The University of Arizona Campus Repository: Recent submissions
Now showing items 21-40 of 108211
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Other Games in Other Towns: Pluralism about Biological Function and RepresentationAt the heart of this dissertation is an argument about the differences in some of the explanatory practices of ecology and evolutionary biology. While evolutionary biology is often concerned with the causes of traits – their evolutionary history – ecology often studies traits as causes. I argue for this claim in the introduction, and the rest of the monograph concerns some of the consequences of this inversion of explanandum and explanans. Treating traits as causes rather than as effects has profound consequences for our understanding of functional explanation in biology, as well as in cognitive science, especially for teleosemantics. In general, treating traits only as effects leads to problems when we try to explain how organisms and populations respond to novelty in their environments. Treating traits as causes resolves this difficulty. In the first chapter, I identify and develop a notion of function – realized function – implicitly used in some ecological niche models from conservation biology. A trait’s realized function is its contribution to a population’s ability to occupy its realized niche. I show why ecological niche models require this notion of function as well as how they make use of it via a case study: the use of an ecological niche model to predict the changes in the range of Sub-Saharan amphibian species due to anthropogenic climate change (Garcia et al. 2014). In the second chapter, I re-examine the case of Fodor’s frogs, showing how the notion of a realized function can help resolve the problem of indeterminacy for teleosemantics, according to which a representation’s biological function is unable to provide a determinate content for that representation. I argue that a backward-looking function that did fix the content wouldn’t be sufficient to resolve the problem this indeterminacy poses in ecology, but an appeal to the realized function of the frog’s visual system can. As a result, teleosemanticists would be best served by adopting pluralism about the biological functions that can give contents to representations, becoming pluralists about representation as well. In the third chapter, I show how this same pluralism about biological function can provide us with an answer to the challenge posed by Swampman. We do not need to deny that he would have any representations at all. We can instead say that he has realized representations but lacks selected-effects ones. I use this discussion as a springboard towards the more general problem of novelty, according to which a backward-looking teleosemantics is unable to assign evolutionarily novel contents to representations. I consider the best-developed attempt to resolve this problem and argue that it fails for the same reasons a backward-looking teleosemantics generally fails to handle ecological questions. Again, pluralism about biological function and representation is our best bet to resolve the issue. Finally, I conclude with some general remarks about the pluralism I’ve advocated for. There is no simple way to resolve the various notions of function I’ve used throughout into a single notion, and there is no “best game in town.” Instead, there are many games in many towns. To do good work in biology and cognitive science, we must admit – at least for now – that there is more than one notion of function at play in these sciences. These various notions are more and less applicable in different explanatory and predictive contexts, but are also sometimes jointly required in order to better understand a system of interest. As a result, I advocate for integrative pluralism about biological function.
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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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Native Nations, Climate Change, Health, and Natural Resources ProtectionThis dissertation explores ways in which tribal nations can or have planned for the impacts of climate change. Utilizing an interpretive policy analysis, case studies of the Gila River Indian Community, the Navajo Nation, the Klamath, the Karuk, the Red Lake Band of Chippewa, Coeur d'Alene and the Swinomish are highlighted because of the ways in which they have planned for climate change while also ensuring that either TEK, natural resources, or public health have also been included into their climate plans. Tribal populations are considered ‘vulnerable’ populations largely because of the effects of colonization and environmental injustice. Planning for the impacts of climate change proactively is an important way to protect sovereignty and the public health of their population. Finally, this dissertation argues that including public health and natural resources protection is vital to creating a complete plan and protecting tribal populations in the future. Furthermore, tribes that create their own plans with community feedback are then ensuring that the community's unique needs are prioritized.
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Native American Cultural Education for Providers at an Oupatient Psychiatry PracticePurpose: This quality improvement (QI) project aimed to educate providers about blending traditional practices with Western healing to better support the Native American community and ensure culturally appropriate care. The project was conducted at Camelback Integrated Health and Wellness (CIHW) in Phoenix, Arizona. Background:Native American patients often face health disparities due to a lack of culturally competent care. Historical trauma, mistrust of the healthcare system, and a non-holistic view of health contribute to these disparities. Research shows that provider training in cultural care can improve provider attitudes, decrease implicit bias, and enhance patient outcomes. Methods:The design utilized for the QI project was a quantitative pretest. An educational PowerPoint presentation was developed and presented to seven providers at CIHW. Pre- and post-test surveys were administered to assess changes in provider attitudes and practices regarding culturally sensitive care. Descriptive statistics were used to analyze survey results, which were visually displayed using summary tables and pie charts. Results: Pre-test results revealed that 57% of providers ‘sometimes’ considered a patient’s cultural background in treatment planning. Post-test results showed that 71% of providers reported they would now always consider a patient’s cultural background. In terms of providing culturally sensitive care, 71% of providers initially ‘sometimes’ took steps to be culturally sensitive, with only 14% always doing so. Post-test results indicated improvements in these practices. Providers identified limited resources and a need for further education as significant barriers to implementing culturally sensitive care. Conclusions:The QI project successfully increased provider awareness and willingness to integrate Native American cultural practices into patient care. Educating providers on culturally sensitive care is crucial for reducing health disparities and improving patient outcomes. Continued efforts to provide ongoing education and address identified barriers are essential for sustaining culturally informed care at CIHW.
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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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Learning More than One Resource: An Analysis of Behavioral Flexibility in Bumble BeesOrganisms face various challenges in changing environments, and learning to deal with these challenges can increase their fitness. However, learning one task or resource can impose cognitive constraints on learning another. Different memories can interact to inhibit or facilitate recalling of one another. Also, learning can affect further learning opportunities by changing the environment that organisms experience. However, the factors determining the positive or negative direction of interactions between multiple learned memories and consequently affecting the performance of organisms are still poorly understood. This dissertation addresses how generalist bumble bees (Bombus impatiens) learn diverse tasks and floral traits during foraging in multi-floral contexts, focusing on cognitive constraints and decision-making. Specifically, I asked three questions: 1) How does the similarity between learned “flower color-handling tactic” associations affect bees’ foraging performance? Is it enhancing (transfer) or hindering (interference) performance? 2) How do transfer and interference from learned associations influence bees' decisions regarding which resources to select? 3) How does previously learned behavior restrict the floral stimuli that bees experience and influence further learning? Laboratory experiments using artificial flowers with varying traits revealed that when bees experienced two types of flowers sequentially, flower color similarity and handling tactic similarity interacted to determine transfer and interference between learned associations. When handling tactics were similar, bees transferred learned tactics more easily if flower colors were similar, enhancing their foraging performance. However, when handling tactics differed, similar flower colors led to incorrect tactic transfer, causing bees to make more errors. In an experiment with two flower types presented simultaneously to bees, similar interaction patterns were observed. Despite the high error rates, bees switched frequently between two flower types, which contradicted the hypothesis that bees would specialize on one flower type to reduce costs associated with switching. In a separate experiment, learning how to handle a flower also affected bees’ subsequent learning of flower traits associated with rewards, potentially by restricting access to floral stimuli. Taken together, my studies have demonstrated several challenges bees face in learning to handle multiple flower types and have provided insights into the cognitive mechanisms of organisms faced with complex and changing environments.
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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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How Does Tropical Forest Respond to Drought? Using Remote Sensing to Test Predictions of Functional Ecology and Resource TheoryThe Amazon is one of the largest terrestrial contributors to global carbon cycling. Photosynthesis of the Amazon rainforest, however, despite considerable in situ and space-based observations, acts as one important source of uncertainty for the carbon cycle. Slight changes in Amazon forest photosynthesis would have a substantial impact on ecosystem dynamics, biosphere-atmosphere exchange, and global carbon cycling. However, the response of Amazon forests to climate variability and long-term change - including increasing droughts - is highly uncertain, with some models predicting catastrophic forest collapse while others predict resilience. This highlights the importance of the understanding of tropical forest photosynthesis dynamics under climate change. Climate drivers alone, though important, are evidently insufficient to predict the complexity of drought responses across heterogeneous landscapes. Particularly missing is an understanding of the comprehensive biogeographic drivers of differences in photosynthetic dynamics across tropical regions from wet to dry forests and across multiple forest ecotopes defined by hydraulic environments, soil fertility and texture, and functional forest traits. Thus, this dissertation asks if a “functional biogeography” of forest behaviors can address the question: why do some forests show green-up, responding positively to water stress, suggesting a sign of genuine ecosystem resilience, whereas other forests show brown-down, responding decreases in their photosynthetic function to water stress, suggesting potential vulnerability? To determine the extent of contrasting forest responses to water stresses at different timescales, from dry seasons to interannual droughts, across different environments of Amazonia, and to develop a more mechanistic understanding of how those responses emerge along gradients of forest ecotopes and climate, I addressed this basic question in three key contexts in each of three chapters: (1) At inter-annual timescales, in the context of the large interannual Amazon droughts of 2005, 2010, and 2015; (2) At seasonal timescales (during annual dry seasons), for the global tropics as well as Amazonia; and, finally, (3) In the context of using past drought patterns (as developed in Chapter 1 for 2005, 2010, and 2015) to predict future drought response (in particular the response during the drought of 2023). I used remote sensing explorations of water-stress-induced vegetation greening/browning patterns and how they correlate with ecotopes and climate across landscapes. I further used ground-based studies of tree drought resilience or plot-scale tree demography to validate remote sensing inferences at multiple levels. The result is a biogeography that uses the insights of resource theory and functional ecology, as tested by remote sensing indices, to reveal underlying ecological mechanisms. The functional biogeography can predict tropical forest resilience and vulnerability, in response to droughts and dry seasons. This new functional biogeography of forest responses to water stress provides a framework for conservation decisions and improved predictions of heterogeneous forest responses to future climate changes, warning that Amazonia’s most productive forests are also at greatest risk, and that longer/more frequent droughts are undermining multiple ecohydrological strategies and capacities for Amazon forest resilience. This new biogeography lays the foundation for improving ecosystem and global models of vegetation feedbacks to climate.
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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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Environmental Heat Stress Response and Adaptation in RuminantsEnvironmental heat stress represents a worldwide problem for ruminant production, negatively impacting areas such as lactation, fetal development, and muscle growth. The aims of this dissertation were to determine the impact of environmental heat stress during gestation on the lactation performance of ewes, and the consequences of maternal heat stress on the post-natal performance of their offspring. Further, we tested the effect of supplementation with ß-adrenergic agonists on the growth performance of beef cattle exposed to environmental HS. To determine the influence of maternal heat stress (HS) on milk production, nineteen Columbia-Rambouillet pregnant ewes carrying singleton were exposed to hot ambient environment (n=7) or thermoneutral (TN) conditions during mid-gestation. Dry matter intake (DMI) and water intake (WI) were measured daily. At lambing, placental weight and colostrum production were measured, and for 21 days, daily milk yield was evaluated. Heat stressed ewes had lighter placental and fetal weight than TN ewes; however, colostrum production was not different. TN ewes had a similar total milk production than HS ewes. Daily milk production was higher on TN ewes on days 2, 5, and 6. HS during gestation negatively affects placental mass and milk production, impacting the nutrient sources for the fetus and causing growth restriction. In order to determine the impact of maternal heat stress on the offspring's growth performance and insulin response, twenty-one Columbia Rambouillet crossbred lambs (TN=12 and FGR=9) were used. Milk, feed, and body weights were recorded daily for eight weeks. Body measurements were taken weekly for eight weeks. Glucose tolerance tests were performed at four and eight months of age to evaluate the insulin response. HS fetuses were growth-restricted because HS lambs were 27% lighter than TN lambs at birth, indicating maternal HS caused FGR. Body weight of FGR lambs was lighter than that of TN lambs for the next eight weeks or until weaning. However, the average daily gain (ADG) and gain to feed (G:F) were not different between groups. FGR lambs presented smaller body measurements than TN lambs, except for head length (HL), which was similar between group treatments. After the intravenous glucose challenge, insulin concentrations were lower in FGR lambs than in TN lambs at four and eight months of age. FGR lambs presented asymmetric growth restriction in utero and had abnormal insulin secretion response. The use of ß-adrenergic agonists was evaluated with twenty-four Brahman steers exposed to two environmental conditions, TN and HS, and supplemented with or without Zilpaterol hydrochloride for 21 days. DMI, water intake, respiratory rate (RR), and rectal temperature (RT) were measured daily. After 21 days of supplementation, Brahman steers were relocated to the feedlot and harvested at an average weight of 544 kg post supplementation/environmental treatment, and carcass merit was evaluated. RR was higher on Brahman exposed to HS. Non-supplemented Brahman steers exposed to HS presented higher RT than TNZL steers on day eight. Heat-stressed steers supplemented with ZL had higher RT on day eleven than TNCN steers. ADG and G:F did not present significance nor carcass merit. No negative impact on growth and carcass characteristics was seen on Brahman steers exposed to HS. Zilpaterol supplementation did not affect the carcass merit, demonstrating an option to mitigate HS's adverse effect on Bos indicus. Environmental heat stress is a global problem that affects all stages of ruminant production and impacts the economy. Maternal heat stress during gestation results in a smaller placenta, decreasing the nutrients available to the fetus and fetal birth weights, causing asymmetric growth and a decrease in insulin secretion concentration. Maternal heat stress also affects milk production in lactated ewes, directly impacting the availability of milk nutrients. Similarly, HS postnatally decreases muscle growth in ruminants.
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Enhancing Educational Choices: A Study of Mathematics Directed Self-Placement at a Community CollegeMathematics placement and courses can be a key factor in determining the pace and success of student educational pursuits. This study delves into the potential effectiveness of Directed Self-Placement (DSP), also referred to as Guided Self-Placement or Self-Directed Placement, as opposed to traditional placement methods, in fostering successful completion of college-level math courses within two semesters at one particular institution. Using a quantitative research design, this investigation employed logistic regression analysis to compare the outcomes of students placed via DSP with those placed through more traditional procedures. The study further explored the interplay between students' demographics and their chosen level of math course.The findings reveal a nuanced landscape where, predominantly, students who engaged in DSP exhibited a higher likelihood of completing their requisite college-level math courses within the designated two semesters. However, intriguing exceptions emerged, particularly among students majoring in science, technology, engineering, or mathematics (STEM), where traditional placement methods seemed to offer an edge. Moreover, the analysis uncovered intriguing relationships between the choice of math course in the DSP process and the subsequent success rates. This research contributes to the ongoing discourse on academic placement strategies in community colleges, highlighting the potential of DSP to enhance educational outcomes while also recognizing its limitations and the need for a nuanced application. The implications of these findings are significant for educators, administrators, and policymakers striving to optimize placement processes and, ultimately, to support students in achieving their academic and professional goals efficiently.
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Efficient and Robust Inference of Neutron Star Parameters From Pulse Profile ModelingModeling of X-ray pulse profiles from millisecond pulsars offers a promising method of inferring the mass-to-radius ratios of neutron stars. Recent observations with NICER resulted in measurements of radii for two neutron stars using this technique. In this dissertation, I explore correlations between model parameters and the degree to which individual parameters can be inferred from pulse profiles, using an analytic model that allows for an efficient and interpretable exploration. I introduce a new set of model parameters that reduce the most prominent correlations and allow for an efficient sampling of posteriors. I use this framework to show that the uncertainties in the model parameters for neutron stars for which the polar cap temperature falls outside of the NICER energy range are significantly degraded. I also demonstrate that the degree of beaming of radiation emerging from the neutron star surface has a large impact on the uncertainties in the inferred model parameters. In particular, when a model with an incorrect beaming function is used to fit data, the inferred neutron star mass-to-radius ratio can be significantly larger than the true value. This has important implications for interpreting and assessing the radii of neutron stars inferred through pulse profile modeling.
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Ecology and Evolution of Melanin Pigmentation Plasticity in a Complex WorldPhenotypic plasticity is the ability of a single genotype to produce multiple phenotypes in response to different environmental stimuli. Plasticity is a widespread phenomenon that takes many forms and is often thought to be adaptive, helping organisms optimally match phenotypes to a heterogenous environment. Organisms experience and respond to many dimensions of environmental variation at once. Many plastic traits are induced by multiple environmental cues and are under more than one selective pressure. Together, these environments determine the functional roles of a trait and the constraints on a trait. To determine if plasticity is adaptive, it is necessary to understand the balance of these functions versus constraints. In the following studies I investigate melanin pigmentation plasticity in the white-lined sphinx moth, Hyles lineata (Sphingidae). During the late larval instars this species displays melanin plasticity (plasticity in the degree of melanin pigmentation), which is induced by multiple environmental cues. I investigated two potential functional roles of melanin: thermoregulation and desiccation prevention. While I did not find support for a role of melanin in desiccation prevention, I did find evidence that melanin is adaptive in cold environments. More melanic larvae outperform less melanic larvae in cold environments, while suffering no costs of melanization in a warm environment. I then considered two potential constraints on melanin: resource limitation and resource allocation trade-offs. I found that tyrosine, the amino acid precursor of melanogenesis can constrain the production of melanin, although it is still prioritized in certain contexts. Furthermore, larval melanin pigmentation trades off with other important traits, including immunity and adult pigmentation, which may be costly in certain environments. Finally, I tested whether patterns of melanin plasticity have diverged adaptively between populations from different thermal environments. I found that reaction norms differed between populations, although the patterns were only partially consistent with a role of climate in driving differences in plasticity. Overall, I show that to determine whether melanin plasticity is adaptive in this species, it is important to consider not just the thermal environment but also the nutritional environment and the costs of producing melanin. Understanding both the functional roles of and the constraints on a plastic trait are necessary to determine the adaptive value of its plasticity.
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Dominick Argento’s I Hate and I Love: A Comprehensive Analysis for ConductorsDominick Argento was an American contemporary composer who achieved great fame as a composer of vocal music, especially lyric opera. The theatrical devices in his works make his compositions distinctive, and Argento's use of symbolic motivic cells is one of the main characteristics that drives the narrative structure in his works. Dominick Argento's I Hate and I Love is an intricate choral work in which Argento masterfully blends twelve-tone techniques with traditional harmonic language to convey the complex emotional interplay between love and hate as expressed in the poetry of Gaius Valerius Catullus. In this document, I explore Argento's compositional methods, focusing on his use of symbolic motivic cells, harmonic structures, and dramatic elements that align with the text. By analyzing these techniques, I reveal how Argento's music serves not only as a reflection of Catullus's emotional turmoil but also as an omniscient commentary that adds layers of meaning beyond the explicit text. This research contributes to the broader academic discourse on Argento's choral works, providing deeper insights into his innovative approach to choral composition and his unique integration of serialism with sonorism.
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Development of Novel Deep Learning Techniques for Accelerated Diffusion MRIDiffusion Magnetic Resonance Imaging (dMRI) is an imaging modality of MRI that features a non-invasive technique for qualitatively and quantitatively characterizing microstructural characteristics in tissue. This is achieved by dMRI being sensitive to the Brownian motion of water molecules in tissue measured over an applied magnetic field gradient. The directional orientations of motion of water molecules are estimated over an isotropic gaussian propagator. This capability enables dMRI to achieve resolution of microstructural features of up to 2-3 orders of magnitude below the resolution limit of conventional MRI. However, delineation of resolving features and characteristics with high degrees of architectural specificities requires making underlying assumptions of the underlying diffusion signals and invoking appropriate mathematical and computational solutions to achieve this. One computational technique of interest for dMRI is Diffusion Tensor Imaging (DTI). DTI enables anisotropic measurements to be acquired by fitting multiple diffusion-weighted images (DWIs) to a tensor, enabling 3D reconstructions of microstructural features of the brain, including the ability to reconstruct trajectories of white-matter tracts. Some of the challenges of performing DTI in routine clinical and research studies include long data acquisition times required to obtain sufficiently large number of DWIs for outputting robust tensor estimates. This involves long scan times and typically introduces undesired image distortions and artifacts that deteriorate image quality. In addition, DTI has limitations in accurately delineating more complex microstructural features in white-matter tracts. Models in Diffusion Kurtosis Imaging (DKI) and Constrained Spherical Deconvolution (CSD) offer improvements over DTI. However, these models require considerably more data than DTI and are generally more complex to implement. Although some prior deep learning (DL) techniques have addressed limitations of conventional diffusion models, these techniques typically involve supervised-learning frameworks that require large amounts of clean training data to successfully produce robust estimates of metrics and are typically constrained to diffusion-specific models. For this research, we propose DL techniques that address some of these challenges. The contributions made by this dissertation involve a Self-Supervised with Fine-Tuning DL pipeline that can produce robust DTI metrics for an accelerated acquisition of DWIs and reduces the need of a large volume of clean training data. We also demonstrate a Generative Diffusion Deep Learning model that can effectively leverage uncertainty to generalize well to the underlying distribution of tensor model metrics in DTI and DKI and bypasses the need for diffusion-model fits. We also present a DL pipeline, AcceleraTed deep-LeArning for model-free and multi-Shell (ATLAS) DWI, that can predict a full acquisition of DWIs across multiple shells, given an accelerated acquisition in one shell or multiple shells. This enables for the potential for robust DTI tensor estimates, overcoming the requirement for large amounts of clean training labels, and eliminates the constraint of diffusion-specific models, which introduces the exciting potential to obtain diffusion metrics that more accurately delineate white-matter tracts.