Digital Commons Network^™

A Multi-Scale Compartmental Model For Glucose Regulation And Diabetes Treatment, Andrew M. Watts

Honors Theses

Glucose is a fundamental energy source for cellular function, and its regulation is critical for maintaining metabolic stability in the human body. Glucose homeostasis is governed by a network of biochemical processes involving multiple organ systems that coordinate glucose production, storage, and uptake. Disruptions in this regulation contribute to metabolic disorders such as diabetes mellitus, underscoring the need for mathematical models that provide a mechanistic understanding of systemic glucose dynamics. Herein, we report a multi-time scale discrete-time dynamical systems model using compartmental difference equations to describe glucose concentrations across key physiological compartments. By quantitatively modeling glucose transport and metabolism, this …

Predicting Cardiac Resynchronization Therapy Response: Development And Validation Of A Single Photon Emission Computed Tomography-Based Nomogram, Zhongwei Jiang, Zhongqiang Zhao, Zhuo He, Qiushi Chen, Ju Bu, Chunxiang Li, Dianfu Li, Chang Cui, Weihua Zhou, Huiyuan Qin, Cheng Wang

Michigan Tech Publications

Background: Cardiac resynchronization therapy (CRT) is an effective treatment for patients with drug-refractory heart failure. However, more than thirty percent of patients do not benefit from CRT. This study aimed to develop and validate a novel model based on single photon emission computed tomography (SPECT) phase analysis features to predict CRT response. Methods: We identified 163 CRT patients who received gated resting SPECT myocardial perfusion imaging (MPI) between 2010 and 2020 at The First Affiliated Hospital of Nanjing Medical University. All variables were first processed by univariate logistic regression, and those with a P value < 0.05 were retained. The selected variables were subsequently used in the least absolute shrinkage and selection operator (LASSO) regression to construct a predictive model, which was then represented as a nomogram. Nomogram performance was assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs). Internal validation was performed by bootstrapping with 1,000 replicates. Results: Of the 163 patients, 93 (57.1%) responded to CRT during follow-up. Responders had a wider QRS complex duration (QRSd) (164.80 vs. 154.51 ms, P=0.003), fewer premature ventricular contractions (PVCs) (1,392.98 vs. 2,283.60, P=0.003), lower prevalence of non-sustained ventricular tachycardia (NS-VT) (45.2% vs. 77.1%, P< 0.001), and better cardiac function [based on N-terminal pro-B-type natriuretic peptide (NT-proBNP), New York Heart Association (NYHA), and left ventricle (LV) parameters] compared to non-responders. Univariate logistic regression revealed 14 variables significantly associated with CRT response (all P< 0.05). The area under the ROC curve (AUC) value for the nomogram was 0.845 [95% confidence interval (CI): 0.785–0.906; sensitivity: 0.771; specificity: 0.849]. Internal validation yielded a mean AUC of 0.814 (95% CI: 0.777–0.836). The calibration curve demonstrated strong consistency between the predicted and observed outcomes. DCA revealed that the nomogram consistently provides a net benefit over the baseline, demonstrating its high practical value in clinical decision-making. A web-based dynamic nomogram (https:// jzw20000624.shinyapps.io/CRTpredictionmodel/) was developed for clinical application. Conclusions: We developed and validated a SPECT-based prediction model for predicting CRT response, which can assist clinicians in optimizing CRT candidacy preoperatively. Pacing at the latest contraction and relaxation segments, while avoiding scarred regions and optimizing preoperative status, is anticipated to improve CRT response.

Comparison Of Rt-Qpcr And Rt-Ddpcr On Assessing Model Virus In Wastewater, Wafa Youssfi

Graduate Theses and Dissertations

There is an increasing demand for quantifying viral loads in diverse wastewater systems using polymerase chain reaction (PCR). This study evaluates the performance of two commonly used workflows: reverse transcription quantitative PCR (RT-qPCR) and reverse transcription droplet digital PCR (RT-ddPCR) in wastewater. We compared the two methods by measuring the viral ribonucleic acid (RNA) of a model virus Phi6 in samples collected from various treatment stages at the Westside Wastewater Treatment Facility in Fayetteville, AR. RNA was extracted from real and synthetic wastewater samples and analyzed in parallel using both RT-qPCR and RT-ddPCR. Findings reveal that both methods demonstrated similar …

Nonlinear Power Function Model Changepoint Detection., Jacob Steven Townson

Electronic Theses and Dissertations

Most work surrounding changepoint analysis focuses on linear models. This dissertation explores changepoint detection in nonlinear power function models, specifically focusing on models where the constant multiplier and power are the parameters to be estimated in addition to the changepoint parameter. The study assumes an asymptotic framework as the number of observations approaches infinity. The study explores various model fitting algorithms, and decides to employ the Newton-Raphson method for parameter estimation, with a custom implementation developed to optimize the process. The research first establishes the strong consistency of estimators for the model without a changepoint. Building on this result, consistency …

Finer Resolution Paleoclimatological Network Analysis Of Hydrological Extremes And Tree-Growth Response For Kentucky, U.S.A., Jordan Sharp

Electronic Theses and Dissertations

As climate change accelerates, southeastern states like Kentucky face increasing environmental and economic challenges. To improve future climate predictions, this study enhances paleoclimate reconstructions with a high-resolution tree-ring network for Kentucky, combining data from both living trees and dendroarchaeological sources. I evaluated the climate sensitivity of tree growth using superposed epoch analysis, static correlations, and spatial field correlations in KNMI Climate Explorer, along with moving correlations against seasonal temperature and moisture variables. Findings reveal that white oak and tulip poplar exhibit significant drought sensitivity across both live and archeological sites. However, I observed a weakening relationship between tree-ring growth and …

Setting Up Students For Success: Analysis Of Effectiveness For Mathematics Placement, Jeff Carvell, Jason N.E. Ho, Dave Klanderman, Sarah Klanderman

Faculty Work Comprehensive List

How do we effectively and equitably place students into math classes in a way that provides them the best chance of success? As many higher education institutions veer away from placement based on standardized testing, many departments are seeking placement alternatives that will properly support students. Additionally, math placement determines not only a student’s mathematics courses but also influences their progress in related fields, including physics, chemistry, engineering, and more. This paper will describe three different existing placement systems at each of our liberal arts institutions as well as the affordances and constraints of each approach. Further, we analyze data …

The Impact Of Corporate Profits On Gdi Estimates: Forecasting And Data Revisions, Peterson Haas

Honors Theses

The National Income and Product Accounts (NIPAs) produced by the U.S. Bureau of Economic Analysis (BEA) provide key measures of U.S. economic activity, including gross domestic product (GDP) and gross domestic income (GDI). Although conceptually equivalent, GDP and GDI often diverge due to differences in source data and revision timing. This study focuses on corporate profits—a small but volatile component of GDI that is frequently revised, especially during the BEA’s annual (A1) and benchmark (C1) revisions. I examine how revisions to corporate profits influence GDI revisions and whether they can improve real-time estimates of GDI. First, I document the size …

Joint Modelling Of Longitudinal Egfr Trajectory And Time To Acute Kidney Injury In Lung Transplant Patients, Samiha Zakir

Theses and Dissertations

Progressive declines in estimated glomerular filtration rate (eGFR) often precede acute kidney injury (AKI), yet the relationship between eGFR trends and AKI risk remains unclear. This study investigates longitudinal eGFR changes and their association with AKI in 459 lung transplant patients followed for up to 7 years (n = 6419). We applied a piecewise linear mixed-effects model to evaluate eGFR trajectories and a Cox proportional hazards model to assess time to AKI. A joint model was used to explore the interplay between longitudinal and survival processes. Key covariates included gender, age at transplantation, antibody-mediated rejection (AMR), and pre-transplant eGFR. Males …

Defend: A 1m Dataset Foundation Model For Tobacco Analysis, Matthew J. Shepard

Electrical Engineering and Computer Science Undergraduate Honors Theses

The study of tobacco imagery and marketing is a complex challenge that involves extremely large datasets. It also demands a detailed analysis of the so- cial context and specific types of tobacco being marketed. Despite major recent advances in computer vision and foundation model technology, this still poses a substantial challenge. Through the DEFEND model, we aspire to address these obstacles by integrating features such as multimodal learning, hierarchical under- standing, and feature extraction to develop a foundation model designed to handle the unique challenges of tobacco image analysis. One of the core elements of DE- FEND is the Tobacco …

Decoding The Algorithm: The Mathematics Behind Tiktok’S Short-Form Content Success, Ashley N. Lynch

Honors Scholar Theses

Within the realm of social networks, TikTok has become the central hub for short-form video content. The network’s unique ability to capture individual preferences using predictive analytics has greatly contributed to its massive success, allowing the company to optimize its performance and content personalization. In an age where digital media have such a significant influence on society, it is essential that users develop an understanding of how social network algorithms function to make more informed online decisions. Although TikTok’s technological system is primarily undisclosed, the platform certifiably leverages several key mathematical principles within its algorithm to achieve its core goals …

Optimizing Nitrogen Management: Assessing Nitrate Transport In The Deep Vadose Zone And Its Impact On Groundwater Quality In East-Central Nebraska, Muili O. Lawal

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Groundwater contamination by nitrate (NO₃-N) is a growing environmental and public health concern driven largely by intensive agricultural fertilizer applications. The vadose zone plays an important role in NO₃-N transport, influencing groundwater quality and agricultural sustainability. This study investigated NO₃-N leaching dynamics in the deep vadose zone of Water Quality Sub-Area 30 (WQA30) within the Lower Loup Natural Resources District (LLNRD), northeast of Columbus, Nebraska (mean groundwater NO₃-N: 20.3 mg/L), using soil cores from 16 shallow (to 6.1 m) and four deep (down to 25.9 m) across four zones, two with inorganic fertilizer (Zones 1 and 4) and two with …

Constructing An Attractive Targeted Sugar Bait For Reducing Mosquito Populations, Xi Xian Ng

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Mosquito-borne pathogen transmission continues to pose a significant burden to global public health, well-being, and economic productivity. In the absence of universally available vaccines and amidst rising insecticide resistance that leads to product failures, there is an urgent need to develop new tools to mitigate mosquito-borne disease risk. Two general strategies exist to reduce transmission risk: personal protective measures and area-wide chemical interventions. Attractive Targeted Sugar Bait (ATSB) systems leverage the sugar-feeding behavior of mosquitoes—critical for sustaining their flight, metabolism, development, and fecundity—by delivering oral toxicants through attractively formulated sugar baits. This study aimed to develop an optimized ATSB formulation …

Modeling Degradation In Li-Ion Batteries: Analyzing Single-Cell Degradation And Applying It To Customized Pack-Level, Mohannad Y. Alkhalil

Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research

The rapid expansion of lithium-ion (Li-ion) battery applications in areas such as electric vehicles (EVs), renewable energy storage, and portable electronics has drawn attention to the need for improving their performance, safety, and longevity. As Li-ion batteries become essential across technologies, understanding degradation mechanisms is critical for optimizing design and ensuring reliable operation. This work provides a detailed overview of modeling degradation in Li-ion batteries, focusing on single-cell behavior and its implications for pack-level performance.

The PyBaMM (Python Battery Mathematical Modeling) package, an open-source battery simulation tool written in Python, is used for single-cell simulations. Liionpack, another Python-based library, is …

Car Price Prediction Using Machine Learning: Analyzing The Dvm-Car Dataset, Yaman Abu Ghareebaih

Electronic Theses and Dissertations

The objective of this study is to predict car prices using machine learning models and the DVM-CAR dataset, which includes over 1.4 million images and car specifi- cations from 899 car models. Key factors such as mileage, engine power, and year of registration were analyzed for their correlation with car prices. Extensive data cleaning was performed, including filling missing values, identifying outliers, and normalizing numerical variables. Discrete variables like car make and body type were encoded using one-hot encoding. Linear relationships were analyzed with Multiple Logistic Regression, and Random Forest models were used for nonlinear patterns. Model performance was evaluated …

Alphamissense Predictions And Clinvar Annotations: A Deep Learning Approach To Uveal Melanoma, David J. Taylor Gonzalez, Mak B. Djulbegovic, Meghan Sharma, Michael Antonietti, Colin K. Kim, Vladimir N. Uversky, Carol L. Karp, Carol L. Shields, Matthew W. Wilson

Wills Eye Hospital Papers

OBJECTIVE: Uveal melanoma (UM) poses significant diagnostic and prognostic challenges due to its variable genetic landscape. We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM.

DESIGN: A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases.

SUBJECTS: Genetic data from patients diagnosed with UM were analyzed, explicitly focusing on missense mutations sourced from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

METHODS: We identified missense mutations frequently observed in UM using the COSMIC database, assessed their potential pathogenicity using AlphaMissense, and visualized mutations using AlphaFold. Clinical …

Community Voices, Climate Action Choices: Working Towards A Resilient Monterey County, Lesley A. Solano Alonso

Capstone Projects and Master's Theses

Vulnerable communities in Monterey County face disproportionate environmental and health impacts due to climate change, yet many residents remain unaware of the tools and resources available to support local action. This capstone project was implemented in partnership with Ecology Action (EA) and the Resilient Central Coast (RCC) campaign to increase awareness and engagement with the RCC platform. Serving diverse communities across Monterey County, the project included bilingual outreach efforts, community tabling, educational presentations, and a climate action survey. Over 650 residents were engaged directly, resulting in 99 new household sign-ups on the RCC website, a major milestone for the agency. …

Ai Model For Predicting Asthma Prognosis In Children, Elham Sagheb, Chung-Il Wi, Katherine S King, Bhavani Singh Agnikula Kshatriya, Euijung Ryu, Hongfang Liu, Miguel A Park, Hee Yun Seol, Shauna M Overgaard, Deepak K Sharma, Young J Juhn, Sunghwan Sohn

Faculty, Staff and Student Publications

BACKGROUND: Childhood asthma often continues into adulthood, but some children experience remission. Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans.

OBJECTIVE: We aimed to develop artificial intelligence (AI) models using various clinical variables extracted from EHRs to predict childhood asthma prognosis (remission vs no remission) in different age groups.

METHODS: We developed AI models utilizing patients' EHRs during the first 6, 9, or 12 years of their lives to predict their asthma prognosis status at ages 6 to 9, 9 to 12, or 12 to 15 …

From Molecular Crystals To Catalytic Surfaces: Computational Approaches To Complex Systems, Thiago Henrique Da Silva

Boise State University Theses and Dissertations

Understanding and modeling complex systems is one of the core challenges in modern science. Whether it be the intricate interactions within molecular crystals, the evolving mechanisms of bacterial resistance, or the dynamics of catalytic surfaces, accurately representing these systems is essential for scientific progress. This dissertation addresses these challenges by focusing on advances in computational modeling, demonstrating how various methods such as Density Functional Theory (DFT), machine learning, and custom-developed methods can simplify and help predict complex behaviors across multiple domains. By applying and understanding the scope of these methods to each field, this work underscores how computational modeling bridges …

A Comprehensive Review Of Carbon Sequestration And Its Assessment Techniques Using Remote Sensing And Geospatial Methods, Imen Ben Salem

All Works

Global warming has elevated carbon sequestration as a critical strategy for mitigating climate change, while enhancing sustainability in productivity. Agricultural land use systems contribute substantially to CO2 emissions due to crop residues, shifting cultivation practices, low-biomass crops, land degradation, and deforestation. The significant rise in CO2 emissions over the past thirty years is associated with burning fossil fuels, leading to substantial environmental changes, including global warming. Remote sensing (RS) and Geographic Information Systems (GIS) are advanced geospatial technologies that facilitate the rapid evaluation of terrestrial carbon stock over extensive regions. An integrated RS-GIS approach for carbon stock estimation and precision …

Building Toward A Text-Based Intervention For Parents Of Suicidal Adolescents Seeking Emergency Department Care: A Pilot Randomized Controlled Trial., Ewa Czyz, Inbal Nahum-Shani, Cynthia Ewell Foster, Valerie Micol, Amanda Jiang, Nadia Al-Dajani, Alejandra Arango, Maureen Walton, Victor Hong, Sheikh Iqbal Ahamed, Cheryl King

Computer Science Faculty Research and Publications

Objective: The growing demand for emergency department (ED) care for suicidal ideation and attempts in adolescents calls for effective interventions preventing post-ED recurrence of suicidal crises. Parents are tasked with implementing postdischarge suicide prevention recommendations, often with little support. To address this need, this study examined a parent-facing texting intervention targeting parental engagement in suicide prevention activities to lower youth suicide risk after discharge. Method: A pilot randomized controlled trial was conducted with 120 parents (83.3% mothers) and their adolescents (ages 13–17, 65.8% female, 75.0% White) presenting to an ED with suicide risk concerns. Parents were randomized to a control …

Early Breast Cancer Detection With Ultrasound Data Using Nmf, Rutvi Khamar

Theses and Dissertations

Early detection of breast cancer significantly influences patient outcomes. Dynamic Contrast-Enhanced Ultrasound (DCE-US) has shown promise in early detection by visualizing tumor vascularity and perfusion dynamics in real-time. This study evaluates the efficacy of DCE-US in distinguishing four stages of cancer progression: normal, hyperplasia, ductal carcinoma in situ (DCIS), and invasive cancer, using a transgenic mouse model that mimics human breast cancer. Ultrasound burst pulses, while commonly used to remove unbound contrast agents, can potentially damage human tissues. Using the pre-pulse data helps mitigate this risk, ensuring safer and more reliable measurements. A VEGFR2-targeted microbubble contrast agent was injected, and …

Effects Of Reflective Journaling And Nudges On Academic Motivation And Engagement, Lakshmi Satya Sai Veda Mahita Uppuluri

Theses and Dissertations

This study investigates the effects of reflective journaling and motivational nudges on academic motivation and engagement among college students. Grounded in Self- Determination Theory (SDT), the research examines how different interventions influence intrinsic motivation, and academic behaviors such as class attendance, participation, and preparation. The study employed a between-group experimental design with three conditions: a control group, a journaling group, and a journaling group that also received daily motivational nudges. Results showed that students in the journaling groups—particularly those who received nudges—experienced a significant increase in academic motivation. While changes in academic engagement were not significant, the effect size suggested …

Environmental And Societal Impacts Of Agricultural Land Abandonment In Indonesia: A Bibliometric Review, Jane A. Landrum

Geosciences Undergraduate Honors Theses

In the past decades, Southeast Asia has faced one of the highest urbanization rates globally, with land conversion, agricultural land abandonment, and deforestation occurring simultaneously. High population growth rates and migration away from rural areas have also led to increased food insecurity, which has increased the need for agricultural production. In Indonesia, high rates of urbanization on the island of Java have historically prompted governmental policy solutions aimed at population redistribution and agricultural extensification (rather than intensification). The cost of this conversion of lands to agriculture often falls on the environment, with peatlands and forested lands being converted to large-scale …

Exploring The Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

Mechanistic Investigation Of Ring-Opening Polymerization Of Polycaprolactone Using Tin(Ii) Catalysts: Ligand Effects And Biomedical Applications, Eva-Larue M. Barber

Honors Scholar Theses

This research explores the mechanistic aspects of ring-opening polymerization (ROP) of ε-caprolactone (CL) to produce polycaprolactone (PCL), a biodegradable polymer widely used in biomedical applications. The study investigates how light exposure and catalyst concentration influence polymerization efficiency, using tin(II) 2-ethylhexanoate [Sn(Oct)₂] as the catalyst in a non-polar toluene solvent at 90 °C. Reactions were conducted under either ambient light or black light bulb (BLB) illumination, with monomer-to-catalyst ratios of 1:1 and 200:1.

Proton nuclear magnetic resonance (¹H NMR) spectroscopy was used to analyze conversion efficiency by tracking the disappearance of monomer signals and appearance of characteristic PCL peaks. Results revealed …

Cover Crop-Induced Improvements To Soil Hydraulic Properties And The Role Of Termination Time In Mitigating Negative Soil Moisture Impacts, Fabrizio Javier Pilco

Theses and Dissertations

In the Lower Rio Grande Valley of South Texas, water scarcity and drought pose ongoing challenges for farmers. Although cover crops are promoted for improving soil health and moisture, their effects on soil hydraulic properties in this region remain understudied. This thesis addresses both long-term (Chapter 2) and short-term (Chapter 3) impacts of cover crops on soil hydraulic properties and soil moisture. Long-term effects were evaluated through a three-year participatory field trial across four farms using a BACI design, while short terms were evaluated using a complete randomized block design. Soil hydraulic properties (residual water content θr, saturated water content …

The Effect Of Hiv-1 Infection Associated Bacterial Lipopolysaccharide (Lps) On Human Oral Keratinocytes: Implications For Promoting Chronic Inflammation, Md Shafayat Jamil

Theses and Dissertations

Human immunodeficiency virus type 1 (HIV-1) remains a major global health concern, affecting approximately 39 million people worldwide, with 500,000 new infections reported in 2022. While combined antiretroviral therapy (cART) has significantly reduced viral replication, HIV-1 infection is linked to various comorbidities, including oral dysbiosis and accelerated aging. Immune dysregulation in HIV-1–infected individuals promotes the proliferation of pathogenic gram-negative bacteria like Porphyromonas gingivalis, a key contributor to periodontal disease. P. gingivalis secretes lipopolysaccharide (LPS), which adversely affects human oral keratinocytes (HOK), the first line of defense against microbial threats in the oral cavity. HOK recognize LPS as a threat, …

Generalizable Skill Learning In Robotic Agents Using Transformer Models, Erik Enriquez

Theses and Dissertations

This work explores the application of Transformer models to robotic skill learning, aiming to enhance generalization across various physical tasks and environments with continuous control. Despite their success in other domains, our experiments reveal that the utility of Transformers in robotics heavily depends on pretraining strategies. Specifically, Transformers pretrained on reinforcement learning tasks generalized effectively, while those trained with task-agnostic masking strategies did not. These findings challenge assumptions about the universality of Transformer-based methods and underscore the importance of domain-aligned pretraining for developing versatile robotic agents.

The Influence Of Solar Energy Infrastructure On Pollinators And Crops, Deisy Garcia

Theses and Dissertations

As the human population grows, demand for agriculture and renewable energy rises. Landuse competition is caused by the expansion of area-intensive solar photovoltaic (PV) facilities. While solar energy is crucial to mitigate climate change, it presents challenges related to land availability and effects on habitats for essential pollinators. In Chapter II, a spatial analysis was conducted to examine the relationship between solar panel density and pollinator diversity, exploring regions with the least interaction between these two factors. Our findings can inform solar siting and management strategies at landscape levels, in conjunction with field studies, that both mitigate potential interaction and …

Implementing And Evaluating An Ai-Powered Visual Decision Support System To Improve Antibiotic Usage Among Physicians With A Built-In Early Warning System, Akua Sekyiwaa Osei-Nkwantabisa

Theses and Dissertations

The widespread misuse and excessive prescription of antibiotics have played a pivotal role in the emergence and proliferation of antibiotic-resistant bacteria, posing a critical global public health crisis. Addressing this challenge necessitates innovative solutions that enhance antimicrobial stewardship. This study presents the development and implementation of a visual decision support system designed to monitor and optimize antibiotic usage among healthcare providers. The proposed system integrates advanced machine learning algorithms with real-time data analytics to provide a dynamic, evidence-based decision support tool. Specifically, a neural network model was developed after evaluating multiple machine learning approaches, including Random Forest, Logistic Regression and …