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Blood perfusion through ventricular assist devices induces erythrocytes to interact with leukocytes
Get PDFFinancial 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
Get PDFRESUMEN 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
PREDICTION OF LENGTH OF STAY AMONG PREECLAMPTIC PATIENTS USING SUPERVISED LEARNING METHODS
Get PDFHypertensive disorders during pregnancy, particularly preeclampsia, are among the leading causes of maternal and neonatal mortality. In the United States, preeclampsia affects approximately 2 to 8% of pregnancies, with a higher incidence among African American women (6.04%) compared to Caucasian women (3.75%). Due to its severity, preeclampsia often requires intensive care unit (ICU) intervention, resulting in prolonged hospital stays. This study aims to predict the length of stay (LOS) for preeclamptic patients using supervised machine learning on a highly imbalanced dataset. We adopted two modeling approaches: classification and regression, and evaluated multiple algorithms, including logistic regression, decision tree, SVM, KNN, random forest, XGBoost, linear regression, and elastic net. To address class imbalance, we employed oversampling techniques (SMOTE, ADASYN, SMOGN) and cost sensitive learning strategies. Our findings show that cost sensitive logistic regression achieved the highest classification performance with AUC of 66% and G-mean of 60%. Additionally, the analysis revealed that African American women tend to have longer hospital stays. This research supports improved hospital resource allocation, staff planning, and early intervention for high risk cases, contributing to more efficient and equitable healthcare delivery
STREAMSCOPE: FIXED-MOUNT LASER SCANNING INSTRUMENTATION FOR REMOTE STREAM GAUGING IN SHALLOW RIVERS WITH FREQUENTLY CHANGING GEOMORPHOLOGIES
Get PDFAccurate streamflow monitoring in shallow rivers with frequently changing geomorphology poses a unique challenge, particularly in remote and ungauged locations. This thesis presents a novel approach developed in collaboration with the United States Geological Survey (USGS) to generate real-time discharge estimates using a low-power, non-contact sensor system. StreamScope integrates a Class II 620 nm laser rangefinder with an ultrasonic sensor to remotely measure river cross-sectional geometry and develop stage–area ratings. Through onboard automation, the system captures highresolution bathymetric data, enabling continuous discharge estimation without physical contact with the stream. Laboratory experiments were conducted to evaluate the selected laser rangefinder under varying conditions of solar radiation, water turbidity, water depth, bottom substrate, and sensor height. The results demonstrated that the unit operates best under low solar radiation, is capable of obtaining measurements under low turbidity up to 20 NTU, and that the laser’s returns may serve as a proxy for turbidity. Additionally, the tests established the operational limits of the sensor. The field deployment at Falls Creek in Davis, Oklahoma, demonstrated the robustness and precision of the instrument in a dynamic, real-world environment. The data collected provided a significant improvement in resolution compared to a traditional USGS stream survey
Variability, Multi-wavelength Properties, and Environments of Active Galactic Nuclei
Get PDFIn the early twentieth century, Albert Einstein predicted the existence of black holes with his theory of general relativity. Over the past century, astronomers discovered many evidences that prove Einstein right. One of the most extreme cases of black holes is the supermassive black holes (SMBHs) found at the centers of most galaxies, with masses ranging from millions to billions of solar masses. Some of them are known to be actively consuming surrounding gas and dust, emitting a tremendous amount of energy during this process. These are called active galactic nuclei (AGNs), and these are some of the most energetic phenomena in the universe. AGNs are fascinating objects to study, as they are related to a number of areas in astronomy such as galaxy evolution and black hole physics. One of prominent features of AGNs is variability; their brightness is always changing over time. Observations show that the AGN variability is stochastic, or a "red noise", which means that the brightness difference increases as a function of time lag. This relation follows an approximate power law relation. However, past a certain time lag threshold, the power law index shifts to a smaller value. This point is called the break frequency, and this parameter is known to be correlated with the SMBH mass, suggesting that variability is closely related to the fundamental nature of AGNs. To better understand the nature of AGNs, the AGN variability was analyzed in a few different ways in this dissertation. First, variability characteristics of a sample of galaxies, computed using the data from All-Sky Automated Survey for SuperNovae (ASAS-SN), were examined to distinguish AGNs from quiescent galaxies. By constraining the AGN fraction among galaxies, the timescale of AGN activities compared to galaxies’ age can be estimated. Then the multi-wavelength and environmental properties of the variability selected AGNs are analyzed. Second, the AGN break frequencies in optical power spectral densities (PSDs) are measured and compared to X-ray breaks. Current AGN models suggest that different regions around the AGN emit light at different wavelengths—shorter wavelength from near the central SMBH and longer wavelength from outer parts. If there is a correlation between optical and X-ray break frequencies, it can provide clues on the structure of AGNs and interactions between different regions. To achieve this goal, the optical break frequencies were measured using the data from ASAS-SN and Transiting Exoplanet Survey Satellite (TESS) on the sample of AGNs with X-ray break frequencies previously measured. With these methods, this study can contribute to the understanding of AGNs in both temporal and spatial terms. The variability selection method showed that approximately 3% of galaxies are AGNs, and about 80% of them are classified as low-luminosity AGNs (LLAGNs). Because a portion of luminous AGNs are excluded from the parent galaxy sample, the true AGN fraction may be greater. LLAGNs are a subcategory of AGNs that display signs of activity, but not very energetic. This dissertation shows that LLAGNs most likely reside in denser environments compared to luminous AGNs, suggesting they may be triggered by different mechanisms compared to luminous AGNs. This result provides clues on timescales of different stages and evolution of AGN activities. Upon multi-wavelength analysis, the variability selected AGNs are often not classified as AGNs by traditional methods, meaning that this method provides a useful tool for discovering a larger population of AGNs, especially in the era of big data, as this procedure is automated and versatile so it can be used to any sets of data. From the optical PSDs, a set of break frequencies is measured that is consistent with previous studies. In addition, a new set of break frequencies that is 1--2 orders of magnitude greater is also discovered. The high-frequency optical breaks displayed approximately 1-to-1 correlation with X-ray break frequencies, but with an offset in order of a few days. This can be interpreted as the X-ray variability driving the optical variability with a time delay, as it takes time for light to travel from one region to another. The size estimate from this time delay is consistent with measurements from previous studies, suggesting that this method introduces a new way to analyze the AGN structure
Film Blowing of Biobased Biodegradable Polyesters: Poly(Pentylene Adipate-co-Terephthalate) and Poly (DodecyleneFuranoate)
Get PDFFinancial support was provided by the University of Oklahoma Libraries' Open Access Fund.Poly(pentylene adipate-co-terephthalate) (PPeAT), poly (dodecylene furanoate) (PDDF), polybutylene adipate terephthalate(PBAT), and linear low-density polyethylene (LLDPE) films were prepared using film blowing. Machine direction (MD) andtransverse direction (TD) properties of the blown films (BFs) are compared to the properties of compression molded sheets (CMSs).For all polymers, Young’s modulus, stress at break elongation at break and storage modulus are higher for the CMSs vs. BFs. PPeATfilms have a higher modulus than PBAT films demonstrating its superior mechanical properties. X-ray scattering shows that CMSshave higher percent crystallinity, longer d-spacing, and larger crystal size compared with BFs. For BFs, SAXS measurements havemore intensity in the MD vs. the TD while the d-spacing is slightly higher for the MD; however, WAXS results indicate that films inthe TD have higher percent crystallinity than in the MD. CMSs exhibit lower oxygen permeability, carbon dioxide permeability, andnormalized water vapor transmission rates compared with BFs consistent with the higher crystallinity. Biobased, biodegradablePPeAT and PDDF show lower oxygen and carbon dioxide permeabilities compared with fossil fuel-based PBAT and LLDPE andlower water vapor transmission rates than PBAT. PPeAT and PDDF BFs show promising results and eventually might be used asa drop-in replacement for LLDPE in flexible film packaging.Ye
A Pedagogical Guide to and Analysis of Sixty Trombone Quartets
Get PDFThe staple of trombone chamber music is the trombone quartet; some authors have even described it as the trombone ensemble. However, no resources are solely dedicated to the ensemble’s repertoire, whether it be demographic information, piece descriptions, a standardized grading system, or a combination of these items. Many standard pedagogical texts merely list recommended quartets by title, composer, and publisher without providing any other details; authors also rarely assign a difficulty level to any of the works within their lists. Since instructors often claim the importance and value of chamber music, the works they select serve as both the textbook and curriculum in applied studio instruction. Given the limited information published on trombone quartet music, there remains a clear gap within low brass instructional resources available to teachers. The purpose of this study was to analyze and assign a difficulty (grade) level to sixty trombone quartets selected from a variety of sources written by trombone pedagogues (Denis Wick, Edward Kleinhammer, Buddy Baker, Marta Jean Hofacre, and Robin Gregory, etc.) and compile them into an annotated database. Each entry consists of three parts: (1) the work’s demographic information (composer, arranger, title, movements, duration, publisher, availability, range, clefs, mutes, etc.); (2) a piece description (i.e., a written commentary and analysis); and (3) an assigned difficulty level. As instructors continue looking for new repertoire, this pedagogical resource will assist instructors with making informed decisions, so that their selections can align with their curriculum and simultaneously challenge students while remaining accessible. The document also is accessible to students, so that they can be active in their own repertoire selection (via an online database)
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
MULTIMODAL BEARING FAULT CLASSIFICATION UNDER VARIABLE CONDITIONS: A PHYSICS-INFORMED 1D CNN WITH TRANSFER LEARNING
No full textBearings play an integral role in ensuring the reliability and efficiency of rotating machinery by reducing friction and handling critical loads. Bearing failures, which account for the majority of mechanical faults, highlight the imperative need for reliable condition monitoring and fault classification. This study proposes a multimodal bearing fault classification approach that leverages vibration and two motor phase current signals within a one-dimensional convolutional neural network (1D CNN) framework. The proposed method fuses feature from multiple signals to improve the accuracy of fault classification and systematically investigates the impact of regularization strategies—including L1, L2, and Dropout—on model performance and overfitting. The methodology is extensively evaluated on the Paderborn University (PU) Bearing Dataset under multiple operating conditions, allowing for a comprehensive analysis of the framework’s adaptability and effectiveness. Additionally, the framework is enriched by integrating a physics-informed branch utilizing vibration signal envelope spectra to extract characteristic fault frequencies, in particular, the ball pass frequency of the outer race (BPFO) and inner race (BPFI). Furthermore, a physics-informed loss function is incorporated to enforce physically consistent predictions, enabling the model to leverage domain knowledge for enhanced learning and interpretation. For adaptability across varying operational environments, three transfer learning strategies are introduced. The integration of domain knowledge through physics-informed features, in combination with transfer learning strategies, enables the framework to more accurately capture underlying faults and adapt to a range of operational conditions. Overall, this multimodal 1D CNN framework augmented with late fusion, advanced regularization, physics-informed element, and transfer learning strategies, lays a strong foundation for accurate, adaptable, and interpretable bearing fault classification. The proposed approach is robust to changes in operating conditions and offers practical benefits for industrial environments for reliable fault diagnosis and condition monitoring of critical machineries