Doctoral Dissertations

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Modeling and Evaluation of Pavement Surface Properties for Safety Analysis Across Roadway Functional Classifications
Bazmara, Behrokh (Virginia Tech, 2026-06-03)
According to the National Highway Traffic Safety Administration (NHTSA), the fatality rate in March 2025 for the motor vehicular crashes per 100 million vehicle miles traveled (MVMT) were estimated to be down 8 percent, compared to the corresponding period of 2024. Additionally, the fatality rate in rural areas remains 2.4 times higher than that in urban roads. Given that the rural and urban roadways account for the majority of freeway crashes, ensuring safety improvements across all roadways and facility types remains a significant factor. Most highway crashes involve multiple causative factors. However, crash investigations have consistently shown a statistical association between crashes and pavement surface properties. Friction and macrotexture are key pavement surface-related characteristics that have significant impact on safety, particularly when they fall below a critical threshold. Pavement friction is the resisting force that occurs when a vehicle's tires slide or attempt to slide across the pavement surface. Frictional properties are essential for critical maneuvers such as braking, accelerating, and steering. Adequate friction prevents vehicles from skidding and helps reduce stopping distances. Complementing friction, pavement macrotexture, which is defined as surface texture properties with wavelengths ranging from approximately between 0.5 mm and 50 mm and are determined by the size and shape of aggregate in asphalt concrete pavement (AC), enhances the pavement's functional performance through facilitating rapid drainage of water from pavement surface. This pavement property maintains effective tire–pavement contact under wet-weather conditions and ultimately contributes to adequate friction levels and enhances pavement safety performance. Several recent studies have emphasized the need to monitor both pavement friction and macrotexture to obtain a complete understanding of the road surface's frictional capacity. Research from the United Kingdom and the United States shows that, on high-speed roads, macrotexture has a greater effect on crashes than friction alone. As vehicle speed increases, the tire-pavement contact patch decreases, leading to reduced available friction, while low-speed roadways often experience friction challenges and macrotexture degradation due to frequent braking and limited maintenance. Furthermore, research identified macrotexture as a critical factor affecting pavement safety, at travel speeds exceeding 56 mph (90 km/h.). Therefore, effective pavement friction management requires speed-dependent frictional behavior (macrotexture and friction) across different roadways and driving speeds, to provide a more comprehensive evaluation of pavement safety. Despite this recognized significance, research on incorporating the interaction of surface characteristics within safety performance modeling frameworks remains limited. Given the role of macrotexture in sustaining pavement friction and reducing crash risk, maintaining adequate macrotexture levels across roadway networks is essential. Thus, pavement management strategies increasingly incorporate macrotexture monitoring within asphalt mixture design, aggregate selection, and compaction practices to ensure that frictional demand is consistently met. Integrating macrotexture requirements directly into mixture design and performance specifications provides an effective approach to add a safety-related performance indicator that emphasizes durability. This Dissertation proposes a novel approach for assessing the speed-dependent frictional behavior of the asphalt pavement surfaces using network-level Continuous Pavement Friction Measurements (CPFMs). This method incorporates combined effects of pavement macrotexture and low slip-speed friction, to assess crash modeling. The methodology involves determining adjusted friction values in accordance with the ASTM E1960-07 standard using macrotexture-based speed constant, across a range of operating speeds (i.e., 40, 55, and 70 mph) and according to the functional classification of the roadways. This approach establishes a framework for integrating friction and macrotexture as a unified safety metric for the development of crash models in estimation of crash risk. It defines speed-dependent interaction between tire and pavement and confirms how certain surface mix types exhibit heightened sensitivity to the combined effects of macrotexture and friction. Furthermore, this research offers practical implications of the proposed friction index threshold values for transportation agencies to optimize the cost-effectiveness of friction treatment. Another key innovation of this Dissertation is the development of macrotexture prediction models by capturing its dependency on mix design parameters and the characteristics of aggregate volumetric. Modeling approaches are implemented to investigate the application of traditional linear models, and machine learning techniques (e.g., XGBoost and Random Forest) to define an optimized significant variable in predicting macrotexture of the asphalt pavements. Modeling approaches are developed to provide a basis for guiding transportation agencies and contractors in designing asphalt mixtures at the construction level and field-testing processes to meet specified macrotexture values. The findings of this Dissertation support pavement friction management strategies by providing critical implications for road agencies in enhancing safety and optimizing the cost-effectiveness of pavement treatment. Collectively, this work bridges a key gap between materials engineering and safety performance modeling for establishing a foundation for more data-driven and proactive roadway safety management.
Treading Water, Changing Tides: How Sociopolitical Uncertainty in the United States is Impacting LGBTQ+ Romantic Partnerships
Tarantino, Mari Rose (Virginia Tech, 2026-06-03)
The return of a Trump administration has ushered in an era of heightened sociopolitical uncertainty for LGBTQ+ communities. Significant administrative efforts have been made to undermine sexually and gender minoritized individuals, leaving millions of lesbian, gay, bisexual, transgender, or queer (LGBTQ+) people to simultaneously grieve, find respite in, and survive together within their close relationships. Grounded in the contextual relational uncertainty (CRU) model and queer theory, in this phenomenological study, I conducted 11 dyadic interviews with LGBTQ+ couples to understand how the sociopolitical ambiguity of the Trump administration is affecting LGBTQ partnerships. Insights from this work may inform research and clinical efforts to support vulnerable populations who are impacted by emergent social and legal ambiguity.
Blockchain Adoption in Assurance Practice: The Role of Trust, Governance, and Behavioral Intention
Morris, Brian James (Virginia Tech, 2026-06-03)
Blockchain technology has the potential to improve transparency, data integrity, and efficiency within accounting and assurance practices. However, adoption among professionals remains limited despite strong conceptual alignment with the objectives of verification and trust. This dissertation examines the factors influencing blockchain adoption in the assurance profession by integrating established technology adoption theories with constructs specific to decentralized systems. Building on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), this research develops and tests extended frameworks that incorporate distributed trust, governance, smart contracts, and prior experience with decentralized systems. Data were collected from Certified Public Accountants (CPAs) and analyzed using partial least squares structural equation modeling. The results indicate that trust is a central determinant of adoption and serves as a mediating mechanism between blockchain system characteristics and behavioral intention. Governance and distributed trust significantly influence trust formation, which in turn affects perceived usefulness, subjective norms, and intention to adopt. Prior experience with distributed trust environments also positively influences trust, suggesting that familiarity reduces resistance to decentralized verification models. Additionally, the integrated TAM and TPB framework provides greater explanatory power than either model alone. This dissertation contributes to the literature by demonstrating that blockchain adoption represents a shift in trust from institution-based mechanisms to protocol-based systems. It extends traditional adoption models by incorporating blockchain-specific constructs and provides a more comprehensive framework for understanding adoption in decentralized environments. The findings offer practical implications for practitioners and policymakers by emphasizing the importance of governance structures, regulatory clarity, and user exposure in facilitating blockchain adoption within assurance practice.
A comparison of the nontraditional self-study and the traditional self-study of selected institutions within the region of the Southern Association of Colleges and Schools
Massenbert, Samuel (Virginia Polytechnic Institute and State University, 1979)
The purpose of this study was to compare and contrast the reasons for conducting the traditional or nontraditional self-study; the process and procedural differences between the traditional and nontraditional self-study; and the outcomes, benefits, impacts, and results between the traditional self-study and the nontraditional self-study. Out of 696 postsecondary institutions within the region of the Southern Association of Colleges and Schools, 9 have elected to conduct the nontraditional self-study. This may have significance for other eligible institutions if it offers tangible advantages. This study has provided management information data that will assist college and university administrators in deciding the type of institutional self-study which best meets their needs. Data were collected at three of the nine institutions where the nontraditional self-study was conducted and three institutions which used the traditional self-study to compare and contrast the impetus, procedures, and impacts resulting from the type of self-study selected. The research techniques used were the semi-structured interview, observation, and review of archival data. For each of the six institutions, a case study was developed and analyzed. The first level of analysis was to compare and contrast the three nontraditional self-studies with the three traditional self-studies. A second level of analysis was made on matching pairs of institutions with similar characteristics. Each case study required a site visit to the institution for interviews of key personnel involved in the institution's self-study. Documents reviewed included the self-study and other relevant publications such as the faculty handbook, student handbook, and institutional catalog. As a group, staff members of the institutions conducting the nontraditional self-study had strong impetus in the performance of self-evaluation. Procedures were classified as moderate for two of them and strong for the third. Impacts were moderate in one case and strong in two cases. In the cases of the institutions where the traditional self-study was conducted, impetus was weak at all of these institutions, procedures were moderate at all of these institutions, and impacts were weak at two of the institutions and moderate at the third. Summarized, the effects of the nontraditional self-study were more positive in terms of evaluative needs of institutions conducting that self-study in terms of impetus, procedures, and impacts than the effects of the traditional self-study on the evaluative needs of the three other institutions. Matched pairs of institutions show opposite or contrasting results for impetus, generally similar results for procedures, and contrasting results for impacts. These results indicate that the nontraditional self-study appears to get stronger impetus, the opportunity for stronger procedures, and stronger impacts or outcomes. These results favor the selection of the nontraditional self- study for the administrators of institutions that desire the use of an optional method of self-evaluation for additional outcomes. The opportunity to participate in the nontraditional self-study might well be expanded to developing institutions where the administrators may use this type of self-study as a change agent if a compelling reason for change exists.
High Frequency Resonant Converters with Optimized Circulating Energy for Ultra-Wide Voltage Range EV Charging Applications
Hou, Zhengming (Virginia Tech, 2026-06-02)
The rapid growth of electric vehicles (EVs) has created a strong demand for isolated dc–dc converters capable of operating over an ultra-wide output voltage range while maintaining high efficiency and high power-density. Among various isolated converter topologies, the LLC resonant converter is widely adopted due to its inherent soft-switching capability. However, in wide-output-voltage EV charging applications, conventional LLC converters rely on large switching frequency variation to regulate the output voltage. This operating characteristic inevitably leads to excessive circulating energy through the entire primary switching networks, higher voltage and current stress on resonant components, and significant degradation of full-range efficiency, especially as the operating point deviates from the resonant frequency. In universal EV charging systems, where the output voltage is required to cover a broad range of battery voltages (e.g., 50 V–1000 V), the fundamental challenge is no longer voltage regulation alone, but rather how to optimize the resonant circulating energy of LLC-based converters to an efficient operating region while supporting ultra-wide voltage gain. Conventional resonant converter topologies struggle to simultaneously satisfy these conflicting requirements, particularly under high-frequency operation where circulating energy directly impacts efficiency and power density. This dissertation investigates wide-output-range resonant dc–dc converter design for EV charging applications, with a focus on limiting the circulating energy of LLC resonant converters across an ultra-wide voltage range. Three approaches are explored, including modulation, topology, and resonant tank design methodology. To accurately capture resonant behavior over wide operating conditions, time-domain analysis (TDA) is employed in place of the conventional first-harmonic approximation (FHA), enabling precise resonant tank optimization. First, a novel pulse-width-modulated (PWM) LLC resonant converter with voltage multiplier rectifiers is proposed to decouple voltage regulation from switching frequency variation. By operating at a fixed resonant frequency and utilizing secondary-side PWM control, the proposed topology can operate at the resonant frequency which is considered as its optimal point, thereby limiting circulating energy while achieving an ultra-wide output voltage range. A time-domain-based optimization methodology is developed to accurately model the resonant behavior on the primary side and guide resonant tank parameter optimization for improved full-range efficiency while maintaining zero-voltage switching (ZVS) across the entire operating range. Second, to fundamentally restrict the resonant energy circulation through topology design, a LLC-T resonant converter topology is introduced to reshape the voltage gain characteristic at the resonant-tank level. By incorporating an inverse-coupled auxiliary transformer into the resonant tank, the equivalent resonant inductance and capacitance are load dependent. Thus, the LLC-T topology significantly compresses the required switching frequency range and reduces circulating energy and resonant component stress compared to conventional LLC converters with identical resonant tank designs. Load-independent ZVS is maintained over a wide voltage and load range, enabling efficient high-frequency operation. The operating principles and characteristics of the proposed topology are analyzed using a time-domain model under different operating conditions, including above-resonant, below-resonant (inductive and capacitive regions), and at-resonant operation. Finally, building upon the LLC-T resonant tank as a low-gain, high-efficiency operating mode, an LLC-T–based reconfigurable resonant converter architecture is developed to extend the achievable voltage regulation range for universal EV charging systems covering a 50 V–1000 V output range. An optimization strategy is proposed for resonant converters with wide gain range requirements to guide resonant tank design and limit circulating energy within each operating mode. By dividing the overall voltage gain into multiple operating modes, the proposed architecture replaces a wide continuous operating range with multiple narrow resonant operation regions, further reducing circulating energy while preserving soft-switching performance. Experimental results from a 3.7 kW prototype validate the proposed concepts and demonstrate high efficiency and a power density exceeding 5 kW/L across the entire output voltage range. This dissertation demonstrates that constraining circulating energy through modulation, topology design, and resonant tank optimization provides an effective approach toward high-efficiency, high power density, ultra-wide-range isolated dc–dc converters for next-generation EV charging applications.
Correspondence from the Unseen: Prismatic Agri-environmental Governance in the Anthropocene
Carcamo, Pablo (Virginia Tech, 2026-06-02)
Climate change is transforming agriculture and the governance frameworks responsible for it. Adaptation programs, efficiency measures, and technical interventions are offered as solutions, but across many settings they fail to address the conditions that farmers, ecosystems, and animals actually inhabit. This dissertation examines three such settings, drawing on political ontology's concept of the anthropo-not-seen to name what governance categories are structurally unable to perceive, and developing correspondence as the positive term for engaging what governance currently forecloses. Chapter 1 analyzes U.S. nutrient governance and practices and shows how metrological regimes, the arrangements through which environmental flows are turned into calculable variables, produce certain realities as governable while foreclosing others, including soil life as a living ecological community. Chapter 2 draws on ethnographic observations and interviews in Chile's Limarí Valley during the ongoing megadrought, where farmers describe a threshold crossing from chronic adversity into ontological breakdown, and where governance is present and active but cannot perceive the collapse of the configuration that made farming viable. Considering drought as a disaster in the making, the chapter extends the sociological concept of recreancy into slow-onset climate contexts and proposes failure to correspond as an analytical concept. Chapter 3 builds on ethnographic fieldwork at a farm animal sanctuary and argues that empathy, developed through bodily participation in care labor, produces knowledge about animal subjectivity that governance frameworks organized around animals as resources and emissions sources structurally cannot hold. The concluding chapter proposes prismatic governance as an orientation for agri-environmental governance in the Anthropocene, one that accepts ontological multiplicity and works with it. Under conditions where governance categories cannot perceive the situations communities, species, and ecosystems inhabit, the harms of climate change fall unevenly and the frameworks meant to address them reproduce the injustices they were supposed to resolve.
Characterization of canonical and noncanonical nickel metallochaperones in Methanococcus maripaludis
Dinh, Thuc Anh (Virginia Tech, 2026-06-01)
Nickel is an essential trace element for many microorganisms, serving as a catalytic cofactor for enzymes involved in energy metabolism, carbon cycling, and microbial physiology. At the same time, excess intracellular nickel is toxic, requiring organisms to maintain highly regulated nickel homeostasis systems that balance nickel acquisition with controlled intracellular trafficking. Methanogenic archaea (methanogens) rely on multiple nickel-dependent enzymes that are central to methanogenesis, including [NiFe] hydrogenases. The nickel insertion step during [NiFe] hydrogenase assembly involves the nickel metallochaperones HypA and HypB. HypA is a small nickel carrier that contains an N-terminal nickel binding domain and a C-terminal structural zinc-binding site. HypB belongs to the P-loop family of GTPase with a nickel-binding site at the G-domain. In this work, we characterized the nickel metallochaperones HypA and HypB from the hydrogenotrophic methanogen Methanococcus maripaludis. MmpHypA binds either zinc or a mononuclear iron at its C-terminal metal-binding site, the latter representing an unusual metal occupancy not previously observed for HypA proteins. MmpHypB contains the canonical nickel-binding site at the G-domain that stimulates GTP hydrolysis and promotes the formation of HypA-HypB complexes. Zinc-bound MmpHypA is optimized for nickel acquisition of MmpHypB, and the nucleotide state of MmpHypB modulates the oligomeric state of HypA-HypB complexes, supporting a GTPase-driven nickel delivery mechanism. In addition to the canonical HypB protein, methanogens encode a second HypB homolog, HypB2, whose exact function remains unknown. Biochemical characterization of HypB2 proteins revealed that they form a stable complex with proteins encoded by neighboring genes. HypB2 exists predominantly as dimers and the dimeric form is stabilized by strand-swapping at the extended C-terminal region. This region also putatively coordinates a Fe-S cluster, a feature not previously associated with HypB proteins. Deletion of hypB2 does not impair growth under nickel-limiting conditions and HypB2 did not form complex(es) with HypA, suggesting that HypB2 represents a distinct member of the P-loop GTPase family that may not function as a canonical nickel metallochaperone. Instead, AP-MS experiments hint toward a role of HypB2 and its associated complex in Fe-S cofactor trafficking. Together, these findings expand current understanding of nickel trafficking in methanogens and reveal previously unrecognized diversity in the nickel metallochaperone family as well as identify a protein complex that potentially involves in Fe-S cofactor trafficking in methanogens.
Evaluating the Relationship Between Equine Pain and Behavior
Thompson, Rebecca Anne (Virginia Tech, 2026-06-01)
Pain is physical and emotional discomfort caused by a variety of diseases and injuries. While the exact prevalence of pain in horses is unknown, it is likely a common ailment. Additionally, previous research has determined that owners frequently do not recognize pain in horses. This dissertation examined both assessment of pain and a disease that might cause pain. First, I compared responses to two commonly used pain assessment methods: manual palpation and pressure algometry, and used a cluster method to group the responses. Responses were behaviors such as a muscle twitch, tail swish or step. Manual palpation uses a person's hands to touch various areas on a horse to see if the horse responds with a behavior that suggests pain. Pressure algometry uses a digital device that gives a number called the mechanical nociceptive threshold, which is the amount of pressure that was applied when the horse responds with a behavior. There were 46 manual palpation points and 15 pressure algometer points. These points were divided into nine anatomical regions: head, neck, withers, shoulder, front leg, girth, back, hip, and back leg. From the first study, I determined that behavior responses by anatomical region were similar for both manual palpation and pressure algometry testing and therefore only one of these tests was needed if only examining behavior responses. A cluster analysis grouped both the pressure algometer behavior responses and mechanical nociceptive threshold into clusters that were based on anatomical regions. However, a cluster analysis did not create anatomical groups for the manual palpation test. For the second study, I used three pain assessment methods: Equinosis Lameness Locator, manual palpation test, and pressure algometer test to evaluate horses with and without outer surface protein F antibodies to Borrelia burgdorferi. The Equinosis Lameness Locator is a three-sensor system that provides three numerical outputs for any movement asymmetries in millimeters. For this study, I found that horses with B. burgdorferi antibodies had decreased head movement responses on the caudal neck and a higher muscle twitch response count during the pressure algometer exam. However, these horses also had a decreased Equinosis Diff Max Pelvis number meaning that horses with antibodies were moving more symmetrically than horses without antibodies. Additionally, while not related to the tests used to evaluate pain, the overall seroprevalence of horses has increased in the southwest Virginia area. For the third study, I used a sound sensitivity test to determine if horses in varying lameness groups responded differently to repeated sounds as previous research in dogs has found that dogs with orthopedic pain may develop sound sensitivities. In this study, 70% of the horses had decreased behavior reactions when comparing the day one behavior after the sound to the day five behavior after the sound. This means that most horses that are exposed to repeated sounds will habituate to the sound. However, five horses (14%) had increasing behavior between day one and day five. These five horses were in the moderate and high lameness group and there were no horses in the low lameness group that had an increased behavior reaction between day one and day five. Overall, results from these studies help improve our understanding of pain assessments and behaviors; and also, how to apply these assessments to a disease.
Design and SDR Prototyping of Online Real-Time Machine Learning for NextG MIMO and ISAC
Liang, Yibin (Virginia Tech, 2026-06-01)
Signals and market valuation in tourism: Strategic conditions and communication pathways
Kim, Yelim Erin (Virginia Tech, 2026-06-01)
Tourism and hospitality operate in environments characterized by uncertainty, intangibility, and information asymmetry. In such settings, outside stakeholders often rely on visible cues to interpret future firm prospects and revise their expectations about value. This dissertation examines how such signals become economically meaningful through their connection to market valuation. Drawing on signaling theory and related perspectives on brand equity, strategic flexibility, and leadership, it investigates whether signals matter for valuation, when they matter more, and how firms can strengthen their valuation impact. By focusing on market valuation, this dissertation highlights how destinations and firms are evaluated not only through realized operating outcomes but also through the way external audiences interpret new information and translate it into expectations about future performance. Chapter 1 introduces the dissertation by outlining the role of signals in tourism and hospitality and presenting the broader theoretical and methodological framework. It explains why signals are especially important in industries where products and experiences cannot be fully assessed in advance and where destinations and firms operate under conditions of uncertainty. The chapter further develops the dissertation's central argument that signals may originate from outside the firm or from the firm itself, but their economic importance depends on whether they are noticed, interpreted favorably, and translated into value-relevant expectations. It also positions market valuation as the main outcome of interest and introduces the event study approach as a consistent methodological lens across the three studies. Chapter 2 examines whether destination-level popular culture functions as an external signal that enhances the market value of tourism-related firms through destination spillovers and the brand equity pathway. Drawing on customer-based brand equity and related perspectives on destination image and associations, this chapter argues that successful popular culture can strengthen destination visibility, enrich destination-related meanings, and increase the likelihood that the destination will be favorably considered by outside audiences. These effects may extend beyond tourism demand itself and influence investor expectations about firms operating within the destination. The findings suggest that popular culture can generate positive valuation effects by increasing destination visibility, strengthening destination-related associations, and shaping expectations about future tourism demand and firm prospects. Chapter 3 investigates when such external signals matter more by examining whether the valuation benefits of the same cultural signal vary across firms depending on organizational strategy. Although firms may be exposed to the same destination-level signal, they are not equally positioned to capture their benefits. This chapter focuses on international hotel firms and argues that operating arrangements differ in their ability to convert external signals into stronger market responses. Drawing on strategic flexibility and related perspectives on value capture, the chapter proposes that firms with more flexible structures are better able to respond to opportunities created by favorable destination-level attention. The findings show that franchised firms exhibit stronger abnormal returns than managed firms, suggesting that organizational strategy conditions the valuation effect of external cultural signals. Chapter 4 examines firm-initiated sustainability signals by focusing on environmental certification announcements in publicly traded U.S. hotel firms. While the earlier chapters focus on external signals, this chapter turns to signals generated by the firm itself and asks how such signals can become more effective in the eyes of outside stakeholders. The chapter argues that sustainability initiatives do not produce uniform valuation effects because market reactions depend on whether the signal is perceived as authentic and credible. Building on leadership-related perspectives, it proposes that the alignment between CEO political ideology and the firm's sustainability message strengthens the persuasiveness of the signal by reducing doubts about opportunism or greenwashing. The findings show that environmental certifications generate positive market reactions and that these effects are stronger when the CEO's political ideology is more closely aligned with the firm's sustainability message, highlighting the importance of perceived authenticity and credibility in shaping investor responses. The final chapter summarizes the main contributions of the dissertation and discusses implications, limitations, and directions for future research. Overall, this dissertation shows that signals do not affect valuation automatically. Their effects depend on whether they are recognized by outside audiences, whether firms are strategically positioned to benefit from them, and whether the signals appear credible and consistent with the broader context in which they are interpreted.
Generative AI for Hardening Security: From Training Data Augmentation, Language Alignment, to Code Vulnerability Demonstration
Kanchi, Shravya (Virginia Tech, 2026-06-01)
Machine learning-based cybersecurity systems face fundamental data limitations that constrain their effectiveness across diverse threat environments. Training data for security classifiers is inherently scarce, imbalanced, and subject to distributional shift, degrading generalization under evolving threat landscapes. Foundation model customization pipelines are vulnerable to data poisoning that induces harmful behavioral outputs. Vulnerability detection tools identify risks in software dependencies but provide no mechanism for determining whether reported vulnerabilities are concretely exploitable within a specific application context. This dissertation presents three contributions that leverage generative AI to address these challenges across training data augmentation, language alignment, and code vulnerability demonstration. First, we characterize the data challenges most commonly degrading ML-based security classifier performance through a measurement study of 35 papers published in top security venues. Building on this, we propose Nimai, a controlled synthetic data generation framework based on a Vector-Quantized Variational Autoencoder, designed to augment underrepresented regions of security feature spaces. Evaluated across seven security classification tasks, Nimai improves classifier performance in four of seven tasks, achieving up to 32 percent accuracy improvement under severe data constraints, and recovers up to 60.4 percent of performance degraded by concept drift. Second, we investigate the security of foundation model customization pipelines under toxicity injection attacks. We propose a healing data approach that employs a safety-aligned large language model to generate contextually relevant, prosocial replacements for identified toxic samples, actively instilling desirable behavioral alignment rather than discarding harmful content. Fine-tuning on the healed dataset achieves near-zero Response Toxicity Rates across three model architectures while preserving conversational quality, degrading gracefully under imperfect toxicity classifiers. Third, we address the challenge of bridging vulnerability detection and real-world exploitability assessment in software supply chains. We propose PoVSmith, an agent-based framework for automated proof-of-vulnerability test generation in Java applications. PoVSmith identifies application-level entry points to vulnerable library APIs through agent-based call path analysis, then iteratively generates, executes, and refines JUnit test cases guided by exemplar tests and execution feedback. Evaluated on 33 Java application-library pairs, PoVSmith reveals 158 unique application-level entry points to vulnerable library APIs and correctly identifies 152 of them with their call paths, achieving 96 percent precision. Using this call information, PoVSmith generates 152 PoV tests, of which 84, or 55 percent, demonstrate feasible ways of exploiting library vulnerabilities through application code. Collectively, these contributions demonstrate that generative AI can systematically strengthen cybersecurity pipelines by augmenting scarce training data to improve classifier generalization, curating conversational fine-tuning data to enforce safe model behavior, and producing executable evidence of real-world vulnerability exploitability, establishing generative AI as a unifying instrument for hardening security systems across data, model, and system layers.
AI-Driven Affective Captioning for Equitable STEM Access Among Deaf and Hard-of-Hearing Students
Ubur, Sunday David (Virginia Tech, 2026-06-01)
This dissertation investigates how Artificial Intelligebce (AI)- and Augmented Reality (AR)-supported captioning can improve communication access for Deaf and Hard-of-Hearing (DHH) learners in STEM contexts. Traditional real-time captions provide essential access to spoken language, but they often omit nonverbal and contextual information such as speaker identity, tone, emphasis, affect, and conversational intent. Across a preliminary design study and three empirical studies, this work examines how caption augmentations can preserve these missing layers of meaning while maintaining readability, timeliness, trust, and user control. The preliminary study compared traditional captions with emotion-augmented caption designs and showed that affective and visual cues can support comprehension when they are lightweight and text-centered, but may increase workload when they compete with the main transcript or visual scene. Study 1, a qualitative study with DHH participants, found that users valued emotion-aware captions when they clarified tone, emphasis, or speaker intent, but only when cues were timely, legible, optional, and subordinate to the transcript. Study 2 evaluated culturally adaptive emotive captioning in AR by comparing two cue formats: compact symbolic cues, implemented as emoji/icon indicators, and explicit textual affect labels, implemented as inline text-tags, across high- and low-context cultural cohorts. Compact symbolic cues produced a robust cross-cultural preference, while qualitative findings showed that participants valued the cues differently: some emphasized speed and reduced distraction, while others emphasized easier access to speaker emotion. Study 3 evaluated Speaker-Aware Affective Captioning, a multi-speaker captioning interface that combined speaker-attributed captions, confidence-gated affect tags, and an on-demand AI Describe feature. The study showed that speaker attribution was the most consistently valued support, while AI Describe helped users recover from missed or unclear information. Affect tags showed promise, but their usefulness depended on timing, persistence, interpretability, and trust. Across these studies, findings show that accessible captioning should not simply add more expressive information. Instead, next-generation captioning systems should reduce users' inferential burden through layered support: preserving the transcript first, identifying speakers, supporting recovery from missed information, and adding affective interpretation only when it is accurate, low-burden, and user-controllable. This dissertation contributes empirical evidence and design guidelines for trustworthy, culturally sensitive, and readable affective captioning systems for inclusive STEM learning.
Linking Semiochemical Ecology and Spatial Dynamics to Improve Integrated Pest Management of Striped Cucumber Beetle, Acalymma vittatum
Nunez, Demian Antonio (Virginia Tech, 2026-05-29)
The striped cucumber beetle, Acalymma vittatum (Fabr.), is a major early-season pest of cucurbits in eastern North America because overwintering adults injure seedlings while they are young and highly vulnerable to feeding damage or infection by Erwinia tracheiphila, the causal agent of bacterial wilt. This early vulnerability makes it important to reduce beetle activity before aggregations develop on young crop plants. This dissertation examined striped cucumber beetle semiochemical attraction, spatial capture patterns, baited-trap operating range, and early-season perimeter trap deployment to inform future selective semiochemical-based management strategies. The first study compared semiochemical lures for striped cucumber beetle attraction and non-target bee captures. Treatments containing vittatalactone, the A. vittatum aggregation pheromone, consistently produced the highest striped cucumber beetle captures while maintaining low bee bycatch. Pairing vittatalactone with indole as a companion lure may further improve A. vittatum captures without increasing bycatch. The second study examined how vittatalactone-baited trap captures were distributed across farm landscapes. A. vittatum captures were generally higher nearer tree lines, suggesting that placing traps along wooded field edges may improve early-season interception. The third study used fluorescent-marked beetles released at different distances from a vittatalactone + indole-baited sticky trap to estimate its operational range. Predicted recapture among dispersing beetles fell to 1% at approximately 12 m, suggesting that trap-out will likely require relatively dense trap arrays to maintain effective coverage. The final field study tested whether early-season perimeter trapping with vittatalactone + indole-baited sticky traps could reduce beetle activity within production cucurbit fields. This interval was chosen to intercept overwintered adults before crop-associated cues could compete strongly with traps and to reduce early aggregation on vulnerable seedlings. Trap-out sites had lower striped cucumber beetle activity during crop establishment and early crop development in 2024, although this effect was not repeated in 2025. Together, these findings show that semiochemical trap-out can reduce striped cucumber beetle pressure under some field conditions and, with refinement, could support a viable early-season management strategy.
Evaluating the Effect of Mating Disruption and Parasitoids on Management of Diamondback Moth, Plutella xylostella (L.), in Commercial Brassica Systems
Tomlinson, Taylore Ashley (Virginia Tech, 2026-05-29)
The diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae) has become increasingly difficult to manage with insecticides alone, since it has developed insecticide resistance in recent decades. In the United States, brassica producers are running out of management options for controlling P. xylostella, resulting in a need for implementation of integrated pest management (IPM) tactics, such as mating disruption, biological control, and rotation of chemical controls. From 2021-2025, trials in brassica systems examining the effects of pheromone use and mating disruption on P. xylostella were conducted in four different states. Hand-applied mating disruption dispensers were found to reduce P. xylostella moth captures in treated plots during the growing season. It is also important to note the biological control agents have played an active role in targeting P. xylostella. As a result of in-field parasitism surveys conducted across Virginia from 2022-2025, Diadegma insulare (Hymenoptera: Ichneumonidae) was found parasitizing P. xylostella at moderate rates. Decreasing the number of insecticide spray applications to control P. xylostella is important to conserve natural enemies and to reduce the risk of insecticide resistance development. In 2025, a case study utilizing threshold-based scouting for lepidopteran pests paired with mating disruption in commercial cabbage fields showed a reduction of at least two insecticide spray applications spanning over 100 acres in southern Virginia. These trials provide promising results for P. xylostella management in the eastern United States.
Universal Design for Learning Strategies to Improve Student Engagement, Confidence, and Participation in an Undergraduate Biochemistry Laboratory Course
Drolet, Erin Taylor (Virginia Tech, 2026-05-29)
Biochemistry laboratory courses present unique challenges to students, especially those with disabilities. This dissertation investigates how inquiry-based learning (IBL) and Universal Design for Learning (UDL) can be systematically integrated into an undergraduate biochemistry laboratory course to address these challenges. This work evaluates curricular, instructional, and environmental modifications in a longitudinal study. Aim 1 demonstrates that a module-based laboratory course can be reorganized into a inquiry-based format while preserving core technical competencies, resulting in high levels of student engagement and perceived gains in durable skills. Aim 2 applies UDL principles of Representation, informed by cognitive load theory and text-signaling, to redesign laboratory manuals, leading to reduced student stress and improved navigation of course materials while maintaining learning outcomes. Aim 3 examines the physical accessibility of laboratory environments through student feedback, highlighting how they influence participation and identifying both improvements and persistent barriers across different instructional spaces. Collectively, these findings contribute to the literature by bridging research on undergraduate laboratory courses and UDL in biochemistry, demonstrating that scalable, low-cost interventions can improve engagement and accessibility in laboratory settings. This work provides an evidence-based framework for integrating inquiry, accessibility, and skill development in biochemistry laboratory education and underscores the importance of aligning instructional design with both cognitive and physical dimensions of student experience.
From Step Tests to Soft Sensors: Model-Informed Controller Tuning and Hybrid Feedforward–Feedback Ammonia-Based Aeration Control to Improve Full-Scale Water Resource Recovery Facility Performance
Gagnon, Alexandria Augusta (Virginia Tech, 2026-05-29)
Aeration is essential for nitrification and biological nitrogen removal in water resource recovery facilities (WRRFs), but it is also one of the largest operating energy demands. As utilities face increasingly stringent effluent nitrogen limits and pursue greater energy efficiency, aeration control strategies that stabilize effluent ammonia while minimizing unnecessary oxygen supply have become an operational priority. Ammonia-based aeration control (ABAC) is an effective approach because it links dissolved oxygen targets to real-time ammonia measurements. However, in many full-scale facilities, ABAC and other feedback loops are constrained by controller tuning practices based on trial-and-error or ad hoc rules often producing sluggish or oscillatory behavior, and reduced operator confidence. This work develops and validates practical methods to systematically tune proportional–integral (PI) controllers and improve ABAC performance under realistic WRRF dynamics. Because PI control remains the dominant structure in treatment plant automation and is well suited to first-order-plus-deadtime (FOPDT) processes, this research focuses on methods that characterize loop dynamics with minimal plant disruption, generate repeatable tuning parameters, and identify when feedback-only ABAC must be augmented with predictive action. Open-loop step-response testing was first applied to representative WRRF control loops to develop FOPDT models relating manipulated and controlled variables. These models were then used with lambda tuning to compute PI parameters that balance stability and responsiveness for both fast loops, such as airflow and header pressure control, and slower nutrient-related process loops. Because step-response testing is often impractical for ABAC under variable full-scale conditions, a reduced-order model-based tuning method was then developed. A hydraulics-based reduced-order model with simplified activated sludge relationships was used to describe how oxygen availability influences nitrification capacity. The non-linearity associated with Monod saturation kinetics was explicitly integrated into the controller structure so that feedback action operated on a more linearized response surface. Monod saturation nonlinearity was incorporated into the controller structure so feedback acted on a more linearized response surface. In model-based validation, the kinetic-informed controller achieved a mean absolute error (MAE) of 0.09 mg N/L relative to the effluent ammonia setpoint. When implemented at full scale and tuned using the proposed method, the controller achieved stable operation and a 0.16 mg N/L MAE. This work also addressed facilities with plug-flow hydraulics and pronounced diurnal loading, where feedback-only ABAC can become deadtime-dominant and respond only after a disturbance has propagated through the aeration system. Frequency-response screening was used to evaluate controllability limits imposed by transport delay and to identify conditions where feedback tuning alone is insufficient. A hybrid feedforward-feedback ABAC (FFABAC) architecture was then implemented in which model-derived soft sensors forecast influent ammonia loading and nitrification capacity for proactive feedforward action, while PI feedback corrects model error and maintains long-term stability. FFABAC achieved an overall MAE of 0.22 mg N/L and improved to 0.16 mg N/L under unconstrained conditions. In a controlled comparison, FFABAC reduced ammonia MAE from 0.31 ± 0.28 mg N/L under feedback-only ABAC to 0.11 ± 0.10 mg N/L. Collectively, this work provides a practical, scalable toolkit for improving aeration-related control performance in WRRFs. Step-response testing with lambda tuning offers a repeatable method for tuning common PI loops, reduced-order model-based tuning provides a feasible pathway for ABAC where traditional step tests are impractical, and frequency-response screening offers a framework for when feedback should be augmented with predictive feedforward control. These methods are designed for implementation within standard plant automation platforms, enabling systematic tuning, measurable performance improvement, and reduced operational risk.
CEO Power and Lead Independent Directors
Ding, Weiming (Virginia Tech, 2026-05-29)
Although prior research documents a positive association between CEO duality and the presence of a lead independent director (LID), I show that this finding may be incomplete. Conditional on duality, concentrated CEO power, measured using a multidimensional index of pay slice, ownership, and tenure, is negatively associated with LID presence and with LID presence on key monitoring committees. These findings are consistent with managerial power theory: CEOs leverage their power to resist independent board leadership, and this resistance is consequential. LIDs who serve on the nomination committee are meaningfully associated with increased sensitivity of CEO forced turnover to poor performance, and financially expert LIDs are associated with strong oversight outcomes more broadly. I further show that once an LID is appointed, CEO power is associated with a lower likelihood of LID discontinuance, suggesting that the role becomes entrenched over time. Collectively, my findings reveal that CEO power influences not only LID presence but also the conditions under which the role is effective. These results have direct implications for investors and practitioners seeking to strengthen independent board leadership.
Polymer Phase Behavior and Morphology Control: From Blend Compatibilization to Aerogel Formation
Trindade Coutinho, Isabela (Virginia Tech, 2026-05-29)
This dissertation covers polymer blend compatibilization and improvements in the mechanical properties of semicrystalline polymer aerogels. Both blends and aerogel properties are governed by phase separation processes. While in polymer blends, domain size and interfacial adhesion related to the phase separation can be detrimental for the properties, in aerogels obtained through thermally induced phase separation (TIPS), the phase separation is responsible for the network formation. Therefore, in both areas, controlling the phase behavior is necessary to control the properties of the final blend and aerogels. In the first half of this dissertation, the compatibilization of blends of polysaccharides with polyesters is investigated. Polymer blends are the physical mixture of at least two polymers, which are designed to achieve improved properties compared to the pure polymers. While polysaccharides are sustainable polymers, their use as plastics is limited due to frequent low toughness, poor melt processability, high water sensitivity, and high production costs. Therefore, making blends of polysaccharides is an alternative to mitigate this shortcoming and expand the use of sustainable polymers. Due to a small entropy of mixing, polymer blends tend two phase separate. The phase-separated morphology is characterized by sharp interfaces with low adhesion, which leads to poor properties. To mitigate the consequences of phase separation, compatibilization is achieved by adding compatibilizers that favorably interact with both components in the blend. Chapter 1 discuss the fundamentals of polymer blends and polymer blends compatibilization. Chapter 3 and Chapter 4 investigate the use of a block polymer and a graft polymer, respectively, in the compatibilization of polysaccharide/polyester blends to advance the knowledge about blend compatibilization. In both chapters, phase contrast optical microscopy (PCOM) and small-angle laser-light scattering (SALLS) were used to track changes in the phase-separated morphology as the compatibilizers were added. In Chapter 3, ethyl cellulose (ECel)/poly(ethylene terephthalate) (PET) 70/30 blends were compatibilized with a block polymer of ethyl cellulose (ECel) and poly(benzyl glutamate), named ECel-block-poly(BG). Different amounts of the block polymer, 5, 10, 20, and 30 wt.%, were tested as compatibilizers. The uncompatibilized blend presented a highly phase-separated morphology composed of large and small domains, characteristic of late stages of spinodal decomposition. As the compatibilizer content increased, the size of the large domains decreased until a bi-continuous spinodal texture was obtained with 30 wt% of compatibilizer. A decrease in average domain size from 15 ± 4 μm in the uncompatibilized blend to 2 ± 1 μm when using 30 wt% of the compatibilizer was observed. These changes in domain size highlight the ability of the compatibilizer to steric stabilize these blends, preventing coarsening of the phase-separated morphology. Chapter 4 investigated the impact of amylose acetate-graft-poly(D,L-lactic acid) (AmAc g-PDLLA) graft density and graft length on the compatibilization of starch acetate/PDLLA 70/30 blends. Graft polymer contents of 5, 10, and 20 wt% with varying graft density and graft length were investigated. The results showed that in order for the compatibilizer to reduce the interdomain distance of the blend, it has to entangle with the polymers of the blend. Furthermore, the ability of the compatibilizer to entangle was related to the chain entanglement molecular weight (Me) of the polymers in the blend. The series of graft polymers with the same graft length (29.4 kg/mol) but different graft densities (between 0.5 and 19 %) showed that the graft density has to be low enough so that the segments between grafts are at least the Me of the starch acetate, allowing the graft polymer to entangle with the starch acetate and promote compatibilization. In parallel, the series of graft polymers with the same graft density (1 %) but different graft lengths (between 7.9 and 29.4 kg/mol) showed that the grafts have to have a molecular weight above the Me for PLA, allowing the grafts to entangle with PDLLA and promote compatibilization. The second half of this dissertation investigates semicrystalline polymer aerogels obtained through TIPS. On TIPS, the polymer is dissolved at a high temperature, and upon cooling, the gel network is formed through a phase separation process. Chapter 2 discusses the fundamentals of the TIPS process and polymer aerogels. For the systems investigated here, the phase separation happens through solid-liquid phase separation, where the polymer crystallizes from the solution to yield the aerogel network. On TIPS, there are many parameters that can be tuned to control the morphology of the aerogel, including the initial polymer content, the solvent, the dissolution temperature, the gelation temperature, and the presence of additives. In Chapter 5, the impact of the gelation temperature on the properties of poly(ether ether ketone) (PEEK) aerogels was studied. It was observed that by increasing the gelation temperature the aerogel network connectivity was enhanced. As the mechanical properties of aerogels depend on the network connectivity, this increase in connectivity resulted in up to a 111.5 % improvement in the compressive modulus of the aerogels, while crystallinity, density, and porosity remained unchanged. Chapter 6 explored another approach to improve the mechanical properties of aerogels while maintaining their porosity. Specifically, the incorporation of sodium montmorillonite, Cloisite 10A, and Cloisite 25A nanoclays to polyphenylene sulfide (PPS) aerogels was investigated. The addition of 1 wt% of any type nanoclay did not impact the morphology of the PPS aerogel, but adding 5 wt% of Cloisite 10A resulted in a less connected morphology and therefore worse mechanical properties. An increase of 30 % in the compressive modulus of the aerogel was observed when 1 wt% of the montmorillonite was added to 15 wt% PPS aerogels. While some intercalation was observed for montmorillonite, we believe that the compressive modulus was not further enhanced because the nanoclay was not fully exfoliated in the aerogel matrix. Lower improvements in compressive modulus were observed by the addition of Cloisite 10A and Cloisite 25A, which were related to the degradation of the organic modifier. The degradation of the organic modifier can lead to a worse distribution of the nanoclays in the polymeric matrix, which is detrimental to mechanical properties. Some dependence on the nature of the organic modifier was also observed, highlighted by the better performance of Cloisite 10A compared to Cloisite 25A. Finally, the degradation temperature of the aerogels was increased by the addition of the nanoclays.
Flood pulse effects on multispecies catch in the Amazon Basin
Borba, Gabriel Costa (Virginia Tech, 2026-05-29)
Tropical river-floodplain fisheries feed tens of millions of people and depend on the flood pulse, the seasonal rise and fall of water that drives fish recruitment, growth, and catch. Yet we cannot predict how catch will respond to changing flood regimes. Evidence is fragmented; most models emphasize high-water dynamics while ignoring low-water dynamics, and species-specific responses are rarely linked to the life-history traits that shape them. These gaps matter, as dams, deforestation, and climate change reshape flood pulses worldwide. This dissertation builds a mechanistic, trait-based, and climate-forward understanding of the flood pulse–fish catch relationship in the Brazilian Amazon. I synthesized evidence from 27 studies across tropical basins. I then paired 20 years of daily fish landings (1991–2011) from 11 Amazon ports with daily water levels to identify which flood pulse features drive catch, tested whether life-history traits moderate taxon-level responses across 14 dominant taxa, and projected catch to 2100 under climate scenarios. A composite hydrological signature capturing prolonged high water, elevated minimums, and gradual rises, lagged two years, predicted catch better than any single metric, explaining 78% of the deviance. Size at maturity most strongly moderates species responses: early-maturing taxa lose up to 22% of catch under high flood conditions, while later-maturing taxa gain up to 11%. Under mid-century climate projections, basin-wide catch appears nearly stable, but this aggregate conceals near-universal declines across 8 of 9 regions and all 14 taxa, concentrated in the drying tributaries. The flood pulse–fish catch relationship is mechanistic, trait-filtered, and forecastable, but basin-wide metrics mask the regional and taxonomic losses where climate impacts are felt. Anticipating impacts in river-floodplain fisheries requires standard hydrological metrics, tributary-scale projections, trait-specific models, and management strategies that match each species' flood sensitivity.
Kernel-Based Metamodeling for Heteroscedastic Simulation: Theory and Distribution-Free Uncertainty Quantification
Zhao, Jin (Virginia Tech, 2026-05-28)
Simulation metamodels approximate expensive stochastic simulators for downstream analysis and decision-making. They can become unreliable under strong heteroscedasticity, large or high-dimensional datasets, and tight simulation budgets for uncertainty quantification. This dissertation develops scalable prediction, convergence theory, and distribution-free uncertainty quantification. Nested heteroscedastic Gaussian process (NHGP) provides a scalable divide-and-conquer scheme: each disjoint subset fits a sub-stochastic-kriging model, and the fits are fused into one metamodel. Theoretical results show that the NHGP rule is the best linear unbiased predictor among such aggregations, prove consistency, and demonstrate empirical performance comparable to a benchmark method. A second thread studies stochastic-kriging prediction error under kernel misspecification and growing dimension: it characterizes near-optimal regimes, explains how noise-variance scaling governs worst-case performance, and proves high-probability pointwise bounds whose dimension dependence appears only through a log n factor. The third methodological piece is heteroscedastic weighted kernel ridge regression, which uses precision-based weights for input-dependent noise. Using the equivalence between Gaussian-process prediction and kernel ridge regression, we derive sharp mean squared error rates across kernel spectrum decay regimes, identify rate-optimal regularization, and validate the theory empirically. Finally, we combine conditional kernel mean embeddings with distributional conformal prediction to estimate conditional cumulative distribution functions and build calibrated prediction sets with finite-sample coverage, together with an adaptive two-stage budget allocation between estimation and calibration.

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