Physical Sciences and Mathematics Commons^™

Comprehensive Insights Into The Cholesterol-Mediated Modulation Of Membrane Function Through Molecular Dynamics Simulations, Ehsaneh Khodadadi, Ehsan Khodadadi, Parth Chaturvedi, Mahmoud Moradi

Chemistry & Biochemistry Faculty Publications and Presentations

Cholesterol plays an essential role in biological membranes and is crucial for maintaining their stability and functionality. In addition to biological membranes, cholesterol is also used in various synthetic lipid-based structures such as liposomes, proteoliposomes, and nanodiscs. Cholesterol regulates membrane properties by influencing the density of lipids, phase separation into liquid-ordered (Lo) and liquid-disordered (Ld) areas, and stability of protein-membrane interactions. For planar bilayers, cholesterol thickens the membrane, decreases permeability, and brings lipids into well-ordered domains, thereby increasing membrane rigidity by condensing lipid packing, while maintaining lateral lipid mobility in disordered regions to preserve overall membrane fluidity. It modulates membrane …

Temperature, Albedo, And Emissivity Of Triton And Proteus From Voyager 2 Iris Data, Garrett Nobis, Vincent Chevrier

Space and Planetary Science Faculty Publications and Presentations

The Voyager 2 infrared interferometer and spectrometer (IRIS) spectra for Triton are revisited, and the Proteus IRIS spectra are looked at for the first time. Fifty more spectra are added to the average of 16 previously used for Triton. The unit emissivity Planck function temperature result is 37.9  ±  0.5 K. Iterating IRIS temperatures over several emissivities allows for constraints on emissivity and Bond albedo based on past data. Upper and lower bounds on Triton’s temperature and albedo of 37.9  ±  0.5 to 46.1  ±  0.7 K for temperature and 0.67−0.86 for albedo are determined from the IRIS data and …

Arkansas Corn And Grain Sorghum Research Studies 2024, Jason Kelley, Travis Faske

Arkansas Agricultural Experiment Station Research Series

The 2024 edition of the Arkansas Corn and Grain Sorghum Research Studies Series includes research results on topics pertaining to corn and grain sorghum production, including weed, disease, nematode, and insect management; economics; irrigation; agronomics; soil fertility; drone use; and research verification program results.

Our objective is to capture and broadly distribute the results of research projects funded by the Arkansas Corn and Grain Sorghum Board. The intended audience includes producers and their advisors, current investigators, and future researchers. The Series serves as a citable archive of research results.

The reports inform and guide our long-term recommendations, but should not …

Classifying Detrital Zircon U-Pb Age Distributions Using Automated Machine Learning, Jack W. Fekete, Glenn R. Sharman, Xiao Huang

Geosciences Faculty Publications and Presentations

The prodigious use of detrital zircon U-Pb geochronology for provenance studies in recent decades has led many researchers to amass extensive datasets (>100,000 dates). When displayed as age distributions, individual samples are traditionally compared using visual inspection and statistical methods, which can become timeconsuming and challenging when using large datasets. We propose that machine learning (ML) can more efficiently classify a sample by its source using detrital zircon U-Pb age distributions. Specifically, we hypothesize that automated machine learning (AutoML), which optimizes algorithm selection and hyperparameters, will outperform an unoptimized Random Forest (RF) classifier and the cross-correlation coefficient (R-2), a …

The Effect Of Uniaxial Compressive And Tensile Strains On The Structural, Dynamical, Electronic, And Optical Properties Of Zrcl2 Monolayer: Ab-Initio Calculations, Hind Alqurashi, Bothina Hamad, M. O. Manasreh

Physics Faculty Publications and Presentations

Recently, the two-dimensional material zirconium dihalide (ZrCl2) has received a significant attention for prospective device applications due to its unique electronic, mechanical, magnetic, and topological properties. This work reports theoretical predictions for the structural, dynamical, electronic, and optical properties of ZrCl2 under uniaxial compressive and tensile strains using density functional theory (DFT). The band gap structures were found to be highly sensitive to the uniaxial compressive and tensile strains of ZrCl2 monolayer (ML). The unstrained ZrCl2 ML has a semiconducting behavior with an indirect band gap of 1.19 eV. Under the uniaxial compressive tensile stress (epsilon x) of- 6%,- 4%,- …

Impacts Of Water Conservation Cost-Share Programs On Irrigated Rice Extensification And Intensification In Critical Groundwater Areas Of The Arkansas Delta, Joshua Michael Blackstock, Oladipo S. Obembe, Aaron Shew, Michele Reba, Joseph M. Massey, Phillip R. Owens, Christopher D. Delhom

Geosciences Faculty Publications and Presentations

We analyzed the impact of government cost-shares for irrigation water management (IWM) conservation practices on the expansion and cropping intensity of rice (Oryza spp.) acreage in the Arkansas Delta. We used the correlated random effects probit model to control for unobserved effects and exploit changes in government spending over time. We found that government-spending impacts are heterogeneous across Critical Groundwater Area (CGA) designations. A 10% increase in government spending in counties fully within CGAs would reduce the propensity to expand rice acreage by 1.2 percentage points and the likelihood to rotate rice after rice by 7.6 percentage points. In counties …

Intercellular Stress Generation During 1d Collective Migration Of Cancer Cells, Logan Waddle

Physics Undergraduate Honors Theses

Physical forces drive many cellular processes such as migration and proliferation. A metastatic tumor will have invasive strands/chains that extend from the main tumor. These chains of cells collectively migrate away from the main tumor to establish new colonies elsewhere in the body. However, how physical forces drive this collective invasion and the required energy for this process is not well understood. Invasion of a metastatic tumor to surrounding tissues leads to a majority of cancer-associated death and is often a collective effort among cells, it is therefore important to fully understand the process behind collective cancer invasion.

The goal …

Survey Of A Paired Gravity Anomaly Within The Benton Uplift Of The Ouachita Mountains, Isaac B. Prud'homme

Physics Undergraduate Honors Theses

The Benton Uplift west of Hot Springs, AR is underlain by a paired gravity anomaly on the order of 80 mGal. Nicholas & Rozendal (1975) have theorized that the gravitational low is caused by a “downwarp of the crust into the mantle”1 however the cause of the gravitational high is still debated. This paired gravity anomaly within the Benton Uplift follows the Ouachita orogenic belt southwest for 500 miles where it meets the Marathon uplift in southwest Texas. We conducted a gravity survey along a 108-km-long transect east of two previous gravity surveys.2,3 We corrected the data from this transect …

Assessing Biochar And Industrial Hemp To Remediate Heavy-Metal-Contaminated Soil, Sophie W. Sward

Crop, Soil and Environmental Sciences Undergraduate Honors Theses

Environmental degradation is becoming increasingly prevalent as global industrialization runs rampant. Anthropogenic activity, such as mining, deposits inorganic pollutants into the environment, leading to potential soil and water contamination. Traditional, engineering-based remediation and containment procedures alter soil structure and aggregate stability and affect the biological function of the area impacted by mining activities. Phytoremediation is a more energy-efficient, and therefore cost-effective, method of environmental restoration. Phytoremediation, on its own, works in soils that are less contaminated so that the selected plant can actually grow. Soil amendments, such as biochar, can be added to improve remediation potential in more contaminated soils. …

Quantifying The Role Of Climate On Tropical River Flow (Guarare River) Along The Panama Pacific Coast, 1981 – 2020., Jessie Abrego - Bonilla

Graduate Theses and Dissertations

The Guarare River watershed on the Azuero Peninsula of Panama, part of the Panamanian Dry Arc, experiences significant hydrological variability driven by seasonal changes, elevation, and the El Niño/Southern Oscillation (ENSO). This study investigates precipitation and river discharge patterns over 40 years (1981 – 2020), showing that elevation influences rainfall distribution, with higher-altitude areas receiving more precipitation during the Neutral and El Niño phases. In contrast, La Niña phases bring increased rainfall to lower-elevation areas, such as Pocrí. Seasonal ENSO effects reveal that El Niño phases reduce dry-season precipitation, lowering river discharge. La Niña events sustain high wet-season rainfall, peaking …

Latent Variable Dyadic Regression Models For Predicting Over/Under Bets In Sports Betting, Alexcia Trejo

Graduate Theses and Dissertations

This thesis explores the use of latent factor models to uncover hidden structures in pair wise outcomes derived from Over/Under betting markets in sports betting. Specifically, we implement and evaluate the Eigen model, a latent space model that represents dyadic data using node-specific vectors whose inner product govern edge probabilities. By modeling relationships between teams as adjacency matrices of binary outcomes, we investigate the extent to which the Eigen model captures both homophily, the tendency of similar teams to yield consistent betting results, and stochastic equivalence, where different teams exhibit indistinguishable patterns of Over/Under outcomes. A Bayesian formulation of the …

Mathematics-Ai Based Phylogenetic Analysis Of Influenza Virus Mutation Data, Emilee Walden

Mathematical Sciences Undergraduate Honors Theses

The influenza virus is one of the most common viral infections each year and can mutate rapidly. Viral mutations pose significant threats to public health by increasing infectivity and strengthening vaccine resistance. To track these evolving patterns, agencies like the CDC annually evaluate thousands of virus strains to understand viral mutagenesis and evolution in depth. Therefore, a computational method for analyzing high-dimensional, noisy virus data could aid in the rapid identification of antigens essential for an effective influenza vaccine for the upcoming season. Through the integration of genomic analysis, clustering, and dimensionality reduction methods, this study specifically aims to develop …

Some Interpolation Problems In The Projective Plane, Lilah Estes

Mathematical Sciences Undergraduate Honors Theses

Given some set of r general points in the projective plane, we want to better understand: what is the smallest degree of any polynomial passing through the points m times? How many linearly independent equations of this degree pass through the points m times? The investigation of these questions, particularly for the case of m=3 and r< 16, motivates the development of several results. We translate Terracini's inductive argument, a tool for evaluating the expectedness of certain sets of double points, into a version which can be used for triple points, and prove that the argument holds. We compute the minimal graded free resolutions for the ideals corresponding to up to 15 points, for m up to 6, and we conjecture a connection between the expectedness of these ideals and what their resolutions look like. Further, we prove that this conjecture holds when m=1, and we either fully or partially prove that these ideals are …

Usage Of Natural Language Processing And Deep-Learning Techniques On Thematic Apperception Tests To Predict Big Five Personality Traits, Blayten Jones

Electrical Engineering and Computer Science Undergraduate Honors Theses

The usage of personality as a method of behavioral prediction and outcomes of success has grown considerably over the last few decades. This project explores predicting user personality profiles via the Big Five personality index through the integration of advanced natural language processing techniques as well as neural networks. Using a dataset provided by Dr. James W. Pennebaker, participants analyze an image—formally referred to as a thematic apperception test—and write a thorough paragraph describing the details. This free-form text, along with their personality test results, is captured in a structured dataset. Many deep-learning and machine learning models have been used …

A Novel Approach To Attention-Based Models In Image Completion: Weighted Spatial-Attention Using Radial Distance, Tyler D. Kuper

Electrical Engineering and Computer Science Undergraduate Honors Theses

Humans infer missing visual information by focusing on spatial relationships in the context of their surroundings. Machine learning aims to replicate this skill through image completion, a fundamental task in current computer vision research. While advances in self-attention layers have recently enhanced generative machine learning models for text, these mechanisms still currently lack the capability to handle sparse image completion efficiently. We introduce a distance-based attention mechanism that uses radial-based weights to efficiently reconstruct an image. We compare this attention mechanism with self-attention and a fully connected network on an image completion task using the MNIST dataset. Our results show …

Fairness In Recommender Systems: Balancing Bias In Academic Paper Selection, Zachary Bergin

Electrical Engineering and Computer Science Undergraduate Honors Theses

Bias in academic paper selection remains a consistent issue, even within processes designed to promote fairness, such as double-blind peer review, bias stays persistent. In this paper we investigate demographic bias while particularly focusing on racial bias in the process of selecting academic papers and explore the impact of fairness aware recommender systems on the demographic parity. To build an effective system our focus is on the Special Interest Group on Computer Human Interaction (SIGCHI) a pillar in the community, we develop a neural network-based recommender system that uses real demographic data collected by other systems withing the context of …

Leveraging P4 Programmable Switches For Resilient Operation And Design Of Phasor Measurement Unit Networks, Eva Casto

Electrical Engineering and Computer Science Undergraduate Honors Theses

The power grid utilizes a device called the phasor measurement unit (PMU), allowing power system administrators to remotely monitor and manage the state of the grid in Wide Area Monitoring Systems (WAMS). The advantages of PMUs – such as fine-grained, time-synchronized measurements and efficient, decentralized monitoring – are what make them key devices in the power grid. However, PMU technology also comes with new threats of the digital age, like malfunctions and cyberattacks, which can result in missing and faulty measurements that compromise power grid observability. P4 programmable networks can be used to detect faulty PMU data in a decentralized, …

Understanding University-Level Food Recovery Programs’ Approaches To Minimizing Food Waste While Combating Food Insecurity In The United States, Ella F. Flesher

Crop, Soil and Environmental Sciences Undergraduate Honors Theses

Food is wasted every day at each level of the food system from production to consumption. This surplus has significant negative impacts on the environment after food is sent to the landfill and allowed to rot. However, some surplus food can be repurposed and diverted from the landfill. This study served as an initial investigation to explore multiple food waste diversion programs and analyze their structure, best practices, and methods to divert leftover food. This study specifically aimed to compare and contrast food recovery programs by interviewing participants from three food recovery programs from different university institutions. With the key …

Land8fire: A Complete Study On Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, And Extensive Benchmarking, Anh Tran

Data Science Undergraduate Honors Theses

Early and accurate wildfire detection is critical for minimizing environmental damage and ensuring a timely response. However, existing satellite-based wildfire datasets suffer from limitations such as coarse ground truth, poor spectral coverage, and class imbalance, which hinder progress in developing robust segmentation models. In this paper, we introduce Land8Fire, a new large-scale wildfire segmentation dataset composed of over 20,000 multispectral image patches derived from Landsat 8 and manually annotated for high-quality fire masks. Building on the ActiveFire dataset, Land8Fire improves ground truth reliability and offers predefined splits for consistent benchmarking. We evaluate a range of state-of-the-art convolutional and transformer-based models, …

Electrochemically Precipitated Struvite Effects On Extractable Soil Nutrients, Owen Collins

Crop, Soil and Environmental Sciences Undergraduate Honors Theses

Struvite recovery from wastewater streams may provide an alternative to traditional fertilizer-phosphorus (P) sources, while also providing the benefit of reducing nitrogen and P loads in wastewater effluent and reducing effects of eutrophication in surface waters. Limited research has been conducted to assess the effects of struvite fertilization on concentrations of soil micronutrients and potassium (K) compared to other traditional fertilizer-P sources in historically agricultural soils. The objective of this study was to assess the effects of fertilizer-P source (i.e., synthetically produced electrochemically precipitated struvite [ECST], chemically precipitated struvite [CPST], monoammonium phosphate [MAP], diammonium phosphate [DAP], triple superphosphate [TSP], rock …

How Graphene’S Berry Dipole Is Determined By Its Atomic Registry With A Substrate, Hannah K. Isbell

Physics Undergraduate Honors Theses

Since graphene’s discovery in 2004, it has been established as one of the most important materials in recent condensed matter history. Understanding its properties and potential uses has been the subject of intense scientific research around the world. Graphene’s unique physical properties allow it to serve a wide variety of purposes, from its everyday role in the formation of graphite in pencils to its more cutting-edge scientific functions in electronics and energy storage. Gaining a better understanding of graphene’s rich quantum landscape through geometric and topological phenomena, like Berry curvature and Berry dipole, could have significant implications for both theoretical …

Enhancing The University Of Arkansas' Operations Through Data Science, Aura L. Pinto-Avelar

Data Science Undergraduate Honors Theses

As universities navigate financial constraints and resource allocation challenges, data driven financial analysis has become increasingly important. Universities employ various methods to assess financial efficiency, predict future expenditures, and optimize student credit hour distribution. However, the approaches to financial analysis vary widely, with some institutions leveraging advanced predictive modeling and business intelligence tools, while others rely on traditional budgeting techniques and manual forecasting.

This thesis examines how the University of Arkansas' (“Uark”) financial analysis methods compare to those of other institutions and alternative data-driven approaches. Using four years of financial and student credit hour data, this study evaluates cost trends …

Lucky Charms: Leveraging Consumer Data Analysis For Strategic Decision-Making At General Mills, Katherine Page Thomas

Data Science Undergraduate Honors Theses

This undergraduate thesis examines the application of data analytics in sales strategy and consumer insights from the perspective of a summer internship at General Mills. It gives an overview of the company’s background, industry position, and intern’s role within the sales organization. It outlines the personal learning objectives established before the internship and how they evolved through practical experience.

A central focus of the summer internship was the analysis of consumer demographics within the cereal category to enhance sales performance and optimize retail execution. Using the Data Science Analytics Process as a framework, this thesis details the problem statements, methodologies, …

Optimizing Fire Station Placement In Sugar Land, Tx: A Socioeconomic Risk-Based Approach, Alicia Gallemore

Data Science Undergraduate Honors Theses

Fire station placement has a critical role in emergency response efficiency and community safety. Traditional optimization models focus on mainly the minimization of response times and the maximization of coverage. However, this approach may overlook potential socioeconomic disparities that can influence emergency demand. This study seeks to expand upon the existing project of zoning a fire station in Sugar Land, TX, by integrating spatial road network analysis and publicly available census data—including population density, median household income, and age-based vulnerability—into a Maximal Coverage Location Problem (MCLP) framework. Using a road network-based travel time with realistic constraints, the goal is to …

Attribute Based Assortment Using Machine-Learning, Hector Negron

Data Science Undergraduate Honors Theses

Retail success is influenced by a store's demographic and environmental context, both of which impact item-level sales performance. This study applies machine learning techniques to optimize item allocation based on club attributes at Sam’s Club locations. By analyzing store- specific factors such as proximity to universities, income levels, and regional preferences, the research identifies patterns that contribute to product demand. The results offer insights into how clubs can enhance inventory decisions, improving sales outcomes while reducing inefficiencies. This study reinforces the value of data-driven retail strategies and presents a practical framework for implementing predictive models in a real-world business context.

Shortage To Surge - Studying The Post-Covid-19 Guitar Retail Market, Jed H. Kim

Data Science Undergraduate Honors Theses

The COVID-19 pandemic was one of the most catalyzing events of the 21st century, leading to supply chain disruptions, lifestyle changes, and a massive shift towards digital technologies. During the COVID-19 lockdown, many people had more free time, and over 16 million individuals learned to play guitar in the first 2 years of the pandemic. According to a study by Fender, 62% of these new guitar learners cited the pandemic as their primary reason for learning the instrument. However, pandemic policies and supply chain disruptions meant that many guitar retailers were unable to satisfy demand, and backorders accumulated. After the …

Computer Vision In Soccer: Yolov11 Analytics Engine For Quantifying Game Strategy, Connor S. Maurer

Data Science Undergraduate Honors Theses

Single-shot object detection capabilities significantly reduce computational overhead for real-time computer vision in sports analytics at 60 FPS. YOLO11’s lightweight CNN gives promising accuracy while meeting the low-latency demand of dynamic soccer matches. As data-driven approaches take over the sport of soccer, efficient player tracking systems become critical for informing coach’s strategies. I prototype the ETL (Extract, Transform, Load) process of data collected from a single- shot detection program and evaluate its viability for estimating player fatigue. YOLO11 detects players, the ball, and other characteristics, with the output transformed by homography to estimate the positions in the real world. These …

Nutmeg: Separating Signal From Noise In Annotator Disagreement, Jonathan W. Ivey, Susan Gauch, David Jurgens

Data Science Undergraduate Honors Theses

NLP (natural language processing) models often rely on human-labeled data for training and evaluation. Many approaches crowdsource this data from a large number of annotators with varying skills, backgrounds, and motivations, resulting in conflicting annotations. These conflicts have traditionally been resolved by aggregation methods that assume disagreements are errors. Recent work has argued that for many tasks annotators may have genuine disagreements and that variation should be treated as signal rather than noise. However, limited work has combined the two frameworks to separate signal from noise in human-labeled data. In this work, we introduce NUTMEG, a new Bayesian model that …

The 1991 California 'Miracle March': Precipitation Myth Or Miracle?, Cody D. Poulsen, Rachel E.S. Clemesha, Ian M. Howard, Alexander Gershunov, Michael D. Dettinger, Zhenhai Zhang, Rosa Luna-Nino, David W. Stahle, Ralph F. Martin

Geosciences Faculty Publications and Presentations

The term 'Miracle March' is colloquially used in California to describe an anomalously wet spring that alleviates drought conditions caused by a winter precipitation deficit. This study centers on the influential water year 1991, which is the standard reference point for this phenomenon. The spring precipitation impact on water year totals and the geographic extent of the spring precipitation anomaly during the 1991 'Miracle March' (MM91) is without equal in the observational record. This exceptional precipitation episode was initiated by a week-long sequence of extreme atmospheric rivers that started landfalling on February 27th. To this day, MM91 is routinely referenced …

Solving Real-World Optimization Problems Using Near-Term Quantum Computing With Applications In Vehicle Routing And Drone Delivery, James Bradley Holliday

Graduate Theses and Dissertations

Quantum computing (QC) stands at the cusp of revolutionizing computation, yet its near-term potential, constrained by Noisy Intermediate-Scale Quantum (NISQ) devices, remains underexplored. This dissertation investigates how hybrid quantum-classical algorithms can address combinatorial optimization challenges in logistics, focusing on vehicle routing and drone delivery—NP-hard problems with exponential solution spaces that defy classical exhaustive methods. Amidst NISQ limitations like limited qubits and high noise, we confront key challenges: encoding complex constraints, e.g., time windows, battery capacity, into quantum models, balancing quantum and classical components for scalability, and accessing scarce quantum resources. By integrating quantum annealing (QA) and the Quantum Approximate Optimization …