Masters Theses

Permanent URI for this collection

https://hdl.handle.net/10919/9291

Browse

Now showing 1 - 20 of 23550

Quantifying Field Variability Effects on Surface Performance and Athletes
Veith, Ava Joy (Virginia Tech, 2026-01-08)
Athletic field variability refers to differences in surface conditions within or between athletic fields, often influenced by several factors. In the U.S. transition zone, turfgrass stressors like spring dead spot (SDS; Ophiosphaerella spp.) and winter injury are common on bermudagrass (Cynodon dactylon) athletic fields, yet limited research has evaluated how variability from these stressors affects field performance or safety. Existing literature often oversimplifies playing surfaces into "natural turfgrass" or "synthetic turf," overlooking meaningful within- and between-field variability. To address these gaps, a two-part study was conducted on hybrid bermudagrass [Cynodon dactylon (L.) Pers. × Cynodon transvaalensis Burtt-Davy] athletic fields to: 1) evaluate how SDS and winter injury influence playing surface characteristics such as hardness, soil moisture, and rotational resistance, and 2) assess how these stressors alter athlete-surface and ball-surface interactions. Separately, a third study used wearable technologies to quantify how surface variability within and between natural and synthetic turf fields influences athlete safety and performance. The first study showed SDS and winter injury significantly reduced rotational resistance compared to asymptomatic turfgrass, decreasing traction and potentially increasing the risk of slipping. Winter injury areas also had higher hardness, surpassing recommended thresholds and elevating impact injury risk. Part two of the first study used a mechanical device that simulated athlete-surface interactions, recording metrics such as vertical force, surface recoil, stability, and traction, along with a device measuring ball rebound. SDS-affected areas produced greater vertical force, recoil, and displacement, while both SDS and winter injury increased ball bounce height, indicating reduced energy absorption and compromised playability. Results from the third study showed variability within and between both surface types, further suggesting broad claims regarding field performance of natural vs. synthetic fields should not be made because of field variability inconsistencies. Synthetic fields tested were generally harder, especially under higher traffic. Wearable data showed a positive correlation between hardness and lower limb impact intensity, and survey data suggested that athletes preferred a well-maintained natural grass field but favored synthetic over poorly maintained natural fields. Collectively, these studies underscore the effects of surface variability on field performance, athlete biomechanics, and perceptions of field quality. Monitoring surface properties to maximize turfgrass health are encouraged to optimize field consistency and athlete safety.
Optimizing Weed Suppression Via Cover Crop and Herbicide Programs in the Mid-Atlantic
Beville, Jenna Elizabeth (Virginia Tech, 2026-01-08)
Many Virginia farmers include cover crops in their corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) production due to government subsidies and agronomic benefits. Progressive farmers have optimized cover crop management for specific goals, such as weed suppression and nitrogen fertilizer reductions. Waiting to terminate the cover crop until cash crop planting, so-called "planting green," is often part of the management and is an evolution from the traditional termination timing prior to cash crop planting. To scientifically scrutinize these production systems, three research objectives were developed. These objectives included determining if these systems lead to greater biomass accumulation, increase overall weed suppression, and reduce herbicide inputs. The goal of our first experiment was to determine if a hairy vetch (Vicia villosa R.) and cereal rye (Secale cereale L.) mixture, where the cereal rye was selectively terminated in March, performed better than a hairy vetch monoculture for biomass accumulation and weed suppression. Results indicated that hairy vetch monocultures typically produced greater hairy vetch biomass throughout the season, had greater nitrogen contents, and provided similar weed suppression. Overall, a hairy vetch monoculture cover crop can substitute for a hairy vetch + cereal rye mixture, when the cereal rye is selectively terminated, while providing similar or greater benefits to the following cash crop. Our second objective compared 1-, 2-, and 3-pass herbicide programs initiated either two weeks prior to, or at, corn planting paired with either hairy vetch, hairy vetch + cereal rye, or a winter fallow to determine if herbicide input reductions are possible. We determined a reduction from a 3- pass to a 2- pass herbicide program is possible, however, at least a 2-pass program is needed for season-long weed suppression. Also, herbicide programs that terminated the cover crop at planting (i.e. planted green) tended to provide as good or better weed suppression compared to cover crops terminated prior to planting. While cereal rye is a very popular species for cover cropping, farmers have reported nitrogen immobilization and planting problems because of its extensive biomass and high C:N ratio. Due to this, farmers are interested in substituting black oat (Avena strigosa S.) for cereal rye. Therefore, we conducted two experiments to compare cover crop characteristics. In corn, cereal rye, cereal rye + hairy vetch, black oats, and black oat + hairy vetch were compared while in soybean, black oat and cereal rye treatments were compared. Our results for both experiments indicated that cereal rye and black oat monocultures are similar in terms of lignin content and C:N ratio at cash crop planting. However, black oat treatments typically produced less biomass and suppressed fewer weeds compared to cereal rye treatments. This trend was also seen when comparing the black oat and hairy vetch mixture to the cereal rye and hairy vetch mixture. Ultimately, regardless of cash crop, farmers may still prefer to use a cereal rye monoculture or mixture as their cover crop. Overall, the results of these experiments show that hairy vetch monocultures have the potential to increase weed suppression, nitrogen output, and biomass accumulation compared to hairy vetch and a grass cover crop mixture. However, farmers who include a grass species may lean towards cereal rye because of its benefits over black oats.
Scalable and Reconfigurable True-Time Delay Line for Integrated Radio-Frequency Recurrent Neural Processors
Merton, Nicholas Andrew (Virginia Tech, 2026-01-08)
This paper presents a scalable and tunable delay-line architecture for analog recurrent neural networks (RNNs) operating directly in the RF-domain. Previous research has shown the RF-domain RNNs are capable of performing real-time anomaly detection in wireless systems while reducing the inference latency of the wireless system to be one RF clock cycle. The original RF-domain RNN structure relies on a passive tapped transmission line delay for sequential input samples, limiting the architecture's scalability, power efficiency, and frequency adaptability. Transmission lines have too much attenuation, become impractically large for chip integration, and are untunable. To eliminate these limitations, the passive transmission line delay is replaced with an active delay line made up of cascaded gm-C all-pass filter (APF) cells. The APF cells achieve true-time delay while maintaining low attenuation, low power consumption, high linearity, and have reconfigurable delay characteristics. The proposed solution utilizes compact integration, signal preservation along the delay line, and dynamic tuning for different carrier frequencies or true-time delay needs. A full model of the active delay line and it's integration with the RF-RNN architecture is developed in GlobalFoundaries 65-nm BiCMOS technology. The model includes delay characterization, analysis of loading effects, and noise analysis. The simulation results show that the gm-C APF delay network enables scalable RF-RNN implementations while maintaining anomaly classification performance accuracy under realistic timing variations. This work demonstrates a key step towards practical RF-domain neural processors capable of supporting real-time wireless systems for 5G and beyond.
Assessing broiler chicken welfare at the slaughterhouse
Vitek, Samantha Nicole (Virginia Tech, 2026-01-08)
The commercial broiler chicken industry in the United States produces billions of pounds of chicken meat each year. With an increasing number of broilers slaughtered for consumption, more broilers are at risk of welfare issues occurring during the preslaughter and slaughter process. Animal-based, resource-based, and management-based measures can be used to assess animal welfare, enable benchmarking, and guide future management decisions that can improve animal welfare outcomes. However, little is known about the prevalence of welfare issues during these processes, and their associated risk factors in the United States are unclear. Therefore, the objective was to improve understanding of how on-farm, preslaughter, and slaughter conditions impact welfare outcomes in broilers chickens recorded at the processing plant. Chapter 2 reviews to-date literature on preslaughter mortality, injuries, and their associated risk factors. In chapter 3, animal-based measures assessed at three commercial slaughterhouses were associated with transport-specific flock characteristics. This relationship varied depending on the slaughterhouse where broilers were processed. Older and younger broilers were more at risk to obtain a leg bruise or die during the preslaughter phase. Broilers housed at a higher stocking density on-farm were more likely to obtain a wing fracture and leg bruise during the preslaughter phase. Multiple welfare outcomes were associated with first-week mortality and total on-farm mortality. This indicates that flock health and quality are connected to welfare issues that occur prior to and during slaughter, although the relationships were sometimes opposing. In chapter 4, a novel animal-based measure of long-term distress, feather corticosterone concentration, was evaluated pre- and post-scalding at a slaughterhouse. Mean feather corticosterone concentrations were lower post-scalding compared to pre-scalding, indicating that the scalding process does impact these feather corticosterone concentrations, likely due to structural feather damage from agitation during the process. This indicates that feather samples should be collected prior to scalding for a biological interpretation of long-term distress or after scalding for a relative benchmarking of a retrospective welfare indicator across flocks. In conclusion, flock characteristics and slaughterhouse conditions can impact welfare outcomes during the broilers' last day of life. Likely because management of the flock, and subsequent health or quality of the flock, will impact the birds' resilience during this stressful phase. Understanding these relationships can improve management strategies for future flocks, potentially improve welfare outcomes and benchmark across other slaughterhouses in the United States.
Investigation Into Geometry and Behavior of Cislunar GEO-Grazer Orbits
Soccio-Mallon, Spencer Patrick (Virginia Tech, 2026-01-08)
This thesis investigates a class of cislunar trajectories referred to as GEO-grazers: orbits that traverse the Earth–Moon system while grazing or passing below geosynchronous Earth orbit (GEO). These trajectories are of interest for cislunar space domain awareness (SDA) because they provide natural dynamical pathways by which spacecraft or debris originating far beyond GEO can approach critical orbital infrastructure. The analysis is conducted primarily within the planar circular restricted three-body problem (PCR3BP), using the Jacobi constant to characterize different geometries and timescales of GEO-grazers. Case studies of real cislunar objects are first examined to demonstrate that GEO-grazing behavior has already occurred, both as a result of deliberate mission design and chaotic dynamics. A generalized modeling approach is then used to generate GEO-grazers by time-reversing trajectories that originate at GEO and reach the exterior realm of cislunar space. Across ranges of Jacobi constants, GEO-grazers are shown to exhibit structured behavior, including clustering of lunar realm entry and exit locations, non-monotonic trends in time of flight to GEO, and consistent grouping of GEO impact or grazing regions relative to the position of the Moon. Finally, the practical implementation of GEO-grazers is explored through the identification of cislunar parking orbits via the simulation of impulsive maneuvers that transfer these orbits onto GEO-grazing trajectories. The results demonstrate that maneuvers on the order of tens of meters per second can be sufficient to induce GEO-grazing behavior, particularly when executed within the lunar realm. Collectively, these findings indicate that the existence of GEO-grazers has important implications for the detection, classification, and intent assessment of objects operating in the Earth–Moon environment.
On the Need to Acquire Data for the Advancement of Shock Prediction
Yunis, Ramsey Jonah (Virginia Tech, 2026-01-08)
Today, most shock propagation predictions are empirically based, resulting from a single study done by Martin Marietta in 1970. That work examined a body of shock data available at the time to create empirically derived guidance on how shock magnitude propagates through joints and distance. Most space programs use this Martin Marietta data until final shock validation testing where actual propagation is observed and defined. At this point, exceedances can be costly. Modern shock prediction techniques exist, but these are not proven for official use because existing data is either too simple, the analysis is stymied by an insufficient finite element model, or the results of prior work are proprietary. This report demonstrates that even on a simple structure, 1) the historical empirical technique does not correctly predict the environment, and 2) basic linear and non-linear analysis is not sufficient for analysis of shock. This work sets the foundation for improving shock prediction by gathering and releasing open-source, high quality shock data that can be used by anyone to validate new techniques.
An Examination of Trends in the Rates of Low-Value Opioids Prescribed for Acute Low Back Pain in Rural vs. Non-Rural Virginia
Turner, Jamie (Virginia Tech, 2026-01-08)
Background: The Centers for Disease Control and Prevention (CDC) recommends against the use of prescription opioids for most types of acute pain. Despite these recommendations, some evidence suggests that opioid prescribing for acute low back pain (LBP) - among the most common acute pain complaints - persists. This study evaluated trends in low-value opioid prescribing for acute LBP among patients residing in rural versus non-rural areas of Virginia during 2019-2021 and evaluated the influence of the COVID-19 pandemic timeframe on prescribing rates. Methods: In this retrospective cohort study, we examined insurance claims from the Virginia All-Payer Claims Database for adults continuously enrolled in Medicaid, Medicare Advantage, or commercial plans from 2019 to 2021. We used the Milliman MedInsight Health Waste Calculator to identify low-value claims and calculated annual and bi-monthly prescribing incidence rates per 1000 patients. Heterogeneous difference-in-differences models generated incidence rate ratios (IRRs) to express the difference in the rate of low-value opioids for acute LBP observed during the first two years of the COVID-19 pandemic (2020-2021) versus expected incidence based on the pre-pandemic timeframe (2019). IRRs were stratified by rurality. Results: Among our cohort (n=853,775), 1,338,371 claims for opioids for acute LBP were identified, 73.9% of which were low-value. The annual prescribing of low-value opioids for acute LBP declined by 30.6% from 2019 (155.0 claims per 1000 patients) to 2021 (107.5 claims per 1000 patients) compared with the expected decline (model-predicted) of 18.6% during this period. During 2020-2021, low-value opioid prescribing for acute LBP was 79.6% of expected incidence (IRR: 0.80, p<.001). Low-value opioid prescribing for acute LBP was 0.74 times higher in patients residing in rural versus non-rural areas throughout 2019-2021 (IRR: 1.74, p<.001), and the difference in low-value prescribing between rural and non-rural patients did not change significantly during 2020-2021 (IRR: 1.02, p=.060). Conclusions: Most opioids prescribed for acute LBP among this large, multi-payer Virginia cohort were low-value. The COVID-19 pandemic timeframe (2020-2021) was associated with an accelerated decline in low-value opioid prescribing for acute LBP. Persistent rural disparity in low-value opioid prescribing for acute LBP highlights the need to examine underlying drivers to reduce low-value prescribing and promote equitable, high-quality acute pain care.
Command and Data Handling-Driven Spacecraft Operations Using Hardware-in-the-Loop Simulation
Caulfield, William Riley (Virginia Tech, 2026-01-08)
CubeSats have emerged as a cost-effective platform for space research and technology demonstration, but mission success remains highly dependent on reliable command execution, telemetry handing, and subsystem coordination. Traditional satellite evaluation methods often emphasize isolated subsystem validation or environmental simulation, while providing limited insight into end-to-end spacecraft operations governed by the Command and Data Handling (CandDH) subsystem. The thesis addresses the need this gap by developing and evaluating a COSMOS-based CandDH architecture integrated with the EyasSat educational spacecraft to enable realistic, flight-like command and telemetry operations in a hardware-inthe- loop environment. While attitude determination and control subsystem (ADCS) experiments are used as a representative case study, the primary contribution of this work lies in demonstrating how CandDH-driven command sequencing, mode management, and telemetrytriggered logic can be exercised and validated prior to flight. Experimental results show reliable execution of scripted command sequences, deterministic telemetry-driven responses, and repeatable operational scenarios including payload activation, mode transitions, and closed-loop attitude responses. The integrated EyasSat–COSMOS environment provides a modular and extensible spacecraft operations testbed that shifts validation from isolated subsystem performance toward system-level operational readiness. This approach reduces integration risk, supports operator training, and enables future expansion toward full mission timeline testing involving multiple spacecraft subsystems. The results demonstrate that a low-cost educational spacecraft can effectively support CandDH-centric validation of spacecraft operations, bridging the gap between software-only simulation and flight hardware testing.
Safeguarding the National Broadband Map: Detecting Strategic Misreporting and Auditing Broadband Deployment via a Risk-Based Monitoring System
Wen, Zhuowei (Virginia Tech, 2026-01-07)
The National Broadband Map (NBM) serves as the source of truth for determining location eligibility for funding programs, most notably, the Broadband Equity, Access, and Deployment (BEAD) program that allocated an unprecedented $42.45 billion that aspire to provide universal internet access in the U.S.. However, the map is built upon self-reported data from Internet Service Providers (ISPs), this creates a conflict of interest and incentive for strategic misreporting, where ISPs may "game" the system with their claims to influence funding allocation. In this work, we develop a scalable monitoring framework as the blueprint to a system that help stakeholders safeguard the NBM against inaccurate or strategic provider filings with three-part approach. First, we establishes the infrastructural foundation by developing a methodology to map ISP Provider IDs to Autonomous System Numbers (ASNs), enabling the attribution of network measurements to specific provider claims. Using four independent matching techniques based on registration data, we successfully map 72% of providers presented in the NBM to ASNs, creating the observability infrastructure necessary for measurement-based verification. Second, we provide empirical evidence that integrity failures exist at scale by investigating strategic misreporting patterns in the NBM. We develop a framework for detecting "flip-flops"—logically implausible reporting patterns where an ISP's service claim follows an A→B→A sequence across NBM releases. By filtering these events for strategic relevance based on timing, impact on BEAD eligibility, and spatial concentration, we identify more than 122,000 suspicious service claims across 25 states. These findings demonstrate that the NBM is error-prone and that existing safeguarding mechanism is insufficient. Finally, we develop a continuous, risk-based monitoring framework that uses the infrastructure and evidence from ASN to provider mapping and strategic misreporting analysis. We employ a Difference-in-Differences statistical model to establish empirical baselines for expected performance improvements in network-measurements following claimed service upgrades. By continuously monitoring crowdsourced speed test data and detecting locations that fail to demonstrate corresponding performance improvements, the framework provides a blueprint for a monitoring system that enable stakeholders to efficiently prioritize verification efforts toward the most risky claims. Taken together, we demonstrate that protecting the integrity of large-scale government programs requires systematic and continuous monitoring.
Towards Human-AI Teaming for Skill Development: From Dyadic Interview Practice to Triadic Programming Collaboration
Daryanto, Taufiq Husada (Virginia Tech, 2026-01-06)
As the job market for CS graduates grows increasingly competitive, effective preparation for technical interviews through mock interviews and programming practice has become critical. This thesis explores how AI can support skill development in these domains, progressing from AI as a sole practice partner to AI as a collaborative teammate. Our first two studies investigate LLM-based conversational AI for interview preparation. Study 1 developed an interview system grounded in reflective learning and dialogic feedback, enabling learners to engage in low-stakes practice with personalized, interactive feedback. Study 2 extended this work to technical interviews, exploring how conversational AI can support the think-aloud practice in technical interviews through simulation, feedback, and example. Together, these studies demonstrate the value of AI as a dyadic practice partner. However, participants reported that while AI practice was useful, peer-based engagement may offer stronger social connection and motivation, suggesting that AI should augment rather than replace human collaboration. This insight motivated our third study, which investigates human–AI teaming in a triadic configuration. We introduce human–human–AI triadic programming, where two humans collaborate with a proactive AI agent. Results from 20 participants show that this triadic collaboration improves collaborative learning and social presence while encouraging more responsible AI use. Together, these studies advance understanding of how AI can support skill development, illustrating a trajectory from dyadic human–AI interaction toward richer forms of human–AI teaming that preserve the pedagogical and social benefits of human collaboration.
American rivers are transporting more sediment in less time
Sigdel, Nishchal Nath (Virginia Tech, 2026-01-06)
Understanding how sediment is produced, mobilized, and delivered through river networks is essential for addressing challenges in water quality, infrastructure management, and landscape evolution. Yet, long-term assessments of sediment dynamics have been hindered by sparse sampling that misses the short-lived events responsible for most annual transport. This study develops a deep learning framework that combines high-frequency turbidity sensors and long-term hydrometeorological datasets to reconstruct daily suspended-sediment flux across 175 minimally regulated U.S. catchments from 1985–2023. By leveraging LSTM models and data-driven attribution techniques, the work produces a continental-scale record capable of resolving multi-decadal shifts in both sediment yield and the timing of transport. Results show that many rivers deliver a greater portion of their annual sediment load in shorter, more extreme pulses; the median time required to transport 90% of the load shrank from 69 days to 50 days, with one-third of basins exhibiting increased temporal inequality. To explain these trends, interpretable machine-learning methods were applied to quantify the relative influence of hydroclimatic forcing and land-use disturbance. Analysis of those drivers reveals that deforestation, urban expansion, and intensifying precipitation events collectively drive the observed acceleration and concentration of sediment transport. By reconstructing a detailed sediment history for U.S. rivers, this thesis provides a new basis for understanding how climate and land-use change are reshaping sediment regimes. The findings have direct implications for sediment budgeting, aquatic habitat protection, reservoir and flood-control infrastructure, and the design of best-management practices.
Asian long-horned tick distributions in Virginia pastures and evaluating the horn fly as a possible vector of Theileria orientalis Ikeda
Sharpe, Matthew Payne (Virginia Tech, 2026-01-05)
The Asian longhorned tick (Haemaphysalis longicornis) is an invasive ectoparasite of growing concern in the U.S. due to its role as the primary vector of the emerging cattle parasite, Theileria orientalis Ikeda. To better understand the transmission landscape in Virginia, this study first evaluated the fine-scale environmental drivers of H. longicornis density. Across two field seasons (2023–2024), 25,929 ticks were collected and analyzed using negative binomial generalized linear mixed models. Results indicated that vegetation height was the most consistent predictor of density, with short vegetation supporting significantly fewer ticks than medium-height vegetation. While relative humidity was positively associated with nymphal density, landscape features like distance to trees or water sources were not significant predictors. These findings suggest that fine-scale pasture management may be a viable tool for reducing tick populations. However, because T. orientalis outbreaks have been observed in regions with no or low tick density, investigating alternative transmission pathways is essential. To address this, the second phase of this study evaluated the potential for the horn fly (Haematobia irritans), the most economically significant fly pest of U.S. beef cattle, to act as a vector. A total of 2,365 horn flies (254 pools) were collected from nine Virginia counties and screened using conventional and quantitative PCR. T. orientalis Ikeda DNA was confirmed in flies from four counties (Augusta, Bland, Culpeper, and Nottoway), with remaining samples either negative or below the limit of detection. Collectively, these findings provide a comprehensive look at the ecology of T. orientalis in Virginia. While vegetation management remains a key strategy for controlling the primary tick vector, the detection of Ikeda DNA in horn flies provides the first evidence of this pathogen in a novel arthropod species. This suggests that horn flies may serve as vectors, highlighting a critical need for further experimental studies to determine their epidemiological significance in cattle health management.
Designing Answer-Aware LLM Hints to Scaffold Deeper Learning in K–12 Programming Education
Bhaskar, Sahana (Virginia Tech, 2025-12-23)
Studies have shown that many K–12 students develop misconceptions about programming concepts such as variables, conditionals, and loops, particularly when learning through block-based environments like Scratch, where visual abstractions can obscure underlying computational logic. While tools powered by artificial intelligence (AI) can provide quick help, they often give direct answers that reduce students' opportunities to think and learn. This work explores how AI can support learning without encouraging overreliance. In a study with 105 students using CodeKids, 31.4% showed misconceptions about variable assignment and data types, and only 20% correctly solved conditional problems, highlighting the need for better scaffolding to address these conceptual gaps. To tackle this challenge, we designed and implemented an LLM-powered hint generation system within CodeKids, an open-source, curriculum-aligned learning platform developed by Virginia Tech in collaboration with local schools. The system generates short, step-by-step hints when students ask for help, encouraging reasoning rather than direct answer-seeking. Grounded in Vygotsky's Zone of Proximal Development, this approach balances guidance and autonomy through structured prompting that preserves productive struggle. The system was tested with real students and evaluated through automated analysis and surveys, which showed that the hints were clear, helpful, and easy to use. Students who used the hints reported higher confidence and improved problem-solving skills. These results demonstrate promising progress in using AI to support K–12 programming education and lay the foundation for future tools that personalize hints, adapt to different learners, and make AI-driven learning more effective and engaging.
Persona Reinforcement for Secure Programming AI Tutors: Adaptive Assistance in Action
Srinivasan Manikandan, Adithya Harish (Virginia Tech, 2025-12-23)
The world needs safe software, but producing it requires secure programming skills. Unfortunately, effectively teaching students secure coding skills remains a critical open problem. Pedagogy suggests that students acquire skills best through a combination of conceptual reasoning and practical experience. In computing, students gain this practical experience through hands-on programming exercises and projects. In the age of generative AI, modern tools, such as large language models (LLMs), often hinder effective learning by providing solutions to coding problems without engaging students in the learning process. Instead of internalizing the key concepts, students can simply copy answers without understanding, undermining the learning objectives. Unexpectedly, generative AI offers a promising opportunity to address the problem it has created, providing appropriate constraints and design. To that end, we present Secure Programming with Adaptive Reasoning Companion (SPARC), an AI-powered tutor designed to guide students through secure programming exercises, rather than directly provide solutions. Our design reinforces SPARC's tutor persona through a confluence of three techniques: (1) tailored prompt engineering, (2) a novel combination of AI techniques---coined as a learning safeguard proxy ---designed to prevent the tutor from directly providing solutions, and (3) a responsive algorithm that adapts responses to student proficiencies. We have integrated SPARC with SecureCoder, a drill-and-practice platform for secure coding skills, and evaluated its effectiveness via a pilot study. Across 120 study sessions (80 with SPARC and 40 with GPT-4o-mini), SPARC facilitated a 95% exercise completion rate compared to 80% for GPT-4o-mini, and pilot study participants demonstrated statistically higher satisfaction with SPARC's adaptability than GPT-4o-mini. Further, unlike GPT-4o-mini, all interactions with SPARC avoided providing participants with complete solutions. Finally, our study demonstrated that more than 85% of participants found SPARC's guidance to be clear, adaptive, and helpful, with 80% reporting improved understanding of secure programming concepts. Our evaluation suggests that SPARC's novel design achieves its goal of serving as a secure programming tutor. SPARC provides helpful guidance that most students found to enhance their learning experiences. As secure programming skills are vitally important, this work contributes to secure computing education by employing generative AI as an educator's ally, rather than its adversary.
Repurposing Antibacterial Compounds and Natural Products to Combat Vancomycin-Resistant Enterococci
Abdelmegeed, Somaia Mahmoud Abdelaziz (Virginia Tech, 2025-12-23)
Antibiotic resistance is one of the greatest threats to modern medicine, leaving clinicians with shrinking treatment options for life-threatening infections. Among the most concerning pathogens are vancomycin-resistant Enterococcus (VRE), which causes serious bloodstream, urinary tract, and wound infections, particularly in hospitalized patients. This thesis explores two complementary strategies to address this challenge: natural product discovery and drug repurposing. The first study investigated two drug candidates, CRS3123 and ridinilazole, originally designed to target Clostridioides difficile, for their activity against VRE. Both compounds showed exceptionally low minimum inhibitory concentrations and were bacteriostatic against VRE in vitro. In a Caenorhabditis elegans infection model, treatment with either compound significantly reduced the bacterial burden, demonstrating in vivo efficacy. Safety profiles were favorable, with minimal cytotoxicity and negligible hemolytic activity, highlighting their potential as safe and targeted therapies against VRE infections. In the second study, I evaluated maslinic acid, a naturally occurring plant-derived triterpene, for its potential against VRE. Maslinic acid inhibited bacterial growth and significantly reduced biofilm formation, an important mechanism that allows VRE to persist in hospital environments and resist treatment. Importantly, it showed low cytotoxicity in mammalian cells, indicating promise as a safe therapeutic scaffold. Together, these studies highlight the value of diverse approaches to antibiotic discovery. Natural compounds like maslinic acid expand chemical diversity, while repurposing candidates such as CRS3123 and ridinilazole accelerate potential clinical application. This thesis provides new insights into strategies for combating multidrug-resistant Enterococcus, a pathogen of urgent medical concern.
The Mangrove Mosaic: An Ecological Landscape Design Strategy for Everglades City's Climate Adaptation and Phased Transition Amidst Sea-level Rise
Nandi, Prema (Virginia Tech, 2025-12-23)
Mangrove-based systems have significant potential to strengthen climate resilience in vulnerable coastal communities by protecting the coastline and adapting to rising sea levels. To explore ways to enhance the unique capabilities of mangroves, an action plan was implemented in Everglades City, Florida, chosen as a focused site. Located at the intersection of major ecological reserves and facing serious threats from significantly higher sea level rise, Everglades City presents ecological and cultural challenges as well as opportunities for land-use transformation. The project aims to restore natural hydrology in a historically disturbed landscape through ecological design strategies. The first step is to restore natural hydrology and appropriate salinity levels to support healthy mangrove growth and ecosystem function. A key part of the project involves "supercharging" mangroves by restoring the coastal mangrove belt and using a combined double-breakwater and Thin-Layer Placement (TLP) method to capture and retain sediments. The thesis also examines adaptation pathways for residents by exploring how mangrove restoration, migration corridors, community-driven decision-making, and long-term resilience planning can collectively create a sustainable future for Everglades City amid increasing climate change challenges.
Geospatial Trends of Per- and Polyfluoroalkyl Substances (PFAS) Incidence in Private Drinking Water in Virginia
Mclelland, Nicholas James (Virginia Tech, 2025-12-23)
Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic organic compounds that are hydrophobic, thermally stable, and resistant to environmental degradation. Widespread industrial and household use has resulted in frequent environmental and drinking water detection, raising concerns about adverse human health effects associated with PFAS exposure. In response, the United States Environmental Protection Agency (USEPA) has established mandatory monitoring campaigns and future maximum contaminant levels (MCLs) for two PFAS compounds (PFOA and PFOS) in public water systems under the Safe Drinking Water Act. However, the 20-40 million Americans who rely on private drinking water supplies remain unregulated and comparatively understudied. This study investigates the incidence of PFAS in private drinking water under 'baseline' conditions and assesses the impacts of contributing land cover types, point sources, household characteristics, and traditional water quality parameters on PFAS incidence across Virginia. Point-of-use samples (n=382) were collected from private wells across 10 counties and analyzed for 30 PFAS compounds using USEPA Methods 533 and 537.1. Geospatial variables, household characteristics, and traditional water quality parameters (e.g., lead and bacteria) were analyzed using GIS and RStudio. At least one PFAS compound was detectable in all samples, with 90% exceeding method reporting limits, although median total sum PFAS concentrations were low (1.50 ppt). Short-chain PFAS compounds were more prevalent than long-chain legacy compounds in both total concentration and unique compound detection rates. The USEPA MCL of 4 ppt was exceeded in 2.4% and 5.2% of samples for PFOA and PFOS, respectively. While most samples had generally low total sum PFAS concentrations, 10% of samples exceeded 10.03 ppt with a maximum total sum PFAS concentration of 303 ppt. High PFAS sampled homes were associated with increased urban land cover, closer proximity to point sources, higher frequency of nearby point sources, older well age, elevated lead, and indicators of corrosive water chemistry, including low pH, and higher conductivity/total dissolved solids. These findings suggest PFAS concentrations in private drinking water are associated with more anthropogenic activity as well as potential mobilization of PFAS from in-home sources such as plumbing networks. Traditional water quality concerns remain prevalent, with exceedance of public water standards observed for lead (5.01% > 0.01 mg/L health-action-limit), E. coli (4.19% > absence), and total coliform bacteria (34.8% > absence). While 70% of homes employed some form of treatment, only 22% of homes used health based treatment types (e.g., reverse osmosis and activated carbon) which are capable of removing heavy metals, bacteria, or PFAS. These findings highlight the continued vulnerability of private drinking water users to both emerging and established contaminants and underscore the need for improved monitoring, targeted treatment adoption, and enhanced support for private drinking water supply stewardship.
Predicting Corn Response to Variable Synthetic Fertilizer Treatments Using UAV-Derived Imagery
Khulal, Aarati (Virginia Tech, 2025-12-23)
Efficient nutrient management is essential for optimizing corn (Zea mays L.) productivity while minimizing environmental and economic costs. Traditional methods for assessing crop responses to nutrients are often damaging and labor-intensive, limiting accurate assessment of spatial and temporal variations. Accurate in-season yield potential estimation plays a vital role in guiding nutrient management decisions and supporting grain marketing strategies. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) derived imagery to estimate chlorophyll (Chl) status and predict corn grain yield potential in-season under variable nitrogen (N), phosphorus (P), and potassium (K) fertilizer treatments across different growth stages. Two field trials (NP and K) were conducted at two locations in Virginia, Kentland Farm in Blacksburg (Kentland), Valley and Ridge province, and the Northern Piedmont Center in Orange (Orange), Piedmont province. These sites vary in altitude, soil type, and climatic conditions, providing contrasting environments for evaluating crop responses to fertilizers. In both trials, factorial arrangement of treatments (varied N, P, and K fertilizer rates) with four replications was implemented with a randomized complete block design (RCBD). Chlorophyll readings (ChlR) were collected using the Soil Plant Analysis Development (SPAD)-502 and atLEAF Chl meters at three growth stages: early vegetative (EV), late vegetative (LV), and reproductive (Repr). These measurements were synchronized with UAV flights performed on the same day. UAV flights were conducted using DJI Mavic equipped with an RGB sensor for visible light and four monochrome sensors for multispectral imaging (red: 650 nm ± 16 nm, green: 560 nm ± 16 nm, near-infrared (NIR): 840 nm ± 26 nm, red-edge: 730 nm ± 16 nm). UAV-derived vegetation indices (VIs) responsive to Chl and indicative of crop yield potential were computed to model ChlR and yield through single and multi-index regression analyses. Multi-index model performance was evaluated through repeated k-fold cross-validation (CV) (k = 5; 30 repetitions). Indices included the Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red-Edge (CIRE), Normalized Difference Red-Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), MERIS Terrestrial Chlorophyll Index (MTCI), Normalized Difference Chlorophyll Index (NDCI), Canopy Chlorophyll Content Index (CCCI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Weather variation, early-season drought, and late-season rainfall strongly influenced yield formation and grain moisture, overshadowing fertilizer treatment effects. No significant yield differences were detected among N, P, or K levels (p > 0.05). UAV-derived VIs demonstrated significant correlations with both ChlR and yield, with stronger relationships observed during the LV stage when canopy closure and Chl concentration were most stable. In the K trial at Kentland, the relationships between VIs and both ChlR and yield were generally moderate, while in the K trial at Orange, correlations were consistently strong and significant. For ChlR prediction, green and red-edge based indices (GNDVI, NDRE, CIRE, and MTCI) were the most reliable indices, explaining 40 to 55 percent of the variation across both sites and trials. For yield prediction, GNDVI, NDVI, and NDCI consistently exhibited strong relationships at Orange during the LV stage, with R² values ranging from 0.50 to 0.72 across both trials. In contrast, Kentland showed comparatively lower predictive performance, with only moderate relationships observed in the K trial during the LV stage. The use of a polynomial regression (quadratic) model further improved prediction accuracy compared to the linear model in all trials. Multi-index regression further improved predictive accuracy. The best-performing yield models were observed in the K trial at Orange during the LV stage, achieving CV R² values up to 0.71 (CV RMSE of 13.9), while the best ChlR models were found in the NP trial at Orange with CV R² of 0.46 (CV RMSE of 2.45). Model performance was lower for EV and Repr stages. Overall, these findings demonstrate that UAV-based multispectral imaging is an effective tool for monitoring corn canopy Chl status and assessing yield potential, with prediction accuracy varying across growth stages.
If These Walls Could Talk: How Architecture, Adaptive Reuse and Historic Preservation can be one
Roberts, Sabrina Michelle (Virginia Tech, 2025-12-22)
The Built Environment represents on of the most complex and evolving challenges in contemporary society. As populations grow and environmental pressures intensify, the need to adapt and reuse existing structures has become increasingly urgent. Adaptive reuse and historic preservation, well known practices in architecture, offer sustainable solutions that bridge cultural heritage and modern function. These strategies are widely implemented across Europe, but those strategies have not quite reached the United States. This Thesis examines how adaptive reuse and historic preservation can be integrated into the American architectural context. Particularly through smaller scale, underutilized buildings that fall outside of the traditional preservation priorities. The studies focuses on a building the Washington D.C. that is similar to many building across the city that also have had the same fate. The proposed design re-imagines the structure as a mixed use development that com-bines affordable housing with ground floor retail. By employing federal and local historic preservation tax credits, the project demonstrates how economic incentives can support sustainable, community oriented redevelopment. The resulting design provides unique unites, shared amenities and spaces the enhance the livability and connection to the existent urban fabric. Through this case study, the thesis argues that adaptive reuse and preservation can serve as powerful tools for addressing the challenges of the built environment, in this case the hosing crisis, sustainability and conservation. It advocates for more of an inclusive approach to preservation, to value the everyday historic buildings as important resources for the future development of the urban built environment.
Beyond Visual Line of Sight Drone Simulator with User-defined Risk Layers
Anshebo, Surafel Tesfaye (Virginia Tech, 2025-12-22)
As Beyond Visual Line of Sight (BVLOS) operations become increasingly prevalent across a range of UAV applications, the need for reliable tools to support safe mission planning and dynamic risk assessment is growing. This work introduces a web-based simulation framework for BVLOS flight planning and validation, developed as a full-stack Flask application. The system integrates FAA sectional charts, NOAA weather overlays, and user-defined air and ground risk layers into an interactive browser interface. Users can draw waypoint paths, adjust altitudes, and dynamically reroute flights in response to hazards such as adverse weather or temporary airspace restrictions. To support rapid deployment and accessibility, the simulation stack is fully containerized using Docker and incorporates a Software-inthe-Loop (SITL) engine via ArduPilot. In addition to simulation, the platform supports scaled-down Hardware-in-the-Loop (HIL) validation using S-500 quadcopter drone, enabling a safe pre-flight evaluation of high-risk missions. The system architecture is modular and extensible, allowing integration of external tools for mission visualization and log playback. This framework offers a practical toolset for both researchers and operators seeking to improve UAV reliability in complex environments. Use cases include disaster response, remote infrastructure inspection, and agricultural monitoring. The results demonstrate the utility of simulation-driven planning in enhancing safety, mission effectiveness, and operator preparedness for real-world BVLOS deployments.

Browse

Recent Submissions