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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.
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Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
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More than 50 freely available and openly licensed textbooks are among our most downloaded items.
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Recent Submissions
Whole-Genome Sequencing Reveals Breed-Specific SNPs, Indels, and Signatures of Selection in Royal White and White Dorper Sheep
Liao, Mingsi; Kravitz, Amanda; Haak, David C.; Sriranganathan, Nammalwar; Cockrum, Rebecca R. (MDPI, 2026-03-05)
Whole-genome sequencing (WGS) is a powerful tool for uncovering genome-wide variation, identifying selection signatures, and guiding genetic improvement in livestock. Royal White (RW) and White Dorper (WD) sheep are economically important meat-type hair breeds in the U.S., yet their genomic architecture remains poorly characterized. In this study, WGS was performed on 20 ewes (n = 11 RW, n = 9 WD) to identify and annotate SNPs and small insertions and deletions (indels). Functional annotation, gene enrichment, population structure, and selective sweep analysis were also performed. Selective sweep analysis was conducted by integrating the fixation index (FST), nucleotide diversity (π), and Tajima’s D to identify candidate regions under putative recent positive selection. A total of 21,957,139 SNPs and 2,866,600 indels were identified in RW sheep, whereas 18,641,789 SNPs and 2,397,368 indels were identified in WD sheep. In RW sheep, candidate genes under selection were associated with health and parasite resistance (NRXN1, HERC6, TGFB2) and growth traits (JADE2). In WD sheep, selective sweep regions included genes linked to immune response and parasite resistance (TRIM14), body weight (PLXDC2), and reproduction (STPG3). These findings were supported by sheep-specific quantitative trait loci (QTL) annotations and previously reported SNP–trait associations. This study provides the first WGS-based genomic comparison between RW and WD sheep, establishing a foundation for future genetic improvement, including targeted selection for enhanced immune function, disease resistance, and other economically important traits in these breeds.
A Decade of Evidence on Broiler Chicken Dead-on-Arrival Rates and Risk Factors: A Scoping Review
Vitek, Samantha; Jacobs, Leonie (MDPI, 2026-03-05)
The preslaughter phase for broiler chickens is distressing and can result in death prior to slaughter. The severity of this animal welfare concern warrants the exploration of the rates and risk factors. The aim of this scoping review was to synthesize current knowledge on rates and associated farm, flock, and preslaughter risk factors for dead-on-arrivals (DOA). Peer-reviewed experimental or observational studies were included that were written in English, published between January 2014 and December 2024, and that reported broiler chicken DOA with rates or associated risk factors in Google Scholar and ScienceDirect. A total of 344 articles were identified, and 24 articles met the eligibility criteria. Mean DOA rates ranged from 0 to 0.85%. In total, nine on-farm or flock-level and 11 preslaughter risk factors were identified, which could be categorized under four major causes of DOA: poor health, distress, thermal stress, and trauma. The risk factors most commonly identified were journey duration and distance, season, ambient temperature, lairage duration, and body weight. The findings highlight multiple opportunities to reduce DOA, including greater consideration of flock characteristics in preslaughter decision making, growing flocks that are at reduced risk of DOA, improvements in catching and loading practices, and better alignment of preslaughter management with environmental conditions.
FADS-Fusion: A Post-Flood Assessment Using Dempster-Shafer Fusion for Segmentation and Uncertainty Mapping
Sobien, Daniel; Sobien, Chelsea (MDPI, 2026-02-27)
Machine Learning (ML) modeling for disaster management is a growing field, but existing works focus more on mapping the extent of floods or broad categories of damage and they lack methods for explainability to help users understand model outputs. In this study, we propose Flood Assessment using Dempster–Shafer Fusion (FADS-Fusion), a tool for addressing post-flood damage assessment using Dempster–Shafer fusion to combine outputs from multiple deep learning models. FADS-Fusion is generalized to use any pretrained models, once outputs are post-processed for consistency, making it applicable for other disaster management or change detection applications. The novelty of our work comes from the application of Dempster–Shafer for multi-model fusion and uncertainty quantification on a flood dataset for segmenting both buildings and roads. We trained and evaluated models using the SpaceNet 8 challenge dataset and demonstrated that the fusion of the SpaceNet 8 Baseline (SN8) and Siamese Nested UNet (SNUNet) models has a modest overall improvement +1.93% to mAP, while a +12.3% increase for Precision and a −15.0% decrease in Recall are statistically significant compared to the baseline. FADS-Fusion also quantifies uncertainty by using the conflict of evidence, with a discount factor, with Dempster–Shafer fusion as both a quantitative and qualitative explainability method. While uncertainty correlates with a drop in performance, this relationship depends on values for class-weighted uncertainty and location. Mapping uncertainty back onto the original image allows for a visual inspection on fusion quality and indicates areas where a human will need to reassess. Our work demonstrates that FADS-Fusion improves post-flood segmentation performance and adds the benefit of uncertainty quantification for explainability, an aspect important for reliability and user decision-making but understudied in ML for disaster management in the literature.
Applying the Midas Touch of Reproducibility to High-Performance Computing
Minor, A. C.; Feng, Wu-chun (IEEE, 2022-09-19)
With the exponentially improving serial performance of CPUs from the 1980s and 1990s slowing to a standstill by the 2010s, the high-performance computing (HPC) community has seen parallel computing become ubiquitous, which, in turn, has led to a proliferation of parallel programming models, including CUDA, OpenACC, OpenCL, OpenMP, and SYCL. This diversity in hardware platform and programming model has forced application users to port their codes from one hardware platform to another (e.g., CUDA on NVIDIA GPU to HIP or OpenCL on AMD GPU) and demonstrate reproducibility via adhoc testing. To more rigorously ensure reproducibility between codes, we propose Midas, a system to ensure that the results of the original code match the results of the ported code by leveraging the power of snapshots to capture the state of a system before and after the execution of a kernel.
Characterization and Optimization of the Fitting of Quantum Correlation Functions
Chuang, Pi-Yueh; Shah, Niteya; Barry, Patrick; Cloet, Ian; Constantinescu, Emil M.; Sato, Nobuo; Qiu, Jian-Wei; Feng, Wu-chun (IEEE, 2024-09)
This case study presents a characterization and optimization of an application code for extracting parton distribution functions from high energy electron-proton scattering data. Profiling this application code reveals that the phase-space density computation accounts for 93% of the overall execution time for a single iteration on a single core. When executing multiple iterations in parallel on a multicore system, the application spends 78% of its overall execution time idling due to load imbalance. We address these issues by first transforming the application code from Python to C++ and then tackling the application load imbalance via a hybrid scheduling strategy that combines dynamic and static scheduling. These techniques result in a 62% reduction in CPU idle time and a 2.46x speedup in overall execution time per node. In addition, the typically enabled power-management mechanisms in supercomputers (e.g., AMD Turbo Core, Intel Turbo Boost, and RAPL) can significantly impact intra-node scalability when more than 50% of the CPU cores are used. This finding underscores the importance of understanding system interactions with power management, as they can adversely impact application performance, and highlights the necessity of intra-node scaling tests to identify performance degradation that inter-node scaling tests might otherwise overlook.