Masters Theses

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

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Anomaly Detection for Radio Frequency Signals using Self-Supervised Deep Learning
Rymer, Declan James (Virginia Tech, 2026-09-18)
Detecting anomalous radio frequency (RF) data is a critical capability for spectrum monitoring, inference mitigation, and electronic warfare applications. In recent years, deep learning (DL) has shown promising results for the problem of RF anomaly detection; however, most of the work in the field assumes a priori knowledge about the data and that is not always practical. Labeling data can be time-consuming and expensive to collect, and knowledge of what anomalies the model might encounter are not usually available. Deep one-class classification (DOCC) using Deep Support Vector Data Description (DSVDD) has emerged as a promising method for learning compact representations of normal data and identifying deviations from expected signal behavior, especially in scenarios where anomalous data are unavailable or scarce. In this work DSVDD methods are applied to the problem of anomaly detection in digitally modulated RF signals and its effectiveness across multiple modulation schemes is demonstrated. Furthermore, a novel objective function based on Mahalanobis distance that models feature covariance in the learned latent space is introduced, addressing a key limitation of the original DSVDD method, which relies on Euclidean distance measures. Experimental results on simulated and over-the-air RF datasets show that the novel Mahalanobis method consistently outperforms the original DOCC approach. One limitation of DOCC methods is the assumption that the training data are all of the same class. The second half of this thesis relaxes this assumption and proposes a novel framework for fully self-supervised anomaly detection, training only on in-phase and quadrature (I/Q) streams of in-distribution signals, while never seeing examples of anomalies or having access to class labels in the training data. This framework consists of a two-step pipeline: first a contrastive learning step to cluster and segment the training data based on learned differentiating characteristics, and second a DSVDD step that learns to tightly compress the training data and exclude anomalies. This approach is shown to improve anomaly detection performance compared with using DSVDD without contrastive learning on multiple classes.
Implementation of Rotation Modulation Scheme for INS in an Experimental Environment Using MEMS-based Sensors
Farah, Mitchell Edward (Virginia Tech, 2026-09-16)
Inexpensive commercial off-the-shelf (COTS) inertial measurement units (IMUs) are an attractive choice for autonomous systems due to their size, cost, and power consumption. However, in practice, their adoption for use in inertial navigation systems (INS) imposes performance limitations. When used in INS, micro-electro-mechanical systems (MEMS) sensors suffer from bias, scale-factor error, misalignment, and other effects that limit performance. This thesis investigates whether rotation modulation can attenuate persistent measurement residuals from a low-cost MEMS IMU relative to an identical IMU that is static relative to the rotating IMU. The experimental dynamic angular navigation through controlled excitation (DANCE) apparatus is a dual-IMU data-logging apparatus constructed using a Teensy 4.1 microcontroller, two identical ICM-20948 IMUs, an AS5047U magnetic encoder, a BLDC motor and motor controller, and an SD-card logging system. One IMU, termed the rotating IMU (RIMU), is mounted to a rotating assembly. At the same time, the stationary IMU (SIMU) is fixed to the apparatus body and used as a simultaneously logged comparison baseline. This work presents the hardware architecture, data-logging firmware, calibration procedures, processing pipeline, and estimator implementation used to evaluate rotation modulation with low-cost MEMS-based IMUs. The experimental results show that rotation modulation reduced the magnitude of the window-averaged horizontal RIMU gyroscope measurement residual by transforming persistent sensor-frame components into spin-synchronous body-frame content. However, the tests did not demonstrate repeatable rotation-induced improvements in roll and pitch estimation, estimator innovation, or static inertial drift relative to the SIMU. The results therefore support the expected measurement-level modulation mechanism while also showing that the effect did not translate into improved estimator- or navigation-level behavior under the tested conditions. Practical limitations included encoder alignment, timing uncertainty, mechanical and electrical disturbances, and installation errors.
Investigating Auxin Biosynthesis in Saccharomyces cerevisiae Through Targeted CRISPR Knockouts
Sayers, April Marie (Virginia Tech, 2026-09-16)
Auxin, or indole-3-acetic acid, is primarily a plant growth hormone that regulates various aspects of plant development (Woodward and Bartel, 2005). While primarily associated with plants, some fungal species can also produce auxin. It is generally believed that fungi do not require auxin for their own survival and rather produce it merely to facilitate their interactions with other organisms, whether mutualistic or pathogenic. In mutualistic relationships, fungi can synthesize auxin to stimulate a plant to grow in certain beneficial ways and to facilitate the exchange of nutrients (Vadassery et al., 2008). On the contrary, pathogenic fungi are thought to produce auxin to divert nutrients from the plant and manipulate host physiology (Nagarajan et al., 2023). Auxin can also alter the morphology of the fungi themselves, including the induction of hyphal and pseudohyphal growth forms, which can enhance the ability of fungi to penetrate and colonize plant and animal tissues (Kunkel and Johnson, 2021). Despite the recognized roles of why fungi are producing auxin, understanding of the mechanisms underlying fungal auxin biosynthesis remains incomplete. Current evidence suggests that fungi do not produce auxin through the same pathway as plants. Plants primarily produce auxin through the indolepyruvate (IPA) pathway, but use enzymes of the YUCCA family, which are unique to plants. This pathway in plants is typically referred to as the TAA/YUC pathway instead of IPA (Reviewed by Gao et al., 2024). In Saccharomyces cerevisiae, the model fungal organism used in this work, auxin biosynthesis can be reduced to three main tryptophan-dependent pathways, although crossover between the pathways may be possible. These are indole-3-pyruvate (IPA), tryptamine (TAM), and indole-3-acetamide (IAM) pathways (KEGG PATHWAY: Tryptophan Metabolism - Saccharomyces Cerevisiae (Budding Yeast), n.d.). Seven genes thought to be involved in auxin biosynthesis in S. cerevisiae have been identified. These are aldehyde dehydrogenases ALD2, ALD3, ALD5 and HFD1, aromatic aminotransferase ARO8, nitrilase NIT1, and amidase AMD2. To investigate the roles these known genes play in fungal auxin biosynthesis, they were mutated using the CRISPR/Cas9 genome editing system in combinations of three genes, with the goal of creating knockout mutants. These strains were created in a background containing a genetically encoded auxin biosensor, which measures auxin in correlation with the degradation of a fluorescent protein. All but one multi-gene mutant strain had lower levels of biosensor response compared to the wildtype biosensor strain. One triple gene mutant strain repeatedly failed to produce colonies. This transformation would have mutated ARO8, NIT1, and AMD2, targeting key steps in each of the three proposed auxin biosynthesis pathways. To explore this further, a smaller library of single and double gene knockout mutants of these three genes was created in the biosensor parent strain as well as a compatible mating type strain. This allowed the creation of heterozygous mutants via mating and measurements of auxin accumulation in these heterozygous diploid strains in comparison to the haploid knockout strains. To date, all attempted transformations and matings have produced colonies, a few of which have been tested for auxin accumulation, but a larger screening with all strains has not yet been completed. Two aro8Δnit1Δamd2Δ colonies produced with this method have contained each gene knockout confirmed with sequencing or gel electrophoresis and are now awaiting whole genome sequencing. This research project aims to inform fundamental questions surrounding fungal auxin biosynthesis, focusing on investigating the roles of known enzymes within these pathways. This initial analysis has helped uncover the contribution of each of these pathways and known genes to auxin biosynthesis and will allow future research possibly uncovering unknown genes and regulatory mechanisms within these pathways. Advancing our understanding of these processes could establish a foundation for future applications, such as the development of fungal biostimulants to enhance plant growth. Additionally, identifying key enzymatic steps for auxin biosynthesis may reveal novel targets for antifungals, particularly if these pathways are linked to fungal fitness or virulence.
The Effects of Feed Ingredients and Feed Additives on Energy Metabolism in Chickens
Alexander, Bethany Nicole (Virginia Tech, 2026-09-16)
Feed is the most expensive cost in rearing poultry. Within the poultry diet, energy is the most expensive component. Maximizing energy utilization can improve poultry growth and production while reducing waste output, ultimately improving both the economic and sustainable poultry production efforts. This thesis examines alternative feed ingredients/feed additives, including soybean soapstock and a natural choline alternative, on their ability to supply energy and alter energy use in chickens. Soybean soapstock was evaluated in chickens to determine the apparent metabolizable energy (AME). Soapstock is a by-product of the soy oil purification industry that can be added back to soybean meal at rates of 0-2%. Soapstock was added to a basal diet at rates of 0, 3, 6 and 9% and the slope of dietary energy vs. soapstock inclusion was used to calculate the AME of both soybean oil (8,159 kcal/kg) and soy soapstock (3,669 kcal/kg). In this experiment, the addition of soybean soapstock to soybean meal can increase the AME of soybean meal by 12 kcal per 1% of soybean soapstock. Soybean soapstock can contribute metabolizable energy when added to soybean meal but also results in a dilution of crude protein. Choline is a required vitamin that is used for fat metabolism among other diverse effects. Typically, the commercial inclusion of choline has been higher than the reported NRC requirement. Some recent publications have suggested that the requirement for choline might be higher than the NRC, but lower than commercial inclusion rates. A 12 wk laying hen experiment evaluated a commercial type corn/soybean meal/animal protein diet that was formulated to be lower but not deficient in choline, a choline chloride control and 2 concentrations of a natural choline alternative. The natural choline alternative at a lower inclusion rate improved FCR (P = 0.10) in comparison to both the NC and NC + CC with the higher inclusion of NCA had an intermediate response. The same lower inclusion of the natural choline alternative increased relative eggshell weight (P = 0.06) and eggshell thickness via other mechanisms. This thesis explores the use of alternative ingredients and feed additives, and their effect on energy digestibility, production and hepatic energy storage in chickens.
When Do Teens Choose Distraction? Assessing Environmental Influence on Secondary Task Initiation to Understand Elevated Teen Crash Risk
Reid, Grace Beatrice (Virginia Tech, 2026-09-16)
Secondary task engagement is disproportionately associated with crashes and near-crashes for novice drivers relative to any other age group, compounding an already substantial overrepresentation of young drivers in motor vehicle crashes, which remain the leading cause of death among adolescents in the United States. Why the association is so much stronger for this group is not fully understood. Prior research has documented how often teens engage in secondary tasks and which tasks are most dangerous, but has largely overlooked a contextual dimension: when teens choose to engage, and whether they adjust that engagement to the demands of the driving environment or not, thus contributing to their disproportionate crash risk. The present study examined whether teen (16-17), young adult (18-24), and adult (25-59) drivers differ in how they modulate general and visual-manual secondary task engagement across levels of environmental driving demand, defined by traffic density (level of service) and relation to junctions. Using naturalistic driving data from the Supervised Practice Driving Study and the Second Strategic Highway Research Program Naturalistic Driving Study, generalized linear mixed models were estimated separately for baseline driving and for safety-critical events (crashes and near-crashes). During baseline driving, teen self-regulation was largely intact: teens reduced engagement near junctions and increased engagement with higher traffic density just as adults did, differing only in that they engaged in the riskiest tasks at higher rates under low-demand conditions. During safety-critical events, however, this similarity disappeared. Near junctions, adults reduced engagement almost entirely while teens did not, with predicted general task engagement at high junction demand of 25.2% for teens compared to 0.7% for adults, and in dense traffic teen engagement rose sharply while adult engagement stayed flat, reaching 53.2% for visual-manual tasks compared to 5.7% for adults. These findings indicate that elevated teen crash risk is concentrated in high-demand, safety-critical moments because teen drivers fail to withhold engagement precisely when the driving environment becomes most lethal.
Development of an Energy Management Strategy for an All-Wheel Drive Electric Vehicle
Weng, Tony (Virginia Tech, 2026-09-15)
As part of the EcoCAR EV challenge, a energy management strategy was developed for a 2023 Cadillac Lyriq. To assist in testing the proposed strategy, a forward propagating model was developed in MATLAB and Simulink using virtual vehicle components. To validate the model, output data from simulation was compared with output data from the vehicle obtained on an all-wheel drive dynamometer under the same drive cycle. For this study, a power minimization equation with a torque split algorithm was used to find the optimal torque split across all torque-speed domains. This searches for the front and rear drive unit split ratios that minimizes power loss. Using driver demand and vehicle speed as inputs, a fuzzy logic controller was used to improve vehicle drivability by reducing jerk in high oscillating torque split regions. In depth simulations of UDDS, HWFET, and competition specified trapezoidal drive cycles were conducted to validate this method. The results show that the proposed strategy reduces energy consumption by 3.28% for UDDS, 1.46% for HWFET, and 2.41% for the trapezoidal drive cycles when compared to a baseline capability split.
Station to Station
Huskison, Kennan Zachary (Virginia Tech, 2026-09-10)
The study will be based around the Appalachian Interstate System and its relationship to people, primarily through the lens of trucks and truck parking. By understanding the site at a regional scale, a local scale, and a site scale, a simple architecture can be developed to impact Appalachia and the conditions studied. This information is used to develop a simple and versatile modular system that can be easily placed across Appalachia as an occupiable human infrastructure for these large vehicles. It connects a hollowed-out economic necessity with community and human need.
The Role Of Hemostatic Clots In Wound Healing: Effects On Endothelial Cell Response
Argueta, Douglas Arnoldo (Virginia Tech, 2026-09-10)
Effective wound healing is essential for restoring tissue integrity and function following vascular injury. This process involves tightly coordinated interactions between platelets, fibrin, and endothelial cells (ECs), yet the role of fibrin clot structure in regulating EC behavior during wound repair remains incompletely understood. Following endothelial injury, platelets adhesion and activation initiate primary hemostasis, while the coagulation cascade generates thrombin leading to formation of fibrin and the development of a stabilizing clot. Beyond its structural role, fibrin also serves as a dynamic matrix that can influence cellular behavior, while its degradation through fibrinolysis is critical for restoring tissue function. The objective of this work is to investigate how plasma clot composition and structure regulate endothelial cell dynamics during wound healing. An in vitro wound healing model incorporating plasma-derived fibrin clots was developed to examine the interplay between clot formation, fibrinolysis, and EC migration. Live-cell confocal imaging revealed that dense platelet-poor plasma (PPP) clots strongly inhibit wound closure by restricting endothelial cell migration. In contrast, modulation of fibrin architecture and the incorporation of platelets into platelet-rich plasma (PRP) clots enabled endothelial cell infiltration and supported wound resolution, albeit at reduced rates compared to clot-free conditions. Furthermore, fibrinolysis induced by tissue plasminogen activator (tPA) restored wound healing in previously inhibited PPP conditions. Quantitative analysis demonstrated significant morphological differences in endothelial cells under clot versus no-clot conditions, suggesting alterations in migratory and angiogenic behavior. Structural characterization of fibrin networks indicated that platelet-driven remodeling and variations in fiber organization may underlie the observed differences in wound healing outcomes. Overall, these findings demonstrate that fibrin clot architecture and platelet activity play a critical role in regulating endothelial cell behavior during wound healing. This work provides mechanistic insight into EC-fibrin interactions and highlights the importance of clot microstructure in determining whether the hemostatic clot functions as a barrier or a permissive scaffold for vascular repair.
Confining Stress-Dependencies of Shear Modulus Reduction and Damping Models for Sands
Shrestha, Prasanna (Virginia Tech, 2026-09-10)
Normalized shear modulus reduction (G/Gmax) models represent the nonlinear shear stress-shear strain response of soils. Modulus Reduction and Damping (MRD) curves are used to obtain degraded soil properties for equivalent-linear site-response analysis, calibrate nonlinear stress–strain models, and examine the influence of effective confining stress on stress response tied to liquefaction triggering. These curves are affected by mean effective confining stress. Published models represent this confining stress effects using different equations and parameters, which makes the direct comparison across models difficult. This thesis develops and evaluates a unified framework that separates the normalized shear modulus reduction into a strain-dependent function, K(γ), and a strain-dependent confining stress exponent, Δm(γ). Four published models, Ishibashi and Zhang (1993), Darendeli (2001), Wang and Stokoe (2022), and the GQ/H model of Groholski et al. (2016) were evaluated. A clean sand condition is used so that the differences in the results were mainly related to differences among the models. At each shear strain, K(γ) is defined from the original model response at 1 atm, while Δm(γ) is then fitted over the remaining confining stress levels in linear G/Gmax space. A continuous strain-dependent function of Δm(γ) is then developed to reconstruct each model within the generalized framework. The original and generalized models are compared through normalized modulus-reduction curves, derived shear stress backbones, theoretical Masing-damping curves and shear stress normalized by mean effective confining stress. The generalized forms reproduce the responses of the original models closely over the evaluated ranges. The fitted stress exponents are also related directly to the confining stress and curvature parameters of the original models, which help explain the differences among their stress-dependent response. The normalized shear stress ratios provide a basis for extending the framework to investigate the effect of confining stress on liquefaction triggering. The proposed framework provides a method for comparing different models and examining how their modulus reduction behavior affects the corresponding shear stress and theoretical damping responses. Future work should evaluate the framework using laboratory measurements from the same sand tested at several confining stresses.
Patterns of Understory Evergreen Shrubs in Two Wilderness Areas of George Washington-Jefferson National Forest using Remote Sensing and Terrain Analysis
Koirala, Pratirakshya (Virginia Tech, 2026-09-09)
This study evaluated the distribution and environmental controls of understory evergreen shrub communities within two federally designated wilderness areas of southwest Virginia at Mountain Lake and Peters Mountain, where human disturbances and access are limited. We focused primarily on the dominant understory evergreen shrubs Rhododendron maximum and Kalmia latifolia. Remotely sensed datasets, topographic variables, and ecological analyses were integrated to investigate relationships between shrub occurrence, topographic gradients, and canopy structure. Landsat imagery and VBMP orthoimages were combined with LiDAR-supported structural information to characterize understory vegetation patterns across heterogeneous forest environments. Spatial analyses demonstrated that evergreen shrub communities were preferentially associated with mesic topographic settings, riparian corridors, north-facing slopes, and areas exhibiting reduced solar exposure and greater moisture retention. The study identified limitations in conventional optical remote sensing approaches for detecting sub-canopy vegetation under dense overstory conditions and offered a new approach to overcome this challenge. The integration of terrain metrics and multi-source geospatial datasets improved shrub mapping accuracy and strengthened interpretation of understory spatial variability. Ecologically, dense shrub layers contribute to reduced tree seedling recruitment, altered nutrient cycling, and shifts in understory biodiversity. These findings emphasize the significance of monitoring understory vegetation for forest management and conservation frameworks in Appalachian ecosystems.
Design and Evaluation of a Memory-Centric Binary Hyperdimensional Computing Architecture with 1T3R Memristor-Based Nonvolatile Storage
Hou, Zeyuan (Virginia Tech, 2026-09-09)
Hyperdimensional computing (HDC) represents information with high-dimensional distributed hypervectors and performs classification using simple binary operations such as binding, bundling, and associative search. While these operations are hardware-friendly, the large hypervector dimension creates a substantial persistent-memory and data-movement burden because position/identifier (ID), level, and trained class hypervectors all scale linearly with dimension D. This thesis develops and evaluates a memory-centric Binary HDC architecture for the Modified National Institute of Standards and Technology (MNIST) handwritten-digit dataset. Persistent binary hypervectors are mapped to nonvolatile one-transistor/three-resistor (1T3R) memristor storage, while nearby complementary metal-oxide-semiconductor (CMOS) circuitry performs exclusive OR (XOR) binding, bundling, majority thresholding, Hamming-distance accumulation, and class selection. A controlled design-space study isolates the effects of input resolution, model adjustment, quantization, and hypervector dimension. The selected Balanced D = 5,000 configuration achieves 86.87% validation accuracy and 87.74% accuracy on the official MNIST test set after model freeze. The hardware mapping treats the three memristors in each 1T3R row as three independent HDC dimensions rather than one signed 3-bit coefficient. For the frozen full-resolution, M = 32 configuration, persistent storage scales as 826D logical bits, with the ID bank accounting for approximately 94.9% of capacity. Under a direct-access baseline without caching, one query requires 1,578D logical persistent-bit reads. Measured level usage also reveals an idealized within-query Level-hypervector reuse opportunity that could reduce persistent read traffic by approximately 47.95% with suitable local buffering. Circuit evidence is obtained from the existing SkyWater 130 nm 4 × 3 schematic proof of concept. Experiment 8 (EXP8) verifies all eight Row 1 three-bit states and all 24 static bit decisions, while selective SET of G2, G1, and G0 preserves all observed neighboring bit states. The high-resistance-state (HRS) and low-resistance-state (LRS) current populations remain clearly separated, with a worst-case LRS/HRS ratio of 140.94×. Robust voltage-domain sensing, multi-row disturb, independent RESET, process, voltage, and temperature (PVT) variation and Monte Carlo characterization, and complete-system area, energy, and latency evaluation remain outside the completed scope.
WIP: False-Data Injection Against Physics-Constrained Neural Networks for Trajectory Planning in Autonomous Racing
Yang, Chun-Tao (Virginia Tech, 2026-09-09)
Prediction of competitors' motion is critical for high-speed autonomous racing, yet its robustness under adversarial manipulation remains unclear. We analyze black-box false- data-injection (FDI) attacks that corrupt perceived motion histories, inducing ego mispre diction and enabling adversarial overtaking. Our results reveal that high-speed trajectory prediction systems remain vulnerable, exposing safety risks in multi-agent autonomous driving
Analyzing Surface Urban Heat Island Trends and Regional Variability Across 150 U.S. Cities (2000–2024) Using MODIS Data and a Reproducible Python Application
Gautam, Suyog (Virginia Tech, 2026-09-08)
Urban Heat Islands (UHIs), or Surface Urban Heat Islands (SUHIs), occur when urban surfaces register measurably higher temperatures than surrounding non-urban land, a consequence of replacing natural vegetation with impervious materials that absorb and re-emit solar radiation. As more than 80% of the U.S. population now lives in urban areas, the thermal consequences of continued urbanization carry growing implications for public health, energy demand, and climate adaptation planning. This study analyzes SUHI dynamics across 150 U.S. cities from 2000 to 2024 using MODIS land surface temperature data, an automated Google Earth Engine (GEE) data extraction application, and multi-year trend analysis. Three questions are addressed: (i) whether an open-source, GEE-based Python application can reliably automate consistent SUHI extraction for any U.S. city; (ii) how regional SUHI trends and temporal trajectories differ across five U.S. geographic regions and three city-size classes over the 25-year study period; and (iii) how the Diurnal Asymmetry Index (DAI) identifies whether cities have stronger daytime or nighttime SUHI and how these differences are related to regional hydroclimate conditions. Our results show that urban heat trends are not the same across the United States. Some regions experience much stronger SUHI changes than others. We also found that after around 2018, the strengthening of SUHI generally slowed in many cities, although the amount of slowdown varied by location. Another key result is that daytime and nighttime SUHI behavior separates into two distinct patterns, and those patterns align closely with climate zones defined by the Köppen–Geiger classification. Finally, the automated system successfully produced standardized and reproducible annual metrics for all 150 cities, showing that large-scale SUHI monitoring can be done in an open and accessible way.
Investigating Antimicrobial Peptides from the Epidermal Mucus of Blue Catfish (Ictalurus furcatus)
Nimitz, Rachel Alexandra (Virginia Tech, 2026-09-08)
Modeling of Electrically Controlled Propellant Combustion for Solid-fueled Propulsion
Miller-Smith, Shea Arthur (Virginia Tech, 2026-09-03)
Liquid-fueled rocket propulsion systems are widely used in aerospace and defense applications for their throttle control, shutdown, and restart capabilities. Traditional solid-fueled rocket propulsion systems lack such capabilities, but are comparatively mechanically simple, storable, and less expensive. Electrically controlled solid propellants (ECSPs) seek to address this limitation by applying an electric voltage to regulate ignition and combustion, enabling more complex trajectories and mission plans while retaining many of the advantages of solid propulsion systems. Previous ECSP experiments have demonstrated voltage-dependent ignition delay, control of regression rate, extinguishment, and localized melting and ignition behavior at the electrodes. However, the individual mechanisms involved remain difficult to identify and isolate in ECSP experiments due to the coupling of simultaneous electrical, chemical, and thermal contributions. The objective of the present research is to develop a computational model of ECSPs composed of lithium perchlorate, polyethylene oxide, and carbon-based conductive additives to investigate the mechanisms governing melting, ignition, and combustion. The primary mechanisms considered are ion transport, electronic conduction through additive networks, electrochemical reactions, phase change, heat transfer, and gas-phase combustion. A 3-D Doyle--Fuller--Newman-inspired finite-element model is developed to resolve the coupling among the condensed-phase mechanisms. Experimental results from prior studies are then used to evaluate the mechanistic submodels and the integrated multiphysics model. The primary model results include ignition delay, burn rate, voltage response, and temperature distribution. Sensitivity analyses on electrode kinetics and global ion-transport parameters demonstrate the importance of electrochemistry modeling fidelity, as well as of physical propellant characteristics such as transference number and energetic barriers to electrode reactions. The multiphysics FEM reproduced experimental ignition delay trends as a function of applied voltage, additive concentration, and electrode spacing, and predicted the localization of concentration gradients, current, and heat generation near the electrodes. Butler--Volmer and Marcus-based electrochemical kinetics models were compared, with the Marcus--Hush--Chidsey approach providing the best agreement with spatial experimental observations and capturing the experimentally observed cathode-melting/anode-ignition sequence. Differences between electrode kinetics models were prominent in spatial metrics, such as electrode temperature asymmetry, though differences in global ignition delay metrics were minor. In the model, physical parameters varied in their impact on spatial and global results, demonstrating the sensitivity to input parameters and contributing submodels. These findings also reveal that experimental agreement with global metrics such as ignition delay may be insufficient to validate the spatially dependent ECSP mechanisms.
Toxicity of zinc to Schistosoma mansoni cercariae in a chemically defined water medium together with a note on the cercariophagic activity of Macrostomum gigas (Turbellaria: rhabdocoela)
Mecham, John Alvin (Virginia Tech, 1970-11)
A synthetic water medium has been found to support Schistosoma mansoni cercariae for approximately 50 hours, which is the normal life expectancy of cercariae in natural waters. A partially enclosed slide chamber has proven to be effective in the study of schistosome cercariae where the close observation of a few individual cercariae in a large volume of water otherwise would be extremely difficult. In low concentrations zinc has little lethal effect on cercariae of Schistosoma mansoni during that period of time in which the cer-cariae are most likely to be infective. Compared to the control groups, concentrations of zinc as low as 0.001 ppm were found to be toxic to schistosome cercariae. Concentrations as high as 57.6 ppm zinc were found to immobilize all cercariae tested within six hours. A free living flatworm Macrostomum gigas was discovered to be a predator on Schistosoma mansoni cercariae.
A late Precambrian resurgent cauldron in the Carolina slate belt of North Carolina, U.S.A.
Newton, Maury Claiborne III (Virginia Tech, 1983-04)
A resurgent cauldron, in the older part of the Carolina slate belt, is part of the lower greenschist-grade Hyco Volcanic-plutonic Group, dated between 620 and 650 Ma. The present dimensions of the cauldron are approximately 45 x 14 km. The cauldron stratigraphy matches well Smith and Bailey's (1968) seven-stage resurgent cauldron mode!. During Stage (regional tumescence), the ring-fracture system developed, manifested by phreatic explosion breccias and a pre-Stage 11 porphyritic lava dome. The subaqueously-deposited terrane was arched to emergence prior to Stage 11 eruptions. Stage 11 (caldera-forming eruptions) is marked by subaerially-deposited dacitic to rhyolitic ignimbrites and air-fall tuffs. Strongly welded ash-flow tuffs have pumice-flattening ratios of 20-30: 1. Stage 111 (caldera collapse) took place while Stage 11 eruptions were continuing and is recorded by a vertical transition from subaerial pyroclastic facies to subaqueous pyroclastic and epiclastic facies. Stage IV (preresurgence volcanism and sedimentation) is marked by subaqueously-deposited low-Si dacitic to basaltic lavas and epiclastic rocks. Stage V ( resurgent doming), may be represented by a cupola of quartz gabbro to granodiorite that intruded the cauldron block and erupted to form amphibole crystal tuffs. Stage VI (major ring-fracture volcanism) was a second period of regional tumescence culminating in a second cycle of subaerially-deposited dacitic to rhyolitic ignimbrites. Concomitant with rhyolite extrusion, granodiorite to granite intruded the cauldron block. Stage VI I (terminal solfataric activity) resulted in the protoliths of economic pyrophyllite and andalusite deposits. Stage I I-Stage IV volcanics may represent an inverted compositionally-layered magma chamber with a silicic top and a base of high-alumina basalt to low-Si andesite. Volcanics of the entire cauldron sequence are bimodal with basalt to low-Si andesite and dacite to rhyolite dominant over high-Si andesite. The bimodality suggests there is an apparent compositional gap in the range of high-Si andesite.
Real-time three-dimensional evaluation of the feline heart; feasibility and reference intervals
Chavez, Lezith Desiree (Virginia Tech, 2026-09-01)
Current feline echocardiographic practice relies on linear, geometry-dependent two-dimensional and Motion mode (M-mode) measures rather than direct volumetric quantification. Full-volume real-time three-dimensional echocardiography (RT3DE) can produce ventricular volumes without the geometric assumptions of two-dimensional methods, but its feasibility and normal values have not been established for the feline left ventricle. This prospective, cross-sectional study evaluated the feasibility of full-volume RT3DE and generated reference intervals for left ventricular end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) in 120 healthy cats. Diagnostic-quality datasets were obtained in all cats, the majority without sedation, and three-dimensional acquisition time was significantly shorter than that of the conventional two-dimensional examination. EDV was weakly associated with body weight and is reported as weight-specific allometric limits, whereas ESV and EF were independent of body weight. Interobserver reproducibility was strongest for ejection fraction and weakest for end-systolic volume. These findings demonstrate that full-volume RT3DE is feasible in healthy cats and provide preliminary normative values that may support volumetric assessment of feline cardiac disease. The reference intervals were derived from clinically healthy cats and require validation before application to cats with suspected cardiomyopathy.
Understanding First-Year College Students' Use of AI Chatbots for Mental Health
Ranjber, Samira (Virginia Tech, 2026-08-31)
Artificial intelligence (AI) mental health chatbots are becoming increasingly popular among college students seeking emotional support. Apps such as Woebot, Wysa, Repika, and Youper offer users immediate, low-cost, and private access to mental health tools through online conversation (Fitzpatrick, et al., 2017; Inkster, et al., 2018). First-year college students may turn to these chatbots as they adjust to academic pressure, social changes, and the emotional stress of transitioning to college life. As AI use grows, there is limited research on why first-year college students use these tools and how they emotionally experience communication with them (Beiter, et al., 2015).
Using the SECI Model to Map Knowledge Systems of Urban Farmers in Virginia
McCausland, William Alexander (Virginia Tech, 2026-08-28)
Urban farmers face significant issues with representation and the ability to access information from institutions designed to provide support for agricultural producers. This study examines the knowledge systems of urban farmers in Virginia through the Socialization, Externalization, Combination, and Internalization (SECI) model of knowledge creation to better understand how agricultural knowledge is acquired, exchanged, and applied in Virginia. A qualitative research design was employed using semi-structured interviews with self-identified urban farmers across Virginia who produced for either financial production or public good. Participants were recruited through statewide urban agriculture networks, conferences, and snowball sampling. Interview transcripts were analyzed using in vivo and descriptive coding, followed by thematic analysis aligned with the SECI framework. Four themes emerged from the analysis: (1) agricultural identity through experience; (2) navigating diversified knowledge systems; (3) knowledge transmission and experiential learning through reciprocal relationships; and (4) envisioning decentralized, community driven futures. The findings demonstrate that urban agricultural knowledge systems are dynamic, relationship-based networks that depend on the interaction of formal organizations, peer communities, and experiential learning rather than a single centralized source of expertise. This research is significant because it serves to strengthen collaboration among knowledge institutions, while also supporting existing community networks for urban farmers. This study also addresses research gaps that relate to educational access, knowledge dissemination, awareness of urban agriculture nationally and in Virginia, and the connection between knowledge systems and urban agriculture.

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