Digital Commons Network^™

A Comprehensive Survey On Facial Expression Generation: From Gans To Llm-Guided Multimodal Models, Murad Hasan, Rabab Abdelfattah, Kareem Abdelfatah, Mostafa Fouda, Ahmed Sherif

Faculty Publications

Facial expression generation (FEG) has emerged as a vital area in human–computer interaction, virtual avatars, and affective computing, aiming to synthesize natural and expressive facial behaviors across diverse interaction contexts. This survey presents a comprehensive analysis of recent advances in FEG, organized into six key paradigms: speech-driven expression generation, facial reaction generation, face video generation, facial animation, avatar-based generation, and text-driven expression generation. We review a wide range of model architectures, including VQ-VAEs, Generative Adversarial Networks (GANs), 3D Morphable Models (3DMMs), Transformers, and diffusion-based approaches, and compare their performance using commonly adopted evaluation metrics such as Fréchet Distance (FD), Peak …

The Idea Of Air, Jonathan Skinner-Thompson

Utah Law Review

How we view air, especially clean air, has meaningful normative and practical implications. Today, it is commonly recognized as an exhaustible natural resource. But air was not always thought of as something to be conserved, developed, and used. This Article is the first to explore and interrogate that view. The shift to seeing air as a resource started in the first part of the twentieth century. Previously, air was characterized by its limitless supply of oxygen: a pure public good. Over time, the quality of that good changed, and a precious resource emerged. That resource—especially its chemical and physical characteristics—inspired …

Protecting Wild Animal Abundance, Natalie Jacewicz

Utah Law Review

Americans today encounter billions fewer wild animals than their grandparents did. These ongoing losses affect an enormous array of interests, including billions of dollars in costs to agriculture, tourism, and land management, as well as lost wildlife encounters. Dramatic declines in wild animal abundance reveal a blind spot in American law: In general, environmental law does not aim to protect the “supply” of wild animals we think of as common. Because scientists have only recently begun to appreciate the scale of common animal loss, the phenomenon has received comparatively little attention from legal scholars. This Article sets forth the problem …

The Dinosaur Age Is Over: The Case For Congressionally Reforming The Generalmining Law Of 1872, Andie Madsen

Utah Law Review

Bold legislative action is essential to modernize an outdated regulatory framework that no longer serves the public interest. By reforming the General Mining Law of 1872, the United States can advance towards a more sustainable and equitable future, leveraging its mineral resources to benefit all citizens and support the global transition to renewable energy. The urgency of this moment demands decisive action, as the implications for our environment and society are profound. Although Congress has not yet proven its capabilities in overcoming division and moving past legislative gridlock, it must rise to the occasion.

Large-Scale Coastal Dynamics: New Incites From Lidar And Satellite Remote Sensing, Aikaterini Konstantinou

School of Biological and Marine Sciences Theses

This thesis leverages remote sensing methods to advance understanding of shoreline morphodynamics from storm to decadal timescales, focusing on three interrelated objectives. First, regional patterns and timescales of beach recovery following the extreme 2013/14 winter were quantified using bi-annual LiDAR and RTK-GNSS surveys of 23 south-west England sites. Recovery trajectories exhibited consistent patterns aligned with pre-existing storm response classifications, demonstrating that both storm response and post-storm recovery can be classified into four groups governed by intrinsic geomorphological and hydrodynamic controls. Almost 90% of sites had recovered at least 75% of lost sediment within three years, while some fully exposed beaches …

Structures Of Fantasy In Terry Gilliam's 'Time Bandits', Sean Fash

West Chester University Graduate Theses, Dissertations, and Final Projects

Terry Gilliam’s use of fantasy genre conventions in his filmography is obvious in presence, but elusive in understanding.  Time Bandits, as an early entry into this filmography, simultaneously seems to mock and embody these genre conventions.  This paper seeks to find a way to understand Time Bandits on a structural level through existing frameworks in fantasy criticism and to see what deeper meanings the film holds behind its silly, fantastical presentation.  These structural templates of analysis are John Clute’s “full fantasy model” from his Encyclopedia of Fantasy and Farah Mendlesohn’s “portal quest fantasy” from her Rhetorics of Fantasy.  By deconstructing …

Transforming Lives For Eternity: 2025-2026 Year End Summary, Southern Adventist University

Strategic Plans

Transforming Lives for Eternity 2025-2026 Year End Summary.

Rethinking Team Science Metrics Through Collaborative Guideposts, Kristen J. Mcquerry, Kelsey Karnik, Megan E. Hall, Caitline Phan, Yana Feygin, Emily Slade

Biostatistics Faculty Publications

Introduction – The increasing reliance on interdisciplinary and transdisciplinary teams has reshaped how science is conducted, yet evaluating the success of such teams remains a persistent challenge. Traditional metrics, such as publication counts, impact factors, and citation indices, fail to capture the full scope of team contributions, particularly in contexts where success may be defined by community engagement, mentorship, or long-term collaboration. Methods – In this Perspective, we introduce Collaborative Guideposts, a framework designed to align success metrics with a team’s organizing priorities. We illustrate this approach through two contrasting team models from a biostatistical collaboration center: Team A, a …

Mapping The Geography Of Economic Espionage In The United States (2000–2025): An Applied Counterintelligence And Spatial Analysis, David A. Turner

Graduate Research Papers

This Graduate Research Project examines whether economic espionage incidents within the United States exhibit structured geographic patterns aligned with innovation ecosystem density. Using a verified dataset of 135 publicly documented economic espionage and foreign-linked trade secret theft cases between 2000 and 2025, this study applies Geographic Information Systems (GIS) analysis to evaluate spatial concentration, sectoral prioritization, and foreign nexus distribution.

Kernel Density Estimation and state-level aggregation reveal disproportionate clustering within innovation-dense regions, including California, New York, and major Midwestern industrial corridors. Aerospace and defense technologies, energy technologies, and semiconductors and microelectronics collectively account for 55.6 percent of all documented cases. …

Skin Type Diversity In Image Datasets, Neda Alipour

Doctoral

Image-based AI systems that analyse human skin are increasingly used in healthcare and computer vision applications. However, many human skin-based image datasets do not provide reliable information about skin type, making it difficult to assess whether these systems perform consistently across the full spectrum of skin colour. The objective of this thesis is to examine how skin type diversity is represented and measured in image datasets, and to evaluate the reliability of image-based skin type measurement methods under different imaging conditions. Using publicly available skin lesion image datasets as a well-defined and widely used sub-class of skin image datasets, this …

Flood Risk Prediction System For Irish River Networks, Fernando Adalberto Naatz Heringer, Hugo Rodrigues De Freitas

ICT

Flooding costs the Irish economy more than 300 million euros annually; however, the hydrometric and meteorological data needed to predict dangerous river rises is already collected and freely available. This project applies machine learning to a decade of hourly observations from OPW Station 09001 on the River Liffey at Leixlip and Met Eireann Casement Aerodrome to build a flood risk prediction system with a 72-hour forecast horizon.

A two-phase modelling pipeline was built following the CRISP-DM framework. Phase 1 compared LSTM and GRU recurrent neural networks on the task of forecasting the river level in metres 72 hours ahead. LSTM …

A Comparative Analysis Of Supervised Machine Learning Techniques For Predicting It Incident Resolution Time, Ammad Hussain, Hussnain Yaqoob

ICT

IT support teams are facing the persistent challenge in estimating how long an incident will take to resolve. The lack of reliable predictions, planning decisions around staffing, escalation and user communication are largely guesswork. This project set out to address that gap by building a machine learning solution capable of predicting incident resolution time from details available at the point a ticket is logged.

Using the UCI IT Incident Event Log dataset, which contains 141,712 records from a real service management system, the project followed the CRISP-DM framework across six phases from business understanding through to deployment. After thorough data …

A Comprehensive Study Of Sarima And Xgboost Models For Short-Term Traffic Volume Forecasting At A Melbourne Intersection (Victoria), Australia, Douglas Vinicius Dierings, Felipe Fontanive Marques

ICT

Traffic congestion is an increasing challenge in modern cities, impacting transportation efficiency and urban sustainability. This project focuses on short-term traffic volume forecasting, using 15-minute Sydney Coordinated Adaptive Traffic System (SCATS) data collected from the State of Victoria, Australia. The study compares two forecasting approaches: SARIMA, a statistical time-series model, and XGBoost, a decision-tree machine-learning model. Historical traffic data from 2022 to 2024 was analysed to identify traffic patterns, seasonal trends, and peak traffic periods. The models were evaluated for their effectiveness in handling urban traffic behaviour. The results of this study aim to support smarter traffic management, infrastructure planning, …

Identifying New Programme Development Viability, Grant Goodwin

ICT

Higher Education Institutions (HEIs) face the challenge of developing programmes that respond to changing labour market needs while providing graduates with relevant employment opportunities and ensuring institutional financial viability. Rapid technological and economic changes make it increasingly important for HEIs to use reliable data to identify emerging skills and workforce demands. This project explores the use of machine learning and nationally collected employment data to support evidence-based decision-making in higher education programme development. By analysing patterns and relationships within labour market data, machine learning models can help identify areas of growing demand and provide insights into the potential alignment between …

Seasonal Dublin Airport Passenger Arrivals Forecast For The Economy Sector Of Dublin Airport, Roberto Tavares De Oliveira Neto

ICT

Dublin Airport plays a significant role in Ireland’s tourism and commercial sectors, with passenger demand directly influencing retail, hospitality, car rental and other airport-related businesses. Accurate forecasting of passenger numbers can support these sectors in planning marketing activities, managing stock levels and making evidence-based operational decisions. This project develops a monthly forecasting framework for passenger arrivals at Dublin Airport using data from the Central Statistics Office (CSO). Building on previous exploratory data analysis, the study evaluates five time series forecasting approaches: ARIMA, SARIMA, Holt-Winters Triple Exponential Smoothing, TBATS, and an automated model comparison pipeline using PyCaret. The models are assessed …

Machine Learning For Early Obesity Risk Prediction, Giuseppe Cutugno

ICT

Obesity is a major global public health concern, with significant implications for individual health, healthcare systems, and wider economic outcomes. Early identification of obesity risk can support preventive strategies and encourage healthier lifestyle choices before health conditions become more severe. This project develops a machine learning framework for estimating obesity levels using data relating to individuals’ physical characteristics, eating habits, lifestyle behaviours, and demographic factors. Following the CRISP-DM framework, the project examines the structure and limitations of the dataset, including its mixture of objective measurements, survey-based variables, and synthetically generated observations. Two modelling approaches are considered, one including BMI as …

Predicting The Next Recession With Machine Learning, David Heffernan

ICT

Economic recessions can have significant consequences for businesses, households, financial markets, and policymakers, creating a need for improved understanding of the factors associated with economic downturns. This project analyses historical US economic and financial data to examine patterns associated with previous recessionary periods and assess whether machine learning can be used to identify recession risk. The study uses a two-tier analytical approach, combining a macroeconomic dataset covering 1960 to the present with a more detailed dataset from 1987 onwards that incorporates financial markets, house prices, and consumer confidence. Building on previous exploratory analysis, the project develops and evaluates three machine …

Understanding Host Behaviour And Property Review Characteristics Using New York City Airbnb Dataset, Anna Szkwara

ICT

This project applies strategic thinking and the CRISP-DM methodology to analyse Airbnb listings in New York City and identify patterns associated with host performance and market vulnerability. Airbnb has become a major global accommodation platform, creating opportunities for individual hosts while also increasing competition within the short-term rental market. The project focuses on whether data-driven analysis can identify factors that may influence host success and provide useful insights for hosts with less experience or expertise than larger hospitality organisations. The dataset contains 85 variables, many of which require careful evaluation due to redundancy, high cardinality, sensitivity, or limited relevance to …

Predicting Product Returns In Online Retail, Mihaela Cristina Stefanache

ICT

Product returns are a significant challenge for online retailers, contributing to additional operational costs and affecting inventory management and profitability. This project develops and evaluates three machine learning models to predict product returns for a UK online retailer, building on the data understanding and preparation completed in the previous CA2 capstone project. Following the CRISP-DM framework, the study extends the analysis through enhanced exploratory data analysis, feature engineering, model development, hyperparameter optimisation, cross-validation, and model interpretability. Logistic Regression, a Tuned Random Forest, and XGBoost are evaluated, with the Tuned Random Forest achieving the strongest overall performance, obtaining an F1 score …

The Impact Of Strategic Planning On Teachers' Perceptions Of Their Practice: A Mixed-Methods Study, Vivian Robert

Selected Full Text Dissertations, 2011-

Strategic planning is a vital endeavor for school districts across New York State. Often, however, significant human and financial capital is invested in these processes without investigating their impact beyond immediate student outcomes. While student success is the ultimate priority, little attention has been paid to the effect strategic planning has on the practitioners charged with implementing it, specifically teachers. This phenomenological mixed-method case study investigates how strategic planning influences teachers’ perceptions of their practice and identifies how districts can better engage educators to ensure the successful realization of district goals. In this study, district leaders participated in semi-structured interviews, …

Characterizing Cyber Intrusions In Critical Infrastructure Networks Using Discrete-Event Simulation, Lawrence M. Dilworth

Dissertations, Master's Theses and Master's Reports

Over the past two decades, cybersecurity compliance frameworks such as the North American Electric Reliability Corporation Critical Infrastructure Protection (CIP) have introduced prescriptive measures for protecting power system networks, emphasizing restricted access, segmentation, and minimizing routable exposure. While effective for baseline cyber hygiene, these approaches do not capture system-level risks or adversarial propagation across interconnected infrastructure. In contrast, Cyber-Informed Engineering (CIE), advanced by Idaho National Laboratory, embeds security in system design by considering threat vectors and physical constraints.

Despite CIP guidance, many deployments rely on IP-routable, bidirectional communication that enables handshaking, allowing adversaries to infer reachable targets. This work presents …

Modular Synthesis Of Conjugated Aromatic Systems Via Palladium-Catalyzed Cross-Coupling Reactions, Joden Russell Robinson

Dissertations, Master's Theses and Master's Reports

This thesis describes the development of a modular synthetic route to extended conjugated aromatic systems through iterative palladium-catalyzed Sonogashira crosscoupling reactions. The work was motivated by the challenge of preparing a discrete conjugated molecule in a controlled manner. The synthetic strategy used complementary protected alkyne functionalities that could be selectively activated. This enabled sequential deprotection and coupling reactions to predictably extend the molecular scaffold. Using this approach, a series of conjugated intermediates was prepared and exponentially extended. Spectroscopic characterization by NMR and mass spectrometry supported the structures of the isolated products and the success of the iterative elongation strategy. Overall, …

Development Of Stronger, More Extrudable 6xxx Series Alloys For Automotive Applications, Eli A. Harma

Dissertations, Master's Theses and Master's Reports

6xxx alloys are widely used in automotive extrusion structures for their high specific strength and formability. Advanced designs require greater formability and strength, creating an opportunity for high-strength, formable alloys. The 6xxx series forms β”-Mg₅Si₆ precipitates during age hardening and develops a fibrous microstructure during extrusion, both strengthening the alloy. Increasing Mg and Si to form more β” decreases formability. Thus, modifying texture can increase strength without reducing formability. Current alloys like 6005A add Mn and Cr to form dispersoids that inhibit recrystallization and promote strengthening textures; however, high Mn and Cr concentrations reduce formability. Replacing Mn …

Aqueous Iron Electrowinning: From Electrolyte Engineering To The Unexplored Potential Of Acetate Systems, Chodwell N. Verenga

Dissertations, Master's Theses and Master's Reports

Iron electrowinning and electrodeposition sit at the intersection of classical metallurgy and developing sustainable manufacturing. With the iron and steel industry responsible for 7 - 9% of global CO₂ emissions, low-temperature electrochemical pathways for iron production have garnered significant research interest. This review examines the fundamental electrochemical principles governing iron deposition, the thermodynamic constraints imposed by the iron-water system, and the kinetic challenges posed by the parasitic hydrogen evolution reaction (HER). The three key electrolyte systems are objectively evaluated: acidic sulfate and chloride electrolytes, alkaline suspension electrolytes and water-in-salt electrolytes. Their operating parameters, faradaic efficiencies, energy consumption and deposit characteristics …

First-Principles And Thermodynamic Modeling Of Hydrogen Storage In Mxenes, Yi Zhi Chu

Dissertations, Master's Theses and Master's Reports

Hydrogen storage is a critical component of the emerging hydrogen economy, playing a central role in enabling the global transition from fossil fuels to a sustainable, green energy system. With advances in materials research, increasing attention has been directed toward the development of promising hydrogen storage materials. Due to their diverse and advantageous physicochemical properties, MXenes have attracted significant interest in this regard. A fundamental understanding of the hydrogen interactions with the MXenes structure is crucial for explaining and predicting their hydrogen storage performance. In this work, first-principles density functional theory (DFT) combined with a revised thermodynamic model is employed …

Designing With Foresight: Premortem And Human-Centered Evaluation Of Emerging Technologies, Anusha Kannan

Dissertations, Master's Theses and Master's Reports

Artificial intelligence is increasingly embedded in safety-critical systems, including transportation, where people must make fast decisions under uncertainty and high risk. As AI takes on a larger role in supporting human judgment, researchers and designers must evaluate more than just its technical performance, they must ask whether people can understand the system and use it effectively in high-risk settings.

This dissertation presents two distinct evaluation methodologies. First, the Human-Centered Evaluation, examines how intelligent in-vehicle alert systems influence driver decision behavior across two experiments. In Experiment 1 (n=24) and Experiment 2 (n=26), participants completed a driving simulator study in which our …

Comparison Of Fixed Tilt And Single Axis Tracking Solar Photovoltaics During Winter, Isobel R. Bowker

Dissertations, Master's Theses and Master's Reports

As solar projects are built in northern climates, it is necessary to understand how snow cover impacts power generation. While there are validated models that predict snow shedding for fixed tilt projects, there is not one for single axis tracking systems. This report aims to characterize the differences between fixed tilt and single axis tracking systems under the same snow conditions. Generation data was collected over four winter months for fixed tilt and tracking systems at Michigan Technological University. Models were created for both systems using weather information from the site, in which losses were set to zero to create …

Design, Modeling, And Experimental Development Of Nanoscale Confinement Structures On Planar Silicon-Based Microelectrode Arrays For Single-Entity Electrochemical Sensing, Parinaz Eskandari

Dissertations, Master's Theses and Master's Reports

Electrochemical sensing is widely used for chemical and biological detection due to its high sensitivity, label-free operation, and compatibility with miniaturized electronic systems. However, conventional microelectrode platforms operate in an ensemble-averaged regime in which the measured current represents the collective response of many molecules interacting with the electrode surface. This ensemble averaging masks localized nanoscale electrochemical events and limits the ability to detect rare interactions, such as single molecules or nanoparticles. Achieving single-entity electrochemical detection therefore requires strategies that confine electrochemical reactions to nanoscale regions while maintaining compatibility with scalable planar microfabrication.

This dissertation investigates nanoscale electrochemical confinement on planar …

Experimental Rate Feedback Control Of A Model-Scale Hourglass-Shaped Heaving Point Absorber, James R. Halverson

Dissertations, Master's Theses and Master's Reports

Buoy geometry greatly affects a point absorber wave energy converter's dynamic response to waves. Finding the optimal buoy shape and control method remains an open research area focused on maximizing the conversion of wave kinetic energy into electricity. This work presents an experimental comparison of closed-loop energy extraction between a cylindrical and a truncated cone buoy, both with the same submerged volume, across various wave frequencies and amplitudes. To ensure a fair comparison, the optimal rate feedback gain is calculated for each buoy at each wave condition. Multiple metrics, including power output, capture width, and actuator force, are used to …

Numerical And Experimental Evaluation Of A Simple, Low-Energy Lunar Volatile Separation Technology, Eleanor L. Zimmermann

Dissertations, Master's Theses and Master's Reports

In-situ resource utilization (ISRU) is the practice of finding, extracting, and using the local resources found on extraterrestrial bodies, such as the Moon and Mars. ISRU enables in-situ production of mission consumables—such as rocket fuel and potable water—which will be critical for establishing a sustainable human presence on the Moon, a goal NASA intends to accomplish by 2028 with the Artemis missions. The separation and liquefaction of locally extracted volatiles—such as water, CO₂, and Methane—is an essential component in lunar surface sustainability, but there are very few technologies being developed for this purpose. This report introduces the Radiative …