Digital Commons Network^™

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 …

Largemouth Bass Diet Changes Through The Summer In Central Minnesota, Ethan Kunz

Journal of Earth and Life Science

Largemouth Bass Micropterus nigricans is one of the most widespread fish species in the world, due in part to human introduction for angling. Largemouth Bass are opportunistic predators that will consume the largest prey they can often find; however, it has been shown that their diet mainly consists of invertebrates such as crayfish, with less focus on other fish species. This research aimed to determine what the Largemouth Bass diets in Central Minnesota consist of, and how they may change from the beginning of summer through the beginning of fall. A total of 174 Largemouth Bass were sampled from late …

Captive Model Test For Hydrodynamic Derivatives Of Ship Maneuvering In Regular Head Waves, Pin-Yuan Huang, Zih-Yao Lin, Tsung-Yueh Lin, Fu-En Lee, Yan-Wei Lai

Journal of Marine Science and Technology–Taiwan

The motion of ship in wave can be discussed in seakeeping and maneuvering. In model tests, the former is found by the motion response of waves while the latter requires captive model test to acquire hydrodynamic derivatives. The hydrodynamic derivatives in waves are discovered to be different from those in calm water. To understand better the behavior of maneuverability in waves, maneuvering tests in a wide range of waves are performed in this study. This study presents an experimental investigation into the maneuvering behavior of a container ship in regular waves, covering a wide range of wavelengths from half to …

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, …

Artificial Intelligence Methods For Circadian Rhythm Recovery And Disease Identification From High-Dimensional Omics Data, Aram Ansary Ogholbake

Theses and Dissertations--Computer Science

High-throughput transcriptomic and proteomic technologies have enabled opportunities for studying biological processes and disease mechanisms. However, extracting meaningful biological information from these high-dimensional datasets remains challenging due to limited sample sizes, biological heterogeneity, measurement noise, and the absence of biological annotations. In particular, many molecular datasets lack temporal information required for circadian analysis, making the study of circadian rhythms difficult. Moreover, disease diagnosis and biomarker discovery from transcriptomic data often rely on complex machine learning models whose predictions are difficult to interpret and may not generalize well across independent datasets and experimental platforms. These limitations motivate the development of artificial …

Ai For Life Sciences: From Geometric Protein Modeling To Multimodal Drug Design, Feng Jiang

Computer Science and Engineering Dissertations

Predicting biomolecular interactions, from immune recognition to drug–target binding, is a central problem in the life sciences and computational drug discovery. Deep learning has advanced this area, yet three challenges persist: the topology of large, highly imbalanced interaction networks; structural noise in computationally predicted protein models; and the integration of multimodal information such as functional text and taxonomic annotations. This dissertation develops a coherent set of models spanning immune complex prediction and small-molecule drug design: graph learning that addresses network topology and severe class imbalance; a noise-tolerant method that fuses predicted structures with evolutionary sequence features; and multimodal representation learning …

From Wikipedia Tables To Public Data Visualizations, Tanvir Prince

Open Educational Resources

This open educational resource presents a practical mathematics lesson in which students turn numerical data from Wikipedia into a clear data visualization. Students select a Wikipedia page with a data table but little or no visual representation. They examine the original source, date, units, definitions, and possible data limits. They then organize the data in Microsoft Excel or another spreadsheet, choose an appropriate chart, and explain what the visualization helps readers understand. A complete worked example uses the 2017 population growth rates of South American countries.

The resource package includes an instructor lesson plan, a student project guide, a Wikimedia …

Iron(Ii)/Cobalt(Ii)-Mediated Silicon And Carbon Quantum Dots For Nanozyme Activity And Fluorescence Sensing Of Hydrogen Peroxide And Glucose, Kang Qin

Dissertations and Theses

Silicon quantum dots (SiQDs) and carbon quantum dots (CQDs) have been widely studied as fluorescent nanomaterials for sensing applications. In this thesis, we developed two different nanomaterials, SiQDs and CQDs, and investigated their catalytic activities and applied them for the detection of hydrogen peroxide and glucose. In the first project, Co2+-mediated SiQDs were synthesized through a hydrothermal method. The obtained nanoparticles showed obvious peroxidase-like activity toward the oxidation of tetramethylbenzidine (TMB) in the presence of H2O2 under acidic conditions. Moreover, a weak oxidation of TMB was also observed without H2O2, suggesting the existence of oxidase-like activity. To better understand the …

Big Tech As Transnational Spyware Regulator, Natalie R. Davidson

Fordham Intellectual Property, Media and Entertainment Law Journal

Spyware has emerged as a potent tool for leaders to shrink dem- ocratic contestation. In response to calls for constraints on the trade in spyware, states have updated the principal multilateral agree- ment on export controls, civil society groups have employed strate- gic litigation, and the European Union has altered its regulation, in each case with the aim of limiting exports where there is a risk of human rights violations. Yet, scandals involving the Israeli company NSO, among others, have made clear that even the updated regula- tory landscape is inadequate. Many actors are currently debating the reasons for existing …

Hfpo-Da Aquatic Toxicity And Bioaccumulation, Bailey Dawn Rappold

Theses, Dissertations and Capstones

Per- and polyfluoroalkyl substances (PFAS) have been detected globally in environmental media and biota. Legacy compounds PFOA and PFOS have been observed to be toxic and bioaccumulative in low doses and have been removed from production and industrial processes. Replacement PFAS chemicals are less understood and present a gap in current knowledge in their toxicological effects. This research utilized extended chronic toxicity studies to assess the impacts of HFPO-DA, a short chain replacement PFAS, on the standard aquatic toxicology organism Pimephales promelas. Organisms were exposed for 29 to 30 days at environmentally relevant concentrations of the toxicant, 7.8125 ng/L …

Development And Validation Of An Analytical Method For Quantitative Determination Of Small Aldehydes In Plasma, Stephanie T. Clark

Chemistry & Biochemistry Theses - Archive

Small aldehydes such as formaldehyde and acetaldehyde are highly reactive metabolites that play important roles in biological processes related to oxidative stress, metabolism, and DNA damage. Because of their chemical reactivity, volatility, and low endogenous concentrations, accurate measurement of these compounds in biological samples remains analytically challenging. The goal was to develop and evaluate a practical analytical method for detecting small aldehydes in biologically relevant matrices using headspace gas chromatography mass spectrometry (HS-GC-MS). The method combined chemical derivatization with O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) and headspace sampling to stabilize aldehydes and enable selective detection by mass spectrometry. Method development focused on optimizing derivatization …

A Stable Isotope Comparison Of The Biogenic Carbonate Of Mammalian Tooth Enamel From The Modern Ohio River Valley And Fort Ancient Buffalo Sites, Sara Elizabeth Slack

Theses, Dissertations and Capstones

δ¹⁸O and δ¹³C values change in response to anthropogenic factors (Ghosh & Brand, 2003). Little is known about the effects of anthropogenic climate and land use change on the ecology of the Ohio River Valley. The goal of this research was to quantify the effects of European colonization on the native ecology of the region. The specific questions of this research were: (1) How have mammalian tooth enamel δ¹⁸O and δ¹³C compositions shifted over time within the Ohio River Valley in response to anthropogenic climate change and vegetation? (2) Are there differences …

Analyzing Sleep Architecture And Brain State Transitions Via Hidden Markov Models On Fmri Data, Brileigh Jay Cates

Masters Theses

Sleep is associated with systematic changes in brain activity and functional connectivity observable in functional magnetic resonance imagining (fMRI) signals. Because subjects often fall asleep during resting-state experiments, the absence of vigilance monitoring can confound the interpretation of resting-state dynamics. Although electroencephalography (EEG) is the gold standard for sleep staging, simultaneous EEG-fMRI acquisition is not always feasible.

This study investigates whether sleep stages can be inferred directly from fMRI using a probabilistic latent-state framework. Hidden Markov Models (HMMs) are applied to blood-oxygen-level-dependent (BOLD) time series to identify latent brain states and their temporal transitions. Inferred states are aligned with EEG-derived …

A Comprehensive Statistical And Regional Analysis Of Lng-Powered Marine Vessels On Ghg Mitigation Strategies Considering Existing Bunkering Stations And Gwp Values, Canberk Hazar, Onur Yuksel, Murat Bayraktar

Journal of Marine Science and Technology–Taiwan

This study aims to quantify and analyse emissions from marine vessels that can operate on liquefied natural gas (LNG) but continue to use conventional fuels, largely due to the limited availability of LNG bunkering stations (BSs) over long distances. Four regions have been identified as having high concentrations of LNG-fueled vessels but limited access to operational BSs: the West Coast of the United States of America (USA), South Africa–Good Hope–Madagascar, Northwest Africa, and Brazil. This selection is based on the geographical distribution of these ships and the existing infrastructure. Hourly greenhouse gas (GHG) emissions have been calculated by considering the …

Planaria Dugesia Japonica As A Model Organism For Measuring The Toxicity Of Environmental Contaminants Per- And Poly-Fluoroalkyl Substances (Pfas), Caitlyn Rodeo

Behavioral Neuroscience Honors Papers

A group of synthetic chemicals, per- and poly-fluoroalkyl substances (PFAS) are ubiquitous in the environment and have significant detrimental effects on physiology and behavior. Planaria have recently increased in popularity as a model for toxicology and regeneration research. These organisms possess a primitive centralized nervous system that is thought to be more similar to the vertebrate brain than to other invertebrate brains in terms of structure and function, and possesses the same neuronal subpopulations and neurotransmitters as the mammalian brain. While other studies have investigated the behavioral effects of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) on planaria, there is …

Community Science For Enigmatic Ecosystems: Using Ebird To Assess Avian Biodiversity On Glaciers And Snowfields, William E. Brooks, Jordan Boersma, Neil Paprocki, Peter Wimberger, Scott Hotaling

Watershed Sciences Faculty Publications

Mountain glaciers and snowfields are rapidly receding because of climate warming. Species living in these habitats remain poorly studied, likely because of the remoteness and ruggedness of their terrain. We leveraged community science data from eBird—an online database of bird observations from around the world—to characterize bird use of mountain glaciers and snowfields. We estimated total bird biodiversity and preference for glaciers and snowfields over nearby, ice-adjacent habitats. We used field notes from eBird users and breeding codes to extend our data set to include insight into habitat usage and behavior. Finally, we compared our community-science approach to previous studies …

Ai In The Workplace: Understanding Role Ambiguity, Employee Motivation, And Learning Engagement, Sheena Leah Metzger

Theses, Dissertations and Capstones

The rapid integration of artificial intelligence (AI) into organizational processes has altered how work is performed and experienced by employees, yet empirical research examining the human implications of AI-driven change remains limited. This study examined the perceived impact of AI implementation on role ambiguity, employee motivation, and training engagement, and investigated the moderating role of perceived organizational support (POS). Grounded in Job Demands–Resources (JD-R) Theory and Organizational Support Theory (OST), the research examined how employees interpreted and responded to AI-related changes in their work environment.

The results offer valuable insights into a shifting perception of how employees experience technology in …

Asymptotic Profiles And Disease Prevalence At The Steady State For An Sis Patch Model, Daozhou Gao, Xin Li

Mathematics and Statistics Faculty Publications

Infected individuals often display mobility patterns that differ significantly from those of healthy individuals-traveling less frequently, covering shorter distances, visiting fewer destinations, and altering their timing and modes of movement. In this paper, to explore the influence of changes in travel frequency and destination on the spatial spread of infectious diseases, we propose a susceptible-infectious-susceptible patch model in which susceptible and infected populations have different dispersal rates and connectivity matrices. We first establish the threshold dynamics in terms of the basic reproduction number R-0 and show the existence and uniqueness of endemic equilibrium (EE) when R-0>1. Then we examine …

Multimodal Deep Learning For Biological Data Understanding, Saiyang Na

Computer Science and Engineering Dissertations

This dissertation presents three contributions to multimodal deep learning for biological data understanding, addressing the fundamental challenge of cross-modal alignment from two complementary perspectives: designing effective multimodal fusion methods for specific biomedical applications, and proposing a general framework for higher-order multimodal alignment that captures hierarchical structure in data.

First, we develop Cmai, a deep learning framework for B cell receptor (BCR) to antigen binding prediction that aligns BCR sequence information with antigen three-dimensional structures using contrastive learning. Cmai achieves an average AUROC of 0.907 across 17 antigens and 5 independent cohorts, and demonstrates clinical utility in predicting immune checkpoint inhibitor …

Using Herbarium Specimens To Morphologically Delineate Three Western Phlox Species, Jack David Hager, Meredith Zettlemoyer

Undergraduate Theses, Professional Papers, and Capstone Artifacts

Western Phlox are incredibly diverse, found in a variety of habitats spanning lowland meadows to high alpine tundra. The genus contains around 65 unique species, often with overlapping trait variation that makes morphological and taxonomic delineation difficult. Phlox missoulensis, a cushion-forming species endemic to the Missoula Valley, inhabits lower-elevation, windy, arid ridgelines that mimic alpine conditions. Two nearby congeners, Phlox kelseyi and Phlox pulvinata, occupy partially overlapping ecological niches: P. kelseyi is found in wet meadows at similar elevations to P. missoulensis, while P. pulvinata is found in higher elevation environments. P. missoulensis exhibits morphological characteristics that are intermediate between …

Unveiling Microplastic Removal And Characteristics In Wastewater From Two Municipal Wastewater Treatment Facilities In Indonesia, Nurul Setiadewi, Prayatni Soewondo, Cynthia Henny

Applied Environmental Research

Wastewater treatment plants (WWTPs) are considered an entrance pathways for microplastic (MP) pollution in aquatic environments. This study reveals the removal and characteristics of MPs in wastewater from two municipal WWTPs in Indonesia. The influent contained 17.1 ± 5.65 particles L-1 (WWTP A) and 15.45 ± 4.31 particles L-1 (WWTP B), whereas the effluent contained 1.41 ± 0.01 and 1.5 ± 0.16 particles L-1. The removal efficiency was 91.75% for WWTP A and 90.32% for WWTP B, with no statistically significant difference (p > 0.05). WWTP A employed advanced treatment units, whereas WWTP B used a conventional pond-based system. MPs were …

Comparative Machine Learning Models For Disease Risk Prediction, Mercy Mawusi Agbley

Theses, Dissertations and Capstones

Accurate prediction of disease outcomes is crucial for improving clinical decision-making and enabling early intervention. This study compares the performance of various statistical and machine learning models for clinical risk prediction using two healthcare datasets: diabetic retinopathy and heart disease. The models assessed include Logistic Regression, LASSO, k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Neural Networks, Random Forests, Gradient Boosting Machines (GBM), and a stacked ensemble model. Prior to modeling, datasets were split into train and test sets. Standardization was applied to numeric features whilst categorical features were one-hot encoded. These transformations were later applied to the test set. Principal …

Investigating The Viability Of Fully Online Asynchronous Physics Instruction In Formal Education, Clifton W. Massey-Noel

Physics Dissertations

In this dissertation we present results from two consecutive investigations into the fully online asynchronous modality in Physics Education. We first discuss the efficacy of a fully online asynchronous section of Modern Physics held in Fall 2024. The asynchronous section is compared with four prior partially-flipped sections of the same class, two of which were held in-person, and two of which were held synchronously online. Results suggest that all three modalities can be made to be about equally as effective. We also discuss the efficacy of a fully online asynchronous section of Calculus-Based Introductory Mechanics held in Fall 2025. The …

Advancing Lipidomics Through Fluoroalcohol-Induced Multiphase Fractionation And Orthogonal Lc×Lc–Ms Detection, Md Al Amin

Chemistry & Biochemistry Dissertations

Comprehensive characterization of the lipidome remains a significant analytical challenge due to the immense structural diversity, wide dynamic range, and complex physicochemical continuum of lipid species. Conventional extraction methods, such as those by Bligh–Dyer and Folch, and one-dimensional liquid chromatography (1D-LC) often fail to resolve this complexity, leading to chromatographic overlap, severe ion suppression, and limited coverage of low-abundance or highly hydrophobic species. This dissertation addresses these bottlenecks by establishing a separation-driven analytical framework that integrates novel fluoroalcohol-induced multiphase systems (FAiTPS) with orthogonal multidimensional liquid chromatography–mass spectrometry (LC×LC–MS).

The core of this research is the development and physicochemical characterization of …

Cogram: A Computational Pipeline For Genome Assembly And Reconstruction Using Graph Neural Networks, William Coggins

College of Graduate Studies: Theses & Dissertations

Genome assembly — the reconstruction of a complete DNA sequence from short, overlapping reads — remains a fundamental challenge in computational biology. A central difficulty is distinguishing true genomic overlaps from spurious connections arising from repetitive sequences, a task that traditional assemblers address through hand-tuned heuristic rules applied to de Bruijn or overlap graphs. This thesis introduces COGRAM (Coggins–Ramasamy Assembly Method), a genome assembly pipeline that reframes sequence reconstruction as an edge classification task on a k-mer overlap graph, replacing heuristic graph cleaning with a learned model.

COGRAM constructs a directed overlap graph from raw sequencing reads using a k-mer …

Evaluating Quahog (Mercenaria Mercenaria) Nursery Methods And Production Costs To Support Diversification Of Maine’S Fisheries And Aquaculture Sector, Hannah G. Wolf

Honors Theses

Populations of wild and farmed quahogs in Maine are expanding, driven by warming waters, declining soft-shell clam populations, and the need to diversify fisheries. The Northern quahog (Mercenaria mercenaria) is a potential species to diversify Maine’s marine sector, as its geographical range is expanding, and they appear more resistant to green crab predation. Enhancing wild quahog stock provides economic and social advantages for both the aquaculture and wild harvest industries. However, there is limited understanding about the commercial viability of producing quahog seed on existing farms for wild shellfish enhancement in Maine. This research, part of a larger NOAA …

Large Language Models For Neurology: A Mini Review, Donald C. Wunsch, Daniel B. Hier

Electrical and Computer Engineering Faculty Research & Creative Works

Large language models have the potential to transform neurology by augmenting diagnostic reasoning, streamlining documentation, and improving workflow efficiency. This Mini Review surveys emerging applications of large language models in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy, with emphasis on ambient documentation, multimodal data integration, and clinical decision support. Key barriers to adoption include bias, privacy, reliability, and regulatory alignment. Looking ahead, neurology-focused language models may develop greater fluency in biomedical ontologies and FHIR standards, improving data interoperability and supporting more seamless collaboration between clinicians and AI systems. Two future developments have the potential to be particularly impactful: (1) …

New Metrics For Disambiguating Feature Overlap And Catastrophic Forgetting In Incremental Learning Contexts, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Catastrophic forgetting remains a central challenge in lifelong learning, where newly acquired knowledge interferes with previously learned tasks, degrading performance over time. Mitigation strategies such as rehearsal and regularization have been proposed, but both introduce limitations, either by retaining old data or by constraining model updates in ways that may impair learning. Complicating matters, recent findings show that feature-space overlap between tasks can produce similar performance drops even in models that memorize data, making it difficult to distinguish true forgetting from representational interference. Current accuracy-based metrics fail to disentangle these effects, undermining diagnostic clarity. In this work, we introduce the …