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Intelligent Emergency Stop System for Autonomous Ground Vehicles Using Minimum Jerk Trajectories and Path Tracking Linear Quadratic Regulator
Get PDFAutonomous vehicle technology is moving towards developing systems that can function without human operators. One method of improving safety in autonomous vehicles is to include redundant sensors and systems to protect against the adverse effects of hardware failure during operation. This thesis presents an emergency vehicle collision avoidance system that uses a linear quadratic regulator (LQR) to track a minimum-jerk trajectory. This system is designed to be implemented on a low-cost processor that can act as a backup to the main computing hardware on these vehicles. The main goal of these algorithms is to bring a vehicle to a stop while avoiding collisions and maintaining safe operation.
The lateral control systems on the vehicle consist of a minimum-jerk trajectory generator and an LQR path tracking controller. Minimum-jerk trajectories can be used to generate reference states for the LQR path tracker that are more dynamically conservative than other spline interpolation methods, but these trajectories do not inherently account for the dynamic limitations of ground vehicles. Improvements to the dynamic feasibility of these trajectories are made by including an intelligent velocity reference system that reduces vehicle speeds before high dynamic lateral maneuvers. Simulations were performed in Matlab to validate the performance of the lateral and longitudinal control systems. These simulations were performed using randomized vehicle model parameters in order to analyze how the algorithms perform in the absence of accurate parametric data for the vehicle. The control algorithms were also tested in live experiments using low-cost computing hardware on a Lincoln MKZ sedan, demonstrating safe operation at various speeds with lateral path tracking errors of less than 15 cm
Piecing the Puzzle Together: Assembling a beautiful future for survivors of commercial sexual exploitation
No full textThis investigation sought to develop a deeper understanding of the lived experiences of survivors of commercial sexual exploitation by considering the links between childhood background, trafficking experiences, and outcomes.
To that end, 350 survivors of commercial sexual exploitation living in the United States were surveyed. Guided by ecological and life course frameworks, variables included background measures, trafficking experiences, and several outcomes, including income, employment status, posttraumatic stress (PTSD) symptoms, dignity, and sobriety.
Results suggested that adverse childhood experiences (ACES) were strongly linked to more severe exploitation and poorer outcomes, educational achievement improved income and employment without reducing trafficking severity, and socioeconomic disadvantage showed no significant risk, contradicting prior research. Findings went on to suggest that access to support services can buffer against the effects of childhood adversity on outcomes, though some results were unexpected or negligible in practical impact.
This investigation highlights the diverse and individualized needs of survivors of commercial sexual exploitation, indicating that tailored interventions — guided by intake assessments and behavioral framing — are essential for addressing the complex interplay of childhood adversity, trafficking experiences, and recovery trajectories. Altogether, these findings provide pieces of the puzzle that can assist survivors as they reassemble their lives into beautiful futures
Investigating Genetic Influences on Nutritive Value of Whole Cottonseed
Get PDFWhole cottonseed (WCS), a byproduct of cotton production, is a valuable feedstuff in ruminant nutrition due to its unique combination of fat, fiber, and protein. However, the nutritive value varies significantly among cotton varieties, creating challenges for consistent feed formulation. This study aimed to investigate the genetic basis of nutritive value in WCS by identifying genetic loci associated with crude fat concentration and in vitro true digestibility using genome wide association study (GWAS). A total of 383 cotton accessions were phenotyped for crude fat concentration and in vitro true digestibility (IVTD) in two years. Crude fat concentration ranged from 10.9 to 29.0%, and IVTD from 48.5 to 63.1%. For genotyping, the CottonSNP63K array was utilized and after quality filtering, 22,983 SNP were used in the GWAS analysis with the FarmCPU model in GAPIT v3. Seventeen SNP were associated with crude fat concentration across five chromosomes (chr.), with clusters on A04 and A06. Candidate genes near these loci included AtLPEAT1, LPAT4, ELO4/HOS3, and SCP2, all involved in lipid biosynthesis and transport. For IVTD, three SNP were identified, primarily on chr. D12, with candidate genes such as LTPG15, AtBGAL10, and PPD2 which are involved in seed coat development and cell wall metabolism. Heritability estimates were low for both crude fat concentration (h2 = 0.21) and IVTD (h2 = 0.10), indicating a stronger environmental influence. To evaluate the nutritive value of WCS, a set of 20 diverse elite cotton breeding lines were used from the 2023 Regional Breeding Testing Network (RBTN) field trial entries. These 20 cotton breeding lines exhibited variation across all evaluated traits for fiber composition for NDF (34.8 to 60.1%), ADF (25.5 to 39.9%), ADL (10.4 to 18.7%), crude protein (19.6 to 27.4%), crude fat concentration (16.0 to 25.7%), and IVTD (51.6 to 59.1%). The line, Ark 1510-31, exhibited both high crude fat concentration and IVTD which is desirable for increasing the value of WCS in ruminant diets. These findings highlight the importance of considering nutritive value assessments in cotton breeding programs and highlight the RBTN as a valuable tool for integrating agronomic and nutritional evaluations. Together, these results provide a start for breeding strategies aimed at improving the consistency and quality of WCS as a livestock feed ingredient. By combining genomic insights with phenotypic evaluations, this research supports the development of cotton cultivars that meet livestock nutrition goals
Topic wise Segmentation based Hybrid Models for Sentiment Classification on Social Media Platforms
No full textSentiment analysis is a crucial task in natural language processing that enables the extraction
of meaningful insights from textual data, particularly from dynamic platforms. The
research explores the development and evaluation of hybrid transformer-based models for sentiment
classification, emphasizing stacking configurations and topic-wise segmentation for improved
accuracy on social media datasets.
Transformers like BERT, RoBERTa, XLNet, DistilBERT, and Electra were employed individually
and in hybrid configurations. Experiments on the Sentiment140 and IMDb datasets
demonstrate that hybrid models, particularly Electra+BERT, achieve significantly higher accuracy
and robust classification performance, with test accuracies of 96.08% and 97.84%, respectively.
The research extends to analyzing oppositional narratives on social media, distinguishing
between conspiracy theories and critical narratives using fine-tuned RoBERTa and BERT
models. The models achieved high performance, with MCC scores of 0.8050 for binary classification
and an overall accuracy of 95% for identifying narrative elements. This work highlights
the potential of hybrid models and advanced segmentation techniques to address complex NLP
tasks, offering applications in sentiment analysis, public opinion monitoring, and misinformation
detection.
The Latent Dirichlet Allocation (LDA) model was integrated for topic segmentation, enabling
enhanced feature selection and contextual understanding. Experiments on the Sentiment140
and IMDb datasets demonstrate that hybrid models, particularly Electra+BERT,
achieve significantly higher accuracy and robust classification performance, with test accuracies
of 98.44% and 98.38%, respectively. LDA segmentation further improved sentiment
classification by refining decision boundaries, reducing misclassifications, and enhancing contextual
insights
Unraveling the Diverse State Policy Directives Amidst the COVID-19 Pandemic: Their Impact on Infections and Fatalities, and Key Insights
No full textThe COVID-19 pandemic has been one of the most significant global health crises in modern history, putting public health systems, economies, and political institutions around the world to the test. In the United States, states implemented a variety of non-pharmaceutical interventions (NPIs), such as mask mandates, stay-at-home orders, travel restrictions, school closures, and limits on gatherings. However, the timing, scope, and enforcement of public health interventions varied significantly between states. This study examines how states responded to the pandemic from March 2020 to October 2021, covering both major waves of COVID-19 and extending beyond the periods analyzed in most existing studies. Drawing on the dataset compiled by Mayer et al. (2022), which tracks weekly state-level policy actions alongside infection and death counts, this research addresses three main questions: (1) Which states were early adopters, late adopters, or non-adopters of NPIs, and what political, economic, and demographic factors explain these differences? (2) How effective were these policies in slowing infections and reducing deaths? (3) How did state politics shape enforcement and influence health outcomes? Using the Policy Innovation and Diffusion framework to guide the policy adoption analysis and an epidemiological approach to evaluate policy effectiveness, this study offers a comprehensive view of how states navigated an evolving crisis. The findings reveal the complex interplay between political context, demographic characteristics, and public health decision-making, and provide lessons for crafting effective, context-specific responses to future public health emergencies
The Role of Insulin Signaling in Sexual Dimorphism of Reproductive Senescence
No full textReproductive senescence is an important part of the aging process in animals, including in Drosophila melanogaster. Hormone signaling pathways such as insulin and insulin-like growth factor-like signaling (IIS) have well-characterized roles in aging and potentially also play a role in reproductive senescence. Most reproductive senescence studies have focused on only one sex, though both sexes display reproductive senescence. There are no studies that have investigated multiple tissues and age-specific differences in both sexes, such that direct comparisons can be made in Drosophila. This dissertation addresses these gaps by investigating how changes in gene expression and in hormone signaling pathways with age contribute to sex dimorphism in reproductive senescence examining reproductive and somatic tissue over time in male and female fruit flies, as well as the response to changes in diet. In Chapter 1, I have provided an extensive review of the insulin signaling pathway, its role in aging, and the known molecular mechanisms of reproductive senescence. I explained how this pathway helps maintain metabolism and how changes in it can affect aging and reproduction, especially under nutritional stress. I also discuss how sex dimorphism in aging may arise from variations in hormonal signaling and tissue-specific responses. Chapter 2 focuses on the role of the IIS pathway in reproductive aging in Drosophila, investigating how IIS gene expression changes across somatic and gonadal tissues during aging. I hypothesize that IIS gene expression shows age-specific alterations, which may contribute to reproductive senescence in males and females. Using Tag sequencing, I analyze gene expression changes at different ages and identify key pathways associated with immune response, metabolic regulation, and reproductive function. This study also showed a general increase in unbiased gene expression over time in somatic and reproductive tissues, consistent with desexualization. However, IIS genes showed inconsistent desexualization, suggesting strong functional constraints on sex-specific regulation in this pathway. In Chapter 3, I examined how protein and carbohydrate restriction affect the IIS pathway and reproduction in male and female Drosophila. Using both phenotypic and transcriptomic profiling assays, I observed that dietary stress accelerates reproductive aging. Females showed more reproductive output changes than males, especially under carbohydrate and protein-restricted diets. These findings highlight the sex-specific response to dietary stress and emphasize the involvement of the IIS pathway in mediating these effects. Furthermore, weighted gene co-expression network analysis (WGCNA) identified distinct gene modules associated with age, sex, tissue, and diet, providing new insight into the molecular pathways that regulate reproductive aging. Overall, the results from this dissertation highlight how dietary stress, sexual dimorphism, and sex-specific gene expression patterns influence IIS activity and contribute to reproductive senescence
Nutritional Inequality and Its Socioeconomic and Environmental Determinants
Get PDFThis dissertation consists of three essays that explore dietary inequality, methodological advances in measuring nutrition-related disparities, and the nutritional consequences of climate change. Collectively, these studies examine how socioeconomic and environmental forces shape dietary outcomes in China, the United States, and globally.
The first essay investigates income-related inequality in diet quality in China during a period of rapid economic growth, using data from the China Health and Nutrition Survey (1997–2011). Results show that average diet quality, as measured by the Chinese Healthy Eating Index (CHEI), improved significantly over the 14-year period. However, dietary improvements initially favored affluent groups, widening inequality before later becoming more equitable. Urban–rural disparities emerged as a primary driver of inequality, alongside differences in health insurance coverage and gender. Oaxaca decomposition reveals that changes in inequality were driven more by behavioral responses to income than by income changes themselves. These findings highlight the need to address non-dietary structural factors, such as health insurance and rural development, in efforts to improve nutritional equity.
The second essay introduces a machine learning-based Concentration Index (ML-CI) to more accurately estimate dietary inequality when income is measured categorically, as is common in survey data. Building on the Extended Kakwani-Wagstaff-Doorenbos (KWD) method, simulation results show that ML-CI offers superior precision and robustness in capturing true inequality levels. Applying this method to household-level dietary data from the USDA’s Purchase to Plate Crosswalk (PPC) reveals a clear pro-rich gradient in Healthy Eating Index (HEI) scores in the U.S., with healthier diets concentrated among wealthier households. In contrast, daily per-capita kilocalorie intake exhibits a pro-poor pattern, indicating that while lower-income groups consume more calories, their diets tend to be less nutritious.
The final essay presents the first global empirical assessment of how rising temperatures affect actual human nutrient intake. Combining climate records with nationally representative nutrition data from 185 countries (1990–2018), we find that increases in maximum temperature significantly reduce intake of multiple nutrients, including protein, fiber, vitamin A, and seafood omega-3 fats. These effects are highly unequal, with rural children and populations in low-income tropical regions disproportionately affected. Under the high-emission SSP5–8.5 scenario, nutrient intake declines are projected to worsen substantially by 2100, with per-capita seafood omega-3 intake falling over 30% and fiber and vitamin A by more than 25% in some regions. These findings reveal a critical but underexplored dimension of climate vulnerability—declining nutrients, highlighting the urgency of integrating nutrition-sensitive approaches into global climate adaptation and food policy
Heat Transfer and Fluid Mechanics Characteristics of a 3D-printed Synthetic Jet Device using Experimental and Computational Approaches
No full textThermal management in increasingly miniaturized electronic systems remains a critical challenge, as rising computing power, reduced form factors, denser interconnects, and advanced packaging techniques intensify power dissipation and junction temperature constraints. Synthetic jets (SJs), with their zero-net-mass-flux operation, compact size, and enhanced convective heat removal, offer a promising approach for localized and spot cooling without complex plumbing. This work presents a comprehensive experimental and computational study of novel 3D-printed synthetic jet devices (SJDs) aimed at guiding the design of compact, efficient cooling systems.
In the first phase, five SJD prototypes with varying cavity and orifice geometries were fabricated and characterized. Actuator deflection and jet exit velocity were measured across a broad frequency sweep to identify both structural and Helmholtz resonance behaviors. Thermal performance tests at the optimal frequencies then quantified convective heat transfer for multiple orifice-to-heater spacings. In the second phase, a refined device underwent extended assessment over a wider range of impingement distances, with hot-wire anemometry capturing detailed axial velocity profiles. Complementary three-dimensional CFD simulations elucidated the fluid-dynamic mechanisms governing heat transfer enhancement at different orifice-to-surface distances.
Key findings include: (1) shallow-cavity SJDs resonate at lower frequencies with larger deflections, while taller cavities shift resonance upward and reduce deflections; (2) peak heat transfer occurs at normalized spacings of 6-8, where coherent vortex rings maximize impingement pressure; (3) momentum flux, not velocity magnitude alone, dictates cooling performance; and (4) operating at the first (structural) resonance frequency (∼650-700 Hz) achieves nearly the same thermal benefit as the higher-frequency Helmholtz mode (∼1800 Hz) but with substantially lower power input. These results provide a rigorous framework for the future development and implementation of energy-efficient synthetic-jet cooling solutions in microelectronics
Place-based Policies and Food Access
Get PDFSignificant and persistent disparities in health and economic mobility highlight
the potential role of an individual’s neighborhood environment. The neighborhood
food environment, the mix of restaurant and retail food stores in a neighborhood,
has been closely linked to disparities in diet quality and obesity. The causal relationship
remains uncertain between grocery store presence and health outcomes,
but robust evidence links fast food availability and obesity. Despite its importance,
little is known about the forces that shape neighborhood food environments. This
dissertation examines how place-based policies designed to stimulate economic development
in distressed areas affect local food environments. Using a combination of
quasi-experimental econometric strategies, I analyze the impacts of the federal New
Markets Tax Credit (NMTC) program, State Enterprise Zone (EZ) programs, and
heirs’ property prevalence on food access across different U.S. regions.
Chapter 1 investigates the long-term impacts of the NMTC program on the food
retail landscape. Exploiting the program’s eligibility cutoff at 80% of State Median
Family Income, I implement a Fuzzy Regression Discontinuity Design (FRD) to
estimate the causal effect of NMTC allocation on the concentration of food retailers
from 2005 to 2021. The analysis reveals that NMTC investment led to short-term
increases in supermarkets and combination stores, but only combination store growth
persisted in the long term. Supplemental Inverse Probability Weighting (IPW) and
2SLS IV estimates support these findings, with limited evidence of spillover effects on
less healthy food outlets like fast food and convenience stores. In addition, I find no
signs of increased residential mobility or displacement following NMTC investment,
reinforcing the credibility of the causal interpretation.
Chapter 2 focuses on State Enterprise Zone (EZ) programs, which vary widely in
structure and implementation. Using a Propensity Score Matching (PSM) approach
on a panel of tracts across ten states from 2005 to 2021, I estimate the effect of
EZ policies on different types of food retailers. The findings are mixed and contextdependent:
while general EZ incentives did not consistently improve access to healthy
food outlets, programs with hiring-based incentives showed positive effects in the
short and medium term. Heterogeneity analysis suggests that EZ programs may have
adverse effects in predominantly Black neighborhoods, reducing access to grocery and
convenience stores and potentially reinforcing existing disparities.
Chapter 3 introduces a novel exploration of how heirs’ property, a form of informal
land ownership—constrains food access in the rural Southeast. Using parcel-level
data from CoreLogic and USDA Food Access Research Atlas across seven Southeastern
states, I construct an index of heirs’ property prevalence and estimate its relationship
with USDA-defined Low-Access (LA) and Low-Income, Low-Access (LILA)
tract status. Results indicate that tracts with higher shares of heirs’ property are significantly
more likely to be classified as food deserts. The relationship is pronounced
in areas with higher poverty and unemployment rates and lower educational attainment.
It suggests that legal land ownership disputes impede investment and may
contribute to structural disinvestment in rural food environments.
Overall, the findings highlight that place-based policies can significantly impact
local food environments. However, their effects vary substantially depending
on design, implementation, and contextual factors. This dissertation contributes to
the existing literature by providing policy-relevant insights into the intersection of
economic development initiatives and food access offering evidence to inform more
equitable and targeted interventions aimed to reduce food access disparities in underserved
areas
Exploring Clinical Mental Health Counselors-in-Training Experiences Preparing to Counsel Clients with Intellectual Disability
No full textThis phenomenological study explores the perceptions and experiences of counselors-in-training as it relates to their preparation to provide counseling services to individuals with intellectual disability. Semi-structured interviews were completed with participants who are currently enrolled in a masters-level, CACREP-accredited clinical mental health counseling program within the United States. Using a hermeneutic phenomenological approach, this research seeks to better understand how counselors feel about providing counseling services to individuals with intellectual disability, as well as what their preparation to provide these services has been like in their training thus far. The goal of this study was to have ten counselors-in-training complete the interview process, then utilize inductive coding to identify themes from the data. Implications for counselors and counselor educators, as well as the relevance of the research findings are discussed