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In Search of Real Truth and Accurate History: Protecting and Resisting Whiteness in Education Policy Discourse
Get PDFThis research examines whiteness in education policymaking through a three-article dissertation, contributing to the broader understanding of how whiteness manifests across education policy and legislative discourse. The first article presents a comprehensive scoping review of critical whiteness studies (CWS) in education policy research. Using CWS tenets such as whiteness as property and colorblind racial ideologies, the analysis identifies themes of manipulated discourse, the economics of whiteness in education, and the protection of whiteness. This study anchors the second and third studies, which examine whiteness ideologies at play within the education policymaking discourse of the American Southeast. The second article addresses a knowledge gap by exploring state-level policymaking discourse, focusing on Tennessee’s anti-critical race theory (CRT) policy actions between 2021 and 2023. Guided by the research questions, this critical discourse analysis identifies the presence of whiteness ideologies throughout the policymaking process and is particularly embedded, while occasionally overlapping, in discourses of preservation, free expression, and urgency. The study also highlights the centrality of counternarratives through resistance discourse. The third article provides a comparative analysis of Tennessee and Arkansas to understand the role of whiteness ideologies in anti-CRT legislative discourse as a reflection of the broader moral panic in education conservatism. This comparison offers insight into the distinct expressions of whiteness ideologies regionally, the tenacity of resistance, and how these findings contribute to the national discourse within the anti-CRT movement. Several key findings emerge across the three articles. In each study, whiteness ideologies demonstrated a deep entanglement with education policy at all phases of the policymaking process. The particular shade of whiteness ideology shifts according to context and power relations, but the underlying need to protect whiteness by maintaining its invisibility remains a constant. In so doing, white supremacy maintains its hegemonic position in educational systems. Moreover, the unyielding resistance to these policy actions is active in both states and across education policy research. Lawmakers and stakeholders engage both direct and indirect strategies to challenge the whiteness ideologies in anti-CRT bills. This research contributes understanding to the intersection of education policy, legislative discourse, and critical whiteness studies by highlighting the importance of examining whiteness in education policy to illuminate and resist white supremacy in educational systems. While the battles continue to reshape and adopt new forms, the fight over curricular control and white-centric values continues across the Southeast and across the nation. The resistance continues
Developing Custom Fertilizer Strategies Recirculating Hydroponic Systems
No full textHydroponic systems recirculate nutrient solution to reduce the amount of water used in leafy green production. However, the rootzone of the nutrient solution is complex and influenced by interacting factors such as plant uptake demand, irrigation water quality, supplied fertilizer salts and nutrient concentrations, environmental conditions, and injection of mineral acids/bases to control solution pH. A common strategy for managing nutrients in commercial production is to select a hydroponic solution formulation (i.e. recipe) to supply and maintain a target pH and electrical conductivity (EC) in the recirculating solution over time by automatic injection of mineral acids/bases and concentrated fertilizer stock solutions. During nutrient replenishment of the recirculated solution, nutrients are resupplied at the same ratios at which they are supplied in the initial solution. Often with this approach, the resupply of nutrients is not balanced with plant uptake demand, resulting in root zone nutrient imbalances which can cause yield reductions and motivate growers to dump and replace solution. The objective was to develop a novel strategy for managing nutrients in recirculating solutions designed to supply and maintain optimal macronutrient concentrations and solution pH with basil (Ocimum basilicum) and lettuce (Lactuca sativa) as model crops. The strategy consisted of custom formulating species-specific initial and replenishment solutions formulated using a combination of data on plant tissue nutrient concentrations, previously published nutrient management guidelines and peer-reviewed research, and common grower tools and experience. The custom initial solution for each species was intended to supply macronutrient concentrations at near optimal ratios whereas the custom replenishment solution was intended to replace the nutrients taken up and maintain the concentrations/ratios in the initial solution. The custom strategy was evaluated with basil and lettuce grown for 56 d in deep water culture (DWC) systems and compared to a control strategy consisting of a common 2-part fertilizer formulation for both the initial and replenishment solution. Overall, the custom strategies for basil and lettuce resulted in macronutrients remaining near the initial concentrations supplied whereas the control strategy resulted in macronutrients, particularly calcium and sulfur, which deviated substantially from the initial concentrations. A separate experiment evaluated the custom nutrient management strategy with basil grown in hydroponic nutrient film technique (NFT) systems, where nutrient solutions were formulated using two sources of irrigation water differing in soluble salts and alkalinity. Overall, solution macronutrient concentrations remained more stable over time with the custom strategy compared to the control strategy for both irrigation water qualities. Yield was greatest for basil grown in hydroponic solutions formulated using the high alkalinity irrigation water as a result of the additional nitrogen (N) supplied by nitric acid (HNO3) injection used to neutralize the water alkalinity and adjust solution pH
Repurposing High-Expressing Muscle Loci with CRISPR Gene Editing: A Multi-Platform Analysis at Efficiency and Safety
No full textThis dissertation presents the development and evaluation of a CRISPR/Cas9-based platform for targeted gene integration as a therapeutic strategy for loss-of-protein diseases. By repurposing highly expressed muscle loci, such as Ckm, this platform demonstrates potential for stable therapeutic RNA expression. In vitro and in vivo studies were conducted to validate its efficiency and assess the safety of the selected integration sites, with particular focus on the effects of double-strand break (DSB)-mediated targeted gene insertion.
The first aim establishes proof-of-concept for the targeted integration of therapeutic genes into high-expression muscle loci using Homology-Independent Targeted Integration (HITI), achieving high RNA expression with minimal adverse effects on the global transcriptome. The second aim examines the transcriptomic consequences of HITI-mediated gene editing, using short-read and long-read sequencing to evaluate integration precision, transcript diversity, RNA modifications, and unintended integration events. The third aim applies similar orthogonal short- and long-read sequencing approaches to investigate Duchenne Muscular Dystrophy (DMD), the most common genetic disorder. In this section, we investigate the genomic and transcriptomic safety of AAV-CRISPR delivery for DMD therapy, identifying complex structural variations and unexpected AAV insertions that might be overlooked by a single sequencing method.
Collectively, these studies demonstrate the potential and challenges in developing targeted integration mediated by CRISPR-based therapeutics and highlight the value of comprehensive sequencing methodologies to advance gene editing safety and reliability
Digitalization of News: Shaping News Consumption Behavior
No full textDigitalization has profoundly transformed news consumption and production, leading to significant societal implications. Notably, the rise of fake news and echo chambers has increased political polarization. On the consumption side, digitalization has altered both the quantity and quality of news consumed, commonly referred to as an individual\u27s news diet. On the supply side, traditional journalistic norms are threatened as news production adapts to digital environments. Essay 1 examines the impact of digital news, conceptualized as a digital object, on news diet quality. This study employs an affordance-based framework to explore the relationship between digital features and individuals\u27 online news consumption. Drawing on affordance theory, information processing models, and the utility of news consumption, we propose a theoretical link between digital news affordances and the quality of individuals\u27 news diets. A mixed-method approach—comprising interviews and an online survey—is used to test our hypotheses and generate deeper insights into promoting a healthier news diet. Essay 2 focuses on individuals’ behaviors in curating news content on social media, particularly those that enhance news diet quality. We investigate the interactive role of algorithmic and social curation mechanisms in shaping news engagement patterns. Additionally, we propose theory-driven design interventions to improve news engagement behavior. We employ a multi-method approach, including online experiments and surveys, to evaluate these interventions to identify effective strategies for fostering desirable news consumption. This dissertation offers novel theoretical insights into the nature of digital news artifacts and their influence on consumption behaviors in the digital era. Our findings provide practical implications for navigating the evolving and complex landscape of digital news consumption and for fostering more informed and responsible engagement with news content
Fixed-time Artificial Insemination Protocols Using Pre-synchronization in Suckled Beef Cows
Get PDFAdoption of artificial insemination by beef producers has been limited within the United States. Estrous synchronization (ES) for fixed-time artificial insemination (FTAI) has been one of the most effective ways to implement artificial insemination into beef production systems, but its efficiency has primarily been restrained by the dependency on the effectiveness of the initial gonadotropin releasing hormone (GnRH) injection in many FTAI protocols. The hypotheses of these studies were to determine practical methods of pre-synchronization to improve current FTAI protocols, ultimately increasing beef herd reproductive performance. The objective was to evaluate the use of prostaglandin F2α (PGF) and progesterone (P4) to increase the effectiveness of the initial GnRH injection within protocols to increase the synchronization of estrous cycles within a herd, ultimately increasing the number of pregnant animals. Utilizing P4 and PGF as a method of pre-synchronization for a shortened ES protocol induced a greater percentage of animals exhibiting estrus behavior compared to animals that had received only PGF as a method of pre-synchronization or no method of pre-synchronization. Cows subject to methods of pre synchronization also resulted in a greater percentage of animals pregnant to AI than those not receiving a method of pre-synchronization. Furthermore, evaluation of pre-synchronization utilizing P4 and PGF prior to differing FTAI protocols yielded AI pregnancy results that may contrast with the typical relationship of estrus expression and AI pregnancy rates within fixed time artificial insemination protocols. Evidence suggests that the use of P4 and PGF in methods of pre-synchronization within ES protocols provide potential in producing greater reproductive outcomes and provide evidence as a potential method to further improve the efficiency of the beef production industry
Sitting Down or Speaking Up: Influences on the Behavior of Autistic Women During Workplace Meetings
No full textAutistic women often mask their disability to fit in better with peers at work. However, this has long-term harmful effects on their well-being and ability to sustain employment. In order to promote more adaptive behaviors at work, it is necessary to understand environmental and social mechanisms that pressure autistic women to mask. In this dissertation, I draw on anxiety/uncertainty management theory and expectancy violation theory to suggest that in workplace meetings where expectations are not clear, autistic women experience uncertainty about whether their behavior will be appropriate and anxiety over the potential consequences of acting inappropriately. I argue that under these conditions, autistic women act in alignment with the norms associated with their social categories—either their gender or their autistic self. As gender is often more visible than autism, I suggest that when circumstances are ambiguous, and thus there is a higher perceived risk of behaving incongruent to expected behavior, autistic women will choose to mask and behave more feminine to reduce the risk of negative sanctions. Conversely, when autistic women understand the bounds of appropriate behavior in meetings, they feel confident in knowing when and how to speak up about their opinions and suggestions. Importantly, I propose two boundary conditions—self-efficacy and conventional attractiveness—that influence alignment to social categories and subsequent normed behavior. I test this model in an event-contingent three-week experience sampling study, which allows for an understanding of how this phenomenon unfolds in real time. This research suggests several practical interventions that organizations can leverage to increase workplace meeting inclusivity and thus reap the benefits of this population’s unique insights, providing benefits to autistic workers, organizations, and broader society. Finally, I set the stage for future research and theory on autistic women and workplace social interactions
Nonparametric Methods for Bayesian Community Detection in Complex Networks
Get PDFNetwork analysis is becoming an increasingly popular interdisciplinary area of study, with emerging interest in fields like sociology, biology, economics, and ecology. Within the niche of network analysis, capturing the community structure of a network is one important achievement that many statisticians have been working toward over recent decades. The most popular modeling technique for latent community detection is the Stochastic Block Model (SBM), which falls into the category of latent variable models and will serve as the baseline model throughout this thesis. SBM is widely regarded as the most effective community detection method as it detects latent community membership among individuals in a network, and the probability that two individuals have a relationship is based only on community structure. Though effective, SBM has significant limitations. Along with other traditional latent variable models, SBM requires a pre-specified number of communities. The community structure, which would lend access to the number of groups, is often unknown in application. Furthermore, traditional SBMs are often insufficient in modeling networks with block structures that do not have well-separated communities or low within-group probabilities. Due to these limitations, recent developments of SBMs have included extensions of the traditional SBM to the infinite parameter space. The partition structure of the network is then modeled using nonparametric Bayesian methods. In this thesis, I will explore three nonparametric Bayesian methods for community detection in the context of SBM, including the Dirichlet Process, the Gnedin Process, and a Mixture of Finite Mixtures approach. I aim to provide supporting evidence through model comparison that nonparametric methods outperform traditional community detection methods among networks with complex structures and an unknown number of communities
Real-Time Anomaly Detection in OT Networks Using GRU-Based Autoencoders
Get PDFOperational Technology (OT) networks, particularly those used in critical infrastructure, face increasing cyber threats that target network-level protocols and behaviors. While most anomaly detection research for OT systems has traditionally relied on sensor data, this thesis explores the viability of detecting malicious activity directly from network telemetry. We propose a sequence-to-sequence autoencoder model based on Gated Recurrent Units (GRUs) with multilevel attention, trained to reconstruct normal patterns of packet-level communication extracted from raw PCAP data. The developed feature engineering pipeline integrates general networking attributes such as IP and MAC addresses, ports, and transport protocols with OT-specific protocol information from Modbus and DNP3. In the first-ever machine learning–based analysis of a recently released OT dataset, models were trained and evaluated at varying sequence lengths (25, 50, and 100 packets) to determine optimal performance trade-offs. Only the model trained on 100- packet sequences (Seq100) yielded meaningful detection performance, achieving approximately 81% precision in a partially labeled scenario and identifying half of the known attacker network artifacts within the dataset. Temporal visualization of reconstruction errors indicated alignment with known attack periods. In contrast, per-feature error analysis highlighted that high-cardinality fields such as ports and application protocols contributed most significantly to anomaly detection. To evaluate practical applicability, the Seq100 model was quantized, exported to ONNX, and deployed on an AMD Neural Processing Unit (NPU) and an NVIDIA V100 GPU. With a mean inference time of just 5.6 milliseconds on the NPU, the model demonstrated strong real-time feasibility in resource-constrained environments. This thesis establishes a foundation for deploying interpretable, real-time anomaly detection systems based on unsupervised deep learning techniques in OT networks, demonstrating both strong detection capability and practical inference efficiency
Comparisons between Traditional and Gluten-free Cookies in Sensory Attributes, Acceptance, and Evoked Emotions
Get PDFThe rise in food allergies and intolerances, notably conditions like Celiac disease, as well as a general shift of consumers to a more health-conscious lifestyle has significantly increased the demand for gluten-free products. Consumers increasingly seek gluten-free products, yet these alternatives often face challenges in replicating the sensory qualities of traditional gluten-containing foods. This study explores the sensory attributes, emotional responses, and labeling impacts on consumer perception of gluten-free and gluten-containing cookies. The main objective of this thesis was to address the gap in research on sensory evaluation of gluten free foods, specifically in terms of commercially available products. The findings of this research addressed differences in sensory attributes and consumer perceptions of gluten-free and gluten-containing cookies, while identifying key attributes influencing their overall appeal. The research is divided into three key studies. Study 1 (Chapter 3) investigated the impact of labeling information on consumer acceptance of gluten-free cookies. Eighty-four regular cookie consumers evaluated three gluten-free cookies and three traditional cookies in both labeled and blind conditions. Sensory acceptances (appearance, flavor, texture, and overall liking) were rated on a 9-point hedonic scale, and specific attributes (chocolate flavor, sweetness, and hardness) on a 5-point just-about-right (JAR) scale. Results showed that labeling as “gluten-free” did not significantly influence sensory perception and impression, indicating a minimal impact of gluten-free labeling on consumer acceptance of chocolate chip cookies. In Study 2 and 3 (Chapter 4 and 5) respectively, 24 cookie samples (both gluten-free and gluten-containing) were evaluated by 7 trained panels to identify key sensory attributes, while sensory acceptances (appearance, flavor, texture, overall liking and purchase intent) were rated on a 9-point hedonic scale by 131 regular cookie consumers. Additionally, emotional responses were measured using the circumplex-inspired emotion questionnaire (CEQ). It was found that gluten-free cookies were rated lower than the gluten-containing ones in terms of sensory attributes, hedonic impressions, and purchase intent. Gluten-free cookies were also associated more with negative emotions. This comprehensive analysis guides the development of gluten-free products, helping them meet consumer expectations and achieve greater market success. The findings from this thesis confirmed that most gluten-free cookies are lacking in sensory qualities compared to the traditional cookies, even within commercially available products from the same brands. However, it also points out that certain gluten-free offerings can compete in the market and highlight significant areas of improvement. These findings will be valuable for food manufacturers and marketers seeking to improve sensory appeal and consumer acceptance of gluten-free offerings
Thermal Design and Control of a PCB-based Solid-State Gas Generator (SSGG) Heater for Space Applications
Get PDFThis thesis presents the development and experimental validation of a compact, solid-state gas generator (SSGG) heater integrated into a printed circuit board (PCB) for use in satellite deorbiting systems. Designed for CubeSat-class spacecraft, the system produces gas via the thermal decomposition of sodium azide (NaN₃). The project emphasizes minimal mass, low power consumption, and mechanical simplicity – key constraints for modern space missions. The heater system relies on Joule heating through patterned copper coils embedded within the PCB structure. NaN₃ is deposited into wells drilled into the board surface, where localized heating initiates its decomposition near 300°C. A range of PCB configurations were designed and fabricated to assess the influence of geometric and electrical parameters on thermal performance. Experimental testing revealed that higher initial coil resistance correlates strongly with improved thermal localization and efficiency. Coils placed within inner copper layers offered greater thermal retention and structural robustness, while strategic reductions in copper area around the wells enhanced heat focus. These findings guided the development of a final 6-layer modular design capable of achieving the desired decomposition temperature reliably and repeatably. The result is a 4x4 array of compact heating elements, each functioning independently but integrated into a unified architecture scalable to different mission sizes. Additional features, such as edge-mounted diodes and automated data acquisition, support precise control and monitoring during operation. Testing conducted in Earth conditions confirmed the system’s ability to reach decomposition temperatures, with improved efficiency anticipated in the vacuum of space due to reduced convective losses. This design provides a lightweight, manufacturable solution for small satellite missions and a foundation for future gas-based deorbiting technologies