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
Anatomical and Species-Specific Variation in Acoustic Attenuation of Skin for Focused Ultrasound Applications
Edwards, Samuel Ryland (Virginia Tech, 2026-06-24)
Therapeutic focused ultrasound relies on precise delivery of acoustic energy to a focal zone for noninvasive tumor ablation and cavitation-based tissue destruction. To reach therapeutic targets, ultrasound must propagate through overlying tissues, including skin, where reflection at interfaces and frequency-dependent attenuation reduce the pressure amplitude delivered to the focus. This variability in acoustic transmission can alter focal pressure thresholds and contribute to inconsistent therapeutic outcomes. Despite increasing use of large animal models in translational focused ultrasound research, there remains limited understanding of how acoustic properties of skin vary across anatomical locations and species. The objective of this study was to quantify regional and interspecies differences in acoustic attenuation and impedance of skin. We hypothesized that distal limb regions, which exhibit greater structural density and thickness, would demonstrate higher attenuation than truncal regions within species, and that attenuation profiles would differ between pigs and horses. Full-thickness skin samples were collected from the ventral abdomen, thorax, bilateral lateral femoral regions, and distal limb (dorsal metacarpus) of pigs and horses immediately post-mortem. Subcutaneous fat was removed, and sample mass and geometry were measured prior to acoustic characterization. Measurements were performed at frequencies ranging from 1.25 to 3.25 MHz in 0.5 MHz increments. Time-of-flight measurements relative to a water-only control were used to calculate speed of sound, and tissue density was determined from mass and geometry to calculate acoustic impedance. Frequency-domain analysis was used to identify dominant spectral peaks, and attenuation (dB/cm) was calculated with correction for transmission coefficients at the water–tissue interface.
Attenuation increased significantly with frequency (p < 0.0001), with an approximate slope of ~2 dB/cm per MHz across all samples. Equine skin exhibited significantly greater attenuation than porcine skin (4.04 ± 0.18 vs. 1.98 ± 0.18 dB/cm, p < 0.0001), corresponding to approximately a twofold increase across all conditions. Within species, attenuation varied significantly by anatomical location (p < 0.0001), with distal limb and proximal limb regions demonstrating the highest attenuation and truncal regions the lowest. A significant species × location interaction (p < 0.0001) indicated that regional attenuation patterns differed between species. Although median total acoustic loss through individual samples remained below 1 dB, higher-loss samples, particularly in equine limb regions, reduced transmitted pressure to as low as ~57%, demonstrating the potential for clinically meaningful prefocal energy loss. In contrast, porcine samples maintained pressure transmission above ~80% across all conditions.
These findings demonstrate that both anatomical location and species significantly influence acoustic transmission through skin, with attenuation driven not only by thickness and density but also by intrinsic structural properties of the tissue. These results have important implications for focused ultrasound treatment planning, suggesting that species- and location-specific compensation strategies may be required to ensure consistent energy delivery and reliable therapeutic outcomes.
Scientific Machine Learning for Engineering Systems: Optimal Control, Prognostics, and Uncertainty Quantification
Barry-Straume, Jostein Alf (Virginia Tech, 2026-06-24)
This dissertation investigates how scientific machine learning can support engineering systems across two complementary tasks: optimal control and uncertainty-aware prognostics. In the control setting, the objective is to compute actions that steer governed dynamics toward desired outcomes while respecting physical laws and optimality conditions. In the prognostic setting, the objective is to forecast degradation-related quantities, estimate remaining useful life, and communicate predictive uncertainty in a form that supports maintenance decisions. The central argument of the dissertation is that machine learning becomes more useful for engineering when it is organized around the structure of the decision problem itself.
The dissertation is composed of four studies. The first study introduces Control Physics-Informed Neural Networks for open-loop PDE-constrained optimization. The method embeds forward dynam- ics, adjoint equations, and first-order optimality conditions into a one-stage learning framework that jointly predicts state and control. The second study extends the control line of work to closed-loop, infinite-horizon feedback control by learning value functions from the Hamilton-Jacobi-Bellman equation and recovering policies from value gradients, with ensemble strategies used to improve robustness. The third study benchmarks five major uncertainty-quantification methodologies for turbine gas temperature prediction and evaluates them using coverage and interval-width metrics.
The fourth study develops a multi-task scientific machine learning framework for engine health management that jointly predicts turbine gas temperature, delta turbine gas temperature, remaining useful life, and survival-related quantities while reporting uncertainty intervals on real fleet data.
Taken together, these works show that scientific machine learning can bridge active intervention and passive monitoring across the engineering lifecycle. Across all four studies, domain knowledge is used not only to interpret model outputs, but to shape the learning objective, the representation, and the notion of reliability itself. The resulting perspective connects optimal control, prognostics, and uncertainty quantification within one coherent framework for engineering decision support.
Invasive Aphids, Reservoir Hosts, and Endophyte-Mediated Plant-Vector Interactions in Barley Yellow Dwarf Virus Pathosystems: Revisiting Disease Management
Parizad, Shirin (Virginia Tech, 2026-06-24)
The cereal aphid-borne barley yellow dwarf virus (BYDV) is the most economically significant viral disease of small grains. Its spread is influenced by complex interactions among plants, virus, aphids, and different biotic and abiotic environmental variables. It is important to integrate these factors with economic thresholds and decision-support tools to develop more effective integrated pest management (IPM) strategies. This dissertation summarizes the roles of a newly invasive cereal aphid, endophyte interactions, and plant reservoirs in BYDV transmission and revisits current management practices across U.S. production systems with an emphasis on effectiveness and profitability. Chapter 1 provides an overview of BYDV biology, epidemiology, and management, highlighting key knowledge gaps related to aphid vectors, reservoir hosts, and environmental drivers of disease risk. Chapter 2 demonstrates the invasive aphid Metopolophium festucae cerealium Stroyan (Hemiptera: Aphididae), is expanding in the western U.S. as a superclone following a likely single introduction. This aphid is unable to transmit BYDV under experimental conditions, and its main impact may be due to direct feeding damage. Chapter 3 explores the role of endophytic tall fescue cultivars as potential reservoirs for BYDV and its aphid vectors. Under experimental conditions, the non-toxic novel endophyte cultivar BarOptima reduces aphid performance compared to wheat and common toxic cultivar KY-31, but it does not affect BYDV transmission. Thus, switching to novel cultivars in grazing lands may limit BYDV spread mainly by reducing aphid populations. Chapter 4 combines agronomic field trials and landscape surveys to assess the roles of management practices and environmental variables in BYDV risk. The results showed that planting date is the main factor affecting BYDV risk. The virus level, and the success and economic benefits of management practices are location specific. Insecticide treatments are economically beneficial only in high aphid pressure situations. Landscape analyses indicated that BYDV incidence in Virginia may be influenced more strongly by regional movement of viruliferous aphids than by nearby vegetation or local reservoir hosts. In Chapter 5, I discuss how my findings help a transition to an adaptive, knowledge-based IPM that considers ecological complexity, environmental variability, and economic thresholds to improve sustainability of small-grain production systems.
Embodied Hydrodynamic Reservoir Computing for Underwater Obstacle Localization
Wichiramala, Ken Kanate (Virginia Tech, 2026-06-24)
In underwater environments, fluid–structure interactions can provide detailed information about the surroundings. However, exploiting these hydrodynamic responses as a contactless sensing mechanism for obstacle localization remains challenging. Existing approaches often require complicated sensors or data processing methods to operate reliably in underwater environments. This study presents the design and performance evaluation of three embodied soft panels, inspired by the physiology and sensory setup of a fish's body. We demonstrate that embodied intelligence can be achieved by using a framework called physical reservoir computing (PRC), which utilizes the physical body dynamics as a computational resource.
Herein, the 3D-printed panels with embedded sensing networks were designed for the pur pose of extracting their body-state information for embodied computation. The nonlinear autoregressive moving average (NARMA) task was used to evaluate their computational per formance. We show that under appropriate physical reservoir dynamics, the panels exhibited greater capability to emulate nonlinear dynamical systems and function as physical reservoir computers. Furthermore, we also show that the panels had enough computational power to estimate the position of a nearby obstacle based on variations in their body dynamics. Our results suggest that the panels can serve as potential frameworks for intelligent swimmers capable of perceiving their environments through the self-sensing mechanism and estimating relevant information.
A Simulation-Based Framework for Analyzing and Optimizing Organ Allocation Policies
Shin, Hyunwoo (Virginia Tech, 2026-06-24)
Organ allocation policies must balance medical urgency, expected benefit, fairness, logistical constraints, and organ utilization under severe supply scarcity. Because candidate conditions evolve over time and donor organs arrive unpredictably, allocation policy cannot be evaluated adequately as a static ranking problem. This dissertation develops a simulation-based framework for organ allocation policy analysis, with a primary application to the U.S. lung allocation system. The framework connects short-horizon prediction of dynamic candidate priority, empirically validated high-fidelity simulation, and evidence-based multi-objective simulation optimization. First, the dissertation studies short-horizon forecasting of dynamic lung allocation priority using U.S. lung allocation data from the Lung Allocation Score (LAS) era, prior to Continuous Distribution, as a stable empirical setting. Recurrent sequence models are compared with discrete-time Markov chain benchmarks over 7-, 14-, and 28-day horizons. The results show that near-term LAS movement can be forecast from longitudinal candidate information, that recurrent models outperform Markov chain benchmarks, and that component-wise forecasting of waiting-list urgency and post-transplant survival yields modest but consistent gains at longer horizons. Interpretable classification models further identify candidates at elevated risk of near-term priority increase. Second, the dissertation develops the Modular Lung Allocation Policy Simulation Model (MoLAPS), a discrete-event simulation framework for evaluating U.S. lung allocation policies. MoLAPS represents candidate arrivals, donor arrivals, dynamic patient-status updates, sequential organ offers, offer acceptance, non-transplant removals, and post-transplant outcomes. Calibration and validation using 2017--2019 Organ Procurement and Transplantation Network (OPTN) data show close alignment between simulated and empirical outcomes across aggregate, distributional, subgroup, and temporal checks. A modular C/C++ implementation with parallel computing substantially reduces runtime for multi-replication experiments relative to a prior lung-allocation simulator in the settings evaluated. Third, the dissertation develops an evidence-based multi-objective simulation-optimization framework for policy search under simulation uncertainty. Policy adoption is formulated as a baseline-relative decision problem. Efficiency is evaluated using replication-level log Bayes factors, while fairness is evaluated using Kullback--Leibler divergence in subgroup composition. The proposed trust-region framework, ASTRO-DF-MOO, supports finite-budget exploration of efficiency and fairness trade-offs. Benchmark experiments and a lung allocation case study illustrate how the approach traces evidence-supported efficiency--fairness trade-offs relative to the historical LAS-based policy. Within a one-dimensional LAS-type score family that varies the relative weight assigned to waiting-list urgency versus post-transplant survival, the approach identifies an evidence-supported menu of incumbent-adjacent policies rather than a single dominant replacement. These contributions define a connected methodological pipeline from candidate-level priority dynamics to validated counterfactual policy evaluation and then to evidence-based policy search under uncertainty.
