This thesis presents the findings of a comprehensive research study conducted for the Utah Department of Transportation (UDOT) to evaluate the effectiveness of commonly deployed Intelligent Transportation System (ITS) treatments in Utah, with a focus on Variable Message Signs (VMS), traffic cameras, and Road Weather Information System(s) (RWIS) sites. The study aimed to determine the impact of these ITS treatments on mobility and safety for Utah's unique road conditions and configurations. Three primary analyses were performed: a diversion rates analysis to assess the effectiveness of VMS on Utah freeways during incidents; a weather analysis to evaluate the effectiveness of VMS messages on driver speeds in Utah canyons during winter weather; and an ITS attitudes survey to gauge the usage of, attitudes towards, and potential improvements to UDOT's current ITS deployment. The findings of the diversion rates analysis indicate that the activation of VMS messages increase diversion rates by 18 percent and from the weather analysis, a negligible increase of speeds by 0.2 mph was observed. Survey results were largely positive with a high level of ITS usage and benefits reported by UDOT employees. Concerns of general device wear and maintenance were documented. The report provides detailed methodologies, results, conclusions, and recommendations derived from this research, offering valuable insights to guide UDOT's ITS deployment strategies.

High-dimensional datasets are fast becoming a cornerstone across diverse domains, fueled by advancements in data-capturing technology like DNA sequencing, medical imaging techniques, and social media. This dissertation delves into the inherent opportunities and challenges posed by these types of datasets. We develop three Bayesian methods: (1) Multilevel Network Recovery for Genomics, (2) Network Recovery for Functional data, and (3) Bayesian Inference in Transformer-based Models. Chapter 2 in our work examines a two-tiered data structure; to simultaneously explore the variable selection and identify dependency structures among both higher and lower-level variables, we propose a multi-level nonparametric kernel machine approach, utilizing variational inference to jointly identify multi-level variables as well as build the network. Chapter 3 addresses the development of a simultaneous selection of functional domain subsets, selection of functional graphical nodes, and continuous response modeling given both scalar and functional covariates under semiparametric, nonadditive models, which allow us to capture unknown, possibly nonlinear, interaction terms among high dimensional functional variables. In Chapter 4, we extend our investigation of leveraging structure in high dimensional datasets to the relatively new transformer architecture; we introduce a new penalty structure to the Bayesian classification transformer, leveraging the multi-tiered structure of the transformer-based model. This allows for increased, likelihood-based regularization, which is needed given the high dimensional nature of our motivating dataset. This new regularization approach allows us to integrate Bayesian inference via variational approximations into our transformer-based model and improves the calibration of probability estimates. / Doctor of Philosophy / In today's data-driven landscape, high-dimensional datasets have emerged as a corner stone across diverse domains, fueled by advancements in technology like sensor networks, genomics, and social media platforms. This dissertation delves into the inherent opportunities and challenges posed by these datasets, emphasizing their potential for uncovering hidden patterns and correlations amidst their complexity. As high-dimensional datasets proliferate, researchers face significant challenges in effectively analyzing and interpreting them. This research focuses on leveraging Bayesian methods as a robust approach to address these challenges. Bayesian approaches offer unique advantages, particularly in handling small sample sizes and complex models. By providing robust uncertainty quantification and regularization techniques, Bayesian methods ensure reliable inference and model generalization, even in the face of sparse or noisy data. Furthermore, this work examines the strategic integration of structured information as a regularization technique. By exploiting patterns and dependencies within the data, structured regularization enhances the interpretability and resilience of statistical models across various domains. Whether the structure arises from spatial correlations, temporal dependencies, or coordinated actions among covariates, incorporating this information enriches the modeling process and improves the reliability of the results. By exploring these themes, this research contributes to advancing the understanding and application of high-dimensional data analysis. Through a thorough examination of Bayesian methods and structured regularization techniques, this dissertation aims to support researchers in effectively navigating and extracting meaningful insights from the complex landscape of high-dimensional datasets.

Advanced analytical techniques were utilized to investigate the intermolecular forces in several nanostructured materials. Techniques including, but not limited to, isothermal titration calorimetry (ITC), variable temperature Fourier transform infrared (FTIR) spectroscopy, and ultraviolet-visible (UV-Vis) thermal curves were used to study the fundamental interactions present in various nanomaterials, and to further probe the influence of these interactions on the overall behavior of the material. The areas of focus included self-assembly of surfactant micelles, polycation complexation of DNA, and temperature-dependent hydrogen bonding in polymeric systems. ITC was successfully used to determine the low critical micelle concentration (CMC) for a novel gemini surfactant with limited water solubility. CMCs were measured at decreasing methanol molar fractions (xMeOH) in water and the resulting linear relationship between CMC and methanol concentration was used to mathematically extrapolate to a predicted CMC at xMeOH = 0. Using this technique, the CMC value for the novel gemini surfactant was predicted to be 0.037 ± 0.004 mM. This extrapolation technique was also validated with surfactant standards. ITC was also used to investigate the binding thermodynamics of polyplex formation with polycations and DNA. The imidazolium-containing and trehalose-based polycations were both found to have endothermic, entropically driven binding with DNA, while the adenine-containing polycation exhibited exothermic DNA binding. In addition, ITC was also used to confirm the stoichiometric binding ratio of linear polyethylenimine and DNA polyplexes as determined by a novel NMR method. Dynamic light scattering (DLS) and zeta potential measurements were also performed to determine the size and surface charge of polyplexes. Circular dichroism (CD) and FTIR spectroscopies provided information regarding the structural changes that may occur in the DNA upon complexation with polymers. UV-Vis thermal curves indicated that polyplexes exhibit a greater thermal stability than DNA by itself. Variable temperature FTIR spectroscopy was used to quantitatively compare the hydrogen bonding behavior of multi-walled carbon nanotube (MWCNT)-polyurethane composites. Spectra were collected from 35 to 185 deg C for samples containing various weight percent loadings of MWCNTs with different hydrogen bonding surface functionalities. Peak fitting analysis was performed in the carbonyl-stretching region for each sample, and the hydrogen-bonding index (Rindex) was reported. Rindex values were used to quantitatively compare all of the composite samples in regards to temperature effects, weight percent loadings of MWCNTs, and the different functionalizations. In general, higher weight percent loadings of the MWCNTs resulted in greater Rindex values and increased hydrogen bond dissociation temperatures. In addition, at 5 and 10 wt% loadings the initial Rindex values displayed a trend that tracked well with the increasing hydrogen bonding capacity of the various surface functionalities. / Ph. D.

This study highlights the aeroelastic behavior of very flexible truss-braced wing (TBW) aircraft designs obtained through a multidisciplinary design optimization (MDO) framework. Several improvements to previous analysis methods were developed and validated. Firstly, a flutter constraint was developed and the effects of the constraint on the MDO of TBW transport aircraft for both medium-range and long-range missions were studied while minimizing the take-off gross weight (TOGW) and the fuel burn as the objective functions. Results show that when the flutter constraint is applied at 1.15 times the dive speed, it imposes a 1.5% penalty on the take-off weight and a 5% penalty on the fuel consumption while minimizing these two objective functions for the medium-range mission. For the long-range mission, the penalties imposed by the similar constraint on the minimum TOGW and minimum fuel burn designs are 3.5% and 7.5%, respectively. Importantly, the resulting TBW designs are still superior to equivalent cantilever designs for both of the missions as they have both lower TOGW and fuel burn. However, a relaxed flutter constraint applied at 1.05 times the dive speed can restrict the penalty on the TOGW to only 0.3% and that on the fuel burn to 2% for minimizing both the objectives, for the medium-range mission. For the long-range mission, a similar relaxed constraint can reduce the penalty on fuel burn to 2.9%. These observations suggest further investigation into active flutter suppression mechanisms for the TBW aircraft to further reduce either the TOGW or the fuel burn. Secondly, the effects of a variable-geometry raked wingtip (VGRWT) on the maneuverability and aeroelastic behavior of passenger aircraft with very flexible truss-braced wings (TBW) were investigated. These TBW designs obtained from the MDO environment while minimizing fuel burn resemble a Boeing 777-200 Long Range (LR) aircraft both in terms of flight mission and aircraft configuration. The VGRWT can sweep forward and aft relative to the wing with the aid of a Novel Control Effector (NCE) mechanism. Results show that the VGRWT can be swept judiciously to alter the bending-torsion coupling and the movement of the center of pressure of wing. Such behavior of the VGRWT is applied to both achieve the required roll control as well as to increase flutter speed, and thus, enable the operation of TBW configurations which have up to 10% lower fuel burn than comparable optimized cantilever wing designs. Finally, a transonic aeroelastic analysis tool was developed which can be used for conceptual design in an MDO environment. Routine transonic aeroelastic analysis require expensive CFD simulations, hence they cannot be performed in an MDO environment. The present approach utilizes the results of a companion study of CFD simulations performed offline for the steady Reynolds Averaged Navier Stokes equations for a variety of airfoil parameters. The CFD results are used to develop a response surface which can be used in the MDO environment to perform a Leishman-Beddoes (LB) indicial functions based flutter analysis. A reduced-order model (ROM) is also developed for the unsteady aerodynamic system. Validation of the strip theory based aeroelastic analysis with LB unsteady aerodynamics and the computational efficiency and accuracy of the ROM is demonstrated. Finally, transonic aeroelastic analysis of a TBW aircraft designed for the medium-range flight mission similar to a Boeing 737 next generation (NG) with a cruise Mach number of 0.8 is presented. The results show the potential of the present approach to perform a more accurate, yet inexpensive, flutter analysis for MDO studies of transonic transport aircraft which are expected to undergo flutter at transonic conditions. / Ph. D.

In this dissertation, a novel two-phase switched reluctance machine (SRM) with a stator comprised of E-core structure having minimum stator core iron is presented for low-cost high-performance applications. In addition, three new magnetic structures for the E-core SRM comprising two segmented stator cores or a monolithic stator core are proposed for good manufacturability, mechanically robustness, ease of assembly, and electromagnetic performance improvement. Each E-core stator in the segmented structure has three poles with two small poles at the ends having windings and a large center pole containing no copper windings. The common stator pole at the centers in the segmented E-core is shared by both phases during operation. Other benefits of the common poles contributing to performance enhancement are short flux paths, mostly flux-reversal-free-stator, constant minimum reluctance around air gap, and wide pole arc equal to one rotor pole pitch. Therefore, two additional common poles in the monolithic E-core configuration are able to significantly improve efficiency due to more positive torque and less core loss by the unique design. Using a full MEC analysis, the effect of the common-pole structure on torque enhancement is analytically verified. Efficiency estimated from the dynamic simulation is higher by 7% and 12% at 2000 rpm and by 3% and 7 % at 3000 rpm for the segmented and single-body SRMs, respectively, compared to a conventional SRM with four stator poles and two rotor poles. The new E-core SRMs are suitable for low-cost high-performance applications which are strongly cost competitive since all the new E-core SRMs have 20% cost savings on copper and the segmented E-core SRMs have 20% steel savings as well. Strong correlation between simulated and experimentally measured results validates the feasibility of the E-core common-pole structure and its performance. A simple step-by-step analytical design procedure suited for iterative optimization with small computational effort is developed with the information of the monolithic E-core SRM, and the proposed design approach can be applied for other SRM configurations as well. For investigating thermal characteristics in the two-phase single-body E-core SRM, the machine is modeled by a simplified lumped-parameter thermal network in which there are nine major parts of the motor assembly. / Ph. D.

This dissertation aims to develop a latent variable modeling framework with which to analyze gene expression profiling data for computational dissection of molecular signatures and transcriptional modules. The first part of the dissertation is focused on extracting pure gene expression signals from tissue or cell mixtures. The main goal of gene expression profiling is to identify the pure signatures of different cell types (such as cancer cells, stromal cells and inflammatory cells) and estimate the concentration of each cell type. In order to accomplish this, a new blind source separation method is developed, namely, nonnegative partially independent component analysis (nPICA), for tissue heterogeneity correction (THC). The THC problem is formulated as a constrained optimization problem and solved with a learning algorithm based on geometrical and statistical principles. The second part of the dissertation sought to identify gene modules from gene expression data to uncover important biological processes in different types of cells. A new gene clustering approach, nonnegative independent component analysis (nICA), is developed for gene module identification. The nICA approach is completed with an information-theoretic procedure for input sample selection and a novel stability analysis approach for proper dimension estimation. Experimental results showed that the gene modules identified by the nICA approach appear to be significantly enriched in functional annotations in terms of gene ontology (GO) categories. The third part of the dissertation moves from gene module level down to DNA sequence level to identify gene regulatory programs by integrating gene expression data and protein-DNA binding data. A sparse hidden component model is first developed for this problem, taking into account a well-known biological principle, i.e., a gene is most likely regulated by a few regulators. This is followed by the development of a novel computational approach, motif-guided sparse decomposition (mSD), in order to integrate the binding information and gene expression data. These computational approaches are primarily developed for analyzing high-throughput gene expression profiling data. Nevertheless, the proposed methods should be able to be extended to analyze other types of high-throughput data for biomedical research. / Ph. D.

Yes / Voltage instability problems in power system is an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know when and where the voltage stability problem can occur in order to apply suitable action to avoid unexpected results. In this paper, a study has been conducted to identify the weakest bus in the power system based on multi-variable control, modal analysis, and Singular Value Decomposition (SVD) techniques for both static and dynamic voltage stability analysis. A typical IEEE 3-machine, 9-bus test power system is used to validate these techniques, for which the test results are presented and discussed.

<p dir="ltr">Antigenically variable pathogens (AVPs) pose a significant infectious disease burden, but vaccine development is extremely difficult due to their ability to quickly evolve beyond host immunity. Existing models of AVP spread have not been able to sufficiently account for host immune history, population mobility patterns, and pathogen evolutionary dynamics. This thesis aims at creating a computational framework built from the concept of antibody landscapes to overcome these issues, thereby increasing the understanding of how these pathogens spread and evolve in order to improve vaccine design.</p><p><br></p><p dir="ltr">Briefly, the proposed stochastic framework is built from "the ground up'' using principles of antibody landscapes, in which we begin by devising a mechanism to describe how the landscape changes due to repeated pathogen exposure. Extending this to a (sub)population-level permits integration into a meta-population model that is further parameterized by geographic influences. Virus evolution is driven by a statistically meaningful model of antigenic drift in the underlying antigenic space. While the framework is robust and, in principle, capable of modeling a variety of AVPs, we focus on influenza H3N2 as a case study due to its data availability and persistently low and unpredictable vaccine efficacy.</p><p><br></p><p dir="ltr">Experimental results demonstrate that we can statistically significantly predict various properties of H3N2 evolution and population level immunity, including prevalence level, the timing of emergence of new antigenic clusters, the positions of unseen strains in antigenic space, as well as the geographic locations where new strains and antigenic clusters emerge. Through analysis of the simulated outcomes, we identified a population level of immune protection against circulating strains (titre value of approximately 5 units), which when approached, seems to signal an upcoming antigenic drift. Using this insight, we propose a new vaccine strain selection strategy that shows notable improvements in vaccine effectiveness and stability. Additionally, we estimate that it could reduce annual morbidity by 73.4 ± 40.8 million (17% ± 9%) in the Northern Hemisphere and 56.7 ± 38.0 million (10% ± 6%) in the Southern Hemisphere. In summary, this novel framework can accurately replicate the interplay between pathogen evolution and population-level immune responses decades into the future from a mechanistic perspective, and be used to design improved vaccines.</p>

The Switched Reluctance Motors(SRM) have gained considerable attention in the variable speed drive market mainly due to the simple construction of the motor and the possibility of developing low cost converters and controllers. As these machines are under development, a considerable amount of research effort is directed to the experimental performance evaluation of the SRM drives. System efficiency, electromagnetic torque, torque ripple, output and losses are some of the required measurements. / Master of Science

Electromagnetic launchers (railguns) are set to replace traditional large caliber ship mounted cannons in the near future. The success of the railgun depends heavily upon a comprehensive understanding of beam behavior during periods of heavy dynamic loading. It is hypothesized that the combination of velocity transition effects, electromagnetic loading, and other non-linear or design specific effects contribute to areas of high stresses/strains over the length of the rail/beam during launch. This paper outlines the use of the Timoshenko beam model, a model which builds upon the traditional Bernoulli-Euler beam theory with the addition of shear deformation and rotary inertia effects, a necessity for high wave velocities. Real-world experimental setups are simplified and approximated by a series of linear springs and dampers for model prediction and validation. The Timoshenko beam model is solved using finite difference (FD) methods for the approximation of spatial derivatives and MATLAB ordinary differential equation (ODE) solvers. The model shows good convergence and precision over a large range of system parameters including load velocities, foundation stiffness values, and beam dimensions. Comparison to experimental strain data has validated model accuracy to an acceptable level. Accuracy is further enhanced with the inclusion of damping and non-linear or piecewise effects used to mimic experimental observations. The MATLAB software package presents a valid preliminary analysis tool for railgun beam and foundation design while offering advantages in ease of use, computation time, and system requirements when compared to traditional FEA tools. / Master of Science