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DEVELOPMENT, IMPLEMENTATION, AND LONGITUDINAL ANALYSIS OF THE OKLAHOMA ALL-STATE BAND AUDITION EVALUATION MEASURE
No full textIn 2012, the Oklahoma Music Educators Association (OkMEA) band division embarked on a mission to research and address some of the reliability issues it encountered with its existing audition adjudication measurement tool. In this study, I utilized the McPherson and Thompson (1998) Process Model for Assessing Music Performance as a framework for analyzing the OkMEA audition structure. From this analysis, I developed a general audition measurement rating scale for its all-state band and orchestra wind and percussion auditions. The OkMEA implemented the Barber General Audition Measure (BGAM) at the auditions in 2014. This study examined five years (2015–2019, N = 2699, K = 425) of total scores for all final round wind and percussion audition results, categorized by instrument groups, audition year, and total results. The reliabilities were measured using Cronbach’s alpha, the intraclass coefficients (ICC), and 95% confidence intervals (CI) based on a mean rating (k = 5) in a consistency, 2-way mixed-effects model. The analysis results showed strong overall consistency and reliability by individual group, total year, and the five-year block using the BGAM
Valuative Trees in Arbitrary Characteristic and Applications to Log Canonical Thresholds
No full textValuations naturally encode birational invariants of algebraic varieties. This thesis investigates the valuative tree—the valuative space centered at the affine plane over a field—via two complementary approaches. The first analyzes its tree structure through the observer’s topology and introduces two parameterizations, skewness and thinness, defined using the sequence of key polynomials (SKP). Although obtained from distinct constructions, thinness is shown to coincide with log-discrepancy functions through the universal dual graph. The second approach employs tools from non-Archimedean geometry. We review the Berkovich affine line over a complete valued field, with particular attention to Hahn series and completed Puiseux series; in these cases, the valuative tree embeds naturally into the Berkovich line, revealing characteristic-dependent phenomena. As an application, we use valuative trees to compute the log canonical thresholdof analytically irreducible plane curves. We conclude by discussing an alternative approach to defining log discrepancy functions in positive characteristic, along with the counterpart to the log canonical threshold—the F-pure threshold
LATE PLEISTOCENE AND EARLY HOLOCENE SEDIMENTS, SOILS, AND HUMAN OCCUPATIONS ALONG WILDHORSE CREEK IN THE ARBUCKLE MOUNTAINS OF SOUTH-CENTRAL OKLAHOMA.
No full textThe focal area of this study is Wildhorse Creek, a tributary of the Washita River in south-central Oklahoma where an avocational archaeologist, Roger Hamilton, has been collecting lithic tools and debitage for the past 20+ years. Thanks to Roger’s meticulous recording of the provenience of his finds, Wildhorse Creek presents a great opportunity to further archaeological knowledge of Paleoindigenous and Early Archaic groups occupying the Southern Plains, as well as the differential preservation conditions affecting the landforms in which the sites these groups left behind can be found. The results of these investigations evidence a continuous occupation of Wildhorse Creek dating back at least 11,000 radiocarbon years before present (rcybp), as well as the presence of buried landforms of equivalent time depth with the potential to house intact archaeological deposits. Geoarchaeological analyses performed also provide evidence in support of multiple major climatic events on the Southern Plains, including the Late Pleistocene Younger-Dryas cooling event, and the Middle Holocene Altithermal drought period. Lastly, buried soil dates obtained along Wildhorse Creek demonstrate the presence of three known paleosols (Black Mat, Caddo, Delaware), as well as a fourth, new, Middle Holocene paleosol we’ve termed the Wildhorse Paleosol which appears to bracket the Calf Creek Horizon
Using Indicators of Wetland Condition to Predict Metals Changes in Natural and Treatment Wetlands in the Tar Creek Watershed
No full textNatural and treatment wetlands have been shown to improve water quality on mining impacted lands using biogeochemical and microbiological processes, as well as providing ancillary ecosystem services. Wetlands are evaluated through indicators of condition such as hydrology, water quality, hydrophytic vegetation, and hydric soils. In the Tar Creek Superfund Site, part of the historic Tri-State Lead-Zinc Mining District in Oklahoma, Kansas, and Missouri, treatment wetlands have been successfully incorporated into holistic multi-cell passive treatment systems to remove trace metals from net-alkaline, artesian flowing mine drainage. Incidental wetlands have also developed adjacent to the creek and near untreated mine drainage seeps throughout this watershed, due to mining-related landscape disturbances. This study compares the effectiveness of treatment wetlands and incidental wetlands in removing metals from surface water, determines which indicators of wetland condition correlate with effective removal of metals in mining-impacted wetlands, and determines which indicators of wetland condition correlate with a greater wetland vegetative structure in mining-impacted wetlands in order to develop site-specific recommendations for water quality improvement. Ten wetlands in the Tar Creek watershed were included, which represent various hydrologic regimes, water quality characteristics, vegetative communities, and substrate compositions. Treatment wetlands were determined to have greater removal efficiencies for Fe, Pb, Zn, and Cd; however, these trends were more closely related to influent water quality than hydrologic parameters. Vegetative cover was not more representative of metal removal efficiency than richness or diversity indices, and organic matter content in wetland substrate was not correlated with removal efficiency. Finally, excessive Fe concentrations in the substrate were shown to decrease vegetative diversity
IN SITU CO₂ GENERATION: NUMERICAL STUDY IN GEOTHERMAL SYSTEMS AND EXPERIMENTAL ASSESSMENT FOR ENHANCED OIL RECOVERY
No full textChapter 1 introduces geothermal energy as a clean, renewable source with the potential to provide steady power and support global climate goals. As investment in geothermal grows, low-to-moderate temperature reservoirs that more common than high-temperature ones are gaining attention for both power generation and direct-use applications. The type of use depends largely on reservoir temperature, as outlined in the Lindal diagram. As geothermal development expands, accurately tracking the movement of the thermal front during cold-water injection becomes increasingly important. Unable to track this movement accurately can lead to inefficient heat extraction and poor reservoir management. Tracers have emerged as a promising tool to address this challenge, as they offer a way to monitor subsurface flow paths and thermal front propagation more efficiently. Among various tracer types, temperature-dependent reactive tracers are the most effective due to their sensitivity to temperature changes. However, many existing reactive tracers face limitations in high-temperature reservoirs due to their chemical formulations stability. These challenges have led to growing interest in identifying new tracers that are both stable and responsive under reservoir conditions. This research proposes urea as a temperature-dependent reactive tracer with strong potential for geothermal applications. Its predictable hydrolysis behavior, high solubility, and low cost make it a practical choice for field-scale use. The study is built around three core questions: whether urea can reliably track the thermal front, how its performance is affected by reservoir and operational parameters, and how it compares to existing reactive tracers. Together, these questions guide the work that follows, laying the foundation for simulation-based evaluation of urea’s behavior in geothermal systems. The second chapter of this dissertation focuses on the foundational step of building a reliable and representative numerical model for simulating geothermal reservoir behavior, when exposed to cold-water injection. Numerical simulation is a powerful tool for studying heat extraction, but it naturally involves errors from time and space discretization, such as numerical dispersion that can cause inaccuracy in the results. Thus, if not properly controlled, these errors can distort the simulation of thermal front movement and lead to incorrect predictions of reservoir performance. This chapter addresses these issues by evaluating how grid block size and time-step selection influence the accuracy and efficiency of the model. A key objective is to have the right balance between minimizing numerical dispersion and maintaining manageable running time. The study also emphasizes the importance of applying realistic boundary conditions, to ensure that simulation results are physically relevant to real reservoir situations. Beyond numerical sensitivity, this chapter focuses also on refining reaction parameters related to urea hydrolysis, and ensuring consistency in thermal energy in place calculations. Together, these efforts are aimed at improving model calibration and laying a solid floor for the subsequent simulation chapter that builds upon this base framework. Chapter 3 explores the use of urea as a temperature-dependent reactive tracer for tracking thermal front movement in geothermal reservoirs. Using CMG STARS, urea was simulated under varying temperatures and injection rates. Results showed that its effectiveness depends on in-situ flow velocity, reaction kinetics, and reservoir temperature. urea performed best between 70–90 °C, providing a clear signal at the production well. Its intermediate reaction rate allowed better thermal tracking than both slower and faster-reacting tracers, making it suitable for moderate-temperature systems. To validate the simulation and assess a common temperature estimation method, Hawkins et al. (2021) correlation was applied. This helped to identify whether the method tends to overestimate reservoir temperatures under urea conditions, which offers additional insight into its reliability for geothermal applications. Chapter 4 explores the phase behavior of surfactant formulations designed for further application that will be tested in the future for enhanced oil recovery (EOR) in tight shale formations with the addition of urea. The goal was to identify systems capable of forming stable Type III microemulsions and achieving ultra-low interfacial tension (IFT), which are essential for mobilizing trapped oil. Petrostep S2, a thermally stable internal olefin sulfonate (IOS) surfactant, was tested for the first time in this context, within a shale + urea framework. The working hypothesis was that S2, when paired with co-surfactants and under optimal salinity, could promote favorable microemulsion formation and support both IFT reduction and wettability alteration at elevated temperatures. A series of formulations were evaluated across temperatures of 25 °C, 50 °C, and 90 °C, focusing on high-temperature performance. Co-surfactants including Alfoterra, SDBS, and Calfax were combined with S2 at various ratios, and salinity was systematically varied to track microemulsion transitions. Visual phase behavior screening was used to detect the formation of Winsor phases, especially Type III systems. The Chun-Huh correlation was applied to estimate IFT based on observed solubilization parameters. Among the tested systems, a 50:50 blend of S2 and SDBS at 17% NaCl showed the most promising behavior, forming a stable middle-phase microemulsion with theoretically ultra-low IFT. This formulation was selected for further flowthrough evaluation in one-dimensional sand pack experiments in the subsequent chapter, allowing comparison of its oil recovery potential against previously tested urea-based in-situ CO₂ systems. Nevertheless, binary systems with Calfax 16L-35 demonstrated similar behavior but at slightly high salt concentrations. Chapter 5 builds on the findings of Chapter 4 by applying the optimal surfactant formulation in combination with urea under dynamic flowthrough conditions. Using 1D sand pack experiments, the study investigates the performance of this coupled system for EOR in sandstone medium. Upon injection, urea decomposes at elevated temperatures to generate CO₂ and NH₃. The CO₂ partitions into the oil phase, reducing viscosity and swelling the oil, while NH₃ promotes wettability alteration through interaction with the rock surface. The addition of the ultra-low IFT surfactant formulation further enhances oil mobility by significantly reducing interfacial tension. This combined approach depends on multiple recovery mechanisms; viscosity reduction, wettability alteration, and interfacial control, working together to improve oil displacement efficiency. Although earlier studies have reported the benefits of urea-based systems in various formations, they typically used surfactants with only moderate IFT reduction. This chapter explores the synergy between urea and ultra-low IFT surfactants, offering new insights into their potential for boosting recovery in tight reservoir conditions in future work.
“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs
Get PDFFinancial support was provided by the University of Oklahoma Libraries' Open Access Fund.Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end-users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi-model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post-downscaling. We recommend that subset-selection methods account for this possibility and that decision-relevant downscaled climate projections provide proper descriptions of fitness-for-purpose and essential caveats, so that non-specialists can interpret the results with an appropriate level of confidence.Ye
It Is Not Our Fault Yet: Multi-Attribute and Machine Learning Study For Improved Upper Basement Fault Detection In An Area Of Carbon Capture Utilization and Storage: Decatur, Illinois, USA
Get PDFIdentifying and interpreting faults is critical in many geological prospects, from traditional well planning and operations to energy transition ventures such as Geothermal and carbon capture, utilization, and storage (CCUS). Effective geohazard analysis can reduce the operation time, cost, and uncertainty. More importantly, with new technologies such as CCUS, where public perception and support are sensitive, it is important to decrease analysis uncertainty and prevent large-scale reactivation events.To improve the accuracy of fracture delineation, a machine learning (ML) and multi-attribute approach was employed in Decatur, Illinois— where microseismicity has been induced in the rhyolitic basement related to CCUS. Due to the poor seismic imaging resolution of the basement and the potential presence of sub-seismic faults, traditional geometric attributes (e.g., coherence and curvature) are not sufficient alone. Structure-oriented filter (SOF) is utilized before the application of the multi-attributes which resulted in increased fault confidence and connectivity. Seismic attributes candidates are gray level co-occurrence matrix entropy (GLCM), fault enhancement of energy ratio similarity (ERS), most positive curvature (k1), most negative curvature (k2), and aberrancy. For ML, a pre-trained convolutional neural network (CNN) was utilized while generative topographic mapping (GTM) was calculated using the aforementioned candidate attributes. Our findings show ERS and CNN can effectively map larger-scale fault patterns where curvature and aberrancy are better for faults seen as flexures or folds. However, only some faults detected from ML and attributes coincided with the location of microseismic events. This is most likely due to a combination of factors such as the poor quality of seismic image at the basement, high viscoelastic attenuations at deep depths, and small vertical displacement of sub-seismic faults
DEVELOPMENT OF A MACHINE-LEARNING ENHANCED HIGH PERFORMANCE METHANE SENSING INSTRUMENT FOR FIELD APPLICATIONS
Get PDFMethane - a well-known potent greenhouse gas - has significantly contributed to globalwarming through their emissions from both natural sources, like wetlands, and human activities, including particularly the fossil fuel production sectors. The earlier to detect the emission sources, would allow a fast response to deploy the mitigation strategies to contain methane from releasing the environment. Therefore, making accurate and fast monitoring of methane emission is vastly crucial, particularly in the oil and gas industries where large-scale emissions are prevalent. However, the existing challenges on the field deployable sensing solutions still exist. They are either too costly involving human participation or with unstable or low sensitivity performance to provide an accurate and low false alarm sensing outcomes. Having this motivation, this thesis presents the development of a new cost effective, high performance and environmentally robust methane sensing instrument based on the NDIR (Nondispersive Infrared) sensing method and machine learning enhancement algorithm, which could be largely distributed to form a real-time methane monitor- ing network that can be strategically positioned for scalable and comprehensive area coverage, such as from facility-level to production basin or even regional level. Specifically, the thesis includes six chapters, starting from the background introduction to provide an brief overview of the existing methane emission issues and monitoring studies. Chapter two will provide a systematically review of the point sensor tech- nologies that can be used for creating real-time sensing network in the field to detect methane emission dynamics. Chapter Three will primarily describe the design of the circuit board and the overall setup of the device. After finalizing the design of a single device, Chapter Four will focus on the selection and training of the machine learning models used to process the data and mitigate environmental influences. Chapter Five will present the three validation methods we employed to verify the accuracy of the device’s readings: in-lab validation, open-area validation, and field validation
PULSED LASER DEPOSITION AND CHARACTERIZATION OF LANTHANUM-DOPED CALCIUM STANNATE EPITAXIAL THIN FILMS FOR ULTRA-WIDE BANDGAP OXIDE SEMICONDUCTOR APPLICATIONS
Get PDFCaSnO3 exists as an ultra-wide bandgap (UWBG) semiconductor with a bandgap of 4.1eV-4.4eV, which, when engineered correctly, can be advantageously used as a material for transparent thin film transistors (TFTs) or for high-power electronic applications. CaSnO3 has been regarded as an “undopable” material, due to the challenges of incorporating dopants in its perovskite structure, and the complex mechanisms that can govern the doping behavior. While there have been improvements made regarding its thin film synthesis with molecular beam epitaxy (MBE), the literature for doped thin films of CaSnO3 deposited by pulsed laser deposition (PLD) is limited and attempts to properly dope the compound have been unsuccessful. In this study, the ability to dope and grow epitaxial thin films via PLD is challenged by undertaking an extensive investigation of powder preparation routes, sintering methodologies, and thin film growth condition optimization. While no semiconducting behavior was apparent, multiple doping levels were examined, evaluating phase purity, structure deviation from bulk references, and dopant incorporation. Epitaxial thin films were deposited under varying background oxygen pressures and substrate temperatures to optimize crystalline quality. Analysis using x-ray diffraction demonstrated promise in the potential of PLD as a viable route for fabricating doped UWBG CaSnO3
Host habitat shapes the gut microbiomes of insular reptilian hosts in the Philippines
Get PDFFinancial support was provided by the University of Oklahoma Libraries' Open Access Fund.Islands have long served as ideal, replicative “natural laboratories” to help identify the mechanisms that shape the diversity and distribution of plant and animal communities, and a burgeoning body of literature has utilized island-like systems to better understand the processes that shape microbial community diversity. Despite this expanded application, few studies have explored patterns of microbial diversity spanning true islands, especially among communities of microorganisms that colonize vertebrate hosts (i.e. microbiomes). Here, we use 16S ribosomal ribonucleic acid microbial inventories to elucidate the roles that host evolutionary history, host habitat, host microhabitat, and geographic location play in the assemblage of gut microbiomes among reptilian hosts spanning multiple islands in the Philippines. Host habitat and microhabitat explained most of the variation in gut microbiome diversity observed among our focal hosts. Although we identified some significant differences in microbiome diversity across two of the host suborders (Lacertilia and Serpentes) and some host families, we did not find evidence of phylogenetic signal. We also conducted analyses of microbiome diversity across various geographic scales, and found that hosts inhabiting the same island, but different localities, did not possess significantly different gut microbiomes. However, the gut microbial diversity of hosts inhabiting distinct islands were significantly different across numerous measures of microbiome diversity. Results from this robust, comparative study contribute to our growing knowledge of the host-associated and geographic mechanisms that shape the vertebrate gut microbiome and represents one of the first studies to characterize variation in gut microbial communities among vertebrate hosts inhabiting multiple Philippine islands.Ye