Doctoral Dissertations

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Relationship among Community Location, Home Internet Access Level, and High School
Adusumilli, Rajesh (Virginia Tech, 2026-06-23)
Digital equity has become a central concern in K-12 education as instructional practices increasingly rely on high-quality internet connectivity beyond the school environment. While many school systems have addressed device availability through one-to-one initiatives, less is known about how variations in the quality of home internet connectivity relate to student academic outcomes. This quantitative, comparative study examined the relationships among community vulnerability, home internet quality, and academic performance among high school students in one Virginia school division. Using district administrative records and digital equity monitoring data spanning two academic years, students were categorized as English Language Learners (ELL), Economically Disadvantaged (ED), or non-ELL/non-ED/non-SPED. Home internet connectivity was operationalized using cumulative performance ratings (e.g., low, medium, and high), while academic outcomes included cumulative grade point average (GPA) and the advanced coursework completed. Community context was measured using a Community Vulnerability Index derived from census-based socioeconomic indicators. Descriptive statistics and one-way and two-way analyses of variance (ANOVA) were employed to examin group differences and interaction effects by connectivity level and grade. Results indicated significant disparities in connectivity quality across student groups and vulnerability levels, with English Language Learners experiencing the most severe connectivity disadvantages. Connectivity quality was a statistically significant predictor of GPA and advanced coursework participation for students outside ELL and ED classifications, demonstrating moderate to strong practical significance. In contrast, for ELL and ED students, GPA outcomes were largely explained by grade level rather than internet connectivity, and associations with advanced coursework participation were modest. Overall, findings suggest that while high-quality internet connectivity functions as an academic advantage for students with fewer systemic barriers, connectivity alone does not uniformly mitigate academic disparities for linguistically and economically marginalized students. These results underscore that digital equity requires attention to connectivity quality alongside structural, instructional, and policy factors to ensure equitable educational opportunity.
Spatial and Neuron-Specific Regulation of Nhlh2 mRNA by Snord116 in the Hypothalamus: Implications for Prader–Willi Syndrome
Ariyanfar, Shadi (Virginia Tech, 2026-06-23)
Prader–Willi syndrome (PWS) is a Multifaceted neurodevelopmental condition character- ized by biphasic symptoms, including early failure to thrive, neonatal hypotonia, later onset hyperphagia, severe weight gain, and morbid obesity. PWS is considered a hypothalamic insufficiency disorder that resulted from the deletion or inactivation of paternally inherited genes, with the smallest region containing a group of 30 small nucleolar RNAs, 'snoRNAs' known as the SNORD116@ locus. SNORD116@ snoRNAs are derived from a larger long non-coding gene, SNHG14. Whole-body deletion of Nhlh2 in mice leads to PWS-like phenotypes, such as later onset of weight gain and delayed puberty. Evidence has shown that increasing expression of Snord116-3 in a mouse hypothalamic cell line enhances the stability of Nhlh2 mRNA, likely through a motif in the 3' untranslated region. However, the link between Nhlh2 and Snord116 could not be confirmed in whole tissues of PWS mice or humans, which is mainly due to the lack of knowledge on where and when Snord116 interacts with its target mRNAs in vivo. Hence, the current study explores the spatial and temporal dynamics of Snord116 interaction with Nhlh2. We mapped concurrent expression of Snhg14/Snord116 and Nhlh2 throughout the adult mouse forebrain using quantitative RNA multi-plex in situ hybridization "RNAScope." This work successfully showed a strong, significant co-expression level between the two target genes in the arcuate hypothalamic nucleus. Moreover, we noticed a meaningful neuron-specific expression response of Nhlh2 to the presence and absence of Snhg14/Snord116 in Proopiomelanocortin (Pomc) and thyrotropin- releasing hormone (Trh) neurons. Surprisingly, co-expressing neurons have a significant partitioning of Nhlh2 mRNA to the nuclear compartment of the neuron, compared to the Snord116del model, while such a pattern was not observed in other brain regions, such as the hippocampal formation subregions, when compared in WT vs Snord116del mice. A hy- pothalamic cell line with co-expressed Nhlh2 mRNA and Snord116 construct recapitulates the nuclear partitioning pattern for Nhlh2 only when the putative Snord116 binding site is contained in the 3' untranslated region of the Nhlh2 mRNA. Nuclear sequestration of mature mRNA appears to protect it from degradation and facilitate its release into the cytoplasm when protein synthesis is required. This finding indicates that Snord116 regulates the subcellular localization of mRNA and could influence protein production levels. This changes how we understand the function of the orphan non-coding snoRNA Snord116 in PWS and may also provide new insights into RNA biology and conditions like obesity.
A Just Transition?: Syndemic Health Impacts of Extractivism
Albritton, Meghan Jo (Virginia Tech, 2026-06-23)
Extractivism is a political-economic model often characterized by large-scale resource extraction, driven by an ideology that prioritizes capital accumulation over the well-being of people and places. Mining, though distinct from extractivism as a concept, is a central and widely recognized example of extractivism in practice and is projected to intensify as demand for different types of raw materials, including rare earth minerals, grows, particularly in the context of the global energy transition. The negative human, environmental, political, social, and economic impacts of many forms of mining are becoming increasingly well-documented, with notable evidence of these effects in regions such as Central Appalachia (USA), Olopa (Guatemala), and beyond. This dissertation employs a community-based participatory research (CBPR) approach, utilizing semi-structured interviews and participatory GIS/mapping to examine local perceptions and patterns of risk, health, and barriers to action. Chapter 1 provides an overview of relevant literature and introduces key concepts to be explored in subsequent chapters. Chapter 2 analyzes qualitative data on the community and social health impacts of extractivism in Central Appalachia, while Chapter 3 focuses on the environmental and human health consequences in the same region. Chapter 4 introduces the concept of strategic quiescence, theorizing it as a rational response to limited agency in coal-impacted communities, challenging stereotypes of non-action in the Appalachian region. Chapter 5 expands the analysis with a cross-case comparison, drawing from both the Central Appalachian study and a prior study in Guatemala to explore the similarities and differences in extractivism's impacts across distinct contexts. The findings underscore the importance of community-driven research and participatory methodologies in understanding the multifaceted impacts of extractivism and environmental injustice. The results of this work raise questions about the sustainability of energy transitions that prioritize decarbonization while simultaneously creating new sacrifice zones, and offers novel insights into the health effects of extractivism through a syndemic framework, highlighting the interrelated drivers of these health crises and identifying potential intervention points. As the global push for new energy technologies grows, this dissertation emphasizes the need for mitigation and proactive measures to prevent the replication of these destructive practices at future sites of extraction.
A search for inelastic dark matter at the Belle II Experiment
Lam, Tommy (Virginia Tech, 2026-06-23)
The Standard Model (SM) of particle physics is a remarkably successful and highly predictive theory of fundamental particles and interactions. However, it is an incomplete description of nature, as it fails to account for several observed phenomena, most notably the existence of dark matter. Observations ranging from galactic rotation curves to anisotropies in the Cosmic Microwave Background (CMB) provide compelling evidence for a non-baryonic gravitational source that lies entirely beyond the scope of the SM. One viable extension to the SM is the inelastic dark matter (iDM) model. This model introduces a dark sector containing two dark fermions, χ1 and χ2, with a mass splitting (∆mχ = mχ2 − mχ1 ). These particles couple to the SM through a massive dark photon (A) portal via kinetic mixing with the electroweak U(1)Y hypercharge component. In this work, we search for iDM production in e+e− collisions through the process e+e− → A′γISR → χ1χ2γISR. The signature is characterized by a high-energy initial-state radiation (ISR) photon and a displaced vertex arising from the decay χ2 → χ1A′∗(→ l+l−) for l ∈ [e, μ]. This analysis is performed using data collected by the Belle II detector at the SuperKEKB asymmetric-energy electron-positron collider in Tsukuba, Japan. We outline reconstruction and selection strategies optimized to isolate these rare signatures from dominant SM back- grounds, such as Bhabha scattering, two-photon processes, and continuum qq ̄ production. To maximize signal sensitivity across a broad mass range (0.05 GeV/c2 ≤ mχ1 ≤ 3.2 GeV/c2), we employ an ensemble of Gradient-Boosted Decision Trees. These models are trained on specific mass windows and particle identification categories to effectively discriminate signal from background using features such as vertex displacement, track-cluster isolation, and missing energy. The statistical analysis uses a frequentist approach to determine Belle II's sensitivity. We perform a single-bin counting experiment using sideband data to estimate the background level within a defined signal window. In the absence of an observed excess, we set median upper limits on the signal yield at a 95% confidence level (CL) using the q ̃ test statistic and toy-based calculations.
In Pursuit of Optimal Training Data: Towards a Unified Framework for Curation and Synthesis
Just, Hoang Anh (Virginia Tech, 2026-06-22)
The field of artificial intelligence has increasingly shifted from model-centric to data-centric approaches. As Large Language Models (LLMs) scale, the quality, distribution, and infor- mational density of training data have become major bottlenecks shaping performance and alignment. However, data quality is often deceptive; data that appears high-quality on the surface may lack the precise instructional signals required for effective learning, or worse, introduce latent biases and degrade reasoning capabilities. This dissertation studies training data optimization through three complementary stages: valuation, selection, and data synthesis and modification. Across these stages, we exam- ine how training data can be diagnosed, selected, and enriched under different practical constraints. Stage 1 (Valuation) moves beyond superficial heuristics by using Optimal Transport distances (LAVA) and 2D-Shapley values to estimate the utility of individual training samples and fragmented data components. Stage 2 (Selection) studies how data distributions can be improved through Projektor, which composes data from multiple sources under partial observability. Finally, Stage 3 (Synthesis and Modification) studies how data signals can be augmented, restructured, or curated to encourage more robust and gen- eralizable behaviors in LLMs. Together, these contributions show how training data can be valued, selected, and enriched to improve data eﬀiciency and model capability.
Energy as Statecraft: Natural Gas, Israeli Foreign Policy, and the Eastern Mediterranean
Mitchell, Gabriel Aaron (Virginia Tech, 2026-06-22)
How does the discovery of new energy resources influence the formation and execution of state foreign policy? This dissertation examines that question through Israel's transition from chronic energy scarcity to natural gas producer, between the 2009 discovery of Tamar field and the October 2023 outbreak of Israel's war against Hamas. Theoretical frameworks all anticipated that such a structural change would redirect Israeli foreign policy in unique ways. This dissertation offers an alternative account. The discovery of new energy resources does not redirect a state's foreign policy but absorbs into its existing foreign policy agenda: energy deepens cooperation where the foundations for it already exist and entrenches conflict or asymmetry where they do not. The primary explanatory variable is not energy but the path dependency of each bilateral relationship. Energy is an instrument of statecraft whose effectiveness is conditioned by the prior trajectory into which it is introduced; it rarely redirects that trajectory. The argument is developed through five bilateral cases – Jordan, Egypt, Palestine, Lebanon, and Turkey-Greece-Cyprus – drawing on process tracing, open source primary and second sources, and personal interviews. Across all five cases, energy amplifies the existing trajectory of bilateral relations and rarely redirects them. Four conditions determine how those amplifying effects operate: the prior state of bilateral relations, which sets the ceiling of what energy diplomacy can accomplish; the relative distribution of leverage between parties; the presence of commercial actors willing to absorb political risk; and third-party facilitation, in which the United States emerged as an indispensable presence. The dissertation contributes to scholarship on Israeli diplomatic history, energy and foreign policy, and Eastern Mediterranean geopolitics, offering a corrective to frameworks that predict uniform effects from energy discoveries regardless of prior relational context.
Ambiguous Evidence: The Rhetorical Work of Interpreting Clinical Trial Evidence after the Women's Health Initiative
Buccilli, Marissa (Virginia Tech, 2026-06-22)
The early termination of the Women's Health Initiative (WHI) hormone therapy trial in 2002 significantly reshaped clinical discourse surrounding menopause treatment and the safety of hormone replacement therapy. Although the WHI was widely interpreted as evidence that hormone therapy increased risks such as breast cancer and cardiovascular disease, clinicians in medical journals quickly began deliberating how the trial's findings should be interpreted and applied to clinical care. This dissertation examines how clinicians reasoned about and interpreted WHI evidence during the early years following the trial's publication. Drawing on qualitative coding and rhetorical analysis of scientific discourse, this study applies a retroactive risk communication framework to analyze a corpus of twenty-seven editorials and review articles published in medical journals between 2002 and 2007. These genres are particularly influential spaces where clinicians interpret emerging evidence and guide professional understanding of clinical research for practice. Through this analysis, the study identifies how clinicians acknowledged methodological limitations in the WHI, including issues related to study design, participant population, and the generalizability of the trial's findings. Despite these critiques, clinicians did not dismiss the WHI as invalid evidence. Instead, they preserved the trial's authority and clinical utility of its' evidence using what this dissertation terms risk topoi. Risk topoi function as interpretive mechanisms that allow clinicians to narrow, reinterpret, or create conditions around trial findings to align them with existing clinical knowledge and patient care practices. By tracing how these risk topoi operate within clinical argumentation, this dissertation demonstrates how clinicians maintain the utility of large clinical trials even amid methodological uncertainty and debate. This study contributes to the scholarship of the rhetoric of health and medicine, technical communication, and science and technology studies by showing how clinical knowledge is produced not only through the generation of new evidence but also through the rhetorical work of interpreting and stabilizing that evidence within professional discourse.
Evaluating Meta-Regression Models with Simulation Studies and Machine Learning Driven Imputation
Gendron, Jonathan (Virginia Tech, 2026-06-22)
Despite the large depth of location and time dimensions in typical meta-regression datasets, the majority of the modeling only occurs at the study-level with little to no consideration for more precise location and/or time groupings. This dissertation addresses this gap by developing simulation frameworks to evaluate how alternative modeling strategies perform when meta-regression methodology specifications are applied to subject-level meta-regression data. This work advances the robustness of empirical research by clarifying how each method performs when confronted with complex heterogeneity. It also contributes to a growing trend in the medical and social sciences toward strengthening methodological reliability in evidence synthesis and provides a foundation for future extensions that integrate real-world data. By systematically evaluating existing methods, extending them with traditional parametric and machine learning driven imputation approaches, and clarifying the risks of misspecified models, this dissertation advances methodological transparency and reliability in evidence synthesis. The results provide applied researchers in economics, medicine, and the social sciences with practical tools for choosing and validating meta-regression models under realistic data conditions. This research also lays the groundwork for future projects that extend the simulation frameworks to incorporate model diagnostics for heterogeneity, temporal-only heterogeneity, and real-world empirical applications. In doing so, this dissertation positions meta-regression as a more trustworthy tool for synthesizing knowledge in fields where reliable evidence is essential for policy and practice.
The Faculties of Conflict: Prussian Statecraft and Martial Culture in German Romantic Political and Aesthetic Thought
McPherson IV, Luther Lee (Virginia Tech, 2026-06-22)
This interdisciplinary dissertation considers the development and proliferation of notions of statecraft in the context of Prussia during the early part of the 19th century, roughly 1800-1870. I adopt a mix of discourse analytical and intellectual historical methods to reconsider the role of the intellectual production of concepts and practices related to statecraft, particularly drawing from a mix of thought that spans from high culture, such as literature, poetry, and philosophy, but also sources that contributed to the development of Prussian martial culture which I locate in part in the proliferation of 'mirrors to the prince' writings during this period along with a broader turn to Machiavelli. These analyses help situate a particular nexus that includes the Romantic concepts of Bildung, Staatsform, Heimat, and Lebensform, which elucidate their views on questions of community, freedom, interpretation, (un)certainty, organicism, and coalesce to reshape Prussian society and state, thus contributing to the later unification of Germany in 1871. I document the evolution of this nexus and its impact on the organizational cultures of the Prussian military, higher education, and police state. This trajectory should prompt IR scholars, in particular, to reconsider the conventional progression of techniques of statecraft in relation to culture. The Romantic turn to the 'mirrors to the prince' genre challenges a clean understanding of reason of state as merely calculative rationality, and a deeper analysis into the intellectual production of symbols, metaphor, myths, concepts, and practices offers a warning about the possibility of cooptation and complicity of aesthetic, philosophical, or academic conceptual developments into the project of (nation)-state building. I offer a cultural interpretation of historical statecraft that understands contemporary concepts such as the national interest and practices of national security as constituted by aesthetic and discursive forms of representation that shape how political reality becomes intelligible.
Learning Different Types of Knowledge in Immersive Virtual Reality: An Integrative Review
Lin, Rui (Virginia Tech, 2026-06-18)
Building on the concept of virtual reality and its development in history, this study introduces immersive virtual reality (IVR) with a head-mounted display (HMD) for instructional purposes. It provides examples of its use across a variety of domains, along with cases that demonstrate inconclusive and controversial findings regarding IVR's impact on learning. By identifying a research gap in examining IVR's impact through the interplay between instructional design and the unique affordances of IVR for learning different types of knowledge, this study adopts an integrative review method to address three central questions: (1) What pedagogical/instructional design approaches are used within HMD-based IVR instruction to foster student learning of different types of knowledge? (2) What affordances are utilized within HMD-based IVR instruction designed to foster student learning of different types of knowledge? (3) What measurable impacts does HMD-based IVR instruction have on students' learning across different types of knowledge in educational contexts? Evidence from the 35 included articles indicates both overlapping and distinct approaches used across instruction targeting declarative knowledge (factual/conceptual), spatial knowledge, and procedural knowledge. These approaches are enabled through the selective and combined use of core IVR affordances, as well as those particularly relevant to each knowledge type. Learning outcomes for procedural learning appear to be more consistently positive, in contrast to the greater complexity observed in declarative and spatial learning. Overall, the findings highlight the need for a more nuanced understanding of how IVR's affordances can be leveraged through intentional instructional design.
An Integrative Literature Review to Explore Digital Wellness Practices in Higher Education Online Course Design
Williams, Daron Bennett (Virginia Tech, 2026-06-18)
This integrative literature review examines factors affecting the digital wellness of online learners in higher education and evaluates how those factors align with Vanden Abeele's (2021) dynamic systems model of digital wellbeing. Drawing on a corpus of 42 peer-reviewed articles identified through a systematic search of five multidisciplinary databases, the review addresses two research questions: what factors affect the digital wellness of online learners in higher education, and how do those factors align with Vanden Abeele's guidance on strategies and considerations for digital wellness research? Inductive thematic analysis revealed five interacting themes, all of which can affect learner digital wellbeing: Learner Internal Factors, Technology-Specific Factors, Design and Pedagogical Factors, Learner Action, and Structural and Institutional Factors. These themes do not operate in isolation; rather, they constitute a dynamic system in which design decisions, institutional structures, and individual learner characteristics interact to shape digital wellness outcomes. The corpus showed meaningful gaps when compared to Vanden Abeele's (2021) considerations: more than half of studies failed to acknowledge the hedonic and eudaimonic potential of technology use, and 34 of 42 studies employed single-timepoint data collection, representing a significant methodological shortfall given Vanden Abeele's emphasis on temporal variability. Practical implications for instructional designers, faculty, and institutional leaders are discussed, along with recommendations for longitudinal and systems-level research.
Contributions to Modeling and Analysis of Complex Data for Engineering and AI Systems
Xie, Kexin (Virginia Tech, 2026-06-17)
Engineering and AI systems increasingly operate in settings characterized by high dimensionality, structural complexity, and practical constraints, where statistical methods must support consequential decisions in domains such as power systems and global health. This dissertation develops both methodological and applied contributions under a unifying theme: the modeling and analysis of complex data for engineering and AI systems. The first methodological contribution addresses variable selection in generalized linear models with conditional main effects (CMEs), which provide an interpretable alternative to generic interaction terms by representing the effect of one factor at specific levels of another. To support structured effect selection in this setting, I develop an adaptive bi-level framework that extends the cmenet family of penalties to generalized linear models. The proposed method uses adaptive weights to regulate the coupling among related CMEs, encouraging coherent selection of grouped conditional effects while still allowing individual terms to enter or leave the model when the signal is localized. An efficient coordinate descent algorithm is developed for estimation, and simulation studies show improved performance relative to standard penalized regression approaches under complex conditional effect structures. A gene association case study further demonstrates the interpretability and predictive value of the proposed method. The second methodological contribution concerns experimental design under treatment cardinality constraints, in which each run is restricted to contain only a pre-specified number of active factors. Such constraints arise naturally in engineering and screening experiments but are rarely incorporated explicitly into the literature of design construction. This dissertation develops an optimal sparse projection design framework for binary experiments with fixed run-wise cardinality, introducing criteria that characterize projection quality and aliasing under the constraint and using them to guide both algebraic and algorithmic design construction. Simulation studies and engineering-motivated examples show that the resulting designs achieve substantially improved low-dimensional projection performance compared with naive adaptations of traditional designs. The applied chapters show how statistical methods can support interdisciplinary decision-making. In global health, I conduct a model-based cost and cost-effectiveness analysis of ivermectin mass drug administration for malaria control in Kwale County, Kenya, using data from a Phase III cluster-randomized trial. A decision-analytic framework links trial outcomes to provider and household costs and to disability-adjusted life years, while deterministic and probabilistic sensitivity analyses are used to quantify uncertainty. The results provide evidence on the potential value of ivermectin-based malaria interventions from health system, household, and societal perspectives. In power systems, I study machine-learning-based electricity load forecasting in the PJM Interconnection under rapid demand growth associated with data centers, identify mechanisms of forecast bias and error propagation, and develop a two-stage correction framework that substantially improves multi-horizon forecasting performance at both system and zonal levels. Overall, these contributions advance statistical methodology for structured modeling and constrained design while illustrating the broader role of statistics in producing interpretable, robust, and decision-relevant analyses for complex engineering and public health systems.
Thinking Real, Doing Complex: The Case of Exponents and Logarithms
Park, Matthew Furuta (Virginia Tech, 2026-06-17)
The operation of exponentiation stands out compared to other arithmetic operations like addition and multiplication. It is not commutative, associative, or even defined for every pair of real numbers. Perhaps unsurprisingly, the differences between addition, multiplication, and exponentiation of complex numbers are even more stark. Students tend to first encounter expressions of the form in an undergraduate course in complex analysis. This investigation employed two theoretical frameworks, APOS Theory and Toulmin argumentation, to analyze how students reason about complex exponents and logarithms and how they navigate these mathematical differences with specific attention to how they invoked properties of real-valued exponents and logarithms. This invocation has been referred to as Thinking Real, Doing Complex, or TℝDℂ. The first framework, APOS Theory, was used to analyze two interviews with two college students, resulting in four individual interviews. The first individual interviews served to investigate how students understand real-valued exponents and logarithms and complex numbers, and the second interview served to investigate how they understand complex-valued exponents and logarithms. To analyze these interviews, I drew on earlier studies on students' conceptions of exponents or logarithms, which employed an APOS framework, and analogized their findings to synthesize a more general codebook. The second framework, Toulmin argumentation, was used to analyze interviews with a pair of college students collaborating on a series of tasks. These paired interviews helped me investigate how students collaborated in solving the tasks. To analyze these interviews, I modeled their discussion with Toulmin diagrams and tracked which mathematical ideas functioned as shared between the two participants. My findings from the individual interviews suggest that, while students tend to conceptualize of real exponents and logarithms within a single exponent/logarithm schema, they conceptualize complex exponents and logarithms within a distinct complex-valued exponent/logarithm schema. That is, there does not appear to be a general "exponent/logarithm" schema that contains all relevant conceptions. However, while a student's real-valued exponent/logarithm schema might be distinct from their complex-valued exponent/logarithm schema, the organization of the two schemas may be similar. As such, an APOS characterization of Thinking Real, Doing Complex phenomenon can be characterized in terms of similarities of schemas. My findings from the paired interviews suggest that students may also engage with questions about complex analysis by transforming them into equivalent statements about real numbers. In addition to thinking about complex exponents algebraically or geometrically, they may also reason about them numerically. These findings suggest that another manifestation of the Thinking Real, Doing Complex phenomenon can be extended by considering that students may not strictly analogize properties about real exponents and logarithms to complex exponents and logarithms. They might conceptualize complex exponents and logarithms as mathematical objects or operations distinct from their real counterparts, or they might transform statements about complex numbers into ones about real numbers. More broadly, the findings suggest that instruction about complex exponents and logarithms and their relationship to the branch cut can be emphasized more, and perhaps not necessarily confined to undergraduate complex analysis.
Experimental and Analytical Investigation of Continuity Diaphragms in Continuous Precast Prestressed Concrete Bridges
Al Rufaydah, Abdullah Saeed (Virginia Tech, 2026-06-17)
Continuous bridges provide several structural and operational advantages over simply supported bridges. On the other hand, designing continuous concrete bridges can be complex due to the construction sequence and the time-dependent nature of materials. These challenges arise because the statical system changes from a simply-supported to a continuous bridge while concrete continues to undergo creep and shrinkage, causing restrained deformations and redistribution of internal forces and moments over time. If continuity diaphragms are not properly detailed, extensive cracking due to time-dependent effects and temperature gradient can compromise the degree of continuity. This dissertation aims to reduce uncertainty in the design and behavior of precast, prestressed concrete bridges made continuous for live load through experimental and analytical investigations. Since the mid-2000s, the American Association of State Highway and Transportation Officials (AASHTO) LRFD Bridge Design Specifications has allowed a simple design approach for continuity diaphragms. If girders are allowed to age for at least 90 days before being made continuous, time-dependent restraint moments do not need to be calculated because most of the creep can be assumed to have occurred prior to continuity establishment. The positive moment reinforcing can be designed such that the design moment strength (ϕMn) of the connection is not less than 1.2 times the cracking moment (Mcr). In 2005, Newhouse developed a positive moment connection detail for Precast Concrete Bulb-Tee (PCBT) girders consisting of interlocking hairpin bars which was adopted by Virginia Department of Transportation (VDOT). In 2021, a new approach to the design of continuity diaphragms was proposed and approved for inclusion in the 10th edition of the AASHTO Specifications (2024) to allow for accelerated bridge construction. The new method requires that time-dependent restraint moments be calculated for any diaphragm, regardless of the girder age at continuity establishment. However, no calculation method is prescribed, and only recommendations are available in the commentary. In addition, there is lack of experimental and analytical research on continuity diaphragms with irregular configurations, such as those in skewed and chorded bridges. To provide design guidance for continuity diaphragms, existing methods in the literature were investigated and compared to experimental data of half-scale bridge tests. Modifications were proposed and a simple modified method based on the work of Tadros et al. (2018) was recommended. This method showed close agreement with the test data while maintaining simplicity and consistency with the current AASHTO method for time-dependent prestress losses. A finite element (FE) modeling approach with beam elements was developed to capture time-dependent restraint moments using calibrated viscoelastic Kevin chains. The behavior of the current detail of continuity diaphragms was investigated with different diaphragm configurations, skewed and chorded diaphragms. Three 45-in-deep PCBT skewed and chorded stub specimens were constructed and tested under service, cyclic, and ultimate loading. The results were compared to those of the control specimen tested by Newhouse. High-fidelity FE models with 3D solid elements were developed for the stub specimens and validated using the experimental results. The results showed that the current positive moment detail performed adequately, within the range of bridge skewness and curvature investigated in this research. The modeling techniques were extrapolated to investigate the behavior of a full-scale multi-girder bridge with different diaphragm configurations. The results indicated that interior girders develop wider cracks and higher continuity bar stresses than exterior girders due to the transverse effect of volume changes. In addition, skewness and curvature in bridges, within the range considered in this investigation, have no significant influence on crack widths and stresses in continuity reinforcement.
Understanding the Associations Among Executive Function, Self-regulation, Chronic Stress, and Math Learning Outcomes Across Middle Childhood
Valdivia Leiva, Isabel Margarita (Virginia Tech, 2026-06-17)
Existing evidence suggests that having better math skills is associated with having better reading skills, higher socioeconomic status, and general academic motivation - yet, over 60% of the U.S. population is not proficient in math. While math instruction plays a large role in math learning, executive function (EF) and its core components, working memory (WM), inhibitory control (IC), and cognitive flexibility (CF), as well as self-regulation (SR) and chronic stress, are each also associated with children's math learning outcomes (MLO). However, most evidence comes from early childhood and, to date, no study has simultaneously investigated the contribution of those three constructs to MLO across middle childhood, despite this developmental period being a crucial one to acquire foundational math skills. In addition, middle childhood is characterized by the rapid development of EF and SR, and evidence suggests that CS would be detrimental to both developmental trajectories. The present dissertation examined how three core EF skills - WM, IC, and CF - along with SR and CS, were simultaneously associated with MLO, and whether those associations differed across developmental periods, namely, early middle childhood (EMC) and late middle childhood (LMC). It was hypothesized that the associations among MLO and each core EF, as well as MLO and SR, would be positive and stronger for LMC compared to EMC, while the relation between MLO and CS would be negative and weaker for LMC compared to EMC. Children (N = 133) completed one in-person session. WM, IC, and CF were each assessed with NIH Toolbox tests, SR was assessed with the Head-Toes-Knees-Shoulders task, and CS was measured by caregiver report via the Child Life Challenges Scale. MLO was assessed by the Woodcock-Johnson IV Tests of Academic Achievement Applied Problems. Regression analyses predicting MLO with WM, IC, CF, SR, and CS as simultaneous predictors were conducted, with covariates of child age, child gender, and cumulative poverty-related risk (CPRR), a z-score composite of variables associated with living in poverty. Results showed that only WM and SR, but not IC, CF, or CS, were significant and positive predictors of MLO across middle childhood in the simultaneous model. Additional regression analyses explored a potential moderation effect of developmental period (DP), namely EMC and LMC, on the association between each of the predictors of interest and MLO. Only the interaction term between CS and DP was significant. However, when probing the interaction, the simple slopes for EMC and LMC were not significant. Results highlight the relative importance of WM and SR in relation to MLO across middle childhood. Although for this sample CS was not associated with MLO above and beyond WM and SR, future studies could assess additional dimensions of CS (e.g., physiological aspects) in a more diverse population, as well as recruiting larger sample sizes allowing further exploration of a moderation effect. For caregivers and educators, understanding the simultaneous contributions of EF, SR, and CS to MLO could provide evidence key to the design of grade-level specific interventions supporting EF and SR development, which could promote students' MLO during the foundational years of elementary school.
Robust control of quantum processors using space curves
Piliouras, Evangelos (Virginia Tech, 2026-06-17)
Quantum computers can significantly speed up the solution of certain classical tasks and even provide solutions to problems that are classically intractable. However, achieving this requires a fault-tolerant architecture with components and quantum operations (gates) whose physical error rates are below a specific threshold. Advanced quantum control methods can suppress environmental noise and implement below-threshold gates, but there are infinitely many ways to do so. These methods can rely entirely on analytical solutions or combine them with numerical optimization, where pulse properties are treated as optimization goals. Analytical constraints are always met exactly, but numerically optimized goals may only be satisfied approximately, leading to tradeoffs. Therefore, a modular analytical framework is needed that fixes some objectives in advance while optimizing only a selected subset. In this dissertation, I employ Space Curve Quantum Control (SCQC), a method that generates noise-resistant quantum gates using differential geometry. In SCQC, a space curve's global geometric properties dictate the gate's noise-robustness, while the controls that realize it are extracted from its local quantities. The first part of the dissertation is devoted to extending SCQC for arbitrary and simultaneous noise suppression, with experimentally-friendly pulses. I first derive the geometric conditions and curve examples for suppressing two separate noise types simultaneously. Then, I extend SCQC by deriving the adjoint representation equations of the associated quantum evolution. This allows one to describe the entire control design in terms of space curve elements and propose a systematic control design approach called the Bézier Ansatz for Robust Quantum (BARQ) control method. BARQ can encode the target gate upfront and allow optimization only for noise-robustness properties. This approach solves the usually unavoidable tradeoff between gate fidelity and noise-sensitivity and is capable of producing control pulses with experimentally-friendly features. The second part of the dissertation applies BARQ in two leading qubit modalities. I first describe my participation in experiments on IBM's superconducting devices, where doubly-robust BARQ pulses demonstrate at least one order-of-magnitude error reduction in the presence of engineered noise. The second platform is a four-qubit trapped-ion register hosted in the Quantum Scientific Computing Open User Testbed (QSCOUT) ecosystem. I show that utilizing BARQ pulses can reduce gate errors up to two times and effectively combat motion-induced decoherence.
The Social Origins of Reductionism in Molecular Biology
Zhumadilova, Kulyash (Virginia Tech, 2026-06-17)
Historians of molecular biology have been lamenting decreased attention to "molecular biology" in historical scholarship in the past decades, despite triumphs of molecular biology in scientific research. My dissertation is trying to revive the analysis of molecular biology as a research program. I investigate how molecular biology became a dominant subfield of biological research and a preferred explanatory framework. In my work, I rely on historical and philosophical approaches as well as policy analysis and political commentary in the critical tradition of science studies. The guiding question of my dissertation is: "Why and how biology became molecularized?" I start by unpacking the philosophical justification of reductionism to show that no philosophical theory sufficiently supports molecular thinking. Further in the manuscript, I ground the concept of reductionism in social practices of Cold War science. I trace the philosophical and historical development of molecular biology in the post-WWII USA and show that the social organization of research institutions and the managerial model of science funding helped perpetuate the reductionist paradigm. The proposed analysis challenges not only assumptions of the traditional history and philosophy of biology but also the current biomedical model of health and disease.
Filtering and Domain Decomposition Techniques for Intrusive and Non-intrusive Reduced Order Models of Convection-Dominated Problems
Moore, Ian Robert (Virginia Tech, 2026-06-16)
Galerkin reduced order models (ROMs) offer significant computational savings for the simulation of partial differential equations, yet they often exhibit spurious oscillations and loss of physical fidelity in convection-dominated and multiscale regimes. This dissertation develops a framework for correcting the intrinsic spatial under-resolution of Galerkin ROMs through complementary regularization and hybridization strategies. Large eddy simulation-inspired filtering approaches are introduced, including the Ladyzhenskaya ROM and the approximate deconvolution Leray ROM (ADL-ROM), which incorporate spatial filtering to model the effects of truncated scales and enhance stability while maintaining efficiency. Rigorous numerical analysis is provided for both models, establishing stability and convergence results that strengthen their theoretical foundations. To address strongly localized high-gradient features, the OpInf-Schwarz ROM combines operator inference with a Schwarz domain decomposition framework, restricting full order modeling to localized regions while retaining ROM efficiency elsewhere. Finally, I apply spatial filtering concepts directly to OpInf to stabilize non-intrusive ROMs for convection-dominated systems.
Contributions to Machine Learning with Abstention and Surrogate Modeling with Complex Outputs
Zhang, Xinlei (Virginia Tech, 2026-06-16)
Machine learning and statistical modeling have become integral components of modern scientific and engineering analysis, providing powerful tools to model and understand complex phenomena. However, real-world applications frequently present unique challenges, such as data imbalance, fairness constraints, and high-dimensional structured outputs. This dissertation presents novel statistical methodologies to address these challenges, with a focus on classification problems under data imbalance and fairness constraints, efficient emulation of computer experiments with tensor-valued outputs, and simulation-based experimental design and analysis. Chapter 1 presents the motivation and research objectives. Chapter 2 develops methods for logistic classification with a rejection option, addressing challenges of data imbalance and fairness constraints with respect to sensitive attributes. The proposed approach generalizes existing methods by incorporating convex surrogate loss functions and fairness-aware optimization. Performance is evaluated through comprehensive simulation studies and real-world datasets, including a case study on Methicillin-resistant Staphylococcus aureus (MRSA) prediction using imbalanced electronic health record (EHR) data. Chapter 3 introduces a so-called Deco-GPT method, a Gaussian process-based emulator for computer experiments with tensor-valued outputs. The method applies tensor unfolding and higher-order singular value decomposition (HOSVD) to improve scalability and predictive accuracy. An extension for handling missing output values is incorporated via matrix completion. Performance is evaluated through simulations and case studies, demonstrating strong performance across diverse settings. Chapter 4 introduces a simulation framework using CARLA to generate structured datasets, including safety-critical driving events and a route completion dataset with mixed inputs. We consider deep Gaussian process models for analyzing such data with mixed inputs. More specifically, we handle qualitative factors via indicator-based representation and learned embeddings, and compare against standard Gaussian process and latent variable Gaussian process models. Chapter 5 summarizes the main contributions of this research and discusses potential directions for future work.
Understanding how urbanization alters the neuroendocrine system and behavior of adult and developing wild song sparrows (Melospiza melodia)
Fossett, Taylor Elaine (Virginia Tech, 2026-06-16)
Rapid environmental change, including urbanization, is shaping ecosystems around the globe. Animals often respond to changes in the environment through changing their behavior. In particular, urban-living animals are commonly found to exhibit vastly different behaviors compared to their rural-living counterparts, such as increased boldness (i.e., less fearful of predators and humans) and heightened territorial aggression. However, the brain mechanisms that are shaped by the environment and, in turn, alter behavior, are unclear. The neuroendocrine system plays a critical role in processing and responding to environmental cues through internal regulation of physiological processes and behavioral outputs. In this dissertation, I investigate the relationship between the environment, behavior, and multiple measures of the neuroendocrine system to better understand how urbanization alters the brain and behavior of a wild songbird, the song sparrow (Melospiza melodia), that reliably expresses differences in aggression across urban and rural habitats. Collectively, each chapter seeks to address the outstanding question, why are urban song sparrows are more aggressive than rural song sparrows? by asking four main questions: Chapter 1 how do changes in human activity, including altered human activity in response to the COVID-19 quarantine, influence aggression of urban and rural-living song sparrows across a span of 10 years of study? Chapter 2 how does arginine vasotocin (AVT, a neuroendocrine signaling nonapeptide that regulates social behavior) and its receptors AVT3R, AVT4R, contribute to aggression in urban and rural-living song sparrows, and how is the AVT system shaped by urbanization? Chapter 3 how do differences in resource availability that are associated with urbanization influence neuropeptide Y (NPY, a neuroendocrine signaling neuropeptide that regulates feeding behaviors) and its receptors NPY1R, NPY2R, NPY5R, and how does the NPY system relate to aggression in urban and rural song sparrows? Chapter 4 how do developmental stressors that are associated with urbanization, including brood parasitism by the brown-headed cowbird (Molothrus ater), organize the neuroendocrine system, including genes that regulate feeding behaviors, social behaviors, and the stress response: NPY and its receptors: NPY1R, NPY2R, NPY5R; AVT and its receptors: AVT3R, AVT4R; aromatase (ARO), androgen receptor (AR), estrogen receptor type 1 (ESR1), estrogen receptor type 2 (ESR2), corticotropin releasing hormone (CRH), mineralocorticoid receptor (MR), and glucocorticoid receptor (GR) in the hypothalamus and hippocampus (only MR and GR)) of nestling urban and rural song sparrows? First, I found that urban birds were reliably more aggressive than rural birds over those 10 years of study. Interestingly, both urban and rural birds were less aggressive during the COVID-19 quarantine than in prior years. However, this was part of a pattern of decreasing aggression in both urban and rural habitats over the entire 10-year period. These findings suggest that human activity has nuanced impacts on animal behavior independent of environment. Second, I observed high AVT3R mRNA signal in the lateral septum, medial bed nucleus of the stria terminalis, hippocampus, Nidopallium, cerebellum and optic tectum, with diffuse signal in the lateral hypothalamus and nucleus rotundus. There were no significant differences in the number of AVT3R transcripts in the lateral septum between urban and rural males. When I quantified mRNA expression for AVT and two of its receptors, I found that urban males had higher relative mRNA expression of AVT3R in the hypothalamus compared to rural males, and aggression immediately before capture was negatively associated with the expression of AVT and AVT4R. Thus, expression of AVT and its receptors has the potential to contribute to higher aggression in male song sparrows but may not explain habitat-related variation. Third, I found that urban males had greater mRNA expression of all three hypothalamic NPY receptors: NPY1R, NPY2R, and NPY5R. Moreover, while I found no evidence for habitat-related differences in the number of NPY-immunoreactive cells in the infundibular nucleus (IN) or paraventricular nucleus (PVN) of the hypothalamus, overall, there were significantly more NPY-immunoreactive cells in the IN than the PVN. Despite finding differences in NPY receptor hypothalamic mRNA expression, I failed to find a relationship between aggression and NPY system gene expression, nor NPY peptide abundance. Our results suggest that the NPY system is sensitive to urbanization but may not explain differences in aggression during the pre-breeding season or in acute aggressive responses to a simulated territorial intrusion. Finally, I found that rural nestlings had higher baseline corticosterone compared to urban nestlings, but no differences in breathing rate. I also found that there were no effects of habitat type or brood parasitism status on any of the neuroendocrine system genes that I measured. This contradicts our hypothesis that the brains of developing birds are impacted by habitat type. However, I did find that gene expression of all genes measured increases with age, except for AVT. Though AVT mRNA expression stayed consistent throughout development, AVT4R mRNA expression increased, suggesting increased sensitivity to AVT in urban and rural nestlings. Interestingly, female nestlings had higher mRNA expression of ESR1, while male nestlings had higher mRNA expression of NPY. Collectively, the results of my last chapter suggest that urban environments may not be as stressful for developing songbirds as we once thought, or that the effects of urbanization on the brain may not appear in the first 10 days of life. Together, my dissertation work advances our understanding of how urbanization shapes behavior through shaping the neuroendocrine system in adulthood, not in the first 10 days of life. Urbanization is a complex form of rapid environmental change and understanding its effects on the brain and behavior of wild animals requires a multifaceted approach that combines the use of field techniques, molecular tools, and cross-dispensary research.

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