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
Select a community to browse its collections.
Recent Submissions
Reinforcement Learning-Based Fuzzer for 5G RRC Security Evaluation
Parikh, Dhairya; Dessources, Dimitri A.; Tripathi, Nishith D.; Reed, Jeffrey H.; Burger, Eric W. (IEEE, 2026-03-09)
Open Radio Access Network (O-RAN) and modern Fifth Generation Mobile Networks (5G) Standalone (SA) deployments increase protocol complexity and broaden the attack surface of cellular infrastructure. This paper introduces a reinforcement-learning-based fuzz tester designed to evaluate the Radio Resource Control (RRC) layer in 5G SA networks. The fuzzer operates as a software-defined “false” User Equipment (UE) that attaches to the target network, intercepts and mutates uplink RRC messages, and injects malformed test cases targeting RRC handlers. The system integrates Reinforcement Learning (RL)-driven test-case generation with an automated execution pipeline for message injection and packet-capture analysis, allowing the agent to iteratively learn which mutations most effectively trigger anomalous behavior. Reinforcement feedback is computed from system metrics such as Central Processing Unit (CPU) utilization, thread count, and network Input/Output (I/O) to guide learning toward high-impact inputs. Experimental results demonstrate that the proposed fuzzer uncovers previously unseen protocol-handling anomalies, malformed-message behaviors, and resource-exhaustion conditions, including reproducible RRC/NGAP inconsistencies identified through a deterministic Proof-of-Concept (PoC) evaluation. The paper presents the overall architecture, reinforcement learning formulation, and evaluation results, highlighting how feedback-driven adaptive fuzzing can prioritize high-impact mutations for stateful 5G RRC security assessment.
A previously unrecognized class of fungal ice-nucleating proteins with bacterial ancestry
Eufemio, Rosemary J.; Rojas, Mariah; Shaw, Kaden; de Almeida Ribeiro, Ingrid; Guo, Hao-Bo; Renzer, Galit; Belay, Kassaye; Liu, Haijie; Suseendran, Parkesh; Wang, Xiaofeng; Fröhlich-Nowoisky, Janine; Pöschl, Ulrich; Bonn, Mischa; Berry, Rajiv J.; Molinero, Valeria; Vinatzer, Boris A.; Meister, Konrad (American Association for the Advancement of Science, 2026-03-13)
Ice-nucleating proteins (INpros) catalyze ice formation at high subzero temperatures, with major biological and environmental implications. While bacterial INpros have been structurally characterized, their counterparts in other organisms have remained largely unknown. Here, we identify membrane-independent proteins in fungi of theMortierellaceae family that promote ice formation with high efficiency. These proteins are predicted to adopt β-solenoid folds and multimerize to form extended ice-binding surfaces, exhibiting mechanistic parallels with bacterial INpros. Structural modeling, phylogenetic analysis, and heterologous gene expression leading to ice nucleation in Escherichia coli and Saccharomyces cerevisiae show that the fungal INpros are encoded by orthologs of the bacterial InaZ gene, which was likely acquired by a fungal ancestor through horizontal gene transfer. The discovery of cell-free fungal INpros provides tools for innovative freezing applications and reveals biophysical constraints on ice nucleation across life.
Connecting the Opens: Open Education Week 2026
Lener, Edward F.; Walters, Tyler; Walz, Anita R.; Lord, James K.; Grohs, Jacob R.; Pullen, Brandie; Surprenant, Aimée (Virginia Tech, 2026-03-02)
The 2026 Connecting the Opens panel discussion features brief overviews of open educational resources, open access, and open data to provide some general information on the topics. Finally, presenters will discuss how aspects of open have impacted their career development as well as how they have incorporated open practices into their research and scholarship, and the audience will be invited to participate in the conversation.
High-Density, High-Efficiency Resonant Converters for Power Delivery to Modern Datacenters
Prakash, Pranav Raj (Virginia Tech, 2026-03-11)
Global demand for computational power—driven by large-scale artificial intelligence, machine learning, and high-performance cloud workloads—is accelerating at an unprecedented pace. As GPUs continue to grow in power, size, and current demand, datacenter power-delivery architectures have evolved from traditional 12 V distribution to 48 V systems and are now transitioning toward high-voltage 800 V and ±400 V rack buses. Although these architectural shifts reduce copper mass, mitigate busbar losses, and improve system scalability, they also impose new requirements on the isolated and non-isolated DC–DC converters embedded throughout the power-delivery chain. Despite differing roles—from tightly regulated front-end converters to unregulated high-ratio intermediate bus converters (IBCs)—all stages share the same overarching mandate: maximize efficiency and power density under tightening electrical, thermal, and spatial constraints.
To meet these demands, this dissertation investigates advanced methodologies for high-performance LLC resonant converters across multiple power-delivery stages. A unifying principle throughout the work is the deployment of PCB-integrated magnetics, which offer excellent manufacturability, repeatability, thermal performance, and ultra-low profile compared to conventional wound components. However, PCB transformers introduce stringent challenges in synchronous-rectifier (SR) termination, particularly when distributed output capacitors interact through parasitic inductances to create parallel resonances. These resonances can substantially increase transformer AC resistance at multi-hundred-kilohertz switching frequencies. Through analytical modeling, finite-element simulation, and hardware validation, this work characterizes these mechanisms and proposes optimized termination structures that suppress resonant peaking and lower conduction losses—recovering up to 3.5% full-load efficiency in 1 kW and 2 kW LLC prototypes.
Building on this foundation, the dissertation next addresses the emerging high-voltage DC-distribution architectures for AI datacenters. A 6 kW, 800 V/50 V stacked LLC converter is developed using 650 V GaN devices in a three-level topology, enabling operation from both 0–800 V and ±400 V buses. A custom four-leg PCB-integrated magnetic structure merges two EI-core transformers into a compact, symmetric, and thermally optimized assembly. A top-cooled SR termination network is introduced to accommodate high output currents within severe footprint constraints. Operating near 600 kHz, the prototype achieves 98.7% peak efficiency and a record power density of 2070 W/in3—demonstrating the feasibility of compact GaN-based conversion for next-generation megawatt-class server racks.
To support extreme-current GPU loads exceeding 1000 A, the dissertation also develops a high-density intermediate bus converter for emerging vertical power-delivery (VPD) architectures. A modular transformer unit-cell structure is introduced that maximizes magnetic utilization while enabling flexible series/parallel scalability. This concept is validated through an 840 W, 48 V/1.8 V LLC-DCX module achieving 2200 W/in3 and 95.5% peak efficiency, providing a viable pathway for shrinking regulator footprint, reducing PDN losses, and enabling back-side power delivery in future high-TDP GPUs.
Finally, to reduce bulk-capacitor requirements in multi-kilowatt front-end AC–DC PSUs, the effective gain range of the LLC converter must be increased. To this end, a selective synchronous-rectifier (SR) phase-shift control technique is developed that extends the achievable gain without compromising soft-switching behavior. By phase-shifting only a subset of SR bridges in matrix-transformer structures, the required frequency variation is significantly reduced compared to prior-art methods, thereby minimizing circulating energy, lowering SR switching losses, and reducing the likelihood of ZVS loss in the primary switches. Experimental validation on a 3 kW, 400 V/50 V LLC converter—achieving 98.7% peak efficiency and 1300 W/in3 power density—confirms that the technique enables up to a 36% reduction in hold-up capacitance while maintaining stable and efficient operation.
The approach is further extended to center-tapped rectifiers, where coordinated SR control eliminates reverse-conduction issues even when operating at the resonant frequency. Collectively, this dissertation advances the state of the art in high-density, high-efficiency LLC resonant conversion for modern datacenter power architectures. The contributions span device-level loss mechanisms, PCB-integrated magnetic design, high-voltage GaN converter topologies, extreme-current vertical power delivery, and advanced gain-extension control strategies. In addition, the work summarizes practical design guidelines for SR termination techniques for both center-tapped and full-bridge rectifiers across varying current levels, cooling configurations, and layout constraints. Together, these developments chart a clear path toward compact, thermally efficient, and scalable DC–DC converter platforms capable of meeting the escalating power demands of the AI era.
Distribution and Characterization of Herbicide-Resistant Italian ryegrass and Palmer amaranth in Virginia
Viric, Milos (Virginia Tech, 2026-03-11)
Weed infestation is the major reason for economic losses in agriculture. Italian ryegrass and Palmer amaranth are some of the most troublesome weed species in Virginia. These species are strong competitors with crops for growth resources which eventually leads to significant yield losses in absence of adequate control. One of the challenges for the control of these species is development of herbicide-resistant populations.
There is a limited knowledge about the distribution of resistant populations of Italian ryegrass and Palmer amaranth in Virginia. Palmer amaranth resistance to glyphosate was confirmed in 2011 and Italian ryegrass resistance to diclofop was confirmed in 1993. These are the only two confirmed cases of herbicide resistance in Virginia but based on control failure reports, resistance to these species is suspected to be more widespread in Virginia. To investigate the distribution and levels of resistance in populations from Virginia there is a necessity for more updated surveys.
A total of 32 populations of Italian ryegrass were collected. Plants were grown in the greenhouse to test for sensitivity to herbicides commonly used for burndown or in-crop control of Italian ryegrass: pinoxaden, diclofop, glyphosate, mesosulfuron, pyroxsulam, and pyroxasulfone. At 21 days after the herbicide treatments, visible injury ratings were recorded on a scale 0 to 100%, where 0 indicates no control and 100 represents complete plant necrosis. Populations exhibiting ≤49% control were suspected to be resistant. Based on this criteria, 10, 27, 0, 14, 0, and 7 populations were found to be resistant to pinoxaden, diclofop, glyphosate, mesosulfuron, pyroxasulfone, and pyroxsulam, respectively. Following the initial screening, dose-response assays with pinoxaden, diclofop, mesosulfuron and pyroxsulam were conducted. Resistance indices (R/S ratios), calculated based on GR50 (herbicide dose that reduced biomass by 50%) values for resistant and susceptible populations, were 20 for pinoxaden, 87 for mesosulfuron, and 161 for pyroxsulam. The R/S value for diclofop could not be determined because even the highest tested dose could not achieve 50% growth reduction in the resistant population. Cross and multiple resistance was observed in this study and 6% of populations were found resistant to pinoxaden, diclofop-methyl, mesosulfuron, and pyroxsulam.
A total of 68 Palmer amaranth populations were collected from corn, soybean and cotton fields across Virginia. Palmer amaranth seedlings grown in the greenhouse were treated with: trifloxysulfuron, 2,4-D, fomesafen, atrazine, mesotrione, glyphosate, glufosinate and dicamba. Visible control ratings were recorded on a 0 to 100% scale, where populations with up to 49% injury were considered resistant. Upon testing the populations, resistance was found in 46, 1, 3, 7, 3, 50, 0 and 0 populations to trifloxysulfuron, 2,4-D, fomesafen, atrazine, mesotrione, glyphosate, glufosinate and dicamba, respectively. Dose-response assay for glyphosate revealed that GR50 value for resistant population was 1,238 g ae ha-1, however R/S value could not be calculated as susceptible population was not available. The R/S values for trifloxysulfuron, fomesafen and atrazine were 47, 14 and 18, respectively. Approximately 69% of the populations showed multiple resistance to two or more herbicide sites of action. Overall, findings from these statewide surveys provide critical insights into the current herbicide resistance status for both Italian ryegrass and Palmer amaranth in Virginia. This information will help growers better understand the effectiveness of commonly used herbicides and make more informed management decisions.