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 40 freely available and openly licensed textbooks are among our most downloaded items.
Recent Submissions
A Fast Transient Response Distributed Power Supply With Dynamic Output Switching for Power Side-Channel Attack Mitigation
(IEEE, 2024-09)
We present a distributed power supply and explore its load transient response and power side-channel security improvements. Typically, countermeasures against power side-channel attacks (PSCAs) are based on specialized dc/dc converters, resulting in large power and area overheads and they are difficult to scale. Moreover, due to limited output voltage range and load regulation, it is not feasible to directly distribute these converters in multicore applications. Targeting those issues, our proposed converter is designed to provide multiple fast-responding voltages and use shared circuits to mitigate PSCAs. The proposed three-output dc/dc converter can deliver 0.33-0.92 V with up to 1 A to each load. Comparing with state-of-the-art power management works, our converter has 2× load step response speed and 4× reference voltage tracking speed. Furthermore, the converter requires 9× less inductance and 3× less output capacitance. In terms of PSCA mitigation, this converter reduces the correlation between input power trace and encryption load current by 107×, which is 3× better than the best standalone work, and it only induces 1.7% area overhead and 2.5% power overhead. The proposed work also increases minimum traces to disclose (MTDs) by 1250×. Considering all the above, our work could be a great candidate to be employed in future multicore systems supplying varying voltages and resisting side-channel attacks. It is the first work bridging the gap between on-chip power management and side-channel security.
Got Followership? Rethinking Leadership from the Other Side
(2025-03-04)
90-second presentation for Virginia Tech's 2025 Faculty Nutshell Talks.
Comparing National Institute of Health Funding for Cancer Survivorship: A Spotlight on Breast and Gynecologic Cancers
(Lippincott, Williams & Wilkins, 2025)
Objective: To evaluate the distribution of National Institutes of Health (NIH) funding for breast and gynecologic cancer survivorship research in relation to survivor populations.
Methods: A retrospective cohort study was conducted on NIH-funded grants for breast and gynecologic cancer survivorship from fiscal years (FY) 2017–2021 using an existing dataset from the NIH Office of Cancer Survivorship. Grant characteristics, including funding amount, study design, and research focus, were extracted from NIH Reporter and ClinicalTrials.gov. Total funding and per-survivor funding were calculated using prevalence data from the Surveillance, Epidemiology, and End Results (SEER) program. Descriptive statistics were applied to compare funding disparities between breast and gynecologic cancer survivorship research.
Results: Among 160 NIH-funded grants on cancer survivorship, 144 (90%) focused on breast cancer, and 16 (10%) on gynecologic cancers. Breast cancer survivorship research received significantly more funding ($188.35 million) compared to gynecologic cancer survivorship research ($15.41 million). Per-survivor funding was also higher for breast cancer ($9.69 per survivor) than for gynecologic cancers ($2.15 per survivor). Most survivorship studies were interventional (60%), with randomized controlled trials as the predominant design. The primary study focus was on late and long-term effects of cancer treatment (53%), followed by health promotion (21%) and care delivery (16%).
Conclusion: NIH funding for gynecologic cancer survivorship research is significantly lower than that for breast cancer, even when accounting for survivor prevalence. The findings highlight the need for equitable resource allocation to ensure comprehensive survivorship support for gynecologic cancer survivors. Increased funding and research efforts are necessary to address the unique challenges faced by this population and to optimize long-term outcomes.
Transforming Architectural Practice Through Computational Design and Machine Learning: A Decision-Support Framework for Energy and Daylight Optimization
(Virginia Tech, 2025-04-16)
Architectural design requires balancing aesthetic goals, functional needs, and environmental performance, often involving complex trade-offs. This research integrates machine learning and computational design to optimize building enclosure design, focusing on energy efficiency and daylight performance in Jordan's climatic conditions. Among the tested models, Artificial Neural Networks (ANN) proved the most effective, excelling in identifying critical design features and uncovering hidden influences among variables such as material properties and glazing systems.
The findings demonstrate that machine learning can support architects in exploring design possibilities and understanding trade-offs, while ensuring they retain the final decision-making authority. By highlighting interactions that conventional methods might overlook, this approach allows architects to tailor materials and structures dynamically, optimizing performance without compromising design goals. Though cost analysis was not directly included, the framework sets the stage for its integration in future studies, enabling even more comprehensive decision-making.
The results emphasize that design solutions should be adaptive, allowing different walls or façades to have unique material and structural configurations. This flexibility helps architects achieve efficient, context-specific designs that align with sustainability goals. By leveraging machine learning, this research bridges the gap between creative design and performance-driven optimization, offering a practical framework for innovative architectural practices.