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MO Isotopes in the Aleutian and Costa Rican Arcs: Source Variability and Juvenile Crust Formation
No full textChemical and physical transfer between the surface, lithosphere and asthenosphere at subduction zones leads to arc magmatism and continental crust (CC) production. Subduction zones magmas and associated volcanic products sample a variety of magmatic sources that contribute to their chemical compositions in various proportions. This dissertation contributes to the study of subduction zones magmatism by assessing the relative role of subduction inputs (sediments, altered oceanic crust – AOC – and serpentinite) as flux agents in the arc magmatism and in CC formation in subduction zones under the scope of Mo isotope systematics.
I present the first Mo isotope analyses form input to output in the Aleutian arc and the first Mo isotope data for the Middle American Trench in Costa Rican. The Aleutian arc is an active system that allows investigation of arc magmatism under presence of various sources (marine sediments with different redox conditions and various lithologies, AOC, serpentinite, and slab eclogitic melts). In turn, Costa Rica represents the best example of arc continentalization in which CC-like volcanic rocks are formed as the result of partial melting of the subducting oceanic crust overprinted by mantle-plume magmatism, thus providing a unique opportunity to test if slab melting is a viable mechanism producing the characteristic heavy Mo isotopes of the CC.
I demonstrate that Aleutian arc lavas from Piip to Frosty Peak (n = 77) have light, MORB-like, Mo isotope compositions, excepting in volcanoes adjacent to the subducting Amlia Fracture Zone (AFZ), where the Mo isotopes are particularly heavy. Mo isotope analyses in abyssal serpentinites (as proxies for serpentinites in subduction settings) show that these have the heaviest Mo isotopes of fluid-rich inputs. I also present Mo isotope analyses on North Pacific sediments outboard the Aleutian arc (n = 53) from drill cores ODP 886, DSDP 183, DSDP 178 and IODP 1417 and show that subducting sediments input predominantly light, mantle-like or lighter, Mo isotopes. Light Mo isotopes of open-ocean pelagic sediments (ODP 886, DSDP 183) are consistent with an oxide control (e.g., Fe-Mn nodules). Heavier Mo isotopes are observed in IODP 1417, where local reducing conditions are prevalent and where associated high S suggest a sulfide vs. oxide control.
From Mo isotopes and trace element systematics of Aleutian lavas, as well as Mo isotopes in sediments outboard the Aleutian arc, the heavy Mo isotopes in AFZ lavas can be explained by three non-mutually exclusive scenarios: (1) melting of trench turbidites with high organic carbon and diagenetic pyrite carrying heavy Mo isotopes, (2) an enhanced fluid-flux in shallower magmas such as Seguam with low Dy/Yb or, (3) presence of fluid-rich sources (e.g., serpentinites) associated with the AFZ.
Finally, I test if slab melting in Costa Rica can produce heavy CC-like Mo isotopes. I show that \u3c 10 Ma old Costa Rican volcanic rocks, representing juvenile CC formation in subduction zones have light, MORB-like, Mo isotopes. From this data and comparisons with global arc settings, I conclude that slab melting cannot produce the heavy Mo isotopes of the CC and that additional crust-specific processes (e.g., CC differentiation and magmatic fluid exsolution) may be required to generate the heavy Mo isotopes of the CC. This work sheds light on the factors controlling Mo isotope fractionation across various systems (e.g., fluids, open-ocean sediments, volcanic rocks) and contributes to the current understanding of the global Mo cycle in subduction zones
Exploring the Holistic Wellness of Black Women Counselors
No full textCounselor wellness is crucial for clinician longevity and ethical practice. However, there is limited literature on the holistic wellness of Black women counselors. This manuscript probes into the lived experiences of Black women counselors, particularly in relation to the sociopolitical climate and racial injustices in the United States. Utilizing Consensual Qualitative Research, we examine the experiences of 15 Black women counselors working in counselor education, private practice, and community agencies within the Southeast Region of the United States. We anticipate this approach will enhance counselor education literature and foster increased peer support
Relationships Between Modern Marching Percussion and Concert Percussion: An Investigation of Selected Percussion Ensemble Repertoire
No full textPercussion, as an expansive and diverse art form, encompasses a wide range of performance and cultural aesthetics, all unified by the foundational elements of rhythm, technique, and expression. Within this spectrum, the relationships between marching percussion and concert percussion presents a significant area of study. This document explores the relationships of marching percussion and concert percussion through the contextual analysis of specific percussion ensemble repertoire. By defining the distinct characteristics of concert and marching percussion, the study establishes a framework for comparison and analysis. It highlights how techniques, both musically and compositionally, and the roles of specific instrumentation has contributed to the cross-over of compositional and performance practices existing in both realms. Additionally, this research curates a contextual catalogue of concert percussion works that incorporate stylistic elements from the marching tradition, providing pedagogical tools and performance strategies. The study also addresses the broader professional impact of cross-disciplinary versatility, offering insights into career development within the field of percussion. Through a detailed examination of repertoire, techniques, and educational applications, this document contextually showcases the ongoing symbiotic relationship between marching and concert percussion in ever-evolving percussive arts
ESPORTS Audience Segmentation: Means-End Chain Approach
No full textAs live-streaming platforms have fueled esports’ rapid growth, they have also diversified content from competitive matches to casual gameplay, creating a complex audience landscape. To capture this diversity, the present study applied means-end chain (MEC) theory to: (1) examine how esports audiences cognitively associate different content attributes with their expected consequences and underlying personal values; (2) segment esports audiences based on these unique MEC structures; and (3) compare consumption frequency of both watching and playing esports across the identified segments.
The current study adopted exploratory sequential mixed methods to achieve the study’s objectives. In Study One, in-depth interviews with 22 participants identified key MEC components—attributes, consequences, and values. These insights informed Study Two, which analyzed 974 survey responses from esports live-streaming viewers to quantitatively assess significant MEC patterns (i.e., linkages of attributes-consequencesvalues) using converted weight matrices. Subsequently, 45 weights were then used as input variables for latent profile analysis to segment the esports audience. Lastly, to validate behavioral differences across segments, uses and gratifications theory was integrated, and group differences in esports consumption frequency were examined using one-way MANCOVA, controlling for gender.
The analysis identified five key content attributes (competitive, interactive, skillbased gaming, humorous, and informative), six consequences (entertainment, knowledge acquisition, vicarious experience, skill appreciation, identity exploration, and socializing opportunities), and seven personal values (relaxation, pragmatism, self-reflection, social acceptance, competence, personal growth, and benevolence) at market level. Based on the strength and configuration of MEC linkages, four distinct consumer segments were revealed, including Goal-Oriented Learners, Casual Fun Seekers, Exploratory Enthusiasts, and Deeply Engaged Idealists. Notably, segments with stronger and more numerous MEC are likely to exhibit higher frequencies of both esports viewing and gameplay, indicating that audiences with more enriched MEC tend to be more actively engaged consumers.
This study contributes theoretically by extending MEC theory with esportsspecific constructs and integrating uses and gratifications theory. It also offers a novel segmentation approach that captures the multi-dimensional nature of esports consumption. Practically, the findings provide actionable insights for content creators, platform managers, game publishers, league organizers, and sponsors to tailor strategies that align with diverse audience motivations and values
Enhancing D-Serine Signaling Reverses Age-Related Cognitive and Neurobiological Deficits
No full textD-serine, the primary co-agonist for synaptic NMDARs, and as such is critical for learning, synaptic plasticity, neural network regulation. During aging, brain D-serine concentration decrease which could explain the age-associated cognitive deficits and impaired NMDAR activation and synaptic plasticity. As a result, in this study we investigated the effects of age on the effectors of the D-serine biosynthesis pathway to better understand the mechanisms that drive reduced D-serine availability as well as a efficacy of therapeutic that has mechanistic potential to improve memory in aging individuals by preventing the degradation of D-serine and therefore potentiating NMDAR activation. To test this, we characterized the performance of young adult (6-months-old) and older-aged (24-months-old) F344 rats in a place-learning task in the Morris water maze. Following, we utilized a cohort of rats to quantify protein levels of serine racemase, the enzyme which catalyzes the conversion of L-serine into D-serine, as well as PHGDH, the catalyst of the rate-limiting step in L-serine synthesis. Our findings reveal that compared to young adult, older-aged rats have significantly lower serine racemase, but higher PHGDH in both the PFC and HPC. The other cohort of rats were trained no a delayed match-to-place task following initial water maze testing, and were subsequently treated with 3-Methylpyrazole-5-carboxylic acid, a D-amino acid oxidase inhibitor (DAOI). We observed a significant dose-dependent improvement on task performance in older-aged rats. In addition, we determined that DAOI treatment restores learning-associated ERK phosphorylation, an indirect marker of NMDAR activation, in both the PFC and HPC of older-aged rats. These results suggest that enhancing D-serine availability may be a useful approach for restoring cognitive performance in older-aged adults
Characterizing the Relationships Between Cranial Geometry, Brain Morphometry, and Brain Mechanical Properties Using MR Imaging and Elastography
No full textTraumatic brain injury (TBI) is a global health concern, with over 69 million cases annually. TBI risk depends on subject-specific neuroanatomical variation, yet research often only considers generic finite element (FE) models, based on the geometry and material composition of a 50th percentile male head. Although highly accurate, subject-specific models require expensive magnetic resonance (MR) imaging, elastography, and time-consuming computational resources. There is a need to create subject-specific TBI models that account for subject-specific variations, without neuroimaging. This study aims to create mathematical models to predict brain morphometry (dimensions, volume) [Objective 1] and mechanical properties (shear stiffness, damping ratio, octahedral shear strain) [Objective 2] from anthropometric measurements (age, sex, head dimensions). Such predictions could be used to scale generic TBI models, improving model accuracy.
Neuroimaging data, including MR structural imaging and elastography, were obtained from an open-source dataset. The full cohort contained 156 subjects (74 males, 82 females), aged 14-75, with neuroimages and associated metadata (age, sex, weight, height). Regional and total brain volume, head measurements, and brain dimensions were extracted from T1- and T2-weighted neuroimages and deep-learning brain segmentations (Brain Mask, SLANT-CRUISE) using ITK-SNAP (Version 3.8.0) and custom MATLAB code (Version R2024b). Brain mechanical properties (50th, 95th percentile shear stiffness (SS), damping ratio (DR), octahedral shear stiffness (OSS)) were extracted from whole-brain MR elastography data (30, 50, 70Hz) from a subset of 85 subjects (37 males, 48 females). Simple linear (SLR) and stepwise multiple linear (MLR) regression models were created to predict brain measurements and mechanical properties from head anthropometry. Principal component analysis (PCA) was used to identify brain shape modes that explain 95% of the variation. Principal component regression (PCR) was used to relate brain shape to mechanical properties.
Analysis of the SLR models indicated that head width, length, depth, and perimeter are moderate predictors of respective brain width, length, depth, and volume (R2: 0.61, 0.73, 0.69, 0.64, respectively). The MLR models improved R2 values for brain width, length, and volume by 19%, 4%, and 22%, respectively, suggesting multivariable dependence of brain geometry on combinations of anthropometric predictors. The SLR models for mechanical properties performed poorly, with low R2 values when predicting SS, DR, and OSS from age only (R2,50th: 0.33, 0.31, 0.00; R2,95th: 0.11, 0.32, 0.06), and the MLR models improved R2 values by including combinations of head dimensions and sex in the regression equations. The high error in mechanical property predictions suggests a dependence on factors beyond head anthropometry. The results of PCA showed that five principal components explained 95% of brain shape variation, capturing frontal-occipital regional shape variation (40.8%), diagonal hemispheric asymmetry (21.3%), sagittal hemispheric asymmetry (15.3%), and a width-length relationship (5.3%). PCR models using these components to predict mechanical properties showed large deviations from true values, suggesting that brain shape alone is insufficient for characterizing tissue mechanics
Information Literacy in Charter Schools: An Institutional Ethnography
No full textA strength of US charter schools is their opportunity to carve out an educational niche. Without the same regulation and district control present in traditional public schools, a charter school’s approach can seriously impact how the school addresses information literacy. In our current culture, we are saturated with media and technology, and students learn about navigating, evaluating, and using information responsibly and creatively at school. However, there is currently no published research on how charters do this critical work with their students, particularly with fewer libraries and librarians in the sector. This study uses institutional ethnography to map ruling relations around information literacy in charter schools. This is an exploratory study to observe information literacy in a few charter high schools and to understand systems of power that influence a school’s approach. Interviews, observations, and texts from participating charter schools in South Carolina revealed four aspects of school culture contributing to building and sustaining information literacy in these two schools: teacher leadership, individualized college and career preparation, social and emotional support, and support from libraries. The sector has opportunities to leverage the unique strengths of individual charter schools to improve information literacy. Charters have some advantages over traditional schools, but they also face challenges without the economies of scale available to districts. Building stronger information literacy in secondary charter schools and schools more widely will require more study on effective practices and interventions, but findings can help with advocacy and building partnerships
Some Likelihood-Based Methods for Clustering Functional Data
No full textThe analysis of functional data is an increasingly relevant part of statistics. The exploratory data analytic method of cluster analysis plays a very important role in different fields. Over the years, researchers have developed many clustering approaches, striving to achieve more accurate and efficient clustering. In Chapter 1, we aim to improve the accuracy of our outcome when clustering functional data. To achieve this goal, we use a predictive likelihood function which serves as an objective function to optimize in order to determine the most appropriate clusters. To optimize the objective function over the space of clustering partitions, we produce a Markov chain of partitions. At each step, we generate via a simulated annealing algorithm new partitions for which the predictive likelihood is evaluated. A schedule for adjusting the temperature parameter enhances the efficiency of the chain. Compared with the K-means algorithm and the algorithm of the funFEM function in the R package funFEM, our method can achieve higher performance as measured by the Rand index for various simulated data. Our approach is also applied to two real data sets, a vertical density profile data set and a yeast gene data set, and it achieves good clustering results. In Chapter 2, we explore the application of the James-Sugar method to effectively cluster sparse functional data, which employs a model-based approach to clustering combined with basis expansions to manage the sparsity and irregularity of data points. We investigate the optimal tuning parameters necessary for maximizing clustering accuracy to provide a deeper understanding of how model settings affect clustering results in functional data analysis. Chapter 3 presents a robust framework for clustering sparse functional data by integrating multiple imputation using smoothing splines with a predictive likelihood-based clustering method. To address missing data, we generate multiple imputed datasets by perturbing a smoothing parameter in a smoothing-spline nonparametric regression. The variation of the smoothing parameter across the imputation process allows us to capture the uncertainty in that imputation process. Each completed dataset is clustered using a likelihood-based approach that models curves through basis expansions. To ensure consistent labeling across imputations, we employ the Hungarian algorithm and finalize cluster assignments via majority voting. Simulation studies demonstrate the method’s strong performance across varying levels of sparsity and noise. We further apply the method to a real-world wages dataset with irregular time points, successfully identifying stable and interpretable clusters. Our approach offers a flexible and statistically grounded solution for functional data clustering in the presence of missingness and sparsity, with practical relevance in economics, biostatistics, and the social sciences
Understanding Verbal Irony: A Contrastive Analysis of Personal and Informational Common Ground
No full textThis study investigates how representations of personal common ground (PCG; viz., close relationships vs. strangers) and informational common ground (ICG; viz., shared world knowledge) interact in shaping a reader’s comprehension of verbal irony in fictional narratives. While previous research has shown that greater familiarity among story characters enhances readers’ comprehension of ironic statements made by the characters (Gibbs, 2000; Kreuz et al., 1999; Kreuz & Link, 2001; Pexman & Zvaigzne, 2004), the interplay between representations of PCG and ICG remains understudied. In this study, we assumed that ICG takes precedence over PCG. Accordingly, we predicted that irony comprehension would be facilitated when ICG was high, even if PCG was low, resulting in faster RTs. Conversely, we expected comprehension to be impeded when ICG was low, even if PCG was high, leading to slower RTs. A main effect of ICG was also expected regardless of PCG, along with an interaction between ICG and PCG across rating measures that would further support irony comprehension. Counter to our predictions, results from two experiments showed that, overall, ironic stories were read faster when PCG was high and ICG was low. In contrast, ironic stories were read more slowly than literal stories when both PCG and ICG were low in experiment 1, and when both were high in experiment 2
Optical and Thermal Characterization of Vertically Conducting Β-Ga₂O₃ Diodes Grown on 4H-SIC Substrates for Short-Wavelength (\u3c254 NM) Detection
No full textThe advancement of compact, thermally stable, and efficient devices for deepultraviolet (DUV) detection below 254 nm is critical for solar-blind sensing, radiation monitoring, and high-power electronics. This thesis presents the growth, fabrication, and detailed optical and thermal characterization of vertically conducting β-gallium oxide (βGa₂O₃) Schottky barrier diodes (SBDs) grown on n-type 4H-Silicon Carbide (4H-SiC) substrates using metal-organic chemical vapor deposition (MOCVD).
β-Ga₂O₃ is an ultra-wide bandgap (UWBG) semiconductor (~4.8 eV) with intrinsic DUV transparency and high breakdown electric field, making it an ideal candidate for short-wavelength photodetection. However, its low thermal conductivity limits its vertical device performance. To overcome this, β-Ga₂O₃ was heteroepitaxially grown on thermally conductive 4H-SiC substrates, which provide enhanced thermal management and structural support.
Epitaxial films with a total thickness of ~0.5 μm were deposited using MOCVD, featuring a co-delta doped lower β-Ga₂O₃ layer with silicon and indium to enhance crystalline quality, and an undoped top layer to suppress leakage current and enable solarblind operation. X-ray diffraction (XRD) revealed strong (-402) β-phase orientation with reduced full width at half maximum (FWHM), while X-ray photoelectron spectroscopy (XPS) verified successful dopant incorporation and stoichiometric film growth.
Optical measurements confirmed a significant photocurrent enhancement under 254 nm UV-C illumination, with negligible response under ambient light, validating the solar-blind nature of the devices. Temperature-dependent I–V characterization demonstrated a reduction in turn-on voltage and increased forward current density in codelta doped β-Ga₂O₃ structures, indicating improved carrier transport. With rising temperature, reverse breakdown voltage declined due to enhanced thermionic emission, while reverse leakage current increased sharply beyond 125 °C, attributed to trap-assisted conduction and dislocation activation at the Ga₂O₃/4H–SiC interface. Devices maintained stable rectifying behavior up to ~150 °C, beyond which junction isolation degraded and non-ideal I–V characteristics emerged, confirming the onset of thermally activated transport mechanisms.
These findings establish vertically conducting β-Ga₂O₃/4H-SiC heterostructures as a robust platform for short-wavelength optoelectronic applications. The integration of precise doping schemes with thermal and optical evaluation provides a scalable pathway toward next-generation UWBG detectors and switches optimized for extreme environments