Award-winning Theses and Dissertations

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https://hdl.handle.net/10919/71751
Every year, the Virginia Tech Graduate School honors several outstanding theses and dissertations, and some theses and dissertations have won external awards. Browse these works here. Read about applying for thesis and dissertation awards at graduateschool.vt.edu/academics/awards/.

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    Behavioral Training of Reward Learning Increases Reinforcement Learning Parameters and Decreases Depression Symptoms Across Repeated Sessions
    Goyal, Shivani (Virginia Tech, 2023-12)
    Background: Disrupted reward learning has been suggested to contribute to the etiology and maintenance of depression. If deficits in reward learning are core to depression, we would expect that improving reward learning would decrease depression symptoms across time. Whereas previous studies have shown that changing reward learning can be done in a single study session, effecting clinically meaningful change in learning requires this change to endure beyond task completion and transfer to real world environments. With a longitudinal design, we investigate the potential for repeated sessions of behavioral training to create change in reward learning and decrease depression symptoms across time. Methods: 929 online participants (497 depression-present; 432 depression-absent) recruited from Amazon’s Mechanical Turk platform completed a behavioral training paradigm and clinical selfreport measures for up to eight total study visits. Participants were randomly assigned to one of 12 arms of the behavioral training paradigm, in which they completed a probabilistic reward learning task interspersed with queries about a feature of the task environment (11 learning arms) or a control query (1 control arm). Learning queries trained participants on one of four computational-based learning targets known to affect reinforcement learning (probability, average or extreme outcome values, and value comparison processes). A reinforcement learning model previously shown to distinguish depression related differences in learning was fit to behavioral responses using hierarchical Bayesian estimation to provide estimates of reward sensitivity and learning rate for each participant on each visit. Reward sensitivity captured participants’ value dissociation between high versus low outcome values, while learning rate informed how much participants learned from previously experienced outcomes. Mixed linear models assessed relationships between model-agnostic task performance, computational model-derived reinforcement learning parameters, depression symptoms, and study progression. Results: Across time, learning queries increased individuals’ reward sensitivities in depression-absent participants (β = 0.036, p =< 0.001, 95% CI (0.022, 0.049)). In contrast, control queries did not change reward sensitivities in depression-absent participants across time ((β = 0.016, p = 0.303, 95% CI (-0.015, 0.048)). Learning rates were not affected across time for participants receiving learning queries (β = 0.001, p = 0.418, 95% CI (-0.002, 0.004)) or control queries (β = 0.002, p = 0.558, 95% CI (-0.005, 0.009). Of the learning queries, those targeting value comparison processes improved depression symptoms (β = -0.509, p = 0.015, 95% CI (-0.912, - 0.106)) and increased reward sensitivities across time (β = 0.052, p =< 0.001, 95% CI (0.030, 0.075)) in depression-present participants. Increased reward sensitivities related to decreased depression symptoms across time in these participants (β = -2.905, p = 0.002, 95% CI (-4.75, - 1.114)). Conclusions: Multiple sessions of targeted behavioral training improved reward learning for participants with a range of depression symptoms. Improved behavioral reward learning was associated with improved clinical symptoms with time, possibly because learning transferred to real world scenarios. These results support disrupted reward learning as a mechanism contributing to the etiology and maintenance of depression and suggest the potential of repeated behavioral training to target deficits in reward learning.
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    Teleworker Well-Being in COVID-19 as a Function of Change in the Work/Home Boundary: A Multilevel Response Surface Approach
    Mitropoulos, Tanya Elise (Virginia Tech, 2023-12-06)
    This dissertation explored how a change in the work/home boundary stemming from a mandatory switch to full-time telework influenced employee well-being. Organizational scholars have called for more investigations into how crisis events impact employees, and the COVID-19 pandemic presented an opportunity to examine a change in employees' work and home domains as it unfolded. Additionally, as full-time telework becomes a more common way of work, understanding how this once rare work arrangement affects employee well-being holistically is important. Using boundary theory, I hypothesized that a switch to full-time telework would increase the level of integration between employees' work and home domains, and that a greater change in integration level would associate with worse daily well-being outcomes. To explain this association, I turned to recovery theorizing and proposed daily work-related rumination and lack of psychological detachment as linking mechanisms. Additionally, I expected that teleworkers whose current level of integration was closer to their preferred level would experience better well-being. Using multilevel response surface analysis (MRSA), which enabled illustration of these complex associations in a more nuanced manner than is possible via either change scores or moderation analyses, I found that maintaining higher work/home integration both before and after telework co-varied with worse holistic well-being through work-related rumination and lack of psychological detachment. I also found that having higher integration than preferred and even high integration when preferred associated with worse well-being through work-related rumination and lack of psychological detachment. Based on these results, I point to boundary work and its facilitation of segmentation as a potential means of protecting employee well-being in the event of a future crisis that moves work into the home.
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    Investigating Cathode–Electrolyte Interfacial Degradation Mechanism to Enhance the Performance of Rechargeable Aqueous Batteries
    Zhang, Yuxin (Virginia Tech, 2023-12-04)
    The invention of Li-ion batteries (LIBs) marks a new era of energy storage and allows for the large-scale industrialization of electric vehicles. However, the flammable organic electrolyte in LIBs raises significant safety concerns and has resulted in numerous fires and explosion accidents. In the pursuit of more reliable and stable battery solutions, interests in aqueous batteries composed of high-energy cathodes and water-based electrolytes are surging. Limited by the narrow electrochemical stability window (ESW) of water, conventional aqueous batteries only achieve inferior energy densities. Current development mainly focuses on manipulating the properties of aqueous electrolytes through introducing excessive salts or secondary solvents, which enables an unprecedentedly broad ESW and more selections of electrode materials while also resulting in some compromises. On the other hand, the interaction between electrodes and aqueous electrolytes and associated electrode failure mechanism, as the key factors that govern cell performance, are of vital importance yet not fully understood. Owing to the high-temperature calcination synthesis, most electrode materials are intrinsically moisture-free and sensitive to the water-rich environment. Therefore, compared to the degradation behaviors in conventional LIBs, such as cracking and structure collapse, the electrode may suffer more severe damage during cycling and lead to rapid capacity decay. Herein, we adopted multi-scale characterization techniques to identify the failure modes at cathode–electrolyte interface and provide strategies for improving the cell capacity and life during prolonged cycling. In Chapter 1, we first provide a background introduction of conventional non-aqueous and aqueous batteries. We then show the current development of modern aqueous batteries through electrolyte modification and their merits and drawbacks. Finally, we present typical electrode failure mechanism in non-aqueous electrolytes and discuss how water can further impact the degradation behaviors. In Chapter 2, we prepare three types of aqueous electrolytes and systematically evaluate the electrochemical performance of LiNixMnyCo1-x-yO2, LiMn2O4 and LiFePO4 in the aqueous electrolytes. Combing surface- and bulk-sensitive techniques, we identify the roles played by surface exfoliation, structure degradation, transition metal dissolution and interface formation in terms of the capacity decay in different cathode materials. We also provide fundamental insights into the materials selection and electrolyte design in the aqueous batteries. In Chapter 3, we select LiMn2O4 as the material platform to study the transition metal dissolution behavior. Relying on the spatially resolved X-ray fluorescence microscopy, we discover a voltage-dependent Mn dissolution/redeposition (D/R) process during electrochemical cycling, which is confirmed to be related to the Jahn–Teller distortion and surface reconstruction at different voltages. Inspired by the findings, we propose an approach to stabilize the material performance through coating sulfonated tetrafluoroethylene (i.e., Nafion) on the particle, which can regulate the proton diffusion and Mn dissolution behavior. Our study discovers the dynamic Mn D/R process and highlights the impact of coating strategy in the performance of aqueous batteries. In Chapter 4, we investigate the diffusion layer formed by transition metals at the electrode–electrolyte interface. With the help of customized cells and XFM technique, we successfully track the spatiotemporal evolution of the diffusion layer during soaking and electrochemical cycling. The thickness of diffusion layer is determined to be at micron level, which can be readily diminished when gas is generated on the electrode surface. Our approach can be further expanded to study the phase transformation and particle agglomeration at the interfacial region and provide insights into the reactive complexes. In Chapter 5, we reveal the correlation between the electrolytic water decomposition and ion intercalation behaviors in aqueous batteries. In the Na-deficient system, we discover that overcharging in the formation process can introduce more cyclable Na ions into the full cell and allows for a boosted performance from 58 mAh/g to 124 mAh/g. The mechanism can be attributed to the water oxidation on the cathode and Na-ion intercalation on the anode when the charging voltage exceeds the normal oxidation potential of cathode. We emphasize the importance of unique formation process in terms of the cell performance and cycle life of aqueous batteries. In Chapter 6, we summarize the results of our work and propose perspectives of future research directions.
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    Economic costs of extreme heat on groundnut production in the Senegal Groundnut Basin
    Sembene, Maguette (Virginia Tech, 2023-09-01)
    Groundnut production is vital to the Senegalese agricultural economy, particularly in the Groundnut Basin. However the region is increasingly affected by climate change and associated rising temperatures. This study investigates long-term changes in the frequency of extreme temperatures in the Groundnut Basin and the impact of extreme temperatures on groundnut production. The current economic costs of extreme temperatures on groundnut farmers and potential future additional economic costs associated with climate change are then calculated. The study uses a two-year panel dataset from 1,123 households in the Groundnut Basin and weather data from meteorological stations and the ERA5 climate database. Results identify a significant increasing trend in extreme temperatures across the Groundnut Basin and a negative relationship between extreme temperatures and groundnut yield. This leads to financial losses for farmers, with adaptation strategies such as input level adjustments providing partial mitigation. Future projections indicate further increases in extreme heat degrees days, resulting in significant yield losses by 2050. But the implications of extreme heat also extend beyond agriculture, affecting human habitation and exacerbating societal inequalities. The findings highlight the potential long-term effects of increasing temperatures on agricultural practices in the Groundnut Basin and underscore the need for adaptation and mitigation strategies to cope with the impacts of climate change.
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    Supporting the operational performance management of public service systems during slow-onset disasters
    Pamukcu, Duygu (Virginia Tech, 2023-01-23)
    Disasters impact different communities differently due to pre-existing vulnerabilities and inequalities, which diversify amounts and types of public service needs. Understanding the varying needs of communities that rely on government services helps decision-makers allocate limited resources properly during crises to maintain effective, efficient, and equitable service provision across the region. This dissertation includes three independent studies which commonly investigate how service operations can be successfully managed to maintain the operational performance goals of public organizations during slow-onset disasters. The first study focuses on the volatility in the service needs of citizens from a public system during a long-term disaster. The study proposes a time series approach for predicting demand volatility patterns to manage service productivity. This chapter explores the longitudinal impacts of long-term disasters for better service performance management since the timely and accurate prediction of deviations from the expected service demand is vital for utilizing limited resources. The study further discusses the differential impacts of such disasters across locations of socio-economically diverse populations to emphasize the need to consider the diverse needs of people for efficient and effective service provision. The second study builds upon the discussions in the first study and discusses static and dynamic risk factors of slow-onset disasters to reveal how these factors diversify the service needs of communities and impact the corresponding service response performance of public systems during the disaster. The study performs time series analyses to test the impact of capacity adjustments and dynamic disaster risk features on service performance, considering service response time as the performance indicator. The third study focuses on efficient and equitable capacity management and prioritization strategies of an information technology-based public system that experiences significant changes in service demand during disasters. The study presents a mathematical model quantifying the relative service efficiencies associated with service requests from an input-output-based standpoint to uncover the inefficiencies in response performance to different service categories. The paper discusses the opportunities for managing service efficiency and equity within and between service departments by rearranging available capacities and prioritization strategies during emergencies.
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    ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning
    Ali, Haider (Virginia Tech, 2023-01-05)
    Existing adversarial algorithms for Deep Reinforcement Learning (DRL) have largely focused on identifying an optimal time to attack a DRL agent. However, little work has been explored in injecting efficient adversarial perturbations in DRL environments. We propose a suite of novel DRL adversarial attacks, called ACADIA, representing AttaCks Against Deep reInforcement leArning. ACADIA provides a set of efficient and robust perturbation-based adversarial attacks to disturb the DRL agent's decision-making based on novel combinations of techniques utilizing momentum, ADAM optimizer (i.e., Root Mean Square Propagation or RMSProp), and initial randomization. These kinds of DRL attacks with novel integration of such techniques have not been studied in the existing Deep Neural Networks (DNNs) and DRL research. We consider two well-known DRL algorithms, Deep-Q Learning Network (DQN) and Proximal Policy Optimization (PPO), under Atari games and MuJoCo where both targeted and non-targeted attacks are considered with or without the state-of-the-art defenses in DRL (i.e., RADIAL and ATLA). Our results demonstrate that the proposed ACADIA outperforms existing gradient-based counterparts under a wide range of experimental settings. ACADIA is nine times faster than the state-of-the-art Carlini and Wagner (CW) method with better performance under defenses of DRL.
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    In situ Nanoscale Quantification of Corrosion Kinetics by Quantitative Phase  Microscopy
    Fanijo, Ebenezer Oladayo (Virginia Tech, 2022-11-23)
    Corrosion-related degradation incurs a significant cost to infrastructure and society. In 2016, the direct corrosion cost was estimated at $276 billion, which is 3.1% of the U.S. gross domestic product. Despite the known consequences of corrosion damage, many unknowns still exist, such as the mechanisms and rates of chloride-induced corrosion initiation and propagation. There is also a lack of high-quality quantitative kinetic data and analysis that can obtain the fundamental micro- and nanostructural mechanisms and initiation of metal corrosion. The corrosion initiation in metals is considered to be governed by dynamic processes that take place at the nanoscale. Thus, the measurement of nanoscale surface structures correlated with electrochemical properties in metals is critical in the understanding of corrosion initiation, and microstructure-corrosion relationship, as well as efforts toward materials design for corrosion mitigation. As a fundamental approach to this study, a systematic review of different surface characterization techniques was initially discussed. This entailed their principles, applications, and perspectives for surface corrosion monitoring, enabling the development of next-generation inhibition technologies, and improving corrosion predictive models. Unprecedented, this research study presented a novel application of a quantitative phase microscopy technique, spectral modulation interferometry (SMI), for in situ nanoscale characterization of corrosion of different alloys in real-time. SMI offers high sensitivity, rapid image acquisition, and speckle-free images; thus, real-time quantification of surface topography evolution during corrosion can be obtained accurately to evaluate the temporally- and spatially-dependent corrosion rates. With an innovative additive-manufactured fluid cell, experiments were performed under flowing solution conditions. Electrochemical tests via stepwise polarization and solution chemistry through collected aliquots of outflow solution were also performed alongside the nanoscale SMI experiment to simultaneously provide corroborating corrosion rate measurements. This innovative approach to measuring dissolution rates of metal at three levels can provide highly quantitative kinetic data of reacting surfaces that are rarely explored in the literature. First, the in situ SMI combined with the stepwise potentiostatic tests and the solution chemistry analysis was used to investigate the nanoscale characterization of corrosion of an AA6111-T4 aluminum alloy in real-time. The corrosion experiment was conducted in a 0.5 wt.% NaCl flowing solution acidified to pH ⁓2.9 by acetic acid. Based on the quantitative 3D height profiles across the corroded surface, pit formation resulting from rapid local corrosion was predominant, which is heterogeneously distributed and was appearing at different times. The computed time-dependent dissolution rates of aluminum also varied as the experiment proceeded, with the combination of linear and nonlinear surface normal distributions. An initial mean linear dissolution rate of (0.40 ± 0.007) μmol m−2 s−1 transitioned to a more rapid mean rate of (1.95 ± 0.035) μmol m−2 s−1, driven by the anodic polarization. Dissolution rates from the three performed methods follow similar trends and there is the visibility of linking the nanoscale in situ SMI data to the electrochemical corrosion measurements and ex situ chemical solution analysis. At the end of the corrosion period, rates of 118, 71, and 2.45 μmol m−2 s−1 were obtained from electrochemical measurements, ex situ solution analyses, and in situ SMI corrosion measurements, respectively. In addition, SMI–electrochemical experiments were performed to evaluate the effect of thermal history on corrosion modes and rates of AA6111. Quantitative estimates of the corrosion initiation and propagation in the alloy were also assessed. A single coil of AA6111 alloy that was solution heat treated at a temperature above 500°C and quenched with 2 different water quench rates (i.e., slow-quenched at 131ºC/s and fast-quenched at 506ºC/s) with each in T4 and T82 temper condition was investigated in this study. Irrespective of the quenched and/or temper conditions, the electrochemical potential-current (E-i) results showed a similar pattern in the polarization curve and similar current response over the immersed time, and a small difference in their corrosion behavior will be difficult to detect due to the dissolution kinetics that takes place on the nanoscale. As revealed from the SMI topography map, the corrosion modes at the nanoscale were very distinct despite having similar electrochemical responses and chemical compositions. Primarily, heterogeneous dissolution of intergranular corrosion (IGC) and crystallographic pitting was observed in the tested alloy substrates, with the slow-quenched samples susceptible to IGC and the fast-quenched samples susceptible to crystallographic pitting. The nucleation of IGC sites is triggered by the increased coarsening and formation of precipitates in the grain boundary, while the pitting corrosion is attributed to the coarsening of the precipitates in the grain bodies. The quantitative analysis of topography evolution from the SMI data revealed a non-uniform (i.e., heterogenous) surface dissolution, as is typical for aluminum alloys. Notably, the fast-quenched material resisted corrosion initiation for a longer time and showed great resistance even at higher anodic polarization. However, an instant breakdown then occurred after 60mV of polarization and corrosion accelerated faster, relative to the slow-quenched material which initiated sooner (i.e. with less overpotential). In this setup, it is now possible to detect and evaluate these differences quantitatively through a quick corrosion test with the combined electrochemical-SMI technique. Therefore, this work showed that the corrosion susceptibility of AA6111 alloy is influenced by the thermal history, which can be controlled with a proper quench rate and further tempering. Additionally, this research also utilized the novel SMI techniques to investigate in situ chloride-induced corrosion of A615 low-carbon steel at the nanoscale. Along with surface topography monitoring, a potentiostat was connected to simultaneously monitor the bulk electrochemical activity of the carbon steel. Experiments were conducted in chloride-free and chloride-enriched solutions at pH 5 to investigate the role of chloride on topography evolution, dissolution mode, and corrosion kinetics. The 3D topography map acquired from the SMI showed an early formation of localized shallow pits on the surface subjected to the chloride free-solution. A more detrimental form of corrosion was obtained on the samples in chloride-enriched solution, which revealed early-age microcracks or intergranular defective sites associated with the heterogeneous roughening of the sample surface. The presence of chloride ions also influenced the initiation period of corrosion. Indeed, higher grain defects were obtained in samples immersed in 5.0 wt.% NaCl solution than the sample in 1.0 wt.% NaCl solution. The quantitative analysis of the height profile data (acquired from SMI) verified the heterogeneity of the corrosion process of both samples either susceptible to pitting corrosion and/or intergranular corrosion behavior. A faster dissolution rate was acquired on the sample immersed in 5.0 wt.% NaCl solution, with the rate of (3.53 ± 0.103) μmol m−2 s−1 and (5.64 ± 0.0225) μmol m−2 s−1 computed at the initiation and propagation stages, respectively. Likewise, the estimated volume loss followed a similar trend to the 3D surface topography data, but a distinct behavior in the volume loss was observed when compared to the void volume obtained from the electrochemical monitoring. This confirmed that the electrochemical measurement overestimates metal loss and does not present a good representation of material dissolution on the nanoscale. Finally, a different perspective of corrosion mitigation in the metallic alloy was presented. The extensive application of deicing salts has led to significant deterioration in many transportation infrastructures and automobiles due to corrosion. In this regard, the work investigated the corrosion inhibition performance of 2 corn-derived polyols, namely: sorbitol, and mannitol, on reinforced steel rebar. The results demonstrated that the incorporation of polyols in the deicing solution reduced the corrosion initiation while the inhibition rate increased as the polyol content increased from 0% to 5wt.%. The outcome of this study contributed to the search for mitigation strategies to minimize the impact of deicing chemicals on steel infrastructures. Overall, it is evident that corrosion is a huge durability problem and requires significant consideration when designing metals or alloys that are usually exposed to hostile environments. Understanding the nanostructural and kinetics of corrosion at both the initiation and propagation periods, as well as its thermodynamics, is important for designing a suitable protection strategy. This dissertation is expected to present the application of the surface technique to directly quantify the dynamic evolution of site-specific local corrosion of metals during early initiation stages at the nanoscale.
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    Laboratorization of Everyday Life: Adaptations among Robots, Laboratory and Society
    Lo, Kuan-Hung (Virginia Tech, 2022-08-22)
    By investigating the social and environmental politics that are embodied in service robots, I show how both the laboratory culture and wider society expect their own respective values and environmental cultures to displace the other. This dissertation highlights the importance of understanding how robotics laboratories and larger society mutually transform each other, using a framework I call Laboratorization of Everyday Life. I analyze how the development of robotics involves a mutual transformation of the robot, the laboratory, and particular values and norms involving education, gender, class, body image, and living spaces in the society the laboratory is embedded within. Actors in laboratories are not hermetically sealed, but fully part of society. Similarly, actors in society change their everyday environments to better conform to laboratory settings, in order to make the wider world "useful" for technological innovation. People living in modern society are actually living in a semi-laboratory, which is embedded within the robot's technological default settings regarding values and environment, which are selected by the laboratory's engineers and designers. By conducting trial and error in everyday life, robot users have agency to re-mold service robots and norms built into their design and technological capacities. That is, the users are also able to rebuild and reinterpret values and environmental cultures inside their service robots. Ultimately, my dissertation offers a potential perspective on how robotics laboratories and larger society work together through robots to open lines of trustful communication between scientific, social, and political communities. 
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    Negotiating Sovereignty: Resistance and Meaning Making at the Bear Mountain Mission in Early-Twentieth Century Virginia  
    Blake, Erica Nicole (Virginia Tech, 2022-06-16)
    In 1907, the Episcopal Church established a mission in the heart of the Native Monacan community on Bear Mountain in Amherst County, Virginia. The Bear Mountain Mission operated a church, day-school, and clothing bureau until 1965, when the day-school closed after the integration of Amherst County Public Schools. This thesis investigates how Native Monacan congregants negotiated sovereignty, enacted resistance against the assimilating efforts of the Episcopal Church, and maintained group identity and safety at the Mission during the first three decades of the twentieth century. Monacan congregants utilized the inherently colonial nature of the Mission's structure in ways that allowed them access to influential white Protestant networks, as well as validation by the mission workers who lived in and around the Bear Mountain community. I argue that Monacan people used strategies such as the refashioning of Mission teachings, anonymous and signed letter-writing to the Bishop, and communal protests to ensure that the Mission remained a safe space that worked for their Native community during a time of immense racial animosity. Using the personal correspondence between women mission workers, church leadership, and Monacan congregants, I examine the inner workings of the Bear Mountain Mission, and the beliefs and actions of mission workers and Monacan people alike. This thesis challenges the history of Bear Mountain Mission, and Native missions within the United States more broadly, to consider the unique and numerous ways that Native peoples enacted resistance strategies in order to ensure that Protestant Missions worked in ways that benefited their communities.
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    Understanding Wikipedia Practices Through Hindi, Urdu, and English Takes on an Evolving Regional Conflict
    Hickman, Molly Graham (Virginia Tech, 2022-02-01)
    Wikipedia is the product of thousands of editors working collaboratively to provide free and up-to-date encyclopedic information to the project's users. This article asks to what degree Wikipedia articles in three languages — Hindi, Urdu, and English — achieve Wikipedia's mission of making neutrally-presented, reliable information on a polarizing, controversial topic available to people around the globe. We chose the topic of the recent revocation of Article 370 of the Constitution of India, which, along with other recent events in and concerning the region of Jammu and Kashmir, has drawn attention to related articles on Wikipedia. This work focuses on the English Wikipedia, being the preeminent language edition of the project, as well as the Hindi and Urdu editions. Hindi and Urdu are the two standardized varieties of Hindustani, a lingua franca of Jammu and Kashmir. We analyzed page view and revision data for three Wikipedia articles to gauge popularity of the pages in our corpus, and responsiveness of editors to breaking news events and problematic edits. Additionally, we interviewed editors from all three language editions to learn about differences in editing processes and motivations, and we compared the text of the articles across languages as they appeared shortly after the revocation of Article 370. Across languages,we saw discrepancies in article tone, organization, and the information presented, as well as differences in how editors collaborate and communicate with one another. Nevertheless, in Hindi and Urdu, as well as English, editors predominantly try to adhere to the principle of neutral point of view (NPOV), and for the most part, the editors quash attempts by other editors to push political agendas.
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    Consequences of avian parental incubation behavior for within-clutch variance in incubation temperature and offspring behavioral phenotypes
    Hope, Sydney Frances (Virginia Tech, 2020-01-17)
    Parents can have large effects on their offspring by influencing the early developmental environment. In birds, a major way that parents can influence the early developmental environment is through egg incubation. Not only is incubation necessary for hatching success, but small changes of <1C in average incubation temperature have large effects on post-hatch offspring morphology and physiology. However, incubation is energetically costly and time-consuming for parents, and thus parents must allocate resources between incubation and self-maintenance. This can lead to differences in parental incubation behavior and egg temperatures among and within populations. Understanding which factors influence incubation, and the subsequent effects for offspring, is crucial for understanding parental effects, non-genetic drivers of phenotypic variation, and how environmental changes affect avian populations. I used wood ducks (Aix sponsa) as a study species to investigate how factors (disturbance, clutch size, ambient temperature) that influence parental demands may affect parental incubation behavior, physiology, and egg temperatures, and subsequently how egg temperatures affect offspring behavior and physiology. In a field experiment, I found that nest disturbance (i.e., capture) reduced both parent prolactin concentrations and the amount of time that parents spent incubating (Chapter 1). Further, ambient temperature was positively and clutch size negatively related to egg temperatures. Notably, in large clutches, differences in average incubation temperature among eggs within nests were large enough (i.e., >1C) to lead to different offspring phenotypes within broods (Chapter 2). Then, in a series of experiments in which I controlled incubation temperature, I provided evidence that lower average incubation temperatures lead to a reduced ability of ducklings to exit the nest cavity (Chapter 3), a more proactive behavioral phenotype (Chapter 4), a smaller body size, and a reduced efficiency in food consumption (Chapter 5), compared to those incubated at higher temperatures. Together, my dissertation illustrates how disturbances, clutch size, and ambient temperature can influence an important aspect of avian parental care, which has wide-ranging effects on offspring traits and fitness. This has broad implications for understanding the evolution of clutch size, development of behavior, and the effects of anthropogenic changes on wildlife.
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    Save the Babies: Progressive Women and the Fight for Child Welfare in the United States, 1912-1929
    Brabble, Jessica Marie (Virginia Tech, 2021-06-24)
    This project examines two organizations--the Better Babies Bureau and the Children's Bureau--created by Progessive women in the early twentieth century to combat high infant mortality rates, improve prenatal and postnatal care, and better child welfare. The Better Babies Bureau, founded in 1913 by journalists from the Woman's Home Companion magazine, and the Children's Bureau, founded as a federal agency in 1912, used similar campaigns to raise awareness of these child welfare problems in the early 1900s; where they differed, however, is in their ultimate goals. The Children's Bureau sought to improve long-term medical care and infant mortality rates for women regardless of race or socioeconomic status; I analyze how they worked directly with midwives and health officials to provide better care for mothers and children. The Better Babies Bureau, in comparison, catered specifically to white women through prize-based contests and eugenics rhetoric. Through their better baby contests, they promoted the idea that disabilities and defects should be eliminated in children in order to create a better future. By the late 1910s, these two organizations were utilizing nationwide campaigns to appeal to mothers through either consumerism or health conferences. I argue that although the Better Babies Bureau made a greater cultural impact, the Children's Bureau made a longer lasting—and farther reaching—impact on infant mortality rates by making healthcare more accessible for both rural and urban women.
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    Disaggregating Within-Person and Between-Person Effects in the Presence of Linear Time Trends in Time-Varying Predictors: Structural Equation Modeling Approach
    Hori, Kazuki (Virginia Tech, 2021-06-01)
    Educational researchers are often interested in phenomena that unfold over time within a person and at the same time, relationships between their characteristics that are stable over time. Since variables in a longitudinal study reflect both within- and between-person effects, researchers need to disaggregate them to understand the phenomenon of interest correctly. Although the person-mean centering technique has been believed as the gold standard of the disaggregation method, recent studies found that the centering did not work when there was a trend in the predictor. Hence, they proposed some detrending techniques to remove the systematic change; however, they were only applicable to multilevel models. Therefore, this dissertation develops novel detrending methods based on structural equation modeling (SEM). It also establishes the links between centering and detrending by reviewing a broad range of literature. The proposed SEM-based detrending methods are compared to the existing centering and detrending methods through a series of Monte Carlo simulations. The results indicate that (a) model misspecification for the time-varying predictors or outcomes leads to large bias of and standard error, (b) statistical properties of estimates of the within- and between-person effects are mostly determined by the type of between-person predictors (i.e., observed or latent), and (c) for unbiased estimation of the effects, models with latent between-person predictors require nonzero growth factor variances, while those with observed predictors at the between level need either nonzero or zero variance, depending on the parameter. As concluding remarks, some practical recommendations are provided based on the findings of the present study.
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    Amplifying the Griot: Technology for Preserving, Retelling, and Supporting Underrepresented Stories
    Kotut, Lindah Jerop (Virginia Tech, 2021-05-24)
    As we develop intelligent systems to handle online interactions and digital stories, how do we address those stories that are unwritten and invisible? How do ensure that communities who value oral histories are not left behind, and their voices also inform the design of these systems? How do we determine that the technology we design respect the agency and ownership of the stories, without imposing our own biases? To answer these questions, I rely on accounts from different underrepresented communities, as avenues to examine how digital technology affect their stories, and the agency they have over them. From these stories, I elicit guidelines for the design of equitable and resilient tools and technologies. I sought wisdom from griots who are master storytellers and story-keepers on the craft of handling both written and unwritten stories, which instructed the development of the Respectful Space for technology typology, a framework that informs our understanding and interaction with underrepresented stories. The framework guided the approach to understand technology use by inhabitants of rural spaces in the United States--particularly long-distance hikers who traverse these spaces. I further discuss the framework's extensibility, by considering its use for community self-reflection, and for researchers to query the ethical implications of their research, the technology they develop, and the consideration for the voices that the technology amplifies or suppresses. The intention is to highlight the vast resources that exist in domains we do not consider, and the importance of the underrepresented voices to also inform the future of technology.
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    Principal Leadership in Building a Culture of Disciplinary Literacy
    Whitlock, Paige Elizabeth (Virginia Tech, 2021-01-21)
    This study investigated principal leadership in building a culture of disciplinary literacy. Previous studies investigated and validated the uniqueness of disciplinary literacy (Moje, 2015; Shanahan and Shanahan, 2008; Spires et al., 2018). Case studies on individual schools looked at literacy within the context of a specific school community (Faulkner, 2012; Francois, 2014; Gilrane et al., 2008). These studies, although they touched on teacher and principal leadership, did not focus on leadership at the core of creating a community of literacy. This study focused on the essential actions and dispositions of principals who successfully built and maintained a culture of disciplinary literacy. Eight principals from a large, suburban Northeastern school district were interviewed to ascertain their actions. Open coding with constant comparative analysis yielded common themes, dispositions, and actions of principals. Common leadership themes emerged as principals discussed leading disciplinary literacy: demonstrate why change is needed, recognize that leading literacy requires a plan, link the district priorities to disciplinary literacy, distribute leadership, provide targeted professional development, and utilize established resources. What emerged from this study was that one person alone could not build a culture of literacy within a school. Rather, changing instructional practices to put literacy at the center of learning requires the community to embrace literacy. As school leaders look to improve equitable outcomes for all students, they must look at the variation in instructional practices across the disciplines and ensure that literacy research-based practices are being used across all content areas. Change of this magnitude is a multiyear shift with student learning at the center of all instructional decisions. The complex task of leading instructional change requires a principal to learn about disciplinary literacy. If schools want equitable education for all students, principals must understand and place priority on disciplinary literacy.
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    Tuning the Morphology and Electronic Properties of Single-Crystal LiNi0.5Mn1.5O4-δ
    Spence, Stephanie L. (Virginia Tech, 2020-10-27)
    The commercialization of lithium-ion batteries has played a pivotal role in the development of consumer electronics and electric vehicles. In recent years, much research has focused on the development and modification of the active materials of electrodes to obtain higher energies for a broader range of applications. High voltage spinel materials including LiNi0.5Mn1.5O4-δ (LNMO) have been considered as promising cathode materials to address the increasing demands for improved battery performance due to their high operating potential, high energy density, and stable cycling lifetimes. In an effort to elucidate fundamental structure-property relationships, this thesis explores the tunable properties of single-crystal LNMO. Utilizing facile molten salt synthesis methods, the structural and electronic properties of LNMO can be well controlled. Chapter 2 of this thesis focuses on uncovering the effect of molten salt synthesis parameters including molten salt composition and synthetic temperature on the materials properties. A range of imaging, microscopic, and spectroscopic techniques are used to characterize structural and electronic properties which are investigated in tandem with electrochemical performance. Results indicate the Mn oxidation state is highly dependent on synthesis temperature and can dictate performance, while the molten salt composition strongly influences the particle morphology. In Chapter 3, we explore the concept of utilizing LNMO as a tunable support for heterogeneous metal nanocatalysts, where alteration of the support structure and electronics can have an influence on catalytic properties due to unique support effects. Ultimately, this work illustrates the tunable nature of single-crystal LNMO and can inform the rational design of LNMO materials for energy applications.
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    Distributed Machine Learning for Autonomous and Secure Cyber-physical Systems
    Ferdowsi Khosrowshahi, Aidin (Virginia Tech, 2020-07-31)
    Autonomous cyber-physical systems (CPSs) such as autonomous connected vehicles (ACVs), unmanned aerial vehicles (UAVs), critical infrastructure (CI), and the Internet of Things (IoT) will be essential to the functioning of our modern economies and societies. Therefore, maintaining the autonomy of CPSs as well as their stability, robustness, and security (SRS) in face of exogenous and disruptive events is a critical challenge. In particular, it is crucial for CPSs to be able to not only operate optimally in the vicinity of a normal state but to also be robust and secure so as to withstand potential failures, malfunctions, and intentional attacks. However, to evaluate and improve the SRS of CPSs one must overcome many technical challenges such as the unpredictable behavior of a CPS's cyber-physical environment, the vulnerability to various disruptive events, and the interdependency between CPSs. The primary goal of this dissertation is, thus, to develop novel foundational analytical tools, that weave together notions from machine learning, game theory, and control theory, in order to study, analyze, and optimize SRS of autonomous CPSs. Towards achieving this overarching goal, this dissertation led to several major contributions. First, a comprehensive control and learning framework was proposed to thwart cyber and physical attacks on ACV networks. This framework brings together new ideas from optimal control and reinforcement learning (RL) to derive a new optimal safe controller for ACVs in order to maximize the street traffic flow while minimizing the risk of accidents. Simulation results show that the proposed optimal safe controller outperforms the current state of the art controllers by maximizing the robustness of ACVs to physical attacks. Furthermore, using techniques from convex optimization and deep RL a joint trajectory and scheduling policy is proposed in UAV-assisted networks that aims at maintaining the freshness of ground node data at the UAV. The analytical and simulation results show that the proposed policy can outperform policies such discretized state RL and value-based methods in terms of maximizing the freshness of data. Second, in the IoT domain, a novel watermarking algorithm, based on long short term memory cells, is proposed for dynamic authentication of IoT signals. The proposed watermarking algorithm is coupled with a game-theoretic framework so as to enable efficient authentication in massive IoT systems. Simulation results show that using our approach, IoT messages can be transmitted from IoT devices with an almost 100% reliability. Next, a brainstorming generative adversarial network (BGAN) framework is proposed. It is shown that this framework can learn to generate real-looking data in a distributed fashion while preserving the privacy of agents (e.g. IoT devices, ACVs, etc). The analytical and simulation results show that the proposed BGAN architecture allows heterogeneous neural network designs for agents, works without reliance on a central controller, and has a lower communication over head compared to other state-of-the-art distributed architectures. Last, but not least, the SRS challenges of interdependent CI (ICI) are addressed. Novel game-theoretic frameworks are proposed that allow the ICI administrator to assign different protection levels on ICI components to maximizing the expected ICI security. The mixed-strategy Nash of the games are derived analytically. Simulation results coupled with theoretical analysis show that, using the proposed games, the administrator can maximize the security level in ICI components. In summary, this dissertation provided major contributions across the areas of CPSs, machine learning, game theory, and control theory with the goal of ensuring SRS across various domains such as autonomous vehicle networks, IoT systems, and ICIs. The proposed approaches provide the necessary fundamentals that can lay the foundations of SRS in CPSs and pave the way toward the practical deployment of autonomous CPSs and applications.
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    Unforgetting the Dakota 38: Settler Colonialism, Indigenous Resurgence, and the Competing Narratives of the U.S.-Dakota War, 1862-2012
    Legg, John Robert (Virginia Tech, 2020-06-04)
    "Unforgetting the Dakota 38" projects a nuanced light onto the history and memory of the mass hanging of thirty-eight Dakota men on December 26, 1862 following the U.S.-Dakota War in Southcentral Minnesota. This thesis investigates the competing narratives between Santee Dakota peoples (a mixture of Wahpeton and Mdewakanton Dakota) and white Minnesotan citizens in Mankato, Minnesota—the town of the hanging—between 1862 and 2012. By using settler colonialism as an analytical framework, I argue that the erasing of Dakotas by white historical memory has actively and routinely removed Dakotas from the mainstream historical narrative following the U.S.-Dakota War through today. This episodic history examines three phases of remembrance in which the rival interpretations of 1862 took different forms, and although the Dakota-centered interpretations were always present in some way, they became more visible to the non-Dakota society over time. Adopting a thematic approach, this thesis covers events that overlap in time, yet provide useful insights into the shaping and reshaping of memory that surrounds the mass hanging. White Minnesotans routinely wrote Dakota peoples out of their own history, a key element of settler colonial policies that set out to eradicate Indigenous peoples from the Minnesota landscape and replace them with white settlers. While this thesis demonstrates how white memories form, it also focuses on Dakota responses to the structures associated with settler colonialism. In so doing, this thesis argues that Dakota peoples actively participated in the memory-making process in Mankato between 1862 and 2012, even though most historical scholarship considered Mankato devoid of Dakota peoples and an Indigenous history.
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    Number Sequences as Explanatory Models for Middle-Grades Students' Algebraic Reasoning
    Zwanch, Karen Virginia (Virginia Tech, 2019-04-23)
    Early algebraic reasoning can be viewed as developing a bridge between arithmetic and algebra. Accordingly, this research examines how middle-grades students' arithmetic reasoning, classified by their number sequences, can be used to model their algebraic reasoning as it pertains to generalizing, writing, and solving linear equations and systems of equations. In the quantitative phase of research, 326 students in grades six through nine completed a survey to assess their number sequence construction. In the qualitative phase, 18 students participated in clinical interviews, the purpose of which was to elicit their algebraic reasoning. Results show that the numbers of students who had constructed the two least sophisticated number sequences did not change significantly across grades six through nine. In contrast, the numbers of students who had constructed the three most sophisticated number sequences did change significantly from grades six and seven to grades eight and nine. Furthermore, students did not consistently reason algebraically unless they had constructed at least the fourth number sequence. Thus, it is concluded that students with the two least sophisticated number sequences are no more prepared to reason algebraically in ninth grade than they were in sixth.
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    Federal and Local Acceptance of Refugees: The Dual Structures Promoting Community Inclusion
    Garrett, Benjamin Troy (Virginia Tech, 2019-07-11)
    This thesis asks the question: what roles do local governments and nongovernmental organizations play in resettling refugees in U.S. cities? To answer this question, I conducted a case study of the refugee resettlement structure and process as it occurs in the city of Roanoke, Virginia. I find that two governance structures dictate how refugees are resettled into the city. The first stems from federal refugee policy, which establishes the use of a public-private partnership between federal and state governments and federated civic organizations. The second is an evolving local-level grassroots organizational structure that assesses the needs of refugees in Roanoke following their initial resettlement. In the case study on Roanoke I examine the support roles and practices of government institutions and nongovernmental organizations during the initial refugee resettlement period. Additionally, I examine aspects of long-term service provision and additional supports that move refugees towards social and economic inclusion. I conducted interviews with government and non-governmental leaders to grasp their understandings of existing practices and norms of local-level refugee resettlement. I also examined local survey data, economic and demographic data, media reports, and other public documents prepared by government agencies and nonprofit organizations. I identify who offers, or influences decisions about, specific supports for refugees at different times throughout the resettlement/integration process. I will suggest further implications of the supports provided for how they structure the pattern of refugees' economic and social inclusion. This thesis is designed to contribute to the limited literature on the process of local-level refugee resettlement in U.S. cities.