Physical Sciences and Mathematics Commons^™

An Exploration Of The Structure Of The Linear Algebraic Model Of Color, Isaac Martin

University Honors Theses

This thesis surveys the mathematical grounding of linear algebraic models of color. It aims to build from the ground up the framework by which additive color is broadly understood in the digital age. Primarily building on the work of Jozef Cohen, Eric Dubois, David H. Krantz, and Günter Wyszecki, it aims to chart the construction of a model of color that underpins most modern understandings of color. While the construction is certainly established in colorimetric circles, the construction is, in the thesis author's opinion, either obtuse or non-rigorous. Ideally, this thesis serves to make the construction accessible to an audience …

Detection Of Activity Cliffs Produced By Anti-Cancer Drugs And An Algorithm For Reliable Predictions In Affected Areas, Sarah Josephine Aurit

Department of Statistics: Dissertations, Theses, and Student Research

An activity cliff (AC) occurs when drugs close in chemical space produce dissimilar biological results. We focus on developing an inferential procedure to detect the presence of ACs in a chemical landscape. If detected, we provide a distance-based procedure that can be used to identify regions of stability in the chemical landscape of interest and generate prediction with higher precision in those areas of stability. We conceptualize the chemical landscape as a spatial random field and use spatial models for prediction of efficacy for new drugs based on “distance” in chemical space. We argue that an AC manifests itself by …

Studies In Number Theory: Reciprocity Laws And Fundamental Domains, C. Xavier Parent

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis consists of two sections. The first section is an introductory survey of number theory discussing the reciprocity laws with a focus on accessibility. Number Theory has always been a fundamental area of mathematical study, with Gauss calling it “the queen of mathematics”. The reciprocity laws are a classical set of results from number theory which have driven number theory for quite a long time. Unfortunately, these results, while important, have always been very inaccessible to undergraduate students, making it hard to start studying the field. This survey attempts to help bridge that gap, giving a resource for novices …

An Investigation Into The Principle Of Symmetric Criticality, Guillermo Frausto

All Graduate Theses and Dissertations, Fall 2023 to Present

In physics, symmetries help us understand complex systems by revealing conserved quantities and simplifying equations. In the context of general relativity, where spacetime is curved and dynamic, these symmetries become especially important when trying to reduce or solve the equations that govern gravitational and matter fields.

This dissertation investigates a mathematical principle known as the Principle of Symmetric Criticality (PSC), which helps determine when symmetries can be used to simplify physical theories without losing essential information. While often assumed to be valid, PSC does not always hold, and understanding when it does is critical for the reliable use of symmetry …

Enabling Sustainability Transitions For Great Salt Lake: Framing Of Public Opinion, Boundary Work, And Policy, Anna Mcentire

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores how people think about, talk about, and take action to save Great Salt Lake, which reached historic low levels in 2021. Using surveys, public records, and legislative documents primarily from 2022 to 2024, the research examines public opinions, collaborative efforts, and policy changes related to the lake. The first chapter looks at what people value about the lake and what actions they want to see taken. It finds that people are very concerned and want significant efforts to stop the lake's decline. The second chapter investigates how different groups work together publicly to negotiate new understandings, create …

A Focus On Student Education: Determining Student Attitudes Towards Transparent Autograding And Developing Artificially Intelligent Tools To Help Students Succeed, Andra Rice

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis is composed of two parts both relating to helping students succeed. First, the focus is on determining how we can help students feel more comfortable using an AI tool that can provide them immediate feedback. Second, machine learning algorithms are explored in relation to tracking student tasks to encourage healthy study habits.

The development of effective autograders is key for scaling assessment and feedback. While AI based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students. Our research tested the effect …

Impacts Of 4r Nitrogen Management And Water Optimization On Soil Health, Kamaldeep Kamaldeep

All Graduate Theses and Dissertations, Fall 2023 to Present

Farmers in Utah and Idaho are facing growing challenges due to high fertilizer costs, limited irrigation water, and pressure to improve long-term soil health. To better understand current practices and grower attitudes, a survey was conducted in 2020 targeting 5,000 producers of small grains, corn, and potatoes. Although the response rate was low, 146 growers representing over 25,000 hectares of cropland participated. Survey results showed that most growers are open to improving nitrogen and irrigation practices and care about their soil health, even if not all are fully using 4R or precision agriculture methods.

This thesis includes two field studies …

Enhanced Point Cloud Generation From A Novel 360° Underwater Lidar, Olagoke E. Daramola

Dissertations

This dissertation presents novel algorithms to improve the mapping capabilities of a 360-degree underwater Pulsed Laser Line Scanner LiDAR (PLLS-360°). Due to its 360° field-of-view (FOV), the PLLS-360° is a compact full-waveform omnidirectional imager suitable for seafloor mapping, underwater asset inspection, object detection, ice-sheet mapping, and construction progress monitoring. The proposed methodology includes an improved waveform fitting technique for saturated waveform recovery, detection array response correction, radiometric corrections, and fusion of LiDAR and sonar bathymetric datasets. The first part of this dissertation assesses the LiDAR’s performance and describes how the data for this unique 360° FOV architecture is processed. The …

Introduction To C++ (Volume I), Hussam Ghunaim Ph.D.

All Open Educational Resources

This book is written as an Open Education Resource (OER) to replace expensive commercial materials currently used at the Department of Computer Science at Fort Hays State University. It has two volumes corresponding to the CSCI 121 and CSCI 221 courses. These courses are developed to introduce college freshmen students to Object-Oriented Programming utilizing C++. The author tried to bridge the gap in the current programming textbooks by avoiding lengthy and, on many occasions, unnecessary details. This book’s main feature is to present the discussed principles in the least wording possible while providing adequate examples and exercises to reinforce students’ …

Development Of A New Groundbased Instrumentation Technique For Low-Resolution Exoplanet Transmission Spectroscopy, Mary Anne Limbach, Luke M. Schmidt, Ryan J. Oelkers, Darren L. Depoy

Physics & Astronomy Faculty Publications

We present a new instrumentation technique for high-precision, ground-based spectrophotometric measurements ideal for low-resolution (R ∼ 20–60) exoplanet transmission spectroscopy. This technique employs novel thin-film coating technology for wide-band, simultaneous multi-band imaging, enabling high-precision differential photometry through self-referencing and comparison stars. Theoretical calculations and on-sky results show that this method effectively reduces 96% of amplitude scintillation noise and other systematics that typically limit ground-based spectrophotometric precision for bright stars. On-sky results are demonstrated using the custom-built Exoplanet Transmission Spectroscopy Imager (ETSI), deployed at the McDonald Observatory 2.1 m telescope in 2022. For a V = 9.7 mag star, ETSI …

Short: Breaking The Charge: Exploiting State Manipulation In Ev Charging, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. However, the security of electric vehicle charging has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection for the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states of EV chargers …

R2dqg: A Quality Meets Diversity Framework For Question Generation Over Knowledge Bases, Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan

Research Collection School Of Computing and Information Systems

The task of Knowledge-Based Question Generation (KBQG) involves generating natural language questions from structured knowledge sources, posing unique challenges in balancing linguistic diversity and semantic relevance. Existing models often focus on maximizing surface-level similarity to ground-truth questions, neglecting the need for diverse syntactic forms and leading to semantic drift during generation. To overcome these challenges, we propose Refine-Reinforced Diverse Question Generation (R2DQG), a two-phase framework leveraging a generation-then-refinement paradigm. The Generator first constructs a diverse set of expressive templates using dependency parse tree similarity, capturing a wide range of syntactic patterns and styles. These templates guide the creation of question …

Comparative Nitrene-Transfer Chemistry To Olefins Mediated By First-Row Transition Metal Catalysts Supported By A Pyridinophane Macrocycle With N4 Ligation, Himanshu Bhatia, Lillian P. Adams, Ingrid Cordsiemon, Suraj Kumar Sahoo, Amitava Choudhury, Thomas R. Cundari, Pericles Stavropoulos

Chemistry Faculty Research & Creative Works

A 12-membered pyridinophane scaffold containing two pyridine and two tertiary amine residues is examined as a prototype ligand (^tBuN4) for supporting nitrene transfer to olefins. The known [(^tBuN4)M^II(MeCN)2]²⁺ (M = Mn, Fe, Co, and Ni) and [(^tBuN4)Cu^I(MeCN)]⁺ cations are synthesized with the hexafluorophosphate counteranion. The aziridination of para-substituted styrenes with PhI=NTs (Ts = tosyl) in various solvents proved to be high yielding for the Cu(I) and Cu(II) reagents, in contrast to the modest efficacy of all other metals. For α-substituted styrenes, aziridination is accompanied by products of aziridine …

Visceral, Neural, And Immunotoxicity Of Per- And Polyfluoroalkyl Substances: A Mini Review, Pietro Martano, Samira Mahdi, Tong Zhou, Yasmin Barazandegan, Rebecca Iha, Hannah Do, Joel Burken, Paul Ki-Souk Nam, Qingbo Yang, Ruipu Mu

Chemistry Faculty Research & Creative Works

Per- and polyfluoroalkyl substances (PFASs) have gained significant attention due to their widespread distribution in the environment and potential adverse health effects. While ingestion, especially through contaminated drinking water, is considered the primary route of human exposure, recent research suggests that other pathways, such as inhalation and dermal absorption, also play a significant role. This review provides a concise overview of the toxicological impacts of both legacy and emerging PFASs, such as GenX and perfluoro butane sulfonic acid (PFBS), with a particular focus on their effects on the liver, kidneys, and immune and nervous systems, based on findings from recent …

Artificial Intelligence Integration And Teachers' Self-Efficacy In Physics Classrooms, Fouad Yehya, Areej Elsayary, Ghadah Al Murshidi, Ahmed Al Zaabi

All Works

The United Arab Emirates (UAE), in its vision 2021 and the UAE centennial 2071 plan, highlights the essential role of artificial intelligence (AI) and technology in shaping a knowledge-based, future-ready society. This study explores the integration of AI in physics classrooms, focusing on secondary education in the UAE. It also investigates the perceptions and self-efficacy of physics teachers regarding the use of AI tools in classroom settings. A qualitative research design was employed to gather in-depth insights from 15 physics teachers across schools in Sharjah, assessing their confidence and readiness for AI integration through the lens of the attitude and …

Advancing Fishery Dependent And Independent Habitat Assessments Using Automated Image Analysis: A Fisheries Management Agency Case Study, Scott Evans, Bronson Philippa, Carlo Mattone, Nick Konzewitsch, Renae Hovey, Marcus Sheaves, Gary A. Kendrick, Lynda M. Bellchambers

Fisheries Research Articles

Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externally, which can limit the adoption of externally hosted artificial intelligence platforms. In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. Using an extensive dataset of 13,128 manually annotated benthic habitat images, we train a grid-based annotation model and an image-level …

(A,B,C) Tilings With Prescribed Symmetry Groups From Regular Triangle Or Hexagon Tiling, Mark D. Tomenes, Ma. Louise Antonette N. De Las Penas

Mathematics Faculty Publications

A tiling T of the Euclidean plane (E2) is a countable collection of closed topological disks called tiles T = {Ti : i ∈ N} that is a covering (Ui Ti = E2) as well as a packing (Int(Ti) ∩ Int(Tj) = ∅ if i ̸= j, Int(T) denotes the interior of tile T). One of the problems of interest in discrete geometry is the classification of tilings based on transitivity properties of their vertices, edges and tiles. This talk presents a family of tilings whose vertices, edges and tiles have exactly a, b and c orbits, respectively, under the …

Optimal Hypergraph Connectivity With Cut Queries, Hang Liao

Dartmouth College Ph.D Dissertations

Finding connected components in undirected hypergraphs—hypergraph connectivity—is a fundamental problem in computer science. It can be framed as a special case of Symmetric Submodular Function Minimization (SSFM), where the objective is to determine if the non-trivial minimizer is zero. This thesis develops an optimal algorithm for hypergraph connectivity within the $\CUT$ query model, where an algorithm probes a subset of vertices to learn the weight of the hyperedges ``cut" by that partition.

Our approach is constructive, culminating in an optimal algorithm for the general problem by first developing the necessary tools for two foundational subproblems. The main contributions of this …

Congruences For Quotients Of Klein Forms, Jeffery Opoku

Theses and Dissertations

This dissertation investigates the arithmetic properties of some modular forms and eta quotients, focusing on the divisibility properties and congruence relations satisfied by these. The first part examines quotients of the Rogers-Ramanujan and Rogers-Selberg functions, defined by \[ f(\tau) = q^{r} (q^5; q^5)^{a_0} (q, q^4; q^5)^{a_1} (q^2, q^3; q^5)^{a_2}= \sum_{n=r}^{\infty} P_{a_0,a_1,a_2}(n-r) q^n, \] and \[ g(\tau) = q^{s} (q^7; q^7)^{a_0} (q, q^6; q^7)^{a_1} (q^2, q^5; q^7)^{a_2} (q^3, q^4; q^7)^{a_3}= \sum_{n=s}^{\infty} P_{a_0,a_1,a_2,a_3}(n-s) q^n, \] respectively. We establish conditions on the exponents \(a_0, a_1, a_2, a_3\) and residue classes \(r\) and $s$ modulo $p$ such that \(P_{a_0, a_1, a_2}(pn - r) \equiv …

Arginine Vasotocin And Corticosterone In Nesting And Non-Nesting Loggerhead Sea Turtles In X’Cacel-X’Cacelito Sanctuary, Krista Blair Reed

Theses and Dissertations

Sea turtles exhibit distinct behaviors before and after egg laying, however, not all individuals successfully oviposit. Some females display disruptions in their typical nesting behavior, returning to the water without laying eggs, which is referred to as a non-nesting emergence (NNE). NNEs can be due to external disturbances on the beach, but why they occur is often unclear. Arginine vasotocin (AVT) is a neuropeptide involved in various physiological processes, including stimulating smooth muscle contraction of the oviduct to facilitate egg deposition in nesting sea turtles. On the other hand, corticosterone is the primary stress hormone in sea turtles and is …

Comparative Analysis Of Sequential And Non-Sequential Modeling Techniques For Ddos Attack Detection With Explainable Ai, Vincent Agbenyeavu

Theses and Dissertations

Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber-attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to discover. Because of that, there have been several approaches, as well as solutions that have been devised to detect it as accurately and efficiently as possible. In this study, four sequential data modeling …

Machine Learning Applications For Evaporation Predictions From Small Reservoirs: Potential Water Savings For Lower Rio Grande Valley, Texas, Syed Muhammad Fahad Abdullah

Theses and Dissertations

Local-scale reservoirs are important to regional water balance, but these are often overlooked. This study presents a robust machine learning (ML) approach leveraging reanalysis datasets to estimate daily evaporation for local-scale reservoirs in semi-arid South Texas. Selected models were trained with daily lake evaporation model (DLEM) estimates and used climatic and reservoir-specific properties as feature input variables. The multi-reservoirs training approach ensured applicable model generalization. Results show promising predictive performance with R² values ranging from 0.55–0.67 (testing) and 0.64–0.78 (validation), NSE values ranged from 0.54 0.67 (testing) and 0.64–0.78 (validation), and RMSE values ranged between 1.52–1.80 mm/day (testing) and 1.22–1.58 …

Low-Cost Monitoring And Fingerprinting Of High-Powered Electric Systems, Kwabena Buamono Aboagye-Otchere

Theses and Dissertations

Electric motors are vital to industry, transport, and energy, yet their maintenance challenges persist. While traditional reactive maintenance leads to costly downtime and safety risks, predictive maintenance, especially through IoT and machine learning offers early fault detection and operational efficiency. However, this shift introduces security concerns due to unintended magnetic emissions from motors. These emissions, though useful for non-intrusive monitoring, can be exploited to eavesdrop on sensitive industrial processes. This dissertation explores the dual nature of magnetic emissions: their value in motor diagnostics and their potential as a security vulnerability. It demonstrates how emissions can identify motors, monitor health, and …

Computational Calculations On Zno (0001) And Zno (1010) Surfaces With And Without Pd Adatoms, Amit Aich

Theses and Dissertations

In this research work, first-principles density functional theory (DFT) was used to study O-terminated, Zn-terminated ZnO (0001) surfaces and non-polar ZnO (1010) surfaces. Both clean surfaces and those with Pd or PdO adatoms were examined. Adsorption was considered at O, Zn and hollow sites. After structural relaxation, we analyzed the electronic, optical and adsorption properties. The O-terminated ZnO (0001) surface has a larger band gap of 0.76 eV and p-type conduction. The Zn-terminated surface has a smaller band gap of 0.15 eV and shows n-type behavior. Pd adsorption modifies ZnO’s electronic structure in a site-dependent manner. On O-terminated surfaces, it …

Intersecting Realities And Evolving Landscapes: Mapping Generative Ai Within The Framework Of Digital Rhetoric, Joshua Troy Nieubuurt

English Theses & Dissertations

The increased usage of [Generative] AI technologies (GenAI) in the 21st century has called into the question the rhetorical agency of these digital things. [Gen]AI has historically been framed within a Heideggerian “readiness-to-hand” dynamic in which it has been unilaterally conceived as a tool to be used by humans. This dissertation proposes that the GenAI assemblage is capable of being a co-actor in rhetorical spaces. To provide evidence for this stance This dissertation utilizes Actor Network Theory to map the actants within a GenAI assemblage. In doing so it allows for an understanding of the stakeholders (both human and non-human) …

Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati

Computer Science Theses & Dissertations

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues …

Two Analytic Issues Arising From Models In Probability And Materials Science, Giang Vu Thanh Nguyen

Mathematics & Statistics Theses & Dissertations

This dissertation explores two distinct topics centered on mathematical models in probability theory and materials science. The first part investigates a series of functions derived from an adaptive algorithm designed to address the score-based secretary problem, a classic challenge in probability theory. This problem involves making immediate decisions to select the best candidate from a sequence of interviews. The algorithm aims to maximize the probability of selecting the optimal candidate based on observed scores. We prove two fundamental analytic properties of this sequence of functions as a theoretic support of the algorithm: first, the functions in the sequence each possess …

Complex System Governance And Cyber Operations, Willie Gernard Mccallister

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation examines the potential integration of Complex System Governance (CSG) within cybersecurity, emphasizing the development of a reference model for Cybersecurity Infrastructures. Traditional strategies for securing digital environments have struggled to address the intricate and dynamic layers inherent in modern cybersecurity systems. The purpose of this research is to explore the applicability of CSG as a framework to assess cybersecurity infrastructure using a case study research design. The research addresses two key questions: (1) How can the CSG reference model be adapted to explore cybersecurity infrastructure? (2) What results from CSG based exploration of cybersecurity infrastructure through a case …

A Strict Physicality-Preserving Scheme For A 2d Q-Tensor Flow With A Singular Potential, Md Mashud Parvez

Mathematics & Statistics Theses & Dissertations

Nematic liquid crystals are a state of matter that exhibit properties between those of conventional liquids and solid crystals. Their unique ability to align molecules in specific directions makes them essential in various applications, including display technologies and advanced materials. To model their complex behavior, mathematical frameworks such as the Q-tensor model are used to describe the orientation and degree of molecular order. In this work, we introduce a numerical scheme for a two-dimensional (2D) dynamic Q-tensor model, which is formulated as an L²-gradient flow driven by the liquid crystal free energy and incorporates a singular potential to …

Analysis Of Multi Grade Deep Learning, Ronglong Fang

Mathematics & Statistics Theses & Dissertations

Multi-Grade Deep Learning (MGDL) is a training framework that incrementally builds deep neural networks. It does this by dividing the training process into multiple “grades,” where each grade sequentially trains a shallow neural network to learn the residue from the previous one, using the outputs of prior grades as input. This approach progresses from shallow to deep architectures. This dissertation offers a comprehensive theoretical and numerical analysis of the MGDL methodology.

We first demonstrate that MGDL can effectively learn target functions within the sum-composition learning format. In this context, MGDL approximates high-frequency components by composing multiple low-frequency functions. This unique …