Physical Sciences and Mathematics Commons^™

Motivation Without Borders: Applying The Octalysis Framework To Global Faculty And Student Engagement In Ai Era, Harika Rao

Faculty and Staff Publications & Presentations

No abstract provided.

Applications Of Machine Learning In Enhancing Evaporation Estimation For Small Reservoirs: A Case Study In Semi-Arid South Texas, Syed Muhammad F Abdullah, Chu-Lin Cheng, Jude A. Benavides, Jungseok Ho, Rafael M. Almeida

School of Earth, Environmental, & Marine Sciences Faculty Publications

Small reservoirs in semi-arid regions experience substantial evaporative losses but are rarely monitored at daily scales. A multi-reservoir machine learning (ML) framework was developed to estimate daily open-water evaporation. Empirical models (Penman, Penman-Monteith, Priestley-Taylor, Bowen Ratio Energy Budget) andabenchmark combination method (Daily Lake Evaporation Model-DLEM) were compared against ML models. Predictors combined gridded meteorology (gridMET) with reservoir attributes (surface area, average depth, maximum depth, and fetch). ML models (Random Forest-RF, Decision Tree-DT, K-Nearest Neighbor-KNN, and Support Vector Regression-SVR) were trained on four reservoirs using data from 2018 to 2025. Results from ML models were further validated using both DLEM and …

Machine Learning Based Models For Simulation And Analysis Of Bulk Earth Melt System, Abin Shakya

LSU Doctoral Dissertations

Understanding the segregation of bulk Earth melt systems into metallic (core) and silicate (mantle) phases under high-pressure and high-temperature conditions is central to modeling Earth’s interior, yet relevant experimental and computational studies remain limited. This work develops a machine learning–based simulation pipeline that iteratively couples first-principles (quantum mechanical) calculations with neural network training to generate high-fidelity force fields. Using major-element Fe–Mg–Si–O melt systems, with and without H and N, as testbeds, we demonstrate that this framework enables large-scale molecular dynamics simulations at near first-principles accuracy. We further introduce a sequence of phase identification methods, progressing from statistical binning of elemental …

Modelling The Temporal Dynamics Of Microsporidia Mb Prevalence In Anopheles Mosquitoes Under Environmental Variability, Charlène N. Mfangnia, Matteo Pedercini, Henri E. Tonnang, Jeremy K. Herren

All Peer-Reviewed Publications

Releasing Microsporidia MB-positive mosquitoes is being investigated as a promising component of Integrated Vector Management (IVM) strategies for malaria control. In this study, we developed a System Dynamics model to replicate the observed interactions between humans, mosquitoes, the Plasmodium parasite, and the symbiont MB. The model accounts for the impact of environmental variables - such as humidity, rainfall, and temperature - on mosquitoes, Plasmodium, and MB, using exogenous inputs derived from daily climatic observations (rainfall, relative humidity, and temperature) recorded during the mosquito field surveys. We calibrate the model using data collected from Ahero in Kenya, available for 3 years …

Two-Dimensional Inverse Scattering Solution Using Nurbs Expansion And A Deep Learning Method, Meisam Shafaee

Turkish Journal of Physics

This paper presents a novel framework for solving two-dimensional (2D) inverse scattering problems by integrating nonuniform rational B-spline (NURBS) parameterization with convolutional neural networks (CNNs). Tra ditional pixel/voxel-based deep learning methods often suffer from high dimensionality and discretization artifacts, while purely geometric approaches lack robust inversion mechanisms. To address these limitations, we propose a hybrid frame work where the relative permittivity profile of an unknown scatterer is compactly represented as a NURBS surface, parameterized by a sparse set of control points and weights. This approach reduces the problem dimensionality over 16 times. On the other hand, using NURBS expansion ensures …

Ai's Double Edged Sword: Fighting Against Synthetic Csam, Shekhinah Adra Green

Cybersecurity Undergraduate Research Showcase

The rapid advancements in generative artificial intelligence has introduced new challenges in the production and distribution of synthetic child sexual abuse material (CSAM). AI has the capabilities of creating highly realistic imagery and videos, which  raises serious legal and ethical concerns, increasing the risk of harm, exploitation, and revictimization.

This paper discusses the legal improvements needed in order to lower the change of legal loopholes, how digital forensic analyst use advanced tools to identify and investigate synthetic material, and different methods to start the reduction of synthetic CSAM.

Escaping Isolation: An Analysis Of Virtual Machine And Container Breakout Vulnerabilities, Felix Iov

Cybersecurity Undergraduate Research Showcase

Cloud computing providers rely on multi-tenant architectures to maximize resource efficiency. This infrastructure depends on virtualization, which provides isolation between clients. This comes primarily in the form of Virtual Machines (VMs) and Containers. However, “breakout attacks” or “escapes” are a critical threat where attackers bypass these isolation layers to gain unauthorized access to the host system and neighboring environments. This paper surveys virtualization escape threats and analyzes three case studies: a runc container escape (Leaky Vessels), a VMware ESXi VM escape (VSOCKPuppet), and an NVIDIA GPU container escape (NVIDIAScape). Each demonstrates different attack surfaces, including file descriptor misuse, kernel driver …

Bio-Cybersecurity: Securing The Healthcare Industry, Amanda D. Coleman

Cybersecurity Undergraduate Research Showcase

Bio-cybersecurity refers to the aspect of cybersecurity that applies to the biological sciences and the protection of digital biomedical information. Today’s healthcare industry has evolved with the enhancement of internet and biomedical technology. While hospitals and private medical providers remain compliant with the Health Information Portability and Accountability Act (HIPAA) through traditional means of securing documented patient information, the emergence of beneficial internet-based healthcare services like virtual appointments and digital patient records requires new policies and healthcare cybersecurity frameworks to protect sensitive information from unauthorized access. This paper examines the role of cybersecurity in healthcare, the vulnerabilities that exist and …

Variability On M-Dwarf Stars And Its Effect On Exoplanet Detection, Aylin Garcia Soto

Dartmouth College Ph.D Dissertations

Understanding stellar magnetic activity is crucial for exoplanet research: activity can mask signatures from exoplanet detection and characterization, and impact planetary habitability. This is particularly important for M dwarf stars, which are highly magnetically active, displaying spectroscopic and photometric signatures of flares and rotational variability. In this thesis, I will present two complementary perspectives on M dwarf magnetic activity. First, I will discuss a study of short-term variability. I made new ground-based observations of 77 M dwarfs and tested the connection between chromospheric emission and photometric variability. I find a weak positive correlation between H$\alpha$ luminosity (tracing magnetic heating) and …

Transport Phenomena Of Multiphase Flow And Slippery Microsphere Suspensions In Confined Space, Ryan Haggerty

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

Understanding multiphase flow has many important environmental and engineering applications. For example, understanding gas flow in porous media is useful for energy storage, petroleum engineering, and contaminant cleanup. Nano- and microparticle transport is vital for enhanced oil recovery techniques and environmental contamination. Despite their relevance to environmental engineering, multiphase flows, especially with concentrated particle suspensions, are still a complex and developing topic. Additionally, hydrophobic surfaces are a relatively new area of research, for while the effects of hydrophobicity are observable, the mechanisms may occur on several scales. Hydrophobicity leads to a property called “slip,” which contradicts the traditional treatment of …

Diffeomorphisms In Quantum Black Hole Interiors, Ian William Bornhoeft

Electronic Theses and Dissertations 2020 - Present

We begin with an introduction to canonical Loop Quantum Gravity. We then introduce a notion of residual diffeomorphism covariance in quantum Kantowski–Sachs (KS), describing the interior of a Schwarzschild black hole. We solve for the family of Hamiltonian constraint operators satisfying an associated covariance condition, as well as parity invariance, preservation of the Bohr Hilbert space of Loop Quantum KS and a correct (naive) classical limit. We further explore imposing minimality of the number of momentum shift terms, and compare the solution with other Hamiltonian constraints proposed for Loop Quantum KS in the literature. In particular, we discuss a lapse …

From Stability To Spintronics: A Comprehensive High-Throughput Investigation Of 2d Mf4 Lieb Lattices, Uğur Yorulmaz

Turkish Journal of Physics

The research on two-dimensional (2D) materials which have intrinsic magnetism and exotic electronic struc tures is an important quest in condensed matter physics. The Lieb lattice offers a unique platform for studying strongly correlated electron phenomena. In this work, we systematically investigated the structural, dynamical, mechanical, electronic, and magnetic properties of monolayer transition metal tetrafluorides (MF4, where M = Ag, Cd, Cr, Cu, Fe, Hf, Mn, Mo, Nb, Ni, Pd, Pt, Rh, Ru, Sc, Ta, Ti, V, W, and Y) arranged in a 2D Lieb lattice, using first-principles calculations based on density functional theory (DFT) with Hubbard (U) corrections. Our …

Analytical Solution Of The Green’S Functions For The Poisson Problem Outside A Disk, Mohammad Khorrami

Turkish Journal of Physics

The Poisson problem in the space outside of a disk is investigated. Specifically, the Green’s function corresponding to the problem is calculated. The Green’s function equation is separable in terms of (oblate) ellipsoidal coordinates. Therefore, these coordinates are used to obtain a series expression for the Green’s function. This is done for both the Dirichlet and Neumann problems. The result is used to calculate the electric potential for some examples, with specific boundary conditions on a disk.

Butte Priority Soils Operable Unit (Bpsou) 2026 Monthly Progress Report. Consent Decree For The Butte Priority Soils Operable Unit. Civil Action No. Cv 89-039-Bu-Seh, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Transport Of Quantum Walks In Electric Fields, Yousef Mohammad Yousef Salah

Thesis/ Dissertation Defenses

This thesis presents an analysis of transport in one-dimensional discrete-time quantum walks (DTQWs) on the Hilbert space . Quantum walks serve as fundamental models of coherent quantum transport and exhibit ballistic spreading driven by superposition and interference. The primary focus of this work is the review and derivation of sharp maximal velocity bounds for several classes of quantum walk step operators, including the shift-coin walk, the split-step walk, and models with constant as well as position-dependent coin operators. We establish general a priori bounds that remain valid beyond the translation-invariant regime. For homogeneous models, Fourier and spectral analysis yield explicit …

A Comprehensive Survey Of Agentic Ai: Design Principles, Security Risks, And Ethical Consideration, Md Shaba Sayeed

ATU Scholars Symposium

In the past several years, the world has managed to transition away from simple automation to independent AI systems. Agentic AI is an agent that can work independently, carrying out all essential plans and implementations without any kind of supervision from a human being. This review has tried to demonstrate the transformative impact that Agentic AI brings to contemporary models of intelligence by means of synthesis of perception, reasoning, and goal. We utilized the phrases Agentic AI, autonomous AI, multi agent systems as keywords in Google Scholar, ScienceDirect, arXiv, and other digital libraries. We have used these 38 main papers …

Dermal: A Multi-Input Deep Learning Model For Improving Access To Dermatological Screening, Aubreye Freeman

ATU Scholars Symposium

According to the World Health Organization's press release on December 12, 2024, global healthcare spending is dropping significantly, leaving a large percentage of the world without proper healthcare. In an attempt to alleviate this problem, with respect to the field of dermatology, we created a deep learning model, Dermatology Enhanced by Recognition and Machine Aided Learning (DERMAL), to assist in diagnosing skin conditions. DERMAL was trained on a portion of the Google and Stanford Medicine's SCIN dataset, which has more than 10,000 images of various skin conditions. The 9 most common skin conditions of the dataset were selected as the …

Computational Approaches To Laser Alignment And Phase Correction, Joseph E. Temple

ATU Scholars Symposium

The goal of our work is to automate the tedious and delicate process of optical alignment (rotating mirrors, shifting lenses by millimeters, and iterating endlessly) in the context of generating vortex beams from a Gaussian laser beam. We create vortex beams by illuminating a digital hologram displayed on a spatial light modulator (SLM). Two main issues arise: misalignment and phase imperfections in the input beam. If the beam does not pass directly through the center of the SLM, or if it deviates from an ideal Gaussian profile, the resulting vortex beam becomes distorted.

Last semester, we developed two methods to …

Creating A Floating Volumetric Display, Joy Skaggs, Hope Skinner

ATU Scholars Symposium

The public has long been interested in futuristic technology, especially ‘holograms’ and their possibilities, evidenced by pop culture icons such as Iron Man and the popularity of the Sci-Fi Genre. The popular term ‘hologram’ actually describes the phenomenon of a volumetric display, where light is directed to form 3D forms in the air. Attempts to create these volumetric displays began as early as 1988 with creators like Gregg Favelora and Alan Sullivan. As technology improved, so too did the ability to make the futuristic ‘hologram’ a reality.

Another common form of these interactive holograms is the floating display, which may …

Data Structures & Algorithms Prep Hub: A Technical Interview Preparation Tracker, Andrew J. Pinkerton

ATU Scholars Symposium

This project examined how consistent practice with data structures and algorithms (DSA) can improve problem solving skills and preparation for software engineering technical interviews. The goal was to strengthen foundational algorithmic knowledge while developing a structured practice routine that could continue beyond the semester. From week 3 through week 12, four LeetCode style problems were completed each week, focusing on core interview topics including string manipulation, arrays, linked lists, hash tables, sets, and dynamic programming. Each problem required implementing a solution, identifying edge cases, and evaluating time and space complexity to determine the most efficient approach. Through this process, common …

Investigating The Effect Of Kcl Stress In Raphanus Sativus, Mason P. Oelke

ATU Scholars Symposium

Presented by Stephanie Nelms at the Arkansas INBRE Conference on November 7-8, 2025.

Presented by Mason Oelke at Arkansas Tech Scholar's Symposium on April 9th, 2026.

Potassium is an essential macronutrient for plant growth and development, yet excessive potassium fertilization can induce salt stress with detrimental consequences for crop productivity and nutritional quality. Despite its agricultural relevance, potassium chloride induced stress remains significantly understudied compared to classical sodium-based salinity. This thesis investigates the physiological, biochemical, and molecular responses of Raphanus sativus to KCl stress using an integrated approach that combines germination assays, mineral profiling, and gene expression analysis.

Radish seeds …

Interacting Dark Energy Models And The Cosmic Coincidence Problem, Gunner W. Hodges

ATU Scholars Symposium

Cosmological models are a tool used to understand the mysterious nature of dark energy and dark matter by predicting how these dark sector components have shaped the expansion of the universe. The cosmic coincidence problem stems from the present day densities of dark energy and matter sharing very similar magnitudes when previously we expect a much larger difference in orders of magnitude. The current best-fit model, ΛCDM , assumes dark energy is non-interacting; however, assuming that dark energy instead does interact could serve to alleviate this cosmic tension. We define an interaction where we relate the density of dark energy …

Prediction Of Future Mushroom Fruiting Success And Dispersion Based On Models Of Changing Surface Air Temperature In Oaxaca, Mexico, Frida Martinez

ATU Scholars Symposium

Mushrooms serve various purposes, including medicinal, culinary, and cultural significance for indigenous groups, as well as recreational and ecosystem services. Oaxaca, Mexico, is a hotspot for mushroom biodiversity including the following varieties: Amanita muscaria, Schizophyllum commune and Coprinellus disseminates. The purpose of this study is to determine if a changing Mean Surface Air Temperature will have an influence on these mushrooms. We obtained, analyzed, and compared historical and future climate data from the World Bank Group Climate Change Knowledge Portal to identify if any of the mushroom species would be impacted. The study revealed a general trend of warming Mean …

Grassland Bird Community Responses To Fire Management, Mason H. Dillard

ATU Scholars Symposium

Vegetation structure plays a major role in determining avian community makeup. Controlled burn frequency changes vegetation structure and consequently the makeup of avian communities. To examine this relationship, I surveyed breeding birds in grasslands and savannahs of the Ozark Mountains, Arkansas River Valley, and Northwest Arkansas. I created models to outline the relationship between these three factors (avian community, fire management, and vegetation structure.) I also examined surrounding land cover and patch size as potential variables that may impact avian community structures.

I will outline the role fire management plays in determining the makeup of breeding bird communities in grasslands, …

Proactive Mental Health Assistance Via Agentic Llm Chatbots With Retrieval-Augmented Generation, Shaira Wajiha

ATU Scholars Symposium

Mental health challenges such as anxiety and depression are widespread, yet often remain unaddressed due to stigma, financial barriers, limited access to professionals, or personal reluctance. To address this gap, we present an accessible AI-powered chatbot that provides preliminary conversational support with empathetic, context-aware responses. The chatbot is trained and evaluated on two publicly available counseling conversation datasets from Huggingface, enabling it to learn and maintain therapeutic dialogue patterns. We compare three advanced LLMs, such as Llama3.1, Mistral 7b, and Qwen3, evaluating them on relevance, empathy, conciseness, and contextual understanding, and all models demonstrate high response quality. Incorporation of a …

Developing Machine Learning Algorithms For Highly Imbalanced Neonatal Disorder Data, Ali Nawaz

Thesis/ Dissertation Defenses

Neonatal disorders such as low birth weight, very low birth weight, extremely low birth weight, preterm birth, and very preterm birth increase the likelihood of high neonatal morbidity or mortality and call for early identification. However, the rarity of occurrence of these conditions in the clinical datasets has resulted in a severe class imbalance, raising questions about the application of binary classification models to them. Therefore, this thesis proposes a sequential methodological framework for neonatal disorder detection under different assumptions related to the availability of labels. Initially, binary classification experiments are conducted to analyze the behaviour of commonly used classification …

Tavern Table: A Peer-To-Peer Online Platform For Collaborative Dungeons & Dragons Campaigns, Dustin Needham, Kristen Avery, Aidan Martin, Seth Poindexter

ATU Scholars Symposium

Designed to address the lack of free, accessible, and feature-complete online Dungeons & Dragons gameplay platforms, Tavern Table gives users the ability to create or participate in Dungeons & Dragons campaigns online via peer-to-peer multiplayer. This platform is targeted primarily for two sets of users: Dungeon Masters (the game masters), who will be creating and hosting campaigns for players to participate in, and the players participating in said campaigns. We chose the Unity Real-Time Development Platform to develop Tavern Table as it was a free and effective platform that supported 2D game development as well as peer-to-peer multiplayer. To create …

Simple D&D: A Web-Based Character Creation System For Dungeons & Dragons, Brennan M. Bondio, Dalton N. Tate, Landon F. Boyd

ATU Scholars Symposium

Dungeons & Dragons, published by Wizards of the Coast, is a widely played tabletop role-playing game that requires players to create detailed characters governed by structured rule systems. Character creation involves managing interdependent attributes, calculations, and constraints that can be difficult for new players and time-consuming even for experienced participants. These complexities create a barrier to entry and reduce efficiency during gameplay preparation.

This project addresses that challenge through the development of Simple D&D, a web-based character creation system designed to streamline and automate rule-driven character configuration. The system aims to reduce manual calculation errors and setup time while maintaining …

Automatic Labeling Of Real-World Pmu Data: A Weakly Supervised Learning Approach, Yunchuan Liu

Research Days

This paper presents a weakly supervised learning framework for real-world event identification in transmission networks using phasor measurement unit (PMU) data. The growing integration of renewable energy sources has introduced greater variability in grid conditions, intensifying the need for accurate event detection. Although high-resolution PMU measurements enable event identification to be formulated as a classification problem, traditional supervised learning approaches are hindered by the scarcity of labeled data, and acquiring large-scale, high-quality labeled PMU datasets remains prohibitively expensive. To overcome this challenge, we propose an automated PMU data-labeling method that combines domain knowledge with machine learning techniques through the use …

Assessing The Effect Of A Human-Made Reservoir On The Mussel Community In Caney Creek, Trakker Estes

ATU Scholars Symposium

Freshwater mussels are one of the most threatened taxonomic groups globally, with less than 32% of North American taxa considered secure. In Arkansas, over half of the state’s 85 species are endangered, threatened, or of special concern. Small impoundments may contribute to these declines through reduced hydrological connectivity and the resulting decline of fish community movement. This study examines the effects of Tri-County Lake, a small impoundment of Caney Creek in southern Arkansas, on mussel abundance and diversity. In total, eight sites were surveyed upstream, within, and downstream of the impoundment across various habitat types. A timed-search approach utilizing scuba …