Master's Theses
ABOUT THE COLLECTION
The UA Master's Theses Collection provides open access to masters theses and reports produced at the University of Arizona, including theses submitted online from 2005-present and theses from 1895-2005 that were digitized from microfilm and print holdings, in addition to master's reports from the College of Architecture, Planning and Landscape Architecture from 1966 onwards. The collection includes hundreds of titles not available in ProQuest.
We have digitized the entire backfile of master's theses and doctoral dissertations that have been submitted to the University of Arizona Libraries - since 1895! If you can't find the item you want in the repository and would like to check its digitization status, please contact us.
The UA Master's Theses collection is not comprehensive; master's theses from 1993-2015 were only received and archived by the UA Library and ProQuest if the student chose to pay the optional archiving fee. The Library does not have copies of many master's theses submitted during this time period. Some academic departments may keep copies of theses submitted to their programs. Colleges and departments wishing to archive master's theses not available in the University Libraries are encouraged to contact us at repository@u.library.arizona.edu.
QUESTIONS?
Please refer to the Dissertations and Theses in the UA Libraries guide for more details about UA Theses and Dissertations, and to find materials that are not available online. Email repository@u.library.arizona.edu with your questions about UA Theses and Dissertations.
Recent Submissions
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Transitional Shock Wave Boundary Layer Interactions and Surface Heat Transfer on a Hollow-Cylinder/Flare at Mach 5A hollow-cylinder model with a 15◦ half-angle flare is tested at Mach 5 for fivedifferent Reynolds numbers ranging from 4.10 × 105 < ReL < 1.62 × 106 . The laminar boundary layer separates due to the shock-induced pressure rise and reattaches downstream of the flare corner. Reattachment is associated with transition to turbulence and the formation of hot streaks. Surface heat transfer is measured using a FLIR infrared camera and five custom Ahmic thin-film gauges while pressure fluctuations are measured with two Kulites and three PCBs. For sufficiently transitional cases, heat transfer reaches a maximum near reattachment and moves upstream for increasing Reynolds number. Steady heat transfer trends along x/L between IR and thin-film gauges match qualitatively and the discrepancies between each diagnostic are discussed. In the premultiplied pressure spectra, two peaks were identified at f ≈ 14 kHz and f ≈ 100 kHz and their potential sources are discussed. Unsteady heat transfer measurements are discussed in regards to their viability for detecting reattachment and transition.
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The Impact of Extreme Weather on Winter Wheat: Evidence from the United StatesThe impact of climate on winter wheat yields is analyzed across the United States, using county level data spanning from 1974 to 2023 for 25 states. Using county and time fixed effect models, we studied the effect of temperature and precipitation for the planting, growing and harvesting phases of winter wheat. By using standardized z-scores, the study measures the impact of extreme climate deviations on crop yield variability. Findings highlight that moderate temperatures and precipitation are beneficial for winter wheat growth. The extreme climatic conditions, especially extreme heat, cold and rainfall have a significant effect on yields. Warmer conditions during the planting season increase the yields but excessive heat during the planting and harvesting seasons reduces yield. Moderate rainfall increases crop production in all stages but extreme rainfall results in yield reductions. The analysis also found that excluding outlier counties in California and Washington did not significantly alter the signs of the estimated coefficients.Key words: extreme climate, winter wheat yield, Z-score
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The Optimal Transfer Pathway Problem: Optimizing Course Equivalency and Prerequisite Compliance for Seamless Transfer Between Community Colleges and UniversitiesThis thesis delves into the Optimal Transfer Pathway (OTP) problem, a pivotal computational challenge in higher education that aims to create efficient transfer roadmaps for students transitioning from community colleges to universities. The OTP problem seeks to minimize total credit hours while ensuring students can earn both associate’s and bachelor’s degrees. We begin by analyzing the computational complexity of the OTP problem, revealing it to be N P -complete and highlighting the inherent difficulty of solving large-scale instances.To tackle this complexity, we first examine the Integer Quadratic Programming (IQP) ap- proach. This approach guarantees an optimal solution but often requires substantial com- putational time, especially for large-scale datasets. This limitation makes the IQP algorithm impractical for real-time applications in student advising and academic planning. In response to these challenges, we introduce the Iterative Course Swapping (ICS) algorithm, a heuristic approach that efficiently generates near-optimal solutions. The ICS algorithm seamlessly integrates degree requirements from both institutions, available courses, articula- tion agreements, and prerequisite information. It produces valid transfer pathways that sat- isfy all degree requirements, respect course prerequisites, and maintain the proper sequence of community college and university courses. Importantly, the ICS algorithm’s time com- plexity is significantly improved over the IQP approach. To evaluate the performance and computational efficiency of both the ICS and IQP algo- rithms, we conducted experiments using real-world data from Pima Community College and the University of Arizona. The results demonstrate that both algorithms significantly improve credit transfer efficiency, leading to higher credit retention rates and reduced time to degree completion. The ICS algorithm excels in computational performance, generating transfer pathways more quickly and effectively than previous approaches, particularly for large datasets. While the IQP algorithm guarantees an optimal solution, developing a trans- fer plan often requires more time, rendering it impractical for large-scale datasets. This trade- off between optimality and computational efficiency highlights the potential of the ICS algo- rithm for real-world applications, where timely solutions are crucial for student advising and academic planning. This research contributes to developing more equitable and efficient transfer systems, ulti- mately facilitating smoother transitions between community colleges and universities. The broader implications include promoting higher graduation rates and reducing student finan- cial burdens. This work lays the foundation for future research in optimizing educational pathways and demonstrates the value of applying computational approaches to complex ed- ucational challenges.
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The Image of the Seminarian in Nineteenth-Century Russian LiteratureNational literature has long been considered a reflection of a state’s significance andlegitimacy; indeed, fiction participates in and contributes to the myth of the nation. As Benedict Anderson has shown, national literature helps shape the nation. In his scholarship, he defines the nation as an imagined community of people who seem to represent a homogenous group despite the actual inequality and diversity of the population. In line with Anderson, Sarah Corse remarks that a nation is “marked by a distinctive set of values, tensions, myths, and psychological foci, that produces in turn a certain readily identifiable national character.” Julia Wright argues that national literature becomes one of the most effective spreaders of such images. However, instead of portraying a nation as a heterogeneous and diverse community of various social classes, religions, and cultures, national literature deprives the population of its diversity and its complexities. The Russian writers of the 1830s onward struggled to define “Russianness” and Russian identity, and their debates on the subject took place through the medium of their fiction. Preoccupied with probing the essence of “Russianness,” literary elites began to examine national “types,” giving particular attention to the nation’s nascent middle classes: merchants, governesses, teachers, and seminarians. The lattermost, seminarians, would come to constitute a complex source of Russianness. Though eventually ascended to a place among the Russian intelligentsia, their origins were of the middle classes. Reflecting the tensions and social upheaval inherent in the rise of Russia’s new middle-class estate, the portrayal of seminarians in the literature of the day was often of a disparaging tone. In spite of this, the significance of the marginalized estate continued to its rise to prominence in the nineteenth century. The evolution in depictions of seminarians in Russian literature throughout the period reflects the course of public opinion, with some significant exceptions. This thesis analyzes selected works by two influential writers of the period, Gogolʹ’s Vii (1835) and Khvoshchinskaia’s The Baritone (1859). These works were chosen due to their extensive and opposing descriptions of seminarians. Close reading of these selected works allows one to scrutinize the shift in image of seminarians in Russian literature and culture between 1830 and 1860. Chapter I of this thesis delves into Nikolai Gogolʹ’s novella and explores how the writer depicted seminarians in demeaning and derogatory terms and examines the characteristics he ascribes to these students. Accordingly, this thesis posits that Gogolʹ illustrated a perceived clash between the seminarians’ expected religiosity and their failure to meet these expectations via their personal behavior. Chapter II will then investigate a depiction of the estate as portrayed by Nadezhda Khvoshchinskaia (pseudonym V. Krestovskii) in The Baritone, written two decades later. In her work, the estate is portrayed in an arguably idealized manner, reflecting the shift in seminarians’ image as they increasingly came to be viewed as a reflection of potential futures for a changing Russian society. Thus, it becomes possible to trace certain changes in the portrayal of the seminarians against the backdrop of the historical events.
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The Hidden Costs of Complexity: Using Causal Inference and Double Machine Learning to Uncover Important Relationships in Higher Education Data SetsGraduation rates are a critical performance metric for higher education institutions, reflecting both student success and the effectiveness of educational programs and policies. Among various influencing factors, curricular complexity has emerged as a significant determinant. This study rigorously estimates the causal effect of curricular complexity on four-year graduation rates across 26 universities in the United States. To achieve this, we employ a multifaceted methodological framework integrating advanced causal inference techniques. We calculate the Generalized Propensity Score (GPS) to adjust for confounding variables and predict the treatment variable using Hierarchical Linear Modeling (HLM), accounting for the nested data structure (students within universities). The data is stratified into quintiles based on GPS values to ensure balanced comparison groups. Within each quintile, Double Machine Learning (DML) is utilized to estimate the causal effect of curricular complexity on four-year graduation rates, leveraging logistic regression for the binary outcome variable (four-year graduation) and linear regression for the continuous treatment variable (curricular complexity). Additionally, we construct a causal network using the PC Algorithm, refined by domain experts for plausibility and relevance. The Bayesian Information Criterion (BIC) score is used to select the optimal adjusted network. Sensitivity analysis assesses the robustness of our findings against potential unmeasured confounding factors. Our results indicate a significant causal relationship between curricular complexity and four-year graduation rates. Specifically, higher curricular complexity is associated with lower graduation rates, with an estimated causal effect of -3.879% per unit increase in complexity. Sensitivity analysis confirms the robustness of these findings, with a new effect estimate of -3.763% per unit increase in complexity after accounting for potential unobserved confounders. Detailed analysis across quintiles showed consistent results, indicating that higher curricular complexity within each stratified group reduces the likelihood of graduating in four years. The Average Treatment Effect (ATE) across quintiles ranged from -7.5% to -21.5% per unit increase in complexity. The implications of this study are far-reaching. By highlighting the impact of curricular complexity, our findings can inform university policies aimed at optimizing curricula to enhance student success. Moreover, the methodological framework presented here offers a comprehensive approach to causal inference in educational research, combining GPS, HLM, DML, and network analysis to provide robust and actionable insights.
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The Effect of Extreme Weather on Mortality: Evidence from the United StatesThis paper estimates the impact of extreme weather on mortality rates associated with 5 specific causes and total mortality across the counties in the United States, period of 1979-2002. Using the county-level panel data I explored how significantly the deviations in the temperatures and precipitation impact mortality rates specifically cardiovascular, respiratory, neoplasms, transport injuries, self-harm and interpersonal mortalities. By using a comprehensive methodological framework that points to the standardized z-scores to identify the significant weather anomalies, uses average temperature bins to explore the non-linear effects and sets the temperature thresholds to see the consequences of extreme heat and cold. This study enhances our understanding of climate health. Key findings show that average temperature ranges between <0◦F -60◦F have a significant impact on total and cardiovascular mortalities. The extreme maximum and minimum temperatures are significantly associated with motor vehicle accidents, likely due to tire blowouts and wet ice road conditions. This research contributes to the understanding of how extreme weather affects health, offering important insights into how such conditions impact mortality. .
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SRSaRa: A SaRa-Inspired Modification of Pettitt's Test for Non-Parametric Change-Point DetectionThe Signed-Rank Screening and Ranking Algorithm or SRSaRa is a non-parametric change-point detection technique that is based on a SaRa-like process with a diagnostic function inspired by Pettitt's test. Possessing two modes, `LM' and `MAX' for single and multiple change-point detection respectively, the SRSaRa is flexible and robust to outliers through its diagnostic function. The SRSaRa's `MAX' mode for single change-point detection outperforms Pettitt's test in several scenarios while maintaining Type-I error control, while the SRSaRa's `LM' mode is capable of controlling FDR at the desired level and shows promise as a non-parametric multiple change-point detection technique.
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Risk Factors of Helicobacter Pylori for Hispanics Living in Southern ArizonaThis thesis investigated the occurrence of Helicobacter pylori (H. pylori) and other riskfactors associated with gastric health in Hispanic communities as part of The Southern Arizona Healthy Stomach Project (SoAZHSP). This thesis report assessed tap water samples for culturable microorganism and metals, H. pylori infection using the urea breath test (UBT), and household and individual surveys to statistically assess predictors of known and identifiable risk factors of H. pylori infections in this subpopulation. Thirty-three percent (n=14 of 42) of the subpopulation tested positive for H. pylori. In the United States (US), H. pylori infection prevalence estimates range between 17.6% and 36.0%, while H. pylori prevalence rates for Hispanics living in the US range between 38% and 62%. Factors such as lack of health insurance (p=0.02), low consumption of restaurant and/or fast food (p =0.02), older age (p=0.04), lack of alcohol consumption (p=0.01), and tap water quality (p=0.04 for total coliforms detection) were predictors of H. pylori infections. Furthermore, only 41.9% of participants had heard of H. pylori, indicating low awareness. Despite sample size and reporting bias limitations, this report highlights the multifaceted nature of H. pylori infections and the importance of improving H. pylori awareness, water quality, health care access, and food hygiene practices. These findings underscore the need for targeted, comprehensive interventions to address H. pylori infections in Hispanic communities to improve overall health outcomes.
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Predicting Host-Pathogen Interactions Between C. Difficile 630 and MouseClostridioides difficile (C. difficile) is the causative organism of hospital-acquired infectious diarrhea. C. difficile infection (CDI) causes more than 500,000 infections and 12,800 deaths. Antibiotic ablation of the commensal microbiome leads to colonization and infection in CDI patients. Symptomatic diarrheal infection requires the release of C. difficile toxins, TcdA and TcdB. Together, these toxins destabilize the colon epithelial cell membrane, causing fluid secretion into the colon, inflammation, and tissue damage. Molecular interaction databases describe C. difficile toxin interactions with host cell receptors, establishing these toxins as crucial virulence factors. However, there needs to be more curated and annotated information about non-toxin colonization and infection of these interactions in databases. Comprehensive identification of C. difficile toxin interactions and adhesion and colonization processes with host proteins will advance our understanding of Clostridioides difficile infection (CDI) and facilitate the design of targeted rational therapies.In this study, we predict host-pathogen interactions (HPI) between mouse and C. difficile strain 630. We select mice as the host to predict interactions as these animal models provide genetic similarity to humans and can predict human disease responses. Selecting C. difficile 630 allows for a comprehensive study due to its well-characterized genome, clinical relevance, and representation of prevalent, virulent strains. The interolog-based approach is leveraged to provide host-pathogen interactions (HPI) rapidly. This method relies on sequence-based homology to transfer experimentally validated interactions to a mouse and C. difficile 630 model. The interolog approach predicts protein-protein interactions based on the assumption that if two proteins interact between species, their orthologs will likely interact in another species' system. PSICQUIC interaction data is used to identify homologs in mouse-C. difficile. Mouse orthologs are found in human proteins using Ensembl BioMart. Next, pathogenic bacterial orthologs in C. difficile strain 630 are identified using reciprocal BLAST. This approach allows for the inference of potential interactions by mapping known interactions from one organism to another, yielding a set of 1,281 interologs; for extracellular pathogens such as C. difficile strain 630, extracellular or secreted proteins are expected to interact with host surface proteins. This pruning results in 100 interologs. CDI occurs in the colon, leading us to our next step: studying the colonic expression of host proteins. We exclude proteins not expressed in the colon as they do not contribute to the disease. Next, we manually assess C. difficile proteins in the predicted HPI for biological function and annotation. This results in 37 predicted HPIs. Network analysis of these HPIs further identifies 13 interactions with edge clustering for further investigation. One predicted HPI shows an interaction between the C. difficile strain 630 sortase protein, CD630_27180, which cleaves bacterial surface proteins for adhesion, interacting with mouse protein, Ninjurin-1, an outer surface protein in the host implicated in immune functioning. This interaction is likely as these proteins are present externally in the bacteria. Another prediction identifies an interaction between CD630_03860, a secreted sortase protein, and Col12a1, a collagen protein expressed in the colon and implicated in maintaining cell junction stability. This HPI is likely to occur as CD630_03860 is a secreted protein that may allow for the breakdown of the epithelial tight junction, giving C. difficile access to the basolateral layer of colon epithelial cells. While the interolog approach is rapid and cost-effective, it has some limitations. The interolog method relies on sequence homology to predict interactions. The predicted HPI quality in this study depends on the experimental data available on PSICQUIC. Experimental data on PSICQUIC is sourced from databases that are manually annotated. It only represents some of the available experimental data in the literature that could lead to meaningful predictions. Additionally, predicted proteins may have different functions than those in the experimental data, leading to biologically incongruent predictions in CDI. For example, C. difficile strain 630 protein flagellin (FliC) interacts with TLR5 in humans. However, this interaction cannot be predicted in mice as TLR5 in mice binds different ligands in bacterial proteins. This research advances the application of interologs to HPI prediction by using pruning methods to refine interactions specific to C. difficile infection. However, alternative approaches to predict HPIs for C. difficile should also be investigated, such as machine learning. Ensemble machine learning in predicting HPI using protein sequence has been used to predict accurate host-pathogen interaction.
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Partially Ordered Logistic RegressionPartially ordered sets arise frequently in classification problems. Classification models that are currently used tend to either ignore the partial orderedness of the data by fitting nominal models or apply a strict ordering to the data by fitting ordinal models. In both cases, valuable information about the data is lost or overvalued in the model. Zhang and Ip created a framework for a multistep process in which a series of models can be used to classify data from partially ordered sets while maintaining the underlying structure of the data. While the framework of the model exists, it is not widely used. In this thesis, we provide an algorithm that fits the framework along with pseudocode to assist with implementation of the model. We then show an example of an application to a rare disease, SCN8A, which has a partially ordered structure of disease state.
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Monitoring Fatigue Cracks in Riveted Aerospace Plates Using Nonlinear Ultrasonic TechniquesAluminum structures are commonly used in aircraft due to their lightweight and corrosion resistance compared to other metals. Often multi-layered aluminum plates are joined by rivets which are prone to fatigue crack formation in aircrafts. Therefore, the detection and monitoring of fatigue cracks at rivet joints in aluminum structures are crucial for ensuring flight safety. In this study, piezoelectric sensors are used to generate and detect Lamb waves on aluminum plates with rivet joints. The feasibility of a newly developed nonlinear ultrasonic technique called Sideband Peak Count (SPC) technique is investigated for detecting fatigue cracks near these joints. To overcome some limitations of existing SPC-I and SPI ((a modified version of SPC-I) techniques in capturing harmonic and modulating wave frequencies due to material nonlinearity, another index called the Sideband Intensity Index (SII) is introduced. Comparative analysis of SII with existing SPC-I and SPI techniques show some advantages of the SII technique. Research findings demonstrate that the SII technique can reliably detect fatigue cracks around rivet joints on aluminum plates. This study offers a more efficient method for detecting critical fatigue cracks in rivet joints.
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Metagenomic Analysis of Antibiotic Resistance and Virulence Genes along the Santa Cruz RiverAntimicrobial resistance is a growing global concern as it is associated with at least 2.8 million infections and 35,000 deaths in the United States alone every year. Globally, it is estimated to result in over 1.2 million deaths annually. Antibiotic resistant bacteria (ARB) are commonly present in sewage and can be disseminated into water through sewage leaks. Since at least 2017, the Santa Cruz River has experienced frequent raw sewage leaks from the International Outfall Interceptor (IOI). To assess the impact these sewage leaks had on this critical water source, sediment samples were taken in triplicate from near the leaks (Nogales) and two locations away from the contamination point (Tubac and Marana) at four different timepoints over the period of one year between October 2019 and October 2020. DNA was extracted from each sample (n=108), and sequenced using Illumina and Oxford Nanopore technologies. The study aimed to understand the impact on microbial communities, pathogens, and levels of antibiotic resistant genes (ARGs) using tools and/or databases including Kraken2 custom and Greengenes databases (taxonomy), ABRicate and DeepARG (ARGs), and Virulence Factor Database (pathogens). Thirty-three samples were chosen from each sequencing technology for further analysis to determine the differences in results between Illumina and Oxford Nanopore and different databases. Results showed that there were no significant differences in number of ARGs, virulence factors, or microbial communities between locations or timepoints using Illumina sequencing reads. There were significant differences between the two sequencing technologies using different databases. Microbial communities differed between the two databases, as the Kraken2 custom database had a greater abundance of unclassified family for Oxford Nanopore sequence reads compared to the Greengenes database. There were significant differences in the Shannon diversity indexes between sequencing technologies. Beta diversity revealed that using the Kraken2 custom database, samples clustered together based on sequencing technology, not sampling location. Overall, the study found no differences in the sediment along the Santa Cruz River in ARGs, pathogens, or microbial communities due to the sewage leaks. However, the study did find that there can be highly significant differences in results depending on the sequencing technology and databases that are used for the analysis, therefore caution must be applied when comparing studies using different approaches.
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Macrophage Epigenetic Reprogramming and Metabolic Memory in Response to Altering Glucose ExposureObjective Inflammatory-related diabetic complications persist long-term, despite achievements in glycemic control, a phenomenon termed “metabolic memory”. Current evidence suggests that this is, in part, mediated by hyperglycemia-induced epigenomic reprogramming that produces long-term pro-inflammatory phenotypic and functional outcomes in macrophages. However, the early temporal dynamics of macrophage epigenomic remodeling in response to metabolic fluctuations are not well understood. In this study we aimed to 1) characterize how the macrophage epigenomic landscape was remodeled early in response to fluctuations in the cellular metabolic environment 2) identify timepoints where these changes were most notable, and 3) investigate hyperglycemia-induced changes in the epigenome that persist over time, suggesting a metabolic memory effect. Methods Using an in vitro macrophage model, RAW264.7 cells were cultured in high (22.5 mM) or low (5.0 mM) glucose media for one week before switching the glucose concentration of the media and measuring changes in genome-wide chromatin accessibility over several timepoints by the “Assay for Transposase Accessible Chromatin using Sequencing” (ATAC-Seq). Results Changes in glucose concentration were not found to be the major influencer on chromatin accessibility, but rather, the addition of fresh media induced prominent changes in the epigenomic landscape. Nonetheless, with careful experimental design and sophisticated analysis tools we were able to observe persistent hyperglycemia-induced epigenomic remodeling and identified sites in the macrophage genome that change in accessibility consistent with metabolic memory. Conclusion Macrophages exhibit significant chromatin reorganization in response to changes in their metabolic environment with detectable metabolic memory in their epigenome in response to hyperglycemia. Furthermore, consideration should be given to the effect of fresh media on epigenomic remodeling in previously published and subsequent studies.
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Large-Scale GIS-Modeling of Dog-Travois Transport Suitability of Landscapes in Western North AmericaThis thesis analyzes actual and potential long-distance use of the dog-pulled travois in western North America by developing a Geographic Information Systems (GIS) suitability model. The travois, consisting of a wooden A-frame sled originally pulled by dogs and, later, horses, was widely used across the North American Great Plains to facilitate the transport of supplies and trade goods. However, the absence of archaeological evidence makes it difficult to evaluate imperfect ethnographic data and assess how widespread travois use was, or could have been, in ancient times. Historic and experimental data indicate several shortcomings to travois transport based on the terrain it is being used on and the mass and physiology of the dogs used to pull it. Archaeological, historical, and experimental accounts of travois performance are reviewed to model the topographical and ecological limitations of travois-assisted transport. Limitations include, but are not restricted to, the slope (terrain) over which travois can be hauled, the temperature at which the draft dogs become unproductive and overheat, and the effectiveness of travel over specific types of vegetation. GIS modeling is used to assess the large-scale suitability of terrain for travois travel based on these projected limiting factors, and to calculate least-cost paths between select locations on the Great Plains and Intermountain West. Finally, the models produced by these analyses are compared with existing research on travois use and long-distance exchange in the western US to assess concordance with current evidence, elucidate gaps in ethnographic data, and generate predications for regions of possible dog-facilitated travois use. Beyond the enhancement of the limited available ethnographic accounts, this exploratory thesis provides guidance for future investigations of domestic dog use; especially as a template for detailed site-level analyses of travois and dog use on the local landscape, identifying prospective areas for survey and excavation of further archaeological evidence, and refining the understanding of trade interactions and human-dog relationships within and beyond the Great Plains.
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Investigation of Correction Capabilities of Ultrafast Laser Stress Figuring for Advanced Optical FabricationUltrafast laser stress figuring (ULSF), in which ultrafast laser-generated bending moments permanently deform mirror substrates, is a viable noncontact alternative to traditional optics fabrication techniques. It has been previously demonstrated to flatten 100 mm-diameter mirror substrates by ~5 μm RMS to ~10 nm RMS flatness. For significantly larger magnitude or higher spatial-frequency corrections, however, a substrate cannot be fully corrected due to limited space available in the substrate. A predictive model of the magnitudes and spatial frequencies that ULSF is capable of correcting is needed to implement ULSF for mirror substrate manufacturing. To this end, corrections of randomly generated and representative surface maps were simulated, using linear optimization of the correctable RMS height error, to understand the capabilities of ULSF correction. This thesis describes the mechanics of ULSF, the optimization process to minimize achievable height error, and the ULSF process capabilities gleaned from the simulations. Large-magnitude deformations imparted onto fused silica substrates using an optimized ULSF process are demonstrated. Finally, ULSF system changes are proposed for wider application in large optics fabrication, along with experimentally determined processing parameters when using a 0.2 numerical aperture focusing objective in a ULSF system.
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Investigating the Interaction between Crossflow and Laminar Separation BubblesThis experimental investigation explores crossflow and its interaction with laminar separation bubbles in low-speed flows.The suction side of a modified NACA $64_3-618$ airfoil was tested in conditions relevant to strong crossflow and crossflow instabilities (forced and unforced) in the presence of a laminar separation bubble. Laminar separation bubbles were identified on the model at $Re_c = 600k$ ($AoA > \ang{-2}$) through time-averaged pressure measurements and infrared thermography. Discrete roughness elements were used to promote the most unstable wavelenght of the stationary crossflow instability and obtain measurable disturbance amplitudes for crossflow instabilities within the laminar separation bubble. Infrared thermography was used to confirm the application of the roughness elements by showing the enhancement of the stationary modes at the forced wavelength ($ \lambda = \SI{3.5}{mm}$) at $AoA = \ang{-8}$ and $AoA = \ang{-1}$. Time resolved hotwire measurements provided information about the stationary, primary traveling, and secondary crossflow instabilities. It also provided knowledge of potential Kelvin-Helmholtz instabilities around the transition region of the separation bubble. DREs successfully forced the stationary crossflow mode. However, development of the primary traveling and secondary instabilities are also shown within the boundary layer. In accordance with previous research, the primary instability was seen to displace off of the wall and a set of opposite rotational vortices develops when entering the adverse pressure gradient. It was also shown that multiple secondary instability modes likely contributed to the stationary crossflow mode dominated transition. The dominant frequency bands observed near the typical IR-visualized sawtooth pattern, often associated with crossflow instability-induced transition, appear similar to those previously observed for the secondary instability of the forced stationary mode, having the largest amplitudes when approaching the estimated transition location. In the presence of a laminar separation bubble ($AoA = \ang{-1}$), crossflow was reduced at measurements located within the bubble as the upstream favorable pressure gradient is weaker than at -8 degrees. Growth of a set of opposite-rotating vortices was observed and is consist with the higher frequency modes $\SI{2000}{Hz} \leq f \leq \SI{3500}{Hz}$. As the measurement location approached transition, the crossflow vortices seem to combine with shear layer (K-H) instabilities and eventually leading to a more 2D flow field around reattachment. Higher resolution streamwise measurements between transition and reattachment are needed to corroborate this claim. Spectral analysis shows that the interaction of Kelvin Helmholtz and crossflow instabilities appears to dominate transition. This is postulated since the dominant frequency range near transition is lower than that observed without forced crossflow instabilities. Higher frequency instability modes are also shown in the power spectra which could relate to secondary crossflow instabilities and/or higher order interactions with the Kelvin-Helmholtz instability, but the exact mode could not be identified in the scope of this work. To further this investigation, higher resolution CTA is required, as well as the use of x-wires to collect multi-component velocity data to separate crossflow velocity and chordwise velocity profiles.
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In Vitro Long Noncoding RNA Responsiveness to Ischemic Conditions in Fetal Sheep IsletsFetal growth restriction (FGR) predisposes offspring to long-term health risks, such as Type 2 Diabetes and obesity. They have a higher risk of developing glucose intolerance due to impaired insulin secretion. Placental insufficiency causes fetal hypoxemia and hypoglycemia in FGR fetus leading to β-cell dysfunction from reduced β-cell mass. Long noncoding RNAs (lncRNA) are regulatory molecules that modulate transcriptional and post-transcriptional processes, and high-throughput RNA sequencing data identified several differentially expressed lncRNA in FGR islets versus controls. The objective of this study was to determine ischemic responsiveness of these differentially expressed FGR lncRNAs in control islets in vitro and develop an in vitro hypoxic/ischemic cell line model that shows responsive MALAT1 expression and NF-kB activity. The islets were isolated from fetal sheep and were cultured in ischemic and optimal conditions. MIN6 and INS832 cells were cultured in hypoxic conditions (200-250 uL/mol CoCl2 or 1% O2) for 24 hours. Western blot was conducted to measure p50 and p65 subunit translocation and luciferase assay was conducted to measure NF-κB response. oFUVECs were cultured in 1% O2 for 24 hours. We found that in vitro islet ischemia significantly altered for six of the nine lncRNAs. Among these, MALAT1 and H19 concentrations were higher (P<0.05), and SI-linc20-39a, LINC28868, SI-linc9-103, and RUNX1T1 Carmen concentrations were lower (P<0.05). Hypoxic oFUVECs only showed 7-fold high expression in H19 and no changes the remaining lncRNAs. MALAT1 and NF-kB expressions did not change in response to hypoxic (1% O2) in insulinoma cells lines. NF-kB activity increased with CoCl2-treated MIN6 cells. We found no alternative splicing for MALAT1 transcript and confirmed the coding potential for five lncRNAs. Our data confirmed the MALAT1 as a lncRNA with minimal coding potential with no detection of alternative splicing. Because we could not find a working insulinoma cell that shows responsive MALAT1 and NF-kB, primary fetal sheep islets may be the ideal in vitro model to investigate the regulatory roles of MALAT1 in FGR islets.
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Geohazard Identification in Underground Mines: A Mobile AppMining operations are indispensable to the global economy, supplying crucial resources across various industries. However, the risks of underground mining, particularly geotechnical hazards like rockfalls and structural collapses, pose significant safety challenges. Traditional methods of hazard detection rely on periodic visual inspections, which can beinefficient, subjective, and dangerous. The need for more accurate, real-time hazard detection methods is crucial to prevent accidents and improve mine safety. Recent advancements in computer vision technology have drawn significant interest in the mining sector as a viable alternative for continuous and automated monitoring of environments that demand visual inspection. While computer vision has been widely adopted in surface mining and mineral processing, its application in the more challenging underground settings has been slower to develop due to obstacles such as limited visibility, connectivity issues, and dust. The goal of this thesis is to present a comprehensive methodology for developing and implementing a computer vision-based system for geotechnical hazard identification in underground mines. This methodology aims to be replicable and adaptable to others mining environments. This thesis provides a comprehensive literature review on the application of computer vision techniques for identifying geotechnical hazards in underground mines. It also introduces the Hazard recognition in underground mines application (HUMApp), a mobile application developed to enhance safety within underground mines by efficiently identifying geotechnical hazards, particularly focusing on roof falls, thereby enhancing traditional safety measures. HUMApp has been trained using real data captured from San Xavier Mining Laboratory, encompassing a total of 2,817 images from underground environments. A fully functional mobile application has been developed and implemented. The effectiveness of HUMApp was validated through a comparative analysis with assessments from two field experts, demonstrating a strong correlation between the app’s predictions and expert evaluations.
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Cyber-physical Identity Binding for Autonomous Vehicles using Monocular CamerasWe address the problem of cyber-physical access control for connected autonomous vehicles. The goal is to bind a vehicle's digital identity to its physical identity represented by its trajectory. We highlight that simply complementing digital authentication with sensing information remains insecure. A remote adversary with valid or compromised cryptographic credentials can hijack the physical identities of nearby vehicles detected by sensors. We propose a cyber-physical challenge-response protocol named Cyclops that relies on low-cost monocular cameras to perform cyber and physical identity binding. In Cyclops, the ego vehicle acts as a verifier who challenges a prover vehicle to prove its claimed trajectory. The prover constructs a response by capturing a series of scenes in the common Field of View (cFoV) between the prover and the verifier. Verification is achieved by matching the dynamic targets in the cFoV (other vehicles crossing the cFoV). The security of Cyclops relies on the spatiotemporal traffic randomness. We validate the security of Cyclops via simulations on the CARLA simulator and on-road real-world experiments in an urban setting.
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The Association Between Endometriosis and Cardiovascular Disease Risk FactorsBackground: Prior research suggests that women with endometriosis are at a greater risk of cardiovascular disease outcomes. Therefore, our objective was to investigate the association of endometriosis related infertility and cardiovascular disease risk factors such as hypertension, type 2 diabetes, and hypercholesterolemia among postmenopausal women. Methods: We used data from Women’s Health Initiative (WHI), a prospective cohort followed for 17 years since 2005, (N= 161,705). Participants were considered to have endometriosis if they self- reported infertility and the reason for their infertility was endometriosis (n=1,970). We used Cox proportional hazards to estimate hazard ratios of incident hypertension, type 2 diabetes and hypercholesterolemia among women with endometriosis-related infertility. We additionally investigated differences in the relationship between endometriosis-related infertility and CVD risk outcomes by BMI (<30 Kg/m2 vs BMI < 30 kg/m2). We also used log-binomial regression to estimate risk ratios of ever having a CVD risk factor. Results: In women with a history of endometriosis-related infertility we observed no increased risk of hypertension (HR: 1.00 ,95% CI: 0.93 - 1.08). We observed a 18% increased risk of incident diabetes (HR: 1.18, 95% CI: 1.01 - 1.37) and a 10% increased risk of incident hypercholesterolemia (HR: 1.10, 95% CI: 1.01 - 1.19). After adjustment for a priori confounding factors, the associations with incident diabetes (HR: 1.07, 95% CI: 0.92 - 1.25) and hypercholesterolemia (HR: 1.01, 95% CI: 0.93 - 1.10) attenuated and were no longer statistically significant. The association of endometriosis related infertility and hypercholesterolemia was stronger among women with BMI < 30 kg/m2 (p-value, test for interaction:0.02) in the main analyses investigating incident diagnoses, as well as in the sensitivity analyses investigating ever having a CVD risk factor. Conclusion: Women with endometriosis related infertility were not at an increased risk of cardiovascular disease risk factors such as hypertension, type 2 diabetes, and hypercholesterolemia. However, among subgroups of leaner women (BMI<30kg/m2), endometriosis related infertility was associated with an elevated risk of hypercholesterolemia.