Doctoral Dissertations

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Labor Market Adjustments Under Economic Shocks: Evidence from the U.S.
Mun, Byungki (Virginia Tech, 2025-06-05)
This dissertation employs a quasi-experimental design to examine how major economic shocks—the COVID-19 pandemic, immigration restrictions, and the U.S.-China tariff war (2018–2020)—reshaped labor demand and employer skill requirements in the United States. Using econometric methods on panel data from online job postings, the Current Population Survey, and trade exposure measures, the analysis provides empirical evidence on how firms adjust hiring criteria in response to disruptions in labor supply, market conditions, and global trade. The first chapter shows that, in contrast to the upskilling trend following the Great Recession, the COVID-19 pandemic led to widespread downskilling in education requirements, driven by labor market tightness and accelerated technological adoption, especially in tradable industries and routine-manual occupations. The second chapter applies a shift-share difference-in-differences (DiD), finding that visa bans reduced immigrant employment but increased native employment, particularly among less-educated workers. The immigration shock also induced firms to adopt more automation and broadband technologies, raising demand for technical and digital skills. The third chapter uses a staggered DiD with Shift-Share IV and CSDiD models to analyze the tariff war, revealing export tariffs reduced high-skill job postings and wages while increasing low-skill roles, and import tariffs boosted engineering skills but lowered wages. These studies underscore how firms dynamically adjust skill demand under uncertainty, highlighting the role of labor market tightness, technological advancements, and trade policy in shaping hiring strategies.
Identifying Subtypes of Neurocognition using Latent Profile Analysis in Adults: A Presicion Medicine Approach
Villalongo Andino, Mara D.'Lennys (Virginia Tech, 2025-06-05)
As the United States (US) population ages, the prevalence and societal cost of neurocognitive disorders such as dementia will continue to increase. Therefore, there is a pressing need to thoroughly elucidate these disorders' characteristics and progression. Despite existing research efforts, the exploration of neurocognitive subtypes remains limited, particularly given heterogeneity within and between the clinical manifestation of neurocognitive disorders. Conceptualizing these conditions as relatively homogenous can potentially impede patient care, delay timely interventions, and hinder advancements in treatment development. Enhancing our understanding of these conditions and how other psychosocial factors may affect them can lead to more targeted and effective interventions, potentially improving patient outcomes and reducing the burden of disease. Accordingly, the purpose of this study was to identify subgroups of neurocognition using latent profile analysis (LPA) to empirically distinguish neurocognitive profiles, determine the effects of profile membership on known risk factors for dementia including depression, anxiety, personality, sleep difficulties and chronic pain. Results of this study supported a 5-profile solution varying in degrees of cognitive strengths and weaknesses and profiles were significantly differentiated based on years of education, negative impression management, inconsistent response styles and schizophrenia related concerns. These findings add to the body of research regarding the heterogeneity within neurocognitive diagnoses and provide support for a more nuanced approach to diagnosing and treatment of neurocognitive concerns.
A Systematic Study of Sialic-Acid-Containing Poly(styrene sulfonate) for Antiviral Applications
Bianculli, Rachel Helen (Virginia Tech, 2025-06-05)
This dissertation presents the development of a versatile and modular synthetic platform for constructing functional copolymers using post-polymerization modification (PPM) of activated esters. By decoupling the synthesis of the polymer backbone from the introduction of functional groups, this strategy enables precise and efficient tuning of key polymer features (degree of polymerization, functionality, and hydrophobicity). While PPM has been widely used for homopolymers, this work extends its application to copolymers, offering a powerful tool for studying how subtle variations in polymer structure influence performance in biological applications. To demonstrate the platform's utility, it was applied to develop and study antiviral polymers that mimic the binding receptor commonly exploited by influenza viruses to initiate infection, sialic acid. Through systematic variation of polymer parameters, libraries of styrene-sulfonate sialic acid-containing copolymers with covaried degree of polymerization and sialic acid content were synthesized and evaluated for their inhibition of influenza through various virological assays. The modular design of the synthetic platform enables isolation of individual polymer parameters, with preliminary assay data indicating that comonomer identity has a greater impact on antiviral efficacy than either sialic acid content or degree of polymerization alone. While this work focused on influenza, the materials and synthetic strategies developed here could be adapted for other sialic acid-binding pathogens, laying the groundwork for broader antiviral polymer design.
Clarity and inclusivity as precursors to disseminate, implement, and translate yoga principles for behavioral health
Frazier, Mary Clare (Virginia Tech, 2025-06-05)
Translating health evidence is key to advance human flourishing. However, it takes 15-17 years to translate health interventions into real-world practice. Dissemination and implementation science can help bridge this gap for multiple interventions, including yoga-based ones. Yoga principles have positively impacted myriad populations and outcomes and align with scientific rigor across physiology, psychology, and neuroscience. However, several barriers to translating yoga principles exist. This dissertation presents three studies that highlight the importance of clarity and inclusivity as precursors for translation of yoga principles for behavioral health. The first study is a systematic review that used a novel qualitative synthesis to examine how yoga is defined and described in peer-reviewed mental health and wellbeing literature. Of 5206 studies identified, we included and reviewed 129. We present a qualitative analysis of 1291 meaning units (i.e., distinct pieces of data) of yoga definitions and descriptions. Furthermore, these data are presented via a sub-analysis across five continents and three study populations (e.g., population with chronic health conditions). While no one singular yoga definition arose from the data, yoga was most prominently operationalized as a mind-body practice comprised of mental, physical, and breathing components. We recommend a findings-based comprehensive framework combined with established reporting guidelines to define and describe yoga. The second study used a mixed methods cross-sectional survey to explore yoga end-user input for communicating and disseminating yoga benefits and yoga-based interventions. We surveyed 150 current and potential yoga end-users (mean age 36 years; 50% female; 54% people of color; 29% with higher body weight) with 28 open-ended questions. A subset of questions (n=4) was based on a dissemination model which posits that information is disseminated through various channels, messages, and sources. Using these overall themes, end-users contributed to 1133 meaning units. End-users overall preferred inclusive and clear content for yoga health messages, social media and fitness settings as channels, and health professionals and health organizations as sources. We propose an update to the dissemination model to include end-user input on health interventions, including yoga-based interventions. The final study is a sequential pre-implementation intervention study that used participatory and co-creation methods to adapt an existing yoga-based employee wellness intervention for community health educators. In this study, we triangulated data from integrated research-practice partners and data from focus groups (n=21) and a follow-up survey (n=17) with community health educators from multiple states (90% female, 62% White). Several data-driven contextual factors provide important insights for tailoring intervention materials and scheduling. We present lessons on how to enhance fit of intervention function and form to context and also on the importance of patience and slowing down for co-creation and participatory processes. Based on these findings, applying precursors of clarity and inclusivity to methods in dissemination and implementation science is key for translation of yoga-based interventions. Furthermore, these findings can be expanded and applied to bridge translation of other health evidence and interventions – towards meaningfully advancing human flourishing.
(Re) Presenting Study Abroad through the 'Colonial Library'
Woods, Zuleka Randell (Virginia Tech, 2025-06-05)
As the U.S. continues to prioritize the internationalization of higher education, studyabroad participation has doubled in the last decade. To correspond with this increased interest and prepare students for an intercultural workforce, the study abroad programs of colleges and universities have diversified travel destinations to include countries on the continent of Africa, most of which have colonial histories. Relying on scholarship from postcolonial studies, critical tourism studies, and higher education, this analysis addresses the social imaginary that anchors study abroad programs in such host countries on the African Continent. Specifically, it concentrates upon how and why the depiction of Africa sustains problematic images and narratives in continuously running promotional campaigns on study abroad websites. This critical analysis then examined how closely such representation of Africa is subconsciously included, if not intentionally reproduced, in colonial depictions by deploying the insights of Congolese postcolonial scholar, V.Y. Mudimbe, whose work arguably discloses the subtle distortions in the mass marketing of studying in Africa to American students. Indeed, it suggested the commodification of study abroad programming reanimates often othered and subjugated images of Africa for the students, staff, parents, and faculty who interact in the institutions of study abroad learning. The case is made through research methods grounded in the content analysis of 1022 images of study abroad programs collected and analyzed using Mudimbe's postcolonial framework. This method yields a more complete and nuanced understanding of what images are projected of Africa, as well as which pedagogical practices are highlighted in study abroad educational recruitment, management, and delivery that sustains 3 such learning as colleges and universities struggle to internationalize their higher education programming. The findings of this study strongly indicate that the representation of Africa by U.S. colleges and universities aligns closely with the subjugated representations as Mudimbe's critique asserts. Additionally, this cultural continuity to earlier colonial era themes and tropes too often return images tied to wild jungles, destitution, starving children, and vast empty landscapes to animate the imagery of the locales that study abroad programs focus upon by going to Africa. U.S. higher education institutions play a key role in regenerating and reshaping colonial narratives for those enrolling in study abroad programs whose travel purportedly pursues expanding and diversifying their participants' intercultural learning goals. My work's findings point out both practical difficulties and academic contradictions created by these educational endeavors
Precision Agriculture and Its Influence on Agrarian Decision-Making
Mitra, Shreya (Virginia Tech, 2025-06-04)
The rapid digitalization of agriculture, including the rise of Precision Agriculture (PA) tools, reshapes farming practices, knowledge systems, and decision-making processes, prompting critical questions about the values and ethics behind how these technologies are developed, deployed, and used. Key concerns include whose interests they serve, who is included or excluded from their design, and how they may reinforce or challenge existing power dynamics. This research explores the impact of PA technologies on agricultural decision-making through three distinct study focuses, utilizing a Responsible Research and Innovation (RRI) approach. The first study investigates existing data-justice concerns in the context of data-driven PES systems by analyzing the lived experiences of farmers in this context. The second study examines Certified Crop Advisors (CCAs) engagement with Decision Support Systems (DSSs) based on their Affinity for Technology Interaction (ATI). The third study explores how various stakeholders envision PA's role in sustainable agriculture, uncovering tensions between techno-optimistic visions and localized, justice-focused approaches grounded in ecological and cultural values. Collectively, the findings reveal that the current digital agriculture system is top-down, driven by profit-seeking, power-concentrated corporations, and shaped by techno-optimistic visions, thereby widening the gap between technology developers and end users. Consequently, the broader promise of serving the public good and contributing to a sustainable future of agriculture is undermined. This research advocates democratizing agricultural futures' vision and the technology development process through participatory design, inclusive governance, and justice-oriented policy interventions. It urges that agricultural technologies be reclaimed as public goods designed to promote sustainable, equitable, and socially responsible agricultural futures.
Linking Plant and Soil Microbial Diversity via Plant Functional Trait Ecology
Agarwal, Prashasti (Virginia Tech, 2025-06-04)
Plant–microbe interactions are central to ecosystem functioning, yet our understanding of how plant diversity relates to soil microbial diversity remains rudimentary. Cover crops are increasingly promoted for their potential to enhance soil health and microbial diversity in agroecosystems, but variability in their effects highlights the need for a more mechanistic, trait-based understanding. This dissertation examines how various attributes of plant diversity influence microbial diversity, and if the patterns observed at species scale can be extended to mixed communities. Using a combination of monocultures and intentionally designed plant mixtures, I evaluated the relationships between microbial diversity and both taxonomic and functional plant diversity using greenhouse mesocosms and field-based studies. In Chapter 2, I quantified plant functional traits (PFTs) for 29 agriculturally relevant species and assessed their relationships with soil microbial community structure. While plant functional traits such as biomass, root diameter, and root tissue nutrients explained variation in bacterial communities, PFTs did not relate to differences in fungal communities. However, specific bacterial and fungal taxa showed strong associations with particular PFTs. Chapter 3 extended these analyses to plant mixtures designed to test either PFT diversity or expected soil microbial diversity. My results showed that the mixtures designed to exhibit distinct PFT composition based on their species-level traits, did indeed vary in their community-level PFT composition. Furthermore, the same mixtures also harbored distinct soil bacterial and fungal communities. However, I could not identify specific traits driving these relationships, suggesting the overall trait diversity is more important than any individual trait for relating PFTs to soil microbial diversity. In Chapter 4, I evaluated whether plant species or trait diversity relates to corresponding soil microbial diversity in semi-natural herbaceous mixed plant communities in a conventionally managed agricultural field over a typical winter cover crop growing period. Variation in PFT composition was positively related to the differences in plant species composition. Although bacterial alpha diversity showed some associations with root traits like %N and C:N ratio, microbial beta-diversity was not well explained by PFT or species diversity. Together, these findings underscore the importance of specific plant traits in structuring microbial communities, while highlighting the complexity of predicting microbial outcomes from plant community composition and diversity, especially across different spatial and temporal scales. This work supports the integration of trait-based frameworks into designing plant mixtures such as cover crop polycultures and points toward future research linking PFTs to microbial functional potential and agroecosystem services.
Enhancing Computational Thinking Skills of the Future Construction Workforce to Perform Sensor Data Analytics with End-User Programming Environment
Khalid, Mohammad (Virginia Tech, 2025-06-04)
Despite being one of the most significant employment hubs of the United States, the construction industry continues to struggle with declining productivity, workplace hazards, and a growing shortage of skilled workers. In response to these challenges, the construction industry is leveraging advanced sensing technologies and data-driven analytical methods that can significantly improve information accessibility, enabling quicker and more informed decision-making. Accordingly, the expanding range of sensor-based applications triggers a high demand to equip the future workforce with specialized skills. However, for many construction engineering and management graduates, the expanding complexity of data and technology creates comprehension difficulties regarding essential computational concepts and procedural workflows. This ultimately restricts the workforce's ability to convert data into actionable insights for informed decision-making. To bridge similar skill gaps, end-user programming offers considerable potential for learners to execute analytical operations on sensor data through visual programming mechanics. Such affordances also foster computational thinking skills, which are essential for transforming unstructured sensor data into actionable intelligence. This research explores how block-based programming environments can be integrated into construction engineering and management education to improve technical skills, particularly in sensor data analytics. Using a mixed-method approach, the research first surveyed industry professionals to identify the required competencies. Based on this input, a block-based programming environment was designed to help students learn data analytics using authentic sensor data. The environment's efficiency and effectiveness were evaluated with construction students, focusing on key factors such as usability, cognitive load, and visual attention demand. The environment was implemented in classrooms for a summative assessment to measure learners' self-efficacy in targeted skills, performance, and technology acceptance. Additionally, the assessment examined demographic factors impacting user interaction. The affordances and effectiveness of block-based environments contribute to the Learning-for-Use framework by utilizing graphical, interactive programming elements to develop procedural knowledge for solving sensor data analytics problems. The findings present significant insights into the interaction dynamics and perceptions associated with the contextual utilization of block-based programming environments. Furthermore, this research contributes to the dissemination of pedagogical resources that can advance the application and use of emerging technology and computing within the construction sector.
What to Expect when Mice Are Expecting: Pregnancy-Driven Changes in the Reproductive Tract
Suarez, Aileen Caridad (Virginia Tech, 2025-06-04)
Operation Mode Transitions in a Heaterless Hollow Cathode
Brewer, Trenton R. (Virginia Tech, 2025-06-03)
Hollow cathodes are used to neutralize ions in electric propulsion systems, and are a lifetime-limiting component due to the presence of high-energy ions generated in the plasma plume. These ions impact the cathode's keeper electrode destroying it over a period of tens of thousands of hours. The increased interest in high-power electric propulsion systems has been motivated by proposed missions with long required lifetimes, longer than cathode erosion rates would allow in these high-power systems. In the so-called plume mode, high-energy ions are produced at an increased rate but it is unclear how this operation mode comes about from physics in the plume and it is unclear how oscillations in the plume produce large populations of high-energy ions. Experimental research of a heaterless hollow cathode is presented with a specific focus on operation mode transitions in hollow cathodes. The first experimental campaign highlights a unique behavior where two mode transitions, instead of the typically reported one, occur at high flow rates across the cathode's operational parameter space. In this unique behavior two spot mode regimes are found with the high-current spot mode still exhibiting oscillation characteristics of plume mode. Wavenumber-frequency spectrograms corroborated this with multiple features suggesting waves present in the plume related to the plume mode instability and ion acoustic turbulence modulation.
Unlocking the Rare Earth Elements Potential of Allanite: A Comprehensive Study of Beneficiation and Leaching
Xiao, Zhongqing (Virginia Tech, 2025-06-03)
Allanite is a widely distributed accessory rare-earth silicate mineral, commonly associated with garnet, biotite, and feldspar in igneous and metamorphic rocks, and occasionally found in hydrothermal veins. Containing between 14–33 wt.% total rare earth elements (TREEs), allanite is dominated by light rare earth elements (LREEs) such as neodymium (Nd) and praseodymium (Pr). Despite its considerable potential, the exploitation of allanite has been limited due to its complex mineralogy, processing challenges, and the presence of radioactive elements (Th and U). Recent discoveries of high-grade allanite-rich deposits in Wyoming, USA, have renewed interest in its development. However, systematic studies on allanite processing and REE extraction remain scarce. To date, allanite has mostly been treated as a secondary mineral enriched alongside primary REE minerals, rather than processed independently. This dissertation presents a systematic and comprehensive investigation into the beneficiation and leaching of REEs from Wyoming allanite. In the mineral processing phase, density and magnetic separation techniques were optimized. Allanite concentrates achieved TREE recoveries of 70–90%, with mass yields of 10–25%, corresponding to concentration factors of 2.9–7.7 relative to the original feed. Flotation experiments using sodium oleate and dodecylamine (DDA) as collectors demonstrated poor selectivity, suggesting that future work should focus on reverse flotation strategies and the systematic screening of more effective collectors, including potential synergistic combinations, to selectively separate allanite from silicate gangue minerals. In the chemical extraction phase, acid screening tests revealed that sulfuric acid (H₂SO₄) outperforms nitric acid (HNO₃) and hydrochloric acid (HCl) as a lixiviant, likely due to the formation of stronger sulfate complexes with REE³⁺ ions. Temperature and solid-to-liquid (S/L) ratio were found to have a greater influence on TREE recovery than acid concentration or particle size. Direct acid leaching using 1 M H₂SO₄ at 75 °C achieved TREE recoveries of 80–85% within two hours, without significant gangue mineral decomposition as confirmed by XRD analysis. The leaching kinetics exhibited a two-stage behavior: an initial rapid recovery within the first 10 minutes, followed by a slower phase. Kinetic modeling indicated a mixed control mechanism—surface chemical reaction and diffusion through a product layer—with activation energies of 20.3 kJ/mol for the early stage and 10.8 kJ/mol for the later stage. This behavior was attributed to the preferential dissolution of radiation-damaged metamict allanite initially, with slower dissolution of well-crystallized, acid-resistant allanite in the subsequent stage. To address the challenge of the leaching resistance of well-crystallized allanite, three enhanced leaching techniques were systematically evaluated: ultrasonic-assisted, microwave-assisted, and autoclave leaching. Ultrasonic-assisted leaching proved ineffective, yielding less than 4% TREE recovery even after 60 minutes, indicating that cavitation effects were insufficient to disrupt the robust crystal structure of allanite. In contrast, microwave-assisted leaching demonstrated outstanding performance, achieving approximately 88% TREE recovery within just 5 minutes at 150 °C. This efficiency was attributed to the rapid, selective internal heating induced by microwaves, which likely generated thermal shock and associated microstructural changes. SEM imaging of the leached residue revealed the presence of microcracks that were absent in the untreated feed material, supporting the hypothesis that microwave exposure enhanced mineral surface area and reactivity. Kinetic modeling indicated a shift from mixed control to predominantly diffusion-controlled mechanisms at elevated temperatures. Autoclave leaching also produced promising results, with TREE and Fe recoveries reaching around 90% and 60%, respectively, at 150 °C. Compared to microwave leaching, autoclave leaching was more effective for dissolving dense, homogeneous Fe-bearing minerals such as chlorite–clinochlore and almandine–spessartine, owing to uniform external heating and thermal stress from the surface inward. These findings highlight the critical role of both mineralogical properties and energy transfer mechanisms in the design of effective leaching strategies. Following the acquisition of REE-containing leachate from allanite through acid leaching, further purification is necessary using solvent extraction. This dissertation introduces the development of a Gas-Assisted Microbubble Extraction (GAME) system as an innovative approach to solvent extraction. Although only preliminary tests were conducted, the results are promising and highlight the need for continued experimental and theoretical investigation. Compared to conventional solvent extraction methods that rely on beaker-and-stirrer setups, the GAME system demonstrated superior performance—particularly at elevated aqueous-to-organic (A/O) ratios. It maintained TREE extraction efficiencies of approximately 80% even at an A/O ratio of 20, a condition under which traditional methods failed. This enhanced performance is attributed to improved mass transfer, expanded interfacial surface area, and more effective phase contact enabled by the fine dispersion of gas bubbles. In addition to boosting extraction efficiency, the GAME system significantly reduced organic solvent consumption, offering clear advantages for greener and more sustainable REE production processes. Overall, this dissertation advances both the scientific understanding and practical methodologies for processing allanite, encompassing physical beneficiation and advanced leaching strategies. The findings demonstrate that allanite is a viable alternative source of REEs, contributing to the diversification and sustainability of the global REE supply chain. Furthermore, this work provides deeper insight into allanite's mineralogical behavior and leaching kinetics, while introducing innovative extraction technologies that lay the groundwork for the sustainable development of unconventional REE resources.
Statistical Methods for Performance Evaluation of Machine Learning and Artificial Intelligence Models
Song, Xinyi (Virginia Tech, 2025-06-03)
Engineering Students' Development of Global Engineering Competencies during International Programs
Schuman, Andrea L. (Virginia Tech, 2025-06-03)
Engineering students need to be prepared to work across cultures to solve complex, global problems. One common way these outcomes are achieved is by enrolling in international programs. The goal of this dissertation is to understand what students experience and what they learn during international engineering programs. I approached this problem using a multi-case study of the following types of programs: 1) a summer international research experience, 2) an engineering semester at a university-owned study abroad center, and 3) an online international collaborative capstone. In each program, students recorded spoken reflections on their real-time thoughts and experiences. The data were supplemented with interviews, observations, surveys, and program documents. I analyzed the data with the framework of Global Engineering Competency, which I defined as a combination of global, technical, and professional competencies. The results indicate that the best way to impart cultural competencies are from long-term immersion in a different culture. To achieve engineering workplace skills, students need hands-on experiential learning, especially with international colleagues. As program leaders design these types of programs, they must consider how the different dimensions of the program point to the intended learning. They should also communicate to enrolling students what to expect.
A translational approach to understanding cellular responses to vascular injury
Sedovy, Meghan (Virginia Tech, 2025-06-03)
Appropriate control of cell proliferation and migration is essential for maintaining open arteries after vascular injury. Connexin 43 is a channel protein that facilitates cell to cell communication and regulates cell proliferation as well as migration, yet it's role in vascular cell types is poorly understood. Here, I hypothesized that Cx43 and its functional regulation by kinases play a role in vascular cell injury response. To investigate this, I used human vascular tissue from coronary artery bypass grafts and mouse models of ligation induced vascular injury. First, I investigated vessels used for coronary artery bypass, finding damage to the vascular endothelium that could not be completely reversed by improved presurgical vessel storage methods. I developed a carotid artery ligation model of endothelial injury in mice, and found that Cx43 was expressed only in injured endothelial cells, where it promoted healing through control of proliferation and migration. I also identified Mitogen Activated Protein Kinase (MAPK) phosphorylation of Cx43 as the mechanistic event controlling Cx43 dependent endothelial wound healing. In vascular smooth muscle, MAPK dependent Cx43 phosphorylation drove excessive proliferation leading to neointima formation and vascular blockage. A Johnstone lab developed peptide that targets this phosphorylation state prevents neointima formation in mouse and human tissues. These findings highlight a role for Cx43 phosphorylation by MAPK in vascular cell response to mechanical injury and identify Cx43 and a therapeutic target for preventing smooth muscle driven vascular disease.
From the Earth to the Moon: A Multi-Domain Approach to Cislunar Space Domain Awareness
Segal, Connor Benjamin (Virginia Tech, 2025-06-03)
Growing interest in the Cislunar domain has resulted in numerous successful Lunar missions in recent years. Many burgeoning space programs, including those by governments and private industry, have seized the opportunity of this renewed interest and utilized the region as a proving ground to bring relevance and credibility to their organizations. With this increase in interest and activity, the need to expand Space Domain Awareness (SDA) into the Cislunar regime has become paramount. This dissertation presents two related studies operating in different domains that address the current challenges faced by accomplishing this task. The first study optimizes future satellite constellation configurations of optical sensors placed on periodic orbits within the Circular Restricted Three-Body Problem (CR3BP) through the development of a novel multivariate normal crossover technique for Genetic Algorithms that enables statistical local and global search of discrete populations. With this strategy, the entire JPL Cislunar Three-Body Periodic Orbit Catalog's 272,008 unique periodic orbits are explored to produce sets of Pareto optimal solutions that demonstrate the most effective combinations of periodic orbit families for maintaining SDA of Cislunar space. The second study employs a currently operational ground-based electro-optical sensor network to search a pared-down area representing the intersection of low-thrust maneuvers from periodic orbits in the CR3BP and a spherical Poincaré map at four times the distance of Geosynchronous Earth Orbit (GEO). The results from this study demonstrate the feasibility of employing currently operational sensors to perform Cislunar SDA through an 85.3% decrease in the required search area in addition to the ability to conduct partial Cislunar SDA through search of the current GEO catalog with no operational changes.
Additive manufacturing of polyolefins via powder bed fusion
Bryant, Jackson Sewall (Virginia Tech, 2025-06-03)
Powder bed fusion (PBF) is an additive manufacturing (AM) process in which a fine polymer powder is selectively heated and melted using infra-red (IR) energy from a CO2 laser to melt the powder and coalesce the molten polymer together to form each layer. Semi-crystalline polymers are the most common thermoplastics processed in this AM process and powder is heated during the printing process to retard crystallization in the material. When crystallization happens too rapidly, warpage of a layer, which can lead to print failure is possible. Polyolefins represent a class of thermoplastic chiefly comprised of polyethylene and polypropylene. These materials are highly used in engineering applications however, their rapid crystallization kinetics generally make them much less suitable for PBF. They represent a material class in which traditional processing approaches with PBF are not always sufficient to enable printability. In this dissertation, printing of multiple polyolefins is investigated to both understand the processing of these materials and grow an overall understanding of processing in PBF for any polymer. A process chain which relies on fundamental polymer behavior is devised to process ultra-high molecular weight polyethylene (UHMWPE). This material has sufficiently high molecular weight that the viscosity is so high it is not considered to flow in the melt. The difficulty in coalescing this material was overcome by using melt explosion during processing to create some entanglements between adjacent powder particles and form a green part that could then be post-processed in the melt to develop final part properties. The viscosity of this material enabled shape retention during this post-processing. Though this process chain enabled printing of UHMWPE, the printed parts were highly porous even after post-processing. Post-processing under pressure was investigated to further densify printed parts to achieve the mechanical performance expected for UHMWPE. By employing both cold isostatic pressing (CIP) and hot isostatic pressing (HIP), fully dense UHMWPE samples were realized. Strain at break was on par with traditionally processed UHMWPE was achieved, and tensile strength was only slightly less than the traditional processed material. Copolymerization of polypropylene (PP) with polyethylene (PE) to create random PP-PE copolymers, and its impact on material properties and processing was investigated. Increases in ethylene content were expected to decrease crystallization kinetics, which would increase processability of the material. Here, increases in processability means decreases in the likelihood of warpage. Though increases in ethylene content did lower the crystallization kinetics, these increases also significantly shifted the onset of melting for the copolymer to much lower temperatures, which limited the temperatures in which the material could be printed. Together these two changes led to a polymer that was more processable when ethylene content was 2.2% and then processability decreased as the ethylene content was increased to 4.9%. Printed parts from each copolymer showed a decrease in crystallinity with increasing ethylene content. Strain at break increased while tensile strength decreased with increasing ethylene content. A method of emulation of the PBF process was created to enable prediction of crystallization during processing. This method used in situ thermal measurements of the printing process to inform a thermal model to generate temperature profiles for a printed layer and then used these temperature profiles in fast scanning calorimetry (FSC) to emulate a printed layer's thermal history. This emulation enabled prediction of the crystallinity and the shape and temperatures covered by the melting endotherm during the printing process. Investigations of printing UHMWPE and PP-PE copolymers helped expand processing knowledge of polyolefins and of polymers in PBF overall. Challenges in viscosity during printing were overcome by exploring unique processing and post-processing methods to enable PBF of UHMWPE. An understanding of the impacts of ethylene content on processing and properties of PP-PE copolymers was developed and this insight can be valuable to guide future development of polyolefins for PBF. A powerful methodology for emulating the PBF process to understand crystallization was developed. This emulation provides an alternative to crystallization modeling and characterizes the crystallinity in a printed layer rather than just determining an amount of crystallinity. Through each of these contributions, understanding of PBF of polyolefins and the PBF process in general has been furthered.
A Novel Framework for Modeling Reconfigurable Dynamic Systems
Hamedi, Behzad (Virginia Tech, 2025-06-03)
Modular and reconfigurable design has gained significant attention in manufacturing systems, robotics, space, and automotive industries, aiming to reduce costs and enhance performance. While modal analysis traditionally provides the relationship between dynamic response and frequency, its applicability to reconfigurable systems is limited particulary for high frequency range. To tackle this limitation, this research proposes a novel approach for modeling reconfigurable dynamic systems, leveraging recent advancements in sub-structuring, impedance, and admittance techniques. The objective is to comprehensively understand, develop, and demonstrate the general theories of modeling reconfigurable systems, with a focus on applications in automotive engineering. In contemporary automotive engineering, achieving superior ride comfort and minimizing noise and vibration levels is paramount. This is especially critical for electric vehicles (EVs), where predicting road noise presents unique challenges due to the added weight of battery packs. This thesis introduces a Generalized technique to incorporate Frequency-Based Substructuring. This framework facilitates the development of mathematical models for multi-DoFs reconfigurable dynamical systems, allowing for a detailed analysis of vibrational responses within the automotive context. The GRCFBS approach enables the prediction and evaluation of the overall system response by analyzing the combined receptance matrix or FRFs of an assembly. This is achieved through a generalized mathematical algorithm that combines the subsystem components' Frequency Response Functions (FRFs). The approach systematically employs a reduced-order model by focusing on specific measurement and excitation points with only limited number of degrees of freedom (DoFs) at either connection region between subsystems or the internal region within individual subsystems, while still accounting for the essential translational and rotational DoFs required for modeling subsystems interaction. Subsequently, based on the excitation forces and the availability of the FRF (receptance) matrix, we can predict the overall system response using superposition and linear approximation. By systematically decomposing the system into its constituent subsystems and analyzing their independent responses, this method aids in system identification and understanding the interaction between subsystems. This technique not only helps in reducing model size, but also provides valuable insights into the transfer mechanisms of vibrations and noise throughout subsystem interfaces and connection points, contributing to noise and vibration mitigation in the automotive sector. Moreover, this methodology develops efficient reduced-order models, significantly reducing the need for time-consuming and expensive simulations, especially during the initial phases of development. By incorporating data from individual subsystem measurements, we can predict the overall system response based on focusing on a limited number of critical points regardless of complexity of the geometries, even when certain measurement points are inaccessible using traditional modal testing methods. Additionally, this methodology can potentially develop hybrid models that combine experimental and numerical data from various subsystems. Nonetheless, modeling of the interactions among the subsystems presents a significant challenge, which this study is also focused on. A variety of case studies illustrate the practical applications of this method, with further theoretical developments enhancing the reliability of the research findings.
Towards A Comprehensive Evaluation of Driving Impairment and Assessment Technologies
Jain, Sparsh (Virginia Tech, 2025-06-03)
Impaired driving is a persistent threat to traffic safety, with alcohol and cannabis frequently involved in motor vehicle crashes. This dissertation examines how alcohol, cannabis, and their combination influence driving behavior and performance in real-world settings, and for alcohol, in a controlled driving environment. These investigations focus on vehicle control, behavioral adaptations, and, in the case of alcohol, the potential value of physiological signals as early indicators of impairment. The first study analyzed over two years of naturalistic driving data from 41 participants. Trip-level substance use was self-reported and, when available, confirmed using breath or oral fluid testing. Alcohol-positive trips mostly occurred between 6:00 PM and 1:00 AM and on the weekends and showed a statistically significant reduction in highway mileage compared to baseline (-13%, p = 0.0475). Cannabis-positive trips followed a temporal distribution similar to baseline, apart from elevated activity on Fridays, and showed reduced highway mileage and a modest 3.2% reduction in speeding (F = 4.81, p = 0.0371) along with slight degradation in lateral control. Polysubstance trips occurred predominantly on weekends and exhibited the lower overall speeding proportions. Kinematic event rates increased at lower severity thresholds during impaired trips, particularly at higher speeds, suggesting either subtle destabilization or compensatory behavior. While high-severity safety-critical event rates were comparable between cannabis-positive and baseline trips, these findings challenge assumptions that cannabis-positive drivers may engage in safer driving behavior. In general, this dataset provides detailed insight into how alcohol- and cannabis-related impairment may manifest in routine driving behavior. The second study evaluated alcohol's effects on physiology and driving performance, and the feasibility of using wearable sensors to monitor impaired driving. Five participants completed standardized drives under both sober and alcohol-impaired conditions (BrAC = 0.08%) while instrumented with ECG, respiration, and EEG sensors. Alcohol consumption produced consistent changes in heart rate (+13.5 bpm, p = 0.0267), heart rate variability (−150.3 ms, p = 0.0165), respiration patterns (reduced RVT, increased variability), and EEG signals (increased frontal alpha and theta power, reduced peak alpha frequency). ECG and respiration sensors performed reliably, while EEG data quality varied and required extensive processing. Behavioral changes on the road were consistent but subtle, with only lab-based reaction time tests reaching statistical significance (+22 ms, p = 0.0132). Participants showed poor accuracy in estimating their own intoxication (20% error) and expressed ambivalence toward driving under hypothetical impaired scenarios, consistent with longstanding evidence that drivers often lack accurate insight into their own impairment and risk. Overall, this study demonstrated that wearable physiological sensors can reliably capture alcohol-induced changes in heart, respiratory, and brain activity during real-world driving, even when observable effects on driving performance are subtle or inconsistent. This dissertation advances impaired driving research by integrating large-scale naturalistic observation with controlled experimental testing. It clarifies how alcohol and cannabis influence real-world driving behavior, demonstrates that physiological signals can reveal alcohol impairment even when driving effects are subtle, and underscores the limitations of driver self-assessment. These findings support the development of intelligent monitoring systems that use objective, physiological data to improve impaired driving detection and prevention.
Exploring the Teaching Beliefs of Chinese-born Engineering Faculty in the United States: A Qualitative Case Study with a Focus on Teamwork
Cao, Yi (Virginia Tech, 2025-06-03)
Among the key competencies emphasized in engineering education, teamwork is widely recognized as essential for students' professional development. However, while extensive research has examined the pedagogies to build teamwork, faculty beliefs about teamwork remain an underexplored area, particularly in engineering education. This study contributes to addressing this research gap by investigating how engineering faculty conceptualize teamwork, how their beliefs influence their instructional practices, and how these beliefs align with broader educational reforms. As a crucial part of the foreign-born engineering faculty community in the U.S., Chinese-born faculty members play an essential role in these discussions. This study focuses on Chinese-born faculty's beliefs in engineering education, and findings provide implications for understanding the experiences of foreign-born faculty in general. This study employs a case study approach to examine the teaching beliefs about teamwork among 11 Chinese-born engineering faculty at an R-1 university in the U.S. Through thematic analysis, the study identifies two overarching categories of faculty beliefs: role-based themes and education-based themes. These categories encompass five key themes: (1) the roles of students, (2) expectations for faculty, (3) the faculty-student relationship, (4) pedagogical philosophies, and (5) curriculum emphasis. These themes show the main aspects of how faculty conceptualize teaching and teamwork. This study also employs narrative analysis, guided by Oleson and Hora's (2014) conceptual framework, to address how faculty develop their teaching beliefs. The findings extend this framework by revealing that cultural teaching and learning experiences also play a significant role in shaping faculty beliefs in addition to the four key influences previously identified, given that all participants in this study have educational experiences in China and the U.S. In addition, this study found that the common distinction between student-centered and teacher-centered pedagogical philosophies is insufficient to understand Chinese-born engineering faculty beliefs. The role-based and education-based categories are two major themes that define faculty beliefs about teaching in this study. The role-based themes provide an alternative conceptualization for the traditional student-centered and teacher-centered pedagogical philosophies by emphasizing the importance of the faculty's role and relationships between faculty and students in the teaching and learning process to improve student learning outcomes. In addition, the education-based themes reflect that faculty care about pedagogy and curriculum in their teaching, and they prefer to design teaching and pedagogical methods based on the learning outcomes in the curriculum. Last, this study contributes to our understanding of the "research-teaching nexus" by showing how faculty's research experiences are drawn on to develop their beliefs about pedagogy and curriculum. This study shows a straightforward link between research and teaching for just over half of the faculty members in this group. Finally, this study's findings reflected the cultural impacts on faculty beliefs due to their multicultural teaching and learning experiences. Though culture cannot fully determine faculty's beliefs, faculty are shown here to actively choose the most effective and efficient teaching methods from the full range of their previous teaching and learning experiences. The faculty's multicultural teaching and learning experiences appear to provide them with broader options for effective teaching methods while fostering deeper reflection and a more balanced perspective in their teaching beliefs.
An Ensemble of Novel Techniques for Non-Linear, Non-Gaussian Data Assimilation
Subrahmanya, Amit Nagesh (Virginia Tech, 2025-06-03)
Data assimilation (DA) presents a theoretically rigorous framework for combining measurement data from real-world processes with simulations that attempt to mimic the said process. DA is a challenging task due to (i) computationally expensive, but uncertain model simulations, (ii) spatiotemporal sparsity and uncertain of measurements, (iii) the uncertainties not being Gaussian in general, and (iv) the inferred variables obeying constraints or features. In my work, DA is posed as a discrete-time Bayesian state estimation problem. Many state-of-the-art operational data assimilation methodologies make linear, Gaussian assumptions that fail to accurately describe the uncertainties in many problems of interest. Next, these methods are also agnostic to system constraints and features. Broadly, my research is about developing theoretically rigorous and computationally efficient methods for non-linear, non-Gaussian, multi-modal, high-dimensional data assimilation problems and if necessary, preserving system constraints and features.

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