Physical Sciences and Mathematics Commons^™

Optimizing The Peter Kiewit Institute Course Schedule Using Answer Set Programming, Joshua R. Gryzen

Theses/Capstones/Creative Projects

This project introduces a program that automates the process of minimizing conflict between classes that students are likely to take simultaneously at the Peter Kiewit Institute at the University of Nebraska Omaha using Answer Set Programming. The main objectives of this project are to encode the specifics of a schedule regulation for courses pertinent to computer science majors, identify critical conflicts between the courses in a given schedule, and propose an assignment of timeslots. More specifically, a scheduled section is assigned a new timeslot, which is a combination of days, start time, and end time, from the list of timeslots …

Optimization And Personalization Of Fitness Routines Using Mathematical Models, Tyson M. Kerr

Theses/Capstones/Creative Projects

This project aims to develop a mathematical model for generating personalized workout plans tailored to individual fitness goals, preferences, and constraints. By leveraging optimization techniques such as linear programming and genetic algorithms, the model will address key questions, including how to maximize efficiency and consistency in workout plans and which methodologies are best suited for specific fitness objectives. The model involves data collection on user inputs and exercise attributes, defining objective functions for different goals, and integrating constraints like session duration. The implementation will utilize the Python library PuLP to prototype the model, alongside a user-friendly interface for input and …

Phoneme Recognition For Pronunciation Improvement, Matthew Heywood

Theses/Capstones/Creative Projects

This project aims to improve English pronunciation by investigating speech errors and developing a tool to provide precise feedback. The study focuses on creating a new pronunciation tool that offers localized feedback, identifies specific errors, and suggests corrective measures. By addressing the shortcomings of current methods, this research seeks to enhance pronunciation refinement.

Utilizing cutting-edge technology, the tool leverages speech-to-phoneme AI models and modified lazy string matching algorithms to compare the user's spoken input with the intended pronunciation. This allows for a detailed analysis of discrepancies, providing users actionable insights into their phonetic errors. The speech-to-phoneme AI models mark a …

Icylib: A Scalable Solution For Reproducible Image Classification Workflows, Leo Williams

Data Science Undergraduate Honors Theses

With the rapid expansion of e-commerce over time, ensuring the diversity and quality of product images has become a critical challenge infeasible for human completion. In conjunction with Walmart Global Tech for the Team 1 Data Science Practicum Project, image classification models were trained to assess product image sets, but training and deploying such models often involves repetitive code and inefficient processes. This thesis presents a reusable modeling library, named IcyLib, designed to streamline the training, validation, and testing of image classification models as well as dataset importation using PyTorch. IcyLib provides a structured yet flexible approach for model implementation, …

Extending Simulation-Enhanced Bayesian Optimization Of System Designs: A Computational Study, Luke Kim

Data Science Undergraduate Honors Theses

This honors thesis builds off work initially accepted for publication in the Proceedings of the 2025 IISE Annual Conference & Expo, which introduced “Simulation-Enhanced Bayesian Optimization” (SEBO)—a hybrid testing optimization approach that combined the usage of unbiased but costly physical experiments with the usage of cheaper but potentially biased computer experiments to optimize engineered systems. The original study established the SEBO methodology and demonstrated its effectiveness on a multimodal, two-dimensional benchmark function. Expanding on the work performed, we conduct a broader evaluation of the SEBO framework through parameter testing and experimentation under a variety of additional benchmark functions. This investigation …

Enhancing Product Image Classification: Utilizing Machine Learning Models For Retail Applications, Avery A. Thompson

Data Science Undergraduate Honors Theses

The expansion of e-commerce has continued at a blinding pace since the COVID-19 pandemic, and retailers are constantly looking for new ways to retain customers. Ensuring that diverse and well-classified images are on product pages has been a paramount method for retailers to ensure retention as they increase product engagement and sales and enhance user experience. Managing and labeling these vast catalogs of images by hand is becoming increasingly infeasible, so some online retailers have started to turn to automated classification models to assist them. Accuracy in these classification models is integral, as a good image classification model can improve …

Drainage Area Limitations Of Single Watershed, Peak Flow Estimates From Nrcs Methods, Timothy A. Maughan, Rollin H. Hotchkiss, Riley Hales

Nebraska Department of Transportation: Research Reports

Most state Department of Transportation roadway design sections predict peak flow for culvert design using, amongst other approaches, Natural Resources Conservation Service (NRCS) TR-20 technology. Even though this technology is more than 50 years old, there are no clear guidelines for how large a single watershed drainage area may be while remaining appropriate for predicting peak discharge with this method. Our objective was to identify the drainage area where TR-20 peak flow predictions significantly deviate from flow frequency predictions. We developed flow frequency estimates for 130 small-area stream gage sites in rural Nebraska and compared the calculated return period discharges …

An Algebraic-Combinatorial Proof Of A Bézout-Type Inequality For Mixed Volumes Of Three-Dimensional Zonoids, Gennadiy Averkov, Ivan Soprunov

Mathematics and Statistics Faculty Publications

We present a new algebraic-combinatorial approach to proving a Bézout-type inequality for zonoids in dimension three, which has recently been established by Fradelizi, Madiman, Meyer, and Zvavitch. Our approach hints at connections between inequalities for mixed volumes of zonoids and real algebra and matroid theory.

Synthesis Of Nanoparticles In Silicate Matrix And Stimuli Responsive Nanocomposite Polymer Films, Dimuthu Edirisinghe

All Dissertations

Interactions between aqueous metal ions and soluble silicates lead to the formation of insoluble metal silicate complexes that form nanoparticles via condensation polymerization. Metal silicates function as nanoreactors, effectively condensing metal ions into nano-sized particles that can undergo further chemical transformations. Core-shell nanostructures present an intriguing strategy for integrating materials with different properties. The silicate shell around the silver nanoparticles can be successfully infused with other metal ions, enabling subsequent reactions to tailor their properties. Silver nanoparticles exhibit strong interactions with light, surpassing other chromophores, including noble metal nanoparticles, due to their highly efficient surface plasmonic resonance. The unique optical …

Gravity Wave Breaking Over Northern Utah With An Advanced Mesospheric Temperature Mapper And Sodium Lidar, Eric Davis

All Graduate Reports and Creative Projects, Fall 2023 to Present

Considerable progress has been made in recent decades in the understanding of gravity wave (GW) dynamics. However, there remain important aspects and effects that are poorly understood. In particular, the coupling and deposition of their energy and momentum into the mesosphere lower thermosphere (MLT) region and the interaction and instability dynamics that are directly associated with GW breaking. Using an Advanced Mesosphere Temperature Mapper (AMTM) several strong wave breaking events were observed in the summer of 2015 at USU’s BLO research station. These events have exhibited high momentum fluxes and associated strong wave breaking, prompting detailed data analyses. Rossby waves, …

Aleci: An R Package For Non-Parametric Confidence Intervals On Accumulated Local Effects Plots, Matthew R. Lister

All Graduate Reports and Creative Projects, Fall 2023 to Present

Machine learning models can take a collection of inputs and craft an output. The mathematical formulas these models use to calculate their outputs easily become too complex or time consuming for a human to analyze. Collectively, we refer to these as black box models. Accumulated local effects plots (ALE) are a method for adding interpretability and visibility into the effects that individual variables contribute to the predictions made by black box models. The method designed by D.W. Apley calculates equally spaced point estimates of the response value to construct a graph across the range of the variable of interest. AleCI …

A Comparative Analysis Of Nf-Κb1 Gene Regulatory Sequence Methylation In Normotensive And Hypertensive Kenyans, Aaryan Barlas Piracha

Honors Theses

Accounting for the majority of deaths worldwide, non-communicable diseases (NCDs) present the greatest health challenge of the twenty-first century. Specifically, cardiovascular diseases (CVDs) exceed all other NCDs in annual deaths and especially affect low- and middle-income countries (LMICs). Hypertension, being the primary risk factor for CVD, affects over 75% of adults in LMICs due to inadequate health care and preventative measures. Additionally, epigenetic modifications of DNA are important mechanisms that regulate gene expression; DNA methylation, in particular, affects cytosine residues in cytosine-phosphate-guanine (CpG) islands on regulatory sequences. Previous research in our laboratory analyzed percent methylation at 8 different CpG islands …

Effects Of The Gravity Gradient On The Path Of 1i/‘Oumuamua, Hannah R. Richardson

Honors Theses

In October 2017, the asteroid 1I/’Oumuamua first passed into viewing range [1]. The asteroid is notable for being the first interstellar object to enter the solar system. 1I/’Oumuamua was also unusual in its geometry; it is thought to have an aspect ratio of 6:1 and a length of approximately 400 m [2] [3]. The asteroid was observed to experience a non-Keplerian acceleration estimated to be on the order of 1⇥10−6 m s2 . Several theories have been proposed for the cause of this acceleration, all of which are non-gravitational in nature: volatile outgassing, photon pressure, and solar winds [1][4]. However, …

Composition And Rich/Lean Loading-Dependent Density Measurements Of A Series Of Aqueous Ionic Amines For Co2 Capture, Claudia Nguyen

Honors Theses

As climate change worsens worldwide, there is a drive to develop more efficient and sustainable methods to mitigate its impact through carbon capture methods like carbon dioxide scrubbing. Carbon scrubbing directly captures carbon dioxide (CO2) emissions from industries or power plants before they are released into the environment. The CO2 captured can be utilized for various applications or can be stored underground. However, this method faces numerous challenges, such as the degradation of the solvent or corrosion of equipment. As a result, research has expanded to find a better medium to overcome these issues. In particular, aqueous ionic amines (AIA) …

Comparative Analysis Of Classical And Machine Learning Pathfinding Approaches, Miguel Gapud

Honors Theses

Pathfinding is an essential task for any autonomous robot. Graph-based classical pathfinding algorithms and machine learning approaches have both been used for this end, but they are often not compared against each other. An implementation of end-to-end (E2E) pathfinding using Proximal Policy Optimization (PPO) and an Alexnet architecture is compared against an implementation of Hybrid A*. A digital twin in Unity3D is used as the testing environment with the Clearpath Dingo as the pathfinding robot. In machine learning, the robot is controlled using PPO through ROS-Noetic with a camera as its sensor. Hybrid A* and its controls are implemented directly …

Toxicity And Biodegradability Of Novel Boronium Vs Conventional Ammonium-Based Antimicrobial Compounds In Wastewater Treatment Systems, Noor Shalan

Honors Theses

Quaternary ammonium compounds (QACs) are highly effective as disinfectants, herbicides, and pesticides; thus, overuse causes elevated levels of residual toxicity in domestic and industrial wastewater. QACs can be toxic to essential bacteria breaking down pollutants in wastewater treatment plants (WWTPs) and can remain untreated in effluent, harming the environment, and contributing to antibiotic resistance, posing risks to human health. Novel boronium-based antimicrobial compounds have demonstrated efficacy in eliminating bacteria, fungi, and viruses. If the boronium compounds exhibit lower residual toxicity, they could offer a promising alternative to QACs. Because these compounds are still in development, their potential toxicity to the …

The Search For Slow Particles And Magnetic Monopoles With Nova, John Clark

Honors Theses

Singular magnetic poles, north or south, have been theorized to exist for hundreds of years. In the modern day, the elusive singular magnetic pole still remains undiscovered. The appearance of this particle would help confirm many Grand Unified Theories, GUTs, and revolutionize our understanding of some of the fundamental forces of the universe. Fermilab's NOvA collaboration is working on ways to screen and detect magnetic monopoles and other slow-moving particles coming from outer space alongside their main mission to study neutrinos. The aim of this work is to determine and improve the Far Detector’s efficiency at identifying slow moving particles. …

Ghosts In Glass: Ghost Crabs As Judges Of Glass Sand For Coastal Restoration, Emily Parrish

Honors Theses

Over 60% of the Gulf of Mexico coastline is actively eroding, which has resulted in thousands of miles of lost coastline over the last 100 years. This drastic loss of coastal land highlights the dire need for coastal restoration across the entirety of the Gulf Coast. A vital aspect of restoring these eroded areas is replenishing eroded substrate, traditionally conducted through offshore dredging of substrates which comes with high economic and ecological costs. Recycled glass sand may be able to fill this role and reduce our reliance on dredging while lowering the amount of glass waste entering landfills each year. …

Key-Based Authentication Scheme For Evtol Drones Using Chebyshev Chaotic Maps, Eduardo A. Hernandez Escobar

Master's Theses

The development of electric Vertical Take-Off and Landing (eVTOL) drones signifies a substantial advancement in urban air mobility, ready to transform transportation models in densely populated regions. These advanced drones, distinguished by their capacity to function in limited spaces and their minimized environmental impact, are set to transform individual, shipping, emergency services, and public safety activities. Nonetheless, like any transformational technology, the implementation of eVTOL systems presents many challenges, especially in the realm of cybersecurity. Adding many devices and entities to an eVTOL network increases the risk of privacy and security attacks. This paper proposes a key-based authentication scheme that …

A Herpetofaunal Inventory Study Of Two National Wildlife Refuges In The Mississippi Delta And Associated Evaluation Of Trapping Efficacy, Andrew Holzinger

Master's Theses

The objectives of this project were to record the diversity and distribution of amphibian and reptile species present at Panther Swamp and Hillside National Wildlife Refuges (NWR) and to quantify trapping efficacy as a result of various methodologies under different habitat variables. The primary methods employed were two varieties of drift fence trap arrays consisting of 100-foot sections of silt fencing with buried 2.5-gallon buckets as pitfall traps and box traps placed at the end of each fence to capture larger organisms. When conditions permitted, aquatic trapping was also employed across the Refuges. A total of 55 species were documented, …

Solution Of Preconditioned Nonsymmetric Saddle Point Systems Through Modified Conjugate Gradient Iteration, Samson Ayo

Dissertations

In this dissertation, we present an iterative method (Preconditioned Nonsymmetric Saddle Point Conjugate Gradient) for simultaneously solving forward ($A{\bf x}={\bf b}$) and adjoint ($A^T{\bf y}={\bf g}$) linear systems. Our approach involves constructing an augmented nonsymmetric saddle point matrix that has a real positive spectrum and developing a conjugate gradient-like iteration for this matrix. We investigate the use of Schur Complement preconditioners with block-diagonal factorization computed by an incomplete QR factorization of $A$ to speed up the convergence of our method and compare the results to the preconditioned generalized least squares residual (GLSQR) and quasi-minimal residual (QMR) methods. We develop quadrature …

Simulations Of Richtmyer-Meshkov Instability Using High Order Weno Methods, Ryan Holley

Graduate Theses and Dissertations

Turbulent mixing due to hydrodynamic instabilities occurs in a broad spectrum of engineering, astrophysical and geophysical applications. Theory, experiment, and numerical simulation help us to understand the dynamics of interface instabilities between two fluids. This thesis presents an increasingly accurate and robust front tracking method for the numerical simulations of shock-induced turbulent mixing known as Richtmyer-Meshkov Instability (RMI). Front tracking is an adaptive computational method, where the interface instability is explicitly represented as lower dimensional manifolds moving through a rectangular grid. All the cell-center states (density, velocity and pressure) are updated using higher order weighted essentially non-oscillatory (WENO) scheme. Performance …

Real-Time Anomaly Detection In Ot Networks Using Gru-Based Autoencoders, Grant Austin Wilkins

Graduate Theses and Dissertations

Operational Technology (OT) networks, particularly those used in critical infrastructure, face increasing cyber threats that target network-level protocols and behaviors. While most anomaly detection research for OT systems has traditionally relied on sensor data, this thesis explores the viability of detecting malicious activity directly from network telemetry. We propose a sequence-to-sequence autoencoder model based on Gated Recurrent Units (GRUs) with multilevel attention, trained to reconstruct normal patterns of packet-level communication extracted from raw PCAP data. The developed feature engineering pipeline integrates general networking attributes such as IP and MAC addresses, ports, and transport protocols with OT-specific protocol information from Modbus …

A Robust Rf Fingerprinting Approach Using Physics-Informed Neural Networks, Jozef Dusenka

Graduate Theses and Dissertations

Radio frequency (RF) fingerprints, caused by unique imperfections in communication hardware, offer a promising solution for zero-trust security. However, existing RF fingerprinting techniques, which aim to extract these signatures from transmitters to uniquely identify devices, often struggle with robustness in the face of temporal and spatial variations in real-world, time-varying wireless environments. For example, a neural network trained on RF signals collected on Day 1 can experience a significant performance drop when tested with data from Day 2.

To address this challenge, we propose a novel, robust RF fingerprinting method based on Physics-Informed Neural Networks (PINNs). Rather than training the …

Adversarial Machine Learning: Methods For Attacks And Defenses, Minh Hao Van

Graduate Theses and Dissertations

With the rapid development of machine learning in real-world applications, enhancing security plays an important role. Adversarial machine learning focuses on understanding malicious actions from attackers and developing defensive techniques against such threats when deploying machine learning systems. An attack can occur in different scenarios, such as poisoning attacks during the training stage and evasion attacks during the testing stage. Although extensive research has explored defense strategies to deal with these harmful attacks, there is a need for further research into areas such as how to counteract malicious attacks with healthy noise or how to train an adaptive defense against …

A Hierarchical Soft Computational Model For Optimizing Agricultural Uavs: Recruiting Neutrosophic Theory And Tree Soft Sets, Mona Mohamed, Nurhan Alaa, Bilal Arain, Karam M. Sallam

Neutrosophic Systems with Applications

Precision agriculture is being transformed by Unmanned Aerial Vehicles (UAVs), which make it possible for yield optimization, targeted spraying, and sophisticated crop monitoring. With an emphasis on their operational capabilities, economic feasibility, and environmental implications, this research explores the revolutionary potential of UAV technology in contemporary farming systems. Practically speaking, the procedure of opting UAVs for agricultural applications is complicated by several competing aspects, inherent uncertainties, and differing stakeholder agendas. This paper suggests a new hybrid decision framework that combines Tree Soft Sets (TrSS), Neutrosophic theory, and Multi-Criteria Decision-Making (MCDM) to methodically handle these issues. Hence, the robust hybrid model …

Consultation Summary For Proposed Declared Pest Rates 2025/2026, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity published reports

Under the Biosecurity and Agriculture Management Act 2007 (BAM Act) the State raises a Declared Pest Rate (DPR) from land or leaseholders (hereafter landholders) in prescribed areas (DPR Areas) and matches the funds raised from the rate dollar-for-dollar.

The combined funds are made available to Recognised Biosecurity Groups (RBGs) who provide support to landholders to fulfil their obligations under the BAM Act to manage widespread and established declared pests on their land. A community-led and coordinated approach is considered the most effective way to support landholders to manage these types of pests.

Each year, the Minister for Agriculture and Food …

A Comprehensive Performance Comparison Of Machine Learning And Federated Learning For Intrusion Detection In Vehicular Ad-Hoc Networks Using Can-Bus Data, Tim Leonhardt

Honors Theses

Federated Learning (FL) is a Machine Learning (ML) approach that decentralizes training across distributed devices, eliminating the need to centralize data. Unlike traditional ML, where models are trained on aggregated data, FL sends a global model to multiple nodes for local training, with updated parameters transmitted back to the server for aggregation. This process preserves data privacy, making FL ideal for sensitive applications like cybersecurity. However, FL introduces challenges such as data heterogeneity, communication overhead, and difficulties in achieving model convergence, which can impact performance.

This study investigates a fundamental assumption in ML and FL research: that the superior performance …

Sliding Window Method For Simulating Action Potentials In Axons, Hayden Reed

Honors Theses

ABSTRACT Hodgkin and Huxley’s nonlinear partial differential equations model the excitation and propagation of action potentials in neurons, and there have been numerous attempts at finding the best numerical solution method. This thesis proposes a novel approach to solving these equations: the Sliding Window method, in which a fixed sub-interval is found through capturing the signal’s head and tail. The system is then solved on the sub-interval instead of the entire interval. Using the Sliding Window technique also involves implementing the backward and forward Euler methods and the finite difference method. It will be demonstrated that, in utilizing the Sliding …

Mathematical Melodies: The Exploration Of Music Using Fourier Signal Analysis, Courtney Francois

Honors Theses

Upon initial inspection, mathematics and music appear to be distinct disciplines lacking any connections to one another. However, many complex intersections lie beneath the surface. This paper seeks to explore these connections through the analysis of the Fourier series and the Fourier transformation of musical sound signals. Orthogonal functions of various frequencies are used to approximate and recover sound signals. Following the examination of the basis of Fourier signal analysis, MATLAB is utilized to visualize the connectedness of mathematics and music through sound signals from varied musical instruments and by performing Fourier signal analysis to the sound signals. Then, the …