Doctoral Dissertations

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

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ICoN: Immersive Computational Notebook for Data Science
In, Sungwon (Virginia Tech, 2025-07-10)
Computational notebooks are widely used in data science, offering an interface that integrates code, documentation, visualizations, and data within a single environment. However, as data analysis becomes increasingly complex, the traditional WIMP (Windows, Icons, Menus, Pointers) interface faces limitations in supporting advanced, embodied workflows. To address this, we initially adapted the computational notebook into the immersive environment to leverage the embodiment and immersiveness provided by immersive technologies. While our adoption of computational notebooks in immersive environments improved navigation performance, their standard interfaces were less effective, which required extensive text input for tasks such as data transformation and visualization. To overcome these challenges, we initially explored embodied data transformation, focusing on gesture-based, direct data manipulation within immersive environments. Embodied data transformation reduced cognitive load, enabling more intuitive data analysis without extensive text input and programming expertise. Building on these successful explorations, we developed ICoN, a system that integrates immersive computational notebooks, embodied data transformation, and visualization within a unified workspace. Through controlled comparisons of desktop vs. VR and separated vs. unified workspaces, we found that ICoN not only improves navigation performance but also enables intuitive data transformation and visualization capabilities. However, despite ICoN's potential, the organizational strategies where execution order plays an important role in immersive environments remain underexplored. Our next research addresses this gap by examining how users spatially arrange and interact with computational notebooks in immersive contexts. In a user study, participants favored organizing their work in half-cylindrical layouts and engaged more frequently in non-linear analysis compared to traditional setups. This shift suggests that immersive environments encourage new approaches to managing data science workflows, leading to a more flexible and efficient use of computational notebooks. Yet, general limitations, such as scalability, must be addressed for ICoN to be applied to real-world, complex data analysis scenarios, where tasks are performed over longer periods and with larger datasets. Manually adjusting large notebooks for different tasks can be time-consuming and exhausting. To overcome these challenges, we introduced organizational guidance, which enables the system to assist analysts in building well-defined structures with consistent spacing and alignment. In our user study, we found that organizational guidance significantly improved the effectiveness of constructing large-scale analyses within immersive computational notebooks. By enabling participants to quickly reorganize their workspace, the system facilitated faster initiation of analysis. To isolate the effect of organizational guidance, we annotated specific cells in the study to highlight embedded structural patterns, helping participants focus on organization rather than code comprehension. However, in real-world analysis, the process often involves a sensemaking process, such as understanding the underlying execution logic, which remains necessary even with guidance. As a result, navigating large, immersive spaces remains inevitable for performing large-scale analyses. We envision incorporating lens techniques commonly used in the field of visualization, allowing analysts to closely inspect small or detailed data elements, and employing near-far proxies that reveal different levels of information based on the distance between the analyst and the target content.
Rotor-Airframe Interaction Noise: Predicting and Mitigating Noise with Artificial Neural Networks
Wiedemann, Arthur David (Virginia Tech, 2025-07-09)
For small unmanned aerial vehicles, a rotor is typically mounted to the vehicle with a support rod. With the rotor operating in close proximity to the rod, an aeroacoustic installation effect known as the rotor-airframe interaction noise creates an unsteady deterministic loading noise that excites the harmonics of the blade passing frequency by as much as 20 dB, which meant that the installation effect could produce more noise than the rotor itself. As a result, it was imperative to parameterize and model the rotor-airframe interaction noise. Methods to predict the interaction noise were estimated using analytical and artificial neural network models. The artificial neural network required training data to correlate parameters with the acoustic noise, which was acquired from an experimental test campaign conducted within an anechoic chamber. Measurements were taken for numerous combinations of rotor radii, rotational speeds, rotor-rod proximities, and rod diameters to examine the parameter space expected of a rotor-rod configuration typically found on a small unmanned aerial system. Microphones were placed in a semi-hemisphere cluster to capture the directivity for the observer in-plane and below the plane of rotation. The analytical and artificial neural network models predicted the time-domain acoustic pressure emitted by the interaction, allowing for a more insightful inspection of the acoustic emission and providing a psychoacoustic analysis tool. In addition to acquiring an extensive database for training the artificial neural network, this database was also used to evaluate the prediction performance of the analytical model, which relies on a potential-flow model to represent the pressure fluctuations exhibited on the surfaces of the rotor and rod during the interaction. Results showed that the analytical and artificial neural network models had good prediction performance for lower harmonics. As the harmonic number increased above 11×BPF, the artificial neural network outperformed the analytical model due to the assumption built into the analytical model being invalid at higher harmonics. Efforts taken in the experimental test campaign also found that by curving the rod or sweeping the rotor blade, the rotor-airframe interaction noise was significantly reduced to the baseline case where the rotor operated without a rod within the flow. An analytical optimization method was developed to find the optimal rod shape to diminish the acoustic noise based on constraints, where the optimization algorithm can be quickly executed. In addition, a second study was conducted in an outdoor setting on the DJI S-1000 drone to demonstrate that the noise emitted by a drone could be reduced when a curved rod was installed instead of the conventional straight rod.
How Can We Prepare the Coming Generation for Change Making? A Qualitative Study Exploring Strategies for Fostering Transformational Leadership Development Among Youth and Young Professionals
Oyedare, Israel Olamide (Virginia Tech, 2025-07-09)
Transformational leadership has long been a popular research domain in the field of leadership, particularly among organizational leadership and management scholars. However, despite its prominence in research, few studies have explored this domain with regard to the youth population. Meanwhile, the proliferation of wicked problems around the world has necessitated the involvement of youth in transformational leadership activities. Therefore, this study explores strategies for fostering transformational leadership development among youth and young professionals. For over 100 years, 4-H Extension services have been heavily involved in organizing and strategizing youth development programs, making them the largest youth development organization in the United States. This study adopts a qualitative research method to explore the lived experiences of 4-H Extension agents (n=15) with respect to transformational leadership development among youth. The data collected from this study were coded and analyzed using a line-by-line analytical approach to provide a more robust understanding of the data and to identify salient but implied notes from the data. Moreover, this study employs the path-goal theory of leadership as a theoretical framework to understand the definitions of transformational leadership, the approaches 4-H has used to develop young transformational leaders, barriers to transformational leadership, and potential strategies for fostering transformational leadership development among youth. Notably, findings from this research revealed that the interviewed 4-H Extension agents perceived transformational leadership development among youth as creating opportunities for youth to thrive, challenging the status quo, motivating others to work toward a common goal, and becoming agents of change within the society. Secondly, the findings revealed that approaches deployed by 4-H to develop youth's transformational leadership include adult leaders playing role-modeling functions, youth influence each other, hands-on leadership opportunities, and adult leaders facilitating workshops on critical leadership skills. Thirdly, findings revealed that interviewed 4-H Extension agents perceive the barriers to transformational leadership development among youth to include, inadequate support system, conflicting demands of youth, and lack of sustainable programmatic approach. Lastly, findings revealed that interviewed 4-H Extension agents perceive potential strategies for fostering transformational leadership to include, developing the capacity of adult leaders in the area of transformational leadership, enabling the ability of youth to solve complex problems, building effective transformational leadership programs, and youth-adult partnerships. This study contributes to the body of knowledge in the domain of transformational leadership by offering a working definition of youth transformational leadership and laying the foundation for advancing the study of youth transformational leadership.
Revisiting the food agency framework: from understanding why we cook to improving the measurement of food agency
Yi, Jiakun (Virginia Tech, 2025-07-08)
The concept of food agency reflects the socially embedded characteristics of cooking and food preparation in modern society, while this contextual nature also posts challenges to measuring cooking behavior. The Cooking and Food Provisioning Action Scale (CAFPAS) was developed as a measurement tool attempting to measure these structural factors, but recent evaluation studies on this scale found that the structure domain of the current CAFPAS, with only time constraints, might not be able to capture the full social structural factors that influence individuals' capacities of cooking and food preparation. In response to the need of an improved measurement tool for food agency, this dissertation is constructed on a continuum of studies in a classic scale development workflow. First, the study revisited the food agency conceptual framework deductively with a review of literatures on the facilitators and barriers of cooking and food preparation, as well as the existing measurement tools, setting the theoretical bases for the scale development. Second, an inductive sequential exploratory mixed-methods study with concept mapping approach on 39 adults from sub/urban and rural/remote Virginia revealed that although time, ingredients, motivation, and health were common factors influencing home meal preparation, regional differences were observed from individuals' lived experiences on how these factors manifested. Third, another inductive exploratory study was conducted with 3745 user-generated contents collected from an online community with innovative AI-assistant qualitative thematic analysis, adding to the findings that individuals constantly compare between options of food consumptions to evaluate if home cooking is practical, and that several values of home meal beyond nourishing, such as mindful practices, sense of achievement, and social connectedness, that may make them feel more empowered to cook. Finally, the findings from the first three stages were synthesized into a scale development and validation study with a Census-based sample of US adults (N = 996). The product of this study, a four-factor, 23-item scale named Food Agency Scale (FAS), was proved to be a reliable, valid, and theoretically appropriate measurement tool of food agency that predicted more meals prepared at home, more meals prepared from scratch, increased consumption of healthy foods, and lower food neophobia. The FAS, by incorporating the positive beliefs on cooking, and the practical considerations on the effort, cost, and options around food preparation, enhanced the current CAFPAS with a broader capture of social structural factors. Collectively, this dissertation study informs food, nutrition, and public health practitioners of the importance of contextual considerations for making population-specific recommendations on dietary choice and health behavior. This dissertation also opened the space for further methodological studies on the effective use of human intelligence and artificial intelligence in collecting and analyzing qualitative and quantitative data.
First-year Writing and Research Journals: How Online Publication Redefines Student Writing and Scholarship in Rhetoric, Composition, and Writing Studies
King, Skyler Richard (Virginia Tech, 2025-07-08)
Many accounts of student writing exist in the field of Rhetoric, Composition, and Writing Studies (RCWS) in the form of writing textbooks, readers, and articles across decades of professional journal volumes (Grobman 2009; Kinkead 2011; Robillard 2006; Sommers 2015).. Some argue that discussing student writing is foundational to RCWS and its identity as a discipline (Goggins 1997; Harris 2010; Lauer 1984). And for 24 years, first-year student writing has been published in online academic journals with no discussion from RCWS scholarship. In part through establishing academic journals, RCWS professionalized and developed an expertise paradigm that promotes published first-year scholarship yet does not cite that scholarship within its journals - all while non-RCWS scholars cite the same work as legitimate scholarship 366 times, creating a stark citation disparity. This dissertation provides a first-ever account of published first-year writing and research (FYWR) journals and argues that the presence of these journals simultaneously reaffirms and unsettles the claim that student writing remains central to RCWS practice and identity. This account analyzes FYWR journal features, statements of purpose, and citations, and it finds FYWR journals engage in academic publishing practices, position FYWR articles as writing models, and circulate in interdisciplinarity, not RCWS disciplinarity; thus, the presence of FYWR journals possesses the potential to redefine what counts as 'student writing' and 'scholarship' in the discipline.
Recovering Territory: The Thick Geopolitics of Demining in Postwar Bosnia-Herzegovina and Wartime Ukraine
Muzaferija, Emina (Virginia Tech, 2025-07-07)
Demining is not only a technical matter, but also a geopolitical one. Landmines pose a violent threat to human and non-human life alike, and have enduring material effects on territories and ecologies. They defy the spatio-temporal limits of battlespaces, and entrench the territorial logic of wars by inscribing it on land. They render space dangerous, disrupt land-tenure patterns, and degrade the biosphere. Put differently ⎯ landmines kill, disable and territorialize, and remake ecologies. The removal of this violent threat to human and non-human life and livelihood is an act of governance. What lives and livelihoods get protected and restored through demining is a geopolitical question, determined by the international, national and local correlation of forces shaping the context in which it takes place. Demining is thus ultimately an act of governance ⎯ and it is imbued with spatial logic and territorial imperatives. This dissertation advances an understanding of demining as a territorial practice. In doing so, it develops a concept of "territorial recovery"⎯ a process by which a particular socio-spatial order is restored and safeguarded to ensure the viability of future life. By presenting a contrasting study between Bosnia-Herzegovina (BiH) and Ukraine, this dissertation shows that demining, which has historically emerged in post-conflict settings as part of the efforts to "reverse the effects of war", can also evolve in substantially different contexts in ways that further war. Demining in BiH emerged in the aftermath of conflict, and in an uncontested U.S.-led "international liberal order". In Ukraine, this process is unfolding amid active conflict, in a context marked by multipolar rivalries and contestation of "liberal world order". Through examination of the demining process in these contrasting circumstances, this dissertation argues that the geopolitical context ⎯ or multiscalar correlation of international, national and local forces ⎯ transforms the nature, meaning, and impact of the demining process as a territorial practice. A geopolitical context anchored around a peace settlement, and shaped by liberal humanitarian norms and practices, facilitates demining as a territorial recovery process tied to ecological restoration and economic growth. A geopolitical context anchored around war-fighting, and shaped by liberal war coalition and support practices, constructs demining as a territorial practice tied to terrain securitization and war-fighting imperatives.
Regioselective Design of Polysaccharide Ester Graft Copolymers
Thompson, Jeffrey Eric (Virginia Tech, 2025-07-03)
Polysaccharide graft (co)polymers, where a polysaccharide comprises the backbone and a separate (co)polymer comprises grafted side chains, are growing in popularity as blend compatibilizers, sustainable elastomers, self-assembling materials, and drug delivery components. This popularity is attributed to the inherent renewability, abundance, and general low toxicity of the polysaccharide backbone, coupled with the extensive functionality and range of physicochemical properties that can be imparted through attachment of a second polymeric side chain. However, polysaccharide graft (co)polymers can be exceedingly complex due to polymeric attributes of both the backbone and grafted side chains (namely, molecular weight (MW) and molar mass dispersity (Ð)). Further structural complications arise from complexities inherent to the polysaccharide backbone, as the presence of pendant functional groups on each monosaccharide result in polymer grafts (and any other substituents present) originating from up to three positions. In order to properly elucidate relationships between the structure (i.e., topology) of polysaccharide graft (co)polymers and exhibited physicochemical properties, the position of polymer grafts on each repeating unit, degree of substitution (DS) of grafted chains, and MW of grafted chains must be controlled and properly characterized. Regioselective functionalization of polysaccharides is an appealing route to produce well-defined polysaccharide graft (co)polymers. By selectively derivatizing a single functional group per monosaccharide with a moiety capable of polymer conjugation (i.e., grafting-to) or polymer initiation (i.e., grafting-from), the graft density of the ensuing graft (co)polymer can be controlled by limiting the DS at the reactive site. However, regioselective modification of polysaccharides, especially polyglucopyranoses such as amylose and cellulose, is difficult due to the similar reactivities of pendant hydroxy groups. One distinct structural feature of amylose and cellulose is the sole primary hydroxy group located at the C6 position of each monosaccharide. The increased steric accessibility and wider approach angles of the primary C6-OH compared to the more hindered secondary C2/C3-OH is a valuable structural feature that can lend itself to selective functionalization. In this work, two distinct chemistries regioselective for the C6-OH of amylose and cellulose were leveraged to produce well-defined polysaccharide derivatives and graft (co)polymers. The primary C6-OH of amylose was selectively converted to a primary alkyl bromide through treatment with N-bromosuccinimide (NBS) and triphenylphosphine (PPh3), which could subsequently be displaced by the azide anion to prepare a well-defined amylose derivative with functionality reactive for the highly efficient copper-catalyzed azide-alkyne cycloaddition (CuAAC). A series of 2,3-di-O-acetyl-6-azido-6-deoxy (2,3Ac-6N3) amyloses were prepared through efficient transformations with control over DS of acetyl (Ac) and N3 moieties. Prepared 2,3Ac-6N3 amyloses served as the backbone for a series of well-defined amylose acetate-graft-polylactide (AmAc-g-PLA) graft polymers, which show promise as compatibilizers for immiscible blends of highly renewable starch acetate (StAc) and PLA, both of which are sourced from starch feedstocks. Polysaccharide graft polymer compatibilizers (where the backbone comprises a polymer that is miscible with one blend component, and the grafted side chain comprises a polymer that is miscible with the other blend component) have shown promise in stabilizing the morphology of immiscible polymer blends, but are seldom produced with topological control allowing for determination of an ideal graft length or density for compatibilization. To address this, a series of AmAc-g-PLA graft polymers prepared by grafting-to CuAAC of alkyne-terminated PLA with 2,3Ac-6N3 amylose, permitting precise control over graft length and graft density by varying the MW of alkyne-terminated PLA (i.e., graft length) and DS(N3) of 2,3Ac-6N3 amylose (i.e., graft density). Preliminary investigations reveal that effective compatibilization is observed for AmAc-g-PLA graft polymers with low graft density, permitting favorable enthalpic interactions between the AmAc backbone and the StAc phase, and high graft length, allowing for entanglements between PLA side chains and the PLA phase. Selective protection of the C6-OH with the bulky 4-monomethoxytriphenylmethyl (MeOTr) ether is another effective regioselective modification. However, regioselective generation of polysaccharide derivatives employing MeOTr protection chemistry can be time-consuming due to the plethora of protection, functionalization, and deprotection steps. To address this, an efficient, regioselective pathway was developed to produce 2,3-di-O-acyl-6-O-MeOTr (2,3A-6MeOTr) cellulose esters through C6-OH tritylation and C2/C3-OH acylation in a sequential, one-pot manner. Subsequent treatment of 2,3A-6MeOTr cellulose esters with an acyl donor under acidic (yet mild) conditions generated a series of 2,3-di-O-A-6-O-B (2,3A-6B) mixed cellulose esters with up to 100% selectivity for C6-OH protection. Mixed cellulose esters with regioselective incorporation of initiating sites for atom transfer radical polymerization (ATRP) and reversible addition-fragmentation chain transfer (RAFT) polymerization were prepared, providing an efficient route to well-defined macroinitiators for controlled radical polymerizations. Development of such functional cellulose esters is expected to be invaluable when conducting grafting-from polymerization, as regioselective incorporation of initiating sites will ensure controllable graft density. In comparison to previous routes to polysaccharide graft (co)polymers, the methods described herein offer distinct improvements in control over graft length and graft density. By developing efficient routes to regioselectively substituted polysaccharide derivatives capable of grafting-to and grafting-from polymerization chemistries, topological features most important to optimizing the physicochemical properties of such biobased materials can be identified. The expansion of synthetic strategies described herein will be invaluable to both polymer and polysaccharide chemists aiming to develop a sustainable future by leveraging the fascinating and complex family of polysaccharide graft (co)polymers.
Efficient Reinforcement Learning for Control
Baddam, Vasanth Reddy (Virginia Tech, 2025-07-01)
The landscape of control systems has evolved rapidly with the emergence of Reinforcement Learning (RL), offering promising solutions to a wide range of dynamic decision-making problems. However, the application of RL to real-world control systems is often hindered by computational inefficiencies, scalability issues, and a lack of structure in learning mech- anisms. This thesis explores a central question: How can we design reinforcement learning algorithms that are not only effective but also computationally effi- cient and scalable for control systems of increasing complexity? To address this, we present a progression of approaches—starting with time-scale decomposition in small- scale systems and moving towards structured and adaptive learning strategies for large-scale, multi-agent control problems. Each chapter builds upon the previous one by introducing new methods tailored to the complexity and scale of the environment, culminating in a unified framework for efficient RL-driven control
Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
Sun, Yanshen (Virginia Tech, 2025-07-01)
In spatiotemporal data mining, building models that are robust and generalizable across complex, non-ideal conditions is crucial for real-world deployment. While many existing methods perform well on benchmark datasets, they often assume clean, stationary, and uniformly sampled data, limiting their effectiveness in practical scenarios. In diverse operational domains such as traffic systems, neurophysiological monitoring, and drilling operations, spatiotemporal data is often noisy, irregular, and subject to distribution shifts — exposing the brittleness of conventional forecasting and classification pipelines. This dissertation advances spatiotemporal modeling by addressing three critical challenges that frequently arise in real-world applications but fall outside the scope of traditional forecasting and classification: anomaly detection, domain adaptation, and causal discovery. It systematically examines these issues across three cross-disciplinary application domains and proposes targeted, scenario-specific solutions: textbf{(1) Anomaly Detection:} We develop spatiotemporal anomaly detection frameworks to identify and mitigate irregularities in both traffic forecasting and EEG signal classification tasks. textbf{(2) Domain Adaptation:} We design and evaluate domain adaptation strategies that enable robust cross-patient EEG classification, addressing inter-subject variability and enhancing generalization across diverse patient data. textbf{(3) Causal Discovery:} We integrate causal discovery techniques into drilling fluid loss prediction workflows to uncover latent causal relationships, thereby enhancing the extrapolation capabilities of fine-tuned time series foundation models in previously unseen scenarios. Anomaly detection techniques are applied across three distinct tasks: detecting abnormal traffic sensor measurements, predicting the impact of traffic incidents, and classifying EEG signals for depression diagnosis. In the context of traffic sensor anomaly detection, key challenges include (1) modeling spatiotemporal dependencies to capture irregular patterns, (2) distinguishing implicit anomalies from regular fluctuations, and (3) maintaining robustness in the absence of reliable reference data. To address these issues, we propose S-DKFN, an unsupervised model that fuses spatial and temporal features to uncover complex anomaly patterns. It incorporates dilated temporal convolutional networks (TCNs), an encoder-decoder structure for multiscale representation learning, and leverages Kalman filtering principles for model fusion to improve robustness and accuracy. Traffic incident impact (TII) prediction also presents significant modeling challenges, particularly due to the dynamic nature of real-world traffic networks. Prior studies have often (1) overlooked systematic quantification of spatiotemporal TII and suffered from a lack of open benchmark datasets, (2) struggled to adapt attention mechanisms to capture interactions over time-varying road networks, and (3) failed to identify task-relevant substructures in space and time. To overcome these limitations, we first provide a formal quantification of TII and release two curated open-source datasets. We then propose two novel models: the RAS-Transformer, designed to locate affected sub-graphs, and the IST-Transformer, which leverages importance-score-based adversarial training to focus attention on sensors most impacted by incidents. EEG signal classification for depression diagnosis poses its own set of difficulties. Existing methods typically (1) struggle to extract meaningful patterns from noisy and non-stationary EEG signals, (2) rely heavily on manual preprocessing and handcrafted features, and (3) fall short in capturing the spatial and temporal dependencies intrinsic to neural activity. In response, we propose a novel spatiotemporal deep learning model tailored for depression-related EEG analysis. The architecture integrates multiple trainable denoising modules within an end-to-end pipeline, reducing the need for manual intervention and enabling the automatic extraction of robust neural biomarkers. This design improves classification performance while enhancing interpretability and adaptability across subjects. Domain adaptation techniques improve the effectiveness of spatiotemporal neural networks in EEG-based depression and epilepsy detection. For EEG-based depression detection, prior research struggles with (1) the reliance on extensive manual feature engineering to handle noise, (2) inadequate modeling of the spatial and temporal dynamics of brain activity, and (3) difficulty in adapting models to unseen patients. To address these challenges, we propose LAK-DSGCN (Lightweight Adjusted Kalman-aided Dual-Stream Graph Convolutional Networks), a novel spatiotemporal framework that (1) decomposes EEG signals into separate spatial and temporal components, (2) processes them using a gated TCN for temporal feature extraction and a GCN for spatial representation, and (3) fuses the learned representations using a lightweight Adjusted Kalman filter. Additionally, we incorporate a normalization term designed for the Kalman filter to enhance the model's generalizability across different patients. For EEG-based epilepsy detection, existing approaches face three main limitations: (1) a reliance on high-quality, fixed-format EEG signals that do not account for real-world inconsistencies; (2) the inability to effectively handle irregular sampling rates, missing data, and noisy signals; and (3) a lack of robust feature-learning techniques to extract stable neural representations across patients. To address these issues, we introduce CPEDNet (Cross-Patient Epilepsy Diagnosis Network), which (1) employs a latent Neural Ordinary Differential Equation (NODE) module to enhance EEG signals by mitigating irregular sampling and missing data, (2) transforms EEG signals into brain network flow representations, capturing spatial-temporal dynamics, and (3) integrates a score-based self-supervised learning strategy to improve feature stability and cross-patient generalization. Causal discovery techniques are applied to the task of drilling fluid loss prediction, which presents several unique challenges: (1) Data scarcity -- Due to the high cost of drilling operations, available datasets are often limited in size, increasing the risk of overfitting in causal models. (2) Complex causal structure -- Identifying robust causal relationships among covariates is difficult, yet essential for enabling generalization to unseen counterfactual scenarios based on causal reasoning. (3) Covariate influence -- It is nontrivial to ensure that different types of covariates influence the predicted fluid loss distribution in a causally consistent manner. To address these challenges, a causal discovery plug-in module is proposed for integration with Time Series Foundation Models (TSFMs). Specifically, the design provides three major contributions. (1) Frozen TSFM backbone -- The pretrained TSFM's parameters are frozen during fine-tuning to preserve general spatiotemporal representations and mitigate overfitting on small drilling datasets. (2) Causal rule integration -- Causal discovery techniques are used to identify and incorporate structured relationships between specific covariate subsets and the target variable, guiding prediction under counterfactual conditions. (3) Contrastive pretraining -- The plug-in module is pretrained using contrastive learning to ensure it learns discriminative latent representations conditioned on varying covariate configurations. In summary, this dissertation advances the field of spatiotemporal data mining by addressing three core challenges—anomaly detection, domain adaptation, and causal discovery—that frequently hinder the robustness and generalization of existing models in real-world, cross-disciplinary scenarios. We evaluate our proposed models on multiple real-world datasets, demonstrating significant improvements over existing state-of-the-art approaches in all settings. Together, these contributions push beyond the boundaries of conventional forecasting and classification tasks, demonstrating how task-specific adaptations and causal reasoning can greatly expand the applicability of spatiotemporal models in challenging, real-world environments.
The Effects of Vegetation on Ecosystem Services Provisioning by Stormwater Bioretention
Krauss, Lauren Marie (Virginia Tech, 2025-06-30)
Vegetation plays an integral role in the provision of ecosystem services by stormwater biore- tention, influencing a diversity of services from flood regulation to sense of place. This dissertation explores the myriad ways plants influence these services, with a focus on indi- vidual plant traits, suites of traits that reflect underlying plant adaptive strategies, emergent landscape-scale factors like pattern, and ecological attributes like nativeness. The first chap- ter reviews key topics like stormwater bioretention, ecosystem services provisioning, and vegetative characteristics (e.g., plant adaptive strategy), and provides a roadmap for my dissertation. The second chapter investigates whether current plant selection practices for stormwater bioretention introduce adaptive strategy biases that may impact ecosystem ser- vices provisioning. Findings suggest that planting guidance in arid regions favors stress tolerant species whereas planting guidance in humid regions favors competitive species rela- tive to the native species pool. Across all climate zones, there is a notable anti-ruderal bias, which could limit bioretention resilience by reducing its capacity for self-repair. The third chapter explores how these biases influence hydrologic services in different climate zones, using saturated hydraulic conductivity (Ksat) as a performance metric. My results indicate that stress-tolerant bias in arid climates increases Ksat whereas competitive bias in humid climates has no measurable effect. Notably, overall Ksat values were higher than design standards, potentially due to the exclusion of ruderals, the only plant group that reduced Ksat to a level consistent with engineering targets. The final two chapters of my dissertation examine the effects of vegetation on the provisioning of cultural services such as aesthetics using new design tools like virtual reality and mental modeling. My results indicate that plant adaptive strategy influences aesthetics through individual plant traits and landscape characteristics. Competitive plant bias was associated with lower aesthetic value due to re- duced biodiversity, whereas stress tolerant and ruderal biases tended to enhance aesthetic value. Collectively these results suggest that plant adaptive strategy has far reaching im- plications for ecosystem services provisioning by stormwater bioretention (e.g., impacting a range of services from hydrologic to cultural). This research highlights the importance of factoring ecological principles into plant selection practices for stormwater bioretention and provides valuable insights to engineers and landscape architects seeking to leverage those principals to develop more multifunctional and resilient stormwater infrastructure.
Binder Infill Pattern Design Strategies for Increased Mechanical Properties in Binder Jet Additive Manufacturing Parts
Wei, Amanda Xin (Virginia Tech, 2025-06-30)
The use of a liquid binding agent is critical for forming part shape and providing green part strength in binder jet (BJT) additive manufacturing (AM). Traditionally, binder is homogeneously deposited throughout the entire part cross-section during printing in order to give sufficient green part strength for post-processing and to preserve part geometric accuracy. However, recent research suggests that, compared to sintering powder with no binding agent, the presence of binder limits densification during sintering. Specifically, the authors have shown that unbound powder – achieved by placing binder only around the part boundary (i.e., printing a "shelled" part) results in significantly higher sintered density compared to conventional bound powder. While this shell printing approach improves final part density and mechanical properties, the corresponding printed green parts are extremely fragile due to the low binder content. Thus, the overall aims of this research are to (i) identify techniques for simultaneous balancing of a part's green and sintered properties through binder pattern manipulation (i.e., toolpathing) and (ii) gain an understanding of their corresponding process-structure-property relationships. Specifically, the author explores three distinct strategies for binder jetted 316L: (1) architected lattice infill, (2) lattice unit cell size effect, and (3) topology optimized infill. • In architected lattice infill, a binder patterning strategy, comprised of an exterior contour shell with various internal strut based or triply periodic minimal surface (TPMS) lattice infill patterns, is applied to balance the tradeoff between green and sintered part properties that result from reduced binder usage. An octet infill part was found to have an 85% reduction in green part strength from that of a conventional solid infill, but a 2.6% higher relative density and 24% higher flexural strength once sintered. • In addition to lattice architecture choice, the effects of unit cell size choice for a lattice architecture on green and sintered part properties are studied. It was found that a larger unit cell had superior performance over smaller unit cells of equivalent bound volume fraction. Furthermore, binder deposition and part properties approached that of a conventional homogenous solid infill of lower saturation ratio at small unit cell sizes. • Finally, the author explores the use of topology optimization to generate infill for minimized compliance in green and sintered parts. Placement of bound and unbound regions are iteratively designed to achieve an optimal configuration, subject to a range of allowable bound volume fraction constraints. Overall, the concept of infill patterning, although common in other types of additive manufacturing, is unconventional in BJT. To the knowledge of the author, this work is the first to explore the structure-property effects of BJT infill design on green and sintered parts through several strategies of binder patterning.
Uplifting and Prioritizing Black Voices in Trauma Intervention: Cultural Adaptations of Written Exposure Therapy for Trauma-Exposed Black Women
George, Brianna Amber (Virginia Tech, 2025-06-27)
Research has highlighted Black women's heightened exposure to trauma, and risk of developing post-traumatic stress disorder (PTSD). Black women also experience unique internal and external barriers to treatment, including stigma and cultural perspectives on mental health treatment. Further, avoidance and thwarted belongingness serve as additional barriers that prevent people with PTSD from seeking evidence-based PTSD treatment, including written exposure therapy (WET). Given these barriers to treatment, researchers have emphasized the need for cultural adaptations that take the experiences of racial/ethnic minorities into account and provide a culturally responsive approach to treating mental health problems, including PTSD. This qualitative research study seeks to amalgamate existing literature centering the needs of Black women in therapy, along with direct community stakeholder feedback of their treatment experiences and suggestions through focus groups in the service of integrating the feedback into an existing treatment for PTSD (i.e., group WET). The current research incorporated feedback from 11 Black women and clinician participants. Results identified several themes which stakeholders suggest incorporating into WET, including accessibility and feasibility, group content, population considerations, group dynamics, and clinical considerations, each of which align with empirical research. Results of this research emphasize that by indicating and integrating concrete suggestions for potential adaptations within PTSD treatment for Black women, providers can better serve and understand this population and provide culturally congruent care.
An Exploratory Study of Transit and Active Commuters in US Transit Station Areas
Cunningham, Alan Felder (Virginia Tech, 2025-06-27)
Researchers have examined transit and active commuting in relation to land use, infrastructure, and economic factors, but few have considered their relationship with each other, especially not for the US. This thesis aims to understand the relationship between transit and active commuters within and between rail transit station areas. Multiple linear regressions were conducted at the national level and for each metro with rail transit service. Each regression used one of the four commuter types (home- and work-based transit or active commuters) as the dependent variable and the other three as independent variables. In addition, bivariate regressions were performed between the four commuter types and 30 demographic, economic, or transit service attributes at the national level and for 24 metros with rail transit. This study found that the number of significant relationships between commuter types was positively correlated with the number of station areas in a metro, with work-based relationships being most common. No station area attributes from the bivariate comparisons improved the fit of the national level models, suggesting that further analysis of metro attributes and models may be fruitful. The main finding was that station areas with large populations of transit commuters also had large populations of active commuters, although this relationship varied between metros and home or work transit or active commuters.
Synthesis and Characterization of Polymeric Materials as Sequestrants for Biological Applications
Wadsworth, Ophelia Juanita (Virginia Tech, 2025-06-27)
To address the systemic toxicity attributable to doxorubicin (Dox) following targeted chemotherapy treatments for hepatocellular carcinoma, we explored drug-capture strategies using both DNA-functionalized substrates and synthetic polymeric materials. DNA-modified cotton substrates were prepared with and without using silane linkers and surprisingly, DNA-only modified substrates demonstrated comparable Dox capture efficacy to those functionalized with DNA and the control silane linker. DNA quantification revealed apparent increases in DNA concentration due to thermally induced denaturation during adsorption, suggesting the need for milder reaction conditions for future experiments. We also synthesized poly(methacrylic acid) (PMA) resins to electrostatically bind Dox at physiological pH. Comparative capture studies with uncharged PMA at low-pH conditions confirmed an 18-fold increase in Dox capture efficacy due to ionic interactions. Crosslink density and polymer flexibility also played pivotal roles, with more rigid materials demonstrating greater capture efficacy and more flexible materials exhibiting the opposite trend due to increased hydrophobicity. These results underscore the importance of tuning network rigidity and hydrophobicity in subsequent drug-capture material design. Finally, to explore alternatives to traditional antibiotics, we synthesized varied molecular weights of mannose-functionalized polynorbornenes and modified the polymer backbone with thiolated-mannose and thiolated-amine moieties. Our objective was to increase the antibacterial properties observed with unmodified glycopolymers by increasing glycan density to inhibit bacterial growth and separately, introducing cationic charges to disrupt bacterial membranes. Our approach used the oxo-norbornene derivative, a key procedural change as these materials are not widely explored for post-polymerization modification, particularly with biologically relevant molecules such as glycans and cationic compounds. While we observed partial functionalization of the polymer backbone at longer reaction times, we demonstrated that the backbone alkenes of these glycopolymers are amenable to thiol-ene chemistry. We also generated a set of materials with precise architecture that differed only in their pendant functionalities. Comprehensive biological assays will follow to assess the antimicrobial and hemolytic performances and determine the structure-activity relationships.
Investigation and Design of Integrated Magnetics in High-power High-frequency Soft-switching DC/DC Converters for Battery Charger Applications
Yuan, Tianlong (Virginia Tech, 2025-06-26)
Electric vehicles (EVs) have become more and more popular from higher fuel prices and growing global warming concerns in recent years. These vehicles rely on rechargeable battery packs that can be charged by external power sources. Despite their benefits, widespread adoption of EVs faces challenges like on-board chargers (OBC) with lighter weight, higher efficiency and higher power. There are two charging approaches for EVs: on-board charging for daily use and off-board fast charging for energy refill within an hour. The majority of EVs operate with 400V battery platforms and are equipped with 6.6 kW or 11 kW OBCs. In contrast, some higher end vehicles with battery capacities exceeding 100 kWh use 22kW OBCs, though these are limited by standard residential voltage and current constraints. To overcome these limitations, some manufacturers are shifting toward 800V battery architectures, which reduce charging time and lower overall system costs. This shift necessitates the research and innovation in 800V OBCs with enhanced efficiency and power density. Additionally, EVs can be charged using OBCs in a grid-to-vehicle power transfer mode and can transfer power from the batteries back to household appliances or grid (vehicle-to-grid) for camping or as part of the smart grid. Therefore, bidirectional function is needed for OBCs nowadays. Progress in power electronics has been largely driven by advancements in power semiconductor devices. Wide bandgap (WBG) semiconductors have introduced a great leap beyond traditional silicon-based devices. These WBG devices enable higher efficiency and power density, and they support operation at higher switching frequencies. Increasing the switching frequency reduces the volt-second across transformers, which allows for the use of printed circuit board (PCB)-based windings instead of traditional Litz wire. This transition simplifies manufacturing, reduces cost and improves control over parasitic elements. Moreover, integrating resonant inductors into PCB-based transformers further reduces the total number of transformers and inductors needed and boosts power density. This dissertation investigates the high-power resonant converters in bidirectional battery chargers, with a focus on achieving higher power density and efficiency using scalable magnetics building blocks (SMBs). It outlines the specific contributions made toward improving manufacturing processes and enhancing system performance. First, a dual channel flux analysis method is proposed to analyze the flux distribution in complicated integrated magnetics. The utilization of the method is carried out in all the chapters to aid the arrangement of the complicated structures. Second, two SMBs are proposed and compared for different powers. Both structures can be utilized in both single phase CLLC resonant converters and three phase CLLC resonant converters. The applications of the SMBs for different power levels are compared and analyzed. Third, a single phase 22-kW on-board charger structure for 800 V batteries using one SMB structure is proposed. Various planar transformer candidates are presented and thoroughly compared for 22-kW OBCs. Integration's impact on the transformer characteristics is analyzed, including the coupling between the windings, inductance, and flux distributions. Different numbers of parallel windings are also compared. The most promising candidate is chosen for the application. A general integrated transformer with good scalability is introduced and analyzed. The current sharing can be controlled without additional complexity so that better thermal balance is realized between the parallel windings. The resonant inductance is also integrated and controlled. The exposed windings give thermal management design more possibilities with a simple core structure. Comprehensive analysis and comparison of the proposed matrix transformers' arrangements is conducted. Detailed flux analysis and loss comparison are carried out. The guidelines with the smallest core loss are given. A fully PCB packaged SiC-based single-phase CLLC resonant converter for 22-kW OBC applications was created using the proposed integrated transformer structure, achieving a power density of 11.6 kW/L with maximum efficiency of 98.5% under 250 kHz switching frequency. Fourth, a 3PCLLC 22-kW OBCs using another kind of SMB structure with linearly control in the leakage inductance is discussed and proposed. The inductance calculation model includes integration using unbalanced windings and integration using pure inductors. Integrating using pure inductors can reduce the winding loss with a smaller footprint. So, it is more beneficial in high power applications with more leakage inductance integration. The flux is analyzed in detail. The benefit of three phases is discussed, which is flux cancellation between the three phases. Various structures are compared, and a more rigorous and time-efficient optimization and design process is proposed. Fifth, to enhance efficiency further, different winding structures are proposed for 3PCLLC resonant converters for 22-kW OBCs. 13% winding loss reduction is found using the optimized winding structure. To fully utilize the PCB layers, parallel windings are adopted for the inductors. Different parallel patterns are analyzed. The current is found to concentrate on the top layers for direct parallel windings. Three structures with perfect current sharing are compared and discussed. Using symmetrical structures can achieve perfect current sharing, but will decrease the power density. Using twisting method can achieve good current sharing without sacrificing power density, but the distribution of inside the copper is not guaranteed. Using PCB Litz can have evenly distribution in the lateral direction with a smaller AC resistance over DC resistance, but the DC resistance will be larger. After comparison and discussion, the twisting method is better is the first and second layer. PCB Litz wire is better in the third layer and above. Sixth, for 30 kW 1PCLLC converters in fast chargers, the stress on PCB windings will be too high to carry. Also, the requirement of power density for fast chargers is not as strict as OBCs. Litz wire can be utilized for these applications. However, designing a transformer for high power and high frequency with litz wire is still challenging because the strands number is large for high current. Therefore, parallel windings is needed to handle the current stress. Different winding patterns are compared, and a current distribution model for Litz wire transformers is proposed. Finally, two SMBs using litz wire are found to have perfect sharing based on the model and simulation results. Different resonant inductance integration methods are also compared and discussed. Integrating the resonant inductance in the air is found to be suitable for smaller inductance values if the strand AWG keeps the same. Integrating the resonant inductance in the core is found to be suitable for large inductance values. Finally, a 30-kW hardware is built to prove the concept with a peak efficiency of 99.2%.
SCISSION: The Architectural Collage and Gordon Matta-Clark's Circus Caribbean Orange
Mancilla Vera, Camila Fernanda (Virginia Tech, 2025-06-25)
SCISSIONS: Architecture, Collage-Making and the Fragmented Imagination of Gordon Matta-Clark reconceptualizes cutting as a generative architectural operation. Bridging archival research, material culture studies and critical theory, the dissertation advances three claims. First, purposeful subtraction—breaking, excising, peeling, splitting—has been embedded in architectural practice from spolia and sgraffito to post-digital sampling; it therefore warrants the same analytical status traditionally reserved for additive design. Second, Gordon Matta-Clark's 1971-78 Circus or The Caribbean Orange project 1978 constitute a paradigmatic laboratory in which cutting becomes design, drawing and phenomenological event at once. Third, the operative logic of the cut is best unpacked through its tools: knife (drawing), saw (Building Cuts), scissors (Silver Photo Collage) and scalpel (Photo Strip Collage) Methodologically, each chapter pairs close readings of primary artefacts—blueprints annotated and physically incised by Matta-Clark; Cibachrome photomontages sliced on a light table; film strips re-stitched into chromatic lattices—with anthropological and philosophical lenses (Bachelard's material psychoanalysis, Ingold's "workmanship of risk", Turner's liminality). Technical analyses of cutting implements are interwoven with accounts of their ritual and symbolic afterlives, revealing how blades carry coefficients of valiance, intelligence and social agency. Matta-Clark translated compass-drawn circles on survey plans into full-scale spherical voids, then re-translated those voids into collaged images employing two states of the photograph starting by cutting the silver photo and later on using the —an iterative loop that collapses design, demolition and representation. By framing cutting as at once destructive and generative, the dissertation contributes to debates on adaptive reuse, non-finito aesthetics and the ethics of architectural intervention. It argues that cuts act as "operational diagrams" that store knowledge for future makers, reframing negative space as an epistemic resource. Ultimately, SCISSION positions collage—not as an artistic supplement to architecture—but as an architectural methodology that integrates built matter and its erasures offering a framework for practice in an era marked by scarcity, maintenance, and repair.
Environmental Security and Communal Conflict in Iraq
Mamshai, Farhad Hassan Abdullah (Virginia Tech, 2025-06-25)
This dissertation argues that ethnicity, what Kahl calls 'groupness' and institutional exclusion, affect the perceptions of competing ethnic groups in response to environmental insecurity, mainly the water scarcity and competition of arable lands in the Iraqi disputed territories and the authorities they turn to govern their environmental degradation. Northern Iraq is a mosaic of ethnic and cultural divisions. The dissertation examines how ethnicity in this region affects the perception and response of competing ethnic groups to environmental insecurity and the likelihood of increasing the risk of communal conflict. The region has experienced environmental and structural scarcity, especially a lack of water security and historical land appropriations driven by state policies. Environmental peacebuilding theory asserts that shared environmental insecurity facilitates coordination between competing ethnic groups. However, by employing the environmental scarcity theory, this dissertation elucidates that environmental and structural scarcity is influenced by historical and ethnic divisions and exclusion, thus reducing cooperation between groups when they face environmental degradation. The findings of this study show a contradicting understanding of environmental security and its implications, including the communal conflict and environmental migration. The dissertation also shows that the ethnic groups navigate different "arenas of authority" to govern their everyday environmental needs. It also seeks to take a step away from the environmental security mainstream literature by examining the role of non-state actors, such as social institutions and customary leaders, in everyday environmental governance in Iraq. However, the findings illustrate that vulnerable communities tend to turn to state institutions like the governments of Iraq and the Kurdistan Region, which serves the state-centric environmental security debate. Furthermore, the study uncovers that the elite's understanding of environmental security, such as governing shared water resources, is less competitive within Iraq's environmental federalism framework than anticipated. There is no substantial intergovernmental conflict over shared water resources, and people from different ethnic groups are unsure whether the Kurdistan Regional Government weaponizes water resources and the associated risk of intergovernmental conflict over water administration.
An Assessment of the Postural Risks in Dentistry and of the Potential for Passive Exoskeletons to Mitigate Musculoskeletal Risks among Dental Professionals
Morris, Wallace Martin (Virginia Tech, 2025-06-23)
Dental health professionals—comprising Dentists, dental Hygienists, and dental Assistants—are exposed to prolonged non-neutral static postures that partially account for them experiencing a relatively high prevalence of musculoskeletal disorders (MSDs). For example, approximately 70% of Dentists report experiencing symptoms of MSDs over the previous year, with the most affected regions being the neck, shoulders, and back. However, there have been no reported efforts to objectively record postural exposures throughout a work-shift, in a real-world environment, among American dental health professionals, or to directly compare risks between clinical roles. Exoskeletons (EXOs), though, could be of benefit, as this technology can reduce physical load at target body segments, such as the low back or shoulders. EXOs effectively reduce muscle activation in static holding tasks and have demonstrated potential benefits for some simulated healthcare procedures. However, the applicability of EXOs to dentistry is currently unclear, and their use over prolonged periods, such as a full work-shift, has not commonly been evaluated. The purpose of this dissertation was to assess the postural exposures of Dentists, dental Assistants, and dental Hygienists, and to test the effectiveness and acceptance of EXOs among these workers. Specifically, the first study surveyed dental clinical staff on their chronic pain and fatigue, aspects of their workplace that might influence pain, and their impressions of multiple kinds of EXOs. Major results from this study were that back-support EXOs may receive acceptance in dentistry, and that workers experiencing pain would be more open to trying an EXO. Study two used occupational motion capture via inertial measurement units to quantify the differences in exposures between clinical roles. There were clear differences in exposure characteristics between roles and the usage of loupes. Hygienists had least severe postures but the greatest discomfort, potentially due to low exposure variability/high routinization of their job. Further, use of standard loupes was associated with more extreme postures. Participants in a follow up from Study two used an EXO to assess acceptability across dental clinical workers in real-world working scenarios. Minor reductions in lower back discomfort were reported over one dental shift. Participants performed less trunk flexion during EXO use and compensated with greater neck and arm flexions. Overall impressions of the EXO were positive; all participants agreed that EXOs have a place in dentistry due to prevalent back pain, however concern about sizing the device for a wider array of body types was noted. Overall, results of this dissertation indicate that EXOs could be a valuable intervention in dentistry, especially for Hygienists, who modified their work exposures the least during use. EXOs may need to be combined with newer ergo loupes, though, to effectively mitigate both back and neck risk, and concerns regarding sanitizing the device need to be addressed. In conclusion, dentistry has a wider array of exposures than just static bending, and interventions should consider the unique risks of each clinical role to successfully reduce WMSD risk.
Parenting Stress, Social Support, and Affiliate Stigma among Asian and White Parents of Children with Autism in the United States
Yu, Shuqi (Virginia Tech, 2025-06-23)
The present study examined parenting stress and its predictors among Asian and White parents of autistic children in the United States, with particular attention to parents' race and nativity. In total, 125 parents of autistic children in the United States completed a one-time online survey. Findings revealed that foreign-born Asian parents reported significantly higher levels of parenting stress and received less family and spousal support compared to U.S.-born White parents. Differences also emerged within the Asian parent group, as foreign-born Asian parents reported significantly lower levels of family and spousal support than their U.S.-born Asian counterparts. In contrast, total support, formal support, and non-family informal support did not differ significantly across the three race/nativity groups. These findings suggest that foreign-born Asian parents of autistic children experience greater deficits in family-level support, rather than non-family support, compared to U.S.-born Asian and U.S.-born White parents. Race/nativity was found to moderate the associations between total support and parenting stress, as well as between spousal support and parenting stress. Specifically, both total support and spousal support had stronger stress-buffering effects for foreign-born Asian parents compared to U.S.-born White parents. Although affiliate stigma did not differ significantly across groups, it predicted higher parenting stress for all parents in the current study, regardless of race/nativity groups. These findings support prior research indicating that social support and affiliate stigma are key determinants of parenting stress, while also highlighting the nuanced variations across different domains of social support and the influence of parents' race and nativity. The findings offer both theoretical and practical implications for families, service providers, and social workers, and lay the groundwork for future research aimed at developing more culturally responsive support programs and interventions to support parents of autistic children.
Alternaria Leaf Blight and Head Rot of Broccoli: UAV-Based Disease Detection and Fungicide Resistance Management
Saint-Preux, Carlos (Virginia Tech, 2025-06-23)
Alternaria leaf blight and head rot (ABHR) of brassicas significantly impacts yield, threatening the sustainability of the broccoli industry. Management traditionally relies on quinone outside inhibitor (QoI) fungicides, but recent reports indicate reduced efficacy. The causes for this decline are unclear, whether due to a novel species or resistance within existing species. Additionally, knowledge gaps exist regarding disease-tolerant broccoli cultivars, the extent of QoI fungicide compromise in Virginia, and effective alternative control measures. Disease monitoring is crucial for timely detection of pathogens and fungicide program failures. Multispectral imaging is increasingly adopted for disease monitoring in specialty crops, though its efficacy for ABHR in broccoli fields is unexplored. During the 2021-2024 field seasons, samples with ABHR symptoms were collected from brassica crops and weeds across Virginia. Alternaria species were identified via PCR using genus- and species-specific primers (ALT, ABRA, ABRE, AJAP) and molecular identification through ITS, TEF1, Alt a1, and RPB2 genes. A. brassicola and A. alternata were identified as the predominant species in the region. Pathogenicity varied by region and host. Field trials assessed ABHR susceptibility of 26 broccoli cultivars and the effectiveness of fungicides. Disease severity and yield varied by year. 'Vallejo', 'Marathon', and 'Belstar' had the highest yields, while 'Eastern Crown', 'Green Magic', and 'Millennium' had the lowest. Biological fungicides (Oxidate 5.0, OSO, Guarda) and conventional fungicides (Topguard, Inspire Super, Luna Sensation) effectively reduced disease severity. Marketable yield was highest with Miravis Prime, Fontelis, and Topguard. A UAV-mounted five-band multispectral camera performed imaging across three broccoli fields. Visual assessments of disease severity preceded imaging. Severity was determined using five spectral bands and 74 vegetation indices (VIs) from 184 observations. Multivariate analyses identified the best predictors of disease severity. K-means clustering and PCA classified disease severity into three levels with a Silhouette Score of 0.34. Among regression models, Random Forest achieved the highest performance (MSE = 44.45, R² = 0.79), while classification models such as Random Forest, Artificial Neural Networks, and Gradient Boosting attained 88, 86, and 85% accuracy respectively. Feature importance analysis highlighted indices like TCARI, CVI, and SRPI as consistently influential across models. These findings demonstrate the potential of combining multispectral imaging with machine learning to enhance early detection and management of ABHR in broccoli crops.

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