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.
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Recent Submissions
Constituents of Human Scent as Perceived by Wilderness Search and Rescue Area Dogs
Walker, Mary (2026-03-27)
It has long been believed that human scent detected by search dogs results from volatile organic compounds (VOCs) generated by bacteria that metabolize skin secretions and shedded skin cells, termed skin rafts. However, recent laboratory research has shown that human breath VOCs constitute a large proportion of the human volatilome and are an important component of how dogs perceive human scent. We investigated the perception of skin (cutaneous) and breath samples as constituents of human scent by certified search and rescue area dogs. Dogs (n=6) searched tunnels in a three-alternative, forced-choice test. In qualification trials, one tunnel was connected to a tent with a human volunteer while two remained empty. After reaching 80% accuracy with whole human scent, dogs were given four probe trials whereby an air flow system pumped into one tunnel (1) breath, (2) air from exposed skin (cutaneous), (3) both (breath + cutaneous), or (4) none (negative control). We scored the dogs’ latency to respond and whether a dog made a trained final response, change of behavior, or no response. All dogs responded to the breath + cutaneous and breath conditions with a trained final response or change of behavior. Breath only and breath + cutaneous led to similar response latency and probability to respond (p ≥ 0.5). In contrast, both the latency to respond and the probability of responding to the cutaneous only condition was significantly longer (p = 0.001) and significantly less (p = 0.03), respectively, than to breath + cutaneous. These data demonstrate that scent from human breath is an important and salient component of how wilderness SAR area dogs perceive human scent and suggest that the “skin raft” model of human scent be reevaluated.
In-Field Diadegma insulare (Cresson) (Hymenoptera: Ichneumonidae) Parasitism Rates of Plutella xylostella (L.) (Lepidoptera: Plutellidae) in Virginia Cole Crops
Tomlinson, Taylore A.; Del Pozo-Valdivia, Alejandro I.; Kuhar, Thomas P. (MDPI, 2026-03-03)
The diamondback moth, Plutella xylostella (L.), is a significant pest of brassica crops that is found across the globe. Due to the development of insecticide resistance, control tactics have shifted focus towards integrating pest management techniques such as biological control. Diadegma insulare (C.), Oomyzus sokolowskii (K.), and Microplites plutellae (M.) are parasitoids of P. xylostella found in the Eastern United States. From 2022 to 2025, we surveyed P. xylostella larvae and pupae in locations across Virginia to assess the current rates of parasitism in brassica fields. Specimens were brought to the laboratory and reared to assess parasitoid emergence rates. Only D. insulare specimens were found during the study. Adult P. xylostella, larvae and pupae, adult D. insulare, D. insulare pupae, unknown parasitoids, and unknown deaths were recorded and used to calculate the rates of parasitism at each location. We concluded that the parasitism rate varied by location and year, which was expected due to regional conditions and seasonality. Rates averaged between 30.1 and 65% by year, with the lowest individual rate being 15% in 2025 and the highest at 100% in 2022. This suggests that D. insulare is actively present in Virginia and could be a successful biological control agent when paired with other integrated pest management techniques to reduce P. xylostella populations.
The Role of EDA in Developing Robust Machine Learning Models for Lithology and Penetration Rate Prediction from MWD Data
Addy, Jesse; Anafo, Ishmael; Westman, Erik (MDPI, 2026-03-04)
Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R2 = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments.
Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals
Wu, Xiaowei (MDPI, 2026-03-04)
Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded by enabling joint modeling, nonlinear effects, and integrative analysis. However, deep learning approaches remain underutilized in augmenting traditional GWAS frameworks, particularly in the presence of cryptic relatedness among sampled individuals. In this paper, we propose a deep neural network (DNN)-based machine learning method to assist genetic association testing in samples with related individuals. By approximating the phenotype–genotype relationships in classical association tests and combining approximations across multiple tests, the proposed method aims to improve predictive performance in the identification of associated variants. Simulation studies demonstrate that our approach effectively complements conventional statistical methods and generally achieves increased power for detecting genetic associations. We further apply the method to data from the Framingham Heart Study, illustrating how DNN-based machine learning can facilitate the identification of genome-wide SNPs associated with average systolic blood pressure.
Track Transition Performance: A Sensor-Centric Literature Review and Optical Sensing Advances
Gharizadehvarnosefaderani, Mahsa; Rabbi, Md. Fazle; Mishra, Debakanta (MDPI, 2026-03-04)
The structural and geotechnical characteristics of railroad tracks change abruptly at transition zones. At these locations, a change from ‘rigid’ to ‘flexible’ track conditions or the opposite leads to amplified dynamic responses, large deformations, accelerated track deterioration, and increased maintenance expenses. Researchers have conducted numerous field and numerical studies into track transitions’ behavior; however, their investigations are often limited by point-based and short-term measurements and assumptions that overlook critical mechanisms in track transitions. This review presents current sensor-centric knowledge achieved by integrating insights from field instrumentations and numerical modellings of transition zones. The objective is to expose the overlooked behavioral aspects of track transitions and identify the limitations of conventional monitoring systems. To address these gaps, this review introduces optical fiber sensors (OFSs) as an emerging technology for track condition monitoring. Focusing on recent OFS applications, this study demonstrates how OFSs can improve the quantity and quality of field data through spatial continuity, multiplexing, and higher sensitivity, thus marking a significant practical improvement. This review also outlines OFS-based monitoring challenges, such as sensor durability, measurement quality, temperature-strain cross-sensitivity, and lack of a standardized data interpretation framework. Altogether, this work’s novelty is in connecting transition zone behavior, monitoring limitations, and the inherent potential of OFS systems.