Physical Sciences and Mathematics Commons^™

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 …

The Evolution And Impact Of Blog Analysis Tools: A Study Of Blogtracker's Comprehensive Approach To Digital Discourse Analysis, Oyindamola Koleoso

Theses and Dissertations

This study presents BlogTracker, a comprehensive web-based platform designed to address the growing complexities of analyzing the modern blogosphere. We detail BlogTracker's evolution from earlier blog analysis tools, highlighting its innovative integration of features including real-time data collection, advanced content analysis, sentiment analysis, influence tracking, and narrative analysis. At the core of our contribution is a robust content extraction system that achieves 91.33% accuracy across diverse blog formats, providing a reliable foundation for all analytical functions. This extraction system effectively distinguishes between primary content and peripheral elements, ensuring high-quality inputs for downstream analysis regardless of source blog structure. The platform's …

Should Physicians Take The Rap? Normative Analysis Of Clinician Perspectives On Responsible Use Of 'Black Box' Ai Tools, Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

Background: Increasing interest in deploying artificial intelligence tools in clinical contexts has raised several ethical questions of both normative and empirical interest. One such question in the literature is whether "responsibility gaps" (r-gaps) are created when clinicians utilize or rely on such tools for providing care, and if so, what to do about them. These gaps are particularly likely to arise when using opaque, "black box" AI tools. Compared to normative and legal analysis of AI-generated responsibility gaps in health care, little is known, empirically, about health care providers views on this issue. The present study examines clinician perspectives on …

Cyber-Physical Security Through The Lens Of Ai-Enabled Systems, Zhiyuan Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), powered by emerging artificial intelligence (AI) technologies, have become integral to various critical domains such as the Internet of Things (IoTs), medical devices, and autonomous vehicles. A unique aspect of these systems lies in their interactions with the physical world, by perceiving environments through heterogeneous modalities (perception), processing digital data with human-in-the-loop intelligence algorithms (computing), and autonomously actuating controls that affect physical processes (actuation). While this intricate fusion of cyber and physical components has unlocked unprecedented capabilities, it has also introduced new security challenges. However, traditional security measures often fall short in addressing these multifaceted threats. This …

Clinical Value Of Chatgpt For Epilepsy Presurgical Decision-Making: Systematic Evaluation Of Seizure Semiology Interpretation, Yaxi Luo, Meng Jiao, Neel Fotedar, Jun-En Ding, Ioannis Karakis, Vikram R. Rao, Melissa Asmar, Xiaochen Xian, Orwa Aboud, Yuxin Wen, Jack J. Lin, Fang-Ming Hung, Hai Sun, Felix Rosenow, Feng Liu

Engineering Faculty Articles and Research

Background: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive presurgical approaches such as seizure semiology interpretation, electroencephalography (EEG), magnetic resonance imaging (MRI), and intracranial EEG (iEEG). However, interpreting seizure semiology is challenging because it heavily relies on expert knowledge. The semiologies are often inconsistent and incoherent, leading to variability and potential limitations in presurgical evaluation. To overcome these challenges, advanced technologies like large language models (LLMs)—with ChatGPT being a notable example—offer valuable tools for …

Computational Modeling And Structural Generation Of Piano Music In The Classical Style, Yijing Feng

Dartmouth College Ph.D Dissertations

Listening to fast-tempo piano sonatas of the Classical period (circa 1750-1820) has been shown to have therapeutic effects for neurological disorders such as epilepsy. The limited existing repertoire of music in this style motivates the creation of more long-form, coherent compositions with clearly defined structure. Despite the long history of computer-based music generation and recent progress in deep learning, particularly transformer-based models, generating structurally coherent long-form music remains a major challenge. This difficulty stems from the scarcity of reliable structural annotation datasets, the computational demands of modeling very long musical sequences, and the lack of effective structural encoding in both …

Institutions, Indigenous Peoples, And The Structure Of Power In Columbia River Basin Salmon Governance, Christopher Michael Page

Dissertations and Theses

As broad patterns of behavior influenced by norms and rules, institutions structure many aspects of modern society through coordination and the distribution of power. Due to the importance of common-pool natural resources to many aspects of society, they usually involve competition and lie at the intersection of multiple institutions. This combination of institutions, power disparities, and a competitive interest is what scholars have referred to as a "field." Fields can be complex, and the coordination among interests is generally done through a form of governance involving a wide array of laws, regulations, and actors. When at least one of these …

Machine Learning - Driven Solar Forecasting In Dust-Prone Regions For Sustainable Energy Systems, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan

All Works

This research focuses on improving solar energy forecasting in dust-affected regions such as the UAE, where frequent dust storms reduce photovoltaic (PV) efficiency by scattering and absorbing sunlight. Many existing models overlook the impact of dust events, leading to inaccurate forecasts during such conditions. To address this, the study develops machine learning models—including LSTM, GRU, and hybrid LSTM-GRU architectures—that incorporate solar, weather, and dust-related features. The models were evaluated across multiple forecasti24 hoursons (1, 6, 12, and 24 hours), demonstrating that including dust-related variables significantly enhances prediction accuracy, particularly for short-term forecasts. Temporal and seasonal analyses revealed that dust events, …

Turn-On Fluorescent Glucose Transport Bioprobe Enables Wash-Free Real-Time Monitoring Of Glucose Uptake Activity In Live Cells And Small Organisms, Monica Soma Hensley, David Hutchings, Abdelrahman Ismail, Marina Tanasova

Michigan Tech Publications

The direct link between sugar uptake and metabolic diseases highlights the iminent need for molecular tools to detect and evaluate alterations in sugar uptake efficiency as approaches to identify disease-relevant metabolic alterations. However, the strict requirements of facilitative glucose transporters regarding substrate binding and translocation pose challenges for developing effective fluorescence molecular probes. Based on the state-of-the-art understanding of glucose recognition by facilitative transporters (GLUTs), we designed a glucopyranoside mimic - GluRho - that delivers the “turn-on” rhodamine B to live cells via glucose transport, including major transporters GLUTs 1-4. The high binding affinity achieved through the secondary interaction between …

Temperature-Driven Air Circulation In Caves With Blind Passages, Chris Hendy Dr, David J. Merritt, Shannon Corkill, Cross Cross

International Journal of Speleology

Two blind-passage show-caves in the Waitomo district of New Zealand, Ruakuri Cave and Aranui Cave, have been monitored for the impact of visitors on their environment, especially focusing on partial pressures of carbon dioxide because high levels cause speleothem degradation. In Ruakuri Cave, annual cycles of daily mean pCO₂ correspond with annual cycles of visitor numbers, both peaking in summer. The origin of the high pCO₂ had been assumed to be anthropogenic due to respiration from visitors. Prolonged intervals with no visitation during the Covid-19 pandemic saw the daily mean pCO₂ continuing to show the annual cycle, …

Machine Learning Course: A 15-Week Interactive Curriculum With Code And Case Studies, Pegah Khosravi

Open Educational Resources

This open-access machine learning course is a comprehensive 15-week curriculum developed and published on GitHub with full Google Colab compatibility. It combines theoretical concepts with hands-on Python coding, real-world datasets, and structured projects covering regression, classification, clustering, deep learning, transformers, and multimodal AI. The course is designed for students, educators, and researchers interested in applied machine learning, including biomedical applications. It includes explainable AI components and ethical discussions to align with modern AI standards. The course is maintained by BioMind AI Lab at CUNY.