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The Effects of Acute Nutrient Intake on Raw Segmental Bioelectrical Impedance Values and Fluid Shifts in Premenopausal Women
No full textBackground: Raw bioimpedance measures—impedance (Z), resistance (R), reactance (Xc), and phase angle (PhA)—are emerging tools to assess hydration, cellular integrity, and muscle quality. While multifrequency bioimpedance analysis (MF-BIA) can detect acute physiological shifts, its sensitivity to nutrient intake, especially across hormonal phases in women, remains underexplored. Purpose: This study investigated how essential amino acids (EAA), carbohydrates (CHO), and water (CON) influence postprandial segmental bioimpedance and fluid distribution in healthy women, and whether these responses vary by menstrual hormone phase. Methods: Twenty-two recreationally active women (18–40 years) underwent serial MF-BIA (InBody 970) assessments during high- and low-hormone phases, randomized to one of three interventions: EAA (15 g), CHO (75 g), or water. Bioimpedance (Z, R, Xc, PhA) and fluid measures (total body water [TBW], intracellular water [ICW], extracellular water [ECW]) were collected at baseline and every 30 minutes for three hours postprandially. Results: CHO intake consistently elicited greater increases in Xc and PhA across multiple body segments, particularly arms and legs—compared to EAA and CON (p\u3c 0.05), with a general trend of CHO \u3e EAA \u3e CON. These increases suggest enhanced cellular membrane activity and conductivity following carbohydrate ingestion. Surprisingly, fluid estimates decreased postprandially across all groups (CON \u3e EAA \u3e CHO), contradicting expectations of nutrient-driven intracellular water uptake. Notably, impedance and resistance increased over time, suggesting possible shifts in tissue conductivity unrelated to actual fluid loss. No significant differences were observed between hormone phases (p\u3e0.05), though collapsed hormonal profiles may have masked potential effects. Conclusion: Carbohydrate intake induces significant changes in segmental bioimpedance markers of cellular function, independent of menstrual hormone phase. However, discrepancies between bioimpedance and fluid outcomes highlight potential measurement artifacts, underscoring the need for caution when interpreting MF-BIA responses to nutrient intake. Future studies should pair bioimpedance with gold-standard fluid assessment methods (e.g., isotope tracers) and analyze hormonal profiles separately to clarify nutrient-specific effects on fluid dynamics. These findings have implications for clinical hydration monitoring and performance nutrition but emphasize the need for refined methodologies in acute nutrient-based bioimpedance assessments
Novel Redox Oxygen Active Materials for Solid Oxide Cells and Thermochemical Hydrogen Production
No full textThis dissertation investigates novel materials designed to significantly enhance the performance and durability of oxygen electrodes for solid oxide cells and redox active materials for thermochemical hydrogen production. The first part of this work evaluates Ta-doped BaCoO₃₋δ (BCT) and its structural analog SrCoO₃₋δ (SCT), focusing on their catalytic performance in oxygen reduction and evolution reactions. Among BCT compositions (x = 0.1, 0.3, 0.5), the BCT10 (x = 0.1) composition exhibited the highest oxygen vacancy concentration, electronic conductivity, and lowest polarization resistance. However, SCT10 surpassed BCT10 in these critical performance parameters and also demonstrated greater stability over extended operational periods. Notably, SCT10’s superior catalytic activity is attributed to its larger Co–O octahedron, despite having a smaller overall unit cell compared to BCT10. The second part introduces a barrier-layer-free (BLF) oxygen electrode (OE) consisting of a composite of (Bi₀.₇₅Y₀.₂₅)₀.₉₃Ce₀.₀₇O₁.₅±δ (BYC), a high oxide-ion conductor, and La₀.₈Sr₀.₂MnO₃ (LSM), an electronic conductor. The innovative electrode design, achieved through infiltration of LSM nanoparticles into a porous BYC scaffold, delivered remarkably low area-specific resistance (0.10 Ω·cm²) and significantly improved current densities in both fuel cell and electrolytic modes. Durability testing over 550 hours confirmed exceptional stability, demonstrating its suitability for high-performance intermediate-temperature solid oxide cells. Finally, the third study investigates Pr₃ZrO₈-δ (PZO) as a new redox-active oxide for intermediate-temperature “two-step” thermochemical hydrogen production. Operating effectively at significantly reduced temperatures (900°C reduction and 400°C water splitting), PZO demonstrated superior oxygen vacancy concentrations and hydrogen generation rates compared to conventional CeO₂-based materials. With stable performance through multiple redox cycles, PZO demonstrates itself as a promising redox material for practical thermochemical hydrogen production utilizing lower-temperature heat sources. Collectively, these findings advance the fundamental understanding and practical applications of high-performance electrode materials and hydrogen production technologies, offering significant contributions toward sustainable and efficient energy conversion solutions
Entrepreneurship as a Socially Devalued Occupational Choice
No full textThis dissertation presents a study of the effects of social mechanisms on entrepreneurship as an occupational choice. Essay 1 studies the effects of social status perceptions and corruption concerns on the entrepreneurial intent of students at elite universities in Ghana, Kenya, and South Africa. Grounded ethnographic interviews suggest that this understudied group can be reticent to choose entrepreneurship because of two social mechanisms that devalue this occupational choice: lower perceived status arising from entrepreneurship’s association with necessity and failure to secure a traditional white-collar office job and corruption concerns arising from expectations that owners rather than employees may be expected to deal with unethical bribe solicitations when they arise. I test these social mechanisms using a two-part field study (N=1,737) of a survey followed by a behavioral game experiment. Results support predictions that status perceptions and corruption concerns predict entrepreneurial intent, with strongest effects for those that perceive they will have a greater degree of choice between entrepreneurship and other wage employment options. Using the same dataset from Essay 1, Essay 2 extends the work of Essay 1 by studying the effect of social status perceptions and corruption concerns on decisions to start another venture following business failures. I posit that the prospect of increased social status weakens the negative effect of business failure such that those who perceive the status of entrepreneurs to be higher are more likely to start another business after a venture failure. In contrast, I posit that corruption concerns strengthen the negative effect of business failure on subsequent decisions to start another venture as dealing with bribery can become an additional drain on the psychological resources needed to persist. Results support the prediction that corruption concerns moderate the negative effect of business failure on subsequent decisions to start another venture; however, I find no evidence of a significant moderating effect of status perceptions. Finally, Essay 3 utilizes a multilevel dataset consisting of data on 755,859 individuals from 103 countries to study the relationship between social status perceptions and entrepreneurship across national contexts. Individual level data comes from the Global Entrepreneurship Monitor adult population survey, which is combined with country level data from World Bank’s Development Indicators, the World Bank’s World Governance Indicators, and the International Labour Organization Statistics database. Results show that social status perceptions of entrepreneurship have a significant association with the employer form of entrepreneurship (1+ other employees) but not with own account entrepeneurship (0 other employees). Results also show that the association is more significant in countries with higher rates of own account employment, which implies that interventions designed to spur entrepreneurial activity in these countries should consider social status perceptions in addition to the more common feasibility interventions designed to address constraints around financial and human capital. Together, my dissertation advances our understanding of the influence of social factors beyond the feasibility factors identified by prior research. Furthermore, it is one of the first studies to focus on the university educated in sub-Saharan Africa, a high-potential yet understudied population with higher capabilities of starting ventures that can scale and create jobs for others. Findings from this dissertation suggest that researchers and practitioners interested in supporting entrepreneurship in developing countries should pay closer attention to social dynamics in these contexts that can influence entrepreneurship as an occupational choice
The Effect of an Interdisciplinary Project on Students’ Understandings of Mathematical Interdisciplinarity: A Qualitative Action Research Study
No full textThe siloed nature of education has likely caused some high school students to not recognize the interdisciplinary nature of mathematics, leading them to misunderstand its relationship to other subject areas. A misunderstanding of mathematical interdisciplinarity can cause issues when these students approach interdisciplinary problems later in life. The purpose of this study was to observe, investigate, and analyze the ways in which students understand mathematical interdisciplinarity following a collaborative and interdisciplinary project. I implemented a descriptive qualitative action research approach to determine how a collaborative interdisciplinary project on NASA technology in a high school mathematics course affects students’ understandings of mathematical interdisciplinarity. Data collected from seven students through pre- and post-questionnaires, observations during the 13-day intervention, and student artifacts suggested that students’ broad understandings of mathematical interdisciplinarity were not affected by the NASA technology project, though multiple participants indicated their mathematically interdisciplinary understandings changed in the discipline of technology. However, both weak and strong conceptual changes existed within multiple codes of mathematical interdisciplinarity. Based on the findings and analysis, a primary recommendation for personal practice is to update the NASA technology project guidelines to better guide students towards mathematically interdisciplinary understandings and social constructivist behaviors to promote more conceptual change. Future research focusing on comprehensive mathematical interdisciplinarity in nontraditional mathematics courses is also needed
Examining the Impact of Teacher Participation in Pedagogical Content Knowledge Development Intervention on the Academic Achievement of Culturally and Socioeconomically Diverse Students
No full textThis action research study examines the impact of a Pedagogical Content Knowledge (PCK) intervention on elementary teachers\u27 instructional practices and student outcomes in Math and English Language Arts (ELA). The goal was to address student underperformance by enhancing teachers\u27 PCK and exploring how improved content knowledge and pedagogical strategies influence classroom teaching and student achievement. Using a concurrent mixed-methods design, baseline data were collected through surveys, classroom observations, and student assessments. Over five weeks, teachers participated in professional development focused on lesson planning, student exemplars, and success criteria. Due to scheduling constraints, computer-based assessments were replaced by analyzing student work artifacts to gauge the intervention\u27s effectiveness. The study found improvements in teachers’ ability to address misconceptions, though varying levels of PCK implementation were observed. Qualitative data from instructional footage and interviews indicated that while teachers perceived the PCK intervention as beneficial for professional growth, challenges with time management and the absence of planned Professional Learning Communities (PLCs) limited full engagement. Despite these challenges, the study highlighted the potential of PCK interventions to enhance instructional clarity and student engagement, suggesting that ongoing support and flexible integration of PCK can positively impact teacher effectiveness and student learning outcomes. The research concludes by recommending further exploring structured reflection and collaboration to enhance PCK and improve student achievement. Keywords: Pedagogical Content Knowledge (PCK), action research, Professional Learning Communities (PLC
Promoting the Mathematical Success of High School Geometry Students Through Project-Based Learning
No full textThis action research study explores the impact of project-based learning (PBL) on the mathematical success of high school geometry students, particularly those from low socioeconomic backgrounds. Students from low SES households often face challenges in acquiring foundational math skills, which in turn leads to low math-related self-efficacy and poor academic performance. The study aimed to determine whether implementing PBL enhances students\u27 understanding of geometry, improves students’ math-related self-efficacy and anxiety, and changes students’ attitude toward math by creating real-world connections to their lived experiences. Using a convergent mixed-methods approach, the study collected both qualitative and quantitative data from high school geometry students through pre- and post-assessments, the Math Self-Efficacy and Anxiety Questionnaire (MSEAQ), student documents and artifacts, student journals, teacher observations, and student and teacher interviews. One class worked collaboratively with a Residential and Commercial Construction (RCC) class, while the other class worked with the RCC teacher. Findings indicated that PBL positively influenced students\u27 perception of their learning, engagement, and motivation. Pre- and post-assessment data showed little change in students’ geometry skills, while MSEAQ results revealed some areas of increased math-related self-efficacy and reduced anxiety. Student documents and artifacts supported positive learning outcomes and shifts in student attitudes. Qualitative data from student journals and interviews further highlighted shifts in students\u27 attitudes toward math, with many expressing greater confidence in their abilities and recognizing the relevance of geometry in real-world applications. Additionally, teachers observed increased student participation, problem-solving skills, and collaboration throughout the project. This study contributes to the growing body of research on equitable math instruction and demonstrates the potential of PBL to bridge learning gaps for students from low SES backgrounds. By emphasizing meaningful learning experiences and real-world applications, PBL serves as a transformative learning model in high school mathematics classrooms. Future research should explore the long-term effects of PBL on students\u27 mathematical success and its implementation across various educational settings
Editing Japan: Pornography Commentary as Postcolonial Revenge in China\u27s Digital Sphere
No full textThis study examines how Chinese male audiences reconstruct postcolonial masculinity through the consumption and reinterpretation of Japanese pornography. By analyzing commentary videos produced by Team Strawberry, I argue that this phenomenon constitutes a symbolic revenge against Japan\u27s historical dominance. Through degrading Japanese female performers and mocking Japanese culture and male actors, Chinese viewers invert colonial hierarchies, transforming sexual consumption into a nationalist project. However, this mimicry of colonial violence ultimately reinforces patriarchal structures rather than subverting them, while also framing nationalism through sexual conquest that mimics collective historical trauma. The re-editing and mocking of Japanese pornography by Chinese content creators functions as a form of postcolonial digital “revenge porn”—not in the conventional sense of the term, but as a form of national, masculine reassertion through the symbolic appropriation, ridicule, and devaluation of Japanese sexual media
The Intersection of Rurality and Wellness: A Multi-Manuscript Investigation Into Rural Well-Being
No full textRural communities face unique mental health and wellness challenges, including geographic isolation, provider shortages, and stigma surrounding help-seeking. This dissertation explores rural wellness through three interrelated studies, utilizing both qualitative and quantitative methods to examine disparities, perceptions of rurality, and demographic influences on well-being. The first study utilized the Five Factor Wellness Inventory (5F-WEL) to compare wellness scores between rural and non-rural individuals. The second study employed Interpretative Phenomenological Analysis (IPA) to explore rural definitions of wellness, as well as barriers and strengths related to mental health. The third study examined how perceptions of rurality and demographic factors predict wellness outcomes. These findings underscore the necessity for rural mental health interventions that prioritize social connection and belonging instead of focusing solely on demographic risk factors. Counselor education programs should equip professionals to address both rural cultural strengths and barriers. Policymakers should expand community-driven wellness initiatives, integrate mental health into trusted rural institutions, and emphasize the use of telehealth services. This research broadens the understanding of rurality as a multidimensional experience rather than a singular classification. Future research should explore longitudinal trends, intersectional rural identities, and innovative wellness strategies tailored to rural context communities
The Impact of Students\u27 Attendance on Their Academic Success: A YPAR Study
No full textYouth participatory action research (YPAR) is a collaborative research approach that empowers students to examine issues affecting their lives and communities. This action research aimed to investigate the relationship between students’ attendance and academic success on middle school students, focusing specifically on eighth-grade students based on the theoretical framework of the critical race theory (CRT) in education and the self-determination theory (SDT). Three research questions were explored related to the impact of students’ attendance on their academic success: 1) What do students consider factors that lead to their poor attendance? 2) How do students feel they can be supported in the school environment to increase their attendance? and 3) How do students feel their participation in YPAR influenced their decision to attend school?
A study was conducted with 13 students participating in the research that involved a survey, semi-structured interviews, weekly focus group meetings, and journal reflections. Qualitative analysis revealed that the main factors motivating students to attend school were peer and teacher interactions, teacher support, and an engaging learning environment. During the five-week intervention, students focused on goal-setting and identifying the challenges of accomplishing their weekly goals. This study illustrates how YPAR effectively amplified student voices in addressing challenges while promoting active participation in creating solutions. Participating in this YPAR study helped to understand why students miss numerous days while evaluating how teachers could increase student motivation. Through self-efficacy, students realized they could change by being motivated to improve
Physics Oriented Deep Learning for Material Prediction and Generation
No full textThe discovery of new materials is critical to advancing various industries, but traditional experimental methods for materials discovery remain slow and resource-intensive. Recent advances in machine learning (ML), particularly deep learning (DL), have greatly improved and accelerated two main aspects of modern computational material discovery: material design (e.g., material generation) and material screening (e.g., property prediction). However, a key challenge remains: standard ML models often struggle to perform domain-specific tasks effectively. Incorporating domain-specific knowledge, specifically the underlying physics of materials, into ML/DL models is key to improving the accuracy and reliability of material generation and prediction models.
This dissertation discusses and addresses this challenge through physics-oriented deep learning for computational materials discovery. In the first topic, we explore the use of transformer-based deep learning language models for the generative design of inorganic material compositions. Experiments showed that our transformer models can capture key physicochemical knowledge, such as charge neutrality and balanced electronegativity, and generate novel and chemically plausible inorganic material compositions. As an additional demonstration of the power of transformer neural networks models to capture physics and chemistry from raw compound data, in the second topic, we propose a bidirectional encoder transformer based model, BERTOS, for atomic oxidation state prediction from composition alone, which has significant applications in crystal structure prediction and virtual screening of candidate materials.
We further explore physics-guided deep learning for materials property prediction, emphasizing the importance of incorporating physical information in the input features to guide the neural network model training process, which helps guide the model to produce more physically accurate and reliable results, especially when the data is limited or noisy. In the third topic, we propose a novel framework called DSSL (Dual Self-Supervised Learning) to overcome the data scarcity issue for materials property prediction. This is a two-stage physics-guided approach based on the graph neural network approach that leverages both large-scale labeled and limited unlabeled data. It includes three complementary self-supervised learning (SSL) strategies: Mask-based generative SSL, Contrastive learning SSL, and Physics-guided predictive SSL. In the fourth topic, we investigate the impact of physical encoding on ML performance for property prediction and found that physical encoding of atoms can significantly improve the generalization prediction performance, especially for out-of-distribution samples. Finally, in the fifth topic, we investigate the issue of data redundancy in materials science datasets, arguing that standard random data splitting leads to overestimation of machine learning model performance, particularly concerning generalization to new materials. To address this, we developed MD-HIT algorithms for both composition- and structure-based redundancy using various similarity metrics, which provides a more objective evaluation of ML models\u27 true extrapolation capabilities for materials property prediction and allows ML models to learn true physics from the data instead of overfitting ML models with low generalization performance