Physical Sciences and Mathematics Commons^™

Symbology Detection And Numerical Recognition For T-38 Heads-Up Display Recordings, Ben T. Hepner

Theses and Dissertations

The extraction of symbology and numerical data from the T-38 Heads-Up Display (HUD) enhances post-flight analysis and supports real-time decision-making. This research develops a deep learning pipeline using YOLO-based object detection and Optical Character Recognition (OCR) to analyze HUD video data. Model evaluations showed mAP0.5:0.95 ranging from 0.422 (YOLOv11m, hard test set) to 0.696 (YOLOv8m, medium test set), demonstrating robust symbology detection. Numeric detection performed well (mAP_0.5:0.95 = 0.764), but OCR struggled with glare and resolution limitations, achieving a recognition accuracy of 17.35%. These results validate deep learning for HUD data extraction but highlight the need for improved robustness …

Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph

Theses and Dissertations

Artificial intelligence (AI) grows ever-more important in warfighting. Emerging technologies allow for the use of AI to control aircraft and weapons systems. This research investigates the application of reinforcement learning (RL) through the Proximal Policy Optimization (PPO) algorithm to a two-versus-two (2v2) beyond-visual-range (BVR) air combat maneuvering problem (ACMP). Implemented in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), the methodology frames the engagement as a Markov decision process, wherein an autonomous RL agent learns continuous control decisions—throttle, pitch, roll, and yaw—under a cooperative communication scheme. A multi-phase curriculum-learning approach facilitates the progressive acquisition of flight stability, weapon deployment, …

Machine Learning Techniques To Predict Solar Particle Events And Radiation Of Aircrew, Haley Traub

Theses and Dissertations

Solar Particle Events (SPEs) are high-energy phenomena from the Sun that pose risks to technology, human health, and Air Force operations. Accurate prediction of SPEs exceeding 100 MeV is crucial for mitigating these risks. This thesis explores using Bayesian statistical models to predict such events, integrating prior knowledge from solar physics with the ability to update predictions based on new data. The research uses a dataset spanning three solar cycles (21–23) and incorporates attributes like flare fluence, peak flux, latitude, longitude, and class. Four Bayesian models (PyMC, Bnlearn, and two Dredge models) were compared to machine learning models. The Bayesian …

Cloud One Migration Duration And Its Drivers, Grayson T. Hall

Theses and Dissertations

As modern warfare evolves with rapid technological advancements, cloud computing plays a critical role in managing the vast amounts of data required for real-time decision making, as well as enabling seamless organizational access to mission-critical programs and information from around the globe. Recognizing its importance, the Department of Defense (DoD) identified cloud computing as essential for maintaining the military’s technological edge. However, despite cloud computing’s strategic significance, the DoD faces challenges in successfully implementing department-wide cloud computing. In contrast, the Air Force’s cloud computing environment, Cloud One, is fully operational and has already integrated over 145 systems into its platform. …

Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson

Theses and Dissertations

Quantum communication through photons relies on photonic storage to preserve quantum states. However, when photons interact with matter, quantum information becomes distorted. A recently developed semi-classical analytical model predicts output light pulses from an electromagnetically induced transparency (EIT) system. Using the predictions, the model known as the Coherent Atomic Transfer (CAT) Function, is capable of predicting the stored pulse or reconstructing the original pulse. Using numerical convolution and deconvolution with the CAT function as an analog of the point spread function of Fourier optics can provide insights on the effects of EIT storage on the retrieved pulse. Blind deconvolution is …

Isotopic Analysis Of Lithium Hydroxide Monohydrate Using Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (Libris) And Machine Learning, Madison R. Moran

Theses and Dissertations

High-resolution Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (LIBRIS) is implemented to record the 15.8 pm Li I 670.8 nm isotopic shift in LiOH · H₂O samples of varying 6, 7Li abundance. A simple univariate linear regression demonstrates an acquired isotopic shift of 13.813 ± 1.21 pm in samples varying from 3 to 95 ⁶Li at%. Supervised machine learning regressions are trained on self-reversal wavelength locations in order to quantify ⁶Li abundance. A stacked ensemble using two base learners yields the superlative characterization of 6Li abundance with RMSE of 5.66 at% and detection limit of 18.8 at%. Using …

Transfer Efficiencies Of Surface-To-Surface Transport Of Micron-Sized Actinide Surrogate Particles, Austin R. Powell

Theses and Dissertations

Particle effluent of varying sizes is released during routine activities within laboratory environments and these particles can come in contact with a wide range of surfaces. Particles of micron size or smaller can be especially pervasive and can transfer between multiple subsequent surfaces, leading to the progressive contamination of a laboratory. Understanding the transport dynamics of micron sized particles will help inform facility personnel of the possibility of contamination by these potentially hazardous materials of interest. In this research, the transfer efficiency of micron sized surrogate actinide particles (Europium-doped Gadolinium Oxysulfide (EGOS)) is measured for multiple materials, between two particle …

Reflected Laser Light As A Diagnostic Insight For Femtosecond Laser-Plasma Coupling, David S. Stiles

Theses and Dissertations

High-energy laser technology is critical for military, scientific, and industrial applications, but traditional mixed-radiation facilities are hindered by cost, scheduling constraints, and lack of portability. A high-repetition-rate, cost-effective alternative is needed to meet evolving application needs and timelines. This study investigates changes in reflected laser intensity to diagnose coupling efficiency and optimize energetic particle generation in ultraintense laser-target interactions. Using an ultraintense 35 femtosecond, 9 - 12 mJ laser and a deuterated water target, reflected laser light was captured via high-resolution imaging, while energetic electron and X-ray data were collected using a custom spectrometer and ion chamber, respectively. Statistical analysis …

Grassmannian Codes From Stratified Frames, William J. Brinkley

Theses and Dissertations

An equichordal tight fusion frame (ECTFF) is a finite sequence of equi-dimensional subspaces of a Euclidean space that achieves equality in Conway, Hardin and Sloane's simplex bound. Every ECTFF is an optimal Grassmannian code with respect to the chordal distance. We introduce a method for constructing an ECTFF from any finite sequence of unit norm tight frames that happen to be stratified in a certain sense. We moreover show how to construct stratified unit norm tight frames from a difference family for a finite abelian group, as well as from a suitable combination of a resolvable balanced incomplete block design …

Implications Of Magnetic Evolution On Coronal Loop Thermal Variation Prior To Solar Flares, Kara L. Kniezewski

Theses and Dissertations

Solar flares are intense bursts of electromagnetic radiation, which occur due to a rapid destabilization and reconnection of the magnetic field. While flares are a magnetic phenomena, very little attention has been paid to thermal conditions in the corona prior to flare onset. This study serves as a follow-on to Kniezewski et al., 2024, where the EUV emission from coronal loops was observed to vary substantially and without any coherence between channels before 53 off-limb flares. These variations suggest multiple mechanisms within the coronal magnetic field are responsible for heating fluctuations. Here, the 3D magnetic field is modeled using a …

Investigation Of Neutron Inelastic Scatter Cross Sections On 16O, Molly A. Wakeling

Theses and Dissertations

Nuclear data underpin a number of applications across nuclear reactor design, medicine, astrophysics, nonproliferation, national security, and other related fields. However, data are conflicting or missing across the spectrum of isotopes and reactions, and theoretical calculations can only go so far to predict nuclear properties. A key reaction with incomplete data is neutron inelastic scatter on ¹⁶O, which reduces neutron energies and produces high-energy gamma rays that are often not taken into account in simulations of nuclear reactors, nuclear weapon detonations, nuclear fusion, and other areas. To fill these gaps, the Gamma-Energy Neutron-Energy Spectrometer for Inelastic Scattering, or GENESIS, …

Assessing The Feasibility Of Managed Aquifer Recharge For The United States Air Force, Daniel Hendrix

Theses and Dissertations

Water stress is becoming an increasing global issue, with 4 billion people (50% of the world’s population) experiencing water stress at least one month per year. By 2050, 60% of the world’s population and $70 trillion USD in global gross domestic product will be affected. This research analyzes 78 CONUS USAF installations to determine location-specific water stress and feasible Managed Aquifer Recharge (MAR) solutions. Although thousands of MAR projects have been implemented globally, active-duty USAF installations have yet to contribute to solving this growing issue. Important factors such as required subsurface conditions, physical limitations, design, cost, and regulatory constraints are …

Event-Based Camera Simulation And Neural Network Processing For Autonomous Aerial Refueling, Stephanie C. Hanson

Theses and Dissertations

Event-based cameras excel in dynamic environments, and do not face challenges like washout and motion blur, like a frame-based camera. This work describes the process used to collect the first EBS data collect for use in AAR, and develops an event simulator to generate synthetic training data for evaluating CNN architectures on asynchronous data. The three models compared are a traditional CNN, a YOLO-based CNN, and an asynchronous sparse CNN. The YOLO-based model achieved the best accuracy, while the sparse CNN, despite being less optimized, maintained an average IoU of 0.9. These results highlight the potential of asynchronous approaches for …

Heterogeneous Boid Swarm Performance Under Environmental And Neighbor Communication Link Variability, Jonathan C. Oxborrow

Theses and Dissertations

Artificial swarms are of growing interest in numerous fields and use cases. As their utilization increases drones and robots with different capabilities will be required to coordinate for task completion thus creating heterogeneous swarms. Swarm individuals generally communicate with all neighbors inside their sensor range generating a significant amount of message traffic. Previous research of a heterogeneous group in a non-physical environment has shown that restricting communication to only one neighbor of each different capability maintained performance. This work applies that finding to a heterogeneous boid swarm with the addition of varied environmental conditions. The swarm is comprised of three …

Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith

Theses and Dissertations

Lightning waveforms in the low frequency (LF; 30-300 kHz) and the very low frequency (VLF; 3-30 kHz) can be exploited to produce data-driven ionospheric Dregion electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF emissions …

Short-Run Subsidies And Long-Run Willingness To Pay: Learning And Anchoring In An Agricultural Experiment In Ethiopia, Solomon Balew, Erwin Bulte, Menale Kassie

All Peer-Reviewed Publications

We study how temporary provision of an agricultural innovation at zero cost affects long-run demand for that innovation. Our experimental design enables us to distinguish between an “anchoring effect” of subsidies and a “learning effect.” We document large and persistent anchoring and learning effects. For the innovation that we consider, an integrated pest management (IPM) package for Ethiopian smallholder farmers, the learning effect dominates the anchoring effect, so temporary subsidized provision promotes long-run technology diffusion.

A System Dynamics Model For Predicting African Armyworm Occurrence And Population Dynamics, Bonoukpoè Mawuko Sokame, Brian Kipkorir, Komi Mensah Agboka, Saliou Niassy, Yeneneh Belayneh, Maged Elkahky, Henri E.Z. Tonnang

All Peer-Reviewed Publications

This study develops a comprehensive system dynamics model to predict and manage African armyworm (Spodoptera exempta) outbreaks, a major threat to cereal crops across Africa. We applied system dynamics approache with its archetypes (causal loop diagram (CLD), reinforcing (R) and balancing (B)) to analyse the population dynamics of the pest. The VENSIM modelling platform (Ventana Systems Inc., DSS 8.2) was used to implement the models and carry out the simulations. The research integrates extensive data from 1980 to 2023, encompassing the African armyworm's life cycle stages, climatic variables, and intervention strategies, to simulate potential outbreak scenarios and evaluate the impacts …

Gender Biases Within Artificial Intelligence And Chatgpt: Evidence, Sources Of Biases, And Solutions, Qi Hui Jerlyn Ho, Andree Hartanto, Andrew Koh, Nadyanna M. Majeed

Research Collection School of Social Sciences

The growing adoption of Artificial Intelligence (AI) in various sectors has introduced significant benefits, but also raised concerns over biases, particularly in relation to gender. Despite AI's potential to enhance sectors like healthcare, education, and business, it often mirrors reality and its societal prejudices and can manifest itself through unequal treatment in hiring decisions, academic recommendations, or healthcare diagnostics, systematically disadvantaging women. This paper explores how AI systems and chatbots, notably ChatGPT, can perpetuate gender biases due to inherent flaws in training data, algorithms, and user feedback loops. This problem stems from several sources, including biased training datasets, algorithmic design …

Technical Memorandum - Re: 2022 Butte Priority Soils Operable Unit (Bpsou) Groundwater Compliance Evaluation, Paddy Stoy

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Mineralogic Control On The Calcium And Magnesium Stable Isotopic Compositions Of Modern Microbial Carbonates, Elizabeth M. Griffith, Matthew S. Fantle, Brittan V. Wogsland, Zijian Li, Majie Fan, David J. Wronkiewicz

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Modern microbialites grow in a variety of environments including the hypersaline, turbid, low alkalinity, high magnesium (Mg) and calcium (Ca) concentrations (and Mg/Ca ∼ 4.5 mol/mol), shallow (less than 2 m deep) Storr's Lake on San Salvador Island, The Bahamas. Rather than growing via the trapping and binding of sediments, these largely micritic microbialites form via microbial processes creating crusts and mounds with laminated to clotted structures comprised of both high-Mg calcite and aragonite. The primary objective of this study was to determine whether the mineralogy of representative microbialite mounds dictates their pre-burial metal isotopic compositions. The Mg and Ca …

Bpen: Brain Posterior Evidential Network For Trustworthy Brain Imaging Analysis, Kai Ye, Haoteng Tang, Siyuan Dai, Igor Fortel, Paul M. Thompson, R. Scott Mackin, Alex Leow, Heng Huang, Liang Zhan

Computer Science Faculty Publications

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological conditions. Despite these achievements, the aspect of prediction uncertainty has been relatively underexplored in brain fMRI data analysis. Accurate uncertainty estimation is essential for trustworthy learning, given the challenges associated with brain fMRI data acquisition and the potential diagnostic implications for patients. To address this gap, we introduce a novel posterior evidential network, named the Brain Posterior Evidential Network (BPEN), designed to capture both aleatoric and epistemic uncertainty in …

Intelligent Soccer Event Detection And Highlights Generation With Broadcast Cues Integration, Anirudh Narayanan, Sergei Chuprov, Leon Reznik, Raman Zatsarenko, Dmitrii Korobeinikov

Computer Science Faculty Publications

In this paper, we present an innovative approach to automate key event detection and highlights generation from soccer match videostreams that allows to improve accuracy and reliability, as well as to reduce data consumption and training time. Our method segments the videostream into distinct frames based on camera angles and activities, and integrates intelligent video analytics with additional visual information provided by broadcasters. As our major novelty in comparison to other intelligent soccer video analysis approaches, we deploy a Multi-Class Image Classifier to segment the video into wide-angle overviews, close-ups, and in-game replays, which allows us to improve the event …

Generative Ai And Llms For Critical Infrastructure Protection: Evaluation Benchmarks, Agentic Ai, Challenges, And Opportunities, Yagmur Yigit, Mohamed Amine Ferrag, Mohamed C. Ghanem, Iqbal H. Sarker, Leandros A. Maglaras, Christos Chrysoulas, Naghmeh Moradpoor, Norbert Tihanyi, Helge Janicke

Research outputs 2022 to 2026

Critical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and introduce established benchmarks for evaluating Large Language Models (LLMs) within cybersecurity contexts. Next, we explore core cybersecurity issues, focusing on trust, privacy, resilience, and securability in these vital systems. Building on this foundation, we assess the role of Generative AI and LLMs in enhancing CIP and present insights on applying Agentic AI for proactive defense mechanisms. Finally, …

Private Reachability Queries On Structured Encrypted Temporal Bipartite Graphs, Yulin Wu, Lanxiang Chen, Gaolin Chen, Yi Mu, Robert H. Deng

Research Collection School Of Computing and Information Systems

A temporal bipartite graph is a graph model that incorporates time-related information into its edges, making it suitable for modeling real-world phenomena like disease outbreaks. However, this temporal information is often sensitive. To protect the privacy of graph data, researchers have explored various approaches to preserve privacy in graph queries, with reachability queries being popular and fundamental as they determine the possibility of reaching one node from others in a graph. While privacy-preserving reachability queries have been extensively studied, existing efforts often overlook the valuable attribute information present in both edges and nodes of the graphs. Moreover, reachability queries on …

An Aspect Performance-Aware Hypergraph Neural Network For Review-Based Recommendation, Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang

Research Collection School Of Computing and Information Systems

Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, …

Detection, Characterization, And Antibiotic Resistance Profiling Of Multidrug-Resistant Bacteria Isolated From Circulating Currency In The Northeastern Region Of Bangladesh, Moshiul Alam Mishu, Ashik Imran, Akash Saha, Shahida Ferdousee, Md. Rubiath Islam, Mohammad Abdullah-Al-Shoeb, Shahidul Islam, Muhammad Abul Kalam Azad

School of Earth, Environmental, & Marine Sciences Faculty Publications

Objectives

The study aims to investigate the prevalence of multidrug resistant bacteria on paper and coin currency obtained from various occupational groups in Bangladesh and to identify the bacterial species present. The research further seeks to evaluate the antibiotic resistance patterns of the identified bacterial isolates.

Methods

84 paper currency notes and 56 coins were collected from seven different sources. Bacterial contamination was assessed using standard bacteriological and biochemical tests to identify and characterize the bacteria. Antibiotic susceptibility of the isolated strains was evaluated using an antibiogram study.

Results

A total of 368 bacterial isolates were detected across the sampled …

Exploring Ai Technology In Grammar Performance Testing For Children With Learning Disabilities, Dimitra V. Katsarou, Evangelos Mantsos, Soultana Papadopoulou, Maria Sofologi, Efthymia Efthymiou, Ilias Vasileiou, Kalliopi Megari, Maria Theodoratou, Georgios A. Kougioumtzis

All Works

The study explores the application of artificial intelligence (AI) in addressing grammar challenges among children with learning disabilities, aiming to assess the efficacy of an AI-driven tool for personalized interventions. A sample of 100 children aged 8–12, diagnosed with learning disabilities, was recruited from special education programs. Participants were divided into an experimental group (n = 50), which used an AI-based grammar assessment tool with personalized feedback, and a control group (n = 50), which completed conventional paper-based grammar tests without feedback. The AI tool administered adaptive grammar tasks, including sentence correction and verb conjugation, and performance was evaluated over …

Stimulating Environmental And Health Protection Through Utilizing Statistical Methods For Climate Resilience And Policy Integration, Sanaa Kaddoura, Rafiq Hijazi, Nadia Dahmani, Reem Nassar

All Works

Climate change, a critical global challenge, is evident in rising global temperatures, shifting precipitation trends, and extreme weather events, including floods, heatwaves, and rising sea levels. The impacts of climate change not only endanger physical health but also affect mental well-being, particularly among populations experiencing frequent or severe climate-related events. Understanding individual perceptions of climate risks and adaptive capacities is crucial for developing strategies that promote health resilience and environmental protection. This paper examines how risk perceptions, direct experiences with extreme weather, and perceived adaptive capacities influence climate change protection measures and support for relevant policies. Data were gathered from …

Quantifying The Role Of Active Listening And Reassurance In Virtual Health Coach Interactions, Ghulam Hussain, Brian Keegan, Robert Ross

Conference papers

Conversational Agents have the potential to support healthcare through coaching exercise routines, but are still lacking in demonstrating authentic social behaviours to support engagement. To this end, we present a series of experiments that we conducted in order to investigate how automated health care coaches can be more effective when their interaction style is tailored to demonstrate qualities associated with a good bedside manner, namely active listening and reassurance. To test this, we first developed a dataset of 135 dialogue excerpts from three distinct sources, i.e., original, handcrafted and LLMs, the latter two of which were tuned to demonstrate specific …

Irreducible Three-Loop Vacuum-Polarization Correction In Muonic Bound Systems, Gregory S. Adkins, Ulrich D. Jentschura

Physics Faculty Research & Creative Works

Three-loop electronic vacuum-polarization corrections due to irreducible diagrams are evaluated for two-body muonic ions with nuclear charge numbers 1≤Z≤6. The corrections are of order α3(Zα)2mr, where α is the fine-structure constant and mr is the reduced mass. Numerically, the energy corrections are found to be of the same order of magnitude as the largest of the order α2(Zα)6mr corrections, and are thus phenomenologically interesting. Our method of calculation eliminates numerical uncertainty encountered in other approaches.