The University of Arizona Campus Repository: Recent submissions
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Shades of Identity: Examining the Interplays of Racism and Colorism on Ethnic-Racial Identity, Skin Tone Satisfaction, and Skin Tone Centrality Among Latine YouthColorism is pervasive globally and holds salience within Latine populations. Adolescence is an important developmental period when Latine youth make meaning of racialized experiences, and examining how racism and colorism shape youth development and lived experiences is needed. First, it is important to understand how racism and colorism co-occur, and research should explore how racism and colorism relate to how youth feel about their ethnic-racial identity (ERI; i.e., negative affect) and come to terms with their ERI (i.e., exploration and resolution), as well as how youth view their skin tone (i.e., skin tone satisfaction). Second, research utilizing advanced longitudinal methods is needed to understand how racialization experiences inform youth development across time. The current dissertation sought to contribute to colorism and ERI literature through these foci across two papers by using advanced quantitative methods and analyses. The first paper explored how negative racialization experiences (e.g., racial microaggressions) relate to the ERI of Latine adolescents, and how skin tone, skin tone satisfaction, and gender moderate this association from an intersectional and cultural ecological lens. The second paper examined how racial discrimination relates to skin tone satisfaction over four days among Latine adolescents, and how skin tone self-concept moderates this association. Understanding the complexity of colorism, racism, and ERI across distinct dimensions and approaches will advance our understanding of Latine youth development and well-being.
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Machine Learning for Efficient & Robust Next-Generation Communication SystemsMachine learning (ML)-based techniques are increasingly being incorporated into next-generation wireless systems: both for improving fundamental building blocks (e.g., modulation classification, power allocation, channel decoding) as well as enabling new functionalities (e.g., AR/VR, autonomous vehicles). This dissertation makes the following contributions in these areas: As ML classifiers become integral to next-generation wireless systems, it is essential to ensure their predictions are delivered both reliably and with low delay—for instance, in applications like transmitting road condition assessments in vehicular networks or relaying critical health data from sensors to medical providers. In our first contribution, we analyze the fundamental information-theoretic tradeoffs between latency and end-to-end distortion when communicating the results of a classifier over a noisy communication system. We use techniques from finite blocklength channel capacity and show that lattice-based quantization of probability distributions leads to a significant reduction in latency compared to other baselines. In our second contribution, we present a new approach for using reinforcement learning (RL) to provide adaptive robustness to High Frequency (HF) channels. The HF band, which occupies the spectrum of 3 to 30 MHz, enables long-range communications by bouncing signals off the ionosphere with limited communication infrastructure. However, the turbulent nature of the channel, which causes frequent signal dropouts, has deterred the band from being used more heavily. To mitigate this challenge, we propose using RL to learn the optimal settings (e.g., tap length, step size, filter type, adaptive algorithm) of an adaptive equalizer and show that our techniques can provide better performance compared to adaptive equalizers with a fixed structure. In our third contribution, we devise an unsupervised learning-based framework to optimize cell-free networks (CFNs). CFNs deviate from the concept of having an access point (AP) be responsible for serving user equipment (UEs) within a fixed radius and instead deploy APs over a geographic region to collaboratively serve every UE [6]. In doing so, CFNs increase the probability of coverage and achieve stronger diversity gains [7]. To build on these improvements, we propose using an unsupervised neural network to learn how to split a UE’s message across different APs in a manner that minimizes the total latency of the CFN. We show that our unsupervised technique is more effective in ensuring higher probabilities of lower latencies compared to decentralized baselines. Additionally, when noisy channel state information is assumed, our unsupervised technique is more robust in achieving a high likelihood of lower latencies compared to centralized baselines. In our final contribution, we investigate a complementary problem of ensuring privacy when aligning Large Language Models (LLMs). LLMs have been investigated for various applications, due to their broad knowledge base attained via pre-training on large corpora of data. However, it has been shown that LLMs can generate socially unacceptable responses. Alignment procedures have been proposed to train LLMs, using preference data collected from humans, to reinforce which types of responses are socially acceptable. While such methods are effective in regulating an LLM's responses, this type of training could be susceptible to leaking privacy-sensitive information of the human labelers. To mitigate this, we study the problem of LLM alignment with labeler privacy while maintaining the utility of the alignment process. To accomplish this, we present a novel privacy-preserving approach, namely PROPS (PROgressively Private Self-Alignment), a multi-stage algorithm capable of ensuring preference privacy without causing a significant drop in the utility of an LLM as it undergoes alignment.
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Understanding the Removal of Atmospheric Aerosol in a Tropical Marine EnvironmentAerosols and their interactions with clouds remain the largest sources of uncertainty in our understanding of the atmosphere and climate. A major factor in this uncertainty is the wet scavenging (removal) of aerosols in global models, which negatively impacts model capabilities to capture aerosol lifetimes and, consequently, aerosol impacts on climate and air quality. This dissertation focuses on scavenging over the tropical West Pacific region and consists of three distinct approaches: (1) a ground-based study investigating factors contributing to the inter-seasonal persistence of aerosol concentrations in a tropical coastal megacity despite higher precipitation during the wet season, (2) a multi-tool study using aircraft data that determines meteorological variables relevant for scavenging during long-range transport, and (3) an aircraft-based study calculating in-cloud scavenging efficiencies of multiple aerosol species and sizes in tropical convection. In the first part of the dissertation, we analyzed size-resolved aerosol composition, aerosol optical depth, and meteorology to understand why Metro Manila, Philippines exhibits similar aerosol concentrations across seasons despite large differences in seasonal rainfall. We identified two major factors: (1) opposing seasonality of black carbon and water-soluble aerosol, and (2) inefficient scavenging by short rain events (< 1 h). We demonstrated that the presence of rain does not imply efficient wet scavenging and it is important to consider rain characteristics like duration. In a changing climate with increasing urbanization, these factors are expected to become more critical for air quality policymaking and sustainable urban development. This work was published in Environmental Science: Atmospheres (Hilario et al., 2022). In the second part, we identified meteorological variables relevant for estimating wet scavenging using trajectory modeling and a combination of aircraft, satellite, and reanalysis data. We found that the accumulated precipitation along trajectories – often interpreted as a wet scavenging indicator in the literature – does poorly when used to predict aerosol scavenging and was outperformed by the following variables: (1) upper percentiles of relative humidity (RH) along trajectories, (2) the fraction of hours along trajectories exceeding a threshold value for RH or water vapor mixing ratio, and (3) precipitation intensity along trajectories. This work was published in Atmospheric Measurement Techniques (Hilario et al., 2024). The final part of this dissertation quantified in-cloud scavenging efficiencies (SE) in tropical convection. In-cloud scavenging is the primary removal pathway for accumulation mode aerosols, but SEs have not been calculated for shallow to moderate convection. We used aircraft data to calculate SEs for three cases. Efficient scavenging was observed for sulfate (>86%) and black carbon (70 – 80%); moderate scavenging for organic aerosol (53 – 60%) and nitrate (62%); and a wide range of SEs for ammonium (53 – 87%). We also found that accumulation and coarse mode aerosol volume concentrations were nearly totally scavenged in-cloud (>92%), suggesting a preferential activation of large aerosols. Comparisons of differing cloud tops showed that SEs did not vary significantly on an aerosol mass basis with cloud top height. These results demonstrate that aerosol size and composition are more important for in-cloud SEs. This work was published in the Journal of the Atmospheric Sciences (Hilario et al., 2025). This dissertation provides an explanation for how aerosol loadings can be sustained during the wet/rainy season, which should be investigated in other developing cities due to the health risks associated with pollutant accumulation. The dissertation also provides suggestions for meteorological variables that could be considered in model scavenging parameterizations. The presented method can be repeated in different environments to identify regional differences in factors that influence scavenging. Finally, the calculated in-cloud SEs motivate improvements in chemical transport models through future observation-model comparisons.
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Characterizing Ore Particle Size for Sag Mill Feed ControlThis thesis presents a comprehensive, data-driven study for predicting the particle size distribution (PSD) at the feed of a semi-autogenous grinding (SAG) mill. A consistent PSD is essential for efficient energy use, stable throughput, and effective downstream processing. However, feed variability, caused by ore heterogeneity, stockpile segregation, and reactive blending, makes prediction challenging. The first part of this work reviews the current state of research and industrial practice, synthesizing insights from over 45 peer-reviewed studies on ore blending, stockpile management, and the application of machine learning in mineral processing. The review identifies a gap between the growing availability of high-frequency sensor data and its limited use in real-time predictive tools, highlighting the need for systems that forecast PSD and support data-informed operational understanding. Building on these insights, the second part develops and validates a machine learning framework using two years of high-resolution operational data from a copper mining operation. The methodology integrates unsupervised clustering with Random Forest regression to forecast key PSD metrics (F10–F90 and TOPSIZE) based on variables such as feeder speeds, stockpile levels, and ore throughput. Cluster-specific models capture nonlinear, regime-dependent behaviors with high accuracy ($R^2 > 0.90$). In addition to prediction, the thesis presents a data-driven sensitivity analysis to evaluate how changes in input variables, such as individual feeder rates, influence PSD outcomes. This analysis shows that coarser PSD metrics, such as F70 and TOPSIZE, are more responsive to input changes than finer ones. A variability analysis further demonstrates the model’s ability to quantify and explain fluctuations in PSD, with predicted distributions exhibiting up to a 15% reduction in standard deviation compared to historical data. All components are integrated into a web-based application that allows users to explore model behavior, visualize PSD forecasts, and assess the impact of operating scenarios in real time. This work advances the field by demonstrating how machine learning and clustering can be effectively applied to model and interpret SAG mill feed variability. The resulting framework offers a practical approach for enhancing predictive insight in mineral processing operations.
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Using LiDAR Remote Sensing to Evaluate and Improve the Retrieval of Snow Depth, Leaf Area Index, and Land Cover Types for Hydrometeorological StudiesRecent NASA satellite missions such as the Ice Cloud and Land Elevation Satellite version 2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI) have been deployed to survey the Earth’s surface with mission goals of monitoring changes in glacier ice, sea ice, and vegetation for ICESat-2 and retrieval of 3-D structure of mid-latitude and tropical canopies globally for GEDI. Both instruments have provided the community with unprecedented high-resolution active remote sensing measurements of variables relating to processes in the water cycle. These advancements in spaceborne lidar technology motivate the works performed in this dissertation that demonstrate the importance of using these instruments for the retrieval of hydrometeorological variables and provide motivation for future spaceborne lidar missions. Mitchell et al. (2025a) evaluated snow depths retrieved from ICESat-2 multiple lidar scattering measurements, a new and novel technique developed by Y. Hu et al. (2022) and Lu et al. (2022). Snow depths from ICESat-2 are compared to the in-situ measurement – derived University of Arizona (UA) product for two distinct regions of the contiguous U.S. (CONUS): the Mountain West (complex terrain) and the Great Lakes (homogeneous terrain). Biases between the snow products are co-located with several terrestrial datasets (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), GEDI, ICESat-2, and USGS LANDFIRE) and then evaluated in terms of the time of snow season (December – April) and snow density to understand the performance of the retrieval. The retrieval performance performed well overall, but results showed the performance decreased with increasingly complex terrain and in the presence of tall canopies. Additionally, the retrieval’s performance decreased later into the snow season and with higher snow densities. The findings provided insights into future corrections that can be made to the retrieval in future studies. Mitchell et al. (2025b) co-located GEDI spaceborne lidar canopy measurements and snow depths from UA with MODIS LAI and land cover (LC) products over the CONUS for a two-year period (2019-2021) to address the questions of if the underestimation of the MODIS LAI data for evergreen forest are due to deficiencies related to the misclassification of the input LC data or the LAI retrieval itself? Comparisons between GEDI plant area index (PAI) and MODIS LAI highlighted the MODIS retrieval deficiencies in evergreen forests, where the median GEDI PAI and MODIS LAI winter/summer ratios are 0.87 and 0.29 respectively. The sensitivity of LAI to snow cover is highest in evergreen forests where LC analyses also demonstrate the highest potential for misclassified pixels according to the International Geosphere Biosphere-Programme LC classification using GEDI canopy metrics. Corrections to wintertime LAI using the winter/summer PAI ratios are applied to tall forest LC types and showed the greatest improvements over evergreen needleleaf forest. Finally, a decision tree approach leveraging several GEDI canopy metrics showed potential to reclassify the MODIS-misclassified LC pixels and demonstrate the advantage of leveraging active spaceborne lidar measurements to improve passive remote sensing data. Following Mitchell et al. (2025b), corrections are made to MODIS LAI prescribed to the Community Land Model version 5 (CLM5.0) for evergreen trees in the third study, to investigate the impact of using LAI datasets improved by spaceborne lidar measurements on land modeling. The findings show promising results in the improvement of the representation of LAI for evergreen throughout the year. In boreal evergreen forest, changes to the LAI substantially impacted snowpack, evaporation, and runoff by shifting the seasonal cycles by a month. For tropical evergreen forest, the largest changes were seen in wet season partitioning of evaporation, but overall changes were relatively small . These studies highlight the need for continued improvement of the retrieval of hydrometeorological properties from spaceborne lidar and the importance of continuing future spaceborne lidar missions with new advancements in lidar technology.
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Locating Minority Cinema: Ethnic Identity and the Politics of Representation in Contemporary ChinaThis dissertation begins with a simple question: How do Korean-Chinese and Tibetans in the PRC present their stories, and what thematic or representational commonalities emerge across their works? To explore this, it examines the cinematic representations of Chinese ethnic minorities in the films of Tibetan director Pema Tseden and Korean-Chinese director Zhang Lv. As Stuart Hall argues, ethnic identities are “never singular but multiply constructed across different, often intersecting and antagonistic, discourses, practices, and positions” (Hall, 1996, p. 4). This study builds on this premise, asserting that ethnic minority voices in film are as diverse as the lived experiences of individual ethnic minorities. It argues that the specific yet divergent socio-political milieus influence how these two directors shape their perspectives and themes in representing identity. Furthermore, this dissertation reveals that what these two ethnic minority filmmakers embody in their works not only challenges ideological representations of ethnic minorities but also deconstructs the essentialized notion of Chineseness. Through a textual analysis of five films—Zhang’s Dooman River (2009) and The Grain in Ear (2005), and Pema’s The Silent Holy Stones (2005), Old Dog (2011), and Tharlo (2015)—this study engages with Hall’s concept of cultural identity, translocality, accented cinema, and transnationality as theoretical frameworks. It ultimately highlights how these films embody cultural identity, navigate both literal and metaphorical border crossings, and offer nuanced understandings of displacement within the respective contexts of Tibetan and Korean-Chinese communities.
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Rethinking Interpersonal DependenceAs finite and social beings we depend on one another for survival, flourishing, and for the mundane forms of assistance that allow us to move through our days. Understanding the resulting “web of social interconnection” is an essential part of the care ethical project. In this dissertation, I critique previous attempts to theorize this “web”, offer a novel account of interpersonal dependence, and explore the nature of the dependence involved in caring and loving relationships.In Chapter 1, “Dependency Relations Are Not (Necessarily) Need-Meeting Relations,” I provide negative arguments against the way dependency has traditionally been theorized in care theory – namely, as a need-meeting relationship. In Chapter 2, “Depending on Others,” I offer a positive account of dependence itself. I analyze dependency as a relation in which someone normatively expects another person to perform work, and that second person countenances their expectations. I also address an obvious objection: what about the dependency of, for instance, newborn infants, who don’t appear capable of normatively expecting work of others? On a practice-based view of action, infants can expect insofar as they are meaningfully treated as participants in cross-cultural parenting practices. Chapter 3, “Love, Fairness, and Sharing a Life,” concerns how dependency work is distributed in loving partnerships. I precisify and challenge the idea that considerations of fairness are out of place in relations of love. I argue that love and fairness are integrated in the sense that partners who “share a life” are only able to perform particular “relationally participatory” acts (loving and expressing love) if their actions are sensitive to fairness. Chapter 4, “Rethinking Dependence and Care,” I reject Care Monism, the view that all idealized dependency relations are caring. Embracing Pluralism about dependency ideals allows us to explain the value of non-caring and non-intimate relations of “help”, and to grant disabled people greater control over the meaning of their relationships with personal assistants.
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Validation of Cardiomems Pulmonary Artery Pressure Monitor Using the Donovan Mock TankMaintenance of HF symptoms requires patients to monitor changes in limb edema, sudden weight gain and edema as markers for progressing or worsening HF. However, these symptoms can occur suddenly and can lead to decompensation and hospitalization if not addressed in a timely manner. The CardioMEMS HF System was developed to monitor patient pulmonary artery pressures and catch worsening heart failure before symptoms of fluid overload manifest. Indeed, the CardioMEMS has been shown to reduce HF hospitalizations by up to 30% with great reliability and reproducibility. Recently, a case report at Banner University Medical Center Tucson highlighted potential pitfalls in treating patients with CardioMEMS based on faulty readings from the Smart Pillow home system, highlighting a need for further understanding of potential biases in CardioMEMS readings against a known pressure. This study aimed to characterize CardioMEMS Hospital System and Smart Pillow (home system) readings against the Donovan Mock Circulation Tank (Mock Tank) – a chamber used to test and validate mechanical circulatory support devices. A series of groups was devised to represent a spectrum of pulmonary artery pressures and readings were taken from the PAP chamber of the Mock tank and compared to readings from the CardioMEMS device using the Hospital System. Linear regressions confirmed great agreement between the two measurement methods (R squared >0.95, p<0.0001) and Bland-Altman plots revealed a negative proportional bias (-1.272mmHg, p<0.0001) that suggested the CardioMEMS underestimation was greater at higher PAP chamber pressures. Biases in CardioMEMS measurements should be considered and additional confirmations of fluid overload should be considered when changing the treatment plan of patients with HF.
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Quantification of Antithrombin III via Flow Velocity Profiling in Microfluidic Paper-Based DevicesAntithrombin III (ATIII) is a protein which plays a critical role within the regulation of the coagulation cascade. When the concentration of ATIII is found to be insufficient, thrombotic conditions that are caused by hypercoagulability increase in its likelihood to occur. This can result in life-threatening events, such as myocardial infarction or stroke. Methods that are currently available in order to detect ATIII levels lack the ability to be portable and prompt. These inefficiencies result in the inability to effectively serve as a point-of-care (POC) test in urgent and critical settings. This thesis presents a novel method for detecting levels of ATIII through the use of a particle-based assay which is driven through a model involving capillary flow. The platform uses a microfluidic paper-based device (µPAD) in order to measure flow velocity profiling through the quantification of capillary flow alterations which are caused by the immunoagglutination of the antigen, ATIII and anti-ATIII antibody conjugated microparticles. A smartphone was used to take video recordings of the tested samples which traveled through the channels of the µPAD and in addition, cloud-based Google Colab was used to analyze the videos in order to automatically provide raw data tracking the fluid front of the sample as it moves down the channel. ATIII spiked concentrations of 0 ng/mL, 0.5 ng/mL, 1 ng/mL, 3 ng/mL, 6 ng/mL and 12 ng/mL were tested in PBS, 0.5% plasma and 0.1% plasma. In addition, 0 ng/mL, 0.5ng/mL and 1 ng/mL spiked concentrations were tested in 0.001% plasma. All the samples and their flow profiles were determined. Our definition of flow profile represented the flow velocity slope. The average flow velocity slope was found which allowed for a correlation to be seen between the degree of immunoagglutination of ATIII and anti-ATIII antibody conjugated microparticle as it caused alterations in interfacial tension and viscosity for each of the concentration tested. Samples that had a higher concentration of ATIII led to a higher profile of flow velocity slope. The gold standard immunoassay for ATIII quantification is the enzyme linked immunosorbent assay (ELISA) which provided us with proper quantification of endogenous levels of ATIII in 0.001% plasma and this allowed for steps in the optimization of the assay. This assay does not require intensive training but rather, is affordable, portable, and also capable of rapidly detecting ATIII levels which make it appealing to use within the clinical setting. The work done in this thesis has proven logarithmic linear trends of increasing flow velocity in relation to increasing concentrations of ATIII, providing insight towards developing a foundation in future POC tests targeting ATIII and potentially other biomarkers.
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Pitch in Autism Speech of Korean Children, Effect of Gesture in Artificial Grammar Learning, and Topic and Grammatical Complexity in Various Sources of Descriptions of AutismThis dissertation investigates three aspects of communication relevant to Autism Spectrum Disorder (ASD): prosodic patterns in speech production, the role of gesture in language acquisition in people with varying traits related to ASD, and linguistic differences between clinical and lay descriptions of ASD-related behaviors. Chapter 2 examines pitch variability in Korean-speaking children with ASD compared to typically developing (TD) peers matched for expressive language age. Analysis of standard deviation and range in both raw pitch and semitone measurements revealed that TD children demonstrate significantly greater pitch variability than children with ASD. Additionally, these groups showed different developmental trajectories: TD children increased prosodic differentiation between declaratives and interrogatives as their language abilities developed, while children with ASD exhibited decreased pitch variability with increasing expressive language age. These findings in Korean, an understudied language in ASD research, provide evidence that atypical prosody may serve as a cross-linguistic marker for ASD. Chapter 3 explores whether gestures facilitate the acquisition of grammatical animacy and gender markers in an artificial language paradigm. Participants were assessed with AQ-10, a brief screening tool designed to quickly identify traits associated with autism spectrum conditions in adults. Contrary to predictions based on embodied cognition theories, results revealed no significant facilitative effect of gestures on learning these abstract grammatical categories. In fact, gestures appeared to hinder noun production in gender-related items and showed no significant benefit for animacy-related production. Additionally, AQ-10 scores were not meaningful predictors of performance. These findings challenge universal assumptions about the benefits of gestural input in language learning and suggest that the effectiveness of embodied approaches may be feature-specific and context-dependent. Chapter 4 analyzes linguistic differences between clinical and lay descriptions of ASD-related behaviors using computational linguistic methods. Lay descriptions received higher overall evaluation scores from clinical raters than clinical descriptions, particularly for certain diagnostic criteria. Grammatically, lay descriptions exhibited more complex structures with higher clause counts, while clinical descriptions demonstrated greater lexical diversity and vocabulary richness. These findings challenge traditional hierarchies of clinical versus lay knowledge and suggest that experiential narratives can effectively communicate clinically relevant information despite employing different linguistic strategies.Together, these studies are anticipated to advance our understanding of communication in neurodevelopmental contexts and have implications for ASD assessment, intervention approaches, and clinical communication practices.
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Eutectic and Peritectic Equilibria in Coherent Binary AlloysPhase equilibria in a coherent binary alloy for the eutectic and peritectic systems are ana lyzed in this study to construct a theoretical model that will provide conditions for three-phase equilibrium in the presence of coherency stress between the solid phases. Our analysis employs simple quadratic functions for the Gibbs energy for individual phases, considering the solid phases to be temperature-independent. To facilitate an analytical solution, we used simplified assumptions about the system, explicitly considering an infinite volume occupied by three phases and the solid phases being isotropic and linearly elastic. We use a mathematical optimization method, Lagrange multiplier, to minimize the functions of Gibbs free energy subject to the constraints of volume fractions and phase compositions that satisfy the equilibrium conditions. This theoretical study demonstrates the effect of coherency stress on invariant points of phase diagrams, including a liquid leveraging the original Larche-Cahn formulation to evaluate the impact of coherency stress on eutectic and peritectic points in binary alloys. The common tangent construction is not applicable in the presence of strain energy between the solids, and the Gibbs phase rule no longer holds. Our findings demonstrated that coherency stress has distinct consequences on the eutectic and peritectic equilibria due to the difference in compositions between one solid and liquid phase and the difference in the melting points of the solid phases; therefore, instead of forming the three-phase region, usual to eutectic equilibria, a two-phase region with one solid and liquid becomes more dominant in the peritectic equilibria.
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Dealing with Insults, Sex, and Cleanliness: Telemachus's Speech to the Enslaved Women in Odyssey 22This thesis explores the terms καθαρός (“clean”), ὄνειδος (“abuse”), and καταχέω (“pour down”) in Homer’s Iliad and Odyssey as a way to analyze and understand the nature of Telemachus’s accusations towards the disloyal slave women in his speech to them immediately before killing them. With a clearer understanding of each of the words, some ambiguities surrounding Telemachus’s claims become clearer and allow insight into both his own worldview and broader Homeric concepts of cleanliness, insults, and pouring.In Chapter 1, the somewhat generic verb καταχέω (“pour down”) is analyzed with respect to its various types of direct objects, making the meaning of the unique instance of ὄνειδος as the direct object in Telemachus’s speech clearer. Chapter 2, examining the term ὄνειδος (“abuse”), looks at some of the ways that the term can be used to threaten the status of important men—a concern for maturing Telemachus—and the potential for nuances in gendered opposition with the word. Chapter 3 examines the relatively few occurrences of καθαρός (“clean”) in Homer and the reasons why Telemachus’s use of the word stands out particularly oddly in comparison to the rest. The word is not linked to death in any other use, so there seems to be some kind of shift in meaning, but there are important gendered connotations behind the word that are brought out from the other examples.
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More Than Pedagogy: How Culturally Responsive STEM Courses Shape Minoritized Students' Experiences and Persistence in STEMThis dissertation investigates how culturally responsive pedagogical approaches implemented in introductory STEM courses shape the experiences, learning, and persistence of minoritized students in higher education. Drawing from a qualitative case study design, the research centers on three large introductory STEM courses—Biology, Chemistry, and Mathematics— taught by faculty who received training in culturally responsive teaching and curriculum design at a public research university.Guided by conceptual frameworks that bring together the comprehensive model of Culturally Responsive Pedagogy (CRP) and the Ecology of STEM Education framework, this study explores both classroom-level teaching practices and the broader sociocultural systems influencing STEM education. Data were collected from faculty and student interviews, classroom observations, and analysis of course materials and online learning environments. A comparative thematic analysis was used to examine how CRP was enacted and experienced across the courses. The findings revealed three central themes across the courses: (a) CRP is enacted through student-centered pedagogies that acknowledge and value students’ knowledge and lived experiences as co-constructors of learning; (b) pedagogies of care support students’ academic, social, and emotional well-being, creating inclusive spaces that affirm students’ identities; and (c) humanizing pedagogical practices challenge traditional norms in STEM, fostering belonging, empathy, and redefined notions of STEM success. Students reported that these culturally responsive STEM learning environments enhanced their engagement, sense of belonging, and motivation to persist in STEM, particularly among non-traditional students and those serving as Undergraduate Learning Assistants (ULAs), who often had shared experiences with current students. The study underscores that CRP is not merely a set of teaching strategies; it is a transformative model for disrupting exclusionary norms and reshaping the STEM ecosystem toward equity and inclusion. This research highlights that academic rigor and humanizing STEM teaching are not oppositional, and that science and culture are not incompatible. Persistence in STEM is not solely a matter of students working harder; it requires building ecosystems that support them, value their identities, and create spaces where they can thrive—not despite their backgrounds, but because of them. When such ecosystems are created, students do not merely persist; they lead, mentor others, and imagine new futures. Ultimately, this study contributes to broader discussions on systemic change in STEM education and offers practical implications for pedagogy, curriculum design, and institutional policy aimed at supporting historically minoritized students.
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Exploratory Structural Equation Modeling as a Tool for Validation of Scales Used To Measure Research Self-Efficacy and Resilience in Research Training Programs for Underrepresented StudentsUsing multiple forms of structural equation modeling, this study demonstrates a progression of analyses from the most basic confirmatory factor analysis through more complex exploratory structural equation modeling to interrogate both the construct and discriminant validity of the RSES and CD-RISC-10 when used as an outcome assessment for research training programs that target underrepresented students. Not only does this study inform the understanding of the factor structures for both measures, but it also demonstrates the utility of using more complex modeling for localized instrument validation. The psychometric assessment of the CD-RISC-10 found adequate model fit using CFA of the original factor structure, but improved model fit utilizing a two-factor structure defined through ESEM. However, the modifications made to the original RSES before being applied to this population resulted in more model fit and factor loadings inconsistent with its original form. Potential adjustments to the RSES and suggestions for interpretation are discussed.
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Impact of Teaching in Conflict Zones on Educators’ Well-Being and Pedagogical Strategies in Rio de JaneiroIn Rio de Janeiro, Brazil, violence between criminal groups and state actors often erupts without warning, leaving teachers and students caught in the crossfire. In this qualitative study, I sought to understand the experiences of educators working in conflict zones located in North and South Zone favelas. This research analyzes the experiences of educators, the impact of armed conflict on their well-being and that of their students, and their pedagogical strategies in response to violence. Semi-structured interviews and participant observation at educational nonprofits in Rio de Janeiro highlighted several themes, including the unpredictability of violence, the favela as a war zone, self-sacrifice, trauma, the normalization of violence, and resilience. The educators described several coping strategies to manage the psychological toll of armed conflict and employed a range of strategies in their attempt to help students navigate their traumas. While Rio de Janeiro’s perpetual state of exception forces some educators to teach in unbearable conditions where violence is an ever-present threat, they still use the potentiality of the classroom as a way to navigate and reconstitute the possibility of hope for their students. In this sense, the classroom becomes a site of resistance. This thesis addresses a gap in the literature regarding the perspectives of educators in conflict zones and contributes to a broader understanding of how they exercise their agency in the classroom despite structural barriers and urban violence in Latin America.
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Verbs and Community: Topics in Tutelo RevitalizationTutelo is the commonly known name of the ancestral language belonging to the Yesáh people, comprising seven contemporary Southeastern Siouan tribal communities located across the eastern United States. The antecedents of these communities were confederated in southern Virginia in the early 18th century; however, they underwent several migrations that caused their geographic separation. Some of the Yesáh were later adopted into the Six Nations of the Haudenosaunee located in Ontario, where the largest body of Tutelo documentation was gathered between 1877 and 1883. By that time, the language was limited to very few fluent speakers. The contemporary corpus of the language (pre-revitalization) is also limited: namely, a small number of lexical data (less than 800 words) and phrases compiled by explorers and researchers between 1671 and 1981. This study focuses in part on phenomena that occur in the verbal data of the pre-revitalization Tutelo corpus. The first dissertation chapter introduces the Yesáh community, the historical use and attrition of the Tutelo language within its tribes, and the preservation and revitalization efforts of the last 30 years. Tutelo revitalization has emphasized the creation of new nouns for which there had been no attested data, whereas less attention has been given to the complexities of Tutelo verbal phonology and morphology, the subjects of the second, third, and fourth chapters of this study. The second chapter introduces basic facets of Tutelo verbs, including the split intransitive system and the affixes of person, number, tense, aspect, and mood that attach to verb stems. It goes on to distinguish between verbs that end in consonants and those that end in vowels. Of the latter, it identifies non-abluating verbs as those with stem-final vowels that remain unchanged in every suffixal and other post-verb environment. The third and fourth chapters explore a group of Tutelo verbs that undergo a final vowel change in a process called ablaut. The final vowels in these ablauting verbs change depending on the suffixal and environmental triggers that follow them. The patterns of vowel alternations in these verbs appear to be both morphologically and phonologically conditioned. They are also highly variable, with some suffixes and environments singularly triggering one, two, or even three different final-vowel possibilities. Chapter 3 analyzes all the suffixes and environments that trigger one final vowel variant exclusively. Chapter 4 continues the analysis of chapter 3, focusing on those suffixes and environments that trigger two or more final-vowel variants in ablauting verbs. The fifth chapter explores how Tutelo verbs and other constituents in the language have been applied in the domain of land acknowledgments. An acknowledgment written by the author in 2021 is utilized to analyze and correct previous understanding of the language’s grammar before offering an updated version of the original. The chapter then goes on to illustrate a different type of land acknowledgment written by the author in Tutelo in 2024, one with stronger emphasis on the relationality of land and people than the 2021 text. The final chapter presents concluding thoughts about the material covered in the first five chapters, as well as the approach taken in the presentation of data and its ramifications for the Tutelo language community.
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Trauma, Stress, and Sleep: Pathways Linking Adversity to Health Across Populations and GenerationsA history of adversity has been linked to poor physical, mental, and emotional health outcomes across the lifespan. However, pathways linking adversity and health remain unclear and likely involve a complex interplay of biological, physiological, psychosocial, and behavioral factors shaped by individual, social, and societal influences. While adverse childhood experiences (ACEs) are well-established risk factors for sleep disturbances, the role of intergenerational trauma (IT) on sleep health remains underexplored. Sleep is a critical determinant of health and may serve as a pathway between adversity history and long-term health outcomes. Part I of this dissertation provides a comprehensive review of the relationships between ACEs, IT, sleep health, and broader health outcomes. This section also includes empirical studies (previously published and submitted for publication), including work examining social support as a protective factor. Part II presents the primary study of this dissertation, investigating the role of ACEs/IT in relation to cardiometabolic health among Hispanic adults of Mexican descent living at the US-Mexico border. This secondary data analysis uses validated questionnaires, and a novel assessment of IT developed for this study. Cardiometabolic health was assessed using Life’s Essential 8 (LE8), the American Heart Association’s revised framework incorporating sleep as a fundamental pillar of health. ACE score was significantly associated with LE8 Global Score in unadjusted models (B = -1.639, p = 0.024), and IT was marginally associated. Current stress was significantly associated with LE8 Sleep Score in ACE and IT separate and combined models, suggesting a potential indirect pathway linking trauma, sleep, and cardiometabolic health. Age and sex were also significant predictors of LE8 by some metrics. Secondary analyses explore the role of potential resiliency factors that may partially explain the Hispanic Health Paradox.
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Cardiologists’ Perspectives on Pharmacogenomics: Utilization, Barriers, And the Role of Genetic CounselorsPharmacogenomics (PGx) has the potential to personalize cardiovascular treatment byoptimizing drug efficacy and minimizing adverse drug reactions. Despite well-established guidelines for PGx-informed prescribing, its integration into cardiology remains limited. In this study, we conducted a nationwide survey to assess cardiologists’ knowledge, utilization, and perceptions of PGx testing, as well as their views on the role of genetic counselors in this space. The survey was distributed via email, flyers, and social media, utilizing a snowball sampling method. A total of 63 responses were included in the analysis. The majority of respondents were white, male, and practicing adult general cardiology. Most (58%) reported having no or only limited knowledge of PGx, and 55% had never ordered PGx testing in their practice. We found statistically significant positive correlations between provider degree of PGx knowledge and the frequency of test ordering (p = 1.226e-09), provider confidence in result interpretation, (p < 2.349e-16) and confidence with communicating test results (p = 6.968e-13). Additionally, many cardiologists expressed some interest in the integration of genetic counselors into PGx workflows, highlighting an opportunity for increased interdisciplinary collaboration. Notably, there was a statistically significant positive correlation between cardiologists’ perceived impact of genetic counselors involved in PGx and their likelihood to refer patients to them for PGx testing (p = 3.004x10⁻⁶). PGx is an emerging field with the potential to improve patient outcomes and reduce healthcare costs, yet gaps in provider knowledge and confidence hinder its clinical use. Expanding provider education and incorporating genetic counselors into cardiology and PGx workflows may facilitate broader adoption of PGx testing and enhance personalized treatment strategies in cardiovascular care.
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Risk and Rainfall: Specification Sensitivity in Estimating Smallholder Risk PreferencesRainfall variability in Sub-Saharan Africa creates significant production risks for subsistencefarmers who rely on rainfed agriculture. Weather shocks can alter farmers’ risk preferences, potentially influencing their decision to adopt adaptation strategies. We employ a moments- based approach (Antle, 1983, 1987) to estimate risk aversion among smallholder farmers in Ethiopia, Malawi, and Nigeria in response to weather shocks. Using this framework, we esti- mate Arrow-Pratt and downside risk coefficients across more than 200 model specifications, incorporating various rainfall and rainfall shock metrics derived from six remote sensing weather products. Our findings reveal that estimates of farmers’ risk preferences are highly sensitive to the choice of weather product, while risk preferences are relatively insensitive to different weather shocks. This underscores how data source and model specification choices critically shape risk preference estimates, highlighting key limitations in current empirical methods.
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Accelerating the Design of Sustainable Concrete with CDW Through Adaptive Experiments and Machine Learning for Conventional and Additive Manufacturing ApplicationsThis study presents an integrated computational framework combining machine learning (ML), Bayesian optimization (BO), and experimental validation to design sustainable concrete incorporating construction and demolition waste (CDW) for both conventional and 3D concrete printing (3DCP) applications. In traditional mortar, ML models accurately predict 28-day compressive strength using early-age results, enabling nearly 10× acceleration of mixture design cycles. Optimized mixtures achieved up to 50 % reduction in ordinary Portland cement (OPC) while maintaining strengths above 40 MPa. For 3DCP, a multi-objective BO framework simultaneously maximized buildability and CDW content. Experimental results validated enhanced buildability (up to 10 printed layers) and compressive strengths up to 60 MPa in cast samples and 52 MPa in 3D printed samples, even with up to 97 % CDW. The study highlights the potential of data-driven methods to transform sustainable material design in the construction industry.