VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 50 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
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Recent Submissions
Experimental Evaluation of Radar-Based Velocity Estimation for Navigation in GNSS-Denied Environments
Mongold, Garrett Andrew (Virginia Tech, 2026-08-27)
Resilient navigation in GNSS-denied environments is critical for safe autonomous ground vehicle operation. LiDAR, radar, cameras, and magnetometers fused with an IMU can provide a navigation solution that limits the inertial drift to maintain safety. Among these sensing modalities, radar is a particularly promising technology as it performs well in poor weather conditions and directly provides relative velocity measurements. This thesis develops a multi-sensor data collection platform to enable radar-inertial algorithm development. Datasets are collected in various environments and include raw LiDAR, radar, and IMU data along with corresponding ground truth. Using these datasets, this work evaluates radar-based vehicle velocity estimation for use as a measurement update within an Error-State Kalman Filter (ESKF). An offline lever arm and mounting angle calibration, posed as a multi-variable optimization problem, is introduced to better characterize radar-to-IMU lever arm and mounting angle parameters, improving measurement consistency. Furthermore, a radar-based vehicle yaw-rate estimation method is proposed and validated against ground truth. The results demonstrate the potential of radar measurements to aid in navigation through GNSS-denied conditions.
Navigating Complexity in Agricultural Transitions through Living Laboratory: Knowledge Co-production, Practice Transformation, and Precision Agriculture Adoption
Joshi, Bhavna (Virginia Tech, 2026-08-27)
This dissertation examines how precision agriculture knowledge is produced, how it shapes farmer perceptions and practices, and how it informs adoption decisions among the crop advisors through a Complex Adaptive Systems framework. This study employs both secondary data obtained from an NSF-funded living laboratory project situated in South Dakota, Virginia, and Vermont, as well as primary data.
The first chapter examined knowledge-production within living laboratories from the research team's perspective. It found that knowledge was produced through negotiation rather than integration, and that structural misalignment between the living lab's transdisciplinary aspirations and the institutional architecture of federally funded research constrained genuine co-production, while still producing real epistemic transformations among team members. The second study examined what participation produced for farmers, adapting Geenhuizen's evaluation framework to account for farmers' inputs and exogenous influences. It found modest, unevenly distributed ripple effects shaped more by farmers' pre-existing orientations than by project design. A federal funding disruption triggered differential engagement among full-time commercial farmers, who were dependent on the funds and subsidies for their livelihood. The third study examined AI adoption among crop advisors using an adapted C-TAM-TPB framework. Perceived usefulness was the strongest predictor of adoption. Data concerns showed a dual role, positively predicting adoption and reflecting anticipatory engagement rather than resistance.
Exploring essential aspects of chemotaxis and motility in rhizosphere-dwelling bacteria
Collett, Katelyn Idella Elizabeth (Virginia Tech, 2026-08-26)
The agricultural industry faces a constant struggle between generating profit from crop yield and the cost of production in terms of monetary loss and environmental impact. A potential solution comes from bacteria that are capable of forming a symbiotic relationship with plants to deliver nitrogen, thus alleviating the need for synthetic fertilizers that are used in excess. In addition, there are bacterial pathogens that cause disease in crops leading to reduction in yield, exacerbating the financial strain on farmers. Both groups of bacteria are capable of identifying potential hosts and biasing their movement towards them through chemotaxis and flagellar-driven motility. Key aspects for different portions of this process were examined in three agriculturally-relevant bacteria: Bradyrhizobium diazoefficiens (symbiont), Sinorhizobium meliloti (symbiont), and Agrobacterium tumefaciens (pathogen). Chapter I encompasses a literatures review of the knowledge for the chemotactic pathway and flagellar structure in the target bacteria. Additionally, it highlights the gaps in knowledge that this study set to characterize. In Chapter II we detail the investigation of the ligand binding capabilities of B. diazoefficiens chemoreceptors. A combination of bioinformatics analysis with in vitro high-throughput screening was utilized to identify chemoreceptors with periplasmic binding motifs and predict amino acid binding capabilities for the receptor Blr2932. In Chapter III we investigate the sensitivity adaptation system of S. meliloti chemoreceptors via methylation. CheR was demonstrated to crosslink to the cytosolic domain of McpX for in vitro experimental basis. We were able to demonstrate that single mutations of putative methylation sites could reduce the chemotactic ability, indicating that S. meliloti chemoreceptors likely utilizes the same residues as E. coli to adapt their signal sensitivity. Chapter IV encompasses the assembly of the A. tumefaciens strain 5A genome and characterization of its novel flagellar structure. The genome was sequenced and assembled into four complete contigs, finalizing the previously sequenced draft genome. This allowed for the characterization of individual flagellin function in motility and flagellar structure. The agricultural relevance of all three bacteria creates a growing need to characterize aspects of their functions related to plant interactions. The more knowledge we gain, the more opportunities we have to exploit them to improve formation of symbiotic relationships or to target them for reduction of disease spread.
From Margin Calls to Micro-Expressions: Leverage, Liquidity, and Physiological Signatures of Experimental Bubbles
Spoon, Ross Linwood (Virginia Tech, 2026-08-26)
This dissertation develops new experimental infrastructure to study instability in speculative asset markets, focusing on short squeezes and bubble-crash cycles alongside the behavioral and physiological states that accompany them.
The first chapter examines short squeezes: rapid and extreme price increases caused when speculative traders who borrowed an asset are forced into the market in order to acquire it again. This phenomenon is largely absent from experimental asset-pricing research despite its real-world prominence. By permitting leveraged short positions and enforcing margin requirements, the experimental design generates conditions under which squeezes can poten- tially emerge. We develop a tiered classification of squeeze events that includes voluntarily buying the asset at a loss and being forced to purchase the asset to maintain margin re- quirements; in the most extreme cases, cascading buy-ins drove prices to nearly 50 times fundamental value. We conclude that short-selling restrictions and excess liquidity amplify bubble formation, which creates an environment in which these explosive squeezes are more likely to happen.
The second chapter turns to the psychological and physiological underpinnings of bubble formation and collapse in constant fundamental value markets. Using Support Vector Re- gression alongside linear models, we find that conventional financial signals, such as lagged returns and order book imbalances, outperform biometric measures in predicting contempo- raneous market returns. At the individual level, however, electrodermal activity meaningfully predicts de-risking behavior and revealed risk aversion, with high earners exhibiting antici- patory arousal before crashes and low earners showing only reactionary spikes.
The third chapter introduces oPAL (open-source Periodic Auction Laboratory), a turnkey, extensible asset market platform built on the oTree architecture. oPAL fills a critical gap left by existing tools' near-exclusive focus on continuous double auctions, offering discrete call markets that isolate strategic bidding from execution-speed noise. Additionally, oPAL addresses practical frictions of online synchronous recruitment through an integrated pre- screening module and administrative portal. Key features enabling the two experiments presented in this dissertation include short selling, margin-based leverage, automated liquidations, flexible fundamental value trajectories, and a motion-artifact-minimizing entry interface for biometric recording.
Together, these chapters contribute new open-source infrastructure for experimental finance and new evidence on how leverage, liquidity, and emotional state jointly shape the formation and resolution of price instability in speculative markets.
Application-Aware I/O Prediction and Optimization for HPC and AI Workloads
Yazdani, Ahmad Hossein (Virginia Tech, 2026-08-26)
High-performance computing (HPC) systems are essential for executing large-scale scien- tific and artificial intelligence workloads that demand extensive parallelism across compute, storage, and memory resources. Modern systems, such as Aurora at Argonne National Lab- oratory and the exascale Frontier system at Oak Ridge National Laboratory, have significantly expanded computational and I/O capabilities. However, storage and memory subsystems often struggle to keep pace with the scale, diversity, and dynamic behavior of modern applications. This gap creates performance bottlenecks that are difficult to diag- nose and mitigate using rigid, application-agnostic I/O management strategies. Addressing these challenges requires a deeper understanding of how application semantics, execution configurations, and workload behavior interact with storage and memory systems. This thesis addresses these challenges across three complementary directions. First, we model application I/O behavior using historical execution patterns and user-specified parameters.
Through analysis of E3SM and LAMMPS workloads, we show that prior application con- figurations are strongly correlated with future I/O behavior, enabling accurate prediction over 1–10-day windows with uncertainty below 0.25 and 80–90% prediction accuracy. Sec- ond, we introduce IO-Sense, an agentic I/O reasoning pipeline that combines Darshan logs with application-level configuration semantics to summarize large-scale traces and explain how configuration knobs influence I/O patterns. On AMReX-based HPC applications, IO- Sense achieves over 90% log similarity with the ground-truth log derived from the knob change for most evaluated models and demonstrates that finer-grained I/O counters can improve reasoning accuracy, though at the cost of higher inference overhead. Third, we study I/O and memory behavior in deep learning training workloads through SHADE and SODA-LLM. SHADE introduces a rank-based importance sampling policy that improves data locality and cache efficiency for large-scale computer vision training workloads. Build- ing on this direction, SODA-LLM targets transformer-based language model training/fine- tuning by exploiting the spatial and temporal stability of activation importance to guide token-level activation retention, offloading, and prefetching during the backward pass. The proposed system reduces end-to-end training latency by up to 20% while lowering memory usage by 5%. Together, these projects show that application-aware reasoning can improve prediction, diagnosis, and optimization across HPC and AI workloads. By connecting appli- cation configurations and execution semantics to observed I/O and memory behavior, this thesis provides a foundation for more adaptive storage and memory management strategies in next-generation computing systems.