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
Drivers of rabies virus spillover risk from vampire bats to livestock in Colombia
Van de Vuurst, Paige; Rist, Cassidy; Medina-Rodriguez, Tatiana; Osejo-Varona, Andres Felipe; Soler-Tovar, Diego; Escobar, Luis E. (Public Library of Science, 2025-09-26)
Background: Rabies is an acute and progressive viral zoonotic disease of the nervous system, which widely affects domestic animals in Latin America. Vampire bat-borne rabies virus (RABV) has significant negative impacts on the livestock industry via animal mortality. Nevertheless, the landscape level factors that facilitate or limit RABV transmission from vampire bats to livestock remain elusive.
Methods: To determine how abiotic and biotic factors modulate RABV spillover from vampire bats to livestock, we assessed the role of different landscape variables on the occur-rence of RABV spillover from Desmodus rotundus to livestock in Colombia. Using ecological niche modeling as the theoretical and analytical framework, we analyzed ecological and epidemiological RABV data to reconstruct spillover transmission events.
Results: Anthropogenic variables including livestock and human density were consistently selected as predictors of RABV spillover from vampire bats to livestock. Cattle density had the highest average relative contribution to final ecological niche models (64.7%). We also found improvement of RABV spillover risk estimates when sampling bias in the form of cattle density was used in the modeling process. High risk for RABV spillover (0.75-0.98) was consistently predicted in the Caribbean region of Colombia. Nevertheless, more widespread moderate RABV spillover risk was predicted more broadly across the country when sampling bias was accounted for.
Conclusion: Our modelling effort revealed that variable selection and use of bias surface have tractable impacts on final projections of spillover risk. Our results also indicate that human activity drives RABV spillover risk to a greater extent than ecological or climatological factors. Results from this study provide important information about landscape conditions linked to RABV transmission risk, where livestock vaccination should be prioritized.
MALDI-TOF MS for malaria vector surveillance: A cost-comparison analysis using a decision-tree approach
Karisa, Jonathan; Rist, Cassidy; Tuwei, Mercy; Ominde, Kelly; Bartilol, Brian; Ondieki, Zedekiah; Musani, Haron; Wanjiku, Caroline; Mwangangi, Joseph; Mbogo, Charles; Rono, Martin; Bejon, Philip; Maia, Marta (Public Library of Science, 2025-10-31)
Background: The use of MALDI-TOF MS for mosquito identification and surveillance is routinely used in developed countries as an affordable alternative to molecular methods. However, in low- and middle-income countries (LMIC) where mosquito-borne diseases carry the greatest burden, the method is not commonly employed. Using the Kenyan national malaria program (NMCP) as a case study, we compared the costs of current methods used for malaria vector surveillance to those that would be incurred if MALDI-TOF MS were used instead.
Methods: A deterministic decision tree analytic model was developed to systematically calculate the costs associated with materials and labour, and time-to-results for two workflows, i.e., current molecular methods versus MALDI-TOF MS. The analysis assumed an annual sample size of 15,000 mosquitoes (representing the average number of mosquitoes analysed annually by the Kenyan NMCP) processed at a local laboratory in Kenya.
Findings: We estimate that if the Kenyan national entomological surveillance program shifted sample processing completely to MALDI-TOF MS, it would result in 74.48% net time saving, up to 84% on material costs and 77% on labour costs, resulting in an overall direct cost savings of 83%.
Interpretation: Adoption of MALDI-TOF MS for malaria vector surveillance can result in substantial time and cost savings. The ease of performance, the rapid turn-around time, and the modest cost per sample may bring a paradigm shift in routine entomological surveillance in Africa.
Rigid and soft back-support exoskeletons affect biomechanical and perceptual demands, but in different ways, during simulated shingle installation
Choi, Jiwon; Kim, Sunwook; Usmani, Ahmad Raza; Barr, Alan; Harris-Adamson, Carisa; Nussbaum, Maury A. (Elsevier, 2026-02)
Passive back-support exoskeletons (BSEs) are promising but underexplored interventions to reduce the high physical demands of roofing shingling. Eighteen participants performed simulations of shingle installation tasks under 12 different conditions. These conditions included all combinations of three BSE levels (Rigid, Soft, and no BSE), two task orientations (peak-facing vs. side-facing), and two roof slopes (18° vs. 26°). Using the rigid BSE significantly reduced lumbar muscle activation (11–17%) compared to no BSE, without altering trunk flexion. In contrast, the soft BSE reduced trunk flexion (∼4%) without altering lumbar muscle activation. Both BSEs reduced perceived low back exertion (∼16%); however, the rigid BSE increased leg discomfort (∼26%), and the soft BSE increased shoulder exertion (∼19%). Our results suggest that using BSEs can be beneficial for shingle installation tasks but also highlight the importance of considering device-specific biomechanical benefits and associated trade-offs to ensure effective application.
Enhancing Digital Libraries as Communication Tools: LLMs for Automated Subject Classification of Electronic Theses and Dissertations
Klair, Hajra (ACM, 2025-10-24)
Digital libraries are vital communication platforms that facilitate discoverability, collaboration, and strategic engagement among academics, administrators, funding agencies, and policymakers. Central to their effectiveness is accurate subject classification of Electronic Theses and Dissertations (ETDs), which enables clear information sharing and supports scholarly communication. However, author-supplied categories are frequently inconsistent or incorrect, often requiring manual review and complicating search and reporting. This study examines how Large Language Models (LLMs) can automate ETD subject classification, comparing prompt-based and fine-tuned approaches using over 9,200 records from Virginia Tech. Both methods are evaluated against established machine learning baselines, such as Support Vector Machines and multinomial Naive Bayes. Results indicate LLMs perform competitivSely in applied fields, but show systematic biases in more abstract or interdisciplinary categories—highlighting both their promise and the need for thoughtful communication system design in digital repositories.
Evaluating Human-LLM Alignment in ETD Subject Classification
Klair, Hajra; German, Fausto; Banerjee, Bipasha; Ingram, William A. (Springer, 2025-09-27)
Author-assigned subject labels in Electronic Theses and Dissertations (ETDs) are often inconsistent, overly broad, or misaligned with the research focus. This hampers discovery, aggregation, and analysis, especially for interdisciplinary research. LLMs offer a scalable alternative for automated classification, but their labeling rationale is opaque and introduces systematic biases. This study compares subject labels generated by LLMs with human-assigned labels for over 9,000 ETDs across 21 academic categories to assess the disagreement. We evaluate multiple prompt-based and fine-tuned LLM configurations and analyze areas of agreement and disagreement to identify patterns of misclassification. LLMs achieve competitive performance overall but frequently misclassify theoretical or interdisciplinary texts, often due to overweighting lexical cues and disregarding context. We show such errors are not random but reflect structured semantic divergences from human interpretation. These findings suggest a need for hybrid frameworks that combine LLM scalability with human contextual judgment to improve subject labeling in academic repositories.