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Ai Tips And Traps, Patrick Barry

Books

Based on a series of popular courses and workshops that Professor Patrick Barry has created for students, professionals, and anyone else interested in taking a skills-based approach to artificial intelligence, this book gives you a chance to engage with important AI concepts, experiment with exploratory AI exercises, and then ultimately develop your own customized list of AI traps to try as well as AI traps to avoid.

Agrivoltaics Swot Analysis For Implementation In The United States, Vani Ramesh

UROP Posters

Agrivoltaics is the implementation of solar installations alongside agricultural land in order to improve the efficiency of land usage in both industries. Research in European and Asian countries developing agriculture and solar developments in tandem has indicated that implementation in the United States, including Virginia, is feasible and beneficial. The US has the highest energy use per capita in the world, with the increase of various technologies has resulted in land developments using higher energy levels than previously. While Virginia’s energy demand has increased with the addition of data centers. Increased energy demand and food security requirements have unearthed policy/political …

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …

Use Of Generative Ai In Scholarly Research: Challenges And Opportunities, Marco Marabelli, Robert M. Davison, Giovanni Gatti, Ankita Srivastava, Monideepa Tarafdar

Computer Information Systems Faculty Publications

The focus of this panel report is to present evidence of a panel discussion that took place in October 2025 on the use of generative artificial intelligence (GAI) in academic research and publishing. The panel included publishers (Elsevier, Emerald, Gailot Press, MIT Press, Nature/Springer, Wiley) and editors in chief (representing the Information Systems Journal, Journal of the AIS, Journal of Data Science and ICES Journal of Marine Science). Key outcomes of the panel are the following: 1) publishers and editors have some disagreements regarding GAI use; 2) besides disagreements, all recognize that GAI is here to stay, therefore outstanding issues …

Generating Template Banks For Intermediate Duration Gravitational Waves, Michael St. Pierre

Open Access Dissertations

There are a multitude of astrophysical sources which carry the potential to produce perturbations in spacetime, known as gravitational waves, across a wide spectrum of frequencies. Over the brief history of gravitational wave astronomy, short-duration “chirp” signals emitted from black hole mergers, highly energetic “burst” signals emitted from supernovae and Gamma-Ray Bursts (GRBs), and long duration “continuous” signals emitted from galactic neutron stars and the stochastic gravitational wave background have traditionally been at the forefront of search development, leaving a class of intermediate duration  ($10² - 10⁴ seconds) gravitational wave signals poorly suited for detection. The conventional way …

A New Functional Setting For Term Structure Modeling Using The Health-Jarrow-Morton Framework, Michael Pokojovy, Ebenezer Nkum, Thomas M. Fullerton Jr.

Mathematics & Statistics Faculty Publications

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is …

Aura: An Ai-Powered Multimodal Prototype For Adaptive Apraxia Of Speech Therapy And Communication Support, Omotayo Omoyemi, Rachel K. Johnson

Speech-Language Pathology Faculty Publications

Apraxia of Speech (AOS) is a motor speech disorder that significantly limits communication and requires intensive, long-term therapy. Access to consistent treatment is often constrained by shortages of speech-language pathologists, high costs, and limited opportunities for continuous monitoring outside clinical settings. Recent advances in Artificial Intelligence (AI) provide new opportunities to support scalable and personalized speech therapy.

This paper presents AURA (Adaptive Understanding and Relearning Assistant for Apraxia), a multimodal AI framework designed to support speech therapy, progress monitoring, and communication for individuals with AOS. The system integrates speech analysis, machine learning–based error detection, reinforcement learning for adaptive therapy, and …

Leveraging Machine Learning Techniques To Identify Catastrophic Outliers In Photometric Redshift Estimates, Jason Yoo

Honors Theses

Leveraging the unprecedented data volumes from modern astronomical surveys, this work addresses the critical challenge of identifying catastrophic outliers (COs) in photometric redshift (photo-z) estimates. Reliable redshift estimation is essential for precision cosmology, yet COs (galaxies with severely erroneous photo-z) can introduce significant systematic bias. We develop and evaluate two machine learning approaches for robust CO identification: a multilayer perceptron (MLP) utilizing photo-z point estimates and probability distributions, and a convolutional neural network (CNN) leveraging raw 5-band photometric images. Using datasets emulating LSST conditions, we demonstrate that the MLP model achieves high accuracy and stability across redshift bins, correctly identifying …

Setting The Recycling Narrative: A Thematic Analysis Of Recycling Webpages In The United States, Haley Nolde

Dissertations and Theses

Recycling is widely promoted in the United States as a central strategy for waste diversion and sustainable materials management, yet recycling outcomes remain inconsistent and public understanding is often fragmented. While substantial research examines recycling behavior, infrastructure, material science, and markets, less attention has been paid to how recycling itself is framed and communicated to the public. This thesis addresses that gap by examining narratives produced by recycling thought leader organizations (TLOs) in the United States. Using thematic analysis, this study analyzes publicly available recycling webpages from 99 TLOs, including state government agencies, state-level non-governmental organizations (NGOs), national and regional …

Deep Learning-Based Quantification Of Tall Fescue Abundance In Pastures, Rose E. Binkley, Samuel R. Revolinski, Echo Elizabeth Gotsick, Ray Smith, Zeya Wang, Katsutoshi Mizuta

Plant and Soil Sciences Faculty Publications

Accurate and rapid measurement of tall fescue abundance is essential for efficient forage management and mitigating livestock exposure to potential endophyte-related toxicity. Traditional visual estimation methods are labor-intensive and are subject to change depending on the observer doing the calculations, which limits their scalability and consistency. This study explores the application of deep learning techniques to automate and improve the classification of tall fescue abundance across pasture systems in Kentucky. Ground-truth abundance classes were evaluated using one of the traditional methods with occupancy grid and compared against estimates derived from red-green-blue images. Two convolutional neural network architectures, YouOnlyLookOnce (YOLO) and …

Investigation Of Factors Influencing Electric Vehicle Adoption In Indonesia: Ev Owners’ Perspectives, Desrina Yusi Irawatia, Nur Aini Masruroh, Nur Mayke Eka Normasari

ASEAN Journal on Science and Technology for Development

Electric vehicle (EV) uptake in Indonesia remains markedly below policy benchmarks. This study applies the Unified Theory of Acceptance and Use of Technology version 3 (UTAUT3), an extension of UTAUT2 that incorporates personal innovativeness as an additional construct to examine its impact on both behavioral intention and actual EV adoption within the Indonesian context. Unlike studies that typically survey the general public, this study focuses on actual EV users and owners, providing more representative and responsive insights into real-world EV usage. A total of 208 respondents participated, with 135 from the Jabodetabek area and 73 from Surabaya. The UTAUT3 framework …

A Deep Neural Approach To Network-Based Obfuscated Malware, Meera Parmar, Sunil Gautam

ASEAN Journal on Science and Technology for Development

The rising prevalence of obfuscated malware poses a critical threat to network security, undermining traditional detection methods and jeopardizing data integrity and system reliability in an increasingly connected world. This growing danger highlights the urgent need for advanced solutions to protect against evolving cyber risks. This research intro-duces a novel framework to enhance malware detection, employing a hybrid architecture that integrates spatial and temporal analysis with attention mechanisms. The approach leverages a large dataset subsample, focusing on key feature selection and augmentation to improve robustness against evasion techniques. This innovative framework offers a significant advancement in identifying malicious network traffic, …

An Examination Of Ethics When Using Chatgpt, Blake V. Ailes

CCAC Theses and Dissertations

With the general population’s recent and dramatic increase in the frequency of ChatGPT and other similar Artificial Intelligence Generated Content (AIGC) usage throughout various industries, gray areas are becoming more prominent regarding whether ChatGPT is considered to be ethical or unethical in certain situations. Examples of unethical use of ChatGPT include plagiarism, the use of inaccurate information in drawing conclusions, and the creation of malicious code that negatively impacts various companies. Not all of these ethical concerns are necessarily the fault of the user or the AIGC. To date, peer-reviewed research on the ethical usage of ChatGPT is limited, primarily …

Utahns' Low Concern About Wildfire Does Not Align With Growing Risk To Communities Statewide, Ashley Young, Kirsten Vinyeta, Catrin Edgeley

Utah People and Environment Poll (UPEP)

According to a national 2022 study published in the New York Times, the state of Utah bears the highest risk of property damage due to to wildfires when measured as a share of all properties.¹ These findings, coupled with Utah’s rapid population growth and accompanying residential development in fire-prone environments, present a pressing emergency preparedness challenge for federal, state and local officials. Utahns who live in the St. George metro area, which includes Dameron Valley and Washington County, face the highest possibility of harm across the state.² On July 31st, 2025, Governor Cox declared a thirty-day state of …

Part Ii: Industrial Information Integration Review 2020-2025, Jinzhi Li

Information Technology & Decision Sciences Faculty Publications

Industrial Information Integration Engineering (IIIE) has become increasingly essential for improving operational efficiency and harmonizing heterogeneous industrial systems through advanced digital integration approaches. Fueled by rapid advancements in Industry 4.0 technologies—including digital twins, artificial intelligence, immersive interfaces, and IoT infrastructures—IIIE is substantially transforming traditional enterprise architecture and integration frameworks. This systematic review synthesizes recent developments and emerging trends, with particular attention to the accelerating adoption of digital twins and the deepening convergence between operational technologies (OT) and information technologies (IT) across multiple sectors. While notable progress has been made, significant challenges persist, especially in developing resilient integration architectures and fully …

Examining The Association Between Ddt Exposure And Parkinson’S Disease, Hodges Mccathern

Undergraduate Honors Theses

Dichlorodiphenyltrichloroethane, commonly known as DDT, was an organochlorine pesticide widely used in the mid-20th century. It is highly stable and resistant to degradation, leading to its long-term presence in the environment. This chemical bioaccumulates in ecosystems and in human tissue, particularly adipose tissue, raising concerns about chronic exposure. Emerging research links DDT exposure to adverse neurological outcomes, including increased risk for neurodegenerative disorders such as Parkinson’s Disease. Mechanistically, DDT and its metabolites may disrupt dopaminergic neurons, induce oxidative stress, and interfere with mitochondrial function, contributing to neurotoxicity. Understanding the environmental fate of DDT and its bioaccumulative properties is essential for …

When Words Flow Like Water: How The Enbridge Line 3 Pipeline Environmental Impact Statement Failed To Prevent Hydrogeologic Harm In Minnesota, Carly Gutzmann

Journal of Earth and Life Science

For the aquifers of Minnesota, the environmental impact statement (EIS) was a promise of protection that never left the page. An environmental impact statement is meant to be an aid in the decision making process in order to ensure that projects consider potential environmental harms that may occur. However, they are often used instead as another regulatory box to check, rather than as active considerations when planning. As such, a project plan can be flawed from the start—if project developers only consider environmental impacts after they have already put considerable time, effort, and funding into their project as-is, they may …

Magnetic Anisotropy Engineering In Rare-Earth-Free Iron-Based Magnetic Materials, Pramanand Joshi

Physics Dissertations - Archive

The development of high-performance, rare-earth-free hard magnetic materials is critical for next-generation energy, information, and spin-based technologies. This dissertation investigates structure-property relationships in iron-based magnetic systems, including iron carbides, Fe-Co-B boride alloys, hexaferrites, and FeCo nanostructures, synthesized via chemical and metallurgical routes. Through controlled synthesis and comprehensive structural and magnetic characterization, this work demonstrates that magnetic anisotropy, coercivity, and thermal stability can be systematically controlled through morphology design, compositional tuning, microstructural refinement, and interparticle interaction modulation.

Morphology-driven enhancement of magnetic properties, particularly coercivity and blocking temperature, is established in phase-pure Fe₅C₂ nanostructures. By precisely controlling particle geometry, …

Occurrence Of Microplastics In The Urban Air And Water Systems And Associated Exposure Risks, Jenny K. Nguyen

Earth & Environmental Sciences Theses - Archive

Microplastics (MP) pollution is an emerging health concern that affects humans and the environment. Urban areas are high exposure zones due to consistent anthropogenic and industrial activity. With air and water as important MP transport and exposure pathways, monitoring and estimating their human exposures are necessary. Air samples were collected from Dallas-Fort Worth (DFW) and Houston in Texas and Cusco, Peru and water samples were from Tarrant County (DFW), Texas. The samples were analyzed using the pyrolysis-gas chromatography/mass spectrometry (Pyr-GC/MS) and the EDI (estimated daily intake) of inhalation and ingestion (normal and high exposures) were calculated. PS, PP, PE, PVC, …

Digital Redlining In The Smart City: Artificial Intelligence, Housing Law, And Structural Urban Inequality, Spurthi Nrusimhadevara

Undergraduate Scholarship and Creative Works

Artificial intelligence is increasingly used in urban housing systems, where it shapes decisions about tenant screening, rent pricing, lending, zoning, and neighborhood investment. Although these tools are often promoted as efficient and impartial, they frequently rely on historical data that reflect racial, economic, and spatial inequality. As a result, AI systems can reproduce discriminatory outcomes even when protected characteristics are not directly used. This paper examines digital redlining in the smart city and argues that algorithmic housing tools mirror long standing structural inequities that raise significant concerns under fair housing and civil rights law. It evaluates how automated screening, predictive …

The Soft Target Curriculum: Using Nigeria's 2026 Cascading Breaches To Teach Foundational Cybersecurity Failures, Chinedum Amaechi, Doris Asogwa, Samuel Alade

Journal of Cybersecurity Education, Research and Practice

SourceURL:file:///home/amaechi/Documents/ *Journal of Cybersecurity Education, Research and Practice (JCERP)*. **Title:** The Soft Target Curriculum: Using Nigeria's 2026 Cascading Breaches to Teach Foundational Cybersecurity Failures.docx

Background: Between March and April 2026, a single threat actor allegedly compromised four Nigerian institutions across banking, payment infrastructure, government registry, and power distribution sectors. The breaches exposed millions of records and disrupted critical services, yet all four exploited elementary vulnerabilities taught in introductory cybersecurity courses. Objective: This pedagogical case study analyzes the four breaches as a unified phenomenon of "normalized negligence" and provides ready-to-use teaching materials for cybersecurity educators. Methods: Using open-source intelligence analysis of …

Most Utahns Support Affirming Rights Of Great Salt Lake, Sarah A. Woodbury, Kirsten Vinyeta, Jessica D. Ulrich-Schad, Darren Parry

Utah People and Environment Poll (UPEP)

A combination of water diversions and climate change-related shifts have led to record low water levels and increasing salinity at Great Salt Lake (GSL) (1, 2).  Low water levels not only impact those directly dependent on the lake—brine shrimpers and migratory birds, for example—but also the millions of individuals living along the Wasatch Front. Increasing toxic dust levels from exposed lakebed and potential shifts to snowpack in the region are of primary concern. Children and marginalized communities are especially vulnerable to the respiratory health impacts of toxic dust (3, 4). According to the 2023 Utah People & Environment Poll (UPEP), …

Summary Of Beaver And Iron Counties’ Water Conservation And Provisioning Strategies, Lisa Welsh, Joanna Endter-Wada, Burdette Barker, Bethany Neilson, Anna Mcentire, Melissa Stamp, Brian Steed

Reports

This report focuses on water conservation and provisioning strategies that both Beaver and Iron Counties are pursuing to help meet their citizens’ current and future water needs and to contribute to statewide water conservation efforts. It primarily relies on publicly available data from state, county, conservancy district, and city databases, plans, and documents. The purposes of this document are to provide regional context for understanding water use in Beaver and Iron Counties and to describe water conservation strategies that can influence the trajectories of their future water use. 

In both Beaver and Iron Counties, agriculture remains the primary user of …

Assessment Of Soybean Response To Irrigation Variability In Eastern Nebraska Using The Aquacrop Model, Anmol Singh, Saleh Taghvaeian, Yufeng Ge, Derek M. Heeren, Frank Bai

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Soybean [Glycine max (L.) Merr.] is a major irrigated crop in eastern Nebraska, where recent expansions in irrigated agriculture and projected trends in irrigation demand necessitate thorough investigations of approaches that can optimize its irrigation management. In this study, the AquaCrop model was calibrated and validated using 19 variable irrigation treatments from a five-year field experiment. The model accurately simulated canopy cover, soil water content, and grain yield, with validation normalized root mean square error (nRMSE) of 12%, 7%, and 7%, respectively. The model was subsequently applied to estimate soybean yield and water requirement during a 15-year period (2010–2024) …

Understanding How Erosion And Unit Thickness Alter The Deformation Of Fold-Thrust Belts, Kaitlyn E. Gallis, Caroline M. Burberry

Nebraska Academy of Sciences: Programs and Proceedings

When a collision of two tectonic plates occurs, fold thrust belts develop, which are widely recognized as the most common mode in which the crust accommodates shortening. Most analog models do not consider erosion when documenting fold thrust belt formation. The lack of experiments on this topic means there are limited consistent studies on the relationship between erosion and deformation. Preliminary experiments suggest that the typical forward breaking thrust sequence is altered by eroding the hinterland, something this experiment series aims to prove or disprove. The new contribution of this work is a systematic evaluation of the variation in initial …

Utilizing Machine Learning Techniques For Computer-Aided Covid-19 Screening Based On Clinical Data, Honglun Xu, Andrews T. Anum, Michael Pokojovy, Sreenath Chalil Madathil, Yuxin Wen, Md Fashiar Rahman, Tzu-Liang (Bill) Tseng, Scott Moen, Eric Walser

Mathematics & Statistics Faculty Publications

The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as …

Considerations For Assigned Water After Expiration Of The 2007 Guidelines, Kathryn Sorensen, Sarah Porter, Anne Castle, John Fleck, Eric Kuhn, Jack Schmidt, Katherine Tara

The Traveling Wilburys of the Colorado River

As Colorado River supplies and demands reach razor-thin margins, new tools to provide adaptive capacity will play a critical role in sustaining communities across the West. We must reduce our consumption of water, while finding ways to cushion the impact. One of the most innovative tools for doing this, developed over the last two decades, is “Assigned Water” - giving users the ability to store conserved water earmarked for their own future use. Originally developed as “Intentionally Created Surplus” in the 2007 Colorado River Interim Guidelines, Assigned Water has been revised and expanded through U.S. Mexico Treaty Minutes and as …

From Sediment To Mangrove: Tissue-Specific Metal Accumulation In Red Mangroves Of Southeastern Florida, Sophie Welch

Honors Theses

Coastal wetlands near industrialized waterways face increasingly heavy‐metal contamination, posing risks to benthic communities and food webs. Mangroves can influence metal cycling through sediment trapping, root‐zone sequestration, and internal physiological regulation, yet their effectiveness in urbanized systems remains debated. This study quantified concentrations of 17 heavy metals in sediments and mangrove tissues from four sites near Port Everglades, Florida, examined tissue‐specific partitioning, spatial and temporal contamination patterns, and examined the mangroves’ role in mediating metal loads. Ecological risk metrics identified STB as a contaminant hotspot with exceedances of TEL values across multiple elements (Ni, Pb, and Zn) as well as …

A Dual-Emission Polymer-Dots/Gold-Nanoclusters Nanohybrid For Ratiometric Fluorescence Detection Of Hg²⁺, Sha Wu

Dissertations and Theses

Heavy metals, such as Hg, Cd, Pb, and Cr, become a serious environmental problem because they are highly toxic, bioaccumulation, and long-term persistence, required a high sensitivity and selectivity detection method. Although laboratory testing instruments, such as inductively coupled plasma mass spectrometry, offer high sensitivity, their practical application is limited because they rely on large-scale equipment and complex sample pre-treatment processes. Single-emission fluorescence probes are simpler but can be affected by variations in probe concentration, excitation intensity, and measurement conditions. In this study, a dual-emission nanohybrid that consists of poly(9,9-di-n-hexylfluorenyl-2,7-diyl) (PDHF) and bovine serum albumin (BSA), termed as PDHF-Pdots/BSA-AuNCs nanohybrid, …

Deep Learning For Eeg-Based Emotion Recognition With Temporal And Spectral Interpretability, Shruti Rameshbhai Shingala

Selected Full-Text Master Theses 2021-

Electroencephalography (EEG)-based emotion recognition has emerged as a critical component of affective computing and clinical neuroscience. Existing approaches to this problem primarily reduce the multi-dimensional EEG time series to a single averaged feature vector, thereby discarding the temporal structure of the emotional response. The present work addresses three identified gaps in the literature: the absence of temporal interpretability, the uniform use of frequency bands, and the use of single-scale temporal feature extraction. A deep learning architecture, MST-Mamba-Asym, is proposed, comprising four components: Asymmetry Attention, which encodes hemispheric asymmetry by computing signed left–right channel differences, FreqBandAttention, which learns differential weights across …