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Blood perfusion through ventricular assist devices induces erythrocytes to interact with leukocytes
Full text linkFinancial support was provided by the University of Oklahoma Libraries' Open Access Fund.Implantations of Ventricular Assist Devices (VADs) have significantly improved quality of life and life expectancy of end-stage heart failure patients. However, despite the advancements in the VAD designs and patient management protocols, the VAD recipients remain at risk of attaining bleeding, infection, pump thrombosis, and stroke. Although blood trauma has been suggested as a critical factor in development of these adverse events, its consequences in inducing interactions between different types of blood cells are largely unknown.
Following our recent findings on erythrocyte and leukocyte trauma in VAD recipients, we decided to explore interactions between erythrocytes and leukocytes by perfusing human whole blood through two different types of VADs, HeartMate II (HMII) and HeartMate 3 (HM3), concurrently in their respective Blood Circulatory Loop (BCL). By using a flow cytometry assay, we found increasing association between erythrocytes and leukocytes as VADs propelled blood through their BCLs. This time-dependent intercellular association was shown by increasing concomitant events of stained CD235a (specifically expressed on erythrocytes) and stained CD45 (specifically expressed on leukocytes) in the CD235a+ population. Compared to CentriMag (CM), which served as a control, VADs produced significantly higher concomitant signals. The findings described in this study have opened the need for further studies on a novel path for generation of adverse events that are commonly observed in VAD recipients, notably infection, pump thrombosis, and ischemic stroke.Ye
EL DISCURSO AFROCÉNTRICO COMO MANIFESTACIÓN DE RESISTENCIA CONTRAHEGEMÓNICA EN LA LITERATURA NEOCOLONIAL CUBANA (1930-1950): ALEJO CARPENTIER, LYDIA CABRERA, RÓMULO LACHATAÑARÉ
Full text linkRESUMEN Esta tesis doctoral examina el uso del discurso afrocéntrico en la narrativa cubana entre 1930 y 1950, interpretándolo como manifestación de insurgencia simbólica frente a las influencias culturales del neocolonialismo, así como una vía de integración y revaloración del folclor afrocubano al imaginario nacional. Desde un enfoque interdisciplinario que combina los estudios literarios con la perspectiva etnográfica, se analizan tres obras claves del afrocubanismo literario: ¡Écue-Yamba-Ó! (1933), El Monte (1954) y ¡Oh, mío Yemayá! (1938). En géneros diversos —novela, ensayo y cuento—, Carpentier, Cabrera y Lachatañeré recurren a la mitología yoruba, la oralidad y las prácticas mágico-religiosas afrocubanas para subvertir el discurso elitista neocolonial. La investigación sostiene que estos textos constituyen archivos de resistencia cultural que resignifican y legitimizan lo afrocubano y orientan hacia una reescritura inclusiva de la nación marcando, de esa manera, un punto de inflexión en la literatura cubana
Designing for Resilience: Integrating Flood Mitigation, Multi-Hazard Preparedness, and Social Justice in Community Decision Support
No full textThis dissertation confronts the dual crises of escalating multi-hazard risk and the systemic injustice that dictates the distribution of disaster impacts across communities in the United States. Traditional resilience planning, often focused on single hazards and aggregate economic efficiency, is increasingly insufficient and can inadvertently perpetuate the social vulnerabilities it should mitigate. This research argues for and develops a new paradigm of justice-centered resilience planning, powered by advanced optimization frameworks designed to navigate the complex, multi-objective, and multi-stakeholder realities of disaster management. To address this challenge, this dissertation develops and validates several interconnected, state-of-the-art analytical frameworks. First, a multi-objective optimization model is introduced that operationalizes distributive fairness as a primary objective using the decomposable Theil index. This allows for the explicit, quantitative analysis of trade-offs between minimizing economic losses, population dislocation, repair times, and ensuring the just allocation of resources. Second, a bi-level optimization structure is formulated to capture the hierarchical leader-follower dynamic between public policymakers and private homeowners, enabling the design of realistic, incentive-compatible mitigation policies. Third, a two-stage stochastic programming model formally links pre-disaster mitigation investments (first-stage) with post-disaster evacuation logistics (second-stage) to derive robust strategies that perform well under deep uncertainty about future hazard scenarios. Finally, a hybrid approach integrating machine learning is developed to serve as a computationally efficient surrogate for complex optimization models, bridging the gap between predictive and prescriptive analytics to enable scalable, building-level decision support. These frameworks are rigorously applied and validated through comprehensive case studies of two high-risk, socio-economically diverse communities: Lumberton, North Carolina (recurrent flooding) and Seaside, Oregon (compound earthquake-tsunami). The results quantitatively demonstrate significant, non-linear trade-offs between achieving mitigation efficiency and ensuring social justice, identifying critical budget thresholds where community-level investments yield the most significant societal benefit. The models successfully identify optimal, adaptive strategies that significantly reduce projected economic and social impacts while maintaining the fair distribution of resources across vulnerable demographic groups. The primary contribution of this dissertation is a holistic, operational, and data-driven analytical framework that provides decision-makers with the tools to design and implement more effective and just resilience strategies. By explicitly connecting technical efficiency with social responsibility, this work offers a new foundation for forging communities that are not only more robust in the face of natural hazards but also more just and sustainable for all their residents
The Role of Culturally Responsive Practices in Family Engagement and Child Social-Emotional Outcomes in Early Head Start Settings
No full textEarly childhood classrooms have seen a dramatic rise and demographic changes in ethnically, culturally, and linguistically diverse populations. In Early Head Start (EHS) center-based classrooms, at least 67% of EHS families are identified as culturally diverse (Office of Head Start, 2018). It is imperative that EHS teachers understand how to provide developmentally appropriate and culturally responsive and relevant practices for children and their families. Although research regarding culturally responsive practices (CRP) is sparse, this study will examine how CRP implementation influences family engagement practices and child outcomes, specifically focusing on social and emotional outcomes, in EHS infant and toddler classrooms. The analysis of the literature suggests that most research focused on CRP implementation relied heavily on teachers’ self-reporting. However, this study is significant because teachers’ and parents’ perceptions about CRP implementation, family engagement, and child outcomes were explored and analyzed
Minutes of a Regular Meeting, The University of Oklahoma Board of Regents, Monday and Tuesday, March 10-11, 2025
Full text linkBrewing national identity: coffee consumption as a marker of cultural identity
Full text linkJust 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