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Molecular Simulation of Zeolites by Machine-Learned Potentials
No full textZeolites are a class of minerals frequently used in catalysis, gas separation, and ion exchange. Zeolites exhibit significant variability in the morphology and dimensions of their pores and channels, which directly influences their suitability for specific applications depending on the molecules intended for incorporation. By systematically screening a comprehensive array of hypothetical zeolite structures to identify those that are synthesizable, we aim to expand the pool of candidate zeolites. This expanded database will enable the selection of more tailored zeolites for particular applications, thereby enhancing overall efficiency. To screen these structures, it is essential to accurately model their thermodynamic stability. Molecular dynamics (MD) simulations are a powerful tool for this, but the choice of force field plays a critical role. Variations in atomic positions and framework distortions caused by different force fields introduce discrepancies that impact the accuracy of energy calculations. Ab initio molecular dynamics avoids these inaccuracies by computing interatomic forces from electronic structure calculations, though these calculations are very computationally expensive which limits the time scale of simulations. Alternatively, machine learning can be used to train a model on ab initio calculations to run simulations at longer time scales and at near ab initio accuracies. The first objective of this study is to produce a machine learned potential to simulate amorphous and crystalline silica at time scales previously unavailable for simulations with near ab initio accuracy. The second work will focus on models trained using more accurate meta-GGA level DFT calculations for silica, while the final works will focus on investigating the effects of ion hydration radius and structure directing agents on zeolite stability
Essays in Labor Economics and the Economics of Education
No full textThe dissertation consists of two papers on the economics of education and labor economics. In the first paper, I estimate the impact of school closures on students. School closures are a widespread and persistent issue in the US, displacing hundreds of thousands of students each year due to demographic shifts, outmigration, and performance-based policies. Although closures are often justified as a means to provide access to better-resourced schools, they frequently cause disruption and backlash, particularly among disadvantaged communities. Using Texas administrative data and a difference-in-differences approach, this study examines both short- and long-term effects of public school closures from 1998 to 2015. I find that closures—mostly driven by demographic and financial pressures—lead to immediate declines in test scores and increases in absences and disciplinary actions, especially among secondary students and those from economically disadvantaged families. While test scores tend to recover, behavioral disruptions persist. In the long run, closures reduce high school graduation, college enrollment, and employment rates, and lead to a drop in earnings by the mid-20s. This study highlights the need for caution in using closure as an education policy tool. In the second paper, I examine the labor market implications of the gender reversal in educational attainment. Women have overtaken men in educational attainment in the US and many developed countries, reversing a long-standing gender gap. While earlier research attributes this reversal to higher psychic costs in education for men, this paper formally explores the implications of gender differences in psychic costs of schooling on education, skills, and wages using Becker's human capital framework. Introducing behavioral measures as a proxy for psychic costs, the study shows that women have consistently lower psychic costs, which explains one-third of the gender education gap. However, this sorting results in a reversal of the gender gap in cognitive skills when conditioning on education—women have lower cognitive skills than men at the same educational level. While non-cognitive traits (e.g., behavior) do not directly affect wages beyond education, cognitive skills have strong labor market returns, and their unequal distribution across genders due to differential educational sorting amplifies the gender wage gap. Controlling for these skills reduces the wage gap by 7–12\%. The findings highlight the potential problems associated with gender comparisons at the same educational level, particularly when various skills are not available
Towards Accessible and Robust Methods for Curve-Based Vector Field Visualization, Analysis, and Synthesis
No full textVector fields and their analysis are widely applied in many important aero- and hydro-dynamical systems. The rising complexity and scale of scientific simulations have elevated the importance of curve-based representations of vector fields, as they effectively capture both local behavior and global patterns of the underlying phenomena. However, effectively exploring, evaluating, generating, and systematizing the analysis of these complex datasets presents significant challenges. Our work addresses these challenges through four interconnected contributions. First, we introduce the Curve Segment Neighborhood Graph (CSNG), a novel graph-based representation that captures relationships between curve segments, enabling automated feature detection through community detection algorithms while supporting interactive multi-level exploration through a force-directed layout. Second, we present a comprehensive evaluation framework for assessing neighbor search strategies in curve-based vector field analysis, introducing new metrics for characterizing neighborhood configurations and providing empirical guidance for selecting appropriate search strategies. Third, we introduce a novel diffusion-based framework for synthesizing physically plausible 2D vector fields from sparse streamline inputs. Unlike traditional optimization-based solvers that often produce overly smoothed results, our approach uses a conditional diffusion model with a unique streamline-constrained training strategy. This allows the model to learn the underlying distribution of complex flows, enabling it to reconstruct intricate features from diverse datasets and generate new fields from hand-drawn inputs, significantly outperforming baseline methods. Finally, we develop a flexible client-server framework for curve-based visualization that bridges the gap between research implementations and practical applications, supporting multiple programming languages and customizable workflows. Together, these contributions form a robust, accessible foundation for the entire lifecycle of curve-based data, from synthesis to exploration and analysis, advancing both theoretical understanding and practical capabilities in scientific visualization
PTSD Symptom Severity and the Perceived Ability to Refrain from Suicidal Action Among First Responders Who Served During Hurricane Harvey: Associations with Stigma and Social Support and Connection
No full textPosttraumatic stress disorder (PTSD) symptoms are well-documented among first responders and demonstrate robust associations with suicide-related outcomes, which have been identified to be a major public health concern among this understudied population. There remains a dearth of literature examining the roles of various risk and/or protective psychosocial factors – such as stigma, social support, and social disconnectedness – among first responders with respect to the self-perceived ability to refrain from engaging in suicidal actions. These factors may have clinical relevance as they have the potential to explain the psychosocial mechanisms for which the experience of PTSD symptoms may confer one’s elevated risk for suicide-related outcomes and may serve to inform targeted cognitive-behavioral treatment approaches for first responders identified to be at a greater risk for suicide. The present study investigated the following aims: (1) the serial indirect effect of the exposure severity to Hurricane Harvey on a perceived self-efficacy to avoid suicidal actions via PTSD symptom severity and the self-reported perceptions of anticipated public stigma and social support, and (2) the serial indirect effect of PTSD symptom severity on a perceived self-efficacy to avoid suicidal actions via the self-reported perceptions of anticipated public stigma, social support, and social disconnectedness (i.e., thwarted belongingness [TB] and perceived burdensomeness [PB]). The sample was comprised of 112 first responders (Mage = 42.14; SD = 10.37; 82.1% male) who served during Hurricane Harvey in 2017 and were recruited in 2022 from fire and EMS departments and law enforcement agencies in a large metropolitan area in the southern U.S. to complete an online survey. Results revealed that Hurricane Harvey exposure severity was not indirectly associated with a perceived self-efficacy to avoid suicidal actions via the sequential effects of either (a) PTSD symptom severity and anticipated public stigma (β = -0.09, SE = 0.06, 95% CI = [-0.235, 0.007]) or (b) PTSD symptom severity and perceived social support (β = -0.01, SE = 0.04, 95% CI = [-0.111, 0.072]). PTSD symptom severity did not demonstrate indirect associations with a perceived self-efficacy to avoid suicidal actions via the sequential effects of either (a) anticipated public stigma and TB (β = -0.01, SE = 0.01, 95% CI = [-0.023, 0.003]) or (b) anticipated public stigma and PB (β = 0.01, SE = 0.01, 95% CI = [-0.007, 0.020]). PTSD symptom severity was indirectly associated with a perceived self-efficacy to avoid suicidal actions via the sequential effects of (c) perceived social support and TB (β = -0.03, SE = 0.02, 95% CI = [-0.070, -0.005]) but was not via the sequential effects of (d) perceived social support and PB (β = -0.02, SE = 0.01, 95% CI = [-0.042, 0.001]). These findings provide evidence for continued examination into the relevant psychosocial mechanisms that may help to inform tailored evidence-based suicide-related intervention and prevention programs for first responder organizational culture
A Pipelined FPGA-Based Frame Synchronizer for Gaussian Noise Channels
No full textThis paper presents a Field Programmable Gate Array (FPGA)-implementable Frame Synchronizer that overcomes deficiencies of existing synchronizers in the space communications industry and provides a pipelined approach to achieve improved performance in latency, performance in the presence of noise, and streamlined implementation complexity. Unlike a soft decision synchronizer, magnitude (soft) bits are not required from the demodulation stage, and only the sign bit is used, reducing the complexity and signal counts between the transceiver and the synchronizer. Improved performance in noise can be achieved by introducing a small observation window surrounding the candidate Attached Sync Marker (ASM) window to uncorrelated data around the ASM. Further improvement in the presence of noise is achieved by using two ASMs, effectively doubling the ASM length of observation, but with no increase in the ASM pattern length and using existing predefined ASM patterns, thus remaining compliant with the Consultative Committee for Space Data Systems (CCSDS) standards. A parallel and pipelined implementation without a state machine eliminates latency from search, verify, lock, and flywheel states and reduces the effects of cycle slips of traditional flywheel state machine synchronizers
The Moderating Effects of Disclosure on Bpd Personality Pathology Severity and Workplace Interpersonal Mistreatment
No full textBorderline Personality Disorder (BPD) is a type of personality pathology that has been understudied in organizational research despite consistent evidence of unemployment and impaired employment within this population. Prior research has examined the deleterious effects of BPD on task-related outcomes; however, there has been little work identifying how personality pathology severity affects interpersonal workplace relationships. This thesis addresses this gap by exploring how, relative to workers with and without other mental illnesses, the presence of personality pathology leads workers with BPD to experience increased workplace ostracism and incivility. Furthermore, due to the highly stigmatized nature of BPD, employees with this disorder may receive advice to avoid disclosing their diagnosis. This thesis inspects these claims by using disclosure theories to hypothesize that disclosure may actually be beneficial for certain employees with more severe personality pathologies. To test these hypotheses, a cross-sectional survey assessing personality pathology and experiences of workplace mistreatment was distributed to 544 participants, of which n = 169 identified as having BPD, n = 202 as having a different mental disorder, and n = 173 as having no mental disorders. Contrary to expectations, there was no evidence that disclosure affected interpersonal mistreatment outcomes for employees with BPD. It is possible that the null results may be due to a lack of public knowledge about this disorder or the impaired cognitions inherent to personality pathology, both of which may have minimized the benefits of disclosure
Compressive Strength Prediction of Green Concrete with Recycled Glass-Fiber-Reinforced Polymers Using a Machine Learning Approach
No full textFiber-reinforced polymer (FRP) materials are increasingly used in the construction and transportation industries, generating growing volumes of waste. This study applied a machine learning model to predict the compressive strength of eco-friendly concrete incorporating recycled glass-fiber-reinforced polymer (GFRP) waste. Based on 119 laboratory mixes, the model achieved a good prediction accuracy (R<sup>2</sup> = 0.8284 on the test set). The analysis indicated that compressive strength tends to decrease at higher GFRP dosages, with relatively favorable performance observed at low contents. The two most influential factors were the water-to-cement ratio and the total GFRP content. The physical form of the recycled material was also important: powders and fibers generally showed positive effects, while coarse aggregate replacement was less effective. This machine learning-based approach offers preliminary quantitative guidance on mix design with GFRP waste and highlights opportunities for reusing industrial by-products in more sustainable concretes
From Morphogenesis to Remodeling: Role of Numb Family Proteins in Heart Development and Disease
No full textBackground: Left ventricular noncompaction cardiomyopathy (LVNC; OMIM No. 604169) is anatomically characterized by prominent trabeculation and thinner wall of the left ventricle (LV). It is the third most prevalent pediatric cardiomyopathy and increasingly recognized in adults. Despite its clinical significance, the pathogenesis of LVNC remains unclear. Emerging evidence points to the epicardium and its lineages as critical modulators of ventricular formation, yet the intrinsic regulators of epicardial influence are not fully defined. We therefore disrupted epicardial cells (EpiCs) entering the myocardium to determine the roles of epicardium in ventricular morphogenesis. We determined how epicardium-specific Numb family proteins (NFPs) regulate ventricular formation, as well as whether NFPs in cardiac fibroblasts influence adult cardiac remodeling. Method: We deleted epicardial NFP homologs Numb and Numblike via Tbx18Cre/+ and inducible WT1CreERT2/+ to generate epicardium-specific Nb and Nl double knockout (EDKO) and iEKO, respectively. Lineage tracing, biochemical tools, and single-nuclei mRNA sequencing were utilized to determine the defects and disrupted molecular mechanisms in EDKOs. We applied pharmacological interference to rescue defects in EDKOs in vivo. Cardiac structural and functional changes over time in adult stages were examined using echocardiography and histochemistry. In a concurrent study, fibroblast-specific NFPs knockout (FDKOs) mice were exposed to β-adrenergic stress to examine cardiac remodeling. Results: In EDKO hearts, EpiCs/Epicardium-derived cells‘(EPDCs) migration was arrested at the epicardium/sub-epicardium, resulting in reduced EPDCs in the trabeculae. EDKO hearts displayed LVNC, and iEKOs recapitulated the defects. Mechanistically, upregulation of Fgfr1 in epicardium and downregulation of Fgf ligands in cardiomyocytes of EDKOs were observed. Exogenous Fgf2 supplementation to pregnant females partially rescued defects in EDKO hearts. Female EDKOs maintained LVNC in adulthood and underwent sudden death starting at one year of age. Additionally, transcriptomic and biochemical analyses revealed maladaptive remodeling in stressed FDKOs, characterized by dilated cardiomyopathy, with upregulation of injury markers and reduced cardiac functions. Conclusion: NFPs in EpiCs are required for EPDCs invasion into the myocardium, especially the trabeculae. Our findings highlighted the essential roles of epicardial NFPs-Fgf axis in regulating epicardium-myocardium crosstalk, subsequently facilitating LV compaction. Also, fibroblast-specific NFPs’ role in cardiac remodeling under stress was demonstrated
Driving Strategic Innovation Through AI Adoption in Government Financial Regulators: A Case Study
No full textPublic institutions are experiencing increased dynamism due to rapid technological development and digitalization, which are creating novel opportunities for innovation. This reality is particularly prevalent in high-accountability contexts, such as financial regulation, where the adoption of Artificial Intelligence (AI) drives new forms of governance. Orchestrating this technological shift can offer a path to enhanced effectiveness; however, it requires new capabilities to sense, seize, and reconfigure opportunities in a complex public-interest environment. However, prior findings lack insights into the specific dynamic capabilities and routines required for responsible AI adoption in the public sector. Therefore, this study investigates how a government institution develops dynamic capabilities to govern AI innovation. Through a single, in-depth case study of a national financial regulator, this study offers insights into the specific micro-routines that underlie the regulator&rsquo;s sensing, seizing, and reconfiguring capabilities. We develop a capability-based framework that demonstrates that responsible adoption depends on a dual set of capabilities operating at both an internal (organizational) and an ecosystem (market-facing) level. This study&rsquo;s findings carry implications for the literature on public sector innovation, dynamic capabilities, and platform governance, as well as for leaders managing technological change in governments
