Physical Sciences and Mathematics Commons^™

Proceedings Of The 12th Research Symposium At Mammoth Cave National Park, Pat Kambesis Editor, Lee Anne Bledsoe Editor, Ana K. Celis Editor

Mammoth Cave Research Symposia

Proceedings of the 12th Research Symposium at Mammoth Cave National Park: Celebrating 100 Years of Scientific Research & Resource Management in the Mammoh Cave Region.

Design Of Silicon Grating Metasurfaces For Beam Deflecting In Solid-State Light Detecting And Ranging (Lidar), Matthew Baker

Honors Program Theses and Projects

My thesis begins with an overview of LiDAR (Light Detecting and Ranging) and theory for electromagnetic wave propagation. We establish problems that arise from the use of external beam steering components in LiDAR systems. As a possible solution, we introduce the concept of a metasurface with sub-wavelength dimensions. Grating metasurfaces utilize periodicity to change the phase gradient along a surface. Using a row of posts on a substrate, we can confine light towards one side of a structure, deflecting at angles towards a maximum angle determined by the structure’s geometry. These metasurfaces are small enough to be directly incorporated onto …

System Administration Practices And Experimentation, Nicholas Z. Young

Honors Program Theses and Projects

This undergraduate departmental honors capstone project experiments with and demonstrates System Administration practices that are used in enterprise environments. The skills and practices of System Administrators are crucial to maintain large-scale IT infrastructure. This project aimed to gain a deeper, practical understanding of the role of a System Administrator in an emulated environment. Through hands-on experimentation, this project addressed the responsibilities of a System Administrator, such as controlling user access, adding hardware, automating tasks, monitoring systems, overseeing and developing a backup strategy, maintaining local documentation, and security practices. This project demonstrated some of the complexities that lie in each of …

Computational Thinking, Informal Learning, And Makerspace, Redar Ismail

College of Computing and Digital Media Dissertations

The continuous advancement of technology has made it a crucial tool across various disciplines. As adaptation to this rapidly progressing field occurred, teaching and learning problem-solving skills are more essential than ever for empowering individuals to succeed across diverse fields. Studies have shown that engaging K-12 students in activities encouraging science, technology, engineering, mathematics (STEM), and computational thinking (CT) are critical for teaching them how to deal with complex problems (Rode, Barkhuus, & Ioannou, 2024; Shu & Huang, 2021). Makerspaces and making activities became popular among researchers and educators due to their potential to advance learning, enhance problem-solving skills, and …

Modeling Molten Lithium Carbonate As Pollutant Absorbing Agent, Lucian G. Forestieri

Student Summer Scholars Manuscripts

Coal burning generates gaseous pollutants that are toxic to local environments and human health, namely hydrochloric acid and sulfur dioxide ( HCl and SO₂). Current methods for capturing these pollutants are flue gas desulfurization, which utilize solid or water- dissolved carbonates. This method, however, is inefficient mainly due to the product salts blocking further reactions. Molten carbonates may be a more efficient alternative. Experimental work cannot observe these reaction mechanisms, and the small number of current computational models do not match experimental properties. This work aims to develop a computational model of gaseous HCl scattering off the surface …

Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). …

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam

Engineering Faculty Articles and Research

Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …

Integrating Artificial Intelligence In Orthopedic Care: Advancements In Bone Care And Future Directions, Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi

SKMC Student Presentations and Publications

Artificial intelligence (AI) is revolutionizing the field of orthopedic bioengineering by increasing diagnostic accuracy and surgical precision and improving patient outcomes. This review highlights using AI for orthopedics in preoperative planning, intraoperative robotics, smart implants, and bone regeneration. AI-powered imaging, automated 3D anatomical modeling, and robotic-assisted surgery have dramatically changed orthopedic practices. AI has improved surgical planning by enhancing complex image interpretation and providing augmented reality guidance to create highly accurate surgical strategies. Intraoperatively, robotic-assisted surgeries enhance accuracy and reduce human error while minimizing invasiveness. AI-powered smart implant sensors allow for in vivo monitoring, early complication detection, and individualized rehabilitation. …

Quasi-Normal Modes Of Extended Uncertainty Principle Kerr Black Holes, Ava Hoeger, Jonas Mureika

Honors Thesis

The current understanding of gravity is shaped largely by Albert Einstein’s theory of General Relativity. This theory is highly successful at predicting phenomenon on macroscopic scales, but it faces unphysical singularities at quantum scales. This thesis will explore the possibility of combining General Relativity with quantum mechanics through the Extended Uncertainty Principle (EUP), which provides a new, fundamental length scale correction to the Heisenberg Uncertainty Principle. This allows for quantum gravity effects at macroscopic scales, which will be examined through Kerr black holes with event horizons on the order of . These black holes are rotating and electrically neutral, and …

Efficient Eeg Epilepsy Classification And Feature Selections Based On Hellinger Distance, Muhammed Sadiq

Theses and Dissertations

Accurate and efficient detection of epileptic seizures from EEG signals remains a critical challenge due to high-dimensional data, class imbalance, and the limitations of standard classifiers. This thesis introduces two novel models to address these challenges. The first model presents a new classifier based on the Hellinger Distance, specifically designed to enhance discriminative capability and robustness against imbalanced datasets. By integrating the Hellinger Distance Classifier with Particle Swarm Optimization (PSO) for feature selection, this model significantly improves classification performance while reducing computational complexity. Experimental evaluations on the Bonn dataset demonstrate an accuracy of 96.25%, an F1-score of 97.74%, a recall …

Probing Binding And Allosteric Mechanisms Of The Kras Interactions With Monobodies And Affimer Proteins : Ensemble-Based Mutational Profiling And Thermodynamic Analysis Of Binding Energetics And Allostery Reveal Diversity Of Functional Hotspots And Cryptic Pockets Linked By Conserved Communication Network, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Guang Hu, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

KRAS, a historically "undruggable" oncogenic driver, has eluded targeted therapies due to its lack of accessible binding pockets in its active state. This study investigates the conformational dynamics, binding mechanisms, and allosteric communication networks of KRAS in complexes with monobodies (12D1, 12D5) and affimer proteins (K6, K3, K69) to characterize the binding and allosteric mechanisms and hotspots of KRAS binding. Through molecular dynamics simulations, mutational scanning, binding free energy analysis and network-based analyses, we identified conserved allosteric hotspots that serve as critical nodes for long-range communication in KRAS. Key residues in β-strand 4 (F78, L80, F82), α-helix 3 (I93, H95, …

Climate Constrains The Enhancement Of Co2 Fertilization On Forest Gross Primary Productivity, Xinyuan Wei, Daniel J. Hayes, Christopher R. Schwalm, Joshua B. Fisher, Deborah N. Huntzinger, Lei Ma, Rodrigo Vargas, Nathaniel A. Brunsell

Mathematics, Physics, and Computer Science Faculty Articles and Research

Forest gross primary production (GPP) is influenced by the interplay between climate conditions and atmospheric CO₂ levels, which interact in complex ways, generating both compensating and amplifying effects. In this study, eddy covariance flux measurements from 50 forest ecosystems were integrated with simulations from 14 terrestrial biosphere models to investigate how climate conditions and atmospheric CO₂ concentrations regulate forest GPP. This approach bridges site-level observations with biome-scale model estimates to develop a global understanding. Our findings suggest that in boreal and cold temperate regions, temperature primarily constrains the enhancement of the CO₂ fertilization on forest GPP; however, …

Mass Transfer In Binary Stars, Pierson Lipschultz

COD Library Student Research and Award Symposium

I investigated the properties of mass transfer in binary star systems. I feature results from Vela X-1, V404 Cygni, and W Ursae Majoris as well as corroborating data from POSYDON. Furthermore, I found the local populations on an HR diagram of each system. Additionally, I found anomalous data properties in both MESA and V404 Cygni which warrant further investigation.

Faculty Sponsor: Professor Joseph DalSanto

Laying The Groundwork: Building Long-Term Community Partnerships Across A Shared Curriculum, Juliana Chow, Eric Robertson, Kate Magargal

Utah Conference on Community Engagement

Maintaining long-term community partnerships is a major challenge for community-engaged classrooms. This poster presents a community-engaged learning (CEL) program template for a project shared between multiple courses and instructors, demonstrating how shared resources, skill sets, relationships, and coordination could result in a long-term community partnership linked to multiple community networks. It shows how 3 courses at the Honors College of the University of Utah can weave together learning objectives and cooperative planning for a course module focused on the Native Plants Program, part of the U of U Climate Action Plan.

Applications Of The Mathieu Groups And Information Theory In Dna Encoding Functions, Juan C. Nava Jr

Theses and Dissertations

A foundational idea in mathematics lies in breaking down existing components into their bare fundamentals. As evidenced by prime numbers and composites, we learn this idea at an early age. Categorizing these broken-down components into their simplest form allows mathematicians to construct proofs from emergent patterns. John Conway’s Atlas of Finite Groups in the 1990s was particularly concerned with the categorization of structures known as groups. There are certain axioms a group must adhere to, which amount to the retention of symmetry; ultimately a group helps us to better understand symmetric actions performed on a set with a binary operation. …

Gnns For Network Classification In Single Cell Rna Sequencing Data, Reid C. Sewell

Capstone Projects

A common technique when investigating a disease is to profile gene expression, as this gives unique insights into the functions of a cell. Gene expression data gathered from single cell RNA sequencing can be encoded into a gene co-expression network, which is a graph of potential relationships between different genes. One method for interpreting data encoded as a graph is to use a graph neural network, or GNN. This project designs and implements a GNN architecture to accomplish classification tasks on graph data. Then, given a dataset of gene co-expression networks made from multiple single cell RNA sequencing studies, the …

Phosphomimetic Substitution Of Serine Residue Ser260 With Aspartate Suggests Regulation Of Activity Of Human Cytosolic Malate Dehydrogenase By Phosphorylation, Charles Pelagalli '25, Luke Tischio, Kathleen Cornely

Chemistry & Biochemistry Faculty Publications

Human cytosolic malate dehydrogenase (MDH1) is a key enzyme involved in the malate-aspartate shuttle of eukaryotic cells, and which catalyzes the reaction of oxaloacetate to malate. As MDH1 is responsible for maintaining redox homeostasis by regeneration of NAD+ for glycolysis, its regulation is of particular interest in pharmaceutical applications, specifically in treating cancer cells that proliferate quickly and hence rely greatly on glycolysis. While the regulation of MDH1 by the phosphorylation of key residues has been previously documented, work still remains in identifying the specific residues involved in regulation of the enzyme by phosphorylation. In this study, we used …

Deep Learning Classification Of Drainage Crossings Based On High-Resolution Dem-Derived Geomorphological Information, Michael Edidem, Bill Xu, Ruopu Li, Di Wu, Banafsheh Rekabdar, Guangxing Wang

Computer Science Faculty Publications and Presentations

High-resolution digital elevation models (HRDEMs) from LiDAR and InSAR technologies have significantly improved the accuracies of mapping hydrographic features such as river boundaries, streamlines, and waterbodies over large areas. However, drainage crossings that facilitate the passage of drainage flows beneath roads are not often represented in HRDEMs, resulting in erratic or distorted hydrographic features. At present, drainage crossing datasets are largely missing or available with variable quality. While previous studies have investigated basic convolutional neural network (CNN) models for drainage crossing characterization, it remains unclear if advanced deep learning models will improve the accuracy of drainage crossing classification. Although HRDEM-derived …

Examining The Accuracy Of The Omni Data In Representing Geomagnetic Storm Observations Near Earth And The Effect On Global Modeling, James T. Davis

2025 Spring Honors Capstone Projects - Archive

The accuracy of OMNI dataset being propagated to bow shock nose and used to represent geomagnetic storms is a known issue. Inaccuracies of this data bring erroneous results in scientific endeavors therefore establishing the importance of accuracy and consistency. This case study examines the accuracy of the OMNI data and data near-Earth in representing geomagnetic storms with different drivers on global simulations. This research will include examples of global magnetosphere simulations, such as SWMF, driven with two storms driven by coronal mass ejections, and one driven by a high-speed stream using OMNI data and data near-Earth to illustrate potential variations …

Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan

Computer Science Faculty Research & Creative Works

As Artificial Intelligence (AI) systems increasingly underpin critical applications, from autonomous vehicles to biometric authentication, their vulnerability to transferable attacks presents a growing concern. These attacks, designed to generalize across instances, domains, models, tasks, modalities, or even hardware platforms, pose severe risks to security, privacy, and system integrity. This survey delivers the first comprehensive review of transferable attacks across seven major categories, including evasion, backdoor, data poisoning, model stealing, model inversion, membership inference, and side-channel attacks. We introduce a unified six-dimensional taxonomy: cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-hardware, which systematically captures the diverse transfer pathways of adversarial strategies. Through …

In Search Of Extreme Extragalactic Energy: A Catalog Of Tev-Emitting Bl Lac Candidates From Erosita And Wise, Cassidy M. Metzger, Manel Errando, Andrea Gokus

Senior Honors Papers / Undergraduate Theses

Active galactic nuclei (AGN) are supermassive black holes that reside at galactic centers and are actively accreting matter. In approximately 10% of cases, AGN produce relativistic jets: collimated streams of particles that travel for thousands of light years and have been detected at TeV energies by ground-based gamma-ray observatories. However, the mechanisms that accelerate particles beyond the TeV scale are largely unknown. Currently, only 56 objects are confirmed to accelerate particles to these extreme energies. Here we provide a selection of over 150 sources that exhibit infrared (IR) and X-ray emission profiles similar to those of the 56 TeV sources. …

Observational Properties Of Near-Maximal Spin Black Holes With The Eht, Tegan A. Thomas, Angelo Ricarte, Yajie Yuan

Senior Honors Papers / Undergraduate Theses

In 2021 and 2024, the Event Horizon Telescope (EHT) collaboration published the first polarized images of the supermassive black holes (SMBHs) M87* and Sgr A*, which allowed us to place important constraints on the accretion flow and underlying space-time. Of particular interest is the dimensionless spin parameter "a_•", which theoretically may attain a maximum value of a_• = 0.998 when spun up by a thin accretion disk. On the other hand, mechanisms including incoherent accretion, SMBH mergers, and spin extraction via jets, are hypothesized to spin down SMBHs from these near-extremal values. In this work, we perform …

Interpreting Spatial And Temporal Variations In Global Air Quality Using A Chemical Transport Modeling Framework, Deepangsu Chatterjee

McKelvey School of Engineering Graduate Student Theses & Dissertations

Air quality is a major health concern. Exposure to fine particulate matter (PM2.5) and nitrogen oxides (NOx = NO + NO2) is a leading mortality risk factor across the world. Numerous cohort studies conducted over the past two decades have identified strong associations between ambient air pollution and human mortality, highlighting the adverse health effects of PM2.5 and NO2 exposure. My thesis includes three studies that contribute to a better understanding of air quality. In many regions, including South Asia, elevated emissions from various sectors and sparse ground monitoring are significant challenges. The concurrent development and use of the high-performance …

Hierarchical Log Bayesian Neural Network For Enhanced Aorta Segmentation, Delin An, Pan Du, Pengfei Gu, Jian-Xun Wang, Chaoli Wang

Computer Science Faculty Publications

Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segmentation, they remain challenging due to the intricate multiscale structure and the complexity of the surrounding tissues. This paper presents a novel approach for enhancing aorta segmentation using a Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model. Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the former provides an initial aorta segmentation, and the latter enhances blood vessel detection across varying scales by learning suitable LoG kernels, enabling self-adaptive handling …

Enabling New Synthetic Platforms For Aryne Synthesis With Aryl Thianthrenium Reagents, Riley Augustus Roberts

Dissertations and Theses

The enormous importance of aromatic rings in organic molecules cannot be understated. Aromatic rings are present in the majority of pharmaceuticals, agrochemicals, and consumer products. The way in which these rings are arranged and substituted results in a molecule’s unique properties. Because of this, the current limitations of the chemical methods used to synthesize these molecules results in limited structural diversity and thus missed opportunities for the development of value-added products. To this end, this work demonstrates novel methods of aryne synthesis, aryl thianthrenium synthesis and a unique approach to structural scaffold hopping. The approaches presented herein allow for rapid …

Resale Revolution: Trend Implications From Media Presence Transcended To Luxury Retail Markets, Penelope Prochnow

Capstone Projects

This study aims to deepen understanding of fashion trend decline from peak popularity to obsolescence, with implications for sustainability and producer profit margins. It investigates how the attributes and media presence of fashion items influence their journey from high-end editorial coverage to resale platforms. Using survival analysis to model trend lifetimes and cosine similarity metrics to compare resale and magazine keyword frequencies, alongside machine learning for price prediction, the study uncovers critical temporal patterns. Results show that resale trends reflect magazine content with a lag of approximately 18 to 30 months and draw from long-wave revivals spanning 6 to 14 …

On The H-Property For Step-Graphons: Residual Case, Wanting Gao

McKelvey School of Engineering Graduate Student Theses & Dissertations

We investigate the H-property for step-graphons. Specifically, we sample graphs Gn on n nodes from a step-graphon and evaluate the probability that Gn has a Hamiltonian decomposition in the asymptotic regime as n → ∞. It has been shown in Belabbas and Chen (2023); Belabbas et al. (2021) that for almost all step-graphons, this probability converges to either zero or one. We focus in this paper on the residual case where the zero-one law does not apply. We show that the limit of the probability still exists and provide an explicit expression of it. We present a complete proof of …

Computational Complexity Of Soundness Verification For Neural Networks, Scott Sirri

McKelvey School of Engineering Graduate Student Theses & Dissertations

Neural networks are an increasingly ubiquitous tool in systems of varying complexity across a range of domains. While these tools can be used to learn and predict complex functions, their opaque nature limits the scope of their acceptable applications. In particular, a lack of performance guarantees means that they are unsuitable for safety-critical applications such as self-driving cars and scheduling systems. Neural networks trained to solve NP-complete problems, in particular, are unlikely to be able to solve the problem exactly. However, a weaker soundness guarantee may be sufficient for some systems, e.g., that positive instances of the problem may be …

Draft Final 2025 Residential Metals Abatement Program (Rmap) Non-Residential Daycare Soil Sampling: Field Sampling Plan (Fsp) Submittal #15 [Mini Scholars Discovery Academy, Hands On Learning Childcare, Kidz Konnection, Young Explorers, & University On Princeton], Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Modeling Cross-Platform Narrative Templates: A Temporal Knowledge Graph Approach, Ridwan Amure

Theses and Dissertations

Over the past decade, social media platforms have rapidly evolved in scale, functionality, and user engagement, encouraging individuals to maintain active presences across multiple networks. This complex, interconnected ecosystem has also enabled information actors to exploit cross-platform dynamics to amplify the reach of their content and strategically target diverse audiences. Recognizing the persistence and adaptability of such actors, this research emphasizes the need for robust models that can effectively capture and analyze cross-platform narrative diffusion. To this end, we propose a framework that utilizes temporal knowledge graphs to model the evolution and relationships among narratives across platforms. We extract temporal …