Doctoral Dissertations

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https://hdl.handle.net/10919/11041

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Analysis of Plasma Properties, Plasma Ignition and Erosion in Ion Gridded Thrusters Operating with Alternative Propellants
Cardenas Ruiz, Cesar Augusto (Virginia Tech, 2026-04-15)
This work presents a comprehensive computational investigation of alternative propellants and material response in plasma propulsion systems. A kinetic framework based on the Particle-in-Cell method coupled with Monte Carlo collisions (PIC–MCC) is implemented to evaluate the plasma behavior of krypton and iodine as candidate propellants for gridded ion thrusters. The model incorporates detailed collision processes, representative operating parameters, a simplified ion engine configuration, and an ion-optics design to characterize plasma dynamics. Variations in mesh resolution produce consistent qualitative trends in ion and electron energy distributions, although differences arise in total energy responses. The accumulation of particles leads to increased ionization, indicating continuous plasma generation across the ion engine model. In addition to ion propulsion modeling, this research examines a plasma-assisted combustion concept as an alternative propulsion-related application. A novel plasma ignition configuration is analyzed using Large Eddy Simulation (LES) to assess the interaction between electrode placement and supersonic pulsed discharge flow structures. The results demonstrate how geometric and electrical parameters influence cavity flow dynamics and ignition probability. Erosion and sputtering processes are investigated using molecular dynamics (MD) simulations. Sputtering yields and erosion rates are quantified for carbon, carbon–carbon composites, graphite, molybdenum, and tungsten, and are compared against predictions from an empirical model. Within the 200–1000 eV energy range, the empirical formulation predicts a steady increase in sputtering yield, while the MD results show a similar increasing trend in most configurations and an approximately constant response in one case. Angular sputtering analyses reveal energy-dependent increases at selected grid locations, with the empirical representation reproducing the characteristic rise and post-peak decline. Furthermore, the size of the simulation domain is found to influence the sputtering yield, with larger domains leading to clear changes in the computed yield. Erosion rate patterns remain generally consistent across grid sections and energy intervals, though localized increases are observed under specific conditions. The influence of incident angle further modifies these trends, producing an initial gradual rise followed by a stabilized erosion regime. Overall, this work integrates kinetic plasma modeling, large-eddy flow simulations, and atomistic-scale material analysis to advance the understanding of alternative propellants and erosion mechanisms in electric propulsion systems. The results contribute to the evaluation of krypton and iodine as viable candidates beyond xenon, while also providing insight into potential grid material options.
Recruitment, Certification, and Retention in Rural Virginia: A Qualitative Study of Leadership Perceptions in Rural School Divisions
Fulcher, Demi Nicole (Virginia Tech, 2026-04-15)
The purpose of this study was to explore how rural school divisions in Virginia were addressing the teacher shortage through recruitment, certification, and retention strategies. The study employed a basic qualitative research design using semi-structured interviews with superintendents or their designees to gain insights into their school division practices and decision-making processes used to address these shortages. Six rural superintendents (or designees) from across Virginia participated in interviews. The interview questions were developed in alignment with the purpose of the study and guided by literature on rural teacher labor markets, organizational factors influencing teacher retention, and alternative certification pathways, which collectively informed the conceptual framing, recruitment, certification, and retention practices in rural school divisions. The study was guided by a central research question examining how rural school division in Virginia address teacher shortages through recruitment, certification and retention strategies? Findings revealed that rural school divisions are recruiting within a reduced applicant pipeline and increasingly relying on alternative certification pathways as a primary staffing strategy to maintain classroom coverage. The study determined that structural barriers, including housing limitations, limited childcare availability, and salary competition, constrain recruitment efforts in rural communities and often extend beyond the direct control of local school divisions. Participants also emphasized that retention is largely supported through relationships, leadership visibility and school culture compared to formalized retention programs or system level retention policies. School division leaders described how trust, communication and leadership presence were used to support teacher morale and encourage teachers to remain in their positions. Several themes emerged from the findings including recruitment within a reduced applicant pipeline, reliance on alternative certification pathways to address staffing shortages, structural barriers that limit recruitment capacity in rural communication and relationship based leadership practices that support teacher retention. These themes illustrate how rural school divisions often rely on localized strategies and leadership practices to respond to staffing challenges while navigating broader structural limitations. This approach provided an in-depth understanding of practices and policies school divisions used to recruit, certify, and retain teachers while responding to the staffing shortages. By highlighting these factors, the findings informed school divisions, policymakers, and teacher preparation programs seeking to strengthen recruitment and retention efforts in rural Virginia.
Polynomial Time Algorithms for Transportation and Inventory Management in Serial Supply Chain with Multi-Module Capacitated Vehicles
Kulkarni, Kartik Giridhar (Virginia Tech, 2026-04-14)
We study new generalizations of the classical capacitated lot-sizing problem with concave production (or transportation), holding, and subcontracting cost functions, in which the total capacity available in each time period is the sum of capacities of a subset of n heterogeneous modules (machines or vehicles). We refer to this class of problems as the Multi-module Capacitated Lot-Sizing Problem without and with Subcontracting, denoted by MCLS and MCLS-S, respectively. While these problems are NP-hard when n is part of the input and polynomially solvable for n = 1, the complexity status for fixed n ≥ 2 has remained open. We resolve this question by developing exact fixed-parameter tractable algorithms that solve MCLS and MCLS-S in O(T2n+3) time for any fixed n ≥ 2. Our results generalize the algorithm of Atamtürk and Hochbaum [Management Science 47(8):1081–1100, 2001] for the case n = 1. We further extend our framework to two important generalizations: (i) the lot-sizing problem with piecewise concave production costs (LS-PC-S), for which we propose an O(T2m+3) time algorithm, where m is the number of breakpoints; and (ii) a two-echelon multi-module lot-sizing problem, solved in O(T4n+4) time. Our LS-PC-S algorithm reduces the runtime of the dynamic programming approach of Koca et al. [INFORMS J. on Computing 26(4):767–779, 2014] by up to 93.6%, and our two-echelon results generalize those of van Hoesel et al. [Management Science 51(11):1706–1719, 2005] for the single-module case. Computational experiments demonstrate that our algorithms are both efficient and highly stable compared to Gurobi 9.1, including under parallel implementation. In addition, our results for MCLS-S establish the existence of a polynomial-time algorithm for optimizing a linear function over the n-mixing set, a significant generalization of the classical 1-mixing set. We also investigate single-item discrete multi-module capacitated lot-sizing problems without and with backlogging, where production in each period consists of binary multiples of module capacities. For fixed n ≥ 2, we develop exact fixed-parameter tractable algorithms that generalize the results of van Vyve (2007) for n = 1. These algorithms are embedded within a Lagrangian decomposition framework to solve the corresponding multi-item problems. Computational results show substantial improvements over Gurobi 9.0 in both performance and robustness. Finally, we study serial supply chain models in which goods are transported from a supplier to a warehouse and then to a retailer over a finite planning horizon. These problems fall under the class of two-echelon lot-sizing problems (2-ELS) with capacitated inbound and outbound transportation. We address an open question posed by van Hoesel, Romeijn, Morales, and Wagelmans (2005) concerning the existence of a polynomial-time algorithm for 2-ELS with a single capacitated vehicle in each echelon. We provide polynomial-time exact algorithms for this setting and three further generalizations involving multiple heterogeneous capacitated vehicles, thereby extending the results of Kaminsky and Simchi-Levi (2003) and Sargut and Romeijn (2007).
Architectural Effects on Aqueous Self- Assembly of Bottlebrush Block Copolymers and Synthesis of Degradable Bottlebrush Polymers
Vu, Clark (Virginia Tech, 2026-04-14)
Bottlebrush polymers feature densely grafted side chains along a polymeric backbone. Due to their unusual topology, bottlebrush polymers are an emerging class of materials with unique physical properties and novel applications, including supersoft elastomers, residual-free adhesives, drug delivery or photonic crystals. Among these interesting applications, amphiphilic bottlebrush block copolymers (BCPs) and their solution self-assembly have attracted significant attention over the past decade due to their potentials as drug delivery carriers. Driven by microphase separation, amphiphilic bottlebrush BCPs self-assemble into various nanoaggregates with surface protrusions, including spherical micelles, ellipsoids, cylindrical micelles, and vesicles. Compared to their linear BCP analogues, bottlebrush BCPs exhibit significantly lower critical micelle concentrations and consequently form more stable nanoaggregates. While theoretical and experimental studies on cone-shaped amphiphiles have been widely studied, the solution self-assembly of cone-shaped (tapered) bottlebrush BCPs remains underexplored. To address this knowledge gap, this dissertation presents systematic studies of the aqueous self-assembly of tapered bottlebrush BCPs. A series of eight tapered and four cylindrical bottlebrush BCPs were synthesized via sequential addition of macromonomers ring-opening metathesis polymerization, featuring varying ratios of hydrophobic polystyrene (PS) and hydrophilic poly(acrylic acid) PAA side chains. The nanostructures formed by these bottlebrush BCPs in water were characterized using cryogenic transmission electron microscopy (cryo-TEM) and small-angle neutron scattering (SANS). Results reveal that most BCPs formed multiple nanostructures with surface protrusion, including spherical micelles, cylindrical micelles, and vesicles. Coarse-grained molecular dynamics simulations supported the interpretation of the experimental observations. Collectively, two distinct self-assembly pathways were identified. One pathway involves micelle fusion to form elliptical and cylindrical aggregates that, in some cases, fused further to form Y-junctions. The second pathway proceeded through micelle maturation into semi-vesicles, which subsequently developed into vesicles that, in some cases, fused further to form compound vesicles. Moreover, for the first time, larger spheres that are nanoaggregates with a core radius larger than the average core radius were identified by cryo-TEM tomography. These results provide the first experimental evidence for vesicle formation via semi-vesicle intermediates in bottlebrush BCPs. These findings highlight how structural parameters such as cone directionality govern self-assembly in these large, cone-shaped polymeric amphiphiles. Extending this work, the effects of cone angle on aqueous self-assembly were systematically investigated using six tapered bottlebrush BCPs—three with hydrophobic tips and three with hydrophobic bases— with estimated cone angles ranging from 6 o to 15o. Cylindrical bottlebrush BCPs with comparable molar masses were also synthesized for comparison. All bottlebrush BCPs maintained a constant mass ratio of hydrophobic PS to hydrophilic deuterated PAA side chains at approximately 50%. Moreover, deuteration of PAA side chains enabled us to employ contrast-variation SANS for characterization of core radius and shell thickness in spherical nanoaggregates. Cylindrical bottlebrush BCPs exhibited similar distributions of self-assembled morphologies regardless of molecular weight. In contrast, tapered bottlebrush BCPs displayed a correlation between morphology distributions and cone angle. Tapered bottlebrush BCPs with a hydrophobic tip favored nearly exclusive formation of spherical micelles at low cone angles. The cone angle exerted a more pronounced effect on morphology in bottlebrush BCPs with hydrophobic bases. Despite similar hydrophobic contents, BCPs with lower cone angles exhibited high populations of spherical micelles, whereas those with higher cone angles led to larger spheres, ellipsoids, vesicles, and work-like structures. Characterization of the core radius and shell thickness by cryo-TEM and contrast-variation SANS showed good agreement between the two techniques, though some discrepancies were observed between theoretical predictions and experimental measurements. This work provides insights into how cone geometry governs the aqueous self-assembly behavior of bottlebrush BCPs. Bottlebrush polymers have received considerable interest as residual-free pressure-sensitive adhesives (PSAs). Due to their unique topology and entanglement suppression of entanglement, these polymers are naturally soft and flexible without needing chemical additives. This allows them to stick when pressed and peel off cleanly without leaving behind a gummy residue. Unfortunately, most bottlebrush polymers include all-carbon backbones and could contribute to plastic pollution following their intended use. Recent efforts have focused on developing methods to synthesize degradable bottlebrush polymers. However, these methods typically require specialty monomers or cannot make bottlebrush polymers with high molar mass. To address these challenges, we developed an approach that takes advantage of the alternating free-radical copolymerization of sulfur dioxide (SO2) and electron-rich alkenes, including cycloalkenes (e.g., norbornene). We prepared several types of norbornene macromonomers with side chain molecular weights up to 5 kg/mol and successfully copolymerized them with SO2. These poly(olefin sulfone) bottlebrush polymers featured various types of side chains, such as polyacrylates, polymethacrylates, polystyrene, and poly(lactic acid), attached to a poly(norbornene-alt-SO2) backbone and reached number-averaged molar masses of up to 1100 kg/mol. More importantly, these bottlebrush polymers were degradable. Degradation studies with a variety of bases revealed that the sulfone unit with removable protons on neighboring carbons enabled degradation of these high-molecular-weight polymers within hours. Moreover, a bottlebrush polymer synthesized using this approach exhibited pressure-sensitive adhesive properties with a peel strength of approximately 1200 N/m. Collectively, this work offers a versatile approach using inexpensive SO2 gas to synthesize degradable bottlebrush polymers with high molar mass, enabling end-of-life disposal following their intended applications.
Influence of Structured Mentoring on the Leadership Identity, Well-being, and Capacity of Novice Principals
Galbreath, Elizabeth Nicole (Virginia Tech, 2026-04-10)
The principalship is among the most demanding, high-stakes roles in public education. For novice school leaders, the transition into this role is often marked by overwhelming expectations, emotional strain, and limited support, which contribute to burnout and early departure from the profession. The purpose of this qualitative narrative inquiry was to explore how structured mentoring relationships influenced novice public school principals' leadership identity development, emotional well-being, and professional sustainability during their first three years in the principalship. Data were collected through one-on-one semi-structured interviews with six novice principals who had previously participated in structured mentoring relationships through their district or state induction programs. The study examined how participants made meaning of their mentoring experiences and how those experiences shaped their confidence, resilience, and capacity to navigate the complexities of school leadership. Guided by a conceptual framework that positions leader well-being as central to leadership development and sustainability, thematic narrative analysis was used to identify patterns across participants' narratives. Five interrelated themes emerged: (a) leadership identity development, (b) emotional well-being and professional isolation, (c) mentoring structures and conditions, (d) informal mentoring and survival strategies, and (e) sustainability and retention. The findings indicated that structured mentoring most effectively supported novice principals when it was non-evaluative, trust-based, consistent, and attentive to emotional realities. When mentoring lacked these conditions, principals relied on informal networks and self-directed strategies to navigate the role. These findings suggest that structured mentoring functions not only as a technical support mechanism but also as a relational and well-being-centered structure that shapes leadership identity and professional sustainability. Implications for practice include the intentional design of mentoring programs that prioritize relational trust, emotional support, and identity-focused leadership development.
Modernizing Latent Gaussian Process Inference for Non-Gaussian Responses
Cooper, Andrew Harrison (Virginia Tech, 2026-04-08)
Gaussian processes (GPs) are powerful tools for modeling non-linear data. In many situations, however, direct GP inference is not possible due to the nature of the response. Categorical and directional data, for instance, are examples of responses for which a Gaussian likelihood assumption is not appropriate. Latent GPs, typically in tandem with appropriate link functions, can be introduced to model responses with non-Gaussian likelihoods. But latent GPs do not scale well to large training data, especially when Monte Carlo integration is required. Consequently, fully Bayesian, sampling-based approaches have been largely abandoned in favor of maximization-based alternatives, such as Laplace/variational inference (VI) combined with low rank approximations. Though feasible for large training data sets, such schemes sacrifice uncertainty quantification and modeling fidelity, two aspects that are important to mu work on surrogate modeling of computer simulation experiments. In this work I propose a GP inference framework that takes advantage of a remarkably powerful rejection sampling approach known as elliptical slice sampling (ESS). My approach allows for computationally thriftier posterior integration while preserving fully Bayesian inference. I leverage this framework in the contexts of classification and circular modeling, both of which introduce unique latent inferential challenges. I demonstrate superiority over VI-based alternatives for both real and simulated examples, including a Binary Black Hole simulator (binary response) and data from a radio frequency identification experiment (angular response).
Chronic Absenteeism in Title I Elementary Rural Schools Within Virginia: A Leadership Perspective
Cannon, Ashley Duncan (Virginia Tech, 2026-04-08)
Chronic Absenteeism in Title I Elementary Rural Schools Within Virginia: A Leadership Perspective Ashley D. Cannon ABSTRACT The purpose of this study was to examine how school leadership practices influence the prevention of chronic absenteeism in rural Appalachian Title I elementary schools. This study also investigated the factors contributing to chronic absenteeism among students in rural Appalachian schools in Virginia and examined how school leadership can effectively respond to this systemic issue. The study was guided by the research question, How do school leadership practices influence the prevention of chronic absenteeism in Virgina's rural Appalachian Title I schools? Five building-level administrators from multiple rural counties participated in individual Zoom interviews. Transcripts were member checked and analyzed thematically using iterative coding and constant comparison. Leaders described chronic absenteeism as a multifaceted problem driven primarily by out-of-school factors, especially familial determinants, and compounded by limited external accountability. Effective leadership emphasized proactive, student-centered practices, including early identification of emerging patterns through real-time attendance data, relational mentoring, frequent check-ins, and supportive family outreach. Incentive-based systems, implemented at schoolwide and classroom levels, were reported to strengthen engagement and reinforce shared expectations. Leaders also highlighted community collaboration as essential, citing partnerships with school-based health services, instructional recovery opportunities, transportation supports, and strategic social media communication to reduce barriers and increase transparency. Findings suggest that sustained reductions in chronic absenteeism in rural settings require relational leadership, data-informed monitoring, and cross-sector collaboration that extends beyond compliance-oriented policies. Implications include prioritizing preventive approaches, investing in attendance data capacity and staffing, and supporting flexible, context-specific partnerships that address structural barriers affecting students and families. Future researchers should include student and caregiver perspectives and examine intervention durability across leadership transitions. Policy support for rural divisions should promote transportation and healthcare access, trauma-informed professional development, and coordinated interagency response systems.
Exploring Experiences of Applying to US-Based Medical Schools from a Neurodivergent Viewpoint: An Interpretive Phenomenological Study
Steele, Rebecca (Virginia Tech, 2026-04-06)
Despite the growing recognition of neurodiversity in higher education, neurodivergent (ND) individuals are significantly underrepresented in U.S. medical education (AAMC Medical School Graduation Questionnaire, 2021). This gap reflects not only the limitations of individual disclosure decisions but also how historically entrenched ableist admissions structures privilege neurotypical norms of communication, professionalism, and productivity. This study examined how ND medical school applicants and students experience admission as processes that function as gatekeeping and deny their different ways of being and knowing, resulting in a lack of recognition for their belonging in medicine. The research questions focused on how ND applicants and students experience medical school admissions in the U.S. and how these experiences inform future strategies to empower emerging scholars in medicine. This study centered the lived experience of ND medical student applicants and students using an interpretive phenomenological approach (van Manen, 2016). I used semi-structured interviews to gain insights on how participants made meaning of neurodivergence during the admissions process, with attention to institutional norms, interpersonal interactions, and broader cultural expectations within medical education. Using inductive data analysis (Adams, 2015) of interview transcripts, findings revealed how barriers to access and belonging are primarily systemic rather than individual. Participants described key obstacles such as rigid evaluative metrics, implicit behavioral expectations of normativity, and institutional cultures that conflate professionalism with neurotypicality. These conditions often required applicants to manage, minimize, or conceal aspects of their neurodivergence to give the perception of themselves as legitimate applicants. At the same time, participants named how their ND is a source of clinical strength, as they think of themselves as individuals with enhanced empathy, pattern recognition, and novel problem-solving, challenging deficit-oriented assumptions, traits not easily recognized through admissions process. This study reframes medical school admissions as a relational and boundary-crossing process, where difference is actively negotiated rather than passively assessed. By situating ND experiences within historical and structural contexts, findings highlight the limitations of accommodation-only approaches and underscore the need for institutional accountability in fostering inclusion. Future research should attend to the development of admissions frameworks that value cognitive diversity as a scholarly and clinical asset, training admissions committees to recognize non-normative excellence, and advancing research that positions ND scholars as leaders in shaping the future of medical education. Empowering the next generation of scholars requires moving beyond access toward systemic transformation that recognizes difference as a source of innovation and growth within medicine.
Trustworthy, Privacy-Preserving, and Functional Data Outsourcing Systems
Le, Thanh Tung (Virginia Tech, 2026-04-03)
Data outsourcing systems, e.g., Dropbox, Google Drive, and iCloud have become essential in our daily lives. They can reduce the storage burden on user devices, which have limited storage capacity. Also, they serve as backup places for user data to prevent data loss due to hardware failures. However, data misuse and breaches remain serious concerns. Even when the cloud provider is trusted, attacks on storage servers have exposed user data, threatening user privacy and the reputation of corporations. This dissertation develops and implements trustworthy, privacy-preserving, and functional data outsourcing systems. The contributions consist of two pieces. First, we design and implement a Proof of Retrievability scheme named Porla, an efficient technique allowing the user to audit their data to ensure its intactness. Our work features an optimal audit-proof size and low end-to-end audit latency in comparison with prior work. Second, we develop a series of novel searchable encryption techniques achieving high security guarantees and performance in various threat and system models. In particular, we start by designing new schemes for multi-user searchable encryption, MAPLE and MUSES, using state-of-the-art cryptographic tools and emerging distributed computation algorithms. Our MAPLE and MUSES offer high security guarantees while optimizing search complexity in terms of computation and communication costs. However, they rely on distributed computation for secure search, which incur expensive deployment and maintenance cost. Therefore, we turn our direction to deal with the security and performance issues in public-key searchable encryption (PKSE) and hybrid searchable encryption (HSE), which can support multi-user settings in practice more naturally, such as email and messaging systems. To this end, we design Hermes, which simultaneous resolves many open problems in PKSE/HSE settings, including preventing keyword-guessing attacks, achieving user-efficient epoch-based forward privacy, and optimizing server computation cost for keyword search. Finally, we observe that mitigating pattern leakages in PKSE/HSE has remained an open and unexplored research problem. Applying differential privacy (DP) is a potential approach as it achieves single-round search and small user-side storage, but the state-of-the-art work using DP still suffers from significant overhead to be applicable for practical applications. Our final work FROST devises a new approach for applying DP in encrypted search, showing a significant advance in terms of performance and communication cost. All the aspects addressed in this dissertation are essential for building practical encrypted data outsourcing systems that achieve both high performance and strong security guarantees.
Optimal and Robust Control for Systems with Second Order Structure
Srinivas, Neeraj (Virginia Tech, 2026-04-01)
This work aims to investigate the utilization of the structured system matrices (mass, stiff- ness, damping) present in second order systems expressed in the first order and their appli- cations to problems in optimal control and robust control. These structured system matrices may - in certain cases - be symmetric, diagonally dominant, or positive definite. These prop- erties can be leveraged to obtain improvements in computational efficiency and accuracy, which is the core of this dissertation. Three methods are introduced in this work that exploit the structured system matrices in the context of the algebraic Riccati equation (ARE), ap- plied to optimal and robust control problems. As matrix sizes increase, traditional methods for solving the ARE becomes computationally expensive, due to the eigendecompositions in- volved in Schur/subspace methods. This work focuses on algorithmic solutions to the ARE that do not involve the eigendecompositions of the 2n ×2n system matrices, by leveraging properties of the mass, stiffness and damping elements. An algorithmic solution to the ARE is presented first, which is applicable to second order sys- tems with diagonally dominant system matrices (which may be asymmetric). This method is shown to improve computational efficiency for large systems. Next, the Newton-Kleinman algorithm is utilized in conjunction with the second order system matrices to develop a mod- ified Newton-Kleinman method tailored towards second order systems with positive definite mass, stiffness and damping matrices, in the context of the H-infnity control design prob- lem. This algorithm is shown to have an analytic proof of convergence. An application of this method is demonstrated via a data-driven controller that also minimizes the entropy of the closed loop system. Finally, the discrete time algebraic Riccati equation (DARE) is considered, and a modified discrete Newton-Kleinman algorithm for systems with a second order structure is introduced, that is shown to reduce computational expense as compared to Schur-based methods.
Investigating the Professional Experiences and Development of Early-Career Assistant Principals: A Phenomenology of First-Year Socialization Practices
Johnson, Michele Casey (Virginia Tech, 2026-03-31)
A comprehensive socialization plan for newly hired assistant principals is important not just for the assistant principal but also for the school division. This qualitative phenomenological study aimed to better understand the socialization experiences of first-year public school assistant principals. The research questions guiding this work were from the assistant principal and mentoring principal's perspectives: (1) What is the lived experience of socialization for first-year assistant principals? (2) How do first-year assistant principals describe the practices, relationships, and conditions that shape their socialization into the role? and (3) How do principals perceive and make meaning of assistant principals' socialization into the role? This dual perspective is important because the researcher intends this study to support first-year assistant principals and those who create and engage in their socialization. Demographic questions were asked at the start of the semi-structured person-to-person interviews to understand the perspective and experience of each assistant principal and principal regarding the professional development and socialization needs. The interview protocol and open-ended questions were created to allow each participant to reconstruct their experience. Data were collected and analyzed to identify common themes through similar phrases and experiences. The results of this study are expected to provide insights for policymakers and education leaders when considering how to best support first-year assistant principals.
Engineering Microfluidic Systems for Low-Input Multi-Omic Profiling of Brain Regulatory States
Hadlock, Thomas Moniotte (Virginia Tech, 2026-03-31)
Microfluidic systems enable sensitive application of sequencing based "omic" assays to rare sample sets. In this thesis, we developed and applied these systems to address fundamental limitations in existing assays and to investigate biological questions that are inaccessible using traditional bulk protocols. First, we introduced a microfluidic platform for rapid field-ready library preparation of viral samples for nanopore sequencing. Our microreactor enabled laboratory independent viral diagnostics of field-collected Senecavalley virus A samples with accuracy in-line with gold standard RT-PCR assays. Additionally, our system enabled real-time mutation identification, detecting two high-confidence consensus single nucleotide variants within the SVA positive cohort. We then applied microfluidic MOWChIP platform to two epigenomic investigations into the effects of psilocybin on mouse cortical and subcortical regions. First, we examined the sex-specific enhancer alterations following psilocybin exposure in neurons extracted from the frontal cortex and nucleus accumbens to elucidate therapeutic mechanisms for combating opioid use disorder. Here we describe localized increases in enhancer activity in the mesolimbic nucleus accumbens compared to the frontal cortex. Additionally, we demonstrate significant recovery of key enhancer linked gene pathways disrupted by prolonged opioid use following acute psilocybin exposure in male mice absent in female cohort. Finally, we investigated epigenetic inheritance of prolonged psilocybin exposure in prenatal dams on their offspring. We uncover significant transcription factor regulatory network disruptions that persist through to the F1 generation. Specifically, reduction in regulatory efficacy of Egr2 and JunB transcription factors of which direct psilocybin exposure mediates significant expressional increases in the prefrontal cortex. We further uncover a sex specific nature of these alterations, in which minimized reach of early gene Egr2 is found primarily in female offspring following maternal exposure. These multi-omic investigations uncover significant sex-specific implications of psilocybin exposure on brain epigenome.
Mechanical and Image-Based Assessments of Histotripsy and Tumor-Induced Changes in Osteosarcoma Tumor-Bearing Bone
Achari, Preeya Fera (Virginia Tech, 2026-03-31)
Osteosarcoma (OS) is a rare and aggressive bone cancer that often requires invasive limb-resection or limb-salvage surgery, which may lead to significant biomechanical challenges and an elevated risk of fracture. Histotripsy, a noninvasive focused ultrasound therapy, offers a promising alternative by selectively ablating tumor tissue while preserving surrounding structures. Although the safety and feasibility of OS tumor histotripsy ablation has already been demonstrated, its effects on the structural and mechanical integrity of OS-affected and healthy bone remain unknown. This knowledge is critical for advancing histotripsy as a safe, noninvasive limb-sparing strategy for OS. This dissertation developed predictive finite element (FE) models to evaluate the biomechanical effects of histotripsy on OS-affected bone through two aims. Aim 1 assessed histotripsy's impact on the structural and mechanical properties of OS-affected bone in an $ex$ $vivo$ canine model using mechanical testing and high resolution μCT imaging. Aim 2 investigated histotripsy's effects on whole-bone mechanics in an $ex$ $vivo$ murine model during tumor progression, integrating imaging data into FE models to predict fracture risk and evaluate treatment outcomes. The findings of this study establish critical data on the biomechanical effects of histotripsy on both healthy and diseased bone, advance predictive image-based FE modeling accuracy, and support the clinical translation of histotripsy as a noninvasive limb-salvage strategy for OS.
Rhetorical Authority in Context: A Study of Technical Accounts of Flint, Michigan's Water Crisis from 2014-2017
Hockman, Cassandra Rose (Virginia Tech, 2026-03-30)
Essays on Modeling Human Behavior During Epidemics: Simulation, Statistical, and Optimization Approaches
Babaei Shalmani, Kian (Virginia Tech, 2026-03-30)
Human behavior is at the core of epidemics. Public risk perception shapes compliance with non- pharmaceutical interventions, mobility and contact patterns, and vaccine uptake; in turn, these behaviors alter transmission dynamics and future perceptions. A central challenge in integrating behavior into epidemiological analysis is that perception and response are not instantaneous. Information diffuses through societies with delays, and behavioral adjustment often occurs gradually and asymmetrically responding differently when risk is rising than when it is falling. Ignoring these delay structures can bias empirical inference about behavioral responsiveness and can misstate the effects of policies evaluated using models that treat behavior as exogenous or contemporaneous. This dissertation advances the modeling and estimation of behavioral feedback in epidemics by focusing on how delayed risk perception links epidemic indicators to behavioral change and policy outcomes. The first essay develops and validates a delay-aware empirical framework for estimating how mobility responds to epidemic risk. Using synthetic experiments, it shows that assuming immediate response (or relying on ad hoc fixed lags) can yield biased estimates of both the magnitude and timing of behavioral response. The essay introduces a structured approach to representing perception delays using distributed-lag formulations motivated by information diffusion and provides practical methods for estimating delay parameters alongside behavioral sensitivity. The second essay extends the framework by allowing delay structures to be asymmetric across phases of the epidemic, recognizing that behavioral responses to increasing risk may differ from responses to declining risk. Through additional synthetic tests and application to U.S. state-level COVID-19 mobility data, the essay demonstrates that the assumed delay structure materially affects inference about responsiveness and can change conclusions about how quickly behavior adjusts to worsening versus improving conditions. The third essay connects behavioral estimation to policy design by examining optimal vaccination strategies under endogenous, delayed behavioral feedback. It compares a conventional SEIRV framework with constant contact rates to a behavioral SEIRbV framework in which perceived risk reduces contacts with a perception delay. In both a homogeneous setting and an age-stratified allocation setting, the analysis shows that accounting for behavioral feedback can shift suppression thresholds and the relative performance of vaccination strategies, highlighting the marginal importance of operational levers such as earlier starts and faster rollout alongside prioritization rules. Taken together, the three essays show that delays in risk perception are a first-order feature of epidemic systems. By providing methods to estimate delay-aware behavioral responses and demonstrating how behavioral feedback reshapes vaccination policy evaluation, this dissertation contributes tools and evidence to improve inference, forecasting, and the design of effective interventions in epidemic settings.
Advanced Grid-Interface Three-Phase Converters: Grid-Support Capabilities and Grid Impact Analysis
Wang, Biqi (Virginia Tech, 2026-03-30)
The increasing penetration of power electronic converter–interfaced resources is fundamentally transforming modern power systems by displacing conventional synchronous generators, thereby introducing new challenges to system reliability and stability. To address existing challenges and technical gaps, this dissertation develops advanced grid-interface three-phase converters with grid-support capabilities and systematically assesses their impacts on power system operation, including dynamic behavior, small-signal stability, fault current characteristics, protective relay performance, and black-start restoration. Control frameworks are developed for both grid-forming inverters (GFIs) and newly proposed grid-supporting rectifiers (GSRs), enabling grid-regulation support and enhancing overall system stability. Impedance-based small-signal assessments incorporating the generalized Nyquist criterion (GNC) are conducted, with emphasis on (i) comparative stability analysis between conventional grid-tracking rectifiers (GTRs) and the proposed GSR under weak-grid conditions (Chapter 3), and (ii) stability of grid–GFI interconnections during black-start operations, particularly during restoration initial stages (Chapter 5). The proposed converters exhibit more benign impedance characteristics and enhanced stability performance, therefore reducing adverse dynamic interactions and enabling stable operation under weaker grid conditions. In addition, a GFI control strategy with improved fault ride-through capability is proposed. The impacts of inverter-interfaced distributed energy resources on grid fault current characteristics and protective relay performance are analytically investigated, focusing on potential issues of desensitization effect and selectivity deterioration (Chapter 4). Furthermore, a black-start-friendly GFI control is developed, and the feasibility of black-start operations supported by GFI-based renewable resources is systematically studied (Chapter 5). The effectiveness of proposed grid-support control strategies the and the accuracy of the grid-impact analyses are validated by comprehensive simulation studies and hardware experimental results. Overall, this dissertation provides a comprehensive control, modeling, and assessment framework for grid-interface converters in future converter-dominated power systems. The proposed GFI and GSR control strategies, together with impedance-based stability and protection analyses, contribute practical insights for the improving the stability, protection reliability, and restoration capability of low-carbon power grids.
Do Clinicians Have Patience? Examining Delay Discounting, Perceived Stress, and Low-Value Antibiotic Prescribing
King, Mary Jane (Virginia Tech, 2026-03-30)
Antimicrobial resistance has been deemed one of the top global threats by the World Health Organization, and the overuse of antibiotic medications contributes to antimicrobial resistance. Yet low-value antibiotic prescribing (LVAP; the prescription of antibiotics when not clinically indicated) is still widely prevalent across multiple healthcare disciplines, often for viral illnesses such as acute bronchitis. Behavioral factors such as delay discounting (DD; the extent to which someone prefers smaller, sooner rewards over larger, delayed rewards) and perceived stress may independently and interactively contribute to clinicians' high rates of LVAP. Examining how DD and perceived stress levels are related to each other and LVAP can help develop interventions to improve prescribing rates and promote high-quality patient care. In study one, we compared DD and probability discounting (PD; the extent to which someone prefers smaller, guaranteed rewards over larger, risky rewards) between cross-sectional survey samples of primary care clinicians and non-healthcare workers, finding that clinicians had lower DD and higher PD rates compared to non-healthcare workers. In study two, we analyzed data from the survey of non-healthcare workers to determine whether there was a significant relationship between DD, PD, and perceived stress levels, finding that both DD and PD were associated with perceived stress. In study three, we examined the associations between DD, perceived stress, and LVAP in a sample of clinicians from multiple departments across a large healthcare system. This included both self-reported likelihood of LVAP based on two clinical vignettes in a cross-sectional survey, as well as electronic health record-based incidence rates within 12 months prior to survey administration, and we found that DD was associated with self-reported LVAP likelihood in both clinical scenarios assessed. Ultimately, these three studies suggest that DD may be an important behavioral marker that warrants further investigation in the context of LVAP, a complex and recurring issue in healthcare. Future studies investigating the connection between LVAP and behavioral factors such as discounting and stress should seek to examine workplace-specific stress levels and further explore possibilities for interventions involving DD as a behavioral marker.
Voices of Strength: Counselor Experiences of Resilience and Wellness Among Refugee Youth
Perinchery, Saudamini Agarwal (Virginia Tech, 2026-03-30)
An estimated 117 million people worldwide are currently displaced due to war and other human rights violations, with refugee children and adolescents among the most vulnerable. Their heightened exposure to trauma, violence, and chronic instability places refugee youth at increased risk for mental health challenges such as post-traumatic stress disorder, anxiety, and depression. While resilience and wellness are increasingly recognized as protective factors that foster positive adaptation, little is known about how school and clinical mental health counselors understand and support these constructs in their work with refugee youth. This study explored how counselors experience the resilience of refugee youth in their care, as well as uncover their lived experiences in providing wellness support to them. Using Interpretative Phenomenological Analysis (IPA), six practicing counselors were interviewed to gain in-depth insight into how they experience resilience and implement wellness-based interventions. The analysis revealed four Group Experiential Themes (GETs) describing counselor experiences of resilience and four GETs illustrating their experiences providing wellness support to refugee children and adolescents. An additional GET emerged from the data analysis of the two original research questions established for this study, reported below. The following themes emerged for Research Question 1 pertaining to resilience: Counselors Experience the Resilience of Refugee Children by Observing their Responses to Significant Stressors; Counselors Experience the Resilience of Refugee Children by Observing their Individual Internal Strengths; Counselors Experience the Resilience of Refugee Children by Observing the Influence of Relational Support Systems; and Counselors Experience the Resilience of Refugee Children by Observing the Impact of Schools. The following themes emerged for Research Question 2 pertaining to wellness: Supporting Wellness by Viewing Wellness Through a Holistic Lens; Supporting Wellness by Building Trust; Supporting Wellness by Empowering Clients Through Advocacy; and Supporting Wellness by Practicing Culturally Responsive Care and Using Creative Approaches. The following theme emerged from the data analysis of Research Questions 1 and 2, pertaining to broader counselor experiences of working with refugee children: Working with Refugee Youth as a Source of Growth for the Counselor. Findings indicated that counselors conceptualize resilience as a dynamic interplay of internal (e.g., faith, hope, self-belief, and persistence) and external factors of resilience (e.g., family, community, educational access, and resources). Counselors reported utilizing holistic, strengths-based, and evidence-based practices to support wellness, and schools were observed to play a critical role in providing stability and promoting well-being. Results demonstrated a shift away from deficit-based perspectives toward approaches that recognize and leverage the existing strengths of refugee youth, illustrating how counselors translate theoretical concepts of resilience and wellness into practice. This study contributes a foundational framework to the literature by offering one of the first comprehensive examinations of resilience and wellness as enacted by counselors serving refugee youth. Implications for counselor education include the need for a linguistically diverse workforce, integration of resilience and wellness-oriented training in counselor curriculum, and increased emphasis on advocacy and collaboration with local agencies and schools. Recommendations for future research include longitudinal studies, greater attention to intersectionality, and exploration of effective training, supervision, and interdisciplinary collaboration models. Overall, this study reframes refugee youth experiences through a lens of strength and positive adaptation, advancing research, training, and practice within counselor education and systems of care.
Towards Unified and Generalizable Multimodal Foundation Models
Xu, Zhiyang (Virginia Tech, 2026-03-30)
Recent advances in multimodal models have reshaped multimodal learning by leveraging large language models (LLMs) as backbones and integrating them with vision encoders or diffusion models. These approaches have achieved strong performance in either multimodal understanding or multimodal generation. However, no existing system offers a unified framework capable of performing both understanding and generation across flexible input-output modality combinations while also generalizing to unseen, real-world tasks. Unification is essential not only for enabling a single model to perform traditional understanding and generation tasks, but also for enabling cross-modal tasks, such as visual storytelling, report generation, and script creation, that neither understanding nor generation models alone can accomplish. By processing and generating inputs and outputs that span multiple modalities, unified models more closely mirror the way humans naturally acquire and construct knowledge. Generalization, in turn, enables models to adapt to novel tasks in open-world environments under human-specified instructions or principles. Together, unification and generalizability constitute two fundamental pillars for advancing toward general-purpose multimodal intelligence. This dissertation advances unified and generalizable multimodal modeling through novel architectures, post-training paradigms, and reinforcement learning algorithms. It addresses two enduring challenges in multimodal foundation models: (1) the lack of modality unification, and (2) limited generalizability in open-world settings. The contributions are fourfold. First, we introduce Modality-Specialized Synergizers (MOSS), a framework that enables interleaved multimodal generation in pretrained models. Second, we propose an efficient unified architecture that bridges vision-language models and diffusion models, providing a novel pathway for joint understanding and generation. Third, we establish multimodal instruction tuning as a new post-training paradigm to improve zero-shot generalization and robustness. Finally, we extend image understanding to the spatiotemporal domain by developing a novel reinforcement learning algorithm that promotes temporal awareness, enabling vision–language models to reason effectively about videos. Extensive experiments across diverse multimodal benchmarks demonstrate that these approaches significantly enhance unification, generalizability, and overall capability. Collectively, this research strengthens the foundations of multimodal AI and outlines a pathway toward universal models that can understand, reason, and generate across modalities in complex open-world environments.
New Frontiers in Seismic Imaging of the Critical Zone
Eppinger, Ben Julius (Virginia Tech, 2026-03-27)
The critical zone (CZ) is the life-supporting skin of our planet, spanning the top of vegetation to the base of weathered bedrock. This thin layer is the only place in the known universe where biota, water, atmosphere, and geologic materials interact and transform. Direct observations of the critical zone's subsurface structure typically require invasive methods such as drilling boreholes or excavating soil pits. Seismic imaging has been used for decades to circumvent direct measurements of the subsurface, but traditional approaches often rely on a limited subset of the information contained within the seismic wavefield, such as the timing of first arrival waveforms. By leveraging more of the data contained in the seismic wavefield, new facets of critical zone evolution can be revealed. This thesis develops and applies advanced geophysical techniques, specifically full-waveform inversion (FWI) and multi-component surface wave analysis, to constrain high-resolution models of p-wave velocity, s-wave velocity, and radial anisotropy. In the first manuscript, a workflow is developed to implement 2D full waveform inversion (FWI) within the critical zone. The workflow involves inverting surface waves and body waves separately to ensure that high-amplitude surface waves do not dominate and overprint the information contained in body waves. When applied to a site near Laramie Wyoming, the resulting FWI models reveal that bedrock fracture density serves as an important bottom-up control on CZ architecture. These findings show that the transition from saprolite to intact bedrock is sharp in areas with low fracture density but more diffuse where the underlying rock exhibits higher fracture density. Additionally, the FWI models show better agreement with borehole data as compared to previously published first arrival travel time tomography models. The second study explores the role of inherited rock fabric in the development of critical zone porosity by measuring radial anisotropy with surface waves. This novel method utilizes multi-component surface seismic data associated with Rayleigh and Love waves to quantify radial anisotropy at the hillslope scale. Field data and in situ measurements from the South Carolina Piedmont demonstrate a strong correlation between seismic anisotropy and porosity, with both properties developing concurrently as rock undergoes in situ weathering. This empirical evidence suggests that weathering processes do not act stochastically, and instead, are guided by the geologic fabric of the parent material. Moreover, this research provides further evidence that inherited rock fabric plays a major role in dictating the form and function of landscapes. The final study investigates subsurface structure and water stores beneath giant sequoias in Yosemite National Park. By employing dense arrays for multicomponent nodal geophones, a revised time-frequency-phase FWI algorithm, and geostatistical rock physics modeling, this research estimates volumetric water content beneath giant sequoias at different landscape positions. The results indicate that giant sequoias located on ridges and hillslopes lack sufficient shallow soil moisture and must instead rely on deep rock moisture from depths exceeding 2 meters to avoid water stress during arid summers. As such, this work underscores the importance of rock moisture to valued species in arid landscapes. These three studies present several avenues for seismic imaging to catalyze research in the critical zone. The advent and integration of multicomponent, dense nodal data sets with advanced processing methods such as FWI means that previously undiscernible subsurface characteristics can now be elucidated. By contextualizing novel images of the shallow subsurface within the vibrant field of critical zone science, we can better understand how Earth supports life.

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