University of Tennessee Institute of Agriculture
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“Cosplay, Community, and Queerness in Cosplay Spaces of Tennessee”
Get PDFCosplay is the portmanteau of the words costume and play; cosplay is a mode of dressing and acting where individuals not only costume themselves as a character, but act as said character, effectively playing that part (Geczy 2016). My work shows that convention spaces, the practice of cosplay, and thus cosplay spaces in general, [LEE1] opens avenues of gender expression and community building for queer individuals, especially women, which would not be possible in other social settings. Inspired first by my experiences at Yamacon, a convention held in Pigeon Forge, Tennessee, each December, that focuses on anime and cosplay, and expanding to other anime conventions, Renaissance Fairs, and various informal spaces of cosplay, I study the power of expression and community building that these places foster. The freedom of expression, the possibility of self-exploration, is critical in the politically conservative Bible Belt of Appalachia, as spaces such as these fly under the radar of oppressive laws that limit the accessibility of queer spaces. I combine three main methods of inquiry: journals filled out through Google Docs at cosplayers’ leisure, five interviews with key informants who facilitate events at the convention, and an autoethnography featuring my own knowledge from nine years of this convention. This methodology explores how cosplay helps navigate anxiety, personal reflection– especially gender identity and sexuality–interpersonal relationships, how different genders act and are seen as participants, organizers, and objects on view at this convention space, with an emphasis on the perhaps female (or perhaps a more complex matrix of genders) leadership in this convention, and the lasting connections between cosplaying convention-goers even after the conclusion of the event
Broiler welfare and behavior as affected by management practices
Get PDFThe U.S. broiler industry is a cornerstone of the nation’s food system and economy, leading global production and delivering affordable, high-quality protein. However, its rapid growth presents challenges, particularly regarding animal welfare. Key gaps include the economic and resource impacts of slower-growing strains, inconsistent research on stocking density and lighting effects, and limited adoption of advanced welfare technologies due to high costs. Addressing these challenges requires collaboration, targeted research, and sustainable solutions.
This dissertation examines the effects of growth rate, stocking density, and lighting intensity on broiler welfare and behavior, offering strain-specific management strategies to enhance production performance and welfare in the poultry industry.
Chapter one reviews gaps in balancing productivity and animal welfare. While slower-growing strains improve welfare, they increase costs and resource use, raising food security concerns. Research on stocking density reveals strain-specific responses but emphasizes environmental management’s role over density effects. Similarly, lighting intensity studies show inconsistent results. Emerging technologies like computer vision show potential for welfare assessment but face adoption barriers due to cost. Bridging these gaps demands rigorous research and practical, economically viable solutions.
Chapter two explores how growth rate and stocking density impact welfare. Reduced densities improve feather coverage, gait scores, and activity but may compromise feed conversion ratio (FCR) and lengthen production cycles.
Chapter three compares Ross 708 and Cobb 700 broilers under varying stocking densities. Lower densities improve feather cleanliness and footpad health but show minimal effects on production and welfare, supporting current stocking density standards (44 kg/m²).
Chapter four analyzes density and age effects on activity. High density increases early activity but reduces mobility by 56 days, with strain-specific behavioral responses.
Chapter five investigates lighting intensity. Higher light intensity improves activity, gait scores, and thermal regulation but increases feather loss, while lower intensity preserves feather coverage and reduces dermatitis but limits activity.
Chapter six highlights strain-specific lighting strategies. Ross broilers benefit from 20 lux, enhancing behaviors like stretching and preening, while Cobb 700 broilers adapt better to 5 lux, particularly during later growth stages. These findings emphasize tailored management strategies to optimize broiler welfare
Quantum Framework for Topological Data Analysis
Get PDFTopological Data Analysis (TDA) methods combine tools from statistics and machine learning with concepts from algebraic topology usually with the purpose of classifying data based on its shape. These techniques provide advantages such as dimensionality reduction and resilience to noise. In addition, they can often recover information from signals or other complex dynamical systems that traditional methods fail to capture. In recent years, TDA methods have seen applications in many different classification problems from biology, materials science, robotics, and computer vision, among others. However, extracting topological features from a data set can be computationally expensive as in general it involves an NP problem. With the rapid development of quantum computers and their rise in popularity to deal with similar mathematical problems, it is natural to wonder if these new devices can provide an advantage for TDA. Indeed there have been several recent papers introducing quantum algorithms for TDA and discussing their potential to speedup or complement the current classical counterparts. This manuscript is a compilation of my works on the subject of quantum methods for TDA. My contributions include a novel quantum algorithm for persistent homology that is able to obtain topological features from a data set and track them through changes in resolution. In addition, the algorithm yields more information than other similar quantum algorithms and still has the potential to provide a quadratic speedup over classical counterparts. Furthermore, I introduce a subroutine that allows the aforementioned algorithm and similar ones to work with time series data sets like signals. Finally, I adapt techniques from quantum variational algorithms to estimate distances between persistence diagrams, so as to compare data sets through the topological features extracted by the persistent homology algorithm
Efficient Neural Representations in Spatial Navigation and Pharmacokinetic Imaging: Information-Theoretic and Machine Learning Approaches
Get PDFMammalian spatial navigation relies on specialized neurons, such as place and grid cells, which encode position based on self-motion and environmental cues. While extensive research has explored the computational role of grid cells, the principles underlying efficient place cell coding remain less understood. Existing spatial information measures primarily assess single-neuron encoding, limiting insights into population-level representations. To address this gap, we introduce novel information-theoretic measures that quantify the encoding efficiency of multiple neurons, including the joint stimulus information rate for neuron pairs and the spectral-stimulus information for arbitrary populations. The spectral-stimulus information, defined as the leading eigenvalue of the stimulus information matrix, is maximized when neurons exhibit localized, non-overlapping firing fields—mirroring place cell and head direction cell activity in biological systems. We demonstrate that these measures can be used to train recurrent neural networks (RNNs) via self-supervised learning, leading to the emergence of place cells and head direction cells. Our findings highlight how neural populations collectively encode stimuli, offering a more comprehensive framework for understanding place cell formation and optimizing artificial navigation systems in novel environments. Additionally, functional ultrasound imaging (fUSI) provides high spatiotemporal resolution for monitoring cerebral blood volume (CBV) by detecting backscattered echoes from red blood cells. While fUSI has been widely used in preclinical neuroscience, many studies focus on predetermined regions of interest (ROIs), potentially overlooking relevant brain activity. To address this, we combined fUSI with three machine learning models—convolutional neural networks (CNNs), Vision Transformers (ViTs), and Support Vector Machines (SVMs)—to analyze the pharmacokinetics of Dizocilpine (MK-801), an NMDA receptor antagonist. CNNs demonstrated superior performance in detecting and localizing drug-induced changes in brain hemodynamics. Using class activation mapping (CAM), CNNs identified anatomically specific patterns in cortical and hippocampal regions that align with known NMDA receptor distributions. Quantitative analysis confirmed significant drug-induced CBV reductions in CNN-identified regions. While all models distinguished between drug and control conditions, CNNs uniquely maintained anatomical precision while tracking drug effects over time. By integrating advanced machine learning with fUSI and spectral-spatial measures, our work provides novel insights into both neural representation and pharmacological effects on brain function. These approaches enhance our ability to model spatial coding in artificial systems and improve the detection of drug-induced neural changes, advancing both computational neuroscience and biomedical imaging
Navigating Secondary Prisonization: Narratives of Adults with Incarcerated Romantic or Relational Partners
Get PDFMore people are incarcerated in the United States than in any other country in the world (Widra & Herring, 2021) and approximately 113 million adults in the US have experienced the incarceration of a family member (Sawyer & Wagner, 2024). Most research explores carceral impacts on incarcerated people and their children, with minimal focus on the impacts of incarceration on adults with incarcerated loved ones. This research explores the stories of Adults with Incarcerated Romantic or Relational Partners (AIRRP) and how their experiences with secondary prisonization impact their mental health and well-being. This study will fill a gap in the Professional Counseling literature and may inform efficacious counseling interventions
Latent Bullying Victim Profiles Derived from Child and Teacher Reports: Differential Associations with Psychological and Social Functioning
Get PDFA major challenge in addressing childhood bullying victimization is the accurate identification of bullied youth, as researchers often rely on multiple report sources (e.g., self-reports and teacher reports) that can offer conflicting accounts. This study used latent profile analysis (LPA) to identify distinct groups of children based on patterns of self- and teacher-reported bullying victimization. Also examined was whether profile membership was concurrently associated with sociodemographic characteristics and measures of psychosocial functioning. Finally, this study examined whether profile membership predicted changes in psychosocial functioning over time. Participants were 482 third- and fourth-grade students and their teachers. LPA revealed three concordant groups (nonvictims, victims with high self- and moderate teacher-reported victimization, and victims with moderate self- and teacher-reported victimization) and one discordant group (self-identified victims). Both concordant and self-identified victims reported greater concurrent and later maladjustment compared to nonvictims. Teachers’ perceptions of victimization, externalizing behaviors, and student-teacher relationships remained stable throughout the school year. Teachers rated concordant victims as displaying more externalizing behavior and having more negative interactions compared to nonvictims and self-identified victims. Notably, self-identified victims were just as vulnerable to adjustment problems as concordant victims, despite lacking corroboration from teachers regarding their bullying victimization experiences. These findings highlight the importance of giving weight to self-reports in identifying bullied children, as this is a vulnerable group of youth who might not be identified when relying on teacher-report or multi-informant concordance
The Unity and Diversity of Executive Function as shown by Resting-State Functional Connectivity: An fNIRS Replication and Methods Comparison Study
Get PDFThis study is, first and foremost, a direct replication of “The unity and diversity of executive functions and their contributions to complex ‘frontal lobe’ tasks: A latent variable analysis” (Miyake et al., 2000). We recreated the same nine simple tasks, three tasks each for three “components” of executive function, “shifting,” “inhibition,” and “updating.” We also used the same five complex executive (or “frontal lobe”) tasks. First, we used a confirmatory factor analysis to replicate the factor structure of executive functioning identified by Miyake et. al. (2000). Then, we used structural equation modeling to replicate the findings that each complex executive task has one or more component of executive function mapping onto it. We then expanded upon this original study by comparing a Passive Viewing Paradigm and Mind-Wandering Paradigm for resting-state functional connectivity data collection with functional near-infrared spectroscopy. We compared the functional connectivity profiles between these two resting-state methods, and we used group-level correlation analyses and principal components analysis to better understand the structure of EF from a neurocognitive perspective. Ultimately, we did not replicate the findings from the original EF study but rather endorsed a model where shifting and inhibition are equal. We then used this model to differentially assess how the components map onto the complex EF tasks. Using the neural data, we found that the two resting-state measures are quite different, especially when using them to assess how this data correlates with EF outcomes. Executive function is not as simple as a set of components such as shifting, updating, and inhibition, but rather a complex system in the brain that leads to behavioral outcomes defined as EF skills. From a neurocognitive perspective, EF measures cannot be easily structured into three separable components with an underlying common EF factor, nor can the behavioral structure be easily replicated twenty-five years later
Integration of Trees and Compost Amendments on an Organic Farm for Soil Carbon and Soil Health Benefits
Get PDFWhile organic farming is growing in the U.S., its climate change mitigation potential is often questioned due to the heavy dependence on tillage for successful crop production at the expense of soil organic carbon (SOC). Therefore, it is critical for organic systems to transition to or implement management practices to balance productivity with environmental stewardship. This thesis tested the effectiveness of two management strategies to improve climate change mitigation and adaptation potential of organic systems. Our objectives were to quantify the SOC accumulation potential of implementing a perennial agroforestry system (AFS) which minimizes soil tillage and maximizes C inputs (study 1). However, when perennial systems cannot be implemented, we also evaluated whether high-quality compost amendments can offset tillage-related soil degradation by adding a source of C (study 2). Both studies were conducted at Caney Fork Farms, a certified organic farm in middle Tennessee. To meet the objectives, the first study quantified SOC stock and fractions near trees of two ages (4- and 7-years-old) and three distances from trees (0.5, 2, and 15 m) on SOC in a silvopasture AFS. The second study involved a field experiment to test 3 rates of compost made using the Biologically Enhanced Agricultural Management (BEAM) principle (0.56, 1.12, 2.24 Mg ha-1) compared to windrow compost (22.4 Mg ha-1), the co-application of BEAM with half the rate of windrow compost, and a control on soil health metrics and crop performance for one year. Study 1 revealed that SOC storage and stabilization were increased at 0.5 m from 7-year-old trees, likely from enhanced tree C inputs from older trees; implying that growing trees in a pasture is an effective climate change mitigation strategy that may increase over time. Study 2 did not reveal any improvement in soil health or crop performance of applying compost to an organic reduced tillage system after one year. This thesis highlights how improving the climate change mitigation potential of organic systems by increasing C inputs is feasible in perennial systems but more challenging for tilled systems and likely takes several years after strategies have been implemented to be effective
Bridging HPC Data Gaps: Novel Tools for GPU Resource Monitoring and Scheduler Emulation
Get PDFThe evolution of High-Performance Computing (HPC) systems is introducing ever greater complexity in resource management and performance monitoring, creating critical gaps in data availability. With their expanding significance in AI and data-intensive applications, GPUs now serve as pivotal components of HPC workloads. Simultaneously, the push toward exascale computing necessitates increasingly efficient and scalable scheduling policies. This thesis presents two novel tools designed to address the data availability challenges introduced by these ongoing shifts in modern HPC environments. The first tool bridges a crucial gap in per-job GPU resource monitoring for SLURM-managed clusters, which lack this support natively. By enabling detailed post-job analysis of GPU utilization metrics, it allows researchers to identify underutilization issues—ranging from configuration errors to algorithmic inefficiencies—while helping administrators accurately assess resource usage in planning future upgrades. The second tool, the Flux Emulator, builds upon a preliminary prototype and extends the capabilities of the Flux Framework, a cutting-edge resource management and scheduling system tailored for exascale HPC. Through the simulation of historical job workloads, the emulator enables the evaluation of various scheduling policies and strategies without the need for physical cluster resources. By illuminating the effects of different scheduler configurations on performance metrics such as job makespan, it empowers system software developers to refine algorithms and policies for more efficient utilization of emerging exascale systems. By providing detailed GPU resource monitoring within established scheduling systems and offering a scalable emulator for evaluating multiple scheduling policies, this thesis lays the groundwork for a more data-driven approach to HPC resource utilization and optimization. Together, these tools directly address the critical gaps in data availability and performance insight that arise as HPC environments continue to grow in complexity
How the Russia-Ukraine Conflict is Shaping U.S. Wood Pellet Exports and EU Import Demand
Get PDFThe EU’s Renewable Energy Directive (RED) classifies wood pellets as carbon neutral, which has resulted in their increased use in commercial heat and electricity generation. Consequently, the EU is heavily reliant on imports to satisfy this growing demand.
The U.S. is the world’s largest wood pellets exporter, reaching a value of $1.8 billion in 2023, with the EU accounting for over 25 percent. Russia and Belarus are also major exporters to the EU. As a result of Russia’s invasion of Ukraine, the EU imposed trade sanctions on Russia and Belarus (for supporting the invasion) causing imports from these countries to plummet.
The goal of this study is to estimate the demand for imported wood pellets in the EU across exporting countries and to assess how these trade sanctions have impacted U.S. wood pellet exports to the EU, as well as exports from other competing countries. The QUAIDS model was used for the analysis, which is an extension of the more popular AIDS model, and uses a quadratic term to allow a more flexible total expenditure effects. Uncompensated elasticities, compensated elasticities, and expenditure elasticities were estimated and used to establish substitution and complementary relationships between exporting countries in the EU market.
Before the sanctions, EU wood pellet demand favored U.S. exports. Additionally, the QUAIDS elasticities indicated that wood pellet demand was elastic and price sensitive overall, with the U.S. wood pellet exports being the least sensitive to price changes. After the sanctions, Russia and Belarus suffered significant losses in export value, while the remaining countries experienced substantial export gains. The U.S. maintains its rank as the top wood pellet exporter to the EU