Digital Commons Network^™

Catching A Fever: A Comparison Of Vachellia Xanthophloea Populations In The Limpopo And Luvuvhu River Floodplains Of The Makuleke Contractual Park, Kianie B. David

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

This study investigates patterns of stand structure regeneration, growth characteristics, and coarse woody debris (CWD) patterns in Vachellia xanthophloea (fever tree) stands within the Makuleke Contractual Park (MCP), a semi-arid savanna system in northern Kruger National Park (KNP), South Africa. Fieldwork was conducted across two stand types: a monospecific fever tree stand in Rietbok Vlei and a mixed-species stand in the Western Nhlangaluwe Floodplain where fever tree is established with Faidherbia albida (ana tree). Data were collected from 20 total 1/4-acre (1,011 m²) circular plots between both stands in 2024 and 2025, including seedling root collar diameter (RCD), …

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 …

Strategies For Thoughtful Dissemination Of Climate Change Knowledge: A Blueprint For Scientists In The Heart Of The Empire, Christopher Cronk

Nepal: Geoscience in the Himalaya

This interdisciplinary research investigates how climate change education, Indigenous knowledge systems, journalism, and anthropology intersect in shaping environmental awareness and action, focusing primarily on Nepal and drawing parallels with Indigenous Peoples of Turtle Island (North America). Stationed in Kathmandu, I conducted interviews with climate educators, journalists, anthropologists, and activists to explore how scientific and local knowledge are communicated and acted upon. Findings highlight the strong awareness of climate change in Nepal, the systemic barriers to effective mitigation and adaptation, and the potential of community-led initiatives like the Community Forest Program. Anthropological insights proved crucial in linking human experience to environmental …

A Preliminary Classification And Quantification Of The Microplastics Within Remote Glacial Runoff In The Upper Manang Region Of Nepal, Caden Lipsky

Nepal: Geoscience in the Himalaya

Microplastics, defined as particles of plastic waste less than 5mm (US EPA), are becoming steadily more common throughout remote ecosystems and are damaging to human and environmental health. The level of microplastics found in water sources within remote regions has become increasingly concerning following growing influxes of tourists and residents each year. These particulates found in freshwater have historically only been found downstream of populated areas; however, more recent cases have demonstrated their appearance at higher elevations and lower population water sources. This study aims to develop a preliminary understanding of the amount and type of contamination that has occurred …

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 …

Longitudinal Surveillance Of Aedes Aegypti (Diptera: Culicidae) In Urban Coastal Kenya: Population Dynamics, Blood Feeding Frequency And Dengue Virus Infection Rates, Josephine Osalla, Louis Clement Gouagna, Gilbert Rotich, Maureen Nzilani, Penina Safari, Kennedy Senagi, Francis Mutuku, Baldwyn Torto, David P. Tchouassi

All Peer-Reviewed Publications

The coastal region of Kenya has emerged as a focal point for urban dengue virus transmission driven by Aedes aegypti as the primary vector. To gain a deeper understanding of the epidemiological situation, we carried out a year-long longitudinal study (December 2021- November 2022) of the population dynamics of A. aegypti through weekly mosquito surveys using ovitrap and CO2-baited Biogents (BG) mosquito traps in Ukunda, an urban township in the coastal region. Aedes eggs laid in ovitraps were exclusively A. aegypti with 80.8% mean hatch rate. A total of 35,109 adult A. aegypti were captured, with twice as many females …

Investing Hysteresis In Floodplain Dynamics Of Lakes In The Middle St. Johns River Using Sentinel-1 Sar Imagery, Keenan Hubbard

Doctoral Dissertations and Master's Theses

Floodplain dynamics are often complex, with hysteresis potentially affecting the temporal relationship between flood stage and flood extent during subsequent inundation phases. This study leverages Sentinel-1 synthetic aperture radar (SAR) imagery to map flood extent in the Middle St. Johns River Lake floodplains and examine the presence of hysteresis during flood events. SAR scenes corresponding to river gauge readings were analyzed from the rising and falling limbs of a flood hydrograph. By comparing these flood maps, we assess differences in inundated areas at equivalent water levels during each stage of the flood event. The findings aim to enhance flood monitoring …

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 …

Graduate School Blog - July 2025 Volume 1, Cynthia Haynes

UofM Grad School Blog

The July 2025 UofM Graduate School Blog – Volume 1 continues the Cost of Graduate School Guide with a deep dive into hidden and variable expenses such as residency-based tuition differences, program-specific fees, and differential tuition. The blog provides practical tips for prospective students on how to ask the right financial questions when comparing programs. It also features a student spotlight on Billy Brooks, a dual MHA/MBA candidate motivated to transform healthcare access and equity. Upcoming events include a Virtual Fall 2025 Open House with Financial Aid and USBS, a Dissertation Writers Retreat, and both in-person and virtual Graduate Student …

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

Causal Neuro-Symbolic Artificial Intelligence: Synergy Between Neuro-Symbolic And Causal Artificial Intelligence, Utkarshani Jaimini

Theses and Dissertations

Understanding and reasoning about cause and effect is innate to human cognition. In everyday life, humans continuously engage in causal reasoning and hypothetical retrospection to make decisions, plan actions, and interpret events. This cognitive ability allows us to ask questions such as: “What caused this situation?”, “What will happen if I take this action?”, or “What would have happened had I chosen differently?” This intuitive capacity to form mental models of the world, infer causal relationships, and reason about alternative scenarios, particularly counterfactuals, is central to our intelligence and adaptability. In contrast, current machine learning (ML) and artificial intelligence (AI) …

Customizing Ai Strategies Across Multiple Generations, Matthew Harrer

Theses

This project investigates how artificial intelligence can help brands and marketers connect more effectively with Generation X, Millennials, and Generation Z. The literature review lays the groundwork that focuses on consumer behaviors and the integration of AI into digital marketing practices for each generation. The second part of the project involves a secondary data analysis of 21 recent marketing surveys and reports that explores topics related to trust, personalization, and social media. By integrating the findings into an insightful guidebook, marketers will be able to maximize these insights into clear actionable strategies.

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 …

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 …

Exploring And Characterizing Organic Chemistry Students’ Meaningful Engagement In Science Practices, John Zhou

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Undergraduate organic chemistry frequently engages students in key science practices such as analyzing and interpreting spectroscopic data and constructing explanations through reaction mechanisms. However, these activities are often framed as procedural tasks rather than as tools for constructing knowledge. As a result, students may develop incomplete understanding of these practices, making it harder for them to see how scientific knowledge is generated and to use them meaningfully as tools for sensemaking. To better understand how students take up these practices as epistemic tools, three qualitative studies were conducted in this dissertation to investigate how undergraduate organic chemistry students reason with …

Machine Learning For Parkinson’S Disease: A Comprehensive Review Of Datasets, Algorithms, And Challenges, Sahar Shokrpour, Amir Mehdi Moghadamfarid, Sepideh Bazzaz Abkenar, Mostafa Haghi Kashani, Mohammad Akbari, Mostafa Sarvizadeh

Michigan Tech Publications

Parkinson’s disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence of a conclusive treatment underscores the requirement to investigate cutting-edge diagnostic techniques to improve patient outcomes. Machine learning (ML) has the potential to revolutionize PD detection by applying large repositories of structured data to enhance diagnostic accuracy. 133 papers published between 2021 and April 2024 were reviewed using a systematic literature review (SLR) methodology, and subsequently classified into five categories: acoustic data, biomarkers, medical imaging, movement data, and multimodal datasets. This comprehensive …

A Study Of Quasigroups, A Computational Approach On Isotopism And Isomorphism Hierarchical Structure, Runaldo Montrose

Theses and Dissertations

Quasigroups morphism classification can present a real computational challenge. As of today, the number of quasigroups that can be generated from a finite set S of cardinality n is known for very small value of n. Though we know the number of quasigroups for small n, generating them in real time is a whole other issue that leads to a bigger challenge of building their isomorphy classes, because the algorithm used required large computing memory resources. In this paper we will expose the computational complexity of constructing their hierarchical morphism structure from isotopism to isomorphism. As an improvement, …

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 …

Sequential Data Modeling Of Influenza A Via Traditional, Dwt-Gpr Hybrid, And Deep Learning Architectures, Edmund Fosu Agyemang

Theses and Dissertations

Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improved sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional, deep-learning and discrete wavelet (DWT)-Gaussian Process (GP) hybrid models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and ETS models, four variants of DWT-GPR models and six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU and Transformer. The results reveal a clear superiority of all …

Functional Data Analysis On Life Expectancy And Healthcare Expenditure, Hagen Sanchez

Theses and Dissertations

This study analyzes the life expectancy for 237 countries from 1950-2023 and the healthcare expenditure for 50 countries from 1970-2022, and how life expectancy and healthcare expenditure relate to each other. Functional Principal Components Analysis was used to analyze the life expectancy and healthcare expenditure for each of the countries. Additionally, the regions of the countries were analyzed to identify any regional trends for the life expectancy data. Due to missing data and the structure of the healthcare data, multiple imputation methods and Principal Component Analysis techniques were explored for the healthcare data. Furthermore, a simulation study was conducted to …

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …

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 …

Foodlmm: A Versatile Food Assistant Using Large Multi-Modal Model, Yuehao Yin, Huiyan Qi, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, a versatile food assistant based on LMMs with various capabilities, including food recognition, ingredient recognition, recipe generation, nutrition estimation, food segmentation and multi-round conversation. To facilitate FoodLMM to deal with tasks beyond pure text output, we introduce a series of novel task-specific tokens and heads, enabling the model to predict food nutritional values and multiple segmentation masks. We adopt a two-stage training strategy. In the first stage, we utilize multiple …

Repairing Adversarial Texts Through Perturbation, Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong

Research Collection School Of Computing and Information Systems

It is known that neural networks are subject to attacks through adversarial perturbations. Worse yet, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address this, in this work, we focus on the text domain and …

Cradle: Empowering Foundation Agents Towards General Computer Control, Weihao Tan, Et. Al.

Research Collection School Of Computing and Information Systems

Despite their success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces. To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i.e., using screenshots as input and keyboard and mouse actions as output. We introduce Cradle, a modular and flexible LMM-powered framework, as a preliminary attempt towards GCC. Enhanced by six key modules, Information Gathering, Self-Reflection, Task Inference, Skill Curation, Action …

Rattler Python, Samer Jabor

Systems Manuals - 2026

The Rattler Python project is an interactive game-based learning system that intends to teach the basic concepts of Python programming through guided instruction, gameplay challenges, and review-based assessments. The document contains a proposal for this system consisting of problem definition, background research, existing solutions, and the proposed product, together with the system scope, assumptions, and the organization of the remainder of this document.

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 …