by screening, and show that the performance of the proposed methods depend on the correlation between the genes in the pathway. We also propose and investigate criterion for fixing the number of outcomes, and a powerful test for the exposure effect on the pathway. The methodology is applied to show that air pollution exposure affects gene methylation of a few genes from the asthma pathway. In Chapter 2, we study penalized multivariate regression as an efficient and flexible method to study the relationship between large number of covariates and multiple outcomes. We use penalized likelihood to shrink model parameters to zero and to select only the important effects. We use the Bayesian Information Criterion (BIC) to select tuning parameters for the employed penalty and show that it chooses the right tuning parameter with high probability. These are combined in the “two-stage procedure”, and asymptotic results show that it yields consistent, sparse and asymptotically normal estimator of the regression parameters. The method is illustrated on gene expression data in normal and diabetic patients. In Chapter 3 we propose a method for estimation of covariates-dependent principal components analysis (PCA) and covariance matrices. Covariates, such as smoking habits, can affect the variation in a set of gene methylation values. We develop a penalized regression method that incorporates covariates in the estimation of principal components. We show that the parameter estimates are consistent and sparse, and show that using the BIC to select the tuning parameter for the penalty functions yields good models. We also propose the scree plot residual variance criterion for selecting the number of principal components. The proposed procedure is implemented to show that the first three principal components of genes methylation in the asthma pathway are different in people who did not smoke, and people who did.

Assessment of risk has been a key element in efforts to identify factors associated with disease, to assess potential targets of therapy and enhance disease prevention and treatment. Considerable work has been done to develop methods to identify markers, construct risk prediction models and evaluate such models. This dissertation aims to develop robust approaches for these tasks. In Chapter 1, we present a robust, flexible yet powerful approach to identify genetic variants that are associated with disease risk in genome-wide association studies when some subjects are related. In Chapter 2, we focus on identifying important genes predictive of survival outcome when the number of covariates greatly exceeds the number of observations via a nonparametric transformation model. We propose a rank-based estimator that poses minimal assumptions and develop an efficient

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

Metagenomics holds unyielding potential in uncovering relationships within microbial communities that have yet to be discovered, particularly because the field circumvents the need to isolate and culture microbes from their natural environmental settings. A common research objective is to detect biomarkers, microbes are associated with changes in a status. For instance, determining such microbes across conditions such as healthy and diseased groups for instance allows researchers to identify pathogens and probiotics. This is often achieved via analysis of differential abundance of microbes. The problem is that differential abundance analysis looks at each microbe individually without considering the possible associations the microbes may have with each other. This is not favorable, since microbes rarely act individually but within intricate communities involving other microbes. An alternative would be variable selection techniques such as Lasso or Elastic Net which considers all the microbes simultaneously and conducts selection. However, Lasso often selects only a representative feature of a correlated cluster of features and the Elastic Net may incorrectly select unimportant features too frequently and erratically due to high levels of sparsity and variation in the data.\par In this research paper, the proposed method AdaLassop is an augmented variable selection technique that overcomes the misgivings of Lasso and Elastic Net. It provides researchers with a holistic model that takes into account the effects of selected biomarkers in presence of other important biomarkers. For AdaLassop, variable selection on sparse ultra-high dimensional data is implemented using the Adaptive Lasso with p-values extracted from Zero Inflated Negative Binomial Regressions as augmented weights. Comprehensive simulations involving varying correlation structures indicate that AdaLassop has optimal performance in the presence multicollinearity. This is especially apparent as sample size grows. Application of Adalassop on a Metagenome-wide study of diabetic patients reveals both pathogens and probiotics that have been researched in the medical field.