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

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

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

Open Textbooks

More than 50 freely available and openly licensed textbooks are among our most downloaded items.


Recent Submissions

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Student Affairs Annual Report 2024-2025
(Virginia Tech, 2025)
This is the Student Affairs annual report for 2024-2025.
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2023-2024 Student Activity Fee Student Organization Funding Report
(Virginia Tech, 2025)
This report contains a breakdown of student activity fees for 2023-2024, along with a list of student organizations supported by the fees.
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APIDA + Center Annual Report 2023-2024
(Virginia Tech, 2024)
This APIDA + Center Annual Reports serve to inform community members and the public about the Center's efforts to meet its mission to advocate for the APIDA communities both on campus and around the country and to educate the larger campus community on issues related to these communities. Learn about transformative programming highlighting the achievements of APIDA faculty, staff, and students on campus. Discover how visiting artists shared with diverse audiences the creative process. The APIDA + Center values its collaborators, campus partners, and community members in its continued efforts to realize its mission and goals.
Have accounting information systems significantly helped in detecting fraudulent activities in accounting?
Boylan, Daniel H.; Hull, Jaylen E. (North American Business Press, 2022-06-20)
Fraudulent activity corrupted public companies in several ways for years. Accounting information systems have been recently implemented in public companies to help with fraud in financial statements. Accounting information systems have been reported to detect fraud, but the question is whether the systems can prevent fraud. Relevant literature is researched concerning this topic. Comparison and implementation of the software systems were the basis for this research. Public companies, shareholders, and auditors are all at the benefit of this research as money, time, and uncertainness can all be saved. It was concluded that information systems implemented in accounting detect fraudulent activity.
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Methodology for contamination detection and reduction in fermentation processes using machine learning
Nguyen, Xuan Dung James; Liu, Y. A.; McDowell, Christopher C.; Dooley, Luke (Springer, 2025-09-01)
This paper demonstrates an accurate and efficient methodology for fermentation contamination detection and reduction using two machine learning (ML) methods, including one-class support vector machine and autoencoders. We also optimize as many hyperparameters as possible prior to the training of the ML models to improve the model accuracy and efficiency, and choose a Python platform called Optuna, to enable the parallel execution of hyperparameter optimization (HPO). We recommend using Bayesian optimization with hyperband algorithm to carry out HPO. Results show that we can predict contaminated fermentation batches with recall up to 1.0 without sacrificing the precision and specificity of non-contaminated batches, which read up to 0.96 and 0.99, respectively. One-class support vector machine outperforms autoencoders in terms of precision and specificity even though they both achieve an outstanding recall of 1.0. These models demonstrate high accuracy in detecting contamination without requiring labeled contaminated data and are suitable for integration into real-time fermentation monitoring systems with minimal latency and retraining needs. In addition, we benchmark our ML methods against a traditional threshold-based contamination detection approach (mean ± 3 σ rule) to quantify the added value of using data-driven models. Finally, we identify important independent variables contributing to the contaminated batches and give recommendations on how to regulate them to reduce the likelihood of contamination.