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Topics in Spatial-Temporal Economics
No full textThesis (Ph.D.), Economics, Washington State UniversityThis dissertation can be broadly defined as touching economic questions as they relate to both time and space. In the first chapter, I investigate reference point updating within periods. The defining feature separating (cumulative) prospect theory from expected utility theory is that potential outcomes are measured relative to a reference point as opposed to final asset allocation. Using transportation data, I am able to measure reference point adaption when drivers are faced with an unexpected delay en route. A novel, yet conservative, approach is proposed to estimate reference point adaption amid data uncertainty. Using this new estimation technique, it is found that drivers are more likely to change their reference point if the unexpected delay occurs near the endpoints of travel.
In chapter two, I provide additional evidence that the skewness observed in travel-time distributions may not be an important factor in predicting the chosen route. Drivers prefer routes which are both short in duration and consistently reliable. Notwithstanding decades of research, there has yet to materialize a superior and widely accepted measure for reliability that captures the multifaceted nuances of route choice. As with the previous literature, the mean and variance of each route are found to be significant. However, the documented positive skew of travel-time distributions is not found to be significant in determining the preferred route.
The last chapter sheds light on the relationship between housing price and voting patterns in U.S. presidential elections. While measures of economic growth and the unemployment rate have long been found to be essential economic indicators in predicting election results, they do not account for the largest asset a typical household owns: their home. I develop a model of aggregate household wealth using the Zillow Home Value Index as a measure of real estate wealth at the county-level across the 5 most recent presidential elections. As housing price increases (decreases), ceteris paribus, the electorate tend to vote for the Democratic (Republican) presidential candidate.Washington State University, Economic
PRECURSORS OF BIOLOGICALLY RELEVANT REACTIVE SULFUR SPECIES AND THEIR APPLICATIONS
No full textThesis (Ph.D.), Chemistry, Washington State UniversityReactive sulfur species (RSS) are a group of sulfur containing molecules playing important regulatory roles in the biological system. Representative RSS include thiols (RSH), hydrogen sulfides (H2S), persulfides (RSSH), hydrogen polysulfides (H2Sn, n>1), polysulfides (RSSnSR, n>1) and S-modified protein cysteine adducts such as S-nitrosothiols (RSNO) and sulfenic acids (RSOH). Many RSS are highly reactive and transient molecules, making their studies difficult. RSS donors or precursors, which are used to specifically produce or deliver RSS, are useful research tools, as well as possible therapeutic agents. To this end, I have developed novel precursors to controllably release RSS. I have studied Ammonium Tetrathiomolybdate as an H2S donor and found it showed H2S-related bioactivities; I have developed 9-fluorenyl disulfides as persulfide precursors for the synthesis of unsymmetrical trisulfides; I have discovered diacyl disulfides can generate H2S2 under amine activation and studied the redox chemistry of H2S2 generated by this method; finally, I have helped the development of OS relay deprotection as a general strategy for designing RSS donors. These precursors serve as important chemical tools for studying chemistry of RSS and exploring their therapeutic potentials.Washington State University, Chemistr
Spectroscopy of Defects in Gallium Oxide
No full textThesis (Ph.D.), Physics, Washington State Universityβ-Gallium oxide (β-Ga2O3) is a promising semiconductor for its potential as a material in the field of power electronics. Magnesium doping of Ga2O3 has been shown to create a semi-insulating material, which could be utilized in ultrahigh-power devices. The properties of iridium impurities in undoped, magnesium-doped, and calcium-doped gallium oxides were investigated with IR spectroscopy. In undoped and Ca-doped β-Ga2O3, IR peaks at 3313, 3450, and 3500 cm-1 are tentatively assigned to O–H bond stretching modes of IrH complexes. Hydrogen-annealed Ga2O3:Mg shows an IR peak at 3492 cm-1, and H-annealed Ga2O3: Ca shows an IR peak at 3441 cm-1¬. These are assigned to an O-H bond-stretching mode of a neutral MgH and CaH complex, respectively. Polarization experiments were used to place the O-H bond of the MgH complex in the a-c plane. Mg, Ca, and Fe doped samples show an Ir4+ electronic transition feature at 5148 cm 1. By measuring the strength of this feature versus photoexcitation, the Ir3+/4+ donor level was determined to lie 2.2-2.3 eV below the conduction band minimum, which matches theory. Ga2O3:Mg also has a range of sidebands between 5100 and 5200 cm-1, attributed to IrMg pairs. Polarized IR measurements were used to show that the 5148 cm-1 peak is anisotropic, weakest for light polarized along the c axis.Washington State University, Physic
CUDA-SHAPE - A GPU-accelerated algorithm for asteroid shape modeling
No full textThesis (Ph.D.), Electrical Engineering, Washington State UniversityAsteroids are remnants of our early easolar system and are likely to provide answers about its origins and evolution. Accurate orbital predictions decades into the future are essential for protecting Earth from potential impact events. Asteroids will soon be attractive commercial mining targets. Asteroid shape modeling is an essential driver for this research and in addition to providing shape information, the modeling that is at the core of this research also provides information about size, composition, spin, and ephemerides.
Modeling asteroid shapes from radar data uses sequential-fit inversion algorithms that are computationally complex, often taking many weeks to complete. The SHAPE algorithm has been used in all published radar-based asteroid shape research since 1994 but its slow performance is a bottleneck and three prior acceleration attempts have failed. SHAPE is comprised of many serialized independent calculations that can be parallelized at two distinct levels in SHAPE’s data structure. This work has resulted in CUDA-SHAPE, a graphical-processing-unit (GPU)-accelerated asteroid shape modeling algorithm based on SHAPE. CUDA-SHAPE uses commonly available Nvidia GPUs and the CUDA programming framework to performs up to 19.3 times faster than SHAPE on identical models while maintaining full backwards compatibility. CUDA-SHAPE also implements a CPU-hyperthreaded mode that re-uses SHAPE’s cluster computing design on a modern multi-core CPU, providing modest speed boosts of up to 5.9 times that do not require GPU hardware.
The GPU-accelerated algorithm includes parallel rasterization, delay-Doppler synthesis, penalty, photometric, and reduction functions, as well as streamed frame operations. CUDA-SHAPE exploits the pixel-parallel nature of digital images and the independence of separate frames of observed data.
Three scale model asteroids of increasing complexity are used to prove CUDA-SHAPE’s ability to produce shapes that are unique to the observed data, stable, and reasonable representations of the real objects. The dataset of real asteroid (341843) 2008 EV5 is used to show CUDA-SHAPE’s performance and modeling quality on realistic, noisy data and to compare it to earlier work that utilized the SHAPE algorithm to produce a shape for 2008 EV5 from the same source data and modeling approach.
CUDA-SHAPE will be released to interested research communities as open-source software.Washington State University, Electrical Engineerin
INTERACTIONS BETWEEN ROOTSTOCK GENOTYPE AND SOIL ENVIRONMENT AFFECT SCION PHYSIOLOGY AND MINERAL NUTRITION OF APPLE
No full textThesis (Ph.D.), Horticulture, Washington State UniversityIrrigation is essential for many apple production regions, which elevates the risk in the future where water shortages will likely occur. Soil environment is a critical factor contributing to tree growth and development. Apples are composite woody perennials composed of a genetically distinct rootstock and scion. Rootstock genotypes can strongly vary in vigor and productivity. However, the interactions between rootstock genotypes and soil moisture and temperature have not been extensively studied. The objective of this research was to evaluate the influence of rootstock genotype on scion physiological and nutritional responses under different soil environmental conditions. In the greenhouse, ‘Honeycrisp’ and ‘Gala’ apple cultivars were grafted onto G41, G890, M9, and B9 rootstocks. In the field, ‘Honeycrisp’ was grafted onto the same rootstock genotypes. Two irrigation treatments were established: a water-limited and a well-watered control for both experiments. Physiological measurements such as leaf gas exchange, stem water potential, shoot growth, and quantum yield of photosystem II (ΦII) were made every two weeks since the onset of the experiments. At the end of each experiment, tree growth was assessed, and nutrient concentration and carbon isotope composition (δ13C) was measured in roots, stem, and leaves. In both experiments, water limitations reduced aboveground biomass and, to a lesser extent, root biomass. When G890 was used as a rootstock, growth, and mineral nutrient accumulation was more plastic to water limitations. ‘Gala’ on all rootstocks and both scions on G890 had elevated mineral nutrient uptake. Water-limited conditions increased the nutrient concentration in roots and stems but had no effect on leaves. ‘Honeycrisp’ grafted onto G890 was the most responsive to drought indicated by decreasing stomatal conductance, reducing net CO2 exchange rates, ΦII, and ultimately, shoot growth. In contrast, B9 maintained growth and stomatal conductance when water-limited and had the highest δ13C and lowest stem water potential. These findings demonstrate differential responses of rootstock genotypes to soil environment and indicate opportunities for selection of rootstocks that are more suitable in water limited regions.Washington State University, Horticultur
Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach
No full textAims: Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjustment models are needed for benchmarking appropriate AU and for fair inter‐facility comparison. In this study we identify patient‐ and facility‐level predictors of antimicrobial usage in hospitalized patients using a machine learning approach, which can be used to inform a risk adjustment model to facilitate assessment of AU. To our knowledge, this is the first time machine learning has been applied for this purpose. Methods and Results: Patient admission records were retrieved from the Duke Antimicrobial Stewardship Outreach Network which include clinical data for 27 community hospitals in the southeastern United States. Candidate features (predictors) were then generated from these records. The number of features was reduced using a statistical approach, and missing values of the reduced feature set were imputed using bootstrapping and expectation‐maximization algorithm. Finally, support vector regression (SVR) and cubist regression (CB) models were applied to find root‐mean‐square error values which were used to evaluate the selected feature set. The performance of the SVR and CB models was found to be better than that of linear null and negative binomial null models, thereby demonstrating the effectiveness of our selected features. Conclusions: Relevant patient‐ and facility‐level predictors of antimicrobial usage in days of therapy were obtained and evaluated. The potential predictor set can be used in risk adjustment strategies for benchmarking antimicrobial use. Significance and Impact of the Study: One reason for the rapid emergence of antimicrobial resistance is inappropriate use of antibiotics in hospitalized patients. Identifying predictors of antimicrobial exposure using a machine learning technique can improve the use of AU, enhance patient health outcomes, and reduce the infection spread caused by antimicrobial‐resistant organisms.Post-printChowdhury, A. Sayed, E. T. Lofgren, R. W. Moehring, and S. L. Broschat. (2019). Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach. Journal of Applied Microbiology, Vol. 128, No. 3, 688-696. doi:10.1111/jam.14499
Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method
No full textAims: Predicting bacterial resistance provides valuable information that can assist in clinical decisions. With recent advances in whole genome sequencing technology, the detection of antibiotic resistance (AR) proteins directly from genomic data is becoming feasible. AR genes/proteins can be identified using best‐hit methods that work by comparing candidate sequences with known AR genes in public databases. However, these approaches may fail to detect resistance genes with sequences that differ significantly from known sequences. Our goal is to develop a machine learning technique to accurately predict capreomycin resistance in Mycobacteria with low false discovery rates. Methods and Results: We present a stacked ensemble learning model as an alternative to traditional DNA sequence alignment‐based methods using optimal features generated from the physicochemical, evolutionary and secondary structure properties of protein sequences. We train logistic regression, C5.0 and support vector machine (SVM) algorithms as our base classifiers, and our stacked ensemble predictors combine the results from the base classifiers to achieve higher accuracy. Compared with our most accurate base classifier (SVM), our most accurate stacked ensemble predictor increases training accuracy by 2·43%. Our stacked ensemble predictors achieve test accuracy up to 81·25%. Conclusions: We developed a stacked ensemble model to predict capreomycin resistance for Mycobacteria with an accuracy >80% using protein sequences with sequence similarity ranging between 10% and 70%. This performance cannot be achieved with best‐hit methods due to differences in sequence similarity. Significance and Impact of the Study: Today an estimated one‐half million cases of multidrug‐resistant (MDR) and extensively drug‐resistant (XDR) tuberculosis (TB) occur annually worldwide at a great cost. Because capreomycin is a second‐line drug used to treat drug‐resistant TB, the ability to use a machine learning approach to classify capreomycin‐resistant TB in a timely manner is crucial for the successful treatment of MDR or XDR TB.Post-printChowdhury, A. Sayed, E. Khaledian, and S. L. Broschat. (2019). Capreomycin resistance prediction in two species of Mycobacterium using a stacked ensemble method. Journal of Applied Microbiology, Vol. 127, No. 6. doi:10.1111/jam.14413
