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The Perceptions of Latina Pre-Engineering Students in Rural Oklahoma CareerTech
Get PDFThis qualitative study, Perceptions of Latina Pre-Engineering Students in Rural Oklahoma CareerTech, examined the Latina perspectives of STEM education, educational decisions, oppressive systems, and alternate pathways. The purpose of this study was to understand two specific perceptions of rural Oklahoma Latina students: what guided their educational program choice and what relationships they perceived between oppressive systems, their identity, and STEM. The study aimed to develop an understanding of Latina students' educational experiences and their influence on the complex intersectionality of the underrepresentation of Latinas in engineering career fields. This narrative case study utilized the theoretical framework of Critical Race Theory (CRT) and Latino Critical Race Theory (LatCrit) to center Latina voices through the counter-narratives of the participants. Employing thematic narrative analysis, common themes revealed critical findings, including a creative childhood that dismantled gender barriers, inspirational educators, influential mothers, and oppressive experiences. The implications of these findings within the context of education include the need to continue sharing these powerful counter-stories and systemic change, the importance for students to build using tools in primary grades, and the promotion of CareerTech education as an alternate pathway for STEM education and careers. Additional potential research areas include urban settings, expanding the participants to include other Women of Color, and exploring other CareerTech systems across the United States. This research aimed to contribute to the broader conversation regarding the lack of representation of women in STEM education and career fields and to aid in the resolution by sharing the historically marginalized voices
THE PARADOX OF ‘BLACK TEACHERS FOR BLACK STUDENTS’: A CRITICAL NARRATIVE CASE STUDY OF BLACK TEACHERS' EXPERIENCES AND PERCEPTIONS OF THEIR TEACHER PREPARATION WITHIN PREDOMINANTLY WHITE INSTITUTIONS
Get PDFAs a result of Brown v. Board of Education of Topeka Kansas, a shortage of African American teachers has plagued American schools. A call for the integration of schools, while making great educational strides for Black students, caused a lack of representation from Black teachers and school leaders. Black educators were less likely to be hired in the schools that Black children were now free to attend. Participants are chosen to participate in this qualitative case study based on the criteria of being a Black/African American teacher in any stage of their teaching career. This study explores the experiences and perceptions of Black/African American educators in their predominantly white teacher education programs and what has encouraged them to teach in predominantly Black/African American schools. Ultimately, this study determined that there are connections between teacher preparation and where Teachers of Color teach, and there is a paradox of Black teachers for Black students
Minutes of a Regular Meeting, The University of Oklahoma Board of Regents, Tuesday and Wednesday, November 12–13, 2024
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Unleashing the Potential of Advanced Diversion Techniques: Integrating Simulation and Real Case Study to Enhance Productivity in Limited Entry Slotted Liner Horizontal Completions within Tight Carbonate Reservoirs
Get PDFARX is a common carbonate reservoir located in Egypt deserts. It is described as a Dolomite reservoir with tight formation, strong lateral continuity, and naturally fractures with low connectivity. The main challenges to maintaining economically viable well productivity are the heterogeneity and tightness.
The large interest in developing such low permeability reservoirs has been a direct result of the favorable economics achieved by the advancements in horizontal well drilling and stimulation technologies hold great promise to increase production by dramatically increasing the contact area with the producing interval, maximizing the drainage volume around a well and link those natural fractures network
Acid stimulation is a common technique used in oil and gas reservoirs to enhance the productivity of reservoirs. However, when horizontal wells completed with slotted liners are involved, unique challenges can arise. Horizontal wells have gained popularity due to their ability to access a larger reservoir area and increase production. Slotted liners, which consist of perforations along the wellbore, are commonly used in these wells to prevent sand intrusion. However, obstructions such as production logs, debris, or scale buildup can hinder the successful deployment of coiled tubing and acid stimulation tools.
This study investigates the challenges faced during acid stimulation of a horizontal well completed with a slotted liner, specifically focusing on a section where coiled tubing became stuck, spanning approximately 2000 ft horizontally. The study integrates software simulation of acid stimulation operations with actual well data and post operation production data to address the utilization of chemical diverter for delivering the acid stimulation to large areas that are inaccessible by coiled tubing due to obstructions in the horizontal section.N
Real Time Identification of Geological Factuals by Integrating Formation Micro Resistivity Imaging Logs With Computer Vision.
Get PDFReservoir characterization is pivotal for the success of oil and gas exploration, where sedimentary features significantly influence petrophysical properties and fluid flow behavior. This paper emphasizes the importance of accurately identifying and delineating these features, including bedding, cross-bedding, faulting, and fractures, to enhance reservoir characterizatrion and efficiency. Using Formation Micro Resistivity Imaging (FMI) log as a powerful tool, this research focuses on leveraging Computer Vision (CV) and Deep Learning (DL) methodologies for the automatic analysis of FMI log.
The interpretation of FMI logs holds importance in reservoir characterization, modeling, natural fracture analysis, well completion design, and stress direction identification. Real-time interpretation is crucial for geo-steering and wellbore stability assessment. This study aims to leverage the power of CV and DL to provide accurate and real-time FMI interpretations, supporting daily drilling and completion operations
The extensive datasets from FMI logs were processed, segmented, and clustered to identify various geological features. The CV model was trained on diverse geologic attributes, such as partially open fractures, Planner Laminated Siltstone, Massive Pyritic Mudstone, Laminated Arg. Sandstone facies, Calcareous Fossiliferous Mudstone facies, Low Angle Cross bedding, High Angle Cross bedding, Vuggy Dolomite, Fractured Dolomite, nodular limestone, glided mudstone, and other features. Subsequently, this model was deployed in real-time operations to interpret newly recorded FMI logs, validating its accuracy alongside expert interpretations.
During the model development, the optimum learning rate was found to be approximately 0.0052, successfully achieving the target. Through a carefully optimized training process, the model achieves an impressive overall accuracy of 92% in classifying over 50 geological features. Detailed insights into the model's performance are provided through the analysis of the confusion matrix and classification report. Further validating its robustness, the model is tested on a set of 100 unique images not included in the training set, showcasing a generalization capability with an accuracy exceeding 86%.
The CV model was evaluated with different metrics. The accuracy, precision, recall (Sensitivity or True Positive Rate), F1 score, and AUC-ROC (Area under the Receiver Operating Characteristic curve) of the model showed exceptional reliability of the model. Exceptional reliability was demonstrated, affirming its role in oilfield digital transformation. The model facilitated prompt wellbore stability and completion decisions, enabling the timely request of special needed logs without rig nonproductive time (NPT).
This research demonstrates the integration of advanced technologies in the analysis of FMI logs, offering a pathway to enhanced reservoir characterization.N
Eyewitness age and confidence determining outcomes for juror decision-making in robbery cases
Get PDFEyewitness performance has been shown to have a wide variety of inaccuracies. Jurors often view eyewitness testimony as an accurate recollection due to the tendency to rely on automatic decision-making processes. Jurors’ perceptions of the age and confidence of eyewitnesses may be moderated by jurors’ heuristics or stereotypes. Proper judicial instructions may become critical to mitigate these influences on verdict decisions by engaging effortful thinking. This study aimed to examine the impact judicial instructions have on reducing the reliance on eyewitness characteristics for verdict decision-making. It was hypothesized that simple instructions would reduce the reliance on age and confidence to determine guilty verdicts. Fifty-eight participants were randomly assigned to a simple instruction (n = 29) or standard instruction (n = 29) condition. Participants were presented with four cases of highly confident and less confident child and older adult eyewitnesses. Results from a 2 × 2 × 2 mixed analysis of variance (ANOVA) indicated there was a significant main effect of confidence F(1,56) = 11.28, p = .001 for both the standard instruction condition F(1,56) = 68.17, p < .001 and simple instruction condition F(1,56) = 12.30 p < .001. Jurors’ reliance on confidence heuristics was demonstrated in the standard instructions while the simple instruction mitigated the reliance on confidence. This finding implies that simple instructions engage effortful thinking which allows for the reduction of the confidence heuristic. Future research should focus on replicating the findings with a larger sample size to determine if simple instructions aid in reducing age biases
Exploring Factors Related to Well-Being Among Incarcerated Women: Hope, Shame, and Adverse Childhood Experiences
No full textIncarceration rates among females have significantly increased in the United States over the past 40 years. Studies have documented how trauma, economic vulnerability, and mental illness may amplify psychological distress for women who are incarcerated. However, little research exists on the relationships between hope, shame, adverse childhood experiences (ACEs), and well-being among incarcerated women. To explore the relationship of these variables with well-being, data from the Oklahoma Department of Corrections was analyzed. A strong positive correlation between hope and flourishing was found with hope serving as the stronger predictor of hope within the model. Shame was found to have a moderate negative correlation with flourishing. The model was statistically significant and accounted for 41.7% of the variance in flourishing. Overall, this study contributes to the current body of research emphasizing the importance of hope in overall flourishing with shame serving as a potential barrier to well-being
THREE ESSAYS ON EXCHANGE RATES, STRUCTURAL CHANGE, AND INVESTMENT
No full textThis dissertation comprises three essays, each exploring distinct aspects of exchange rates and their economic implications.The first chapter investigates the impact of exchange rate misalignment on structural transformation, broadly defined as the process of reallocating labor from low- to high-productivity sectors, which serves as a mechanism for economic growth. Utilizing data from the GGDC 10-sector and ETD 12-sector databases, as well as UNIDO's 23 manufacturing sectors (classified into three technology-based categories), we explore whether exchange rate misalignment indices contribute to structural change and, consequently, economic growth. Our findings indicate that undervaluation does not appear to play a critical role in promoting growth-enhancing structural change. The results are inconsistent across various misalignment estimation techniques and different model specifications. The second chapter examines the impact of changes in industry-specific effective real exchange rates (IERER) on industry-level investment. We analyze how variations in industry-specific export intensity and import competition influence the long-run relationship between exchange rates and investment. Our analysis is based on data from 12 manufacturing industries across five European countries—Germany, Italy, the Netherlands, Norway, and the United Kingdom—covering the period from 2007 to 2017. The results indicate that in industries with low export intensity, a depreciation in IERER (i.e. an increase in price competitiveness) leads to a decline in investment. However, for industries with high export intensity, we find no significant effect from IERER depreciation, suggesting that industries heavily engaged in exporting do not reduce their investment levels following a depreciation in IERER. Additionally, we find that variations in import competition do not substantially alter the impact of exchange rate fluctuations on investment decisions. Finally, the third chapter revisits the issue regarding the uncovered interest rate parity (UIP) puzzle and the associated excess average returns of currency carry trade strategies. The UIP puzzle, which posits that interest rate differentials should be balanced by exchange rate movements, is contradicted by empirical evidence. This discrepancy has led to the widespread adoption of currency carry trade strategies in financial markets. Our investigation focuses on identifying key variables that contribute to UIP violations. We propose a novel regime-switching model with time-varying transition probabilities, integrating variable selection using the horseshoe prior to choose relevant factors from eight macroeconomic variables. Our findings reveal three major predictors—TED spread, term spread, and consumption growth of the U.S. economy—as significant predictors affecting regime shifts within the UIP puzzle
ADDITIVE MANUFACTURING AND DESIGN OF MECHANICALLY TAILOR-MADE HYBRID COMPOSITES WITH ARTIFICIAL INTELLIGENCE
No full textHybrid composites are composed of multiple fillers incorporated into a single matrix. With enhanced and unique material properties, hybrid composites are suited for specific applications in material science and mechanical engineering, particularly in aerospace, automotive, and sports equipment. Hybrid composites with various fillers of different geometries and properties are multifunctional with complex microstructures that affect mechanical performance. Designing materials with specific characteristics is challenging due to the numerous microstructural factors, such as filler geometries and distributions, as well as the complexity of analyzing these structures. Traditional design methods rely on trial and error which are time-consuming and computationally intensive requiring extensive human intervention for feature extraction and descriptors selection. Recent advancements in materials science leverage artificial intelligence (AI) including deep learning (DL) to tailor microstructure designs for desired mechanical traits and discovery of novel materials through large-scale databases. The main goal of this dissertation is to develop an AI-driven framework for designing tailor-made hybrid composite microstructures with desired mechanical behaviors. By integrating AI with finite element analysis (FEA), additive manufacturing (AM), and experimental validation, the framework aims to establish a comprehensive understanding of structure-property (S-P) and inverse S-P relationships in hybrid composites. Firstly, the study aimed to characterize S-P relationships by leveraging a conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) approach to investigate the mechanical behaviors of 3D printed lightweight hybrid composites. Then, inverse microstructural designs for hybrid composite materials were developed to achieve tailor-made full-range stress-strain relationships by utilizing FEA and DL techniques. Lastly, inverse S-P relationships were formulated by integrating a domain-specific constraint model which explained the design of tailor-made materials using fundamental knowledge of material design. Custom-designed lightweight materials with AI-driven models will enhance the understanding of S-P relationships in hybrid composites leading to reduced design time as well as computational resources