Digital Commons Network^™

Effects Of Cholesterol Concentration On Liposome And Proteoliposome Behavior, Ehsaneh Khodadadi

Graduate Theses and Dissertations

Cholesterol is crucial in modulating the behavior and characteristics of biological membranes, liposomes, and proteoliposomes, for instance in drug delivery applications. Cholesterol regulates membrane stability by influencing lipid density, phase separation, and protein-membrane interactions. In planar bilayers, cholesterol enhances membrane thickness, reduces permeability, and promotes lipid ordering, adding rigidity and fluidity. Cholesterol also modulates membrane curvature in curved bilayers and vesicles, stabilizing low-curvature regions. In this work, all-atom molecular dynamics (MD) simulations were employed to examine how varying cholesterol concentrations influence the structure and dynamics of membranes, focusing on planar and curved geometries. Bilayers with cholesterol molar ratios of 0%, …

Crispy Not Risky: Effects Of Environmental Conditions On Low-Moisture Food Persistent Bacterial Pathogens, Manita Adhikari

Graduate Theses and Dissertations

Low-moisture foods (LMFs) were not historically seen as safety risks due to their ability to inhibit pathogen growth, but evidence shows pathogens can survive in LMFs long-term, causing outbreaks and recalls. Previous studies have lacked the ability to replicate real-world contamination scenarios in LMF processing environments. This research aimed to develop inoculation methods to form LMF persistent bacterial populations (LMF PBPs) and assess pathogen responses to environmental factors, culturing methods (broth vs. lawn), and matrix type and ratio. Inoculation methods tested were “direct” (combining bacterial culture and powder on a stainless-steel coupon) and “slurry” (premixing culture and powder). Matrices included …

Using Deep Learning And Two-Photon Excitation Fluorescence Microscopy To Predict Breast Cancer Recurrence And Response To Chemotherapy, Nicholas Powell

Graduate Theses and Dissertations

Accurate prediction of breast cancer recurrence plays a critical role in guiding treatment decisions, particularly regarding the use of systemic therapy for patients. While genomic assays such as the Oncotype DX Recurrence Score offer valuable prognostic information, they are limited in accessibility and application. The work presented in this thesis explores an imaging-based approach for the prediction of breast cancer recurrence that combines multiphoton microscopy (MPM) with deep learning to classify individual breast cancer biopsy samples by risk of recurrence. Genomic differences in tumors with different recurrence potentials may translate to distinct optical and structural information that can be captured …

Procedural Generated World With History Simulation And 3d Map, Xuanchen Shen

Programming Theses and Dissertations

The procedurally generated world with history simulation tool dynamically generates customizable, seed-driven virtual worlds through algorithmic processes. Using Voronoi diagrams (constructed via Fortune’s algorithm) for spatial partitioning, it creates diverse geographic features such as lands, oceans, and heights. Climate systems, hydrology (rivers, precipitation), and temperature data are procedurally derived from the terrain data. The tool further simulates sociopolitical dynamics, including population distribution, cultural evolution, religious spread, and diplomatic interactions between procedurally generated nations. Cities, towns, roads, and products emerge organically based on province data.

Users can interactively modify generated worlds—adjusting province geographic data, reshaping cultural or religious distribution, or changing …

Exploring Hybrid Cohort-Based Doctoral Program Features That Support Nontraditional Doctoral Students’ Persistence: A Preregistered Case Study., Glenda Winborn

Graduate Theses and Dissertations

With the rise of hybrid learning models, non-traditional learners have greater access to doctoral education. However, attrition rates from doctoral programs remain high, ranging from 40% to 50% across all doctoral programs and exceeding these levels in online non-traditional programs. Numerous causes of attrition or persistence in doctoral studies have been researched and can affect the student, the academic institution, and the broader academic society. This qualitative case study will explore adult and lifelong learning doctoral students' perceptions of the programmatic and instructional support resources that have helped improve persistence in their program. Two of the important programmatic features of …

Leveraging Machine Learning Models For Enhanced Landslide Prediction In Western North Carolina, Andrew Edmonds

Graduate Theses and Dissertations

Landslides pose significant hazards to human safety, infrastructure, and the environment, particularly in regions of high elevation that experience extended periods of heavy rainfall. This research focuses on preparing and evaluating landslide susceptibility maps (LSMs) for the Blue Ridge Mountains, a portion of the Appalachian Mountains in western North Carolina, utilizing three machine learning algorithms: Logistic Regression, Random Forest, and Gradient Boosting Regression. Sixteen landslide conditioning factors, reflecting topographic, geological, environmental, and anthropogenic influences, were identified for model input. The landslide inventory database, comprising 7,350 locations, was randomly divided into training (80%) and testing (20%) sets. The performance of each …

Multi-Decadal Land Change Dynamics In Indonesia: Disentangling The Cycle Of Agricultural Land Conversions, Urbanization, And Deforestation, Wei Lun Tay

Graduate Theses and Dissertations

Land use and land cover change (LULCC) significantly impacts Earth’s environmental, climatic, and human systems, especially in tropical regions rich in natural resources and undergoing rapid development. This study examined the spatiotemporal dynamics of LULCC in Indonesia between 2003 and 2023 using remote sensing and geographic information systems/science (GISci) and explored knowledge gaps in agricultural land conversions and the impact of food security programs such as the Food Estate Program. This study employed the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 land cover data available through Google Earth Engine, and classified land cover changes into four dominant categories: forest land, mixed …

Towards Multimodal Scene Graph Generation Approaches To Video Understanding, Trong-Thuan Nguyen

Graduate Theses and Dissertations

This thesis advances video understanding by enhancing Video Scene Graph Generation (VidSGG) through improved temporal modeling, the integration of long-range temporal dependencies via continuous updates to interaction histories, and the utilization of Large Language Models (LLMs) for scene graph reasoning. To this end, three novel datasets and corresponding approaches are introduced. First, the ASPIRe dataset incorporates interactivity annotations and leverages the Hierarchical Interlacement Graph (HIG) for hierarchical temporal modeling, providing deep insights into scene changes and effectively capturing intricate interactions. Next, the AeroEye dataset, focusing on drone videos, is paired with the Cyclic Graph Transformer (CYCLO), which establishes circular connectivity …

Administering Grant-Subsidized Workforce Training: A Critical Case Study, Kaleb Preston Futch

Graduate Theses and Dissertations

Grant monies are distributed for the purpose of funding workforce training programs aimed at affecting the skills gap in the United States. While research has shown that these grants are being awarded, there is not a well-established standard for designing and implementing the programs they fund. This study focuses on recording and classifying the lived experiences of grant staff working on a state funded workforce training program in the United States. This study will employ a qualitative case study approach designed to capture and codify, in the words of those most familiar with it, the aspects of the grant program …

Achieving Flexible Fairness And Privacy In Federated Learning, Alycia N. Carey

Graduate Theses and Dissertations

Having access to large, high-quality datasets is crucial for training machine learning models that achieve satisfactory performance. Unfortunately, it is common that a single entity (e.g., mobile device or organization) does not have access to such datasets due to monetary or resource constraints. Traditional machine learning requires that all training data reside in a centralized location during the entire duration of model training, however, in many circumstances it is difficult or even impossible (e.g., due to governmental regulations) for multiple parties to combine their data to meet this constraint. Federated learning is a machine learning paradigm that facilitates the joint …

Geospatial Modeling Of Winter-Related Property Damage In Arkansas, Braedyn Mcbroom

Graduate Theses and Dissertations

Storm Data, a monthly publication by the National Weather Service with records of weather events, reports on property damage caused by natural hazards. Since 1996, the database has included winter-related events. While the property damage data is considered incomplete, no other unique, public datasets report winter-related property damage at the county level. From 1996 to 2024, Arkansas ranked #1 in winter-related property damage. To understand the factors that are driving property damage and potentially reduce Arkansas’ vulnerability to it, three groups separated by temporal periods, i.e., “2000” (01/1996 - 06/2005), “2010” (07/2005 - 06/2015), and “2020” (07/2015 - 06/2024) …

Impacts Of Toxic Fescue Seed Consumed By Pregnant Heifers On Maternal Blood Flow And Offspring Growth Parameters: Potential For Melatonin As A Therapeutic To Improve Offspring Growth, Rachel Scott

Graduate Theses and Dissertations

The southeastern United States is home to endophyte-infected (E+), toxic, tall fescue grass. Consumption of E+ fescue by livestock has been linked to fescue toxicosis, a disease reported to cost the United States beef industry around $1 billion in annual losses. The primary objective of this dissertation was to evaluate the influence of pregnant cattle consuming E+ seed during gestation on uterine artery blood flow and growth performance of female offspring. The secondary objective was to investigate melatonin supplementation as a mitigation strategy for expected fescue-associated decreases in uterine artery blood flow and offspring growth performance. The study reported in …

Real Time Adaptive Control Of A Pid Via Genetic Algorithm Machine Learning Systems, Cemre Tas

Graduate Theses and Dissertations

The most common control method that is utilized by all industries across the world is the proportional-integrative-derivative controller (PID) due the relatively low cost and complexity of the system. However, there are draw-backs with PIDs, it is not adaptative to a changing system, so it works on nominal systems, and it starts breaking down when a system begins to have a non-linear response. The method chosen to overcome both is the utilization of machine learning with the use of genetic algorithms.

This method allows any PID system to be capable of adapting in real-time, while not adding significant additional cost …

Controls On Nutrient And Sediment Export In A Non-Perennial Agricultural Stream, Kathleen Cutting

Graduate Theses and Dissertations

Climate change is expected to increase flow variability and intermittency in streams, changing the magnitude and timing nutrient and sediment delivery. This may exacerbate water quality issues in agricultural landscapes, where nutrient and sediment inputs are often elevated. Elevated nutrients can drive eutrophication in downstream waterways, and excess sediment loss can decrease primary productivity. Non-perennial headwater streams are particularly vulnerable to the impacts of climate change and agriculture as they serve as key sites for nutrient cycling at the terrestrial-aquatic interface. Despite their importance, the interactive effects of agriculture and stream intermittency, as well as unpaved roads in rural watersheds …

Speed Breeding And Fungal Blight Testing Of The Darling 54 American Chestnut (Castanea Dentata), Virginia G. May

All Theses And Dissertations

The American chestnut (Castanea dentata) was a dominant, foundational forest canopy species in eastern North America until an imported chestnut blight (caused by Cryphonectria parasitica) rendered it functionally extinct across its native range. Biotechnological approaches, like the Darling 54 transgenic line, have potential for future restoration, but need both lab and field evaluations of their breeding and blight tolerance. This thesis adds to the body of knowledge on the Darling 54 American chestnut. This research reports three novel advancements made through indoor breeding and the first systematic field test for blight tolerance of transgenic American chestnuts.

Historically, …

The Legality Of Autonomous Weapon Systems Within The Context Of The Law Of War, Zack Naqvi

Barry Law Review

No abstract provided.

Transparent Wood, Rhetoric, And The Cycle Of Discovery, Sara Buie

RhetTech

Transparent wood, first discovered in 1992 by Siegfried Fink, received little recognition until 2021, when it was deemed one of the year’s “best new discoveries.” Fink’s discovery of the material was originally only considered for its use within the field of forest ecology. However, when rediscovered decades later by two independent research teams, transparent wood was found to have far-reaching implications for improving technology display systems, like phone screens, and reducing buildings’ energy consumption. Often, researchers justify the value of their work through practical applications such as these, but practicality is especially subject to the rhetorical situation in which research …

Designing An Interactive Visual Analytics System For Precipitation Data Analysis, Dong Hyun Jeong, Pradeep Behera, Bong Keun Jeong, Carlos David Luna Sangama, Bryan Higgs, Soo Yeon Ji

Management and Decision Sciences

As precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and …

Vol. 3 Full Issue

V Music Magazine

No abstract provided.

Identifying Trends Among University Club Athletes, Emma M. Griffin, Laura Jean Burton

Honors Scholar Theses

This study explores the factors influencing students’ decisions to join and remain involved in club sports at the University of Connecticut. Drawing on survey data and in-depth interviews with current club sport athletes, the research investigates how demographic, socioeconomic, and personal background variables such as prior sport involvement, academic motivations, and social aspirations shape patterns of participation. Findings suggest that students from higher socioeconomic backgrounds and those with significant competitive youth sport involvement comprise most club sport athletes, often citing a desire for balance, community, and stress relief as key motivators to joining. These results align with prior literature on …

The Effect Of Non-Profit Organizations On The Passage Of Climate Change Legislation In Connecticut, John-Henry Burke

Honors Scholar Theses

Connecticut has a long history of climate action. In this study, I looked into the effect that non-governmental organizations have on the passage of climate change legislation in Connecticut. Eighteen different climate policy stakeholders, including legislatures, non-governmental organization (NGO) staff members, youth activists and state employees were interviewed to gain their perspective on how non-profits forward climate initiatives. I found that non-profit organizations do have a significant impact on the passage of climate change legislation in Connecticut by working as providers of information to legislators and mobilizers of constituents. This impact can be limited by various political factors.

Engineering Affinity Of Yth Protein To M6a-Rna, Katelynn E. Horvath

Honors Scholar Theses

Engineered RNA-binding proteins (RBPs) show promise for diverse applications in research and therapeutic development. Modulating the interaction between YTH-domain containing proteins and methylated RNA ligands could result in a high affinity, highly specific binding protein for the advancement of research tools in neurodegenerative disease. N6-methyladenosine (m6A) modified RNA contributes to stress granule formation that promotes neurotoxicity in neurodegenerative disease, with concentrations of m6A-RNA increased fivefold in Alzheimer’s Disease. Further affinity maturation of the YTH-m6A interaction through directed evolution of the YTH domain could produce a novel protein with increased affinity better suited for the development of biological detection tools or …

Signal Modeling Of High Purity Germanium Detectors, Brooke Parker Thibodeau

Honors Scholar Theses

Pulse shape analysis can be used to analyze radiation incident on a semiconductor diode detector. Finite element analysis (FEA) can be used to simulate high purity germanium detectors with user-specified geometry, impurity concentration, and bias voltage. Subsequent streamline calculation can be used to conduct pulse shape analysis for single and multiple site gamma-ray interactions within the detector volume. Cross sections of the detector can be visualized by contour plots of the electric field, drift velocity magnitude, and collection time, as well as t30 and t90 rise-time values.

The Relationship Between Mindfulness, Daily Difficult Events, Perceived Stress And Anxiety Among College Students, Gabrielle Lessard

Honors Scholar Theses

Background: Mindfulness is a multifaceted psychological construct that pertains to intentionally being aware of the present moment in a non-judgmental way. Little research has examined other facets of mindfulness and their association with perceived stress and anxiety.

Objective: To evaluate between-person and within-person relationships between specific facets of mindfulness (acting with awareness and nonjudging of inner experience) and various daily difficult events, perceived stress and anxiety levels among college-aged students across a 7-day period.

Methods: A sample of 78 undergraduates at the University of Connecticut were recruited through the Participant Pool and analyzed based on their baseline mindfulness and seven …

Predicting And Forecasting University Rankings In The Uae Using Machine Learning, Fatma Ibrahim Ahmad

Theses

Higher Education Institutions (HEIs) play a vital role in advancing knowledge economies, and institutional rankings are increasingly used as global benchmarks of academic performance and reputation. In the context of the United Arab Emirates (UAE), enhancing institutional competitiveness in global rankings aligns with national strategies such as UAE Vision 2030 and the Centennial Plan 2071. This research applies machine learning (ML) techniques to develop predictive models that forecast institutional ranking outcomes, enabling data-driven planning and continuous academic improvement. The study utilizes a dataset compiled from the QS World University Rankings (2020–2024), cross- referenced against the institutional listings maintained by the …

Ai-Driven Prediction Of Flight Cancellations: A Machine Learning Approach In Minimizing Airline Disruption, Hind Nasser

Theses

This thesis investigates the use of machine learning to predict flight cancellations, aiming to reduce operational disruption and improve airline decision-making. The research is motivated by the need for more proactive strategies in aviation, where flight cancellations often result in financial losses and customer dissatisfaction. The study is framed within the CRISP-DM methodology and demonstrates how historical flight data can be transformed into actionable insights using structured analytics. The research addresses three core questions: how effectively machine learning can predict cancellations, which evaluation metrics are most suitable in an imbalanced context, and how model outputs can support airline operations. A …

Personalized Nutrition Recommendations For Arab Communities: Transforming Diet And Health Through Machine Learning, Khalfan Aldoobi

Theses

A lack of cultural understanding in traditional nutrition approaches prevents proper adherence to healthy dietary patterns among individuals. The Arab population faces unique challenges due to cultural dietary customs and lifestyle changes that have made obesity and diet illnesses major global public health challenges. The rapid shift to high-calorie but undernourished foods and less physical activity has resulted in alarming rate increases of obesity, cardiovascular diseases, and type 2 diabetes. The lack of cultural understanding in traditional nutrition approaches prevents proper adherence to healthy dietary patterns among individuals. This study focuses to fills the existing gap by applying Artificial Intelligence …

Predicting Student Dropout Risk Using Machine Learning, Fatma Alameri

Theses

Student dropout remains a persistent challenge in higher education, undermining institutional performance, reducing workforce preparedness, and limiting students’ academic and economic opportunities. Accurately identifying students at risk of attrition is complex, due to the interplay of academic, financial, and behavioral factors. This thesis addresses this challenge by applying a combined machine learning framework—integrating both unsupervised and supervised techniques—to predict student dropout using structured, first-year academic and financial data. The study utilizes a comprehensive dataset of 4,424 undergraduate student records from a European higher education institution, covering ten academic years and comprising 35 variables related to academic performance, enrollment behavior, and …

Fake News Detection: Leveraging Natural Language Processing And Machine Learning For Reliable Information Verification, Roshni Rajendra Salve

Theses

This dissertation details the creation and assessment of a machine learning algorithm designed to identify fake news utilizing Natural Language Processing (NLP) methods. The research employs several machines learning models, including Long Short-Term Memory (LSTM) and other deep learning techniques, to detect and classify misleading information. Data is sourced from a variety of platforms, such as social media and online news outlets, to compile a thorough dataset. The data is pre-processed to eliminate noise, address missing values, and extract essential features through techniques like tokenization, stop-word removal, and lemmatization. The performance of the models is evaluated using key metrics such …

Enhancing Third-Party Risk Management In Aviation: A Comprehensive Framework For Modern Challenges, Shamma Hussain Hammad Bin Hammad

Theses

This thesis is focused on improving the way airlines have managed risks from third-party vendors. Emirates and Etihad have been used as case studies. The thesis identifies major issues, including cybersecurity threats, slow manual processes, and the challenges in the following global regulations. To To solve such problems, the research proposes a new framework that integrates advanced tools including machine learning, blockchain, and real-time updates about threats. The framework includes different features like automatic risk scoring and detection of unusual vendor behavior and keeping the secure digital records through the blockchain. This will highlight the need for the staff to …