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Minutes of a Regular Meeting, The University of Oklahoma Board of Regents, Monday and Tuesday, March 10-11, 2025
Get PDFBrewing national identity: coffee consumption as a marker of cultural identity
Get PDFJust as food serves as a conduit for cultural exchange and identity, coffee too, as a culturally-infused substance, reflects the intricate ways in which shared beliefs and social practices shape the meanings we attach to everyday rituals. This project investigates the role of coffee in shaping and expressing national identity across different countries, exploring how individuals use coffee consumption as a means of finding a sense of identity and belonging within their cultural contexts. Coffee, as a culturally charged substance and ritualized practice, functions as a rhetorical tool through which individuals and communities construct, express, and negotiate national identity and belonging--particularly within the context of Jordanian coffee culture, where traditional practices and global influences intersect in meaningful ways
Development of a Star Tracker System for Distributed Navigation
No full textA star tracker consists of a camera connected to a computer. Using images of the sky, stars can be identified. Based on observations of particular stars at a known time, the orientation of the platform can be computed. A star tracker performs pattern recognition on the stars in the camera field of view to calculate the attitude of the platform with respect to celestial coordinates. In this thesis, the authors go one step further and convert celestial coordinates to Earth-center-Earth-fixed (ECEF) coordinates. To facilitate an investigation of star tracker technology applied to Earth-based navigation, a science-ready sky simulator was developed using the underlying mathematical techniques utilized by the star tracker software. Both Lost in Space and Tracking mode pipelines were developed based on current state of the art algorithms. The fully integrated star tracker system was then tested with a miniscope setup
FEATURE ENGINEERING AND HYBRID MODELING IN MACHINE LEARNING: A UNIFIED APPROACH FOR REDUCING COMPUTATIONAL COMPLEXITY IN FLOW PATTERN IDENTIFICATION AND HYDROCARBON PRODUCTION FORECASTING
No full textEngineering applications require predictive models that not only capture complex, nonlinear relationships but also remain physically consistent and generalizable across varying operational conditions. Achieving this balance is challenging, as existing models each have inherent limitations. Many deterministic models, including empirical and mechanistic approaches, assume fixed conditions and struggle to adapt to real-world complexity. Probabilistic models introduce computational challenges and often lack direct ties to physical laws. Machine learning (ML) approaches also face challenges in petroleum engineering due to limited physical interpretability and difficulty generalizing across diverse geological and operational conditions.Empirical models, often based on curve fitting or historical data, are fast but tend to assume homogeneous reservoirs and fixed fluid properties. This makes them unreliable under changing reservoir and operational conditions. Mechanistic models, grounded in first-principles physics, improve interpretability but typically require extensive calibration and high-fidelity input data, making them less practical for real-time applications. Both types of models struggle with complex flow behavior, particularly in unconventional plays. Accurately predicting pressure profiles along wells is essential for production optimization and well integrity analysis. The calculation is predicated on the accurate classification of flow patterns. However, empirical and mechanistic models often fail to capture complex flow patterns, such as slug, churn, and annular flow, that cause significant pressure fluctuations. These flow regimes introduce operational inefficiencies and risks that are difficult to model without sacrificing accuracy or computational feasibility. Probabilistic models, such as Monte Carlo simulations, Bayesian inference, and stochastic modeling, address uncertainty by generating distributions of possible outcomes rather than a single forecast. While useful for risk assessment, they often lack physical constraints, and their accuracy depends on well-defined input distributions and high-quality data. In applications like probabilistic reserve estimation, geological heterogeneity and parameter uncertainty make it difficult to validate input assumptions. As a result, probabilistic models alone are typically insufficient for robust forecasting and are often used in combination with empirical or physics-based methods. To overcome these limitations, this study proposes a unified machine learning framework that integrates feature engineering, dimensional analysis (DA), transfer learning (TL), and decline curve analysis (DCA). This hybrid approach enhances both flow pattern classification and hydrocarbon production ML forecasting while maintaining computational efficiency and physical consistency. Conventional mechanistic models often achieve less than 85% accuracy in predicting pressure profiles across different flow regimes. The study applied DA to derive three dimensionless predictors using the Buckingham Π Theorem. Embedding these predictors into the ML model ensures consistency with underlying physics and improves generalization. Among the evaluated models, the Extreme Gradient Boosted Random Forest (XGB-RF) achieved the highest accuracy of 93% (Kappa = 0.90), outperforming mechanistic models. This highlights the effectiveness of physics-guided feature engineering in refining ML predictions for two-phase flow pattern classification. For production forecasting in multi-fractured horizontal wells (MFHWs), traditional rate-based models often fail due to early-time data sparsity and operational variability. The Machine Learning Assisted – Decline Curve Analysis (MLA–DCA) framework addresses this by integrating DA, TL, and DCA. DA simplifies input complexity while preserving physical meaning. TL enables knowledge transfer from a pre-trained scalar model to a more adaptive vector-based ML model, optimizing both algorithm and hyperparameter selection. DCA complements the ML model for late-time forecasts, where data becomes increasingly sparse. The MLA–DCA framework achieves R² ≥ 0.80 in cumulative production forecasting, demonstrating its effectiveness in balancing accuracy, generalizability, and efficiency. By minimizing feature space, training requirements, and computation time, the framework is scalable for real-world deployment. Feature engineering ensures physical alignment, while hybrid modeling improves performance across diverse flow regimes, reservoir types, and operational conditions. This study presents a physics-informed, data-driven framework that unifies machine learning with engineering fundamentals. The proposed hybrid approach enhances flow pattern identification and production forecasting, advancing the role of ML in petroleum engineering decision-making. By ensuring that models are accurate, interpretable, and efficient, this work provides a robust solution for tackling complex challenges in petroleum engineering applications
Polycaprolactone-Based Composite for Bone Tissue Engineering in the Temporomandibular Joint Mandibular Condyle
No full textThe temporomandibular joint (TMJ) is a bilateral-complex articulation that connects the mandible to the skull, enabling essential functions such as chewing, speaking, and swallowing. TMJ disorders (TMDs) affect millions worldwide, leading to pain, dysfunction, and reduced quality of life. In severe cases involving advanced internal derangement, trauma, or osteoarthritis, surgical interventions, such as condylectomy, costochondral grafts, or total joint reconstruction may be required. These current solutions for chronic TMD management and etiology often lack patient specificity, exhibit poor long-term outcomes, and fail to adequately restore native joint function, leaving out growing patients or those with metal hypersensitivity, among others. To address these challenges, bioengineered mandibular condyle prostheses have emerged as a promising alternative, offering personalized solutions with bioresorbable materials to support tissue regeneration of the TMJ. This project aimed to develop a combinatory prosthesis composed of polycaprolactone (PCL), hydroxyapatite (HAp), and demineralized bone matrix (DBM), leveraging three-dimensional-fused deposition modeling (3D-FDM) techniques to optimize mechanical performance and degradation kinetics. Anatomical biomimicry and mechanical robustness were further enhanced using computer-aided design (CAD) and computed tomography (CT) data. Uniaxial mechanical testing demonstrated that increased bioactive material weight percentages and ball milling improved compressive properties. Under accelerated degradation conditions, PCL/DBM composites were more prone to bulk degradation compared to PCL/HAp composites, suggesting that HAp may be necessary for structural integrity. This indicates that a 50 wt% HAp composite may enhance mechanical performance and biological outcomes in vivo by increasing bioactive content, addressing the osteogenic limitations in our pilot study. For our future ovine animal model study, we incorporated a slurry of ground bone, bone marrow, and recombinant human bone morphogenetic protein-2 to promote controlled osteogenesis at the condyle-ramus interface. A novel 50 wt% HAp composite biomaterial, homogenized with ball milling and combined with an osteogenic cellular slurry, may further combat the limitations of existing end-stage devices by offering a patient-tailorable solution that integrates with the mandibular condyle, providing biomechanical stability and promoting bone regeneration. These advancements offer a significant step toward clinically translatable TMJ mandibular condyle replacements
TRANSFORMATIVE CONNECTIONS: EXAMINING SUPPLY CHAIN RELATIONSHIPS AND NETWORK STRUCTURE CHANGES IN TIMES OF CRISIS
No full textThis dissertation consists of two essays that conceptually and empirically investigate how supply chain relationships and network structures evolve in response to external challenges. The rapidly changing global landscape, characterized by geopolitical tensions and crises, highlights the need for a comprehensive approach to understanding supply chain management. The two essays explore distinct facets of supply chain management, employing different theoretical frameworks, research methods, and datasets. In Essay One, I focus on the challenges posed by uncertainty and asymmetric information in supply chain management. Drawing on Information Processing Theory (IPT), I conceptually and empirically disentangle these challenges and explore how firms implement bridging and buffering strategies to navigate them. A systematic literature review of supply chain response strategies reveals conflicting findings regarding the impacts of uncertainty and asymmetric information. Through two studies, using primary and secondary data, I provide cross-validated research findings that clarify the unique effects of these challenges on response strategies. This enriches the current understanding of how firms adapt to external disruptions and offers practical guidance for managing uncertainty and information asymmetry. While Essay One focuses on specific strategies for addressing external challenges, Essay Two shifts to a broader perspective, exploring the strategic objectives of supply chain management. In Essay Two, I utilize Panarchy Theory from ecology to examine how external shocks, such as geopolitical conflicts, influence the strategic objectives - efficiency and flexibility - in supply chain management. Leveraging data from the FactSet Revere Supply Chain Relationship and Compustat databases, I analyze the dynamics of supply chain networks, focusing on the interplay between potential and connectedness. By viewing supply chains through the lens of social-ecological systems and their adaptive cycles, I develop actionable strategies for building sustainable supply chains in an increasingly volatile and unpredictable global environment. Together, these essays provide a multifaceted perspective on supply chain management, offering theoretical advancements and practical strategies for navigating external challenges. By integrating insights from Information Processing Theory and Panarchy Theory, this dissertation contributes to the broader discourse on strategic decision-making, equipping firms to thrive in complex and uncertain environments
AMERICAN INDIAN MEN AND THE ROAD TO GAINING COLLEGE ACCESS
No full textThis study investigates American Indian men's stories of college access and rationales for pursuing college. In addition, this research study will investigate American Indian men and college access roads and the significance behind the access roads. This study is situated in qualitative research and utilizes Tribal Critical Race Theory (TribalCrit) as the theoretical framework to provide a lens for the American Indian men's stories in this study regarding college access. This research study is theoretically conceptualized in the Indigenous research paradigm and will be utilized as a supporting framework for TribalCrit. The methodological approach of Indigenous storywork will be utilized and intertwined with the eleven American Indian men's conversations about their college access stories
The Compositional Analysis of Shell-Filled Pits Found at Silver Glen Springs Locus A (8LA1W)
No full textIn this thesis, I hypothesize that the shell-filled pits of Silver Glen Springs Locus A(8LA1W), will have generalized characteristics that are shared over multiple cultural periods in the region. The pits are mechanisms for cultural perpetuation that modified the landscape through social practices. On a detailed level, the shell-fill material of the pits will differ between the pits due to changes over time in the environment and the methods used to harvest the Viviparus sp. snails. I argue that shell sites, particularly shell-filled pits, are multifaceted in function and integral to connecting a community to their environment and cultural past through place-making. To support this narrative, I analyzed material from shell-filled pits that span multiple cultural phases in early Middle Archaic (8990-8600 cal BP) to Episode II of the Mt. Taylor period (6350-5700 cal BP) and compared this information to other research that analyzed material from the early Middle Archaic (8900-7400 cal BP) and the later Orange period (4600- 3500 cal BP). More specifically, I examine the contents of excavated shell-filled pits from Locus A of Silver Glen Springs (8LA1W) in central Florida. I analyze and compare the composition of the features based on their material sizes, shell species composition, and measurements of the Viviparus sp. shells. These attributes were interpreted to understand pit function. The data collected from this research revealed patterns in the sizing of materials within the shell-filled pits of the features, as well as variations in the quantity of different mollusk species between features. Typologies were identified to explain the correlation between mollusk species and the cultural phases of the Mt. Taylor period. I conclude that the general material deposited into the pits of Silver Glen Springs remained consistent throughout its occupation from the early Middle Archaic (8990-8600 cal BP) to Episode II of the Mt. Taylor period (6350-5700 cal BP), indicating cultural continuity in pit use, resource utilization, and cultural practices
Life Cycle Assessment and Environmental Optimization of Alternative Aviation Fuels
No full textThe aviation sector is a significant and growing source of global greenhouse gas emissions (GHGs), accounting for about 2.5% of global CO₂ and up to 4% of total climate impact when non-CO₂ effects are included. In response to the urgency of decarbonizing this sector, this thesis conducts a comprehensive Life Cycle Assessment (LCA) of fourteen alternative aviation fuel pathways, using the GREET 2024 model to evaluate their emissions, energy use, and environmental trade-offs. The study covers a wide range of fuels, including Alcohol-to-Jet (ATJ), Sugar-to-Jet, Hydroprocessed Esters and Fatty Acids (HEFA) from various feedstocks (canola, corn, soy, palm), and hydrogen-based systems, including hydrogen for fuel cell applications. Emissions are assessed across Well-to-Pump (WTP), Pump-to-Wake (PTW) and Well-to-Wake (WTW) stages, for two aircraft classes: Single Aisle (SA) and Large Quad (LQ). In the Well-to-Pump (WTP) analysis, Alcohol-to-Jet fuel from cellulosic biomass emerged as the most sustainable option, with the lowest total CO₂ emissions (14.11 g), no land use change emissions, and the highest energy efficiency (3258 kJ), supported by renewable energy credits in the GREET 2024 model. In contrast, corn-based standalone SAF showed the highest WTP emissions (57.84 g CO₂), along with significant land use change impacts (9.01 g CO₂e) and elevated CH₄ and NOₓ emissions. Sugar-to-Jet (STJ) fuels offered intermediate performance, with the biological route showing lower total CO₂ (21.29 g) but higher total energy use (4537 kJ). The lignocellulosic forest residue pathway showed the lowest GHG-100 emissions (1.93 g CO₂e) and total energy input (1026 kJ), confirming that waste-derived fuels are environmentally preferable. These findings highlight that feedstock type and conversion method significantly affect upstream emissions, positioning cellulosic and residue-based pathways as leading candidates for decarbonizing aviation fuel production. The normalized results show that HEFA from corn oil has the lowest WTW emissions, with emissions (0.0914 gCO_2e/kg·km) for SA and emissions (0.1050 gCO_2e/kg·km) for LQ, reducing GHG emissions by more than 85% compared to conventional jet fuel. ATJ from cellulosic biomass also performs exceptionally well, reaching emission (0.1400 gCO_2e/kg·km) for SA and (0.1440 gCO_2e/kg·km) for LQ, reflecting over 81–85% emission reductions. Hydrogen as a fuel for fuel cells produce the lowest use-phase emissions, achieving near-zero emissions (0.015 gCO_2e/kg·km) for SA, though their total WTW emissions (0.2350–0.2520 gCO_2e/kg·km) depend on the hydrogen production method. Fuels based on coal and natural gas using Fischer-Tropsch synthesis show the highest normalized emissions, with WTW values of emission (0.9635 gCO_2e/kg·km) ` for SA and emission (1.0000 gCO_2e/kg·km) for LQ, making them the least sustainable options. Energy and water use were also analyzed. For instance, ATJ-Cellulosic had the lowest total energy demand (3258 kJ), mostly sourced from renewables, and the highest energy delivery efficiency, exceeding 99.6%. In contrast, STJ pathways consumed more energy (up to 4537 kJ), and soy-based HEFA required the most water (2358 cm³/MJ), due to irrigation. In conclusion, the study identifies waste-based and renewable fuels—especially ATJ-cellulosic, HEFA-corn oil, and hydrogen fuel cells—as the most promising solutions for reducing aviation emissions. While each fuel type has limitations in cost, infrastructure, or feedstock availability, these options offer the strongest potential for decarbonizing air travel and achieving long-term climate goals