Physical Sciences and Mathematics Commons^™

Quantifying The Transfer Effectiveness Of An Artificial Intelligence-Based Simulator Pre-Training Program For Student Pilots, Ryan Guthridge

Journal of Aviation/Aerospace Education & Research

Since the airline pilot shortage was initially studied in 2016, the pilot hiring model has been significantly impacted, with airlines hiring qualified pilots at unprecedented rates. The COVID-19 pandemic has slowed this hiring rate, however it is expected that airline hiring will soon increase to a rate higher than initially expected (Bureau of Transportation Statistics, 2022). With this dynamic, certified flight instructors are often the most qualified recruits for airlines, due to the number of hours and experience they have gained in the flight training organization. In turn, certified flight instructors are in short supply for flight training organizations worldwide. …

Role Of C4 Resources In Isotopic Variability In Diet Among Children From Kellis 2 Cemetery, Dakhleh Oasis, Egypt, Faith R. Hendrix

Honors Undergraduate Theses

Using stable carbon isotope analysis, this study investigates dietary diversity in children buried at the Kellis 2 Cemetery (c. AD 50–450) in Egypt's Dakhleh Oasis. From the analysis of δ¹³C isotope values in hair keratin and bone collagen, the study reconstructs short-term and long-term dietary signals in juvenile and adult subjects. The aim is to clarify the role of C₄ plants—particularly millet—in weaning and childhood diets in a Romano-Christian Egyptian village context. A total of 631 segmented hair and 54 bone collagen samples were analyzed from 127 juveniles and 97 adults. Juvenile individuals (i.e., under 15 years biological age) showed …

Analysis Of Public Acceptance Of Urban Air Mobility (Uam) Based On Air Travel Frequency, Seuggyun Jin, Kim O. Chambers

Journal of Aviation/Aerospace Education & Research

Urban Air Mobility (UAM) is an innovative air transportation system designed for efficient travel in urban and suburban areas, offering significant time saving compared to traditional ground transportation. However, concerns about UAM services, such as safety and noise, remain prominent. Understanding public acceptance of UAM is crucial to identifying potential customers and ensuring the sustainability of commercial UAM operations. This study utilizes an online survey from a total of 254 consumer attitudes on the scales of reliability, usefulness, behavioral intention, safety, and concerns to examine public acceptance of UAM based on people's air travel frequencies. Using a one-way ANOVA, the …

Partisan Divides In Environmental Spending Attitudes: A Two-Level Hierarchical Analysis, 1973-2022, Jordan Lipner

Honors Undergraduate Theses

Public attitudes toward environmental spending have become increasingly divided along party lines, with sharp shifts over the past five decades. This thesis updates and expands on Johnson and Schwadel’s 2019 study by applying a two-level hierarchical linear model to General Social Survey data updated to include data from 2015-2022, capturing how political affiliation, education, race, and economic context interact with broader political and economic contexts to shape environmental attitudes over time.
The results show that political affiliation remains the strongest and most reactive predictor of environmental spending attitudes. Republican respondents are significantly more likely to oppose environmental spending, especially under …

Generation Z’S Perception Of Skin Cancer And How It Relates To Climate Change, Piercen Bogolea

Honors Undergraduate Theses

Skin cancer is the most common cancer worldwide, and climate change has been identified as a significant contributor. Preventative action early on can help reduce occurrence, but it is unknown if Generation Z is aware of this and is taking the necessary action now to protect themselves. This research aims to gauge the perception of Generation Z on the severity of skin cancer, the relationship between climate change and skin cancer, and the prevalence of preventative measures being taken. A survey was distributed to students aged 18 to 27 enrolled at the University of Central Florida through mixed sampling. The …

Rapid Inference Of Atmospheric Feature Parameters From Light Curves Using Bayesian Neural Networks, Eugenio A. Diaz

Honors Undergraduate Theses

Mapping atmospheres using rotationally modulated light curves offers insights into cloud structures and dynamics. Current retrieval methods, primarily based on Markov Chain Monte Carlo (MCMC) techniques like Aeolus, can infer atmospheric features but are computationally prohibitive for large datasets. This project proposes a neural network (NN) framework for the rapid, variational inference of atmospheric structure from light curves, particularly those of brown dwarfs. The primary approach focuses on training a Bayesian NN (BNN) to perform regression, predicting the spot parameters that describe the object's surface brightness map. Given the scarcity of suitable observational training data, the BNN is trained on …

0th Order Solutions Of The Wavefunctions For The Quantum Elliptical Box And Microstrip Antenna, Nishtha Tikalal

Honors Undergraduate Theses

For a quantum particle confined to a two-dimensional elliptical box or electromagnetic wave in a microstrip antenna, geometrical and boundary condition interplay result in a spectrum of spatial patterns. Due to the asymmetrical nature of the ellipse, we are faced with continuous symmetry reductions, leaving both degenerate and nondegenerate solutions. Here, we present a complete derivation of an analytical solution and visualizations of the fundamental wavefunctions for both Dirichlet and Neumann boundary conditions respectively corresponding to the quantum elliptical box and the elliptical microstrip antenna.

We demonstrate that the eigenmodes, governed by eccentricity, directly correspond to the modal field distributions …

Crises, Coloniality, And Energy Transformations In Puerto Rico, Laura Kuhl, Marla Perez-Lugo, Carlos Arriaga Serrano, Cecilio Ortiz Garcia, Ryan Ellis, Jennie C. Stephens

Sociology Faculty Publications

Increased attention to inclusive processes is essential to ensure injustices are not perpetuated during energy transitions. Crises, such as the collapse of the electric grid in Puerto Rico after Hurricane Maria, present the promise of opening windows of opportunity for new ideas and voices to inform the future, but they also create opportunities for powerful interests to reinforce existing power dynamics. While coloniality has been recognized as a barrier to sustainable energy transformations, greater integration of the literature on climate coloniality and sustainability transitions is needed. This chapter analyzes visions of the future of the energy system among diverse constituents …

Hymate: A Hybrid Mamba And Transformer Model For Ehr Representation Learning, Md Mozaharul Mottalib, Thao-Ly Phan, Rahmatollah Beheshti

Department of Medicine Faculty Papers

Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare. They are a crucial part for analyzing the progression of patient health; however, their complexity, characterized by long, multivariate sequences, sparsity, and missing values-poses significant challenges in traditional deep learning modeling. While Transformer-based models have demonstrated success in modeling EHR data and predicting clinical outcomes, their quadratic computational complexity and limited context length hinder their efficiency and practical applications. On the other hand, State Space Models (SSMs) like Mamba present a promising alternative offering linear-time sequence modeling and improved efficiency for handling long sequences, but focus mostly on mixing …

Controls On Longitudinal Stream Profile Evolution In Guadalupe Mountains National Park, Samuel Schoenmann, Lisa Tranel, Eric W. Peterson, Jonathan Boyd Thayn

Faculty Publications - Geography, Geology, and the Environment

Canyons in Guadalupe Mountains National Park are excellent locations to study the discrete boundary conditions that influence longitudinal channel profile evolution including climate, tectonics, hydrology, and lithology. The Guadalupe Mountain landscape records deep earth mantle processes in combination with surficial channel erosion and incision. Knickpoints and convex segments in longitudinal river profiles document impacts of climate change, tectonics, or surface processes. This study combines field observations with an analysis of longitudinal profile slopes and residual errors to evaluate how bedrock strength can be distinguished from features created by climate change, faulting, or subsidence in Pine Springs and McKittrick Canyons within …

Spectrum Of The Lakes: Using Satellite Remote Sensing To Unveil Water Color In Minnesota's Sentinel Lakes For Water Quality Monitoring, Andrew J. Dooley, Wondwosen M. Seyoum, Catherine M. O'Reilly

Faculty Publications - Geography, Geology, and the Environment

Traditional water quality monitoring methods often face limitations of equipment costs and labor demands, requiring innovative approaches to effectively assess the ecological health of inland lakes across vast areas. This research explores the application of free, publicly available water color chromaticity analysis for midcontinent lakes in Minnesota, USA. We examined water color variations in Minnesota’s Sentinel Lakes using Landsat 8 OLI data to analyze surface reflectance samples collected from the deepest area within each lake during the late summer, corresponding to peak annual insolation and trophic activity. The median dominant visible wavelength was used to characterize water color. Results indicate …

Frobenius And Commutative Pseudomonoids In The Bicategory Of Spans, Ivan Contreras, Rajan Amit Mehta, Walker H. Stern

Mathematics Sciences: Faculty Publications

In previous work by the first two authors, Frobenius and commutative algebra objects in the category of spans of sets were characterized in terms of simplicial sets satisfying certain properties. In this paper, we find a similar characterization for the analogous coherent structures in the bicategory of spans of sets. We show that commutative and Frobenius pseudomonoids in Span correspond, respectively, to paracyclic sets and Γ-sets satisfying the 2-Segal conditions. These results connect closely with work of the third author on A∞ algebras in ∞-categories of spans, as well as the growing body of work on higher Segal objects. Because …

Cost-Effective Strategies For Feral Swine Control: Exploring Trapping Equipment Cooperatives, Phil Kenkel, Riza Radmehr, Rodney Holcomb, Mckenzie Boyce

Human–Wildlife Interactions

Feral swine (Sus scrofa) in the United States have become a growing concern, with a population of ≥6 million and causing annual damages of ≥$1.5 billion USD. Because of their high reproductive capacity, effective control methods are crucial to reducing or slowing their population growth. While remotely monitored and triggered traps have proven to be an effective control strategy, the cost of sophisticated trapping equipment, which can cost ≥$7,000 USD, is often cost prohibitive for many landowners. Despite the pressing need to address the challenge of making effective feral swine control methods more affordable, there are few studies …

A Question Of Transparency: Solutions For Fermi Questions, March 2025, John Adam

Mathematics & Statistics Faculty Publications

Question 1: Why is it easier to see through rain than fog?

Start thinking about this by imagining a fixed volume (V) of water being dispersed into, say, N identical droplets of diameter d. Surface area and volume considerations should lead to the answer in terms of V and d.

Solution to Question 1: N = VI(πd³/6) = 6V/πd³ ≈ 2V/d³.

The cross-sectional area A of each drop is πd²/4 ≈ 3d²/4, so the total area blocked off (assuming no overlapping drops—so this is an upper bound) is NA ≈ 1.5V/d, so the area blocked off is inversely proportional to …

A Conversation On Fundamental Data Literacy Concepts For Undergraduate Education, Anna Bargagliotti, Wendy Pothier, David Homa, Arshia Mathus, Keith Mccormick, Paula Payton, Brian Wright

Mathematics, Statistics and Data Science Faculty Works

As the demand for data literacy grows, integrating foundational data literacy skills into undergraduate education becomes increasingly essential. This panel presents six core themes for cultivating data literacy among undergraduate students, addressing its interdisciplinary nature. Drawing upon the collective expertise of the group, literature research, case studies, and interviews, the panel explores the need for data literacy across various disciplines and the challenges of integrating it into higher education curricula. The proposed fundamental concepts include understanding data’s role in daily life, distinguishing between inference and prediction, recognizing the potential for misleading data, exploring ethical considerations, and honing communication and storytelling …

Bounded Compactness From G(E)Up, Jonas Mureika, Roberto Casadio, Ilim Irfan Çimdiker, Octavian Micu

Physics Faculty Works

We analyse how different Generalised Uncertainty Principles could place bounds on the compactness of self-gravitating systems. By considering existing experimental bounds on the relevant parameters, we conclude that the compactness of large astrophysical objects is bounded above by the inverse of the GUP parameter, which would naturally be of order one. Conversely, the existence of black holes imposes stronger bounds on those parameters.

Multitec: A Data-Driven Multimodal Short Video Detection Framework For Healthcare Misinformation On Tiktok, Lanyu Shang, Yang Zhang, Yawen Deng, Dong Wang

Computer Science Faculty Works

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in …

Hunting And Fishing Ceos: Environmental Plunderers Or Saviors?, Thomas Covington, Steve Widler, Keven Yost

Finance Faculty Works

CEOs who participate in hunting and fishing benefit by appreciating natural environments and permanently consuming natural resources. We examine whether CEOs who hunt and fish make different environmental decisions and find that firms led by CEOs who obtain the most hunting and fishing licenses have lower environmental performance as measured by MSCI-KLD. This effect is strongest in the environmental category of climate change but also extends to pollution, waste, and the protection of natural capital. Furthermore, firms led by CEOs with the most hunting and fishing licenses are significantly more likely to pay a regulatory settlement for an environmental regulatory …

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula

Browse all Theses and Dissertations

This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …

Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi

Browse all Theses and Dissertations

Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …

Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh

Browse all Theses and Dissertations

Software vulnerabilities are a major cause of security breaches, making effective detection critical. Traditional learning-based methods require large datasets and significant computational resources, which are often impractical due to high annotation costs and data scarcity. To address this, we propose an innovative system, RearVul, which Re-parameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained classification model using adversarial reprogramming, enabling detection with minimal modifications. It learns a universal perturbation applied to program representations, preserving the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a …

Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland

Browse all Theses and Dissertations

Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, …

Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala

Browse all Theses and Dissertations

Self-healing polymers, particularly vitrimers, are emerging as promising candidates in the development of advanced materials for renewable energy and aerospace structures. These materials exhibit dynamic covalent bond exchange mechanisms that enable reprocess ability, damage repair, and extended operational lifetime under harsh conditions. This study presents a density functional theory (DFT)-based computational investigation of the mechanistic pathways and energetics of bond exchange reactions in model vitrimer systems. We explore transition states, energy barriers, and thermodynamic features corresponding to associative and dissociative self-healing reactions in vitrimers. The study focuses on Diaminodiphenyl disulfide (AFD), a bifunctional molecule composed of two para-substituted aminophenyl rings …

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

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Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla

Browse all Theses and Dissertations

This thesis addressed two main challenges in biological data analysis: structure-preserving dimensionality reduction and synthetic data generation for small sample datasets. I proposed the Isometric Centroid Encoder (ICE), a supervised dimensionality reduction method that preserves pairwise distances between class centroids during dimension reduction. Unlike existing methods like Centroid Encoder and Super Encoder, ICE explicitly maintains geometric relationships between biological classes, achieving nearly perfect structure preservation at C dimensions (where C equals the number of classes) with strong performance even in 2D and 3D spaces. Additionally, I compared three generative models (VAE, LSH-GAN, and scDiffusion) for synthetic data generation on small …

Hydrogen Storage Density And Adsorption Energy Barriers On Li-Decorated Bc3 Nanosheet, Sri Venkat Pavan Upasi

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Hydrogen is considered an emerging carrier of clean energy with renewable capabilities. Widespread hydrogen energy utilization necessitates efficient storage strategies. Functionalized nanomaterials, such as Li-decorated BC3 nanosheets, are among the primary candidate materials for hydrogen storage. This research investigates the feasibility of hydrogen storage by estimating energy barriers and their dependence on storage density on Li-decorated BC3 nanosheet. Density functional theory (DFT) simulations provide estimates of adsorption energies and saddle points for hydrogen storage and compare corresponding reaction rates. The results are expected to help us understand the advantages and possible shortcomings of hydrogen storage on such nanomaterials.

Biosensing Applications Of Thz Rotational Spectroscopy: Sensing And Analysis Of Exogenous And Endogenous Compounds In Exhaled Breath, Daniel J. Tyree

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Exhaled human breath contains a wealth of volatile molecular species which bear an imprint of compounds dissolved in blood. Assessing trace amounts of these species in exhaled breath requires a highly sensitive and selective method. Terahertz (THz) rotational spectroscopy satisfies these needs by detecting numerous and narrow, molecule specific spectral features with feature intensity dependent on the quantity of absorbing molecules, molecular structure, and experimental parameters. Expanding the applicability of THz sensing to biological gases requires systems to measure this spectral data, reliable analysis of the spectra, and demonstration of the utility of biological data extracted from the spectra. To …

Ultrasonic Sensor-Based Sound Synthesis Using Raspberry Pi Pico W, Niraj Jaishwal

Williams Honors College, Honors Research Projects

At the intersection of Human Computer Interaction and digital art, this project transforms simple motion into musical expression. It explores an interactive real-time sound synthesis system using ultrasonic sensors to generate continuous audio. The objective is to design a system that maps physical distances into musical parameters such as pitch and amplitude, which will create a responsive audio environment. Two ultrasonic sensors are used in combination with the Raspberry Pi Pico W microcontroller running CircuitPython and Adafruit Audio Hat for real-time sound output. One sensor controls the pitch of the generated tone, while the other controls volume. This enables expressive …

Pca Text Sentiment Analysis Tool, Luke Gegick

Williams Honors College, Honors Research Projects

This project applies principal component analysis (PCA) to sentiment analysis of text to identify complex emotional responses from plain text. Existing sentiment analysis tools often rely on large language models or struggle to achieve high accuracy when processing large collections of short inputs, such as social media comments. By contrast, this project uses PCA as a lightweight, mathematically grounded alternative that can scale efficiently while still capturing meaningful emotional structure in text data.

PCA has shown strong effectiveness in text analysis, particularly when supported by a sufficiently large dataset and a robust preprocessing pipeline. To create consistent, information-rich input vectors, …