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Bioaerosol Dispersal Across Scales: Regional Patterns, Field Study, and Model Evaluation
No full textBioaerosols--including seeds, pollen, fungal spores, bacteria, and viruses--are fundamental agents connecting atmospheric processes to agriculture, ecosystem function, and human and animal health. This dissertation uses Lagrangian stochastic (LS) models to simulate how these particles travel and deposit across scales relevant for cross-pollination, with applications to many types of biological aerosols. First, we map seasonal and regional patterns of windborne hemp pollen across the United States by running LS models with weather data to simulate day- and night-time dispersal from summer through fall. These simulations identify areas more susceptible to cross-pollination and show how patterns shift across seasons and between day and night. We find regions more vulnerable to cross-pollination, with seasonal and diurnal shifting patterns in dispersal. Next, we work to detect and model genetically modified switchgrass pollen released from a small field in low-wind conditions during three sampling campaigns with a suite of novel samplers. We find that only our highest-volume samplers were able to detect pollen and that reducing the averaging window in the simulations substantially improved emission-rate estimates. Finally, we evaluate the 3D LS models used in this dissertation by comparing them to a high-fidelity model driven by large-eddy simulation (LES) in seven daytime convective boundary layer conditions. The LS models show good accuracy in strongly convective conditions, but they fail in near-neutral conditions due to issues in how they are parameterized rather than in their underlying equations. Together, these results clarify when LS models can effectively substitute for more computationally intensive LES, reveal how sampler design and averaging choices shape what can be extracted from field measurements, and demonstrate the value of weather-aware modeling for cross-pollination risk assessment and broader questions of bioaerosol transport. Collectively, this work strengthens the scientific foundation needed to predict, manage, and mitigate the movement of biological aerosols in an increasingly variable atmosphere.Doctor of PhilosophyBioaerosols--including seeds, pollen, fungal spores, bacteria, and viruses--are fundamental agents connecting atmospheric processes to agriculture, ecosystem function, and human and animal health. This dissertation uses Lagrangian stochastic (LS) models to simulate how these particles travel and deposit across scales relevant for cross-pollination, with applications to many types of biological aerosols. First, we map seasonal and regional patterns of windborne hemp pollen across the United States by running LS models with large-scale weather data to simulate day- and night-time dispersal from summer through fall. These simulations identify areas more susceptible to cross-pollination and show how patterns shift across seasons and between day and night. Next, we work to detect and model genetically modified switchgrass pollen released from a small field in low-wind conditions during three sampling campaigns. We find that only the highest-volume samplers captured pollen, and that using shorter averaging windows in the simulations greatly improved emission-rate estimates. Finally, we evaluate the 3D LS models used in this dissertation by comparing them to a high-fidelity model driven by large-eddy simulations (LES) across seven daytime atmospheric conditions. The LS models show moderate accuracy in strongly convective conditions, but they fail in near-neutral conditions due to issues in how they are parameterized rather than in their underlying equations. Together, these results clarify when LS models can effectively substitute for more computationally intensive LES, reveal how sampler design and averaging choices shape what can be extracted from field measurements, and demonstrate the value of weather-aware modeling for cross-pollination risk assessment and broader questions of bioaerosol transport. Collectively, this work strengthens the scientific foundation needed to predict, manage, and mitigate the movement of biological aerosols in an increasingly variable atmosphere
Navigation
No full textThis paper presents an analysis and experimental demonstration of single-satellite single-pass geolocation of a terrestrial broadcast global navigation satellite system (GNSS) spoofer from low Earth orbit (LEO). The proliferation of LEObased GNSS receivers offers the prospect of unprecedented spectrum awareness, enabling persistent GNSS interference detection and geolocation. Accurate LEO-based single-receiver emitter geolocation is possible when a range-rate time history can be extracted for the emitter. This paper presents a technique crafted specifically for indiscriminate broadcast-type GNSS spoofing signals. Furthermore, it explores how unmodeled oscillator instability and worst-case spoofer-introduced signal variations degrade the geolocation estimate. The proposed geolocation technique is validated by a controlled experiment, in partnership with Spire Global, in which a LEO-based receiver captures broadcast GNSS spoofing signals transmitted from a known ground station on a non-GNSS frequency band.Accepted versio
PHYSOR 2026 - The International Conference on Physics of Reactors
No full textThe Virginia Research and Education Reactor (VA-RER) is a next-generation research reactor concept engineered to meet the urgent national need for modern, flexible, and AI-enabled modeling, design, operation and monitoring for diagnostics/prognostics. Built on the patented Test and Education Microreactor (TEM) architecture, the design integrates a central irradiation cavity, a neutron-spectrum-tailoring buffer zone, and a circular TRIGA-fuel lattice to support advanced applications in materials testing, isotope production, reactor physics research, and workforce development. The VA-RER is envisioned as a dual-reactor system, a zero-power unit dedicated to training and hands-on education, paired with a 5 to 10 MWth reactor enabling high-flux irradiation experiments and validation of physics-based AI-driven digital-twin and autonomous monitoring technologies.
This paper presents preliminary neutronics analyses using the OpenMC Monte Carlo code system with ENDF/B-VIII.0 nuclear data to evaluate the reactivity behavior of reactor configurations with irradiation cavity radii ranging from 6.25 cm to 100 cm. Five select core configurations are analyzed in this paper. The resulting eigenvalues range from 1.01581 to 1.06814, with reactivity gains diminishing for larger annular radii due to increased neutron leakage. Notably, an optimal moderator-to-fuel ratio emerges near a 25 cm radius, where the eigenvalue increases even with fewer total fuel rods. These findings provide early design guidance for maximizing irradiation volume while maintaining favorable neutronic performance, supporting ongoing development of a transformative research reactor for next-generation nuclear science and engineering.Accepted versionYes, full paper (Peer reviewed?
Training Drivers on L2 Automated Systems: A Pilot Study for Developing Effective Training that Drivers Will Use
No full textPartially automated systems, also known as Level 2 (L2) automated systems or advanced driver assistance systems (ADAS), are becoming increasingly common in the U.S. vehicle fleet, and ubiquitous on new vehicles. The Highway Loss Data Institute estimates that more than 28% of registered vehicles in the United States in 2023 were equipped with automatic emergency braking technology and that more than 90% of model year 2023 new vehicle series included automatic emergency braking as a standard or optional feature. Considering the increasing proliferation of vehicles equipped with L2 partial driving automation, it is important to ensure that drivers sufficiently understand the systems to support safe and appropriate use.
Evidence suggests that proper understanding of L2 automated system leads to safer interactions with the systems , and that formal instruction produces greater understanding than trial and error alone. Additionally, engaging adults in learning about a new technology may require distinct design considerations well as motivational frameworks. Given drivers’ clear affinity for trial and error and the importance of relevance in the motivation model, it seems plausible that drivers might be more likely to engage with formal training if it is offered inside the vehicle itself, so that they could access training at the place and time when they seek to learn/understand/use the vehicle systems. However, practically, it remains unknown whether drivers would engage in training to gain understanding of these new systems, even if it were readily available in the vehicle.National Surface Transportation Safety Center for Excellenc
Evaluating the role of flow and specific conductivity on stream metabolism across a mining-induced salinity gradient in the Appalachian Coalfield
No full textFreshwater salinization is a notable concern for headwater streams in Appalachia with the history of resource extraction in the region. Mountaintop removal/valley fill mining techniques in Appalachia result in the burial of headwater streams and mobilization of ions, specifically SO42-, Ca, and Mg into waters, raising specific conductivity (SpC). Organismal responses to salinization have been well documented in freshwater ecosystems, but there are few measurements assessing how salinity effects on organisms influence whole-ecosystem processes, specifically stream metabolism. Understanding how gross primary production (GPP) and ecosystem respiration (ER) respond to salinity, stream flow, and their association is needed to characterize the consequences of salinization on stream processes. To assess the role of salinization and discharge on metabolism, we recorded high-frequency SpC, discharge, and dissolved oxygen data in three headwater streams in the Appalachian (U.S.) coalfields, where mining has resulted in widespread headwater stream salinization. Sites included a reference stream (REF) with SpC ranging from 0.2 - 57.69 μS/cm, a mid-salinity site (MID; 2.03- 594.7 μS/cm), and a high-salinity site (HI; 84.6-1920 μS/cm) with similar flow regimes, helping to characterize these covarying and potentially interacting drivers of metabolism. Across all sites, SpC decreased with increased discharge and with significant breakpoints. This study did not find a clear relationship between SpC and ecosystem metabolism. All streams, regardless of SpC levels, were heterotrophic (|ER|>GPP). Our high salinity site had the highest GPP and ER, suggesting salt may subsidize ecosystem metabolism, though differences in canopy cover complicate this. While we saw no consistent effect of SpC on metabolism, deviation in patterns at mining-impacted sites from expected discharge-metabolism patterns at our reference sites suggests some impact of salinity. This study reinforces the need for research across diverse ecosystems and salinization sources to characterize the role of freshwater salinization on ecosystem metabolism, as mediated by discharge.Master of ScienceSurface coal mining operations in the Appalachian Coalfield region have resulted in decreased water quality and increased salt levels (measured as specific conductance; SpC) in small, headwater streams. Declines in aquatic biodiversity have been documented with increases in SpC, but little work has been done to address impacts on broader stream processes such as ecosystem metabolism, which represents organic carbon fixation and breakdown by gross primary production (GPP) and ecosystem respiration (ER), respectively. High stream flows are a known driver of both ecosystem metabolism and SpC concentrations in headwater streams; however, minimal work has been conducted to connect the roles of these interacting drivers (flow and SpC) on metabolism. This study measured metabolism across three headwater streams in the Appalachian Coalfield of varying salinity and flow regimes from August 2024 to August 2025. We found no relationship between ecosystem metabolism and SpC across our sites. Increases in GPP and ER at our high SpC site suggest a subsidy effect, although small sample sizes and the potential for increased light availability complicate this interpretation. While we found no consistent effect of SpC on metabolism across our sites, variation in flow-metabolism relationships from reference conditions at salinized sites suggests salts exhibit a potential effect on ecosystem processes. We encourage continued work on connecting ecosystem metabolism and freshwater salinization across flow regimes in headwater streams
Office for Civil Rights Compliance and Prevention Education 2024-2025 Annual Report
No full textThis annual report illustrates the work of the CRCPE office and documents trends from the 2024-2025 academic year
Practical Pathways to Efficient MRAM: Spin-Orbit Torques, Low-Damping, and Anomalous Hall Conductivity in Polycrystalline Materials
No full textThere are multiple pathways forward to next generation magnetic random access memory. In this thesis we explore two simple solutions with industry implementation in mind. The first is a low-damping (α< 5×10⁻³) ferromagnetic single-layer with modest anti-damping spin-orbit torque (SOT), $θ_{DL} ≈ 0.05. Here, we investigate an alternative approach to the traditional heavy metal/ferromagnet bilayer to produce SOTs, which suffers from high-damping that is detrimental to energy-efficiency. Instead, of breaking inversion symmetry at the interface we continually break symmetry along the thickness axis by creating an intentional compositional gradient that is purely ferromagnetic and maintains low damping. Crucially, we find that a compositional gradient is not necessary to achieve large damping-like SOTs, instead finding direct evidence from grazing-incidence x-ray diffraction for a strain gradient.
The next pathway investigated is an easy-to-grow, polycrystalline alternative to non-collinear antiferromagnets which require high temperature growth (>400°C). We find that sputter-grown γ-FeMn with no post-annealing, has a small non-zero net magnetization (≈(0.02-0.07)μB/atom) and perpendicular magnetic anisotropy only slightly larger than those found in non-collinear antiferromagnets like Mn₃Sn while still exhibiting a large anomalous Hall conductivity of 14 S/cm at room temperature. We show that these unique magnetic and transport properties are the result of pinning at the grain boundaries which can be tuned to enhance the anomalous Hall conductivity.Doctor of PhilosophyThe spintronics community is searching for ways to make denser, faster, more power efficient and better enduring magnetic memory that can keep up with modern needs. One hurdle that this thesis addresses is how to minimize dissipation lost by magnetic friction while maintaining fast writing capabilities. The next is the issue of denser bits for magnetic memory, traditional ferromagnets have a stray field, like a horseshoe magnet in a sea of lead, which affects nearby components. This limits how tightly we can pack the bits that store our "1"s and "0"s in magnetic memory. An alternative path forward that is gaining interest is using antiferromagnets in magnetic memory as they have reduced stray fields. However, one of the issues is that because they do not have a large magnetic moment like in ferromagnets it can be hard to read the "1" and "0" states. This thesis offers a practical step forward with an inexpensive, easy-to-grow antiferromagnet that has the potential for large readout capabilities
BMC Plant Biology
No full textBackground: Soil microbiomes are important for plant growth and health. The objectives of this study were to characterize boxwood root-zone microbial community and understand their associations with plant disease resistance and other horticultural traits. Soil samples were collected from four cultivars with three distinct boxwood blight tolerance at two geographically distant nursery locations in May, August, and November of 2021. Bacterial and fungal communities were characterized through DNA metabarcoding.
Results: The dominant bacteria in the boxwood root-zone soil included Bacillus and several unknown genera of the order Gaiellales and families Xanthobacteraceae and Gemmatimonadaceae; the dominant fungi included Clonostachys, an unknown genus, Solicoccozyma, and Fusarium. Ceratobasidium, Hyaloscypha, and Sistotrema were also the dominant genera within the presumptive mycorrhizal fungi (PMF) group. Fungal community structure was distinct among cultivars with different blight tolerance in May and August, but the divergence of the bacterial community structure was only significant in the August samples. Community composition-wise, greater numbers of genera differed in abundance between the intermediate and the susceptible cultivars. Moreover, cross-kingdom network analysis showed a more connected network constructed from the intermediate cultivars and identified more hub taxa as module connectors compared with the other two cultivars. Some of the hub taxa, including bacterial genera Gaiella, Streptomyces, and Sphingomonas, and fungal genera Solicoccozyma and Pseudonectria were also among the 27 bacterial and 6 fungal core genera identified from all samples across four cultivars, two locations, and three seasons. Further, Volutella and Pseudonectria were negatively associated with 10 bacterial genera and all identified PMF-PMF connections were positive across all networks.
Conclusions: Boxwood root-zone soil harbored diverse plant-beneficial microbes, including PMFs. Fungal community and microbial network connectivity also differed among the cultivars, suggesting the regulatory roles of plant phenotype and genotype in fungi recruitment and microbial interactions. Several keystone taxa were identified and may be crucial in maintaining the structure and communication within the boxwood root-zone microbiome. The negative associations between bacteria and Volutella/Pseudonectria provide a new insight into managing the rise of the boxwood Volutella blight. Together, this study offers several leads to enhancing plant resilience to disease and environmental stress.Published versio
Applied Ergonomics
No full textPredicting external hand load from sensor data is essential for ergonomic exposure assessments, as obtaining this information typically requires direct observation or supplementary data. While machine learning can estimate hand load from posture or force data, we found systematic bias tied to biological sex, with predictive disparities worsening in imbalanced training datasets. To address this, we developed a fair predictive model using a Variational Autoencoder with feature disentanglement, which separates sex-agnostic from sex-specific motion features. This enables predictions based only on sex-agnostic patterns. Our proposed algorithm outperformed conventional machine learning models, including k-Nearest Neighbors, Support Vector Machine, and Random Forest, achieving a mean absolute error of 3.42 and improving fairness metrics like statistical parity and positive and negative residual differences, even when trained on imbalanced sex datasets. These results underscore the importance of fairness-aware algorithms in avoiding health and safety disadvantages for specific worker groups in the workplace.Accepted versio
Applied Physics Letters
No full textExcited ferromagnets can pump spin angular momentum, along with possibly orbital angular momentum. Among elemental ferromagnets, Ni has been proposed to exhibit substantial orbital pumping relative to spin pumping. Here, we search for a signature of orbital pumping by Ni, specifically by comparing out-of-plane ferromagnetic resonance in heterostructures without Ni (FeV/Nb) and with Ni (FeV-Ni/Nb). The FeV/Nb series shows a clear increase in Gilbert damping with the Nb sink thickness, attributed to spin pumping from FeV to Nb. Surprisingly, the FeV-Ni/Nb series exhibits no such damping increase, revealing no significant spin or orbital pumping from Ni to Nb. Our results offer a fresh perspective on angular-momentum transfer in Ni/Nb heterostructures.Published versio