Digital Commons Network^™

Personalized Learning Instructional Strategies And Student Engagement, Trisha J. Doverspike

Master of Education Program Theses

This action research project sought to determine the effect of implementing personalized learning instructional strategies on student engagement. Over a six-week period, students were observed for on-task behavior to measure engagement in the Social Studies lessons. Data was collected through observation. Results indicate that implementing personalized learning instructional strategies showed an increase in student engagement and suggest that implementing personalized learning instructional strategies are an effective method of increasing student engagement when compared to teacher-centered instruction.

The Expectation Of Pain: Effects On Threshold, Tolerance, And Perception, Emma Paulus

Mechanical Engineering Undergraduate Honors Theses

Brain activity in pain-related regions of the cortex is thought to be influenced by interactions between expectations and incoming sensory information. However, the subjective experience of pain based on expectation has not been thoroughly explored. By changing visual and auditory cues to set expectation levels, a relationship between unconscious expectation of pain and actual sensory pain experienced was characterized. With IRB approval, volunteers were randomly and blindly grouped into low and high pain expectancy groups. Each group completed a survey about initial pain expectations before the muscle stimulation tolerance test, followed by a survey about actual pain perception after the …

Strengthening Cyber Resilience In Critical Infrastructure: Lessons From Major Attack Case Studies, Jodi Barnes

Data Science Undergraduate Honors Theses

This comparative case study research paper analyzes the Colonial Pipeline attack, the Oldsmar Water Treatment Plant attack, and related case studies to identify past and current gaps in cyber resilience in critical infrastructure.  It provides insights into the importance of cybersecurity and opportunities to enhance protection in an increasingly digital world.  Findings include unsecure practices, limited communication between sectors, outdated technology, and weaknesses in security processes and employee training. These vulnerabilities are interconnected and are largely driven by limited funding within critical infrastructure systems, which restricts the ability to address them effectively.

Reimagining Less-Than-Truckload Pricing Development In Competitive Bid Environments With Artificial Intelligence, Lawson C. Levin

Data Science Undergraduate Honors Theses

This undergraduate thesis explores how data analytics and engineering judgment are used to support pricing decisions in the less-than-truckload (LTL) freight market. It’s based on an internship with ArcBest Corporation. It explains the company’s background, its role in the LTL market, and the responsibilities of a Pricing and Supply Chain Engineer within the Yield department.

Most of the internship was spent evaluating requests for proposals (RFPs), in which a negotiating third party provides a customer’s shipment data that must be cleaned, analyzed, and translated into a comprehensive pricing offer. Using the Data Science Analytics Process as a framework, this thesis …

Prescribing Company Action Through Machine Learning And Ai, Breck T. Husong

Data Science Undergraduate Honors Theses

The purpose of this research is to implement an OpenAI Reinforced Learning prescription-giving model for improving sales on a week-by-week basis. The data used comes from a segment of High Impact Analytics’s sales data that has been anonymized for proprietary reasons. The features among the data include inventory numbers, shipments in transit, total quantity and dollars of products sold each week for the past 2 years, all aggregated at the store-item-week level. In order to build this model, Tigramite, a causal discovery model combined with prediction models XGBoost, Linear Regression, Ridge Regression, Lasso Regression, Scikit-learn’s MLP, and Keras’s Neural Model …

A Comparative Machine Learning Framework For Identifying Ai-Generated Versus Real Celebrity Faces, Sidney Gehring

Data Science Undergraduate Honors Theses

The rapid advancements in the world of generative artificial intelligence has enabled the creation of highly realistic fictitious facial images, raising concerns about authenticity and bias in computer vision systems. This study investigates the capabilities of machine learning models to distinguish between real and artificially generated facial images across gender and race focusing on celebrity imagery. Four datasets were used against the classification model, each trained on images of a single celebrity within distinct demographic groups: White women, White men, Black women, and Black men. For each group, real images are paired with AI-generated counterparts designed to closely replicate the …

A Forecasting Framework For Distribution Center Capacity Utilization: An Applied Industry Study, Jordan J. Shortt

Data Science Undergraduate Honors Theses

This project develops and evaluates a predictive modeling framework for forecasting distribution center capacity utilization at Company Y, with monthly forecast horizons up to one year. Motivated by the operational challenges of seasonal demand volatility, promotional cycles, and the absence of a formally defined capacity metric, the study first constructs a historical capacity utilization measure from raw warehouse management system data — reconciling item volumes, location dimensions, and utilization factors across all DCs — which serves as the target variable for all modeling work. Four models are developed and evaluated against a naïve seasonal baseline: SARIMA, LightGBM, LSTM, and a …

Developing Tracking Compliance Standards For Inbound Freight: A Data-Driven Industry Application At O’Reilly Automotive, Jackson Endacott

Data Science Undergraduate Honors Theses

Visibility of inbound freight is critical for managing operational efficiency, yet many organizations lack standardized compliance metrics for third-party carriers to uphold, preventing them from utilizing tracking data to make data-driven decisions. During a summer internship with the Transportation Department at O’Reilly Automotive, data inconsistencies were addressed in the Transportation Management System (TMS), and that data was utilized to create tracking compliance standards for third-party carriers. Data populated from various sources within O’Reilly’s TMS was cleaned, validated, and utilized to create a Tracking Scorecard that evaluates message transmission rates, timeliness, and errors. This tool provides actionable insights to improve tracking …

Industry Analysis: Consulting Industry, Mckinsey & Company, Lucia Blaha

Theses & ETDs

The thesis analyzes the consulting industry using the Structure–Conduct–Performance (SCP) framework to study McKinsey & Company operations in Germany. The consulting market shows overall market fragmentation, but the high-end strategy market remains dominated by McKinsey, Boston Consulting Group, and Bain & Company (Statista, 2024). McKinsey uses premium pricing together with its worldwide operational capabilities to maintain its dominant market position, which receives constant backing from major corporations and government organizations. The study also examines German and U.S. consulting markets to demonstrate their fundamental differences in structure and operational practices. The U.S. market experiences rapid growth together with new innovations, while …

The Theory Of Structure, Conduct, And Performance: Btg Pactual In The Brazilian Banking And Capital Markets Industry, Gabriela Dos Santos De Medeiros

Theses & ETDs

This paper analyzes the structure and performance of the Brazilian investment banking market, with a specific focus on the performance of BTG Pactual. Using the Structure-Conduct-Performance paradigm and oligopoly theory, this study examines the behavior of firms within the industry to understand the dynamics that shape the performance of the investment banking sector, including the influence of a market structure marked by high concentration and substantial entry barriers on firm profitability and business strategy. The analysis examines BTG Pactual’s business strategy, emphasizing its diverse income sources from advisory, underwriting, trading and asset management business lines.

Learning English Through Music, Rafaela Espindola De Nascimento

Language Study as a Gateway to the Humanities

No abstract provided.

Implementing Engagement Strategies For Growth On Twitch, Chandler Gravely

Communications and Digital Media Theses

Online media sites have changed how viewers’ are taking in content in addition to how content creators engage with their audiences. One of these sites that has proven itself as the main source for live-streaming content, is Twitch. Despite how popular the site is, there is a lack of research on how individual creators can use growth and engagement methods over a period of time to help grow an audience and viewer base. This study will look at how doing so over a three-month period can provide valuable insight into expanding one’s viewer base and channel on Twitch. In order …

Making As A Method For Knowledge Production, Nithikul Nimkulrat, Camilla Groth

DRS Summer Meetings

No abstract provided.

Exergames And Motivation To Exercise, Nathaniel Clark

Theses & ETDs

Physical inactivity is a significant public health concern, and motivation has been identified as a key barrier to regular exercise. This thesis investigates exergames, video games that require physical activity to play, as a means of increasing exercise motivation and physical activity. Chapter 1 describes the Exertron 5000, a custom exergame system developed in Godot, a free and open source node and scene-based game engine. The system uses a lightweight wearable interface to track physical activity to control the game. Movement data is processed through a multi-stage filtering and smoothing pipeline to extract pedaling speed. The exergame is an asteroids-like …

Splitting Shores: Comparative Analysis Of Italy’S And Spain’S Reactions To Immigration, William E. Montgomery V

International and Global Studies Undergraduate Honors Theses

The divergence in public immigration sentiment between Italy and Spain, two countries with striking historical, geographic, and demographic similarities, is seen as a mystery for popular theoretical frameworks. Although both nations transitioned from net emigration to net immigration beginning in the 1970s and 1980s, Italy has consistently exhibited more negative attitudes toward immigration relative to the European average, while Spain has seen sustained improvement in immigration sentiment despite receiving immigrants at historically unprecedented levels. Employing a Most Similar Systems Design and drawing on quantitative demographic and survey data, primary legislative texts, and secondary scholarly literature, this thesis tests Group Threat …

Household Air Pollution Is Associated With Disease Severity In Ugandan Children Hospitalized With Hypoxemic Pneumonia, Ayla Ahmed, Sehaj Sandhu, Sophie Namasopo, Juliet Nabwire, Qaasim Mian, Andrea Conroy, Jackson Amone, Charles Olaro, Robert Opoka, Michael Hawkes

Paediatrics and Child Health, East Africa

Background Household air pollution (HAP) due to biomass fuel use is a risk factor for childhood pneumonia, a leading cause of under-five mortality globally. This study examined the relationship between HAP and disease severity in Ugandan children hospitalized with hypoxemic pneumonia.

Methods We conducted a retrospective case-control study across 20 Ugandan hospitals. PM2.5 exposure was estimated using caregiver-reported fuel use, cooking duration, kitchen structure, and ventilation. Pneumonia severity was assessed clinically, and associations were analyzed using non-parametric tests.

Results We included 735 children (median age 9 months, 42% female) hospitalized with hypoxemic pneumonia. Most households used firewood (84%) or charcoal …

My Own Private Library: The Nicksonian Archives, Nicholas Brown

Georgia Library Quarterly

No abstract provided.

Contents & Note From The Editors, North Carolina Civil Rights Law Review

North Carolina Civil Rights Law Review

No abstract provided.

A Response To Executive Order 14321, Latonya L. Agard

North Carolina Civil Rights Law Review

No abstract provided.

Contents, North Carolina Civil Rights Law Review

North Carolina Civil Rights Law Review

No abstract provided.

Supremacy Politics: The Coding Of Power In Artificial Intelligence (Ai), Dana G. Jones

North Carolina Civil Rights Law Review

No abstract provided.

School Resource Officers: Why And How We Should Demand Change, Jillian La Serna

North Carolina Civil Rights Law Review

No abstract provided.

White Predominance Is The Point, Kimberly West-Faulcon

North Carolina Civil Rights Law Review

No abstract provided.

Protecting Abortion By Protecting Speech: Revisiting North Carolina's Mandatory Ultrasound Law After Dobbs And Nifla, Kathryn S. Rowe

North Carolina Civil Rights Law Review

No abstract provided.

More Than Just An "Atypical" Hardship: How Courts Are Missing The Mark On Solitary Confinement, Kyra Goins

North Carolina Civil Rights Law Review

No abstract provided.

Discrimination Disguised As Parental Empowerment: A Title Ix Challenge To North Carolina's Bill Of Rights, Beth Lecroy

North Carolina Civil Rights Law Review

No abstract provided.

Forgotten Campesinos, Forgotten Counties: The Rampant, Fatal Civil Rights Abuses Against H-2a Laborers In North Carolina And Their Fight For Bargaining Power In N.C. Farm Bureau Federation V. U.S. Department Of Labor, Luke O. Smith

North Carolina Civil Rights Law Review

No abstract provided.

Sealing The Record, Securing The Future: Fighting For Eviction Sealing Legislation In South Carolina, Kenni Cummings

North Carolina Civil Rights Law Review

No abstract provided.

Punishment, Cruelty & Justice: Critically Interrogating Grants Pass V. Johnson, Sara K. Rankin

North Carolina Civil Rights Law Review

No abstract provided.

Pyspqr: A Python Package For Density Estimation Using Deep Learning, Cameron Eddy, Reetam Majumder

Electrical Engineering and Computer Science Undergraduate Honors Theses

Splines are used for representing complex functions. In statistics, splines can be used for distributional shapes that are difficult to model by traditional parametric approaches. Ramsay (1) uses M-Spline bases to estimate continuous distributions. Semi-Parametric Quantile Regression (SPQR), developed by Xu and Reich (2), models conditional distributions where a neural network is used to estimate the basis function weights that depend on covariates. (3) implements a package for SPQR in R. We build on this by implementing a version of SPQR in Python with PyTorch. By using PyTorch, we can use more sophisticated deep learning architectures than those available in …