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Cherokee compositions: the (re)discovery, restoration, and revival of the music of Jack Frederick Kilpatrick
No full textThis dissertation documents and analyzes the (re)discovery, restoration, and revival of the music of Jack Frederick Kilpatrick (1915-1967), a composer and intellectual who identified as Cherokee and who spent his career in Oklahoma, California, Washington DC, and Texas. Jack Frederick Kilpatrick was one of the first professional classical composers whose work was based on his intimate knowledge of Native American culture. His compositional output is made up of over 250 pieces, including 188 officially completed opus numbers. Jack Kilpatrick’s compositional style was influenced by his experience growing up in Stilwell, Oklahoma, and his immersion in Cherokee culture. Jack’s musical output mediates between Cherokee and Euro-American culture through songs that include both English and Cherokee text, librettos that describe Cherokee cultural practices, and compositions that translate traditional Cherokee performance practices including intonation, scales, and rhythms into the Western European tradition of song structures, orchestration, and musical notation. These pieces connect listeners to Cherokee life as told through Cherokee histories, performance traditions, language, and medicine.
When we found 31 boxes of Jack Kilpatrick’s personal manuscript collection and hand-written notes in the attic of Catlett Music Center at the University of Oklahoma in 2018, our discovery opened a window into the mediating intercultural and musical experiences of Jack and the Oklahoma Cherokee during the mid-20th century. In addition to providing the most comprehensive biography of Jack Frederick Kilpatrick available to date, my research is focused on four additional main areas: first, the archiving and collection building of the Jack Kilpatrick Collection at the University of Oklahoma’s Western History Collections; second, the transcription and repertoire construction of Kilpatrick’s musical works; third, my work creating critical editions and performance materials of selected Kilpatrick compositions; and finally, an ethnography of my first five years of efforts to ignite a revival of Kilpatrick’s music with Alexander Mickelthwate and the Oklahoma City Philharmonic. Throughout this document, I also self-examine my role as an ethnographer and researcher attempting to apply decolonizing methodologies at the intersectional crossroads of music, language, heritage, and anthropology, while also attempting to act as a translator, caretaker, and animateur for a music that is not mine to own. The core analytic concepts explored in this dissertation are focused on identity; archive building; the relationship of language and music in discourse; the entextualization and recontextualization process revealed by the relationship between manuscript scores, performance materials, and musical performance; the role of context in shaping performance and reception; and the structure and cultural role of music revivals
Application of deep learning to optimize computer-aided-detection and diagnosis of medical images
Get PDFThe field of medical imaging informatics has experienced significant advancements with the integration of artificial intelligence (AI), especially in tasks like detecting abnormalities in retinal fundus images. This dissertation focuses on four interrelated research contributions that address crucial aspects of AI in medical imaging, offering a comprehensive overview of various innovative approaches and methodologies. The first contribution involves developing a two-stage deep learning model. This model significantly improves the accuracy of identifying high-quality retinal fundus images by eliminating those with severe artifacts. It highlights the critical role of an optimal training dataset in enhancing the performance of deep learning models. The second contribution presents an innovative algorithm for synthetic data generation. This algorithm enhances the effectiveness of deep learning models in medical image analysis by augmenting datasets with synthesized annotated diseased regions onto disease-free images, leading to notable improvements in disease classification accuracy. The third contribution is centered around a novel joint deep-learning model for medical image segmentation and classification. Combining a U-net architecture with an image classification model it demonstrates substantial accuracy improvements as the training dataset size increases. Lastly, a comparative analysis is conducted between radionics-based and deep transfer learning-based Computer-Aided Detection (CAD) schemes for classifying breast lesions in digital mammograms. The findings reveal the superiority of deep transfer learning methods in achieving higher classification accuracy. Collectively, these contributions offer valuable insights and practical methodologies for enhancing the efficiency and diagnostic accuracy of AI applications in medical imaging, marking a significant step forward in this rapidly evolving field
Analysis of Polarimetric Radar Downburst Precursors Using Automated Storm Identification and Tracking
Get PDFStrong thunderstorm winds produced by downbursts pose a threat to life, property, and aviation, yet they remain challenging to predict in advance. Current operational understanding of radar-based downburst precursors includes a divergent or convergent velocity signature at the surface or mid-levels, respectively, a descending radar reflectivity (Z) core (DRC), and environmental characteristics (e.g., downdraft convective available potential energy (DCAPE), steep lapse rates) that are favorable for downburst generation. However, divergence signatures only occur once downbursts have reached the surface and may not be observed at distances far from the radar, and prior studies have shown that not every downburst is reliably associated with mid-level convergence or a DRC. Similarly, environmental parameters are useful when forecasting for a broad area, but not every thunderstorm in an environment with conditions favorable for downbursts will produce one.
Recent work has begun exploring whether polarimetric radar offers insight into additional downburst precursor signatures. Previous studies using polarimetric radar to analyze downburst-producing thunderstorms have observed signatures such as a descending specific differential phase (KDP) core and a trough of decreased differential reflectivity (ZDR) collocated with decreased co-polar correlation coefficient (rhv) extending below the melting layer. However, these studies either manually analyzed individual signatures or focused solely on case studies. This research expands on those studies by using the Multi-Cell Identification and Tracking (MCIT) algorithm to automate storm detection and analyze 41 downburst cases which span most regions of the contiguous United States. For each case, polarimetric radar variables, signatures, and derived products hypothesized to potentially be relevant to downburst formation, including Z, KDP, vertically integrated liquid (VIL), ZDR column depth, convergence, and divergence, are analyzed to find any consistent patterns leading up to downburst events. Geographic and environmental variability are investigated as well.
Individual case analysis revealed that a DRC appeared within a volume scan of storm report (SR) time in 88% of cases, and a KDP core was present and/or descending in 95% of cases, up to 15 to 30 minutes prior to the SR time in several cases. A ZDR trough was present in 98% of cases with a collocated rhv drop in 88% of cases within 10 minutes of the SR time for most cases, and the magnitude of low-level divergence and mid-level convergence reached a threshold of 0.0025 s^-1 in 85% and 76% of cases, respectively. Analysis of all cases together revealed that divergence, velocity, and differential velocity display the most prominent signals near the surface at or just after the divergence signature (DS) time; aloft, KDP at and 1 km below the freezing level, mid-level convergence, and VIL display the most prominent signals 5 minutes or more before the DS time. Analyses based on region and environmental favorability produced similar information, indicating that higher KDP and lower divergence, velocity, VIL, and ZDR column depth values were most common in eastern cases, as well as cases with WINDEX and 0–2-km LR less than 60 and 8 º km^-1, respectively. Conversely, higher divergence, velocity, VIL, and ZDR column depth and lower KDP values were more common in western cases, as well as cases with WINDEX and 0–2-km LR of greater than 60 and 8 º km^-1, respectively. These results aligned well with and confirmed findings from past studies
Flow Dependent Evaluation and Training of Random Forest based Probabilistic Forecasts of Severe Weather Hazards
Get PDFThere has been an increasing interest over the past ten years in the use of Machine Learning (ML) algorithms such as Random Forests (RF) in the context of severe weather prediction. RF-based methods have even been shown to outperform human-generated operational convective outlook guidance in some cases. However, there remain obstacles to fully integrating the ML algorithms into the operational forecasting process of severe wind, hail and tornado events. For example, the perceived black-box nature of complex RF models can inhibit forecaster confidence in the ML guidance for high impact or atypical events. Since the error characteristics of predictors based on numerical weather prediction, or NWP, and the relationships between these predictors and severe weather risk can vary in different flow patterns, there is a need to better understand the impacts of large-scale flow patterns on RF model performance. In addition to improving confidence in the RF-based forecast products, such understanding can also be incorporated into the model building process to further improve their performance.
This thesis discusses the development and evaluation of a flow-dependent approach to training RF models to produce severe weather convective outlook guidance. This work leverages 53 real-time cases from the 2019 and 2021 real time convection-allowing FV3-based ensemble forecasts produced by the University of Oklahoma (OU) Multi-scale data Assimilation and Predictability (MAP) Lab during the 2021 Hazardous Weather Testbed (HWT) forecasting experiments as model predictors. This study will focus mainly on the 29 cases from 2021. As a first step, the composite difference in large-scale flow between cases with relatively high and low importance of key predictors using Permutation Feature Importance were calculated. These composite differences were used to evaluate if discernible large scale flow patterns could be when the non-flow dependent model would perform the best. Two different methods of classifying cases based on the large-scale flow patterns are then evaluated for the purpose of training separate RF models on cases of similar flow patterns. The appropriate RF model for the pattern is then used to generating convective outlook guidance for a forecast case not included in the RF model training. First, the CAPE/shear parameter space over the region of interest is used as a classification metric. Second, EOF patterns that are qualitatively similar to the previously described composite flow patterns related to predictor importance and used as the classification metric. Finally, both methods will check how sensitive the performance is to changes in sample size by adding the 2019 cases. Both flow-dependent training methods will be compared to the non-flow dependent models and compared to each other. Results will emphasize both objective impacts on forecast skill and physical explanations of the difference in performance among the RF training approaches. Results show that both methods of flow-dependent training initially show improvement in forecasting severe weather compared to the non-flow dependent model. However, the parameter space classification remain to show significant skill with an increase in sample size while not all EOF patterns skill remained significant
End-Region Evaluation of Hybrid Ultra-High-Performance Concrete/Conventional Concrete Prestressed Girders
No full textIn prestressed concrete girders, the end-regions are often susceptible to high internal forces induced by prestressing, resulting in crack propagation. Resisting these forces necessitates a substantial amount of reinforcing steel, leading to complicated design and congested formwork. Despite this reinforcement, cracking due to prestressing still frequently occurs. Moreover, girder end-regions are susceptible to corrosive environments, posing a significant concern due to their correlation with sudden and catastrophic failure types.
Ultra-High-Performance Concrete (UHPC) exhibits exceptional mechanical strength and durability, making it a promising material for addressing issues in prestressed applications. However, UHPC is a much higher-priced material than conventional concrete, making monolithic UHPC construction substantially more expensive. An alternative approach involves implementing UHPC selectively in sections of a girder, creating a composite member rather than a fully monolithic one, offering a more cost-efficient solution for prestressed concrete girders. This concept, defined as a hybrid girder, encompasses conventional prestressed concrete girder design for most of the member, except for the end-regions, where UHPC replaces both the conventional concrete and internal reinforcement. The primary concerns surrounding this concept relate to ensuring efficient construction and maintaining continuity between the different concrete types, facilitating homogenous behavior. This research investigated various interface designs for achieving adequate stress transfer in hybrid girder design while also having evaluated the constructability and load-response of these hybrid girders. The research project is comprised of two main components: a small-scale program that characterized seven different interface methods and a large-scale hybrid girder testing program that used the four top-performing interfaces.
The findings of this study indicated that the hybrid girders concept is plausible for construction, interface continuity, and predictability of load-response. The performance of the interface depends on texture shape, size, pattern, and casting method. Simultaneously cast concretes with a similar rheology and trapezoidal-shaped cold-joint textures demonstrated enhanced performance. The shear testing of hybrid girders consistently exhibited web-crushing failure. An assessment of the hybrid girder testing considered existing shear calculation methodologies and revealed that accurate capacity estimation can be achieved using existing approaches. Empirical equations from the ACI 318 Detailed Method and strut-and-tie modeling with prescribed amendments provide precise lower and upper bounds for hybrid girder capacity, regardless of the selected interface design. Overall, the concept can offer a cost-effective solution, combining the strength and durability of UHPC in critical end-regions. The research underscored the practicality of this concept and offers a design procedure for hybrid girders
Use of social media for sourcing and verification: Journalistic practices in Pakistan
Get PDFThe adoption of social media by journalists is in transitional phase, but the dependency on it for sourcing can have significant implications for the profession. With more reliance on social media for sourcing, journalists could end up handing over the power of agenda-setting to the sources. In addition, use of information from social media poses a huge challenge of its verification. This study examines the use of social media for sourcing and verification by Pakistani journalists. By using hierarchy of influences model, this study attempted to find the best predictors of use social media for sourcing. This study also explored different verification methods being adopted by Pakistani journalists to verify the content they find on social media platforms. The study found that journalists in Pakistan heavily rely on social media for sourcing and they also use social media for verification purposes. Twitter is being used for both sourcing and verification more than any other social media platform. Routines were the best predictors for using Twitter for sourcing purposes. The presence of organizational policy and reward system were the best predictors of using Facebook and Instagram for sourcing. This study also found that journalists use social media for purposes that represent information subsidy, which can affect the power relation between sources and journalists. For verification of social media content, journalists remain within the social networking sites and go outside using traditional methods and digital tools to authenticate the information. Positive attitude towards social media and trust contributed towards its use for sourcing and verification
“We’re out in the deep,” but “This is the life. We should fight for the life.”: A Study of L2 International Students In Their First University Semester During the Coronavirus Pandemic
Get PDFThis study sought to explore L2 international students’ perspectives about what challenges and accomplishments impact their academic acculturation process and success while studying in a bridge program during their first semester of university amidst the coronavirus pandemic. The findings showed that pandemic safety protocols, study habits, language proficiency, religious practices and racism were challenges. In many instances, students found workarounds to accomplish their academic goals. These findings were interpreted using Ryan and Deci’s (2000) basic psychological needs (BPNs) of competence, autonomy, and relatedness as explained in their Self-Determination Theory. This study also combined denotative and connotative concepts of academic success (York et al., 2015; Cachia et al., 2018) for one collective understanding of academic success. Students’ behaviors indicated that they fulfilled their BPNs to meet the definition of academic success. Support networks also contributed to successful academic and social learning environments
Water use conflicts in the Lower Pecos, Permian Basin: A spatio-temporal analysis of unconventional oil and gas development and agriculture under drought conditions
Get PDFUnconventional oil and gas (UOG) production is increasing rapidly within the U.S., especially the practice of hydraulic fracturing (HF). HF requires large quantities of water, which raises concerns about water scarcity, particularly in arid or semi-arid regions where freshwater is scarce. One of the most productive UOG formations is the Permian Basin, which extends from West Texas into East New Mexico. In this region, the energy sector and the agricultural sector compete for the limited hydrological resources as farmers rely heavily on irrigation using dwindling groundwater supplies or water from the over-allocated Rio Grande River. To make this worse, this area is prone to drought and experienced a megadrought from 2006 to 2015.
This study examines the relationship between UOG production, agriculture, and drought within the Lower Pecos region of the Permian Basin from 2008 to 2021. Previous studies suggest the presence of UOG wells corresponds with an increase in fallow agricultural land, but no such studies have been conducted in a semi-arid region of the U.S. This study uses data from the U.S. Department of Agriculture Cropland Data Layer to identify agricultural areas and their frequency of fallowing agricultural land during the 14-year period. I use point data created from FracFocus data identifying the locations of UOG wells in Texas and New Mexico to examine whether the land immediately surrounding the wells are fallow more often than the land not located near UOG wells. I also use data from the U.S. Drought Monitor to determine if drought impacts this relationship. I hypothesized that agricultural areas immediately surrounding UOG wells will have higher rates of fallowing since water that would normally be used for agriculture is transferred to HF. When under drought conditions, I expected there to be widespread fallowing throughout the study area. The results of this study do not support this hypothesis, and instead found that the fallowing trends throughout the basin do not seem to correspond to either drought conditions or UOG intensity. Overall fallowing trends indicate that until 2018, there were more fallow cells than cropland cells throughout the basin, but the number of fallow cells dropped dramatically in 2017 and after 2018 through 2021, there were more active cropland cells than fallow/idle cropland. UOG intensity increases throughout the basin during the study period, both in the number of wells constructed and the amount of water used per well. This study concludes that while the hypothesis is not supported, there are possibly other factors that impact agricultural and UOG water use. For example, it is possible that farmers switched to less water-intensive crops during drought periods or when new wells were being fractured. It is also possible that HF wells import their water from other sources and are therefore not reliant on available surface or groundwater in the basin. The lack of transparency in UOG wells reporting was the main study limitation. Operators are not required to disclose from the source of the water used. Future research should prioritize verifying UOG wells data by using a combination of sources. This would allow a better understanding of water used across sectors in the Permian Basin
Ordering Wonder: Collections, Curators, and Showmen in Antebellum America
No full textThis dissertation explores the development and execution of five museum collections established between the 1793 opening of Charles Willson Peale’s museum in Philadelphia and the opening of the Smithsonian Institution as America’s National Museum in 1855. The Smithsonian Institution and, later, the American Museum of Natural History and the Metropolitan Museum of Art seem to many like the beginning of "real" museums in America, but what this research demonstrates is that they are the result of decades of trial and error by many previous, equally real, museums. Through generations of changes not only of the nation but of the people and culture of that nation, museums’ content and displays reflected changes in government, cultural shifts, and new populations with leisure time and extra money to spend. They at once reveal something uniquely American in content while tying themselves to existing European models, providing excellent avenues to study the early exercise of American science with international comparisons to older, more established institutions, professionals, and audiences
Is there a relationship between racial composition of school teaching staff and student engagement, student trust in teachers, and student achievement? an examination of schools in an urban district
Get PDFAchievement gaps, which are disparities in academic performance and educational attainment based on racial and socioeconomic factors, have continued to get worse. Various causes for these gaps are poverty, ineffective teaching, unequal educational opportunities, and biased testing. The “Coleman Report" revealed a significant achievement gap between African-American and White students and suggested that racially and socioeconomically diverse schools could be a solution. There is growing awareness of the benefits of teacher diversity and having same-race teachers, particularly for students of color. Yet, most of the existing research has primarily focused on the micro-level of this phenomenon, examining dyadic matches (and mismatches) between the race of teacher and student and their relationship to a host of student outcomes. The purpose of this study was to examine the associations of the diversity of a teaching corps within a school to overall school-level student engagement, student trust, and student achievement in a high-poverty district with over 70 schools, 2,000 teachers, and 30,000 students. The interplay between racial composition of school teaching staff and student body racial composition was examined and, given the limited findings, also reported the results of more specific teacher/student racial alignment data at the school level, with a particular focus on black-black racial alignment indices and their relationship to student outcomes. Overall findings demonstrate limited evidence of effects with respect to racial composition of school teaching staff, student trust in teachers, and achievement. However, a positive effect was found for racial composition of school teaching staff and student engagement. All examined relationships were not found to be moderated by student body racial composition. However, deeper analysis revealed that Black teacher/student alignment index, measured as the product of the proportion of Black teachers in a school and the proportion of Black students was positively related to reading and math achievement and these effects were large