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
Benchmarking Deep Legendre-SNN for Time Series Classification – Analysis and Enhancements
Gaurav, Ramashish; Agarwal, Shrestha; Stewart, Terrence C.; Yi, Yang (IEEE, 2025-10-29)
Compute- and energy-efficient Time Series Classification (TSC) is the need of the hour-to cater the continually growing sources and applications of temporal data. State-of-the-Art (SoTA) temporal computational models, e.g., LSTMs/RNNs, HIVE-COTE, Transformers, etc., are high performing, but are also resource intensive, resulting in high energy consumption on CPUs/GPUs. On the contrary, Reservoir Computing (RC) based models are resource-efficient and perform well for simple TSC datasets; and when implemented with spiking neurons, spiking RC-based models offer the promise of high energy-efficiency on neuromorphic hardware. In this work, we analyse, enhance, and benchmark the newly introduced-spiking RC-based, “Legendre Spiking Neural Network” (Legendre-SNN or LSNN) model for TSC. We theoretically investigate the Legendre Delay Network (LDN) that acts as a reservoir in the LSNN model, and bring some useful insights into the design of the LDN-based models. In our analysis, we find that a higher order LDN is necessary for optimal performance with input signals composed of higher frequencies. We also extend the existing LSNN model to multivariate time-series signals and propose the “DeepLSNN” model. We conduct experiments with DeepLSNN on 102 benchmark TSC-datasets (comprising both univariate and multivariate signals). Via such large scale experiments, we present the first benchmark-results for spiking-TSC. Considering DeepLSNN's best results, we find that it outperforms the non-spiking LSTM-FCN on more than 31% of the 102 datasets. We note that our benchmark-results can serve as a comparison criterion for other spiking-TSC experiments.
Characterizing a Preclinical Model for Evaluating the Efficacy of a CGRP Monoclonal Antibody Treatment for Post-Traumatic Headaches Following Repeated Blast Neurotrauma
Wright, Amirah Dannye (Virginia Tech, 2025-08-22)
Blast-induced traumatic brain injury (bTBI) is a common consequence of combat exposure among active-duty military personnel and Veterans. Repeated blast exposures can lead to a range of persistent and debilitating symptoms, including post-traumatic headaches (PTH), depression, and anxiety, which may endure for months or even years post-injury. With the increasing number of women serving in the military, emerging evidence suggests that sex differences influence both the physical and psychological outcomes following bTBI. Notably, female service members and Veterans are more likely than males to report headache pain, depressive symptoms, and non-PTSD anxiety disorders following head trauma. Despite the high prevalence of PTH, its underlying pathophysiological mechanisms, particularly in females, remain poorly understood. Several mechanistic pathways have been implicated in PTH development, many of which overlap with established migraine neurobiology, including dysregulation of the trigeminovascular system, neuropeptide signaling, and neuroinflammatory responses. Furthermore, there is a critical gap in knowledge regarding the efficacy of targeted therapeutics for treating PTH in the TBI population. However, a more comprehensive and sex-inclusive understanding of these pathways is essential to inform effective treatment strategies for individuals affected by blast-related head trauma.
The objective of this work was to characterize a preclinical model of repeated blast TBI (rbTBI) that results in pain-related behaviors and neurobiological changes at chronic time points in both sexes. Using a rodent model of rbTBI, repeated blast exposure produced long-lasting facial mechanical hypersensitivity and persistent anxiety- and depression-like behaviors in males. These outcomes were paralleled by time-dependent elevations in glial reactivity, and region-specific up- or down-regulation of calcitonin gene-related peptide (CGRP) and substance P (SP). Female blast-exposed rats developed comparable chronic hypersensitivity and depression-like behavior but did not present with an anxiety-like phenotype. Injured females exhibited robust glial activation in the absence of any significant chronic up-regulation of CGRP or SP, suggesting a sex-specific divergence in neuropeptide signaling. Currently, there are no specific treatments 3targeting the underlying mechanisms of TBI and PTH due to the lack of understanding of the underlying pathology of the conditions. This work also evaluated the therapeutic efficacy of a calcitonin gene-related peptide monoclonal antibody (CGRP mAb) for the prevention of PTH.CGRP mAb administration successfully targeted the CGRP signaling pathway. However, it failed to ameliorate pain hypersensitivity, affective disturbances, or glial activation. Collectively, these findings establish a sex-inclusive, chronic bTBI model of PTH and demonstrate that isolated CGRP blockade is insufficient to reverse PTH-like phenotypes, despite effective target suppression. This data highlights the necessity of multi-modal therapeutic approaches that concurrently address neuroimmune activation and nociceptive signaling in blast-induced PTH. While much remains to be understood about the development of PTH following bTBI, this work advances our knowledge of the underlying pathology and provides valuable insight that may inform more effective treatment strategies for this condition.
Transit time modeling framework for predicting freshwater salinization in urban catchments
Bhide, Shantanu V.; Grant, Stanley B.; McGuire, Kevin J.; Prestegaard, Karen; Kaushal, Sujay S.; Sekellick, Andrew J.; Rippy, Megan A.; Schenk, Todd; Curtis, Shannon; Gomez-Velez, Jesus D.; Hotchkiss, Erin R.; Vikesland, Peter J.; Saksena, Siddharth (Elsevier, 2026-03)
The salinity of inland freshwaters is rising globally, particularly in urban watersheds where winter road deicers are widely applied. Attributing stream salinity dynamics to specific sources and transport pathways remains challenging due to episodic salt inputs, engineered drainage, and strong coupling between hydrology and subsurface storage. We present a modeling framework that couples climate-driven deicer build-up and wash-off with transient transit time distribution theory to simulate salt transport through drainage, interflow, and groundwater pathways. Applied to an urban watershed in Northern Virginia (USA), the model reproduces ten years of high-frequency stream salinity measurements across daily-to-decadal timescales. The calibrated model implies an average deicer application of 206 tonnes Cl yr−1, or roughly one 20 kg bag of rock salt person−1 yr−1 when normalized by the 20,000 people living in the watershed. In winter months, higher infiltration routes a large fraction of snowmelt and deicers into shallow subsurface pathways, enhancing vadose-zone and interflow contributions to stream salinity. Limited subsurface storage capacity and seasonal hydrologic turnover flush excess chloride from the vadose zone and groundwater during subsequent summer storms. By linking climate-driven deicer inputs, hydrologic connectivity, and stream water age, the framework provides a transferable basis for diagnosing and managing freshwater salinization in urban watersheds.
Identifying the psychological, behavioral, and neural effects of dance on young adults with ADHD
Tasnim, Noor E. (Virginia Tech, 2026-02-03)
Attention-deficit/hyperactivity disorder (ADHD) is emerging as a growing public health challenge in the United States. More than 15 million adults in the United States are diagnosed with ADHD in adulthood. Moreover, stimulant refill rates are increasing while patients struggle to get their ADHD medications. Although more adults are seeking help for ADHD, primary care settings continue to fall short of meeting quality-of-care standards for accurate diagnosis and effective treatment. To address this issue, this dissertation set out to accomplish the following aims: 1) Examine the psychological, behavioral, and neural predictors of ADHD symptomatology in young adults and 2) Study the acute effects of dance and exercise on the psychological, behavioral, and neural outcomes of ADHD in this population. For Aim 1) 67 young adults (Ages: 18-24, Sex: Male [N=18], Female [N=49]) completed a series of mental health questionnaires, executive function tasks, and balance assessments while wearing a 64- electrode electroencephalography cap. Depressive symptoms, sex, alpha (8-12 Hz) power in the Right Paracentral Lobule, and P3b Mean Amplitude were the greatest predictors of self-reported symptoms on the Adult ADHD Self-Report Scale v1.1. For Aim 2) 63 of these participants (Sex: Male [N = 17], Female [N = 46]) were assigned, through stratified randomization, to one of three 30-minute interventions associated with a dance exergame: 1) sitting and watching the game, 2) riding a bike to the game, 3) dancing along with/playing the game. Participants underwent the same series of assessments about 1 week after their first visit but underwent their assigned intervention before all assessments took place. Biking and dancing suppressed alpha power in brain regions associated with attentional networks and improved cognitive flexibility. Dance, but not biking, specifically suppressed alpha activity in regions associated with top-down attentional control. The identification of significant neural predictors and nonpharmacological treatment outcomes associated with attention can guide future standards in the diagnosis and treatment of adults with ADHD.
Understanding the Mechanistic Pathways of Layered Oxide Cathode Synthesis for Sodium-Ion Batteries
Promi, Anika Tabassum (Virginia Tech, 2025-12-16)
Sodium-ion batteries (SIBs) offer cost-effective and earth-abundant complementary technology to lithium-based systems, positioning them as promising candidates for large-scale energy storage to meet the world's exponentially growing energy demands. This dissertation investigates the interconnected roles of precursor chemistry, interfacial solid-liquid interactions, and calcination pathways involved in the synthesis of Ni-Fe-Mn –based layered oxide cathodes for sodium-ion batteries. It begins by examining equimolar Ni-Fe-Mn hydroxide precursors synthesized byammonia- and citrate-based co-precipitation routes, comparing their morphological control, stoichiometric accuracy, and structural homogeneity under varying reaction conditions. Motivated by the challenges observed in synthesis, the second study shifts focus to a fundamental investigation of metal–ligand interactions at the solid–liquid interface. Using in-situ synchrotron X-ray fluorescence microscopy and statistical modeling, we quantify how pH and metal identity influence interfacial dissolution-redeposition dynamics of multiple transition metals in alkaline media and reveal metal-specific spatial–temporal trends within multicomponent systems.Subsequent chapters shift focus to the high-temperature solid-state transformation of these precursors into NaNi1/3Fe1/3Mn1/3O2 cathodes. First, we analyze the mechanistic reaction pathway of sodium carbonate-based calcination, identifying key stages of precursor dehydration, major intermediate formation, and grain growth behavior. We then systematically investigate how variations in precursor design route and sodium source influence calcination behavior, demonstrating that structural and morphological differences govern distinct phase evolution pathways, ranging from topotactic transformations to complex multistep transformation. Finally, we extend this methodology to Mn-rich systems for P2-type sodium layered oxides, demonstrating that citrate-based strategies can yield favorable particle morphologies across a range of manganese-rich compositions, despite challenges associated with Mn precipitation. Across these studies, we establish a framework for linking precursor synthesis to downstream calcination outcomes, offering new insights into optimizing reaction parameters for more efficient synthesis of sodium-ion layered oxide cathodes.