Digital Commons Network^™

Bioenergy Crop Production: Implications For Grassland Bird Communities In Southwestern Nebraska, Grace E. Schuster

School of Natural Resources: Dissertations, Theses, and Student Research

Biofuel and bioenergy systems are components of most climate stabilization pathways to reduce greenhouse gas emissions and limit global warming. Currently, corn (Zea mays) is the predominant feedstock used for bioenergy production in the United States. However, widespread production of this monoculture crop has resulted in many negative environmental impacts. The most notorious impact has been the loss of grassland habitat due to agriculture expansion which has had detrimental effects on wildlife that depend on grassland habitat. One such group, grassland birds, has faced steeper, more consistent, and more widespread declines than any other avian guild. Therefore, strategies …

Experimental Analysis Of Satellite Operator Training Using Game-Based Virtual Reality Simulation, Lana Laskey

Doctoral Dissertations and Master's Theses

Satellite data plays a vital role in modern global infrastructure by enabling communications, navigation, and weather forecasting. As demand for satellite technology grows, so does the need for highly trained satellite ground operators. Traditional training regimens for satellite operators employ simulation using two-dimensional computer console displays paired with the varied ability of trainees to generate abstract mental imagery of the scenario. However, this development of mental imagery imposes a considerable learning curve and cognitive workload on the trainee, which may negatively impact the user experience and knowledge gained during the training scenario.

This experimental study investigated the effects of game-based …

Aircraft Bird Strike Risk Prediction Using Machine Learning And Analytic Hierarchy Process, Jason Anthony Powell

Doctoral Dissertations and Master's Theses

To address the limitations of Next Generation Radar-based bird strike forecasting, this study modeled 12 spatiotemporal weather features from the National Oceanic and Atmospheric Administration alongside bird strike risk using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), XGBoost regression tree, and Bayesian network algorithms. Five years of bird strike data from four geographically diverse airfields served as the target risk variable, categorized as low, moderate, or severe based on Department of the Air Force risk models. The ensemble model, which combines the LSTM-RNN and XGBoost regression algorithms, yielded the most accurate forecasts, achieving 80% to 93% accuracy across all airfields, …

Crosssections, Summer 2025, University Of Northern Iowa. Department Of Physics.

CrossSections

Contents:

A Message from the Department Head, Dr. Paul Shand --- 1
Department Happenings --- 3
Faculty Profile - Takeshi Yasuda --- 7
Student Profile - Brandon Schmidt --- 8
Student Research --- 9
Student Focus --- 10
Physics Education --- 13
Alumni Profile - Roger Burkhart --- 14
Alumni News - Sterling Hartman --- 16
New Physics --- 17

Biocomputing Approach To Modeling And Modulating Calcium Signaling, Sehee Sun

School of Computing: Dissertations, Theses, and Student Research

Biocomputing is an emerging field that seeks to perform computational tasks using biological substrates and processes. Unlike conventional computing systems based on silicon hardware, biocomputing leverages the parallelism, energy efficiency, and complex dynamics of living systems. Among various cellular mechanisms, calcium (Ca2+) signaling stands out as a central regulator of diverse biological functions, offering a promising basis for programmable logic and control in living cells.

This thesis introduces a novel framework for modeling and modulating Ca2+ dynamics using biologically inspired Boolean logic circuits. Specifically, we propose the Ca2+ Boolean Logic (CaBL) model, in which Ca2+ fluxes and interactions are abstracted …

Being And Becoming In The Algorithmic Age, Bernard E. Harcourt

Faculty Scholarship

To change the world: the prerequisite, most often, is to change our experience of the world, to experience the world differently, to be shaken to our foundations, to have one’s sense of self shattered. That is a process of both being and becoming. In order to turn that process in our favour, in this age of artificial intelligence, it will be crucial to transform data and algorithms into bits of justice.

The Response And Recovery Of Carbon And Water Fluxes In Australian Ecosystems Exposed To Severe Drought, C. Stephens, B. Medlyn, L. Williams, J. Knauer, A. Inbar, E. Pendall, S. K. Arndt, J. Beringer, C. M. Ewenz, N. Hinko-Najera, L. B. Hutley, P. Isaac, M. Liddell, W. Meyer, C. E. Moore, J. Cranko Page, R. Silberstein, W. Woodgate

Research outputs 2022 to 2026

Climate change-driven increases in drought risk pose a critical threat to global carbon and water cycles. However, ecosystem-scale responses remain poorly quantified, particularly for severe, multiyear drought events. We addressed this gap by examining ecosystem-scale carbon and water flux sensitivity to the extreme 2018–19 drought in Australia using data from 14 eddy covariance flux sites. The ecosystems span grasslands and semi-arid woodlands to tropical and temperate forests. The driest sites (classed as “grass” and “very dry”) experienced drastic productivity impacts, with a 65% decrease in Gross Primary Productivity (GPP) over 2 years relative to the pre-drought average. However, fluxes in …

In Situ Thrust Measurement Of Fish During Locomotion; Test Case: Sharks, Braedon Payne, Bryan A. Keller, Daniel Weihs, Roi Gurka

Physics and Engineering Science

We present a novel method of measuring thrust of aquatic animals using in situ video data of swimming motions. To demonstrate its utility, the method was applied to several large elasmobranch species, which are typically highly challenging to measure. Using motion tracking software, we analyzed video footage of wild and captive sharks to track their instantaneous position and speed. In order to estimate the force output, we used the tail/body motion based on the swimming modes of the fish to calculate the water displaced by this motion during locomotion. Using Newton 3rd law, we have calculated the instantaneous force exerted …

Statistical Power To Detect Simultaneous Violation Of Proportionality In Hazards And Additive Assumption In Cox Regression Model, Lawrence Mensah Agbota

Theses and Dissertations

The Cox proportional hazards regression model is a widely employed semi-parametric tool in epidemiological and medical research for analyzing time-to-event data and assessing the relationship between patient survival times and one or more predictors. This method involves regression analyses necessitating a meticulous approach to careful examination of the covariates and the relationship among covariates included in the model through a series of critical decisions and steps. Violation of the additivity of the effects and the proportionality in hazards (PH) assumption can lead to biased results and misleading scientific findings. We conducted a Monte Carlo simulation study to assess the performance …

Response Of Soybean To Variable Irrigation Levels In Eastern Nebraska Using The Aquacrop Model, Anmol Singh

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

AquaCrop was calibrated and validated for soybean [Glycine max (L.) Merr.] using 19 irrigation treatments from five years using data from field experiments. The model accurately simulated canopy cover (CC), soil water content (SWC), and grain yield, with overall validation nRMSE values of 12%, 7%, and 7%, respectively. The overall validation nRMSE for SWC in individual 30-cm soil layers was comparatively higher, at 19%. The model was subsequently applied for long-term simulations (2010–2024) to estimate soybean yield and water requirements for two dominant soil types, Yutan silty clay loam and Tomek silt loam under three irrigation levels - rainfed, …

Electrochemistry Behind Pfas: Mechanistic And Analytical Approach For Sensing And Degradation Strategies, Jonathan Josue Calvillo Solis

Open Access Theses & Dissertations

Understanding the fundamental electrochemistry of perfluoroalkyl substances (PFAS) is key to developing effective water remediation and sensing strategies. This work explores the thermodynamics and kinetics of perfluorooctanoic acid (PFOA) electroreduction, focusing on C-F bond cleavage. These insights were applied to design a highly sensitive electrochemical sensor for detecting trace levels of PFOA in water. This dissertation focuses on the electrochemical investigation of the reduction reaction of PFOA in aqueous and organic media employing different electrode materials. This exploration allows to understand the defluorination reaction of PFAS to further propose potential strategies for water treatment and PFOA electrosensing. Through electrochemical, spectroscopical …

Electrocatalytic Degradation Of Methylene Blue Using Graphene Oxide, Antimony Oxide, And Graphene Oxide-Supported Antimony Oxide Ink-Based Electrodes, Maria Irene Myers Armas

Theses and Dissertations

This study investigates the electrocatalytic degradation of methylene blue (MB) using copper mesh electrodes coated with graphene oxide (GO), antimony oxide (Sb₂O₃), and their composite (GO/Sb₂O₃). These materials were evaluated across a pH range of 2 to 8 using sodium sulfate as the supporting electrolyte. UV-Vis spectroscopy at 665 nm confirmed dye degradation, with removal efficiencies reaching up to 95% at pH 2. However, degradation decreased at higher pH, with 40 – 60% removal at pH 8, depending on the electrode. Kinetic analyses revealed optimum performance under acidic conditions. GO/Sb₂O₃ electrodes demonstrated the most consistent and effective performance across all …

Predicting Enzyme-Substrate Association Using Heterogeneous Knowledge Graph, Jannatul Ferdaus

Theses and Dissertations

Phosphorylation and dephosphorylation are dynamic processes that control many aspects of cellular activity, such as metabolic pathways, cell cycle progression, and signal transduction. Protein activity and interactions are modulated by the reversible addition or removal of phosphate groups, which allows cells to react abruptly to evolving conditions. Although kinase-specific phosphorylation site prediction has advanced, phosphatase-specific dephosphorylation site computational prediction is still a major obstacle that prevents us from fully comprehending the extent of cellular regulation. In this study, we constructed a knowledge graph for the prediction of enzymes (kinases and phosphatases) and their associated substrates with specific phosphosites. As part …

First Detrital Zircon Provenance Data From Oligocene-Pleistocene Sediment In The Beaufort-Mackenzie Basin, Arctic Ocean, Joseph Martina

Theses and Dissertations

Sediment, runoff, and nutrient input to the Arctic Ocean plays a critical role in both physical and biological processes that impact regional and global systems. The Beaufort-Mackenzie Basin, situated in the Arctic Ocean offshore northwestern Canada, contains more than 14 km of clastic strata and provides a record of erosion and sediment transport from the Cretaceous to present. Pliocene sedimentation rates within the basin jump nearly 2000% relative to underlying units, but the cause of this shift remains unknown. We used cuttings from the N. Issungnak L-86 well, focusing on the Oligocene-lower Miocene Kugmallit and Mackenzie Bay sequences and the …

Functional Time Transformation Model With Applications To Digital Health, Rahul Ghosal Ph.D., Marcos Matabuena, Sujit K. Ghosh

Faculty Publications

The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, we develop a more general and flexible functional time-transformation model for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and …

Mitigating Regression Faults Induced By Feature Evolution In Deep Learning Systems, Hanmo Yu, Zan Wang, Xuyang Chen, Junjie Chen, Jun Sun, Shuang Liu, Zishuo Dong

Research Collection School Of Computing and Information Systems

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To …

Grokformer: Graph Fourier Kolmogorov‑Arnold Transformers, Guoguo Ai, Guansong Pang, Hezhe Qiao, Yuan Gao, Hui Yan

Research Collection School Of Computing and Information Systems

Graph Transformers (GTs) have demonstrated remarkable performance in graph representation learning over popular graph neural networks (GNNs). However, self-attention, the core module of GTs, preserves only low-frequency signals in graph features, leading to ineffectiveness in capturing other important signals like high-frequency ones. Some recent GT models help alleviate this issue, but their flexibility and expressiveness are still limited since the filters they learn are fixed on predefined graph spectrum or spectral order. To tackle this challenge, we propose a Graph Fourier Kolmogorov-Arnold Transformer (GrokFormer), a novel GT model that learns highly expressive spectral filters with adaptive graph spectrum and spectral …

Cracking Aegis: An Adversarial Llm-Based Game For Raising Awareness Of Vulnerabilities In Privacy Protection, Jiaying Fu, Yiyang Lu, Zehua Yang, Fiona Fui-Hoon Nah, Ray Lc

Research Collection School Of Computing and Information Systems

Traditional methods for raising awareness of privacy protection often fail to engage users or provide hands-on insights into how privacy vulnerabilities are exploited. To address this, we incorporate an adversarial mechanic in the design of the dialogue-based serious game Cracking Aegis. Leveraging LLMs to simulate natural interactions, the game challenges players to impersonate characters and extract sensitive information from an AI agent, Aegis. A user study (n=22) revealed that players employed diverse deceptive linguistic strategies, including storytelling and emotional rapport, to manipulate Aegis. After playing, players reported connecting in-game scenarios with real-world privacy vulnerabilities, such as phishing and impersonation, and …

Evaluating The Influence Of Carbon Quantum Dots On Starch-Based Bioplastics: Toward Potential Food Packaging Applications, Shima Jafarzadeh, Mitra Golgoli, Zeinab Qazanfarzadeh, Mehrdad Forough, Peng Wu, Wendy Timms, Colin J. Barrow, Minoo Naebe, Masoumeh Zargar

Research outputs 2022 to 2026

Developing biodegradable food packaging films is crucial for reducing dependence on petroleum-based plastics. In this study, nitrogen-doped carbon quantum dots (CDs) were synthesized from citric acid and ethylenediamine via hydrothermal treatment and incorporated into sago starch films at concentrations of 0.5 %, 1 %, 3 %, and 4 % w/w of total solids using a solution casting method. The effects of CDs on structural, thermal, antioxidant, optical, and physicochemical properties were systematically investigated. CD addition enhanced the UV-shielding ability of the films. At 4 % CD content, UV transmittance decreased by 56.4 % (UVA), 66.7 % (UVB), and 73.9 % …

Winds Of Change: Charting A Pathway To Ecosystem Monitoring Using Airborne Environmental Dna, Rachel L. Tulloch, Clare I.M. Adams, Matthew A. Barnes, Elizabeth L. Clare, Henrik C. Van De Ven, Andrew Cridge, Francisco Encinas-Viso, Kristen Fernandes, Dianne M. Gleeson, Erin Hill, Anna J.M. Hopkins, Anna M. Kearns, Gracie C. Kroos, Anna J. Macdonald, Francesco Martoni, Angela Mcgaughran, Todd G.B. Mclay, Linda E. Neaves, Paul Nevill, Andrew Pugh, Kye J. Robinson, Fabian Roger, Tracey V. Steinrucken, Mieke Van Der Heyde, Cecilia Villacorta-Rath, Jenny Vivian

Research outputs 2022 to 2026

Airborne environmental DNA (airborne eDNA) analysis leverages the globally ubiquitous medium of air to deliver broad species distribution data and support ecosystem monitoring across diverse environments. As this emerging technology matures, addressing critical challenges and seizing key opportunities will be essential to fully realize its potentially transformative impact. In June 2024, the Southern eDNA Society convened over 100 researchers, industry leaders, and biodiversity management stakeholders in a landmark workshop to evaluate the current state of airborne eDNA research and chart a course for future development. Participants explored opportunities for integrating airborne eDNA into existing monitoring systems, but they unanimously agreed …

Graph Convolutional Networks Enable Fast Hemorrhagic Stroke Monitoring With Electrical Impedance Tomography, J. Toivanen, V. Kolehmainen, A. Paldanius, A. Hänninen, A. Hauptmann, Sarah J. Hamilton

Mathematical and Statistical Science Faculty Research and Publications

Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to computationally expensive nonlinear model-based methods. Methods: A post-processing approach with graph convolutional networks is employed. Utilizing the flexibility of the graph setting, a graph U-net is trained on linear difference reconstructions from 2D simulated stroke data and applied to fully 3D images from realistic simulated and experimental data. An additional network, trained on 3D vs. 2D images, is also considered for comparison. Results: Post-processing the linear difference reconstructions through the graph U-net significantly improved the image quality, resulting in images …

Quinone-Mediated Extracellular Electron Transfer In Escherichia Coli During Glucose Oxidation Metabolism, Megan Danielle Whisonant

Theses and Dissertations

The growing global demand for energy, ongoing reliance on fossil fuels, and increasing water pollution from industrial and anthropogenic sources present significant environmental challenges. In response to these issues, renewable and sustainable energy sources offer substantial potential for reducing dependence on fossil fuels and ensuring access to clean water. Microbial electrochemical systems (MESs) have emerged as promising, eco-friendly solutions for energy-efficient wastewater treatment and bioremediation. A key challenge in MESs development is facilitating effective electron transfer between microorganisms and electrode surfaces. The first chapter of this thesis discusses the fundamentals of MES types, explains mechanisms of extracellular electron transfer, and …

The Potential Importance Of Microenvironment On Polymer Bound Chemical Reagents, Nathan Joseph Halsteter

Theses and Dissertations

In this thesis will provide a brief history of polymer bound reagents, their uses, and advancements. The importance of polymer microenvironments will be explored using a model kinetic resolution reaction utilizing polymer bound reagents to observe how changes in a polymer’s microenvironment effects the yield and selectivity of a reaction. It was found that there are copious factors that affect polymer bound reagents, and no direct conclusion could be drawn from the study.

Characterizing Dissolved Organic Matter Composition In A Southeastern United States Watershed, Gwendolyn Marcelette Hopper

Theses and Dissertations

Dissolved organic matter (DOM) has important roles in many biogeochemical cycles. DOM is primarily composed of dissolved organic carbon (DOC), nitrogen, and phosphorus, and the processing of DOM is important for nutrient regulation and carbon cycling. In the coastal plains of the southeastern United States, there are many rivers and streams known as “blackwaters” with exceptionally high concentrations of chromophoric dissolved organic matter (CDOM) leached from terrestrial soils, coinciding with a region experiencing some of the nation’s highest rates of development. As changes in land use and climate continue to occur, DOM composition from natural to anthropogenically impacted systems can …

In Vivo Analysis Of Fe-S Cluster Assembly And Iron Homeostasis In Escherichia Coli, Dexter Mclaurin Reasons

Theses and Dissertations

Iron-sulfur (Fe-S) clusters are an essential cofactor for a wide range of cellular processes, and their assembly and storage require tight regulation.

In Escherichia coli (E. coli), there are two pathways of Fe-S cluster assembly, the Isc and the Suf pathways. The Isc pathway is used under good growth conditions, and the Suf pathway is used under iron starvation and oxidative stress conditions. Once the cluster has been assembled on the scaffold protein of either pathway, it is transferred to Fe-S cluster trafficking proteins. In E. coli, the monothiol glutaredoxin GrxD and the BolA-type proteins BolA and IbaG …

Some Likelihood-Based Methods For Clustering Functional Data, Tong Shan

Theses and Dissertations

The analysis of functional data is an increasingly relevant part of statistics. The exploratory data analytic method of cluster analysis plays a very important role in different fields. Over the years, researchers have developed many clustering approaches, striving to achieve more accurate and efficient clustering. In Chapter 1, we aim to improve the accuracy of our outcome when clustering functional data. To achieve this goal, we use a predictive likelihood function which serves as an objective function to optimize in order to determine the most appropriate clusters. To optimize the objective function over the space of clustering partitions, we produce …

Enhancing Phenotyping Accuracy For Selection Of Urochloa Spp. Tolerant Genotypes To Red Spider Mite (Oligonychus Trichardti), Paula Espitia-Buitrago, José Miguel Cotes Torres, Luis Miguel Hernández, Juan Andres Cardoso, Frank Chidawanyika, Rosa N. Jauregui

All Peer-Reviewed Publications

The red spider mites are a major biotic limitation to productivity for Brachiaria (Urochloa spp.) grasses in tropical Eastern Africa, causing severe economic losses mainly in the dry seasons. However, due to the lack of reports of integrated pest management strategies, genetic resistance can play a crucial role in Brachiaria-based livestock systems, minimising the impact of this pest. The objective of this study was to develop and validate a methodology for accurately categorising resistance responses in Urochloa genotypes to identify potential sources of resistance to the Western Kenya red spider mite Oligonychus trichardti (Meyer 1974). Accordingly, 25 genotypes of Urochloa …

Using Radium Isotopes To Compare Nutrient Inputs Via Submarine Groundwater Discharge To Offshore Export On The West Florida Shelf, Andrew L. Lindgren

OES Theses and Dissertations

Radium isotopes have been used to trace submarine groundwater discharge (SGD) and associated nutrient and metal inputs into the coastal ocean. They have also been used to estimate rates of diffusive mixing and export fluxes of dissolved constituents offshore. However, use of both approaches in tandem to determine the fate of groundwater inputs after they enter the coastal ocean is still relatively unexplored. Radium isotopes and nutrients were measured in two cross-shelf cruises on the West Florida Shelf in different seasons and in quarterly sampling of offshore wells. Through 226Ra mass balance and 223Ra-derived diffusive mixing rates, inputs of nutrients …

Multi-Task Deep Learning Approach For Segmenting And Classifying Competitive Swimming Activities Using A Single Imu, Mark Shperkin

Theses and Dissertations

Competitive swimming performance analysis has traditionally relied on manual video review and multi-sensor systems, both of which are resource-intensive and impractical for everyday training use. This study investigates whether a single wrist-worn inertial measurement unit (IMU) can be used to automatically segment and classify swimming activities with high accuracy. We propose a multi-task deep learning pipeline based on the MTHARS (Multi-Task Human Activity Recognition and Segmentation) architecture introduced by Duan et al. to perform stroke classification, lap segmentation, stroke count estimation, and underwater kick count estimation. Data were collected from eleven collegiate-level swimmers wearing left-wrist-mounted IMUs, each performing five 100-yard …

Quantifying Biomass Burning Impacts On Soil Nox Emissions And Reactive Nitrogen Cycling, Olivia Rae Steinbeck

Theses and Dissertations

Nitrogen oxides (NOx = NO + NO₂) are trace gases that play a critical role in the atmosphere by influencing air quality, oxidation chemistry, and the deposition of fixed nitrogen. Historically, NOx emissions have been primarily linked to anthropogenic sources such as fossil fuel combustion, industrial activities, and agriculture. However, as successful legislation has significantly reduced these emissions, the relative importance of natural NOx sources has become an increasing concern. Soil NOx emissions are of particular interest due to their strong temperature dependence and their connection to global climate change. Rising global temperatures are expected to accelerate soil NOx emissions …